diff --git a/.env b/.env new file mode 100644 index 0000000..57119ac --- /dev/null +++ b/.env @@ -0,0 +1,16 @@ +##### GENERAL ENVS ##### +GPU_SUPPORTED=True +GPU_ENABLED=True + +##### AUTH ENVS ##### +AUTH_ENABLED=False +REQUIRED_AUTH_ROLES=video-synthesis + +##### BACKEND ENVS ##### +FFMPEG_DOCKER=True # Python will use a different version of ffmpeg, therefore we need to redirect to /usr/bin/ffmpeg +BACKEND_URL=http://localhost:5000 + +#### AVATAR SETTINGS #### +AVAILABLE_AVATARS="" # Comma separated list of all Avatar names +AVATAR_MODELS=neuralVoice,motionGan # All options: "neuralVoice,motionGan" + diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..a1842ad --- /dev/null +++ b/.gitattributes @@ -0,0 +1,13 @@ +*.jpg filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.mp4 filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.mat filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text + diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..a52f1ed --- /dev/null +++ b/.gitignore @@ -0,0 +1,34 @@ +## Ignore Visual Studio temporary files, build results, and +## files generated by popular Visual Studio add-ons. +## +## Get latest from https://github.com/github/gitignore/blob/master/VisualStudio.gitignore + +.idea + +# Logging +error.log + +# Visual Studio 2015/2017 cache/options directory +.vscode/ + +#Pycharm directory +avatar_backend_api/.idea/ + +# log folder +sbatch_log/ +.out + +# Python Tools for Visual Studio (PTVS) +__pycache__/ +*.pyc + +# train cluster +*.job +# *.sh + +# all mac os files ._ +._* +*.DS_Store + +# Data dir +data/** diff --git a/AUTHORS.txt b/AUTHORS.txt new file mode 100644 index 0000000..df870c1 --- /dev/null +++ b/AUTHORS.txt @@ -0,0 +1,7 @@ +Contributions: + +* Alberto Pennino : Research and backend developement. +* Thomas Steinmann : Frontend and API developement. +* Marc Willhaus : Frontend developement. +* Clara Labrador-Fernandez : Supervision. + diff --git a/Dockerfile-motion-gan b/Dockerfile-motion-gan new file mode 100644 index 0000000..a472c83 --- /dev/null +++ b/Dockerfile-motion-gan @@ -0,0 +1,89 @@ +FROM nvidia/cuda:11.4.1-base-ubuntu20.04 as base +ENV DEBIAN_FRONTEND noninteractive + +ENV PATH /opt/conda/bin:$PATH + +# Update & install packages +RUN apt-get -qq update --fix-missing && \ + apt-get -qq install -y bzip2 ca-certificates wget curl git build-essential && \ + apt-get -qq clean && \ + rm -rf /var/lib/apt/lists/* + +SHELL ["/bin/bash", "-c"] +WORKDIR /app + +# Install miniconda +RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-py38_4.12.0-Linux-x86_64.sh -O ~/miniconda.sh && \ + /bin/bash ~/miniconda.sh -b -p /opt/conda && \ + rm ~/miniconda.sh && \ + /opt/conda/bin/conda clean --quiet -tipsy && \ + ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \ + echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \ + echo "conda activate base" >> ~/.bashrc + +# Set python env vars +ENV PYTHONDONTWRITEBYTECODE 1 +ENV PYTHONUNBUFFERED 1 +ENV PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python +ENV FORCE_CUDA 1 +ENV PYTORCH_CUDA_ALLOC_CONF max_split_size_mb:512 + +RUN apt-get -qq update && \ + apt-get -qq install -y python3-opencv libsm6 libxext6 && \ + apt-get -qq clean && \ + rm -rf /var/lib/apt/lists/* + +FROM base as backend +WORKDIR /app + +### deepspeech ### +RUN conda create -n deepspeech python=3.7 +# Make run command use the new environment +SHELL ["conda", "run", "-n", "deepspeech", "/bin/bash", "-c"] + +RUN conda install pysoundfile -c conda-forge && \ + pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html && \ + conda install x264=='1!152.20180717' ffmpeg=4.0.2 -c conda-forge + +COPY requirements_deepspeech.txt . +RUN pip install -r requirements_deepspeech.txt + +############################################## + +### pyenv ### +RUN conda create -n pyenv python=3.9 + +# Make run command use the new environment +SHELL ["conda", "run", "-n", "pyenv", "/bin/bash", "-c"] + +RUN conda install pysoundfile -c conda-forge && \ + conda install pytorch=1.13.0 torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia && \ + conda install -c fvcore -c iopath -c conda-forge fvcore iopath && \ + conda install x264=='1!152.20180717' ffmpeg=4.0.2 -c conda-forge && \ + conda install pytorch3d -c pytorch3d + +COPY requirements.txt . +RUN pip install -r requirements.txt + +# Make run command use the new environment +SHELL ["conda", "run", "-n", "base", "/bin/bash", "-c"] + +COPY requirements.api.txt . +RUN pip install -r requirements.api.txt + +COPY avatar-api ./avatar-api +RUN pip install ./avatar-api --use-feature=in-tree-build && \ + rm -rf avatar-api + +# continue +COPY motion-gan-pipeline/GFPGAN ./GFPGAN +COPY motion-gan-pipeline/preprocessing ./preprocessing +COPY motion-gan-pipeline/motion-generation ./motion-generation +COPY motion-gan-pipeline/ImageToImage ./ImageToImage + +COPY motion-gan-pipeline/gunicorn.conf.py . +COPY motion-gan-pipeline/motion_gan_backend_api ./motion_gan_backend_api +COPY motion-gan-pipeline/full_pipeline* ./ +COPY motion-gan-pipeline/*.py ./ + +CMD [ "conda", "run", "--no-capture-output", "-n", "base", "gunicorn", "-c", "gunicorn.conf.py", "--chdir", "./motion_gan_backend_api", "-k", "uvicorn.workers.UvicornWorker", "app:app" ] diff --git a/Dockerfile-neural-voice b/Dockerfile-neural-voice new file mode 100644 index 0000000..e327d33 --- /dev/null +++ b/Dockerfile-neural-voice @@ -0,0 +1,83 @@ +FROM nvidia/cuda:11.4.1-base-ubuntu20.04 as base +ENV DEBIAN_FRONTEND noninteractive + +ENV PATH /opt/conda/bin:$PATH + +# Update & install packages +RUN apt-get -qq update --fix-missing && \ + apt-get -qq install -y bzip2 ca-certificates wget curl git build-essential && \ + apt-get -qq clean && \ + rm -rf /var/lib/apt/lists/* + +SHELL ["/bin/bash", "-c"] +WORKDIR /app + +# Install miniconda +RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-py38_4.12.0-Linux-x86_64.sh -O ~/miniconda.sh && \ + /bin/bash ~/miniconda.sh -b -p /opt/conda && \ + rm ~/miniconda.sh && \ + /opt/conda/bin/conda clean --quiet -tipsy && \ + ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \ + echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \ + echo "conda activate base" >> ~/.bashrc + +# Set python env vars +ENV PYTHONDONTWRITEBYTECODE 1 +ENV PYTHONUNBUFFERED 1 +ENV PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python +ENV FORCE_CUDA 1 +ENV PYTORCH_CUDA_ALLOC_CONF max_split_size_mb:512 + +RUN apt-get -qq update && \ + apt-get -qq install -y python3-opencv libsm6 libxext6 && \ + apt-get -qq clean && \ + rm -rf /var/lib/apt/lists/* + +FROM base as backend +WORKDIR /app + +### deepspeech ### +RUN conda create -n deepspeech python=3.7 +# Make run command use the new environment +SHELL ["conda", "run", "-n", "deepspeech", "/bin/bash", "-c"] + +RUN conda install pysoundfile -c conda-forge && \ + pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html && \ + conda install x264=='1!152.20180717' ffmpeg=4.0.2 -c conda-forge + +COPY requirements_deepspeech.txt . +RUN pip install -r requirements_deepspeech.txt + +############################################## + +### pyenv ### +RUN conda create -n pyenv python=3.9 + +# Make run command use the new environment +SHELL ["conda", "run", "-n", "pyenv", "/bin/bash", "-c"] + +RUN conda install pysoundfile -c conda-forge && \ + conda install pytorch=1.13.0 torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia && \ + conda install -c fvcore -c iopath -c conda-forge fvcore iopath && \ + conda install x264=='1!152.20180717' ffmpeg=4.0.2 -c conda-forge && \ + conda install pytorch3d -c pytorch3d + +COPY requirements.txt . +RUN pip install -r requirements.txt + +# Make run command use the new environment +SHELL ["conda", "run", "-n", "base", "/bin/bash", "-c"] + +COPY requirements.api.txt . +RUN pip install -r requirements.api.txt + +COPY avatar-api ./avatar-api +RUN pip install ./avatar-api --use-feature=in-tree-build && \ + rm -rf avatar-api + +COPY NeuralVoicePuppetry/neural-code ./neural-code + +COPY NeuralVoicePuppetry/neural_voice_backend_api ./neural_voice_backend_api + +COPY NeuralVoicePuppetry/gunicorn.conf.py . +CMD [ "conda", "run", "--no-capture-output", "-n", "base", "gunicorn", "-c", "gunicorn.conf.py", "--chdir", "./neural_voice_backend_api", "-k", "uvicorn.workers.UvicornWorker", "app:app" ] diff --git a/NeuralVoicePuppetry/.dockerignore b/NeuralVoicePuppetry/.dockerignore new file mode 100644 index 0000000..0a71680 --- /dev/null +++ b/NeuralVoicePuppetry/.dockerignore @@ -0,0 +1,5 @@ +.idea + +**__pycache__** + +**.git diff --git a/NeuralVoicePuppetry/.env b/NeuralVoicePuppetry/.env new file mode 100644 index 0000000..b17fd6d --- /dev/null +++ b/NeuralVoicePuppetry/.env @@ -0,0 +1,13 @@ +##### GENERAL ENVS ##### +GPU_SUPPORTED=True +GPU_ENABLED=True + +##### AUTH ENVS ##### +AUTH_ENABLED=False +REQUIRED_AUTH_ROLES=video-synthesis + +##### BACKEND ENVS ##### +FFMPEG_DOCKER=True # Python will use a different version of ffmpeg, therefore we need to redirect to /usr/bin/ffmpeg + +##### REACT ENVS ##### +REACT_APP_ENVIRONMENT=prod diff --git a/NeuralVoicePuppetry/AUTHORS.txt b/NeuralVoicePuppetry/AUTHORS.txt new file mode 100644 index 0000000..df870c1 --- /dev/null +++ b/NeuralVoicePuppetry/AUTHORS.txt @@ -0,0 +1,7 @@ +Contributions: + +* Alberto Pennino : Research and backend developement. +* Thomas Steinmann : Frontend and API developement. +* Marc Willhaus : Frontend developement. +* Clara Labrador-Fernandez : Supervision. + diff --git a/NeuralVoicePuppetry/LICENSE b/NeuralVoicePuppetry/LICENSE new file mode 100644 index 0000000..46aadda --- /dev/null +++ b/NeuralVoicePuppetry/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 ETH Zurich + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/NeuralVoicePuppetry/README.md b/NeuralVoicePuppetry/README.md new file mode 100644 index 0000000..545186c --- /dev/null +++ b/NeuralVoicePuppetry/README.md @@ -0,0 +1,199 @@ +# NeuralVoicePuppetry + +This repository contains the end-to-end "Neural Voice Puppetry" for the Audio-driven Video Synthesis project. + +You can find additional information on the method in the following [paper](https://arxiv.org/abs/1912.05566). + +![NeuralVoicePuppetry](media/pipeline.png "NeuralVoicePuppetry") + +## Requirements +Create a new environment: + +```bash +# Deepspeech environment +conda create -n deepspeech python=3.7 +conda activate deepspeech +conda install pysoundfile -c conda-forge +pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html +conda install x264=='1!152.20180717' ffmpeg=4.0.2 -c conda-forge +pip install -r requirements_deepspeech.txt +conda deactivate + +# pyenv environment +conda create -n pyenv python=3.9 +conda activate pyenv +conda install pysoundfile -c conda-forge +conda install pytorch=1.13.0 torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia +conda install -c fvcore -c iopath -c conda-forge fvcore iopath +conda install x264=='1!152.20180717' ffmpeg=4.0.2 -c conda-forge +conda install pytorch3d -c pytorch3d +pip install -r requirements.txt +``` + +## Data +If you plan on using this code with the already available and pre-trained moderators, you will only have to provide the audio data. + +Please follow these instructions on data quality: +- Audio + - Provide a recording of a person speaking (audio of any duration is accepted). + - The cleaner the audio signal the better: audio with background noise will result in unmatching lip-sync. + - Avoid recording multiple people talking: the model is unable to distinguish between multiple voice signals. +- Video + - Provide a video of your desired avatar character talking. + - Minimum video duration: 3 minutes. + - Longer videos will results in longer training time. + - The background is irrelevant. + +## Data structure + +Place your input data using the following structure: + +``` +input_data +│ +└───video +│ └─── NAME_VIDEO_FILE +│ │ └─ NAME_VIDEO_FILE.mp4 +│ |... +│ +└───audio +│ └─── NAME_AUDIO_FILE +│ | └─ NAME_AUDIO_FILE.mp3/wav/mp4 +│ |... +│ +``` + +Video files can only be mp4. Moreover for the time being input videos must have 25 fps. + +Input audio files can have the following formats: mp4, mp3 or wav. + +## Usage +The same command can be used for both training and inference. If the trained checkpoints for the requested avatar are not found, the code will launch the training procedure. + +Use the following call to launch the code: +```bash +bash full_pipeline_nvp.sh $AUDIO_NAME $VIDEO_NAME $DATAROOT +``` +Where: +- $AUDIO_NAME: name of the audio file (without extension). +- $VIDEO_NAME: name of the video file (without extension). This will also be the name of the Avatar. +- $DATAROOT: path to the data folder. + +## Train new Avatars +In order to train a new avatar follow this easy steps: + +1. Record a new video following the instructions above. \ + Eg: VIDEONAME = my_new_avatar +2. Move your file to your data folder inside the video subfolder. + +3. Choose any audio file. \ + Eg: AUDIONAME = my_audio_sample + +4. Start training (and inference) with the following command: + ```bash + bash full_pipeline_nvp.sh my_audio_sample my_new_avatar $DATAROOT + ``` + +5. Wait until it's finished (if training, the process will be long). +6. Enjoy your new Avatar! \ + You can now use the same avatar with any other audio file. + +## Run using pretrained model +In order to run the model on a pretrained (video) identity, you need the following data: +- `./NeuralRenderingNetwork/checkpoints/DynamicNeuralTextures-NAME_VIDEO_FILE` - this should contain the pretrained model +- `./output_data/features/NAME_VIDEO_FILE` - this should contain the DECA codedics, the original video frames, an h5 file and the tform.npy + +## Computational time +Estimated computational time : +* Preprocessing: approx 30 minutes for a 5 minutes video. Varies depending on the frame size. +* Audio2Expression Training: approx 5 minutes for a 5 minutes video. +* Audio2Expression Inference: approx 30 seconds for a 5 minutes video. +* Neural Rendering Training: apporx 8 hours for a 5 minutes video. Change number of epochs for longer videos to keep training time constant. +* Neural Rendering Inference: approx 15 minutes for a 5 minutes video. +* Postprocessing: approx 20 minutes for a 5 minutes video. + +## LICENSE + +This implementation NVP Pipeline is free and open source! All code in this repository is licensed under: + +* [MIT](LICENSE) License. + +This pipeline relies and is inspired by the following works, and therefore refers to their individual licenses: + +- [Neural Voice Puppetry](https://web.archive.org/web/20201113014501/https://github.com/JustusThies/NeuralVoicePuppetry): [license](https://gitlab.ethz.ch/mtc/video-synthesis/.NeuralVoicePuppetry/-/blob/0fc75ba2edfdfd5655984f1515bfe06de880d91d/LICENSE), internal path `/neural-code/Audio2ExpressionNet`, `/neural-code/NeuralRenderingNetwork`. +- [DECA](https://github.com/YadiraF/DECA): [license](https://github.com/YadiraF/DECA/blob/master/LICENSE), internal path `/neural-code/third/DECA`. +- [VOCA](https://github.com/TimoBolkart/voca): [license](https://voca.is.tue.mpg.de/license), used internally by DECA. +- [DeepSpeech](https://github.com/mozilla/DeepSpeech): [license](https://github.com/mozilla/DeepSpeech/blob/master/LICENSE), used by VOCA. +- [CycleGAN and Pix2Pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix): [license](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/LICENSE), used as skeleton for GAN training. + + +## Contact information + +Alberto Pennino: [alberto.pennino@inf.ethz.ch](alberto.pennino@inf.ethz.ch) + + +## Reproduce env from scratch + +1. Install [gcc](https://linuxize.com/post/how-to-install-gcc-compiler-on-ubuntu-18-04/) + +2. Disable [Nouveau kernel driver](https://linuxconfig.org/how-to-disable-nouveau-nvidia-driver-on-ubuntu-18-04-bionic-beaver-linux/) - always reboot + +3. Install [CUDA 11.1](https://developer.nvidia.com/cuda-11.1.0-download-archive?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1804&target_type=runfilelocal) + +Run the following: + +``` +$ wget https://developer.download.nvidia.com/compute/cuda/11.1.0/local_installers/cuda_11.1.0_455.23.05_linux.run +$ sudo sh cuda_11.1.0_455.23.05_linux.run +$ export PATH="/usr/local/cuda-11.1/bin:$PATH" export +$ LD_LIBRARY_PATH="/usr/local/cuda-11.1/lib64:$LD_LIBRARY_PATH" +``` + +4. Clone repo: git clone https://gitlab.ethz.ch/mtc/NeuralVoicePuppetry.git + +5. Install [Miniconda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html) + +6. Reproduce env + +``` +conda create -n NAME python=3.8 +conda activate NAME +conda install numpy +conda install -c anaconda scipy +conda install -c conda-forge opencv +conda install -c conda-forge tqdm +conda install -c conda-forge resampy +conda install scikit-image +conda install h5py +pip install --upgrade tensorflow +pip install python_speech_features +conda install -c conda-forge moviepy +conda install -c conda-forge pydub +conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch +conda install -c conda-forge librosa +conda install -c fvcore -c iopath -c conda-forge fvcore iopath +conda install -c bottler nvidiacub +pip install "git+https://github.com/facebookresearch/pytorch3d.git" +pip install face-alignment +conda install -c conda-forge dominate +pip install chumpy +conda install -c conda-forge progressbar2 +pip install flask +chmod a+x full_pipeline_nvp.sh +pip install gunicorn +``` + + + + + diff --git a/NeuralVoicePuppetry/gunicorn.conf.py b/NeuralVoicePuppetry/gunicorn.conf.py new file mode 100644 index 0000000..9bc8d9c --- /dev/null +++ b/NeuralVoicePuppetry/gunicorn.conf.py @@ -0,0 +1,19 @@ +# https://github.com/benoitc/gunicorn/blob/master/examples/example_config.py + +# Bind & deployment + +bind = '0.0.0.0:5000' +reload = False + +# Connections +# The dashboard backend should be capable of supporting multiple workers, +# however initialization is currently an issue when running in multiple threads. +workers = 1 # if DashboardConfig.debug else 4 +threads = 4 +backlog = 64 + +timeout = 300 +TIMEOUT = 300 + +# Logging +loglevel = 'info' diff --git a/NeuralVoicePuppetry/media/audio2expression_net.jpg b/NeuralVoicePuppetry/media/audio2expression_net.jpg new file mode 100644 index 0000000..f5298b3 --- /dev/null +++ b/NeuralVoicePuppetry/media/audio2expression_net.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:27b5086adab995c64b451d19623a436a9c0b5724e98d49751aea256eb85e4aff +size 236375 diff 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b/NeuralVoicePuppetry/media/teaser.jpg new file mode 100644 index 0000000..1d2108b --- /dev/null +++ b/NeuralVoicePuppetry/media/teaser.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f085e8851a012b69e3aba2d69196d2e69a55b1daec3f5215ab6f8a4794a4b4db +size 543929 diff --git a/NeuralVoicePuppetry/neural-code/.gitignore b/NeuralVoicePuppetry/neural-code/.gitignore new file mode 100644 index 0000000..2f0d06f --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/.gitignore @@ -0,0 +1 @@ +output_data \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/BaselModel/__init__.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/BaselModel/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/BaselModel/basel_model.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/BaselModel/basel_model.py new file mode 100644 index 0000000..26a50c0 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/BaselModel/basel_model.py @@ -0,0 +1,94 @@ +import os +import torch +import torch.nn as nn +import torchvision.transforms as transforms +import numpy as np + +######################### +N_EXPRESSIONS=76 # <<<<<< NEEDS TO BE SPECIFIED ACCORDING TO THE USED FACE MODEL +# N_EXPRESSIONS=299 +######################### + +#import soft_renderer as sr +# +#class MorphableModel(nn.Module): +# def __init__(self, filename_average=''): +# super(MorphableModel, self).__init__() +# +# print('Load Morphable Model (Basel)') +# +# #filename_mesh = os.path.join(opt.dataroot, opt.phase + '/average_model.obj') +# filename_mesh = filename_average +# if filename_average=='': +# print('use default identity') +# filename_mesh = './BaselModel/average.obj' +# mesh = sr.Mesh.from_obj(filename_mesh, normalization=False, load_texture=True) +# self.average_vertices = mesh.vertices[0] +# self.faces = mesh.faces[0] +# self.average_vertices = self.average_vertices[None, :, :] # [num_vertices, XYZ] -> [batch_size=1, num_vertices, XYZ] +# self.faces = self.faces[None, :, :] # [num_faces, 3] -> [batch_size=1, num_faces, 3] +# self.textures = mesh.textures +# +# self.num_vertices = self.average_vertices.shape[1] +# self.num_faces = self.faces.shape[1] +# print('vertices:', self.average_vertices.shape) +# print('faces:', self.faces.shape) +# +# ## basis function +# self.expression_basis = np.memmap('./BaselModel/ExpressionBasis.matrix', dtype='float32', mode='r').__array__()[1:] # first entry is the size +# self.expression_basis = np.resize(self.expression_basis, (N_EXPRESSIONS, self.num_vertices, 4))[:,:,0:3] +# self.expression_basis = torch.tensor(self.expression_basis.astype(np.float32)).cuda() # N_EXPRESSIONS x num_vertices x 3 +# self.expression_basis = torch.transpose(self.expression_basis,0,2) # transpose for matmul +# print('expression_basis', self.expression_basis.shape) +# +# # default expression +# zeroExpr = torch.zeros(1, N_EXPRESSIONS, dtype=torch.float32).cuda() +# self.morph(zeroExpr) +# +# +# def save_model_to_obj_file(self, filename, mask=None): +# faces_cpu = self.faces.detach().cpu().numpy() +# vertices_cpu = self.vertices.detach().cpu().numpy() +# +# mask_cpu = None +# if not type(mask) == type(None): +# mask_cpu = mask.detach().cpu().numpy() +# +# f = open(filename, 'w') +# if type(mask) == type(None): +# for i in range(0, self.num_vertices): +# f.write('v ' + str(vertices_cpu[0, i, 0]) + ' ' + str(vertices_cpu[0, i, 1]) + ' ' + str(vertices_cpu[0, i, 2]) + '\n') +# else: +# for i in range(0, self.num_vertices): +# f.write('v ' + str(vertices_cpu[0, i, 0]) + ' ' + str(vertices_cpu[0, i, 1]) + ' ' + str(vertices_cpu[0, i, 2]) + ' ' + str(mask_cpu[i]) + ' ' + str(mask_cpu[i]) + ' ' + str(mask_cpu[i]) + ' 1'+ '\n') +# +# for i in range(0, self.num_faces): +# f.write('f ' + str(faces_cpu[0, i, 0]+1) + '// ' + str(faces_cpu[0, i, 1]+1) + '// ' + str(faces_cpu[0, i, 2]+1) + '//\n') +# +# f.close() +# +# def compute_expression_delta(self, expressions): +# return torch.transpose(torch.matmul(self.expression_basis, torch.transpose(expressions, 0,1)), 0, 2) # note that matmul wants to have this order: (a x b x c) x (c x m) => (a x b x m) +# +# def morph(self, expressions): +# self.vertices = self.average_vertices + self.compute_expression_delta(expressions) +# return self.vertices +# +# +# +# def LoadMask(self, filename=''): +# if filename=='': +# print('use default mask') +# filename = './BaselModel/mask/defaultMask_mouth.obj' +# +# mask = np.zeros(self.num_vertices) +# file = open(filename, 'r') +# i=0 +# for line in file: +# if line[0] == 'v': +# floats = [float(x) for x in line[1:].split()] +# if floats[3] == 1.0 and floats[4] == 0.0 and floats[5] == 0.0: +# mask[i] = 1.0 +# i += 1 +# file.close() +# return torch.tensor(mask.astype(np.float32)).cuda() \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/ReadMe.txt b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/ReadMe.txt new file mode 100644 index 0000000..9f5dd53 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/ReadMe.txt @@ -0,0 +1,21 @@ +This code runs the inference given a source audio and a target video that has been tracked. +The process is started using "transfer.sh" (where you can also specify the target sequences -> TARGET_ACTOR_LIST). +In the "transfer.py" you can specify the source sequences (search for "source_actors"). +Make sure that you specify the dimensions of your blendshape model in "BaselModel/basel_model.py" (-> N_EXPRESSIONS). + +Note that you have to extract the deepspeech features in a preprocessing step. +In the datasets folder you will find an example how the data should look like. +The deepspeech features are provided as npy files, while for the target sequence you also have to provide + the expressions (visually tracked blendshape coefficients). +If you have a different data format you need to adapt the data loader (data/face_dataset.py). + +Once you have the prepared data you can run the script. +It will optimize for the mapping from the audio-expression space to your blendshape model space. +The mapping is stored in the "mappings" folder (note: that it caches the mappings there and reuses it for the next run. + If you change something, you need to delete this cache). +The final output is stored in the "datasets/TRANSFERS" folder as a list of estimated expressions using the + source audio features. + +Given these expressions you need to generate new uv maps for the target video using the rigid pose + of the target video (only replacing the expressions). +These can then be used in the deferred neural rendering framework. \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/__init__.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/audio2expr_with_map.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/audio2expr_with_map.py new file mode 100644 index 0000000..fd323c2 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/audio2expr_with_map.py @@ -0,0 +1,107 @@ +import os +import os.path +from options.transfer_options import Audio2ExprOptions +from data import CreateDataLoader +from data.face_dataset import FaceDataset +from data.audio_dataset import AudioDataset +from models import create_model +from util.visualizer import save_images +from util import html +import torch +import torch.nn as nn +import torchvision.transforms as transforms +import numpy as np +from PIL import Image +import time +import random +import progressbar +import copy +from shutil import copyfile + +from BaselModel.basel_model import * + + +def load_model(opt): + opt.output_audio_expressions = True + opt.nTrainObjects = 116 + + print('#train objects = %d' % opt.nTrainObjects) + + print('>>> create model <<<') + model = create_model(opt) + print('>>> setup model <<<') + model.setup(opt) + + return model + +def load_source_sequence(opt): + opt_source = copy.copy(opt) # create a clone + opt_source.dataroot = opt.source_actor # overwrite root directory + print(opt_source.dataroot) + opt_source.dataset_mode = 'audio' + opt_source.phase = 'train' + + dataset_source = AudioDataset() + + dataset_source.initialize(opt_source) + + dataloader = torch.utils.data.DataLoader( + dataset_source, + batch_size=opt.batch_size, + shuffle=not opt.serial_batches, + num_workers=int(opt.num_threads)) + + return dataset_source, dataloader + + +if __name__ == '__main__': + # read options + opt = Audio2ExprOptions().parse() + + # load model + model = load_model(opt) + print('model version:', opt.name) + + if opt.use_mapping: + # read mapping + mapping_fn = opt.mapping_path + + # load mapping from file + map_cpu = np.load(mapping_fn + '.npy') + mapping = torch.tensor(map_cpu.astype(np.float32)).cuda() + print('loaded mapping from file', mapping.shape) + + # make outdir + source_name = opt.source_actor.split("/")[-1] + out_dir = opt.out_dir + source_name + os.makedirs(out_dir, exist_ok=True) + audio2expr_dir = os.path.join(out_dir, 'audio2expr') + os.makedirs(audio2expr_dir, exist_ok=True) + if opt.use_mapping: + expr_dir = os.path.join(out_dir, 'expr') + os.makedirs(expr_dir, exist_ok=True) + + # read source sequence + dataset_source, data_loader_source = load_source_sequence(opt) + dataset_source_size = len(dataset_source) + print('#source_actor frames = %d' % dataset_source_size) + + expression_multiplier = 1.0 # default + + # run over data + with progressbar.ProgressBar(max_value=len(dataset_source)) as bar: + for i, data in enumerate(data_loader_source): + bar.update(i) + model.set_input(data) + model.test() + audio_expression = model.fake_expressions.data[0, :, 0] + + if opt.use_mapping: + expression = expression_multiplier * 10.0 * torch.matmul(mapping, audio_expression) + expression = expression[None, :] + np.save(os.path.join(expr_dir, f'expr_{i}.npy'), expression.cpu().numpy()) + + audio_expression = audio_expression[None, :] + np.save(os.path.join(audio2expr_dir, f'audioexpr_{i}.npy'), audio_expression.cpu().numpy()) + + exit() \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/checkpoints/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead/10_netG.pth b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/checkpoints/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead/10_netG.pth new file mode 100644 index 0000000..86f917f --- 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b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/checkpoints/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead/loss_log.txt @@ -0,0 +1,7569 @@ +================ Training Loss (Tue Nov 5 11:55:26 2019) ================ +(epoch: 1, iters: 2000, time: 0.527, data: 0.543) G_L1: 20.533 G_L1_ABSOLUTE: 6.121 G_L1_RELATIVE: 14.412 G_Regularizer: 0.000 validation_error: 26.195 +(epoch: 1, iters: 4000, time: 0.519, data: 0.000) G_L1: 22.297 G_L1_ABSOLUTE: 7.213 G_L1_RELATIVE: 15.085 G_Regularizer: 0.000 validation_error: 26.295 +(epoch: 1, iters: 6000, time: 0.523, data: 0.000) G_L1: 26.460 G_L1_ABSOLUTE: 7.193 G_L1_RELATIVE: 19.267 G_Regularizer: 0.000 validation_error: 26.111 +(epoch: 1, iters: 8000, time: 0.525, data: 0.000) G_L1: 25.699 G_L1_ABSOLUTE: 6.721 G_L1_RELATIVE: 18.978 G_Regularizer: 0.000 validation_error: 25.961 +(epoch: 1, iters: 10000, time: 0.522, data: 0.000) G_L1: 25.790 G_L1_ABSOLUTE: 6.689 G_L1_RELATIVE: 19.102 G_Regularizer: 0.000 validation_error: 26.259 +(epoch: 1, iters: 12000, time: 0.524, data: 0.000) G_L1: 21.361 G_L1_ABSOLUTE: 6.775 G_L1_RELATIVE: 14.586 G_Regularizer: 0.000 validation_error: 25.803 +(epoch: 1, iters: 14000, time: 0.524, data: 0.000) G_L1: 26.366 G_L1_ABSOLUTE: 6.800 G_L1_RELATIVE: 19.566 G_Regularizer: 0.000 validation_error: 26.035 +(epoch: 1, iters: 16000, time: 0.521, data: 0.000) G_L1: 22.563 G_L1_ABSOLUTE: 6.146 G_L1_RELATIVE: 16.416 G_Regularizer: 0.000 validation_error: 25.958 +(epoch: 1, iters: 18000, time: 0.521, data: 0.000) G_L1: 23.015 G_L1_ABSOLUTE: 6.598 G_L1_RELATIVE: 16.417 G_Regularizer: 0.000 validation_error: 26.145 +(epoch: 1, iters: 20000, time: 0.521, data: 0.000) G_L1: 27.604 G_L1_ABSOLUTE: 6.411 G_L1_RELATIVE: 21.193 G_Regularizer: 0.000 validation_error: 26.835 +(epoch: 1, iters: 22000, time: 0.524, data: 0.000) G_L1: 23.692 G_L1_ABSOLUTE: 6.645 G_L1_RELATIVE: 17.046 G_Regularizer: 0.000 validation_error: 26.400 +(epoch: 1, iters: 24000, time: 0.525, data: 0.000) G_L1: 31.754 G_L1_ABSOLUTE: 6.221 G_L1_RELATIVE: 25.533 G_Regularizer: 0.000 validation_error: 25.967 +(epoch: 1, iters: 26000, time: 0.534, data: 0.000) G_L1: 20.770 G_L1_ABSOLUTE: 6.981 G_L1_RELATIVE: 13.789 G_Regularizer: 0.000 validation_error: 26.519 +(epoch: 1, iters: 28000, time: 0.521, data: 0.000) G_L1: 21.386 G_L1_ABSOLUTE: 5.921 G_L1_RELATIVE: 15.465 G_Regularizer: 0.000 validation_error: 26.218 +(epoch: 1, iters: 30000, time: 0.527, data: 0.000) G_L1: 20.580 G_L1_ABSOLUTE: 5.905 G_L1_RELATIVE: 14.675 G_Regularizer: 0.000 validation_error: 26.542 +(epoch: 1, iters: 32000, time: 0.528, data: 0.000) G_L1: 22.306 G_L1_ABSOLUTE: 5.767 G_L1_RELATIVE: 16.538 G_Regularizer: 0.000 validation_error: 25.869 +(epoch: 1, iters: 34000, time: 0.530, data: 0.000) G_L1: 27.841 G_L1_ABSOLUTE: 6.845 G_L1_RELATIVE: 20.997 G_Regularizer: 0.000 validation_error: 25.692 +(epoch: 1, iters: 36000, time: 0.528, data: 0.000) G_L1: 24.150 G_L1_ABSOLUTE: 6.119 G_L1_RELATIVE: 18.030 G_Regularizer: 0.000 validation_error: 25.631 +(epoch: 1, iters: 38000, time: 0.530, data: 0.000) G_L1: 28.152 G_L1_ABSOLUTE: 5.918 G_L1_RELATIVE: 22.234 G_Regularizer: 0.000 validation_error: 26.156 +(epoch: 1, iters: 40000, time: 0.525, data: 0.000) G_L1: 25.895 G_L1_ABSOLUTE: 5.195 G_L1_RELATIVE: 20.700 G_Regularizer: 0.000 validation_error: 25.479 +(epoch: 1, iters: 42000, time: 0.530, data: 0.000) G_L1: 23.754 G_L1_ABSOLUTE: 5.785 G_L1_RELATIVE: 17.969 G_Regularizer: 0.000 validation_error: 25.697 +(epoch: 1, iters: 44000, time: 0.524, data: 0.000) G_L1: 23.181 G_L1_ABSOLUTE: 5.283 G_L1_RELATIVE: 17.897 G_Regularizer: 0.000 validation_error: 25.307 +(epoch: 1, iters: 46000, time: 0.528, data: 0.000) G_L1: 23.469 G_L1_ABSOLUTE: 6.536 G_L1_RELATIVE: 16.933 G_Regularizer: 0.000 validation_error: 25.563 +(epoch: 1, iters: 48000, time: 0.532, data: 0.000) G_L1: 24.980 G_L1_ABSOLUTE: 6.330 G_L1_RELATIVE: 18.649 G_Regularizer: 0.000 validation_error: 25.355 +(epoch: 1, iters: 50000, time: 0.528, data: 0.000) G_L1: 22.681 G_L1_ABSOLUTE: 5.501 G_L1_RELATIVE: 17.180 G_Regularizer: 0.000 validation_error: 25.428 +(epoch: 1, iters: 52000, time: 0.528, data: 0.000) G_L1: 18.113 G_L1_ABSOLUTE: 5.679 G_L1_RELATIVE: 12.434 G_Regularizer: 0.000 validation_error: 25.698 +(epoch: 1, iters: 54000, time: 0.527, data: 0.000) G_L1: 19.100 G_L1_ABSOLUTE: 5.162 G_L1_RELATIVE: 13.939 G_Regularizer: 0.000 validation_error: 25.779 +(epoch: 1, iters: 56000, time: 0.522, data: 0.000) G_L1: 24.206 G_L1_ABSOLUTE: 5.879 G_L1_RELATIVE: 18.327 G_Regularizer: 0.000 validation_error: 26.205 +(epoch: 1, iters: 58000, time: 0.532, data: 0.000) G_L1: 27.497 G_L1_ABSOLUTE: 5.718 G_L1_RELATIVE: 21.779 G_Regularizer: 0.000 validation_error: 25.873 +(epoch: 1, iters: 60000, time: 0.528, data: 0.000) G_L1: 23.159 G_L1_ABSOLUTE: 6.134 G_L1_RELATIVE: 17.025 G_Regularizer: 0.000 validation_error: 26.379 +(epoch: 1, iters: 62000, time: 0.533, data: 0.000) G_L1: 19.434 G_L1_ABSOLUTE: 4.600 G_L1_RELATIVE: 14.834 G_Regularizer: 0.000 validation_error: 25.891 +(epoch: 1, iters: 64000, time: 0.525, data: 0.001) G_L1: 23.563 G_L1_ABSOLUTE: 5.571 G_L1_RELATIVE: 17.992 G_Regularizer: 0.000 validation_error: 26.009 +(epoch: 1, iters: 66000, time: 0.531, data: 0.000) G_L1: 20.647 G_L1_ABSOLUTE: 5.063 G_L1_RELATIVE: 15.585 G_Regularizer: 0.000 validation_error: 25.800 +(epoch: 1, iters: 68000, time: 0.532, data: 0.000) G_L1: 24.282 G_L1_ABSOLUTE: 5.969 G_L1_RELATIVE: 18.312 G_Regularizer: 0.000 validation_error: 25.929 +(epoch: 1, iters: 70000, time: 0.530, data: 0.000) G_L1: 23.425 G_L1_ABSOLUTE: 5.420 G_L1_RELATIVE: 18.005 G_Regularizer: 0.000 validation_error: 26.189 +(epoch: 1, iters: 72000, time: 0.523, data: 0.000) G_L1: 19.160 G_L1_ABSOLUTE: 5.065 G_L1_RELATIVE: 14.095 G_Regularizer: 0.000 validation_error: 25.471 +(epoch: 1, iters: 74000, time: 0.526, data: 0.000) G_L1: 21.785 G_L1_ABSOLUTE: 5.365 G_L1_RELATIVE: 16.420 G_Regularizer: 0.000 validation_error: 25.427 +(epoch: 1, iters: 76000, time: 0.531, data: 0.000) G_L1: 24.412 G_L1_ABSOLUTE: 4.703 G_L1_RELATIVE: 19.709 G_Regularizer: 0.000 validation_error: 25.933 +(epoch: 1, iters: 78000, time: 0.523, data: 0.000) G_L1: 20.388 G_L1_ABSOLUTE: 4.958 G_L1_RELATIVE: 15.430 G_Regularizer: 0.000 validation_error: 25.459 +(epoch: 1, iters: 80000, time: 0.527, data: 0.000) G_L1: 24.715 G_L1_ABSOLUTE: 4.889 G_L1_RELATIVE: 19.826 G_Regularizer: 0.000 validation_error: 25.478 +(epoch: 1, iters: 82000, time: 0.531, data: 0.000) G_L1: 17.904 G_L1_ABSOLUTE: 4.281 G_L1_RELATIVE: 13.622 G_Regularizer: 0.000 validation_error: 25.462 +(epoch: 1, iters: 84000, time: 0.525, data: 0.000) G_L1: 23.077 G_L1_ABSOLUTE: 4.637 G_L1_RELATIVE: 18.440 G_Regularizer: 0.000 validation_error: 25.672 +(epoch: 1, iters: 86000, time: 0.538, data: 0.000) G_L1: 25.415 G_L1_ABSOLUTE: 5.170 G_L1_RELATIVE: 20.246 G_Regularizer: 0.000 validation_error: 26.016 +(epoch: 1, iters: 88000, time: 0.525, data: 0.000) G_L1: 21.505 G_L1_ABSOLUTE: 4.944 G_L1_RELATIVE: 16.561 G_Regularizer: 0.000 validation_error: 25.608 +(epoch: 1, iters: 90000, time: 0.522, data: 0.000) G_L1: 22.340 G_L1_ABSOLUTE: 4.808 G_L1_RELATIVE: 17.533 G_Regularizer: 0.000 validation_error: 25.641 +(epoch: 1, iters: 92000, time: 0.528, data: 0.000) G_L1: 17.184 G_L1_ABSOLUTE: 5.004 G_L1_RELATIVE: 12.179 G_Regularizer: 0.000 validation_error: 25.229 +(epoch: 1, iters: 94000, time: 0.526, data: 0.000) G_L1: 22.259 G_L1_ABSOLUTE: 4.649 G_L1_RELATIVE: 17.610 G_Regularizer: 0.000 validation_error: 25.663 +(epoch: 1, iters: 96000, time: 0.531, data: 0.000) G_L1: 20.482 G_L1_ABSOLUTE: 4.829 G_L1_RELATIVE: 15.653 G_Regularizer: 0.000 validation_error: 25.542 +(epoch: 1, iters: 98000, time: 0.526, data: 0.000) G_L1: 20.044 G_L1_ABSOLUTE: 4.605 G_L1_RELATIVE: 15.439 G_Regularizer: 0.000 validation_error: 25.474 +(epoch: 1, iters: 100000, time: 0.524, data: 0.000) G_L1: 24.498 G_L1_ABSOLUTE: 4.666 G_L1_RELATIVE: 19.831 G_Regularizer: 0.000 validation_error: 25.764 +(epoch: 1, iters: 102000, time: 0.529, data: 0.000) G_L1: 28.585 G_L1_ABSOLUTE: 5.306 G_L1_RELATIVE: 23.278 G_Regularizer: 0.000 validation_error: 25.472 +(epoch: 1, iters: 104000, time: 0.525, data: 0.000) G_L1: 22.490 G_L1_ABSOLUTE: 4.104 G_L1_RELATIVE: 18.385 G_Regularizer: 0.000 validation_error: 25.019 +(epoch: 1, iters: 106000, time: 0.530, data: 0.000) G_L1: 23.004 G_L1_ABSOLUTE: 5.039 G_L1_RELATIVE: 17.965 G_Regularizer: 0.000 validation_error: 25.303 +(epoch: 1, iters: 108000, time: 0.530, data: 0.000) G_L1: 27.354 G_L1_ABSOLUTE: 4.710 G_L1_RELATIVE: 22.644 G_Regularizer: 0.000 validation_error: 25.447 +(epoch: 1, iters: 110000, time: 0.523, data: 0.000) G_L1: 21.328 G_L1_ABSOLUTE: 4.957 G_L1_RELATIVE: 16.371 G_Regularizer: 0.000 validation_error: 25.714 +(epoch: 1, iters: 112000, time: 0.525, data: 0.000) G_L1: 19.985 G_L1_ABSOLUTE: 4.154 G_L1_RELATIVE: 15.831 G_Regularizer: 0.000 validation_error: 25.122 +(epoch: 1, iters: 114000, time: 0.529, data: 0.000) G_L1: 22.220 G_L1_ABSOLUTE: 4.470 G_L1_RELATIVE: 17.750 G_Regularizer: 0.000 validation_error: 25.690 +(epoch: 1, iters: 116000, time: 0.527, data: 0.000) G_L1: 20.264 G_L1_ABSOLUTE: 3.744 G_L1_RELATIVE: 16.520 G_Regularizer: 0.000 validation_error: 25.616 +(epoch: 1, iters: 118000, time: 0.524, data: 0.000) G_L1: 23.164 G_L1_ABSOLUTE: 4.250 G_L1_RELATIVE: 18.915 G_Regularizer: 0.000 validation_error: 25.461 +(epoch: 1, iters: 120000, time: 0.535, data: 0.000) G_L1: 24.064 G_L1_ABSOLUTE: 4.503 G_L1_RELATIVE: 19.561 G_Regularizer: 0.000 validation_error: 25.242 +(epoch: 1, iters: 122000, time: 0.524, data: 0.000) G_L1: 20.198 G_L1_ABSOLUTE: 3.989 G_L1_RELATIVE: 16.209 G_Regularizer: 0.000 validation_error: 25.856 +(epoch: 1, iters: 124000, time: 0.535, data: 0.000) G_L1: 22.604 G_L1_ABSOLUTE: 4.396 G_L1_RELATIVE: 18.208 G_Regularizer: 0.000 validation_error: 25.615 +(epoch: 1, iters: 126000, time: 0.532, data: 0.000) G_L1: 17.857 G_L1_ABSOLUTE: 3.804 G_L1_RELATIVE: 14.053 G_Regularizer: 0.000 validation_error: 24.820 +(epoch: 1, iters: 128000, time: 0.527, data: 0.000) G_L1: 22.076 G_L1_ABSOLUTE: 4.793 G_L1_RELATIVE: 17.284 G_Regularizer: 0.000 validation_error: 25.246 +(epoch: 1, iters: 130000, time: 0.526, data: 0.000) G_L1: 22.058 G_L1_ABSOLUTE: 4.431 G_L1_RELATIVE: 17.628 G_Regularizer: 0.000 validation_error: 24.596 +(epoch: 1, iters: 132000, time: 0.524, data: 0.000) G_L1: 25.289 G_L1_ABSOLUTE: 4.253 G_L1_RELATIVE: 21.036 G_Regularizer: 0.000 validation_error: 25.387 +(epoch: 1, iters: 134000, time: 0.527, data: 0.000) G_L1: 21.595 G_L1_ABSOLUTE: 3.987 G_L1_RELATIVE: 17.607 G_Regularizer: 0.000 validation_error: 25.077 +(epoch: 1, iters: 136000, time: 0.525, data: 0.000) G_L1: 21.235 G_L1_ABSOLUTE: 4.392 G_L1_RELATIVE: 16.843 G_Regularizer: 0.000 validation_error: 24.855 +(epoch: 1, iters: 138000, time: 0.534, data: 0.000) G_L1: 22.488 G_L1_ABSOLUTE: 4.404 G_L1_RELATIVE: 18.084 G_Regularizer: 0.000 validation_error: 25.866 +(epoch: 1, iters: 140000, time: 0.528, data: 0.000) G_L1: 23.761 G_L1_ABSOLUTE: 4.339 G_L1_RELATIVE: 19.422 G_Regularizer: 0.000 validation_error: 25.059 +(epoch: 1, iters: 142000, time: 0.527, data: 0.001) G_L1: 18.897 G_L1_ABSOLUTE: 4.298 G_L1_RELATIVE: 14.599 G_Regularizer: 0.000 validation_error: 25.060 +(epoch: 1, iters: 144000, time: 0.529, data: 0.000) G_L1: 18.984 G_L1_ABSOLUTE: 4.345 G_L1_RELATIVE: 14.639 G_Regularizer: 0.000 validation_error: 25.088 +(epoch: 1, iters: 146000, time: 0.531, data: 0.000) G_L1: 17.956 G_L1_ABSOLUTE: 4.423 G_L1_RELATIVE: 13.533 G_Regularizer: 0.000 validation_error: 25.111 +(epoch: 1, iters: 148000, time: 0.530, data: 0.000) G_L1: 19.417 G_L1_ABSOLUTE: 4.514 G_L1_RELATIVE: 14.903 G_Regularizer: 0.000 validation_error: 24.421 +(epoch: 1, iters: 150000, time: 0.525, data: 0.000) G_L1: 19.327 G_L1_ABSOLUTE: 4.618 G_L1_RELATIVE: 14.709 G_Regularizer: 0.000 validation_error: 24.444 +(epoch: 1, iters: 152000, time: 0.529, data: 0.000) G_L1: 22.617 G_L1_ABSOLUTE: 4.578 G_L1_RELATIVE: 18.040 G_Regularizer: 0.000 validation_error: 24.705 +(epoch: 1, iters: 154000, time: 0.524, data: 0.000) G_L1: 24.957 G_L1_ABSOLUTE: 3.963 G_L1_RELATIVE: 20.994 G_Regularizer: 0.000 validation_error: 24.430 +(epoch: 1, iters: 156000, time: 0.521, data: 0.000) G_L1: 20.307 G_L1_ABSOLUTE: 3.911 G_L1_RELATIVE: 16.396 G_Regularizer: 0.000 validation_error: 24.632 +(epoch: 1, iters: 158000, time: 0.527, data: 0.000) G_L1: 18.463 G_L1_ABSOLUTE: 4.156 G_L1_RELATIVE: 14.307 G_Regularizer: 0.000 validation_error: 24.343 +(epoch: 1, iters: 160000, time: 0.531, data: 0.000) G_L1: 20.475 G_L1_ABSOLUTE: 4.195 G_L1_RELATIVE: 16.280 G_Regularizer: 0.000 validation_error: 24.677 +(epoch: 1, iters: 162000, time: 0.525, data: 0.000) G_L1: 20.776 G_L1_ABSOLUTE: 4.367 G_L1_RELATIVE: 16.409 G_Regularizer: 0.000 validation_error: 24.303 +(epoch: 1, iters: 164000, time: 0.531, data: 0.000) G_L1: 18.067 G_L1_ABSOLUTE: 4.091 G_L1_RELATIVE: 13.976 G_Regularizer: 0.000 validation_error: 24.560 +(epoch: 1, iters: 166000, time: 0.526, data: 0.000) G_L1: 22.843 G_L1_ABSOLUTE: 4.290 G_L1_RELATIVE: 18.553 G_Regularizer: 0.000 validation_error: 24.721 +(epoch: 1, iters: 168000, time: 0.524, data: 0.000) G_L1: 20.924 G_L1_ABSOLUTE: 3.742 G_L1_RELATIVE: 17.182 G_Regularizer: 0.000 validation_error: 24.209 +(epoch: 1, iters: 170000, time: 0.526, data: 0.000) G_L1: 17.669 G_L1_ABSOLUTE: 4.038 G_L1_RELATIVE: 13.631 G_Regularizer: 0.000 validation_error: 24.377 +(epoch: 1, iters: 172000, time: 0.526, data: 0.000) G_L1: 20.277 G_L1_ABSOLUTE: 4.201 G_L1_RELATIVE: 16.076 G_Regularizer: 0.000 validation_error: 24.538 +(epoch: 1, iters: 174000, time: 0.527, data: 0.000) G_L1: 22.542 G_L1_ABSOLUTE: 3.949 G_L1_RELATIVE: 18.593 G_Regularizer: 0.000 validation_error: 24.260 +(epoch: 1, iters: 176000, time: 0.529, data: 0.000) G_L1: 21.529 G_L1_ABSOLUTE: 3.585 G_L1_RELATIVE: 17.944 G_Regularizer: 0.000 validation_error: 24.700 +(epoch: 1, iters: 178000, time: 0.529, data: 0.000) G_L1: 21.695 G_L1_ABSOLUTE: 4.256 G_L1_RELATIVE: 17.439 G_Regularizer: 0.000 validation_error: 24.722 +(epoch: 1, iters: 180000, time: 0.528, data: 0.000) G_L1: 18.981 G_L1_ABSOLUTE: 3.781 G_L1_RELATIVE: 15.200 G_Regularizer: 0.000 validation_error: 24.588 +(epoch: 1, iters: 182000, time: 0.531, data: 0.000) G_L1: 25.291 G_L1_ABSOLUTE: 4.210 G_L1_RELATIVE: 21.081 G_Regularizer: 0.000 validation_error: 24.406 +(epoch: 1, iters: 184000, time: 0.522, data: 0.000) G_L1: 19.536 G_L1_ABSOLUTE: 3.747 G_L1_RELATIVE: 15.789 G_Regularizer: 0.000 validation_error: 24.321 +(epoch: 1, iters: 186000, time: 0.525, data: 0.000) G_L1: 20.605 G_L1_ABSOLUTE: 4.199 G_L1_RELATIVE: 16.406 G_Regularizer: 0.000 validation_error: 24.567 +(epoch: 1, iters: 188000, time: 0.524, data: 0.000) G_L1: 18.809 G_L1_ABSOLUTE: 3.932 G_L1_RELATIVE: 14.877 G_Regularizer: 0.000 validation_error: 24.504 +(epoch: 1, iters: 190000, time: 0.527, data: 0.000) G_L1: 18.435 G_L1_ABSOLUTE: 4.072 G_L1_RELATIVE: 14.363 G_Regularizer: 0.000 validation_error: 24.471 +(epoch: 1, iters: 192000, time: 0.527, data: 0.000) G_L1: 24.383 G_L1_ABSOLUTE: 3.989 G_L1_RELATIVE: 20.394 G_Regularizer: 0.000 validation_error: 24.320 +(epoch: 1, iters: 194000, time: 0.524, data: 0.000) G_L1: 18.557 G_L1_ABSOLUTE: 4.491 G_L1_RELATIVE: 14.066 G_Regularizer: 0.000 validation_error: 24.349 +(epoch: 1, iters: 196000, time: 0.527, data: 0.000) G_L1: 22.087 G_L1_ABSOLUTE: 4.181 G_L1_RELATIVE: 17.906 G_Regularizer: 0.000 validation_error: 24.555 +(epoch: 1, iters: 198000, time: 0.532, data: 0.001) G_L1: 21.366 G_L1_ABSOLUTE: 3.752 G_L1_RELATIVE: 17.614 G_Regularizer: 0.000 validation_error: 24.559 +(epoch: 1, iters: 200000, time: 0.524, data: 0.000) G_L1: 19.948 G_L1_ABSOLUTE: 4.048 G_L1_RELATIVE: 15.900 G_Regularizer: 0.000 validation_error: 24.242 +(epoch: 1, iters: 202000, time: 0.530, data: 0.000) G_L1: 20.864 G_L1_ABSOLUTE: 3.799 G_L1_RELATIVE: 17.066 G_Regularizer: 0.000 validation_error: 24.322 +(epoch: 1, iters: 204000, time: 0.531, data: 0.000) G_L1: 20.734 G_L1_ABSOLUTE: 3.939 G_L1_RELATIVE: 16.795 G_Regularizer: 0.000 validation_error: 23.832 +(epoch: 1, iters: 206000, time: 0.527, data: 0.000) G_L1: 17.837 G_L1_ABSOLUTE: 4.024 G_L1_RELATIVE: 13.813 G_Regularizer: 0.000 validation_error: 24.326 +(epoch: 1, iters: 208000, time: 0.523, data: 0.000) G_L1: 21.147 G_L1_ABSOLUTE: 3.896 G_L1_RELATIVE: 17.251 G_Regularizer: 0.000 validation_error: 24.181 +(epoch: 1, iters: 210000, time: 0.527, data: 0.000) G_L1: 18.972 G_L1_ABSOLUTE: 3.557 G_L1_RELATIVE: 15.415 G_Regularizer: 0.000 validation_error: 24.457 +(epoch: 1, iters: 212000, time: 0.524, data: 0.000) G_L1: 21.846 G_L1_ABSOLUTE: 3.925 G_L1_RELATIVE: 17.921 G_Regularizer: 0.000 validation_error: 24.408 +(epoch: 1, iters: 214000, time: 0.528, data: 0.000) G_L1: 19.201 G_L1_ABSOLUTE: 3.623 G_L1_RELATIVE: 15.577 G_Regularizer: 0.000 validation_error: 24.239 +(epoch: 1, iters: 216000, time: 0.531, data: 0.000) G_L1: 17.034 G_L1_ABSOLUTE: 4.041 G_L1_RELATIVE: 12.993 G_Regularizer: 0.000 validation_error: 24.544 +(epoch: 1, iters: 218000, time: 0.525, data: 0.000) G_L1: 22.975 G_L1_ABSOLUTE: 3.899 G_L1_RELATIVE: 19.076 G_Regularizer: 0.000 validation_error: 23.697 +(epoch: 1, iters: 220000, time: 0.523, data: 0.000) G_L1: 18.786 G_L1_ABSOLUTE: 3.943 G_L1_RELATIVE: 14.844 G_Regularizer: 0.000 validation_error: 24.047 +(epoch: 1, iters: 222000, time: 0.527, data: 0.000) G_L1: 16.579 G_L1_ABSOLUTE: 4.034 G_L1_RELATIVE: 12.545 G_Regularizer: 0.000 validation_error: 23.986 +(epoch: 1, iters: 224000, time: 0.530, data: 0.000) G_L1: 18.551 G_L1_ABSOLUTE: 3.978 G_L1_RELATIVE: 14.573 G_Regularizer: 0.000 validation_error: 24.706 +(epoch: 1, iters: 226000, time: 0.526, data: 0.000) G_L1: 21.904 G_L1_ABSOLUTE: 3.952 G_L1_RELATIVE: 17.952 G_Regularizer: 0.000 validation_error: 24.628 +(epoch: 1, iters: 228000, time: 0.531, data: 0.000) G_L1: 18.624 G_L1_ABSOLUTE: 3.560 G_L1_RELATIVE: 15.064 G_Regularizer: 0.000 validation_error: 23.511 +(epoch: 1, iters: 230000, time: 0.524, data: 0.000) G_L1: 17.772 G_L1_ABSOLUTE: 3.938 G_L1_RELATIVE: 13.834 G_Regularizer: 0.000 validation_error: 23.734 +(epoch: 1, iters: 232000, time: 0.531, data: 0.000) G_L1: 20.067 G_L1_ABSOLUTE: 4.079 G_L1_RELATIVE: 15.988 G_Regularizer: 0.000 validation_error: 24.111 +(epoch: 1, iters: 234000, time: 0.529, data: 0.000) G_L1: 17.543 G_L1_ABSOLUTE: 4.035 G_L1_RELATIVE: 13.507 G_Regularizer: 0.000 validation_error: 24.288 +(epoch: 1, iters: 236000, time: 0.527, data: 0.000) G_L1: 23.722 G_L1_ABSOLUTE: 3.983 G_L1_RELATIVE: 19.739 G_Regularizer: 0.000 validation_error: 23.954 +(epoch: 1, iters: 238000, time: 0.526, data: 0.000) G_L1: 19.177 G_L1_ABSOLUTE: 3.632 G_L1_RELATIVE: 15.546 G_Regularizer: 0.000 validation_error: 24.831 +(epoch: 1, iters: 240000, time: 0.531, data: 0.000) G_L1: 18.756 G_L1_ABSOLUTE: 3.548 G_L1_RELATIVE: 15.207 G_Regularizer: 0.000 validation_error: 24.057 +(epoch: 1, iters: 242000, time: 0.526, data: 0.000) G_L1: 15.468 G_L1_ABSOLUTE: 3.767 G_L1_RELATIVE: 11.701 G_Regularizer: 0.000 validation_error: 24.242 +(epoch: 1, iters: 244000, time: 0.529, data: 0.000) G_L1: 18.866 G_L1_ABSOLUTE: 3.760 G_L1_RELATIVE: 15.105 G_Regularizer: 0.000 validation_error: 24.002 +(epoch: 1, iters: 246000, time: 0.522, data: 0.001) G_L1: 18.390 G_L1_ABSOLUTE: 3.824 G_L1_RELATIVE: 14.566 G_Regularizer: 0.000 validation_error: 23.662 +(epoch: 1, iters: 248000, time: 0.524, data: 0.000) G_L1: 19.327 G_L1_ABSOLUTE: 3.222 G_L1_RELATIVE: 16.105 G_Regularizer: 0.000 validation_error: 23.933 +(epoch: 1, iters: 250000, time: 0.525, data: 0.000) G_L1: 16.488 G_L1_ABSOLUTE: 4.071 G_L1_RELATIVE: 12.417 G_Regularizer: 0.000 validation_error: 23.823 +(epoch: 1, iters: 252000, time: 0.529, data: 0.001) G_L1: 19.932 G_L1_ABSOLUTE: 3.844 G_L1_RELATIVE: 16.088 G_Regularizer: 0.000 validation_error: 24.064 +(epoch: 1, iters: 254000, time: 0.528, data: 0.000) G_L1: 21.046 G_L1_ABSOLUTE: 3.934 G_L1_RELATIVE: 17.112 G_Regularizer: 0.000 validation_error: 23.750 +(epoch: 1, iters: 256000, time: 0.523, data: 0.000) G_L1: 20.878 G_L1_ABSOLUTE: 4.014 G_L1_RELATIVE: 16.864 G_Regularizer: 0.000 validation_error: 24.444 +(epoch: 1, iters: 258000, time: 0.525, data: 0.000) G_L1: 15.624 G_L1_ABSOLUTE: 3.351 G_L1_RELATIVE: 12.272 G_Regularizer: 0.000 validation_error: 23.646 +(epoch: 1, iters: 260000, time: 0.522, data: 0.000) G_L1: 21.103 G_L1_ABSOLUTE: 3.355 G_L1_RELATIVE: 17.748 G_Regularizer: 0.000 validation_error: 24.173 +(epoch: 1, iters: 262000, time: 0.528, data: 0.000) G_L1: 15.254 G_L1_ABSOLUTE: 3.802 G_L1_RELATIVE: 11.452 G_Regularizer: 0.000 validation_error: 24.097 +(epoch: 1, iters: 264000, time: 0.529, data: 0.000) G_L1: 17.711 G_L1_ABSOLUTE: 3.441 G_L1_RELATIVE: 14.270 G_Regularizer: 0.000 validation_error: 23.639 +(epoch: 1, iters: 266000, time: 0.531, data: 0.000) G_L1: 22.043 G_L1_ABSOLUTE: 3.804 G_L1_RELATIVE: 18.239 G_Regularizer: 0.000 validation_error: 24.509 +(epoch: 1, iters: 268000, time: 0.529, data: 0.000) G_L1: 15.759 G_L1_ABSOLUTE: 3.534 G_L1_RELATIVE: 12.226 G_Regularizer: 0.000 validation_error: 23.430 +(epoch: 1, iters: 270000, time: 0.527, data: 0.000) G_L1: 19.133 G_L1_ABSOLUTE: 4.115 G_L1_RELATIVE: 15.018 G_Regularizer: 0.000 validation_error: 24.018 +(epoch: 1, iters: 272000, time: 0.529, data: 0.000) G_L1: 15.650 G_L1_ABSOLUTE: 3.156 G_L1_RELATIVE: 12.494 G_Regularizer: 0.000 validation_error: 24.029 +(epoch: 1, iters: 274000, time: 0.532, data: 0.000) G_L1: 22.627 G_L1_ABSOLUTE: 3.961 G_L1_RELATIVE: 18.666 G_Regularizer: 0.000 validation_error: 24.322 +(epoch: 1, iters: 276000, time: 0.530, data: 0.000) G_L1: 21.083 G_L1_ABSOLUTE: 4.061 G_L1_RELATIVE: 17.022 G_Regularizer: 0.000 validation_error: 23.667 +(epoch: 1, iters: 278000, time: 0.531, data: 0.000) G_L1: 20.596 G_L1_ABSOLUTE: 3.878 G_L1_RELATIVE: 16.718 G_Regularizer: 0.000 validation_error: 24.293 +(epoch: 1, iters: 280000, time: 0.534, data: 0.001) G_L1: 22.605 G_L1_ABSOLUTE: 3.707 G_L1_RELATIVE: 18.898 G_Regularizer: 0.000 validation_error: 23.754 +(epoch: 1, iters: 282000, time: 0.527, data: 0.000) G_L1: 15.923 G_L1_ABSOLUTE: 3.235 G_L1_RELATIVE: 12.688 G_Regularizer: 0.000 validation_error: 23.480 +(epoch: 1, iters: 284000, time: 0.531, data: 0.000) G_L1: 20.187 G_L1_ABSOLUTE: 4.283 G_L1_RELATIVE: 15.904 G_Regularizer: 0.000 validation_error: 23.692 +(epoch: 1, iters: 286000, time: 0.528, data: 0.000) G_L1: 21.816 G_L1_ABSOLUTE: 3.407 G_L1_RELATIVE: 18.409 G_Regularizer: 0.000 validation_error: 23.527 +(epoch: 1, iters: 288000, time: 0.527, data: 0.000) G_L1: 19.219 G_L1_ABSOLUTE: 3.813 G_L1_RELATIVE: 15.405 G_Regularizer: 0.000 validation_error: 23.934 +(epoch: 1, iters: 290000, time: 0.531, data: 0.000) G_L1: 15.926 G_L1_ABSOLUTE: 3.455 G_L1_RELATIVE: 12.470 G_Regularizer: 0.000 validation_error: 23.804 +(epoch: 1, iters: 292000, time: 0.525, data: 0.000) G_L1: 23.940 G_L1_ABSOLUTE: 3.763 G_L1_RELATIVE: 20.177 G_Regularizer: 0.000 validation_error: 23.613 +(epoch: 1, iters: 294000, time: 0.532, data: 0.000) G_L1: 19.805 G_L1_ABSOLUTE: 3.906 G_L1_RELATIVE: 15.899 G_Regularizer: 0.000 validation_error: 23.512 +(epoch: 1, iters: 296000, time: 0.526, data: 0.000) G_L1: 18.564 G_L1_ABSOLUTE: 3.842 G_L1_RELATIVE: 14.722 G_Regularizer: 0.000 validation_error: 24.080 +(epoch: 1, iters: 298000, time: 0.523, data: 0.000) G_L1: 21.355 G_L1_ABSOLUTE: 4.216 G_L1_RELATIVE: 17.139 G_Regularizer: 0.000 validation_error: 23.654 +(epoch: 1, iters: 300000, time: 0.524, data: 0.000) G_L1: 16.615 G_L1_ABSOLUTE: 3.394 G_L1_RELATIVE: 13.222 G_Regularizer: 0.000 validation_error: 23.533 +(epoch: 1, iters: 302000, time: 0.531, data: 0.000) G_L1: 22.100 G_L1_ABSOLUTE: 4.024 G_L1_RELATIVE: 18.076 G_Regularizer: 0.000 validation_error: 23.328 +(epoch: 2, iters: 1248, time: 0.531, data: 0.000) G_L1: 17.071 G_L1_ABSOLUTE: 3.598 G_L1_RELATIVE: 13.473 G_Regularizer: 0.000 validation_error: 23.080 +(epoch: 2, iters: 3248, time: 0.527, data: 0.000) G_L1: 20.515 G_L1_ABSOLUTE: 3.594 G_L1_RELATIVE: 16.921 G_Regularizer: 0.000 validation_error: 23.704 +(epoch: 2, iters: 5248, time: 0.527, data: 0.000) G_L1: 16.561 G_L1_ABSOLUTE: 3.182 G_L1_RELATIVE: 13.379 G_Regularizer: 0.000 validation_error: 23.516 +(epoch: 2, iters: 7248, time: 0.531, data: 0.000) G_L1: 15.340 G_L1_ABSOLUTE: 3.116 G_L1_RELATIVE: 12.225 G_Regularizer: 0.000 validation_error: 23.368 +(epoch: 2, iters: 9248, time: 0.531, data: 0.000) G_L1: 21.704 G_L1_ABSOLUTE: 3.737 G_L1_RELATIVE: 17.967 G_Regularizer: 0.000 validation_error: 23.156 +(epoch: 2, iters: 11248, time: 0.531, data: 0.000) G_L1: 16.057 G_L1_ABSOLUTE: 3.588 G_L1_RELATIVE: 12.469 G_Regularizer: 0.000 validation_error: 23.250 +(epoch: 2, iters: 13248, time: 0.528, data: 0.000) G_L1: 21.139 G_L1_ABSOLUTE: 3.705 G_L1_RELATIVE: 17.434 G_Regularizer: 0.000 validation_error: 23.455 +(epoch: 2, iters: 15248, time: 0.527, data: 0.000) G_L1: 17.701 G_L1_ABSOLUTE: 3.750 G_L1_RELATIVE: 13.951 G_Regularizer: 0.000 validation_error: 23.150 +(epoch: 2, iters: 17248, time: 0.521, data: 0.000) G_L1: 18.372 G_L1_ABSOLUTE: 3.927 G_L1_RELATIVE: 14.445 G_Regularizer: 0.000 validation_error: 22.785 +(epoch: 2, iters: 19248, time: 0.531, data: 0.000) G_L1: 22.116 G_L1_ABSOLUTE: 3.609 G_L1_RELATIVE: 18.507 G_Regularizer: 0.000 validation_error: 23.102 +(epoch: 2, iters: 21248, time: 0.531, data: 0.000) G_L1: 21.331 G_L1_ABSOLUTE: 4.043 G_L1_RELATIVE: 17.288 G_Regularizer: 0.000 validation_error: 23.610 +(epoch: 2, iters: 23248, time: 0.532, data: 0.000) G_L1: 20.874 G_L1_ABSOLUTE: 3.990 G_L1_RELATIVE: 16.884 G_Regularizer: 0.000 validation_error: 23.080 +(epoch: 2, iters: 25248, time: 0.530, data: 0.000) G_L1: 20.500 G_L1_ABSOLUTE: 4.043 G_L1_RELATIVE: 16.458 G_Regularizer: 0.000 validation_error: 22.868 +(epoch: 2, iters: 27248, time: 0.521, data: 0.000) G_L1: 23.737 G_L1_ABSOLUTE: 3.618 G_L1_RELATIVE: 20.119 G_Regularizer: 0.000 validation_error: 23.424 +(epoch: 2, iters: 29248, time: 0.528, data: 0.000) G_L1: 16.543 G_L1_ABSOLUTE: 3.437 G_L1_RELATIVE: 13.107 G_Regularizer: 0.000 validation_error: 23.103 +(epoch: 2, iters: 31248, time: 0.532, data: 0.000) G_L1: 18.316 G_L1_ABSOLUTE: 3.081 G_L1_RELATIVE: 15.235 G_Regularizer: 0.000 validation_error: 23.809 +(epoch: 2, iters: 33248, time: 0.523, data: 0.000) G_L1: 19.593 G_L1_ABSOLUTE: 3.451 G_L1_RELATIVE: 16.142 G_Regularizer: 0.000 validation_error: 23.355 +(epoch: 2, iters: 35248, time: 0.529, data: 0.000) G_L1: 20.985 G_L1_ABSOLUTE: 3.616 G_L1_RELATIVE: 17.369 G_Regularizer: 0.000 validation_error: 23.562 +(epoch: 2, iters: 37248, time: 0.520, data: 0.000) G_L1: 19.825 G_L1_ABSOLUTE: 3.324 G_L1_RELATIVE: 16.501 G_Regularizer: 0.000 validation_error: 23.243 +(epoch: 2, iters: 39248, time: 0.524, data: 0.000) G_L1: 14.658 G_L1_ABSOLUTE: 3.161 G_L1_RELATIVE: 11.497 G_Regularizer: 0.000 validation_error: 23.368 +(epoch: 2, iters: 41248, time: 0.534, data: 0.000) G_L1: 17.936 G_L1_ABSOLUTE: 4.023 G_L1_RELATIVE: 13.913 G_Regularizer: 0.000 validation_error: 23.358 +(epoch: 2, iters: 43248, time: 0.525, data: 0.000) G_L1: 18.571 G_L1_ABSOLUTE: 3.913 G_L1_RELATIVE: 14.659 G_Regularizer: 0.000 validation_error: 23.244 +(epoch: 2, iters: 45248, time: 0.524, data: 0.000) G_L1: 21.359 G_L1_ABSOLUTE: 4.021 G_L1_RELATIVE: 17.338 G_Regularizer: 0.000 validation_error: 23.670 +(epoch: 2, iters: 47248, time: 0.523, data: 0.000) G_L1: 17.232 G_L1_ABSOLUTE: 3.526 G_L1_RELATIVE: 13.706 G_Regularizer: 0.000 validation_error: 23.741 +(epoch: 2, iters: 49248, time: 0.525, data: 0.000) G_L1: 19.561 G_L1_ABSOLUTE: 3.473 G_L1_RELATIVE: 16.088 G_Regularizer: 0.000 validation_error: 22.551 +(epoch: 2, iters: 51248, time: 0.527, data: 0.000) G_L1: 18.534 G_L1_ABSOLUTE: 3.302 G_L1_RELATIVE: 15.232 G_Regularizer: 0.000 validation_error: 23.145 +(epoch: 2, iters: 53248, time: 0.525, data: 0.000) G_L1: 16.864 G_L1_ABSOLUTE: 3.113 G_L1_RELATIVE: 13.750 G_Regularizer: 0.000 validation_error: 23.217 +(epoch: 2, iters: 55248, time: 0.528, data: 0.000) G_L1: 17.958 G_L1_ABSOLUTE: 3.384 G_L1_RELATIVE: 14.574 G_Regularizer: 0.000 validation_error: 23.627 +(epoch: 2, iters: 57248, time: 0.522, data: 0.000) G_L1: 19.117 G_L1_ABSOLUTE: 3.716 G_L1_RELATIVE: 15.401 G_Regularizer: 0.000 validation_error: 23.102 +(epoch: 2, iters: 59248, time: 0.532, data: 0.000) G_L1: 19.338 G_L1_ABSOLUTE: 3.483 G_L1_RELATIVE: 15.856 G_Regularizer: 0.000 validation_error: 23.934 +(epoch: 2, iters: 61248, time: 0.531, data: 0.000) G_L1: 20.639 G_L1_ABSOLUTE: 3.676 G_L1_RELATIVE: 16.963 G_Regularizer: 0.000 validation_error: 23.446 +(epoch: 2, iters: 63248, time: 0.528, data: 0.000) G_L1: 19.802 G_L1_ABSOLUTE: 3.931 G_L1_RELATIVE: 15.870 G_Regularizer: 0.000 validation_error: 23.258 +(epoch: 2, iters: 65248, time: 0.528, data: 0.000) G_L1: 19.555 G_L1_ABSOLUTE: 3.193 G_L1_RELATIVE: 16.362 G_Regularizer: 0.000 validation_error: 22.816 +(epoch: 2, iters: 67248, time: 0.529, data: 0.000) G_L1: 15.286 G_L1_ABSOLUTE: 3.287 G_L1_RELATIVE: 11.998 G_Regularizer: 0.000 validation_error: 23.291 +(epoch: 2, iters: 69248, time: 0.527, data: 0.000) G_L1: 16.987 G_L1_ABSOLUTE: 3.303 G_L1_RELATIVE: 13.684 G_Regularizer: 0.000 validation_error: 22.931 +(epoch: 2, iters: 71248, time: 0.522, data: 0.000) G_L1: 15.496 G_L1_ABSOLUTE: 4.119 G_L1_RELATIVE: 11.377 G_Regularizer: 0.000 validation_error: 23.433 +(epoch: 2, iters: 73248, time: 0.531, data: 0.000) G_L1: 22.068 G_L1_ABSOLUTE: 3.235 G_L1_RELATIVE: 18.833 G_Regularizer: 0.000 validation_error: 22.599 +(epoch: 2, iters: 75248, time: 0.524, data: 0.000) G_L1: 14.787 G_L1_ABSOLUTE: 3.646 G_L1_RELATIVE: 11.141 G_Regularizer: 0.000 validation_error: 23.038 +(epoch: 2, iters: 77248, time: 0.525, data: 0.000) G_L1: 19.725 G_L1_ABSOLUTE: 3.895 G_L1_RELATIVE: 15.830 G_Regularizer: 0.000 validation_error: 22.628 +(epoch: 2, iters: 79248, time: 0.524, data: 0.000) G_L1: 17.329 G_L1_ABSOLUTE: 3.965 G_L1_RELATIVE: 13.364 G_Regularizer: 0.000 validation_error: 22.406 +(epoch: 2, iters: 81248, time: 0.521, data: 0.000) G_L1: 20.568 G_L1_ABSOLUTE: 3.516 G_L1_RELATIVE: 17.052 G_Regularizer: 0.000 validation_error: 22.773 +(epoch: 2, iters: 83248, time: 0.530, data: 0.000) G_L1: 19.684 G_L1_ABSOLUTE: 3.353 G_L1_RELATIVE: 16.330 G_Regularizer: 0.000 validation_error: 22.096 +(epoch: 2, iters: 85248, time: 0.526, data: 0.000) G_L1: 18.110 G_L1_ABSOLUTE: 3.267 G_L1_RELATIVE: 14.843 G_Regularizer: 0.000 validation_error: 22.325 +(epoch: 2, iters: 87248, time: 0.523, data: 0.000) G_L1: 20.769 G_L1_ABSOLUTE: 3.615 G_L1_RELATIVE: 17.154 G_Regularizer: 0.000 validation_error: 22.466 +(epoch: 2, iters: 89248, time: 0.524, data: 0.000) G_L1: 21.016 G_L1_ABSOLUTE: 3.439 G_L1_RELATIVE: 17.577 G_Regularizer: 0.000 validation_error: 23.083 +(epoch: 2, iters: 91248, time: 0.523, data: 0.000) G_L1: 16.418 G_L1_ABSOLUTE: 3.475 G_L1_RELATIVE: 12.942 G_Regularizer: 0.000 validation_error: 22.785 +(epoch: 2, iters: 93248, time: 0.529, data: 0.000) G_L1: 16.625 G_L1_ABSOLUTE: 3.387 G_L1_RELATIVE: 13.238 G_Regularizer: 0.000 validation_error: 22.552 +(epoch: 2, iters: 95248, time: 0.524, data: 0.000) G_L1: 16.428 G_L1_ABSOLUTE: 3.582 G_L1_RELATIVE: 12.846 G_Regularizer: 0.000 validation_error: 22.430 +(epoch: 2, iters: 97248, time: 0.525, data: 0.000) G_L1: 19.261 G_L1_ABSOLUTE: 3.571 G_L1_RELATIVE: 15.690 G_Regularizer: 0.000 validation_error: 22.355 +(epoch: 2, iters: 99248, time: 0.527, data: 0.000) G_L1: 15.921 G_L1_ABSOLUTE: 3.310 G_L1_RELATIVE: 12.611 G_Regularizer: 0.000 validation_error: 22.780 +(epoch: 2, iters: 101248, time: 0.523, data: 0.000) G_L1: 18.042 G_L1_ABSOLUTE: 3.489 G_L1_RELATIVE: 14.554 G_Regularizer: 0.000 validation_error: 23.101 +(epoch: 2, iters: 103248, time: 0.529, data: 0.000) G_L1: 20.851 G_L1_ABSOLUTE: 3.513 G_L1_RELATIVE: 17.338 G_Regularizer: 0.000 validation_error: 22.728 +(epoch: 2, iters: 105248, time: 0.528, data: 0.000) G_L1: 16.144 G_L1_ABSOLUTE: 3.327 G_L1_RELATIVE: 12.817 G_Regularizer: 0.000 validation_error: 22.925 +(epoch: 2, iters: 107248, time: 0.521, data: 0.000) G_L1: 16.086 G_L1_ABSOLUTE: 3.030 G_L1_RELATIVE: 13.057 G_Regularizer: 0.000 validation_error: 22.383 +(epoch: 2, iters: 109248, time: 0.525, data: 0.000) G_L1: 19.658 G_L1_ABSOLUTE: 3.804 G_L1_RELATIVE: 15.854 G_Regularizer: 0.000 validation_error: 23.037 +(epoch: 2, iters: 111248, time: 0.524, data: 0.000) G_L1: 18.013 G_L1_ABSOLUTE: 3.070 G_L1_RELATIVE: 14.944 G_Regularizer: 0.000 validation_error: 23.241 +(epoch: 2, iters: 113248, time: 0.521, data: 0.000) G_L1: 16.377 G_L1_ABSOLUTE: 3.382 G_L1_RELATIVE: 12.995 G_Regularizer: 0.000 validation_error: 22.550 +(epoch: 2, iters: 115248, time: 0.527, data: 0.000) G_L1: 22.772 G_L1_ABSOLUTE: 3.368 G_L1_RELATIVE: 19.404 G_Regularizer: 0.000 validation_error: 22.492 +(epoch: 2, iters: 117248, time: 0.529, data: 0.000) G_L1: 17.029 G_L1_ABSOLUTE: 3.176 G_L1_RELATIVE: 13.853 G_Regularizer: 0.000 validation_error: 22.841 +(epoch: 2, iters: 119248, time: 0.533, data: 0.000) G_L1: 19.395 G_L1_ABSOLUTE: 3.149 G_L1_RELATIVE: 16.245 G_Regularizer: 0.000 validation_error: 22.454 +(epoch: 2, iters: 121248, time: 0.522, data: 0.001) G_L1: 23.174 G_L1_ABSOLUTE: 3.557 G_L1_RELATIVE: 19.618 G_Regularizer: 0.000 validation_error: 23.497 +(epoch: 2, iters: 123248, time: 0.528, data: 0.000) G_L1: 16.769 G_L1_ABSOLUTE: 3.241 G_L1_RELATIVE: 13.528 G_Regularizer: 0.000 validation_error: 22.927 +(epoch: 2, iters: 125248, time: 0.526, data: 0.000) G_L1: 19.578 G_L1_ABSOLUTE: 3.705 G_L1_RELATIVE: 15.873 G_Regularizer: 0.000 validation_error: 23.087 +(epoch: 2, iters: 127248, time: 0.529, data: 0.000) G_L1: 21.452 G_L1_ABSOLUTE: 3.541 G_L1_RELATIVE: 17.911 G_Regularizer: 0.000 validation_error: 23.492 +(epoch: 2, iters: 129248, time: 0.528, data: 0.000) G_L1: 18.801 G_L1_ABSOLUTE: 3.398 G_L1_RELATIVE: 15.404 G_Regularizer: 0.000 validation_error: 22.291 +(epoch: 2, iters: 131248, time: 0.534, data: 0.000) G_L1: 16.445 G_L1_ABSOLUTE: 3.393 G_L1_RELATIVE: 13.052 G_Regularizer: 0.000 validation_error: 21.906 +(epoch: 2, iters: 133248, time: 0.525, data: 0.000) G_L1: 18.212 G_L1_ABSOLUTE: 3.306 G_L1_RELATIVE: 14.906 G_Regularizer: 0.000 validation_error: 22.626 +(epoch: 2, iters: 135248, time: 0.525, data: 0.000) G_L1: 16.989 G_L1_ABSOLUTE: 2.998 G_L1_RELATIVE: 13.991 G_Regularizer: 0.000 validation_error: 22.980 +(epoch: 2, iters: 137248, time: 0.531, data: 0.000) G_L1: 23.062 G_L1_ABSOLUTE: 3.350 G_L1_RELATIVE: 19.711 G_Regularizer: 0.000 validation_error: 22.804 +(epoch: 2, iters: 139248, time: 0.527, data: 0.000) G_L1: 13.959 G_L1_ABSOLUTE: 3.508 G_L1_RELATIVE: 10.451 G_Regularizer: 0.000 validation_error: 22.635 +(epoch: 2, iters: 141248, time: 0.527, data: 0.001) G_L1: 15.024 G_L1_ABSOLUTE: 3.493 G_L1_RELATIVE: 11.531 G_Regularizer: 0.000 validation_error: 22.477 +(epoch: 2, iters: 143248, time: 0.530, data: 0.000) G_L1: 19.662 G_L1_ABSOLUTE: 3.676 G_L1_RELATIVE: 15.986 G_Regularizer: 0.000 validation_error: 22.946 +(epoch: 2, iters: 145248, time: 0.527, data: 0.000) G_L1: 16.094 G_L1_ABSOLUTE: 3.977 G_L1_RELATIVE: 12.117 G_Regularizer: 0.000 validation_error: 22.937 +(epoch: 2, iters: 147248, time: 0.522, data: 0.000) G_L1: 19.488 G_L1_ABSOLUTE: 3.716 G_L1_RELATIVE: 15.772 G_Regularizer: 0.000 validation_error: 22.969 +(epoch: 2, iters: 149248, time: 0.525, data: 0.000) G_L1: 16.429 G_L1_ABSOLUTE: 3.325 G_L1_RELATIVE: 13.104 G_Regularizer: 0.000 validation_error: 22.564 +(epoch: 2, iters: 151248, time: 0.525, data: 0.000) G_L1: 19.893 G_L1_ABSOLUTE: 3.890 G_L1_RELATIVE: 16.003 G_Regularizer: 0.000 validation_error: 22.257 +(epoch: 2, iters: 153248, time: 0.527, data: 0.000) G_L1: 21.929 G_L1_ABSOLUTE: 3.938 G_L1_RELATIVE: 17.991 G_Regularizer: 0.000 validation_error: 22.666 +(epoch: 2, iters: 155248, time: 0.529, data: 0.000) G_L1: 17.609 G_L1_ABSOLUTE: 3.660 G_L1_RELATIVE: 13.949 G_Regularizer: 0.000 validation_error: 22.663 +(epoch: 2, iters: 157248, time: 0.527, data: 0.000) G_L1: 19.777 G_L1_ABSOLUTE: 3.402 G_L1_RELATIVE: 16.375 G_Regularizer: 0.000 validation_error: 21.971 +(epoch: 2, iters: 159248, time: 0.523, data: 0.000) G_L1: 16.893 G_L1_ABSOLUTE: 4.129 G_L1_RELATIVE: 12.764 G_Regularizer: 0.000 validation_error: 22.675 +(epoch: 2, iters: 161248, time: 0.526, data: 0.000) G_L1: 15.298 G_L1_ABSOLUTE: 3.091 G_L1_RELATIVE: 12.207 G_Regularizer: 0.000 validation_error: 22.544 +(epoch: 2, iters: 163248, time: 0.531, data: 0.000) G_L1: 21.016 G_L1_ABSOLUTE: 3.173 G_L1_RELATIVE: 17.843 G_Regularizer: 0.000 validation_error: 22.432 +(epoch: 2, iters: 165248, time: 0.525, data: 0.000) G_L1: 23.441 G_L1_ABSOLUTE: 3.821 G_L1_RELATIVE: 19.620 G_Regularizer: 0.000 validation_error: 22.382 +(epoch: 2, iters: 167248, time: 0.528, data: 0.000) G_L1: 16.872 G_L1_ABSOLUTE: 3.352 G_L1_RELATIVE: 13.520 G_Regularizer: 0.000 validation_error: 22.550 +(epoch: 2, iters: 169248, time: 0.519, data: 0.000) G_L1: 21.007 G_L1_ABSOLUTE: 3.747 G_L1_RELATIVE: 17.260 G_Regularizer: 0.000 validation_error: 22.080 +(epoch: 2, iters: 171248, time: 0.527, data: 0.000) G_L1: 17.800 G_L1_ABSOLUTE: 3.790 G_L1_RELATIVE: 14.010 G_Regularizer: 0.000 validation_error: 22.413 +(epoch: 2, iters: 173248, time: 0.525, data: 0.001) G_L1: 19.482 G_L1_ABSOLUTE: 3.269 G_L1_RELATIVE: 16.213 G_Regularizer: 0.000 validation_error: 22.347 +(epoch: 2, iters: 175248, time: 0.527, data: 0.000) G_L1: 18.551 G_L1_ABSOLUTE: 3.339 G_L1_RELATIVE: 15.212 G_Regularizer: 0.000 validation_error: 22.609 +(epoch: 2, iters: 177248, time: 0.526, data: 0.000) G_L1: 16.063 G_L1_ABSOLUTE: 2.951 G_L1_RELATIVE: 13.112 G_Regularizer: 0.000 validation_error: 23.254 +(epoch: 2, iters: 179248, time: 0.529, data: 0.000) G_L1: 13.859 G_L1_ABSOLUTE: 3.016 G_L1_RELATIVE: 10.844 G_Regularizer: 0.000 validation_error: 22.355 +(epoch: 2, iters: 181248, time: 0.526, data: 0.000) G_L1: 17.330 G_L1_ABSOLUTE: 3.341 G_L1_RELATIVE: 13.989 G_Regularizer: 0.000 validation_error: 22.650 +(epoch: 2, iters: 183248, time: 0.524, data: 0.000) G_L1: 21.017 G_L1_ABSOLUTE: 3.245 G_L1_RELATIVE: 17.773 G_Regularizer: 0.000 validation_error: 22.689 +(epoch: 2, iters: 185248, time: 0.527, data: 0.000) G_L1: 18.863 G_L1_ABSOLUTE: 3.178 G_L1_RELATIVE: 15.685 G_Regularizer: 0.000 validation_error: 22.150 +(epoch: 2, iters: 187248, time: 0.530, data: 0.000) G_L1: 18.754 G_L1_ABSOLUTE: 3.526 G_L1_RELATIVE: 15.229 G_Regularizer: 0.000 validation_error: 21.877 +(epoch: 2, iters: 189248, time: 0.526, data: 0.000) G_L1: 16.535 G_L1_ABSOLUTE: 2.914 G_L1_RELATIVE: 13.620 G_Regularizer: 0.000 validation_error: 22.509 +(epoch: 2, iters: 191248, time: 0.531, data: 0.000) G_L1: 19.439 G_L1_ABSOLUTE: 3.230 G_L1_RELATIVE: 16.209 G_Regularizer: 0.000 validation_error: 22.330 +(epoch: 2, iters: 193248, time: 0.530, data: 0.000) G_L1: 18.030 G_L1_ABSOLUTE: 3.428 G_L1_RELATIVE: 14.602 G_Regularizer: 0.000 validation_error: 22.655 +(epoch: 2, iters: 195248, time: 0.524, data: 0.000) G_L1: 19.138 G_L1_ABSOLUTE: 3.059 G_L1_RELATIVE: 16.079 G_Regularizer: 0.000 validation_error: 22.307 +(epoch: 2, iters: 197248, time: 0.530, data: 0.000) G_L1: 14.541 G_L1_ABSOLUTE: 3.254 G_L1_RELATIVE: 11.287 G_Regularizer: 0.000 validation_error: 22.990 +(epoch: 2, iters: 199248, time: 0.536, data: 0.000) G_L1: 19.795 G_L1_ABSOLUTE: 3.369 G_L1_RELATIVE: 16.426 G_Regularizer: 0.000 validation_error: 23.097 +(epoch: 2, iters: 201248, time: 0.528, data: 0.000) G_L1: 19.176 G_L1_ABSOLUTE: 3.318 G_L1_RELATIVE: 15.858 G_Regularizer: 0.000 validation_error: 22.049 +(epoch: 2, iters: 203248, time: 0.524, data: 0.000) G_L1: 18.021 G_L1_ABSOLUTE: 3.264 G_L1_RELATIVE: 14.757 G_Regularizer: 0.000 validation_error: 22.431 +(epoch: 2, iters: 205248, time: 0.528, data: 0.000) G_L1: 16.555 G_L1_ABSOLUTE: 3.110 G_L1_RELATIVE: 13.445 G_Regularizer: 0.000 validation_error: 23.123 +(epoch: 2, iters: 207248, time: 0.529, data: 0.000) G_L1: 16.991 G_L1_ABSOLUTE: 3.376 G_L1_RELATIVE: 13.616 G_Regularizer: 0.000 validation_error: 22.364 +(epoch: 2, iters: 209248, time: 0.525, data: 0.000) G_L1: 18.126 G_L1_ABSOLUTE: 3.660 G_L1_RELATIVE: 14.466 G_Regularizer: 0.000 validation_error: 22.964 +(epoch: 2, iters: 211248, time: 0.527, data: 0.000) G_L1: 18.180 G_L1_ABSOLUTE: 3.626 G_L1_RELATIVE: 14.554 G_Regularizer: 0.000 validation_error: 22.443 +(epoch: 2, iters: 213248, time: 0.531, data: 0.000) G_L1: 18.043 G_L1_ABSOLUTE: 3.998 G_L1_RELATIVE: 14.044 G_Regularizer: 0.000 validation_error: 21.764 +(epoch: 2, iters: 215248, time: 0.523, data: 0.000) G_L1: 20.519 G_L1_ABSOLUTE: 3.863 G_L1_RELATIVE: 16.656 G_Regularizer: 0.000 validation_error: 22.037 +(epoch: 2, iters: 217248, time: 0.532, data: 0.000) G_L1: 16.253 G_L1_ABSOLUTE: 2.979 G_L1_RELATIVE: 13.274 G_Regularizer: 0.000 validation_error: 22.081 +(epoch: 2, iters: 219248, time: 0.528, data: 0.000) G_L1: 18.731 G_L1_ABSOLUTE: 3.423 G_L1_RELATIVE: 15.307 G_Regularizer: 0.000 validation_error: 21.826 +(epoch: 2, iters: 221248, time: 0.532, data: 0.000) G_L1: 17.620 G_L1_ABSOLUTE: 3.538 G_L1_RELATIVE: 14.082 G_Regularizer: 0.000 validation_error: 22.069 +(epoch: 2, iters: 223248, time: 0.529, data: 0.000) G_L1: 19.015 G_L1_ABSOLUTE: 2.803 G_L1_RELATIVE: 16.212 G_Regularizer: 0.000 validation_error: 22.037 +(epoch: 2, iters: 225248, time: 0.531, data: 0.000) G_L1: 15.481 G_L1_ABSOLUTE: 3.166 G_L1_RELATIVE: 12.315 G_Regularizer: 0.000 validation_error: 22.581 +(epoch: 2, iters: 227248, time: 0.530, data: 0.000) G_L1: 15.880 G_L1_ABSOLUTE: 3.149 G_L1_RELATIVE: 12.730 G_Regularizer: 0.000 validation_error: 22.800 +(epoch: 2, iters: 229248, time: 0.528, data: 0.001) G_L1: 14.752 G_L1_ABSOLUTE: 3.355 G_L1_RELATIVE: 11.397 G_Regularizer: 0.000 validation_error: 22.253 +(epoch: 2, iters: 231248, time: 0.527, data: 0.000) G_L1: 17.916 G_L1_ABSOLUTE: 3.297 G_L1_RELATIVE: 14.620 G_Regularizer: 0.000 validation_error: 22.567 +(epoch: 2, iters: 233248, time: 0.531, data: 0.000) G_L1: 19.253 G_L1_ABSOLUTE: 2.872 G_L1_RELATIVE: 16.381 G_Regularizer: 0.000 validation_error: 21.814 +(epoch: 2, iters: 235248, time: 0.528, data: 0.000) G_L1: 15.784 G_L1_ABSOLUTE: 2.995 G_L1_RELATIVE: 12.789 G_Regularizer: 0.000 validation_error: 22.220 +(epoch: 2, iters: 237248, time: 0.525, data: 0.000) G_L1: 18.267 G_L1_ABSOLUTE: 3.682 G_L1_RELATIVE: 14.585 G_Regularizer: 0.000 validation_error: 22.397 +(epoch: 2, iters: 239248, time: 0.531, data: 0.000) G_L1: 13.722 G_L1_ABSOLUTE: 3.123 G_L1_RELATIVE: 10.598 G_Regularizer: 0.000 validation_error: 22.534 +(epoch: 2, iters: 241248, time: 0.527, data: 0.000) G_L1: 18.989 G_L1_ABSOLUTE: 3.674 G_L1_RELATIVE: 15.315 G_Regularizer: 0.000 validation_error: 22.702 +(epoch: 2, iters: 243248, time: 0.528, data: 0.000) G_L1: 19.537 G_L1_ABSOLUTE: 3.492 G_L1_RELATIVE: 16.045 G_Regularizer: 0.000 validation_error: 22.421 +(epoch: 2, iters: 245248, time: 0.525, data: 0.000) G_L1: 16.567 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 13.790 G_Regularizer: 0.000 validation_error: 22.657 +(epoch: 2, iters: 247248, time: 0.528, data: 0.000) G_L1: 19.837 G_L1_ABSOLUTE: 3.300 G_L1_RELATIVE: 16.537 G_Regularizer: 0.000 validation_error: 22.231 +(epoch: 2, iters: 249248, time: 0.525, data: 0.000) G_L1: 17.652 G_L1_ABSOLUTE: 2.874 G_L1_RELATIVE: 14.778 G_Regularizer: 0.000 validation_error: 22.521 +(epoch: 2, iters: 251248, time: 0.536, data: 0.000) G_L1: 14.739 G_L1_ABSOLUTE: 3.446 G_L1_RELATIVE: 11.293 G_Regularizer: 0.000 validation_error: 22.760 +(epoch: 2, iters: 253248, time: 0.525, data: 0.000) G_L1: 15.555 G_L1_ABSOLUTE: 3.050 G_L1_RELATIVE: 12.505 G_Regularizer: 0.000 validation_error: 21.859 +(epoch: 2, iters: 255248, time: 0.536, data: 0.000) G_L1: 17.642 G_L1_ABSOLUTE: 2.963 G_L1_RELATIVE: 14.678 G_Regularizer: 0.000 validation_error: 22.491 +(epoch: 2, iters: 257248, time: 0.537, data: 0.000) G_L1: 15.999 G_L1_ABSOLUTE: 3.013 G_L1_RELATIVE: 12.986 G_Regularizer: 0.000 validation_error: 21.722 +(epoch: 2, iters: 259248, time: 0.561, data: 0.000) G_L1: 18.104 G_L1_ABSOLUTE: 3.312 G_L1_RELATIVE: 14.792 G_Regularizer: 0.000 validation_error: 21.919 +(epoch: 2, iters: 261248, time: 0.531, data: 0.000) G_L1: 19.184 G_L1_ABSOLUTE: 3.353 G_L1_RELATIVE: 15.831 G_Regularizer: 0.000 validation_error: 22.411 +(epoch: 2, iters: 263248, time: 0.532, data: 0.000) G_L1: 19.732 G_L1_ABSOLUTE: 3.769 G_L1_RELATIVE: 15.963 G_Regularizer: 0.000 validation_error: 22.091 +(epoch: 2, iters: 265248, time: 0.531, data: 0.000) G_L1: 17.633 G_L1_ABSOLUTE: 2.899 G_L1_RELATIVE: 14.734 G_Regularizer: 0.000 validation_error: 22.655 +(epoch: 2, iters: 267248, time: 0.529, data: 0.000) G_L1: 15.106 G_L1_ABSOLUTE: 3.098 G_L1_RELATIVE: 12.008 G_Regularizer: 0.000 validation_error: 22.111 +(epoch: 2, iters: 269248, time: 0.527, data: 0.000) G_L1: 15.622 G_L1_ABSOLUTE: 3.032 G_L1_RELATIVE: 12.589 G_Regularizer: 0.000 validation_error: 22.442 +(epoch: 2, iters: 271248, time: 0.527, data: 0.000) G_L1: 19.038 G_L1_ABSOLUTE: 3.374 G_L1_RELATIVE: 15.664 G_Regularizer: 0.000 validation_error: 21.477 +(epoch: 2, iters: 273248, time: 0.529, data: 0.000) G_L1: 15.576 G_L1_ABSOLUTE: 3.535 G_L1_RELATIVE: 12.041 G_Regularizer: 0.000 validation_error: 22.186 +(epoch: 2, iters: 275248, time: 0.530, data: 0.000) G_L1: 20.849 G_L1_ABSOLUTE: 3.242 G_L1_RELATIVE: 17.607 G_Regularizer: 0.000 validation_error: 22.657 +(epoch: 2, iters: 277248, time: 0.541, data: 0.000) G_L1: 16.140 G_L1_ABSOLUTE: 3.040 G_L1_RELATIVE: 13.099 G_Regularizer: 0.000 validation_error: 21.840 +(epoch: 2, iters: 279248, time: 0.545, data: 0.000) G_L1: 17.551 G_L1_ABSOLUTE: 3.423 G_L1_RELATIVE: 14.128 G_Regularizer: 0.000 validation_error: 22.008 +(epoch: 2, iters: 281248, time: 0.539, data: 0.000) G_L1: 18.892 G_L1_ABSOLUTE: 3.339 G_L1_RELATIVE: 15.553 G_Regularizer: 0.000 validation_error: 22.458 +(epoch: 2, iters: 283248, time: 0.535, data: 0.000) G_L1: 16.882 G_L1_ABSOLUTE: 3.635 G_L1_RELATIVE: 13.247 G_Regularizer: 0.000 validation_error: 22.282 +(epoch: 2, iters: 285248, time: 0.534, data: 0.000) G_L1: 19.265 G_L1_ABSOLUTE: 3.230 G_L1_RELATIVE: 16.036 G_Regularizer: 0.000 validation_error: 21.618 +(epoch: 2, iters: 287248, time: 0.534, data: 0.000) G_L1: 16.428 G_L1_ABSOLUTE: 2.852 G_L1_RELATIVE: 13.575 G_Regularizer: 0.000 validation_error: 21.424 +(epoch: 2, iters: 289248, time: 0.538, data: 0.000) G_L1: 17.082 G_L1_ABSOLUTE: 3.566 G_L1_RELATIVE: 13.516 G_Regularizer: 0.000 validation_error: 21.639 +(epoch: 2, iters: 291248, time: 0.534, data: 0.000) G_L1: 19.217 G_L1_ABSOLUTE: 3.050 G_L1_RELATIVE: 16.167 G_Regularizer: 0.000 validation_error: 21.731 +(epoch: 2, iters: 293248, time: 0.537, data: 0.000) G_L1: 17.839 G_L1_ABSOLUTE: 3.147 G_L1_RELATIVE: 14.692 G_Regularizer: 0.000 validation_error: 21.612 +(epoch: 2, iters: 295248, time: 0.549, data: 0.000) G_L1: 16.399 G_L1_ABSOLUTE: 2.961 G_L1_RELATIVE: 13.438 G_Regularizer: 0.000 validation_error: 22.383 +(epoch: 2, iters: 297248, time: 0.529, data: 0.000) G_L1: 18.376 G_L1_ABSOLUTE: 3.261 G_L1_RELATIVE: 15.115 G_Regularizer: 0.000 validation_error: 22.006 +(epoch: 2, iters: 299248, time: 0.539, data: 0.000) G_L1: 19.169 G_L1_ABSOLUTE: 3.104 G_L1_RELATIVE: 16.066 G_Regularizer: 0.000 validation_error: 22.928 +(epoch: 2, iters: 301248, time: 0.537, data: 0.000) G_L1: 18.682 G_L1_ABSOLUTE: 3.289 G_L1_RELATIVE: 15.393 G_Regularizer: 0.000 validation_error: 21.984 +(epoch: 3, iters: 496, time: 0.539, data: 0.000) G_L1: 16.565 G_L1_ABSOLUTE: 3.553 G_L1_RELATIVE: 13.012 G_Regularizer: 0.000 validation_error: 22.182 +(epoch: 3, iters: 2496, time: 0.870, data: 0.000) G_L1: 21.922 G_L1_ABSOLUTE: 3.485 G_L1_RELATIVE: 18.437 G_Regularizer: 0.000 validation_error: 22.160 +(epoch: 3, iters: 4496, time: 0.926, data: 0.000) G_L1: 17.519 G_L1_ABSOLUTE: 3.493 G_L1_RELATIVE: 14.026 G_Regularizer: 0.000 validation_error: 21.903 +(epoch: 3, iters: 6496, time: 0.929, data: 0.000) G_L1: 17.317 G_L1_ABSOLUTE: 2.849 G_L1_RELATIVE: 14.468 G_Regularizer: 0.000 validation_error: 22.253 +(epoch: 3, iters: 8496, time: 0.933, data: 0.000) G_L1: 16.292 G_L1_ABSOLUTE: 3.001 G_L1_RELATIVE: 13.291 G_Regularizer: 0.000 validation_error: 21.738 +(epoch: 3, iters: 10496, time: 0.928, data: 0.000) G_L1: 17.132 G_L1_ABSOLUTE: 3.135 G_L1_RELATIVE: 13.997 G_Regularizer: 0.000 validation_error: 21.739 +(epoch: 3, iters: 12496, time: 0.933, data: 0.000) G_L1: 20.222 G_L1_ABSOLUTE: 3.591 G_L1_RELATIVE: 16.631 G_Regularizer: 0.000 validation_error: 22.351 +(epoch: 3, iters: 14496, time: 0.937, data: 0.000) G_L1: 22.101 G_L1_ABSOLUTE: 4.080 G_L1_RELATIVE: 18.021 G_Regularizer: 0.000 validation_error: 21.922 +(epoch: 3, iters: 16496, time: 0.939, data: 0.000) G_L1: 20.988 G_L1_ABSOLUTE: 3.306 G_L1_RELATIVE: 17.682 G_Regularizer: 0.000 validation_error: 22.179 +(epoch: 3, iters: 18496, time: 0.941, data: 0.000) G_L1: 18.434 G_L1_ABSOLUTE: 2.928 G_L1_RELATIVE: 15.506 G_Regularizer: 0.000 validation_error: 21.850 +(epoch: 3, iters: 20496, time: 0.936, data: 0.000) G_L1: 17.564 G_L1_ABSOLUTE: 3.211 G_L1_RELATIVE: 14.353 G_Regularizer: 0.000 validation_error: 22.317 +(epoch: 3, iters: 22496, time: 0.941, data: 0.000) G_L1: 16.734 G_L1_ABSOLUTE: 3.227 G_L1_RELATIVE: 13.506 G_Regularizer: 0.000 validation_error: 21.512 +(epoch: 3, iters: 24496, time: 0.943, data: 0.000) G_L1: 15.422 G_L1_ABSOLUTE: 3.271 G_L1_RELATIVE: 12.151 G_Regularizer: 0.000 validation_error: 21.823 +(epoch: 3, iters: 26496, time: 0.943, data: 0.000) G_L1: 16.184 G_L1_ABSOLUTE: 3.400 G_L1_RELATIVE: 12.784 G_Regularizer: 0.000 validation_error: 22.031 +(epoch: 3, iters: 28496, time: 0.945, data: 0.000) G_L1: 16.703 G_L1_ABSOLUTE: 3.048 G_L1_RELATIVE: 13.654 G_Regularizer: 0.000 validation_error: 21.609 +(epoch: 3, iters: 30496, time: 0.936, data: 0.000) G_L1: 15.875 G_L1_ABSOLUTE: 3.278 G_L1_RELATIVE: 12.597 G_Regularizer: 0.000 validation_error: 22.192 +(epoch: 3, iters: 32496, time: 0.939, data: 0.000) G_L1: 17.809 G_L1_ABSOLUTE: 3.370 G_L1_RELATIVE: 14.440 G_Regularizer: 0.000 validation_error: 21.414 +(epoch: 3, iters: 34496, time: 0.945, data: 0.000) G_L1: 18.109 G_L1_ABSOLUTE: 3.659 G_L1_RELATIVE: 14.450 G_Regularizer: 0.000 validation_error: 21.787 +(epoch: 3, iters: 36496, time: 0.942, data: 0.000) G_L1: 15.798 G_L1_ABSOLUTE: 2.897 G_L1_RELATIVE: 12.901 G_Regularizer: 0.000 validation_error: 21.267 +(epoch: 3, iters: 38496, time: 0.947, data: 0.000) G_L1: 17.948 G_L1_ABSOLUTE: 3.016 G_L1_RELATIVE: 14.932 G_Regularizer: 0.000 validation_error: 21.690 +(epoch: 3, iters: 40496, time: 0.948, data: 0.000) G_L1: 15.979 G_L1_ABSOLUTE: 3.450 G_L1_RELATIVE: 12.529 G_Regularizer: 0.000 validation_error: 21.998 +(epoch: 3, iters: 42496, time: 0.942, data: 0.000) G_L1: 17.612 G_L1_ABSOLUTE: 3.498 G_L1_RELATIVE: 14.114 G_Regularizer: 0.000 validation_error: 21.841 +(epoch: 3, iters: 44496, time: 0.947, data: 0.000) G_L1: 15.908 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 13.152 G_Regularizer: 0.000 validation_error: 21.506 +(epoch: 3, iters: 46496, time: 0.941, data: 0.000) G_L1: 16.567 G_L1_ABSOLUTE: 3.162 G_L1_RELATIVE: 13.405 G_Regularizer: 0.000 validation_error: 21.237 +(epoch: 3, iters: 48496, time: 0.951, data: 0.000) G_L1: 18.775 G_L1_ABSOLUTE: 3.128 G_L1_RELATIVE: 15.647 G_Regularizer: 0.000 validation_error: 22.224 +(epoch: 3, iters: 50496, time: 0.942, data: 0.000) G_L1: 16.853 G_L1_ABSOLUTE: 3.432 G_L1_RELATIVE: 13.421 G_Regularizer: 0.000 validation_error: 21.711 +(epoch: 3, iters: 52496, time: 0.943, data: 0.000) G_L1: 15.349 G_L1_ABSOLUTE: 2.979 G_L1_RELATIVE: 12.369 G_Regularizer: 0.000 validation_error: 22.062 +(epoch: 3, iters: 54496, time: 0.950, data: 0.000) G_L1: 15.604 G_L1_ABSOLUTE: 3.300 G_L1_RELATIVE: 12.304 G_Regularizer: 0.000 validation_error: 21.327 +(epoch: 3, iters: 56496, time: 0.947, data: 0.000) G_L1: 19.685 G_L1_ABSOLUTE: 3.272 G_L1_RELATIVE: 16.413 G_Regularizer: 0.000 validation_error: 22.060 +(epoch: 3, iters: 58496, time: 0.943, data: 0.000) G_L1: 18.089 G_L1_ABSOLUTE: 3.214 G_L1_RELATIVE: 14.875 G_Regularizer: 0.000 validation_error: 21.277 +(epoch: 3, iters: 60496, time: 0.946, data: 0.000) G_L1: 17.823 G_L1_ABSOLUTE: 3.441 G_L1_RELATIVE: 14.382 G_Regularizer: 0.000 validation_error: 21.906 +(epoch: 3, iters: 62496, time: 0.943, data: 0.000) G_L1: 16.541 G_L1_ABSOLUTE: 3.503 G_L1_RELATIVE: 13.038 G_Regularizer: 0.000 validation_error: 21.393 +(epoch: 3, iters: 64496, time: 0.941, data: 0.000) G_L1: 18.917 G_L1_ABSOLUTE: 3.573 G_L1_RELATIVE: 15.344 G_Regularizer: 0.000 validation_error: 21.662 +(epoch: 3, iters: 66496, time: 0.941, data: 0.000) G_L1: 15.879 G_L1_ABSOLUTE: 2.843 G_L1_RELATIVE: 13.036 G_Regularizer: 0.000 validation_error: 22.285 +(epoch: 3, iters: 68496, time: 0.946, data: 0.000) G_L1: 19.113 G_L1_ABSOLUTE: 2.847 G_L1_RELATIVE: 16.266 G_Regularizer: 0.000 validation_error: 21.788 +(epoch: 3, iters: 70496, time: 0.941, data: 0.000) G_L1: 18.363 G_L1_ABSOLUTE: 2.796 G_L1_RELATIVE: 15.567 G_Regularizer: 0.000 validation_error: 22.055 +(epoch: 3, iters: 72496, time: 0.946, data: 0.000) G_L1: 15.999 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 13.262 G_Regularizer: 0.000 validation_error: 21.737 +(epoch: 3, iters: 74496, time: 0.946, data: 0.000) G_L1: 18.528 G_L1_ABSOLUTE: 3.078 G_L1_RELATIVE: 15.450 G_Regularizer: 0.000 validation_error: 21.563 +(epoch: 3, iters: 76496, time: 0.951, data: 0.000) G_L1: 13.771 G_L1_ABSOLUTE: 3.166 G_L1_RELATIVE: 10.605 G_Regularizer: 0.000 validation_error: 21.817 +(epoch: 3, iters: 78496, time: 0.947, data: 0.000) G_L1: 21.609 G_L1_ABSOLUTE: 3.524 G_L1_RELATIVE: 18.085 G_Regularizer: 0.000 validation_error: 21.049 +(epoch: 3, iters: 80496, time: 0.943, data: 0.000) G_L1: 19.271 G_L1_ABSOLUTE: 3.639 G_L1_RELATIVE: 15.632 G_Regularizer: 0.000 validation_error: 22.086 +(epoch: 3, iters: 82496, time: 0.576, data: 0.000) G_L1: 15.880 G_L1_ABSOLUTE: 3.172 G_L1_RELATIVE: 12.708 G_Regularizer: 0.000 validation_error: 21.125 +(epoch: 3, iters: 84496, time: 0.586, data: 0.000) G_L1: 17.768 G_L1_ABSOLUTE: 3.853 G_L1_RELATIVE: 13.915 G_Regularizer: 0.000 validation_error: 21.905 +(epoch: 3, iters: 86496, time: 0.534, data: 0.000) G_L1: 18.121 G_L1_ABSOLUTE: 3.262 G_L1_RELATIVE: 14.859 G_Regularizer: 0.000 validation_error: 21.787 +(epoch: 3, iters: 88496, time: 0.527, data: 0.000) G_L1: 18.524 G_L1_ABSOLUTE: 2.971 G_L1_RELATIVE: 15.553 G_Regularizer: 0.000 validation_error: 22.054 +(epoch: 3, iters: 90496, time: 0.528, data: 0.000) G_L1: 21.795 G_L1_ABSOLUTE: 3.187 G_L1_RELATIVE: 18.609 G_Regularizer: 0.000 validation_error: 21.429 +(epoch: 3, iters: 92496, time: 0.528, data: 0.000) G_L1: 19.608 G_L1_ABSOLUTE: 2.978 G_L1_RELATIVE: 16.630 G_Regularizer: 0.000 validation_error: 21.598 +(epoch: 3, iters: 94496, time: 0.526, data: 0.000) G_L1: 16.730 G_L1_ABSOLUTE: 3.196 G_L1_RELATIVE: 13.534 G_Regularizer: 0.000 validation_error: 22.201 +(epoch: 3, iters: 96496, time: 0.531, data: 0.000) G_L1: 16.715 G_L1_ABSOLUTE: 2.896 G_L1_RELATIVE: 13.819 G_Regularizer: 0.000 validation_error: 21.440 +(epoch: 3, iters: 98496, time: 0.521, data: 0.000) G_L1: 18.180 G_L1_ABSOLUTE: 3.171 G_L1_RELATIVE: 15.009 G_Regularizer: 0.000 validation_error: 21.957 +(epoch: 3, iters: 100496, time: 0.529, data: 0.000) G_L1: 15.614 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 12.661 G_Regularizer: 0.000 validation_error: 22.274 +(epoch: 3, iters: 102496, time: 0.527, data: 0.000) G_L1: 18.841 G_L1_ABSOLUTE: 3.175 G_L1_RELATIVE: 15.666 G_Regularizer: 0.000 validation_error: 21.431 +(epoch: 3, iters: 104496, time: 0.527, data: 0.000) G_L1: 16.661 G_L1_ABSOLUTE: 3.162 G_L1_RELATIVE: 13.500 G_Regularizer: 0.000 validation_error: 21.564 +(epoch: 3, iters: 106496, time: 0.521, data: 0.000) G_L1: 20.301 G_L1_ABSOLUTE: 3.380 G_L1_RELATIVE: 16.921 G_Regularizer: 0.000 validation_error: 21.512 +(epoch: 3, iters: 108496, time: 0.526, data: 0.000) G_L1: 18.253 G_L1_ABSOLUTE: 3.154 G_L1_RELATIVE: 15.099 G_Regularizer: 0.000 validation_error: 21.463 +(epoch: 3, iters: 110496, time: 0.525, data: 0.000) G_L1: 14.442 G_L1_ABSOLUTE: 2.799 G_L1_RELATIVE: 11.644 G_Regularizer: 0.000 validation_error: 21.249 +(epoch: 3, iters: 112496, time: 0.533, data: 0.000) G_L1: 14.927 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 12.211 G_Regularizer: 0.000 validation_error: 21.361 +(epoch: 3, iters: 114496, time: 0.526, data: 0.000) G_L1: 19.963 G_L1_ABSOLUTE: 2.703 G_L1_RELATIVE: 17.259 G_Regularizer: 0.000 validation_error: 21.335 +(epoch: 3, iters: 116496, time: 0.531, data: 0.000) G_L1: 17.456 G_L1_ABSOLUTE: 2.980 G_L1_RELATIVE: 14.477 G_Regularizer: 0.000 validation_error: 21.344 +(epoch: 3, iters: 118496, time: 0.531, data: 0.000) G_L1: 21.592 G_L1_ABSOLUTE: 2.833 G_L1_RELATIVE: 18.759 G_Regularizer: 0.000 validation_error: 21.740 +(epoch: 3, iters: 120496, time: 0.526, data: 0.000) G_L1: 16.403 G_L1_ABSOLUTE: 3.315 G_L1_RELATIVE: 13.088 G_Regularizer: 0.000 validation_error: 21.648 +(epoch: 3, iters: 122496, time: 0.524, data: 0.000) G_L1: 16.938 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 14.061 G_Regularizer: 0.000 validation_error: 21.674 +(epoch: 3, iters: 124496, time: 0.529, data: 0.000) G_L1: 22.409 G_L1_ABSOLUTE: 3.748 G_L1_RELATIVE: 18.661 G_Regularizer: 0.000 validation_error: 21.116 +(epoch: 3, iters: 126496, time: 0.534, data: 0.000) G_L1: 20.650 G_L1_ABSOLUTE: 3.539 G_L1_RELATIVE: 17.111 G_Regularizer: 0.000 validation_error: 21.293 +(epoch: 3, iters: 128496, time: 0.528, data: 0.000) G_L1: 18.110 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 15.225 G_Regularizer: 0.000 validation_error: 21.701 +(epoch: 3, iters: 130496, time: 0.524, data: 0.000) G_L1: 20.200 G_L1_ABSOLUTE: 3.020 G_L1_RELATIVE: 17.180 G_Regularizer: 0.000 validation_error: 21.355 +(epoch: 3, iters: 132496, time: 0.533, data: 0.000) G_L1: 18.152 G_L1_ABSOLUTE: 3.460 G_L1_RELATIVE: 14.692 G_Regularizer: 0.000 validation_error: 21.807 +(epoch: 3, iters: 134496, time: 0.529, data: 0.000) G_L1: 21.824 G_L1_ABSOLUTE: 3.691 G_L1_RELATIVE: 18.133 G_Regularizer: 0.000 validation_error: 21.716 +(epoch: 3, iters: 136496, time: 0.528, data: 0.000) G_L1: 16.460 G_L1_ABSOLUTE: 3.176 G_L1_RELATIVE: 13.284 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 3, iters: 138496, time: 0.531, data: 0.000) G_L1: 14.454 G_L1_ABSOLUTE: 3.432 G_L1_RELATIVE: 11.022 G_Regularizer: 0.000 validation_error: 21.599 +(epoch: 3, iters: 140496, time: 0.535, data: 0.000) G_L1: 14.606 G_L1_ABSOLUTE: 3.254 G_L1_RELATIVE: 11.352 G_Regularizer: 0.000 validation_error: 22.054 +(epoch: 3, iters: 142496, time: 0.529, data: 0.000) G_L1: 15.329 G_L1_ABSOLUTE: 3.208 G_L1_RELATIVE: 12.121 G_Regularizer: 0.000 validation_error: 21.600 +(epoch: 3, iters: 144496, time: 0.532, data: 0.000) G_L1: 15.620 G_L1_ABSOLUTE: 3.564 G_L1_RELATIVE: 12.055 G_Regularizer: 0.000 validation_error: 21.926 +(epoch: 3, iters: 146496, time: 0.529, data: 0.000) G_L1: 19.399 G_L1_ABSOLUTE: 3.785 G_L1_RELATIVE: 15.614 G_Regularizer: 0.000 validation_error: 21.624 +(epoch: 3, iters: 148496, time: 0.526, data: 0.000) G_L1: 22.538 G_L1_ABSOLUTE: 3.848 G_L1_RELATIVE: 18.689 G_Regularizer: 0.000 validation_error: 21.926 +(epoch: 3, iters: 150496, time: 0.566, data: 0.000) G_L1: 15.952 G_L1_ABSOLUTE: 2.944 G_L1_RELATIVE: 13.009 G_Regularizer: 0.000 validation_error: 21.905 +(epoch: 3, iters: 152496, time: 0.633, data: 0.000) G_L1: 20.764 G_L1_ABSOLUTE: 3.469 G_L1_RELATIVE: 17.295 G_Regularizer: 0.000 validation_error: 21.631 +(epoch: 3, iters: 154496, time: 0.621, data: 0.000) G_L1: 17.313 G_L1_ABSOLUTE: 3.185 G_L1_RELATIVE: 14.128 G_Regularizer: 0.000 validation_error: 21.827 +(epoch: 3, iters: 156496, time: 0.627, data: 0.000) G_L1: 19.455 G_L1_ABSOLUTE: 3.362 G_L1_RELATIVE: 16.094 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 3, iters: 158496, time: 0.589, data: 0.000) G_L1: 17.687 G_L1_ABSOLUTE: 2.788 G_L1_RELATIVE: 14.899 G_Regularizer: 0.000 validation_error: 21.908 +(epoch: 3, iters: 160496, time: 0.632, data: 0.000) G_L1: 16.040 G_L1_ABSOLUTE: 2.956 G_L1_RELATIVE: 13.084 G_Regularizer: 0.000 validation_error: 21.339 +(epoch: 3, iters: 162496, time: 0.630, data: 0.000) G_L1: 15.586 G_L1_ABSOLUTE: 2.893 G_L1_RELATIVE: 12.693 G_Regularizer: 0.000 validation_error: 21.502 +(epoch: 3, iters: 164496, time: 0.636, data: 0.000) G_L1: 15.950 G_L1_ABSOLUTE: 2.898 G_L1_RELATIVE: 13.052 G_Regularizer: 0.000 validation_error: 21.982 +(epoch: 3, iters: 166496, time: 0.622, data: 0.000) G_L1: 14.508 G_L1_ABSOLUTE: 2.720 G_L1_RELATIVE: 11.788 G_Regularizer: 0.000 validation_error: 20.869 +(epoch: 3, iters: 168496, time: 0.585, data: 0.000) G_L1: 15.632 G_L1_ABSOLUTE: 3.198 G_L1_RELATIVE: 12.434 G_Regularizer: 0.000 validation_error: 21.817 +(epoch: 3, iters: 170496, time: 0.630, data: 0.000) G_L1: 16.885 G_L1_ABSOLUTE: 2.935 G_L1_RELATIVE: 13.951 G_Regularizer: 0.000 validation_error: 21.575 +(epoch: 3, iters: 172496, time: 0.621, data: 0.000) G_L1: 15.546 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 12.896 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 3, iters: 174496, time: 0.600, data: 0.000) G_L1: 15.884 G_L1_ABSOLUTE: 3.753 G_L1_RELATIVE: 12.131 G_Regularizer: 0.000 validation_error: 21.523 +(epoch: 3, iters: 176496, time: 0.602, data: 0.000) G_L1: 14.790 G_L1_ABSOLUTE: 2.998 G_L1_RELATIVE: 11.792 G_Regularizer: 0.000 validation_error: 21.165 +(epoch: 3, iters: 178496, time: 0.548, data: 0.000) G_L1: 16.725 G_L1_ABSOLUTE: 2.855 G_L1_RELATIVE: 13.870 G_Regularizer: 0.000 validation_error: 21.179 +(epoch: 3, iters: 180496, time: 0.525, data: 0.000) G_L1: 17.632 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 14.856 G_Regularizer: 0.000 validation_error: 21.914 +(epoch: 3, iters: 182496, time: 0.527, data: 0.000) G_L1: 20.213 G_L1_ABSOLUTE: 3.651 G_L1_RELATIVE: 16.562 G_Regularizer: 0.000 validation_error: 21.575 +(epoch: 3, iters: 184496, time: 0.530, data: 0.000) G_L1: 17.158 G_L1_ABSOLUTE: 3.185 G_L1_RELATIVE: 13.974 G_Regularizer: 0.000 validation_error: 21.702 +(epoch: 3, iters: 186496, time: 0.528, data: 0.000) G_L1: 15.908 G_L1_ABSOLUTE: 3.113 G_L1_RELATIVE: 12.795 G_Regularizer: 0.000 validation_error: 21.021 +(epoch: 3, iters: 188496, time: 0.529, data: 0.000) G_L1: 17.625 G_L1_ABSOLUTE: 3.218 G_L1_RELATIVE: 14.407 G_Regularizer: 0.000 validation_error: 21.256 +(epoch: 3, iters: 190496, time: 0.531, data: 0.000) G_L1: 20.941 G_L1_ABSOLUTE: 3.107 G_L1_RELATIVE: 17.834 G_Regularizer: 0.000 validation_error: 21.122 +(epoch: 3, iters: 192496, time: 0.527, data: 0.000) G_L1: 18.809 G_L1_ABSOLUTE: 2.966 G_L1_RELATIVE: 15.843 G_Regularizer: 0.000 validation_error: 21.592 +(epoch: 3, iters: 194496, time: 0.524, data: 0.000) G_L1: 15.818 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 12.955 G_Regularizer: 0.000 validation_error: 21.230 +(epoch: 3, iters: 196496, time: 0.537, data: 0.000) G_L1: 16.811 G_L1_ABSOLUTE: 3.618 G_L1_RELATIVE: 13.194 G_Regularizer: 0.000 validation_error: 21.419 +(epoch: 3, iters: 198496, time: 0.529, data: 0.000) G_L1: 17.341 G_L1_ABSOLUTE: 2.854 G_L1_RELATIVE: 14.487 G_Regularizer: 0.000 validation_error: 21.866 +(epoch: 3, iters: 200496, time: 0.533, data: 0.000) G_L1: 17.436 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 14.652 G_Regularizer: 0.000 validation_error: 22.129 +(epoch: 3, iters: 202496, time: 0.531, data: 0.000) G_L1: 17.934 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 15.306 G_Regularizer: 0.000 validation_error: 22.251 +(epoch: 3, iters: 204496, time: 0.531, data: 0.000) G_L1: 16.221 G_L1_ABSOLUTE: 3.189 G_L1_RELATIVE: 13.032 G_Regularizer: 0.000 validation_error: 21.341 +(epoch: 3, iters: 206496, time: 0.527, data: 0.000) G_L1: 22.852 G_L1_ABSOLUTE: 3.629 G_L1_RELATIVE: 19.223 G_Regularizer: 0.000 validation_error: 22.227 +(epoch: 3, iters: 208496, time: 0.525, data: 0.000) G_L1: 15.474 G_L1_ABSOLUTE: 3.251 G_L1_RELATIVE: 12.223 G_Regularizer: 0.000 validation_error: 21.987 +(epoch: 3, iters: 210496, time: 0.528, data: 0.000) G_L1: 19.697 G_L1_ABSOLUTE: 3.347 G_L1_RELATIVE: 16.350 G_Regularizer: 0.000 validation_error: 21.738 +(epoch: 3, iters: 212496, time: 0.522, data: 0.000) G_L1: 15.983 G_L1_ABSOLUTE: 3.269 G_L1_RELATIVE: 12.714 G_Regularizer: 0.000 validation_error: 21.409 +(epoch: 3, iters: 214496, time: 0.532, data: 0.000) G_L1: 15.208 G_L1_ABSOLUTE: 2.860 G_L1_RELATIVE: 12.348 G_Regularizer: 0.000 validation_error: 21.843 +(epoch: 3, iters: 216496, time: 0.530, data: 0.000) G_L1: 14.705 G_L1_ABSOLUTE: 2.995 G_L1_RELATIVE: 11.710 G_Regularizer: 0.000 validation_error: 21.481 +(epoch: 3, iters: 218496, time: 0.523, data: 0.000) G_L1: 16.089 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 13.359 G_Regularizer: 0.000 validation_error: 21.103 +(epoch: 3, iters: 220496, time: 0.524, data: 0.000) G_L1: 19.863 G_L1_ABSOLUTE: 3.676 G_L1_RELATIVE: 16.187 G_Regularizer: 0.000 validation_error: 21.371 +(epoch: 3, iters: 222496, time: 0.525, data: 0.000) G_L1: 18.292 G_L1_ABSOLUTE: 3.061 G_L1_RELATIVE: 15.231 G_Regularizer: 0.000 validation_error: 21.496 +(epoch: 3, iters: 224496, time: 0.536, data: 0.000) G_L1: 16.496 G_L1_ABSOLUTE: 3.288 G_L1_RELATIVE: 13.207 G_Regularizer: 0.000 validation_error: 21.179 +(epoch: 3, iters: 226496, time: 0.534, data: 0.000) G_L1: 17.648 G_L1_ABSOLUTE: 3.121 G_L1_RELATIVE: 14.527 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 3, iters: 228496, time: 0.528, data: 0.000) G_L1: 15.308 G_L1_ABSOLUTE: 3.200 G_L1_RELATIVE: 12.108 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 3, iters: 230496, time: 0.535, data: 0.000) G_L1: 18.682 G_L1_ABSOLUTE: 3.223 G_L1_RELATIVE: 15.459 G_Regularizer: 0.000 validation_error: 21.030 +(epoch: 3, iters: 232496, time: 0.530, data: 0.000) G_L1: 18.377 G_L1_ABSOLUTE: 3.179 G_L1_RELATIVE: 15.198 G_Regularizer: 0.000 validation_error: 21.094 +(epoch: 3, iters: 234496, time: 0.526, data: 0.001) G_L1: 17.366 G_L1_ABSOLUTE: 3.299 G_L1_RELATIVE: 14.067 G_Regularizer: 0.000 validation_error: 21.876 +(epoch: 3, iters: 236496, time: 0.535, data: 0.000) G_L1: 21.660 G_L1_ABSOLUTE: 3.146 G_L1_RELATIVE: 18.513 G_Regularizer: 0.000 validation_error: 21.934 +(epoch: 3, iters: 238496, time: 0.535, data: 0.000) G_L1: 17.813 G_L1_ABSOLUTE: 2.901 G_L1_RELATIVE: 14.913 G_Regularizer: 0.000 validation_error: 21.841 +(epoch: 3, iters: 240496, time: 0.529, data: 0.000) G_L1: 16.320 G_L1_ABSOLUTE: 3.248 G_L1_RELATIVE: 13.072 G_Regularizer: 0.000 validation_error: 21.190 +(epoch: 3, iters: 242496, time: 0.530, data: 0.000) G_L1: 16.815 G_L1_ABSOLUTE: 3.004 G_L1_RELATIVE: 13.811 G_Regularizer: 0.000 validation_error: 21.124 +(epoch: 3, iters: 244496, time: 0.533, data: 0.000) G_L1: 15.978 G_L1_ABSOLUTE: 3.700 G_L1_RELATIVE: 12.278 G_Regularizer: 0.000 validation_error: 20.896 +(epoch: 3, iters: 246496, time: 0.527, data: 0.000) G_L1: 16.128 G_L1_ABSOLUTE: 3.414 G_L1_RELATIVE: 12.714 G_Regularizer: 0.000 validation_error: 21.267 +(epoch: 3, iters: 248496, time: 0.530, data: 0.000) G_L1: 14.625 G_L1_ABSOLUTE: 2.884 G_L1_RELATIVE: 11.741 G_Regularizer: 0.000 validation_error: 21.809 +(epoch: 3, iters: 250496, time: 0.530, data: 0.000) G_L1: 15.678 G_L1_ABSOLUTE: 3.089 G_L1_RELATIVE: 12.590 G_Regularizer: 0.000 validation_error: 21.367 +(epoch: 3, iters: 252496, time: 0.527, data: 0.000) G_L1: 13.569 G_L1_ABSOLUTE: 2.763 G_L1_RELATIVE: 10.806 G_Regularizer: 0.000 validation_error: 21.784 +(epoch: 3, iters: 254496, time: 0.531, data: 0.000) G_L1: 16.397 G_L1_ABSOLUTE: 2.992 G_L1_RELATIVE: 13.405 G_Regularizer: 0.000 validation_error: 21.863 +(epoch: 3, iters: 256496, time: 0.535, data: 0.000) G_L1: 15.573 G_L1_ABSOLUTE: 3.270 G_L1_RELATIVE: 12.303 G_Regularizer: 0.000 validation_error: 21.346 +(epoch: 3, iters: 258496, time: 0.530, data: 0.000) G_L1: 18.379 G_L1_ABSOLUTE: 3.364 G_L1_RELATIVE: 15.016 G_Regularizer: 0.000 validation_error: 21.315 +(epoch: 3, iters: 260496, time: 0.530, data: 0.000) G_L1: 16.494 G_L1_ABSOLUTE: 2.729 G_L1_RELATIVE: 13.766 G_Regularizer: 0.000 validation_error: 21.313 +(epoch: 3, iters: 262496, time: 0.527, data: 0.000) G_L1: 16.103 G_L1_ABSOLUTE: 3.063 G_L1_RELATIVE: 13.040 G_Regularizer: 0.000 validation_error: 22.026 +(epoch: 3, iters: 264496, time: 0.545, data: 0.000) G_L1: 16.610 G_L1_ABSOLUTE: 2.852 G_L1_RELATIVE: 13.758 G_Regularizer: 0.000 validation_error: 22.296 +(epoch: 3, iters: 266496, time: 0.633, data: 0.000) G_L1: 18.416 G_L1_ABSOLUTE: 3.152 G_L1_RELATIVE: 15.264 G_Regularizer: 0.000 validation_error: 21.743 +(epoch: 3, iters: 268496, time: 0.630, data: 0.000) G_L1: 13.812 G_L1_ABSOLUTE: 2.699 G_L1_RELATIVE: 11.113 G_Regularizer: 0.000 validation_error: 21.977 +(epoch: 3, iters: 270496, time: 0.628, data: 0.001) G_L1: 15.533 G_L1_ABSOLUTE: 3.260 G_L1_RELATIVE: 12.273 G_Regularizer: 0.000 validation_error: 22.139 +(epoch: 3, iters: 272496, time: 0.633, data: 0.000) G_L1: 16.503 G_L1_ABSOLUTE: 2.832 G_L1_RELATIVE: 13.671 G_Regularizer: 0.000 validation_error: 21.469 +(epoch: 3, iters: 274496, time: 0.627, data: 0.000) G_L1: 24.646 G_L1_ABSOLUTE: 3.712 G_L1_RELATIVE: 20.934 G_Regularizer: 0.000 validation_error: 21.617 +(epoch: 3, iters: 276496, time: 0.620, data: 0.000) G_L1: 14.582 G_L1_ABSOLUTE: 2.820 G_L1_RELATIVE: 11.762 G_Regularizer: 0.000 validation_error: 21.755 +(epoch: 3, iters: 278496, time: 0.629, data: 0.000) G_L1: 20.605 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 17.868 G_Regularizer: 0.000 validation_error: 21.140 +(epoch: 3, iters: 280496, time: 0.591, data: 0.000) G_L1: 18.820 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 16.083 G_Regularizer: 0.000 validation_error: 21.585 +(epoch: 3, iters: 282496, time: 0.627, data: 0.000) G_L1: 17.572 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 14.702 G_Regularizer: 0.000 validation_error: 21.225 +(epoch: 3, iters: 284496, time: 0.586, data: 0.000) G_L1: 16.094 G_L1_ABSOLUTE: 2.991 G_L1_RELATIVE: 13.103 G_Regularizer: 0.000 validation_error: 21.506 +(epoch: 3, iters: 286496, time: 0.528, data: 0.000) G_L1: 19.949 G_L1_ABSOLUTE: 4.025 G_L1_RELATIVE: 15.924 G_Regularizer: 0.000 validation_error: 21.667 +(epoch: 3, iters: 288496, time: 0.526, data: 0.000) G_L1: 22.480 G_L1_ABSOLUTE: 3.173 G_L1_RELATIVE: 19.307 G_Regularizer: 0.000 validation_error: 22.052 +(epoch: 3, iters: 290496, time: 0.527, data: 0.000) G_L1: 16.482 G_L1_ABSOLUTE: 2.931 G_L1_RELATIVE: 13.551 G_Regularizer: 0.000 validation_error: 21.638 +(epoch: 3, iters: 292496, time: 0.534, data: 0.000) G_L1: 16.128 G_L1_ABSOLUTE: 3.177 G_L1_RELATIVE: 12.952 G_Regularizer: 0.000 validation_error: 22.034 +(epoch: 3, iters: 294496, time: 0.522, data: 0.000) G_L1: 14.329 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 11.340 G_Regularizer: 0.000 validation_error: 21.526 +(epoch: 3, iters: 296496, time: 0.545, data: 0.000) G_L1: 19.908 G_L1_ABSOLUTE: 3.168 G_L1_RELATIVE: 16.740 G_Regularizer: 0.000 validation_error: 21.747 +(epoch: 3, iters: 298496, time: 0.624, data: 0.000) G_L1: 17.762 G_L1_ABSOLUTE: 3.407 G_L1_RELATIVE: 14.355 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 3, iters: 300496, time: 0.575, data: 0.000) G_L1: 16.927 G_L1_ABSOLUTE: 2.953 G_L1_RELATIVE: 13.974 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 3, iters: 302496, time: 0.530, data: 0.000) G_L1: 16.064 G_L1_ABSOLUTE: 2.922 G_L1_RELATIVE: 13.141 G_Regularizer: 0.000 validation_error: 21.503 +(epoch: 4, iters: 1744, time: 0.527, data: 0.000) G_L1: 17.335 G_L1_ABSOLUTE: 3.433 G_L1_RELATIVE: 13.902 G_Regularizer: 0.000 validation_error: 21.591 +(epoch: 4, iters: 3744, time: 0.530, data: 0.000) G_L1: 22.076 G_L1_ABSOLUTE: 3.282 G_L1_RELATIVE: 18.794 G_Regularizer: 0.000 validation_error: 21.497 +(epoch: 4, iters: 5744, time: 0.532, data: 0.000) G_L1: 14.521 G_L1_ABSOLUTE: 3.097 G_L1_RELATIVE: 11.425 G_Regularizer: 0.000 validation_error: 21.582 +(epoch: 4, iters: 7744, time: 0.532, data: 0.000) G_L1: 16.551 G_L1_ABSOLUTE: 2.796 G_L1_RELATIVE: 13.755 G_Regularizer: 0.000 validation_error: 21.678 +(epoch: 4, iters: 9744, time: 0.532, data: 0.000) G_L1: 16.196 G_L1_ABSOLUTE: 2.996 G_L1_RELATIVE: 13.200 G_Regularizer: 0.000 validation_error: 21.293 +(epoch: 4, iters: 11744, time: 0.524, data: 0.000) G_L1: 16.523 G_L1_ABSOLUTE: 2.955 G_L1_RELATIVE: 13.568 G_Regularizer: 0.000 validation_error: 21.899 +(epoch: 4, iters: 13744, time: 0.524, data: 0.000) G_L1: 16.354 G_L1_ABSOLUTE: 3.167 G_L1_RELATIVE: 13.187 G_Regularizer: 0.000 validation_error: 21.277 +(epoch: 4, iters: 15744, time: 0.521, data: 0.000) G_L1: 18.166 G_L1_ABSOLUTE: 2.860 G_L1_RELATIVE: 15.306 G_Regularizer: 0.000 validation_error: 21.599 +(epoch: 4, iters: 17744, time: 0.537, data: 0.000) G_L1: 13.495 G_L1_ABSOLUTE: 3.052 G_L1_RELATIVE: 10.444 G_Regularizer: 0.000 validation_error: 21.734 +(epoch: 4, iters: 19744, time: 0.525, data: 0.000) G_L1: 16.257 G_L1_ABSOLUTE: 3.035 G_L1_RELATIVE: 13.223 G_Regularizer: 0.000 validation_error: 20.847 +(epoch: 4, iters: 21744, time: 0.531, data: 0.000) G_L1: 16.905 G_L1_ABSOLUTE: 3.068 G_L1_RELATIVE: 13.837 G_Regularizer: 0.000 validation_error: 21.744 +(epoch: 4, iters: 23744, time: 0.527, data: 0.000) G_L1: 16.217 G_L1_ABSOLUTE: 3.039 G_L1_RELATIVE: 13.178 G_Regularizer: 0.000 validation_error: 21.468 +(epoch: 4, iters: 25744, time: 0.529, data: 0.000) G_L1: 19.822 G_L1_ABSOLUTE: 3.210 G_L1_RELATIVE: 16.611 G_Regularizer: 0.000 validation_error: 21.523 +(epoch: 4, iters: 27744, time: 0.528, data: 0.000) G_L1: 14.909 G_L1_ABSOLUTE: 2.909 G_L1_RELATIVE: 11.999 G_Regularizer: 0.000 validation_error: 22.234 +(epoch: 4, iters: 29744, time: 0.528, data: 0.000) G_L1: 17.506 G_L1_ABSOLUTE: 3.258 G_L1_RELATIVE: 14.248 G_Regularizer: 0.000 validation_error: 21.357 +(epoch: 4, iters: 31744, time: 0.528, data: 0.000) G_L1: 13.906 G_L1_ABSOLUTE: 2.935 G_L1_RELATIVE: 10.971 G_Regularizer: 0.000 validation_error: 21.327 +(epoch: 4, iters: 33744, time: 0.533, data: 0.000) G_L1: 15.678 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 12.886 G_Regularizer: 0.000 validation_error: 21.464 +(epoch: 4, iters: 35744, time: 0.536, data: 0.000) G_L1: 19.474 G_L1_ABSOLUTE: 2.922 G_L1_RELATIVE: 16.553 G_Regularizer: 0.000 validation_error: 21.793 +(epoch: 4, iters: 37744, time: 0.532, data: 0.001) G_L1: 16.319 G_L1_ABSOLUTE: 2.996 G_L1_RELATIVE: 13.323 G_Regularizer: 0.000 validation_error: 21.906 +(epoch: 4, iters: 39744, time: 0.529, data: 0.000) G_L1: 16.452 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 13.746 G_Regularizer: 0.000 validation_error: 22.104 +(epoch: 4, iters: 41744, time: 0.533, data: 0.000) G_L1: 18.475 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 15.583 G_Regularizer: 0.000 validation_error: 21.524 +(epoch: 4, iters: 43744, time: 0.532, data: 0.000) G_L1: 17.585 G_L1_ABSOLUTE: 3.704 G_L1_RELATIVE: 13.881 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 4, iters: 45744, time: 0.534, data: 0.000) G_L1: 15.930 G_L1_ABSOLUTE: 3.445 G_L1_RELATIVE: 12.484 G_Regularizer: 0.000 validation_error: 21.105 +(epoch: 4, iters: 47744, time: 0.529, data: 0.000) G_L1: 18.882 G_L1_ABSOLUTE: 3.354 G_L1_RELATIVE: 15.527 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 4, iters: 49744, time: 0.532, data: 0.000) G_L1: 16.443 G_L1_ABSOLUTE: 2.837 G_L1_RELATIVE: 13.606 G_Regularizer: 0.000 validation_error: 21.443 +(epoch: 4, iters: 51744, time: 0.528, data: 0.000) G_L1: 18.152 G_L1_ABSOLUTE: 2.770 G_L1_RELATIVE: 15.381 G_Regularizer: 0.000 validation_error: 21.118 +(epoch: 4, iters: 53744, time: 0.536, data: 0.000) G_L1: 18.071 G_L1_ABSOLUTE: 3.799 G_L1_RELATIVE: 14.272 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 4, iters: 55744, time: 0.534, data: 0.000) G_L1: 21.285 G_L1_ABSOLUTE: 3.057 G_L1_RELATIVE: 18.228 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 4, iters: 57744, time: 0.533, data: 0.000) G_L1: 16.574 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 13.952 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 4, iters: 59744, time: 0.533, data: 0.000) G_L1: 15.908 G_L1_ABSOLUTE: 3.138 G_L1_RELATIVE: 12.771 G_Regularizer: 0.000 validation_error: 21.746 +(epoch: 4, iters: 61744, time: 0.526, data: 0.000) G_L1: 15.686 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 13.182 G_Regularizer: 0.000 validation_error: 21.012 +(epoch: 4, iters: 63744, time: 0.533, data: 0.000) G_L1: 18.535 G_L1_ABSOLUTE: 2.883 G_L1_RELATIVE: 15.652 G_Regularizer: 0.000 validation_error: 21.244 +(epoch: 4, iters: 65744, time: 0.527, data: 0.000) G_L1: 14.268 G_L1_ABSOLUTE: 2.974 G_L1_RELATIVE: 11.294 G_Regularizer: 0.000 validation_error: 21.373 +(epoch: 4, iters: 67744, time: 0.536, data: 0.000) G_L1: 15.090 G_L1_ABSOLUTE: 2.807 G_L1_RELATIVE: 12.283 G_Regularizer: 0.000 validation_error: 20.765 +(epoch: 4, iters: 69744, time: 0.531, data: 0.000) G_L1: 16.255 G_L1_ABSOLUTE: 2.993 G_L1_RELATIVE: 13.263 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 4, iters: 71744, time: 0.532, data: 0.000) G_L1: 21.011 G_L1_ABSOLUTE: 3.050 G_L1_RELATIVE: 17.961 G_Regularizer: 0.000 validation_error: 21.989 +(epoch: 4, iters: 73744, time: 0.527, data: 0.000) G_L1: 17.072 G_L1_ABSOLUTE: 3.097 G_L1_RELATIVE: 13.975 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 4, iters: 75744, time: 0.530, data: 0.000) G_L1: 17.241 G_L1_ABSOLUTE: 2.941 G_L1_RELATIVE: 14.299 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 4, iters: 77744, time: 0.532, data: 0.000) G_L1: 19.436 G_L1_ABSOLUTE: 3.997 G_L1_RELATIVE: 15.439 G_Regularizer: 0.000 validation_error: 21.525 +(epoch: 4, iters: 79744, time: 0.533, data: 0.000) G_L1: 13.746 G_L1_ABSOLUTE: 3.092 G_L1_RELATIVE: 10.655 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 4, iters: 81744, time: 0.529, data: 0.000) G_L1: 19.946 G_L1_ABSOLUTE: 3.184 G_L1_RELATIVE: 16.762 G_Regularizer: 0.000 validation_error: 21.729 +(epoch: 4, iters: 83744, time: 0.530, data: 0.000) G_L1: 16.279 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 13.443 G_Regularizer: 0.000 validation_error: 21.631 +(epoch: 4, iters: 85744, time: 0.533, data: 0.000) G_L1: 19.037 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 16.152 G_Regularizer: 0.000 validation_error: 21.862 +(epoch: 4, iters: 87744, time: 0.529, data: 0.000) G_L1: 15.886 G_L1_ABSOLUTE: 2.925 G_L1_RELATIVE: 12.961 G_Regularizer: 0.000 validation_error: 21.782 +(epoch: 4, iters: 89744, time: 0.531, data: 0.000) G_L1: 15.377 G_L1_ABSOLUTE: 3.318 G_L1_RELATIVE: 12.059 G_Regularizer: 0.000 validation_error: 22.207 +(epoch: 4, iters: 91744, time: 0.530, data: 0.000) G_L1: 16.127 G_L1_ABSOLUTE: 3.342 G_L1_RELATIVE: 12.785 G_Regularizer: 0.000 validation_error: 21.526 +(epoch: 4, iters: 93744, time: 0.535, data: 0.000) G_L1: 18.349 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 15.460 G_Regularizer: 0.000 validation_error: 20.724 +(epoch: 4, iters: 95744, time: 0.536, data: 0.000) G_L1: 18.555 G_L1_ABSOLUTE: 3.254 G_L1_RELATIVE: 15.301 G_Regularizer: 0.000 validation_error: 21.752 +(epoch: 4, iters: 97744, time: 0.533, data: 0.000) G_L1: 17.785 G_L1_ABSOLUTE: 3.590 G_L1_RELATIVE: 14.194 G_Regularizer: 0.000 validation_error: 20.822 +(epoch: 4, iters: 99744, time: 0.529, data: 0.000) G_L1: 20.644 G_L1_ABSOLUTE: 3.022 G_L1_RELATIVE: 17.622 G_Regularizer: 0.000 validation_error: 22.042 +(epoch: 4, iters: 101744, time: 0.529, data: 0.000) G_L1: 14.574 G_L1_ABSOLUTE: 2.860 G_L1_RELATIVE: 11.714 G_Regularizer: 0.000 validation_error: 21.241 +(epoch: 4, iters: 103744, time: 0.525, data: 0.000) G_L1: 17.009 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 14.263 G_Regularizer: 0.000 validation_error: 21.882 +(epoch: 4, iters: 105744, time: 0.521, data: 0.000) G_L1: 15.014 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 12.202 G_Regularizer: 0.000 validation_error: 21.068 +(epoch: 4, iters: 107744, time: 0.536, data: 0.000) G_L1: 19.033 G_L1_ABSOLUTE: 2.993 G_L1_RELATIVE: 16.040 G_Regularizer: 0.000 validation_error: 21.504 +(epoch: 4, iters: 109744, time: 0.531, data: 0.000) G_L1: 14.486 G_L1_ABSOLUTE: 3.258 G_L1_RELATIVE: 11.228 G_Regularizer: 0.000 validation_error: 21.350 +(epoch: 4, iters: 111744, time: 0.527, data: 0.000) G_L1: 18.351 G_L1_ABSOLUTE: 3.072 G_L1_RELATIVE: 15.280 G_Regularizer: 0.000 validation_error: 21.327 +(epoch: 4, iters: 113744, time: 0.528, data: 0.000) G_L1: 20.497 G_L1_ABSOLUTE: 2.910 G_L1_RELATIVE: 17.587 G_Regularizer: 0.000 validation_error: 21.737 +(epoch: 4, iters: 115744, time: 0.532, data: 0.000) G_L1: 15.026 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 12.375 G_Regularizer: 0.000 validation_error: 21.063 +(epoch: 4, iters: 117744, time: 0.526, data: 0.000) G_L1: 14.562 G_L1_ABSOLUTE: 2.875 G_L1_RELATIVE: 11.687 G_Regularizer: 0.000 validation_error: 21.202 +(epoch: 4, iters: 119744, time: 0.530, data: 0.001) G_L1: 17.048 G_L1_ABSOLUTE: 3.127 G_L1_RELATIVE: 13.921 G_Regularizer: 0.000 validation_error: 21.372 +(epoch: 4, iters: 121744, time: 0.532, data: 0.000) G_L1: 16.221 G_L1_ABSOLUTE: 3.169 G_L1_RELATIVE: 13.052 G_Regularizer: 0.000 validation_error: 21.882 +(epoch: 4, iters: 123744, time: 0.532, data: 0.000) G_L1: 15.657 G_L1_ABSOLUTE: 3.314 G_L1_RELATIVE: 12.342 G_Regularizer: 0.000 validation_error: 21.763 +(epoch: 4, iters: 125744, time: 0.538, data: 0.000) G_L1: 19.169 G_L1_ABSOLUTE: 3.709 G_L1_RELATIVE: 15.460 G_Regularizer: 0.000 validation_error: 21.599 +(epoch: 4, iters: 127744, time: 0.521, data: 0.000) G_L1: 19.321 G_L1_ABSOLUTE: 3.477 G_L1_RELATIVE: 15.844 G_Regularizer: 0.000 validation_error: 21.398 +(epoch: 4, iters: 129744, time: 0.527, data: 0.000) G_L1: 15.468 G_L1_ABSOLUTE: 3.087 G_L1_RELATIVE: 12.382 G_Regularizer: 0.000 validation_error: 20.620 +(epoch: 4, iters: 131744, time: 0.530, data: 0.000) G_L1: 15.220 G_L1_ABSOLUTE: 2.956 G_L1_RELATIVE: 12.264 G_Regularizer: 0.000 validation_error: 21.105 +(epoch: 4, iters: 133744, time: 0.526, data: 0.000) G_L1: 17.284 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 14.530 G_Regularizer: 0.000 validation_error: 21.734 +(epoch: 4, iters: 135744, time: 0.528, data: 0.000) G_L1: 16.186 G_L1_ABSOLUTE: 2.850 G_L1_RELATIVE: 13.336 G_Regularizer: 0.000 validation_error: 21.040 +(epoch: 4, iters: 137744, time: 0.533, data: 0.000) G_L1: 19.734 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 16.974 G_Regularizer: 0.000 validation_error: 20.510 +(epoch: 4, iters: 139744, time: 0.524, data: 0.000) G_L1: 18.493 G_L1_ABSOLUTE: 2.643 G_L1_RELATIVE: 15.849 G_Regularizer: 0.000 validation_error: 21.152 +(epoch: 4, iters: 141744, time: 0.533, data: 0.000) G_L1: 16.483 G_L1_ABSOLUTE: 2.991 G_L1_RELATIVE: 13.493 G_Regularizer: 0.000 validation_error: 21.489 +(epoch: 4, iters: 143744, time: 0.533, data: 0.000) G_L1: 21.410 G_L1_ABSOLUTE: 3.390 G_L1_RELATIVE: 18.019 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 4, iters: 145744, time: 0.529, data: 0.000) G_L1: 19.219 G_L1_ABSOLUTE: 3.381 G_L1_RELATIVE: 15.838 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 4, iters: 147744, time: 0.533, data: 0.000) G_L1: 17.485 G_L1_ABSOLUTE: 3.072 G_L1_RELATIVE: 14.413 G_Regularizer: 0.000 validation_error: 21.507 +(epoch: 4, iters: 149744, time: 0.530, data: 0.000) G_L1: 17.711 G_L1_ABSOLUTE: 3.031 G_L1_RELATIVE: 14.680 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 4, iters: 151744, time: 0.529, data: 0.000) G_L1: 19.921 G_L1_ABSOLUTE: 3.418 G_L1_RELATIVE: 16.503 G_Regularizer: 0.000 validation_error: 20.691 +(epoch: 4, iters: 153744, time: 0.525, data: 0.000) G_L1: 17.161 G_L1_ABSOLUTE: 2.845 G_L1_RELATIVE: 14.316 G_Regularizer: 0.000 validation_error: 21.186 +(epoch: 4, iters: 155744, time: 0.526, data: 0.000) G_L1: 17.292 G_L1_ABSOLUTE: 3.476 G_L1_RELATIVE: 13.816 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 4, iters: 157744, time: 0.529, data: 0.000) G_L1: 14.090 G_L1_ABSOLUTE: 3.078 G_L1_RELATIVE: 11.012 G_Regularizer: 0.000 validation_error: 21.323 +(epoch: 4, iters: 159744, time: 0.527, data: 0.000) G_L1: 14.811 G_L1_ABSOLUTE: 2.711 G_L1_RELATIVE: 12.100 G_Regularizer: 0.000 validation_error: 21.647 +(epoch: 4, iters: 161744, time: 0.529, data: 0.000) G_L1: 16.950 G_L1_ABSOLUTE: 3.252 G_L1_RELATIVE: 13.699 G_Regularizer: 0.000 validation_error: 21.162 +(epoch: 4, iters: 163744, time: 0.528, data: 0.000) G_L1: 14.781 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 12.027 G_Regularizer: 0.000 validation_error: 21.249 +(epoch: 4, iters: 165744, time: 0.529, data: 0.000) G_L1: 18.643 G_L1_ABSOLUTE: 3.176 G_L1_RELATIVE: 15.467 G_Regularizer: 0.000 validation_error: 20.768 +(epoch: 4, iters: 167744, time: 0.529, data: 0.001) G_L1: 16.096 G_L1_ABSOLUTE: 2.799 G_L1_RELATIVE: 13.297 G_Regularizer: 0.000 validation_error: 20.323 +(epoch: 4, iters: 169744, time: 0.532, data: 0.000) G_L1: 23.842 G_L1_ABSOLUTE: 3.075 G_L1_RELATIVE: 20.766 G_Regularizer: 0.000 validation_error: 21.659 +(epoch: 4, iters: 171744, time: 0.527, data: 0.000) G_L1: 18.038 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 15.162 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 4, iters: 173744, time: 0.529, data: 0.000) G_L1: 17.841 G_L1_ABSOLUTE: 3.190 G_L1_RELATIVE: 14.650 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 4, iters: 175744, time: 0.531, data: 0.000) G_L1: 18.511 G_L1_ABSOLUTE: 3.043 G_L1_RELATIVE: 15.468 G_Regularizer: 0.000 validation_error: 20.358 +(epoch: 4, iters: 177744, time: 0.527, data: 0.000) G_L1: 15.785 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 13.116 G_Regularizer: 0.000 validation_error: 21.161 +(epoch: 4, iters: 179744, time: 0.526, data: 0.000) G_L1: 15.732 G_L1_ABSOLUTE: 2.769 G_L1_RELATIVE: 12.963 G_Regularizer: 0.000 validation_error: 21.321 +(epoch: 4, iters: 181744, time: 0.530, data: 0.000) G_L1: 17.038 G_L1_ABSOLUTE: 3.448 G_L1_RELATIVE: 13.590 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 4, iters: 183744, time: 0.527, data: 0.000) G_L1: 17.348 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 14.611 G_Regularizer: 0.000 validation_error: 21.898 +(epoch: 4, iters: 185744, time: 0.528, data: 0.000) G_L1: 16.733 G_L1_ABSOLUTE: 3.018 G_L1_RELATIVE: 13.715 G_Regularizer: 0.000 validation_error: 21.037 +(epoch: 4, iters: 187744, time: 0.537, data: 0.000) G_L1: 15.632 G_L1_ABSOLUTE: 3.297 G_L1_RELATIVE: 12.335 G_Regularizer: 0.000 validation_error: 21.629 +(epoch: 4, iters: 189744, time: 0.527, data: 0.000) G_L1: 15.297 G_L1_ABSOLUTE: 2.958 G_L1_RELATIVE: 12.340 G_Regularizer: 0.000 validation_error: 21.024 +(epoch: 4, iters: 191744, time: 0.529, data: 0.000) G_L1: 17.970 G_L1_ABSOLUTE: 3.312 G_L1_RELATIVE: 14.657 G_Regularizer: 0.000 validation_error: 20.910 +(epoch: 4, iters: 193744, time: 0.527, data: 0.000) G_L1: 16.879 G_L1_ABSOLUTE: 3.324 G_L1_RELATIVE: 13.555 G_Regularizer: 0.000 validation_error: 20.430 +(epoch: 4, iters: 195744, time: 0.527, data: 0.000) G_L1: 15.272 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 12.623 G_Regularizer: 0.000 validation_error: 20.946 +(epoch: 4, iters: 197744, time: 0.533, data: 0.000) G_L1: 17.341 G_L1_ABSOLUTE: 3.230 G_L1_RELATIVE: 14.112 G_Regularizer: 0.000 validation_error: 21.608 +(epoch: 4, iters: 199744, time: 0.529, data: 0.000) G_L1: 17.586 G_L1_ABSOLUTE: 2.879 G_L1_RELATIVE: 14.707 G_Regularizer: 0.000 validation_error: 21.232 +(epoch: 4, iters: 201744, time: 0.542, data: 0.000) G_L1: 14.913 G_L1_ABSOLUTE: 3.138 G_L1_RELATIVE: 11.774 G_Regularizer: 0.000 validation_error: 21.487 +(epoch: 4, iters: 203744, time: 0.530, data: 0.000) G_L1: 15.769 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 12.992 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 4, iters: 205744, time: 0.532, data: 0.000) G_L1: 19.274 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 16.516 G_Regularizer: 0.000 validation_error: 21.382 +(epoch: 4, iters: 207744, time: 0.529, data: 0.000) G_L1: 16.524 G_L1_ABSOLUTE: 3.157 G_L1_RELATIVE: 13.367 G_Regularizer: 0.000 validation_error: 21.272 +(epoch: 4, iters: 209744, time: 0.529, data: 0.000) G_L1: 14.329 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 11.671 G_Regularizer: 0.000 validation_error: 21.864 +(epoch: 4, iters: 211744, time: 0.530, data: 0.000) G_L1: 18.949 G_L1_ABSOLUTE: 2.921 G_L1_RELATIVE: 16.028 G_Regularizer: 0.000 validation_error: 21.542 +(epoch: 4, iters: 213744, time: 0.532, data: 0.000) G_L1: 15.176 G_L1_ABSOLUTE: 3.045 G_L1_RELATIVE: 12.131 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 4, iters: 215744, time: 0.529, data: 0.000) G_L1: 21.322 G_L1_ABSOLUTE: 2.912 G_L1_RELATIVE: 18.410 G_Regularizer: 0.000 validation_error: 21.861 +(epoch: 4, iters: 217744, time: 0.527, data: 0.000) G_L1: 17.395 G_L1_ABSOLUTE: 3.124 G_L1_RELATIVE: 14.271 G_Regularizer: 0.000 validation_error: 21.358 +(epoch: 4, iters: 219744, time: 0.527, data: 0.000) G_L1: 21.469 G_L1_ABSOLUTE: 2.615 G_L1_RELATIVE: 18.855 G_Regularizer: 0.000 validation_error: 21.423 +(epoch: 4, iters: 221744, time: 0.532, data: 0.000) G_L1: 14.882 G_L1_ABSOLUTE: 3.062 G_L1_RELATIVE: 11.820 G_Regularizer: 0.000 validation_error: 21.286 +(epoch: 4, iters: 223744, time: 0.527, data: 0.000) G_L1: 17.217 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 14.567 G_Regularizer: 0.000 validation_error: 21.546 +(epoch: 4, iters: 225744, time: 0.529, data: 0.000) G_L1: 19.188 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 16.644 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 4, iters: 227744, time: 0.534, data: 0.000) G_L1: 17.153 G_L1_ABSOLUTE: 3.044 G_L1_RELATIVE: 14.110 G_Regularizer: 0.000 validation_error: 21.080 +(epoch: 4, iters: 229744, time: 0.524, data: 0.000) G_L1: 17.809 G_L1_ABSOLUTE: 3.119 G_L1_RELATIVE: 14.690 G_Regularizer: 0.000 validation_error: 21.191 +(epoch: 4, iters: 231744, time: 0.528, data: 0.000) G_L1: 19.376 G_L1_ABSOLUTE: 2.941 G_L1_RELATIVE: 16.436 G_Regularizer: 0.000 validation_error: 21.467 +(epoch: 4, iters: 233744, time: 0.536, data: 0.000) G_L1: 16.162 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 13.173 G_Regularizer: 0.000 validation_error: 20.596 +(epoch: 4, iters: 235744, time: 0.529, data: 0.000) G_L1: 15.474 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 12.714 G_Regularizer: 0.000 validation_error: 21.654 +(epoch: 4, iters: 237744, time: 0.528, data: 0.000) G_L1: 14.889 G_L1_ABSOLUTE: 2.779 G_L1_RELATIVE: 12.110 G_Regularizer: 0.000 validation_error: 21.755 +(epoch: 4, iters: 239744, time: 0.527, data: 0.000) G_L1: 15.106 G_L1_ABSOLUTE: 2.891 G_L1_RELATIVE: 12.215 G_Regularizer: 0.000 validation_error: 21.126 +(epoch: 4, iters: 241744, time: 0.530, data: 0.000) G_L1: 13.297 G_L1_ABSOLUTE: 3.214 G_L1_RELATIVE: 10.083 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 4, iters: 243744, time: 0.535, data: 0.000) G_L1: 17.786 G_L1_ABSOLUTE: 3.253 G_L1_RELATIVE: 14.533 G_Regularizer: 0.000 validation_error: 21.769 +(epoch: 4, iters: 245744, time: 0.523, data: 0.000) G_L1: 17.610 G_L1_ABSOLUTE: 3.050 G_L1_RELATIVE: 14.560 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 4, iters: 247744, time: 0.532, data: 0.000) G_L1: 18.648 G_L1_ABSOLUTE: 3.270 G_L1_RELATIVE: 15.378 G_Regularizer: 0.000 validation_error: 21.160 +(epoch: 4, iters: 249744, time: 0.529, data: 0.000) G_L1: 15.973 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 13.229 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 4, iters: 251744, time: 0.527, data: 0.000) G_L1: 18.756 G_L1_ABSOLUTE: 3.282 G_L1_RELATIVE: 15.474 G_Regularizer: 0.000 validation_error: 21.351 +(epoch: 4, iters: 253744, time: 0.530, data: 0.000) G_L1: 14.392 G_L1_ABSOLUTE: 2.802 G_L1_RELATIVE: 11.590 G_Regularizer: 0.000 validation_error: 21.784 +(epoch: 4, iters: 255744, time: 0.528, data: 0.000) G_L1: 18.928 G_L1_ABSOLUTE: 3.237 G_L1_RELATIVE: 15.691 G_Regularizer: 0.000 validation_error: 21.813 +(epoch: 4, iters: 257744, time: 0.532, data: 0.000) G_L1: 15.665 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 12.840 G_Regularizer: 0.000 validation_error: 20.761 +(epoch: 4, iters: 259744, time: 0.531, data: 0.000) G_L1: 15.276 G_L1_ABSOLUTE: 2.671 G_L1_RELATIVE: 12.605 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 4, iters: 261744, time: 0.526, data: 0.000) G_L1: 16.180 G_L1_ABSOLUTE: 3.049 G_L1_RELATIVE: 13.132 G_Regularizer: 0.000 validation_error: 21.627 +(epoch: 4, iters: 263744, time: 0.527, data: 0.000) G_L1: 14.778 G_L1_ABSOLUTE: 2.682 G_L1_RELATIVE: 12.097 G_Regularizer: 0.000 validation_error: 20.331 +(epoch: 4, iters: 265744, time: 0.532, data: 0.000) G_L1: 15.793 G_L1_ABSOLUTE: 2.893 G_L1_RELATIVE: 12.900 G_Regularizer: 0.000 validation_error: 21.787 +(epoch: 4, iters: 267744, time: 0.531, data: 0.000) G_L1: 16.914 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 14.253 G_Regularizer: 0.000 validation_error: 21.357 +(epoch: 4, iters: 269744, time: 0.531, data: 0.000) G_L1: 13.687 G_L1_ABSOLUTE: 3.093 G_L1_RELATIVE: 10.594 G_Regularizer: 0.000 validation_error: 21.418 +(epoch: 4, iters: 271744, time: 0.529, data: 0.000) G_L1: 15.516 G_L1_ABSOLUTE: 3.515 G_L1_RELATIVE: 12.001 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 4, iters: 273744, time: 0.534, data: 0.000) G_L1: 16.192 G_L1_ABSOLUTE: 3.194 G_L1_RELATIVE: 12.998 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 4, iters: 275744, time: 0.531, data: 0.000) G_L1: 18.883 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 16.246 G_Regularizer: 0.000 validation_error: 20.991 +(epoch: 4, iters: 277744, time: 0.527, data: 0.000) G_L1: 15.228 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 12.642 G_Regularizer: 0.000 validation_error: 21.353 +(epoch: 4, iters: 279744, time: 0.533, data: 0.000) G_L1: 19.209 G_L1_ABSOLUTE: 3.341 G_L1_RELATIVE: 15.868 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 4, iters: 281744, time: 0.533, data: 0.000) G_L1: 17.870 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 15.218 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 4, iters: 283744, time: 0.527, data: 0.000) G_L1: 18.653 G_L1_ABSOLUTE: 3.365 G_L1_RELATIVE: 15.287 G_Regularizer: 0.000 validation_error: 21.291 +(epoch: 4, iters: 285744, time: 0.527, data: 0.000) G_L1: 12.783 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 9.926 G_Regularizer: 0.000 validation_error: 21.252 +(epoch: 4, iters: 287744, time: 0.530, data: 0.000) G_L1: 16.768 G_L1_ABSOLUTE: 3.043 G_L1_RELATIVE: 13.724 G_Regularizer: 0.000 validation_error: 21.249 +(epoch: 4, iters: 289744, time: 0.521, data: 0.000) G_L1: 18.966 G_L1_ABSOLUTE: 3.215 G_L1_RELATIVE: 15.751 G_Regularizer: 0.000 validation_error: 21.186 +(epoch: 4, iters: 291744, time: 0.531, data: 0.000) G_L1: 19.073 G_L1_ABSOLUTE: 3.205 G_L1_RELATIVE: 15.868 G_Regularizer: 0.000 validation_error: 20.856 +(epoch: 4, iters: 293744, time: 0.531, data: 0.000) G_L1: 16.995 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 14.102 G_Regularizer: 0.000 validation_error: 20.483 +(epoch: 4, iters: 295744, time: 0.527, data: 0.000) G_L1: 14.628 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 12.072 G_Regularizer: 0.000 validation_error: 21.348 +(epoch: 4, iters: 297744, time: 0.530, data: 0.000) G_L1: 16.006 G_L1_ABSOLUTE: 3.033 G_L1_RELATIVE: 12.973 G_Regularizer: 0.000 validation_error: 21.497 +(epoch: 4, iters: 299744, time: 0.526, data: 0.000) G_L1: 18.891 G_L1_ABSOLUTE: 3.243 G_L1_RELATIVE: 15.647 G_Regularizer: 0.000 validation_error: 20.483 +(epoch: 4, iters: 301744, time: 0.525, data: 0.000) G_L1: 17.897 G_L1_ABSOLUTE: 3.047 G_L1_RELATIVE: 14.850 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 5, iters: 992, time: 0.534, data: 0.000) G_L1: 14.018 G_L1_ABSOLUTE: 3.353 G_L1_RELATIVE: 10.665 G_Regularizer: 0.000 validation_error: 21.132 +(epoch: 5, iters: 2992, time: 0.537, data: 0.000) G_L1: 16.055 G_L1_ABSOLUTE: 2.983 G_L1_RELATIVE: 13.072 G_Regularizer: 0.000 validation_error: 22.075 +(epoch: 5, iters: 4992, time: 0.533, data: 0.000) G_L1: 17.554 G_L1_ABSOLUTE: 2.966 G_L1_RELATIVE: 14.588 G_Regularizer: 0.000 validation_error: 21.824 +(epoch: 5, iters: 6992, time: 0.536, data: 0.000) G_L1: 16.833 G_L1_ABSOLUTE: 2.699 G_L1_RELATIVE: 14.133 G_Regularizer: 0.000 validation_error: 21.336 +(epoch: 5, iters: 8992, time: 0.527, data: 0.000) G_L1: 17.394 G_L1_ABSOLUTE: 2.753 G_L1_RELATIVE: 14.640 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 5, iters: 10992, time: 0.528, data: 0.000) G_L1: 15.535 G_L1_ABSOLUTE: 3.328 G_L1_RELATIVE: 12.207 G_Regularizer: 0.000 validation_error: 21.223 +(epoch: 5, iters: 12992, time: 0.531, data: 0.000) G_L1: 14.813 G_L1_ABSOLUTE: 2.557 G_L1_RELATIVE: 12.256 G_Regularizer: 0.000 validation_error: 21.005 +(epoch: 5, iters: 14992, time: 0.536, data: 0.000) G_L1: 19.054 G_L1_ABSOLUTE: 3.761 G_L1_RELATIVE: 15.293 G_Regularizer: 0.000 validation_error: 21.727 +(epoch: 5, iters: 16992, time: 0.528, data: 0.000) G_L1: 16.415 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 13.823 G_Regularizer: 0.000 validation_error: 21.115 +(epoch: 5, iters: 18992, time: 0.529, data: 0.000) G_L1: 12.564 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 9.915 G_Regularizer: 0.000 validation_error: 20.410 +(epoch: 5, iters: 20992, time: 0.533, data: 0.000) G_L1: 15.131 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 12.426 G_Regularizer: 0.000 validation_error: 20.820 +(epoch: 5, iters: 22992, time: 0.533, data: 0.000) G_L1: 15.767 G_L1_ABSOLUTE: 2.759 G_L1_RELATIVE: 13.008 G_Regularizer: 0.000 validation_error: 21.057 +(epoch: 5, iters: 24992, time: 0.530, data: 0.000) G_L1: 14.083 G_L1_ABSOLUTE: 2.993 G_L1_RELATIVE: 11.090 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 5, iters: 26992, time: 0.528, data: 0.000) G_L1: 16.393 G_L1_ABSOLUTE: 2.693 G_L1_RELATIVE: 13.701 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 5, iters: 28992, time: 0.531, data: 0.000) G_L1: 14.112 G_L1_ABSOLUTE: 2.931 G_L1_RELATIVE: 11.180 G_Regularizer: 0.000 validation_error: 21.705 +(epoch: 5, iters: 30992, time: 0.531, data: 0.000) G_L1: 17.419 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 14.549 G_Regularizer: 0.000 validation_error: 21.454 +(epoch: 5, iters: 32992, time: 0.529, data: 0.000) G_L1: 17.585 G_L1_ABSOLUTE: 3.425 G_L1_RELATIVE: 14.160 G_Regularizer: 0.000 validation_error: 21.233 +(epoch: 5, iters: 34992, time: 0.530, data: 0.000) G_L1: 14.840 G_L1_ABSOLUTE: 2.803 G_L1_RELATIVE: 12.037 G_Regularizer: 0.000 validation_error: 21.131 +(epoch: 5, iters: 36992, time: 0.528, data: 0.000) G_L1: 14.474 G_L1_ABSOLUTE: 2.846 G_L1_RELATIVE: 11.628 G_Regularizer: 0.000 validation_error: 21.431 +(epoch: 5, iters: 38992, time: 0.531, data: 0.000) G_L1: 20.901 G_L1_ABSOLUTE: 3.795 G_L1_RELATIVE: 17.106 G_Regularizer: 0.000 validation_error: 21.382 +(epoch: 5, iters: 40992, time: 0.531, data: 0.000) G_L1: 13.466 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 10.982 G_Regularizer: 0.000 validation_error: 21.642 +(epoch: 5, iters: 42992, time: 0.533, data: 0.000) G_L1: 14.916 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 12.178 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 5, iters: 44992, time: 0.527, data: 0.000) G_L1: 13.906 G_L1_ABSOLUTE: 3.123 G_L1_RELATIVE: 10.784 G_Regularizer: 0.000 validation_error: 21.438 +(epoch: 5, iters: 46992, time: 0.532, data: 0.000) G_L1: 12.649 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 10.308 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 5, iters: 48992, time: 0.534, data: 0.000) G_L1: 15.013 G_L1_ABSOLUTE: 2.636 G_L1_RELATIVE: 12.377 G_Regularizer: 0.000 validation_error: 21.041 +(epoch: 5, iters: 50992, time: 0.532, data: 0.000) G_L1: 17.935 G_L1_ABSOLUTE: 3.055 G_L1_RELATIVE: 14.881 G_Regularizer: 0.000 validation_error: 21.096 +(epoch: 5, iters: 52992, time: 0.534, data: 0.000) G_L1: 18.116 G_L1_ABSOLUTE: 3.148 G_L1_RELATIVE: 14.968 G_Regularizer: 0.000 validation_error: 21.188 +(epoch: 5, iters: 54992, time: 0.537, data: 0.000) G_L1: 15.619 G_L1_ABSOLUTE: 3.478 G_L1_RELATIVE: 12.141 G_Regularizer: 0.000 validation_error: 21.112 +(epoch: 5, iters: 56992, time: 0.531, data: 0.000) G_L1: 16.092 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 13.371 G_Regularizer: 0.000 validation_error: 21.418 +(epoch: 5, iters: 58992, time: 0.520, data: 0.000) G_L1: 19.706 G_L1_ABSOLUTE: 3.103 G_L1_RELATIVE: 16.603 G_Regularizer: 0.000 validation_error: 22.348 +(epoch: 5, iters: 60992, time: 0.524, data: 0.000) G_L1: 14.858 G_L1_ABSOLUTE: 2.913 G_L1_RELATIVE: 11.945 G_Regularizer: 0.000 validation_error: 20.937 +(epoch: 5, iters: 62992, time: 0.525, data: 0.000) G_L1: 16.800 G_L1_ABSOLUTE: 3.368 G_L1_RELATIVE: 13.432 G_Regularizer: 0.000 validation_error: 21.463 +(epoch: 5, iters: 64992, time: 0.525, data: 0.000) G_L1: 15.168 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 12.483 G_Regularizer: 0.000 validation_error: 21.306 +(epoch: 5, iters: 66992, time: 0.521, data: 0.000) G_L1: 14.167 G_L1_ABSOLUTE: 3.052 G_L1_RELATIVE: 11.115 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 5, iters: 68992, time: 0.524, data: 0.000) G_L1: 16.224 G_L1_ABSOLUTE: 3.014 G_L1_RELATIVE: 13.210 G_Regularizer: 0.000 validation_error: 21.593 +(epoch: 5, iters: 70992, time: 0.525, data: 0.000) G_L1: 16.905 G_L1_ABSOLUTE: 2.849 G_L1_RELATIVE: 14.057 G_Regularizer: 0.000 validation_error: 21.296 +(epoch: 5, iters: 72992, time: 0.526, data: 0.000) G_L1: 15.040 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 12.472 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 5, iters: 74992, time: 0.526, data: 0.000) G_L1: 15.518 G_L1_ABSOLUTE: 2.873 G_L1_RELATIVE: 12.645 G_Regularizer: 0.000 validation_error: 20.984 +(epoch: 5, iters: 76992, time: 0.532, data: 0.000) G_L1: 15.827 G_L1_ABSOLUTE: 2.922 G_L1_RELATIVE: 12.904 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 5, iters: 78992, time: 0.530, data: 0.000) G_L1: 14.993 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 12.372 G_Regularizer: 0.000 validation_error: 21.610 +(epoch: 5, iters: 80992, time: 0.530, data: 0.000) G_L1: 15.138 G_L1_ABSOLUTE: 2.687 G_L1_RELATIVE: 12.451 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 5, iters: 82992, time: 0.532, data: 0.000) G_L1: 16.424 G_L1_ABSOLUTE: 3.520 G_L1_RELATIVE: 12.904 G_Regularizer: 0.000 validation_error: 21.014 +(epoch: 5, iters: 84992, time: 0.527, data: 0.000) G_L1: 13.955 G_L1_ABSOLUTE: 2.840 G_L1_RELATIVE: 11.115 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 5, iters: 86992, time: 0.534, data: 0.000) G_L1: 19.030 G_L1_ABSOLUTE: 3.118 G_L1_RELATIVE: 15.912 G_Regularizer: 0.000 validation_error: 21.587 +(epoch: 5, iters: 88992, time: 0.528, data: 0.000) G_L1: 14.833 G_L1_ABSOLUTE: 2.819 G_L1_RELATIVE: 12.014 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 5, iters: 90992, time: 0.532, data: 0.000) G_L1: 17.091 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 14.310 G_Regularizer: 0.000 validation_error: 20.106 +(epoch: 5, iters: 92992, time: 0.530, data: 0.000) G_L1: 17.745 G_L1_ABSOLUTE: 2.801 G_L1_RELATIVE: 14.944 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 5, iters: 94992, time: 0.531, data: 0.000) G_L1: 17.968 G_L1_ABSOLUTE: 3.460 G_L1_RELATIVE: 14.508 G_Regularizer: 0.000 validation_error: 21.294 +(epoch: 5, iters: 96992, time: 0.516, data: 0.000) G_L1: 18.130 G_L1_ABSOLUTE: 3.123 G_L1_RELATIVE: 15.007 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 5, iters: 98992, time: 0.520, data: 0.000) G_L1: 17.821 G_L1_ABSOLUTE: 2.968 G_L1_RELATIVE: 14.853 G_Regularizer: 0.000 validation_error: 21.699 +(epoch: 5, iters: 100992, time: 0.517, data: 0.000) G_L1: 15.793 G_L1_ABSOLUTE: 2.487 G_L1_RELATIVE: 13.306 G_Regularizer: 0.000 validation_error: 21.338 +(epoch: 5, iters: 102992, time: 0.516, data: 0.000) G_L1: 18.522 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 15.700 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 5, iters: 104992, time: 0.536, data: 0.000) G_L1: 18.343 G_L1_ABSOLUTE: 3.039 G_L1_RELATIVE: 15.304 G_Regularizer: 0.000 validation_error: 21.680 +(epoch: 5, iters: 106992, time: 0.525, data: 0.000) G_L1: 16.912 G_L1_ABSOLUTE: 2.909 G_L1_RELATIVE: 14.002 G_Regularizer: 0.000 validation_error: 21.150 +(epoch: 5, iters: 108992, time: 0.524, data: 0.000) G_L1: 19.879 G_L1_ABSOLUTE: 3.014 G_L1_RELATIVE: 16.865 G_Regularizer: 0.000 validation_error: 21.211 +(epoch: 5, iters: 110992, time: 0.521, data: 0.000) G_L1: 17.852 G_L1_ABSOLUTE: 3.022 G_L1_RELATIVE: 14.829 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 5, iters: 112992, time: 0.524, data: 0.000) G_L1: 15.363 G_L1_ABSOLUTE: 3.192 G_L1_RELATIVE: 12.171 G_Regularizer: 0.000 validation_error: 20.461 +(epoch: 5, iters: 114992, time: 0.521, data: 0.000) G_L1: 14.951 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 12.386 G_Regularizer: 0.000 validation_error: 20.140 +(epoch: 5, iters: 116992, time: 0.528, data: 0.000) G_L1: 15.852 G_L1_ABSOLUTE: 3.393 G_L1_RELATIVE: 12.459 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 5, iters: 118992, time: 0.526, data: 0.000) G_L1: 15.606 G_L1_ABSOLUTE: 2.689 G_L1_RELATIVE: 12.917 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 5, iters: 120992, time: 0.527, data: 0.000) G_L1: 17.720 G_L1_ABSOLUTE: 2.948 G_L1_RELATIVE: 14.772 G_Regularizer: 0.000 validation_error: 21.456 +(epoch: 5, iters: 122992, time: 0.537, data: 0.000) G_L1: 14.697 G_L1_ABSOLUTE: 2.932 G_L1_RELATIVE: 11.765 G_Regularizer: 0.000 validation_error: 20.899 +(epoch: 5, iters: 124992, time: 0.535, data: 0.000) G_L1: 13.932 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 10.980 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 5, iters: 126992, time: 0.522, data: 0.000) G_L1: 16.197 G_L1_ABSOLUTE: 3.104 G_L1_RELATIVE: 13.093 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 5, iters: 128992, time: 0.527, data: 0.000) G_L1: 14.652 G_L1_ABSOLUTE: 2.826 G_L1_RELATIVE: 11.826 G_Regularizer: 0.000 validation_error: 21.165 +(epoch: 5, iters: 130992, time: 0.527, data: 0.000) G_L1: 17.918 G_L1_ABSOLUTE: 3.116 G_L1_RELATIVE: 14.802 G_Regularizer: 0.000 validation_error: 20.602 +(epoch: 5, iters: 132992, time: 0.525, data: 0.000) G_L1: 17.076 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 14.167 G_Regularizer: 0.000 validation_error: 20.011 +(epoch: 5, iters: 134992, time: 0.529, data: 0.000) G_L1: 16.228 G_L1_ABSOLUTE: 2.605 G_L1_RELATIVE: 13.623 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 5, iters: 136992, time: 0.526, data: 0.000) G_L1: 15.628 G_L1_ABSOLUTE: 3.199 G_L1_RELATIVE: 12.429 G_Regularizer: 0.000 validation_error: 21.596 +(epoch: 5, iters: 138992, time: 0.528, data: 0.000) G_L1: 16.223 G_L1_ABSOLUTE: 3.144 G_L1_RELATIVE: 13.080 G_Regularizer: 0.000 validation_error: 20.500 +(epoch: 5, iters: 140992, time: 0.532, data: 0.000) G_L1: 19.719 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 17.058 G_Regularizer: 0.000 validation_error: 21.485 +(epoch: 5, iters: 142992, time: 0.532, data: 0.000) G_L1: 18.376 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 15.642 G_Regularizer: 0.000 validation_error: 21.511 +(epoch: 5, iters: 144992, time: 0.528, data: 0.000) G_L1: 16.820 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 14.171 G_Regularizer: 0.000 validation_error: 20.644 +(epoch: 5, iters: 146992, time: 0.528, data: 0.000) G_L1: 15.625 G_L1_ABSOLUTE: 3.514 G_L1_RELATIVE: 12.111 G_Regularizer: 0.000 validation_error: 20.464 +(epoch: 5, iters: 148992, time: 0.534, data: 0.000) G_L1: 15.255 G_L1_ABSOLUTE: 2.739 G_L1_RELATIVE: 12.516 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 5, iters: 150992, time: 0.525, data: 0.000) G_L1: 15.172 G_L1_ABSOLUTE: 2.842 G_L1_RELATIVE: 12.329 G_Regularizer: 0.000 validation_error: 20.742 +(epoch: 5, iters: 152992, time: 0.525, data: 0.000) G_L1: 17.636 G_L1_ABSOLUTE: 3.128 G_L1_RELATIVE: 14.509 G_Regularizer: 0.000 validation_error: 20.232 +(epoch: 5, iters: 154992, time: 0.529, data: 0.000) G_L1: 19.175 G_L1_ABSOLUTE: 3.514 G_L1_RELATIVE: 15.661 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 5, iters: 156992, time: 0.529, data: 0.000) G_L1: 16.192 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 13.521 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 5, iters: 158992, time: 0.533, data: 0.000) G_L1: 16.512 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 14.096 G_Regularizer: 0.000 validation_error: 21.003 +(epoch: 5, iters: 160992, time: 0.538, data: 0.000) G_L1: 17.621 G_L1_ABSOLUTE: 2.973 G_L1_RELATIVE: 14.649 G_Regularizer: 0.000 validation_error: 20.191 +(epoch: 5, iters: 162992, time: 0.527, data: 0.000) G_L1: 18.010 G_L1_ABSOLUTE: 3.079 G_L1_RELATIVE: 14.931 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 5, iters: 164992, time: 0.532, data: 0.000) G_L1: 12.928 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 10.315 G_Regularizer: 0.000 validation_error: 21.217 +(epoch: 5, iters: 166992, time: 0.527, data: 0.000) G_L1: 15.709 G_L1_ABSOLUTE: 3.197 G_L1_RELATIVE: 12.512 G_Regularizer: 0.000 validation_error: 21.032 +(epoch: 5, iters: 168992, time: 0.530, data: 0.000) G_L1: 14.896 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 12.200 G_Regularizer: 0.000 validation_error: 20.744 +(epoch: 5, iters: 170992, time: 0.531, data: 0.000) G_L1: 17.794 G_L1_ABSOLUTE: 2.842 G_L1_RELATIVE: 14.951 G_Regularizer: 0.000 validation_error: 20.515 +(epoch: 5, iters: 172992, time: 0.535, data: 0.000) G_L1: 16.485 G_L1_ABSOLUTE: 2.786 G_L1_RELATIVE: 13.700 G_Regularizer: 0.000 validation_error: 21.376 +(epoch: 5, iters: 174992, time: 0.530, data: 0.000) G_L1: 16.218 G_L1_ABSOLUTE: 3.070 G_L1_RELATIVE: 13.148 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 5, iters: 176992, time: 0.528, data: 0.000) G_L1: 17.142 G_L1_ABSOLUTE: 2.713 G_L1_RELATIVE: 14.428 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 5, iters: 178992, time: 0.526, data: 0.000) G_L1: 14.390 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 11.513 G_Regularizer: 0.000 validation_error: 20.437 +(epoch: 5, iters: 180992, time: 0.528, data: 0.000) G_L1: 17.505 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 14.864 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 5, iters: 182992, time: 0.530, data: 0.000) G_L1: 16.481 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 13.749 G_Regularizer: 0.000 validation_error: 21.315 +(epoch: 5, iters: 184992, time: 0.530, data: 0.000) G_L1: 15.490 G_L1_ABSOLUTE: 3.180 G_L1_RELATIVE: 12.310 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 5, iters: 186992, time: 0.523, data: 0.000) G_L1: 19.083 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 16.414 G_Regularizer: 0.000 validation_error: 21.482 +(epoch: 5, iters: 188992, time: 0.522, data: 0.000) G_L1: 12.842 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 10.120 G_Regularizer: 0.000 validation_error: 21.589 +(epoch: 5, iters: 190992, time: 0.523, data: 0.000) G_L1: 17.215 G_L1_ABSOLUTE: 3.183 G_L1_RELATIVE: 14.031 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 5, iters: 192992, time: 0.532, data: 0.000) G_L1: 17.388 G_L1_ABSOLUTE: 3.215 G_L1_RELATIVE: 14.173 G_Regularizer: 0.000 validation_error: 20.514 +(epoch: 5, iters: 194992, time: 0.523, data: 0.000) G_L1: 15.192 G_L1_ABSOLUTE: 3.067 G_L1_RELATIVE: 12.125 G_Regularizer: 0.000 validation_error: 21.642 +(epoch: 5, iters: 196992, time: 0.524, data: 0.000) G_L1: 13.670 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 11.081 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 5, iters: 198992, time: 0.531, data: 0.000) G_L1: 13.167 G_L1_ABSOLUTE: 2.790 G_L1_RELATIVE: 10.378 G_Regularizer: 0.000 validation_error: 20.930 +(epoch: 5, iters: 200992, time: 0.549, data: 0.000) G_L1: 17.505 G_L1_ABSOLUTE: 3.022 G_L1_RELATIVE: 14.482 G_Regularizer: 0.000 validation_error: 21.435 +(epoch: 5, iters: 202992, time: 0.543, data: 0.000) G_L1: 16.052 G_L1_ABSOLUTE: 2.844 G_L1_RELATIVE: 13.207 G_Regularizer: 0.000 validation_error: 19.793 +(epoch: 5, iters: 204992, time: 0.549, data: 0.000) G_L1: 16.025 G_L1_ABSOLUTE: 3.137 G_L1_RELATIVE: 12.888 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 5, iters: 206992, time: 0.545, data: 0.001) G_L1: 15.921 G_L1_ABSOLUTE: 3.198 G_L1_RELATIVE: 12.723 G_Regularizer: 0.000 validation_error: 21.422 +(epoch: 5, iters: 208992, time: 0.550, data: 0.000) G_L1: 17.209 G_L1_ABSOLUTE: 3.196 G_L1_RELATIVE: 14.013 G_Regularizer: 0.000 validation_error: 20.998 +(epoch: 5, iters: 210992, time: 0.544, data: 0.000) G_L1: 15.802 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 13.077 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 5, iters: 212992, time: 0.551, data: 0.000) G_L1: 20.727 G_L1_ABSOLUTE: 3.232 G_L1_RELATIVE: 17.495 G_Regularizer: 0.000 validation_error: 21.001 +(epoch: 5, iters: 214992, time: 0.546, data: 0.000) G_L1: 15.088 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 12.297 G_Regularizer: 0.000 validation_error: 21.220 +(epoch: 5, iters: 216992, time: 0.542, data: 0.000) G_L1: 19.945 G_L1_ABSOLUTE: 2.997 G_L1_RELATIVE: 16.947 G_Regularizer: 0.000 validation_error: 21.522 +(epoch: 5, iters: 218992, time: 0.541, data: 0.000) G_L1: 16.690 G_L1_ABSOLUTE: 3.029 G_L1_RELATIVE: 13.661 G_Regularizer: 0.000 validation_error: 20.675 +(epoch: 5, iters: 220992, time: 0.542, data: 0.000) G_L1: 15.623 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 12.866 G_Regularizer: 0.000 validation_error: 20.291 +(epoch: 5, iters: 222992, time: 0.549, data: 0.000) G_L1: 14.722 G_L1_ABSOLUTE: 3.237 G_L1_RELATIVE: 11.485 G_Regularizer: 0.000 validation_error: 21.328 +(epoch: 5, iters: 224992, time: 0.543, data: 0.000) G_L1: 15.755 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 13.361 G_Regularizer: 0.000 validation_error: 21.384 +(epoch: 5, iters: 226992, time: 0.541, data: 0.000) G_L1: 15.782 G_L1_ABSOLUTE: 3.088 G_L1_RELATIVE: 12.694 G_Regularizer: 0.000 validation_error: 21.200 +(epoch: 5, iters: 228992, time: 0.542, data: 0.000) G_L1: 14.813 G_L1_ABSOLUTE: 2.869 G_L1_RELATIVE: 11.944 G_Regularizer: 0.000 validation_error: 21.240 +(epoch: 5, iters: 230992, time: 0.545, data: 0.000) G_L1: 17.611 G_L1_ABSOLUTE: 2.796 G_L1_RELATIVE: 14.816 G_Regularizer: 0.000 validation_error: 21.975 +(epoch: 5, iters: 232992, time: 0.549, data: 0.000) G_L1: 14.335 G_L1_ABSOLUTE: 2.866 G_L1_RELATIVE: 11.470 G_Regularizer: 0.000 validation_error: 21.296 +(epoch: 5, iters: 234992, time: 0.543, data: 0.000) G_L1: 16.163 G_L1_ABSOLUTE: 3.306 G_L1_RELATIVE: 12.857 G_Regularizer: 0.000 validation_error: 21.280 +(epoch: 5, iters: 236992, time: 0.544, data: 0.000) G_L1: 13.168 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 10.658 G_Regularizer: 0.000 validation_error: 21.811 +(epoch: 5, iters: 238992, time: 0.544, data: 0.000) G_L1: 15.478 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 12.570 G_Regularizer: 0.000 validation_error: 21.095 +(epoch: 5, iters: 240992, time: 0.537, data: 0.000) G_L1: 16.343 G_L1_ABSOLUTE: 2.980 G_L1_RELATIVE: 13.363 G_Regularizer: 0.000 validation_error: 21.650 +(epoch: 5, iters: 242992, time: 0.543, data: 0.000) G_L1: 17.232 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 14.557 G_Regularizer: 0.000 validation_error: 21.806 +(epoch: 5, iters: 244992, time: 0.541, data: 0.000) G_L1: 12.887 G_L1_ABSOLUTE: 2.793 G_L1_RELATIVE: 10.095 G_Regularizer: 0.000 validation_error: 21.543 +(epoch: 5, iters: 246992, time: 0.551, data: 0.000) G_L1: 18.945 G_L1_ABSOLUTE: 2.895 G_L1_RELATIVE: 16.051 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 5, iters: 248992, time: 0.543, data: 0.000) G_L1: 17.340 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 14.518 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 5, iters: 250992, time: 0.546, data: 0.000) G_L1: 13.879 G_L1_ABSOLUTE: 2.608 G_L1_RELATIVE: 11.272 G_Regularizer: 0.000 validation_error: 21.294 +(epoch: 5, iters: 252992, time: 0.548, data: 0.000) G_L1: 16.900 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 14.029 G_Regularizer: 0.000 validation_error: 21.443 +(epoch: 5, iters: 254992, time: 0.545, data: 0.001) G_L1: 15.761 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 12.856 G_Regularizer: 0.000 validation_error: 21.263 +(epoch: 5, iters: 256992, time: 0.547, data: 0.000) G_L1: 15.379 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 12.390 G_Regularizer: 0.000 validation_error: 21.476 +(epoch: 5, iters: 258992, time: 0.549, data: 0.000) G_L1: 18.406 G_L1_ABSOLUTE: 3.294 G_L1_RELATIVE: 15.112 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 5, iters: 260992, time: 0.554, data: 0.000) G_L1: 15.002 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 12.255 G_Regularizer: 0.000 validation_error: 21.839 +(epoch: 5, iters: 262992, time: 0.541, data: 0.000) G_L1: 18.592 G_L1_ABSOLUTE: 2.828 G_L1_RELATIVE: 15.764 G_Regularizer: 0.000 validation_error: 21.559 +(epoch: 5, iters: 264992, time: 0.548, data: 0.000) G_L1: 16.400 G_L1_ABSOLUTE: 2.888 G_L1_RELATIVE: 13.512 G_Regularizer: 0.000 validation_error: 21.228 +(epoch: 5, iters: 266992, time: 0.539, data: 0.000) G_L1: 16.922 G_L1_ABSOLUTE: 3.311 G_L1_RELATIVE: 13.611 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 5, iters: 268992, time: 0.547, data: 0.000) G_L1: 16.334 G_L1_ABSOLUTE: 3.304 G_L1_RELATIVE: 13.030 G_Regularizer: 0.000 validation_error: 21.511 +(epoch: 5, iters: 270992, time: 0.545, data: 0.000) G_L1: 16.556 G_L1_ABSOLUTE: 2.924 G_L1_RELATIVE: 13.632 G_Regularizer: 0.000 validation_error: 20.518 +(epoch: 5, iters: 272992, time: 0.555, data: 0.000) G_L1: 20.760 G_L1_ABSOLUTE: 3.002 G_L1_RELATIVE: 17.758 G_Regularizer: 0.000 validation_error: 20.601 +(epoch: 5, iters: 274992, time: 0.545, data: 0.000) G_L1: 17.560 G_L1_ABSOLUTE: 3.227 G_L1_RELATIVE: 14.333 G_Regularizer: 0.000 validation_error: 21.247 +(epoch: 5, iters: 276992, time: 0.540, data: 0.000) G_L1: 14.889 G_L1_ABSOLUTE: 2.448 G_L1_RELATIVE: 12.441 G_Regularizer: 0.000 validation_error: 21.005 +(epoch: 5, iters: 278992, time: 0.544, data: 0.000) G_L1: 13.757 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 10.947 G_Regularizer: 0.000 validation_error: 21.299 +(epoch: 5, iters: 280992, time: 0.533, data: 0.000) G_L1: 18.719 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 15.828 G_Regularizer: 0.000 validation_error: 20.717 +(epoch: 5, iters: 282992, time: 0.544, data: 0.000) G_L1: 19.028 G_L1_ABSOLUTE: 3.058 G_L1_RELATIVE: 15.970 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 5, iters: 284992, time: 0.540, data: 0.000) G_L1: 16.437 G_L1_ABSOLUTE: 3.398 G_L1_RELATIVE: 13.039 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 5, iters: 286992, time: 0.544, data: 0.000) G_L1: 16.851 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 14.196 G_Regularizer: 0.000 validation_error: 20.939 +(epoch: 5, iters: 288992, time: 0.544, data: 0.000) G_L1: 17.301 G_L1_ABSOLUTE: 2.647 G_L1_RELATIVE: 14.654 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 5, iters: 290992, time: 0.538, data: 0.000) G_L1: 18.381 G_L1_ABSOLUTE: 3.082 G_L1_RELATIVE: 15.299 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 5, iters: 292992, time: 0.539, data: 0.000) G_L1: 19.436 G_L1_ABSOLUTE: 3.374 G_L1_RELATIVE: 16.062 G_Regularizer: 0.000 validation_error: 21.706 +(epoch: 5, iters: 294992, time: 0.548, data: 0.000) G_L1: 15.186 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 12.294 G_Regularizer: 0.000 validation_error: 20.577 +(epoch: 5, iters: 296992, time: 0.535, data: 0.000) G_L1: 16.207 G_L1_ABSOLUTE: 2.579 G_L1_RELATIVE: 13.628 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 5, iters: 298992, time: 0.543, data: 0.000) G_L1: 15.561 G_L1_ABSOLUTE: 3.015 G_L1_RELATIVE: 12.546 G_Regularizer: 0.000 validation_error: 20.748 +(epoch: 5, iters: 300992, time: 0.545, data: 0.000) G_L1: 17.729 G_L1_ABSOLUTE: 2.823 G_L1_RELATIVE: 14.906 G_Regularizer: 0.000 validation_error: 20.768 +(epoch: 6, iters: 240, time: 0.542, data: 0.000) G_L1: 16.193 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 13.507 G_Regularizer: 0.000 validation_error: 21.195 +(epoch: 6, iters: 2240, time: 0.549, data: 0.000) G_L1: 15.056 G_L1_ABSOLUTE: 2.957 G_L1_RELATIVE: 12.099 G_Regularizer: 0.000 validation_error: 20.636 +(epoch: 6, iters: 4240, time: 0.548, data: 0.000) G_L1: 17.886 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 15.023 G_Regularizer: 0.000 validation_error: 21.398 +(epoch: 6, iters: 6240, time: 0.543, data: 0.000) G_L1: 18.168 G_L1_ABSOLUTE: 2.751 G_L1_RELATIVE: 15.416 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 6, iters: 8240, time: 0.547, data: 0.000) G_L1: 15.935 G_L1_ABSOLUTE: 3.023 G_L1_RELATIVE: 12.912 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 6, iters: 10240, time: 0.540, data: 0.000) G_L1: 19.362 G_L1_ABSOLUTE: 3.176 G_L1_RELATIVE: 16.186 G_Regularizer: 0.000 validation_error: 22.686 +(epoch: 6, iters: 12240, time: 0.546, data: 0.000) G_L1: 14.796 G_L1_ABSOLUTE: 3.008 G_L1_RELATIVE: 11.788 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 6, iters: 14240, time: 0.540, data: 0.001) G_L1: 16.099 G_L1_ABSOLUTE: 2.815 G_L1_RELATIVE: 13.284 G_Regularizer: 0.000 validation_error: 21.051 +(epoch: 6, iters: 16240, time: 0.543, data: 0.000) G_L1: 16.948 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 14.180 G_Regularizer: 0.000 validation_error: 21.257 +(epoch: 6, iters: 18240, time: 0.550, data: 0.000) G_L1: 16.655 G_L1_ABSOLUTE: 3.055 G_L1_RELATIVE: 13.601 G_Regularizer: 0.000 validation_error: 20.445 +(epoch: 6, iters: 20240, time: 0.542, data: 0.000) G_L1: 15.364 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 12.842 G_Regularizer: 0.000 validation_error: 21.024 +(epoch: 6, iters: 22240, time: 0.543, data: 0.000) G_L1: 17.821 G_L1_ABSOLUTE: 2.992 G_L1_RELATIVE: 14.829 G_Regularizer: 0.000 validation_error: 20.789 +(epoch: 6, iters: 24240, time: 0.543, data: 0.000) G_L1: 14.774 G_L1_ABSOLUTE: 2.638 G_L1_RELATIVE: 12.135 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 6, iters: 26240, time: 0.542, data: 0.000) G_L1: 21.330 G_L1_ABSOLUTE: 3.396 G_L1_RELATIVE: 17.934 G_Regularizer: 0.000 validation_error: 21.287 +(epoch: 6, iters: 28240, time: 0.539, data: 0.000) G_L1: 17.168 G_L1_ABSOLUTE: 3.573 G_L1_RELATIVE: 13.594 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 6, iters: 30240, time: 0.534, data: 0.000) G_L1: 13.083 G_L1_ABSOLUTE: 2.807 G_L1_RELATIVE: 10.276 G_Regularizer: 0.000 validation_error: 20.281 +(epoch: 6, iters: 32240, time: 0.546, data: 0.000) G_L1: 14.334 G_L1_ABSOLUTE: 2.988 G_L1_RELATIVE: 11.347 G_Regularizer: 0.000 validation_error: 21.197 +(epoch: 6, iters: 34240, time: 0.545, data: 0.000) G_L1: 15.807 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 12.913 G_Regularizer: 0.000 validation_error: 21.105 +(epoch: 6, iters: 36240, time: 0.540, data: 0.000) G_L1: 16.104 G_L1_ABSOLUTE: 3.217 G_L1_RELATIVE: 12.887 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 6, iters: 38240, time: 0.540, data: 0.000) G_L1: 13.744 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 11.011 G_Regularizer: 0.000 validation_error: 21.162 +(epoch: 6, iters: 40240, time: 0.546, data: 0.000) G_L1: 16.080 G_L1_ABSOLUTE: 3.224 G_L1_RELATIVE: 12.856 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 6, iters: 42240, time: 0.544, data: 0.000) G_L1: 13.914 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 11.665 G_Regularizer: 0.000 validation_error: 20.292 +(epoch: 6, iters: 44240, time: 0.546, data: 0.000) G_L1: 17.629 G_L1_ABSOLUTE: 2.979 G_L1_RELATIVE: 14.650 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 6, iters: 46240, time: 0.547, data: 0.000) G_L1: 17.605 G_L1_ABSOLUTE: 2.720 G_L1_RELATIVE: 14.884 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 6, iters: 48240, time: 0.546, data: 0.000) G_L1: 15.357 G_L1_ABSOLUTE: 3.188 G_L1_RELATIVE: 12.169 G_Regularizer: 0.000 validation_error: 21.207 +(epoch: 6, iters: 50240, time: 0.549, data: 0.000) G_L1: 16.898 G_L1_ABSOLUTE: 3.118 G_L1_RELATIVE: 13.780 G_Regularizer: 0.000 validation_error: 21.300 +(epoch: 6, iters: 52240, time: 0.541, data: 0.000) G_L1: 19.373 G_L1_ABSOLUTE: 3.246 G_L1_RELATIVE: 16.127 G_Regularizer: 0.000 validation_error: 20.442 +(epoch: 6, iters: 54240, time: 0.547, data: 0.000) G_L1: 18.230 G_L1_ABSOLUTE: 2.957 G_L1_RELATIVE: 15.272 G_Regularizer: 0.000 validation_error: 21.316 +(epoch: 6, iters: 56240, time: 0.543, data: 0.000) G_L1: 14.731 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 12.177 G_Regularizer: 0.000 validation_error: 20.653 +(epoch: 6, iters: 58240, time: 0.549, data: 0.000) G_L1: 17.763 G_L1_ABSOLUTE: 3.005 G_L1_RELATIVE: 14.758 G_Regularizer: 0.000 validation_error: 20.795 +(epoch: 6, iters: 60240, time: 0.539, data: 0.000) G_L1: 14.467 G_L1_ABSOLUTE: 2.906 G_L1_RELATIVE: 11.561 G_Regularizer: 0.000 validation_error: 21.407 +(epoch: 6, iters: 62240, time: 0.547, data: 0.000) G_L1: 16.870 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 14.213 G_Regularizer: 0.000 validation_error: 20.559 +(epoch: 6, iters: 64240, time: 0.546, data: 0.000) G_L1: 18.308 G_L1_ABSOLUTE: 2.776 G_L1_RELATIVE: 15.531 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 6, iters: 66240, time: 0.541, data: 0.000) G_L1: 16.301 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 13.576 G_Regularizer: 0.000 validation_error: 21.027 +(epoch: 6, iters: 68240, time: 0.547, data: 0.000) G_L1: 17.460 G_L1_ABSOLUTE: 2.747 G_L1_RELATIVE: 14.712 G_Regularizer: 0.000 validation_error: 21.079 +(epoch: 6, iters: 70240, time: 0.549, data: 0.000) G_L1: 20.230 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 17.353 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 6, iters: 72240, time: 0.547, data: 0.000) G_L1: 18.004 G_L1_ABSOLUTE: 3.023 G_L1_RELATIVE: 14.981 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 6, iters: 74240, time: 0.548, data: 0.000) G_L1: 15.911 G_L1_ABSOLUTE: 2.925 G_L1_RELATIVE: 12.986 G_Regularizer: 0.000 validation_error: 20.854 +(epoch: 6, iters: 76240, time: 0.542, data: 0.000) G_L1: 16.619 G_L1_ABSOLUTE: 2.946 G_L1_RELATIVE: 13.673 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 6, iters: 78240, time: 0.546, data: 0.000) G_L1: 15.331 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 12.743 G_Regularizer: 0.000 validation_error: 21.296 +(epoch: 6, iters: 80240, time: 0.542, data: 0.000) G_L1: 14.614 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 12.046 G_Regularizer: 0.000 validation_error: 21.117 +(epoch: 6, iters: 82240, time: 0.547, data: 0.000) G_L1: 15.960 G_L1_ABSOLUTE: 2.543 G_L1_RELATIVE: 13.417 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 6, iters: 84240, time: 0.550, data: 0.000) G_L1: 15.596 G_L1_ABSOLUTE: 2.662 G_L1_RELATIVE: 12.934 G_Regularizer: 0.000 validation_error: 21.694 +(epoch: 6, iters: 86240, time: 0.542, data: 0.000) G_L1: 16.010 G_L1_ABSOLUTE: 2.647 G_L1_RELATIVE: 13.362 G_Regularizer: 0.000 validation_error: 21.115 +(epoch: 6, iters: 88240, time: 0.544, data: 0.000) G_L1: 18.031 G_L1_ABSOLUTE: 3.014 G_L1_RELATIVE: 15.016 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 6, iters: 90240, time: 0.546, data: 0.001) G_L1: 17.389 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 14.739 G_Regularizer: 0.000 validation_error: 20.744 +(epoch: 6, iters: 92240, time: 0.542, data: 0.000) G_L1: 14.932 G_L1_ABSOLUTE: 2.692 G_L1_RELATIVE: 12.240 G_Regularizer: 0.000 validation_error: 20.896 +(epoch: 6, iters: 94240, time: 0.548, data: 0.000) G_L1: 15.241 G_L1_ABSOLUTE: 3.093 G_L1_RELATIVE: 12.147 G_Regularizer: 0.000 validation_error: 20.566 +(epoch: 6, iters: 96240, time: 0.547, data: 0.000) G_L1: 18.588 G_L1_ABSOLUTE: 2.930 G_L1_RELATIVE: 15.658 G_Regularizer: 0.000 validation_error: 21.328 +(epoch: 6, iters: 98240, time: 0.544, data: 0.000) G_L1: 18.605 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 15.748 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 6, iters: 100240, time: 0.543, data: 0.000) G_L1: 15.938 G_L1_ABSOLUTE: 2.886 G_L1_RELATIVE: 13.053 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 6, iters: 102240, time: 0.542, data: 0.000) G_L1: 15.017 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 12.326 G_Regularizer: 0.000 validation_error: 21.923 +(epoch: 6, iters: 104240, time: 0.555, data: 0.000) G_L1: 17.065 G_L1_ABSOLUTE: 3.598 G_L1_RELATIVE: 13.467 G_Regularizer: 0.000 validation_error: 21.277 +(epoch: 6, iters: 106240, time: 0.542, data: 0.000) G_L1: 16.373 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 13.639 G_Regularizer: 0.000 validation_error: 20.512 +(epoch: 6, iters: 108240, time: 0.547, data: 0.000) G_L1: 17.522 G_L1_ABSOLUTE: 2.490 G_L1_RELATIVE: 15.031 G_Regularizer: 0.000 validation_error: 20.971 +(epoch: 6, iters: 110240, time: 0.548, data: 0.000) G_L1: 16.326 G_L1_ABSOLUTE: 3.191 G_L1_RELATIVE: 13.135 G_Regularizer: 0.000 validation_error: 21.113 +(epoch: 6, iters: 112240, time: 0.542, data: 0.000) G_L1: 15.629 G_L1_ABSOLUTE: 3.486 G_L1_RELATIVE: 12.143 G_Regularizer: 0.000 validation_error: 21.058 +(epoch: 6, iters: 114240, time: 0.544, data: 0.000) G_L1: 18.839 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 16.026 G_Regularizer: 0.000 validation_error: 21.161 +(epoch: 6, iters: 116240, time: 0.541, data: 0.000) G_L1: 13.580 G_L1_ABSOLUTE: 2.425 G_L1_RELATIVE: 11.155 G_Regularizer: 0.000 validation_error: 21.138 +(epoch: 6, iters: 118240, time: 0.546, data: 0.000) G_L1: 16.520 G_L1_ABSOLUTE: 2.534 G_L1_RELATIVE: 13.986 G_Regularizer: 0.000 validation_error: 21.329 +(epoch: 6, iters: 120240, time: 0.549, data: 0.000) G_L1: 16.249 G_L1_ABSOLUTE: 2.978 G_L1_RELATIVE: 13.270 G_Regularizer: 0.000 validation_error: 20.401 +(epoch: 6, iters: 122240, time: 0.544, data: 0.000) G_L1: 14.663 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 12.039 G_Regularizer: 0.000 validation_error: 20.699 +(epoch: 6, iters: 124240, time: 0.536, data: 0.000) G_L1: 15.925 G_L1_ABSOLUTE: 3.021 G_L1_RELATIVE: 12.903 G_Regularizer: 0.000 validation_error: 21.166 +(epoch: 6, iters: 126240, time: 0.539, data: 0.000) G_L1: 21.046 G_L1_ABSOLUTE: 2.758 G_L1_RELATIVE: 18.287 G_Regularizer: 0.000 validation_error: 21.553 +(epoch: 6, iters: 128240, time: 0.547, data: 0.000) G_L1: 16.882 G_L1_ABSOLUTE: 3.099 G_L1_RELATIVE: 13.783 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 6, iters: 130240, time: 0.555, data: 0.000) G_L1: 16.164 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 13.256 G_Regularizer: 0.000 validation_error: 20.737 +(epoch: 6, iters: 132240, time: 0.549, data: 0.000) G_L1: 14.700 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 12.128 G_Regularizer: 0.000 validation_error: 21.349 +(epoch: 6, iters: 134240, time: 0.541, data: 0.000) G_L1: 15.702 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 13.032 G_Regularizer: 0.000 validation_error: 21.009 +(epoch: 6, iters: 136240, time: 0.553, data: 0.000) G_L1: 21.830 G_L1_ABSOLUTE: 3.493 G_L1_RELATIVE: 18.337 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 6, iters: 138240, time: 0.549, data: 0.000) G_L1: 16.293 G_L1_ABSOLUTE: 3.088 G_L1_RELATIVE: 13.205 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 6, iters: 140240, time: 0.542, data: 0.000) G_L1: 13.667 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 11.381 G_Regularizer: 0.000 validation_error: 21.096 +(epoch: 6, iters: 142240, time: 0.552, data: 0.000) G_L1: 21.931 G_L1_ABSOLUTE: 2.770 G_L1_RELATIVE: 19.161 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 6, iters: 144240, time: 0.543, data: 0.000) G_L1: 16.510 G_L1_ABSOLUTE: 2.922 G_L1_RELATIVE: 13.588 G_Regularizer: 0.000 validation_error: 20.791 +(epoch: 6, iters: 146240, time: 0.550, data: 0.000) G_L1: 15.841 G_L1_ABSOLUTE: 2.599 G_L1_RELATIVE: 13.241 G_Regularizer: 0.000 validation_error: 21.473 +(epoch: 6, iters: 148240, time: 0.541, data: 0.000) G_L1: 16.553 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 13.816 G_Regularizer: 0.000 validation_error: 21.066 +(epoch: 6, iters: 150240, time: 0.542, data: 0.000) G_L1: 15.483 G_L1_ABSOLUTE: 2.921 G_L1_RELATIVE: 12.562 G_Regularizer: 0.000 validation_error: 22.378 +(epoch: 6, iters: 152240, time: 0.547, data: 0.000) G_L1: 16.069 G_L1_ABSOLUTE: 3.265 G_L1_RELATIVE: 12.803 G_Regularizer: 0.000 validation_error: 21.070 +(epoch: 6, iters: 154240, time: 0.539, data: 0.000) G_L1: 16.541 G_L1_ABSOLUTE: 2.372 G_L1_RELATIVE: 14.169 G_Regularizer: 0.000 validation_error: 21.590 +(epoch: 6, iters: 156240, time: 0.548, data: 0.000) G_L1: 17.000 G_L1_ABSOLUTE: 2.788 G_L1_RELATIVE: 14.212 G_Regularizer: 0.000 validation_error: 21.530 +(epoch: 6, iters: 158240, time: 0.535, data: 0.000) G_L1: 13.460 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 11.087 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 6, iters: 160240, time: 0.543, data: 0.001) G_L1: 14.255 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 11.808 G_Regularizer: 0.000 validation_error: 21.350 +(epoch: 6, iters: 162240, time: 0.545, data: 0.000) G_L1: 17.160 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 14.436 G_Regularizer: 0.000 validation_error: 21.780 +(epoch: 6, iters: 164240, time: 0.540, data: 0.000) G_L1: 17.760 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 15.114 G_Regularizer: 0.000 validation_error: 20.557 +(epoch: 6, iters: 166240, time: 0.542, data: 0.000) G_L1: 15.203 G_L1_ABSOLUTE: 2.727 G_L1_RELATIVE: 12.476 G_Regularizer: 0.000 validation_error: 20.299 +(epoch: 6, iters: 168240, time: 0.542, data: 0.000) G_L1: 17.053 G_L1_ABSOLUTE: 3.043 G_L1_RELATIVE: 14.010 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 6, iters: 170240, time: 0.548, data: 0.000) G_L1: 17.217 G_L1_ABSOLUTE: 2.785 G_L1_RELATIVE: 14.432 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 6, iters: 172240, time: 0.537, data: 0.000) G_L1: 25.567 G_L1_ABSOLUTE: 2.867 G_L1_RELATIVE: 22.699 G_Regularizer: 0.000 validation_error: 20.464 +(epoch: 6, iters: 174240, time: 0.547, data: 0.000) G_L1: 14.403 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 11.817 G_Regularizer: 0.000 validation_error: 20.336 +(epoch: 6, iters: 176240, time: 0.549, data: 0.000) G_L1: 15.679 G_L1_ABSOLUTE: 2.913 G_L1_RELATIVE: 12.766 G_Regularizer: 0.000 validation_error: 20.993 +(epoch: 6, iters: 178240, time: 0.551, data: 0.000) G_L1: 15.552 G_L1_ABSOLUTE: 2.854 G_L1_RELATIVE: 12.698 G_Regularizer: 0.000 validation_error: 21.157 +(epoch: 6, iters: 180240, time: 0.550, data: 0.000) G_L1: 17.262 G_L1_ABSOLUTE: 3.042 G_L1_RELATIVE: 14.220 G_Regularizer: 0.000 validation_error: 20.596 +(epoch: 6, iters: 182240, time: 0.546, data: 0.000) G_L1: 16.092 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 13.478 G_Regularizer: 0.000 validation_error: 21.095 +(epoch: 6, iters: 184240, time: 0.553, data: 0.000) G_L1: 16.253 G_L1_ABSOLUTE: 3.051 G_L1_RELATIVE: 13.202 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 6, iters: 186240, time: 0.548, data: 0.000) G_L1: 17.756 G_L1_ABSOLUTE: 2.643 G_L1_RELATIVE: 15.113 G_Regularizer: 0.000 validation_error: 21.175 +(epoch: 6, iters: 188240, time: 0.536, data: 0.000) G_L1: 15.022 G_L1_ABSOLUTE: 2.514 G_L1_RELATIVE: 12.509 G_Regularizer: 0.000 validation_error: 21.060 +(epoch: 6, iters: 190240, time: 0.544, data: 0.000) G_L1: 15.218 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 12.655 G_Regularizer: 0.000 validation_error: 21.270 +(epoch: 6, iters: 192240, time: 0.546, data: 0.000) G_L1: 16.688 G_L1_ABSOLUTE: 2.937 G_L1_RELATIVE: 13.751 G_Regularizer: 0.000 validation_error: 20.366 +(epoch: 6, iters: 194240, time: 0.546, data: 0.000) G_L1: 16.584 G_L1_ABSOLUTE: 2.699 G_L1_RELATIVE: 13.886 G_Regularizer: 0.000 validation_error: 21.354 +(epoch: 6, iters: 196240, time: 0.545, data: 0.000) G_L1: 15.798 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 13.059 G_Regularizer: 0.000 validation_error: 21.148 +(epoch: 6, iters: 198240, time: 0.541, data: 0.000) G_L1: 16.513 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 13.994 G_Regularizer: 0.000 validation_error: 21.365 +(epoch: 6, iters: 200240, time: 0.548, data: 0.000) G_L1: 16.807 G_L1_ABSOLUTE: 3.509 G_L1_RELATIVE: 13.298 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 6, iters: 202240, time: 0.544, data: 0.000) G_L1: 16.552 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 14.004 G_Regularizer: 0.000 validation_error: 21.044 +(epoch: 6, iters: 204240, time: 0.544, data: 0.000) G_L1: 14.405 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 11.581 G_Regularizer: 0.000 validation_error: 21.549 +(epoch: 6, iters: 206240, time: 0.540, data: 0.000) G_L1: 16.689 G_L1_ABSOLUTE: 2.982 G_L1_RELATIVE: 13.707 G_Regularizer: 0.000 validation_error: 20.910 +(epoch: 6, iters: 208240, time: 0.544, data: 0.001) G_L1: 15.751 G_L1_ABSOLUTE: 2.970 G_L1_RELATIVE: 12.781 G_Regularizer: 0.000 validation_error: 20.422 +(epoch: 6, iters: 210240, time: 0.544, data: 0.000) G_L1: 13.601 G_L1_ABSOLUTE: 2.441 G_L1_RELATIVE: 11.160 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 6, iters: 212240, time: 0.549, data: 0.000) G_L1: 15.976 G_L1_ABSOLUTE: 2.613 G_L1_RELATIVE: 13.363 G_Regularizer: 0.000 validation_error: 20.545 +(epoch: 6, iters: 214240, time: 0.544, data: 0.000) G_L1: 15.391 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 12.683 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 6, iters: 216240, time: 0.546, data: 0.000) G_L1: 17.680 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 14.884 G_Regularizer: 0.000 validation_error: 20.636 +(epoch: 6, iters: 218240, time: 0.543, data: 0.000) G_L1: 11.809 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 9.031 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 6, iters: 220240, time: 0.544, data: 0.000) G_L1: 15.956 G_L1_ABSOLUTE: 3.297 G_L1_RELATIVE: 12.659 G_Regularizer: 0.000 validation_error: 21.823 +(epoch: 6, iters: 222240, time: 0.542, data: 0.000) G_L1: 16.555 G_L1_ABSOLUTE: 3.246 G_L1_RELATIVE: 13.308 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 6, iters: 224240, time: 0.548, data: 0.000) G_L1: 14.384 G_L1_ABSOLUTE: 2.859 G_L1_RELATIVE: 11.525 G_Regularizer: 0.000 validation_error: 21.103 +(epoch: 6, iters: 226240, time: 0.546, data: 0.000) G_L1: 14.149 G_L1_ABSOLUTE: 2.693 G_L1_RELATIVE: 11.456 G_Regularizer: 0.000 validation_error: 20.217 +(epoch: 6, iters: 228240, time: 0.542, data: 0.000) G_L1: 17.708 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 15.118 G_Regularizer: 0.000 validation_error: 20.540 +(epoch: 6, iters: 230240, time: 0.544, data: 0.000) G_L1: 19.234 G_L1_ABSOLUTE: 2.976 G_L1_RELATIVE: 16.258 G_Regularizer: 0.000 validation_error: 21.519 +(epoch: 6, iters: 232240, time: 0.542, data: 0.000) G_L1: 15.313 G_L1_ABSOLUTE: 2.770 G_L1_RELATIVE: 12.543 G_Regularizer: 0.000 validation_error: 20.979 +(epoch: 6, iters: 234240, time: 0.540, data: 0.000) G_L1: 16.593 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 13.832 G_Regularizer: 0.000 validation_error: 21.124 +(epoch: 6, iters: 236240, time: 0.546, data: 0.000) G_L1: 15.655 G_L1_ABSOLUTE: 3.120 G_L1_RELATIVE: 12.535 G_Regularizer: 0.000 validation_error: 20.913 +(epoch: 6, iters: 238240, time: 0.549, data: 0.000) G_L1: 23.141 G_L1_ABSOLUTE: 3.225 G_L1_RELATIVE: 19.915 G_Regularizer: 0.000 validation_error: 21.092 +(epoch: 6, iters: 240240, time: 0.546, data: 0.000) G_L1: 16.912 G_L1_ABSOLUTE: 2.991 G_L1_RELATIVE: 13.921 G_Regularizer: 0.000 validation_error: 20.274 +(epoch: 6, iters: 242240, time: 0.547, data: 0.000) G_L1: 15.885 G_L1_ABSOLUTE: 2.699 G_L1_RELATIVE: 13.186 G_Regularizer: 0.000 validation_error: 21.735 +(epoch: 6, iters: 244240, time: 0.548, data: 0.000) G_L1: 17.707 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 14.907 G_Regularizer: 0.000 validation_error: 20.557 +(epoch: 6, iters: 246240, time: 0.543, data: 0.000) G_L1: 19.591 G_L1_ABSOLUTE: 2.865 G_L1_RELATIVE: 16.726 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 6, iters: 248240, time: 0.552, data: 0.000) G_L1: 18.637 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 16.097 G_Regularizer: 0.000 validation_error: 21.345 +(epoch: 6, iters: 250240, time: 0.547, data: 0.000) G_L1: 16.012 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 13.235 G_Regularizer: 0.000 validation_error: 20.373 +(epoch: 6, iters: 252240, time: 0.545, data: 0.000) G_L1: 17.392 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 14.661 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 6, iters: 254240, time: 0.544, data: 0.000) G_L1: 15.459 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 12.648 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 6, iters: 256240, time: 0.548, data: 0.000) G_L1: 15.094 G_L1_ABSOLUTE: 2.984 G_L1_RELATIVE: 12.110 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 6, iters: 258240, time: 0.548, data: 0.000) G_L1: 16.904 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 14.378 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 6, iters: 260240, time: 0.544, data: 0.001) G_L1: 13.811 G_L1_ABSOLUTE: 2.348 G_L1_RELATIVE: 11.463 G_Regularizer: 0.000 validation_error: 20.306 +(epoch: 6, iters: 262240, time: 0.535, data: 0.000) G_L1: 12.540 G_L1_ABSOLUTE: 2.617 G_L1_RELATIVE: 9.923 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 6, iters: 264240, time: 0.546, data: 0.000) G_L1: 14.167 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 11.457 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 6, iters: 266240, time: 0.544, data: 0.000) G_L1: 18.652 G_L1_ABSOLUTE: 2.944 G_L1_RELATIVE: 15.707 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 6, iters: 268240, time: 0.540, data: 0.000) G_L1: 19.862 G_L1_ABSOLUTE: 2.538 G_L1_RELATIVE: 17.324 G_Regularizer: 0.000 validation_error: 21.056 +(epoch: 6, iters: 270240, time: 0.540, data: 0.000) G_L1: 13.911 G_L1_ABSOLUTE: 3.011 G_L1_RELATIVE: 10.901 G_Regularizer: 0.000 validation_error: 20.904 +(epoch: 6, iters: 272240, time: 0.543, data: 0.000) G_L1: 15.678 G_L1_ABSOLUTE: 2.334 G_L1_RELATIVE: 13.343 G_Regularizer: 0.000 validation_error: 21.303 +(epoch: 6, iters: 274240, time: 0.541, data: 0.000) G_L1: 15.646 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 13.046 G_Regularizer: 0.000 validation_error: 20.612 +(epoch: 6, iters: 276240, time: 0.547, data: 0.000) G_L1: 15.791 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 12.928 G_Regularizer: 0.000 validation_error: 21.014 +(epoch: 6, iters: 278240, time: 0.542, data: 0.000) G_L1: 16.174 G_L1_ABSOLUTE: 3.083 G_L1_RELATIVE: 13.091 G_Regularizer: 0.000 validation_error: 20.386 +(epoch: 6, iters: 280240, time: 0.549, data: 0.000) G_L1: 19.565 G_L1_ABSOLUTE: 3.290 G_L1_RELATIVE: 16.276 G_Regularizer: 0.000 validation_error: 20.907 +(epoch: 6, iters: 282240, time: 0.543, data: 0.000) G_L1: 12.985 G_L1_ABSOLUTE: 3.002 G_L1_RELATIVE: 9.982 G_Regularizer: 0.000 validation_error: 21.824 +(epoch: 6, iters: 284240, time: 0.542, data: 0.000) G_L1: 13.479 G_L1_ABSOLUTE: 2.392 G_L1_RELATIVE: 11.087 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 6, iters: 286240, time: 0.548, data: 0.000) G_L1: 14.722 G_L1_ABSOLUTE: 2.695 G_L1_RELATIVE: 12.026 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 6, iters: 288240, time: 0.544, data: 0.000) G_L1: 15.923 G_L1_ABSOLUTE: 2.603 G_L1_RELATIVE: 13.320 G_Regularizer: 0.000 validation_error: 20.170 +(epoch: 6, iters: 290240, time: 0.544, data: 0.000) G_L1: 14.956 G_L1_ABSOLUTE: 3.179 G_L1_RELATIVE: 11.777 G_Regularizer: 0.000 validation_error: 20.532 +(epoch: 6, iters: 292240, time: 0.546, data: 0.000) G_L1: 19.723 G_L1_ABSOLUTE: 3.184 G_L1_RELATIVE: 16.539 G_Regularizer: 0.000 validation_error: 20.377 +(epoch: 6, iters: 294240, time: 0.549, data: 0.000) G_L1: 16.031 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 13.151 G_Regularizer: 0.000 validation_error: 21.341 +(epoch: 6, iters: 296240, time: 0.542, data: 0.000) G_L1: 18.362 G_L1_ABSOLUTE: 3.022 G_L1_RELATIVE: 15.340 G_Regularizer: 0.000 validation_error: 21.096 +(epoch: 6, iters: 298240, time: 0.546, data: 0.000) G_L1: 20.405 G_L1_ABSOLUTE: 3.140 G_L1_RELATIVE: 17.264 G_Regularizer: 0.000 validation_error: 21.096 +(epoch: 6, iters: 300240, time: 0.544, data: 0.000) G_L1: 19.462 G_L1_ABSOLUTE: 3.233 G_L1_RELATIVE: 16.229 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 6, iters: 302240, time: 0.545, data: 0.000) G_L1: 18.690 G_L1_ABSOLUTE: 3.216 G_L1_RELATIVE: 15.474 G_Regularizer: 0.000 validation_error: 21.379 +(epoch: 7, iters: 1488, time: 0.539, data: 0.000) G_L1: 15.882 G_L1_ABSOLUTE: 2.817 G_L1_RELATIVE: 13.064 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 7, iters: 3488, time: 0.543, data: 0.000) G_L1: 16.438 G_L1_ABSOLUTE: 2.997 G_L1_RELATIVE: 13.440 G_Regularizer: 0.000 validation_error: 20.629 +(epoch: 7, iters: 5488, time: 0.549, data: 0.000) G_L1: 15.449 G_L1_ABSOLUTE: 2.615 G_L1_RELATIVE: 12.833 G_Regularizer: 0.000 validation_error: 20.333 +(epoch: 7, iters: 7488, time: 0.542, data: 0.000) G_L1: 16.196 G_L1_ABSOLUTE: 2.796 G_L1_RELATIVE: 13.400 G_Regularizer: 0.000 validation_error: 20.108 +(epoch: 7, iters: 9488, time: 0.540, data: 0.000) G_L1: 14.910 G_L1_ABSOLUTE: 2.872 G_L1_RELATIVE: 12.038 G_Regularizer: 0.000 validation_error: 20.521 +(epoch: 7, iters: 11488, time: 0.547, data: 0.000) G_L1: 17.306 G_L1_ABSOLUTE: 2.950 G_L1_RELATIVE: 14.356 G_Regularizer: 0.000 validation_error: 21.329 +(epoch: 7, iters: 13488, time: 0.540, data: 0.000) G_L1: 16.909 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 14.289 G_Regularizer: 0.000 validation_error: 21.121 +(epoch: 7, iters: 15488, time: 0.546, data: 0.000) G_L1: 15.133 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 12.525 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 7, iters: 17488, time: 0.546, data: 0.000) G_L1: 16.227 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 13.846 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 7, iters: 19488, time: 0.548, data: 0.000) G_L1: 13.446 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 11.031 G_Regularizer: 0.000 validation_error: 20.445 +(epoch: 7, iters: 21488, time: 0.546, data: 0.000) G_L1: 16.821 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 14.043 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 7, iters: 23488, time: 0.547, data: 0.000) G_L1: 16.599 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 14.136 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 7, iters: 25488, time: 0.547, data: 0.000) G_L1: 13.995 G_L1_ABSOLUTE: 2.537 G_L1_RELATIVE: 11.458 G_Regularizer: 0.000 validation_error: 20.340 +(epoch: 7, iters: 27488, time: 0.542, data: 0.000) G_L1: 18.691 G_L1_ABSOLUTE: 2.950 G_L1_RELATIVE: 15.741 G_Regularizer: 0.000 validation_error: 20.634 +(epoch: 7, iters: 29488, time: 0.543, data: 0.000) G_L1: 17.314 G_L1_ABSOLUTE: 2.854 G_L1_RELATIVE: 14.460 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 7, iters: 31488, time: 0.552, data: 0.000) G_L1: 15.181 G_L1_ABSOLUTE: 2.743 G_L1_RELATIVE: 12.438 G_Regularizer: 0.000 validation_error: 21.165 +(epoch: 7, iters: 33488, time: 0.543, data: 0.000) G_L1: 15.187 G_L1_ABSOLUTE: 2.926 G_L1_RELATIVE: 12.261 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 7, iters: 35488, time: 0.545, data: 0.001) G_L1: 14.766 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 11.885 G_Regularizer: 0.000 validation_error: 21.656 +(epoch: 7, iters: 37488, time: 0.547, data: 0.000) G_L1: 15.962 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 13.278 G_Regularizer: 0.000 validation_error: 20.946 +(epoch: 7, iters: 39488, time: 0.547, data: 0.000) G_L1: 18.470 G_L1_ABSOLUTE: 3.467 G_L1_RELATIVE: 15.002 G_Regularizer: 0.000 validation_error: 21.208 +(epoch: 7, iters: 41488, time: 0.545, data: 0.000) G_L1: 14.587 G_L1_ABSOLUTE: 3.014 G_L1_RELATIVE: 11.572 G_Regularizer: 0.000 validation_error: 21.597 +(epoch: 7, iters: 43488, time: 0.541, data: 0.000) G_L1: 18.191 G_L1_ABSOLUTE: 3.081 G_L1_RELATIVE: 15.110 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 7, iters: 45488, time: 0.550, data: 0.000) G_L1: 16.190 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 13.646 G_Regularizer: 0.000 validation_error: 21.036 +(epoch: 7, iters: 47488, time: 0.545, data: 0.000) G_L1: 19.757 G_L1_ABSOLUTE: 3.506 G_L1_RELATIVE: 16.251 G_Regularizer: 0.000 validation_error: 21.215 +(epoch: 7, iters: 49488, time: 0.550, data: 0.000) G_L1: 16.511 G_L1_ABSOLUTE: 2.932 G_L1_RELATIVE: 13.579 G_Regularizer: 0.000 validation_error: 20.791 +(epoch: 7, iters: 51488, time: 0.545, data: 0.000) G_L1: 16.738 G_L1_ABSOLUTE: 3.006 G_L1_RELATIVE: 13.733 G_Regularizer: 0.000 validation_error: 20.469 +(epoch: 7, iters: 53488, time: 0.545, data: 0.000) G_L1: 18.144 G_L1_ABSOLUTE: 3.038 G_L1_RELATIVE: 15.106 G_Regularizer: 0.000 validation_error: 21.288 +(epoch: 7, iters: 55488, time: 0.549, data: 0.000) G_L1: 15.120 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 12.625 G_Regularizer: 0.000 validation_error: 21.472 +(epoch: 7, iters: 57488, time: 0.552, data: 0.000) G_L1: 14.521 G_L1_ABSOLUTE: 2.823 G_L1_RELATIVE: 11.698 G_Regularizer: 0.000 validation_error: 20.431 +(epoch: 7, iters: 59488, time: 0.545, data: 0.000) G_L1: 16.929 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 14.115 G_Regularizer: 0.000 validation_error: 21.118 +(epoch: 7, iters: 61488, time: 0.559, data: 0.000) G_L1: 15.309 G_L1_ABSOLUTE: 2.562 G_L1_RELATIVE: 12.746 G_Regularizer: 0.000 validation_error: 21.073 +(epoch: 7, iters: 63488, time: 0.551, data: 0.000) G_L1: 17.289 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 14.841 G_Regularizer: 0.000 validation_error: 20.844 +(epoch: 7, iters: 65488, time: 0.554, data: 0.000) G_L1: 16.505 G_L1_ABSOLUTE: 2.874 G_L1_RELATIVE: 13.630 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 7, iters: 67488, time: 0.552, data: 0.000) G_L1: 13.462 G_L1_ABSOLUTE: 2.853 G_L1_RELATIVE: 10.609 G_Regularizer: 0.000 validation_error: 21.350 +(epoch: 7, iters: 69488, time: 0.550, data: 0.000) G_L1: 19.528 G_L1_ABSOLUTE: 3.199 G_L1_RELATIVE: 16.329 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 7, iters: 71488, time: 0.546, data: 0.000) G_L1: 17.029 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 14.281 G_Regularizer: 0.000 validation_error: 21.144 +(epoch: 7, iters: 73488, time: 0.546, data: 0.000) G_L1: 16.107 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 13.296 G_Regularizer: 0.000 validation_error: 21.319 +(epoch: 7, iters: 75488, time: 0.554, data: 0.000) G_L1: 15.360 G_L1_ABSOLUTE: 2.868 G_L1_RELATIVE: 12.492 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 7, iters: 77488, time: 0.549, data: 0.001) G_L1: 17.641 G_L1_ABSOLUTE: 2.821 G_L1_RELATIVE: 14.820 G_Regularizer: 0.000 validation_error: 21.300 +(epoch: 7, iters: 79488, time: 0.550, data: 0.000) G_L1: 16.862 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 14.099 G_Regularizer: 0.000 validation_error: 20.632 +(epoch: 7, iters: 81488, time: 0.549, data: 0.000) G_L1: 15.642 G_L1_ABSOLUTE: 2.794 G_L1_RELATIVE: 12.848 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 7, iters: 83488, time: 0.556, data: 0.001) G_L1: 16.978 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 14.315 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 7, iters: 85488, time: 0.549, data: 0.000) G_L1: 17.241 G_L1_ABSOLUTE: 2.966 G_L1_RELATIVE: 14.274 G_Regularizer: 0.000 validation_error: 20.216 +(epoch: 7, iters: 87488, time: 0.561, data: 0.000) G_L1: 17.939 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 15.125 G_Regularizer: 0.000 validation_error: 20.442 +(epoch: 7, iters: 89488, time: 0.543, data: 0.000) G_L1: 12.685 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 10.216 G_Regularizer: 0.000 validation_error: 21.291 +(epoch: 7, iters: 91488, time: 0.548, data: 0.000) G_L1: 15.271 G_L1_ABSOLUTE: 2.602 G_L1_RELATIVE: 12.669 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 7, iters: 93488, time: 0.545, data: 0.000) G_L1: 16.596 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 14.102 G_Regularizer: 0.000 validation_error: 20.430 +(epoch: 7, iters: 95488, time: 0.542, data: 0.000) G_L1: 15.808 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 13.331 G_Regularizer: 0.000 validation_error: 21.248 +(epoch: 7, iters: 97488, time: 0.543, data: 0.000) G_L1: 17.778 G_L1_ABSOLUTE: 3.207 G_L1_RELATIVE: 14.571 G_Regularizer: 0.000 validation_error: 21.453 +(epoch: 7, iters: 99488, time: 0.541, data: 0.000) G_L1: 17.883 G_L1_ABSOLUTE: 3.667 G_L1_RELATIVE: 14.216 G_Regularizer: 0.000 validation_error: 21.235 +(epoch: 7, iters: 101488, time: 0.546, data: 0.000) G_L1: 15.022 G_L1_ABSOLUTE: 2.801 G_L1_RELATIVE: 12.221 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 7, iters: 103488, time: 0.543, data: 0.000) G_L1: 16.312 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 13.567 G_Regularizer: 0.000 validation_error: 21.468 +(epoch: 7, iters: 105488, time: 0.545, data: 0.000) G_L1: 15.513 G_L1_ABSOLUTE: 3.156 G_L1_RELATIVE: 12.356 G_Regularizer: 0.000 validation_error: 20.670 +(epoch: 7, iters: 107488, time: 0.541, data: 0.000) G_L1: 16.795 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 13.984 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 7, iters: 109488, time: 0.540, data: 0.000) G_L1: 12.669 G_L1_ABSOLUTE: 2.980 G_L1_RELATIVE: 9.689 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 7, iters: 111488, time: 0.550, data: 0.000) G_L1: 22.348 G_L1_ABSOLUTE: 2.791 G_L1_RELATIVE: 19.557 G_Regularizer: 0.000 validation_error: 20.577 +(epoch: 7, iters: 113488, time: 0.542, data: 0.000) G_L1: 13.866 G_L1_ABSOLUTE: 3.062 G_L1_RELATIVE: 10.804 G_Regularizer: 0.000 validation_error: 20.384 +(epoch: 7, iters: 115488, time: 0.542, data: 0.000) G_L1: 15.793 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 13.257 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 7, iters: 117488, time: 0.543, data: 0.000) G_L1: 14.482 G_L1_ABSOLUTE: 2.719 G_L1_RELATIVE: 11.763 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 7, iters: 119488, time: 0.539, data: 0.000) G_L1: 14.201 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 11.578 G_Regularizer: 0.000 validation_error: 20.291 +(epoch: 7, iters: 121488, time: 0.540, data: 0.000) G_L1: 15.631 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 13.023 G_Regularizer: 0.000 validation_error: 21.198 +(epoch: 7, iters: 123488, time: 0.539, data: 0.000) G_L1: 15.074 G_L1_ABSOLUTE: 2.869 G_L1_RELATIVE: 12.205 G_Regularizer: 0.000 validation_error: 19.916 +(epoch: 7, iters: 125488, time: 0.547, data: 0.000) G_L1: 16.335 G_L1_ABSOLUTE: 3.257 G_L1_RELATIVE: 13.077 G_Regularizer: 0.000 validation_error: 21.910 +(epoch: 7, iters: 127488, time: 0.536, data: 0.000) G_L1: 16.518 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 14.253 G_Regularizer: 0.000 validation_error: 21.432 +(epoch: 7, iters: 129488, time: 0.541, data: 0.000) G_L1: 16.800 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 14.158 G_Regularizer: 0.000 validation_error: 20.313 +(epoch: 7, iters: 131488, time: 0.543, data: 0.000) G_L1: 14.252 G_L1_ABSOLUTE: 2.902 G_L1_RELATIVE: 11.350 G_Regularizer: 0.000 validation_error: 21.259 +(epoch: 7, iters: 133488, time: 0.540, data: 0.000) G_L1: 14.494 G_L1_ABSOLUTE: 2.831 G_L1_RELATIVE: 11.663 G_Regularizer: 0.000 validation_error: 21.392 +(epoch: 7, iters: 135488, time: 0.546, data: 0.000) G_L1: 15.983 G_L1_ABSOLUTE: 2.700 G_L1_RELATIVE: 13.283 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 7, iters: 137488, time: 0.543, data: 0.000) G_L1: 13.936 G_L1_ABSOLUTE: 2.297 G_L1_RELATIVE: 11.639 G_Regularizer: 0.000 validation_error: 20.782 +(epoch: 7, iters: 139488, time: 0.541, data: 0.000) G_L1: 16.582 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 14.199 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 7, iters: 141488, time: 0.547, data: 0.000) G_L1: 14.801 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 12.183 G_Regularizer: 0.000 validation_error: 20.370 +(epoch: 7, iters: 143488, time: 0.543, data: 0.000) G_L1: 13.359 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 10.722 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 7, iters: 145488, time: 0.540, data: 0.000) G_L1: 14.873 G_L1_ABSOLUTE: 2.772 G_L1_RELATIVE: 12.102 G_Regularizer: 0.000 validation_error: 20.768 +(epoch: 7, iters: 147488, time: 0.552, data: 0.000) G_L1: 15.189 G_L1_ABSOLUTE: 2.775 G_L1_RELATIVE: 12.414 G_Regularizer: 0.000 validation_error: 20.601 +(epoch: 7, iters: 149488, time: 0.541, data: 0.000) G_L1: 16.811 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 14.392 G_Regularizer: 0.000 validation_error: 21.655 +(epoch: 7, iters: 151488, time: 0.543, data: 0.000) G_L1: 13.832 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 11.147 G_Regularizer: 0.000 validation_error: 21.180 +(epoch: 7, iters: 153488, time: 0.543, data: 0.000) G_L1: 16.855 G_L1_ABSOLUTE: 3.334 G_L1_RELATIVE: 13.521 G_Regularizer: 0.000 validation_error: 20.895 +(epoch: 7, iters: 155488, time: 0.543, data: 0.000) G_L1: 13.690 G_L1_ABSOLUTE: 2.351 G_L1_RELATIVE: 11.339 G_Regularizer: 0.000 validation_error: 21.328 +(epoch: 7, iters: 157488, time: 0.545, data: 0.000) G_L1: 13.795 G_L1_ABSOLUTE: 2.808 G_L1_RELATIVE: 10.986 G_Regularizer: 0.000 validation_error: 20.917 +(epoch: 7, iters: 159488, time: 0.544, data: 0.000) G_L1: 12.846 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 10.214 G_Regularizer: 0.000 validation_error: 21.252 +(epoch: 7, iters: 161488, time: 0.538, data: 0.000) G_L1: 20.032 G_L1_ABSOLUTE: 3.413 G_L1_RELATIVE: 16.619 G_Regularizer: 0.000 validation_error: 21.123 +(epoch: 7, iters: 163488, time: 0.546, data: 0.000) G_L1: 13.905 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 11.444 G_Regularizer: 0.000 validation_error: 21.584 +(epoch: 7, iters: 165488, time: 0.540, data: 0.000) G_L1: 15.271 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 12.822 G_Regularizer: 0.000 validation_error: 20.738 +(epoch: 7, iters: 167488, time: 0.537, data: 0.000) G_L1: 17.307 G_L1_ABSOLUTE: 2.514 G_L1_RELATIVE: 14.793 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 7, iters: 169488, time: 0.535, data: 0.000) G_L1: 17.404 G_L1_ABSOLUTE: 3.677 G_L1_RELATIVE: 13.727 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 7, iters: 171488, time: 0.544, data: 0.000) G_L1: 14.203 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 11.446 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 7, iters: 173488, time: 0.538, data: 0.000) G_L1: 14.211 G_L1_ABSOLUTE: 2.241 G_L1_RELATIVE: 11.970 G_Regularizer: 0.000 validation_error: 21.401 +(epoch: 7, iters: 175488, time: 0.543, data: 0.000) G_L1: 15.308 G_L1_ABSOLUTE: 2.838 G_L1_RELATIVE: 12.471 G_Regularizer: 0.000 validation_error: 21.791 +(epoch: 7, iters: 177488, time: 0.548, data: 0.000) G_L1: 18.719 G_L1_ABSOLUTE: 3.481 G_L1_RELATIVE: 15.238 G_Regularizer: 0.000 validation_error: 21.239 +(epoch: 7, iters: 179488, time: 0.539, data: 0.000) G_L1: 16.404 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 13.637 G_Regularizer: 0.000 validation_error: 20.776 +(epoch: 7, iters: 181488, time: 0.557, data: 0.000) G_L1: 14.715 G_L1_ABSOLUTE: 2.865 G_L1_RELATIVE: 11.850 G_Regularizer: 0.000 validation_error: 20.639 +(epoch: 7, iters: 183488, time: 0.555, data: 0.000) G_L1: 13.604 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 11.151 G_Regularizer: 0.000 validation_error: 21.630 +(epoch: 7, iters: 185488, time: 0.539, data: 0.000) G_L1: 15.135 G_L1_ABSOLUTE: 2.842 G_L1_RELATIVE: 12.293 G_Regularizer: 0.000 validation_error: 20.922 +(epoch: 7, iters: 187488, time: 0.546, data: 0.000) G_L1: 16.448 G_L1_ABSOLUTE: 2.936 G_L1_RELATIVE: 13.512 G_Regularizer: 0.000 validation_error: 20.867 +(epoch: 7, iters: 189488, time: 0.542, data: 0.000) G_L1: 16.333 G_L1_ABSOLUTE: 2.936 G_L1_RELATIVE: 13.397 G_Regularizer: 0.000 validation_error: 20.362 +(epoch: 7, iters: 191488, time: 0.547, data: 0.000) G_L1: 16.083 G_L1_ABSOLUTE: 2.358 G_L1_RELATIVE: 13.725 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 7, iters: 193488, time: 0.547, data: 0.000) G_L1: 15.457 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 12.549 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 7, iters: 195488, time: 0.543, data: 0.000) G_L1: 15.379 G_L1_ABSOLUTE: 2.272 G_L1_RELATIVE: 13.108 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 7, iters: 197488, time: 0.543, data: 0.000) G_L1: 13.654 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 10.819 G_Regularizer: 0.000 validation_error: 21.407 +(epoch: 7, iters: 199488, time: 0.548, data: 0.000) G_L1: 17.973 G_L1_ABSOLUTE: 3.258 G_L1_RELATIVE: 14.716 G_Regularizer: 0.000 validation_error: 19.863 +(epoch: 7, iters: 201488, time: 0.540, data: 0.000) G_L1: 15.333 G_L1_ABSOLUTE: 2.806 G_L1_RELATIVE: 12.528 G_Regularizer: 0.000 validation_error: 21.430 +(epoch: 7, iters: 203488, time: 0.534, data: 0.000) G_L1: 15.083 G_L1_ABSOLUTE: 2.636 G_L1_RELATIVE: 12.447 G_Regularizer: 0.000 validation_error: 21.256 +(epoch: 7, iters: 205488, time: 0.534, data: 0.000) G_L1: 14.415 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 11.507 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 7, iters: 207488, time: 0.551, data: 0.000) G_L1: 15.934 G_L1_ABSOLUTE: 2.718 G_L1_RELATIVE: 13.216 G_Regularizer: 0.000 validation_error: 20.611 +(epoch: 7, iters: 209488, time: 0.536, data: 0.000) G_L1: 17.534 G_L1_ABSOLUTE: 2.711 G_L1_RELATIVE: 14.822 G_Regularizer: 0.000 validation_error: 19.974 +(epoch: 7, iters: 211488, time: 0.542, data: 0.000) G_L1: 15.730 G_L1_ABSOLUTE: 2.859 G_L1_RELATIVE: 12.872 G_Regularizer: 0.000 validation_error: 20.745 +(epoch: 7, iters: 213488, time: 0.952, data: 0.000) G_L1: 15.402 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 12.784 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 7, iters: 215488, time: 0.950, data: 0.000) G_L1: 17.083 G_L1_ABSOLUTE: 2.329 G_L1_RELATIVE: 14.754 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 7, iters: 217488, time: 0.952, data: 0.000) G_L1: 17.356 G_L1_ABSOLUTE: 3.062 G_L1_RELATIVE: 14.293 G_Regularizer: 0.000 validation_error: 20.979 +(epoch: 7, iters: 219488, time: 0.945, data: 0.000) G_L1: 18.794 G_L1_ABSOLUTE: 3.116 G_L1_RELATIVE: 15.678 G_Regularizer: 0.000 validation_error: 21.487 +(epoch: 7, iters: 221488, time: 0.946, data: 0.000) G_L1: 16.169 G_L1_ABSOLUTE: 2.854 G_L1_RELATIVE: 13.315 G_Regularizer: 0.000 validation_error: 20.513 +(epoch: 7, iters: 223488, time: 0.952, data: 0.000) G_L1: 16.591 G_L1_ABSOLUTE: 2.643 G_L1_RELATIVE: 13.949 G_Regularizer: 0.000 validation_error: 21.398 +(epoch: 7, iters: 225488, time: 0.950, data: 0.000) G_L1: 13.806 G_L1_ABSOLUTE: 2.444 G_L1_RELATIVE: 11.361 G_Regularizer: 0.000 validation_error: 21.101 +(epoch: 7, iters: 227488, time: 0.950, data: 0.000) G_L1: 15.240 G_L1_ABSOLUTE: 3.177 G_L1_RELATIVE: 12.062 G_Regularizer: 0.000 validation_error: 21.106 +(epoch: 7, iters: 229488, time: 0.959, data: 0.000) G_L1: 16.177 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 13.365 G_Regularizer: 0.000 validation_error: 20.564 +(epoch: 7, iters: 231488, time: 0.956, data: 0.000) G_L1: 21.500 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 18.707 G_Regularizer: 0.000 validation_error: 21.081 +(epoch: 7, iters: 233488, time: 0.960, data: 0.000) G_L1: 16.471 G_L1_ABSOLUTE: 3.149 G_L1_RELATIVE: 13.322 G_Regularizer: 0.000 validation_error: 20.582 +(epoch: 7, iters: 235488, time: 0.948, data: 0.000) G_L1: 14.604 G_L1_ABSOLUTE: 2.768 G_L1_RELATIVE: 11.835 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 7, iters: 237488, time: 0.962, data: 0.000) G_L1: 17.029 G_L1_ABSOLUTE: 3.118 G_L1_RELATIVE: 13.911 G_Regularizer: 0.000 validation_error: 20.459 +(epoch: 7, iters: 239488, time: 0.957, data: 0.000) G_L1: 14.547 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 11.905 G_Regularizer: 0.000 validation_error: 20.421 +(epoch: 7, iters: 241488, time: 0.966, data: 0.000) G_L1: 16.705 G_L1_ABSOLUTE: 2.906 G_L1_RELATIVE: 13.800 G_Regularizer: 0.000 validation_error: 21.885 +(epoch: 7, iters: 243488, time: 0.954, data: 0.000) G_L1: 14.955 G_L1_ABSOLUTE: 2.868 G_L1_RELATIVE: 12.088 G_Regularizer: 0.000 validation_error: 20.867 +(epoch: 7, iters: 245488, time: 0.964, data: 0.000) G_L1: 17.937 G_L1_ABSOLUTE: 3.135 G_L1_RELATIVE: 14.803 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 7, iters: 247488, time: 0.961, data: 0.000) G_L1: 14.924 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 12.354 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 7, iters: 249488, time: 0.961, data: 0.000) G_L1: 16.443 G_L1_ABSOLUTE: 3.196 G_L1_RELATIVE: 13.246 G_Regularizer: 0.000 validation_error: 20.158 +(epoch: 7, iters: 251488, time: 0.964, data: 0.004) G_L1: 13.191 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 10.580 G_Regularizer: 0.000 validation_error: 21.441 +(epoch: 7, iters: 253488, time: 0.967, data: 0.000) G_L1: 16.307 G_L1_ABSOLUTE: 3.398 G_L1_RELATIVE: 12.909 G_Regularizer: 0.000 validation_error: 19.433 +(epoch: 7, iters: 255488, time: 0.960, data: 0.000) G_L1: 13.085 G_L1_ABSOLUTE: 3.145 G_L1_RELATIVE: 9.940 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 7, iters: 257488, time: 0.964, data: 0.000) G_L1: 15.551 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 12.759 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 7, iters: 259488, time: 0.962, data: 0.000) G_L1: 13.729 G_L1_ABSOLUTE: 3.238 G_L1_RELATIVE: 10.491 G_Regularizer: 0.000 validation_error: 20.112 +(epoch: 7, iters: 261488, time: 0.961, data: 0.000) G_L1: 14.181 G_L1_ABSOLUTE: 2.983 G_L1_RELATIVE: 11.198 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 7, iters: 263488, time: 0.964, data: 0.000) G_L1: 16.138 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 13.459 G_Regularizer: 0.000 validation_error: 20.066 +(epoch: 7, iters: 265488, time: 0.963, data: 0.000) G_L1: 18.784 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 15.974 G_Regularizer: 0.000 validation_error: 21.068 +(epoch: 7, iters: 267488, time: 0.960, data: 0.000) G_L1: 14.018 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 11.220 G_Regularizer: 0.000 validation_error: 20.661 +(epoch: 7, iters: 269488, time: 0.962, data: 0.000) G_L1: 20.672 G_L1_ABSOLUTE: 3.066 G_L1_RELATIVE: 17.606 G_Regularizer: 0.000 validation_error: 21.336 +(epoch: 7, iters: 271488, time: 0.966, data: 0.000) G_L1: 16.259 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 13.370 G_Regularizer: 0.000 validation_error: 20.412 +(epoch: 7, iters: 273488, time: 0.957, data: 0.000) G_L1: 15.082 G_L1_ABSOLUTE: 3.035 G_L1_RELATIVE: 12.047 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 7, iters: 275488, time: 0.966, data: 0.000) G_L1: 14.281 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 11.689 G_Regularizer: 0.000 validation_error: 21.335 +(epoch: 7, iters: 277488, time: 0.968, data: 0.000) G_L1: 16.842 G_L1_ABSOLUTE: 3.163 G_L1_RELATIVE: 13.679 G_Regularizer: 0.000 validation_error: 20.936 +(epoch: 7, iters: 279488, time: 0.962, data: 0.000) G_L1: 17.019 G_L1_ABSOLUTE: 2.404 G_L1_RELATIVE: 14.615 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 7, iters: 281488, time: 0.969, data: 0.000) G_L1: 19.542 G_L1_ABSOLUTE: 2.802 G_L1_RELATIVE: 16.740 G_Regularizer: 0.000 validation_error: 20.332 +(epoch: 7, iters: 283488, time: 0.963, data: 0.000) G_L1: 13.776 G_L1_ABSOLUTE: 2.583 G_L1_RELATIVE: 11.193 G_Regularizer: 0.000 validation_error: 20.893 +(epoch: 7, iters: 285488, time: 0.967, data: 0.000) G_L1: 13.699 G_L1_ABSOLUTE: 2.873 G_L1_RELATIVE: 10.826 G_Regularizer: 0.000 validation_error: 20.716 +(epoch: 7, iters: 287488, time: 0.970, data: 0.000) G_L1: 14.708 G_L1_ABSOLUTE: 3.108 G_L1_RELATIVE: 11.600 G_Regularizer: 0.000 validation_error: 20.441 +(epoch: 7, iters: 289488, time: 0.969, data: 0.000) G_L1: 15.487 G_L1_ABSOLUTE: 3.355 G_L1_RELATIVE: 12.132 G_Regularizer: 0.000 validation_error: 21.359 +(epoch: 7, iters: 291488, time: 0.960, data: 0.000) G_L1: 16.891 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 14.379 G_Regularizer: 0.000 validation_error: 20.388 +(epoch: 7, iters: 293488, time: 0.960, data: 0.000) G_L1: 15.866 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 13.009 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 7, iters: 295488, time: 0.968, data: 0.000) G_L1: 14.271 G_L1_ABSOLUTE: 2.465 G_L1_RELATIVE: 11.807 G_Regularizer: 0.000 validation_error: 20.226 +(epoch: 7, iters: 297488, time: 0.969, data: 0.000) G_L1: 16.518 G_L1_ABSOLUTE: 3.038 G_L1_RELATIVE: 13.480 G_Regularizer: 0.000 validation_error: 20.260 +(epoch: 7, iters: 299488, time: 0.968, data: 0.000) G_L1: 18.973 G_L1_ABSOLUTE: 3.642 G_L1_RELATIVE: 15.332 G_Regularizer: 0.000 validation_error: 20.323 +(epoch: 7, iters: 301488, time: 0.965, data: 0.000) G_L1: 16.323 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 13.565 G_Regularizer: 0.000 validation_error: 20.480 +(epoch: 8, iters: 736, time: 0.967, data: 0.000) G_L1: 13.612 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 11.073 G_Regularizer: 0.000 validation_error: 20.894 +(epoch: 8, iters: 2736, time: 0.966, data: 0.000) G_L1: 14.812 G_L1_ABSOLUTE: 2.783 G_L1_RELATIVE: 12.029 G_Regularizer: 0.000 validation_error: 20.003 +(epoch: 8, iters: 4736, time: 0.962, data: 0.000) G_L1: 13.494 G_L1_ABSOLUTE: 2.641 G_L1_RELATIVE: 10.853 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 8, iters: 6736, time: 0.968, data: 0.000) G_L1: 13.571 G_L1_ABSOLUTE: 2.840 G_L1_RELATIVE: 10.731 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 8, iters: 8736, time: 0.963, data: 0.000) G_L1: 17.155 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 14.275 G_Regularizer: 0.000 validation_error: 19.969 +(epoch: 8, iters: 10736, time: 0.967, data: 0.000) G_L1: 17.185 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 14.659 G_Regularizer: 0.000 validation_error: 20.665 +(epoch: 8, iters: 12736, time: 0.963, data: 0.000) G_L1: 15.634 G_L1_ABSOLUTE: 3.076 G_L1_RELATIVE: 12.558 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 8, iters: 14736, time: 0.965, data: 0.000) G_L1: 17.600 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 14.744 G_Regularizer: 0.000 validation_error: 21.009 +(epoch: 8, iters: 16736, time: 0.964, data: 0.000) G_L1: 14.371 G_L1_ABSOLUTE: 2.717 G_L1_RELATIVE: 11.654 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 8, iters: 18736, time: 0.968, data: 0.000) G_L1: 15.998 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 13.551 G_Regularizer: 0.000 validation_error: 22.041 +(epoch: 8, iters: 20736, time: 0.967, data: 0.000) G_L1: 16.580 G_L1_ABSOLUTE: 3.075 G_L1_RELATIVE: 13.505 G_Regularizer: 0.000 validation_error: 20.944 +(epoch: 8, iters: 22736, time: 0.967, data: 0.000) G_L1: 15.155 G_L1_ABSOLUTE: 2.605 G_L1_RELATIVE: 12.550 G_Regularizer: 0.000 validation_error: 20.506 +(epoch: 8, iters: 24736, time: 0.966, data: 0.001) G_L1: 19.403 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 16.629 G_Regularizer: 0.000 validation_error: 20.771 +(epoch: 8, iters: 26736, time: 0.969, data: 0.000) G_L1: 12.731 G_L1_ABSOLUTE: 2.154 G_L1_RELATIVE: 10.578 G_Regularizer: 0.000 validation_error: 21.552 +(epoch: 8, iters: 28736, time: 0.965, data: 0.002) G_L1: 14.734 G_L1_ABSOLUTE: 3.341 G_L1_RELATIVE: 11.393 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 8, iters: 30736, time: 0.961, data: 0.000) G_L1: 15.440 G_L1_ABSOLUTE: 3.515 G_L1_RELATIVE: 11.925 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 8, iters: 32736, time: 0.965, data: 0.000) G_L1: 14.674 G_L1_ABSOLUTE: 2.771 G_L1_RELATIVE: 11.903 G_Regularizer: 0.000 validation_error: 21.198 +(epoch: 8, iters: 34736, time: 0.955, data: 0.000) G_L1: 16.497 G_L1_ABSOLUTE: 2.688 G_L1_RELATIVE: 13.809 G_Regularizer: 0.000 validation_error: 21.251 +(epoch: 8, iters: 36736, time: 0.957, data: 0.000) G_L1: 13.661 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 11.315 G_Regularizer: 0.000 validation_error: 20.202 +(epoch: 8, iters: 38736, time: 0.966, data: 0.000) G_L1: 17.127 G_L1_ABSOLUTE: 3.039 G_L1_RELATIVE: 14.088 G_Regularizer: 0.000 validation_error: 20.592 +(epoch: 8, iters: 40736, time: 0.960, data: 0.000) G_L1: 13.988 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 11.569 G_Regularizer: 0.000 validation_error: 20.324 +(epoch: 8, iters: 42736, time: 0.967, data: 0.000) G_L1: 14.757 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 12.184 G_Regularizer: 0.000 validation_error: 20.436 +(epoch: 8, iters: 44736, time: 0.962, data: 0.000) G_L1: 15.931 G_L1_ABSOLUTE: 3.102 G_L1_RELATIVE: 12.828 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 8, iters: 46736, time: 0.968, data: 0.000) G_L1: 15.296 G_L1_ABSOLUTE: 3.449 G_L1_RELATIVE: 11.846 G_Regularizer: 0.000 validation_error: 20.290 +(epoch: 8, iters: 48736, time: 0.962, data: 0.000) G_L1: 15.891 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 13.231 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 8, iters: 50736, time: 0.966, data: 0.000) G_L1: 16.759 G_L1_ABSOLUTE: 2.961 G_L1_RELATIVE: 13.798 G_Regularizer: 0.000 validation_error: 20.594 +(epoch: 8, iters: 52736, time: 0.964, data: 0.000) G_L1: 14.306 G_L1_ABSOLUTE: 2.745 G_L1_RELATIVE: 11.561 G_Regularizer: 0.000 validation_error: 20.297 +(epoch: 8, iters: 54736, time: 0.963, data: 0.000) G_L1: 16.300 G_L1_ABSOLUTE: 3.089 G_L1_RELATIVE: 13.211 G_Regularizer: 0.000 validation_error: 20.478 +(epoch: 8, iters: 56736, time: 0.969, data: 0.000) G_L1: 12.454 G_L1_ABSOLUTE: 2.156 G_L1_RELATIVE: 10.299 G_Regularizer: 0.000 validation_error: 20.183 +(epoch: 8, iters: 58736, time: 0.963, data: 0.000) G_L1: 18.585 G_L1_ABSOLUTE: 2.876 G_L1_RELATIVE: 15.710 G_Regularizer: 0.000 validation_error: 21.060 +(epoch: 8, iters: 60736, time: 0.970, data: 0.000) G_L1: 13.725 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 10.969 G_Regularizer: 0.000 validation_error: 21.520 +(epoch: 8, iters: 62736, time: 0.965, data: 0.000) G_L1: 17.687 G_L1_ABSOLUTE: 3.004 G_L1_RELATIVE: 14.683 G_Regularizer: 0.000 validation_error: 20.616 +(epoch: 8, iters: 64736, time: 0.964, data: 0.000) G_L1: 15.836 G_L1_ABSOLUTE: 3.160 G_L1_RELATIVE: 12.676 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 8, iters: 66736, time: 0.972, data: 0.000) G_L1: 15.817 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 13.297 G_Regularizer: 0.000 validation_error: 20.188 +(epoch: 8, iters: 68736, time: 0.966, data: 0.000) G_L1: 13.620 G_L1_ABSOLUTE: 2.895 G_L1_RELATIVE: 10.725 G_Regularizer: 0.000 validation_error: 20.218 +(epoch: 8, iters: 70736, time: 0.967, data: 0.000) G_L1: 16.255 G_L1_ABSOLUTE: 3.095 G_L1_RELATIVE: 13.160 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 8, iters: 72736, time: 0.966, data: 0.000) G_L1: 19.138 G_L1_ABSOLUTE: 2.639 G_L1_RELATIVE: 16.500 G_Regularizer: 0.000 validation_error: 20.409 +(epoch: 8, iters: 74736, time: 0.965, data: 0.000) G_L1: 15.723 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 13.448 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 8, iters: 76736, time: 0.965, data: 0.000) G_L1: 13.382 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 10.736 G_Regularizer: 0.000 validation_error: 21.307 +(epoch: 8, iters: 78736, time: 0.964, data: 0.000) G_L1: 14.600 G_L1_ABSOLUTE: 2.252 G_L1_RELATIVE: 12.348 G_Regularizer: 0.000 validation_error: 21.214 +(epoch: 8, iters: 80736, time: 0.965, data: 0.000) G_L1: 17.443 G_L1_ABSOLUTE: 3.210 G_L1_RELATIVE: 14.232 G_Regularizer: 0.000 validation_error: 20.518 +(epoch: 8, iters: 82736, time: 0.957, data: 0.000) G_L1: 17.354 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 14.491 G_Regularizer: 0.000 validation_error: 21.371 +(epoch: 8, iters: 84736, time: 0.972, data: 0.000) G_L1: 14.949 G_L1_ABSOLUTE: 3.037 G_L1_RELATIVE: 11.912 G_Regularizer: 0.000 validation_error: 20.509 +(epoch: 8, iters: 86736, time: 0.963, data: 0.000) G_L1: 15.421 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 12.898 G_Regularizer: 0.000 validation_error: 20.156 +(epoch: 8, iters: 88736, time: 0.966, data: 0.000) G_L1: 16.252 G_L1_ABSOLUTE: 2.960 G_L1_RELATIVE: 13.292 G_Regularizer: 0.000 validation_error: 20.066 +(epoch: 8, iters: 90736, time: 0.711, data: 0.000) G_L1: 15.504 G_L1_ABSOLUTE: 2.514 G_L1_RELATIVE: 12.989 G_Regularizer: 0.000 validation_error: 19.965 +(epoch: 8, iters: 92736, time: 0.557, data: 0.000) G_L1: 16.518 G_L1_ABSOLUTE: 2.833 G_L1_RELATIVE: 13.686 G_Regularizer: 0.000 validation_error: 19.995 +(epoch: 8, iters: 94736, time: 0.573, data: 0.000) G_L1: 18.142 G_L1_ABSOLUTE: 2.728 G_L1_RELATIVE: 15.414 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 8, iters: 96736, time: 0.542, data: 0.000) G_L1: 14.603 G_L1_ABSOLUTE: 2.965 G_L1_RELATIVE: 11.637 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 8, iters: 98736, time: 0.551, data: 0.000) G_L1: 17.956 G_L1_ABSOLUTE: 2.862 G_L1_RELATIVE: 15.095 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 8, iters: 100736, time: 0.543, data: 0.000) G_L1: 17.222 G_L1_ABSOLUTE: 2.848 G_L1_RELATIVE: 14.375 G_Regularizer: 0.000 validation_error: 21.441 +(epoch: 8, iters: 102736, time: 0.547, data: 0.000) G_L1: 15.981 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 13.409 G_Regularizer: 0.000 validation_error: 21.885 +(epoch: 8, iters: 104736, time: 0.542, data: 0.000) G_L1: 14.450 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 11.900 G_Regularizer: 0.000 validation_error: 20.391 +(epoch: 8, iters: 106736, time: 0.540, data: 0.000) G_L1: 13.132 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 10.591 G_Regularizer: 0.000 validation_error: 20.402 +(epoch: 8, iters: 108736, time: 0.544, data: 0.000) G_L1: 16.296 G_L1_ABSOLUTE: 3.268 G_L1_RELATIVE: 13.028 G_Regularizer: 0.000 validation_error: 20.400 +(epoch: 8, iters: 110736, time: 0.548, data: 0.000) G_L1: 17.367 G_L1_ABSOLUTE: 3.012 G_L1_RELATIVE: 14.355 G_Regularizer: 0.000 validation_error: 21.134 +(epoch: 8, iters: 112736, time: 0.550, data: 0.000) G_L1: 14.403 G_L1_ABSOLUTE: 2.986 G_L1_RELATIVE: 11.416 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 8, iters: 114736, time: 0.540, data: 0.000) G_L1: 15.408 G_L1_ABSOLUTE: 3.190 G_L1_RELATIVE: 12.218 G_Regularizer: 0.000 validation_error: 20.336 +(epoch: 8, iters: 116736, time: 0.551, data: 0.000) G_L1: 17.879 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 15.158 G_Regularizer: 0.000 validation_error: 20.932 +(epoch: 8, iters: 118736, time: 0.542, data: 0.000) G_L1: 15.972 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 13.382 G_Regularizer: 0.000 validation_error: 20.459 +(epoch: 8, iters: 120736, time: 0.548, data: 0.000) G_L1: 14.268 G_L1_ABSOLUTE: 2.862 G_L1_RELATIVE: 11.406 G_Regularizer: 0.000 validation_error: 20.406 +(epoch: 8, iters: 122736, time: 0.542, data: 0.000) G_L1: 14.050 G_L1_ABSOLUTE: 2.529 G_L1_RELATIVE: 11.521 G_Regularizer: 0.000 validation_error: 20.415 +(epoch: 8, iters: 124736, time: 0.547, data: 0.000) G_L1: 15.425 G_L1_ABSOLUTE: 2.988 G_L1_RELATIVE: 12.437 G_Regularizer: 0.000 validation_error: 20.573 +(epoch: 8, iters: 126736, time: 0.553, data: 0.000) G_L1: 16.021 G_L1_ABSOLUTE: 3.221 G_L1_RELATIVE: 12.800 G_Regularizer: 0.000 validation_error: 20.475 +(epoch: 8, iters: 128736, time: 0.549, data: 0.000) G_L1: 16.918 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 14.239 G_Regularizer: 0.000 validation_error: 20.598 +(epoch: 8, iters: 130736, time: 0.552, data: 0.000) G_L1: 16.067 G_L1_ABSOLUTE: 2.993 G_L1_RELATIVE: 13.075 G_Regularizer: 0.000 validation_error: 20.427 +(epoch: 8, iters: 132736, time: 0.547, data: 0.000) G_L1: 14.398 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 11.508 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 8, iters: 134736, time: 0.545, data: 0.000) G_L1: 16.672 G_L1_ABSOLUTE: 3.076 G_L1_RELATIVE: 13.596 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 8, iters: 136736, time: 0.546, data: 0.000) G_L1: 16.482 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 13.605 G_Regularizer: 0.000 validation_error: 20.708 +(epoch: 8, iters: 138736, time: 0.550, data: 0.000) G_L1: 17.175 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 14.604 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 8, iters: 140736, time: 0.546, data: 0.000) G_L1: 13.729 G_L1_ABSOLUTE: 2.359 G_L1_RELATIVE: 11.371 G_Regularizer: 0.000 validation_error: 21.048 +(epoch: 8, iters: 142736, time: 0.545, data: 0.000) G_L1: 18.831 G_L1_ABSOLUTE: 3.262 G_L1_RELATIVE: 15.570 G_Regularizer: 0.000 validation_error: 19.818 +(epoch: 8, iters: 144736, time: 0.544, data: 0.000) G_L1: 16.613 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 13.978 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 8, iters: 146736, time: 0.545, data: 0.000) G_L1: 15.680 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 13.235 G_Regularizer: 0.000 validation_error: 20.455 +(epoch: 8, iters: 148736, time: 0.549, data: 0.000) G_L1: 16.197 G_L1_ABSOLUTE: 3.263 G_L1_RELATIVE: 12.934 G_Regularizer: 0.000 validation_error: 20.628 +(epoch: 8, iters: 150736, time: 0.544, data: 0.000) G_L1: 19.209 G_L1_ABSOLUTE: 3.083 G_L1_RELATIVE: 16.126 G_Regularizer: 0.000 validation_error: 20.394 +(epoch: 8, iters: 152736, time: 0.536, data: 0.000) G_L1: 14.369 G_L1_ABSOLUTE: 2.768 G_L1_RELATIVE: 11.601 G_Regularizer: 0.000 validation_error: 20.639 +(epoch: 8, iters: 154736, time: 0.547, data: 0.000) G_L1: 15.404 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 12.741 G_Regularizer: 0.000 validation_error: 20.092 +(epoch: 8, iters: 156736, time: 0.551, data: 0.000) G_L1: 15.602 G_L1_ABSOLUTE: 3.300 G_L1_RELATIVE: 12.302 G_Regularizer: 0.000 validation_error: 20.547 +(epoch: 8, iters: 158736, time: 0.543, data: 0.000) G_L1: 16.330 G_L1_ABSOLUTE: 2.772 G_L1_RELATIVE: 13.559 G_Regularizer: 0.000 validation_error: 21.238 +(epoch: 8, iters: 160736, time: 0.545, data: 0.000) G_L1: 19.179 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 16.437 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 8, iters: 162736, time: 0.551, data: 0.000) G_L1: 15.590 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 13.191 G_Regularizer: 0.000 validation_error: 20.494 +(epoch: 8, iters: 164736, time: 0.542, data: 0.000) G_L1: 14.680 G_L1_ABSOLUTE: 2.391 G_L1_RELATIVE: 12.289 G_Regularizer: 0.000 validation_error: 21.076 +(epoch: 8, iters: 166736, time: 0.539, data: 0.000) G_L1: 18.449 G_L1_ABSOLUTE: 3.356 G_L1_RELATIVE: 15.092 G_Regularizer: 0.000 validation_error: 20.249 +(epoch: 8, iters: 168736, time: 0.543, data: 0.000) G_L1: 17.683 G_L1_ABSOLUTE: 3.361 G_L1_RELATIVE: 14.322 G_Regularizer: 0.000 validation_error: 20.285 +(epoch: 8, iters: 170736, time: 0.554, data: 0.000) G_L1: 13.039 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 10.485 G_Regularizer: 0.000 validation_error: 19.896 +(epoch: 8, iters: 172736, time: 0.555, data: 0.000) G_L1: 13.817 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 11.313 G_Regularizer: 0.000 validation_error: 21.357 +(epoch: 8, iters: 174736, time: 0.555, data: 0.001) G_L1: 12.515 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 10.103 G_Regularizer: 0.000 validation_error: 20.604 +(epoch: 8, iters: 176736, time: 0.545, data: 0.000) G_L1: 15.166 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 12.509 G_Regularizer: 0.000 validation_error: 21.538 +(epoch: 8, iters: 178736, time: 0.549, data: 0.000) G_L1: 18.090 G_L1_ABSOLUTE: 2.695 G_L1_RELATIVE: 15.395 G_Regularizer: 0.000 validation_error: 21.251 +(epoch: 8, iters: 180736, time: 0.544, data: 0.000) G_L1: 16.903 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 14.285 G_Regularizer: 0.000 validation_error: 21.493 +(epoch: 8, iters: 182736, time: 0.537, data: 0.000) G_L1: 13.108 G_L1_ABSOLUTE: 2.411 G_L1_RELATIVE: 10.696 G_Regularizer: 0.000 validation_error: 19.816 +(epoch: 8, iters: 184736, time: 0.539, data: 0.000) G_L1: 18.393 G_L1_ABSOLUTE: 3.445 G_L1_RELATIVE: 14.949 G_Regularizer: 0.000 validation_error: 20.791 +(epoch: 8, iters: 186736, time: 0.549, data: 0.000) G_L1: 17.608 G_L1_ABSOLUTE: 2.939 G_L1_RELATIVE: 14.669 G_Regularizer: 0.000 validation_error: 20.057 +(epoch: 8, iters: 188736, time: 0.549, data: 0.000) G_L1: 15.536 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 12.967 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 8, iters: 190736, time: 0.543, data: 0.000) G_L1: 15.097 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 12.843 G_Regularizer: 0.000 validation_error: 21.409 +(epoch: 8, iters: 192736, time: 0.543, data: 0.000) G_L1: 15.143 G_L1_ABSOLUTE: 2.852 G_L1_RELATIVE: 12.292 G_Regularizer: 0.000 validation_error: 20.420 +(epoch: 8, iters: 194736, time: 0.546, data: 0.000) G_L1: 15.580 G_L1_ABSOLUTE: 2.916 G_L1_RELATIVE: 12.664 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 8, iters: 196736, time: 0.546, data: 0.000) G_L1: 12.428 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 9.913 G_Regularizer: 0.000 validation_error: 21.258 +(epoch: 8, iters: 198736, time: 0.545, data: 0.000) G_L1: 13.673 G_L1_ABSOLUTE: 3.224 G_L1_RELATIVE: 10.450 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 8, iters: 200736, time: 0.553, data: 0.000) G_L1: 15.773 G_L1_ABSOLUTE: 4.077 G_L1_RELATIVE: 11.695 G_Regularizer: 0.000 validation_error: 20.588 +(epoch: 8, iters: 202736, time: 0.541, data: 0.000) G_L1: 15.781 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 13.218 G_Regularizer: 0.000 validation_error: 20.315 +(epoch: 8, iters: 204736, time: 0.543, data: 0.000) G_L1: 15.969 G_L1_ABSOLUTE: 2.423 G_L1_RELATIVE: 13.545 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 8, iters: 206736, time: 0.547, data: 0.000) G_L1: 14.095 G_L1_ABSOLUTE: 2.471 G_L1_RELATIVE: 11.624 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 8, iters: 208736, time: 0.553, data: 0.000) G_L1: 17.238 G_L1_ABSOLUTE: 3.357 G_L1_RELATIVE: 13.881 G_Regularizer: 0.000 validation_error: 20.240 +(epoch: 8, iters: 210736, time: 0.542, data: 0.000) G_L1: 13.716 G_L1_ABSOLUTE: 2.307 G_L1_RELATIVE: 11.408 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 8, iters: 212736, time: 0.544, data: 0.000) G_L1: 15.258 G_L1_ABSOLUTE: 2.969 G_L1_RELATIVE: 12.289 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 8, iters: 214736, time: 0.537, data: 0.000) G_L1: 15.738 G_L1_ABSOLUTE: 2.545 G_L1_RELATIVE: 13.193 G_Regularizer: 0.000 validation_error: 20.583 +(epoch: 8, iters: 216736, time: 0.546, data: 0.000) G_L1: 13.430 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 11.028 G_Regularizer: 0.000 validation_error: 20.533 +(epoch: 8, iters: 218736, time: 0.950, data: 0.000) G_L1: 18.152 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 15.502 G_Regularizer: 0.000 validation_error: 20.148 +(epoch: 8, iters: 220736, time: 0.955, data: 0.000) G_L1: 15.518 G_L1_ABSOLUTE: 2.898 G_L1_RELATIVE: 12.620 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 8, iters: 222736, time: 0.950, data: 0.000) G_L1: 10.679 G_L1_ABSOLUTE: 2.643 G_L1_RELATIVE: 8.035 G_Regularizer: 0.000 validation_error: 21.310 +(epoch: 8, iters: 224736, time: 0.955, data: 0.000) G_L1: 18.038 G_L1_ABSOLUTE: 2.934 G_L1_RELATIVE: 15.104 G_Regularizer: 0.000 validation_error: 21.303 +(epoch: 8, iters: 226736, time: 0.954, data: 0.000) G_L1: 17.917 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 14.966 G_Regularizer: 0.000 validation_error: 21.494 +(epoch: 8, iters: 228736, time: 0.953, data: 0.000) G_L1: 16.140 G_L1_ABSOLUTE: 2.838 G_L1_RELATIVE: 13.302 G_Regularizer: 0.000 validation_error: 20.266 +(epoch: 8, iters: 230736, time: 0.962, data: 0.000) G_L1: 14.914 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 12.236 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 8, iters: 232736, time: 0.965, data: 0.000) G_L1: 13.346 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 10.817 G_Regularizer: 0.000 validation_error: 21.059 +(epoch: 8, iters: 234736, time: 0.962, data: 0.000) G_L1: 17.994 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 15.359 G_Regularizer: 0.000 validation_error: 21.063 +(epoch: 8, iters: 236736, time: 0.962, data: 0.000) G_L1: 15.085 G_L1_ABSOLUTE: 3.021 G_L1_RELATIVE: 12.064 G_Regularizer: 0.000 validation_error: 19.728 +(epoch: 8, iters: 238736, time: 0.962, data: 0.000) G_L1: 15.655 G_L1_ABSOLUTE: 3.387 G_L1_RELATIVE: 12.268 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 8, iters: 240736, time: 0.964, data: 0.000) G_L1: 18.510 G_L1_ABSOLUTE: 3.149 G_L1_RELATIVE: 15.361 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 8, iters: 242736, time: 0.960, data: 0.000) G_L1: 13.298 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 10.779 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 8, iters: 244736, time: 0.958, data: 0.000) G_L1: 14.388 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 11.678 G_Regularizer: 0.000 validation_error: 20.631 +(epoch: 8, iters: 246736, time: 0.966, data: 0.000) G_L1: 14.198 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 11.488 G_Regularizer: 0.000 validation_error: 20.568 +(epoch: 8, iters: 248736, time: 0.965, data: 0.000) G_L1: 16.713 G_L1_ABSOLUTE: 3.385 G_L1_RELATIVE: 13.328 G_Regularizer: 0.000 validation_error: 20.020 +(epoch: 8, iters: 250736, time: 0.963, data: 0.000) G_L1: 14.918 G_L1_ABSOLUTE: 2.751 G_L1_RELATIVE: 12.167 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 8, iters: 252736, time: 0.962, data: 0.000) G_L1: 19.178 G_L1_ABSOLUTE: 2.752 G_L1_RELATIVE: 16.426 G_Regularizer: 0.000 validation_error: 20.505 +(epoch: 8, iters: 254736, time: 0.962, data: 0.000) G_L1: 12.395 G_L1_ABSOLUTE: 2.560 G_L1_RELATIVE: 9.835 G_Regularizer: 0.000 validation_error: 20.434 +(epoch: 8, iters: 256736, time: 0.964, data: 0.000) G_L1: 18.127 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 15.497 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 8, iters: 258736, time: 0.966, data: 0.000) G_L1: 13.785 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 11.148 G_Regularizer: 0.000 validation_error: 20.716 +(epoch: 8, iters: 260736, time: 0.960, data: 0.000) G_L1: 14.304 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 11.780 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 8, iters: 262736, time: 0.965, data: 0.000) G_L1: 14.036 G_L1_ABSOLUTE: 2.692 G_L1_RELATIVE: 11.344 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 8, iters: 264736, time: 0.965, data: 0.000) G_L1: 16.708 G_L1_ABSOLUTE: 2.776 G_L1_RELATIVE: 13.932 G_Regularizer: 0.000 validation_error: 20.542 +(epoch: 8, iters: 266736, time: 0.963, data: 0.000) G_L1: 15.014 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 12.426 G_Regularizer: 0.000 validation_error: 20.406 +(epoch: 8, iters: 268736, time: 0.968, data: 0.000) G_L1: 15.856 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 13.021 G_Regularizer: 0.000 validation_error: 21.142 +(epoch: 8, iters: 270736, time: 0.969, data: 0.000) G_L1: 17.382 G_L1_ABSOLUTE: 3.273 G_L1_RELATIVE: 14.110 G_Regularizer: 0.000 validation_error: 21.355 +(epoch: 8, iters: 272736, time: 0.963, data: 0.000) G_L1: 16.861 G_L1_ABSOLUTE: 3.114 G_L1_RELATIVE: 13.747 G_Regularizer: 0.000 validation_error: 20.128 +(epoch: 8, iters: 274736, time: 0.966, data: 0.000) G_L1: 15.006 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 12.360 G_Regularizer: 0.000 validation_error: 20.285 +(epoch: 8, iters: 276736, time: 0.958, data: 0.001) G_L1: 16.904 G_L1_ABSOLUTE: 2.578 G_L1_RELATIVE: 14.325 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 8, iters: 278736, time: 0.964, data: 0.000) G_L1: 18.011 G_L1_ABSOLUTE: 3.172 G_L1_RELATIVE: 14.839 G_Regularizer: 0.000 validation_error: 20.392 +(epoch: 8, iters: 280736, time: 0.970, data: 0.000) G_L1: 17.662 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 15.165 G_Regularizer: 0.000 validation_error: 20.215 +(epoch: 8, iters: 282736, time: 0.968, data: 0.000) G_L1: 14.436 G_L1_ABSOLUTE: 2.816 G_L1_RELATIVE: 11.621 G_Regularizer: 0.000 validation_error: 21.121 +(epoch: 8, iters: 284736, time: 0.971, data: 0.000) G_L1: 15.203 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 12.740 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 8, iters: 286736, time: 0.962, data: 0.000) G_L1: 16.904 G_L1_ABSOLUTE: 2.726 G_L1_RELATIVE: 14.178 G_Regularizer: 0.000 validation_error: 19.925 +(epoch: 8, iters: 288736, time: 0.964, data: 0.000) G_L1: 15.568 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 13.180 G_Regularizer: 0.000 validation_error: 20.339 +(epoch: 8, iters: 290736, time: 0.962, data: 0.000) G_L1: 17.917 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 15.082 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 8, iters: 292736, time: 0.973, data: 0.000) G_L1: 16.667 G_L1_ABSOLUTE: 3.062 G_L1_RELATIVE: 13.605 G_Regularizer: 0.000 validation_error: 20.607 +(epoch: 8, iters: 294736, time: 0.966, data: 0.000) G_L1: 18.075 G_L1_ABSOLUTE: 2.771 G_L1_RELATIVE: 15.304 G_Regularizer: 0.000 validation_error: 20.076 +(epoch: 8, iters: 296736, time: 0.968, data: 0.000) G_L1: 16.862 G_L1_ABSOLUTE: 2.793 G_L1_RELATIVE: 14.069 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 8, iters: 298736, time: 0.967, data: 0.000) G_L1: 17.603 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 14.895 G_Regularizer: 0.000 validation_error: 20.511 +(epoch: 8, iters: 300736, time: 0.967, data: 0.000) G_L1: 15.650 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 12.745 G_Regularizer: 0.000 validation_error: 20.432 +(epoch: 8, iters: 302736, time: 0.966, data: 0.000) G_L1: 15.968 G_L1_ABSOLUTE: 2.739 G_L1_RELATIVE: 13.229 G_Regularizer: 0.000 validation_error: 20.544 +(epoch: 9, iters: 1984, time: 0.960, data: 0.000) G_L1: 15.391 G_L1_ABSOLUTE: 3.043 G_L1_RELATIVE: 12.348 G_Regularizer: 0.000 validation_error: 20.588 +(epoch: 9, iters: 3984, time: 0.964, data: 0.000) G_L1: 15.321 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 12.854 G_Regularizer: 0.000 validation_error: 21.430 +(epoch: 9, iters: 5984, time: 0.969, data: 0.000) G_L1: 12.995 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 10.235 G_Regularizer: 0.000 validation_error: 20.382 +(epoch: 9, iters: 7984, time: 0.970, data: 0.000) G_L1: 17.092 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 14.458 G_Regularizer: 0.000 validation_error: 20.504 +(epoch: 9, iters: 9984, time: 0.970, data: 0.000) G_L1: 16.882 G_L1_ABSOLUTE: 2.956 G_L1_RELATIVE: 13.926 G_Regularizer: 0.000 validation_error: 20.484 +(epoch: 9, iters: 11984, time: 0.965, data: 0.000) G_L1: 22.417 G_L1_ABSOLUTE: 3.428 G_L1_RELATIVE: 18.989 G_Regularizer: 0.000 validation_error: 20.157 +(epoch: 9, iters: 13984, time: 0.967, data: 0.000) G_L1: 18.115 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 15.531 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 9, iters: 15984, time: 0.968, data: 0.000) G_L1: 17.238 G_L1_ABSOLUTE: 3.226 G_L1_RELATIVE: 14.013 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 9, iters: 17984, time: 0.966, data: 0.000) G_L1: 12.049 G_L1_ABSOLUTE: 2.225 G_L1_RELATIVE: 9.824 G_Regularizer: 0.000 validation_error: 20.956 +(epoch: 9, iters: 19984, time: 0.967, data: 0.000) G_L1: 14.266 G_L1_ABSOLUTE: 2.346 G_L1_RELATIVE: 11.920 G_Regularizer: 0.000 validation_error: 20.479 +(epoch: 9, iters: 21984, time: 0.969, data: 0.000) G_L1: 15.944 G_L1_ABSOLUTE: 3.077 G_L1_RELATIVE: 12.867 G_Regularizer: 0.000 validation_error: 20.566 +(epoch: 9, iters: 23984, time: 0.971, data: 0.000) G_L1: 19.269 G_L1_ABSOLUTE: 3.584 G_L1_RELATIVE: 15.684 G_Regularizer: 0.000 validation_error: 20.180 +(epoch: 9, iters: 25984, time: 0.968, data: 0.000) G_L1: 17.748 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 15.041 G_Regularizer: 0.000 validation_error: 21.080 +(epoch: 9, iters: 27984, time: 0.967, data: 0.000) G_L1: 16.678 G_L1_ABSOLUTE: 2.747 G_L1_RELATIVE: 13.931 G_Regularizer: 0.000 validation_error: 20.578 +(epoch: 9, iters: 29984, time: 0.958, data: 0.000) G_L1: 16.547 G_L1_ABSOLUTE: 3.203 G_L1_RELATIVE: 13.344 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 9, iters: 31984, time: 0.964, data: 0.000) G_L1: 13.849 G_L1_ABSOLUTE: 2.232 G_L1_RELATIVE: 11.617 G_Regularizer: 0.000 validation_error: 20.741 +(epoch: 9, iters: 33984, time: 0.968, data: 0.000) G_L1: 18.135 G_L1_ABSOLUTE: 3.049 G_L1_RELATIVE: 15.086 G_Regularizer: 0.000 validation_error: 20.022 +(epoch: 9, iters: 35984, time: 0.965, data: 0.000) G_L1: 12.726 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 10.320 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 9, iters: 37984, time: 0.964, data: 0.000) G_L1: 18.169 G_L1_ABSOLUTE: 3.230 G_L1_RELATIVE: 14.939 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 9, iters: 39984, time: 0.963, data: 0.000) G_L1: 17.010 G_L1_ABSOLUTE: 3.178 G_L1_RELATIVE: 13.832 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 9, iters: 41984, time: 0.969, data: 0.000) G_L1: 15.596 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 13.227 G_Regularizer: 0.000 validation_error: 19.750 +(epoch: 9, iters: 43984, time: 0.965, data: 0.000) G_L1: 17.123 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 14.542 G_Regularizer: 0.000 validation_error: 20.237 +(epoch: 9, iters: 45984, time: 0.965, data: 0.000) G_L1: 17.597 G_L1_ABSOLUTE: 3.007 G_L1_RELATIVE: 14.590 G_Regularizer: 0.000 validation_error: 20.175 +(epoch: 9, iters: 47984, time: 0.966, data: 0.000) G_L1: 14.714 G_L1_ABSOLUTE: 2.771 G_L1_RELATIVE: 11.943 G_Regularizer: 0.000 validation_error: 20.571 +(epoch: 9, iters: 49984, time: 0.963, data: 0.000) G_L1: 14.604 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 12.053 G_Regularizer: 0.000 validation_error: 21.180 +(epoch: 9, iters: 51984, time: 0.963, data: 0.000) G_L1: 14.205 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 11.536 G_Regularizer: 0.000 validation_error: 20.072 +(epoch: 9, iters: 53984, time: 0.965, data: 0.000) G_L1: 15.603 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 12.718 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 9, iters: 55984, time: 0.961, data: 0.000) G_L1: 18.314 G_L1_ABSOLUTE: 3.172 G_L1_RELATIVE: 15.142 G_Regularizer: 0.000 validation_error: 20.462 +(epoch: 9, iters: 57984, time: 0.965, data: 0.000) G_L1: 17.680 G_L1_ABSOLUTE: 2.689 G_L1_RELATIVE: 14.991 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 9, iters: 59984, time: 0.969, data: 0.000) G_L1: 15.235 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 12.552 G_Regularizer: 0.000 validation_error: 20.628 +(epoch: 9, iters: 61984, time: 0.967, data: 0.000) G_L1: 14.881 G_L1_ABSOLUTE: 2.555 G_L1_RELATIVE: 12.326 G_Regularizer: 0.000 validation_error: 20.616 +(epoch: 9, iters: 63984, time: 0.969, data: 0.000) G_L1: 14.579 G_L1_ABSOLUTE: 2.831 G_L1_RELATIVE: 11.748 G_Regularizer: 0.000 validation_error: 20.340 +(epoch: 9, iters: 65984, time: 0.969, data: 0.000) G_L1: 14.290 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 11.664 G_Regularizer: 0.000 validation_error: 20.917 +(epoch: 9, iters: 67984, time: 0.963, data: 0.000) G_L1: 15.652 G_L1_ABSOLUTE: 2.865 G_L1_RELATIVE: 12.787 G_Regularizer: 0.000 validation_error: 21.268 +(epoch: 9, iters: 69984, time: 0.962, data: 0.000) G_L1: 17.347 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 14.776 G_Regularizer: 0.000 validation_error: 20.386 +(epoch: 9, iters: 71984, time: 0.968, data: 0.000) G_L1: 17.116 G_L1_ABSOLUTE: 2.727 G_L1_RELATIVE: 14.389 G_Regularizer: 0.000 validation_error: 20.591 +(epoch: 9, iters: 73984, time: 0.967, data: 0.000) G_L1: 14.543 G_L1_ABSOLUTE: 3.130 G_L1_RELATIVE: 11.413 G_Regularizer: 0.000 validation_error: 20.029 +(epoch: 9, iters: 75984, time: 0.968, data: 0.000) G_L1: 17.660 G_L1_ABSOLUTE: 2.923 G_L1_RELATIVE: 14.737 G_Regularizer: 0.000 validation_error: 21.179 +(epoch: 9, iters: 77984, time: 0.966, data: 0.000) G_L1: 13.225 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 10.547 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 9, iters: 79984, time: 0.967, data: 0.000) G_L1: 15.474 G_L1_ABSOLUTE: 2.785 G_L1_RELATIVE: 12.689 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 9, iters: 81984, time: 0.964, data: 0.000) G_L1: 18.693 G_L1_ABSOLUTE: 2.860 G_L1_RELATIVE: 15.834 G_Regularizer: 0.000 validation_error: 20.523 +(epoch: 9, iters: 83984, time: 0.960, data: 0.000) G_L1: 17.049 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 14.408 G_Regularizer: 0.000 validation_error: 20.922 +(epoch: 9, iters: 85984, time: 0.959, data: 0.001) G_L1: 14.629 G_L1_ABSOLUTE: 2.826 G_L1_RELATIVE: 11.803 G_Regularizer: 0.000 validation_error: 20.564 +(epoch: 9, iters: 87984, time: 0.968, data: 0.001) G_L1: 16.568 G_L1_ABSOLUTE: 2.440 G_L1_RELATIVE: 14.128 G_Regularizer: 0.000 validation_error: 20.076 +(epoch: 9, iters: 89984, time: 0.969, data: 0.000) G_L1: 17.583 G_L1_ABSOLUTE: 2.981 G_L1_RELATIVE: 14.602 G_Regularizer: 0.000 validation_error: 21.361 +(epoch: 9, iters: 91984, time: 0.966, data: 0.000) G_L1: 17.062 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 14.321 G_Regularizer: 0.000 validation_error: 20.198 +(epoch: 9, iters: 93984, time: 0.965, data: 0.000) G_L1: 16.944 G_L1_ABSOLUTE: 2.690 G_L1_RELATIVE: 14.254 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 9, iters: 95984, time: 0.969, data: 0.000) G_L1: 15.196 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 12.622 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 9, iters: 97984, time: 0.968, data: 0.000) G_L1: 19.242 G_L1_ABSOLUTE: 3.369 G_L1_RELATIVE: 15.872 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 9, iters: 99984, time: 0.968, data: 0.000) G_L1: 17.298 G_L1_ABSOLUTE: 3.143 G_L1_RELATIVE: 14.155 G_Regularizer: 0.000 validation_error: 21.179 +(epoch: 9, iters: 101984, time: 0.972, data: 0.000) G_L1: 18.865 G_L1_ABSOLUTE: 3.097 G_L1_RELATIVE: 15.768 G_Regularizer: 0.000 validation_error: 20.525 +(epoch: 9, iters: 103984, time: 0.962, data: 0.000) G_L1: 15.893 G_L1_ABSOLUTE: 3.196 G_L1_RELATIVE: 12.697 G_Regularizer: 0.000 validation_error: 21.222 +(epoch: 9, iters: 105984, time: 0.969, data: 0.000) G_L1: 16.690 G_L1_ABSOLUTE: 2.795 G_L1_RELATIVE: 13.895 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 9, iters: 107984, time: 0.970, data: 0.000) G_L1: 15.500 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 12.794 G_Regularizer: 0.000 validation_error: 20.929 +(epoch: 9, iters: 109984, time: 0.965, data: 0.000) G_L1: 16.343 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 13.959 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 9, iters: 111984, time: 0.963, data: 0.000) G_L1: 15.450 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 12.658 G_Regularizer: 0.000 validation_error: 20.946 +(epoch: 9, iters: 113984, time: 0.966, data: 0.000) G_L1: 18.040 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 15.201 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 9, iters: 115984, time: 0.685, data: 0.000) G_L1: 17.283 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 14.413 G_Regularizer: 0.000 validation_error: 21.065 +(epoch: 9, iters: 117984, time: 0.546, data: 0.000) G_L1: 13.555 G_L1_ABSOLUTE: 2.613 G_L1_RELATIVE: 10.942 G_Regularizer: 0.000 validation_error: 20.506 +(epoch: 9, iters: 119984, time: 0.539, data: 0.000) G_L1: 16.151 G_L1_ABSOLUTE: 3.106 G_L1_RELATIVE: 13.045 G_Regularizer: 0.000 validation_error: 20.723 +(epoch: 9, iters: 121984, time: 0.553, data: 0.000) G_L1: 15.498 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 12.962 G_Regularizer: 0.000 validation_error: 20.471 +(epoch: 9, iters: 123984, time: 0.554, data: 0.000) G_L1: 15.203 G_L1_ABSOLUTE: 2.441 G_L1_RELATIVE: 12.763 G_Regularizer: 0.000 validation_error: 20.070 +(epoch: 9, iters: 125984, time: 0.555, data: 0.000) G_L1: 15.588 G_L1_ABSOLUTE: 2.986 G_L1_RELATIVE: 12.602 G_Regularizer: 0.000 validation_error: 20.765 +(epoch: 9, iters: 127984, time: 0.549, data: 0.000) G_L1: 15.423 G_L1_ABSOLUTE: 2.604 G_L1_RELATIVE: 12.819 G_Regularizer: 0.000 validation_error: 20.575 +(epoch: 9, iters: 129984, time: 0.553, data: 0.000) G_L1: 14.916 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 12.381 G_Regularizer: 0.000 validation_error: 20.572 +(epoch: 9, iters: 131984, time: 0.546, data: 0.000) G_L1: 16.137 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 13.593 G_Regularizer: 0.000 validation_error: 20.159 +(epoch: 9, iters: 133984, time: 0.545, data: 0.000) G_L1: 15.612 G_L1_ABSOLUTE: 2.543 G_L1_RELATIVE: 13.070 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 9, iters: 135984, time: 0.545, data: 0.000) G_L1: 14.613 G_L1_ABSOLUTE: 2.440 G_L1_RELATIVE: 12.174 G_Regularizer: 0.000 validation_error: 21.282 +(epoch: 9, iters: 137984, time: 0.548, data: 0.000) G_L1: 15.895 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 13.160 G_Regularizer: 0.000 validation_error: 21.194 +(epoch: 9, iters: 139984, time: 0.544, data: 0.000) G_L1: 13.625 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 11.038 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 9, iters: 141984, time: 0.549, data: 0.000) G_L1: 15.495 G_L1_ABSOLUTE: 2.868 G_L1_RELATIVE: 12.627 G_Regularizer: 0.000 validation_error: 21.056 +(epoch: 9, iters: 143984, time: 0.546, data: 0.000) G_L1: 13.926 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 11.560 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 9, iters: 145984, time: 0.548, data: 0.000) G_L1: 15.235 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 12.665 G_Regularizer: 0.000 validation_error: 20.537 +(epoch: 9, iters: 147984, time: 0.550, data: 0.000) G_L1: 14.352 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 11.937 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 9, iters: 149984, time: 0.544, data: 0.000) G_L1: 18.128 G_L1_ABSOLUTE: 2.832 G_L1_RELATIVE: 15.297 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 9, iters: 151984, time: 0.546, data: 0.000) G_L1: 16.792 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 14.253 G_Regularizer: 0.000 validation_error: 21.160 +(epoch: 9, iters: 153984, time: 0.553, data: 0.000) G_L1: 16.713 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 14.148 G_Regularizer: 0.000 validation_error: 20.294 +(epoch: 9, iters: 155984, time: 0.550, data: 0.000) G_L1: 17.000 G_L1_ABSOLUTE: 3.398 G_L1_RELATIVE: 13.602 G_Regularizer: 0.000 validation_error: 21.202 +(epoch: 9, iters: 157984, time: 0.546, data: 0.000) G_L1: 14.628 G_L1_ABSOLUTE: 3.023 G_L1_RELATIVE: 11.605 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 9, iters: 159984, time: 0.549, data: 0.000) G_L1: 15.870 G_L1_ABSOLUTE: 2.558 G_L1_RELATIVE: 13.312 G_Regularizer: 0.000 validation_error: 21.116 +(epoch: 9, iters: 161984, time: 0.542, data: 0.000) G_L1: 13.638 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 10.980 G_Regularizer: 0.000 validation_error: 20.561 +(epoch: 9, iters: 163984, time: 0.549, data: 0.000) G_L1: 13.394 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 10.992 G_Regularizer: 0.000 validation_error: 20.508 +(epoch: 9, iters: 165984, time: 0.547, data: 0.000) G_L1: 14.899 G_L1_ABSOLUTE: 2.992 G_L1_RELATIVE: 11.907 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 9, iters: 167984, time: 0.550, data: 0.000) G_L1: 17.892 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 15.036 G_Regularizer: 0.000 validation_error: 21.693 +(epoch: 9, iters: 169984, time: 0.543, data: 0.000) G_L1: 14.069 G_L1_ABSOLUTE: 2.545 G_L1_RELATIVE: 11.524 G_Regularizer: 0.000 validation_error: 20.057 +(epoch: 9, iters: 171984, time: 0.542, data: 0.000) G_L1: 16.640 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 14.013 G_Regularizer: 0.000 validation_error: 21.367 +(epoch: 9, iters: 173984, time: 0.542, data: 0.000) G_L1: 14.436 G_L1_ABSOLUTE: 2.393 G_L1_RELATIVE: 12.044 G_Regularizer: 0.000 validation_error: 20.504 +(epoch: 9, iters: 175984, time: 0.551, data: 0.000) G_L1: 13.468 G_L1_ABSOLUTE: 2.392 G_L1_RELATIVE: 11.075 G_Regularizer: 0.000 validation_error: 20.037 +(epoch: 9, iters: 177984, time: 0.554, data: 0.000) G_L1: 15.018 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 12.556 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 9, iters: 179984, time: 0.541, data: 0.000) G_L1: 18.841 G_L1_ABSOLUTE: 3.417 G_L1_RELATIVE: 15.424 G_Regularizer: 0.000 validation_error: 21.483 +(epoch: 9, iters: 181984, time: 0.542, data: 0.000) G_L1: 13.313 G_L1_ABSOLUTE: 2.827 G_L1_RELATIVE: 10.486 G_Regularizer: 0.000 validation_error: 20.972 +(epoch: 9, iters: 183984, time: 0.543, data: 0.000) G_L1: 14.184 G_L1_ABSOLUTE: 2.875 G_L1_RELATIVE: 11.309 G_Regularizer: 0.000 validation_error: 20.609 +(epoch: 9, iters: 185984, time: 0.548, data: 0.000) G_L1: 15.670 G_L1_ABSOLUTE: 2.330 G_L1_RELATIVE: 13.341 G_Regularizer: 0.000 validation_error: 21.245 +(epoch: 9, iters: 187984, time: 0.542, data: 0.000) G_L1: 14.869 G_L1_ABSOLUTE: 2.346 G_L1_RELATIVE: 12.524 G_Regularizer: 0.000 validation_error: 20.964 +(epoch: 9, iters: 189984, time: 0.542, data: 0.000) G_L1: 17.299 G_L1_ABSOLUTE: 3.104 G_L1_RELATIVE: 14.195 G_Regularizer: 0.000 validation_error: 20.745 +(epoch: 9, iters: 191984, time: 0.552, data: 0.000) G_L1: 14.024 G_L1_ABSOLUTE: 2.350 G_L1_RELATIVE: 11.674 G_Regularizer: 0.000 validation_error: 20.832 +(epoch: 9, iters: 193984, time: 0.543, data: 0.000) G_L1: 16.882 G_L1_ABSOLUTE: 2.353 G_L1_RELATIVE: 14.529 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 9, iters: 195984, time: 0.546, data: 0.000) G_L1: 13.199 G_L1_ABSOLUTE: 2.644 G_L1_RELATIVE: 10.555 G_Regularizer: 0.000 validation_error: 20.653 +(epoch: 9, iters: 197984, time: 0.548, data: 0.000) G_L1: 14.760 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 12.227 G_Regularizer: 0.000 validation_error: 21.057 +(epoch: 9, iters: 199984, time: 0.545, data: 0.000) G_L1: 18.370 G_L1_ABSOLUTE: 2.959 G_L1_RELATIVE: 15.411 G_Regularizer: 0.000 validation_error: 20.337 +(epoch: 9, iters: 201984, time: 0.548, data: 0.000) G_L1: 11.193 G_L1_ABSOLUTE: 2.337 G_L1_RELATIVE: 8.856 G_Regularizer: 0.000 validation_error: 21.236 +(epoch: 9, iters: 203984, time: 0.544, data: 0.000) G_L1: 15.613 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 12.976 G_Regularizer: 0.000 validation_error: 20.735 +(epoch: 9, iters: 205984, time: 0.554, data: 0.000) G_L1: 18.866 G_L1_ABSOLUTE: 3.138 G_L1_RELATIVE: 15.728 G_Regularizer: 0.000 validation_error: 21.234 +(epoch: 9, iters: 207984, time: 0.544, data: 0.000) G_L1: 15.386 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 12.884 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 9, iters: 209984, time: 0.549, data: 0.000) G_L1: 17.763 G_L1_ABSOLUTE: 2.775 G_L1_RELATIVE: 14.988 G_Regularizer: 0.000 validation_error: 21.195 +(epoch: 9, iters: 211984, time: 0.543, data: 0.000) G_L1: 15.748 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 13.037 G_Regularizer: 0.000 validation_error: 20.436 +(epoch: 9, iters: 213984, time: 0.549, data: 0.000) G_L1: 14.852 G_L1_ABSOLUTE: 3.001 G_L1_RELATIVE: 11.852 G_Regularizer: 0.000 validation_error: 21.267 +(epoch: 9, iters: 215984, time: 0.548, data: 0.000) G_L1: 17.066 G_L1_ABSOLUTE: 2.567 G_L1_RELATIVE: 14.500 G_Regularizer: 0.000 validation_error: 20.058 +(epoch: 9, iters: 217984, time: 0.549, data: 0.000) G_L1: 15.206 G_L1_ABSOLUTE: 2.472 G_L1_RELATIVE: 12.735 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 9, iters: 219984, time: 0.539, data: 0.000) G_L1: 18.198 G_L1_ABSOLUTE: 2.922 G_L1_RELATIVE: 15.276 G_Regularizer: 0.000 validation_error: 20.740 +(epoch: 9, iters: 221984, time: 0.541, data: 0.000) G_L1: 16.940 G_L1_ABSOLUTE: 3.160 G_L1_RELATIVE: 13.781 G_Regularizer: 0.000 validation_error: 20.947 +(epoch: 9, iters: 223984, time: 0.552, data: 0.000) G_L1: 13.963 G_L1_ABSOLUTE: 2.356 G_L1_RELATIVE: 11.606 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 9, iters: 225984, time: 0.555, data: 0.000) G_L1: 13.250 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 10.915 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 9, iters: 227984, time: 0.545, data: 0.001) G_L1: 13.509 G_L1_ABSOLUTE: 3.088 G_L1_RELATIVE: 10.421 G_Regularizer: 0.000 validation_error: 20.153 +(epoch: 9, iters: 229984, time: 0.546, data: 0.000) G_L1: 15.049 G_L1_ABSOLUTE: 2.466 G_L1_RELATIVE: 12.584 G_Regularizer: 0.000 validation_error: 20.307 +(epoch: 9, iters: 231984, time: 0.630, data: 0.000) G_L1: 13.619 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 10.949 G_Regularizer: 0.000 validation_error: 20.469 +(epoch: 9, iters: 233984, time: 0.624, data: 0.000) G_L1: 15.607 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 13.017 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 9, iters: 235984, time: 0.617, data: 0.000) G_L1: 14.109 G_L1_ABSOLUTE: 3.144 G_L1_RELATIVE: 10.965 G_Regularizer: 0.000 validation_error: 20.604 +(epoch: 9, iters: 237984, time: 0.548, data: 0.000) G_L1: 19.180 G_L1_ABSOLUTE: 2.959 G_L1_RELATIVE: 16.222 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 9, iters: 239984, time: 0.545, data: 0.000) G_L1: 13.808 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 11.168 G_Regularizer: 0.000 validation_error: 20.471 +(epoch: 9, iters: 241984, time: 0.550, data: 0.000) G_L1: 12.543 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 9.923 G_Regularizer: 0.000 validation_error: 21.082 +(epoch: 9, iters: 243984, time: 0.551, data: 0.000) G_L1: 15.811 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 13.225 G_Regularizer: 0.000 validation_error: 20.474 +(epoch: 9, iters: 245984, time: 0.545, data: 0.000) G_L1: 14.515 G_L1_ABSOLUTE: 3.198 G_L1_RELATIVE: 11.317 G_Regularizer: 0.000 validation_error: 20.509 +(epoch: 9, iters: 247984, time: 0.538, data: 0.000) G_L1: 12.108 G_L1_ABSOLUTE: 3.111 G_L1_RELATIVE: 8.996 G_Regularizer: 0.000 validation_error: 20.513 +(epoch: 9, iters: 249984, time: 0.540, data: 0.000) G_L1: 15.355 G_L1_ABSOLUTE: 2.145 G_L1_RELATIVE: 13.210 G_Regularizer: 0.000 validation_error: 20.125 +(epoch: 9, iters: 251984, time: 0.543, data: 0.000) G_L1: 16.205 G_L1_ABSOLUTE: 3.125 G_L1_RELATIVE: 13.081 G_Regularizer: 0.000 validation_error: 20.205 +(epoch: 9, iters: 253984, time: 0.548, data: 0.000) G_L1: 13.934 G_L1_ABSOLUTE: 2.552 G_L1_RELATIVE: 11.382 G_Regularizer: 0.000 validation_error: 20.574 +(epoch: 9, iters: 255984, time: 0.548, data: 0.000) G_L1: 12.349 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 9.647 G_Regularizer: 0.000 validation_error: 21.705 +(epoch: 9, iters: 257984, time: 0.545, data: 0.001) G_L1: 14.679 G_L1_ABSOLUTE: 2.703 G_L1_RELATIVE: 11.976 G_Regularizer: 0.000 validation_error: 20.335 +(epoch: 9, iters: 259984, time: 0.545, data: 0.000) G_L1: 13.881 G_L1_ABSOLUTE: 2.299 G_L1_RELATIVE: 11.582 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 9, iters: 261984, time: 0.543, data: 0.000) G_L1: 15.650 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 13.040 G_Regularizer: 0.000 validation_error: 20.567 +(epoch: 9, iters: 263984, time: 0.546, data: 0.000) G_L1: 15.620 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 12.998 G_Regularizer: 0.000 validation_error: 20.940 +(epoch: 9, iters: 265984, time: 0.549, data: 0.000) G_L1: 15.056 G_L1_ABSOLUTE: 2.268 G_L1_RELATIVE: 12.788 G_Regularizer: 0.000 validation_error: 20.753 +(epoch: 9, iters: 267984, time: 0.546, data: 0.000) G_L1: 15.834 G_L1_ABSOLUTE: 2.958 G_L1_RELATIVE: 12.875 G_Regularizer: 0.000 validation_error: 19.715 +(epoch: 9, iters: 269984, time: 0.546, data: 0.000) G_L1: 16.128 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 13.233 G_Regularizer: 0.000 validation_error: 20.263 +(epoch: 9, iters: 271984, time: 0.549, data: 0.000) G_L1: 15.477 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 13.058 G_Regularizer: 0.000 validation_error: 21.156 +(epoch: 9, iters: 273984, time: 0.548, data: 0.000) G_L1: 14.259 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 11.553 G_Regularizer: 0.000 validation_error: 21.190 +(epoch: 9, iters: 275984, time: 0.545, data: 0.000) G_L1: 14.822 G_L1_ABSOLUTE: 2.796 G_L1_RELATIVE: 12.026 G_Regularizer: 0.000 validation_error: 20.103 +(epoch: 9, iters: 277984, time: 0.545, data: 0.000) G_L1: 14.809 G_L1_ABSOLUTE: 2.932 G_L1_RELATIVE: 11.877 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 9, iters: 279984, time: 0.546, data: 0.000) G_L1: 15.399 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 12.871 G_Regularizer: 0.000 validation_error: 20.763 +(epoch: 9, iters: 281984, time: 0.547, data: 0.000) G_L1: 15.017 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 12.403 G_Regularizer: 0.000 validation_error: 20.419 +(epoch: 9, iters: 283984, time: 0.542, data: 0.000) G_L1: 22.202 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 19.572 G_Regularizer: 0.000 validation_error: 20.691 +(epoch: 9, iters: 285984, time: 0.551, data: 0.000) G_L1: 15.115 G_L1_ABSOLUTE: 2.395 G_L1_RELATIVE: 12.721 G_Regularizer: 0.000 validation_error: 19.677 +(epoch: 9, iters: 287984, time: 0.546, data: 0.000) G_L1: 14.265 G_L1_ABSOLUTE: 3.070 G_L1_RELATIVE: 11.195 G_Regularizer: 0.000 validation_error: 20.434 +(epoch: 9, iters: 289984, time: 0.542, data: 0.000) G_L1: 15.141 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 12.616 G_Regularizer: 0.000 validation_error: 20.186 +(epoch: 9, iters: 291984, time: 0.547, data: 0.000) G_L1: 15.511 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 12.781 G_Regularizer: 0.000 validation_error: 20.067 +(epoch: 9, iters: 293984, time: 0.550, data: 0.000) G_L1: 15.141 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 12.674 G_Regularizer: 0.000 validation_error: 21.343 +(epoch: 9, iters: 295984, time: 0.548, data: 0.000) G_L1: 15.472 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 12.789 G_Regularizer: 0.000 validation_error: 20.261 +(epoch: 9, iters: 297984, time: 0.549, data: 0.000) G_L1: 17.730 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 14.953 G_Regularizer: 0.000 validation_error: 20.460 +(epoch: 9, iters: 299984, time: 0.548, data: 0.000) G_L1: 17.515 G_L1_ABSOLUTE: 2.872 G_L1_RELATIVE: 14.643 G_Regularizer: 0.000 validation_error: 20.756 +(epoch: 9, iters: 301984, time: 0.542, data: 0.000) G_L1: 17.828 G_L1_ABSOLUTE: 3.263 G_L1_RELATIVE: 14.565 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 10, iters: 1232, time: 0.544, data: 0.000) G_L1: 15.572 G_L1_ABSOLUTE: 2.912 G_L1_RELATIVE: 12.661 G_Regularizer: 0.000 validation_error: 20.653 +(epoch: 10, iters: 3232, time: 0.551, data: 0.000) G_L1: 17.238 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 14.611 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 10, iters: 5232, time: 0.545, data: 0.000) G_L1: 15.144 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 12.453 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 10, iters: 7232, time: 0.547, data: 0.001) G_L1: 15.155 G_L1_ABSOLUTE: 3.141 G_L1_RELATIVE: 12.014 G_Regularizer: 0.000 validation_error: 20.582 +(epoch: 10, iters: 9232, time: 0.548, data: 0.000) G_L1: 18.825 G_L1_ABSOLUTE: 2.671 G_L1_RELATIVE: 16.154 G_Regularizer: 0.000 validation_error: 20.148 +(epoch: 10, iters: 11232, time: 0.548, data: 0.000) G_L1: 14.238 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 11.716 G_Regularizer: 0.000 validation_error: 20.464 +(epoch: 10, iters: 13232, time: 0.539, data: 0.000) G_L1: 19.797 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 17.278 G_Regularizer: 0.000 validation_error: 20.471 +(epoch: 10, iters: 15232, time: 0.545, data: 0.001) G_L1: 15.785 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 13.122 G_Regularizer: 0.000 validation_error: 20.022 +(epoch: 10, iters: 17232, time: 0.547, data: 0.000) G_L1: 14.396 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 11.719 G_Regularizer: 0.000 validation_error: 20.340 +(epoch: 10, iters: 19232, time: 0.550, data: 0.000) G_L1: 16.141 G_L1_ABSOLUTE: 3.529 G_L1_RELATIVE: 12.612 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 10, iters: 21232, time: 0.547, data: 0.000) G_L1: 17.689 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 15.227 G_Regularizer: 0.000 validation_error: 21.075 +(epoch: 10, iters: 23232, time: 0.540, data: 0.000) G_L1: 17.677 G_L1_ABSOLUTE: 2.750 G_L1_RELATIVE: 14.928 G_Regularizer: 0.000 validation_error: 21.038 +(epoch: 10, iters: 25232, time: 0.545, data: 0.000) G_L1: 15.482 G_L1_ABSOLUTE: 2.788 G_L1_RELATIVE: 12.693 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 10, iters: 27232, time: 0.542, data: 0.000) G_L1: 16.848 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 14.320 G_Regularizer: 0.000 validation_error: 20.402 +(epoch: 10, iters: 29232, time: 0.542, data: 0.000) G_L1: 18.471 G_L1_ABSOLUTE: 3.160 G_L1_RELATIVE: 15.311 G_Regularizer: 0.000 validation_error: 20.398 +(epoch: 10, iters: 31232, time: 0.550, data: 0.000) G_L1: 13.583 G_L1_ABSOLUTE: 2.638 G_L1_RELATIVE: 10.945 G_Regularizer: 0.000 validation_error: 21.071 +(epoch: 10, iters: 33232, time: 0.545, data: 0.000) G_L1: 18.439 G_L1_ABSOLUTE: 2.983 G_L1_RELATIVE: 15.456 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 10, iters: 35232, time: 0.547, data: 0.000) G_L1: 14.395 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 11.942 G_Regularizer: 0.000 validation_error: 21.390 +(epoch: 10, iters: 37232, time: 0.552, data: 0.000) G_L1: 15.150 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 12.388 G_Regularizer: 0.000 validation_error: 20.524 +(epoch: 10, iters: 39232, time: 0.538, data: 0.000) G_L1: 15.819 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 12.881 G_Regularizer: 0.000 validation_error: 21.298 +(epoch: 10, iters: 41232, time: 0.543, data: 0.000) G_L1: 14.680 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 12.264 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 10, iters: 43232, time: 0.553, data: 0.000) G_L1: 15.394 G_L1_ABSOLUTE: 2.847 G_L1_RELATIVE: 12.547 G_Regularizer: 0.000 validation_error: 20.938 +(epoch: 10, iters: 45232, time: 0.542, data: 0.000) G_L1: 17.995 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 15.449 G_Regularizer: 0.000 validation_error: 20.217 +(epoch: 10, iters: 47232, time: 0.547, data: 0.000) G_L1: 14.848 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 12.247 G_Regularizer: 0.000 validation_error: 20.282 +(epoch: 10, iters: 49232, time: 0.545, data: 0.000) G_L1: 15.107 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 12.708 G_Regularizer: 0.000 validation_error: 21.832 +(epoch: 10, iters: 51232, time: 0.541, data: 0.000) G_L1: 16.661 G_L1_ABSOLUTE: 2.961 G_L1_RELATIVE: 13.700 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 10, iters: 53232, time: 0.546, data: 0.000) G_L1: 15.706 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 13.008 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 10, iters: 55232, time: 0.546, data: 0.000) G_L1: 16.812 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 14.243 G_Regularizer: 0.000 validation_error: 21.406 +(epoch: 10, iters: 57232, time: 0.548, data: 0.000) G_L1: 14.104 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 11.426 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 10, iters: 59232, time: 0.548, data: 0.000) G_L1: 14.951 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 12.362 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 10, iters: 61232, time: 0.549, data: 0.000) G_L1: 14.276 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 11.560 G_Regularizer: 0.000 validation_error: 20.417 +(epoch: 10, iters: 63232, time: 0.544, data: 0.000) G_L1: 16.909 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 14.203 G_Regularizer: 0.000 validation_error: 20.492 +(epoch: 10, iters: 65232, time: 0.543, data: 0.000) G_L1: 17.253 G_L1_ABSOLUTE: 2.288 G_L1_RELATIVE: 14.965 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 10, iters: 67232, time: 0.554, data: 0.000) G_L1: 14.807 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 12.144 G_Regularizer: 0.000 validation_error: 21.235 +(epoch: 10, iters: 69232, time: 0.542, data: 0.000) G_L1: 14.487 G_L1_ABSOLUTE: 2.508 G_L1_RELATIVE: 11.979 G_Regularizer: 0.000 validation_error: 20.401 +(epoch: 10, iters: 71232, time: 0.549, data: 0.000) G_L1: 14.774 G_L1_ABSOLUTE: 2.907 G_L1_RELATIVE: 11.868 G_Regularizer: 0.000 validation_error: 20.380 +(epoch: 10, iters: 73232, time: 0.542, data: 0.000) G_L1: 13.222 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 10.658 G_Regularizer: 0.000 validation_error: 21.290 +(epoch: 10, iters: 75232, time: 0.542, data: 0.001) G_L1: 14.659 G_L1_ABSOLUTE: 2.263 G_L1_RELATIVE: 12.395 G_Regularizer: 0.000 validation_error: 19.907 +(epoch: 10, iters: 77232, time: 0.550, data: 0.000) G_L1: 21.355 G_L1_ABSOLUTE: 3.249 G_L1_RELATIVE: 18.107 G_Regularizer: 0.000 validation_error: 20.249 +(epoch: 10, iters: 79232, time: 0.556, data: 0.000) G_L1: 15.954 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 13.326 G_Regularizer: 0.000 validation_error: 21.347 +(epoch: 10, iters: 81232, time: 0.547, data: 0.000) G_L1: 17.125 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 14.448 G_Regularizer: 0.000 validation_error: 20.140 +(epoch: 10, iters: 83232, time: 0.539, data: 0.000) G_L1: 16.122 G_L1_ABSOLUTE: 2.933 G_L1_RELATIVE: 13.189 G_Regularizer: 0.000 validation_error: 20.130 +(epoch: 10, iters: 85232, time: 0.544, data: 0.000) G_L1: 14.880 G_L1_ABSOLUTE: 3.152 G_L1_RELATIVE: 11.728 G_Regularizer: 0.000 validation_error: 20.672 +(epoch: 10, iters: 87232, time: 0.545, data: 0.000) G_L1: 18.042 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 15.459 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 10, iters: 89232, time: 0.543, data: 0.000) G_L1: 15.052 G_L1_ABSOLUTE: 2.379 G_L1_RELATIVE: 12.674 G_Regularizer: 0.000 validation_error: 22.012 +(epoch: 10, iters: 91232, time: 0.541, data: 0.000) G_L1: 14.189 G_L1_ABSOLUTE: 3.181 G_L1_RELATIVE: 11.009 G_Regularizer: 0.000 validation_error: 20.145 +(epoch: 10, iters: 93232, time: 0.545, data: 0.000) G_L1: 15.443 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 12.824 G_Regularizer: 0.000 validation_error: 20.939 +(epoch: 10, iters: 95232, time: 0.545, data: 0.000) G_L1: 16.188 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 13.685 G_Regularizer: 0.000 validation_error: 20.505 +(epoch: 10, iters: 97232, time: 0.544, data: 0.000) G_L1: 18.122 G_L1_ABSOLUTE: 3.304 G_L1_RELATIVE: 14.818 G_Regularizer: 0.000 validation_error: 20.263 +(epoch: 10, iters: 99232, time: 0.544, data: 0.000) G_L1: 19.433 G_L1_ABSOLUTE: 2.788 G_L1_RELATIVE: 16.645 G_Regularizer: 0.000 validation_error: 21.333 +(epoch: 10, iters: 101232, time: 0.539, data: 0.000) G_L1: 16.613 G_L1_ABSOLUTE: 2.299 G_L1_RELATIVE: 14.314 G_Regularizer: 0.000 validation_error: 20.554 +(epoch: 10, iters: 103232, time: 0.539, data: 0.000) G_L1: 22.337 G_L1_ABSOLUTE: 2.918 G_L1_RELATIVE: 19.419 G_Regularizer: 0.000 validation_error: 20.227 +(epoch: 10, iters: 105232, time: 0.548, data: 0.000) G_L1: 13.335 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 10.987 G_Regularizer: 0.000 validation_error: 20.627 +(epoch: 10, iters: 107232, time: 0.551, data: 0.000) G_L1: 17.967 G_L1_ABSOLUTE: 3.361 G_L1_RELATIVE: 14.606 G_Regularizer: 0.000 validation_error: 21.174 +(epoch: 10, iters: 109232, time: 0.550, data: 0.000) G_L1: 18.124 G_L1_ABSOLUTE: 3.704 G_L1_RELATIVE: 14.420 G_Regularizer: 0.000 validation_error: 20.492 +(epoch: 10, iters: 111232, time: 0.544, data: 0.000) G_L1: 13.830 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 11.052 G_Regularizer: 0.000 validation_error: 19.846 +(epoch: 10, iters: 113232, time: 0.542, data: 0.000) G_L1: 15.722 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 12.828 G_Regularizer: 0.000 validation_error: 20.707 +(epoch: 10, iters: 115232, time: 0.542, data: 0.000) G_L1: 14.628 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 12.163 G_Regularizer: 0.000 validation_error: 20.289 +(epoch: 10, iters: 117232, time: 0.549, data: 0.000) G_L1: 16.276 G_L1_ABSOLUTE: 2.963 G_L1_RELATIVE: 13.313 G_Regularizer: 0.000 validation_error: 20.735 +(epoch: 10, iters: 119232, time: 0.545, data: 0.000) G_L1: 16.165 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 13.360 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 10, iters: 121232, time: 0.541, data: 0.000) G_L1: 15.414 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 12.617 G_Regularizer: 0.000 validation_error: 20.455 +(epoch: 10, iters: 123232, time: 0.541, data: 0.000) G_L1: 17.959 G_L1_ABSOLUTE: 3.439 G_L1_RELATIVE: 14.519 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 10, iters: 125232, time: 0.543, data: 0.000) G_L1: 16.408 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 13.750 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 10, iters: 127232, time: 0.549, data: 0.000) G_L1: 15.866 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 13.322 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 10, iters: 129232, time: 0.545, data: 0.000) G_L1: 14.576 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 11.699 G_Regularizer: 0.000 validation_error: 20.377 +(epoch: 10, iters: 131232, time: 0.552, data: 0.000) G_L1: 18.244 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 15.726 G_Regularizer: 0.000 validation_error: 21.169 +(epoch: 10, iters: 133232, time: 0.543, data: 0.000) G_L1: 14.349 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 11.711 G_Regularizer: 0.000 validation_error: 20.050 +(epoch: 10, iters: 135232, time: 0.546, data: 0.000) G_L1: 14.903 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 12.092 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 10, iters: 137232, time: 0.554, data: 0.000) G_L1: 17.455 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 14.836 G_Regularizer: 0.000 validation_error: 20.345 +(epoch: 10, iters: 139232, time: 0.547, data: 0.000) G_L1: 14.852 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 12.339 G_Regularizer: 0.000 validation_error: 20.586 +(epoch: 10, iters: 141232, time: 0.545, data: 0.000) G_L1: 14.591 G_L1_ABSOLUTE: 2.423 G_L1_RELATIVE: 12.168 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 10, iters: 143232, time: 0.542, data: 0.001) G_L1: 12.710 G_L1_ABSOLUTE: 2.120 G_L1_RELATIVE: 10.590 G_Regularizer: 0.000 validation_error: 20.434 +(epoch: 10, iters: 145232, time: 0.546, data: 0.000) G_L1: 12.996 G_L1_ABSOLUTE: 2.380 G_L1_RELATIVE: 10.617 G_Regularizer: 0.000 validation_error: 21.059 +(epoch: 10, iters: 147232, time: 0.550, data: 0.000) G_L1: 16.103 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 13.443 G_Regularizer: 0.000 validation_error: 20.179 +(epoch: 10, iters: 149232, time: 0.545, data: 0.000) G_L1: 15.736 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 13.277 G_Regularizer: 0.000 validation_error: 20.741 +(epoch: 10, iters: 151232, time: 0.548, data: 0.000) G_L1: 15.790 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 13.176 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 10, iters: 153232, time: 0.548, data: 0.000) G_L1: 19.871 G_L1_ABSOLUTE: 3.389 G_L1_RELATIVE: 16.481 G_Regularizer: 0.000 validation_error: 20.670 +(epoch: 10, iters: 155232, time: 0.546, data: 0.000) G_L1: 14.880 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 12.421 G_Regularizer: 0.000 validation_error: 20.503 +(epoch: 10, iters: 157232, time: 0.544, data: 0.000) G_L1: 16.626 G_L1_ABSOLUTE: 2.906 G_L1_RELATIVE: 13.720 G_Regularizer: 0.000 validation_error: 20.956 +(epoch: 10, iters: 159232, time: 0.547, data: 0.000) G_L1: 14.700 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 11.864 G_Regularizer: 0.000 validation_error: 21.093 +(epoch: 10, iters: 161232, time: 0.541, data: 0.000) G_L1: 16.012 G_L1_ABSOLUTE: 2.472 G_L1_RELATIVE: 13.540 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 10, iters: 163232, time: 0.540, data: 0.000) G_L1: 16.534 G_L1_ABSOLUTE: 3.063 G_L1_RELATIVE: 13.471 G_Regularizer: 0.000 validation_error: 20.047 +(epoch: 10, iters: 165232, time: 0.555, data: 0.001) G_L1: 17.346 G_L1_ABSOLUTE: 3.091 G_L1_RELATIVE: 14.255 G_Regularizer: 0.000 validation_error: 20.836 +(epoch: 10, iters: 167232, time: 0.553, data: 0.000) G_L1: 14.109 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 11.556 G_Regularizer: 0.000 validation_error: 20.547 +(epoch: 10, iters: 169232, time: 0.544, data: 0.000) G_L1: 14.122 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 11.391 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 10, iters: 171232, time: 0.545, data: 0.000) G_L1: 12.755 G_L1_ABSOLUTE: 2.328 G_L1_RELATIVE: 10.427 G_Regularizer: 0.000 validation_error: 20.380 +(epoch: 10, iters: 173232, time: 0.549, data: 0.000) G_L1: 17.591 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 14.918 G_Regularizer: 0.000 validation_error: 20.791 +(epoch: 10, iters: 175232, time: 0.545, data: 0.000) G_L1: 16.113 G_L1_ABSOLUTE: 2.831 G_L1_RELATIVE: 13.282 G_Regularizer: 0.000 validation_error: 20.408 +(epoch: 10, iters: 177232, time: 0.549, data: 0.000) G_L1: 13.938 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 11.182 G_Regularizer: 0.000 validation_error: 21.442 +(epoch: 10, iters: 179232, time: 0.544, data: 0.000) G_L1: 12.992 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 10.590 G_Regularizer: 0.000 validation_error: 20.125 +(epoch: 10, iters: 181232, time: 0.544, data: 0.000) G_L1: 14.906 G_L1_ABSOLUTE: 3.289 G_L1_RELATIVE: 11.617 G_Regularizer: 0.000 validation_error: 21.198 +(epoch: 10, iters: 183232, time: 0.543, data: 0.000) G_L1: 14.732 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 11.970 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 10, iters: 185232, time: 0.547, data: 0.000) G_L1: 14.420 G_L1_ABSOLUTE: 3.417 G_L1_RELATIVE: 11.002 G_Regularizer: 0.000 validation_error: 20.052 +(epoch: 10, iters: 187232, time: 0.547, data: 0.000) G_L1: 14.213 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 11.768 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 10, iters: 189232, time: 0.549, data: 0.000) G_L1: 13.537 G_L1_ABSOLUTE: 2.356 G_L1_RELATIVE: 11.181 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 10, iters: 191232, time: 0.545, data: 0.000) G_L1: 18.029 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 15.523 G_Regularizer: 0.000 validation_error: 20.999 +(epoch: 10, iters: 193232, time: 0.546, data: 0.000) G_L1: 12.655 G_L1_ABSOLUTE: 2.248 G_L1_RELATIVE: 10.407 G_Regularizer: 0.000 validation_error: 21.423 +(epoch: 10, iters: 195232, time: 0.549, data: 0.000) G_L1: 14.996 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 12.236 G_Regularizer: 0.000 validation_error: 20.178 +(epoch: 10, iters: 197232, time: 0.548, data: 0.000) G_L1: 14.274 G_L1_ABSOLUTE: 3.032 G_L1_RELATIVE: 11.242 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 10, iters: 199232, time: 0.545, data: 0.000) G_L1: 13.755 G_L1_ABSOLUTE: 2.391 G_L1_RELATIVE: 11.365 G_Regularizer: 0.000 validation_error: 20.429 +(epoch: 10, iters: 201232, time: 0.551, data: 0.000) G_L1: 14.799 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 12.258 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 10, iters: 203232, time: 0.545, data: 0.000) G_L1: 13.486 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 10.871 G_Regularizer: 0.000 validation_error: 20.114 +(epoch: 10, iters: 205232, time: 0.546, data: 0.000) G_L1: 15.124 G_L1_ABSOLUTE: 2.929 G_L1_RELATIVE: 12.194 G_Regularizer: 0.000 validation_error: 20.145 +(epoch: 10, iters: 207232, time: 0.553, data: 0.000) G_L1: 18.527 G_L1_ABSOLUTE: 3.160 G_L1_RELATIVE: 15.367 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 10, iters: 209232, time: 0.547, data: 0.000) G_L1: 14.778 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 12.479 G_Regularizer: 0.000 validation_error: 20.730 +(epoch: 10, iters: 211232, time: 0.547, data: 0.001) G_L1: 15.017 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 12.508 G_Regularizer: 0.000 validation_error: 20.552 +(epoch: 10, iters: 213232, time: 0.554, data: 0.000) G_L1: 17.296 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 14.661 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 10, iters: 215232, time: 0.552, data: 0.000) G_L1: 15.949 G_L1_ABSOLUTE: 2.191 G_L1_RELATIVE: 13.758 G_Regularizer: 0.000 validation_error: 20.483 +(epoch: 10, iters: 217232, time: 0.551, data: 0.000) G_L1: 14.761 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 12.112 G_Regularizer: 0.000 validation_error: 21.312 +(epoch: 10, iters: 219232, time: 0.553, data: 0.001) G_L1: 12.814 G_L1_ABSOLUTE: 2.382 G_L1_RELATIVE: 10.433 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 10, iters: 221232, time: 0.556, data: 0.000) G_L1: 15.400 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 12.916 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 10, iters: 223232, time: 0.549, data: 0.000) G_L1: 15.091 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 12.752 G_Regularizer: 0.000 validation_error: 21.217 +(epoch: 10, iters: 225232, time: 0.565, data: 0.000) G_L1: 17.512 G_L1_ABSOLUTE: 2.936 G_L1_RELATIVE: 14.576 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 10, iters: 227232, time: 0.553, data: 0.000) G_L1: 16.970 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 14.232 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 10, iters: 229232, time: 0.554, data: 0.000) G_L1: 17.336 G_L1_ABSOLUTE: 3.443 G_L1_RELATIVE: 13.893 G_Regularizer: 0.000 validation_error: 20.342 +(epoch: 10, iters: 231232, time: 0.554, data: 0.000) G_L1: 14.998 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 12.562 G_Regularizer: 0.000 validation_error: 20.045 +(epoch: 10, iters: 233232, time: 0.551, data: 0.000) G_L1: 16.179 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 13.381 G_Regularizer: 0.000 validation_error: 20.145 +(epoch: 10, iters: 235232, time: 0.552, data: 0.000) G_L1: 14.076 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 11.503 G_Regularizer: 0.000 validation_error: 20.510 +(epoch: 10, iters: 237232, time: 0.545, data: 0.000) G_L1: 16.731 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 13.920 G_Regularizer: 0.000 validation_error: 20.528 +(epoch: 10, iters: 239232, time: 0.553, data: 0.000) G_L1: 14.125 G_L1_ABSOLUTE: 2.992 G_L1_RELATIVE: 11.134 G_Regularizer: 0.000 validation_error: 20.737 +(epoch: 10, iters: 241232, time: 0.549, data: 0.000) G_L1: 15.174 G_L1_ABSOLUTE: 3.535 G_L1_RELATIVE: 11.639 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 10, iters: 243232, time: 0.545, data: 0.000) G_L1: 16.767 G_L1_ABSOLUTE: 2.897 G_L1_RELATIVE: 13.869 G_Regularizer: 0.000 validation_error: 20.551 +(epoch: 10, iters: 245232, time: 0.553, data: 0.000) G_L1: 23.020 G_L1_ABSOLUTE: 2.900 G_L1_RELATIVE: 20.120 G_Regularizer: 0.000 validation_error: 19.363 +(epoch: 10, iters: 247232, time: 0.552, data: 0.000) G_L1: 17.410 G_L1_ABSOLUTE: 2.860 G_L1_RELATIVE: 14.550 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 10, iters: 249232, time: 0.545, data: 0.000) G_L1: 14.205 G_L1_ABSOLUTE: 2.802 G_L1_RELATIVE: 11.403 G_Regularizer: 0.000 validation_error: 19.963 +(epoch: 10, iters: 251232, time: 0.548, data: 0.000) G_L1: 17.507 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 14.753 G_Regularizer: 0.000 validation_error: 19.682 +(epoch: 10, iters: 253232, time: 0.555, data: 0.000) G_L1: 14.642 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 11.974 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 10, iters: 255232, time: 0.552, data: 0.000) G_L1: 14.878 G_L1_ABSOLUTE: 3.030 G_L1_RELATIVE: 11.847 G_Regularizer: 0.000 validation_error: 20.699 +(epoch: 10, iters: 257232, time: 0.547, data: 0.000) G_L1: 13.846 G_L1_ABSOLUTE: 2.307 G_L1_RELATIVE: 11.538 G_Regularizer: 0.000 validation_error: 21.463 +(epoch: 10, iters: 259232, time: 0.545, data: 0.000) G_L1: 11.461 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 8.911 G_Regularizer: 0.000 validation_error: 20.281 +(epoch: 10, iters: 261232, time: 0.545, data: 0.000) G_L1: 16.568 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 14.028 G_Regularizer: 0.000 validation_error: 19.805 +(epoch: 10, iters: 263232, time: 0.550, data: 0.000) G_L1: 14.315 G_L1_ABSOLUTE: 2.639 G_L1_RELATIVE: 11.676 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 10, iters: 265232, time: 0.541, data: 0.000) G_L1: 15.470 G_L1_ABSOLUTE: 2.918 G_L1_RELATIVE: 12.551 G_Regularizer: 0.000 validation_error: 20.637 +(epoch: 10, iters: 267232, time: 0.551, data: 0.000) G_L1: 15.038 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 12.278 G_Regularizer: 0.000 validation_error: 21.138 +(epoch: 10, iters: 269232, time: 0.549, data: 0.000) G_L1: 15.097 G_L1_ABSOLUTE: 2.815 G_L1_RELATIVE: 12.282 G_Regularizer: 0.000 validation_error: 20.063 +(epoch: 10, iters: 271232, time: 0.547, data: 0.000) G_L1: 16.487 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 13.820 G_Regularizer: 0.000 validation_error: 20.431 +(epoch: 10, iters: 273232, time: 0.545, data: 0.000) G_L1: 13.480 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 10.773 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 10, iters: 275232, time: 0.545, data: 0.000) G_L1: 17.855 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 15.399 G_Regularizer: 0.000 validation_error: 21.204 +(epoch: 10, iters: 277232, time: 0.547, data: 0.000) G_L1: 13.515 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 11.099 G_Regularizer: 0.000 validation_error: 20.197 +(epoch: 10, iters: 279232, time: 0.538, data: 0.000) G_L1: 18.028 G_L1_ABSOLUTE: 2.765 G_L1_RELATIVE: 15.263 G_Regularizer: 0.000 validation_error: 20.173 +(epoch: 10, iters: 281232, time: 0.553, data: 0.000) G_L1: 15.266 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 12.561 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 10, iters: 283232, time: 0.551, data: 0.000) G_L1: 15.726 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 13.080 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 10, iters: 285232, time: 0.549, data: 0.000) G_L1: 17.751 G_L1_ABSOLUTE: 3.109 G_L1_RELATIVE: 14.642 G_Regularizer: 0.000 validation_error: 20.437 +(epoch: 10, iters: 287232, time: 0.552, data: 0.001) G_L1: 14.509 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 11.840 G_Regularizer: 0.000 validation_error: 20.600 +(epoch: 10, iters: 289232, time: 0.536, data: 0.000) G_L1: 17.136 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 14.279 G_Regularizer: 0.000 validation_error: 21.193 +(epoch: 10, iters: 291232, time: 0.545, data: 0.000) G_L1: 17.186 G_L1_ABSOLUTE: 2.701 G_L1_RELATIVE: 14.485 G_Regularizer: 0.000 validation_error: 21.151 +(epoch: 10, iters: 293232, time: 0.547, data: 0.000) G_L1: 14.177 G_L1_ABSOLUTE: 3.148 G_L1_RELATIVE: 11.029 G_Regularizer: 0.000 validation_error: 20.417 +(epoch: 10, iters: 295232, time: 0.550, data: 0.000) G_L1: 13.360 G_L1_ABSOLUTE: 2.162 G_L1_RELATIVE: 11.198 G_Regularizer: 0.000 validation_error: 19.833 +(epoch: 10, iters: 297232, time: 0.545, data: 0.000) G_L1: 13.799 G_L1_ABSOLUTE: 2.319 G_L1_RELATIVE: 11.479 G_Regularizer: 0.000 validation_error: 20.525 +(epoch: 10, iters: 299232, time: 0.546, data: 0.000) G_L1: 14.907 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 12.217 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 10, iters: 301232, time: 0.544, data: 0.000) G_L1: 13.607 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 10.997 G_Regularizer: 0.000 validation_error: 20.512 +(epoch: 11, iters: 480, time: 0.549, data: 0.000) G_L1: 16.376 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 13.707 G_Regularizer: 0.000 validation_error: 20.742 +(epoch: 11, iters: 2480, time: 0.546, data: 0.000) G_L1: 13.902 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 11.401 G_Regularizer: 0.000 validation_error: 21.817 +(epoch: 11, iters: 4480, time: 0.542, data: 0.000) G_L1: 18.020 G_L1_ABSOLUTE: 3.025 G_L1_RELATIVE: 14.994 G_Regularizer: 0.000 validation_error: 20.607 +(epoch: 11, iters: 6480, time: 0.547, data: 0.000) G_L1: 13.064 G_L1_ABSOLUTE: 2.252 G_L1_RELATIVE: 10.812 G_Regularizer: 0.000 validation_error: 20.303 +(epoch: 11, iters: 8480, time: 0.548, data: 0.000) G_L1: 17.276 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 14.656 G_Regularizer: 0.000 validation_error: 20.267 +(epoch: 11, iters: 10480, time: 0.549, data: 0.000) G_L1: 17.090 G_L1_ABSOLUTE: 2.688 G_L1_RELATIVE: 14.402 G_Regularizer: 0.000 validation_error: 21.273 +(epoch: 11, iters: 12480, time: 0.555, data: 0.000) G_L1: 14.087 G_L1_ABSOLUTE: 2.709 G_L1_RELATIVE: 11.378 G_Regularizer: 0.000 validation_error: 21.315 +(epoch: 11, iters: 14480, time: 0.554, data: 0.000) G_L1: 14.267 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 11.792 G_Regularizer: 0.000 validation_error: 20.320 +(epoch: 11, iters: 16480, time: 0.546, data: 0.001) G_L1: 15.473 G_L1_ABSOLUTE: 2.939 G_L1_RELATIVE: 12.534 G_Regularizer: 0.000 validation_error: 19.640 +(epoch: 11, iters: 18480, time: 0.540, data: 0.000) G_L1: 17.398 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 14.765 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 11, iters: 20480, time: 0.543, data: 0.000) G_L1: 14.396 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 11.765 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 11, iters: 22480, time: 0.546, data: 0.000) G_L1: 14.633 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 12.010 G_Regularizer: 0.000 validation_error: 22.118 +(epoch: 11, iters: 24480, time: 0.546, data: 0.000) G_L1: 13.481 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 10.681 G_Regularizer: 0.000 validation_error: 21.266 +(epoch: 11, iters: 26480, time: 0.553, data: 0.000) G_L1: 13.165 G_L1_ABSOLUTE: 2.745 G_L1_RELATIVE: 10.420 G_Regularizer: 0.000 validation_error: 20.287 +(epoch: 11, iters: 28480, time: 0.547, data: 0.001) G_L1: 14.899 G_L1_ABSOLUTE: 2.477 G_L1_RELATIVE: 12.422 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 11, iters: 30480, time: 0.543, data: 0.000) G_L1: 18.560 G_L1_ABSOLUTE: 3.533 G_L1_RELATIVE: 15.027 G_Regularizer: 0.000 validation_error: 20.532 +(epoch: 11, iters: 32480, time: 0.542, data: 0.000) G_L1: 15.031 G_L1_ABSOLUTE: 2.764 G_L1_RELATIVE: 12.267 G_Regularizer: 0.000 validation_error: 20.572 +(epoch: 11, iters: 34480, time: 0.545, data: 0.000) G_L1: 17.692 G_L1_ABSOLUTE: 2.758 G_L1_RELATIVE: 14.934 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 11, iters: 36480, time: 0.549, data: 0.000) G_L1: 16.194 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 13.679 G_Regularizer: 0.000 validation_error: 20.206 +(epoch: 11, iters: 38480, time: 0.546, data: 0.000) G_L1: 19.133 G_L1_ABSOLUTE: 2.913 G_L1_RELATIVE: 16.220 G_Regularizer: 0.000 validation_error: 20.541 +(epoch: 11, iters: 40480, time: 0.543, data: 0.000) G_L1: 16.560 G_L1_ABSOLUTE: 2.455 G_L1_RELATIVE: 14.104 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 11, iters: 42480, time: 0.550, data: 0.000) G_L1: 15.410 G_L1_ABSOLUTE: 2.205 G_L1_RELATIVE: 13.205 G_Regularizer: 0.000 validation_error: 20.208 +(epoch: 11, iters: 44480, time: 0.543, data: 0.000) G_L1: 18.799 G_L1_ABSOLUTE: 3.002 G_L1_RELATIVE: 15.797 G_Regularizer: 0.000 validation_error: 20.021 +(epoch: 11, iters: 46480, time: 0.549, data: 0.000) G_L1: 16.024 G_L1_ABSOLUTE: 2.487 G_L1_RELATIVE: 13.537 G_Regularizer: 0.000 validation_error: 21.482 +(epoch: 11, iters: 48480, time: 0.546, data: 0.000) G_L1: 16.181 G_L1_ABSOLUTE: 2.806 G_L1_RELATIVE: 13.375 G_Regularizer: 0.000 validation_error: 20.675 +(epoch: 11, iters: 50480, time: 0.547, data: 0.000) G_L1: 15.226 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 12.823 G_Regularizer: 0.000 validation_error: 20.059 +(epoch: 11, iters: 52480, time: 0.551, data: 0.000) G_L1: 14.939 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 12.485 G_Regularizer: 0.000 validation_error: 20.214 +(epoch: 11, iters: 54480, time: 0.547, data: 0.000) G_L1: 18.968 G_L1_ABSOLUTE: 2.351 G_L1_RELATIVE: 16.616 G_Regularizer: 0.000 validation_error: 20.430 +(epoch: 11, iters: 56480, time: 0.543, data: 0.000) G_L1: 16.062 G_L1_ABSOLUTE: 2.405 G_L1_RELATIVE: 13.657 G_Regularizer: 0.000 validation_error: 20.357 +(epoch: 11, iters: 58480, time: 0.547, data: 0.001) G_L1: 14.969 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 12.172 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 11, iters: 60480, time: 0.547, data: 0.000) G_L1: 16.544 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 13.848 G_Regularizer: 0.000 validation_error: 20.776 +(epoch: 11, iters: 62480, time: 0.544, data: 0.000) G_L1: 15.426 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 12.932 G_Regularizer: 0.000 validation_error: 20.785 +(epoch: 11, iters: 64480, time: 0.549, data: 0.000) G_L1: 13.320 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 10.769 G_Regularizer: 0.000 validation_error: 20.560 +(epoch: 11, iters: 66480, time: 0.548, data: 0.000) G_L1: 15.972 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 13.115 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 11, iters: 68480, time: 0.545, data: 0.000) G_L1: 13.796 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 11.354 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 11, iters: 70480, time: 0.550, data: 0.000) G_L1: 14.291 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 11.909 G_Regularizer: 0.000 validation_error: 21.171 +(epoch: 11, iters: 72480, time: 0.542, data: 0.000) G_L1: 15.998 G_L1_ABSOLUTE: 2.923 G_L1_RELATIVE: 13.075 G_Regularizer: 0.000 validation_error: 21.129 +(epoch: 11, iters: 74480, time: 0.560, data: 0.000) G_L1: 13.909 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 11.284 G_Regularizer: 0.000 validation_error: 20.157 +(epoch: 11, iters: 76480, time: 0.547, data: 0.000) G_L1: 13.925 G_L1_ABSOLUTE: 2.875 G_L1_RELATIVE: 11.050 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 11, iters: 78480, time: 0.550, data: 0.000) G_L1: 16.667 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 13.935 G_Regularizer: 0.000 validation_error: 20.937 +(epoch: 11, iters: 80480, time: 0.548, data: 0.000) G_L1: 18.635 G_L1_ABSOLUTE: 3.035 G_L1_RELATIVE: 15.600 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 11, iters: 82480, time: 0.539, data: 0.000) G_L1: 14.653 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 12.306 G_Regularizer: 0.000 validation_error: 21.124 +(epoch: 11, iters: 84480, time: 0.536, data: 0.000) G_L1: 15.658 G_L1_ABSOLUTE: 2.220 G_L1_RELATIVE: 13.438 G_Regularizer: 0.000 validation_error: 20.509 +(epoch: 11, iters: 86480, time: 0.548, data: 0.000) G_L1: 14.230 G_L1_ABSOLUTE: 2.821 G_L1_RELATIVE: 11.409 G_Regularizer: 0.000 validation_error: 20.718 +(epoch: 11, iters: 88480, time: 0.544, data: 0.000) G_L1: 15.662 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 13.112 G_Regularizer: 0.000 validation_error: 20.400 +(epoch: 11, iters: 90480, time: 0.544, data: 0.000) G_L1: 13.886 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 11.087 G_Regularizer: 0.000 validation_error: 19.992 +(epoch: 11, iters: 92480, time: 0.533, data: 0.000) G_L1: 12.838 G_L1_ABSOLUTE: 2.654 G_L1_RELATIVE: 10.183 G_Regularizer: 0.000 validation_error: 20.913 +(epoch: 11, iters: 94480, time: 0.546, data: 0.000) G_L1: 17.600 G_L1_ABSOLUTE: 3.057 G_L1_RELATIVE: 14.543 G_Regularizer: 0.000 validation_error: 20.485 +(epoch: 11, iters: 96480, time: 0.535, data: 0.000) G_L1: 14.893 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 12.139 G_Regularizer: 0.000 validation_error: 21.407 +(epoch: 11, iters: 98480, time: 0.530, data: 0.000) G_L1: 14.051 G_L1_ABSOLUTE: 3.233 G_L1_RELATIVE: 10.818 G_Regularizer: 0.000 validation_error: 21.251 +(epoch: 11, iters: 100480, time: 0.534, data: 0.000) G_L1: 13.616 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 11.019 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 11, iters: 102480, time: 0.536, data: 0.000) G_L1: 15.465 G_L1_ABSOLUTE: 2.761 G_L1_RELATIVE: 12.704 G_Regularizer: 0.000 validation_error: 20.061 +(epoch: 11, iters: 104480, time: 0.529, data: 0.000) G_L1: 16.854 G_L1_ABSOLUTE: 2.210 G_L1_RELATIVE: 14.644 G_Regularizer: 0.000 validation_error: 20.299 +(epoch: 11, iters: 106480, time: 0.544, data: 0.000) G_L1: 14.665 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 12.064 G_Regularizer: 0.000 validation_error: 20.782 +(epoch: 11, iters: 108480, time: 0.534, data: 0.000) G_L1: 14.821 G_L1_ABSOLUTE: 3.057 G_L1_RELATIVE: 11.764 G_Regularizer: 0.000 validation_error: 20.255 +(epoch: 11, iters: 110480, time: 0.526, data: 0.000) G_L1: 15.236 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 12.803 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 11, iters: 112480, time: 0.548, data: 0.000) G_L1: 14.774 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 12.212 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 11, iters: 114480, time: 0.539, data: 0.000) G_L1: 13.771 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 11.293 G_Regularizer: 0.000 validation_error: 19.755 +(epoch: 11, iters: 116480, time: 0.535, data: 0.000) G_L1: 16.205 G_L1_ABSOLUTE: 2.886 G_L1_RELATIVE: 13.318 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 11, iters: 118480, time: 0.522, data: 0.000) G_L1: 12.835 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 10.559 G_Regularizer: 0.000 validation_error: 20.497 +(epoch: 11, iters: 120480, time: 0.544, data: 0.000) G_L1: 16.972 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 14.508 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 11, iters: 122480, time: 0.542, data: 0.000) G_L1: 13.825 G_L1_ABSOLUTE: 3.029 G_L1_RELATIVE: 10.796 G_Regularizer: 0.000 validation_error: 21.800 +(epoch: 11, iters: 124480, time: 0.531, data: 0.000) G_L1: 16.790 G_L1_ABSOLUTE: 2.346 G_L1_RELATIVE: 14.443 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 11, iters: 126480, time: 0.536, data: 0.000) G_L1: 15.606 G_L1_ABSOLUTE: 2.775 G_L1_RELATIVE: 12.831 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 11, iters: 128480, time: 0.544, data: 0.000) G_L1: 13.453 G_L1_ABSOLUTE: 2.277 G_L1_RELATIVE: 11.176 G_Regularizer: 0.000 validation_error: 20.572 +(epoch: 11, iters: 130480, time: 0.540, data: 0.000) G_L1: 16.980 G_L1_ABSOLUTE: 2.972 G_L1_RELATIVE: 14.008 G_Regularizer: 0.000 validation_error: 21.207 +(epoch: 11, iters: 132480, time: 0.905, data: 0.000) G_L1: 13.569 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 11.194 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 11, iters: 134480, time: 0.918, data: 0.000) G_L1: 16.084 G_L1_ABSOLUTE: 2.718 G_L1_RELATIVE: 13.366 G_Regularizer: 0.000 validation_error: 20.218 +(epoch: 11, iters: 136480, time: 0.920, data: 0.000) G_L1: 14.828 G_L1_ABSOLUTE: 3.328 G_L1_RELATIVE: 11.500 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 11, iters: 138480, time: 0.920, data: 0.000) G_L1: 20.499 G_L1_ABSOLUTE: 2.876 G_L1_RELATIVE: 17.623 G_Regularizer: 0.000 validation_error: 20.491 +(epoch: 11, iters: 140480, time: 0.920, data: 0.000) G_L1: 14.632 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 12.000 G_Regularizer: 0.000 validation_error: 20.408 +(epoch: 11, iters: 142480, time: 0.919, data: 0.000) G_L1: 16.573 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 13.679 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 11, iters: 144480, time: 0.918, data: 0.000) G_L1: 16.041 G_L1_ABSOLUTE: 2.957 G_L1_RELATIVE: 13.084 G_Regularizer: 0.000 validation_error: 20.929 +(epoch: 11, iters: 146480, time: 0.925, data: 0.001) G_L1: 14.816 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 12.533 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 11, iters: 148480, time: 0.918, data: 0.000) G_L1: 17.811 G_L1_ABSOLUTE: 2.959 G_L1_RELATIVE: 14.853 G_Regularizer: 0.000 validation_error: 21.003 +(epoch: 11, iters: 150480, time: 0.919, data: 0.000) G_L1: 13.809 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 11.355 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 11, iters: 152480, time: 0.919, data: 0.000) G_L1: 13.772 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 11.270 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 11, iters: 154480, time: 0.923, data: 0.000) G_L1: 15.495 G_L1_ABSOLUTE: 2.855 G_L1_RELATIVE: 12.640 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 11, iters: 156480, time: 0.927, data: 0.000) G_L1: 14.256 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 11.881 G_Regularizer: 0.000 validation_error: 19.980 +(epoch: 11, iters: 158480, time: 0.920, data: 0.000) G_L1: 16.756 G_L1_ABSOLUTE: 2.562 G_L1_RELATIVE: 14.194 G_Regularizer: 0.000 validation_error: 20.596 +(epoch: 11, iters: 160480, time: 0.925, data: 0.000) G_L1: 17.864 G_L1_ABSOLUTE: 2.916 G_L1_RELATIVE: 14.947 G_Regularizer: 0.000 validation_error: 20.221 +(epoch: 11, iters: 162480, time: 0.917, data: 0.000) G_L1: 15.868 G_L1_ABSOLUTE: 2.927 G_L1_RELATIVE: 12.941 G_Regularizer: 0.000 validation_error: 20.288 +(epoch: 11, iters: 164480, time: 0.919, data: 0.000) G_L1: 12.488 G_L1_ABSOLUTE: 2.594 G_L1_RELATIVE: 9.894 G_Regularizer: 0.000 validation_error: 20.631 +(epoch: 11, iters: 166480, time: 0.925, data: 0.000) G_L1: 18.878 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 16.249 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 11, iters: 168480, time: 0.923, data: 0.000) G_L1: 15.654 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 13.055 G_Regularizer: 0.000 validation_error: 20.369 +(epoch: 11, iters: 170480, time: 0.923, data: 0.000) G_L1: 15.099 G_L1_ABSOLUTE: 2.821 G_L1_RELATIVE: 12.279 G_Regularizer: 0.000 validation_error: 20.030 +(epoch: 11, iters: 172480, time: 0.922, data: 0.000) G_L1: 16.376 G_L1_ABSOLUTE: 2.560 G_L1_RELATIVE: 13.815 G_Regularizer: 0.000 validation_error: 20.749 +(epoch: 11, iters: 174480, time: 0.924, data: 0.000) G_L1: 13.603 G_L1_ABSOLUTE: 2.692 G_L1_RELATIVE: 10.911 G_Regularizer: 0.000 validation_error: 19.996 +(epoch: 11, iters: 176480, time: 0.930, data: 0.000) G_L1: 16.873 G_L1_ABSOLUTE: 2.692 G_L1_RELATIVE: 14.181 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 11, iters: 178480, time: 0.923, data: 0.000) G_L1: 19.851 G_L1_ABSOLUTE: 3.333 G_L1_RELATIVE: 16.518 G_Regularizer: 0.000 validation_error: 20.615 +(epoch: 11, iters: 180480, time: 0.928, data: 0.000) G_L1: 17.791 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 15.049 G_Regularizer: 0.000 validation_error: 20.285 +(epoch: 11, iters: 182480, time: 0.919, data: 0.000) G_L1: 19.061 G_L1_ABSOLUTE: 3.048 G_L1_RELATIVE: 16.013 G_Regularizer: 0.000 validation_error: 21.129 +(epoch: 11, iters: 184480, time: 0.915, data: 0.000) G_L1: 15.264 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 12.638 G_Regularizer: 0.000 validation_error: 20.644 +(epoch: 11, iters: 186480, time: 0.920, data: 0.000) G_L1: 13.892 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 11.555 G_Regularizer: 0.000 validation_error: 21.263 +(epoch: 11, iters: 188480, time: 0.921, data: 0.000) G_L1: 15.035 G_L1_ABSOLUTE: 2.827 G_L1_RELATIVE: 12.209 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 11, iters: 190480, time: 0.925, data: 0.000) G_L1: 15.177 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 12.713 G_Regularizer: 0.000 validation_error: 21.315 +(epoch: 11, iters: 192480, time: 0.924, data: 0.000) G_L1: 15.585 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 13.320 G_Regularizer: 0.000 validation_error: 21.604 +(epoch: 11, iters: 194480, time: 0.926, data: 0.000) G_L1: 12.338 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 9.768 G_Regularizer: 0.000 validation_error: 21.072 +(epoch: 11, iters: 196480, time: 0.921, data: 0.000) G_L1: 18.636 G_L1_ABSOLUTE: 2.819 G_L1_RELATIVE: 15.817 G_Regularizer: 0.000 validation_error: 20.552 +(epoch: 11, iters: 198480, time: 0.925, data: 0.000) G_L1: 14.545 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 12.021 G_Regularizer: 0.000 validation_error: 20.528 +(epoch: 11, iters: 200480, time: 0.920, data: 0.000) G_L1: 20.975 G_L1_ABSOLUTE: 3.117 G_L1_RELATIVE: 17.858 G_Regularizer: 0.000 validation_error: 20.569 +(epoch: 11, iters: 202480, time: 0.928, data: 0.000) G_L1: 13.589 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 11.132 G_Regularizer: 0.000 validation_error: 21.420 +(epoch: 11, iters: 204480, time: 0.921, data: 0.000) G_L1: 13.505 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 11.010 G_Regularizer: 0.000 validation_error: 20.684 +(epoch: 11, iters: 206480, time: 0.926, data: 0.000) G_L1: 16.471 G_L1_ABSOLUTE: 2.922 G_L1_RELATIVE: 13.549 G_Regularizer: 0.000 validation_error: 20.504 +(epoch: 11, iters: 208480, time: 0.921, data: 0.000) G_L1: 16.531 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 13.865 G_Regularizer: 0.000 validation_error: 20.447 +(epoch: 11, iters: 210480, time: 0.886, data: 0.000) G_L1: 15.495 G_L1_ABSOLUTE: 2.795 G_L1_RELATIVE: 12.700 G_Regularizer: 0.000 validation_error: 21.027 +(epoch: 11, iters: 212480, time: 0.557, data: 0.000) G_L1: 15.242 G_L1_ABSOLUTE: 3.111 G_L1_RELATIVE: 12.131 G_Regularizer: 0.000 validation_error: 21.078 +(epoch: 11, iters: 214480, time: 0.533, data: 0.000) G_L1: 17.768 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 15.428 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 11, iters: 216480, time: 0.532, data: 0.001) G_L1: 17.826 G_L1_ABSOLUTE: 2.813 G_L1_RELATIVE: 15.012 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 11, iters: 218480, time: 0.529, data: 0.001) G_L1: 26.968 G_L1_ABSOLUTE: 3.524 G_L1_RELATIVE: 23.444 G_Regularizer: 0.000 validation_error: 20.216 +(epoch: 11, iters: 220480, time: 0.526, data: 0.000) G_L1: 16.722 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 14.228 G_Regularizer: 0.000 validation_error: 19.986 +(epoch: 11, iters: 222480, time: 0.529, data: 0.000) G_L1: 15.934 G_L1_ABSOLUTE: 2.429 G_L1_RELATIVE: 13.505 G_Regularizer: 0.000 validation_error: 20.684 +(epoch: 11, iters: 224480, time: 0.528, data: 0.000) G_L1: 16.382 G_L1_ABSOLUTE: 2.853 G_L1_RELATIVE: 13.529 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 11, iters: 226480, time: 0.530, data: 0.001) G_L1: 15.439 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 12.813 G_Regularizer: 0.000 validation_error: 21.076 +(epoch: 11, iters: 228480, time: 0.533, data: 0.000) G_L1: 14.611 G_L1_ABSOLUTE: 2.051 G_L1_RELATIVE: 12.560 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 11, iters: 230480, time: 0.528, data: 0.001) G_L1: 15.223 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 12.546 G_Regularizer: 0.000 validation_error: 20.168 +(epoch: 11, iters: 232480, time: 0.521, data: 0.000) G_L1: 18.622 G_L1_ABSOLUTE: 2.998 G_L1_RELATIVE: 15.624 G_Regularizer: 0.000 validation_error: 20.415 +(epoch: 11, iters: 234480, time: 0.530, data: 0.000) G_L1: 17.019 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 14.343 G_Regularizer: 0.000 validation_error: 19.730 +(epoch: 11, iters: 236480, time: 0.525, data: 0.000) G_L1: 11.582 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 8.746 G_Regularizer: 0.000 validation_error: 20.749 +(epoch: 11, iters: 238480, time: 0.528, data: 0.000) G_L1: 17.524 G_L1_ABSOLUTE: 3.012 G_L1_RELATIVE: 14.511 G_Regularizer: 0.000 validation_error: 20.177 +(epoch: 11, iters: 240480, time: 0.530, data: 0.000) G_L1: 16.383 G_L1_ABSOLUTE: 2.977 G_L1_RELATIVE: 13.406 G_Regularizer: 0.000 validation_error: 21.028 +(epoch: 11, iters: 242480, time: 0.531, data: 0.000) G_L1: 12.936 G_L1_ABSOLUTE: 3.339 G_L1_RELATIVE: 9.596 G_Regularizer: 0.000 validation_error: 20.097 +(epoch: 11, iters: 244480, time: 0.526, data: 0.000) G_L1: 16.490 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 14.145 G_Regularizer: 0.000 validation_error: 21.038 +(epoch: 11, iters: 246480, time: 0.526, data: 0.000) G_L1: 19.234 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 16.453 G_Regularizer: 0.000 validation_error: 21.165 +(epoch: 11, iters: 248480, time: 0.528, data: 0.000) G_L1: 13.478 G_L1_ABSOLUTE: 2.899 G_L1_RELATIVE: 10.579 G_Regularizer: 0.000 validation_error: 19.934 +(epoch: 11, iters: 250480, time: 0.530, data: 0.000) G_L1: 14.532 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 11.949 G_Regularizer: 0.000 validation_error: 20.152 +(epoch: 11, iters: 252480, time: 0.528, data: 0.000) G_L1: 12.507 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 10.015 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 11, iters: 254480, time: 0.526, data: 0.000) G_L1: 13.786 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 11.160 G_Regularizer: 0.000 validation_error: 20.327 +(epoch: 11, iters: 256480, time: 0.527, data: 0.000) G_L1: 14.052 G_L1_ABSOLUTE: 2.538 G_L1_RELATIVE: 11.514 G_Regularizer: 0.000 validation_error: 20.498 +(epoch: 11, iters: 258480, time: 0.530, data: 0.000) G_L1: 14.081 G_L1_ABSOLUTE: 2.356 G_L1_RELATIVE: 11.725 G_Regularizer: 0.000 validation_error: 20.433 +(epoch: 11, iters: 260480, time: 0.524, data: 0.000) G_L1: 15.754 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 12.932 G_Regularizer: 0.000 validation_error: 20.470 +(epoch: 11, iters: 262480, time: 0.526, data: 0.000) G_L1: 16.718 G_L1_ABSOLUTE: 3.036 G_L1_RELATIVE: 13.682 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 11, iters: 264480, time: 0.530, data: 0.000) G_L1: 13.801 G_L1_ABSOLUTE: 2.472 G_L1_RELATIVE: 11.329 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 11, iters: 266480, time: 0.527, data: 0.000) G_L1: 14.400 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 11.864 G_Regularizer: 0.000 validation_error: 20.235 +(epoch: 11, iters: 268480, time: 0.533, data: 0.000) G_L1: 13.516 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 11.005 G_Regularizer: 0.000 validation_error: 20.992 +(epoch: 11, iters: 270480, time: 0.528, data: 0.000) G_L1: 16.431 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 13.550 G_Regularizer: 0.000 validation_error: 20.257 +(epoch: 11, iters: 272480, time: 0.526, data: 0.000) G_L1: 16.999 G_L1_ABSOLUTE: 3.061 G_L1_RELATIVE: 13.938 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 11, iters: 274480, time: 0.527, data: 0.000) G_L1: 14.973 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 12.235 G_Regularizer: 0.000 validation_error: 20.146 +(epoch: 11, iters: 276480, time: 0.528, data: 0.000) G_L1: 20.296 G_L1_ABSOLUTE: 2.959 G_L1_RELATIVE: 17.337 G_Regularizer: 0.000 validation_error: 21.501 +(epoch: 11, iters: 278480, time: 0.522, data: 0.000) G_L1: 13.399 G_L1_ABSOLUTE: 3.155 G_L1_RELATIVE: 10.243 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 11, iters: 280480, time: 0.529, data: 0.000) G_L1: 15.377 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 12.610 G_Regularizer: 0.000 validation_error: 21.688 +(epoch: 11, iters: 282480, time: 0.530, data: 0.000) G_L1: 17.750 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 15.281 G_Regularizer: 0.000 validation_error: 20.327 +(epoch: 11, iters: 284480, time: 0.531, data: 0.000) G_L1: 17.927 G_L1_ABSOLUTE: 2.972 G_L1_RELATIVE: 14.955 G_Regularizer: 0.000 validation_error: 20.068 +(epoch: 11, iters: 286480, time: 0.523, data: 0.000) G_L1: 13.003 G_L1_ABSOLUTE: 2.220 G_L1_RELATIVE: 10.782 G_Regularizer: 0.000 validation_error: 20.509 +(epoch: 11, iters: 288480, time: 0.530, data: 0.000) G_L1: 14.933 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 12.226 G_Regularizer: 0.000 validation_error: 21.162 +(epoch: 11, iters: 290480, time: 0.536, data: 0.000) G_L1: 17.147 G_L1_ABSOLUTE: 3.229 G_L1_RELATIVE: 13.917 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 11, iters: 292480, time: 0.525, data: 0.000) G_L1: 13.382 G_L1_ABSOLUTE: 2.441 G_L1_RELATIVE: 10.942 G_Regularizer: 0.000 validation_error: 20.436 +(epoch: 11, iters: 294480, time: 0.524, data: 0.000) G_L1: 19.117 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 16.372 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 11, iters: 296480, time: 0.529, data: 0.000) G_L1: 14.440 G_L1_ABSOLUTE: 3.084 G_L1_RELATIVE: 11.356 G_Regularizer: 0.000 validation_error: 20.279 +(epoch: 11, iters: 298480, time: 0.531, data: 0.000) G_L1: 14.145 G_L1_ABSOLUTE: 2.807 G_L1_RELATIVE: 11.338 G_Regularizer: 0.000 validation_error: 21.221 +(epoch: 11, iters: 300480, time: 0.530, data: 0.000) G_L1: 16.273 G_L1_ABSOLUTE: 2.911 G_L1_RELATIVE: 13.362 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 11, iters: 302480, time: 0.534, data: 0.000) G_L1: 15.915 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 13.282 G_Regularizer: 0.000 validation_error: 20.481 +(epoch: 12, iters: 1728, time: 0.522, data: 0.000) G_L1: 15.724 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 13.210 G_Regularizer: 0.000 validation_error: 21.135 +(epoch: 12, iters: 3728, time: 0.532, data: 0.000) G_L1: 17.741 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 15.019 G_Regularizer: 0.000 validation_error: 19.812 +(epoch: 12, iters: 5728, time: 0.535, data: 0.000) G_L1: 12.924 G_L1_ABSOLUTE: 2.351 G_L1_RELATIVE: 10.573 G_Regularizer: 0.000 validation_error: 20.099 +(epoch: 12, iters: 7728, time: 0.525, data: 0.000) G_L1: 14.210 G_L1_ABSOLUTE: 2.361 G_L1_RELATIVE: 11.850 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 12, iters: 9728, time: 0.527, data: 0.000) G_L1: 16.350 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 13.870 G_Regularizer: 0.000 validation_error: 21.565 +(epoch: 12, iters: 11728, time: 0.529, data: 0.000) G_L1: 13.257 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 10.620 G_Regularizer: 0.000 validation_error: 20.657 +(epoch: 12, iters: 13728, time: 0.533, data: 0.000) G_L1: 15.167 G_L1_ABSOLUTE: 2.471 G_L1_RELATIVE: 12.696 G_Regularizer: 0.000 validation_error: 22.384 +(epoch: 12, iters: 15728, time: 0.530, data: 0.000) G_L1: 13.293 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 10.660 G_Regularizer: 0.000 validation_error: 20.582 +(epoch: 12, iters: 17728, time: 0.526, data: 0.000) G_L1: 14.108 G_L1_ABSOLUTE: 2.909 G_L1_RELATIVE: 11.200 G_Regularizer: 0.000 validation_error: 20.206 +(epoch: 12, iters: 19728, time: 0.521, data: 0.000) G_L1: 15.960 G_L1_ABSOLUTE: 2.685 G_L1_RELATIVE: 13.274 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 12, iters: 21728, time: 0.523, data: 0.000) G_L1: 13.859 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 11.471 G_Regularizer: 0.000 validation_error: 20.943 +(epoch: 12, iters: 23728, time: 0.527, data: 0.000) G_L1: 14.965 G_L1_ABSOLUTE: 2.251 G_L1_RELATIVE: 12.714 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 12, iters: 25728, time: 0.532, data: 0.000) G_L1: 13.731 G_L1_ABSOLUTE: 2.560 G_L1_RELATIVE: 11.171 G_Regularizer: 0.000 validation_error: 20.535 +(epoch: 12, iters: 27728, time: 0.522, data: 0.000) G_L1: 15.238 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 12.575 G_Regularizer: 0.000 validation_error: 20.522 +(epoch: 12, iters: 29728, time: 0.523, data: 0.000) G_L1: 16.831 G_L1_ABSOLUTE: 2.886 G_L1_RELATIVE: 13.945 G_Regularizer: 0.000 validation_error: 20.735 +(epoch: 12, iters: 31728, time: 0.529, data: 0.000) G_L1: 14.177 G_L1_ABSOLUTE: 2.996 G_L1_RELATIVE: 11.181 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 12, iters: 33728, time: 0.527, data: 0.000) G_L1: 18.937 G_L1_ABSOLUTE: 2.977 G_L1_RELATIVE: 15.960 G_Regularizer: 0.000 validation_error: 21.125 +(epoch: 12, iters: 35728, time: 0.527, data: 0.000) G_L1: 14.674 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 12.141 G_Regularizer: 0.000 validation_error: 21.017 +(epoch: 12, iters: 37728, time: 0.522, data: 0.000) G_L1: 17.610 G_L1_ABSOLUTE: 2.907 G_L1_RELATIVE: 14.703 G_Regularizer: 0.000 validation_error: 20.417 +(epoch: 12, iters: 39728, time: 0.525, data: 0.001) G_L1: 15.945 G_L1_ABSOLUTE: 2.330 G_L1_RELATIVE: 13.615 G_Regularizer: 0.000 validation_error: 19.909 +(epoch: 12, iters: 41728, time: 0.523, data: 0.000) G_L1: 17.535 G_L1_ABSOLUTE: 2.929 G_L1_RELATIVE: 14.606 G_Regularizer: 0.000 validation_error: 20.520 +(epoch: 12, iters: 43728, time: 0.529, data: 0.000) G_L1: 16.762 G_L1_ABSOLUTE: 2.672 G_L1_RELATIVE: 14.090 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 12, iters: 45728, time: 0.533, data: 0.000) G_L1: 16.834 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 14.252 G_Regularizer: 0.000 validation_error: 20.778 +(epoch: 12, iters: 47728, time: 0.530, data: 0.000) G_L1: 17.534 G_L1_ABSOLUTE: 2.719 G_L1_RELATIVE: 14.815 G_Regularizer: 0.000 validation_error: 20.067 +(epoch: 12, iters: 49728, time: 0.529, data: 0.000) G_L1: 15.151 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 12.765 G_Regularizer: 0.000 validation_error: 20.490 +(epoch: 12, iters: 51728, time: 0.526, data: 0.000) G_L1: 14.129 G_L1_ABSOLUTE: 2.848 G_L1_RELATIVE: 11.281 G_Regularizer: 0.000 validation_error: 20.452 +(epoch: 12, iters: 53728, time: 0.528, data: 0.000) G_L1: 14.174 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 11.572 G_Regularizer: 0.000 validation_error: 20.471 +(epoch: 12, iters: 55728, time: 0.528, data: 0.000) G_L1: 17.472 G_L1_ABSOLUTE: 2.875 G_L1_RELATIVE: 14.597 G_Regularizer: 0.000 validation_error: 21.149 +(epoch: 12, iters: 57728, time: 0.533, data: 0.000) G_L1: 16.179 G_L1_ABSOLUTE: 2.851 G_L1_RELATIVE: 13.328 G_Regularizer: 0.000 validation_error: 21.284 +(epoch: 12, iters: 59728, time: 0.534, data: 0.000) G_L1: 18.631 G_L1_ABSOLUTE: 2.775 G_L1_RELATIVE: 15.856 G_Regularizer: 0.000 validation_error: 20.998 +(epoch: 12, iters: 61728, time: 0.528, data: 0.000) G_L1: 14.799 G_L1_ABSOLUTE: 3.099 G_L1_RELATIVE: 11.700 G_Regularizer: 0.000 validation_error: 19.772 +(epoch: 12, iters: 63728, time: 0.525, data: 0.000) G_L1: 14.629 G_L1_ABSOLUTE: 2.606 G_L1_RELATIVE: 12.023 G_Regularizer: 0.000 validation_error: 21.674 +(epoch: 12, iters: 65728, time: 0.526, data: 0.001) G_L1: 15.409 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 12.797 G_Regularizer: 0.000 validation_error: 20.369 +(epoch: 12, iters: 67728, time: 0.528, data: 0.000) G_L1: 13.566 G_L1_ABSOLUTE: 2.931 G_L1_RELATIVE: 10.635 G_Regularizer: 0.000 validation_error: 20.993 +(epoch: 12, iters: 69728, time: 0.530, data: 0.000) G_L1: 15.074 G_L1_ABSOLUTE: 3.131 G_L1_RELATIVE: 11.943 G_Regularizer: 0.000 validation_error: 20.041 +(epoch: 12, iters: 71728, time: 0.526, data: 0.000) G_L1: 13.059 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 10.395 G_Regularizer: 0.000 validation_error: 20.370 +(epoch: 12, iters: 73728, time: 0.524, data: 0.000) G_L1: 12.749 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 10.337 G_Regularizer: 0.000 validation_error: 20.185 +(epoch: 12, iters: 75728, time: 0.527, data: 0.000) G_L1: 15.351 G_L1_ABSOLUTE: 2.779 G_L1_RELATIVE: 12.572 G_Regularizer: 0.000 validation_error: 20.555 +(epoch: 12, iters: 77728, time: 0.527, data: 0.000) G_L1: 13.469 G_L1_ABSOLUTE: 2.444 G_L1_RELATIVE: 11.025 G_Regularizer: 0.000 validation_error: 20.747 +(epoch: 12, iters: 79728, time: 0.530, data: 0.001) G_L1: 17.070 G_L1_ABSOLUTE: 2.727 G_L1_RELATIVE: 14.342 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 12, iters: 81728, time: 0.527, data: 0.000) G_L1: 17.607 G_L1_ABSOLUTE: 2.362 G_L1_RELATIVE: 15.244 G_Regularizer: 0.000 validation_error: 20.839 +(epoch: 12, iters: 83728, time: 0.527, data: 0.000) G_L1: 16.949 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 14.365 G_Regularizer: 0.000 validation_error: 20.403 +(epoch: 12, iters: 85728, time: 0.531, data: 0.000) G_L1: 15.851 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 13.227 G_Regularizer: 0.000 validation_error: 21.052 +(epoch: 12, iters: 87728, time: 0.527, data: 0.000) G_L1: 16.581 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 13.835 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 12, iters: 89728, time: 0.529, data: 0.000) G_L1: 13.167 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 10.461 G_Regularizer: 0.000 validation_error: 20.096 +(epoch: 12, iters: 91728, time: 0.532, data: 0.000) G_L1: 15.076 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 12.662 G_Regularizer: 0.000 validation_error: 21.315 +(epoch: 12, iters: 93728, time: 0.526, data: 0.000) G_L1: 17.040 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 14.394 G_Regularizer: 0.000 validation_error: 21.336 +(epoch: 12, iters: 95728, time: 0.526, data: 0.000) G_L1: 12.777 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 10.295 G_Regularizer: 0.000 validation_error: 20.283 +(epoch: 12, iters: 97728, time: 0.529, data: 0.000) G_L1: 19.675 G_L1_ABSOLUTE: 3.133 G_L1_RELATIVE: 16.542 G_Regularizer: 0.000 validation_error: 20.522 +(epoch: 12, iters: 99728, time: 0.531, data: 0.000) G_L1: 18.210 G_L1_ABSOLUTE: 3.050 G_L1_RELATIVE: 15.160 G_Regularizer: 0.000 validation_error: 21.378 +(epoch: 12, iters: 101728, time: 0.528, data: 0.000) G_L1: 13.621 G_L1_ABSOLUTE: 2.500 G_L1_RELATIVE: 11.121 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 12, iters: 103728, time: 0.527, data: 0.000) G_L1: 14.532 G_L1_ABSOLUTE: 2.985 G_L1_RELATIVE: 11.547 G_Regularizer: 0.000 validation_error: 20.692 +(epoch: 12, iters: 105728, time: 0.526, data: 0.000) G_L1: 16.584 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 13.958 G_Regularizer: 0.000 validation_error: 21.020 +(epoch: 12, iters: 107728, time: 0.526, data: 0.000) G_L1: 15.125 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 12.497 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 12, iters: 109728, time: 0.526, data: 0.000) G_L1: 13.830 G_L1_ABSOLUTE: 2.643 G_L1_RELATIVE: 11.187 G_Regularizer: 0.000 validation_error: 20.117 +(epoch: 12, iters: 111728, time: 0.528, data: 0.000) G_L1: 14.338 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 11.765 G_Regularizer: 0.000 validation_error: 19.777 +(epoch: 12, iters: 113728, time: 0.529, data: 0.000) G_L1: 15.024 G_L1_ABSOLUTE: 2.434 G_L1_RELATIVE: 12.590 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 12, iters: 115728, time: 0.523, data: 0.000) G_L1: 14.143 G_L1_ABSOLUTE: 2.887 G_L1_RELATIVE: 11.256 G_Regularizer: 0.000 validation_error: 20.357 +(epoch: 12, iters: 117728, time: 0.531, data: 0.000) G_L1: 13.189 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 10.531 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 12, iters: 119728, time: 0.527, data: 0.000) G_L1: 15.810 G_L1_ABSOLUTE: 2.939 G_L1_RELATIVE: 12.871 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 12, iters: 121728, time: 0.527, data: 0.000) G_L1: 14.398 G_L1_ABSOLUTE: 2.257 G_L1_RELATIVE: 12.141 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 12, iters: 123728, time: 0.536, data: 0.000) G_L1: 18.415 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 15.760 G_Regularizer: 0.000 validation_error: 20.453 +(epoch: 12, iters: 125728, time: 0.525, data: 0.000) G_L1: 16.323 G_L1_ABSOLUTE: 2.793 G_L1_RELATIVE: 13.530 G_Regularizer: 0.000 validation_error: 20.003 +(epoch: 12, iters: 127728, time: 0.524, data: 0.000) G_L1: 17.923 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 15.289 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 12, iters: 129728, time: 0.529, data: 0.000) G_L1: 13.750 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 10.928 G_Regularizer: 0.000 validation_error: 20.490 +(epoch: 12, iters: 131728, time: 0.523, data: 0.000) G_L1: 12.818 G_L1_ABSOLUTE: 2.312 G_L1_RELATIVE: 10.506 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 12, iters: 133728, time: 0.527, data: 0.000) G_L1: 18.953 G_L1_ABSOLUTE: 3.169 G_L1_RELATIVE: 15.784 G_Regularizer: 0.000 validation_error: 20.982 +(epoch: 12, iters: 135728, time: 0.528, data: 0.000) G_L1: 16.005 G_L1_ABSOLUTE: 2.552 G_L1_RELATIVE: 13.453 G_Regularizer: 0.000 validation_error: 20.369 +(epoch: 12, iters: 137728, time: 0.526, data: 0.000) G_L1: 14.852 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 12.188 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 12, iters: 139728, time: 0.527, data: 0.000) G_L1: 14.041 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 11.367 G_Regularizer: 0.000 validation_error: 20.450 +(epoch: 12, iters: 141728, time: 0.524, data: 0.000) G_L1: 15.628 G_L1_ABSOLUTE: 3.054 G_L1_RELATIVE: 12.574 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 12, iters: 143728, time: 0.532, data: 0.000) G_L1: 16.777 G_L1_ABSOLUTE: 2.605 G_L1_RELATIVE: 14.172 G_Regularizer: 0.000 validation_error: 19.954 +(epoch: 12, iters: 145728, time: 0.526, data: 0.000) G_L1: 16.162 G_L1_ABSOLUTE: 2.557 G_L1_RELATIVE: 13.605 G_Regularizer: 0.000 validation_error: 20.296 +(epoch: 12, iters: 147728, time: 0.531, data: 0.000) G_L1: 16.454 G_L1_ABSOLUTE: 2.851 G_L1_RELATIVE: 13.603 G_Regularizer: 0.000 validation_error: 20.313 +(epoch: 12, iters: 149728, time: 0.529, data: 0.000) G_L1: 15.254 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 12.265 G_Regularizer: 0.000 validation_error: 19.959 +(epoch: 12, iters: 151728, time: 0.529, data: 0.000) G_L1: 16.641 G_L1_ABSOLUTE: 3.272 G_L1_RELATIVE: 13.370 G_Regularizer: 0.000 validation_error: 21.006 +(epoch: 12, iters: 153728, time: 0.529, data: 0.000) G_L1: 15.600 G_L1_ABSOLUTE: 2.813 G_L1_RELATIVE: 12.787 G_Regularizer: 0.000 validation_error: 19.959 +(epoch: 12, iters: 155728, time: 0.528, data: 0.000) G_L1: 15.437 G_L1_ABSOLUTE: 3.343 G_L1_RELATIVE: 12.094 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 12, iters: 157728, time: 0.525, data: 0.000) G_L1: 20.543 G_L1_ABSOLUTE: 2.935 G_L1_RELATIVE: 17.608 G_Regularizer: 0.000 validation_error: 20.566 +(epoch: 12, iters: 159728, time: 0.530, data: 0.000) G_L1: 14.562 G_L1_ABSOLUTE: 2.534 G_L1_RELATIVE: 12.028 G_Regularizer: 0.000 validation_error: 20.600 +(epoch: 12, iters: 161728, time: 0.528, data: 0.000) G_L1: 12.041 G_L1_ABSOLUTE: 2.456 G_L1_RELATIVE: 9.585 G_Regularizer: 0.000 validation_error: 20.802 +(epoch: 12, iters: 163728, time: 0.529, data: 0.000) G_L1: 14.755 G_L1_ABSOLUTE: 3.024 G_L1_RELATIVE: 11.731 G_Regularizer: 0.000 validation_error: 20.393 +(epoch: 12, iters: 165728, time: 0.542, data: 0.000) G_L1: 12.314 G_L1_ABSOLUTE: 2.372 G_L1_RELATIVE: 9.943 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 12, iters: 167728, time: 0.578, data: 0.000) G_L1: 16.751 G_L1_ABSOLUTE: 2.829 G_L1_RELATIVE: 13.922 G_Regularizer: 0.000 validation_error: 21.163 +(epoch: 12, iters: 169728, time: 0.586, data: 0.000) G_L1: 15.225 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 12.697 G_Regularizer: 0.000 validation_error: 20.814 +(epoch: 12, iters: 171728, time: 0.577, data: 0.000) G_L1: 13.867 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 11.210 G_Regularizer: 0.000 validation_error: 20.297 +(epoch: 12, iters: 173728, time: 0.571, data: 0.000) G_L1: 15.300 G_L1_ABSOLUTE: 2.825 G_L1_RELATIVE: 12.475 G_Regularizer: 0.000 validation_error: 20.375 +(epoch: 12, iters: 175728, time: 0.570, data: 0.000) G_L1: 21.425 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 18.890 G_Regularizer: 0.000 validation_error: 20.098 +(epoch: 12, iters: 177728, time: 0.584, data: 0.000) G_L1: 13.583 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 10.974 G_Regularizer: 0.000 validation_error: 20.568 +(epoch: 12, iters: 179728, time: 0.580, data: 0.000) G_L1: 14.473 G_L1_ABSOLUTE: 2.358 G_L1_RELATIVE: 12.115 G_Regularizer: 0.000 validation_error: 20.723 +(epoch: 12, iters: 181728, time: 0.587, data: 0.000) G_L1: 16.857 G_L1_ABSOLUTE: 2.949 G_L1_RELATIVE: 13.908 G_Regularizer: 0.000 validation_error: 20.352 +(epoch: 12, iters: 183728, time: 0.586, data: 0.000) G_L1: 11.880 G_L1_ABSOLUTE: 2.180 G_L1_RELATIVE: 9.700 G_Regularizer: 0.000 validation_error: 20.911 +(epoch: 12, iters: 185728, time: 0.527, data: 0.000) G_L1: 16.344 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 13.850 G_Regularizer: 0.000 validation_error: 20.724 +(epoch: 12, iters: 187728, time: 0.526, data: 0.000) G_L1: 13.986 G_L1_ABSOLUTE: 2.465 G_L1_RELATIVE: 11.522 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 12, iters: 189728, time: 0.525, data: 0.000) G_L1: 17.501 G_L1_ABSOLUTE: 3.148 G_L1_RELATIVE: 14.353 G_Regularizer: 0.000 validation_error: 21.521 +(epoch: 12, iters: 191728, time: 0.529, data: 0.000) G_L1: 13.334 G_L1_ABSOLUTE: 2.828 G_L1_RELATIVE: 10.505 G_Regularizer: 0.000 validation_error: 21.024 +(epoch: 12, iters: 193728, time: 0.526, data: 0.000) G_L1: 13.050 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 10.522 G_Regularizer: 0.000 validation_error: 20.246 +(epoch: 12, iters: 195728, time: 0.527, data: 0.000) G_L1: 15.878 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 13.538 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 12, iters: 197728, time: 0.527, data: 0.000) G_L1: 14.032 G_L1_ABSOLUTE: 2.179 G_L1_RELATIVE: 11.852 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 12, iters: 199728, time: 0.528, data: 0.000) G_L1: 15.836 G_L1_ABSOLUTE: 2.838 G_L1_RELATIVE: 12.998 G_Regularizer: 0.000 validation_error: 21.012 +(epoch: 12, iters: 201728, time: 0.526, data: 0.000) G_L1: 16.718 G_L1_ABSOLUTE: 2.281 G_L1_RELATIVE: 14.438 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 12, iters: 203728, time: 0.533, data: 0.000) G_L1: 17.392 G_L1_ABSOLUTE: 2.752 G_L1_RELATIVE: 14.640 G_Regularizer: 0.000 validation_error: 20.563 +(epoch: 12, iters: 205728, time: 0.529, data: 0.001) G_L1: 15.247 G_L1_ABSOLUTE: 2.769 G_L1_RELATIVE: 12.478 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 12, iters: 207728, time: 0.529, data: 0.000) G_L1: 15.448 G_L1_ABSOLUTE: 2.872 G_L1_RELATIVE: 12.576 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 12, iters: 209728, time: 0.537, data: 0.000) G_L1: 14.785 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 12.171 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 12, iters: 211728, time: 0.524, data: 0.000) G_L1: 17.222 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 14.498 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 12, iters: 213728, time: 0.532, data: 0.000) G_L1: 14.576 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 11.980 G_Regularizer: 0.000 validation_error: 19.973 +(epoch: 12, iters: 215728, time: 0.533, data: 0.000) G_L1: 16.782 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 14.147 G_Regularizer: 0.000 validation_error: 19.712 +(epoch: 12, iters: 217728, time: 0.528, data: 0.000) G_L1: 13.155 G_L1_ABSOLUTE: 2.285 G_L1_RELATIVE: 10.869 G_Regularizer: 0.000 validation_error: 20.357 +(epoch: 12, iters: 219728, time: 0.528, data: 0.000) G_L1: 14.675 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 12.107 G_Regularizer: 0.000 validation_error: 20.791 +(epoch: 12, iters: 221728, time: 0.527, data: 0.000) G_L1: 15.247 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 12.463 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 12, iters: 223728, time: 0.541, data: 0.000) G_L1: 15.439 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 13.085 G_Regularizer: 0.000 validation_error: 20.293 +(epoch: 12, iters: 225728, time: 0.544, data: 0.000) G_L1: 15.914 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 13.582 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 12, iters: 227728, time: 0.547, data: 0.000) G_L1: 14.425 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 11.766 G_Regularizer: 0.000 validation_error: 21.276 +(epoch: 12, iters: 229728, time: 0.555, data: 0.000) G_L1: 15.796 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 12.919 G_Regularizer: 0.000 validation_error: 20.359 +(epoch: 12, iters: 231728, time: 0.527, data: 0.000) G_L1: 13.090 G_L1_ABSOLUTE: 2.452 G_L1_RELATIVE: 10.638 G_Regularizer: 0.000 validation_error: 19.841 +(epoch: 12, iters: 233728, time: 0.535, data: 0.000) G_L1: 14.464 G_L1_ABSOLUTE: 2.808 G_L1_RELATIVE: 11.656 G_Regularizer: 0.000 validation_error: 21.254 +(epoch: 12, iters: 235728, time: 0.554, data: 0.000) G_L1: 16.927 G_L1_ABSOLUTE: 2.803 G_L1_RELATIVE: 14.124 G_Regularizer: 0.000 validation_error: 20.020 +(epoch: 12, iters: 237728, time: 0.551, data: 0.000) G_L1: 18.946 G_L1_ABSOLUTE: 3.049 G_L1_RELATIVE: 15.897 G_Regularizer: 0.000 validation_error: 19.887 +(epoch: 12, iters: 239728, time: 0.544, data: 0.000) G_L1: 14.554 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 12.121 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 12, iters: 241728, time: 0.550, data: 0.000) G_L1: 14.955 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 12.482 G_Regularizer: 0.000 validation_error: 20.197 +(epoch: 12, iters: 243728, time: 0.556, data: 0.000) G_L1: 15.971 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 12.982 G_Regularizer: 0.000 validation_error: 20.488 +(epoch: 12, iters: 245728, time: 0.554, data: 0.000) G_L1: 17.786 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 14.951 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 12, iters: 247728, time: 0.559, data: 0.000) G_L1: 15.590 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 12.875 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 12, iters: 249728, time: 0.530, data: 0.001) G_L1: 15.817 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 13.144 G_Regularizer: 0.000 validation_error: 20.531 +(epoch: 12, iters: 251728, time: 0.545, data: 0.000) G_L1: 17.129 G_L1_ABSOLUTE: 2.779 G_L1_RELATIVE: 14.351 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 12, iters: 253728, time: 0.562, data: 0.000) G_L1: 15.595 G_L1_ABSOLUTE: 3.490 G_L1_RELATIVE: 12.105 G_Regularizer: 0.000 validation_error: 20.769 +(epoch: 12, iters: 255728, time: 0.554, data: 0.000) G_L1: 16.554 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 13.888 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 12, iters: 257728, time: 0.530, data: 0.000) G_L1: 14.065 G_L1_ABSOLUTE: 2.874 G_L1_RELATIVE: 11.191 G_Regularizer: 0.000 validation_error: 20.533 +(epoch: 12, iters: 259728, time: 0.559, data: 0.000) G_L1: 14.618 G_L1_ABSOLUTE: 2.899 G_L1_RELATIVE: 11.719 G_Regularizer: 0.000 validation_error: 21.422 +(epoch: 12, iters: 261728, time: 0.565, data: 0.000) G_L1: 14.307 G_L1_ABSOLUTE: 3.278 G_L1_RELATIVE: 11.029 G_Regularizer: 0.000 validation_error: 21.353 +(epoch: 12, iters: 263728, time: 0.556, data: 0.000) G_L1: 12.485 G_L1_ABSOLUTE: 2.318 G_L1_RELATIVE: 10.167 G_Regularizer: 0.000 validation_error: 20.369 +(epoch: 12, iters: 265728, time: 0.526, data: 0.000) G_L1: 14.852 G_L1_ABSOLUTE: 2.833 G_L1_RELATIVE: 12.019 G_Regularizer: 0.000 validation_error: 20.661 +(epoch: 12, iters: 267728, time: 0.532, data: 0.000) G_L1: 14.449 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 11.773 G_Regularizer: 0.000 validation_error: 20.399 +(epoch: 12, iters: 269728, time: 0.538, data: 0.000) G_L1: 14.787 G_L1_ABSOLUTE: 2.422 G_L1_RELATIVE: 12.365 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 12, iters: 271728, time: 0.550, data: 0.000) G_L1: 14.876 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 12.140 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 12, iters: 273728, time: 0.543, data: 0.001) G_L1: 14.125 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 11.704 G_Regularizer: 0.000 validation_error: 20.573 +(epoch: 12, iters: 275728, time: 0.542, data: 0.000) G_L1: 16.684 G_L1_ABSOLUTE: 3.195 G_L1_RELATIVE: 13.489 G_Regularizer: 0.000 validation_error: 20.960 +(epoch: 12, iters: 277728, time: 0.561, data: 0.000) G_L1: 16.461 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 13.737 G_Regularizer: 0.000 validation_error: 21.175 +(epoch: 12, iters: 279728, time: 0.537, data: 0.000) G_L1: 17.806 G_L1_ABSOLUTE: 2.990 G_L1_RELATIVE: 14.816 G_Regularizer: 0.000 validation_error: 21.170 +(epoch: 12, iters: 281728, time: 0.537, data: 0.000) G_L1: 14.342 G_L1_ABSOLUTE: 2.961 G_L1_RELATIVE: 11.381 G_Regularizer: 0.000 validation_error: 20.239 +(epoch: 12, iters: 283728, time: 0.532, data: 0.000) G_L1: 18.945 G_L1_ABSOLUTE: 2.579 G_L1_RELATIVE: 16.366 G_Regularizer: 0.000 validation_error: 20.480 +(epoch: 12, iters: 285728, time: 0.531, data: 0.000) G_L1: 12.938 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 10.281 G_Regularizer: 0.000 validation_error: 21.294 +(epoch: 12, iters: 287728, time: 0.547, data: 0.000) G_L1: 17.512 G_L1_ABSOLUTE: 2.974 G_L1_RELATIVE: 14.537 G_Regularizer: 0.000 validation_error: 20.894 +(epoch: 12, iters: 289728, time: 0.545, data: 0.000) G_L1: 16.963 G_L1_ABSOLUTE: 3.036 G_L1_RELATIVE: 13.927 G_Regularizer: 0.000 validation_error: 22.002 +(epoch: 12, iters: 291728, time: 0.540, data: 0.000) G_L1: 15.221 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 12.696 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 12, iters: 293728, time: 0.555, data: 0.001) G_L1: 14.782 G_L1_ABSOLUTE: 3.108 G_L1_RELATIVE: 11.674 G_Regularizer: 0.000 validation_error: 20.818 +(epoch: 12, iters: 295728, time: 0.554, data: 0.000) G_L1: 14.291 G_L1_ABSOLUTE: 2.499 G_L1_RELATIVE: 11.792 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 12, iters: 297728, time: 0.558, data: 0.000) G_L1: 17.145 G_L1_ABSOLUTE: 2.511 G_L1_RELATIVE: 14.634 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 12, iters: 299728, time: 0.549, data: 0.000) G_L1: 17.186 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 14.316 G_Regularizer: 0.000 validation_error: 20.579 +(epoch: 12, iters: 301728, time: 0.561, data: 0.000) G_L1: 13.919 G_L1_ABSOLUTE: 2.747 G_L1_RELATIVE: 11.172 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 13, iters: 976, time: 0.544, data: 0.000) G_L1: 16.858 G_L1_ABSOLUTE: 3.082 G_L1_RELATIVE: 13.777 G_Regularizer: 0.000 validation_error: 20.114 +(epoch: 13, iters: 2976, time: 0.562, data: 0.000) G_L1: 15.040 G_L1_ABSOLUTE: 2.553 G_L1_RELATIVE: 12.487 G_Regularizer: 0.000 validation_error: 20.042 +(epoch: 13, iters: 4976, time: 0.540, data: 0.000) G_L1: 15.250 G_L1_ABSOLUTE: 3.119 G_L1_RELATIVE: 12.131 G_Regularizer: 0.000 validation_error: 21.293 +(epoch: 13, iters: 6976, time: 0.554, data: 0.000) G_L1: 16.756 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 14.099 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 13, iters: 8976, time: 0.549, data: 0.000) G_L1: 15.237 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 12.647 G_Regularizer: 0.000 validation_error: 20.301 +(epoch: 13, iters: 10976, time: 0.550, data: 0.000) G_L1: 19.038 G_L1_ABSOLUTE: 3.167 G_L1_RELATIVE: 15.871 G_Regularizer: 0.000 validation_error: 20.470 +(epoch: 13, iters: 12976, time: 0.541, data: 0.000) G_L1: 17.155 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 14.604 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 13, iters: 14976, time: 0.549, data: 0.000) G_L1: 13.155 G_L1_ABSOLUTE: 2.163 G_L1_RELATIVE: 10.992 G_Regularizer: 0.000 validation_error: 20.383 +(epoch: 13, iters: 16976, time: 0.554, data: 0.000) G_L1: 12.324 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 9.879 G_Regularizer: 0.000 validation_error: 20.944 +(epoch: 13, iters: 18976, time: 0.555, data: 0.000) G_L1: 13.218 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 10.766 G_Regularizer: 0.000 validation_error: 21.233 +(epoch: 13, iters: 20976, time: 0.548, data: 0.000) G_L1: 15.478 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 12.588 G_Regularizer: 0.000 validation_error: 20.501 +(epoch: 13, iters: 22976, time: 0.556, data: 0.000) G_L1: 15.290 G_L1_ABSOLUTE: 3.475 G_L1_RELATIVE: 11.815 G_Regularizer: 0.000 validation_error: 21.195 +(epoch: 13, iters: 24976, time: 0.561, data: 0.000) G_L1: 13.541 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 10.744 G_Regularizer: 0.000 validation_error: 20.109 +(epoch: 13, iters: 26976, time: 0.558, data: 0.000) G_L1: 13.602 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 11.129 G_Regularizer: 0.000 validation_error: 20.068 +(epoch: 13, iters: 28976, time: 0.526, data: 0.000) G_L1: 17.087 G_L1_ABSOLUTE: 2.764 G_L1_RELATIVE: 14.323 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 13, iters: 30976, time: 0.538, data: 0.000) G_L1: 15.520 G_L1_ABSOLUTE: 3.129 G_L1_RELATIVE: 12.391 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 13, iters: 32976, time: 0.556, data: 0.000) G_L1: 17.732 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 14.986 G_Regularizer: 0.000 validation_error: 21.526 +(epoch: 13, iters: 34976, time: 0.554, data: 0.000) G_L1: 14.897 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 12.477 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 13, iters: 36976, time: 0.564, data: 0.000) G_L1: 16.279 G_L1_ABSOLUTE: 2.770 G_L1_RELATIVE: 13.509 G_Regularizer: 0.000 validation_error: 21.424 +(epoch: 13, iters: 38976, time: 0.550, data: 0.001) G_L1: 16.573 G_L1_ABSOLUTE: 3.176 G_L1_RELATIVE: 13.397 G_Regularizer: 0.000 validation_error: 20.671 +(epoch: 13, iters: 40976, time: 0.549, data: 0.000) G_L1: 15.057 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 12.468 G_Regularizer: 0.000 validation_error: 20.276 +(epoch: 13, iters: 42976, time: 0.556, data: 0.000) G_L1: 15.123 G_L1_ABSOLUTE: 2.745 G_L1_RELATIVE: 12.377 G_Regularizer: 0.000 validation_error: 20.620 +(epoch: 13, iters: 44976, time: 0.556, data: 0.000) G_L1: 16.213 G_L1_ABSOLUTE: 2.662 G_L1_RELATIVE: 13.551 G_Regularizer: 0.000 validation_error: 20.859 +(epoch: 13, iters: 46976, time: 0.538, data: 0.000) G_L1: 12.090 G_L1_ABSOLUTE: 2.532 G_L1_RELATIVE: 9.558 G_Regularizer: 0.000 validation_error: 20.435 +(epoch: 13, iters: 48976, time: 0.531, data: 0.000) G_L1: 15.107 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 12.526 G_Regularizer: 0.000 validation_error: 20.920 +(epoch: 13, iters: 50976, time: 0.547, data: 0.000) G_L1: 16.637 G_L1_ABSOLUTE: 2.963 G_L1_RELATIVE: 13.675 G_Regularizer: 0.000 validation_error: 20.904 +(epoch: 13, iters: 52976, time: 0.551, data: 0.000) G_L1: 14.541 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 12.243 G_Regularizer: 0.000 validation_error: 20.662 +(epoch: 13, iters: 54976, time: 0.544, data: 0.001) G_L1: 16.875 G_L1_ABSOLUTE: 2.448 G_L1_RELATIVE: 14.427 G_Regularizer: 0.000 validation_error: 20.852 +(epoch: 13, iters: 56976, time: 0.537, data: 0.000) G_L1: 18.710 G_L1_ABSOLUTE: 3.124 G_L1_RELATIVE: 15.586 G_Regularizer: 0.000 validation_error: 20.432 +(epoch: 13, iters: 58976, time: 0.552, data: 0.000) G_L1: 17.448 G_L1_ABSOLUTE: 3.101 G_L1_RELATIVE: 14.347 G_Regularizer: 0.000 validation_error: 21.337 +(epoch: 13, iters: 60976, time: 0.555, data: 0.000) G_L1: 13.407 G_L1_ABSOLUTE: 2.411 G_L1_RELATIVE: 10.996 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 13, iters: 62976, time: 0.550, data: 0.000) G_L1: 16.745 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 14.161 G_Regularizer: 0.000 validation_error: 20.230 +(epoch: 13, iters: 64976, time: 0.540, data: 0.000) G_L1: 14.258 G_L1_ABSOLUTE: 2.364 G_L1_RELATIVE: 11.894 G_Regularizer: 0.000 validation_error: 20.502 +(epoch: 13, iters: 66976, time: 0.530, data: 0.000) G_L1: 15.699 G_L1_ABSOLUTE: 2.821 G_L1_RELATIVE: 12.878 G_Regularizer: 0.000 validation_error: 20.006 +(epoch: 13, iters: 68976, time: 0.545, data: 0.000) G_L1: 13.681 G_L1_ABSOLUTE: 2.440 G_L1_RELATIVE: 11.241 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 13, iters: 70976, time: 0.539, data: 0.000) G_L1: 14.305 G_L1_ABSOLUTE: 2.851 G_L1_RELATIVE: 11.454 G_Regularizer: 0.000 validation_error: 20.435 +(epoch: 13, iters: 72976, time: 0.542, data: 0.000) G_L1: 13.469 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 11.050 G_Regularizer: 0.000 validation_error: 20.148 +(epoch: 13, iters: 74976, time: 0.543, data: 0.000) G_L1: 16.010 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 13.675 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 13, iters: 76976, time: 0.549, data: 0.000) G_L1: 14.021 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 11.366 G_Regularizer: 0.000 validation_error: 19.921 +(epoch: 13, iters: 78976, time: 0.554, data: 0.000) G_L1: 16.178 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 13.814 G_Regularizer: 0.000 validation_error: 20.473 +(epoch: 13, iters: 80976, time: 0.540, data: 0.000) G_L1: 14.161 G_L1_ABSOLUTE: 2.135 G_L1_RELATIVE: 12.027 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 13, iters: 82976, time: 0.539, data: 0.000) G_L1: 14.157 G_L1_ABSOLUTE: 2.843 G_L1_RELATIVE: 11.314 G_Regularizer: 0.000 validation_error: 20.412 +(epoch: 13, iters: 84976, time: 0.558, data: 0.000) G_L1: 16.261 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 13.602 G_Regularizer: 0.000 validation_error: 20.473 +(epoch: 13, iters: 86976, time: 0.555, data: 0.001) G_L1: 18.562 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 16.230 G_Regularizer: 0.000 validation_error: 21.262 +(epoch: 13, iters: 88976, time: 0.531, data: 0.000) G_L1: 13.676 G_L1_ABSOLUTE: 2.299 G_L1_RELATIVE: 11.377 G_Regularizer: 0.000 validation_error: 20.505 +(epoch: 13, iters: 90976, time: 0.552, data: 0.000) G_L1: 13.200 G_L1_ABSOLUTE: 2.987 G_L1_RELATIVE: 10.213 G_Regularizer: 0.000 validation_error: 20.622 +(epoch: 13, iters: 92976, time: 0.553, data: 0.000) G_L1: 14.376 G_L1_ABSOLUTE: 2.775 G_L1_RELATIVE: 11.601 G_Regularizer: 0.000 validation_error: 21.009 +(epoch: 13, iters: 94976, time: 0.554, data: 0.000) G_L1: 14.770 G_L1_ABSOLUTE: 2.562 G_L1_RELATIVE: 12.208 G_Regularizer: 0.000 validation_error: 20.313 +(epoch: 13, iters: 96976, time: 0.540, data: 0.000) G_L1: 15.290 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 12.922 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 13, iters: 98976, time: 0.535, data: 0.000) G_L1: 14.612 G_L1_ABSOLUTE: 2.939 G_L1_RELATIVE: 11.673 G_Regularizer: 0.000 validation_error: 20.456 +(epoch: 13, iters: 100976, time: 0.553, data: 0.000) G_L1: 17.365 G_L1_ABSOLUTE: 3.105 G_L1_RELATIVE: 14.260 G_Regularizer: 0.000 validation_error: 20.457 +(epoch: 13, iters: 102976, time: 0.551, data: 0.000) G_L1: 15.983 G_L1_ABSOLUTE: 3.114 G_L1_RELATIVE: 12.868 G_Regularizer: 0.000 validation_error: 20.552 +(epoch: 13, iters: 104976, time: 0.540, data: 0.000) G_L1: 12.562 G_L1_ABSOLUTE: 2.594 G_L1_RELATIVE: 9.968 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 13, iters: 106976, time: 0.550, data: 0.000) G_L1: 14.977 G_L1_ABSOLUTE: 2.884 G_L1_RELATIVE: 12.093 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 13, iters: 108976, time: 0.561, data: 0.001) G_L1: 15.194 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 12.597 G_Regularizer: 0.000 validation_error: 20.220 +(epoch: 13, iters: 110976, time: 0.556, data: 0.000) G_L1: 15.224 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 12.698 G_Regularizer: 0.000 validation_error: 20.510 +(epoch: 13, iters: 112976, time: 0.554, data: 0.000) G_L1: 12.690 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 10.277 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 13, iters: 114976, time: 0.542, data: 0.000) G_L1: 19.616 G_L1_ABSOLUTE: 3.087 G_L1_RELATIVE: 16.529 G_Regularizer: 0.000 validation_error: 21.171 +(epoch: 13, iters: 116976, time: 0.553, data: 0.001) G_L1: 12.822 G_L1_ABSOLUTE: 2.315 G_L1_RELATIVE: 10.508 G_Regularizer: 0.000 validation_error: 20.274 +(epoch: 13, iters: 118976, time: 0.563, data: 0.000) G_L1: 17.822 G_L1_ABSOLUTE: 2.681 G_L1_RELATIVE: 15.141 G_Regularizer: 0.000 validation_error: 22.026 +(epoch: 13, iters: 120976, time: 0.562, data: 0.000) G_L1: 13.213 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 10.664 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 13, iters: 122976, time: 0.529, data: 0.000) G_L1: 15.571 G_L1_ABSOLUTE: 3.059 G_L1_RELATIVE: 12.512 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 13, iters: 124976, time: 0.533, data: 0.000) G_L1: 18.208 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 15.800 G_Regularizer: 0.000 validation_error: 21.545 +(epoch: 13, iters: 126976, time: 0.544, data: 0.000) G_L1: 14.760 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 12.172 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 13, iters: 128976, time: 0.562, data: 0.000) G_L1: 15.249 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 12.738 G_Regularizer: 0.000 validation_error: 20.782 +(epoch: 13, iters: 130976, time: 0.531, data: 0.000) G_L1: 15.656 G_L1_ABSOLUTE: 2.593 G_L1_RELATIVE: 13.063 G_Regularizer: 0.000 validation_error: 19.508 +(epoch: 13, iters: 132976, time: 0.543, data: 0.000) G_L1: 13.916 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 11.407 G_Regularizer: 0.000 validation_error: 20.651 +(epoch: 13, iters: 134976, time: 0.555, data: 0.000) G_L1: 13.957 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 11.460 G_Regularizer: 0.000 validation_error: 20.404 +(epoch: 13, iters: 136976, time: 0.554, data: 0.000) G_L1: 15.795 G_L1_ABSOLUTE: 2.290 G_L1_RELATIVE: 13.505 G_Regularizer: 0.000 validation_error: 20.251 +(epoch: 13, iters: 138976, time: 0.547, data: 0.000) G_L1: 14.870 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 12.327 G_Regularizer: 0.000 validation_error: 20.366 +(epoch: 13, iters: 140976, time: 0.550, data: 0.000) G_L1: 18.950 G_L1_ABSOLUTE: 3.091 G_L1_RELATIVE: 15.859 G_Regularizer: 0.000 validation_error: 21.057 +(epoch: 13, iters: 142976, time: 0.561, data: 0.000) G_L1: 15.685 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 13.168 G_Regularizer: 0.000 validation_error: 20.146 +(epoch: 13, iters: 144976, time: 0.552, data: 0.000) G_L1: 12.027 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 9.689 G_Regularizer: 0.000 validation_error: 20.240 +(epoch: 13, iters: 146976, time: 0.542, data: 0.000) G_L1: 15.488 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 12.819 G_Regularizer: 0.000 validation_error: 20.413 +(epoch: 13, iters: 148976, time: 0.560, data: 0.001) G_L1: 17.268 G_L1_ABSOLUTE: 2.956 G_L1_RELATIVE: 14.312 G_Regularizer: 0.000 validation_error: 20.271 +(epoch: 13, iters: 150976, time: 0.558, data: 0.000) G_L1: 16.290 G_L1_ABSOLUTE: 2.804 G_L1_RELATIVE: 13.486 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 13, iters: 152976, time: 0.563, data: 0.000) G_L1: 15.585 G_L1_ABSOLUTE: 2.929 G_L1_RELATIVE: 12.657 G_Regularizer: 0.000 validation_error: 20.342 +(epoch: 13, iters: 154976, time: 0.548, data: 0.000) G_L1: 15.288 G_L1_ABSOLUTE: 3.083 G_L1_RELATIVE: 12.205 G_Regularizer: 0.000 validation_error: 21.066 +(epoch: 13, iters: 156976, time: 0.535, data: 0.000) G_L1: 15.031 G_L1_ABSOLUTE: 2.307 G_L1_RELATIVE: 12.724 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 13, iters: 158976, time: 0.560, data: 0.000) G_L1: 13.922 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 11.220 G_Regularizer: 0.000 validation_error: 20.862 +(epoch: 13, iters: 160976, time: 0.558, data: 0.000) G_L1: 14.386 G_L1_ABSOLUTE: 2.712 G_L1_RELATIVE: 11.674 G_Regularizer: 0.000 validation_error: 20.471 +(epoch: 13, iters: 162976, time: 0.553, data: 0.000) G_L1: 15.780 G_L1_ABSOLUTE: 2.932 G_L1_RELATIVE: 12.849 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 13, iters: 164976, time: 0.541, data: 0.000) G_L1: 17.217 G_L1_ABSOLUTE: 3.031 G_L1_RELATIVE: 14.186 G_Regularizer: 0.000 validation_error: 21.197 +(epoch: 13, iters: 166976, time: 0.532, data: 0.000) G_L1: 14.763 G_L1_ABSOLUTE: 2.820 G_L1_RELATIVE: 11.943 G_Regularizer: 0.000 validation_error: 20.441 +(epoch: 13, iters: 168976, time: 0.542, data: 0.000) G_L1: 12.840 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 10.523 G_Regularizer: 0.000 validation_error: 21.131 +(epoch: 13, iters: 170976, time: 0.532, data: 0.000) G_L1: 12.160 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 9.483 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 13, iters: 172976, time: 0.527, data: 0.000) G_L1: 15.002 G_L1_ABSOLUTE: 2.959 G_L1_RELATIVE: 12.044 G_Regularizer: 0.000 validation_error: 20.702 +(epoch: 13, iters: 174976, time: 0.544, data: 0.000) G_L1: 17.234 G_L1_ABSOLUTE: 2.703 G_L1_RELATIVE: 14.532 G_Regularizer: 0.000 validation_error: 20.904 +(epoch: 13, iters: 176976, time: 0.553, data: 0.000) G_L1: 15.465 G_L1_ABSOLUTE: 3.074 G_L1_RELATIVE: 12.391 G_Regularizer: 0.000 validation_error: 20.205 +(epoch: 13, iters: 178976, time: 0.558, data: 0.000) G_L1: 20.354 G_L1_ABSOLUTE: 3.295 G_L1_RELATIVE: 17.059 G_Regularizer: 0.000 validation_error: 20.416 +(epoch: 13, iters: 180976, time: 0.540, data: 0.000) G_L1: 13.678 G_L1_ABSOLUTE: 2.213 G_L1_RELATIVE: 11.465 G_Regularizer: 0.000 validation_error: 20.651 +(epoch: 13, iters: 182976, time: 0.548, data: 0.000) G_L1: 14.003 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 11.377 G_Regularizer: 0.000 validation_error: 20.396 +(epoch: 13, iters: 184976, time: 0.560, data: 0.000) G_L1: 13.682 G_L1_ABSOLUTE: 2.300 G_L1_RELATIVE: 11.382 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 13, iters: 186976, time: 0.558, data: 0.000) G_L1: 15.921 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 13.459 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 13, iters: 188976, time: 0.533, data: 0.000) G_L1: 14.549 G_L1_ABSOLUTE: 3.106 G_L1_RELATIVE: 11.443 G_Regularizer: 0.000 validation_error: 21.312 +(epoch: 13, iters: 190976, time: 0.549, data: 0.000) G_L1: 19.071 G_L1_ABSOLUTE: 3.293 G_L1_RELATIVE: 15.778 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 13, iters: 192976, time: 0.558, data: 0.000) G_L1: 12.730 G_L1_ABSOLUTE: 2.217 G_L1_RELATIVE: 10.513 G_Regularizer: 0.000 validation_error: 20.683 +(epoch: 13, iters: 194976, time: 0.563, data: 0.000) G_L1: 15.409 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 13.040 G_Regularizer: 0.000 validation_error: 20.395 +(epoch: 13, iters: 196976, time: 0.551, data: 0.000) G_L1: 11.787 G_L1_ABSOLUTE: 2.161 G_L1_RELATIVE: 9.626 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 13, iters: 198976, time: 0.554, data: 0.000) G_L1: 15.777 G_L1_ABSOLUTE: 3.023 G_L1_RELATIVE: 12.754 G_Regularizer: 0.000 validation_error: 20.496 +(epoch: 13, iters: 200976, time: 0.558, data: 0.000) G_L1: 15.247 G_L1_ABSOLUTE: 2.817 G_L1_RELATIVE: 12.430 G_Regularizer: 0.000 validation_error: 21.662 +(epoch: 13, iters: 202976, time: 0.555, data: 0.000) G_L1: 19.820 G_L1_ABSOLUTE: 3.537 G_L1_RELATIVE: 16.283 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 13, iters: 204976, time: 0.552, data: 0.001) G_L1: 16.276 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 13.570 G_Regularizer: 0.000 validation_error: 20.453 +(epoch: 13, iters: 206976, time: 0.535, data: 0.000) G_L1: 14.404 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 11.974 G_Regularizer: 0.000 validation_error: 21.069 +(epoch: 13, iters: 208976, time: 0.528, data: 0.000) G_L1: 15.424 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 12.644 G_Regularizer: 0.000 validation_error: 20.246 +(epoch: 13, iters: 210976, time: 0.537, data: 0.001) G_L1: 13.346 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 10.489 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 13, iters: 212976, time: 0.559, data: 0.000) G_L1: 12.006 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 9.497 G_Regularizer: 0.000 validation_error: 20.972 +(epoch: 13, iters: 214976, time: 0.530, data: 0.000) G_L1: 14.671 G_L1_ABSOLUTE: 2.196 G_L1_RELATIVE: 12.475 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 13, iters: 216976, time: 0.553, data: 0.000) G_L1: 17.917 G_L1_ABSOLUTE: 3.110 G_L1_RELATIVE: 14.807 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 13, iters: 218976, time: 0.547, data: 0.000) G_L1: 15.271 G_L1_ABSOLUTE: 3.704 G_L1_RELATIVE: 11.568 G_Regularizer: 0.000 validation_error: 20.354 +(epoch: 13, iters: 220976, time: 0.561, data: 0.000) G_L1: 14.334 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 11.999 G_Regularizer: 0.000 validation_error: 21.103 +(epoch: 13, iters: 222976, time: 0.535, data: 0.000) G_L1: 15.187 G_L1_ABSOLUTE: 3.125 G_L1_RELATIVE: 12.062 G_Regularizer: 0.000 validation_error: 20.728 +(epoch: 13, iters: 224976, time: 0.559, data: 0.000) G_L1: 16.925 G_L1_ABSOLUTE: 3.393 G_L1_RELATIVE: 13.532 G_Regularizer: 0.000 validation_error: 20.367 +(epoch: 13, iters: 226976, time: 0.553, data: 0.000) G_L1: 15.152 G_L1_ABSOLUTE: 2.709 G_L1_RELATIVE: 12.443 G_Regularizer: 0.000 validation_error: 20.564 +(epoch: 13, iters: 228976, time: 0.551, data: 0.000) G_L1: 14.851 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 12.040 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 13, iters: 230976, time: 0.540, data: 0.000) G_L1: 19.207 G_L1_ABSOLUTE: 3.127 G_L1_RELATIVE: 16.080 G_Regularizer: 0.000 validation_error: 20.479 +(epoch: 13, iters: 232976, time: 0.544, data: 0.000) G_L1: 15.016 G_L1_ABSOLUTE: 2.878 G_L1_RELATIVE: 12.138 G_Regularizer: 0.000 validation_error: 20.984 +(epoch: 13, iters: 234976, time: 0.552, data: 0.001) G_L1: 14.377 G_L1_ABSOLUTE: 2.834 G_L1_RELATIVE: 11.543 G_Regularizer: 0.000 validation_error: 21.381 +(epoch: 13, iters: 236976, time: 0.552, data: 0.000) G_L1: 14.846 G_L1_ABSOLUTE: 2.729 G_L1_RELATIVE: 12.117 G_Regularizer: 0.000 validation_error: 20.464 +(epoch: 13, iters: 238976, time: 0.559, data: 0.000) G_L1: 15.752 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 13.303 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 13, iters: 240976, time: 0.552, data: 0.000) G_L1: 16.469 G_L1_ABSOLUTE: 3.031 G_L1_RELATIVE: 13.438 G_Regularizer: 0.000 validation_error: 20.025 +(epoch: 13, iters: 242976, time: 0.550, data: 0.000) G_L1: 15.704 G_L1_ABSOLUTE: 2.309 G_L1_RELATIVE: 13.395 G_Regularizer: 0.000 validation_error: 20.190 +(epoch: 13, iters: 244976, time: 0.555, data: 0.000) G_L1: 14.215 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 11.757 G_Regularizer: 0.000 validation_error: 20.452 +(epoch: 13, iters: 246976, time: 0.560, data: 0.000) G_L1: 14.174 G_L1_ABSOLUTE: 3.193 G_L1_RELATIVE: 10.982 G_Regularizer: 0.000 validation_error: 20.322 +(epoch: 13, iters: 248976, time: 0.542, data: 0.000) G_L1: 14.630 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 11.641 G_Regularizer: 0.000 validation_error: 20.240 +(epoch: 13, iters: 250976, time: 0.547, data: 0.001) G_L1: 16.934 G_L1_ABSOLUTE: 2.794 G_L1_RELATIVE: 14.139 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 13, iters: 252976, time: 0.555, data: 0.000) G_L1: 17.732 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 15.401 G_Regularizer: 0.000 validation_error: 20.559 +(epoch: 13, iters: 254976, time: 0.565, data: 0.000) G_L1: 16.282 G_L1_ABSOLUTE: 2.964 G_L1_RELATIVE: 13.317 G_Regularizer: 0.000 validation_error: 20.568 +(epoch: 13, iters: 256976, time: 0.535, data: 0.000) G_L1: 15.360 G_L1_ABSOLUTE: 3.134 G_L1_RELATIVE: 12.226 G_Regularizer: 0.000 validation_error: 20.449 +(epoch: 13, iters: 258976, time: 0.531, data: 0.000) G_L1: 16.790 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 14.153 G_Regularizer: 0.000 validation_error: 20.742 +(epoch: 13, iters: 260976, time: 0.558, data: 0.001) G_L1: 13.683 G_L1_ABSOLUTE: 2.579 G_L1_RELATIVE: 11.105 G_Regularizer: 0.000 validation_error: 20.139 +(epoch: 13, iters: 262976, time: 0.557, data: 0.000) G_L1: 17.135 G_L1_ABSOLUTE: 2.128 G_L1_RELATIVE: 15.007 G_Regularizer: 0.000 validation_error: 20.347 +(epoch: 13, iters: 264976, time: 0.543, data: 0.000) G_L1: 18.879 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 16.203 G_Regularizer: 0.000 validation_error: 21.254 +(epoch: 13, iters: 266976, time: 0.534, data: 0.000) G_L1: 14.312 G_L1_ABSOLUTE: 2.873 G_L1_RELATIVE: 11.438 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 13, iters: 268976, time: 0.547, data: 0.001) G_L1: 17.528 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 14.792 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 13, iters: 270976, time: 0.557, data: 0.000) G_L1: 15.771 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 13.170 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 13, iters: 272976, time: 0.548, data: 0.000) G_L1: 17.449 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 14.829 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 13, iters: 274976, time: 0.552, data: 0.000) G_L1: 16.237 G_L1_ABSOLUTE: 2.641 G_L1_RELATIVE: 13.596 G_Regularizer: 0.000 validation_error: 20.163 +(epoch: 13, iters: 276976, time: 0.562, data: 0.000) G_L1: 16.604 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 13.963 G_Regularizer: 0.000 validation_error: 20.237 +(epoch: 13, iters: 278976, time: 0.549, data: 0.000) G_L1: 16.955 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 14.341 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 13, iters: 280976, time: 0.542, data: 0.000) G_L1: 16.656 G_L1_ABSOLUTE: 2.816 G_L1_RELATIVE: 13.839 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 13, iters: 282976, time: 0.533, data: 0.000) G_L1: 15.946 G_L1_ABSOLUTE: 2.557 G_L1_RELATIVE: 13.389 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 13, iters: 284976, time: 0.557, data: 0.000) G_L1: 16.758 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 14.129 G_Regularizer: 0.000 validation_error: 20.746 +(epoch: 13, iters: 286976, time: 0.549, data: 0.000) G_L1: 15.261 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 12.630 G_Regularizer: 0.000 validation_error: 21.089 +(epoch: 13, iters: 288976, time: 0.563, data: 0.000) G_L1: 13.659 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 10.980 G_Regularizer: 0.000 validation_error: 20.469 +(epoch: 13, iters: 290976, time: 0.531, data: 0.000) G_L1: 13.772 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 11.171 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 13, iters: 292976, time: 0.549, data: 0.000) G_L1: 12.840 G_L1_ABSOLUTE: 2.326 G_L1_RELATIVE: 10.514 G_Regularizer: 0.000 validation_error: 20.628 +(epoch: 13, iters: 294976, time: 0.554, data: 0.000) G_L1: 15.227 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 12.543 G_Regularizer: 0.000 validation_error: 21.275 +(epoch: 13, iters: 296976, time: 0.553, data: 0.000) G_L1: 13.760 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 11.132 G_Regularizer: 0.000 validation_error: 20.595 +(epoch: 13, iters: 298976, time: 0.532, data: 0.001) G_L1: 13.696 G_L1_ABSOLUTE: 2.295 G_L1_RELATIVE: 11.401 G_Regularizer: 0.000 validation_error: 20.447 +(epoch: 13, iters: 300976, time: 0.538, data: 0.000) G_L1: 17.344 G_L1_ABSOLUTE: 3.016 G_L1_RELATIVE: 14.327 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 14, iters: 224, time: 0.532, data: 0.000) G_L1: 13.684 G_L1_ABSOLUTE: 2.511 G_L1_RELATIVE: 11.173 G_Regularizer: 0.000 validation_error: 20.417 +(epoch: 14, iters: 2224, time: 0.561, data: 0.000) G_L1: 14.627 G_L1_ABSOLUTE: 2.888 G_L1_RELATIVE: 11.739 G_Regularizer: 0.000 validation_error: 21.105 +(epoch: 14, iters: 4224, time: 0.542, data: 0.000) G_L1: 13.497 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 11.154 G_Regularizer: 0.000 validation_error: 20.458 +(epoch: 14, iters: 6224, time: 0.535, data: 0.000) G_L1: 13.816 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 11.157 G_Regularizer: 0.000 validation_error: 20.100 +(epoch: 14, iters: 8224, time: 0.558, data: 0.000) G_L1: 15.192 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 12.542 G_Regularizer: 0.000 validation_error: 21.564 +(epoch: 14, iters: 10224, time: 0.551, data: 0.000) G_L1: 15.258 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 12.640 G_Regularizer: 0.000 validation_error: 20.359 +(epoch: 14, iters: 12224, time: 0.551, data: 0.000) G_L1: 14.358 G_L1_ABSOLUTE: 2.842 G_L1_RELATIVE: 11.516 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 14, iters: 14224, time: 0.536, data: 0.000) G_L1: 13.070 G_L1_ABSOLUTE: 2.475 G_L1_RELATIVE: 10.595 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 14, iters: 16224, time: 0.561, data: 0.001) G_L1: 16.479 G_L1_ABSOLUTE: 2.830 G_L1_RELATIVE: 13.649 G_Regularizer: 0.000 validation_error: 20.582 +(epoch: 14, iters: 18224, time: 0.565, data: 0.000) G_L1: 16.760 G_L1_ABSOLUTE: 3.008 G_L1_RELATIVE: 13.753 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 14, iters: 20224, time: 0.548, data: 0.000) G_L1: 18.038 G_L1_ABSOLUTE: 3.077 G_L1_RELATIVE: 14.961 G_Regularizer: 0.000 validation_error: 20.699 +(epoch: 14, iters: 22224, time: 0.543, data: 0.000) G_L1: 15.946 G_L1_ABSOLUTE: 2.244 G_L1_RELATIVE: 13.703 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 14, iters: 24224, time: 0.554, data: 0.001) G_L1: 15.020 G_L1_ABSOLUTE: 2.939 G_L1_RELATIVE: 12.081 G_Regularizer: 0.000 validation_error: 21.037 +(epoch: 14, iters: 26224, time: 0.534, data: 0.000) G_L1: 14.694 G_L1_ABSOLUTE: 2.695 G_L1_RELATIVE: 12.000 G_Regularizer: 0.000 validation_error: 20.377 +(epoch: 14, iters: 28224, time: 0.561, data: 0.000) G_L1: 19.784 G_L1_ABSOLUTE: 3.016 G_L1_RELATIVE: 16.768 G_Regularizer: 0.000 validation_error: 20.063 +(epoch: 14, iters: 30224, time: 0.530, data: 0.000) G_L1: 14.794 G_L1_ABSOLUTE: 2.327 G_L1_RELATIVE: 12.467 G_Regularizer: 0.000 validation_error: 20.854 +(epoch: 14, iters: 32224, time: 0.549, data: 0.000) G_L1: 15.858 G_L1_ABSOLUTE: 2.976 G_L1_RELATIVE: 12.881 G_Regularizer: 0.000 validation_error: 20.735 +(epoch: 14, iters: 34224, time: 0.556, data: 0.000) G_L1: 14.560 G_L1_ABSOLUTE: 2.815 G_L1_RELATIVE: 11.745 G_Regularizer: 0.000 validation_error: 20.597 +(epoch: 14, iters: 36224, time: 0.559, data: 0.000) G_L1: 14.073 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 11.628 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 14, iters: 38224, time: 0.541, data: 0.000) G_L1: 18.371 G_L1_ABSOLUTE: 3.390 G_L1_RELATIVE: 14.981 G_Regularizer: 0.000 validation_error: 20.252 +(epoch: 14, iters: 40224, time: 0.562, data: 0.000) G_L1: 16.922 G_L1_ABSOLUTE: 2.779 G_L1_RELATIVE: 14.143 G_Regularizer: 0.000 validation_error: 21.084 +(epoch: 14, iters: 42224, time: 0.557, data: 0.000) G_L1: 16.187 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 13.634 G_Regularizer: 0.000 validation_error: 20.217 +(epoch: 14, iters: 44224, time: 0.560, data: 0.000) G_L1: 14.905 G_L1_ABSOLUTE: 3.128 G_L1_RELATIVE: 11.777 G_Regularizer: 0.000 validation_error: 21.246 +(epoch: 14, iters: 46224, time: 0.537, data: 0.000) G_L1: 30.416 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 27.830 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 14, iters: 48224, time: 0.558, data: 0.000) G_L1: 14.523 G_L1_ABSOLUTE: 2.675 G_L1_RELATIVE: 11.848 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 14, iters: 50224, time: 0.556, data: 0.000) G_L1: 13.315 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 10.974 G_Regularizer: 0.000 validation_error: 20.577 +(epoch: 14, iters: 52224, time: 0.559, data: 0.000) G_L1: 15.785 G_L1_ABSOLUTE: 2.425 G_L1_RELATIVE: 13.360 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 14, iters: 54224, time: 0.544, data: 0.000) G_L1: 17.110 G_L1_ABSOLUTE: 3.062 G_L1_RELATIVE: 14.048 G_Regularizer: 0.000 validation_error: 21.070 +(epoch: 14, iters: 56224, time: 0.552, data: 0.000) G_L1: 14.542 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 12.032 G_Regularizer: 0.000 validation_error: 21.195 +(epoch: 14, iters: 58224, time: 0.562, data: 0.000) G_L1: 15.618 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 12.885 G_Regularizer: 0.000 validation_error: 21.401 +(epoch: 14, iters: 60224, time: 0.552, data: 0.000) G_L1: 14.941 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 12.131 G_Regularizer: 0.000 validation_error: 20.571 +(epoch: 14, iters: 62224, time: 0.549, data: 0.001) G_L1: 15.085 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 12.516 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 14, iters: 64224, time: 0.532, data: 0.000) G_L1: 12.951 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 10.490 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 14, iters: 66224, time: 0.546, data: 0.000) G_L1: 15.685 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 13.121 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 14, iters: 68224, time: 0.557, data: 0.000) G_L1: 14.376 G_L1_ABSOLUTE: 2.423 G_L1_RELATIVE: 11.954 G_Regularizer: 0.000 validation_error: 21.306 +(epoch: 14, iters: 70224, time: 0.567, data: 0.000) G_L1: 14.034 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 11.328 G_Regularizer: 0.000 validation_error: 20.702 +(epoch: 14, iters: 72224, time: 0.528, data: 0.000) G_L1: 12.247 G_L1_ABSOLUTE: 2.203 G_L1_RELATIVE: 10.043 G_Regularizer: 0.000 validation_error: 20.385 +(epoch: 14, iters: 74224, time: 0.531, data: 0.000) G_L1: 15.744 G_L1_ABSOLUTE: 2.821 G_L1_RELATIVE: 12.923 G_Regularizer: 0.000 validation_error: 21.137 +(epoch: 14, iters: 76224, time: 0.554, data: 0.000) G_L1: 16.642 G_L1_ABSOLUTE: 3.066 G_L1_RELATIVE: 13.575 G_Regularizer: 0.000 validation_error: 20.893 +(epoch: 14, iters: 78224, time: 0.558, data: 0.000) G_L1: 15.446 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 12.712 G_Regularizer: 0.000 validation_error: 21.316 +(epoch: 14, iters: 80224, time: 0.533, data: 0.000) G_L1: 11.685 G_L1_ABSOLUTE: 2.253 G_L1_RELATIVE: 9.432 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 14, iters: 82224, time: 0.536, data: 0.000) G_L1: 14.890 G_L1_ABSOLUTE: 2.411 G_L1_RELATIVE: 12.479 G_Regularizer: 0.000 validation_error: 20.565 +(epoch: 14, iters: 84224, time: 0.548, data: 0.000) G_L1: 13.770 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 11.377 G_Regularizer: 0.000 validation_error: 20.631 +(epoch: 14, iters: 86224, time: 0.556, data: 0.000) G_L1: 17.046 G_L1_ABSOLUTE: 3.220 G_L1_RELATIVE: 13.827 G_Regularizer: 0.000 validation_error: 20.874 +(epoch: 14, iters: 88224, time: 0.541, data: 0.000) G_L1: 14.803 G_L1_ABSOLUTE: 2.441 G_L1_RELATIVE: 12.362 G_Regularizer: 0.000 validation_error: 21.492 +(epoch: 14, iters: 90224, time: 0.549, data: 0.000) G_L1: 16.064 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 13.283 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 14, iters: 92224, time: 0.545, data: 0.000) G_L1: 15.302 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 12.860 G_Regularizer: 0.000 validation_error: 20.288 +(epoch: 14, iters: 94224, time: 0.535, data: 0.000) G_L1: 15.026 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 12.252 G_Regularizer: 0.000 validation_error: 20.693 +(epoch: 14, iters: 96224, time: 0.550, data: 0.000) G_L1: 16.932 G_L1_ABSOLUTE: 2.935 G_L1_RELATIVE: 13.996 G_Regularizer: 0.000 validation_error: 20.729 +(epoch: 14, iters: 98224, time: 0.541, data: 0.000) G_L1: 16.517 G_L1_ABSOLUTE: 2.263 G_L1_RELATIVE: 14.254 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 14, iters: 100224, time: 0.558, data: 0.000) G_L1: 14.135 G_L1_ABSOLUTE: 2.953 G_L1_RELATIVE: 11.182 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 14, iters: 102224, time: 0.554, data: 0.001) G_L1: 14.996 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 12.174 G_Regularizer: 0.000 validation_error: 19.977 +(epoch: 14, iters: 104224, time: 0.562, data: 0.000) G_L1: 14.915 G_L1_ABSOLUTE: 2.490 G_L1_RELATIVE: 12.426 G_Regularizer: 0.000 validation_error: 20.161 +(epoch: 14, iters: 106224, time: 0.532, data: 0.000) G_L1: 16.644 G_L1_ABSOLUTE: 2.306 G_L1_RELATIVE: 14.338 G_Regularizer: 0.000 validation_error: 20.285 +(epoch: 14, iters: 108224, time: 0.528, data: 0.000) G_L1: 15.151 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 12.580 G_Regularizer: 0.000 validation_error: 20.085 +(epoch: 14, iters: 110224, time: 0.546, data: 0.000) G_L1: 14.887 G_L1_ABSOLUTE: 2.783 G_L1_RELATIVE: 12.104 G_Regularizer: 0.000 validation_error: 20.357 +(epoch: 14, iters: 112224, time: 0.547, data: 0.001) G_L1: 15.276 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 12.800 G_Regularizer: 0.000 validation_error: 20.650 +(epoch: 14, iters: 114224, time: 0.535, data: 0.000) G_L1: 13.229 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 10.711 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 14, iters: 116224, time: 0.559, data: 0.000) G_L1: 15.714 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 13.221 G_Regularizer: 0.000 validation_error: 20.663 +(epoch: 14, iters: 118224, time: 0.560, data: 0.000) G_L1: 16.992 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 14.603 G_Regularizer: 0.000 validation_error: 21.071 +(epoch: 14, iters: 120224, time: 0.556, data: 0.000) G_L1: 15.779 G_L1_ABSOLUTE: 2.608 G_L1_RELATIVE: 13.171 G_Regularizer: 0.000 validation_error: 20.491 +(epoch: 14, iters: 122224, time: 0.533, data: 0.001) G_L1: 14.952 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 12.320 G_Regularizer: 0.000 validation_error: 20.212 +(epoch: 14, iters: 124224, time: 0.552, data: 0.000) G_L1: 22.967 G_L1_ABSOLUTE: 2.943 G_L1_RELATIVE: 20.024 G_Regularizer: 0.000 validation_error: 21.244 +(epoch: 14, iters: 126224, time: 0.563, data: 0.000) G_L1: 18.744 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 16.011 G_Regularizer: 0.000 validation_error: 20.537 +(epoch: 14, iters: 128224, time: 0.551, data: 0.001) G_L1: 15.218 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 12.627 G_Regularizer: 0.000 validation_error: 20.076 +(epoch: 14, iters: 130224, time: 0.549, data: 0.001) G_L1: 14.472 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 11.766 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 14, iters: 132224, time: 0.551, data: 0.000) G_L1: 16.892 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 14.324 G_Regularizer: 0.000 validation_error: 20.415 +(epoch: 14, iters: 134224, time: 0.537, data: 0.000) G_L1: 16.180 G_L1_ABSOLUTE: 2.825 G_L1_RELATIVE: 13.356 G_Regularizer: 0.000 validation_error: 20.552 +(epoch: 14, iters: 136224, time: 0.560, data: 0.000) G_L1: 12.685 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 10.114 G_Regularizer: 0.000 validation_error: 20.090 +(epoch: 14, iters: 138224, time: 0.547, data: 0.000) G_L1: 18.553 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 16.033 G_Regularizer: 0.000 validation_error: 19.844 +(epoch: 14, iters: 140224, time: 0.533, data: 0.000) G_L1: 13.461 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 10.940 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 14, iters: 142224, time: 0.553, data: 0.000) G_L1: 14.492 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 12.032 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 14, iters: 144224, time: 0.561, data: 0.000) G_L1: 15.609 G_L1_ABSOLUTE: 2.818 G_L1_RELATIVE: 12.791 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 14, iters: 146224, time: 0.556, data: 0.000) G_L1: 30.928 G_L1_ABSOLUTE: 2.967 G_L1_RELATIVE: 27.962 G_Regularizer: 0.000 validation_error: 21.021 +(epoch: 14, iters: 148224, time: 0.555, data: 0.000) G_L1: 12.491 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 10.093 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 14, iters: 150224, time: 0.559, data: 0.000) G_L1: 15.139 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 12.656 G_Regularizer: 0.000 validation_error: 20.500 +(epoch: 14, iters: 152224, time: 0.560, data: 0.000) G_L1: 16.724 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 14.177 G_Regularizer: 0.000 validation_error: 20.225 +(epoch: 14, iters: 154224, time: 0.553, data: 0.000) G_L1: 15.024 G_L1_ABSOLUTE: 2.277 G_L1_RELATIVE: 12.746 G_Regularizer: 0.000 validation_error: 21.184 +(epoch: 14, iters: 156224, time: 0.534, data: 0.000) G_L1: 16.824 G_L1_ABSOLUTE: 2.901 G_L1_RELATIVE: 13.923 G_Regularizer: 0.000 validation_error: 20.680 +(epoch: 14, iters: 158224, time: 0.541, data: 0.000) G_L1: 12.779 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 10.297 G_Regularizer: 0.000 validation_error: 20.394 +(epoch: 14, iters: 160224, time: 0.535, data: 0.000) G_L1: 14.315 G_L1_ABSOLUTE: 3.194 G_L1_RELATIVE: 11.121 G_Regularizer: 0.000 validation_error: 21.799 +(epoch: 14, iters: 162224, time: 0.555, data: 0.000) G_L1: 14.730 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 12.247 G_Regularizer: 0.000 validation_error: 20.036 +(epoch: 14, iters: 164224, time: 0.541, data: 0.000) G_L1: 16.525 G_L1_ABSOLUTE: 2.807 G_L1_RELATIVE: 13.718 G_Regularizer: 0.000 validation_error: 20.573 +(epoch: 14, iters: 166224, time: 0.556, data: 0.000) G_L1: 12.857 G_L1_ABSOLUTE: 3.065 G_L1_RELATIVE: 9.792 G_Regularizer: 0.000 validation_error: 20.506 +(epoch: 14, iters: 168224, time: 0.533, data: 0.000) G_L1: 16.778 G_L1_ABSOLUTE: 2.534 G_L1_RELATIVE: 14.244 G_Regularizer: 0.000 validation_error: 20.100 +(epoch: 14, iters: 170224, time: 0.533, data: 0.000) G_L1: 14.420 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 11.606 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 14, iters: 172224, time: 0.539, data: 0.000) G_L1: 17.305 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 14.570 G_Regularizer: 0.000 validation_error: 21.149 +(epoch: 14, iters: 174224, time: 0.550, data: 0.000) G_L1: 15.405 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 12.844 G_Regularizer: 0.000 validation_error: 21.260 +(epoch: 14, iters: 176224, time: 0.548, data: 0.000) G_L1: 13.658 G_L1_ABSOLUTE: 2.602 G_L1_RELATIVE: 11.056 G_Regularizer: 0.000 validation_error: 21.290 +(epoch: 14, iters: 178224, time: 0.562, data: 0.000) G_L1: 13.634 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 11.277 G_Regularizer: 0.000 validation_error: 20.202 +(epoch: 14, iters: 180224, time: 0.543, data: 0.000) G_L1: 13.252 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 10.824 G_Regularizer: 0.000 validation_error: 20.100 +(epoch: 14, iters: 182224, time: 0.547, data: 0.000) G_L1: 15.642 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 12.908 G_Regularizer: 0.000 validation_error: 20.310 +(epoch: 14, iters: 184224, time: 0.557, data: 0.000) G_L1: 13.468 G_L1_ABSOLUTE: 2.949 G_L1_RELATIVE: 10.518 G_Regularizer: 0.000 validation_error: 21.233 +(epoch: 14, iters: 186224, time: 0.542, data: 0.001) G_L1: 16.542 G_L1_ABSOLUTE: 2.826 G_L1_RELATIVE: 13.716 G_Regularizer: 0.000 validation_error: 21.418 +(epoch: 14, iters: 188224, time: 0.557, data: 0.000) G_L1: 17.850 G_L1_ABSOLUTE: 2.817 G_L1_RELATIVE: 15.033 G_Regularizer: 0.000 validation_error: 20.894 +(epoch: 14, iters: 190224, time: 0.531, data: 0.000) G_L1: 12.831 G_L1_ABSOLUTE: 2.313 G_L1_RELATIVE: 10.518 G_Regularizer: 0.000 validation_error: 21.340 +(epoch: 14, iters: 192224, time: 0.557, data: 0.000) G_L1: 15.403 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 12.719 G_Regularizer: 0.000 validation_error: 20.593 +(epoch: 14, iters: 194224, time: 0.560, data: 0.000) G_L1: 17.601 G_L1_ABSOLUTE: 2.794 G_L1_RELATIVE: 14.807 G_Regularizer: 0.000 validation_error: 20.466 +(epoch: 14, iters: 196224, time: 0.556, data: 0.000) G_L1: 15.566 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 12.804 G_Regularizer: 0.000 validation_error: 20.077 +(epoch: 14, iters: 198224, time: 0.530, data: 0.000) G_L1: 15.816 G_L1_ABSOLUTE: 2.575 G_L1_RELATIVE: 13.241 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 14, iters: 200224, time: 0.551, data: 0.000) G_L1: 15.988 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 13.418 G_Regularizer: 0.000 validation_error: 20.469 +(epoch: 14, iters: 202224, time: 0.544, data: 0.000) G_L1: 16.594 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 13.656 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 14, iters: 204224, time: 0.533, data: 0.001) G_L1: 15.443 G_L1_ABSOLUTE: 2.807 G_L1_RELATIVE: 12.636 G_Regularizer: 0.000 validation_error: 21.076 +(epoch: 14, iters: 206224, time: 0.540, data: 0.000) G_L1: 14.741 G_L1_ABSOLUTE: 2.751 G_L1_RELATIVE: 11.991 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 14, iters: 208224, time: 0.531, data: 0.000) G_L1: 16.643 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 13.763 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 14, iters: 210224, time: 0.534, data: 0.000) G_L1: 13.667 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 10.983 G_Regularizer: 0.000 validation_error: 20.226 +(epoch: 14, iters: 212224, time: 0.548, data: 0.000) G_L1: 13.905 G_L1_ABSOLUTE: 2.507 G_L1_RELATIVE: 11.398 G_Regularizer: 0.000 validation_error: 20.373 +(epoch: 14, iters: 214224, time: 0.553, data: 0.000) G_L1: 13.585 G_L1_ABSOLUTE: 2.374 G_L1_RELATIVE: 11.211 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 14, iters: 216224, time: 0.547, data: 0.000) G_L1: 15.356 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 12.955 G_Regularizer: 0.000 validation_error: 21.687 +(epoch: 14, iters: 218224, time: 0.556, data: 0.000) G_L1: 14.291 G_L1_ABSOLUTE: 2.376 G_L1_RELATIVE: 11.915 G_Regularizer: 0.000 validation_error: 21.087 +(epoch: 14, iters: 220224, time: 0.549, data: 0.000) G_L1: 17.474 G_L1_ABSOLUTE: 3.008 G_L1_RELATIVE: 14.466 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 14, iters: 222224, time: 0.548, data: 0.000) G_L1: 14.063 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 11.413 G_Regularizer: 0.000 validation_error: 20.204 +(epoch: 14, iters: 224224, time: 0.532, data: 0.000) G_L1: 13.770 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 11.423 G_Regularizer: 0.000 validation_error: 21.275 +(epoch: 14, iters: 226224, time: 0.565, data: 0.000) G_L1: 18.916 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 16.296 G_Regularizer: 0.000 validation_error: 20.632 +(epoch: 14, iters: 228224, time: 0.562, data: 0.000) G_L1: 16.020 G_L1_ABSOLUTE: 2.913 G_L1_RELATIVE: 13.106 G_Regularizer: 0.000 validation_error: 20.003 +(epoch: 14, iters: 230224, time: 0.542, data: 0.000) G_L1: 14.386 G_L1_ABSOLUTE: 3.006 G_L1_RELATIVE: 11.380 G_Regularizer: 0.000 validation_error: 20.365 +(epoch: 14, iters: 232224, time: 0.537, data: 0.000) G_L1: 17.274 G_L1_ABSOLUTE: 3.228 G_L1_RELATIVE: 14.045 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 14, iters: 234224, time: 0.551, data: 0.000) G_L1: 14.806 G_L1_ABSOLUTE: 2.438 G_L1_RELATIVE: 12.368 G_Regularizer: 0.000 validation_error: 20.425 +(epoch: 14, iters: 236224, time: 0.537, data: 0.000) G_L1: 13.294 G_L1_ABSOLUTE: 2.250 G_L1_RELATIVE: 11.044 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 14, iters: 238224, time: 0.533, data: 0.000) G_L1: 14.870 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 12.065 G_Regularizer: 0.000 validation_error: 20.802 +(epoch: 14, iters: 240224, time: 0.529, data: 0.000) G_L1: 15.175 G_L1_ABSOLUTE: 2.376 G_L1_RELATIVE: 12.799 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 14, iters: 242224, time: 0.560, data: 0.000) G_L1: 11.894 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 9.488 G_Regularizer: 0.000 validation_error: 21.107 +(epoch: 14, iters: 244224, time: 0.557, data: 0.000) G_L1: 15.985 G_L1_ABSOLUTE: 2.472 G_L1_RELATIVE: 13.513 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 14, iters: 246224, time: 0.558, data: 0.000) G_L1: 14.717 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 12.125 G_Regularizer: 0.000 validation_error: 20.559 +(epoch: 14, iters: 248224, time: 0.544, data: 0.000) G_L1: 12.281 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 10.032 G_Regularizer: 0.000 validation_error: 20.250 +(epoch: 14, iters: 250224, time: 0.537, data: 0.000) G_L1: 16.302 G_L1_ABSOLUTE: 3.177 G_L1_RELATIVE: 13.124 G_Regularizer: 0.000 validation_error: 20.457 +(epoch: 14, iters: 252224, time: 0.548, data: 0.000) G_L1: 14.173 G_L1_ABSOLUTE: 3.021 G_L1_RELATIVE: 11.152 G_Regularizer: 0.000 validation_error: 20.010 +(epoch: 14, iters: 254224, time: 0.559, data: 0.000) G_L1: 15.591 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 13.098 G_Regularizer: 0.000 validation_error: 20.111 +(epoch: 14, iters: 256224, time: 0.554, data: 0.000) G_L1: 14.896 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 12.298 G_Regularizer: 0.000 validation_error: 20.202 +(epoch: 14, iters: 258224, time: 0.530, data: 0.000) G_L1: 15.855 G_L1_ABSOLUTE: 2.605 G_L1_RELATIVE: 13.249 G_Regularizer: 0.000 validation_error: 20.258 +(epoch: 14, iters: 260224, time: 0.547, data: 0.000) G_L1: 16.619 G_L1_ABSOLUTE: 2.965 G_L1_RELATIVE: 13.654 G_Regularizer: 0.000 validation_error: 20.707 +(epoch: 14, iters: 262224, time: 0.551, data: 0.000) G_L1: 14.176 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 11.462 G_Regularizer: 0.000 validation_error: 20.537 +(epoch: 14, iters: 264224, time: 0.545, data: 0.000) G_L1: 15.293 G_L1_ABSOLUTE: 2.771 G_L1_RELATIVE: 12.523 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 14, iters: 266224, time: 0.535, data: 0.000) G_L1: 16.441 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 13.791 G_Regularizer: 0.000 validation_error: 21.230 +(epoch: 14, iters: 268224, time: 0.558, data: 0.000) G_L1: 16.647 G_L1_ABSOLUTE: 3.582 G_L1_RELATIVE: 13.064 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 14, iters: 270224, time: 0.524, data: 0.000) G_L1: 14.827 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 12.060 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 14, iters: 272224, time: 0.554, data: 0.000) G_L1: 14.202 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 11.345 G_Regularizer: 0.000 validation_error: 20.892 +(epoch: 14, iters: 274224, time: 0.534, data: 0.001) G_L1: 17.657 G_L1_ABSOLUTE: 3.009 G_L1_RELATIVE: 14.648 G_Regularizer: 0.000 validation_error: 21.150 +(epoch: 14, iters: 276224, time: 0.534, data: 0.000) G_L1: 15.536 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 12.758 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 14, iters: 278224, time: 0.542, data: 0.000) G_L1: 15.977 G_L1_ABSOLUTE: 3.250 G_L1_RELATIVE: 12.727 G_Regularizer: 0.000 validation_error: 21.009 +(epoch: 14, iters: 280224, time: 0.544, data: 0.001) G_L1: 18.644 G_L1_ABSOLUTE: 2.893 G_L1_RELATIVE: 15.751 G_Regularizer: 0.000 validation_error: 20.105 +(epoch: 14, iters: 282224, time: 0.545, data: 0.000) G_L1: 13.348 G_L1_ABSOLUTE: 2.318 G_L1_RELATIVE: 11.030 G_Regularizer: 0.000 validation_error: 20.538 +(epoch: 14, iters: 284224, time: 0.553, data: 0.001) G_L1: 14.323 G_L1_ABSOLUTE: 2.296 G_L1_RELATIVE: 12.027 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 14, iters: 286224, time: 0.554, data: 0.000) G_L1: 13.136 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 10.527 G_Regularizer: 0.000 validation_error: 21.321 +(epoch: 14, iters: 288224, time: 0.556, data: 0.000) G_L1: 12.455 G_L1_ABSOLUTE: 2.156 G_L1_RELATIVE: 10.299 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 14, iters: 290224, time: 0.551, data: 0.001) G_L1: 15.343 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 12.753 G_Regularizer: 0.000 validation_error: 20.200 +(epoch: 14, iters: 292224, time: 0.534, data: 0.000) G_L1: 16.609 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 14.120 G_Regularizer: 0.000 validation_error: 21.462 +(epoch: 14, iters: 294224, time: 0.533, data: 0.000) G_L1: 13.999 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 11.668 G_Regularizer: 0.000 validation_error: 20.362 +(epoch: 14, iters: 296224, time: 0.551, data: 0.000) G_L1: 14.865 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 12.311 G_Regularizer: 0.000 validation_error: 20.525 +(epoch: 14, iters: 298224, time: 0.555, data: 0.000) G_L1: 17.743 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 14.907 G_Regularizer: 0.000 validation_error: 20.222 +(epoch: 14, iters: 300224, time: 0.535, data: 0.000) G_L1: 15.517 G_L1_ABSOLUTE: 2.126 G_L1_RELATIVE: 13.392 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 14, iters: 302224, time: 0.553, data: 0.000) G_L1: 14.543 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 11.923 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 15, iters: 1472, time: 0.553, data: 0.000) G_L1: 15.186 G_L1_ABSOLUTE: 2.562 G_L1_RELATIVE: 12.624 G_Regularizer: 0.000 validation_error: 20.566 +(epoch: 15, iters: 3472, time: 0.548, data: 0.001) G_L1: 13.347 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 10.721 G_Regularizer: 0.000 validation_error: 20.254 +(epoch: 15, iters: 5472, time: 0.539, data: 0.000) G_L1: 13.893 G_L1_ABSOLUTE: 2.246 G_L1_RELATIVE: 11.647 G_Regularizer: 0.000 validation_error: 20.409 +(epoch: 15, iters: 7472, time: 0.537, data: 0.000) G_L1: 12.412 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 9.815 G_Regularizer: 0.000 validation_error: 20.101 +(epoch: 15, iters: 9472, time: 0.535, data: 0.000) G_L1: 14.820 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 12.484 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 15, iters: 11472, time: 0.543, data: 0.000) G_L1: 15.621 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 12.847 G_Regularizer: 0.000 validation_error: 21.129 +(epoch: 15, iters: 13472, time: 0.549, data: 0.000) G_L1: 16.442 G_L1_ABSOLUTE: 2.834 G_L1_RELATIVE: 13.608 G_Regularizer: 0.000 validation_error: 20.632 +(epoch: 15, iters: 15472, time: 0.538, data: 0.000) G_L1: 14.048 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 11.425 G_Regularizer: 0.000 validation_error: 21.495 +(epoch: 15, iters: 17472, time: 0.559, data: 0.000) G_L1: 17.324 G_L1_ABSOLUTE: 3.275 G_L1_RELATIVE: 14.049 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 15, iters: 19472, time: 0.562, data: 0.000) G_L1: 16.229 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 13.572 G_Regularizer: 0.000 validation_error: 21.073 +(epoch: 15, iters: 21472, time: 0.541, data: 0.000) G_L1: 14.771 G_L1_ABSOLUTE: 2.803 G_L1_RELATIVE: 11.969 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 15, iters: 23472, time: 0.560, data: 0.000) G_L1: 14.570 G_L1_ABSOLUTE: 2.900 G_L1_RELATIVE: 11.670 G_Regularizer: 0.000 validation_error: 20.954 +(epoch: 15, iters: 25472, time: 0.552, data: 0.000) G_L1: 17.073 G_L1_ABSOLUTE: 2.873 G_L1_RELATIVE: 14.200 G_Regularizer: 0.000 validation_error: 21.467 +(epoch: 15, iters: 27472, time: 0.552, data: 0.000) G_L1: 15.421 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 12.754 G_Regularizer: 0.000 validation_error: 20.599 +(epoch: 15, iters: 29472, time: 0.551, data: 0.000) G_L1: 14.491 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 11.793 G_Regularizer: 0.000 validation_error: 20.260 +(epoch: 15, iters: 31472, time: 0.531, data: 0.000) G_L1: 13.707 G_L1_ABSOLUTE: 2.404 G_L1_RELATIVE: 11.304 G_Regularizer: 0.000 validation_error: 19.965 +(epoch: 15, iters: 33472, time: 0.556, data: 0.000) G_L1: 14.878 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 11.984 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 15, iters: 35472, time: 0.557, data: 0.000) G_L1: 14.907 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 12.374 G_Regularizer: 0.000 validation_error: 20.938 +(epoch: 15, iters: 37472, time: 0.556, data: 0.001) G_L1: 15.687 G_L1_ABSOLUTE: 2.929 G_L1_RELATIVE: 12.758 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 15, iters: 39472, time: 0.558, data: 0.000) G_L1: 13.030 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 10.462 G_Regularizer: 0.000 validation_error: 21.352 +(epoch: 15, iters: 41472, time: 0.551, data: 0.000) G_L1: 18.711 G_L1_ABSOLUTE: 2.718 G_L1_RELATIVE: 15.993 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 15, iters: 43472, time: 0.540, data: 0.000) G_L1: 16.082 G_L1_ABSOLUTE: 2.321 G_L1_RELATIVE: 13.761 G_Regularizer: 0.000 validation_error: 20.229 +(epoch: 15, iters: 45472, time: 0.558, data: 0.000) G_L1: 13.439 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 10.628 G_Regularizer: 0.000 validation_error: 20.856 +(epoch: 15, iters: 47472, time: 0.532, data: 0.000) G_L1: 14.323 G_L1_ABSOLUTE: 3.060 G_L1_RELATIVE: 11.263 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 15, iters: 49472, time: 0.530, data: 0.000) G_L1: 14.825 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 12.234 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 15, iters: 51472, time: 0.563, data: 0.000) G_L1: 16.566 G_L1_ABSOLUTE: 3.555 G_L1_RELATIVE: 13.011 G_Regularizer: 0.000 validation_error: 21.219 +(epoch: 15, iters: 53472, time: 0.537, data: 0.000) G_L1: 15.074 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 12.454 G_Regularizer: 0.000 validation_error: 21.111 +(epoch: 15, iters: 55472, time: 0.537, data: 0.000) G_L1: 15.761 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 13.302 G_Regularizer: 0.000 validation_error: 20.423 +(epoch: 15, iters: 57472, time: 0.532, data: 0.000) G_L1: 17.178 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 14.364 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 15, iters: 59472, time: 0.556, data: 0.000) G_L1: 14.585 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 12.013 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 15, iters: 61472, time: 0.535, data: 0.000) G_L1: 12.946 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 10.354 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 15, iters: 63472, time: 0.546, data: 0.000) G_L1: 16.376 G_L1_ABSOLUTE: 3.204 G_L1_RELATIVE: 13.172 G_Regularizer: 0.000 validation_error: 20.435 +(epoch: 15, iters: 65472, time: 0.539, data: 0.000) G_L1: 13.115 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 10.784 G_Regularizer: 0.000 validation_error: 21.298 +(epoch: 15, iters: 67472, time: 0.532, data: 0.000) G_L1: 13.339 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 10.607 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 15, iters: 69472, time: 0.560, data: 0.000) G_L1: 16.132 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 13.409 G_Regularizer: 0.000 validation_error: 20.346 +(epoch: 15, iters: 71472, time: 0.541, data: 0.001) G_L1: 15.479 G_L1_ABSOLUTE: 2.704 G_L1_RELATIVE: 12.775 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 15, iters: 73472, time: 0.545, data: 0.000) G_L1: 15.688 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 12.957 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 15, iters: 75472, time: 0.560, data: 0.000) G_L1: 16.235 G_L1_ABSOLUTE: 3.098 G_L1_RELATIVE: 13.137 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 15, iters: 77472, time: 0.548, data: 0.000) G_L1: 16.214 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 13.536 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 15, iters: 79472, time: 0.559, data: 0.000) G_L1: 16.152 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 13.521 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 15, iters: 81472, time: 0.541, data: 0.000) G_L1: 13.228 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 10.755 G_Regularizer: 0.000 validation_error: 21.006 +(epoch: 15, iters: 83472, time: 0.547, data: 0.000) G_L1: 17.289 G_L1_ABSOLUTE: 2.813 G_L1_RELATIVE: 14.476 G_Regularizer: 0.000 validation_error: 20.583 +(epoch: 15, iters: 85472, time: 0.565, data: 0.000) G_L1: 14.868 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 12.480 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 15, iters: 87472, time: 0.559, data: 0.000) G_L1: 15.980 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 13.648 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 15, iters: 89472, time: 0.544, data: 0.000) G_L1: 13.769 G_L1_ABSOLUTE: 2.308 G_L1_RELATIVE: 11.461 G_Regularizer: 0.000 validation_error: 20.474 +(epoch: 15, iters: 91472, time: 0.539, data: 0.000) G_L1: 14.901 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 12.125 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 15, iters: 93472, time: 0.553, data: 0.000) G_L1: 14.701 G_L1_ABSOLUTE: 2.352 G_L1_RELATIVE: 12.349 G_Regularizer: 0.000 validation_error: 20.351 +(epoch: 15, iters: 95472, time: 0.547, data: 0.000) G_L1: 16.253 G_L1_ABSOLUTE: 2.931 G_L1_RELATIVE: 13.322 G_Regularizer: 0.000 validation_error: 20.452 +(epoch: 15, iters: 97472, time: 0.543, data: 0.000) G_L1: 13.488 G_L1_ABSOLUTE: 2.337 G_L1_RELATIVE: 11.152 G_Regularizer: 0.000 validation_error: 20.618 +(epoch: 15, iters: 99472, time: 0.550, data: 0.000) G_L1: 15.606 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 12.795 G_Regularizer: 0.000 validation_error: 20.326 +(epoch: 15, iters: 101472, time: 0.551, data: 0.000) G_L1: 14.492 G_L1_ABSOLUTE: 2.654 G_L1_RELATIVE: 11.838 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 15, iters: 103472, time: 0.559, data: 0.000) G_L1: 13.631 G_L1_ABSOLUTE: 3.128 G_L1_RELATIVE: 10.503 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 15, iters: 105472, time: 0.554, data: 0.000) G_L1: 14.674 G_L1_ABSOLUTE: 3.176 G_L1_RELATIVE: 11.498 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 15, iters: 107472, time: 0.535, data: 0.000) G_L1: 15.979 G_L1_ABSOLUTE: 3.190 G_L1_RELATIVE: 12.789 G_Regularizer: 0.000 validation_error: 20.396 +(epoch: 15, iters: 109472, time: 0.547, data: 0.000) G_L1: 15.654 G_L1_ABSOLUTE: 2.904 G_L1_RELATIVE: 12.750 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 15, iters: 111472, time: 0.544, data: 0.000) G_L1: 14.869 G_L1_ABSOLUTE: 2.645 G_L1_RELATIVE: 12.224 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 15, iters: 113472, time: 0.546, data: 0.000) G_L1: 15.339 G_L1_ABSOLUTE: 2.876 G_L1_RELATIVE: 12.463 G_Regularizer: 0.000 validation_error: 20.944 +(epoch: 15, iters: 115472, time: 0.531, data: 0.000) G_L1: 13.937 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 11.474 G_Regularizer: 0.000 validation_error: 19.658 +(epoch: 15, iters: 117472, time: 0.557, data: 0.000) G_L1: 17.793 G_L1_ABSOLUTE: 3.152 G_L1_RELATIVE: 14.641 G_Regularizer: 0.000 validation_error: 20.536 +(epoch: 15, iters: 119472, time: 0.555, data: 0.000) G_L1: 15.269 G_L1_ABSOLUTE: 2.487 G_L1_RELATIVE: 12.781 G_Regularizer: 0.000 validation_error: 21.558 +(epoch: 15, iters: 121472, time: 0.542, data: 0.000) G_L1: 15.236 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 12.599 G_Regularizer: 0.000 validation_error: 19.879 +(epoch: 15, iters: 123472, time: 0.549, data: 0.000) G_L1: 15.470 G_L1_ABSOLUTE: 2.739 G_L1_RELATIVE: 12.732 G_Regularizer: 0.000 validation_error: 20.528 +(epoch: 15, iters: 125472, time: 0.563, data: 0.000) G_L1: 14.923 G_L1_ABSOLUTE: 3.223 G_L1_RELATIVE: 11.701 G_Regularizer: 0.000 validation_error: 20.444 +(epoch: 15, iters: 127472, time: 0.546, data: 0.000) G_L1: 26.215 G_L1_ABSOLUTE: 2.356 G_L1_RELATIVE: 23.859 G_Regularizer: 0.000 validation_error: 20.420 +(epoch: 15, iters: 129472, time: 0.558, data: 0.000) G_L1: 17.561 G_L1_ABSOLUTE: 3.160 G_L1_RELATIVE: 14.401 G_Regularizer: 0.000 validation_error: 20.373 +(epoch: 15, iters: 131472, time: 0.542, data: 0.000) G_L1: 14.723 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 12.093 G_Regularizer: 0.000 validation_error: 19.950 +(epoch: 15, iters: 133472, time: 0.560, data: 0.000) G_L1: 12.727 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 10.092 G_Regularizer: 0.000 validation_error: 19.751 +(epoch: 15, iters: 135472, time: 0.560, data: 0.000) G_L1: 15.908 G_L1_ABSOLUTE: 2.844 G_L1_RELATIVE: 13.065 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 15, iters: 137472, time: 0.558, data: 0.000) G_L1: 14.417 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 11.764 G_Regularizer: 0.000 validation_error: 20.797 +(epoch: 15, iters: 139472, time: 0.542, data: 0.000) G_L1: 13.710 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 11.184 G_Regularizer: 0.000 validation_error: 19.684 +(epoch: 15, iters: 141472, time: 0.562, data: 0.000) G_L1: 15.427 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 12.992 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 15, iters: 143472, time: 0.559, data: 0.000) G_L1: 14.828 G_L1_ABSOLUTE: 2.765 G_L1_RELATIVE: 12.063 G_Regularizer: 0.000 validation_error: 19.963 +(epoch: 15, iters: 145472, time: 0.559, data: 0.000) G_L1: 15.792 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 13.192 G_Regularizer: 0.000 validation_error: 20.550 +(epoch: 15, iters: 147472, time: 0.545, data: 0.000) G_L1: 17.714 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 14.966 G_Regularizer: 0.000 validation_error: 20.238 +(epoch: 15, iters: 149472, time: 0.534, data: 0.000) G_L1: 15.828 G_L1_ABSOLUTE: 2.770 G_L1_RELATIVE: 13.058 G_Regularizer: 0.000 validation_error: 20.618 +(epoch: 15, iters: 151472, time: 0.533, data: 0.000) G_L1: 14.684 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 12.027 G_Regularizer: 0.000 validation_error: 20.303 +(epoch: 15, iters: 153472, time: 0.556, data: 0.000) G_L1: 17.120 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 14.284 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 15, iters: 155472, time: 0.541, data: 0.000) G_L1: 15.430 G_L1_ABSOLUTE: 2.479 G_L1_RELATIVE: 12.951 G_Regularizer: 0.000 validation_error: 20.744 +(epoch: 15, iters: 157472, time: 0.534, data: 0.000) G_L1: 15.051 G_L1_ABSOLUTE: 2.990 G_L1_RELATIVE: 12.061 G_Regularizer: 0.000 validation_error: 20.867 +(epoch: 15, iters: 159472, time: 0.560, data: 0.000) G_L1: 14.118 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 11.255 G_Regularizer: 0.000 validation_error: 20.757 +(epoch: 15, iters: 161472, time: 0.557, data: 0.000) G_L1: 15.586 G_L1_ABSOLUTE: 2.910 G_L1_RELATIVE: 12.675 G_Regularizer: 0.000 validation_error: 20.439 +(epoch: 15, iters: 163472, time: 0.566, data: 0.000) G_L1: 18.545 G_L1_ABSOLUTE: 3.261 G_L1_RELATIVE: 15.285 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 15, iters: 165472, time: 0.535, data: 0.000) G_L1: 14.515 G_L1_ABSOLUTE: 2.846 G_L1_RELATIVE: 11.669 G_Regularizer: 0.000 validation_error: 20.972 +(epoch: 15, iters: 167472, time: 0.539, data: 0.000) G_L1: 13.231 G_L1_ABSOLUTE: 2.396 G_L1_RELATIVE: 10.834 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 15, iters: 169472, time: 0.554, data: 0.000) G_L1: 14.182 G_L1_ABSOLUTE: 2.567 G_L1_RELATIVE: 11.615 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 15, iters: 171472, time: 0.564, data: 0.000) G_L1: 17.483 G_L1_ABSOLUTE: 3.067 G_L1_RELATIVE: 14.417 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 15, iters: 173472, time: 0.533, data: 0.001) G_L1: 16.094 G_L1_ABSOLUTE: 2.831 G_L1_RELATIVE: 13.263 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 15, iters: 175472, time: 0.550, data: 0.000) G_L1: 13.102 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 10.772 G_Regularizer: 0.000 validation_error: 20.769 +(epoch: 15, iters: 177472, time: 0.554, data: 0.000) G_L1: 16.650 G_L1_ABSOLUTE: 2.933 G_L1_RELATIVE: 13.716 G_Regularizer: 0.000 validation_error: 20.180 +(epoch: 15, iters: 179472, time: 0.550, data: 0.000) G_L1: 16.171 G_L1_ABSOLUTE: 2.977 G_L1_RELATIVE: 13.194 G_Regularizer: 0.000 validation_error: 20.300 +(epoch: 15, iters: 181472, time: 0.542, data: 0.000) G_L1: 14.661 G_L1_ABSOLUTE: 2.769 G_L1_RELATIVE: 11.892 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 15, iters: 183472, time: 0.555, data: 0.000) G_L1: 18.622 G_L1_ABSOLUTE: 2.817 G_L1_RELATIVE: 15.805 G_Regularizer: 0.000 validation_error: 20.513 +(epoch: 15, iters: 185472, time: 0.543, data: 0.000) G_L1: 13.532 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 11.270 G_Regularizer: 0.000 validation_error: 20.405 +(epoch: 15, iters: 187472, time: 0.559, data: 0.000) G_L1: 14.766 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 11.988 G_Regularizer: 0.000 validation_error: 20.122 +(epoch: 15, iters: 189472, time: 0.552, data: 0.000) G_L1: 16.124 G_L1_ABSOLUTE: 2.826 G_L1_RELATIVE: 13.297 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 15, iters: 191472, time: 0.559, data: 0.000) G_L1: 15.698 G_L1_ABSOLUTE: 2.067 G_L1_RELATIVE: 13.631 G_Regularizer: 0.000 validation_error: 20.789 +(epoch: 15, iters: 193472, time: 0.557, data: 0.000) G_L1: 12.478 G_L1_ABSOLUTE: 2.662 G_L1_RELATIVE: 9.817 G_Regularizer: 0.000 validation_error: 20.756 +(epoch: 15, iters: 195472, time: 0.558, data: 0.000) G_L1: 14.350 G_L1_ABSOLUTE: 2.147 G_L1_RELATIVE: 12.203 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 15, iters: 197472, time: 0.550, data: 0.000) G_L1: 17.161 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 14.749 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 15, iters: 199472, time: 0.536, data: 0.000) G_L1: 17.677 G_L1_ABSOLUTE: 2.654 G_L1_RELATIVE: 15.023 G_Regularizer: 0.000 validation_error: 21.571 +(epoch: 15, iters: 201472, time: 0.560, data: 0.000) G_L1: 14.986 G_L1_ABSOLUTE: 3.197 G_L1_RELATIVE: 11.789 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 15, iters: 203472, time: 0.559, data: 0.000) G_L1: 15.386 G_L1_ABSOLUTE: 3.146 G_L1_RELATIVE: 12.241 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 15, iters: 205472, time: 0.556, data: 0.000) G_L1: 15.276 G_L1_ABSOLUTE: 2.823 G_L1_RELATIVE: 12.453 G_Regularizer: 0.000 validation_error: 20.298 +(epoch: 15, iters: 207472, time: 0.549, data: 0.000) G_L1: 14.406 G_L1_ABSOLUTE: 2.617 G_L1_RELATIVE: 11.789 G_Regularizer: 0.000 validation_error: 20.629 +(epoch: 15, iters: 209472, time: 0.556, data: 0.000) G_L1: 14.142 G_L1_ABSOLUTE: 2.166 G_L1_RELATIVE: 11.976 G_Regularizer: 0.000 validation_error: 21.290 +(epoch: 15, iters: 211472, time: 0.541, data: 0.000) G_L1: 14.233 G_L1_ABSOLUTE: 2.392 G_L1_RELATIVE: 11.841 G_Regularizer: 0.000 validation_error: 21.752 +(epoch: 15, iters: 213472, time: 0.550, data: 0.001) G_L1: 14.178 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 11.298 G_Regularizer: 0.000 validation_error: 20.306 +(epoch: 15, iters: 215472, time: 0.547, data: 0.000) G_L1: 16.368 G_L1_ABSOLUTE: 2.580 G_L1_RELATIVE: 13.787 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 15, iters: 217472, time: 0.535, data: 0.000) G_L1: 12.199 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 9.766 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 15, iters: 219472, time: 0.532, data: 0.001) G_L1: 14.260 G_L1_ABSOLUTE: 2.973 G_L1_RELATIVE: 11.286 G_Regularizer: 0.000 validation_error: 21.014 +(epoch: 15, iters: 221472, time: 0.555, data: 0.000) G_L1: 14.878 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 12.124 G_Regularizer: 0.000 validation_error: 20.326 +(epoch: 15, iters: 223472, time: 0.545, data: 0.001) G_L1: 17.159 G_L1_ABSOLUTE: 3.035 G_L1_RELATIVE: 14.124 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 15, iters: 225472, time: 0.560, data: 0.000) G_L1: 15.612 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 13.001 G_Regularizer: 0.000 validation_error: 20.145 +(epoch: 15, iters: 227472, time: 0.561, data: 0.000) G_L1: 14.660 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 12.104 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 15, iters: 229472, time: 0.538, data: 0.000) G_L1: 14.824 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 12.459 G_Regularizer: 0.000 validation_error: 19.850 +(epoch: 15, iters: 231472, time: 0.534, data: 0.000) G_L1: 17.321 G_L1_ABSOLUTE: 3.581 G_L1_RELATIVE: 13.740 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 15, iters: 233472, time: 0.532, data: 0.000) G_L1: 14.237 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 11.822 G_Regularizer: 0.000 validation_error: 21.171 +(epoch: 15, iters: 235472, time: 0.541, data: 0.000) G_L1: 16.495 G_L1_ABSOLUTE: 2.913 G_L1_RELATIVE: 13.582 G_Regularizer: 0.000 validation_error: 20.304 +(epoch: 15, iters: 237472, time: 0.537, data: 0.000) G_L1: 13.676 G_L1_ABSOLUTE: 2.441 G_L1_RELATIVE: 11.235 G_Regularizer: 0.000 validation_error: 20.770 +(epoch: 15, iters: 239472, time: 0.556, data: 0.000) G_L1: 13.043 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 10.827 G_Regularizer: 0.000 validation_error: 20.675 +(epoch: 15, iters: 241472, time: 0.550, data: 0.000) G_L1: 14.686 G_L1_ABSOLUTE: 2.617 G_L1_RELATIVE: 12.069 G_Regularizer: 0.000 validation_error: 20.284 +(epoch: 15, iters: 243472, time: 0.565, data: 0.000) G_L1: 15.374 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 13.004 G_Regularizer: 0.000 validation_error: 20.375 +(epoch: 15, iters: 245472, time: 0.549, data: 0.000) G_L1: 13.655 G_L1_ABSOLUTE: 2.772 G_L1_RELATIVE: 10.883 G_Regularizer: 0.000 validation_error: 20.598 +(epoch: 15, iters: 247472, time: 0.564, data: 0.000) G_L1: 12.804 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 10.457 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 15, iters: 249472, time: 0.537, data: 0.000) G_L1: 13.787 G_L1_ABSOLUTE: 2.727 G_L1_RELATIVE: 11.060 G_Regularizer: 0.000 validation_error: 20.352 +(epoch: 15, iters: 251472, time: 0.559, data: 0.000) G_L1: 17.749 G_L1_ABSOLUTE: 2.786 G_L1_RELATIVE: 14.963 G_Regularizer: 0.000 validation_error: 21.260 +(epoch: 15, iters: 253472, time: 0.566, data: 0.000) G_L1: 15.926 G_L1_ABSOLUTE: 2.411 G_L1_RELATIVE: 13.515 G_Regularizer: 0.000 validation_error: 19.741 +(epoch: 15, iters: 255472, time: 0.558, data: 0.000) G_L1: 13.374 G_L1_ABSOLUTE: 2.396 G_L1_RELATIVE: 10.978 G_Regularizer: 0.000 validation_error: 20.073 +(epoch: 15, iters: 257472, time: 0.543, data: 0.000) G_L1: 16.039 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 13.513 G_Regularizer: 0.000 validation_error: 20.683 +(epoch: 15, iters: 259472, time: 0.560, data: 0.000) G_L1: 15.081 G_L1_ABSOLUTE: 3.131 G_L1_RELATIVE: 11.950 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 15, iters: 261472, time: 0.556, data: 0.000) G_L1: 12.990 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 10.707 G_Regularizer: 0.000 validation_error: 20.537 +(epoch: 15, iters: 263472, time: 0.540, data: 0.000) G_L1: 13.021 G_L1_ABSOLUTE: 2.313 G_L1_RELATIVE: 10.708 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 15, iters: 265472, time: 0.543, data: 0.001) G_L1: 14.745 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 12.428 G_Regularizer: 0.000 validation_error: 20.096 +(epoch: 15, iters: 267472, time: 0.559, data: 0.000) G_L1: 15.903 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 13.327 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 15, iters: 269472, time: 0.549, data: 0.000) G_L1: 16.743 G_L1_ABSOLUTE: 3.025 G_L1_RELATIVE: 13.718 G_Regularizer: 0.000 validation_error: 20.451 +(epoch: 15, iters: 271472, time: 0.541, data: 0.000) G_L1: 20.410 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 17.752 G_Regularizer: 0.000 validation_error: 20.430 +(epoch: 15, iters: 273472, time: 0.536, data: 0.000) G_L1: 13.005 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 10.555 G_Regularizer: 0.000 validation_error: 20.445 +(epoch: 15, iters: 275472, time: 0.553, data: 0.000) G_L1: 13.376 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 11.032 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 15, iters: 277472, time: 0.544, data: 0.000) G_L1: 15.083 G_L1_ABSOLUTE: 3.236 G_L1_RELATIVE: 11.846 G_Regularizer: 0.000 validation_error: 19.986 +(epoch: 15, iters: 279472, time: 0.560, data: 0.000) G_L1: 17.279 G_L1_ABSOLUTE: 2.514 G_L1_RELATIVE: 14.764 G_Regularizer: 0.000 validation_error: 20.893 +(epoch: 15, iters: 281472, time: 0.550, data: 0.000) G_L1: 17.204 G_L1_ABSOLUTE: 3.145 G_L1_RELATIVE: 14.059 G_Regularizer: 0.000 validation_error: 19.612 +(epoch: 15, iters: 283472, time: 0.554, data: 0.001) G_L1: 15.606 G_L1_ABSOLUTE: 2.927 G_L1_RELATIVE: 12.679 G_Regularizer: 0.000 validation_error: 20.637 +(epoch: 15, iters: 285472, time: 0.558, data: 0.000) G_L1: 14.598 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 11.925 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 15, iters: 287472, time: 0.549, data: 0.000) G_L1: 17.151 G_L1_ABSOLUTE: 3.517 G_L1_RELATIVE: 13.634 G_Regularizer: 0.000 validation_error: 20.366 +(epoch: 15, iters: 289472, time: 0.558, data: 0.001) G_L1: 14.643 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 12.182 G_Regularizer: 0.000 validation_error: 20.107 +(epoch: 15, iters: 291472, time: 0.533, data: 0.000) G_L1: 15.612 G_L1_ABSOLUTE: 2.245 G_L1_RELATIVE: 13.367 G_Regularizer: 0.000 validation_error: 20.902 +(epoch: 15, iters: 293472, time: 0.549, data: 0.000) G_L1: 15.987 G_L1_ABSOLUTE: 2.311 G_L1_RELATIVE: 13.675 G_Regularizer: 0.000 validation_error: 19.834 +(epoch: 15, iters: 295472, time: 0.547, data: 0.000) G_L1: 16.113 G_L1_ABSOLUTE: 2.685 G_L1_RELATIVE: 13.428 G_Regularizer: 0.000 validation_error: 20.233 +(epoch: 15, iters: 297472, time: 0.554, data: 0.000) G_L1: 14.056 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 11.675 G_Regularizer: 0.000 validation_error: 20.371 +(epoch: 15, iters: 299472, time: 0.539, data: 0.000) G_L1: 16.233 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 13.765 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 15, iters: 301472, time: 0.559, data: 0.000) G_L1: 12.279 G_L1_ABSOLUTE: 3.032 G_L1_RELATIVE: 9.247 G_Regularizer: 0.000 validation_error: 20.330 +(epoch: 16, iters: 720, time: 0.529, data: 0.000) G_L1: 16.885 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 14.273 G_Regularizer: 0.000 validation_error: 20.362 +(epoch: 16, iters: 2720, time: 0.560, data: 0.000) G_L1: 11.384 G_L1_ABSOLUTE: 2.176 G_L1_RELATIVE: 9.208 G_Regularizer: 0.000 validation_error: 20.468 +(epoch: 16, iters: 4720, time: 0.543, data: 0.000) G_L1: 17.008 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 14.563 G_Regularizer: 0.000 validation_error: 21.485 +(epoch: 16, iters: 6720, time: 0.554, data: 0.000) G_L1: 14.577 G_L1_ABSOLUTE: 3.018 G_L1_RELATIVE: 11.559 G_Regularizer: 0.000 validation_error: 20.296 +(epoch: 16, iters: 8720, time: 0.558, data: 0.000) G_L1: 17.480 G_L1_ABSOLUTE: 2.384 G_L1_RELATIVE: 15.096 G_Regularizer: 0.000 validation_error: 21.332 +(epoch: 16, iters: 10720, time: 0.554, data: 0.000) G_L1: 15.768 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 13.249 G_Regularizer: 0.000 validation_error: 20.545 +(epoch: 16, iters: 12720, time: 0.538, data: 0.000) G_L1: 15.892 G_L1_ABSOLUTE: 2.488 G_L1_RELATIVE: 13.403 G_Regularizer: 0.000 validation_error: 21.166 +(epoch: 16, iters: 14720, time: 0.555, data: 0.000) G_L1: 19.379 G_L1_ABSOLUTE: 2.667 G_L1_RELATIVE: 16.712 G_Regularizer: 0.000 validation_error: 20.631 +(epoch: 16, iters: 16720, time: 0.550, data: 0.000) G_L1: 17.897 G_L1_ABSOLUTE: 3.307 G_L1_RELATIVE: 14.590 G_Regularizer: 0.000 validation_error: 21.294 +(epoch: 16, iters: 18720, time: 0.551, data: 0.000) G_L1: 15.584 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 12.595 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 16, iters: 20720, time: 0.553, data: 0.000) G_L1: 14.203 G_L1_ABSOLUTE: 2.741 G_L1_RELATIVE: 11.462 G_Regularizer: 0.000 validation_error: 21.029 +(epoch: 16, iters: 22720, time: 0.539, data: 0.000) G_L1: 14.078 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 11.347 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 16, iters: 24720, time: 0.558, data: 0.000) G_L1: 16.643 G_L1_ABSOLUTE: 3.107 G_L1_RELATIVE: 13.536 G_Regularizer: 0.000 validation_error: 20.228 +(epoch: 16, iters: 26720, time: 0.563, data: 0.000) G_L1: 13.955 G_L1_ABSOLUTE: 2.099 G_L1_RELATIVE: 11.856 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 16, iters: 28720, time: 0.557, data: 0.000) G_L1: 14.943 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 12.594 G_Regularizer: 0.000 validation_error: 21.253 +(epoch: 16, iters: 30720, time: 0.559, data: 0.000) G_L1: 15.303 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 12.728 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 16, iters: 32720, time: 0.568, data: 0.000) G_L1: 16.298 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 13.725 G_Regularizer: 0.000 validation_error: 20.816 +(epoch: 16, iters: 34720, time: 0.556, data: 0.000) G_L1: 21.052 G_L1_ABSOLUTE: 3.165 G_L1_RELATIVE: 17.887 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 16, iters: 36720, time: 0.562, data: 0.000) G_L1: 15.074 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 12.260 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 16, iters: 38720, time: 0.535, data: 0.000) G_L1: 14.326 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 11.658 G_Regularizer: 0.000 validation_error: 20.344 +(epoch: 16, iters: 40720, time: 0.564, data: 0.000) G_L1: 17.524 G_L1_ABSOLUTE: 3.234 G_L1_RELATIVE: 14.290 G_Regularizer: 0.000 validation_error: 21.422 +(epoch: 16, iters: 42720, time: 0.564, data: 0.000) G_L1: 14.633 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 12.225 G_Regularizer: 0.000 validation_error: 20.699 +(epoch: 16, iters: 44720, time: 0.559, data: 0.000) G_L1: 17.483 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 14.620 G_Regularizer: 0.000 validation_error: 19.984 +(epoch: 16, iters: 46720, time: 0.531, data: 0.000) G_L1: 15.632 G_L1_ABSOLUTE: 2.662 G_L1_RELATIVE: 12.970 G_Regularizer: 0.000 validation_error: 20.311 +(epoch: 16, iters: 48720, time: 0.536, data: 0.000) G_L1: 16.441 G_L1_ABSOLUTE: 2.882 G_L1_RELATIVE: 13.559 G_Regularizer: 0.000 validation_error: 20.579 +(epoch: 16, iters: 50720, time: 0.551, data: 0.000) G_L1: 14.654 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 12.191 G_Regularizer: 0.000 validation_error: 20.602 +(epoch: 16, iters: 52720, time: 0.551, data: 0.000) G_L1: 13.702 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 11.218 G_Regularizer: 0.000 validation_error: 20.571 +(epoch: 16, iters: 54720, time: 0.544, data: 0.000) G_L1: 15.200 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 12.811 G_Regularizer: 0.000 validation_error: 20.317 +(epoch: 16, iters: 56720, time: 0.542, data: 0.000) G_L1: 13.088 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 10.725 G_Regularizer: 0.000 validation_error: 20.112 +(epoch: 16, iters: 58720, time: 0.567, data: 0.000) G_L1: 15.605 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 13.101 G_Regularizer: 0.000 validation_error: 20.572 +(epoch: 16, iters: 60720, time: 0.558, data: 0.000) G_L1: 15.511 G_L1_ABSOLUTE: 2.431 G_L1_RELATIVE: 13.080 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 16, iters: 62720, time: 0.548, data: 0.000) G_L1: 16.407 G_L1_ABSOLUTE: 2.917 G_L1_RELATIVE: 13.489 G_Regularizer: 0.000 validation_error: 20.244 +(epoch: 16, iters: 64720, time: 0.553, data: 0.000) G_L1: 15.366 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 12.907 G_Regularizer: 0.000 validation_error: 20.309 +(epoch: 16, iters: 66720, time: 0.557, data: 0.000) G_L1: 18.665 G_L1_ABSOLUTE: 2.695 G_L1_RELATIVE: 15.969 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 16, iters: 68720, time: 0.557, data: 0.000) G_L1: 13.910 G_L1_ABSOLUTE: 2.293 G_L1_RELATIVE: 11.617 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 16, iters: 70720, time: 0.553, data: 0.000) G_L1: 14.873 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 12.449 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 16, iters: 72720, time: 0.543, data: 0.000) G_L1: 13.579 G_L1_ABSOLUTE: 2.212 G_L1_RELATIVE: 11.367 G_Regularizer: 0.000 validation_error: 20.463 +(epoch: 16, iters: 74720, time: 0.537, data: 0.000) G_L1: 15.235 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 12.611 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 16, iters: 76720, time: 0.550, data: 0.000) G_L1: 14.233 G_L1_ABSOLUTE: 2.180 G_L1_RELATIVE: 12.053 G_Regularizer: 0.000 validation_error: 19.776 +(epoch: 16, iters: 78720, time: 0.564, data: 0.000) G_L1: 13.733 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 11.241 G_Regularizer: 0.000 validation_error: 20.120 +(epoch: 16, iters: 80720, time: 0.533, data: 0.000) G_L1: 14.295 G_L1_ABSOLUTE: 2.713 G_L1_RELATIVE: 11.582 G_Regularizer: 0.000 validation_error: 21.179 +(epoch: 16, iters: 82720, time: 0.551, data: 0.000) G_L1: 14.630 G_L1_ABSOLUTE: 2.799 G_L1_RELATIVE: 11.831 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 16, iters: 84720, time: 0.559, data: 0.000) G_L1: 23.292 G_L1_ABSOLUTE: 2.741 G_L1_RELATIVE: 20.550 G_Regularizer: 0.000 validation_error: 19.963 +(epoch: 16, iters: 86720, time: 0.557, data: 0.000) G_L1: 16.564 G_L1_ABSOLUTE: 2.852 G_L1_RELATIVE: 13.713 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 16, iters: 88720, time: 0.532, data: 0.000) G_L1: 17.371 G_L1_ABSOLUTE: 2.845 G_L1_RELATIVE: 14.526 G_Regularizer: 0.000 validation_error: 20.331 +(epoch: 16, iters: 90720, time: 0.561, data: 0.001) G_L1: 16.049 G_L1_ABSOLUTE: 2.719 G_L1_RELATIVE: 13.330 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 16, iters: 92720, time: 0.558, data: 0.003) G_L1: 13.792 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 11.457 G_Regularizer: 0.000 validation_error: 20.620 +(epoch: 16, iters: 94720, time: 0.558, data: 0.000) G_L1: 16.870 G_L1_ABSOLUTE: 2.993 G_L1_RELATIVE: 13.877 G_Regularizer: 0.000 validation_error: 21.130 +(epoch: 16, iters: 96720, time: 0.547, data: 0.000) G_L1: 14.726 G_L1_ABSOLUTE: 2.751 G_L1_RELATIVE: 11.975 G_Regularizer: 0.000 validation_error: 20.240 +(epoch: 16, iters: 98720, time: 0.558, data: 0.000) G_L1: 17.494 G_L1_ABSOLUTE: 3.166 G_L1_RELATIVE: 14.327 G_Regularizer: 0.000 validation_error: 20.665 +(epoch: 16, iters: 100720, time: 0.561, data: 0.000) G_L1: 18.075 G_L1_ABSOLUTE: 3.031 G_L1_RELATIVE: 15.044 G_Regularizer: 0.000 validation_error: 20.292 +(epoch: 16, iters: 102720, time: 0.558, data: 0.000) G_L1: 16.573 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 13.983 G_Regularizer: 0.000 validation_error: 20.221 +(epoch: 16, iters: 104720, time: 0.541, data: 0.000) G_L1: 15.126 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 12.690 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 16, iters: 106720, time: 0.535, data: 0.000) G_L1: 15.484 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 13.099 G_Regularizer: 0.000 validation_error: 20.765 +(epoch: 16, iters: 108720, time: 0.548, data: 0.000) G_L1: 17.463 G_L1_ABSOLUTE: 2.813 G_L1_RELATIVE: 14.650 G_Regularizer: 0.000 validation_error: 21.038 +(epoch: 16, iters: 110720, time: 0.556, data: 0.000) G_L1: 13.720 G_L1_ABSOLUTE: 3.238 G_L1_RELATIVE: 10.482 G_Regularizer: 0.000 validation_error: 20.202 +(epoch: 16, iters: 112720, time: 0.544, data: 0.000) G_L1: 15.258 G_L1_ABSOLUTE: 2.496 G_L1_RELATIVE: 12.762 G_Regularizer: 0.000 validation_error: 20.320 +(epoch: 16, iters: 114720, time: 0.544, data: 0.000) G_L1: 15.533 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 13.028 G_Regularizer: 0.000 validation_error: 21.620 +(epoch: 16, iters: 116720, time: 0.545, data: 0.000) G_L1: 14.462 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 11.830 G_Regularizer: 0.000 validation_error: 20.455 +(epoch: 16, iters: 118720, time: 0.560, data: 0.000) G_L1: 13.131 G_L1_ABSOLUTE: 2.765 G_L1_RELATIVE: 10.367 G_Regularizer: 0.000 validation_error: 20.187 +(epoch: 16, iters: 120720, time: 0.548, data: 0.000) G_L1: 15.572 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 13.118 G_Regularizer: 0.000 validation_error: 20.012 +(epoch: 16, iters: 122720, time: 0.534, data: 0.000) G_L1: 16.460 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 13.993 G_Regularizer: 0.000 validation_error: 20.167 +(epoch: 16, iters: 124720, time: 0.550, data: 0.000) G_L1: 14.460 G_L1_ABSOLUTE: 2.809 G_L1_RELATIVE: 11.651 G_Regularizer: 0.000 validation_error: 19.905 +(epoch: 16, iters: 126720, time: 0.555, data: 0.001) G_L1: 13.489 G_L1_ABSOLUTE: 2.465 G_L1_RELATIVE: 11.024 G_Regularizer: 0.000 validation_error: 20.026 +(epoch: 16, iters: 128720, time: 0.559, data: 0.000) G_L1: 14.814 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 11.909 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 16, iters: 130720, time: 0.553, data: 0.000) G_L1: 16.321 G_L1_ABSOLUTE: 2.321 G_L1_RELATIVE: 13.999 G_Regularizer: 0.000 validation_error: 20.522 +(epoch: 16, iters: 132720, time: 0.554, data: 0.000) G_L1: 15.255 G_L1_ABSOLUTE: 2.898 G_L1_RELATIVE: 12.357 G_Regularizer: 0.000 validation_error: 20.939 +(epoch: 16, iters: 134720, time: 0.537, data: 0.000) G_L1: 17.156 G_L1_ABSOLUTE: 3.070 G_L1_RELATIVE: 14.086 G_Regularizer: 0.000 validation_error: 20.517 +(epoch: 16, iters: 136720, time: 0.544, data: 0.000) G_L1: 12.925 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 10.536 G_Regularizer: 0.000 validation_error: 20.453 +(epoch: 16, iters: 138720, time: 0.536, data: 0.000) G_L1: 16.760 G_L1_ABSOLUTE: 3.422 G_L1_RELATIVE: 13.338 G_Regularizer: 0.000 validation_error: 20.411 +(epoch: 16, iters: 140720, time: 0.556, data: 0.000) G_L1: 13.775 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 11.392 G_Regularizer: 0.000 validation_error: 20.108 +(epoch: 16, iters: 142720, time: 0.529, data: 0.000) G_L1: 15.551 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 13.114 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 16, iters: 144720, time: 0.537, data: 0.000) G_L1: 13.227 G_L1_ABSOLUTE: 2.934 G_L1_RELATIVE: 10.293 G_Regularizer: 0.000 validation_error: 21.079 +(epoch: 16, iters: 146720, time: 0.555, data: 0.000) G_L1: 14.019 G_L1_ABSOLUTE: 2.980 G_L1_RELATIVE: 11.039 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 16, iters: 148720, time: 0.553, data: 0.000) G_L1: 17.428 G_L1_ABSOLUTE: 3.080 G_L1_RELATIVE: 14.348 G_Regularizer: 0.000 validation_error: 21.541 +(epoch: 16, iters: 150720, time: 0.536, data: 0.000) G_L1: 11.972 G_L1_ABSOLUTE: 2.114 G_L1_RELATIVE: 9.858 G_Regularizer: 0.000 validation_error: 20.913 +(epoch: 16, iters: 152720, time: 0.548, data: 0.000) G_L1: 15.307 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 12.560 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 16, iters: 154720, time: 0.543, data: 0.000) G_L1: 14.417 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 11.788 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 16, iters: 156720, time: 0.546, data: 0.000) G_L1: 16.045 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 13.343 G_Regularizer: 0.000 validation_error: 20.342 +(epoch: 16, iters: 158720, time: 0.558, data: 0.000) G_L1: 16.841 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 14.213 G_Regularizer: 0.000 validation_error: 20.771 +(epoch: 16, iters: 160720, time: 0.547, data: 0.000) G_L1: 15.947 G_L1_ABSOLUTE: 2.038 G_L1_RELATIVE: 13.909 G_Regularizer: 0.000 validation_error: 19.876 +(epoch: 16, iters: 162720, time: 0.537, data: 0.000) G_L1: 16.784 G_L1_ABSOLUTE: 2.912 G_L1_RELATIVE: 13.872 G_Regularizer: 0.000 validation_error: 20.312 +(epoch: 16, iters: 164720, time: 0.539, data: 0.000) G_L1: 18.464 G_L1_ABSOLUTE: 2.804 G_L1_RELATIVE: 15.660 G_Regularizer: 0.000 validation_error: 21.184 +(epoch: 16, iters: 166720, time: 0.560, data: 0.000) G_L1: 15.393 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 12.999 G_Regularizer: 0.000 validation_error: 21.163 +(epoch: 16, iters: 168720, time: 0.558, data: 0.000) G_L1: 14.255 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 11.694 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 16, iters: 170720, time: 0.553, data: 0.000) G_L1: 17.315 G_L1_ABSOLUTE: 2.553 G_L1_RELATIVE: 14.761 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 16, iters: 172720, time: 0.532, data: 0.000) G_L1: 15.116 G_L1_ABSOLUTE: 2.186 G_L1_RELATIVE: 12.930 G_Regularizer: 0.000 validation_error: 21.568 +(epoch: 16, iters: 174720, time: 0.533, data: 0.000) G_L1: 16.115 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 13.384 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 16, iters: 176720, time: 0.537, data: 0.000) G_L1: 14.471 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 11.647 G_Regularizer: 0.000 validation_error: 20.380 +(epoch: 16, iters: 178720, time: 0.556, data: 0.000) G_L1: 14.840 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 12.327 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 16, iters: 180720, time: 0.548, data: 0.000) G_L1: 15.483 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 12.944 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 16, iters: 182720, time: 0.561, data: 0.000) G_L1: 13.437 G_L1_ABSOLUTE: 2.957 G_L1_RELATIVE: 10.480 G_Regularizer: 0.000 validation_error: 20.717 +(epoch: 16, iters: 184720, time: 0.557, data: 0.000) G_L1: 13.706 G_L1_ABSOLUTE: 2.864 G_L1_RELATIVE: 10.842 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 16, iters: 186720, time: 0.543, data: 0.000) G_L1: 13.478 G_L1_ABSOLUTE: 3.309 G_L1_RELATIVE: 10.168 G_Regularizer: 0.000 validation_error: 20.468 +(epoch: 16, iters: 188720, time: 0.546, data: 0.000) G_L1: 14.468 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 11.921 G_Regularizer: 0.000 validation_error: 20.320 +(epoch: 16, iters: 190720, time: 0.560, data: 0.000) G_L1: 13.578 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 10.905 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 16, iters: 192720, time: 0.555, data: 0.000) G_L1: 15.430 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 12.818 G_Regularizer: 0.000 validation_error: 20.691 +(epoch: 16, iters: 194720, time: 0.552, data: 0.000) G_L1: 17.654 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 15.089 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 16, iters: 196720, time: 0.546, data: 0.000) G_L1: 12.817 G_L1_ABSOLUTE: 2.246 G_L1_RELATIVE: 10.571 G_Regularizer: 0.000 validation_error: 19.939 +(epoch: 16, iters: 198720, time: 0.536, data: 0.000) G_L1: 16.361 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 13.561 G_Regularizer: 0.000 validation_error: 21.146 +(epoch: 16, iters: 200720, time: 0.532, data: 0.000) G_L1: 13.658 G_L1_ABSOLUTE: 2.606 G_L1_RELATIVE: 11.052 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 16, iters: 202720, time: 0.552, data: 0.000) G_L1: 16.023 G_L1_ABSOLUTE: 2.360 G_L1_RELATIVE: 13.663 G_Regularizer: 0.000 validation_error: 21.128 +(epoch: 16, iters: 204720, time: 0.561, data: 0.000) G_L1: 15.265 G_L1_ABSOLUTE: 3.236 G_L1_RELATIVE: 12.028 G_Regularizer: 0.000 validation_error: 20.401 +(epoch: 16, iters: 206720, time: 0.528, data: 0.000) G_L1: 16.003 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 13.511 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 16, iters: 208720, time: 0.539, data: 0.000) G_L1: 14.003 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 11.550 G_Regularizer: 0.000 validation_error: 19.942 +(epoch: 16, iters: 210720, time: 0.548, data: 0.000) G_L1: 16.765 G_L1_ABSOLUTE: 3.080 G_L1_RELATIVE: 13.686 G_Regularizer: 0.000 validation_error: 20.114 +(epoch: 16, iters: 212720, time: 0.536, data: 0.000) G_L1: 13.262 G_L1_ABSOLUTE: 2.813 G_L1_RELATIVE: 10.449 G_Regularizer: 0.000 validation_error: 20.501 +(epoch: 16, iters: 214720, time: 0.533, data: 0.000) G_L1: 16.756 G_L1_ABSOLUTE: 2.891 G_L1_RELATIVE: 13.865 G_Regularizer: 0.000 validation_error: 20.385 +(epoch: 16, iters: 216720, time: 0.555, data: 0.000) G_L1: 15.796 G_L1_ABSOLUTE: 2.802 G_L1_RELATIVE: 12.994 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 16, iters: 218720, time: 0.552, data: 0.000) G_L1: 14.234 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 11.538 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 16, iters: 220720, time: 0.559, data: 0.000) G_L1: 19.184 G_L1_ABSOLUTE: 3.437 G_L1_RELATIVE: 15.747 G_Regularizer: 0.000 validation_error: 20.367 +(epoch: 16, iters: 222720, time: 0.545, data: 0.000) G_L1: 14.050 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 11.424 G_Regularizer: 0.000 validation_error: 20.554 +(epoch: 16, iters: 224720, time: 0.559, data: 0.000) G_L1: 14.392 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 11.850 G_Regularizer: 0.000 validation_error: 20.261 +(epoch: 16, iters: 226720, time: 0.560, data: 0.000) G_L1: 16.596 G_L1_ABSOLUTE: 2.899 G_L1_RELATIVE: 13.697 G_Regularizer: 0.000 validation_error: 20.415 +(epoch: 16, iters: 228720, time: 0.563, data: 0.000) G_L1: 13.605 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 11.075 G_Regularizer: 0.000 validation_error: 20.021 +(epoch: 16, iters: 230720, time: 0.550, data: 0.000) G_L1: 15.686 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 13.278 G_Regularizer: 0.000 validation_error: 21.021 +(epoch: 16, iters: 232720, time: 0.558, data: 0.000) G_L1: 14.688 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 12.199 G_Regularizer: 0.000 validation_error: 19.775 +(epoch: 16, iters: 234720, time: 0.563, data: 0.002) G_L1: 15.675 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 13.160 G_Regularizer: 0.000 validation_error: 20.201 +(epoch: 16, iters: 236720, time: 0.544, data: 0.000) G_L1: 14.422 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 11.882 G_Regularizer: 0.000 validation_error: 20.182 +(epoch: 16, iters: 238720, time: 0.554, data: 0.000) G_L1: 15.008 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 12.515 G_Regularizer: 0.000 validation_error: 20.468 +(epoch: 16, iters: 240720, time: 0.540, data: 0.000) G_L1: 14.674 G_L1_ABSOLUTE: 2.837 G_L1_RELATIVE: 11.837 G_Regularizer: 0.000 validation_error: 20.745 +(epoch: 16, iters: 242720, time: 0.548, data: 0.000) G_L1: 14.527 G_L1_ABSOLUTE: 2.804 G_L1_RELATIVE: 11.723 G_Regularizer: 0.000 validation_error: 21.132 +(epoch: 16, iters: 244720, time: 0.545, data: 0.000) G_L1: 12.441 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 10.033 G_Regularizer: 0.000 validation_error: 20.225 +(epoch: 16, iters: 246720, time: 0.560, data: 0.001) G_L1: 13.330 G_L1_ABSOLUTE: 2.256 G_L1_RELATIVE: 11.074 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 16, iters: 248720, time: 0.559, data: 0.000) G_L1: 19.203 G_L1_ABSOLUTE: 3.099 G_L1_RELATIVE: 16.104 G_Regularizer: 0.000 validation_error: 20.244 +(epoch: 16, iters: 250720, time: 0.553, data: 0.000) G_L1: 15.022 G_L1_ABSOLUTE: 2.514 G_L1_RELATIVE: 12.508 G_Regularizer: 0.000 validation_error: 20.288 +(epoch: 16, iters: 252720, time: 0.529, data: 0.000) G_L1: 16.017 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 13.261 G_Regularizer: 0.000 validation_error: 20.707 +(epoch: 16, iters: 254720, time: 0.548, data: 0.000) G_L1: 13.318 G_L1_ABSOLUTE: 2.232 G_L1_RELATIVE: 11.086 G_Regularizer: 0.000 validation_error: 20.183 +(epoch: 16, iters: 256720, time: 0.541, data: 0.000) G_L1: 14.801 G_L1_ABSOLUTE: 3.037 G_L1_RELATIVE: 11.764 G_Regularizer: 0.000 validation_error: 20.579 +(epoch: 16, iters: 258720, time: 0.563, data: 0.000) G_L1: 13.756 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 11.174 G_Regularizer: 0.000 validation_error: 19.838 +(epoch: 16, iters: 260720, time: 0.558, data: 0.000) G_L1: 17.510 G_L1_ABSOLUTE: 2.605 G_L1_RELATIVE: 14.905 G_Regularizer: 0.000 validation_error: 19.520 +(epoch: 16, iters: 262720, time: 0.555, data: 0.001) G_L1: 13.722 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 10.898 G_Regularizer: 0.000 validation_error: 20.350 +(epoch: 16, iters: 264720, time: 0.534, data: 0.000) G_L1: 15.876 G_L1_ABSOLUTE: 2.638 G_L1_RELATIVE: 13.237 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 16, iters: 266720, time: 0.559, data: 0.000) G_L1: 15.286 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 12.513 G_Regularizer: 0.000 validation_error: 19.742 +(epoch: 16, iters: 268720, time: 0.545, data: 0.000) G_L1: 14.128 G_L1_ABSOLUTE: 2.776 G_L1_RELATIVE: 11.353 G_Regularizer: 0.000 validation_error: 21.087 +(epoch: 16, iters: 270720, time: 0.555, data: 0.000) G_L1: 17.456 G_L1_ABSOLUTE: 3.059 G_L1_RELATIVE: 14.397 G_Regularizer: 0.000 validation_error: 19.679 +(epoch: 16, iters: 272720, time: 0.540, data: 0.000) G_L1: 15.453 G_L1_ABSOLUTE: 2.924 G_L1_RELATIVE: 12.529 G_Regularizer: 0.000 validation_error: 20.869 +(epoch: 16, iters: 274720, time: 0.555, data: 0.000) G_L1: 17.890 G_L1_ABSOLUTE: 3.044 G_L1_RELATIVE: 14.845 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 16, iters: 276720, time: 0.549, data: 0.000) G_L1: 16.135 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 13.254 G_Regularizer: 0.000 validation_error: 20.979 +(epoch: 16, iters: 278720, time: 0.556, data: 0.000) G_L1: 15.921 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 13.107 G_Regularizer: 0.000 validation_error: 20.116 +(epoch: 16, iters: 280720, time: 0.553, data: 0.000) G_L1: 13.633 G_L1_ABSOLUTE: 3.046 G_L1_RELATIVE: 10.587 G_Regularizer: 0.000 validation_error: 20.333 +(epoch: 16, iters: 282720, time: 0.564, data: 0.000) G_L1: 13.566 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 11.164 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 16, iters: 284720, time: 0.552, data: 0.000) G_L1: 15.336 G_L1_ABSOLUTE: 2.825 G_L1_RELATIVE: 12.511 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 16, iters: 286720, time: 0.560, data: 0.000) G_L1: 13.536 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 10.680 G_Regularizer: 0.000 validation_error: 20.155 +(epoch: 16, iters: 288720, time: 0.538, data: 0.000) G_L1: 23.352 G_L1_ABSOLUTE: 2.919 G_L1_RELATIVE: 20.433 G_Regularizer: 0.000 validation_error: 20.100 +(epoch: 16, iters: 290720, time: 0.537, data: 0.000) G_L1: 13.752 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 11.133 G_Regularizer: 0.000 validation_error: 20.518 +(epoch: 16, iters: 292720, time: 0.546, data: 0.000) G_L1: 14.433 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 11.859 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 16, iters: 294720, time: 0.563, data: 0.000) G_L1: 15.785 G_L1_ABSOLUTE: 2.950 G_L1_RELATIVE: 12.834 G_Regularizer: 0.000 validation_error: 20.279 +(epoch: 16, iters: 296720, time: 0.559, data: 0.001) G_L1: 14.784 G_L1_ABSOLUTE: 2.977 G_L1_RELATIVE: 11.807 G_Regularizer: 0.000 validation_error: 20.326 +(epoch: 16, iters: 298720, time: 0.528, data: 0.000) G_L1: 12.828 G_L1_ABSOLUTE: 2.411 G_L1_RELATIVE: 10.417 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 16, iters: 300720, time: 0.553, data: 0.000) G_L1: 17.238 G_L1_ABSOLUTE: 3.026 G_L1_RELATIVE: 14.212 G_Regularizer: 0.000 validation_error: 20.386 +(epoch: 16, iters: 302720, time: 0.557, data: 0.000) G_L1: 15.511 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 12.603 G_Regularizer: 0.000 validation_error: 20.427 +(epoch: 17, iters: 1968, time: 0.557, data: 0.000) G_L1: 11.920 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 9.096 G_Regularizer: 0.000 validation_error: 19.892 +(epoch: 17, iters: 3968, time: 0.546, data: 0.000) G_L1: 19.525 G_L1_ABSOLUTE: 2.653 G_L1_RELATIVE: 16.872 G_Regularizer: 0.000 validation_error: 19.894 +(epoch: 17, iters: 5968, time: 0.560, data: 0.000) G_L1: 16.621 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 13.989 G_Regularizer: 0.000 validation_error: 20.372 +(epoch: 17, iters: 7968, time: 0.554, data: 0.000) G_L1: 15.289 G_L1_ABSOLUTE: 2.769 G_L1_RELATIVE: 12.519 G_Regularizer: 0.000 validation_error: 20.667 +(epoch: 17, iters: 9968, time: 0.558, data: 0.000) G_L1: 15.042 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 12.783 G_Regularizer: 0.000 validation_error: 20.298 +(epoch: 17, iters: 11968, time: 0.537, data: 0.000) G_L1: 14.694 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 12.043 G_Regularizer: 0.000 validation_error: 20.377 +(epoch: 17, iters: 13968, time: 0.563, data: 0.000) G_L1: 15.037 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 12.722 G_Regularizer: 0.000 validation_error: 20.276 +(epoch: 17, iters: 15968, time: 0.552, data: 0.000) G_L1: 13.194 G_L1_ABSOLUTE: 2.869 G_L1_RELATIVE: 10.325 G_Regularizer: 0.000 validation_error: 21.020 +(epoch: 17, iters: 17968, time: 0.559, data: 0.000) G_L1: 18.673 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 15.892 G_Regularizer: 0.000 validation_error: 20.261 +(epoch: 17, iters: 19968, time: 0.549, data: 0.000) G_L1: 15.147 G_L1_ABSOLUTE: 3.294 G_L1_RELATIVE: 11.852 G_Regularizer: 0.000 validation_error: 20.494 +(epoch: 17, iters: 21968, time: 0.534, data: 0.000) G_L1: 12.430 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 10.090 G_Regularizer: 0.000 validation_error: 20.157 +(epoch: 17, iters: 23968, time: 0.538, data: 0.000) G_L1: 15.988 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 13.576 G_Regularizer: 0.000 validation_error: 20.552 +(epoch: 17, iters: 25968, time: 0.540, data: 0.000) G_L1: 16.052 G_L1_ABSOLUTE: 3.089 G_L1_RELATIVE: 12.962 G_Regularizer: 0.000 validation_error: 20.218 +(epoch: 17, iters: 27968, time: 0.554, data: 0.000) G_L1: 15.197 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 12.532 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 17, iters: 29968, time: 0.541, data: 0.000) G_L1: 13.195 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 10.946 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 17, iters: 31968, time: 0.543, data: 0.000) G_L1: 16.461 G_L1_ABSOLUTE: 2.689 G_L1_RELATIVE: 13.772 G_Regularizer: 0.000 validation_error: 20.102 +(epoch: 17, iters: 33968, time: 0.540, data: 0.000) G_L1: 14.395 G_L1_ABSOLUTE: 2.799 G_L1_RELATIVE: 11.596 G_Regularizer: 0.000 validation_error: 20.578 +(epoch: 17, iters: 35968, time: 0.548, data: 0.000) G_L1: 12.853 G_L1_ABSOLUTE: 2.238 G_L1_RELATIVE: 10.615 G_Regularizer: 0.000 validation_error: 20.708 +(epoch: 17, iters: 37968, time: 0.538, data: 0.000) G_L1: 14.160 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 11.252 G_Regularizer: 0.000 validation_error: 20.349 +(epoch: 17, iters: 39968, time: 0.556, data: 0.000) G_L1: 12.741 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 10.459 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 17, iters: 41968, time: 0.540, data: 0.000) G_L1: 15.529 G_L1_ABSOLUTE: 3.307 G_L1_RELATIVE: 12.222 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 17, iters: 43968, time: 0.554, data: 0.000) G_L1: 18.339 G_L1_ABSOLUTE: 3.094 G_L1_RELATIVE: 15.245 G_Regularizer: 0.000 validation_error: 19.930 +(epoch: 17, iters: 45968, time: 0.537, data: 0.000) G_L1: 17.984 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 15.150 G_Regularizer: 0.000 validation_error: 20.168 +(epoch: 17, iters: 47968, time: 0.531, data: 0.000) G_L1: 18.809 G_L1_ABSOLUTE: 2.300 G_L1_RELATIVE: 16.509 G_Regularizer: 0.000 validation_error: 21.123 +(epoch: 17, iters: 49968, time: 0.552, data: 0.000) G_L1: 16.118 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 13.596 G_Regularizer: 0.000 validation_error: 21.257 +(epoch: 17, iters: 51968, time: 0.553, data: 0.000) G_L1: 17.296 G_L1_ABSOLUTE: 2.688 G_L1_RELATIVE: 14.608 G_Regularizer: 0.000 validation_error: 20.086 +(epoch: 17, iters: 53968, time: 0.539, data: 0.000) G_L1: 13.852 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 11.374 G_Regularizer: 0.000 validation_error: 20.639 +(epoch: 17, iters: 55968, time: 0.560, data: 0.000) G_L1: 13.626 G_L1_ABSOLUTE: 2.204 G_L1_RELATIVE: 11.421 G_Regularizer: 0.000 validation_error: 20.791 +(epoch: 17, iters: 57968, time: 0.561, data: 0.000) G_L1: 15.592 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 12.792 G_Regularizer: 0.000 validation_error: 20.227 +(epoch: 17, iters: 59968, time: 0.541, data: 0.000) G_L1: 13.263 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 10.909 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 17, iters: 61968, time: 0.537, data: 0.000) G_L1: 14.913 G_L1_ABSOLUTE: 3.380 G_L1_RELATIVE: 11.533 G_Regularizer: 0.000 validation_error: 20.181 +(epoch: 17, iters: 63968, time: 0.544, data: 0.000) G_L1: 14.263 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 11.622 G_Regularizer: 0.000 validation_error: 20.114 +(epoch: 17, iters: 65968, time: 0.555, data: 0.000) G_L1: 14.417 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 11.853 G_Regularizer: 0.000 validation_error: 19.570 +(epoch: 17, iters: 67968, time: 0.557, data: 0.000) G_L1: 14.157 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 11.523 G_Regularizer: 0.000 validation_error: 20.097 +(epoch: 17, iters: 69968, time: 0.553, data: 0.000) G_L1: 17.874 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 15.201 G_Regularizer: 0.000 validation_error: 20.745 +(epoch: 17, iters: 71968, time: 0.551, data: 0.000) G_L1: 14.719 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 11.919 G_Regularizer: 0.000 validation_error: 20.586 +(epoch: 17, iters: 73968, time: 0.552, data: 0.000) G_L1: 15.799 G_L1_ABSOLUTE: 3.008 G_L1_RELATIVE: 12.791 G_Regularizer: 0.000 validation_error: 19.934 +(epoch: 17, iters: 75968, time: 0.559, data: 0.000) G_L1: 13.372 G_L1_ABSOLUTE: 2.763 G_L1_RELATIVE: 10.609 G_Regularizer: 0.000 validation_error: 20.629 +(epoch: 17, iters: 77968, time: 0.566, data: 0.000) G_L1: 15.469 G_L1_ABSOLUTE: 3.039 G_L1_RELATIVE: 12.430 G_Regularizer: 0.000 validation_error: 20.494 +(epoch: 17, iters: 79968, time: 0.540, data: 0.001) G_L1: 11.991 G_L1_ABSOLUTE: 1.882 G_L1_RELATIVE: 10.109 G_Regularizer: 0.000 validation_error: 20.113 +(epoch: 17, iters: 81968, time: 0.555, data: 0.000) G_L1: 14.806 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 12.220 G_Regularizer: 0.000 validation_error: 20.285 +(epoch: 17, iters: 83968, time: 0.540, data: 0.000) G_L1: 14.778 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 12.336 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 17, iters: 85968, time: 0.563, data: 0.000) G_L1: 17.656 G_L1_ABSOLUTE: 2.452 G_L1_RELATIVE: 15.204 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 17, iters: 87968, time: 0.535, data: 0.000) G_L1: 14.340 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 11.722 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 17, iters: 89968, time: 0.536, data: 0.000) G_L1: 15.230 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 12.910 G_Regularizer: 0.000 validation_error: 20.491 +(epoch: 17, iters: 91968, time: 0.553, data: 0.000) G_L1: 12.511 G_L1_ABSOLUTE: 2.207 G_L1_RELATIVE: 10.304 G_Regularizer: 0.000 validation_error: 20.627 +(epoch: 17, iters: 93968, time: 0.559, data: 0.001) G_L1: 12.857 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 10.334 G_Regularizer: 0.000 validation_error: 19.803 +(epoch: 17, iters: 95968, time: 0.538, data: 0.000) G_L1: 14.705 G_L1_ABSOLUTE: 2.834 G_L1_RELATIVE: 11.870 G_Regularizer: 0.000 validation_error: 20.515 +(epoch: 17, iters: 97968, time: 0.532, data: 0.000) G_L1: 12.738 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 10.330 G_Regularizer: 0.000 validation_error: 19.981 +(epoch: 17, iters: 99968, time: 0.531, data: 0.000) G_L1: 16.750 G_L1_ABSOLUTE: 3.009 G_L1_RELATIVE: 13.741 G_Regularizer: 0.000 validation_error: 19.988 +(epoch: 17, iters: 101968, time: 0.534, data: 0.000) G_L1: 18.399 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 15.676 G_Regularizer: 0.000 validation_error: 20.515 +(epoch: 17, iters: 103968, time: 0.529, data: 0.000) G_L1: 15.688 G_L1_ABSOLUTE: 3.399 G_L1_RELATIVE: 12.289 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 17, iters: 105968, time: 0.534, data: 0.000) G_L1: 16.067 G_L1_ABSOLUTE: 2.895 G_L1_RELATIVE: 13.172 G_Regularizer: 0.000 validation_error: 19.969 +(epoch: 17, iters: 107968, time: 0.532, data: 0.001) G_L1: 15.416 G_L1_ABSOLUTE: 2.585 G_L1_RELATIVE: 12.831 G_Regularizer: 0.000 validation_error: 21.027 +(epoch: 17, iters: 109968, time: 0.556, data: 0.000) G_L1: 16.782 G_L1_ABSOLUTE: 2.972 G_L1_RELATIVE: 13.810 G_Regularizer: 0.000 validation_error: 20.361 +(epoch: 17, iters: 111968, time: 0.563, data: 0.000) G_L1: 13.588 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 11.019 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 17, iters: 113968, time: 0.534, data: 0.000) G_L1: 14.154 G_L1_ABSOLUTE: 3.342 G_L1_RELATIVE: 10.812 G_Regularizer: 0.000 validation_error: 20.355 +(epoch: 17, iters: 115968, time: 0.543, data: 0.000) G_L1: 15.585 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 13.036 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 17, iters: 117968, time: 0.566, data: 0.000) G_L1: 13.967 G_L1_ABSOLUTE: 2.611 G_L1_RELATIVE: 11.356 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 17, iters: 119968, time: 0.551, data: 0.000) G_L1: 16.432 G_L1_ABSOLUTE: 2.507 G_L1_RELATIVE: 13.926 G_Regularizer: 0.000 validation_error: 20.480 +(epoch: 17, iters: 121968, time: 0.542, data: 0.000) G_L1: 16.600 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 14.180 G_Regularizer: 0.000 validation_error: 20.882 +(epoch: 17, iters: 123968, time: 0.559, data: 0.000) G_L1: 13.620 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 10.904 G_Regularizer: 0.000 validation_error: 20.785 +(epoch: 17, iters: 125968, time: 0.547, data: 0.000) G_L1: 13.021 G_L1_ABSOLUTE: 2.077 G_L1_RELATIVE: 10.944 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 17, iters: 127968, time: 0.554, data: 0.000) G_L1: 13.997 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 11.323 G_Regularizer: 0.000 validation_error: 21.351 +(epoch: 17, iters: 129968, time: 0.550, data: 0.000) G_L1: 15.293 G_L1_ABSOLUTE: 2.119 G_L1_RELATIVE: 13.174 G_Regularizer: 0.000 validation_error: 20.310 +(epoch: 17, iters: 131968, time: 0.556, data: 0.000) G_L1: 15.260 G_L1_ABSOLUTE: 3.054 G_L1_RELATIVE: 12.206 G_Regularizer: 0.000 validation_error: 20.449 +(epoch: 17, iters: 133968, time: 0.565, data: 0.000) G_L1: 16.629 G_L1_ABSOLUTE: 2.715 G_L1_RELATIVE: 13.914 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 17, iters: 135968, time: 0.554, data: 0.001) G_L1: 15.744 G_L1_ABSOLUTE: 2.753 G_L1_RELATIVE: 12.991 G_Regularizer: 0.000 validation_error: 21.411 +(epoch: 17, iters: 137968, time: 0.545, data: 0.001) G_L1: 15.926 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 13.121 G_Regularizer: 0.000 validation_error: 20.982 +(epoch: 17, iters: 139968, time: 0.552, data: 0.000) G_L1: 15.891 G_L1_ABSOLUTE: 2.971 G_L1_RELATIVE: 12.919 G_Regularizer: 0.000 validation_error: 20.543 +(epoch: 17, iters: 141968, time: 0.562, data: 0.000) G_L1: 13.408 G_L1_ABSOLUTE: 2.770 G_L1_RELATIVE: 10.638 G_Regularizer: 0.000 validation_error: 20.457 +(epoch: 17, iters: 143968, time: 0.564, data: 0.000) G_L1: 14.026 G_L1_ABSOLUTE: 2.425 G_L1_RELATIVE: 11.601 G_Regularizer: 0.000 validation_error: 20.320 +(epoch: 17, iters: 145968, time: 0.548, data: 0.000) G_L1: 16.352 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 13.951 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 17, iters: 147968, time: 0.555, data: 0.000) G_L1: 13.706 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 11.165 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 17, iters: 149968, time: 0.548, data: 0.000) G_L1: 14.768 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 12.194 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 17, iters: 151968, time: 0.551, data: 0.000) G_L1: 14.084 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 11.405 G_Regularizer: 0.000 validation_error: 20.028 +(epoch: 17, iters: 153968, time: 0.560, data: 0.000) G_L1: 13.537 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 11.076 G_Regularizer: 0.000 validation_error: 20.499 +(epoch: 17, iters: 155968, time: 0.538, data: 0.000) G_L1: 15.545 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 13.061 G_Regularizer: 0.000 validation_error: 20.798 +(epoch: 17, iters: 157968, time: 0.555, data: 0.000) G_L1: 14.579 G_L1_ABSOLUTE: 2.288 G_L1_RELATIVE: 12.291 G_Regularizer: 0.000 validation_error: 21.051 +(epoch: 17, iters: 159968, time: 0.557, data: 0.000) G_L1: 15.402 G_L1_ABSOLUTE: 3.150 G_L1_RELATIVE: 12.252 G_Regularizer: 0.000 validation_error: 20.325 +(epoch: 17, iters: 161968, time: 0.558, data: 0.000) G_L1: 16.114 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 13.468 G_Regularizer: 0.000 validation_error: 20.220 +(epoch: 17, iters: 163968, time: 0.536, data: 0.001) G_L1: 16.297 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 13.486 G_Regularizer: 0.000 validation_error: 20.723 +(epoch: 17, iters: 165968, time: 0.540, data: 0.000) G_L1: 12.516 G_L1_ABSOLUTE: 2.671 G_L1_RELATIVE: 9.845 G_Regularizer: 0.000 validation_error: 22.236 +(epoch: 17, iters: 167968, time: 0.553, data: 0.000) G_L1: 14.838 G_L1_ABSOLUTE: 2.850 G_L1_RELATIVE: 11.989 G_Regularizer: 0.000 validation_error: 20.175 +(epoch: 17, iters: 169968, time: 0.557, data: 0.000) G_L1: 16.682 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 13.948 G_Regularizer: 0.000 validation_error: 20.221 +(epoch: 17, iters: 171968, time: 0.543, data: 0.000) G_L1: 13.896 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 11.286 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 17, iters: 173968, time: 0.553, data: 0.000) G_L1: 13.196 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 10.646 G_Regularizer: 0.000 validation_error: 21.175 +(epoch: 17, iters: 175968, time: 0.551, data: 0.000) G_L1: 17.553 G_L1_ABSOLUTE: 2.859 G_L1_RELATIVE: 14.695 G_Regularizer: 0.000 validation_error: 20.714 +(epoch: 17, iters: 177968, time: 0.540, data: 0.000) G_L1: 16.289 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 13.603 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 17, iters: 179968, time: 0.540, data: 0.001) G_L1: 13.996 G_L1_ABSOLUTE: 2.446 G_L1_RELATIVE: 11.551 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 17, iters: 181968, time: 0.546, data: 0.000) G_L1: 12.996 G_L1_ABSOLUTE: 2.567 G_L1_RELATIVE: 10.430 G_Regularizer: 0.000 validation_error: 20.439 +(epoch: 17, iters: 183968, time: 0.545, data: 0.000) G_L1: 14.641 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 12.064 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 17, iters: 185968, time: 0.565, data: 0.000) G_L1: 15.966 G_L1_ABSOLUTE: 2.979 G_L1_RELATIVE: 12.987 G_Regularizer: 0.000 validation_error: 20.597 +(epoch: 17, iters: 187968, time: 0.540, data: 0.000) G_L1: 16.219 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 13.446 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 17, iters: 189968, time: 0.550, data: 0.000) G_L1: 13.807 G_L1_ABSOLUTE: 2.921 G_L1_RELATIVE: 10.887 G_Regularizer: 0.000 validation_error: 21.272 +(epoch: 17, iters: 191968, time: 0.557, data: 0.000) G_L1: 14.659 G_L1_ABSOLUTE: 2.465 G_L1_RELATIVE: 12.194 G_Regularizer: 0.000 validation_error: 20.120 +(epoch: 17, iters: 193968, time: 0.560, data: 0.000) G_L1: 14.254 G_L1_ABSOLUTE: 2.490 G_L1_RELATIVE: 11.764 G_Regularizer: 0.000 validation_error: 20.234 +(epoch: 17, iters: 195968, time: 0.555, data: 0.000) G_L1: 15.314 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 12.957 G_Regularizer: 0.000 validation_error: 20.490 +(epoch: 17, iters: 197968, time: 0.532, data: 0.000) G_L1: 16.058 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 13.562 G_Regularizer: 0.000 validation_error: 20.564 +(epoch: 17, iters: 199968, time: 0.552, data: 0.000) G_L1: 19.091 G_L1_ABSOLUTE: 3.121 G_L1_RELATIVE: 15.970 G_Regularizer: 0.000 validation_error: 20.167 +(epoch: 17, iters: 201968, time: 0.560, data: 0.000) G_L1: 13.293 G_L1_ABSOLUTE: 2.840 G_L1_RELATIVE: 10.453 G_Regularizer: 0.000 validation_error: 20.874 +(epoch: 17, iters: 203968, time: 0.560, data: 0.000) G_L1: 13.949 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 11.367 G_Regularizer: 0.000 validation_error: 20.357 +(epoch: 17, iters: 205968, time: 0.530, data: 0.000) G_L1: 16.915 G_L1_ABSOLUTE: 3.088 G_L1_RELATIVE: 13.826 G_Regularizer: 0.000 validation_error: 20.849 +(epoch: 17, iters: 207968, time: 0.555, data: 0.000) G_L1: 16.828 G_L1_ABSOLUTE: 3.042 G_L1_RELATIVE: 13.785 G_Regularizer: 0.000 validation_error: 20.137 +(epoch: 17, iters: 209968, time: 0.560, data: 0.000) G_L1: 14.972 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 12.695 G_Regularizer: 0.000 validation_error: 20.365 +(epoch: 17, iters: 211968, time: 0.557, data: 0.000) G_L1: 14.074 G_L1_ABSOLUTE: 2.511 G_L1_RELATIVE: 11.563 G_Regularizer: 0.000 validation_error: 19.955 +(epoch: 17, iters: 213968, time: 0.528, data: 0.000) G_L1: 15.085 G_L1_ABSOLUTE: 2.703 G_L1_RELATIVE: 12.382 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 17, iters: 215968, time: 0.554, data: 0.000) G_L1: 14.957 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 12.357 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 17, iters: 217968, time: 0.562, data: 0.000) G_L1: 16.228 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 13.494 G_Regularizer: 0.000 validation_error: 19.508 +(epoch: 17, iters: 219968, time: 0.541, data: 0.000) G_L1: 26.527 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 23.819 G_Regularizer: 0.000 validation_error: 20.338 +(epoch: 17, iters: 221968, time: 0.546, data: 0.000) G_L1: 15.703 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 12.995 G_Regularizer: 0.000 validation_error: 20.376 +(epoch: 17, iters: 223968, time: 0.531, data: 0.000) G_L1: 14.644 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 12.083 G_Regularizer: 0.000 validation_error: 20.094 +(epoch: 17, iters: 225968, time: 0.535, data: 0.000) G_L1: 16.403 G_L1_ABSOLUTE: 2.984 G_L1_RELATIVE: 13.419 G_Regularizer: 0.000 validation_error: 20.410 +(epoch: 17, iters: 227968, time: 0.539, data: 0.000) G_L1: 13.928 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 11.389 G_Regularizer: 0.000 validation_error: 20.420 +(epoch: 17, iters: 229968, time: 0.537, data: 0.000) G_L1: 17.750 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 14.952 G_Regularizer: 0.000 validation_error: 20.428 +(epoch: 17, iters: 231968, time: 0.543, data: 0.000) G_L1: 12.163 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 9.831 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 17, iters: 233968, time: 0.558, data: 0.000) G_L1: 13.506 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 11.082 G_Regularizer: 0.000 validation_error: 20.072 +(epoch: 17, iters: 235968, time: 0.557, data: 0.001) G_L1: 12.007 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 9.632 G_Regularizer: 0.000 validation_error: 21.491 +(epoch: 17, iters: 237968, time: 0.562, data: 0.000) G_L1: 15.590 G_L1_ABSOLUTE: 2.750 G_L1_RELATIVE: 12.839 G_Regularizer: 0.000 validation_error: 20.424 +(epoch: 17, iters: 239968, time: 0.540, data: 0.000) G_L1: 17.080 G_L1_ABSOLUTE: 2.790 G_L1_RELATIVE: 14.290 G_Regularizer: 0.000 validation_error: 21.168 +(epoch: 17, iters: 241968, time: 0.543, data: 0.000) G_L1: 13.154 G_L1_ABSOLUTE: 2.204 G_L1_RELATIVE: 10.950 G_Regularizer: 0.000 validation_error: 21.441 +(epoch: 17, iters: 243968, time: 0.542, data: 0.000) G_L1: 15.872 G_L1_ABSOLUTE: 3.244 G_L1_RELATIVE: 12.628 G_Regularizer: 0.000 validation_error: 20.419 +(epoch: 17, iters: 245968, time: 0.549, data: 0.000) G_L1: 14.376 G_L1_ABSOLUTE: 2.427 G_L1_RELATIVE: 11.949 G_Regularizer: 0.000 validation_error: 20.284 +(epoch: 17, iters: 247968, time: 0.540, data: 0.000) G_L1: 15.255 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 12.643 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 17, iters: 249968, time: 0.529, data: 0.000) G_L1: 17.225 G_L1_ABSOLUTE: 3.111 G_L1_RELATIVE: 14.114 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 17, iters: 251968, time: 0.563, data: 0.000) G_L1: 15.128 G_L1_ABSOLUTE: 3.274 G_L1_RELATIVE: 11.854 G_Regularizer: 0.000 validation_error: 19.942 +(epoch: 17, iters: 253968, time: 0.537, data: 0.000) G_L1: 14.670 G_L1_ABSOLUTE: 2.979 G_L1_RELATIVE: 11.692 G_Regularizer: 0.000 validation_error: 20.444 +(epoch: 17, iters: 255968, time: 0.553, data: 0.000) G_L1: 15.665 G_L1_ABSOLUTE: 2.648 G_L1_RELATIVE: 13.016 G_Regularizer: 0.000 validation_error: 20.738 +(epoch: 17, iters: 257968, time: 0.558, data: 0.000) G_L1: 16.477 G_L1_ABSOLUTE: 2.783 G_L1_RELATIVE: 13.694 G_Regularizer: 0.000 validation_error: 20.482 +(epoch: 17, iters: 259968, time: 0.563, data: 0.000) G_L1: 13.218 G_L1_ABSOLUTE: 2.695 G_L1_RELATIVE: 10.524 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 17, iters: 261968, time: 0.552, data: 0.001) G_L1: 16.081 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 13.429 G_Regularizer: 0.000 validation_error: 20.404 +(epoch: 17, iters: 263968, time: 0.548, data: 0.000) G_L1: 14.530 G_L1_ABSOLUTE: 2.636 G_L1_RELATIVE: 11.894 G_Regularizer: 0.000 validation_error: 21.071 +(epoch: 17, iters: 265968, time: 0.544, data: 0.000) G_L1: 14.587 G_L1_ABSOLUTE: 2.779 G_L1_RELATIVE: 11.809 G_Regularizer: 0.000 validation_error: 20.227 +(epoch: 17, iters: 267968, time: 0.562, data: 0.000) G_L1: 12.878 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 10.257 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 17, iters: 269968, time: 0.562, data: 0.000) G_L1: 15.606 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 13.009 G_Regularizer: 0.000 validation_error: 20.416 +(epoch: 17, iters: 271968, time: 0.556, data: 0.000) G_L1: 15.309 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 12.712 G_Regularizer: 0.000 validation_error: 20.253 +(epoch: 17, iters: 273968, time: 0.554, data: 0.000) G_L1: 15.118 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 12.440 G_Regularizer: 0.000 validation_error: 20.747 +(epoch: 17, iters: 275968, time: 0.558, data: 0.000) G_L1: 16.377 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 13.623 G_Regularizer: 0.000 validation_error: 20.456 +(epoch: 17, iters: 277968, time: 0.558, data: 0.001) G_L1: 13.261 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 10.586 G_Regularizer: 0.000 validation_error: 19.896 +(epoch: 17, iters: 279968, time: 0.560, data: 0.000) G_L1: 14.669 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 12.129 G_Regularizer: 0.000 validation_error: 20.038 +(epoch: 17, iters: 281968, time: 0.545, data: 0.000) G_L1: 13.214 G_L1_ABSOLUTE: 2.384 G_L1_RELATIVE: 10.830 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 17, iters: 283968, time: 0.559, data: 0.000) G_L1: 16.798 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 14.242 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 17, iters: 285968, time: 0.560, data: 0.000) G_L1: 15.719 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 13.051 G_Regularizer: 0.000 validation_error: 20.663 +(epoch: 17, iters: 287968, time: 0.559, data: 0.000) G_L1: 16.138 G_L1_ABSOLUTE: 2.985 G_L1_RELATIVE: 13.153 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 17, iters: 289968, time: 0.531, data: 0.000) G_L1: 14.275 G_L1_ABSOLUTE: 2.264 G_L1_RELATIVE: 12.011 G_Regularizer: 0.000 validation_error: 20.113 +(epoch: 17, iters: 291968, time: 0.553, data: 0.000) G_L1: 15.133 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 12.519 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 17, iters: 293968, time: 0.555, data: 0.000) G_L1: 15.072 G_L1_ABSOLUTE: 3.032 G_L1_RELATIVE: 12.040 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 17, iters: 295968, time: 0.564, data: 0.000) G_L1: 16.255 G_L1_ABSOLUTE: 2.704 G_L1_RELATIVE: 13.551 G_Regularizer: 0.000 validation_error: 20.987 +(epoch: 17, iters: 297968, time: 0.549, data: 0.000) G_L1: 14.665 G_L1_ABSOLUTE: 2.529 G_L1_RELATIVE: 12.136 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 17, iters: 299968, time: 0.559, data: 0.000) G_L1: 14.294 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 11.928 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 17, iters: 301968, time: 0.560, data: 0.001) G_L1: 17.263 G_L1_ABSOLUTE: 3.306 G_L1_RELATIVE: 13.958 G_Regularizer: 0.000 validation_error: 20.609 +(epoch: 18, iters: 1216, time: 0.558, data: 0.000) G_L1: 16.176 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 13.337 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 18, iters: 3216, time: 0.543, data: 0.000) G_L1: 14.646 G_L1_ABSOLUTE: 2.604 G_L1_RELATIVE: 12.042 G_Regularizer: 0.000 validation_error: 21.444 +(epoch: 18, iters: 5216, time: 0.563, data: 0.000) G_L1: 14.041 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 11.635 G_Regularizer: 0.000 validation_error: 20.387 +(epoch: 18, iters: 7216, time: 0.559, data: 0.000) G_L1: 13.743 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 11.142 G_Regularizer: 0.000 validation_error: 20.406 +(epoch: 18, iters: 9216, time: 0.558, data: 0.000) G_L1: 12.023 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 9.603 G_Regularizer: 0.000 validation_error: 20.997 +(epoch: 18, iters: 11216, time: 0.543, data: 0.000) G_L1: 14.247 G_L1_ABSOLUTE: 2.898 G_L1_RELATIVE: 11.349 G_Regularizer: 0.000 validation_error: 20.785 +(epoch: 18, iters: 13216, time: 0.545, data: 0.000) G_L1: 12.613 G_L1_ABSOLUTE: 2.496 G_L1_RELATIVE: 10.117 G_Regularizer: 0.000 validation_error: 20.375 +(epoch: 18, iters: 15216, time: 0.555, data: 0.000) G_L1: 13.729 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 11.139 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 18, iters: 17216, time: 0.568, data: 0.001) G_L1: 14.390 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 11.842 G_Regularizer: 0.000 validation_error: 20.540 +(epoch: 18, iters: 19216, time: 0.553, data: 0.000) G_L1: 12.291 G_L1_ABSOLUTE: 2.082 G_L1_RELATIVE: 10.208 G_Regularizer: 0.000 validation_error: 20.345 +(epoch: 18, iters: 21216, time: 0.534, data: 0.000) G_L1: 16.179 G_L1_ABSOLUTE: 3.110 G_L1_RELATIVE: 13.069 G_Regularizer: 0.000 validation_error: 21.002 +(epoch: 18, iters: 23216, time: 0.557, data: 0.000) G_L1: 12.535 G_L1_ABSOLUTE: 2.210 G_L1_RELATIVE: 10.325 G_Regularizer: 0.000 validation_error: 20.621 +(epoch: 18, iters: 25216, time: 0.564, data: 0.000) G_L1: 15.132 G_L1_ABSOLUTE: 2.215 G_L1_RELATIVE: 12.917 G_Regularizer: 0.000 validation_error: 20.598 +(epoch: 18, iters: 27216, time: 0.541, data: 0.001) G_L1: 13.412 G_L1_ABSOLUTE: 2.727 G_L1_RELATIVE: 10.684 G_Regularizer: 0.000 validation_error: 21.135 +(epoch: 18, iters: 29216, time: 0.533, data: 0.000) G_L1: 14.811 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 12.272 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 18, iters: 31216, time: 0.556, data: 0.000) G_L1: 14.716 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 12.119 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 18, iters: 33216, time: 0.556, data: 0.000) G_L1: 16.690 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 14.114 G_Regularizer: 0.000 validation_error: 20.367 +(epoch: 18, iters: 35216, time: 0.561, data: 0.000) G_L1: 14.212 G_L1_ABSOLUTE: 3.041 G_L1_RELATIVE: 11.171 G_Regularizer: 0.000 validation_error: 20.480 +(epoch: 18, iters: 37216, time: 0.538, data: 0.000) G_L1: 15.275 G_L1_ABSOLUTE: 2.991 G_L1_RELATIVE: 12.284 G_Regularizer: 0.000 validation_error: 21.030 +(epoch: 18, iters: 39216, time: 0.561, data: 0.000) G_L1: 12.672 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 10.331 G_Regularizer: 0.000 validation_error: 21.241 +(epoch: 18, iters: 41216, time: 0.555, data: 0.000) G_L1: 13.011 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 10.671 G_Regularizer: 0.000 validation_error: 20.723 +(epoch: 18, iters: 43216, time: 0.557, data: 0.000) G_L1: 15.643 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 13.101 G_Regularizer: 0.000 validation_error: 21.163 +(epoch: 18, iters: 45216, time: 0.530, data: 0.000) G_L1: 13.586 G_L1_ABSOLUTE: 2.984 G_L1_RELATIVE: 10.603 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 18, iters: 47216, time: 0.556, data: 0.000) G_L1: 15.771 G_L1_ABSOLUTE: 2.789 G_L1_RELATIVE: 12.982 G_Regularizer: 0.000 validation_error: 20.293 +(epoch: 18, iters: 49216, time: 0.562, data: 0.000) G_L1: 14.453 G_L1_ABSOLUTE: 2.726 G_L1_RELATIVE: 11.727 G_Regularizer: 0.000 validation_error: 20.249 +(epoch: 18, iters: 51216, time: 0.564, data: 0.000) G_L1: 11.702 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 9.160 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 18, iters: 53216, time: 0.546, data: 0.000) G_L1: 15.676 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 13.234 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 18, iters: 55216, time: 0.546, data: 0.000) G_L1: 17.108 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 14.231 G_Regularizer: 0.000 validation_error: 21.528 +(epoch: 18, iters: 57216, time: 0.562, data: 0.001) G_L1: 16.457 G_L1_ABSOLUTE: 2.358 G_L1_RELATIVE: 14.099 G_Regularizer: 0.000 validation_error: 21.925 +(epoch: 18, iters: 59216, time: 0.561, data: 0.000) G_L1: 17.877 G_L1_ABSOLUTE: 2.496 G_L1_RELATIVE: 15.381 G_Regularizer: 0.000 validation_error: 20.492 +(epoch: 18, iters: 61216, time: 0.549, data: 0.000) G_L1: 15.558 G_L1_ABSOLUTE: 2.826 G_L1_RELATIVE: 12.732 G_Regularizer: 0.000 validation_error: 20.249 +(epoch: 18, iters: 63216, time: 0.533, data: 0.000) G_L1: 14.077 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 11.640 G_Regularizer: 0.000 validation_error: 20.995 +(epoch: 18, iters: 65216, time: 0.553, data: 0.000) G_L1: 15.755 G_L1_ABSOLUTE: 3.205 G_L1_RELATIVE: 12.550 G_Regularizer: 0.000 validation_error: 20.008 +(epoch: 18, iters: 67216, time: 0.546, data: 0.000) G_L1: 11.631 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 9.148 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 18, iters: 69216, time: 0.540, data: 0.000) G_L1: 14.422 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 11.612 G_Regularizer: 0.000 validation_error: 20.674 +(epoch: 18, iters: 71216, time: 0.542, data: 0.000) G_L1: 15.464 G_L1_ABSOLUTE: 3.263 G_L1_RELATIVE: 12.201 G_Regularizer: 0.000 validation_error: 20.669 +(epoch: 18, iters: 73216, time: 0.549, data: 0.000) G_L1: 14.880 G_L1_ABSOLUTE: 3.110 G_L1_RELATIVE: 11.770 G_Regularizer: 0.000 validation_error: 21.127 +(epoch: 18, iters: 75216, time: 0.563, data: 0.000) G_L1: 17.239 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 14.555 G_Regularizer: 0.000 validation_error: 20.222 +(epoch: 18, iters: 77216, time: 0.558, data: 0.000) G_L1: 13.780 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 11.046 G_Regularizer: 0.000 validation_error: 20.310 +(epoch: 18, iters: 79216, time: 0.552, data: 0.000) G_L1: 15.787 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 13.190 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 18, iters: 81216, time: 0.554, data: 0.000) G_L1: 14.247 G_L1_ABSOLUTE: 2.417 G_L1_RELATIVE: 11.830 G_Regularizer: 0.000 validation_error: 21.327 +(epoch: 18, iters: 83216, time: 0.531, data: 0.000) G_L1: 15.223 G_L1_ABSOLUTE: 3.059 G_L1_RELATIVE: 12.164 G_Regularizer: 0.000 validation_error: 20.062 +(epoch: 18, iters: 85216, time: 0.559, data: 0.000) G_L1: 18.024 G_L1_ABSOLUTE: 3.053 G_L1_RELATIVE: 14.971 G_Regularizer: 0.000 validation_error: 20.288 +(epoch: 18, iters: 87216, time: 0.532, data: 0.001) G_L1: 13.467 G_L1_ABSOLUTE: 2.174 G_L1_RELATIVE: 11.293 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 18, iters: 89216, time: 0.531, data: 0.000) G_L1: 17.445 G_L1_ABSOLUTE: 2.942 G_L1_RELATIVE: 14.503 G_Regularizer: 0.000 validation_error: 20.483 +(epoch: 18, iters: 91216, time: 0.573, data: 0.000) G_L1: 13.877 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 11.268 G_Regularizer: 0.000 validation_error: 20.346 +(epoch: 18, iters: 93216, time: 0.557, data: 0.000) G_L1: 13.989 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 11.559 G_Regularizer: 0.000 validation_error: 21.003 +(epoch: 18, iters: 95216, time: 0.546, data: 0.000) G_L1: 15.038 G_L1_ABSOLUTE: 2.583 G_L1_RELATIVE: 12.455 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 18, iters: 97216, time: 0.566, data: 0.000) G_L1: 14.544 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 12.130 G_Regularizer: 0.000 validation_error: 21.269 +(epoch: 18, iters: 99216, time: 0.546, data: 0.000) G_L1: 17.851 G_L1_ABSOLUTE: 2.675 G_L1_RELATIVE: 15.176 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 18, iters: 101216, time: 0.547, data: 0.000) G_L1: 15.127 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 12.237 G_Regularizer: 0.000 validation_error: 20.463 +(epoch: 18, iters: 103216, time: 0.552, data: 0.000) G_L1: 14.622 G_L1_ABSOLUTE: 2.396 G_L1_RELATIVE: 12.226 G_Regularizer: 0.000 validation_error: 20.927 +(epoch: 18, iters: 105216, time: 0.549, data: 0.000) G_L1: 16.605 G_L1_ABSOLUTE: 2.787 G_L1_RELATIVE: 13.818 G_Regularizer: 0.000 validation_error: 20.429 +(epoch: 18, iters: 107216, time: 0.560, data: 0.001) G_L1: 17.746 G_L1_ABSOLUTE: 3.226 G_L1_RELATIVE: 14.520 G_Regularizer: 0.000 validation_error: 21.239 +(epoch: 18, iters: 109216, time: 0.556, data: 0.000) G_L1: 16.168 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 13.546 G_Regularizer: 0.000 validation_error: 20.418 +(epoch: 18, iters: 111216, time: 0.541, data: 0.000) G_L1: 15.366 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 12.912 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 18, iters: 113216, time: 0.536, data: 0.000) G_L1: 13.260 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 10.995 G_Regularizer: 0.000 validation_error: 20.334 +(epoch: 18, iters: 115216, time: 0.560, data: 0.000) G_L1: 16.590 G_L1_ABSOLUTE: 2.903 G_L1_RELATIVE: 13.686 G_Regularizer: 0.000 validation_error: 20.425 +(epoch: 18, iters: 117216, time: 0.560, data: 0.000) G_L1: 14.155 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 11.664 G_Regularizer: 0.000 validation_error: 21.314 +(epoch: 18, iters: 119216, time: 0.566, data: 0.000) G_L1: 14.299 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 11.671 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 18, iters: 121216, time: 0.537, data: 0.000) G_L1: 14.783 G_L1_ABSOLUTE: 2.787 G_L1_RELATIVE: 11.995 G_Regularizer: 0.000 validation_error: 19.758 +(epoch: 18, iters: 123216, time: 0.558, data: 0.000) G_L1: 14.921 G_L1_ABSOLUTE: 3.135 G_L1_RELATIVE: 11.787 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 18, iters: 125216, time: 0.564, data: 0.000) G_L1: 17.450 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 14.905 G_Regularizer: 0.000 validation_error: 20.258 +(epoch: 18, iters: 127216, time: 0.554, data: 0.000) G_L1: 13.985 G_L1_ABSOLUTE: 2.928 G_L1_RELATIVE: 11.058 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 18, iters: 129216, time: 0.531, data: 0.000) G_L1: 15.816 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 13.220 G_Regularizer: 0.000 validation_error: 20.407 +(epoch: 18, iters: 131216, time: 0.543, data: 0.000) G_L1: 16.696 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 13.989 G_Regularizer: 0.000 validation_error: 20.761 +(epoch: 18, iters: 133216, time: 0.565, data: 0.000) G_L1: 18.754 G_L1_ABSOLUTE: 3.401 G_L1_RELATIVE: 15.354 G_Regularizer: 0.000 validation_error: 21.752 +(epoch: 18, iters: 135216, time: 0.561, data: 0.001) G_L1: 14.709 G_L1_ABSOLUTE: 2.240 G_L1_RELATIVE: 12.470 G_Regularizer: 0.000 validation_error: 20.276 +(epoch: 18, iters: 137216, time: 0.536, data: 0.001) G_L1: 13.303 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 10.706 G_Regularizer: 0.000 validation_error: 20.342 +(epoch: 18, iters: 139216, time: 0.559, data: 0.000) G_L1: 15.003 G_L1_ABSOLUTE: 3.174 G_L1_RELATIVE: 11.829 G_Regularizer: 0.000 validation_error: 20.982 +(epoch: 18, iters: 141216, time: 0.559, data: 0.001) G_L1: 17.664 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 15.301 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 18, iters: 143216, time: 0.556, data: 0.000) G_L1: 12.305 G_L1_ABSOLUTE: 2.076 G_L1_RELATIVE: 10.228 G_Regularizer: 0.000 validation_error: 20.056 +(epoch: 18, iters: 145216, time: 0.553, data: 0.000) G_L1: 15.035 G_L1_ABSOLUTE: 2.376 G_L1_RELATIVE: 12.659 G_Regularizer: 0.000 validation_error: 21.290 +(epoch: 18, iters: 147216, time: 0.559, data: 0.000) G_L1: 20.920 G_L1_ABSOLUTE: 2.934 G_L1_RELATIVE: 17.987 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 18, iters: 149216, time: 0.558, data: 0.000) G_L1: 14.125 G_L1_ABSOLUTE: 2.350 G_L1_RELATIVE: 11.775 G_Regularizer: 0.000 validation_error: 21.245 +(epoch: 18, iters: 151216, time: 0.553, data: 0.000) G_L1: 15.524 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 13.041 G_Regularizer: 0.000 validation_error: 21.081 +(epoch: 18, iters: 153216, time: 0.554, data: 0.000) G_L1: 23.109 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 20.298 G_Regularizer: 0.000 validation_error: 20.291 +(epoch: 18, iters: 155216, time: 0.545, data: 0.001) G_L1: 16.714 G_L1_ABSOLUTE: 2.723 G_L1_RELATIVE: 13.991 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 18, iters: 157216, time: 0.560, data: 0.000) G_L1: 15.369 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 12.488 G_Regularizer: 0.000 validation_error: 20.535 +(epoch: 18, iters: 159216, time: 0.549, data: 0.000) G_L1: 14.507 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 12.013 G_Regularizer: 0.000 validation_error: 20.067 +(epoch: 18, iters: 161216, time: 0.552, data: 0.001) G_L1: 17.323 G_L1_ABSOLUTE: 3.133 G_L1_RELATIVE: 14.190 G_Regularizer: 0.000 validation_error: 20.533 +(epoch: 18, iters: 163216, time: 0.561, data: 0.000) G_L1: 11.816 G_L1_ABSOLUTE: 2.844 G_L1_RELATIVE: 8.972 G_Regularizer: 0.000 validation_error: 20.425 +(epoch: 18, iters: 165216, time: 0.558, data: 0.000) G_L1: 14.764 G_L1_ABSOLUTE: 2.602 G_L1_RELATIVE: 12.162 G_Regularizer: 0.000 validation_error: 20.955 +(epoch: 18, iters: 167216, time: 0.549, data: 0.000) G_L1: 11.201 G_L1_ABSOLUTE: 2.072 G_L1_RELATIVE: 9.129 G_Regularizer: 0.000 validation_error: 21.024 +(epoch: 18, iters: 169216, time: 0.553, data: 0.000) G_L1: 17.063 G_L1_ABSOLUTE: 2.799 G_L1_RELATIVE: 14.264 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 18, iters: 171216, time: 0.551, data: 0.001) G_L1: 17.420 G_L1_ABSOLUTE: 2.971 G_L1_RELATIVE: 14.449 G_Regularizer: 0.000 validation_error: 20.160 +(epoch: 18, iters: 173216, time: 0.539, data: 0.000) G_L1: 17.074 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 14.453 G_Regularizer: 0.000 validation_error: 20.329 +(epoch: 18, iters: 175216, time: 0.555, data: 0.000) G_L1: 14.226 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 11.465 G_Regularizer: 0.000 validation_error: 21.001 +(epoch: 18, iters: 177216, time: 0.553, data: 0.000) G_L1: 16.457 G_L1_ABSOLUTE: 3.215 G_L1_RELATIVE: 13.242 G_Regularizer: 0.000 validation_error: 20.255 +(epoch: 18, iters: 179216, time: 0.550, data: 0.000) G_L1: 17.378 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 14.580 G_Regularizer: 0.000 validation_error: 20.267 +(epoch: 18, iters: 181216, time: 0.560, data: 0.001) G_L1: 15.439 G_L1_ABSOLUTE: 2.861 G_L1_RELATIVE: 12.578 G_Regularizer: 0.000 validation_error: 20.372 +(epoch: 18, iters: 183216, time: 0.561, data: 0.000) G_L1: 12.987 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 10.647 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 18, iters: 185216, time: 0.541, data: 0.000) G_L1: 17.012 G_L1_ABSOLUTE: 2.427 G_L1_RELATIVE: 14.585 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 18, iters: 187216, time: 0.553, data: 0.000) G_L1: 15.516 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 12.898 G_Regularizer: 0.000 validation_error: 20.450 +(epoch: 18, iters: 189216, time: 0.559, data: 0.001) G_L1: 16.233 G_L1_ABSOLUTE: 2.288 G_L1_RELATIVE: 13.945 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 18, iters: 191216, time: 0.557, data: 0.000) G_L1: 14.026 G_L1_ABSOLUTE: 2.815 G_L1_RELATIVE: 11.212 G_Regularizer: 0.000 validation_error: 20.025 +(epoch: 18, iters: 193216, time: 0.558, data: 0.000) G_L1: 15.920 G_L1_ABSOLUTE: 2.838 G_L1_RELATIVE: 13.082 G_Regularizer: 0.000 validation_error: 20.295 +(epoch: 18, iters: 195216, time: 0.540, data: 0.000) G_L1: 15.112 G_L1_ABSOLUTE: 2.263 G_L1_RELATIVE: 12.849 G_Regularizer: 0.000 validation_error: 20.416 +(epoch: 18, iters: 197216, time: 0.539, data: 0.000) G_L1: 11.663 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 9.295 G_Regularizer: 0.000 validation_error: 19.968 +(epoch: 18, iters: 199216, time: 0.556, data: 0.001) G_L1: 17.890 G_L1_ABSOLUTE: 2.643 G_L1_RELATIVE: 15.247 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 18, iters: 201216, time: 0.554, data: 0.000) G_L1: 11.252 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 8.950 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 18, iters: 203216, time: 0.552, data: 0.000) G_L1: 14.577 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 11.683 G_Regularizer: 0.000 validation_error: 20.569 +(epoch: 18, iters: 205216, time: 0.543, data: 0.000) G_L1: 15.436 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 12.692 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 18, iters: 207216, time: 0.559, data: 0.000) G_L1: 12.824 G_L1_ABSOLUTE: 2.281 G_L1_RELATIVE: 10.544 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 18, iters: 209216, time: 0.564, data: 0.000) G_L1: 13.086 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 10.305 G_Regularizer: 0.000 validation_error: 19.755 +(epoch: 18, iters: 211216, time: 0.548, data: 0.000) G_L1: 14.105 G_L1_ABSOLUTE: 2.559 G_L1_RELATIVE: 11.545 G_Regularizer: 0.000 validation_error: 21.111 +(epoch: 18, iters: 213216, time: 0.538, data: 0.000) G_L1: 13.234 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 10.819 G_Regularizer: 0.000 validation_error: 20.550 +(epoch: 18, iters: 215216, time: 0.549, data: 0.000) G_L1: 13.325 G_L1_ABSOLUTE: 2.155 G_L1_RELATIVE: 11.170 G_Regularizer: 0.000 validation_error: 21.060 +(epoch: 18, iters: 217216, time: 0.538, data: 0.000) G_L1: 15.643 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 12.960 G_Regularizer: 0.000 validation_error: 20.599 +(epoch: 18, iters: 219216, time: 0.550, data: 0.000) G_L1: 12.902 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 10.397 G_Regularizer: 0.000 validation_error: 20.178 +(epoch: 18, iters: 221216, time: 0.538, data: 0.000) G_L1: 12.255 G_L1_ABSOLUTE: 2.327 G_L1_RELATIVE: 9.928 G_Regularizer: 0.000 validation_error: 20.984 +(epoch: 18, iters: 223216, time: 0.548, data: 0.001) G_L1: 16.002 G_L1_ABSOLUTE: 3.029 G_L1_RELATIVE: 12.973 G_Regularizer: 0.000 validation_error: 20.392 +(epoch: 18, iters: 225216, time: 0.563, data: 0.000) G_L1: 17.544 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 14.664 G_Regularizer: 0.000 validation_error: 20.049 +(epoch: 18, iters: 227216, time: 0.553, data: 0.000) G_L1: 13.862 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 11.358 G_Regularizer: 0.000 validation_error: 20.255 +(epoch: 18, iters: 229216, time: 0.538, data: 0.000) G_L1: 14.428 G_L1_ABSOLUTE: 2.832 G_L1_RELATIVE: 11.596 G_Regularizer: 0.000 validation_error: 20.107 +(epoch: 18, iters: 231216, time: 0.559, data: 0.000) G_L1: 17.832 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 15.172 G_Regularizer: 0.000 validation_error: 21.467 +(epoch: 18, iters: 233216, time: 0.556, data: 0.000) G_L1: 13.670 G_L1_ABSOLUTE: 2.761 G_L1_RELATIVE: 10.909 G_Regularizer: 0.000 validation_error: 20.486 +(epoch: 18, iters: 235216, time: 0.538, data: 0.000) G_L1: 16.803 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 14.006 G_Regularizer: 0.000 validation_error: 20.393 +(epoch: 18, iters: 237216, time: 0.561, data: 0.000) G_L1: 13.685 G_L1_ABSOLUTE: 2.064 G_L1_RELATIVE: 11.620 G_Regularizer: 0.000 validation_error: 21.200 +(epoch: 18, iters: 239216, time: 0.531, data: 0.000) G_L1: 13.574 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 11.003 G_Regularizer: 0.000 validation_error: 20.065 +(epoch: 18, iters: 241216, time: 0.556, data: 0.000) G_L1: 16.014 G_L1_ABSOLUTE: 3.533 G_L1_RELATIVE: 12.481 G_Regularizer: 0.000 validation_error: 20.607 +(epoch: 18, iters: 243216, time: 0.546, data: 0.000) G_L1: 13.984 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 11.645 G_Regularizer: 0.000 validation_error: 20.289 +(epoch: 18, iters: 245216, time: 0.562, data: 0.000) G_L1: 18.028 G_L1_ABSOLUTE: 2.604 G_L1_RELATIVE: 15.424 G_Regularizer: 0.000 validation_error: 21.240 +(epoch: 18, iters: 247216, time: 0.537, data: 0.000) G_L1: 13.711 G_L1_ABSOLUTE: 2.177 G_L1_RELATIVE: 11.534 G_Regularizer: 0.000 validation_error: 21.157 +(epoch: 18, iters: 249216, time: 0.559, data: 0.000) G_L1: 16.200 G_L1_ABSOLUTE: 2.771 G_L1_RELATIVE: 13.429 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 18, iters: 251216, time: 0.553, data: 0.000) G_L1: 15.787 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 13.201 G_Regularizer: 0.000 validation_error: 20.544 +(epoch: 18, iters: 253216, time: 0.553, data: 0.000) G_L1: 15.760 G_L1_ABSOLUTE: 2.779 G_L1_RELATIVE: 12.981 G_Regularizer: 0.000 validation_error: 21.431 +(epoch: 18, iters: 255216, time: 0.542, data: 0.000) G_L1: 30.076 G_L1_ABSOLUTE: 2.713 G_L1_RELATIVE: 27.364 G_Regularizer: 0.000 validation_error: 21.041 +(epoch: 18, iters: 257216, time: 0.551, data: 0.000) G_L1: 18.217 G_L1_ABSOLUTE: 2.901 G_L1_RELATIVE: 15.316 G_Regularizer: 0.000 validation_error: 20.549 +(epoch: 18, iters: 259216, time: 0.547, data: 0.000) G_L1: 21.037 G_L1_ABSOLUTE: 3.265 G_L1_RELATIVE: 17.772 G_Regularizer: 0.000 validation_error: 20.288 +(epoch: 18, iters: 261216, time: 0.559, data: 0.000) G_L1: 14.459 G_L1_ABSOLUTE: 2.270 G_L1_RELATIVE: 12.190 G_Regularizer: 0.000 validation_error: 20.007 +(epoch: 18, iters: 263216, time: 0.539, data: 0.000) G_L1: 15.024 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 12.478 G_Regularizer: 0.000 validation_error: 20.560 +(epoch: 18, iters: 265216, time: 0.556, data: 0.002) G_L1: 15.508 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 12.844 G_Regularizer: 0.000 validation_error: 20.646 +(epoch: 18, iters: 267216, time: 0.559, data: 0.000) G_L1: 15.475 G_L1_ABSOLUTE: 2.701 G_L1_RELATIVE: 12.774 G_Regularizer: 0.000 validation_error: 20.175 +(epoch: 18, iters: 269216, time: 0.559, data: 0.000) G_L1: 14.271 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 11.916 G_Regularizer: 0.000 validation_error: 21.079 +(epoch: 18, iters: 271216, time: 0.540, data: 0.000) G_L1: 18.815 G_L1_ABSOLUTE: 2.441 G_L1_RELATIVE: 16.374 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 18, iters: 273216, time: 0.553, data: 0.000) G_L1: 18.615 G_L1_ABSOLUTE: 2.833 G_L1_RELATIVE: 15.782 G_Regularizer: 0.000 validation_error: 20.525 +(epoch: 18, iters: 275216, time: 0.537, data: 0.000) G_L1: 15.630 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 12.985 G_Regularizer: 0.000 validation_error: 21.069 +(epoch: 18, iters: 277216, time: 0.540, data: 0.000) G_L1: 14.254 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 11.667 G_Regularizer: 0.000 validation_error: 20.609 +(epoch: 18, iters: 279216, time: 0.548, data: 0.000) G_L1: 16.233 G_L1_ABSOLUTE: 2.405 G_L1_RELATIVE: 13.828 G_Regularizer: 0.000 validation_error: 20.647 +(epoch: 18, iters: 281216, time: 0.555, data: 0.000) G_L1: 15.125 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 12.628 G_Regularizer: 0.000 validation_error: 20.525 +(epoch: 18, iters: 283216, time: 0.561, data: 0.000) G_L1: 16.377 G_L1_ABSOLUTE: 2.289 G_L1_RELATIVE: 14.088 G_Regularizer: 0.000 validation_error: 21.426 +(epoch: 18, iters: 285216, time: 0.561, data: 0.000) G_L1: 14.843 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 12.095 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 18, iters: 287216, time: 0.560, data: 0.000) G_L1: 15.489 G_L1_ABSOLUTE: 2.866 G_L1_RELATIVE: 12.623 G_Regularizer: 0.000 validation_error: 21.113 +(epoch: 18, iters: 289216, time: 0.537, data: 0.000) G_L1: 18.288 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 15.604 G_Regularizer: 0.000 validation_error: 20.504 +(epoch: 18, iters: 291216, time: 0.565, data: 0.000) G_L1: 17.055 G_L1_ABSOLUTE: 3.277 G_L1_RELATIVE: 13.779 G_Regularizer: 0.000 validation_error: 21.628 +(epoch: 18, iters: 293216, time: 0.552, data: 0.000) G_L1: 15.469 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 12.913 G_Regularizer: 0.000 validation_error: 20.241 +(epoch: 18, iters: 295216, time: 0.534, data: 0.000) G_L1: 18.294 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 15.752 G_Regularizer: 0.000 validation_error: 20.582 +(epoch: 18, iters: 297216, time: 0.537, data: 0.000) G_L1: 13.243 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 10.940 G_Regularizer: 0.000 validation_error: 20.117 +(epoch: 18, iters: 299216, time: 0.553, data: 0.000) G_L1: 14.747 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 12.232 G_Regularizer: 0.000 validation_error: 20.449 +(epoch: 18, iters: 301216, time: 0.556, data: 0.000) G_L1: 16.174 G_L1_ABSOLUTE: 2.869 G_L1_RELATIVE: 13.305 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 19, iters: 464, time: 0.551, data: 0.000) G_L1: 15.367 G_L1_ABSOLUTE: 3.116 G_L1_RELATIVE: 12.251 G_Regularizer: 0.000 validation_error: 20.358 +(epoch: 19, iters: 2464, time: 0.540, data: 0.000) G_L1: 15.152 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 12.720 G_Regularizer: 0.000 validation_error: 20.692 +(epoch: 19, iters: 4464, time: 0.556, data: 0.000) G_L1: 12.386 G_L1_ABSOLUTE: 2.500 G_L1_RELATIVE: 9.886 G_Regularizer: 0.000 validation_error: 20.871 +(epoch: 19, iters: 6464, time: 0.549, data: 0.000) G_L1: 14.758 G_L1_ABSOLUTE: 2.293 G_L1_RELATIVE: 12.465 G_Regularizer: 0.000 validation_error: 20.409 +(epoch: 19, iters: 8464, time: 0.550, data: 0.000) G_L1: 16.797 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 14.225 G_Regularizer: 0.000 validation_error: 21.302 +(epoch: 19, iters: 10464, time: 0.547, data: 0.000) G_L1: 14.510 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 12.137 G_Regularizer: 0.000 validation_error: 20.913 +(epoch: 19, iters: 12464, time: 0.533, data: 0.000) G_L1: 22.304 G_L1_ABSOLUTE: 3.062 G_L1_RELATIVE: 19.242 G_Regularizer: 0.000 validation_error: 20.399 +(epoch: 19, iters: 14464, time: 0.546, data: 0.000) G_L1: 14.920 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 12.302 G_Regularizer: 0.000 validation_error: 20.402 +(epoch: 19, iters: 16464, time: 0.558, data: 0.000) G_L1: 14.168 G_L1_ABSOLUTE: 2.758 G_L1_RELATIVE: 11.410 G_Regularizer: 0.000 validation_error: 20.291 +(epoch: 19, iters: 18464, time: 0.545, data: 0.000) G_L1: 13.241 G_L1_ABSOLUTE: 2.970 G_L1_RELATIVE: 10.271 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 19, iters: 20464, time: 0.547, data: 0.000) G_L1: 13.955 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 11.291 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 19, iters: 22464, time: 0.553, data: 0.000) G_L1: 16.244 G_L1_ABSOLUTE: 3.079 G_L1_RELATIVE: 13.165 G_Regularizer: 0.000 validation_error: 20.496 +(epoch: 19, iters: 24464, time: 0.553, data: 0.000) G_L1: 14.189 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 11.765 G_Regularizer: 0.000 validation_error: 21.137 +(epoch: 19, iters: 26464, time: 0.554, data: 0.000) G_L1: 17.305 G_L1_ABSOLUTE: 2.921 G_L1_RELATIVE: 14.384 G_Regularizer: 0.000 validation_error: 20.158 +(epoch: 19, iters: 28464, time: 0.539, data: 0.000) G_L1: 14.232 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 11.778 G_Regularizer: 0.000 validation_error: 20.618 +(epoch: 19, iters: 30464, time: 0.561, data: 0.000) G_L1: 18.771 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 16.199 G_Regularizer: 0.000 validation_error: 20.516 +(epoch: 19, iters: 32464, time: 0.565, data: 0.000) G_L1: 16.101 G_L1_ABSOLUTE: 3.052 G_L1_RELATIVE: 13.049 G_Regularizer: 0.000 validation_error: 20.404 +(epoch: 19, iters: 34464, time: 0.552, data: 0.001) G_L1: 15.848 G_L1_ABSOLUTE: 2.790 G_L1_RELATIVE: 13.057 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 19, iters: 36464, time: 0.541, data: 0.000) G_L1: 12.889 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 10.212 G_Regularizer: 0.000 validation_error: 21.344 +(epoch: 19, iters: 38464, time: 0.557, data: 0.000) G_L1: 12.939 G_L1_ABSOLUTE: 2.697 G_L1_RELATIVE: 10.241 G_Regularizer: 0.000 validation_error: 20.546 +(epoch: 19, iters: 40464, time: 0.562, data: 0.000) G_L1: 12.421 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 9.967 G_Regularizer: 0.000 validation_error: 20.007 +(epoch: 19, iters: 42464, time: 0.546, data: 0.000) G_L1: 14.580 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 11.818 G_Regularizer: 0.000 validation_error: 20.814 +(epoch: 19, iters: 44464, time: 0.537, data: 0.000) G_L1: 14.803 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 12.135 G_Regularizer: 0.000 validation_error: 20.194 +(epoch: 19, iters: 46464, time: 0.560, data: 0.000) G_L1: 15.727 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 13.256 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 19, iters: 48464, time: 0.558, data: 0.000) G_L1: 16.829 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 14.238 G_Regularizer: 0.000 validation_error: 20.325 +(epoch: 19, iters: 50464, time: 0.553, data: 0.000) G_L1: 14.140 G_L1_ABSOLUTE: 2.648 G_L1_RELATIVE: 11.492 G_Regularizer: 0.000 validation_error: 20.430 +(epoch: 19, iters: 52464, time: 0.530, data: 0.000) G_L1: 16.743 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 13.986 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 19, iters: 54464, time: 0.560, data: 0.000) G_L1: 16.116 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 13.277 G_Regularizer: 0.000 validation_error: 20.520 +(epoch: 19, iters: 56464, time: 0.552, data: 0.000) G_L1: 13.345 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 10.758 G_Regularizer: 0.000 validation_error: 21.104 +(epoch: 19, iters: 58464, time: 0.558, data: 0.000) G_L1: 15.797 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 13.521 G_Regularizer: 0.000 validation_error: 20.150 +(epoch: 19, iters: 60464, time: 0.533, data: 0.000) G_L1: 16.582 G_L1_ABSOLUTE: 2.884 G_L1_RELATIVE: 13.698 G_Regularizer: 0.000 validation_error: 20.565 +(epoch: 19, iters: 62464, time: 0.531, data: 0.000) G_L1: 14.153 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 11.871 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 19, iters: 64464, time: 0.551, data: 0.000) G_L1: 12.850 G_L1_ABSOLUTE: 2.180 G_L1_RELATIVE: 10.670 G_Regularizer: 0.000 validation_error: 20.508 +(epoch: 19, iters: 66464, time: 0.558, data: 0.000) G_L1: 14.965 G_L1_ABSOLUTE: 2.978 G_L1_RELATIVE: 11.987 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 19, iters: 68464, time: 0.537, data: 0.000) G_L1: 15.289 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 12.692 G_Regularizer: 0.000 validation_error: 20.697 +(epoch: 19, iters: 70464, time: 0.546, data: 0.001) G_L1: 12.357 G_L1_ABSOLUTE: 2.241 G_L1_RELATIVE: 10.116 G_Regularizer: 0.000 validation_error: 20.994 +(epoch: 19, iters: 72464, time: 0.563, data: 0.001) G_L1: 14.091 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 11.752 G_Regularizer: 0.000 validation_error: 20.434 +(epoch: 19, iters: 74464, time: 0.557, data: 0.000) G_L1: 14.433 G_L1_ABSOLUTE: 2.324 G_L1_RELATIVE: 12.109 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 19, iters: 76464, time: 0.563, data: 0.000) G_L1: 17.195 G_L1_ABSOLUTE: 2.205 G_L1_RELATIVE: 14.990 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 19, iters: 78464, time: 0.552, data: 0.000) G_L1: 15.788 G_L1_ABSOLUTE: 2.661 G_L1_RELATIVE: 13.126 G_Regularizer: 0.000 validation_error: 21.358 +(epoch: 19, iters: 80464, time: 0.564, data: 0.000) G_L1: 14.927 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 12.329 G_Regularizer: 0.000 validation_error: 20.387 +(epoch: 19, iters: 82464, time: 0.561, data: 0.000) G_L1: 19.138 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 16.718 G_Regularizer: 0.000 validation_error: 20.237 +(epoch: 19, iters: 84464, time: 0.557, data: 0.000) G_L1: 16.363 G_L1_ABSOLUTE: 2.794 G_L1_RELATIVE: 13.569 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 19, iters: 86464, time: 0.548, data: 0.000) G_L1: 15.052 G_L1_ABSOLUTE: 2.933 G_L1_RELATIVE: 12.119 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 19, iters: 88464, time: 0.554, data: 0.000) G_L1: 16.366 G_L1_ABSOLUTE: 2.758 G_L1_RELATIVE: 13.608 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 19, iters: 90464, time: 0.561, data: 0.000) G_L1: 16.883 G_L1_ABSOLUTE: 2.534 G_L1_RELATIVE: 14.348 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 19, iters: 92464, time: 0.534, data: 0.000) G_L1: 13.485 G_L1_ABSOLUTE: 2.834 G_L1_RELATIVE: 10.651 G_Regularizer: 0.000 validation_error: 20.450 +(epoch: 19, iters: 94464, time: 0.536, data: 0.000) G_L1: 14.322 G_L1_ABSOLUTE: 3.047 G_L1_RELATIVE: 11.275 G_Regularizer: 0.000 validation_error: 21.266 +(epoch: 19, iters: 96464, time: 0.538, data: 0.000) G_L1: 13.121 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 10.785 G_Regularizer: 0.000 validation_error: 20.367 +(epoch: 19, iters: 98464, time: 0.551, data: 0.000) G_L1: 16.684 G_L1_ABSOLUTE: 2.853 G_L1_RELATIVE: 13.831 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 19, iters: 100464, time: 0.563, data: 0.000) G_L1: 13.774 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 10.977 G_Regularizer: 0.000 validation_error: 19.794 +(epoch: 19, iters: 102464, time: 0.547, data: 0.000) G_L1: 18.478 G_L1_ABSOLUTE: 3.217 G_L1_RELATIVE: 15.261 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 19, iters: 104464, time: 0.546, data: 0.001) G_L1: 15.116 G_L1_ABSOLUTE: 3.028 G_L1_RELATIVE: 12.088 G_Regularizer: 0.000 validation_error: 20.502 +(epoch: 19, iters: 106464, time: 0.560, data: 0.000) G_L1: 16.216 G_L1_ABSOLUTE: 3.163 G_L1_RELATIVE: 13.053 G_Regularizer: 0.000 validation_error: 20.441 +(epoch: 19, iters: 108464, time: 0.559, data: 0.000) G_L1: 15.052 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 12.529 G_Regularizer: 0.000 validation_error: 20.667 +(epoch: 19, iters: 110464, time: 0.543, data: 0.000) G_L1: 18.565 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 15.991 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 19, iters: 112464, time: 0.562, data: 0.000) G_L1: 14.327 G_L1_ABSOLUTE: 2.583 G_L1_RELATIVE: 11.744 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 19, iters: 114464, time: 0.561, data: 0.000) G_L1: 13.605 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 11.152 G_Regularizer: 0.000 validation_error: 20.575 +(epoch: 19, iters: 116464, time: 0.557, data: 0.000) G_L1: 14.028 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 11.562 G_Regularizer: 0.000 validation_error: 20.554 +(epoch: 19, iters: 118464, time: 0.552, data: 0.000) G_L1: 16.283 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 13.734 G_Regularizer: 0.000 validation_error: 20.895 +(epoch: 19, iters: 120464, time: 0.532, data: 0.000) G_L1: 14.463 G_L1_ABSOLUTE: 3.139 G_L1_RELATIVE: 11.324 G_Regularizer: 0.000 validation_error: 20.627 +(epoch: 19, iters: 122464, time: 0.561, data: 0.000) G_L1: 16.128 G_L1_ABSOLUTE: 2.975 G_L1_RELATIVE: 13.154 G_Regularizer: 0.000 validation_error: 20.032 +(epoch: 19, iters: 124464, time: 0.562, data: 0.000) G_L1: 13.943 G_L1_ABSOLUTE: 2.222 G_L1_RELATIVE: 11.721 G_Regularizer: 0.000 validation_error: 20.681 +(epoch: 19, iters: 126464, time: 0.552, data: 0.000) G_L1: 11.543 G_L1_ABSOLUTE: 2.207 G_L1_RELATIVE: 9.336 G_Regularizer: 0.000 validation_error: 20.372 +(epoch: 19, iters: 128464, time: 0.539, data: 0.000) G_L1: 13.510 G_L1_ABSOLUTE: 2.898 G_L1_RELATIVE: 10.611 G_Regularizer: 0.000 validation_error: 21.280 +(epoch: 19, iters: 130464, time: 0.558, data: 0.000) G_L1: 14.039 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 11.554 G_Regularizer: 0.000 validation_error: 20.204 +(epoch: 19, iters: 132464, time: 0.566, data: 0.000) G_L1: 15.535 G_L1_ABSOLUTE: 2.833 G_L1_RELATIVE: 12.702 G_Regularizer: 0.000 validation_error: 21.550 +(epoch: 19, iters: 134464, time: 0.563, data: 0.000) G_L1: 19.217 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 16.768 G_Regularizer: 0.000 validation_error: 21.616 +(epoch: 19, iters: 136464, time: 0.534, data: 0.000) G_L1: 14.811 G_L1_ABSOLUTE: 2.361 G_L1_RELATIVE: 12.450 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 19, iters: 138464, time: 0.537, data: 0.000) G_L1: 15.159 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 12.816 G_Regularizer: 0.000 validation_error: 20.502 +(epoch: 19, iters: 140464, time: 0.558, data: 0.000) G_L1: 16.230 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 13.598 G_Regularizer: 0.000 validation_error: 20.622 +(epoch: 19, iters: 142464, time: 0.556, data: 0.001) G_L1: 13.698 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 11.086 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 19, iters: 144464, time: 0.551, data: 0.000) G_L1: 14.963 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 12.544 G_Regularizer: 0.000 validation_error: 20.305 +(epoch: 19, iters: 146464, time: 0.533, data: 0.000) G_L1: 11.375 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 8.962 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 19, iters: 148464, time: 0.538, data: 0.000) G_L1: 14.273 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 11.738 G_Regularizer: 0.000 validation_error: 20.407 +(epoch: 19, iters: 150464, time: 0.557, data: 0.000) G_L1: 14.813 G_L1_ABSOLUTE: 2.247 G_L1_RELATIVE: 12.565 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 19, iters: 152464, time: 0.547, data: 0.000) G_L1: 16.240 G_L1_ABSOLUTE: 2.896 G_L1_RELATIVE: 13.343 G_Regularizer: 0.000 validation_error: 20.470 +(epoch: 19, iters: 154464, time: 0.551, data: 0.000) G_L1: 13.387 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 10.929 G_Regularizer: 0.000 validation_error: 20.117 +(epoch: 19, iters: 156464, time: 0.559, data: 0.000) G_L1: 15.117 G_L1_ABSOLUTE: 2.969 G_L1_RELATIVE: 12.148 G_Regularizer: 0.000 validation_error: 21.090 +(epoch: 19, iters: 158464, time: 0.556, data: 0.000) G_L1: 13.023 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 10.325 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 19, iters: 160464, time: 0.528, data: 0.000) G_L1: 14.413 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 12.006 G_Regularizer: 0.000 validation_error: 20.160 +(epoch: 19, iters: 162464, time: 0.549, data: 0.000) G_L1: 15.285 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 12.886 G_Regularizer: 0.000 validation_error: 20.410 +(epoch: 19, iters: 164464, time: 0.553, data: 0.000) G_L1: 15.926 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 13.353 G_Regularizer: 0.000 validation_error: 21.419 +(epoch: 19, iters: 166464, time: 0.558, data: 0.000) G_L1: 15.601 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 13.107 G_Regularizer: 0.000 validation_error: 20.482 +(epoch: 19, iters: 168464, time: 0.555, data: 0.000) G_L1: 11.379 G_L1_ABSOLUTE: 2.123 G_L1_RELATIVE: 9.256 G_Regularizer: 0.000 validation_error: 20.094 +(epoch: 19, iters: 170464, time: 0.561, data: 0.000) G_L1: 15.207 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 12.677 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 19, iters: 172464, time: 0.556, data: 0.000) G_L1: 13.897 G_L1_ABSOLUTE: 2.799 G_L1_RELATIVE: 11.098 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 19, iters: 174464, time: 0.553, data: 0.000) G_L1: 15.087 G_L1_ABSOLUTE: 2.585 G_L1_RELATIVE: 12.501 G_Regularizer: 0.000 validation_error: 21.157 +(epoch: 19, iters: 176464, time: 0.561, data: 0.000) G_L1: 16.418 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 14.004 G_Regularizer: 0.000 validation_error: 21.435 +(epoch: 19, iters: 178464, time: 0.549, data: 0.000) G_L1: 16.224 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 13.389 G_Regularizer: 0.000 validation_error: 21.063 +(epoch: 19, iters: 180464, time: 0.556, data: 0.000) G_L1: 18.171 G_L1_ABSOLUTE: 3.185 G_L1_RELATIVE: 14.986 G_Regularizer: 0.000 validation_error: 20.400 +(epoch: 19, iters: 182464, time: 0.562, data: 0.000) G_L1: 19.761 G_L1_ABSOLUTE: 3.100 G_L1_RELATIVE: 16.661 G_Regularizer: 0.000 validation_error: 20.464 +(epoch: 19, iters: 184464, time: 0.569, data: 0.000) G_L1: 15.198 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 12.694 G_Regularizer: 0.000 validation_error: 21.203 +(epoch: 19, iters: 186464, time: 0.549, data: 0.000) G_L1: 13.735 G_L1_ABSOLUTE: 2.395 G_L1_RELATIVE: 11.340 G_Regularizer: 0.000 validation_error: 20.449 +(epoch: 19, iters: 188464, time: 0.542, data: 0.000) G_L1: 16.593 G_L1_ABSOLUTE: 2.967 G_L1_RELATIVE: 13.626 G_Regularizer: 0.000 validation_error: 20.775 +(epoch: 19, iters: 190464, time: 0.558, data: 0.000) G_L1: 13.408 G_L1_ABSOLUTE: 2.644 G_L1_RELATIVE: 10.765 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 19, iters: 192464, time: 0.536, data: 0.001) G_L1: 13.301 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 10.895 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 19, iters: 194464, time: 0.553, data: 0.000) G_L1: 16.433 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 14.044 G_Regularizer: 0.000 validation_error: 20.224 +(epoch: 19, iters: 196464, time: 0.552, data: 0.000) G_L1: 15.682 G_L1_ABSOLUTE: 2.661 G_L1_RELATIVE: 13.021 G_Regularizer: 0.000 validation_error: 20.469 +(epoch: 19, iters: 198464, time: 0.540, data: 0.000) G_L1: 14.566 G_L1_ABSOLUTE: 2.808 G_L1_RELATIVE: 11.758 G_Regularizer: 0.000 validation_error: 21.055 +(epoch: 19, iters: 200464, time: 0.543, data: 0.000) G_L1: 12.281 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 9.573 G_Regularizer: 0.000 validation_error: 20.428 +(epoch: 19, iters: 202464, time: 0.556, data: 0.000) G_L1: 14.448 G_L1_ABSOLUTE: 2.534 G_L1_RELATIVE: 11.914 G_Regularizer: 0.000 validation_error: 20.551 +(epoch: 19, iters: 204464, time: 0.543, data: 0.000) G_L1: 13.210 G_L1_ABSOLUTE: 2.585 G_L1_RELATIVE: 10.625 G_Regularizer: 0.000 validation_error: 20.378 +(epoch: 19, iters: 206464, time: 0.560, data: 0.000) G_L1: 16.016 G_L1_ABSOLUTE: 2.749 G_L1_RELATIVE: 13.267 G_Regularizer: 0.000 validation_error: 20.729 +(epoch: 19, iters: 208464, time: 0.555, data: 0.001) G_L1: 15.054 G_L1_ABSOLUTE: 2.675 G_L1_RELATIVE: 12.380 G_Regularizer: 0.000 validation_error: 20.596 +(epoch: 19, iters: 210464, time: 0.561, data: 0.000) G_L1: 14.048 G_L1_ABSOLUTE: 2.024 G_L1_RELATIVE: 12.024 G_Regularizer: 0.000 validation_error: 20.126 +(epoch: 19, iters: 212464, time: 0.530, data: 0.000) G_L1: 12.941 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 10.586 G_Regularizer: 0.000 validation_error: 21.458 +(epoch: 19, iters: 214464, time: 0.562, data: 0.000) G_L1: 14.947 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 12.351 G_Regularizer: 0.000 validation_error: 20.632 +(epoch: 19, iters: 216464, time: 0.556, data: 0.000) G_L1: 11.938 G_L1_ABSOLUTE: 2.244 G_L1_RELATIVE: 9.694 G_Regularizer: 0.000 validation_error: 20.312 +(epoch: 19, iters: 218464, time: 0.560, data: 0.000) G_L1: 15.287 G_L1_ABSOLUTE: 2.996 G_L1_RELATIVE: 12.291 G_Regularizer: 0.000 validation_error: 21.428 +(epoch: 19, iters: 220464, time: 0.531, data: 0.000) G_L1: 16.462 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 13.944 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 19, iters: 222464, time: 0.548, data: 0.001) G_L1: 16.299 G_L1_ABSOLUTE: 2.862 G_L1_RELATIVE: 13.437 G_Regularizer: 0.000 validation_error: 21.230 +(epoch: 19, iters: 224464, time: 0.566, data: 0.000) G_L1: 14.828 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 12.205 G_Regularizer: 0.000 validation_error: 20.512 +(epoch: 19, iters: 226464, time: 0.562, data: 0.000) G_L1: 15.328 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 12.698 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 19, iters: 228464, time: 0.540, data: 0.000) G_L1: 14.298 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 11.709 G_Regularizer: 0.000 validation_error: 21.248 +(epoch: 19, iters: 230464, time: 0.550, data: 0.000) G_L1: 16.469 G_L1_ABSOLUTE: 2.062 G_L1_RELATIVE: 14.407 G_Regularizer: 0.000 validation_error: 20.657 +(epoch: 19, iters: 232464, time: 0.541, data: 0.000) G_L1: 18.113 G_L1_ABSOLUTE: 3.111 G_L1_RELATIVE: 15.003 G_Regularizer: 0.000 validation_error: 21.186 +(epoch: 19, iters: 234464, time: 0.554, data: 0.000) G_L1: 19.876 G_L1_ABSOLUTE: 3.223 G_L1_RELATIVE: 16.653 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 19, iters: 236464, time: 0.553, data: 0.000) G_L1: 14.758 G_L1_ABSOLUTE: 2.980 G_L1_RELATIVE: 11.778 G_Regularizer: 0.000 validation_error: 20.672 +(epoch: 19, iters: 238464, time: 0.559, data: 0.000) G_L1: 13.041 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 10.444 G_Regularizer: 0.000 validation_error: 21.080 +(epoch: 19, iters: 240464, time: 0.559, data: 0.001) G_L1: 16.267 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 13.861 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 19, iters: 242464, time: 0.552, data: 0.000) G_L1: 14.442 G_L1_ABSOLUTE: 2.444 G_L1_RELATIVE: 11.998 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 19, iters: 244464, time: 0.552, data: 0.000) G_L1: 13.572 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 11.314 G_Regularizer: 0.000 validation_error: 20.002 +(epoch: 19, iters: 246464, time: 0.543, data: 0.000) G_L1: 16.713 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 13.997 G_Regularizer: 0.000 validation_error: 19.997 +(epoch: 19, iters: 248464, time: 0.559, data: 0.001) G_L1: 16.037 G_L1_ABSOLUTE: 3.002 G_L1_RELATIVE: 13.035 G_Regularizer: 0.000 validation_error: 20.414 +(epoch: 19, iters: 250464, time: 0.561, data: 0.000) G_L1: 14.102 G_L1_ABSOLUTE: 3.022 G_L1_RELATIVE: 11.080 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 19, iters: 252464, time: 0.559, data: 0.000) G_L1: 11.413 G_L1_ABSOLUTE: 2.161 G_L1_RELATIVE: 9.252 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 19, iters: 254464, time: 0.564, data: 0.000) G_L1: 13.805 G_L1_ABSOLUTE: 2.940 G_L1_RELATIVE: 10.865 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 19, iters: 256464, time: 0.562, data: 0.000) G_L1: 15.724 G_L1_ABSOLUTE: 3.154 G_L1_RELATIVE: 12.571 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 19, iters: 258464, time: 0.556, data: 0.001) G_L1: 13.393 G_L1_ABSOLUTE: 2.828 G_L1_RELATIVE: 10.564 G_Regularizer: 0.000 validation_error: 21.063 +(epoch: 19, iters: 260464, time: 0.570, data: 0.000) G_L1: 12.988 G_L1_ABSOLUTE: 2.703 G_L1_RELATIVE: 10.285 G_Regularizer: 0.000 validation_error: 21.557 +(epoch: 19, iters: 262464, time: 0.539, data: 0.000) G_L1: 15.013 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 12.236 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 19, iters: 264464, time: 0.555, data: 0.000) G_L1: 14.775 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 12.515 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 19, iters: 266464, time: 0.566, data: 0.000) G_L1: 15.779 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 13.127 G_Regularizer: 0.000 validation_error: 21.028 +(epoch: 19, iters: 268464, time: 0.563, data: 0.000) G_L1: 14.394 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 11.699 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 19, iters: 270464, time: 0.550, data: 0.001) G_L1: 12.722 G_L1_ABSOLUTE: 2.203 G_L1_RELATIVE: 10.519 G_Regularizer: 0.000 validation_error: 21.519 +(epoch: 19, iters: 272464, time: 0.562, data: 0.000) G_L1: 12.909 G_L1_ABSOLUTE: 2.322 G_L1_RELATIVE: 10.587 G_Regularizer: 0.000 validation_error: 20.732 +(epoch: 19, iters: 274464, time: 0.561, data: 0.000) G_L1: 15.075 G_L1_ABSOLUTE: 2.382 G_L1_RELATIVE: 12.693 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 19, iters: 276464, time: 0.552, data: 0.000) G_L1: 16.129 G_L1_ABSOLUTE: 3.120 G_L1_RELATIVE: 13.009 G_Regularizer: 0.000 validation_error: 21.236 +(epoch: 19, iters: 278464, time: 0.553, data: 0.000) G_L1: 15.697 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 13.303 G_Regularizer: 0.000 validation_error: 20.442 +(epoch: 19, iters: 280464, time: 0.558, data: 0.000) G_L1: 12.489 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 10.075 G_Regularizer: 0.000 validation_error: 20.594 +(epoch: 19, iters: 282464, time: 0.560, data: 0.000) G_L1: 15.038 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 12.240 G_Regularizer: 0.000 validation_error: 20.316 +(epoch: 19, iters: 284464, time: 0.561, data: 0.000) G_L1: 14.210 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 11.728 G_Regularizer: 0.000 validation_error: 19.859 +(epoch: 19, iters: 286464, time: 0.540, data: 0.001) G_L1: 14.200 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 11.705 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 19, iters: 288464, time: 0.547, data: 0.000) G_L1: 17.944 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 15.144 G_Regularizer: 0.000 validation_error: 20.043 +(epoch: 19, iters: 290464, time: 0.565, data: 0.000) G_L1: 15.249 G_L1_ABSOLUTE: 2.804 G_L1_RELATIVE: 12.445 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 19, iters: 292464, time: 0.544, data: 0.000) G_L1: 13.994 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 11.578 G_Regularizer: 0.000 validation_error: 20.051 +(epoch: 19, iters: 294464, time: 0.542, data: 0.000) G_L1: 14.150 G_L1_ABSOLUTE: 2.173 G_L1_RELATIVE: 11.977 G_Regularizer: 0.000 validation_error: 20.356 +(epoch: 19, iters: 296464, time: 0.553, data: 0.000) G_L1: 14.967 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 12.482 G_Regularizer: 0.000 validation_error: 20.412 +(epoch: 19, iters: 298464, time: 0.556, data: 0.000) G_L1: 13.791 G_L1_ABSOLUTE: 2.580 G_L1_RELATIVE: 11.210 G_Regularizer: 0.000 validation_error: 20.647 +(epoch: 19, iters: 300464, time: 0.557, data: 0.000) G_L1: 16.250 G_L1_ABSOLUTE: 2.929 G_L1_RELATIVE: 13.321 G_Regularizer: 0.000 validation_error: 20.461 +(epoch: 19, iters: 302464, time: 0.558, data: 0.001) G_L1: 16.661 G_L1_ABSOLUTE: 2.951 G_L1_RELATIVE: 13.710 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 20, iters: 1712, time: 0.542, data: 0.000) G_L1: 13.294 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 10.718 G_Regularizer: 0.000 validation_error: 19.992 +(epoch: 20, iters: 3712, time: 0.550, data: 0.000) G_L1: 14.482 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 11.910 G_Regularizer: 0.000 validation_error: 20.263 +(epoch: 20, iters: 5712, time: 0.548, data: 0.000) G_L1: 15.929 G_L1_ABSOLUTE: 2.295 G_L1_RELATIVE: 13.634 G_Regularizer: 0.000 validation_error: 20.357 +(epoch: 20, iters: 7712, time: 0.570, data: 0.001) G_L1: 12.130 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 9.693 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 20, iters: 9712, time: 0.541, data: 0.000) G_L1: 13.645 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 11.239 G_Regularizer: 0.000 validation_error: 20.322 +(epoch: 20, iters: 11712, time: 0.545, data: 0.000) G_L1: 15.685 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 12.941 G_Regularizer: 0.000 validation_error: 19.595 +(epoch: 20, iters: 13712, time: 0.561, data: 0.000) G_L1: 16.539 G_L1_ABSOLUTE: 3.046 G_L1_RELATIVE: 13.492 G_Regularizer: 0.000 validation_error: 20.558 +(epoch: 20, iters: 15712, time: 0.566, data: 0.000) G_L1: 13.760 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 11.318 G_Regularizer: 0.000 validation_error: 20.357 +(epoch: 20, iters: 17712, time: 0.528, data: 0.000) G_L1: 17.161 G_L1_ABSOLUTE: 2.641 G_L1_RELATIVE: 14.520 G_Regularizer: 0.000 validation_error: 20.256 +(epoch: 20, iters: 19712, time: 0.556, data: 0.000) G_L1: 13.413 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 10.787 G_Regularizer: 0.000 validation_error: 20.276 +(epoch: 20, iters: 21712, time: 0.557, data: 0.000) G_L1: 14.865 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 12.318 G_Regularizer: 0.000 validation_error: 20.280 +(epoch: 20, iters: 23712, time: 0.538, data: 0.000) G_L1: 12.584 G_L1_ABSOLUTE: 2.203 G_L1_RELATIVE: 10.380 G_Regularizer: 0.000 validation_error: 20.168 +(epoch: 20, iters: 25712, time: 0.549, data: 0.000) G_L1: 14.139 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 11.650 G_Regularizer: 0.000 validation_error: 20.220 +(epoch: 20, iters: 27712, time: 0.545, data: 0.000) G_L1: 14.669 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 12.038 G_Regularizer: 0.000 validation_error: 21.688 +(epoch: 20, iters: 29712, time: 0.562, data: 0.000) G_L1: 14.389 G_L1_ABSOLUTE: 1.899 G_L1_RELATIVE: 12.490 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 20, iters: 31712, time: 0.548, data: 0.000) G_L1: 17.641 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 14.801 G_Regularizer: 0.000 validation_error: 20.637 +(epoch: 20, iters: 33712, time: 0.551, data: 0.000) G_L1: 16.171 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 13.653 G_Regularizer: 0.000 validation_error: 19.895 +(epoch: 20, iters: 35712, time: 0.559, data: 0.000) G_L1: 14.244 G_L1_ABSOLUTE: 2.405 G_L1_RELATIVE: 11.839 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 20, iters: 37712, time: 0.564, data: 0.000) G_L1: 16.852 G_L1_ABSOLUTE: 2.913 G_L1_RELATIVE: 13.938 G_Regularizer: 0.000 validation_error: 21.262 +(epoch: 20, iters: 39712, time: 0.560, data: 0.000) G_L1: 13.470 G_L1_ABSOLUTE: 3.007 G_L1_RELATIVE: 10.463 G_Regularizer: 0.000 validation_error: 20.346 +(epoch: 20, iters: 41712, time: 0.545, data: 0.000) G_L1: 13.327 G_L1_ABSOLUTE: 2.386 G_L1_RELATIVE: 10.941 G_Regularizer: 0.000 validation_error: 20.286 +(epoch: 20, iters: 43712, time: 0.541, data: 0.000) G_L1: 14.136 G_L1_ABSOLUTE: 2.795 G_L1_RELATIVE: 11.341 G_Regularizer: 0.000 validation_error: 19.731 +(epoch: 20, iters: 45712, time: 0.538, data: 0.001) G_L1: 14.796 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 12.317 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 20, iters: 47712, time: 0.538, data: 0.000) G_L1: 16.569 G_L1_ABSOLUTE: 3.045 G_L1_RELATIVE: 13.523 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 20, iters: 49712, time: 0.543, data: 0.000) G_L1: 14.120 G_L1_ABSOLUTE: 3.059 G_L1_RELATIVE: 11.060 G_Regularizer: 0.000 validation_error: 20.500 +(epoch: 20, iters: 51712, time: 0.548, data: 0.000) G_L1: 19.758 G_L1_ABSOLUTE: 2.875 G_L1_RELATIVE: 16.883 G_Regularizer: 0.000 validation_error: 21.265 +(epoch: 20, iters: 53712, time: 0.561, data: 0.000) G_L1: 16.173 G_L1_ABSOLUTE: 2.902 G_L1_RELATIVE: 13.272 G_Regularizer: 0.000 validation_error: 21.558 +(epoch: 20, iters: 55712, time: 0.558, data: 0.000) G_L1: 18.107 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 15.216 G_Regularizer: 0.000 validation_error: 21.047 +(epoch: 20, iters: 57712, time: 0.559, data: 0.000) G_L1: 16.029 G_L1_ABSOLUTE: 2.270 G_L1_RELATIVE: 13.759 G_Regularizer: 0.000 validation_error: 20.738 +(epoch: 20, iters: 59712, time: 0.546, data: 0.000) G_L1: 15.108 G_L1_ABSOLUTE: 2.850 G_L1_RELATIVE: 12.259 G_Regularizer: 0.000 validation_error: 20.466 +(epoch: 20, iters: 61712, time: 0.562, data: 0.001) G_L1: 13.365 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 10.620 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 20, iters: 63712, time: 0.555, data: 0.000) G_L1: 12.254 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 9.841 G_Regularizer: 0.000 validation_error: 20.498 +(epoch: 20, iters: 65712, time: 0.557, data: 0.001) G_L1: 12.039 G_L1_ABSOLUTE: 2.681 G_L1_RELATIVE: 9.358 G_Regularizer: 0.000 validation_error: 20.496 +(epoch: 20, iters: 67712, time: 0.546, data: 0.001) G_L1: 12.054 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 9.370 G_Regularizer: 0.000 validation_error: 20.070 +(epoch: 20, iters: 69712, time: 0.553, data: 0.000) G_L1: 15.241 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 12.790 G_Regularizer: 0.000 validation_error: 21.048 +(epoch: 20, iters: 71712, time: 0.556, data: 0.000) G_L1: 13.573 G_L1_ABSOLUTE: 2.532 G_L1_RELATIVE: 11.041 G_Regularizer: 0.000 validation_error: 20.464 +(epoch: 20, iters: 73712, time: 0.558, data: 0.000) G_L1: 14.532 G_L1_ABSOLUTE: 2.763 G_L1_RELATIVE: 11.769 G_Regularizer: 0.000 validation_error: 20.206 +(epoch: 20, iters: 75712, time: 0.545, data: 0.001) G_L1: 14.106 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 11.735 G_Regularizer: 0.000 validation_error: 20.494 +(epoch: 20, iters: 77712, time: 0.534, data: 0.000) G_L1: 12.649 G_L1_ABSOLUTE: 2.599 G_L1_RELATIVE: 10.050 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 20, iters: 79712, time: 0.557, data: 0.000) G_L1: 16.299 G_L1_ABSOLUTE: 2.700 G_L1_RELATIVE: 13.599 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 20, iters: 81712, time: 0.561, data: 0.000) G_L1: 14.904 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 12.374 G_Regularizer: 0.000 validation_error: 20.959 +(epoch: 20, iters: 83712, time: 0.547, data: 0.000) G_L1: 14.523 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 11.942 G_Regularizer: 0.000 validation_error: 20.551 +(epoch: 20, iters: 85712, time: 0.561, data: 0.000) G_L1: 13.507 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 11.139 G_Regularizer: 0.000 validation_error: 20.573 +(epoch: 20, iters: 87712, time: 0.554, data: 0.000) G_L1: 13.606 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 10.871 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 20, iters: 89712, time: 0.559, data: 0.000) G_L1: 17.434 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 15.069 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 20, iters: 91712, time: 0.545, data: 0.001) G_L1: 12.433 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 9.930 G_Regularizer: 0.000 validation_error: 20.372 +(epoch: 20, iters: 93712, time: 0.537, data: 0.000) G_L1: 15.699 G_L1_ABSOLUTE: 3.113 G_L1_RELATIVE: 12.585 G_Regularizer: 0.000 validation_error: 20.575 +(epoch: 20, iters: 95712, time: 0.535, data: 0.000) G_L1: 14.181 G_L1_ABSOLUTE: 2.431 G_L1_RELATIVE: 11.750 G_Regularizer: 0.000 validation_error: 20.028 +(epoch: 20, iters: 97712, time: 0.566, data: 0.000) G_L1: 17.770 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 15.297 G_Regularizer: 0.000 validation_error: 20.944 +(epoch: 20, iters: 99712, time: 0.547, data: 0.000) G_L1: 12.943 G_L1_ABSOLUTE: 2.398 G_L1_RELATIVE: 10.545 G_Regularizer: 0.000 validation_error: 20.304 +(epoch: 20, iters: 101712, time: 0.531, data: 0.000) G_L1: 14.338 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 11.672 G_Regularizer: 0.000 validation_error: 20.970 +(epoch: 20, iters: 103712, time: 0.541, data: 0.000) G_L1: 14.233 G_L1_ABSOLUTE: 2.808 G_L1_RELATIVE: 11.426 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 20, iters: 105712, time: 0.559, data: 0.000) G_L1: 13.696 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 11.155 G_Regularizer: 0.000 validation_error: 21.181 +(epoch: 20, iters: 107712, time: 0.558, data: 0.000) G_L1: 15.298 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 12.836 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 20, iters: 109712, time: 0.533, data: 0.000) G_L1: 15.229 G_L1_ABSOLUTE: 2.532 G_L1_RELATIVE: 12.697 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 20, iters: 111712, time: 0.536, data: 0.000) G_L1: 15.135 G_L1_ABSOLUTE: 2.906 G_L1_RELATIVE: 12.229 G_Regularizer: 0.000 validation_error: 20.312 +(epoch: 20, iters: 113712, time: 0.559, data: 0.000) G_L1: 14.096 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 11.282 G_Regularizer: 0.000 validation_error: 19.982 +(epoch: 20, iters: 115712, time: 0.549, data: 0.001) G_L1: 16.537 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 13.879 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 20, iters: 117712, time: 0.560, data: 0.000) G_L1: 14.532 G_L1_ABSOLUTE: 2.917 G_L1_RELATIVE: 11.615 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 20, iters: 119712, time: 0.560, data: 0.000) G_L1: 16.893 G_L1_ABSOLUTE: 2.337 G_L1_RELATIVE: 14.556 G_Regularizer: 0.000 validation_error: 20.462 +(epoch: 20, iters: 121712, time: 0.558, data: 0.000) G_L1: 12.785 G_L1_ABSOLUTE: 2.127 G_L1_RELATIVE: 10.658 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 20, iters: 123712, time: 0.553, data: 0.000) G_L1: 13.511 G_L1_ABSOLUTE: 2.900 G_L1_RELATIVE: 10.612 G_Regularizer: 0.000 validation_error: 20.595 +(epoch: 20, iters: 125712, time: 0.554, data: 0.000) G_L1: 15.083 G_L1_ABSOLUTE: 2.184 G_L1_RELATIVE: 12.899 G_Regularizer: 0.000 validation_error: 20.305 +(epoch: 20, iters: 127712, time: 0.551, data: 0.000) G_L1: 15.096 G_L1_ABSOLUTE: 2.740 G_L1_RELATIVE: 12.356 G_Regularizer: 0.000 validation_error: 20.015 +(epoch: 20, iters: 129712, time: 0.553, data: 0.000) G_L1: 13.688 G_L1_ABSOLUTE: 2.068 G_L1_RELATIVE: 11.620 G_Regularizer: 0.000 validation_error: 20.409 +(epoch: 20, iters: 131712, time: 0.551, data: 0.000) G_L1: 15.090 G_L1_ABSOLUTE: 2.946 G_L1_RELATIVE: 12.144 G_Regularizer: 0.000 validation_error: 20.453 +(epoch: 20, iters: 133712, time: 0.547, data: 0.000) G_L1: 13.897 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 11.230 G_Regularizer: 0.000 validation_error: 21.005 +(epoch: 20, iters: 135712, time: 0.550, data: 0.000) G_L1: 14.331 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 12.017 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 20, iters: 137712, time: 0.562, data: 0.000) G_L1: 13.897 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 11.357 G_Regularizer: 0.000 validation_error: 20.795 +(epoch: 20, iters: 139712, time: 0.559, data: 0.000) G_L1: 15.935 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 13.415 G_Regularizer: 0.000 validation_error: 20.971 +(epoch: 20, iters: 141712, time: 0.558, data: 0.000) G_L1: 13.656 G_L1_ABSOLUTE: 2.052 G_L1_RELATIVE: 11.603 G_Regularizer: 0.000 validation_error: 20.500 +(epoch: 20, iters: 143712, time: 0.530, data: 0.000) G_L1: 15.467 G_L1_ABSOLUTE: 2.578 G_L1_RELATIVE: 12.888 G_Regularizer: 0.000 validation_error: 20.269 +(epoch: 20, iters: 145712, time: 0.562, data: 0.000) G_L1: 17.814 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 15.272 G_Regularizer: 0.000 validation_error: 19.935 +(epoch: 20, iters: 147712, time: 0.551, data: 0.000) G_L1: 13.837 G_L1_ABSOLUTE: 2.275 G_L1_RELATIVE: 11.561 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 20, iters: 149712, time: 0.550, data: 0.000) G_L1: 14.251 G_L1_ABSOLUTE: 2.223 G_L1_RELATIVE: 12.028 G_Regularizer: 0.000 validation_error: 21.090 +(epoch: 20, iters: 151712, time: 0.554, data: 0.000) G_L1: 13.915 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 11.288 G_Regularizer: 0.000 validation_error: 20.133 +(epoch: 20, iters: 153712, time: 0.548, data: 0.000) G_L1: 12.948 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 10.431 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 20, iters: 155712, time: 0.561, data: 0.000) G_L1: 14.393 G_L1_ABSOLUTE: 2.690 G_L1_RELATIVE: 11.703 G_Regularizer: 0.000 validation_error: 20.046 +(epoch: 20, iters: 157712, time: 0.560, data: 0.000) G_L1: 15.802 G_L1_ABSOLUTE: 2.878 G_L1_RELATIVE: 12.924 G_Regularizer: 0.000 validation_error: 20.309 +(epoch: 20, iters: 159712, time: 0.551, data: 0.000) G_L1: 15.897 G_L1_ABSOLUTE: 3.229 G_L1_RELATIVE: 12.668 G_Regularizer: 0.000 validation_error: 20.640 +(epoch: 20, iters: 161712, time: 0.560, data: 0.000) G_L1: 12.938 G_L1_ABSOLUTE: 2.181 G_L1_RELATIVE: 10.758 G_Regularizer: 0.000 validation_error: 20.378 +(epoch: 20, iters: 163712, time: 0.557, data: 0.000) G_L1: 13.757 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 11.168 G_Regularizer: 0.000 validation_error: 20.204 +(epoch: 20, iters: 165712, time: 0.559, data: 0.000) G_L1: 14.497 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 11.776 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 20, iters: 167712, time: 0.549, data: 0.000) G_L1: 13.481 G_L1_ABSOLUTE: 2.201 G_L1_RELATIVE: 11.280 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 20, iters: 169712, time: 0.548, data: 0.000) G_L1: 12.913 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 10.416 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 20, iters: 171712, time: 0.557, data: 0.000) G_L1: 15.840 G_L1_ABSOLUTE: 2.889 G_L1_RELATIVE: 12.951 G_Regularizer: 0.000 validation_error: 20.615 +(epoch: 20, iters: 173712, time: 0.559, data: 0.000) G_L1: 15.864 G_L1_ABSOLUTE: 2.680 G_L1_RELATIVE: 13.184 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 20, iters: 175712, time: 0.547, data: 0.001) G_L1: 16.734 G_L1_ABSOLUTE: 2.675 G_L1_RELATIVE: 14.059 G_Regularizer: 0.000 validation_error: 20.284 +(epoch: 20, iters: 177712, time: 0.549, data: 0.000) G_L1: 13.263 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 10.779 G_Regularizer: 0.000 validation_error: 19.910 +(epoch: 20, iters: 179712, time: 0.564, data: 0.000) G_L1: 16.104 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 13.517 G_Regularizer: 0.000 validation_error: 20.350 +(epoch: 20, iters: 181712, time: 0.548, data: 0.000) G_L1: 16.790 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 14.300 G_Regularizer: 0.000 validation_error: 19.638 +(epoch: 20, iters: 183712, time: 0.558, data: 0.000) G_L1: 14.402 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 11.741 G_Regularizer: 0.000 validation_error: 20.432 +(epoch: 20, iters: 185712, time: 0.539, data: 0.000) G_L1: 14.274 G_L1_ABSOLUTE: 2.920 G_L1_RELATIVE: 11.354 G_Regularizer: 0.000 validation_error: 20.143 +(epoch: 20, iters: 187712, time: 0.561, data: 0.000) G_L1: 15.507 G_L1_ABSOLUTE: 2.806 G_L1_RELATIVE: 12.701 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 20, iters: 189712, time: 0.556, data: 0.000) G_L1: 15.384 G_L1_ABSOLUTE: 2.253 G_L1_RELATIVE: 13.131 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 20, iters: 191712, time: 0.548, data: 0.000) G_L1: 13.230 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 10.477 G_Regularizer: 0.000 validation_error: 20.061 +(epoch: 20, iters: 193712, time: 0.530, data: 0.000) G_L1: 11.959 G_L1_ABSOLUTE: 2.685 G_L1_RELATIVE: 9.273 G_Regularizer: 0.000 validation_error: 20.280 +(epoch: 20, iters: 195712, time: 0.538, data: 0.000) G_L1: 15.844 G_L1_ABSOLUTE: 2.852 G_L1_RELATIVE: 12.992 G_Regularizer: 0.000 validation_error: 20.519 +(epoch: 20, iters: 197712, time: 0.551, data: 0.000) G_L1: 12.650 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 10.303 G_Regularizer: 0.000 validation_error: 20.993 +(epoch: 20, iters: 199712, time: 0.561, data: 0.000) G_L1: 12.087 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 9.667 G_Regularizer: 0.000 validation_error: 20.099 +(epoch: 20, iters: 201712, time: 0.549, data: 0.001) G_L1: 13.922 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 11.325 G_Regularizer: 0.000 validation_error: 19.985 +(epoch: 20, iters: 203712, time: 0.538, data: 0.000) G_L1: 14.822 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 12.184 G_Regularizer: 0.000 validation_error: 21.059 +(epoch: 20, iters: 205712, time: 0.561, data: 0.000) G_L1: 15.803 G_L1_ABSOLUTE: 2.672 G_L1_RELATIVE: 13.131 G_Regularizer: 0.000 validation_error: 20.279 +(epoch: 20, iters: 207712, time: 0.538, data: 0.000) G_L1: 12.743 G_L1_ABSOLUTE: 2.417 G_L1_RELATIVE: 10.325 G_Regularizer: 0.000 validation_error: 20.137 +(epoch: 20, iters: 209712, time: 0.537, data: 0.000) G_L1: 14.386 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 11.928 G_Regularizer: 0.000 validation_error: 20.241 +(epoch: 20, iters: 211712, time: 0.559, data: 0.001) G_L1: 17.438 G_L1_ABSOLUTE: 3.186 G_L1_RELATIVE: 14.252 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 20, iters: 213712, time: 0.546, data: 0.001) G_L1: 13.378 G_L1_ABSOLUTE: 2.334 G_L1_RELATIVE: 11.044 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 20, iters: 215712, time: 0.552, data: 0.001) G_L1: 15.050 G_L1_ABSOLUTE: 3.258 G_L1_RELATIVE: 11.792 G_Regularizer: 0.000 validation_error: 20.281 +(epoch: 20, iters: 217712, time: 0.551, data: 0.000) G_L1: 15.921 G_L1_ABSOLUTE: 2.715 G_L1_RELATIVE: 13.205 G_Regularizer: 0.000 validation_error: 21.289 +(epoch: 20, iters: 219712, time: 0.556, data: 0.000) G_L1: 14.646 G_L1_ABSOLUTE: 2.983 G_L1_RELATIVE: 11.663 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 20, iters: 221712, time: 0.549, data: 0.000) G_L1: 15.619 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 13.224 G_Regularizer: 0.000 validation_error: 20.747 +(epoch: 20, iters: 223712, time: 0.536, data: 0.000) G_L1: 14.936 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 12.231 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 20, iters: 225712, time: 0.559, data: 0.000) G_L1: 13.936 G_L1_ABSOLUTE: 2.552 G_L1_RELATIVE: 11.384 G_Regularizer: 0.000 validation_error: 20.641 +(epoch: 20, iters: 227712, time: 0.537, data: 0.000) G_L1: 14.771 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 12.176 G_Regularizer: 0.000 validation_error: 20.533 +(epoch: 20, iters: 229712, time: 0.549, data: 0.000) G_L1: 16.245 G_L1_ABSOLUTE: 2.682 G_L1_RELATIVE: 13.563 G_Regularizer: 0.000 validation_error: 21.293 +(epoch: 20, iters: 231712, time: 0.548, data: 0.000) G_L1: 13.700 G_L1_ABSOLUTE: 2.238 G_L1_RELATIVE: 11.462 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 20, iters: 233712, time: 0.566, data: 0.000) G_L1: 13.444 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 10.608 G_Regularizer: 0.000 validation_error: 20.183 +(epoch: 20, iters: 235712, time: 0.533, data: 0.000) G_L1: 16.799 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 14.148 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 20, iters: 237712, time: 0.557, data: 0.001) G_L1: 14.784 G_L1_ABSOLUTE: 2.300 G_L1_RELATIVE: 12.484 G_Regularizer: 0.000 validation_error: 20.871 +(epoch: 20, iters: 239712, time: 0.560, data: 0.000) G_L1: 12.940 G_L1_ABSOLUTE: 2.768 G_L1_RELATIVE: 10.172 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 20, iters: 241712, time: 0.557, data: 0.001) G_L1: 14.575 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 12.151 G_Regularizer: 0.000 validation_error: 21.450 +(epoch: 20, iters: 243712, time: 0.546, data: 0.000) G_L1: 13.789 G_L1_ABSOLUTE: 2.593 G_L1_RELATIVE: 11.196 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 20, iters: 245712, time: 0.561, data: 0.000) G_L1: 15.898 G_L1_ABSOLUTE: 2.166 G_L1_RELATIVE: 13.732 G_Regularizer: 0.000 validation_error: 20.343 +(epoch: 20, iters: 247712, time: 0.560, data: 0.000) G_L1: 15.946 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 13.320 G_Regularizer: 0.000 validation_error: 20.712 +(epoch: 20, iters: 249712, time: 0.556, data: 0.000) G_L1: 16.146 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 13.545 G_Regularizer: 0.000 validation_error: 20.516 +(epoch: 20, iters: 251712, time: 0.544, data: 0.001) G_L1: 15.664 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 13.040 G_Regularizer: 0.000 validation_error: 20.531 +(epoch: 20, iters: 253712, time: 0.565, data: 0.001) G_L1: 15.903 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 13.444 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 20, iters: 255712, time: 0.561, data: 0.000) G_L1: 16.753 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 14.036 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 20, iters: 257712, time: 0.561, data: 0.000) G_L1: 14.228 G_L1_ABSOLUTE: 2.848 G_L1_RELATIVE: 11.380 G_Regularizer: 0.000 validation_error: 20.257 +(epoch: 20, iters: 259712, time: 0.546, data: 0.000) G_L1: 15.523 G_L1_ABSOLUTE: 2.558 G_L1_RELATIVE: 12.964 G_Regularizer: 0.000 validation_error: 20.487 +(epoch: 20, iters: 261712, time: 0.535, data: 0.000) G_L1: 12.375 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 9.789 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 20, iters: 263712, time: 0.563, data: 0.000) G_L1: 16.328 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 13.928 G_Regularizer: 0.000 validation_error: 20.196 +(epoch: 20, iters: 265712, time: 0.564, data: 0.000) G_L1: 19.714 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 17.160 G_Regularizer: 0.000 validation_error: 20.498 +(epoch: 20, iters: 267712, time: 0.551, data: 0.000) G_L1: 17.411 G_L1_ABSOLUTE: 2.267 G_L1_RELATIVE: 15.144 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 20, iters: 269712, time: 0.554, data: 0.000) G_L1: 18.151 G_L1_ABSOLUTE: 2.980 G_L1_RELATIVE: 15.172 G_Regularizer: 0.000 validation_error: 20.222 +(epoch: 20, iters: 271712, time: 0.559, data: 0.000) G_L1: 15.165 G_L1_ABSOLUTE: 2.232 G_L1_RELATIVE: 12.934 G_Regularizer: 0.000 validation_error: 20.573 +(epoch: 20, iters: 273712, time: 0.556, data: 0.001) G_L1: 13.962 G_L1_ABSOLUTE: 2.608 G_L1_RELATIVE: 11.354 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 20, iters: 275712, time: 0.560, data: 0.000) G_L1: 14.854 G_L1_ABSOLUTE: 2.655 G_L1_RELATIVE: 12.199 G_Regularizer: 0.000 validation_error: 19.767 +(epoch: 20, iters: 277712, time: 0.537, data: 0.000) G_L1: 14.725 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 11.988 G_Regularizer: 0.000 validation_error: 21.058 +(epoch: 20, iters: 279712, time: 0.556, data: 0.000) G_L1: 12.802 G_L1_ABSOLUTE: 2.879 G_L1_RELATIVE: 9.923 G_Regularizer: 0.000 validation_error: 20.366 +(epoch: 20, iters: 281712, time: 0.549, data: 0.000) G_L1: 15.423 G_L1_ABSOLUTE: 2.904 G_L1_RELATIVE: 12.519 G_Regularizer: 0.000 validation_error: 20.397 +(epoch: 20, iters: 283712, time: 0.560, data: 0.000) G_L1: 15.268 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 12.893 G_Regularizer: 0.000 validation_error: 19.939 +(epoch: 20, iters: 285712, time: 0.553, data: 0.000) G_L1: 14.921 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 12.272 G_Regularizer: 0.000 validation_error: 20.387 +(epoch: 20, iters: 287712, time: 0.556, data: 0.000) G_L1: 13.322 G_L1_ABSOLUTE: 2.287 G_L1_RELATIVE: 11.035 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 20, iters: 289712, time: 0.548, data: 0.000) G_L1: 15.256 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 12.699 G_Regularizer: 0.000 validation_error: 20.276 +(epoch: 20, iters: 291712, time: 0.557, data: 0.000) G_L1: 12.531 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 9.891 G_Regularizer: 0.000 validation_error: 21.067 +(epoch: 20, iters: 293712, time: 0.560, data: 0.000) G_L1: 14.645 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 12.153 G_Regularizer: 0.000 validation_error: 20.753 +(epoch: 20, iters: 295712, time: 0.551, data: 0.000) G_L1: 14.970 G_L1_ABSOLUTE: 2.644 G_L1_RELATIVE: 12.326 G_Regularizer: 0.000 validation_error: 20.363 +(epoch: 20, iters: 297712, time: 0.559, data: 0.000) G_L1: 13.845 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 11.424 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 20, iters: 299712, time: 0.555, data: 0.000) G_L1: 15.202 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 12.458 G_Regularizer: 0.000 validation_error: 20.319 +(epoch: 20, iters: 301712, time: 0.559, data: 0.000) G_L1: 14.606 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 12.091 G_Regularizer: 0.000 validation_error: 20.307 +(epoch: 21, iters: 960, time: 0.549, data: 0.000) G_L1: 14.657 G_L1_ABSOLUTE: 2.723 G_L1_RELATIVE: 11.933 G_Regularizer: 0.000 validation_error: 20.526 +(epoch: 21, iters: 2960, time: 0.561, data: 0.000) G_L1: 16.028 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 13.372 G_Regularizer: 0.000 validation_error: 21.096 +(epoch: 21, iters: 4960, time: 0.554, data: 0.000) G_L1: 15.241 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 12.562 G_Regularizer: 0.000 validation_error: 20.206 +(epoch: 21, iters: 6960, time: 0.566, data: 0.000) G_L1: 15.466 G_L1_ABSOLUTE: 3.025 G_L1_RELATIVE: 12.441 G_Regularizer: 0.000 validation_error: 21.060 +(epoch: 21, iters: 8960, time: 0.554, data: 0.000) G_L1: 14.840 G_L1_ABSOLUTE: 2.728 G_L1_RELATIVE: 12.113 G_Regularizer: 0.000 validation_error: 21.051 +(epoch: 21, iters: 10960, time: 0.560, data: 0.000) G_L1: 14.623 G_L1_ABSOLUTE: 3.103 G_L1_RELATIVE: 11.520 G_Regularizer: 0.000 validation_error: 20.194 +(epoch: 21, iters: 12960, time: 0.552, data: 0.000) G_L1: 14.090 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 11.681 G_Regularizer: 0.000 validation_error: 20.493 +(epoch: 21, iters: 14960, time: 0.568, data: 0.000) G_L1: 13.330 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 10.911 G_Regularizer: 0.000 validation_error: 20.316 +(epoch: 21, iters: 16960, time: 0.553, data: 0.000) G_L1: 16.091 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 13.359 G_Regularizer: 0.000 validation_error: 20.184 +(epoch: 21, iters: 18960, time: 0.552, data: 0.000) G_L1: 17.419 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 14.430 G_Regularizer: 0.000 validation_error: 20.262 +(epoch: 21, iters: 20960, time: 0.553, data: 0.000) G_L1: 13.049 G_L1_ABSOLUTE: 2.079 G_L1_RELATIVE: 10.970 G_Regularizer: 0.000 validation_error: 20.441 +(epoch: 21, iters: 22960, time: 0.555, data: 0.000) G_L1: 14.094 G_L1_ABSOLUTE: 2.250 G_L1_RELATIVE: 11.843 G_Regularizer: 0.000 validation_error: 20.347 +(epoch: 21, iters: 24960, time: 0.556, data: 0.000) G_L1: 15.238 G_L1_ABSOLUTE: 3.027 G_L1_RELATIVE: 12.211 G_Regularizer: 0.000 validation_error: 20.641 +(epoch: 21, iters: 26960, time: 0.550, data: 0.000) G_L1: 14.844 G_L1_ABSOLUTE: 2.532 G_L1_RELATIVE: 12.312 G_Regularizer: 0.000 validation_error: 20.778 +(epoch: 21, iters: 28960, time: 0.556, data: 0.000) G_L1: 18.415 G_L1_ABSOLUTE: 3.177 G_L1_RELATIVE: 15.238 G_Regularizer: 0.000 validation_error: 20.818 +(epoch: 21, iters: 30960, time: 0.561, data: 0.000) G_L1: 16.895 G_L1_ABSOLUTE: 3.039 G_L1_RELATIVE: 13.856 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 21, iters: 32960, time: 0.565, data: 0.000) G_L1: 14.095 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 11.436 G_Regularizer: 0.000 validation_error: 20.609 +(epoch: 21, iters: 34960, time: 0.573, data: 0.000) G_L1: 14.357 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 11.975 G_Regularizer: 0.000 validation_error: 20.307 +(epoch: 21, iters: 36960, time: 0.572, data: 0.000) G_L1: 15.365 G_L1_ABSOLUTE: 2.819 G_L1_RELATIVE: 12.545 G_Regularizer: 0.000 validation_error: 20.405 +(epoch: 21, iters: 38960, time: 0.584, data: 0.000) G_L1: 11.983 G_L1_ABSOLUTE: 2.173 G_L1_RELATIVE: 9.810 G_Regularizer: 0.000 validation_error: 20.400 +(epoch: 21, iters: 40960, time: 0.584, data: 0.000) G_L1: 15.344 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 12.831 G_Regularizer: 0.000 validation_error: 20.407 +(epoch: 21, iters: 42960, time: 0.574, data: 0.000) G_L1: 16.427 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 13.967 G_Regularizer: 0.000 validation_error: 20.146 +(epoch: 21, iters: 44960, time: 0.574, data: 0.000) G_L1: 14.184 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 11.452 G_Regularizer: 0.000 validation_error: 20.276 +(epoch: 21, iters: 46960, time: 0.581, data: 0.000) G_L1: 15.763 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 12.939 G_Regularizer: 0.000 validation_error: 20.420 +(epoch: 21, iters: 48960, time: 0.575, data: 0.000) G_L1: 15.683 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 13.000 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 21, iters: 50960, time: 0.575, data: 0.000) G_L1: 15.563 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 13.182 G_Regularizer: 0.000 validation_error: 20.892 +(epoch: 21, iters: 52960, time: 0.583, data: 0.000) G_L1: 16.392 G_L1_ABSOLUTE: 2.700 G_L1_RELATIVE: 13.692 G_Regularizer: 0.000 validation_error: 19.954 +(epoch: 21, iters: 54960, time: 0.575, data: 0.000) G_L1: 14.211 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 11.701 G_Regularizer: 0.000 validation_error: 19.679 +(epoch: 21, iters: 56960, time: 0.573, data: 0.000) G_L1: 14.834 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 12.098 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 21, iters: 58960, time: 0.573, data: 0.000) G_L1: 14.887 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 12.258 G_Regularizer: 0.000 validation_error: 20.214 +(epoch: 21, iters: 60960, time: 0.573, data: 0.001) G_L1: 15.122 G_L1_ABSOLUTE: 2.972 G_L1_RELATIVE: 12.150 G_Regularizer: 0.000 validation_error: 21.343 +(epoch: 21, iters: 62960, time: 0.593, data: 0.000) G_L1: 11.192 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 8.827 G_Regularizer: 0.000 validation_error: 20.569 +(epoch: 21, iters: 64960, time: 0.574, data: 0.000) G_L1: 14.716 G_L1_ABSOLUTE: 2.740 G_L1_RELATIVE: 11.975 G_Regularizer: 0.000 validation_error: 19.854 +(epoch: 21, iters: 66960, time: 0.580, data: 0.000) G_L1: 13.322 G_L1_ABSOLUTE: 2.639 G_L1_RELATIVE: 10.683 G_Regularizer: 0.000 validation_error: 20.418 +(epoch: 21, iters: 68960, time: 0.569, data: 0.000) G_L1: 13.937 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 11.417 G_Regularizer: 0.000 validation_error: 20.374 +(epoch: 21, iters: 70960, time: 0.573, data: 0.000) G_L1: 16.676 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 14.111 G_Regularizer: 0.000 validation_error: 20.457 +(epoch: 21, iters: 72960, time: 0.572, data: 0.000) G_L1: 18.013 G_L1_ABSOLUTE: 2.655 G_L1_RELATIVE: 15.358 G_Regularizer: 0.000 validation_error: 20.543 +(epoch: 21, iters: 74960, time: 0.576, data: 0.000) G_L1: 18.551 G_L1_ABSOLUTE: 2.508 G_L1_RELATIVE: 16.043 G_Regularizer: 0.000 validation_error: 20.871 +(epoch: 21, iters: 76960, time: 0.581, data: 0.000) G_L1: 15.418 G_L1_ABSOLUTE: 2.324 G_L1_RELATIVE: 13.093 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 21, iters: 78960, time: 0.583, data: 0.000) G_L1: 15.758 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 13.168 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 21, iters: 80960, time: 0.570, data: 0.000) G_L1: 12.358 G_L1_ABSOLUTE: 2.305 G_L1_RELATIVE: 10.054 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 21, iters: 82960, time: 0.573, data: 0.000) G_L1: 17.076 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 14.352 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 21, iters: 84960, time: 0.576, data: 0.000) G_L1: 15.612 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 13.200 G_Regularizer: 0.000 validation_error: 19.960 +(epoch: 21, iters: 86960, time: 0.567, data: 0.000) G_L1: 16.900 G_L1_ABSOLUTE: 2.802 G_L1_RELATIVE: 14.098 G_Regularizer: 0.000 validation_error: 19.428 +(epoch: 21, iters: 88960, time: 0.577, data: 0.000) G_L1: 14.592 G_L1_ABSOLUTE: 2.972 G_L1_RELATIVE: 11.620 G_Regularizer: 0.000 validation_error: 20.434 +(epoch: 21, iters: 90960, time: 0.571, data: 0.000) G_L1: 16.350 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 13.701 G_Regularizer: 0.000 validation_error: 20.541 +(epoch: 21, iters: 92960, time: 0.569, data: 0.000) G_L1: 17.157 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 14.616 G_Regularizer: 0.000 validation_error: 20.439 +(epoch: 21, iters: 94960, time: 0.585, data: 0.000) G_L1: 12.284 G_L1_ABSOLUTE: 2.191 G_L1_RELATIVE: 10.094 G_Regularizer: 0.000 validation_error: 20.511 +(epoch: 21, iters: 96960, time: 0.576, data: 0.000) G_L1: 14.595 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 11.858 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 21, iters: 98960, time: 0.567, data: 0.000) G_L1: 14.613 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 12.339 G_Regularizer: 0.000 validation_error: 19.728 +(epoch: 21, iters: 100960, time: 0.573, data: 0.000) G_L1: 14.365 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 11.837 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 21, iters: 102960, time: 0.581, data: 0.000) G_L1: 12.860 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 10.401 G_Regularizer: 0.000 validation_error: 19.775 +(epoch: 21, iters: 104960, time: 0.573, data: 0.001) G_L1: 15.804 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 13.330 G_Regularizer: 0.000 validation_error: 20.370 +(epoch: 21, iters: 106960, time: 0.573, data: 0.000) G_L1: 14.789 G_L1_ABSOLUTE: 2.869 G_L1_RELATIVE: 11.920 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 21, iters: 108960, time: 0.576, data: 0.000) G_L1: 14.283 G_L1_ABSOLUTE: 2.393 G_L1_RELATIVE: 11.890 G_Regularizer: 0.000 validation_error: 20.018 +(epoch: 21, iters: 110960, time: 0.574, data: 0.000) G_L1: 14.779 G_L1_ABSOLUTE: 2.448 G_L1_RELATIVE: 12.331 G_Regularizer: 0.000 validation_error: 20.261 +(epoch: 21, iters: 112960, time: 0.583, data: 0.000) G_L1: 12.205 G_L1_ABSOLUTE: 2.304 G_L1_RELATIVE: 9.900 G_Regularizer: 0.000 validation_error: 20.604 +(epoch: 21, iters: 114960, time: 0.570, data: 0.000) G_L1: 15.022 G_L1_ABSOLUTE: 3.463 G_L1_RELATIVE: 11.560 G_Regularizer: 0.000 validation_error: 19.131 +(epoch: 21, iters: 116960, time: 0.571, data: 0.000) G_L1: 14.601 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 11.910 G_Regularizer: 0.000 validation_error: 20.317 +(epoch: 21, iters: 118960, time: 0.572, data: 0.000) G_L1: 16.187 G_L1_ABSOLUTE: 3.353 G_L1_RELATIVE: 12.834 G_Regularizer: 0.000 validation_error: 20.050 +(epoch: 21, iters: 120960, time: 0.578, data: 0.000) G_L1: 15.491 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 12.839 G_Regularizer: 0.000 validation_error: 20.246 +(epoch: 21, iters: 122960, time: 0.571, data: 0.000) G_L1: 15.616 G_L1_ABSOLUTE: 2.739 G_L1_RELATIVE: 12.878 G_Regularizer: 0.000 validation_error: 20.259 +(epoch: 21, iters: 124960, time: 0.577, data: 0.000) G_L1: 15.792 G_L1_ABSOLUTE: 2.527 G_L1_RELATIVE: 13.265 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 21, iters: 126960, time: 0.574, data: 0.000) G_L1: 15.560 G_L1_ABSOLUTE: 2.924 G_L1_RELATIVE: 12.637 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 21, iters: 128960, time: 0.585, data: 0.000) G_L1: 13.490 G_L1_ABSOLUTE: 2.575 G_L1_RELATIVE: 10.916 G_Regularizer: 0.000 validation_error: 20.185 +(epoch: 21, iters: 130960, time: 0.568, data: 0.000) G_L1: 15.531 G_L1_ABSOLUTE: 2.616 G_L1_RELATIVE: 12.915 G_Regularizer: 0.000 validation_error: 20.054 +(epoch: 21, iters: 132960, time: 0.573, data: 0.000) G_L1: 15.307 G_L1_ABSOLUTE: 3.090 G_L1_RELATIVE: 12.217 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 21, iters: 134960, time: 0.576, data: 0.000) G_L1: 15.118 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 12.381 G_Regularizer: 0.000 validation_error: 20.300 +(epoch: 21, iters: 136960, time: 0.570, data: 0.000) G_L1: 14.705 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 12.350 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 21, iters: 138960, time: 0.580, data: 0.000) G_L1: 14.849 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 12.229 G_Regularizer: 0.000 validation_error: 20.674 +(epoch: 21, iters: 140960, time: 0.575, data: 0.000) G_L1: 16.150 G_L1_ABSOLUTE: 2.689 G_L1_RELATIVE: 13.461 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 21, iters: 142960, time: 0.578, data: 0.000) G_L1: 13.754 G_L1_ABSOLUTE: 2.607 G_L1_RELATIVE: 11.147 G_Regularizer: 0.000 validation_error: 20.607 +(epoch: 21, iters: 144960, time: 0.575, data: 0.000) G_L1: 14.477 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 12.063 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 21, iters: 146960, time: 0.570, data: 0.000) G_L1: 14.357 G_L1_ABSOLUTE: 3.352 G_L1_RELATIVE: 11.005 G_Regularizer: 0.000 validation_error: 20.315 +(epoch: 21, iters: 148960, time: 0.576, data: 0.000) G_L1: 15.390 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 12.905 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 21, iters: 150960, time: 0.585, data: 0.000) G_L1: 14.044 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 11.233 G_Regularizer: 0.000 validation_error: 20.618 +(epoch: 21, iters: 152960, time: 0.572, data: 0.000) G_L1: 16.000 G_L1_ABSOLUTE: 2.786 G_L1_RELATIVE: 13.214 G_Regularizer: 0.000 validation_error: 20.680 +(epoch: 21, iters: 154960, time: 0.569, data: 0.000) G_L1: 16.367 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 13.429 G_Regularizer: 0.000 validation_error: 20.716 +(epoch: 21, iters: 156960, time: 0.574, data: 0.000) G_L1: 16.136 G_L1_ABSOLUTE: 3.180 G_L1_RELATIVE: 12.956 G_Regularizer: 0.000 validation_error: 20.413 +(epoch: 21, iters: 158960, time: 0.569, data: 0.000) G_L1: 13.863 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 11.161 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 21, iters: 160960, time: 0.579, data: 0.000) G_L1: 16.223 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 13.700 G_Regularizer: 0.000 validation_error: 20.490 +(epoch: 21, iters: 162960, time: 0.582, data: 0.000) G_L1: 12.864 G_L1_ABSOLUTE: 2.487 G_L1_RELATIVE: 10.377 G_Regularizer: 0.000 validation_error: 20.460 +(epoch: 21, iters: 164960, time: 0.583, data: 0.000) G_L1: 16.661 G_L1_ABSOLUTE: 2.348 G_L1_RELATIVE: 14.313 G_Regularizer: 0.000 validation_error: 20.349 +(epoch: 21, iters: 166960, time: 0.572, data: 0.000) G_L1: 16.378 G_L1_ABSOLUTE: 2.653 G_L1_RELATIVE: 13.725 G_Regularizer: 0.000 validation_error: 20.399 +(epoch: 21, iters: 168960, time: 0.574, data: 0.000) G_L1: 16.458 G_L1_ABSOLUTE: 3.036 G_L1_RELATIVE: 13.422 G_Regularizer: 0.000 validation_error: 20.674 +(epoch: 21, iters: 170960, time: 0.576, data: 0.000) G_L1: 19.124 G_L1_ABSOLUTE: 2.769 G_L1_RELATIVE: 16.355 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 21, iters: 172960, time: 0.572, data: 0.000) G_L1: 16.306 G_L1_ABSOLUTE: 3.212 G_L1_RELATIVE: 13.094 G_Regularizer: 0.000 validation_error: 20.693 +(epoch: 21, iters: 174960, time: 0.577, data: 0.000) G_L1: 14.540 G_L1_ABSOLUTE: 2.986 G_L1_RELATIVE: 11.554 G_Regularizer: 0.000 validation_error: 20.260 +(epoch: 21, iters: 176960, time: 0.578, data: 0.000) G_L1: 13.875 G_L1_ABSOLUTE: 2.653 G_L1_RELATIVE: 11.222 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 21, iters: 178960, time: 0.589, data: 0.000) G_L1: 16.822 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 14.090 G_Regularizer: 0.000 validation_error: 20.178 +(epoch: 21, iters: 180960, time: 0.576, data: 0.000) G_L1: 12.724 G_L1_ABSOLUTE: 2.287 G_L1_RELATIVE: 10.437 G_Regularizer: 0.000 validation_error: 20.268 +(epoch: 21, iters: 182960, time: 0.578, data: 0.000) G_L1: 13.672 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 11.128 G_Regularizer: 0.000 validation_error: 20.251 +(epoch: 21, iters: 184960, time: 0.571, data: 0.000) G_L1: 15.223 G_L1_ABSOLUTE: 2.945 G_L1_RELATIVE: 12.278 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 21, iters: 186960, time: 0.575, data: 0.000) G_L1: 17.398 G_L1_ABSOLUTE: 2.994 G_L1_RELATIVE: 14.404 G_Regularizer: 0.000 validation_error: 20.079 +(epoch: 21, iters: 188960, time: 0.574, data: 0.000) G_L1: 15.853 G_L1_ABSOLUTE: 2.808 G_L1_RELATIVE: 13.044 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 21, iters: 190960, time: 0.574, data: 0.000) G_L1: 18.669 G_L1_ABSOLUTE: 3.504 G_L1_RELATIVE: 15.165 G_Regularizer: 0.000 validation_error: 20.719 +(epoch: 21, iters: 192960, time: 0.579, data: 0.000) G_L1: 14.501 G_L1_ABSOLUTE: 2.889 G_L1_RELATIVE: 11.612 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 21, iters: 194960, time: 0.598, data: 0.000) G_L1: 16.251 G_L1_ABSOLUTE: 2.990 G_L1_RELATIVE: 13.261 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 21, iters: 196960, time: 0.585, data: 0.000) G_L1: 18.594 G_L1_ABSOLUTE: 2.790 G_L1_RELATIVE: 15.804 G_Regularizer: 0.000 validation_error: 20.412 +(epoch: 21, iters: 198960, time: 0.576, data: 0.000) G_L1: 17.582 G_L1_ABSOLUTE: 2.653 G_L1_RELATIVE: 14.930 G_Regularizer: 0.000 validation_error: 21.273 +(epoch: 21, iters: 200960, time: 0.570, data: 0.000) G_L1: 12.575 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 9.958 G_Regularizer: 0.000 validation_error: 21.299 +(epoch: 21, iters: 202960, time: 0.572, data: 0.000) G_L1: 19.067 G_L1_ABSOLUTE: 3.030 G_L1_RELATIVE: 16.037 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 21, iters: 204960, time: 0.577, data: 0.000) G_L1: 16.324 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 13.759 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 21, iters: 206960, time: 0.573, data: 0.001) G_L1: 16.264 G_L1_ABSOLUTE: 2.816 G_L1_RELATIVE: 13.448 G_Regularizer: 0.000 validation_error: 20.947 +(epoch: 21, iters: 208960, time: 0.566, data: 0.000) G_L1: 16.862 G_L1_ABSOLUTE: 2.275 G_L1_RELATIVE: 14.588 G_Regularizer: 0.000 validation_error: 21.278 +(epoch: 21, iters: 210960, time: 0.567, data: 0.000) G_L1: 14.952 G_L1_ABSOLUTE: 2.829 G_L1_RELATIVE: 12.122 G_Regularizer: 0.000 validation_error: 20.732 +(epoch: 21, iters: 212960, time: 0.600, data: 0.000) G_L1: 15.201 G_L1_ABSOLUTE: 2.709 G_L1_RELATIVE: 12.492 G_Regularizer: 0.000 validation_error: 20.765 +(epoch: 21, iters: 214960, time: 0.579, data: 0.001) G_L1: 16.019 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 13.349 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 21, iters: 216960, time: 0.570, data: 0.000) G_L1: 12.810 G_L1_ABSOLUTE: 2.681 G_L1_RELATIVE: 10.129 G_Regularizer: 0.000 validation_error: 20.490 +(epoch: 21, iters: 218960, time: 0.577, data: 0.000) G_L1: 14.482 G_L1_ABSOLUTE: 1.966 G_L1_RELATIVE: 12.515 G_Regularizer: 0.000 validation_error: 20.016 +(epoch: 21, iters: 220960, time: 0.586, data: 0.000) G_L1: 13.491 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 10.694 G_Regularizer: 0.000 validation_error: 20.304 +(epoch: 21, iters: 222960, time: 0.570, data: 0.000) G_L1: 16.252 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 13.536 G_Regularizer: 0.000 validation_error: 20.205 +(epoch: 21, iters: 224960, time: 0.568, data: 0.000) G_L1: 14.137 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 11.252 G_Regularizer: 0.000 validation_error: 20.273 +(epoch: 21, iters: 226960, time: 0.568, data: 0.000) G_L1: 12.263 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 9.754 G_Regularizer: 0.000 validation_error: 21.229 +(epoch: 21, iters: 228960, time: 0.593, data: 0.000) G_L1: 14.767 G_L1_ABSOLUTE: 2.902 G_L1_RELATIVE: 11.865 G_Regularizer: 0.000 validation_error: 21.277 +(epoch: 21, iters: 230960, time: 0.579, data: 0.000) G_L1: 14.950 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 12.321 G_Regularizer: 0.000 validation_error: 20.281 +(epoch: 21, iters: 232960, time: 0.565, data: 0.000) G_L1: 17.562 G_L1_ABSOLUTE: 2.786 G_L1_RELATIVE: 14.776 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 21, iters: 234960, time: 0.577, data: 0.000) G_L1: 17.497 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 14.864 G_Regularizer: 0.000 validation_error: 19.722 +(epoch: 21, iters: 236960, time: 0.576, data: 0.000) G_L1: 15.994 G_L1_ABSOLUTE: 2.723 G_L1_RELATIVE: 13.271 G_Regularizer: 0.000 validation_error: 20.190 +(epoch: 21, iters: 238960, time: 0.572, data: 0.001) G_L1: 16.431 G_L1_ABSOLUTE: 3.000 G_L1_RELATIVE: 13.431 G_Regularizer: 0.000 validation_error: 20.234 +(epoch: 21, iters: 240960, time: 0.569, data: 0.000) G_L1: 13.958 G_L1_ABSOLUTE: 2.183 G_L1_RELATIVE: 11.775 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 21, iters: 242960, time: 0.569, data: 0.000) G_L1: 13.054 G_L1_ABSOLUTE: 2.392 G_L1_RELATIVE: 10.662 G_Regularizer: 0.000 validation_error: 21.270 +(epoch: 21, iters: 244960, time: 0.841, data: 0.000) G_L1: 14.084 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 11.348 G_Regularizer: 0.000 validation_error: 20.360 +(epoch: 21, iters: 246960, time: 0.570, data: 0.000) G_L1: 12.456 G_L1_ABSOLUTE: 2.193 G_L1_RELATIVE: 10.264 G_Regularizer: 0.000 validation_error: 20.157 +(epoch: 21, iters: 248960, time: 0.575, data: 0.000) G_L1: 13.866 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 11.344 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 21, iters: 250960, time: 0.575, data: 0.000) G_L1: 16.931 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 14.224 G_Regularizer: 0.000 validation_error: 20.745 +(epoch: 21, iters: 252960, time: 0.568, data: 0.000) G_L1: 21.860 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 19.425 G_Regularizer: 0.000 validation_error: 20.662 +(epoch: 21, iters: 254960, time: 0.573, data: 0.000) G_L1: 17.362 G_L1_ABSOLUTE: 2.973 G_L1_RELATIVE: 14.389 G_Regularizer: 0.000 validation_error: 20.383 +(epoch: 21, iters: 256960, time: 0.570, data: 0.000) G_L1: 12.647 G_L1_ABSOLUTE: 2.076 G_L1_RELATIVE: 10.570 G_Regularizer: 0.000 validation_error: 20.578 +(epoch: 21, iters: 258960, time: 0.577, data: 0.000) G_L1: 15.212 G_L1_ABSOLUTE: 2.941 G_L1_RELATIVE: 12.271 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 21, iters: 260960, time: 0.579, data: 0.000) G_L1: 13.113 G_L1_ABSOLUTE: 2.308 G_L1_RELATIVE: 10.805 G_Regularizer: 0.000 validation_error: 20.557 +(epoch: 21, iters: 262960, time: 0.567, data: 0.000) G_L1: 14.773 G_L1_ABSOLUTE: 2.379 G_L1_RELATIVE: 12.394 G_Regularizer: 0.000 validation_error: 21.484 +(epoch: 21, iters: 264960, time: 0.573, data: 0.000) G_L1: 13.611 G_L1_ABSOLUTE: 3.133 G_L1_RELATIVE: 10.478 G_Regularizer: 0.000 validation_error: 21.320 +(epoch: 21, iters: 266960, time: 0.568, data: 0.000) G_L1: 15.283 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 12.859 G_Regularizer: 0.000 validation_error: 20.300 +(epoch: 21, iters: 268960, time: 0.566, data: 0.000) G_L1: 14.007 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 11.553 G_Regularizer: 0.000 validation_error: 20.614 +(epoch: 21, iters: 270960, time: 0.571, data: 0.000) G_L1: 12.995 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 10.513 G_Regularizer: 0.000 validation_error: 20.728 +(epoch: 21, iters: 272960, time: 0.588, data: 0.000) G_L1: 12.453 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 9.876 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 21, iters: 274960, time: 0.569, data: 0.000) G_L1: 12.002 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 9.671 G_Regularizer: 0.000 validation_error: 20.515 +(epoch: 21, iters: 276960, time: 0.568, data: 0.000) G_L1: 13.820 G_L1_ABSOLUTE: 2.197 G_L1_RELATIVE: 11.623 G_Regularizer: 0.000 validation_error: 20.532 +(epoch: 21, iters: 278960, time: 0.597, data: 0.000) G_L1: 14.299 G_L1_ABSOLUTE: 2.903 G_L1_RELATIVE: 11.396 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 21, iters: 280960, time: 0.580, data: 0.000) G_L1: 16.800 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 14.205 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 21, iters: 282960, time: 0.567, data: 0.000) G_L1: 16.754 G_L1_ABSOLUTE: 3.119 G_L1_RELATIVE: 13.635 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 21, iters: 284960, time: 0.573, data: 0.000) G_L1: 17.310 G_L1_ABSOLUTE: 2.849 G_L1_RELATIVE: 14.461 G_Regularizer: 0.000 validation_error: 21.149 +(epoch: 21, iters: 286960, time: 0.575, data: 0.000) G_L1: 16.583 G_L1_ABSOLUTE: 3.059 G_L1_RELATIVE: 13.524 G_Regularizer: 0.000 validation_error: 20.423 +(epoch: 21, iters: 288960, time: 0.569, data: 0.000) G_L1: 16.779 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 14.438 G_Regularizer: 0.000 validation_error: 20.482 +(epoch: 21, iters: 290960, time: 0.570, data: 0.000) G_L1: 22.240 G_L1_ABSOLUTE: 3.778 G_L1_RELATIVE: 18.461 G_Regularizer: 0.000 validation_error: 20.423 +(epoch: 21, iters: 292960, time: 0.574, data: 0.000) G_L1: 14.951 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 12.660 G_Regularizer: 0.000 validation_error: 20.524 +(epoch: 21, iters: 294960, time: 0.597, data: 0.000) G_L1: 13.017 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 10.347 G_Regularizer: 0.000 validation_error: 21.043 +(epoch: 21, iters: 296960, time: 0.578, data: 0.000) G_L1: 15.471 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 12.940 G_Regularizer: 0.000 validation_error: 21.172 +(epoch: 21, iters: 298960, time: 0.570, data: 0.000) G_L1: 17.117 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 14.457 G_Regularizer: 0.000 validation_error: 20.197 +(epoch: 21, iters: 300960, time: 0.572, data: 0.000) G_L1: 15.888 G_L1_ABSOLUTE: 2.776 G_L1_RELATIVE: 13.111 G_Regularizer: 0.000 validation_error: 20.555 +(epoch: 22, iters: 208, time: 0.581, data: 0.000) G_L1: 13.767 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 11.084 G_Regularizer: 0.000 validation_error: 20.404 +(epoch: 22, iters: 2208, time: 0.578, data: 0.000) G_L1: 11.624 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 9.206 G_Regularizer: 0.000 validation_error: 20.377 +(epoch: 22, iters: 4208, time: 0.570, data: 0.000) G_L1: 13.907 G_L1_ABSOLUTE: 2.508 G_L1_RELATIVE: 11.399 G_Regularizer: 0.000 validation_error: 20.513 +(epoch: 22, iters: 6208, time: 0.577, data: 0.000) G_L1: 15.842 G_L1_ABSOLUTE: 2.559 G_L1_RELATIVE: 13.282 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 22, iters: 8208, time: 0.586, data: 0.001) G_L1: 13.831 G_L1_ABSOLUTE: 2.578 G_L1_RELATIVE: 11.253 G_Regularizer: 0.000 validation_error: 20.089 +(epoch: 22, iters: 10208, time: 0.569, data: 0.000) G_L1: 13.905 G_L1_ABSOLUTE: 2.771 G_L1_RELATIVE: 11.134 G_Regularizer: 0.000 validation_error: 20.664 +(epoch: 22, iters: 12208, time: 0.571, data: 0.000) G_L1: 14.782 G_L1_ABSOLUTE: 2.690 G_L1_RELATIVE: 12.092 G_Regularizer: 0.000 validation_error: 20.198 +(epoch: 22, iters: 14208, time: 0.574, data: 0.000) G_L1: 17.022 G_L1_ABSOLUTE: 2.935 G_L1_RELATIVE: 14.087 G_Regularizer: 0.000 validation_error: 20.714 +(epoch: 22, iters: 16208, time: 0.574, data: 0.000) G_L1: 12.987 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 10.448 G_Regularizer: 0.000 validation_error: 20.663 +(epoch: 22, iters: 18208, time: 0.570, data: 0.000) G_L1: 15.535 G_L1_ABSOLUTE: 3.347 G_L1_RELATIVE: 12.188 G_Regularizer: 0.000 validation_error: 20.937 +(epoch: 22, iters: 20208, time: 0.568, data: 0.000) G_L1: 14.662 G_L1_ABSOLUTE: 2.718 G_L1_RELATIVE: 11.944 G_Regularizer: 0.000 validation_error: 20.849 +(epoch: 22, iters: 22208, time: 0.571, data: 0.000) G_L1: 14.410 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 11.740 G_Regularizer: 0.000 validation_error: 20.125 +(epoch: 22, iters: 24208, time: 0.571, data: 0.000) G_L1: 14.574 G_L1_ABSOLUTE: 2.081 G_L1_RELATIVE: 12.493 G_Regularizer: 0.000 validation_error: 20.535 +(epoch: 22, iters: 26208, time: 0.572, data: 0.000) G_L1: 13.919 G_L1_ABSOLUTE: 2.250 G_L1_RELATIVE: 11.669 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 22, iters: 28208, time: 0.579, data: 0.000) G_L1: 16.660 G_L1_ABSOLUTE: 2.847 G_L1_RELATIVE: 13.813 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 22, iters: 30208, time: 0.581, data: 0.000) G_L1: 13.343 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 10.929 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 22, iters: 32208, time: 0.569, data: 0.000) G_L1: 16.373 G_L1_ABSOLUTE: 2.701 G_L1_RELATIVE: 13.673 G_Regularizer: 0.000 validation_error: 20.521 +(epoch: 22, iters: 34208, time: 0.572, data: 0.000) G_L1: 15.778 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 13.132 G_Regularizer: 0.000 validation_error: 20.795 +(epoch: 22, iters: 36208, time: 0.573, data: 0.000) G_L1: 18.115 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 15.378 G_Regularizer: 0.000 validation_error: 20.577 +(epoch: 22, iters: 38208, time: 0.577, data: 0.000) G_L1: 13.812 G_L1_ABSOLUTE: 2.750 G_L1_RELATIVE: 11.062 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 22, iters: 40208, time: 0.578, data: 0.000) G_L1: 15.650 G_L1_ABSOLUTE: 2.636 G_L1_RELATIVE: 13.014 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 22, iters: 42208, time: 0.596, data: 0.000) G_L1: 15.177 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 12.393 G_Regularizer: 0.000 validation_error: 20.303 +(epoch: 22, iters: 44208, time: 0.577, data: 0.000) G_L1: 13.245 G_L1_ABSOLUTE: 2.937 G_L1_RELATIVE: 10.308 G_Regularizer: 0.000 validation_error: 20.586 +(epoch: 22, iters: 46208, time: 0.565, data: 0.000) G_L1: 17.448 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 14.712 G_Regularizer: 0.000 validation_error: 20.407 +(epoch: 22, iters: 48208, time: 0.575, data: 0.000) G_L1: 14.781 G_L1_ABSOLUTE: 2.410 G_L1_RELATIVE: 12.371 G_Regularizer: 0.000 validation_error: 20.546 +(epoch: 22, iters: 50208, time: 0.570, data: 0.000) G_L1: 17.763 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 15.102 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 22, iters: 52208, time: 0.571, data: 0.000) G_L1: 14.533 G_L1_ABSOLUTE: 2.934 G_L1_RELATIVE: 11.599 G_Regularizer: 0.000 validation_error: 20.583 +(epoch: 22, iters: 54208, time: 0.569, data: 0.000) G_L1: 16.015 G_L1_ABSOLUTE: 2.146 G_L1_RELATIVE: 13.869 G_Regularizer: 0.000 validation_error: 21.094 +(epoch: 22, iters: 56208, time: 0.585, data: 0.000) G_L1: 15.299 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 12.566 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 22, iters: 58208, time: 0.596, data: 0.000) G_L1: 16.195 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 13.669 G_Regularizer: 0.000 validation_error: 21.326 +(epoch: 22, iters: 60208, time: 0.578, data: 0.000) G_L1: 13.140 G_L1_ABSOLUTE: 2.685 G_L1_RELATIVE: 10.454 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 22, iters: 62208, time: 0.578, data: 0.000) G_L1: 15.618 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 12.885 G_Regularizer: 0.000 validation_error: 20.423 +(epoch: 22, iters: 64208, time: 0.564, data: 0.000) G_L1: 16.610 G_L1_ABSOLUTE: 2.599 G_L1_RELATIVE: 14.011 G_Regularizer: 0.000 validation_error: 20.450 +(epoch: 22, iters: 66208, time: 0.581, data: 0.000) G_L1: 13.698 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 11.032 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 22, iters: 68208, time: 0.579, data: 0.000) G_L1: 14.884 G_L1_ABSOLUTE: 3.019 G_L1_RELATIVE: 11.865 G_Regularizer: 0.000 validation_error: 20.385 +(epoch: 22, iters: 70208, time: 0.573, data: 0.000) G_L1: 16.106 G_L1_ABSOLUTE: 2.789 G_L1_RELATIVE: 13.316 G_Regularizer: 0.000 validation_error: 20.435 +(epoch: 22, iters: 72208, time: 0.580, data: 0.000) G_L1: 13.456 G_L1_ABSOLUTE: 2.715 G_L1_RELATIVE: 10.741 G_Regularizer: 0.000 validation_error: 21.499 +(epoch: 22, iters: 74208, time: 0.569, data: 0.000) G_L1: 14.068 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 11.754 G_Regularizer: 0.000 validation_error: 20.775 +(epoch: 22, iters: 76208, time: 0.571, data: 0.000) G_L1: 17.691 G_L1_ABSOLUTE: 2.982 G_L1_RELATIVE: 14.709 G_Regularizer: 0.000 validation_error: 21.433 +(epoch: 22, iters: 78208, time: 0.568, data: 0.000) G_L1: 14.402 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 11.901 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 22, iters: 80208, time: 0.576, data: 0.000) G_L1: 21.391 G_L1_ABSOLUTE: 3.044 G_L1_RELATIVE: 18.347 G_Regularizer: 0.000 validation_error: 21.178 +(epoch: 22, iters: 82208, time: 0.573, data: 0.000) G_L1: 15.181 G_L1_ABSOLUTE: 2.443 G_L1_RELATIVE: 12.738 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 22, iters: 84208, time: 0.568, data: 0.000) G_L1: 13.031 G_L1_ABSOLUTE: 2.240 G_L1_RELATIVE: 10.791 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 22, iters: 86208, time: 0.575, data: 0.000) G_L1: 15.170 G_L1_ABSOLUTE: 2.789 G_L1_RELATIVE: 12.381 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 22, iters: 88208, time: 0.569, data: 0.000) G_L1: 19.579 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 16.641 G_Regularizer: 0.000 validation_error: 21.381 +(epoch: 22, iters: 90208, time: 0.571, data: 0.000) G_L1: 16.322 G_L1_ABSOLUTE: 2.562 G_L1_RELATIVE: 13.760 G_Regularizer: 0.000 validation_error: 20.183 +(epoch: 22, iters: 92208, time: 0.598, data: 0.000) G_L1: 14.492 G_L1_ABSOLUTE: 2.829 G_L1_RELATIVE: 11.663 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 22, iters: 94208, time: 0.574, data: 0.000) G_L1: 16.454 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 13.866 G_Regularizer: 0.000 validation_error: 20.422 +(epoch: 22, iters: 96208, time: 0.563, data: 0.000) G_L1: 16.045 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 13.338 G_Regularizer: 0.000 validation_error: 21.268 +(epoch: 22, iters: 98208, time: 0.580, data: 0.000) G_L1: 15.223 G_L1_ABSOLUTE: 2.745 G_L1_RELATIVE: 12.478 G_Regularizer: 0.000 validation_error: 21.412 +(epoch: 22, iters: 100208, time: 0.570, data: 0.000) G_L1: 16.753 G_L1_ABSOLUTE: 2.861 G_L1_RELATIVE: 13.892 G_Regularizer: 0.000 validation_error: 21.237 +(epoch: 22, iters: 102208, time: 0.571, data: 0.000) G_L1: 15.389 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 12.519 G_Regularizer: 0.000 validation_error: 21.167 +(epoch: 22, iters: 104208, time: 0.573, data: 0.000) G_L1: 14.735 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 12.225 G_Regularizer: 0.000 validation_error: 20.261 +(epoch: 22, iters: 106208, time: 0.576, data: 0.001) G_L1: 17.569 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 14.861 G_Regularizer: 0.000 validation_error: 20.466 +(epoch: 22, iters: 108208, time: 0.590, data: 0.000) G_L1: 14.956 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 12.004 G_Regularizer: 0.000 validation_error: 21.253 +(epoch: 22, iters: 110208, time: 0.563, data: 0.000) G_L1: 13.005 G_L1_ABSOLUTE: 2.647 G_L1_RELATIVE: 10.358 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 22, iters: 112208, time: 0.572, data: 0.000) G_L1: 14.331 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 11.722 G_Regularizer: 0.000 validation_error: 20.977 +(epoch: 22, iters: 114208, time: 0.568, data: 0.000) G_L1: 12.098 G_L1_ABSOLUTE: 2.984 G_L1_RELATIVE: 9.114 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 22, iters: 116208, time: 0.581, data: 0.000) G_L1: 14.154 G_L1_ABSOLUTE: 2.712 G_L1_RELATIVE: 11.442 G_Regularizer: 0.000 validation_error: 20.237 +(epoch: 22, iters: 118208, time: 0.576, data: 0.000) G_L1: 14.685 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 12.116 G_Regularizer: 0.000 validation_error: 20.396 +(epoch: 22, iters: 120208, time: 0.583, data: 0.000) G_L1: 15.159 G_L1_ABSOLUTE: 2.500 G_L1_RELATIVE: 12.659 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 22, iters: 122208, time: 0.570, data: 0.000) G_L1: 14.941 G_L1_ABSOLUTE: 2.271 G_L1_RELATIVE: 12.671 G_Regularizer: 0.000 validation_error: 20.482 +(epoch: 22, iters: 124208, time: 0.585, data: 0.001) G_L1: 14.165 G_L1_ABSOLUTE: 2.507 G_L1_RELATIVE: 11.658 G_Regularizer: 0.000 validation_error: 20.360 +(epoch: 22, iters: 126208, time: 0.570, data: 0.000) G_L1: 15.853 G_L1_ABSOLUTE: 3.241 G_L1_RELATIVE: 12.612 G_Regularizer: 0.000 validation_error: 20.387 +(epoch: 22, iters: 128208, time: 0.567, data: 0.000) G_L1: 15.820 G_L1_ABSOLUTE: 3.012 G_L1_RELATIVE: 12.808 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 22, iters: 130208, time: 0.579, data: 0.000) G_L1: 14.438 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 11.854 G_Regularizer: 0.000 validation_error: 20.220 +(epoch: 22, iters: 132208, time: 0.576, data: 0.000) G_L1: 15.887 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 13.252 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 22, iters: 134208, time: 0.570, data: 0.000) G_L1: 17.251 G_L1_ABSOLUTE: 2.603 G_L1_RELATIVE: 14.648 G_Regularizer: 0.000 validation_error: 20.650 +(epoch: 22, iters: 136208, time: 0.577, data: 0.000) G_L1: 15.337 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 12.788 G_Regularizer: 0.000 validation_error: 20.529 +(epoch: 22, iters: 138208, time: 0.575, data: 0.000) G_L1: 13.904 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 11.234 G_Regularizer: 0.000 validation_error: 21.091 +(epoch: 22, iters: 140208, time: 0.569, data: 0.000) G_L1: 13.335 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 10.898 G_Regularizer: 0.000 validation_error: 21.438 +(epoch: 22, iters: 142208, time: 0.574, data: 0.000) G_L1: 14.563 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 11.849 G_Regularizer: 0.000 validation_error: 20.052 +(epoch: 22, iters: 144208, time: 0.573, data: 0.000) G_L1: 17.985 G_L1_ABSOLUTE: 2.514 G_L1_RELATIVE: 15.471 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 22, iters: 146208, time: 0.573, data: 0.000) G_L1: 13.857 G_L1_ABSOLUTE: 2.500 G_L1_RELATIVE: 11.357 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 22, iters: 148208, time: 0.570, data: 0.000) G_L1: 15.106 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 12.432 G_Regularizer: 0.000 validation_error: 19.957 +(epoch: 22, iters: 150208, time: 0.573, data: 0.000) G_L1: 14.317 G_L1_ABSOLUTE: 3.207 G_L1_RELATIVE: 11.110 G_Regularizer: 0.000 validation_error: 20.544 +(epoch: 22, iters: 152208, time: 0.572, data: 0.000) G_L1: 16.559 G_L1_ABSOLUTE: 3.074 G_L1_RELATIVE: 13.485 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 22, iters: 154208, time: 0.574, data: 0.000) G_L1: 15.326 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 12.605 G_Regularizer: 0.000 validation_error: 20.054 +(epoch: 22, iters: 156208, time: 0.569, data: 0.000) G_L1: 13.580 G_L1_ABSOLUTE: 2.671 G_L1_RELATIVE: 10.909 G_Regularizer: 0.000 validation_error: 20.284 +(epoch: 22, iters: 158208, time: 0.599, data: 0.000) G_L1: 15.024 G_L1_ABSOLUTE: 2.529 G_L1_RELATIVE: 12.495 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 22, iters: 160208, time: 0.576, data: 0.000) G_L1: 17.291 G_L1_ABSOLUTE: 2.872 G_L1_RELATIVE: 14.419 G_Regularizer: 0.000 validation_error: 20.231 +(epoch: 22, iters: 162208, time: 0.571, data: 0.000) G_L1: 14.548 G_L1_ABSOLUTE: 2.500 G_L1_RELATIVE: 12.047 G_Regularizer: 0.000 validation_error: 21.089 +(epoch: 22, iters: 164208, time: 0.567, data: 0.001) G_L1: 14.304 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 11.845 G_Regularizer: 0.000 validation_error: 20.469 +(epoch: 22, iters: 166208, time: 0.585, data: 0.000) G_L1: 13.681 G_L1_ABSOLUTE: 2.648 G_L1_RELATIVE: 11.033 G_Regularizer: 0.000 validation_error: 20.275 +(epoch: 22, iters: 168208, time: 0.578, data: 0.000) G_L1: 17.491 G_L1_ABSOLUTE: 3.211 G_L1_RELATIVE: 14.280 G_Regularizer: 0.000 validation_error: 20.382 +(epoch: 22, iters: 170208, time: 0.568, data: 0.000) G_L1: 16.265 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 13.528 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 22, iters: 172208, time: 0.572, data: 0.000) G_L1: 15.112 G_L1_ABSOLUTE: 2.228 G_L1_RELATIVE: 12.884 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 22, iters: 174208, time: 0.595, data: 0.000) G_L1: 15.892 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 13.619 G_Regularizer: 0.000 validation_error: 20.315 +(epoch: 22, iters: 176208, time: 0.571, data: 0.000) G_L1: 15.324 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 12.807 G_Regularizer: 0.000 validation_error: 20.197 +(epoch: 22, iters: 178208, time: 0.575, data: 0.000) G_L1: 14.730 G_L1_ABSOLUTE: 2.616 G_L1_RELATIVE: 12.115 G_Regularizer: 0.000 validation_error: 21.161 +(epoch: 22, iters: 180208, time: 0.566, data: 0.000) G_L1: 14.645 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 12.135 G_Regularizer: 0.000 validation_error: 21.628 +(epoch: 22, iters: 182208, time: 0.585, data: 0.000) G_L1: 14.469 G_L1_ABSOLUTE: 2.841 G_L1_RELATIVE: 11.628 G_Regularizer: 0.000 validation_error: 20.338 +(epoch: 22, iters: 184208, time: 0.573, data: 0.000) G_L1: 15.051 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 12.455 G_Regularizer: 0.000 validation_error: 20.761 +(epoch: 22, iters: 186208, time: 0.577, data: 0.001) G_L1: 14.330 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 11.729 G_Regularizer: 0.000 validation_error: 21.162 +(epoch: 22, iters: 188208, time: 0.571, data: 0.000) G_L1: 16.372 G_L1_ABSOLUTE: 3.071 G_L1_RELATIVE: 13.301 G_Regularizer: 0.000 validation_error: 20.983 +(epoch: 22, iters: 190208, time: 0.585, data: 0.000) G_L1: 17.225 G_L1_ABSOLUTE: 2.806 G_L1_RELATIVE: 14.418 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 22, iters: 192208, time: 0.574, data: 0.000) G_L1: 12.951 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 10.081 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 22, iters: 194208, time: 0.571, data: 0.000) G_L1: 13.193 G_L1_ABSOLUTE: 2.964 G_L1_RELATIVE: 10.229 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 22, iters: 196208, time: 0.579, data: 0.000) G_L1: 13.304 G_L1_ABSOLUTE: 2.997 G_L1_RELATIVE: 10.307 G_Regularizer: 0.000 validation_error: 20.415 +(epoch: 22, iters: 198208, time: 0.576, data: 0.000) G_L1: 16.164 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 13.308 G_Regularizer: 0.000 validation_error: 20.463 +(epoch: 22, iters: 200208, time: 0.568, data: 0.000) G_L1: 13.908 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 11.545 G_Regularizer: 0.000 validation_error: 20.277 +(epoch: 22, iters: 202208, time: 0.579, data: 0.000) G_L1: 16.757 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 14.239 G_Regularizer: 0.000 validation_error: 20.318 +(epoch: 22, iters: 204208, time: 0.572, data: 0.000) G_L1: 14.372 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 11.960 G_Regularizer: 0.000 validation_error: 20.490 +(epoch: 22, iters: 206208, time: 0.566, data: 0.000) G_L1: 16.698 G_L1_ABSOLUTE: 2.770 G_L1_RELATIVE: 13.927 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 22, iters: 208208, time: 0.585, data: 0.000) G_L1: 14.706 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 11.816 G_Regularizer: 0.000 validation_error: 21.336 +(epoch: 22, iters: 210208, time: 0.574, data: 0.000) G_L1: 15.137 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 12.655 G_Regularizer: 0.000 validation_error: 21.339 +(epoch: 22, iters: 212208, time: 0.573, data: 0.000) G_L1: 15.206 G_L1_ABSOLUTE: 2.720 G_L1_RELATIVE: 12.485 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 22, iters: 214208, time: 0.578, data: 0.000) G_L1: 19.794 G_L1_ABSOLUTE: 2.301 G_L1_RELATIVE: 17.494 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 22, iters: 216208, time: 0.574, data: 0.000) G_L1: 14.741 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 12.098 G_Regularizer: 0.000 validation_error: 21.149 +(epoch: 22, iters: 218208, time: 0.578, data: 0.000) G_L1: 15.228 G_L1_ABSOLUTE: 2.157 G_L1_RELATIVE: 13.071 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 22, iters: 220208, time: 0.562, data: 0.000) G_L1: 14.777 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 12.189 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 22, iters: 222208, time: 0.566, data: 0.000) G_L1: 16.897 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 14.347 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 22, iters: 224208, time: 0.584, data: 0.000) G_L1: 13.679 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 11.128 G_Regularizer: 0.000 validation_error: 20.558 +(epoch: 22, iters: 226208, time: 0.575, data: 0.000) G_L1: 12.343 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 9.936 G_Regularizer: 0.000 validation_error: 20.332 +(epoch: 22, iters: 228208, time: 0.569, data: 0.000) G_L1: 15.391 G_L1_ABSOLUTE: 3.176 G_L1_RELATIVE: 12.214 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 22, iters: 230208, time: 0.578, data: 0.000) G_L1: 13.300 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 10.827 G_Regularizer: 0.000 validation_error: 20.924 +(epoch: 22, iters: 232208, time: 0.574, data: 0.000) G_L1: 13.914 G_L1_ABSOLUTE: 2.269 G_L1_RELATIVE: 11.645 G_Regularizer: 0.000 validation_error: 20.849 +(epoch: 22, iters: 234208, time: 0.565, data: 0.000) G_L1: 15.530 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 12.911 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 22, iters: 236208, time: 0.575, data: 0.000) G_L1: 16.802 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 14.277 G_Regularizer: 0.000 validation_error: 21.191 +(epoch: 22, iters: 238208, time: 0.564, data: 0.001) G_L1: 15.094 G_L1_ABSOLUTE: 3.021 G_L1_RELATIVE: 12.073 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 22, iters: 240208, time: 0.576, data: 0.000) G_L1: 17.860 G_L1_ABSOLUTE: 2.807 G_L1_RELATIVE: 15.053 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 22, iters: 242208, time: 0.576, data: 0.000) G_L1: 14.657 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 11.884 G_Regularizer: 0.000 validation_error: 20.423 +(epoch: 22, iters: 244208, time: 0.568, data: 0.000) G_L1: 14.489 G_L1_ABSOLUTE: 2.718 G_L1_RELATIVE: 11.771 G_Regularizer: 0.000 validation_error: 20.614 +(epoch: 22, iters: 246208, time: 0.572, data: 0.000) G_L1: 15.719 G_L1_ABSOLUTE: 3.348 G_L1_RELATIVE: 12.371 G_Regularizer: 0.000 validation_error: 20.354 +(epoch: 22, iters: 248208, time: 0.572, data: 0.000) G_L1: 13.449 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 11.147 G_Regularizer: 0.000 validation_error: 20.401 +(epoch: 22, iters: 250208, time: 0.572, data: 0.000) G_L1: 19.439 G_L1_ABSOLUTE: 2.923 G_L1_RELATIVE: 16.516 G_Regularizer: 0.000 validation_error: 20.596 +(epoch: 22, iters: 252208, time: 0.576, data: 0.000) G_L1: 20.473 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 17.929 G_Regularizer: 0.000 validation_error: 20.345 +(epoch: 22, iters: 254208, time: 0.578, data: 0.000) G_L1: 13.250 G_L1_ABSOLUTE: 2.361 G_L1_RELATIVE: 10.889 G_Regularizer: 0.000 validation_error: 19.922 +(epoch: 22, iters: 256208, time: 0.578, data: 0.000) G_L1: 14.300 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 11.919 G_Regularizer: 0.000 validation_error: 21.463 +(epoch: 22, iters: 258208, time: 0.594, data: 0.000) G_L1: 16.455 G_L1_ABSOLUTE: 2.439 G_L1_RELATIVE: 14.016 G_Regularizer: 0.000 validation_error: 21.107 +(epoch: 22, iters: 260208, time: 0.574, data: 0.000) G_L1: 17.511 G_L1_ABSOLUTE: 3.236 G_L1_RELATIVE: 14.275 G_Regularizer: 0.000 validation_error: 20.033 +(epoch: 22, iters: 262208, time: 0.580, data: 0.000) G_L1: 16.892 G_L1_ABSOLUTE: 3.088 G_L1_RELATIVE: 13.803 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 22, iters: 264208, time: 0.576, data: 0.000) G_L1: 16.247 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 13.515 G_Regularizer: 0.000 validation_error: 20.622 +(epoch: 22, iters: 266208, time: 0.575, data: 0.000) G_L1: 11.013 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 8.562 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 22, iters: 268208, time: 0.579, data: 0.000) G_L1: 12.126 G_L1_ABSOLUTE: 2.097 G_L1_RELATIVE: 10.029 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 22, iters: 270208, time: 0.559, data: 0.000) G_L1: 13.119 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 10.628 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 22, iters: 272208, time: 0.554, data: 0.000) G_L1: 16.110 G_L1_ABSOLUTE: 2.555 G_L1_RELATIVE: 13.555 G_Regularizer: 0.000 validation_error: 20.491 +(epoch: 22, iters: 274208, time: 0.578, data: 0.000) G_L1: 14.058 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 11.284 G_Regularizer: 0.000 validation_error: 20.390 +(epoch: 22, iters: 276208, time: 0.564, data: 0.000) G_L1: 13.771 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 11.480 G_Regularizer: 0.000 validation_error: 21.177 +(epoch: 22, iters: 278208, time: 0.551, data: 0.000) G_L1: 15.467 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 12.827 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 22, iters: 280208, time: 0.560, data: 0.000) G_L1: 12.856 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 10.591 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 22, iters: 282208, time: 0.566, data: 0.000) G_L1: 15.769 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 13.227 G_Regularizer: 0.000 validation_error: 20.404 +(epoch: 22, iters: 284208, time: 0.555, data: 0.000) G_L1: 14.615 G_L1_ABSOLUTE: 2.806 G_L1_RELATIVE: 11.809 G_Regularizer: 0.000 validation_error: 20.317 +(epoch: 22, iters: 286208, time: 0.559, data: 0.000) G_L1: 13.354 G_L1_ABSOLUTE: 2.439 G_L1_RELATIVE: 10.915 G_Regularizer: 0.000 validation_error: 20.728 +(epoch: 22, iters: 288208, time: 0.556, data: 0.000) G_L1: 14.620 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 12.047 G_Regularizer: 0.000 validation_error: 20.308 +(epoch: 22, iters: 290208, time: 0.560, data: 0.000) G_L1: 14.149 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 11.601 G_Regularizer: 0.000 validation_error: 20.183 +(epoch: 22, iters: 292208, time: 0.565, data: 0.001) G_L1: 14.548 G_L1_ABSOLUTE: 2.387 G_L1_RELATIVE: 12.161 G_Regularizer: 0.000 validation_error: 21.161 +(epoch: 22, iters: 294208, time: 0.565, data: 0.000) G_L1: 17.412 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 14.653 G_Regularizer: 0.000 validation_error: 21.128 +(epoch: 22, iters: 296208, time: 0.568, data: 0.000) G_L1: 16.536 G_L1_ABSOLUTE: 2.874 G_L1_RELATIVE: 13.662 G_Regularizer: 0.000 validation_error: 21.395 +(epoch: 22, iters: 298208, time: 0.559, data: 0.000) G_L1: 13.809 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 11.281 G_Regularizer: 0.000 validation_error: 21.273 +(epoch: 22, iters: 300208, time: 0.556, data: 0.000) G_L1: 17.023 G_L1_ABSOLUTE: 2.865 G_L1_RELATIVE: 14.158 G_Regularizer: 0.000 validation_error: 20.576 +(epoch: 22, iters: 302208, time: 0.554, data: 0.000) G_L1: 13.653 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 11.173 G_Regularizer: 0.000 validation_error: 20.612 +(epoch: 23, iters: 1456, time: 0.553, data: 0.000) G_L1: 14.903 G_L1_ABSOLUTE: 2.769 G_L1_RELATIVE: 12.133 G_Regularizer: 0.000 validation_error: 20.371 +(epoch: 23, iters: 3456, time: 0.556, data: 0.000) G_L1: 15.112 G_L1_ABSOLUTE: 3.050 G_L1_RELATIVE: 12.061 G_Regularizer: 0.000 validation_error: 21.096 +(epoch: 23, iters: 5456, time: 0.564, data: 0.000) G_L1: 15.330 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 12.802 G_Regularizer: 0.000 validation_error: 21.416 +(epoch: 23, iters: 7456, time: 0.562, data: 0.000) G_L1: 12.981 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 10.399 G_Regularizer: 0.000 validation_error: 20.816 +(epoch: 23, iters: 9456, time: 0.558, data: 0.000) G_L1: 15.927 G_L1_ABSOLUTE: 2.717 G_L1_RELATIVE: 13.210 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 23, iters: 11456, time: 0.549, data: 0.000) G_L1: 14.318 G_L1_ABSOLUTE: 2.150 G_L1_RELATIVE: 12.168 G_Regularizer: 0.000 validation_error: 20.687 +(epoch: 23, iters: 13456, time: 0.556, data: 0.000) G_L1: 11.802 G_L1_ABSOLUTE: 2.034 G_L1_RELATIVE: 9.767 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 23, iters: 15456, time: 0.564, data: 0.000) G_L1: 16.303 G_L1_ABSOLUTE: 2.599 G_L1_RELATIVE: 13.704 G_Regularizer: 0.000 validation_error: 20.654 +(epoch: 23, iters: 17456, time: 0.561, data: 0.000) G_L1: 14.655 G_L1_ABSOLUTE: 2.718 G_L1_RELATIVE: 11.937 G_Regularizer: 0.000 validation_error: 21.117 +(epoch: 23, iters: 19456, time: 0.563, data: 0.000) G_L1: 14.235 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 11.478 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 23, iters: 21456, time: 0.562, data: 0.000) G_L1: 13.999 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 11.514 G_Regularizer: 0.000 validation_error: 20.555 +(epoch: 23, iters: 23456, time: 0.569, data: 0.000) G_L1: 15.028 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 12.534 G_Regularizer: 0.000 validation_error: 20.498 +(epoch: 23, iters: 25456, time: 0.557, data: 0.000) G_L1: 15.079 G_L1_ABSOLUTE: 2.439 G_L1_RELATIVE: 12.639 G_Regularizer: 0.000 validation_error: 20.540 +(epoch: 23, iters: 27456, time: 0.554, data: 0.000) G_L1: 17.180 G_L1_ABSOLUTE: 2.900 G_L1_RELATIVE: 14.281 G_Regularizer: 0.000 validation_error: 21.392 +(epoch: 23, iters: 29456, time: 0.568, data: 0.000) G_L1: 16.802 G_L1_ABSOLUTE: 2.933 G_L1_RELATIVE: 13.869 G_Regularizer: 0.000 validation_error: 20.398 +(epoch: 23, iters: 31456, time: 0.558, data: 0.000) G_L1: 16.203 G_L1_ABSOLUTE: 2.560 G_L1_RELATIVE: 13.643 G_Regularizer: 0.000 validation_error: 20.441 +(epoch: 23, iters: 33456, time: 0.553, data: 0.000) G_L1: 13.631 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 11.033 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 23, iters: 35456, time: 0.559, data: 0.000) G_L1: 11.032 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 8.699 G_Regularizer: 0.000 validation_error: 21.133 +(epoch: 23, iters: 37456, time: 0.555, data: 0.000) G_L1: 12.836 G_L1_ABSOLUTE: 2.362 G_L1_RELATIVE: 10.473 G_Regularizer: 0.000 validation_error: 20.687 +(epoch: 23, iters: 39456, time: 0.566, data: 0.000) G_L1: 13.384 G_L1_ABSOLUTE: 2.917 G_L1_RELATIVE: 10.467 G_Regularizer: 0.000 validation_error: 20.393 +(epoch: 23, iters: 41456, time: 0.547, data: 0.000) G_L1: 14.499 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 12.023 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 23, iters: 43456, time: 0.555, data: 0.000) G_L1: 14.552 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 11.681 G_Regularizer: 0.000 validation_error: 20.330 +(epoch: 23, iters: 45456, time: 0.552, data: 0.000) G_L1: 15.767 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 13.259 G_Regularizer: 0.000 validation_error: 20.593 +(epoch: 23, iters: 47456, time: 0.562, data: 0.000) G_L1: 20.930 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 18.565 G_Regularizer: 0.000 validation_error: 20.489 +(epoch: 23, iters: 49456, time: 0.555, data: 0.000) G_L1: 19.710 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 17.014 G_Regularizer: 0.000 validation_error: 19.938 +(epoch: 23, iters: 51456, time: 0.555, data: 0.000) G_L1: 13.471 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 10.993 G_Regularizer: 0.000 validation_error: 20.495 +(epoch: 23, iters: 53456, time: 0.560, data: 0.000) G_L1: 15.839 G_L1_ABSOLUTE: 3.018 G_L1_RELATIVE: 12.820 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 23, iters: 55456, time: 0.566, data: 0.000) G_L1: 13.349 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 11.018 G_Regularizer: 0.000 validation_error: 21.056 +(epoch: 23, iters: 57456, time: 0.571, data: 0.000) G_L1: 14.110 G_L1_ABSOLUTE: 2.279 G_L1_RELATIVE: 11.831 G_Regularizer: 0.000 validation_error: 20.386 +(epoch: 23, iters: 59456, time: 0.554, data: 0.000) G_L1: 15.851 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 13.233 G_Regularizer: 0.000 validation_error: 20.577 +(epoch: 23, iters: 61456, time: 0.556, data: 0.001) G_L1: 13.561 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 10.788 G_Regularizer: 0.000 validation_error: 20.307 +(epoch: 23, iters: 63456, time: 0.561, data: 0.000) G_L1: 17.033 G_L1_ABSOLUTE: 2.769 G_L1_RELATIVE: 14.264 G_Regularizer: 0.000 validation_error: 20.742 +(epoch: 23, iters: 65456, time: 0.559, data: 0.000) G_L1: 16.582 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 13.878 G_Regularizer: 0.000 validation_error: 20.233 +(epoch: 23, iters: 67456, time: 0.558, data: 0.000) G_L1: 15.147 G_L1_ABSOLUTE: 2.896 G_L1_RELATIVE: 12.250 G_Regularizer: 0.000 validation_error: 20.009 +(epoch: 23, iters: 69456, time: 0.557, data: 0.000) G_L1: 14.285 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 11.471 G_Regularizer: 0.000 validation_error: 20.364 +(epoch: 23, iters: 71456, time: 0.564, data: 0.000) G_L1: 13.552 G_L1_ABSOLUTE: 2.203 G_L1_RELATIVE: 11.349 G_Regularizer: 0.000 validation_error: 21.166 +(epoch: 23, iters: 73456, time: 0.578, data: 0.000) G_L1: 15.057 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 12.716 G_Regularizer: 0.000 validation_error: 20.451 +(epoch: 23, iters: 75456, time: 0.558, data: 0.000) G_L1: 13.736 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 11.078 G_Regularizer: 0.000 validation_error: 20.577 +(epoch: 23, iters: 77456, time: 0.557, data: 0.000) G_L1: 14.478 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 11.901 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 23, iters: 79456, time: 0.559, data: 0.000) G_L1: 15.038 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 12.303 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 23, iters: 81456, time: 0.552, data: 0.000) G_L1: 13.107 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 10.551 G_Regularizer: 0.000 validation_error: 20.512 +(epoch: 23, iters: 83456, time: 0.547, data: 0.000) G_L1: 16.529 G_L1_ABSOLUTE: 2.367 G_L1_RELATIVE: 14.161 G_Regularizer: 0.000 validation_error: 20.509 +(epoch: 23, iters: 85456, time: 0.561, data: 0.000) G_L1: 15.137 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 12.701 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 23, iters: 87456, time: 0.559, data: 0.000) G_L1: 12.414 G_L1_ABSOLUTE: 2.475 G_L1_RELATIVE: 9.939 G_Regularizer: 0.000 validation_error: 20.514 +(epoch: 23, iters: 89456, time: 0.558, data: 0.000) G_L1: 15.780 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 13.184 G_Regularizer: 0.000 validation_error: 20.927 +(epoch: 23, iters: 91456, time: 0.560, data: 0.000) G_L1: 14.742 G_L1_ABSOLUTE: 2.749 G_L1_RELATIVE: 11.993 G_Regularizer: 0.000 validation_error: 20.667 +(epoch: 23, iters: 93456, time: 0.557, data: 0.000) G_L1: 13.654 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 10.791 G_Regularizer: 0.000 validation_error: 20.079 +(epoch: 23, iters: 95456, time: 0.551, data: 0.000) G_L1: 14.073 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 11.740 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 23, iters: 97456, time: 0.558, data: 0.000) G_L1: 15.919 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 13.402 G_Regularizer: 0.000 validation_error: 20.174 +(epoch: 23, iters: 99456, time: 0.557, data: 0.000) G_L1: 12.953 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 10.503 G_Regularizer: 0.000 validation_error: 20.749 +(epoch: 23, iters: 101456, time: 0.553, data: 0.000) G_L1: 15.671 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 12.973 G_Regularizer: 0.000 validation_error: 20.712 +(epoch: 23, iters: 103456, time: 0.557, data: 0.000) G_L1: 14.997 G_L1_ABSOLUTE: 2.321 G_L1_RELATIVE: 12.676 G_Regularizer: 0.000 validation_error: 20.744 +(epoch: 23, iters: 105456, time: 0.561, data: 0.000) G_L1: 13.304 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 11.106 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 23, iters: 107456, time: 0.582, data: 0.000) G_L1: 13.757 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 11.253 G_Regularizer: 0.000 validation_error: 21.773 +(epoch: 23, iters: 109456, time: 0.552, data: 0.000) G_L1: 14.261 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 11.632 G_Regularizer: 0.000 validation_error: 20.621 +(epoch: 23, iters: 111456, time: 0.563, data: 0.000) G_L1: 15.512 G_L1_ABSOLUTE: 2.704 G_L1_RELATIVE: 12.808 G_Regularizer: 0.000 validation_error: 20.513 +(epoch: 23, iters: 113456, time: 0.561, data: 0.000) G_L1: 17.521 G_L1_ABSOLUTE: 2.831 G_L1_RELATIVE: 14.690 G_Regularizer: 0.000 validation_error: 20.707 +(epoch: 23, iters: 115456, time: 0.550, data: 0.000) G_L1: 13.892 G_L1_ABSOLUTE: 2.260 G_L1_RELATIVE: 11.632 G_Regularizer: 0.000 validation_error: 20.582 +(epoch: 23, iters: 117456, time: 0.556, data: 0.000) G_L1: 13.963 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 11.151 G_Regularizer: 0.000 validation_error: 20.216 +(epoch: 23, iters: 119456, time: 0.547, data: 0.000) G_L1: 13.331 G_L1_ABSOLUTE: 2.329 G_L1_RELATIVE: 11.002 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 23, iters: 121456, time: 0.557, data: 0.000) G_L1: 14.324 G_L1_ABSOLUTE: 3.214 G_L1_RELATIVE: 11.110 G_Regularizer: 0.000 validation_error: 20.091 +(epoch: 23, iters: 123456, time: 0.565, data: 0.000) G_L1: 15.333 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 12.743 G_Regularizer: 0.000 validation_error: 20.622 +(epoch: 23, iters: 125456, time: 0.578, data: 0.000) G_L1: 14.077 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 11.630 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 23, iters: 127456, time: 0.557, data: 0.000) G_L1: 14.390 G_L1_ABSOLUTE: 2.422 G_L1_RELATIVE: 11.969 G_Regularizer: 0.000 validation_error: 20.859 +(epoch: 23, iters: 129456, time: 0.559, data: 0.000) G_L1: 17.098 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 14.241 G_Regularizer: 0.000 validation_error: 20.732 +(epoch: 23, iters: 131456, time: 0.562, data: 0.000) G_L1: 18.305 G_L1_ABSOLUTE: 3.021 G_L1_RELATIVE: 15.283 G_Regularizer: 0.000 validation_error: 20.391 +(epoch: 23, iters: 133456, time: 0.556, data: 0.000) G_L1: 14.170 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 11.593 G_Regularizer: 0.000 validation_error: 20.354 +(epoch: 23, iters: 135456, time: 0.555, data: 0.000) G_L1: 13.717 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 11.250 G_Regularizer: 0.000 validation_error: 20.454 +(epoch: 23, iters: 137456, time: 0.553, data: 0.000) G_L1: 14.577 G_L1_ABSOLUTE: 2.543 G_L1_RELATIVE: 12.034 G_Regularizer: 0.000 validation_error: 20.170 +(epoch: 23, iters: 139456, time: 0.564, data: 0.000) G_L1: 13.808 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 11.262 G_Regularizer: 0.000 validation_error: 21.105 +(epoch: 23, iters: 141456, time: 0.574, data: 0.000) G_L1: 15.080 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 12.651 G_Regularizer: 0.000 validation_error: 20.779 +(epoch: 23, iters: 143456, time: 0.557, data: 0.000) G_L1: 14.016 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 11.131 G_Regularizer: 0.000 validation_error: 21.800 +(epoch: 23, iters: 145456, time: 0.555, data: 0.000) G_L1: 17.481 G_L1_ABSOLUTE: 3.489 G_L1_RELATIVE: 13.992 G_Regularizer: 0.000 validation_error: 20.616 +(epoch: 23, iters: 147456, time: 0.561, data: 0.000) G_L1: 13.330 G_L1_ABSOLUTE: 2.602 G_L1_RELATIVE: 10.727 G_Regularizer: 0.000 validation_error: 20.591 +(epoch: 23, iters: 149456, time: 0.563, data: 0.000) G_L1: 23.808 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 21.158 G_Regularizer: 0.000 validation_error: 19.820 +(epoch: 23, iters: 151456, time: 0.561, data: 0.000) G_L1: 14.457 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 11.774 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 23, iters: 153456, time: 0.559, data: 0.000) G_L1: 13.211 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 10.516 G_Regularizer: 0.000 validation_error: 21.164 +(epoch: 23, iters: 155456, time: 0.559, data: 0.000) G_L1: 16.166 G_L1_ABSOLUTE: 2.289 G_L1_RELATIVE: 13.876 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 23, iters: 157456, time: 0.552, data: 0.000) G_L1: 12.921 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 10.406 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 23, iters: 159456, time: 0.578, data: 0.000) G_L1: 16.799 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 14.279 G_Regularizer: 0.000 validation_error: 21.566 +(epoch: 23, iters: 161456, time: 0.552, data: 0.000) G_L1: 13.658 G_L1_ABSOLUTE: 2.384 G_L1_RELATIVE: 11.274 G_Regularizer: 0.000 validation_error: 21.284 +(epoch: 23, iters: 163456, time: 0.558, data: 0.000) G_L1: 16.607 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 14.036 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 23, iters: 165456, time: 0.563, data: 0.000) G_L1: 15.910 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 13.567 G_Regularizer: 0.000 validation_error: 22.094 +(epoch: 23, iters: 167456, time: 0.555, data: 0.000) G_L1: 14.702 G_L1_ABSOLUTE: 2.472 G_L1_RELATIVE: 12.230 G_Regularizer: 0.000 validation_error: 20.756 +(epoch: 23, iters: 169456, time: 0.561, data: 0.000) G_L1: 13.708 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 11.227 G_Regularizer: 0.000 validation_error: 21.033 +(epoch: 23, iters: 171456, time: 0.552, data: 0.001) G_L1: 14.870 G_L1_ABSOLUTE: 2.187 G_L1_RELATIVE: 12.682 G_Regularizer: 0.000 validation_error: 20.329 +(epoch: 23, iters: 173456, time: 0.566, data: 0.000) G_L1: 15.420 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 12.760 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 23, iters: 175456, time: 0.574, data: 0.000) G_L1: 16.386 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 13.664 G_Regularizer: 0.000 validation_error: 20.932 +(epoch: 23, iters: 177456, time: 0.554, data: 0.000) G_L1: 15.572 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 12.835 G_Regularizer: 0.000 validation_error: 20.771 +(epoch: 23, iters: 179456, time: 0.558, data: 0.000) G_L1: 16.214 G_L1_ABSOLUTE: 2.304 G_L1_RELATIVE: 13.910 G_Regularizer: 0.000 validation_error: 21.309 +(epoch: 23, iters: 181456, time: 0.557, data: 0.000) G_L1: 12.737 G_L1_ABSOLUTE: 2.292 G_L1_RELATIVE: 10.446 G_Regularizer: 0.000 validation_error: 21.051 +(epoch: 23, iters: 183456, time: 0.559, data: 0.000) G_L1: 17.185 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 14.393 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 23, iters: 185456, time: 0.561, data: 0.000) G_L1: 14.596 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 12.149 G_Regularizer: 0.000 validation_error: 21.241 +(epoch: 23, iters: 187456, time: 0.561, data: 0.000) G_L1: 12.859 G_L1_ABSOLUTE: 2.998 G_L1_RELATIVE: 9.861 G_Regularizer: 0.000 validation_error: 20.328 +(epoch: 23, iters: 189456, time: 0.563, data: 0.000) G_L1: 17.788 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 15.258 G_Regularizer: 0.000 validation_error: 20.757 +(epoch: 23, iters: 191456, time: 0.558, data: 0.000) G_L1: 12.839 G_L1_ABSOLUTE: 2.311 G_L1_RELATIVE: 10.529 G_Regularizer: 0.000 validation_error: 20.343 +(epoch: 23, iters: 193456, time: 0.574, data: 0.000) G_L1: 17.296 G_L1_ABSOLUTE: 3.072 G_L1_RELATIVE: 14.224 G_Regularizer: 0.000 validation_error: 21.222 +(epoch: 23, iters: 195456, time: 0.561, data: 0.000) G_L1: 13.738 G_L1_ABSOLUTE: 2.210 G_L1_RELATIVE: 11.528 G_Regularizer: 0.000 validation_error: 21.024 +(epoch: 23, iters: 197456, time: 0.561, data: 0.000) G_L1: 13.004 G_L1_ABSOLUTE: 2.232 G_L1_RELATIVE: 10.772 G_Regularizer: 0.000 validation_error: 21.131 +(epoch: 23, iters: 199456, time: 0.559, data: 0.000) G_L1: 13.265 G_L1_ABSOLUTE: 2.392 G_L1_RELATIVE: 10.872 G_Regularizer: 0.000 validation_error: 20.959 +(epoch: 23, iters: 201456, time: 0.561, data: 0.000) G_L1: 13.755 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 11.235 G_Regularizer: 0.000 validation_error: 20.346 +(epoch: 23, iters: 203456, time: 0.577, data: 0.000) G_L1: 15.805 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 13.458 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 23, iters: 205456, time: 0.551, data: 0.000) G_L1: 14.326 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 11.848 G_Regularizer: 0.000 validation_error: 19.950 +(epoch: 23, iters: 207456, time: 0.557, data: 0.000) G_L1: 20.621 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 17.891 G_Regularizer: 0.000 validation_error: 20.447 +(epoch: 23, iters: 209456, time: 0.570, data: 0.000) G_L1: 18.799 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 16.120 G_Regularizer: 0.000 validation_error: 20.761 +(epoch: 23, iters: 211456, time: 0.557, data: 0.000) G_L1: 16.145 G_L1_ABSOLUTE: 3.031 G_L1_RELATIVE: 13.114 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 23, iters: 213456, time: 0.560, data: 0.000) G_L1: 14.570 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 11.748 G_Regularizer: 0.000 validation_error: 20.910 +(epoch: 23, iters: 215456, time: 0.565, data: 0.000) G_L1: 15.458 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 13.050 G_Regularizer: 0.000 validation_error: 20.251 +(epoch: 23, iters: 217456, time: 0.562, data: 0.000) G_L1: 14.459 G_L1_ABSOLUTE: 2.562 G_L1_RELATIVE: 11.897 G_Regularizer: 0.000 validation_error: 20.465 +(epoch: 23, iters: 219456, time: 0.559, data: 0.000) G_L1: 14.497 G_L1_ABSOLUTE: 2.884 G_L1_RELATIVE: 11.614 G_Regularizer: 0.000 validation_error: 21.918 +(epoch: 23, iters: 221456, time: 0.561, data: 0.000) G_L1: 13.540 G_L1_ABSOLUTE: 2.752 G_L1_RELATIVE: 10.788 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 23, iters: 223456, time: 0.577, data: 0.000) G_L1: 11.678 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 9.215 G_Regularizer: 0.000 validation_error: 21.082 +(epoch: 23, iters: 225456, time: 0.556, data: 0.000) G_L1: 16.451 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 13.925 G_Regularizer: 0.000 validation_error: 20.154 +(epoch: 23, iters: 227456, time: 0.574, data: 0.000) G_L1: 15.848 G_L1_ABSOLUTE: 2.786 G_L1_RELATIVE: 13.063 G_Regularizer: 0.000 validation_error: 20.640 +(epoch: 23, iters: 229456, time: 0.559, data: 0.000) G_L1: 16.631 G_L1_ABSOLUTE: 3.117 G_L1_RELATIVE: 13.514 G_Regularizer: 0.000 validation_error: 20.021 +(epoch: 23, iters: 231456, time: 0.566, data: 0.000) G_L1: 15.471 G_L1_ABSOLUTE: 2.906 G_L1_RELATIVE: 12.565 G_Regularizer: 0.000 validation_error: 20.376 +(epoch: 23, iters: 233456, time: 0.563, data: 0.000) G_L1: 13.764 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 11.394 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 23, iters: 235456, time: 0.556, data: 0.000) G_L1: 16.741 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 13.986 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 23, iters: 237456, time: 0.557, data: 0.000) G_L1: 13.736 G_L1_ABSOLUTE: 2.152 G_L1_RELATIVE: 11.584 G_Regularizer: 0.000 validation_error: 20.593 +(epoch: 23, iters: 239456, time: 0.564, data: 0.000) G_L1: 16.643 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 14.182 G_Regularizer: 0.000 validation_error: 20.247 +(epoch: 23, iters: 241456, time: 0.556, data: 0.000) G_L1: 14.922 G_L1_ABSOLUTE: 2.171 G_L1_RELATIVE: 12.751 G_Regularizer: 0.000 validation_error: 20.554 +(epoch: 23, iters: 243456, time: 0.567, data: 0.000) G_L1: 14.584 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 11.823 G_Regularizer: 0.000 validation_error: 20.128 +(epoch: 23, iters: 245456, time: 0.556, data: 0.000) G_L1: 12.958 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 10.478 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 23, iters: 247456, time: 0.562, data: 0.000) G_L1: 16.286 G_L1_ABSOLUTE: 2.927 G_L1_RELATIVE: 13.359 G_Regularizer: 0.000 validation_error: 20.105 +(epoch: 23, iters: 249456, time: 0.548, data: 0.000) G_L1: 16.571 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 13.861 G_Regularizer: 0.000 validation_error: 20.146 +(epoch: 23, iters: 251456, time: 0.554, data: 0.000) G_L1: 15.254 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 12.602 G_Regularizer: 0.000 validation_error: 20.185 +(epoch: 23, iters: 253456, time: 0.563, data: 0.000) G_L1: 14.676 G_L1_ABSOLUTE: 2.965 G_L1_RELATIVE: 11.711 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 23, iters: 255456, time: 0.565, data: 0.000) G_L1: 16.715 G_L1_ABSOLUTE: 2.566 G_L1_RELATIVE: 14.149 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 23, iters: 257456, time: 0.564, data: 0.000) G_L1: 13.777 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 11.515 G_Regularizer: 0.000 validation_error: 20.578 +(epoch: 23, iters: 259456, time: 0.551, data: 0.000) G_L1: 15.047 G_L1_ABSOLUTE: 2.455 G_L1_RELATIVE: 12.592 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 23, iters: 261456, time: 0.577, data: 0.000) G_L1: 15.325 G_L1_ABSOLUTE: 2.617 G_L1_RELATIVE: 12.709 G_Regularizer: 0.000 validation_error: 20.329 +(epoch: 23, iters: 263456, time: 0.559, data: 0.000) G_L1: 12.947 G_L1_ABSOLUTE: 2.281 G_L1_RELATIVE: 10.666 G_Regularizer: 0.000 validation_error: 20.885 +(epoch: 23, iters: 265456, time: 0.563, data: 0.000) G_L1: 15.869 G_L1_ABSOLUTE: 2.801 G_L1_RELATIVE: 13.068 G_Regularizer: 0.000 validation_error: 20.419 +(epoch: 23, iters: 267456, time: 0.554, data: 0.000) G_L1: 20.493 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 17.929 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 23, iters: 269456, time: 0.562, data: 0.000) G_L1: 12.860 G_L1_ABSOLUTE: 2.218 G_L1_RELATIVE: 10.642 G_Regularizer: 0.000 validation_error: 20.666 +(epoch: 23, iters: 271456, time: 0.557, data: 0.000) G_L1: 16.405 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 13.729 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 23, iters: 273456, time: 0.564, data: 0.000) G_L1: 14.032 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 11.398 G_Regularizer: 0.000 validation_error: 21.418 +(epoch: 23, iters: 275456, time: 0.553, data: 0.000) G_L1: 13.872 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 11.240 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 23, iters: 277456, time: 0.572, data: 0.000) G_L1: 16.094 G_L1_ABSOLUTE: 2.359 G_L1_RELATIVE: 13.735 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 23, iters: 279456, time: 0.555, data: 0.000) G_L1: 16.458 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 14.059 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 23, iters: 281456, time: 0.556, data: 0.000) G_L1: 15.113 G_L1_ABSOLUTE: 2.594 G_L1_RELATIVE: 12.519 G_Regularizer: 0.000 validation_error: 20.267 +(epoch: 23, iters: 283456, time: 0.562, data: 0.000) G_L1: 16.488 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 13.908 G_Regularizer: 0.000 validation_error: 20.325 +(epoch: 23, iters: 285456, time: 0.559, data: 0.000) G_L1: 13.824 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 11.314 G_Regularizer: 0.000 validation_error: 20.373 +(epoch: 23, iters: 287456, time: 0.560, data: 0.000) G_L1: 13.868 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 11.246 G_Regularizer: 0.000 validation_error: 20.758 +(epoch: 23, iters: 289456, time: 0.558, data: 0.000) G_L1: 14.106 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 11.594 G_Regularizer: 0.000 validation_error: 21.119 +(epoch: 23, iters: 291456, time: 0.558, data: 0.000) G_L1: 15.317 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 13.058 G_Regularizer: 0.000 validation_error: 20.786 +(epoch: 23, iters: 293456, time: 0.557, data: 0.000) G_L1: 14.033 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 11.657 G_Regularizer: 0.000 validation_error: 21.267 +(epoch: 23, iters: 295456, time: 0.581, data: 0.000) G_L1: 13.531 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 11.013 G_Regularizer: 0.000 validation_error: 20.395 +(epoch: 23, iters: 297456, time: 0.558, data: 0.000) G_L1: 14.668 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 12.047 G_Regularizer: 0.000 validation_error: 20.163 +(epoch: 23, iters: 299456, time: 0.562, data: 0.000) G_L1: 18.088 G_L1_ABSOLUTE: 2.745 G_L1_RELATIVE: 15.343 G_Regularizer: 0.000 validation_error: 20.351 +(epoch: 23, iters: 301456, time: 0.561, data: 0.000) G_L1: 14.324 G_L1_ABSOLUTE: 2.444 G_L1_RELATIVE: 11.879 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 24, iters: 704, time: 0.559, data: 0.000) G_L1: 16.026 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 13.300 G_Regularizer: 0.000 validation_error: 21.425 +(epoch: 24, iters: 2704, time: 0.566, data: 0.000) G_L1: 15.383 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 12.848 G_Regularizer: 0.000 validation_error: 20.120 +(epoch: 24, iters: 4704, time: 0.564, data: 0.000) G_L1: 14.147 G_L1_ABSOLUTE: 2.973 G_L1_RELATIVE: 11.173 G_Regularizer: 0.000 validation_error: 21.167 +(epoch: 24, iters: 6704, time: 0.560, data: 0.000) G_L1: 15.940 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 13.350 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 24, iters: 8704, time: 0.574, data: 0.001) G_L1: 12.715 G_L1_ABSOLUTE: 2.611 G_L1_RELATIVE: 10.104 G_Regularizer: 0.000 validation_error: 21.032 +(epoch: 24, iters: 10704, time: 0.561, data: 0.000) G_L1: 15.848 G_L1_ABSOLUTE: 2.455 G_L1_RELATIVE: 13.393 G_Regularizer: 0.000 validation_error: 20.456 +(epoch: 24, iters: 12704, time: 0.558, data: 0.000) G_L1: 15.634 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 13.111 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 24, iters: 14704, time: 0.557, data: 0.000) G_L1: 16.633 G_L1_ABSOLUTE: 2.690 G_L1_RELATIVE: 13.942 G_Regularizer: 0.000 validation_error: 21.277 +(epoch: 24, iters: 16704, time: 0.559, data: 0.000) G_L1: 14.336 G_L1_ABSOLUTE: 2.237 G_L1_RELATIVE: 12.099 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 24, iters: 18704, time: 0.555, data: 0.000) G_L1: 19.221 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 16.386 G_Regularizer: 0.000 validation_error: 20.361 +(epoch: 24, iters: 20704, time: 0.555, data: 0.000) G_L1: 14.520 G_L1_ABSOLUTE: 2.267 G_L1_RELATIVE: 12.254 G_Regularizer: 0.000 validation_error: 21.255 +(epoch: 24, iters: 22704, time: 0.565, data: 0.000) G_L1: 12.642 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 9.760 G_Regularizer: 0.000 validation_error: 20.331 +(epoch: 24, iters: 24704, time: 0.555, data: 0.000) G_L1: 13.993 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 11.543 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 24, iters: 26704, time: 0.584, data: 0.000) G_L1: 15.209 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 12.614 G_Regularizer: 0.000 validation_error: 21.081 +(epoch: 24, iters: 28704, time: 0.556, data: 0.000) G_L1: 12.063 G_L1_ABSOLUTE: 3.180 G_L1_RELATIVE: 8.883 G_Regularizer: 0.000 validation_error: 20.429 +(epoch: 24, iters: 30704, time: 0.555, data: 0.000) G_L1: 14.699 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 12.113 G_Regularizer: 0.000 validation_error: 20.719 +(epoch: 24, iters: 32704, time: 0.554, data: 0.000) G_L1: 15.142 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 12.757 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 24, iters: 34704, time: 0.556, data: 0.000) G_L1: 13.133 G_L1_ABSOLUTE: 2.471 G_L1_RELATIVE: 10.662 G_Regularizer: 0.000 validation_error: 21.045 +(epoch: 24, iters: 36704, time: 0.551, data: 0.000) G_L1: 13.107 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 10.734 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 24, iters: 38704, time: 0.562, data: 0.000) G_L1: 15.375 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 12.878 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 24, iters: 40704, time: 0.559, data: 0.000) G_L1: 14.742 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 12.142 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 24, iters: 42704, time: 0.577, data: 0.000) G_L1: 13.693 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 11.081 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 24, iters: 44704, time: 0.555, data: 0.000) G_L1: 14.181 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 11.627 G_Regularizer: 0.000 validation_error: 20.984 +(epoch: 24, iters: 46704, time: 0.567, data: 0.000) G_L1: 15.434 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 12.895 G_Regularizer: 0.000 validation_error: 20.114 +(epoch: 24, iters: 48704, time: 0.559, data: 0.000) G_L1: 13.183 G_L1_ABSOLUTE: 2.606 G_L1_RELATIVE: 10.577 G_Regularizer: 0.000 validation_error: 20.422 +(epoch: 24, iters: 50704, time: 0.561, data: 0.002) G_L1: 14.344 G_L1_ABSOLUTE: 2.557 G_L1_RELATIVE: 11.788 G_Regularizer: 0.000 validation_error: 20.565 +(epoch: 24, iters: 52704, time: 0.561, data: 0.000) G_L1: 13.094 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 10.820 G_Regularizer: 0.000 validation_error: 20.629 +(epoch: 24, iters: 54704, time: 0.561, data: 0.000) G_L1: 12.628 G_L1_ABSOLUTE: 1.923 G_L1_RELATIVE: 10.705 G_Regularizer: 0.000 validation_error: 20.560 +(epoch: 24, iters: 56704, time: 0.554, data: 0.000) G_L1: 15.932 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 13.355 G_Regularizer: 0.000 validation_error: 20.712 +(epoch: 24, iters: 58704, time: 0.558, data: 0.000) G_L1: 15.586 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 13.050 G_Regularizer: 0.000 validation_error: 20.571 +(epoch: 24, iters: 60704, time: 0.581, data: 0.000) G_L1: 14.326 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 11.784 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 24, iters: 62704, time: 0.564, data: 0.000) G_L1: 12.218 G_L1_ABSOLUTE: 2.064 G_L1_RELATIVE: 10.154 G_Regularizer: 0.000 validation_error: 20.559 +(epoch: 24, iters: 64704, time: 0.557, data: 0.000) G_L1: 13.757 G_L1_ABSOLUTE: 2.603 G_L1_RELATIVE: 11.154 G_Regularizer: 0.000 validation_error: 20.413 +(epoch: 24, iters: 66704, time: 0.562, data: 0.000) G_L1: 15.032 G_L1_ABSOLUTE: 2.466 G_L1_RELATIVE: 12.566 G_Regularizer: 0.000 validation_error: 20.705 +(epoch: 24, iters: 68704, time: 0.567, data: 0.000) G_L1: 13.397 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 10.878 G_Regularizer: 0.000 validation_error: 20.308 +(epoch: 24, iters: 70704, time: 0.556, data: 0.000) G_L1: 15.283 G_L1_ABSOLUTE: 3.241 G_L1_RELATIVE: 12.042 G_Regularizer: 0.000 validation_error: 20.116 +(epoch: 24, iters: 72704, time: 0.554, data: 0.000) G_L1: 12.996 G_L1_ABSOLUTE: 2.060 G_L1_RELATIVE: 10.935 G_Regularizer: 0.000 validation_error: 20.231 +(epoch: 24, iters: 74704, time: 0.558, data: 0.000) G_L1: 15.953 G_L1_ABSOLUTE: 3.092 G_L1_RELATIVE: 12.861 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 24, iters: 76704, time: 0.563, data: 0.000) G_L1: 17.268 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 14.755 G_Regularizer: 0.000 validation_error: 20.748 +(epoch: 24, iters: 78704, time: 0.559, data: 0.000) G_L1: 19.120 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 16.456 G_Regularizer: 0.000 validation_error: 20.496 +(epoch: 24, iters: 80704, time: 0.559, data: 0.000) G_L1: 14.164 G_L1_ABSOLUTE: 2.212 G_L1_RELATIVE: 11.953 G_Regularizer: 0.000 validation_error: 20.508 +(epoch: 24, iters: 82704, time: 0.553, data: 0.000) G_L1: 16.816 G_L1_ABSOLUTE: 2.918 G_L1_RELATIVE: 13.898 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 24, iters: 84704, time: 0.572, data: 0.000) G_L1: 15.151 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 12.246 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 24, iters: 86704, time: 0.557, data: 0.000) G_L1: 13.900 G_L1_ABSOLUTE: 2.553 G_L1_RELATIVE: 11.347 G_Regularizer: 0.000 validation_error: 21.016 +(epoch: 24, iters: 88704, time: 0.566, data: 0.000) G_L1: 13.983 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 11.480 G_Regularizer: 0.000 validation_error: 20.134 +(epoch: 24, iters: 90704, time: 0.549, data: 0.000) G_L1: 14.020 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 11.658 G_Regularizer: 0.000 validation_error: 20.553 +(epoch: 24, iters: 92704, time: 0.560, data: 0.000) G_L1: 13.211 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 10.793 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 24, iters: 94704, time: 0.574, data: 0.000) G_L1: 17.891 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 15.149 G_Regularizer: 0.000 validation_error: 20.398 +(epoch: 24, iters: 96704, time: 0.572, data: 0.000) G_L1: 13.850 G_L1_ABSOLUTE: 2.585 G_L1_RELATIVE: 11.265 G_Regularizer: 0.000 validation_error: 20.770 +(epoch: 24, iters: 98704, time: 0.558, data: 0.000) G_L1: 16.148 G_L1_ABSOLUTE: 3.617 G_L1_RELATIVE: 12.531 G_Regularizer: 0.000 validation_error: 20.564 +(epoch: 24, iters: 100704, time: 0.559, data: 0.000) G_L1: 17.273 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 14.810 G_Regularizer: 0.000 validation_error: 19.873 +(epoch: 24, iters: 102704, time: 0.570, data: 0.000) G_L1: 12.051 G_L1_ABSOLUTE: 2.248 G_L1_RELATIVE: 9.803 G_Regularizer: 0.000 validation_error: 21.108 +(epoch: 24, iters: 104704, time: 0.562, data: 0.000) G_L1: 14.975 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 12.343 G_Regularizer: 0.000 validation_error: 20.948 +(epoch: 24, iters: 106704, time: 0.563, data: 0.000) G_L1: 15.431 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 12.998 G_Regularizer: 0.000 validation_error: 21.456 +(epoch: 24, iters: 108704, time: 0.551, data: 0.000) G_L1: 15.512 G_L1_ABSOLUTE: 1.910 G_L1_RELATIVE: 13.602 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 24, iters: 110704, time: 0.571, data: 0.000) G_L1: 16.515 G_L1_ABSOLUTE: 2.681 G_L1_RELATIVE: 13.834 G_Regularizer: 0.000 validation_error: 20.112 +(epoch: 24, iters: 112704, time: 0.563, data: 0.000) G_L1: 14.358 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 11.807 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 24, iters: 114704, time: 0.560, data: 0.000) G_L1: 13.907 G_L1_ABSOLUTE: 2.308 G_L1_RELATIVE: 11.599 G_Regularizer: 0.000 validation_error: 20.020 +(epoch: 24, iters: 116704, time: 0.563, data: 0.000) G_L1: 19.225 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 16.711 G_Regularizer: 0.000 validation_error: 20.375 +(epoch: 24, iters: 118704, time: 0.569, data: 0.000) G_L1: 15.135 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 12.452 G_Regularizer: 0.000 validation_error: 21.361 +(epoch: 24, iters: 120704, time: 0.561, data: 0.000) G_L1: 15.855 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 13.118 G_Regularizer: 0.000 validation_error: 20.683 +(epoch: 24, iters: 122704, time: 0.571, data: 0.000) G_L1: 16.073 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 13.589 G_Regularizer: 0.000 validation_error: 20.551 +(epoch: 24, iters: 124704, time: 0.554, data: 0.000) G_L1: 12.422 G_L1_ABSOLUTE: 2.508 G_L1_RELATIVE: 9.913 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 24, iters: 126704, time: 0.566, data: 0.000) G_L1: 12.164 G_L1_ABSOLUTE: 2.316 G_L1_RELATIVE: 9.848 G_Regularizer: 0.000 validation_error: 20.869 +(epoch: 24, iters: 128704, time: 0.576, data: 0.000) G_L1: 12.460 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 9.992 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 24, iters: 130704, time: 0.573, data: 0.000) G_L1: 14.777 G_L1_ABSOLUTE: 2.229 G_L1_RELATIVE: 12.548 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 24, iters: 132704, time: 0.563, data: 0.000) G_L1: 15.096 G_L1_ABSOLUTE: 2.949 G_L1_RELATIVE: 12.147 G_Regularizer: 0.000 validation_error: 20.202 +(epoch: 24, iters: 134704, time: 0.563, data: 0.000) G_L1: 15.063 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 12.660 G_Regularizer: 0.000 validation_error: 20.100 +(epoch: 24, iters: 136704, time: 0.558, data: 0.000) G_L1: 13.086 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 10.594 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 24, iters: 138704, time: 0.570, data: 0.000) G_L1: 14.226 G_L1_ABSOLUTE: 2.808 G_L1_RELATIVE: 11.418 G_Regularizer: 0.000 validation_error: 20.070 +(epoch: 24, iters: 140704, time: 0.561, data: 0.000) G_L1: 13.786 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 11.163 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 24, iters: 142704, time: 0.566, data: 0.000) G_L1: 18.055 G_L1_ABSOLUTE: 3.263 G_L1_RELATIVE: 14.792 G_Regularizer: 0.000 validation_error: 21.038 +(epoch: 24, iters: 144704, time: 0.576, data: 0.000) G_L1: 14.387 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 11.799 G_Regularizer: 0.000 validation_error: 20.728 +(epoch: 24, iters: 146704, time: 0.557, data: 0.000) G_L1: 15.297 G_L1_ABSOLUTE: 2.255 G_L1_RELATIVE: 13.042 G_Regularizer: 0.000 validation_error: 20.861 +(epoch: 24, iters: 148704, time: 0.555, data: 0.000) G_L1: 16.317 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 13.766 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 24, iters: 150704, time: 0.573, data: 0.000) G_L1: 14.623 G_L1_ABSOLUTE: 3.099 G_L1_RELATIVE: 11.524 G_Regularizer: 0.000 validation_error: 20.980 +(epoch: 24, iters: 152704, time: 0.557, data: 0.000) G_L1: 13.387 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 10.985 G_Regularizer: 0.000 validation_error: 20.129 +(epoch: 24, iters: 154704, time: 0.563, data: 0.000) G_L1: 15.086 G_L1_ABSOLUTE: 2.477 G_L1_RELATIVE: 12.609 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 24, iters: 156704, time: 0.555, data: 0.000) G_L1: 15.020 G_L1_ABSOLUTE: 2.575 G_L1_RELATIVE: 12.446 G_Regularizer: 0.000 validation_error: 20.750 +(epoch: 24, iters: 158704, time: 0.558, data: 0.001) G_L1: 15.231 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 12.655 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 24, iters: 160704, time: 0.562, data: 0.000) G_L1: 12.787 G_L1_ABSOLUTE: 2.452 G_L1_RELATIVE: 10.335 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 24, iters: 162704, time: 0.565, data: 0.000) G_L1: 12.796 G_L1_ABSOLUTE: 2.802 G_L1_RELATIVE: 9.995 G_Regularizer: 0.000 validation_error: 20.340 +(epoch: 24, iters: 164704, time: 0.564, data: 0.000) G_L1: 14.526 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 12.035 G_Regularizer: 0.000 validation_error: 20.397 +(epoch: 24, iters: 166704, time: 0.565, data: 0.000) G_L1: 15.657 G_L1_ABSOLUTE: 2.206 G_L1_RELATIVE: 13.452 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 24, iters: 168704, time: 0.563, data: 0.000) G_L1: 15.639 G_L1_ABSOLUTE: 2.897 G_L1_RELATIVE: 12.742 G_Regularizer: 0.000 validation_error: 20.230 +(epoch: 24, iters: 170704, time: 0.562, data: 0.000) G_L1: 14.529 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 12.037 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 24, iters: 172704, time: 0.567, data: 0.000) G_L1: 15.020 G_L1_ABSOLUTE: 2.475 G_L1_RELATIVE: 12.545 G_Regularizer: 0.000 validation_error: 20.921 +(epoch: 24, iters: 174704, time: 0.563, data: 0.000) G_L1: 16.078 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 13.505 G_Regularizer: 0.000 validation_error: 21.276 +(epoch: 24, iters: 176704, time: 0.561, data: 0.000) G_L1: 12.635 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 10.221 G_Regularizer: 0.000 validation_error: 21.314 +(epoch: 24, iters: 178704, time: 0.573, data: 0.000) G_L1: 14.076 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 11.657 G_Regularizer: 0.000 validation_error: 20.482 +(epoch: 24, iters: 180704, time: 0.564, data: 0.000) G_L1: 16.631 G_L1_ABSOLUTE: 2.764 G_L1_RELATIVE: 13.866 G_Regularizer: 0.000 validation_error: 21.043 +(epoch: 24, iters: 182704, time: 0.555, data: 0.000) G_L1: 17.755 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 15.366 G_Regularizer: 0.000 validation_error: 20.874 +(epoch: 24, iters: 184704, time: 0.557, data: 0.000) G_L1: 15.627 G_L1_ABSOLUTE: 2.723 G_L1_RELATIVE: 12.904 G_Regularizer: 0.000 validation_error: 21.361 +(epoch: 24, iters: 186704, time: 0.563, data: 0.001) G_L1: 15.070 G_L1_ABSOLUTE: 2.316 G_L1_RELATIVE: 12.754 G_Regularizer: 0.000 validation_error: 20.093 +(epoch: 24, iters: 188704, time: 0.565, data: 0.000) G_L1: 14.186 G_L1_ABSOLUTE: 3.432 G_L1_RELATIVE: 10.754 G_Regularizer: 0.000 validation_error: 20.337 +(epoch: 24, iters: 190704, time: 0.563, data: 0.000) G_L1: 13.941 G_L1_ABSOLUTE: 2.362 G_L1_RELATIVE: 11.579 G_Regularizer: 0.000 validation_error: 21.670 +(epoch: 24, iters: 192704, time: 0.560, data: 0.000) G_L1: 13.593 G_L1_ABSOLUTE: 2.372 G_L1_RELATIVE: 11.221 G_Regularizer: 0.000 validation_error: 20.559 +(epoch: 24, iters: 194704, time: 0.565, data: 0.000) G_L1: 14.358 G_L1_ABSOLUTE: 2.752 G_L1_RELATIVE: 11.605 G_Regularizer: 0.000 validation_error: 20.538 +(epoch: 24, iters: 196704, time: 0.559, data: 0.000) G_L1: 13.918 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 11.054 G_Regularizer: 0.000 validation_error: 20.202 +(epoch: 24, iters: 198704, time: 0.551, data: 0.000) G_L1: 14.000 G_L1_ABSOLUTE: 3.021 G_L1_RELATIVE: 10.978 G_Regularizer: 0.000 validation_error: 20.332 +(epoch: 24, iters: 200704, time: 0.570, data: 0.000) G_L1: 13.345 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 10.799 G_Regularizer: 0.000 validation_error: 20.839 +(epoch: 24, iters: 202704, time: 0.559, data: 0.000) G_L1: 15.501 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 13.000 G_Regularizer: 0.000 validation_error: 20.992 +(epoch: 24, iters: 204704, time: 0.561, data: 0.000) G_L1: 13.382 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 10.963 G_Regularizer: 0.000 validation_error: 20.500 +(epoch: 24, iters: 206704, time: 0.563, data: 0.000) G_L1: 13.188 G_L1_ABSOLUTE: 2.149 G_L1_RELATIVE: 11.040 G_Regularizer: 0.000 validation_error: 21.278 +(epoch: 24, iters: 208704, time: 0.561, data: 0.000) G_L1: 18.588 G_L1_ABSOLUTE: 2.821 G_L1_RELATIVE: 15.767 G_Regularizer: 0.000 validation_error: 20.485 +(epoch: 24, iters: 210704, time: 0.561, data: 0.000) G_L1: 12.726 G_L1_ABSOLUTE: 2.907 G_L1_RELATIVE: 9.820 G_Regularizer: 0.000 validation_error: 20.207 +(epoch: 24, iters: 212704, time: 0.580, data: 0.000) G_L1: 14.909 G_L1_ABSOLUTE: 2.223 G_L1_RELATIVE: 12.687 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 24, iters: 214704, time: 0.559, data: 0.000) G_L1: 14.428 G_L1_ABSOLUTE: 2.309 G_L1_RELATIVE: 12.119 G_Regularizer: 0.000 validation_error: 21.191 +(epoch: 24, iters: 216704, time: 0.570, data: 0.000) G_L1: 14.761 G_L1_ABSOLUTE: 2.268 G_L1_RELATIVE: 12.493 G_Regularizer: 0.000 validation_error: 20.453 +(epoch: 24, iters: 218704, time: 0.562, data: 0.000) G_L1: 16.550 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 13.940 G_Regularizer: 0.000 validation_error: 21.610 +(epoch: 24, iters: 220704, time: 0.559, data: 0.000) G_L1: 15.994 G_L1_ABSOLUTE: 2.891 G_L1_RELATIVE: 13.103 G_Regularizer: 0.000 validation_error: 20.462 +(epoch: 24, iters: 222704, time: 0.570, data: 0.000) G_L1: 14.513 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 12.125 G_Regularizer: 0.000 validation_error: 21.009 +(epoch: 24, iters: 224704, time: 0.558, data: 0.000) G_L1: 17.063 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 14.612 G_Regularizer: 0.000 validation_error: 20.523 +(epoch: 24, iters: 226704, time: 0.561, data: 0.001) G_L1: 12.676 G_L1_ABSOLUTE: 2.328 G_L1_RELATIVE: 10.348 G_Regularizer: 0.000 validation_error: 21.187 +(epoch: 24, iters: 228704, time: 0.577, data: 0.000) G_L1: 13.067 G_L1_ABSOLUTE: 2.197 G_L1_RELATIVE: 10.870 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 24, iters: 230704, time: 0.565, data: 0.000) G_L1: 16.636 G_L1_ABSOLUTE: 2.471 G_L1_RELATIVE: 14.166 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 24, iters: 232704, time: 0.575, data: 0.000) G_L1: 16.097 G_L1_ABSOLUTE: 3.122 G_L1_RELATIVE: 12.975 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 24, iters: 234704, time: 0.562, data: 0.000) G_L1: 17.661 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 14.998 G_Regularizer: 0.000 validation_error: 20.313 +(epoch: 24, iters: 236704, time: 0.559, data: 0.000) G_L1: 11.944 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 9.746 G_Regularizer: 0.000 validation_error: 20.937 +(epoch: 24, iters: 238704, time: 0.558, data: 0.000) G_L1: 16.410 G_L1_ABSOLUTE: 2.719 G_L1_RELATIVE: 13.691 G_Regularizer: 0.000 validation_error: 20.449 +(epoch: 24, iters: 240704, time: 0.558, data: 0.000) G_L1: 13.702 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 11.224 G_Regularizer: 0.000 validation_error: 20.564 +(epoch: 24, iters: 242704, time: 0.563, data: 0.000) G_L1: 17.343 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 14.769 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 24, iters: 244704, time: 0.565, data: 0.000) G_L1: 12.764 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 10.133 G_Regularizer: 0.000 validation_error: 20.303 +(epoch: 24, iters: 246704, time: 0.576, data: 0.000) G_L1: 14.188 G_L1_ABSOLUTE: 2.785 G_L1_RELATIVE: 11.403 G_Regularizer: 0.000 validation_error: 20.797 +(epoch: 24, iters: 248704, time: 0.575, data: 0.000) G_L1: 14.787 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 12.129 G_Regularizer: 0.000 validation_error: 20.737 +(epoch: 24, iters: 250704, time: 0.553, data: 0.000) G_L1: 14.373 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 12.157 G_Regularizer: 0.000 validation_error: 21.296 +(epoch: 24, iters: 252704, time: 0.544, data: 0.000) G_L1: 15.521 G_L1_ABSOLUTE: 2.488 G_L1_RELATIVE: 13.033 G_Regularizer: 0.000 validation_error: 20.509 +(epoch: 24, iters: 254704, time: 0.543, data: 0.000) G_L1: 14.207 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 11.715 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 24, iters: 256704, time: 0.549, data: 0.000) G_L1: 16.004 G_L1_ABSOLUTE: 2.763 G_L1_RELATIVE: 13.240 G_Regularizer: 0.000 validation_error: 21.286 +(epoch: 24, iters: 258704, time: 0.548, data: 0.000) G_L1: 16.068 G_L1_ABSOLUTE: 2.847 G_L1_RELATIVE: 13.222 G_Regularizer: 0.000 validation_error: 20.416 +(epoch: 24, iters: 260704, time: 0.539, data: 0.000) G_L1: 16.207 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 13.704 G_Regularizer: 0.000 validation_error: 20.565 +(epoch: 24, iters: 262704, time: 0.543, data: 0.000) G_L1: 15.212 G_L1_ABSOLUTE: 2.956 G_L1_RELATIVE: 12.256 G_Regularizer: 0.000 validation_error: 20.585 +(epoch: 24, iters: 264704, time: 0.547, data: 0.000) G_L1: 14.536 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 11.965 G_Regularizer: 0.000 validation_error: 20.461 +(epoch: 24, iters: 266704, time: 0.548, data: 0.000) G_L1: 12.771 G_L1_ABSOLUTE: 2.011 G_L1_RELATIVE: 10.760 G_Regularizer: 0.000 validation_error: 20.092 +(epoch: 24, iters: 268704, time: 0.549, data: 0.000) G_L1: 15.456 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 12.632 G_Regularizer: 0.000 validation_error: 20.382 +(epoch: 24, iters: 270704, time: 0.545, data: 0.000) G_L1: 12.213 G_L1_ABSOLUTE: 2.030 G_L1_RELATIVE: 10.184 G_Regularizer: 0.000 validation_error: 20.902 +(epoch: 24, iters: 272704, time: 0.536, data: 0.000) G_L1: 13.201 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 10.487 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 24, iters: 274704, time: 0.548, data: 0.000) G_L1: 13.934 G_L1_ABSOLUTE: 2.864 G_L1_RELATIVE: 11.070 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 24, iters: 276704, time: 0.543, data: 0.000) G_L1: 13.380 G_L1_ABSOLUTE: 2.749 G_L1_RELATIVE: 10.631 G_Regularizer: 0.000 validation_error: 20.089 +(epoch: 24, iters: 278704, time: 0.550, data: 0.000) G_L1: 14.844 G_L1_ABSOLUTE: 2.782 G_L1_RELATIVE: 12.062 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 24, iters: 280704, time: 0.552, data: 0.000) G_L1: 15.046 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 12.683 G_Regularizer: 0.000 validation_error: 20.708 +(epoch: 24, iters: 282704, time: 0.548, data: 0.001) G_L1: 12.310 G_L1_ABSOLUTE: 2.030 G_L1_RELATIVE: 10.279 G_Regularizer: 0.000 validation_error: 20.618 +(epoch: 24, iters: 284704, time: 0.555, data: 0.000) G_L1: 15.632 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 13.292 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 24, iters: 286704, time: 0.546, data: 0.000) G_L1: 14.568 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 12.102 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 24, iters: 288704, time: 0.547, data: 0.000) G_L1: 13.261 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 10.886 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 24, iters: 290704, time: 0.548, data: 0.000) G_L1: 12.579 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 10.043 G_Regularizer: 0.000 validation_error: 20.595 +(epoch: 24, iters: 292704, time: 0.549, data: 0.000) G_L1: 16.031 G_L1_ABSOLUTE: 2.997 G_L1_RELATIVE: 13.034 G_Regularizer: 0.000 validation_error: 21.705 +(epoch: 24, iters: 294704, time: 0.546, data: 0.000) G_L1: 13.506 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 10.966 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 24, iters: 296704, time: 0.545, data: 0.000) G_L1: 12.504 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 10.069 G_Regularizer: 0.000 validation_error: 20.456 +(epoch: 24, iters: 298704, time: 0.547, data: 0.001) G_L1: 14.962 G_L1_ABSOLUTE: 2.579 G_L1_RELATIVE: 12.383 G_Regularizer: 0.000 validation_error: 20.574 +(epoch: 24, iters: 300704, time: 0.550, data: 0.000) G_L1: 14.775 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 12.361 G_Regularizer: 0.000 validation_error: 20.667 +(epoch: 24, iters: 302704, time: 0.547, data: 0.000) G_L1: 13.865 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 11.197 G_Regularizer: 0.000 validation_error: 20.438 +(epoch: 25, iters: 1952, time: 0.546, data: 0.000) G_L1: 16.715 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 14.064 G_Regularizer: 0.000 validation_error: 20.354 +(epoch: 25, iters: 3952, time: 0.549, data: 0.000) G_L1: 18.925 G_L1_ABSOLUTE: 2.796 G_L1_RELATIVE: 16.129 G_Regularizer: 0.000 validation_error: 20.198 +(epoch: 25, iters: 5952, time: 0.550, data: 0.000) G_L1: 14.260 G_L1_ABSOLUTE: 3.075 G_L1_RELATIVE: 11.185 G_Regularizer: 0.000 validation_error: 20.948 +(epoch: 25, iters: 7952, time: 0.546, data: 0.001) G_L1: 13.572 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 11.169 G_Regularizer: 0.000 validation_error: 20.370 +(epoch: 25, iters: 9952, time: 0.545, data: 0.000) G_L1: 17.079 G_L1_ABSOLUTE: 3.210 G_L1_RELATIVE: 13.869 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 25, iters: 11952, time: 0.550, data: 0.000) G_L1: 14.941 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 12.379 G_Regularizer: 0.000 validation_error: 20.099 +(epoch: 25, iters: 13952, time: 0.547, data: 0.000) G_L1: 12.610 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 10.104 G_Regularizer: 0.000 validation_error: 20.946 +(epoch: 25, iters: 15952, time: 0.549, data: 0.000) G_L1: 23.800 G_L1_ABSOLUTE: 2.837 G_L1_RELATIVE: 20.963 G_Regularizer: 0.000 validation_error: 20.311 +(epoch: 25, iters: 17952, time: 0.552, data: 0.000) G_L1: 13.479 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 10.939 G_Regularizer: 0.000 validation_error: 20.415 +(epoch: 25, iters: 19952, time: 0.550, data: 0.000) G_L1: 13.971 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 11.019 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 25, iters: 21952, time: 0.552, data: 0.000) G_L1: 15.496 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 12.771 G_Regularizer: 0.000 validation_error: 19.754 +(epoch: 25, iters: 23952, time: 0.544, data: 0.000) G_L1: 14.165 G_L1_ABSOLUTE: 2.807 G_L1_RELATIVE: 11.358 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 25, iters: 25952, time: 0.548, data: 0.000) G_L1: 16.510 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 13.964 G_Regularizer: 0.000 validation_error: 20.315 +(epoch: 25, iters: 27952, time: 0.544, data: 0.000) G_L1: 13.271 G_L1_ABSOLUTE: 2.396 G_L1_RELATIVE: 10.875 G_Regularizer: 0.000 validation_error: 20.520 +(epoch: 25, iters: 29952, time: 0.543, data: 0.000) G_L1: 15.635 G_L1_ABSOLUTE: 2.176 G_L1_RELATIVE: 13.459 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 25, iters: 31952, time: 0.541, data: 0.001) G_L1: 15.312 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 12.847 G_Regularizer: 0.000 validation_error: 20.651 +(epoch: 25, iters: 33952, time: 0.546, data: 0.000) G_L1: 11.945 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 9.435 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 25, iters: 35952, time: 0.551, data: 0.000) G_L1: 15.506 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 12.762 G_Regularizer: 0.000 validation_error: 20.555 +(epoch: 25, iters: 37952, time: 0.542, data: 0.000) G_L1: 12.867 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 10.525 G_Regularizer: 0.000 validation_error: 20.484 +(epoch: 25, iters: 39952, time: 0.547, data: 0.001) G_L1: 15.521 G_L1_ABSOLUTE: 2.769 G_L1_RELATIVE: 12.752 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 25, iters: 41952, time: 0.549, data: 0.000) G_L1: 13.969 G_L1_ABSOLUTE: 2.199 G_L1_RELATIVE: 11.770 G_Regularizer: 0.000 validation_error: 20.564 +(epoch: 25, iters: 43952, time: 0.543, data: 0.000) G_L1: 16.354 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 13.935 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 25, iters: 45952, time: 0.548, data: 0.000) G_L1: 16.821 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 14.143 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 25, iters: 47952, time: 0.548, data: 0.000) G_L1: 13.068 G_L1_ABSOLUTE: 2.047 G_L1_RELATIVE: 11.022 G_Regularizer: 0.000 validation_error: 20.238 +(epoch: 25, iters: 49952, time: 0.542, data: 0.000) G_L1: 14.608 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 11.884 G_Regularizer: 0.000 validation_error: 20.591 +(epoch: 25, iters: 51952, time: 0.549, data: 0.000) G_L1: 13.869 G_L1_ABSOLUTE: 2.499 G_L1_RELATIVE: 11.370 G_Regularizer: 0.000 validation_error: 20.288 +(epoch: 25, iters: 53952, time: 0.547, data: 0.000) G_L1: 11.907 G_L1_ABSOLUTE: 2.311 G_L1_RELATIVE: 9.596 G_Regularizer: 0.000 validation_error: 20.600 +(epoch: 25, iters: 55952, time: 0.550, data: 0.000) G_L1: 16.315 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 13.693 G_Regularizer: 0.000 validation_error: 20.261 +(epoch: 25, iters: 57952, time: 0.542, data: 0.000) G_L1: 12.949 G_L1_ABSOLUTE: 2.325 G_L1_RELATIVE: 10.624 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 25, iters: 59952, time: 0.542, data: 0.000) G_L1: 13.782 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 11.281 G_Regularizer: 0.000 validation_error: 20.241 +(epoch: 25, iters: 61952, time: 0.547, data: 0.000) G_L1: 15.093 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 12.636 G_Regularizer: 0.000 validation_error: 20.158 +(epoch: 25, iters: 63952, time: 0.547, data: 0.000) G_L1: 12.885 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 10.180 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 25, iters: 65952, time: 0.551, data: 0.000) G_L1: 16.276 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 13.759 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 25, iters: 67952, time: 0.555, data: 0.000) G_L1: 14.900 G_L1_ABSOLUTE: 2.689 G_L1_RELATIVE: 12.211 G_Regularizer: 0.000 validation_error: 20.139 +(epoch: 25, iters: 69952, time: 0.546, data: 0.000) G_L1: 19.067 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 16.610 G_Regularizer: 0.000 validation_error: 20.381 +(epoch: 25, iters: 71952, time: 0.545, data: 0.000) G_L1: 13.194 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 10.492 G_Regularizer: 0.000 validation_error: 21.068 +(epoch: 25, iters: 73952, time: 0.556, data: 0.000) G_L1: 13.975 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 11.265 G_Regularizer: 0.000 validation_error: 20.512 +(epoch: 25, iters: 75952, time: 0.548, data: 0.000) G_L1: 16.101 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 13.466 G_Regularizer: 0.000 validation_error: 20.379 +(epoch: 25, iters: 77952, time: 0.556, data: 0.000) G_L1: 14.420 G_L1_ABSOLUTE: 2.322 G_L1_RELATIVE: 12.098 G_Regularizer: 0.000 validation_error: 20.497 +(epoch: 25, iters: 79952, time: 0.552, data: 0.000) G_L1: 16.715 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 14.248 G_Regularizer: 0.000 validation_error: 20.640 +(epoch: 25, iters: 81952, time: 0.544, data: 0.000) G_L1: 17.056 G_L1_ABSOLUTE: 2.919 G_L1_RELATIVE: 14.137 G_Regularizer: 0.000 validation_error: 20.750 +(epoch: 25, iters: 83952, time: 0.546, data: 0.000) G_L1: 20.427 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 17.622 G_Regularizer: 0.000 validation_error: 20.378 +(epoch: 25, iters: 85952, time: 0.547, data: 0.000) G_L1: 14.589 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 11.998 G_Regularizer: 0.000 validation_error: 20.499 +(epoch: 25, iters: 87952, time: 0.546, data: 0.001) G_L1: 14.603 G_L1_ABSOLUTE: 2.438 G_L1_RELATIVE: 12.166 G_Regularizer: 0.000 validation_error: 20.041 +(epoch: 25, iters: 89952, time: 0.550, data: 0.000) G_L1: 13.411 G_L1_ABSOLUTE: 2.285 G_L1_RELATIVE: 11.126 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 25, iters: 91952, time: 0.554, data: 0.000) G_L1: 15.037 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 12.702 G_Regularizer: 0.000 validation_error: 19.758 +(epoch: 25, iters: 93952, time: 0.545, data: 0.000) G_L1: 15.665 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 13.006 G_Regularizer: 0.000 validation_error: 20.452 +(epoch: 25, iters: 95952, time: 0.546, data: 0.000) G_L1: 16.046 G_L1_ABSOLUTE: 2.849 G_L1_RELATIVE: 13.197 G_Regularizer: 0.000 validation_error: 21.242 +(epoch: 25, iters: 97952, time: 0.549, data: 0.000) G_L1: 12.299 G_L1_ABSOLUTE: 2.189 G_L1_RELATIVE: 10.110 G_Regularizer: 0.000 validation_error: 20.861 +(epoch: 25, iters: 99952, time: 0.546, data: 0.000) G_L1: 15.122 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 12.643 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 25, iters: 101952, time: 0.545, data: 0.000) G_L1: 12.570 G_L1_ABSOLUTE: 2.580 G_L1_RELATIVE: 9.990 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 25, iters: 103952, time: 0.546, data: 0.000) G_L1: 14.672 G_L1_ABSOLUTE: 2.829 G_L1_RELATIVE: 11.843 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 25, iters: 105952, time: 0.552, data: 0.000) G_L1: 13.057 G_L1_ABSOLUTE: 2.398 G_L1_RELATIVE: 10.659 G_Regularizer: 0.000 validation_error: 21.183 +(epoch: 25, iters: 107952, time: 0.544, data: 0.000) G_L1: 13.316 G_L1_ABSOLUTE: 1.974 G_L1_RELATIVE: 11.342 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 25, iters: 109952, time: 0.543, data: 0.000) G_L1: 14.259 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 11.945 G_Regularizer: 0.000 validation_error: 20.457 +(epoch: 25, iters: 111952, time: 0.541, data: 0.000) G_L1: 14.341 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 11.665 G_Regularizer: 0.000 validation_error: 20.737 +(epoch: 25, iters: 113952, time: 0.546, data: 0.000) G_L1: 14.547 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 12.080 G_Regularizer: 0.000 validation_error: 21.192 +(epoch: 25, iters: 115952, time: 0.543, data: 0.000) G_L1: 15.822 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 13.333 G_Regularizer: 0.000 validation_error: 20.771 +(epoch: 25, iters: 117952, time: 0.542, data: 0.000) G_L1: 12.656 G_L1_ABSOLUTE: 2.102 G_L1_RELATIVE: 10.554 G_Regularizer: 0.000 validation_error: 20.859 +(epoch: 25, iters: 119952, time: 0.538, data: 0.000) G_L1: 15.247 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 12.989 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 25, iters: 121952, time: 0.548, data: 0.000) G_L1: 12.808 G_L1_ABSOLUTE: 2.179 G_L1_RELATIVE: 10.629 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 25, iters: 123952, time: 0.545, data: 0.000) G_L1: 11.810 G_L1_ABSOLUTE: 2.166 G_L1_RELATIVE: 9.644 G_Regularizer: 0.000 validation_error: 19.865 +(epoch: 25, iters: 125952, time: 0.548, data: 0.000) G_L1: 16.592 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 14.179 G_Regularizer: 0.000 validation_error: 20.483 +(epoch: 25, iters: 127952, time: 0.551, data: 0.001) G_L1: 15.898 G_L1_ABSOLUTE: 3.202 G_L1_RELATIVE: 12.696 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 25, iters: 129952, time: 0.542, data: 0.000) G_L1: 14.299 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 11.463 G_Regularizer: 0.000 validation_error: 20.181 +(epoch: 25, iters: 131952, time: 0.537, data: 0.000) G_L1: 13.003 G_L1_ABSOLUTE: 2.134 G_L1_RELATIVE: 10.870 G_Regularizer: 0.000 validation_error: 20.569 +(epoch: 25, iters: 133952, time: 0.546, data: 0.000) G_L1: 12.881 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 10.491 G_Regularizer: 0.000 validation_error: 20.601 +(epoch: 25, iters: 135952, time: 0.544, data: 0.000) G_L1: 17.695 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 15.064 G_Regularizer: 0.000 validation_error: 20.486 +(epoch: 25, iters: 137952, time: 0.549, data: 0.000) G_L1: 15.002 G_L1_ABSOLUTE: 2.545 G_L1_RELATIVE: 12.457 G_Regularizer: 0.000 validation_error: 20.314 +(epoch: 25, iters: 139952, time: 0.545, data: 0.000) G_L1: 12.675 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 10.255 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 25, iters: 141952, time: 0.556, data: 0.000) G_L1: 17.942 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 15.141 G_Regularizer: 0.000 validation_error: 20.489 +(epoch: 25, iters: 143952, time: 0.540, data: 0.000) G_L1: 16.373 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 13.636 G_Regularizer: 0.000 validation_error: 20.541 +(epoch: 25, iters: 145952, time: 0.539, data: 0.000) G_L1: 11.442 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 8.932 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 25, iters: 147952, time: 0.547, data: 0.001) G_L1: 13.761 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 11.161 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 25, iters: 149952, time: 0.550, data: 0.000) G_L1: 14.855 G_L1_ABSOLUTE: 2.846 G_L1_RELATIVE: 12.009 G_Regularizer: 0.000 validation_error: 20.789 +(epoch: 25, iters: 151952, time: 0.551, data: 0.000) G_L1: 16.074 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 13.553 G_Regularizer: 0.000 validation_error: 20.418 +(epoch: 25, iters: 153952, time: 0.556, data: 0.000) G_L1: 14.377 G_L1_ABSOLUTE: 2.575 G_L1_RELATIVE: 11.802 G_Regularizer: 0.000 validation_error: 20.404 +(epoch: 25, iters: 155952, time: 0.546, data: 0.000) G_L1: 12.834 G_L1_ABSOLUTE: 2.059 G_L1_RELATIVE: 10.775 G_Regularizer: 0.000 validation_error: 20.009 +(epoch: 25, iters: 157952, time: 0.548, data: 0.000) G_L1: 15.957 G_L1_ABSOLUTE: 2.883 G_L1_RELATIVE: 13.074 G_Regularizer: 0.000 validation_error: 20.911 +(epoch: 25, iters: 159952, time: 0.546, data: 0.001) G_L1: 14.605 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 12.329 G_Regularizer: 0.000 validation_error: 21.007 +(epoch: 25, iters: 161952, time: 0.545, data: 0.000) G_L1: 15.289 G_L1_ABSOLUTE: 2.936 G_L1_RELATIVE: 12.353 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 25, iters: 163952, time: 0.550, data: 0.000) G_L1: 13.254 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 10.750 G_Regularizer: 0.000 validation_error: 20.414 +(epoch: 25, iters: 165952, time: 0.544, data: 0.000) G_L1: 15.712 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 12.982 G_Regularizer: 0.000 validation_error: 20.207 +(epoch: 25, iters: 167952, time: 0.545, data: 0.000) G_L1: 16.877 G_L1_ABSOLUTE: 2.917 G_L1_RELATIVE: 13.960 G_Regularizer: 0.000 validation_error: 21.030 +(epoch: 25, iters: 169952, time: 0.542, data: 0.000) G_L1: 15.824 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 13.295 G_Regularizer: 0.000 validation_error: 21.383 +(epoch: 25, iters: 171952, time: 0.548, data: 0.000) G_L1: 13.776 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 11.355 G_Regularizer: 0.000 validation_error: 21.416 +(epoch: 25, iters: 173952, time: 0.546, data: 0.000) G_L1: 13.602 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 11.041 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 25, iters: 175952, time: 0.546, data: 0.000) G_L1: 15.279 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 12.877 G_Regularizer: 0.000 validation_error: 21.287 +(epoch: 25, iters: 177952, time: 0.550, data: 0.000) G_L1: 16.209 G_L1_ABSOLUTE: 2.965 G_L1_RELATIVE: 13.244 G_Regularizer: 0.000 validation_error: 21.056 +(epoch: 25, iters: 179952, time: 0.539, data: 0.000) G_L1: 14.468 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 12.095 G_Regularizer: 0.000 validation_error: 20.992 +(epoch: 25, iters: 181952, time: 0.539, data: 0.000) G_L1: 14.712 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 11.877 G_Regularizer: 0.000 validation_error: 20.561 +(epoch: 25, iters: 183952, time: 0.542, data: 0.000) G_L1: 13.878 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 11.255 G_Regularizer: 0.000 validation_error: 20.940 +(epoch: 25, iters: 185952, time: 0.548, data: 0.000) G_L1: 14.138 G_L1_ABSOLUTE: 2.146 G_L1_RELATIVE: 11.991 G_Regularizer: 0.000 validation_error: 20.104 +(epoch: 25, iters: 187952, time: 0.545, data: 0.000) G_L1: 15.302 G_L1_ABSOLUTE: 2.322 G_L1_RELATIVE: 12.980 G_Regularizer: 0.000 validation_error: 20.142 +(epoch: 25, iters: 189952, time: 0.553, data: 0.001) G_L1: 14.616 G_L1_ABSOLUTE: 2.639 G_L1_RELATIVE: 11.977 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 25, iters: 191952, time: 0.545, data: 0.000) G_L1: 16.370 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 13.831 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 25, iters: 193952, time: 0.544, data: 0.000) G_L1: 12.403 G_L1_ABSOLUTE: 2.183 G_L1_RELATIVE: 10.220 G_Regularizer: 0.000 validation_error: 20.006 +(epoch: 25, iters: 195952, time: 0.544, data: 0.000) G_L1: 12.550 G_L1_ABSOLUTE: 2.285 G_L1_RELATIVE: 10.265 G_Regularizer: 0.000 validation_error: 20.427 +(epoch: 25, iters: 197952, time: 0.541, data: 0.000) G_L1: 15.400 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 12.810 G_Regularizer: 0.000 validation_error: 20.943 +(epoch: 25, iters: 199952, time: 0.548, data: 0.000) G_L1: 13.333 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 10.647 G_Regularizer: 0.000 validation_error: 20.598 +(epoch: 25, iters: 201952, time: 0.545, data: 0.000) G_L1: 12.835 G_L1_ABSOLUTE: 2.763 G_L1_RELATIVE: 10.072 G_Regularizer: 0.000 validation_error: 21.254 +(epoch: 25, iters: 203952, time: 0.543, data: 0.000) G_L1: 15.029 G_L1_ABSOLUTE: 2.823 G_L1_RELATIVE: 12.206 G_Regularizer: 0.000 validation_error: 20.956 +(epoch: 25, iters: 205952, time: 0.546, data: 0.000) G_L1: 15.837 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 13.356 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 25, iters: 207952, time: 0.549, data: 0.000) G_L1: 19.813 G_L1_ABSOLUTE: 2.868 G_L1_RELATIVE: 16.946 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 25, iters: 209952, time: 0.545, data: 0.000) G_L1: 15.073 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 12.178 G_Regularizer: 0.000 validation_error: 20.342 +(epoch: 25, iters: 211952, time: 0.553, data: 0.000) G_L1: 16.697 G_L1_ABSOLUTE: 2.776 G_L1_RELATIVE: 13.921 G_Regularizer: 0.000 validation_error: 21.111 +(epoch: 25, iters: 213952, time: 0.553, data: 0.000) G_L1: 12.403 G_L1_ABSOLUTE: 2.352 G_L1_RELATIVE: 10.051 G_Regularizer: 0.000 validation_error: 20.069 +(epoch: 25, iters: 215952, time: 0.550, data: 0.000) G_L1: 13.453 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 10.994 G_Regularizer: 0.000 validation_error: 21.216 +(epoch: 25, iters: 217952, time: 0.546, data: 0.000) G_L1: 13.543 G_L1_ABSOLUTE: 2.566 G_L1_RELATIVE: 10.977 G_Regularizer: 0.000 validation_error: 20.594 +(epoch: 25, iters: 219952, time: 0.541, data: 0.000) G_L1: 14.260 G_L1_ABSOLUTE: 2.128 G_L1_RELATIVE: 12.132 G_Regularizer: 0.000 validation_error: 21.248 +(epoch: 25, iters: 221952, time: 0.543, data: 0.000) G_L1: 15.988 G_L1_ABSOLUTE: 2.613 G_L1_RELATIVE: 13.375 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 25, iters: 223952, time: 0.539, data: 0.000) G_L1: 13.488 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 10.863 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 25, iters: 225952, time: 0.548, data: 0.000) G_L1: 14.501 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 11.723 G_Regularizer: 0.000 validation_error: 20.549 +(epoch: 25, iters: 227952, time: 0.542, data: 0.000) G_L1: 14.266 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 11.567 G_Regularizer: 0.000 validation_error: 20.583 +(epoch: 25, iters: 229952, time: 0.544, data: 0.000) G_L1: 15.227 G_L1_ABSOLUTE: 2.186 G_L1_RELATIVE: 13.041 G_Regularizer: 0.000 validation_error: 20.238 +(epoch: 25, iters: 231952, time: 0.550, data: 0.000) G_L1: 14.301 G_L1_ABSOLUTE: 2.693 G_L1_RELATIVE: 11.608 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 25, iters: 233952, time: 0.537, data: 0.000) G_L1: 19.735 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 16.987 G_Regularizer: 0.000 validation_error: 21.341 +(epoch: 25, iters: 235952, time: 0.542, data: 0.000) G_L1: 12.368 G_L1_ABSOLUTE: 2.306 G_L1_RELATIVE: 10.062 G_Regularizer: 0.000 validation_error: 20.566 +(epoch: 25, iters: 237952, time: 0.548, data: 0.001) G_L1: 15.184 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 12.589 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 25, iters: 239952, time: 0.541, data: 0.000) G_L1: 14.809 G_L1_ABSOLUTE: 2.765 G_L1_RELATIVE: 12.044 G_Regularizer: 0.000 validation_error: 21.258 +(epoch: 25, iters: 241952, time: 0.539, data: 0.000) G_L1: 13.245 G_L1_ABSOLUTE: 2.532 G_L1_RELATIVE: 10.713 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 25, iters: 243952, time: 0.548, data: 0.000) G_L1: 14.554 G_L1_ABSOLUTE: 2.661 G_L1_RELATIVE: 11.893 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 25, iters: 245952, time: 0.548, data: 0.000) G_L1: 12.945 G_L1_ABSOLUTE: 2.155 G_L1_RELATIVE: 10.790 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 25, iters: 247952, time: 0.541, data: 0.000) G_L1: 10.626 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 8.344 G_Regularizer: 0.000 validation_error: 20.536 +(epoch: 25, iters: 249952, time: 0.548, data: 0.000) G_L1: 13.284 G_L1_ABSOLUTE: 2.019 G_L1_RELATIVE: 11.265 G_Regularizer: 0.000 validation_error: 20.339 +(epoch: 25, iters: 251952, time: 0.540, data: 0.000) G_L1: 14.619 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 11.814 G_Regularizer: 0.000 validation_error: 21.218 +(epoch: 25, iters: 253952, time: 0.548, data: 0.000) G_L1: 11.994 G_L1_ABSOLUTE: 2.052 G_L1_RELATIVE: 9.942 G_Regularizer: 0.000 validation_error: 20.401 +(epoch: 25, iters: 255952, time: 0.545, data: 0.000) G_L1: 15.365 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 12.643 G_Regularizer: 0.000 validation_error: 20.283 +(epoch: 25, iters: 257952, time: 0.537, data: 0.000) G_L1: 14.425 G_L1_ABSOLUTE: 3.009 G_L1_RELATIVE: 11.416 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 25, iters: 259952, time: 0.546, data: 0.000) G_L1: 13.229 G_L1_ABSOLUTE: 2.098 G_L1_RELATIVE: 11.131 G_Regularizer: 0.000 validation_error: 20.186 +(epoch: 25, iters: 261952, time: 0.543, data: 0.000) G_L1: 13.845 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 11.364 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 25, iters: 263952, time: 0.547, data: 0.000) G_L1: 14.886 G_L1_ABSOLUTE: 2.498 G_L1_RELATIVE: 12.388 G_Regularizer: 0.000 validation_error: 20.093 +(epoch: 25, iters: 265952, time: 0.549, data: 0.000) G_L1: 14.481 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 12.078 G_Regularizer: 0.000 validation_error: 20.432 +(epoch: 25, iters: 267952, time: 0.537, data: 0.000) G_L1: 16.035 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 13.340 G_Regularizer: 0.000 validation_error: 20.377 +(epoch: 25, iters: 269952, time: 0.537, data: 0.000) G_L1: 17.378 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 14.539 G_Regularizer: 0.000 validation_error: 19.787 +(epoch: 25, iters: 271952, time: 0.545, data: 0.000) G_L1: 14.124 G_L1_ABSOLUTE: 2.466 G_L1_RELATIVE: 11.658 G_Regularizer: 0.000 validation_error: 20.638 +(epoch: 25, iters: 273952, time: 0.557, data: 0.000) G_L1: 13.374 G_L1_ABSOLUTE: 2.975 G_L1_RELATIVE: 10.399 G_Regularizer: 0.000 validation_error: 21.318 +(epoch: 25, iters: 275952, time: 0.550, data: 0.000) G_L1: 12.762 G_L1_ABSOLUTE: 2.244 G_L1_RELATIVE: 10.518 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 25, iters: 277952, time: 0.547, data: 0.000) G_L1: 16.992 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 14.367 G_Regularizer: 0.000 validation_error: 20.382 +(epoch: 25, iters: 279952, time: 0.545, data: 0.000) G_L1: 13.341 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 10.835 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 25, iters: 281952, time: 0.543, data: 0.000) G_L1: 14.834 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 12.366 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 25, iters: 283952, time: 0.543, data: 0.000) G_L1: 17.367 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 14.876 G_Regularizer: 0.000 validation_error: 20.432 +(epoch: 25, iters: 285952, time: 0.546, data: 0.000) G_L1: 17.617 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 15.071 G_Regularizer: 0.000 validation_error: 20.355 +(epoch: 25, iters: 287952, time: 0.550, data: 0.000) G_L1: 14.524 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 11.950 G_Regularizer: 0.000 validation_error: 21.399 +(epoch: 25, iters: 289952, time: 0.538, data: 0.000) G_L1: 16.944 G_L1_ABSOLUTE: 2.900 G_L1_RELATIVE: 14.044 G_Regularizer: 0.000 validation_error: 21.237 +(epoch: 25, iters: 291952, time: 0.551, data: 0.000) G_L1: 13.965 G_L1_ABSOLUTE: 2.981 G_L1_RELATIVE: 10.984 G_Regularizer: 0.000 validation_error: 20.345 +(epoch: 25, iters: 293952, time: 0.546, data: 0.000) G_L1: 16.071 G_L1_ABSOLUTE: 2.879 G_L1_RELATIVE: 13.193 G_Regularizer: 0.000 validation_error: 21.338 +(epoch: 25, iters: 295952, time: 0.538, data: 0.000) G_L1: 14.207 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 11.302 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 25, iters: 297952, time: 0.550, data: 0.000) G_L1: 14.575 G_L1_ABSOLUTE: 2.334 G_L1_RELATIVE: 12.241 G_Regularizer: 0.000 validation_error: 21.365 +(epoch: 25, iters: 299952, time: 0.542, data: 0.000) G_L1: 15.937 G_L1_ABSOLUTE: 3.362 G_L1_RELATIVE: 12.575 G_Regularizer: 0.000 validation_error: 20.360 +(epoch: 25, iters: 301952, time: 0.541, data: 0.000) G_L1: 11.934 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 9.718 G_Regularizer: 0.000 validation_error: 20.271 +(epoch: 26, iters: 1200, time: 0.547, data: 0.001) G_L1: 17.731 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 15.272 G_Regularizer: 0.000 validation_error: 20.519 +(epoch: 26, iters: 3200, time: 0.543, data: 0.000) G_L1: 13.515 G_L1_ABSOLUTE: 2.788 G_L1_RELATIVE: 10.728 G_Regularizer: 0.000 validation_error: 20.033 +(epoch: 26, iters: 5200, time: 0.551, data: 0.000) G_L1: 15.812 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 13.369 G_Regularizer: 0.000 validation_error: 20.323 +(epoch: 26, iters: 7200, time: 0.546, data: 0.000) G_L1: 11.655 G_L1_ABSOLUTE: 2.727 G_L1_RELATIVE: 8.928 G_Regularizer: 0.000 validation_error: 20.559 +(epoch: 26, iters: 9200, time: 0.546, data: 0.000) G_L1: 12.563 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 10.279 G_Regularizer: 0.000 validation_error: 20.309 +(epoch: 26, iters: 11200, time: 0.542, data: 0.000) G_L1: 14.576 G_L1_ABSOLUTE: 2.379 G_L1_RELATIVE: 12.197 G_Regularizer: 0.000 validation_error: 20.699 +(epoch: 26, iters: 13200, time: 0.545, data: 0.000) G_L1: 14.302 G_L1_ABSOLUTE: 2.661 G_L1_RELATIVE: 11.641 G_Regularizer: 0.000 validation_error: 20.191 +(epoch: 26, iters: 15200, time: 0.541, data: 0.000) G_L1: 14.633 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 11.747 G_Regularizer: 0.000 validation_error: 20.184 +(epoch: 26, iters: 17200, time: 0.540, data: 0.000) G_L1: 13.829 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 11.245 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 26, iters: 19200, time: 0.548, data: 0.000) G_L1: 16.028 G_L1_ABSOLUTE: 3.242 G_L1_RELATIVE: 12.785 G_Regularizer: 0.000 validation_error: 20.394 +(epoch: 26, iters: 21200, time: 0.544, data: 0.000) G_L1: 11.484 G_L1_ABSOLUTE: 2.245 G_L1_RELATIVE: 9.239 G_Regularizer: 0.000 validation_error: 20.894 +(epoch: 26, iters: 23200, time: 0.552, data: 0.000) G_L1: 11.320 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 8.698 G_Regularizer: 0.000 validation_error: 21.237 +(epoch: 26, iters: 25200, time: 0.541, data: 0.000) G_L1: 15.500 G_L1_ABSOLUTE: 2.915 G_L1_RELATIVE: 12.586 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 26, iters: 27200, time: 0.541, data: 0.000) G_L1: 13.222 G_L1_ABSOLUTE: 2.308 G_L1_RELATIVE: 10.914 G_Regularizer: 0.000 validation_error: 21.151 +(epoch: 26, iters: 29200, time: 0.546, data: 0.000) G_L1: 14.034 G_L1_ABSOLUTE: 2.018 G_L1_RELATIVE: 12.016 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 26, iters: 31200, time: 0.555, data: 0.000) G_L1: 15.462 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 12.912 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 26, iters: 33200, time: 0.541, data: 0.000) G_L1: 16.336 G_L1_ABSOLUTE: 2.834 G_L1_RELATIVE: 13.502 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 26, iters: 35200, time: 0.546, data: 0.000) G_L1: 15.006 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 12.250 G_Regularizer: 0.000 validation_error: 20.639 +(epoch: 26, iters: 37200, time: 0.551, data: 0.000) G_L1: 15.101 G_L1_ABSOLUTE: 2.329 G_L1_RELATIVE: 12.773 G_Regularizer: 0.000 validation_error: 20.779 +(epoch: 26, iters: 39200, time: 0.550, data: 0.000) G_L1: 12.957 G_L1_ABSOLUTE: 2.266 G_L1_RELATIVE: 10.691 G_Regularizer: 0.000 validation_error: 19.895 +(epoch: 26, iters: 41200, time: 0.545, data: 0.000) G_L1: 14.701 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 12.176 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 26, iters: 43200, time: 0.540, data: 0.000) G_L1: 18.566 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 15.902 G_Regularizer: 0.000 validation_error: 21.293 +(epoch: 26, iters: 45200, time: 0.547, data: 0.000) G_L1: 12.798 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 10.506 G_Regularizer: 0.000 validation_error: 20.735 +(epoch: 26, iters: 47200, time: 0.538, data: 0.000) G_L1: 14.607 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 12.126 G_Regularizer: 0.000 validation_error: 21.059 +(epoch: 26, iters: 49200, time: 0.550, data: 0.000) G_L1: 15.208 G_L1_ABSOLUTE: 2.167 G_L1_RELATIVE: 13.041 G_Regularizer: 0.000 validation_error: 21.204 +(epoch: 26, iters: 51200, time: 0.542, data: 0.000) G_L1: 16.370 G_L1_ABSOLUTE: 2.374 G_L1_RELATIVE: 13.996 G_Regularizer: 0.000 validation_error: 20.775 +(epoch: 26, iters: 53200, time: 0.550, data: 0.000) G_L1: 15.528 G_L1_ABSOLUTE: 2.841 G_L1_RELATIVE: 12.687 G_Regularizer: 0.000 validation_error: 20.594 +(epoch: 26, iters: 55200, time: 0.555, data: 0.000) G_L1: 14.653 G_L1_ABSOLUTE: 2.296 G_L1_RELATIVE: 12.357 G_Regularizer: 0.000 validation_error: 20.162 +(epoch: 26, iters: 57200, time: 0.547, data: 0.000) G_L1: 15.623 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 13.193 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 26, iters: 59200, time: 0.544, data: 0.000) G_L1: 12.558 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 10.271 G_Regularizer: 0.000 validation_error: 20.936 +(epoch: 26, iters: 61200, time: 0.540, data: 0.000) G_L1: 15.960 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 13.469 G_Regularizer: 0.000 validation_error: 20.636 +(epoch: 26, iters: 63200, time: 0.547, data: 0.000) G_L1: 15.280 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 13.026 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 26, iters: 65200, time: 0.540, data: 0.000) G_L1: 12.284 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 9.655 G_Regularizer: 0.000 validation_error: 20.516 +(epoch: 26, iters: 67200, time: 0.540, data: 0.000) G_L1: 13.738 G_L1_ABSOLUTE: 2.277 G_L1_RELATIVE: 11.461 G_Regularizer: 0.000 validation_error: 21.355 +(epoch: 26, iters: 69200, time: 0.545, data: 0.000) G_L1: 14.205 G_L1_ABSOLUTE: 2.992 G_L1_RELATIVE: 11.213 G_Regularizer: 0.000 validation_error: 20.600 +(epoch: 26, iters: 71200, time: 0.550, data: 0.000) G_L1: 14.195 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 11.706 G_Regularizer: 0.000 validation_error: 20.331 +(epoch: 26, iters: 73200, time: 0.542, data: 0.000) G_L1: 15.752 G_L1_ABSOLUTE: 2.697 G_L1_RELATIVE: 13.055 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 26, iters: 75200, time: 0.556, data: 0.000) G_L1: 16.121 G_L1_ABSOLUTE: 2.603 G_L1_RELATIVE: 13.518 G_Regularizer: 0.000 validation_error: 20.241 +(epoch: 26, iters: 77200, time: 0.538, data: 0.000) G_L1: 14.030 G_L1_ABSOLUTE: 2.775 G_L1_RELATIVE: 11.256 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 26, iters: 79200, time: 0.540, data: 0.000) G_L1: 16.021 G_L1_ABSOLUTE: 2.665 G_L1_RELATIVE: 13.357 G_Regularizer: 0.000 validation_error: 20.457 +(epoch: 26, iters: 81200, time: 0.551, data: 0.000) G_L1: 14.119 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 11.440 G_Regularizer: 0.000 validation_error: 20.575 +(epoch: 26, iters: 83200, time: 0.550, data: 0.000) G_L1: 15.805 G_L1_ABSOLUTE: 2.720 G_L1_RELATIVE: 13.086 G_Regularizer: 0.000 validation_error: 20.421 +(epoch: 26, iters: 85200, time: 0.546, data: 0.000) G_L1: 11.093 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 8.537 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 26, iters: 87200, time: 0.542, data: 0.000) G_L1: 15.693 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 13.224 G_Regularizer: 0.000 validation_error: 20.338 +(epoch: 26, iters: 89200, time: 0.548, data: 0.000) G_L1: 17.452 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 14.742 G_Regularizer: 0.000 validation_error: 20.403 +(epoch: 26, iters: 91200, time: 0.550, data: 0.000) G_L1: 15.264 G_L1_ABSOLUTE: 2.215 G_L1_RELATIVE: 13.049 G_Regularizer: 0.000 validation_error: 20.443 +(epoch: 26, iters: 93200, time: 0.543, data: 0.000) G_L1: 14.834 G_L1_ABSOLUTE: 2.507 G_L1_RELATIVE: 12.327 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 26, iters: 95200, time: 0.549, data: 0.001) G_L1: 14.425 G_L1_ABSOLUTE: 2.700 G_L1_RELATIVE: 11.725 G_Regularizer: 0.000 validation_error: 21.058 +(epoch: 26, iters: 97200, time: 0.543, data: 0.000) G_L1: 16.007 G_L1_ABSOLUTE: 2.644 G_L1_RELATIVE: 13.363 G_Regularizer: 0.000 validation_error: 20.438 +(epoch: 26, iters: 99200, time: 0.546, data: 0.000) G_L1: 14.259 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 11.978 G_Regularizer: 0.000 validation_error: 20.168 +(epoch: 26, iters: 101200, time: 0.555, data: 0.001) G_L1: 14.692 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 12.290 G_Regularizer: 0.000 validation_error: 20.213 +(epoch: 26, iters: 103200, time: 0.544, data: 0.000) G_L1: 17.673 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 15.119 G_Regularizer: 0.000 validation_error: 20.747 +(epoch: 26, iters: 105200, time: 0.545, data: 0.000) G_L1: 14.042 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 11.585 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 26, iters: 107200, time: 0.544, data: 0.000) G_L1: 13.302 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 10.558 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 26, iters: 109200, time: 0.551, data: 0.000) G_L1: 14.729 G_L1_ABSOLUTE: 2.422 G_L1_RELATIVE: 12.307 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 26, iters: 111200, time: 0.544, data: 0.000) G_L1: 15.444 G_L1_ABSOLUTE: 2.850 G_L1_RELATIVE: 12.594 G_Regularizer: 0.000 validation_error: 20.637 +(epoch: 26, iters: 113200, time: 0.547, data: 0.000) G_L1: 14.586 G_L1_ABSOLUTE: 3.393 G_L1_RELATIVE: 11.193 G_Regularizer: 0.000 validation_error: 20.563 +(epoch: 26, iters: 115200, time: 0.556, data: 0.000) G_L1: 14.339 G_L1_ABSOLUTE: 2.791 G_L1_RELATIVE: 11.549 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 26, iters: 117200, time: 0.539, data: 0.000) G_L1: 12.470 G_L1_ABSOLUTE: 2.096 G_L1_RELATIVE: 10.374 G_Regularizer: 0.000 validation_error: 20.576 +(epoch: 26, iters: 119200, time: 0.539, data: 0.000) G_L1: 28.228 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 25.394 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 26, iters: 121200, time: 0.546, data: 0.000) G_L1: 14.364 G_L1_ABSOLUTE: 2.700 G_L1_RELATIVE: 11.664 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 26, iters: 123200, time: 0.553, data: 0.000) G_L1: 13.052 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 10.559 G_Regularizer: 0.000 validation_error: 20.460 +(epoch: 26, iters: 125200, time: 0.548, data: 0.000) G_L1: 15.790 G_L1_ABSOLUTE: 2.815 G_L1_RELATIVE: 12.975 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 26, iters: 127200, time: 0.541, data: 0.000) G_L1: 26.302 G_L1_ABSOLUTE: 2.775 G_L1_RELATIVE: 23.527 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 26, iters: 129200, time: 0.542, data: 0.000) G_L1: 15.641 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 13.029 G_Regularizer: 0.000 validation_error: 21.323 +(epoch: 26, iters: 131200, time: 0.545, data: 0.000) G_L1: 15.445 G_L1_ABSOLUTE: 2.667 G_L1_RELATIVE: 12.778 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 26, iters: 133200, time: 0.541, data: 0.000) G_L1: 15.923 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 13.166 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 26, iters: 135200, time: 0.543, data: 0.000) G_L1: 13.783 G_L1_ABSOLUTE: 2.726 G_L1_RELATIVE: 11.057 G_Regularizer: 0.000 validation_error: 20.563 +(epoch: 26, iters: 137200, time: 0.543, data: 0.000) G_L1: 12.564 G_L1_ABSOLUTE: 2.559 G_L1_RELATIVE: 10.005 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 26, iters: 139200, time: 0.546, data: 0.000) G_L1: 14.751 G_L1_ABSOLUTE: 2.817 G_L1_RELATIVE: 11.934 G_Regularizer: 0.000 validation_error: 21.395 +(epoch: 26, iters: 141200, time: 0.563, data: 0.001) G_L1: 16.133 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 13.417 G_Regularizer: 0.000 validation_error: 20.400 +(epoch: 26, iters: 143200, time: 0.548, data: 0.000) G_L1: 12.502 G_L1_ABSOLUTE: 2.246 G_L1_RELATIVE: 10.256 G_Regularizer: 0.000 validation_error: 20.304 +(epoch: 26, iters: 145200, time: 0.546, data: 0.000) G_L1: 15.244 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 12.565 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 26, iters: 147200, time: 0.543, data: 0.000) G_L1: 16.972 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 14.494 G_Regularizer: 0.000 validation_error: 21.227 +(epoch: 26, iters: 149200, time: 0.543, data: 0.000) G_L1: 15.682 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 12.923 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 26, iters: 151200, time: 0.546, data: 0.000) G_L1: 14.424 G_L1_ABSOLUTE: 2.538 G_L1_RELATIVE: 11.886 G_Regularizer: 0.000 validation_error: 20.537 +(epoch: 26, iters: 153200, time: 0.546, data: 0.000) G_L1: 14.488 G_L1_ABSOLUTE: 2.496 G_L1_RELATIVE: 11.992 G_Regularizer: 0.000 validation_error: 20.279 +(epoch: 26, iters: 155200, time: 0.541, data: 0.000) G_L1: 13.382 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 10.928 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 26, iters: 157200, time: 0.551, data: 0.000) G_L1: 13.709 G_L1_ABSOLUTE: 2.100 G_L1_RELATIVE: 11.609 G_Regularizer: 0.000 validation_error: 21.339 +(epoch: 26, iters: 159200, time: 0.544, data: 0.000) G_L1: 14.290 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 11.793 G_Regularizer: 0.000 validation_error: 20.520 +(epoch: 26, iters: 161200, time: 0.546, data: 0.000) G_L1: 13.713 G_L1_ABSOLUTE: 2.239 G_L1_RELATIVE: 11.474 G_Regularizer: 0.000 validation_error: 20.503 +(epoch: 26, iters: 163200, time: 0.543, data: 0.000) G_L1: 15.597 G_L1_ABSOLUTE: 2.141 G_L1_RELATIVE: 13.455 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 26, iters: 165200, time: 0.540, data: 0.000) G_L1: 15.178 G_L1_ABSOLUTE: 2.988 G_L1_RELATIVE: 12.190 G_Regularizer: 0.000 validation_error: 21.046 +(epoch: 26, iters: 167200, time: 0.544, data: 0.000) G_L1: 16.894 G_L1_ABSOLUTE: 2.779 G_L1_RELATIVE: 14.115 G_Regularizer: 0.000 validation_error: 20.355 +(epoch: 26, iters: 169200, time: 0.542, data: 0.000) G_L1: 13.856 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 11.280 G_Regularizer: 0.000 validation_error: 20.572 +(epoch: 26, iters: 171200, time: 0.554, data: 0.000) G_L1: 14.541 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 12.090 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 26, iters: 173200, time: 0.540, data: 0.000) G_L1: 13.012 G_L1_ABSOLUTE: 2.233 G_L1_RELATIVE: 10.779 G_Regularizer: 0.000 validation_error: 20.748 +(epoch: 26, iters: 175200, time: 0.550, data: 0.000) G_L1: 11.372 G_L1_ABSOLUTE: 2.093 G_L1_RELATIVE: 9.279 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 26, iters: 177200, time: 0.550, data: 0.000) G_L1: 13.548 G_L1_ABSOLUTE: 2.230 G_L1_RELATIVE: 11.318 G_Regularizer: 0.000 validation_error: 20.579 +(epoch: 26, iters: 179200, time: 0.545, data: 0.000) G_L1: 16.508 G_L1_ABSOLUTE: 2.417 G_L1_RELATIVE: 14.091 G_Regularizer: 0.000 validation_error: 20.758 +(epoch: 26, iters: 181200, time: 0.545, data: 0.000) G_L1: 13.808 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 11.273 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 26, iters: 183200, time: 0.548, data: 0.000) G_L1: 16.096 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 13.520 G_Regularizer: 0.000 validation_error: 20.480 +(epoch: 26, iters: 185200, time: 0.545, data: 0.001) G_L1: 16.210 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 13.726 G_Regularizer: 0.000 validation_error: 20.782 +(epoch: 26, iters: 187200, time: 0.543, data: 0.001) G_L1: 15.265 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 12.723 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 26, iters: 189200, time: 0.548, data: 0.000) G_L1: 16.285 G_L1_ABSOLUTE: 2.953 G_L1_RELATIVE: 13.332 G_Regularizer: 0.000 validation_error: 21.345 +(epoch: 26, iters: 191200, time: 0.554, data: 0.000) G_L1: 17.032 G_L1_ABSOLUTE: 2.709 G_L1_RELATIVE: 14.323 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 26, iters: 193200, time: 0.545, data: 0.000) G_L1: 15.819 G_L1_ABSOLUTE: 2.477 G_L1_RELATIVE: 13.342 G_Regularizer: 0.000 validation_error: 21.332 +(epoch: 26, iters: 195200, time: 0.542, data: 0.000) G_L1: 14.127 G_L1_ABSOLUTE: 2.973 G_L1_RELATIVE: 11.154 G_Regularizer: 0.000 validation_error: 20.365 +(epoch: 26, iters: 197200, time: 0.545, data: 0.000) G_L1: 15.976 G_L1_ABSOLUTE: 2.557 G_L1_RELATIVE: 13.419 G_Regularizer: 0.000 validation_error: 20.785 +(epoch: 26, iters: 199200, time: 0.540, data: 0.000) G_L1: 15.561 G_L1_ABSOLUTE: 2.324 G_L1_RELATIVE: 13.237 G_Regularizer: 0.000 validation_error: 20.559 +(epoch: 26, iters: 201200, time: 0.557, data: 0.000) G_L1: 15.706 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 13.142 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 26, iters: 203200, time: 0.546, data: 0.000) G_L1: 15.360 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 13.021 G_Regularizer: 0.000 validation_error: 21.071 +(epoch: 26, iters: 205200, time: 0.545, data: 0.000) G_L1: 14.373 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 11.625 G_Regularizer: 0.000 validation_error: 20.449 +(epoch: 26, iters: 207200, time: 0.536, data: 0.000) G_L1: 13.921 G_L1_ABSOLUTE: 2.426 G_L1_RELATIVE: 11.495 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 26, iters: 209200, time: 0.547, data: 0.000) G_L1: 12.059 G_L1_ABSOLUTE: 2.074 G_L1_RELATIVE: 9.985 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 26, iters: 211200, time: 0.544, data: 0.000) G_L1: 16.363 G_L1_ABSOLUTE: 2.923 G_L1_RELATIVE: 13.440 G_Regularizer: 0.000 validation_error: 20.607 +(epoch: 26, iters: 213200, time: 0.545, data: 0.000) G_L1: 15.442 G_L1_ABSOLUTE: 2.813 G_L1_RELATIVE: 12.629 G_Regularizer: 0.000 validation_error: 21.784 +(epoch: 26, iters: 215200, time: 0.544, data: 0.000) G_L1: 16.341 G_L1_ABSOLUTE: 2.946 G_L1_RELATIVE: 13.395 G_Regularizer: 0.000 validation_error: 20.121 +(epoch: 26, iters: 217200, time: 0.542, data: 0.000) G_L1: 16.771 G_L1_ABSOLUTE: 2.759 G_L1_RELATIVE: 14.012 G_Regularizer: 0.000 validation_error: 20.322 +(epoch: 26, iters: 219200, time: 0.549, data: 0.000) G_L1: 15.739 G_L1_ABSOLUTE: 2.793 G_L1_RELATIVE: 12.947 G_Regularizer: 0.000 validation_error: 20.436 +(epoch: 26, iters: 221200, time: 0.539, data: 0.000) G_L1: 13.654 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 11.255 G_Regularizer: 0.000 validation_error: 21.284 +(epoch: 26, iters: 223200, time: 0.543, data: 0.001) G_L1: 14.085 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 11.567 G_Regularizer: 0.000 validation_error: 21.489 +(epoch: 26, iters: 225200, time: 0.547, data: 0.000) G_L1: 15.109 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 12.605 G_Regularizer: 0.000 validation_error: 21.141 +(epoch: 26, iters: 227200, time: 0.544, data: 0.000) G_L1: 14.358 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 12.060 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 26, iters: 229200, time: 0.542, data: 0.000) G_L1: 13.022 G_L1_ABSOLUTE: 2.776 G_L1_RELATIVE: 10.246 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 26, iters: 231200, time: 0.546, data: 0.000) G_L1: 14.675 G_L1_ABSOLUTE: 3.004 G_L1_RELATIVE: 11.671 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 26, iters: 233200, time: 0.549, data: 0.001) G_L1: 12.705 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 10.200 G_Regularizer: 0.000 validation_error: 20.456 +(epoch: 26, iters: 235200, time: 0.609, data: 0.000) G_L1: 11.844 G_L1_ABSOLUTE: 2.426 G_L1_RELATIVE: 9.418 G_Regularizer: 0.000 validation_error: 21.056 +(epoch: 26, iters: 237200, time: 0.532, data: 0.000) G_L1: 16.622 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 13.973 G_Regularizer: 0.000 validation_error: 20.418 +(epoch: 26, iters: 239200, time: 0.540, data: 0.000) G_L1: 14.724 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 12.129 G_Regularizer: 0.000 validation_error: 21.137 +(epoch: 26, iters: 241200, time: 0.545, data: 0.000) G_L1: 14.158 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 11.661 G_Regularizer: 0.000 validation_error: 21.256 +(epoch: 26, iters: 243200, time: 0.548, data: 0.000) G_L1: 13.462 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 11.027 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 26, iters: 245200, time: 0.542, data: 0.000) G_L1: 11.069 G_L1_ABSOLUTE: 2.338 G_L1_RELATIVE: 8.731 G_Regularizer: 0.000 validation_error: 20.263 +(epoch: 26, iters: 247200, time: 0.547, data: 0.000) G_L1: 15.064 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 12.677 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 26, iters: 249200, time: 0.541, data: 0.000) G_L1: 15.840 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 13.310 G_Regularizer: 0.000 validation_error: 21.022 +(epoch: 26, iters: 251200, time: 0.545, data: 0.000) G_L1: 12.541 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 9.977 G_Regularizer: 0.000 validation_error: 20.947 +(epoch: 26, iters: 253200, time: 0.544, data: 0.000) G_L1: 16.257 G_L1_ABSOLUTE: 2.701 G_L1_RELATIVE: 13.556 G_Regularizer: 0.000 validation_error: 20.731 +(epoch: 26, iters: 255200, time: 0.545, data: 0.000) G_L1: 15.291 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 12.822 G_Regularizer: 0.000 validation_error: 20.431 +(epoch: 26, iters: 257200, time: 0.535, data: 0.000) G_L1: 15.446 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 12.852 G_Regularizer: 0.000 validation_error: 20.284 +(epoch: 26, iters: 259200, time: 0.541, data: 0.000) G_L1: 14.829 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 12.365 G_Regularizer: 0.000 validation_error: 20.654 +(epoch: 26, iters: 261200, time: 0.549, data: 0.000) G_L1: 13.339 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 10.853 G_Regularizer: 0.000 validation_error: 20.519 +(epoch: 26, iters: 263200, time: 0.544, data: 0.000) G_L1: 14.754 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 12.400 G_Regularizer: 0.000 validation_error: 20.775 +(epoch: 26, iters: 265200, time: 0.549, data: 0.000) G_L1: 14.671 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 12.051 G_Regularizer: 0.000 validation_error: 20.693 +(epoch: 26, iters: 267200, time: 0.548, data: 0.000) G_L1: 12.848 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 10.479 G_Regularizer: 0.000 validation_error: 20.415 +(epoch: 26, iters: 269200, time: 0.549, data: 0.000) G_L1: 16.247 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 13.391 G_Regularizer: 0.000 validation_error: 20.542 +(epoch: 26, iters: 271200, time: 0.538, data: 0.000) G_L1: 12.614 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 10.186 G_Regularizer: 0.000 validation_error: 20.286 +(epoch: 26, iters: 273200, time: 0.551, data: 0.000) G_L1: 14.505 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 11.868 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 26, iters: 275200, time: 0.544, data: 0.000) G_L1: 14.805 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 12.170 G_Regularizer: 0.000 validation_error: 20.176 +(epoch: 26, iters: 277200, time: 0.546, data: 0.000) G_L1: 16.320 G_L1_ABSOLUTE: 2.851 G_L1_RELATIVE: 13.469 G_Regularizer: 0.000 validation_error: 20.379 +(epoch: 26, iters: 279200, time: 0.543, data: 0.000) G_L1: 15.443 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 13.025 G_Regularizer: 0.000 validation_error: 20.417 +(epoch: 26, iters: 281200, time: 0.548, data: 0.000) G_L1: 16.069 G_L1_ABSOLUTE: 2.728 G_L1_RELATIVE: 13.340 G_Regularizer: 0.000 validation_error: 21.047 +(epoch: 26, iters: 283200, time: 0.542, data: 0.001) G_L1: 13.604 G_L1_ABSOLUTE: 2.060 G_L1_RELATIVE: 11.544 G_Regularizer: 0.000 validation_error: 20.666 +(epoch: 26, iters: 285200, time: 0.551, data: 0.000) G_L1: 15.449 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 13.106 G_Regularizer: 0.000 validation_error: 20.523 +(epoch: 26, iters: 287200, time: 0.555, data: 0.000) G_L1: 13.753 G_L1_ABSOLUTE: 2.181 G_L1_RELATIVE: 11.572 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 26, iters: 289200, time: 0.539, data: 0.000) G_L1: 14.601 G_L1_ABSOLUTE: 2.907 G_L1_RELATIVE: 11.694 G_Regularizer: 0.000 validation_error: 20.216 +(epoch: 26, iters: 291200, time: 0.544, data: 0.000) G_L1: 14.594 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 12.130 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 26, iters: 293200, time: 0.543, data: 0.000) G_L1: 14.140 G_L1_ABSOLUTE: 2.543 G_L1_RELATIVE: 11.597 G_Regularizer: 0.000 validation_error: 20.466 +(epoch: 26, iters: 295200, time: 0.546, data: 0.000) G_L1: 13.739 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 11.476 G_Regularizer: 0.000 validation_error: 21.508 +(epoch: 26, iters: 297200, time: 0.542, data: 0.000) G_L1: 17.267 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 14.719 G_Regularizer: 0.000 validation_error: 21.108 +(epoch: 26, iters: 299200, time: 0.549, data: 0.000) G_L1: 11.134 G_L1_ABSOLUTE: 1.970 G_L1_RELATIVE: 9.164 G_Regularizer: 0.000 validation_error: 20.826 +(epoch: 26, iters: 301200, time: 0.541, data: 0.000) G_L1: 13.578 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 10.843 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 27, iters: 448, time: 0.539, data: 0.000) G_L1: 16.217 G_L1_ABSOLUTE: 2.543 G_L1_RELATIVE: 13.674 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 27, iters: 2448, time: 0.546, data: 0.000) G_L1: 14.076 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 11.562 G_Regularizer: 0.000 validation_error: 20.490 +(epoch: 27, iters: 4448, time: 0.546, data: 0.000) G_L1: 16.323 G_L1_ABSOLUTE: 2.947 G_L1_RELATIVE: 13.376 G_Regularizer: 0.000 validation_error: 19.992 +(epoch: 27, iters: 6448, time: 0.544, data: 0.000) G_L1: 13.348 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 10.865 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 27, iters: 8448, time: 0.551, data: 0.001) G_L1: 12.721 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 10.244 G_Regularizer: 0.000 validation_error: 20.151 +(epoch: 27, iters: 10448, time: 0.540, data: 0.000) G_L1: 12.463 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 9.966 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 27, iters: 12448, time: 0.545, data: 0.000) G_L1: 15.999 G_L1_ABSOLUTE: 2.828 G_L1_RELATIVE: 13.171 G_Regularizer: 0.000 validation_error: 21.436 +(epoch: 27, iters: 14448, time: 0.545, data: 0.000) G_L1: 11.687 G_L1_ABSOLUTE: 1.926 G_L1_RELATIVE: 9.761 G_Regularizer: 0.000 validation_error: 20.122 +(epoch: 27, iters: 16448, time: 0.535, data: 0.000) G_L1: 16.158 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 13.942 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 27, iters: 18448, time: 0.550, data: 0.000) G_L1: 15.476 G_L1_ABSOLUTE: 2.868 G_L1_RELATIVE: 12.608 G_Regularizer: 0.000 validation_error: 20.794 +(epoch: 27, iters: 20448, time: 0.549, data: 0.000) G_L1: 11.684 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 9.315 G_Regularizer: 0.000 validation_error: 20.621 +(epoch: 27, iters: 22448, time: 0.542, data: 0.000) G_L1: 13.060 G_L1_ABSOLUTE: 2.110 G_L1_RELATIVE: 10.950 G_Regularizer: 0.000 validation_error: 20.548 +(epoch: 27, iters: 24448, time: 0.546, data: 0.000) G_L1: 12.763 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 10.254 G_Regularizer: 0.000 validation_error: 20.136 +(epoch: 27, iters: 26448, time: 0.551, data: 0.000) G_L1: 14.683 G_L1_ABSOLUTE: 2.455 G_L1_RELATIVE: 12.229 G_Regularizer: 0.000 validation_error: 20.745 +(epoch: 27, iters: 28448, time: 0.539, data: 0.000) G_L1: 13.817 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 11.192 G_Regularizer: 0.000 validation_error: 19.994 +(epoch: 27, iters: 30448, time: 0.544, data: 0.000) G_L1: 15.856 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 13.449 G_Regularizer: 0.000 validation_error: 20.194 +(epoch: 27, iters: 32448, time: 0.542, data: 0.000) G_L1: 14.978 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 12.605 G_Regularizer: 0.000 validation_error: 21.392 +(epoch: 27, iters: 34448, time: 0.541, data: 0.000) G_L1: 15.978 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 13.458 G_Regularizer: 0.000 validation_error: 20.195 +(epoch: 27, iters: 36448, time: 0.543, data: 0.000) G_L1: 14.425 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 12.043 G_Regularizer: 0.000 validation_error: 20.527 +(epoch: 27, iters: 38448, time: 0.547, data: 0.000) G_L1: 14.608 G_L1_ABSOLUTE: 2.194 G_L1_RELATIVE: 12.414 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 27, iters: 40448, time: 0.543, data: 0.000) G_L1: 14.977 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 12.430 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 27, iters: 42448, time: 0.543, data: 0.000) G_L1: 17.086 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 14.437 G_Regularizer: 0.000 validation_error: 21.025 +(epoch: 27, iters: 44448, time: 0.548, data: 0.001) G_L1: 13.298 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 10.674 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 27, iters: 46448, time: 0.551, data: 0.000) G_L1: 15.198 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 12.475 G_Regularizer: 0.000 validation_error: 20.078 +(epoch: 27, iters: 48448, time: 0.544, data: 0.000) G_L1: 14.075 G_L1_ABSOLUTE: 2.719 G_L1_RELATIVE: 11.355 G_Regularizer: 0.000 validation_error: 20.344 +(epoch: 27, iters: 50448, time: 0.542, data: 0.000) G_L1: 15.117 G_L1_ABSOLUTE: 2.380 G_L1_RELATIVE: 12.737 G_Regularizer: 0.000 validation_error: 20.406 +(epoch: 27, iters: 52448, time: 0.546, data: 0.000) G_L1: 14.462 G_L1_ABSOLUTE: 2.358 G_L1_RELATIVE: 12.105 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 27, iters: 54448, time: 0.551, data: 0.000) G_L1: 13.081 G_L1_ABSOLUTE: 2.272 G_L1_RELATIVE: 10.809 G_Regularizer: 0.000 validation_error: 20.461 +(epoch: 27, iters: 56448, time: 0.546, data: 0.000) G_L1: 13.450 G_L1_ABSOLUTE: 2.303 G_L1_RELATIVE: 11.148 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 27, iters: 58448, time: 0.540, data: 0.000) G_L1: 15.751 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 13.130 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 27, iters: 60448, time: 0.547, data: 0.000) G_L1: 12.128 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 9.716 G_Regularizer: 0.000 validation_error: 21.781 +(epoch: 27, iters: 62448, time: 0.541, data: 0.000) G_L1: 13.082 G_L1_ABSOLUTE: 2.055 G_L1_RELATIVE: 11.027 G_Regularizer: 0.000 validation_error: 21.784 +(epoch: 27, iters: 64448, time: 0.542, data: 0.000) G_L1: 13.702 G_L1_ABSOLUTE: 2.717 G_L1_RELATIVE: 10.985 G_Regularizer: 0.000 validation_error: 20.671 +(epoch: 27, iters: 66448, time: 0.537, data: 0.000) G_L1: 15.008 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 12.641 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 27, iters: 68448, time: 0.542, data: 0.001) G_L1: 14.308 G_L1_ABSOLUTE: 2.567 G_L1_RELATIVE: 11.741 G_Regularizer: 0.000 validation_error: 21.469 +(epoch: 27, iters: 70448, time: 0.557, data: 0.000) G_L1: 13.099 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 10.616 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 27, iters: 72448, time: 0.536, data: 0.000) G_L1: 13.134 G_L1_ABSOLUTE: 3.055 G_L1_RELATIVE: 10.079 G_Regularizer: 0.000 validation_error: 20.421 +(epoch: 27, iters: 74448, time: 0.542, data: 0.000) G_L1: 16.029 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 13.519 G_Regularizer: 0.000 validation_error: 21.231 +(epoch: 27, iters: 76448, time: 0.541, data: 0.000) G_L1: 16.189 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 13.309 G_Regularizer: 0.000 validation_error: 20.681 +(epoch: 27, iters: 78448, time: 0.548, data: 0.000) G_L1: 12.902 G_L1_ABSOLUTE: 2.636 G_L1_RELATIVE: 10.265 G_Regularizer: 0.000 validation_error: 20.964 +(epoch: 27, iters: 80448, time: 0.547, data: 0.000) G_L1: 13.733 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 11.296 G_Regularizer: 0.000 validation_error: 20.359 +(epoch: 27, iters: 82448, time: 0.544, data: 0.000) G_L1: 13.420 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 10.785 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 27, iters: 84448, time: 0.543, data: 0.000) G_L1: 13.639 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 11.366 G_Regularizer: 0.000 validation_error: 20.247 +(epoch: 27, iters: 86448, time: 0.548, data: 0.000) G_L1: 15.903 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 13.332 G_Regularizer: 0.000 validation_error: 20.195 +(epoch: 27, iters: 88448, time: 0.546, data: 0.000) G_L1: 13.779 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 11.492 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 27, iters: 90448, time: 0.548, data: 0.000) G_L1: 18.633 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 16.172 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 27, iters: 92448, time: 0.546, data: 0.000) G_L1: 14.887 G_L1_ABSOLUTE: 2.575 G_L1_RELATIVE: 12.311 G_Regularizer: 0.000 validation_error: 21.125 +(epoch: 27, iters: 94448, time: 0.539, data: 0.000) G_L1: 13.500 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 10.964 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 27, iters: 96448, time: 0.539, data: 0.000) G_L1: 12.768 G_L1_ABSOLUTE: 2.055 G_L1_RELATIVE: 10.713 G_Regularizer: 0.000 validation_error: 21.382 +(epoch: 27, iters: 98448, time: 0.544, data: 0.000) G_L1: 13.590 G_L1_ABSOLUTE: 2.278 G_L1_RELATIVE: 11.312 G_Regularizer: 0.000 validation_error: 21.358 +(epoch: 27, iters: 100448, time: 0.541, data: 0.000) G_L1: 13.560 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 11.229 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 27, iters: 102448, time: 0.550, data: 0.000) G_L1: 13.789 G_L1_ABSOLUTE: 2.655 G_L1_RELATIVE: 11.134 G_Regularizer: 0.000 validation_error: 20.481 +(epoch: 27, iters: 104448, time: 0.546, data: 0.000) G_L1: 13.524 G_L1_ABSOLUTE: 2.303 G_L1_RELATIVE: 11.221 G_Regularizer: 0.000 validation_error: 20.538 +(epoch: 27, iters: 106448, time: 0.546, data: 0.000) G_L1: 15.241 G_L1_ABSOLUTE: 2.222 G_L1_RELATIVE: 13.019 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 27, iters: 108448, time: 0.551, data: 0.000) G_L1: 12.794 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 10.385 G_Regularizer: 0.000 validation_error: 20.714 +(epoch: 27, iters: 110448, time: 0.551, data: 0.000) G_L1: 14.867 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 12.237 G_Regularizer: 0.000 validation_error: 20.429 +(epoch: 27, iters: 112448, time: 0.547, data: 0.000) G_L1: 14.473 G_L1_ABSOLUTE: 2.843 G_L1_RELATIVE: 11.630 G_Regularizer: 0.000 validation_error: 20.548 +(epoch: 27, iters: 114448, time: 0.550, data: 0.000) G_L1: 14.742 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 11.791 G_Regularizer: 0.000 validation_error: 20.549 +(epoch: 27, iters: 116448, time: 0.547, data: 0.000) G_L1: 14.700 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 12.264 G_Regularizer: 0.000 validation_error: 19.920 +(epoch: 27, iters: 118448, time: 0.544, data: 0.000) G_L1: 13.651 G_L1_ABSOLUTE: 2.607 G_L1_RELATIVE: 11.044 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 27, iters: 120448, time: 0.539, data: 0.000) G_L1: 16.959 G_L1_ABSOLUTE: 2.534 G_L1_RELATIVE: 14.425 G_Regularizer: 0.000 validation_error: 20.741 +(epoch: 27, iters: 122448, time: 0.542, data: 0.000) G_L1: 15.101 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 12.452 G_Regularizer: 0.000 validation_error: 20.263 +(epoch: 27, iters: 124448, time: 0.542, data: 0.000) G_L1: 16.394 G_L1_ABSOLUTE: 2.804 G_L1_RELATIVE: 13.590 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 27, iters: 126448, time: 0.543, data: 0.001) G_L1: 14.281 G_L1_ABSOLUTE: 2.653 G_L1_RELATIVE: 11.628 G_Regularizer: 0.000 validation_error: 21.049 +(epoch: 27, iters: 128448, time: 0.542, data: 0.000) G_L1: 13.161 G_L1_ABSOLUTE: 2.202 G_L1_RELATIVE: 10.959 G_Regularizer: 0.000 validation_error: 20.634 +(epoch: 27, iters: 130448, time: 0.552, data: 0.000) G_L1: 15.239 G_L1_ABSOLUTE: 3.082 G_L1_RELATIVE: 12.157 G_Regularizer: 0.000 validation_error: 21.066 +(epoch: 27, iters: 132448, time: 0.543, data: 0.000) G_L1: 14.367 G_L1_ABSOLUTE: 2.712 G_L1_RELATIVE: 11.654 G_Regularizer: 0.000 validation_error: 21.046 +(epoch: 27, iters: 134448, time: 0.547, data: 0.000) G_L1: 13.851 G_L1_ABSOLUTE: 2.921 G_L1_RELATIVE: 10.930 G_Regularizer: 0.000 validation_error: 21.254 +(epoch: 27, iters: 136448, time: 0.547, data: 0.000) G_L1: 14.202 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 11.689 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 27, iters: 138448, time: 0.548, data: 0.000) G_L1: 13.871 G_L1_ABSOLUTE: 1.988 G_L1_RELATIVE: 11.883 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 27, iters: 140448, time: 0.542, data: 0.000) G_L1: 14.385 G_L1_ABSOLUTE: 3.218 G_L1_RELATIVE: 11.166 G_Regularizer: 0.000 validation_error: 20.273 +(epoch: 27, iters: 142448, time: 0.548, data: 0.000) G_L1: 13.903 G_L1_ABSOLUTE: 2.239 G_L1_RELATIVE: 11.664 G_Regularizer: 0.000 validation_error: 20.251 +(epoch: 27, iters: 144448, time: 0.546, data: 0.000) G_L1: 14.992 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 12.282 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 27, iters: 146448, time: 0.541, data: 0.000) G_L1: 15.290 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 12.920 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 27, iters: 148448, time: 0.544, data: 0.000) G_L1: 14.233 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 11.847 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 27, iters: 150448, time: 0.554, data: 0.000) G_L1: 13.004 G_L1_ABSOLUTE: 2.207 G_L1_RELATIVE: 10.797 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 27, iters: 152448, time: 0.549, data: 0.000) G_L1: 13.517 G_L1_ABSOLUTE: 1.930 G_L1_RELATIVE: 11.588 G_Regularizer: 0.000 validation_error: 21.092 +(epoch: 27, iters: 154448, time: 0.543, data: 0.000) G_L1: 14.781 G_L1_ABSOLUTE: 2.882 G_L1_RELATIVE: 11.899 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 27, iters: 156448, time: 0.543, data: 0.000) G_L1: 13.633 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 11.187 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 27, iters: 158448, time: 0.550, data: 0.000) G_L1: 14.732 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 12.251 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 27, iters: 160448, time: 0.538, data: 0.000) G_L1: 13.907 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 11.424 G_Regularizer: 0.000 validation_error: 20.616 +(epoch: 27, iters: 162448, time: 0.544, data: 0.000) G_L1: 14.725 G_L1_ABSOLUTE: 2.118 G_L1_RELATIVE: 12.607 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 27, iters: 164448, time: 0.545, data: 0.000) G_L1: 13.966 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 11.374 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 27, iters: 166448, time: 0.545, data: 0.000) G_L1: 15.247 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 12.443 G_Regularizer: 0.000 validation_error: 20.427 +(epoch: 27, iters: 168448, time: 0.547, data: 0.001) G_L1: 14.234 G_L1_ABSOLUTE: 2.726 G_L1_RELATIVE: 11.509 G_Regularizer: 0.000 validation_error: 21.210 +(epoch: 27, iters: 170448, time: 0.545, data: 0.000) G_L1: 14.903 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 12.355 G_Regularizer: 0.000 validation_error: 21.277 +(epoch: 27, iters: 172448, time: 0.548, data: 0.000) G_L1: 15.826 G_L1_ABSOLUTE: 3.004 G_L1_RELATIVE: 12.823 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 27, iters: 174448, time: 0.544, data: 0.000) G_L1: 13.223 G_L1_ABSOLUTE: 2.516 G_L1_RELATIVE: 10.707 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 27, iters: 176448, time: 0.546, data: 0.000) G_L1: 13.849 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 11.277 G_Regularizer: 0.000 validation_error: 20.594 +(epoch: 27, iters: 178448, time: 0.545, data: 0.000) G_L1: 13.122 G_L1_ABSOLUTE: 2.342 G_L1_RELATIVE: 10.780 G_Regularizer: 0.000 validation_error: 20.940 +(epoch: 27, iters: 180448, time: 0.542, data: 0.000) G_L1: 14.672 G_L1_ABSOLUTE: 2.358 G_L1_RELATIVE: 12.315 G_Regularizer: 0.000 validation_error: 20.516 +(epoch: 27, iters: 182448, time: 0.538, data: 0.000) G_L1: 15.489 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 12.869 G_Regularizer: 0.000 validation_error: 20.424 +(epoch: 27, iters: 184448, time: 0.549, data: 0.000) G_L1: 12.518 G_L1_ABSOLUTE: 2.310 G_L1_RELATIVE: 10.208 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 27, iters: 186448, time: 0.538, data: 0.000) G_L1: 16.759 G_L1_ABSOLUTE: 3.110 G_L1_RELATIVE: 13.649 G_Regularizer: 0.000 validation_error: 20.663 +(epoch: 27, iters: 188448, time: 0.548, data: 0.000) G_L1: 13.250 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 10.752 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 27, iters: 190448, time: 0.546, data: 0.000) G_L1: 11.783 G_L1_ABSOLUTE: 2.440 G_L1_RELATIVE: 9.343 G_Regularizer: 0.000 validation_error: 21.755 +(epoch: 27, iters: 192448, time: 0.543, data: 0.000) G_L1: 14.930 G_L1_ABSOLUTE: 2.977 G_L1_RELATIVE: 11.953 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 27, iters: 194448, time: 0.546, data: 0.000) G_L1: 14.583 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 11.778 G_Regularizer: 0.000 validation_error: 21.003 +(epoch: 27, iters: 196448, time: 0.550, data: 0.001) G_L1: 12.034 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 9.522 G_Regularizer: 0.000 validation_error: 20.044 +(epoch: 27, iters: 198448, time: 0.549, data: 0.000) G_L1: 15.646 G_L1_ABSOLUTE: 2.915 G_L1_RELATIVE: 12.731 G_Regularizer: 0.000 validation_error: 20.383 +(epoch: 27, iters: 200448, time: 0.539, data: 0.000) G_L1: 13.578 G_L1_ABSOLUTE: 2.793 G_L1_RELATIVE: 10.785 G_Regularizer: 0.000 validation_error: 20.301 +(epoch: 27, iters: 202448, time: 0.542, data: 0.000) G_L1: 13.811 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 10.872 G_Regularizer: 0.000 validation_error: 20.993 +(epoch: 27, iters: 204448, time: 0.545, data: 0.000) G_L1: 13.987 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 11.565 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 27, iters: 206448, time: 0.543, data: 0.000) G_L1: 14.170 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 11.835 G_Regularizer: 0.000 validation_error: 20.462 +(epoch: 27, iters: 208448, time: 0.548, data: 0.000) G_L1: 14.103 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 11.594 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 27, iters: 210448, time: 0.547, data: 0.000) G_L1: 13.652 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 11.284 G_Regularizer: 0.000 validation_error: 20.731 +(epoch: 27, iters: 212448, time: 0.550, data: 0.000) G_L1: 13.366 G_L1_ABSOLUTE: 3.042 G_L1_RELATIVE: 10.324 G_Regularizer: 0.000 validation_error: 20.398 +(epoch: 27, iters: 214448, time: 0.543, data: 0.000) G_L1: 14.637 G_L1_ABSOLUTE: 2.191 G_L1_RELATIVE: 12.447 G_Regularizer: 0.000 validation_error: 20.476 +(epoch: 27, iters: 216448, time: 0.547, data: 0.000) G_L1: 17.143 G_L1_ABSOLUTE: 2.806 G_L1_RELATIVE: 14.337 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 27, iters: 218448, time: 0.544, data: 0.000) G_L1: 15.904 G_L1_ABSOLUTE: 3.098 G_L1_RELATIVE: 12.806 G_Regularizer: 0.000 validation_error: 20.495 +(epoch: 27, iters: 220448, time: 0.550, data: 0.000) G_L1: 12.901 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 10.539 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 27, iters: 222448, time: 0.551, data: 0.000) G_L1: 14.060 G_L1_ABSOLUTE: 2.507 G_L1_RELATIVE: 11.553 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 27, iters: 224448, time: 0.540, data: 0.000) G_L1: 12.161 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 9.540 G_Regularizer: 0.000 validation_error: 20.206 +(epoch: 27, iters: 226448, time: 0.540, data: 0.000) G_L1: 15.672 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 13.423 G_Regularizer: 0.000 validation_error: 20.362 +(epoch: 27, iters: 228448, time: 0.546, data: 0.000) G_L1: 11.906 G_L1_ABSOLUTE: 2.193 G_L1_RELATIVE: 9.713 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 27, iters: 230448, time: 0.537, data: 0.000) G_L1: 16.354 G_L1_ABSOLUTE: 2.884 G_L1_RELATIVE: 13.470 G_Regularizer: 0.000 validation_error: 20.611 +(epoch: 27, iters: 232448, time: 0.547, data: 0.000) G_L1: 14.434 G_L1_ABSOLUTE: 2.295 G_L1_RELATIVE: 12.138 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 27, iters: 234448, time: 0.541, data: 0.000) G_L1: 15.380 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 12.649 G_Regularizer: 0.000 validation_error: 21.126 +(epoch: 27, iters: 236448, time: 0.545, data: 0.000) G_L1: 12.197 G_L1_ABSOLUTE: 2.084 G_L1_RELATIVE: 10.113 G_Regularizer: 0.000 validation_error: 20.922 +(epoch: 27, iters: 238448, time: 0.538, data: 0.000) G_L1: 11.949 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 9.379 G_Regularizer: 0.000 validation_error: 20.277 +(epoch: 27, iters: 240448, time: 0.541, data: 0.000) G_L1: 14.986 G_L1_ABSOLUTE: 2.196 G_L1_RELATIVE: 12.791 G_Regularizer: 0.000 validation_error: 21.303 +(epoch: 27, iters: 242448, time: 0.549, data: 0.001) G_L1: 13.793 G_L1_ABSOLUTE: 2.422 G_L1_RELATIVE: 11.371 G_Regularizer: 0.000 validation_error: 20.495 +(epoch: 27, iters: 244448, time: 0.542, data: 0.000) G_L1: 16.133 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 13.557 G_Regularizer: 0.000 validation_error: 22.036 +(epoch: 27, iters: 246448, time: 0.543, data: 0.000) G_L1: 13.984 G_L1_ABSOLUTE: 2.799 G_L1_RELATIVE: 11.184 G_Regularizer: 0.000 validation_error: 20.576 +(epoch: 27, iters: 248448, time: 0.544, data: 0.000) G_L1: 12.124 G_L1_ABSOLUTE: 2.311 G_L1_RELATIVE: 9.813 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 27, iters: 250448, time: 0.549, data: 0.000) G_L1: 13.900 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 11.586 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 27, iters: 252448, time: 0.548, data: 0.000) G_L1: 16.025 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 13.295 G_Regularizer: 0.000 validation_error: 20.446 +(epoch: 27, iters: 254448, time: 0.541, data: 0.000) G_L1: 16.162 G_L1_ABSOLUTE: 2.884 G_L1_RELATIVE: 13.277 G_Regularizer: 0.000 validation_error: 20.478 +(epoch: 27, iters: 256448, time: 0.546, data: 0.000) G_L1: 17.459 G_L1_ABSOLUTE: 3.209 G_L1_RELATIVE: 14.250 G_Regularizer: 0.000 validation_error: 21.225 +(epoch: 27, iters: 258448, time: 0.547, data: 0.000) G_L1: 13.863 G_L1_ABSOLUTE: 2.740 G_L1_RELATIVE: 11.123 G_Regularizer: 0.000 validation_error: 20.138 +(epoch: 27, iters: 260448, time: 0.540, data: 0.000) G_L1: 15.102 G_L1_ABSOLUTE: 2.726 G_L1_RELATIVE: 12.376 G_Regularizer: 0.000 validation_error: 21.196 +(epoch: 27, iters: 262448, time: 0.539, data: 0.000) G_L1: 14.970 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 12.273 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 27, iters: 264448, time: 0.547, data: 0.000) G_L1: 16.592 G_L1_ABSOLUTE: 2.861 G_L1_RELATIVE: 13.731 G_Regularizer: 0.000 validation_error: 20.563 +(epoch: 27, iters: 266448, time: 0.546, data: 0.000) G_L1: 14.695 G_L1_ABSOLUTE: 2.580 G_L1_RELATIVE: 12.115 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 27, iters: 268448, time: 0.537, data: 0.000) G_L1: 11.287 G_L1_ABSOLUTE: 2.338 G_L1_RELATIVE: 8.950 G_Regularizer: 0.000 validation_error: 20.159 +(epoch: 27, iters: 270448, time: 0.539, data: 0.000) G_L1: 11.582 G_L1_ABSOLUTE: 2.382 G_L1_RELATIVE: 9.200 G_Regularizer: 0.000 validation_error: 21.043 +(epoch: 27, iters: 272448, time: 0.547, data: 0.000) G_L1: 13.537 G_L1_ABSOLUTE: 3.089 G_L1_RELATIVE: 10.447 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 27, iters: 274448, time: 0.555, data: 0.000) G_L1: 16.325 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 13.468 G_Regularizer: 0.000 validation_error: 21.605 +(epoch: 27, iters: 276448, time: 0.551, data: 0.001) G_L1: 14.123 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 11.450 G_Regularizer: 0.000 validation_error: 21.040 +(epoch: 27, iters: 278448, time: 0.541, data: 0.000) G_L1: 14.788 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 12.086 G_Regularizer: 0.000 validation_error: 20.084 +(epoch: 27, iters: 280448, time: 0.547, data: 0.000) G_L1: 15.491 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 13.038 G_Regularizer: 0.000 validation_error: 21.260 +(epoch: 27, iters: 282448, time: 0.552, data: 0.000) G_L1: 15.821 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 13.327 G_Regularizer: 0.000 validation_error: 20.699 +(epoch: 27, iters: 284448, time: 0.537, data: 0.000) G_L1: 15.459 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 12.982 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 27, iters: 286448, time: 0.543, data: 0.000) G_L1: 16.494 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 13.696 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 27, iters: 288448, time: 0.550, data: 0.000) G_L1: 15.917 G_L1_ABSOLUTE: 2.648 G_L1_RELATIVE: 13.268 G_Regularizer: 0.000 validation_error: 20.794 +(epoch: 27, iters: 290448, time: 0.545, data: 0.000) G_L1: 12.894 G_L1_ABSOLUTE: 2.172 G_L1_RELATIVE: 10.722 G_Regularizer: 0.000 validation_error: 20.757 +(epoch: 27, iters: 292448, time: 0.542, data: 0.000) G_L1: 16.555 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 14.110 G_Regularizer: 0.000 validation_error: 20.301 +(epoch: 27, iters: 294448, time: 0.541, data: 0.000) G_L1: 14.297 G_L1_ABSOLUTE: 2.728 G_L1_RELATIVE: 11.569 G_Regularizer: 0.000 validation_error: 21.617 +(epoch: 27, iters: 296448, time: 0.540, data: 0.000) G_L1: 12.925 G_L1_ABSOLUTE: 2.315 G_L1_RELATIVE: 10.610 G_Regularizer: 0.000 validation_error: 21.087 +(epoch: 27, iters: 298448, time: 0.550, data: 0.000) G_L1: 12.797 G_L1_ABSOLUTE: 2.247 G_L1_RELATIVE: 10.551 G_Regularizer: 0.000 validation_error: 20.300 +(epoch: 27, iters: 300448, time: 0.548, data: 0.000) G_L1: 13.827 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 11.357 G_Regularizer: 0.000 validation_error: 20.479 +(epoch: 27, iters: 302448, time: 0.547, data: 0.000) G_L1: 14.407 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 11.615 G_Regularizer: 0.000 validation_error: 21.435 +(epoch: 28, iters: 1696, time: 0.541, data: 0.000) G_L1: 16.847 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 14.099 G_Regularizer: 0.000 validation_error: 20.432 +(epoch: 28, iters: 3696, time: 0.553, data: 0.000) G_L1: 14.083 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 11.472 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 28, iters: 5696, time: 0.546, data: 0.000) G_L1: 14.381 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 11.769 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 28, iters: 7696, time: 0.544, data: 0.000) G_L1: 15.810 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 13.402 G_Regularizer: 0.000 validation_error: 21.154 +(epoch: 28, iters: 9696, time: 0.546, data: 0.000) G_L1: 16.450 G_L1_ABSOLUTE: 2.709 G_L1_RELATIVE: 13.741 G_Regularizer: 0.000 validation_error: 21.007 +(epoch: 28, iters: 11696, time: 0.548, data: 0.000) G_L1: 13.376 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 11.141 G_Regularizer: 0.000 validation_error: 21.241 +(epoch: 28, iters: 13696, time: 0.542, data: 0.000) G_L1: 13.447 G_L1_ABSOLUTE: 2.324 G_L1_RELATIVE: 11.123 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 28, iters: 15696, time: 0.549, data: 0.000) G_L1: 14.224 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 11.771 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 28, iters: 17696, time: 0.543, data: 0.000) G_L1: 15.751 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 13.043 G_Regularizer: 0.000 validation_error: 21.133 +(epoch: 28, iters: 19696, time: 0.549, data: 0.000) G_L1: 12.690 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 10.275 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 28, iters: 21696, time: 0.544, data: 0.000) G_L1: 17.179 G_L1_ABSOLUTE: 2.701 G_L1_RELATIVE: 14.478 G_Regularizer: 0.000 validation_error: 20.453 +(epoch: 28, iters: 23696, time: 0.544, data: 0.000) G_L1: 16.326 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 13.888 G_Regularizer: 0.000 validation_error: 20.654 +(epoch: 28, iters: 25696, time: 0.541, data: 0.000) G_L1: 15.768 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 13.194 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 28, iters: 27696, time: 0.538, data: 0.000) G_L1: 14.396 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 11.728 G_Regularizer: 0.000 validation_error: 20.641 +(epoch: 28, iters: 29696, time: 0.544, data: 0.000) G_L1: 15.326 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 12.611 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 28, iters: 31696, time: 0.540, data: 0.000) G_L1: 15.457 G_L1_ABSOLUTE: 2.717 G_L1_RELATIVE: 12.740 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 28, iters: 33696, time: 0.544, data: 0.000) G_L1: 11.802 G_L1_ABSOLUTE: 2.405 G_L1_RELATIVE: 9.398 G_Regularizer: 0.000 validation_error: 20.653 +(epoch: 28, iters: 35696, time: 0.544, data: 0.000) G_L1: 17.464 G_L1_ABSOLUTE: 2.888 G_L1_RELATIVE: 14.576 G_Regularizer: 0.000 validation_error: 20.222 +(epoch: 28, iters: 37696, time: 0.545, data: 0.000) G_L1: 15.017 G_L1_ABSOLUTE: 2.888 G_L1_RELATIVE: 12.128 G_Regularizer: 0.000 validation_error: 20.323 +(epoch: 28, iters: 39696, time: 0.543, data: 0.001) G_L1: 13.474 G_L1_ABSOLUTE: 2.671 G_L1_RELATIVE: 10.803 G_Regularizer: 0.000 validation_error: 21.069 +(epoch: 28, iters: 41696, time: 0.545, data: 0.000) G_L1: 15.910 G_L1_ABSOLUTE: 2.959 G_L1_RELATIVE: 12.951 G_Regularizer: 0.000 validation_error: 21.087 +(epoch: 28, iters: 43696, time: 0.537, data: 0.000) G_L1: 14.471 G_L1_ABSOLUTE: 2.743 G_L1_RELATIVE: 11.728 G_Regularizer: 0.000 validation_error: 20.553 +(epoch: 28, iters: 45696, time: 0.551, data: 0.001) G_L1: 13.407 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 10.607 G_Regularizer: 0.000 validation_error: 20.382 +(epoch: 28, iters: 47696, time: 0.548, data: 0.000) G_L1: 13.171 G_L1_ABSOLUTE: 2.423 G_L1_RELATIVE: 10.748 G_Regularizer: 0.000 validation_error: 20.489 +(epoch: 28, iters: 49696, time: 0.536, data: 0.000) G_L1: 14.030 G_L1_ABSOLUTE: 2.342 G_L1_RELATIVE: 11.688 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 28, iters: 51696, time: 0.546, data: 0.000) G_L1: 13.750 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 11.154 G_Regularizer: 0.000 validation_error: 21.091 +(epoch: 28, iters: 53696, time: 0.540, data: 0.000) G_L1: 15.999 G_L1_ABSOLUTE: 2.796 G_L1_RELATIVE: 13.202 G_Regularizer: 0.000 validation_error: 21.351 +(epoch: 28, iters: 55696, time: 0.544, data: 0.000) G_L1: 13.970 G_L1_ABSOLUTE: 2.330 G_L1_RELATIVE: 11.640 G_Regularizer: 0.000 validation_error: 20.650 +(epoch: 28, iters: 57696, time: 0.545, data: 0.000) G_L1: 15.411 G_L1_ABSOLUTE: 2.917 G_L1_RELATIVE: 12.494 G_Regularizer: 0.000 validation_error: 20.712 +(epoch: 28, iters: 59696, time: 0.547, data: 0.000) G_L1: 12.091 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 9.756 G_Regularizer: 0.000 validation_error: 20.388 +(epoch: 28, iters: 61696, time: 0.541, data: 0.000) G_L1: 19.008 G_L1_ABSOLUTE: 2.717 G_L1_RELATIVE: 16.291 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 28, iters: 63696, time: 0.548, data: 0.000) G_L1: 12.466 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 10.021 G_Regularizer: 0.000 validation_error: 21.262 +(epoch: 28, iters: 65696, time: 0.543, data: 0.000) G_L1: 14.919 G_L1_ABSOLUTE: 2.692 G_L1_RELATIVE: 12.227 G_Regularizer: 0.000 validation_error: 20.309 +(epoch: 28, iters: 67696, time: 0.557, data: 0.000) G_L1: 15.407 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 13.013 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 28, iters: 69696, time: 0.536, data: 0.000) G_L1: 13.233 G_L1_ABSOLUTE: 3.164 G_L1_RELATIVE: 10.069 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 28, iters: 71696, time: 0.539, data: 0.000) G_L1: 15.579 G_L1_ABSOLUTE: 2.808 G_L1_RELATIVE: 12.771 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 28, iters: 73696, time: 0.544, data: 0.000) G_L1: 13.295 G_L1_ABSOLUTE: 2.108 G_L1_RELATIVE: 11.187 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 28, iters: 75696, time: 0.552, data: 0.000) G_L1: 14.383 G_L1_ABSOLUTE: 2.348 G_L1_RELATIVE: 12.035 G_Regularizer: 0.000 validation_error: 20.303 +(epoch: 28, iters: 77696, time: 0.539, data: 0.000) G_L1: 14.285 G_L1_ABSOLUTE: 3.004 G_L1_RELATIVE: 11.281 G_Regularizer: 0.000 validation_error: 21.360 +(epoch: 28, iters: 79696, time: 0.551, data: 0.000) G_L1: 13.302 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 10.679 G_Regularizer: 0.000 validation_error: 20.893 +(epoch: 28, iters: 81696, time: 0.546, data: 0.000) G_L1: 16.662 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 14.179 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 28, iters: 83696, time: 0.545, data: 0.000) G_L1: 11.143 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 8.660 G_Regularizer: 0.000 validation_error: 21.236 +(epoch: 28, iters: 85696, time: 0.551, data: 0.000) G_L1: 15.131 G_L1_ABSOLUTE: 3.040 G_L1_RELATIVE: 12.091 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 28, iters: 87696, time: 0.550, data: 0.000) G_L1: 15.833 G_L1_ABSOLUTE: 2.318 G_L1_RELATIVE: 13.515 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 28, iters: 89696, time: 0.548, data: 0.000) G_L1: 13.583 G_L1_ABSOLUTE: 2.531 G_L1_RELATIVE: 11.053 G_Regularizer: 0.000 validation_error: 20.189 +(epoch: 28, iters: 91696, time: 0.547, data: 0.000) G_L1: 15.882 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 13.148 G_Regularizer: 0.000 validation_error: 20.874 +(epoch: 28, iters: 93696, time: 0.540, data: 0.000) G_L1: 14.932 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 12.384 G_Regularizer: 0.000 validation_error: 21.236 +(epoch: 28, iters: 95696, time: 0.548, data: 0.000) G_L1: 14.063 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 11.723 G_Regularizer: 0.000 validation_error: 21.194 +(epoch: 28, iters: 97696, time: 0.544, data: 0.000) G_L1: 16.807 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 14.241 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 28, iters: 99696, time: 0.547, data: 0.000) G_L1: 13.905 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 11.275 G_Regularizer: 0.000 validation_error: 20.733 +(epoch: 28, iters: 101696, time: 0.546, data: 0.001) G_L1: 15.005 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 12.531 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 28, iters: 103696, time: 0.543, data: 0.000) G_L1: 12.282 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 9.695 G_Regularizer: 0.000 validation_error: 20.544 +(epoch: 28, iters: 105696, time: 0.543, data: 0.001) G_L1: 16.014 G_L1_ABSOLUTE: 3.374 G_L1_RELATIVE: 12.640 G_Regularizer: 0.000 validation_error: 20.079 +(epoch: 28, iters: 107696, time: 0.552, data: 0.000) G_L1: 14.152 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 11.703 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 28, iters: 109696, time: 0.544, data: 0.000) G_L1: 12.903 G_L1_ABSOLUTE: 2.608 G_L1_RELATIVE: 10.295 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 28, iters: 111696, time: 0.555, data: 0.000) G_L1: 16.142 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 13.489 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 28, iters: 113696, time: 0.543, data: 0.000) G_L1: 15.277 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 12.828 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 28, iters: 115696, time: 0.546, data: 0.000) G_L1: 16.657 G_L1_ABSOLUTE: 2.753 G_L1_RELATIVE: 13.904 G_Regularizer: 0.000 validation_error: 21.056 +(epoch: 28, iters: 117696, time: 0.543, data: 0.000) G_L1: 16.223 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 13.728 G_Regularizer: 0.000 validation_error: 20.284 +(epoch: 28, iters: 119696, time: 0.546, data: 0.000) G_L1: 16.767 G_L1_ABSOLUTE: 2.844 G_L1_RELATIVE: 13.923 G_Regularizer: 0.000 validation_error: 21.318 +(epoch: 28, iters: 121696, time: 0.545, data: 0.000) G_L1: 18.802 G_L1_ABSOLUTE: 3.270 G_L1_RELATIVE: 15.531 G_Regularizer: 0.000 validation_error: 20.844 +(epoch: 28, iters: 123696, time: 0.550, data: 0.001) G_L1: 12.158 G_L1_ABSOLUTE: 2.338 G_L1_RELATIVE: 9.820 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 28, iters: 125696, time: 0.543, data: 0.000) G_L1: 14.648 G_L1_ABSOLUTE: 2.831 G_L1_RELATIVE: 11.817 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 28, iters: 127696, time: 0.543, data: 0.000) G_L1: 13.391 G_L1_ABSOLUTE: 2.109 G_L1_RELATIVE: 11.282 G_Regularizer: 0.000 validation_error: 20.107 +(epoch: 28, iters: 129696, time: 0.544, data: 0.000) G_L1: 12.770 G_L1_ABSOLUTE: 2.372 G_L1_RELATIVE: 10.397 G_Regularizer: 0.000 validation_error: 21.369 +(epoch: 28, iters: 131696, time: 0.540, data: 0.000) G_L1: 13.142 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 10.662 G_Regularizer: 0.000 validation_error: 20.485 +(epoch: 28, iters: 133696, time: 0.541, data: 0.000) G_L1: 16.728 G_L1_ABSOLUTE: 2.791 G_L1_RELATIVE: 13.937 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 28, iters: 135696, time: 0.542, data: 0.000) G_L1: 16.139 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 13.571 G_Regularizer: 0.000 validation_error: 21.212 +(epoch: 28, iters: 137696, time: 0.547, data: 0.000) G_L1: 14.723 G_L1_ABSOLUTE: 2.853 G_L1_RELATIVE: 11.869 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 28, iters: 139696, time: 0.543, data: 0.000) G_L1: 15.737 G_L1_ABSOLUTE: 2.973 G_L1_RELATIVE: 12.763 G_Regularizer: 0.000 validation_error: 20.598 +(epoch: 28, iters: 141696, time: 0.547, data: 0.000) G_L1: 13.670 G_L1_ABSOLUTE: 2.783 G_L1_RELATIVE: 10.887 G_Regularizer: 0.000 validation_error: 20.596 +(epoch: 28, iters: 143696, time: 0.537, data: 0.000) G_L1: 15.923 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 13.290 G_Regularizer: 0.000 validation_error: 20.572 +(epoch: 28, iters: 145696, time: 0.546, data: 0.000) G_L1: 12.602 G_L1_ABSOLUTE: 2.260 G_L1_RELATIVE: 10.342 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 28, iters: 147696, time: 0.538, data: 0.000) G_L1: 18.570 G_L1_ABSOLUTE: 3.407 G_L1_RELATIVE: 15.163 G_Regularizer: 0.000 validation_error: 20.364 +(epoch: 28, iters: 149696, time: 0.539, data: 0.000) G_L1: 13.627 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 11.178 G_Regularizer: 0.000 validation_error: 20.043 +(epoch: 28, iters: 151696, time: 0.547, data: 0.000) G_L1: 14.241 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 11.612 G_Regularizer: 0.000 validation_error: 19.906 +(epoch: 28, iters: 153696, time: 0.549, data: 0.000) G_L1: 14.662 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 12.188 G_Regularizer: 0.000 validation_error: 21.253 +(epoch: 28, iters: 155696, time: 0.541, data: 0.001) G_L1: 13.812 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 11.339 G_Regularizer: 0.000 validation_error: 21.256 +(epoch: 28, iters: 157696, time: 0.541, data: 0.000) G_L1: 15.984 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 13.437 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 28, iters: 159696, time: 0.542, data: 0.001) G_L1: 15.607 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 12.930 G_Regularizer: 0.000 validation_error: 21.162 +(epoch: 28, iters: 161696, time: 0.546, data: 0.000) G_L1: 16.120 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 13.543 G_Regularizer: 0.000 validation_error: 20.749 +(epoch: 28, iters: 163696, time: 0.549, data: 0.001) G_L1: 15.788 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 13.160 G_Regularizer: 0.000 validation_error: 20.257 +(epoch: 28, iters: 165696, time: 0.544, data: 0.000) G_L1: 13.577 G_L1_ABSOLUTE: 2.396 G_L1_RELATIVE: 11.180 G_Regularizer: 0.000 validation_error: 21.215 +(epoch: 28, iters: 167696, time: 0.546, data: 0.000) G_L1: 17.069 G_L1_ABSOLUTE: 3.256 G_L1_RELATIVE: 13.813 G_Regularizer: 0.000 validation_error: 20.437 +(epoch: 28, iters: 169696, time: 0.542, data: 0.000) G_L1: 16.515 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 13.857 G_Regularizer: 0.000 validation_error: 21.392 +(epoch: 28, iters: 171696, time: 0.549, data: 0.000) G_L1: 14.691 G_L1_ABSOLUTE: 2.545 G_L1_RELATIVE: 12.146 G_Regularizer: 0.000 validation_error: 21.302 +(epoch: 28, iters: 173696, time: 0.543, data: 0.000) G_L1: 12.286 G_L1_ABSOLUTE: 2.025 G_L1_RELATIVE: 10.260 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 28, iters: 175696, time: 0.545, data: 0.000) G_L1: 13.660 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 10.906 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 28, iters: 177696, time: 0.540, data: 0.000) G_L1: 15.050 G_L1_ABSOLUTE: 2.944 G_L1_RELATIVE: 12.106 G_Regularizer: 0.000 validation_error: 21.179 +(epoch: 28, iters: 179696, time: 0.539, data: 0.000) G_L1: 10.556 G_L1_ABSOLUTE: 1.943 G_L1_RELATIVE: 8.614 G_Regularizer: 0.000 validation_error: 20.422 +(epoch: 28, iters: 181696, time: 0.539, data: 0.000) G_L1: 13.187 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 10.667 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 28, iters: 183696, time: 0.540, data: 0.000) G_L1: 12.725 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 10.266 G_Regularizer: 0.000 validation_error: 19.901 +(epoch: 28, iters: 185696, time: 0.545, data: 0.000) G_L1: 15.240 G_L1_ABSOLUTE: 2.315 G_L1_RELATIVE: 12.925 G_Regularizer: 0.000 validation_error: 20.545 +(epoch: 28, iters: 187696, time: 0.547, data: 0.000) G_L1: 14.390 G_L1_ABSOLUTE: 2.604 G_L1_RELATIVE: 11.787 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 28, iters: 189696, time: 0.543, data: 0.000) G_L1: 18.476 G_L1_ABSOLUTE: 2.205 G_L1_RELATIVE: 16.271 G_Regularizer: 0.000 validation_error: 20.297 +(epoch: 28, iters: 191696, time: 0.539, data: 0.000) G_L1: 12.897 G_L1_ABSOLUTE: 2.257 G_L1_RELATIVE: 10.640 G_Regularizer: 0.000 validation_error: 20.538 +(epoch: 28, iters: 193696, time: 0.548, data: 0.000) G_L1: 12.322 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 9.892 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 28, iters: 195696, time: 0.537, data: 0.000) G_L1: 16.990 G_L1_ABSOLUTE: 2.434 G_L1_RELATIVE: 14.556 G_Regularizer: 0.000 validation_error: 20.455 +(epoch: 28, iters: 197696, time: 0.552, data: 0.000) G_L1: 16.008 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 13.513 G_Regularizer: 0.000 validation_error: 20.299 +(epoch: 28, iters: 199696, time: 0.547, data: 0.000) G_L1: 14.336 G_L1_ABSOLUTE: 2.393 G_L1_RELATIVE: 11.943 G_Regularizer: 0.000 validation_error: 20.653 +(epoch: 28, iters: 201696, time: 0.549, data: 0.000) G_L1: 12.775 G_L1_ABSOLUTE: 2.163 G_L1_RELATIVE: 10.612 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 28, iters: 203696, time: 0.536, data: 0.000) G_L1: 17.169 G_L1_ABSOLUTE: 3.224 G_L1_RELATIVE: 13.945 G_Regularizer: 0.000 validation_error: 20.959 +(epoch: 28, iters: 205696, time: 0.548, data: 0.000) G_L1: 13.948 G_L1_ABSOLUTE: 2.376 G_L1_RELATIVE: 11.571 G_Regularizer: 0.000 validation_error: 20.287 +(epoch: 28, iters: 207696, time: 0.542, data: 0.000) G_L1: 13.001 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 10.585 G_Regularizer: 0.000 validation_error: 20.037 +(epoch: 28, iters: 209696, time: 0.541, data: 0.000) G_L1: 15.556 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 13.187 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 28, iters: 211696, time: 0.545, data: 0.000) G_L1: 16.728 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 14.231 G_Regularizer: 0.000 validation_error: 20.442 +(epoch: 28, iters: 213696, time: 0.540, data: 0.000) G_L1: 17.605 G_L1_ABSOLUTE: 2.516 G_L1_RELATIVE: 15.090 G_Regularizer: 0.000 validation_error: 20.871 +(epoch: 28, iters: 215696, time: 0.537, data: 0.000) G_L1: 14.490 G_L1_ABSOLUTE: 2.338 G_L1_RELATIVE: 12.152 G_Regularizer: 0.000 validation_error: 20.379 +(epoch: 28, iters: 217696, time: 0.547, data: 0.000) G_L1: 16.151 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 13.426 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 28, iters: 219696, time: 0.553, data: 0.000) G_L1: 14.544 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 11.639 G_Regularizer: 0.000 validation_error: 20.343 +(epoch: 28, iters: 221696, time: 0.548, data: 0.000) G_L1: 15.586 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 12.788 G_Regularizer: 0.000 validation_error: 20.168 +(epoch: 28, iters: 223696, time: 0.550, data: 0.000) G_L1: 13.016 G_L1_ABSOLUTE: 2.277 G_L1_RELATIVE: 10.738 G_Regularizer: 0.000 validation_error: 21.314 +(epoch: 28, iters: 225696, time: 0.547, data: 0.000) G_L1: 16.028 G_L1_ABSOLUTE: 2.266 G_L1_RELATIVE: 13.763 G_Regularizer: 0.000 validation_error: 20.992 +(epoch: 28, iters: 227696, time: 0.550, data: 0.000) G_L1: 13.567 G_L1_ABSOLUTE: 2.718 G_L1_RELATIVE: 10.849 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 28, iters: 229696, time: 0.541, data: 0.000) G_L1: 16.846 G_L1_ABSOLUTE: 3.201 G_L1_RELATIVE: 13.645 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 28, iters: 231696, time: 0.554, data: 0.000) G_L1: 14.361 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 11.730 G_Regularizer: 0.000 validation_error: 20.692 +(epoch: 28, iters: 233696, time: 0.552, data: 0.000) G_L1: 14.936 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 12.426 G_Regularizer: 0.000 validation_error: 20.567 +(epoch: 28, iters: 235696, time: 0.540, data: 0.000) G_L1: 17.507 G_L1_ABSOLUTE: 2.825 G_L1_RELATIVE: 14.682 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 28, iters: 237696, time: 0.540, data: 0.000) G_L1: 14.675 G_L1_ABSOLUTE: 2.500 G_L1_RELATIVE: 12.175 G_Regularizer: 0.000 validation_error: 20.389 +(epoch: 28, iters: 239696, time: 0.548, data: 0.000) G_L1: 14.594 G_L1_ABSOLUTE: 2.638 G_L1_RELATIVE: 11.957 G_Regularizer: 0.000 validation_error: 20.475 +(epoch: 28, iters: 241696, time: 0.542, data: 0.000) G_L1: 12.876 G_L1_ABSOLUTE: 2.159 G_L1_RELATIVE: 10.718 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 28, iters: 243696, time: 0.545, data: 0.000) G_L1: 16.207 G_L1_ABSOLUTE: 2.997 G_L1_RELATIVE: 13.210 G_Regularizer: 0.000 validation_error: 20.389 +(epoch: 28, iters: 245696, time: 0.559, data: 0.000) G_L1: 15.270 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 12.614 G_Regularizer: 0.000 validation_error: 20.234 +(epoch: 28, iters: 247696, time: 0.541, data: 0.000) G_L1: 13.788 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 11.082 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 28, iters: 249696, time: 0.546, data: 0.000) G_L1: 14.391 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 11.806 G_Regularizer: 0.000 validation_error: 20.532 +(epoch: 28, iters: 251696, time: 0.548, data: 0.000) G_L1: 15.266 G_L1_ABSOLUTE: 2.308 G_L1_RELATIVE: 12.958 G_Regularizer: 0.000 validation_error: 20.312 +(epoch: 28, iters: 253696, time: 0.556, data: 0.000) G_L1: 13.227 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 10.861 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 28, iters: 255696, time: 0.546, data: 0.000) G_L1: 13.636 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 11.062 G_Regularizer: 0.000 validation_error: 20.653 +(epoch: 28, iters: 257696, time: 0.547, data: 0.001) G_L1: 16.406 G_L1_ABSOLUTE: 2.750 G_L1_RELATIVE: 13.656 G_Regularizer: 0.000 validation_error: 20.421 +(epoch: 28, iters: 259696, time: 0.547, data: 0.000) G_L1: 12.322 G_L1_ABSOLUTE: 2.228 G_L1_RELATIVE: 10.094 G_Regularizer: 0.000 validation_error: 20.397 +(epoch: 28, iters: 261696, time: 0.543, data: 0.000) G_L1: 14.052 G_L1_ABSOLUTE: 2.234 G_L1_RELATIVE: 11.818 G_Regularizer: 0.000 validation_error: 21.172 +(epoch: 28, iters: 263696, time: 0.544, data: 0.000) G_L1: 15.915 G_L1_ABSOLUTE: 2.879 G_L1_RELATIVE: 13.036 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 28, iters: 265696, time: 0.559, data: 0.000) G_L1: 14.742 G_L1_ABSOLUTE: 3.041 G_L1_RELATIVE: 11.701 G_Regularizer: 0.000 validation_error: 21.300 +(epoch: 28, iters: 267696, time: 0.542, data: 0.000) G_L1: 17.566 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 15.112 G_Regularizer: 0.000 validation_error: 20.634 +(epoch: 28, iters: 269696, time: 0.541, data: 0.000) G_L1: 16.793 G_L1_ABSOLUTE: 2.987 G_L1_RELATIVE: 13.806 G_Regularizer: 0.000 validation_error: 20.529 +(epoch: 28, iters: 271696, time: 0.540, data: 0.000) G_L1: 11.008 G_L1_ABSOLUTE: 2.054 G_L1_RELATIVE: 8.955 G_Regularizer: 0.000 validation_error: 20.748 +(epoch: 28, iters: 273696, time: 0.550, data: 0.001) G_L1: 14.266 G_L1_ABSOLUTE: 2.293 G_L1_RELATIVE: 11.974 G_Regularizer: 0.000 validation_error: 21.194 +(epoch: 28, iters: 275696, time: 0.547, data: 0.000) G_L1: 11.475 G_L1_ABSOLUTE: 2.141 G_L1_RELATIVE: 9.334 G_Regularizer: 0.000 validation_error: 20.854 +(epoch: 28, iters: 277696, time: 0.539, data: 0.000) G_L1: 14.498 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 12.153 G_Regularizer: 0.000 validation_error: 20.576 +(epoch: 28, iters: 279696, time: 0.558, data: 0.000) G_L1: 15.444 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 12.919 G_Regularizer: 0.000 validation_error: 21.060 +(epoch: 28, iters: 281696, time: 0.538, data: 0.001) G_L1: 18.425 G_L1_ABSOLUTE: 2.313 G_L1_RELATIVE: 16.113 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 28, iters: 283696, time: 0.549, data: 0.000) G_L1: 15.748 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 13.264 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 28, iters: 285696, time: 0.541, data: 0.000) G_L1: 13.062 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 10.463 G_Regularizer: 0.000 validation_error: 20.927 +(epoch: 28, iters: 287696, time: 0.552, data: 0.001) G_L1: 13.333 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 10.596 G_Regularizer: 0.000 validation_error: 20.768 +(epoch: 28, iters: 289696, time: 0.542, data: 0.000) G_L1: 14.396 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 11.813 G_Regularizer: 0.000 validation_error: 20.292 +(epoch: 28, iters: 291696, time: 0.548, data: 0.000) G_L1: 15.197 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 12.616 G_Regularizer: 0.000 validation_error: 20.672 +(epoch: 28, iters: 293696, time: 0.542, data: 0.000) G_L1: 15.412 G_L1_ABSOLUTE: 2.270 G_L1_RELATIVE: 13.142 G_Regularizer: 0.000 validation_error: 20.288 +(epoch: 28, iters: 295696, time: 0.546, data: 0.000) G_L1: 17.772 G_L1_ABSOLUTE: 3.231 G_L1_RELATIVE: 14.542 G_Regularizer: 0.000 validation_error: 20.325 +(epoch: 28, iters: 297696, time: 0.545, data: 0.000) G_L1: 15.716 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 13.160 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 28, iters: 299696, time: 0.549, data: 0.000) G_L1: 14.640 G_L1_ABSOLUTE: 2.205 G_L1_RELATIVE: 12.435 G_Regularizer: 0.000 validation_error: 20.207 +(epoch: 28, iters: 301696, time: 0.538, data: 0.000) G_L1: 15.122 G_L1_ABSOLUTE: 3.334 G_L1_RELATIVE: 11.788 G_Regularizer: 0.000 validation_error: 20.309 +(epoch: 29, iters: 944, time: 0.538, data: 0.000) G_L1: 15.783 G_L1_ABSOLUTE: 3.073 G_L1_RELATIVE: 12.710 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 29, iters: 2944, time: 0.544, data: 0.000) G_L1: 13.689 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 11.153 G_Regularizer: 0.000 validation_error: 21.223 +(epoch: 29, iters: 4944, time: 0.548, data: 0.000) G_L1: 14.162 G_L1_ABSOLUTE: 2.917 G_L1_RELATIVE: 11.245 G_Regularizer: 0.000 validation_error: 19.976 +(epoch: 29, iters: 6944, time: 0.545, data: 0.001) G_L1: 14.831 G_L1_ABSOLUTE: 3.379 G_L1_RELATIVE: 11.452 G_Regularizer: 0.000 validation_error: 21.496 +(epoch: 29, iters: 8944, time: 0.543, data: 0.000) G_L1: 14.772 G_L1_ABSOLUTE: 2.529 G_L1_RELATIVE: 12.243 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 29, iters: 10944, time: 0.549, data: 0.000) G_L1: 14.717 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 12.146 G_Regularizer: 0.000 validation_error: 20.939 +(epoch: 29, iters: 12944, time: 0.541, data: 0.000) G_L1: 12.274 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 9.614 G_Regularizer: 0.000 validation_error: 20.661 +(epoch: 29, iters: 14944, time: 0.551, data: 0.000) G_L1: 13.604 G_L1_ABSOLUTE: 2.359 G_L1_RELATIVE: 11.245 G_Regularizer: 0.000 validation_error: 20.429 +(epoch: 29, iters: 16944, time: 0.541, data: 0.000) G_L1: 12.651 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 10.110 G_Regularizer: 0.000 validation_error: 21.190 +(epoch: 29, iters: 18944, time: 0.546, data: 0.001) G_L1: 14.415 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 11.789 G_Regularizer: 0.000 validation_error: 20.413 +(epoch: 29, iters: 20944, time: 0.537, data: 0.000) G_L1: 14.138 G_L1_ABSOLUTE: 2.465 G_L1_RELATIVE: 11.673 G_Regularizer: 0.000 validation_error: 20.475 +(epoch: 29, iters: 22944, time: 0.545, data: 0.000) G_L1: 17.081 G_L1_ABSOLUTE: 2.906 G_L1_RELATIVE: 14.175 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 29, iters: 24944, time: 0.551, data: 0.000) G_L1: 15.952 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 13.230 G_Regularizer: 0.000 validation_error: 21.090 +(epoch: 29, iters: 26944, time: 0.546, data: 0.000) G_L1: 24.085 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 21.588 G_Regularizer: 0.000 validation_error: 20.798 +(epoch: 29, iters: 28944, time: 0.544, data: 0.000) G_L1: 14.598 G_L1_ABSOLUTE: 2.324 G_L1_RELATIVE: 12.274 G_Regularizer: 0.000 validation_error: 20.920 +(epoch: 29, iters: 30944, time: 0.534, data: 0.000) G_L1: 16.065 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 13.253 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 29, iters: 32944, time: 0.559, data: 0.000) G_L1: 13.906 G_L1_ABSOLUTE: 2.862 G_L1_RELATIVE: 11.044 G_Regularizer: 0.000 validation_error: 21.089 +(epoch: 29, iters: 34944, time: 0.541, data: 0.001) G_L1: 12.031 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 9.684 G_Regularizer: 0.000 validation_error: 21.318 +(epoch: 29, iters: 36944, time: 0.539, data: 0.000) G_L1: 12.281 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 9.658 G_Regularizer: 0.000 validation_error: 20.602 +(epoch: 29, iters: 38944, time: 0.549, data: 0.000) G_L1: 14.355 G_L1_ABSOLUTE: 2.330 G_L1_RELATIVE: 12.025 G_Regularizer: 0.000 validation_error: 20.459 +(epoch: 29, iters: 40944, time: 0.547, data: 0.000) G_L1: 15.190 G_L1_ABSOLUTE: 2.479 G_L1_RELATIVE: 12.711 G_Regularizer: 0.000 validation_error: 20.674 +(epoch: 29, iters: 42944, time: 0.544, data: 0.000) G_L1: 17.793 G_L1_ABSOLUTE: 3.034 G_L1_RELATIVE: 14.759 G_Regularizer: 0.000 validation_error: 20.339 +(epoch: 29, iters: 44944, time: 0.548, data: 0.000) G_L1: 13.982 G_L1_ABSOLUTE: 2.896 G_L1_RELATIVE: 11.086 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 29, iters: 46944, time: 0.551, data: 0.000) G_L1: 16.979 G_L1_ABSOLUTE: 2.934 G_L1_RELATIVE: 14.046 G_Regularizer: 0.000 validation_error: 20.854 +(epoch: 29, iters: 48944, time: 0.547, data: 0.000) G_L1: 14.975 G_L1_ABSOLUTE: 3.475 G_L1_RELATIVE: 11.500 G_Regularizer: 0.000 validation_error: 20.206 +(epoch: 29, iters: 50944, time: 0.544, data: 0.000) G_L1: 14.249 G_L1_ABSOLUTE: 2.163 G_L1_RELATIVE: 12.086 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 29, iters: 52944, time: 0.548, data: 0.000) G_L1: 14.180 G_L1_ABSOLUTE: 2.477 G_L1_RELATIVE: 11.703 G_Regularizer: 0.000 validation_error: 20.597 +(epoch: 29, iters: 54944, time: 0.547, data: 0.000) G_L1: 15.496 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 13.001 G_Regularizer: 0.000 validation_error: 21.246 +(epoch: 29, iters: 56944, time: 0.543, data: 0.000) G_L1: 14.402 G_L1_ABSOLUTE: 2.654 G_L1_RELATIVE: 11.748 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 29, iters: 58944, time: 0.543, data: 0.000) G_L1: 16.604 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 13.806 G_Regularizer: 0.000 validation_error: 20.258 +(epoch: 29, iters: 60944, time: 0.544, data: 0.000) G_L1: 13.965 G_L1_ABSOLUTE: 2.766 G_L1_RELATIVE: 11.199 G_Regularizer: 0.000 validation_error: 21.063 +(epoch: 29, iters: 62944, time: 0.549, data: 0.000) G_L1: 13.309 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 10.674 G_Regularizer: 0.000 validation_error: 20.424 +(epoch: 29, iters: 64944, time: 0.539, data: 0.000) G_L1: 11.475 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 9.221 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 29, iters: 66944, time: 0.538, data: 0.000) G_L1: 18.099 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 15.606 G_Regularizer: 0.000 validation_error: 20.943 +(epoch: 29, iters: 68944, time: 0.541, data: 0.000) G_L1: 16.482 G_L1_ABSOLUTE: 2.690 G_L1_RELATIVE: 13.793 G_Regularizer: 0.000 validation_error: 20.681 +(epoch: 29, iters: 70944, time: 0.546, data: 0.000) G_L1: 13.784 G_L1_ABSOLUTE: 2.608 G_L1_RELATIVE: 11.176 G_Regularizer: 0.000 validation_error: 20.380 +(epoch: 29, iters: 72944, time: 0.541, data: 0.000) G_L1: 13.097 G_L1_ABSOLUTE: 2.109 G_L1_RELATIVE: 10.988 G_Regularizer: 0.000 validation_error: 21.043 +(epoch: 29, iters: 74944, time: 0.559, data: 0.001) G_L1: 17.087 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 14.669 G_Regularizer: 0.000 validation_error: 20.383 +(epoch: 29, iters: 76944, time: 0.539, data: 0.000) G_L1: 14.200 G_L1_ABSOLUTE: 2.532 G_L1_RELATIVE: 11.667 G_Regularizer: 0.000 validation_error: 20.374 +(epoch: 29, iters: 78944, time: 0.541, data: 0.000) G_L1: 14.180 G_L1_ABSOLUTE: 3.126 G_L1_RELATIVE: 11.053 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 29, iters: 80944, time: 0.553, data: 0.000) G_L1: 15.635 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 13.014 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 29, iters: 82944, time: 0.543, data: 0.000) G_L1: 14.037 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 11.388 G_Regularizer: 0.000 validation_error: 20.553 +(epoch: 29, iters: 84944, time: 0.539, data: 0.000) G_L1: 11.372 G_L1_ABSOLUTE: 2.244 G_L1_RELATIVE: 9.129 G_Regularizer: 0.000 validation_error: 20.674 +(epoch: 29, iters: 86944, time: 0.542, data: 0.000) G_L1: 12.872 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 10.316 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 29, iters: 88944, time: 0.551, data: 0.000) G_L1: 12.699 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 10.110 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 29, iters: 90944, time: 0.548, data: 0.000) G_L1: 13.365 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 10.621 G_Regularizer: 0.000 validation_error: 20.527 +(epoch: 29, iters: 92944, time: 0.545, data: 0.000) G_L1: 14.232 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 11.595 G_Regularizer: 0.000 validation_error: 20.229 +(epoch: 29, iters: 94944, time: 0.545, data: 0.000) G_L1: 20.259 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 17.590 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 29, iters: 96944, time: 0.544, data: 0.000) G_L1: 15.416 G_L1_ABSOLUTE: 2.761 G_L1_RELATIVE: 12.655 G_Regularizer: 0.000 validation_error: 20.038 +(epoch: 29, iters: 98944, time: 0.537, data: 0.000) G_L1: 13.431 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 10.723 G_Regularizer: 0.000 validation_error: 21.228 +(epoch: 29, iters: 100944, time: 0.545, data: 0.000) G_L1: 14.055 G_L1_ABSOLUTE: 2.427 G_L1_RELATIVE: 11.627 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 29, iters: 102944, time: 0.547, data: 0.001) G_L1: 15.922 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 13.396 G_Regularizer: 0.000 validation_error: 20.323 +(epoch: 29, iters: 104944, time: 0.548, data: 0.000) G_L1: 16.599 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 14.093 G_Regularizer: 0.000 validation_error: 20.027 +(epoch: 29, iters: 106944, time: 0.542, data: 0.000) G_L1: 16.517 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 13.763 G_Regularizer: 0.000 validation_error: 20.421 +(epoch: 29, iters: 108944, time: 0.551, data: 0.000) G_L1: 14.025 G_L1_ABSOLUTE: 2.267 G_L1_RELATIVE: 11.758 G_Regularizer: 0.000 validation_error: 21.238 +(epoch: 29, iters: 110944, time: 0.540, data: 0.000) G_L1: 16.654 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 14.114 G_Regularizer: 0.000 validation_error: 19.846 +(epoch: 29, iters: 112944, time: 0.549, data: 0.000) G_L1: 12.593 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 10.239 G_Regularizer: 0.000 validation_error: 20.408 +(epoch: 29, iters: 114944, time: 0.549, data: 0.000) G_L1: 12.488 G_L1_ABSOLUTE: 2.353 G_L1_RELATIVE: 10.135 G_Regularizer: 0.000 validation_error: 21.152 +(epoch: 29, iters: 116944, time: 0.545, data: 0.000) G_L1: 16.239 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 13.625 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 29, iters: 118944, time: 0.547, data: 0.000) G_L1: 13.338 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 10.629 G_Regularizer: 0.000 validation_error: 21.249 +(epoch: 29, iters: 120944, time: 0.548, data: 0.001) G_L1: 13.866 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 11.131 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 29, iters: 122944, time: 0.554, data: 0.000) G_L1: 13.125 G_L1_ABSOLUTE: 2.204 G_L1_RELATIVE: 10.921 G_Regularizer: 0.000 validation_error: 20.622 +(epoch: 29, iters: 124944, time: 0.542, data: 0.000) G_L1: 12.550 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 9.864 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 29, iters: 126944, time: 0.554, data: 0.000) G_L1: 15.320 G_L1_ABSOLUTE: 2.575 G_L1_RELATIVE: 12.745 G_Regularizer: 0.000 validation_error: 20.972 +(epoch: 29, iters: 128944, time: 0.544, data: 0.000) G_L1: 11.659 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 8.847 G_Regularizer: 0.000 validation_error: 21.224 +(epoch: 29, iters: 130944, time: 0.545, data: 0.000) G_L1: 13.934 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 11.534 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 29, iters: 132944, time: 0.538, data: 0.000) G_L1: 14.750 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 12.349 G_Regularizer: 0.000 validation_error: 20.697 +(epoch: 29, iters: 134944, time: 0.539, data: 0.000) G_L1: 14.704 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 11.990 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 29, iters: 136944, time: 0.544, data: 0.000) G_L1: 14.151 G_L1_ABSOLUTE: 2.241 G_L1_RELATIVE: 11.910 G_Regularizer: 0.000 validation_error: 20.357 +(epoch: 29, iters: 138944, time: 0.549, data: 0.000) G_L1: 16.096 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 13.467 G_Regularizer: 0.000 validation_error: 20.364 +(epoch: 29, iters: 140944, time: 0.549, data: 0.000) G_L1: 13.122 G_L1_ABSOLUTE: 2.155 G_L1_RELATIVE: 10.967 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 29, iters: 142944, time: 0.543, data: 0.000) G_L1: 13.117 G_L1_ABSOLUTE: 2.667 G_L1_RELATIVE: 10.450 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 29, iters: 144944, time: 0.543, data: 0.000) G_L1: 13.839 G_L1_ABSOLUTE: 2.704 G_L1_RELATIVE: 11.135 G_Regularizer: 0.000 validation_error: 20.747 +(epoch: 29, iters: 146944, time: 0.542, data: 0.000) G_L1: 14.990 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 12.522 G_Regularizer: 0.000 validation_error: 21.075 +(epoch: 29, iters: 148944, time: 0.552, data: 0.000) G_L1: 15.735 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 12.993 G_Regularizer: 0.000 validation_error: 20.867 +(epoch: 29, iters: 150944, time: 0.538, data: 0.000) G_L1: 15.098 G_L1_ABSOLUTE: 2.711 G_L1_RELATIVE: 12.387 G_Regularizer: 0.000 validation_error: 21.192 +(epoch: 29, iters: 152944, time: 0.542, data: 0.000) G_L1: 14.173 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 11.915 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 29, iters: 154944, time: 0.538, data: 0.000) G_L1: 14.265 G_L1_ABSOLUTE: 2.617 G_L1_RELATIVE: 11.649 G_Regularizer: 0.000 validation_error: 20.730 +(epoch: 29, iters: 156944, time: 0.569, data: 0.000) G_L1: 13.822 G_L1_ABSOLUTE: 2.170 G_L1_RELATIVE: 11.651 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 29, iters: 158944, time: 0.539, data: 0.000) G_L1: 12.739 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 10.272 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 29, iters: 160944, time: 0.549, data: 0.001) G_L1: 17.458 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 14.833 G_Regularizer: 0.000 validation_error: 21.370 +(epoch: 29, iters: 162944, time: 0.541, data: 0.000) G_L1: 12.440 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 10.182 G_Regularizer: 0.000 validation_error: 20.377 +(epoch: 29, iters: 164944, time: 0.547, data: 0.000) G_L1: 16.039 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 13.356 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 29, iters: 166944, time: 0.553, data: 0.000) G_L1: 14.918 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 12.286 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 29, iters: 168944, time: 0.546, data: 0.000) G_L1: 14.001 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 11.612 G_Regularizer: 0.000 validation_error: 21.360 +(epoch: 29, iters: 170944, time: 0.546, data: 0.000) G_L1: 12.809 G_L1_ABSOLUTE: 2.608 G_L1_RELATIVE: 10.202 G_Regularizer: 0.000 validation_error: 20.626 +(epoch: 29, iters: 172944, time: 0.547, data: 0.000) G_L1: 14.471 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 12.136 G_Regularizer: 0.000 validation_error: 20.508 +(epoch: 29, iters: 174944, time: 0.549, data: 0.000) G_L1: 15.480 G_L1_ABSOLUTE: 3.167 G_L1_RELATIVE: 12.313 G_Regularizer: 0.000 validation_error: 20.181 +(epoch: 29, iters: 176944, time: 0.552, data: 0.000) G_L1: 13.173 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 10.832 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 29, iters: 178944, time: 0.540, data: 0.000) G_L1: 14.595 G_L1_ABSOLUTE: 2.693 G_L1_RELATIVE: 11.902 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 29, iters: 180944, time: 0.555, data: 0.000) G_L1: 13.217 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 10.757 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 29, iters: 182944, time: 0.546, data: 0.000) G_L1: 13.267 G_L1_ABSOLUTE: 2.334 G_L1_RELATIVE: 10.932 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 29, iters: 184944, time: 0.550, data: 0.000) G_L1: 13.577 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 11.006 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 29, iters: 186944, time: 0.541, data: 0.000) G_L1: 13.685 G_L1_ABSOLUTE: 2.218 G_L1_RELATIVE: 11.467 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 29, iters: 188944, time: 0.540, data: 0.000) G_L1: 16.126 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 13.396 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 29, iters: 190944, time: 0.543, data: 0.000) G_L1: 15.471 G_L1_ABSOLUTE: 2.942 G_L1_RELATIVE: 12.530 G_Regularizer: 0.000 validation_error: 20.708 +(epoch: 29, iters: 192944, time: 0.538, data: 0.000) G_L1: 14.545 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 12.091 G_Regularizer: 0.000 validation_error: 20.161 +(epoch: 29, iters: 194944, time: 0.555, data: 0.000) G_L1: 12.990 G_L1_ABSOLUTE: 2.089 G_L1_RELATIVE: 10.901 G_Regularizer: 0.000 validation_error: 20.503 +(epoch: 29, iters: 196944, time: 0.545, data: 0.000) G_L1: 13.842 G_L1_ABSOLUTE: 2.788 G_L1_RELATIVE: 11.054 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 29, iters: 198944, time: 0.539, data: 0.000) G_L1: 16.051 G_L1_ABSOLUTE: 2.507 G_L1_RELATIVE: 13.544 G_Regularizer: 0.000 validation_error: 20.455 +(epoch: 29, iters: 200944, time: 0.545, data: 0.000) G_L1: 14.118 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 11.408 G_Regularizer: 0.000 validation_error: 21.144 +(epoch: 29, iters: 202944, time: 0.546, data: 0.000) G_L1: 13.683 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 10.937 G_Regularizer: 0.000 validation_error: 20.515 +(epoch: 29, iters: 204944, time: 0.538, data: 0.001) G_L1: 14.252 G_L1_ABSOLUTE: 2.943 G_L1_RELATIVE: 11.310 G_Regularizer: 0.000 validation_error: 21.027 +(epoch: 29, iters: 206944, time: 0.542, data: 0.000) G_L1: 14.931 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 12.657 G_Regularizer: 0.000 validation_error: 20.502 +(epoch: 29, iters: 208944, time: 0.547, data: 0.000) G_L1: 13.522 G_L1_ABSOLUTE: 2.211 G_L1_RELATIVE: 11.310 G_Regularizer: 0.000 validation_error: 20.513 +(epoch: 29, iters: 210944, time: 0.535, data: 0.001) G_L1: 27.837 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 25.083 G_Regularizer: 0.000 validation_error: 20.395 +(epoch: 29, iters: 212944, time: 0.554, data: 0.000) G_L1: 13.080 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 10.645 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 29, iters: 214944, time: 0.545, data: 0.000) G_L1: 13.408 G_L1_ABSOLUTE: 2.653 G_L1_RELATIVE: 10.754 G_Regularizer: 0.000 validation_error: 19.890 +(epoch: 29, iters: 216944, time: 0.538, data: 0.001) G_L1: 13.051 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 10.400 G_Regularizer: 0.000 validation_error: 20.712 +(epoch: 29, iters: 218944, time: 0.538, data: 0.000) G_L1: 14.443 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 11.826 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 29, iters: 220944, time: 0.555, data: 0.000) G_L1: 12.196 G_L1_ABSOLUTE: 2.671 G_L1_RELATIVE: 9.524 G_Regularizer: 0.000 validation_error: 21.364 +(epoch: 29, iters: 222944, time: 0.542, data: 0.000) G_L1: 17.012 G_L1_ABSOLUTE: 2.552 G_L1_RELATIVE: 14.460 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 29, iters: 224944, time: 0.547, data: 0.000) G_L1: 13.753 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 11.470 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 29, iters: 226944, time: 0.549, data: 0.000) G_L1: 14.797 G_L1_ABSOLUTE: 2.993 G_L1_RELATIVE: 11.804 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 29, iters: 228944, time: 0.547, data: 0.000) G_L1: 14.709 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 12.090 G_Regularizer: 0.000 validation_error: 20.716 +(epoch: 29, iters: 230944, time: 0.543, data: 0.000) G_L1: 15.298 G_L1_ABSOLUTE: 2.323 G_L1_RELATIVE: 12.975 G_Regularizer: 0.000 validation_error: 20.867 +(epoch: 29, iters: 232944, time: 0.542, data: 0.000) G_L1: 10.488 G_L1_ABSOLUTE: 2.380 G_L1_RELATIVE: 8.108 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 29, iters: 234944, time: 0.554, data: 0.000) G_L1: 15.791 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 13.203 G_Regularizer: 0.000 validation_error: 20.675 +(epoch: 29, iters: 236944, time: 0.540, data: 0.000) G_L1: 15.349 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 12.709 G_Regularizer: 0.000 validation_error: 20.478 +(epoch: 29, iters: 238944, time: 0.544, data: 0.000) G_L1: 13.044 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 10.778 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 29, iters: 240944, time: 0.539, data: 0.000) G_L1: 15.243 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 12.840 G_Regularizer: 0.000 validation_error: 21.164 +(epoch: 29, iters: 242944, time: 0.553, data: 0.000) G_L1: 14.975 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 12.439 G_Regularizer: 0.000 validation_error: 20.564 +(epoch: 29, iters: 244944, time: 0.544, data: 0.000) G_L1: 15.308 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 12.937 G_Regularizer: 0.000 validation_error: 20.533 +(epoch: 29, iters: 246944, time: 0.557, data: 0.000) G_L1: 15.104 G_L1_ABSOLUTE: 2.848 G_L1_RELATIVE: 12.256 G_Regularizer: 0.000 validation_error: 20.611 +(epoch: 29, iters: 248944, time: 0.546, data: 0.000) G_L1: 14.972 G_L1_ABSOLUTE: 2.338 G_L1_RELATIVE: 12.634 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 29, iters: 250944, time: 0.546, data: 0.000) G_L1: 11.544 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 8.800 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 29, iters: 252944, time: 0.542, data: 0.000) G_L1: 15.329 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 12.951 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 29, iters: 254944, time: 0.551, data: 0.000) G_L1: 13.078 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 10.671 G_Regularizer: 0.000 validation_error: 21.055 +(epoch: 29, iters: 256944, time: 0.539, data: 0.000) G_L1: 13.231 G_L1_ABSOLUTE: 2.655 G_L1_RELATIVE: 10.577 G_Regularizer: 0.000 validation_error: 20.305 +(epoch: 29, iters: 258944, time: 0.543, data: 0.000) G_L1: 13.720 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 10.972 G_Regularizer: 0.000 validation_error: 21.093 +(epoch: 29, iters: 260944, time: 0.553, data: 0.000) G_L1: 15.331 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 12.692 G_Regularizer: 0.000 validation_error: 20.538 +(epoch: 29, iters: 262944, time: 0.541, data: 0.000) G_L1: 14.216 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 11.833 G_Regularizer: 0.000 validation_error: 21.278 +(epoch: 29, iters: 264944, time: 0.545, data: 0.000) G_L1: 13.856 G_L1_ABSOLUTE: 2.511 G_L1_RELATIVE: 11.345 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 29, iters: 266944, time: 0.542, data: 0.000) G_L1: 15.001 G_L1_ABSOLUTE: 2.962 G_L1_RELATIVE: 12.039 G_Regularizer: 0.000 validation_error: 20.971 +(epoch: 29, iters: 268944, time: 0.548, data: 0.000) G_L1: 14.325 G_L1_ABSOLUTE: 2.700 G_L1_RELATIVE: 11.625 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 29, iters: 270944, time: 0.534, data: 0.000) G_L1: 18.325 G_L1_ABSOLUTE: 3.427 G_L1_RELATIVE: 14.899 G_Regularizer: 0.000 validation_error: 21.179 +(epoch: 29, iters: 272944, time: 0.547, data: 0.000) G_L1: 12.054 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 9.541 G_Regularizer: 0.000 validation_error: 20.529 +(epoch: 29, iters: 274944, time: 0.547, data: 0.000) G_L1: 13.416 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 11.046 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 29, iters: 276944, time: 0.545, data: 0.000) G_L1: 15.214 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 12.594 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 29, iters: 278944, time: 0.549, data: 0.000) G_L1: 14.631 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 12.107 G_Regularizer: 0.000 validation_error: 21.150 +(epoch: 29, iters: 280944, time: 0.548, data: 0.000) G_L1: 14.953 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 12.169 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 29, iters: 282944, time: 0.547, data: 0.000) G_L1: 11.592 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 9.172 G_Regularizer: 0.000 validation_error: 20.692 +(epoch: 29, iters: 284944, time: 0.546, data: 0.000) G_L1: 15.213 G_L1_ABSOLUTE: 2.173 G_L1_RELATIVE: 13.039 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 29, iters: 286944, time: 0.550, data: 0.000) G_L1: 13.939 G_L1_ABSOLUTE: 2.279 G_L1_RELATIVE: 11.660 G_Regularizer: 0.000 validation_error: 20.259 +(epoch: 29, iters: 288944, time: 0.547, data: 0.000) G_L1: 15.641 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 12.837 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 29, iters: 290944, time: 0.548, data: 0.000) G_L1: 13.848 G_L1_ABSOLUTE: 3.074 G_L1_RELATIVE: 10.774 G_Regularizer: 0.000 validation_error: 20.316 +(epoch: 29, iters: 292944, time: 0.548, data: 0.000) G_L1: 14.348 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 11.698 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 29, iters: 294944, time: 0.544, data: 0.000) G_L1: 13.913 G_L1_ABSOLUTE: 2.411 G_L1_RELATIVE: 11.502 G_Regularizer: 0.000 validation_error: 20.531 +(epoch: 29, iters: 296944, time: 0.545, data: 0.000) G_L1: 12.746 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 10.491 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 29, iters: 298944, time: 0.547, data: 0.000) G_L1: 17.247 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 14.583 G_Regularizer: 0.000 validation_error: 20.342 +(epoch: 29, iters: 300944, time: 0.545, data: 0.000) G_L1: 15.133 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 12.647 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 30, iters: 192, time: 0.544, data: 0.000) G_L1: 15.310 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 12.828 G_Regularizer: 0.000 validation_error: 20.912 +(epoch: 30, iters: 2192, time: 0.544, data: 0.000) G_L1: 14.362 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 11.867 G_Regularizer: 0.000 validation_error: 20.445 +(epoch: 30, iters: 4192, time: 0.550, data: 0.000) G_L1: 11.628 G_L1_ABSOLUTE: 2.353 G_L1_RELATIVE: 9.276 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 30, iters: 6192, time: 0.541, data: 0.000) G_L1: 23.910 G_L1_ABSOLUTE: 2.935 G_L1_RELATIVE: 20.975 G_Regularizer: 0.000 validation_error: 20.758 +(epoch: 30, iters: 8192, time: 0.542, data: 0.000) G_L1: 16.729 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 13.983 G_Regularizer: 0.000 validation_error: 21.262 +(epoch: 30, iters: 10192, time: 0.543, data: 0.000) G_L1: 15.147 G_L1_ABSOLUTE: 3.024 G_L1_RELATIVE: 12.124 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 30, iters: 12192, time: 0.558, data: 0.000) G_L1: 12.274 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 9.853 G_Regularizer: 0.000 validation_error: 20.861 +(epoch: 30, iters: 14192, time: 0.549, data: 0.000) G_L1: 14.458 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 11.975 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 30, iters: 16192, time: 0.548, data: 0.000) G_L1: 14.216 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 11.874 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 30, iters: 18192, time: 0.547, data: 0.000) G_L1: 14.198 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 11.728 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 30, iters: 20192, time: 0.548, data: 0.000) G_L1: 13.245 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 10.549 G_Regularizer: 0.000 validation_error: 21.215 +(epoch: 30, iters: 22192, time: 0.544, data: 0.000) G_L1: 13.640 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 10.963 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 30, iters: 24192, time: 0.542, data: 0.000) G_L1: 15.972 G_L1_ABSOLUTE: 3.341 G_L1_RELATIVE: 12.630 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 30, iters: 26192, time: 0.545, data: 0.000) G_L1: 11.734 G_L1_ABSOLUTE: 1.850 G_L1_RELATIVE: 9.884 G_Regularizer: 0.000 validation_error: 20.687 +(epoch: 30, iters: 28192, time: 0.545, data: 0.000) G_L1: 17.166 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 14.743 G_Regularizer: 0.000 validation_error: 21.116 +(epoch: 30, iters: 30192, time: 0.546, data: 0.000) G_L1: 12.942 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 10.345 G_Regularizer: 0.000 validation_error: 20.595 +(epoch: 30, iters: 32192, time: 0.549, data: 0.000) G_L1: 15.928 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 13.049 G_Regularizer: 0.000 validation_error: 20.463 +(epoch: 30, iters: 34192, time: 0.544, data: 0.000) G_L1: 14.299 G_L1_ABSOLUTE: 2.695 G_L1_RELATIVE: 11.604 G_Regularizer: 0.000 validation_error: 20.027 +(epoch: 30, iters: 36192, time: 0.541, data: 0.000) G_L1: 13.821 G_L1_ABSOLUTE: 2.255 G_L1_RELATIVE: 11.566 G_Regularizer: 0.000 validation_error: 20.491 +(epoch: 30, iters: 38192, time: 0.547, data: 0.000) G_L1: 14.256 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 11.715 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 30, iters: 40192, time: 0.550, data: 0.000) G_L1: 12.104 G_L1_ABSOLUTE: 2.325 G_L1_RELATIVE: 9.779 G_Regularizer: 0.000 validation_error: 21.229 +(epoch: 30, iters: 42192, time: 0.547, data: 0.000) G_L1: 12.979 G_L1_ABSOLUTE: 2.156 G_L1_RELATIVE: 10.822 G_Regularizer: 0.000 validation_error: 21.224 +(epoch: 30, iters: 44192, time: 0.547, data: 0.000) G_L1: 14.828 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 12.395 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 30, iters: 46192, time: 0.546, data: 0.000) G_L1: 14.724 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 12.147 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 30, iters: 48192, time: 0.539, data: 0.000) G_L1: 15.215 G_L1_ABSOLUTE: 2.243 G_L1_RELATIVE: 12.972 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 30, iters: 50192, time: 0.540, data: 0.001) G_L1: 13.391 G_L1_ABSOLUTE: 2.980 G_L1_RELATIVE: 10.411 G_Regularizer: 0.000 validation_error: 20.684 +(epoch: 30, iters: 52192, time: 0.549, data: 0.000) G_L1: 13.780 G_L1_ABSOLUTE: 2.566 G_L1_RELATIVE: 11.214 G_Regularizer: 0.000 validation_error: 20.659 +(epoch: 30, iters: 54192, time: 0.538, data: 0.000) G_L1: 16.405 G_L1_ABSOLUTE: 3.036 G_L1_RELATIVE: 13.369 G_Regularizer: 0.000 validation_error: 21.271 +(epoch: 30, iters: 56192, time: 0.548, data: 0.000) G_L1: 14.091 G_L1_ABSOLUTE: 3.002 G_L1_RELATIVE: 11.090 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 30, iters: 58192, time: 0.547, data: 0.000) G_L1: 14.600 G_L1_ABSOLUTE: 2.448 G_L1_RELATIVE: 12.152 G_Regularizer: 0.000 validation_error: 20.169 +(epoch: 30, iters: 60192, time: 0.542, data: 0.000) G_L1: 14.532 G_L1_ABSOLUTE: 2.130 G_L1_RELATIVE: 12.402 G_Regularizer: 0.000 validation_error: 21.194 +(epoch: 30, iters: 62192, time: 0.550, data: 0.001) G_L1: 15.906 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 13.025 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 30, iters: 64192, time: 0.549, data: 0.000) G_L1: 14.577 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 11.841 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 30, iters: 66192, time: 0.540, data: 0.001) G_L1: 13.675 G_L1_ABSOLUTE: 2.776 G_L1_RELATIVE: 10.898 G_Regularizer: 0.000 validation_error: 20.957 +(epoch: 30, iters: 68192, time: 0.545, data: 0.001) G_L1: 15.177 G_L1_ABSOLUTE: 2.242 G_L1_RELATIVE: 12.935 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 30, iters: 70192, time: 0.564, data: 0.001) G_L1: 18.073 G_L1_ABSOLUTE: 2.750 G_L1_RELATIVE: 15.323 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 30, iters: 72192, time: 0.546, data: 0.000) G_L1: 14.435 G_L1_ABSOLUTE: 3.039 G_L1_RELATIVE: 11.396 G_Regularizer: 0.000 validation_error: 20.659 +(epoch: 30, iters: 74192, time: 0.544, data: 0.000) G_L1: 15.635 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 13.220 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 30, iters: 76192, time: 0.544, data: 0.000) G_L1: 11.577 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 9.091 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 30, iters: 78192, time: 0.541, data: 0.000) G_L1: 17.850 G_L1_ABSOLUTE: 2.776 G_L1_RELATIVE: 15.074 G_Regularizer: 0.000 validation_error: 21.188 +(epoch: 30, iters: 80192, time: 0.545, data: 0.000) G_L1: 29.229 G_L1_ABSOLUTE: 3.360 G_L1_RELATIVE: 25.869 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 30, iters: 82192, time: 0.546, data: 0.000) G_L1: 17.498 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 14.862 G_Regularizer: 0.000 validation_error: 20.145 +(epoch: 30, iters: 84192, time: 0.540, data: 0.000) G_L1: 14.515 G_L1_ABSOLUTE: 3.195 G_L1_RELATIVE: 11.319 G_Regularizer: 0.000 validation_error: 20.327 +(epoch: 30, iters: 86192, time: 0.551, data: 0.000) G_L1: 13.770 G_L1_ABSOLUTE: 2.391 G_L1_RELATIVE: 11.379 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 30, iters: 88192, time: 0.543, data: 0.000) G_L1: 12.906 G_L1_ABSOLUTE: 2.146 G_L1_RELATIVE: 10.760 G_Regularizer: 0.000 validation_error: 20.541 +(epoch: 30, iters: 90192, time: 0.547, data: 0.000) G_L1: 13.634 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 11.046 G_Regularizer: 0.000 validation_error: 20.699 +(epoch: 30, iters: 92192, time: 0.550, data: 0.000) G_L1: 16.852 G_L1_ABSOLUTE: 2.975 G_L1_RELATIVE: 13.877 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 30, iters: 94192, time: 0.550, data: 0.000) G_L1: 13.199 G_L1_ABSOLUTE: 2.342 G_L1_RELATIVE: 10.857 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 30, iters: 96192, time: 0.542, data: 0.000) G_L1: 13.920 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 11.600 G_Regularizer: 0.000 validation_error: 20.977 +(epoch: 30, iters: 98192, time: 0.553, data: 0.000) G_L1: 14.092 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 11.671 G_Regularizer: 0.000 validation_error: 20.600 +(epoch: 30, iters: 100192, time: 0.548, data: 0.000) G_L1: 11.938 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 9.298 G_Regularizer: 0.000 validation_error: 20.681 +(epoch: 30, iters: 102192, time: 0.542, data: 0.000) G_L1: 14.652 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 12.131 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 30, iters: 104192, time: 0.550, data: 0.000) G_L1: 14.278 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 12.019 G_Regularizer: 0.000 validation_error: 20.982 +(epoch: 30, iters: 106192, time: 0.541, data: 0.000) G_L1: 13.384 G_L1_ABSOLUTE: 2.311 G_L1_RELATIVE: 11.073 G_Regularizer: 0.000 validation_error: 21.059 +(epoch: 30, iters: 108192, time: 0.538, data: 0.001) G_L1: 13.742 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 10.975 G_Regularizer: 0.000 validation_error: 20.550 +(epoch: 30, iters: 110192, time: 0.546, data: 0.000) G_L1: 14.492 G_L1_ABSOLUTE: 2.681 G_L1_RELATIVE: 11.811 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 30, iters: 112192, time: 0.548, data: 0.000) G_L1: 15.726 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 13.139 G_Regularizer: 0.000 validation_error: 20.560 +(epoch: 30, iters: 114192, time: 0.542, data: 0.000) G_L1: 13.824 G_L1_ABSOLUTE: 2.682 G_L1_RELATIVE: 11.142 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 30, iters: 116192, time: 0.547, data: 0.000) G_L1: 14.053 G_L1_ABSOLUTE: 2.374 G_L1_RELATIVE: 11.679 G_Regularizer: 0.000 validation_error: 20.653 +(epoch: 30, iters: 118192, time: 0.543, data: 0.000) G_L1: 13.528 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 11.064 G_Regularizer: 0.000 validation_error: 20.509 +(epoch: 30, iters: 120192, time: 0.546, data: 0.000) G_L1: 12.066 G_L1_ABSOLUTE: 2.384 G_L1_RELATIVE: 9.682 G_Regularizer: 0.000 validation_error: 21.084 +(epoch: 30, iters: 122192, time: 0.550, data: 0.000) G_L1: 14.032 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 11.557 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 30, iters: 124192, time: 0.544, data: 0.000) G_L1: 12.804 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 10.368 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 30, iters: 126192, time: 0.547, data: 0.000) G_L1: 15.183 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 12.732 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 30, iters: 128192, time: 0.536, data: 0.000) G_L1: 16.607 G_L1_ABSOLUTE: 2.832 G_L1_RELATIVE: 13.776 G_Regularizer: 0.000 validation_error: 20.960 +(epoch: 30, iters: 130192, time: 0.548, data: 0.000) G_L1: 13.462 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 10.820 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 30, iters: 132192, time: 0.542, data: 0.000) G_L1: 12.991 G_L1_ABSOLUTE: 2.154 G_L1_RELATIVE: 10.837 G_Regularizer: 0.000 validation_error: 20.496 +(epoch: 30, iters: 134192, time: 0.545, data: 0.000) G_L1: 12.391 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 10.010 G_Regularizer: 0.000 validation_error: 20.431 +(epoch: 30, iters: 136192, time: 0.548, data: 0.000) G_L1: 15.861 G_L1_ABSOLUTE: 2.715 G_L1_RELATIVE: 13.146 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 30, iters: 138192, time: 0.552, data: 0.000) G_L1: 14.146 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 11.683 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 30, iters: 140192, time: 0.542, data: 0.000) G_L1: 13.303 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 10.601 G_Regularizer: 0.000 validation_error: 21.349 +(epoch: 30, iters: 142192, time: 0.549, data: 0.001) G_L1: 14.247 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 11.703 G_Regularizer: 0.000 validation_error: 21.032 +(epoch: 30, iters: 144192, time: 0.547, data: 0.000) G_L1: 11.657 G_L1_ABSOLUTE: 2.312 G_L1_RELATIVE: 9.345 G_Regularizer: 0.000 validation_error: 21.345 +(epoch: 30, iters: 146192, time: 0.542, data: 0.000) G_L1: 15.758 G_L1_ABSOLUTE: 2.295 G_L1_RELATIVE: 13.463 G_Regularizer: 0.000 validation_error: 20.412 +(epoch: 30, iters: 148192, time: 0.543, data: 0.000) G_L1: 15.643 G_L1_ABSOLUTE: 2.901 G_L1_RELATIVE: 12.742 G_Regularizer: 0.000 validation_error: 21.291 +(epoch: 30, iters: 150192, time: 0.544, data: 0.000) G_L1: 12.995 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 10.398 G_Regularizer: 0.000 validation_error: 21.241 +(epoch: 30, iters: 152192, time: 0.541, data: 0.000) G_L1: 14.608 G_L1_ABSOLUTE: 2.648 G_L1_RELATIVE: 11.960 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 30, iters: 154192, time: 0.554, data: 0.000) G_L1: 24.356 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 21.693 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 30, iters: 156192, time: 0.544, data: 0.000) G_L1: 15.083 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 12.461 G_Regularizer: 0.000 validation_error: 20.724 +(epoch: 30, iters: 158192, time: 0.544, data: 0.001) G_L1: 14.519 G_L1_ABSOLUTE: 2.156 G_L1_RELATIVE: 12.363 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 30, iters: 160192, time: 0.540, data: 0.001) G_L1: 13.068 G_L1_ABSOLUTE: 2.825 G_L1_RELATIVE: 10.243 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 30, iters: 162192, time: 0.542, data: 0.000) G_L1: 15.199 G_L1_ABSOLUTE: 2.965 G_L1_RELATIVE: 12.234 G_Regularizer: 0.000 validation_error: 20.321 +(epoch: 30, iters: 164192, time: 0.541, data: 0.000) G_L1: 17.267 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 14.698 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 30, iters: 166192, time: 0.541, data: 0.000) G_L1: 13.261 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 10.694 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 30, iters: 168192, time: 0.544, data: 0.000) G_L1: 13.988 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 11.579 G_Regularizer: 0.000 validation_error: 20.620 +(epoch: 30, iters: 170192, time: 0.545, data: 0.000) G_L1: 15.456 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 12.631 G_Regularizer: 0.000 validation_error: 21.144 +(epoch: 30, iters: 172192, time: 0.548, data: 0.000) G_L1: 12.206 G_L1_ABSOLUTE: 2.307 G_L1_RELATIVE: 9.899 G_Regularizer: 0.000 validation_error: 20.612 +(epoch: 30, iters: 174192, time: 0.547, data: 0.000) G_L1: 13.960 G_L1_ABSOLUTE: 2.417 G_L1_RELATIVE: 11.544 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 30, iters: 176192, time: 0.543, data: 0.000) G_L1: 16.451 G_L1_ABSOLUTE: 2.393 G_L1_RELATIVE: 14.057 G_Regularizer: 0.000 validation_error: 21.047 +(epoch: 30, iters: 178192, time: 0.545, data: 0.000) G_L1: 14.124 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 11.390 G_Regularizer: 0.000 validation_error: 20.538 +(epoch: 30, iters: 180192, time: 0.549, data: 0.000) G_L1: 15.308 G_L1_ABSOLUTE: 2.930 G_L1_RELATIVE: 12.378 G_Regularizer: 0.000 validation_error: 20.644 +(epoch: 30, iters: 182192, time: 0.548, data: 0.001) G_L1: 16.418 G_L1_ABSOLUTE: 2.946 G_L1_RELATIVE: 13.472 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 30, iters: 184192, time: 0.550, data: 0.001) G_L1: 14.245 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 11.725 G_Regularizer: 0.000 validation_error: 20.332 +(epoch: 30, iters: 186192, time: 0.546, data: 0.000) G_L1: 14.132 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 11.747 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 30, iters: 188192, time: 0.552, data: 0.000) G_L1: 17.100 G_L1_ABSOLUTE: 3.022 G_L1_RELATIVE: 14.078 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 30, iters: 190192, time: 0.542, data: 0.000) G_L1: 13.068 G_L1_ABSOLUTE: 2.231 G_L1_RELATIVE: 10.837 G_Regularizer: 0.000 validation_error: 20.932 +(epoch: 30, iters: 192192, time: 0.550, data: 0.000) G_L1: 12.071 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 9.769 G_Regularizer: 0.000 validation_error: 20.702 +(epoch: 30, iters: 194192, time: 0.549, data: 0.000) G_L1: 15.276 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 12.600 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 30, iters: 196192, time: 0.546, data: 0.000) G_L1: 15.834 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 13.558 G_Regularizer: 0.000 validation_error: 20.344 +(epoch: 30, iters: 198192, time: 0.548, data: 0.000) G_L1: 14.914 G_L1_ABSOLUTE: 2.189 G_L1_RELATIVE: 12.725 G_Regularizer: 0.000 validation_error: 20.560 +(epoch: 30, iters: 200192, time: 0.538, data: 0.000) G_L1: 14.316 G_L1_ABSOLUTE: 2.786 G_L1_RELATIVE: 11.531 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 30, iters: 202192, time: 0.548, data: 0.000) G_L1: 14.288 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 11.872 G_Regularizer: 0.000 validation_error: 21.105 +(epoch: 30, iters: 204192, time: 0.541, data: 0.001) G_L1: 16.212 G_L1_ABSOLUTE: 2.215 G_L1_RELATIVE: 13.997 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 30, iters: 206192, time: 0.541, data: 0.000) G_L1: 14.991 G_L1_ABSOLUTE: 2.636 G_L1_RELATIVE: 12.355 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 30, iters: 208192, time: 0.544, data: 0.000) G_L1: 15.000 G_L1_ABSOLUTE: 2.177 G_L1_RELATIVE: 12.823 G_Regularizer: 0.000 validation_error: 20.567 +(epoch: 30, iters: 210192, time: 0.546, data: 0.000) G_L1: 13.916 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 11.374 G_Regularizer: 0.000 validation_error: 20.452 +(epoch: 30, iters: 212192, time: 0.543, data: 0.001) G_L1: 13.664 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 10.978 G_Regularizer: 0.000 validation_error: 20.899 +(epoch: 30, iters: 214192, time: 0.549, data: 0.000) G_L1: 14.181 G_L1_ABSOLUTE: 2.361 G_L1_RELATIVE: 11.820 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 30, iters: 216192, time: 0.542, data: 0.000) G_L1: 13.448 G_L1_ABSOLUTE: 2.329 G_L1_RELATIVE: 11.119 G_Regularizer: 0.000 validation_error: 20.528 +(epoch: 30, iters: 218192, time: 0.554, data: 0.000) G_L1: 15.203 G_L1_ABSOLUTE: 2.783 G_L1_RELATIVE: 12.421 G_Regularizer: 0.000 validation_error: 21.239 +(epoch: 30, iters: 220192, time: 0.542, data: 0.000) G_L1: 12.132 G_L1_ABSOLUTE: 2.869 G_L1_RELATIVE: 9.264 G_Regularizer: 0.000 validation_error: 21.286 +(epoch: 30, iters: 222192, time: 0.545, data: 0.000) G_L1: 16.070 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 13.498 G_Regularizer: 0.000 validation_error: 21.268 +(epoch: 30, iters: 224192, time: 0.555, data: 0.000) G_L1: 16.334 G_L1_ABSOLUTE: 2.970 G_L1_RELATIVE: 13.365 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 30, iters: 226192, time: 0.539, data: 0.000) G_L1: 12.728 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 10.203 G_Regularizer: 0.000 validation_error: 20.991 +(epoch: 30, iters: 228192, time: 0.558, data: 0.000) G_L1: 14.609 G_L1_ABSOLUTE: 2.271 G_L1_RELATIVE: 12.338 G_Regularizer: 0.000 validation_error: 21.066 +(epoch: 30, iters: 230192, time: 0.551, data: 0.000) G_L1: 13.980 G_L1_ABSOLUTE: 3.189 G_L1_RELATIVE: 10.791 G_Regularizer: 0.000 validation_error: 20.977 +(epoch: 30, iters: 232192, time: 0.553, data: 0.000) G_L1: 13.544 G_L1_ABSOLUTE: 2.247 G_L1_RELATIVE: 11.297 G_Regularizer: 0.000 validation_error: 20.285 +(epoch: 30, iters: 234192, time: 0.539, data: 0.001) G_L1: 13.285 G_L1_ABSOLUTE: 2.228 G_L1_RELATIVE: 11.057 G_Regularizer: 0.000 validation_error: 20.276 +(epoch: 30, iters: 236192, time: 0.548, data: 0.000) G_L1: 14.005 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 11.375 G_Regularizer: 0.000 validation_error: 20.623 +(epoch: 30, iters: 238192, time: 0.544, data: 0.000) G_L1: 13.833 G_L1_ABSOLUTE: 2.208 G_L1_RELATIVE: 11.625 G_Regularizer: 0.000 validation_error: 20.741 +(epoch: 30, iters: 240192, time: 0.540, data: 0.000) G_L1: 14.473 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 11.837 G_Regularizer: 0.000 validation_error: 20.483 +(epoch: 30, iters: 242192, time: 0.545, data: 0.000) G_L1: 13.537 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 10.965 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 30, iters: 244192, time: 0.549, data: 0.000) G_L1: 15.545 G_L1_ABSOLUTE: 2.417 G_L1_RELATIVE: 13.128 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 30, iters: 246192, time: 0.539, data: 0.000) G_L1: 14.572 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 11.960 G_Regularizer: 0.000 validation_error: 20.585 +(epoch: 30, iters: 248192, time: 0.546, data: 0.000) G_L1: 15.845 G_L1_ABSOLUTE: 2.579 G_L1_RELATIVE: 13.267 G_Regularizer: 0.000 validation_error: 20.987 +(epoch: 30, iters: 250192, time: 0.544, data: 0.000) G_L1: 15.287 G_L1_ABSOLUTE: 2.680 G_L1_RELATIVE: 12.607 G_Regularizer: 0.000 validation_error: 20.768 +(epoch: 30, iters: 252192, time: 0.545, data: 0.000) G_L1: 13.651 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 11.158 G_Regularizer: 0.000 validation_error: 20.363 +(epoch: 30, iters: 254192, time: 0.540, data: 0.000) G_L1: 12.626 G_L1_ABSOLUTE: 2.204 G_L1_RELATIVE: 10.422 G_Regularizer: 0.000 validation_error: 20.203 +(epoch: 30, iters: 256192, time: 0.550, data: 0.000) G_L1: 16.060 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 13.743 G_Regularizer: 0.000 validation_error: 20.820 +(epoch: 30, iters: 258192, time: 0.540, data: 0.000) G_L1: 13.435 G_L1_ABSOLUTE: 2.157 G_L1_RELATIVE: 11.278 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 30, iters: 260192, time: 0.539, data: 0.000) G_L1: 14.946 G_L1_ABSOLUTE: 2.712 G_L1_RELATIVE: 12.234 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 30, iters: 262192, time: 0.547, data: 0.001) G_L1: 14.251 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 11.577 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 30, iters: 264192, time: 0.542, data: 0.000) G_L1: 13.953 G_L1_ABSOLUTE: 2.346 G_L1_RELATIVE: 11.607 G_Regularizer: 0.000 validation_error: 20.214 +(epoch: 30, iters: 266192, time: 0.544, data: 0.000) G_L1: 15.665 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 13.172 G_Regularizer: 0.000 validation_error: 20.245 +(epoch: 30, iters: 268192, time: 0.551, data: 0.000) G_L1: 14.015 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 11.580 G_Regularizer: 0.000 validation_error: 20.156 +(epoch: 30, iters: 270192, time: 0.560, data: 0.000) G_L1: 13.780 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 11.097 G_Regularizer: 0.000 validation_error: 19.974 +(epoch: 30, iters: 272192, time: 0.540, data: 0.000) G_L1: 13.332 G_L1_ABSOLUTE: 2.342 G_L1_RELATIVE: 10.990 G_Regularizer: 0.000 validation_error: 20.414 +(epoch: 30, iters: 274192, time: 0.546, data: 0.000) G_L1: 12.581 G_L1_ABSOLUTE: 2.061 G_L1_RELATIVE: 10.520 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 30, iters: 276192, time: 0.552, data: 0.000) G_L1: 13.195 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 10.561 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 30, iters: 278192, time: 0.540, data: 0.000) G_L1: 13.686 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 11.354 G_Regularizer: 0.000 validation_error: 20.805 +(epoch: 30, iters: 280192, time: 0.540, data: 0.000) G_L1: 12.855 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 10.157 G_Regularizer: 0.000 validation_error: 21.163 +(epoch: 30, iters: 282192, time: 0.552, data: 0.001) G_L1: 14.369 G_L1_ABSOLUTE: 2.496 G_L1_RELATIVE: 11.873 G_Regularizer: 0.000 validation_error: 20.367 +(epoch: 30, iters: 284192, time: 0.556, data: 0.001) G_L1: 13.699 G_L1_ABSOLUTE: 2.264 G_L1_RELATIVE: 11.435 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 30, iters: 286192, time: 0.540, data: 0.000) G_L1: 14.483 G_L1_ABSOLUTE: 3.027 G_L1_RELATIVE: 11.456 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 30, iters: 288192, time: 0.546, data: 0.000) G_L1: 16.015 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 13.260 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 30, iters: 290192, time: 0.546, data: 0.000) G_L1: 14.025 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 11.477 G_Regularizer: 0.000 validation_error: 21.372 +(epoch: 30, iters: 292192, time: 0.546, data: 0.000) G_L1: 17.001 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 14.375 G_Regularizer: 0.000 validation_error: 20.575 +(epoch: 30, iters: 294192, time: 0.545, data: 0.000) G_L1: 15.014 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 12.258 G_Regularizer: 0.000 validation_error: 20.430 +(epoch: 30, iters: 296192, time: 0.553, data: 0.000) G_L1: 13.765 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 11.433 G_Regularizer: 0.000 validation_error: 20.697 +(epoch: 30, iters: 298192, time: 0.543, data: 0.000) G_L1: 13.902 G_L1_ABSOLUTE: 2.321 G_L1_RELATIVE: 11.581 G_Regularizer: 0.000 validation_error: 20.219 +(epoch: 30, iters: 300192, time: 0.548, data: 0.000) G_L1: 13.175 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 10.893 G_Regularizer: 0.000 validation_error: 21.028 +(epoch: 30, iters: 302192, time: 0.542, data: 0.000) G_L1: 12.268 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 9.890 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 31, iters: 1440, time: 0.553, data: 0.000) G_L1: 15.434 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 12.865 G_Regularizer: 0.000 validation_error: 21.134 +(epoch: 31, iters: 3440, time: 0.546, data: 0.000) G_L1: 16.577 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 14.065 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 31, iters: 5440, time: 0.551, data: 0.001) G_L1: 19.288 G_L1_ABSOLUTE: 3.330 G_L1_RELATIVE: 15.958 G_Regularizer: 0.000 validation_error: 20.264 +(epoch: 31, iters: 7440, time: 0.545, data: 0.000) G_L1: 13.993 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 11.653 G_Regularizer: 0.000 validation_error: 20.330 +(epoch: 31, iters: 9440, time: 0.542, data: 0.000) G_L1: 12.465 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 10.130 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 31, iters: 11440, time: 0.547, data: 0.000) G_L1: 13.523 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 10.684 G_Regularizer: 0.000 validation_error: 21.027 +(epoch: 31, iters: 13440, time: 0.541, data: 0.000) G_L1: 17.036 G_L1_ABSOLUTE: 2.886 G_L1_RELATIVE: 14.150 G_Regularizer: 0.000 validation_error: 20.242 +(epoch: 31, iters: 15440, time: 0.546, data: 0.000) G_L1: 14.027 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 11.768 G_Regularizer: 0.000 validation_error: 20.603 +(epoch: 31, iters: 17440, time: 0.540, data: 0.000) G_L1: 18.270 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 15.633 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 31, iters: 19440, time: 0.548, data: 0.000) G_L1: 22.899 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 20.640 G_Regularizer: 0.000 validation_error: 20.400 +(epoch: 31, iters: 21440, time: 0.548, data: 0.000) G_L1: 12.428 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 10.092 G_Regularizer: 0.000 validation_error: 20.429 +(epoch: 31, iters: 23440, time: 0.555, data: 0.000) G_L1: 14.527 G_L1_ABSOLUTE: 2.924 G_L1_RELATIVE: 11.603 G_Regularizer: 0.000 validation_error: 20.473 +(epoch: 31, iters: 25440, time: 0.546, data: 0.000) G_L1: 15.899 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 13.022 G_Regularizer: 0.000 validation_error: 20.548 +(epoch: 31, iters: 27440, time: 0.543, data: 0.000) G_L1: 15.554 G_L1_ABSOLUTE: 2.689 G_L1_RELATIVE: 12.865 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 31, iters: 29440, time: 0.537, data: 0.000) G_L1: 18.762 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 16.025 G_Regularizer: 0.000 validation_error: 20.354 +(epoch: 31, iters: 31440, time: 0.542, data: 0.000) G_L1: 21.914 G_L1_ABSOLUTE: 2.936 G_L1_RELATIVE: 18.978 G_Regularizer: 0.000 validation_error: 20.271 +(epoch: 31, iters: 33440, time: 0.541, data: 0.001) G_L1: 14.733 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 12.320 G_Regularizer: 0.000 validation_error: 20.574 +(epoch: 31, iters: 35440, time: 0.545, data: 0.000) G_L1: 15.080 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 12.616 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 31, iters: 37440, time: 0.556, data: 0.000) G_L1: 14.983 G_L1_ABSOLUTE: 2.559 G_L1_RELATIVE: 12.424 G_Regularizer: 0.000 validation_error: 20.948 +(epoch: 31, iters: 39440, time: 0.544, data: 0.000) G_L1: 16.416 G_L1_ABSOLUTE: 2.578 G_L1_RELATIVE: 13.838 G_Regularizer: 0.000 validation_error: 20.311 +(epoch: 31, iters: 41440, time: 0.545, data: 0.000) G_L1: 16.003 G_L1_ABSOLUTE: 3.008 G_L1_RELATIVE: 12.995 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 31, iters: 43440, time: 0.535, data: 0.000) G_L1: 14.084 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 11.310 G_Regularizer: 0.000 validation_error: 20.508 +(epoch: 31, iters: 45440, time: 0.543, data: 0.000) G_L1: 14.728 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 12.258 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 31, iters: 47440, time: 0.549, data: 0.000) G_L1: 15.850 G_L1_ABSOLUTE: 2.645 G_L1_RELATIVE: 13.205 G_Regularizer: 0.000 validation_error: 20.983 +(epoch: 31, iters: 49440, time: 0.542, data: 0.001) G_L1: 13.453 G_L1_ABSOLUTE: 2.059 G_L1_RELATIVE: 11.393 G_Regularizer: 0.000 validation_error: 20.757 +(epoch: 31, iters: 51440, time: 0.545, data: 0.000) G_L1: 14.042 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 11.296 G_Regularizer: 0.000 validation_error: 20.920 +(epoch: 31, iters: 53440, time: 0.547, data: 0.000) G_L1: 14.859 G_L1_ABSOLUTE: 2.786 G_L1_RELATIVE: 12.072 G_Regularizer: 0.000 validation_error: 21.458 +(epoch: 31, iters: 55440, time: 0.545, data: 0.000) G_L1: 13.159 G_L1_ABSOLUTE: 2.479 G_L1_RELATIVE: 10.680 G_Regularizer: 0.000 validation_error: 20.672 +(epoch: 31, iters: 57440, time: 0.540, data: 0.000) G_L1: 14.141 G_L1_ABSOLUTE: 2.037 G_L1_RELATIVE: 12.104 G_Regularizer: 0.000 validation_error: 20.708 +(epoch: 31, iters: 59440, time: 0.551, data: 0.000) G_L1: 15.777 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 12.921 G_Regularizer: 0.000 validation_error: 21.281 +(epoch: 31, iters: 61440, time: 0.540, data: 0.000) G_L1: 13.814 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 11.528 G_Regularizer: 0.000 validation_error: 20.463 +(epoch: 31, iters: 63440, time: 0.543, data: 0.000) G_L1: 15.590 G_L1_ABSOLUTE: 2.699 G_L1_RELATIVE: 12.891 G_Regularizer: 0.000 validation_error: 21.075 +(epoch: 31, iters: 65440, time: 0.546, data: 0.000) G_L1: 14.573 G_L1_ABSOLUTE: 2.827 G_L1_RELATIVE: 11.746 G_Regularizer: 0.000 validation_error: 21.196 +(epoch: 31, iters: 67440, time: 0.548, data: 0.000) G_L1: 15.223 G_L1_ABSOLUTE: 2.638 G_L1_RELATIVE: 12.585 G_Regularizer: 0.000 validation_error: 20.310 +(epoch: 31, iters: 69440, time: 0.540, data: 0.000) G_L1: 24.533 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 21.991 G_Regularizer: 0.000 validation_error: 20.324 +(epoch: 31, iters: 71440, time: 0.549, data: 0.000) G_L1: 13.004 G_L1_ABSOLUTE: 2.143 G_L1_RELATIVE: 10.861 G_Regularizer: 0.000 validation_error: 20.616 +(epoch: 31, iters: 73440, time: 0.541, data: 0.000) G_L1: 14.813 G_L1_ABSOLUTE: 2.426 G_L1_RELATIVE: 12.386 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 31, iters: 75440, time: 0.543, data: 0.001) G_L1: 11.469 G_L1_ABSOLUTE: 2.281 G_L1_RELATIVE: 9.188 G_Regularizer: 0.000 validation_error: 20.450 +(epoch: 31, iters: 77440, time: 0.537, data: 0.000) G_L1: 15.981 G_L1_ABSOLUTE: 3.171 G_L1_RELATIVE: 12.810 G_Regularizer: 0.000 validation_error: 21.421 +(epoch: 31, iters: 79440, time: 0.549, data: 0.000) G_L1: 15.608 G_L1_ABSOLUTE: 2.995 G_L1_RELATIVE: 12.613 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 31, iters: 81440, time: 0.540, data: 0.000) G_L1: 15.966 G_L1_ABSOLUTE: 3.113 G_L1_RELATIVE: 12.853 G_Regularizer: 0.000 validation_error: 20.746 +(epoch: 31, iters: 83440, time: 0.542, data: 0.000) G_L1: 14.199 G_L1_ABSOLUTE: 2.359 G_L1_RELATIVE: 11.840 G_Regularizer: 0.000 validation_error: 20.980 +(epoch: 31, iters: 85440, time: 0.545, data: 0.000) G_L1: 11.848 G_L1_ABSOLUTE: 2.477 G_L1_RELATIVE: 9.371 G_Regularizer: 0.000 validation_error: 20.646 +(epoch: 31, iters: 87440, time: 0.546, data: 0.001) G_L1: 15.115 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 12.540 G_Regularizer: 0.000 validation_error: 20.885 +(epoch: 31, iters: 89440, time: 0.549, data: 0.000) G_L1: 14.289 G_L1_ABSOLUTE: 2.438 G_L1_RELATIVE: 11.851 G_Regularizer: 0.000 validation_error: 20.749 +(epoch: 31, iters: 91440, time: 0.553, data: 0.000) G_L1: 13.378 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 10.768 G_Regularizer: 0.000 validation_error: 20.640 +(epoch: 31, iters: 93440, time: 0.546, data: 0.000) G_L1: 13.935 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 11.594 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 31, iters: 95440, time: 0.538, data: 0.001) G_L1: 13.643 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 11.445 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 31, iters: 97440, time: 0.546, data: 0.000) G_L1: 14.737 G_L1_ABSOLUTE: 2.440 G_L1_RELATIVE: 12.296 G_Regularizer: 0.000 validation_error: 20.899 +(epoch: 31, iters: 99440, time: 0.558, data: 0.000) G_L1: 13.818 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 11.402 G_Regularizer: 0.000 validation_error: 21.150 +(epoch: 31, iters: 101440, time: 0.543, data: 0.000) G_L1: 14.930 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 12.273 G_Regularizer: 0.000 validation_error: 21.045 +(epoch: 31, iters: 103440, time: 0.544, data: 0.000) G_L1: 16.166 G_L1_ABSOLUTE: 2.889 G_L1_RELATIVE: 13.278 G_Regularizer: 0.000 validation_error: 20.248 +(epoch: 31, iters: 105440, time: 0.555, data: 0.000) G_L1: 13.641 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 10.959 G_Regularizer: 0.000 validation_error: 20.787 +(epoch: 31, iters: 107440, time: 0.541, data: 0.000) G_L1: 16.349 G_L1_ABSOLUTE: 2.113 G_L1_RELATIVE: 14.236 G_Regularizer: 0.000 validation_error: 20.651 +(epoch: 31, iters: 109440, time: 0.543, data: 0.000) G_L1: 14.956 G_L1_ABSOLUTE: 2.443 G_L1_RELATIVE: 12.513 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 31, iters: 111440, time: 0.547, data: 0.000) G_L1: 14.041 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 11.508 G_Regularizer: 0.000 validation_error: 21.146 +(epoch: 31, iters: 113440, time: 0.547, data: 0.000) G_L1: 14.198 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 11.822 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 31, iters: 115440, time: 0.543, data: 0.000) G_L1: 12.266 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 9.900 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 31, iters: 117440, time: 0.546, data: 0.001) G_L1: 14.075 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 11.453 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 31, iters: 119440, time: 0.537, data: 0.000) G_L1: 13.390 G_L1_ABSOLUTE: 2.410 G_L1_RELATIVE: 10.980 G_Regularizer: 0.000 validation_error: 21.235 +(epoch: 31, iters: 121440, time: 0.541, data: 0.000) G_L1: 13.559 G_L1_ABSOLUTE: 2.615 G_L1_RELATIVE: 10.944 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 31, iters: 123440, time: 0.556, data: 0.000) G_L1: 16.467 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 13.839 G_Regularizer: 0.000 validation_error: 21.761 +(epoch: 31, iters: 125440, time: 0.542, data: 0.000) G_L1: 16.186 G_L1_ABSOLUTE: 2.771 G_L1_RELATIVE: 13.416 G_Regularizer: 0.000 validation_error: 20.315 +(epoch: 31, iters: 127440, time: 0.542, data: 0.001) G_L1: 15.462 G_L1_ABSOLUTE: 2.292 G_L1_RELATIVE: 13.169 G_Regularizer: 0.000 validation_error: 20.475 +(epoch: 31, iters: 129440, time: 0.548, data: 0.000) G_L1: 15.405 G_L1_ABSOLUTE: 2.987 G_L1_RELATIVE: 12.418 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 31, iters: 131440, time: 0.554, data: 0.000) G_L1: 14.183 G_L1_ABSOLUTE: 3.031 G_L1_RELATIVE: 11.151 G_Regularizer: 0.000 validation_error: 20.661 +(epoch: 31, iters: 133440, time: 0.548, data: 0.000) G_L1: 13.904 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 11.236 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 31, iters: 135440, time: 0.542, data: 0.001) G_L1: 13.359 G_L1_ABSOLUTE: 2.166 G_L1_RELATIVE: 11.193 G_Regularizer: 0.000 validation_error: 21.101 +(epoch: 31, iters: 137440, time: 0.556, data: 0.000) G_L1: 17.182 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 14.427 G_Regularizer: 0.000 validation_error: 20.327 +(epoch: 31, iters: 139440, time: 0.551, data: 0.001) G_L1: 13.251 G_L1_ABSOLUTE: 2.982 G_L1_RELATIVE: 10.268 G_Regularizer: 0.000 validation_error: 21.214 +(epoch: 31, iters: 141440, time: 0.539, data: 0.000) G_L1: 14.122 G_L1_ABSOLUTE: 3.042 G_L1_RELATIVE: 11.080 G_Regularizer: 0.000 validation_error: 20.270 +(epoch: 31, iters: 143440, time: 0.547, data: 0.000) G_L1: 15.564 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 12.707 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 31, iters: 145440, time: 0.541, data: 0.000) G_L1: 13.296 G_L1_ABSOLUTE: 2.644 G_L1_RELATIVE: 10.652 G_Regularizer: 0.000 validation_error: 20.546 +(epoch: 31, iters: 147440, time: 0.547, data: 0.001) G_L1: 16.899 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 14.120 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 31, iters: 149440, time: 0.541, data: 0.000) G_L1: 12.817 G_L1_ABSOLUTE: 2.260 G_L1_RELATIVE: 10.557 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 31, iters: 151440, time: 0.550, data: 0.000) G_L1: 13.392 G_L1_ABSOLUTE: 2.559 G_L1_RELATIVE: 10.833 G_Regularizer: 0.000 validation_error: 20.748 +(epoch: 31, iters: 153440, time: 0.550, data: 0.000) G_L1: 15.198 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 12.633 G_Regularizer: 0.000 validation_error: 20.963 +(epoch: 31, iters: 155440, time: 0.544, data: 0.000) G_L1: 13.105 G_L1_ABSOLUTE: 2.252 G_L1_RELATIVE: 10.853 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 31, iters: 157440, time: 0.545, data: 0.000) G_L1: 15.714 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 13.244 G_Regularizer: 0.000 validation_error: 20.867 +(epoch: 31, iters: 159440, time: 0.549, data: 0.000) G_L1: 13.129 G_L1_ABSOLUTE: 2.201 G_L1_RELATIVE: 10.928 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 31, iters: 161440, time: 0.540, data: 0.000) G_L1: 14.800 G_L1_ABSOLUTE: 2.977 G_L1_RELATIVE: 11.823 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 31, iters: 163440, time: 0.544, data: 0.000) G_L1: 12.636 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 10.010 G_Regularizer: 0.000 validation_error: 21.075 +(epoch: 31, iters: 165440, time: 0.545, data: 0.000) G_L1: 13.435 G_L1_ABSOLUTE: 2.490 G_L1_RELATIVE: 10.945 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 31, iters: 167440, time: 0.548, data: 0.000) G_L1: 17.262 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 14.734 G_Regularizer: 0.000 validation_error: 21.028 +(epoch: 31, iters: 169440, time: 0.546, data: 0.001) G_L1: 13.210 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 10.827 G_Regularizer: 0.000 validation_error: 20.729 +(epoch: 31, iters: 171440, time: 0.548, data: 0.000) G_L1: 14.519 G_L1_ABSOLUTE: 2.188 G_L1_RELATIVE: 12.331 G_Regularizer: 0.000 validation_error: 21.162 +(epoch: 31, iters: 173440, time: 0.548, data: 0.000) G_L1: 15.137 G_L1_ABSOLUTE: 2.477 G_L1_RELATIVE: 12.660 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 31, iters: 175440, time: 0.538, data: 0.000) G_L1: 14.146 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 11.436 G_Regularizer: 0.000 validation_error: 21.497 +(epoch: 31, iters: 177440, time: 0.554, data: 0.000) G_L1: 12.884 G_L1_ABSOLUTE: 2.176 G_L1_RELATIVE: 10.708 G_Regularizer: 0.000 validation_error: 20.789 +(epoch: 31, iters: 179440, time: 0.568, data: 0.000) G_L1: 12.651 G_L1_ABSOLUTE: 2.380 G_L1_RELATIVE: 10.271 G_Regularizer: 0.000 validation_error: 21.057 +(epoch: 31, iters: 181440, time: 0.542, data: 0.000) G_L1: 18.021 G_L1_ABSOLUTE: 2.844 G_L1_RELATIVE: 15.177 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 31, iters: 183440, time: 0.550, data: 0.000) G_L1: 14.459 G_L1_ABSOLUTE: 3.212 G_L1_RELATIVE: 11.246 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 31, iters: 185440, time: 0.548, data: 0.000) G_L1: 15.211 G_L1_ABSOLUTE: 2.266 G_L1_RELATIVE: 12.945 G_Regularizer: 0.000 validation_error: 21.377 +(epoch: 31, iters: 187440, time: 0.544, data: 0.000) G_L1: 12.814 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 10.338 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 31, iters: 189440, time: 0.540, data: 0.000) G_L1: 13.989 G_L1_ABSOLUTE: 2.297 G_L1_RELATIVE: 11.692 G_Regularizer: 0.000 validation_error: 20.994 +(epoch: 31, iters: 191440, time: 0.547, data: 0.000) G_L1: 15.546 G_L1_ABSOLUTE: 3.495 G_L1_RELATIVE: 12.051 G_Regularizer: 0.000 validation_error: 21.090 +(epoch: 31, iters: 193440, time: 0.539, data: 0.000) G_L1: 14.442 G_L1_ABSOLUTE: 2.172 G_L1_RELATIVE: 12.270 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 31, iters: 195440, time: 0.544, data: 0.000) G_L1: 14.517 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 11.956 G_Regularizer: 0.000 validation_error: 20.997 +(epoch: 31, iters: 197440, time: 0.553, data: 0.000) G_L1: 10.857 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 8.565 G_Regularizer: 0.000 validation_error: 20.224 +(epoch: 31, iters: 199440, time: 0.546, data: 0.000) G_L1: 14.373 G_L1_ABSOLUTE: 2.287 G_L1_RELATIVE: 12.086 G_Regularizer: 0.000 validation_error: 20.794 +(epoch: 31, iters: 201440, time: 0.551, data: 0.000) G_L1: 14.260 G_L1_ABSOLUTE: 2.904 G_L1_RELATIVE: 11.356 G_Regularizer: 0.000 validation_error: 20.927 +(epoch: 31, iters: 203440, time: 0.537, data: 0.001) G_L1: 14.945 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 12.529 G_Regularizer: 0.000 validation_error: 21.655 +(epoch: 31, iters: 205440, time: 0.546, data: 0.001) G_L1: 15.469 G_L1_ABSOLUTE: 2.319 G_L1_RELATIVE: 13.150 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 31, iters: 207440, time: 0.535, data: 0.000) G_L1: 15.561 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 13.326 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 31, iters: 209440, time: 0.550, data: 0.001) G_L1: 15.538 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 12.906 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 31, iters: 211440, time: 0.547, data: 0.000) G_L1: 13.534 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 10.966 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 31, iters: 213440, time: 0.547, data: 0.000) G_L1: 15.084 G_L1_ABSOLUTE: 2.163 G_L1_RELATIVE: 12.920 G_Regularizer: 0.000 validation_error: 20.763 +(epoch: 31, iters: 215440, time: 0.542, data: 0.000) G_L1: 14.579 G_L1_ABSOLUTE: 2.845 G_L1_RELATIVE: 11.734 G_Regularizer: 0.000 validation_error: 20.028 +(epoch: 31, iters: 217440, time: 0.547, data: 0.000) G_L1: 14.830 G_L1_ABSOLUTE: 2.272 G_L1_RELATIVE: 12.558 G_Regularizer: 0.000 validation_error: 20.591 +(epoch: 31, iters: 219440, time: 0.539, data: 0.000) G_L1: 13.070 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 10.542 G_Regularizer: 0.000 validation_error: 20.143 +(epoch: 31, iters: 221440, time: 0.541, data: 0.000) G_L1: 15.771 G_L1_ABSOLUTE: 3.115 G_L1_RELATIVE: 12.656 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 31, iters: 223440, time: 0.543, data: 0.000) G_L1: 13.083 G_L1_ABSOLUTE: 2.425 G_L1_RELATIVE: 10.659 G_Regularizer: 0.000 validation_error: 20.312 +(epoch: 31, iters: 225440, time: 0.546, data: 0.000) G_L1: 12.079 G_L1_ABSOLUTE: 2.498 G_L1_RELATIVE: 9.581 G_Regularizer: 0.000 validation_error: 20.226 +(epoch: 31, iters: 227440, time: 0.537, data: 0.001) G_L1: 11.687 G_L1_ABSOLUTE: 2.061 G_L1_RELATIVE: 9.627 G_Regularizer: 0.000 validation_error: 21.388 +(epoch: 31, iters: 229440, time: 0.552, data: 0.000) G_L1: 12.275 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 9.735 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 31, iters: 231440, time: 0.548, data: 0.001) G_L1: 15.704 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 12.982 G_Regularizer: 0.000 validation_error: 21.033 +(epoch: 31, iters: 233440, time: 0.543, data: 0.000) G_L1: 11.945 G_L1_ABSOLUTE: 2.431 G_L1_RELATIVE: 9.514 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 31, iters: 235440, time: 0.537, data: 0.000) G_L1: 17.086 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 14.517 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 31, iters: 237440, time: 0.550, data: 0.000) G_L1: 14.511 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 11.886 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 31, iters: 239440, time: 0.544, data: 0.000) G_L1: 12.547 G_L1_ABSOLUTE: 2.638 G_L1_RELATIVE: 9.909 G_Regularizer: 0.000 validation_error: 20.339 +(epoch: 31, iters: 241440, time: 0.546, data: 0.000) G_L1: 17.247 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 15.031 G_Regularizer: 0.000 validation_error: 20.079 +(epoch: 31, iters: 243440, time: 0.553, data: 0.000) G_L1: 15.518 G_L1_ABSOLUTE: 2.994 G_L1_RELATIVE: 12.524 G_Regularizer: 0.000 validation_error: 20.546 +(epoch: 31, iters: 245440, time: 0.549, data: 0.000) G_L1: 13.652 G_L1_ABSOLUTE: 2.328 G_L1_RELATIVE: 11.324 G_Regularizer: 0.000 validation_error: 20.553 +(epoch: 31, iters: 247440, time: 0.548, data: 0.000) G_L1: 23.853 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 21.129 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 31, iters: 249440, time: 0.538, data: 0.000) G_L1: 14.342 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 11.545 G_Regularizer: 0.000 validation_error: 20.405 +(epoch: 31, iters: 251440, time: 0.555, data: 0.001) G_L1: 13.841 G_L1_ABSOLUTE: 2.279 G_L1_RELATIVE: 11.562 G_Regularizer: 0.000 validation_error: 21.196 +(epoch: 31, iters: 253440, time: 0.542, data: 0.000) G_L1: 13.652 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 11.082 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 31, iters: 255440, time: 0.542, data: 0.000) G_L1: 25.598 G_L1_ABSOLUTE: 2.970 G_L1_RELATIVE: 22.629 G_Regularizer: 0.000 validation_error: 21.666 +(epoch: 31, iters: 257440, time: 0.559, data: 0.000) G_L1: 12.904 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 10.173 G_Regularizer: 0.000 validation_error: 20.641 +(epoch: 31, iters: 259440, time: 0.552, data: 0.000) G_L1: 13.977 G_L1_ABSOLUTE: 2.188 G_L1_RELATIVE: 11.790 G_Regularizer: 0.000 validation_error: 20.500 +(epoch: 31, iters: 261440, time: 0.546, data: 0.000) G_L1: 13.721 G_L1_ABSOLUTE: 2.772 G_L1_RELATIVE: 10.949 G_Regularizer: 0.000 validation_error: 20.716 +(epoch: 31, iters: 263440, time: 0.537, data: 0.000) G_L1: 13.652 G_L1_ABSOLUTE: 2.488 G_L1_RELATIVE: 11.165 G_Regularizer: 0.000 validation_error: 21.073 +(epoch: 31, iters: 265440, time: 0.547, data: 0.000) G_L1: 11.899 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 9.533 G_Regularizer: 0.000 validation_error: 20.620 +(epoch: 31, iters: 267440, time: 0.544, data: 0.000) G_L1: 14.270 G_L1_ABSOLUTE: 2.599 G_L1_RELATIVE: 11.671 G_Regularizer: 0.000 validation_error: 21.065 +(epoch: 31, iters: 269440, time: 0.550, data: 0.000) G_L1: 14.495 G_L1_ABSOLUTE: 2.292 G_L1_RELATIVE: 12.202 G_Regularizer: 0.000 validation_error: 21.185 +(epoch: 31, iters: 271440, time: 0.545, data: 0.000) G_L1: 15.408 G_L1_ABSOLUTE: 2.682 G_L1_RELATIVE: 12.726 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 31, iters: 273440, time: 0.554, data: 0.000) G_L1: 12.439 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 10.096 G_Regularizer: 0.000 validation_error: 20.175 +(epoch: 31, iters: 275440, time: 0.542, data: 0.000) G_L1: 12.176 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 9.894 G_Regularizer: 0.000 validation_error: 20.119 +(epoch: 31, iters: 277440, time: 0.541, data: 0.000) G_L1: 15.300 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 12.311 G_Regularizer: 0.000 validation_error: 20.907 +(epoch: 31, iters: 279440, time: 0.541, data: 0.000) G_L1: 15.100 G_L1_ABSOLUTE: 2.878 G_L1_RELATIVE: 12.222 G_Regularizer: 0.000 validation_error: 20.320 +(epoch: 31, iters: 281440, time: 0.549, data: 0.000) G_L1: 15.950 G_L1_ABSOLUTE: 2.802 G_L1_RELATIVE: 13.148 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 31, iters: 283440, time: 0.551, data: 0.000) G_L1: 14.677 G_L1_ABSOLUTE: 2.186 G_L1_RELATIVE: 12.491 G_Regularizer: 0.000 validation_error: 20.302 +(epoch: 31, iters: 285440, time: 0.550, data: 0.000) G_L1: 13.018 G_L1_ABSOLUTE: 2.593 G_L1_RELATIVE: 10.425 G_Regularizer: 0.000 validation_error: 20.572 +(epoch: 31, iters: 287440, time: 0.547, data: 0.000) G_L1: 14.301 G_L1_ABSOLUTE: 2.817 G_L1_RELATIVE: 11.484 G_Regularizer: 0.000 validation_error: 20.856 +(epoch: 31, iters: 289440, time: 0.545, data: 0.000) G_L1: 15.117 G_L1_ABSOLUTE: 2.465 G_L1_RELATIVE: 12.652 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 31, iters: 291440, time: 0.548, data: 0.000) G_L1: 16.981 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 14.273 G_Regularizer: 0.000 validation_error: 20.592 +(epoch: 31, iters: 293440, time: 0.545, data: 0.001) G_L1: 17.295 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 14.719 G_Regularizer: 0.000 validation_error: 20.565 +(epoch: 31, iters: 295440, time: 0.542, data: 0.001) G_L1: 13.315 G_L1_ABSOLUTE: 2.137 G_L1_RELATIVE: 11.178 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 31, iters: 297440, time: 0.552, data: 0.000) G_L1: 13.286 G_L1_ABSOLUTE: 2.069 G_L1_RELATIVE: 11.217 G_Regularizer: 0.000 validation_error: 20.517 +(epoch: 31, iters: 299440, time: 0.559, data: 0.000) G_L1: 12.168 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 9.582 G_Regularizer: 0.000 validation_error: 21.457 +(epoch: 31, iters: 301440, time: 0.545, data: 0.000) G_L1: 14.315 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 11.983 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 32, iters: 688, time: 0.559, data: 0.000) G_L1: 13.400 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 11.146 G_Regularizer: 0.000 validation_error: 20.670 +(epoch: 32, iters: 2688, time: 0.548, data: 0.000) G_L1: 14.849 G_L1_ABSOLUTE: 2.871 G_L1_RELATIVE: 11.978 G_Regularizer: 0.000 validation_error: 20.638 +(epoch: 32, iters: 4688, time: 0.545, data: 0.001) G_L1: 13.882 G_L1_ABSOLUTE: 2.422 G_L1_RELATIVE: 11.461 G_Regularizer: 0.000 validation_error: 20.134 +(epoch: 32, iters: 6688, time: 0.542, data: 0.000) G_L1: 14.857 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 12.407 G_Regularizer: 0.000 validation_error: 20.553 +(epoch: 32, iters: 8688, time: 0.550, data: 0.000) G_L1: 14.870 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 12.007 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 32, iters: 10688, time: 0.550, data: 0.000) G_L1: 12.436 G_L1_ABSOLUTE: 2.261 G_L1_RELATIVE: 10.175 G_Regularizer: 0.000 validation_error: 20.972 +(epoch: 32, iters: 12688, time: 0.540, data: 0.000) G_L1: 15.911 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 13.248 G_Regularizer: 0.000 validation_error: 20.687 +(epoch: 32, iters: 14688, time: 0.542, data: 0.000) G_L1: 14.270 G_L1_ABSOLUTE: 2.219 G_L1_RELATIVE: 12.051 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 32, iters: 16688, time: 0.553, data: 0.000) G_L1: 13.647 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 10.791 G_Regularizer: 0.000 validation_error: 20.535 +(epoch: 32, iters: 18688, time: 0.552, data: 0.000) G_L1: 14.976 G_L1_ABSOLUTE: 2.911 G_L1_RELATIVE: 12.065 G_Regularizer: 0.000 validation_error: 20.320 +(epoch: 32, iters: 20688, time: 0.551, data: 0.000) G_L1: 15.050 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 12.376 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 32, iters: 22688, time: 0.545, data: 0.000) G_L1: 15.020 G_L1_ABSOLUTE: 2.709 G_L1_RELATIVE: 12.312 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 32, iters: 24688, time: 0.545, data: 0.000) G_L1: 12.586 G_L1_ABSOLUTE: 2.531 G_L1_RELATIVE: 10.054 G_Regularizer: 0.000 validation_error: 20.946 +(epoch: 32, iters: 26688, time: 0.540, data: 0.000) G_L1: 14.275 G_L1_ABSOLUTE: 2.783 G_L1_RELATIVE: 11.492 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 32, iters: 28688, time: 0.539, data: 0.000) G_L1: 14.102 G_L1_ABSOLUTE: 2.299 G_L1_RELATIVE: 11.804 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 32, iters: 30688, time: 0.550, data: 0.000) G_L1: 13.777 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 11.350 G_Regularizer: 0.000 validation_error: 20.560 +(epoch: 32, iters: 32688, time: 0.537, data: 0.000) G_L1: 16.939 G_L1_ABSOLUTE: 2.934 G_L1_RELATIVE: 14.006 G_Regularizer: 0.000 validation_error: 20.899 +(epoch: 32, iters: 34688, time: 0.549, data: 0.000) G_L1: 15.492 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 12.988 G_Regularizer: 0.000 validation_error: 21.394 +(epoch: 32, iters: 36688, time: 0.537, data: 0.000) G_L1: 14.049 G_L1_ABSOLUTE: 3.010 G_L1_RELATIVE: 11.039 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 32, iters: 38688, time: 0.546, data: 0.000) G_L1: 16.644 G_L1_ABSOLUTE: 3.173 G_L1_RELATIVE: 13.472 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 32, iters: 40688, time: 0.545, data: 0.000) G_L1: 18.708 G_L1_ABSOLUTE: 3.011 G_L1_RELATIVE: 15.697 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 32, iters: 42688, time: 0.547, data: 0.000) G_L1: 12.636 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 10.139 G_Regularizer: 0.000 validation_error: 20.666 +(epoch: 32, iters: 44688, time: 0.544, data: 0.000) G_L1: 14.435 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 11.721 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 32, iters: 46688, time: 0.545, data: 0.000) G_L1: 14.676 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 12.275 G_Regularizer: 0.000 validation_error: 20.797 +(epoch: 32, iters: 48688, time: 0.549, data: 0.000) G_L1: 16.271 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 13.377 G_Regularizer: 0.000 validation_error: 20.746 +(epoch: 32, iters: 50688, time: 0.545, data: 0.000) G_L1: 18.568 G_L1_ABSOLUTE: 3.174 G_L1_RELATIVE: 15.394 G_Regularizer: 0.000 validation_error: 20.523 +(epoch: 32, iters: 52688, time: 0.547, data: 0.000) G_L1: 13.803 G_L1_ABSOLUTE: 2.751 G_L1_RELATIVE: 11.052 G_Regularizer: 0.000 validation_error: 21.169 +(epoch: 32, iters: 54688, time: 0.545, data: 0.000) G_L1: 14.388 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 11.710 G_Regularizer: 0.000 validation_error: 20.542 +(epoch: 32, iters: 56688, time: 0.550, data: 0.000) G_L1: 13.372 G_L1_ABSOLUTE: 2.801 G_L1_RELATIVE: 10.572 G_Regularizer: 0.000 validation_error: 21.048 +(epoch: 32, iters: 58688, time: 0.547, data: 0.000) G_L1: 15.227 G_L1_ABSOLUTE: 2.189 G_L1_RELATIVE: 13.038 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 32, iters: 60688, time: 0.550, data: 0.000) G_L1: 12.307 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 9.860 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 32, iters: 62688, time: 0.547, data: 0.000) G_L1: 14.540 G_L1_ABSOLUTE: 2.787 G_L1_RELATIVE: 11.753 G_Regularizer: 0.000 validation_error: 21.120 +(epoch: 32, iters: 64688, time: 0.544, data: 0.000) G_L1: 13.082 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 10.552 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 32, iters: 66688, time: 0.547, data: 0.000) G_L1: 16.768 G_L1_ABSOLUTE: 2.991 G_L1_RELATIVE: 13.777 G_Regularizer: 0.000 validation_error: 20.972 +(epoch: 32, iters: 68688, time: 0.548, data: 0.000) G_L1: 16.751 G_L1_ABSOLUTE: 3.600 G_L1_RELATIVE: 13.151 G_Regularizer: 0.000 validation_error: 20.077 +(epoch: 32, iters: 70688, time: 0.539, data: 0.000) G_L1: 13.511 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 10.962 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 32, iters: 72688, time: 0.549, data: 0.000) G_L1: 11.979 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 9.567 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 32, iters: 74688, time: 0.542, data: 0.000) G_L1: 12.890 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 10.342 G_Regularizer: 0.000 validation_error: 20.369 +(epoch: 32, iters: 76688, time: 0.539, data: 0.000) G_L1: 12.758 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 10.482 G_Regularizer: 0.000 validation_error: 20.691 +(epoch: 32, iters: 78688, time: 0.542, data: 0.000) G_L1: 12.683 G_L1_ABSOLUTE: 1.921 G_L1_RELATIVE: 10.761 G_Regularizer: 0.000 validation_error: 20.423 +(epoch: 32, iters: 80688, time: 0.551, data: 0.000) G_L1: 13.188 G_L1_ABSOLUTE: 2.390 G_L1_RELATIVE: 10.799 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 32, iters: 82688, time: 0.543, data: 0.000) G_L1: 14.750 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 12.204 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 32, iters: 84688, time: 0.538, data: 0.000) G_L1: 14.953 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 12.362 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 32, iters: 86688, time: 0.551, data: 0.000) G_L1: 12.258 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 10.042 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 32, iters: 88688, time: 0.543, data: 0.001) G_L1: 13.914 G_L1_ABSOLUTE: 2.144 G_L1_RELATIVE: 11.770 G_Regularizer: 0.000 validation_error: 21.074 +(epoch: 32, iters: 90688, time: 0.548, data: 0.000) G_L1: 13.129 G_L1_ABSOLUTE: 1.969 G_L1_RELATIVE: 11.159 G_Regularizer: 0.000 validation_error: 21.156 +(epoch: 32, iters: 92688, time: 0.546, data: 0.000) G_L1: 13.964 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 11.440 G_Regularizer: 0.000 validation_error: 20.936 +(epoch: 32, iters: 94688, time: 0.551, data: 0.000) G_L1: 16.338 G_L1_ABSOLUTE: 3.060 G_L1_RELATIVE: 13.278 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 32, iters: 96688, time: 0.541, data: 0.001) G_L1: 15.931 G_L1_ABSOLUTE: 2.959 G_L1_RELATIVE: 12.972 G_Regularizer: 0.000 validation_error: 21.036 +(epoch: 32, iters: 98688, time: 0.545, data: 0.001) G_L1: 14.770 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 12.248 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 32, iters: 100688, time: 0.558, data: 0.000) G_L1: 11.763 G_L1_ABSOLUTE: 2.242 G_L1_RELATIVE: 9.521 G_Regularizer: 0.000 validation_error: 20.115 +(epoch: 32, iters: 102688, time: 0.550, data: 0.000) G_L1: 12.021 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 9.547 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 32, iters: 104688, time: 0.538, data: 0.000) G_L1: 13.468 G_L1_ABSOLUTE: 2.095 G_L1_RELATIVE: 11.373 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 32, iters: 106688, time: 0.541, data: 0.000) G_L1: 14.633 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 11.751 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 32, iters: 108688, time: 0.552, data: 0.000) G_L1: 15.795 G_L1_ABSOLUTE: 2.866 G_L1_RELATIVE: 12.928 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 32, iters: 110688, time: 0.546, data: 0.000) G_L1: 13.843 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 11.392 G_Regularizer: 0.000 validation_error: 20.463 +(epoch: 32, iters: 112688, time: 0.538, data: 0.000) G_L1: 13.016 G_L1_ABSOLUTE: 2.444 G_L1_RELATIVE: 10.572 G_Regularizer: 0.000 validation_error: 20.744 +(epoch: 32, iters: 114688, time: 0.550, data: 0.000) G_L1: 14.111 G_L1_ABSOLUTE: 2.098 G_L1_RELATIVE: 12.013 G_Regularizer: 0.000 validation_error: 20.604 +(epoch: 32, iters: 116688, time: 0.555, data: 0.000) G_L1: 15.896 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 13.189 G_Regularizer: 0.000 validation_error: 20.374 +(epoch: 32, iters: 118688, time: 0.542, data: 0.000) G_L1: 15.725 G_L1_ABSOLUTE: 2.832 G_L1_RELATIVE: 12.893 G_Regularizer: 0.000 validation_error: 20.384 +(epoch: 32, iters: 120688, time: 0.544, data: 0.000) G_L1: 16.500 G_L1_ABSOLUTE: 3.475 G_L1_RELATIVE: 13.025 G_Regularizer: 0.000 validation_error: 20.210 +(epoch: 32, iters: 122688, time: 0.547, data: 0.000) G_L1: 16.913 G_L1_ABSOLUTE: 2.604 G_L1_RELATIVE: 14.309 G_Regularizer: 0.000 validation_error: 21.131 +(epoch: 32, iters: 124688, time: 0.542, data: 0.000) G_L1: 13.215 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 10.559 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 32, iters: 126688, time: 0.540, data: 0.000) G_L1: 14.580 G_L1_ABSOLUTE: 2.750 G_L1_RELATIVE: 11.830 G_Regularizer: 0.000 validation_error: 20.310 +(epoch: 32, iters: 128688, time: 0.546, data: 0.000) G_L1: 16.992 G_L1_ABSOLUTE: 3.004 G_L1_RELATIVE: 13.989 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 32, iters: 130688, time: 0.545, data: 0.000) G_L1: 13.562 G_L1_ABSOLUTE: 2.239 G_L1_RELATIVE: 11.323 G_Regularizer: 0.000 validation_error: 20.318 +(epoch: 32, iters: 132688, time: 0.544, data: 0.000) G_L1: 15.200 G_L1_ABSOLUTE: 2.238 G_L1_RELATIVE: 12.962 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 32, iters: 134688, time: 0.552, data: 0.001) G_L1: 15.393 G_L1_ABSOLUTE: 3.627 G_L1_RELATIVE: 11.766 G_Regularizer: 0.000 validation_error: 20.595 +(epoch: 32, iters: 136688, time: 0.543, data: 0.000) G_L1: 13.503 G_L1_ABSOLUTE: 2.301 G_L1_RELATIVE: 11.201 G_Regularizer: 0.000 validation_error: 20.340 +(epoch: 32, iters: 138688, time: 0.549, data: 0.000) G_L1: 13.169 G_L1_ABSOLUTE: 2.844 G_L1_RELATIVE: 10.325 G_Regularizer: 0.000 validation_error: 21.112 +(epoch: 32, iters: 140688, time: 0.544, data: 0.000) G_L1: 14.620 G_L1_ABSOLUTE: 2.606 G_L1_RELATIVE: 12.014 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 32, iters: 142688, time: 0.549, data: 0.000) G_L1: 15.324 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 12.854 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 32, iters: 144688, time: 0.545, data: 0.000) G_L1: 15.071 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 12.387 G_Regularizer: 0.000 validation_error: 20.593 +(epoch: 32, iters: 146688, time: 0.542, data: 0.001) G_L1: 15.299 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 12.839 G_Regularizer: 0.000 validation_error: 20.597 +(epoch: 32, iters: 148688, time: 0.548, data: 0.000) G_L1: 14.960 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 12.605 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 32, iters: 150688, time: 0.545, data: 0.000) G_L1: 13.038 G_L1_ABSOLUTE: 2.268 G_L1_RELATIVE: 10.770 G_Regularizer: 0.000 validation_error: 20.662 +(epoch: 32, iters: 152688, time: 0.544, data: 0.000) G_L1: 15.606 G_L1_ABSOLUTE: 3.091 G_L1_RELATIVE: 12.514 G_Regularizer: 0.000 validation_error: 20.346 +(epoch: 32, iters: 154688, time: 0.548, data: 0.000) G_L1: 13.577 G_L1_ABSOLUTE: 2.753 G_L1_RELATIVE: 10.824 G_Regularizer: 0.000 validation_error: 20.490 +(epoch: 32, iters: 156688, time: 0.546, data: 0.000) G_L1: 14.621 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 12.119 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 32, iters: 158688, time: 0.558, data: 0.000) G_L1: 16.494 G_L1_ABSOLUTE: 2.203 G_L1_RELATIVE: 14.292 G_Regularizer: 0.000 validation_error: 20.234 +(epoch: 32, iters: 160688, time: 0.544, data: 0.000) G_L1: 15.431 G_L1_ABSOLUTE: 2.854 G_L1_RELATIVE: 12.577 G_Regularizer: 0.000 validation_error: 20.337 +(epoch: 32, iters: 162688, time: 0.546, data: 0.000) G_L1: 13.904 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 11.603 G_Regularizer: 0.000 validation_error: 20.626 +(epoch: 32, iters: 164688, time: 0.541, data: 0.001) G_L1: 13.935 G_L1_ABSOLUTE: 2.104 G_L1_RELATIVE: 11.831 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 32, iters: 166688, time: 0.544, data: 0.000) G_L1: 17.123 G_L1_ABSOLUTE: 3.043 G_L1_RELATIVE: 14.080 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 32, iters: 168688, time: 0.546, data: 0.000) G_L1: 12.744 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 10.330 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 32, iters: 170688, time: 0.541, data: 0.000) G_L1: 15.111 G_L1_ABSOLUTE: 2.790 G_L1_RELATIVE: 12.320 G_Regularizer: 0.000 validation_error: 20.465 +(epoch: 32, iters: 172688, time: 0.550, data: 0.000) G_L1: 15.355 G_L1_ABSOLUTE: 2.719 G_L1_RELATIVE: 12.636 G_Regularizer: 0.000 validation_error: 20.280 +(epoch: 32, iters: 174688, time: 0.546, data: 0.000) G_L1: 12.118 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 9.786 G_Regularizer: 0.000 validation_error: 20.684 +(epoch: 32, iters: 176688, time: 0.551, data: 0.000) G_L1: 15.378 G_L1_ABSOLUTE: 2.964 G_L1_RELATIVE: 12.415 G_Regularizer: 0.000 validation_error: 21.288 +(epoch: 32, iters: 178688, time: 0.543, data: 0.000) G_L1: 15.049 G_L1_ABSOLUTE: 2.241 G_L1_RELATIVE: 12.808 G_Regularizer: 0.000 validation_error: 20.892 +(epoch: 32, iters: 180688, time: 0.544, data: 0.000) G_L1: 16.692 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 14.216 G_Regularizer: 0.000 validation_error: 20.440 +(epoch: 32, iters: 182688, time: 0.549, data: 0.000) G_L1: 13.916 G_L1_ABSOLUTE: 3.170 G_L1_RELATIVE: 10.746 G_Regularizer: 0.000 validation_error: 20.662 +(epoch: 32, iters: 184688, time: 0.549, data: 0.000) G_L1: 13.190 G_L1_ABSOLUTE: 2.387 G_L1_RELATIVE: 10.803 G_Regularizer: 0.000 validation_error: 20.149 +(epoch: 32, iters: 186688, time: 0.551, data: 0.000) G_L1: 12.744 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 10.208 G_Regularizer: 0.000 validation_error: 20.457 +(epoch: 32, iters: 188688, time: 0.552, data: 0.001) G_L1: 15.539 G_L1_ABSOLUTE: 2.898 G_L1_RELATIVE: 12.641 G_Regularizer: 0.000 validation_error: 20.392 +(epoch: 32, iters: 190688, time: 0.545, data: 0.000) G_L1: 15.924 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 13.265 G_Regularizer: 0.000 validation_error: 20.080 +(epoch: 32, iters: 192688, time: 0.545, data: 0.000) G_L1: 16.039 G_L1_ABSOLUTE: 3.031 G_L1_RELATIVE: 13.008 G_Regularizer: 0.000 validation_error: 21.080 +(epoch: 32, iters: 194688, time: 0.540, data: 0.000) G_L1: 13.209 G_L1_ABSOLUTE: 2.309 G_L1_RELATIVE: 10.900 G_Regularizer: 0.000 validation_error: 19.919 +(epoch: 32, iters: 196688, time: 0.552, data: 0.000) G_L1: 14.111 G_L1_ABSOLUTE: 2.037 G_L1_RELATIVE: 12.074 G_Regularizer: 0.000 validation_error: 20.351 +(epoch: 32, iters: 198688, time: 0.540, data: 0.001) G_L1: 13.787 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 11.485 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 32, iters: 200688, time: 0.540, data: 0.000) G_L1: 16.914 G_L1_ABSOLUTE: 3.042 G_L1_RELATIVE: 13.872 G_Regularizer: 0.000 validation_error: 20.594 +(epoch: 32, iters: 202688, time: 0.557, data: 0.000) G_L1: 13.284 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 10.806 G_Regularizer: 0.000 validation_error: 20.559 +(epoch: 32, iters: 204688, time: 0.544, data: 0.000) G_L1: 14.461 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 11.968 G_Regularizer: 0.000 validation_error: 20.536 +(epoch: 32, iters: 206688, time: 0.547, data: 0.000) G_L1: 12.262 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 9.734 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 32, iters: 208688, time: 0.544, data: 0.000) G_L1: 13.697 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 11.365 G_Regularizer: 0.000 validation_error: 21.244 +(epoch: 32, iters: 210688, time: 0.544, data: 0.000) G_L1: 15.230 G_L1_ABSOLUTE: 3.028 G_L1_RELATIVE: 12.202 G_Regularizer: 0.000 validation_error: 20.524 +(epoch: 32, iters: 212688, time: 0.544, data: 0.000) G_L1: 16.565 G_L1_ABSOLUTE: 2.479 G_L1_RELATIVE: 14.086 G_Regularizer: 0.000 validation_error: 20.650 +(epoch: 32, iters: 214688, time: 0.544, data: 0.001) G_L1: 13.447 G_L1_ABSOLUTE: 2.104 G_L1_RELATIVE: 11.343 G_Regularizer: 0.000 validation_error: 20.896 +(epoch: 32, iters: 216688, time: 0.544, data: 0.000) G_L1: 16.394 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 13.818 G_Regularizer: 0.000 validation_error: 20.612 +(epoch: 32, iters: 218688, time: 0.542, data: 0.000) G_L1: 15.679 G_L1_ABSOLUTE: 2.361 G_L1_RELATIVE: 13.318 G_Regularizer: 0.000 validation_error: 20.983 +(epoch: 32, iters: 220688, time: 0.545, data: 0.000) G_L1: 13.574 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 11.320 G_Regularizer: 0.000 validation_error: 20.820 +(epoch: 32, iters: 222688, time: 0.550, data: 0.000) G_L1: 15.023 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 12.538 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 32, iters: 224688, time: 0.544, data: 0.000) G_L1: 14.424 G_L1_ABSOLUTE: 2.545 G_L1_RELATIVE: 11.878 G_Regularizer: 0.000 validation_error: 20.659 +(epoch: 32, iters: 226688, time: 0.544, data: 0.000) G_L1: 12.131 G_L1_ABSOLUTE: 2.021 G_L1_RELATIVE: 10.110 G_Regularizer: 0.000 validation_error: 21.043 +(epoch: 32, iters: 228688, time: 0.540, data: 0.000) G_L1: 13.827 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 11.470 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 32, iters: 230688, time: 0.549, data: 0.001) G_L1: 15.114 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 12.560 G_Regularizer: 0.000 validation_error: 21.387 +(epoch: 32, iters: 232688, time: 0.555, data: 0.000) G_L1: 13.571 G_L1_ABSOLUTE: 2.296 G_L1_RELATIVE: 11.275 G_Regularizer: 0.000 validation_error: 21.091 +(epoch: 32, iters: 234688, time: 0.546, data: 0.000) G_L1: 12.361 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 9.842 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 32, iters: 236688, time: 0.550, data: 0.001) G_L1: 14.613 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 12.245 G_Regularizer: 0.000 validation_error: 21.153 +(epoch: 32, iters: 238688, time: 0.542, data: 0.000) G_L1: 17.433 G_L1_ABSOLUTE: 2.306 G_L1_RELATIVE: 15.126 G_Regularizer: 0.000 validation_error: 21.147 +(epoch: 32, iters: 240688, time: 0.554, data: 0.000) G_L1: 13.187 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 10.462 G_Regularizer: 0.000 validation_error: 21.063 +(epoch: 32, iters: 242688, time: 0.541, data: 0.000) G_L1: 12.326 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 9.773 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 32, iters: 244688, time: 0.544, data: 0.000) G_L1: 13.364 G_L1_ABSOLUTE: 2.566 G_L1_RELATIVE: 10.798 G_Regularizer: 0.000 validation_error: 21.338 +(epoch: 32, iters: 246688, time: 0.547, data: 0.000) G_L1: 11.285 G_L1_ABSOLUTE: 2.042 G_L1_RELATIVE: 9.243 G_Regularizer: 0.000 validation_error: 20.714 +(epoch: 32, iters: 248688, time: 0.555, data: 0.000) G_L1: 17.801 G_L1_ABSOLUTE: 2.968 G_L1_RELATIVE: 14.833 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 32, iters: 250688, time: 0.539, data: 0.000) G_L1: 17.351 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 14.931 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 32, iters: 252688, time: 0.547, data: 0.000) G_L1: 13.652 G_L1_ABSOLUTE: 2.135 G_L1_RELATIVE: 11.517 G_Regularizer: 0.000 validation_error: 20.911 +(epoch: 32, iters: 254688, time: 0.547, data: 0.000) G_L1: 15.311 G_L1_ABSOLUTE: 2.953 G_L1_RELATIVE: 12.358 G_Regularizer: 0.000 validation_error: 21.229 +(epoch: 32, iters: 256688, time: 0.541, data: 0.000) G_L1: 15.517 G_L1_ABSOLUTE: 2.499 G_L1_RELATIVE: 13.018 G_Regularizer: 0.000 validation_error: 20.504 +(epoch: 32, iters: 258688, time: 0.539, data: 0.000) G_L1: 16.318 G_L1_ABSOLUTE: 2.645 G_L1_RELATIVE: 13.673 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 32, iters: 260688, time: 0.542, data: 0.000) G_L1: 12.333 G_L1_ABSOLUTE: 2.387 G_L1_RELATIVE: 9.946 G_Regularizer: 0.000 validation_error: 20.305 +(epoch: 32, iters: 262688, time: 0.536, data: 0.000) G_L1: 11.829 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 9.452 G_Regularizer: 0.000 validation_error: 20.405 +(epoch: 32, iters: 264688, time: 0.543, data: 0.000) G_L1: 12.642 G_L1_ABSOLUTE: 2.167 G_L1_RELATIVE: 10.474 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 32, iters: 266688, time: 0.548, data: 0.000) G_L1: 13.674 G_L1_ABSOLUTE: 2.241 G_L1_RELATIVE: 11.433 G_Regularizer: 0.000 validation_error: 20.621 +(epoch: 32, iters: 268688, time: 0.553, data: 0.000) G_L1: 15.524 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 13.021 G_Regularizer: 0.000 validation_error: 20.496 +(epoch: 32, iters: 270688, time: 0.540, data: 0.000) G_L1: 13.685 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 10.971 G_Regularizer: 0.000 validation_error: 20.748 +(epoch: 32, iters: 272688, time: 0.549, data: 0.000) G_L1: 16.412 G_L1_ABSOLUTE: 3.004 G_L1_RELATIVE: 13.408 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 32, iters: 274688, time: 0.552, data: 0.000) G_L1: 17.846 G_L1_ABSOLUTE: 3.076 G_L1_RELATIVE: 14.770 G_Regularizer: 0.000 validation_error: 20.763 +(epoch: 32, iters: 276688, time: 0.542, data: 0.000) G_L1: 15.899 G_L1_ABSOLUTE: 2.741 G_L1_RELATIVE: 13.158 G_Regularizer: 0.000 validation_error: 21.252 +(epoch: 32, iters: 278688, time: 0.542, data: 0.000) G_L1: 12.479 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 10.106 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 32, iters: 280688, time: 0.549, data: 0.000) G_L1: 14.473 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 12.042 G_Regularizer: 0.000 validation_error: 20.848 +(epoch: 32, iters: 282688, time: 0.549, data: 0.000) G_L1: 16.495 G_L1_ABSOLUTE: 2.655 G_L1_RELATIVE: 13.840 G_Regularizer: 0.000 validation_error: 20.730 +(epoch: 32, iters: 284688, time: 0.545, data: 0.000) G_L1: 15.645 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 13.025 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 32, iters: 286688, time: 0.551, data: 0.000) G_L1: 14.766 G_L1_ABSOLUTE: 2.251 G_L1_RELATIVE: 12.515 G_Regularizer: 0.000 validation_error: 20.485 +(epoch: 32, iters: 288688, time: 0.553, data: 0.000) G_L1: 14.682 G_L1_ABSOLUTE: 2.593 G_L1_RELATIVE: 12.090 G_Regularizer: 0.000 validation_error: 21.310 +(epoch: 32, iters: 290688, time: 0.544, data: 0.000) G_L1: 14.097 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 11.595 G_Regularizer: 0.000 validation_error: 21.100 +(epoch: 32, iters: 292688, time: 0.550, data: 0.000) G_L1: 15.115 G_L1_ABSOLUTE: 2.806 G_L1_RELATIVE: 12.309 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 32, iters: 294688, time: 0.548, data: 0.000) G_L1: 14.533 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 11.776 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 32, iters: 296688, time: 0.545, data: 0.000) G_L1: 12.034 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 9.635 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 32, iters: 298688, time: 0.550, data: 0.000) G_L1: 14.842 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 12.395 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 32, iters: 300688, time: 0.554, data: 0.000) G_L1: 18.641 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 15.867 G_Regularizer: 0.000 validation_error: 21.033 +(epoch: 32, iters: 302688, time: 0.542, data: 0.001) G_L1: 13.871 G_L1_ABSOLUTE: 2.088 G_L1_RELATIVE: 11.783 G_Regularizer: 0.000 validation_error: 20.503 +(epoch: 33, iters: 1936, time: 0.546, data: 0.000) G_L1: 13.409 G_L1_ABSOLUTE: 2.122 G_L1_RELATIVE: 11.287 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 33, iters: 3936, time: 0.547, data: 0.000) G_L1: 15.309 G_L1_ABSOLUTE: 2.929 G_L1_RELATIVE: 12.379 G_Regularizer: 0.000 validation_error: 20.475 +(epoch: 33, iters: 5936, time: 0.551, data: 0.000) G_L1: 13.525 G_L1_ABSOLUTE: 2.148 G_L1_RELATIVE: 11.377 G_Regularizer: 0.000 validation_error: 20.178 +(epoch: 33, iters: 7936, time: 0.541, data: 0.000) G_L1: 14.559 G_L1_ABSOLUTE: 2.296 G_L1_RELATIVE: 12.263 G_Regularizer: 0.000 validation_error: 21.116 +(epoch: 33, iters: 9936, time: 0.545, data: 0.000) G_L1: 15.649 G_L1_ABSOLUTE: 2.904 G_L1_RELATIVE: 12.745 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 33, iters: 11936, time: 0.545, data: 0.000) G_L1: 12.280 G_L1_ABSOLUTE: 2.120 G_L1_RELATIVE: 10.159 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 33, iters: 13936, time: 0.543, data: 0.000) G_L1: 14.211 G_L1_ABSOLUTE: 2.932 G_L1_RELATIVE: 11.279 G_Regularizer: 0.000 validation_error: 20.371 +(epoch: 33, iters: 15936, time: 0.552, data: 0.000) G_L1: 14.902 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 12.396 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 33, iters: 17936, time: 0.544, data: 0.000) G_L1: 15.253 G_L1_ABSOLUTE: 2.207 G_L1_RELATIVE: 13.046 G_Regularizer: 0.000 validation_error: 20.782 +(epoch: 33, iters: 19936, time: 0.552, data: 0.000) G_L1: 16.224 G_L1_ABSOLUTE: 3.257 G_L1_RELATIVE: 12.967 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 33, iters: 21936, time: 0.547, data: 0.000) G_L1: 13.255 G_L1_ABSOLUTE: 2.884 G_L1_RELATIVE: 10.371 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 33, iters: 23936, time: 0.553, data: 0.000) G_L1: 15.811 G_L1_ABSOLUTE: 2.209 G_L1_RELATIVE: 13.601 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 33, iters: 25936, time: 0.544, data: 0.000) G_L1: 17.588 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 15.189 G_Regularizer: 0.000 validation_error: 21.231 +(epoch: 33, iters: 27936, time: 0.553, data: 0.000) G_L1: 12.939 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 10.218 G_Regularizer: 0.000 validation_error: 20.705 +(epoch: 33, iters: 29936, time: 0.556, data: 0.000) G_L1: 24.473 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 21.968 G_Regularizer: 0.000 validation_error: 20.922 +(epoch: 33, iters: 31936, time: 0.554, data: 0.000) G_L1: 19.335 G_L1_ABSOLUTE: 3.288 G_L1_RELATIVE: 16.048 G_Regularizer: 0.000 validation_error: 21.261 +(epoch: 33, iters: 33936, time: 0.541, data: 0.000) G_L1: 15.943 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 13.474 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 33, iters: 35936, time: 0.545, data: 0.000) G_L1: 17.470 G_L1_ABSOLUTE: 2.192 G_L1_RELATIVE: 15.279 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 33, iters: 37936, time: 0.546, data: 0.000) G_L1: 14.971 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 12.385 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 33, iters: 39936, time: 0.544, data: 0.000) G_L1: 17.201 G_L1_ABSOLUTE: 2.417 G_L1_RELATIVE: 14.784 G_Regularizer: 0.000 validation_error: 20.776 +(epoch: 33, iters: 41936, time: 0.550, data: 0.000) G_L1: 12.462 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 10.092 G_Regularizer: 0.000 validation_error: 21.161 +(epoch: 33, iters: 43936, time: 0.543, data: 0.000) G_L1: 15.364 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 12.773 G_Regularizer: 0.000 validation_error: 21.223 +(epoch: 33, iters: 45936, time: 0.547, data: 0.000) G_L1: 16.958 G_L1_ABSOLUTE: 3.378 G_L1_RELATIVE: 13.580 G_Regularizer: 0.000 validation_error: 21.549 +(epoch: 33, iters: 47936, time: 0.546, data: 0.000) G_L1: 14.058 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 11.461 G_Regularizer: 0.000 validation_error: 20.539 +(epoch: 33, iters: 49936, time: 0.546, data: 0.000) G_L1: 18.969 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 16.350 G_Regularizer: 0.000 validation_error: 20.480 +(epoch: 33, iters: 51936, time: 0.546, data: 0.000) G_L1: 16.446 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 13.664 G_Regularizer: 0.000 validation_error: 20.637 +(epoch: 33, iters: 53936, time: 0.546, data: 0.000) G_L1: 16.173 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 13.754 G_Regularizer: 0.000 validation_error: 20.461 +(epoch: 33, iters: 55936, time: 0.545, data: 0.000) G_L1: 12.979 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 10.446 G_Regularizer: 0.000 validation_error: 21.299 +(epoch: 33, iters: 57936, time: 0.552, data: 0.000) G_L1: 15.505 G_L1_ABSOLUTE: 2.717 G_L1_RELATIVE: 12.788 G_Regularizer: 0.000 validation_error: 21.066 +(epoch: 33, iters: 59936, time: 0.544, data: 0.000) G_L1: 11.402 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 9.069 G_Regularizer: 0.000 validation_error: 21.067 +(epoch: 33, iters: 61936, time: 0.537, data: 0.000) G_L1: 15.358 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 12.768 G_Regularizer: 0.000 validation_error: 20.327 +(epoch: 33, iters: 63936, time: 0.553, data: 0.000) G_L1: 13.175 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 10.607 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 33, iters: 65936, time: 0.550, data: 0.000) G_L1: 13.051 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 10.616 G_Regularizer: 0.000 validation_error: 20.650 +(epoch: 33, iters: 67936, time: 0.543, data: 0.000) G_L1: 14.593 G_L1_ABSOLUTE: 2.239 G_L1_RELATIVE: 12.354 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 33, iters: 69936, time: 0.546, data: 0.000) G_L1: 14.735 G_L1_ABSOLUTE: 2.039 G_L1_RELATIVE: 12.696 G_Regularizer: 0.000 validation_error: 20.400 +(epoch: 33, iters: 71936, time: 0.551, data: 0.000) G_L1: 13.781 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 11.255 G_Regularizer: 0.000 validation_error: 21.063 +(epoch: 33, iters: 73936, time: 0.543, data: 0.000) G_L1: 13.518 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 11.227 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 33, iters: 75936, time: 0.554, data: 0.000) G_L1: 14.428 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 12.098 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 33, iters: 77936, time: 0.541, data: 0.000) G_L1: 14.429 G_L1_ABSOLUTE: 2.239 G_L1_RELATIVE: 12.190 G_Regularizer: 0.000 validation_error: 20.545 +(epoch: 33, iters: 79936, time: 0.549, data: 0.000) G_L1: 15.632 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 12.752 G_Regularizer: 0.000 validation_error: 21.076 +(epoch: 33, iters: 81936, time: 0.550, data: 0.000) G_L1: 15.446 G_L1_ABSOLUTE: 3.144 G_L1_RELATIVE: 12.302 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 33, iters: 83936, time: 0.546, data: 0.000) G_L1: 15.278 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 12.863 G_Regularizer: 0.000 validation_error: 20.957 +(epoch: 33, iters: 85936, time: 0.539, data: 0.000) G_L1: 13.841 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 11.576 G_Regularizer: 0.000 validation_error: 21.012 +(epoch: 33, iters: 87936, time: 0.545, data: 0.000) G_L1: 17.099 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 14.318 G_Regularizer: 0.000 validation_error: 21.172 +(epoch: 33, iters: 89936, time: 0.560, data: 0.000) G_L1: 13.707 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 11.282 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 33, iters: 91936, time: 0.558, data: 0.000) G_L1: 14.734 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 12.319 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 33, iters: 93936, time: 0.547, data: 0.000) G_L1: 13.566 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 11.041 G_Regularizer: 0.000 validation_error: 21.175 +(epoch: 33, iters: 95936, time: 0.547, data: 0.000) G_L1: 13.512 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 10.877 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 33, iters: 97936, time: 0.547, data: 0.000) G_L1: 12.153 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 9.691 G_Regularizer: 0.000 validation_error: 20.497 +(epoch: 33, iters: 99936, time: 0.539, data: 0.000) G_L1: 15.071 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 12.420 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 33, iters: 101936, time: 0.542, data: 0.000) G_L1: 15.074 G_L1_ABSOLUTE: 3.011 G_L1_RELATIVE: 12.063 G_Regularizer: 0.000 validation_error: 20.456 +(epoch: 33, iters: 103936, time: 0.549, data: 0.000) G_L1: 14.412 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 11.784 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 33, iters: 105936, time: 0.547, data: 0.000) G_L1: 14.999 G_L1_ABSOLUTE: 2.567 G_L1_RELATIVE: 12.431 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 33, iters: 107936, time: 0.544, data: 0.000) G_L1: 15.014 G_L1_ABSOLUTE: 2.379 G_L1_RELATIVE: 12.635 G_Regularizer: 0.000 validation_error: 20.601 +(epoch: 33, iters: 109936, time: 0.550, data: 0.000) G_L1: 14.181 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 11.564 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 33, iters: 111936, time: 0.546, data: 0.001) G_L1: 13.038 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 10.629 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 33, iters: 113936, time: 0.542, data: 0.000) G_L1: 14.570 G_L1_ABSOLUTE: 2.834 G_L1_RELATIVE: 11.736 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 33, iters: 115936, time: 0.546, data: 0.000) G_L1: 13.842 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 11.502 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 33, iters: 117936, time: 0.556, data: 0.000) G_L1: 13.724 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 11.228 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 33, iters: 119936, time: 0.547, data: 0.000) G_L1: 14.401 G_L1_ABSOLUTE: 2.771 G_L1_RELATIVE: 11.630 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 33, iters: 121936, time: 0.539, data: 0.000) G_L1: 15.212 G_L1_ABSOLUTE: 2.654 G_L1_RELATIVE: 12.558 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 33, iters: 123936, time: 0.545, data: 0.000) G_L1: 13.879 G_L1_ABSOLUTE: 2.723 G_L1_RELATIVE: 11.156 G_Regularizer: 0.000 validation_error: 20.503 +(epoch: 33, iters: 125936, time: 0.550, data: 0.000) G_L1: 14.953 G_L1_ABSOLUTE: 2.741 G_L1_RELATIVE: 12.212 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 33, iters: 127936, time: 0.545, data: 0.000) G_L1: 16.142 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 13.757 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 33, iters: 129936, time: 0.547, data: 0.000) G_L1: 14.769 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 12.055 G_Regularizer: 0.000 validation_error: 20.349 +(epoch: 33, iters: 131936, time: 0.549, data: 0.000) G_L1: 16.240 G_L1_ABSOLUTE: 2.823 G_L1_RELATIVE: 13.417 G_Regularizer: 0.000 validation_error: 21.006 +(epoch: 33, iters: 133936, time: 0.542, data: 0.000) G_L1: 12.864 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 10.605 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 33, iters: 135936, time: 0.548, data: 0.000) G_L1: 13.053 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 10.768 G_Regularizer: 0.000 validation_error: 20.183 +(epoch: 33, iters: 137936, time: 0.546, data: 0.001) G_L1: 11.928 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 9.730 G_Regularizer: 0.000 validation_error: 20.681 +(epoch: 33, iters: 139936, time: 0.541, data: 0.000) G_L1: 15.216 G_L1_ABSOLUTE: 2.871 G_L1_RELATIVE: 12.345 G_Regularizer: 0.000 validation_error: 20.998 +(epoch: 33, iters: 141936, time: 0.537, data: 0.000) G_L1: 13.808 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 11.156 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 33, iters: 143936, time: 0.545, data: 0.000) G_L1: 13.653 G_L1_ABSOLUTE: 2.846 G_L1_RELATIVE: 10.808 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 33, iters: 145936, time: 0.542, data: 0.000) G_L1: 14.972 G_L1_ABSOLUTE: 2.465 G_L1_RELATIVE: 12.506 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 33, iters: 147936, time: 0.553, data: 0.000) G_L1: 10.777 G_L1_ABSOLUTE: 1.930 G_L1_RELATIVE: 8.848 G_Regularizer: 0.000 validation_error: 20.377 +(epoch: 33, iters: 149936, time: 0.544, data: 0.000) G_L1: 14.503 G_L1_ABSOLUTE: 2.182 G_L1_RELATIVE: 12.321 G_Regularizer: 0.000 validation_error: 20.391 +(epoch: 33, iters: 151936, time: 0.544, data: 0.000) G_L1: 16.129 G_L1_ABSOLUTE: 2.560 G_L1_RELATIVE: 13.569 G_Regularizer: 0.000 validation_error: 21.069 +(epoch: 33, iters: 153936, time: 0.547, data: 0.001) G_L1: 16.473 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 14.024 G_Regularizer: 0.000 validation_error: 20.519 +(epoch: 33, iters: 155936, time: 0.541, data: 0.000) G_L1: 15.486 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 13.075 G_Regularizer: 0.000 validation_error: 21.350 +(epoch: 33, iters: 157936, time: 0.550, data: 0.000) G_L1: 12.938 G_L1_ABSOLUTE: 2.128 G_L1_RELATIVE: 10.810 G_Regularizer: 0.000 validation_error: 20.957 +(epoch: 33, iters: 159936, time: 0.547, data: 0.000) G_L1: 13.856 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 11.454 G_Regularizer: 0.000 validation_error: 20.527 +(epoch: 33, iters: 161936, time: 0.563, data: 0.000) G_L1: 13.801 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 11.110 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 33, iters: 163936, time: 0.545, data: 0.000) G_L1: 15.292 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 12.978 G_Regularizer: 0.000 validation_error: 20.854 +(epoch: 33, iters: 165936, time: 0.546, data: 0.000) G_L1: 12.393 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 10.073 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 33, iters: 167936, time: 0.543, data: 0.000) G_L1: 14.948 G_L1_ABSOLUTE: 2.944 G_L1_RELATIVE: 12.004 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 33, iters: 169936, time: 0.547, data: 0.000) G_L1: 16.082 G_L1_ABSOLUTE: 3.403 G_L1_RELATIVE: 12.679 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 33, iters: 171936, time: 0.544, data: 0.000) G_L1: 13.112 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 10.744 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 33, iters: 173936, time: 0.546, data: 0.000) G_L1: 14.250 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 11.883 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 33, iters: 175936, time: 0.547, data: 0.000) G_L1: 15.091 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 12.553 G_Regularizer: 0.000 validation_error: 20.785 +(epoch: 33, iters: 177936, time: 0.554, data: 0.000) G_L1: 15.981 G_L1_ABSOLUTE: 2.838 G_L1_RELATIVE: 13.143 G_Regularizer: 0.000 validation_error: 21.251 +(epoch: 33, iters: 179936, time: 0.541, data: 0.000) G_L1: 15.346 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 12.812 G_Regularizer: 0.000 validation_error: 21.156 +(epoch: 33, iters: 181936, time: 0.546, data: 0.000) G_L1: 15.805 G_L1_ABSOLUTE: 2.912 G_L1_RELATIVE: 12.893 G_Regularizer: 0.000 validation_error: 20.565 +(epoch: 33, iters: 183936, time: 0.546, data: 0.000) G_L1: 13.610 G_L1_ABSOLUTE: 2.287 G_L1_RELATIVE: 11.323 G_Regularizer: 0.000 validation_error: 21.227 +(epoch: 33, iters: 185936, time: 0.544, data: 0.000) G_L1: 17.934 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 15.308 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 33, iters: 187936, time: 0.549, data: 0.000) G_L1: 14.328 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 12.051 G_Regularizer: 0.000 validation_error: 20.671 +(epoch: 33, iters: 189936, time: 0.548, data: 0.001) G_L1: 15.319 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 12.641 G_Regularizer: 0.000 validation_error: 20.847 +(epoch: 33, iters: 191936, time: 0.547, data: 0.000) G_L1: 12.611 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 10.040 G_Regularizer: 0.000 validation_error: 20.551 +(epoch: 33, iters: 193936, time: 0.549, data: 0.000) G_L1: 9.905 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 7.558 G_Regularizer: 0.000 validation_error: 21.098 +(epoch: 33, iters: 195936, time: 0.544, data: 0.000) G_L1: 15.420 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 12.902 G_Regularizer: 0.000 validation_error: 20.569 +(epoch: 33, iters: 197936, time: 0.545, data: 0.000) G_L1: 14.987 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 12.256 G_Regularizer: 0.000 validation_error: 20.461 +(epoch: 33, iters: 199936, time: 0.540, data: 0.000) G_L1: 15.013 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 12.530 G_Regularizer: 0.000 validation_error: 21.404 +(epoch: 33, iters: 201936, time: 0.550, data: 0.000) G_L1: 12.909 G_L1_ABSOLUTE: 2.763 G_L1_RELATIVE: 10.145 G_Regularizer: 0.000 validation_error: 20.532 +(epoch: 33, iters: 203936, time: 0.546, data: 0.000) G_L1: 14.679 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 12.430 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 33, iters: 205936, time: 0.551, data: 0.000) G_L1: 14.088 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 11.596 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 33, iters: 207936, time: 0.549, data: 0.000) G_L1: 14.133 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 11.816 G_Regularizer: 0.000 validation_error: 20.555 +(epoch: 33, iters: 209936, time: 0.550, data: 0.000) G_L1: 14.676 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 12.336 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 33, iters: 211936, time: 0.546, data: 0.000) G_L1: 17.943 G_L1_ABSOLUTE: 2.398 G_L1_RELATIVE: 15.544 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 33, iters: 213936, time: 0.538, data: 0.000) G_L1: 14.725 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 12.186 G_Regularizer: 0.000 validation_error: 21.211 +(epoch: 33, iters: 215936, time: 0.545, data: 0.000) G_L1: 13.449 G_L1_ABSOLUTE: 2.239 G_L1_RELATIVE: 11.210 G_Regularizer: 0.000 validation_error: 20.598 +(epoch: 33, iters: 217936, time: 0.540, data: 0.000) G_L1: 14.112 G_L1_ABSOLUTE: 2.272 G_L1_RELATIVE: 11.839 G_Regularizer: 0.000 validation_error: 21.074 +(epoch: 33, iters: 219936, time: 0.547, data: 0.000) G_L1: 12.961 G_L1_ABSOLUTE: 2.393 G_L1_RELATIVE: 10.568 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 33, iters: 221936, time: 0.553, data: 0.000) G_L1: 12.151 G_L1_ABSOLUTE: 1.813 G_L1_RELATIVE: 10.338 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 33, iters: 223936, time: 0.548, data: 0.000) G_L1: 14.776 G_L1_ABSOLUTE: 2.899 G_L1_RELATIVE: 11.877 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 33, iters: 225936, time: 0.540, data: 0.000) G_L1: 13.661 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 11.212 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 33, iters: 227936, time: 0.549, data: 0.000) G_L1: 17.337 G_L1_ABSOLUTE: 2.937 G_L1_RELATIVE: 14.400 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 33, iters: 229936, time: 0.549, data: 0.001) G_L1: 14.612 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 11.980 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 33, iters: 231936, time: 0.543, data: 0.000) G_L1: 13.629 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 11.222 G_Regularizer: 0.000 validation_error: 20.381 +(epoch: 33, iters: 233936, time: 0.543, data: 0.000) G_L1: 16.069 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 13.264 G_Regularizer: 0.000 validation_error: 20.574 +(epoch: 33, iters: 235936, time: 0.543, data: 0.000) G_L1: 14.173 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 11.743 G_Regularizer: 0.000 validation_error: 19.987 +(epoch: 33, iters: 237936, time: 0.547, data: 0.000) G_L1: 14.772 G_L1_ABSOLUTE: 2.175 G_L1_RELATIVE: 12.597 G_Regularizer: 0.000 validation_error: 20.548 +(epoch: 33, iters: 239936, time: 0.543, data: 0.001) G_L1: 12.691 G_L1_ABSOLUTE: 2.241 G_L1_RELATIVE: 10.450 G_Regularizer: 0.000 validation_error: 20.628 +(epoch: 33, iters: 241936, time: 0.549, data: 0.000) G_L1: 16.423 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 13.824 G_Regularizer: 0.000 validation_error: 20.484 +(epoch: 33, iters: 243936, time: 0.542, data: 0.000) G_L1: 15.230 G_L1_ABSOLUTE: 2.197 G_L1_RELATIVE: 13.033 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 33, iters: 245936, time: 0.548, data: 0.000) G_L1: 12.223 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 9.969 G_Regularizer: 0.000 validation_error: 21.022 +(epoch: 33, iters: 247936, time: 0.546, data: 0.000) G_L1: 13.160 G_L1_ABSOLUTE: 2.338 G_L1_RELATIVE: 10.822 G_Regularizer: 0.000 validation_error: 19.880 +(epoch: 33, iters: 249936, time: 0.543, data: 0.000) G_L1: 15.686 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 13.258 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 33, iters: 251936, time: 0.548, data: 0.000) G_L1: 14.987 G_L1_ABSOLUTE: 2.749 G_L1_RELATIVE: 12.239 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 33, iters: 253936, time: 0.545, data: 0.000) G_L1: 16.745 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 14.249 G_Regularizer: 0.000 validation_error: 20.515 +(epoch: 33, iters: 255936, time: 0.546, data: 0.000) G_L1: 12.479 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 10.094 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 33, iters: 257936, time: 0.550, data: 0.000) G_L1: 14.069 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 11.442 G_Regularizer: 0.000 validation_error: 20.298 +(epoch: 33, iters: 259936, time: 0.540, data: 0.001) G_L1: 14.659 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 12.217 G_Regularizer: 0.000 validation_error: 20.893 +(epoch: 33, iters: 261936, time: 0.559, data: 0.001) G_L1: 14.342 G_L1_ABSOLUTE: 3.172 G_L1_RELATIVE: 11.170 G_Regularizer: 0.000 validation_error: 20.637 +(epoch: 33, iters: 263936, time: 0.546, data: 0.000) G_L1: 13.462 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 10.730 G_Regularizer: 0.000 validation_error: 21.515 +(epoch: 33, iters: 265936, time: 0.544, data: 0.000) G_L1: 14.168 G_L1_ABSOLUTE: 2.290 G_L1_RELATIVE: 11.877 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 33, iters: 267936, time: 0.553, data: 0.000) G_L1: 15.194 G_L1_ABSOLUTE: 2.806 G_L1_RELATIVE: 12.389 G_Regularizer: 0.000 validation_error: 20.408 +(epoch: 33, iters: 269936, time: 0.551, data: 0.000) G_L1: 15.737 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 13.095 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 33, iters: 271936, time: 0.543, data: 0.000) G_L1: 13.815 G_L1_ABSOLUTE: 2.195 G_L1_RELATIVE: 11.620 G_Regularizer: 0.000 validation_error: 20.741 +(epoch: 33, iters: 273936, time: 0.538, data: 0.000) G_L1: 16.555 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 14.033 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 33, iters: 275936, time: 0.548, data: 0.000) G_L1: 15.634 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 13.060 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 33, iters: 277936, time: 0.542, data: 0.000) G_L1: 13.981 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 11.490 G_Regularizer: 0.000 validation_error: 21.032 +(epoch: 33, iters: 279936, time: 0.540, data: 0.000) G_L1: 16.058 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 13.372 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 33, iters: 281936, time: 0.552, data: 0.000) G_L1: 14.263 G_L1_ABSOLUTE: 2.555 G_L1_RELATIVE: 11.707 G_Regularizer: 0.000 validation_error: 20.355 +(epoch: 33, iters: 283936, time: 0.545, data: 0.000) G_L1: 17.923 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 15.443 G_Regularizer: 0.000 validation_error: 20.141 +(epoch: 33, iters: 285936, time: 0.551, data: 0.001) G_L1: 11.887 G_L1_ABSOLUTE: 1.917 G_L1_RELATIVE: 9.970 G_Regularizer: 0.000 validation_error: 20.588 +(epoch: 33, iters: 287936, time: 0.550, data: 0.000) G_L1: 12.751 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 10.431 G_Regularizer: 0.000 validation_error: 20.350 +(epoch: 33, iters: 289936, time: 0.553, data: 0.000) G_L1: 13.549 G_L1_ABSOLUTE: 2.508 G_L1_RELATIVE: 11.041 G_Regularizer: 0.000 validation_error: 20.995 +(epoch: 33, iters: 291936, time: 0.542, data: 0.000) G_L1: 14.457 G_L1_ABSOLUTE: 2.215 G_L1_RELATIVE: 12.242 G_Regularizer: 0.000 validation_error: 20.367 +(epoch: 33, iters: 293936, time: 0.544, data: 0.000) G_L1: 16.487 G_L1_ABSOLUTE: 2.092 G_L1_RELATIVE: 14.394 G_Regularizer: 0.000 validation_error: 20.445 +(epoch: 33, iters: 295936, time: 0.548, data: 0.001) G_L1: 13.280 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 10.646 G_Regularizer: 0.000 validation_error: 20.554 +(epoch: 33, iters: 297936, time: 0.546, data: 0.000) G_L1: 15.739 G_L1_ABSOLUTE: 2.655 G_L1_RELATIVE: 13.083 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 33, iters: 299936, time: 0.546, data: 0.000) G_L1: 14.956 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 12.422 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 33, iters: 301936, time: 0.546, data: 0.000) G_L1: 12.875 G_L1_ABSOLUTE: 2.111 G_L1_RELATIVE: 10.764 G_Regularizer: 0.000 validation_error: 21.353 +(epoch: 34, iters: 1184, time: 0.548, data: 0.000) G_L1: 15.059 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 12.549 G_Regularizer: 0.000 validation_error: 20.452 +(epoch: 34, iters: 3184, time: 0.548, data: 0.000) G_L1: 15.093 G_L1_ABSOLUTE: 2.269 G_L1_RELATIVE: 12.825 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 34, iters: 5184, time: 0.546, data: 0.000) G_L1: 15.700 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 12.969 G_Regularizer: 0.000 validation_error: 20.665 +(epoch: 34, iters: 7184, time: 0.544, data: 0.000) G_L1: 14.791 G_L1_ABSOLUTE: 2.761 G_L1_RELATIVE: 12.030 G_Regularizer: 0.000 validation_error: 20.731 +(epoch: 34, iters: 9184, time: 0.548, data: 0.000) G_L1: 17.464 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 14.739 G_Regularizer: 0.000 validation_error: 20.687 +(epoch: 34, iters: 11184, time: 0.545, data: 0.000) G_L1: 16.011 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 13.554 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 34, iters: 13184, time: 0.549, data: 0.000) G_L1: 17.306 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 14.675 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 34, iters: 15184, time: 0.547, data: 0.000) G_L1: 16.258 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 13.662 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 34, iters: 17184, time: 0.542, data: 0.000) G_L1: 15.093 G_L1_ABSOLUTE: 2.516 G_L1_RELATIVE: 12.576 G_Regularizer: 0.000 validation_error: 21.060 +(epoch: 34, iters: 19184, time: 0.547, data: 0.000) G_L1: 12.898 G_L1_ABSOLUTE: 2.379 G_L1_RELATIVE: 10.519 G_Regularizer: 0.000 validation_error: 20.657 +(epoch: 34, iters: 21184, time: 0.547, data: 0.000) G_L1: 14.618 G_L1_ABSOLUTE: 2.439 G_L1_RELATIVE: 12.179 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 34, iters: 23184, time: 0.551, data: 0.000) G_L1: 14.822 G_L1_ABSOLUTE: 2.358 G_L1_RELATIVE: 12.464 G_Regularizer: 0.000 validation_error: 21.133 +(epoch: 34, iters: 25184, time: 0.544, data: 0.000) G_L1: 15.176 G_L1_ABSOLUTE: 2.995 G_L1_RELATIVE: 12.181 G_Regularizer: 0.000 validation_error: 20.549 +(epoch: 34, iters: 27184, time: 0.547, data: 0.000) G_L1: 16.341 G_L1_ABSOLUTE: 2.851 G_L1_RELATIVE: 13.490 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 34, iters: 29184, time: 0.552, data: 0.000) G_L1: 12.301 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 9.981 G_Regularizer: 0.000 validation_error: 20.261 +(epoch: 34, iters: 31184, time: 0.547, data: 0.000) G_L1: 14.375 G_L1_ABSOLUTE: 2.729 G_L1_RELATIVE: 11.646 G_Regularizer: 0.000 validation_error: 20.669 +(epoch: 34, iters: 33184, time: 0.547, data: 0.000) G_L1: 15.799 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 13.346 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 34, iters: 35184, time: 0.546, data: 0.000) G_L1: 14.437 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 11.806 G_Regularizer: 0.000 validation_error: 20.594 +(epoch: 34, iters: 37184, time: 0.536, data: 0.000) G_L1: 11.891 G_L1_ABSOLUTE: 2.225 G_L1_RELATIVE: 9.666 G_Regularizer: 0.000 validation_error: 20.839 +(epoch: 34, iters: 39184, time: 0.550, data: 0.000) G_L1: 14.314 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 11.840 G_Regularizer: 0.000 validation_error: 20.521 +(epoch: 34, iters: 41184, time: 0.537, data: 0.000) G_L1: 15.686 G_L1_ABSOLUTE: 2.901 G_L1_RELATIVE: 12.785 G_Regularizer: 0.000 validation_error: 20.693 +(epoch: 34, iters: 43184, time: 0.551, data: 0.000) G_L1: 15.393 G_L1_ABSOLUTE: 2.878 G_L1_RELATIVE: 12.514 G_Regularizer: 0.000 validation_error: 20.347 +(epoch: 34, iters: 45184, time: 0.544, data: 0.000) G_L1: 15.652 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 13.148 G_Regularizer: 0.000 validation_error: 20.462 +(epoch: 34, iters: 47184, time: 0.546, data: 0.000) G_L1: 13.669 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 11.097 G_Regularizer: 0.000 validation_error: 20.546 +(epoch: 34, iters: 49184, time: 0.548, data: 0.000) G_L1: 11.164 G_L1_ABSOLUTE: 2.088 G_L1_RELATIVE: 9.076 G_Regularizer: 0.000 validation_error: 20.449 +(epoch: 34, iters: 51184, time: 0.538, data: 0.000) G_L1: 12.824 G_L1_ABSOLUTE: 2.247 G_L1_RELATIVE: 10.577 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 34, iters: 53184, time: 0.551, data: 0.000) G_L1: 18.164 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 15.634 G_Regularizer: 0.000 validation_error: 20.475 +(epoch: 34, iters: 55184, time: 0.544, data: 0.000) G_L1: 16.480 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 13.706 G_Regularizer: 0.000 validation_error: 20.626 +(epoch: 34, iters: 57184, time: 0.550, data: 0.000) G_L1: 17.145 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 14.619 G_Regularizer: 0.000 validation_error: 20.431 +(epoch: 34, iters: 59184, time: 0.547, data: 0.000) G_L1: 13.581 G_L1_ABSOLUTE: 2.575 G_L1_RELATIVE: 11.006 G_Regularizer: 0.000 validation_error: 20.176 +(epoch: 34, iters: 61184, time: 0.543, data: 0.000) G_L1: 13.479 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 11.061 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 34, iters: 63184, time: 0.543, data: 0.001) G_L1: 14.007 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 11.420 G_Regularizer: 0.000 validation_error: 20.566 +(epoch: 34, iters: 65184, time: 0.545, data: 0.000) G_L1: 11.177 G_L1_ABSOLUTE: 1.836 G_L1_RELATIVE: 9.341 G_Regularizer: 0.000 validation_error: 20.820 +(epoch: 34, iters: 67184, time: 0.549, data: 0.000) G_L1: 13.192 G_L1_ABSOLUTE: 2.545 G_L1_RELATIVE: 10.647 G_Regularizer: 0.000 validation_error: 20.567 +(epoch: 34, iters: 69184, time: 0.552, data: 0.000) G_L1: 13.467 G_L1_ABSOLUTE: 2.301 G_L1_RELATIVE: 11.166 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 34, iters: 71184, time: 0.538, data: 0.000) G_L1: 22.214 G_L1_ABSOLUTE: 2.170 G_L1_RELATIVE: 20.045 G_Regularizer: 0.000 validation_error: 20.506 +(epoch: 34, iters: 73184, time: 0.551, data: 0.000) G_L1: 15.430 G_L1_ABSOLUTE: 2.222 G_L1_RELATIVE: 13.208 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 34, iters: 75184, time: 0.543, data: 0.001) G_L1: 11.367 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 8.973 G_Regularizer: 0.000 validation_error: 19.999 +(epoch: 34, iters: 77184, time: 0.543, data: 0.001) G_L1: 13.287 G_L1_ABSOLUTE: 2.277 G_L1_RELATIVE: 11.010 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 34, iters: 79184, time: 0.556, data: 0.000) G_L1: 15.083 G_L1_ABSOLUTE: 2.538 G_L1_RELATIVE: 12.545 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 34, iters: 81184, time: 0.549, data: 0.000) G_L1: 14.232 G_L1_ABSOLUTE: 2.496 G_L1_RELATIVE: 11.736 G_Regularizer: 0.000 validation_error: 20.913 +(epoch: 34, iters: 83184, time: 0.545, data: 0.000) G_L1: 12.045 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 9.688 G_Regularizer: 0.000 validation_error: 20.740 +(epoch: 34, iters: 85184, time: 0.543, data: 0.000) G_L1: 11.533 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 8.874 G_Regularizer: 0.000 validation_error: 20.309 +(epoch: 34, iters: 87184, time: 0.550, data: 0.000) G_L1: 16.227 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 13.467 G_Regularizer: 0.000 validation_error: 20.386 +(epoch: 34, iters: 89184, time: 0.550, data: 0.000) G_L1: 13.429 G_L1_ABSOLUTE: 2.688 G_L1_RELATIVE: 10.741 G_Regularizer: 0.000 validation_error: 20.672 +(epoch: 34, iters: 91184, time: 0.556, data: 0.000) G_L1: 16.072 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 13.657 G_Regularizer: 0.000 validation_error: 20.612 +(epoch: 34, iters: 93184, time: 0.542, data: 0.000) G_L1: 14.348 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 11.534 G_Regularizer: 0.000 validation_error: 20.359 +(epoch: 34, iters: 95184, time: 0.545, data: 0.000) G_L1: 13.312 G_L1_ABSOLUTE: 2.171 G_L1_RELATIVE: 11.140 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 34, iters: 97184, time: 0.545, data: 0.000) G_L1: 16.660 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 14.000 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 34, iters: 99184, time: 0.559, data: 0.000) G_L1: 13.822 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 11.282 G_Regularizer: 0.000 validation_error: 20.460 +(epoch: 34, iters: 101184, time: 0.550, data: 0.000) G_L1: 13.258 G_L1_ABSOLUTE: 2.350 G_L1_RELATIVE: 10.908 G_Regularizer: 0.000 validation_error: 20.412 +(epoch: 34, iters: 103184, time: 0.553, data: 0.000) G_L1: 13.720 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 11.214 G_Regularizer: 0.000 validation_error: 21.352 +(epoch: 34, iters: 105184, time: 0.539, data: 0.000) G_L1: 14.298 G_L1_ABSOLUTE: 2.367 G_L1_RELATIVE: 11.931 G_Regularizer: 0.000 validation_error: 20.267 +(epoch: 34, iters: 107184, time: 0.563, data: 0.000) G_L1: 17.242 G_L1_ABSOLUTE: 3.000 G_L1_RELATIVE: 14.242 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 34, iters: 109184, time: 0.547, data: 0.001) G_L1: 14.901 G_L1_ABSOLUTE: 3.010 G_L1_RELATIVE: 11.891 G_Regularizer: 0.000 validation_error: 20.586 +(epoch: 34, iters: 111184, time: 0.539, data: 0.000) G_L1: 13.142 G_L1_ABSOLUTE: 2.692 G_L1_RELATIVE: 10.449 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 34, iters: 113184, time: 0.545, data: 0.000) G_L1: 11.338 G_L1_ABSOLUTE: 2.367 G_L1_RELATIVE: 8.971 G_Regularizer: 0.000 validation_error: 20.786 +(epoch: 34, iters: 115184, time: 0.553, data: 0.000) G_L1: 13.905 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 11.622 G_Regularizer: 0.000 validation_error: 20.718 +(epoch: 34, iters: 117184, time: 0.540, data: 0.000) G_L1: 15.692 G_L1_ABSOLUTE: 2.255 G_L1_RELATIVE: 13.437 G_Regularizer: 0.000 validation_error: 20.455 +(epoch: 34, iters: 119184, time: 0.548, data: 0.000) G_L1: 12.918 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 10.395 G_Regularizer: 0.000 validation_error: 20.601 +(epoch: 34, iters: 121184, time: 0.547, data: 0.000) G_L1: 15.171 G_L1_ABSOLUTE: 2.926 G_L1_RELATIVE: 12.245 G_Regularizer: 0.000 validation_error: 20.844 +(epoch: 34, iters: 123184, time: 0.541, data: 0.000) G_L1: 10.889 G_L1_ABSOLUTE: 1.943 G_L1_RELATIVE: 8.945 G_Regularizer: 0.000 validation_error: 20.569 +(epoch: 34, iters: 125184, time: 0.541, data: 0.000) G_L1: 15.342 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 12.603 G_Regularizer: 0.000 validation_error: 20.602 +(epoch: 34, iters: 127184, time: 0.543, data: 0.000) G_L1: 14.748 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 12.128 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 34, iters: 129184, time: 0.548, data: 0.000) G_L1: 15.600 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 12.867 G_Regularizer: 0.000 validation_error: 21.193 +(epoch: 34, iters: 131184, time: 0.548, data: 0.000) G_L1: 15.209 G_L1_ABSOLUTE: 2.145 G_L1_RELATIVE: 13.064 G_Regularizer: 0.000 validation_error: 20.852 +(epoch: 34, iters: 133184, time: 0.553, data: 0.000) G_L1: 14.867 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 12.199 G_Regularizer: 0.000 validation_error: 20.539 +(epoch: 34, iters: 135184, time: 0.542, data: 0.000) G_L1: 13.998 G_L1_ABSOLUTE: 2.219 G_L1_RELATIVE: 11.779 G_Regularizer: 0.000 validation_error: 20.718 +(epoch: 34, iters: 137184, time: 0.543, data: 0.000) G_L1: 15.199 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 12.830 G_Regularizer: 0.000 validation_error: 20.399 +(epoch: 34, iters: 139184, time: 0.549, data: 0.000) G_L1: 15.387 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 12.920 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 34, iters: 141184, time: 0.554, data: 0.000) G_L1: 12.215 G_L1_ABSOLUTE: 2.471 G_L1_RELATIVE: 9.744 G_Regularizer: 0.000 validation_error: 20.514 +(epoch: 34, iters: 143184, time: 0.544, data: 0.000) G_L1: 13.411 G_L1_ABSOLUTE: 2.694 G_L1_RELATIVE: 10.717 G_Regularizer: 0.000 validation_error: 20.515 +(epoch: 34, iters: 145184, time: 0.545, data: 0.000) G_L1: 11.331 G_L1_ABSOLUTE: 2.312 G_L1_RELATIVE: 9.018 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 34, iters: 147184, time: 0.547, data: 0.000) G_L1: 14.001 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 11.499 G_Regularizer: 0.000 validation_error: 20.455 +(epoch: 34, iters: 149184, time: 0.541, data: 0.000) G_L1: 15.105 G_L1_ABSOLUTE: 2.318 G_L1_RELATIVE: 12.787 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 34, iters: 151184, time: 0.544, data: 0.000) G_L1: 12.803 G_L1_ABSOLUTE: 2.196 G_L1_RELATIVE: 10.607 G_Regularizer: 0.000 validation_error: 20.567 +(epoch: 34, iters: 153184, time: 0.548, data: 0.000) G_L1: 15.405 G_L1_ABSOLUTE: 2.761 G_L1_RELATIVE: 12.644 G_Regularizer: 0.000 validation_error: 21.112 +(epoch: 34, iters: 155184, time: 0.547, data: 0.000) G_L1: 13.326 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 10.798 G_Regularizer: 0.000 validation_error: 21.130 +(epoch: 34, iters: 157184, time: 0.547, data: 0.000) G_L1: 13.622 G_L1_ABSOLUTE: 2.266 G_L1_RELATIVE: 11.356 G_Regularizer: 0.000 validation_error: 20.214 +(epoch: 34, iters: 159184, time: 0.552, data: 0.000) G_L1: 12.488 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 10.027 G_Regularizer: 0.000 validation_error: 20.626 +(epoch: 34, iters: 161184, time: 0.544, data: 0.001) G_L1: 13.222 G_L1_ABSOLUTE: 2.221 G_L1_RELATIVE: 11.000 G_Regularizer: 0.000 validation_error: 20.814 +(epoch: 34, iters: 163184, time: 0.549, data: 0.000) G_L1: 14.559 G_L1_ABSOLUTE: 2.209 G_L1_RELATIVE: 12.350 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 34, iters: 165184, time: 0.544, data: 0.001) G_L1: 15.095 G_L1_ABSOLUTE: 2.360 G_L1_RELATIVE: 12.735 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 34, iters: 167184, time: 0.550, data: 0.001) G_L1: 16.368 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 13.611 G_Regularizer: 0.000 validation_error: 21.242 +(epoch: 34, iters: 169184, time: 0.545, data: 0.000) G_L1: 11.490 G_L1_ABSOLUTE: 2.431 G_L1_RELATIVE: 9.059 G_Regularizer: 0.000 validation_error: 20.427 +(epoch: 34, iters: 171184, time: 0.550, data: 0.000) G_L1: 14.567 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 11.865 G_Regularizer: 0.000 validation_error: 20.485 +(epoch: 34, iters: 173184, time: 0.559, data: 0.000) G_L1: 16.130 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 13.604 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 34, iters: 175184, time: 0.544, data: 0.000) G_L1: 12.206 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 9.803 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 34, iters: 177184, time: 0.554, data: 0.000) G_L1: 14.025 G_L1_ABSOLUTE: 2.808 G_L1_RELATIVE: 11.217 G_Regularizer: 0.000 validation_error: 21.048 +(epoch: 34, iters: 179184, time: 0.541, data: 0.000) G_L1: 12.317 G_L1_ABSOLUTE: 2.238 G_L1_RELATIVE: 10.079 G_Regularizer: 0.000 validation_error: 20.844 +(epoch: 34, iters: 181184, time: 0.549, data: 0.000) G_L1: 12.714 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 10.346 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 34, iters: 183184, time: 0.543, data: 0.000) G_L1: 14.825 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 12.216 G_Regularizer: 0.000 validation_error: 21.106 +(epoch: 34, iters: 185184, time: 0.539, data: 0.000) G_L1: 17.890 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 15.053 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 34, iters: 187184, time: 0.543, data: 0.000) G_L1: 14.988 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 12.366 G_Regularizer: 0.000 validation_error: 20.665 +(epoch: 34, iters: 189184, time: 0.550, data: 0.000) G_L1: 14.471 G_L1_ABSOLUTE: 2.711 G_L1_RELATIVE: 11.760 G_Regularizer: 0.000 validation_error: 20.363 +(epoch: 34, iters: 191184, time: 0.544, data: 0.000) G_L1: 13.362 G_L1_ABSOLUTE: 3.077 G_L1_RELATIVE: 10.284 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 34, iters: 193184, time: 0.547, data: 0.001) G_L1: 14.773 G_L1_ABSOLUTE: 3.029 G_L1_RELATIVE: 11.744 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 34, iters: 195184, time: 0.554, data: 0.000) G_L1: 16.561 G_L1_ABSOLUTE: 2.692 G_L1_RELATIVE: 13.869 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 34, iters: 197184, time: 0.542, data: 0.000) G_L1: 14.922 G_L1_ABSOLUTE: 2.223 G_L1_RELATIVE: 12.699 G_Regularizer: 0.000 validation_error: 20.464 +(epoch: 34, iters: 199184, time: 0.550, data: 0.000) G_L1: 15.048 G_L1_ABSOLUTE: 3.136 G_L1_RELATIVE: 11.912 G_Regularizer: 0.000 validation_error: 21.065 +(epoch: 34, iters: 201184, time: 0.546, data: 0.000) G_L1: 16.401 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 14.058 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 34, iters: 203184, time: 0.543, data: 0.000) G_L1: 15.534 G_L1_ABSOLUTE: 3.041 G_L1_RELATIVE: 12.493 G_Regularizer: 0.000 validation_error: 20.836 +(epoch: 34, iters: 205184, time: 0.551, data: 0.000) G_L1: 13.815 G_L1_ABSOLUTE: 2.003 G_L1_RELATIVE: 11.812 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 34, iters: 207184, time: 0.556, data: 0.000) G_L1: 15.092 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 12.542 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 34, iters: 209184, time: 0.543, data: 0.000) G_L1: 13.630 G_L1_ABSOLUTE: 2.329 G_L1_RELATIVE: 11.301 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 34, iters: 211184, time: 0.542, data: 0.000) G_L1: 14.107 G_L1_ABSOLUTE: 2.200 G_L1_RELATIVE: 11.907 G_Regularizer: 0.000 validation_error: 20.936 +(epoch: 34, iters: 213184, time: 0.541, data: 0.000) G_L1: 16.203 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 13.426 G_Regularizer: 0.000 validation_error: 20.789 +(epoch: 34, iters: 215184, time: 0.555, data: 0.000) G_L1: 12.858 G_L1_ABSOLUTE: 1.922 G_L1_RELATIVE: 10.935 G_Regularizer: 0.000 validation_error: 20.816 +(epoch: 34, iters: 217184, time: 0.547, data: 0.000) G_L1: 15.288 G_L1_ABSOLUTE: 3.170 G_L1_RELATIVE: 12.118 G_Regularizer: 0.000 validation_error: 21.492 +(epoch: 34, iters: 219184, time: 0.560, data: 0.000) G_L1: 13.499 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 11.167 G_Regularizer: 0.000 validation_error: 20.552 +(epoch: 34, iters: 221184, time: 0.541, data: 0.000) G_L1: 13.337 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 10.900 G_Regularizer: 0.000 validation_error: 21.290 +(epoch: 34, iters: 223184, time: 0.550, data: 0.000) G_L1: 15.404 G_L1_ABSOLUTE: 2.694 G_L1_RELATIVE: 12.710 G_Regularizer: 0.000 validation_error: 20.922 +(epoch: 34, iters: 225184, time: 0.558, data: 0.000) G_L1: 14.200 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 11.737 G_Regularizer: 0.000 validation_error: 20.498 +(epoch: 34, iters: 227184, time: 0.556, data: 0.000) G_L1: 14.652 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 12.201 G_Regularizer: 0.000 validation_error: 20.614 +(epoch: 34, iters: 229184, time: 0.546, data: 0.000) G_L1: 12.858 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 9.995 G_Regularizer: 0.000 validation_error: 21.277 +(epoch: 34, iters: 231184, time: 0.543, data: 0.000) G_L1: 13.539 G_L1_ABSOLUTE: 2.303 G_L1_RELATIVE: 11.236 G_Regularizer: 0.000 validation_error: 20.814 +(epoch: 34, iters: 233184, time: 0.547, data: 0.000) G_L1: 14.691 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 12.158 G_Regularizer: 0.000 validation_error: 20.492 +(epoch: 34, iters: 235184, time: 0.555, data: 0.000) G_L1: 13.630 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 11.194 G_Regularizer: 0.000 validation_error: 20.702 +(epoch: 34, iters: 237184, time: 0.546, data: 0.000) G_L1: 16.811 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 14.285 G_Regularizer: 0.000 validation_error: 19.916 +(epoch: 34, iters: 239184, time: 0.548, data: 0.000) G_L1: 14.660 G_L1_ABSOLUTE: 2.886 G_L1_RELATIVE: 11.774 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 34, iters: 241184, time: 0.554, data: 0.000) G_L1: 13.469 G_L1_ABSOLUTE: 2.448 G_L1_RELATIVE: 11.020 G_Regularizer: 0.000 validation_error: 20.342 +(epoch: 34, iters: 243184, time: 0.547, data: 0.000) G_L1: 15.489 G_L1_ABSOLUTE: 2.095 G_L1_RELATIVE: 13.393 G_Regularizer: 0.000 validation_error: 20.657 +(epoch: 34, iters: 245184, time: 0.550, data: 0.000) G_L1: 16.412 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 13.691 G_Regularizer: 0.000 validation_error: 20.266 +(epoch: 34, iters: 247184, time: 0.543, data: 0.000) G_L1: 14.881 G_L1_ABSOLUTE: 2.560 G_L1_RELATIVE: 12.321 G_Regularizer: 0.000 validation_error: 20.669 +(epoch: 34, iters: 249184, time: 0.542, data: 0.000) G_L1: 14.717 G_L1_ABSOLUTE: 2.585 G_L1_RELATIVE: 12.132 G_Regularizer: 0.000 validation_error: 20.744 +(epoch: 34, iters: 251184, time: 0.549, data: 0.000) G_L1: 13.605 G_L1_ABSOLUTE: 2.846 G_L1_RELATIVE: 10.759 G_Regularizer: 0.000 validation_error: 20.798 +(epoch: 34, iters: 253184, time: 0.554, data: 0.001) G_L1: 10.945 G_L1_ABSOLUTE: 2.246 G_L1_RELATIVE: 8.699 G_Regularizer: 0.000 validation_error: 21.468 +(epoch: 34, iters: 255184, time: 0.546, data: 0.000) G_L1: 15.271 G_L1_ABSOLUTE: 2.281 G_L1_RELATIVE: 12.990 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 34, iters: 257184, time: 0.552, data: 0.000) G_L1: 23.669 G_L1_ABSOLUTE: 2.943 G_L1_RELATIVE: 20.726 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 34, iters: 259184, time: 0.543, data: 0.000) G_L1: 13.679 G_L1_ABSOLUTE: 2.386 G_L1_RELATIVE: 11.293 G_Regularizer: 0.000 validation_error: 21.213 +(epoch: 34, iters: 261184, time: 0.548, data: 0.000) G_L1: 12.633 G_L1_ABSOLUTE: 2.180 G_L1_RELATIVE: 10.453 G_Regularizer: 0.000 validation_error: 20.658 +(epoch: 34, iters: 263184, time: 0.556, data: 0.000) G_L1: 14.253 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 11.633 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 34, iters: 265184, time: 0.543, data: 0.000) G_L1: 13.934 G_L1_ABSOLUTE: 2.947 G_L1_RELATIVE: 10.987 G_Regularizer: 0.000 validation_error: 20.575 +(epoch: 34, iters: 267184, time: 0.553, data: 0.000) G_L1: 13.701 G_L1_ABSOLUTE: 2.384 G_L1_RELATIVE: 11.316 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 34, iters: 269184, time: 0.551, data: 0.000) G_L1: 16.795 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 14.158 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 34, iters: 271184, time: 0.556, data: 0.000) G_L1: 15.317 G_L1_ABSOLUTE: 2.699 G_L1_RELATIVE: 12.618 G_Regularizer: 0.000 validation_error: 21.102 +(epoch: 34, iters: 273184, time: 0.543, data: 0.000) G_L1: 13.312 G_L1_ABSOLUTE: 2.158 G_L1_RELATIVE: 11.155 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 34, iters: 275184, time: 0.555, data: 0.000) G_L1: 12.181 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 9.811 G_Regularizer: 0.000 validation_error: 20.963 +(epoch: 34, iters: 277184, time: 0.541, data: 0.000) G_L1: 13.430 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 11.195 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 34, iters: 279184, time: 0.549, data: 0.000) G_L1: 15.151 G_L1_ABSOLUTE: 2.319 G_L1_RELATIVE: 12.831 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 34, iters: 281184, time: 0.550, data: 0.000) G_L1: 14.077 G_L1_ABSOLUTE: 2.580 G_L1_RELATIVE: 11.497 G_Regularizer: 0.000 validation_error: 20.948 +(epoch: 34, iters: 283184, time: 0.550, data: 0.000) G_L1: 13.556 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 10.718 G_Regularizer: 0.000 validation_error: 20.394 +(epoch: 34, iters: 285184, time: 0.540, data: 0.000) G_L1: 16.008 G_L1_ABSOLUTE: 3.011 G_L1_RELATIVE: 12.997 G_Regularizer: 0.000 validation_error: 20.404 +(epoch: 34, iters: 287184, time: 0.540, data: 0.000) G_L1: 13.901 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 11.441 G_Regularizer: 0.000 validation_error: 20.586 +(epoch: 34, iters: 289184, time: 0.545, data: 0.000) G_L1: 16.710 G_L1_ABSOLUTE: 3.333 G_L1_RELATIVE: 13.377 G_Regularizer: 0.000 validation_error: 20.471 +(epoch: 34, iters: 291184, time: 0.544, data: 0.000) G_L1: 14.125 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 11.631 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 34, iters: 293184, time: 0.550, data: 0.000) G_L1: 16.259 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 13.874 G_Regularizer: 0.000 validation_error: 20.761 +(epoch: 34, iters: 295184, time: 0.548, data: 0.000) G_L1: 13.856 G_L1_ABSOLUTE: 2.487 G_L1_RELATIVE: 11.368 G_Regularizer: 0.000 validation_error: 21.087 +(epoch: 34, iters: 297184, time: 0.539, data: 0.000) G_L1: 16.735 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 14.138 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 34, iters: 299184, time: 0.551, data: 0.000) G_L1: 14.549 G_L1_ABSOLUTE: 2.741 G_L1_RELATIVE: 11.808 G_Regularizer: 0.000 validation_error: 20.287 +(epoch: 34, iters: 301184, time: 0.546, data: 0.000) G_L1: 14.377 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 11.599 G_Regularizer: 0.000 validation_error: 20.341 +(epoch: 35, iters: 432, time: 0.561, data: 0.000) G_L1: 15.108 G_L1_ABSOLUTE: 2.605 G_L1_RELATIVE: 12.504 G_Regularizer: 0.000 validation_error: 21.294 +(epoch: 35, iters: 2432, time: 0.544, data: 0.000) G_L1: 13.208 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 10.732 G_Regularizer: 0.000 validation_error: 21.066 +(epoch: 35, iters: 4432, time: 0.547, data: 0.000) G_L1: 13.142 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 10.785 G_Regularizer: 0.000 validation_error: 20.497 +(epoch: 35, iters: 6432, time: 0.547, data: 0.000) G_L1: 13.918 G_L1_ABSOLUTE: 2.319 G_L1_RELATIVE: 11.599 G_Regularizer: 0.000 validation_error: 20.547 +(epoch: 35, iters: 8432, time: 0.543, data: 0.000) G_L1: 14.711 G_L1_ABSOLUTE: 2.553 G_L1_RELATIVE: 12.158 G_Regularizer: 0.000 validation_error: 20.705 +(epoch: 35, iters: 10432, time: 0.540, data: 0.000) G_L1: 12.538 G_L1_ABSOLUTE: 2.367 G_L1_RELATIVE: 10.172 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 35, iters: 12432, time: 0.550, data: 0.000) G_L1: 14.281 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 11.681 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 35, iters: 14432, time: 0.553, data: 0.000) G_L1: 15.156 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 12.505 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 35, iters: 16432, time: 0.541, data: 0.000) G_L1: 14.692 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 11.971 G_Regularizer: 0.000 validation_error: 20.552 +(epoch: 35, iters: 18432, time: 0.548, data: 0.000) G_L1: 14.659 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 11.848 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 35, iters: 20432, time: 0.546, data: 0.000) G_L1: 27.341 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 24.860 G_Regularizer: 0.000 validation_error: 21.140 +(epoch: 35, iters: 22432, time: 0.544, data: 0.000) G_L1: 17.350 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 14.880 G_Regularizer: 0.000 validation_error: 21.065 +(epoch: 35, iters: 24432, time: 0.551, data: 0.000) G_L1: 10.966 G_L1_ABSOLUTE: 2.247 G_L1_RELATIVE: 8.719 G_Regularizer: 0.000 validation_error: 21.127 +(epoch: 35, iters: 26432, time: 0.547, data: 0.000) G_L1: 13.850 G_L1_ABSOLUTE: 2.404 G_L1_RELATIVE: 11.446 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 35, iters: 28432, time: 0.545, data: 0.000) G_L1: 13.254 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 10.841 G_Regularizer: 0.000 validation_error: 20.601 +(epoch: 35, iters: 30432, time: 0.546, data: 0.000) G_L1: 14.489 G_L1_ABSOLUTE: 2.955 G_L1_RELATIVE: 11.534 G_Regularizer: 0.000 validation_error: 20.693 +(epoch: 35, iters: 32432, time: 0.556, data: 0.000) G_L1: 17.096 G_L1_ABSOLUTE: 2.813 G_L1_RELATIVE: 14.283 G_Regularizer: 0.000 validation_error: 21.144 +(epoch: 35, iters: 34432, time: 0.553, data: 0.000) G_L1: 13.948 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 11.579 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 35, iters: 36432, time: 0.547, data: 0.001) G_L1: 13.091 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 10.633 G_Regularizer: 0.000 validation_error: 20.591 +(epoch: 35, iters: 38432, time: 0.546, data: 0.000) G_L1: 14.237 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 11.892 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 35, iters: 40432, time: 0.548, data: 0.000) G_L1: 12.291 G_L1_ABSOLUTE: 2.087 G_L1_RELATIVE: 10.205 G_Regularizer: 0.000 validation_error: 21.088 +(epoch: 35, iters: 42432, time: 0.538, data: 0.000) G_L1: 14.414 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 11.961 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 35, iters: 44432, time: 0.547, data: 0.000) G_L1: 15.494 G_L1_ABSOLUTE: 2.312 G_L1_RELATIVE: 13.182 G_Regularizer: 0.000 validation_error: 21.045 +(epoch: 35, iters: 46432, time: 0.543, data: 0.000) G_L1: 18.387 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 15.713 G_Regularizer: 0.000 validation_error: 20.824 +(epoch: 35, iters: 48432, time: 0.546, data: 0.001) G_L1: 12.920 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 10.324 G_Regularizer: 0.000 validation_error: 20.383 +(epoch: 35, iters: 50432, time: 0.546, data: 0.000) G_L1: 13.810 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 11.284 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 35, iters: 52432, time: 0.548, data: 0.000) G_L1: 13.354 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 10.592 G_Regularizer: 0.000 validation_error: 21.333 +(epoch: 35, iters: 54432, time: 0.547, data: 0.001) G_L1: 10.727 G_L1_ABSOLUTE: 2.441 G_L1_RELATIVE: 8.286 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 35, iters: 56432, time: 0.547, data: 0.000) G_L1: 14.973 G_L1_ABSOLUTE: 2.604 G_L1_RELATIVE: 12.369 G_Regularizer: 0.000 validation_error: 21.081 +(epoch: 35, iters: 58432, time: 0.546, data: 0.000) G_L1: 15.700 G_L1_ABSOLUTE: 3.142 G_L1_RELATIVE: 12.557 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 35, iters: 60432, time: 0.550, data: 0.000) G_L1: 12.650 G_L1_ABSOLUTE: 2.194 G_L1_RELATIVE: 10.456 G_Regularizer: 0.000 validation_error: 21.641 +(epoch: 35, iters: 62432, time: 0.545, data: 0.000) G_L1: 14.885 G_L1_ABSOLUTE: 2.809 G_L1_RELATIVE: 12.076 G_Regularizer: 0.000 validation_error: 21.093 +(epoch: 35, iters: 64432, time: 0.539, data: 0.000) G_L1: 12.725 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 10.115 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 35, iters: 66432, time: 0.548, data: 0.000) G_L1: 15.069 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 12.706 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 35, iters: 68432, time: 0.544, data: 0.000) G_L1: 12.469 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 10.012 G_Regularizer: 0.000 validation_error: 20.547 +(epoch: 35, iters: 70432, time: 0.544, data: 0.001) G_L1: 13.441 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 10.845 G_Regularizer: 0.000 validation_error: 20.430 +(epoch: 35, iters: 72432, time: 0.542, data: 0.001) G_L1: 14.156 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 11.676 G_Regularizer: 0.000 validation_error: 20.778 +(epoch: 35, iters: 74432, time: 0.548, data: 0.000) G_L1: 14.899 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 12.535 G_Regularizer: 0.000 validation_error: 20.467 +(epoch: 35, iters: 76432, time: 0.550, data: 0.000) G_L1: 11.970 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 9.688 G_Regularizer: 0.000 validation_error: 21.268 +(epoch: 35, iters: 78432, time: 0.550, data: 0.000) G_L1: 12.159 G_L1_ABSOLUTE: 2.187 G_L1_RELATIVE: 9.972 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 35, iters: 80432, time: 0.546, data: 0.001) G_L1: 14.338 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 11.854 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 35, iters: 82432, time: 0.543, data: 0.000) G_L1: 14.446 G_L1_ABSOLUTE: 2.981 G_L1_RELATIVE: 11.465 G_Regularizer: 0.000 validation_error: 21.268 +(epoch: 35, iters: 84432, time: 0.541, data: 0.000) G_L1: 14.229 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 11.657 G_Regularizer: 0.000 validation_error: 21.146 +(epoch: 35, iters: 86432, time: 0.545, data: 0.000) G_L1: 14.126 G_L1_ABSOLUTE: 2.386 G_L1_RELATIVE: 11.739 G_Regularizer: 0.000 validation_error: 20.595 +(epoch: 35, iters: 88432, time: 0.546, data: 0.000) G_L1: 13.250 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 10.837 G_Regularizer: 0.000 validation_error: 21.387 +(epoch: 35, iters: 90432, time: 0.542, data: 0.000) G_L1: 13.639 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 10.829 G_Regularizer: 0.000 validation_error: 20.779 +(epoch: 35, iters: 92432, time: 0.542, data: 0.000) G_L1: 14.748 G_L1_ABSOLUTE: 3.043 G_L1_RELATIVE: 11.705 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 35, iters: 94432, time: 0.541, data: 0.000) G_L1: 12.252 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 9.828 G_Regularizer: 0.000 validation_error: 20.540 +(epoch: 35, iters: 96432, time: 0.547, data: 0.000) G_L1: 15.842 G_L1_ABSOLUTE: 2.269 G_L1_RELATIVE: 13.573 G_Regularizer: 0.000 validation_error: 20.732 +(epoch: 35, iters: 98432, time: 0.546, data: 0.000) G_L1: 15.805 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 13.176 G_Regularizer: 0.000 validation_error: 20.674 +(epoch: 35, iters: 100432, time: 0.546, data: 0.000) G_L1: 13.318 G_L1_ABSOLUTE: 2.207 G_L1_RELATIVE: 11.111 G_Regularizer: 0.000 validation_error: 20.403 +(epoch: 35, iters: 102432, time: 0.547, data: 0.000) G_L1: 17.161 G_L1_ABSOLUTE: 2.749 G_L1_RELATIVE: 14.412 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 35, iters: 104432, time: 0.546, data: 0.000) G_L1: 13.643 G_L1_ABSOLUTE: 2.815 G_L1_RELATIVE: 10.828 G_Regularizer: 0.000 validation_error: 21.214 +(epoch: 35, iters: 106432, time: 0.552, data: 0.000) G_L1: 16.303 G_L1_ABSOLUTE: 2.220 G_L1_RELATIVE: 14.083 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 35, iters: 108432, time: 0.549, data: 0.000) G_L1: 14.148 G_L1_ABSOLUTE: 1.935 G_L1_RELATIVE: 12.214 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 35, iters: 110432, time: 0.548, data: 0.000) G_L1: 14.823 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 12.354 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 35, iters: 112432, time: 0.546, data: 0.000) G_L1: 14.131 G_L1_ABSOLUTE: 2.472 G_L1_RELATIVE: 11.659 G_Regularizer: 0.000 validation_error: 20.744 +(epoch: 35, iters: 114432, time: 0.544, data: 0.000) G_L1: 13.438 G_L1_ABSOLUTE: 2.529 G_L1_RELATIVE: 10.909 G_Regularizer: 0.000 validation_error: 20.763 +(epoch: 35, iters: 116432, time: 0.545, data: 0.000) G_L1: 13.549 G_L1_ABSOLUTE: 2.186 G_L1_RELATIVE: 11.362 G_Regularizer: 0.000 validation_error: 20.763 +(epoch: 35, iters: 118432, time: 0.549, data: 0.001) G_L1: 14.729 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 11.930 G_Regularizer: 0.000 validation_error: 20.456 +(epoch: 35, iters: 120432, time: 0.544, data: 0.000) G_L1: 13.506 G_L1_ABSOLUTE: 2.479 G_L1_RELATIVE: 11.027 G_Regularizer: 0.000 validation_error: 21.247 +(epoch: 35, iters: 122432, time: 0.542, data: 0.000) G_L1: 15.021 G_L1_ABSOLUTE: 2.555 G_L1_RELATIVE: 12.467 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 35, iters: 124432, time: 0.547, data: 0.000) G_L1: 12.166 G_L1_ABSOLUTE: 2.228 G_L1_RELATIVE: 9.938 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 35, iters: 126432, time: 0.543, data: 0.000) G_L1: 13.504 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 11.251 G_Regularizer: 0.000 validation_error: 21.091 +(epoch: 35, iters: 128432, time: 0.561, data: 0.000) G_L1: 13.455 G_L1_ABSOLUTE: 2.271 G_L1_RELATIVE: 11.184 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 35, iters: 130432, time: 0.550, data: 0.000) G_L1: 15.314 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 12.651 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 35, iters: 132432, time: 0.545, data: 0.000) G_L1: 15.193 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 12.625 G_Regularizer: 0.000 validation_error: 20.638 +(epoch: 35, iters: 134432, time: 0.544, data: 0.000) G_L1: 14.937 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 12.504 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 35, iters: 136432, time: 0.544, data: 0.000) G_L1: 12.729 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 10.132 G_Regularizer: 0.000 validation_error: 21.278 +(epoch: 35, iters: 138432, time: 0.547, data: 0.000) G_L1: 13.802 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 10.967 G_Regularizer: 0.000 validation_error: 21.155 +(epoch: 35, iters: 140432, time: 0.547, data: 0.000) G_L1: 15.749 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 13.367 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 35, iters: 142432, time: 0.549, data: 0.001) G_L1: 12.536 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 9.788 G_Regularizer: 0.000 validation_error: 21.081 +(epoch: 35, iters: 144432, time: 0.547, data: 0.000) G_L1: 15.147 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 12.350 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 35, iters: 146432, time: 0.538, data: 0.000) G_L1: 14.023 G_L1_ABSOLUTE: 2.583 G_L1_RELATIVE: 11.439 G_Regularizer: 0.000 validation_error: 20.396 +(epoch: 35, iters: 148432, time: 0.546, data: 0.000) G_L1: 14.647 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 12.135 G_Regularizer: 0.000 validation_error: 20.425 +(epoch: 35, iters: 150432, time: 0.549, data: 0.000) G_L1: 14.835 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 12.344 G_Regularizer: 0.000 validation_error: 20.492 +(epoch: 35, iters: 152432, time: 0.552, data: 0.000) G_L1: 13.585 G_L1_ABSOLUTE: 2.391 G_L1_RELATIVE: 11.195 G_Regularizer: 0.000 validation_error: 20.750 +(epoch: 35, iters: 154432, time: 0.543, data: 0.001) G_L1: 15.351 G_L1_ABSOLUTE: 2.325 G_L1_RELATIVE: 13.025 G_Regularizer: 0.000 validation_error: 20.782 +(epoch: 35, iters: 156432, time: 0.548, data: 0.001) G_L1: 11.860 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 9.577 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 35, iters: 158432, time: 0.547, data: 0.000) G_L1: 17.214 G_L1_ABSOLUTE: 2.974 G_L1_RELATIVE: 14.240 G_Regularizer: 0.000 validation_error: 21.128 +(epoch: 35, iters: 160432, time: 0.551, data: 0.000) G_L1: 13.221 G_L1_ABSOLUTE: 2.431 G_L1_RELATIVE: 10.790 G_Regularizer: 0.000 validation_error: 21.157 +(epoch: 35, iters: 162432, time: 0.550, data: 0.000) G_L1: 14.750 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 12.026 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 35, iters: 164432, time: 0.548, data: 0.000) G_L1: 12.771 G_L1_ABSOLUTE: 2.456 G_L1_RELATIVE: 10.315 G_Regularizer: 0.000 validation_error: 20.885 +(epoch: 35, iters: 166432, time: 0.550, data: 0.000) G_L1: 13.837 G_L1_ABSOLUTE: 2.242 G_L1_RELATIVE: 11.595 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 35, iters: 168432, time: 0.543, data: 0.000) G_L1: 15.765 G_L1_ABSOLUTE: 2.086 G_L1_RELATIVE: 13.678 G_Regularizer: 0.000 validation_error: 20.937 +(epoch: 35, iters: 170432, time: 0.543, data: 0.000) G_L1: 12.664 G_L1_ABSOLUTE: 2.761 G_L1_RELATIVE: 9.903 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 35, iters: 172432, time: 0.547, data: 0.000) G_L1: 16.045 G_L1_ABSOLUTE: 2.823 G_L1_RELATIVE: 13.222 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 35, iters: 174432, time: 0.546, data: 0.000) G_L1: 13.900 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 11.580 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 35, iters: 176432, time: 0.540, data: 0.000) G_L1: 15.201 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 12.749 G_Regularizer: 0.000 validation_error: 21.176 +(epoch: 35, iters: 178432, time: 0.549, data: 0.000) G_L1: 15.717 G_L1_ABSOLUTE: 3.291 G_L1_RELATIVE: 12.427 G_Regularizer: 0.000 validation_error: 21.272 +(epoch: 35, iters: 180432, time: 0.545, data: 0.000) G_L1: 15.519 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 12.974 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 35, iters: 182432, time: 0.551, data: 0.000) G_L1: 12.836 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 10.185 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 35, iters: 184432, time: 0.540, data: 0.000) G_L1: 13.647 G_L1_ABSOLUTE: 2.426 G_L1_RELATIVE: 11.220 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 35, iters: 186432, time: 0.546, data: 0.000) G_L1: 14.176 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 11.885 G_Regularizer: 0.000 validation_error: 21.331 +(epoch: 35, iters: 188432, time: 0.548, data: 0.000) G_L1: 14.045 G_L1_ABSOLUTE: 2.199 G_L1_RELATIVE: 11.845 G_Regularizer: 0.000 validation_error: 20.779 +(epoch: 35, iters: 190432, time: 0.547, data: 0.000) G_L1: 13.398 G_L1_ABSOLUTE: 2.356 G_L1_RELATIVE: 11.043 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 35, iters: 192432, time: 0.548, data: 0.000) G_L1: 13.529 G_L1_ABSOLUTE: 2.187 G_L1_RELATIVE: 11.342 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 35, iters: 194432, time: 0.541, data: 0.000) G_L1: 12.448 G_L1_ABSOLUTE: 2.611 G_L1_RELATIVE: 9.838 G_Regularizer: 0.000 validation_error: 20.575 +(epoch: 35, iters: 196432, time: 0.557, data: 0.000) G_L1: 14.495 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 12.178 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 35, iters: 198432, time: 0.557, data: 0.000) G_L1: 16.566 G_L1_ABSOLUTE: 2.296 G_L1_RELATIVE: 14.270 G_Regularizer: 0.000 validation_error: 20.729 +(epoch: 35, iters: 200432, time: 0.539, data: 0.000) G_L1: 13.388 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 11.017 G_Regularizer: 0.000 validation_error: 21.287 +(epoch: 35, iters: 202432, time: 0.553, data: 0.000) G_L1: 14.485 G_L1_ABSOLUTE: 2.783 G_L1_RELATIVE: 11.702 G_Regularizer: 0.000 validation_error: 20.253 +(epoch: 35, iters: 204432, time: 0.547, data: 0.001) G_L1: 14.549 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 11.693 G_Regularizer: 0.000 validation_error: 20.936 +(epoch: 35, iters: 206432, time: 0.547, data: 0.000) G_L1: 11.949 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 9.684 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 35, iters: 208432, time: 0.551, data: 0.000) G_L1: 16.691 G_L1_ABSOLUTE: 3.128 G_L1_RELATIVE: 13.563 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 35, iters: 210432, time: 0.539, data: 0.000) G_L1: 16.314 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 13.727 G_Regularizer: 0.000 validation_error: 20.896 +(epoch: 35, iters: 212432, time: 0.552, data: 0.000) G_L1: 14.663 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 12.024 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 35, iters: 214432, time: 0.544, data: 0.000) G_L1: 20.001 G_L1_ABSOLUTE: 2.579 G_L1_RELATIVE: 17.422 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 35, iters: 216432, time: 0.560, data: 0.000) G_L1: 15.776 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 13.042 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 35, iters: 218432, time: 0.540, data: 0.000) G_L1: 12.126 G_L1_ABSOLUTE: 2.248 G_L1_RELATIVE: 9.878 G_Regularizer: 0.000 validation_error: 21.323 +(epoch: 35, iters: 220432, time: 0.544, data: 0.000) G_L1: 14.225 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 11.817 G_Regularizer: 0.000 validation_error: 20.522 +(epoch: 35, iters: 222432, time: 0.549, data: 0.000) G_L1: 13.570 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 10.885 G_Regularizer: 0.000 validation_error: 21.216 +(epoch: 35, iters: 224432, time: 0.551, data: 0.000) G_L1: 15.088 G_L1_ABSOLUTE: 2.928 G_L1_RELATIVE: 12.160 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 35, iters: 226432, time: 0.539, data: 0.000) G_L1: 14.787 G_L1_ABSOLUTE: 2.555 G_L1_RELATIVE: 12.232 G_Regularizer: 0.000 validation_error: 20.852 +(epoch: 35, iters: 228432, time: 0.546, data: 0.000) G_L1: 14.327 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 12.065 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 35, iters: 230432, time: 0.547, data: 0.000) G_L1: 11.794 G_L1_ABSOLUTE: 2.165 G_L1_RELATIVE: 9.629 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 35, iters: 232432, time: 0.551, data: 0.000) G_L1: 12.711 G_L1_ABSOLUTE: 2.122 G_L1_RELATIVE: 10.590 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 35, iters: 234432, time: 0.539, data: 0.000) G_L1: 26.445 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 23.896 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 35, iters: 236432, time: 0.542, data: 0.000) G_L1: 11.979 G_L1_ABSOLUTE: 2.328 G_L1_RELATIVE: 9.651 G_Regularizer: 0.000 validation_error: 20.604 +(epoch: 35, iters: 238432, time: 0.545, data: 0.000) G_L1: 15.302 G_L1_ABSOLUTE: 2.988 G_L1_RELATIVE: 12.314 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 35, iters: 240432, time: 0.543, data: 0.001) G_L1: 12.377 G_L1_ABSOLUTE: 2.353 G_L1_RELATIVE: 10.024 G_Regularizer: 0.000 validation_error: 20.599 +(epoch: 35, iters: 242432, time: 0.546, data: 0.000) G_L1: 17.676 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 15.175 G_Regularizer: 0.000 validation_error: 20.416 +(epoch: 35, iters: 244432, time: 0.544, data: 0.000) G_L1: 13.397 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 10.740 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 35, iters: 246432, time: 0.540, data: 0.001) G_L1: 12.560 G_L1_ABSOLUTE: 2.261 G_L1_RELATIVE: 10.299 G_Regularizer: 0.000 validation_error: 20.770 +(epoch: 35, iters: 248432, time: 0.549, data: 0.000) G_L1: 17.413 G_L1_ABSOLUTE: 3.502 G_L1_RELATIVE: 13.911 G_Regularizer: 0.000 validation_error: 20.671 +(epoch: 35, iters: 250432, time: 0.555, data: 0.000) G_L1: 14.278 G_L1_ABSOLUTE: 3.166 G_L1_RELATIVE: 11.112 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 35, iters: 252432, time: 0.547, data: 0.000) G_L1: 13.589 G_L1_ABSOLUTE: 2.197 G_L1_RELATIVE: 11.392 G_Regularizer: 0.000 validation_error: 21.255 +(epoch: 35, iters: 254432, time: 0.551, data: 0.001) G_L1: 13.261 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 10.768 G_Regularizer: 0.000 validation_error: 20.692 +(epoch: 35, iters: 256432, time: 0.543, data: 0.000) G_L1: 14.996 G_L1_ABSOLUTE: 2.553 G_L1_RELATIVE: 12.443 G_Regularizer: 0.000 validation_error: 20.629 +(epoch: 35, iters: 258432, time: 0.548, data: 0.000) G_L1: 15.326 G_L1_ABSOLUTE: 3.128 G_L1_RELATIVE: 12.198 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 35, iters: 260432, time: 0.541, data: 0.001) G_L1: 13.579 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 11.178 G_Regularizer: 0.000 validation_error: 20.536 +(epoch: 35, iters: 262432, time: 0.542, data: 0.000) G_L1: 14.027 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 11.696 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 35, iters: 264432, time: 0.542, data: 0.002) G_L1: 14.784 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 12.316 G_Regularizer: 0.000 validation_error: 21.156 +(epoch: 35, iters: 266432, time: 0.545, data: 0.000) G_L1: 14.524 G_L1_ABSOLUTE: 3.165 G_L1_RELATIVE: 11.359 G_Regularizer: 0.000 validation_error: 20.524 +(epoch: 35, iters: 268432, time: 0.544, data: 0.000) G_L1: 13.273 G_L1_ABSOLUTE: 2.830 G_L1_RELATIVE: 10.443 G_Regularizer: 0.000 validation_error: 21.072 +(epoch: 35, iters: 270432, time: 0.550, data: 0.001) G_L1: 13.977 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 11.575 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 35, iters: 272432, time: 0.549, data: 0.000) G_L1: 12.767 G_L1_ABSOLUTE: 2.025 G_L1_RELATIVE: 10.741 G_Regularizer: 0.000 validation_error: 20.592 +(epoch: 35, iters: 274432, time: 0.545, data: 0.000) G_L1: 18.408 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 15.523 G_Regularizer: 0.000 validation_error: 20.719 +(epoch: 35, iters: 276432, time: 0.552, data: 0.001) G_L1: 14.640 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 11.909 G_Regularizer: 0.000 validation_error: 20.599 +(epoch: 35, iters: 278432, time: 0.547, data: 0.001) G_L1: 16.662 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 14.136 G_Regularizer: 0.000 validation_error: 20.444 +(epoch: 35, iters: 280432, time: 0.545, data: 0.000) G_L1: 15.315 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 12.979 G_Regularizer: 0.000 validation_error: 20.472 +(epoch: 35, iters: 282432, time: 0.557, data: 0.000) G_L1: 13.674 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 11.111 G_Regularizer: 0.000 validation_error: 20.186 +(epoch: 35, iters: 284432, time: 0.547, data: 0.000) G_L1: 16.963 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 14.219 G_Regularizer: 0.000 validation_error: 20.406 +(epoch: 35, iters: 286432, time: 0.541, data: 0.000) G_L1: 19.652 G_L1_ABSOLUTE: 2.860 G_L1_RELATIVE: 16.791 G_Regularizer: 0.000 validation_error: 20.647 +(epoch: 35, iters: 288432, time: 0.552, data: 0.000) G_L1: 16.436 G_L1_ABSOLUTE: 2.953 G_L1_RELATIVE: 13.482 G_Regularizer: 0.000 validation_error: 20.203 +(epoch: 35, iters: 290432, time: 0.552, data: 0.000) G_L1: 14.637 G_L1_ABSOLUTE: 1.958 G_L1_RELATIVE: 12.679 G_Regularizer: 0.000 validation_error: 20.546 +(epoch: 35, iters: 292432, time: 0.540, data: 0.000) G_L1: 13.801 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 11.080 G_Regularizer: 0.000 validation_error: 20.385 +(epoch: 35, iters: 294432, time: 0.541, data: 0.000) G_L1: 13.243 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 10.601 G_Regularizer: 0.000 validation_error: 20.691 +(epoch: 35, iters: 296432, time: 0.541, data: 0.001) G_L1: 15.631 G_L1_ABSOLUTE: 2.567 G_L1_RELATIVE: 13.063 G_Regularizer: 0.000 validation_error: 20.786 +(epoch: 35, iters: 298432, time: 0.547, data: 0.000) G_L1: 12.644 G_L1_ABSOLUTE: 2.559 G_L1_RELATIVE: 10.084 G_Regularizer: 0.000 validation_error: 20.325 +(epoch: 35, iters: 300432, time: 0.542, data: 0.001) G_L1: 14.638 G_L1_ABSOLUTE: 2.322 G_L1_RELATIVE: 12.316 G_Regularizer: 0.000 validation_error: 20.907 +(epoch: 35, iters: 302432, time: 0.556, data: 0.000) G_L1: 13.227 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 10.706 G_Regularizer: 0.000 validation_error: 20.582 +(epoch: 36, iters: 1680, time: 0.549, data: 0.000) G_L1: 15.592 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 13.139 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 36, iters: 3680, time: 0.547, data: 0.000) G_L1: 14.185 G_L1_ABSOLUTE: 2.957 G_L1_RELATIVE: 11.228 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 36, iters: 5680, time: 0.552, data: 0.000) G_L1: 11.535 G_L1_ABSOLUTE: 2.173 G_L1_RELATIVE: 9.361 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 36, iters: 7680, time: 0.545, data: 0.000) G_L1: 13.719 G_L1_ABSOLUTE: 2.199 G_L1_RELATIVE: 11.520 G_Regularizer: 0.000 validation_error: 20.454 +(epoch: 36, iters: 9680, time: 0.546, data: 0.000) G_L1: 14.041 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 11.368 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 36, iters: 11680, time: 0.549, data: 0.000) G_L1: 16.669 G_L1_ABSOLUTE: 2.825 G_L1_RELATIVE: 13.844 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 36, iters: 13680, time: 0.548, data: 0.000) G_L1: 12.521 G_L1_ABSOLUTE: 2.064 G_L1_RELATIVE: 10.457 G_Regularizer: 0.000 validation_error: 21.065 +(epoch: 36, iters: 15680, time: 0.552, data: 0.000) G_L1: 14.798 G_L1_ABSOLUTE: 2.611 G_L1_RELATIVE: 12.187 G_Regularizer: 0.000 validation_error: 20.731 +(epoch: 36, iters: 17680, time: 0.549, data: 0.000) G_L1: 15.035 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 12.515 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 36, iters: 19680, time: 0.546, data: 0.000) G_L1: 16.540 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 13.819 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 36, iters: 21680, time: 0.544, data: 0.000) G_L1: 12.153 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 9.735 G_Regularizer: 0.000 validation_error: 20.548 +(epoch: 36, iters: 23680, time: 0.547, data: 0.000) G_L1: 15.721 G_L1_ABSOLUTE: 2.909 G_L1_RELATIVE: 12.812 G_Regularizer: 0.000 validation_error: 20.599 +(epoch: 36, iters: 25680, time: 0.538, data: 0.000) G_L1: 13.707 G_L1_ABSOLUTE: 2.295 G_L1_RELATIVE: 11.412 G_Regularizer: 0.000 validation_error: 20.595 +(epoch: 36, iters: 27680, time: 0.542, data: 0.000) G_L1: 12.990 G_L1_ABSOLUTE: 2.151 G_L1_RELATIVE: 10.839 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 36, iters: 29680, time: 0.539, data: 0.000) G_L1: 14.621 G_L1_ABSOLUTE: 2.740 G_L1_RELATIVE: 11.881 G_Regularizer: 0.000 validation_error: 20.596 +(epoch: 36, iters: 31680, time: 0.547, data: 0.000) G_L1: 14.413 G_L1_ABSOLUTE: 2.201 G_L1_RELATIVE: 12.212 G_Regularizer: 0.000 validation_error: 20.592 +(epoch: 36, iters: 33680, time: 0.538, data: 0.000) G_L1: 13.441 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 10.817 G_Regularizer: 0.000 validation_error: 20.578 +(epoch: 36, iters: 35680, time: 0.544, data: 0.000) G_L1: 14.226 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 11.964 G_Regularizer: 0.000 validation_error: 20.956 +(epoch: 36, iters: 37680, time: 0.539, data: 0.000) G_L1: 14.391 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 11.659 G_Regularizer: 0.000 validation_error: 20.576 +(epoch: 36, iters: 39680, time: 0.551, data: 0.000) G_L1: 16.365 G_L1_ABSOLUTE: 2.390 G_L1_RELATIVE: 13.975 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 36, iters: 41680, time: 0.552, data: 0.000) G_L1: 12.881 G_L1_ABSOLUTE: 2.148 G_L1_RELATIVE: 10.733 G_Regularizer: 0.000 validation_error: 20.924 +(epoch: 36, iters: 43680, time: 0.544, data: 0.000) G_L1: 12.676 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 10.252 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 36, iters: 45680, time: 0.548, data: 0.000) G_L1: 13.897 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 11.446 G_Regularizer: 0.000 validation_error: 21.078 +(epoch: 36, iters: 47680, time: 0.551, data: 0.000) G_L1: 12.347 G_L1_ABSOLUTE: 2.290 G_L1_RELATIVE: 10.057 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 36, iters: 49680, time: 0.551, data: 0.000) G_L1: 12.085 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 9.681 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 36, iters: 51680, time: 0.552, data: 0.000) G_L1: 14.464 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 11.704 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 36, iters: 53680, time: 0.542, data: 0.000) G_L1: 12.095 G_L1_ABSOLUTE: 2.315 G_L1_RELATIVE: 9.779 G_Regularizer: 0.000 validation_error: 20.607 +(epoch: 36, iters: 55680, time: 0.549, data: 0.000) G_L1: 13.488 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 11.052 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 36, iters: 57680, time: 0.541, data: 0.000) G_L1: 14.588 G_L1_ABSOLUTE: 2.129 G_L1_RELATIVE: 12.460 G_Regularizer: 0.000 validation_error: 20.920 +(epoch: 36, iters: 59680, time: 0.558, data: 0.000) G_L1: 12.554 G_L1_ABSOLUTE: 2.593 G_L1_RELATIVE: 9.961 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 36, iters: 61680, time: 0.545, data: 0.000) G_L1: 14.162 G_L1_ABSOLUTE: 2.913 G_L1_RELATIVE: 11.249 G_Regularizer: 0.000 validation_error: 21.139 +(epoch: 36, iters: 63680, time: 0.550, data: 0.000) G_L1: 14.355 G_L1_ABSOLUTE: 2.499 G_L1_RELATIVE: 11.857 G_Regularizer: 0.000 validation_error: 20.749 +(epoch: 36, iters: 65680, time: 0.545, data: 0.000) G_L1: 16.490 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 13.944 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 36, iters: 67680, time: 0.548, data: 0.000) G_L1: 13.661 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 11.072 G_Regularizer: 0.000 validation_error: 20.515 +(epoch: 36, iters: 69680, time: 0.549, data: 0.000) G_L1: 14.511 G_L1_ABSOLUTE: 2.033 G_L1_RELATIVE: 12.478 G_Regularizer: 0.000 validation_error: 21.267 +(epoch: 36, iters: 71680, time: 0.544, data: 0.000) G_L1: 15.471 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 13.189 G_Regularizer: 0.000 validation_error: 21.280 +(epoch: 36, iters: 73680, time: 0.552, data: 0.000) G_L1: 17.074 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 14.732 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 36, iters: 75680, time: 0.547, data: 0.000) G_L1: 14.070 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 11.697 G_Regularizer: 0.000 validation_error: 20.707 +(epoch: 36, iters: 77680, time: 0.543, data: 0.000) G_L1: 14.237 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 11.961 G_Regularizer: 0.000 validation_error: 20.991 +(epoch: 36, iters: 79680, time: 0.543, data: 0.000) G_L1: 14.036 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 11.558 G_Regularizer: 0.000 validation_error: 20.912 +(epoch: 36, iters: 81680, time: 0.547, data: 0.001) G_L1: 13.799 G_L1_ABSOLUTE: 2.537 G_L1_RELATIVE: 11.262 G_Regularizer: 0.000 validation_error: 21.281 +(epoch: 36, iters: 83680, time: 0.543, data: 0.000) G_L1: 12.351 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 9.620 G_Regularizer: 0.000 validation_error: 21.517 +(epoch: 36, iters: 85680, time: 0.546, data: 0.000) G_L1: 12.763 G_L1_ABSOLUTE: 2.828 G_L1_RELATIVE: 9.935 G_Regularizer: 0.000 validation_error: 20.545 +(epoch: 36, iters: 87680, time: 0.553, data: 0.000) G_L1: 14.524 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 12.009 G_Regularizer: 0.000 validation_error: 20.644 +(epoch: 36, iters: 89680, time: 0.542, data: 0.000) G_L1: 16.921 G_L1_ABSOLUTE: 2.699 G_L1_RELATIVE: 14.222 G_Regularizer: 0.000 validation_error: 20.573 +(epoch: 36, iters: 91680, time: 0.541, data: 0.000) G_L1: 13.842 G_L1_ABSOLUTE: 2.034 G_L1_RELATIVE: 11.808 G_Regularizer: 0.000 validation_error: 21.049 +(epoch: 36, iters: 93680, time: 0.555, data: 0.000) G_L1: 14.418 G_L1_ABSOLUTE: 3.457 G_L1_RELATIVE: 10.961 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 36, iters: 95680, time: 0.549, data: 0.000) G_L1: 14.342 G_L1_ABSOLUTE: 2.182 G_L1_RELATIVE: 12.161 G_Regularizer: 0.000 validation_error: 21.313 +(epoch: 36, iters: 97680, time: 0.541, data: 0.000) G_L1: 15.866 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 13.129 G_Regularizer: 0.000 validation_error: 20.938 +(epoch: 36, iters: 99680, time: 0.544, data: 0.000) G_L1: 14.401 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 11.916 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 36, iters: 101680, time: 0.551, data: 0.001) G_L1: 13.188 G_L1_ABSOLUTE: 2.545 G_L1_RELATIVE: 10.644 G_Regularizer: 0.000 validation_error: 20.563 +(epoch: 36, iters: 103680, time: 0.547, data: 0.000) G_L1: 14.381 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 11.901 G_Regularizer: 0.000 validation_error: 20.920 +(epoch: 36, iters: 105680, time: 0.544, data: 0.000) G_L1: 14.364 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 11.841 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 36, iters: 107680, time: 0.547, data: 0.000) G_L1: 13.436 G_L1_ABSOLUTE: 2.327 G_L1_RELATIVE: 11.109 G_Regularizer: 0.000 validation_error: 20.795 +(epoch: 36, iters: 109680, time: 0.542, data: 0.000) G_L1: 12.987 G_L1_ABSOLUTE: 2.154 G_L1_RELATIVE: 10.833 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 36, iters: 111680, time: 0.541, data: 0.000) G_L1: 16.860 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 14.204 G_Regularizer: 0.000 validation_error: 20.646 +(epoch: 36, iters: 113680, time: 0.547, data: 0.001) G_L1: 14.083 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 11.615 G_Regularizer: 0.000 validation_error: 21.161 +(epoch: 36, iters: 115680, time: 0.548, data: 0.000) G_L1: 14.176 G_L1_ABSOLUTE: 2.479 G_L1_RELATIVE: 11.697 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 36, iters: 117680, time: 0.538, data: 0.000) G_L1: 14.236 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 11.373 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 36, iters: 119680, time: 0.551, data: 0.000) G_L1: 13.760 G_L1_ABSOLUTE: 2.127 G_L1_RELATIVE: 11.634 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 36, iters: 121680, time: 0.553, data: 0.000) G_L1: 12.979 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 10.370 G_Regularizer: 0.000 validation_error: 21.073 +(epoch: 36, iters: 123680, time: 0.552, data: 0.000) G_L1: 14.568 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 12.228 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 36, iters: 125680, time: 0.549, data: 0.000) G_L1: 16.296 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 13.814 G_Regularizer: 0.000 validation_error: 20.301 +(epoch: 36, iters: 127680, time: 0.549, data: 0.000) G_L1: 13.548 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 11.047 G_Regularizer: 0.000 validation_error: 20.733 +(epoch: 36, iters: 129680, time: 0.550, data: 0.001) G_L1: 12.949 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 10.535 G_Regularizer: 0.000 validation_error: 20.297 +(epoch: 36, iters: 131680, time: 0.543, data: 0.000) G_L1: 13.813 G_L1_ABSOLUTE: 2.456 G_L1_RELATIVE: 11.358 G_Regularizer: 0.000 validation_error: 20.543 +(epoch: 36, iters: 133680, time: 0.545, data: 0.000) G_L1: 12.497 G_L1_ABSOLUTE: 2.272 G_L1_RELATIVE: 10.224 G_Regularizer: 0.000 validation_error: 21.380 +(epoch: 36, iters: 135680, time: 0.551, data: 0.000) G_L1: 13.479 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 11.022 G_Regularizer: 0.000 validation_error: 21.230 +(epoch: 36, iters: 137680, time: 0.542, data: 0.000) G_L1: 13.454 G_L1_ABSOLUTE: 3.182 G_L1_RELATIVE: 10.271 G_Regularizer: 0.000 validation_error: 21.007 +(epoch: 36, iters: 139680, time: 0.543, data: 0.000) G_L1: 16.195 G_L1_ABSOLUTE: 3.217 G_L1_RELATIVE: 12.978 G_Regularizer: 0.000 validation_error: 21.069 +(epoch: 36, iters: 141680, time: 0.545, data: 0.000) G_L1: 12.818 G_L1_ABSOLUTE: 2.346 G_L1_RELATIVE: 10.471 G_Regularizer: 0.000 validation_error: 20.735 +(epoch: 36, iters: 143680, time: 0.544, data: 0.000) G_L1: 15.261 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 12.524 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 36, iters: 145680, time: 0.548, data: 0.000) G_L1: 14.384 G_L1_ABSOLUTE: 2.897 G_L1_RELATIVE: 11.486 G_Regularizer: 0.000 validation_error: 21.248 +(epoch: 36, iters: 147680, time: 0.547, data: 0.000) G_L1: 16.200 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 13.602 G_Regularizer: 0.000 validation_error: 20.355 +(epoch: 36, iters: 149680, time: 0.547, data: 0.000) G_L1: 12.884 G_L1_ABSOLUTE: 2.266 G_L1_RELATIVE: 10.618 G_Regularizer: 0.000 validation_error: 20.611 +(epoch: 36, iters: 151680, time: 0.549, data: 0.000) G_L1: 16.105 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 13.581 G_Regularizer: 0.000 validation_error: 21.071 +(epoch: 36, iters: 153680, time: 0.547, data: 0.001) G_L1: 12.350 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 9.606 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 36, iters: 155680, time: 0.552, data: 0.000) G_L1: 13.861 G_L1_ABSOLUTE: 2.789 G_L1_RELATIVE: 11.071 G_Regularizer: 0.000 validation_error: 21.484 +(epoch: 36, iters: 157680, time: 0.553, data: 0.000) G_L1: 12.906 G_L1_ABSOLUTE: 2.393 G_L1_RELATIVE: 10.513 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 36, iters: 159680, time: 0.552, data: 0.000) G_L1: 15.220 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 12.550 G_Regularizer: 0.000 validation_error: 20.847 +(epoch: 36, iters: 161680, time: 0.554, data: 0.000) G_L1: 13.658 G_L1_ABSOLUTE: 2.557 G_L1_RELATIVE: 11.100 G_Regularizer: 0.000 validation_error: 20.993 +(epoch: 36, iters: 163680, time: 0.548, data: 0.000) G_L1: 16.049 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 13.678 G_Regularizer: 0.000 validation_error: 20.683 +(epoch: 36, iters: 165680, time: 0.545, data: 0.000) G_L1: 11.625 G_L1_ABSOLUTE: 2.297 G_L1_RELATIVE: 9.328 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 36, iters: 167680, time: 0.546, data: 0.000) G_L1: 14.289 G_L1_ABSOLUTE: 2.261 G_L1_RELATIVE: 12.027 G_Regularizer: 0.000 validation_error: 20.591 +(epoch: 36, iters: 169680, time: 0.540, data: 0.000) G_L1: 14.768 G_L1_ABSOLUTE: 2.361 G_L1_RELATIVE: 12.407 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 36, iters: 171680, time: 0.548, data: 0.000) G_L1: 20.242 G_L1_ABSOLUTE: 3.288 G_L1_RELATIVE: 16.954 G_Regularizer: 0.000 validation_error: 20.946 +(epoch: 36, iters: 173680, time: 0.544, data: 0.000) G_L1: 13.411 G_L1_ABSOLUTE: 2.250 G_L1_RELATIVE: 11.161 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 36, iters: 175680, time: 0.554, data: 0.000) G_L1: 16.492 G_L1_ABSOLUTE: 2.639 G_L1_RELATIVE: 13.854 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 36, iters: 177680, time: 0.558, data: 0.000) G_L1: 14.755 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 12.221 G_Regularizer: 0.000 validation_error: 20.046 +(epoch: 36, iters: 179680, time: 0.542, data: 0.000) G_L1: 16.950 G_L1_ABSOLUTE: 3.028 G_L1_RELATIVE: 13.922 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 36, iters: 181680, time: 0.544, data: 0.000) G_L1: 10.602 G_L1_ABSOLUTE: 2.179 G_L1_RELATIVE: 8.422 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 36, iters: 183680, time: 0.546, data: 0.000) G_L1: 14.370 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 11.897 G_Regularizer: 0.000 validation_error: 20.732 +(epoch: 36, iters: 185680, time: 0.551, data: 0.000) G_L1: 13.858 G_L1_ABSOLUTE: 2.309 G_L1_RELATIVE: 11.549 G_Regularizer: 0.000 validation_error: 20.418 +(epoch: 36, iters: 187680, time: 0.555, data: 0.001) G_L1: 16.004 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 13.529 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 36, iters: 189680, time: 0.544, data: 0.000) G_L1: 15.760 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 13.235 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 36, iters: 191680, time: 0.549, data: 0.000) G_L1: 13.238 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 10.643 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 36, iters: 193680, time: 0.556, data: 0.000) G_L1: 14.353 G_L1_ABSOLUTE: 2.975 G_L1_RELATIVE: 11.378 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 36, iters: 195680, time: 0.547, data: 0.000) G_L1: 14.086 G_L1_ABSOLUTE: 2.288 G_L1_RELATIVE: 11.798 G_Regularizer: 0.000 validation_error: 20.520 +(epoch: 36, iters: 197680, time: 0.536, data: 0.000) G_L1: 13.319 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 10.912 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 36, iters: 199680, time: 0.550, data: 0.000) G_L1: 11.218 G_L1_ABSOLUTE: 2.128 G_L1_RELATIVE: 9.090 G_Regularizer: 0.000 validation_error: 20.954 +(epoch: 36, iters: 201680, time: 0.541, data: 0.001) G_L1: 13.131 G_L1_ABSOLUTE: 2.210 G_L1_RELATIVE: 10.921 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 36, iters: 203680, time: 0.549, data: 0.000) G_L1: 15.191 G_L1_ABSOLUTE: 2.356 G_L1_RELATIVE: 12.835 G_Regularizer: 0.000 validation_error: 20.493 +(epoch: 36, iters: 205680, time: 0.555, data: 0.000) G_L1: 16.060 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 13.268 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 36, iters: 207680, time: 0.546, data: 0.000) G_L1: 14.089 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 11.621 G_Regularizer: 0.000 validation_error: 20.669 +(epoch: 36, iters: 209680, time: 0.547, data: 0.000) G_L1: 15.208 G_L1_ABSOLUTE: 2.537 G_L1_RELATIVE: 12.671 G_Regularizer: 0.000 validation_error: 21.075 +(epoch: 36, iters: 211680, time: 0.548, data: 0.000) G_L1: 16.146 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 13.682 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 36, iters: 213680, time: 0.555, data: 0.000) G_L1: 14.347 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 11.799 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 36, iters: 215680, time: 0.536, data: 0.000) G_L1: 13.851 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 11.463 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 36, iters: 217680, time: 0.549, data: 0.000) G_L1: 15.306 G_L1_ABSOLUTE: 2.768 G_L1_RELATIVE: 12.537 G_Regularizer: 0.000 validation_error: 20.554 +(epoch: 36, iters: 219680, time: 0.550, data: 0.000) G_L1: 17.994 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 15.365 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 36, iters: 221680, time: 0.547, data: 0.000) G_L1: 15.728 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 13.304 G_Regularizer: 0.000 validation_error: 20.954 +(epoch: 36, iters: 223680, time: 0.545, data: 0.000) G_L1: 13.260 G_L1_ABSOLUTE: 2.074 G_L1_RELATIVE: 11.186 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 36, iters: 225680, time: 0.544, data: 0.000) G_L1: 15.579 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 13.009 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 36, iters: 227680, time: 0.552, data: 0.000) G_L1: 15.285 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 12.580 G_Regularizer: 0.000 validation_error: 20.797 +(epoch: 36, iters: 229680, time: 0.548, data: 0.001) G_L1: 13.415 G_L1_ABSOLUTE: 2.883 G_L1_RELATIVE: 10.532 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 36, iters: 231680, time: 0.547, data: 0.000) G_L1: 13.090 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 10.655 G_Regularizer: 0.000 validation_error: 21.211 +(epoch: 36, iters: 233680, time: 0.545, data: 0.000) G_L1: 15.319 G_L1_ABSOLUTE: 2.555 G_L1_RELATIVE: 12.764 G_Regularizer: 0.000 validation_error: 20.480 +(epoch: 36, iters: 235680, time: 0.542, data: 0.000) G_L1: 14.656 G_L1_ABSOLUTE: 2.021 G_L1_RELATIVE: 12.634 G_Regularizer: 0.000 validation_error: 20.956 +(epoch: 36, iters: 237680, time: 0.548, data: 0.000) G_L1: 15.900 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 13.126 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 36, iters: 239680, time: 0.545, data: 0.000) G_L1: 13.728 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 11.334 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 36, iters: 241680, time: 0.542, data: 0.000) G_L1: 13.463 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 10.889 G_Regularizer: 0.000 validation_error: 21.165 +(epoch: 36, iters: 243680, time: 0.546, data: 0.001) G_L1: 12.777 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 10.495 G_Regularizer: 0.000 validation_error: 21.306 +(epoch: 36, iters: 245680, time: 0.551, data: 0.000) G_L1: 15.934 G_L1_ABSOLUTE: 2.920 G_L1_RELATIVE: 13.014 G_Regularizer: 0.000 validation_error: 20.979 +(epoch: 36, iters: 247680, time: 0.563, data: 0.000) G_L1: 15.549 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 12.897 G_Regularizer: 0.000 validation_error: 20.761 +(epoch: 36, iters: 249680, time: 0.546, data: 0.000) G_L1: 11.263 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 8.761 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 36, iters: 251680, time: 0.544, data: 0.000) G_L1: 13.584 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 11.248 G_Regularizer: 0.000 validation_error: 20.802 +(epoch: 36, iters: 253680, time: 0.555, data: 0.000) G_L1: 14.620 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 12.200 G_Regularizer: 0.000 validation_error: 20.455 +(epoch: 36, iters: 255680, time: 0.550, data: 0.000) G_L1: 11.185 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 8.923 G_Regularizer: 0.000 validation_error: 20.536 +(epoch: 36, iters: 257680, time: 0.547, data: 0.000) G_L1: 13.534 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 11.140 G_Regularizer: 0.000 validation_error: 21.278 +(epoch: 36, iters: 259680, time: 0.549, data: 0.000) G_L1: 14.603 G_L1_ABSOLUTE: 2.963 G_L1_RELATIVE: 11.640 G_Regularizer: 0.000 validation_error: 21.027 +(epoch: 36, iters: 261680, time: 0.555, data: 0.000) G_L1: 17.107 G_L1_ABSOLUTE: 3.222 G_L1_RELATIVE: 13.885 G_Regularizer: 0.000 validation_error: 21.161 +(epoch: 36, iters: 263680, time: 0.544, data: 0.000) G_L1: 13.101 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 10.644 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 36, iters: 265680, time: 0.557, data: 0.000) G_L1: 14.716 G_L1_ABSOLUTE: 2.616 G_L1_RELATIVE: 12.100 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 36, iters: 267680, time: 0.539, data: 0.000) G_L1: 13.044 G_L1_ABSOLUTE: 2.148 G_L1_RELATIVE: 10.896 G_Regularizer: 0.000 validation_error: 20.816 +(epoch: 36, iters: 269680, time: 0.543, data: 0.000) G_L1: 15.949 G_L1_ABSOLUTE: 2.912 G_L1_RELATIVE: 13.037 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 36, iters: 271680, time: 0.539, data: 0.000) G_L1: 12.048 G_L1_ABSOLUTE: 2.270 G_L1_RELATIVE: 9.778 G_Regularizer: 0.000 validation_error: 20.525 +(epoch: 36, iters: 273680, time: 0.545, data: 0.000) G_L1: 13.558 G_L1_ABSOLUTE: 2.665 G_L1_RELATIVE: 10.893 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 36, iters: 275680, time: 0.554, data: 0.000) G_L1: 17.435 G_L1_ABSOLUTE: 3.108 G_L1_RELATIVE: 14.327 G_Regularizer: 0.000 validation_error: 20.848 +(epoch: 36, iters: 277680, time: 0.546, data: 0.000) G_L1: 12.425 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 9.862 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 36, iters: 279680, time: 0.547, data: 0.000) G_L1: 14.006 G_L1_ABSOLUTE: 2.280 G_L1_RELATIVE: 11.726 G_Regularizer: 0.000 validation_error: 21.126 +(epoch: 36, iters: 281680, time: 0.552, data: 0.000) G_L1: 12.728 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 10.453 G_Regularizer: 0.000 validation_error: 20.552 +(epoch: 36, iters: 283680, time: 0.548, data: 0.000) G_L1: 13.367 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 10.827 G_Regularizer: 0.000 validation_error: 20.603 +(epoch: 36, iters: 285680, time: 0.545, data: 0.000) G_L1: 14.505 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 11.908 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 36, iters: 287680, time: 0.547, data: 0.000) G_L1: 12.516 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 9.999 G_Regularizer: 0.000 validation_error: 20.862 +(epoch: 36, iters: 289680, time: 0.545, data: 0.000) G_L1: 11.822 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 9.386 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 36, iters: 291680, time: 0.545, data: 0.000) G_L1: 13.045 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 10.630 G_Regularizer: 0.000 validation_error: 21.254 +(epoch: 36, iters: 293680, time: 0.545, data: 0.000) G_L1: 13.524 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 11.051 G_Regularizer: 0.000 validation_error: 21.239 +(epoch: 36, iters: 295680, time: 0.552, data: 0.001) G_L1: 13.582 G_L1_ABSOLUTE: 2.359 G_L1_RELATIVE: 11.223 G_Regularizer: 0.000 validation_error: 20.874 +(epoch: 36, iters: 297680, time: 0.545, data: 0.000) G_L1: 16.238 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 13.582 G_Regularizer: 0.000 validation_error: 20.269 +(epoch: 36, iters: 299680, time: 0.547, data: 0.000) G_L1: 11.939 G_L1_ABSOLUTE: 2.280 G_L1_RELATIVE: 9.659 G_Regularizer: 0.000 validation_error: 21.025 +(epoch: 36, iters: 301680, time: 0.543, data: 0.000) G_L1: 13.939 G_L1_ABSOLUTE: 2.157 G_L1_RELATIVE: 11.783 G_Regularizer: 0.000 validation_error: 20.408 +(epoch: 37, iters: 928, time: 0.551, data: 0.000) G_L1: 12.967 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 10.732 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 37, iters: 2928, time: 0.550, data: 0.000) G_L1: 17.113 G_L1_ABSOLUTE: 3.076 G_L1_RELATIVE: 14.037 G_Regularizer: 0.000 validation_error: 20.344 +(epoch: 37, iters: 4928, time: 0.555, data: 0.000) G_L1: 11.039 G_L1_ABSOLUTE: 2.358 G_L1_RELATIVE: 8.681 G_Regularizer: 0.000 validation_error: 21.347 +(epoch: 37, iters: 6928, time: 0.550, data: 0.000) G_L1: 13.640 G_L1_ABSOLUTE: 2.029 G_L1_RELATIVE: 11.611 G_Regularizer: 0.000 validation_error: 21.055 +(epoch: 37, iters: 8928, time: 0.559, data: 0.000) G_L1: 14.808 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 12.465 G_Regularizer: 0.000 validation_error: 20.571 +(epoch: 37, iters: 10928, time: 0.541, data: 0.000) G_L1: 13.467 G_L1_ABSOLUTE: 2.882 G_L1_RELATIVE: 10.585 G_Regularizer: 0.000 validation_error: 20.446 +(epoch: 37, iters: 12928, time: 0.556, data: 0.000) G_L1: 14.200 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 11.946 G_Regularizer: 0.000 validation_error: 20.397 +(epoch: 37, iters: 14928, time: 0.543, data: 0.000) G_L1: 13.905 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 11.556 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 37, iters: 16928, time: 0.555, data: 0.000) G_L1: 13.272 G_L1_ABSOLUTE: 2.256 G_L1_RELATIVE: 11.016 G_Regularizer: 0.000 validation_error: 21.051 +(epoch: 37, iters: 18928, time: 0.546, data: 0.000) G_L1: 16.778 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 14.275 G_Regularizer: 0.000 validation_error: 21.199 +(epoch: 37, iters: 20928, time: 0.544, data: 0.000) G_L1: 12.605 G_L1_ABSOLUTE: 2.257 G_L1_RELATIVE: 10.348 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 37, iters: 22928, time: 0.546, data: 0.000) G_L1: 14.604 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 12.133 G_Regularizer: 0.000 validation_error: 20.611 +(epoch: 37, iters: 24928, time: 0.545, data: 0.000) G_L1: 13.154 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 10.651 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 37, iters: 26928, time: 0.546, data: 0.001) G_L1: 12.146 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 9.811 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 37, iters: 28928, time: 0.544, data: 0.000) G_L1: 13.214 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 10.857 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 37, iters: 30928, time: 0.549, data: 0.000) G_L1: 14.946 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 12.337 G_Regularizer: 0.000 validation_error: 20.669 +(epoch: 37, iters: 32928, time: 0.543, data: 0.000) G_L1: 15.114 G_L1_ABSOLUTE: 2.508 G_L1_RELATIVE: 12.606 G_Regularizer: 0.000 validation_error: 20.740 +(epoch: 37, iters: 34928, time: 0.550, data: 0.000) G_L1: 11.943 G_L1_ABSOLUTE: 2.050 G_L1_RELATIVE: 9.893 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 37, iters: 36928, time: 0.546, data: 0.000) G_L1: 14.628 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 11.994 G_Regularizer: 0.000 validation_error: 21.566 +(epoch: 37, iters: 38928, time: 0.551, data: 0.000) G_L1: 14.147 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 11.774 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 37, iters: 40928, time: 0.545, data: 0.000) G_L1: 15.369 G_L1_ABSOLUTE: 2.552 G_L1_RELATIVE: 12.816 G_Regularizer: 0.000 validation_error: 20.639 +(epoch: 37, iters: 42928, time: 0.546, data: 0.000) G_L1: 11.599 G_L1_ABSOLUTE: 2.236 G_L1_RELATIVE: 9.364 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 37, iters: 44928, time: 0.550, data: 0.000) G_L1: 13.443 G_L1_ABSOLUTE: 2.950 G_L1_RELATIVE: 10.492 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 37, iters: 46928, time: 0.539, data: 0.000) G_L1: 12.944 G_L1_ABSOLUTE: 2.374 G_L1_RELATIVE: 10.569 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 37, iters: 48928, time: 0.538, data: 0.000) G_L1: 13.358 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 10.898 G_Regularizer: 0.000 validation_error: 20.691 +(epoch: 37, iters: 50928, time: 0.549, data: 0.000) G_L1: 16.174 G_L1_ABSOLUTE: 3.209 G_L1_RELATIVE: 12.965 G_Regularizer: 0.000 validation_error: 21.353 +(epoch: 37, iters: 52928, time: 0.548, data: 0.000) G_L1: 11.681 G_L1_ABSOLUTE: 1.838 G_L1_RELATIVE: 9.842 G_Regularizer: 0.000 validation_error: 20.585 +(epoch: 37, iters: 54928, time: 0.544, data: 0.000) G_L1: 14.113 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 11.643 G_Regularizer: 0.000 validation_error: 21.262 +(epoch: 37, iters: 56928, time: 0.548, data: 0.000) G_L1: 13.541 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 11.043 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 37, iters: 58928, time: 0.548, data: 0.000) G_L1: 16.033 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 13.573 G_Regularizer: 0.000 validation_error: 20.702 +(epoch: 37, iters: 60928, time: 0.540, data: 0.000) G_L1: 13.711 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 11.099 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 37, iters: 62928, time: 0.560, data: 0.000) G_L1: 12.815 G_L1_ABSOLUTE: 2.231 G_L1_RELATIVE: 10.584 G_Regularizer: 0.000 validation_error: 21.328 +(epoch: 37, iters: 64928, time: 0.550, data: 0.000) G_L1: 14.106 G_L1_ABSOLUTE: 2.212 G_L1_RELATIVE: 11.894 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 37, iters: 66928, time: 0.552, data: 0.000) G_L1: 14.485 G_L1_ABSOLUTE: 2.267 G_L1_RELATIVE: 12.218 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 37, iters: 68928, time: 0.550, data: 0.000) G_L1: 14.771 G_L1_ABSOLUTE: 2.236 G_L1_RELATIVE: 12.535 G_Regularizer: 0.000 validation_error: 21.139 +(epoch: 37, iters: 70928, time: 0.547, data: 0.000) G_L1: 15.445 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 12.811 G_Regularizer: 0.000 validation_error: 20.553 +(epoch: 37, iters: 72928, time: 0.546, data: 0.000) G_L1: 13.060 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 10.652 G_Regularizer: 0.000 validation_error: 21.058 +(epoch: 37, iters: 74928, time: 0.544, data: 0.000) G_L1: 14.045 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 11.701 G_Regularizer: 0.000 validation_error: 21.095 +(epoch: 37, iters: 76928, time: 0.547, data: 0.000) G_L1: 15.547 G_L1_ABSOLUTE: 2.053 G_L1_RELATIVE: 13.493 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 37, iters: 78928, time: 0.550, data: 0.000) G_L1: 16.004 G_L1_ABSOLUTE: 2.129 G_L1_RELATIVE: 13.875 G_Regularizer: 0.000 validation_error: 20.153 +(epoch: 37, iters: 80928, time: 0.543, data: 0.001) G_L1: 17.452 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 14.774 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 37, iters: 82928, time: 0.544, data: 0.000) G_L1: 14.551 G_L1_ABSOLUTE: 2.915 G_L1_RELATIVE: 11.637 G_Regularizer: 0.000 validation_error: 20.684 +(epoch: 37, iters: 84928, time: 0.552, data: 0.000) G_L1: 17.284 G_L1_ABSOLUTE: 3.144 G_L1_RELATIVE: 14.140 G_Regularizer: 0.000 validation_error: 21.184 +(epoch: 37, iters: 86928, time: 0.542, data: 0.000) G_L1: 14.708 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 12.359 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 37, iters: 88928, time: 0.542, data: 0.000) G_L1: 14.674 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 11.796 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 37, iters: 90928, time: 0.546, data: 0.000) G_L1: 12.175 G_L1_ABSOLUTE: 2.181 G_L1_RELATIVE: 9.994 G_Regularizer: 0.000 validation_error: 20.980 +(epoch: 37, iters: 92928, time: 0.540, data: 0.000) G_L1: 13.612 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 10.907 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 37, iters: 94928, time: 0.545, data: 0.000) G_L1: 11.470 G_L1_ABSOLUTE: 2.064 G_L1_RELATIVE: 9.406 G_Regularizer: 0.000 validation_error: 21.327 +(epoch: 37, iters: 96928, time: 0.547, data: 0.000) G_L1: 16.009 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 13.444 G_Regularizer: 0.000 validation_error: 20.627 +(epoch: 37, iters: 98928, time: 0.555, data: 0.000) G_L1: 16.093 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 13.516 G_Regularizer: 0.000 validation_error: 21.059 +(epoch: 37, iters: 100928, time: 0.541, data: 0.000) G_L1: 14.228 G_L1_ABSOLUTE: 3.043 G_L1_RELATIVE: 11.184 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 37, iters: 102928, time: 0.548, data: 0.000) G_L1: 16.499 G_L1_ABSOLUTE: 3.352 G_L1_RELATIVE: 13.147 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 37, iters: 104928, time: 0.556, data: 0.000) G_L1: 13.859 G_L1_ABSOLUTE: 2.185 G_L1_RELATIVE: 11.674 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 37, iters: 106928, time: 0.542, data: 0.000) G_L1: 13.762 G_L1_ABSOLUTE: 2.197 G_L1_RELATIVE: 11.565 G_Regularizer: 0.000 validation_error: 21.361 +(epoch: 37, iters: 108928, time: 0.538, data: 0.000) G_L1: 14.099 G_L1_ABSOLUTE: 2.334 G_L1_RELATIVE: 11.765 G_Regularizer: 0.000 validation_error: 20.505 +(epoch: 37, iters: 110928, time: 0.544, data: 0.000) G_L1: 12.755 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 10.282 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 37, iters: 112928, time: 0.551, data: 0.000) G_L1: 15.362 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 12.793 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 37, iters: 114928, time: 0.547, data: 0.000) G_L1: 14.409 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 11.631 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 37, iters: 116928, time: 0.546, data: 0.001) G_L1: 13.312 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 10.661 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 37, iters: 118928, time: 0.548, data: 0.000) G_L1: 17.696 G_L1_ABSOLUTE: 2.558 G_L1_RELATIVE: 15.138 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 37, iters: 120928, time: 0.553, data: 0.000) G_L1: 15.186 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 12.780 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 37, iters: 122928, time: 0.558, data: 0.000) G_L1: 12.544 G_L1_ABSOLUTE: 2.644 G_L1_RELATIVE: 9.900 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 37, iters: 124928, time: 0.556, data: 0.000) G_L1: 12.290 G_L1_ABSOLUTE: 1.966 G_L1_RELATIVE: 10.323 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 37, iters: 126928, time: 0.544, data: 0.000) G_L1: 12.523 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 10.264 G_Regularizer: 0.000 validation_error: 21.110 +(epoch: 37, iters: 128928, time: 0.547, data: 0.000) G_L1: 15.895 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 13.296 G_Regularizer: 0.000 validation_error: 21.340 +(epoch: 37, iters: 130928, time: 0.553, data: 0.000) G_L1: 14.848 G_L1_ABSOLUTE: 2.638 G_L1_RELATIVE: 12.209 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 37, iters: 132928, time: 0.541, data: 0.000) G_L1: 16.628 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 13.914 G_Regularizer: 0.000 validation_error: 21.169 +(epoch: 37, iters: 134928, time: 0.549, data: 0.000) G_L1: 12.203 G_L1_ABSOLUTE: 2.241 G_L1_RELATIVE: 9.962 G_Regularizer: 0.000 validation_error: 21.029 +(epoch: 37, iters: 136928, time: 0.547, data: 0.001) G_L1: 15.411 G_L1_ABSOLUTE: 2.665 G_L1_RELATIVE: 12.745 G_Regularizer: 0.000 validation_error: 20.535 +(epoch: 37, iters: 138928, time: 0.548, data: 0.000) G_L1: 14.649 G_L1_ABSOLUTE: 2.580 G_L1_RELATIVE: 12.069 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 37, iters: 140928, time: 0.542, data: 0.001) G_L1: 12.124 G_L1_ABSOLUTE: 1.944 G_L1_RELATIVE: 10.180 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 37, iters: 142928, time: 0.554, data: 0.000) G_L1: 13.249 G_L1_ABSOLUTE: 2.956 G_L1_RELATIVE: 10.292 G_Regularizer: 0.000 validation_error: 21.278 +(epoch: 37, iters: 144928, time: 0.547, data: 0.001) G_L1: 14.035 G_L1_ABSOLUTE: 2.747 G_L1_RELATIVE: 11.288 G_Regularizer: 0.000 validation_error: 20.548 +(epoch: 37, iters: 146928, time: 0.547, data: 0.000) G_L1: 13.470 G_L1_ABSOLUTE: 2.384 G_L1_RELATIVE: 11.086 G_Regularizer: 0.000 validation_error: 20.947 +(epoch: 37, iters: 148928, time: 0.550, data: 0.000) G_L1: 15.007 G_L1_ABSOLUTE: 2.602 G_L1_RELATIVE: 12.405 G_Regularizer: 0.000 validation_error: 21.312 +(epoch: 37, iters: 150928, time: 0.553, data: 0.000) G_L1: 13.359 G_L1_ABSOLUTE: 2.236 G_L1_RELATIVE: 11.123 G_Regularizer: 0.000 validation_error: 21.126 +(epoch: 37, iters: 152928, time: 0.548, data: 0.001) G_L1: 12.775 G_L1_ABSOLUTE: 2.322 G_L1_RELATIVE: 10.453 G_Regularizer: 0.000 validation_error: 20.560 +(epoch: 37, iters: 154928, time: 0.559, data: 0.000) G_L1: 13.748 G_L1_ABSOLUTE: 2.219 G_L1_RELATIVE: 11.529 G_Regularizer: 0.000 validation_error: 20.714 +(epoch: 37, iters: 156928, time: 0.544, data: 0.000) G_L1: 15.873 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 13.275 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 37, iters: 158928, time: 0.553, data: 0.000) G_L1: 14.122 G_L1_ABSOLUTE: 2.073 G_L1_RELATIVE: 12.050 G_Regularizer: 0.000 validation_error: 20.844 +(epoch: 37, iters: 160928, time: 0.551, data: 0.000) G_L1: 14.958 G_L1_ABSOLUTE: 3.084 G_L1_RELATIVE: 11.874 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 37, iters: 162928, time: 0.548, data: 0.001) G_L1: 16.528 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 13.716 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 37, iters: 164928, time: 0.550, data: 0.000) G_L1: 17.369 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 14.746 G_Regularizer: 0.000 validation_error: 20.690 +(epoch: 37, iters: 166928, time: 0.543, data: 0.000) G_L1: 14.611 G_L1_ABSOLUTE: 2.969 G_L1_RELATIVE: 11.642 G_Regularizer: 0.000 validation_error: 20.576 +(epoch: 37, iters: 168928, time: 0.536, data: 0.000) G_L1: 14.118 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 11.609 G_Regularizer: 0.000 validation_error: 20.621 +(epoch: 37, iters: 170928, time: 0.544, data: 0.000) G_L1: 15.144 G_L1_ABSOLUTE: 2.790 G_L1_RELATIVE: 12.354 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 37, iters: 172928, time: 0.557, data: 0.000) G_L1: 15.802 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 13.138 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 37, iters: 174928, time: 0.547, data: 0.000) G_L1: 14.387 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 11.589 G_Regularizer: 0.000 validation_error: 20.930 +(epoch: 37, iters: 176928, time: 0.549, data: 0.001) G_L1: 14.969 G_L1_ABSOLUTE: 2.840 G_L1_RELATIVE: 12.128 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 37, iters: 178928, time: 0.548, data: 0.000) G_L1: 11.070 G_L1_ABSOLUTE: 2.271 G_L1_RELATIVE: 8.799 G_Regularizer: 0.000 validation_error: 20.588 +(epoch: 37, iters: 180928, time: 0.545, data: 0.000) G_L1: 13.724 G_L1_ABSOLUTE: 2.456 G_L1_RELATIVE: 11.268 G_Regularizer: 0.000 validation_error: 20.960 +(epoch: 37, iters: 182928, time: 0.547, data: 0.000) G_L1: 14.327 G_L1_ABSOLUTE: 2.103 G_L1_RELATIVE: 12.224 G_Regularizer: 0.000 validation_error: 21.053 +(epoch: 37, iters: 184928, time: 0.555, data: 0.000) G_L1: 13.489 G_L1_ABSOLUTE: 2.288 G_L1_RELATIVE: 11.202 G_Regularizer: 0.000 validation_error: 20.346 +(epoch: 37, iters: 186928, time: 0.551, data: 0.000) G_L1: 12.048 G_L1_ABSOLUTE: 2.223 G_L1_RELATIVE: 9.825 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 37, iters: 188928, time: 0.550, data: 0.000) G_L1: 12.823 G_L1_ABSOLUTE: 2.325 G_L1_RELATIVE: 10.498 G_Regularizer: 0.000 validation_error: 21.005 +(epoch: 37, iters: 190928, time: 0.546, data: 0.000) G_L1: 16.678 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 13.976 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 37, iters: 192928, time: 0.554, data: 0.000) G_L1: 14.917 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 12.287 G_Regularizer: 0.000 validation_error: 20.693 +(epoch: 37, iters: 194928, time: 0.548, data: 0.000) G_L1: 22.297 G_L1_ABSOLUTE: 3.000 G_L1_RELATIVE: 19.296 G_Regularizer: 0.000 validation_error: 20.921 +(epoch: 37, iters: 196928, time: 0.541, data: 0.000) G_L1: 14.453 G_L1_ABSOLUTE: 2.310 G_L1_RELATIVE: 12.143 G_Regularizer: 0.000 validation_error: 20.621 +(epoch: 37, iters: 198928, time: 0.547, data: 0.000) G_L1: 13.898 G_L1_ABSOLUTE: 2.529 G_L1_RELATIVE: 11.369 G_Regularizer: 0.000 validation_error: 20.228 +(epoch: 37, iters: 200928, time: 0.546, data: 0.000) G_L1: 14.344 G_L1_ABSOLUTE: 2.219 G_L1_RELATIVE: 12.125 G_Regularizer: 0.000 validation_error: 20.466 +(epoch: 37, iters: 202928, time: 0.538, data: 0.000) G_L1: 12.804 G_L1_ABSOLUTE: 2.050 G_L1_RELATIVE: 10.754 G_Regularizer: 0.000 validation_error: 21.129 +(epoch: 37, iters: 204928, time: 0.545, data: 0.000) G_L1: 14.758 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 12.475 G_Regularizer: 0.000 validation_error: 20.744 +(epoch: 37, iters: 206928, time: 0.553, data: 0.000) G_L1: 14.930 G_L1_ABSOLUTE: 2.211 G_L1_RELATIVE: 12.719 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 37, iters: 208928, time: 0.542, data: 0.000) G_L1: 13.251 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 10.994 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 37, iters: 210928, time: 0.549, data: 0.000) G_L1: 12.444 G_L1_ABSOLUTE: 2.825 G_L1_RELATIVE: 9.619 G_Regularizer: 0.000 validation_error: 20.533 +(epoch: 37, iters: 212928, time: 0.537, data: 0.000) G_L1: 14.678 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 12.058 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 37, iters: 214928, time: 0.549, data: 0.000) G_L1: 13.076 G_L1_ABSOLUTE: 2.353 G_L1_RELATIVE: 10.722 G_Regularizer: 0.000 validation_error: 21.088 +(epoch: 37, iters: 216928, time: 0.546, data: 0.000) G_L1: 13.338 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 10.764 G_Regularizer: 0.000 validation_error: 20.621 +(epoch: 37, iters: 218928, time: 0.554, data: 0.001) G_L1: 16.410 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 13.936 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 37, iters: 220928, time: 0.546, data: 0.000) G_L1: 10.908 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 8.610 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 37, iters: 222928, time: 0.546, data: 0.000) G_L1: 12.444 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 9.830 G_Regularizer: 0.000 validation_error: 21.130 +(epoch: 37, iters: 224928, time: 0.550, data: 0.000) G_L1: 14.714 G_L1_ABSOLUTE: 2.709 G_L1_RELATIVE: 12.005 G_Regularizer: 0.000 validation_error: 20.944 +(epoch: 37, iters: 226928, time: 0.538, data: 0.000) G_L1: 16.877 G_L1_ABSOLUTE: 2.917 G_L1_RELATIVE: 13.960 G_Regularizer: 0.000 validation_error: 20.307 +(epoch: 37, iters: 228928, time: 0.542, data: 0.000) G_L1: 12.911 G_L1_ABSOLUTE: 2.854 G_L1_RELATIVE: 10.057 G_Regularizer: 0.000 validation_error: 21.094 +(epoch: 37, iters: 230928, time: 0.548, data: 0.000) G_L1: 12.581 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 9.991 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 37, iters: 232928, time: 0.550, data: 0.000) G_L1: 15.459 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 12.955 G_Regularizer: 0.000 validation_error: 21.189 +(epoch: 37, iters: 234928, time: 0.543, data: 0.000) G_L1: 16.508 G_L1_ABSOLUTE: 3.179 G_L1_RELATIVE: 13.329 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 37, iters: 236928, time: 0.554, data: 0.000) G_L1: 13.954 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 11.541 G_Regularizer: 0.000 validation_error: 20.421 +(epoch: 37, iters: 238928, time: 0.550, data: 0.000) G_L1: 12.614 G_L1_ABSOLUTE: 2.393 G_L1_RELATIVE: 10.222 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 37, iters: 240928, time: 0.546, data: 0.000) G_L1: 14.030 G_L1_ABSOLUTE: 2.063 G_L1_RELATIVE: 11.968 G_Regularizer: 0.000 validation_error: 21.043 +(epoch: 37, iters: 242928, time: 0.540, data: 0.000) G_L1: 15.094 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 12.531 G_Regularizer: 0.000 validation_error: 20.525 +(epoch: 37, iters: 244928, time: 0.555, data: 0.000) G_L1: 16.561 G_L1_ABSOLUTE: 2.426 G_L1_RELATIVE: 14.135 G_Regularizer: 0.000 validation_error: 20.441 +(epoch: 37, iters: 246928, time: 0.544, data: 0.000) G_L1: 15.541 G_L1_ABSOLUTE: 2.034 G_L1_RELATIVE: 13.507 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 37, iters: 248928, time: 0.545, data: 0.000) G_L1: 14.270 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 11.809 G_Regularizer: 0.000 validation_error: 20.497 +(epoch: 37, iters: 250928, time: 0.548, data: 0.000) G_L1: 14.143 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 11.685 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 37, iters: 252928, time: 0.544, data: 0.000) G_L1: 15.520 G_L1_ABSOLUTE: 2.813 G_L1_RELATIVE: 12.707 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 37, iters: 254928, time: 0.545, data: 0.000) G_L1: 13.282 G_L1_ABSOLUTE: 2.834 G_L1_RELATIVE: 10.447 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 37, iters: 256928, time: 0.546, data: 0.000) G_L1: 15.505 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 12.916 G_Regularizer: 0.000 validation_error: 20.636 +(epoch: 37, iters: 258928, time: 0.551, data: 0.000) G_L1: 14.054 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 11.340 G_Regularizer: 0.000 validation_error: 20.599 +(epoch: 37, iters: 260928, time: 0.544, data: 0.000) G_L1: 12.391 G_L1_ABSOLUTE: 2.236 G_L1_RELATIVE: 10.155 G_Regularizer: 0.000 validation_error: 20.300 +(epoch: 37, iters: 262928, time: 0.558, data: 0.000) G_L1: 13.390 G_L1_ABSOLUTE: 2.745 G_L1_RELATIVE: 10.645 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 37, iters: 264928, time: 0.546, data: 0.000) G_L1: 14.348 G_L1_ABSOLUTE: 2.883 G_L1_RELATIVE: 11.465 G_Regularizer: 0.000 validation_error: 20.193 +(epoch: 37, iters: 266928, time: 0.540, data: 0.000) G_L1: 15.885 G_L1_ABSOLUTE: 2.616 G_L1_RELATIVE: 13.268 G_Regularizer: 0.000 validation_error: 20.484 +(epoch: 37, iters: 268928, time: 0.544, data: 0.000) G_L1: 13.083 G_L1_ABSOLUTE: 2.602 G_L1_RELATIVE: 10.480 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 37, iters: 270928, time: 0.550, data: 0.000) G_L1: 14.129 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 11.697 G_Regularizer: 0.000 validation_error: 19.990 +(epoch: 37, iters: 272928, time: 0.541, data: 0.000) G_L1: 20.214 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 17.509 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 37, iters: 274928, time: 0.543, data: 0.000) G_L1: 13.674 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 11.131 G_Regularizer: 0.000 validation_error: 21.386 +(epoch: 37, iters: 276928, time: 0.546, data: 0.000) G_L1: 11.363 G_L1_ABSOLUTE: 2.348 G_L1_RELATIVE: 9.015 G_Regularizer: 0.000 validation_error: 21.065 +(epoch: 37, iters: 278928, time: 0.552, data: 0.000) G_L1: 14.836 G_L1_ABSOLUTE: 2.220 G_L1_RELATIVE: 12.616 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 37, iters: 280928, time: 0.543, data: 0.000) G_L1: 14.157 G_L1_ABSOLUTE: 2.750 G_L1_RELATIVE: 11.407 G_Regularizer: 0.000 validation_error: 20.513 +(epoch: 37, iters: 282928, time: 0.542, data: 0.000) G_L1: 13.533 G_L1_ABSOLUTE: 2.872 G_L1_RELATIVE: 10.661 G_Regularizer: 0.000 validation_error: 20.480 +(epoch: 37, iters: 284928, time: 0.547, data: 0.000) G_L1: 13.805 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 11.185 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 37, iters: 286928, time: 0.540, data: 0.000) G_L1: 13.148 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 10.899 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 37, iters: 288928, time: 0.545, data: 0.001) G_L1: 14.926 G_L1_ABSOLUTE: 2.410 G_L1_RELATIVE: 12.516 G_Regularizer: 0.000 validation_error: 21.186 +(epoch: 37, iters: 290928, time: 0.541, data: 0.000) G_L1: 12.482 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 10.134 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 37, iters: 292928, time: 0.548, data: 0.000) G_L1: 10.319 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 7.866 G_Regularizer: 0.000 validation_error: 20.395 +(epoch: 37, iters: 294928, time: 0.546, data: 0.000) G_L1: 12.993 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 10.575 G_Regularizer: 0.000 validation_error: 20.502 +(epoch: 37, iters: 296928, time: 0.549, data: 0.001) G_L1: 13.037 G_L1_ABSOLUTE: 2.141 G_L1_RELATIVE: 10.896 G_Regularizer: 0.000 validation_error: 20.658 +(epoch: 37, iters: 298928, time: 0.548, data: 0.000) G_L1: 12.847 G_L1_ABSOLUTE: 2.641 G_L1_RELATIVE: 10.207 G_Regularizer: 0.000 validation_error: 21.357 +(epoch: 37, iters: 300928, time: 0.548, data: 0.000) G_L1: 14.922 G_L1_ABSOLUTE: 2.531 G_L1_RELATIVE: 12.391 G_Regularizer: 0.000 validation_error: 20.658 +(epoch: 38, iters: 176, time: 0.547, data: 0.000) G_L1: 15.220 G_L1_ABSOLUTE: 2.898 G_L1_RELATIVE: 12.323 G_Regularizer: 0.000 validation_error: 21.216 +(epoch: 38, iters: 2176, time: 0.552, data: 0.001) G_L1: 13.925 G_L1_ABSOLUTE: 2.545 G_L1_RELATIVE: 11.380 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 38, iters: 4176, time: 0.546, data: 0.000) G_L1: 14.061 G_L1_ABSOLUTE: 2.466 G_L1_RELATIVE: 11.595 G_Regularizer: 0.000 validation_error: 21.230 +(epoch: 38, iters: 6176, time: 0.544, data: 0.000) G_L1: 12.640 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 10.307 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 38, iters: 8176, time: 0.548, data: 0.000) G_L1: 15.937 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 13.221 G_Regularizer: 0.000 validation_error: 20.719 +(epoch: 38, iters: 10176, time: 0.547, data: 0.000) G_L1: 12.983 G_L1_ABSOLUTE: 2.647 G_L1_RELATIVE: 10.336 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 38, iters: 12176, time: 0.543, data: 0.000) G_L1: 13.620 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 11.221 G_Regularizer: 0.000 validation_error: 20.323 +(epoch: 38, iters: 14176, time: 0.549, data: 0.000) G_L1: 11.918 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 9.582 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 38, iters: 16176, time: 0.555, data: 0.000) G_L1: 14.488 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 12.173 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 38, iters: 18176, time: 0.541, data: 0.000) G_L1: 17.008 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 14.693 G_Regularizer: 0.000 validation_error: 20.512 +(epoch: 38, iters: 20176, time: 0.552, data: 0.000) G_L1: 15.017 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 12.287 G_Regularizer: 0.000 validation_error: 20.705 +(epoch: 38, iters: 22176, time: 0.540, data: 0.000) G_L1: 12.597 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 10.096 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 38, iters: 24176, time: 0.548, data: 0.000) G_L1: 14.606 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 12.015 G_Regularizer: 0.000 validation_error: 20.457 +(epoch: 38, iters: 26176, time: 0.551, data: 0.001) G_L1: 14.208 G_L1_ABSOLUTE: 2.188 G_L1_RELATIVE: 12.020 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 38, iters: 28176, time: 0.552, data: 0.000) G_L1: 16.070 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 13.519 G_Regularizer: 0.000 validation_error: 20.982 +(epoch: 38, iters: 30176, time: 0.547, data: 0.000) G_L1: 15.815 G_L1_ABSOLUTE: 2.842 G_L1_RELATIVE: 12.972 G_Regularizer: 0.000 validation_error: 21.146 +(epoch: 38, iters: 32176, time: 0.550, data: 0.000) G_L1: 13.450 G_L1_ABSOLUTE: 2.386 G_L1_RELATIVE: 11.064 G_Regularizer: 0.000 validation_error: 20.603 +(epoch: 38, iters: 34176, time: 0.564, data: 0.000) G_L1: 15.928 G_L1_ABSOLUTE: 2.372 G_L1_RELATIVE: 13.556 G_Regularizer: 0.000 validation_error: 20.770 +(epoch: 38, iters: 36176, time: 0.551, data: 0.000) G_L1: 14.883 G_L1_ABSOLUTE: 2.444 G_L1_RELATIVE: 12.439 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 38, iters: 38176, time: 0.550, data: 0.000) G_L1: 18.228 G_L1_ABSOLUTE: 3.090 G_L1_RELATIVE: 15.138 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 38, iters: 40176, time: 0.549, data: 0.000) G_L1: 18.201 G_L1_ABSOLUTE: 2.257 G_L1_RELATIVE: 15.943 G_Regularizer: 0.000 validation_error: 21.069 +(epoch: 38, iters: 42176, time: 0.552, data: 0.000) G_L1: 12.995 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 10.625 G_Regularizer: 0.000 validation_error: 20.852 +(epoch: 38, iters: 44176, time: 0.544, data: 0.000) G_L1: 11.206 G_L1_ABSOLUTE: 2.240 G_L1_RELATIVE: 8.966 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 38, iters: 46176, time: 0.546, data: 0.000) G_L1: 15.798 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 13.025 G_Regularizer: 0.000 validation_error: 20.374 +(epoch: 38, iters: 48176, time: 0.550, data: 0.001) G_L1: 16.572 G_L1_ABSOLUTE: 2.351 G_L1_RELATIVE: 14.221 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 38, iters: 50176, time: 0.548, data: 0.000) G_L1: 14.366 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 11.917 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 38, iters: 52176, time: 0.546, data: 0.000) G_L1: 15.267 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 12.711 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 38, iters: 54176, time: 0.555, data: 0.001) G_L1: 11.563 G_L1_ABSOLUTE: 2.029 G_L1_RELATIVE: 9.534 G_Regularizer: 0.000 validation_error: 20.532 +(epoch: 38, iters: 56176, time: 0.550, data: 0.000) G_L1: 13.524 G_L1_ABSOLUTE: 2.307 G_L1_RELATIVE: 11.218 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 38, iters: 58176, time: 0.549, data: 0.000) G_L1: 15.926 G_L1_ABSOLUTE: 2.998 G_L1_RELATIVE: 12.928 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 38, iters: 60176, time: 0.539, data: 0.000) G_L1: 13.947 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 11.431 G_Regularizer: 0.000 validation_error: 21.002 +(epoch: 38, iters: 62176, time: 0.554, data: 0.000) G_L1: 14.023 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 11.688 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 38, iters: 64176, time: 0.543, data: 0.000) G_L1: 14.440 G_L1_ABSOLUTE: 1.935 G_L1_RELATIVE: 12.505 G_Regularizer: 0.000 validation_error: 21.267 +(epoch: 38, iters: 66176, time: 0.544, data: 0.001) G_L1: 14.853 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 12.068 G_Regularizer: 0.000 validation_error: 21.002 +(epoch: 38, iters: 68176, time: 0.557, data: 0.000) G_L1: 14.449 G_L1_ABSOLUTE: 2.832 G_L1_RELATIVE: 11.617 G_Regularizer: 0.000 validation_error: 20.816 +(epoch: 38, iters: 70176, time: 0.547, data: 0.000) G_L1: 13.842 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 11.213 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 38, iters: 72176, time: 0.550, data: 0.000) G_L1: 14.803 G_L1_ABSOLUTE: 3.007 G_L1_RELATIVE: 11.796 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 38, iters: 74176, time: 0.542, data: 0.000) G_L1: 12.441 G_L1_ABSOLUTE: 2.293 G_L1_RELATIVE: 10.147 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 38, iters: 76176, time: 0.552, data: 0.000) G_L1: 14.937 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 12.530 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 38, iters: 78176, time: 0.552, data: 0.000) G_L1: 12.647 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 10.097 G_Regularizer: 0.000 validation_error: 21.110 +(epoch: 38, iters: 80176, time: 0.558, data: 0.000) G_L1: 14.093 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 11.424 G_Regularizer: 0.000 validation_error: 20.789 +(epoch: 38, iters: 82176, time: 0.541, data: 0.000) G_L1: 13.347 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 10.862 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 38, iters: 84176, time: 0.588, data: 0.000) G_L1: 17.222 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 14.544 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 38, iters: 86176, time: 0.606, data: 0.000) G_L1: 19.185 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 16.322 G_Regularizer: 0.000 validation_error: 20.995 +(epoch: 38, iters: 88176, time: 0.588, data: 0.000) G_L1: 13.624 G_L1_ABSOLUTE: 2.164 G_L1_RELATIVE: 11.460 G_Regularizer: 0.000 validation_error: 20.514 +(epoch: 38, iters: 90176, time: 0.611, data: 0.000) G_L1: 10.437 G_L1_ABSOLUTE: 2.200 G_L1_RELATIVE: 8.237 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 38, iters: 92176, time: 0.608, data: 0.000) G_L1: 12.581 G_L1_ABSOLUTE: 2.290 G_L1_RELATIVE: 10.291 G_Regularizer: 0.000 validation_error: 21.365 +(epoch: 38, iters: 94176, time: 0.604, data: 0.000) G_L1: 12.218 G_L1_ABSOLUTE: 2.196 G_L1_RELATIVE: 10.021 G_Regularizer: 0.000 validation_error: 21.034 +(epoch: 38, iters: 96176, time: 0.610, data: 0.000) G_L1: 15.028 G_L1_ABSOLUTE: 2.139 G_L1_RELATIVE: 12.889 G_Regularizer: 0.000 validation_error: 21.223 +(epoch: 38, iters: 98176, time: 0.611, data: 0.000) G_L1: 16.481 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 13.761 G_Regularizer: 0.000 validation_error: 20.594 +(epoch: 38, iters: 100176, time: 0.567, data: 0.000) G_L1: 14.994 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 12.619 G_Regularizer: 0.000 validation_error: 20.650 +(epoch: 38, iters: 102176, time: 0.614, data: 0.000) G_L1: 16.942 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 14.217 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 38, iters: 104176, time: 0.608, data: 0.000) G_L1: 13.322 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 10.989 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 38, iters: 106176, time: 0.610, data: 0.000) G_L1: 14.850 G_L1_ABSOLUTE: 3.199 G_L1_RELATIVE: 11.652 G_Regularizer: 0.000 validation_error: 20.902 +(epoch: 38, iters: 108176, time: 0.594, data: 0.000) G_L1: 12.929 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 10.516 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 38, iters: 110176, time: 0.601, data: 0.000) G_L1: 14.459 G_L1_ABSOLUTE: 2.967 G_L1_RELATIVE: 11.493 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 38, iters: 112176, time: 0.606, data: 0.000) G_L1: 11.923 G_L1_ABSOLUTE: 2.062 G_L1_RELATIVE: 9.861 G_Regularizer: 0.000 validation_error: 20.849 +(epoch: 38, iters: 114176, time: 0.607, data: 0.000) G_L1: 15.638 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 13.184 G_Regularizer: 0.000 validation_error: 20.907 +(epoch: 38, iters: 116176, time: 0.608, data: 0.000) G_L1: 13.054 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 10.819 G_Regularizer: 0.000 validation_error: 20.546 +(epoch: 38, iters: 118176, time: 0.612, data: 0.000) G_L1: 13.862 G_L1_ABSOLUTE: 2.720 G_L1_RELATIVE: 11.142 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 38, iters: 120176, time: 0.561, data: 0.000) G_L1: 12.793 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 9.971 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 38, iters: 122176, time: 0.604, data: 0.000) G_L1: 16.206 G_L1_ABSOLUTE: 2.309 G_L1_RELATIVE: 13.897 G_Regularizer: 0.000 validation_error: 21.165 +(epoch: 38, iters: 124176, time: 0.578, data: 0.000) G_L1: 14.435 G_L1_ABSOLUTE: 3.003 G_L1_RELATIVE: 11.432 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 38, iters: 126176, time: 0.607, data: 0.001) G_L1: 14.751 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 12.045 G_Regularizer: 0.000 validation_error: 20.532 +(epoch: 38, iters: 128176, time: 0.608, data: 0.000) G_L1: 15.299 G_L1_ABSOLUTE: 2.661 G_L1_RELATIVE: 12.638 G_Regularizer: 0.000 validation_error: 20.691 +(epoch: 38, iters: 130176, time: 0.608, data: 0.000) G_L1: 14.575 G_L1_ABSOLUTE: 2.953 G_L1_RELATIVE: 11.623 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 38, iters: 132176, time: 0.613, data: 0.000) G_L1: 14.889 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 12.591 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 38, iters: 134176, time: 0.609, data: 0.000) G_L1: 13.422 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 10.648 G_Regularizer: 0.000 validation_error: 20.563 +(epoch: 38, iters: 136176, time: 0.611, data: 0.000) G_L1: 16.604 G_L1_ABSOLUTE: 2.991 G_L1_RELATIVE: 13.613 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 38, iters: 138176, time: 0.613, data: 0.000) G_L1: 12.279 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 9.732 G_Regularizer: 0.000 validation_error: 21.006 +(epoch: 38, iters: 140176, time: 0.589, data: 0.000) G_L1: 12.235 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 9.895 G_Regularizer: 0.000 validation_error: 20.599 +(epoch: 38, iters: 142176, time: 0.603, data: 0.000) G_L1: 12.970 G_L1_ABSOLUTE: 2.398 G_L1_RELATIVE: 10.572 G_Regularizer: 0.000 validation_error: 21.033 +(epoch: 38, iters: 144176, time: 0.610, data: 0.000) G_L1: 15.953 G_L1_ABSOLUTE: 2.832 G_L1_RELATIVE: 13.121 G_Regularizer: 0.000 validation_error: 20.615 +(epoch: 38, iters: 146176, time: 0.599, data: 0.000) G_L1: 13.399 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 10.990 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 38, iters: 148176, time: 0.546, data: 0.000) G_L1: 18.064 G_L1_ABSOLUTE: 3.088 G_L1_RELATIVE: 14.976 G_Regularizer: 0.000 validation_error: 20.892 +(epoch: 38, iters: 150176, time: 0.541, data: 0.000) G_L1: 12.437 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 9.840 G_Regularizer: 0.000 validation_error: 20.738 +(epoch: 38, iters: 152176, time: 0.541, data: 0.000) G_L1: 15.600 G_L1_ABSOLUTE: 3.037 G_L1_RELATIVE: 12.564 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 38, iters: 154176, time: 0.540, data: 0.000) G_L1: 17.176 G_L1_ABSOLUTE: 3.004 G_L1_RELATIVE: 14.172 G_Regularizer: 0.000 validation_error: 21.127 +(epoch: 38, iters: 156176, time: 0.546, data: 0.000) G_L1: 14.082 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 11.716 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 38, iters: 158176, time: 0.534, data: 0.000) G_L1: 15.048 G_L1_ABSOLUTE: 2.661 G_L1_RELATIVE: 12.387 G_Regularizer: 0.000 validation_error: 21.406 +(epoch: 38, iters: 160176, time: 0.543, data: 0.000) G_L1: 16.212 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 13.475 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 38, iters: 162176, time: 0.544, data: 0.000) G_L1: 14.247 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 11.758 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 38, iters: 164176, time: 0.538, data: 0.000) G_L1: 14.005 G_L1_ABSOLUTE: 2.313 G_L1_RELATIVE: 11.692 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 38, iters: 166176, time: 0.533, data: 0.000) G_L1: 15.798 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 13.074 G_Regularizer: 0.000 validation_error: 20.482 +(epoch: 38, iters: 168176, time: 0.540, data: 0.000) G_L1: 15.040 G_L1_ABSOLUTE: 2.310 G_L1_RELATIVE: 12.730 G_Regularizer: 0.000 validation_error: 21.048 +(epoch: 38, iters: 170176, time: 0.544, data: 0.000) G_L1: 13.183 G_L1_ABSOLUTE: 2.228 G_L1_RELATIVE: 10.955 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 38, iters: 172176, time: 0.549, data: 0.000) G_L1: 14.113 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 11.484 G_Regularizer: 0.000 validation_error: 21.258 +(epoch: 38, iters: 174176, time: 0.532, data: 0.000) G_L1: 13.811 G_L1_ABSOLUTE: 2.924 G_L1_RELATIVE: 10.887 G_Regularizer: 0.000 validation_error: 20.970 +(epoch: 38, iters: 176176, time: 0.540, data: 0.000) G_L1: 14.389 G_L1_ABSOLUTE: 2.496 G_L1_RELATIVE: 11.893 G_Regularizer: 0.000 validation_error: 20.716 +(epoch: 38, iters: 178176, time: 0.547, data: 0.000) G_L1: 14.586 G_L1_ABSOLUTE: 2.687 G_L1_RELATIVE: 11.898 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 38, iters: 180176, time: 0.542, data: 0.000) G_L1: 13.691 G_L1_ABSOLUTE: 2.300 G_L1_RELATIVE: 11.391 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 38, iters: 182176, time: 0.543, data: 0.000) G_L1: 14.191 G_L1_ABSOLUTE: 2.073 G_L1_RELATIVE: 12.119 G_Regularizer: 0.000 validation_error: 21.025 +(epoch: 38, iters: 184176, time: 0.532, data: 0.000) G_L1: 13.878 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 11.277 G_Regularizer: 0.000 validation_error: 20.737 +(epoch: 38, iters: 186176, time: 0.539, data: 0.000) G_L1: 12.997 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 10.739 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 38, iters: 188176, time: 0.538, data: 0.000) G_L1: 14.409 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 12.125 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 38, iters: 190176, time: 0.544, data: 0.000) G_L1: 14.019 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 11.444 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 38, iters: 192176, time: 0.536, data: 0.001) G_L1: 13.911 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 11.653 G_Regularizer: 0.000 validation_error: 21.366 +(epoch: 38, iters: 194176, time: 0.542, data: 0.000) G_L1: 15.196 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 12.288 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 38, iters: 196176, time: 0.537, data: 0.000) G_L1: 20.692 G_L1_ABSOLUTE: 2.904 G_L1_RELATIVE: 17.788 G_Regularizer: 0.000 validation_error: 20.936 +(epoch: 38, iters: 198176, time: 0.547, data: 0.000) G_L1: 12.780 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 10.256 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 38, iters: 200176, time: 0.537, data: 0.000) G_L1: 13.877 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 11.364 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 38, iters: 202176, time: 0.541, data: 0.000) G_L1: 14.712 G_L1_ABSOLUTE: 2.688 G_L1_RELATIVE: 12.024 G_Regularizer: 0.000 validation_error: 21.329 +(epoch: 38, iters: 204176, time: 0.540, data: 0.000) G_L1: 14.898 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 12.456 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 38, iters: 206176, time: 0.543, data: 0.000) G_L1: 12.426 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 9.924 G_Regularizer: 0.000 validation_error: 20.964 +(epoch: 38, iters: 208176, time: 0.539, data: 0.000) G_L1: 13.448 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 10.946 G_Regularizer: 0.000 validation_error: 20.979 +(epoch: 38, iters: 210176, time: 0.530, data: 0.000) G_L1: 13.728 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 11.361 G_Regularizer: 0.000 validation_error: 20.651 +(epoch: 38, iters: 212176, time: 0.543, data: 0.000) G_L1: 13.261 G_L1_ABSOLUTE: 2.193 G_L1_RELATIVE: 11.068 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 38, iters: 214176, time: 0.538, data: 0.001) G_L1: 16.541 G_L1_ABSOLUTE: 2.826 G_L1_RELATIVE: 13.715 G_Regularizer: 0.000 validation_error: 21.139 +(epoch: 38, iters: 216176, time: 0.539, data: 0.000) G_L1: 16.186 G_L1_ABSOLUTE: 2.818 G_L1_RELATIVE: 13.367 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 38, iters: 218176, time: 0.538, data: 0.000) G_L1: 14.750 G_L1_ABSOLUTE: 2.226 G_L1_RELATIVE: 12.523 G_Regularizer: 0.000 validation_error: 20.657 +(epoch: 38, iters: 220176, time: 0.539, data: 0.000) G_L1: 14.231 G_L1_ABSOLUTE: 2.224 G_L1_RELATIVE: 12.008 G_Regularizer: 0.000 validation_error: 20.469 +(epoch: 38, iters: 222176, time: 0.539, data: 0.000) G_L1: 13.746 G_L1_ABSOLUTE: 3.117 G_L1_RELATIVE: 10.629 G_Regularizer: 0.000 validation_error: 21.262 +(epoch: 38, iters: 224176, time: 0.542, data: 0.000) G_L1: 15.530 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 13.057 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 38, iters: 226176, time: 0.535, data: 0.000) G_L1: 15.602 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 13.155 G_Regularizer: 0.000 validation_error: 20.651 +(epoch: 38, iters: 228176, time: 0.541, data: 0.000) G_L1: 20.649 G_L1_ABSOLUTE: 2.326 G_L1_RELATIVE: 18.323 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 38, iters: 230176, time: 0.542, data: 0.000) G_L1: 15.236 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 12.649 G_Regularizer: 0.000 validation_error: 20.386 +(epoch: 38, iters: 232176, time: 0.539, data: 0.000) G_L1: 15.066 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 12.592 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 38, iters: 234176, time: 0.539, data: 0.001) G_L1: 14.797 G_L1_ABSOLUTE: 2.434 G_L1_RELATIVE: 12.363 G_Regularizer: 0.000 validation_error: 21.187 +(epoch: 38, iters: 236176, time: 0.545, data: 0.000) G_L1: 12.323 G_L1_ABSOLUTE: 2.594 G_L1_RELATIVE: 9.729 G_Regularizer: 0.000 validation_error: 20.940 +(epoch: 38, iters: 238176, time: 0.541, data: 0.000) G_L1: 13.334 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 10.898 G_Regularizer: 0.000 validation_error: 20.373 +(epoch: 38, iters: 240176, time: 0.546, data: 0.000) G_L1: 13.343 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 10.935 G_Regularizer: 0.000 validation_error: 20.674 +(epoch: 38, iters: 242176, time: 0.541, data: 0.000) G_L1: 14.764 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 12.196 G_Regularizer: 0.000 validation_error: 21.050 +(epoch: 38, iters: 244176, time: 0.547, data: 0.000) G_L1: 15.758 G_L1_ABSOLUTE: 2.386 G_L1_RELATIVE: 13.371 G_Regularizer: 0.000 validation_error: 20.384 +(epoch: 38, iters: 246176, time: 0.546, data: 0.000) G_L1: 13.677 G_L1_ABSOLUTE: 2.165 G_L1_RELATIVE: 11.512 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 38, iters: 248176, time: 0.541, data: 0.000) G_L1: 12.901 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 10.603 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 38, iters: 250176, time: 0.547, data: 0.000) G_L1: 16.396 G_L1_ABSOLUTE: 2.352 G_L1_RELATIVE: 14.044 G_Regularizer: 0.000 validation_error: 20.816 +(epoch: 38, iters: 252176, time: 0.542, data: 0.000) G_L1: 14.126 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 11.388 G_Regularizer: 0.000 validation_error: 20.640 +(epoch: 38, iters: 254176, time: 0.539, data: 0.000) G_L1: 15.742 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 13.220 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 38, iters: 256176, time: 0.539, data: 0.000) G_L1: 14.959 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 12.385 G_Regularizer: 0.000 validation_error: 20.692 +(epoch: 38, iters: 258176, time: 0.542, data: 0.000) G_L1: 14.760 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 12.027 G_Regularizer: 0.000 validation_error: 20.957 +(epoch: 38, iters: 260176, time: 0.543, data: 0.000) G_L1: 14.052 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 11.468 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 38, iters: 262176, time: 0.541, data: 0.000) G_L1: 15.193 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 12.382 G_Regularizer: 0.000 validation_error: 20.782 +(epoch: 38, iters: 264176, time: 0.541, data: 0.000) G_L1: 13.122 G_L1_ABSOLUTE: 2.188 G_L1_RELATIVE: 10.934 G_Regularizer: 0.000 validation_error: 20.849 +(epoch: 38, iters: 266176, time: 0.538, data: 0.000) G_L1: 13.409 G_L1_ABSOLUTE: 2.139 G_L1_RELATIVE: 11.270 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 38, iters: 268176, time: 0.546, data: 0.001) G_L1: 13.196 G_L1_ABSOLUTE: 2.338 G_L1_RELATIVE: 10.858 G_Regularizer: 0.000 validation_error: 20.970 +(epoch: 38, iters: 270176, time: 0.534, data: 0.000) G_L1: 15.325 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 12.800 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 38, iters: 272176, time: 0.543, data: 0.000) G_L1: 15.162 G_L1_ABSOLUTE: 2.823 G_L1_RELATIVE: 12.339 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 38, iters: 274176, time: 0.546, data: 0.000) G_L1: 11.841 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 9.581 G_Regularizer: 0.000 validation_error: 21.324 +(epoch: 38, iters: 276176, time: 0.549, data: 0.000) G_L1: 13.307 G_L1_ABSOLUTE: 2.098 G_L1_RELATIVE: 11.208 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 38, iters: 278176, time: 0.524, data: 0.000) G_L1: 13.719 G_L1_ABSOLUTE: 2.780 G_L1_RELATIVE: 10.939 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 38, iters: 280176, time: 0.534, data: 0.000) G_L1: 14.010 G_L1_ABSOLUTE: 3.018 G_L1_RELATIVE: 10.992 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 38, iters: 282176, time: 0.538, data: 0.000) G_L1: 15.020 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 12.606 G_Regularizer: 0.000 validation_error: 20.707 +(epoch: 38, iters: 284176, time: 0.541, data: 0.000) G_L1: 13.529 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 11.026 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 38, iters: 286176, time: 0.542, data: 0.000) G_L1: 15.800 G_L1_ABSOLUTE: 2.616 G_L1_RELATIVE: 13.184 G_Regularizer: 0.000 validation_error: 20.520 +(epoch: 38, iters: 288176, time: 0.544, data: 0.000) G_L1: 12.757 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 10.304 G_Regularizer: 0.000 validation_error: 21.168 +(epoch: 38, iters: 290176, time: 0.551, data: 0.000) G_L1: 13.296 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 10.925 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 38, iters: 292176, time: 0.536, data: 0.000) G_L1: 14.027 G_L1_ABSOLUTE: 2.939 G_L1_RELATIVE: 11.089 G_Regularizer: 0.000 validation_error: 20.893 +(epoch: 38, iters: 294176, time: 0.534, data: 0.000) G_L1: 11.779 G_L1_ABSOLUTE: 1.975 G_L1_RELATIVE: 9.804 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 38, iters: 296176, time: 0.541, data: 0.000) G_L1: 15.051 G_L1_ABSOLUTE: 2.427 G_L1_RELATIVE: 12.624 G_Regularizer: 0.000 validation_error: 20.644 +(epoch: 38, iters: 298176, time: 0.544, data: 0.000) G_L1: 12.588 G_L1_ABSOLUTE: 2.134 G_L1_RELATIVE: 10.455 G_Regularizer: 0.000 validation_error: 20.460 +(epoch: 38, iters: 300176, time: 0.545, data: 0.000) G_L1: 14.091 G_L1_ABSOLUTE: 2.384 G_L1_RELATIVE: 11.708 G_Regularizer: 0.000 validation_error: 21.418 +(epoch: 38, iters: 302176, time: 0.545, data: 0.000) G_L1: 14.047 G_L1_ABSOLUTE: 2.150 G_L1_RELATIVE: 11.897 G_Regularizer: 0.000 validation_error: 20.683 +(epoch: 39, iters: 1424, time: 0.532, data: 0.000) G_L1: 14.844 G_L1_ABSOLUTE: 2.183 G_L1_RELATIVE: 12.661 G_Regularizer: 0.000 validation_error: 20.911 +(epoch: 39, iters: 3424, time: 0.548, data: 0.000) G_L1: 12.174 G_L1_ABSOLUTE: 2.487 G_L1_RELATIVE: 9.687 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 39, iters: 5424, time: 0.542, data: 0.000) G_L1: 11.743 G_L1_ABSOLUTE: 1.980 G_L1_RELATIVE: 9.763 G_Regularizer: 0.000 validation_error: 20.802 +(epoch: 39, iters: 7424, time: 0.548, data: 0.000) G_L1: 14.694 G_L1_ABSOLUTE: 2.125 G_L1_RELATIVE: 12.569 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 39, iters: 9424, time: 0.538, data: 0.001) G_L1: 14.174 G_L1_ABSOLUTE: 2.285 G_L1_RELATIVE: 11.889 G_Regularizer: 0.000 validation_error: 21.087 +(epoch: 39, iters: 11424, time: 0.543, data: 0.001) G_L1: 14.830 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 12.352 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 39, iters: 13424, time: 0.533, data: 0.000) G_L1: 13.771 G_L1_ABSOLUTE: 2.865 G_L1_RELATIVE: 10.906 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 39, iters: 15424, time: 0.546, data: 0.000) G_L1: 14.888 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 12.499 G_Regularizer: 0.000 validation_error: 20.576 +(epoch: 39, iters: 17424, time: 0.547, data: 0.000) G_L1: 13.508 G_L1_ABSOLUTE: 2.982 G_L1_RELATIVE: 10.526 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 39, iters: 19424, time: 0.537, data: 0.000) G_L1: 13.126 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 10.613 G_Regularizer: 0.000 validation_error: 20.712 +(epoch: 39, iters: 21424, time: 0.545, data: 0.000) G_L1: 14.570 G_L1_ABSOLUTE: 3.111 G_L1_RELATIVE: 11.459 G_Regularizer: 0.000 validation_error: 20.592 +(epoch: 39, iters: 23424, time: 0.540, data: 0.000) G_L1: 13.792 G_L1_ABSOLUTE: 2.380 G_L1_RELATIVE: 11.412 G_Regularizer: 0.000 validation_error: 21.383 +(epoch: 39, iters: 25424, time: 0.546, data: 0.000) G_L1: 14.204 G_L1_ABSOLUTE: 2.680 G_L1_RELATIVE: 11.524 G_Regularizer: 0.000 validation_error: 20.432 +(epoch: 39, iters: 27424, time: 0.541, data: 0.000) G_L1: 13.481 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 10.744 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 39, iters: 29424, time: 0.541, data: 0.000) G_L1: 14.124 G_L1_ABSOLUTE: 2.770 G_L1_RELATIVE: 11.354 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 39, iters: 31424, time: 0.541, data: 0.000) G_L1: 14.669 G_L1_ABSOLUTE: 2.580 G_L1_RELATIVE: 12.089 G_Regularizer: 0.000 validation_error: 20.729 +(epoch: 39, iters: 33424, time: 0.542, data: 0.000) G_L1: 15.470 G_L1_ABSOLUTE: 2.955 G_L1_RELATIVE: 12.515 G_Regularizer: 0.000 validation_error: 21.068 +(epoch: 39, iters: 35424, time: 0.541, data: 0.000) G_L1: 13.683 G_L1_ABSOLUTE: 2.937 G_L1_RELATIVE: 10.746 G_Regularizer: 0.000 validation_error: 20.987 +(epoch: 39, iters: 37424, time: 0.544, data: 0.000) G_L1: 13.217 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 10.815 G_Regularizer: 0.000 validation_error: 20.899 +(epoch: 39, iters: 39424, time: 0.538, data: 0.000) G_L1: 24.063 G_L1_ABSOLUTE: 2.740 G_L1_RELATIVE: 21.323 G_Regularizer: 0.000 validation_error: 20.666 +(epoch: 39, iters: 41424, time: 0.541, data: 0.000) G_L1: 16.581 G_L1_ABSOLUTE: 2.452 G_L1_RELATIVE: 14.129 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 39, iters: 43424, time: 0.542, data: 0.001) G_L1: 14.113 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 11.666 G_Regularizer: 0.000 validation_error: 21.416 +(epoch: 39, iters: 45424, time: 0.545, data: 0.000) G_L1: 11.419 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 9.046 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 39, iters: 47424, time: 0.538, data: 0.001) G_L1: 12.732 G_L1_ABSOLUTE: 2.255 G_L1_RELATIVE: 10.476 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 39, iters: 49424, time: 0.541, data: 0.000) G_L1: 15.876 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 13.302 G_Regularizer: 0.000 validation_error: 21.171 +(epoch: 39, iters: 51424, time: 0.541, data: 0.000) G_L1: 14.098 G_L1_ABSOLUTE: 2.911 G_L1_RELATIVE: 11.186 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 39, iters: 53424, time: 0.544, data: 0.000) G_L1: 13.286 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 10.746 G_Regularizer: 0.000 validation_error: 20.466 +(epoch: 39, iters: 55424, time: 0.542, data: 0.000) G_L1: 12.995 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 10.336 G_Regularizer: 0.000 validation_error: 21.227 +(epoch: 39, iters: 57424, time: 0.542, data: 0.001) G_L1: 17.724 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 15.354 G_Regularizer: 0.000 validation_error: 20.386 +(epoch: 39, iters: 59424, time: 0.540, data: 0.000) G_L1: 15.137 G_L1_ABSOLUTE: 2.376 G_L1_RELATIVE: 12.761 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 39, iters: 61424, time: 0.537, data: 0.000) G_L1: 14.749 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 12.311 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 39, iters: 63424, time: 0.543, data: 0.001) G_L1: 13.935 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 11.676 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 39, iters: 65424, time: 0.543, data: 0.000) G_L1: 12.096 G_L1_ABSOLUTE: 2.153 G_L1_RELATIVE: 9.943 G_Regularizer: 0.000 validation_error: 20.509 +(epoch: 39, iters: 67424, time: 0.545, data: 0.000) G_L1: 14.398 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 12.065 G_Regularizer: 0.000 validation_error: 20.826 +(epoch: 39, iters: 69424, time: 0.537, data: 0.001) G_L1: 14.968 G_L1_ABSOLUTE: 3.076 G_L1_RELATIVE: 11.892 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 39, iters: 71424, time: 0.536, data: 0.001) G_L1: 14.318 G_L1_ABSOLUTE: 2.327 G_L1_RELATIVE: 11.991 G_Regularizer: 0.000 validation_error: 21.104 +(epoch: 39, iters: 73424, time: 0.542, data: 0.000) G_L1: 13.797 G_L1_ABSOLUTE: 2.821 G_L1_RELATIVE: 10.976 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 39, iters: 75424, time: 0.544, data: 0.001) G_L1: 15.121 G_L1_ABSOLUTE: 3.140 G_L1_RELATIVE: 11.981 G_Regularizer: 0.000 validation_error: 21.007 +(epoch: 39, iters: 77424, time: 0.540, data: 0.000) G_L1: 13.510 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 10.991 G_Regularizer: 0.000 validation_error: 20.778 +(epoch: 39, iters: 79424, time: 0.544, data: 0.000) G_L1: 14.367 G_L1_ABSOLUTE: 2.323 G_L1_RELATIVE: 12.044 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 39, iters: 81424, time: 0.540, data: 0.000) G_L1: 15.942 G_L1_ABSOLUTE: 2.936 G_L1_RELATIVE: 13.006 G_Regularizer: 0.000 validation_error: 20.634 +(epoch: 39, iters: 83424, time: 0.542, data: 0.000) G_L1: 14.078 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 11.624 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 39, iters: 85424, time: 0.542, data: 0.000) G_L1: 14.920 G_L1_ABSOLUTE: 2.122 G_L1_RELATIVE: 12.797 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 39, iters: 87424, time: 0.540, data: 0.000) G_L1: 14.167 G_L1_ABSOLUTE: 2.752 G_L1_RELATIVE: 11.415 G_Regularizer: 0.000 validation_error: 20.539 +(epoch: 39, iters: 89424, time: 0.545, data: 0.000) G_L1: 11.949 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 9.446 G_Regularizer: 0.000 validation_error: 20.946 +(epoch: 39, iters: 91424, time: 0.537, data: 0.000) G_L1: 11.065 G_L1_ABSOLUTE: 2.293 G_L1_RELATIVE: 8.772 G_Regularizer: 0.000 validation_error: 21.169 +(epoch: 39, iters: 93424, time: 0.545, data: 0.000) G_L1: 11.189 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 8.620 G_Regularizer: 0.000 validation_error: 21.112 +(epoch: 39, iters: 95424, time: 0.549, data: 0.001) G_L1: 15.107 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 12.230 G_Regularizer: 0.000 validation_error: 20.553 +(epoch: 39, iters: 97424, time: 0.542, data: 0.000) G_L1: 15.318 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 12.800 G_Regularizer: 0.000 validation_error: 20.927 +(epoch: 39, iters: 99424, time: 0.543, data: 0.000) G_L1: 13.345 G_L1_ABSOLUTE: 2.138 G_L1_RELATIVE: 11.207 G_Regularizer: 0.000 validation_error: 21.212 +(epoch: 39, iters: 101424, time: 0.543, data: 0.001) G_L1: 13.989 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 11.247 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 39, iters: 103424, time: 0.540, data: 0.000) G_L1: 14.894 G_L1_ABSOLUTE: 3.144 G_L1_RELATIVE: 11.750 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 39, iters: 105424, time: 0.539, data: 0.000) G_L1: 13.646 G_L1_ABSOLUTE: 2.516 G_L1_RELATIVE: 11.131 G_Regularizer: 0.000 validation_error: 20.566 +(epoch: 39, iters: 107424, time: 0.542, data: 0.000) G_L1: 12.621 G_L1_ABSOLUTE: 2.228 G_L1_RELATIVE: 10.393 G_Regularizer: 0.000 validation_error: 21.103 +(epoch: 39, iters: 109424, time: 0.538, data: 0.000) G_L1: 14.827 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 12.107 G_Regularizer: 0.000 validation_error: 20.616 +(epoch: 39, iters: 111424, time: 0.542, data: 0.000) G_L1: 14.047 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 11.237 G_Regularizer: 0.000 validation_error: 21.190 +(epoch: 39, iters: 113424, time: 0.524, data: 0.001) G_L1: 14.037 G_L1_ABSOLUTE: 2.321 G_L1_RELATIVE: 11.716 G_Regularizer: 0.000 validation_error: 20.892 +(epoch: 39, iters: 115424, time: 0.540, data: 0.000) G_L1: 15.471 G_L1_ABSOLUTE: 2.827 G_L1_RELATIVE: 12.644 G_Regularizer: 0.000 validation_error: 21.159 +(epoch: 39, iters: 117424, time: 0.544, data: 0.000) G_L1: 15.030 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 12.525 G_Regularizer: 0.000 validation_error: 20.924 +(epoch: 39, iters: 119424, time: 0.542, data: 0.000) G_L1: 15.885 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 13.516 G_Regularizer: 0.000 validation_error: 21.244 +(epoch: 39, iters: 121424, time: 0.535, data: 0.000) G_L1: 15.028 G_L1_ABSOLUTE: 2.390 G_L1_RELATIVE: 12.639 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 39, iters: 123424, time: 0.541, data: 0.000) G_L1: 14.175 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 11.754 G_Regularizer: 0.000 validation_error: 21.165 +(epoch: 39, iters: 125424, time: 0.541, data: 0.000) G_L1: 12.162 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 9.870 G_Regularizer: 0.000 validation_error: 21.172 +(epoch: 39, iters: 127424, time: 0.543, data: 0.000) G_L1: 16.847 G_L1_ABSOLUTE: 3.025 G_L1_RELATIVE: 13.822 G_Regularizer: 0.000 validation_error: 20.511 +(epoch: 39, iters: 129424, time: 0.542, data: 0.000) G_L1: 12.637 G_L1_ABSOLUTE: 2.076 G_L1_RELATIVE: 10.561 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 39, iters: 131424, time: 0.536, data: 0.000) G_L1: 13.061 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 10.543 G_Regularizer: 0.000 validation_error: 21.331 +(epoch: 39, iters: 133424, time: 0.543, data: 0.000) G_L1: 14.440 G_L1_ABSOLUTE: 2.567 G_L1_RELATIVE: 11.873 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 39, iters: 135424, time: 0.541, data: 0.000) G_L1: 15.554 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 13.186 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 39, iters: 137424, time: 0.543, data: 0.000) G_L1: 13.646 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 11.062 G_Regularizer: 0.000 validation_error: 20.576 +(epoch: 39, iters: 139424, time: 0.535, data: 0.000) G_L1: 12.292 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 9.890 G_Regularizer: 0.000 validation_error: 21.135 +(epoch: 39, iters: 141424, time: 0.538, data: 0.000) G_L1: 13.164 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 10.505 G_Regularizer: 0.000 validation_error: 20.527 +(epoch: 39, iters: 143424, time: 0.542, data: 0.000) G_L1: 14.892 G_L1_ABSOLUTE: 2.322 G_L1_RELATIVE: 12.571 G_Regularizer: 0.000 validation_error: 21.454 +(epoch: 39, iters: 145424, time: 0.541, data: 0.000) G_L1: 12.512 G_L1_ABSOLUTE: 2.360 G_L1_RELATIVE: 10.151 G_Regularizer: 0.000 validation_error: 20.518 +(epoch: 39, iters: 147424, time: 0.537, data: 0.000) G_L1: 15.870 G_L1_ABSOLUTE: 2.426 G_L1_RELATIVE: 13.444 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 39, iters: 149424, time: 0.540, data: 0.000) G_L1: 15.958 G_L1_ABSOLUTE: 2.425 G_L1_RELATIVE: 13.533 G_Regularizer: 0.000 validation_error: 20.188 +(epoch: 39, iters: 151424, time: 0.544, data: 0.000) G_L1: 16.256 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 13.614 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 39, iters: 153424, time: 0.542, data: 0.000) G_L1: 14.178 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 11.442 G_Regularizer: 0.000 validation_error: 21.381 +(epoch: 39, iters: 155424, time: 0.539, data: 0.000) G_L1: 12.463 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 9.916 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 39, iters: 157424, time: 0.530, data: 0.000) G_L1: 13.798 G_L1_ABSOLUTE: 2.397 G_L1_RELATIVE: 11.401 G_Regularizer: 0.000 validation_error: 20.627 +(epoch: 39, iters: 159424, time: 0.538, data: 0.000) G_L1: 14.811 G_L1_ABSOLUTE: 2.334 G_L1_RELATIVE: 12.477 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 39, iters: 161424, time: 0.539, data: 0.000) G_L1: 14.122 G_L1_ABSOLUTE: 2.425 G_L1_RELATIVE: 11.697 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 39, iters: 163424, time: 0.541, data: 0.000) G_L1: 13.110 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 10.486 G_Regularizer: 0.000 validation_error: 20.894 +(epoch: 39, iters: 165424, time: 0.537, data: 0.000) G_L1: 14.945 G_L1_ABSOLUTE: 2.279 G_L1_RELATIVE: 12.666 G_Regularizer: 0.000 validation_error: 21.020 +(epoch: 39, iters: 167424, time: 0.544, data: 0.000) G_L1: 12.862 G_L1_ABSOLUTE: 2.391 G_L1_RELATIVE: 10.472 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 39, iters: 169424, time: 0.543, data: 0.000) G_L1: 11.935 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 9.300 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 39, iters: 171424, time: 0.544, data: 0.000) G_L1: 14.485 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 12.008 G_Regularizer: 0.000 validation_error: 20.601 +(epoch: 39, iters: 173424, time: 0.539, data: 0.001) G_L1: 14.638 G_L1_ABSOLUTE: 2.559 G_L1_RELATIVE: 12.079 G_Regularizer: 0.000 validation_error: 20.932 +(epoch: 39, iters: 175424, time: 0.537, data: 0.000) G_L1: 15.872 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 13.311 G_Regularizer: 0.000 validation_error: 20.554 +(epoch: 39, iters: 177424, time: 0.549, data: 0.000) G_L1: 11.961 G_L1_ABSOLUTE: 2.120 G_L1_RELATIVE: 9.841 G_Regularizer: 0.000 validation_error: 20.623 +(epoch: 39, iters: 179424, time: 0.538, data: 0.000) G_L1: 15.014 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 12.453 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 39, iters: 181424, time: 0.535, data: 0.000) G_L1: 15.708 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 12.935 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 39, iters: 183424, time: 0.537, data: 0.001) G_L1: 12.920 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 10.458 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 39, iters: 185424, time: 0.539, data: 0.000) G_L1: 13.314 G_L1_ABSOLUTE: 2.636 G_L1_RELATIVE: 10.678 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 39, iters: 187424, time: 0.540, data: 0.001) G_L1: 12.708 G_L1_ABSOLUTE: 2.051 G_L1_RELATIVE: 10.657 G_Regularizer: 0.000 validation_error: 20.636 +(epoch: 39, iters: 189424, time: 0.546, data: 0.000) G_L1: 16.990 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 14.304 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 39, iters: 191424, time: 0.536, data: 0.000) G_L1: 13.874 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 11.093 G_Regularizer: 0.000 validation_error: 20.304 +(epoch: 39, iters: 193424, time: 0.539, data: 0.000) G_L1: 14.722 G_L1_ABSOLUTE: 2.543 G_L1_RELATIVE: 12.179 G_Regularizer: 0.000 validation_error: 20.487 +(epoch: 39, iters: 195424, time: 0.543, data: 0.000) G_L1: 15.065 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 12.515 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 39, iters: 197424, time: 0.547, data: 0.000) G_L1: 11.881 G_L1_ABSOLUTE: 2.171 G_L1_RELATIVE: 9.711 G_Regularizer: 0.000 validation_error: 20.955 +(epoch: 39, iters: 199424, time: 0.539, data: 0.000) G_L1: 13.134 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 10.359 G_Regularizer: 0.000 validation_error: 21.057 +(epoch: 39, iters: 201424, time: 0.544, data: 0.000) G_L1: 15.879 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 13.473 G_Regularizer: 0.000 validation_error: 20.651 +(epoch: 39, iters: 203424, time: 0.545, data: 0.000) G_L1: 14.215 G_L1_ABSOLUTE: 2.685 G_L1_RELATIVE: 11.530 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 39, iters: 205424, time: 0.539, data: 0.000) G_L1: 15.453 G_L1_ABSOLUTE: 2.174 G_L1_RELATIVE: 13.279 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 39, iters: 207424, time: 0.538, data: 0.000) G_L1: 15.155 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 12.705 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 39, iters: 209424, time: 0.538, data: 0.000) G_L1: 14.244 G_L1_ABSOLUTE: 2.443 G_L1_RELATIVE: 11.801 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 39, iters: 211424, time: 0.547, data: 0.001) G_L1: 13.553 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 11.073 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 39, iters: 213424, time: 0.546, data: 0.000) G_L1: 12.236 G_L1_ABSOLUTE: 2.089 G_L1_RELATIVE: 10.148 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 39, iters: 215424, time: 0.544, data: 0.000) G_L1: 14.512 G_L1_ABSOLUTE: 2.095 G_L1_RELATIVE: 12.417 G_Regularizer: 0.000 validation_error: 20.438 +(epoch: 39, iters: 217424, time: 0.537, data: 0.000) G_L1: 12.221 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 9.649 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 39, iters: 219424, time: 0.550, data: 0.000) G_L1: 14.180 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 11.275 G_Regularizer: 0.000 validation_error: 20.983 +(epoch: 39, iters: 221424, time: 0.558, data: 0.000) G_L1: 14.195 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 11.453 G_Regularizer: 0.000 validation_error: 20.826 +(epoch: 39, iters: 223424, time: 0.556, data: 0.001) G_L1: 13.123 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 10.745 G_Regularizer: 0.000 validation_error: 20.433 +(epoch: 39, iters: 225424, time: 0.538, data: 0.000) G_L1: 15.493 G_L1_ABSOLUTE: 2.954 G_L1_RELATIVE: 12.539 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 39, iters: 227424, time: 0.554, data: 0.000) G_L1: 14.385 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 12.004 G_Regularizer: 0.000 validation_error: 20.602 +(epoch: 39, iters: 229424, time: 0.543, data: 0.000) G_L1: 14.897 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 12.490 G_Regularizer: 0.000 validation_error: 21.317 +(epoch: 39, iters: 231424, time: 0.556, data: 0.000) G_L1: 13.115 G_L1_ABSOLUTE: 2.210 G_L1_RELATIVE: 10.905 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 39, iters: 233424, time: 0.548, data: 0.000) G_L1: 13.395 G_L1_ABSOLUTE: 2.605 G_L1_RELATIVE: 10.790 G_Regularizer: 0.000 validation_error: 20.487 +(epoch: 39, iters: 235424, time: 0.551, data: 0.001) G_L1: 13.899 G_L1_ABSOLUTE: 2.452 G_L1_RELATIVE: 11.446 G_Regularizer: 0.000 validation_error: 20.531 +(epoch: 39, iters: 237424, time: 0.551, data: 0.000) G_L1: 13.591 G_L1_ABSOLUTE: 2.766 G_L1_RELATIVE: 10.825 G_Regularizer: 0.000 validation_error: 20.647 +(epoch: 39, iters: 239424, time: 0.551, data: 0.000) G_L1: 14.379 G_L1_ABSOLUTE: 2.382 G_L1_RELATIVE: 11.997 G_Regularizer: 0.000 validation_error: 20.565 +(epoch: 39, iters: 241424, time: 0.547, data: 0.000) G_L1: 15.929 G_L1_ABSOLUTE: 2.936 G_L1_RELATIVE: 12.993 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 39, iters: 243424, time: 0.556, data: 0.000) G_L1: 12.747 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 10.005 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 39, iters: 245424, time: 0.555, data: 0.000) G_L1: 12.735 G_L1_ABSOLUTE: 2.261 G_L1_RELATIVE: 10.474 G_Regularizer: 0.000 validation_error: 21.088 +(epoch: 39, iters: 247424, time: 0.554, data: 0.000) G_L1: 13.421 G_L1_ABSOLUTE: 2.934 G_L1_RELATIVE: 10.488 G_Regularizer: 0.000 validation_error: 21.091 +(epoch: 39, iters: 249424, time: 0.555, data: 0.000) G_L1: 14.009 G_L1_ABSOLUTE: 2.316 G_L1_RELATIVE: 11.693 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 39, iters: 251424, time: 0.563, data: 0.000) G_L1: 17.074 G_L1_ABSOLUTE: 2.921 G_L1_RELATIVE: 14.154 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 39, iters: 253424, time: 0.551, data: 0.000) G_L1: 15.411 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 12.864 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 39, iters: 255424, time: 0.555, data: 0.000) G_L1: 13.196 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 10.796 G_Regularizer: 0.000 validation_error: 20.738 +(epoch: 39, iters: 257424, time: 0.554, data: 0.000) G_L1: 14.129 G_L1_ABSOLUTE: 2.794 G_L1_RELATIVE: 11.335 G_Regularizer: 0.000 validation_error: 21.329 +(epoch: 39, iters: 259424, time: 0.548, data: 0.000) G_L1: 14.082 G_L1_ABSOLUTE: 2.616 G_L1_RELATIVE: 11.466 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 39, iters: 261424, time: 0.548, data: 0.000) G_L1: 15.934 G_L1_ABSOLUTE: 2.841 G_L1_RELATIVE: 13.093 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 39, iters: 263424, time: 0.550, data: 0.000) G_L1: 15.937 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 12.999 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 39, iters: 265424, time: 0.555, data: 0.000) G_L1: 15.392 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 12.723 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 39, iters: 267424, time: 0.550, data: 0.000) G_L1: 12.420 G_L1_ABSOLUTE: 2.322 G_L1_RELATIVE: 10.098 G_Regularizer: 0.000 validation_error: 20.970 +(epoch: 39, iters: 269424, time: 0.553, data: 0.000) G_L1: 15.722 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 13.194 G_Regularizer: 0.000 validation_error: 20.483 +(epoch: 39, iters: 271424, time: 0.556, data: 0.000) G_L1: 13.586 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 11.184 G_Regularizer: 0.000 validation_error: 20.636 +(epoch: 39, iters: 273424, time: 0.541, data: 0.000) G_L1: 14.064 G_L1_ABSOLUTE: 2.439 G_L1_RELATIVE: 11.625 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 39, iters: 275424, time: 0.556, data: 0.000) G_L1: 15.616 G_L1_ABSOLUTE: 2.130 G_L1_RELATIVE: 13.486 G_Regularizer: 0.000 validation_error: 20.670 +(epoch: 39, iters: 277424, time: 0.549, data: 0.000) G_L1: 12.700 G_L1_ABSOLUTE: 2.359 G_L1_RELATIVE: 10.342 G_Regularizer: 0.000 validation_error: 20.523 +(epoch: 39, iters: 279424, time: 0.555, data: 0.000) G_L1: 13.904 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 11.303 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 39, iters: 281424, time: 0.558, data: 0.000) G_L1: 12.649 G_L1_ABSOLUTE: 2.434 G_L1_RELATIVE: 10.214 G_Regularizer: 0.000 validation_error: 20.998 +(epoch: 39, iters: 283424, time: 0.547, data: 0.000) G_L1: 13.580 G_L1_ABSOLUTE: 2.217 G_L1_RELATIVE: 11.362 G_Regularizer: 0.000 validation_error: 20.658 +(epoch: 39, iters: 285424, time: 0.549, data: 0.000) G_L1: 13.243 G_L1_ABSOLUTE: 2.195 G_L1_RELATIVE: 11.048 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 39, iters: 287424, time: 0.561, data: 0.001) G_L1: 17.081 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 14.599 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 39, iters: 289424, time: 0.652, data: 0.000) G_L1: 14.443 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 11.727 G_Regularizer: 0.000 validation_error: 20.579 +(epoch: 39, iters: 291424, time: 0.647, data: 0.000) G_L1: 12.757 G_L1_ABSOLUTE: 2.921 G_L1_RELATIVE: 9.836 G_Regularizer: 0.000 validation_error: 21.050 +(epoch: 39, iters: 293424, time: 0.592, data: 0.000) G_L1: 14.040 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 11.286 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 39, iters: 295424, time: 0.614, data: 0.000) G_L1: 14.847 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 12.332 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 39, iters: 297424, time: 0.563, data: 0.000) G_L1: 12.949 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 10.413 G_Regularizer: 0.000 validation_error: 20.735 +(epoch: 39, iters: 299424, time: 0.619, data: 0.000) G_L1: 13.104 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 10.214 G_Regularizer: 0.000 validation_error: 20.746 +(epoch: 39, iters: 301424, time: 0.606, data: 0.000) G_L1: 11.411 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 8.928 G_Regularizer: 0.000 validation_error: 20.500 +(epoch: 40, iters: 672, time: 0.619, data: 0.000) G_L1: 13.078 G_L1_ABSOLUTE: 2.410 G_L1_RELATIVE: 10.667 G_Regularizer: 0.000 validation_error: 20.737 +(epoch: 40, iters: 2672, time: 0.603, data: 0.000) G_L1: 16.802 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 14.047 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 40, iters: 4672, time: 0.614, data: 0.000) G_L1: 13.812 G_L1_ABSOLUTE: 2.429 G_L1_RELATIVE: 11.383 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 40, iters: 6672, time: 0.612, data: 0.000) G_L1: 14.037 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 11.233 G_Regularizer: 0.000 validation_error: 20.957 +(epoch: 40, iters: 8672, time: 0.611, data: 0.000) G_L1: 12.319 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 9.685 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 40, iters: 10672, time: 0.575, data: 0.000) G_L1: 12.714 G_L1_ABSOLUTE: 2.422 G_L1_RELATIVE: 10.292 G_Regularizer: 0.000 validation_error: 20.798 +(epoch: 40, iters: 12672, time: 0.605, data: 0.000) G_L1: 13.719 G_L1_ABSOLUTE: 2.304 G_L1_RELATIVE: 11.415 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 40, iters: 14672, time: 0.610, data: 0.000) G_L1: 13.007 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 10.444 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 40, iters: 16672, time: 0.610, data: 0.000) G_L1: 13.234 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 10.980 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 40, iters: 18672, time: 0.600, data: 0.000) G_L1: 13.697 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 11.354 G_Regularizer: 0.000 validation_error: 21.176 +(epoch: 40, iters: 20672, time: 0.581, data: 0.000) G_L1: 13.538 G_L1_ABSOLUTE: 2.391 G_L1_RELATIVE: 11.147 G_Regularizer: 0.000 validation_error: 20.493 +(epoch: 40, iters: 22672, time: 0.601, data: 0.000) G_L1: 12.109 G_L1_ABSOLUTE: 2.192 G_L1_RELATIVE: 9.917 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 40, iters: 24672, time: 0.576, data: 0.000) G_L1: 14.281 G_L1_ABSOLUTE: 2.115 G_L1_RELATIVE: 12.166 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 40, iters: 26672, time: 0.614, data: 0.000) G_L1: 13.699 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 11.047 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 40, iters: 28672, time: 0.618, data: 0.000) G_L1: 16.170 G_L1_ABSOLUTE: 3.070 G_L1_RELATIVE: 13.100 G_Regularizer: 0.000 validation_error: 20.391 +(epoch: 40, iters: 30672, time: 0.611, data: 0.000) G_L1: 15.237 G_L1_ABSOLUTE: 2.194 G_L1_RELATIVE: 13.043 G_Regularizer: 0.000 validation_error: 21.147 +(epoch: 40, iters: 32672, time: 0.603, data: 0.000) G_L1: 14.975 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 12.606 G_Regularizer: 0.000 validation_error: 21.032 +(epoch: 40, iters: 34672, time: 0.579, data: 0.000) G_L1: 14.283 G_L1_ABSOLUTE: 2.943 G_L1_RELATIVE: 11.340 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 40, iters: 36672, time: 0.619, data: 0.000) G_L1: 14.336 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 11.718 G_Regularizer: 0.000 validation_error: 21.384 +(epoch: 40, iters: 38672, time: 0.604, data: 0.000) G_L1: 11.601 G_L1_ABSOLUTE: 2.046 G_L1_RELATIVE: 9.555 G_Regularizer: 0.000 validation_error: 20.885 +(epoch: 40, iters: 40672, time: 0.620, data: 0.000) G_L1: 14.937 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 12.288 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 40, iters: 42672, time: 0.593, data: 0.000) G_L1: 14.351 G_L1_ABSOLUTE: 2.741 G_L1_RELATIVE: 11.610 G_Regularizer: 0.000 validation_error: 21.124 +(epoch: 40, iters: 44672, time: 0.615, data: 0.000) G_L1: 17.192 G_L1_ABSOLUTE: 2.689 G_L1_RELATIVE: 14.503 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 40, iters: 46672, time: 0.611, data: 0.000) G_L1: 14.012 G_L1_ABSOLUTE: 3.186 G_L1_RELATIVE: 10.826 G_Regularizer: 0.000 validation_error: 20.939 +(epoch: 40, iters: 48672, time: 0.597, data: 0.000) G_L1: 15.074 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 12.341 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 40, iters: 50672, time: 0.529, data: 0.000) G_L1: 14.695 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 11.859 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 40, iters: 52672, time: 0.537, data: 0.000) G_L1: 12.661 G_L1_ABSOLUTE: 2.759 G_L1_RELATIVE: 9.902 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 40, iters: 54672, time: 0.545, data: 0.000) G_L1: 12.950 G_L1_ABSOLUTE: 2.466 G_L1_RELATIVE: 10.484 G_Regularizer: 0.000 validation_error: 21.123 +(epoch: 40, iters: 56672, time: 0.539, data: 0.000) G_L1: 15.742 G_L1_ABSOLUTE: 2.654 G_L1_RELATIVE: 13.089 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 40, iters: 58672, time: 0.540, data: 0.001) G_L1: 15.573 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 12.789 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 40, iters: 60672, time: 0.540, data: 0.000) G_L1: 13.603 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 10.968 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 40, iters: 62672, time: 0.531, data: 0.000) G_L1: 14.146 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 11.829 G_Regularizer: 0.000 validation_error: 20.672 +(epoch: 40, iters: 64672, time: 0.538, data: 0.000) G_L1: 16.657 G_L1_ABSOLUTE: 2.562 G_L1_RELATIVE: 14.094 G_Regularizer: 0.000 validation_error: 20.753 +(epoch: 40, iters: 66672, time: 0.550, data: 0.000) G_L1: 15.610 G_L1_ABSOLUTE: 2.224 G_L1_RELATIVE: 13.386 G_Regularizer: 0.000 validation_error: 21.139 +(epoch: 40, iters: 68672, time: 0.543, data: 0.001) G_L1: 13.831 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 11.082 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 40, iters: 70672, time: 0.528, data: 0.000) G_L1: 12.770 G_L1_ABSOLUTE: 2.141 G_L1_RELATIVE: 10.630 G_Regularizer: 0.000 validation_error: 20.957 +(epoch: 40, iters: 72672, time: 0.545, data: 0.000) G_L1: 13.850 G_L1_ABSOLUTE: 2.395 G_L1_RELATIVE: 11.454 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 40, iters: 74672, time: 0.542, data: 0.000) G_L1: 13.191 G_L1_ABSOLUTE: 2.313 G_L1_RELATIVE: 10.878 G_Regularizer: 0.000 validation_error: 20.920 +(epoch: 40, iters: 76672, time: 0.538, data: 0.000) G_L1: 15.712 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 12.957 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 40, iters: 78672, time: 0.537, data: 0.000) G_L1: 14.055 G_L1_ABSOLUTE: 2.440 G_L1_RELATIVE: 11.615 G_Regularizer: 0.000 validation_error: 20.979 +(epoch: 40, iters: 80672, time: 0.542, data: 0.000) G_L1: 15.825 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 13.355 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 40, iters: 82672, time: 0.539, data: 0.000) G_L1: 14.311 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 11.825 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 40, iters: 84672, time: 0.543, data: 0.000) G_L1: 12.777 G_L1_ABSOLUTE: 2.236 G_L1_RELATIVE: 10.541 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 40, iters: 86672, time: 0.546, data: 0.000) G_L1: 12.341 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 9.574 G_Regularizer: 0.000 validation_error: 20.757 +(epoch: 40, iters: 88672, time: 0.548, data: 0.000) G_L1: 13.642 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 10.969 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 40, iters: 90672, time: 0.538, data: 0.000) G_L1: 16.614 G_L1_ABSOLUTE: 2.944 G_L1_RELATIVE: 13.670 G_Regularizer: 0.000 validation_error: 21.009 +(epoch: 40, iters: 92672, time: 0.539, data: 0.000) G_L1: 15.413 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 13.014 G_Regularizer: 0.000 validation_error: 21.162 +(epoch: 40, iters: 94672, time: 0.538, data: 0.000) G_L1: 14.949 G_L1_ABSOLUTE: 2.417 G_L1_RELATIVE: 12.532 G_Regularizer: 0.000 validation_error: 21.158 +(epoch: 40, iters: 96672, time: 0.549, data: 0.000) G_L1: 14.418 G_L1_ABSOLUTE: 2.789 G_L1_RELATIVE: 11.629 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 40, iters: 98672, time: 0.548, data: 0.000) G_L1: 15.354 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 12.842 G_Regularizer: 0.000 validation_error: 21.181 +(epoch: 40, iters: 100672, time: 0.542, data: 0.000) G_L1: 13.249 G_L1_ABSOLUTE: 2.329 G_L1_RELATIVE: 10.920 G_Regularizer: 0.000 validation_error: 21.020 +(epoch: 40, iters: 102672, time: 0.548, data: 0.000) G_L1: 14.609 G_L1_ABSOLUTE: 2.167 G_L1_RELATIVE: 12.442 G_Regularizer: 0.000 validation_error: 20.653 +(epoch: 40, iters: 104672, time: 0.544, data: 0.001) G_L1: 12.020 G_L1_ABSOLUTE: 2.183 G_L1_RELATIVE: 9.837 G_Regularizer: 0.000 validation_error: 21.367 +(epoch: 40, iters: 106672, time: 0.541, data: 0.000) G_L1: 14.735 G_L1_ABSOLUTE: 3.030 G_L1_RELATIVE: 11.705 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 40, iters: 108672, time: 0.542, data: 0.000) G_L1: 17.364 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 14.964 G_Regularizer: 0.000 validation_error: 20.902 +(epoch: 40, iters: 110672, time: 0.548, data: 0.000) G_L1: 13.443 G_L1_ABSOLUTE: 2.898 G_L1_RELATIVE: 10.545 G_Regularizer: 0.000 validation_error: 21.269 +(epoch: 40, iters: 112672, time: 0.543, data: 0.000) G_L1: 13.810 G_L1_ABSOLUTE: 2.238 G_L1_RELATIVE: 11.572 G_Regularizer: 0.000 validation_error: 21.411 +(epoch: 40, iters: 114672, time: 0.552, data: 0.000) G_L1: 12.922 G_L1_ABSOLUTE: 2.191 G_L1_RELATIVE: 10.731 G_Regularizer: 0.000 validation_error: 21.044 +(epoch: 40, iters: 116672, time: 0.545, data: 0.000) G_L1: 15.168 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 12.837 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 40, iters: 118672, time: 0.545, data: 0.001) G_L1: 14.672 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 11.861 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 40, iters: 120672, time: 0.536, data: 0.000) G_L1: 13.127 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 10.929 G_Regularizer: 0.000 validation_error: 20.922 +(epoch: 40, iters: 122672, time: 0.534, data: 0.000) G_L1: 12.248 G_L1_ABSOLUTE: 2.144 G_L1_RELATIVE: 10.105 G_Regularizer: 0.000 validation_error: 20.431 +(epoch: 40, iters: 124672, time: 0.547, data: 0.000) G_L1: 11.208 G_L1_ABSOLUTE: 2.328 G_L1_RELATIVE: 8.881 G_Regularizer: 0.000 validation_error: 21.215 +(epoch: 40, iters: 126672, time: 0.542, data: 0.000) G_L1: 17.507 G_L1_ABSOLUTE: 2.695 G_L1_RELATIVE: 14.812 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 40, iters: 128672, time: 0.549, data: 0.000) G_L1: 14.393 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 11.854 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 40, iters: 130672, time: 0.550, data: 0.001) G_L1: 14.427 G_L1_ABSOLUTE: 2.316 G_L1_RELATIVE: 12.111 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 40, iters: 132672, time: 0.539, data: 0.000) G_L1: 13.453 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 10.864 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 40, iters: 134672, time: 0.544, data: 0.000) G_L1: 13.472 G_L1_ABSOLUTE: 2.077 G_L1_RELATIVE: 11.394 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 40, iters: 136672, time: 0.542, data: 0.001) G_L1: 13.380 G_L1_ABSOLUTE: 2.859 G_L1_RELATIVE: 10.521 G_Regularizer: 0.000 validation_error: 21.014 +(epoch: 40, iters: 138672, time: 0.537, data: 0.000) G_L1: 11.557 G_L1_ABSOLUTE: 2.163 G_L1_RELATIVE: 9.394 G_Regularizer: 0.000 validation_error: 21.187 +(epoch: 40, iters: 140672, time: 0.538, data: 0.000) G_L1: 12.548 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 10.137 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 40, iters: 142672, time: 0.541, data: 0.000) G_L1: 12.271 G_L1_ABSOLUTE: 2.212 G_L1_RELATIVE: 10.058 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 40, iters: 144672, time: 0.547, data: 0.000) G_L1: 13.786 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 11.223 G_Regularizer: 0.000 validation_error: 20.367 +(epoch: 40, iters: 146672, time: 0.543, data: 0.000) G_L1: 13.371 G_L1_ABSOLUTE: 2.360 G_L1_RELATIVE: 11.011 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 40, iters: 148672, time: 0.541, data: 0.000) G_L1: 14.804 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 12.177 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 40, iters: 150672, time: 0.539, data: 0.000) G_L1: 17.266 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 14.624 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 40, iters: 152672, time: 0.544, data: 0.000) G_L1: 13.602 G_L1_ABSOLUTE: 1.813 G_L1_RELATIVE: 11.789 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 40, iters: 154672, time: 0.544, data: 0.000) G_L1: 14.020 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 11.313 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 40, iters: 156672, time: 0.530, data: 0.001) G_L1: 13.597 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 10.921 G_Regularizer: 0.000 validation_error: 20.748 +(epoch: 40, iters: 158672, time: 0.540, data: 0.000) G_L1: 14.663 G_L1_ABSOLUTE: 2.511 G_L1_RELATIVE: 12.152 G_Regularizer: 0.000 validation_error: 20.959 +(epoch: 40, iters: 160672, time: 0.537, data: 0.000) G_L1: 18.136 G_L1_ABSOLUTE: 3.774 G_L1_RELATIVE: 14.362 G_Regularizer: 0.000 validation_error: 20.522 +(epoch: 40, iters: 162672, time: 0.543, data: 0.000) G_L1: 11.588 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 9.083 G_Regularizer: 0.000 validation_error: 20.674 +(epoch: 40, iters: 164672, time: 0.538, data: 0.000) G_L1: 12.127 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 9.668 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 40, iters: 166672, time: 0.543, data: 0.000) G_L1: 13.542 G_L1_ABSOLUTE: 2.139 G_L1_RELATIVE: 11.403 G_Regularizer: 0.000 validation_error: 21.041 +(epoch: 40, iters: 168672, time: 0.545, data: 0.000) G_L1: 15.490 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 13.089 G_Regularizer: 0.000 validation_error: 20.516 +(epoch: 40, iters: 170672, time: 0.538, data: 0.000) G_L1: 15.693 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 13.041 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 40, iters: 172672, time: 0.543, data: 0.000) G_L1: 14.590 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 11.992 G_Regularizer: 0.000 validation_error: 20.740 +(epoch: 40, iters: 174672, time: 0.539, data: 0.000) G_L1: 13.389 G_L1_ABSOLUTE: 2.208 G_L1_RELATIVE: 11.180 G_Regularizer: 0.000 validation_error: 20.573 +(epoch: 40, iters: 176672, time: 0.546, data: 0.000) G_L1: 13.479 G_L1_ABSOLUTE: 2.329 G_L1_RELATIVE: 11.150 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 40, iters: 178672, time: 0.544, data: 0.000) G_L1: 14.187 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 11.752 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 40, iters: 180672, time: 0.543, data: 0.000) G_L1: 14.545 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 11.653 G_Regularizer: 0.000 validation_error: 21.057 +(epoch: 40, iters: 182672, time: 0.541, data: 0.000) G_L1: 12.671 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 10.095 G_Regularizer: 0.000 validation_error: 20.176 +(epoch: 40, iters: 184672, time: 0.537, data: 0.000) G_L1: 13.153 G_L1_ABSOLUTE: 2.064 G_L1_RELATIVE: 11.088 G_Regularizer: 0.000 validation_error: 21.239 +(epoch: 40, iters: 186672, time: 0.537, data: 0.000) G_L1: 14.732 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 12.262 G_Regularizer: 0.000 validation_error: 20.284 +(epoch: 40, iters: 188672, time: 0.550, data: 0.000) G_L1: 15.211 G_L1_ABSOLUTE: 2.514 G_L1_RELATIVE: 12.697 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 40, iters: 190672, time: 0.548, data: 0.000) G_L1: 11.419 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 9.128 G_Regularizer: 0.000 validation_error: 21.177 +(epoch: 40, iters: 192672, time: 0.547, data: 0.000) G_L1: 13.893 G_L1_ABSOLUTE: 2.324 G_L1_RELATIVE: 11.569 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 40, iters: 194672, time: 0.544, data: 0.000) G_L1: 12.214 G_L1_ABSOLUTE: 2.067 G_L1_RELATIVE: 10.147 G_Regularizer: 0.000 validation_error: 20.994 +(epoch: 40, iters: 196672, time: 0.539, data: 0.001) G_L1: 15.905 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 13.496 G_Regularizer: 0.000 validation_error: 21.317 +(epoch: 40, iters: 198672, time: 0.544, data: 0.001) G_L1: 11.825 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 9.471 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 40, iters: 200672, time: 0.545, data: 0.000) G_L1: 13.308 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 11.007 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 40, iters: 202672, time: 0.540, data: 0.000) G_L1: 16.156 G_L1_ABSOLUTE: 2.920 G_L1_RELATIVE: 13.236 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 40, iters: 204672, time: 0.539, data: 0.000) G_L1: 14.554 G_L1_ABSOLUTE: 2.446 G_L1_RELATIVE: 12.108 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 40, iters: 206672, time: 0.554, data: 0.001) G_L1: 14.706 G_L1_ABSOLUTE: 2.231 G_L1_RELATIVE: 12.475 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 40, iters: 208672, time: 0.538, data: 0.000) G_L1: 11.998 G_L1_ABSOLUTE: 2.351 G_L1_RELATIVE: 9.646 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 40, iters: 210672, time: 0.540, data: 0.000) G_L1: 13.910 G_L1_ABSOLUTE: 2.301 G_L1_RELATIVE: 11.609 G_Regularizer: 0.000 validation_error: 20.627 +(epoch: 40, iters: 212672, time: 0.533, data: 0.000) G_L1: 12.840 G_L1_ABSOLUTE: 2.173 G_L1_RELATIVE: 10.667 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 40, iters: 214672, time: 0.542, data: 0.000) G_L1: 15.175 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 12.427 G_Regularizer: 0.000 validation_error: 20.575 +(epoch: 40, iters: 216672, time: 0.537, data: 0.000) G_L1: 12.762 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 10.232 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 40, iters: 218672, time: 0.544, data: 0.000) G_L1: 13.906 G_L1_ABSOLUTE: 2.252 G_L1_RELATIVE: 11.655 G_Regularizer: 0.000 validation_error: 21.040 +(epoch: 40, iters: 220672, time: 0.544, data: 0.000) G_L1: 14.043 G_L1_ABSOLUTE: 2.255 G_L1_RELATIVE: 11.788 G_Regularizer: 0.000 validation_error: 20.538 +(epoch: 40, iters: 222672, time: 0.541, data: 0.000) G_L1: 11.845 G_L1_ABSOLUTE: 1.986 G_L1_RELATIVE: 9.859 G_Regularizer: 0.000 validation_error: 20.758 +(epoch: 40, iters: 224672, time: 0.532, data: 0.000) G_L1: 13.444 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 11.050 G_Regularizer: 0.000 validation_error: 20.874 +(epoch: 40, iters: 226672, time: 0.546, data: 0.000) G_L1: 12.270 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 9.633 G_Regularizer: 0.000 validation_error: 20.882 +(epoch: 40, iters: 228672, time: 0.541, data: 0.001) G_L1: 11.422 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 8.964 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 40, iters: 230672, time: 0.541, data: 0.000) G_L1: 16.320 G_L1_ABSOLUTE: 2.982 G_L1_RELATIVE: 13.339 G_Regularizer: 0.000 validation_error: 21.021 +(epoch: 40, iters: 232672, time: 0.550, data: 0.001) G_L1: 13.957 G_L1_ABSOLUTE: 2.147 G_L1_RELATIVE: 11.810 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 40, iters: 234672, time: 0.539, data: 0.000) G_L1: 14.591 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 11.951 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 40, iters: 236672, time: 0.546, data: 0.000) G_L1: 12.607 G_L1_ABSOLUTE: 2.228 G_L1_RELATIVE: 10.379 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 40, iters: 238672, time: 0.537, data: 0.000) G_L1: 13.054 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 10.856 G_Regularizer: 0.000 validation_error: 20.992 +(epoch: 40, iters: 240672, time: 0.545, data: 0.000) G_L1: 17.009 G_L1_ABSOLUTE: 2.141 G_L1_RELATIVE: 14.868 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 40, iters: 242672, time: 0.528, data: 0.000) G_L1: 13.186 G_L1_ABSOLUTE: 2.741 G_L1_RELATIVE: 10.445 G_Regularizer: 0.000 validation_error: 21.145 +(epoch: 40, iters: 244672, time: 0.539, data: 0.000) G_L1: 14.491 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 11.882 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 40, iters: 246672, time: 0.543, data: 0.001) G_L1: 15.506 G_L1_ABSOLUTE: 2.285 G_L1_RELATIVE: 13.220 G_Regularizer: 0.000 validation_error: 20.753 +(epoch: 40, iters: 248672, time: 0.542, data: 0.000) G_L1: 15.389 G_L1_ABSOLUTE: 2.875 G_L1_RELATIVE: 12.515 G_Regularizer: 0.000 validation_error: 21.071 +(epoch: 40, iters: 250672, time: 0.542, data: 0.000) G_L1: 14.497 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 11.831 G_Regularizer: 0.000 validation_error: 20.545 +(epoch: 40, iters: 252672, time: 0.538, data: 0.000) G_L1: 13.287 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 10.910 G_Regularizer: 0.000 validation_error: 20.472 +(epoch: 40, iters: 254672, time: 0.543, data: 0.000) G_L1: 12.912 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 10.395 G_Regularizer: 0.000 validation_error: 20.498 +(epoch: 40, iters: 256672, time: 0.547, data: 0.000) G_L1: 12.527 G_L1_ABSOLUTE: 2.082 G_L1_RELATIVE: 10.445 G_Regularizer: 0.000 validation_error: 20.510 +(epoch: 40, iters: 258672, time: 0.544, data: 0.000) G_L1: 14.253 G_L1_ABSOLUTE: 3.104 G_L1_RELATIVE: 11.148 G_Regularizer: 0.000 validation_error: 21.270 +(epoch: 40, iters: 260672, time: 0.543, data: 0.000) G_L1: 14.204 G_L1_ABSOLUTE: 2.434 G_L1_RELATIVE: 11.769 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 40, iters: 262672, time: 0.545, data: 0.001) G_L1: 12.295 G_L1_ABSOLUTE: 2.261 G_L1_RELATIVE: 10.034 G_Regularizer: 0.000 validation_error: 20.487 +(epoch: 40, iters: 264672, time: 0.542, data: 0.000) G_L1: 12.326 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 9.928 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 40, iters: 266672, time: 0.543, data: 0.000) G_L1: 16.281 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 13.784 G_Regularizer: 0.000 validation_error: 20.484 +(epoch: 40, iters: 268672, time: 0.526, data: 0.000) G_L1: 15.234 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 12.709 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 40, iters: 270672, time: 0.546, data: 0.000) G_L1: 13.575 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 11.299 G_Regularizer: 0.000 validation_error: 20.425 +(epoch: 40, iters: 272672, time: 0.544, data: 0.000) G_L1: 12.616 G_L1_ABSOLUTE: 2.759 G_L1_RELATIVE: 9.858 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 40, iters: 274672, time: 0.547, data: 0.000) G_L1: 15.339 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 12.741 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 40, iters: 276672, time: 0.537, data: 0.000) G_L1: 15.659 G_L1_ABSOLUTE: 2.887 G_L1_RELATIVE: 12.772 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 40, iters: 278672, time: 0.547, data: 0.000) G_L1: 11.873 G_L1_ABSOLUTE: 2.636 G_L1_RELATIVE: 9.237 G_Regularizer: 0.000 validation_error: 20.778 +(epoch: 40, iters: 280672, time: 0.547, data: 0.000) G_L1: 14.515 G_L1_ABSOLUTE: 2.237 G_L1_RELATIVE: 12.278 G_Regularizer: 0.000 validation_error: 20.531 +(epoch: 40, iters: 282672, time: 0.545, data: 0.000) G_L1: 13.618 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 10.985 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 40, iters: 284672, time: 0.544, data: 0.000) G_L1: 13.044 G_L1_ABSOLUTE: 2.959 G_L1_RELATIVE: 10.085 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 40, iters: 286672, time: 0.540, data: 0.000) G_L1: 15.020 G_L1_ABSOLUTE: 2.983 G_L1_RELATIVE: 12.037 G_Regularizer: 0.000 validation_error: 20.667 +(epoch: 40, iters: 288672, time: 0.543, data: 0.000) G_L1: 15.352 G_L1_ABSOLUTE: 2.187 G_L1_RELATIVE: 13.165 G_Regularizer: 0.000 validation_error: 21.306 +(epoch: 40, iters: 290672, time: 0.543, data: 0.000) G_L1: 15.308 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 12.802 G_Regularizer: 0.000 validation_error: 20.716 +(epoch: 40, iters: 292672, time: 0.541, data: 0.000) G_L1: 13.123 G_L1_ABSOLUTE: 2.323 G_L1_RELATIVE: 10.800 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 40, iters: 294672, time: 0.540, data: 0.000) G_L1: 15.378 G_L1_ABSOLUTE: 2.410 G_L1_RELATIVE: 12.967 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 40, iters: 296672, time: 0.548, data: 0.000) G_L1: 15.537 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 13.017 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 40, iters: 298672, time: 0.540, data: 0.000) G_L1: 13.935 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 11.452 G_Regularizer: 0.000 validation_error: 20.384 +(epoch: 40, iters: 300672, time: 0.546, data: 0.000) G_L1: 14.812 G_L1_ABSOLUTE: 2.608 G_L1_RELATIVE: 12.204 G_Regularizer: 0.000 validation_error: 20.659 +(epoch: 40, iters: 302672, time: 0.531, data: 0.000) G_L1: 13.715 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 11.174 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 41, iters: 1920, time: 0.546, data: 0.000) G_L1: 15.368 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 12.701 G_Regularizer: 0.000 validation_error: 20.638 +(epoch: 41, iters: 3920, time: 0.539, data: 0.000) G_L1: 14.953 G_L1_ABSOLUTE: 2.552 G_L1_RELATIVE: 12.402 G_Regularizer: 0.000 validation_error: 20.904 +(epoch: 41, iters: 5920, time: 0.545, data: 0.000) G_L1: 14.879 G_L1_ABSOLUTE: 2.790 G_L1_RELATIVE: 12.090 G_Regularizer: 0.000 validation_error: 20.423 +(epoch: 41, iters: 7920, time: 0.539, data: 0.000) G_L1: 13.578 G_L1_ABSOLUTE: 2.475 G_L1_RELATIVE: 11.103 G_Regularizer: 0.000 validation_error: 20.697 +(epoch: 41, iters: 9920, time: 0.545, data: 0.000) G_L1: 12.208 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 9.864 G_Regularizer: 0.000 validation_error: 20.894 +(epoch: 41, iters: 11920, time: 0.540, data: 0.000) G_L1: 15.097 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 12.496 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 41, iters: 13920, time: 0.542, data: 0.000) G_L1: 13.298 G_L1_ABSOLUTE: 2.372 G_L1_RELATIVE: 10.927 G_Regularizer: 0.000 validation_error: 20.474 +(epoch: 41, iters: 15920, time: 0.546, data: 0.000) G_L1: 12.808 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 10.285 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 41, iters: 17920, time: 0.539, data: 0.000) G_L1: 13.554 G_L1_ABSOLUTE: 2.361 G_L1_RELATIVE: 11.192 G_Regularizer: 0.000 validation_error: 20.824 +(epoch: 41, iters: 19920, time: 0.541, data: 0.000) G_L1: 13.445 G_L1_ABSOLUTE: 2.018 G_L1_RELATIVE: 11.428 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 41, iters: 21920, time: 0.540, data: 0.000) G_L1: 14.210 G_L1_ABSOLUTE: 2.352 G_L1_RELATIVE: 11.858 G_Regularizer: 0.000 validation_error: 21.121 +(epoch: 41, iters: 23920, time: 0.539, data: 0.000) G_L1: 13.030 G_L1_ABSOLUTE: 1.957 G_L1_RELATIVE: 11.073 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 41, iters: 25920, time: 0.536, data: 0.000) G_L1: 13.076 G_L1_ABSOLUTE: 2.304 G_L1_RELATIVE: 10.773 G_Regularizer: 0.000 validation_error: 20.987 +(epoch: 41, iters: 27920, time: 0.545, data: 0.000) G_L1: 13.921 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 11.638 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 41, iters: 29920, time: 0.547, data: 0.001) G_L1: 15.518 G_L1_ABSOLUTE: 2.599 G_L1_RELATIVE: 12.920 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 41, iters: 31920, time: 0.538, data: 0.001) G_L1: 11.288 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 8.955 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 41, iters: 33920, time: 0.534, data: 0.000) G_L1: 13.922 G_L1_ABSOLUTE: 2.318 G_L1_RELATIVE: 11.604 G_Regularizer: 0.000 validation_error: 20.954 +(epoch: 41, iters: 35920, time: 0.544, data: 0.000) G_L1: 14.840 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 12.352 G_Regularizer: 0.000 validation_error: 20.980 +(epoch: 41, iters: 37920, time: 0.542, data: 0.001) G_L1: 12.792 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 10.256 G_Regularizer: 0.000 validation_error: 21.192 +(epoch: 41, iters: 39920, time: 0.545, data: 0.000) G_L1: 12.906 G_L1_ABSOLUTE: 2.685 G_L1_RELATIVE: 10.220 G_Regularizer: 0.000 validation_error: 20.895 +(epoch: 41, iters: 41920, time: 0.537, data: 0.000) G_L1: 14.645 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 12.049 G_Regularizer: 0.000 validation_error: 20.856 +(epoch: 41, iters: 43920, time: 0.546, data: 0.000) G_L1: 24.490 G_L1_ABSOLUTE: 2.920 G_L1_RELATIVE: 21.570 G_Regularizer: 0.000 validation_error: 20.166 +(epoch: 41, iters: 45920, time: 0.541, data: 0.000) G_L1: 13.116 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 10.699 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 41, iters: 47920, time: 0.546, data: 0.000) G_L1: 14.506 G_L1_ABSOLUTE: 3.201 G_L1_RELATIVE: 11.305 G_Regularizer: 0.000 validation_error: 21.069 +(epoch: 41, iters: 49920, time: 0.542, data: 0.001) G_L1: 13.297 G_L1_ABSOLUTE: 2.431 G_L1_RELATIVE: 10.866 G_Regularizer: 0.000 validation_error: 20.848 +(epoch: 41, iters: 51920, time: 0.528, data: 0.000) G_L1: 13.154 G_L1_ABSOLUTE: 2.795 G_L1_RELATIVE: 10.359 G_Regularizer: 0.000 validation_error: 20.620 +(epoch: 41, iters: 53920, time: 0.543, data: 0.000) G_L1: 12.910 G_L1_ABSOLUTE: 2.192 G_L1_RELATIVE: 10.719 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 41, iters: 55920, time: 0.546, data: 0.000) G_L1: 14.887 G_L1_ABSOLUTE: 2.379 G_L1_RELATIVE: 12.508 G_Regularizer: 0.000 validation_error: 21.139 +(epoch: 41, iters: 57920, time: 0.535, data: 0.000) G_L1: 13.194 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 10.945 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 41, iters: 59920, time: 0.532, data: 0.000) G_L1: 14.837 G_L1_ABSOLUTE: 3.009 G_L1_RELATIVE: 11.828 G_Regularizer: 0.000 validation_error: 21.175 +(epoch: 41, iters: 61920, time: 0.540, data: 0.000) G_L1: 14.714 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 12.223 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 41, iters: 63920, time: 0.532, data: 0.000) G_L1: 13.255 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 10.739 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 41, iters: 65920, time: 0.546, data: 0.000) G_L1: 14.609 G_L1_ABSOLUTE: 2.171 G_L1_RELATIVE: 12.439 G_Regularizer: 0.000 validation_error: 20.621 +(epoch: 41, iters: 67920, time: 0.535, data: 0.000) G_L1: 14.591 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 12.124 G_Regularizer: 0.000 validation_error: 20.740 +(epoch: 41, iters: 69920, time: 0.542, data: 0.000) G_L1: 13.949 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 11.606 G_Regularizer: 0.000 validation_error: 20.591 +(epoch: 41, iters: 71920, time: 0.547, data: 0.001) G_L1: 12.103 G_L1_ABSOLUTE: 2.557 G_L1_RELATIVE: 9.546 G_Regularizer: 0.000 validation_error: 20.997 +(epoch: 41, iters: 73920, time: 0.545, data: 0.000) G_L1: 12.799 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 10.331 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 41, iters: 75920, time: 0.544, data: 0.000) G_L1: 16.727 G_L1_ABSOLUTE: 2.726 G_L1_RELATIVE: 14.001 G_Regularizer: 0.000 validation_error: 20.832 +(epoch: 41, iters: 77920, time: 0.538, data: 0.000) G_L1: 13.721 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 11.180 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 41, iters: 79920, time: 0.551, data: 0.000) G_L1: 13.400 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 10.968 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 41, iters: 81920, time: 0.541, data: 0.000) G_L1: 12.837 G_L1_ABSOLUTE: 2.221 G_L1_RELATIVE: 10.616 G_Regularizer: 0.000 validation_error: 21.051 +(epoch: 41, iters: 83920, time: 0.547, data: 0.000) G_L1: 13.586 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 11.237 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 41, iters: 85920, time: 0.540, data: 0.000) G_L1: 12.719 G_L1_ABSOLUTE: 2.080 G_L1_RELATIVE: 10.639 G_Regularizer: 0.000 validation_error: 21.063 +(epoch: 41, iters: 87920, time: 0.545, data: 0.000) G_L1: 13.034 G_L1_ABSOLUTE: 2.061 G_L1_RELATIVE: 10.973 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 41, iters: 89920, time: 0.542, data: 0.000) G_L1: 13.021 G_L1_ABSOLUTE: 2.285 G_L1_RELATIVE: 10.736 G_Regularizer: 0.000 validation_error: 20.670 +(epoch: 41, iters: 91920, time: 0.550, data: 0.000) G_L1: 12.626 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 10.256 G_Regularizer: 0.000 validation_error: 21.345 +(epoch: 41, iters: 93920, time: 0.543, data: 0.000) G_L1: 13.590 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 10.894 G_Regularizer: 0.000 validation_error: 21.147 +(epoch: 41, iters: 95920, time: 0.541, data: 0.000) G_L1: 13.292 G_L1_ABSOLUTE: 2.247 G_L1_RELATIVE: 11.045 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 41, iters: 97920, time: 0.546, data: 0.000) G_L1: 14.344 G_L1_ABSOLUTE: 2.431 G_L1_RELATIVE: 11.913 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 41, iters: 99920, time: 0.542, data: 0.000) G_L1: 16.477 G_L1_ABSOLUTE: 2.156 G_L1_RELATIVE: 14.321 G_Regularizer: 0.000 validation_error: 20.626 +(epoch: 41, iters: 101920, time: 0.544, data: 0.000) G_L1: 13.518 G_L1_ABSOLUTE: 2.693 G_L1_RELATIVE: 10.825 G_Regularizer: 0.000 validation_error: 20.939 +(epoch: 41, iters: 103920, time: 0.538, data: 0.001) G_L1: 14.605 G_L1_ABSOLUTE: 1.966 G_L1_RELATIVE: 12.639 G_Regularizer: 0.000 validation_error: 21.306 +(epoch: 41, iters: 105920, time: 0.546, data: 0.000) G_L1: 13.908 G_L1_ABSOLUTE: 2.015 G_L1_RELATIVE: 11.893 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 41, iters: 107920, time: 0.547, data: 0.001) G_L1: 14.094 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 11.643 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 41, iters: 109920, time: 0.536, data: 0.000) G_L1: 16.367 G_L1_ABSOLUTE: 3.152 G_L1_RELATIVE: 13.216 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 41, iters: 111920, time: 0.541, data: 0.000) G_L1: 12.847 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 10.498 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 41, iters: 113920, time: 0.542, data: 0.000) G_L1: 14.352 G_L1_ABSOLUTE: 2.395 G_L1_RELATIVE: 11.957 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 41, iters: 115920, time: 0.541, data: 0.000) G_L1: 15.487 G_L1_ABSOLUTE: 2.558 G_L1_RELATIVE: 12.929 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 41, iters: 117920, time: 0.542, data: 0.001) G_L1: 14.679 G_L1_ABSOLUTE: 2.434 G_L1_RELATIVE: 12.245 G_Regularizer: 0.000 validation_error: 21.258 +(epoch: 41, iters: 119920, time: 0.534, data: 0.000) G_L1: 14.116 G_L1_ABSOLUTE: 2.723 G_L1_RELATIVE: 11.394 G_Regularizer: 0.000 validation_error: 21.093 +(epoch: 41, iters: 121920, time: 0.550, data: 0.001) G_L1: 13.570 G_L1_ABSOLUTE: 2.396 G_L1_RELATIVE: 11.174 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 41, iters: 123920, time: 0.549, data: 0.000) G_L1: 12.472 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 10.093 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 41, iters: 125920, time: 0.545, data: 0.000) G_L1: 12.734 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 10.010 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 41, iters: 127920, time: 0.538, data: 0.001) G_L1: 14.107 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 11.498 G_Regularizer: 0.000 validation_error: 21.108 +(epoch: 41, iters: 129920, time: 0.533, data: 0.001) G_L1: 13.977 G_L1_ABSOLUTE: 2.252 G_L1_RELATIVE: 11.725 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 41, iters: 131920, time: 0.537, data: 0.000) G_L1: 14.809 G_L1_ABSOLUTE: 2.817 G_L1_RELATIVE: 11.993 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 41, iters: 133920, time: 0.544, data: 0.001) G_L1: 13.871 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 11.517 G_Regularizer: 0.000 validation_error: 21.073 +(epoch: 41, iters: 135920, time: 0.541, data: 0.000) G_L1: 15.046 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 12.483 G_Regularizer: 0.000 validation_error: 20.761 +(epoch: 41, iters: 137920, time: 0.539, data: 0.000) G_L1: 13.278 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 10.683 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 41, iters: 139920, time: 0.541, data: 0.000) G_L1: 14.789 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 12.281 G_Regularizer: 0.000 validation_error: 21.143 +(epoch: 41, iters: 141920, time: 0.546, data: 0.000) G_L1: 14.524 G_L1_ABSOLUTE: 2.325 G_L1_RELATIVE: 12.199 G_Regularizer: 0.000 validation_error: 20.468 +(epoch: 41, iters: 143920, time: 0.539, data: 0.000) G_L1: 14.327 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 11.522 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 41, iters: 145920, time: 0.541, data: 0.001) G_L1: 12.166 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 9.653 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 41, iters: 147920, time: 0.540, data: 0.001) G_L1: 13.152 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 10.646 G_Regularizer: 0.000 validation_error: 20.662 +(epoch: 41, iters: 149920, time: 0.539, data: 0.000) G_L1: 13.092 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 10.603 G_Regularizer: 0.000 validation_error: 20.882 +(epoch: 41, iters: 151920, time: 0.544, data: 0.000) G_L1: 14.673 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 12.228 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 41, iters: 153920, time: 0.538, data: 0.000) G_L1: 13.313 G_L1_ABSOLUTE: 2.423 G_L1_RELATIVE: 10.891 G_Regularizer: 0.000 validation_error: 20.577 +(epoch: 41, iters: 155920, time: 0.548, data: 0.000) G_L1: 12.737 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 10.402 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 41, iters: 157920, time: 0.537, data: 0.000) G_L1: 13.158 G_L1_ABSOLUTE: 2.168 G_L1_RELATIVE: 10.990 G_Regularizer: 0.000 validation_error: 20.602 +(epoch: 41, iters: 159920, time: 0.541, data: 0.000) G_L1: 10.480 G_L1_ABSOLUTE: 2.154 G_L1_RELATIVE: 8.326 G_Regularizer: 0.000 validation_error: 20.770 +(epoch: 41, iters: 161920, time: 0.540, data: 0.000) G_L1: 15.453 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 12.568 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 41, iters: 163920, time: 0.543, data: 0.000) G_L1: 14.635 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 12.386 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 41, iters: 165920, time: 0.540, data: 0.000) G_L1: 16.296 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 13.907 G_Regularizer: 0.000 validation_error: 20.604 +(epoch: 41, iters: 167920, time: 0.544, data: 0.000) G_L1: 12.955 G_L1_ABSOLUTE: 2.604 G_L1_RELATIVE: 10.351 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 41, iters: 169920, time: 0.542, data: 0.000) G_L1: 14.811 G_L1_ABSOLUTE: 2.987 G_L1_RELATIVE: 11.824 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 41, iters: 171920, time: 0.532, data: 0.001) G_L1: 13.791 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 11.018 G_Regularizer: 0.000 validation_error: 20.930 +(epoch: 41, iters: 173920, time: 0.546, data: 0.000) G_L1: 14.836 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 11.944 G_Regularizer: 0.000 validation_error: 21.214 +(epoch: 41, iters: 175920, time: 0.546, data: 0.000) G_L1: 14.719 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 12.184 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 41, iters: 177920, time: 0.543, data: 0.000) G_L1: 15.604 G_L1_ABSOLUTE: 2.841 G_L1_RELATIVE: 12.763 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 41, iters: 179920, time: 0.539, data: 0.000) G_L1: 15.723 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 13.291 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 41, iters: 181920, time: 0.543, data: 0.000) G_L1: 14.724 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 12.339 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 41, iters: 183920, time: 0.541, data: 0.000) G_L1: 14.425 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 12.151 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 41, iters: 185920, time: 0.547, data: 0.000) G_L1: 15.123 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 12.608 G_Regularizer: 0.000 validation_error: 20.639 +(epoch: 41, iters: 187920, time: 0.541, data: 0.000) G_L1: 16.249 G_L1_ABSOLUTE: 2.143 G_L1_RELATIVE: 14.106 G_Regularizer: 0.000 validation_error: 21.005 +(epoch: 41, iters: 189920, time: 0.545, data: 0.000) G_L1: 14.550 G_L1_ABSOLUTE: 2.359 G_L1_RELATIVE: 12.192 G_Regularizer: 0.000 validation_error: 21.313 +(epoch: 41, iters: 191920, time: 0.545, data: 0.000) G_L1: 13.311 G_L1_ABSOLUTE: 2.207 G_L1_RELATIVE: 11.104 G_Regularizer: 0.000 validation_error: 21.030 +(epoch: 41, iters: 193920, time: 0.538, data: 0.000) G_L1: 16.444 G_L1_ABSOLUTE: 2.292 G_L1_RELATIVE: 14.153 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 41, iters: 195920, time: 0.541, data: 0.000) G_L1: 13.255 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 10.877 G_Regularizer: 0.000 validation_error: 20.568 +(epoch: 41, iters: 197920, time: 0.544, data: 0.000) G_L1: 16.000 G_L1_ABSOLUTE: 2.616 G_L1_RELATIVE: 13.385 G_Regularizer: 0.000 validation_error: 20.832 +(epoch: 41, iters: 199920, time: 0.543, data: 0.001) G_L1: 12.830 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 10.194 G_Regularizer: 0.000 validation_error: 20.808 +(epoch: 41, iters: 201920, time: 0.537, data: 0.000) G_L1: 11.620 G_L1_ABSOLUTE: 2.116 G_L1_RELATIVE: 9.504 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 41, iters: 203920, time: 0.545, data: 0.000) G_L1: 14.841 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 12.074 G_Regularizer: 0.000 validation_error: 20.740 +(epoch: 41, iters: 205920, time: 0.538, data: 0.001) G_L1: 15.624 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 13.305 G_Regularizer: 0.000 validation_error: 21.363 +(epoch: 41, iters: 207920, time: 0.538, data: 0.000) G_L1: 16.028 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 13.542 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 41, iters: 209920, time: 0.543, data: 0.000) G_L1: 13.495 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 11.002 G_Regularizer: 0.000 validation_error: 21.293 +(epoch: 41, iters: 211920, time: 0.548, data: 0.000) G_L1: 15.630 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 13.162 G_Regularizer: 0.000 validation_error: 20.874 +(epoch: 41, iters: 213920, time: 0.536, data: 0.000) G_L1: 14.036 G_L1_ABSOLUTE: 2.114 G_L1_RELATIVE: 11.922 G_Regularizer: 0.000 validation_error: 20.402 +(epoch: 41, iters: 215920, time: 0.538, data: 0.001) G_L1: 14.125 G_L1_ABSOLUTE: 2.827 G_L1_RELATIVE: 11.298 G_Regularizer: 0.000 validation_error: 21.380 +(epoch: 41, iters: 217920, time: 0.537, data: 0.000) G_L1: 11.927 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 9.610 G_Regularizer: 0.000 validation_error: 20.697 +(epoch: 41, iters: 219920, time: 0.540, data: 0.000) G_L1: 14.071 G_L1_ABSOLUTE: 2.184 G_L1_RELATIVE: 11.887 G_Regularizer: 0.000 validation_error: 20.640 +(epoch: 41, iters: 221920, time: 0.547, data: 0.000) G_L1: 16.713 G_L1_ABSOLUTE: 3.001 G_L1_RELATIVE: 13.712 G_Regularizer: 0.000 validation_error: 20.948 +(epoch: 41, iters: 223920, time: 0.538, data: 0.000) G_L1: 14.712 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 11.956 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 41, iters: 225920, time: 0.539, data: 0.000) G_L1: 15.197 G_L1_ABSOLUTE: 2.752 G_L1_RELATIVE: 12.445 G_Regularizer: 0.000 validation_error: 21.130 +(epoch: 41, iters: 227920, time: 0.533, data: 0.000) G_L1: 13.016 G_L1_ABSOLUTE: 2.374 G_L1_RELATIVE: 10.642 G_Regularizer: 0.000 validation_error: 20.963 +(epoch: 41, iters: 229920, time: 0.535, data: 0.000) G_L1: 14.113 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 11.637 G_Regularizer: 0.000 validation_error: 21.294 +(epoch: 41, iters: 231920, time: 0.532, data: 0.000) G_L1: 12.419 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 9.824 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 41, iters: 233920, time: 0.547, data: 0.000) G_L1: 12.612 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 10.017 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 41, iters: 235920, time: 0.542, data: 0.000) G_L1: 12.278 G_L1_ABSOLUTE: 2.330 G_L1_RELATIVE: 9.948 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 41, iters: 237920, time: 0.548, data: 0.000) G_L1: 16.593 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 13.895 G_Regularizer: 0.000 validation_error: 20.644 +(epoch: 41, iters: 239920, time: 0.543, data: 0.000) G_L1: 15.663 G_L1_ABSOLUTE: 2.962 G_L1_RELATIVE: 12.701 G_Regularizer: 0.000 validation_error: 20.776 +(epoch: 41, iters: 241920, time: 0.541, data: 0.000) G_L1: 13.155 G_L1_ABSOLUTE: 2.092 G_L1_RELATIVE: 11.063 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 41, iters: 243920, time: 0.544, data: 0.000) G_L1: 17.743 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 14.999 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 41, iters: 245920, time: 0.540, data: 0.000) G_L1: 14.676 G_L1_ABSOLUTE: 3.065 G_L1_RELATIVE: 11.610 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 41, iters: 247920, time: 0.531, data: 0.000) G_L1: 15.601 G_L1_ABSOLUTE: 2.466 G_L1_RELATIVE: 13.135 G_Regularizer: 0.000 validation_error: 20.779 +(epoch: 41, iters: 249920, time: 0.535, data: 0.001) G_L1: 11.738 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 9.325 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 41, iters: 251920, time: 0.546, data: 0.000) G_L1: 13.309 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 10.906 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 41, iters: 253920, time: 0.547, data: 0.000) G_L1: 13.060 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 10.774 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 41, iters: 255920, time: 0.547, data: 0.000) G_L1: 12.577 G_L1_ABSOLUTE: 2.263 G_L1_RELATIVE: 10.313 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 41, iters: 257920, time: 0.538, data: 0.000) G_L1: 12.586 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 9.994 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 41, iters: 259920, time: 0.545, data: 0.000) G_L1: 14.196 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 11.606 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 41, iters: 261920, time: 0.546, data: 0.001) G_L1: 13.981 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 11.494 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 41, iters: 263920, time: 0.541, data: 0.000) G_L1: 15.267 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 13.050 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 41, iters: 265920, time: 0.535, data: 0.000) G_L1: 13.087 G_L1_ABSOLUTE: 2.185 G_L1_RELATIVE: 10.902 G_Regularizer: 0.000 validation_error: 20.737 +(epoch: 41, iters: 267920, time: 0.548, data: 0.000) G_L1: 15.162 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 12.652 G_Regularizer: 0.000 validation_error: 20.601 +(epoch: 41, iters: 269920, time: 0.545, data: 0.000) G_L1: 14.791 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 12.424 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 41, iters: 271920, time: 0.543, data: 0.000) G_L1: 14.261 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 11.773 G_Regularizer: 0.000 validation_error: 20.947 +(epoch: 41, iters: 273920, time: 0.540, data: 0.000) G_L1: 13.169 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 10.915 G_Regularizer: 0.000 validation_error: 20.943 +(epoch: 41, iters: 275920, time: 0.546, data: 0.000) G_L1: 15.720 G_L1_ABSOLUTE: 2.861 G_L1_RELATIVE: 12.859 G_Regularizer: 0.000 validation_error: 20.641 +(epoch: 41, iters: 277920, time: 0.544, data: 0.001) G_L1: 13.598 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 11.265 G_Regularizer: 0.000 validation_error: 21.320 +(epoch: 41, iters: 279920, time: 0.546, data: 0.000) G_L1: 13.220 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 10.738 G_Regularizer: 0.000 validation_error: 21.036 +(epoch: 41, iters: 281920, time: 0.553, data: 0.000) G_L1: 11.818 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 9.391 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 41, iters: 283920, time: 0.543, data: 0.000) G_L1: 13.965 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 11.529 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 41, iters: 285920, time: 0.543, data: 0.000) G_L1: 14.259 G_L1_ABSOLUTE: 2.816 G_L1_RELATIVE: 11.443 G_Regularizer: 0.000 validation_error: 20.210 +(epoch: 41, iters: 287920, time: 0.542, data: 0.001) G_L1: 15.088 G_L1_ABSOLUTE: 2.583 G_L1_RELATIVE: 12.505 G_Regularizer: 0.000 validation_error: 20.523 +(epoch: 41, iters: 289920, time: 0.538, data: 0.001) G_L1: 14.560 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 12.010 G_Regularizer: 0.000 validation_error: 20.776 +(epoch: 41, iters: 291920, time: 0.530, data: 0.000) G_L1: 12.680 G_L1_ABSOLUTE: 2.665 G_L1_RELATIVE: 10.015 G_Regularizer: 0.000 validation_error: 21.195 +(epoch: 41, iters: 293920, time: 0.529, data: 0.000) G_L1: 14.786 G_L1_ABSOLUTE: 2.143 G_L1_RELATIVE: 12.643 G_Regularizer: 0.000 validation_error: 20.592 +(epoch: 41, iters: 295920, time: 0.528, data: 0.000) G_L1: 12.972 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 10.342 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 41, iters: 297920, time: 0.538, data: 0.001) G_L1: 12.231 G_L1_ABSOLUTE: 2.615 G_L1_RELATIVE: 9.616 G_Regularizer: 0.000 validation_error: 20.778 +(epoch: 41, iters: 299920, time: 0.531, data: 0.001) G_L1: 15.068 G_L1_ABSOLUTE: 3.068 G_L1_RELATIVE: 12.000 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 41, iters: 301920, time: 0.538, data: 0.000) G_L1: 12.908 G_L1_ABSOLUTE: 2.222 G_L1_RELATIVE: 10.686 G_Regularizer: 0.000 validation_error: 21.135 +(epoch: 42, iters: 1168, time: 0.531, data: 0.000) G_L1: 14.898 G_L1_ABSOLUTE: 3.260 G_L1_RELATIVE: 11.639 G_Regularizer: 0.000 validation_error: 21.053 +(epoch: 42, iters: 3168, time: 0.531, data: 0.001) G_L1: 15.221 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 12.459 G_Regularizer: 0.000 validation_error: 20.907 +(epoch: 42, iters: 5168, time: 0.533, data: 0.001) G_L1: 13.587 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 11.015 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 42, iters: 7168, time: 0.531, data: 0.001) G_L1: 13.430 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 10.976 G_Regularizer: 0.000 validation_error: 20.492 +(epoch: 42, iters: 9168, time: 0.537, data: 0.000) G_L1: 13.689 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 11.277 G_Regularizer: 0.000 validation_error: 21.145 +(epoch: 42, iters: 11168, time: 0.531, data: 0.000) G_L1: 13.307 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 10.811 G_Regularizer: 0.000 validation_error: 20.508 +(epoch: 42, iters: 13168, time: 0.531, data: 0.000) G_L1: 14.809 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 12.135 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 42, iters: 15168, time: 0.544, data: 0.000) G_L1: 12.463 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 10.116 G_Regularizer: 0.000 validation_error: 21.055 +(epoch: 42, iters: 17168, time: 0.532, data: 0.001) G_L1: 16.653 G_L1_ABSOLUTE: 2.919 G_L1_RELATIVE: 13.734 G_Regularizer: 0.000 validation_error: 20.716 +(epoch: 42, iters: 19168, time: 0.531, data: 0.001) G_L1: 13.070 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 10.565 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 42, iters: 21168, time: 0.529, data: 0.000) G_L1: 14.784 G_L1_ABSOLUTE: 2.793 G_L1_RELATIVE: 11.991 G_Regularizer: 0.000 validation_error: 21.078 +(epoch: 42, iters: 23168, time: 0.527, data: 0.000) G_L1: 14.689 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 11.905 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 42, iters: 25168, time: 0.540, data: 0.000) G_L1: 15.421 G_L1_ABSOLUTE: 2.785 G_L1_RELATIVE: 12.636 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 42, iters: 27168, time: 0.533, data: 0.000) G_L1: 12.748 G_L1_ABSOLUTE: 2.213 G_L1_RELATIVE: 10.535 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 42, iters: 29168, time: 0.532, data: 0.000) G_L1: 12.323 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 9.861 G_Regularizer: 0.000 validation_error: 20.561 +(epoch: 42, iters: 31168, time: 0.541, data: 0.000) G_L1: 16.265 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 13.632 G_Regularizer: 0.000 validation_error: 20.547 +(epoch: 42, iters: 33168, time: 0.536, data: 0.000) G_L1: 15.132 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 12.585 G_Regularizer: 0.000 validation_error: 20.740 +(epoch: 42, iters: 35168, time: 0.535, data: 0.000) G_L1: 13.779 G_L1_ABSOLUTE: 2.440 G_L1_RELATIVE: 11.339 G_Regularizer: 0.000 validation_error: 21.196 +(epoch: 42, iters: 37168, time: 0.540, data: 0.001) G_L1: 14.525 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 11.879 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 42, iters: 39168, time: 0.526, data: 0.000) G_L1: 13.796 G_L1_ABSOLUTE: 2.499 G_L1_RELATIVE: 11.297 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 42, iters: 41168, time: 0.534, data: 0.000) G_L1: 14.898 G_L1_ABSOLUTE: 2.029 G_L1_RELATIVE: 12.869 G_Regularizer: 0.000 validation_error: 20.560 +(epoch: 42, iters: 43168, time: 0.532, data: 0.001) G_L1: 11.622 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 9.024 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 42, iters: 45168, time: 0.534, data: 0.000) G_L1: 12.213 G_L1_ABSOLUTE: 2.163 G_L1_RELATIVE: 10.050 G_Regularizer: 0.000 validation_error: 21.198 +(epoch: 42, iters: 47168, time: 0.529, data: 0.000) G_L1: 15.624 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 13.142 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 42, iters: 49168, time: 0.531, data: 0.000) G_L1: 16.454 G_L1_ABSOLUTE: 2.238 G_L1_RELATIVE: 14.216 G_Regularizer: 0.000 validation_error: 20.464 +(epoch: 42, iters: 51168, time: 0.537, data: 0.000) G_L1: 24.276 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 21.384 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 42, iters: 53168, time: 0.532, data: 0.000) G_L1: 15.067 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 12.371 G_Regularizer: 0.000 validation_error: 20.690 +(epoch: 42, iters: 55168, time: 0.536, data: 0.000) G_L1: 16.208 G_L1_ABSOLUTE: 2.264 G_L1_RELATIVE: 13.944 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 42, iters: 57168, time: 0.532, data: 0.000) G_L1: 14.603 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 11.933 G_Regularizer: 0.000 validation_error: 21.144 +(epoch: 42, iters: 59168, time: 0.531, data: 0.000) G_L1: 17.747 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 15.488 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 42, iters: 61168, time: 0.534, data: 0.001) G_L1: 12.610 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 10.232 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 42, iters: 63168, time: 0.531, data: 0.000) G_L1: 15.618 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 13.205 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 42, iters: 65168, time: 0.534, data: 0.000) G_L1: 13.746 G_L1_ABSOLUTE: 2.204 G_L1_RELATIVE: 11.542 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 42, iters: 67168, time: 0.533, data: 0.000) G_L1: 13.690 G_L1_ABSOLUTE: 2.490 G_L1_RELATIVE: 11.201 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 42, iters: 69168, time: 0.532, data: 0.000) G_L1: 14.227 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 11.764 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 42, iters: 71168, time: 0.530, data: 0.000) G_L1: 11.916 G_L1_ABSOLUTE: 2.613 G_L1_RELATIVE: 9.303 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 42, iters: 73168, time: 0.543, data: 0.000) G_L1: 14.582 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 11.962 G_Regularizer: 0.000 validation_error: 21.435 +(epoch: 42, iters: 75168, time: 0.535, data: 0.000) G_L1: 11.818 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 9.277 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 42, iters: 77168, time: 0.530, data: 0.000) G_L1: 13.539 G_L1_ABSOLUTE: 2.305 G_L1_RELATIVE: 11.234 G_Regularizer: 0.000 validation_error: 20.995 +(epoch: 42, iters: 79168, time: 0.533, data: 0.000) G_L1: 13.848 G_L1_ABSOLUTE: 2.305 G_L1_RELATIVE: 11.543 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 42, iters: 81168, time: 0.530, data: 0.000) G_L1: 13.983 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 11.074 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 42, iters: 83168, time: 0.531, data: 0.000) G_L1: 14.572 G_L1_ABSOLUTE: 2.507 G_L1_RELATIVE: 12.066 G_Regularizer: 0.000 validation_error: 20.920 +(epoch: 42, iters: 85168, time: 0.534, data: 0.000) G_L1: 12.953 G_L1_ABSOLUTE: 2.362 G_L1_RELATIVE: 10.591 G_Regularizer: 0.000 validation_error: 20.667 +(epoch: 42, iters: 87168, time: 0.537, data: 0.001) G_L1: 17.711 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 15.119 G_Regularizer: 0.000 validation_error: 20.995 +(epoch: 42, iters: 89168, time: 0.532, data: 0.000) G_L1: 13.802 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 11.192 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 42, iters: 91168, time: 0.535, data: 0.000) G_L1: 11.532 G_L1_ABSOLUTE: 2.116 G_L1_RELATIVE: 9.416 G_Regularizer: 0.000 validation_error: 21.163 +(epoch: 42, iters: 93168, time: 0.539, data: 0.000) G_L1: 15.567 G_L1_ABSOLUTE: 2.933 G_L1_RELATIVE: 12.634 G_Regularizer: 0.000 validation_error: 20.611 +(epoch: 42, iters: 95168, time: 0.531, data: 0.001) G_L1: 15.912 G_L1_ABSOLUTE: 2.703 G_L1_RELATIVE: 13.210 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 42, iters: 97168, time: 0.535, data: 0.000) G_L1: 11.685 G_L1_ABSOLUTE: 2.273 G_L1_RELATIVE: 9.412 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 42, iters: 99168, time: 0.531, data: 0.000) G_L1: 15.493 G_L1_ABSOLUTE: 3.470 G_L1_RELATIVE: 12.023 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 42, iters: 101168, time: 0.528, data: 0.000) G_L1: 14.381 G_L1_ABSOLUTE: 2.266 G_L1_RELATIVE: 12.115 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 42, iters: 103168, time: 0.537, data: 0.000) G_L1: 11.017 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 8.752 G_Regularizer: 0.000 validation_error: 20.761 +(epoch: 42, iters: 105168, time: 0.532, data: 0.000) G_L1: 12.457 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 9.805 G_Regularizer: 0.000 validation_error: 21.221 +(epoch: 42, iters: 107168, time: 0.530, data: 0.000) G_L1: 13.843 G_L1_ABSOLUTE: 2.045 G_L1_RELATIVE: 11.798 G_Regularizer: 0.000 validation_error: 21.109 +(epoch: 42, iters: 109168, time: 0.529, data: 0.000) G_L1: 12.759 G_L1_ABSOLUTE: 2.396 G_L1_RELATIVE: 10.363 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 42, iters: 111168, time: 0.536, data: 0.001) G_L1: 13.539 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 11.035 G_Regularizer: 0.000 validation_error: 21.256 +(epoch: 42, iters: 113168, time: 0.543, data: 0.001) G_L1: 16.596 G_L1_ABSOLUTE: 2.692 G_L1_RELATIVE: 13.904 G_Regularizer: 0.000 validation_error: 20.728 +(epoch: 42, iters: 115168, time: 0.535, data: 0.000) G_L1: 13.268 G_L1_ABSOLUTE: 2.834 G_L1_RELATIVE: 10.434 G_Regularizer: 0.000 validation_error: 21.127 +(epoch: 42, iters: 117168, time: 0.534, data: 0.000) G_L1: 15.633 G_L1_ABSOLUTE: 2.717 G_L1_RELATIVE: 12.916 G_Regularizer: 0.000 validation_error: 21.236 +(epoch: 42, iters: 119168, time: 0.536, data: 0.000) G_L1: 14.495 G_L1_ABSOLUTE: 2.701 G_L1_RELATIVE: 11.794 G_Regularizer: 0.000 validation_error: 20.746 +(epoch: 42, iters: 121168, time: 0.539, data: 0.000) G_L1: 14.744 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 12.287 G_Regularizer: 0.000 validation_error: 20.786 +(epoch: 42, iters: 123168, time: 0.536, data: 0.000) G_L1: 13.618 G_L1_ABSOLUTE: 2.255 G_L1_RELATIVE: 11.363 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 42, iters: 125168, time: 0.537, data: 0.000) G_L1: 15.296 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 12.846 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 42, iters: 127168, time: 0.539, data: 0.000) G_L1: 14.226 G_L1_ABSOLUTE: 3.105 G_L1_RELATIVE: 11.121 G_Regularizer: 0.000 validation_error: 21.070 +(epoch: 42, iters: 129168, time: 0.535, data: 0.000) G_L1: 12.424 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 9.873 G_Regularizer: 0.000 validation_error: 21.070 +(epoch: 42, iters: 131168, time: 0.535, data: 0.000) G_L1: 15.546 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 13.347 G_Regularizer: 0.000 validation_error: 20.971 +(epoch: 42, iters: 133168, time: 0.534, data: 0.000) G_L1: 11.841 G_L1_ABSOLUTE: 2.184 G_L1_RELATIVE: 9.657 G_Regularizer: 0.000 validation_error: 20.994 +(epoch: 42, iters: 135168, time: 0.541, data: 0.000) G_L1: 14.553 G_L1_ABSOLUTE: 2.785 G_L1_RELATIVE: 11.768 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 42, iters: 137168, time: 0.532, data: 0.000) G_L1: 12.794 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 10.265 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 42, iters: 139168, time: 0.532, data: 0.000) G_L1: 12.897 G_L1_ABSOLUTE: 2.615 G_L1_RELATIVE: 10.282 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 42, iters: 141168, time: 0.551, data: 0.000) G_L1: 16.315 G_L1_ABSOLUTE: 2.897 G_L1_RELATIVE: 13.418 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 42, iters: 143168, time: 0.580, data: 0.000) G_L1: 14.513 G_L1_ABSOLUTE: 2.161 G_L1_RELATIVE: 12.353 G_Regularizer: 0.000 validation_error: 21.194 +(epoch: 42, iters: 145168, time: 0.595, data: 0.000) G_L1: 14.164 G_L1_ABSOLUTE: 2.879 G_L1_RELATIVE: 11.285 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 42, iters: 147168, time: 0.580, data: 0.000) G_L1: 13.181 G_L1_ABSOLUTE: 2.047 G_L1_RELATIVE: 11.133 G_Regularizer: 0.000 validation_error: 20.861 +(epoch: 42, iters: 149168, time: 0.551, data: 0.000) G_L1: 15.500 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 12.888 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 42, iters: 151168, time: 0.565, data: 0.000) G_L1: 13.768 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 11.250 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 42, iters: 153168, time: 0.589, data: 0.000) G_L1: 14.585 G_L1_ABSOLUTE: 2.925 G_L1_RELATIVE: 11.660 G_Regularizer: 0.000 validation_error: 21.302 +(epoch: 42, iters: 155168, time: 0.567, data: 0.000) G_L1: 14.202 G_L1_ABSOLUTE: 2.384 G_L1_RELATIVE: 11.818 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 42, iters: 157168, time: 0.581, data: 0.000) G_L1: 14.411 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 12.195 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 42, iters: 159168, time: 0.531, data: 0.000) G_L1: 14.604 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 11.974 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 42, iters: 161168, time: 0.527, data: 0.000) G_L1: 16.121 G_L1_ABSOLUTE: 2.871 G_L1_RELATIVE: 13.250 G_Regularizer: 0.000 validation_error: 20.929 +(epoch: 42, iters: 163168, time: 0.539, data: 0.000) G_L1: 15.832 G_L1_ABSOLUTE: 2.862 G_L1_RELATIVE: 12.969 G_Regularizer: 0.000 validation_error: 20.508 +(epoch: 42, iters: 165168, time: 0.534, data: 0.000) G_L1: 14.615 G_L1_ABSOLUTE: 2.085 G_L1_RELATIVE: 12.530 G_Regularizer: 0.000 validation_error: 21.125 +(epoch: 42, iters: 167168, time: 0.540, data: 0.000) G_L1: 13.989 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 11.546 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 42, iters: 169168, time: 0.533, data: 0.000) G_L1: 15.605 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 13.120 G_Regularizer: 0.000 validation_error: 20.446 +(epoch: 42, iters: 171168, time: 0.534, data: 0.000) G_L1: 13.519 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 10.950 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 42, iters: 173168, time: 0.536, data: 0.001) G_L1: 15.572 G_L1_ABSOLUTE: 2.829 G_L1_RELATIVE: 12.744 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 42, iters: 175168, time: 0.528, data: 0.000) G_L1: 12.501 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 10.073 G_Regularizer: 0.000 validation_error: 20.999 +(epoch: 42, iters: 177168, time: 0.539, data: 0.000) G_L1: 14.300 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 11.831 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 42, iters: 179168, time: 0.527, data: 0.001) G_L1: 14.476 G_L1_ABSOLUTE: 2.175 G_L1_RELATIVE: 12.301 G_Regularizer: 0.000 validation_error: 21.133 +(epoch: 42, iters: 181168, time: 0.539, data: 0.000) G_L1: 13.121 G_L1_ABSOLUTE: 2.039 G_L1_RELATIVE: 11.082 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 42, iters: 183168, time: 0.533, data: 0.001) G_L1: 12.156 G_L1_ABSOLUTE: 2.192 G_L1_RELATIVE: 9.964 G_Regularizer: 0.000 validation_error: 21.007 +(epoch: 42, iters: 185168, time: 0.537, data: 0.001) G_L1: 13.700 G_L1_ABSOLUTE: 2.700 G_L1_RELATIVE: 11.000 G_Regularizer: 0.000 validation_error: 21.193 +(epoch: 42, iters: 187168, time: 0.538, data: 0.000) G_L1: 13.698 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 11.115 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 42, iters: 189168, time: 0.535, data: 0.000) G_L1: 13.041 G_L1_ABSOLUTE: 2.527 G_L1_RELATIVE: 10.513 G_Regularizer: 0.000 validation_error: 21.148 +(epoch: 42, iters: 191168, time: 0.534, data: 0.000) G_L1: 14.259 G_L1_ABSOLUTE: 2.240 G_L1_RELATIVE: 12.018 G_Regularizer: 0.000 validation_error: 20.808 +(epoch: 42, iters: 193168, time: 0.530, data: 0.000) G_L1: 13.404 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 11.121 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 42, iters: 195168, time: 0.531, data: 0.000) G_L1: 15.108 G_L1_ABSOLUTE: 3.216 G_L1_RELATIVE: 11.892 G_Regularizer: 0.000 validation_error: 20.852 +(epoch: 42, iters: 197168, time: 0.532, data: 0.001) G_L1: 17.183 G_L1_ABSOLUTE: 2.516 G_L1_RELATIVE: 14.668 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 42, iters: 199168, time: 0.536, data: 0.000) G_L1: 13.602 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 10.945 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 42, iters: 201168, time: 0.531, data: 0.000) G_L1: 13.978 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 11.595 G_Regularizer: 0.000 validation_error: 20.651 +(epoch: 42, iters: 203168, time: 0.530, data: 0.000) G_L1: 17.911 G_L1_ABSOLUTE: 2.929 G_L1_RELATIVE: 14.982 G_Regularizer: 0.000 validation_error: 20.984 +(epoch: 42, iters: 205168, time: 0.533, data: 0.000) G_L1: 14.804 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 12.459 G_Regularizer: 0.000 validation_error: 21.128 +(epoch: 42, iters: 207168, time: 0.538, data: 0.000) G_L1: 13.170 G_L1_ABSOLUTE: 2.279 G_L1_RELATIVE: 10.891 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 42, iters: 209168, time: 0.537, data: 0.000) G_L1: 13.825 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 11.003 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 42, iters: 211168, time: 0.533, data: 0.000) G_L1: 15.966 G_L1_ABSOLUTE: 2.185 G_L1_RELATIVE: 13.781 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 42, iters: 213168, time: 0.531, data: 0.000) G_L1: 14.473 G_L1_ABSOLUTE: 3.027 G_L1_RELATIVE: 11.446 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 42, iters: 215168, time: 0.536, data: 0.001) G_L1: 14.682 G_L1_ABSOLUTE: 2.321 G_L1_RELATIVE: 12.360 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 42, iters: 217168, time: 0.531, data: 0.001) G_L1: 14.106 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 11.556 G_Regularizer: 0.000 validation_error: 21.158 +(epoch: 42, iters: 219168, time: 0.538, data: 0.001) G_L1: 13.832 G_L1_ABSOLUTE: 2.192 G_L1_RELATIVE: 11.641 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 42, iters: 221168, time: 0.539, data: 0.000) G_L1: 13.313 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 10.999 G_Regularizer: 0.000 validation_error: 20.904 +(epoch: 42, iters: 223168, time: 0.530, data: 0.001) G_L1: 15.922 G_L1_ABSOLUTE: 2.615 G_L1_RELATIVE: 13.308 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 42, iters: 225168, time: 0.536, data: 0.000) G_L1: 14.113 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 11.639 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 42, iters: 227168, time: 0.528, data: 0.001) G_L1: 12.425 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 9.958 G_Regularizer: 0.000 validation_error: 21.524 +(epoch: 42, iters: 229168, time: 0.542, data: 0.000) G_L1: 12.262 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 9.372 G_Regularizer: 0.000 validation_error: 21.311 +(epoch: 42, iters: 231168, time: 0.530, data: 0.000) G_L1: 13.269 G_L1_ABSOLUTE: 2.230 G_L1_RELATIVE: 11.039 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 42, iters: 233168, time: 0.542, data: 0.000) G_L1: 12.916 G_L1_ABSOLUTE: 1.891 G_L1_RELATIVE: 11.025 G_Regularizer: 0.000 validation_error: 20.719 +(epoch: 42, iters: 235168, time: 0.536, data: 0.000) G_L1: 11.145 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 8.606 G_Regularizer: 0.000 validation_error: 21.202 +(epoch: 42, iters: 237168, time: 0.539, data: 0.001) G_L1: 14.368 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 12.134 G_Regularizer: 0.000 validation_error: 21.124 +(epoch: 42, iters: 239168, time: 0.536, data: 0.000) G_L1: 18.056 G_L1_ABSOLUTE: 2.500 G_L1_RELATIVE: 15.556 G_Regularizer: 0.000 validation_error: 21.165 +(epoch: 42, iters: 241168, time: 0.534, data: 0.001) G_L1: 15.834 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 13.292 G_Regularizer: 0.000 validation_error: 20.954 +(epoch: 42, iters: 243168, time: 0.550, data: 0.000) G_L1: 12.678 G_L1_ABSOLUTE: 2.139 G_L1_RELATIVE: 10.538 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 42, iters: 245168, time: 0.553, data: 0.000) G_L1: 11.597 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 9.093 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 42, iters: 247168, time: 0.556, data: 0.000) G_L1: 13.666 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 10.999 G_Regularizer: 0.000 validation_error: 21.202 +(epoch: 42, iters: 249168, time: 0.536, data: 0.000) G_L1: 12.560 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 10.298 G_Regularizer: 0.000 validation_error: 20.747 +(epoch: 42, iters: 251168, time: 0.552, data: 0.000) G_L1: 17.271 G_L1_ABSOLUTE: 2.838 G_L1_RELATIVE: 14.433 G_Regularizer: 0.000 validation_error: 21.006 +(epoch: 42, iters: 253168, time: 0.555, data: 0.000) G_L1: 13.828 G_L1_ABSOLUTE: 2.237 G_L1_RELATIVE: 11.591 G_Regularizer: 0.000 validation_error: 21.268 +(epoch: 42, iters: 255168, time: 0.552, data: 0.000) G_L1: 14.657 G_L1_ABSOLUTE: 2.264 G_L1_RELATIVE: 12.393 G_Regularizer: 0.000 validation_error: 21.155 +(epoch: 42, iters: 257168, time: 0.558, data: 0.000) G_L1: 12.840 G_L1_ABSOLUTE: 2.229 G_L1_RELATIVE: 10.611 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 42, iters: 259168, time: 0.550, data: 0.000) G_L1: 14.897 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 12.239 G_Regularizer: 0.000 validation_error: 21.428 +(epoch: 42, iters: 261168, time: 0.547, data: 0.000) G_L1: 12.898 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 10.415 G_Regularizer: 0.000 validation_error: 21.192 +(epoch: 42, iters: 263168, time: 0.539, data: 0.000) G_L1: 17.170 G_L1_ABSOLUTE: 3.259 G_L1_RELATIVE: 13.911 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 42, iters: 265168, time: 0.540, data: 0.000) G_L1: 16.443 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 14.088 G_Regularizer: 0.000 validation_error: 21.057 +(epoch: 42, iters: 267168, time: 0.546, data: 0.000) G_L1: 12.907 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 10.230 G_Regularizer: 0.000 validation_error: 20.960 +(epoch: 42, iters: 269168, time: 0.547, data: 0.000) G_L1: 12.705 G_L1_ABSOLUTE: 2.260 G_L1_RELATIVE: 10.446 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 42, iters: 271168, time: 0.551, data: 0.000) G_L1: 15.396 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 12.819 G_Regularizer: 0.000 validation_error: 21.166 +(epoch: 42, iters: 273168, time: 0.545, data: 0.000) G_L1: 14.342 G_L1_ABSOLUTE: 2.358 G_L1_RELATIVE: 11.984 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 42, iters: 275168, time: 0.540, data: 0.000) G_L1: 14.749 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 12.092 G_Regularizer: 0.000 validation_error: 21.155 +(epoch: 42, iters: 277168, time: 0.547, data: 0.000) G_L1: 11.636 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 8.945 G_Regularizer: 0.000 validation_error: 20.805 +(epoch: 42, iters: 279168, time: 0.553, data: 0.000) G_L1: 12.489 G_L1_ABSOLUTE: 2.148 G_L1_RELATIVE: 10.341 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 42, iters: 281168, time: 0.555, data: 0.000) G_L1: 11.346 G_L1_ABSOLUTE: 2.217 G_L1_RELATIVE: 9.129 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 42, iters: 283168, time: 0.542, data: 0.000) G_L1: 13.733 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 11.498 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 42, iters: 285168, time: 0.534, data: 0.000) G_L1: 13.730 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 11.167 G_Regularizer: 0.000 validation_error: 20.671 +(epoch: 42, iters: 287168, time: 0.552, data: 0.000) G_L1: 12.243 G_L1_ABSOLUTE: 2.290 G_L1_RELATIVE: 9.954 G_Regularizer: 0.000 validation_error: 21.153 +(epoch: 42, iters: 289168, time: 0.561, data: 0.001) G_L1: 16.455 G_L1_ABSOLUTE: 3.102 G_L1_RELATIVE: 13.352 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 42, iters: 291168, time: 0.548, data: 0.000) G_L1: 14.505 G_L1_ABSOLUTE: 2.083 G_L1_RELATIVE: 12.422 G_Regularizer: 0.000 validation_error: 20.984 +(epoch: 42, iters: 293168, time: 0.548, data: 0.000) G_L1: 12.925 G_L1_ABSOLUTE: 1.966 G_L1_RELATIVE: 10.960 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 42, iters: 295168, time: 0.553, data: 0.000) G_L1: 14.089 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 11.488 G_Regularizer: 0.000 validation_error: 21.310 +(epoch: 42, iters: 297168, time: 0.550, data: 0.000) G_L1: 14.222 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 11.780 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 42, iters: 299168, time: 0.536, data: 0.000) G_L1: 16.529 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 13.937 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 42, iters: 301168, time: 0.521, data: 0.000) G_L1: 14.893 G_L1_ABSOLUTE: 3.190 G_L1_RELATIVE: 11.703 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 43, iters: 416, time: 0.552, data: 0.000) G_L1: 15.790 G_L1_ABSOLUTE: 2.907 G_L1_RELATIVE: 12.883 G_Regularizer: 0.000 validation_error: 21.137 +(epoch: 43, iters: 2416, time: 0.550, data: 0.000) G_L1: 13.883 G_L1_ABSOLUTE: 2.260 G_L1_RELATIVE: 11.623 G_Regularizer: 0.000 validation_error: 21.040 +(epoch: 43, iters: 4416, time: 0.557, data: 0.000) G_L1: 14.368 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 11.691 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 43, iters: 6416, time: 0.536, data: 0.001) G_L1: 15.613 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 12.869 G_Regularizer: 0.000 validation_error: 21.251 +(epoch: 43, iters: 8416, time: 0.544, data: 0.000) G_L1: 12.993 G_L1_ABSOLUTE: 2.180 G_L1_RELATIVE: 10.812 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 43, iters: 10416, time: 0.552, data: 0.000) G_L1: 15.451 G_L1_ABSOLUTE: 2.749 G_L1_RELATIVE: 12.702 G_Regularizer: 0.000 validation_error: 21.103 +(epoch: 43, iters: 12416, time: 0.546, data: 0.001) G_L1: 13.129 G_L1_ABSOLUTE: 2.516 G_L1_RELATIVE: 10.612 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 43, iters: 14416, time: 0.557, data: 0.000) G_L1: 12.447 G_L1_ABSOLUTE: 2.219 G_L1_RELATIVE: 10.227 G_Regularizer: 0.000 validation_error: 20.818 +(epoch: 43, iters: 16416, time: 0.545, data: 0.000) G_L1: 14.548 G_L1_ABSOLUTE: 2.508 G_L1_RELATIVE: 12.040 G_Regularizer: 0.000 validation_error: 21.148 +(epoch: 43, iters: 18416, time: 0.551, data: 0.000) G_L1: 14.733 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 12.390 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 43, iters: 20416, time: 0.551, data: 0.000) G_L1: 12.578 G_L1_ABSOLUTE: 2.553 G_L1_RELATIVE: 10.025 G_Regularizer: 0.000 validation_error: 20.785 +(epoch: 43, iters: 22416, time: 0.560, data: 0.000) G_L1: 13.393 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 10.986 G_Regularizer: 0.000 validation_error: 20.797 +(epoch: 43, iters: 24416, time: 0.553, data: 0.000) G_L1: 14.071 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 11.610 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 43, iters: 26416, time: 0.551, data: 0.000) G_L1: 14.614 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 12.379 G_Regularizer: 0.000 validation_error: 20.756 +(epoch: 43, iters: 28416, time: 0.544, data: 0.000) G_L1: 11.688 G_L1_ABSOLUTE: 2.794 G_L1_RELATIVE: 8.894 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 43, iters: 30416, time: 0.554, data: 0.000) G_L1: 13.910 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 10.972 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 43, iters: 32416, time: 0.557, data: 0.000) G_L1: 11.773 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 9.252 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 43, iters: 34416, time: 0.549, data: 0.000) G_L1: 14.544 G_L1_ABSOLUTE: 2.487 G_L1_RELATIVE: 12.057 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 43, iters: 36416, time: 0.553, data: 0.000) G_L1: 13.890 G_L1_ABSOLUTE: 2.285 G_L1_RELATIVE: 11.605 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 43, iters: 38416, time: 0.556, data: 0.000) G_L1: 13.093 G_L1_ABSOLUTE: 2.136 G_L1_RELATIVE: 10.957 G_Regularizer: 0.000 validation_error: 20.991 +(epoch: 43, iters: 40416, time: 0.567, data: 0.001) G_L1: 10.620 G_L1_ABSOLUTE: 2.076 G_L1_RELATIVE: 8.545 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 43, iters: 42416, time: 0.548, data: 0.000) G_L1: 11.518 G_L1_ABSOLUTE: 2.117 G_L1_RELATIVE: 9.401 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 43, iters: 44416, time: 0.549, data: 0.000) G_L1: 15.369 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 12.558 G_Regularizer: 0.000 validation_error: 20.964 +(epoch: 43, iters: 46416, time: 0.553, data: 0.000) G_L1: 11.842 G_L1_ABSOLUTE: 2.211 G_L1_RELATIVE: 9.631 G_Regularizer: 0.000 validation_error: 21.389 +(epoch: 43, iters: 48416, time: 0.565, data: 0.000) G_L1: 19.041 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 16.469 G_Regularizer: 0.000 validation_error: 20.498 +(epoch: 43, iters: 50416, time: 0.552, data: 0.000) G_L1: 15.270 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 12.925 G_Regularizer: 0.000 validation_error: 20.683 +(epoch: 43, iters: 52416, time: 0.550, data: 0.000) G_L1: 13.026 G_L1_ABSOLUTE: 2.055 G_L1_RELATIVE: 10.971 G_Regularizer: 0.000 validation_error: 20.644 +(epoch: 43, iters: 54416, time: 0.551, data: 0.000) G_L1: 12.111 G_L1_ABSOLUTE: 2.397 G_L1_RELATIVE: 9.714 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 43, iters: 56416, time: 0.554, data: 0.000) G_L1: 12.857 G_L1_ABSOLUTE: 2.255 G_L1_RELATIVE: 10.603 G_Regularizer: 0.000 validation_error: 20.571 +(epoch: 43, iters: 58416, time: 0.549, data: 0.000) G_L1: 15.180 G_L1_ABSOLUTE: 2.244 G_L1_RELATIVE: 12.935 G_Regularizer: 0.000 validation_error: 21.345 +(epoch: 43, iters: 60416, time: 0.541, data: 0.000) G_L1: 13.603 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 11.254 G_Regularizer: 0.000 validation_error: 21.151 +(epoch: 43, iters: 62416, time: 0.541, data: 0.000) G_L1: 12.914 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 10.089 G_Regularizer: 0.000 validation_error: 21.068 +(epoch: 43, iters: 64416, time: 0.545, data: 0.000) G_L1: 14.504 G_L1_ABSOLUTE: 2.713 G_L1_RELATIVE: 11.791 G_Regularizer: 0.000 validation_error: 21.090 +(epoch: 43, iters: 66416, time: 0.537, data: 0.000) G_L1: 13.085 G_L1_ABSOLUTE: 2.727 G_L1_RELATIVE: 10.359 G_Regularizer: 0.000 validation_error: 20.964 +(epoch: 43, iters: 68416, time: 0.559, data: 0.000) G_L1: 15.026 G_L1_ABSOLUTE: 2.297 G_L1_RELATIVE: 12.729 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 43, iters: 70416, time: 0.563, data: 0.000) G_L1: 12.017 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 9.309 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 43, iters: 72416, time: 0.558, data: 0.000) G_L1: 16.321 G_L1_ABSOLUTE: 2.968 G_L1_RELATIVE: 13.353 G_Regularizer: 0.000 validation_error: 20.992 +(epoch: 43, iters: 74416, time: 0.554, data: 0.000) G_L1: 13.189 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 10.730 G_Regularizer: 0.000 validation_error: 21.219 +(epoch: 43, iters: 76416, time: 0.553, data: 0.000) G_L1: 13.080 G_L1_ABSOLUTE: 2.137 G_L1_RELATIVE: 10.943 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 43, iters: 78416, time: 0.561, data: 0.000) G_L1: 12.084 G_L1_ABSOLUTE: 2.191 G_L1_RELATIVE: 9.893 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 43, iters: 80416, time: 0.556, data: 0.000) G_L1: 13.273 G_L1_ABSOLUTE: 2.293 G_L1_RELATIVE: 10.980 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 43, iters: 82416, time: 0.544, data: 0.000) G_L1: 12.943 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 10.241 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 43, iters: 84416, time: 0.562, data: 0.000) G_L1: 16.056 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 13.171 G_Regularizer: 0.000 validation_error: 20.980 +(epoch: 43, iters: 86416, time: 0.539, data: 0.000) G_L1: 14.271 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 11.778 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 43, iters: 88416, time: 0.564, data: 0.000) G_L1: 12.287 G_L1_ABSOLUTE: 2.452 G_L1_RELATIVE: 9.835 G_Regularizer: 0.000 validation_error: 20.588 +(epoch: 43, iters: 90416, time: 0.552, data: 0.001) G_L1: 14.875 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 12.540 G_Regularizer: 0.000 validation_error: 20.921 +(epoch: 43, iters: 92416, time: 0.544, data: 0.000) G_L1: 13.125 G_L1_ABSOLUTE: 3.086 G_L1_RELATIVE: 10.040 G_Regularizer: 0.000 validation_error: 21.058 +(epoch: 43, iters: 94416, time: 0.559, data: 0.000) G_L1: 14.287 G_L1_ABSOLUTE: 2.791 G_L1_RELATIVE: 11.496 G_Regularizer: 0.000 validation_error: 21.029 +(epoch: 43, iters: 96416, time: 0.565, data: 0.001) G_L1: 14.310 G_L1_ABSOLUTE: 2.790 G_L1_RELATIVE: 11.520 G_Regularizer: 0.000 validation_error: 21.033 +(epoch: 43, iters: 98416, time: 0.569, data: 0.000) G_L1: 12.250 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 9.714 G_Regularizer: 0.000 validation_error: 21.041 +(epoch: 43, iters: 100416, time: 0.533, data: 0.000) G_L1: 16.172 G_L1_ABSOLUTE: 3.402 G_L1_RELATIVE: 12.770 G_Regularizer: 0.000 validation_error: 20.808 +(epoch: 43, iters: 102416, time: 0.556, data: 0.000) G_L1: 12.235 G_L1_ABSOLUTE: 2.153 G_L1_RELATIVE: 10.082 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 43, iters: 104416, time: 0.543, data: 0.000) G_L1: 14.404 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 11.713 G_Regularizer: 0.000 validation_error: 20.984 +(epoch: 43, iters: 106416, time: 0.543, data: 0.000) G_L1: 11.852 G_L1_ABSOLUTE: 2.326 G_L1_RELATIVE: 9.525 G_Regularizer: 0.000 validation_error: 20.929 +(epoch: 43, iters: 108416, time: 0.541, data: 0.000) G_L1: 14.888 G_L1_ABSOLUTE: 2.910 G_L1_RELATIVE: 11.977 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 43, iters: 110416, time: 0.550, data: 0.000) G_L1: 16.410 G_L1_ABSOLUTE: 3.048 G_L1_RELATIVE: 13.362 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 43, iters: 112416, time: 0.555, data: 0.000) G_L1: 14.724 G_L1_ABSOLUTE: 2.294 G_L1_RELATIVE: 12.431 G_Regularizer: 0.000 validation_error: 20.681 +(epoch: 43, iters: 114416, time: 0.555, data: 0.000) G_L1: 16.235 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 13.758 G_Regularizer: 0.000 validation_error: 20.458 +(epoch: 43, iters: 116416, time: 0.552, data: 0.000) G_L1: 13.759 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 11.218 G_Regularizer: 0.000 validation_error: 20.622 +(epoch: 43, iters: 118416, time: 0.558, data: 0.000) G_L1: 15.833 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 13.298 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 43, iters: 120416, time: 0.533, data: 0.000) G_L1: 13.304 G_L1_ABSOLUTE: 2.747 G_L1_RELATIVE: 10.557 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 43, iters: 122416, time: 0.537, data: 0.000) G_L1: 12.839 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 10.498 G_Regularizer: 0.000 validation_error: 21.108 +(epoch: 43, iters: 124416, time: 0.543, data: 0.001) G_L1: 16.667 G_L1_ABSOLUTE: 2.819 G_L1_RELATIVE: 13.849 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 43, iters: 126416, time: 0.557, data: 0.000) G_L1: 14.240 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 11.790 G_Regularizer: 0.000 validation_error: 21.075 +(epoch: 43, iters: 128416, time: 0.549, data: 0.000) G_L1: 13.758 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 11.460 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 43, iters: 130416, time: 0.563, data: 0.000) G_L1: 15.255 G_L1_ABSOLUTE: 2.949 G_L1_RELATIVE: 12.306 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 43, iters: 132416, time: 0.559, data: 0.000) G_L1: 13.908 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 11.451 G_Regularizer: 0.000 validation_error: 20.869 +(epoch: 43, iters: 134416, time: 0.540, data: 0.000) G_L1: 13.171 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 10.266 G_Regularizer: 0.000 validation_error: 21.120 +(epoch: 43, iters: 136416, time: 0.537, data: 0.000) G_L1: 14.653 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 12.028 G_Regularizer: 0.000 validation_error: 21.074 +(epoch: 43, iters: 138416, time: 0.561, data: 0.001) G_L1: 12.042 G_L1_ABSOLUTE: 3.019 G_L1_RELATIVE: 9.024 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 43, iters: 140416, time: 0.556, data: 0.000) G_L1: 13.130 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 10.559 G_Regularizer: 0.000 validation_error: 21.137 +(epoch: 43, iters: 142416, time: 0.540, data: 0.002) G_L1: 12.754 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 10.024 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 43, iters: 144416, time: 0.560, data: 0.000) G_L1: 15.613 G_L1_ABSOLUTE: 2.687 G_L1_RELATIVE: 12.926 G_Regularizer: 0.000 validation_error: 21.459 +(epoch: 43, iters: 146416, time: 0.553, data: 0.000) G_L1: 13.586 G_L1_ABSOLUTE: 2.374 G_L1_RELATIVE: 11.212 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 43, iters: 148416, time: 0.536, data: 0.000) G_L1: 14.672 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 12.328 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 43, iters: 150416, time: 0.544, data: 0.000) G_L1: 12.208 G_L1_ABSOLUTE: 2.268 G_L1_RELATIVE: 9.940 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 43, iters: 152416, time: 0.548, data: 0.000) G_L1: 13.934 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 11.305 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 43, iters: 154416, time: 0.556, data: 0.000) G_L1: 12.636 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 10.055 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 43, iters: 156416, time: 0.547, data: 0.001) G_L1: 14.918 G_L1_ABSOLUTE: 2.337 G_L1_RELATIVE: 12.581 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 43, iters: 158416, time: 0.536, data: 0.000) G_L1: 14.234 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 11.610 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 43, iters: 160416, time: 0.553, data: 0.000) G_L1: 16.868 G_L1_ABSOLUTE: 2.972 G_L1_RELATIVE: 13.897 G_Regularizer: 0.000 validation_error: 20.535 +(epoch: 43, iters: 162416, time: 0.565, data: 0.000) G_L1: 14.377 G_L1_ABSOLUTE: 2.263 G_L1_RELATIVE: 12.114 G_Regularizer: 0.000 validation_error: 20.753 +(epoch: 43, iters: 164416, time: 0.562, data: 0.000) G_L1: 12.621 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 10.246 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 43, iters: 166416, time: 0.559, data: 0.000) G_L1: 11.947 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 9.711 G_Regularizer: 0.000 validation_error: 20.972 +(epoch: 43, iters: 168416, time: 0.540, data: 0.000) G_L1: 15.267 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 12.883 G_Regularizer: 0.000 validation_error: 20.902 +(epoch: 43, iters: 170416, time: 0.543, data: 0.000) G_L1: 12.812 G_L1_ABSOLUTE: 2.280 G_L1_RELATIVE: 10.531 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 43, iters: 172416, time: 0.550, data: 0.000) G_L1: 12.279 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 9.858 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 43, iters: 174416, time: 0.556, data: 0.000) G_L1: 11.145 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 8.731 G_Regularizer: 0.000 validation_error: 21.222 +(epoch: 43, iters: 176416, time: 0.556, data: 0.000) G_L1: 16.240 G_L1_ABSOLUTE: 2.583 G_L1_RELATIVE: 13.658 G_Regularizer: 0.000 validation_error: 21.119 +(epoch: 43, iters: 178416, time: 0.555, data: 0.000) G_L1: 12.569 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 10.109 G_Regularizer: 0.000 validation_error: 20.795 +(epoch: 43, iters: 180416, time: 0.559, data: 0.001) G_L1: 13.105 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 10.695 G_Regularizer: 0.000 validation_error: 21.095 +(epoch: 43, iters: 182416, time: 0.556, data: 0.001) G_L1: 12.657 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 10.202 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 43, iters: 184416, time: 0.544, data: 0.000) G_L1: 14.547 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 12.021 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 43, iters: 186416, time: 0.558, data: 0.000) G_L1: 12.185 G_L1_ABSOLUTE: 2.096 G_L1_RELATIVE: 10.089 G_Regularizer: 0.000 validation_error: 20.836 +(epoch: 43, iters: 188416, time: 0.564, data: 0.000) G_L1: 15.334 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 12.806 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 43, iters: 190416, time: 0.564, data: 0.001) G_L1: 15.090 G_L1_ABSOLUTE: 2.277 G_L1_RELATIVE: 12.813 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 43, iters: 192416, time: 0.559, data: 0.000) G_L1: 11.732 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 9.449 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 43, iters: 194416, time: 0.555, data: 0.001) G_L1: 21.905 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 19.024 G_Regularizer: 0.000 validation_error: 20.628 +(epoch: 43, iters: 196416, time: 0.549, data: 0.000) G_L1: 16.290 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 13.640 G_Regularizer: 0.000 validation_error: 20.893 +(epoch: 43, iters: 198416, time: 0.540, data: 0.000) G_L1: 13.504 G_L1_ABSOLUTE: 2.438 G_L1_RELATIVE: 11.066 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 43, iters: 200416, time: 0.570, data: 0.000) G_L1: 15.594 G_L1_ABSOLUTE: 2.200 G_L1_RELATIVE: 13.394 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 43, iters: 202416, time: 0.545, data: 0.000) G_L1: 15.267 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 12.897 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 43, iters: 204416, time: 0.556, data: 0.000) G_L1: 14.213 G_L1_ABSOLUTE: 2.280 G_L1_RELATIVE: 11.933 G_Regularizer: 0.000 validation_error: 20.892 +(epoch: 43, iters: 206416, time: 0.562, data: 0.000) G_L1: 13.420 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 10.968 G_Regularizer: 0.000 validation_error: 21.029 +(epoch: 43, iters: 208416, time: 0.563, data: 0.000) G_L1: 13.414 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 10.723 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 43, iters: 210416, time: 0.541, data: 0.000) G_L1: 15.183 G_L1_ABSOLUTE: 2.092 G_L1_RELATIVE: 13.091 G_Regularizer: 0.000 validation_error: 21.076 +(epoch: 43, iters: 212416, time: 0.545, data: 0.000) G_L1: 11.589 G_L1_ABSOLUTE: 2.292 G_L1_RELATIVE: 9.297 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 43, iters: 214416, time: 0.553, data: 0.000) G_L1: 13.032 G_L1_ABSOLUTE: 2.138 G_L1_RELATIVE: 10.893 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 43, iters: 216416, time: 0.556, data: 0.000) G_L1: 16.056 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 13.539 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 43, iters: 218416, time: 0.549, data: 0.000) G_L1: 13.361 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 10.739 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 43, iters: 220416, time: 0.548, data: 0.000) G_L1: 13.177 G_L1_ABSOLUTE: 3.134 G_L1_RELATIVE: 10.043 G_Regularizer: 0.000 validation_error: 20.938 +(epoch: 43, iters: 222416, time: 0.565, data: 0.000) G_L1: 12.793 G_L1_ABSOLUTE: 2.032 G_L1_RELATIVE: 10.761 G_Regularizer: 0.000 validation_error: 20.805 +(epoch: 43, iters: 224416, time: 0.561, data: 0.000) G_L1: 13.973 G_L1_ABSOLUTE: 2.743 G_L1_RELATIVE: 11.230 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 43, iters: 226416, time: 0.540, data: 0.000) G_L1: 14.540 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 12.157 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 43, iters: 228416, time: 0.545, data: 0.000) G_L1: 13.696 G_L1_ABSOLUTE: 2.294 G_L1_RELATIVE: 11.402 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 43, iters: 230416, time: 0.555, data: 0.000) G_L1: 12.669 G_L1_ABSOLUTE: 2.303 G_L1_RELATIVE: 10.366 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 43, iters: 232416, time: 0.553, data: 0.000) G_L1: 14.723 G_L1_ABSOLUTE: 2.316 G_L1_RELATIVE: 12.407 G_Regularizer: 0.000 validation_error: 20.542 +(epoch: 43, iters: 234416, time: 0.562, data: 0.000) G_L1: 17.991 G_L1_ABSOLUTE: 2.915 G_L1_RELATIVE: 15.076 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 43, iters: 236416, time: 0.534, data: 0.000) G_L1: 15.218 G_L1_ABSOLUTE: 2.193 G_L1_RELATIVE: 13.026 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 43, iters: 238416, time: 0.559, data: 0.000) G_L1: 15.558 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 13.111 G_Regularizer: 0.000 validation_error: 21.423 +(epoch: 43, iters: 240416, time: 0.564, data: 0.000) G_L1: 13.123 G_L1_ABSOLUTE: 2.257 G_L1_RELATIVE: 10.865 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 43, iters: 242416, time: 0.553, data: 0.000) G_L1: 15.231 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 12.645 G_Regularizer: 0.000 validation_error: 21.022 +(epoch: 43, iters: 244416, time: 0.558, data: 0.000) G_L1: 16.928 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 14.446 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 43, iters: 246416, time: 0.562, data: 0.000) G_L1: 12.564 G_L1_ABSOLUTE: 2.142 G_L1_RELATIVE: 10.422 G_Regularizer: 0.000 validation_error: 20.485 +(epoch: 43, iters: 248416, time: 0.562, data: 0.000) G_L1: 14.410 G_L1_ABSOLUTE: 2.613 G_L1_RELATIVE: 11.797 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 43, iters: 250416, time: 0.550, data: 0.000) G_L1: 14.934 G_L1_ABSOLUTE: 2.980 G_L1_RELATIVE: 11.954 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 43, iters: 252416, time: 0.563, data: 0.000) G_L1: 14.875 G_L1_ABSOLUTE: 2.685 G_L1_RELATIVE: 12.190 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 43, iters: 254416, time: 0.535, data: 0.000) G_L1: 13.767 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 11.394 G_Regularizer: 0.000 validation_error: 20.729 +(epoch: 43, iters: 256416, time: 0.558, data: 0.000) G_L1: 11.073 G_L1_ABSOLUTE: 1.991 G_L1_RELATIVE: 9.082 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 43, iters: 258416, time: 0.553, data: 0.000) G_L1: 13.420 G_L1_ABSOLUTE: 2.234 G_L1_RELATIVE: 11.185 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 43, iters: 260416, time: 0.561, data: 0.000) G_L1: 15.477 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 12.745 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 43, iters: 262416, time: 0.544, data: 0.000) G_L1: 13.963 G_L1_ABSOLUTE: 2.443 G_L1_RELATIVE: 11.520 G_Regularizer: 0.000 validation_error: 20.848 +(epoch: 43, iters: 264416, time: 0.535, data: 0.000) G_L1: 13.710 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 11.199 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 43, iters: 266416, time: 0.557, data: 0.000) G_L1: 14.061 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 11.319 G_Regularizer: 0.000 validation_error: 21.073 +(epoch: 43, iters: 268416, time: 0.551, data: 0.000) G_L1: 14.393 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 11.994 G_Regularizer: 0.000 validation_error: 20.533 +(epoch: 43, iters: 270416, time: 0.548, data: 0.000) G_L1: 13.754 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 11.434 G_Regularizer: 0.000 validation_error: 20.410 +(epoch: 43, iters: 272416, time: 0.551, data: 0.000) G_L1: 16.454 G_L1_ABSOLUTE: 2.499 G_L1_RELATIVE: 13.954 G_Regularizer: 0.000 validation_error: 21.116 +(epoch: 43, iters: 274416, time: 0.549, data: 0.000) G_L1: 15.649 G_L1_ABSOLUTE: 2.878 G_L1_RELATIVE: 12.771 G_Regularizer: 0.000 validation_error: 20.669 +(epoch: 43, iters: 276416, time: 0.541, data: 0.000) G_L1: 14.685 G_L1_ABSOLUTE: 2.655 G_L1_RELATIVE: 12.030 G_Regularizer: 0.000 validation_error: 20.994 +(epoch: 43, iters: 278416, time: 0.533, data: 0.001) G_L1: 17.176 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 14.492 G_Regularizer: 0.000 validation_error: 21.146 +(epoch: 43, iters: 280416, time: 0.558, data: 0.000) G_L1: 13.515 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 10.710 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 43, iters: 282416, time: 0.557, data: 0.000) G_L1: 15.595 G_L1_ABSOLUTE: 2.313 G_L1_RELATIVE: 13.282 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 43, iters: 284416, time: 0.562, data: 0.000) G_L1: 15.134 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 12.566 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 43, iters: 286416, time: 0.544, data: 0.000) G_L1: 15.731 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 13.134 G_Regularizer: 0.000 validation_error: 20.452 +(epoch: 43, iters: 288416, time: 0.529, data: 0.000) G_L1: 16.617 G_L1_ABSOLUTE: 2.234 G_L1_RELATIVE: 14.383 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 43, iters: 290416, time: 0.561, data: 0.000) G_L1: 13.604 G_L1_ABSOLUTE: 3.127 G_L1_RELATIVE: 10.477 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 43, iters: 292416, time: 0.547, data: 0.000) G_L1: 14.011 G_L1_ABSOLUTE: 2.224 G_L1_RELATIVE: 11.786 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 43, iters: 294416, time: 0.542, data: 0.000) G_L1: 17.116 G_L1_ABSOLUTE: 2.988 G_L1_RELATIVE: 14.128 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 43, iters: 296416, time: 0.563, data: 0.000) G_L1: 14.414 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 12.026 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 43, iters: 298416, time: 0.553, data: 0.000) G_L1: 13.677 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 10.998 G_Regularizer: 0.000 validation_error: 21.028 +(epoch: 43, iters: 300416, time: 0.551, data: 0.000) G_L1: 13.808 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 11.305 G_Regularizer: 0.000 validation_error: 20.631 +(epoch: 43, iters: 302416, time: 0.545, data: 0.000) G_L1: 18.610 G_L1_ABSOLUTE: 2.981 G_L1_RELATIVE: 15.629 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 44, iters: 1664, time: 0.556, data: 0.000) G_L1: 12.910 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 10.619 G_Regularizer: 0.000 validation_error: 20.470 +(epoch: 44, iters: 3664, time: 0.543, data: 0.000) G_L1: 14.913 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 12.536 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 44, iters: 5664, time: 0.553, data: 0.000) G_L1: 11.004 G_L1_ABSOLUTE: 2.122 G_L1_RELATIVE: 8.883 G_Regularizer: 0.000 validation_error: 20.738 +(epoch: 44, iters: 7664, time: 0.560, data: 0.000) G_L1: 13.712 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 11.324 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 44, iters: 9664, time: 0.531, data: 0.000) G_L1: 13.678 G_L1_ABSOLUTE: 3.108 G_L1_RELATIVE: 10.570 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 44, iters: 11664, time: 0.563, data: 0.000) G_L1: 15.823 G_L1_ABSOLUTE: 2.953 G_L1_RELATIVE: 12.870 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 44, iters: 13664, time: 0.549, data: 0.000) G_L1: 13.809 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 11.176 G_Regularizer: 0.000 validation_error: 20.690 +(epoch: 44, iters: 15664, time: 0.559, data: 0.000) G_L1: 14.197 G_L1_ABSOLUTE: 2.301 G_L1_RELATIVE: 11.896 G_Regularizer: 0.000 validation_error: 20.705 +(epoch: 44, iters: 17664, time: 0.540, data: 0.000) G_L1: 13.534 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 10.875 G_Regularizer: 0.000 validation_error: 20.818 +(epoch: 44, iters: 19664, time: 0.556, data: 0.000) G_L1: 14.900 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 12.427 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 44, iters: 21664, time: 0.553, data: 0.000) G_L1: 13.066 G_L1_ABSOLUTE: 2.359 G_L1_RELATIVE: 10.707 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 44, iters: 23664, time: 0.560, data: 0.000) G_L1: 14.756 G_L1_ABSOLUTE: 2.604 G_L1_RELATIVE: 12.152 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 44, iters: 25664, time: 0.555, data: 0.000) G_L1: 16.557 G_L1_ABSOLUTE: 2.578 G_L1_RELATIVE: 13.979 G_Regularizer: 0.000 validation_error: 21.288 +(epoch: 44, iters: 27664, time: 0.550, data: 0.000) G_L1: 16.587 G_L1_ABSOLUTE: 2.599 G_L1_RELATIVE: 13.987 G_Regularizer: 0.000 validation_error: 20.775 +(epoch: 44, iters: 29664, time: 0.555, data: 0.000) G_L1: 14.444 G_L1_ABSOLUTE: 3.279 G_L1_RELATIVE: 11.165 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 44, iters: 31664, time: 0.556, data: 0.000) G_L1: 15.145 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 12.774 G_Regularizer: 0.000 validation_error: 21.114 +(epoch: 44, iters: 33664, time: 0.535, data: 0.000) G_L1: 14.363 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 11.992 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 44, iters: 35664, time: 0.553, data: 0.000) G_L1: 14.682 G_L1_ABSOLUTE: 2.288 G_L1_RELATIVE: 12.394 G_Regularizer: 0.000 validation_error: 20.416 +(epoch: 44, iters: 37664, time: 0.537, data: 0.000) G_L1: 13.645 G_L1_ABSOLUTE: 2.280 G_L1_RELATIVE: 11.365 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 44, iters: 39664, time: 0.539, data: 0.000) G_L1: 13.659 G_L1_ABSOLUTE: 2.466 G_L1_RELATIVE: 11.193 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 44, iters: 41664, time: 0.544, data: 0.000) G_L1: 13.728 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 11.382 G_Regularizer: 0.000 validation_error: 21.115 +(epoch: 44, iters: 43664, time: 0.534, data: 0.000) G_L1: 15.018 G_L1_ABSOLUTE: 2.593 G_L1_RELATIVE: 12.425 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 44, iters: 45664, time: 0.538, data: 0.000) G_L1: 13.819 G_L1_ABSOLUTE: 2.854 G_L1_RELATIVE: 10.965 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 44, iters: 47664, time: 0.557, data: 0.000) G_L1: 12.634 G_L1_ABSOLUTE: 2.184 G_L1_RELATIVE: 10.450 G_Regularizer: 0.000 validation_error: 21.017 +(epoch: 44, iters: 49664, time: 0.538, data: 0.000) G_L1: 15.692 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 13.246 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 44, iters: 51664, time: 0.539, data: 0.000) G_L1: 13.981 G_L1_ABSOLUTE: 3.000 G_L1_RELATIVE: 10.981 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 44, iters: 53664, time: 0.531, data: 0.000) G_L1: 12.315 G_L1_ABSOLUTE: 2.066 G_L1_RELATIVE: 10.249 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 44, iters: 55664, time: 0.563, data: 0.000) G_L1: 11.918 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 9.413 G_Regularizer: 0.000 validation_error: 20.765 +(epoch: 44, iters: 57664, time: 0.545, data: 0.000) G_L1: 13.255 G_L1_ABSOLUTE: 2.537 G_L1_RELATIVE: 10.718 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 44, iters: 59664, time: 0.531, data: 0.000) G_L1: 12.950 G_L1_ABSOLUTE: 2.207 G_L1_RELATIVE: 10.743 G_Regularizer: 0.000 validation_error: 20.465 +(epoch: 44, iters: 61664, time: 0.551, data: 0.000) G_L1: 15.299 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 12.575 G_Regularizer: 0.000 validation_error: 20.513 +(epoch: 44, iters: 63664, time: 0.556, data: 0.000) G_L1: 15.545 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 12.919 G_Regularizer: 0.000 validation_error: 20.824 +(epoch: 44, iters: 65664, time: 0.557, data: 0.000) G_L1: 11.876 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 9.504 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 44, iters: 67664, time: 0.539, data: 0.000) G_L1: 16.032 G_L1_ABSOLUTE: 2.653 G_L1_RELATIVE: 13.379 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 44, iters: 69664, time: 0.540, data: 0.000) G_L1: 14.275 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 11.904 G_Regularizer: 0.000 validation_error: 20.680 +(epoch: 44, iters: 71664, time: 0.551, data: 0.000) G_L1: 13.500 G_L1_ABSOLUTE: 2.878 G_L1_RELATIVE: 10.622 G_Regularizer: 0.000 validation_error: 20.714 +(epoch: 44, iters: 73664, time: 0.549, data: 0.000) G_L1: 14.199 G_L1_ABSOLUTE: 2.793 G_L1_RELATIVE: 11.406 G_Regularizer: 0.000 validation_error: 20.520 +(epoch: 44, iters: 75664, time: 0.525, data: 0.000) G_L1: 14.983 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 12.548 G_Regularizer: 0.000 validation_error: 21.081 +(epoch: 44, iters: 77664, time: 0.535, data: 0.000) G_L1: 13.512 G_L1_ABSOLUTE: 2.605 G_L1_RELATIVE: 10.907 G_Regularizer: 0.000 validation_error: 20.728 +(epoch: 44, iters: 79664, time: 0.550, data: 0.000) G_L1: 13.438 G_L1_ABSOLUTE: 2.100 G_L1_RELATIVE: 11.338 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 44, iters: 81664, time: 0.553, data: 0.000) G_L1: 14.166 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 11.706 G_Regularizer: 0.000 validation_error: 20.348 +(epoch: 44, iters: 83664, time: 0.538, data: 0.000) G_L1: 13.733 G_L1_ABSOLUTE: 2.475 G_L1_RELATIVE: 11.258 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 44, iters: 85664, time: 0.668, data: 0.000) G_L1: 14.807 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 12.045 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 44, iters: 87664, time: 0.680, data: 0.000) G_L1: 10.322 G_L1_ABSOLUTE: 2.159 G_L1_RELATIVE: 8.163 G_Regularizer: 0.000 validation_error: 21.139 +(epoch: 44, iters: 89664, time: 0.668, data: 0.000) G_L1: 11.637 G_L1_ABSOLUTE: 2.560 G_L1_RELATIVE: 9.077 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 44, iters: 91664, time: 0.650, data: 0.000) G_L1: 13.373 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 10.992 G_Regularizer: 0.000 validation_error: 20.794 +(epoch: 44, iters: 93664, time: 0.638, data: 0.000) G_L1: 15.732 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 12.951 G_Regularizer: 0.000 validation_error: 20.856 +(epoch: 44, iters: 95664, time: 0.658, data: 0.000) G_L1: 14.252 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 11.965 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 44, iters: 97664, time: 0.687, data: 0.000) G_L1: 12.953 G_L1_ABSOLUTE: 2.538 G_L1_RELATIVE: 10.416 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 44, iters: 99664, time: 0.688, data: 0.000) G_L1: 12.121 G_L1_ABSOLUTE: 2.465 G_L1_RELATIVE: 9.657 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 44, iters: 101664, time: 0.672, data: 0.000) G_L1: 12.312 G_L1_ABSOLUTE: 2.031 G_L1_RELATIVE: 10.281 G_Regularizer: 0.000 validation_error: 20.578 +(epoch: 44, iters: 103664, time: 0.643, data: 0.000) G_L1: 12.856 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 10.481 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 44, iters: 105664, time: 0.673, data: 0.000) G_L1: 16.290 G_L1_ABSOLUTE: 2.743 G_L1_RELATIVE: 13.548 G_Regularizer: 0.000 validation_error: 21.179 +(epoch: 44, iters: 107664, time: 0.679, data: 0.000) G_L1: 17.360 G_L1_ABSOLUTE: 2.903 G_L1_RELATIVE: 14.457 G_Regularizer: 0.000 validation_error: 20.733 +(epoch: 44, iters: 109664, time: 0.675, data: 0.000) G_L1: 13.873 G_L1_ABSOLUTE: 2.912 G_L1_RELATIVE: 10.962 G_Regularizer: 0.000 validation_error: 21.051 +(epoch: 44, iters: 111664, time: 0.663, data: 0.001) G_L1: 15.268 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 12.635 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 44, iters: 113664, time: 0.686, data: 0.000) G_L1: 14.383 G_L1_ABSOLUTE: 2.593 G_L1_RELATIVE: 11.790 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 44, iters: 115664, time: 0.671, data: 0.000) G_L1: 12.953 G_L1_ABSOLUTE: 2.065 G_L1_RELATIVE: 10.888 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 44, iters: 117664, time: 0.674, data: 0.000) G_L1: 15.540 G_L1_ABSOLUTE: 2.948 G_L1_RELATIVE: 12.592 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 44, iters: 119664, time: 0.565, data: 0.000) G_L1: 15.911 G_L1_ABSOLUTE: 2.321 G_L1_RELATIVE: 13.590 G_Regularizer: 0.000 validation_error: 21.038 +(epoch: 44, iters: 121664, time: 0.554, data: 0.000) G_L1: 18.023 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 15.288 G_Regularizer: 0.000 validation_error: 20.626 +(epoch: 44, iters: 123664, time: 0.531, data: 0.000) G_L1: 14.718 G_L1_ABSOLUTE: 2.477 G_L1_RELATIVE: 12.241 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 44, iters: 125664, time: 0.559, data: 0.000) G_L1: 12.288 G_L1_ABSOLUTE: 2.603 G_L1_RELATIVE: 9.684 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 44, iters: 127664, time: 0.553, data: 0.001) G_L1: 14.185 G_L1_ABSOLUTE: 2.243 G_L1_RELATIVE: 11.942 G_Regularizer: 0.000 validation_error: 20.274 +(epoch: 44, iters: 129664, time: 0.552, data: 0.001) G_L1: 12.771 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 10.175 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 44, iters: 131664, time: 0.539, data: 0.000) G_L1: 13.106 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 10.741 G_Regularizer: 0.000 validation_error: 21.270 +(epoch: 44, iters: 133664, time: 0.547, data: 0.000) G_L1: 14.906 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 12.476 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 44, iters: 135664, time: 0.543, data: 0.001) G_L1: 13.279 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 10.796 G_Regularizer: 0.000 validation_error: 21.028 +(epoch: 44, iters: 137664, time: 0.548, data: 0.000) G_L1: 15.095 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 12.658 G_Regularizer: 0.000 validation_error: 21.094 +(epoch: 44, iters: 139664, time: 0.555, data: 0.000) G_L1: 13.736 G_L1_ABSOLUTE: 2.825 G_L1_RELATIVE: 10.911 G_Regularizer: 0.000 validation_error: 20.907 +(epoch: 44, iters: 141664, time: 0.546, data: 0.000) G_L1: 12.980 G_L1_ABSOLUTE: 2.701 G_L1_RELATIVE: 10.279 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 44, iters: 143664, time: 0.553, data: 0.000) G_L1: 13.982 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 11.261 G_Regularizer: 0.000 validation_error: 21.071 +(epoch: 44, iters: 145664, time: 0.556, data: 0.000) G_L1: 12.555 G_L1_ABSOLUTE: 2.098 G_L1_RELATIVE: 10.457 G_Regularizer: 0.000 validation_error: 21.142 +(epoch: 44, iters: 147664, time: 0.557, data: 0.000) G_L1: 13.550 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 11.115 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 44, iters: 149664, time: 0.530, data: 0.001) G_L1: 15.160 G_L1_ABSOLUTE: 3.024 G_L1_RELATIVE: 12.135 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 44, iters: 151664, time: 0.557, data: 0.000) G_L1: 13.735 G_L1_ABSOLUTE: 2.411 G_L1_RELATIVE: 11.325 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 44, iters: 153664, time: 0.540, data: 0.000) G_L1: 11.744 G_L1_ABSOLUTE: 1.894 G_L1_RELATIVE: 9.850 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 44, iters: 155664, time: 0.539, data: 0.000) G_L1: 14.952 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 12.554 G_Regularizer: 0.000 validation_error: 21.082 +(epoch: 44, iters: 157664, time: 0.547, data: 0.001) G_L1: 14.598 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 12.161 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 44, iters: 159664, time: 0.549, data: 0.001) G_L1: 14.374 G_L1_ABSOLUTE: 2.318 G_L1_RELATIVE: 12.056 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 44, iters: 161664, time: 0.533, data: 0.000) G_L1: 14.563 G_L1_ABSOLUTE: 2.682 G_L1_RELATIVE: 11.881 G_Regularizer: 0.000 validation_error: 21.153 +(epoch: 44, iters: 163664, time: 0.550, data: 0.002) G_L1: 14.036 G_L1_ABSOLUTE: 2.142 G_L1_RELATIVE: 11.895 G_Regularizer: 0.000 validation_error: 20.697 +(epoch: 44, iters: 165664, time: 0.539, data: 0.001) G_L1: 14.653 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 11.886 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 44, iters: 167664, time: 0.530, data: 0.000) G_L1: 24.341 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 21.668 G_Regularizer: 0.000 validation_error: 21.029 +(epoch: 44, iters: 169664, time: 0.532, data: 0.000) G_L1: 14.539 G_L1_ABSOLUTE: 2.498 G_L1_RELATIVE: 12.041 G_Regularizer: 0.000 validation_error: 21.235 +(epoch: 44, iters: 171664, time: 0.539, data: 0.000) G_L1: 12.021 G_L1_ABSOLUTE: 2.353 G_L1_RELATIVE: 9.668 G_Regularizer: 0.000 validation_error: 20.547 +(epoch: 44, iters: 173664, time: 0.545, data: 0.000) G_L1: 13.551 G_L1_ABSOLUTE: 2.697 G_L1_RELATIVE: 10.854 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 44, iters: 175664, time: 0.548, data: 0.000) G_L1: 13.379 G_L1_ABSOLUTE: 2.390 G_L1_RELATIVE: 10.989 G_Regularizer: 0.000 validation_error: 20.913 +(epoch: 44, iters: 177664, time: 0.545, data: 0.000) G_L1: 12.556 G_L1_ABSOLUTE: 2.232 G_L1_RELATIVE: 10.323 G_Regularizer: 0.000 validation_error: 21.121 +(epoch: 44, iters: 179664, time: 0.538, data: 0.000) G_L1: 12.779 G_L1_ABSOLUTE: 2.558 G_L1_RELATIVE: 10.221 G_Regularizer: 0.000 validation_error: 21.146 +(epoch: 44, iters: 181664, time: 0.525, data: 0.001) G_L1: 14.219 G_L1_ABSOLUTE: 2.516 G_L1_RELATIVE: 11.704 G_Regularizer: 0.000 validation_error: 21.173 +(epoch: 44, iters: 183664, time: 0.549, data: 0.000) G_L1: 15.731 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 13.009 G_Regularizer: 0.000 validation_error: 21.138 +(epoch: 44, iters: 185664, time: 0.558, data: 0.000) G_L1: 15.280 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 12.710 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 44, iters: 187664, time: 0.559, data: 0.000) G_L1: 14.145 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 11.423 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 44, iters: 189664, time: 0.552, data: 0.000) G_L1: 15.451 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 12.826 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 44, iters: 191664, time: 0.547, data: 0.000) G_L1: 15.158 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 12.357 G_Regularizer: 0.000 validation_error: 20.566 +(epoch: 44, iters: 193664, time: 0.559, data: 0.000) G_L1: 14.323 G_L1_ABSOLUTE: 2.616 G_L1_RELATIVE: 11.706 G_Regularizer: 0.000 validation_error: 20.992 +(epoch: 44, iters: 195664, time: 0.555, data: 0.000) G_L1: 13.411 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 11.002 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 44, iters: 197664, time: 0.562, data: 0.000) G_L1: 13.375 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 10.861 G_Regularizer: 0.000 validation_error: 21.196 +(epoch: 44, iters: 199664, time: 0.545, data: 0.000) G_L1: 13.807 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 11.130 G_Regularizer: 0.000 validation_error: 21.006 +(epoch: 44, iters: 201664, time: 0.546, data: 0.000) G_L1: 15.030 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 12.510 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 44, iters: 203664, time: 0.531, data: 0.000) G_L1: 12.952 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 10.456 G_Regularizer: 0.000 validation_error: 21.170 +(epoch: 44, iters: 205664, time: 0.546, data: 0.000) G_L1: 13.989 G_L1_ABSOLUTE: 2.260 G_L1_RELATIVE: 11.729 G_Regularizer: 0.000 validation_error: 21.033 +(epoch: 44, iters: 207664, time: 0.538, data: 0.000) G_L1: 14.157 G_L1_ABSOLUTE: 2.654 G_L1_RELATIVE: 11.503 G_Regularizer: 0.000 validation_error: 20.822 +(epoch: 44, iters: 209664, time: 0.549, data: 0.000) G_L1: 13.158 G_L1_ABSOLUTE: 2.278 G_L1_RELATIVE: 10.880 G_Regularizer: 0.000 validation_error: 21.007 +(epoch: 44, iters: 211664, time: 0.525, data: 0.000) G_L1: 14.107 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 11.702 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 44, iters: 213664, time: 0.542, data: 0.001) G_L1: 12.167 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 9.881 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 44, iters: 215664, time: 0.550, data: 0.000) G_L1: 15.549 G_L1_ABSOLUTE: 2.455 G_L1_RELATIVE: 13.094 G_Regularizer: 0.000 validation_error: 21.116 +(epoch: 44, iters: 217664, time: 0.538, data: 0.000) G_L1: 12.672 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 10.315 G_Regularizer: 0.000 validation_error: 21.268 +(epoch: 44, iters: 219664, time: 0.553, data: 0.000) G_L1: 13.563 G_L1_ABSOLUTE: 2.775 G_L1_RELATIVE: 10.788 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 44, iters: 221664, time: 0.552, data: 0.000) G_L1: 13.774 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 11.386 G_Regularizer: 0.000 validation_error: 21.132 +(epoch: 44, iters: 223664, time: 0.553, data: 0.000) G_L1: 12.195 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 9.672 G_Regularizer: 0.000 validation_error: 20.629 +(epoch: 44, iters: 225664, time: 0.552, data: 0.000) G_L1: 14.490 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 11.967 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 44, iters: 227664, time: 0.554, data: 0.000) G_L1: 13.406 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 11.121 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 44, iters: 229664, time: 0.553, data: 0.000) G_L1: 14.638 G_L1_ABSOLUTE: 2.662 G_L1_RELATIVE: 11.977 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 44, iters: 231664, time: 0.532, data: 0.000) G_L1: 13.355 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 11.053 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 44, iters: 233664, time: 0.551, data: 0.000) G_L1: 12.180 G_L1_ABSOLUTE: 1.990 G_L1_RELATIVE: 10.190 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 44, iters: 235664, time: 0.548, data: 0.000) G_L1: 14.377 G_L1_ABSOLUTE: 2.772 G_L1_RELATIVE: 11.605 G_Regularizer: 0.000 validation_error: 20.683 +(epoch: 44, iters: 237664, time: 0.539, data: 0.000) G_L1: 16.523 G_L1_ABSOLUTE: 2.152 G_L1_RELATIVE: 14.371 G_Regularizer: 0.000 validation_error: 20.938 +(epoch: 44, iters: 239664, time: 0.558, data: 0.000) G_L1: 16.653 G_L1_ABSOLUTE: 2.648 G_L1_RELATIVE: 14.004 G_Regularizer: 0.000 validation_error: 21.054 +(epoch: 44, iters: 241664, time: 0.558, data: 0.000) G_L1: 11.480 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 9.068 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 44, iters: 243664, time: 0.532, data: 0.000) G_L1: 20.372 G_L1_ABSOLUTE: 2.788 G_L1_RELATIVE: 17.583 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 44, iters: 245664, time: 0.533, data: 0.000) G_L1: 11.691 G_L1_ABSOLUTE: 2.137 G_L1_RELATIVE: 9.554 G_Regularizer: 0.000 validation_error: 20.818 +(epoch: 44, iters: 247664, time: 0.551, data: 0.000) G_L1: 12.576 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 10.071 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 44, iters: 249664, time: 0.559, data: 0.000) G_L1: 13.963 G_L1_ABSOLUTE: 2.514 G_L1_RELATIVE: 11.449 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 44, iters: 251664, time: 0.545, data: 0.001) G_L1: 11.396 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 8.967 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 44, iters: 253664, time: 0.538, data: 0.000) G_L1: 13.734 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 11.088 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 44, iters: 255664, time: 0.557, data: 0.000) G_L1: 17.421 G_L1_ABSOLUTE: 2.253 G_L1_RELATIVE: 15.169 G_Regularizer: 0.000 validation_error: 20.778 +(epoch: 44, iters: 257664, time: 0.561, data: 0.000) G_L1: 13.732 G_L1_ABSOLUTE: 2.471 G_L1_RELATIVE: 11.261 G_Regularizer: 0.000 validation_error: 20.874 +(epoch: 44, iters: 259664, time: 0.554, data: 0.000) G_L1: 12.591 G_L1_ABSOLUTE: 2.148 G_L1_RELATIVE: 10.443 G_Regularizer: 0.000 validation_error: 20.911 +(epoch: 44, iters: 261664, time: 0.543, data: 0.000) G_L1: 12.428 G_L1_ABSOLUTE: 2.257 G_L1_RELATIVE: 10.170 G_Regularizer: 0.000 validation_error: 20.460 +(epoch: 44, iters: 263664, time: 0.534, data: 0.000) G_L1: 14.386 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 11.871 G_Regularizer: 0.000 validation_error: 21.080 +(epoch: 44, iters: 265664, time: 0.552, data: 0.001) G_L1: 10.812 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 8.326 G_Regularizer: 0.000 validation_error: 20.970 +(epoch: 44, iters: 267664, time: 0.539, data: 0.000) G_L1: 13.236 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 10.974 G_Regularizer: 0.000 validation_error: 20.567 +(epoch: 44, iters: 269664, time: 0.536, data: 0.000) G_L1: 14.057 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 11.610 G_Regularizer: 0.000 validation_error: 21.106 +(epoch: 44, iters: 271664, time: 0.554, data: 0.000) G_L1: 14.849 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 12.651 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 44, iters: 273664, time: 0.562, data: 0.001) G_L1: 14.149 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 11.579 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 44, iters: 275664, time: 0.557, data: 0.000) G_L1: 13.989 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 11.243 G_Regularizer: 0.000 validation_error: 21.130 +(epoch: 44, iters: 277664, time: 0.539, data: 0.000) G_L1: 14.709 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 12.161 G_Regularizer: 0.000 validation_error: 20.728 +(epoch: 44, iters: 279664, time: 0.550, data: 0.000) G_L1: 14.411 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 11.820 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 44, iters: 281664, time: 0.551, data: 0.001) G_L1: 14.643 G_L1_ABSOLUTE: 2.032 G_L1_RELATIVE: 12.611 G_Regularizer: 0.000 validation_error: 20.769 +(epoch: 44, iters: 283664, time: 0.540, data: 0.000) G_L1: 15.154 G_L1_ABSOLUTE: 2.661 G_L1_RELATIVE: 12.494 G_Regularizer: 0.000 validation_error: 20.791 +(epoch: 44, iters: 285664, time: 0.542, data: 0.000) G_L1: 15.561 G_L1_ABSOLUTE: 3.076 G_L1_RELATIVE: 12.484 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 44, iters: 287664, time: 0.531, data: 0.000) G_L1: 14.634 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 12.165 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 44, iters: 289664, time: 0.536, data: 0.000) G_L1: 16.457 G_L1_ABSOLUTE: 3.252 G_L1_RELATIVE: 13.206 G_Regularizer: 0.000 validation_error: 20.778 +(epoch: 44, iters: 291664, time: 0.550, data: 0.000) G_L1: 14.604 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 12.248 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 44, iters: 293664, time: 0.538, data: 0.000) G_L1: 16.174 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 13.681 G_Regularizer: 0.000 validation_error: 21.114 +(epoch: 44, iters: 295664, time: 0.527, data: 0.000) G_L1: 13.417 G_L1_ABSOLUTE: 2.434 G_L1_RELATIVE: 10.983 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 44, iters: 297664, time: 0.544, data: 0.000) G_L1: 11.399 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 8.942 G_Regularizer: 0.000 validation_error: 20.991 +(epoch: 44, iters: 299664, time: 0.535, data: 0.000) G_L1: 15.657 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 13.287 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 44, iters: 301664, time: 0.538, data: 0.000) G_L1: 15.529 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 13.121 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 45, iters: 912, time: 0.531, data: 0.000) G_L1: 12.325 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 10.051 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 45, iters: 2912, time: 0.556, data: 0.000) G_L1: 11.568 G_L1_ABSOLUTE: 2.163 G_L1_RELATIVE: 9.405 G_Regularizer: 0.000 validation_error: 20.577 +(epoch: 45, iters: 4912, time: 0.530, data: 0.000) G_L1: 14.774 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 12.420 G_Regularizer: 0.000 validation_error: 20.964 +(epoch: 45, iters: 6912, time: 0.549, data: 0.000) G_L1: 19.406 G_L1_ABSOLUTE: 2.531 G_L1_RELATIVE: 16.875 G_Regularizer: 0.000 validation_error: 20.987 +(epoch: 45, iters: 8912, time: 0.554, data: 0.000) G_L1: 13.827 G_L1_ABSOLUTE: 1.972 G_L1_RELATIVE: 11.855 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 45, iters: 10912, time: 0.554, data: 0.000) G_L1: 13.889 G_L1_ABSOLUTE: 2.963 G_L1_RELATIVE: 10.925 G_Regularizer: 0.000 validation_error: 20.657 +(epoch: 45, iters: 12912, time: 0.546, data: 0.000) G_L1: 13.482 G_L1_ABSOLUTE: 2.667 G_L1_RELATIVE: 10.815 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 45, iters: 14912, time: 0.561, data: 0.000) G_L1: 13.473 G_L1_ABSOLUTE: 2.786 G_L1_RELATIVE: 10.687 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 45, iters: 16912, time: 0.554, data: 0.000) G_L1: 13.540 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 10.992 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 45, iters: 18912, time: 0.560, data: 0.000) G_L1: 14.248 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 11.827 G_Regularizer: 0.000 validation_error: 20.614 +(epoch: 45, iters: 20912, time: 0.554, data: 0.000) G_L1: 13.981 G_L1_ABSOLUTE: 2.785 G_L1_RELATIVE: 11.196 G_Regularizer: 0.000 validation_error: 21.024 +(epoch: 45, iters: 22912, time: 0.541, data: 0.000) G_L1: 11.274 G_L1_ABSOLUTE: 2.188 G_L1_RELATIVE: 9.085 G_Regularizer: 0.000 validation_error: 20.856 +(epoch: 45, iters: 24912, time: 0.545, data: 0.000) G_L1: 14.602 G_L1_ABSOLUTE: 2.906 G_L1_RELATIVE: 11.696 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 45, iters: 26912, time: 0.544, data: 0.000) G_L1: 12.710 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 10.122 G_Regularizer: 0.000 validation_error: 20.753 +(epoch: 45, iters: 28912, time: 0.549, data: 0.000) G_L1: 13.804 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 11.298 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 45, iters: 30912, time: 0.543, data: 0.001) G_L1: 13.549 G_L1_ABSOLUTE: 2.305 G_L1_RELATIVE: 11.245 G_Regularizer: 0.000 validation_error: 20.798 +(epoch: 45, iters: 32912, time: 0.547, data: 0.000) G_L1: 13.908 G_L1_ABSOLUTE: 2.382 G_L1_RELATIVE: 11.527 G_Regularizer: 0.000 validation_error: 20.593 +(epoch: 45, iters: 34912, time: 0.544, data: 0.000) G_L1: 15.413 G_L1_ABSOLUTE: 2.145 G_L1_RELATIVE: 13.268 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 45, iters: 36912, time: 0.555, data: 0.000) G_L1: 12.771 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 10.387 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 45, iters: 38912, time: 0.555, data: 0.000) G_L1: 12.948 G_L1_ABSOLUTE: 2.337 G_L1_RELATIVE: 10.611 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 45, iters: 40912, time: 0.547, data: 0.000) G_L1: 12.964 G_L1_ABSOLUTE: 2.937 G_L1_RELATIVE: 10.027 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 45, iters: 42912, time: 0.546, data: 0.000) G_L1: 16.626 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 14.220 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 45, iters: 44912, time: 0.553, data: 0.000) G_L1: 12.110 G_L1_ABSOLUTE: 2.104 G_L1_RELATIVE: 10.006 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 45, iters: 46912, time: 0.555, data: 0.000) G_L1: 12.978 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 10.578 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 45, iters: 48912, time: 0.561, data: 0.000) G_L1: 16.011 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 13.244 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 45, iters: 50912, time: 0.533, data: 0.000) G_L1: 12.741 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 10.244 G_Regularizer: 0.000 validation_error: 20.859 +(epoch: 45, iters: 52912, time: 0.561, data: 0.000) G_L1: 14.835 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 12.200 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 45, iters: 54912, time: 0.552, data: 0.000) G_L1: 14.650 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 12.176 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 45, iters: 56912, time: 0.560, data: 0.000) G_L1: 11.943 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 9.626 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 45, iters: 58912, time: 0.551, data: 0.000) G_L1: 16.132 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 13.352 G_Regularizer: 0.000 validation_error: 20.994 +(epoch: 45, iters: 60912, time: 0.548, data: 0.000) G_L1: 11.620 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 9.289 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 45, iters: 62912, time: 0.558, data: 0.000) G_L1: 15.430 G_L1_ABSOLUTE: 2.139 G_L1_RELATIVE: 13.292 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 45, iters: 64912, time: 0.555, data: 0.000) G_L1: 14.492 G_L1_ABSOLUTE: 2.475 G_L1_RELATIVE: 12.018 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 45, iters: 66912, time: 0.555, data: 0.000) G_L1: 12.844 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 10.188 G_Regularizer: 0.000 validation_error: 20.675 +(epoch: 45, iters: 68912, time: 0.530, data: 0.000) G_L1: 14.981 G_L1_ABSOLUTE: 2.326 G_L1_RELATIVE: 12.654 G_Regularizer: 0.000 validation_error: 21.107 +(epoch: 45, iters: 70912, time: 0.548, data: 0.000) G_L1: 14.608 G_L1_ABSOLUTE: 2.257 G_L1_RELATIVE: 12.351 G_Regularizer: 0.000 validation_error: 20.592 +(epoch: 45, iters: 72912, time: 0.559, data: 0.000) G_L1: 15.087 G_L1_ABSOLUTE: 3.155 G_L1_RELATIVE: 11.932 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 45, iters: 74912, time: 0.532, data: 0.000) G_L1: 14.655 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 12.035 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 45, iters: 76912, time: 0.535, data: 0.001) G_L1: 13.630 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 11.180 G_Regularizer: 0.000 validation_error: 20.680 +(epoch: 45, iters: 78912, time: 0.558, data: 0.000) G_L1: 12.824 G_L1_ABSOLUTE: 2.123 G_L1_RELATIVE: 10.701 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 45, iters: 80912, time: 0.551, data: 0.000) G_L1: 12.364 G_L1_ABSOLUTE: 2.199 G_L1_RELATIVE: 10.165 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 45, iters: 82912, time: 0.544, data: 0.001) G_L1: 15.620 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 12.922 G_Regularizer: 0.000 validation_error: 20.693 +(epoch: 45, iters: 84912, time: 0.537, data: 0.000) G_L1: 13.472 G_L1_ABSOLUTE: 2.099 G_L1_RELATIVE: 11.373 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 45, iters: 86912, time: 0.561, data: 0.000) G_L1: 12.495 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 10.211 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 45, iters: 88912, time: 0.559, data: 0.001) G_L1: 13.591 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 11.214 G_Regularizer: 0.000 validation_error: 20.669 +(epoch: 45, iters: 90912, time: 0.547, data: 0.000) G_L1: 13.515 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 10.831 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 45, iters: 92912, time: 0.548, data: 0.000) G_L1: 15.287 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 12.509 G_Regularizer: 0.000 validation_error: 20.913 +(epoch: 45, iters: 94912, time: 0.530, data: 0.000) G_L1: 12.667 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 10.045 G_Regularizer: 0.000 validation_error: 20.826 +(epoch: 45, iters: 96912, time: 0.533, data: 0.000) G_L1: 14.379 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 11.737 G_Regularizer: 0.000 validation_error: 20.982 +(epoch: 45, iters: 98912, time: 0.551, data: 0.000) G_L1: 13.414 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 10.609 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 45, iters: 100912, time: 0.553, data: 0.000) G_L1: 12.269 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 9.863 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 45, iters: 102912, time: 0.542, data: 0.000) G_L1: 15.669 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 13.210 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 45, iters: 104912, time: 0.558, data: 0.000) G_L1: 13.533 G_L1_ABSOLUTE: 2.749 G_L1_RELATIVE: 10.784 G_Regularizer: 0.000 validation_error: 20.756 +(epoch: 45, iters: 106912, time: 0.553, data: 0.000) G_L1: 14.889 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 12.487 G_Regularizer: 0.000 validation_error: 20.472 +(epoch: 45, iters: 108912, time: 0.539, data: 0.000) G_L1: 13.449 G_L1_ABSOLUTE: 2.688 G_L1_RELATIVE: 10.761 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 45, iters: 110912, time: 0.547, data: 0.000) G_L1: 14.344 G_L1_ABSOLUTE: 2.374 G_L1_RELATIVE: 11.970 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 45, iters: 112912, time: 0.558, data: 0.000) G_L1: 13.522 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 11.109 G_Regularizer: 0.000 validation_error: 20.691 +(epoch: 45, iters: 114912, time: 0.559, data: 0.000) G_L1: 12.916 G_L1_ABSOLUTE: 2.146 G_L1_RELATIVE: 10.771 G_Regularizer: 0.000 validation_error: 21.095 +(epoch: 45, iters: 116912, time: 0.536, data: 0.000) G_L1: 13.311 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 10.638 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 45, iters: 118912, time: 0.548, data: 0.000) G_L1: 12.679 G_L1_ABSOLUTE: 2.025 G_L1_RELATIVE: 10.654 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 45, iters: 120912, time: 0.553, data: 0.000) G_L1: 15.030 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 12.636 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 45, iters: 122912, time: 0.539, data: 0.000) G_L1: 13.833 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 11.245 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 45, iters: 124912, time: 0.543, data: 0.000) G_L1: 16.347 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 13.747 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 45, iters: 126912, time: 0.562, data: 0.000) G_L1: 13.368 G_L1_ABSOLUTE: 2.613 G_L1_RELATIVE: 10.755 G_Regularizer: 0.000 validation_error: 20.693 +(epoch: 45, iters: 128912, time: 0.539, data: 0.000) G_L1: 13.526 G_L1_ABSOLUTE: 2.205 G_L1_RELATIVE: 11.321 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 45, iters: 130912, time: 0.556, data: 0.000) G_L1: 13.877 G_L1_ABSOLUTE: 2.410 G_L1_RELATIVE: 11.467 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 45, iters: 132912, time: 0.540, data: 0.000) G_L1: 13.944 G_L1_ABSOLUTE: 2.277 G_L1_RELATIVE: 11.667 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 45, iters: 134912, time: 0.547, data: 0.000) G_L1: 13.832 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 11.283 G_Regularizer: 0.000 validation_error: 20.598 +(epoch: 45, iters: 136912, time: 0.528, data: 0.000) G_L1: 15.013 G_L1_ABSOLUTE: 2.090 G_L1_RELATIVE: 12.923 G_Regularizer: 0.000 validation_error: 20.462 +(epoch: 45, iters: 138912, time: 0.538, data: 0.000) G_L1: 18.113 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 15.590 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 45, iters: 140912, time: 0.536, data: 0.000) G_L1: 14.581 G_L1_ABSOLUTE: 2.593 G_L1_RELATIVE: 11.988 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 45, iters: 142912, time: 0.551, data: 0.000) G_L1: 12.635 G_L1_ABSOLUTE: 2.174 G_L1_RELATIVE: 10.461 G_Regularizer: 0.000 validation_error: 20.862 +(epoch: 45, iters: 144912, time: 0.540, data: 0.000) G_L1: 15.563 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 13.265 G_Regularizer: 0.000 validation_error: 20.602 +(epoch: 45, iters: 146912, time: 0.544, data: 0.000) G_L1: 14.898 G_L1_ABSOLUTE: 2.199 G_L1_RELATIVE: 12.699 G_Regularizer: 0.000 validation_error: 21.024 +(epoch: 45, iters: 148912, time: 0.549, data: 0.000) G_L1: 14.120 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 11.854 G_Regularizer: 0.000 validation_error: 20.747 +(epoch: 45, iters: 150912, time: 0.552, data: 0.000) G_L1: 15.065 G_L1_ABSOLUTE: 2.779 G_L1_RELATIVE: 12.287 G_Regularizer: 0.000 validation_error: 20.665 +(epoch: 45, iters: 152912, time: 0.550, data: 0.000) G_L1: 14.803 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 12.319 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 45, iters: 154912, time: 0.533, data: 0.000) G_L1: 14.263 G_L1_ABSOLUTE: 2.186 G_L1_RELATIVE: 12.077 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 45, iters: 156912, time: 0.537, data: 0.000) G_L1: 13.356 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 10.543 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 45, iters: 158912, time: 0.531, data: 0.000) G_L1: 13.681 G_L1_ABSOLUTE: 2.130 G_L1_RELATIVE: 11.551 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 45, iters: 160912, time: 0.558, data: 0.000) G_L1: 13.346 G_L1_ABSOLUTE: 2.693 G_L1_RELATIVE: 10.653 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 45, iters: 162912, time: 0.547, data: 0.000) G_L1: 11.734 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 9.476 G_Regularizer: 0.000 validation_error: 21.017 +(epoch: 45, iters: 164912, time: 0.551, data: 0.001) G_L1: 14.742 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 12.367 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 45, iters: 166912, time: 0.541, data: 0.000) G_L1: 12.975 G_L1_ABSOLUTE: 2.362 G_L1_RELATIVE: 10.614 G_Regularizer: 0.000 validation_error: 20.971 +(epoch: 45, iters: 168912, time: 0.560, data: 0.000) G_L1: 14.345 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 11.455 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 45, iters: 170912, time: 0.551, data: 0.000) G_L1: 15.889 G_L1_ABSOLUTE: 2.743 G_L1_RELATIVE: 13.146 G_Regularizer: 0.000 validation_error: 20.757 +(epoch: 45, iters: 172912, time: 0.543, data: 0.000) G_L1: 13.414 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 10.723 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 45, iters: 174912, time: 0.564, data: 0.000) G_L1: 13.832 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 11.331 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 45, iters: 176912, time: 0.557, data: 0.001) G_L1: 13.623 G_L1_ABSOLUTE: 1.989 G_L1_RELATIVE: 11.633 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 45, iters: 178912, time: 0.547, data: 0.000) G_L1: 12.384 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 10.101 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 45, iters: 180912, time: 0.554, data: 0.000) G_L1: 12.701 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 10.129 G_Regularizer: 0.000 validation_error: 20.859 +(epoch: 45, iters: 182912, time: 0.545, data: 0.000) G_L1: 14.088 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 11.700 G_Regularizer: 0.000 validation_error: 21.223 +(epoch: 45, iters: 184912, time: 0.541, data: 0.000) G_L1: 13.657 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 11.188 G_Regularizer: 0.000 validation_error: 21.324 +(epoch: 45, iters: 186912, time: 0.554, data: 0.000) G_L1: 11.856 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 9.541 G_Regularizer: 0.000 validation_error: 21.297 +(epoch: 45, iters: 188912, time: 0.545, data: 0.000) G_L1: 14.098 G_L1_ABSOLUTE: 2.920 G_L1_RELATIVE: 11.178 G_Regularizer: 0.000 validation_error: 20.911 +(epoch: 45, iters: 190912, time: 0.529, data: 0.000) G_L1: 14.718 G_L1_ABSOLUTE: 2.954 G_L1_RELATIVE: 11.764 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 45, iters: 192912, time: 0.541, data: 0.000) G_L1: 15.457 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 12.741 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 45, iters: 194912, time: 0.559, data: 0.000) G_L1: 13.891 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 11.298 G_Regularizer: 0.000 validation_error: 20.662 +(epoch: 45, iters: 196912, time: 0.544, data: 0.000) G_L1: 12.764 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 10.213 G_Regularizer: 0.000 validation_error: 21.146 +(epoch: 45, iters: 198912, time: 0.552, data: 0.000) G_L1: 13.252 G_L1_ABSOLUTE: 2.681 G_L1_RELATIVE: 10.571 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 45, iters: 200912, time: 0.555, data: 0.000) G_L1: 13.043 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 10.376 G_Regularizer: 0.000 validation_error: 21.076 +(epoch: 45, iters: 202912, time: 0.565, data: 0.000) G_L1: 14.355 G_L1_ABSOLUTE: 2.178 G_L1_RELATIVE: 12.177 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 45, iters: 204912, time: 0.537, data: 0.000) G_L1: 14.718 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 12.256 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 45, iters: 206912, time: 0.558, data: 0.000) G_L1: 12.417 G_L1_ABSOLUTE: 2.207 G_L1_RELATIVE: 10.210 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 45, iters: 208912, time: 0.535, data: 0.000) G_L1: 13.597 G_L1_ABSOLUTE: 2.475 G_L1_RELATIVE: 11.122 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 45, iters: 210912, time: 0.543, data: 0.000) G_L1: 13.592 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 11.247 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 45, iters: 212912, time: 0.538, data: 0.000) G_L1: 13.934 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 11.559 G_Regularizer: 0.000 validation_error: 21.025 +(epoch: 45, iters: 214912, time: 0.560, data: 0.000) G_L1: 14.406 G_L1_ABSOLUTE: 2.197 G_L1_RELATIVE: 12.209 G_Regularizer: 0.000 validation_error: 20.963 +(epoch: 45, iters: 216912, time: 0.546, data: 0.000) G_L1: 14.902 G_L1_ABSOLUTE: 2.740 G_L1_RELATIVE: 12.162 G_Regularizer: 0.000 validation_error: 21.038 +(epoch: 45, iters: 218912, time: 0.552, data: 0.000) G_L1: 13.231 G_L1_ABSOLUTE: 2.238 G_L1_RELATIVE: 10.993 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 45, iters: 220912, time: 0.555, data: 0.000) G_L1: 14.574 G_L1_ABSOLUTE: 2.387 G_L1_RELATIVE: 12.188 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 45, iters: 222912, time: 0.558, data: 0.000) G_L1: 13.484 G_L1_ABSOLUTE: 2.647 G_L1_RELATIVE: 10.837 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 45, iters: 224912, time: 0.538, data: 0.001) G_L1: 13.402 G_L1_ABSOLUTE: 2.285 G_L1_RELATIVE: 11.116 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 45, iters: 226912, time: 0.536, data: 0.000) G_L1: 13.634 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 11.179 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 45, iters: 228912, time: 0.558, data: 0.000) G_L1: 14.295 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 11.661 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 45, iters: 230912, time: 0.542, data: 0.000) G_L1: 15.519 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 12.984 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 45, iters: 232912, time: 0.544, data: 0.000) G_L1: 13.650 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 11.368 G_Regularizer: 0.000 validation_error: 21.155 +(epoch: 45, iters: 234912, time: 0.549, data: 0.000) G_L1: 16.477 G_L1_ABSOLUTE: 3.072 G_L1_RELATIVE: 13.405 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 45, iters: 236912, time: 0.542, data: 0.000) G_L1: 15.186 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 12.614 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 45, iters: 238912, time: 0.540, data: 0.000) G_L1: 12.063 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 9.800 G_Regularizer: 0.000 validation_error: 21.243 +(epoch: 45, iters: 240912, time: 0.544, data: 0.000) G_L1: 13.855 G_L1_ABSOLUTE: 2.261 G_L1_RELATIVE: 11.594 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 45, iters: 242912, time: 0.549, data: 0.000) G_L1: 14.809 G_L1_ABSOLUTE: 2.801 G_L1_RELATIVE: 12.008 G_Regularizer: 0.000 validation_error: 20.826 +(epoch: 45, iters: 244912, time: 0.544, data: 0.000) G_L1: 15.088 G_L1_ABSOLUTE: 2.434 G_L1_RELATIVE: 12.654 G_Regularizer: 0.000 validation_error: 20.604 +(epoch: 45, iters: 246912, time: 0.544, data: 0.000) G_L1: 13.458 G_L1_ABSOLUTE: 2.312 G_L1_RELATIVE: 11.146 G_Regularizer: 0.000 validation_error: 20.959 +(epoch: 45, iters: 248912, time: 0.527, data: 0.000) G_L1: 12.984 G_L1_ABSOLUTE: 2.859 G_L1_RELATIVE: 10.126 G_Regularizer: 0.000 validation_error: 20.954 +(epoch: 45, iters: 250912, time: 0.541, data: 0.000) G_L1: 14.836 G_L1_ABSOLUTE: 2.780 G_L1_RELATIVE: 12.056 G_Regularizer: 0.000 validation_error: 20.647 +(epoch: 45, iters: 252912, time: 0.526, data: 0.000) G_L1: 15.372 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 12.599 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 45, iters: 254912, time: 0.551, data: 0.000) G_L1: 15.427 G_L1_ABSOLUTE: 3.341 G_L1_RELATIVE: 12.086 G_Regularizer: 0.000 validation_error: 20.957 +(epoch: 45, iters: 256912, time: 0.552, data: 0.000) G_L1: 14.768 G_L1_ABSOLUTE: 2.920 G_L1_RELATIVE: 11.848 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 45, iters: 258912, time: 0.536, data: 0.000) G_L1: 15.252 G_L1_ABSOLUTE: 2.477 G_L1_RELATIVE: 12.775 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 45, iters: 260912, time: 0.553, data: 0.000) G_L1: 11.715 G_L1_ABSOLUTE: 2.337 G_L1_RELATIVE: 9.379 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 45, iters: 262912, time: 0.556, data: 0.000) G_L1: 14.512 G_L1_ABSOLUTE: 3.017 G_L1_RELATIVE: 11.495 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 45, iters: 264912, time: 0.535, data: 0.000) G_L1: 13.746 G_L1_ABSOLUTE: 2.006 G_L1_RELATIVE: 11.740 G_Regularizer: 0.000 validation_error: 20.944 +(epoch: 45, iters: 266912, time: 0.548, data: 0.000) G_L1: 16.370 G_L1_ABSOLUTE: 2.886 G_L1_RELATIVE: 13.484 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 45, iters: 268912, time: 0.555, data: 0.001) G_L1: 14.656 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 11.954 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 45, iters: 270912, time: 0.549, data: 0.000) G_L1: 10.988 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 8.705 G_Regularizer: 0.000 validation_error: 21.072 +(epoch: 45, iters: 272912, time: 0.538, data: 0.000) G_L1: 14.963 G_L1_ABSOLUTE: 2.398 G_L1_RELATIVE: 12.566 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 45, iters: 274912, time: 0.545, data: 0.000) G_L1: 16.807 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 14.295 G_Regularizer: 0.000 validation_error: 21.297 +(epoch: 45, iters: 276912, time: 0.538, data: 0.000) G_L1: 13.994 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 11.372 G_Regularizer: 0.000 validation_error: 21.251 +(epoch: 45, iters: 278912, time: 0.559, data: 0.000) G_L1: 17.085 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 14.377 G_Regularizer: 0.000 validation_error: 21.353 +(epoch: 45, iters: 280912, time: 0.555, data: 0.001) G_L1: 14.177 G_L1_ABSOLUTE: 2.142 G_L1_RELATIVE: 12.034 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 45, iters: 282912, time: 0.541, data: 0.000) G_L1: 12.332 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 9.823 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 45, iters: 284912, time: 0.534, data: 0.000) G_L1: 14.091 G_L1_ABSOLUTE: 2.139 G_L1_RELATIVE: 11.951 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 45, iters: 286912, time: 0.548, data: 0.000) G_L1: 14.436 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 11.761 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 45, iters: 288912, time: 0.559, data: 0.001) G_L1: 14.049 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 11.325 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 45, iters: 290912, time: 0.536, data: 0.000) G_L1: 14.785 G_L1_ABSOLUTE: 2.681 G_L1_RELATIVE: 12.104 G_Regularizer: 0.000 validation_error: 21.153 +(epoch: 45, iters: 292912, time: 0.560, data: 0.000) G_L1: 14.719 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 12.364 G_Regularizer: 0.000 validation_error: 20.808 +(epoch: 45, iters: 294912, time: 0.552, data: 0.000) G_L1: 13.824 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 11.118 G_Regularizer: 0.000 validation_error: 21.014 +(epoch: 45, iters: 296912, time: 0.557, data: 0.000) G_L1: 13.322 G_L1_ABSOLUTE: 2.711 G_L1_RELATIVE: 10.610 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 45, iters: 298912, time: 0.533, data: 0.000) G_L1: 12.308 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 9.935 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 45, iters: 300912, time: 0.548, data: 0.000) G_L1: 13.233 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 10.950 G_Regularizer: 0.000 validation_error: 20.959 +(epoch: 46, iters: 160, time: 0.546, data: 0.000) G_L1: 12.205 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 9.709 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 46, iters: 2160, time: 0.553, data: 0.000) G_L1: 15.929 G_L1_ABSOLUTE: 2.362 G_L1_RELATIVE: 13.567 G_Regularizer: 0.000 validation_error: 20.650 +(epoch: 46, iters: 4160, time: 0.535, data: 0.000) G_L1: 11.617 G_L1_ABSOLUTE: 2.253 G_L1_RELATIVE: 9.364 G_Regularizer: 0.000 validation_error: 20.588 +(epoch: 46, iters: 6160, time: 0.556, data: 0.001) G_L1: 14.480 G_L1_ABSOLUTE: 3.309 G_L1_RELATIVE: 11.171 G_Regularizer: 0.000 validation_error: 21.056 +(epoch: 46, iters: 8160, time: 0.545, data: 0.000) G_L1: 10.192 G_L1_ABSOLUTE: 2.131 G_L1_RELATIVE: 8.061 G_Regularizer: 0.000 validation_error: 21.174 +(epoch: 46, iters: 10160, time: 0.558, data: 0.000) G_L1: 14.256 G_L1_ABSOLUTE: 2.127 G_L1_RELATIVE: 12.130 G_Regularizer: 0.000 validation_error: 20.474 +(epoch: 46, iters: 12160, time: 0.547, data: 0.000) G_L1: 14.516 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 11.743 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 46, iters: 14160, time: 0.548, data: 0.000) G_L1: 15.049 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 12.684 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 46, iters: 16160, time: 0.547, data: 0.001) G_L1: 13.996 G_L1_ABSOLUTE: 2.256 G_L1_RELATIVE: 11.739 G_Regularizer: 0.000 validation_error: 21.088 +(epoch: 46, iters: 18160, time: 0.555, data: 0.000) G_L1: 12.049 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 9.513 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 46, iters: 20160, time: 0.565, data: 0.000) G_L1: 14.241 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 11.875 G_Regularizer: 0.000 validation_error: 21.029 +(epoch: 46, iters: 22160, time: 0.533, data: 0.000) G_L1: 14.137 G_L1_ABSOLUTE: 2.444 G_L1_RELATIVE: 11.692 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 46, iters: 24160, time: 0.556, data: 0.000) G_L1: 14.732 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 12.318 G_Regularizer: 0.000 validation_error: 20.957 +(epoch: 46, iters: 26160, time: 0.556, data: 0.000) G_L1: 13.747 G_L1_ABSOLUTE: 2.566 G_L1_RELATIVE: 11.180 G_Regularizer: 0.000 validation_error: 20.637 +(epoch: 46, iters: 28160, time: 0.566, data: 0.000) G_L1: 15.750 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 13.317 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 46, iters: 30160, time: 0.532, data: 0.000) G_L1: 25.271 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 22.390 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 46, iters: 32160, time: 0.556, data: 0.000) G_L1: 14.843 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 12.413 G_Regularizer: 0.000 validation_error: 21.285 +(epoch: 46, iters: 34160, time: 0.555, data: 0.000) G_L1: 13.749 G_L1_ABSOLUTE: 2.953 G_L1_RELATIVE: 10.797 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 46, iters: 36160, time: 0.544, data: 0.000) G_L1: 14.151 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 11.258 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 46, iters: 38160, time: 0.552, data: 0.000) G_L1: 14.118 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 11.735 G_Regularizer: 0.000 validation_error: 20.854 +(epoch: 46, iters: 40160, time: 0.559, data: 0.000) G_L1: 16.428 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 14.075 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 46, iters: 42160, time: 0.550, data: 0.000) G_L1: 14.367 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 11.739 G_Regularizer: 0.000 validation_error: 20.955 +(epoch: 46, iters: 44160, time: 0.534, data: 0.000) G_L1: 14.633 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 12.220 G_Regularizer: 0.000 validation_error: 20.939 +(epoch: 46, iters: 46160, time: 0.534, data: 0.000) G_L1: 14.090 G_L1_ABSOLUTE: 2.537 G_L1_RELATIVE: 11.553 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 46, iters: 48160, time: 0.544, data: 0.000) G_L1: 11.956 G_L1_ABSOLUTE: 2.245 G_L1_RELATIVE: 9.711 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 46, iters: 50160, time: 0.552, data: 0.000) G_L1: 13.873 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 11.470 G_Regularizer: 0.000 validation_error: 20.763 +(epoch: 46, iters: 52160, time: 0.542, data: 0.000) G_L1: 11.227 G_L1_ABSOLUTE: 2.296 G_L1_RELATIVE: 8.930 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 46, iters: 54160, time: 0.549, data: 0.000) G_L1: 13.779 G_L1_ABSOLUTE: 2.202 G_L1_RELATIVE: 11.577 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 46, iters: 56160, time: 0.554, data: 0.000) G_L1: 13.107 G_L1_ABSOLUTE: 2.196 G_L1_RELATIVE: 10.911 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 46, iters: 58160, time: 0.553, data: 0.000) G_L1: 14.125 G_L1_ABSOLUTE: 2.971 G_L1_RELATIVE: 11.153 G_Regularizer: 0.000 validation_error: 20.899 +(epoch: 46, iters: 60160, time: 0.532, data: 0.000) G_L1: 17.177 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 14.559 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 46, iters: 62160, time: 0.550, data: 0.000) G_L1: 13.997 G_L1_ABSOLUTE: 2.146 G_L1_RELATIVE: 11.852 G_Regularizer: 0.000 validation_error: 20.569 +(epoch: 46, iters: 64160, time: 0.541, data: 0.000) G_L1: 13.882 G_L1_ABSOLUTE: 2.179 G_L1_RELATIVE: 11.703 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 46, iters: 66160, time: 0.553, data: 0.001) G_L1: 15.299 G_L1_ABSOLUTE: 2.868 G_L1_RELATIVE: 12.431 G_Regularizer: 0.000 validation_error: 20.820 +(epoch: 46, iters: 68160, time: 0.551, data: 0.000) G_L1: 12.648 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 10.147 G_Regularizer: 0.000 validation_error: 20.932 +(epoch: 46, iters: 70160, time: 0.554, data: 0.000) G_L1: 14.140 G_L1_ABSOLUTE: 2.184 G_L1_RELATIVE: 11.956 G_Regularizer: 0.000 validation_error: 20.836 +(epoch: 46, iters: 72160, time: 0.550, data: 0.000) G_L1: 15.911 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 12.960 G_Regularizer: 0.000 validation_error: 20.775 +(epoch: 46, iters: 74160, time: 0.558, data: 0.000) G_L1: 14.518 G_L1_ABSOLUTE: 2.647 G_L1_RELATIVE: 11.871 G_Regularizer: 0.000 validation_error: 21.132 +(epoch: 46, iters: 76160, time: 0.555, data: 0.000) G_L1: 14.460 G_L1_ABSOLUTE: 2.728 G_L1_RELATIVE: 11.733 G_Regularizer: 0.000 validation_error: 20.789 +(epoch: 46, iters: 78160, time: 0.549, data: 0.000) G_L1: 13.665 G_L1_ABSOLUTE: 2.350 G_L1_RELATIVE: 11.314 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 46, iters: 80160, time: 0.534, data: 0.000) G_L1: 14.599 G_L1_ABSOLUTE: 2.837 G_L1_RELATIVE: 11.762 G_Regularizer: 0.000 validation_error: 20.742 +(epoch: 46, iters: 82160, time: 0.562, data: 0.000) G_L1: 13.851 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 11.370 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 46, iters: 84160, time: 0.554, data: 0.000) G_L1: 16.545 G_L1_ABSOLUTE: 2.398 G_L1_RELATIVE: 14.147 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 46, iters: 86160, time: 0.540, data: 0.000) G_L1: 14.633 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 12.294 G_Regularizer: 0.000 validation_error: 20.669 +(epoch: 46, iters: 88160, time: 0.541, data: 0.000) G_L1: 17.945 G_L1_ABSOLUTE: 3.102 G_L1_RELATIVE: 14.843 G_Regularizer: 0.000 validation_error: 20.847 +(epoch: 46, iters: 90160, time: 0.549, data: 0.000) G_L1: 14.786 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 12.318 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 46, iters: 92160, time: 0.553, data: 0.000) G_L1: 13.286 G_L1_ABSOLUTE: 2.315 G_L1_RELATIVE: 10.971 G_Regularizer: 0.000 validation_error: 21.193 +(epoch: 46, iters: 94160, time: 0.552, data: 0.001) G_L1: 15.157 G_L1_ABSOLUTE: 2.667 G_L1_RELATIVE: 12.490 G_Regularizer: 0.000 validation_error: 21.021 +(epoch: 46, iters: 96160, time: 0.534, data: 0.000) G_L1: 13.560 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 10.655 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 46, iters: 98160, time: 0.546, data: 0.000) G_L1: 13.281 G_L1_ABSOLUTE: 1.972 G_L1_RELATIVE: 11.309 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 46, iters: 100160, time: 0.562, data: 0.000) G_L1: 14.769 G_L1_ABSOLUTE: 2.134 G_L1_RELATIVE: 12.635 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 46, iters: 102160, time: 0.546, data: 0.000) G_L1: 14.546 G_L1_ABSOLUTE: 2.841 G_L1_RELATIVE: 11.705 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 46, iters: 104160, time: 0.537, data: 0.000) G_L1: 13.381 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 10.820 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 46, iters: 106160, time: 0.534, data: 0.000) G_L1: 14.442 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 12.010 G_Regularizer: 0.000 validation_error: 21.104 +(epoch: 46, iters: 108160, time: 0.557, data: 0.000) G_L1: 14.541 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 12.138 G_Regularizer: 0.000 validation_error: 21.040 +(epoch: 46, iters: 110160, time: 0.558, data: 0.000) G_L1: 13.826 G_L1_ABSOLUTE: 2.876 G_L1_RELATIVE: 10.949 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 46, iters: 112160, time: 0.549, data: 0.001) G_L1: 13.467 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 10.966 G_Regularizer: 0.000 validation_error: 21.108 +(epoch: 46, iters: 114160, time: 0.545, data: 0.000) G_L1: 15.713 G_L1_ABSOLUTE: 2.229 G_L1_RELATIVE: 13.484 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 46, iters: 116160, time: 0.531, data: 0.000) G_L1: 14.656 G_L1_ABSOLUTE: 2.739 G_L1_RELATIVE: 11.917 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 46, iters: 118160, time: 0.548, data: 0.000) G_L1: 14.223 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 11.518 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 46, iters: 120160, time: 0.556, data: 0.000) G_L1: 13.219 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 10.887 G_Regularizer: 0.000 validation_error: 20.862 +(epoch: 46, iters: 122160, time: 0.555, data: 0.000) G_L1: 14.154 G_L1_ABSOLUTE: 2.537 G_L1_RELATIVE: 11.617 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 46, iters: 124160, time: 0.543, data: 0.000) G_L1: 16.266 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 13.889 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 46, iters: 126160, time: 0.561, data: 0.000) G_L1: 12.310 G_L1_ABSOLUTE: 2.092 G_L1_RELATIVE: 10.219 G_Regularizer: 0.000 validation_error: 20.818 +(epoch: 46, iters: 128160, time: 0.551, data: 0.000) G_L1: 13.216 G_L1_ABSOLUTE: 2.218 G_L1_RELATIVE: 10.998 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 46, iters: 130160, time: 0.541, data: 0.001) G_L1: 14.190 G_L1_ABSOLUTE: 3.018 G_L1_RELATIVE: 11.171 G_Regularizer: 0.000 validation_error: 20.936 +(epoch: 46, iters: 132160, time: 0.528, data: 0.001) G_L1: 13.241 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 10.817 G_Regularizer: 0.000 validation_error: 20.885 +(epoch: 46, iters: 134160, time: 0.565, data: 0.001) G_L1: 15.186 G_L1_ABSOLUTE: 2.175 G_L1_RELATIVE: 13.011 G_Regularizer: 0.000 validation_error: 21.184 +(epoch: 46, iters: 136160, time: 0.562, data: 0.000) G_L1: 13.719 G_L1_ABSOLUTE: 2.700 G_L1_RELATIVE: 11.019 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 46, iters: 138160, time: 0.541, data: 0.000) G_L1: 12.734 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 10.019 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 46, iters: 140160, time: 0.544, data: 0.000) G_L1: 15.116 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 12.753 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 46, iters: 142160, time: 0.539, data: 0.000) G_L1: 13.739 G_L1_ABSOLUTE: 2.695 G_L1_RELATIVE: 11.044 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 46, iters: 144160, time: 0.548, data: 0.001) G_L1: 15.581 G_L1_ABSOLUTE: 2.391 G_L1_RELATIVE: 13.190 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 46, iters: 146160, time: 0.539, data: 0.000) G_L1: 14.450 G_L1_ABSOLUTE: 2.364 G_L1_RELATIVE: 12.086 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 46, iters: 148160, time: 0.542, data: 0.000) G_L1: 15.531 G_L1_ABSOLUTE: 2.241 G_L1_RELATIVE: 13.290 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 46, iters: 150160, time: 0.538, data: 0.000) G_L1: 14.299 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 11.719 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 46, iters: 152160, time: 0.537, data: 0.000) G_L1: 14.310 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 11.816 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 46, iters: 154160, time: 0.538, data: 0.000) G_L1: 16.373 G_L1_ABSOLUTE: 3.089 G_L1_RELATIVE: 13.284 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 46, iters: 156160, time: 0.554, data: 0.000) G_L1: 13.353 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 10.922 G_Regularizer: 0.000 validation_error: 21.252 +(epoch: 46, iters: 158160, time: 0.543, data: 0.001) G_L1: 14.793 G_L1_ABSOLUTE: 2.087 G_L1_RELATIVE: 12.706 G_Regularizer: 0.000 validation_error: 20.867 +(epoch: 46, iters: 160160, time: 0.549, data: 0.000) G_L1: 14.896 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 12.529 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 46, iters: 162160, time: 0.546, data: 0.000) G_L1: 12.189 G_L1_ABSOLUTE: 2.496 G_L1_RELATIVE: 9.693 G_Regularizer: 0.000 validation_error: 21.266 +(epoch: 46, iters: 164160, time: 0.551, data: 0.000) G_L1: 17.667 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 15.181 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 46, iters: 166160, time: 0.537, data: 0.000) G_L1: 14.345 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 11.825 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 46, iters: 168160, time: 0.555, data: 0.000) G_L1: 14.146 G_L1_ABSOLUTE: 2.367 G_L1_RELATIVE: 11.779 G_Regularizer: 0.000 validation_error: 21.224 +(epoch: 46, iters: 170160, time: 0.558, data: 0.000) G_L1: 16.307 G_L1_ABSOLUTE: 2.694 G_L1_RELATIVE: 13.613 G_Regularizer: 0.000 validation_error: 20.980 +(epoch: 46, iters: 172160, time: 0.549, data: 0.001) G_L1: 16.617 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 14.148 G_Regularizer: 0.000 validation_error: 21.002 +(epoch: 46, iters: 174160, time: 0.541, data: 0.001) G_L1: 14.651 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 12.167 G_Regularizer: 0.000 validation_error: 20.741 +(epoch: 46, iters: 176160, time: 0.562, data: 0.001) G_L1: 14.125 G_L1_ABSOLUTE: 2.553 G_L1_RELATIVE: 11.573 G_Regularizer: 0.000 validation_error: 20.583 +(epoch: 46, iters: 178160, time: 0.558, data: 0.000) G_L1: 13.338 G_L1_ABSOLUTE: 2.928 G_L1_RELATIVE: 10.410 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 46, iters: 180160, time: 0.535, data: 0.000) G_L1: 17.832 G_L1_ABSOLUTE: 2.859 G_L1_RELATIVE: 14.973 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 46, iters: 182160, time: 0.537, data: 0.000) G_L1: 12.465 G_L1_ABSOLUTE: 1.986 G_L1_RELATIVE: 10.479 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 46, iters: 184160, time: 0.551, data: 0.000) G_L1: 14.832 G_L1_ABSOLUTE: 3.165 G_L1_RELATIVE: 11.667 G_Regularizer: 0.000 validation_error: 20.987 +(epoch: 46, iters: 186160, time: 0.561, data: 0.000) G_L1: 15.628 G_L1_ABSOLUTE: 2.987 G_L1_RELATIVE: 12.640 G_Regularizer: 0.000 validation_error: 20.814 +(epoch: 46, iters: 188160, time: 0.530, data: 0.000) G_L1: 16.298 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 13.418 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 46, iters: 190160, time: 0.543, data: 0.000) G_L1: 13.556 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 11.074 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 46, iters: 192160, time: 0.533, data: 0.000) G_L1: 15.481 G_L1_ABSOLUTE: 2.325 G_L1_RELATIVE: 13.156 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 46, iters: 194160, time: 0.552, data: 0.000) G_L1: 11.629 G_L1_ABSOLUTE: 2.094 G_L1_RELATIVE: 9.535 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 46, iters: 196160, time: 0.535, data: 0.000) G_L1: 13.976 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 11.184 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 46, iters: 198160, time: 0.557, data: 0.000) G_L1: 13.653 G_L1_ABSOLUTE: 2.084 G_L1_RELATIVE: 11.569 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 46, iters: 200160, time: 0.535, data: 0.000) G_L1: 17.706 G_L1_ABSOLUTE: 3.464 G_L1_RELATIVE: 14.242 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 46, iters: 202160, time: 0.550, data: 0.000) G_L1: 14.049 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 11.850 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 46, iters: 204160, time: 0.557, data: 0.001) G_L1: 11.970 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 9.772 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 46, iters: 206160, time: 0.561, data: 0.000) G_L1: 11.703 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 9.318 G_Regularizer: 0.000 validation_error: 21.151 +(epoch: 46, iters: 208160, time: 0.553, data: 0.000) G_L1: 12.781 G_L1_ABSOLUTE: 2.301 G_L1_RELATIVE: 10.480 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 46, iters: 210160, time: 0.532, data: 0.000) G_L1: 13.413 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 11.029 G_Regularizer: 0.000 validation_error: 21.278 +(epoch: 46, iters: 212160, time: 0.548, data: 0.000) G_L1: 11.529 G_L1_ABSOLUTE: 2.538 G_L1_RELATIVE: 8.991 G_Regularizer: 0.000 validation_error: 20.657 +(epoch: 46, iters: 214160, time: 0.554, data: 0.000) G_L1: 13.389 G_L1_ABSOLUTE: 2.427 G_L1_RELATIVE: 10.962 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 46, iters: 216160, time: 0.560, data: 0.000) G_L1: 13.063 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 10.646 G_Regularizer: 0.000 validation_error: 20.571 +(epoch: 46, iters: 218160, time: 0.547, data: 0.000) G_L1: 15.455 G_L1_ABSOLUTE: 2.289 G_L1_RELATIVE: 13.167 G_Regularizer: 0.000 validation_error: 21.003 +(epoch: 46, iters: 220160, time: 0.555, data: 0.000) G_L1: 14.340 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 11.984 G_Regularizer: 0.000 validation_error: 20.940 +(epoch: 46, iters: 222160, time: 0.557, data: 0.000) G_L1: 13.434 G_L1_ABSOLUTE: 2.537 G_L1_RELATIVE: 10.896 G_Regularizer: 0.000 validation_error: 20.832 +(epoch: 46, iters: 224160, time: 0.551, data: 0.000) G_L1: 12.900 G_L1_ABSOLUTE: 2.189 G_L1_RELATIVE: 10.711 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 46, iters: 226160, time: 0.541, data: 0.000) G_L1: 16.123 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 13.599 G_Regularizer: 0.000 validation_error: 21.003 +(epoch: 46, iters: 228160, time: 0.557, data: 0.001) G_L1: 13.802 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 10.864 G_Regularizer: 0.000 validation_error: 21.177 +(epoch: 46, iters: 230160, time: 0.532, data: 0.000) G_L1: 17.382 G_L1_ABSOLUTE: 2.641 G_L1_RELATIVE: 14.741 G_Regularizer: 0.000 validation_error: 20.930 +(epoch: 46, iters: 232160, time: 0.555, data: 0.001) G_L1: 13.207 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 10.620 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 46, iters: 234160, time: 0.542, data: 0.000) G_L1: 12.219 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 10.003 G_Regularizer: 0.000 validation_error: 20.818 +(epoch: 46, iters: 236160, time: 0.545, data: 0.000) G_L1: 13.487 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 10.649 G_Regularizer: 0.000 validation_error: 21.078 +(epoch: 46, iters: 238160, time: 0.533, data: 0.001) G_L1: 12.732 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 10.224 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 46, iters: 240160, time: 0.548, data: 0.000) G_L1: 14.951 G_L1_ABSOLUTE: 3.240 G_L1_RELATIVE: 11.710 G_Regularizer: 0.000 validation_error: 20.503 +(epoch: 46, iters: 242160, time: 0.557, data: 0.000) G_L1: 14.910 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 12.309 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 46, iters: 244160, time: 0.560, data: 0.000) G_L1: 16.740 G_L1_ABSOLUTE: 2.667 G_L1_RELATIVE: 14.073 G_Regularizer: 0.000 validation_error: 21.231 +(epoch: 46, iters: 246160, time: 0.539, data: 0.000) G_L1: 12.884 G_L1_ABSOLUTE: 2.280 G_L1_RELATIVE: 10.604 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 46, iters: 248160, time: 0.563, data: 0.000) G_L1: 12.877 G_L1_ABSOLUTE: 2.182 G_L1_RELATIVE: 10.696 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 46, iters: 250160, time: 0.557, data: 0.000) G_L1: 11.678 G_L1_ABSOLUTE: 2.133 G_L1_RELATIVE: 9.545 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 46, iters: 252160, time: 0.533, data: 0.000) G_L1: 15.731 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 13.376 G_Regularizer: 0.000 validation_error: 20.932 +(epoch: 46, iters: 254160, time: 0.541, data: 0.000) G_L1: 14.257 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 11.837 G_Regularizer: 0.000 validation_error: 21.001 +(epoch: 46, iters: 256160, time: 0.561, data: 0.000) G_L1: 13.311 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 10.978 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 46, iters: 258160, time: 0.556, data: 0.000) G_L1: 12.909 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 10.199 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 46, iters: 260160, time: 0.545, data: 0.001) G_L1: 14.195 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 11.797 G_Regularizer: 0.000 validation_error: 20.604 +(epoch: 46, iters: 262160, time: 0.543, data: 0.000) G_L1: 19.543 G_L1_ABSOLUTE: 2.997 G_L1_RELATIVE: 16.546 G_Regularizer: 0.000 validation_error: 20.672 +(epoch: 46, iters: 264160, time: 0.526, data: 0.000) G_L1: 12.245 G_L1_ABSOLUTE: 2.226 G_L1_RELATIVE: 10.019 G_Regularizer: 0.000 validation_error: 21.087 +(epoch: 46, iters: 266160, time: 0.533, data: 0.000) G_L1: 13.813 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 11.448 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 46, iters: 268160, time: 0.547, data: 0.001) G_L1: 12.655 G_L1_ABSOLUTE: 2.151 G_L1_RELATIVE: 10.504 G_Regularizer: 0.000 validation_error: 20.675 +(epoch: 46, iters: 270160, time: 0.525, data: 0.000) G_L1: 14.083 G_L1_ABSOLUTE: 2.580 G_L1_RELATIVE: 11.503 G_Regularizer: 0.000 validation_error: 21.072 +(epoch: 46, iters: 272160, time: 0.554, data: 0.000) G_L1: 11.637 G_L1_ABSOLUTE: 2.529 G_L1_RELATIVE: 9.109 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 46, iters: 274160, time: 0.552, data: 0.000) G_L1: 14.829 G_L1_ABSOLUTE: 2.682 G_L1_RELATIVE: 12.147 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 46, iters: 276160, time: 0.540, data: 0.000) G_L1: 14.370 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 11.890 G_Regularizer: 0.000 validation_error: 20.979 +(epoch: 46, iters: 278160, time: 0.531, data: 0.000) G_L1: 15.222 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 12.638 G_Regularizer: 0.000 validation_error: 20.747 +(epoch: 46, iters: 280160, time: 0.535, data: 0.000) G_L1: 12.378 G_L1_ABSOLUTE: 2.154 G_L1_RELATIVE: 10.223 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 46, iters: 282160, time: 0.554, data: 0.000) G_L1: 16.971 G_L1_ABSOLUTE: 2.186 G_L1_RELATIVE: 14.784 G_Regularizer: 0.000 validation_error: 21.177 +(epoch: 46, iters: 284160, time: 0.547, data: 0.001) G_L1: 11.308 G_L1_ABSOLUTE: 2.242 G_L1_RELATIVE: 9.066 G_Regularizer: 0.000 validation_error: 20.738 +(epoch: 46, iters: 286160, time: 0.545, data: 0.000) G_L1: 14.867 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 12.267 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 46, iters: 288160, time: 0.543, data: 0.000) G_L1: 15.869 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 13.505 G_Regularizer: 0.000 validation_error: 21.118 +(epoch: 46, iters: 290160, time: 0.552, data: 0.000) G_L1: 13.980 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 11.315 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 46, iters: 292160, time: 0.558, data: 0.000) G_L1: 14.178 G_L1_ABSOLUTE: 2.456 G_L1_RELATIVE: 11.722 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 46, iters: 294160, time: 0.555, data: 0.000) G_L1: 13.050 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 10.633 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 46, iters: 296160, time: 0.542, data: 0.000) G_L1: 13.002 G_L1_ABSOLUTE: 2.295 G_L1_RELATIVE: 10.707 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 46, iters: 298160, time: 0.557, data: 0.000) G_L1: 13.162 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 10.778 G_Regularizer: 0.000 validation_error: 20.795 +(epoch: 46, iters: 300160, time: 0.557, data: 0.000) G_L1: 17.280 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 14.523 G_Regularizer: 0.000 validation_error: 20.776 +(epoch: 46, iters: 302160, time: 0.538, data: 0.001) G_L1: 13.382 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 10.968 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 47, iters: 1408, time: 0.532, data: 0.000) G_L1: 14.104 G_L1_ABSOLUTE: 2.306 G_L1_RELATIVE: 11.798 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 47, iters: 3408, time: 0.559, data: 0.001) G_L1: 16.565 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 13.672 G_Regularizer: 0.000 validation_error: 21.022 +(epoch: 47, iters: 5408, time: 0.552, data: 0.000) G_L1: 11.763 G_L1_ABSOLUTE: 1.965 G_L1_RELATIVE: 9.797 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 47, iters: 7408, time: 0.545, data: 0.000) G_L1: 14.375 G_L1_ABSOLUTE: 2.851 G_L1_RELATIVE: 11.524 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 47, iters: 9408, time: 0.545, data: 0.000) G_L1: 17.191 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 14.489 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 47, iters: 11408, time: 0.532, data: 0.000) G_L1: 13.082 G_L1_ABSOLUTE: 2.263 G_L1_RELATIVE: 10.819 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 47, iters: 13408, time: 0.533, data: 0.000) G_L1: 14.603 G_L1_ABSOLUTE: 2.560 G_L1_RELATIVE: 12.043 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 47, iters: 15408, time: 0.553, data: 0.000) G_L1: 12.431 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 10.182 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 47, iters: 17408, time: 0.553, data: 0.000) G_L1: 13.540 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 11.110 G_Regularizer: 0.000 validation_error: 20.836 +(epoch: 47, iters: 19408, time: 0.535, data: 0.000) G_L1: 13.446 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 10.970 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 47, iters: 21408, time: 0.548, data: 0.000) G_L1: 16.318 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 13.688 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 47, iters: 23408, time: 0.555, data: 0.000) G_L1: 14.793 G_L1_ABSOLUTE: 2.883 G_L1_RELATIVE: 11.909 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 47, iters: 25408, time: 0.535, data: 0.000) G_L1: 15.210 G_L1_ABSOLUTE: 2.455 G_L1_RELATIVE: 12.755 G_Regularizer: 0.000 validation_error: 20.927 +(epoch: 47, iters: 27408, time: 0.534, data: 0.000) G_L1: 12.666 G_L1_ABSOLUTE: 2.704 G_L1_RELATIVE: 9.962 G_Regularizer: 0.000 validation_error: 21.033 +(epoch: 47, iters: 29408, time: 0.548, data: 0.000) G_L1: 12.741 G_L1_ABSOLUTE: 2.327 G_L1_RELATIVE: 10.414 G_Regularizer: 0.000 validation_error: 20.667 +(epoch: 47, iters: 31408, time: 0.555, data: 0.000) G_L1: 14.120 G_L1_ABSOLUTE: 2.753 G_L1_RELATIVE: 11.367 G_Regularizer: 0.000 validation_error: 20.983 +(epoch: 47, iters: 33408, time: 0.548, data: 0.000) G_L1: 14.030 G_L1_ABSOLUTE: 2.397 G_L1_RELATIVE: 11.633 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 47, iters: 35408, time: 0.571, data: 0.000) G_L1: 11.744 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 9.375 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 47, iters: 37408, time: 0.557, data: 0.000) G_L1: 14.338 G_L1_ABSOLUTE: 3.048 G_L1_RELATIVE: 11.290 G_Regularizer: 0.000 validation_error: 20.922 +(epoch: 47, iters: 39408, time: 0.551, data: 0.000) G_L1: 14.526 G_L1_ABSOLUTE: 2.318 G_L1_RELATIVE: 12.208 G_Regularizer: 0.000 validation_error: 21.129 +(epoch: 47, iters: 41408, time: 0.559, data: 0.000) G_L1: 12.454 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 10.111 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 47, iters: 43408, time: 0.538, data: 0.001) G_L1: 12.883 G_L1_ABSOLUTE: 1.771 G_L1_RELATIVE: 11.111 G_Regularizer: 0.000 validation_error: 20.852 +(epoch: 47, iters: 45408, time: 0.551, data: 0.000) G_L1: 15.913 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 13.281 G_Regularizer: 0.000 validation_error: 21.107 +(epoch: 47, iters: 47408, time: 0.555, data: 0.000) G_L1: 15.051 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 12.442 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 47, iters: 49408, time: 0.546, data: 0.000) G_L1: 14.884 G_L1_ABSOLUTE: 2.665 G_L1_RELATIVE: 12.219 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 47, iters: 51408, time: 0.550, data: 0.000) G_L1: 13.978 G_L1_ABSOLUTE: 2.826 G_L1_RELATIVE: 11.151 G_Regularizer: 0.000 validation_error: 20.844 +(epoch: 47, iters: 53408, time: 0.536, data: 0.000) G_L1: 12.330 G_L1_ABSOLUTE: 2.164 G_L1_RELATIVE: 10.166 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 47, iters: 55408, time: 0.559, data: 0.000) G_L1: 12.410 G_L1_ABSOLUTE: 1.940 G_L1_RELATIVE: 10.470 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 47, iters: 57408, time: 0.534, data: 0.000) G_L1: 13.164 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 10.532 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 47, iters: 59408, time: 0.539, data: 0.001) G_L1: 15.502 G_L1_ABSOLUTE: 3.134 G_L1_RELATIVE: 12.368 G_Regularizer: 0.000 validation_error: 21.133 +(epoch: 47, iters: 61408, time: 0.547, data: 0.000) G_L1: 12.568 G_L1_ABSOLUTE: 2.426 G_L1_RELATIVE: 10.142 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 47, iters: 63408, time: 0.553, data: 0.000) G_L1: 11.820 G_L1_ABSOLUTE: 2.179 G_L1_RELATIVE: 9.641 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 47, iters: 65408, time: 0.556, data: 0.000) G_L1: 12.960 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 10.478 G_Regularizer: 0.000 validation_error: 20.723 +(epoch: 47, iters: 67408, time: 0.550, data: 0.000) G_L1: 12.496 G_L1_ABSOLUTE: 2.078 G_L1_RELATIVE: 10.418 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 47, iters: 69408, time: 0.554, data: 0.001) G_L1: 13.477 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 11.044 G_Regularizer: 0.000 validation_error: 20.746 +(epoch: 47, iters: 71408, time: 0.541, data: 0.001) G_L1: 13.205 G_L1_ABSOLUTE: 2.069 G_L1_RELATIVE: 11.136 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 47, iters: 73408, time: 0.557, data: 0.000) G_L1: 15.519 G_L1_ABSOLUTE: 2.488 G_L1_RELATIVE: 13.032 G_Regularizer: 0.000 validation_error: 20.805 +(epoch: 47, iters: 75408, time: 0.574, data: 0.001) G_L1: 16.120 G_L1_ABSOLUTE: 2.933 G_L1_RELATIVE: 13.187 G_Regularizer: 0.000 validation_error: 21.053 +(epoch: 47, iters: 77408, time: 0.550, data: 0.000) G_L1: 13.096 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 10.812 G_Regularizer: 0.000 validation_error: 20.917 +(epoch: 47, iters: 79408, time: 0.551, data: 0.001) G_L1: 13.960 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 11.405 G_Regularizer: 0.000 validation_error: 20.940 +(epoch: 47, iters: 81408, time: 0.548, data: 0.000) G_L1: 14.104 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 11.517 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 47, iters: 83408, time: 0.551, data: 0.000) G_L1: 14.406 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 12.006 G_Regularizer: 0.000 validation_error: 20.836 +(epoch: 47, iters: 85408, time: 0.537, data: 0.001) G_L1: 13.791 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 11.226 G_Regularizer: 0.000 validation_error: 21.014 +(epoch: 47, iters: 87408, time: 0.554, data: 0.000) G_L1: 13.983 G_L1_ABSOLUTE: 2.356 G_L1_RELATIVE: 11.628 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 47, iters: 89408, time: 0.550, data: 0.000) G_L1: 12.670 G_L1_ABSOLUTE: 2.426 G_L1_RELATIVE: 10.244 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 47, iters: 91408, time: 0.553, data: 0.000) G_L1: 11.894 G_L1_ABSOLUTE: 2.374 G_L1_RELATIVE: 9.520 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 47, iters: 93408, time: 0.533, data: 0.000) G_L1: 16.572 G_L1_ABSOLUTE: 2.815 G_L1_RELATIVE: 13.757 G_Regularizer: 0.000 validation_error: 20.982 +(epoch: 47, iters: 95408, time: 0.541, data: 0.000) G_L1: 13.587 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 10.766 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 47, iters: 97408, time: 0.538, data: 0.000) G_L1: 14.780 G_L1_ABSOLUTE: 2.896 G_L1_RELATIVE: 11.883 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 47, iters: 99408, time: 0.548, data: 0.000) G_L1: 14.149 G_L1_ABSOLUTE: 2.896 G_L1_RELATIVE: 11.252 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 47, iters: 101408, time: 0.540, data: 0.001) G_L1: 12.224 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 9.809 G_Regularizer: 0.000 validation_error: 20.555 +(epoch: 47, iters: 103408, time: 0.543, data: 0.000) G_L1: 12.564 G_L1_ABSOLUTE: 2.255 G_L1_RELATIVE: 10.309 G_Regularizer: 0.000 validation_error: 21.009 +(epoch: 47, iters: 105408, time: 0.545, data: 0.000) G_L1: 13.255 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 10.898 G_Regularizer: 0.000 validation_error: 20.789 +(epoch: 47, iters: 107408, time: 0.553, data: 0.000) G_L1: 15.414 G_L1_ABSOLUTE: 2.688 G_L1_RELATIVE: 12.727 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 47, iters: 109408, time: 0.552, data: 0.000) G_L1: 12.213 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 9.843 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 47, iters: 111408, time: 0.529, data: 0.000) G_L1: 14.098 G_L1_ABSOLUTE: 2.876 G_L1_RELATIVE: 11.222 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 47, iters: 113408, time: 0.546, data: 0.000) G_L1: 13.347 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 10.645 G_Regularizer: 0.000 validation_error: 20.376 +(epoch: 47, iters: 115408, time: 0.557, data: 0.000) G_L1: 15.296 G_L1_ABSOLUTE: 2.452 G_L1_RELATIVE: 12.844 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 47, iters: 117408, time: 0.549, data: 0.000) G_L1: 12.658 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 10.172 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 47, iters: 119408, time: 0.540, data: 0.000) G_L1: 13.671 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 11.422 G_Regularizer: 0.000 validation_error: 20.993 +(epoch: 47, iters: 121408, time: 0.556, data: 0.000) G_L1: 14.940 G_L1_ABSOLUTE: 2.508 G_L1_RELATIVE: 12.431 G_Regularizer: 0.000 validation_error: 21.183 +(epoch: 47, iters: 123408, time: 0.558, data: 0.000) G_L1: 12.359 G_L1_ABSOLUTE: 2.606 G_L1_RELATIVE: 9.752 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 47, iters: 125408, time: 0.549, data: 0.000) G_L1: 11.674 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 9.216 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 47, iters: 127408, time: 0.540, data: 0.000) G_L1: 13.552 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 11.132 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 47, iters: 129408, time: 0.563, data: 0.000) G_L1: 14.697 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 12.247 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 47, iters: 131408, time: 0.558, data: 0.000) G_L1: 13.827 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 11.228 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 47, iters: 133408, time: 0.562, data: 0.000) G_L1: 11.654 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 9.455 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 47, iters: 135408, time: 0.534, data: 0.000) G_L1: 14.003 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 11.705 G_Regularizer: 0.000 validation_error: 21.049 +(epoch: 47, iters: 137408, time: 0.571, data: 0.000) G_L1: 12.175 G_L1_ABSOLUTE: 2.192 G_L1_RELATIVE: 9.984 G_Regularizer: 0.000 validation_error: 20.626 +(epoch: 47, iters: 139408, time: 0.537, data: 0.000) G_L1: 16.023 G_L1_ABSOLUTE: 2.206 G_L1_RELATIVE: 13.817 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 47, iters: 141408, time: 0.550, data: 0.000) G_L1: 14.130 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 11.931 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 47, iters: 143408, time: 0.546, data: 0.000) G_L1: 12.628 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 10.104 G_Regularizer: 0.000 validation_error: 20.943 +(epoch: 47, iters: 145408, time: 0.542, data: 0.000) G_L1: 14.340 G_L1_ABSOLUTE: 2.133 G_L1_RELATIVE: 12.208 G_Regularizer: 0.000 validation_error: 20.536 +(epoch: 47, iters: 147408, time: 0.556, data: 0.000) G_L1: 12.750 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 10.120 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 47, iters: 149408, time: 0.560, data: 0.000) G_L1: 15.616 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 13.030 G_Regularizer: 0.000 validation_error: 21.173 +(epoch: 47, iters: 151408, time: 0.532, data: 0.001) G_L1: 13.719 G_L1_ABSOLUTE: 2.321 G_L1_RELATIVE: 11.398 G_Regularizer: 0.000 validation_error: 21.127 +(epoch: 47, iters: 153408, time: 0.536, data: 0.000) G_L1: 15.255 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 12.737 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 47, iters: 155408, time: 0.548, data: 0.000) G_L1: 12.808 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 10.278 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 47, iters: 157408, time: 0.549, data: 0.001) G_L1: 15.417 G_L1_ABSOLUTE: 2.334 G_L1_RELATIVE: 13.083 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 47, iters: 159408, time: 0.545, data: 0.001) G_L1: 20.813 G_L1_ABSOLUTE: 2.850 G_L1_RELATIVE: 17.963 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 47, iters: 161408, time: 0.541, data: 0.001) G_L1: 13.836 G_L1_ABSOLUTE: 2.956 G_L1_RELATIVE: 10.880 G_Regularizer: 0.000 validation_error: 21.045 +(epoch: 47, iters: 163408, time: 0.555, data: 0.000) G_L1: 12.773 G_L1_ABSOLUTE: 2.361 G_L1_RELATIVE: 10.413 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 47, iters: 165408, time: 0.554, data: 0.000) G_L1: 13.057 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 10.439 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 47, iters: 167408, time: 0.547, data: 0.000) G_L1: 12.377 G_L1_ABSOLUTE: 2.324 G_L1_RELATIVE: 10.052 G_Regularizer: 0.000 validation_error: 20.937 +(epoch: 47, iters: 169408, time: 0.551, data: 0.000) G_L1: 11.914 G_L1_ABSOLUTE: 2.110 G_L1_RELATIVE: 9.804 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 47, iters: 171408, time: 0.553, data: 0.000) G_L1: 13.415 G_L1_ABSOLUTE: 2.844 G_L1_RELATIVE: 10.572 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 47, iters: 173408, time: 0.551, data: 0.000) G_L1: 14.099 G_L1_ABSOLUTE: 2.115 G_L1_RELATIVE: 11.984 G_Regularizer: 0.000 validation_error: 21.054 +(epoch: 47, iters: 175408, time: 0.549, data: 0.000) G_L1: 14.728 G_L1_ABSOLUTE: 2.429 G_L1_RELATIVE: 12.299 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 47, iters: 177408, time: 0.558, data: 0.000) G_L1: 15.051 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 12.274 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 47, iters: 179408, time: 0.532, data: 0.000) G_L1: 14.385 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 11.948 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 47, iters: 181408, time: 0.533, data: 0.000) G_L1: 13.918 G_L1_ABSOLUTE: 2.723 G_L1_RELATIVE: 11.195 G_Regularizer: 0.000 validation_error: 20.779 +(epoch: 47, iters: 183408, time: 0.548, data: 0.000) G_L1: 13.733 G_L1_ABSOLUTE: 2.030 G_L1_RELATIVE: 11.703 G_Regularizer: 0.000 validation_error: 20.960 +(epoch: 47, iters: 185408, time: 0.557, data: 0.000) G_L1: 14.363 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 11.786 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 47, iters: 187408, time: 0.529, data: 0.000) G_L1: 15.495 G_L1_ABSOLUTE: 2.452 G_L1_RELATIVE: 13.043 G_Regularizer: 0.000 validation_error: 20.924 +(epoch: 47, iters: 189408, time: 0.556, data: 0.000) G_L1: 14.321 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 11.791 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 47, iters: 191408, time: 0.559, data: 0.000) G_L1: 15.200 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 12.822 G_Regularizer: 0.000 validation_error: 21.050 +(epoch: 47, iters: 193408, time: 0.547, data: 0.000) G_L1: 12.572 G_L1_ABSOLUTE: 2.004 G_L1_RELATIVE: 10.568 G_Regularizer: 0.000 validation_error: 21.073 +(epoch: 47, iters: 195408, time: 0.540, data: 0.000) G_L1: 14.585 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 12.099 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 47, iters: 197408, time: 0.560, data: 0.000) G_L1: 13.173 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 10.823 G_Regularizer: 0.000 validation_error: 20.892 +(epoch: 47, iters: 199408, time: 0.539, data: 0.000) G_L1: 13.287 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 10.752 G_Regularizer: 0.000 validation_error: 20.982 +(epoch: 47, iters: 201408, time: 0.542, data: 0.000) G_L1: 14.310 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 11.544 G_Regularizer: 0.000 validation_error: 20.911 +(epoch: 47, iters: 203408, time: 0.556, data: 0.000) G_L1: 13.023 G_L1_ABSOLUTE: 2.206 G_L1_RELATIVE: 10.817 G_Regularizer: 0.000 validation_error: 20.956 +(epoch: 47, iters: 205408, time: 0.545, data: 0.000) G_L1: 15.485 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 12.701 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 47, iters: 207408, time: 0.531, data: 0.000) G_L1: 12.024 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 9.618 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 47, iters: 209408, time: 0.537, data: 0.000) G_L1: 14.493 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 11.883 G_Regularizer: 0.000 validation_error: 21.189 +(epoch: 47, iters: 211408, time: 0.548, data: 0.000) G_L1: 11.333 G_L1_ABSOLUTE: 2.074 G_L1_RELATIVE: 9.259 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 47, iters: 213408, time: 0.541, data: 0.000) G_L1: 13.648 G_L1_ABSOLUTE: 2.559 G_L1_RELATIVE: 11.089 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 47, iters: 215408, time: 0.556, data: 0.000) G_L1: 13.757 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 11.126 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 47, iters: 217408, time: 0.555, data: 0.000) G_L1: 14.165 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 11.641 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 47, iters: 219408, time: 0.558, data: 0.000) G_L1: 15.206 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 12.429 G_Regularizer: 0.000 validation_error: 21.037 +(epoch: 47, iters: 221408, time: 0.539, data: 0.000) G_L1: 15.954 G_L1_ABSOLUTE: 2.395 G_L1_RELATIVE: 13.559 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 47, iters: 223408, time: 0.531, data: 0.000) G_L1: 13.023 G_L1_ABSOLUTE: 2.195 G_L1_RELATIVE: 10.828 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 47, iters: 225408, time: 0.556, data: 0.000) G_L1: 15.072 G_L1_ABSOLUTE: 2.780 G_L1_RELATIVE: 12.292 G_Regularizer: 0.000 validation_error: 21.050 +(epoch: 47, iters: 227408, time: 0.538, data: 0.000) G_L1: 13.501 G_L1_ABSOLUTE: 2.213 G_L1_RELATIVE: 11.288 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 47, iters: 229408, time: 0.545, data: 0.000) G_L1: 14.215 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 11.826 G_Regularizer: 0.000 validation_error: 21.189 +(epoch: 47, iters: 231408, time: 0.537, data: 0.000) G_L1: 13.624 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 11.082 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 47, iters: 233408, time: 0.534, data: 0.000) G_L1: 14.455 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 11.997 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 47, iters: 235408, time: 0.540, data: 0.000) G_L1: 15.438 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 13.029 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 47, iters: 237408, time: 0.546, data: 0.000) G_L1: 23.324 G_L1_ABSOLUTE: 2.531 G_L1_RELATIVE: 20.792 G_Regularizer: 0.000 validation_error: 20.930 +(epoch: 47, iters: 239408, time: 0.547, data: 0.000) G_L1: 15.218 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 12.554 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 47, iters: 241408, time: 0.559, data: 0.000) G_L1: 14.062 G_L1_ABSOLUTE: 1.943 G_L1_RELATIVE: 12.118 G_Regularizer: 0.000 validation_error: 20.983 +(epoch: 47, iters: 243408, time: 0.536, data: 0.000) G_L1: 16.495 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 13.894 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 47, iters: 245408, time: 0.554, data: 0.001) G_L1: 12.721 G_L1_ABSOLUTE: 2.538 G_L1_RELATIVE: 10.184 G_Regularizer: 0.000 validation_error: 21.130 +(epoch: 47, iters: 247408, time: 0.558, data: 0.000) G_L1: 13.595 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 11.311 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 47, iters: 249408, time: 0.559, data: 0.000) G_L1: 15.289 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 12.533 G_Regularizer: 0.000 validation_error: 20.871 +(epoch: 47, iters: 251408, time: 0.551, data: 0.000) G_L1: 12.865 G_L1_ABSOLUTE: 2.209 G_L1_RELATIVE: 10.656 G_Regularizer: 0.000 validation_error: 20.786 +(epoch: 47, iters: 253408, time: 0.555, data: 0.000) G_L1: 18.581 G_L1_ABSOLUTE: 2.139 G_L1_RELATIVE: 16.442 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 47, iters: 255408, time: 0.539, data: 0.000) G_L1: 13.438 G_L1_ABSOLUTE: 2.606 G_L1_RELATIVE: 10.832 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 47, iters: 257408, time: 0.552, data: 0.000) G_L1: 14.660 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 11.900 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 47, iters: 259408, time: 0.548, data: 0.000) G_L1: 11.916 G_L1_ABSOLUTE: 2.167 G_L1_RELATIVE: 9.748 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 47, iters: 261408, time: 0.556, data: 0.000) G_L1: 13.628 G_L1_ABSOLUTE: 2.704 G_L1_RELATIVE: 10.924 G_Regularizer: 0.000 validation_error: 20.659 +(epoch: 47, iters: 263408, time: 0.554, data: 0.000) G_L1: 14.608 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 12.310 G_Regularizer: 0.000 validation_error: 20.756 +(epoch: 47, iters: 265408, time: 0.544, data: 0.000) G_L1: 15.293 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 12.662 G_Regularizer: 0.000 validation_error: 21.075 +(epoch: 47, iters: 267408, time: 0.554, data: 0.000) G_L1: 14.477 G_L1_ABSOLUTE: 2.585 G_L1_RELATIVE: 11.892 G_Regularizer: 0.000 validation_error: 21.014 +(epoch: 47, iters: 269408, time: 0.550, data: 0.000) G_L1: 11.432 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 9.004 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 47, iters: 271408, time: 0.565, data: 0.000) G_L1: 12.547 G_L1_ABSOLUTE: 2.026 G_L1_RELATIVE: 10.522 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 47, iters: 273408, time: 0.552, data: 0.000) G_L1: 13.879 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 11.405 G_Regularizer: 0.000 validation_error: 21.027 +(epoch: 47, iters: 275408, time: 0.554, data: 0.000) G_L1: 16.228 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 13.761 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 47, iters: 277408, time: 0.543, data: 0.000) G_L1: 16.246 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 13.812 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 47, iters: 279408, time: 0.557, data: 0.000) G_L1: 13.866 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 11.235 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 47, iters: 281408, time: 0.530, data: 0.000) G_L1: 12.102 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 9.737 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 47, iters: 283408, time: 0.563, data: 0.000) G_L1: 13.211 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 10.727 G_Regularizer: 0.000 validation_error: 21.058 +(epoch: 47, iters: 285408, time: 0.541, data: 0.000) G_L1: 15.288 G_L1_ABSOLUTE: 2.219 G_L1_RELATIVE: 13.069 G_Regularizer: 0.000 validation_error: 20.467 +(epoch: 47, iters: 287408, time: 0.553, data: 0.000) G_L1: 12.115 G_L1_ABSOLUTE: 2.367 G_L1_RELATIVE: 9.748 G_Regularizer: 0.000 validation_error: 20.849 +(epoch: 47, iters: 289408, time: 0.542, data: 0.000) G_L1: 15.578 G_L1_ABSOLUTE: 2.404 G_L1_RELATIVE: 13.174 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 47, iters: 291408, time: 0.562, data: 0.000) G_L1: 13.177 G_L1_ABSOLUTE: 2.438 G_L1_RELATIVE: 10.739 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 47, iters: 293408, time: 0.530, data: 0.000) G_L1: 14.891 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 12.477 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 47, iters: 295408, time: 0.559, data: 0.000) G_L1: 15.205 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 12.432 G_Regularizer: 0.000 validation_error: 20.740 +(epoch: 47, iters: 297408, time: 0.557, data: 0.000) G_L1: 13.349 G_L1_ABSOLUTE: 2.425 G_L1_RELATIVE: 10.924 G_Regularizer: 0.000 validation_error: 20.847 +(epoch: 47, iters: 299408, time: 0.560, data: 0.000) G_L1: 13.582 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 11.243 G_Regularizer: 0.000 validation_error: 20.836 +(epoch: 47, iters: 301408, time: 0.539, data: 0.000) G_L1: 15.124 G_L1_ABSOLUTE: 2.984 G_L1_RELATIVE: 12.140 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 48, iters: 656, time: 0.542, data: 0.000) G_L1: 13.665 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 11.008 G_Regularizer: 0.000 validation_error: 21.029 +(epoch: 48, iters: 2656, time: 0.552, data: 0.000) G_L1: 14.486 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 11.891 G_Regularizer: 0.000 validation_error: 20.826 +(epoch: 48, iters: 4656, time: 0.535, data: 0.000) G_L1: 12.929 G_L1_ABSOLUTE: 2.516 G_L1_RELATIVE: 10.413 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 48, iters: 6656, time: 0.539, data: 0.000) G_L1: 14.075 G_L1_ABSOLUTE: 2.552 G_L1_RELATIVE: 11.523 G_Regularizer: 0.000 validation_error: 20.820 +(epoch: 48, iters: 8656, time: 0.552, data: 0.000) G_L1: 15.876 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 13.312 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 48, iters: 10656, time: 0.569, data: 0.000) G_L1: 14.472 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 11.966 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 48, iters: 12656, time: 0.535, data: 0.000) G_L1: 13.844 G_L1_ABSOLUTE: 3.055 G_L1_RELATIVE: 10.790 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 48, iters: 14656, time: 0.536, data: 0.000) G_L1: 14.081 G_L1_ABSOLUTE: 3.025 G_L1_RELATIVE: 11.057 G_Regularizer: 0.000 validation_error: 21.133 +(epoch: 48, iters: 16656, time: 0.536, data: 0.000) G_L1: 13.959 G_L1_ABSOLUTE: 2.289 G_L1_RELATIVE: 11.670 G_Regularizer: 0.000 validation_error: 20.814 +(epoch: 48, iters: 18656, time: 0.540, data: 0.000) G_L1: 16.811 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 14.233 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 48, iters: 20656, time: 0.541, data: 0.000) G_L1: 12.896 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 10.556 G_Regularizer: 0.000 validation_error: 21.105 +(epoch: 48, iters: 22656, time: 0.543, data: 0.000) G_L1: 12.357 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 9.907 G_Regularizer: 0.000 validation_error: 20.786 +(epoch: 48, iters: 24656, time: 0.548, data: 0.001) G_L1: 12.884 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 10.449 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 48, iters: 26656, time: 0.556, data: 0.001) G_L1: 14.310 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 11.440 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 48, iters: 28656, time: 0.550, data: 0.000) G_L1: 16.712 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 14.089 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 48, iters: 30656, time: 0.534, data: 0.000) G_L1: 15.419 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 12.686 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 48, iters: 32656, time: 0.528, data: 0.000) G_L1: 16.417 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 13.926 G_Regularizer: 0.000 validation_error: 20.797 +(epoch: 48, iters: 34656, time: 0.565, data: 0.000) G_L1: 15.450 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 12.720 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 48, iters: 36656, time: 0.541, data: 0.000) G_L1: 15.602 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 13.224 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 48, iters: 38656, time: 0.532, data: 0.000) G_L1: 14.467 G_L1_ABSOLUTE: 2.750 G_L1_RELATIVE: 11.717 G_Regularizer: 0.000 validation_error: 21.106 +(epoch: 48, iters: 40656, time: 0.539, data: 0.000) G_L1: 14.569 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 11.864 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 48, iters: 42656, time: 0.559, data: 0.000) G_L1: 15.759 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 13.026 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 48, iters: 44656, time: 0.561, data: 0.000) G_L1: 14.941 G_L1_ABSOLUTE: 3.087 G_L1_RELATIVE: 11.854 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 48, iters: 46656, time: 0.548, data: 0.001) G_L1: 15.772 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 13.150 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 48, iters: 48656, time: 0.537, data: 0.000) G_L1: 13.331 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 10.576 G_Regularizer: 0.000 validation_error: 20.769 +(epoch: 48, iters: 50656, time: 0.533, data: 0.001) G_L1: 12.280 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 9.759 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 48, iters: 52656, time: 0.545, data: 0.000) G_L1: 12.602 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 10.240 G_Regularizer: 0.000 validation_error: 20.895 +(epoch: 48, iters: 54656, time: 0.549, data: 0.000) G_L1: 14.524 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 11.964 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 48, iters: 56656, time: 0.535, data: 0.000) G_L1: 13.512 G_L1_ABSOLUTE: 2.404 G_L1_RELATIVE: 11.108 G_Regularizer: 0.000 validation_error: 21.071 +(epoch: 48, iters: 58656, time: 0.535, data: 0.000) G_L1: 11.933 G_L1_ABSOLUTE: 2.247 G_L1_RELATIVE: 9.686 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 48, iters: 60656, time: 0.553, data: 0.000) G_L1: 12.891 G_L1_ABSOLUTE: 3.088 G_L1_RELATIVE: 9.803 G_Regularizer: 0.000 validation_error: 20.749 +(epoch: 48, iters: 62656, time: 0.559, data: 0.000) G_L1: 13.460 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 11.106 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 48, iters: 64656, time: 0.557, data: 0.000) G_L1: 15.884 G_L1_ABSOLUTE: 2.293 G_L1_RELATIVE: 13.591 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 48, iters: 66656, time: 0.527, data: 0.000) G_L1: 12.567 G_L1_ABSOLUTE: 2.310 G_L1_RELATIVE: 10.258 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 48, iters: 68656, time: 0.557, data: 0.000) G_L1: 13.751 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 11.306 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 48, iters: 70656, time: 0.556, data: 0.000) G_L1: 14.940 G_L1_ABSOLUTE: 2.364 G_L1_RELATIVE: 12.576 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 48, iters: 72656, time: 0.542, data: 0.000) G_L1: 15.313 G_L1_ABSOLUTE: 2.768 G_L1_RELATIVE: 12.545 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 48, iters: 74656, time: 0.554, data: 0.000) G_L1: 13.672 G_L1_ABSOLUTE: 2.387 G_L1_RELATIVE: 11.284 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 48, iters: 76656, time: 0.556, data: 0.000) G_L1: 16.601 G_L1_ABSOLUTE: 2.398 G_L1_RELATIVE: 14.203 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 48, iters: 78656, time: 0.558, data: 0.000) G_L1: 14.090 G_L1_ABSOLUTE: 2.160 G_L1_RELATIVE: 11.930 G_Regularizer: 0.000 validation_error: 20.861 +(epoch: 48, iters: 80656, time: 0.543, data: 0.000) G_L1: 12.661 G_L1_ABSOLUTE: 2.379 G_L1_RELATIVE: 10.283 G_Regularizer: 0.000 validation_error: 20.687 +(epoch: 48, iters: 82656, time: 0.535, data: 0.000) G_L1: 10.948 G_L1_ABSOLUTE: 2.080 G_L1_RELATIVE: 8.868 G_Regularizer: 0.000 validation_error: 20.862 +(epoch: 48, iters: 84656, time: 0.550, data: 0.000) G_L1: 12.662 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 10.111 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 48, iters: 86656, time: 0.561, data: 0.000) G_L1: 13.155 G_L1_ABSOLUTE: 2.261 G_L1_RELATIVE: 10.894 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 48, iters: 88656, time: 0.554, data: 0.000) G_L1: 14.460 G_L1_ABSOLUTE: 2.195 G_L1_RELATIVE: 12.265 G_Regularizer: 0.000 validation_error: 21.046 +(epoch: 48, iters: 90656, time: 0.537, data: 0.000) G_L1: 12.292 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 9.597 G_Regularizer: 0.000 validation_error: 20.895 +(epoch: 48, iters: 92656, time: 0.561, data: 0.000) G_L1: 14.466 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 11.946 G_Regularizer: 0.000 validation_error: 20.912 +(epoch: 48, iters: 94656, time: 0.552, data: 0.000) G_L1: 12.726 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 10.201 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 48, iters: 96656, time: 0.561, data: 0.000) G_L1: 17.071 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 14.635 G_Regularizer: 0.000 validation_error: 20.671 +(epoch: 48, iters: 98656, time: 0.536, data: 0.000) G_L1: 12.858 G_L1_ABSOLUTE: 2.062 G_L1_RELATIVE: 10.796 G_Regularizer: 0.000 validation_error: 21.102 +(epoch: 48, iters: 100656, time: 0.559, data: 0.000) G_L1: 13.393 G_L1_ABSOLUTE: 2.279 G_L1_RELATIVE: 11.114 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 48, iters: 102656, time: 0.560, data: 0.000) G_L1: 12.866 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 10.347 G_Regularizer: 0.000 validation_error: 20.816 +(epoch: 48, iters: 104656, time: 0.559, data: 0.000) G_L1: 13.026 G_L1_ABSOLUTE: 2.167 G_L1_RELATIVE: 10.859 G_Regularizer: 0.000 validation_error: 20.924 +(epoch: 48, iters: 106656, time: 0.542, data: 0.001) G_L1: 14.272 G_L1_ABSOLUTE: 2.351 G_L1_RELATIVE: 11.921 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 48, iters: 108656, time: 0.555, data: 0.000) G_L1: 16.128 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 13.580 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 48, iters: 110656, time: 0.552, data: 0.001) G_L1: 12.689 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 10.226 G_Regularizer: 0.000 validation_error: 20.692 +(epoch: 48, iters: 112656, time: 0.563, data: 0.000) G_L1: 13.948 G_L1_ABSOLUTE: 2.439 G_L1_RELATIVE: 11.508 G_Regularizer: 0.000 validation_error: 21.054 +(epoch: 48, iters: 114656, time: 0.544, data: 0.000) G_L1: 13.888 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 11.437 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 48, iters: 116656, time: 0.563, data: 0.000) G_L1: 14.214 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 11.564 G_Regularizer: 0.000 validation_error: 20.702 +(epoch: 48, iters: 118656, time: 0.552, data: 0.000) G_L1: 19.357 G_L1_ABSOLUTE: 3.049 G_L1_RELATIVE: 16.308 G_Regularizer: 0.000 validation_error: 20.756 +(epoch: 48, iters: 120656, time: 0.555, data: 0.000) G_L1: 14.225 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 11.723 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 48, iters: 122656, time: 0.545, data: 0.000) G_L1: 14.400 G_L1_ABSOLUTE: 2.562 G_L1_RELATIVE: 11.838 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 48, iters: 124656, time: 0.558, data: 0.000) G_L1: 14.816 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 12.226 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 48, iters: 126656, time: 0.559, data: 0.000) G_L1: 13.514 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 11.010 G_Regularizer: 0.000 validation_error: 20.639 +(epoch: 48, iters: 128656, time: 0.557, data: 0.000) G_L1: 13.873 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 11.466 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 48, iters: 130656, time: 0.545, data: 0.000) G_L1: 15.862 G_L1_ABSOLUTE: 3.140 G_L1_RELATIVE: 12.723 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 48, iters: 132656, time: 0.527, data: 0.000) G_L1: 13.523 G_L1_ABSOLUTE: 2.213 G_L1_RELATIVE: 11.309 G_Regularizer: 0.000 validation_error: 20.907 +(epoch: 48, iters: 134656, time: 0.541, data: 0.000) G_L1: 14.828 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 12.132 G_Regularizer: 0.000 validation_error: 20.912 +(epoch: 48, iters: 136656, time: 0.531, data: 0.000) G_L1: 14.793 G_L1_ABSOLUTE: 2.704 G_L1_RELATIVE: 12.090 G_Regularizer: 0.000 validation_error: 20.768 +(epoch: 48, iters: 138656, time: 0.543, data: 0.000) G_L1: 14.514 G_L1_ABSOLUTE: 2.752 G_L1_RELATIVE: 11.762 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 48, iters: 140656, time: 0.539, data: 0.000) G_L1: 15.677 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 13.174 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 48, iters: 142656, time: 0.535, data: 0.000) G_L1: 13.754 G_L1_ABSOLUTE: 2.227 G_L1_RELATIVE: 11.527 G_Regularizer: 0.000 validation_error: 20.588 +(epoch: 48, iters: 144656, time: 0.555, data: 0.000) G_L1: 26.933 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 24.384 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 48, iters: 146656, time: 0.560, data: 0.002) G_L1: 12.350 G_L1_ABSOLUTE: 2.301 G_L1_RELATIVE: 10.049 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 48, iters: 148656, time: 0.541, data: 0.000) G_L1: 14.605 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 12.146 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 48, iters: 150656, time: 0.531, data: 0.000) G_L1: 14.682 G_L1_ABSOLUTE: 2.147 G_L1_RELATIVE: 12.535 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 48, iters: 152656, time: 0.541, data: 0.000) G_L1: 14.205 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 11.683 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 48, iters: 154656, time: 0.551, data: 0.000) G_L1: 16.240 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 13.964 G_Regularizer: 0.000 validation_error: 21.048 +(epoch: 48, iters: 156656, time: 0.547, data: 0.000) G_L1: 27.442 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 24.802 G_Regularizer: 0.000 validation_error: 21.055 +(epoch: 48, iters: 158656, time: 0.541, data: 0.000) G_L1: 15.127 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 12.622 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 48, iters: 160656, time: 0.543, data: 0.001) G_L1: 14.967 G_L1_ABSOLUTE: 2.796 G_L1_RELATIVE: 12.171 G_Regularizer: 0.000 validation_error: 20.912 +(epoch: 48, iters: 162656, time: 0.558, data: 0.000) G_L1: 14.070 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 11.717 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 48, iters: 164656, time: 0.552, data: 0.001) G_L1: 13.150 G_L1_ABSOLUTE: 2.334 G_L1_RELATIVE: 10.817 G_Regularizer: 0.000 validation_error: 21.126 +(epoch: 48, iters: 166656, time: 0.554, data: 0.000) G_L1: 14.276 G_L1_ABSOLUTE: 2.315 G_L1_RELATIVE: 11.961 G_Regularizer: 0.000 validation_error: 20.776 +(epoch: 48, iters: 168656, time: 0.546, data: 0.000) G_L1: 12.856 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 10.372 G_Regularizer: 0.000 validation_error: 20.977 +(epoch: 48, iters: 170656, time: 0.556, data: 0.000) G_L1: 12.938 G_L1_ABSOLUTE: 2.144 G_L1_RELATIVE: 10.794 G_Regularizer: 0.000 validation_error: 20.742 +(epoch: 48, iters: 172656, time: 0.553, data: 0.000) G_L1: 13.878 G_L1_ABSOLUTE: 2.386 G_L1_RELATIVE: 11.492 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 48, iters: 174656, time: 0.543, data: 0.000) G_L1: 12.340 G_L1_ABSOLUTE: 2.185 G_L1_RELATIVE: 10.155 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 48, iters: 176656, time: 0.545, data: 0.001) G_L1: 17.896 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 15.277 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 48, iters: 178656, time: 0.547, data: 0.000) G_L1: 15.512 G_L1_ABSOLUTE: 2.747 G_L1_RELATIVE: 12.765 G_Regularizer: 0.000 validation_error: 21.044 +(epoch: 48, iters: 180656, time: 0.538, data: 0.000) G_L1: 14.918 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 12.486 G_Regularizer: 0.000 validation_error: 21.058 +(epoch: 48, iters: 182656, time: 0.546, data: 0.000) G_L1: 14.545 G_L1_ABSOLUTE: 2.064 G_L1_RELATIVE: 12.481 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 48, iters: 184656, time: 0.557, data: 0.000) G_L1: 13.449 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 10.715 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 48, iters: 186656, time: 0.554, data: 0.000) G_L1: 15.108 G_L1_ABSOLUTE: 2.768 G_L1_RELATIVE: 12.340 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 48, iters: 188656, time: 0.558, data: 0.000) G_L1: 12.296 G_L1_ABSOLUTE: 2.511 G_L1_RELATIVE: 9.785 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 48, iters: 190656, time: 0.527, data: 0.000) G_L1: 13.134 G_L1_ABSOLUTE: 2.713 G_L1_RELATIVE: 10.421 G_Regularizer: 0.000 validation_error: 21.120 +(epoch: 48, iters: 192656, time: 0.563, data: 0.000) G_L1: 14.674 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 12.306 G_Regularizer: 0.000 validation_error: 20.787 +(epoch: 48, iters: 194656, time: 0.553, data: 0.000) G_L1: 13.659 G_L1_ABSOLUTE: 2.386 G_L1_RELATIVE: 11.274 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 48, iters: 196656, time: 0.556, data: 0.000) G_L1: 12.328 G_L1_ABSOLUTE: 2.307 G_L1_RELATIVE: 10.022 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 48, iters: 198656, time: 0.548, data: 0.000) G_L1: 14.231 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 11.824 G_Regularizer: 0.000 validation_error: 21.116 +(epoch: 48, iters: 200656, time: 0.533, data: 0.000) G_L1: 13.626 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 11.054 G_Regularizer: 0.000 validation_error: 21.045 +(epoch: 48, iters: 202656, time: 0.539, data: 0.000) G_L1: 11.540 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 9.286 G_Regularizer: 0.000 validation_error: 20.820 +(epoch: 48, iters: 204656, time: 0.554, data: 0.000) G_L1: 12.710 G_L1_ABSOLUTE: 2.693 G_L1_RELATIVE: 10.018 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 48, iters: 206656, time: 0.559, data: 0.000) G_L1: 12.429 G_L1_ABSOLUTE: 2.117 G_L1_RELATIVE: 10.312 G_Regularizer: 0.000 validation_error: 20.639 +(epoch: 48, iters: 208656, time: 0.531, data: 0.000) G_L1: 13.704 G_L1_ABSOLUTE: 2.239 G_L1_RELATIVE: 11.465 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 48, iters: 210656, time: 0.562, data: 0.000) G_L1: 13.644 G_L1_ABSOLUTE: 3.132 G_L1_RELATIVE: 10.512 G_Regularizer: 0.000 validation_error: 20.991 +(epoch: 48, iters: 212656, time: 0.551, data: 0.001) G_L1: 12.194 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 9.731 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 48, iters: 214656, time: 0.547, data: 0.001) G_L1: 16.337 G_L1_ABSOLUTE: 1.998 G_L1_RELATIVE: 14.339 G_Regularizer: 0.000 validation_error: 21.080 +(epoch: 48, iters: 216656, time: 0.546, data: 0.000) G_L1: 14.736 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 12.175 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 48, iters: 218656, time: 0.534, data: 0.000) G_L1: 14.381 G_L1_ABSOLUTE: 2.479 G_L1_RELATIVE: 11.902 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 48, iters: 220656, time: 0.542, data: 0.000) G_L1: 15.921 G_L1_ABSOLUTE: 2.745 G_L1_RELATIVE: 13.175 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 48, iters: 222656, time: 0.562, data: 0.000) G_L1: 11.972 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 9.390 G_Regularizer: 0.000 validation_error: 20.785 +(epoch: 48, iters: 224656, time: 0.534, data: 0.000) G_L1: 13.156 G_L1_ABSOLUTE: 2.270 G_L1_RELATIVE: 10.886 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 48, iters: 226656, time: 0.553, data: 0.000) G_L1: 13.792 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 11.437 G_Regularizer: 0.000 validation_error: 21.020 +(epoch: 48, iters: 228656, time: 0.561, data: 0.000) G_L1: 13.376 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 10.520 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 48, iters: 230656, time: 0.557, data: 0.000) G_L1: 14.795 G_L1_ABSOLUTE: 2.273 G_L1_RELATIVE: 12.522 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 48, iters: 232656, time: 0.542, data: 0.000) G_L1: 13.533 G_L1_ABSOLUTE: 2.427 G_L1_RELATIVE: 11.106 G_Regularizer: 0.000 validation_error: 20.565 +(epoch: 48, iters: 234656, time: 0.553, data: 0.000) G_L1: 13.277 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 10.676 G_Regularizer: 0.000 validation_error: 21.014 +(epoch: 48, iters: 236656, time: 0.554, data: 0.000) G_L1: 11.555 G_L1_ABSOLUTE: 2.211 G_L1_RELATIVE: 9.344 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 48, iters: 238656, time: 0.544, data: 0.001) G_L1: 13.956 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 11.524 G_Regularizer: 0.000 validation_error: 20.782 +(epoch: 48, iters: 240656, time: 0.560, data: 0.000) G_L1: 13.908 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 11.321 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 48, iters: 242656, time: 0.551, data: 0.000) G_L1: 13.177 G_L1_ABSOLUTE: 2.223 G_L1_RELATIVE: 10.953 G_Regularizer: 0.000 validation_error: 20.871 +(epoch: 48, iters: 244656, time: 0.566, data: 0.000) G_L1: 14.607 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 11.878 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 48, iters: 246656, time: 0.558, data: 0.000) G_L1: 13.182 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 10.842 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 48, iters: 248656, time: 0.547, data: 0.000) G_L1: 14.058 G_L1_ABSOLUTE: 2.498 G_L1_RELATIVE: 11.561 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 48, iters: 250656, time: 0.548, data: 0.000) G_L1: 13.218 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 10.712 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 48, iters: 252656, time: 0.544, data: 0.000) G_L1: 12.762 G_L1_ABSOLUTE: 1.940 G_L1_RELATIVE: 10.822 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 48, iters: 254656, time: 0.572, data: 0.000) G_L1: 13.513 G_L1_ABSOLUTE: 2.268 G_L1_RELATIVE: 11.245 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 48, iters: 256656, time: 0.545, data: 0.000) G_L1: 13.616 G_L1_ABSOLUTE: 2.129 G_L1_RELATIVE: 11.487 G_Regularizer: 0.000 validation_error: 20.922 +(epoch: 48, iters: 258656, time: 0.541, data: 0.000) G_L1: 14.539 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 11.654 G_Regularizer: 0.000 validation_error: 20.844 +(epoch: 48, iters: 260656, time: 0.543, data: 0.000) G_L1: 15.157 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 12.435 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 48, iters: 262656, time: 0.544, data: 0.000) G_L1: 14.287 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 11.696 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 48, iters: 264656, time: 0.550, data: 0.000) G_L1: 11.775 G_L1_ABSOLUTE: 2.237 G_L1_RELATIVE: 9.538 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 48, iters: 266656, time: 0.557, data: 0.000) G_L1: 13.859 G_L1_ABSOLUTE: 2.338 G_L1_RELATIVE: 11.521 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 48, iters: 268656, time: 0.550, data: 0.000) G_L1: 12.632 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 9.992 G_Regularizer: 0.000 validation_error: 20.861 +(epoch: 48, iters: 270656, time: 0.555, data: 0.000) G_L1: 14.029 G_L1_ABSOLUTE: 3.007 G_L1_RELATIVE: 11.022 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 48, iters: 272656, time: 0.552, data: 0.000) G_L1: 14.219 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 11.921 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 48, iters: 274656, time: 0.553, data: 0.000) G_L1: 14.481 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 12.111 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 48, iters: 276656, time: 0.534, data: 0.000) G_L1: 16.469 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 13.715 G_Regularizer: 0.000 validation_error: 20.786 +(epoch: 48, iters: 278656, time: 0.537, data: 0.001) G_L1: 15.058 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 12.323 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 48, iters: 280656, time: 0.558, data: 0.000) G_L1: 13.037 G_L1_ABSOLUTE: 2.448 G_L1_RELATIVE: 10.589 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 48, iters: 282656, time: 0.560, data: 0.000) G_L1: 15.770 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 13.084 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 48, iters: 284656, time: 0.532, data: 0.000) G_L1: 15.240 G_L1_ABSOLUTE: 2.153 G_L1_RELATIVE: 13.088 G_Regularizer: 0.000 validation_error: 20.707 +(epoch: 48, iters: 286656, time: 0.559, data: 0.001) G_L1: 13.817 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 11.180 G_Regularizer: 0.000 validation_error: 20.802 +(epoch: 48, iters: 288656, time: 0.554, data: 0.000) G_L1: 12.126 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 9.496 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 48, iters: 290656, time: 0.538, data: 0.000) G_L1: 15.244 G_L1_ABSOLUTE: 2.578 G_L1_RELATIVE: 12.666 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 48, iters: 292656, time: 0.548, data: 0.000) G_L1: 11.808 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 9.359 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 48, iters: 294656, time: 0.532, data: 0.000) G_L1: 11.594 G_L1_ABSOLUTE: 2.230 G_L1_RELATIVE: 9.364 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 48, iters: 296656, time: 0.564, data: 0.000) G_L1: 13.473 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 11.119 G_Regularizer: 0.000 validation_error: 20.893 +(epoch: 48, iters: 298656, time: 0.534, data: 0.001) G_L1: 13.508 G_L1_ABSOLUTE: 2.148 G_L1_RELATIVE: 11.360 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 48, iters: 300656, time: 0.569, data: 0.000) G_L1: 12.742 G_L1_ABSOLUTE: 2.152 G_L1_RELATIVE: 10.590 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 48, iters: 302656, time: 0.544, data: 0.001) G_L1: 16.292 G_L1_ABSOLUTE: 2.617 G_L1_RELATIVE: 13.675 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 49, iters: 1904, time: 0.549, data: 0.000) G_L1: 13.613 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 11.273 G_Regularizer: 0.000 validation_error: 20.808 +(epoch: 49, iters: 3904, time: 0.553, data: 0.000) G_L1: 12.797 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 10.365 G_Regularizer: 0.000 validation_error: 20.902 +(epoch: 49, iters: 5904, time: 0.561, data: 0.000) G_L1: 13.107 G_L1_ABSOLUTE: 2.183 G_L1_RELATIVE: 10.924 G_Regularizer: 0.000 validation_error: 20.932 +(epoch: 49, iters: 7904, time: 0.537, data: 0.000) G_L1: 14.259 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 11.753 G_Regularizer: 0.000 validation_error: 20.822 +(epoch: 49, iters: 9904, time: 0.557, data: 0.000) G_L1: 17.067 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 14.793 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 49, iters: 11904, time: 0.546, data: 0.000) G_L1: 12.834 G_L1_ABSOLUTE: 2.147 G_L1_RELATIVE: 10.687 G_Regularizer: 0.000 validation_error: 21.168 +(epoch: 49, iters: 13904, time: 0.541, data: 0.000) G_L1: 17.336 G_L1_ABSOLUTE: 2.488 G_L1_RELATIVE: 14.848 G_Regularizer: 0.000 validation_error: 20.745 +(epoch: 49, iters: 15904, time: 0.534, data: 0.000) G_L1: 14.037 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 11.265 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 49, iters: 17904, time: 0.549, data: 0.000) G_L1: 17.021 G_L1_ABSOLUTE: 2.538 G_L1_RELATIVE: 14.482 G_Regularizer: 0.000 validation_error: 20.892 +(epoch: 49, iters: 19904, time: 0.546, data: 0.000) G_L1: 13.703 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 11.181 G_Regularizer: 0.000 validation_error: 21.002 +(epoch: 49, iters: 21904, time: 0.559, data: 0.000) G_L1: 13.633 G_L1_ABSOLUTE: 2.981 G_L1_RELATIVE: 10.652 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 49, iters: 23904, time: 0.537, data: 0.001) G_L1: 15.637 G_L1_ABSOLUTE: 2.367 G_L1_RELATIVE: 13.271 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 49, iters: 25904, time: 0.530, data: 0.000) G_L1: 14.873 G_L1_ABSOLUTE: 2.552 G_L1_RELATIVE: 12.321 G_Regularizer: 0.000 validation_error: 20.749 +(epoch: 49, iters: 27904, time: 0.550, data: 0.000) G_L1: 12.277 G_L1_ABSOLUTE: 2.203 G_L1_RELATIVE: 10.074 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 49, iters: 29904, time: 0.552, data: 0.000) G_L1: 14.633 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 12.108 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 49, iters: 31904, time: 0.550, data: 0.000) G_L1: 15.170 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 12.507 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 49, iters: 33904, time: 0.535, data: 0.000) G_L1: 14.249 G_L1_ABSOLUTE: 2.712 G_L1_RELATIVE: 11.537 G_Regularizer: 0.000 validation_error: 21.047 +(epoch: 49, iters: 35904, time: 0.557, data: 0.000) G_L1: 13.938 G_L1_ABSOLUTE: 2.048 G_L1_RELATIVE: 11.890 G_Regularizer: 0.000 validation_error: 20.600 +(epoch: 49, iters: 37904, time: 0.549, data: 0.000) G_L1: 13.543 G_L1_ABSOLUTE: 2.101 G_L1_RELATIVE: 11.442 G_Regularizer: 0.000 validation_error: 20.750 +(epoch: 49, iters: 39904, time: 0.550, data: 0.000) G_L1: 13.573 G_L1_ABSOLUTE: 2.123 G_L1_RELATIVE: 11.450 G_Regularizer: 0.000 validation_error: 21.045 +(epoch: 49, iters: 41904, time: 0.534, data: 0.000) G_L1: 16.738 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 14.234 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 49, iters: 43904, time: 0.558, data: 0.000) G_L1: 15.040 G_L1_ABSOLUTE: 2.318 G_L1_RELATIVE: 12.722 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 49, iters: 45904, time: 0.537, data: 0.000) G_L1: 14.812 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 12.133 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 49, iters: 47904, time: 0.569, data: 0.000) G_L1: 13.980 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 11.677 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 49, iters: 49904, time: 0.540, data: 0.000) G_L1: 12.633 G_L1_ABSOLUTE: 2.228 G_L1_RELATIVE: 10.405 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 49, iters: 51904, time: 0.559, data: 0.000) G_L1: 12.430 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 10.052 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 49, iters: 53904, time: 0.547, data: 0.000) G_L1: 13.977 G_L1_ABSOLUTE: 2.681 G_L1_RELATIVE: 11.296 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 49, iters: 55904, time: 0.546, data: 0.001) G_L1: 11.549 G_L1_ABSOLUTE: 2.475 G_L1_RELATIVE: 9.074 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 49, iters: 57904, time: 0.546, data: 0.001) G_L1: 12.847 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 10.063 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 49, iters: 59904, time: 0.558, data: 0.000) G_L1: 13.375 G_L1_ABSOLUTE: 2.131 G_L1_RELATIVE: 11.243 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 49, iters: 61904, time: 0.553, data: 0.000) G_L1: 13.330 G_L1_ABSOLUTE: 2.125 G_L1_RELATIVE: 11.205 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 49, iters: 63904, time: 0.554, data: 0.000) G_L1: 11.689 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 9.321 G_Regularizer: 0.000 validation_error: 20.964 +(epoch: 49, iters: 65904, time: 0.553, data: 0.000) G_L1: 15.371 G_L1_ABSOLUTE: 2.034 G_L1_RELATIVE: 13.337 G_Regularizer: 0.000 validation_error: 20.920 +(epoch: 49, iters: 67904, time: 0.536, data: 0.000) G_L1: 17.632 G_L1_ABSOLUTE: 3.010 G_L1_RELATIVE: 14.622 G_Regularizer: 0.000 validation_error: 20.779 +(epoch: 49, iters: 69904, time: 0.548, data: 0.000) G_L1: 13.731 G_L1_ABSOLUTE: 2.379 G_L1_RELATIVE: 11.352 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 49, iters: 71904, time: 0.557, data: 0.000) G_L1: 15.937 G_L1_ABSOLUTE: 2.234 G_L1_RELATIVE: 13.702 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 49, iters: 73904, time: 0.560, data: 0.000) G_L1: 11.998 G_L1_ABSOLUTE: 2.069 G_L1_RELATIVE: 9.929 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 49, iters: 75904, time: 0.549, data: 0.000) G_L1: 12.983 G_L1_ABSOLUTE: 2.205 G_L1_RELATIVE: 10.778 G_Regularizer: 0.000 validation_error: 20.826 +(epoch: 49, iters: 77904, time: 0.535, data: 0.000) G_L1: 14.605 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 11.981 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 49, iters: 79904, time: 0.541, data: 0.000) G_L1: 12.581 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 10.319 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 49, iters: 81904, time: 0.534, data: 0.000) G_L1: 13.376 G_L1_ABSOLUTE: 2.223 G_L1_RELATIVE: 11.153 G_Regularizer: 0.000 validation_error: 21.084 +(epoch: 49, iters: 83904, time: 0.530, data: 0.001) G_L1: 13.694 G_L1_ABSOLUTE: 1.927 G_L1_RELATIVE: 11.767 G_Regularizer: 0.000 validation_error: 20.650 +(epoch: 49, iters: 85904, time: 0.559, data: 0.000) G_L1: 14.352 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 11.767 G_Regularizer: 0.000 validation_error: 20.987 +(epoch: 49, iters: 87904, time: 0.541, data: 0.000) G_L1: 14.997 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 12.596 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 49, iters: 89904, time: 0.535, data: 0.000) G_L1: 13.576 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 10.696 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 49, iters: 91904, time: 0.544, data: 0.000) G_L1: 11.584 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 9.002 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 49, iters: 93904, time: 0.551, data: 0.000) G_L1: 13.397 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 10.778 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 49, iters: 95904, time: 0.540, data: 0.000) G_L1: 14.491 G_L1_ABSOLUTE: 2.611 G_L1_RELATIVE: 11.880 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 49, iters: 97904, time: 0.558, data: 0.001) G_L1: 15.263 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 12.325 G_Regularizer: 0.000 validation_error: 20.684 +(epoch: 49, iters: 99904, time: 0.552, data: 0.000) G_L1: 13.163 G_L1_ABSOLUTE: 3.027 G_L1_RELATIVE: 10.136 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 49, iters: 101904, time: 0.538, data: 0.000) G_L1: 10.810 G_L1_ABSOLUTE: 2.130 G_L1_RELATIVE: 8.680 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 49, iters: 103904, time: 0.542, data: 0.001) G_L1: 17.370 G_L1_ABSOLUTE: 2.861 G_L1_RELATIVE: 14.509 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 49, iters: 105904, time: 0.565, data: 0.001) G_L1: 11.656 G_L1_ABSOLUTE: 2.312 G_L1_RELATIVE: 9.344 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 49, iters: 107904, time: 0.564, data: 0.000) G_L1: 14.769 G_L1_ABSOLUTE: 2.602 G_L1_RELATIVE: 12.167 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 49, iters: 109904, time: 0.545, data: 0.000) G_L1: 12.786 G_L1_ABSOLUTE: 2.234 G_L1_RELATIVE: 10.552 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 49, iters: 111904, time: 0.543, data: 0.000) G_L1: 15.174 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 12.774 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 49, iters: 113904, time: 0.552, data: 0.000) G_L1: 13.885 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 11.427 G_Regularizer: 0.000 validation_error: 20.963 +(epoch: 49, iters: 115904, time: 0.555, data: 0.000) G_L1: 14.428 G_L1_ABSOLUTE: 2.387 G_L1_RELATIVE: 12.040 G_Regularizer: 0.000 validation_error: 21.016 +(epoch: 49, iters: 117904, time: 0.542, data: 0.001) G_L1: 14.676 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 12.179 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 49, iters: 119904, time: 0.546, data: 0.000) G_L1: 13.709 G_L1_ABSOLUTE: 2.507 G_L1_RELATIVE: 11.202 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 49, iters: 121904, time: 0.550, data: 0.000) G_L1: 13.924 G_L1_ABSOLUTE: 2.129 G_L1_RELATIVE: 11.795 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 49, iters: 123904, time: 0.551, data: 0.000) G_L1: 12.596 G_L1_ABSOLUTE: 1.983 G_L1_RELATIVE: 10.612 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 49, iters: 125904, time: 0.538, data: 0.000) G_L1: 11.128 G_L1_ABSOLUTE: 2.289 G_L1_RELATIVE: 8.839 G_Regularizer: 0.000 validation_error: 20.820 +(epoch: 49, iters: 127904, time: 0.551, data: 0.000) G_L1: 14.323 G_L1_ABSOLUTE: 2.827 G_L1_RELATIVE: 11.496 G_Regularizer: 0.000 validation_error: 21.104 +(epoch: 49, iters: 129904, time: 0.532, data: 0.000) G_L1: 15.281 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 12.772 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 49, iters: 131904, time: 0.551, data: 0.000) G_L1: 13.254 G_L1_ABSOLUTE: 2.075 G_L1_RELATIVE: 11.179 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 49, iters: 133904, time: 0.556, data: 0.001) G_L1: 13.731 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 11.285 G_Regularizer: 0.000 validation_error: 21.002 +(epoch: 49, iters: 135904, time: 0.552, data: 0.000) G_L1: 14.572 G_L1_ABSOLUTE: 2.280 G_L1_RELATIVE: 12.292 G_Regularizer: 0.000 validation_error: 21.034 +(epoch: 49, iters: 137904, time: 0.542, data: 0.000) G_L1: 12.657 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 10.080 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 49, iters: 139904, time: 0.557, data: 0.001) G_L1: 12.236 G_L1_ABSOLUTE: 2.427 G_L1_RELATIVE: 9.809 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 49, iters: 141904, time: 0.557, data: 0.000) G_L1: 15.094 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 12.717 G_Regularizer: 0.000 validation_error: 20.946 +(epoch: 49, iters: 143904, time: 0.532, data: 0.000) G_L1: 15.125 G_L1_ABSOLUTE: 3.032 G_L1_RELATIVE: 12.093 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 49, iters: 145904, time: 0.536, data: 0.000) G_L1: 14.029 G_L1_ABSOLUTE: 2.615 G_L1_RELATIVE: 11.414 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 49, iters: 147904, time: 0.551, data: 0.000) G_L1: 13.184 G_L1_ABSOLUTE: 2.204 G_L1_RELATIVE: 10.980 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 49, iters: 149904, time: 0.563, data: 0.000) G_L1: 11.877 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 9.502 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 49, iters: 151904, time: 0.555, data: 0.000) G_L1: 13.322 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 10.817 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 49, iters: 153904, time: 0.541, data: 0.000) G_L1: 14.177 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 11.623 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 49, iters: 155904, time: 0.558, data: 0.000) G_L1: 13.299 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 11.101 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 49, iters: 157904, time: 0.558, data: 0.000) G_L1: 16.129 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 13.470 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 49, iters: 159904, time: 0.542, data: 0.000) G_L1: 13.056 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 10.530 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 49, iters: 161904, time: 0.531, data: 0.000) G_L1: 13.267 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 10.744 G_Regularizer: 0.000 validation_error: 21.113 +(epoch: 49, iters: 163904, time: 0.541, data: 0.000) G_L1: 14.652 G_L1_ABSOLUTE: 2.175 G_L1_RELATIVE: 12.477 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 49, iters: 165904, time: 0.539, data: 0.000) G_L1: 14.093 G_L1_ABSOLUTE: 2.766 G_L1_RELATIVE: 11.328 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 49, iters: 167904, time: 0.566, data: 0.000) G_L1: 13.120 G_L1_ABSOLUTE: 2.893 G_L1_RELATIVE: 10.227 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 49, iters: 169904, time: 0.535, data: 0.000) G_L1: 11.731 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 9.205 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 49, iters: 171904, time: 0.546, data: 0.000) G_L1: 16.165 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 13.595 G_Regularizer: 0.000 validation_error: 20.970 +(epoch: 49, iters: 173904, time: 0.557, data: 0.001) G_L1: 15.285 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 12.643 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 49, iters: 175904, time: 0.552, data: 0.000) G_L1: 14.278 G_L1_ABSOLUTE: 2.145 G_L1_RELATIVE: 12.133 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 49, iters: 177904, time: 0.539, data: 0.000) G_L1: 14.081 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 11.595 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 49, iters: 179904, time: 0.542, data: 0.000) G_L1: 13.539 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 11.102 G_Regularizer: 0.000 validation_error: 20.867 +(epoch: 49, iters: 181904, time: 0.530, data: 0.000) G_L1: 13.544 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 10.979 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 49, iters: 183904, time: 0.531, data: 0.000) G_L1: 14.716 G_L1_ABSOLUTE: 2.718 G_L1_RELATIVE: 11.998 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 49, iters: 185904, time: 0.538, data: 0.000) G_L1: 13.016 G_L1_ABSOLUTE: 2.309 G_L1_RELATIVE: 10.707 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 49, iters: 187904, time: 0.540, data: 0.000) G_L1: 15.010 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 12.248 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 49, iters: 189904, time: 0.535, data: 0.000) G_L1: 14.609 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 11.936 G_Regularizer: 0.000 validation_error: 20.634 +(epoch: 49, iters: 191904, time: 0.552, data: 0.001) G_L1: 13.307 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 10.932 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 49, iters: 193904, time: 0.554, data: 0.001) G_L1: 12.827 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 10.395 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 49, iters: 195904, time: 0.542, data: 0.000) G_L1: 13.582 G_L1_ABSOLUTE: 2.208 G_L1_RELATIVE: 11.373 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 49, iters: 197904, time: 0.558, data: 0.000) G_L1: 15.217 G_L1_ABSOLUTE: 2.312 G_L1_RELATIVE: 12.905 G_Regularizer: 0.000 validation_error: 20.927 +(epoch: 49, iters: 199904, time: 0.555, data: 0.000) G_L1: 15.170 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 12.702 G_Regularizer: 0.000 validation_error: 21.103 +(epoch: 49, iters: 201904, time: 0.567, data: 0.000) G_L1: 14.614 G_L1_ABSOLUTE: 2.372 G_L1_RELATIVE: 12.242 G_Regularizer: 0.000 validation_error: 21.134 +(epoch: 49, iters: 203904, time: 0.540, data: 0.000) G_L1: 12.198 G_L1_ABSOLUTE: 2.263 G_L1_RELATIVE: 9.935 G_Regularizer: 0.000 validation_error: 20.995 +(epoch: 49, iters: 205904, time: 0.560, data: 0.000) G_L1: 12.741 G_L1_ABSOLUTE: 2.219 G_L1_RELATIVE: 10.522 G_Regularizer: 0.000 validation_error: 21.091 +(epoch: 49, iters: 207904, time: 0.538, data: 0.000) G_L1: 15.057 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 12.624 G_Regularizer: 0.000 validation_error: 21.248 +(epoch: 49, iters: 209904, time: 0.546, data: 0.000) G_L1: 12.408 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 9.927 G_Regularizer: 0.000 validation_error: 20.871 +(epoch: 49, iters: 211904, time: 0.542, data: 0.000) G_L1: 14.120 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 11.781 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 49, iters: 213904, time: 0.560, data: 0.000) G_L1: 14.100 G_L1_ABSOLUTE: 2.398 G_L1_RELATIVE: 11.702 G_Regularizer: 0.000 validation_error: 21.002 +(epoch: 49, iters: 215904, time: 0.560, data: 0.000) G_L1: 12.077 G_L1_ABSOLUTE: 2.063 G_L1_RELATIVE: 10.014 G_Regularizer: 0.000 validation_error: 21.082 +(epoch: 49, iters: 217904, time: 0.558, data: 0.000) G_L1: 13.099 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 10.638 G_Regularizer: 0.000 validation_error: 21.170 +(epoch: 49, iters: 219904, time: 0.540, data: 0.000) G_L1: 13.035 G_L1_ABSOLUTE: 2.017 G_L1_RELATIVE: 11.018 G_Regularizer: 0.000 validation_error: 20.902 +(epoch: 49, iters: 221904, time: 0.542, data: 0.000) G_L1: 14.600 G_L1_ABSOLUTE: 2.310 G_L1_RELATIVE: 12.290 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 49, iters: 223904, time: 0.554, data: 0.000) G_L1: 13.688 G_L1_ABSOLUTE: 2.828 G_L1_RELATIVE: 10.860 G_Regularizer: 0.000 validation_error: 21.045 +(epoch: 49, iters: 225904, time: 0.556, data: 0.001) G_L1: 11.710 G_L1_ABSOLUTE: 1.925 G_L1_RELATIVE: 9.785 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 49, iters: 227904, time: 0.555, data: 0.000) G_L1: 13.748 G_L1_ABSOLUTE: 2.987 G_L1_RELATIVE: 10.761 G_Regularizer: 0.000 validation_error: 21.104 +(epoch: 49, iters: 229904, time: 0.544, data: 0.000) G_L1: 11.599 G_L1_ABSOLUTE: 2.162 G_L1_RELATIVE: 9.437 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 49, iters: 231904, time: 0.553, data: 0.000) G_L1: 13.653 G_L1_ABSOLUTE: 2.602 G_L1_RELATIVE: 11.051 G_Regularizer: 0.000 validation_error: 21.082 +(epoch: 49, iters: 233904, time: 0.559, data: 0.000) G_L1: 13.177 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 10.820 G_Regularizer: 0.000 validation_error: 20.779 +(epoch: 49, iters: 235904, time: 0.562, data: 0.001) G_L1: 14.612 G_L1_ABSOLUTE: 2.243 G_L1_RELATIVE: 12.368 G_Regularizer: 0.000 validation_error: 21.001 +(epoch: 49, iters: 237904, time: 0.530, data: 0.000) G_L1: 13.244 G_L1_ABSOLUTE: 2.296 G_L1_RELATIVE: 10.948 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 49, iters: 239904, time: 0.537, data: 0.000) G_L1: 13.886 G_L1_ABSOLUTE: 2.205 G_L1_RELATIVE: 11.681 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 49, iters: 241904, time: 0.556, data: 0.000) G_L1: 13.006 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 10.617 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 49, iters: 243904, time: 0.555, data: 0.000) G_L1: 14.110 G_L1_ABSOLUTE: 2.946 G_L1_RELATIVE: 11.163 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 49, iters: 245904, time: 0.535, data: 0.000) G_L1: 11.671 G_L1_ABSOLUTE: 1.877 G_L1_RELATIVE: 9.794 G_Regularizer: 0.000 validation_error: 21.156 +(epoch: 49, iters: 247904, time: 0.554, data: 0.000) G_L1: 13.502 G_L1_ABSOLUTE: 2.372 G_L1_RELATIVE: 11.130 G_Regularizer: 0.000 validation_error: 20.971 +(epoch: 49, iters: 249904, time: 0.549, data: 0.000) G_L1: 12.961 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 10.187 G_Regularizer: 0.000 validation_error: 20.930 +(epoch: 49, iters: 251904, time: 0.555, data: 0.000) G_L1: 13.851 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 11.430 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 49, iters: 253904, time: 0.548, data: 0.000) G_L1: 15.253 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 12.550 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 49, iters: 255904, time: 0.560, data: 0.000) G_L1: 11.771 G_L1_ABSOLUTE: 2.297 G_L1_RELATIVE: 9.474 G_Regularizer: 0.000 validation_error: 20.896 +(epoch: 49, iters: 257904, time: 0.551, data: 0.000) G_L1: 12.998 G_L1_ABSOLUTE: 2.874 G_L1_RELATIVE: 10.123 G_Regularizer: 0.000 validation_error: 20.795 +(epoch: 49, iters: 259904, time: 0.552, data: 0.001) G_L1: 17.093 G_L1_ABSOLUTE: 2.448 G_L1_RELATIVE: 14.646 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 49, iters: 261904, time: 0.549, data: 0.000) G_L1: 13.355 G_L1_ABSOLUTE: 2.807 G_L1_RELATIVE: 10.548 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 49, iters: 263904, time: 0.555, data: 0.000) G_L1: 14.607 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 12.085 G_Regularizer: 0.000 validation_error: 20.714 +(epoch: 49, iters: 265904, time: 0.563, data: 0.000) G_L1: 14.565 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 12.048 G_Regularizer: 0.000 validation_error: 20.907 +(epoch: 49, iters: 267904, time: 0.559, data: 0.000) G_L1: 14.950 G_L1_ABSOLUTE: 2.655 G_L1_RELATIVE: 12.295 G_Regularizer: 0.000 validation_error: 21.087 +(epoch: 49, iters: 269904, time: 0.554, data: 0.001) G_L1: 14.625 G_L1_ABSOLUTE: 2.832 G_L1_RELATIVE: 11.793 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 49, iters: 271904, time: 0.549, data: 0.000) G_L1: 17.761 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 15.006 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 49, iters: 273904, time: 0.556, data: 0.000) G_L1: 14.041 G_L1_ABSOLUTE: 2.405 G_L1_RELATIVE: 11.636 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 49, iters: 275904, time: 0.559, data: 0.000) G_L1: 13.499 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 11.054 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 49, iters: 277904, time: 0.561, data: 0.000) G_L1: 14.529 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 12.128 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 49, iters: 279904, time: 0.540, data: 0.000) G_L1: 14.537 G_L1_ABSOLUTE: 2.382 G_L1_RELATIVE: 12.155 G_Regularizer: 0.000 validation_error: 20.797 +(epoch: 49, iters: 281904, time: 0.563, data: 0.000) G_L1: 13.450 G_L1_ABSOLUTE: 2.248 G_L1_RELATIVE: 11.202 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 49, iters: 283904, time: 0.558, data: 0.000) G_L1: 14.795 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 12.422 G_Regularizer: 0.000 validation_error: 20.899 +(epoch: 49, iters: 285904, time: 0.679, data: 0.000) G_L1: 11.269 G_L1_ABSOLUTE: 2.105 G_L1_RELATIVE: 9.164 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 49, iters: 287904, time: 0.530, data: 0.000) G_L1: 17.003 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 14.438 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 49, iters: 289904, time: 0.535, data: 0.000) G_L1: 12.346 G_L1_ABSOLUTE: 2.606 G_L1_RELATIVE: 9.741 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 49, iters: 291904, time: 0.547, data: 0.000) G_L1: 12.442 G_L1_ABSOLUTE: 2.689 G_L1_RELATIVE: 9.752 G_Regularizer: 0.000 validation_error: 20.741 +(epoch: 49, iters: 293904, time: 0.566, data: 0.000) G_L1: 14.000 G_L1_ABSOLUTE: 3.031 G_L1_RELATIVE: 10.969 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 49, iters: 295904, time: 0.551, data: 0.000) G_L1: 13.322 G_L1_ABSOLUTE: 2.566 G_L1_RELATIVE: 10.756 G_Regularizer: 0.000 validation_error: 20.771 +(epoch: 49, iters: 297904, time: 0.553, data: 0.000) G_L1: 13.791 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 11.328 G_Regularizer: 0.000 validation_error: 20.936 +(epoch: 49, iters: 299904, time: 0.564, data: 0.000) G_L1: 14.164 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 11.948 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 49, iters: 301904, time: 0.557, data: 0.000) G_L1: 15.067 G_L1_ABSOLUTE: 3.471 G_L1_RELATIVE: 11.597 G_Regularizer: 0.000 validation_error: 21.283 +(epoch: 50, iters: 1152, time: 0.533, data: 0.000) G_L1: 14.371 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 11.849 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 50, iters: 3152, time: 0.554, data: 0.000) G_L1: 13.130 G_L1_ABSOLUTE: 2.278 G_L1_RELATIVE: 10.853 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 50, iters: 5152, time: 0.554, data: 0.000) G_L1: 14.526 G_L1_ABSOLUTE: 2.749 G_L1_RELATIVE: 11.777 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 50, iters: 7152, time: 0.556, data: 0.000) G_L1: 14.835 G_L1_ABSOLUTE: 2.280 G_L1_RELATIVE: 12.555 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 50, iters: 9152, time: 0.535, data: 0.000) G_L1: 11.811 G_L1_ABSOLUTE: 2.200 G_L1_RELATIVE: 9.611 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 50, iters: 11152, time: 0.543, data: 0.001) G_L1: 12.810 G_L1_ABSOLUTE: 2.041 G_L1_RELATIVE: 10.769 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 50, iters: 13152, time: 0.556, data: 0.000) G_L1: 15.019 G_L1_ABSOLUTE: 2.815 G_L1_RELATIVE: 12.204 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 50, iters: 15152, time: 0.561, data: 0.000) G_L1: 18.882 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 15.930 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 50, iters: 17152, time: 0.540, data: 0.000) G_L1: 13.298 G_L1_ABSOLUTE: 2.134 G_L1_RELATIVE: 11.164 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 50, iters: 19152, time: 0.536, data: 0.000) G_L1: 15.481 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 12.821 G_Regularizer: 0.000 validation_error: 20.972 +(epoch: 50, iters: 21152, time: 0.548, data: 0.000) G_L1: 14.504 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 11.974 G_Regularizer: 0.000 validation_error: 20.794 +(epoch: 50, iters: 23152, time: 0.554, data: 0.000) G_L1: 13.488 G_L1_ABSOLUTE: 2.795 G_L1_RELATIVE: 10.693 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 50, iters: 25152, time: 0.549, data: 0.000) G_L1: 13.609 G_L1_ABSOLUTE: 2.325 G_L1_RELATIVE: 11.283 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 50, iters: 27152, time: 0.534, data: 0.000) G_L1: 15.034 G_L1_ABSOLUTE: 2.993 G_L1_RELATIVE: 12.041 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 50, iters: 29152, time: 0.558, data: 0.000) G_L1: 12.685 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 10.340 G_Regularizer: 0.000 validation_error: 20.738 +(epoch: 50, iters: 31152, time: 0.540, data: 0.000) G_L1: 14.289 G_L1_ABSOLUTE: 2.687 G_L1_RELATIVE: 11.602 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 50, iters: 33152, time: 0.563, data: 0.000) G_L1: 12.960 G_L1_ABSOLUTE: 2.189 G_L1_RELATIVE: 10.771 G_Regularizer: 0.000 validation_error: 20.859 +(epoch: 50, iters: 35152, time: 0.541, data: 0.000) G_L1: 10.776 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 8.514 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 50, iters: 37152, time: 0.547, data: 0.000) G_L1: 11.814 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 9.081 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 50, iters: 39152, time: 0.552, data: 0.000) G_L1: 13.315 G_L1_ABSOLUTE: 2.082 G_L1_RELATIVE: 11.233 G_Regularizer: 0.000 validation_error: 21.025 +(epoch: 50, iters: 41152, time: 0.558, data: 0.000) G_L1: 12.997 G_L1_ABSOLUTE: 2.941 G_L1_RELATIVE: 10.056 G_Regularizer: 0.000 validation_error: 21.070 +(epoch: 50, iters: 43152, time: 0.551, data: 0.000) G_L1: 13.694 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 11.208 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 50, iters: 45152, time: 0.544, data: 0.000) G_L1: 14.561 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 11.979 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 50, iters: 47152, time: 0.533, data: 0.001) G_L1: 12.045 G_L1_ABSOLUTE: 2.292 G_L1_RELATIVE: 9.752 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 50, iters: 49152, time: 0.539, data: 0.000) G_L1: 14.838 G_L1_ABSOLUTE: 2.765 G_L1_RELATIVE: 12.073 G_Regularizer: 0.000 validation_error: 20.948 +(epoch: 50, iters: 51152, time: 0.542, data: 0.000) G_L1: 13.778 G_L1_ABSOLUTE: 2.233 G_L1_RELATIVE: 11.545 G_Regularizer: 0.000 validation_error: 20.849 +(epoch: 50, iters: 53152, time: 0.535, data: 0.000) G_L1: 14.896 G_L1_ABSOLUTE: 2.222 G_L1_RELATIVE: 12.674 G_Regularizer: 0.000 validation_error: 20.847 +(epoch: 50, iters: 55152, time: 0.535, data: 0.000) G_L1: 13.896 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 11.300 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 50, iters: 57152, time: 0.534, data: 0.001) G_L1: 14.520 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 11.971 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 50, iters: 59152, time: 0.550, data: 0.000) G_L1: 14.624 G_L1_ABSOLUTE: 2.440 G_L1_RELATIVE: 12.185 G_Regularizer: 0.000 validation_error: 20.798 +(epoch: 50, iters: 61152, time: 0.541, data: 0.000) G_L1: 12.540 G_L1_ABSOLUTE: 2.209 G_L1_RELATIVE: 10.331 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 50, iters: 63152, time: 0.552, data: 0.000) G_L1: 12.599 G_L1_ABSOLUTE: 2.245 G_L1_RELATIVE: 10.354 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 50, iters: 65152, time: 0.552, data: 0.000) G_L1: 14.447 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 11.935 G_Regularizer: 0.000 validation_error: 20.805 +(epoch: 50, iters: 67152, time: 0.553, data: 0.000) G_L1: 12.909 G_L1_ABSOLUTE: 2.350 G_L1_RELATIVE: 10.559 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 50, iters: 69152, time: 0.562, data: 0.001) G_L1: 14.041 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 11.440 G_Regularizer: 0.000 validation_error: 20.776 +(epoch: 50, iters: 71152, time: 0.545, data: 0.000) G_L1: 13.394 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 11.032 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 50, iters: 73152, time: 0.541, data: 0.000) G_L1: 12.909 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 10.389 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 50, iters: 75152, time: 0.554, data: 0.000) G_L1: 16.826 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 14.196 G_Regularizer: 0.000 validation_error: 20.836 +(epoch: 50, iters: 77152, time: 0.557, data: 0.000) G_L1: 13.076 G_L1_ABSOLUTE: 2.187 G_L1_RELATIVE: 10.889 G_Regularizer: 0.000 validation_error: 20.847 +(epoch: 50, iters: 79152, time: 0.533, data: 0.000) G_L1: 12.667 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 10.069 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 50, iters: 81152, time: 0.562, data: 0.000) G_L1: 16.399 G_L1_ABSOLUTE: 3.145 G_L1_RELATIVE: 13.254 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 50, iters: 83152, time: 0.555, data: 0.000) G_L1: 17.972 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 15.421 G_Regularizer: 0.000 validation_error: 21.033 +(epoch: 50, iters: 85152, time: 0.538, data: 0.001) G_L1: 14.157 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 11.644 G_Regularizer: 0.000 validation_error: 21.007 +(epoch: 50, iters: 87152, time: 0.531, data: 0.001) G_L1: 14.550 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 11.976 G_Regularizer: 0.000 validation_error: 20.895 +(epoch: 50, iters: 89152, time: 0.564, data: 0.000) G_L1: 12.514 G_L1_ABSOLUTE: 2.362 G_L1_RELATIVE: 10.152 G_Regularizer: 0.000 validation_error: 20.824 +(epoch: 50, iters: 91152, time: 0.551, data: 0.000) G_L1: 12.283 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 9.789 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 50, iters: 93152, time: 0.552, data: 0.001) G_L1: 11.937 G_L1_ABSOLUTE: 2.155 G_L1_RELATIVE: 9.781 G_Regularizer: 0.000 validation_error: 20.768 +(epoch: 50, iters: 95152, time: 0.548, data: 0.000) G_L1: 11.778 G_L1_ABSOLUTE: 2.242 G_L1_RELATIVE: 9.536 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 50, iters: 97152, time: 0.558, data: 0.000) G_L1: 14.091 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 11.721 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 50, iters: 99152, time: 0.552, data: 0.000) G_L1: 12.872 G_L1_ABSOLUTE: 2.511 G_L1_RELATIVE: 10.361 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 50, iters: 101152, time: 0.531, data: 0.001) G_L1: 15.522 G_L1_ABSOLUTE: 2.422 G_L1_RELATIVE: 13.100 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 50, iters: 103152, time: 0.551, data: 0.001) G_L1: 12.428 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 10.169 G_Regularizer: 0.000 validation_error: 20.910 +(epoch: 50, iters: 105152, time: 0.545, data: 0.000) G_L1: 15.354 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 12.804 G_Regularizer: 0.000 validation_error: 20.929 +(epoch: 50, iters: 107152, time: 0.559, data: 0.000) G_L1: 13.092 G_L1_ABSOLUTE: 2.171 G_L1_RELATIVE: 10.922 G_Regularizer: 0.000 validation_error: 20.795 +(epoch: 50, iters: 109152, time: 0.559, data: 0.000) G_L1: 16.573 G_L1_ABSOLUTE: 3.057 G_L1_RELATIVE: 13.517 G_Regularizer: 0.000 validation_error: 20.895 +(epoch: 50, iters: 111152, time: 0.563, data: 0.000) G_L1: 13.745 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 11.165 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 50, iters: 113152, time: 0.542, data: 0.000) G_L1: 14.991 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 12.307 G_Regularizer: 0.000 validation_error: 20.917 +(epoch: 50, iters: 115152, time: 0.533, data: 0.000) G_L1: 14.602 G_L1_ABSOLUTE: 2.578 G_L1_RELATIVE: 12.024 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 50, iters: 117152, time: 0.540, data: 0.001) G_L1: 12.585 G_L1_ABSOLUTE: 2.405 G_L1_RELATIVE: 10.180 G_Regularizer: 0.000 validation_error: 21.089 +(epoch: 50, iters: 119152, time: 0.546, data: 0.000) G_L1: 13.344 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 10.755 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 50, iters: 121152, time: 0.539, data: 0.000) G_L1: 14.067 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 11.690 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 50, iters: 123152, time: 0.529, data: 0.000) G_L1: 12.631 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 10.345 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 50, iters: 125152, time: 0.543, data: 0.000) G_L1: 12.825 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 10.124 G_Regularizer: 0.000 validation_error: 21.034 +(epoch: 50, iters: 127152, time: 0.563, data: 0.000) G_L1: 14.406 G_L1_ABSOLUTE: 2.613 G_L1_RELATIVE: 11.793 G_Regularizer: 0.000 validation_error: 21.003 +(epoch: 50, iters: 129152, time: 0.542, data: 0.002) G_L1: 13.469 G_L1_ABSOLUTE: 2.165 G_L1_RELATIVE: 11.304 G_Regularizer: 0.000 validation_error: 20.970 +(epoch: 50, iters: 131152, time: 0.559, data: 0.000) G_L1: 13.021 G_L1_ABSOLUTE: 3.026 G_L1_RELATIVE: 9.995 G_Regularizer: 0.000 validation_error: 21.050 +(epoch: 50, iters: 133152, time: 0.552, data: 0.000) G_L1: 12.392 G_L1_ABSOLUTE: 2.456 G_L1_RELATIVE: 9.936 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 50, iters: 135152, time: 0.553, data: 0.000) G_L1: 14.762 G_L1_ABSOLUTE: 2.860 G_L1_RELATIVE: 11.902 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 50, iters: 137152, time: 0.555, data: 0.000) G_L1: 12.442 G_L1_ABSOLUTE: 2.155 G_L1_RELATIVE: 10.287 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 50, iters: 139152, time: 0.544, data: 0.000) G_L1: 13.416 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 10.892 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 50, iters: 141152, time: 0.559, data: 0.000) G_L1: 11.459 G_L1_ABSOLUTE: 2.348 G_L1_RELATIVE: 9.111 G_Regularizer: 0.000 validation_error: 21.022 +(epoch: 50, iters: 143152, time: 0.537, data: 0.000) G_L1: 13.826 G_L1_ABSOLUTE: 2.156 G_L1_RELATIVE: 11.669 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 50, iters: 145152, time: 0.559, data: 0.000) G_L1: 17.179 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 14.694 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 50, iters: 147152, time: 0.530, data: 0.000) G_L1: 11.547 G_L1_ABSOLUTE: 2.557 G_L1_RELATIVE: 8.990 G_Regularizer: 0.000 validation_error: 20.983 +(epoch: 50, iters: 149152, time: 0.539, data: 0.001) G_L1: 14.324 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 11.788 G_Regularizer: 0.000 validation_error: 21.112 +(epoch: 50, iters: 151152, time: 0.541, data: 0.000) G_L1: 14.865 G_L1_ABSOLUTE: 2.404 G_L1_RELATIVE: 12.461 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 50, iters: 153152, time: 0.559, data: 0.000) G_L1: 14.809 G_L1_ABSOLUTE: 2.531 G_L1_RELATIVE: 12.278 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 50, iters: 155152, time: 0.549, data: 0.000) G_L1: 12.128 G_L1_ABSOLUTE: 2.213 G_L1_RELATIVE: 9.915 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 50, iters: 157152, time: 0.545, data: 0.000) G_L1: 13.782 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 10.983 G_Regularizer: 0.000 validation_error: 20.750 +(epoch: 50, iters: 159152, time: 0.555, data: 0.000) G_L1: 13.700 G_L1_ABSOLUTE: 2.423 G_L1_RELATIVE: 11.276 G_Regularizer: 0.000 validation_error: 20.862 +(epoch: 50, iters: 161152, time: 0.559, data: 0.000) G_L1: 16.878 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 14.068 G_Regularizer: 0.000 validation_error: 21.028 +(epoch: 50, iters: 163152, time: 0.545, data: 0.000) G_L1: 14.249 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 11.894 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 50, iters: 165152, time: 0.546, data: 0.000) G_L1: 13.474 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 11.064 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 50, iters: 167152, time: 0.541, data: 0.000) G_L1: 14.579 G_L1_ABSOLUTE: 2.638 G_L1_RELATIVE: 11.941 G_Regularizer: 0.000 validation_error: 21.046 +(epoch: 50, iters: 169152, time: 0.552, data: 0.000) G_L1: 13.612 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 11.295 G_Regularizer: 0.000 validation_error: 20.797 +(epoch: 50, iters: 171152, time: 0.550, data: 0.000) G_L1: 13.918 G_L1_ABSOLUTE: 2.350 G_L1_RELATIVE: 11.568 G_Regularizer: 0.000 validation_error: 21.055 +(epoch: 50, iters: 173152, time: 0.559, data: 0.000) G_L1: 14.002 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 11.582 G_Regularizer: 0.000 validation_error: 21.025 +(epoch: 50, iters: 175152, time: 0.544, data: 0.000) G_L1: 12.535 G_L1_ABSOLUTE: 3.139 G_L1_RELATIVE: 9.397 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 50, iters: 177152, time: 0.540, data: 0.000) G_L1: 15.029 G_L1_ABSOLUTE: 3.106 G_L1_RELATIVE: 11.923 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 50, iters: 179152, time: 0.562, data: 0.001) G_L1: 13.282 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 10.908 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 50, iters: 181152, time: 0.538, data: 0.001) G_L1: 12.506 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 9.982 G_Regularizer: 0.000 validation_error: 20.818 +(epoch: 50, iters: 183152, time: 0.549, data: 0.000) G_L1: 13.664 G_L1_ABSOLUTE: 2.346 G_L1_RELATIVE: 11.318 G_Regularizer: 0.000 validation_error: 20.921 +(epoch: 50, iters: 185152, time: 0.554, data: 0.000) G_L1: 12.013 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 9.635 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 50, iters: 187152, time: 0.541, data: 0.000) G_L1: 13.810 G_L1_ABSOLUTE: 2.155 G_L1_RELATIVE: 11.655 G_Regularizer: 0.000 validation_error: 20.929 +(epoch: 50, iters: 189152, time: 0.546, data: 0.000) G_L1: 12.634 G_L1_ABSOLUTE: 2.278 G_L1_RELATIVE: 10.356 G_Regularizer: 0.000 validation_error: 20.871 +(epoch: 50, iters: 191152, time: 0.549, data: 0.000) G_L1: 12.997 G_L1_ABSOLUTE: 2.296 G_L1_RELATIVE: 10.701 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 50, iters: 193152, time: 0.535, data: 0.000) G_L1: 11.561 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 9.277 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 50, iters: 195152, time: 0.557, data: 0.001) G_L1: 9.892 G_L1_ABSOLUTE: 2.195 G_L1_RELATIVE: 7.697 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 50, iters: 197152, time: 0.540, data: 0.000) G_L1: 14.692 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 12.211 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 50, iters: 199152, time: 0.558, data: 0.000) G_L1: 13.356 G_L1_ABSOLUTE: 2.219 G_L1_RELATIVE: 11.137 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 50, iters: 201152, time: 0.539, data: 0.000) G_L1: 10.934 G_L1_ABSOLUTE: 2.351 G_L1_RELATIVE: 8.584 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 50, iters: 203152, time: 0.532, data: 0.000) G_L1: 14.364 G_L1_ABSOLUTE: 2.226 G_L1_RELATIVE: 12.138 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 50, iters: 205152, time: 0.544, data: 0.000) G_L1: 13.461 G_L1_ABSOLUTE: 2.390 G_L1_RELATIVE: 11.071 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 50, iters: 207152, time: 0.547, data: 0.000) G_L1: 16.535 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 13.865 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 50, iters: 209152, time: 0.543, data: 0.001) G_L1: 14.066 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 11.393 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 50, iters: 211152, time: 0.545, data: 0.000) G_L1: 14.960 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 12.282 G_Regularizer: 0.000 validation_error: 21.038 +(epoch: 50, iters: 213152, time: 0.552, data: 0.000) G_L1: 13.359 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 10.782 G_Regularizer: 0.000 validation_error: 21.114 +(epoch: 50, iters: 215152, time: 0.550, data: 0.000) G_L1: 13.462 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 10.623 G_Regularizer: 0.000 validation_error: 21.005 +(epoch: 50, iters: 217152, time: 0.534, data: 0.001) G_L1: 14.286 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 11.870 G_Regularizer: 0.000 validation_error: 21.007 +(epoch: 50, iters: 219152, time: 0.549, data: 0.001) G_L1: 13.220 G_L1_ABSOLUTE: 2.181 G_L1_RELATIVE: 11.039 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 50, iters: 221152, time: 0.549, data: 0.000) G_L1: 14.465 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 11.791 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 50, iters: 223152, time: 0.543, data: 0.000) G_L1: 15.709 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 13.220 G_Regularizer: 0.000 validation_error: 20.687 +(epoch: 50, iters: 225152, time: 0.552, data: 0.000) G_L1: 14.548 G_L1_ABSOLUTE: 2.997 G_L1_RELATIVE: 11.551 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 50, iters: 227152, time: 0.551, data: 0.000) G_L1: 16.420 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 13.907 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 50, iters: 229152, time: 0.553, data: 0.000) G_L1: 14.365 G_L1_ABSOLUTE: 2.567 G_L1_RELATIVE: 11.798 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 50, iters: 231152, time: 0.554, data: 0.000) G_L1: 13.529 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 11.120 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 50, iters: 233152, time: 0.541, data: 0.000) G_L1: 13.512 G_L1_ABSOLUTE: 2.270 G_L1_RELATIVE: 11.242 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 50, iters: 235152, time: 0.546, data: 0.000) G_L1: 14.784 G_L1_ABSOLUTE: 2.499 G_L1_RELATIVE: 12.284 G_Regularizer: 0.000 validation_error: 20.963 +(epoch: 50, iters: 237152, time: 0.540, data: 0.001) G_L1: 17.443 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 14.966 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 50, iters: 239152, time: 0.536, data: 0.000) G_L1: 14.884 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 12.125 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 50, iters: 241152, time: 0.546, data: 0.000) G_L1: 13.389 G_L1_ABSOLUTE: 2.895 G_L1_RELATIVE: 10.495 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 50, iters: 243152, time: 0.544, data: 0.000) G_L1: 17.264 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 14.614 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 50, iters: 245152, time: 0.563, data: 0.000) G_L1: 13.888 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 11.461 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 50, iters: 247152, time: 0.559, data: 0.000) G_L1: 12.697 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 10.132 G_Regularizer: 0.000 validation_error: 21.009 +(epoch: 50, iters: 249152, time: 0.539, data: 0.000) G_L1: 14.719 G_L1_ABSOLUTE: 2.404 G_L1_RELATIVE: 12.315 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 50, iters: 251152, time: 0.540, data: 0.000) G_L1: 14.773 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 12.453 G_Regularizer: 0.000 validation_error: 20.998 +(epoch: 50, iters: 253152, time: 0.541, data: 0.000) G_L1: 14.338 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 11.679 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 50, iters: 255152, time: 0.562, data: 0.000) G_L1: 15.671 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 13.166 G_Regularizer: 0.000 validation_error: 20.839 +(epoch: 50, iters: 257152, time: 0.546, data: 0.001) G_L1: 14.734 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 11.783 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 50, iters: 259152, time: 0.546, data: 0.000) G_L1: 14.127 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 11.393 G_Regularizer: 0.000 validation_error: 20.826 +(epoch: 50, iters: 261152, time: 0.560, data: 0.000) G_L1: 21.629 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 19.002 G_Regularizer: 0.000 validation_error: 20.832 +(epoch: 50, iters: 263152, time: 0.561, data: 0.000) G_L1: 11.584 G_L1_ABSOLUTE: 2.200 G_L1_RELATIVE: 9.384 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 50, iters: 265152, time: 0.547, data: 0.000) G_L1: 10.555 G_L1_ABSOLUTE: 2.069 G_L1_RELATIVE: 8.486 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 50, iters: 267152, time: 0.545, data: 0.000) G_L1: 11.750 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 9.152 G_Regularizer: 0.000 validation_error: 20.791 +(epoch: 50, iters: 269152, time: 0.542, data: 0.000) G_L1: 13.131 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 10.662 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 50, iters: 271152, time: 0.551, data: 0.000) G_L1: 15.054 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 12.691 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 50, iters: 273152, time: 0.553, data: 0.000) G_L1: 14.395 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 12.021 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 50, iters: 275152, time: 0.531, data: 0.000) G_L1: 16.199 G_L1_ABSOLUTE: 2.816 G_L1_RELATIVE: 13.383 G_Regularizer: 0.000 validation_error: 21.037 +(epoch: 50, iters: 277152, time: 0.537, data: 0.000) G_L1: 13.642 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 11.180 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 50, iters: 279152, time: 0.554, data: 0.000) G_L1: 15.279 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 12.803 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 50, iters: 281152, time: 0.552, data: 0.000) G_L1: 13.569 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 11.202 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 50, iters: 283152, time: 0.550, data: 0.001) G_L1: 14.213 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 11.543 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 50, iters: 285152, time: 0.555, data: 0.000) G_L1: 15.793 G_L1_ABSOLUTE: 2.404 G_L1_RELATIVE: 13.390 G_Regularizer: 0.000 validation_error: 20.636 +(epoch: 50, iters: 287152, time: 0.553, data: 0.000) G_L1: 12.423 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 10.046 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 50, iters: 289152, time: 0.563, data: 0.000) G_L1: 12.481 G_L1_ABSOLUTE: 2.242 G_L1_RELATIVE: 10.239 G_Regularizer: 0.000 validation_error: 20.723 +(epoch: 50, iters: 291152, time: 0.554, data: 0.000) G_L1: 14.564 G_L1_ABSOLUTE: 2.944 G_L1_RELATIVE: 11.619 G_Regularizer: 0.000 validation_error: 20.637 +(epoch: 50, iters: 293152, time: 0.540, data: 0.000) G_L1: 13.002 G_L1_ABSOLUTE: 2.353 G_L1_RELATIVE: 10.648 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 50, iters: 295152, time: 0.550, data: 0.001) G_L1: 15.001 G_L1_ABSOLUTE: 2.648 G_L1_RELATIVE: 12.353 G_Regularizer: 0.000 validation_error: 20.943 +(epoch: 50, iters: 297152, time: 0.560, data: 0.001) G_L1: 13.265 G_L1_ABSOLUTE: 2.233 G_L1_RELATIVE: 11.032 G_Regularizer: 0.000 validation_error: 21.106 +(epoch: 50, iters: 299152, time: 0.539, data: 0.000) G_L1: 14.484 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 11.886 G_Regularizer: 0.000 validation_error: 20.849 +(epoch: 50, iters: 301152, time: 0.535, data: 0.000) G_L1: 15.200 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 12.917 G_Regularizer: 0.000 validation_error: 20.902 diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/checkpoints/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead/opt.txt b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/checkpoints/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead/opt.txt new file mode 100644 index 0000000..4c31766 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/checkpoints/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead/opt.txt @@ -0,0 +1,60 @@ +----------------- Options --------------- + aspect_ratio: 1.0 + audio_window_size: 16 + batch_size: 1 + cached_images: False + checkpoints_dir: ./checkpoints + dataroot: /ARD_ZDF [default: None] + dataset_mode: multi_face_audio_eq_tmp_cached [default: aligned] + direction: AtoB + display_winsize: 512 [default: 256] + epoch: latest + erosionFactor: 1.0 + eval: False + fineSize: 512 + fix_renderer: False + gpu_ids: 0 + hierarchicalTex: False + init_gain: 0.02 + init_type: xavier + input_nc: 3 + isTrain: False [default: None] + loadSize: 512 + load_iter: 0 [default: 0] + look_ahead: True [default: False] + lossType: RMS [default: L1] + max_dataset_size: inf + model: audio2ExpressionsAttentionTMP4 [default: test] + n_layers_D: 3 + name: audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead [default: experiment_name] + ndf: 64 + netD: basic + netG: unet_256 + ngf: 64 + no_augmentation: False + no_dropout: False + norm: instance + ntest: inf + num_test: 50 + num_threads: 4 + output_audio_expressions: False + output_nc: 3 + phase: test + renderer: no_renderer + rendererType: estimatorAttention [default: UNET_5_level] + resize_or_crop: resize_and_crop + results_dir: ./results/ + seq_len: 8 [default: 1] + serial_batches: False + source_actor: /code/neural-code/output_data/features/fgsdfsdf [default: ] + source_dir: ./datasets/ + suffix: + target_actor: /code/neural-code/output_data/features/Jennifer_355_9415 [default: ] + tex_dim: 256 + tex_features: 16 +tex_features_intermediate: 16 + textureModel: DynamicNeuralTextureAudio + transfer_path: /code/neural-code/output_data/TRANSFERS/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead [default: None] + verbose: False + write_no_images: True [default: False] +----------------- End ------------------- diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/checkpoints/audio2ExpressionsAttentionTMP4/opt.txt b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/checkpoints/audio2ExpressionsAttentionTMP4/opt.txt new file mode 100644 index 0000000..caf26b3 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/checkpoints/audio2ExpressionsAttentionTMP4/opt.txt @@ -0,0 +1,59 @@ +----------------- Options --------------- + aspect_ratio: 1.0 + audio_window_size: 16 + batch_size: 1 + cached_images: False + checkpoints_dir: ./checkpoints + dataroot: /ARD_ZDF [default: None] + dataset_mode: multi_face_audio_eq_tmp_cached [default: aligned] + direction: AtoB + display_winsize: 512 [default: 256] + epoch: latest + erosionFactor: 1.0 + eval: False + fineSize: 512 + fix_renderer: False + gpu_ids: 0 + hierarchicalTex: False + init_gain: 0.02 + init_type: xavier + input_nc: 3 + isTrain: False [default: None] + loadSize: 512 + load_iter: 0 [default: 0] + look_ahead: True [default: False] + lossType: RMS [default: L1] + max_dataset_size: inf + model: audio2ExpressionsAttentionTMP4 [default: test] + n_layers_D: 3 + name: audio2ExpressionsAttentionTMP4 [default: experiment_name] + ndf: 64 + netD: basic + netG: unet_256 + ngf: 64 + no_augmentation: False + no_dropout: False + norm: instance + ntest: inf + num_test: 50 + num_threads: 4 + output_audio_expressions: False + output_nc: 3 + phase: test + renderer: no_renderer + rendererType: estimatorAttention [default: UNET_5_level] + resize_or_crop: resize_and_crop + results_dir: ./results/ + seq_len: 8 [default: 1] + serial_batches: False + source_actor: /home/alberto/NeuralVoicePuppetry/datasets//External/Sekunden_Wissen_01 [default: ] + source_dir: ./datasets/ + suffix: + target_actor: /home/alberto/NeuralVoicePuppetry/datasets//External/Russian_guy [default: ] + tex_dim: 256 + tex_features: 16 +tex_features_intermediate: 16 + textureModel: DynamicNeuralTextureAudio + verbose: False + write_no_images: True [default: False] +----------------- End ------------------- diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/__init__.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/__init__.py new file mode 100644 index 0000000..3735ac1 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/__init__.py @@ -0,0 +1,90 @@ +import importlib +import torch.utils.data +from data.base_data_loader import BaseDataLoader +from data.base_dataset import BaseDataset + + +def find_dataset_using_name(dataset_name): + # Given the option --dataset_mode [datasetname], + # the file "data/datasetname_dataset.py" + # will be imported. + dataset_filename = "data." + dataset_name + "_dataset" + datasetlib = importlib.import_module(dataset_filename) + + # In the file, the class called DatasetNameDataset() will + # be instantiated. It has to be a subclass of BaseDataset, + # and it is case-insensitive. + dataset = None + target_dataset_name = dataset_name.replace('_', '') + 'dataset' + for name, cls in datasetlib.__dict__.items(): + if name.lower() == target_dataset_name.lower() \ + and issubclass(cls, BaseDataset): + dataset = cls + + if dataset is None: + print("In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase." % (dataset_filename, target_dataset_name)) + exit(0) + + return dataset + + +def get_option_setter(dataset_name): + dataset_class = find_dataset_using_name(dataset_name) + return dataset_class.modify_commandline_options + + +def create_dataset(opt): + dataset = find_dataset_using_name(opt.dataset_mode) + instance = dataset() + instance.initialize(opt) + print("dataset [%s] was created" % (instance.name())) + return instance + + +def CreateDataLoader(opt): + data_loader = CustomDatasetDataLoader() + data_loader.initialize(opt) + return data_loader + + +# Wrapper class of Dataset class that performs +# multi-threaded data loading +class CustomDatasetDataLoader(BaseDataLoader): + def name(self): + return 'CustomDatasetDataLoader' + + def initialize(self, opt): + BaseDataLoader.initialize(self, opt) + self.dataset = create_dataset(opt) + if opt.serial_batches: + self.dataloader = torch.utils.data.DataLoader( + self.dataset, + batch_size=opt.batch_size, + shuffle=not opt.serial_batches, + num_workers=int(opt.num_threads)) + else: + #weights = make_weights_for_balanced_classes(dataset_train.imgs, len(dataset_train.classes)) + weights = self.dataset.getSampleWeights() + weights = torch.DoubleTensor(weights) + sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights)) + + self.dataloader = torch.utils.data.DataLoader( + self.dataset, + batch_size=opt.batch_size, + #shuffle=True, + sampler=sampler, + pin_memory=True, + num_workers=int(opt.num_threads)) + + + def load_data(self): + return self + + def __len__(self): + return min(len(self.dataset), self.opt.max_dataset_size) + + def __iter__(self): + for i, data in enumerate(self.dataloader): + if i * self.opt.batch_size >= self.opt.max_dataset_size: + break + yield data diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/audio.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/audio.py new file mode 100644 index 0000000..4932c94 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/audio.py @@ -0,0 +1,77 @@ +import time +import random +import math + +import numpy as np + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.transforms as transforms + +import torchaudio +import torchaudio.transforms + +import librosa +import scipy.signal +import librosa.display +import matplotlib.pyplot as plt + + +class Audio(): + def name(self): + return 'Audio' + + def __init__(self, filename, write_mel_spectogram = False): + self.n_mels=128 + self.fmax=8000 + self.hop_length_ms = 20 + + sound, sample_rate = librosa.load(filename)#torchaudio.load(filename) + self.raw_audio = sound + self.sample_rate = sample_rate + print('sample_rate = %d' % self.sample_rate) + self.n_samples = sound.shape[0] + self.time_total = self.n_samples / self.sample_rate + print('length = %ds' % self.time_total) + + print('compute mel spectrogram...') + self.hop_length = int(sample_rate / 1000.0 * self.hop_length_ms) + print('hop_length: ', self.hop_length) + self.mel_spectrogram = librosa.feature.melspectrogram(y=self.raw_audio, sr=self.sample_rate, hop_length=self.hop_length, n_mels=self.n_mels, fmax=self.fmax) + + + if write_mel_spectogram: + print('write spectrogram to file') + plt.figure(figsize=(100, 15)) + librosa.display.specshow(librosa.power_to_db(self.mel_spectrogram, ref=np.max), y_axis='mel', fmax=self.fmax, x_axis='time') + plt.colorbar(format='%+2.0f dB') + plt.title('Mel spectrogram') + plt.tight_layout() + plt.savefig('mel_features.png', dpi=None, facecolor='w', edgecolor='w', orientation='portrait', papertype=None, format=None, transparent=False, bbox_inches=None, pad_inches=0.1, frameon=None, metadata=None) + + print('mel: ', self.mel_spectrogram.shape) # (128, 18441) + self.n_mel_frames = self.mel_spectrogram.shape[1] + self.mel_sample_rate = self.mel_spectrogram.shape[1] / self.time_total + print('n_mel_frames: ', self.n_mel_frames) + print('mel_sample_rate: ', self.mel_sample_rate) + + # convert to torch + self.mel_spectrogram = torch.FloatTensor(self.mel_spectrogram) + + def getWindow(self, mel_frame_idx, window_size): + # get audio mel sample window + audio_start = mel_frame_idx - (window_size//2) + audio_end = mel_frame_idx + (window_size//2) + if audio_start < 0: + audio_input = self.mel_spectrogram[0:self.n_mels, 0:audio_end] + zeros = torch.zeros((self.n_mels,-audio_start)) + audio_input = torch.cat([zeros, audio_input], 1) + elif audio_end >= self.n_mel_frames: + audio_input = self.mel_spectrogram[:, audio_start:-1] + zeros = torch.zeros((self.n_mels,audio_end-self.n_mel_frames + 1)) + audio_input = torch.cat([audio_input, zeros], 1) + else: + audio_input = self.mel_spectrogram[:, audio_start:audio_end] + + return torch.reshape(audio_input, (1, 1, self.n_mels, window_size)) \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/audio_dataset.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/audio_dataset.py new file mode 100644 index 0000000..3ff5e14 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/audio_dataset.py @@ -0,0 +1,143 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from data.base_dataset import BaseDataset +from data.audio import Audio +#from data.image_folder import make_dataset +from PIL import Image +import h5py + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.deepspeech.npy']): + id_str = fname[:-15] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.deepspeech.npy' + path = os.path.join(root, fname) + images.append(path) + return images + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + #id_str = fname[l+1:-4] + id_str = fname[l+1:-15] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + +class AudioDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + self.root = opt.dataroot + self.h5py_path = os.path.join(opt.dataroot, opt.dataroot.split("/")[-1]+'.h5') + + print('\th5py_path:', self.h5py_path) + self.data = h5py.File(self.h5py_path, 'r') + + opt.nObjects = 1 + opt.nTrainObjects = 116 # TODO + opt.nTestObjects = 1 + opt.test_sequence_names = [[opt.dataroot.split("/")[-1], 'test']] + assert(opt.resize_or_crop == 'resize_and_crop') + + if opt.isTrain: + print('ERROR: audio_dataset only allowed for test') + exit() + + def getSampleWeights(self): + weights = np.ones((len(self.frame_paths))) + return weights + + def getAudioFilename(self): + return os.path.join(self.root, 'audio.wav') + + def getAudioFeatureFilename(self, idx): + return self.frame_paths[idx % len(self.frame_paths)] + + def __getitem__(self, index): + + #print('GET ITEM: ', index) + + # load deepspeech feature + feature_array = self.data["dsf"][index] + dsf_np = np.resize(feature_array, (16,29,1)) + dsf = transforms.ToTensor()(dsf_np.astype(np.float32)) + + + # load sequence data if necessary + if self.opt.look_ahead:# use prev and following frame infos + r = self.opt.seq_len//2 + for i in range(1,r): # prev frames + index_seq = index - i + if index_seq < 0: index_seq = 0 + + feature_array = self.data["dsf"][index_seq] + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + for i in range(1,self.opt.seq_len - r + 1): # following frames + index_seq = index + i + max_idx = len(self.data["dsf"])-1 + if index_seq > max_idx: index_seq = max_idx + + feature_array = self.data["dsf"][index_seq] + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf, dsf_seq], 0) # seq_len x 16 x 29 + # note the ordering [old ... current ... future] + else: + for i in range(1,self.opt.seq_len): + index_seq = index - i + if index_seq < 0: index_seq = 0 + + feature_array = self.data["dsf"][index_seq] + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + + ################################# + zeroIdentity = torch.zeros(100) + zeroExpressions = torch.zeros(76) + + target_id = -1 + internal_sequence_id = 0 + + weight = 1.0 / len(self.data["dsf"]) + + return {'paths': '', + 'expressions': zeroExpressions, + 'identity': zeroIdentity, + 'intrinsics': np.zeros((4)), + 'extrinsics': np.zeros((4,4)), + 'audio_deepspeech': dsf, # deepspeech feature + 'target_id':target_id, + 'internal_id':internal_sequence_id, + 'weight': np.array([weight]).astype(np.float32)} + + def __len__(self): + return len(self.data["dsf"]) + + def name(self): + return 'AudioDataset' diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/base_data_loader.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/base_data_loader.py new file mode 100644 index 0000000..ae5a168 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/base_data_loader.py @@ -0,0 +1,10 @@ +class BaseDataLoader(): + def __init__(self): + pass + + def initialize(self, opt): + self.opt = opt + pass + + def load_data(): + return None diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/base_dataset.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/base_dataset.py new file mode 100644 index 0000000..25c7c8c --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/base_dataset.py @@ -0,0 +1,105 @@ +import torch.utils.data as data +from PIL import Image +import torchvision.transforms as transforms + + +class BaseDataset(data.Dataset): + def __init__(self): + super(BaseDataset, self).__init__() + + def name(self): + return 'BaseDataset' + + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + pass + + def getSampleWeights(self): + return torch.ones((len(self))) + + def __len__(self): + return 0 + + +def get_transform(opt): + transform_list = [] + if opt.resize_or_crop == 'resize_and_crop': + osize = [opt.loadSize, opt.loadSize] + transform_list.append(transforms.Resize(osize, Image.BICUBIC)) + transform_list.append(transforms.RandomCrop(opt.fineSize)) + elif opt.resize_or_crop == 'crop': + transform_list.append(transforms.RandomCrop(opt.fineSize)) + elif opt.resize_or_crop == 'scale_width': + transform_list.append(transforms.Lambda( + lambda img: __scale_width(img, opt.fineSize))) + elif opt.resize_or_crop == 'scale_width_and_crop': + transform_list.append(transforms.Lambda( + lambda img: __scale_width(img, opt.loadSize))) + transform_list.append(transforms.RandomCrop(opt.fineSize)) + elif opt.resize_or_crop == 'none': + transform_list.append(transforms.Lambda( + lambda img: __adjust(img))) + else: + raise ValueError('--resize_or_crop %s is not a valid option.' % opt.resize_or_crop) + + if opt.isTrain and not opt.no_flip: + transform_list.append(transforms.RandomHorizontalFlip()) + + transform_list += [transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), + (0.5, 0.5, 0.5))] + return transforms.Compose(transform_list) + + +# just modify the width and height to be multiple of 4 +def __adjust(img): + ow, oh = img.size + + # the size needs to be a multiple of this number, + # because going through generator network may change img size + # and eventually cause size mismatch error + mult = 4 + if ow % mult == 0 and oh % mult == 0: + return img + w = (ow - 1) // mult + w = (w + 1) * mult + h = (oh - 1) // mult + h = (h + 1) * mult + + if ow != w or oh != h: + __print_size_warning(ow, oh, w, h) + + return img.resize((w, h), Image.BICUBIC) + + +def __scale_width(img, target_width): + ow, oh = img.size + + # the size needs to be a multiple of this number, + # because going through generator network may change img size + # and eventually cause size mismatch error + mult = 4 + assert target_width % mult == 0, "the target width needs to be multiple of %d." % mult + if (ow == target_width and oh % mult == 0): + return img + w = target_width + target_height = int(target_width * oh / ow) + m = (target_height - 1) // mult + h = (m + 1) * mult + + if target_height != h: + __print_size_warning(target_width, target_height, w, h) + + return img.resize((w, h), Image.BICUBIC) + + +def __print_size_warning(ow, oh, w, h): + if not hasattr(__print_size_warning, 'has_printed'): + print("The image size needs to be a multiple of 4. " + "The loaded image size was (%d, %d), so it was adjusted to " + "(%d, %d). This adjustment will be done to all images " + "whose sizes are not multiples of 4" % (ow, oh, w, h)) + __print_size_warning.has_printed = True diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/face_dataset.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/face_dataset.py new file mode 100644 index 0000000..66abe90 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/face_dataset.py @@ -0,0 +1,179 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from data.base_dataset import BaseDataset +from data.audio import Audio +from PIL import Image +from util import util +import h5py + + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.npy', '.NPY']): + #.deepspeech.npy + id_str = fname[:-15] #4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.deepspeech.npy' + path = os.path.join(root, fname) + images.append(path) + return images + +def make_expr_dataset(dir): + expressions = [] + num_expr = len([name for name in os.listdir(dir) if os.path.isfile(name)]) + for i in range(num_expr): + fname = f'expr_{i}.npy' + path = os.path.join(dir, fname) + expressions.append(path) + return expressions + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + id_str = fname[l+1:-15]#4] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + + +# def load_expressions(input_dir): +# file = open(input_dir+"/expressions/", "r") +# expressions = [[float(x) for x in line.split()] for line in file] +# file.close() +# return expressions + +class FaceDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + + # directories + self.dataroot = opt.dataroot + self.h5py_path = os.path.join(opt.dataroot, opt.dataroot.split("/")[-1]+'.h5') + + # debug print + print('load sequence:', self.dataroot) + print('\th5py_path:', self.h5py_path) + + self.data = h5py.File(self.h5py_path, 'r') + + # set data + self.n_frames_total = min(len(self.data["dsf"]), len(self.data["ep"])) + + print('\tnum frames:', self.n_frames_total) + + + opt.nTrainObjects = 1 + opt.nValObjects = 1 + opt.nTestObjects = 1 + + opt.test_sequence_names = [[opt.dataroot.split("/")[-1], 'train']] + assert(opt.resize_or_crop == 'resize_and_crop') + + def getSampleWeights(self): + weights = np.ones((self.n_frames_total)) + return weights + + + def getAudioFilename(self): + return os.path.join(self.dataroot, 'audio.wav') + + def getAudioFeatureFilename(self, idx): + #return self.frame_paths[idx % len(self.frame_paths)] + audio_id = self.audio_ids[idx] + return os.path.join(self.audio_feature_dir, str(audio_id) + '.deepspeech.npy') + + + def __getitem__(self, global_index): + # select frame from sequence + index = global_index + + # intrinsics and extrinsics + intrinsics = np.zeros((4)) # not used + extrinsics = np.zeros((4,4))# not used + + # expressions + expressions = self.data["ep"][index] + expressions = torch.tensor(expressions) + + # identity + identity = torch.zeros(100) # not used + + # load deepspeech feature + feature_array = self.data["dsf"][index] + dsf_np = np.resize(feature_array, (16,29,1)) + dsf = transforms.ToTensor()(dsf_np.astype(np.float32)) + + + # load sequence data if necessary + if self.opt.look_ahead:# use prev and following frame infos + r = self.opt.seq_len//2 + for i in range(1,r): # prev frames + index_seq = index - i + if index_seq < 0: index_seq = 0 + + feature_array = self.data["dsf"][index_seq] + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + for i in range(1,self.opt.seq_len - r + 1): # following frames + index_seq = index + i + max_idx = len(self.data["dsf"])-1 + if index_seq > max_idx: index_seq = max_idx + + feature_array = self.data["dsf"][index_seq] + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf, dsf_seq], 0) # seq_len x 16 x 29 + # note the ordering [old ... current ... future] + else: + last_valid_idx = audio_id + for i in range(1,self.opt.seq_len): + index_seq = index - i + if index_seq < 0: index_seq = 0 + + feature_array = self.data["dsf"][index_seq] + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + + ################################# + weight = 1.0 / self.n_frames_total + + return {#'TARGET': TARGET, 'UV': UV, + 'paths': '', #img_path, + 'intrinsics': np.array(intrinsics), + 'extrinsics': np.array(extrinsics), + 'expressions': expressions, + 'identity': identity, + 'audio_deepspeech': dsf, # deepspeech feature + 'target_id': -1, + 'internal_id': 0, + 'weight': np.array([weight]).astype(np.float32)} + + def __len__(self): + return self.n_frames_total + + def name(self): + return 'FaceDataset' diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/multi_face_audio_eq_tmp_cached_dataset.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/multi_face_audio_eq_tmp_cached_dataset.py new file mode 100644 index 0000000..59c4a89 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/data/multi_face_audio_eq_tmp_cached_dataset.py @@ -0,0 +1,458 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from data.base_dataset import BaseDataset +from data.audio import Audio +#from data.image_folder import make_dataset +from PIL import Image +from tqdm import tqdm + +#def make_dataset(dir): +# images = [] +# assert os.path.isdir(dir), '%s is not a valid directory' % dir +# for root, _, fnames in sorted(os.walk(dir)): +# for fname in fnames: +# if any(fname.endswith(extension) for extension in ['.bin', '.BIN']): +# path = os.path.join(root, fname) +# images.append(path) +# return sorted(images) + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.npy', '.NPY']): + #.deepspeech.npy + id_str = fname[:-15] #4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.deepspeech.npy' + path = os.path.join(root, fname) + images.append(path) + return images + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + id_str = fname[l+1:-15]#4] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + +def make_dataset_ids_png(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.png', '.png']): + id_str = fname[:-4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + return ids + +def load_intrinsics(input_dir): + file = open(input_dir+"/intrinsics.txt", "r") + intrinsics = [[(float(x) for x in line.split())] for line in file] + file.close() + intrinsics = list(intrinsics[0][0]) + return intrinsics + +def load_rigids(input_dir): + file = open(input_dir+"/rigid.txt", "r") + rigid_floats = [[float(x) for x in line.split()] for line in file] # note that it stores 5 lines per matrix (blank line) + file.close() + all_rigids = [ [rigid_floats[4*idx + 0],rigid_floats[4*idx + 1],rigid_floats[4*idx + 2],rigid_floats[4*idx + 3]] for idx in range(0, len(rigid_floats)//4) ] + return all_rigids + +def load_expressions(input_dir): + file = open(input_dir+"/expression.txt", "r") + expressions = [[float(x) for x in line.split()] for line in file] + file.close() + return expressions + +def load_identity(input_dir): + file = open(input_dir+"/identities.txt", "r") + identities = [[float(x) for x in line.split()] for line in file] + file.close() + return identities + + +class MultiFaceAudioEQTmpCachedDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + self.root = opt.dataroot + + # read dataset file that contains the filenames for the train, val and test lists + file = open(self.root+"/dataset.txt", "r") + self.filename_train_list, self.filename_val_list, self.filename_test_list = [str(line) for line in file] + file.close() + if self.filename_train_list[-1] == '\n': self.filename_train_list = self.filename_train_list[:-1] + if self.filename_val_list[-1] == '\n': self.filename_val_list = self.filename_val_list[:-1] + if self.filename_test_list[-1] == '\n': self.filename_test_list = self.filename_test_list[:-1] + + + + # get list of train sequences + file = open(self.root+"/" + self.filename_train_list, "r") + self.train_sequence_names = [str(line) for line in file] + file.close() + for i in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[i][-1] == '\n': + self.train_sequence_names[i] = self.train_sequence_names[i][:-1] + + # get list of val sequences + file = open(self.root+"/" + self.filename_val_list, "r") + self.val_sequence_names = [[str(w) for w in line.split()] for line in file] + file.close() + for i in range(0,len(self.val_sequence_names)): + if self.val_sequence_names[i][0][-1] == '\n': self.val_sequence_names[i][0] = self.val_sequence_names[i][0][:-1] + if self.val_sequence_names[i][1][-1] == '\n': self.val_sequence_names[i][1] = self.val_sequence_names[i][1][:-1] + + # get list of test sequences + file = open(self.root+"/" + self.filename_test_list, "r") + self.test_sequence_names = [[str(w) for w in line.split()] for line in file] + if opt.output_audio_expressions: self.test_sequence_names = self.test_sequence_names[0:1] + file.close() + for i in range(0,len(self.test_sequence_names)): + if self.test_sequence_names[i][0][-1] == '\n': self.test_sequence_names[i][0] = self.test_sequence_names[i][0][:-1] + if self.test_sequence_names[i][1][-1] == '\n': self.test_sequence_names[i][1] = self.test_sequence_names[i][1][:-1] + + # print some stats + print('filename_train_list:', self.filename_train_list) + print('\tnum_seq:', len(self.train_sequence_names)) + print('filename_val_list: ', self.filename_val_list) + print('\tnum_seq:', len(self.val_sequence_names)) + print('filename_test_list: ', self.filename_test_list) + print('\tnum_seq:', len(self.test_sequence_names)) + + opt.train_sequence_names = self.train_sequence_names + opt.val_sequence_names = self.val_sequence_names + opt.test_sequence_names = self.test_sequence_names + + # search mapping from val, test to train sequences that are used as targets + self.val_sequence_targets = [] + for i in range(0,len(self.val_sequence_names)): + target_name = self.val_sequence_names[i][1] + target_id = -1 + for j in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[j] == target_name: + target_id = j + break + if target_id == -1: + print('Target sequence not in train set! ', target_name) + exit() + self.val_sequence_targets.append(target_id) + + self.test_sequence_targets = [] + for i in range(0,len(self.test_sequence_names)): + target_name = self.test_sequence_names[i][1] + target_id = -1 + for j in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[j] == target_name: + target_id = j + break + if target_id == -1: + print('Target sequence not in train set! ', target_name) + exit() + self.test_sequence_targets.append(target_id) + print('test: ', self.test_sequence_names[i]) + print('\t target:', target_id) + + # store len values + opt.nTrainObjects = len(self.train_sequence_names) + opt.nValObjects = len(self.val_sequence_names) + opt.nTestObjects = len(self.test_sequence_names) + + ################################################ + ################################################ + ################################################ + + # prepare dataloader paths / data + self.audio_feature_dir = [] + self.image_dir = [] + self.uvs_dir = [] + self.audio_ids = [] + self.image_ids = [] + self.intrinsics = [] + self.extrinsics = [] + self.expressions = [] + self.identities = [] + self.target_id = [] + self.n_frames_total = 0 + + if opt.phase == 'train': + self.sequence_names = self.train_sequence_names + for i in range(0,len(self.train_sequence_names)): + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[i]) + audio_feature_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'uvs') + print('load train sequence:', self.train_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + + audio_ids = make_ids(make_dataset(audio_feature_dir), dataroot) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(dataroot) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(i) + + self.n_frames_total += min_len + elif opt.phase == 'val': + for i in range(0,len(self.val_sequence_names)): + target_id = self.val_sequence_targets[i] + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[target_id]) + audio_feature_dir = os.path.join(opt.dataroot, self.val_sequence_names[i][0], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'uvs') + print('load val sequence:', self.val_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + audio_ids = make_ids(make_dataset(audio_feature_dir), os.path.join(opt.dataroot, self.val_sequence_names[i][0])) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(os.path.join(opt.dataroot, self.val_sequence_names[i][0])) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(target_id) + + self.n_frames_total += min_len + else: # test + for i in range(0,len(self.test_sequence_names)): + target_id = self.test_sequence_targets[i] + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[target_id]) + audio_feature_dir = os.path.join(opt.dataroot, self.test_sequence_names[i][0], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'uvs') + print('load test sequence:', self.test_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + audio_ids = make_ids(make_dataset(audio_feature_dir), os.path.join(opt.dataroot, self.test_sequence_names[i][0])) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(os.path.join(opt.dataroot, self.test_sequence_names[i][0])) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(target_id) + + self.n_frames_total += min_len + + + print('frames_total:', self.n_frames_total) + + + #global_target_ids = [] + #for i in range(0,len(self.audio_ids)): + # for j in range(0,len(self.audio_ids[i])): + # global_target_ids.append(self.target_id[i]) + #global_target_ids=np.array(global_target_ids) + #self.weights = np.where(global_target_ids==2, 1.0 * np.ones((self.n_frames_total)), 0.01 * np.ones((self.n_frames_total)) ) + self.weights = [] + for i in range(0,len(self.audio_ids)): + l = len(self.audio_ids[i]) + for j in range(0,l): + self.weights.append(1.0 / l) + self.weights = np.array(self.weights) + + assert(opt.resize_or_crop == 'resize_and_crop') + + # mapping global to internal + self.mapping_global2internal = [] + self.mapping_global2internal_offset = [] + self.dsf = [] + offset = 0 + + for i in tqdm(range(0,len(self.audio_ids))): + l = len(self.audio_ids[i]) + dsf_seq = [] + for k in range(0,l): + self.mapping_global2internal.append(i) + self.mapping_global2internal_offset.append(offset) + dsf_fname = os.path.join(self.audio_feature_dir[i], str(self.audio_ids[i][k]) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq.append(dsf_np.astype(np.float32)) + bar.update(offset + k) + self.dsf.append(dsf_seq) + offset += l + + + def getSampleWeights(self): + return self.weights + + def getitem(self, global_index): + + # select sequence + internal_sequence_id = self.mapping_global2internal[global_index] + sum_frames = self.mapping_global2internal_offset[global_index] + + # select frame from sequence + index = (global_index-sum_frames) % len(self.audio_ids[internal_sequence_id]) + + # get data ids + audio_id = self.audio_ids[internal_sequence_id][index] + image_id = self.image_ids[internal_sequence_id][index] + + #print('GET ITEM: ', index) + #img_path = self.frame_paths[sequence_id][index] + + # intrinsics and extrinsics + intrinsics = self.intrinsics[internal_sequence_id] + extrinsics = self.extrinsics[internal_sequence_id][image_id] + + # expressions + expressions = np.asarray(self.expressions[internal_sequence_id][audio_id], dtype=np.float32) + #print('expressions:', expressions.shape) + expressions[32] *= 0.0 # remove eye brow movements + expressions[41] *= 0.0 # remove eye brow movements + expressions[71:75] *= 0.0 # remove eye brow movements + expressions = torch.tensor(expressions) + + # identity + identity = torch.tensor(self.identities[internal_sequence_id][image_id]) + target_id = self.target_id[internal_sequence_id] # sequence id refers to the target sequence (of the training corpus) + + # load deepspeech feature + #print('audio_id', audio_id) + dsf_fname = os.path.join(self.audio_feature_dir[internal_sequence_id], str(audio_id) + '.deepspeech.npy') + + dsf_np = self.dsf[internal_sequence_id][index] + dsf = transforms.ToTensor()(dsf_np) + + # load sequence data if necessary + if self.opt.look_ahead:# use prev and following frame infos + r = self.opt.seq_len//2 + for i in range(1,r): # prev frames + index_seq = index - i + if index_seq < 0: index_seq = 0 + dsf_np = self.dsf[internal_sequence_id][index_seq] + dsf_seq = transforms.ToTensor()(dsf_np) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + for i in range(1,self.opt.seq_len - r + 1): # following frames + index_seq = index + i + max_idx = len(self.audio_ids[internal_sequence_id])-1 + if index_seq > max_idx: index_seq = max_idx + dsf_np = self.dsf[internal_sequence_id][index_seq] + dsf_seq = transforms.ToTensor()(dsf_np) # 1 x 16 x 29 + dsf = torch.cat([dsf, dsf_seq], 0) # seq_len x 16 x 29 + # note the ordering [old ... current ... future] + else: + for i in range(1,self.opt.seq_len): + index_seq = index - i + if index_seq < 0: index_seq = 0 + dsf_np = self.dsf[internal_sequence_id][index_seq] + dsf_seq = transforms.ToTensor()(dsf_np) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + #weight = 1.0 / len(self.audio_feature_dir[internal_sequence_id]) + weight = self.weights[global_index] + + return {'paths': dsf_fname, #img_path, + 'intrinsics': np.array(intrinsics), + 'extrinsics': np.array(extrinsics), + 'expressions': expressions, + 'identity': identity, + 'audio_deepspeech': dsf, # deepspeech feature + 'target_id':target_id, + 'internal_id':internal_sequence_id, + + 'weight': np.array([weight]).astype(np.float32)} + + + def __getitem__(self, global_index): + # select frame from sequence + index = global_index + current = self.getitem(index) + prv = self.getitem(max(index-1, 0)) + nxt = self.getitem(min(index+1, self.n_frames_total-1)) + + return { + 'paths': current['paths'], #img_path, + 'target_id': current['target_id'], + 'internal_id': current['internal_id'], + 'weight': current['weight'], + 'identity': current['identity'], + 'intrinsics': current['intrinsics'], + + 'extrinsics': current['extrinsics'], + 'expressions': current['expressions'], + 'audio_deepspeech': current['audio_deepspeech'], + + 'extrinsics_prv': prv['extrinsics'], + 'expressions_prv': prv['expressions'], + 'audio_deepspeech_prv': prv['audio_deepspeech'], + + 'extrinsics_nxt': nxt['extrinsics'], + 'expressions_nxt': nxt['expressions'], + 'audio_deepspeech_nxt': nxt['audio_deepspeech'], + } + + + def __len__(self): + return self.n_frames_total #len(self.frame_paths[0]) + + def name(self): + return 'MultiFaceAudioEQTmpCachedDataset' diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/models/__init__.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/models/__init__.py new file mode 100644 index 0000000..4d92091 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/models/__init__.py @@ -0,0 +1,39 @@ +import importlib +from models.base_model import BaseModel + + +def find_model_using_name(model_name): + # Given the option --model [modelname], + # the file "models/modelname_model.py" + # will be imported. + model_filename = "models." + model_name + "_model" + modellib = importlib.import_module(model_filename) + + # In the file, the class called ModelNameModel() will + # be instantiated. It has to be a subclass of BaseModel, + # and it is case-insensitive. + model = None + target_model_name = model_name.replace('_', '') + 'model' + for name, cls in modellib.__dict__.items(): + if name.lower() == target_model_name.lower() \ + and issubclass(cls, BaseModel): + model = cls + + if model is None: + print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name)) + exit(0) + + return model + + +def get_option_setter(model_name): + model_class = find_model_using_name(model_name) + return model_class.modify_commandline_options + + +def create_model(opt): + model = find_model_using_name(opt.model) + instance = model() + instance.initialize(opt) + print("model [%s] was created" % (instance.name())) + return instance diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/models/audio2ExpressionsAttentionTMP4_model.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/models/audio2ExpressionsAttentionTMP4_model.py new file mode 100644 index 0000000..8c8b6f2 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/models/audio2ExpressionsAttentionTMP4_model.py @@ -0,0 +1,342 @@ +import torch +import torch.nn as nn +from util.image_pool import ImagePool +from .base_model import BaseModel +from . import networks + +from BaselModel.basel_model import * + +INVALID_UV = -1.0 + + +class ExpressionEstimator_Attention(nn.Module): + def __init__(self, n_output_expressions, nIdentities, seq_len, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(ExpressionEstimator_Attention, self).__init__() + print('Estimator Attention') + ################################# + ######## audio net ########## + ################################# + self.seq_len = seq_len + + dropout_rate = 0.0 + if use_dropout == True: + #dropout_rate = 0.5 + dropout_rate = 0.25 + + self.convNet = nn.Sequential( + nn.Conv2d(29, 32, kernel_size=(3,1), stride=(2,1), padding=(1,0), bias=True), # 29 x 16 x 1 => 32 x 8 x 1 + nn.LeakyReLU(0.02, True), + nn.Conv2d(32, 32, kernel_size=(3,1), stride=(2,1), padding=(1,0), bias=True), # 32 x 8 x 1 => 32 x 4 x 1 + nn.LeakyReLU(0.02, True), + nn.Conv2d(32, 64, kernel_size=(3,1), stride=(2,1), padding=(1,0), bias=True), # 32 x 4 x 1 => 64 x 2 x 1 + nn.LeakyReLU(0.2, True), + nn.Conv2d(64, 64, kernel_size=(3,1), stride=(2,1), padding=(1,0), bias=True), # 64 x 2 x 1 => 64 x 1 x 1 + nn.LeakyReLU(0.2, True), + ) + + fullNet_input_size = 64 + + self.subspace_dim = 32 # number of audio expressions + print('fullNet_input_size: ', fullNet_input_size) + self.fullNet = nn.Sequential( + nn.Linear(in_features = fullNet_input_size, out_features=128, bias = True), + nn.LeakyReLU(0.02), + + nn.Linear(in_features = 128, out_features=64, bias = True), + nn.LeakyReLU(0.02), + + nn.Linear(in_features = 64, out_features=self.subspace_dim, bias = True), + nn.Tanh() + ) + + + # mapping from subspace to full expression space + self.register_parameter('mapping', torch.nn.Parameter(torch.randn(1, nIdentities, N_EXPRESSIONS, self.subspace_dim, requires_grad=True))) + + # attention + self.attentionConvNet = nn.Sequential( # b x subspace_dim x seq_len + nn.Conv1d(self.subspace_dim, 16, kernel_size=3, stride=1, padding=1, bias=True), + nn.LeakyReLU(0.02, True), + nn.Conv1d(16, 8, kernel_size=3, stride=1, padding=1, bias=True), + nn.LeakyReLU(0.02, True), + nn.Conv1d(8, 4, kernel_size=3, stride=1, padding=1, bias=True), + nn.LeakyReLU(0.02, True), + nn.Conv1d(4, 2, kernel_size=3, stride=1, padding=1, bias=True), + nn.LeakyReLU(0.02, True), + nn.Conv1d(2, 1, kernel_size=3, stride=1, padding=1, bias=True), + nn.LeakyReLU(0.02, True) + ) + self.attentionNet = nn.Sequential( + nn.Linear(in_features = self.seq_len, out_features=self.seq_len, bias = True), + nn.Softmax(dim=1) + ) + #self.hidden2subspace = nn.Linear(self.subspace_dim,self.subspace_dim) + + def forward_internal(self, audio_features_sequence, identity_id): + result_subspace, intermediate_expression = self.getAudioExpressions_internal(audio_features_sequence) + mapping = torch.index_select(self.mapping[0], dim = 0, index = identity_id) + result = 10.0 * torch.bmm(mapping, result_subspace)[:,:,0] + result_intermediate = 10.0 * torch.bmm(mapping, intermediate_expression)[:,:,0] + #exit(-1) + return result, result_intermediate + + def forward(self, audio_features_sequence, identity_id): + result_subspace = self.getAudioExpressions(audio_features_sequence) + mapping = torch.index_select(self.mapping[0], dim = 0, index = identity_id) + result = torch.bmm(mapping, result_subspace)[:,:,0] + #exit(-1) + return 10.0 * result + + def getAudioExpressions_internal(self, audio_features_sequence): + # audio_features_sequence: b x seq_len x 16 x 29 + b = audio_features_sequence.shape[0] # batchsize + audio_features_sequence = audio_features_sequence.view(b * self.seq_len, 1, 16, 29) # b * seq_len x 1 x 16 x 29 + audio_features_sequence = torch.transpose(audio_features_sequence, 1, 3) # b* seq_len x 29 x 16 x 1 + conv_res = self.convNet( audio_features_sequence ) + conv_res = torch.reshape( conv_res, (b * self.seq_len, 1, -1)) + result_subspace = self.fullNet(conv_res)[:,0,:] # b * seq_len x subspace_dim + result_subspace = result_subspace.view(b, self.seq_len, self.subspace_dim)# b x seq_len x subspace_dim + + ################# + ### attention ### + ################# + result_subspace_T = torch.transpose(result_subspace, 1, 2) # b x subspace_dim x seq_len + intermediate_expression = result_subspace_T[:,:,(self.seq_len // 2):(self.seq_len // 2) + 1] + att_conv_res = self.attentionConvNet(result_subspace_T) + #print('att_conv_res', att_conv_res.shape) + attention = self.attentionNet(att_conv_res.view(b, self.seq_len)).view(b, self.seq_len, 1) # b x seq_len x 1 + #print('attention', attention.shape) + # pooling along the sequence dimension + result_subspace = torch.bmm(result_subspace_T, attention) + #print('result_subspace', result_subspace.shape) + ### + + return result_subspace.view(b, self.subspace_dim, 1), intermediate_expression + + def getAudioExpressions(self, audio_features_sequence): + expr, _ = self.getAudioExpressions_internal(audio_features_sequence) + return expr + + def regularizer(self): + #reg = torch.norm(self.mapping) + reg_mapping = torch.mean(torch.abs(self.mapping)) + + # one could also enforce orthogonality here + + # s_browExpressions[] = { 32, 41, 71, 72, 73, 74, 75 }; + reg_eye_brow = torch.mean(torch.abs( self.mapping[0,:,[32, 41, 71, 72, 73, 74, 75],:] )) + #return 0.01 * reg_mapping + 1.0 * reg_eye_brow + return 0.0 * reg_mapping + + + +def define_ExpressionEstimator(estimatorType='estimatorDefault', nIdentities=1, seq_len=1, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]): + net = None + print('EstimatorType: ', estimatorType) + if estimatorType=='estimatorAttention': net = ExpressionEstimator_Attention(N_EXPRESSIONS,nIdentities, seq_len) + + return networks.init_net(net, init_type, init_gain, gpu_ids) + + +class Audio2ExpressionsAttentionTMP4Model(BaseModel): + def name(self): + return 'Audio2ExpressionsAttentionTMP4Model' + + @staticmethod + def modify_commandline_options(parser, is_train=True): + + # changing the default values to match the pix2pix paper + # (https://phillipi.github.io/pix2pix/) + #parser.set_defaults(norm='batch', netG='unet_256') + parser.set_defaults(norm='instance', netG='unet_256') + parser.set_defaults(dataset_mode='aligned') + if is_train: + parser.set_defaults(pool_size=0, no_lsgan=True) + parser.add_argument('--lambda_L1', type=float, default=100.0, help='weight for L1 loss') + + return parser + + def initialize(self, opt): + BaseModel.initialize(self, opt) + self.isTrain = opt.isTrain + + self.trainRenderer = not opt.fix_renderer + + # specify the training losses you want to print out. The program will call base_model.get_current_losses + self.loss_names = ['G_L1','G_L1_ABSOLUTE','G_L1_RELATIVE', 'G_Regularizer'] + + # specify the images you want to save/display. The program will call base_model.get_current_visuals + #self.visual_names = ['input_uv', 'fake', 'target'] + self.visual_names = ['zeros'] + self.zeros = torch.zeros(1,3,2,2) + + # specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks + if self.isTrain: + self.model_names = ['netG'] + else: # during test time, only load Gs + self.model_names = ['netG'] + + self.fake_expressions = None + self.fake_expressions_prv = None + self.fake_expressions_nxt = None + + if self.isTrain: + self.morphable_model = MorphableModel() + self.mask = self.morphable_model.LoadMask() + + nIdentities=opt.nTrainObjects + + # load/define networks + self.netG = define_ExpressionEstimator(estimatorType=opt.rendererType, nIdentities=nIdentities, seq_len=opt.seq_len, gpu_ids=self.gpu_ids) + + if self.isTrain: + use_sigmoid = opt.no_lsgan + self.fake_AB_pool = ImagePool(opt.pool_size) + + # define loss functions + self.criterionL1 = torch.nn.L1Loss() + self.criterionL1Smooth = torch.nn.SmoothL1Loss() + self.criterionL2 = torch.nn.MSELoss() + + # initialize optimizers + self.optimizers = [] + #self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999), weight_decay=0.1 )#10.0) + self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999), weight_decay=0.0 )#10.0) + self.optimizers.append(self.optimizer_G) + + + def set_input(self, input): + #self.image_paths = input['paths'] + + self.expressions = input['expressions'].cuda() + self.audio_features = input['audio_deepspeech'].cuda() # b x seq_len x 16 x 29 + + if self.isTrain: + self.expressions_prv = input['expressions_prv'].cuda() + self.audio_features_prv = input['audio_deepspeech_prv'].cuda() # b x seq_len x 16 x 29 + + self.expressions_nxt = input['expressions_nxt'].cuda() + self.audio_features_nxt = input['audio_deepspeech_nxt'].cuda() # b x seq_len x 16 x 29 + + self.target_id = input['target_id'].cuda() + + + def forward(self): + # estimate expressions + if self.opt.output_audio_expressions: #self.opt.dataset_mode=='audio': + self.fake_expressions = self.netG.getAudioExpressions(self.audio_features) + if self.isTrain: + self.fake_expressions_prv = self.netG.getAudioExpressions(self.audio_features_prv) + self.fake_expressions_nxt = self.netG.getAudioExpressions(self.audio_features_nxt) + else: + self.fake_expressions, self.fake_expressions_intermediate = self.netG.forward_internal(self.audio_features, self.target_id) + if self.isTrain: + self.fake_expressions_prv = self.netG(self.audio_features_prv, self.target_id) + self.fake_expressions_nxt = self.netG(self.audio_features_nxt, self.target_id) + + + + def backward_G(self, epoch): + + # Second, G(A) = B + #self.loss_G_L1 = self.criterionL1(self.fake_expressions, self.expressions) + + # difference in vertex space + mask = torch.cat([self.mask[:,None],self.mask[:,None],self.mask[:,None]], 1) + mask = mask + 0.1 * torch.ones_like(mask) # priority for the mask region, but other region should also be constrained + + # absolute (single timesteps) + diff_expression = self.fake_expressions - self.expressions + diff_vertices = self.morphable_model.compute_expression_delta(diff_expression) + + + diff_expression_intermediate = self.fake_expressions_intermediate - self.expressions + diff_vertices_intermediate = self.morphable_model.compute_expression_delta(diff_expression_intermediate) + + + diff_expression_prv = self.fake_expressions_prv - self.expressions_prv + diff_vertices_prv = self.morphable_model.compute_expression_delta(diff_expression_prv) + + diff_expression_nxt = self.fake_expressions_nxt - self.expressions_nxt + diff_vertices_nxt = self.morphable_model.compute_expression_delta(diff_expression_nxt) + + # relative (temporal 1 timestep) cur - nxt and prv - cur + diff_expression_tmp_cur_nxt = (self.fake_expressions - self.fake_expressions_nxt) - (self.expressions - self.expressions_nxt) + diff_vertices_tmp_cur_nxt = self.morphable_model.compute_expression_delta(diff_expression_tmp_cur_nxt) + diff_expression_tmp_prv_cur = (self.fake_expressions_prv - self.fake_expressions) - (self.expressions_prv - self.expressions) + diff_vertices_tmp_prv_cur = self.morphable_model.compute_expression_delta(diff_expression_tmp_prv_cur) + + # relative (temporal 2 timesteps) nxt - prv + diff_expression_tmp_nxt_prv = (self.fake_expressions_nxt - self.fake_expressions_prv) - (self.expressions_nxt - self.expressions_prv) + diff_vertices_tmp_nxt_prv = self.morphable_model.compute_expression_delta(diff_expression_tmp_nxt_prv) + + #print('mask: ', mask.shape) + #print('diff_vertices: ', diff_vertices.shape) + + self.loss_G_L1_ABSOLUTE = 0.0 + self.loss_G_L1_RELATIVE = 0.0 + if self.opt.lossType == 'L1': + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices)) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices_prv)) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices_nxt)) + + self.loss_G_L1_ABSOLUTE += 3000.0 * torch.mean(mask * torch.abs(diff_vertices_intermediate)) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_cur_nxt)) + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_prv_cur)) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_nxt_prv)) + + elif self.opt.lossType == 'L2': + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * diff_vertices * diff_vertices) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * diff_vertices_prv * diff_vertices_prv) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * diff_vertices_nxt * diff_vertices_nxt) + + self.loss_G_L1_ABSOLUTE += 3000.0 * torch.mean(mask * diff_vertices_intermediate * diff_vertices_intermediate) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * diff_vertices_tmp_cur_nxt * diff_vertices_tmp_cur_nxt) + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * diff_vertices_tmp_prv_cur * diff_vertices_tmp_prv_cur) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * diff_vertices_tmp_nxt_prv * diff_vertices_tmp_nxt_prv) + + elif self.opt.lossType == 'RMS': + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.sqrt(torch.mean(mask * diff_vertices * diff_vertices)) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.sqrt(torch.mean(mask * diff_vertices_prv * diff_vertices_prv)) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.sqrt(torch.mean(mask * diff_vertices_nxt * diff_vertices_nxt)) + + self.loss_G_L1_ABSOLUTE += 3000.0 * torch.sqrt(torch.mean(mask * diff_vertices_intermediate * diff_vertices_intermediate)) + + + self.loss_G_L1_RELATIVE += 20000.0 * torch.sqrt(torch.mean(mask * diff_vertices_tmp_cur_nxt * diff_vertices_tmp_cur_nxt)) + self.loss_G_L1_RELATIVE += 20000.0 * torch.sqrt(torch.mean(mask * diff_vertices_tmp_prv_cur * diff_vertices_tmp_prv_cur)) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.sqrt(torch.mean(mask * diff_vertices_tmp_nxt_prv * diff_vertices_tmp_nxt_prv)) + + else: # L1 + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices)) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices_prv)) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices_nxt)) + + self.loss_G_L1_ABSOLUTE += 3000.0 * torch.mean(mask * torch.abs(diff_vertices_intermediate)) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_cur_nxt)) + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_prv_cur)) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_nxt_prv)) + + self.loss_G_L1 = self.loss_G_L1_ABSOLUTE + self.loss_G_L1_RELATIVE + self.loss_G_Regularizer = self.netG.regularizer() + + self.loss_G = self.loss_G_L1 + self.loss_G_Regularizer #self.loss_G_GAN + self.loss_G_L1 + self.regularizerTex + + + self.loss_G.backward() + + def optimize_parameters(self, epoch_iter): + self.forward() + + # update Generator + self.optimizer_G.zero_grad() + self.backward_G(epoch_iter) + self.optimizer_G.step() + diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/models/base_model.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/models/base_model.py new file mode 100644 index 0000000..04489f6 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/models/base_model.py @@ -0,0 +1,222 @@ +import os +import torch +from collections import OrderedDict +from . import networks +import numpy as np +from PIL import Image + +def save_tensor_image(input_image, image_path): + if isinstance(input_image, torch.Tensor): + image_tensor = input_image.data + image_numpy = image_tensor[0].cpu().float().numpy() + if image_numpy.shape[0] == 1: + image_numpy = np.tile(image_numpy, (3, 1, 1)) + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 + else: + image_numpy = input_image + image_numpy = image_numpy.astype(np.uint8) + image_pil = Image.fromarray(image_numpy) + image_pil.save(image_path) + +class BaseModel(): + + # modify parser to add command line options, + # and also change the default values if needed + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def name(self): + return 'BaseModel' + + def initialize(self, opt): + self.opt = opt + self.gpu_ids = opt.gpu_ids + self.isTrain = opt.isTrain + self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu') + self.load_dir = os.path.join(opt.checkpoints_dir, opt.name) + self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) + if opt.resize_or_crop != 'scale_width': + torch.backends.cudnn.benchmark = True + self.loss_names = [] + self.model_names = [] + self.visual_names = [] + self.image_paths = [] + + def set_input(self, input): + pass + + def forward(self): + pass + + # load and print networks; create schedulers + def setup(self, opt, parser=None): + if self.isTrain: + self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers] + if not self.isTrain or opt.continue_train: + load_suffix = 'iter_%d' % opt.load_iter if opt.load_iter > 0 else opt.epoch + self.load_networks(load_suffix) + self.print_networks(opt.verbose) + + + + # load specific moudles + def loadModules(self, opt, model_name, module_names): + for name in module_names: + if isinstance(name, str): + load_dir = os.path.join(opt.checkpoints_dir, model_name) + load_filename = 'latest_%s.pth' % (name) + load_path = os.path.join(load_dir, load_filename) + net = getattr(self, name) + if isinstance(net, torch.Tensor): + print('loading the tensor from %s' % load_path) + net_loaded = torch.load(load_path, map_location=str(self.device)) + net.copy_(net_loaded) + else: + # if isinstance(net, torch.nn.DataParallel): + # net = net.module + print('loading the module from %s' % load_path) + # if you are using PyTorch newer than 0.4 (e.g., built from + # GitHub source), you can remove str() on self.device + state_dict = torch.load(load_path, map_location=str(self.device)) + if hasattr(state_dict, '_metadata'): + del state_dict._metadata + + # patch InstanceNorm checkpoints prior to 0.4 + for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop + self.__patch_instance_norm_state_dict(state_dict, net, key.split('.')) + net.load_state_dict(state_dict) + + + + + # make models eval mode during test time + def eval(self): + for name in self.model_names: + if isinstance(name, str): + net = getattr(self, name) + net.eval() + + # used in test time, wrapping `forward` in no_grad() so we don't save + # intermediate steps for backprop + def test(self): + with torch.no_grad(): + self.forward() + + # get image paths + def get_image_paths(self): + return self.image_paths + + def optimize_parameters(self): + pass + + # update learning rate (called once every epoch) + def update_learning_rate(self): + for scheduler in self.schedulers: + scheduler.step() + lr = self.optimizers[0].param_groups[0]['lr'] + print('learning rate = %.7f' % lr) + + # return visualization images. train.py will display these images, and save the images to a html + def get_current_visuals(self): + visual_ret = OrderedDict() + for name in self.visual_names: + if isinstance(name, str): + visual_ret[name] = getattr(self, name) + return visual_ret + + # return traning losses/errors. train.py will print out these errors as debugging information + def get_current_losses(self): + errors_ret = OrderedDict() + for name in self.loss_names: + if isinstance(name, str): + # float(...) works for both scalar tensor and float number + errors_ret[name] = float(getattr(self, 'loss_' + name)) + return errors_ret + + # save models to the disk + def save_networks(self, epoch): + for name in self.model_names: + if isinstance(name, str): + save_filename = '%s_%s.pth' % (epoch, name) + save_path = os.path.join(self.save_dir, save_filename) + net = getattr(self, name) + + if isinstance(net, torch.Tensor): + #torch.save(net.state_dict(), save_path) + torch.save(net, save_path) + for i in range(0, list(net.size())[0]): + save_tensor_image(net[i:i+1,0:3,:,:], save_path+str(i)+'.png') + else: + if len(self.gpu_ids) > 0 and torch.cuda.is_available(): + #torch.save(net.module.cpu().state_dict(), save_path) # << original + torch.save(net.cpu().state_dict(), save_path) + net.cuda(self.gpu_ids[0]) + else: + torch.save(net.cpu().state_dict(), save_path) + + def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0): + key = keys[i] + if i + 1 == len(keys): # at the end, pointing to a parameter/buffer + if module.__class__.__name__.startswith('InstanceNorm') and \ + (key == 'running_mean' or key == 'running_var'): + if getattr(module, key) is None: + state_dict.pop('.'.join(keys)) + if module.__class__.__name__.startswith('InstanceNorm') and \ + (key == 'num_batches_tracked'): + state_dict.pop('.'.join(keys)) + else: + self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1) + + # load models from the disk + def load_networks(self, epoch): + for name in self.model_names: + if isinstance(name, str): + load_filename = '%s_%s.pth' % (epoch, name) + load_path = os.path.join(self.load_dir, load_filename) + net = getattr(self, name) + if isinstance(net, torch.Tensor): + print('loading the tensor from %s' % load_path) + net_loaded = torch.load(load_path, map_location=str(self.device)) + net.copy_(net_loaded) + else: + # if isinstance(net, torch.nn.DataParallel): + # net = net.module + print('loading the module from %s' % load_path) + # if you are using PyTorch newer than 0.4 (e.g., built from + # GitHub source), you can remove str() on self.device + state_dict = torch.load(load_path, map_location=str(self.device)) + if hasattr(state_dict, '_metadata'): + del state_dict._metadata + + # patch InstanceNorm checkpoints prior to 0.4 + for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop + self.__patch_instance_norm_state_dict(state_dict, net, key.split('.')) + net.load_state_dict(state_dict) + + # print network information + def print_networks(self, verbose): + print('---------- Networks initialized -------------') + for name in self.model_names: + if isinstance(name, str): + net = getattr(self, name) + if isinstance(net, torch.Tensor): + num_params = net.numel() + print('[Tensor %s] Total number of parameters : %.3f M' % (name, num_params / 1e6)) + else: + num_params = 0 + for param in net.parameters(): + num_params += param.numel() + if verbose: + print(net) + print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6)) + print('-----------------------------------------------') + + # set requies_grad=False to avoid computation + def set_requires_grad(self, nets, requires_grad=False): + if not isinstance(nets, list): + nets = [nets] + for net in nets: + if net is not None: + for param in net.parameters(): + param.requires_grad = requires_grad diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/models/networks.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/models/networks.py new file mode 100644 index 0000000..a8c6505 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/models/networks.py @@ -0,0 +1,383 @@ +import torch +import torch.nn as nn +from torch.nn import init +import functools +from torch.optim import lr_scheduler + +############################################################################### +# Helper Functions +############################################################################### + + +def get_norm_layer(norm_type='instance'): + if norm_type == 'batch': + norm_layer = functools.partial(nn.BatchNorm2d, affine=True) + elif norm_type == 'instance': + norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False) + elif norm_type == 'none': + norm_layer = None + else: + raise NotImplementedError('normalization layer [%s] is not found' % norm_type) + return norm_layer + + +def get_scheduler(optimizer, opt): + if opt.lr_policy == 'lambda': + def lambda_rule(epoch): + lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.niter) / float(opt.niter_decay + 1) + return lr_l + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule) + elif opt.lr_policy == 'step': + scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1) + elif opt.lr_policy == 'plateau': + scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5) + elif opt.lr_policy == 'cosine': + scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.niter, eta_min=0) + else: + return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy) + return scheduler + + +def init_weights(net, init_type='normal', gain=0.02): + def init_func(m): + classname = m.__class__.__name__ + if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): + if init_type == 'normal': + init.normal_(m.weight.data, 0.0, gain) + elif init_type == 'xavier': + init.xavier_normal_(m.weight.data, gain=gain) + elif init_type == 'kaiming': + init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') + elif init_type == 'orthogonal': + init.orthogonal_(m.weight.data, gain=gain) + else: + raise NotImplementedError('initialization method [%s] is not implemented' % init_type) + if hasattr(m, 'bias') and m.bias is not None: + init.constant_(m.bias.data, 0.0) + elif classname.find('BatchNorm2d') != -1: + init.normal_(m.weight.data, 1.0, gain) + init.constant_(m.bias.data, 0.0) + + print('initialize network with %s' % init_type) + net.apply(init_func) + + +def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]): + if len(gpu_ids) > 0: + assert(torch.cuda.is_available()) + net.to(gpu_ids[0]) + #net = torch.nn.DataParallel(net, gpu_ids) + init_weights(net, init_type, gain=init_gain) + return net + + +def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]): + net = None + norm_layer = get_norm_layer(norm_type=norm) + + if netG == 'resnet_9blocks': + net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9) + elif netG == 'resnet_6blocks': + net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6) + elif netG == 'unet_128': + net = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout) + elif netG == 'unet_256': + net = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout) + else: + raise NotImplementedError('Generator model name [%s] is not recognized' % netG) + return init_net(net, init_type, init_gain, gpu_ids) + + +def define_D(input_nc, ndf, netD, + n_layers_D=3, norm='batch', use_sigmoid=False, init_type='normal', init_gain=0.02, gpu_ids=[]): + net = None + norm_layer = get_norm_layer(norm_type=norm) + + if netD == 'basic': + net = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer, use_sigmoid=use_sigmoid) + elif netD == 'n_layers': + net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer, use_sigmoid=use_sigmoid) + elif netD == 'pixel': + net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer, use_sigmoid=use_sigmoid) + else: + raise NotImplementedError('Discriminator model name [%s] is not recognized' % net) + return init_net(net, init_type, init_gain, gpu_ids) + + +############################################################################## +# Classes +############################################################################## + + +# Defines the GAN loss which uses either LSGAN or the regular GAN. +# When LSGAN is used, it is basically same as MSELoss, +# but it abstracts away the need to create the target label tensor +# that has the same size as the input +class GANLoss(nn.Module): + def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0): + super(GANLoss, self).__init__() + self.register_buffer('real_label', torch.tensor(target_real_label)) + self.register_buffer('fake_label', torch.tensor(target_fake_label)) + if use_lsgan: + self.loss = nn.MSELoss() + else: + self.loss = nn.BCELoss() + + def get_target_tensor(self, input, target_is_real): + if target_is_real: + target_tensor = self.real_label + else: + target_tensor = self.fake_label + return target_tensor.expand_as(input) + + def __call__(self, input, target_is_real): + target_tensor = self.get_target_tensor(input, target_is_real) + return self.loss(input, target_tensor) + + +# Defines the generator that consists of Resnet blocks between a few +# downsampling/upsampling operations. +# Code and idea originally from Justin Johnson's architecture. +# https://github.com/jcjohnson/fast-neural-style/ +class ResnetGenerator(nn.Module): + def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'): + assert(n_blocks >= 0) + super(ResnetGenerator, self).__init__() + self.input_nc = input_nc + self.output_nc = output_nc + self.ngf = ngf + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + + model = [nn.ReflectionPad2d(3), + nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, + bias=use_bias), + norm_layer(ngf), + nn.ReLU(True)] + + n_downsampling = 2 + for i in range(n_downsampling): + mult = 2**i + model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, + stride=2, padding=1, bias=use_bias), + norm_layer(ngf * mult * 2), + nn.ReLU(True)] + + mult = 2**n_downsampling + for i in range(n_blocks): + model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)] + + for i in range(n_downsampling): + mult = 2**(n_downsampling - i) + model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), + kernel_size=3, stride=2, + padding=1, output_padding=1, + bias=use_bias), + norm_layer(int(ngf * mult / 2)), + nn.ReLU(True)] + model += [nn.ReflectionPad2d(3)] + model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] + model += [nn.Tanh()] + + self.model = nn.Sequential(*model) + + def forward(self, input): + return self.model(input) + + +# Define a resnet block +class ResnetBlock(nn.Module): + def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias): + super(ResnetBlock, self).__init__() + self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias) + + def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias): + conv_block = [] + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(1)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(1)] + elif padding_type == 'zero': + p = 1 + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + + conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), + norm_layer(dim), + nn.ReLU(True)] + if use_dropout: + conv_block += [nn.Dropout(0.5)] + + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(1)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(1)] + elif padding_type == 'zero': + p = 1 + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), + norm_layer(dim)] + + return nn.Sequential(*conv_block) + + def forward(self, x): + out = x + self.conv_block(x) + return out + + +# Defines the Unet generator. +# |num_downs|: number of downsamplings in UNet. For example, +# if |num_downs| == 7, image of size 128x128 will become of size 1x1 +# at the bottleneck +class UnetGenerator(nn.Module): + def __init__(self, input_nc, output_nc, num_downs, ngf=64, + norm_layer=nn.BatchNorm2d, use_dropout=False): + super(UnetGenerator, self).__init__() + + # construct unet structure + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) + for i in range(num_downs - 5): + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) + unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) + + self.model = unet_block + + def forward(self, input): + return self.model(input) + + +# Defines the submodule with skip connection. +# X -------------------identity---------------------- X +# |-- downsampling -- |submodule| -- upsampling --| +class UnetSkipConnectionBlock(nn.Module): + def __init__(self, outer_nc, inner_nc, input_nc=None, + submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(UnetSkipConnectionBlock, self).__init__() + self.outermost = outermost + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + if input_nc is None: + input_nc = outer_nc + downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, + stride=2, padding=1, bias=use_bias) + downrelu = nn.LeakyReLU(0.2, True) + downnorm = norm_layer(inner_nc) + uprelu = nn.ReLU(True) + upnorm = norm_layer(outer_nc) + + if outermost: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1) + down = [downconv] + up = [uprelu, upconv, nn.Tanh()] + model = down + [submodule] + up + elif innermost: + upconv = nn.ConvTranspose2d(inner_nc, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv] + up = [uprelu, upconv, upnorm] + model = down + up + else: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv, downnorm] + up = [uprelu, upconv, upnorm] + + if use_dropout: + model = down + [submodule] + up + [nn.Dropout(0.5)] + else: + model = down + [submodule] + up + + self.model = nn.Sequential(*model) + + def forward(self, x): + if self.outermost: + return self.model(x) + else: + return torch.cat([x, self.model(x)], 1) + + +# Defines the PatchGAN discriminator with the specified arguments. +class NLayerDiscriminator(nn.Module): + def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False): + super(NLayerDiscriminator, self).__init__() + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + + kw = 4 + padw = 1 + sequence = [ + nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), + nn.LeakyReLU(0.2, True) + ] + + nf_mult = 1 + nf_mult_prev = 1 + for n in range(1, n_layers): + nf_mult_prev = nf_mult + nf_mult = min(2**n, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, + kernel_size=kw, stride=2, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + nf_mult_prev = nf_mult + nf_mult = min(2**n_layers, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, + kernel_size=kw, stride=1, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] + + if use_sigmoid: + sequence += [nn.Sigmoid()] + + self.model = nn.Sequential(*sequence) + + def forward(self, input): + return self.model(input) + + +class PixelDiscriminator(nn.Module): + def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d, use_sigmoid=False): + super(PixelDiscriminator, self).__init__() + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + + self.net = [ + nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0), + nn.LeakyReLU(0.2, True), + nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias), + norm_layer(ndf * 2), + nn.LeakyReLU(0.2, True), + nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)] + + if use_sigmoid: + self.net.append(nn.Sigmoid()) + + self.net = nn.Sequential(*self.net) + + def forward(self, input): + return self.net(input) diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/options/__init__.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/options/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/options/base_options.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/options/base_options.py new file mode 100644 index 0000000..b85c6ce --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/options/base_options.py @@ -0,0 +1,138 @@ +import argparse +import os +from util import util +import torch +import models +import data + + +class BaseOptions(): + def __init__(self): + self.initialized = False + + def initialize(self, parser): + parser.add_argument('--dataroot', required=True, help='path to images (should have subfolders trainA, trainB, valA, valB, etc)') + parser.add_argument('--batch_size', type=int, default=1, help='input batch size') + parser.add_argument('--seq_len', type=int, default=1, help='sequence length (if applicable)') + parser.add_argument('--fineSize', type=int, default=512, help='then crop to this size') + parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML') + parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels') + parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels') + parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer') + parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer') + parser.add_argument('--netD', type=str, default='basic', help='selects model to use for netD') + parser.add_argument('--netG', type=str, default='resnet_9blocks', help='selects model to use for netG') + parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers') + parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU') + parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models') + parser.add_argument('--renderer', type=str, default='no_renderer', help='name of the renderer to load the models from') + parser.add_argument('--fix_renderer', action='store_true', help='renderer is fixed') + parser.add_argument('--dataset_mode', type=str, default='aligned', help='chooses how datasets are loaded. [aligned | multi]') + parser.add_argument('--model', type=str, default='cycle_gan', help='chooses which model to use. cycle_gan, pix2pix, test') + parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA') + parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') + parser.add_argument('--load_iter', type=int, default='0', help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]') + parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data') + parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here') + parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization') + parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly') + parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator') + parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.') + parser.add_argument('--resize_or_crop', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop|none]') + parser.add_argument('--no_augmentation', action='store_true', help='if specified, no data augmentation') + #parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal|xavier|kaiming|orthogonal]') + parser.add_argument('--init_type', type=str, default='xavier', help='network initialization [normal|xavier|kaiming|orthogonal]') + parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.') + parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information') + parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{loadSize}') + parser.add_argument('--tex_dim', type=int, default=256, help='neural texture dimensions') + parser.add_argument('--tex_features_intermediate', type=int, default=16, help='# intermediate neural texture features when using dynamic textures') + parser.add_argument('--tex_features', type=int, default=16, help='# neural texture features') + parser.add_argument('--textureModel', type=str, default='DynamicNeuralTextureAudio', help='texture model') + parser.add_argument('--rendererType', type=str, default='UNET_5_level', help='neural renderer network') + parser.add_argument('--lossType', type=str, default='L1', help='loss type for the final output') + + parser.add_argument('--hierarchicalTex', action='store_true', help='if specified, hierachical neural textures are used') + + parser.add_argument('--output_audio_expressions', action='store_true', help='if specified, no sh layers are used') + + parser.add_argument('--erosionFactor', type=float, default=1.0, help='scaling factor for erosion of the background.') + + parser.add_argument('--audio_window_size', type=float, default=16, help='audio window size = #mel feature bins') + + parser.add_argument('--look_ahead', action='store_true', help='cache images in numpy format') + + parser.add_argument('--cached_images', action='store_true', help='cache images in numpy format') + + self.initialized = True + return parser + + def gather_options(self): + # initialize parser with basic options + if not self.initialized: + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser = self.initialize(parser) + + # get the basic options + opt, _ = parser.parse_known_args() + + # modify model-related parser options + model_name = opt.model + model_option_setter = models.get_option_setter(model_name) + parser = model_option_setter(parser, self.isTrain) + opt, _ = parser.parse_known_args() # parse again with the new defaults + + # modify dataset-related parser options + dataset_name = opt.dataset_mode + dataset_option_setter = data.get_option_setter(dataset_name) + parser = dataset_option_setter(parser, self.isTrain) + + self.parser = parser + + return parser.parse_args() + + def print_options(self, opt): + message = '' + message += '----------------- Options ---------------\n' + for k, v in sorted(vars(opt).items()): + comment = '' + default = self.parser.get_default(k) + if v != default: + comment = '\t[default: %s]' % str(default) + message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment) + message += '----------------- End -------------------' + print(message) + + # save to the disk + expr_dir = os.path.join(opt.checkpoints_dir, opt.name) + util.mkdirs(expr_dir) + file_name = os.path.join(expr_dir, 'opt.txt') + with open(file_name, 'wt') as opt_file: + opt_file.write(message) + opt_file.write('\n') + + def parse(self): + + opt = self.gather_options() + opt.isTrain = self.isTrain # train or test + + # process opt.suffix + if opt.suffix: + suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else '' + opt.name = opt.name + suffix + + self.print_options(opt) + + # set gpu ids + str_ids = opt.gpu_ids.split(',') + opt.gpu_ids = [] + for str_id in str_ids: + id = int(str_id) + if id >= 0: + opt.gpu_ids.append(id) + if len(opt.gpu_ids) > 0: + torch.cuda.set_device(opt.gpu_ids[0]) + + self.opt = opt + return self.opt diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/options/test_options.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/options/test_options.py new file mode 100644 index 0000000..d21da35 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/options/test_options.py @@ -0,0 +1,27 @@ +from .base_options import BaseOptions + + +class TestOptions(BaseOptions): + def initialize(self, parser): + parser = BaseOptions.initialize(self, parser) + parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.') + parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.') + parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images') + parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') + # Dropout and Batchnorm has different behavioir during training and test. + parser.add_argument('--eval', action='store_true', help='use eval mode during test time.') + parser.add_argument('--num_test', type=int, default=50, help='how many test images to run') + + parser.add_argument('--write_no_images', action='store_true', help='compute validation') + + parser.add_argument('--write_video', action='store_true', help='write video') + parser.add_argument('--video_fps', type=float, default=25.0, help='video fps') + + + parser.add_argument('--source_dir', type=str, default='./datasets/', help='loads source files (expressions, audio, uvs).') + + parser.set_defaults(model='test') + # To avoid cropping, the loadSize should be the same as fineSize + parser.set_defaults(loadSize=parser.get_default('fineSize')) + self.isTrain = False + return parser diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/options/train_options.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/options/train_options.py new file mode 100644 index 0000000..c91d50d --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/options/train_options.py @@ -0,0 +1,37 @@ +from .base_options import BaseOptions + + +class TrainOptions(BaseOptions): + def initialize(self, parser): + parser = BaseOptions.initialize(self, parser) + parser.add_argument('--display_freq', type=int, default=400, help='frequency of showing training results on screen') + parser.add_argument('--display_ncols', type=int, default=4, help='if positive, display all images in a single visdom web panel with certain number of images per row.') + parser.add_argument('--display_id', type=int, default=1, help='window id of the web display') + parser.add_argument('--display_server', type=str, default="http://localhost", help='visdom server of the web display') + parser.add_argument('--display_env', type=str, default='main', help='visdom display environment name (default is "main")') + parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display') + + parser.add_argument('--compute_val', action='store_true', help='compute validation') + parser.add_argument('--input_noise_augmentation', action='store_true', help='add input noise') + + parser.add_argument('--update_html_freq', type=int, default=1000, help='frequency of saving training results to html') + parser.add_argument('--print_freq', type=int, default=500, help='frequency of showing training results on console') + parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results') + parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs') + parser.add_argument('--save_by_iter', action='store_true', help='whether saves model by iteration') + + parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model') + parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by , +, ...') + parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') + parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate') + parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero') + parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam') + parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam') + parser.add_argument('--no_lsgan', action='store_true', help='do *not* use least square GAN, if false, use vanilla GAN') + parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images') + parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/') + parser.add_argument('--lr_policy', type=str, default='lambda', help='learning rate policy: lambda|step|plateau|cosine') + parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations') + + self.isTrain = True + return parser diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/options/transfer_options.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/options/transfer_options.py new file mode 100644 index 0000000..25e6832 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/options/transfer_options.py @@ -0,0 +1,42 @@ +from .base_options import BaseOptions + + +class TransferOptions(BaseOptions): + def initialize(self, parser): + parser = BaseOptions.initialize(self, parser) + parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.') + parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.') + parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images') + parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') + # Dropout and Batchnorm has different behavioir during training and test. + parser.add_argument('--eval', action='store_true', help='use eval mode during test time.') + parser.add_argument('--num_test', type=int, default=50, help='how many test images to run') + + parser.add_argument('--write_no_images', action='store_true', help='compute validation') + + + parser.add_argument('--source_dir', type=str, default='./datasets/', help='loads source files (expressions, audio, uvs).') + + parser.add_argument('--source_actor', type=str, default='', help='source actor directory') + parser.add_argument('--target_actor', type=str, default='', help='target actor directory') + + parser.add_argument('--transfer_path', type=str, help='path to output the transfer files') + parser.add_argument('--base_path', type = str, default= '../..', help='path for mappings folder') + + parser.set_defaults(model='test') + # To avoid cropping, the loadSize should be the same as fineSize + parser.set_defaults(loadSize=parser.get_default('fineSize')) + self.isTrain = False + return parser + +class Audio2ExprOptions(TransferOptions): + def initialize(self, parser): + parser = TransferOptions.initialize(self, parser) + parser.add_argument('--use_mapping', action='store_true', help='use mapping function.') + parser.add_argument('--mapping_path', type=str, default='', help='path to mapping function.') + parser.add_argument('--out_dir', type=str, default='', help='path to output directory.') + parser.set_defaults(model='test') + # To avoid cropping, the loadSize should be the same as fineSize + parser.set_defaults(loadSize=parser.get_default('fineSize')) + self.isTrain = False + return parser \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/transfer.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/transfer.py new file mode 100644 index 0000000..1532d82 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/transfer.py @@ -0,0 +1,247 @@ +import os +import os.path +from options.transfer_options import TransferOptions +from data.face_dataset import FaceDataset +from data.audio_dataset import AudioDataset +from models import create_model +import torch +import numpy as np +import random +from tqdm import tqdm +import copy + +from BaselModel.basel_model import * + + +def load_model(opt): + opt.output_audio_expressions = True + opt.nTrainObjects = 116 + + print('#train objects = %d' % opt.nTrainObjects) + + print('>>> create model <<<') + model = create_model(opt) + print('>>> setup model <<<') + model.setup(opt) + + return model + +def load_target_sequence(opt): + opt_target = copy.copy(opt) # create a clone + opt_target.dataroot = opt.target_actor # overwrite root directory + opt_target.dataset_mode = 'face' + opt_target.phase = 'train' + dataset_target = FaceDataset() + dataset_target.initialize(opt_target) + + dataloader = torch.utils.data.DataLoader( + dataset_target, + batch_size=opt.batch_size, + shuffle=not opt.serial_batches, + num_workers=int(opt.num_threads)) + + return dataset_target, dataloader + + +def load_source_sequence(opt): + opt_source = copy.copy(opt) # create a clone + opt_source.dataroot = opt.source_actor # overwrite root directory + print(opt_source.dataroot) + opt_source.dataset_mode = 'audio' + opt_source.phase = 'train' + + dataset_source = AudioDataset() + + dataset_source.initialize(opt_source) + + dataloader = torch.utils.data.DataLoader( + dataset_source, + batch_size=opt.batch_size, + shuffle=not opt.serial_batches, + num_workers=int(opt.num_threads)) + + return dataset_source, dataloader + + +if __name__ == '__main__': + # read options + opt = TransferOptions().parse() + + target_name = opt.target_actor.split("/")[-1] + + # hard-code some parameters for test + opt.num_threads = 1 # test code only supports num_threads = 1 + opt.batch_size = 1 # test code only supports batch_size = 1 + opt.serial_batches = True # no shuffle + opt.no_augmentation = True # no flip + opt.display_id = -1 # no visdom display + + + # load model + model = load_model(opt) + print('model version:', opt.name) + + + # # load face model + # morphable_model = MorphableModel() + # mask = morphable_model.LoadMask() + # mask = mask + 0.1 * torch.ones_like(mask) + + + # read target sequence + dataset_target, data_loader_target = load_target_sequence(opt) + dataset_target_size = len(dataset_target) + print('#target_actor frames = %d' % dataset_target_size) + + ################################## + ####### create mapping ####### + ################################## + base_path = opt.base_path + # base_path = '../..' #'/home/alberto/NeuralVoicePuppetry' + + os.makedirs(base_path+'/mappings/'+opt.name, exist_ok=True) + mapping_fn = base_path+'/mappings/'+opt.name+'/'+'mapping_'+target_name + print(mapping_fn) + #not_exists = True + not_exists = not os.path.exists(mapping_fn+'.npy') + if not_exists: + # collect data + print('collect data') + audio_expressions = None + gt_expressions = None + for i, data in tqdm(enumerate(data_loader_target)): + model.set_input(data) + model.test() + + ae = model.fake_expressions.data[:,:,0] + if type(audio_expressions) == type(None): + audio_expressions = ae + e = model.expressions.data + gt_expressions = e + else: + audio_expressions = torch.cat([audio_expressions,ae],dim=0) + e = model.expressions.data + gt_expressions = torch.cat([gt_expressions,e],dim=0) + + # solve for mapping + print('solve for mapping') + optimize_in_parameter_space = True #False + if optimize_in_parameter_space: +# A = audio_expressions +# B = gt_expressions +# # solve lstsq ||AX - B|| +# X, _ = torch.gels(B, A, out=None) +# #X, _ = torch.lstsq(B, A) # requires pytorch 1.2 +# X = X[0:A.shape[1],:] +# mapping = X.t() + + # use gradient descent method + n = audio_expressions.shape[0] + print(n) + subspace_dim = 32 + + # TODO: patch + n_expr = 53 + + X = torch.nn.Parameter(torch.randn(n_expr, subspace_dim, requires_grad=True).cuda()) + optimizer = torch.optim.Adam([X], lr=0.01) + lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1) + num_epochs = 90 + random_range = [k for k in range(0,n)] + + for ep in tqdm(range(0,num_epochs)): + random.shuffle(random_range) + for j in random_range: + expressions = gt_expressions[j] + fake_expressions = 10.0 * torch.matmul(X, audio_expressions[j]) + diff_expression = fake_expressions - expressions + loss = torch.mean(diff_expression * diff_expression) # L2 + optimizer.zero_grad() + loss.backward() + optimizer.step() + lr_scheduler.step() + mapping = X.data + + + +# else: # optimize in vertex space +# # use gradient descent method +# n = audio_expressions.shape[0] +# subspace_dim = 32 +# X = torch.nn.Parameter(torch.randn(N_EXPRESSIONS, subspace_dim, requires_grad=True).cuda()) +# optimizer = torch.optim.Adam([X], lr=0.01) +# lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1) +# num_epochs = 90 +# random_range = [k for k in range(0,n)] +# with progressbar.ProgressBar(max_value=num_epochs) as bar: +# for ep in range(0,num_epochs): +# bar.update(ep) +# random.shuffle(random_range) +# for j in random_range: +# expressions = gt_expressions[j] +# fake_expressions = 10.0 * torch.matmul(X, audio_expressions[j]) +# diff_expression = fake_expressions - expressions +# diff_vertices = torch.matmul(morphable_model.expression_basis, diff_expression) +# #loss = torch.sqrt(torch.mean(mask * diff_vertices * diff_vertices)) # RMS +# loss = torch.mean(mask * diff_vertices * diff_vertices) # L2 +# # +# optimizer.zero_grad() +# loss.backward() +# optimizer.step() +# lr_scheduler.step() +# +# mapping = X.data + + map_cpu = mapping.data.cpu().numpy() + print(map_cpu.shape) + file_out=open(mapping_fn+'.txt', 'w') + np.savetxt(file_out, map_cpu, delimiter=' ') + file_out.close() + np.save(mapping_fn+'.npy', map_cpu) + else: + # load mapping from file + map_cpu = np.load(mapping_fn+'.npy') + mapping = torch.tensor(map_cpu.astype(np.float32)).cuda() + print('loaded mapping from file', mapping.shape) + + # ############# + # map_cpu = np.load(base_path + '/mappings/' + opt.name + '/' + 'mapping_Close_355_9105.npy') + # mapping = torch.tensor(map_cpu.astype(np.float32)).cuda() + # print('loaded mapping from file', mapping.shape) + # ############# + + # process source sequence (opt.source_actor) + source_actors = [opt.source_actor] + + os.makedirs(opt.transfer_path, exist_ok=True) + list_transfer = open(opt.transfer_path+'/list_transfer.txt', "a") + + target_actor_offset = 0 # default + expression_multiplier = 1.0 # default + + if target_actor_offset != 0.0: + target_name = target_name + '--offset' + if expression_multiplier != 1.0: + target_name = target_name + '-X' + + for source_actor in source_actors: + opt.source_actor = source_actor + source_name = opt.source_actor.split("/")[-1] + # read source sequence + dataset_source, data_loader_source = load_source_sequence(opt) + dataset_source_size = len(dataset_source) + print('#source_actor frames = %d' % dataset_source_size) + list_transfer.write(source_name+'--'+target_name+'\n') + out_dir = opt.transfer_path + '/' + source_name+'--'+target_name+'/expressions' + if not os.path.isdir(out_dir): + os.makedirs(out_dir, exist_ok=True) + for i, data in tqdm(enumerate(data_loader_source)): + model.set_input(data) + model.test() + audio_expression = model.fake_expressions.data[0,:,0] + expression = expression_multiplier * 10.0 * torch.matmul(mapping, audio_expression) + expression = expression[None,:] + np.save(os.path.join(out_dir, f'expr_{i}.npy'), expression.cpu().numpy()) + + list_transfer.close() + exit() diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/transfer.sh b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/transfer.sh new file mode 100644 index 0000000..2726e84 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/transfer.sh @@ -0,0 +1,57 @@ +set -ex +# . transfer.sh & +GPUID=0 + +###################################################### +################## SPECIFY MODEL ################## +###################################################### + +## audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead # <<<<<<<<<<<<< +DATASETS_DIR=/home/alberto/NeuralVoicePuppetry/datasets +OBJECT=ARD_ZDF +LR=0.00001 +N_ITER=150 +N_ITER_LR_DECAY=50 +RENDERER=$OBJECT +EROSION=1.0 +BATCH_SIZE=16 +MODEL=audio2ExpressionsAttentionTMP4 +RENDERER_TYPE=estimatorAttention +DATASET_MODE=multi_face_audio_eq_tmp_cached +LOSS=RMS +SEQ_LEN=8 +DATE_WITH_TIME=20191105-115332 +NAME=$MODEL-$RENDERER_TYPE-SL$SEQ_LEN-BS$BATCH_SIZE-$OBJECT-$DATASET_MODE-$LOSS-$DATE_WITH_TIME-look_ahead + +# --look_ahead +EPOCH=latest + +############################################################### +###################### SPECIFY TARGET ###################### +############################################################### + +# target actors +#SOURCE_ACTOR_LIST=(/home/alberto/NeuralVoicePuppetry/datasets/External/Alberto_videos_uno /home/alberto/NeuralVoicePuppetry/datasets/External/Alberto_videos_due /home/alberto/NeuralVoicePuppetry/datasets/External/Alberto_videos_tre) +SOURCE_ACTOR_LIST=(/home/alberto/NeuralVoicePuppetry/datasets/External/Clara_audios_one /home/alberto/NeuralVoicePuppetry/datasets/External/Clara_audios_two /home/alberto/NeuralVoicePuppetry/datasets/External/Clara_audios_three) + +#TARGET_ACTOR_LIST[1]=/home/alberto/NeuralVoicePuppetry/datasets/External/Youtube_Russian_guy +TARGET_ACTOR_LIST[1]=/home/alberto/NeuralVoicePuppetry/datasets/SRF_anchor_short/Halbtotale_355_9415 + + +rm -f ./datasets/TRANSFERS/$NAME/list_transfer.txt +for TARGET_ACTOR in "${TARGET_ACTOR_LIST[@]}" +do + echo $TARGET_ACTOR + + for SOURCE_ACTOR in "${SOURCE_ACTOR_LIST[@]}" + do + echo $SOURCE_ACTOR + # --look_ahead + python transfer.py --look_ahead --seq_len $SEQ_LEN --source_actor $SOURCE_ACTOR --target_actor $TARGET_ACTOR --write_no_images --name $NAME --erosionFactor $EROSION --epoch $EPOCH --display_winsize 512 --rendererType $RENDERER_TYPE --lossType $LOSS --dataroot $DATASETS_DIR/$OBJECT --model $MODEL --netG unet_256 --dataset_mode $DATASET_MODE --norm instance --gpu_ids $GPUID + done +done + + +############################################################### +############################################################### +############################################################### \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/util/__init__.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/util/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/util/get_data.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/util/get_data.py new file mode 100644 index 0000000..6325605 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/util/get_data.py @@ -0,0 +1,115 @@ +from __future__ import print_function +import os +import tarfile +import requests +from warnings import warn +from zipfile import ZipFile +from bs4 import BeautifulSoup +from os.path import abspath, isdir, join, basename + + +class GetData(object): + """ + + Download CycleGAN or Pix2Pix Data. + + Args: + technique : str + One of: 'cyclegan' or 'pix2pix'. + verbose : bool + If True, print additional information. + + Examples: + >>> from util.get_data import GetData + >>> gd = GetData(technique='cyclegan') + >>> new_data_path = gd.get(save_path='./datasets') # options will be displayed. + + """ + + def __init__(self, technique='cyclegan', verbose=True): + url_dict = { + 'pix2pix': 'https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets', + 'cyclegan': 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets' + } + self.url = url_dict.get(technique.lower()) + self._verbose = verbose + + def _print(self, text): + if self._verbose: + print(text) + + @staticmethod + def _get_options(r): + soup = BeautifulSoup(r.text, 'lxml') + options = [h.text for h in soup.find_all('a', href=True) + if h.text.endswith(('.zip', 'tar.gz'))] + return options + + def _present_options(self): + r = requests.get(self.url) + options = self._get_options(r) + print('Options:\n') + for i, o in enumerate(options): + print("{0}: {1}".format(i, o)) + choice = input("\nPlease enter the number of the " + "dataset above you wish to download:") + return options[int(choice)] + + def _download_data(self, dataset_url, save_path): + if not isdir(save_path): + os.makedirs(save_path) + + base = basename(dataset_url) + temp_save_path = join(save_path, base) + + with open(temp_save_path, "wb") as f: + r = requests.get(dataset_url) + f.write(r.content) + + if base.endswith('.tar.gz'): + obj = tarfile.open(temp_save_path) + elif base.endswith('.zip'): + obj = ZipFile(temp_save_path, 'r') + else: + raise ValueError("Unknown File Type: {0}.".format(base)) + + self._print("Unpacking Data...") + obj.extractall(save_path) + obj.close() + os.remove(temp_save_path) + + def get(self, save_path, dataset=None): + """ + + Download a dataset. + + Args: + save_path : str + A directory to save the data to. + dataset : str, optional + A specific dataset to download. + Note: this must include the file extension. + If None, options will be presented for you + to choose from. + + Returns: + save_path_full : str + The absolute path to the downloaded data. + + """ + if dataset is None: + selected_dataset = self._present_options() + else: + selected_dataset = dataset + + save_path_full = join(save_path, selected_dataset.split('.')[0]) + + if isdir(save_path_full): + warn("\n'{0}' already exists. Voiding Download.".format( + save_path_full)) + else: + self._print('Downloading Data...') + url = "{0}/{1}".format(self.url, selected_dataset) + self._download_data(url, save_path=save_path) + + return abspath(save_path_full) diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/util/html.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/util/html.py new file mode 100644 index 0000000..1e7aab9 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/util/html.py @@ -0,0 +1,64 @@ +import dominate +from dominate.tags import meta, h3, table, tr, td, p, a, img, br +import os + + +class HTML: + def __init__(self, web_dir, title, reflesh=0): + self.title = title + self.web_dir = web_dir + self.img_dir = os.path.join(self.web_dir, 'images') + if not os.path.exists(self.web_dir): + os.makedirs(self.web_dir) + if not os.path.exists(self.img_dir): + os.makedirs(self.img_dir) + # print(self.img_dir) + + self.doc = dominate.document(title=title) + if reflesh > 0: + with self.doc.head: + meta(http_equiv="reflesh", content=str(reflesh)) + + def get_image_dir(self): + return self.img_dir + + def add_header(self, str): + with self.doc: + h3(str) + + def add_table(self, border=1): + self.t = table(border=border, style="table-layout: fixed;") + self.doc.add(self.t) + + def add_images(self, ims, txts, links, width=400): + self.add_table() + with self.t: + with tr(): + for im, txt, link in zip(ims, txts, links): + with td(style="word-wrap: break-word;", halign="center", valign="top"): + with p(): + with a(href=os.path.join('images', link)): + img(style="width:%dpx" % width, src=os.path.join('images', im)) + br() + p(txt) + + def save(self): + html_file = '%s/index.html' % self.web_dir + f = open(html_file, 'wt') + f.write(self.doc.render()) + f.close() + + +if __name__ == '__main__': + html = HTML('web/', 'test_html') + html.add_header('hello world') + + ims = [] + txts = [] + links = [] + for n in range(4): + ims.append('image_%d.png' % n) + txts.append('text_%d' % n) + links.append('image_%d.png' % n) + html.add_images(ims, txts, links) + html.save() diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/util/image_pool.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/util/image_pool.py new file mode 100644 index 0000000..52413e0 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/util/image_pool.py @@ -0,0 +1,32 @@ +import random +import torch + + +class ImagePool(): + def __init__(self, pool_size): + self.pool_size = pool_size + if self.pool_size > 0: + self.num_imgs = 0 + self.images = [] + + def query(self, images): + if self.pool_size == 0: + return images + return_images = [] + for image in images: + image = torch.unsqueeze(image.data, 0) + if self.num_imgs < self.pool_size: + self.num_imgs = self.num_imgs + 1 + self.images.append(image) + return_images.append(image) + else: + p = random.uniform(0, 1) + if p > 0.5: + random_id = random.randint(0, self.pool_size - 1) # randint is inclusive + tmp = self.images[random_id].clone() + self.images[random_id] = image + return_images.append(tmp) + else: + return_images.append(image) + return_images = torch.cat(return_images, 0) + return return_images diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/util/util.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/util/util.py new file mode 100644 index 0000000..df72b66 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/util/util.py @@ -0,0 +1,82 @@ +from __future__ import print_function +import torch +import numpy as np +from PIL import Image +import os +import sys +import array +# import OpenEXR +# import Imath + +'''def load_exr(image_path): + # Open the input file + file = OpenEXR.InputFile(image_path) + + # Compute the size + dw = file.header()['dataWindow'] + w, h = (dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1) + + # Read the three color channels as 32-bit floats + FLOAT = Imath.PixelType(Imath.PixelType.FLOAT) + #(R,G,B) = [np.array(array.array('f', file.channel(Chan, FLOAT)).tolist()).reshape((w, h, 1)) for Chan in ("R", "G", "B") ] + + (r, g, b) = file.channels("RGB") + R = np.array(array.array('f', r).tolist()).reshape((w, h, 1)) + G = np.array(array.array('f', g).tolist()).reshape((w, h, 1)) + B = np.array(array.array('f', b).tolist()).reshape((w, h, 1)) + + return np.concatenate((R, G, B), axis=2)''' + +# Converts a Tensor into an image array (numpy) +# |imtype|: the desired type of the converted numpy array +def tensor2im(input_image, imtype=np.uint8): + if isinstance(input_image, torch.Tensor): + input_image = torch.clamp(input_image, -1.0, 1.0) + image_tensor = input_image.data + else: + return input_image + image_numpy = image_tensor[0].cpu().float().numpy() + if image_numpy.shape[0] == 1: + image_numpy = np.tile(image_numpy, (3, 1, 1)) + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 + return image_numpy.astype(imtype) + + +def diagnose_network(net, name='network'): + mean = 0.0 + count = 0 + for param in net.parameters(): + if param.grad is not None: + mean += torch.mean(torch.abs(param.grad.data)) + count += 1 + if count > 0: + mean = mean / count + print(name) + print(mean) + + +def save_image(image_numpy, image_path): + image_pil = Image.fromarray(image_numpy) + image_pil.save(image_path) + +def print_numpy(x, val=True, shp=False): + x = x.astype(np.float64) + if shp: + print('shape,', x.shape) + if val: + x = x.flatten() + print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % ( + np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x))) + + +def mkdirs(paths): + if isinstance(paths, list) and not isinstance(paths, str): + for path in paths: + mkdir(path) + else: + mkdir(paths) + + +def mkdir(path): + if not os.path.exists(path): + os.makedirs(path) diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/util/visualizer.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/util/visualizer.py new file mode 100644 index 0000000..7cb7750 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Inference/util/visualizer.py @@ -0,0 +1,207 @@ +import numpy as np +import os +import sys +import ntpath +import time +from . import util +from . import html +#from scipy.misc import imresize +import cv2 + +if sys.version_info[0] == 2: + VisdomExceptionBase = Exception +else: + VisdomExceptionBase = ConnectionError + + +# save image to the disk +def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256): + image_dir = webpage.get_image_dir() + short_path = ntpath.basename(image_path[0]) + name = os.path.splitext(short_path)[0] + + webpage.add_header(name) + ims, txts, links = [], [], [] + + for label, im_data in visuals.items(): + im = util.tensor2im(im_data) + image_name = '%s_%s.png' % (name, label) + save_path = os.path.join(image_dir, image_name) + h, w, _ = im.shape + + height = int(width * h / float(w)) + #im = imresize(im, (height,width), interp='bicubic') + im = cv2.resize(src=im, dsize=(height,width), interpolation=cv2.INTER_CUBIC) + + #im = imresize(im, (height,widht), interp='bicubic') + #if aspect_ratio > 1.0: + # im = imresize(im, (h, int(w * aspect_ratio)), interp='bicubic') + #if aspect_ratio < 1.0: + # im = imresize(im, (int(h / aspect_ratio), w), interp='bicubic') + + util.save_image(im, save_path) + + ims.append(image_name) + txts.append(label) + links.append(image_name) + webpage.add_images(ims, txts, links, width=width) + + +class Visualizer(): + def __init__(self, opt): + self.display_id = opt.display_id + self.use_html = opt.isTrain and not opt.no_html + self.win_size = opt.display_winsize + self.name = opt.name + self.opt = opt + self.saved = False + if self.display_id > 0: + import visdom + self.ncols = opt.display_ncols + self.vis = visdom.Visdom(server=opt.display_server, port=opt.display_port, env=opt.display_env, raise_exceptions=True) + + if self.use_html: + self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web') + self.img_dir = os.path.join(self.web_dir, 'images') + print('create web directory %s...' % self.web_dir) + util.mkdirs([self.web_dir, self.img_dir]) + self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt') + with open(self.log_name, "a") as log_file: + now = time.strftime("%c") + log_file.write('================ Training Loss (%s) ================\n' % now) + + def reset(self): + self.saved = False + + def throw_visdom_connection_error(self): + print('\n\nCould not connect to Visdom server (https://github.com/facebookresearch/visdom) for displaying training progress.\nYou can suppress connection to Visdom using the option --display_id -1. To install visdom, run \n$ pip install visdom\n, and start the server by \n$ python -m visdom.server.\n\n') + exit(1) + + # |visuals|: dictionary of images to display or save + def display_current_results(self, visuals, epoch, save_result, aspect_ratio=1.0, width=256): + if self.display_id > 0: # show images in the browser + ncols = self.ncols + if ncols > 0: + ncols = min(ncols, len(visuals)) + h, w = next(iter(visuals.values())).shape[2:4] + height = int(width * h / float(w)) + h = height + w = width + table_css = """""" % (w, h) + title = self.name + label_html = '' + label_html_row = '' + images = [] + idx = 0 + for label, image in visuals.items(): + # + image_numpy = util.tensor2im(image) + #image_numpy = imresize(image_numpy, (h, w), interp='bicubic') + image_numpy = cv2.resize(src=image_numpy, dsize=(h, w), interpolation=cv2.INTER_CUBIC) + image_numpy = image_numpy.transpose([2, 0, 1]) + label_html_row += '%s' % label + images.append(image_numpy) + idx += 1 + if idx % ncols == 0: + label_html += '%s' % label_html_row + label_html_row = '' + white_image = np.ones_like(image_numpy) * 255 + while idx % ncols != 0: + images.append(white_image) + label_html_row += '' + idx += 1 + if label_html_row != '': + label_html += '%s' % label_html_row + # pane col = image row + try: + self.vis.images(images, nrow=ncols, win=self.display_id + 1, padding=2, opts=dict(title=title + ' images')) + label_html = '%s
' % label_html + self.vis.text(table_css + label_html, win=self.display_id + 2, + opts=dict(title=title + ' labels')) + except VisdomExceptionBase: + self.throw_visdom_connection_error() + + else: + idx = 1 + for label, image in visuals.items(): + image_numpy = util.tensor2im(image) + self.vis.image(image_numpy.transpose([2, 0, 1]), opts=dict(title=label), + win=self.display_id + idx) + idx += 1 + + if self.use_html and (save_result or not self.saved): # save images to a html file + self.saved = True + for label, image in visuals.items(): + image_numpy = util.tensor2im(image) + img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label)) + util.save_image(image_numpy, img_path) + # update website + webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, reflesh=1) + for n in range(epoch, 0, -1): + webpage.add_header('epoch [%d]' % n) + ims, txts, links = [], [], [] + + for label, image_numpy in visuals.items(): + image_numpy = util.tensor2im(image) + img_path = 'epoch%.3d_%s.png' % (n, label) + ims.append(img_path) + txts.append(label) + links.append(img_path) + webpage.add_images(ims, txts, links, width=self.win_size) + webpage.save() + + # losses: dictionary of error labels and values + def plot_current_losses(self, epoch, counter_ratio, opt, losses): + if not hasattr(self, 'plot_data'): + self.plot_data = {'X': [], 'Y': [], 'legend': list(losses.keys())} + self.plot_data['X'].append(epoch + counter_ratio) + self.plot_data['Y'].append([losses[k] for k in self.plot_data['legend']]) + try: + self.vis.line( + X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1), + Y=np.array(self.plot_data['Y']), + opts={ + 'title': self.name + ' loss over time', + 'legend': self.plot_data['legend'], + 'xlabel': 'epoch', + 'ylabel': 'loss'}, + win=self.display_id) + except VisdomExceptionBase: + self.throw_visdom_connection_error() + + # losses: same format as |losses| of plot_current_losses + def print_current_losses(self, epoch, i, losses, t, t_data): + message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, i, t, t_data) + for k, v in losses.items(): + message += '%s: %.3f ' % (k, v) + + print(message) + with open(self.log_name, "a") as log_file: + log_file.write('%s\n' % message) + + + + + + + # losses: dictionary of error labels and values + def plot_current_validation_error(self, epoch, counter_ratio, losses): + if not hasattr(self, 'plot_validation_data'): + self.plot_validation_data = {'X': [], 'Y': [], 'legend': list(losses.keys())} + self.plot_validation_data['X'].append(epoch + counter_ratio) + self.plot_validation_data['Y'].append([losses[k] for k in self.plot_validation_data['legend']]) + try: + self.vis.line( + X=np.stack([np.array(self.plot_validation_data['X'])] * len(self.plot_validation_data['legend']), 1), + Y=np.array(self.plot_validation_data['Y']), + opts={ + 'title': self.name + ' validation error over time', + 'legend': self.plot_validation_data['legend'], + 'xlabel': 'epoch', + 'ylabel': 'error'}, + win=self.display_id+1) + except VisdomExceptionBase: + self.throw_visdom_connection_error() \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/BaselModel/__init__.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/BaselModel/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/BaselModel/basel_model.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/BaselModel/basel_model.py new file mode 100644 index 0000000..876b871 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/BaselModel/basel_model.py @@ -0,0 +1,106 @@ +import os +import torch +import torch.nn as nn +import torchvision.transforms as transforms +import numpy as np + +#import soft_renderer as sr +import renderer as sr + +N_EXPRESSIONS=76 + +class MorphableModel(nn.Module): + def __init__(self, filename_average=''): + super(MorphableModel, self).__init__() + + print('Load Morphable Model (Basel)') + + #filename_mesh = os.path.join(opt.dataroot, opt.phase + '/average_model.obj') + filename_mesh = filename_average + if filename_average=='': + print('use default identity') + filename_mesh = './BaselModel/average.obj' + mesh = sr.Mesh.from_obj(filename_mesh, normalization=False, load_texture=True) + self.average_vertices = mesh.vertices[0] + self.faces = mesh.faces[0] + self.average_vertices = self.average_vertices[None, :, :] # [num_vertices, XYZ] -> [batch_size=1, num_vertices, XYZ] + self.faces = self.faces[None, :, :] # [num_faces, 3] -> [batch_size=1, num_faces, 3] + self.textures = mesh.textures + + self.num_vertices = self.average_vertices.shape[1] + self.num_faces = self.faces.shape[1] + print('vertices:', self.average_vertices.shape) + print('faces:', self.faces.shape) + + ## basis function + self.expression_basis = np.memmap('./BaselModel/ExpressionBasis.matrix', dtype='float32', mode='r').__array__()[1:] # first entry is the size + self.expression_basis = np.resize(self.expression_basis, (N_EXPRESSIONS, self.num_vertices, 4))[:,:,0:3] + self.expression_basis = torch.tensor(self.expression_basis.astype(np.float32)).cuda() # N_EXPRESSIONS x num_vertices x 3 + self.expression_basis = torch.transpose(self.expression_basis,0,2) # transpose for matmul + print('expression_basis', self.expression_basis.shape) + + + #texture_size = 2 + #self.textures = torch.ones(1, self.faces.shape[1], texture_size, texture_size, texture_size, 3, dtype=torch.float32).cuda() + #print('textures:', self.textures.shape) + + ## ## debug + ## zeroExpr = torch.zeros(1, N_EXPRESSIONS, dtype=torch.float32).cuda() + ## self.morph(zeroExpr) + ## self.save_model_to_obj_file('model_zero_expression.obj') + ## ## + ## onesExpr = torch.ones(1, N_EXPRESSIONS, dtype=torch.float32).cuda() + ## self.morph(onesExpr) + ## self.save_model_to_obj_file('model_ones_expression.obj') + ## exit() + ## ## + + # default expression + zeroExpr = torch.zeros(1, N_EXPRESSIONS, dtype=torch.float32).cuda() + self.morph(zeroExpr) + + + def save_model_to_obj_file(self, filename, mask=None): + faces_cpu = self.faces.detach().cpu().numpy() + vertices_cpu = self.vertices.detach().cpu().numpy() + + mask_cpu = None + if not type(mask) == type(None): + mask_cpu = mask.detach().cpu().numpy() + + f = open(filename, 'w') + if type(mask) == type(None): + for i in range(0, self.num_vertices): + f.write('v ' + str(vertices_cpu[0, i, 0]) + ' ' + str(vertices_cpu[0, i, 1]) + ' ' + str(vertices_cpu[0, i, 2]) + '\n') + else: + for i in range(0, self.num_vertices): + f.write('v ' + str(vertices_cpu[0, i, 0]) + ' ' + str(vertices_cpu[0, i, 1]) + ' ' + str(vertices_cpu[0, i, 2]) + ' ' + str(mask_cpu[i]) + ' ' + str(mask_cpu[i]) + ' ' + str(mask_cpu[i]) + ' 1'+ '\n') + + for i in range(0, self.num_faces): + f.write('f ' + str(faces_cpu[0, i, 0]+1) + '// ' + str(faces_cpu[0, i, 1]+1) + '// ' + str(faces_cpu[0, i, 2]+1) + '//\n') + + f.close() + + def compute_expression_delta(self, expressions): + return torch.transpose(torch.matmul(self.expression_basis, torch.transpose(expressions, 0,1)), 0, 2) # note that matmul wants to have this order: (a x b x c) x (c x m) => (a x b x m) + + def morph(self, expressions): + self.vertices = self.average_vertices + self.compute_expression_delta(expressions) + return self.vertices + + def LoadMask(self, filename=''): + if filename=='': + print('use default mask') + filename = './BaselModel/mask/defaultMask_mouth.obj' + + mask = np.zeros(self.num_vertices) + file = open(filename, 'r') + i=0 + for line in file: + if line[0] == 'v': + floats = [float(x) for x in line[1:].split()] + if floats[3] == 1.0 and floats[4] == 0.0 and floats[5] == 0.0: + mask[i] = 1.0 + i += 1 + file.close() + return torch.tensor(mask.astype(np.float32)).cuda() \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/ReadMe.md b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/ReadMe.md new file mode 100644 index 0000000..4165ff6 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/ReadMe.md @@ -0,0 +1,6 @@ +This code is used to train the audio to expression network. +-> train_audio2expressionsAttentionTMP.sh +You need to provide the training data that fits your face model. +The face model is defined in "BaselModel", you need to provide the average model, a mask for the mouth and the basis vectors. + +As training data you need to provide data that fits the "data/multi_face_audio_eq_tmp_cached_dataset.py" data loader. \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/__init__.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/alphabet.txt b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/alphabet.txt new file mode 100644 index 0000000..46aa35e --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/alphabet.txt @@ -0,0 +1,33 @@ +# Each line in this file represents the Unicode codepoint (UTF-8 encoded) +# associated with a numeric label. +# A line that starts with # is a comment. You can escape it with \# if you wish +# to use '#' as a label. + +a +b +c +d +e +f +g +h +i +j +k +l +m +n +o +p +q +r +s +t +u +v +w +x +y +z +' +# The last (non-comment) line needs to end with a newline. diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/__init__.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/__init__.py new file mode 100644 index 0000000..3735ac1 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/__init__.py @@ -0,0 +1,90 @@ +import importlib +import torch.utils.data +from data.base_data_loader import BaseDataLoader +from data.base_dataset import BaseDataset + + +def find_dataset_using_name(dataset_name): + # Given the option --dataset_mode [datasetname], + # the file "data/datasetname_dataset.py" + # will be imported. + dataset_filename = "data." + dataset_name + "_dataset" + datasetlib = importlib.import_module(dataset_filename) + + # In the file, the class called DatasetNameDataset() will + # be instantiated. It has to be a subclass of BaseDataset, + # and it is case-insensitive. + dataset = None + target_dataset_name = dataset_name.replace('_', '') + 'dataset' + for name, cls in datasetlib.__dict__.items(): + if name.lower() == target_dataset_name.lower() \ + and issubclass(cls, BaseDataset): + dataset = cls + + if dataset is None: + print("In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase." % (dataset_filename, target_dataset_name)) + exit(0) + + return dataset + + +def get_option_setter(dataset_name): + dataset_class = find_dataset_using_name(dataset_name) + return dataset_class.modify_commandline_options + + +def create_dataset(opt): + dataset = find_dataset_using_name(opt.dataset_mode) + instance = dataset() + instance.initialize(opt) + print("dataset [%s] was created" % (instance.name())) + return instance + + +def CreateDataLoader(opt): + data_loader = CustomDatasetDataLoader() + data_loader.initialize(opt) + return data_loader + + +# Wrapper class of Dataset class that performs +# multi-threaded data loading +class CustomDatasetDataLoader(BaseDataLoader): + def name(self): + return 'CustomDatasetDataLoader' + + def initialize(self, opt): + BaseDataLoader.initialize(self, opt) + self.dataset = create_dataset(opt) + if opt.serial_batches: + self.dataloader = torch.utils.data.DataLoader( + self.dataset, + batch_size=opt.batch_size, + shuffle=not opt.serial_batches, + num_workers=int(opt.num_threads)) + else: + #weights = make_weights_for_balanced_classes(dataset_train.imgs, len(dataset_train.classes)) + weights = self.dataset.getSampleWeights() + weights = torch.DoubleTensor(weights) + sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights)) + + self.dataloader = torch.utils.data.DataLoader( + self.dataset, + batch_size=opt.batch_size, + #shuffle=True, + sampler=sampler, + pin_memory=True, + num_workers=int(opt.num_threads)) + + + def load_data(self): + return self + + def __len__(self): + return min(len(self.dataset), self.opt.max_dataset_size) + + def __iter__(self): + for i, data in enumerate(self.dataloader): + if i * self.opt.batch_size >= self.opt.max_dataset_size: + break + yield data diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/aligned_dataset.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/aligned_dataset.py new file mode 100644 index 0000000..b4cfa17 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/aligned_dataset.py @@ -0,0 +1,208 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from data.base_dataset import BaseDataset +from data.audio import Audio +#from data.image_folder import make_dataset +from PIL import Image + +#def make_dataset(dir): +# images = [] +# assert os.path.isdir(dir), '%s is not a valid directory' % dir +# for root, _, fnames in sorted(os.walk(dir)): +# for fname in fnames: +# if any(fname.endswith(extension) for extension in ['.bin', '.BIN']): +# path = os.path.join(root, fname) +# images.append(path) +# return sorted(images) + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.bin', '.BIN']): + id_str = fname[:-4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.bin' + path = os.path.join(root, fname) + images.append(path) + return images + +def load_intrinsics(input_dir): + file = open(input_dir+"/intrinsics.txt", "r") + intrinsics = [[(float(x) for x in line.split())] for line in file] + file.close() + intrinsics = list(intrinsics[0][0]) + return intrinsics + +def load_rigids(input_dir): + file = open(input_dir+"/rigid.txt", "r") + rigid_floats = [[float(x) for x in line.split()] for line in file] # note that it stores 5 lines per matrix (blank line) + file.close() + all_rigids = [ [rigid_floats[4*idx + 0],rigid_floats[4*idx + 1],rigid_floats[4*idx + 2],rigid_floats[4*idx + 3]] for idx in range(0, len(rigid_floats)//4) ] + return all_rigids + +def load_expressions(input_dir): + file = open(input_dir+"/expression.txt", "r") + expressions = [[float(x) for x in line.split()] for line in file] + file.close() + return expressions + +def load_audio(input_dir): + audio = Audio(input_dir + '/audio.mp3', write_mel_spectogram = False) + #audio = Audio(input_dir + '/audio.mp3', write_mel_spectogram = True) + + return audio + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + id_str = fname[l+1:-4] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + +class AlignedDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + self.root = opt.dataroot + self.data_dir = os.path.join(opt.dataroot, opt.phase) + self.frame_paths = make_dataset(self.data_dir) + self.frame_ids = make_ids(self.frame_paths, self.root) + self.intrinsics = load_intrinsics(self.data_dir) + self.extrinsics = load_rigids(self.data_dir) + self.expressions = load_expressions(self.data_dir) + + self.audio = load_audio(self.data_dir) + self.audio_window_size = opt.audio_window_size + + opt.nObjects = 1 + assert(opt.resize_or_crop == 'resize_and_crop') + + def __getitem__(self, index): + + # get video data + frame_id = index + + #print('GET ITEM: ', index) + img_path = self.frame_paths[index] + frame_id = self.frame_ids[index] + + # intrinsics and extrinsics + intrinsics = self.intrinsics + extrinsics = self.extrinsics[frame_id] + + # get audio mel sample window + frame_rate = 24#29.97 + #frame_rate = len(self.expressions) / self.audio.time_total + mel_frame_idx = int((frame_id / frame_rate) * self.audio.mel_sample_rate) + mels = self.audio.getWindow(mel_frame_idx, self.audio_window_size) + + + # expressions + expressions = torch.tensor(self.expressions[frame_id]) + + # default image dimensions + IMG_DIM_X = 512 + IMG_DIM_Y = 512 + + # load image data + #assert(IMG_DIM == self.opt.fineSize) + img_array = np.memmap(img_path, dtype='float32', mode='r').__array__() + if img_array.size != IMG_DIM_X * IMG_DIM_Y * 5: + IMG_DIM_X = int(img_array[0]) + IMG_DIM_Y = int(img_array[1]) + img_array = img_array[2:] + intrinsics = img_array[0:4] + img_array = img_array[4:] + + img_array = np.clip(img_array, 0.0, 1.0) + img = np.resize(img_array, (IMG_DIM_Y, IMG_DIM_X, 5)) + A = img[:,:,0:3] + B = img[:,:,3:5] + B = np.concatenate((B, np.zeros((IMG_DIM_Y, IMG_DIM_X, 1))), axis=2) + + TARGET = transforms.ToTensor()(A.astype(np.float32)) + UV = transforms.ToTensor()(B.astype(np.float32)) + + TARGET = 2.0 * TARGET - 1.0 + UV = 2.0 * UV - 1.0 + + + # load deepspeech feature + dsf_fname = img_path[:-4] + '.deepspeech.npy' +# print('dsf_fname:', dsf_fname) + feature_array = np.load(dsf_fname) + #feature_array = np.memmap(dsf_fname, dtype='float32', mode='r').__array__() +# print('feature_array shape: ', feature_array.shape) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf = transforms.ToTensor()(dsf_np.astype(np.float32)) + + ################################# + ####### apply augmentation ###### + ################################# + if not self.opt.no_augmentation: + # random dimensions + new_dim_x = np.random.randint(int(IMG_DIM_X * 0.75), IMG_DIM_X+1) + new_dim_y = np.random.randint(int(IMG_DIM_Y * 0.75), IMG_DIM_Y+1) + new_dim_x = int(np.floor(new_dim_x / 64.0) * 64 ) # << dependent on the network structure !! 64 => 6 layers + new_dim_y = int(np.floor(new_dim_y / 64.0) * 64 ) + if new_dim_x > IMG_DIM_X: new_dim_x -= 64 + if new_dim_y > IMG_DIM_Y: new_dim_y -= 64 + + # random pos + if IMG_DIM_X == new_dim_x: offset_x = 0 + else: offset_x = np.random.randint(0, IMG_DIM_X-new_dim_x) + if IMG_DIM_Y == new_dim_y: offset_y = 0 + else: offset_y = np.random.randint(0, IMG_DIM_Y-new_dim_y) + + # select subwindow + TARGET = TARGET[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + UV = UV[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + + # compute new intrinsics + # TODO: atm not needed but maybe later + + else: + new_dim_x = int(np.floor(IMG_DIM_X / 64.0) * 64 ) # << dependent on the network structure !! 64 => 6 layers + new_dim_y = int(np.floor(IMG_DIM_Y / 64.0) * 64 ) + offset_x = 0 + offset_y = 0 + # select subwindow + TARGET = TARGET[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + UV = UV[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + + # compute new intrinsics + # TODO: atm not needed but maybe later + + ################################# + + return {'TARGET': TARGET, 'UV': UV, + 'paths': self.frame_paths[index],#img_path, + 'intrinsics': intrinsics, + 'extrinsics': extrinsics, + 'expressions': expressions, + 'audio_mels': mels, + 'audio_deepspeech': dsf, # deepspeech feature + 'object_id':0} + + def __len__(self): + return len(self.frame_paths) + + def name(self): + return 'AlignedDataset' diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/audio.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/audio.py new file mode 100644 index 0000000..4932c94 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/audio.py @@ -0,0 +1,77 @@ +import time +import random +import math + +import numpy as np + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.transforms as transforms + +import torchaudio +import torchaudio.transforms + +import librosa +import scipy.signal +import librosa.display +import matplotlib.pyplot as plt + + +class Audio(): + def name(self): + return 'Audio' + + def __init__(self, filename, write_mel_spectogram = False): + self.n_mels=128 + self.fmax=8000 + self.hop_length_ms = 20 + + sound, sample_rate = librosa.load(filename)#torchaudio.load(filename) + self.raw_audio = sound + self.sample_rate = sample_rate + print('sample_rate = %d' % self.sample_rate) + self.n_samples = sound.shape[0] + self.time_total = self.n_samples / self.sample_rate + print('length = %ds' % self.time_total) + + print('compute mel spectrogram...') + self.hop_length = int(sample_rate / 1000.0 * self.hop_length_ms) + print('hop_length: ', self.hop_length) + self.mel_spectrogram = librosa.feature.melspectrogram(y=self.raw_audio, sr=self.sample_rate, hop_length=self.hop_length, n_mels=self.n_mels, fmax=self.fmax) + + + if write_mel_spectogram: + print('write spectrogram to file') + plt.figure(figsize=(100, 15)) + librosa.display.specshow(librosa.power_to_db(self.mel_spectrogram, ref=np.max), y_axis='mel', fmax=self.fmax, x_axis='time') + plt.colorbar(format='%+2.0f dB') + plt.title('Mel spectrogram') + plt.tight_layout() + plt.savefig('mel_features.png', dpi=None, facecolor='w', edgecolor='w', orientation='portrait', papertype=None, format=None, transparent=False, bbox_inches=None, pad_inches=0.1, frameon=None, metadata=None) + + print('mel: ', self.mel_spectrogram.shape) # (128, 18441) + self.n_mel_frames = self.mel_spectrogram.shape[1] + self.mel_sample_rate = self.mel_spectrogram.shape[1] / self.time_total + print('n_mel_frames: ', self.n_mel_frames) + print('mel_sample_rate: ', self.mel_sample_rate) + + # convert to torch + self.mel_spectrogram = torch.FloatTensor(self.mel_spectrogram) + + def getWindow(self, mel_frame_idx, window_size): + # get audio mel sample window + audio_start = mel_frame_idx - (window_size//2) + audio_end = mel_frame_idx + (window_size//2) + if audio_start < 0: + audio_input = self.mel_spectrogram[0:self.n_mels, 0:audio_end] + zeros = torch.zeros((self.n_mels,-audio_start)) + audio_input = torch.cat([zeros, audio_input], 1) + elif audio_end >= self.n_mel_frames: + audio_input = self.mel_spectrogram[:, audio_start:-1] + zeros = torch.zeros((self.n_mels,audio_end-self.n_mel_frames + 1)) + audio_input = torch.cat([audio_input, zeros], 1) + else: + audio_input = self.mel_spectrogram[:, audio_start:audio_end] + + return torch.reshape(audio_input, (1, 1, self.n_mels, window_size)) \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/audio_dataset.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/audio_dataset.py new file mode 100644 index 0000000..ba0d603 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/audio_dataset.py @@ -0,0 +1,145 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from data.base_dataset import BaseDataset +from data.audio import Audio +#from data.image_folder import make_dataset +from PIL import Image + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.deepspeech.npy']): + id_str = fname[:-15] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.deepspeech.npy' + path = os.path.join(root, fname) + images.append(path) + return images + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + #id_str = fname[l+1:-4] + id_str = fname[l+1:-15] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + +class AudioDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + self.root = opt.dataroot + self.data_dir = os.path.join(opt.dataroot, 'audio_feature') + self.frame_paths = make_dataset(self.data_dir) + self.frame_ids = make_ids(self.frame_paths, self.root) + + opt.nObjects = 1 + opt.nTrainObjects = 116 # TODO + opt.nTestObjects = 1 + opt.test_sequence_names = [[opt.dataroot.split("/")[-1], 'test']] + assert(opt.resize_or_crop == 'resize_and_crop') + + if opt.isTrain: + print('ERROR: audio_dataset only allowed for test') + exit() + + def getSampleWeights(self): + weights = np.ones((len(self.frame_paths))) + return weights + + def getAudioFilename(self): + return os.path.join(self.root, 'audio.wav') + + def getAudioFeatureFilename(self, idx): + return self.frame_paths[idx % len(self.frame_paths)] + + def __getitem__(self, index): + + #print('GET ITEM: ', index) + frame_path = self.frame_paths[index] + frame_id = self.frame_ids[index] + + # load deepspeech feature + feature_array = np.load(frame_path) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf = transforms.ToTensor()(dsf_np.astype(np.float32)) + + + # load sequence data if necessary + if self.opt.look_ahead:# use prev and following frame infos + r = self.opt.seq_len//2 + for i in range(1,r): # prev frames + index_seq = index - i + if index_seq < 0: index_seq = 0 + + dsf_fname = self.frame_paths[index_seq] + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + for i in range(1,self.opt.seq_len - r + 1): # following frames + index_seq = index + i + max_idx = len(self.frame_paths)-1 + if index_seq > max_idx: index_seq = max_idx + + dsf_fname = self.frame_paths[index_seq] + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf, dsf_seq], 0) # seq_len x 16 x 29 + # note the ordering [old ... current ... future] + else: + for i in range(1,self.opt.seq_len): + index_seq = index - i + if index_seq < 0: index_seq = 0 + dsf_fname = self.frame_paths[index_seq] + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + + ################################# + zeroIdentity = torch.zeros(100) + zeroExpressions = torch.zeros(76) + + target_id = -1 + internal_sequence_id = 0 + + weight = 1.0 / len(self.frame_paths) + + return {'paths': frame_path, + 'expressions': zeroExpressions, + 'identity': zeroIdentity, + 'intrinsics': np.zeros((4)), + 'extrinsics': np.zeros((4,4)), + 'audio_deepspeech': dsf, # deepspeech feature + 'target_id':target_id, + 'internal_id':internal_sequence_id, + 'weight': np.array([weight]).astype(np.float32)} + + def __len__(self): + return len(self.frame_paths) + + def name(self): + return 'AudioDataset' diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/base_data_loader.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/base_data_loader.py new file mode 100644 index 0000000..ae5a168 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/base_data_loader.py @@ -0,0 +1,10 @@ +class BaseDataLoader(): + def __init__(self): + pass + + def initialize(self, opt): + self.opt = opt + pass + + def load_data(): + return None diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/base_dataset.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/base_dataset.py new file mode 100644 index 0000000..25c7c8c --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/base_dataset.py @@ -0,0 +1,105 @@ +import torch.utils.data as data +from PIL import Image +import torchvision.transforms as transforms + + +class BaseDataset(data.Dataset): + def __init__(self): + super(BaseDataset, self).__init__() + + def name(self): + return 'BaseDataset' + + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + pass + + def getSampleWeights(self): + return torch.ones((len(self))) + + def __len__(self): + return 0 + + +def get_transform(opt): + transform_list = [] + if opt.resize_or_crop == 'resize_and_crop': + osize = [opt.loadSize, opt.loadSize] + transform_list.append(transforms.Resize(osize, Image.BICUBIC)) + transform_list.append(transforms.RandomCrop(opt.fineSize)) + elif opt.resize_or_crop == 'crop': + transform_list.append(transforms.RandomCrop(opt.fineSize)) + elif opt.resize_or_crop == 'scale_width': + transform_list.append(transforms.Lambda( + lambda img: __scale_width(img, opt.fineSize))) + elif opt.resize_or_crop == 'scale_width_and_crop': + transform_list.append(transforms.Lambda( + lambda img: __scale_width(img, opt.loadSize))) + transform_list.append(transforms.RandomCrop(opt.fineSize)) + elif opt.resize_or_crop == 'none': + transform_list.append(transforms.Lambda( + lambda img: __adjust(img))) + else: + raise ValueError('--resize_or_crop %s is not a valid option.' % opt.resize_or_crop) + + if opt.isTrain and not opt.no_flip: + transform_list.append(transforms.RandomHorizontalFlip()) + + transform_list += [transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), + (0.5, 0.5, 0.5))] + return transforms.Compose(transform_list) + + +# just modify the width and height to be multiple of 4 +def __adjust(img): + ow, oh = img.size + + # the size needs to be a multiple of this number, + # because going through generator network may change img size + # and eventually cause size mismatch error + mult = 4 + if ow % mult == 0 and oh % mult == 0: + return img + w = (ow - 1) // mult + w = (w + 1) * mult + h = (oh - 1) // mult + h = (h + 1) * mult + + if ow != w or oh != h: + __print_size_warning(ow, oh, w, h) + + return img.resize((w, h), Image.BICUBIC) + + +def __scale_width(img, target_width): + ow, oh = img.size + + # the size needs to be a multiple of this number, + # because going through generator network may change img size + # and eventually cause size mismatch error + mult = 4 + assert target_width % mult == 0, "the target width needs to be multiple of %d." % mult + if (ow == target_width and oh % mult == 0): + return img + w = target_width + target_height = int(target_width * oh / ow) + m = (target_height - 1) // mult + h = (m + 1) * mult + + if target_height != h: + __print_size_warning(target_width, target_height, w, h) + + return img.resize((w, h), Image.BICUBIC) + + +def __print_size_warning(ow, oh, w, h): + if not hasattr(__print_size_warning, 'has_printed'): + print("The image size needs to be a multiple of 4. " + "The loaded image size was (%d, %d), so it was adjusted to " + "(%d, %d). This adjustment will be done to all images " + "whose sizes are not multiples of 4" % (ow, oh, w, h)) + __print_size_warning.has_printed = True diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/face_dataset.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/face_dataset.py new file mode 100644 index 0000000..a89c931 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/face_dataset.py @@ -0,0 +1,382 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from data.base_dataset import BaseDataset +from data.audio import Audio +#from data.image_folder import make_dataset +from PIL import Image +from util import util + +#def make_dataset(dir): +# images = [] +# assert os.path.isdir(dir), '%s is not a valid directory' % dir +# for root, _, fnames in sorted(os.walk(dir)): +# for fname in fnames: +# if any(fname.endswith(extension) for extension in ['.bin', '.BIN']): +# path = os.path.join(root, fname) +# images.append(path) +# return sorted(images) + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.npy', '.NPY']): + #.deepspeech.npy + id_str = fname[:-15] #4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.deepspeech.npy' + path = os.path.join(root, fname) + images.append(path) + return images + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + id_str = fname[l+1:-15]#4] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + +def make_dataset_png_ids(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.png', '.PNG']): + id_str = fname[:-4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + return ids + +def make_dataset_exr_ids(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.exr', '.EXR']): + id_str = fname[:-4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + return ids + + +def load_intrinsics(input_dir): + file = open(input_dir+"/intrinsics.txt", "r") + intrinsics = [[(float(x) for x in line.split())] for line in file] + file.close() + intrinsics = list(intrinsics[0][0]) + return intrinsics + +def load_rigids(input_dir): + file = open(input_dir+"/rigid.txt", "r") + rigid_floats = [[float(x) for x in line.split()] for line in file] # note that it stores 5 lines per matrix (blank line) + file.close() + all_rigids = [ [rigid_floats[4*idx + 0],rigid_floats[4*idx + 1],rigid_floats[4*idx + 2],rigid_floats[4*idx + 3]] for idx in range(0, len(rigid_floats)//4) ] + return all_rigids + +def load_expressions(input_dir): + file = open(input_dir+"/expression.txt", "r") + expressions = [[float(x) for x in line.split()] for line in file] + file.close() + return expressions + +def load_identity(input_dir): + file = open(input_dir+"/identities.txt", "r") + identity = [[float(x) for x in line.split()] for line in file] + file.close() + return identity + + + +class FaceDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + + # directories + self.dataroot = opt.dataroot + self.audio_feature_dir = os.path.join(opt.dataroot, 'audio_feature') + self.image_dir = os.path.join(opt.dataroot, 'images') + self.uvs_dir = os.path.join(opt.dataroot, 'uvs') + + # debug print + print('load sequence:', self.dataroot) + print('\taudio_feature_dir:', self.audio_feature_dir) + print('\timage_dir:', self.image_dir) + print('\tuvs_dir:', self.uvs_dir) + + # generate index maps + audio_ids = make_ids(make_dataset(self.audio_feature_dir), self.dataroot) + image_ids = make_dataset_png_ids(self.image_dir) + uvs_ids = make_dataset_exr_ids(self.uvs_dir) + + # get model parameters + intrinsics = load_intrinsics(self.dataroot) + extrinsics = load_rigids(self.dataroot) + expressions = load_expressions(self.dataroot) + identities = load_identity(self.dataroot) + + if opt.phase == 'test': # test overwrites the audio and uv files, as well as expressions + print('Test mode. Overwriting audio, uv and expressions') + print('source sequence:', opt.source_dir) + dataroot = opt.source_dir + self.audio_feature_dir = os.path.join(dataroot, 'audio_feature') + self.uvs_dir = os.path.join(dataroot, 'uvs') + audio_ids = make_ids(make_dataset(self.audio_feature_dir), dataroot) + uvs_ids = make_dataset_exr_ids(self.uvs_dir) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(dataroot) + identities = load_identity(dataroot) + + print('\tnum audio_ids:', len(audio_ids)) + print('\tnum image_ids:', len(image_ids)) + print('\tnum uvs_ids:', len(uvs_ids)) + + + # set data + min_len = min(len(audio_ids), len(image_ids), len(uvs_ids), len(extrinsics), len(expressions)) + self.audio_ids = audio_ids[:min_len] + self.image_ids = image_ids[:min_len] + self.uvs_ids = uvs_ids[:min_len] + self.intrinsics = intrinsics + self.extrinsics = extrinsics[:] #extrinsics[:min_len] + self.expressions = expressions[:] #expressions[:min_len] + self.identities = identities[:] #identities[:min_len] + self.n_frames_total = min_len + + print('\tnum frames:', self.n_frames_total) + + + opt.nTrainObjects = 1 + opt.nValObjects = 1 + opt.nTestObjects = 1 + + + opt.test_sequence_names = [[opt.dataroot.split("/")[-1], 'train']] + if opt.phase == 'test': + opt.test_sequence_names = [[opt.source_dir.split("/")[-1], 'test']] + print('test:', opt.test_sequence_names) + + assert(opt.resize_or_crop == 'resize_and_crop') + + def getSampleWeights(self): + weights = np.ones((self.n_frames_total)) + return weights + + def getExtrinsics(self, idx): + return self.extrinsics[self.uvs_ids[idx % self.n_frames_total]] + def getIntrinsics(self, idx): + return self.intrinsics + def getIdentities(self, idx): + return self.identities[self.uvs_ids[idx % self.n_frames_total]] + def getExpressions(self, idx): + return self.expressions[self.uvs_ids[idx % self.n_frames_total]] + + def getAudioFilename(self): + return os.path.join(self.dataroot, 'audio.wav') + + def getImageFilename(self, idx): + image_id = self.image_ids[idx % self.n_frames_total] + img_fname = os.path.join(self.image_dir, str(image_id).zfill(5) + '.png') + return img_fname + #img_numpy = np.asarray(Image.open(img_fname)) + #TARGET = 2.0 * transforms.ToTensor()(img_numpy.astype(np.float32))/255.0 - 1.0 + + def getAudioFeatureFilename(self, idx): + #return self.frame_paths[idx % len(self.frame_paths)] + audio_id = self.audio_ids[idx] + return os.path.join(self.audio_feature_dir, str(audio_id) + '.deepspeech.npy') + + + def computeCrop(self, mask, MULTIPLE_OF=64, random_size=False): + IMG_DIM_X = mask.shape[2] + IMG_DIM_Y = mask.shape[1] + if random_size: + # random dimensions + new_dim_x = np.random.randint(int(IMG_DIM_X * 0.75), IMG_DIM_X+1) + new_dim_y = np.random.randint(int(IMG_DIM_Y * 0.75), IMG_DIM_Y+1) + new_dim_x = int(np.floor(new_dim_x / float(MULTIPLE_OF)) * MULTIPLE_OF ) + new_dim_y = int(np.floor(new_dim_y / float(MULTIPLE_OF)) * MULTIPLE_OF ) + else: + new_dim_x = 3 * MULTIPLE_OF + new_dim_y = 3 * MULTIPLE_OF + + # check dims + if new_dim_x > IMG_DIM_X: new_dim_x -= MULTIPLE_OF + if new_dim_y > IMG_DIM_Y: new_dim_y -= MULTIPLE_OF + + # random pos + mask_indices = torch.nonzero(mask) + _, bb_mid_point_y, bb_mid_point_x = mask_indices[np.random.randint(0, mask_indices.shape[0])].data.cpu() + #print('bb_mid_point', bb_mid_point_x, bb_mid_point_y) + + offset_x = bb_mid_point_x - new_dim_x/2 + offset_y = bb_mid_point_y - new_dim_y/2 + + + if IMG_DIM_X == new_dim_x: offset_x = 0 + if offset_x < 0: offset_x = 0 + if offset_x+new_dim_x >= IMG_DIM_X: offset_x = IMG_DIM_X-new_dim_x + + if IMG_DIM_Y == new_dim_y: offset_y = 0 + if offset_y < 0: offset_y = 0 + if offset_y+new_dim_y >= IMG_DIM_Y: offset_y = IMG_DIM_Y-new_dim_y + + return np.array([int(offset_x),int(offset_y),int(new_dim_x), int(new_dim_y)]) + + + def __getitem__(self, global_index): + # select frame from sequence + index = global_index + + # get data ids + audio_id = self.audio_ids[index] + image_id = self.image_ids[index] + uv_id = self.uvs_ids[index] + + + + #print('GET ITEM: ', index) + img_fname = os.path.join(self.image_dir, str(image_id).zfill(5) + '.png') + img_numpy = np.asarray(Image.open(img_fname)) + TARGET = 2.0 * transforms.ToTensor()(img_numpy.astype(np.float32))/255.0 - 1.0 + + uv_fname = os.path.join(self.uvs_dir, str(uv_id).zfill(5) + '.exr') + uv_numpy = util.load_exr(uv_fname) + UV = transforms.ToTensor()(uv_numpy.astype(np.float32)) + UV = torch.where(UV > 1.0, torch.zeros_like(UV), UV) + UV = torch.where(UV < 0.0, torch.zeros_like(UV), UV) + UV = 2.0 * UV - 1.0 + + #print('img_fname:', img_fname) + #print('uv_fname:', uv_fname) + + # intrinsics and extrinsics + intrinsics = self.intrinsics + extrinsics = self.extrinsics[uv_id] + + # expressions + expressions = np.asarray(self.expressions[audio_id], dtype=np.float32) + #print('expressions:', expressions.shape) + expressions[32] *= 0.0 # remove eye brow movements + expressions[41] *= 0.0 # remove eye brow movements + expressions[71:75] *= 0.0 # remove eye brow movements + expressions = torch.tensor(expressions) + + # identity + identity = torch.tensor(self.identities[audio_id]) + + # load deepspeech feature + dsf_fname = os.path.join(self.audio_feature_dir, str(audio_id) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf = transforms.ToTensor()(dsf_np.astype(np.float32)) + + + # load sequence data if necessary + if self.opt.look_ahead:# use prev and following frame infos + r = self.opt.seq_len//2 + last_valid_idx = audio_id + for i in range(1,r): # prev frames + index_seq = index - i + if index_seq < 0: index_seq = 0 + audio_id_seq = self.audio_ids[index_seq] + if audio_id_seq == audio_id - i: last_valid_idx = audio_id_seq + else: audio_id_seq = last_valid_idx + + dsf_fname = os.path.join(self.audio_feature_dir, str(audio_id_seq) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + last_valid_idx = audio_id + for i in range(1,self.opt.seq_len - r + 1): # following frames + index_seq = index + i + max_idx = len(self.audio_ids)-1 + if index_seq > max_idx: index_seq = max_idx + audio_id_seq = self.audio_ids[index_seq] + if audio_id_seq == audio_id + i: last_valid_idx = audio_id_seq + else: audio_id_seq = last_valid_idx + + dsf_fname = os.path.join(self.audio_feature_dir, str(audio_id_seq) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf, dsf_seq], 0) # seq_len x 16 x 29 + # note the ordering [old ... current ... future] + else: + last_valid_idx = audio_id + for i in range(1,self.opt.seq_len): + index_seq = index - i + if index_seq < 0: index_seq = 0 + audio_id_seq = self.audio_ids[index_seq] + if audio_id_seq == audio_id - i: last_valid_idx = audio_id_seq + else: audio_id_seq = last_valid_idx + + dsf_fname = os.path.join(self.audio_feature_dir, str(audio_id_seq) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + + ################################# + ####### apply augmentation ###### + ################################# + crop = np.array([0,0,UV.shape[2],UV.shape[1]]) + if not self.opt.no_augmentation: + INVALID_UV = -1 + mask = ( (UV[0:1,:,:] != INVALID_UV) | (UV[1:2,:,:] != INVALID_UV) ) + crop = self.computeCrop(mask, MULTIPLE_OF=64) # << dependent on the network structure !! 64 => 6 layers + + offset_x,offset_y,new_dim_x, new_dim_y = crop + # select subwindow + TARGET = TARGET[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + UV = UV[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + + # compute new intrinsics + # TODO: atm not needed but maybe later + + ################################# + weight = 1.0 / self.n_frames_total + + return {'TARGET': TARGET, 'UV': UV, + 'paths': dsf_fname, #img_path, + 'intrinsics': np.array(intrinsics), + 'extrinsics': np.array(extrinsics), + 'expressions': expressions, + 'identity': identity, + 'audio_deepspeech': dsf, # deepspeech feature + 'target_id': -1, + 'crop': crop, + 'internal_id': 0, + 'weight': np.array([weight]).astype(np.float32)} + + def __len__(self): + return self.n_frames_total + + def name(self): + return 'FaceDataset' diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/facetmp_dataset.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/facetmp_dataset.py new file mode 100644 index 0000000..bf2baeb --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/facetmp_dataset.py @@ -0,0 +1,394 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from data.base_dataset import BaseDataset +from data.audio import Audio +#from data.image_folder import make_dataset +from PIL import Image +from util import util + +#def make_dataset(dir): +# images = [] +# assert os.path.isdir(dir), '%s is not a valid directory' % dir +# for root, _, fnames in sorted(os.walk(dir)): +# for fname in fnames: +# if any(fname.endswith(extension) for extension in ['.bin', '.BIN']): +# path = os.path.join(root, fname) +# images.append(path) +# return sorted(images) + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.npy', '.NPY']): + #.deepspeech.npy + id_str = fname[:-15] #4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.deepspeech.npy' + path = os.path.join(root, fname) + images.append(path) + return images + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + id_str = fname[l+1:-15]#4] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + +def make_dataset_png_ids(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.png', '.PNG']): + id_str = fname[:-4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + return ids + +def make_dataset_exr_ids(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.exr', '.EXR']): + id_str = fname[:-4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + return ids + + +def load_intrinsics(input_dir): + file = open(input_dir+"/intrinsics.txt", "r") + intrinsics = [[(float(x) for x in line.split())] for line in file] + file.close() + intrinsics = list(intrinsics[0][0]) + return intrinsics + +def load_rigids(input_dir): + file = open(input_dir+"/rigid.txt", "r") + rigid_floats = [[float(x) for x in line.split()] for line in file] # note that it stores 5 lines per matrix (blank line) + file.close() + all_rigids = [ [rigid_floats[4*idx + 0],rigid_floats[4*idx + 1],rigid_floats[4*idx + 2],rigid_floats[4*idx + 3]] for idx in range(0, len(rigid_floats)//4) ] + return all_rigids + +def load_expressions(input_dir): + file = open(input_dir+"/expression.txt", "r") + expressions = [[float(x) for x in line.split()] for line in file] + file.close() + return expressions + +def load_identity(input_dir): + file = open(input_dir+"/identities.txt", "r") + identity = [[float(x) for x in line.split()] for line in file] + file.close() + return identity + + + +class FaceTmpDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + + # directories + self.dataroot = opt.dataroot + self.audio_feature_dir = os.path.join(opt.dataroot, 'audio_feature') + self.image_dir = os.path.join(opt.dataroot, 'images') + self.uvs_dir = os.path.join(opt.dataroot, 'uvs') + + # debug print + print('load sequence:', self.dataroot) + print('\taudio_feature_dir:', self.audio_feature_dir) + print('\timage_dir:', self.image_dir) + print('\tuvs_dir:', self.uvs_dir) + + # generate index maps + audio_ids = make_ids(make_dataset(self.audio_feature_dir), self.dataroot) + image_ids = make_dataset_png_ids(self.image_dir) + uvs_ids = make_dataset_exr_ids(self.uvs_dir) + + # get model parameters + intrinsics = load_intrinsics(self.dataroot) + extrinsics = load_rigids(self.dataroot) + expressions = load_expressions(self.dataroot) + identities = load_identity(self.dataroot) + + if opt.phase == 'test': # test overwrites the audio and uv files, as well as expressions + print('Test mode. Overwriting audio, uv and expressions') + print('source sequence:', opt.source_dir) + dataroot = opt.source_dir + self.audio_feature_dir = os.path.join(dataroot, 'audio_feature') + self.uvs_dir = os.path.join(dataroot, 'uvs') + audio_ids = make_ids(make_dataset(self.audio_feature_dir), dataroot) + uvs_ids = make_dataset_exr_ids(self.uvs_dir) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(dataroot) + identities = load_identity(dataroot) + + print('\tnum audio_ids:', len(audio_ids)) + print('\tnum image_ids:', len(image_ids)) + print('\tnum uvs_ids:', len(uvs_ids)) + + + # set data + min_len = min(len(audio_ids), len(image_ids), len(uvs_ids), len(extrinsics), len(expressions)) + self.audio_ids = audio_ids[:min_len] + self.image_ids = image_ids[:min_len] + self.uvs_ids = uvs_ids[:min_len] + self.intrinsics = intrinsics + self.extrinsics = extrinsics[:] # extrinsics[:min_len] + self.expressions = expressions[:] # expressions[:min_len] + self.identities = identities[:] #identities[:min_len] + self.n_frames_total = min_len + + print('\tnum frames:', self.n_frames_total) + + + opt.nTrainObjects = 1 + opt.nValObjects = 1 + opt.nTestObjects = 1 + + + opt.test_sequence_names = [[opt.dataroot.split("/")[-1], 'train']] + if opt.phase == 'test': + + opt.test_sequence_names = [[opt.source_dir.split("/")[-1], 'test']] + print('test:', opt.test_sequence_names) + + assert(opt.resize_or_crop == 'resize_and_crop') + + def getSampleWeights(self): + weights = np.ones((self.n_frames_total)) + return weights + + def computeCrop(self, mask, MULTIPLE_OF=64, random_size=False): + IMG_DIM_X = mask.shape[2] + IMG_DIM_Y = mask.shape[1] + if random_size: + # random dimensions + new_dim_x = np.random.randint(int(IMG_DIM_X * 0.75), IMG_DIM_X+1) + new_dim_y = np.random.randint(int(IMG_DIM_Y * 0.75), IMG_DIM_Y+1) + new_dim_x = int(np.floor(new_dim_x / float(MULTIPLE_OF)) * MULTIPLE_OF ) + new_dim_y = int(np.floor(new_dim_y / float(MULTIPLE_OF)) * MULTIPLE_OF ) + else: + new_dim_x = 2 * MULTIPLE_OF + new_dim_y = 2 * MULTIPLE_OF + + # check dims + if new_dim_x > IMG_DIM_X: new_dim_x -= MULTIPLE_OF + if new_dim_y > IMG_DIM_Y: new_dim_y -= MULTIPLE_OF + + # random pos + mask_indices = torch.nonzero(mask) + _, bb_mid_point_y, bb_mid_point_x = mask_indices[np.random.randint(0, mask_indices.shape[0])].data.cpu() + #print('bb_mid_point', bb_mid_point_x, bb_mid_point_y) + + offset_x = bb_mid_point_x - new_dim_x/2 + offset_y = bb_mid_point_y - new_dim_y/2 + + + if IMG_DIM_X == new_dim_x: offset_x = 0 + if offset_x < 0: offset_x = 0 + if offset_x+new_dim_x >= IMG_DIM_X: offset_x = IMG_DIM_X-new_dim_x + + if IMG_DIM_Y == new_dim_y: offset_y = 0 + if offset_y < 0: offset_y = 0 + if offset_y+new_dim_y >= IMG_DIM_Y: offset_y = IMG_DIM_Y-new_dim_y + + + return np.array([int(offset_x),int(offset_y),int(new_dim_x), int(new_dim_y)]) + + def getitem(self, global_index, crop=None): + # select frame from sequence + index = global_index + + # get data ids + audio_id = self.audio_ids[index] + image_id = self.image_ids[index] + uv_id = self.uvs_ids[index] + + #print('GET ITEM: ', index) + img_fname = os.path.join(self.image_dir, str(image_id).zfill(5) + '.png') + img_numpy = np.asarray(Image.open(img_fname)) + TARGET = 2.0 * transforms.ToTensor()(img_numpy.astype(np.float32))/255.0 - 1.0 + + uv_fname = os.path.join(self.uvs_dir, str(uv_id).zfill(5) + '.exr') + uv_numpy = util.load_exr(uv_fname) + UV = transforms.ToTensor()(uv_numpy.astype(np.float32)) + UV = torch.where(UV > 1.0, torch.zeros_like(UV), UV) + UV = torch.where(UV < 0.0, torch.zeros_like(UV), UV) + UV = 2.0 * UV - 1.0 + + + # intrinsics and extrinsics + intrinsics = self.intrinsics + extrinsics = self.extrinsics[uv_id] + + # expressions + expressions = np.asarray(self.expressions[audio_id], dtype=np.float32) + #print('expressions:', expressions.shape) + expressions[32] *= 0.0 # remove eye brow movements + expressions[41] *= 0.0 # remove eye brow movements + expressions[71:75] *= 0.0 # remove eye brow movements + expressions = torch.tensor(expressions) + + # identity + identity = torch.tensor(self.identities[audio_id]) + + # load deepspeech feature + dsf_fname = os.path.join(self.audio_feature_dir, str(audio_id) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf = transforms.ToTensor()(dsf_np.astype(np.float32)) + + + # load sequence data if necessary + if self.opt.look_ahead:# use prev and following frame infos + r = self.opt.seq_len//2 + last_valid_idx = audio_id + for i in range(1,r): # prev frames + index_seq = index - i + if index_seq < 0: index_seq = 0 + audio_id_seq = self.audio_ids[index_seq] + if audio_id_seq == audio_id - i: last_valid_idx = audio_id_seq + else: audio_id_seq = last_valid_idx + + dsf_fname = os.path.join(self.audio_feature_dir, str(audio_id_seq) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + last_valid_idx = audio_id + for i in range(1,self.opt.seq_len - r + 1): # following frames + index_seq = index + i + max_idx = len(self.audio_ids)-1 + if index_seq > max_idx: index_seq = max_idx + audio_id_seq = self.audio_ids[index_seq] + if audio_id_seq == audio_id + i: last_valid_idx = audio_id_seq + else: audio_id_seq = last_valid_idx + + dsf_fname = os.path.join(self.audio_feature_dir, str(audio_id_seq) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf, dsf_seq], 0) # seq_len x 16 x 29 + # note the ordering [old ... current ... future] + else: + last_valid_idx = audio_id + for i in range(1,self.opt.seq_len): + index_seq = index - i + if index_seq < 0: index_seq = 0 + audio_id_seq = self.audio_ids[index_seq] + if audio_id_seq == audio_id - i: last_valid_idx = audio_id_seq + else: audio_id_seq = last_valid_idx + + dsf_fname = os.path.join(self.audio_feature_dir, str(audio_id_seq) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + ################################# + ####### apply augmentation ###### + ################################# + + if not self.opt.no_augmentation: + if type(crop) == type(None): + INVALID_UV = -1 + mask = ( (UV[0:1,:,:] != INVALID_UV) | (UV[1:2,:,:] != INVALID_UV) ) + crop = self.computeCrop(mask, MULTIPLE_OF=64) # << dependent on the network structure !! 64 => 6 layers + + offset_x,offset_y,new_dim_x, new_dim_y = crop + + # select subwindow + TARGET = TARGET[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + UV = UV[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + + # compute new intrinsics + # TODO: atm not needed but maybe later + + ################################# + + + return {'TARGET': TARGET, 'UV': UV, + 'paths': dsf_fname, #img_path, + 'intrinsics': np.array(intrinsics), + 'extrinsics': np.array(extrinsics), + 'expressions': expressions, + 'identity': identity, + 'audio_deepspeech': dsf, # deepspeech feature + #'target_id':target_id, + 'crop': crop, + + 'internal_id': 0} + + + def __getitem__(self, global_index): + # select frame from sequence + index = global_index + + current = self.getitem(index) + crop = current['crop'] + prv = self.getitem(max(index-1, 0), crop) + nxt = self.getitem(min(index+1, self.n_frames_total-1), crop) + + if type(crop) == type(None): + crop = np.array([0,0,current['UV'].shape[2],current['UV'].shape[1]]) + + weight = 1.0 / self.n_frames_total + + return {'TARGET': current['TARGET'], + 'UV': current['UV'], + 'paths': current['paths'], + 'intrinsics': current['intrinsics'], + 'extrinsics': current['extrinsics'], + 'expressions': current['expressions'], + 'identity': current['identity'], + 'audio_deepspeech': current['audio_deepspeech'], + + 'prv_TARGET': prv['TARGET'], + 'prv_UV': prv['UV'], + 'prv_audio_deepspeech': prv['audio_deepspeech'], + 'prv_expressions': prv['expressions'], + + 'nxt_TARGET': nxt['TARGET'], + 'nxt_UV': nxt['UV'], + 'nxt_audio_deepspeech': nxt['audio_deepspeech'], + 'nxt_expressions': nxt['expressions'], + + 'crop': crop, + + 'internal_id': current['internal_id'], + 'weight': np.array([weight]).astype(np.float32)} + + def __len__(self): + return self.n_frames_total + + def name(self): + return 'FaceTmpDataset' diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/multi_face_audio_eq_tmp_cached_dataset.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/multi_face_audio_eq_tmp_cached_dataset.py new file mode 100644 index 0000000..9bb4504 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/multi_face_audio_eq_tmp_cached_dataset.py @@ -0,0 +1,458 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from data.base_dataset import BaseDataset +from data.audio import Audio +#from data.image_folder import make_dataset +from PIL import Image +import progressbar + +#def make_dataset(dir): +# images = [] +# assert os.path.isdir(dir), '%s is not a valid directory' % dir +# for root, _, fnames in sorted(os.walk(dir)): +# for fname in fnames: +# if any(fname.endswith(extension) for extension in ['.bin', '.BIN']): +# path = os.path.join(root, fname) +# images.append(path) +# return sorted(images) + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.npy', '.NPY']): + #.deepspeech.npy + id_str = fname[:-15] #4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.deepspeech.npy' + path = os.path.join(root, fname) + images.append(path) + return images + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + id_str = fname[l+1:-15]#4] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + +def make_dataset_ids_png(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.png', '.png']): + id_str = fname[:-4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + return ids + +def load_intrinsics(input_dir): + file = open(input_dir+"/intrinsics.txt", "r") + intrinsics = [[(float(x) for x in line.split())] for line in file] + file.close() + intrinsics = list(intrinsics[0][0]) + return intrinsics + +def load_rigids(input_dir): + file = open(input_dir+"/rigid.txt", "r") + rigid_floats = [[float(x) for x in line.split()] for line in file] # note that it stores 5 lines per matrix (blank line) + file.close() + all_rigids = [ [rigid_floats[4*idx + 0],rigid_floats[4*idx + 1],rigid_floats[4*idx + 2],rigid_floats[4*idx + 3]] for idx in range(0, len(rigid_floats)//4) ] + return all_rigids + +def load_expressions(input_dir): + file = open(input_dir+"/expression.txt", "r") + expressions = [[float(x) for x in line.split()] for line in file] + file.close() + return expressions + +def load_identity(input_dir): + file = open(input_dir+"/identities.txt", "r") + identities = [[float(x) for x in line.split()] for line in file] + file.close() + return identities + + +class MultiFaceAudioEQTmpCachedDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + self.root = opt.dataroot + + # read dataset file that contains the filenames for the train, val and test lists + file = open(self.root+"/dataset.txt", "r") + self.filename_train_list, self.filename_val_list, self.filename_test_list = [str(line) for line in file] + file.close() + if self.filename_train_list[-1] == '\n': self.filename_train_list = self.filename_train_list[:-1] + if self.filename_val_list[-1] == '\n': self.filename_val_list = self.filename_val_list[:-1] + if self.filename_test_list[-1] == '\n': self.filename_test_list = self.filename_test_list[:-1] + + + + # get list of train sequences + file = open(self.root+"/" + self.filename_train_list, "r") + self.train_sequence_names = [str(line) for line in file] + file.close() + for i in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[i][-1] == '\n': + self.train_sequence_names[i] = self.train_sequence_names[i][:-1] + + # get list of val sequences + file = open(self.root+"/" + self.filename_val_list, "r") + self.val_sequence_names = [[str(w) for w in line.split()] for line in file] + file.close() + for i in range(0,len(self.val_sequence_names)): + if self.val_sequence_names[i][0][-1] == '\n': self.val_sequence_names[i][0] = self.val_sequence_names[i][0][:-1] + if self.val_sequence_names[i][1][-1] == '\n': self.val_sequence_names[i][1] = self.val_sequence_names[i][1][:-1] + + # get list of test sequences + file = open(self.root+"/" + self.filename_test_list, "r") + self.test_sequence_names = [[str(w) for w in line.split()] for line in file] + if opt.output_audio_expressions: self.test_sequence_names = self.test_sequence_names[0:1] + file.close() + for i in range(0,len(self.test_sequence_names)): + if self.test_sequence_names[i][0][-1] == '\n': self.test_sequence_names[i][0] = self.test_sequence_names[i][0][:-1] + if self.test_sequence_names[i][1][-1] == '\n': self.test_sequence_names[i][1] = self.test_sequence_names[i][1][:-1] + + # print some stats + print('filename_train_list:', self.filename_train_list) + print('\tnum_seq:', len(self.train_sequence_names)) + print('filename_val_list: ', self.filename_val_list) + print('\tnum_seq:', len(self.val_sequence_names)) + print('filename_test_list: ', self.filename_test_list) + print('\tnum_seq:', len(self.test_sequence_names)) + + opt.train_sequence_names = self.train_sequence_names + opt.val_sequence_names = self.val_sequence_names + opt.test_sequence_names = self.test_sequence_names + + # search mapping from val, test to train sequences that are used as targets + self.val_sequence_targets = [] + for i in range(0,len(self.val_sequence_names)): + target_name = self.val_sequence_names[i][1] + target_id = -1 + for j in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[j] == target_name: + target_id = j + break + if target_id == -1: + print('Target sequence not in train set! ', target_name) + exit() + self.val_sequence_targets.append(target_id) + + self.test_sequence_targets = [] + for i in range(0,len(self.test_sequence_names)): + target_name = self.test_sequence_names[i][1] + target_id = -1 + for j in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[j] == target_name: + target_id = j + break + if target_id == -1: + print('Target sequence not in train set! ', target_name) + exit() + self.test_sequence_targets.append(target_id) + print('test: ', self.test_sequence_names[i]) + print('\t target:', target_id) + + # store len values + opt.nTrainObjects = len(self.train_sequence_names) + opt.nValObjects = len(self.val_sequence_names) + opt.nTestObjects = len(self.test_sequence_names) + + ################################################ + ################################################ + ################################################ + + # prepare dataloader paths / data + self.audio_feature_dir = [] + self.image_dir = [] + self.uvs_dir = [] + self.audio_ids = [] + self.image_ids = [] + self.intrinsics = [] + self.extrinsics = [] + self.expressions = [] + self.identities = [] + self.target_id = [] + self.n_frames_total = 0 + + if opt.phase == 'train': + self.sequence_names = self.train_sequence_names + for i in range(0,len(self.train_sequence_names)): + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[i]) + audio_feature_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'uvs') + print('load train sequence:', self.train_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + + audio_ids = make_ids(make_dataset(audio_feature_dir), dataroot) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(dataroot) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(i) + + self.n_frames_total += min_len + elif opt.phase == 'val': + for i in range(0,len(self.val_sequence_names)): + target_id = self.val_sequence_targets[i] + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[target_id]) + audio_feature_dir = os.path.join(opt.dataroot, self.val_sequence_names[i][0], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'uvs') + print('load val sequence:', self.val_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + audio_ids = make_ids(make_dataset(audio_feature_dir), os.path.join(opt.dataroot, self.val_sequence_names[i][0])) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(os.path.join(opt.dataroot, self.val_sequence_names[i][0])) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(target_id) + + self.n_frames_total += min_len + else: # test + for i in range(0,len(self.test_sequence_names)): + target_id = self.test_sequence_targets[i] + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[target_id]) + audio_feature_dir = os.path.join(opt.dataroot, self.test_sequence_names[i][0], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'uvs') + print('load test sequence:', self.test_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + audio_ids = make_ids(make_dataset(audio_feature_dir), os.path.join(opt.dataroot, self.test_sequence_names[i][0])) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(os.path.join(opt.dataroot, self.test_sequence_names[i][0])) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(target_id) + + self.n_frames_total += min_len + + + print('frames_total:', self.n_frames_total) + + + #global_target_ids = [] + #for i in range(0,len(self.audio_ids)): + # for j in range(0,len(self.audio_ids[i])): + # global_target_ids.append(self.target_id[i]) + #global_target_ids=np.array(global_target_ids) + #self.weights = np.where(global_target_ids==2, 1.0 * np.ones((self.n_frames_total)), 0.01 * np.ones((self.n_frames_total)) ) + self.weights = [] + for i in range(0,len(self.audio_ids)): + l = len(self.audio_ids[i]) + for j in range(0,l): + self.weights.append(1.0 / l) + self.weights = np.array(self.weights) + + assert(opt.resize_or_crop == 'resize_and_crop') + + # mapping global to internal + self.mapping_global2internal = [] + self.mapping_global2internal_offset = [] + self.dsf = [] + offset = 0 + with progressbar.ProgressBar(max_value=self.n_frames_total) as bar: + for i in range(0,len(self.audio_ids)): + l = len(self.audio_ids[i]) + dsf_seq = [] + for k in range(0,l): + self.mapping_global2internal.append(i) + self.mapping_global2internal_offset.append(offset) + dsf_fname = os.path.join(self.audio_feature_dir[i], str(self.audio_ids[i][k]) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq.append(dsf_np.astype(np.float32)) + bar.update(offset + k) + self.dsf.append(dsf_seq) + offset += l + + + def getSampleWeights(self): + return self.weights + + def getitem(self, global_index): + + # select sequence + internal_sequence_id = self.mapping_global2internal[global_index] + sum_frames = self.mapping_global2internal_offset[global_index] + + # select frame from sequence + index = (global_index-sum_frames) % len(self.audio_ids[internal_sequence_id]) + + # get data ids + audio_id = self.audio_ids[internal_sequence_id][index] + image_id = self.image_ids[internal_sequence_id][index] + + #print('GET ITEM: ', index) + #img_path = self.frame_paths[sequence_id][index] + + # intrinsics and extrinsics + intrinsics = self.intrinsics[internal_sequence_id] + extrinsics = self.extrinsics[internal_sequence_id][image_id] + + # expressions + expressions = np.asarray(self.expressions[internal_sequence_id][audio_id], dtype=np.float32) + #print('expressions:', expressions.shape) + expressions[32] *= 0.0 # remove eye brow movements + expressions[41] *= 0.0 # remove eye brow movements + expressions[71:75] *= 0.0 # remove eye brow movements + expressions = torch.tensor(expressions) + + # identity + identity = torch.tensor(self.identities[internal_sequence_id][image_id]) + target_id = self.target_id[internal_sequence_id] # sequence id refers to the target sequence (of the training corpus) + + # load deepspeech feature + #print('audio_id', audio_id) + dsf_fname = os.path.join(self.audio_feature_dir[internal_sequence_id], str(audio_id) + '.deepspeech.npy') + + dsf_np = self.dsf[internal_sequence_id][index] + dsf = transforms.ToTensor()(dsf_np) + + # load sequence data if necessary + if self.opt.look_ahead:# use prev and following frame infos + r = self.opt.seq_len//2 + for i in range(1,r): # prev frames + index_seq = index - i + if index_seq < 0: index_seq = 0 + dsf_np = self.dsf[internal_sequence_id][index_seq] + dsf_seq = transforms.ToTensor()(dsf_np) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + for i in range(1,self.opt.seq_len - r + 1): # following frames + index_seq = index + i + max_idx = len(self.audio_ids[internal_sequence_id])-1 + if index_seq > max_idx: index_seq = max_idx + dsf_np = self.dsf[internal_sequence_id][index_seq] + dsf_seq = transforms.ToTensor()(dsf_np) # 1 x 16 x 29 + dsf = torch.cat([dsf, dsf_seq], 0) # seq_len x 16 x 29 + # note the ordering [old ... current ... future] + else: + for i in range(1,self.opt.seq_len): + index_seq = index - i + if index_seq < 0: index_seq = 0 + dsf_np = self.dsf[internal_sequence_id][index_seq] + dsf_seq = transforms.ToTensor()(dsf_np) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + #weight = 1.0 / len(self.audio_feature_dir[internal_sequence_id]) + weight = self.weights[global_index] + + return {'paths': dsf_fname, #img_path, + 'intrinsics': np.array(intrinsics), + 'extrinsics': np.array(extrinsics), + 'expressions': expressions, + 'identity': identity, + 'audio_deepspeech': dsf, # deepspeech feature + 'target_id':target_id, + 'internal_id':internal_sequence_id, + + 'weight': np.array([weight]).astype(np.float32)} + + + def __getitem__(self, global_index): + # select frame from sequence + index = global_index + current = self.getitem(index) + prv = self.getitem(max(index-1, 0)) + nxt = self.getitem(min(index+1, self.n_frames_total-1)) + + return { + 'paths': current['paths'], #img_path, + 'target_id': current['target_id'], + 'internal_id': current['internal_id'], + 'weight': current['weight'], + 'identity': current['identity'], + 'intrinsics': current['intrinsics'], + + 'extrinsics': current['extrinsics'], + 'expressions': current['expressions'], + 'audio_deepspeech': current['audio_deepspeech'], + + 'extrinsics_prv': prv['extrinsics'], + 'expressions_prv': prv['expressions'], + 'audio_deepspeech_prv': prv['audio_deepspeech'], + + 'extrinsics_nxt': nxt['extrinsics'], + 'expressions_nxt': nxt['expressions'], + 'audio_deepspeech_nxt': nxt['audio_deepspeech'], + } + + + def __len__(self): + return self.n_frames_total #len(self.frame_paths[0]) + + def name(self): + return 'MultiFaceAudioEQTmpCachedDataset' diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/multi_face_audio_eq_tmp_dataset.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/multi_face_audio_eq_tmp_dataset.py new file mode 100644 index 0000000..e42f155 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/data/multi_face_audio_eq_tmp_dataset.py @@ -0,0 +1,435 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from data.base_dataset import BaseDataset +from data.audio import Audio +#from data.image_folder import make_dataset +from PIL import Image + +#def make_dataset(dir): +# images = [] +# assert os.path.isdir(dir), '%s is not a valid directory' % dir +# for root, _, fnames in sorted(os.walk(dir)): +# for fname in fnames: +# if any(fname.endswith(extension) for extension in ['.bin', '.BIN']): +# path = os.path.join(root, fname) +# images.append(path) +# return sorted(images) + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.npy', '.NPY']): + #.deepspeech.npy + id_str = fname[:-15] #4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.deepspeech.npy' + path = os.path.join(root, fname) + images.append(path) + return images + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + id_str = fname[l+1:-15]#4] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + +def make_dataset_ids_png(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.png', '.png']): + id_str = fname[:-4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + return ids + +def load_intrinsics(input_dir): + file = open(input_dir+"/intrinsics.txt", "r") + intrinsics = [[(float(x) for x in line.split())] for line in file] + file.close() + intrinsics = list(intrinsics[0][0]) + return intrinsics + +def load_rigids(input_dir): + file = open(input_dir+"/rigid.txt", "r") + rigid_floats = [[float(x) for x in line.split()] for line in file] # note that it stores 5 lines per matrix (blank line) + file.close() + all_rigids = [ [rigid_floats[4*idx + 0],rigid_floats[4*idx + 1],rigid_floats[4*idx + 2],rigid_floats[4*idx + 3]] for idx in range(0, len(rigid_floats)//4) ] + return all_rigids + +def load_expressions(input_dir): + file = open(input_dir+"/expression.txt", "r") + expressions = [[float(x) for x in line.split()] for line in file] + file.close() + return expressions + +def load_identity(input_dir): + file = open(input_dir+"/identities.txt", "r") + identities = [[float(x) for x in line.split()] for line in file] + file.close() + return identities + + +class MultiFaceAudioEQTmpDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + self.root = opt.dataroot + + # read dataset file that contains the filenames for the train, val and test lists + file = open(self.root+"/dataset.txt", "r") + self.filename_train_list, self.filename_val_list, self.filename_test_list = [str(line) for line in file] + file.close() + if self.filename_train_list[-1] == '\n': self.filename_train_list = self.filename_train_list[:-1] + if self.filename_val_list[-1] == '\n': self.filename_val_list = self.filename_val_list[:-1] + if self.filename_test_list[-1] == '\n': self.filename_test_list = self.filename_test_list[:-1] + + + + # get list of train sequences + file = open(self.root+"/" + self.filename_train_list, "r") + self.train_sequence_names = [str(line) for line in file] + file.close() + for i in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[i][-1] == '\n': + self.train_sequence_names[i] = self.train_sequence_names[i][:-1] + + # get list of val sequences + file = open(self.root+"/" + self.filename_val_list, "r") + self.val_sequence_names = [[str(w) for w in line.split()] for line in file] + file.close() + for i in range(0,len(self.val_sequence_names)): + if self.val_sequence_names[i][0][-1] == '\n': self.val_sequence_names[i][0] = self.val_sequence_names[i][0][:-1] + if self.val_sequence_names[i][1][-1] == '\n': self.val_sequence_names[i][1] = self.val_sequence_names[i][1][:-1] + + # get list of test sequences + file = open(self.root+"/" + self.filename_test_list, "r") + self.test_sequence_names = [[str(w) for w in line.split()] for line in file] + if opt.output_audio_expressions: self.test_sequence_names = self.test_sequence_names[0:1] + file.close() + for i in range(0,len(self.test_sequence_names)): + if self.test_sequence_names[i][0][-1] == '\n': self.test_sequence_names[i][0] = self.test_sequence_names[i][0][:-1] + if self.test_sequence_names[i][1][-1] == '\n': self.test_sequence_names[i][1] = self.test_sequence_names[i][1][:-1] + + # print some stats + print('filename_train_list:', self.filename_train_list) + print('\tnum_seq:', len(self.train_sequence_names)) + print('filename_val_list: ', self.filename_val_list) + print('\tnum_seq:', len(self.val_sequence_names)) + print('filename_test_list: ', self.filename_test_list) + print('\tnum_seq:', len(self.test_sequence_names)) + + opt.train_sequence_names = self.train_sequence_names + opt.val_sequence_names = self.val_sequence_names + opt.test_sequence_names = self.test_sequence_names + + # search mapping from val, test to train sequences that are used as targets + self.val_sequence_targets = [] + for i in range(0,len(self.val_sequence_names)): + target_name = self.val_sequence_names[i][1] + target_id = -1 + for j in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[j] == target_name: + target_id = j + break + if target_id == -1: + print('Target sequence not in train set! ', target_name) + exit() + self.val_sequence_targets.append(target_id) + + self.test_sequence_targets = [] + for i in range(0,len(self.test_sequence_names)): + target_name = self.test_sequence_names[i][1] + target_id = -1 + for j in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[j] == target_name: + target_id = j + break + if target_id == -1: + print('Target sequence not in train set! ', target_name) + exit() + self.test_sequence_targets.append(target_id) + print('test: ', self.test_sequence_names[i]) + print('\t target:', target_id) + + # store len values + opt.nTrainObjects = len(self.train_sequence_names) + opt.nValObjects = len(self.val_sequence_names) + opt.nTestObjects = len(self.test_sequence_names) + + ################################################ + ################################################ + ################################################ + + # prepare dataloader paths / data + self.audio_feature_dir = [] + self.image_dir = [] + self.uvs_dir = [] + self.audio_ids = [] + self.image_ids = [] + self.intrinsics = [] + self.extrinsics = [] + self.expressions = [] + self.identities = [] + self.target_id = [] + self.n_frames_total = 0 + + if opt.phase == 'train': + self.sequence_names = self.train_sequence_names + for i in range(0,len(self.train_sequence_names)): + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[i]) + audio_feature_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'uvs') + print('load train sequence:', self.train_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + + audio_ids = make_ids(make_dataset(audio_feature_dir), dataroot) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(dataroot) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(i) + + self.n_frames_total += min_len + elif opt.phase == 'val': + for i in range(0,len(self.val_sequence_names)): + target_id = self.val_sequence_targets[i] + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[target_id]) + audio_feature_dir = os.path.join(opt.dataroot, self.val_sequence_names[i][0], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'uvs') + print('load val sequence:', self.val_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + audio_ids = make_ids(make_dataset(audio_feature_dir), os.path.join(opt.dataroot, self.val_sequence_names[i][0])) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(os.path.join(opt.dataroot, self.val_sequence_names[i][0])) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(target_id) + + self.n_frames_total += min_len + else: # test + for i in range(0,len(self.test_sequence_names)): + target_id = self.test_sequence_targets[i] + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[target_id]) + audio_feature_dir = os.path.join(opt.dataroot, self.test_sequence_names[i][0], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'uvs') + print('load test sequence:', self.test_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + audio_ids = make_ids(make_dataset(audio_feature_dir), os.path.join(opt.dataroot, self.test_sequence_names[i][0])) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(os.path.join(opt.dataroot, self.test_sequence_names[i][0])) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(target_id) + + self.n_frames_total += min_len + + + print('frames_total:', self.n_frames_total) + + + #global_target_ids = [] + #for i in range(0,len(self.audio_ids)): + # for j in range(0,len(self.audio_ids[i])): + # global_target_ids.append(self.target_id[i]) + #global_target_ids=np.array(global_target_ids) + #self.weights = np.where(global_target_ids==2, 1.0 * np.ones((self.n_frames_total)), 0.01 * np.ones((self.n_frames_total)) ) + self.weights = [] + for i in range(0,len(self.audio_ids)): + l = len(self.audio_ids[i]) + for j in range(0,l): + self.weights.append(1.0 / l) + self.weights = np.array(self.weights) + + assert(opt.resize_or_crop == 'resize_and_crop') + + def getSampleWeights(self): + return self.weights + + def getitem(self, global_index): + + # select sequence + internal_sequence_id = 0 + sum_frames = 0 + for i in range(0,len(self.audio_ids)): + l = len(self.audio_ids[i]) + if (global_index-sum_frames) < l: + internal_sequence_id = i + break + else: + sum_frames += len(self.audio_ids[i]) + + # select frame from sequence + index = (global_index-sum_frames) % len(self.audio_ids[internal_sequence_id]) + + # get data ids + audio_id = self.audio_ids[internal_sequence_id][index] + image_id = self.image_ids[internal_sequence_id][index] + + #print('GET ITEM: ', index) + #img_path = self.frame_paths[sequence_id][index] + + # intrinsics and extrinsics + intrinsics = self.intrinsics[internal_sequence_id] + extrinsics = self.extrinsics[internal_sequence_id][image_id] + + # expressions + expressions = np.asarray(self.expressions[internal_sequence_id][audio_id], dtype=np.float32) + #print('expressions:', expressions.shape) + expressions[32] *= 0.0 # remove eye brow movements + expressions[41] *= 0.0 # remove eye brow movements + expressions[71:75] *= 0.0 # remove eye brow movements + expressions = torch.tensor(expressions) + + + + # identity + identity = torch.tensor(self.identities[internal_sequence_id][image_id]) + target_id = self.target_id[internal_sequence_id] # sequence id refers to the target sequence (of the training corpus) + + # load deepspeech feature + #print('audio_id', audio_id) + dsf_fname = os.path.join(self.audio_feature_dir[internal_sequence_id], str(audio_id) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf = transforms.ToTensor()(dsf_np.astype(np.float32)) + + # load sequence data if necessary + last_valid_idx = audio_id + for i in range(1,self.opt.seq_len): + index_seq = index - i + if index_seq < 0: index_seq = 0 + audio_id_seq = self.audio_ids[internal_sequence_id][index_seq] + if audio_id_seq == audio_id - i: last_valid_idx = audio_id_seq + else: audio_id_seq = last_valid_idx + + dsf_fname = os.path.join(self.audio_feature_dir[internal_sequence_id], str(audio_id_seq) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + + + #weight = 1.0 / len(self.audio_feature_dir[internal_sequence_id]) + weight = self.weights[global_index] + + return {'paths': dsf_fname, #img_path, + 'intrinsics': np.array(intrinsics), + 'extrinsics': np.array(extrinsics), + 'expressions': expressions, + 'identity': identity, + 'audio_deepspeech': dsf, # deepspeech feature + 'target_id':target_id, + 'internal_id':internal_sequence_id, + + 'weight': np.array([weight]).astype(np.float32)} + + + def __getitem__(self, global_index): + # select frame from sequence + index = global_index + current = self.getitem(index) + prv = self.getitem(max(index-1, 0)) + nxt = self.getitem(min(index+1, self.n_frames_total-1)) + + return { + 'paths': current['paths'], #img_path, + 'target_id': current['target_id'], + 'internal_id': current['internal_id'], + 'weight': current['weight'], + 'identity': current['identity'], + 'intrinsics': current['intrinsics'], + + 'extrinsics': current['extrinsics'], + 'expressions': current['expressions'], + 'audio_deepspeech': current['audio_deepspeech'], + + 'extrinsics_prv': prv['extrinsics'], + 'expressions_prv': prv['expressions'], + 'audio_deepspeech_prv': prv['audio_deepspeech'], + + 'extrinsics_nxt': nxt['extrinsics'], + 'expressions_nxt': nxt['expressions'], + 'audio_deepspeech_nxt': nxt['audio_deepspeech'], + } + + + def __len__(self): + return self.n_frames_total #len(self.frame_paths[0]) + + def name(self): + return 'MultiFaceAudioEQTmpDataset' diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/models/__init__.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/models/__init__.py new file mode 100644 index 0000000..4d92091 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/models/__init__.py @@ -0,0 +1,39 @@ +import importlib +from models.base_model import BaseModel + + +def find_model_using_name(model_name): + # Given the option --model [modelname], + # the file "models/modelname_model.py" + # will be imported. + model_filename = "models." + model_name + "_model" + modellib = importlib.import_module(model_filename) + + # In the file, the class called ModelNameModel() will + # be instantiated. It has to be a subclass of BaseModel, + # and it is case-insensitive. + model = None + target_model_name = model_name.replace('_', '') + 'model' + for name, cls in modellib.__dict__.items(): + if name.lower() == target_model_name.lower() \ + and issubclass(cls, BaseModel): + model = cls + + if model is None: + print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name)) + exit(0) + + return model + + +def get_option_setter(model_name): + model_class = find_model_using_name(model_name) + return model_class.modify_commandline_options + + +def create_model(opt): + model = find_model_using_name(opt.model) + instance = model() + instance.initialize(opt) + print("model [%s] was created" % (instance.name())) + return instance diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/models/audio2ExpressionsAttentionTMP4_model.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/models/audio2ExpressionsAttentionTMP4_model.py new file mode 100644 index 0000000..7ba8d49 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/models/audio2ExpressionsAttentionTMP4_model.py @@ -0,0 +1,342 @@ +import torch +import torch.nn as nn +from util.image_pool import ImagePool +from .base_model import BaseModel +from . import networks + +from BaselModel.basel_model import * + +################ +### HELPER ### +################ + +INVALID_UV = -1.0 + + +class ExpressionEstimator_Attention(nn.Module): + def __init__(self, n_output_expressions, nIdentities, seq_len, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(ExpressionEstimator_Attention, self).__init__() + print('Estimator Attention') + ################################# + ######## audio net ########## + ################################# + self.seq_len = seq_len + + dropout_rate = 0.0 + if use_dropout == True: + #dropout_rate = 0.5 + dropout_rate = 0.25 + + self.convNet = nn.Sequential( + nn.Conv2d(29, 32, kernel_size=(3,1), stride=(2,1), padding=(1,0), bias=True), # 29 x 16 x 1 => 32 x 8 x 1 + nn.LeakyReLU(0.02, True), + nn.Conv2d(32, 32, kernel_size=(3,1), stride=(2,1), padding=(1,0), bias=True), # 32 x 8 x 1 => 32 x 4 x 1 + nn.LeakyReLU(0.02, True), + nn.Conv2d(32, 64, kernel_size=(3,1), stride=(2,1), padding=(1,0), bias=True), # 32 x 4 x 1 => 64 x 2 x 1 + nn.LeakyReLU(0.2, True), + nn.Conv2d(64, 64, kernel_size=(3,1), stride=(2,1), padding=(1,0), bias=True), # 64 x 2 x 1 => 64 x 1 x 1 + nn.LeakyReLU(0.2, True), + ) + + fullNet_input_size = 64 + + self.subspace_dim = 32 # number of audio expressions + print('fullNet_input_size: ', fullNet_input_size) + self.fullNet = nn.Sequential( + nn.Linear(in_features = fullNet_input_size, out_features=128, bias = True), + nn.LeakyReLU(0.02), + + nn.Linear(in_features = 128, out_features=64, bias = True), + nn.LeakyReLU(0.02), + + nn.Linear(in_features = 64, out_features=self.subspace_dim, bias = True), + nn.Tanh() + ) + + + # mapping from subspace to full expression space + self.register_parameter('mapping', torch.nn.Parameter(torch.randn(1, nIdentities, N_EXPRESSIONS, self.subspace_dim, requires_grad=True))) + + # attention + self.attentionConvNet = nn.Sequential( # b x subspace_dim x seq_len + nn.Conv1d(self.subspace_dim, 16, kernel_size=3, stride=1, padding=1, bias=True), + nn.LeakyReLU(0.02, True), + nn.Conv1d(16, 8, kernel_size=3, stride=1, padding=1, bias=True), + nn.LeakyReLU(0.02, True), + nn.Conv1d(8, 4, kernel_size=3, stride=1, padding=1, bias=True), + nn.LeakyReLU(0.02, True), + nn.Conv1d(4, 2, kernel_size=3, stride=1, padding=1, bias=True), + nn.LeakyReLU(0.02, True), + nn.Conv1d(2, 1, kernel_size=3, stride=1, padding=1, bias=True), + nn.LeakyReLU(0.02, True) + ) + self.attentionNet = nn.Sequential( + nn.Linear(in_features = self.seq_len, out_features=self.seq_len, bias = True), + nn.Softmax(dim=1) + ) + #self.hidden2subspace = nn.Linear(self.subspace_dim,self.subspace_dim) + + def forward_internal(self, audio_features_sequence, identity_id): + result_subspace, intermediate_expression = self.getAudioExpressions_internal(audio_features_sequence) + mapping = torch.index_select(self.mapping[0], dim = 0, index = identity_id) + result = 10.0 * torch.bmm(mapping, result_subspace)[:,:,0] + result_intermediate = 10.0 * torch.bmm(mapping, intermediate_expression)[:,:,0] + return result, result_intermediate + + def forward(self, audio_features_sequence, identity_id): + result_subspace = self.getAudioExpressions(audio_features_sequence) + mapping = torch.index_select(self.mapping[0], dim = 0, index = identity_id) + result = torch.bmm(mapping, result_subspace)[:,:,0] + return 10.0 * result + + def getAudioExpressions_internal(self, audio_features_sequence): + # audio_features_sequence: b x seq_len x 16 x 29 + b = audio_features_sequence.shape[0] # batchsize + audio_features_sequence = audio_features_sequence.view(b * self.seq_len, 1, 16, 29) # b * seq_len x 1 x 16 x 29 + audio_features_sequence = torch.transpose(audio_features_sequence, 1, 3) # b* seq_len x 29 x 16 x 1 + conv_res = self.convNet( audio_features_sequence ) + conv_res = torch.reshape( conv_res, (b * self.seq_len, 1, -1)) + result_subspace = self.fullNet(conv_res)[:,0,:] # b * seq_len x subspace_dim + result_subspace = result_subspace.view(b, self.seq_len, self.subspace_dim)# b x seq_len x subspace_dim + + ################# + ### attention ### + ################# + result_subspace_T = torch.transpose(result_subspace, 1, 2) # b x subspace_dim x seq_len + intermediate_expression = result_subspace_T[:,:,(self.seq_len // 2):(self.seq_len // 2) + 1] + att_conv_res = self.attentionConvNet(result_subspace_T) + #print('att_conv_res', att_conv_res.shape) + attention = self.attentionNet(att_conv_res.view(b, self.seq_len)).view(b, self.seq_len, 1) # b x seq_len x 1 + #print('attention', attention.shape) + # pooling along the sequence dimension + result_subspace = torch.bmm(result_subspace_T, attention) + #print('result_subspace', result_subspace.shape) + ### + + return result_subspace.view(b, self.subspace_dim, 1), intermediate_expression + + def getAudioExpressions(self, audio_features_sequence): + expr, _ = self.getAudioExpressions_internal(audio_features_sequence) + return expr + + def regularizer(self): + #reg = torch.norm(self.mapping) + reg_mapping = torch.mean(torch.abs(self.mapping)) + + # one could also enforce orthogonality here + + # s_browExpressions[] = { 32, 41, 71, 72, 73, 74, 75 }; + reg_eye_brow = torch.mean(torch.abs( self.mapping[0,:,[32, 41, 71, 72, 73, 74, 75],:] )) + #return 0.01 * reg_mapping + 1.0 * reg_eye_brow + return 0.0 * reg_mapping + + + +def define_ExpressionEstimator(estimatorType='estimatorDefault', nIdentities=1, seq_len=1, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]): + net = None + print('EstimatorType: ', estimatorType) + if estimatorType=='estimatorAttention': net = ExpressionEstimator_Attention(N_EXPRESSIONS,nIdentities, seq_len) + + return networks.init_net(net, init_type, init_gain, gpu_ids) + + +class Audio2ExpressionsAttentionTMP4Model(BaseModel): + def name(self): + return 'Audio2ExpressionsAttentionTMP4Model' + + @staticmethod + def modify_commandline_options(parser, is_train=True): + + # changing the default values to match the pix2pix paper + # (https://phillipi.github.io/pix2pix/) + #parser.set_defaults(norm='batch', netG='unet_256') + parser.set_defaults(norm='instance', netG='unet_256') + parser.set_defaults(dataset_mode='aligned') + if is_train: + parser.set_defaults(pool_size=0, no_lsgan=True) + parser.add_argument('--lambda_L1', type=float, default=100.0, help='weight for L1 loss') + + return parser + + def initialize(self, opt): + BaseModel.initialize(self, opt) + self.isTrain = opt.isTrain + + self.trainRenderer = not opt.fix_renderer + + # specify the training losses you want to print out. The program will call base_model.get_current_losses + self.loss_names = ['G_L1','G_L1_ABSOLUTE','G_L1_RELATIVE', 'G_Regularizer'] + + # specify the images you want to save/display. The program will call base_model.get_current_visuals + #self.visual_names = ['input_uv', 'fake', 'target'] + self.visual_names = ['zeros'] + self.zeros = torch.zeros(1,3,2,2) + + # specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks + if self.isTrain: + self.model_names = ['netG'] + else: # during test time, only load Gs + self.model_names = ['netG'] + + self.fake_expressions = None + self.fake_expressions_prv = None + self.fake_expressions_nxt = None + + self.morphable_model = MorphableModel() + self.mask = self.morphable_model.LoadMask() + + nIdentities=opt.nTrainObjects + + # load/define networks + self.netG = define_ExpressionEstimator(estimatorType=opt.rendererType, nIdentities=nIdentities, seq_len=opt.seq_len, gpu_ids=self.gpu_ids) + + if self.isTrain: + use_sigmoid = opt.no_lsgan + self.fake_AB_pool = ImagePool(opt.pool_size) + + # define loss functions + self.criterionL1 = torch.nn.L1Loss() + self.criterionL1Smooth = torch.nn.SmoothL1Loss() + self.criterionL2 = torch.nn.MSELoss() + + # initialize optimizers + self.optimizers = [] + self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999), weight_decay=0.0 ) + self.optimizers.append(self.optimizer_G) + + + def set_input(self, input): + self.image_paths = input['paths'] + + self.expressions = input['expressions'].cuda() + self.audio_features = input['audio_deepspeech'].cuda() # b x seq_len x 16 x 29 + + if self.isTrain: + self.expressions_prv = input['expressions_prv'].cuda() + self.audio_features_prv = input['audio_deepspeech_prv'].cuda() # b x seq_len x 16 x 29 + + self.expressions_nxt = input['expressions_nxt'].cuda() + self.audio_features_nxt = input['audio_deepspeech_nxt'].cuda() # b x seq_len x 16 x 29 + + self.target_id = input['target_id'].cuda() + + + def forward(self): + # estimate expressions + if self.opt.output_audio_expressions: #self.opt.dataset_mode=='audio': + self.fake_expressions = self.netG.getAudioExpressions(self.audio_features) + if self.isTrain: + self.fake_expressions_prv = self.netG.getAudioExpressions(self.audio_features_prv) + self.fake_expressions_nxt = self.netG.getAudioExpressions(self.audio_features_nxt) + else: + self.fake_expressions, self.fake_expressions_intermediate = self.netG.forward_internal(self.audio_features, self.target_id) + if self.isTrain: + self.fake_expressions_prv = self.netG(self.audio_features_prv, self.target_id) + self.fake_expressions_nxt = self.netG(self.audio_features_nxt, self.target_id) + + + + def backward_G(self, epoch): + + # Second, G(A) = B + #self.loss_G_L1 = self.criterionL1(self.fake_expressions, self.expressions) + + # difference in vertex space + mask = torch.cat([self.mask[:,None],self.mask[:,None],self.mask[:,None]], 1) + mask = mask + 0.1 * torch.ones_like(mask) # priority for the mask region, but other region should also be constrained + + # absolute (single timesteps) + diff_expression = self.fake_expressions - self.expressions + diff_vertices = self.morphable_model.compute_expression_delta(diff_expression) + + + diff_expression_intermediate = self.fake_expressions_intermediate - self.expressions + diff_vertices_intermediate = self.morphable_model.compute_expression_delta(diff_expression_intermediate) + + + diff_expression_prv = self.fake_expressions_prv - self.expressions_prv + diff_vertices_prv = self.morphable_model.compute_expression_delta(diff_expression_prv) + + diff_expression_nxt = self.fake_expressions_nxt - self.expressions_nxt + diff_vertices_nxt = self.morphable_model.compute_expression_delta(diff_expression_nxt) + + # relative (temporal 1 timestep) cur - nxt and prv - cur + diff_expression_tmp_cur_nxt = (self.fake_expressions - self.fake_expressions_nxt) - (self.expressions - self.expressions_nxt) + diff_vertices_tmp_cur_nxt = self.morphable_model.compute_expression_delta(diff_expression_tmp_cur_nxt) + diff_expression_tmp_prv_cur = (self.fake_expressions_prv - self.fake_expressions) - (self.expressions_prv - self.expressions) + diff_vertices_tmp_prv_cur = self.morphable_model.compute_expression_delta(diff_expression_tmp_prv_cur) + + # relative (temporal 2 timesteps) nxt - prv + diff_expression_tmp_nxt_prv = (self.fake_expressions_nxt - self.fake_expressions_prv) - (self.expressions_nxt - self.expressions_prv) + diff_vertices_tmp_nxt_prv = self.morphable_model.compute_expression_delta(diff_expression_tmp_nxt_prv) + + #print('mask: ', mask.shape) + #print('diff_vertices: ', diff_vertices.shape) + + self.loss_G_L1_ABSOLUTE = 0.0 + self.loss_G_L1_RELATIVE = 0.0 + if self.opt.lossType == 'L1': + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices)) # scale brings meters to millimeters + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices_prv)) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices_nxt)) + + self.loss_G_L1_ABSOLUTE += 3000.0 * torch.mean(mask * torch.abs(diff_vertices_intermediate)) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_cur_nxt)) + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_prv_cur)) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_nxt_prv)) + + elif self.opt.lossType == 'L2': + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * diff_vertices * diff_vertices) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * diff_vertices_prv * diff_vertices_prv) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * diff_vertices_nxt * diff_vertices_nxt) + + self.loss_G_L1_ABSOLUTE += 3000.0 * torch.mean(mask * diff_vertices_intermediate * diff_vertices_intermediate) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * diff_vertices_tmp_cur_nxt * diff_vertices_tmp_cur_nxt) + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * diff_vertices_tmp_prv_cur * diff_vertices_tmp_prv_cur) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * diff_vertices_tmp_nxt_prv * diff_vertices_tmp_nxt_prv) + + elif self.opt.lossType == 'RMS': + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.sqrt(torch.mean(mask * diff_vertices * diff_vertices)) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.sqrt(torch.mean(mask * diff_vertices_prv * diff_vertices_prv)) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.sqrt(torch.mean(mask * diff_vertices_nxt * diff_vertices_nxt)) + + self.loss_G_L1_ABSOLUTE += 3000.0 * torch.sqrt(torch.mean(mask * diff_vertices_intermediate * diff_vertices_intermediate)) + + + self.loss_G_L1_RELATIVE += 20000.0 * torch.sqrt(torch.mean(mask * diff_vertices_tmp_cur_nxt * diff_vertices_tmp_cur_nxt)) + self.loss_G_L1_RELATIVE += 20000.0 * torch.sqrt(torch.mean(mask * diff_vertices_tmp_prv_cur * diff_vertices_tmp_prv_cur)) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.sqrt(torch.mean(mask * diff_vertices_tmp_nxt_prv * diff_vertices_tmp_nxt_prv)) + + else: # L1 + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices)) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices_prv)) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices_nxt)) + + self.loss_G_L1_ABSOLUTE += 3000.0 * torch.mean(mask * torch.abs(diff_vertices_intermediate)) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_cur_nxt)) + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_prv_cur)) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_nxt_prv)) + + self.loss_G_L1 = self.loss_G_L1_ABSOLUTE + self.loss_G_L1_RELATIVE + self.loss_G_Regularizer = self.netG.regularizer() + + self.loss_G = self.loss_G_L1 + self.loss_G_Regularizer + + + self.loss_G.backward() + + def optimize_parameters(self, epoch_iter): + self.forward() + + # update Generator + self.optimizer_G.zero_grad() + self.backward_G(epoch_iter) + self.optimizer_G.step() + diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/models/base_model.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/models/base_model.py new file mode 100644 index 0000000..04489f6 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/models/base_model.py @@ -0,0 +1,222 @@ +import os +import torch +from collections import OrderedDict +from . import networks +import numpy as np +from PIL import Image + +def save_tensor_image(input_image, image_path): + if isinstance(input_image, torch.Tensor): + image_tensor = input_image.data + image_numpy = image_tensor[0].cpu().float().numpy() + if image_numpy.shape[0] == 1: + image_numpy = np.tile(image_numpy, (3, 1, 1)) + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 + else: + image_numpy = input_image + image_numpy = image_numpy.astype(np.uint8) + image_pil = Image.fromarray(image_numpy) + image_pil.save(image_path) + +class BaseModel(): + + # modify parser to add command line options, + # and also change the default values if needed + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def name(self): + return 'BaseModel' + + def initialize(self, opt): + self.opt = opt + self.gpu_ids = opt.gpu_ids + self.isTrain = opt.isTrain + self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu') + self.load_dir = os.path.join(opt.checkpoints_dir, opt.name) + self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) + if opt.resize_or_crop != 'scale_width': + torch.backends.cudnn.benchmark = True + self.loss_names = [] + self.model_names = [] + self.visual_names = [] + self.image_paths = [] + + def set_input(self, input): + pass + + def forward(self): + pass + + # load and print networks; create schedulers + def setup(self, opt, parser=None): + if self.isTrain: + self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers] + if not self.isTrain or opt.continue_train: + load_suffix = 'iter_%d' % opt.load_iter if opt.load_iter > 0 else opt.epoch + self.load_networks(load_suffix) + self.print_networks(opt.verbose) + + + + # load specific moudles + def loadModules(self, opt, model_name, module_names): + for name in module_names: + if isinstance(name, str): + load_dir = os.path.join(opt.checkpoints_dir, model_name) + load_filename = 'latest_%s.pth' % (name) + load_path = os.path.join(load_dir, load_filename) + net = getattr(self, name) + if isinstance(net, torch.Tensor): + print('loading the tensor from %s' % load_path) + net_loaded = torch.load(load_path, map_location=str(self.device)) + net.copy_(net_loaded) + else: + # if isinstance(net, torch.nn.DataParallel): + # net = net.module + print('loading the module from %s' % load_path) + # if you are using PyTorch newer than 0.4 (e.g., built from + # GitHub source), you can remove str() on self.device + state_dict = torch.load(load_path, map_location=str(self.device)) + if hasattr(state_dict, '_metadata'): + del state_dict._metadata + + # patch InstanceNorm checkpoints prior to 0.4 + for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop + self.__patch_instance_norm_state_dict(state_dict, net, key.split('.')) + net.load_state_dict(state_dict) + + + + + # make models eval mode during test time + def eval(self): + for name in self.model_names: + if isinstance(name, str): + net = getattr(self, name) + net.eval() + + # used in test time, wrapping `forward` in no_grad() so we don't save + # intermediate steps for backprop + def test(self): + with torch.no_grad(): + self.forward() + + # get image paths + def get_image_paths(self): + return self.image_paths + + def optimize_parameters(self): + pass + + # update learning rate (called once every epoch) + def update_learning_rate(self): + for scheduler in self.schedulers: + scheduler.step() + lr = self.optimizers[0].param_groups[0]['lr'] + print('learning rate = %.7f' % lr) + + # return visualization images. train.py will display these images, and save the images to a html + def get_current_visuals(self): + visual_ret = OrderedDict() + for name in self.visual_names: + if isinstance(name, str): + visual_ret[name] = getattr(self, name) + return visual_ret + + # return traning losses/errors. train.py will print out these errors as debugging information + def get_current_losses(self): + errors_ret = OrderedDict() + for name in self.loss_names: + if isinstance(name, str): + # float(...) works for both scalar tensor and float number + errors_ret[name] = float(getattr(self, 'loss_' + name)) + return errors_ret + + # save models to the disk + def save_networks(self, epoch): + for name in self.model_names: + if isinstance(name, str): + save_filename = '%s_%s.pth' % (epoch, name) + save_path = os.path.join(self.save_dir, save_filename) + net = getattr(self, name) + + if isinstance(net, torch.Tensor): + #torch.save(net.state_dict(), save_path) + torch.save(net, save_path) + for i in range(0, list(net.size())[0]): + save_tensor_image(net[i:i+1,0:3,:,:], save_path+str(i)+'.png') + else: + if len(self.gpu_ids) > 0 and torch.cuda.is_available(): + #torch.save(net.module.cpu().state_dict(), save_path) # << original + torch.save(net.cpu().state_dict(), save_path) + net.cuda(self.gpu_ids[0]) + else: + torch.save(net.cpu().state_dict(), save_path) + + def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0): + key = keys[i] + if i + 1 == len(keys): # at the end, pointing to a parameter/buffer + if module.__class__.__name__.startswith('InstanceNorm') and \ + (key == 'running_mean' or key == 'running_var'): + if getattr(module, key) is None: + state_dict.pop('.'.join(keys)) + if module.__class__.__name__.startswith('InstanceNorm') and \ + (key == 'num_batches_tracked'): + state_dict.pop('.'.join(keys)) + else: + self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1) + + # load models from the disk + def load_networks(self, epoch): + for name in self.model_names: + if isinstance(name, str): + load_filename = '%s_%s.pth' % (epoch, name) + load_path = os.path.join(self.load_dir, load_filename) + net = getattr(self, name) + if isinstance(net, torch.Tensor): + print('loading the tensor from %s' % load_path) + net_loaded = torch.load(load_path, map_location=str(self.device)) + net.copy_(net_loaded) + else: + # if isinstance(net, torch.nn.DataParallel): + # net = net.module + print('loading the module from %s' % load_path) + # if you are using PyTorch newer than 0.4 (e.g., built from + # GitHub source), you can remove str() on self.device + state_dict = torch.load(load_path, map_location=str(self.device)) + if hasattr(state_dict, '_metadata'): + del state_dict._metadata + + # patch InstanceNorm checkpoints prior to 0.4 + for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop + self.__patch_instance_norm_state_dict(state_dict, net, key.split('.')) + net.load_state_dict(state_dict) + + # print network information + def print_networks(self, verbose): + print('---------- Networks initialized -------------') + for name in self.model_names: + if isinstance(name, str): + net = getattr(self, name) + if isinstance(net, torch.Tensor): + num_params = net.numel() + print('[Tensor %s] Total number of parameters : %.3f M' % (name, num_params / 1e6)) + else: + num_params = 0 + for param in net.parameters(): + num_params += param.numel() + if verbose: + print(net) + print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6)) + print('-----------------------------------------------') + + # set requies_grad=False to avoid computation + def set_requires_grad(self, nets, requires_grad=False): + if not isinstance(nets, list): + nets = [nets] + for net in nets: + if net is not None: + for param in net.parameters(): + param.requires_grad = requires_grad diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/models/networks.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/models/networks.py new file mode 100644 index 0000000..a8c6505 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/models/networks.py @@ -0,0 +1,383 @@ +import torch +import torch.nn as nn +from torch.nn import init +import functools +from torch.optim import lr_scheduler + +############################################################################### +# Helper Functions +############################################################################### + + +def get_norm_layer(norm_type='instance'): + if norm_type == 'batch': + norm_layer = functools.partial(nn.BatchNorm2d, affine=True) + elif norm_type == 'instance': + norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False) + elif norm_type == 'none': + norm_layer = None + else: + raise NotImplementedError('normalization layer [%s] is not found' % norm_type) + return norm_layer + + +def get_scheduler(optimizer, opt): + if opt.lr_policy == 'lambda': + def lambda_rule(epoch): + lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.niter) / float(opt.niter_decay + 1) + return lr_l + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule) + elif opt.lr_policy == 'step': + scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1) + elif opt.lr_policy == 'plateau': + scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5) + elif opt.lr_policy == 'cosine': + scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.niter, eta_min=0) + else: + return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy) + return scheduler + + +def init_weights(net, init_type='normal', gain=0.02): + def init_func(m): + classname = m.__class__.__name__ + if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): + if init_type == 'normal': + init.normal_(m.weight.data, 0.0, gain) + elif init_type == 'xavier': + init.xavier_normal_(m.weight.data, gain=gain) + elif init_type == 'kaiming': + init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') + elif init_type == 'orthogonal': + init.orthogonal_(m.weight.data, gain=gain) + else: + raise NotImplementedError('initialization method [%s] is not implemented' % init_type) + if hasattr(m, 'bias') and m.bias is not None: + init.constant_(m.bias.data, 0.0) + elif classname.find('BatchNorm2d') != -1: + init.normal_(m.weight.data, 1.0, gain) + init.constant_(m.bias.data, 0.0) + + print('initialize network with %s' % init_type) + net.apply(init_func) + + +def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]): + if len(gpu_ids) > 0: + assert(torch.cuda.is_available()) + net.to(gpu_ids[0]) + #net = torch.nn.DataParallel(net, gpu_ids) + init_weights(net, init_type, gain=init_gain) + return net + + +def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]): + net = None + norm_layer = get_norm_layer(norm_type=norm) + + if netG == 'resnet_9blocks': + net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9) + elif netG == 'resnet_6blocks': + net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6) + elif netG == 'unet_128': + net = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout) + elif netG == 'unet_256': + net = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout) + else: + raise NotImplementedError('Generator model name [%s] is not recognized' % netG) + return init_net(net, init_type, init_gain, gpu_ids) + + +def define_D(input_nc, ndf, netD, + n_layers_D=3, norm='batch', use_sigmoid=False, init_type='normal', init_gain=0.02, gpu_ids=[]): + net = None + norm_layer = get_norm_layer(norm_type=norm) + + if netD == 'basic': + net = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer, use_sigmoid=use_sigmoid) + elif netD == 'n_layers': + net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer, use_sigmoid=use_sigmoid) + elif netD == 'pixel': + net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer, use_sigmoid=use_sigmoid) + else: + raise NotImplementedError('Discriminator model name [%s] is not recognized' % net) + return init_net(net, init_type, init_gain, gpu_ids) + + +############################################################################## +# Classes +############################################################################## + + +# Defines the GAN loss which uses either LSGAN or the regular GAN. +# When LSGAN is used, it is basically same as MSELoss, +# but it abstracts away the need to create the target label tensor +# that has the same size as the input +class GANLoss(nn.Module): + def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0): + super(GANLoss, self).__init__() + self.register_buffer('real_label', torch.tensor(target_real_label)) + self.register_buffer('fake_label', torch.tensor(target_fake_label)) + if use_lsgan: + self.loss = nn.MSELoss() + else: + self.loss = nn.BCELoss() + + def get_target_tensor(self, input, target_is_real): + if target_is_real: + target_tensor = self.real_label + else: + target_tensor = self.fake_label + return target_tensor.expand_as(input) + + def __call__(self, input, target_is_real): + target_tensor = self.get_target_tensor(input, target_is_real) + return self.loss(input, target_tensor) + + +# Defines the generator that consists of Resnet blocks between a few +# downsampling/upsampling operations. +# Code and idea originally from Justin Johnson's architecture. +# https://github.com/jcjohnson/fast-neural-style/ +class ResnetGenerator(nn.Module): + def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'): + assert(n_blocks >= 0) + super(ResnetGenerator, self).__init__() + self.input_nc = input_nc + self.output_nc = output_nc + self.ngf = ngf + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + + model = [nn.ReflectionPad2d(3), + nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, + bias=use_bias), + norm_layer(ngf), + nn.ReLU(True)] + + n_downsampling = 2 + for i in range(n_downsampling): + mult = 2**i + model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, + stride=2, padding=1, bias=use_bias), + norm_layer(ngf * mult * 2), + nn.ReLU(True)] + + mult = 2**n_downsampling + for i in range(n_blocks): + model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)] + + for i in range(n_downsampling): + mult = 2**(n_downsampling - i) + model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), + kernel_size=3, stride=2, + padding=1, output_padding=1, + bias=use_bias), + norm_layer(int(ngf * mult / 2)), + nn.ReLU(True)] + model += [nn.ReflectionPad2d(3)] + model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] + model += [nn.Tanh()] + + self.model = nn.Sequential(*model) + + def forward(self, input): + return self.model(input) + + +# Define a resnet block +class ResnetBlock(nn.Module): + def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias): + super(ResnetBlock, self).__init__() + self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias) + + def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias): + conv_block = [] + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(1)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(1)] + elif padding_type == 'zero': + p = 1 + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + + conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), + norm_layer(dim), + nn.ReLU(True)] + if use_dropout: + conv_block += [nn.Dropout(0.5)] + + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(1)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(1)] + elif padding_type == 'zero': + p = 1 + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), + norm_layer(dim)] + + return nn.Sequential(*conv_block) + + def forward(self, x): + out = x + self.conv_block(x) + return out + + +# Defines the Unet generator. +# |num_downs|: number of downsamplings in UNet. For example, +# if |num_downs| == 7, image of size 128x128 will become of size 1x1 +# at the bottleneck +class UnetGenerator(nn.Module): + def __init__(self, input_nc, output_nc, num_downs, ngf=64, + norm_layer=nn.BatchNorm2d, use_dropout=False): + super(UnetGenerator, self).__init__() + + # construct unet structure + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) + for i in range(num_downs - 5): + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) + unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) + + self.model = unet_block + + def forward(self, input): + return self.model(input) + + +# Defines the submodule with skip connection. +# X -------------------identity---------------------- X +# |-- downsampling -- |submodule| -- upsampling --| +class UnetSkipConnectionBlock(nn.Module): + def __init__(self, outer_nc, inner_nc, input_nc=None, + submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(UnetSkipConnectionBlock, self).__init__() + self.outermost = outermost + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + if input_nc is None: + input_nc = outer_nc + downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, + stride=2, padding=1, bias=use_bias) + downrelu = nn.LeakyReLU(0.2, True) + downnorm = norm_layer(inner_nc) + uprelu = nn.ReLU(True) + upnorm = norm_layer(outer_nc) + + if outermost: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1) + down = [downconv] + up = [uprelu, upconv, nn.Tanh()] + model = down + [submodule] + up + elif innermost: + upconv = nn.ConvTranspose2d(inner_nc, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv] + up = [uprelu, upconv, upnorm] + model = down + up + else: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv, downnorm] + up = [uprelu, upconv, upnorm] + + if use_dropout: + model = down + [submodule] + up + [nn.Dropout(0.5)] + else: + model = down + [submodule] + up + + self.model = nn.Sequential(*model) + + def forward(self, x): + if self.outermost: + return self.model(x) + else: + return torch.cat([x, self.model(x)], 1) + + +# Defines the PatchGAN discriminator with the specified arguments. +class NLayerDiscriminator(nn.Module): + def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False): + super(NLayerDiscriminator, self).__init__() + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + + kw = 4 + padw = 1 + sequence = [ + nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), + nn.LeakyReLU(0.2, True) + ] + + nf_mult = 1 + nf_mult_prev = 1 + for n in range(1, n_layers): + nf_mult_prev = nf_mult + nf_mult = min(2**n, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, + kernel_size=kw, stride=2, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + nf_mult_prev = nf_mult + nf_mult = min(2**n_layers, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, + kernel_size=kw, stride=1, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] + + if use_sigmoid: + sequence += [nn.Sigmoid()] + + self.model = nn.Sequential(*sequence) + + def forward(self, input): + return self.model(input) + + +class PixelDiscriminator(nn.Module): + def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d, use_sigmoid=False): + super(PixelDiscriminator, self).__init__() + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + + self.net = [ + nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0), + nn.LeakyReLU(0.2, True), + nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias), + norm_layer(ndf * 2), + nn.LeakyReLU(0.2, True), + nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)] + + if use_sigmoid: + self.net.append(nn.Sigmoid()) + + self.net = nn.Sequential(*self.net) + + def forward(self, input): + return self.net(input) diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/options/__init__.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/options/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/options/base_options.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/options/base_options.py new file mode 100644 index 0000000..b85c6ce --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/options/base_options.py @@ -0,0 +1,138 @@ +import argparse +import os +from util import util +import torch +import models +import data + + +class BaseOptions(): + def __init__(self): + self.initialized = False + + def initialize(self, parser): + parser.add_argument('--dataroot', required=True, help='path to images (should have subfolders trainA, trainB, valA, valB, etc)') + parser.add_argument('--batch_size', type=int, default=1, help='input batch size') + parser.add_argument('--seq_len', type=int, default=1, help='sequence length (if applicable)') + parser.add_argument('--fineSize', type=int, default=512, help='then crop to this size') + parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML') + parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels') + parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels') + parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer') + parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer') + parser.add_argument('--netD', type=str, default='basic', help='selects model to use for netD') + parser.add_argument('--netG', type=str, default='resnet_9blocks', help='selects model to use for netG') + parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers') + parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU') + parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models') + parser.add_argument('--renderer', type=str, default='no_renderer', help='name of the renderer to load the models from') + parser.add_argument('--fix_renderer', action='store_true', help='renderer is fixed') + parser.add_argument('--dataset_mode', type=str, default='aligned', help='chooses how datasets are loaded. [aligned | multi]') + parser.add_argument('--model', type=str, default='cycle_gan', help='chooses which model to use. cycle_gan, pix2pix, test') + parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA') + parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') + parser.add_argument('--load_iter', type=int, default='0', help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]') + parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data') + parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here') + parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization') + parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly') + parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator') + parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.') + parser.add_argument('--resize_or_crop', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop|none]') + parser.add_argument('--no_augmentation', action='store_true', help='if specified, no data augmentation') + #parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal|xavier|kaiming|orthogonal]') + parser.add_argument('--init_type', type=str, default='xavier', help='network initialization [normal|xavier|kaiming|orthogonal]') + parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.') + parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information') + parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{loadSize}') + parser.add_argument('--tex_dim', type=int, default=256, help='neural texture dimensions') + parser.add_argument('--tex_features_intermediate', type=int, default=16, help='# intermediate neural texture features when using dynamic textures') + parser.add_argument('--tex_features', type=int, default=16, help='# neural texture features') + parser.add_argument('--textureModel', type=str, default='DynamicNeuralTextureAudio', help='texture model') + parser.add_argument('--rendererType', type=str, default='UNET_5_level', help='neural renderer network') + parser.add_argument('--lossType', type=str, default='L1', help='loss type for the final output') + + parser.add_argument('--hierarchicalTex', action='store_true', help='if specified, hierachical neural textures are used') + + parser.add_argument('--output_audio_expressions', action='store_true', help='if specified, no sh layers are used') + + parser.add_argument('--erosionFactor', type=float, default=1.0, help='scaling factor for erosion of the background.') + + parser.add_argument('--audio_window_size', type=float, default=16, help='audio window size = #mel feature bins') + + parser.add_argument('--look_ahead', action='store_true', help='cache images in numpy format') + + parser.add_argument('--cached_images', action='store_true', help='cache images in numpy format') + + self.initialized = True + return parser + + def gather_options(self): + # initialize parser with basic options + if not self.initialized: + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser = self.initialize(parser) + + # get the basic options + opt, _ = parser.parse_known_args() + + # modify model-related parser options + model_name = opt.model + model_option_setter = models.get_option_setter(model_name) + parser = model_option_setter(parser, self.isTrain) + opt, _ = parser.parse_known_args() # parse again with the new defaults + + # modify dataset-related parser options + dataset_name = opt.dataset_mode + dataset_option_setter = data.get_option_setter(dataset_name) + parser = dataset_option_setter(parser, self.isTrain) + + self.parser = parser + + return parser.parse_args() + + def print_options(self, opt): + message = '' + message += '----------------- Options ---------------\n' + for k, v in sorted(vars(opt).items()): + comment = '' + default = self.parser.get_default(k) + if v != default: + comment = '\t[default: %s]' % str(default) + message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment) + message += '----------------- End -------------------' + print(message) + + # save to the disk + expr_dir = os.path.join(opt.checkpoints_dir, opt.name) + util.mkdirs(expr_dir) + file_name = os.path.join(expr_dir, 'opt.txt') + with open(file_name, 'wt') as opt_file: + opt_file.write(message) + opt_file.write('\n') + + def parse(self): + + opt = self.gather_options() + opt.isTrain = self.isTrain # train or test + + # process opt.suffix + if opt.suffix: + suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else '' + opt.name = opt.name + suffix + + self.print_options(opt) + + # set gpu ids + str_ids = opt.gpu_ids.split(',') + opt.gpu_ids = [] + for str_id in str_ids: + id = int(str_id) + if id >= 0: + opt.gpu_ids.append(id) + if len(opt.gpu_ids) > 0: + torch.cuda.set_device(opt.gpu_ids[0]) + + self.opt = opt + return self.opt diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/options/test_options.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/options/test_options.py new file mode 100644 index 0000000..d21da35 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/options/test_options.py @@ -0,0 +1,27 @@ +from .base_options import BaseOptions + + +class TestOptions(BaseOptions): + def initialize(self, parser): + parser = BaseOptions.initialize(self, parser) + parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.') + parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.') + parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images') + parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') + # Dropout and Batchnorm has different behavioir during training and test. + parser.add_argument('--eval', action='store_true', help='use eval mode during test time.') + parser.add_argument('--num_test', type=int, default=50, help='how many test images to run') + + parser.add_argument('--write_no_images', action='store_true', help='compute validation') + + parser.add_argument('--write_video', action='store_true', help='write video') + parser.add_argument('--video_fps', type=float, default=25.0, help='video fps') + + + parser.add_argument('--source_dir', type=str, default='./datasets/', help='loads source files (expressions, audio, uvs).') + + parser.set_defaults(model='test') + # To avoid cropping, the loadSize should be the same as fineSize + parser.set_defaults(loadSize=parser.get_default('fineSize')) + self.isTrain = False + return parser diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/options/train_options.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/options/train_options.py new file mode 100644 index 0000000..c91d50d --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/options/train_options.py @@ -0,0 +1,37 @@ +from .base_options import BaseOptions + + +class TrainOptions(BaseOptions): + def initialize(self, parser): + parser = BaseOptions.initialize(self, parser) + parser.add_argument('--display_freq', type=int, default=400, help='frequency of showing training results on screen') + parser.add_argument('--display_ncols', type=int, default=4, help='if positive, display all images in a single visdom web panel with certain number of images per row.') + parser.add_argument('--display_id', type=int, default=1, help='window id of the web display') + parser.add_argument('--display_server', type=str, default="http://localhost", help='visdom server of the web display') + parser.add_argument('--display_env', type=str, default='main', help='visdom display environment name (default is "main")') + parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display') + + parser.add_argument('--compute_val', action='store_true', help='compute validation') + parser.add_argument('--input_noise_augmentation', action='store_true', help='add input noise') + + parser.add_argument('--update_html_freq', type=int, default=1000, help='frequency of saving training results to html') + parser.add_argument('--print_freq', type=int, default=500, help='frequency of showing training results on console') + parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results') + parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs') + parser.add_argument('--save_by_iter', action='store_true', help='whether saves model by iteration') + + parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model') + parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by , +, ...') + parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') + parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate') + parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero') + parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam') + parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam') + parser.add_argument('--no_lsgan', action='store_true', help='do *not* use least square GAN, if false, use vanilla GAN') + parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images') + parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/') + parser.add_argument('--lr_policy', type=str, default='lambda', help='learning rate policy: lambda|step|plateau|cosine') + parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations') + + self.isTrain = True + return parser diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/options/transfer_options.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/options/transfer_options.py new file mode 100644 index 0000000..3fb0709 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/options/transfer_options.py @@ -0,0 +1,30 @@ +from .base_options import BaseOptions + + +class TransferOptions(BaseOptions): + def initialize(self, parser): + parser = BaseOptions.initialize(self, parser) + parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.') + parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.') + parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images') + parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') + # Dropout and Batchnorm has different behavioir during training and test. + parser.add_argument('--eval', action='store_true', help='use eval mode during test time.') + parser.add_argument('--num_test', type=int, default=50, help='how many test images to run') + + parser.add_argument('--write_no_images', action='store_true', help='compute validation') + + + parser.add_argument('--source_dir', type=str, default='./datasets/', help='loads source files (expressions, audio, uvs).') + + + + parser.add_argument('--source_actor', type=str, default='', help='source actor directory') + parser.add_argument('--target_actor', type=str, default='', help='target actor directory') + + + parser.set_defaults(model='test') + # To avoid cropping, the loadSize should be the same as fineSize + parser.set_defaults(loadSize=parser.get_default('fineSize')) + self.isTrain = False + return parser diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/req.txt b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/req.txt new file mode 100644 index 0000000..5252532 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/req.txt @@ -0,0 +1,60 @@ +audioread==2.1.6 +certifi==2018.11.29 +cffi==1.12.1 +chardet==3.0.4 +chumpy==0.68 +cycler==0.10.0 +decorator==4.3.2 +dominate==2.3.5 +EasyProcess==0.2.5 +freetype-py==2.1.0.post1 +future==0.17.1 +idna==2.8 +imageio==2.5.0 +joblib==0.13.2 +kiwisolver==1.0.1 +librosa==0.6.3 +llvmlite==0.27.1 +matplotlib==3.0.3 +mkl-fft==1.0.6 +mkl-random==1.0.1 +moderngl==5.5.0 +networkx==2.2 +neural-renderer-pytorch==1.1.3 +numba==0.42.1 +numpy==1.16.1 +olefile==0.46 +opencv-python==4.1.1.26 +OpenEXR==1.3.2 +Pillow==5.4.1 +progressbar2==3.46.1 +pycparser==2.19 +pyglet==1.4.0b1 +PyOpenGL==3.1.0 +pyparsing==2.3.1 +pyrender==0.1.24 +python-dateutil==2.8.0 +python-Levenshtein==0.12.0 +python-utils==2.3.0 +PyVirtualDisplay==0.2.1 +PyWavelets==1.0.3 +pyzmq==18.0.0 +requests==2.21.0 +resampy==0.2.1 +scikit-image==0.15.0 +scikit-learn==0.20.2 +scipy==1.2.1 +Shapely==1.6.4.post2 +six==1.12.0 +soft-renderer==1.0.0 +torch==1.0.1.post2 +torchaudio==0.2 +torchfile==0.1.0 +torchvision==0.2.1 +tornado==5.1.1 +tqdm==4.31.1 +trimesh==2.38.19 +urllib3==1.24.1 +visdom==0.1.8.8 +websocket-client==0.54.0 +wget==3.2 diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/requirements.txt b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/requirements.txt new file mode 100644 index 0000000..072d027 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/requirements.txt @@ -0,0 +1,4 @@ +torch>=0.4.0 +torchvision>=0.2.1 +dominate>=2.3.1 +visdom>=0.1.8.3 diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/test.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/test.py new file mode 100644 index 0000000..13c1bf7 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/test.py @@ -0,0 +1,169 @@ +import os +from options.test_options import TestOptions +from data import CreateDataLoader +from models import create_model +from util.visualizer import save_images +from util import html +from util import util +from scipy.misc import imresize + +import torch +import numpy as np +from PIL import Image +import time +import cv2 + + +def save_tensor_image(input_image, image_path): + if isinstance(input_image, torch.Tensor): + image_tensor = input_image.data + image_numpy = image_tensor[0].cpu().float().numpy() + if image_numpy.shape[0] == 1: + image_numpy = np.tile(image_numpy, (3, 1, 1)) + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 + else: + image_numpy = input_image + image_numpy = image_numpy.astype(np.uint8) + image_pil = Image.fromarray(image_numpy) + image_pil.save(image_path) + +if __name__ == '__main__': + opt = TestOptions().parse() + # hard-code some parameters for test + opt.num_threads = 1 # test code only supports num_threads = 1 + opt.batch_size = 1 # test code only supports batch_size = 1 + opt.serial_batches = True # no shuffle + opt.no_augmentation = True # no flip + opt.display_id = -1 # no visdom display + + data_loader = CreateDataLoader(opt) + dataset = data_loader.load_data() + dataset_size = len(data_loader) + print('#test images = %d' % dataset_size) + print('#train objects = %d' % opt.nTrainObjects) + print('#test objects = %d' % opt.nTestObjects) + + + print('>>> create model <<<') + model = create_model(opt) + print('>>> setup model <<<') + model.setup(opt) + #save_tensor_image(model.texture.data[0:1,0:3,:,:], 'load_test1.png') + + + sum_time = 0 + total_runs = dataset_size + warm_up = 50 + + # create a website + web_dirs = [] + webpages = [] + file_expressions = [] + file_fake_expressions = [] + file_rigids = [] + file_intrinsics = [] + file_identities = [] + video_writer = [] + + print('>>> create a websites and output directories <<<') + for i in range(0, opt.nTestObjects): + #web_dir = os.path.join(opt.results_dir, opt.name, '%s--%s__%s_%s' % (opt.test_sequence_names[i][0], opt.test_sequence_names[i][1], opt.phase, opt.epoch) ) + web_dir = os.path.join(opt.results_dir, opt.name, '%s--%s' % (opt.test_sequence_names[i][0], opt.test_sequence_names[i][1]) ) + webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch)) + + ## + output_file_expressions=None + output_file_fake_expressions=None + output_file_rigids = None + output_file_intrinsics = None + output_file_identities = None + if hasattr(model, 'fake_expressions'): + output_file_expressions=open(os.path.join(web_dir, 'gt_expressions.txt'), 'w') + output_file_fake_expressions=open(os.path.join(web_dir, 'expression.txt'), 'w') + output_file_rigids=open(os.path.join(web_dir, 'rigid.txt'), 'w') + output_file_intrinsics=open(os.path.join(web_dir, 'intrinsics.txt'), 'w') + output_file_identities=open(os.path.join(web_dir, 'identities.txt'), 'w') + ## + + web_dirs.append(web_dir) + webpages.append(webpage) + file_expressions.append(output_file_expressions) + file_fake_expressions.append(output_file_fake_expressions) + file_rigids.append(output_file_rigids) + file_intrinsics.append(output_file_intrinsics) + file_identities.append(output_file_identities) + + if opt.write_video: + writer = cv2.VideoWriter(web_dir+'.mp4', cv2.VideoWriter_fourcc(*"mp4v"), 25.0,(opt.display_winsize,opt.display_winsize)) + video_writer.append(writer) + + # test with eval mode. This only affects layers like batchnorm and dropout. + # pix2pix: we use batchnorm and dropout in the original pix2pix. You can experiment it with and without eval() mode. + # CycleGAN: It should not affect CycleGAN as CycleGAN uses instancenorm without dropout. + if opt.eval: + model.eval() + for i, data in enumerate(dataset): + #if i >= opt.num_test: + # break + model.set_input(data) + test_sequence_id = data['internal_id'].cpu() + + + torch.cuda.synchronize() + a = time.perf_counter() + + model.test() + + torch.cuda.synchronize() # added sync + b = time.perf_counter() + + if i > warm_up: # give torch some time to warm up + sum_time += ((b-a) * 1000) + + visuals = model.get_current_visuals() + img_path = model.get_image_paths() + if i % 5 == 0: + print('processing (%04d)-th image... %s' % (i, img_path)) + #if not hasattr(model, 'fake_expressions'): + if not opt.write_no_images: + save_images(webpages[test_sequence_id], visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize) + + if opt.write_video: + fake = visuals['fake'] + im = util.tensor2im(fake) + im = imresize(im, (opt.display_winsize,opt.display_winsize), interp='bicubic') + im = np.concatenate([im[:,:,2:3], im[:,:,1:2], im[:,:,0:1]],axis=2) + #video_writer[test_sequence_id].write(np.random.randint(0, 255, (opt.display_winsize,opt.display_winsize,3)).astype('uint8')) + video_writer[test_sequence_id].write(im.astype('uint8')) + + if hasattr(model, 'fake_expressions'): + #print('contains fake expressions') + np.savetxt(file_fake_expressions[test_sequence_id], model.fake_expressions.data.cpu().numpy(), delimiter=' ') + if hasattr(model, 'expressions'): + np.savetxt(file_expressions[test_sequence_id], model.expressions.data.cpu().numpy(), delimiter=' ') + + np.savetxt(file_rigids[test_sequence_id], data['extrinsics'][0].data.cpu().numpy(), delimiter=' ') + np.savetxt(file_intrinsics[test_sequence_id], data['intrinsics'].data.cpu().numpy(), delimiter=' ') + np.savetxt(file_identities[test_sequence_id], data['identity'].data.cpu().numpy(), delimiter=' ') + + + #if i < 50: + # if hasattr(model, 'face_model'): + # image_dir = webpage.get_image_dir() + # name = os.path.splitext(short_path)[0] + # + # filename_mesh = name + '.obj' + # #filename_tex = name + '_audio_texture.png' + # #filename_mat = name + '.mtl' + # #model.face_model.save_model_to_obj_file(image_dir, filename_mesh, filename_mat, filename_tex) + # model.face_model.save_model_to_obj_file(image_dir, filename_mesh) + + print('mean eval time (ms): ', (sum_time / (total_runs - warm_up))) + + # save the website + for webpage in webpages: + webpage.save() + + if opt.write_video: + for writer in video_writer: + writer.release() diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/test.sh b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/test.sh new file mode 100644 index 0000000..2a5d23a --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/test.sh @@ -0,0 +1,2 @@ +set -ex +python test.py --dataroot ./datasets/facades --name facades_pix2pix --model neuralRenderer --netG unet_256 --direction BtoA --dataset_mode aligned --norm batch diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/train.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/train.py new file mode 100644 index 0000000..dfdd454 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/train.py @@ -0,0 +1,114 @@ +import time +import copy +import torch +from options.train_options import TrainOptions +from data import CreateDataLoader +from models import create_model +from util.visualizer import Visualizer + +if __name__ == '__main__': + # training dataset + opt = TrainOptions().parse() + data_loader = CreateDataLoader(opt) + dataset = data_loader.load_data() + dataset_size = len(data_loader) + print('#training images = %d' % dataset_size) + print('#training objects = %d' % opt.nTrainObjects) + + ## validation dataset + if opt.compute_val: + opt_validation = copy.copy(opt) # create a clone + opt_validation.phase = 'val' + opt_validation.serial_batches = True + opt_validation.isTrain = False + data_loader_validation = CreateDataLoader(opt_validation) + dataset_validation = data_loader_validation.load_data() + dataset_size_validation = len(data_loader_validation) + print('#validation images = %d' % dataset_size_validation) + print('#validation objects = %d' % opt_validation.nValObjects) + + # model + model = create_model(opt) + model.setup(opt) + + if opt.renderer != 'no_renderer': + print('load renderer') + model.loadModules(opt, opt.renderer, ['netD', 'netG']) + + visualizer = Visualizer(opt) + total_steps = 0 + + for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1): + epoch_start_time = time.time() + iter_data_time = time.time() + epoch_iter = 0 # iterator within an epoch + + for i, data in enumerate(dataset): + iter_start_time = time.time() + if total_steps % opt.print_freq == 0: + t_data = iter_start_time - iter_data_time + visualizer.reset() + total_steps += opt.batch_size + epoch_iter += opt.batch_size + + model.set_input(data) + model.optimize_parameters(epoch) + + if total_steps % opt.display_freq == 0: + save_result = total_steps % opt.update_html_freq == 0 + visualizer.display_current_results(model.get_current_visuals(), epoch, save_result) + + if total_steps % opt.print_freq == 0: + losses = model.get_current_losses() + + if opt.compute_val: + validation_error = 0 + cnt = 0 + for i, data in enumerate(dataset_validation): + model.set_input(data) + model.forward() + model.backward_G(epoch) # be carefull with the gradients (are zeroed in the optimization step) + validation_error += model.loss_G.detach().cpu() + cnt += 1.0 + validation_error /= cnt + #print('Validation Error:', validation_error) + #visualizer.plot_current_validation_error(epoch, float(epoch_iter) / dataset_size, {'validation_error': validation_error}) + losses.update({'validation_error': validation_error}) + + t = (time.time() - iter_start_time) / opt.batch_size + visualizer.print_current_losses(epoch, epoch_iter, losses, t, t_data) + if opt.display_id > 0: + visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, opt, losses) + + if total_steps % opt.save_latest_freq == 0: + print('saving the latest model (epoch %d, total_steps %d)' % (epoch, total_steps)) + save_suffix = 'iter_%d' % total_steps if opt.save_by_iter else 'latest' + model.save_networks(save_suffix) + + iter_data_time = time.time() + + + if epoch % opt.save_epoch_freq == 0: + print('saving the model at the end of epoch %d, iters %d' % (epoch, total_steps)) + model.save_networks('latest') + model.save_networks(epoch) + + print('End of epoch %d / %d \t Time Taken: %d sec' % + (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time)) + model.update_learning_rate() + + # run validation + #if epoch % opt.save_epoch_freq == 0: + # if opt.compute_val: + # validation_error = 0 + # cnt = 0 + # for i, data in enumerate(dataset_validation): + # model.set_input(data) + # model.forward() + # model.backward_G(epoch) + # validation_error += model.loss_G.detach().cpu() + # cnt += 1.0 + # + # validation_error /= cnt + # print('Validation Error:', validation_error) + # visualizer.plot_current_validation_error(epoch, {'validation_error': validation_error}) diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/train_audio2expressionsAttentionTMP.sh b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/train_audio2expressionsAttentionTMP.sh new file mode 100644 index 0000000..520edae --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/train_audio2expressionsAttentionTMP.sh @@ -0,0 +1,58 @@ +set -ex +# . train_audio2expressionsAttentionTMP.sh & +GPUID=0 +DATASETS_DIR=./datasets +DATASET_MODE=multi_face_audio_eq_tmp_cached +OBJECT=ARD_ZDF + +# neural texture, not used here +TEX_DIM=128 +TEX_FEATURES=16 + +# loss +#LOSS=VGG +#LOSS=L1 +LOSS=RMS +#LOSS=L4 + +# models +MODEL=audio2ExpressionsAttentionTMP4 +RENDERER_TYPE=estimatorAttention + + +# optimizer parameters +#LR=0.00001 +LR=0.0001 + +#N_ITER=150 #50 #N_ITER=150 +#N_ITER_LR_DECAY=50 + +N_ITER=20 #50 #N_ITER=150 +N_ITER_LR_DECAY=30 + +BATCH_SIZE=16 +SEQ_LEN=8 + + +RENDERER=$OBJECT +EROSION=1.0 + +################################################################################ +################################################################################ +################################################################################ +DATE_WITH_TIME=`date "+%Y%m%d-%H%M%S"` +NAME=$MODEL-$RENDERER_TYPE-SL$SEQ_LEN-BS$BATCH_SIZE-$OBJECT-$DATASET_MODE-$LOSS-$DATE_WITH_TIME-look_ahead +DISPLAY_NAME=${MODEL}-$DATASET_MODE_${OBJECT}-${RENDERER_TYPE}-SL$SEQ_LEN-BS$BATCH_SIZE-${LOSS}-look_ahead + + +# training +# --input_noise_augmentation +python train.py --look_ahead --seq_len $SEQ_LEN --save_latest_freq 100000 --no_augmentation --compute_val --name $NAME --erosionFactor $EROSION --tex_dim $TEX_DIM --tex_features $TEX_FEATURES --rendererType $RENDERER_TYPE --lossType $LOSS --display_env $DISPLAY_NAME --niter $N_ITER --niter_decay $N_ITER_LR_DECAY --dataroot $DATASETS_DIR/$OBJECT --model $MODEL --netG unet_256 --lambda_L1 100 --dataset_mode $DATASET_MODE --no_lsgan --norm instance --pool_size 0 --gpu_ids $GPUID --lr $LR --batch_size $BATCH_SIZE + +# # testing +#EPOCH=latest +#python test.py --seq_len $SEQ_LEN --write_no_images --name $NAME --erosionFactor $EROSION --epoch $EPOCH --display_winsize 512 --tex_dim $TEX_DIM --tex_features $TEX_FEATURES --rendererType $RENDERER_TYPE --lossType $LOSS --dataroot $DATASETS_DIR/$OBJECT --model $MODEL --netG unet_256 --dataset_mode $DATASET_MODE --norm instance --gpu_ids $GPUID + +################################################################################ +################################################################################ +################################################################################ \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/util/__init__.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/util/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/util/get_data.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/util/get_data.py new file mode 100644 index 0000000..6325605 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/util/get_data.py @@ -0,0 +1,115 @@ +from __future__ import print_function +import os +import tarfile +import requests +from warnings import warn +from zipfile import ZipFile +from bs4 import BeautifulSoup +from os.path import abspath, isdir, join, basename + + +class GetData(object): + """ + + Download CycleGAN or Pix2Pix Data. + + Args: + technique : str + One of: 'cyclegan' or 'pix2pix'. + verbose : bool + If True, print additional information. + + Examples: + >>> from util.get_data import GetData + >>> gd = GetData(technique='cyclegan') + >>> new_data_path = gd.get(save_path='./datasets') # options will be displayed. + + """ + + def __init__(self, technique='cyclegan', verbose=True): + url_dict = { + 'pix2pix': 'https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets', + 'cyclegan': 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets' + } + self.url = url_dict.get(technique.lower()) + self._verbose = verbose + + def _print(self, text): + if self._verbose: + print(text) + + @staticmethod + def _get_options(r): + soup = BeautifulSoup(r.text, 'lxml') + options = [h.text for h in soup.find_all('a', href=True) + if h.text.endswith(('.zip', 'tar.gz'))] + return options + + def _present_options(self): + r = requests.get(self.url) + options = self._get_options(r) + print('Options:\n') + for i, o in enumerate(options): + print("{0}: {1}".format(i, o)) + choice = input("\nPlease enter the number of the " + "dataset above you wish to download:") + return options[int(choice)] + + def _download_data(self, dataset_url, save_path): + if not isdir(save_path): + os.makedirs(save_path) + + base = basename(dataset_url) + temp_save_path = join(save_path, base) + + with open(temp_save_path, "wb") as f: + r = requests.get(dataset_url) + f.write(r.content) + + if base.endswith('.tar.gz'): + obj = tarfile.open(temp_save_path) + elif base.endswith('.zip'): + obj = ZipFile(temp_save_path, 'r') + else: + raise ValueError("Unknown File Type: {0}.".format(base)) + + self._print("Unpacking Data...") + obj.extractall(save_path) + obj.close() + os.remove(temp_save_path) + + def get(self, save_path, dataset=None): + """ + + Download a dataset. + + Args: + save_path : str + A directory to save the data to. + dataset : str, optional + A specific dataset to download. + Note: this must include the file extension. + If None, options will be presented for you + to choose from. + + Returns: + save_path_full : str + The absolute path to the downloaded data. + + """ + if dataset is None: + selected_dataset = self._present_options() + else: + selected_dataset = dataset + + save_path_full = join(save_path, selected_dataset.split('.')[0]) + + if isdir(save_path_full): + warn("\n'{0}' already exists. Voiding Download.".format( + save_path_full)) + else: + self._print('Downloading Data...') + url = "{0}/{1}".format(self.url, selected_dataset) + self._download_data(url, save_path=save_path) + + return abspath(save_path_full) diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/util/html.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/util/html.py new file mode 100644 index 0000000..1e7aab9 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/util/html.py @@ -0,0 +1,64 @@ +import dominate +from dominate.tags import meta, h3, table, tr, td, p, a, img, br +import os + + +class HTML: + def __init__(self, web_dir, title, reflesh=0): + self.title = title + self.web_dir = web_dir + self.img_dir = os.path.join(self.web_dir, 'images') + if not os.path.exists(self.web_dir): + os.makedirs(self.web_dir) + if not os.path.exists(self.img_dir): + os.makedirs(self.img_dir) + # print(self.img_dir) + + self.doc = dominate.document(title=title) + if reflesh > 0: + with self.doc.head: + meta(http_equiv="reflesh", content=str(reflesh)) + + def get_image_dir(self): + return self.img_dir + + def add_header(self, str): + with self.doc: + h3(str) + + def add_table(self, border=1): + self.t = table(border=border, style="table-layout: fixed;") + self.doc.add(self.t) + + def add_images(self, ims, txts, links, width=400): + self.add_table() + with self.t: + with tr(): + for im, txt, link in zip(ims, txts, links): + with td(style="word-wrap: break-word;", halign="center", valign="top"): + with p(): + with a(href=os.path.join('images', link)): + img(style="width:%dpx" % width, src=os.path.join('images', im)) + br() + p(txt) + + def save(self): + html_file = '%s/index.html' % self.web_dir + f = open(html_file, 'wt') + f.write(self.doc.render()) + f.close() + + +if __name__ == '__main__': + html = HTML('web/', 'test_html') + html.add_header('hello world') + + ims = [] + txts = [] + links = [] + for n in range(4): + ims.append('image_%d.png' % n) + txts.append('text_%d' % n) + links.append('image_%d.png' % n) + html.add_images(ims, txts, links) + html.save() diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/util/image_pool.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/util/image_pool.py new file mode 100644 index 0000000..52413e0 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/util/image_pool.py @@ -0,0 +1,32 @@ +import random +import torch + + +class ImagePool(): + def __init__(self, pool_size): + self.pool_size = pool_size + if self.pool_size > 0: + self.num_imgs = 0 + self.images = [] + + def query(self, images): + if self.pool_size == 0: + return images + return_images = [] + for image in images: + image = torch.unsqueeze(image.data, 0) + if self.num_imgs < self.pool_size: + self.num_imgs = self.num_imgs + 1 + self.images.append(image) + return_images.append(image) + else: + p = random.uniform(0, 1) + if p > 0.5: + random_id = random.randint(0, self.pool_size - 1) # randint is inclusive + tmp = self.images[random_id].clone() + self.images[random_id] = image + return_images.append(tmp) + else: + return_images.append(image) + return_images = torch.cat(return_images, 0) + return return_images diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/util/util.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/util/util.py new file mode 100644 index 0000000..4f5ca3b --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/util/util.py @@ -0,0 +1,82 @@ +from __future__ import print_function +import torch +import numpy as np +from PIL import Image +import os +import sys +import array +import OpenEXR +import Imath + +def load_exr(image_path): + # Open the input file + file = OpenEXR.InputFile(image_path) + + # Compute the size + dw = file.header()['dataWindow'] + w, h = (dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1) + + # Read the three color channels as 32-bit floats + FLOAT = Imath.PixelType(Imath.PixelType.FLOAT) + #(R,G,B) = [np.array(array.array('f', file.channel(Chan, FLOAT)).tolist()).reshape((w, h, 1)) for Chan in ("R", "G", "B") ] + + (r, g, b) = file.channels("RGB") + R = np.array(array.array('f', r).tolist()).reshape((w, h, 1)) + G = np.array(array.array('f', g).tolist()).reshape((w, h, 1)) + B = np.array(array.array('f', b).tolist()).reshape((w, h, 1)) + + return np.concatenate((R, G, B), axis=2) + +# Converts a Tensor into an image array (numpy) +# |imtype|: the desired type of the converted numpy array +def tensor2im(input_image, imtype=np.uint8): + if isinstance(input_image, torch.Tensor): + input_image = torch.clamp(input_image, -1.0, 1.0) + image_tensor = input_image.data + else: + return input_image + image_numpy = image_tensor[0].cpu().float().numpy() + if image_numpy.shape[0] == 1: + image_numpy = np.tile(image_numpy, (3, 1, 1)) + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 + return image_numpy.astype(imtype) + + +def diagnose_network(net, name='network'): + mean = 0.0 + count = 0 + for param in net.parameters(): + if param.grad is not None: + mean += torch.mean(torch.abs(param.grad.data)) + count += 1 + if count > 0: + mean = mean / count + print(name) + print(mean) + + +def save_image(image_numpy, image_path): + image_pil = Image.fromarray(image_numpy) + image_pil.save(image_path) + +def print_numpy(x, val=True, shp=False): + x = x.astype(np.float64) + if shp: + print('shape,', x.shape) + if val: + x = x.flatten() + print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % ( + np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x))) + + +def mkdirs(paths): + if isinstance(paths, list) and not isinstance(paths, str): + for path in paths: + mkdir(path) + else: + mkdir(paths) + + +def mkdir(path): + if not os.path.exists(path): + os.makedirs(path) diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/util/visualizer.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/util/visualizer.py new file mode 100644 index 0000000..7cb7750 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/Training Code/util/visualizer.py @@ -0,0 +1,207 @@ +import numpy as np +import os +import sys +import ntpath +import time +from . import util +from . import html +#from scipy.misc import imresize +import cv2 + +if sys.version_info[0] == 2: + VisdomExceptionBase = Exception +else: + VisdomExceptionBase = ConnectionError + + +# save image to the disk +def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256): + image_dir = webpage.get_image_dir() + short_path = ntpath.basename(image_path[0]) + name = os.path.splitext(short_path)[0] + + webpage.add_header(name) + ims, txts, links = [], [], [] + + for label, im_data in visuals.items(): + im = util.tensor2im(im_data) + image_name = '%s_%s.png' % (name, label) + save_path = os.path.join(image_dir, image_name) + h, w, _ = im.shape + + height = int(width * h / float(w)) + #im = imresize(im, (height,width), interp='bicubic') + im = cv2.resize(src=im, dsize=(height,width), interpolation=cv2.INTER_CUBIC) + + #im = imresize(im, (height,widht), interp='bicubic') + #if aspect_ratio > 1.0: + # im = imresize(im, (h, int(w * aspect_ratio)), interp='bicubic') + #if aspect_ratio < 1.0: + # im = imresize(im, (int(h / aspect_ratio), w), interp='bicubic') + + util.save_image(im, save_path) + + ims.append(image_name) + txts.append(label) + links.append(image_name) + webpage.add_images(ims, txts, links, width=width) + + +class Visualizer(): + def __init__(self, opt): + self.display_id = opt.display_id + self.use_html = opt.isTrain and not opt.no_html + self.win_size = opt.display_winsize + self.name = opt.name + self.opt = opt + self.saved = False + if self.display_id > 0: + import visdom + self.ncols = opt.display_ncols + self.vis = visdom.Visdom(server=opt.display_server, port=opt.display_port, env=opt.display_env, raise_exceptions=True) + + if self.use_html: + self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web') + self.img_dir = os.path.join(self.web_dir, 'images') + print('create web directory %s...' % self.web_dir) + util.mkdirs([self.web_dir, self.img_dir]) + self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt') + with open(self.log_name, "a") as log_file: + now = time.strftime("%c") + log_file.write('================ Training Loss (%s) ================\n' % now) + + def reset(self): + self.saved = False + + def throw_visdom_connection_error(self): + print('\n\nCould not connect to Visdom server (https://github.com/facebookresearch/visdom) for displaying training progress.\nYou can suppress connection to Visdom using the option --display_id -1. To install visdom, run \n$ pip install visdom\n, and start the server by \n$ python -m visdom.server.\n\n') + exit(1) + + # |visuals|: dictionary of images to display or save + def display_current_results(self, visuals, epoch, save_result, aspect_ratio=1.0, width=256): + if self.display_id > 0: # show images in the browser + ncols = self.ncols + if ncols > 0: + ncols = min(ncols, len(visuals)) + h, w = next(iter(visuals.values())).shape[2:4] + height = int(width * h / float(w)) + h = height + w = width + table_css = """""" % (w, h) + title = self.name + label_html = '' + label_html_row = '' + images = [] + idx = 0 + for label, image in visuals.items(): + # + image_numpy = util.tensor2im(image) + #image_numpy = imresize(image_numpy, (h, w), interp='bicubic') + image_numpy = cv2.resize(src=image_numpy, dsize=(h, w), interpolation=cv2.INTER_CUBIC) + image_numpy = image_numpy.transpose([2, 0, 1]) + label_html_row += '%s' % label + images.append(image_numpy) + idx += 1 + if idx % ncols == 0: + label_html += '%s' % label_html_row + label_html_row = '' + white_image = np.ones_like(image_numpy) * 255 + while idx % ncols != 0: + images.append(white_image) + label_html_row += '' + idx += 1 + if label_html_row != '': + label_html += '%s' % label_html_row + # pane col = image row + try: + self.vis.images(images, nrow=ncols, win=self.display_id + 1, padding=2, opts=dict(title=title + ' images')) + label_html = '%s
' % label_html + self.vis.text(table_css + label_html, win=self.display_id + 2, + opts=dict(title=title + ' labels')) + except VisdomExceptionBase: + self.throw_visdom_connection_error() + + else: + idx = 1 + for label, image in visuals.items(): + image_numpy = util.tensor2im(image) + self.vis.image(image_numpy.transpose([2, 0, 1]), opts=dict(title=label), + win=self.display_id + idx) + idx += 1 + + if self.use_html and (save_result or not self.saved): # save images to a html file + self.saved = True + for label, image in visuals.items(): + image_numpy = util.tensor2im(image) + img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label)) + util.save_image(image_numpy, img_path) + # update website + webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, reflesh=1) + for n in range(epoch, 0, -1): + webpage.add_header('epoch [%d]' % n) + ims, txts, links = [], [], [] + + for label, image_numpy in visuals.items(): + image_numpy = util.tensor2im(image) + img_path = 'epoch%.3d_%s.png' % (n, label) + ims.append(img_path) + txts.append(label) + links.append(img_path) + webpage.add_images(ims, txts, links, width=self.win_size) + webpage.save() + + # losses: dictionary of error labels and values + def plot_current_losses(self, epoch, counter_ratio, opt, losses): + if not hasattr(self, 'plot_data'): + self.plot_data = {'X': [], 'Y': [], 'legend': list(losses.keys())} + self.plot_data['X'].append(epoch + counter_ratio) + self.plot_data['Y'].append([losses[k] for k in self.plot_data['legend']]) + try: + self.vis.line( + X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1), + Y=np.array(self.plot_data['Y']), + opts={ + 'title': self.name + ' loss over time', + 'legend': self.plot_data['legend'], + 'xlabel': 'epoch', + 'ylabel': 'loss'}, + win=self.display_id) + except VisdomExceptionBase: + self.throw_visdom_connection_error() + + # losses: same format as |losses| of plot_current_losses + def print_current_losses(self, epoch, i, losses, t, t_data): + message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, i, t, t_data) + for k, v in losses.items(): + message += '%s: %.3f ' % (k, v) + + print(message) + with open(self.log_name, "a") as log_file: + log_file.write('%s\n' % message) + + + + + + + # losses: dictionary of error labels and values + def plot_current_validation_error(self, epoch, counter_ratio, losses): + if not hasattr(self, 'plot_validation_data'): + self.plot_validation_data = {'X': [], 'Y': [], 'legend': list(losses.keys())} + self.plot_validation_data['X'].append(epoch + counter_ratio) + self.plot_validation_data['Y'].append([losses[k] for k in self.plot_validation_data['legend']]) + try: + self.vis.line( + X=np.stack([np.array(self.plot_validation_data['X'])] * len(self.plot_validation_data['legend']), 1), + Y=np.array(self.plot_validation_data['Y']), + opts={ + 'title': self.name + ' validation error over time', + 'legend': self.plot_validation_data['legend'], + 'xlabel': 'epoch', + 'ylabel': 'error'}, + win=self.display_id+1) + except VisdomExceptionBase: + self.throw_visdom_connection_error() \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/__init__.py b/NeuralVoicePuppetry/neural-code/Audio2ExpressionNet/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/DECA/FLAME2020.zip b/NeuralVoicePuppetry/neural-code/DECA/FLAME2020.zip new file mode 100644 index 0000000..3a00762 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/DECA/FLAME2020.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9bfa073763c38d02068e26f72818aec8401d5d306c56b42503af4d8bffac2225 +size 153808252 diff --git a/NeuralVoicePuppetry/neural-code/DECA/deca_model.tar b/NeuralVoicePuppetry/neural-code/DECA/deca_model.tar new file mode 100644 index 0000000..c04160f --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/DECA/deca_model.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e714ed293054cba5eea9c96bd3b6b57880074cd84b3fd00d606cbaf0bee7c5c2 +size 434142943 diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/.gitignore b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/.gitignore new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/BaselModel/__init__.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/BaselModel/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/BaselModel/basel_model.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/BaselModel/basel_model.py new file mode 100644 index 0000000..c84016d --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/BaselModel/basel_model.py @@ -0,0 +1,108 @@ +import os +import torch +import torch.nn as nn +import torchvision.transforms as transforms +import numpy as np + +# import soft_renderer as sr +import renderer as sr + +N_EXPRESSIONS=76 + +class MorphableModel(nn.Module): + def __init__(self, filename_average=''): + super(MorphableModel, self).__init__() + + print('Load Morphable Model (Basel)') + + #filename_mesh = os.path.join(opt.dataroot, opt.phase + '/average_model.obj') + filename_mesh = filename_average + if filename_average=='': + print('use default identity') + filename_mesh = './BaselModel/average.obj' + mesh = sr.Mesh.from_obj(filename_mesh, normalization=False, load_texture=True) + self.average_vertices = mesh.vertices[0] + self.faces = mesh.faces[0] + self.average_vertices = self.average_vertices[None, :, :] # [num_vertices, XYZ] -> [batch_size=1, num_vertices, XYZ] + self.faces = self.faces[None, :, :] # [num_faces, 3] -> [batch_size=1, num_faces, 3] + self.textures = mesh.textures + + self.num_vertices = self.average_vertices.shape[1] + self.num_faces = self.faces.shape[1] + print('vertices:', self.average_vertices.shape) + print('faces:', self.faces.shape) + + ## basis function + self.expression_basis = np.memmap('./BaselModel/ExpressionBasis.matrix', dtype='float32', mode='r').__array__()[1:] # first entry is the size + self.expression_basis = np.resize(self.expression_basis, (N_EXPRESSIONS, self.num_vertices, 4))[:,:,0:3] + self.expression_basis = torch.tensor(self.expression_basis.astype(np.float32)).cuda() # N_EXPRESSIONS x num_vertices x 3 + self.expression_basis = torch.transpose(self.expression_basis,0,2) # transpose for matmul + print('expression_basis', self.expression_basis.shape) + + + #texture_size = 2 + #self.textures = torch.ones(1, self.faces.shape[1], texture_size, texture_size, texture_size, 3, dtype=torch.float32).cuda() + #print('textures:', self.textures.shape) + + ## ## debug + ## zeroExpr = torch.zeros(1, N_EXPRESSIONS, dtype=torch.float32).cuda() + ## self.morph(zeroExpr) + ## self.save_model_to_obj_file('model_zero_expression.obj') + ## ## + ## onesExpr = torch.ones(1, N_EXPRESSIONS, dtype=torch.float32).cuda() + ## self.morph(onesExpr) + ## self.save_model_to_obj_file('model_ones_expression.obj') + ## exit() + ## ## + + # default expression + zeroExpr = torch.zeros(1, N_EXPRESSIONS, dtype=torch.float32).cuda() + self.morph(zeroExpr) + + + def save_model_to_obj_file(self, filename, mask=None): + faces_cpu = self.faces.detach().cpu().numpy() + vertices_cpu = self.vertices.detach().cpu().numpy() + + mask_cpu = None + if not type(mask) == type(None): + mask_cpu = mask.detach().cpu().numpy() + + f = open(filename, 'w') + if type(mask) == type(None): + for i in range(0, self.num_vertices): + f.write('v ' + str(vertices_cpu[0, i, 0]) + ' ' + str(vertices_cpu[0, i, 1]) + ' ' + str(vertices_cpu[0, i, 2]) + '\n') + else: + for i in range(0, self.num_vertices): + f.write('v ' + str(vertices_cpu[0, i, 0]) + ' ' + str(vertices_cpu[0, i, 1]) + ' ' + str(vertices_cpu[0, i, 2]) + ' ' + str(mask_cpu[i]) + ' ' + str(mask_cpu[i]) + ' ' + str(mask_cpu[i]) + ' 1'+ '\n') + + for i in range(0, self.num_faces): + f.write('f ' + str(faces_cpu[0, i, 0]+1) + '// ' + str(faces_cpu[0, i, 1]+1) + '// ' + str(faces_cpu[0, i, 2]+1) + '//\n') + + f.close() + + def compute_expression_delta(self, expressions): + return torch.transpose(torch.matmul(self.expression_basis, torch.transpose(expressions, 0,1)), 0, 2) # note that matmul wants to have this order: (a x b x c) x (c x m) => (a x b x m) + + def morph(self, expressions): + self.vertices = self.average_vertices + self.compute_expression_delta(expressions) + return self.vertices + + + + def LoadMask(self, filename=''): + if filename=='': + print('use default mask') + filename = './BaselModel/mask/defaultMask_mouth.obj' + + mask = np.zeros(self.num_vertices) + file = open(filename, 'r') + i=0 + for line in file: + if line[0] == 'v': + floats = [float(x) for x in line[1:].split()] + if floats[3] == 1.0 and floats[4] == 0.0 and floats[5] == 0.0: + mask[i] = 1.0 + i += 1 + file.close() + return torch.tensor(mask.astype(np.float32)).cuda() \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/ReadMe.md b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/ReadMe.md new file mode 100644 index 0000000..4165ff6 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/ReadMe.md @@ -0,0 +1,6 @@ +This code is used to train the audio to expression network. +-> train_audio2expressionsAttentionTMP.sh +You need to provide the training data that fits your face model. +The face model is defined in "BaselModel", you need to provide the average model, a mask for the mouth and the basis vectors. + +As training data you need to provide data that fits the "data/multi_face_audio_eq_tmp_cached_dataset.py" data loader. \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/__init__.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/__init__.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/__init__.py new file mode 100644 index 0000000..3735ac1 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/__init__.py @@ -0,0 +1,90 @@ +import importlib +import torch.utils.data +from data.base_data_loader import BaseDataLoader +from data.base_dataset import BaseDataset + + +def find_dataset_using_name(dataset_name): + # Given the option --dataset_mode [datasetname], + # the file "data/datasetname_dataset.py" + # will be imported. + dataset_filename = "data." + dataset_name + "_dataset" + datasetlib = importlib.import_module(dataset_filename) + + # In the file, the class called DatasetNameDataset() will + # be instantiated. It has to be a subclass of BaseDataset, + # and it is case-insensitive. + dataset = None + target_dataset_name = dataset_name.replace('_', '') + 'dataset' + for name, cls in datasetlib.__dict__.items(): + if name.lower() == target_dataset_name.lower() \ + and issubclass(cls, BaseDataset): + dataset = cls + + if dataset is None: + print("In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase." % (dataset_filename, target_dataset_name)) + exit(0) + + return dataset + + +def get_option_setter(dataset_name): + dataset_class = find_dataset_using_name(dataset_name) + return dataset_class.modify_commandline_options + + +def create_dataset(opt): + dataset = find_dataset_using_name(opt.dataset_mode) + instance = dataset() + instance.initialize(opt) + print("dataset [%s] was created" % (instance.name())) + return instance + + +def CreateDataLoader(opt): + data_loader = CustomDatasetDataLoader() + data_loader.initialize(opt) + return data_loader + + +# Wrapper class of Dataset class that performs +# multi-threaded data loading +class CustomDatasetDataLoader(BaseDataLoader): + def name(self): + return 'CustomDatasetDataLoader' + + def initialize(self, opt): + BaseDataLoader.initialize(self, opt) + self.dataset = create_dataset(opt) + if opt.serial_batches: + self.dataloader = torch.utils.data.DataLoader( + self.dataset, + batch_size=opt.batch_size, + shuffle=not opt.serial_batches, + num_workers=int(opt.num_threads)) + else: + #weights = make_weights_for_balanced_classes(dataset_train.imgs, len(dataset_train.classes)) + weights = self.dataset.getSampleWeights() + weights = torch.DoubleTensor(weights) + sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights)) + + self.dataloader = torch.utils.data.DataLoader( + self.dataset, + batch_size=opt.batch_size, + #shuffle=True, + sampler=sampler, + pin_memory=True, + num_workers=int(opt.num_threads)) + + + def load_data(self): + return self + + def __len__(self): + return min(len(self.dataset), self.opt.max_dataset_size) + + def __iter__(self): + for i, data in enumerate(self.dataloader): + if i * self.opt.batch_size >= self.opt.max_dataset_size: + break + yield data diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/aligned_dataset.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/aligned_dataset.py new file mode 100644 index 0000000..cfc1a74 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/aligned_dataset.py @@ -0,0 +1,209 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from data.base_dataset import BaseDataset +from data.audio import Audio +#from data.image_folder import make_dataset +from PIL import Image + +#def make_dataset(dir): +# images = [] +# assert os.path.isdir(dir), '%s is not a valid directory' % dir +# for root, _, fnames in sorted(os.walk(dir)): +# for fname in fnames: +# if any(fname.endswith(extension) for extension in ['.bin', '.BIN']): +# path = os.path.join(root, fname) +# images.append(path) +# return sorted(images) + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.bin', '.BIN']): + id_str = fname[:-4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.bin' + path = os.path.join(root, fname) + images.append(path) + return images + +def load_intrinsics(input_dir): + file = open(input_dir+"/intrinsics.txt", "r") + intrinsics = [[(float(x) for x in line.split())] for line in file] + file.close() + intrinsics = list(intrinsics[0][0]) + return intrinsics + +def load_rigids(input_dir): + file = open(input_dir+"/rigid.txt", "r") + rigid_floats = [[float(x) for x in line.split()] for line in file] # note that it stores 5 lines per matrix (blank line) + file.close() + all_rigids = [ [rigid_floats[4*idx + 0],rigid_floats[4*idx + 1],rigid_floats[4*idx + 2],rigid_floats[4*idx + 3]] for idx in range(0, len(rigid_floats)//4) ] + return all_rigids + +def load_expressions(input_dir): + file = open(input_dir+"/expression.txt", "r") + expressions = [[float(x) for x in line.split()] for line in file] + file.close() + return expressions + +def load_audio(input_dir): + audio = Audio(input_dir + '/audio.mp3', write_mel_spectogram = False) + #audio = Audio(input_dir + '/audio.mp3', write_mel_spectogram = True) + + return audio + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + id_str = fname[l+1:-4] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + +class AlignedDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + self.root = opt.dataroot + self.data_dir = os.path.join(opt.dataroot, opt.phase) + self.frame_paths = make_dataset(self.data_dir) + self.frame_ids = make_ids(self.frame_paths, self.root) + self.intrinsics = load_intrinsics(self.data_dir) + self.extrinsics = load_rigids(self.data_dir) + self.expressions = load_expressions(self.data_dir) + + self.audio = load_audio(self.data_dir) + self.audio_window_size = opt.audio_window_size + + opt.nObjects = 1 + assert(opt.resize_or_crop == 'resize_and_crop') + + def __getitem__(self, index): + + # get video data + frame_id = index + + #print('GET ITEM: ', index) + img_path = self.frame_paths[index] + frame_id = self.frame_ids[index] + + # intrinsics and extrinsics + intrinsics = self.intrinsics + extrinsics = self.extrinsics[frame_id] + + # get audio mel sample window + frame_rate = 24#29.97 + #frame_rate = len(self.expressions) / self.audio.time_total + mel_frame_idx = int((frame_id / frame_rate) * self.audio.mel_sample_rate) + mels = self.audio.getWindow(mel_frame_idx, self.audio_window_size) + + + # expressions + expressions = torch.tensor(self.expressions[frame_id]) + + # default image dimensions + IMG_DIM_X = 512 + IMG_DIM_Y = 512 + + # load image data + #assert(IMG_DIM == self.opt.fineSize) + img_array = np.memmap(img_path, dtype='float32', mode='r').__array__() + if img_array.size != IMG_DIM_X * IMG_DIM_Y * 5: + IMG_DIM_X = int(img_array[0]) + IMG_DIM_Y = int(img_array[1]) + img_array = img_array[2:] + intrinsics = img_array[0:4] + img_array = img_array[4:] + + img_array = np.clip(img_array, 0.0, 1.0) + img = np.resize(img_array, (IMG_DIM_Y, IMG_DIM_X, 5)) + A = img[:,:,0:3] + B = img[:,:,3:5] + B = np.concatenate((B, np.zeros((IMG_DIM_Y, IMG_DIM_X, 1))), axis=2) + + TARGET = transforms.ToTensor()(A.astype(np.float32)) + UV = transforms.ToTensor()(B.astype(np.float32)) + + TARGET = 2.0 * TARGET - 1.0 + UV = 2.0 * UV - 1.0 + + + # load deepspeech feature + dsf_fname = img_path[:-4] + '.deepspeech.npy' +# print('dsf_fname:', dsf_fname) + feature_array = np.load(dsf_fname) + #feature_array = np.memmap(dsf_fname, dtype='float32', mode='r').__array__() +# print('feature_array shape: ', feature_array.shape) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf = transforms.ToTensor()(dsf_np.astype(np.float32)) + + ################################# + ####### apply augmentation ###### + ################################# + if not self.opt.no_augmentation: + # random dimensions + new_dim_x = np.random.randint(int(IMG_DIM_X * 0.75), IMG_DIM_X+1) + new_dim_y = np.random.randint(int(IMG_DIM_Y * 0.75), IMG_DIM_Y+1) + new_dim_x = int(np.floor(new_dim_x / 64.0) * 64 ) # << dependent on the network structure !! 64 => 6 layers + new_dim_y = int(np.floor(new_dim_y / 64.0) * 64 ) + if new_dim_x > IMG_DIM_X: new_dim_x -= 64 + if new_dim_y > IMG_DIM_Y: new_dim_y -= 64 + + # random pos + if IMG_DIM_X == new_dim_x: offset_x = 0 + else: offset_x = np.random.randint(0, IMG_DIM_X-new_dim_x) + if IMG_DIM_Y == new_dim_y: offset_y = 0 + else: offset_y = np.random.randint(0, IMG_DIM_Y-new_dim_y) + + # select subwindow + TARGET = TARGET[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + UV = UV[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + + # compute new intrinsics + # TODO: atm not needed but maybe later + + else: + new_dim_x = int(np.floor(IMG_DIM_X / 64.0) * 64 ) # << dependent on the network structure !! 64 => 6 layers + new_dim_y = int(np.floor(IMG_DIM_Y / 64.0) * 64 ) + offset_x = 0 + offset_y = 0 + # select subwindow + TARGET = TARGET[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + UV = UV[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + + # compute new intrinsics + # TODO: atm not needed but maybe later + + ################################# + + return {'TARGET': TARGET, + 'UV': UV, + 'paths': self.frame_paths[index],#img_path, + 'intrinsics': intrinsics, + 'extrinsics': extrinsics, + 'expressions': expressions, + 'audio_mels': mels, + 'audio_deepspeech': dsf, # deepspeech feature + 'object_id':0} + + def __len__(self): + return len(self.frame_paths) + + def name(self): + return 'AlignedDataset' diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/audio.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/audio.py new file mode 100644 index 0000000..4932c94 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/audio.py @@ -0,0 +1,77 @@ +import time +import random +import math + +import numpy as np + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.transforms as transforms + +import torchaudio +import torchaudio.transforms + +import librosa +import scipy.signal +import librosa.display +import matplotlib.pyplot as plt + + +class Audio(): + def name(self): + return 'Audio' + + def __init__(self, filename, write_mel_spectogram = False): + self.n_mels=128 + self.fmax=8000 + self.hop_length_ms = 20 + + sound, sample_rate = librosa.load(filename)#torchaudio.load(filename) + self.raw_audio = sound + self.sample_rate = sample_rate + print('sample_rate = %d' % self.sample_rate) + self.n_samples = sound.shape[0] + self.time_total = self.n_samples / self.sample_rate + print('length = %ds' % self.time_total) + + print('compute mel spectrogram...') + self.hop_length = int(sample_rate / 1000.0 * self.hop_length_ms) + print('hop_length: ', self.hop_length) + self.mel_spectrogram = librosa.feature.melspectrogram(y=self.raw_audio, sr=self.sample_rate, hop_length=self.hop_length, n_mels=self.n_mels, fmax=self.fmax) + + + if write_mel_spectogram: + print('write spectrogram to file') + plt.figure(figsize=(100, 15)) + librosa.display.specshow(librosa.power_to_db(self.mel_spectrogram, ref=np.max), y_axis='mel', fmax=self.fmax, x_axis='time') + plt.colorbar(format='%+2.0f dB') + plt.title('Mel spectrogram') + plt.tight_layout() + plt.savefig('mel_features.png', dpi=None, facecolor='w', edgecolor='w', orientation='portrait', papertype=None, format=None, transparent=False, bbox_inches=None, pad_inches=0.1, frameon=None, metadata=None) + + print('mel: ', self.mel_spectrogram.shape) # (128, 18441) + self.n_mel_frames = self.mel_spectrogram.shape[1] + self.mel_sample_rate = self.mel_spectrogram.shape[1] / self.time_total + print('n_mel_frames: ', self.n_mel_frames) + print('mel_sample_rate: ', self.mel_sample_rate) + + # convert to torch + self.mel_spectrogram = torch.FloatTensor(self.mel_spectrogram) + + def getWindow(self, mel_frame_idx, window_size): + # get audio mel sample window + audio_start = mel_frame_idx - (window_size//2) + audio_end = mel_frame_idx + (window_size//2) + if audio_start < 0: + audio_input = self.mel_spectrogram[0:self.n_mels, 0:audio_end] + zeros = torch.zeros((self.n_mels,-audio_start)) + audio_input = torch.cat([zeros, audio_input], 1) + elif audio_end >= self.n_mel_frames: + audio_input = self.mel_spectrogram[:, audio_start:-1] + zeros = torch.zeros((self.n_mels,audio_end-self.n_mel_frames + 1)) + audio_input = torch.cat([audio_input, zeros], 1) + else: + audio_input = self.mel_spectrogram[:, audio_start:audio_end] + + return torch.reshape(audio_input, (1, 1, self.n_mels, window_size)) \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/audio_dataset.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/audio_dataset.py new file mode 100644 index 0000000..ba0d603 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/audio_dataset.py @@ -0,0 +1,145 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from data.base_dataset import BaseDataset +from data.audio import Audio +#from data.image_folder import make_dataset +from PIL import Image + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.deepspeech.npy']): + id_str = fname[:-15] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.deepspeech.npy' + path = os.path.join(root, fname) + images.append(path) + return images + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + #id_str = fname[l+1:-4] + id_str = fname[l+1:-15] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + +class AudioDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + self.root = opt.dataroot + self.data_dir = os.path.join(opt.dataroot, 'audio_feature') + self.frame_paths = make_dataset(self.data_dir) + self.frame_ids = make_ids(self.frame_paths, self.root) + + opt.nObjects = 1 + opt.nTrainObjects = 116 # TODO + opt.nTestObjects = 1 + opt.test_sequence_names = [[opt.dataroot.split("/")[-1], 'test']] + assert(opt.resize_or_crop == 'resize_and_crop') + + if opt.isTrain: + print('ERROR: audio_dataset only allowed for test') + exit() + + def getSampleWeights(self): + weights = np.ones((len(self.frame_paths))) + return weights + + def getAudioFilename(self): + return os.path.join(self.root, 'audio.wav') + + def getAudioFeatureFilename(self, idx): + return self.frame_paths[idx % len(self.frame_paths)] + + def __getitem__(self, index): + + #print('GET ITEM: ', index) + frame_path = self.frame_paths[index] + frame_id = self.frame_ids[index] + + # load deepspeech feature + feature_array = np.load(frame_path) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf = transforms.ToTensor()(dsf_np.astype(np.float32)) + + + # load sequence data if necessary + if self.opt.look_ahead:# use prev and following frame infos + r = self.opt.seq_len//2 + for i in range(1,r): # prev frames + index_seq = index - i + if index_seq < 0: index_seq = 0 + + dsf_fname = self.frame_paths[index_seq] + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + for i in range(1,self.opt.seq_len - r + 1): # following frames + index_seq = index + i + max_idx = len(self.frame_paths)-1 + if index_seq > max_idx: index_seq = max_idx + + dsf_fname = self.frame_paths[index_seq] + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf, dsf_seq], 0) # seq_len x 16 x 29 + # note the ordering [old ... current ... future] + else: + for i in range(1,self.opt.seq_len): + index_seq = index - i + if index_seq < 0: index_seq = 0 + dsf_fname = self.frame_paths[index_seq] + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + + ################################# + zeroIdentity = torch.zeros(100) + zeroExpressions = torch.zeros(76) + + target_id = -1 + internal_sequence_id = 0 + + weight = 1.0 / len(self.frame_paths) + + return {'paths': frame_path, + 'expressions': zeroExpressions, + 'identity': zeroIdentity, + 'intrinsics': np.zeros((4)), + 'extrinsics': np.zeros((4,4)), + 'audio_deepspeech': dsf, # deepspeech feature + 'target_id':target_id, + 'internal_id':internal_sequence_id, + 'weight': np.array([weight]).astype(np.float32)} + + def __len__(self): + return len(self.frame_paths) + + def name(self): + return 'AudioDataset' diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/base_data_loader.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/base_data_loader.py new file mode 100644 index 0000000..ae5a168 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/base_data_loader.py @@ -0,0 +1,10 @@ +class BaseDataLoader(): + def __init__(self): + pass + + def initialize(self, opt): + self.opt = opt + pass + + def load_data(): + return None diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/base_dataset.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/base_dataset.py new file mode 100644 index 0000000..4a714be --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/base_dataset.py @@ -0,0 +1,106 @@ +import torch.utils.data as data +from PIL import Image +import torchvision.transforms as transforms +import torch + + +class BaseDataset(data.Dataset): + def __init__(self): + super(BaseDataset, self).__init__() + + def name(self): + return 'BaseDataset' + + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + pass + + def getSampleWeights(self): + return torch.ones((len(self))).double() + + def __len__(self): + return 0 + + +def get_transform(opt): + transform_list = [] + if opt.resize_or_crop == 'resize_and_crop': + osize = [opt.loadSize, opt.loadSize] + transform_list.append(transforms.Resize(osize, Image.BICUBIC)) + transform_list.append(transforms.RandomCrop(opt.fineSize)) + elif opt.resize_or_crop == 'crop': + transform_list.append(transforms.RandomCrop(opt.fineSize)) + elif opt.resize_or_crop == 'scale_width': + transform_list.append(transforms.Lambda( + lambda img: __scale_width(img, opt.fineSize))) + elif opt.resize_or_crop == 'scale_width_and_crop': + transform_list.append(transforms.Lambda( + lambda img: __scale_width(img, opt.loadSize))) + transform_list.append(transforms.RandomCrop(opt.fineSize)) + elif opt.resize_or_crop == 'none': + transform_list.append(transforms.Lambda( + lambda img: __adjust(img))) + else: + raise ValueError('--resize_or_crop %s is not a valid option.' % opt.resize_or_crop) + + if opt.isTrain and not opt.no_flip: + transform_list.append(transforms.RandomHorizontalFlip()) + + transform_list += [transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), + (0.5, 0.5, 0.5))] + return transforms.Compose(transform_list) + + +# just modify the width and height to be multiple of 4 +def __adjust(img): + ow, oh = img.size + + # the size needs to be a multiple of this number, + # because going through generator network may change img size + # and eventually cause size mismatch error + mult = 4 + if ow % mult == 0 and oh % mult == 0: + return img + w = (ow - 1) // mult + w = (w + 1) * mult + h = (oh - 1) // mult + h = (h + 1) * mult + + if ow != w or oh != h: + __print_size_warning(ow, oh, w, h) + + return img.resize((w, h), Image.BICUBIC) + + +def __scale_width(img, target_width): + ow, oh = img.size + + # the size needs to be a multiple of this number, + # because going through generator network may change img size + # and eventually cause size mismatch error + mult = 4 + assert target_width % mult == 0, "the target width needs to be multiple of %d." % mult + if (ow == target_width and oh % mult == 0): + return img + w = target_width + target_height = int(target_width * oh / ow) + m = (target_height - 1) // mult + h = (m + 1) * mult + + if target_height != h: + __print_size_warning(target_width, target_height, w, h) + + return img.resize((w, h), Image.BICUBIC) + + +def __print_size_warning(ow, oh, w, h): + if not hasattr(__print_size_warning, 'has_printed'): + print("The image size needs to be a multiple of 4. " + "The loaded image size was (%d, %d), so it was adjusted to " + "(%d, %d). This adjustment will be done to all images " + "whose sizes are not multiples of 4" % (ow, oh, w, h)) + __print_size_warning.has_printed = True diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/custom_aligned_dataset.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/custom_aligned_dataset.py new file mode 100644 index 0000000..a88669e --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/custom_aligned_dataset.py @@ -0,0 +1,238 @@ +import os.path + +import h5py +import random +import torchvision.transforms as transforms +import torch +import numpy as np + +# from mutil.bfm2017 import BFM2017 +# from mutil.np_util import adjust_width + +from data.base_dataset import BaseDataset +from data.audio import Audio +import cv2 +from scipy.ndimage.morphology import binary_fill_holes +from scipy.ndimage.morphology import binary_erosion +#from data.image_folder import make_dataset +from PIL import Image + +#def make_dataset(dir): +# images = [] +# assert os.path.isdir(dir), '%s is not a valid directory' % dir +# for root, _, fnames in sorted(os.walk(dir)): +# for fname in fnames: +# if any(fname.endswith(extension) for extension in ['.bin', '.BIN']): +# path = os.path.join(root, fname) +# images.append(path) +# return sorted(images) +# from mutil.pytorch_utils import to_tensor + + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.bin', '.BIN']): + id_str = fname[:-4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.bin' + path = os.path.join(root, fname) + images.append(path) + return images + +def load_intrinsics(input_dir): + file = open(input_dir+"/intrinsics.txt", "r") + intrinsics = [[(float(x) for x in line.split())] for line in file] + file.close() + intrinsics = list(intrinsics[0][0]) + return intrinsics + +def load_rigids(input_dir): + file = open(input_dir+"/rigid.txt", "r") + rigid_floats = [[float(x) for x in line.split()] for line in file] # note that it stores 5 lines per matrix (blank line) + file.close() + all_rigids = [ [rigid_floats[4*idx + 0],rigid_floats[4*idx + 1],rigid_floats[4*idx + 2],rigid_floats[4*idx + 3]] for idx in range(0, len(rigid_floats)//4) ] + return all_rigids + +def load_expressions(input_dir): + file = open(input_dir+"/expression.txt", "r") + expressions = [[float(x) for x in line.split()] for line in file] + file.close() + return expressions + +def load_audio(input_dir): + audio = Audio(input_dir + '/audio.mp3', write_mel_spectogram = False) + #audio = Audio(input_dir + '/audio.mp3', write_mel_spectogram = True) + + return audio + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + id_str = fname[l+1:-4] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + +class CustomAlignedDataset(BaseDataset): + IMG_DIM_Y = 256 + IMG_DIM_X = 256 + + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + self.root = opt.dataroot + + self.data = h5py.File(self.root, 'r') + print(self.data.keys()) + # self.audio = load_audio(self.data_dir) + # self.audio_window_size = opt.audio_window_size + + opt.nObjects = 1 + + def preprocess_image(self, frame_id): + img = self.data["frame"][frame_id] + img = img.astype(np.float) / 255 + if img.shape[0] != self.IMG_DIM_Y or img.shape[1] != self.IMG_DIM_X: + # print("Image needs resizing from {} to {},{}".format(img.shape, self.IMG_DIM_Y, self.IMG_DIM_X)) + img = cv2.resize(img, (self.IMG_DIM_Y, self.IMG_DIM_X)) # TODO: np.resize vs. cv2.resize + + assert 0 <= img.min() and img.max() <= 1 + img_tensor = transforms.ToTensor()(img.astype(np.float32)) + img_tensor = 2.0 * img_tensor - 1.0 + return img_tensor + + def preprocess_uv(self, frame_id): + uv = self.data["uv"][frame_id] # TODO: how to store uv? Do we need the same dims? + + if uv.shape[0] != self.IMG_DIM_Y or uv.shape[1] != self.IMG_DIM_X: + # print("UV needs resizing from {} to {},{}".format(uv.shape, self.IMG_DIM_Y, self.IMG_DIM_X)) + uv = cv2.resize(uv, (self.IMG_DIM_Y, self.IMG_DIM_X)) # TODO: np.resize vs. cv2.resize + + # issue only happens with multiple concurrent reads + if not (-1 <= uv.min() and uv.max() <= 1): + print("frame invalid", frame_id) + uv_tensor = transforms.ToTensor()(uv.astype(np.float32)) + + assert -1 <= uv.min() and uv.max() <= 1, "UV not in range [-1, 1]! min: {} max: {}".format(uv.min(), uv.max()) + return uv_tensor + + def preprocess_expressions(self, frame_id): + return torch.tensor(self.data['ep'][frame_id]) + + def preprocess_audio_features(self, frame_id): + # load deepspeech feature + feature_array = self.data["dsf"][frame_id] + assert feature_array.shape == (16, 29) + dsf_np = np.expand_dims(feature_array, 2) + dsf_tensor = transforms.ToTensor()(dsf_np.astype(np.float32)) + return dsf_tensor + + def preprocess_deca_details(self, frame_id): + deca_details = self.data["deca_details"][frame_id] # TODO: how to store uv? Do we need the same dims? + + if deca_details.shape[0] != self.IMG_DIM_Y or deca_details.shape[1] != self.IMG_DIM_X: + # print("UV needs resizing from {} to {},{}".format(uv.shape, self.IMG_DIM_Y, self.IMG_DIM_X)) + deca_details = cv2.resize(deca_details, (self.IMG_DIM_Y, self.IMG_DIM_X)) # TODO: np.resize vs. cv2.resize + + deca_details_tensor = transforms.ToTensor()(deca_details.astype(np.float32)) + return deca_details_tensor + + def preprocess_mask(self, frame_id): + mask = self.data["mask"][frame_id] # TODO: how to store uv? Do we need the same dims? + + if mask.shape[0] != self.IMG_DIM_Y or mask.shape[1] != self.IMG_DIM_X: + # print("UV needs resizing from {} to {},{}".format(uv.shape, self.IMG_DIM_Y, self.IMG_DIM_X)) + mask = cv2.resize(mask, (self.IMG_DIM_Y, self.IMG_DIM_X)) + + mask = mask>0 + mask = binary_fill_holes(mask).astype(np.float32) + mask = binary_erosion(mask, iterations=8) + mask_tensor = transforms.ToTensor()(mask.astype(np.float32)) + + return mask_tensor + + def __getitem__(self, index): + frame_id = index + # expressions + expression_tensor = self.preprocess_expressions(frame_id) + img_tensor = self.preprocess_image(frame_id) + uv_tensor = self.preprocess_uv(frame_id) + mask_tensor = self.preprocess_mask(frame_id) + dsf_tensor = self.preprocess_audio_features(frame_id) + + if self.opt.deca_details: + deca_details_tensor = self.preprocess_deca_details(frame_id) + + ################################# + ####### apply augmentation ###### + ################################# + if not self.opt.no_augmentation: + # random dimensions + new_dim_x = np.random.randint(int(self.IMG_DIM_X * 0.75), self.IMG_DIM_X+1) + new_dim_y = np.random.randint(int(self.IMG_DIM_Y * 0.75), self.IMG_DIM_Y+1) + new_dim_x = int(np.floor(new_dim_x / 64.0) * 64 ) # << dependent on the network structure !! 64 => 6 layers + new_dim_y = int(np.floor(new_dim_y / 64.0) * 64 ) + if new_dim_x > self.IMG_DIM_X: new_dim_x -= 64 + if new_dim_y > self.IMG_DIM_Y: new_dim_y -= 64 + + # random pos + if self.IMG_DIM_X == new_dim_x: offset_x = 0 + else: offset_x = np.random.randint(0, self.IMG_DIM_X-new_dim_x) + if self.IMG_DIM_Y == new_dim_y: offset_y = 0 + else: offset_y = np.random.randint(0, self.IMG_DIM_Y-new_dim_y) + + # select subwindow + img_tensor = img_tensor[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + uv_tensor = uv_tensor[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + + # compute new intrinsics + # TODO: atm not needed but maybe later + + else: + new_dim_x = int(np.floor(self.IMG_DIM_X / 64.0) * 64 ) # << dependent on the network structure !! 64 => 6 layers + new_dim_y = int(np.floor(self.IMG_DIM_Y / 64.0) * 64 ) + offset_x = 0 + offset_y = 0 + # select subwindow + img_tensor = img_tensor[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + uv_tensor = uv_tensor[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + + # compute new intrinsics + # TODO: atm not needed but maybe later + + ################################# + return_dict = {'TARGET': img_tensor, + 'UV': uv_tensor * mask_tensor, + # 'paths': self.frame_paths[index],#img_path, + # 'intrinsics': intrinsics, + # 'extrinsics': extrinsics, + 'expressions': expression_tensor, + # 'audio_mels': mels, + 'audio_deepspeech': dsf_tensor, # deepspeech feature + } + + if self.opt.deca_details: + return_dict['deca_details'] = deca_details_tensor * mask_tensor + + return return_dict + + def __len__(self): + return self.data["dsf"].shape[0] + + def name(self): + return 'CustomAlignedDataset' + diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/custom_aligned_inference.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/custom_aligned_inference.py new file mode 100644 index 0000000..2626e9c --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/custom_aligned_inference.py @@ -0,0 +1,78 @@ +import os.path + +import h5py +import random +import torchvision.transforms as transforms +import torch +import numpy as np + +from mutil.bfm2017 import BFM2017 +from mutil.np_util import adjust_width + +from data.base_dataset import BaseDataset +from data.audio import Audio +import cv2 +from pytorch3d.renderer import look_at_view_transform +from pytorch3d.structures import Meshes +#from data.image_folder import make_dataset +from PIL import Image + +from data.custom_aligned_dataset import CustomAlignedDataset + + +class CustomAlignedInferenceDataset(CustomAlignedDataset): + def initialize(self, opt): + super().initialize(opt) + self.data_source = h5py.File(opt.dataroot_source, 'r') + + self.bfm = BFM2017(opt.path_bfm) + self.uv_renderer = None + + def create_uv_renderer(self, height): + from mutil.renderer import BFMUVRenderer + R, T = look_at_view_transform(eye=((0, 0, 0),), at=((0, 0, -1),), up=((0, 1, 0),)) + self.uv_renderer = BFMUVRenderer(self.opt.path_uv, device="cuda" if torch.cuda.is_available() else "cpu", fov=63, image_size=height, R=R, T=T) + + + def render_uv(self, mesh, height, width): + if self.uv_renderer is None: + # We do this because we only now the height now. + self.uv_renderer = self.create_uv_renderer(height) + + rendered_uv_np = self.uv_renderer.render(mesh) + rendered_uv_np = adjust_width(rendered_uv_np, width, is_uv=True) + return rendered_uv_np + + def __getitem__(self, index): + item = super().__getitem__(index) + frame_id = index + + # # Expressions + # source_expressions_tensor = torch.tensor(self.data_source['ep'][frame_id]) + # item['expressions'] = source_expressions_tensor + # + # # Audio features + # feature_array = self.data_source["dsf"][frame_id] + # dsf_np = np.expand_dims(feature_array, 2) + # dsf_tensor = transforms.ToTensor()(dsf_np.astype(np.float32)) + # item['audio_deepspeech'] = dsf_tensor # deepspeech feature + # + # # image and UV texture map (rendered) + # new_dim_x = int(np.floor(self.IMG_DIM_X / 64.0) * 64) # << dependent on the network structure !! 64 => 6 layers + # new_dim_y = int(np.floor(self.IMG_DIM_Y / 64.0) * 64) + # img_source = self.data_source["frame"][frame_id] + # img_tensor_source = self.preprocess_image(img_source) + # item['SOURCE'] = img_tensor_source + + shape_coeff = self.data['sp'][frame_id] + expression_coeff = self.data_source['ep'][frame_id] + verts = self.bfm.generate_vertices(shape_coeff, expression_coeff) + R, t, s = self.data['R'][frame_id], self.data['t'][frame_id], self.data['s'][frame_id] + verts = self.bfm.apply_Rts(verts, R, t, s) + mesh = Meshes([verts], [self.bfm.faces]) + + # uv = self.render_uv(mesh, img_source.shape[0], img_source.shape[1]) + # uv_tensor = transforms.ToTensor()(uv.astype(np.float32)) + # uv_tensor = uv_tensor[:, 0: new_dim_y, 0:new_dim_x] + # item['UV'] = uv_tensor + return item \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/custom_aligned_inference_dataset.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/custom_aligned_inference_dataset.py new file mode 100644 index 0000000..d570542 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/custom_aligned_inference_dataset.py @@ -0,0 +1,77 @@ +import os.path + +import h5py +import random +import torchvision.transforms as transforms +import torch +import numpy as np + + +from data.base_dataset import BaseDataset +from data.audio import Audio +import cv2 +from pytorch3d.renderer import look_at_view_transform +from pytorch3d.structures import Meshes +#from data.image_folder import make_dataset +from PIL import Image + +from data.custom_aligned_dataset import CustomAlignedDataset + + +class CustomAlignedInferenceDataset(CustomAlignedDataset): + def initialize(self, opt): + super().initialize(opt) + self.data_source = h5py.File(opt.dataroot_source, 'r') + + # self.bfm = BFM2017(opt.path_bfm) + # self.uv_renderer = None + # + # def create_uv_renderer(self, height): + # from mutil.renderer import BFMUVRenderer + # R, T = look_at_view_transform(eye=((0, 0, 0),), at=((0, 0, -1),), up=((0, 1, 0),)) + # self.uv_renderer = BFMUVRenderer(self.opt.path_uv, device="cuda" if torch.cuda.is_available() else "cpu", fov=63, image_size=height, R=R, T=T) + # + # + # def render_uv(self, mesh, height, width): + # if self.uv_renderer is None: + # # We do this because we only now the height now. + # self.uv_renderer = self.create_uv_renderer(height) + # + # rendered_uv_np = self.uv_renderer.render(mesh) + # rendered_uv_np = adjust_width(rendered_uv_np, width, is_uv=True) + # return rendered_uv_np + + def __getitem__(self, index): + item = super().__getitem__(index) + frame_id = index + # + # # Expressions + # source_expressions_tensor = torch.tensor(self.data_source['ep'][frame_id]) + # item['expressions'] = source_expressions_tensor + # + # # Audio features + # feature_array = self.data_source["dsf"][frame_id] + # dsf_np = np.expand_dims(feature_array, 2) + # dsf_tensor = transforms.ToTensor()(dsf_np.astype(np.float32)) + # item['audio_deepspeech'] = dsf_tensor # deepspeech feature + # + # # image and UV texture map (rendered) + # new_dim_x = int(np.floor(self.IMG_DIM_X / 64.0) * 64) # << dependent on the network structure !! 64 => 6 layers + # new_dim_y = int(np.floor(self.IMG_DIM_Y / 64.0) * 64) + # img_source = self.data_source["frame"][frame_id] + # img_tensor_source = self.preprocess_image(img_source) + # item['SOURCE'] = img_tensor_source + # + # shape_coeff = self.data['sp'][frame_id] + # expression_coeff = self.data_source['ep'][frame_id] + # verts = self.bfm.generate_vertices(shape_coeff, expression_coeff) + # R, t, s = self.data['R'][frame_id], self.data['t'][frame_id], self.data['s'][frame_id] + # verts = self.bfm.apply_Rts(verts, R, t, s).astype(np.float) + # mesh = Meshes([to_tensor(verts, "cuda")], [to_tensor(self.bfm.faces, "cuda").long()]) + # + # uv = self.render_uv(mesh, item["TARGET"].shape[-2], item["TARGET"].shape[-1]) + + # uv_tensor = transforms.ToTensor()(uv.astype(np.float32)) + # uv_tensor = uv_tensor[:, 0: new_dim_y, 0:new_dim_x] + # item['UV'] = uv_tensor + return item \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/face_dataset.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/face_dataset.py new file mode 100644 index 0000000..619c088 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/face_dataset.py @@ -0,0 +1,382 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from data.base_dataset import BaseDataset +from data.audio import Audio +#from data.image_folder import make_dataset +from PIL import Image +from util import util + +#def make_dataset(dir): +# images = [] +# assert os.path.isdir(dir), '%s is not a valid directory' % dir +# for root, _, fnames in sorted(os.walk(dir)): +# for fname in fnames: +# if any(fname.endswith(extension) for extension in ['.bin', '.BIN']): +# path = os.path.join(root, fname) +# images.append(path) +# return sorted(images) + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.npy', '.NPY']): + #.deepspeech.npy + id_str = fname[:-15] #4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.deepspeech.npy' + path = os.path.join(root, fname) + images.append(path) + return images + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + id_str = fname[l+1:-15]#4] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + +def make_dataset_png_ids(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.png', '.PNG']): + id_str = fname[:-4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + return ids + +def make_dataset_exr_ids(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.exr', '.EXR']): + id_str = fname[:-4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + return ids + + +def load_intrinsics(input_dir): + file = open(input_dir+"/intrinsics.txt", "r") + intrinsics = [[(float(x) for x in line.split())] for line in file] + file.close() + intrinsics = list(intrinsics[0][0]) + return intrinsics + +def load_rigids(input_dir): + file = open(input_dir+"/rigid.txt", "r") + rigid_floats = [[float(x) for x in line.split()] for line in file] # note that it stores 5 lines per matrix (blank line) + file.close() + all_rigids = [ [rigid_floats[4*idx + 0],rigid_floats[4*idx + 1],rigid_floats[4*idx + 2],rigid_floats[4*idx + 3]] for idx in range(0, len(rigid_floats)//4) ] + return all_rigids + +def load_expressions(input_dir): + file = open(input_dir+"/expression.txt", "r") + expressions = [[float(x) for x in line.split()] for line in file] + file.close() + return expressions + +def load_identity(input_dir): + file = open(input_dir+"/identities.txt", "r") + identity = [[float(x) for x in line.split()] for line in file] + file.close() + return identity + + + +class FaceDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + + # directories + self.dataroot = opt.dataroot + self.audio_feature_dir = os.path.join(opt.dataroot, 'audio_feature') + self.image_dir = os.path.join(opt.dataroot, 'images') + self.uvs_dir = os.path.join(opt.dataroot, 'uvs') + + # debug print + print('load sequence:', self.dataroot) + print('\taudio_feature_dir:', self.audio_feature_dir) + print('\timage_dir:', self.image_dir) + print('\tuvs_dir:', self.uvs_dir) + + # generate index maps + audio_ids = make_ids(make_dataset(self.audio_feature_dir), self.dataroot) + image_ids = make_dataset_png_ids(self.image_dir) + uvs_ids = make_dataset_exr_ids(self.uvs_dir) + + # get model parameters + intrinsics = load_intrinsics(self.dataroot) + extrinsics = load_rigids(self.dataroot) + expressions = load_expressions(self.dataroot) + identities = load_identity(self.dataroot) + + if opt.phase == 'test': # test overwrites the audio and uv files, as well as expressions + print('Test mode. Overwriting audio, uv and expressions') + print('source sequence:', opt.source_dir) + dataroot = opt.source_dir + self.audio_feature_dir = os.path.join(dataroot, 'audio_feature') + self.uvs_dir = os.path.join(dataroot, 'uvs') + audio_ids = make_ids(make_dataset(self.audio_feature_dir), dataroot) + uvs_ids = make_dataset_exr_ids(self.uvs_dir) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(dataroot) + identities = load_identity(dataroot) + + print('\tnum audio_ids:', len(audio_ids)) + print('\tnum image_ids:', len(image_ids)) + print('\tnum uvs_ids:', len(uvs_ids)) + + + # set data + min_len = min(len(audio_ids), len(image_ids), len(uvs_ids), len(extrinsics), len(expressions)) + self.audio_ids = audio_ids[:min_len] + self.image_ids = image_ids[:min_len] + self.uvs_ids = uvs_ids[:min_len] + self.intrinsics = intrinsics + self.extrinsics = extrinsics[:] #extrinsics[:min_len] + self.expressions = expressions[:] #expressions[:min_len] + self.identities = identities[:] #identities[:min_len] + self.n_frames_total = min_len + + print('\tnum frames:', self.n_frames_total) + + + opt.nTrainObjects = 1 + opt.nValObjects = 1 + opt.nTestObjects = 1 + + + opt.test_sequence_names = [[opt.dataroot.split("/")[-1], 'train']] + if opt.phase == 'test': + opt.test_sequence_names = [[opt.source_dir.split("/")[-1], 'test']] + print('test:', opt.test_sequence_names) + + assert(opt.resize_or_crop == 'resize_and_crop') + + def getSampleWeights(self): + weights = np.ones((self.n_frames_total)) + return weights + + def getExtrinsics(self, idx): + return self.extrinsics[self.uvs_ids[idx % self.n_frames_total]] + def getIntrinsics(self, idx): + return self.intrinsics + def getIdentities(self, idx): + return self.identities[self.uvs_ids[idx % self.n_frames_total]] + def getExpressions(self, idx): + return self.expressions[self.uvs_ids[idx % self.n_frames_total]] + + def getAudioFilename(self): + return os.path.join(self.dataroot, 'audio.wav') + + def getImageFilename(self, idx): + image_id = self.image_ids[idx % self.n_frames_total] + img_fname = os.path.join(self.image_dir, str(image_id).zfill(5) + '.png') + return img_fname + #img_numpy = np.asarray(Image.open(img_fname)) + #TARGET = 2.0 * transforms.ToTensor()(img_numpy.astype(np.float32))/255.0 - 1.0 + + def getAudioFeatureFilename(self, idx): + #return self.frame_paths[idx % len(self.frame_paths)] + audio_id = self.audio_ids[idx] + return os.path.join(self.audio_feature_dir, str(audio_id) + '.deepspeech.npy') + + + def computeCrop(self, mask, MULTIPLE_OF=64, random_size=False): + IMG_DIM_X = mask.shape[2] + IMG_DIM_Y = mask.shape[1] + if random_size: + # random dimensions + new_dim_x = np.random.randint(int(IMG_DIM_X * 0.75), IMG_DIM_X+1) + new_dim_y = np.random.randint(int(IMG_DIM_Y * 0.75), IMG_DIM_Y+1) + new_dim_x = int(np.floor(new_dim_x / float(MULTIPLE_OF)) * MULTIPLE_OF ) + new_dim_y = int(np.floor(new_dim_y / float(MULTIPLE_OF)) * MULTIPLE_OF ) + else: + new_dim_x = 3 * MULTIPLE_OF + new_dim_y = 3 * MULTIPLE_OF + + # check dims + if new_dim_x > IMG_DIM_X: new_dim_x -= MULTIPLE_OF + if new_dim_y > IMG_DIM_Y: new_dim_y -= MULTIPLE_OF + + # random pos + mask_indices = torch.nonzero(mask) + _, bb_mid_point_y, bb_mid_point_x = mask_indices[np.random.randint(0, mask_indices.shape[0])].data_source.cpu() + #print('bb_mid_point', bb_mid_point_x, bb_mid_point_y) + + offset_x = bb_mid_point_x - new_dim_x/2 + offset_y = bb_mid_point_y - new_dim_y/2 + + + if IMG_DIM_X == new_dim_x: offset_x = 0 + if offset_x < 0: offset_x = 0 + if offset_x+new_dim_x >= IMG_DIM_X: offset_x = IMG_DIM_X-new_dim_x + + if IMG_DIM_Y == new_dim_y: offset_y = 0 + if offset_y < 0: offset_y = 0 + if offset_y+new_dim_y >= IMG_DIM_Y: offset_y = IMG_DIM_Y-new_dim_y + + return np.array([int(offset_x),int(offset_y),int(new_dim_x), int(new_dim_y)]) + + + def __getitem__(self, global_index): + # select frame from sequence + index = global_index + + # get data ids + audio_id = self.audio_ids[index] + image_id = self.image_ids[index] + uv_id = self.uvs_ids[index] + + + + #print('GET ITEM: ', index) + img_fname = os.path.join(self.image_dir, str(image_id).zfill(5) + '.png') + img_numpy = np.asarray(Image.open(img_fname)) + TARGET = 2.0 * transforms.ToTensor()(img_numpy.astype(np.float32))/255.0 - 1.0 + + uv_fname = os.path.join(self.uvs_dir, str(uv_id).zfill(5) + '.exr') + uv_numpy = util.load_exr(uv_fname) + UV = transforms.ToTensor()(uv_numpy.astype(np.float32)) + UV = torch.where(UV > 1.0, torch.zeros_like(UV), UV) + UV = torch.where(UV < 0.0, torch.zeros_like(UV), UV) + UV = 2.0 * UV - 1.0 + + #print('img_fname:', img_fname) + #print('uv_fname:', uv_fname) + + # intrinsics and extrinsics + intrinsics = self.intrinsics + extrinsics = self.extrinsics[uv_id] + + # expressions + expressions = np.asarray(self.expressions[audio_id], dtype=np.float32) + #print('expressions:', expressions.shape) + expressions[32] *= 0.0 # remove eye brow movements + expressions[41] *= 0.0 # remove eye brow movements + expressions[71:75] *= 0.0 # remove eye brow movements + expressions = torch.tensor(expressions) + + # identity + identity = torch.tensor(self.identities[audio_id]) + + # load deepspeech feature + dsf_fname = os.path.join(self.audio_feature_dir, str(audio_id) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf = transforms.ToTensor()(dsf_np.astype(np.float32)) + + + # load sequence data if necessary + if self.opt.look_ahead:# use prev and following frame infos + r = self.opt.seq_len//2 + last_valid_idx = audio_id + for i in range(1,r): # prev frames + index_seq = index - i + if index_seq < 0: index_seq = 0 + audio_id_seq = self.audio_ids[index_seq] + if audio_id_seq == audio_id - i: last_valid_idx = audio_id_seq + else: audio_id_seq = last_valid_idx + + dsf_fname = os.path.join(self.audio_feature_dir, str(audio_id_seq) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + last_valid_idx = audio_id + for i in range(1,self.opt.seq_len - r + 1): # following frames + index_seq = index + i + max_idx = len(self.audio_ids)-1 + if index_seq > max_idx: index_seq = max_idx + audio_id_seq = self.audio_ids[index_seq] + if audio_id_seq == audio_id + i: last_valid_idx = audio_id_seq + else: audio_id_seq = last_valid_idx + + dsf_fname = os.path.join(self.audio_feature_dir, str(audio_id_seq) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf, dsf_seq], 0) # seq_len x 16 x 29 + # note the ordering [old ... current ... future] + else: + last_valid_idx = audio_id + for i in range(1,self.opt.seq_len): + index_seq = index - i + if index_seq < 0: index_seq = 0 + audio_id_seq = self.audio_ids[index_seq] + if audio_id_seq == audio_id - i: last_valid_idx = audio_id_seq + else: audio_id_seq = last_valid_idx + + dsf_fname = os.path.join(self.audio_feature_dir, str(audio_id_seq) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + + ################################# + ####### apply augmentation ###### + ################################# + crop = np.array([0,0,UV.shape[2],UV.shape[1]]) + if not self.opt.no_augmentation: + INVALID_UV = -1 + mask = ( (UV[0:1,:,:] != INVALID_UV) | (UV[1:2,:,:] != INVALID_UV) ) + crop = self.computeCrop(mask, MULTIPLE_OF=64) # << dependent on the network structure !! 64 => 6 layers + + offset_x,offset_y,new_dim_x, new_dim_y = crop + # select subwindow + TARGET = TARGET[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + UV = UV[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + + # compute new intrinsics + # TODO: atm not needed but maybe later + + ################################# + weight = 1.0 / self.n_frames_total + + return {'TARGET': TARGET, 'UV': UV, + 'paths': dsf_fname, #img_path, + 'intrinsics': np.array(intrinsics), + 'extrinsics': np.array(extrinsics), + 'expressions': expressions, + 'identity': identity, + 'audio_deepspeech': dsf, # deepspeech feature + 'target_id': -1, + 'crop': crop, + 'internal_id': 0, + 'weight': np.array([weight]).astype(np.float32)} + + def __len__(self): + return self.n_frames_total + + def name(self): + return 'FaceDataset' diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/facetmp_dataset.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/facetmp_dataset.py new file mode 100644 index 0000000..d771116 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/facetmp_dataset.py @@ -0,0 +1,394 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from data.base_dataset import BaseDataset +from data.audio import Audio +#from data.image_folder import make_dataset +from PIL import Image +from util import util + +#def make_dataset(dir): +# images = [] +# assert os.path.isdir(dir), '%s is not a valid directory' % dir +# for root, _, fnames in sorted(os.walk(dir)): +# for fname in fnames: +# if any(fname.endswith(extension) for extension in ['.bin', '.BIN']): +# path = os.path.join(root, fname) +# images.append(path) +# return sorted(images) + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.npy', '.NPY']): + #.deepspeech.npy + id_str = fname[:-15] #4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.deepspeech.npy' + path = os.path.join(root, fname) + images.append(path) + return images + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + id_str = fname[l+1:-15]#4] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + +def make_dataset_png_ids(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.png', '.PNG']): + id_str = fname[:-4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + return ids + +def make_dataset_exr_ids(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.exr', '.EXR']): + id_str = fname[:-4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + return ids + + +def load_intrinsics(input_dir): + file = open(input_dir+"/intrinsics.txt", "r") + intrinsics = [[(float(x) for x in line.split())] for line in file] + file.close() + intrinsics = list(intrinsics[0][0]) + return intrinsics + +def load_rigids(input_dir): + file = open(input_dir+"/rigid.txt", "r") + rigid_floats = [[float(x) for x in line.split()] for line in file] # note that it stores 5 lines per matrix (blank line) + file.close() + all_rigids = [ [rigid_floats[4*idx + 0],rigid_floats[4*idx + 1],rigid_floats[4*idx + 2],rigid_floats[4*idx + 3]] for idx in range(0, len(rigid_floats)//4) ] + return all_rigids + +def load_expressions(input_dir): + file = open(input_dir+"/expression.txt", "r") + expressions = [[float(x) for x in line.split()] for line in file] + file.close() + return expressions + +def load_identity(input_dir): + file = open(input_dir+"/identities.txt", "r") + identity = [[float(x) for x in line.split()] for line in file] + file.close() + return identity + + + +class FaceTmpDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + + # directories + self.dataroot = opt.dataroot + self.audio_feature_dir = os.path.join(opt.dataroot, 'audio_feature') + self.image_dir = os.path.join(opt.dataroot, 'images') + self.uvs_dir = os.path.join(opt.dataroot, 'uvs') + + # debug print + print('load sequence:', self.dataroot) + print('\taudio_feature_dir:', self.audio_feature_dir) + print('\timage_dir:', self.image_dir) + print('\tuvs_dir:', self.uvs_dir) + + # generate index maps + audio_ids = make_ids(make_dataset(self.audio_feature_dir), self.dataroot) + image_ids = make_dataset_png_ids(self.image_dir) + uvs_ids = make_dataset_exr_ids(self.uvs_dir) + + # get model parameters + intrinsics = load_intrinsics(self.dataroot) + extrinsics = load_rigids(self.dataroot) + expressions = load_expressions(self.dataroot) + identities = load_identity(self.dataroot) + + if opt.phase == 'test': # test overwrites the audio and uv files, as well as expressions + print('Test mode. Overwriting audio, uv and expressions') + print('source sequence:', opt.source_dir) + dataroot = opt.source_dir + self.audio_feature_dir = os.path.join(dataroot, 'audio_feature') + self.uvs_dir = os.path.join(dataroot, 'uvs') + audio_ids = make_ids(make_dataset(self.audio_feature_dir), dataroot) + uvs_ids = make_dataset_exr_ids(self.uvs_dir) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(dataroot) + identities = load_identity(dataroot) + + print('\tnum audio_ids:', len(audio_ids)) + print('\tnum image_ids:', len(image_ids)) + print('\tnum uvs_ids:', len(uvs_ids)) + + + # set data + min_len = min(len(audio_ids), len(image_ids), len(uvs_ids), len(extrinsics), len(expressions)) + self.audio_ids = audio_ids[:min_len] + self.image_ids = image_ids[:min_len] + self.uvs_ids = uvs_ids[:min_len] + self.intrinsics = intrinsics + self.extrinsics = extrinsics[:] # extrinsics[:min_len] + self.expressions = expressions[:] # expressions[:min_len] + self.identities = identities[:] #identities[:min_len] + self.n_frames_total = min_len + + print('\tnum frames:', self.n_frames_total) + + + opt.nTrainObjects = 1 + opt.nValObjects = 1 + opt.nTestObjects = 1 + + + opt.test_sequence_names = [[opt.dataroot.split("/")[-1], 'train']] + if opt.phase == 'test': + + opt.test_sequence_names = [[opt.source_dir.split("/")[-1], 'test']] + print('test:', opt.test_sequence_names) + + assert(opt.resize_or_crop == 'resize_and_crop') + + def getSampleWeights(self): + weights = np.ones((self.n_frames_total)) + return weights + + def computeCrop(self, mask, MULTIPLE_OF=64, random_size=False): + IMG_DIM_X = mask.shape[2] + IMG_DIM_Y = mask.shape[1] + if random_size: + # random dimensions + new_dim_x = np.random.randint(int(IMG_DIM_X * 0.75), IMG_DIM_X+1) + new_dim_y = np.random.randint(int(IMG_DIM_Y * 0.75), IMG_DIM_Y+1) + new_dim_x = int(np.floor(new_dim_x / float(MULTIPLE_OF)) * MULTIPLE_OF ) + new_dim_y = int(np.floor(new_dim_y / float(MULTIPLE_OF)) * MULTIPLE_OF ) + else: + new_dim_x = 2 * MULTIPLE_OF + new_dim_y = 2 * MULTIPLE_OF + + # check dims + if new_dim_x > IMG_DIM_X: new_dim_x -= MULTIPLE_OF + if new_dim_y > IMG_DIM_Y: new_dim_y -= MULTIPLE_OF + + # random pos + mask_indices = torch.nonzero(mask) + _, bb_mid_point_y, bb_mid_point_x = mask_indices[np.random.randint(0, mask_indices.shape[0])].data_source.cpu() + #print('bb_mid_point', bb_mid_point_x, bb_mid_point_y) + + offset_x = bb_mid_point_x - new_dim_x/2 + offset_y = bb_mid_point_y - new_dim_y/2 + + + if IMG_DIM_X == new_dim_x: offset_x = 0 + if offset_x < 0: offset_x = 0 + if offset_x+new_dim_x >= IMG_DIM_X: offset_x = IMG_DIM_X-new_dim_x + + if IMG_DIM_Y == new_dim_y: offset_y = 0 + if offset_y < 0: offset_y = 0 + if offset_y+new_dim_y >= IMG_DIM_Y: offset_y = IMG_DIM_Y-new_dim_y + + + return np.array([int(offset_x),int(offset_y),int(new_dim_x), int(new_dim_y)]) + + def getitem(self, global_index, crop=None): + # select frame from sequence + index = global_index + + # get data ids + audio_id = self.audio_ids[index] + image_id = self.image_ids[index] + uv_id = self.uvs_ids[index] + + #print('GET ITEM: ', index) + img_fname = os.path.join(self.image_dir, str(image_id).zfill(5) + '.png') + img_numpy = np.asarray(Image.open(img_fname)) + TARGET = 2.0 * transforms.ToTensor()(img_numpy.astype(np.float32))/255.0 - 1.0 + + uv_fname = os.path.join(self.uvs_dir, str(uv_id).zfill(5) + '.exr') + uv_numpy = util.load_exr(uv_fname) + UV = transforms.ToTensor()(uv_numpy.astype(np.float32)) + UV = torch.where(UV > 1.0, torch.zeros_like(UV), UV) + UV = torch.where(UV < 0.0, torch.zeros_like(UV), UV) + UV = 2.0 * UV - 1.0 + + + # intrinsics and extrinsics + intrinsics = self.intrinsics + extrinsics = self.extrinsics[uv_id] + + # expressions + expressions = np.asarray(self.expressions[audio_id], dtype=np.float32) + #print('expressions:', expressions.shape) + expressions[32] *= 0.0 # remove eye brow movements + expressions[41] *= 0.0 # remove eye brow movements + expressions[71:75] *= 0.0 # remove eye brow movements + expressions = torch.tensor(expressions) + + # identity + identity = torch.tensor(self.identities[audio_id]) + + # load deepspeech feature + dsf_fname = os.path.join(self.audio_feature_dir, str(audio_id) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf = transforms.ToTensor()(dsf_np.astype(np.float32)) + + + # load sequence data if necessary + if self.opt.look_ahead:# use prev and following frame infos + r = self.opt.seq_len//2 + last_valid_idx = audio_id + for i in range(1,r): # prev frames + index_seq = index - i + if index_seq < 0: index_seq = 0 + audio_id_seq = self.audio_ids[index_seq] + if audio_id_seq == audio_id - i: last_valid_idx = audio_id_seq + else: audio_id_seq = last_valid_idx + + dsf_fname = os.path.join(self.audio_feature_dir, str(audio_id_seq) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + last_valid_idx = audio_id + for i in range(1,self.opt.seq_len - r + 1): # following frames + index_seq = index + i + max_idx = len(self.audio_ids)-1 + if index_seq > max_idx: index_seq = max_idx + audio_id_seq = self.audio_ids[index_seq] + if audio_id_seq == audio_id + i: last_valid_idx = audio_id_seq + else: audio_id_seq = last_valid_idx + + dsf_fname = os.path.join(self.audio_feature_dir, str(audio_id_seq) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf, dsf_seq], 0) # seq_len x 16 x 29 + # note the ordering [old ... current ... future] + else: + last_valid_idx = audio_id + for i in range(1,self.opt.seq_len): + index_seq = index - i + if index_seq < 0: index_seq = 0 + audio_id_seq = self.audio_ids[index_seq] + if audio_id_seq == audio_id - i: last_valid_idx = audio_id_seq + else: audio_id_seq = last_valid_idx + + dsf_fname = os.path.join(self.audio_feature_dir, str(audio_id_seq) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + ################################# + ####### apply augmentation ###### + ################################# + + if not self.opt.no_augmentation: + if type(crop) == type(None): + INVALID_UV = -1 + mask = ( (UV[0:1,:,:] != INVALID_UV) | (UV[1:2,:,:] != INVALID_UV) ) + crop = self.computeCrop(mask, MULTIPLE_OF=64) # << dependent on the network structure !! 64 => 6 layers + + offset_x,offset_y,new_dim_x, new_dim_y = crop + + # select subwindow + TARGET = TARGET[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + UV = UV[:, offset_y:offset_y+new_dim_y, offset_x:offset_x+new_dim_x] + + # compute new intrinsics + # TODO: atm not needed but maybe later + + ################################# + + + return {'TARGET': TARGET, 'UV': UV, + 'paths': dsf_fname, #img_path, + 'intrinsics': np.array(intrinsics), + 'extrinsics': np.array(extrinsics), + 'expressions': expressions, + 'identity': identity, + 'audio_deepspeech': dsf, # deepspeech feature + #'target_id':target_id, + 'crop': crop, + + 'internal_id': 0} + + + def __getitem__(self, global_index): + # select frame from sequence + index = global_index + + current = self.getitem(index) + crop = current['crop'] + prv = self.getitem(max(index-1, 0), crop) + nxt = self.getitem(min(index+1, self.n_frames_total-1), crop) + + if type(crop) == type(None): + crop = np.array([0,0,current['UV'].shape[2],current['UV'].shape[1]]) + + weight = 1.0 / self.n_frames_total + + return {'TARGET': current['TARGET'], + 'UV': current['UV'], + 'paths': current['paths'], + 'intrinsics': current['intrinsics'], + 'extrinsics': current['extrinsics'], + 'expressions': current['expressions'], + 'identity': current['identity'], + 'audio_deepspeech': current['audio_deepspeech'], + + 'prv_TARGET': prv['TARGET'], + 'prv_UV': prv['UV'], + 'prv_audio_deepspeech': prv['audio_deepspeech'], + 'prv_expressions': prv['expressions'], + + 'nxt_TARGET': nxt['TARGET'], + 'nxt_UV': nxt['UV'], + 'nxt_audio_deepspeech': nxt['audio_deepspeech'], + 'nxt_expressions': nxt['expressions'], + + 'crop': crop, + + 'internal_id': current['internal_id'], + 'weight': np.array([weight]).astype(np.float32)} + + def __len__(self): + return self.n_frames_total + + def name(self): + return 'FaceTmpDataset' diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/multi_face_audio_eq_tmp_cached_dataset.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/multi_face_audio_eq_tmp_cached_dataset.py new file mode 100644 index 0000000..9bb4504 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/multi_face_audio_eq_tmp_cached_dataset.py @@ -0,0 +1,458 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from data.base_dataset import BaseDataset +from data.audio import Audio +#from data.image_folder import make_dataset +from PIL import Image +import progressbar + +#def make_dataset(dir): +# images = [] +# assert os.path.isdir(dir), '%s is not a valid directory' % dir +# for root, _, fnames in sorted(os.walk(dir)): +# for fname in fnames: +# if any(fname.endswith(extension) for extension in ['.bin', '.BIN']): +# path = os.path.join(root, fname) +# images.append(path) +# return sorted(images) + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.npy', '.NPY']): + #.deepspeech.npy + id_str = fname[:-15] #4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.deepspeech.npy' + path = os.path.join(root, fname) + images.append(path) + return images + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + id_str = fname[l+1:-15]#4] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + +def make_dataset_ids_png(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.png', '.png']): + id_str = fname[:-4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + return ids + +def load_intrinsics(input_dir): + file = open(input_dir+"/intrinsics.txt", "r") + intrinsics = [[(float(x) for x in line.split())] for line in file] + file.close() + intrinsics = list(intrinsics[0][0]) + return intrinsics + +def load_rigids(input_dir): + file = open(input_dir+"/rigid.txt", "r") + rigid_floats = [[float(x) for x in line.split()] for line in file] # note that it stores 5 lines per matrix (blank line) + file.close() + all_rigids = [ [rigid_floats[4*idx + 0],rigid_floats[4*idx + 1],rigid_floats[4*idx + 2],rigid_floats[4*idx + 3]] for idx in range(0, len(rigid_floats)//4) ] + return all_rigids + +def load_expressions(input_dir): + file = open(input_dir+"/expression.txt", "r") + expressions = [[float(x) for x in line.split()] for line in file] + file.close() + return expressions + +def load_identity(input_dir): + file = open(input_dir+"/identities.txt", "r") + identities = [[float(x) for x in line.split()] for line in file] + file.close() + return identities + + +class MultiFaceAudioEQTmpCachedDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + self.root = opt.dataroot + + # read dataset file that contains the filenames for the train, val and test lists + file = open(self.root+"/dataset.txt", "r") + self.filename_train_list, self.filename_val_list, self.filename_test_list = [str(line) for line in file] + file.close() + if self.filename_train_list[-1] == '\n': self.filename_train_list = self.filename_train_list[:-1] + if self.filename_val_list[-1] == '\n': self.filename_val_list = self.filename_val_list[:-1] + if self.filename_test_list[-1] == '\n': self.filename_test_list = self.filename_test_list[:-1] + + + + # get list of train sequences + file = open(self.root+"/" + self.filename_train_list, "r") + self.train_sequence_names = [str(line) for line in file] + file.close() + for i in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[i][-1] == '\n': + self.train_sequence_names[i] = self.train_sequence_names[i][:-1] + + # get list of val sequences + file = open(self.root+"/" + self.filename_val_list, "r") + self.val_sequence_names = [[str(w) for w in line.split()] for line in file] + file.close() + for i in range(0,len(self.val_sequence_names)): + if self.val_sequence_names[i][0][-1] == '\n': self.val_sequence_names[i][0] = self.val_sequence_names[i][0][:-1] + if self.val_sequence_names[i][1][-1] == '\n': self.val_sequence_names[i][1] = self.val_sequence_names[i][1][:-1] + + # get list of test sequences + file = open(self.root+"/" + self.filename_test_list, "r") + self.test_sequence_names = [[str(w) for w in line.split()] for line in file] + if opt.output_audio_expressions: self.test_sequence_names = self.test_sequence_names[0:1] + file.close() + for i in range(0,len(self.test_sequence_names)): + if self.test_sequence_names[i][0][-1] == '\n': self.test_sequence_names[i][0] = self.test_sequence_names[i][0][:-1] + if self.test_sequence_names[i][1][-1] == '\n': self.test_sequence_names[i][1] = self.test_sequence_names[i][1][:-1] + + # print some stats + print('filename_train_list:', self.filename_train_list) + print('\tnum_seq:', len(self.train_sequence_names)) + print('filename_val_list: ', self.filename_val_list) + print('\tnum_seq:', len(self.val_sequence_names)) + print('filename_test_list: ', self.filename_test_list) + print('\tnum_seq:', len(self.test_sequence_names)) + + opt.train_sequence_names = self.train_sequence_names + opt.val_sequence_names = self.val_sequence_names + opt.test_sequence_names = self.test_sequence_names + + # search mapping from val, test to train sequences that are used as targets + self.val_sequence_targets = [] + for i in range(0,len(self.val_sequence_names)): + target_name = self.val_sequence_names[i][1] + target_id = -1 + for j in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[j] == target_name: + target_id = j + break + if target_id == -1: + print('Target sequence not in train set! ', target_name) + exit() + self.val_sequence_targets.append(target_id) + + self.test_sequence_targets = [] + for i in range(0,len(self.test_sequence_names)): + target_name = self.test_sequence_names[i][1] + target_id = -1 + for j in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[j] == target_name: + target_id = j + break + if target_id == -1: + print('Target sequence not in train set! ', target_name) + exit() + self.test_sequence_targets.append(target_id) + print('test: ', self.test_sequence_names[i]) + print('\t target:', target_id) + + # store len values + opt.nTrainObjects = len(self.train_sequence_names) + opt.nValObjects = len(self.val_sequence_names) + opt.nTestObjects = len(self.test_sequence_names) + + ################################################ + ################################################ + ################################################ + + # prepare dataloader paths / data + self.audio_feature_dir = [] + self.image_dir = [] + self.uvs_dir = [] + self.audio_ids = [] + self.image_ids = [] + self.intrinsics = [] + self.extrinsics = [] + self.expressions = [] + self.identities = [] + self.target_id = [] + self.n_frames_total = 0 + + if opt.phase == 'train': + self.sequence_names = self.train_sequence_names + for i in range(0,len(self.train_sequence_names)): + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[i]) + audio_feature_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'uvs') + print('load train sequence:', self.train_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + + audio_ids = make_ids(make_dataset(audio_feature_dir), dataroot) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(dataroot) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(i) + + self.n_frames_total += min_len + elif opt.phase == 'val': + for i in range(0,len(self.val_sequence_names)): + target_id = self.val_sequence_targets[i] + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[target_id]) + audio_feature_dir = os.path.join(opt.dataroot, self.val_sequence_names[i][0], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'uvs') + print('load val sequence:', self.val_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + audio_ids = make_ids(make_dataset(audio_feature_dir), os.path.join(opt.dataroot, self.val_sequence_names[i][0])) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(os.path.join(opt.dataroot, self.val_sequence_names[i][0])) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(target_id) + + self.n_frames_total += min_len + else: # test + for i in range(0,len(self.test_sequence_names)): + target_id = self.test_sequence_targets[i] + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[target_id]) + audio_feature_dir = os.path.join(opt.dataroot, self.test_sequence_names[i][0], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'uvs') + print('load test sequence:', self.test_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + audio_ids = make_ids(make_dataset(audio_feature_dir), os.path.join(opt.dataroot, self.test_sequence_names[i][0])) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(os.path.join(opt.dataroot, self.test_sequence_names[i][0])) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(target_id) + + self.n_frames_total += min_len + + + print('frames_total:', self.n_frames_total) + + + #global_target_ids = [] + #for i in range(0,len(self.audio_ids)): + # for j in range(0,len(self.audio_ids[i])): + # global_target_ids.append(self.target_id[i]) + #global_target_ids=np.array(global_target_ids) + #self.weights = np.where(global_target_ids==2, 1.0 * np.ones((self.n_frames_total)), 0.01 * np.ones((self.n_frames_total)) ) + self.weights = [] + for i in range(0,len(self.audio_ids)): + l = len(self.audio_ids[i]) + for j in range(0,l): + self.weights.append(1.0 / l) + self.weights = np.array(self.weights) + + assert(opt.resize_or_crop == 'resize_and_crop') + + # mapping global to internal + self.mapping_global2internal = [] + self.mapping_global2internal_offset = [] + self.dsf = [] + offset = 0 + with progressbar.ProgressBar(max_value=self.n_frames_total) as bar: + for i in range(0,len(self.audio_ids)): + l = len(self.audio_ids[i]) + dsf_seq = [] + for k in range(0,l): + self.mapping_global2internal.append(i) + self.mapping_global2internal_offset.append(offset) + dsf_fname = os.path.join(self.audio_feature_dir[i], str(self.audio_ids[i][k]) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq.append(dsf_np.astype(np.float32)) + bar.update(offset + k) + self.dsf.append(dsf_seq) + offset += l + + + def getSampleWeights(self): + return self.weights + + def getitem(self, global_index): + + # select sequence + internal_sequence_id = self.mapping_global2internal[global_index] + sum_frames = self.mapping_global2internal_offset[global_index] + + # select frame from sequence + index = (global_index-sum_frames) % len(self.audio_ids[internal_sequence_id]) + + # get data ids + audio_id = self.audio_ids[internal_sequence_id][index] + image_id = self.image_ids[internal_sequence_id][index] + + #print('GET ITEM: ', index) + #img_path = self.frame_paths[sequence_id][index] + + # intrinsics and extrinsics + intrinsics = self.intrinsics[internal_sequence_id] + extrinsics = self.extrinsics[internal_sequence_id][image_id] + + # expressions + expressions = np.asarray(self.expressions[internal_sequence_id][audio_id], dtype=np.float32) + #print('expressions:', expressions.shape) + expressions[32] *= 0.0 # remove eye brow movements + expressions[41] *= 0.0 # remove eye brow movements + expressions[71:75] *= 0.0 # remove eye brow movements + expressions = torch.tensor(expressions) + + # identity + identity = torch.tensor(self.identities[internal_sequence_id][image_id]) + target_id = self.target_id[internal_sequence_id] # sequence id refers to the target sequence (of the training corpus) + + # load deepspeech feature + #print('audio_id', audio_id) + dsf_fname = os.path.join(self.audio_feature_dir[internal_sequence_id], str(audio_id) + '.deepspeech.npy') + + dsf_np = self.dsf[internal_sequence_id][index] + dsf = transforms.ToTensor()(dsf_np) + + # load sequence data if necessary + if self.opt.look_ahead:# use prev and following frame infos + r = self.opt.seq_len//2 + for i in range(1,r): # prev frames + index_seq = index - i + if index_seq < 0: index_seq = 0 + dsf_np = self.dsf[internal_sequence_id][index_seq] + dsf_seq = transforms.ToTensor()(dsf_np) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + for i in range(1,self.opt.seq_len - r + 1): # following frames + index_seq = index + i + max_idx = len(self.audio_ids[internal_sequence_id])-1 + if index_seq > max_idx: index_seq = max_idx + dsf_np = self.dsf[internal_sequence_id][index_seq] + dsf_seq = transforms.ToTensor()(dsf_np) # 1 x 16 x 29 + dsf = torch.cat([dsf, dsf_seq], 0) # seq_len x 16 x 29 + # note the ordering [old ... current ... future] + else: + for i in range(1,self.opt.seq_len): + index_seq = index - i + if index_seq < 0: index_seq = 0 + dsf_np = self.dsf[internal_sequence_id][index_seq] + dsf_seq = transforms.ToTensor()(dsf_np) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + #weight = 1.0 / len(self.audio_feature_dir[internal_sequence_id]) + weight = self.weights[global_index] + + return {'paths': dsf_fname, #img_path, + 'intrinsics': np.array(intrinsics), + 'extrinsics': np.array(extrinsics), + 'expressions': expressions, + 'identity': identity, + 'audio_deepspeech': dsf, # deepspeech feature + 'target_id':target_id, + 'internal_id':internal_sequence_id, + + 'weight': np.array([weight]).astype(np.float32)} + + + def __getitem__(self, global_index): + # select frame from sequence + index = global_index + current = self.getitem(index) + prv = self.getitem(max(index-1, 0)) + nxt = self.getitem(min(index+1, self.n_frames_total-1)) + + return { + 'paths': current['paths'], #img_path, + 'target_id': current['target_id'], + 'internal_id': current['internal_id'], + 'weight': current['weight'], + 'identity': current['identity'], + 'intrinsics': current['intrinsics'], + + 'extrinsics': current['extrinsics'], + 'expressions': current['expressions'], + 'audio_deepspeech': current['audio_deepspeech'], + + 'extrinsics_prv': prv['extrinsics'], + 'expressions_prv': prv['expressions'], + 'audio_deepspeech_prv': prv['audio_deepspeech'], + + 'extrinsics_nxt': nxt['extrinsics'], + 'expressions_nxt': nxt['expressions'], + 'audio_deepspeech_nxt': nxt['audio_deepspeech'], + } + + + def __len__(self): + return self.n_frames_total #len(self.frame_paths[0]) + + def name(self): + return 'MultiFaceAudioEQTmpCachedDataset' diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/multi_face_audio_eq_tmp_dataset.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/multi_face_audio_eq_tmp_dataset.py new file mode 100644 index 0000000..e42f155 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/data/multi_face_audio_eq_tmp_dataset.py @@ -0,0 +1,435 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from data.base_dataset import BaseDataset +from data.audio import Audio +#from data.image_folder import make_dataset +from PIL import Image + +#def make_dataset(dir): +# images = [] +# assert os.path.isdir(dir), '%s is not a valid directory' % dir +# for root, _, fnames in sorted(os.walk(dir)): +# for fname in fnames: +# if any(fname.endswith(extension) for extension in ['.bin', '.BIN']): +# path = os.path.join(root, fname) +# images.append(path) +# return sorted(images) + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.npy', '.NPY']): + #.deepspeech.npy + id_str = fname[:-15] #4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.deepspeech.npy' + path = os.path.join(root, fname) + images.append(path) + return images + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + id_str = fname[l+1:-15]#4] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + +def make_dataset_ids_png(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.png', '.png']): + id_str = fname[:-4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + return ids + +def load_intrinsics(input_dir): + file = open(input_dir+"/intrinsics.txt", "r") + intrinsics = [[(float(x) for x in line.split())] for line in file] + file.close() + intrinsics = list(intrinsics[0][0]) + return intrinsics + +def load_rigids(input_dir): + file = open(input_dir+"/rigid.txt", "r") + rigid_floats = [[float(x) for x in line.split()] for line in file] # note that it stores 5 lines per matrix (blank line) + file.close() + all_rigids = [ [rigid_floats[4*idx + 0],rigid_floats[4*idx + 1],rigid_floats[4*idx + 2],rigid_floats[4*idx + 3]] for idx in range(0, len(rigid_floats)//4) ] + return all_rigids + +def load_expressions(input_dir): + file = open(input_dir+"/expression.txt", "r") + expressions = [[float(x) for x in line.split()] for line in file] + file.close() + return expressions + +def load_identity(input_dir): + file = open(input_dir+"/identities.txt", "r") + identities = [[float(x) for x in line.split()] for line in file] + file.close() + return identities + + +class MultiFaceAudioEQTmpDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + self.root = opt.dataroot + + # read dataset file that contains the filenames for the train, val and test lists + file = open(self.root+"/dataset.txt", "r") + self.filename_train_list, self.filename_val_list, self.filename_test_list = [str(line) for line in file] + file.close() + if self.filename_train_list[-1] == '\n': self.filename_train_list = self.filename_train_list[:-1] + if self.filename_val_list[-1] == '\n': self.filename_val_list = self.filename_val_list[:-1] + if self.filename_test_list[-1] == '\n': self.filename_test_list = self.filename_test_list[:-1] + + + + # get list of train sequences + file = open(self.root+"/" + self.filename_train_list, "r") + self.train_sequence_names = [str(line) for line in file] + file.close() + for i in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[i][-1] == '\n': + self.train_sequence_names[i] = self.train_sequence_names[i][:-1] + + # get list of val sequences + file = open(self.root+"/" + self.filename_val_list, "r") + self.val_sequence_names = [[str(w) for w in line.split()] for line in file] + file.close() + for i in range(0,len(self.val_sequence_names)): + if self.val_sequence_names[i][0][-1] == '\n': self.val_sequence_names[i][0] = self.val_sequence_names[i][0][:-1] + if self.val_sequence_names[i][1][-1] == '\n': self.val_sequence_names[i][1] = self.val_sequence_names[i][1][:-1] + + # get list of test sequences + file = open(self.root+"/" + self.filename_test_list, "r") + self.test_sequence_names = [[str(w) for w in line.split()] for line in file] + if opt.output_audio_expressions: self.test_sequence_names = self.test_sequence_names[0:1] + file.close() + for i in range(0,len(self.test_sequence_names)): + if self.test_sequence_names[i][0][-1] == '\n': self.test_sequence_names[i][0] = self.test_sequence_names[i][0][:-1] + if self.test_sequence_names[i][1][-1] == '\n': self.test_sequence_names[i][1] = self.test_sequence_names[i][1][:-1] + + # print some stats + print('filename_train_list:', self.filename_train_list) + print('\tnum_seq:', len(self.train_sequence_names)) + print('filename_val_list: ', self.filename_val_list) + print('\tnum_seq:', len(self.val_sequence_names)) + print('filename_test_list: ', self.filename_test_list) + print('\tnum_seq:', len(self.test_sequence_names)) + + opt.train_sequence_names = self.train_sequence_names + opt.val_sequence_names = self.val_sequence_names + opt.test_sequence_names = self.test_sequence_names + + # search mapping from val, test to train sequences that are used as targets + self.val_sequence_targets = [] + for i in range(0,len(self.val_sequence_names)): + target_name = self.val_sequence_names[i][1] + target_id = -1 + for j in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[j] == target_name: + target_id = j + break + if target_id == -1: + print('Target sequence not in train set! ', target_name) + exit() + self.val_sequence_targets.append(target_id) + + self.test_sequence_targets = [] + for i in range(0,len(self.test_sequence_names)): + target_name = self.test_sequence_names[i][1] + target_id = -1 + for j in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[j] == target_name: + target_id = j + break + if target_id == -1: + print('Target sequence not in train set! ', target_name) + exit() + self.test_sequence_targets.append(target_id) + print('test: ', self.test_sequence_names[i]) + print('\t target:', target_id) + + # store len values + opt.nTrainObjects = len(self.train_sequence_names) + opt.nValObjects = len(self.val_sequence_names) + opt.nTestObjects = len(self.test_sequence_names) + + ################################################ + ################################################ + ################################################ + + # prepare dataloader paths / data + self.audio_feature_dir = [] + self.image_dir = [] + self.uvs_dir = [] + self.audio_ids = [] + self.image_ids = [] + self.intrinsics = [] + self.extrinsics = [] + self.expressions = [] + self.identities = [] + self.target_id = [] + self.n_frames_total = 0 + + if opt.phase == 'train': + self.sequence_names = self.train_sequence_names + for i in range(0,len(self.train_sequence_names)): + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[i]) + audio_feature_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'uvs') + print('load train sequence:', self.train_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + + audio_ids = make_ids(make_dataset(audio_feature_dir), dataroot) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(dataroot) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(i) + + self.n_frames_total += min_len + elif opt.phase == 'val': + for i in range(0,len(self.val_sequence_names)): + target_id = self.val_sequence_targets[i] + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[target_id]) + audio_feature_dir = os.path.join(opt.dataroot, self.val_sequence_names[i][0], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'uvs') + print('load val sequence:', self.val_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + audio_ids = make_ids(make_dataset(audio_feature_dir), os.path.join(opt.dataroot, self.val_sequence_names[i][0])) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(os.path.join(opt.dataroot, self.val_sequence_names[i][0])) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(target_id) + + self.n_frames_total += min_len + else: # test + for i in range(0,len(self.test_sequence_names)): + target_id = self.test_sequence_targets[i] + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[target_id]) + audio_feature_dir = os.path.join(opt.dataroot, self.test_sequence_names[i][0], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'uvs') + print('load test sequence:', self.test_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + audio_ids = make_ids(make_dataset(audio_feature_dir), os.path.join(opt.dataroot, self.test_sequence_names[i][0])) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(os.path.join(opt.dataroot, self.test_sequence_names[i][0])) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(target_id) + + self.n_frames_total += min_len + + + print('frames_total:', self.n_frames_total) + + + #global_target_ids = [] + #for i in range(0,len(self.audio_ids)): + # for j in range(0,len(self.audio_ids[i])): + # global_target_ids.append(self.target_id[i]) + #global_target_ids=np.array(global_target_ids) + #self.weights = np.where(global_target_ids==2, 1.0 * np.ones((self.n_frames_total)), 0.01 * np.ones((self.n_frames_total)) ) + self.weights = [] + for i in range(0,len(self.audio_ids)): + l = len(self.audio_ids[i]) + for j in range(0,l): + self.weights.append(1.0 / l) + self.weights = np.array(self.weights) + + assert(opt.resize_or_crop == 'resize_and_crop') + + def getSampleWeights(self): + return self.weights + + def getitem(self, global_index): + + # select sequence + internal_sequence_id = 0 + sum_frames = 0 + for i in range(0,len(self.audio_ids)): + l = len(self.audio_ids[i]) + if (global_index-sum_frames) < l: + internal_sequence_id = i + break + else: + sum_frames += len(self.audio_ids[i]) + + # select frame from sequence + index = (global_index-sum_frames) % len(self.audio_ids[internal_sequence_id]) + + # get data ids + audio_id = self.audio_ids[internal_sequence_id][index] + image_id = self.image_ids[internal_sequence_id][index] + + #print('GET ITEM: ', index) + #img_path = self.frame_paths[sequence_id][index] + + # intrinsics and extrinsics + intrinsics = self.intrinsics[internal_sequence_id] + extrinsics = self.extrinsics[internal_sequence_id][image_id] + + # expressions + expressions = np.asarray(self.expressions[internal_sequence_id][audio_id], dtype=np.float32) + #print('expressions:', expressions.shape) + expressions[32] *= 0.0 # remove eye brow movements + expressions[41] *= 0.0 # remove eye brow movements + expressions[71:75] *= 0.0 # remove eye brow movements + expressions = torch.tensor(expressions) + + + + # identity + identity = torch.tensor(self.identities[internal_sequence_id][image_id]) + target_id = self.target_id[internal_sequence_id] # sequence id refers to the target sequence (of the training corpus) + + # load deepspeech feature + #print('audio_id', audio_id) + dsf_fname = os.path.join(self.audio_feature_dir[internal_sequence_id], str(audio_id) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf = transforms.ToTensor()(dsf_np.astype(np.float32)) + + # load sequence data if necessary + last_valid_idx = audio_id + for i in range(1,self.opt.seq_len): + index_seq = index - i + if index_seq < 0: index_seq = 0 + audio_id_seq = self.audio_ids[internal_sequence_id][index_seq] + if audio_id_seq == audio_id - i: last_valid_idx = audio_id_seq + else: audio_id_seq = last_valid_idx + + dsf_fname = os.path.join(self.audio_feature_dir[internal_sequence_id], str(audio_id_seq) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + + + #weight = 1.0 / len(self.audio_feature_dir[internal_sequence_id]) + weight = self.weights[global_index] + + return {'paths': dsf_fname, #img_path, + 'intrinsics': np.array(intrinsics), + 'extrinsics': np.array(extrinsics), + 'expressions': expressions, + 'identity': identity, + 'audio_deepspeech': dsf, # deepspeech feature + 'target_id':target_id, + 'internal_id':internal_sequence_id, + + 'weight': np.array([weight]).astype(np.float32)} + + + def __getitem__(self, global_index): + # select frame from sequence + index = global_index + current = self.getitem(index) + prv = self.getitem(max(index-1, 0)) + nxt = self.getitem(min(index+1, self.n_frames_total-1)) + + return { + 'paths': current['paths'], #img_path, + 'target_id': current['target_id'], + 'internal_id': current['internal_id'], + 'weight': current['weight'], + 'identity': current['identity'], + 'intrinsics': current['intrinsics'], + + 'extrinsics': current['extrinsics'], + 'expressions': current['expressions'], + 'audio_deepspeech': current['audio_deepspeech'], + + 'extrinsics_prv': prv['extrinsics'], + 'expressions_prv': prv['expressions'], + 'audio_deepspeech_prv': prv['audio_deepspeech'], + + 'extrinsics_nxt': nxt['extrinsics'], + 'expressions_nxt': nxt['expressions'], + 'audio_deepspeech_nxt': nxt['audio_deepspeech'], + } + + + def __len__(self): + return self.n_frames_total #len(self.frame_paths[0]) + + def name(self): + return 'MultiFaceAudioEQTmpDataset' diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/inference_renderer.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/inference_renderer.py new file mode 100644 index 0000000..a8ceb25 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/inference_renderer.py @@ -0,0 +1,221 @@ +import copy +import numpy as np +import torch +import torchvision.transforms as transforms +import sys +import os +import cv2 + +from tqdm import tqdm +from scipy.ndimage import binary_fill_holes +from scipy.ndimage import binary_erosion + +from data.custom_aligned_dataset import CustomAlignedDataset +from options.test_options import TestOptions +from models import create_model +from util.visualizer import InferenceVisualizer + +# deca import +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../third/DECA/'))) +from decalib.deca import * +from decalib.utils.config import cfg as deca_cfg + +class InferenceManager: + + def __init__(self, opt): + self.opt = opt + + deca_cfg.model.use_tex = False + self.deca = DECA(config=deca_cfg, device="cuda" if torch.cuda.is_available() else "cpu") + + self.model = create_model(opt) + self.model.setup(opt) + + # self.new_expressions = np.loadtxt(opt.expr_path) + # print(self.new_expressions.shape) + + if opt.textureModel == 'DynamicNeuralTextureAudio': + self.load_source = True + opt_source = copy.copy(opt) + opt_source.dataroot = opt.source_dataroot + self.dataset_source = CustomAlignedDataset() + self.dataset_source.initialize(opt_source) + opt_target = copy.copy(opt) + opt_target.dataroot = opt.target_dataroot + self.dataset_target = CustomAlignedDataset() + self.dataset_target.initialize(opt_target) + + else: + self.load_source = False + opt_target = copy.copy(opt) + opt_target.dataroot = opt.target_dataroot + self.dataset_target = CustomAlignedDataset() + self.dataset_target.initialize(opt_target) + opt_source = copy.copy(opt) + opt_source.dataroot = opt.source_dataroot + self.dataset_source = CustomAlignedDataset() + self.dataset_source.initialize(opt_source) + + + self.frame_id_source = opt.frame_id_source + self.frame_id_target = opt.frame_id_target + print("Running inference with the following config:") + print("SOURCE: frame {} in {}".format(self.frame_id_source, opt.source_dataroot)) + print("TARGET: frame {} in {}".format(self.frame_id_target, opt.target_dataroot)) + + self.visualizer = InferenceVisualizer(opt) + + + def get_codedict(self, frame_id): + + codedict = torch.load(os.path.join(self.opt.dataroot, 'DECA_codedicts', f'codedict_{frame_id}.pt')) + + return codedict + + def get_expr(self, frame_id): + + expr = np.load(os.path.join(self.opt.expr_path, f'expr_{frame_id}.npy'))[0] + + return expr + + def preprocess_uv(self, uv): + IMG_DIM_Y = 256 + IMG_DIM_X = 256 + + if uv.shape[0] != IMG_DIM_Y or uv.shape[1] != IMG_DIM_X: + # print("UV needs resizing from {} to {},{}".format(uv.shape, self.IMG_DIM_Y, self.IMG_DIM_X)) + uv = cv2.resize(uv, (IMG_DIM_Y, IMG_DIM_X)) + + # issue only happens with multiple concurrent reads + if not (-1 <= uv.min() and uv.max() <= 1): + print("frame invalid", frame_id) + + assert -1 <= uv.min() and uv.max() <= 1, "UV not in range [-1, 1]! min: {} max: {}".format(uv.min(), uv.max()) + uv_tensor = transforms.ToTensor()(uv.astype(np.float32)) + + return uv_tensor.cuda() + + def preprocess_deca_details(self, deca_details): + IMG_DIM_Y = 256 + IMG_DIM_X = 256 + + if deca_details.shape[0] != IMG_DIM_Y or deca_details.shape[1] != IMG_DIM_X: + # print("UV needs resizing from {} to {},{}".format(uv.shape, self.IMG_DIM_Y, self.IMG_DIM_X)) + deca_details = cv2.resize(deca_details, (IMG_DIM_Y, IMG_DIM_X)) + + deca_details_tensor = transforms.ToTensor()(deca_details.astype(np.float32)) + + return deca_details_tensor.cuda() + + def preprocess_mask(self, mask): + IMG_DIM_Y = 256 + IMG_DIM_X = 256 + + if mask.shape[0] != IMG_DIM_Y or mask.shape[1] != IMG_DIM_X: + # print("UV needs resizing from {} to {},{}".format(uv.shape, self.IMG_DIM_Y, self.IMG_DIM_X)) + mask = cv2.resize(mask, (IMG_DIM_Y, IMG_DIM_X)) + + mask = mask>0 + mask = binary_fill_holes(mask).astype(np.float32) + mask = binary_erosion(mask, iterations=8) + mask_tensor = transforms.ToTensor()(mask.astype(np.float32)) + + return mask_tensor.cuda() + + def run_frame(self, frame_id_source, frame_id_target): + + new_expr = self.get_expr(frame_id_source) + codedict = self.get_codedict(frame_id_target) + + # Render new UV map with shape and pose from target, and expression from source + exp = torch.tensor(new_expr[:-3], dtype=torch.float32).cuda().unsqueeze(0) + + pose = codedict['pose'][:, :-3] + jaw_pose = torch.tensor(new_expr[-3:], dtype=torch.float32).cuda().unsqueeze(0) + new_pose = torch.cat((pose, jaw_pose), dim=1).cuda() + + if self.opt.deca_details: + uv, deca_details = self.deca.render_uv_details(codedict, exp, new_pose) + mask = self.deca.render_mask(uv) + mask = self.preprocess_mask(mask.cpu().detach().numpy()) + uv = self.preprocess_uv(uv[0].cpu().numpy()) + deca_details = self.preprocess_deca_details(deca_details[0].permute(1, 2, 0).cpu().detach().numpy()) + + uv = uv * mask + deca_details = deca_details * mask + + else: + uv = self.deca.render_uv(codedict, exp, new_pose)[0] + uv = self.preprocess_uv(uv.cpu().numpy()) + + if self.load_source: + source = self.dataset_source[frame_id_source] + target = self.dataset_target[frame_id_target] + + item = source + + item["SOURCE"] = source['TARGET'].cuda().unsqueeze(0) + item["TARGET"] = target['TARGET'].cuda().unsqueeze(0) + item["UV"] = uv.cuda().unsqueeze(0) + item["expressions"] = torch.tensor(new_expr).cuda().unsqueeze(0) + item["audio_deepspeech"] = item["audio_deepspeech"].cuda().unsqueeze(0) + + else: + target = self.dataset_target[frame_id_target] + + item = target + + item["SOURCE"] = target['TARGET'].cuda().unsqueeze(0) + item["TARGET"] = target['TARGET'].cuda().unsqueeze(0) + item["UV"] = uv.cuda().unsqueeze(0) + item["expressions"] = torch.tensor(new_expr).cuda().unsqueeze(0) + item["audio_deepspeech"] = item["audio_deepspeech"].cuda().unsqueeze(0) + + if self.opt.deca_details: + item["deca_details"] = deca_details.cuda().unsqueeze(0) + + self.model.set_input(item) + self.model.forward() + results = self.model.get_current_visuals() + + # results = None + + return results + + + def run_inference(self): + + # Run inference for all frames + try: + n_frames = min(len(self.dataset_source), len(self.dataset_target)) + + except AttributeError: + n_frames = len(self.dataset_target) + + if self.frame_id_source >= 0 and self.frame_id_target >= 0: + + for frame_id in tqdm(range(self.frame_id_source, self.frame_id_target)): + results = self.run_frame(frame_id, frame_id) + self.visualizer.reset() + self.visualizer.display_current_results(results, frame_id, save_result=True) + + else: + + for frame_id in tqdm(range(n_frames)): + results = self.run_frame(frame_id, frame_id) + self.visualizer.reset() + self.visualizer.display_current_results(results, frame_id, save_result=True) + + +if __name__ == '__main__': + # training dataset + opt = TestOptions().parse() + opt.serial_batches = True + opt.display_id = 0 + + # model + manager = InferenceManager(opt) + manager.run_inference() + + + diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/inference_renderer.sh b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/inference_renderer.sh new file mode 100644 index 0000000..d2cb02f --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/inference_renderer.sh @@ -0,0 +1,91 @@ +set -ex + +DP=/home/alberto/NeuralVoicePuppetry/datasets/SRF_anchor_short +DT=/home/alberto/NeuralVoicePuppetry/datasets/TRANSFERS/ +TRANSFER_PATH=$DT/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead +CHECKPOINT_DIR=/home/alberto/NeuralVoicePuppetry/NeuralRenderingNetwork/checkpoints + +FILE_ID_SOURCE=Italian_vicky_audio +FILE_ID_TARGET=Halbtotale_355_9415 + +# . train_audio2expressionsAttentionTMP.sh & +#EROSION=0.2 +NAME=DynamicNeuralTextures-UNET_6_level-DynamicNeuralTextureExpression-SL8-BS8-Halbtotale_355_9415.h5-custom_aligned-VGG-20210302-144030-look_ahead +EROSION=0.6 + +GPUID=0 +DATASET_MODE=custom_aligned + +DATAROOT=$DP/$FILE_ID_TARGET/ +TARGET_DATAROOT=$DP/$FILE_ID_TARGET/$FILE_ID_TARGET.h5 +SOURCE_DATAROOT=$DP/$FILE_ID_SOURCE/$FILE_ID_SOURCE.h5 +EXPR_PATH=$TRANSFER_PATH/$FILE_ID_SOURCE--$FILE_ID_TARGET/expression.txt + +IMAGES_TARGET_DIR=/home/alberto/NeuralVoicePuppetry/results/inference/$NAME/$FILE_ID_SOURCE"_to_"$FILE_ID_TARGET + +# neural texture, not used here +TEX_DIM=128 +TEX_FEATURES=16 +TEXTUREMODEL=DynamicNeuralTextureExpression +INPUT_NC=2 +NUM_THREADS=1 + +LOSS=VGG + +# models +MODEL=DynamicNeuralTextures +RENDERER_TYPE=UNET_6_level # There are many more of these TODO + +BATCH_SIZE=1 +SEQ_LEN=8 + +################################################################################ +################################################################################ +################################################################################ + + +#DISPLAY_NAME=DynamicNeuralTextures-UNET_6_level-SL8-BS16-201125_TV-20190114-2245-5701_24fps_cropped_short.h5-custom_aligned-RMS-20201123-185215-look_ahead +DISPLAY_ID=0 + +FRAME_ID_SOURCE=-1 +FRAME_ID_TARGET=-1 +#FRAME_ID_SOURCE=6540 +#FRAME_ID_TARGET=6540 + +# training +# --input_noise_augmentation +echo "---------" + +CUDA_VISIBLE_DEVICES=0 python \ +inference_renderer.py \ +--num_threads $NUM_THREADS \ +--input_nc $INPUT_NC \ +--look_ahead \ +--seq_len $SEQ_LEN \ +--no_augmentation \ +--name $NAME \ +--checkpoints_dir $CHECKPOINT_DIR \ +--erosionFactor $EROSION \ +--tex_dim $TEX_DIM \ +--tex_features $TEX_FEATURES \ +--rendererType $RENDERER_TYPE \ +--lossType $LOSS \ +--model $MODEL \ +--netG unet_256 \ +--dataset_mode $DATASET_MODE \ +--norm instance \ +--gpu_ids $GPUID \ +--batch_size $BATCH_SIZE \ +--epoch latest \ +--textureModel $TEXTUREMODEL \ +--dataroot $DATAROOT \ +--target_dataroot $TARGET_DATAROOT \ +--source_dataroot $SOURCE_DATAROOT \ +--expr_path $EXPR_PATH \ +--images_target_dir $IMAGES_TARGET_DIR \ +--frame_id_source $FRAME_ID_SOURCE \ +--frame_id_target $FRAME_ID_TARGET \ + +################################################################################ +################################################################################ +################################################################################ diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/inference_renderer_deca_details.sh b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/inference_renderer_deca_details.sh new file mode 100644 index 0000000..f2cffde --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/inference_renderer_deca_details.sh @@ -0,0 +1,113 @@ +set -ex + +DP=/home/alberto/NeuralVoicePuppetry/datasets/SRF_anchor_short +DPS=/home/alberto/NeuralVoicePuppetry/datasets/External +DT=/home/alberto/NeuralVoicePuppetry/datasets/TRANSFERS/ +TRANSFER_PATH=$DT/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead +CHECKPOINT_DIR=/home/alberto/NeuralVoicePuppetry/NeuralRenderingNetwork/checkpoints + +#FILE_ID_SOURCE=Halbtotale_355_9414 +#FILE_ID_SOURCE=Italian_vicky_audio + +FILE_ID_SOURCE_LIST=(Alberto_videos_uno Alberto_videos_due Alberto_videos_tre) +#FILE_ID_SOURCE_LIST=(Clara_audios_two) +#FILE_ID_TARGET=Halbtotale_355_9415 +FILE_ID_TARGET=Youtube_Russian_guy + +# . train_audio2expressionsAttentionTMP.sh & +#EROSION=0.2 +# Name for standard model +NAME=DynamicNeuralTextures-UNET_6_level-DynamicNeuralTextureExpression-SL8-BS8-Halbtotale_355_9415.h5-custom_aligned-VGG-20210302-144030-look_ahead +# Name for deca details model +NAME=DynamicNeuralTextures-UNET_6_level-DynamicNeuralTextureExpression-SL8-BS8-Halbtotale_355_9415.h5-custom_aligned-VGG-20210308-004445-look_ahead +# Name for deca details masked model +NAME=DynamicNeuralTextures-UNET_6_level-DynamicNeuralTextureExpression-SL8-BS8-Halbtotale_355_9415.h5-custom_aligned-VGG-20210309-184531-look_ahead_masked +# Name for deca details of masked mouth SRF moderator +#NAME=DynamicNeuralTextures-UNET_6_level-DynamicNeuralTextureExpression-SL8-BS8-Halbtotale_355_9415.h5-custom_aligned-VGG-20210311-095742-look_ahead_mask_mouth +# Name for deca details of masked mouth Russian guy +#NAME=DynamicNeuralTextures-UNET_6_level-DynamicNeuralTextureExpression-SL8-BS8-Youtube_Russian_guy.h5-custom_aligned-VGG-20210315-231511-look_ahead_mask_mouth + +EROSION=0.6 + +GPUID=0 +DATASET_MODE=custom_aligned + +for FILE_ID_SOURCE in "${FILE_ID_SOURCE_LIST[@]}" +do + DATAROOT=$DPS/$FILE_ID_TARGET/ + TARGET_DATAROOT=$DPS/$FILE_ID_TARGET/$FILE_ID_TARGET.h5 + #SOURCE_DATAROOT=$DP/$FILE_ID_SOURCE/$FILE_ID_SOURCE.h5 + SOURCE_DATAROOT=$DPS/$FILE_ID_SOURCE/$FILE_ID_SOURCE.h5 + EXPR_PATH=$TRANSFER_PATH/$FILE_ID_SOURCE--$FILE_ID_TARGET/expression.txt + + IMAGES_TARGET_DIR=/home/alberto/NeuralVoicePuppetry/results/inference/$NAME/$FILE_ID_SOURCE"_to_"$FILE_ID_TARGET + + # neural texture, not used here + TEX_DIM=128 + TEX_FEATURES=16 + TEXTUREMODEL=DynamicNeuralTextureExpression + INPUT_NC=5 + NUM_THREADS=1 + + LOSS=VGG + + # models + MODEL=DynamicNeuralTextures + RENDERER_TYPE=UNET_6_level # There are many more of these TODO + + BATCH_SIZE=1 + SEQ_LEN=8 + + ################################################################################ + ################################################################################ + ################################################################################ + + + #DISPLAY_NAME=DynamicNeuralTextures-UNET_6_level-SL8-BS16-201125_TV-20190114-2245-5701_24fps_cropped_short.h5-custom_aligned-RMS-20201123-185215-look_ahead + DISPLAY_ID=0 + + # Used as start and end + FRAME_ID_SOURCE=-1 + FRAME_ID_TARGET=-1 + + + # training + # --input_noise_augmentation + echo "---------" + + CUDA_VISIBLE_DEVICES=0 python \ + inference_renderer.py \ + --num_threads $NUM_THREADS \ + --input_nc $INPUT_NC \ + --look_ahead \ + --seq_len $SEQ_LEN \ + --no_augmentation \ + --name $NAME \ + --checkpoints_dir $CHECKPOINT_DIR \ + --erosionFactor $EROSION \ + --tex_dim $TEX_DIM \ + --tex_features $TEX_FEATURES \ + --rendererType $RENDERER_TYPE \ + --lossType $LOSS \ + --model $MODEL \ + --netG unet_256 \ + --dataset_mode $DATASET_MODE \ + --norm instance \ + --gpu_ids $GPUID \ + --batch_size $BATCH_SIZE \ + --epoch latest \ + --textureModel $TEXTUREMODEL \ + --dataroot $DATAROOT \ + --target_dataroot $TARGET_DATAROOT \ + --source_dataroot $SOURCE_DATAROOT \ + --expr_path $EXPR_PATH \ + --images_target_dir $IMAGES_TARGET_DIR \ + --frame_id_source $FRAME_ID_SOURCE \ + --frame_id_target $FRAME_ID_TARGET \ + --deca_details + +done + +################################################################################ +################################################################################ +################################################################################ diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/DynamicNeuralTextures_model.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/DynamicNeuralTextures_model.py new file mode 100644 index 0000000..8cf232b --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/DynamicNeuralTextures_model.py @@ -0,0 +1,328 @@ +from util.image_pool import ImagePool +import torch +################ +### HELPER ### +################ +# from BaselModel.basel_model import * +from . import NeuralTexture +from . import UNET +from . import VGG_LOSS +from . import networks +from .base_model import BaseModel +import matplotlib.pyplot as plt +import numpy as np + +INVALID_UV = 0 + + +def define_Texture(opt, gpu_ids=[]): + net = None + + if opt.textureModel == 'DynamicNeuralTextureAudio': + net = NeuralTexture.DynamicNeuralTextureAudio(texture_dimensions=opt.tex_dim, texture_features_intermediate=opt.tex_features_intermediate, texture_features=opt.tex_features) + elif opt.textureModel == 'DynamicNeuralTextureExpression': + net = NeuralTexture.DynamicNeuralTextureExpression(texture_dimensions=opt.tex_dim, texture_features_intermediate=opt.tex_features_intermediate, texture_features=opt.tex_features) + elif opt.textureModel == 'StaticNeuralTexture': + net = NeuralTexture.StaticNeuralTexture(texture_dimensions=opt.tex_dim, texture_features=opt.tex_features) + + return networks.init_net(net, opt.init_type, opt.init_gain, gpu_ids) + + +def define_TextureDecoder(renderer, n_feature, ngf, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]): + net = None + norm_layer = networks.get_norm_layer(norm_type=norm) + N_OUT = 3 + #renderer=='UNET_5_level' + net = UNET.UnetRenderer(renderer, n_feature, N_OUT, ngf, norm_layer=norm_layer, use_dropout=use_dropout) + + return networks.init_net(net, init_type, init_gain, gpu_ids) + + +def define_Inpainter(renderer, n_feature, ngf, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]): + net = None + norm_layer = networks.get_norm_layer(norm_type=norm) + N_OUT = 3 + #renderer=='UNET_5_level' + net = UNET.UnetRenderer(renderer, n_feature, N_OUT, ngf, norm_layer=norm_layer, use_dropout=use_dropout) + + return networks.init_net(net, init_type, init_gain, gpu_ids) + + + +class DynamicNeuralTexturesModel(BaseModel): + def name(self): + return 'DynamicNeuralTexturesModel' + + @staticmethod + def modify_commandline_options(parser, is_train=True): + + # changing the default values to match the pix2pix paper + # (https://phillipi.github.io/pix2pix/) + #parser.set_defaults(norm='batch', netG='unet_256') + parser.set_defaults(norm='instance', netG='unet_256') + parser.set_defaults(dataset_mode='aligned') + if is_train: + parser.set_defaults(pool_size=0, no_lsgan=True) + parser.add_argument('--lambda_L1', type=float, default=100.0, help='weight for L1 loss') + + return parser + + def initialize(self, opt): + BaseModel.initialize(self, opt) + self.isTrain = opt.isTrain + + self.trainRenderer = not opt.fix_renderer + + # specify the training losses you want to print out. The program will call base_model.get_current_losses + self.loss_names = ['G_total', 'G_L1_Rendering', 'G_VGG_Rendering', 'G_GAN'] + + # specify the images you want to save/display. The program will call base_model.get_current_visuals + if self.isTrain: + self.visual_names = ['input_uv', 'features', 'fake', 'target'] + else: + self.visual_names = ['input_uv', 'fake', 'target', 'source'] + + # specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks + if self.isTrain: + self.model_names = ['texture', 'texture_decoder', 'inpainter' ,'netD'] + else: # during test time, only load Gs + self.model_names = ['texture', 'texture_decoder', 'inpainter'] + + # load/define networks + self.texture = define_Texture(opt, self.gpu_ids) + if self.opt.deca_details: + self.texture_decoder = define_TextureDecoder(opt.rendererType, opt.tex_features + 6, opt.ngf, opt.norm, not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids) + else: + self.texture_decoder = define_TextureDecoder(opt.rendererType, opt.tex_features + 3, opt.ngf, opt.norm, + not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids) + self.inpainter = define_Inpainter(opt.rendererType, 6, opt.ngf, opt.norm, not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids) + + + # optimizer + self.loss_G_GAN = 0.0 + + if self.isTrain: + use_sigmoid = opt.no_lsgan + print(opt.input_nc) + print(opt.output_nc) + print(opt.input_nc + opt.output_nc) + self.netD = networks.define_D(opt.input_nc + opt.output_nc, opt.ndf, opt.netD, opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, opt.init_gain, self.gpu_ids) + self.fake_AB_pool = ImagePool(opt.pool_size) + # define loss functions + self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan).to(self.device) + self.criterionL1 = torch.nn.L1Loss(reduction='mean') + self.criterionL1Smooth = torch.nn.SmoothL1Loss(reduction='mean') + self.criterionL2 = torch.nn.MSELoss(reduction='mean') + + if self.opt.lossType == 'VGG': + self.vggloss = VGG_LOSS.VGGLOSS().to(self.device) + + # initialize optimizers + self.optimizers = [] + + self.optimizer_texture_decoder = torch.optim.Adam(self.texture_decoder.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999)) + self.optimizers.append(self.optimizer_texture_decoder) + + self.optimizer_inpainter = torch.optim.Adam(self.inpainter.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999)) + self.optimizers.append(self.optimizer_inpainter) + + self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999)) + self.optimizers.append(self.optimizer_D) + + self.optimizer_T = torch.optim.Adam(self.texture.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999)) + self.optimizers.append(self.optimizer_T) + + + def maskErosion(self, mask, erosionFactor): + offsetY = int(erosionFactor * 40) + # throat + mask2 = mask[:,:,0:-offsetY,:] + mask2 = torch.cat([torch.ones_like(mask[:,:,0:offsetY,:]), mask2], 2) + # forehead + offsetY = int(erosionFactor * 8) #<<<< + mask3 = mask[:,:,offsetY:,:] + mask3 = torch.cat([mask3, torch.ones_like(mask[:,:,0:offsetY,:])], 2) + mask = mask * mask2 * mask3 + + offsetX = int(erosionFactor * 15) + # left + mask4 = mask[:,:,:,0:-offsetX] + mask4 = torch.cat([torch.ones_like(mask[:,:,:,0:offsetX]), mask4], 3) + # right + mask5 = mask[:,:,:,offsetX:] + mask5 = torch.cat([mask5,torch.ones_like(mask[:,:,:,0:offsetX])], 3) + return mask * mask4 * mask5 + + def set_input(self, input): + self.target = input['TARGET'].to(self.device) + self.input_uv = input['UV'].to(self.device) + # self.intrinsics = input['intrinsics'] + # self.extrinsics = input['extrinsics'] + self.expressions = input['expressions'].cuda() + if self.opt.deca_details : + self.deca_details = input['deca_details'].cuda() + + # self.image_paths = input['paths'] + self.audio_features = input['audio_deepspeech'].cuda() + + if "SOURCE" in input: + self.source = input["SOURCE"].to(self.device) + if "REFERENCE_POSE" in input: + self.reference_pose = input["REFERENCE_POSE"].to(self.device) + + ## in training phase introduce some noise + #if self.isTrain: + # if self.opt.input_noise_augmentation: + # audio_noise = torch.randn_like(self.audio_features)*0.05 # check magnitude of noise + # self.audio_features = self.audio_features + audio_noise + + + def forward(self, alpha=1.0): + # background + mask = (self.input_uv[:,0:1,:,:] == INVALID_UV) & (self.input_uv[:,1:2,:,:] == INVALID_UV) + mask = self.maskErosion(mask, self.opt.erosionFactor) + mask = torch.cat([mask,mask,mask], 1) + self.background = torch.where(mask, self.target, torch.zeros_like(self.target)) + + if 0: + fig = plt.figure() + fig.add_subplot(241) + plt.imshow(self.input_uv[0,0:1,:,:].permute(1, 2, 0).cpu().numpy()) + fig.add_subplot(242) + plt.imshow(self.input_uv[0, 1:2, :, :].permute(1, 2, 0).cpu().numpy()) + fig.add_subplot(243) + plt.imshow(mask[0].permute(1, 2, 0).cpu().numpy().astype(np.float32)) + fig.add_subplot(244) + plt.imshow((((self.background[0].permute(1, 2, 0).cpu().numpy() + 1.) / 2.) * 255.).astype(np.uint8)) + fig.add_subplot(245) + plt.imshow((((self.target[0].permute(1, 2, 0).cpu().numpy() + 1.) / 2.) * 255.).astype(np.uint8)) + fig.add_subplot(246) + plt.imshow(torch.zeros_like(self.target)[0].permute(1, 2, 0).cpu().numpy()) + plt.show() + + # loop over batch elements + batch_size = self.target.shape[0] + self.features = [] + self.intermediate_fake = [] + self.fake = [] + for b in range(0,batch_size): + feat = self.texture(self.expressions[b:b+1], self.audio_features[b:b+1], self.input_uv[b:b+1]) + + if self.opt.deca_details: + feat = torch.cat((feat, self.deca_details[b:b+1]), dim=1) + + + self.features.append(feat) + + + intermediate_fake = self.texture_decoder(self.expressions[b:b+1], self.audio_features[b:b+1], feat, self.background[b:b+1]) + self.intermediate_fake.append(intermediate_fake) + + fake = self.inpainter(self.expressions[b:b+1], self.audio_features[b:b+1], intermediate_fake, self.background[b:b+1]) + self.fake.append(fake) + + + self.features = torch.cat(self.features, dim=0) + self.intermediate_fake = torch.cat(self.intermediate_fake, dim=0) + self.fake = torch.cat(self.fake, dim=0) + + self.fake = torch.where(mask, self.background, self.fake) + + + def backward_D(self): + mask = ( (self.input_uv[:,0:1,:,:] != INVALID_UV) | (self.input_uv[:,1:2,:,:] != INVALID_UV) ) + mask = torch.cat([mask,mask,mask], 1) + + def masked(img): + return torch.where(mask, img, torch.zeros_like(img)) + # Fake + # stop backprop to the generator by detaching fake_B + if self.opt.deca_details: + fake_AB = self.fake_AB_pool.query(torch.cat((self.input_uv, self.deca_details, masked(self.fake)), 1)) + + else: + fake_AB = self.fake_AB_pool.query(torch.cat((self.input_uv, masked(self.fake)), 1)) + + pred_fake = self.netD(fake_AB.detach()) + self.loss_D_fake = self.criterionGAN(pred_fake, False) + + + # Real + if self.opt.deca_details: + real_AB = torch.cat((self.input_uv, self.deca_details, masked(self.target)), 1) + + else: + real_AB = torch.cat((self.input_uv, masked(self.target)), 1) + + pred_real = self.netD(real_AB) + self.loss_D_real = self.criterionGAN(pred_real, True) + + + # Combined loss + self.loss_D = (self.loss_D_fake + self.loss_D_real) * 0.5 + + self.loss_D.backward() + + def backward_G(self, epoch): + + mask = ( (self.input_uv[:,0:1,:,:] != INVALID_UV) | (self.input_uv[:,1:2,:,:] != INVALID_UV) ) + sum_mask = torch.sum(mask) + d = mask.shape[1] + mask_weight = (d*d) / sum_mask + mask = torch.cat([mask,mask,mask], 1) + def masked(img): + return torch.where(mask, img, torch.zeros_like(img)) + + # First, G(A) should fake the discriminator + if self.opt.deca_details: + fake_AB = torch.cat((self.input_uv, self.deca_details, masked(self.fake)), 1) + + else: + fake_AB = torch.cat((self.input_uv, masked(self.fake)), 1) + + pred_fake = self.netD(fake_AB) + self.loss_G_GAN = self.criterionGAN(pred_fake, True) * 0.0 # disabled GAN + + + # Second, G(A) = B + self.loss_G_L1_Rendering = 0.0 + self.loss_G_L1_Rendering = 1.0 * self.criterionL1(masked(self.features[:,0:3,:,:]), masked(self.target) ) * mask_weight + self.loss_G_L1_Rendering += 5.0 * self.criterionL1(masked(self.intermediate_fake), masked(self.target) ) * mask_weight + self.loss_G_L1_Rendering += 10.0 * self.criterionL1(self.fake, self.target) + + self.loss_G_VGG_Rendering = 0.0 + if self.opt.lossType == 'VGG': + self.loss_G_VGG_Rendering += 10.0 * self.vggloss(self.fake, self.target) + + self.loss_G_total = self.loss_G_L1_Rendering + self.loss_G_VGG_Rendering + self.loss_G_GAN + + self.loss_G_total.backward() + + def optimize_parameters(self, epoch_iter): + alpha = (epoch_iter-5) / 50.0 + if alpha < 0.0: alpha = 0.0 + if alpha > 1.0: alpha = 1.0 + self.forward(alpha) + + + updateDiscriminator = self.loss_G_GAN < 1.0#0.1 + + # update Discriminator + if updateDiscriminator: + self.set_requires_grad(self.netD, True) + self.optimizer_D.zero_grad() + self.backward_D() + self.optimizer_D.step() + + # update Generator + self.set_requires_grad(self.netD, False) + self.optimizer_texture_decoder.zero_grad() + self.optimizer_inpainter.zero_grad() + self.optimizer_T.zero_grad() + + self.backward_G(epoch_iter) + + self.optimizer_texture_decoder.step() + self.optimizer_inpainter.step() + self.optimizer_T.step() + diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/NeuralTexture.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/NeuralTexture.py new file mode 100644 index 0000000..0f0b56a --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/NeuralTexture.py @@ -0,0 +1,134 @@ +import os +import torch +import torch.nn as nn +import torchvision.transforms as transforms +from util.image_pool import ImagePool +from .base_model import BaseModel +from . import networks +import numpy as np +import functools +from PIL import Image +from util import util +from torchvision import models +from collections import namedtuple + +################ +### HELPER ### +################ +# from BaselModel.basel_model import * +INVALID_UV = -1.0 + + + +##################################### +######## static texture ######### +##################################### +class StaticNeuralTexture(nn.Module): + def __init__(self, texture_dimensions, texture_features): + super(StaticNeuralTexture, self).__init__() + self.texture_dimensions = texture_dimensions #256 #texture dimensions + self.out_ch = texture_features # output feature, after evaluating the texture + + + self.register_parameter('data', torch.nn.Parameter(torch.randn(1, self.out_ch, self.texture_dimensions, self.texture_dimensions, requires_grad=True))) + #### + + def forward(self, expressions, audio_features, uv_inputs): + b = audio_features.shape[0] # batchsize + if b != 1: + print('ERROR: NeuralTexture forward only implemented for batchsize==1') + exit(-1) + uvs = torch.stack([uv_inputs[:,0,:,:], uv_inputs[:,1,:,:]], 3) + return torch.nn.functional.grid_sample(self.data, uvs, mode='bilinear', padding_mode='border') + + +##################################### +######## audio texture ########## +##################################### +class DynamicNeuralTextureAudio(nn.Module): + def __init__(self, texture_dimensions, texture_features_intermediate, texture_features): + super(DynamicNeuralTextureAudio, self).__init__() + self.texture_features_intermediate = texture_features_intermediate #16 #features stored in texture + self.texture_dimensions = texture_dimensions #256 #texture dimensions + self.out_ch = texture_features # output feature, after evaluating the texture + + # input 16 x 29 + self.convNet = nn.Sequential( + nn.Conv2d(29, 32, kernel_size=(3,1), stride=(2,1), padding=(1,0), bias=True), # 29 x 16 x 1 => 32 x 8 x 1 + nn.LeakyReLU(0.02, True), + nn.Conv2d(32, 32, kernel_size=(3,1), stride=(2,1), padding=(1,0), bias=True), # 32 x 8 x 1 => 32 x 4 x 1 + nn.LeakyReLU(0.02, True), + nn.Conv2d(32, 64, kernel_size=(3,1), stride=(2,1), padding=(1,0), bias=True), # 32 x 4 x 1 => 64 x 2 x 1 + nn.LeakyReLU(0.2, True), + nn.Conv2d(64, 64, kernel_size=(3,1), stride=(2,1), padding=(1,0), bias=True), # 64 x 2 x 1 => 64 x 1 x 1 + nn.LeakyReLU(0.2, True), + ) + conv_output_size = 64 + self.fullNet = nn.Sequential( + nn.Linear(in_features = conv_output_size, out_features=128, bias = True), + nn.LeakyReLU(0.02), + nn.Linear(in_features = 128, out_features=64, bias = True), + nn.LeakyReLU(0.02), + nn.Linear(in_features = 64, out_features=self.out_ch*4*4*self.texture_features_intermediate, bias = True), + nn.Tanh() + ) + + self.register_parameter('data', torch.nn.Parameter(torch.randn(1, self.texture_features_intermediate, self.texture_dimensions, self.texture_dimensions, requires_grad=True))) + #### + + def forward(self, expressions, audio_features, uv_inputs): + b = audio_features.shape[0] # batchsize + if b != 1: + print('ERROR: NeuralTexture forward only implemented for batchsize==1') + exit(-1) + # b x 1 x 16 x 29 --> transpose + audio_features = torch.transpose(audio_features, 1, 3) + audio_conv_res = self.convNet( audio_features ) + conv_filter = torch.reshape( self.fullNet( torch.reshape( audio_conv_res, (b,1,-1))), (self.out_ch,self.texture_features_intermediate,4,4)) + self.tex_eval = nn.functional.conv2d(self.data, conv_filter, stride=1, padding=2) + uvs = torch.stack([uv_inputs[:,0,:,:], uv_inputs[:,1,:,:]], 3) + + return torch.nn.functional.grid_sample(self.tex_eval, uvs, mode='bilinear', padding_mode='border') + + +##################################### +###### expression texture ####### +##################################### + +class DynamicNeuralTextureExpression(nn.Module): + def __init__(self, texture_dimensions, texture_features_intermediate, texture_features): + super(DynamicNeuralTextureExpression, self).__init__() + self.texture_features_intermediate = texture_features_intermediate #16 #features stored in texture + self.texture_dimensions = texture_dimensions #256 #texture dimensions + self.out_ch = texture_features # output feature, after evaluating the texture + + # input: 76 + input_size = 53 + self.fullNet = nn.Sequential( + nn.Linear(in_features = input_size, out_features=128, bias = True), + nn.LeakyReLU(0.02), + nn.Linear(in_features = 128, out_features=64, bias = True), + nn.LeakyReLU(0.02), + nn.Linear(in_features = 64, out_features=self.out_ch*4*4*self.texture_features_intermediate, bias = True), + nn.Tanh() + ).cuda() + + self.register_parameter('data', torch.nn.Parameter(torch.randn(1, self.texture_features_intermediate, self.texture_dimensions, self.texture_dimensions, requires_grad=True))) + #### + + def forward(self, expressions, audio_features, uv_inputs): + b = expressions.shape[0] # batchsize + if b != 1: + print('ERROR: NeuralTexture forward only implemented for batchsize==1') + exit(-1) + + expressions = expressions.type(torch.FloatTensor) + + tmp = self.fullNet(torch.reshape( expressions, (1,1,-1)).cuda()) + + conv_filter = torch.reshape(tmp, (self.out_ch,self.texture_features_intermediate,4,4)) + self.tex_eval = nn.functional.conv2d(self.data, conv_filter, stride=1, padding=2) + uvs = torch.stack([uv_inputs[:,0,:,:], uv_inputs[:,1,:,:]], 3) + + return torch.nn.functional.grid_sample(self.tex_eval, uvs, mode='bilinear', padding_mode='border') + diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/UNET.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/UNET.py new file mode 100644 index 0000000..0b0724d --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/UNET.py @@ -0,0 +1,316 @@ +import os +import torch +import torch.nn as nn +import torchvision.transforms as transforms +from util.image_pool import ImagePool +from .base_model import BaseModel +from . import networks +import numpy as np +import functools +from PIL import Image +from util import util +from torchvision import models +from collections import namedtuple + + +class UnetSkipConnectionBlock(nn.Module): + def __init__(self, outer_nc, inner_nc, input_nc=None, submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(UnetSkipConnectionBlock, self).__init__() + self.outermost = outermost + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + if input_nc is None: + input_nc = outer_nc + + #use_norm = False + use_norm = True + + downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, stride=2, padding=1, bias=use_bias) + downrelu = nn.LeakyReLU(0.2, True) + + if use_norm: downnorm = norm_layer(inner_nc) + uprelu = nn.ReLU(True) + if use_norm: upnorm = norm_layer(outer_nc) + + if outermost: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1) + down = [downconv] + up = [uprelu, upconv, nn.Tanh()] + model = down + [submodule] + up + elif innermost: + upconv = nn.ConvTranspose2d(inner_nc, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias) + down = [downrelu, downconv] + if use_norm: up = [uprelu, upconv, upnorm] + else: up = [uprelu, upconv] + model = down + up + else: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias) + if use_norm: down = [downrelu, downconv, downnorm] + down = [downrelu, downconv] + if use_norm: up = [uprelu, upconv, upnorm] + else: up = [uprelu, upconv] + + if use_dropout: + model = down + [submodule] + up + [nn.Dropout(0.5)] + else: + model = down + [submodule] + up + + self.model = nn.Sequential(*model) + + def forward(self, x): + if self.outermost: + return self.model(x) + else: + return torch.cat([x, self.model(x)], 1) + + +# with bilinear upsampling +class UnetSkipConnectionBlock_BU(nn.Module): + def __init__(self, outer_nc, inner_nc, input_nc=None, submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(UnetSkipConnectionBlock_BU, self).__init__() + self.outermost = outermost + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + if input_nc is None: + input_nc = outer_nc + + #use_norm = False + use_norm = True + + downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, stride=2, padding=1, bias=use_bias) + downrelu = nn.LeakyReLU(0.2, True) + + if use_norm: downnorm = norm_layer(inner_nc) + uprelu = nn.ReLU(True) + if use_norm: upnorm = norm_layer(outer_nc) + + if outermost: + #upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1) + upconv = nn.Sequential( + nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False), + nn.Conv2d(inner_nc * 2, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias), + nn.LeakyReLU(0.2, True) + ) + + down = [downconv] + up = [uprelu, upconv, nn.Tanh()] + model = down + [submodule] + up + elif innermost: + #upconv = nn.ConvTranspose2d(inner_nc, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias) + upconv = nn.Sequential( + nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False), + nn.Conv2d(inner_nc, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias), + nn.LeakyReLU(0.2, True) + ) + + down = [downrelu, downconv] + if use_norm: up = [uprelu, upconv, upnorm] + else: up = [uprelu, upconv] + model = down + up + else: + #upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias) + upconv = nn.Sequential( + nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False), + nn.Conv2d(inner_nc * 2, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias), + nn.LeakyReLU(0.2, True) + ) + + if use_norm: down = [downrelu, downconv, downnorm] + down = [downrelu, downconv] + if use_norm: up = [uprelu, upconv, upnorm] + else: up = [uprelu, upconv] + + if use_dropout: + model = down + [submodule] + up + [nn.Dropout(0.5)] + else: + model = down + [submodule] + up + + self.model = nn.Sequential(*model) + + def forward(self, x): + if self.outermost: + return self.model(x) + else: + return torch.cat([x, self.model(x)], 1) + + +# dilated convs, without downsampling +class UnetSkipConnectionBlock_DC(nn.Module): + def __init__(self, outer_nc, inner_nc, input_nc=None, submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False, dilation=1): + super(UnetSkipConnectionBlock_DC, self).__init__() + self.outermost = outermost + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + if input_nc is None: + input_nc = outer_nc + + #use_norm = False + use_norm = True + + #downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, stride=2, dilation=dilation, padding=1, bias=use_bias) + downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=3, stride=1, dilation=dilation, padding=1*dilation, bias=use_bias) + downrelu = nn.LeakyReLU(0.2, True) + + if use_norm: downnorm = norm_layer(inner_nc) + uprelu = nn.ReLU(True) + if use_norm: upnorm = norm_layer(outer_nc) + + if outermost: + #upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1) + upconv = nn.Sequential( + nn.Conv2d(inner_nc * 2, outer_nc, kernel_size=3, stride=1, dilation=1, padding=1, bias=use_bias), + nn.LeakyReLU(0.2, True) + ) + + down = [downconv] + up = [uprelu, upconv, nn.Tanh()] + model = down + [submodule] + up + elif innermost: + #upconv = nn.ConvTranspose2d(inner_nc, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias) + upconv = nn.Sequential( + nn.Conv2d(inner_nc, outer_nc, kernel_size=3, stride=1, dilation=1, padding=1, bias=use_bias), + nn.LeakyReLU(0.2, True) + ) + + down = [downrelu, downconv] + if use_norm: up = [uprelu, upconv, upnorm] + else: up = [uprelu, upconv] + model = down + up + else: + #upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias) + upconv = nn.Sequential( + nn.Conv2d(inner_nc * 2, outer_nc, kernel_size=3, stride=1, dilation=1, padding=1, bias=use_bias), + nn.LeakyReLU(0.2, True) + ) + + if use_norm: down = [downrelu, downconv, downnorm] + down = [downrelu, downconv] + if use_norm: up = [uprelu, upconv, upnorm] + else: up = [uprelu, upconv] + + if use_dropout: + model = down + [submodule] + up + [nn.Dropout(0.5)] + else: + model = down + [submodule] + up + + self.model = nn.Sequential(*model) + + def forward(self, x): + if self.outermost: + return self.model(x) + else: + return torch.cat([x, self.model(x)], 1) + + +class UnetRenderer(nn.Module): + def __init__(self, renderer, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(UnetRenderer, self).__init__() + # construct unet structure + if renderer=='UNET_8_level_BU': + print('>>>> UNET_8_level_BU <<<<') + num_downs = 8 + unet_block = UnetSkipConnectionBlock_BU(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) + for i in range(num_downs - 5): + unet_block = UnetSkipConnectionBlock_BU(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) + unet_block = UnetSkipConnectionBlock_BU(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock_BU(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock_BU(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock_BU(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) + elif renderer=='UNET_6_level_BU': + print('>>>> UNET_6_level_BU <<<<') + num_downs = 6 + unet_block = UnetSkipConnectionBlock_BU(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) + for i in range(num_downs - 5): + unet_block = UnetSkipConnectionBlock_BU(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) + unet_block = UnetSkipConnectionBlock_BU(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock_BU(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock_BU(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock_BU(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) + elif renderer=='UNET_5_level_BU': + print('>>>> UNET_5_level_BU <<<<') + num_downs = 5 + unet_block = UnetSkipConnectionBlock_BU(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) + for i in range(num_downs - 5): + unet_block = UnetSkipConnectionBlock_BU(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) + unet_block = UnetSkipConnectionBlock_BU(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock_BU(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock_BU(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock_BU(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) + elif renderer=='UNET_3_level_BU': + print('>>>> UNET_3_level_BU <<<<') + unet_block = UnetSkipConnectionBlock_BU(ngf * 2, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) + unet_block = UnetSkipConnectionBlock_BU(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock_BU(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) + + elif renderer=='UNET_8_level': + print('>>>> UNET_8_level <<<<') + num_downs = 8 + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) + for i in range(num_downs - 5): + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) + unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) + elif renderer=='UNET_6_level': + print('>>>> UNET_6_level <<<<') + num_downs = 6 + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) + for i in range(num_downs - 5): + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) + unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) + elif renderer=='UNET_5_level': + print('>>>> UNET_5_level <<<<') + num_downs = 5 + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) + for i in range(num_downs - 5): + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) + unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) + elif renderer=='UNET_3_level': + print('>>>> UNET_3_level <<<<') + unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) + unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) + + elif renderer=='UNET_5_level_DC': + print('>>>> UNET_5_level_DC <<<<') + num_downs = 5 + dilation = 1 + unet_block = UnetSkipConnectionBlock_DC(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True, dilation=dilation) + for i in range(num_downs - 5): + dilation *= 2 + unet_block = UnetSkipConnectionBlock_DC(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout, dilation=dilation) + dilation *= 2 + unet_block = UnetSkipConnectionBlock_DC(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, dilation=dilation) + dilation *= 2 + unet_block = UnetSkipConnectionBlock_DC(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer, dilation=dilation) + dilation *= 2 + unet_block = UnetSkipConnectionBlock_DC(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer, dilation=dilation) + dilation *= 2 + unet_block = UnetSkipConnectionBlock_DC(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer, dilation=dilation) + elif renderer=='UNET_3_level_DC': + print('>>>> UNET_3_level_DC <<<<') + dilation = 1 + unet_block = UnetSkipConnectionBlock_DC(ngf * 2, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True, dilation=dilation) + dilation *= 2 + unet_block = UnetSkipConnectionBlock_DC(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer, dilation=dilation) + dilation *= 2 + unet_block = UnetSkipConnectionBlock_DC(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer, dilation=dilation) + + self.model = unet_block + + def forward(self, expressions, audio_features, features, background): + unet_input = torch.cat([features, background], 1) + return self.model(unet_input) \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/VGG_LOSS.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/VGG_LOSS.py new file mode 100644 index 0000000..f426c37 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/VGG_LOSS.py @@ -0,0 +1,79 @@ +import os +import torch +import torch.nn as nn +import torchvision.transforms as transforms +from util.image_pool import ImagePool +from .base_model import BaseModel +from . import networks +import numpy as np +import functools +from torchvision import models +from collections import namedtuple + +class VGG16(torch.nn.Module): + def __init__(self, requires_grad=False): + super(VGG16, self).__init__() + vgg_pretrained_features = models.vgg16(pretrained=True).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + for x in range(4): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(4, 9): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(9, 16): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(16, 23): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + #normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + #X=normalize(X) + X = 0.5 * (X + 1.0) # map to [0,1] + + h = self.slice1(X) + h_relu1_2 = h + h = self.slice2(h) + h_relu2_2 = h + h = self.slice3(h) + h_relu3_3 = h + h = self.slice4(h) + h_relu4_3 = h + vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3']) + out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3) + return out + +def gram_matrix(y): + (b, ch, h, w) = y.size() + features = y.view(b, ch, w * h) + features_t = features.transpose(1, 2) + gram = features.bmm(features_t) / (ch * h * w) + return gram + + + +class VGGLOSS(torch.nn.Module): + def __init__(self): + super(VGGLOSS, self).__init__() + self.model = VGG16() + self.criterionL2 = torch.nn.MSELoss(reduction='mean') + + def forward(self, fake, target, content_weight = 1.0, style_weight = 1.0): + vgg_fake = self.model(fake) + vgg_target = self.model(target) + + content_loss = self.criterionL2(vgg_target.relu2_2, vgg_fake.relu2_2) + + # gram_matrix + gram_style = [gram_matrix(y) for y in vgg_target] + style_loss = 0.0 + for ft_y, gm_s in zip(vgg_fake, gram_style): + gm_y = gram_matrix(ft_y) + style_loss += self.criterionL2(gm_y, gm_s) + + total_loss = content_weight * content_loss + style_weight * style_loss + return total_loss \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/__init__.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/__init__.py new file mode 100644 index 0000000..4d92091 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/__init__.py @@ -0,0 +1,39 @@ +import importlib +from models.base_model import BaseModel + + +def find_model_using_name(model_name): + # Given the option --model [modelname], + # the file "models/modelname_model.py" + # will be imported. + model_filename = "models." + model_name + "_model" + modellib = importlib.import_module(model_filename) + + # In the file, the class called ModelNameModel() will + # be instantiated. It has to be a subclass of BaseModel, + # and it is case-insensitive. + model = None + target_model_name = model_name.replace('_', '') + 'model' + for name, cls in modellib.__dict__.items(): + if name.lower() == target_model_name.lower() \ + and issubclass(cls, BaseModel): + model = cls + + if model is None: + print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name)) + exit(0) + + return model + + +def get_option_setter(model_name): + model_class = find_model_using_name(model_name) + return model_class.modify_commandline_options + + +def create_model(opt): + model = find_model_using_name(opt.model) + instance = model() + instance.initialize(opt) + print("model [%s] was created" % (instance.name())) + return instance diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/base_model.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/base_model.py new file mode 100644 index 0000000..5d3f9fa --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/base_model.py @@ -0,0 +1,254 @@ +import os +import torch +from collections import OrderedDict +from . import networks +import numpy as np +from PIL import Image +import shutil + +def save_tensor_image(input_image, image_path): + if isinstance(input_image, torch.Tensor): + image_tensor = input_image.data + image_numpy = image_tensor[0].cpu().float().numpy() + if image_numpy.shape[0] == 1: + image_numpy = np.tile(image_numpy, (3, 1, 1)) + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 + else: + image_numpy = input_image + image_numpy = image_numpy.astype(np.uint8) + image_pil = Image.fromarray(image_numpy) + image_pil.save(image_path) + +class BaseModel(): + + # modify parser to add command line options, + # and also change the default values if needed + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def name(self): + return 'BaseModel' + + def initialize(self, opt): + self.opt = opt + self.gpu_ids = opt.gpu_ids + self.isTrain = opt.isTrain + self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu') + self.load_dir = os.path.join(opt.checkpoints_dir, opt.name) + self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) + if opt.resize_or_crop != 'scale_width': + torch.backends.cudnn.benchmark = True + self.loss_names = [] + self.model_names = [] + self.visual_names = [] + self.image_paths = [] + + def set_input(self, input): + pass + + def forward(self): + pass + + # load and print networks; create schedulers + def setup(self, opt, parser=None): + if self.isTrain: + self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers] + if not self.isTrain or opt.continue_train: + load_suffix = 'iter_%d' % opt.load_iter if opt.load_iter > 0 else opt.epoch + self.load_networks(load_suffix) + self.print_networks(opt.verbose) + + + + # load specific moudles + def loadModules(self, opt, model_name, module_names): + for name in module_names: + if isinstance(name, str): + load_dir = os.path.join(opt.checkpoints_dir, model_name) + load_filename = 'latest_%s.pth' % (name) + load_path = os.path.join(load_dir, load_filename) + net = getattr(self, name) + if isinstance(net, torch.Tensor): + print('loading the tensor from %s' % load_path) + net_loaded = torch.load(load_path, map_location=str(self.device)) + net.copy_(net_loaded) + else: + # if isinstance(net, torch.nn.DataParallel): + # net = net.module + print('loading the module from %s' % load_path) + # if you are using PyTorch newer than 0.4 (e.g., built from + # GitHub source), you can remove str() on self.device + state_dict = torch.load(load_path, map_location=str(self.device)) + if hasattr(state_dict, '_metadata'): + del state_dict._metadata + + # patch InstanceNorm checkpoints prior to 0.4 + for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop + self.__patch_instance_norm_state_dict(state_dict, net, key.split('.')) + net.load_state_dict(state_dict) + + + + + # make models eval mode during test time + def eval(self): + for name in self.model_names: + if isinstance(name, str): + net = getattr(self, name) + net.eval() + + # used in test time, wrapping `forward` in no_grad() so we don't save + # intermediate steps for backprop + def test(self): + with torch.no_grad(): + self.forward() + + # get image paths + def get_image_paths(self): + return self.image_paths + + def optimize_parameters(self): + pass + + # update learning rate (called once every epoch) + def update_learning_rate(self): + for scheduler in self.schedulers: + scheduler.step() + lr = self.optimizers[0].param_groups[0]['lr'] + print('learning rate = %.7f' % lr) + + # return visualization images. train.py will display these images, and save the images to a html + def get_current_visuals(self): + visual_ret = OrderedDict() + for name in self.visual_names: + if isinstance(name, str): + visual_ret[name] = getattr(self, name) + return visual_ret + + # return traning losses/errors. train.py will print out these errors as debugging information + def get_current_losses(self): + errors_ret = OrderedDict() + for name in self.loss_names: + if isinstance(name, str): + # float(...) works for both scalar tensor and float number + errors_ret[name] = float(getattr(self, 'loss_' + name)) + return errors_ret + + # save models to the disk + def save_networks(self, epoch): + for name in self.model_names: + if isinstance(name, str): + save_filename = '%s_%s.pth' % (epoch, name) + save_path = os.path.join(self.save_dir, save_filename) + net = getattr(self, name) + torch.save(net.state_dict(), save_path) + + # if isinstance(net, torch.Tensor): + # #torch.save(net.state_dict(), save_path) + # torch.save(net, save_path) + # for i in range(0, list(net.size())[0]): + # save_tensor_image(net[i:i+1,0:3,:,:], save_path+str(i)+'.png') + # else: + # if len(self.gpu_ids) > 0 and torch.cuda.is_available(): + # #torch.save(net.module.cpu().state_dict(), save_path) # << original + # torch.save(net.cpu().state_dict(), save_path) + # net.cuda(self.gpu_ids[0]) + # else: + # torch.save(net.cpu().state_dict(), save_path) + + # clean checkpoints + def clean_checkpoints(self, total_epochs): + for name in self.model_names: + if isinstance(name, str): + for epoch in range(total_epochs): + delete_filename = '%s_%s.pth' % (epoch, name) + save_path = os.path.join(self.save_dir, delete_filename) + if os.path.isfile(save_path): + os.remove(save_path) + + shutil.rmtree(os.path.join(self.save_dir, 'web')) + + + + def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0): + key = keys[i] + if i + 1 == len(keys): # at the end, pointing to a parameter/buffer + if module.__class__.__name__.startswith('InstanceNorm') and \ + (key == 'running_mean' or key == 'running_var'): + if getattr(module, key) is None: + state_dict.pop('.'.join(keys)) + if module.__class__.__name__.startswith('InstanceNorm') and \ + (key == 'num_batches_tracked'): + state_dict.pop('.'.join(keys)) + else: + self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1) + + # load models from the disk + def load_networks(self, epoch): + for name in self.model_names: + if isinstance(name, str): + load_filename = '%s_%s.pth' % (epoch, name) + load_path = os.path.join(self.load_dir, load_filename) + net = getattr(self, name) + net_loaded = torch.load(load_path, map_location=str(self.device)) + net.load_state_dict(net_loaded) + + + # print('loading the tensor from %s' % load_path) + # net_loaded = torch.load(load_path, map_location=str(self.device)) + + # for i, k in zip(net.state_dict().keys(), net_loaded.keys()): + # print(i, k) + # + # net.state_dict()[i] = net_loaded[k] + + + # if isinstance(net, torch.Tensor): + # print('loading the tensor from %s' % load_path) + # net_loaded = torch.load(load_path, map_location=str(self.device)) + # net.copy_(net_loaded) + # + # else: + # # if isinstance(net, torch.nn.DataParallel): + # # net = net.module + # print('loading the module from %s' % load_path) + # # if you are using PyTorch newer than 0.4 (e.g., built from + # # GitHub source), you can remove str() on self.device + # state_dict = torch.load(load_path, map_location=str(self.device)) + # if hasattr(state_dict, '_metadata'): + # del state_dict._metadata + # + # # patch InstanceNorm checkpoints prior to 0.4 + # for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop + # print(key) + # self.__patch_instance_norm_state_dict(state_dict, net, key.split('.')) + # + # net.load_state_dict(net_loaded.state_dict()) + + # print network information + def print_networks(self, verbose): + print('---------- Networks initialized -------------') + for name in self.model_names: + if isinstance(name, str): + net = getattr(self, name) + if isinstance(net, torch.Tensor): + num_params = net.numel() + print('[Tensor %s] Total number of parameters : %.3f M' % (name, num_params / 1e6)) + else: + num_params = 0 + for param in net.parameters(): + num_params += param.numel() + if verbose: + print(net) + print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6)) + print('-----------------------------------------------') + + # set requies_grad=False to avoid computation + def set_requires_grad(self, nets, requires_grad=False): + if not isinstance(nets, list): + nets = [nets] + for net in nets: + if net is not None: + for param in net.parameters(): + param.requires_grad = requires_grad diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/networks.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/networks.py new file mode 100644 index 0000000..a8c6505 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/models/networks.py @@ -0,0 +1,383 @@ +import torch +import torch.nn as nn +from torch.nn import init +import functools +from torch.optim import lr_scheduler + +############################################################################### +# Helper Functions +############################################################################### + + +def get_norm_layer(norm_type='instance'): + if norm_type == 'batch': + norm_layer = functools.partial(nn.BatchNorm2d, affine=True) + elif norm_type == 'instance': + norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False) + elif norm_type == 'none': + norm_layer = None + else: + raise NotImplementedError('normalization layer [%s] is not found' % norm_type) + return norm_layer + + +def get_scheduler(optimizer, opt): + if opt.lr_policy == 'lambda': + def lambda_rule(epoch): + lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.niter) / float(opt.niter_decay + 1) + return lr_l + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule) + elif opt.lr_policy == 'step': + scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1) + elif opt.lr_policy == 'plateau': + scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5) + elif opt.lr_policy == 'cosine': + scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.niter, eta_min=0) + else: + return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy) + return scheduler + + +def init_weights(net, init_type='normal', gain=0.02): + def init_func(m): + classname = m.__class__.__name__ + if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): + if init_type == 'normal': + init.normal_(m.weight.data, 0.0, gain) + elif init_type == 'xavier': + init.xavier_normal_(m.weight.data, gain=gain) + elif init_type == 'kaiming': + init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') + elif init_type == 'orthogonal': + init.orthogonal_(m.weight.data, gain=gain) + else: + raise NotImplementedError('initialization method [%s] is not implemented' % init_type) + if hasattr(m, 'bias') and m.bias is not None: + init.constant_(m.bias.data, 0.0) + elif classname.find('BatchNorm2d') != -1: + init.normal_(m.weight.data, 1.0, gain) + init.constant_(m.bias.data, 0.0) + + print('initialize network with %s' % init_type) + net.apply(init_func) + + +def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]): + if len(gpu_ids) > 0: + assert(torch.cuda.is_available()) + net.to(gpu_ids[0]) + #net = torch.nn.DataParallel(net, gpu_ids) + init_weights(net, init_type, gain=init_gain) + return net + + +def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]): + net = None + norm_layer = get_norm_layer(norm_type=norm) + + if netG == 'resnet_9blocks': + net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9) + elif netG == 'resnet_6blocks': + net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6) + elif netG == 'unet_128': + net = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout) + elif netG == 'unet_256': + net = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout) + else: + raise NotImplementedError('Generator model name [%s] is not recognized' % netG) + return init_net(net, init_type, init_gain, gpu_ids) + + +def define_D(input_nc, ndf, netD, + n_layers_D=3, norm='batch', use_sigmoid=False, init_type='normal', init_gain=0.02, gpu_ids=[]): + net = None + norm_layer = get_norm_layer(norm_type=norm) + + if netD == 'basic': + net = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer, use_sigmoid=use_sigmoid) + elif netD == 'n_layers': + net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer, use_sigmoid=use_sigmoid) + elif netD == 'pixel': + net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer, use_sigmoid=use_sigmoid) + else: + raise NotImplementedError('Discriminator model name [%s] is not recognized' % net) + return init_net(net, init_type, init_gain, gpu_ids) + + +############################################################################## +# Classes +############################################################################## + + +# Defines the GAN loss which uses either LSGAN or the regular GAN. +# When LSGAN is used, it is basically same as MSELoss, +# but it abstracts away the need to create the target label tensor +# that has the same size as the input +class GANLoss(nn.Module): + def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0): + super(GANLoss, self).__init__() + self.register_buffer('real_label', torch.tensor(target_real_label)) + self.register_buffer('fake_label', torch.tensor(target_fake_label)) + if use_lsgan: + self.loss = nn.MSELoss() + else: + self.loss = nn.BCELoss() + + def get_target_tensor(self, input, target_is_real): + if target_is_real: + target_tensor = self.real_label + else: + target_tensor = self.fake_label + return target_tensor.expand_as(input) + + def __call__(self, input, target_is_real): + target_tensor = self.get_target_tensor(input, target_is_real) + return self.loss(input, target_tensor) + + +# Defines the generator that consists of Resnet blocks between a few +# downsampling/upsampling operations. +# Code and idea originally from Justin Johnson's architecture. +# https://github.com/jcjohnson/fast-neural-style/ +class ResnetGenerator(nn.Module): + def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'): + assert(n_blocks >= 0) + super(ResnetGenerator, self).__init__() + self.input_nc = input_nc + self.output_nc = output_nc + self.ngf = ngf + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + + model = [nn.ReflectionPad2d(3), + nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, + bias=use_bias), + norm_layer(ngf), + nn.ReLU(True)] + + n_downsampling = 2 + for i in range(n_downsampling): + mult = 2**i + model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, + stride=2, padding=1, bias=use_bias), + norm_layer(ngf * mult * 2), + nn.ReLU(True)] + + mult = 2**n_downsampling + for i in range(n_blocks): + model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)] + + for i in range(n_downsampling): + mult = 2**(n_downsampling - i) + model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), + kernel_size=3, stride=2, + padding=1, output_padding=1, + bias=use_bias), + norm_layer(int(ngf * mult / 2)), + nn.ReLU(True)] + model += [nn.ReflectionPad2d(3)] + model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] + model += [nn.Tanh()] + + self.model = nn.Sequential(*model) + + def forward(self, input): + return self.model(input) + + +# Define a resnet block +class ResnetBlock(nn.Module): + def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias): + super(ResnetBlock, self).__init__() + self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias) + + def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias): + conv_block = [] + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(1)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(1)] + elif padding_type == 'zero': + p = 1 + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + + conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), + norm_layer(dim), + nn.ReLU(True)] + if use_dropout: + conv_block += [nn.Dropout(0.5)] + + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(1)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(1)] + elif padding_type == 'zero': + p = 1 + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), + norm_layer(dim)] + + return nn.Sequential(*conv_block) + + def forward(self, x): + out = x + self.conv_block(x) + return out + + +# Defines the Unet generator. +# |num_downs|: number of downsamplings in UNet. For example, +# if |num_downs| == 7, image of size 128x128 will become of size 1x1 +# at the bottleneck +class UnetGenerator(nn.Module): + def __init__(self, input_nc, output_nc, num_downs, ngf=64, + norm_layer=nn.BatchNorm2d, use_dropout=False): + super(UnetGenerator, self).__init__() + + # construct unet structure + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) + for i in range(num_downs - 5): + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) + unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) + + self.model = unet_block + + def forward(self, input): + return self.model(input) + + +# Defines the submodule with skip connection. +# X -------------------identity---------------------- X +# |-- downsampling -- |submodule| -- upsampling --| +class UnetSkipConnectionBlock(nn.Module): + def __init__(self, outer_nc, inner_nc, input_nc=None, + submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(UnetSkipConnectionBlock, self).__init__() + self.outermost = outermost + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + if input_nc is None: + input_nc = outer_nc + downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, + stride=2, padding=1, bias=use_bias) + downrelu = nn.LeakyReLU(0.2, True) + downnorm = norm_layer(inner_nc) + uprelu = nn.ReLU(True) + upnorm = norm_layer(outer_nc) + + if outermost: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1) + down = [downconv] + up = [uprelu, upconv, nn.Tanh()] + model = down + [submodule] + up + elif innermost: + upconv = nn.ConvTranspose2d(inner_nc, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv] + up = [uprelu, upconv, upnorm] + model = down + up + else: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv, downnorm] + up = [uprelu, upconv, upnorm] + + if use_dropout: + model = down + [submodule] + up + [nn.Dropout(0.5)] + else: + model = down + [submodule] + up + + self.model = nn.Sequential(*model) + + def forward(self, x): + if self.outermost: + return self.model(x) + else: + return torch.cat([x, self.model(x)], 1) + + +# Defines the PatchGAN discriminator with the specified arguments. +class NLayerDiscriminator(nn.Module): + def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False): + super(NLayerDiscriminator, self).__init__() + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + + kw = 4 + padw = 1 + sequence = [ + nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), + nn.LeakyReLU(0.2, True) + ] + + nf_mult = 1 + nf_mult_prev = 1 + for n in range(1, n_layers): + nf_mult_prev = nf_mult + nf_mult = min(2**n, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, + kernel_size=kw, stride=2, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + nf_mult_prev = nf_mult + nf_mult = min(2**n_layers, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, + kernel_size=kw, stride=1, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] + + if use_sigmoid: + sequence += [nn.Sigmoid()] + + self.model = nn.Sequential(*sequence) + + def forward(self, input): + return self.model(input) + + +class PixelDiscriminator(nn.Module): + def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d, use_sigmoid=False): + super(PixelDiscriminator, self).__init__() + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + + self.net = [ + nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0), + nn.LeakyReLU(0.2, True), + nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias), + norm_layer(ndf * 2), + nn.LeakyReLU(0.2, True), + nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)] + + if use_sigmoid: + self.net.append(nn.Sigmoid()) + + self.net = nn.Sequential(*self.net) + + def forward(self, input): + return self.net(input) diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/options/__init__.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/options/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/options/base_options.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/options/base_options.py new file mode 100644 index 0000000..e56ae40 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/options/base_options.py @@ -0,0 +1,141 @@ +import argparse +import os +from util import util +import torch +import models +import data + + +class BaseOptions(): + def __init__(self): + self.initialized = False + + def initialize(self, parser): + parser.add_argument('--dataroot', required=True, help='path to images (should have subfolders trainA, trainB, valA, valB, etc)') + parser.add_argument('--batch_size', type=int, default=1, help='input batch size') + parser.add_argument('--seq_len', type=int, default=1, help='sequence length (if applicable)') + parser.add_argument('--fineSize', type=int, default=512, help='then crop to this size') + parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML') + parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels') + parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels') + parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer') + parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer') + parser.add_argument('--netD', type=str, default='basic', help='selects model to use for netD') + parser.add_argument('--netG', type=str, default='resnet_9blocks', help='selects model to use for netG') + parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers') + parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU') + parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models') + parser.add_argument('--renderer', type=str, default='no_renderer', help='name of the renderer to load the models from') + parser.add_argument('--fix_renderer', action='store_true', help='renderer is fixed') + parser.add_argument('--dataset_mode', type=str, default='aligned', help='chooses how datasets are loaded. [aligned | multi]') + parser.add_argument('--model', type=str, default='cycle_gan', help='chooses which model to use. cycle_gan, pix2pix, test') + parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA') + parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') + parser.add_argument('--load_iter', type=int, default='0', help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]') + parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data') + parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here') + parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization') + parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly') + parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator') + parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.') + parser.add_argument('--resize_or_crop', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop|none]') + parser.add_argument('--no_augmentation', action='store_true', help='if specified, no data augmentation') + #parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal|xavier|kaiming|orthogonal]') + parser.add_argument('--init_type', type=str, default='xavier', help='network initialization [normal|xavier|kaiming|orthogonal]') + parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.') + parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information') + parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{loadSize}') + parser.add_argument('--tex_dim', type=int, default=256, help='neural texture dimensions') + parser.add_argument('--tex_features_intermediate', type=int, default=16, help='# intermediate neural texture features when using dynamic textures') + parser.add_argument('--tex_features', type=int, default=16, help='# neural texture features') + parser.add_argument('--textureModel', type=str, default='DynamicNeuralTextureAudio', help='texture model') + parser.add_argument('--rendererType', type=str, default='UNET_5_level', help='neural renderer network') + parser.add_argument('--lossType', type=str, default='L1', help='loss type for the final output') + + parser.add_argument('--hierarchicalTex', action='store_true', help='if specified, hierachical neural textures are used') + + parser.add_argument('--output_audio_expressions', action='store_true', help='if specified, no sh layers are used') + + parser.add_argument('--erosionFactor', type=float, default=1.0, help='scaling factor for erosion of the background.') + + parser.add_argument('--audio_window_size', type=float, default=16, help='audio window size = #mel feature bins') + + parser.add_argument('--look_ahead', action='store_true', help='cache images in numpy format') + + parser.add_argument('--cached_images', action='store_true', help='cache images in numpy format') + + # add option for deca details + parser.add_argument('--deca_details', action='store_true', help='use deca details as addidtional input') + + self.initialized = True + return parser + + def gather_options(self): + # initialize parser with basic options + if not self.initialized: + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser = self.initialize(parser) + + # get the basic options + opt, _ = parser.parse_known_args() + + # modify model-related parser options + model_name = opt.model + model_option_setter = models.get_option_setter(model_name) + parser = model_option_setter(parser, self.isTrain) + opt, _ = parser.parse_known_args() # parse again with the new defaults + + # modify dataset-related parser options + dataset_name = opt.dataset_mode + dataset_option_setter = data.get_option_setter(dataset_name) + parser = dataset_option_setter(parser, self.isTrain) + + self.parser = parser + + return parser.parse_args() + + def print_options(self, opt): + message = '' + message += '----------------- Options ---------------\n' + for k, v in sorted(vars(opt).items()): + comment = '' + default = self.parser.get_default(k) + if v != default: + comment = '\t[default: %s]' % str(default) + message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment) + message += '----------------- End -------------------' + print(message) + + # save to the disk + expr_dir = os.path.join(opt.checkpoints_dir, opt.name) + util.mkdirs(expr_dir) + file_name = os.path.join(expr_dir, 'opt.txt') + with open(file_name, 'wt') as opt_file: + opt_file.write(message) + opt_file.write('\n') + + def parse(self): + + opt = self.gather_options() + opt.isTrain = self.isTrain # train or test + + # process opt.suffix + if opt.suffix: + suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else '' + opt.name = opt.name + suffix + + self.print_options(opt) + + # set gpu ids + str_ids = opt.gpu_ids.split(',') + opt.gpu_ids = [] + for str_id in str_ids: + id = int(str_id) + if id >= 0: + opt.gpu_ids.append(id) + if len(opt.gpu_ids) > 0: + torch.cuda.set_device(opt.gpu_ids[0]) + + self.opt = opt + return self.opt diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/options/test_options.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/options/test_options.py new file mode 100644 index 0000000..2c35acf --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/options/test_options.py @@ -0,0 +1,32 @@ +from .base_options import BaseOptions + + +class TestOptions(BaseOptions): + def initialize(self, parser): + parser = BaseOptions.initialize(self, parser) + parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.') + parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.') + parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images') + parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') + # Dropout and Batchnorm has different behavioir during training and test. + parser.add_argument('--eval', action='store_true', help='use eval mode during test time.') + parser.add_argument('--num_test', type=int, default=50, help='how many test images to run') + + parser.add_argument('--write_no_images', action='store_true', help='compute validation') + + parser.add_argument('--write_video', action='store_true', help='write video') + parser.add_argument('--video_fps', type=float, default=25.0, help='video fps') + + parser.add_argument('--target_dataroot', type=str, default=None) + parser.add_argument('--source_dataroot', type=str, default='./datasets/', help='loads source files (expressions, audio, uvs).') + parser.add_argument('--expr_path', type=str, default=None) + parser.add_argument('--images_target_dir', type=str, default=None, help='path to save images.') + + parser.add_argument('--frame_id_source', type=int, default=-1) + parser.add_argument('--frame_id_target', type=int, default=-1) + + parser.set_defaults(model='test') + # To avoid cropping, the loadSize should be the same as fineSize + parser.set_defaults(loadSize=parser.get_default('fineSize')) + self.isTrain = False + return parser diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/options/train_options.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/options/train_options.py new file mode 100644 index 0000000..c91d50d --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/options/train_options.py @@ -0,0 +1,37 @@ +from .base_options import BaseOptions + + +class TrainOptions(BaseOptions): + def initialize(self, parser): + parser = BaseOptions.initialize(self, parser) + parser.add_argument('--display_freq', type=int, default=400, help='frequency of showing training results on screen') + parser.add_argument('--display_ncols', type=int, default=4, help='if positive, display all images in a single visdom web panel with certain number of images per row.') + parser.add_argument('--display_id', type=int, default=1, help='window id of the web display') + parser.add_argument('--display_server', type=str, default="http://localhost", help='visdom server of the web display') + parser.add_argument('--display_env', type=str, default='main', help='visdom display environment name (default is "main")') + parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display') + + parser.add_argument('--compute_val', action='store_true', help='compute validation') + parser.add_argument('--input_noise_augmentation', action='store_true', help='add input noise') + + parser.add_argument('--update_html_freq', type=int, default=1000, help='frequency of saving training results to html') + parser.add_argument('--print_freq', type=int, default=500, help='frequency of showing training results on console') + parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results') + parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs') + parser.add_argument('--save_by_iter', action='store_true', help='whether saves model by iteration') + + parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model') + parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by , +, ...') + parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') + parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate') + parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero') + parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam') + parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam') + parser.add_argument('--no_lsgan', action='store_true', help='do *not* use least square GAN, if false, use vanilla GAN') + parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images') + parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/') + parser.add_argument('--lr_policy', type=str, default='lambda', help='learning rate policy: lambda|step|plateau|cosine') + parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations') + + self.isTrain = True + return parser diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/options/transfer_options.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/options/transfer_options.py new file mode 100644 index 0000000..3fb0709 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/options/transfer_options.py @@ -0,0 +1,30 @@ +from .base_options import BaseOptions + + +class TransferOptions(BaseOptions): + def initialize(self, parser): + parser = BaseOptions.initialize(self, parser) + parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.') + parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.') + parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images') + parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') + # Dropout and Batchnorm has different behavioir during training and test. + parser.add_argument('--eval', action='store_true', help='use eval mode during test time.') + parser.add_argument('--num_test', type=int, default=50, help='how many test images to run') + + parser.add_argument('--write_no_images', action='store_true', help='compute validation') + + + parser.add_argument('--source_dir', type=str, default='./datasets/', help='loads source files (expressions, audio, uvs).') + + + + parser.add_argument('--source_actor', type=str, default='', help='source actor directory') + parser.add_argument('--target_actor', type=str, default='', help='target actor directory') + + + parser.set_defaults(model='test') + # To avoid cropping, the loadSize should be the same as fineSize + parser.set_defaults(loadSize=parser.get_default('fineSize')) + self.isTrain = False + return parser diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/req.txt b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/req.txt new file mode 100644 index 0000000..5252532 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/req.txt @@ -0,0 +1,60 @@ +audioread==2.1.6 +certifi==2018.11.29 +cffi==1.12.1 +chardet==3.0.4 +chumpy==0.68 +cycler==0.10.0 +decorator==4.3.2 +dominate==2.3.5 +EasyProcess==0.2.5 +freetype-py==2.1.0.post1 +future==0.17.1 +idna==2.8 +imageio==2.5.0 +joblib==0.13.2 +kiwisolver==1.0.1 +librosa==0.6.3 +llvmlite==0.27.1 +matplotlib==3.0.3 +mkl-fft==1.0.6 +mkl-random==1.0.1 +moderngl==5.5.0 +networkx==2.2 +neural-renderer-pytorch==1.1.3 +numba==0.42.1 +numpy==1.16.1 +olefile==0.46 +opencv-python==4.1.1.26 +OpenEXR==1.3.2 +Pillow==5.4.1 +progressbar2==3.46.1 +pycparser==2.19 +pyglet==1.4.0b1 +PyOpenGL==3.1.0 +pyparsing==2.3.1 +pyrender==0.1.24 +python-dateutil==2.8.0 +python-Levenshtein==0.12.0 +python-utils==2.3.0 +PyVirtualDisplay==0.2.1 +PyWavelets==1.0.3 +pyzmq==18.0.0 +requests==2.21.0 +resampy==0.2.1 +scikit-image==0.15.0 +scikit-learn==0.20.2 +scipy==1.2.1 +Shapely==1.6.4.post2 +six==1.12.0 +soft-renderer==1.0.0 +torch==1.0.1.post2 +torchaudio==0.2 +torchfile==0.1.0 +torchvision==0.2.1 +tornado==5.1.1 +tqdm==4.31.1 +trimesh==2.38.19 +urllib3==1.24.1 +visdom==0.1.8.8 +websocket-client==0.54.0 +wget==3.2 diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/requirements.txt b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/requirements.txt new file mode 100644 index 0000000..072d027 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/requirements.txt @@ -0,0 +1,4 @@ +torch>=0.4.0 +torchvision>=0.2.1 +dominate>=2.3.1 +visdom>=0.1.8.3 diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/train_renderer.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/train_renderer.py new file mode 100644 index 0000000..f9911b5 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/train_renderer.py @@ -0,0 +1,105 @@ +import time +import copy +from options.train_options import TrainOptions +from data import CreateDataLoader +from models import create_model +from util.visualizer import Visualizer + + +if __name__ == '__main__': + # training dataset + opt = TrainOptions().parse() + data_loader = CreateDataLoader(opt) + dataset = data_loader.load_data() + dataset_size = len(data_loader) + print('#training images = %d' % dataset_size) + # print('#training objects = %d' % opt.nTrainObjects) + + ## validation dataset + if opt.compute_val: + opt_validation = copy.copy(opt) # create a clone + opt_validation.phase = 'val' + opt_validation.serial_batches = True + opt_validation.isTrain = False + data_loader_validation = CreateDataLoader(opt_validation) + dataset_validation = data_loader_validation.load_data() + dataset_size_validation = len(data_loader_validation) + print('#validation images = %d' % dataset_size_validation) + print('#validation objects = %d' % opt_validation.nValObjects) + + # model + model = create_model(opt) + model.setup(opt) + + if opt.renderer != 'no_renderer': + print('load renderer') + model.loadModules(opt, opt.renderer, ['netD','netG']) + + visualizer = Visualizer(opt) + total_steps = 0 + + for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1): + epoch_start_time = time.time() + iter_data_time = time.time() + epoch_iter = 0 # iterator within an epoch + + for i, data in enumerate(dataset): + iter_start_time = time.time() + if total_steps % opt.print_freq == 0: + t_data = iter_start_time - iter_data_time + visualizer.reset() + total_steps += opt.batch_size + epoch_iter += opt.batch_size + + model.set_input(data) + model.optimize_parameters(epoch) + + if total_steps % opt.display_freq == 0: + save_result = total_steps % opt.update_html_freq == 0 + visualizer.display_current_results(model.get_current_visuals(), epoch, save_result) + + if total_steps % opt.print_freq == 0: + losses = model.get_current_losses() + + + if opt.compute_val: + validation_error = 0 + cnt = 0 + for i, data in enumerate(dataset_validation): + model.set_input(data) + model.forward() + model.backward_G(epoch) # be carefull with the gradients (are zeroed in the optimization step) + validation_error += model.loss_G.detach().cpu() + cnt += 1.0 + validation_error /= cnt + #print('Validation Error:', validation_error) + #visualizer.plot_current_validation_error(epoch, float(epoch_iter) / dataset_size, {'validation_error': validation_error}) + losses.update({'validation_error': validation_error}) + + t = (time.time() - iter_start_time) / opt.batch_size + visualizer.print_current_losses(epoch, epoch_iter, losses, t, t_data) + if opt.display_id > 0: + visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, opt, losses) + + if total_steps % opt.save_latest_freq == 0: + print('saving the latest model (epoch %d, total_steps %d)' % (epoch, total_steps)) + save_suffix = 'iter_%d' % total_steps if opt.save_by_iter else 'latest' + model.save_networks(save_suffix) + + iter_data_time = time.time() + + + + if epoch % opt.save_epoch_freq == 0: + print('saving the model at the end of epoch %d, iters %d' % (epoch, total_steps)) + model.save_networks('latest') + model.save_networks(epoch) + + print('End of epoch %d / %d \t Time Taken: %d sec' % + (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time)) + model.update_learning_rate() + + model.save_networks('latest') + model.save_networks(epoch) + model.clean_checkpoints(opt.niter + opt.niter_decay + 1) + diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/train_renderer.sh b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/train_renderer.sh new file mode 100644 index 0000000..dbe4dcd --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/train_renderer.sh @@ -0,0 +1,70 @@ +set -ex +# . train_audio2expressionsAttentionTMP.sh & +GPUID=0 + +DP=/home/alberto/NeuralVoicePuppetry/datasets/SRF_anchor_short +SUB=Halbtotale_355_9415 + +DATASETS_DIR=$DP/$SUB +DATASET_MODE=custom_aligned +OBJECT=Halbtotale_355_9415.h5 + +# neural texture, not used here +TEX_DIM=128 +TEX_FEATURES=16 + +INPUT_NC=2 +NUM_THREADS=1 + +# loss +LOSS=VGG +#LOSS=L1 +#LOSS=RMS +#LOSS=L4 + +# models +TEXTUREMODEL=DynamicNeuralTextureExpression +#TEXTUREMODEL=DynamicNeuralTextureAudio +MODEL=DynamicNeuralTextures +RENDERER_TYPE=UNET_6_level # There are many more of these TODO + + +# optimizer parameters +#LR=0.00001 +LR=0.0001 + +#N_ITER=150 #50 #N_ITER=150 +#N_ITER_LR_DECAY=50 + +N_ITER=50 #50 #N_ITER=150 +N_ITER_LR_DECAY=50 + +BATCH_SIZE=8 +SEQ_LEN=8 + + +RENDERER=$OBJECT +EROSION=0.6 + +################################################################################ +################################################################################ +################################################################################ +DATE_WITH_TIME=`date "+%Y%m%d-%H%M%S"` +NAME=$MODEL-$RENDERER_TYPE-$TEXTUREMODEL-SL$SEQ_LEN-BS$BATCH_SIZE-$OBJECT-$DATASET_MODE-$LOSS-$DATE_WITH_TIME-look_ahead +DISPLAY_NAME=${MODEL}-$DATASET_MODE_${OBJECT}-${RENDERER_TYPE}-SL$SEQ_LEN-BS$BATCH_SIZE-${LOSS}-look_ahead +DISPLAY_ID=0 + + + +# training +# --input_noise_augmentation +echo "---------" +CUDA_VISIBLE_DEVICES=0 python train_renderer.py --textureModel $TEXTUREMODEL --num_threads $NUM_THREADS --input_nc $INPUT_NC --display_id $DISPLAY_ID --look_ahead --seq_len $SEQ_LEN --save_latest_freq 100000 --no_augmentation --name $NAME --erosionFactor $EROSION --tex_dim $TEX_DIM --tex_features $TEX_FEATURES --rendererType $RENDERER_TYPE --lossType $LOSS --display_env $DISPLAY_NAME --niter $N_ITER --niter_decay $N_ITER_LR_DECAY --dataroot $DATASETS_DIR/$OBJECT --model $MODEL --netG unet_256 --lambda_L1 100 --dataset_mode $DATASET_MODE --no_lsgan --norm instance --pool_size 0 --gpu_ids $GPUID --lr $LR --batch_size $BATCH_SIZE + +# # testing +#EPOCH=latest +#python test.py --seq_len $SEQ_LEN --write_no_images --name $NAME --erosionFactor $EROSION --epoch $EPOCH --display_winsize 512 --tex_dim $TEX_DIM --tex_features $TEX_FEATURES --rendererType $RENDERER_TYPE --lossType $LOSS --dataroot $DATASETS_DIR/$OBJECT --model $MODEL --netG unet_256 --dataset_mode $DATASET_MODE --norm instance --gpu_ids $GPUID + +################################################################################ +################################################################################ +################################################################################ diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/train_renderer_deca_details.sh b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/train_renderer_deca_details.sh new file mode 100644 index 0000000..1f5a191 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/train_renderer_deca_details.sh @@ -0,0 +1,104 @@ +set -ex +# . train_audio2expressionsAttentionTMP.sh & +GPUID=0 + +DP=/home/alberto/NeuralVoicePuppetry/datasets/External +SUB=Youtube_Russian_guy + +DATASETS_DIR=$DP/$SUB +DATASET_MODE=custom_aligned +OBJECT=Youtube_Russian_guy.h5 + +# neural texture, not used here +TEX_DIM=128 +TEX_FEATURES=16 + +INPUT_NC=5 +NUM_THREADS=1 + +# loss +LOSS=VGG +#LOSS=L1 +#LOSS=RMS +#LOSS=L4 + +# models +TEXTUREMODEL=DynamicNeuralTextureExpression +#TEXTUREMODEL=DynamicNeuralTextureAudio +MODEL=DynamicNeuralTextures +RENDERER_TYPE=UNET_6_level # There are many more of these TODO + + +# optimizer parameters +#LR=0.00001 +LR=0.0001 + +#N_ITER=150 #50 #N_ITER=150 +#N_ITER_LR_DECAY=50 + +N_ITER=50 #50 #N_ITER=150 +N_ITER_LR_DECAY=50 + +BATCH_SIZE=8 +SEQ_LEN=8 + + +RENDERER=$OBJECT +EROSION=0.6 + +################################################################################ +################################################################################ +################################################################################ +DATE_WITH_TIME=`date "+%Y%m%d-%H%M%S"` +NAME=$MODEL-$RENDERER_TYPE-$TEXTUREMODEL-SL$SEQ_LEN-BS$BATCH_SIZE-$OBJECT-$DATASET_MODE-$LOSS-$DATE_WITH_TIME-look_ahead_mask_mouth +#NAME=DynamicNeuralTextures-UNET_6_level-DynamicNeuralTextureExpression-SL8-BS8-Halbtotale_355_9415.h5-custom_aligned-VGG-20210308-004445-look_ahead +#NAME=DynamicNeuralTextures-UNET_6_level-DynamicNeuralTextureExpression-SL8-BS8-Halbtotale_355_9415.h5-custom_aligned-VGG-20210309-184531-look_ahead_masked +DISPLAY_NAME=${MODEL}-$DATASET_MODE_${OBJECT}-${RENDERER_TYPE}-SL$SEQ_LEN-BS$BATCH_SIZE-${LOSS}-look_ahead +DISPLAY_ID=0 + + + +# training +# --input_noise_augmentation +echo "---------" +CUDA_VISIBLE_DEVICES=0 python \ +train_renderer.py \ +--textureModel $TEXTUREMODEL \ +--num_threads $NUM_THREADS \ +--input_nc $INPUT_NC \ +--display_id $DISPLAY_ID \ +--look_ahead \ +--seq_len $SEQ_LEN \ +--save_latest_freq 100000 \ +--no_augmentation \ +--name $NAME \ +--erosionFactor $EROSION \ +--tex_dim $TEX_DIM \ +--tex_features $TEX_FEATURES \ +--rendererType $RENDERER_TYPE \ +--lossType $LOSS \ +--display_env $DISPLAY_NAME \ +--niter $N_ITER \ +--niter_decay $N_ITER_LR_DECAY \ +--dataroot $DATASETS_DIR/$OBJECT \ +--model $MODEL \ +--netG unet_256 \ +--lambda_L1 100 \ +--dataset_mode $DATASET_MODE \ +--no_lsgan \ +--norm instance \ +--pool_size 0 \ +--gpu_ids $GPUID \ +--lr $LR \ +--batch_size $BATCH_SIZE \ +--deca_details \ +#--continue_train \ +#--epoch_count 15 \ + +# # testing +#EPOCH=latest +#python test.py --seq_len $SEQ_LEN --write_no_images --name $NAME --erosionFactor $EROSION --epoch $EPOCH --display_winsize 512 --tex_dim $TEX_DIM --tex_features $TEX_FEATURES --rendererType $RENDERER_TYPE --lossType $LOSS --dataroot $DATASETS_DIR/$OBJECT --model $MODEL --netG unet_256 --dataset_mode $DATASET_MODE --norm instance --gpu_ids $GPUID + +################################################################################ +################################################################################ +################################################################################ diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/util/__init__.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/util/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/util/get_data.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/util/get_data.py new file mode 100644 index 0000000..6325605 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/util/get_data.py @@ -0,0 +1,115 @@ +from __future__ import print_function +import os +import tarfile +import requests +from warnings import warn +from zipfile import ZipFile +from bs4 import BeautifulSoup +from os.path import abspath, isdir, join, basename + + +class GetData(object): + """ + + Download CycleGAN or Pix2Pix Data. + + Args: + technique : str + One of: 'cyclegan' or 'pix2pix'. + verbose : bool + If True, print additional information. + + Examples: + >>> from util.get_data import GetData + >>> gd = GetData(technique='cyclegan') + >>> new_data_path = gd.get(save_path='./datasets') # options will be displayed. + + """ + + def __init__(self, technique='cyclegan', verbose=True): + url_dict = { + 'pix2pix': 'https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets', + 'cyclegan': 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets' + } + self.url = url_dict.get(technique.lower()) + self._verbose = verbose + + def _print(self, text): + if self._verbose: + print(text) + + @staticmethod + def _get_options(r): + soup = BeautifulSoup(r.text, 'lxml') + options = [h.text for h in soup.find_all('a', href=True) + if h.text.endswith(('.zip', 'tar.gz'))] + return options + + def _present_options(self): + r = requests.get(self.url) + options = self._get_options(r) + print('Options:\n') + for i, o in enumerate(options): + print("{0}: {1}".format(i, o)) + choice = input("\nPlease enter the number of the " + "dataset above you wish to download:") + return options[int(choice)] + + def _download_data(self, dataset_url, save_path): + if not isdir(save_path): + os.makedirs(save_path) + + base = basename(dataset_url) + temp_save_path = join(save_path, base) + + with open(temp_save_path, "wb") as f: + r = requests.get(dataset_url) + f.write(r.content) + + if base.endswith('.tar.gz'): + obj = tarfile.open(temp_save_path) + elif base.endswith('.zip'): + obj = ZipFile(temp_save_path, 'r') + else: + raise ValueError("Unknown File Type: {0}.".format(base)) + + self._print("Unpacking Data...") + obj.extractall(save_path) + obj.close() + os.remove(temp_save_path) + + def get(self, save_path, dataset=None): + """ + + Download a dataset. + + Args: + save_path : str + A directory to save the data to. + dataset : str, optional + A specific dataset to download. + Note: this must include the file extension. + If None, options will be presented for you + to choose from. + + Returns: + save_path_full : str + The absolute path to the downloaded data. + + """ + if dataset is None: + selected_dataset = self._present_options() + else: + selected_dataset = dataset + + save_path_full = join(save_path, selected_dataset.split('.')[0]) + + if isdir(save_path_full): + warn("\n'{0}' already exists. Voiding Download.".format( + save_path_full)) + else: + self._print('Downloading Data...') + url = "{0}/{1}".format(self.url, selected_dataset) + self._download_data(url, save_path=save_path) + + return abspath(save_path_full) diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/util/image_pool.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/util/image_pool.py new file mode 100644 index 0000000..4a525fc --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/util/image_pool.py @@ -0,0 +1,32 @@ +import random +import torch + + +class ImagePool(): + def __init__(self, pool_size): + self.pool_size = pool_size + if self.pool_size > 0: + self.num_imgs = 0 + self.images = [] + + def query(self, images): + if self.pool_size == 0: + return images + return_images = [] + for image in images: + image = torch.unsqueeze(image.data_source, 0) + if self.num_imgs < self.pool_size: + self.num_imgs = self.num_imgs + 1 + self.images.append(image) + return_images.append(image) + else: + p = random.uniform(0, 1) + if p > 0.5: + random_id = random.randint(0, self.pool_size - 1) # randint is inclusive + tmp = self.images[random_id].clone() + self.images[random_id] = image + return_images.append(tmp) + else: + return_images.append(image) + return_images = torch.cat(return_images, 0) + return return_images diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/util/ownhtml.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/util/ownhtml.py new file mode 100644 index 0000000..41eabdd --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/util/ownhtml.py @@ -0,0 +1,64 @@ +import dominate +from dominate.tags import meta, h3, table, tr, td, p, a, img, br +import os + + +class OwnHTML: + def __init__(self, web_dir, title, reflesh=0): + self.title = title + self.web_dir = web_dir + self.img_dir = os.path.join(self.web_dir, 'images') + if not os.path.exists(self.web_dir): + os.makedirs(self.web_dir) + if not os.path.exists(self.img_dir): + os.makedirs(self.img_dir) + # print(self.img_dir) + + self.doc = dominate.document(title=title) + if reflesh > 0: + with self.doc.head: + meta(http_equiv="reflesh", content=str(reflesh)) + + def get_image_dir(self): + return self.img_dir + + def add_header(self, str): + with self.doc: + h3(str) + + def add_table(self, border=1): + self.t = table(border=border, style="table-layout: fixed;") + self.doc.add(self.t) + + def add_images(self, ims, txts, links, width=400): + self.add_table() + with self.t: + with tr(): + for im, txt, link in zip(ims, txts, links): + with td(style="word-wrap: break-word;", halign="center", valign="top"): + with p(): + with a(href=os.path.join('images', link)): + img(style="width:%dpx" % width, src=os.path.join('images', im)) + br() + p(txt) + + def save(self): + html_file = '%s/index.html' % self.web_dir + f = open(html_file, 'wt') + f.write(self.doc.render()) + f.close() + + +if __name__ == '__main__': + html = OwnHTML('web/', 'test_html') + html.add_header('hello world') + + ims = [] + txts = [] + links = [] + for n in range(4): + ims.append('image_%d.png' % n) + txts.append('text_%d' % n) + links.append('image_%d.png' % n) + html.add_images(ims, txts, links) + html.save() diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/util/util.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/util/util.py new file mode 100644 index 0000000..0509edf --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/util/util.py @@ -0,0 +1,82 @@ +from __future__ import print_function +import torch +import numpy as np +from PIL import Image +import os +import sys +import array +# import OpenEXR +# import Imath + +# def load_exr(image_path): +# # Open the input file +# file = OpenEXR.InputFile(image_path) +# +# # Compute the size +# dw = file.header()['dataWindow'] +# w, h = (dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1) +# +# # Read the three color channels as 32-bit floats +# FLOAT = Imath.PixelType(Imath.PixelType.FLOAT) +# #(R,G,B) = [np.array(array.array('f', file.channel(Chan, FLOAT)).tolist()).reshape((w, h, 1)) for Chan in ("R", "G", "B") ] +# +# (r, g, b) = file.channels("RGB") +# R = np.array(array.array('f', r).tolist()).reshape((w, h, 1)) +# G = np.array(array.array('f', g).tolist()).reshape((w, h, 1)) +# B = np.array(array.array('f', b).tolist()).reshape((w, h, 1)) +# +# return np.concatenate((R, G, B), axis=2) + +# Converts a Tensor into an image array (numpy) +# |imtype|: the desired type of the converted numpy array +def tensor2im(input_image, imtype=np.uint8): + if isinstance(input_image, torch.Tensor): + input_image = torch.clamp(input_image, -1.0, 1.0) + image_tensor = input_image.data + else: + return input_image + image_numpy = image_tensor[0].cpu().float().numpy() + if image_numpy.shape[0] == 1: + image_numpy = np.tile(image_numpy, (3, 1, 1)) + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 + return image_numpy.astype(imtype) + + +def diagnose_network(net, name='network'): + mean = 0.0 + count = 0 + for param in net.parameters(): + if param.grad is not None: + mean += torch.mean(torch.abs(param.grad.data_source)) + count += 1 + if count > 0: + mean = mean / count + print(name) + print(mean) + + +def save_image(image_numpy, image_path): + image_pil = Image.fromarray(image_numpy) + image_pil.save(image_path) + +def print_numpy(x, val=True, shp=False): + x = x.astype(np.float64) + if shp: + print('shape,', x.shape) + if val: + x = x.flatten() + print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % ( + np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x))) + + +def mkdirs(paths): + if isinstance(paths, list) and not isinstance(paths, str): + for path in paths: + mkdir(path) + else: + mkdir(paths) + + +def mkdir(path): + if not os.path.exists(path): + os.makedirs(path) diff --git a/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/util/visualizer.py b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/util/visualizer.py new file mode 100644 index 0000000..cc301d3 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/NeuralRenderingNetwork/util/visualizer.py @@ -0,0 +1,233 @@ +import numpy as np +import os +import sys +import ntpath +import time +from . import util +from . import ownhtml +# from scipy.misc import imresize + +def imresize(arr, shape, method): + return numpy.array(Image.fromarray(arr).resize(shape[::-1], method)) + + +if sys.version_info[0] == 2: + VisdomExceptionBase = Exception +else: + VisdomExceptionBase = ConnectionError + + +# # save image to the disk +# def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256): +# image_dir = webpage.get_image_dir() +# short_path = ntpath.basename(image_path[0]) +# name = os.path.splitext(short_path)[0] +# +# webpage.add_header(name) +# ims, txts, links = [], [], [] +# +# for label, im_data in visuals.items(): +# im = util.tensor2im(im_data) +# image_name = '%s_%s.png' % (name, label) +# save_path = os.path.join(image_dir, image_name) +# h, w, _ = im.shape +# +# height = int(width * h / float(w)) +# im = imresize(im, (height,width), interp='bicubic') +# +# #im = imresize(im, (height,widht), interp='bicubic') +# #if aspect_ratio > 1.0: +# # im = imresize(im, (h, int(w * aspect_ratio)), interp='bicubic') +# #if aspect_ratio < 1.0: +# # im = imresize(im, (int(h / aspect_ratio), w), interp='bicubic') +# +# util.save_image(im, save_path) +# +# ims.append(image_name) +# txts.append(label) +# links.append(image_name) +# webpage.add_images(ims, txts, links, width=width) + +def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256): + image_dir = webpage.get_image_dir() + short_path = ntpath.basename(image_path[0]) + name = os.path.splitext(short_path)[0] + + ims, txts, links = [], [], [] + + for label, im_data in visuals.items(): + im = util.tensor2im(im_data) + image_name = '%s_%s.png' % (name, label) + save_path = os.path.join(image_dir, image_name) + h, w, _ = im.shape + + height = int(width * h / float(w)) + im = imresize(im, (height, width), interp='bicubic') + util.save_image(im, save_path) + +class Visualizer(): + def __init__(self, opt): + print("vis") + self.display_id = opt.display_id + # self.use_html = opt.isTrain and not opt.no_html + self.use_html = True + self.win_size = opt.display_winsize + self.name = opt.name + self.opt = opt + self.saved = False + if self.display_id > 0: + import visdom + self.ncols = opt.display_ncols + self.vis = visdom.Visdom(server=opt.display_server, port=opt.display_port, env=opt.display_env, raise_exceptions=True) + + if self.use_html: + self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web', opt.phase) + if "images_target_dir" in opt: + self.img_dir = os.path.join(opt.images_target_dir, 'images') + else: + self.img_dir = os.path.join(self.web_dir, 'images') + print('create web directory %s...' % self.web_dir) + util.mkdirs([self.web_dir, self.img_dir]) + self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt') + with open(self.log_name, "a") as log_file: + now = time.strftime("%c") + log_file.write('================ Training Loss (%s) ================\n' % now) + + def reset(self): + self.saved = False + + def throw_visdom_connection_error(self): + print('\n\nCould not connect to Visdom server (https://github.com/facebookresearch/visdom) for displaying training progress.\nYou can suppress connection to Visdom using the option --display_id -1. To install visdom, run \n$ pip install visdom\n, and start the server by \n$ python -m visdom.server.\n\n') + exit(1) + + # |visuals|: dictionary of images to display or save + def display_current_results(self, visuals, epoch, save_result, aspect_ratio=1.0, width=256): + if self.display_id > 0: # show images in the browser + ncols = self.ncols + if ncols > 0: + ncols = min(ncols, len(visuals)) + h, w = next(iter(visuals.values())).shape[2:4] + height = int(width * h / float(w)) + h = height + w = width + table_css = """""" % (w, h) + title = self.name + label_html = '' + label_html_row = '' + images = [] + idx = 0 + for label, image in visuals.items(): + # + image_numpy = util.tensor2im(image) + image_numpy = imresize(image_numpy, (h, w), interp='bicubic') + image_numpy = image_numpy.transpose([2, 0, 1]) + label_html_row += '%s' % label + images.append(image_numpy) + idx += 1 + if idx % ncols == 0: + label_html += '%s' % label_html_row + label_html_row = '' + white_image = np.ones_like(image_numpy) * 255 + while idx % ncols != 0: + images.append(white_image) + label_html_row += '' + idx += 1 + if label_html_row != '': + label_html += '%s' % label_html_row + # pane col = image row + try: + self.vis.images(images, nrow=ncols, win=self.display_id + 1, padding=2, opts=dict(title=title + ' images')) + label_html = '%s
' % label_html + self.vis.text(table_css + label_html, win=self.display_id + 2, + opts=dict(title=title + ' labels')) + except VisdomExceptionBase: + self.throw_visdom_connection_error() + + else: + idx = 1 + for label, image in visuals.items(): + image_numpy = util.tensor2im(image) + self.vis.image(image_numpy.transpose([2, 0, 1]), opts=dict(title=label), + win=self.display_id + idx) + idx += 1 + + if self.use_html and (save_result or not self.saved): # save images to a html file + self.saved = True + for label, image in visuals.items(): + if label == "features": + image = image[:, :3] + image_numpy = util.tensor2im(image) + img_path = os.path.join(self.img_dir, 'epoch%.5d_%s.png' % (epoch, label)) + util.save_image(image_numpy, img_path) + # update website + webpage = ownhtml.OwnHTML(self.web_dir, 'Experiment name = %s' % self.name, reflesh=1) + for n in range(epoch, 0, -1): + webpage.add_header('epoch [%d]' % n) + ims, txts, links = [], [], [] + + for label, image_numpy in visuals.items(): + image_numpy = util.tensor2im(image) + img_path = 'epoch%.5d_%s.png' % (n, label) + ims.append(img_path) + txts.append(label) + links.append(img_path) + webpage.add_images(ims, txts, links, width=self.win_size) + webpage.save() + + # losses: dictionary of error labels and values + def plot_current_losses(self, epoch, counter_ratio, opt, losses): + if not hasattr(self, 'plot_data'): + self.plot_data = {'X': [], 'Y': [], 'legend': list(losses.keys())} + self.plot_data['X'].append(epoch + counter_ratio) + self.plot_data['Y'].append([losses[k] for k in self.plot_data['legend']]) + try: + self.vis.line( + X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1), + Y=np.array(self.plot_data['Y']), + opts={ + 'title': self.name + ' loss over time', + 'legend': self.plot_data['legend'], + 'xlabel': 'epoch', + 'ylabel': 'loss'}, + win=self.display_id) + except VisdomExceptionBase: + self.throw_visdom_connection_error() + + # losses: same format as |losses| of plot_current_losses + def print_current_losses(self, epoch, i, losses, t, t_data): + message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, i, t, t_data) + for k, v in losses.items(): + message += '%s: %.3f ' % (k, v) + + print(message) + with open(self.log_name, "a") as log_file: + log_file.write('%s\n' % message) + + # losses: dictionary of error labels and values + def plot_current_validation_error(self, epoch, counter_ratio, losses): + if not hasattr(self, 'plot_validation_data'): + self.plot_validation_data = {'X': [], 'Y': [], 'legend': list(losses.keys())} + self.plot_validation_data['X'].append(epoch + counter_ratio) + self.plot_validation_data['Y'].append([losses[k] for k in self.plot_validation_data['legend']]) + try: + self.vis.line( + X=np.stack([np.array(self.plot_validation_data['X'])] * len(self.plot_validation_data['legend']), 1), + Y=np.array(self.plot_validation_data['Y']), + opts={ + 'title': self.name + ' validation error over time', + 'legend': self.plot_validation_data['legend'], + 'xlabel': 'epoch', + 'ylabel': 'error'}, + win=self.display_id+1) + except VisdomExceptionBase: + self.throw_visdom_connection_error() + +class InferenceVisualizer(Visualizer): + def display_current_results(self, visuals, epoch, save_result, aspect_ratio=1.0, width=256): + image = visuals['fake'] + image_numpy = util.tensor2im(image) + img_path = os.path.join(self.img_dir, '%05d.png' %epoch) + util.save_image(image_numpy, img_path) \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/__init__.py b/NeuralVoicePuppetry/neural-code/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/api_test.sh b/NeuralVoicePuppetry/neural-code/api_test.sh new file mode 100755 index 0000000..55aeac9 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/api_test.sh @@ -0,0 +1,13 @@ +#!/bin/bash +echo 'Hello World' + +NAME_AUDIO_FILE=$1 +NAME_VIDEO_FILE=$2 +echo "NAME_AUDIO_FILE: $1" +echo "NAME_VIDEO_FILE: $2" + +cd ../neural-code + +BASE=`pwd` +echo "BASE DIRECTORY: "$BASE + diff --git a/NeuralVoicePuppetry/neural-code/autils/__init__.py b/NeuralVoicePuppetry/neural-code/autils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/autils/combine_rendering_frames.py b/NeuralVoicePuppetry/neural-code/autils/combine_rendering_frames.py new file mode 100644 index 0000000..f56db5e --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/autils/combine_rendering_frames.py @@ -0,0 +1,59 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os +import numpy as np +import cv2 +from tqdm import tqdm +import matplotlib.pyplot as plt +from skimage.transform import warp +from skimage.transform import SimilarityTransform + +def warp_back(image, oldimage, tform): + + alpha = 0.6 + + oldimage = oldimage.astype(np.float64) /255. + new_size = oldimage.shape + + dst_image = warp(image, tform, output_shape=new_size) + + # Mask of non-black pixels. + mask = np.where(np.all(dst_image == [0, 0, 0], axis=-1)) + dst_image[mask] = oldimage[mask] + + res = cv2.addWeighted(oldimage, 1 - alpha, dst_image, alpha, 0) + res = res[:, :, ::-1] + + return res + +images_out_path = 'results/face_reconstruction/images' +os.makedirs(images_out_path, exist_ok=True) + +tform_path = '/home/alberto/NeuralVoicePuppetry/datasets/SRF_anchor_short/Halbtotale_355_9415/tform.npy' + +frames_path = '/home/alberto/data/videosynth/SRF_anchor_short/Halbtotale/355_9415' + +combined_images_out_path = 'results/face_reconstruction/combines_images' +os.makedirs(combined_images_out_path, exist_ok=True) + +tform = np.load(tform_path) + +for image_path, frame in zip(tqdm(sorted(os.listdir(images_out_path))), sorted(os.listdir(frames_path))): + + index = int(image_path[:-4]) + f_tform = SimilarityTransform(matrix=tform[index]) + + image = cv2.imread(os.path.join(images_out_path,image_path)) + old_image = cv2.imread(os.path.join(frames_path, frame)) + + res = warp_back(image, old_image, f_tform) + res = res * 255. + res = res.astype('uint8') + res = cv2.cvtColor(res, cv2.COLOR_BGR2RGB) + + + file_name = os.path.join(combined_images_out_path, '%04d.jpg' % index) + cv2.imwrite(file_name, res) + + diff --git a/NeuralVoicePuppetry/neural-code/autils/crop_videos.py b/NeuralVoicePuppetry/neural-code/autils/crop_videos.py new file mode 100644 index 0000000..1878699 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/autils/crop_videos.py @@ -0,0 +1,43 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip +import os +from moviepy.editor import * +from subprocess import call +from math import ceil + +dataset = 'External' +data_path = f'/home/alberto/data/videosynth/{dataset}' +target_base = f'/home/alberto/data/videosynth/{dataset}' + +for sub in ['Close', 'Halbtotale', 'Totale']: + os.makedirs(os.path.join(target_base, sub), exist_ok=True) + +video_list = ['Youtube/Russian_guy.mp4', ] + +# Start and end time in minutes +start_time = 0 +end_time = 5 + +for video in video_list: + input_file = os.path.join(data_path, video) + output_file = os.path.join(target_base, video) + + clip = VideoFileClip(input_file) + print('FPS: ', clip.fps) + # end_time = ceil((7500/clip.fps))/60 + # print('End time: ', end_time) + + try: + cut = clip.subclip(start_time*60, int(end_time*60)) + + cut.write_videofile(output_file) + + except ValueError: + start_time = 0 + end_time = 5 + + cut = clip.subclip(start_time * 60, int(end_time*60)) + cut.write_videofile(output_file) + diff --git a/NeuralVoicePuppetry/neural-code/autils/deca_flame_fitting.py b/NeuralVoicePuppetry/neural-code/autils/deca_flame_fitting.py new file mode 100644 index 0000000..5247128 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/autils/deca_flame_fitting.py @@ -0,0 +1,63 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os, sys +import cv2 +import numpy as np +from time import time +from scipy.io import savemat +import argparse +from tqdm import tqdm +import matplotlib.pyplot as plt +from skimage.transform import estimate_transform, warp, resize, rescale +import torch + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../third/DECA/'))) +from decalib.deca import DECA +from decalib.datasets import datasets +from decalib.utils import util +from decalib.utils.config import cfg as deca_cfg + + +def warp_back(image, oldimage, tform): + + alpha = 0.6 + + oldimage = oldimage.astype(np.float64) /255. + new_size = oldimage.shape + + dst_image = warp(image, tform, output_shape=new_size) + + # Mask of non-black pixels. + mask = np.where(np.all(dst_image == [0, 0, 0], axis=-1)) + dst_image[mask] = oldimage[mask] + + res = cv2.addWeighted(oldimage, 1 - alpha, dst_image, alpha, 0) + res = res[:, :, ::-1] + + return res + + +class DECA_tracker: + def __init__(self, video_path, target_dir=None): + + # load test images + self.testdata = datasets.TestData(video_path, iscrop=True, face_detector='fan', target_dir=target_dir) + self.device = "cuda" if torch.cuda.is_available() else "cpu" + + # run DECA + deca_cfg.model.use_tex = False + self.deca = DECA(config=deca_cfg, device=self.device) + + def __call__(self, images, tform=None): + + codedict = self.deca.encode(images) + + opdict, visdict = self.deca.decode(codedict, tform) + + mask = self.deca.render_mask(opdict['grid']) + + # for key in opdict.keys(): + # print(key, opdict[key].size()) + + return codedict, opdict, mask diff --git a/NeuralVoicePuppetry/neural-code/autils/deepspeech_features.py b/NeuralVoicePuppetry/neural-code/autils/deepspeech_features.py new file mode 100644 index 0000000..a16f0f7 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/autils/deepspeech_features.py @@ -0,0 +1,351 @@ +""" +20.10.14 +Code collection from VOCA: +https://github.com/TimoBolkart/voca +""" +import argparse +import re +import subprocess + +from scipy.io import wavfile +import os +import copy +import resampy +import numpy as np +import tensorflow as tf +from python_speech_features import mfcc +import moviepy.editor as mp +from pydub import AudioSegment +from subprocess import call + +class AudioHandler: + def __init__(self, config): + self.config = config + self.audio_feature_type = config['audio_feature_type'] + self.num_audio_features = config['num_audio_features'] + self.audio_window_size = config['audio_window_size'] + self.audio_window_stride = config['audio_window_stride'] + self.target_fps = config["target_fps"] + + def process(self, audio): + if self.audio_feature_type.lower() == "none": + return None + elif self.audio_feature_type.lower() == 'deepspeech': + return self.convert_to_deepspeech(audio) + else: + raise NotImplementedError("Audio features not supported") + + def convert_to_deepspeech(self, audio): + def audioToInputVector(audio, fs, numcep, numcontext): + # Get mfcc coefficients + features = mfcc(audio, samplerate=fs, numcep=numcep) + + # We only keep every second feature (BiRNN stride = 2) + features = features[::2] + + # One stride per time step in the input + num_strides = len(features) + + # Add empty initial and final contexts + empty_context = np.zeros((numcontext, numcep), dtype=features.dtype) + features = np.concatenate((empty_context, features, empty_context)) + + # Create a view into the array with overlapping strides of size + # numcontext (past) + 1 (present) + numcontext (future) + window_size = 2 * numcontext + 1 + train_inputs = np.lib.stride_tricks.as_strided( + features, + (num_strides, window_size, numcep), + (features.strides[0], features.strides[0], features.strides[1]), + writeable=False) + + # Flatten the second and third dimensions + train_inputs = np.reshape(train_inputs, [num_strides, -1]) + + train_inputs = np.copy(train_inputs) + train_inputs = (train_inputs - np.mean(train_inputs)) / np.std(train_inputs) + + # Return results + return train_inputs + + if type(audio) == dict: + pass + else: + raise ValueError('Wrong type for audio') + + # Load graph and place_holders + + with tf.io.gfile.GFile(self.config['deepspeech_graph_fname'], "rb") as f: + graph_def = tf.compat.v1.GraphDef() + graph_def.ParseFromString(f.read()) + + from tensorflow.python.framework.ops import get_default_graph + graph = get_default_graph() + tf.import_graph_def(graph_def, name="deepspeech") + input_tensor = graph.get_tensor_by_name('deepspeech/input_node:0') + # input_tensor = graph.get_tensor_by_name('input_node:0') + seq_length = graph.get_tensor_by_name('deepspeech/input_lengths:0') + layer_6 = graph.get_tensor_by_name('deepspeech/logits:0') + + n_input = 26 + n_context = 9 + + processed_audio = copy.deepcopy(audio) + with tf.compat.v1.Session(graph=graph) as sess: + for subj in audio.keys(): + for seq in audio[subj].keys(): + print('process audio: %s - %s' % (subj, seq)) + + audio_sample = audio[subj][seq]['audio'] + sample_rate = audio[subj][seq]['sample_rate'] + resampled_audio = resampy.resample(audio_sample.astype(float), sample_rate, 16000) + input_vector = audioToInputVector(resampled_audio.astype('int16'), 16000, n_input, n_context) + + network_output = sess.run(layer_6, feed_dict={input_tensor: input_vector[np.newaxis, ...], + seq_length: [input_vector.shape[0]]}) + + # Resample network output from 50 fps to 60 fps + audio_len_s = float(audio_sample.shape[0]) / sample_rate + num_frames = int(round(audio_len_s * self.target_fps)) + network_output = self.interpolate_features(network_output[:, 0], 50, self.target_fps, + output_len=num_frames) + + # Make windows + zero_pad = np.zeros((int(self.audio_window_size / 2), network_output.shape[1])) + network_output = np.concatenate((zero_pad, network_output, zero_pad), axis=0) + windows = [] + for window_index in range(0, network_output.shape[0] - self.audio_window_size, + self.audio_window_stride): + windows.append(network_output[window_index:window_index + self.audio_window_size]) + + processed_audio[subj][seq]['audio'] = np.array(windows) + return processed_audio + + def interpolate_features(self, features, input_rate, output_rate, output_len=None): + num_features = features.shape[1] + input_len = features.shape[0] + seq_len = input_len / float(input_rate) + if output_len is None: + output_len = int(seq_len * output_rate) + input_timestamps = np.arange(input_len) / float(input_rate) + output_timestamps = np.arange(output_len) / float(output_rate) + output_features = np.zeros((output_len, num_features)) + for feat in range(num_features): + output_features[:, feat] = np.interp(output_timestamps, + input_timestamps, + features[:, feat]) + return output_features + + +class AudioFeatures: + def __init__(self, config): + self.audio_handler = AudioHandler(config) + + def process_audio(self, audio, sample_rate): + tmp_audio = {'subj': {'seq': {'audio': audio, 'sample_rate': sample_rate}}} + return self.audio_handler.process(tmp_audio)['subj']['seq']['audio'] + + def inference_interpolate_styles(self, audio_fname): + # sample_rate, audio = wavfile.read(audio_fname) + import soundfile as sf + import librosa + import wavio + + x,_ = librosa.load(audio_fname, sr=16000) + sf.write(audio_fname, x, 16000) + print('Successfully converted.') + + wav = wavio.read(audio_fname) + audio, sample_rate = wav.data, wav.rate + print("sample rate: ", sample_rate) + if audio.ndim != 1: + print('Audio has multiple channels, only first channel is considered') + audio = audio[:, 0] + + processed_audio = self.process_audio(audio, sample_rate) + return processed_audio + + def run(self, audio_fname): + features = self.inference_interpolate_styles(audio_fname) + return features + + +class FFMPEGMetaReader: + """ + Uses ffprobe to extract metadata from audio and video + """ + + def __init__(cls, fps): + cls.DEFAULTS = { + "samplerate": 48000, + "bitrate": 96, + "duration": "00", + "fps": fps + } + + cls.PATTERNS = { + "sample_and_bitrate": r'Audio:.*\s(\d+)\sHz.*\s(\d+)\skb/s', + "duration": r"Duration:\s(\d{2}):(\d{2}):(\d{2})\.(\d{2})", + "fps": r"Video:.*\s(\d+\.?\d*)\sfps" + } + + def _run_ffmpeg(cls, path): + process = subprocess.Popen(['ffprobe', '-i', path, '-hide_banner'], stdout=subprocess.PIPE, + stderr=subprocess.PIPE) + stdout, stderr = process.communicate() + output = stderr.decode('utf-8') + return output + + def _sample_and_bitrate(cls, path, default_samplerate=None, default_bitrate=None, output=None): + if output is None: + output = cls._run_ffmpeg(path) + match = re.search(cls.PATTERNS["sample_and_bitrate"], output) + + samplerate = default_samplerate if default_samplerate else cls.DEFAULTS["samplerate"] + bitrate = default_bitrate if default_bitrate else cls.DEFAULTS["bitrate"] + + if match: + samplerate = match.group(1) if match.group(1) else samplerate + bitrate = match.group(2) if match.group(2) else bitrate + return samplerate, bitrate + + def _duration(cls, path, output=None): + if output is None: + output = cls._run_ffmpeg(path) + + duration = cls.DEFAULTS["duration"] + + match = re.search(cls.PATTERNS["duration"], output) + + if match and match.group(4): + duration = "{}:{}:{}.{}".format(*[match.group(i) if match.group(i) else duration for i in range(1, 5)]) + return duration + + def _fps(cls, video_path, output=None): + if output is None: + output = cls._run_ffmpeg(video_path) + + fps = cls.DEFAULTS["fps"] + + match = re.search(cls.PATTERNS["fps"], output) + + if match: + fps = match.group(1) if match.group(1) else fps + else: + raise Warning("No fps found.") + return fps + + def extract_meta(cls, path): + output = cls._run_ffmpeg(path) + samplerate, bitrate = cls._sample_and_bitrate(path, output=output) + duration = cls._duration(path, output=output) + fps = cls._fps(path, output=output) + return { + "samplerate": samplerate, + "bitrate": bitrate, + "duration": duration, + "fps": fps + } + + +def get_parser(): + parser = argparse.ArgumentParser() + parser.add_argument("--dataset", type=str, required=True) + parser.add_argument("--video_id", type=str, required=True) + return parser + + +def get_config(): + config = {} + config['deepspeech_graph_fname'] = os.path.join( + "./third/DeepSpeech/models/", "output_graph.pb") + config['audio_feature_type'] = 'deepspeech' + config['num_audio_features'] = 29 + + config['audio_window_size'] = 16 + config['audio_window_stride'] = 1 + # config['target_fps'] = target_fps + return config + + +def mkdir(path): + if not os.path.exists(path): + os.makedirs(path, exist_ok=True) + +def extract_ds(folder_videos, file_id, target_base, target_name, type, target_fps): + + config = get_config() + + # First convert mp4 to wav + if type == 'video': + + path_wav = '{}/{}.wav'.format(folder_videos, file_id) + if not os.path.isfile(path_wav): + path_video_in = '{}/{}.mp4'.format(folder_videos, file_id) + + if not os.path.isfile(path_video_in): + path_video_in = '{}/{}.avi'.format(folder_videos, file_id) + + # cmd = "ffmpeg -i input-video.avi -vn -acodec copy output-audio.aac" + + cmd = ('ffmpeg' + f' -i {path_video_in} {path_wav}').split() + call(cmd) + + else: + path_wav = '{}/{}.wav'.format(folder_videos, file_id) + if not os.path.isfile(path_wav): + try: + path_mp3 = '{}/{}.mp3'.format(folder_videos, file_id) + sound = AudioSegment.from_mp3(path_mp3) + sound.export(path_wav, format="wav") + + except FileNotFoundError: + print('Audio file not found. File format should be mp3 or wav.') + + + config["target_fps"] = target_fps + + # Compute features + features = AudioFeatures(config=config).run(path_wav) + folder_out = os.path.join(target_base, target_name, "audio_feature") + + # Save features + mkdir(folder_out) + for i, feature in enumerate(features): + fn_out = "{}.deepspeech.npy".format(i) + np.save(os.path.join(folder_out, fn_out), feature) + print("Written {} files to '{}'".format(features.shape[0], folder_out)) + + +if __name__ == "__main__": + args = get_parser().parse_args() + folder_videos = args.dataset + file_id = args.video_id + folder_nvp = "TARGETS" + + # First convert mp4 to wav + path_video_in = '{}/{}.mp4'.format(folder_videos, file_id) + path_wav = '{}/{}.wav'.format(folder_videos, file_id) + path_features = os.path.join(folder_videos, "deepspeech", "{}.npy".format(file_id)) + clip = mp.VideoFileClip(path_video_in) + clip.audio.write_audiofile(path_wav) + + # Get the meta data via ffmpeg + config = get_config() + metadata = FFMPEGMetaReader.extract_meta(path_video_in) + if int(metadata["fps"]) != float(metadata["fps"]): + raise Warning("Careful: fps is not an integer ({})".format(metadata["fps"])) + config["target_fps"] = int(metadata["fps"]) + + # Compute features + features = AudioFeatures(config=config).run(path_wav) + folder_out = os.path.join( + './NeuralVoicePuppetry/Audio2ExpressionNet/Inference/datasets/{}'.format( + folder_nvp), file_id, "audio_feature") + + # Save features + mkdir(folder_out) + for i, feature in enumerate(features): + fn_out = "{}.deepspeech.npy".format(i) + np.save(os.path.join(folder_out, fn_out), feature) + print("Written {} files to '{}'".format(features.shape[0], folder_out)) diff --git a/NeuralVoicePuppetry/neural-code/autils/eos_tracker.py b/NeuralVoicePuppetry/neural-code/autils/eos_tracker.py new file mode 100644 index 0000000..ae1403e --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/autils/eos_tracker.py @@ -0,0 +1,168 @@ +import numpy as np +import dlib +import eos +import os +from imageio import imread, imwrite +from skimage.draw import disk +import matplotlib.pyplot as plt +import cv2 +import trimesh + +PREDICTOR_PATH = 'tracker/eos/shape_predictor_68_face_landmarks.dat' +PATH_TO_EOS = 'tracker/eos/share' + +# get viewport matrix +def viewport_matrix(w, h): + viewport = np.array([0, h, w, -h]) + + # scale + S = np.identity(4, dtype=np.float32) + S[0][0] *= viewport[2] / 2 + S[1][1] *= viewport[3] / 2 + S[2][2] *= 0.5 + + # translate + T = np.identity(4, dtype=np.float32) + T[3][0] = viewport[0] + (viewport[2] / 2) + T[3][1] = viewport[1] + (viewport[3] / 2) + T[3][2] = 0.5 + return S @ T + + +def draw_circle(canvas, x, y, r=8, color=(255,255,255)): + rr,cc = disk((x,y), r, shape=canvas.shape) + canvas[rr,cc] = color + + +class EOS_Tracker: + def __init__(self, path_to_eos, predictor_path): + + # Dlib tracker + self.detector = dlib.get_frontal_face_detector() + self.shape_predictor = dlib.shape_predictor(predictor_path) + + # EOS model + self.model = eos.morphablemodel.load_model(f"{path_to_eos}/scripts/bfm2017-1_bfm_nomouth.bin") + self.landmark_mapper = eos.core.LandmarkMapper(f'{path_to_eos}/ibug_to_bfm2017-1_bfm_nomouth.txt') + self.edge_topology = eos.morphablemodel.load_edge_topology(f'{path_to_eos}/scripts/bfm2017.json') + self.contour_landmarks = eos.fitting.ContourLandmarks.load(f'{path_to_eos}/ibug_to_bfm2017-1_bfm_nomouth.txt') + self.model_contour = eos.fitting.ModelContour.load(f'{path_to_eos}/bfm2017-1_bfm_nomouth_model_contours.json') + + def get_facial_landmarks(self, img): + # get bounding box and facial landmarks + boxes = self.detector(img) + lp = [] + for box in boxes: + shape = self.shape_predictor(img, box) + index = 1 + landmarks = [] + for part in shape.parts(): + landmarks.append(eos.core.Landmark(str(index), [float(part.y), float(part.x)])) + index += 1 + lp.append(landmarks) + return lp + + def fit_shape_and_pose(self, img, landmarks): + h, w = img.shape[:2] + mesh, pose, shape_coeffs, blendshape_coeffs = eos.fitting.fit_shape_and_pose(self.model, + landmarks, + self.landmark_mapper, + w, + h, + self.edge_topology, + self.contour_landmarks, + self.model_contour) + + return mesh, pose, shape_coeffs, blendshape_coeffs + + def project_on_img(self, img, mesh, pose): + canvas = img.copy() + h, w = img.shape[:2] + p, mv, vm, fm = self.get_transformation(img, pose) + + # get vertices from mesh + sampled_verts = mesh.vertices + for i in sampled_verts: + tmp = fm @ np.append(i, 1) + # disregard z and draw 2d pt + x, y = (int(w / 2 + tmp[0]), int(h / 2 + tmp[1])) + draw_circle(canvas, x, y) + return canvas + + def get_transformation(self, img, pose): + h, w = img.shape[:2] + vm = viewport_matrix(w, h) + + # get pose and transform to img space + p = pose.get_projection() # from world coordinates to view coordinates + mv = pose.get_modelview() # From object to world coordinates + fm = vm @ p @ mv + + return p, mv, vm, fm + + def get_mesh_from_coeffs(self, shape_coeffs, expr_coeffs): + mesh = self.model.draw_sample(shape_coeffs, expr_coeffs) + return mesh + + def __call__(self, img, show_on_img=False, show_landmarks=False, save_ply=False): + + # call landmark detector + landmarks = self.get_facial_landmarks(img) + + if show_landmarks: + canvas = img.copy() + for lp in landmarks: + for point in lp: draw_circle(canvas, point.coordinates[0], point.coordinates[1]) + plt.imshow(canvas) + plt.show() + + # call eos fitter + try: + landmarks = landmarks[0] + mesh, pose, shape_coeffs, blendshape_coeffs = self.fit_shape_and_pose(img, landmarks) + # Mesh vertices + vertices = mesh.vertices + # print(f'vertices: {len(vertices)}') + + # Triangle vertex indices + faces = mesh.tvi + # print(f'faces: {len(faces)}') + + if save_ply: + mesh_tri = trimesh.Trimesh(vertices=vertices, faces=faces) + mesh_tri.export('mesh.ply') + + # Get pose + # rotation = pose.get_rotation() + # print(f'rotation:\n{rotation}') + # projection = pose.get_projection() + # print(f'projection:\n{projection}') + + # print(f'shape_coeffs: {len(shape_coeffs)}') + # print(f'blendshape_coeffs: {len(blendshape_coeffs)}') + + if show_on_img: + img_with_mesh = self.project_on_img(img, mesh, pose) + plt.imshow(img_with_mesh) + plt.show() + + return mesh, pose, shape_coeffs, blendshape_coeffs + + except IndexError: + # plt.imshow(img) + # plt.show() + imwrite('error.png', img) + + return None, None, np.zeros(100), np.zeros(199) + + +if __name__ == "__main__": + img_path = 'IMG_3046.JPG' + # img = cv2.imread(img_path) + img = imread(img_path) + tracker = EOS_Tracker(PATH_TO_EOS, PREDICTOR_PATH) + + mesh, pose, shape_coeffs, blendshape_coeffs = tracker(img) + + + diff --git a/NeuralVoicePuppetry/neural-code/autils/face12.json b/NeuralVoicePuppetry/neural-code/autils/face12.json new file mode 100644 index 0000000..252b542 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/autils/face12.json @@ -0,0 +1,10 @@ +{ + "triangles": [[0, 1, 130], [0, 130, 129], [1, 2, 130], 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[1974, 1975, 2104], [1974, 2104, 2103], [1975, 1976, 2104], [1976, 2105, 2104], [1976, 1977, 2106], [1976, 2106, 2105], [1977, 1978, 2106], [1978, 2107, 2106], [1978, 1979, 2108], [1978, 2108, 2107], [1979, 1980, 2108], [1980, 2109, 2108], [1980, 1981, 2110], [1980, 2110, 2109], [1981, 1982, 2110], [1982, 2111, 2110], [1982, 1983, 2112], [1982, 2112, 2111], [1983, 1984, 2112], [1984, 2113, 2112], [1984, 1985, 2114], [1984, 2114, 2113], [1985, 1986, 2114], [1986, 2115, 2114], [1986, 1987, 2116], [1986, 2116, 2115], [1987, 1988, 2116], [1988, 2117, 2116], [1988, 1989, 2118], [1988, 2118, 2117], [1989, 1990, 2118], [1990, 2119, 2118], [1990, 1991, 2120], [1990, 2120, 2119], [1991, 1992, 2120], [1992, 2121, 2120], [1992, 1993, 2122], [1992, 2122, 2121], [1993, 1994, 2122], [1994, 2123, 2122], [1994, 1995, 2124], [1994, 2124, 2123], [1995, 1996, 2124], [1996, 2125, 2124], [1996, 1997, 2126], [1996, 2126, 2125], [1997, 1998, 2126], [1998, 2127, 2126], [1998, 1999, 2128], [1998, 2128, 2127], 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[1818, 1819, 1948], [1818, 1948, 1947], [1819, 1820, 1948], [1820, 1949, 1948], [1820, 1821, 1950], [1820, 1950, 1949], [1821, 1822, 1950], [1822, 1951, 1950], [1822, 1823, 1952], [1822, 1952, 1951], [1823, 1824, 1952], [1824, 1953, 1952], [1824, 1825, 1954], [1824, 1954, 1953], [1825, 1826, 1954], [1826, 1955, 1954], [1826, 1827, 1956], [1826, 1956, 1955], [1827, 1828, 1956], [1828, 1957, 1956], [1828, 1829, 1958], [1828, 1958, 1957], [1829, 1830, 1958], [1830, 1959, 1958], [1830, 1831, 1960], [1830, 1960, 1959], [1831, 1832, 1960], [1832, 1961, 1960], [1832, 1833, 1962], [1832, 1962, 1961], [1833, 1834, 1962], [1834, 1963, 1962], [1834, 1835, 1964], [1834, 1964, 1963], [1835, 1836, 1964], [1836, 1965, 1964], [1836, 1837, 1966], [1836, 1966, 1965], [1837, 1838, 1966], [1838, 1967, 1966], [1838, 1839, 1968], [1838, 1968, 1967], [1839, 1840, 1968], [1840, 1969, 1968], [1840, 1841, 1970], [1840, 1970, 1969], [1841, 1842, 1970], [1842, 1971, 1970], [1842, 1843, 1972], [1842, 1972, 1971], 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[1893, 1894, 2022], [1894, 2023, 2022], [1894, 1895, 2024], [1894, 2024, 2023], [1895, 1896, 2024], [1896, 2025, 2024], [1896, 1897, 2026], [1896, 2026, 2025], [1897, 1898, 2026], [1898, 2027, 2026], [1898, 1899, 2028], [1898, 2028, 2027], [1899, 1900, 2028], [1900, 2029, 2028], [1900, 1901, 2030], [1900, 2030, 2029], [1901, 1902, 2030], [1902, 2031, 2030], [1902, 1903, 2032], [1902, 2032, 2031], [1903, 1904, 2032], [1904, 2033, 2032], [1904, 1905, 2034], [1904, 2034, 2033], [1905, 1906, 2034], [1906, 2035, 2034], [1906, 1907, 2036], [1906, 2036, 2035], [1907, 1908, 2036], [1908, 2037, 2036], [1908, 1909, 2038], [1908, 2038, 2037], [1909, 1910, 2038], [1910, 2039, 2038], [1910, 1911, 2040], [1910, 2040, 2039], [1911, 1912, 2040], [1912, 2041, 2040], [1912, 1913, 2042], [1912, 2042, 2041], [1913, 1914, 2042], [1914, 2043, 2042], [1914, 1915, 2044], [1914, 2044, 2043], [1915, 1916, 2044], [1916, 2045, 2044], [1916, 1917, 2046], [1916, 2046, 2045], [1917, 1918, 2046], [1918, 2047, 2046], [1918, 1919, 2048], [1918, 2048, 2047], [1919, 1920, 2048], [1920, 2049, 2048], [1920, 1921, 2050], [1920, 2050, 2049], [1921, 1922, 2050], [1922, 2051, 2050], [1922, 1923, 2052], [1922, 2052, 2051], [1923, 1924, 2052], [1924, 2053, 2052], [1924, 1925, 2054], [1924, 2054, 2053], [1925, 1926, 2054], [1926, 2055, 2054], [1926, 1927, 2056], [1926, 2056, 2055], [1927, 1928, 2056], [1928, 2057, 2056], [1928, 1929, 2058], [1928, 2058, 2057], [1929, 1930, 2058], [1930, 2059, 2058], [1930, 1931, 2060], [1930, 2060, 2059], [1931, 1932, 2060], [1932, 2061, 2060], [1932, 1933, 2062], [1932, 2062, 2061], [1933, 1934, 2062], [1934, 2063, 2062], [1935, 1936, 2064], [1936, 2065, 2064], [1936, 1937, 2066], [1936, 2066, 2065], [1937, 1938, 2066], [1938, 2067, 2066], [1938, 1939, 2068], [1938, 2068, 2067], [1939, 1940, 2068], [1940, 2069, 2068], [1940, 1941, 2070], [1940, 2070, 2069], [1941, 1942, 2070], [1942, 2071, 2070], [1942, 1943, 2072], [1942, 2072, 2071], [1943, 1944, 2072], [1944, 2073, 2072], [1944, 1945, 2074], [1944, 2074, 2073], [1945, 1946, 2074], [1946, 2075, 2074], [1946, 1947, 2076], [1946, 2076, 2075], [1947, 1948, 2076], [1948, 2077, 2076], [1948, 1949, 2078], [1948, 2078, 2077], [1949, 1950, 2078], [1950, 2079, 2078], [1950, 1951, 2080], [1950, 2080, 2079], [1951, 1952, 2080], [1952, 2081, 2080], [1952, 1953, 2082], [1952, 2082, 2081], [1953, 1954, 2082], [1954, 2083, 2082], [1954, 1955, 2084], [1954, 2084, 2083], [1955, 1956, 2084], [1956, 2085, 2084], [1956, 1957, 2086], [1956, 2086, 2085], [1957, 1958, 2086], [1958, 2087, 2086], [1958, 1959, 2088], [1958, 2088, 2087], [1959, 1960, 2088], [1960, 2089, 2088], [1960, 1961, 2090], [1960, 2090, 2089], [1961, 1962, 2090], [1962, 2091, 2090], [1962, 1963, 2092], [1962, 2092, 2091], [1963, 1964, 2092], [1964, 2093, 2092], [1964, 1965, 2094], [1964, 2094, 2093], [1965, 1966, 2094], [1966, 2095, 2094], [1966, 1967, 2096], [1966, 2096, 2095], [1967, 1968, 2096], [1968, 2097, 2096], [1968, 1969, 2098], [1968, 2098, 2097], [1969, 1970, 2098], [1970, 2099, 2098], [1970, 1971, 2100], [1970, 2100, 2099], [1971, 1972, 2100], [1972, 2101, 2100], [1972, 1973, 2102], [1972, 2102, 2101], [1973, 1974, 2102], [1974, 2103, 2102], [1974, 1975, 2104], [1974, 2104, 2103], [1975, 1976, 2104], [1976, 2105, 2104], [1976, 1977, 2106], [1976, 2106, 2105], [1977, 1978, 2106], [1978, 2107, 2106], [1978, 1979, 2108], [1978, 2108, 2107], [1979, 1980, 2108], [1980, 2109, 2108], [1980, 1981, 2110], [1980, 2110, 2109], [1981, 1982, 2110], [1982, 2111, 2110], [1982, 1983, 2112], [1982, 2112, 2111], [1983, 1984, 2112], [1984, 2113, 2112], [1984, 1985, 2114], [1984, 2114, 2113], [1985, 1986, 2114], [1986, 2115, 2114], [1986, 1987, 2116], [1986, 2116, 2115], [1987, 1988, 2116], [1988, 2117, 2116], [1988, 1989, 2118], [1988, 2118, 2117], [1989, 1990, 2118], [1990, 2119, 2118], [1990, 1991, 2120], [1990, 2120, 2119], [1991, 1992, 2120], [1992, 2121, 2120], [1992, 1993, 2122], [1992, 2122, 2121], [1993, 1994, 2122], [1994, 2123, 2122], [1994, 1995, 2124], [1994, 2124, 2123], [1995, 1996, 2124], [1996, 2125, 2124], [1996, 1997, 2126], [1996, 2126, 2125], [1997, 1998, 2126], [1998, 2127, 2126], [1998, 1999, 2128], [1998, 2128, 2127], [1999, 2000, 2128], [2000, 2129, 2128], [2000, 2001, 2130], [2000, 2130, 2129], [2001, 2002, 2130], [2002, 2131, 2130], [2002, 2003, 2132], [2002, 2132, 2131], [2003, 2004, 2132], [2004, 2133, 2132], [2004, 2005, 2134], [2004, 2134, 2133], [2005, 2006, 2134], [2006, 2135, 2134], [2006, 2007, 2136], [2006, 2136, 2135], [2007, 2008, 2136], [2008, 2137, 2136], [2008, 2009, 2138], [2008, 2138, 2137], [2009, 2010, 2138], [2010, 2139, 2138], [2010, 2011, 2140], [2010, 2140, 2139], [2011, 2012, 2140], [2012, 2141, 2140], [2012, 2013, 2142], [2012, 2142, 2141], [2013, 2014, 2142], [2014, 2143, 2142], [2014, 2015, 2144], [2014, 2144, 2143], [2015, 2016, 2144], [2016, 2145, 2144], [2016, 2017, 2146], [2016, 2146, 2145], [2017, 2018, 2146], [2018, 2147, 2146], [2018, 2019, 2148], [2018, 2148, 2147], [2019, 2020, 2148], [2020, 2149, 2148], [2020, 2021, 2150], [2020, 2150, 2149], [2021, 2022, 2150], [2022, 2151, 2150], [2022, 2023, 2152], [2022, 2152, 2151], [2023, 2024, 2152], [2024, 2153, 2152], [2024, 2025, 2154], [2024, 2154, 2153], [2025, 2026, 2154], [2026, 2155, 2154], [2026, 2027, 2156], [2026, 2156, 2155], [2027, 2028, 2156], [2028, 2157, 2156], [2028, 2029, 2158], [2028, 2158, 2157], [2029, 2030, 2158], [2030, 2159, 2158], [2030, 2031, 2160], [2030, 2160, 2159], [2031, 2032, 2160], [2032, 2161, 2160], [2032, 2033, 2162], [2032, 2162, 2161], [2033, 2034, 2162], [2034, 2163, 2162], [2034, 2035, 2164], [2034, 2164, 2163], [2035, 2036, 2164], [2036, 2165, 2164], [2036, 2037, 2166], [2036, 2166, 2165], [2037, 2038, 2166], [2038, 2167, 2166], [2038, 2039, 2168], [2038, 2168, 2167], [2039, 2040, 2168], [2040, 2169, 2168], [2040, 2041, 2170], [2040, 2170, 2169], [2041, 2042, 2170], [2042, 2171, 2170], [2042, 2043, 2172], [2042, 2172, 2171], [2043, 2044, 2172], [2044, 2173, 2172], [2044, 2045, 2174], [2044, 2174, 2173], [2045, 2046, 2174], [2046, 2175, 2174], [2046, 2047, 2176], [2046, 2176, 2175], [2047, 2048, 2176], [2048, 2177, 2176], [2048, 2049, 2178], [2048, 2178, 2177], [2049, 2050, 2178], [2050, 2179, 2178], [2050, 2051, 2180], [2050, 2180, 2179], [2051, 2052, 2180], [2052, 2181, 2180], [2052, 2053, 2182], [2052, 2182, 2181], [2053, 2054, 2182], [2054, 2183, 2182], [2054, 2055, 2184], [2054, 2184, 2183], [2055, 2056, 2184], [2056, 2185, 2184], [2056, 2057, 2186], [2056, 2186, 2185], [2057, 2058, 2186], [2058, 2187, 2186], [2058, 2059, 2188], [2058, 2188, 2187], [2059, 2060, 2188], [2060, 2189, 2188], [2060, 2061, 2190], [2060, 2190, 2189], [2061, 2062, 2190], [2062, 2191, 2190], [2062, 2063, 2192], [2062, 2192, 2191], [2064, 2065, 2194], [2064, 2194, 2193], [2065, 2066, 2194], [2066, 2195, 2194], [2066, 2067, 2196], [2066, 2196, 2195], [2067, 2068, 2196], [2068, 2197, 2196], [2068, 2069, 2198], [2068, 2198, 2197], [2069, 2070, 2198], [2070, 2199, 2198], [2070, 2071, 2200], [2070, 2200, 2199], [2071, 2072, 2200], [2072, 2201, 2200], [2072, 2073, 2202], [2072, 2202, 2201], [2073, 2074, 2202], [2074, 2203, 2202], [2074, 2075, 2204], [2074, 2204, 2203], [2075, 2076, 2204], [2076, 2205, 2204], [2076, 2077, 2206], [2076, 2206, 2205], [2077, 2078, 2206], [2078, 2207, 2206], [2078, 2079, 2208], [2078, 2208, 2207], [2079, 2080, 2208], [2080, 2209, 2208], [2080, 2081, 2210], [2080, 2210, 2209], [2081, 2082, 2210], [2082, 2211, 2210], [2082, 2083, 2212], [2082, 2212, 2211], [2083, 2084, 2212], [2084, 2213, 2212], [2084, 2085, 2214], [2084, 2214, 2213], [2085, 2086, 2214], [2086, 2215, 2214], [2086, 2087, 2216], [2086, 2216, 2215], [2087, 2088, 2216], [2088, 2217, 2216], [2088, 2089, 2218], [2088, 2218, 2217], [2089, 2090, 2218], [2090, 2219, 2218], [2090, 2091, 2220], [2090, 2220, 2219], [2091, 2092, 2220], [2092, 2221, 2220], [2092, 2093, 2222], [2092, 2222, 2221], [2093, 2094, 2222], [2094, 2223, 2222], [2094, 2095, 2224], [2094, 2224, 2223], [2095, 2096, 2224], [2096, 2225, 2224], [2096, 2097, 2226], [2096, 2226, 2225], [2097, 2098, 2226], [2098, 2227, 2226], [2098, 2099, 2228], [2098, 2228, 2227], [2099, 2100, 2228], [2100, 2229, 2228], [2100, 2101, 2230], [2100, 2230, 2229], [2101, 2102, 2230], [2102, 2231, 2230], [2102, 2103, 2232], [2102, 2232, 2231], [2103, 2104, 2232], [2104, 2233, 2232], [2104, 2105, 2234], [2104, 2234, 2233], [2105, 2106, 2234], [2106, 2235, 2234], [2106, 2107, 2236], [2106, 2236, 2235], [2107, 2108, 2236], [2108, 2237, 2236], [2108, 2109, 2238], [2108, 2238, 2237], [2109, 2110, 2238], [2110, 2239, 2238], [2110, 2111, 2240], [2110, 2240, 2239], [2111, 2112, 2240], [2112, 2241, 2240], [2112, 2113, 2242], [2112, 2242, 2241], [2113, 2114, 2242], [2114, 2243, 2242], [2114, 2115, 2244], [2114, 2244, 2243], [2115, 2116, 2244], [2116, 2245, 2244], [2116, 2117, 2246], [2116, 2246, 2245], [2117, 2118, 2246], [2118, 2247, 2246], [2118, 2119, 2248], [2118, 2248, 2247], [2119, 2120, 2248], [2120, 2249, 2248], [2120, 2121, 2250], [2120, 2250, 2249], [2121, 2122, 2250], [2122, 2251, 2250], [2122, 2123, 2252], [2122, 2252, 2251], [2123, 2124, 2252], [2124, 2253, 2252], [2124, 2125, 2254], [2124, 2254, 2253], [2125, 2126, 2254], [2126, 2255, 2254], [2126, 2127, 2256], [2126, 2256, 2255], [2127, 2128, 2256], [2128, 2257, 2256], [2128, 2129, 2258], [2128, 2258, 2257], [2129, 2130, 2258], [2130, 2259, 2258], [2130, 2131, 2260], [2130, 2260, 2259], [2131, 2132, 2260], [2132, 2261, 2260], [2132, 2133, 2262], [2132, 2262, 2261], [2133, 2134, 2262], [2134, 2263, 2262], [2134, 2135, 2264], [2134, 2264, 2263], [2135, 2136, 2264], [2136, 2265, 2264], [2136, 2137, 2266], [2136, 2266, 2265], [2137, 2138, 2266], [2138, 2267, 2266], [2138, 2139, 2268], [2138, 2268, 2267], [2139, 2140, 2268], [2140, 2269, 2268], [2140, 2141, 2270], [2140, 2270, 2269], [2141, 2142, 2270], [2142, 2271, 2270], [2142, 2143, 2272], [2142, 2272, 2271], [2143, 2144, 2272], [2144, 2273, 2272], [2144, 2145, 2274], [2144, 2274, 2273], [2145, 2146, 2274], [2146, 2275, 2274], [2146, 2147, 2276], [2146, 2276, 2275], [2147, 2148, 2276], [2148, 2277, 2276], [2148, 2149, 2278], [2148, 2278, 2277], [2149, 2150, 2278], [2150, 2279, 2278], [2150, 2151, 2280], [2150, 2280, 2279], [2151, 2152, 2280], [2152, 2281, 2280], [2152, 2153, 2282], [2152, 2282, 2281], [2153, 2154, 2282], [2154, 2283, 2282], [2154, 2155, 2284], [2154, 2284, 2283], [2155, 2156, 2284], [2156, 2285, 2284], [2156, 2157, 2286], [2156, 2286, 2285], [2157, 2158, 2286], [2158, 2287, 2286], [2158, 2159, 2288], [2158, 2288, 2287], [2159, 2160, 2288], [2160, 2289, 2288], [2160, 2161, 2290], [2160, 2290, 2289], [2161, 2162, 2290], [2162, 2291, 2290], [2162, 2163, 2292], [2162, 2292, 2291], [2163, 2164, 2292], [2164, 2293, 2292], [2164, 2165, 2294], [2164, 2294, 2293], [2165, 2166, 2294], [2166, 2295, 2294], [2166, 2167, 2296], [2166, 2296, 2295], [2167, 2168, 2296], [2168, 2297, 2296], [2168, 2169, 2298], [2168, 2298, 2297], [2169, 2170, 2298], [2170, 2299, 2298], [2170, 2171, 2300], [2170, 2300, 2299], [2171, 2172, 2300], [2172, 2301, 2300], [2172, 2173, 2302], [2172, 2302, 2301], [2173, 2174, 2302], [2174, 2303, 2302], [2174, 2175, 2304], [2174, 2304, 2303], [2175, 2176, 2304], [2176, 2305, 2304], [2176, 2177, 2306], [2176, 2306, 2305], [2177, 2178, 2306], [2178, 2307, 2306], [2178, 2179, 2308], [2178, 2308, 2307], [2179, 2180, 2308], [2180, 2309, 2308], [2180, 2181, 2310], [2180, 2310, 2309], [2181, 2182, 2310], [2182, 2311, 2310], [2182, 2183, 2312], [2182, 2312, 2311], [2183, 2184, 2312], [2184, 2313, 2312], [2184, 2185, 2314], [2184, 2314, 2313], [2185, 2186, 2314], [2186, 2315, 2314], [2186, 2187, 2316], [2186, 2316, 2315], [2187, 2188, 2316], [2188, 2317, 2316], [2188, 2189, 2318], [2188, 2318, 2317], [2189, 2190, 2318], [2190, 2319, 2318], [2190, 2191, 2320], [2190, 2320, 2319], [2191, 2192, 2320], [2192, 2321, 2320], [2193, 2194, 2322], [2194, 2323, 2322], [2194, 2195, 2324], [2194, 2324, 2323], [2195, 2196, 2324], [2196, 2325, 2324], [2196, 2197, 2326], [2196, 2326, 2325], [2197, 2198, 2326], [2198, 2327, 2326], [2198, 2199, 2328], [2198, 2328, 2327], [2199, 2200, 2328], [2200, 2329, 2328], [2200, 2201, 2330], [2200, 2330, 2329], [2201, 2202, 2330], [2202, 2331, 2330], [2202, 2203, 2332], [2202, 2332, 2331], [2203, 2204, 2332], [2204, 2333, 2332], [2204, 2205, 2334], [2204, 2334, 2333], [2205, 2206, 2334], [2206, 2335, 2334], [2206, 2207, 2336], [2206, 2336, 2335], [2207, 2208, 2336], [2208, 2337, 2336], [2208, 2209, 2338], [2208, 2338, 2337], [2209, 2210, 2338], [2210, 2339, 2338], [2210, 2211, 2340], [2210, 2340, 2339], [2211, 2212, 2340], [2212, 2341, 2340], [2212, 2213, 2342], [2212, 2342, 2341], [2213, 2214, 2342], [2214, 2343, 2342], [2214, 2215, 2344], [2214, 2344, 2343], [2215, 2216, 2344], [2216, 2345, 2344], [2216, 2217, 2346], [2216, 2346, 2345], [2217, 2218, 2346], [2218, 2347, 2346], [2218, 2219, 2348], [2218, 2348, 2347], [2219, 2220, 2348], [2220, 2349, 2348], [2220, 2221, 2350], [2220, 2350, 2349], [2221, 2222, 2350], [2222, 2351, 2350], [2222, 2223, 2352], [2222, 2352, 2351], [2223, 2224, 2352], [2224, 2353, 2352], [2224, 2225, 2354], [2224, 2354, 2353], [2225, 2226, 2354], [2226, 2355, 2354], [2226, 2227, 2356], [2226, 2356, 2355], [2227, 2228, 2356], [2228, 2357, 2356], [2228, 2229, 2358], [2228, 2358, 2357], [2229, 2230, 2358], [2230, 2359, 2358], [2230, 2231, 2360], [2230, 2360, 2359], [2231, 2232, 2360], [2232, 2361, 2360], [2232, 2233, 2362], [2232, 2362, 2361], [2233, 2234, 2362], [2234, 2363, 2362], [2234, 2235, 2364], [2234, 2364, 2363], [2235, 2236, 2364], [2236, 2365, 2364], [2236, 2237, 2366], [2236, 2366, 2365], [2237, 2238, 2366], [2238, 2367, 2366], [2238, 2239, 2368], [2238, 2368, 2367], [2239, 2240, 2368], [2240, 2369, 2368], [2240, 2241, 2370], [2240, 2370, 2369], [2241, 2242, 2370], [2242, 2371, 2370], [2242, 2243, 2372], [2242, 2372, 2371], [2243, 2244, 2372], [2244, 2373, 2372], [2244, 2245, 2374], [2244, 2374, 2373], [2245, 2246, 2374], [2246, 2375, 2374], [2246, 2247, 2376], [2246, 2376, 2375], [2247, 2248, 2376], [2248, 2377, 2376], [2248, 2249, 2378], [2248, 2378, 2377], [2249, 2250, 2378], [2250, 2379, 2378], [2250, 2251, 2380], [2250, 2380, 2379], [2251, 2252, 2380], [2252, 2381, 2380], [2252, 2253, 2382], [2252, 2382, 2381], [2253, 2254, 2382], [2254, 2383, 2382], [2254, 2255, 2384], [2254, 2384, 2383], [2255, 2256, 2384], [2256, 2385, 2384], [2256, 2257, 2386], [2256, 2386, 2385], [2257, 2258, 2386], [2258, 2387, 2386], [2258, 2259, 2388], [2258, 2388, 2387], [2259, 2260, 2388], [2260, 2389, 2388], [2260, 2261, 2390], [2260, 2390, 2389], [2261, 2262, 2390], [2262, 2391, 2390], [2262, 2263, 2392], [2262, 2392, 2391], [2263, 2264, 2392], [2264, 2393, 2392], [2264, 2265, 2394], [2264, 2394, 2393], [2265, 2266, 2394], [2266, 2395, 2394], [2266, 2267, 2396], [2266, 2396, 2395], [2267, 2268, 2396], [2268, 2397, 2396], [2268, 2269, 2398], [2268, 2398, 2397], [2269, 2270, 2398], [2270, 2399, 2398], [2270, 2271, 2400], [2270, 2400, 2399], [2271, 2272, 2400], [2272, 2401, 2400], [2272, 2273, 2402], [2272, 2402, 2401], [2273, 2274, 2402], [2274, 2403, 2402], [2274, 2275, 2404], [2274, 2404, 2403], [2275, 2276, 2404], [2276, 2405, 2404], [2276, 2277, 2406], [2276, 2406, 2405], [2277, 2278, 2406], [2278, 2407, 2406], [2278, 2279, 2408], [2278, 2408, 2407], [2279, 2280, 2408], [2280, 2409, 2408], [2280, 2281, 2410], [2280, 2410, 2409], [2281, 2282, 2410], [2282, 2411, 2410], [2282, 2283, 2412], [2282, 2412, 2411], [2283, 2284, 2412], [2284, 2413, 2412], [2284, 2285, 2414], [2284, 2414, 2413], [2285, 2286, 2414], [2286, 2415, 2414], [2286, 2287, 2416], [2286, 2416, 2415], [2287, 2288, 2416], [2288, 2417, 2416], [2288, 2289, 2418], [2288, 2418, 2417], [2289, 2290, 2418], [2290, 2419, 2418], [2290, 2291, 2420], [2290, 2420, 2419], [2291, 2292, 2420], [2292, 2421, 2420], [2292, 2293, 2422], [2292, 2422, 2421], [2293, 2294, 2422], [2294, 2423, 2422], [2294, 2295, 2424], [2294, 2424, 2423], [2295, 2296, 2424], [2296, 2425, 2424], [2296, 2297, 2426], [2296, 2426, 2425], [2297, 2298, 2426], [2298, 2427, 2426], [2298, 2299, 2428], [2298, 2428, 2427], [2299, 2300, 2428], [2300, 2429, 2428], [2300, 2301, 2430], [2300, 2430, 2429], [2301, 2302, 2430], [2302, 2431, 2430], [2302, 2303, 2432], [2302, 2432, 2431], [2303, 2304, 2432], [2304, 2433, 2432], [2304, 2305, 2434], [2304, 2434, 2433], [2305, 2306, 2434], [2306, 2435, 2434], [2306, 2307, 2436], [2306, 2436, 2435], [2307, 2308, 2436], [2308, 2437, 2436], [2308, 2309, 2438], [2308, 2438, 2437], [2309, 2310, 2438], [2310, 2439, 2438], [2310, 2311, 2440], [2310, 2440, 2439], [2311, 2312, 2440], [2312, 2441, 2440], [2312, 2313, 2442], [2312, 2442, 2441], [2313, 2314, 2442], [2314, 2443, 2442], [2314, 2315, 2444], [2314, 2444, 2443], [2315, 2316, 2444], [2316, 2445, 2444], [2316, 2317, 2446], [2316, 2446, 2445], [2317, 2318, 2446], [2318, 2447, 2446], [2318, 2319, 2448], [2318, 2448, 2447], [2319, 2320, 2448], [2320, 2449, 2448], [2320, 2321, 2450], [2320, 2450, 2449], 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[28584, 22233, 22104], [28585, 28586, 22233], [28586, 22362, 22233], [28586, 28587, 22491], [28586, 22491, 22362]], + "@type": "VertexPropertyPerTriangle" + }, + "@type": "TextureMappedProperty" +} \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/autils/get_one_frame.py b/NeuralVoicePuppetry/neural-code/autils/get_one_frame.py new file mode 100644 index 0000000..0838f73 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/autils/get_one_frame.py @@ -0,0 +1,30 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip +import os +from moviepy.editor import * + +dataset = 'SRF_anchor' +data_path = f'/home/alberto/data/videosynth/{dataset}' +target_base = f'/home/alberto/data/videosynth/{dataset}_frames' + +for sub in ['Close', 'Halbtotale', 'Totale']: + os.makedirs(os.path.join(target_base, sub), exist_ok=True) + +video_list = ['Close/355_9105.mp4', 'Close/355_9106.mp4', + 'Halbtotale/355_9414.mp4', 'Halbtotale/355_9415.mp4', + 'Totale/355_7615.mp4', 'Totale/355_7616.mp4'] + +# Start and end time in minutes +frame_n = 200 + +for video in video_list: + input_file = os.path.join(data_path, video) + output_file = os.path.join(target_base, video) + # print(os.path.join(target_base, video)) + # ffmpeg_extract_subclip(os.path.join(data_path, video), start_time*60, end_time*60, + # targetname=os.path.join(target_base, video)) + + clip = VideoFileClip(input_file) + clip.save_frame(f'{target_base}/{video[:-4]}.jpeg', t=frame_n) # save frame at t=2 as JPEG diff --git a/NeuralVoicePuppetry/neural-code/autils/make_h5py.py b/NeuralVoicePuppetry/neural-code/autils/make_h5py.py new file mode 100644 index 0000000..a545724 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/autils/make_h5py.py @@ -0,0 +1,120 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os +import h5py +import numpy as np +from PIL import Image +from tqdm import tqdm +import shutil + +def remove_folders(folders): + print('\n\nCleaning folders..\n\n') + for folder in folders: + try: + shutil.rmtree(folder) + + except: + print(folder) + pass + return + +def create_h5py_dataset(target_dir, clean=False): + + ds_path = os.path.join(target_dir, 'audio_feature') + expr_path = os.path.join(target_dir, 'expressions') + frames_path = os.path.join(target_dir, 'frames') + uv_path = os.path.join(target_dir, 'uv') + mask_path = os.path.join(target_dir, 'mask_mouth') + deca_details_path = os.path.join(target_dir, 'deca_details') + + h5py_out_file = os.path.join(target_dir, f'{target_dir.split("/")[-1]}.h5') + if os.path.isfile(h5py_out_file): + print('H5py file already exists.') + if clean: + folders = [ds_path, expr_path, frames_path, uv_path, mask_path, deca_details_path] + remove_folders(folders) + return + + f = h5py.File(h5py_out_file, 'w') + + dataset_size = len(os.listdir(ds_path)) - 1 + print(f'Dataset size: {dataset_size}') + + # Load deepspeech data + dsf_data = [] + for i in tqdm(range(dataset_size)): + dsf_data.append(np.load(os.path.join(ds_path, f'{i}.deepspeech.npy'))) + + dsf_data = np.array(dsf_data) + grp = f.create_dataset("dsf", dsf_data.shape, data=dsf_data) + + # Load expression data + try: + ep_data = [] + for i in tqdm(range(dataset_size)): + ep_data.append(np.load(os.path.join(expr_path, f'expr_{i}.npy'))) + + ep_data = np.array(ep_data) + grp = f.create_dataset("ep", ep_data.shape, data=ep_data) + + except FileNotFoundError: + pass + + try: + frame_data = [] + grp = f.create_dataset("frame", (dataset_size, 224, 224, 3)) + for i in tqdm(range(dataset_size)): + im = np.asarray(Image.open(os.path.join(frames_path, '%04d.jpg' % i))) + f["frame"][i] = im + + except FileNotFoundError: + pass + + try: + uv_data = [] + grp = f.create_dataset("uv", (dataset_size, 224, 224, 2)) + for i in tqdm(range(dataset_size)): + im = np.asarray(np.load(os.path.join(uv_path, f'uv_{i}.npy'))) + f["uv"][i] = im + + except FileNotFoundError: + pass + + try: + mask_data = [] + grp = f.create_dataset("mask", (dataset_size, 224, 224)) + for i in tqdm(range(dataset_size)): + im = np.asarray(np.load(os.path.join(mask_path, f'mask_{i}.npy'))) + f["mask"][i] = im + + except FileNotFoundError: + pass + + try: + deca_details_data = [] + grp = f.create_dataset("deca_details", (dataset_size, 224, 224, 3)) + for i in tqdm(range(dataset_size)): + im = np.asarray(np.load(os.path.join(deca_details_path, f'deca_details_{i}.npy'))) + f["deca_details"][i] = im + + except FileNotFoundError: + pass + + f.close() + + print('Created h5 file at : ', h5py_out_file) + + if clean: + folders = [ds_path, expr_path, frames_path, uv_path, mask_path, deca_details_path] + remove_folders(folders) + + + +if __name__ == '__main__': + + dataset_type = 'SRF_anchor_short' + dataset_name = 'Halbtotale_355_9414' + dataset_path = '/home/alberto/NeuralVoicePuppetry/datasets' + + create_h5py_dataset(dataset_type, dataset_name, dataset_path) diff --git a/NeuralVoicePuppetry/neural-code/autils/make_video_from_frames.py b/NeuralVoicePuppetry/neural-code/autils/make_video_from_frames.py new file mode 100644 index 0000000..e2e220d --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/autils/make_video_from_frames.py @@ -0,0 +1,67 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os +from psbody.mesh import Mesh +import cv2 +import pyrender +import trimesh +import tempfile +import numpy as np +import matplotlib as mpl +import matplotlib.pyplot as plt +import matplotlib.cm as cm +from psbody.mesh import Mesh +import tempfile +from subprocess import call +from tqdm import tqdm +from PIL import Image + + +def make_video_seq(audio_fname, images_out_path, video_out_path, name): + + image = cv2.imread(os.path.join(images_out_path, sorted(os.listdir(images_out_path))[0])) + size = (image.shape[1], image.shape[0]) + print('Video size: ', size) + + tmp_video_file = tempfile.NamedTemporaryFile('w', suffix='.mp4', dir=video_out_path) + if int(cv2.__version__[0]) < 3: + writer = cv2.VideoWriter(tmp_video_file.name, cv2.cv.CV_FOURCC(*'mp4v'), 25, size, True) + else: + writer = cv2.VideoWriter(tmp_video_file.name, cv2.VideoWriter_fourcc(*'mp4v'), 25, size, True) + + images_list = sorted(os.listdir(images_out_path)) + indexes = [] + + for i in images_list: + if i.startswith('.'): + images_list.remove(i) + + else: + indexes.append(int(i[:-4])) + + print(np.max(indexes)) + + for i in tqdm(range(np.max(indexes))): + + file_name = os.path.join(images_out_path, '%04d.jpg' % i) + im = cv2.imread(file_name) + writer.write(im) + + writer.release() + + video_fname = os.path.join(video_out_path, name) + cmd = ('ffmpeg' + ' -i {0} -i {1} -vcodec h264 -ac 2 -channel_layout stereo -pix_fmt yuv420p {2}'.format( + audio_fname, tmp_video_file.name, video_fname)).split() + call(cmd) + + +if __name__ == '__main__': + audio_fname = '/home/alberto/data/videosynth/SRF_anchor_short/Halbtotale/355_9415.wav' + video_out_path = 'results/face_reconstruction/video' + os.makedirs(video_out_path, exist_ok=True) + # images_out_path = 'results/face_reconstruction/images' + images_out_path = 'results/face_reconstruction/combined_images' + os.makedirs(images_out_path, exist_ok=True) + + make_video_seq(audio_fname, images_out_path, video_out_path) diff --git a/NeuralVoicePuppetry/neural-code/autils/renderer.py b/NeuralVoicePuppetry/neural-code/autils/renderer.py new file mode 100644 index 0000000..81495af --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/autils/renderer.py @@ -0,0 +1,258 @@ +from typing import Union, List, Tuple + +import json +import numpy as np +import torch +from PIL import Image +from pytorch3d.io import load_obj +from pytorch3d.ops import interpolate_face_attributes +from pytorch3d.renderer import ( + look_at_view_transform, + FoVPerspectiveCameras, + FoVOrthographicCameras, + PointLights, + RasterizationSettings, + MeshRenderer, + MeshRasterizer, + SoftPhongShader, + Textures, TexturesUV +) +from pytorch3d.structures import Meshes + + +def to_tensor(a, device, dtype=None): + if isinstance(a, torch.Tensor): + return a.to(device) + return torch.from_numpy(np.array(a)).to(device) + + +class Renderer: + def __init__(self, device, dist=2, elev=0, azimuth=180, fov=40, image_size=256, aspect_ratio=1, R=None, T=None, K=None, cameras=None): + # If you provide R and T, you don't need dist, elev, azimuth, fov + self.device = device + + # Data structures and functions for rendering + if R is None and T is None: + R, T = look_at_view_transform(dist, elev, azimuth) + if cameras is None: + if K is None: + cameras = FoVPerspectiveCameras(device=device, R=R, T=T, znear=1, zfar=10000, aspect_ratio=aspect_ratio, + fov=fov, degrees=True) + else: + cameras = FoVOrthographicCameras(device=device, R=R, T=T, K=K) + + raster_settings = RasterizationSettings( + image_size=image_size, + blur_radius=0.0, + faces_per_pixel=1, + perspective_correct=False + ) + + dist = cameras.T # Place lights at the same point as the camera + lights = PointLights(ambient_color=((0.3, 0.3, 0.3),), diffuse_color=((0.7, 0.7, 0.7),), device=device, + location=dist) + + # Create a phong renderer by composing a rasterizer and a shader. The textured phong shader will + # interpolate the texture uv coordinates for each vertex, sample from a texture image and + # apply the Phong lighting model + self.mesh_rasterizer = MeshRasterizer( + cameras=cameras, + raster_settings=raster_settings + ) + self._renderer = MeshRenderer( + rasterizer=self.mesh_rasterizer, + shader=SoftPhongShader( + device=device, + cameras=cameras, + lights=lights + ) + ) + + def _flatten(self, a): + return torch.from_numpy(np.array(a)).reshape(-1, 1).to(self.device) + + def render_bfm(self, sp, ep, shape_mu, shape_pcab, expression_pcab, faces, save_to=None): + sp = self._flatten(sp).double() + ep = self._flatten(ep).double() + shape_mu = self._flatten(shape_mu).double() + shape_pcab = torch.from_numpy(shape_pcab).to(self.device).double() + expression_pcab = torch.from_numpy(expression_pcab).to(self.device).double() + faces = torch.from_numpy(np.array(faces)).to(self.device) + + verts_flat = shape_mu + \ + shape_pcab.mm(sp) + \ + expression_pcab.mm(ep) + verts_flat = verts_flat.view(1, -1, 3) + faces = faces.view(1, -1, 3) + return self.render_mesh(verts_flat, faces, save_to) + + def render_mesh(self, verts, faces, textures=None, save_to=None, return_format="pil", standardize=False, device="cuda" if torch.cuda.is_available() else "cpu"): + if textures is not None: + assert len(textures.shape) == 3, "Please use batches" + textures = to_tensor(textures, device) + verts = to_tensor(verts, device, dtype=np.float) + faces = to_tensor(faces, device, dtype=np.long) + + assert len(verts.shape) == 3, "Please use batches" + assert len(faces.shape) == 3, "Please use batches" + bs = verts.shape[0] + + # device + if verts.device != self.device: + verts = verts.to(self.device) + if faces.device != self.device: + faces = faces.to(self.device) + + # verts = verts / verts.abs().max() + # standardize + if standardize: + verts = verts - verts.mean(1) + verts = verts / verts.abs().max() + + # Initialize a camera. + # With world coordinates +Y up, +X left and +Z in, the front of the cow is facing the -Z direction. + # So we move the camera by 180 in the azimuth direction so it is facing the front of the cow. + verts = verts.float() + faces = faces.int() + verts_rgb = torch.stack([torch.ones_like(verts[i]) for i in range(bs)]) # (1, V, 3) + if textures is None: + textures = Textures(verts_rgb=verts_rgb.to(self.device)) + mesh = Meshes(verts=verts, faces=faces, textures=textures) + + images = self._renderer(mesh) + image = images[0, ..., :3].detach().cpu().numpy() + + image = (image.clip(0, 1) * 255).astype(np.uint8) + image_pil = Image.fromarray(image) + if save_to: + image_pil.save(save_to) + if return_format.lower() == "pil": + return image_pil + elif return_format.lower() == "np": + return image + return image + + +class ImagelessTexturesUV(TexturesUV): + def __init__(self, + faces_uvs: Union[torch.Tensor, List[torch.Tensor], Tuple[torch.Tensor]], + verts_uvs: Union[torch.Tensor, List[torch.Tensor], Tuple[torch.Tensor]], + padding_mode: str = "border", + align_corners: bool = True, + ): + maps = torch.zeros(1, 2,2, 3).to(faces_uvs[0].device) # This is simply to instantiate a texture, but it is not used. + super().__init__(maps=maps, faces_uvs=faces_uvs, verts_uvs=verts_uvs, padding_mode=padding_mode, align_corners=align_corners) + + def sample_pixel_uvs(self, fragments, **kwargs) -> torch.Tensor: + """ + Copied from super().sample_textures and adapted to output pixel_uvs instead of the sampled texture. + + Args: + fragments: + The outputs of rasterization. From this we use + + - pix_to_face: LongTensor of shape (N, H, W, K) specifying the indices + of the faces (in the packed representation) which + overlap each pixel in the image. + - barycentric_coords: FloatTensor of shape (N, H, W, K, 3) specifying + the barycentric coordianates of each pixel + relative to the faces (in the packed + representation) which overlap the pixel. + + Returns: + texels: tensor of shape (N, H, W, K, C) giving the interpolated + texture for each pixel in the rasterized image. + """ + if self.isempty(): + faces_verts_uvs = torch.zeros( + (self._N, 3, 2), dtype=torch.float32, device=self.device + ) + else: + packing_list = [ + i[j] for i, j in zip(self.verts_uvs_list(), self.faces_uvs_list()) + ] + faces_verts_uvs = torch.cat(packing_list) + texture_maps = self.maps_padded() + + # pixel_uvs: (N, H, W, K, 2) + pixel_uvs = interpolate_face_attributes( + fragments.pix_to_face, fragments.bary_coords, faces_verts_uvs + ) + + N, H_out, W_out, K = fragments.pix_to_face.shape + N, H_in, W_in, C = texture_maps.shape # 3 for RGB + + # pixel_uvs: (N, H, W, K, 2) -> (N, K, H, W, 2) -> (NK, H, W, 2) + pixel_uvs = pixel_uvs.permute(0, 3, 1, 2, 4).reshape(N * K, H_out, W_out, 2) + + # textures.map: + # (N, H, W, C) -> (N, C, H, W) -> (1, N, C, H, W) + # -> expand (K, N, C, H, W) -> reshape (N*K, C, H, W) + texture_maps = ( + texture_maps.permute(0, 3, 1, 2)[None, ...] + .expand(K, -1, -1, -1, -1) + .transpose(0, 1) + .reshape(N * K, C, H_in, W_in) + ) + + # Textures: (N*K, C, H, W), pixel_uvs: (N*K, H, W, 2) + # Now need to format the pixel uvs and the texture map correctly! + # From pytorch docs, grid_sample takes `grid` and `input`: + # grid specifies the sampling pixel locations normalized by + # the input spatial dimensions It should have most + # values in the range of [-1, 1]. Values x = -1, y = -1 + # is the left-top pixel of input, and values x = 1, y = 1 is the + # right-bottom pixel of input. + + pixel_uvs = pixel_uvs * 2.0 - 1.0 + return pixel_uvs + + +class UVRenderer(Renderer): + def __init__(self, obj_path, device, dist=2, elev=0, azimuth=180, fov=40, image_size=256, R=None, T=None, cameras=None): + # obj path should be the path to the obj file with the UV parametrization + super().__init__(device=device, dist=dist, elev=elev, azimuth=azimuth, fov=fov, image_size=image_size, R=R, T=T, cameras=cameras) + # p = "/media/data/facemodels/basel_facemodel/xucong_bfm/BFM 2017/BFM_face_2017.obj" + v, f, aux = load_obj(obj_path) + verts_uvs = aux.verts_uvs.to(device) # (V, 2) + faces_uvs = f.textures_idx.to(device) # (F, 3) + self.texture = ImagelessTexturesUV(verts_uvs=[verts_uvs], faces_uvs=[faces_uvs]) + + def render(self, meshes): + # Currently only supports one mesh in meshes + fragments = self.mesh_rasterizer(meshes) + rendered_uv = self.texture.sample_pixel_uvs(fragments).detach().cpu().numpy()[0] + return rendered_uv + + +class BFMUVRenderer(Renderer): + def __init__(self, json_path, device, dist=2, elev=0, azimuth=180, fov=40, image_size=256, R=None, T=None, cameras=None): + # json_path = ".../face12.json" + super().__init__(device=device, dist=dist, elev=elev, azimuth=azimuth, fov=fov, image_size=image_size, R=R, T=T, cameras=cameras) + + with open(json_path, 'r') as f: + uv_para = json.load(f) + verts_uvs = np.array(uv_para['textureMapping']['pointData']) + faces_uvs = np.array(uv_para['textureMapping']['triangles']) + verts_uvs = to_tensor(verts_uvs, device).float() + faces_uvs = to_tensor(faces_uvs, device) + self.texture = ImagelessTexturesUV(verts_uvs=[verts_uvs], faces_uvs=[faces_uvs]) + + def render(self, meshes): + # Currently only supports one mesh in meshes + fragments = self.mesh_rasterizer(meshes) + rendered_uv = self.texture.sample_pixel_uvs(fragments).detach().cpu().numpy()[0] + return rendered_uv + + +if __name__ == "__main__": + height = 256 + path_uv = "face12.json" + new_bfm_vertices = ... # vertices of BFM + bfm_faces_tensor = ... # triangle faces of BFM + + R, T = look_at_view_transform(eye=((0, 0, 0),), at=((0, 0, -1),), up=((0, 1, 0),)) + uv_renderer = BFMUVRenderer(path_uv, device="cuda" if torch.cuda.is_available() else "cpu", fov=63, image_size=height, R=R, T=T) + new_bfm_mesh = Meshes(new_bfm_vertices, bfm_faces_tensor) + rendered_uv_np = uv_renderer.render(new_bfm_mesh) + diff --git a/NeuralVoicePuppetry/neural-code/autils/resample_audio.py b/NeuralVoicePuppetry/neural-code/autils/resample_audio.py new file mode 100644 index 0000000..980ce2f --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/autils/resample_audio.py @@ -0,0 +1,13 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from scipy.io import wavfile + +audio_fname = '/home/alberto/data/videosynth/SRF_anchor_short/Halbtotale/355_9415.wav' +new_audio = '/home/alberto/data/videosynth/SRF_anchor_short/Halbtotale/355_9415_short_Clara.wav' + +samplerate, data = wavfile.read(audio_fname) + +new_data = data[:20*samplerate, :] + +wavfile.write(new_audio, samplerate, new_data) diff --git a/NeuralVoicePuppetry/neural-code/autils/voca_helpers.py b/NeuralVoicePuppetry/neural-code/autils/voca_helpers.py new file mode 100644 index 0000000..5820b2a --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/autils/voca_helpers.py @@ -0,0 +1,183 @@ +from __future__ import division +import os +os.environ['PYOPENGL_PLATFORM'] = 'egl' # Uncommnet this line while running remotely +import cv2 +import pyrender +import trimesh +import tempfile +import numpy as np +import matplotlib as mpl +import matplotlib.pyplot as plt +import matplotlib.cm as cm +from psbody.mesh import Mesh +import tempfile +from subprocess import call +from tqdm import tqdm +from PIL import Image + +def get_unit_factor(unit): + if unit == 'mm': + return 1000.0 + elif unit == 'cm': + return 100.0 + elif unit == 'm': + return 1.0 + else: + raise ValueError('Unit not supported') + + +def render_mesh_helper(mesh, t_center, rot=np.zeros(3), tex_img=None, v_colors=None, errors=None, error_unit='m', min_dist_in_mm=0.0, max_dist_in_mm=3.0, z_offset=0): + camera_params = {'c': np.array([400, 400]), + 'k': np.array([-0.19816071, 0.92822711, 0, 0, 0]), + 'f': np.array([4754.97941935 / 2, 4754.97941935 / 2])} + + frustum = {'near': 0.01, 'far': 3.0, 'height': 800, 'width': 800} + + mesh_copy = Mesh(mesh.v, mesh.f) + mesh_copy.v[:] = cv2.Rodrigues(rot)[0].dot((mesh_copy.v-t_center).T).T+t_center + + texture_rendering = tex_img is not None and hasattr(mesh, 'vt') and hasattr(mesh, 'ft') + if texture_rendering: + intensity = 0.5 + tex = pyrender.Texture(source=tex_img, source_channels='RGB') + material = pyrender.material.MetallicRoughnessMaterial(baseColorTexture=tex) + + # Workaround as pyrender requires number of vertices and uv coordinates to be the same + temp_filename = '%s.obj' % next(tempfile._get_candidate_names()) + mesh.write_obj(temp_filename) + tri_mesh = trimesh.load(temp_filename, process=False) + try: + os.remove(temp_filename) + except: + print('Failed deleting temporary file - %s' % temp_filename) + render_mesh = pyrender.Mesh.from_trimesh(tri_mesh, material=material) + elif errors is not None: + intensity = 0.5 + unit_factor = get_unit_factor('mm')/get_unit_factor(error_unit) + errors = unit_factor*errors + + norm = mpl.colors.Normalize(vmin=min_dist_in_mm, vmax=max_dist_in_mm) + cmap = cm.get_cmap(name='jet') + colormapper = cm.ScalarMappable(norm=norm, cmap=cmap) + rgba_per_v = colormapper.to_rgba(errors) + rgb_per_v = rgba_per_v[:, 0:3] + elif v_colors is not None: + intensity = 0.5 + rgb_per_v = v_colors + else: + intensity = 1.5 + rgb_per_v = None + + if not texture_rendering: + tri_mesh = trimesh.Trimesh(vertices=mesh_copy.v, faces=mesh_copy.f, vertex_colors=rgb_per_v) + render_mesh = pyrender.Mesh.from_trimesh(tri_mesh, smooth=True) + + scene = pyrender.Scene(ambient_light=[.2, .2, .2], bg_color=[255, 255, 255]) + camera = pyrender.IntrinsicsCamera(fx=camera_params['f'][0], + fy=camera_params['f'][1], + cx=camera_params['c'][0], + cy=camera_params['c'][1], + znear=frustum['near'], + zfar=frustum['far']) + + scene.add(render_mesh, pose=np.eye(4)) + + camera_pose = np.eye(4) + camera_pose[:3,3] = np.array([0, 0, 1.0-z_offset]) + scene.add(camera, pose=[[1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, 1, 1], + [0, 0, 0, 1]]) + + angle = np.pi / 6.0 + pos = camera_pose[:3,3] + light_color = np.array([1., 1., 1.]) + light = pyrender.PointLight(color=light_color, intensity=intensity) + + light_pose = np.eye(4) + light_pose[:3,3] = pos + scene.add(light, pose=light_pose.copy()) + + light_pose[:3,3] = cv2.Rodrigues(np.array([angle, 0, 0]))[0].dot(pos) + scene.add(light, pose=light_pose.copy()) + + light_pose[:3,3] = cv2.Rodrigues(np.array([-angle, 0, 0]))[0].dot(pos) + scene.add(light, pose=light_pose.copy()) + + light_pose[:3,3] = cv2.Rodrigues(np.array([0, -angle, 0]))[0].dot(pos) + scene.add(light, pose=light_pose.copy()) + + light_pose[:3,3] = cv2.Rodrigues(np.array([0, angle, 0]))[0].dot(pos) + scene.add(light, pose=light_pose.copy()) + + flags = pyrender.RenderFlags.SKIP_CULL_FACES + try: + r = pyrender.OffscreenRenderer(viewport_width=frustum['width'], viewport_height=frustum['height']) + color, _ = r.render(scene, flags=flags) + except: + print('pyrender: Failed rendering frame') + color = np.zeros((frustum['height'], frustum['width'], 3), dtype='uint8') + + return color[..., ::-1] + + +def render_sequence_meshes(audio_fname, sequence_vertices, template, images_out_path, video_out_path, + uv_template_fname='', texture_img_fname=''): + + if not os.path.exists(video_out_path): + os.makedirs(video_out_path) + + if not os.path.exists(images_out_path): + os.makedirs(images_out_path) + + # tmp_video_file = tempfile.NamedTemporaryFile('w', suffix='.mp4', dir=video_out_path) + # if int(cv2.__version__[0]) < 3: + # writer = cv2.VideoWriter(tmp_video_file.name, cv2.cv.CV_FOURCC(*'mp4v'), 60, (800, 800), True) + # else: + # writer = cv2.VideoWriter(tmp_video_file.name, cv2.VideoWriter_fourcc(*'mp4v'), 60, (800, 800), True) + + if os.path.exists(uv_template_fname) and os.path.exists(texture_img_fname): + uv_template = Mesh(filename=uv_template_fname) + vt, ft = uv_template.vt, uv_template.ft + tex_img = cv2.imread(texture_img_fname)[:,:,::-1] + else: + vt, ft = None, None + tex_img = None + + num_frames = sequence_vertices.shape[0] + center = np.mean(sequence_vertices[0], axis=0) + for i_frame in tqdm(range(num_frames)): + + if i_frame >= 0: + + render_mesh = Mesh(sequence_vertices[i_frame], template.f) + if vt is not None and ft is not None: + render_mesh.vt, render_mesh.ft = vt, ft + + im_tar_path = os.path.join(images_out_path, f"frame_{i_frame}.png") + + if os.path.isfile(im_tar_path): + + im = Image.open(im_tar_path) + singular = np.unique(im) + + if len(singular) >= 2: + pass + + else: + print(f"Rendering {i_frame} because it's black") + + + img = render_mesh_helper(render_mesh, center, tex_img=tex_img) + + im = Image.fromarray(img) + im.save(im_tar_path) + + # writer.write(img) + + # writer.release() + # + # video_fname = os.path.join(out_path, 'video.mp4') + # cmd = ('ffmpeg' + ' -i {0} -i {1} -vcodec h264 -ac 2 -channel_layout stereo -pix_fmt yuv420p {2}'.format( + # audio_fname, tmp_video_file.name, video_fname)).split() + # call(cmd) diff --git a/NeuralVoicePuppetry/neural-code/base_options.py b/NeuralVoicePuppetry/neural-code/base_options.py new file mode 100644 index 0000000..ac18b11 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/base_options.py @@ -0,0 +1,111 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import argparse +import os + +class Options(): + def __init__(self): + self.initialized = False + + def initialize(self, parser): + self.initialized = True + return parser + + def gather_options(self): + # initialize parser with basic options + if not self.initialized: + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser = self.initialize(parser) + + # get the basic options + opt, _ = parser.parse_known_args() + + self.parser = parser + + return parser.parse_args() + + def print_options(self, opt): + message = '' + message += '----------------- Options ---------------\n' + for k, v in sorted(vars(opt).items()): + comment = '' + default = self.parser.get_default(k) + if v != default: + comment = '\t[default: %s]' % str(default) + message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment) + message += '----------------- End -------------------' + print(message) + + + def parse(self): + + opt = self.gather_options() + + self.print_options(opt) + + self.opt = opt + return self.opt + +class PostprocessingOptions(Options): + + def initialize(self, parser): + + parser = Options.initialize(self, parser) + parser.add_argument('--file_id_source', required=True, help='Name of source') + parser.add_argument('--file_id_target', required=True, help='Name of target') + parser.add_argument('--model_name', required=True, help='Name of the rendering model') + + parser.add_argument('--frames_path', required=True, help='path to target orignal frames') + parser.add_argument('--audio_fname', required=True, help='path to input audio file') + parser.add_argument('--dataset_target', required=True, help='path to target dataset folder') + parser.add_argument('--target_fps', type=int, default=25, help='Fps of target video') + parser.add_argument('--clean', action='store_true', help='True to clean all data and inference folders') + parser.add_argument('--results_out_dir', required=True, help='path to output folder') + + return parser + + +class PreprocessingOptions(Options): + + def initialize(self, parser): + + parser = Options.initialize(self, parser) + parser.add_argument('--dataroot', required=True, default='/home/alberto/data/videosynth/', + help='path to data folder') + parser.add_argument('--dataset_path', required=True, default='/home/alberto/NeuralVoicePuppetry/datasets/', + help='path to the dataset folder') + parser.add_argument('--dataset', required=True, help='name of the dataset cathegory') + parser.add_argument('--name', required=True, help='name of video/audio to process') + + parser.add_argument('--preprocess_ds', action='store_true', help='True to run deepspeach preprocessing') + parser.add_argument('--preprocess_tracking', action='store_true', help='True to run tracking preprocessing') + parser.add_argument('--skip_h5py', action='store_true', help='True to skip h5py creation') + parser.add_argument('--target_fps', type=int, default=25, help='Fps of target video') + parser.add_argument('--clean', action='store_true', help='True to clean all datset folders and leave only h5py') + + return parser + +class FaceReconstructionOptions(Options): + + def initialize(self, parser): + parser = Options.initialize(self, parser) + parser.add_argument('--source_name', required=True, help='name of source dataset') + parser.add_argument('--target_name', required=True, help='name of target dataset') + parser.add_argument('--audio_path', required=True, help='path to source audio file') + parser.add_argument('--expression_path', required=True, help='path to expressions dir') + parser.add_argument('--codedicts_path', required=True, help='path to codedicts dir') + parser.add_argument('--tform_path', required=True, help='path to tform file') + parser.add_argument('--frames_path', required=True, help='path to frames dir') + + return parser + +class Text2SpeachOptions(PreprocessingOptions): + def initialize(self, parser): + + parser = PreprocessingOptions.initialize(self, parser) + parser.add_argument('--language_source', type=str, default='en', help='Original language of text file') + parser.add_argument('--language_target', type=str, default='en', help='Target language for audio file') + + return parser diff --git a/NeuralVoicePuppetry/neural-code/codedump/NeRF_audio2expression.sh b/NeuralVoicePuppetry/neural-code/codedump/NeRF_audio2expression.sh new file mode 100644 index 0000000..82d39ab --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/codedump/NeRF_audio2expression.sh @@ -0,0 +1,57 @@ +GPUID=0 + +BASE=`pwd` +echo $BASE + +# DATA PATHS +DATAROOT=/home/alberto/data/videosynth/ +DATASETPATH=/home/alberto/NeuralVoicePuppetry/datasets/ + +### SOURCE ### + +# Severin Videos +DATASET_SOURCE=External +VIDEOTYPE_SOURCE=Severin_videos +NAME_SOURCE_LIST=( transformers_lecture ) + +TARGET_FPS=25 +##################### PREPROCESSING ##################### +for NAME_SOURCE in "${NAME_SOURCE_LIST[@]}" +do + echo 'Preprocessing source video: '$DATAROOT$DATASET_SOURCE/$VIDEOTYPE_SOURCE/$NAME_SOURCE + python preprocessing.py \ + --dataroot $DATAROOT \ + --dataset_path $DATASETPATH \ + --dataset $DATASET_SOURCE \ + --video_type $VIDEOTYPE_SOURCE \ + --name $NAME_SOURCE \ + --preprocess_ds \ + --target_fps $TARGET_FPS \ + --clean \ + + + ##################### AUDIO2EXPRESSION ##################### + echo -e '\n--------------AUDIO2EXPRESSION---------------\n' + OBJECT=ARD_ZDF + N_ITER=150 + N_ITER_LR_DECAY=50 + RENDERER=$OBJECT + EROSION=1.0 + BATCH_SIZE=16 + MODEL=audio2ExpressionsAttentionTMP4 + RENDERER_TYPE=estimatorAttention + DATASET_MODE=multi_face_audio_eq_tmp_cached + LOSS=RMS + SEQ_LEN=8 + DATE_WITH_TIME=20191105-115332 + A2E_NAME=$MODEL-$RENDERER_TYPE-SL$SEQ_LEN-BS$BATCH_SIZE-$OBJECT-$DATASET_MODE-$LOSS-$DATE_WITH_TIME-look_ahead + EPOCH=latest + + # SOURCE + SOURCE_ACTOR=$DATASETPATH$DATASET_SOURCE/$VIDEOTYPE_SOURCE'_'$NAME_SOURCE + MAPPING_PATH=/home/alberto/NeuralVoicePuppetry/mappings/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead/mapping_Severin_videos_SC + OUTDIR=/home/alberto/data/dave_fxfy/audio2exprNVP/ + + cd Audio2ExpressionNet/Inference/ + python audio2expr_with_map.py --use_mapping --mapping_path $MAPPING_PATH --out_dir $OUTDIR --look_ahead --seq_len $SEQ_LEN --source_actor $SOURCE_ACTOR --write_no_images --name $A2E_NAME --erosionFactor $EROSION --epoch $EPOCH --display_winsize 512 --rendererType $RENDERER_TYPE --lossType $LOSS --dataroot $DATASETS_DIR/$OBJECT --model $MODEL --netG unet_256 --dataset_mode $DATASET_MODE --norm instance --gpu_ids $GPUID +done diff --git a/NeuralVoicePuppetry/neural-code/codedump/__init__.py b/NeuralVoicePuppetry/neural-code/codedump/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/codedump/find_closest_frames.py b/NeuralVoicePuppetry/neural-code/codedump/find_closest_frames.py new file mode 100644 index 0000000..8c2cd61 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/codedump/find_closest_frames.py @@ -0,0 +1,83 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os +import cv2 +from skimage.metrics import structural_similarity +import matplotlib.pyplot as plt +from tqdm import tqdm +import sys, getopt + +def compute_diff(image1, image2): + # Convert images to grayscale + image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY) + image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY) + + # Compute SSIM between two images + (score, diff) = structural_similarity(image1, image2, full=True) + diff = (diff * 255).astype("uint8") + + return score, diff + +def main(argv): + + fake_video_path = argv[0] + real_video_path = argv[1] + + print(f'Fake video path: {fake_video_path}') + print(f'Real video path: {real_video_path}') + + name = fake_video_path.split('/')[-1][:-4] + + vidcap_fake = cv2.VideoCapture(fake_video_path) + vidcap_real = cv2.VideoCapture(real_video_path) + + length = min(int(vidcap_fake.get(cv2.CAP_PROP_FRAME_COUNT)), int(vidcap_real.get(cv2.CAP_PROP_FRAME_COUNT))) + + success_fake,image_fake = vidcap_fake.read() + success_real,image_real = vidcap_real.read() + + best_pair = { + 'index': 0, + 'score': -1, + 'diff': None, + 'real_img': None, + 'fake_img': None, + } + + for count in tqdm(range(length)): + + score, diff = compute_diff(image_fake, image_real) + if score > best_pair["score"]: + best_pair["index"] = count + best_pair["score"] = score + best_pair["diff"] = diff + best_pair["real_img"] = image_real + best_pair["fake_img"] = image_fake + + success_fake, image_fake = vidcap_fake.read() + success_real, image_real = vidcap_real.read() + + # best_pair["real_img"] = cv2.cvtColor(best_pair["real_img"], cv2.COLOR_BGR2RGB) + # best_pair["fake_img"] = cv2.cvtColor(best_pair["fake_img"], cv2.COLOR_BGR2RGB) + + # fig=plt.figure(figsize=(14, 6)) + # fig.suptitle(f'Index: {best_pair["index"]}, Score: {best_pair["score"]}') + # fig.add_subplot(131) + # plt.imshow(best_pair["real_img"]) + # fig.add_subplot(132) + # plt.imshow(best_pair["fake_img"]) + # fig.add_subplot(133) + # plt.imshow(best_pair["diff"]) + # plt.show() + + os.makedirs('../results/differences/', exist_ok=True) + + cv2.imwrite('results/differences/' + name + '_real_img.png', best_pair["real_img"]) + cv2.imwrite('results/differences/' + name + '_fake_img.png', best_pair["fake_img"]) + cv2.imwrite('results/differences/' + name + '_diff_img.png', best_pair["diff"]) + + +# python find_closest_frames.py '/home/alberto/NeuralVoicePuppetry/results/inference/DynamicNeuralTextures-Demo_Male_moderator_1/Demo_Male_moderator_1_to_Demo_Male_moderator_1/Demo_Male_moderator_1_to_Demo_Male_moderator_1.mp4' '/home/alberto/data/videosynth/External/Demo/Male_moderator_1.mp4' +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/NeuralVoicePuppetry/neural-code/codedump/find_closest_frames.sh b/NeuralVoicePuppetry/neural-code/codedump/find_closest_frames.sh new file mode 100644 index 0000000..a3c4662 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/codedump/find_closest_frames.sh @@ -0,0 +1,10 @@ + +DATASET_SOURCE=Original_NVP +VIDEOTYPE_SOURCE=Videos +NAME_SOURCE_LIST=( mpi_franziska_neutral_eng obama sequence002 ) + +for NAME in "${NAME_SOURCE_LIST[@]}" +do + VIDEONAME=$VIDEOTYPE_SOURCE'_'$NAME + python find_closest_frames.py '/home/alberto/NeuralVoicePuppetry/results/inference/DynamicNeuralTextures-'$VIDEONAME/$VIDEONAME'_to_'$VIDEONAME/$VIDEONAME'_to_'$VIDEONAME'.mp4' '/home/alberto/data/videosynth/'$DATASET_SOURCE/$VIDEOTYPE_SOURCE/$NAME'.mp4' +done \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/codedump/make_various_audios.py b/NeuralVoicePuppetry/neural-code/codedump/make_various_audios.py new file mode 100644 index 0000000..bb931fc --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/codedump/make_various_audios.py @@ -0,0 +1,13 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from subprocess import call + +name = "transformers_lecture" +file_path = "/home/alberto/data/videosynth/External/Severin_videos/" + +durations = [ 10 ] + +for dur in durations: + cmd = (f"ffmpeg -i {file_path}{name}.wav -ss 0 -to {dur} -c copy {file_path}{name}_{dur}.wav").split() + call(cmd) diff --git a/NeuralVoicePuppetry/neural-code/codedump/reconstruct_faces.py b/NeuralVoicePuppetry/neural-code/codedump/reconstruct_faces.py new file mode 100644 index 0000000..8b7661f --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/codedump/reconstruct_faces.py @@ -0,0 +1,170 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os +import tqdm +import torch +import numpy as np +import json +from PIL import Image +from psbody.mesh import Mesh +from autils.voca_helpers import * +from autils.make_video_from_frames import make_video_seq +from third.DECA.decalib.utils.config import cfg +from third.DECA.decalib.utils.renderer import SRenderY +from third.DECA.decalib.models.FLAME import FLAME +from third.DECA.decalib.utils.util import write_obj, batch_orth_proj +from skimage.transform import warp +from skimage.transform import SimilarityTransform +from base_options import FaceReconstructionOptions + +def warp_back(image, oldimage, tform): + + alpha = 0.6 + + oldimage = oldimage.astype(np.float64) /255. + new_size = oldimage.shape + + dst_image = warp(image, tform, output_shape=new_size) + + # Mask of non-black pixels. + mask = np.where(np.all(dst_image <= [0.2, 0.2, 0.2], axis=-1)) + dst_image[mask] = oldimage[mask] + + res = cv2.addWeighted(oldimage, 1 - alpha, dst_image, alpha, 0) + res = res[:, :, ::-1] + + return res + +if __name__ == '__main__': + opt = FaceReconstructionOptions().parse() + + source_name = opt.source_name + target_name = opt.target_name + expression_path = opt.expression_path + pd_files_path = opt.codedicts_path + audio_fname = opt.audio_input_dir + frames_path = opt.frames_path + tform_path = opt.tform_path + + transfer_name = source_name+'--'target_name + + meshes_out_path = f'results/face_reconstruction/{transfer_name}/meshes' + os.makedirs(meshes_out_path, exist_ok=True) + video_out_path = f'results/face_reconstruction/{transfer_name}/video' + os.makedirs(video_out_path, exist_ok=True) + images_out_path = f'results/face_reconstruction/{transfer_name}/images' + os.makedirs(images_out_path, exist_ok=True) + json_out_path = f'results/face_reconstruction/{transfer_name}/talking_head.json' + + # deca or voca + mode = 'deca' + # mode = 'voca' + + # skip saving obj + skip_obj = True + + # initialize flame + model_cfg = cfg.model + flame = FLAME(model_cfg).cuda() + image_size = cfg.dataset.image_size + render = SRenderY(image_size, obj_filename=model_cfg.topology_path, uv_size=model_cfg.uv_size).cuda() + faces = render.faces[0].cpu().numpy() + + # get mean shape + print('Getting mean shape..') + pd_files = [f for f in sorted(os.listdir(pd_files_path)) + if os.path.isfile(os.path.join(pd_files_path, f))] + + shapes = torch.tensor([]).cuda() + codedicts = {} + + for pd_file in tqdm(pd_files): + codedict = torch.load(os.path.join(pd_files_path, pd_file)) + index = int(pd_file[9:-3]) + codedicts[index] = codedict + shape = codedict['shape'] + shapes = torch.cat((shapes, shape)) + + mean_shape = torch.mean(shapes, dim=0) + mean_shape = torch.unsqueeze(mean_shape, dim=0) + + + # get expressions + num_expressions = len([name for name in os.listdir(expression_path)]) + sequence_vertices = [] + + # make objs + print('Making meshes..') + + for index in tqdm(range(num_expressions)): + + codedict = codedicts[index] + expression = np.load(os.path.join(expression_path, f'expr_{index}.npy'))[0] + + pose = codedict['pose'][0, :-3] + expr = torch.unsqueeze(torch.tensor(expression[:-3], dtype=torch.float32), dim=0).cuda() + jaw_pose = torch.tensor(expression[-3:], dtype=torch.float32).cuda() + new_pose = torch.unsqueeze(torch.cat((pose, jaw_pose), dim=0), dim=0) + + # get flame model + vertices, landmarks2d, landmarks3d = flame(mean_shape, expr, new_pose) + + if not skip_obj: + filename = os.path.join(meshes_out_path, '%04d.obj'%index) + write_obj(filename, vertices[0], faces) + + if mode == 'voca': + # append vertices + frame = Mesh(filename=filename) + sequence_vertices.append(frame.v) + + elif mode == 'deca': + # save images + trans_verts = batch_orth_proj(vertices, codedict['cam']) + trans_verts[:, :, 1:] = -trans_verts[:, :, 1:] + shape_images = render.render_shape(vertices, trans_verts)[0] + shape_images = shape_images.permute(1,2,0).cpu().numpy() + shape_images = shape_images * 255. + shape_images = shape_images.astype(np.uint8) + + filename = os.path.join(images_out_path, '%04d.jpg' % index) + + img = Image.fromarray(shape_images) + img.save(filename) + + # Render images + print('Rendering images and making video..') + if mode == 'voca': + template = Mesh(sequence_vertices[0], f) + sequence_vertices = np.stack(sequence_vertices) + render_sequence_meshes(audio_fname, sequence_vertices, template, images_out_path, video_out_path, + uv_template_fname='', texture_img_fname='') + + make_video_seq(audio_fname, images_out_path, video_out_path, 'talking_head.mp4') + + + combined_images_out_path = f'results/face_reconstruction/{transfer_name}/combined_images' + os.makedirs(combined_images_out_path, exist_ok=True) + + tform = np.load(tform_path) + + for image_path, frame in zip(tqdm(sorted(os.listdir(images_out_path))), sorted(os.listdir(frames_path))): + + index = int(image_path[:-4]) + f_tform = SimilarityTransform(matrix=tform[index]) + + image = cv2.imread(os.path.join(images_out_path,image_path)) + old_image = cv2.imread(os.path.join(frames_path, frame)) + + res = warp_back(image, old_image, f_tform) + res = res * 255. + res = res.astype('uint8') + res = cv2.cvtColor(res, cv2.COLOR_BGR2RGB) + + + file_name = os.path.join(combined_images_out_path, '%04d.jpg' % index) + cv2.imwrite(file_name, res) + + make_video_seq(audio_fname, combined_images_out_path, video_out_path, 'video_combined.mp4') + diff --git a/NeuralVoicePuppetry/neural-code/codedump/reconstruct_faces.sh b/NeuralVoicePuppetry/neural-code/codedump/reconstruct_faces.sh new file mode 100644 index 0000000..bfd9955 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/codedump/reconstruct_faces.sh @@ -0,0 +1,37 @@ +GPUID=0 + +BASE=`pwd` +# DATA PATHS +DATAROOT=/home/alberto/data/videosynth/ +DATASETPATH=/home/alberto/NeuralVoicePuppetry/datasets/ + +# SOURCE +DATASET_SOURCE=SRF_anchor_short +VIDEOTYPE_SOURCE=Halbtotale +NAME_SOURCE=355_9415 + +# TARGET +DATASET_TARGET=SRF_anchor_short +VIDEOTYPE_TARGET=Close +NAME_TARGET=355_9105 + +############################################# +SOURCE_NAME=$VIDEOTYPE_SOURCE'_'$NAME_SOURCE +TARGET_NAME=$VIDEOTYPE_TARGET'_'$NAME_TARGET +MODEL='TRANSFERS/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead' + +AUDIO_PATH = $DATAROOT$DATASET_SOURCE/$VIDEOTYPE_SOURCE/$NAME_SOURCE'.wav' +EXPRESSION_PATH = $DATASETPATH$MODEL/$SOURCE_NAME'--'$TARGET_NAME/'expressions' +CODEDICTS_PATH = $DATASETPATH$DATASET_TARGET/$TARGET_NAME/'DECA_codedicts' +TFORM_PATH = $DATASETPATH$DATASET_TARGET/$TARGET_NAME/'tform.npy' +FRAMES_PATH = $DATAROOT$DATASET_TARGET/$VIDEOTYPE_TARGET/$NAME_TARGET + +echo 'Reconstructing face: '$SOURCE_NAME'--'$TARGET_NAME +python reconstruct_faces.py \ +--source_name $SOURCE_NAME \ +--target_name $TARGET_NAME +--audio_path $AUDIO_PATH \ +--expression_path $EXPRESSION_PATH \ +--codedicts_path $CODEDICTS_PATH \ +--tform_path $TFORM_PATH \ +--frames_path $FRAMES_PATH \ diff --git a/NeuralVoicePuppetry/neural-code/codedump/test_compression.py b/NeuralVoicePuppetry/neural-code/codedump/test_compression.py new file mode 100644 index 0000000..899b654 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/codedump/test_compression.py @@ -0,0 +1,59 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os +from subprocess import call +import cv2 +import matplotlib.pyplot as plt +from tqdm import tqdm +from skimage.metrics import structural_similarity + + +def check_mkdir(path): + if not os.path.exists(path): + print('creating %s' % path) + os.makedirs(path) + + +def video2sequence_lossless(video_path): + videofolder = video_path.split('.')[0] + check_mkdir(videofolder) + video_name = video_path.split('/')[-1].split('.')[0] + cmd = (f'ffmpeg -i {video_path} -vf fps=25 {videofolder}/{video_name}_frame%04d.png').split() + call(cmd) + imagepath_list = [os.path.join(videofolder, f) for f in os.listdir(videofolder)] + print('video frames are stored in {}'.format(videofolder)) + return imagepath_list + + +def compute_diff(image1, image2, text): + # Convert images to grayscale + image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY) + image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY) + + # Compute SSIM between two images + (score, diff) = structural_similarity(image1, image2, full=True) + print(text, ' SSIM: ', score) + diff = (diff * 255).astype("uint8") + + plt.imshow(diff) + plt.show() + + +NAME = 'DynamicNeuralTextures-Demo_Female_moderator_1' +FILE_ID_SOURCE = 'Clara_audios_Resemble_clara' +FILE_ID_TARGET = 'Demo_Female_moderator_1' + +video_out_path = f'results/inference/{NAME}/{FILE_ID_SOURCE}_to_{FILE_ID_TARGET}/' +video_fname = os.path.join(video_out_path, f'{FILE_ID_SOURCE}_to_{FILE_ID_TARGET}.mp4') +frames_path = '/home/alberto/data/videosynth/External/Demo/Female_moderator_1/' + +imagepath_list = video2sequence_lossless(video_fname) + +for gen_frame, old_frame in zip(tqdm(sorted(imagepath_list)), sorted(os.listdir(frames_path))): + + old_image = cv2.imread(os.path.join(frames_path, old_frame)) + gen_image = cv2.imread(gen_frame) + + compute_diff(old_image, gen_image, 'old_image - gen_image') + diff --git a/NeuralVoicePuppetry/neural-code/full_pipeline_nvp.sh b/NeuralVoicePuppetry/neural-code/full_pipeline_nvp.sh new file mode 100755 index 0000000..fe28d6c --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/full_pipeline_nvp.sh @@ -0,0 +1,279 @@ +#!/bin/bash + +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +set -e +eval "$(conda shell.bash hook)" +# cd ../neural-code + +GPUID=0 +BASE_DIR="${3:-$(pwd)}" +echo "BASE_DIR=$BASE_DIR" + +NAME_AUDIO_FILE=$1 +NAME_VIDEO_FILE=$2 +echo "NAME_AUDIO_FILE: $1" +echo "NAME_VIDEO_FILE: $2" + +# DATA PATHS +INPUTDATAPATH=$BASE_DIR/input_data +OUTPUTDATAPATH=$BASE_DIR/output_data +FEATURESPATH=features + +#Testing output path +OUTPUT_TOOL_DATA_PATH=$BASE_DIR/output_data/videos +#mkdir -p $OUTPUT_TOOL_DATA_PATH + +IMAGES_TARGET_DIR=$OUTPUT_TOOL_DATA_PATH + +###AUDIO### +AUDIO_PATH=audio +NAME_AUDIOS_LIST=($NAME_AUDIO_FILE) + +### VIDEO ### +VIDEO_PATH=video +NAME_VIDEOS_LIST=($NAME_VIDEO_FILE) + +TARGET_FPS=25 + +##################### PREPROCESSING ##################### +for NAME_AUDIO in "${NAME_AUDIOS_LIST[@]}"; do + for NAME_VIDEO in "${NAME_VIDEOS_LIST[@]}"; do + # Set time variable + SECONDS=0 + + conda activate deepspeech + if [ $NAME_AUDIO != $NAME_VIDEO ]; then + echo 'Preprocessing source audio: '$INPUTDATAPATH/$AUDIO_PATH/$NAME_AUDIO + python preprocessing.py \ + --dataroot $INPUTDATAPATH \ + --dataset_path $OUTPUTDATAPATH/$FEATURESPATH \ + --dataset $AUDIO_PATH \ + --name $NAME_AUDIO \ + --preprocess_ds \ + --target_fps $TARGET_FPS \ + --clean + + else + echo $NAME_AUDIO'='$NAME_VIDEO + + fi + + echo 'Preprocessing target video: '$INPUTDATAPATH/$VIDEO_PATH/$NAME_VIDEO + python preprocessing.py \ + --dataroot $INPUTDATAPATH \ + --dataset_path $OUTPUTDATAPATH/$FEATURESPATH \ + --dataset $VIDEO_PATH \ + --name $NAME_VIDEO \ + --preprocess_ds \ + --target_fps $TARGET_FPS \ + --skip_h5py \ + + conda deactivate + + # Twice for memory issues + conda activate pyenv + python preprocessing.py \ + --dataroot $INPUTDATAPATH \ + --dataset_path $OUTPUTDATAPATH/$FEATURESPATH \ + --dataset $VIDEO_PATH \ + --name $NAME_VIDEO \ + --preprocess_tracking \ + --target_fps $TARGET_FPS \ + --clean \ + + ##################### AUDIO2EXPRESSION ##################### + echo -e '\n--------------AUDIO2EXPRESSION---------------\n' + OBJECT=ARD_ZDF + LR=0.00001 + N_ITER=150 + N_ITER_LR_DECAY=50 + RENDERER=$OBJECT + EROSION=1.0 + BATCH_SIZE=16 + MODEL=audio2ExpressionsAttentionTMP4 + RENDERER_TYPE=estimatorAttention + DATASET_MODE=multi_face_audio_eq_tmp_cached + LOSS=RMS + SEQ_LEN=8 + DATE_WITH_TIME=20191105-115332 + A2E_NAME=$MODEL-$RENDERER_TYPE-SL$SEQ_LEN-BS$BATCH_SIZE-$OBJECT-$DATASET_MODE-$LOSS-$DATE_WITH_TIME-look_ahead + EPOCH=latest + + DT=$OUTPUTDATAPATH/'TRANSFERS' + TRANSFER_PATH=$DT/$A2E_NAME + + # AUDIO + AUDIO_LIST=($OUTPUTDATAPATH/$FEATURESPATH/$NAME_AUDIO) + + # VIDEO + VIDEO_LIST=($OUTPUTDATAPATH/$FEATURESPATH/$NAME_VIDEO) + + rm -f $OUTPUTDATAPATH/TRANSFERS/$NAME/list_transfer.txt + cd Audio2ExpressionNet/Inference/ + pwd + for TARGET_ACTOR in "${VIDEO_LIST[@]}"; do + echo 'Training for Target: '$TARGET_ACTOR + + for SOURCE_ACTOR in "${AUDIO_LIST[@]}"; do + echo 'Training for Source: '$SOURCE_ACTOR + # --look_ahead + python transfer.py --look_ahead --base_path $OUTPUTDATAPATH --seq_len $SEQ_LEN --source_actor $SOURCE_ACTOR --target_actor $TARGET_ACTOR --write_no_images --name $A2E_NAME --erosionFactor $EROSION --epoch $EPOCH --display_winsize 512 --rendererType $RENDERER_TYPE --lossType $LOSS --dataroot $DATASETS_DIR/$OBJECT --model $MODEL --netG unet_256 --dataset_mode $DATASET_MODE --norm instance --gpu_ids $GPUID --transfer_path $TRANSFER_PATH + done + done + cd .. + cd .. + + ##################### NEURAL RENDERING ##################### + + echo -e '\n--------------Neural Rendering Training---------------\n' + ############ TRAINING ############ + DP=$OUTPUTDATAPATH/$FEATURESPATH + DATASETS_DIR=$DP/$NAME_VIDEO + OBJECT=$NAME_VIDEO'.h5' + + ############# CHECKPOINTS PATH ############ + CHECKPOINT_DIR=$OUTPUTDATAPATH/checkpoints + + #dataset mode + DATASET_MODE=custom_aligned + # neural texture, not used here + TEX_DIM=128 + TEX_FEATURES=16 + INPUT_NC=5 + NUM_THREADS=1 + # loss + LOSS=VGG + # models + TEXTUREMODEL=DynamicNeuralTextureExpression + # TEXTUREMODEL=DynamicNeuralTextureAudio + MODEL=DynamicNeuralTextures + RENDERER_TYPE=UNET_6_level + # optimizer parameters + LR=0.0001 + N_ITER=50 + N_ITER_LR_DECAY=50 + BATCH_SIZE=8 + SEQ_LEN=8 + RENDERER=$OBJECT + EROSION=0.6 + + # Model name + NAME=$MODEL-$NAME_VIDEO + + DISPLAY_NAME=$MODEL-$NAME_VIDEO + DISPLAY_ID=0 + + cd NeuralRenderingNetwork + + if [ -f "$CHECKPOINT_DIR/$NAME/latest_texture.pth" ]; then + echo "Model $NAME already exists." + + else + echo "Training model $NAME..." + + CUDA_VISIBLE_DEVICES=0 python \ + train_renderer.py \ + --textureModel $TEXTUREMODEL \ + --num_threads $NUM_THREADS \ + --input_nc $INPUT_NC \ + --checkpoints_dir $CHECKPOINT_DIR \ + --display_id $DISPLAY_ID \ + --look_ahead \ + --seq_len $SEQ_LEN \ + --save_latest_freq 100000 \ + --no_augmentation \ + --name $NAME \ + --erosionFactor $EROSION \ + --tex_dim $TEX_DIM \ + --tex_features $TEX_FEATURES \ + --rendererType $RENDERER_TYPE \ + --lossType $LOSS \ + --display_env $DISPLAY_NAME \ + --niter $N_ITER \ + --niter_decay $N_ITER_LR_DECAY \ + --dataroot $DATASETS_DIR/$OBJECT \ + --model $MODEL \ + --netG unet_256 \ + --lambda_L1 100 \ + --dataset_mode $DATASET_MODE \ + --no_lsgan \ + --norm instance \ + --pool_size 0 \ + --gpu_ids $GPUID \ + --lr $LR \ + --batch_size $BATCH_SIZE \ + --deca_details + #--continue_train \ + #--epoch_count 40 \ + + fi + + ############ INFERENCE ############ + echo -e '\n--------------Neural Rendering Inference---------------\n' + + FILE_ID_SOURCE=$NAME_AUDIO + FILE_ID_TARGET=$NAME_VIDEO + + DR=$OUTPUTDATAPATH/$FEATURESPATH/$FILE_ID_TARGET/ + SOURCE_DATAROOT=$OUTPUTDATAPATH/$FEATURESPATH/$FILE_ID_SOURCE/$FILE_ID_SOURCE.h5 + TARGET_DATAROOT=$OUTPUTDATAPATH/$FEATURESPATH/$FILE_ID_TARGET/$FILE_ID_TARGET.h5 + EXPR_PATH=$TRANSFER_PATH/$FILE_ID_SOURCE--$FILE_ID_TARGET/expressions + + # Used as start and end + FRAME_ID_SOURCE=-1 + FRAME_ID_TARGET=-1 + + CUDA_VISIBLE_DEVICES=0 python \ + inference_renderer.py \ + --num_threads $NUM_THREADS \ + --input_nc $INPUT_NC \ + --look_ahead \ + --seq_len $SEQ_LEN \ + --no_augmentation \ + --name $NAME \ + --checkpoints_dir $CHECKPOINT_DIR \ + --erosionFactor $EROSION \ + --tex_dim $TEX_DIM \ + --tex_features $TEX_FEATURES \ + --rendererType $RENDERER_TYPE \ + --lossType $LOSS \ + --model $MODEL \ + --netG unet_256 \ + --dataset_mode $DATASET_MODE \ + --norm instance \ + --gpu_ids $GPUID \ + --batch_size $BATCH_SIZE \ + --epoch latest \ + --textureModel $TEXTUREMODEL \ + --dataroot $DR \ + --target_dataroot $TARGET_DATAROOT \ + --source_dataroot $SOURCE_DATAROOT \ + --expr_path $EXPR_PATH \ + --images_target_dir $OUTPUTDATAPATH/$FEATURESPATH \ + --frame_id_source $FRAME_ID_SOURCE \ + --frame_id_target $FRAME_ID_TARGET \ + --deca_details + + #--images_target_dir $IMAGES_TARGET_DIR \ + cd .. + + ##################### POSTPROCESSING ##################### + echo -e '\n--------------Postprocessing---------------\n' + python postprocessing.py \ + --file_id_source $FILE_ID_SOURCE \ + --file_id_target $FILE_ID_TARGET \ + --model_name $NAME \ + --frames_path $OUTPUTDATAPATH/$FEATURESPATH/$NAME_VIDEO/'og_frames/' \ + --audio_fname $INPUTDATAPATH/$AUDIO_PATH/$NAME_AUDIO/$NAME_AUDIO'.wav' \ + --dataset_target $OUTPUTDATAPATH/$FEATURESPATH \ + --target_fps $TARGET_FPS \ + --results_out_dir $IMAGES_TARGET_DIR \ + --clean + + done + +done + +conda deactivate diff --git a/NeuralVoicePuppetry/neural-code/full_pipeline_nvp.sh.sb-4428aa14-X9ZNZh/.fuse_hidden0000003300000001 b/NeuralVoicePuppetry/neural-code/full_pipeline_nvp.sh.sb-4428aa14-X9ZNZh/.fuse_hidden0000003300000001 new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/postprocessing.py b/NeuralVoicePuppetry/neural-code/postprocessing.py new file mode 100644 index 0000000..527e192 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/postprocessing.py @@ -0,0 +1,155 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os +import shutil +import subprocess + +import cv2 +import h5py +import numpy as np +from scipy.ndimage.morphology import binary_erosion +from skimage.metrics import structural_similarity +from skimage.transform import SimilarityTransform +from skimage.transform import warp +from tqdm import tqdm + +from base_options import PostprocessingOptions + + +def warp_back(image, oldimage, tform): + alpha = 1 + new_size = oldimage.shape + + dst_image = (warp(image, tform, output_shape=new_size) * 255.).astype(np.uint8) + + # Mask of non-black pixels. + ones = np.ones_like(dst_image[:, :, 0]).astype(np.uint8) + indexes = np.where(np.all(dst_image == [0, 0, 0], axis=-1)) + ones[indexes] = 0 + eroded_mask = binary_erosion(ones, iterations=5).astype(np.uint8) + + dst_image[eroded_mask == 0] = oldimage[eroded_mask == 0] + + return dst_image + + +def compute_diff(image1, image2, text): + # Convert images to grayscale + image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY) + image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY) + + # Compute SSIM between two images + (score, diff) = structural_similarity(image1, image2, full=True) + print(text, ' SSIM: ', score) + diff = (diff * 255).astype("uint8") + + +def write_video_with_audio(save_root, audio_path, output_path, h=512, w=512, fps=25): + font = cv2.FONT_HERSHEY_SIMPLEX + fontScale = 1.0 + fontColor = (255, 255, 255) + thickness = 1 + lineType = 2 + text = 'This video has been manipulated.' + + label_width, label_height = cv2.getTextSize(text, font, 1, 2)[0] + print(label_width, label_height) + bottomLeftCornerOfText = (int((w - label_width) / 2), int(h - label_height - 20)) + + fourcc = cv2.VideoWriter_fourcc(*'DIVX') + # fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G') + video_tmp_path = os.path.join(save_root, 'tmp.avi') + out = cv2.VideoWriter(video_tmp_path, fourcc, fps, (w, h)) + for j in tqdm(range(num_images), position=0, desc='writing video'): + img = cv2.imread(os.path.join(save_root, '%05d.png' % j)) + img = cv2.putText( + img, + text, + bottomLeftCornerOfText, + font, + fontScale, + fontColor, + thickness, + lineType, + ) + out.write(img) + + out.release() + + print("ffmpeg version:") + subprocess.call("ffmpeg -version", shell=True) + + # TODO: Set proper size of video: [-s {w}x{h}] + cmd = f'ffmpeg -y -i "{video_tmp_path}" -i "{audio_path}" -acodec aac -vcodec h264 -shortest -threads 0 -s {w}x{h} "{output_path}"' + # "-pix_fmt yuv420p -profile:v baseline -level 3" + + print(f"ffmpeg cmd: {cmd}") + return_code = subprocess.call(cmd, shell=True) + + if return_code > 0: + raise Exception(f"An error occurred when assembling the output video: ffmpeg return_code={return_code}") + + try: + os.remove(video_tmp_path) # remove the template video + + except FileNotFoundError: + return + + +if __name__ == '__main__': + + opt = PostprocessingOptions().parse() + + DP = opt.dataset_target + NAME = opt.model_name + FILE_ID_SOURCE = opt.file_id_source + FILE_ID_TARGET = opt.file_id_target + frames_path = opt.frames_path + audio_fname = opt.audio_fname + target_fps = opt.target_fps + results_out_dir = opt.results_out_dir + + h5py_path = os.path.join(DP, FILE_ID_TARGET, FILE_ID_TARGET + '.h5') + + cropped = h5py.File(h5py_path, 'r')["frame"] + crop_image_shape = cropped[0].shape[:-1] + + images_out_path = os.path.join(DP, 'images') # results_out_dir + tform_path = os.path.join(DP, FILE_ID_TARGET, 'tform.npy') + + tform = np.load(tform_path) + + final_file = os.path.join(results_out_dir, f'{FILE_ID_SOURCE}_to_{FILE_ID_TARGET}.mp4') + frames_out_path = os.path.join(DP, 'generated_frames') # results_out_dir + os.makedirs(frames_out_path, exist_ok=True) + + num_images = int(len(os.listdir(images_out_path))) + + for index, frame in zip(tqdm(range(num_images)), sorted(os.listdir(frames_path))): + image_path = '%05d.png' % index + + image = cv2.imread(os.path.join(images_out_path, image_path)) + # resize image to cropped image size + image = cv2.resize(image, crop_image_shape) + + old_image = cv2.imread(os.path.join(frames_path, frame)) + + f_tform = SimilarityTransform(matrix=tform[index]) + + res = warp_back(image, old_image, f_tform) + + cv2.imwrite(os.path.join(frames_out_path, image_path), res) + + h, w, _ = res.shape + + write_video_with_audio(frames_out_path, audio_fname, final_file, h=h, w=w, fps=target_fps) + + try: + if opt.clean: + shutil.rmtree(images_out_path) + shutil.rmtree(frames_out_path) + shutil.rmtree(frames_path) + + except Exception as e: + print(e) diff --git a/NeuralVoicePuppetry/neural-code/preprocessing.py b/NeuralVoicePuppetry/neural-code/preprocessing.py new file mode 100644 index 0000000..3bd9622 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/preprocessing.py @@ -0,0 +1,209 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details +import os +import numpy as np +import h5py +import torch +from PIL import Image +from tqdm import tqdm + +from autils.make_h5py import create_h5py_dataset +from base_options import PreprocessingOptions + + +def find_file_ext(data_path, dataset_name, name): + dataset = dataset_name + '/' + name + tmp = os.path.join(data_path, dataset, name + '.mp4') + + if os.path.isfile(tmp): + return name + '.mp4' + + else: + tmp = os.path.join(data_path, dataset, name + '.mp3') + + if os.path.isfile(tmp): + return name + '.mp3' + + else: + tmp = os.path.join(data_path, dataset, name + '.wav') + if os.path.isfile(tmp): + return name + '.wav' + + else: + tmp = os.path.join(data_path, dataset, name + '.avi') + + if os.path.isfile(tmp): + return name + '.avi' + + else: + print('No input file with given name') + exit() + + +def deepspeech_preprocessing(name, target_dir, folder_videos, video_name, target_base, type, target_fps): + from autils.deepspeech_features import extract_ds + print(f'\n\nExtracting DeepSpeech features for {name}..\n\n') + + if not os.path.isfile(os.path.join(target_dir, target_dir.split("/")[-1] + '.h5')): + extract_ds(folder_videos=folder_videos, + file_id=video_name.split("/")[-1][:-4], + target_base=target_base, + target_name=name, + type=type, + target_fps=target_fps) + + else: + print('Already done.') + + +def video_tracking(tracker, target_dir): + + exp_dir = os.path.join(target_dir, 'expressions') + os.makedirs(exp_dir, exist_ok=True) + + h5_path = os.path.join(target_dir, target_dir.split("/")[-1] + '.h5') + if os.path.isfile(h5_path): + data = h5py.File(h5_path, 'r') + if "ep" in data.keys(): + print('Already done.') + return + + codedict_dir = os.path.join(target_dir, 'DECA_codedicts') + os.makedirs(codedict_dir, exist_ok=True) + + uv_dir = os.path.join(target_dir, 'uv') + os.makedirs(uv_dir, exist_ok=True) + + frames_dir = os.path.join(target_dir, 'frames') + os.makedirs(frames_dir, exist_ok=True) + + deca_details_dir = os.path.join(target_dir, 'deca_details') + os.makedirs(deca_details_dir, exist_ok=True) + + mask_dir = os.path.join(target_dir, 'mask_mouth') + os.makedirs(mask_dir, exist_ok=True) + + tforms_file = os.path.join(target_dir, 'tform.npy') + + expressions = [] + tforms = [] + + # Load video + for i in tqdm(range(len(tracker.testdata))): + images = tracker.testdata[i]['image'].to(tracker.device)[None, ...] + tform = tracker.testdata[i]['tform'] + tform = np.array(tform.params) + # og_image = tracker.testdata[i]['original_image'].cpu() + name = tracker.testdata[i]['imagename'] + + # save image and tfrom + to_save = images[0].permute(1, 2, 0).cpu().numpy() + to_save = to_save * 255. + to_save = to_save.astype(np.uint8) + + filename = os.path.join(frames_dir, '%04d.jpg' % i) + img = Image.fromarray(to_save) + img.save(filename) + + tforms.append(tform) + + # call deca + codedict, opdict, mask = tracker(images) + + # Save codedict + pt_file = os.path.join(codedict_dir, f'codedict_{i}.pt') + torch.save(codedict, pt_file) + + # Save expressions + expression_params = codedict['exp'].cpu().numpy()[0] + pose_params = codedict['pose'].cpu().numpy()[0, 3:] + new_expr = np.concatenate((expression_params, pose_params), axis=0) + + exp_file = os.path.join(exp_dir, f'expr_{i}.npy') + np.save(exp_file, new_expr) + + # Save uv + uv_img = opdict['grid'].cpu()[0] + npy_file = os.path.join(uv_dir, f'uv_{i}.npy') + np.save(npy_file, uv_img) + + # Save mask + mask_img = mask.cpu() + npy_file = os.path.join(mask_dir, f'mask_{i}.npy') + np.save(npy_file, mask_img) + + # Save deca details + deca_details_img = opdict['detail_normal_images'][0].permute(1, 2, 0).cpu().numpy() + npy_file = os.path.join(deca_details_dir, f'deca_details_{i}.npy') + np.save(npy_file, deca_details_img) + + tforms = np.array(tforms) + print(tforms.shape) + np.save(tforms_file, tforms) + + +def preprocess_video(name, folder_videos, target_base, video_name, preprocess_ds, preprocess_tracking, skip_h5py, type, target_fps, clean): + print('Video name: ', video_name) + target_dir = os.path.join(target_base, name) + print('target_dir: ', target_dir) + target_file_name = video_name.split("/")[-1][:-4] + print('target_file_name: ', target_file_name) + print('folder_videos: ', folder_videos) + + frames_folder = target_base + '/' + target_file_name + '/og_frames' + print('Video folder: ', frames_folder) + + if preprocess_ds: + deepspeech_preprocessing(name, target_dir, folder_videos, video_name, target_base, type, target_fps) + + if preprocess_tracking: + from autils.deca_flame_fitting import DECA_tracker + + print(f'\n\nExtracting Tracking information for {name}..\n\n') + if os.path.isdir(frames_folder): + + tracker = DECA_tracker(frames_folder) + + else: + tracker = DECA_tracker(folder_videos + '/' + target_file_name + '.mp4', target_dir=frames_folder) + + video_tracking(tracker, target_dir) + + if skip_h5py: + return + + else: + create_h5py_dataset(target_dir, clean) + + +if __name__ == '__main__': + + opt = PreprocessingOptions().parse() + + data_path = opt.dataroot + dataset_path = opt.dataset_path + dataset = opt.dataset + name = opt.name + target_fps = opt.target_fps + skip_h5py = opt.skip_h5py + clean = opt.clean + + full_path = find_file_ext(data_path, dataset, name) + + target_base = dataset_path + + # Flags to control preprocessing + preprocess_ds = opt.preprocess_ds + preprocess_tracking = opt.preprocess_tracking + + file_name = f'{full_path.split("/")[-1][:-4]}' + print(file_name) + folder_videos = os.path.join(data_path, dataset, file_name) + + if full_path.endswith("mp3") or full_path.endswith("wav"): + preprocess_video(file_name, folder_videos, target_base, full_path, + preprocess_ds, False, skip_h5py, 'audio', target_fps, clean) + + else: + preprocess_video(file_name, folder_videos, target_base, full_path, + preprocess_ds, preprocess_tracking, skip_h5py, 'video', target_fps, clean) diff --git a/NeuralVoicePuppetry/neural-code/preprocessing.sh b/NeuralVoicePuppetry/neural-code/preprocessing.sh new file mode 100644 index 0000000..d53f59d --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/preprocessing.sh @@ -0,0 +1,25 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +GPUID=0 + +BASE=`pwd` +# DATA PATHS +DATAROOT=/home/alberto/data/videosynth/ +DATASETPATH=/home/alberto/NeuralVoicePuppetry/datasets/ + +# SOURCE +DATASET_SOURCE=External +VIDEOTYPE_SOURCE=Youtube +#NAME_SOURCE=Russian_guy +NAME_SOURCE=Bill_Maher + +##################### PREPROCESSING ##################### + +echo 'Preprocessing video: '$DATAROOT$DATASET_SOURCE/$VIDEOTYPE_SOURCE/$NAME_SOURCE +python preprocessing.py \ +--dataroot $DATAROOT \ +--dataset_path $DATASETPATH \ +--dataset $DATASET_SOURCE \ +--video_type $VIDEOTYPE_SOURCE \ +--name $NAME_SOURCE \ diff --git a/NeuralVoicePuppetry/neural-code/tests/DECA_uv_test.py b/NeuralVoicePuppetry/neural-code/tests/DECA_uv_test.py new file mode 100644 index 0000000..80426e6 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/tests/DECA_uv_test.py @@ -0,0 +1,25 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os +import subprocess +from autils.deca_flame_fitting import * +import cv2 +from tqdm import tqdm +import json +import torch + + +test_sample = '/home/alberto/NeuralVoicePuppetry/third/DECA/TestSamples/SRF/355_9414.jpg' +# Flags to control preprocessing + +# Create Tracker object +tracker = DECA_tracker(test_sample) + +# extract face tracking information + + +# Load video +for i in tqdm(range(len(tracker.testdata))): + images = tracker.testdata[i]['image'].to(tracker.device)[None, ...] + codedict = tracker(images) diff --git a/NeuralVoicePuppetry/neural-code/tests/__init__.py b/NeuralVoicePuppetry/neural-code/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/tests/test_rendering_3DDFA.py b/NeuralVoicePuppetry/neural-code/tests/test_rendering_3DDFA.py new file mode 100644 index 0000000..c62d5ee --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/tests/test_rendering_3DDFA.py @@ -0,0 +1,96 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import torch + +from autils.eos_tracker import * +from third._3DDFA_V2.DDVa_tracker import * +from autils.renderer import Renderer +from pytorch3d.renderer import look_at_view_transform + + +def call_renderer(mesh, R, T, K=None): + new_bfm_vertices = torch.FloatTensor(mesh.vertices).unsqueeze(0) + try: + bfm_faces_tensor = torch.FloatTensor(mesh.tvi).unsqueeze(0) + + except AttributeError: + bfm_faces_tensor = torch.FloatTensor(mesh.faces).unsqueeze(0) + + R = torch.FloatTensor(R).unsqueeze(0) + T = torch.FloatTensor(T).unsqueeze(0) + if K is not None: + K = torch.FloatTensor(K).unsqueeze(0) + + print(f'Vertices shape: {new_bfm_vertices.shape}') + print(f'Faces shape: {bfm_faces_tensor.shape}') + print(f'R shape: {R.shape}') + print(f'T shape: {T.shape}') + + # Render with white texture to check if R and T are correct + mesh_renderer = Renderer(device="cuda" if torch.cuda.is_available() else "cpu", fov=60, aspect_ratio=h/w, image_size=(h, w), R=R, T=T, K=K) + rendered_np = mesh_renderer.render_mesh(new_bfm_vertices.cuda(), bfm_faces_tensor.cuda()) + + return rendered_np + + +def combine(img, face_render, alpha=0.6): + final_img = np.array(img) + face_render = np.array(face_render) + mask = np.where(np.all(face_render != (255,255,255), axis=-1)) + final_img[mask] = (1-alpha) * final_img[mask] + (alpha) * face_render[mask] + return final_img + +os.environ['PYOPENGL_PLATFORM'] = 'egl' + +img_path = 'obama_test.jpeg' +img = imread(img_path) +h, w = img.shape[:2] + +print(f'Height: {h}, Width: {w}') +# +# 3DDVA tracker +_3ddva_tracker = DDVA_Tracker() +mesh_tri, R, T, alpha_shp, alpha_exp = _3ddva_tracker(img) +mesh_tri.show() +print(f'\nRotation:\n{R}') +print(f'\nTranslation matrix:\n{T}') + + +# rendered_np = call_renderer(mesh_tri, R, T) +# plt.imshow(rendered_np) +# plt.savefig('render.png') +# plt.show() +# +# rendered_np = call_renderer(mesh_tri, np.linalg.inv(R), -T) +# plt.imshow(rendered_np) +# plt.savefig('render.png') +# plt.show() + +camera = trimesh.scene.cameras.Camera(resolution=(h, w), fov=(63, 63), z_near=0.01, z_far=1000.0) +transform = camera.look_at(mesh_tri.vertices) + +R = np.identity(3) +print(f'\nR:\n{R}') + +T = np.zeros(3) +print(f'\nT:\n{T}') + +rendered_np = call_renderer(mesh_tri, R, T) +plt.imshow(rendered_np) +plt.savefig('render.png') +plt.show() +# +# for i in range(3): +# R = - R +# R[i,i] = - R[i,i] +# rendered_np = call_renderer(mesh_tri, R, T) +# plt.imshow(rendered_np) +# plt.savefig('render.png') +# plt.show() + +########################## + + + + diff --git a/NeuralVoicePuppetry/neural-code/tests/test_rendering_EOS.py b/NeuralVoicePuppetry/neural-code/tests/test_rendering_EOS.py new file mode 100644 index 0000000..a8424cf --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/tests/test_rendering_EOS.py @@ -0,0 +1,122 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import torch + +from autils.eos_tracker import * +from third._3DDFA_V2.DDVa_tracker import * +from autils.renderer import Renderer +from pytorch3d.renderer import look_at_view_transform +import pytorch3d +from scipy.spatial.transform import Rotation + + +def call_renderer(mesh, R, T, K=None): + new_bfm_vertices = torch.FloatTensor(mesh.vertices).unsqueeze(0) + try: + bfm_faces_tensor = torch.FloatTensor(mesh.tvi).unsqueeze(0) + + except AttributeError: + bfm_faces_tensor = torch.FloatTensor(mesh.faces).unsqueeze(0) + + R = torch.FloatTensor(R).unsqueeze(0) + T = torch.FloatTensor(T).unsqueeze(0) + if K is not None: + K = torch.FloatTensor(K).unsqueeze(0) + + print(f'Vertices shape: {new_bfm_vertices.shape}') + print(f'Faces shape: {bfm_faces_tensor.shape}') + print(f'R shape: {R.shape}') + print(f'T shape: {T.shape}') + + # Render with white texture to check if R and T are correct + mesh_renderer = Renderer(device="cuda" if torch.cuda.is_available() else "cpu", fov=60, aspect_ratio=h/w, image_size=(h, w), R=R, T=T, K=K) + rendered_np = mesh_renderer.render_mesh(new_bfm_vertices.cuda(), bfm_faces_tensor.cuda()) + + return rendered_np + + +def combine(img, face_render, alpha=0.6): + final_img = np.array(img) + face_render = np.array(face_render) + mask = np.where(np.all(face_render != (255,255,255), axis=-1)) + final_img[mask] = (1-alpha) * final_img[mask] + (alpha) * face_render[mask] + return final_img + +os.environ['PYOPENGL_PLATFORM'] = 'egl' + +img_path = 'obama_test.jpeg' +img = imread(img_path) +h, w = img.shape[:2] + +print(f'Height: {h}, Width: {w}') +# +# Launch tracker to get BFM model +eos_tracker = EOS_Tracker(PATH_TO_EOS, PREDICTOR_PATH) +mesh, pose, shape_coeffs, blendshape_coeffs = eos_tracker(img) + +# # Store mesh in trimesh +mesh_tri = trimesh.Trimesh(vertices=mesh.vertices, faces=mesh.tvi) + +camera = trimesh.scene.cameras.Camera(resolution=(h,w), fov=(63, 63), z_near=0.01, z_far=1000.0) +transform = camera.look_at(mesh_tri.vertices) + +# R_trimesh = - transform[0:3, 0:3] +# R_trimesh[1,1] = - R_trimesh[1,1] # flip z to look from other side +# print(f'\nR_trimesh:\n{R_trimesh}') +# +# T_trimesh = transform[0:3, -1] +# print(f'\nT_trimesh:\n{T_trimesh}') + +p = pose.get_projection() # from world coordinates to view coordinates +mv = pose.get_modelview() # From object to world coordinates +mv_inverse = np.linalg.inv(mv) +rot = pose.get_rotation() +vm = viewport_matrix(w, h) +fm = vm @ p @ mv + +v = np.asarray(mesh.vertices) +v = np.hstack([v, np.ones(v.shape[0])]) +print(v.shape) +print(v[0]) + +# # Add rotation +# R_z = Rotation.from_euler('xyz', [0, 0, 90], degrees=True).as_matrix() +# R_y = Rotation.from_euler('xyz', [0, -180, 0], degrees=True).as_matrix() +# +# # R = mv_inverse[0:3, 0:3] @ R_z +# R = mv[0:3, 0:3] @ R_z +# # R = R_pytorch +# +# # T = mv_inverse[0:3, -1] @ R_z +# T = mv[0:3, -1] @ R_z +# # T = T_trimesh + +R = np.identity(3) +print(f'\nR:\n{R}') + +T = np.zeros(3) +print(f'\nT:\n{T}') + +print(f'\nRotation:\n{R}') +print(f'\nTranslation matrix:\n{T}') + +rendered_np = call_renderer(mesh, R, T) +plt.imshow(rendered_np) +plt.savefig('render.png') +plt.show() + +# v = np.asarray(mesh.vertices) +# R_z = Rotation.from_euler('xyz', [0, 0, 90], degrees=True).as_matrix() +# v2 = v @ mv[0:3,0:3] @ p[0:3,0:3] @ R_z +# plt.figure(figsize=(5,5)) +# plt.axis('equal') +# plt.scatter(v2[:,0], v2[:,1], s=0.5) +# plt.savefig('adios.png') +# plt.show() + +# ''' try combine function to put mask on img ''' +# combined_img = combine(img, rendered_np) +# plt.imshow(combined_img) +# plt.show() + diff --git a/NeuralVoicePuppetry/neural-code/text_to_face_pipeline.sh b/NeuralVoicePuppetry/neural-code/text_to_face_pipeline.sh new file mode 100644 index 0000000..fa2cdf8 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/text_to_face_pipeline.sh @@ -0,0 +1,293 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +GPUID=0 + +BASE=`pwd` +echo $BASE +export GOOGLE_APPLICATION_CREDENTIALS="/home/alberto/ttsnvp-a5777a0c2445.json" + +# DATA PATHS +DATAROOT=/home/alberto/data/videosynth/ +DATASETPATH=/home/alberto/NeuralVoicePuppetry/datasets/ + +### SOURCE ### +# Italian +DATASET_SOURCE=Synthetic +VIDEOTYPE_SOURCE=Italian +NAME_SOURCE_LIST=( clara ) +LANGUAGE_SOURCE='it' +LANGUAGE_TARGET_LIST=( 'it' 'en' 'es' ) + +## English +#DATASET_SOURCE=Synthetic +#VIDEOTYPE_SOURCE=English +#NAME_SOURCE_LIST=( how_are_you ) +#LANGUAGE_SOURCE='en' +#LANGUAGE_TARGET='it' + +###################################### + +### TARGET ### +# Jennifer +DATASET_TARGET=SRF_anchor_short +VIDEOTYPE_TARGET=Halbtotale +NAME_TARGET=355_9415 + +## Vicky +#DATASET_TARGET=External +#VIDEOTYPE_TARGET=Italian +#NAME_TARGET=Vicky_EAC + +TARGET_FPS=25 + +##################### PREPROCESSING ##################### +for NAME_SOURCE in "${NAME_SOURCE_LIST[@]}" +do + OG_NAME=$NAME_SOURCE + for LANGUAGE_TARGET in "${LANGUAGE_TARGET_LIST[@]}" + do + echo 'Generating audio from source text: '$DATAROOT$DATASET_SOURCE/$VIDEOTYPE_SOURCE/$NAME_SOURCE + python text_to_speach.py \ + --dataroot $DATAROOT \ + --dataset_path $DATASETPATH \ + --dataset $DATASET_SOURCE \ + --video_type $VIDEOTYPE_SOURCE \ + --name $NAME_SOURCE \ + --language_source $LANGUAGE_SOURCE \ + --language_target $LANGUAGE_TARGET \ + + NAME_SOURCE=$OG_NAME'_'$LANGUAGE_TARGET + + echo 'Preprocessing source video: '$DATAROOT$DATASET_SOURCE/$VIDEOTYPE_SOURCE/$NAME_SOURCE + python preprocessing.py \ + --dataroot $DATAROOT \ + --dataset_path $DATASETPATH \ + --dataset $DATASET_SOURCE \ + --video_type $VIDEOTYPE_SOURCE \ + --name $NAME_SOURCE \ + --preprocess_ds \ + --target_fps $TARGET_FPS \ + + echo 'Preprocessing target video: '$DATAROOT$DATASET_TARGET/$VIDEOTYPE_TARGET/$NAME_TARGET + python preprocessing.py \ + --dataroot $DATAROOT \ + --dataset_path $DATASETPATH \ + --dataset $DATASET_TARGET \ + --video_type $VIDEOTYPE_TARGET \ + --name $NAME_TARGET \ + --preprocess_ds \ + --target_fps $TARGET_FPS \ + --skip_h5py \ + + # Twice for memory issues + python preprocessing.py \ + --dataroot $DATAROOT \ + --dataset_path $DATASETPATH \ + --dataset $DATASET_TARGET \ + --video_type $VIDEOTYPE_TARGET \ + --name $NAME_TARGET \ + --preprocess_tracking \ + --target_fps $TARGET_FPS \ + + ##################### AUDIO2EXPRESSION ##################### + echo -e '\n--------------AUDIO2EXPRESSION---------------\n' + OBJECT=ARD_ZDF + LR=0.00001 + N_ITER=150 + N_ITER_LR_DECAY=50 + RENDERER=$OBJECT + EROSION=1.0 + BATCH_SIZE=16 + MODEL=audio2ExpressionsAttentionTMP4 + RENDERER_TYPE=estimatorAttention + DATASET_MODE=multi_face_audio_eq_tmp_cached + LOSS=RMS + SEQ_LEN=8 + DATE_WITH_TIME=20191105-115332 + A2E_NAME=$MODEL-$RENDERER_TYPE-SL$SEQ_LEN-BS$BATCH_SIZE-$OBJECT-$DATASET_MODE-$LOSS-$DATE_WITH_TIME-look_ahead + EPOCH=latest + + # SOURCE + SOURCE_ACTOR_LIST=( \ + $DATASETPATH$DATASET_SOURCE/$VIDEOTYPE_SOURCE'_'$NAME_SOURCE \ + ) + + # TARGET + TARGET_ACTOR_LIST=( \ + $DATASETPATH$DATASET_TARGET/$VIDEOTYPE_TARGET'_'$NAME_TARGET \ + ) + + rm -f ./datasets/TRANSFERS/$NAME/list_transfer.txt + cd Audio2ExpressionNet/Inference/ + pwd + for TARGET_ACTOR in "${TARGET_ACTOR_LIST[@]}" + do + echo 'Training for Target: '$TARGET_ACTOR + + for SOURCE_ACTOR in "${SOURCE_ACTOR_LIST[@]}" + do + echo 'Training for Source: '$SOURCE_ACTOR + # --look_ahead + python transfer.py --look_ahead --seq_len $SEQ_LEN --source_actor $SOURCE_ACTOR --target_actor $TARGET_ACTOR --write_no_images --name $A2E_NAME --erosionFactor $EROSION --epoch $EPOCH --display_winsize 512 --rendererType $RENDERER_TYPE --lossType $LOSS --dataroot $DATASETS_DIR/$OBJECT --model $MODEL --netG unet_256 --dataset_mode $DATASET_MODE --norm instance --gpu_ids $GPUID + done + done + cd .. + cd .. + + ##################### NEURAL RENDERING ##################### + + echo -e '\n--------------Neural Rendering Training---------------\n' + ############ TRAINING ############ + DP=$DATASETPATH$DATASET_TARGET + SUB=$VIDEOTYPE_TARGET + DATASETS_DIR=$DP/$VIDEOTYPE_TARGET'_'$NAME_TARGET + OBJECT=$VIDEOTYPE_TARGET'_'$NAME_TARGET'.h5' + + #dataset mode + DATASET_MODE=custom_aligned + # neural texture, not used here + TEX_DIM=128 + TEX_FEATURES=16 + INPUT_NC=5 + NUM_THREADS=1 + # loss + LOSS=VGG + # models + TEXTUREMODEL=DynamicNeuralTextureExpression + # TEXTUREMODEL=DynamicNeuralTextureAudio + MODEL=DynamicNeuralTextures + RENDERER_TYPE=UNET_6_level + # optimizer parameters + LR=0.0001 + N_ITER=50 + N_ITER_LR_DECAY=50 + BATCH_SIZE=8 + SEQ_LEN=8 + RENDERER=$OBJECT + EROSION=0.6 + + # Model name + NAME=$MODEL-$VIDEOTYPE_TARGET'_'$NAME_TARGET + #NAME=$TEXTUREMODEL-$VIDEOTYPE_TARGET'_'$NAME_TARGET + #NAME=DynamicNeuralTextures-Halbtotale_355_9415 + + DISPLAY_NAME=$MODEL-$VIDEOTYPE_TARGET'_'$NAME_TARGET + #DISPLAY_NAME=$TEXTUREMODEL-$VIDEOTYPE_TARGET'_'$NAME_TARGET + DISPLAY_ID=0 + #NAME=DynamicNeuralTextures-UNET_6_level-DynamicNeuralTextureExpression-SL8-BS8-Halbtotale_355_9415.h5-custom_aligned-VGG-20210311-095742-look_ahead_mask_mouth + + cd NeuralRenderingNetwork + + if [ -d "checkpoints/$NAME" ]; then + echo "Model $NAME already exists." + + + else + echo "Training model $NAME..." + + CUDA_VISIBLE_DEVICES=0 python \ + train_renderer.py \ + --textureModel $TEXTUREMODEL \ + --num_threads $NUM_THREADS \ + --input_nc $INPUT_NC \ + --display_id $DISPLAY_ID \ + --look_ahead \ + --seq_len $SEQ_LEN \ + --save_latest_freq 100000 \ + --no_augmentation \ + --name $NAME \ + --erosionFactor $EROSION \ + --tex_dim $TEX_DIM \ + --tex_features $TEX_FEATURES \ + --rendererType $RENDERER_TYPE \ + --lossType $LOSS \ + --display_env $DISPLAY_NAME \ + --niter $N_ITER \ + --niter_decay $N_ITER_LR_DECAY \ + --dataroot $DATASETS_DIR/$OBJECT \ + --model $MODEL \ + --netG unet_256 \ + --lambda_L1 100 \ + --dataset_mode $DATASET_MODE \ + --no_lsgan \ + --norm instance \ + --pool_size 0 \ + --gpu_ids $GPUID \ + --lr $LR \ + --batch_size $BATCH_SIZE \ + --deca_details \ + #--continue_train \ + #--epoch_count 15 \ + + fi + + ############ INFERENCE ############ + echo -e '\n--------------Neural Rendering Inference---------------\n' + + DT=$DATASETPATH'TRANSFERS' + TRANSFER_PATH=$DT/$A2E_NAME + CHECKPOINT_DIR=$BASE/NeuralRenderingNetwork/checkpoints + + FILE_ID_SOURCE=$VIDEOTYPE_SOURCE'_'$NAME_SOURCE + FILE_ID_TARGET=$VIDEOTYPE_TARGET'_'$NAME_TARGET + + DR=$DATASETPATH$DATASET_TARGET/$FILE_ID_TARGET/ + SOURCE_DATAROOT=$DATASETPATH$DATASET_SOURCE/$FILE_ID_SOURCE/$FILE_ID_SOURCE.h5 + TARGET_DATAROOT=$DATASETPATH$DATASET_TARGET/$FILE_ID_TARGET/$FILE_ID_TARGET.h5 + EXPR_PATH=$TRANSFER_PATH/$FILE_ID_SOURCE--$FILE_ID_TARGET/expressions + + IMAGES_TARGET_DIR=$BASE/results/inference/$NAME/$FILE_ID_SOURCE"_to_"$FILE_ID_TARGET + + # Used as start and end + FRAME_ID_SOURCE=-1 + FRAME_ID_TARGET=-1 + + CUDA_VISIBLE_DEVICES=0 python \ + inference_renderer.py \ + --num_threads $NUM_THREADS \ + --input_nc $INPUT_NC \ + --look_ahead \ + --seq_len $SEQ_LEN \ + --no_augmentation \ + --name $NAME \ + --checkpoints_dir $CHECKPOINT_DIR \ + --erosionFactor $EROSION \ + --tex_dim $TEX_DIM \ + --tex_features $TEX_FEATURES \ + --rendererType $RENDERER_TYPE \ + --lossType $LOSS \ + --model $MODEL \ + --netG unet_256 \ + --dataset_mode $DATASET_MODE \ + --norm instance \ + --gpu_ids $GPUID \ + --batch_size $BATCH_SIZE \ + --epoch latest \ + --textureModel $TEXTUREMODEL \ + --dataroot $DR \ + --target_dataroot $TARGET_DATAROOT \ + --source_dataroot $SOURCE_DATAROOT \ + --expr_path $EXPR_PATH \ + --images_target_dir $IMAGES_TARGET_DIR \ + --frame_id_source $FRAME_ID_SOURCE \ + --frame_id_target $FRAME_ID_TARGET \ + --deca_details \ + + cd .. + + ##################### POSTPROCESSING ##################### + echo -e '\n--------------Postprocessing---------------\n' + python postprocessing.py \ + --file_id_source $FILE_ID_SOURCE \ + --file_id_target $FILE_ID_TARGET \ + --model_name $NAME \ + --frames_path $DATAROOT$DATASET_TARGET/$VIDEOTYPE_TARGET/$NAME_TARGET \ + --audio_fname $DATAROOT$DATASET_SOURCE/$VIDEOTYPE_SOURCE/$NAME_SOURCE'.wav' \ + --dataset_target $DATASETPATH$DATASET_TARGET \ + --target_fps $TARGET_FPS + + done +done + + diff --git a/NeuralVoicePuppetry/neural-code/text_to_speach.py b/NeuralVoicePuppetry/neural-code/text_to_speach.py new file mode 100644 index 0000000..5cfc0c2 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/text_to_speach.py @@ -0,0 +1,124 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os +import json +import glob +from base_options import Text2SpeachOptions +from googletrans import Translator +from google.cloud import texttospeech + + +def find_file_ext(data_path, dataset, video_type, name): + tmp = os.path.join(data_path, dataset, video_type, name + '.txt') + + if os.path.isfile(tmp): + return tmp + + else: + print('No text file!') + exit() + +def translate(source_path, target_path, language_source, language_target): + f = open(source_path, 'r') + source_txt = f.read() + f.close() + + if not(language_source==language_target): + translator = Translator() + print(f'Source text in {language_source}:\n{source_txt}') + translation = translator.translate(source_txt,src=language_source, dest=language_target).text + print(f'Translated text in {language_target}:\n{translation}') + + else: + translation = source_txt + + f = open(target_path, 'w') + f.write(translation) + f.close() + + return + +def list_voices(language_code=None): + client = texttospeech.TextToSpeechClient() + response = client.list_voices(language_code=language_code) + voices = sorted(response.voices, key=lambda voice: voice.name) + + print(f" Voices: {len(voices)} ".center(60, "-")) + for i, voice in enumerate(voices): + languages = ", ".join(voice.language_codes) + name = voice.name + gender = texttospeech.SsmlVoiceGender(voice.ssml_gender).name + rate = voice.natural_sample_rate_hertz + print(f"{i} | {languages:<8} | {name:<24} | {gender:<8} | {rate:,} Hz") + + return voices + +def text_to_wav(voice_name, text, target_audio_path): + language_code = "-".join(voice_name.split("-")[:2]) + text_input = texttospeech.SynthesisInput(text=text) + voice_params = texttospeech.VoiceSelectionParams( + language_code=language_code, name=voice_name + ) + audio_config = texttospeech.AudioConfig( + audio_encoding=texttospeech.AudioEncoding.LINEAR16 + ) + + client = texttospeech.TextToSpeechClient() + response = client.synthesize_speech( + input=text_input, voice=voice_params, audio_config=audio_config + ) + + filename = f"{target_audio_path}" + with open(filename, "wb") as out: + out.write(response.audio_content) + print(f'Audio content written to "{filename}"') + +# def text2speach(text_path, language): +# f = open(text_path, 'r') +# mytext = f.read() +# f.close() +# +# myobj = gTTS(text=mytext, lang=language, slow=False) +# target_audio_path = text_path[:-4]+'.wav' +# myobj.save(target_audio_path) +# +# return + +def text2speach(text_path, language): + f = open(text_path, 'r') + mytext = f.read() + f.close() + + voices = list_voices(language) + idx = input('Pick voice index: ') + voice = voices[int(idx)] + + target_audio_path = text_path[:-4]+'.wav' + text_to_wav(voice.name, mytext, target_audio_path) + + return + +if __name__ == '__main__': + + opt = Text2SpeachOptions().parse() + + data_path = opt.dataroot + dataset_path = opt.dataset_path + dataset = opt.dataset + video_type = opt.video_type + name = opt.name + language_source = opt.language_source + language_target = opt.language_target + + source_path = find_file_ext(data_path, dataset, video_type, name) + target_path = source_path[:-4]+'_'+language_target+'.txt' + + # Translate input txt to target language + translate(source_path, target_path, language_source, language_target) + + # Text to speech + text2speach(target_path, language_target) + + + diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/Doc/images/DECA_evaluation_github.png b/NeuralVoicePuppetry/neural-code/third/DECA/Doc/images/DECA_evaluation_github.png new file mode 100644 index 0000000..a690d88 Binary files /dev/null and b/NeuralVoicePuppetry/neural-code/third/DECA/Doc/images/DECA_evaluation_github.png differ diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/Doc/images/DECA_performance.png b/NeuralVoicePuppetry/neural-code/third/DECA/Doc/images/DECA_performance.png new file mode 100755 index 0000000..1d483c6 Binary files /dev/null and b/NeuralVoicePuppetry/neural-code/third/DECA/Doc/images/DECA_performance.png differ diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/Doc/images/soubhik.gif b/NeuralVoicePuppetry/neural-code/third/DECA/Doc/images/soubhik.gif new file mode 100644 index 0000000..9cee13f Binary files /dev/null and b/NeuralVoicePuppetry/neural-code/third/DECA/Doc/images/soubhik.gif differ diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/README.md b/NeuralVoicePuppetry/neural-code/third/DECA/README.md new file mode 100755 index 0000000..359dce6 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/README.md @@ -0,0 +1,125 @@ +# DECA: Detailed Expression Capture and Animation + +

+ +

+

input image, aligned reconstruction, animation with various poses & expressions

+ +This is the official Pytorch implementation of DECA. + +DECA reconstructs a 3D head model with detailed facial geometry from a single input image. The resulting 3D head model can be easily animated. Please refer to the [arXiv paper](https://arxiv.org/abs/2012.04012) for more details. + +The main features: + +* **Reconstruction:** produces head pose, shape, detailed face geometry, and lighting information from a single image. +* **Animation:** animate the face with realistic wrinkle deformations. +* **Robustness:** tested on facial images in unconstrained conditions. Our method is robust to various poses, illuminations and occlusions. +* **Accurate:** state-of-the-art 3D face shape reconstruction on the [NoW Challenge](https://ringnet.is.tue.mpg.de/challenge) benchmark dataset. + +## Getting Started +Clone the repo: + ```bash + git clone https://github.com/YadiraF/DECA + cd DECA + ``` + +### Requirements +* Python 3.7 (numpy, skimage, scipy, opencv) +* PyTorch >= 1.6 (pytorch3d) +* face-alignment (Optional for detecting face) + You can run + ```bash + pip install -r requirements.txt + ``` + Or use virtual environment by runing + ```bash + bash install_pip.sh + ``` + Then follow the instruction to install [pytorch3d](https://github.com/facebookresearch/pytorch3d/blob/master/INSTALL.md). + +### Usage +1. Prepare data + a. download [FLAME model](https://flame.is.tue.mpg.de/downloads), choose **FLAME 2020** and unzip it, copy 'generic_model.pkl' into ./data + b. download [DECA trained model](https://drive.google.com/file/d/1rp8kdyLPvErw2dTmqtjISRVvQLj6Yzje/view?usp=sharing), and put it in ./data (**no unzip required**) + c. (Optional) follow the instructions for the [Albedo model](https://github.com/TimoBolkart/BFM_to_FLAME) to get 'FLAME_albedo_from_BFM.npz', put it into ./data + +2. Run demos + a. **reconstruction** + ```bash + python demos/demo_reconstruct.py -i TestSamples/examples --saveDepth True --saveObj True + ``` + to visualize the predicted 2D landmanks, 3D landmarks (red means non-visible points), coarse geometry, detailed geometry, and depth. +

+ +

+

+ +

+ You can also generate an obj file (which can be opened with Meshlab) that includes extracted texture from the input image. + + Please run `python demos/demo_reconstruct.py --help` for more details. + + b. **expression transfer** + ```bash + python demos/demo_transfer.py + ``` + Given an image, you can reconstruct its 3D face, then animate it by tranfering expressions from other images. + Using Meshlab to open the detailed mesh obj file, you can see something like that: +

+ +

+ (Thank Soubhik for allowing me to use his face ^_^) + + Note that, you need to set '--useTex True' to get full texture. + + c. for the [teaser gif](https://github.com/YadiraF/DECA/results/teaser.gif) (**reposing** and **animation**) + ```bash + python demos/demo_teaser.py + ``` + + More demos and training code coming soon. + +## Evaluation +DECA (ours) achieves 9% lower mean shape reconstruction error on the [NoW Challenge](https://ringnet.is.tue.mpg.de/challenge) dataset compared to the previous state-of-the-art method. +The left figure compares the cumulative error of our approach and other recent methods (RingNet and Deng et al. have nearly identitical performance, so their curves overlap each other). Here we use point-to-surface distance as the error metric, following the NoW Challenge. +

+ +

+ +For more details of the evaluation, please check our [arXiv paper](https://arxiv.org/abs/2012.04012). + +## Citation +If you find our work useful to your research, please consider citing: +``` +@inproceedings{deca2020, + title={Learning an Animatable Detailed {3D} Face Model from In-The-Wild Images}, + author={Feng, Yao and Feng, Haiwen and Black, Michael J. and Bolkart, Timo}, + booktitle = {arxiv}, + month = {Dec}, + year = {2020} +} +``` + + + +## License +This code and model are available for non-commercial scientific research purposes as defined in the [LICENSE](https://github.com/YadiraF/DECA/blob/master/LICENSE) file. +By downloading and using the code and model you agree to the terms in the [LICENSE](https://github.com/YadiraF/DECA/blob/master/LICENSE). + +## Acknowledgements +For functions or scripts that are based on external sources, we acknowledge the origin individually in each file. +Here are some great resources we benefit: +- [FLAME_PyTorch](https://github.com/soubhiksanyal/FLAME_PyTorch) and [TF_FLAME](https://github.com/TimoBolkart/TF_FLAME) for the FLAME model +- [Pytorch3D](https://pytorch3d.org/), [neural_renderer](https://github.com/daniilidis-group/neural_renderer), [SoftRas](https://github.com/ShichenLiu/SoftRas) for rendering +- [kornia](https://github.com/kornia/kornia) for image/rotation processing +- [face-alignment](https://github.com/1adrianb/face-alignment) for cropping + +We would also like to thank other recent public 3D face reconstruction works that allow us to easily perform quantitative and qualitative comparisons :) +[RingNet](https://github.com/soubhiksanyal/RingNet), +[Deep3DFaceReconstruction](https://github.com/microsoft/Deep3DFaceReconstruction/blob/master/renderer/rasterize_triangles.py), +[Nonlinear_Face_3DMM](https://github.com/tranluan/Nonlinear_Face_3DMM), +[3DDFA-v2](https://github.com/cleardusk/3DDFA_V2), +[extreme_3d_faces](https://github.com/anhttran/extreme_3d_faces), +[facescape](https://github.com/zhuhao-nju/facescape) + diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/__init__.py b/NeuralVoicePuppetry/neural-code/third/DECA/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/data/deca_model.tar b/NeuralVoicePuppetry/neural-code/third/DECA/data/deca_model.tar new file mode 100644 index 0000000..c04160f --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/data/deca_model.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e714ed293054cba5eea9c96bd3b6b57880074cd84b3fd00d606cbaf0bee7c5c2 +size 434142943 diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/data/fixed_displacement_256.npy b/NeuralVoicePuppetry/neural-code/third/DECA/data/fixed_displacement_256.npy new file mode 100755 index 0000000..a13befd --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/data/fixed_displacement_256.npy @@ -0,0 +1,3 @@ +version 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+#-*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import os +import torch +from torch.utils.data import Dataset +import numpy as np +import cv2 +import scipy +from skimage.io import imread +from skimage.transform import estimate_transform, warp +from glob import glob +import scipy.io +from subprocess import call + +from . import detectors + + +def check_mkdir(path): + if not os.path.exists(path): + print('creating %s' % path) + os.makedirs(path) + +def video2sequence(video_path): + videofolder = video_path.split('.')[0] + check_mkdir(videofolder) + video_name = video_path.split('/')[-1].split('.')[0] + vidcap = cv2.VideoCapture(video_path) + success,image = vidcap.read() + count = 0 + imagepath_list = [] + while success: + imagepath = '{}/{}_frame{:04d}.png'.format(videofolder, video_name, count) + cv2.imwrite(imagepath, image) # save frame as PNG file + success,image = vidcap.read() + count += 1 + imagepath_list.append(imagepath) + print('video frames are stored in {}'.format(videofolder)) + return imagepath_list + + +def video2sequence_lossless(video_path, target_dir): + if target_dir is not None: + videofolder = target_dir + check_mkdir(videofolder) + video_name = video_path.split('/')[-1].split('.')[0] + + else: + videofolder = video_path.split('.')[0] + check_mkdir(videofolder) + video_name = video_path.split('/')[-1].split('.')[0] + + cmd = (f'ffmpeg -i {video_path} -vf fps=25 {videofolder}/{video_name}_frame%04d.png').split() + call(cmd) + imagepath_list = [os.path.join(videofolder, f) for f in os.listdir(videofolder)] + print('video frames are stored in {}'.format(videofolder)) + return imagepath_list + + +class TestData(Dataset): + def __init__(self, testpath, iscrop=True, crop_size=224, scale=1.25, face_detector='mtcnn', target_dir=None): + ''' + testpath: folder, imagepath_list, image path, video path + ''' + print('testpath: ', testpath) + if isinstance(testpath, list): + self.imagepath_list = testpath + elif os.path.isdir(testpath): + self.imagepath_list = glob(testpath + '/*.jpg') + glob(testpath + '/*.png') + glob(testpath + '/*.bmp') + elif os.path.isfile(testpath) and (testpath[-3:] in ['jpg', 'png', 'bmp']): + self.imagepath_list = [testpath] + elif os.path.isfile(testpath) and (testpath[-3:] in ['mp4', 'csv', 'vid', 'ebm']): + self.imagepath_list = video2sequence_lossless(testpath, target_dir) + else: + print(f'please check the test path: {testpath}') + exit() + print('total {} images'.format(len(self.imagepath_list))) + self.imagepath_list = sorted(self.imagepath_list) + self.crop_size = crop_size + self.scale = scale + self.iscrop = iscrop + self.resolution_inp = crop_size + if face_detector == 'fan': + self.face_detector = detectors.FAN() + # elif face_detector == 'mtcnn': + # self.face_detector = detectors.MTCNN() + else: + print(f'please check the detector: {face_detector}') + exit() + + def __len__(self): + return len(self.imagepath_list) + + def bbox2point(self, left, right, top, bottom, type='bbox'): + ''' bbox from detector and landmarks are different + ''' + if type=='kpt68': + old_size = (right - left + bottom - top)/2*1.1 + center = np.array([right - (right - left) / 2.0, bottom - (bottom - top) / 2.0 ]) + elif type=='bbox': + old_size = (right - left + bottom - top)/2 + center = np.array([right - (right - left) / 2.0, bottom - (bottom - top) / 2.0 + old_size*0.12]) + else: + raise NotImplementedError + return old_size, center + + def __getitem__(self, index): + imagepath = self.imagepath_list[index] + imagename = imagepath.split('/')[-1].split('.')[0] + + image = np.array(imread(imagepath)) + if len(image.shape) == 2: + image = image[:,:,None].repeat(1,1,3) + if len(image.shape) == 3 and image.shape[2] > 3: + image = image[:,:,:3] + + h, w, _ = image.shape + if self.iscrop: + # provide kpt as txt file, or mat file (for AFLW2000) + kpt_matpath = imagepath.replace('.jpg', '.mat').replace('.png', '.mat') + kpt_txtpath = imagepath.replace('.jpg', '.txt').replace('.png', '.txt') + if os.path.exists(kpt_matpath): + kpt = scipy.io.loadmat(kpt_matpath)['pt3d_68'].T + left = np.min(kpt[:,0]); right = np.max(kpt[:,0]); + top = np.min(kpt[:,1]); bottom = np.max(kpt[:,1]) + old_size, center = self.bbox2point(left, right, top, bottom, type='kpt68') + elif os.path.exists(kpt_txtpath): + kpt = np.loadtxt(kptpath) + left = np.min(kpt[:,0]); right = np.max(kpt[:,0]); + top = np.min(kpt[:,1]); bottom = np.max(kpt[:,1]) + old_size, center = self.bbox2point(left, right, top, bottom, type='kpt68') + else: + try: + bbox, bbox_type = self.face_detector.run(image) + + + except ValueError: + bbox = self.face_detector.run(image) + bbox_type = 'kpt68' + + if len(bbox) < 4: + print('no face detected! run original image') + left = 0; right = h-1; top=0; bottom=w-1 + else: + left = bbox[0]; right=bbox[2] + top = bbox[1]; bottom=bbox[3] + old_size, center = self.bbox2point(left, right, top, bottom, type=bbox_type) + + size = int(old_size*self.scale) + src_pts = np.array([[center[0]-size/2, center[1]-size/2], [center[0] - size/2, center[1]+size/2], [center[0]+size/2, center[1]-size/2]]) + else: + src_pts = np.array([[0, 0], [0, h-1], [w-1, 0]]) + + DST_PTS = np.array([[0,0], [0,self.resolution_inp - 1], [self.resolution_inp - 1, 0]]) + tform = estimate_transform('similarity', src_pts, DST_PTS) + + image = image/255. + dst_image = warp(image, tform.inverse, output_shape=(self.resolution_inp, self.resolution_inp)) + dst_image = dst_image.transpose(2,0,1) + return {'image': torch.tensor(dst_image).float(), + 'imagename': imagename, + 'tform': tform, + 'original_image': torch.tensor(image.transpose(2,0,1)).float(), + } \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/decalib/datasets/detectors.py b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/datasets/detectors.py new file mode 100755 index 0000000..0b6358d --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/datasets/detectors.py @@ -0,0 +1,60 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import numpy as np +import torch + +class FAN(object): + def __init__(self): + import face_alignment + self.model = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False) + + def run(self, image): + ''' + image: 0-255, uint8, rgb, [h, w, 3] + return: detected box list + ''' + out = self.model.get_landmarks(image) + if out is None: + return [0] + else: + kpt = out[0].squeeze() + left = np.min(kpt[:,0]); right = np.max(kpt[:,0]); + top = np.min(kpt[:,1]); bottom = np.max(kpt[:,1]) + bbox = [left,top, right, bottom] + return bbox, 'kpt68' + +class MTCNN(object): + def __init__(self, device = "cuda" if torch.cuda.is_available() else "cpu"): + ''' + https://github.com/timesler/facenet-pytorch/blob/master/examples/infer.ipynb + ''' + from facenet_pytorch import MTCNN as mtcnn + self.device = device + self.model = mtcnn(keep_all=True) + def run(self, input): + ''' + image: 0-255, uint8, rgb, [h, w, 3] + return: detected box + ''' + out = self.model.detect(input[None,...]) + if out[0][0] is None: + return [0] + else: + bbox = out[0][0].squeeze() + return bbox, 'bbox' + + + diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/decalib/deca.py b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/deca.py new file mode 100644 index 0000000..2019daa --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/deca.py @@ -0,0 +1,466 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import os +import torch +import torchvision +from torchvision import transforms +from PIL import Image +import torch.nn.functional as F +from skimage.transform import warp +import numpy as np +from skimage.io import imread + +from .utils.renderer import SRenderY +from .models.encoders import ResnetEncoder +from .models.FLAME import FLAME, FLAMETex +from .models.decoders import Generator +from .utils import util +from .utils.config import cfg +torch.backends.cudnn.benchmark = True + +class DECA(object): + def __init__(self, config=None, device="cuda" if torch.cuda.is_available() else "cpu"): + if config is None: + self.cfg = cfg + else: + self.cfg = config + self.device = device + self.image_size = self.cfg.dataset.image_size + self.uv_size = self.cfg.model.uv_size + + self._create_model(self.cfg.model) + self._setup_renderer(self.cfg.model) + + def _setup_renderer(self, model_cfg): + self.render = SRenderY(self.image_size, obj_filename=model_cfg.topology_path, uv_size=model_cfg.uv_size).to(self.device) + # face mask for rendering details + mask = imread(model_cfg.face_eye_mask_path).astype(np.float32)/255.; mask = torch.from_numpy(mask[:,:,0])[None,None,:,:].contiguous() + self.uv_face_eye_mask = F.interpolate(mask, [model_cfg.uv_size, model_cfg.uv_size]).to(self.device) + mask = imread(model_cfg.face_mask_path).astype(np.float32)/255.; mask = torch.from_numpy(mask[:,:,0])[None,None,:,:].contiguous() + self.uv_face_mask = F.interpolate(mask, [model_cfg.uv_size, model_cfg.uv_size]).to(self.device) + # displacement correction + fixed_dis = np.load(model_cfg.fixed_displacement_path) + self.fixed_uv_dis = torch.tensor(fixed_dis).float().to(self.device) + # mean texture + mean_texture = imread(model_cfg.mean_tex_path).astype(np.float32)/255.; mean_texture = torch.from_numpy(mean_texture.transpose(2,0,1))[None,:,:,:].contiguous() + self.mean_texture = F.interpolate(mean_texture, [model_cfg.uv_size, model_cfg.uv_size]).to(self.device) + # dense mesh template, for save detail mesh + self.dense_template = np.load(model_cfg.dense_template_path, allow_pickle=True, encoding='latin1').item() + + def _create_model(self, model_cfg): + # set up parameters + self.n_param = model_cfg.n_shape+model_cfg.n_tex+model_cfg.n_exp+model_cfg.n_pose+model_cfg.n_cam+model_cfg.n_light + self.n_detail = model_cfg.n_detail + self.n_cond = model_cfg.n_exp + 3 # exp + jaw pose + self.num_list = [model_cfg.n_shape, model_cfg.n_tex, model_cfg.n_exp, model_cfg.n_pose, model_cfg.n_cam, model_cfg.n_light] + self.param_dict = {i:model_cfg.get('n_' + i) for i in model_cfg.param_list} + + # encoders + self.E_flame = ResnetEncoder(outsize=self.n_param).to(self.device) + self.E_detail = ResnetEncoder(outsize=self.n_detail).to(self.device) + # decoders + self.flame = FLAME(model_cfg).to(self.device) + if model_cfg.use_tex: + self.flametex = FLAMETex(model_cfg).to(self.device) + self.D_detail = Generator(latent_dim=self.n_detail+self.n_cond, out_channels=1, out_scale=model_cfg.max_z, sample_mode = 'bilinear').to(self.device) + # resume model + model_path = self.cfg.pretrained_modelpath + if os.path.exists(model_path): + print(f'trained model found. load {model_path}') + checkpoint = torch.load(model_path) + self.checkpoint = checkpoint + util.copy_state_dict(self.E_flame.state_dict(), checkpoint['E_flame']) + util.copy_state_dict(self.E_detail.state_dict(), checkpoint['E_detail']) + util.copy_state_dict(self.D_detail.state_dict(), checkpoint['D_detail']) + else: + print(f'please check model path: {model_path}') + exit() + # eval mode + self.E_flame.eval() + self.E_detail.eval() + self.D_detail.eval() + + def decompose_code(self, code, num_dict): + ''' Convert a flattened parameter vector to a dictionary of parameters + code_dict.keys() = ['shape', 'tex', 'exp', 'pose', 'cam', 'light'] + ''' + code_dict = {} + start = 0 + for key in num_dict: + end = start+int(num_dict[key]) + code_dict[key] = code[:, start:end] + start = end + if key == 'light': + code_dict[key] = code_dict[key].reshape(code_dict[key].shape[0], 9, 3) + return code_dict + + def displacement2normal(self, uv_z, coarse_verts, coarse_normals): + ''' Convert displacement map into detail normal map + ''' + batch_size = uv_z.shape[0] + uv_coarse_vertices = self.render.world2uv(coarse_verts).detach() + uv_coarse_normals = self.render.world2uv(coarse_normals).detach() + + uv_z = uv_z*self.uv_face_eye_mask + uv_detail_vertices = uv_coarse_vertices + uv_z*uv_coarse_normals + self.fixed_uv_dis[None,None,:,:]*uv_coarse_normals.detach() + dense_vertices = uv_detail_vertices.permute(0,2,3,1).reshape([batch_size, -1, 3]) + uv_detail_normals = util.vertex_normals(dense_vertices, self.render.dense_faces.expand(batch_size, -1, -1)) + uv_detail_normals = uv_detail_normals.reshape([batch_size, uv_coarse_vertices.shape[2], uv_coarse_vertices.shape[3], 3]).permute(0,3,1,2) + return uv_detail_normals + + def displacement2vertex(self, uv_z, coarse_verts, coarse_normals): + ''' Convert displacement map into detail vertices + ''' + batch_size = uv_z.shape[0] + uv_coarse_vertices = self.render.world2uv(coarse_verts).detach() + uv_coarse_normals = self.render.world2uv(coarse_normals).detach() + + uv_z = uv_z*self.uv_face_eye_mask + uv_detail_vertices = uv_coarse_vertices + uv_z*uv_coarse_normals + self.fixed_uv_dis[None,None,:,:]*uv_coarse_normals.detach() + dense_vertices = uv_detail_vertices.permute(0,2,3,1).reshape([batch_size, -1, 3]) + # uv_detail_normals = util.vertex_normals(dense_vertices, self.render.dense_faces.expand(batch_size, -1, -1)) + # uv_detail_normals = uv_detail_normals.reshape([batch_size, uv_coarse_vertices.shape[2], uv_coarse_vertices.shape[3], 3]).permute(0,3,1,2) + detail_faces = self.render.dense_faces + return dense_vertices, detail_faces + + def visofp(self, normals): + ''' visibility of keypoints, based on the normal direction + ''' + normals68 = self.flame.seletec_3d68(normals) + vis68 = (normals68[:,:,2:] < 0.1).float() + return vis68 + + @torch.no_grad() + def encode(self, images): + batch_size = images.shape[0] + parameters = self.E_flame(images) + detailcode = self.E_detail(images) + codedict = self.decompose_code(parameters, self.param_dict) + codedict['detail'] = detailcode + codedict['images'] = images + return codedict + + @torch.no_grad() + def decode(self, codedict, tform=None): + images = codedict['images'] + batch_size = images.shape[0] + + # pose = codedict['pose'] + # print(f'Pose: {pose}') + # camera = codedict['cam'] + # print(f'Camera: {camera}') + + ## decode + verts, landmarks2d, landmarks3d = self.flame(shape_params=codedict['shape'], expression_params=codedict['exp'], pose_params=codedict['pose']) + uv_z = self.D_detail(torch.cat([codedict['pose'][:,3:], codedict['exp'], codedict['detail']], dim=1)) + if self.cfg.model.use_tex: + albedo = self.flametex(codedict['tex']) + else: + albedo = torch.zeros([batch_size, 3, self.uv_size, self.uv_size], device=images.device) + + # pdb.set_trace() + + ## projection + landmarks2d = util.batch_orth_proj(landmarks2d, codedict['cam'])[:, :, :2]; landmarks2d[:, :, 1:] = -landmarks2d[:, :, 1:]; landmarks2d = landmarks2d * self.image_size / 2 + self.image_size / 2 + landmarks3d = util.batch_orth_proj(landmarks3d, codedict['cam']); landmarks3d[:, :, 1:] = -landmarks3d[:, :, 1:]; landmarks3d = landmarks3d * self.image_size / 2 + self.image_size / 2 + trans_verts = util.batch_orth_proj(verts, codedict['cam']); trans_verts[:, :, 1:] = -trans_verts[:, :, 1:] + + if tform is not None: + + tform_tensor = torch.tensor(tform.params, dtype=torch.float32).cuda() + dst_image = warp(trans_verts[0,:,1:].cpu().numpy(), tform) + trans_verts = torch.cat((trans_verts[0,:,:1], torch.tensor(dst_image, dtype=torch.float32).cuda()), dim = 1) + trans_verts = torch.unsqueeze(trans_verts, dim=0) + + ## rendering + ops = self.render(verts, trans_verts, albedo, codedict['light']) + uv_detail_normals = self.displacement2normal(uv_z, verts, ops['normals']) + uv_shading = self.render.add_SHlight(uv_detail_normals, codedict['light']) + uv_texture = albedo*uv_shading + + landmarks3d_vis = self.visofp(ops['transformed_normals']) + landmarks3d = torch.cat([landmarks3d, landmarks3d_vis], dim=2) + + ## render shape + shape_images = self.render.render_shape(verts, trans_verts) + + # new_shape = shape_images[0].permute(1, 2, 0).cpu().numpy() + # plt.imshow(new_shape) + # plt.show() + + detail_normal_images = F.grid_sample(uv_detail_normals, ops['grid'], align_corners=False)*ops['alpha_images'] + shape_detail_images = self.render.render_shape(verts, trans_verts, detail_normal_images=detail_normal_images) + + ## extract texture + ## TODO: current resolution 256x256, support higher resolution, and add visibility + uv_pverts = self.render.world2uv(trans_verts) + uv_gt = F.grid_sample(images, uv_pverts.permute(0,2,3,1)[:,:,:,:2], mode='bilinear') + if self.cfg.model.use_tex: + ## TODO: poisson blending should give better-looking results + uv_texture_gt = uv_gt[:,:3,:,:]*self.uv_face_eye_mask + (uv_texture[:,:3,:,:]*(1-self.uv_face_eye_mask)*0.7) + else: + uv_texture_gt = uv_gt[:,:3,:,:]*self.uv_face_eye_mask + (torch.ones_like(uv_gt[:,:3,:,:])*(1-self.uv_face_eye_mask)*0.7) + + ## output + opdict = { + 'vertices': verts, + 'normals': ops['normals'], + 'grid': ops['grid'], + 'transformed_vertices': trans_verts, + 'landmarks2d': landmarks2d, + 'landmarks3d': landmarks3d, + 'uv_detail_normals': uv_detail_normals, + 'uv_texture_gt': uv_texture_gt, + 'displacement_map': uv_z+self.fixed_uv_dis[None,None,:,:], + 'detail_normal_images': detail_normal_images, + } + + if self.cfg.model.use_tex: + opdict['albedo'] = albedo + opdict['uv_texture'] = uv_texture + + visdict = { + 'inputs': images, + 'landmarks2d': util.tensor_vis_landmarks(images, landmarks2d, isScale=False), + 'landmarks3d': util.tensor_vis_landmarks(images, landmarks3d, isScale=False), + 'shape_images': shape_images, + 'shape_detail_images': shape_detail_images, + } + + if self.cfg.model.use_tex: + visdict['rendered_images'] = ops['images'] + + return opdict, visdict + + @torch.no_grad() + def decode_eyes(self, codedict, tform=None): + + images = codedict['images'] + batch_size = images.shape[0] + + pose = codedict['pose'] + print(f'Pose: {pose}') + camera = codedict['cam'] + print(f'Camera: {camera}') + + eye_pose_params = torch.tensor([[0, 0, 0, 0, 0, 0]], device="cuda" if torch.cuda.is_available() else "cpu") + eye_pose_params = -20 * pose + print(f'eye_pose_params: {eye_pose_params}') + + ## decode + verts, landmarks2d, landmarks3d = self.flame(shape_params=codedict['shape'], expression_params=codedict['exp'], + pose_params=codedict['pose'], eye_pose_params=eye_pose_params) + + uv_z = self.D_detail(torch.cat([codedict['pose'][:, 3:], codedict['exp'], codedict['detail']], dim=1)) + if self.cfg.model.use_tex: + albedo = self.flametex(codedict['tex']) + else: + albedo = torch.zeros([batch_size, 3, self.uv_size, self.uv_size], device=images.device) + + # pdb.set_trace() + + ## projection + landmarks2d = util.batch_orth_proj(landmarks2d, codedict['cam'])[:, :, :2]; + landmarks2d[:, :, 1:] = -landmarks2d[:, :, 1:]; + landmarks2d = landmarks2d * self.image_size / 2 + self.image_size / 2 + landmarks3d = util.batch_orth_proj(landmarks3d, codedict['cam']); + landmarks3d[:, :, 1:] = -landmarks3d[:, :, 1:]; + landmarks3d = landmarks3d * self.image_size / 2 + self.image_size / 2 + trans_verts = util.batch_orth_proj(verts, codedict['cam']); + trans_verts[:, :, 1:] = -trans_verts[:, :, 1:] + + if tform is not None: + tform_tensor = torch.tensor(tform.params, dtype=torch.float32).cuda() + dst_image = warp(trans_verts[0, :, 1:].cpu().numpy(), tform) + trans_verts = torch.cat((trans_verts[0, :, :1], torch.tensor(dst_image, dtype=torch.float32).cuda()), dim=1) + trans_verts = torch.unsqueeze(trans_verts, dim=0) + + ## rendering + ops = self.render(verts, trans_verts, albedo, codedict['light']) + uv_detail_normals = self.displacement2normal(uv_z, verts, ops['normals']) + uv_shading = self.render.add_SHlight(uv_detail_normals, codedict['light']) + uv_texture = albedo * uv_shading + + landmarks3d_vis = self.visofp(ops['transformed_normals']) + landmarks3d = torch.cat([landmarks3d, landmarks3d_vis], dim=2) + + ## render shape + shape_images = self.render.render_shape(verts, trans_verts) + + # new_shape = shape_images[0].permute(1, 2, 0).cpu().numpy() + # plt.imshow(new_shape) + # plt.show() + + detail_normal_images = F.grid_sample(uv_detail_normals, ops['grid'], align_corners=False) * ops['alpha_images'] + shape_detail_images = self.render.render_shape(verts, trans_verts, detail_normal_images=detail_normal_images) + + ## extract texture + ## TODO: current resolution 256x256, support higher resolution, and add visibility + uv_pverts = self.render.world2uv(trans_verts) + uv_gt = F.grid_sample(images, uv_pverts.permute(0, 2, 3, 1)[:, :, :, :2], mode='bilinear') + if self.cfg.model.use_tex: + ## TODO: poisson blending should give better-looking results + uv_texture_gt = uv_gt[:, :3, :, :] * self.uv_face_eye_mask + ( + uv_texture[:, :3, :, :] * (1 - self.uv_face_eye_mask) * 0.7) + else: + uv_texture_gt = uv_gt[:, :3, :, :] * self.uv_face_eye_mask + ( + torch.ones_like(uv_gt[:, :3, :, :]) * (1 - self.uv_face_eye_mask) * 0.7) + + ## output + opdict = { + 'vertices': verts, + 'normals': ops['normals'], + 'grid': ops['grid'], + 'transformed_vertices': trans_verts, + 'landmarks2d': landmarks2d, + 'landmarks3d': landmarks3d, + 'uv_detail_normals': uv_detail_normals, + 'uv_texture_gt': uv_texture_gt, + 'displacement_map': uv_z + self.fixed_uv_dis[None, None, :, :], + 'detail_normal_images': detail_normal_images, + + } + + if self.cfg.model.use_tex: + opdict['albedo'] = albedo + opdict['uv_texture'] = uv_texture + + visdict = { + 'inputs': images, + 'landmarks2d': util.tensor_vis_landmarks(images, landmarks2d, isScale=False), + 'landmarks3d': util.tensor_vis_landmarks(images, landmarks3d, isScale=False), + 'shape_images': shape_images, + 'shape_detail_images': shape_detail_images, + } + + if self.cfg.model.use_tex: + visdict['rendered_images'] = ops['images'] + + return opdict, visdict + + def render_uv(self, codedict, exp, pose): + + images = codedict['images'] + batch_size = images.shape[0] + + ## decode + verts, landmarks2d, landmarks3d = self.flame(shape_params=codedict['shape'], expression_params=exp, + pose_params=pose) + + if self.cfg.model.use_tex: + albedo = self.flametex(codedict['tex']) + else: + albedo = torch.zeros([batch_size, 3, self.uv_size, self.uv_size], device=images.device) + + ## projection + trans_verts = util.batch_orth_proj(verts, codedict['cam']) + trans_verts[:, :, 1:] = -trans_verts[:, :, 1:] + + ## rendering + ops = self.render(verts, trans_verts, albedo, codedict['light']) + + return ops['grid'] + + def render_uv_details(self, codedict, exp, pose): + + images = codedict['images'] + batch_size = images.shape[0] + + ## decode + verts, landmarks2d, landmarks3d = self.flame(shape_params=codedict['shape'], expression_params=exp, + pose_params=pose) + uv_z = self.D_detail(torch.cat([pose[:, 3:], exp, codedict['detail']], dim=1)) + + if self.cfg.model.use_tex: + albedo = self.flametex(codedict['tex']) + else: + albedo = torch.zeros([batch_size, 3, self.uv_size, self.uv_size], device=images.device) + + ## projection + trans_verts = util.batch_orth_proj(verts, codedict['cam']); + trans_verts[:, :, 1:] = -trans_verts[:, :, 1:] + + ## rendering + ops = self.render(verts, trans_verts, albedo, codedict['light']) + uv_detail_normals = self.displacement2normal(uv_z, verts, ops['normals']) + + detail_normal_images = F.grid_sample(uv_detail_normals, ops['grid'], align_corners=False) * ops['alpha_images'] + + return ops['grid'], detail_normal_images + + def render_mask(self, grid): + + # image = Image.open('./third/DECA/data/mask_face.png') + # image = Image.open('./third/DECA/data/mask_3.png') + current_path = os.getcwd() + try : + head_tail = os.path.split(current_path) + image = Image.open(head_tail[0] + '/third/DECA/data/mask_mouth_2.png') + + except FileNotFoundError: + image = Image.open(current_path + '/third/DECA/data/mask_mouth_2.png') + + trans = transforms.ToTensor() + tmp = trans(image) + image_tensor = trans(image).cuda().unsqueeze(0) + mask_1 = (grid[:, :, :, 0:1] != 0.0) & (grid[:, :, :, 1:2] != 0.0) + mask_1 = mask_1.permute(0, 3, 1, 2) + + mask = F.grid_sample(image_tensor, grid, align_corners=False, padding_mode='zeros') + mask = mask_1 * mask + + return mask[0,0,:,:] + + def visualize(self, visdict, size=None): + grids = {} + if size is None: + size = self.image_size + for key in visdict: + grids[key] = torchvision.utils.make_grid(F.interpolate(visdict[key], [size, size])).detach().cpu() + grid = torch.cat(list(grids.values()), 2) + grid_image = (grid.numpy().transpose(1,2,0).copy()*255)[:,:,[2,1,0]] + grid_image = np.minimum(np.maximum(grid_image, 0), 255).astype(np.uint8) + return grid_image + + def save_obj(self, filename, opdict): + ''' + vertices: [nv, 3], tensor + texture: [3, h, w], tensor + ''' + i = 0 + vertices = opdict['vertices'][i].cpu().numpy() + faces = self.render.faces[0].cpu().numpy() + texture = util.tensor2image(opdict['uv_texture_gt'][i]) + uvcoords = self.render.raw_uvcoords[0].cpu().numpy() + uvfaces = self.render.uvfaces[0].cpu().numpy() + # save coarse mesh, with texture and normal map + normal_map = util.tensor2image(opdict['uv_detail_normals'][i] * 0.5 + 0.5) + util.write_obj(filename, vertices, faces, + texture=texture, + uvcoords=uvcoords, + uvfaces=uvfaces, + normal_map=normal_map) + # upsample mesh, save detailed mesh + texture = texture[:,:,[2,1,0]] + normals = opdict['normals'][i].cpu().numpy() + displacement_map = opdict['displacement_map'][i].cpu().numpy().squeeze() + dense_vertices, dense_colors, dense_faces = util.upsample_mesh(vertices, normals, faces, displacement_map, + texture, self.dense_template) + util.write_obj(filename.replace('.obj', '_detail.obj'), + dense_vertices, + dense_faces, + colors=dense_colors, + inverse_face_order=True) diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/decalib/models/FLAME.py b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/models/FLAME.py new file mode 100755 index 0000000..7e2821a --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/models/FLAME.py @@ -0,0 +1,262 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import torch +import torch.nn as nn +import numpy as np +import pickle +import torch.nn.functional as F + +from .lbs import lbs, batch_rodrigues, vertices2landmarks, rot_mat_to_euler + +def to_tensor(array, dtype=torch.float32): + if 'torch.tensor' not in str(type(array)): + return torch.tensor(array, dtype=dtype) +def to_np(array, dtype=np.float32): + if 'scipy.sparse' in str(type(array)): + array = array.todense() + return np.array(array, dtype=dtype) + +class Struct(object): + def __init__(self, **kwargs): + for key, val in kwargs.items(): + setattr(self, key, val) + +class FLAME(nn.Module): + """ + borrowed from https://github.com/soubhiksanyal/FLAME_PyTorch/blob/master/FLAME.py + Given flame parameters this class generates a differentiable FLAME function + which outputs the a mesh and 2D/3D facial landmarks + """ + def __init__(self, config): + super(FLAME, self).__init__() + print("creating the FLAME Decoder") + with open(config.flame_model_path, 'rb') as f: + ss = pickle.load(f, encoding='latin1') + flame_model = Struct(**ss) + + self.dtype = torch.float32 + self.register_buffer('faces_tensor', to_tensor(to_np(flame_model.f, dtype=np.int64), dtype=torch.long)) + # The vertices of the template model + self.register_buffer('v_template', to_tensor(to_np(flame_model.v_template), dtype=self.dtype)) + # The shape components and expression + shapedirs = to_tensor(to_np(flame_model.shapedirs), dtype=self.dtype) + shapedirs = torch.cat([shapedirs[:,:,:config.n_shape], shapedirs[:,:,300:300+config.n_exp]], 2) + self.register_buffer('shapedirs', shapedirs) + # The pose components + num_pose_basis = flame_model.posedirs.shape[-1] + posedirs = np.reshape(flame_model.posedirs, [-1, num_pose_basis]).T + self.register_buffer('posedirs', to_tensor(to_np(posedirs), dtype=self.dtype)) + # + self.register_buffer('J_regressor', to_tensor(to_np(flame_model.J_regressor), dtype=self.dtype)) + parents = to_tensor(to_np(flame_model.kintree_table[0])).long(); parents[0] = -1 + self.register_buffer('parents', parents) + self.register_buffer('lbs_weights', to_tensor(to_np(flame_model.weights), dtype=self.dtype)) + + # Fixing Eyeball and neck rotation + default_eyball_pose = torch.zeros([1, 6], dtype=self.dtype, requires_grad=False) + self.register_parameter('eye_pose', nn.Parameter(default_eyball_pose, + requires_grad=False)) + default_neck_pose = torch.zeros([1, 3], dtype=self.dtype, requires_grad=False) + self.register_parameter('neck_pose', nn.Parameter(default_neck_pose, + requires_grad=False)) + + # Static and Dynamic Landmark embeddings for FLAME + lmk_embeddings = np.load(config.flame_lmk_embedding_path, allow_pickle=True, encoding='latin1') + lmk_embeddings = lmk_embeddings[()] + self.register_buffer('lmk_faces_idx', torch.from_numpy(lmk_embeddings['static_lmk_faces_idx']).long()) + self.register_buffer('lmk_bary_coords', torch.from_numpy(lmk_embeddings['static_lmk_bary_coords']).to(self.dtype)) + self.register_buffer('dynamic_lmk_faces_idx', lmk_embeddings['dynamic_lmk_faces_idx'].long()) + self.register_buffer('dynamic_lmk_bary_coords', lmk_embeddings['dynamic_lmk_bary_coords'].to(self.dtype)) + self.register_buffer('full_lmk_faces_idx', torch.from_numpy(lmk_embeddings['full_lmk_faces_idx']).long()) + self.register_buffer('full_lmk_bary_coords', torch.from_numpy(lmk_embeddings['full_lmk_bary_coords']).to(self.dtype)) + + neck_kin_chain = []; NECK_IDX=1 + curr_idx = torch.tensor(NECK_IDX, dtype=torch.long) + while curr_idx != -1: + neck_kin_chain.append(curr_idx) + curr_idx = self.parents[curr_idx] + self.register_buffer('neck_kin_chain', torch.stack(neck_kin_chain)) + + def _find_dynamic_lmk_idx_and_bcoords(self, pose, dynamic_lmk_faces_idx, + dynamic_lmk_b_coords, + neck_kin_chain, dtype=torch.float32): + """ + Selects the face contour depending on the reletive position of the head + Input: + vertices: N X num_of_vertices X 3 + pose: N X full pose + dynamic_lmk_faces_idx: The list of contour face indexes + dynamic_lmk_b_coords: The list of contour barycentric weights + neck_kin_chain: The tree to consider for the relative rotation + dtype: Data type + return: + The contour face indexes and the corresponding barycentric weights + """ + + batch_size = pose.shape[0] + + aa_pose = torch.index_select(pose.view(batch_size, -1, 3), 1, + neck_kin_chain) + rot_mats = batch_rodrigues( + aa_pose.view(-1, 3), dtype=dtype).view(batch_size, -1, 3, 3) + + rel_rot_mat = torch.eye(3, device=pose.device, + dtype=dtype).unsqueeze_(dim=0).expand(batch_size, -1, -1) + for idx in range(len(neck_kin_chain)): + rel_rot_mat = torch.bmm(rot_mats[:, idx], rel_rot_mat) + + y_rot_angle = torch.round( + torch.clamp(rot_mat_to_euler(rel_rot_mat) * 180.0 / np.pi, + max=39)).to(dtype=torch.long) + + neg_mask = y_rot_angle.lt(0).to(dtype=torch.long) + mask = y_rot_angle.lt(-39).to(dtype=torch.long) + neg_vals = mask * 78 + (1 - mask) * (39 - y_rot_angle) + y_rot_angle = (neg_mask * neg_vals + + (1 - neg_mask) * y_rot_angle) + + dyn_lmk_faces_idx = torch.index_select(dynamic_lmk_faces_idx, + 0, y_rot_angle) + dyn_lmk_b_coords = torch.index_select(dynamic_lmk_b_coords, + 0, y_rot_angle) + return dyn_lmk_faces_idx, dyn_lmk_b_coords + + def _vertices2landmarks(self, vertices, faces, lmk_faces_idx, lmk_bary_coords): + """ + Calculates landmarks by barycentric interpolation + Input: + vertices: torch.tensor NxVx3, dtype = torch.float32 + The tensor of input vertices + faces: torch.tensor (N*F)x3, dtype = torch.long + The faces of the mesh + lmk_faces_idx: torch.tensor N X L, dtype = torch.long + The tensor with the indices of the faces used to calculate the + landmarks. + lmk_bary_coords: torch.tensor N X L X 3, dtype = torch.float32 + The tensor of barycentric coordinates that are used to interpolate + the landmarks + + Returns: + landmarks: torch.tensor NxLx3, dtype = torch.float32 + The coordinates of the landmarks for each mesh in the batch + """ + # Extract the indices of the vertices for each face + # NxLx3 + batch_size, num_verts = vertices.shape[:dd2] + lmk_faces = torch.index_select(faces, 0, lmk_faces_idx.view(-1)).view( + 1, -1, 3).view(batch_size, lmk_faces_idx.shape[1], -1) + + lmk_faces += torch.arange(batch_size, dtype=torch.long).view(-1, 1, 1).to( + device=vertices.device) * num_verts + + lmk_vertices = vertices.view(-1, 3)[lmk_faces] + landmarks = torch.einsum('blfi,blf->bli', [lmk_vertices, lmk_bary_coords]) + return landmarks + + def seletec_3d68(self, vertices): + landmarks3d = vertices2landmarks(vertices, self.faces_tensor, + self.full_lmk_faces_idx.repeat(vertices.shape[0], 1), + self.full_lmk_bary_coords.repeat(vertices.shape[0], 1, 1)) + return landmarks3d + + def forward(self, shape_params=None, expression_params=None, pose_params=None, eye_pose_params=None): + """ + Input: + shape_params: N X number of shape parameters + expression_params: N X number of expression parameters + pose_params: N X number of pose parameters (6) + return:d + vertices: N X V X 3 + landmarks: N X number of landmarks X 3 + """ + batch_size = shape_params.shape[0] + if eye_pose_params is None: + eye_pose_params = self.eye_pose.expand(batch_size, -1) + betas = torch.cat([shape_params, expression_params], dim=1) + full_pose = torch.cat([pose_params[:, :3], self.neck_pose.expand(batch_size, -1), pose_params[:, 3:], eye_pose_params], dim=1) + template_vertices = self.v_template.unsqueeze(0).expand(batch_size, -1, -1) + + vertices, _ = lbs(betas, full_pose, template_vertices, + self.shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, dtype=self.dtype) + + lmk_faces_idx = self.lmk_faces_idx.unsqueeze(dim=0).expand(batch_size, -1) + lmk_bary_coords = self.lmk_bary_coords.unsqueeze(dim=0).expand(batch_size, -1, -1) + + dyn_lmk_faces_idx, dyn_lmk_bary_coords = self._find_dynamic_lmk_idx_and_bcoords( + full_pose, self.dynamic_lmk_faces_idx, + self.dynamic_lmk_bary_coords, + self.neck_kin_chain, dtype=self.dtype) + lmk_faces_idx = torch.cat([dyn_lmk_faces_idx, lmk_faces_idx], 1) + lmk_bary_coords = torch.cat([dyn_lmk_bary_coords, lmk_bary_coords], 1) + + landmarks2d = vertices2landmarks(vertices, self.faces_tensor, + lmk_faces_idx, + lmk_bary_coords) + bz = vertices.shape[0] + landmarks3d = vertices2landmarks(vertices, self.faces_tensor, + self.full_lmk_faces_idx.repeat(bz, 1), + self.full_lmk_bary_coords.repeat(bz, 1, 1)) + return vertices, landmarks2d, landmarks3d + +class FLAMETex(nn.Module): + """ + FLAME texture: + https://github.com/TimoBolkart/TF_FLAME/blob/ade0ab152300ec5f0e8555d6765411555c5ed43d/sample_texture.py#L64 + FLAME texture converted from BFM: + https://github.com/TimoBolkart/BFM_to_FLAME + """ + def __init__(self, config): + super(FLAMETex, self).__init__() + if config.tex_type == 'BFM': + mu_key = 'MU' + pc_key = 'PC' + n_pc = 199 + tex_path = config.tex_path + tex_space = np.load(tex_path) + texture_mean = tex_space[mu_key].reshape(1, -1) + texture_basis = tex_space[pc_key].reshape(-1, n_pc) + + elif config.tex_type == 'FLAME': + mu_key = 'mean' + pc_key = 'tex_dir' + n_pc = 200 + tex_path = config.flame_tex_path + tex_space = np.load(tex_path) + texture_mean = tex_space[mu_key].reshape(1, -1)/255. + texture_basis = tex_space[pc_key].reshape(-1, n_pc)/255. + else: + print('texture type ', config.tex_type, 'not exist!') + raise NotImplementedError + + n_tex = config.n_tex + num_components = texture_basis.shape[1] + texture_mean = torch.from_numpy(texture_mean).float()[None,...] + texture_basis = torch.from_numpy(texture_basis[:,:n_tex]).float()[None,...] + self.register_buffer('texture_mean', texture_mean) + self.register_buffer('texture_basis', texture_basis) + + def forward(self, texcode): + ''' + texcode: [batchsize, n_tex] + texture: [bz, 3, 256, 256], range: 0-1 + ''' + texture = self.texture_mean + (self.texture_basis*texcode[:,None,:]).sum(-1) + texture = texture.reshape(texcode.shape[0], 512, 512, 3).permute(0,3,1,2) + texture = F.interpolate(texture, [256, 256]) + texture = texture[:,[2,1,0], :,:] + return texture \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/decalib/models/__init__.py b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/decalib/models/decoders.py b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/models/decoders.py new file mode 100755 index 0000000..e1cdfa5 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/models/decoders.py @@ -0,0 +1,55 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import torch.nn as nn + +class Generator(nn.Module): + def __init__(self, latent_dim=100, out_channels=1, out_scale=0.01, sample_mode = 'bilinear'): + super(Generator, self).__init__() + self.out_scale = out_scale + + self.init_size = 32 // 4 # Initial size before upsampling + self.l1 = nn.Sequential(nn.Linear(latent_dim, 128 * self.init_size ** 2)) + self.conv_blocks = nn.Sequential( + nn.BatchNorm2d(128), + nn.Upsample(scale_factor=2, mode=sample_mode), #16 + nn.Conv2d(128, 128, 3, stride=1, padding=1), + nn.BatchNorm2d(128, 0.8), + nn.LeakyReLU(0.2, inplace=True), + nn.Upsample(scale_factor=2, mode=sample_mode), #32 + nn.Conv2d(128, 64, 3, stride=1, padding=1), + nn.BatchNorm2d(64, 0.8), + nn.LeakyReLU(0.2, inplace=True), + nn.Upsample(scale_factor=2, mode=sample_mode), #64 + nn.Conv2d(64, 64, 3, stride=1, padding=1), + nn.BatchNorm2d(64, 0.8), + nn.LeakyReLU(0.2, inplace=True), + nn.Upsample(scale_factor=2, mode=sample_mode), #128 + nn.Conv2d(64, 32, 3, stride=1, padding=1), + nn.BatchNorm2d(32, 0.8), + nn.LeakyReLU(0.2, inplace=True), + nn.Upsample(scale_factor=2, mode=sample_mode), #256 + nn.Conv2d(32, 16, 3, stride=1, padding=1), + nn.BatchNorm2d(16, 0.8), + nn.LeakyReLU(0.2, inplace=True), + nn.Conv2d(16, out_channels, 3, stride=1, padding=1), + nn.Tanh(), + ) + + def forward(self, noise): + out = self.l1(noise) + out = out.view(out.shape[0], 128, self.init_size, self.init_size) + img = self.conv_blocks(out) + return img*self.out_scale \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/decalib/models/encoders.py b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/models/encoders.py new file mode 100755 index 0000000..53331fd --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/models/encoders.py @@ -0,0 +1,37 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import torch.nn as nn +from . import resnet + +class ResnetEncoder(nn.Module): + def __init__(self, outsize, last_op=None): + super(ResnetEncoder, self).__init__() + feature_size = 2048 + self.encoder = resnet.load_ResNet50Model() #out: 2048 + ### regressor + self.layers = nn.Sequential( + nn.Linear(feature_size, 1024), + nn.ReLU(), + nn.Linear(1024, outsize) + ) + self.last_op = last_op + + def forward(self, inputs): + features = self.encoder(inputs) + parameters = self.layers(features) + if self.last_op: + parameters = self.last_op(parameters) + return parameters diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/decalib/models/lbs.py b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/models/lbs.py new file mode 100755 index 0000000..df55ab3 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/models/lbs.py @@ -0,0 +1,378 @@ +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import numpy as np + +import torch +import torch.nn.functional as F + +def rot_mat_to_euler(rot_mats): + # Calculates rotation matrix to euler angles + # Careful for extreme cases of eular angles like [0.0, pi, 0.0] + + sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] + + rot_mats[:, 1, 0] * rot_mats[:, 1, 0]) + return torch.atan2(-rot_mats[:, 2, 0], sy) + +def find_dynamic_lmk_idx_and_bcoords(vertices, pose, dynamic_lmk_faces_idx, + dynamic_lmk_b_coords, + neck_kin_chain, dtype=torch.float32): + ''' Compute the faces, barycentric coordinates for the dynamic landmarks + + + To do so, we first compute the rotation of the neck around the y-axis + and then use a pre-computed look-up table to find the faces and the + barycentric coordinates that will be used. + + Special thanks to Soubhik Sanyal (soubhik.sanyal@tuebingen.mpg.de) + for providing the original TensorFlow implementation and for the LUT. + + Parameters + ---------- + vertices: torch.tensor BxVx3, dtype = torch.float32 + The tensor of input vertices + pose: torch.tensor Bx(Jx3), dtype = torch.float32 + The current pose of the body model + dynamic_lmk_faces_idx: torch.tensor L, dtype = torch.long + The look-up table from neck rotation to faces + dynamic_lmk_b_coords: torch.tensor Lx3, dtype = torch.float32 + The look-up table from neck rotation to barycentric coordinates + neck_kin_chain: list + A python list that contains the indices of the joints that form the + kinematic chain of the neck. + dtype: torch.dtype, optional + + Returns + ------- + dyn_lmk_faces_idx: torch.tensor, dtype = torch.long + A tensor of size BxL that contains the indices of the faces that + will be used to compute the current dynamic landmarks. + dyn_lmk_b_coords: torch.tensor, dtype = torch.float32 + A tensor of size BxL that contains the indices of the faces that + will be used to compute the current dynamic landmarks. + ''' + + batch_size = vertices.shape[0] + + aa_pose = torch.index_select(pose.view(batch_size, -1, 3), 1, + neck_kin_chain) + rot_mats = batch_rodrigues( + aa_pose.view(-1, 3), dtype=dtype).view(batch_size, -1, 3, 3) + + rel_rot_mat = torch.eye(3, device=vertices.device, + dtype=dtype).unsqueeze_(dim=0) + for idx in range(len(neck_kin_chain)): + rel_rot_mat = torch.bmm(rot_mats[:, idx], rel_rot_mat) + + y_rot_angle = torch.round( + torch.clamp(-rot_mat_to_euler(rel_rot_mat) * 180.0 / np.pi, + max=39)).to(dtype=torch.long) + neg_mask = y_rot_angle.lt(0).to(dtype=torch.long) + mask = y_rot_angle.lt(-39).to(dtype=torch.long) + neg_vals = mask * 78 + (1 - mask) * (39 - y_rot_angle) + y_rot_angle = (neg_mask * neg_vals + + (1 - neg_mask) * y_rot_angle) + + dyn_lmk_faces_idx = torch.index_select(dynamic_lmk_faces_idx, + 0, y_rot_angle) + dyn_lmk_b_coords = torch.index_select(dynamic_lmk_b_coords, + 0, y_rot_angle) + + return dyn_lmk_faces_idx, dyn_lmk_b_coords + + +def vertices2landmarks(vertices, faces, lmk_faces_idx, lmk_bary_coords): + ''' Calculates landmarks by barycentric interpolation + + Parameters + ---------- + vertices: torch.tensor BxVx3, dtype = torch.float32 + The tensor of input vertices + faces: torch.tensor Fx3, dtype = torch.long + The faces of the mesh + lmk_faces_idx: torch.tensor L, dtype = torch.long + The tensor with the indices of the faces used to calculate the + landmarks. + lmk_bary_coords: torch.tensor Lx3, dtype = torch.float32 + The tensor of barycentric coordinates that are used to interpolate + the landmarks + + Returns + ------- + landmarks: torch.tensor BxLx3, dtype = torch.float32 + The coordinates of the landmarks for each mesh in the batch + ''' + # Extract the indices of the vertices for each face + # BxLx3 + batch_size, num_verts = vertices.shape[:2] + device = vertices.device + + lmk_faces = torch.index_select(faces, 0, lmk_faces_idx.view(-1)).view( + batch_size, -1, 3) + + lmk_faces += torch.arange( + batch_size, dtype=torch.long, device=device).view(-1, 1, 1) * num_verts + + lmk_vertices = vertices.view(-1, 3)[lmk_faces].view( + batch_size, -1, 3, 3) + + landmarks = torch.einsum('blfi,blf->bli', [lmk_vertices, lmk_bary_coords]) + return landmarks + + +def lbs(betas, pose, v_template, shapedirs, posedirs, J_regressor, parents, + lbs_weights, pose2rot=True, dtype=torch.float32): + ''' Performs Linear Blend Skinning with the given shape and pose parameters + + Parameters + ---------- + betas : torch.tensor BxNB + The tensor of shape parameters + pose : torch.tensor Bx(J + 1) * 3 + The pose parameters in axis-angle format + v_template torch.tensor BxVx3 + The template mesh that will be deformed + shapedirs : torch.tensor 1xNB + The tensor of PCA shape displacements + posedirs : torch.tensor Px(V * 3) + The pose PCA coefficients + J_regressor : torch.tensor JxV + The regressor array that is used to calculate the joints from + the position of the vertices + parents: torch.tensor J + The array that describes the kinematic tree for the model + lbs_weights: torch.tensor N x V x (J + 1) + The linear blend skinning weights that represent how much the + rotation matrix of each part affects each vertex + pose2rot: bool, optional + Flag on whether to convert the input pose tensor to rotation + matrices. The default value is True. If False, then the pose tensor + should already contain rotation matrices and have a size of + Bx(J + 1)x9 + dtype: torch.dtype, optional + + Returns + ------- + verts: torch.tensor BxVx3 + The vertices of the mesh after applying the shape and pose + displacements. + joints: torch.tensor BxJx3 + The joints of the model + ''' + + batch_size = max(betas.shape[0], pose.shape[0]) + device = betas.device + + # Add shape contribution + v_shaped = v_template + blend_shapes(betas, shapedirs) + + # Get the joints + # NxJx3 array + J = vertices2joints(J_regressor, v_shaped) + + # 3. Add pose blend shapes + # N x J x 3 x 3 + ident = torch.eye(3, dtype=dtype, device=device) + if pose2rot: + rot_mats = batch_rodrigues( + pose.view(-1, 3), dtype=dtype).view([batch_size, -1, 3, 3]) + + pose_feature = (rot_mats[:, 1:, :, :] - ident).view([batch_size, -1]) + # (N x P) x (P, V * 3) -> N x V x 3 + pose_offsets = torch.matmul(pose_feature, posedirs) \ + .view(batch_size, -1, 3) + else: + pose_feature = pose[:, 1:].view(batch_size, -1, 3, 3) - ident + rot_mats = pose.view(batch_size, -1, 3, 3) + + pose_offsets = torch.matmul(pose_feature.view(batch_size, -1), + posedirs).view(batch_size, -1, 3) + + v_posed = pose_offsets + v_shaped + # 4. Get the global joint location + J_transformed, A = batch_rigid_transform(rot_mats, J, parents, dtype=dtype) + + # 5. Do skinning: + # W is N x V x (J + 1) + W = lbs_weights.unsqueeze(dim=0).expand([batch_size, -1, -1]) + # (N x V x (J + 1)) x (N x (J + 1) x 16) + num_joints = J_regressor.shape[0] + T = torch.matmul(W, A.view(batch_size, num_joints, 16)) \ + .view(batch_size, -1, 4, 4) + + homogen_coord = torch.ones([batch_size, v_posed.shape[1], 1], + dtype=dtype, device=device) + v_posed_homo = torch.cat([v_posed, homogen_coord], dim=2) + v_homo = torch.matmul(T, torch.unsqueeze(v_posed_homo, dim=-1)) + + verts = v_homo[:, :, :3, 0] + + return verts, J_transformed + + +def vertices2joints(J_regressor, vertices): + ''' Calculates the 3D joint locations from the vertices + + Parameters + ---------- + J_regressor : torch.tensor JxV + The regressor array that is used to calculate the joints from the + position of the vertices + vertices : torch.tensor BxVx3 + The tensor of mesh vertices + + Returns + ------- + torch.tensor BxJx3 + The location of the joints + ''' + + return torch.einsum('bik,ji->bjk', [vertices, J_regressor]) + + +def blend_shapes(betas, shape_disps): + ''' Calculates the per vertex displacement due to the blend shapes + + + Parameters + ---------- + betas : torch.tensor Bx(num_betas) + Blend shape coefficients + shape_disps: torch.tensor Vx3x(num_betas) + Blend shapes + + Returns + ------- + torch.tensor BxVx3 + The per-vertex displacement due to shape deformation + ''' + + # Displacement[b, m, k] = sum_{l} betas[b, l] * shape_disps[m, k, l] + # i.e. Multiply each shape displacement by its corresponding beta and + # then sum them. + blend_shape = torch.einsum('bl,mkl->bmk', [betas, shape_disps]) + return blend_shape + + +def batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32): + ''' Calculates the rotation matrices for a batch of rotation vectors + Parameters + ---------- + rot_vecs: torch.tensor Nx3 + array of N axis-angle vectors + Returns + ------- + R: torch.tensor Nx3x3 + The rotation matrices for the given axis-angle parameters + ''' + + batch_size = rot_vecs.shape[0] + device = rot_vecs.device + + angle = torch.norm(rot_vecs + 1e-8, dim=1, keepdim=True) + rot_dir = rot_vecs / angle + + cos = torch.unsqueeze(torch.cos(angle), dim=1) + sin = torch.unsqueeze(torch.sin(angle), dim=1) + + # Bx1 arrays + rx, ry, rz = torch.split(rot_dir, 1, dim=1) + K = torch.zeros((batch_size, 3, 3), dtype=dtype, device=device) + + zeros = torch.zeros((batch_size, 1), dtype=dtype, device=device) + K = torch.cat([zeros, -rz, ry, rz, zeros, -rx, -ry, rx, zeros], dim=1) \ + .view((batch_size, 3, 3)) + + ident = torch.eye(3, dtype=dtype, device=device).unsqueeze(dim=0) + rot_mat = ident + sin * K + (1 - cos) * torch.bmm(K, K) + return rot_mat + + +def transform_mat(R, t): + ''' Creates a batch of transformation matrices + Args: + - R: Bx3x3 array of a batch of rotation matrices + - t: Bx3x1 array of a batch of translation vectors + Returns: + - T: Bx4x4 Transformation matrix + ''' + # No padding left or right, only add an extra row + return torch.cat([F.pad(R, [0, 0, 0, 1]), + F.pad(t, [0, 0, 0, 1], value=1)], dim=2) + + +def batch_rigid_transform(rot_mats, joints, parents, dtype=torch.float32): + """ + Applies a batch of rigid transformations to the joints + + Parameters + ---------- + rot_mats : torch.tensor BxNx3x3 + Tensor of rotation matrices + joints : torch.tensor BxNx3 + Locations of joints + parents : torch.tensor BxN + The kinematic tree of each object + dtype : torch.dtype, optional: + The data type of the created tensors, the default is torch.float32 + + Returns + ------- + posed_joints : torch.tensor BxNx3 + The locations of the joints after applying the pose rotations + rel_transforms : torch.tensor BxNx4x4 + The relative (with respect to the root joint) rigid transformations + for all the joints + """ + + joints = torch.unsqueeze(joints, dim=-1) + + rel_joints = joints.clone() + rel_joints[:, 1:] -= joints[:, parents[1:]] + + # transforms_mat = transform_mat( + # rot_mats.view(-1, 3, 3), + # rel_joints.view(-1, 3, 1)).view(-1, joints.shape[1], 4, 4) + transforms_mat = transform_mat( + rot_mats.view(-1, 3, 3), + rel_joints.reshape(-1, 3, 1)).reshape(-1, joints.shape[1], 4, 4) + + transform_chain = [transforms_mat[:, 0]] + for i in range(1, parents.shape[0]): + # Subtract the joint location at the rest pose + # No need for rotation, since it's identity when at rest + curr_res = torch.matmul(transform_chain[parents[i]], + transforms_mat[:, i]) + transform_chain.append(curr_res) + + transforms = torch.stack(transform_chain, dim=1) + + # The last column of the transformations contains the posed joints + posed_joints = transforms[:, :, :3, 3] + + # The last column of the transformations contains the posed joints + posed_joints = transforms[:, :, :3, 3] + + joints_homogen = F.pad(joints, [0, 0, 0, 1]) + + rel_transforms = transforms - F.pad( + torch.matmul(transforms, joints_homogen), [3, 0, 0, 0, 0, 0, 0, 0]) + + return posed_joints, rel_transforms \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/decalib/models/resnet.py b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/models/resnet.py new file mode 100755 index 0000000..039bab0 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/models/resnet.py @@ -0,0 +1,291 @@ +""" +Author: Soubhik Sanyal +Copyright (c) 2019, Soubhik Sanyal +All rights reserved. +Loads different resnet models +""" +''' + file: Resnet.py + date: 2018_05_02 + author: zhangxiong(1025679612@qq.com) + mark: copied from pytorch source code +''' + +import torch.nn as nn +import torch.nn.functional as F +import torch +from torch.nn.parameter import Parameter +import torch.optim as optim +import numpy as np +import math +import torchvision + +class ResNet(nn.Module): + def __init__(self, block, layers, num_classes=1000): + self.inplanes = 64 + super(ResNet, self).__init__() + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AvgPool2d(7, stride=1) + # self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x1 = self.layer4(x) + + x2 = self.avgpool(x1) + x2 = x2.view(x2.size(0), -1) + # x = self.fc(x) + ## x2: [bz, 2048] for shape + ## x1: [bz, 2048, 7, 7] for texture + return x2 + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + +def copy_parameter_from_resnet(model, resnet_dict): + cur_state_dict = model.state_dict() + # import ipdb; ipdb.set_trace() + for name, param in list(resnet_dict.items())[0:None]: + if name not in cur_state_dict: + print(name, ' not available in reconstructed resnet') + continue + if isinstance(param, Parameter): + param = param.data + try: + cur_state_dict[name].copy_(param) + except: + print(name, ' is inconsistent!') + continue + # print('copy resnet state dict finished!') + # import ipdb; ipdb.set_trace() + +def load_ResNet50Model(): + model = ResNet(Bottleneck, [3, 4, 6, 3]) + copy_parameter_from_resnet(model, torchvision.models.resnet50(pretrained = True).state_dict()) + return model + +def load_ResNet101Model(): + model = ResNet(Bottleneck, [3, 4, 23, 3]) + copy_parameter_from_resnet(model, torchvision.models.resnet101(pretrained = True).state_dict()) + return model + +def load_ResNet152Model(): + model = ResNet(Bottleneck, [3, 8, 36, 3]) + copy_parameter_from_resnet(model, torchvision.models.resnet152(pretrained = True).state_dict()) + return model + +# model.load_state_dict(checkpoint['model_state_dict']) + + +######## Unet + +class DoubleConv(nn.Module): + """(convolution => [BN] => ReLU) * 2""" + + def __init__(self, in_channels, out_channels): + super().__init__() + self.double_conv = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True) + ) + + def forward(self, x): + return self.double_conv(x) + + +class Down(nn.Module): + """Downscaling with maxpool then double conv""" + + def __init__(self, in_channels, out_channels): + super().__init__() + self.maxpool_conv = nn.Sequential( + nn.MaxPool2d(2), + DoubleConv(in_channels, out_channels) + ) + + def forward(self, x): + return self.maxpool_conv(x) + + +class Up(nn.Module): + """Upscaling then double conv""" + + def __init__(self, in_channels, out_channels, bilinear=True): + super().__init__() + + # if bilinear, use the normal convolutions to reduce the number of channels + if bilinear: + self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) + else: + self.up = nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2) + + self.conv = DoubleConv(in_channels, out_channels) + + def forward(self, x1, x2): + x1 = self.up(x1) + # input is CHW + diffY = x2.size()[2] - x1.size()[2] + diffX = x2.size()[3] - x1.size()[3] + + x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, + diffY // 2, diffY - diffY // 2]) + # if you have padding issues, see + # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a + # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd + x = torch.cat([x2, x1], dim=1) + return self.conv(x) + + +class OutConv(nn.Module): + def __init__(self, in_channels, out_channels): + super(OutConv, self).__init__() + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1) + + def forward(self, x): + return self.conv(x) + +class UNet(nn.Module): + def __init__(self, n_channels, n_classes, bilinear=True): + super(UNet, self).__init__() + self.n_channels = n_channels + self.n_classes = n_classes + self.bilinear = bilinear + + self.inc = DoubleConv(n_channels, 64) + self.down1 = Down(64, 128) + self.down2 = Down(128, 256) + self.down3 = Down(256, 512) + self.down4 = Down(512, 512) + self.up1 = Up(1024, 256, bilinear) + self.up2 = Up(512, 128, bilinear) + self.up3 = Up(256, 64, bilinear) + self.up4 = Up(128, 64, bilinear) + self.outc = OutConv(64, n_classes) + + def forward(self, x): + x1 = self.inc(x) + x2 = self.down1(x1) + x3 = self.down2(x2) + x4 = self.down3(x3) + x5 = self.down4(x4) + x = self.up1(x5, x4) + x = self.up2(x, x3) + x = self.up3(x, x2) + x = self.up4(x, x1) + x = F.normalize(x) + return x \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/decalib/utils/__init__.py b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/decalib/utils/config.py b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/utils/config.py new file mode 100644 index 0000000..e305b21 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/utils/config.py @@ -0,0 +1,80 @@ +''' +Default config for DECA +''' +from yacs.config import CfgNode as CN +import argparse +import yaml +import os +import torch + +cfg = CN() + +abs_deca_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) +cfg.deca_dir = abs_deca_dir +cfg.device = "cuda" if torch.cuda.is_available() else "cpu" +cfg.device_id = '0' + +cfg.pretrained_modelpath = os.path.join(cfg.deca_dir, 'data', 'deca_model.tar') + +# ---------------------------------------------------------------------------- # +# Options for Face model +# ---------------------------------------------------------------------------- # +cfg.model = CN() +cfg.model.topology_path = os.path.join(cfg.deca_dir, 'data', 'head_template.obj') +# texture data original from http://files.is.tue.mpg.de/tbolkart/FLAME/FLAME_texture_data.zip +cfg.model.dense_template_path = os.path.join(cfg.deca_dir, 'data', 'texture_data_256.npy') +cfg.model.fixed_displacement_path = os.path.join(cfg.deca_dir, 'data', 'fixed_displacement_256.npy') +cfg.model.flame_model_path = os.path.join(cfg.deca_dir, 'data', 'generic_model.pkl') +cfg.model.flame_lmk_embedding_path = os.path.join(cfg.deca_dir, 'data', 'landmark_embedding.npy') +cfg.model.face_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_mask.png') +cfg.model.face_eye_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_eye_mask.png') +cfg.model.mean_tex_path = os.path.join(cfg.deca_dir, 'data', 'mean_texture.jpg') +cfg.model.tex_path = os.path.join(cfg.deca_dir, 'data', 'FLAME_albedo_from_BFM.npz') +cfg.model.tex_type = 'BFM' # BFM, FLAME, albedoMM +cfg.model.uv_size = 256 +cfg.model.param_list = ['shape', 'tex', 'exp', 'pose', 'cam', 'light'] +cfg.model.n_shape = 100 +cfg.model.n_tex = 50 +cfg.model.n_exp = 50 +cfg.model.n_cam = 3 +cfg.model.n_pose = 6 +cfg.model.n_light = 27 +cfg.model.use_tex = False +cfg.model.jaw_type = 'aa' # default use axis angle, another option: euler + +## details +cfg.model.n_detail = 128 +cfg.model.max_z = 0.01 + +# ---------------------------------------------------------------------------- # +# Options for Dataset +# ---------------------------------------------------------------------------- # +cfg.dataset = CN() +cfg.dataset.batch_size = 24 +cfg.dataset.num_workers = 2 +cfg.dataset.image_size = 224 + +def get_cfg_defaults(): + """Get a yacs CfgNode object with default values for my_project.""" + # Return a clone so that the defaults will not be altered + # This is for the "local variable" use pattern + return cfg.clone() + +def update_cfg(cfg, cfg_file): + cfg.merge_from_file(cfg_file) + return cfg.clone() + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--cfg', type=str, help='cfg file path') + + args = parser.parse_args() + print(args, end='\n\n') + + cfg = get_cfg_defaults() + if args.cfg is not None: + cfg_file = args.cfg + cfg = update_cfg(cfg, args.cfg) + cfg.cfg_file = cfg_file + + return cfg diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/decalib/utils/renderer.py b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/utils/renderer.py new file mode 100755 index 0000000..7da0b83 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/utils/renderer.py @@ -0,0 +1,338 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from pytorch3d.structures import Meshes +from pytorch3d.io import load_obj +from pytorch3d.renderer.mesh import rasterize_meshes +from . import util +# from .rasterizer.standard_rasterize_cuda import standard_rasterize + +class Pytorch3dRasterizer(nn.Module): + """ Borrowed from https://github.com/facebookresearch/pytorch3d + Notice: + x,y,z are in image space, normalized + can only render squared image now + """ + + def __init__(self, image_size=224): + """ + use fixed raster_settings for rendering faces + """ + super().__init__() + raster_settings = { + 'image_size': image_size, + 'blur_radius': 0.0, + 'faces_per_pixel': 1, + 'bin_size': None, + 'max_faces_per_bin': None, + 'perspective_correct': False, + } + raster_settings = util.dict2obj(raster_settings) + self.raster_settings = raster_settings + + def forward(self, vertices, faces, attributes=None): + fixed_vertices = vertices.clone() + fixed_vertices[...,:2] = -fixed_vertices[...,:2] + meshes_screen = Meshes(verts=fixed_vertices.float(), faces=faces.long()) + raster_settings = self.raster_settings + pix_to_face, zbuf, bary_coords, dists = rasterize_meshes( + meshes_screen, + image_size=raster_settings.image_size, + blur_radius=raster_settings.blur_radius, + faces_per_pixel=raster_settings.faces_per_pixel, + bin_size=raster_settings.bin_size, + max_faces_per_bin=raster_settings.max_faces_per_bin, + perspective_correct=raster_settings.perspective_correct, + ) + vismask = (pix_to_face > -1).float() + D = attributes.shape[-1] + attributes = attributes.clone(); attributes = attributes.view(attributes.shape[0]*attributes.shape[1], 3, attributes.shape[-1]) + N, H, W, K, _ = bary_coords.shape + mask = pix_to_face == -1 + pix_to_face = pix_to_face.clone() + pix_to_face[mask] = 0 + idx = pix_to_face.view(N * H * W * K, 1, 1).expand(N * H * W * K, 3, D) + pixel_face_vals = attributes.gather(0, idx).view(N, H, W, K, 3, D) + pixel_vals = (bary_coords[..., None] * pixel_face_vals).sum(dim=-2) + pixel_vals[mask] = 0 # Replace masked values in output. + pixel_vals = pixel_vals[:,:,:,0].permute(0,3,1,2) + pixel_vals = torch.cat([pixel_vals, vismask[:,:,:,0][:,None,:,:]], dim=1) + return pixel_vals + +class SRenderY(nn.Module): + def __init__(self, image_size, obj_filename, uv_size=256, rasterizer_type='pytorch3d'): + super(SRenderY, self).__init__() + self.image_size = image_size + self.uv_size = uv_size + + verts, faces, aux = load_obj(obj_filename) + uvcoords = aux.verts_uvs[None, ...] # (N, V, 2) + uvfaces = faces.textures_idx[None, ...] # (N, F, 3) + faces = faces.verts_idx[None,...] + + if rasterizer_type == 'pytorch3d': + self.rasterizer = Pytorch3dRasterizer(image_size) + self.uv_rasterizer = Pytorch3dRasterizer(uv_size) + + # faces + dense_triangles = util.generate_triangles(uv_size, uv_size) + self.register_buffer('dense_faces', torch.from_numpy(dense_triangles).long()[None,:,:]) + self.register_buffer('faces', faces) + self.register_buffer('raw_uvcoords', uvcoords) + + # uv coords + uvcoords = torch.cat([uvcoords, uvcoords[:,:,0:1]*0.+1.], -1) #[bz, ntv, 3] + uvcoords = uvcoords*2 - 1; uvcoords[...,1] = -uvcoords[...,1] + face_uvcoords = util.face_vertices(uvcoords, uvfaces) + self.register_buffer('uvcoords', uvcoords) + self.register_buffer('uvfaces', uvfaces) + self.register_buffer('face_uvcoords', face_uvcoords) + + # shape colors, for rendering shape overlay + colors = torch.tensor([180, 180, 180])[None, None, :].repeat(1, faces.max()+1, 1).float()/255. + face_colors = util.face_vertices(colors, faces) + self.register_buffer('face_colors', face_colors) + + ## SH factors for lighting + pi = np.pi + constant_factor = torch.tensor([1/np.sqrt(4*pi), ((2*pi)/3)*(np.sqrt(3/(4*pi))), ((2*pi)/3)*(np.sqrt(3/(4*pi))),\ + ((2*pi)/3)*(np.sqrt(3/(4*pi))), (pi/4)*(3)*(np.sqrt(5/(12*pi))), (pi/4)*(3)*(np.sqrt(5/(12*pi))),\ + (pi/4)*(3)*(np.sqrt(5/(12*pi))), (pi/4)*(3/2)*(np.sqrt(5/(12*pi))), (pi/4)*(1/2)*(np.sqrt(5/(4*pi)))]).float() + self.register_buffer('constant_factor', constant_factor) + + def forward(self, vertices, transformed_vertices, albedos, lights=None, light_type='point'): + ''' + -- Texture Rendering + vertices: [batch_size, V, 3], vertices in world space, for calculating normals, then shading + transformed_vertices: [batch_size, V, 3], range:normalized to [-1,1], projected vertices in image space (that is aligned to the iamge pixel), for rasterization + albedos: [batch_size, 3, h, w], uv map + lights: + spherical homarnic: [N, 9(shcoeff), 3(rgb)] + points/directional lighting: [N, n_lights, 6(xyzrgb)] + light_type: + point or directional + ''' + batch_size = vertices.shape[0] + ## rasterizer near 0 far 100. move mesh so minz larger than 0 + transformed_vertices[:,:,2] = transformed_vertices[:,:,2] + 10 + # attributes + face_vertices = util.face_vertices(vertices, self.faces.expand(batch_size, -1, -1)) + normals = util.vertex_normals(vertices, self.faces.expand(batch_size, -1, -1)); face_normals = util.face_vertices(normals, self.faces.expand(batch_size, -1, -1)) + transformed_normals = util.vertex_normals(transformed_vertices, self.faces.expand(batch_size, -1, -1)); transformed_face_normals = util.face_vertices(transformed_normals, self.faces.expand(batch_size, -1, -1)) + + attributes = torch.cat([self.face_uvcoords.expand(batch_size, -1, -1, -1), + transformed_face_normals.detach(), + face_vertices.detach(), + face_normals], + -1) + # rasterize + rendering = self.rasterizer(transformed_vertices, self.faces.expand(batch_size, -1, -1), attributes) + + #### + # vis mask + alpha_images = rendering[:, -1, :, :][:, None, :, :].detach() + + # albedo + #pdb.set_trace() + uvcoords_images = rendering[:, :3, :, :]; grid = (uvcoords_images).permute(0, 2, 3, 1)[:, :, :, :2] + albedo_images = F.grid_sample(albedos, grid, align_corners=False) + + # visible mask for pixels with positive normal direction + transformed_normal_map = rendering[:, 3:6, :, :].detach() + pos_mask = (transformed_normal_map[:, 2:, :, :] < -0.05).float() + + # shading + normal_images = rendering[:, 9:12, :, :] + if lights is not None: + if lights.shape[1] == 9: + shading_images = self.add_SHlight(normal_images, lights) + else: + if light_type=='point': + vertice_images = rendering[:, 6:9, :, :].detach() + shading = self.add_pointlight(vertice_images.permute(0,2,3,1).reshape([batch_size, -1, 3]), normal_images.permute(0,2,3,1).reshape([batch_size, -1, 3]), lights) + shading_images = shading.reshape([batch_size, albedo_images.shape[2], albedo_images.shape[3], 3]).permute(0,3,1,2) + else: + shading = self.add_directionlight(normal_images.permute(0,2,3,1).reshape([batch_size, -1, 3]), lights) + shading_images = shading.reshape([batch_size, albedo_images.shape[2], albedo_images.shape[3], 3]).permute(0,3,1,2) + images = albedo_images*shading_images + else: + images = albedo_images + shading_images = images.detach()*0. + + outputs = { + 'images': images*alpha_images, + 'albedo_images': albedo_images*alpha_images, + 'alpha_images': alpha_images, + 'pos_mask': pos_mask, + 'shading_images': shading_images, + 'grid': grid, + 'normals': normals, + 'normal_images': normal_images*alpha_images, + 'transformed_normals': transformed_normals, + } + + return outputs + + def add_SHlight(self, normal_images, sh_coeff): + ''' + sh_coeff: [bz, 9, 3] + ''' + N = normal_images + sh = torch.stack([ + N[:,0]*0.+1., N[:,0], N[:,1], \ + N[:,2], N[:,0]*N[:,1], N[:,0]*N[:,2], + N[:,1]*N[:,2], N[:,0]**2 - N[:,1]**2, 3*(N[:,2]**2) - 1 + ], + 1) # [bz, 9, h, w] + sh = sh*self.constant_factor[None,:,None,None] + shading = torch.sum(sh_coeff[:,:,:,None,None]*sh[:,:,None,:,:], 1) # [bz, 9, 3, h, w] + return shading + + def add_pointlight(self, vertices, normals, lights): + ''' + vertices: [bz, nv, 3] + lights: [bz, nlight, 6] + returns: + shading: [bz, nv, 3] + ''' + light_positions = lights[:,:,:3]; light_intensities = lights[:,:,3:] + directions_to_lights = F.normalize(light_positions[:,:,None,:] - vertices[:,None,:,:], dim=3) + # normals_dot_lights = torch.clamp((normals[:,None,:,:]*directions_to_lights).sum(dim=3), 0., 1.) + normals_dot_lights = (normals[:,None,:,:]*directions_to_lights).sum(dim=3) + shading = normals_dot_lights[:,:,:,None]*light_intensities[:,:,None,:] + return shading.mean(1) + + def add_directionlight(self, normals, lights): + ''' + normals: [bz, nv, 3] + lights: [bz, nlight, 6] + returns: + shading: [bz, nv, 3] + ''' + light_direction = lights[:,:,:3]; light_intensities = lights[:,:,3:] + directions_to_lights = F.normalize(light_direction[:,:,None,:].expand(-1,-1,normals.shape[1],-1), dim=3) + # normals_dot_lights = torch.clamp((normals[:,None,:,:]*directions_to_lights).sum(dim=3), 0., 1.) + # normals_dot_lights = (normals[:,None,:,:]*directions_to_lights).sum(dim=3) + normals_dot_lights = torch.clamp((normals[:,None,:,:]*directions_to_lights).sum(dim=3), 0., 1.) + shading = normals_dot_lights[:,:,:,None]*light_intensities[:,:,None,:] + return shading.mean(1) + + def render_shape(self, vertices, transformed_vertices, images=None, detail_normal_images=None, lights=None): + ''' + -- rendering shape with detail normal map + ''' + batch_size = vertices.shape[0] + # set lighting + if lights is None: + light_positions = torch.tensor( + [ + [-1,1,1], + [1,1,1], + [-1,-1,1], + [1,-1,1], + [0,0,1] + ] + )[None,:,:].expand(batch_size, -1, -1).float() + light_intensities = torch.ones_like(light_positions).float()*1.7 + lights = torch.cat((light_positions, light_intensities), 2).to(vertices.device) + transformed_vertices[:,:,2] = transformed_vertices[:,:,2] + 10 + + # Attributes + face_vertices = util.face_vertices(vertices, self.faces.expand(batch_size, -1, -1)) + normals = util.vertex_normals(vertices, self.faces.expand(batch_size, -1, -1)); face_normals = util.face_vertices(normals, self.faces.expand(batch_size, -1, -1)) + transformed_normals = util.vertex_normals(transformed_vertices, self.faces.expand(batch_size, -1, -1)); transformed_face_normals = util.face_vertices(transformed_normals, self.faces.expand(batch_size, -1, -1)) + attributes = torch.cat([self.face_colors.expand(batch_size, -1, -1, -1), + transformed_face_normals.detach(), + face_vertices.detach(), + face_normals], + -1) + # rasterize + rendering = self.rasterizer(transformed_vertices, self.faces.expand(batch_size, -1, -1), attributes) + + #### + alpha_images = rendering[:, -1, :, :][:, None, :, :].detach() + + # albedo + albedo_images = rendering[:, :3, :, :] + # mask + transformed_normal_map = rendering[:, 3:6, :, :].detach() + pos_mask = (transformed_normal_map[:, 2:, :, :] < 0.15).float() + + # shading + normal_images = rendering[:, 9:12, :, :].detach() + vertice_images = rendering[:, 6:9, :, :].detach() + if detail_normal_images is not None: + normal_images = detail_normal_images + + shading = self.add_directionlight(normal_images.permute(0,2,3,1).reshape([batch_size, -1, 3]), lights) + shading_images = shading.reshape([batch_size, albedo_images.shape[2], albedo_images.shape[3], 3]).permute(0,3,1,2).contiguous() + shaded_images = albedo_images*shading_images + + alpha_images = alpha_images*pos_mask + if images is None: + shape_images = shaded_images*alpha_images + torch.zeros_like(shaded_images).to(vertices.device)*(1-alpha_images) + else: + shape_images = shaded_images*alpha_images + images*(1-alpha_images) + return shape_images + + def render_depth(self, transformed_vertices): + ''' + -- rendering depth + ''' + batch_size = transformed_vertices.shape[0] + + transformed_vertices[:,:,2] = transformed_vertices[:,:,2] - transformed_vertices[:,:,2].min() + z = -transformed_vertices[:,:,2:].repeat(1,1,3).clone() + z = z-z.min() + z = z/z.max() + # Attributes + attributes = util.face_vertices(z, self.faces.expand(batch_size, -1, -1)) + # rasterize + transformed_vertices[:,:,2] = transformed_vertices[:,:,2] + 10 + rendering = self.rasterizer(transformed_vertices, self.faces.expand(batch_size, -1, -1), attributes) + + #### + alpha_images = rendering[:, -1, :, :][:, None, :, :].detach() + depth_images = rendering[:, :1, :, :] + return depth_images + + def render_normal(self, transformed_vertices, normals): + ''' + -- rendering normal + ''' + batch_size = normals.shape[0] + + # Attributes + attributes = util.face_vertices(normals, self.faces.expand(batch_size, -1, -1)) + # rasterize + rendering = self.rasterizer(transformed_vertices, self.faces.expand(batch_size, -1, -1), attributes) + #### + alpha_images = rendering[:, -1, :, :][:, None, :, :].detach() + normal_images = rendering[:, :3, :, :] + return normal_images + + def world2uv(self, vertices): + ''' + warp vertices from world space to uv space + vertices: [bz, V, 3] + uv_vertices: [bz, 3, h, w] + ''' + batch_size = vertices.shape[0] + face_vertices = util.face_vertices(vertices, self.faces.expand(batch_size, -1, -1)) + uv_vertices = self.uv_rasterizer(self.uvcoords.expand(batch_size, -1, -1), self.uvfaces.expand(batch_size, -1, -1), face_vertices)[:, :3] + return uv_vertices diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/decalib/utils/rotation_converter.py b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/utils/rotation_converter.py new file mode 100644 index 0000000..89756d9 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/utils/rotation_converter.py @@ -0,0 +1,381 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import torch +import pdb + +''' Rotation Converter +Representations: + euler angle(3), angle axis(3), rotation matrix(3x3), quaternion(4), continous repre +Ref: + https://kornia.readthedocs.io/en/v0.1.2/_modules/torchgeometry/core/conversions.html# + smplx/lbs +''' + +pi = torch.Tensor([3.14159265358979323846]) +def rad2deg(tensor): + """Function that converts angles from radians to degrees. + + See :class:`~torchgeometry.RadToDeg` for details. + + Args: + tensor (Tensor): Tensor of arbitrary shape. + + Returns: + Tensor: Tensor with same shape as input. + + Example: + >>> input = tgm.pi * torch.rand(1, 3, 3) + >>> output = tgm.rad2deg(input) + """ + if not torch.is_tensor(tensor): + raise TypeError("Input type is not a torch.Tensor. Got {}" + .format(type(tensor))) + + return 180. * tensor / pi.to(tensor.device).type(tensor.dtype) + +def deg2rad(tensor): + """Function that converts angles from degrees to radians. + + See :class:`~torchgeometry.DegToRad` for details. + + Args: + tensor (Tensor): Tensor of arbitrary shape. + + Returns: + Tensor: Tensor with same shape as input. + + Examples:: + + >>> input = 360. * torch.rand(1, 3, 3) + >>> output = tgm.deg2rad(input) + """ + if not torch.is_tensor(tensor): + raise TypeError("Input type is not a torch.Tensor. Got {}" + .format(type(tensor))) + + return tensor * pi.to(tensor.device).type(tensor.dtype) / 180. + +######### to quaternion +def euler_to_quaternion(r): + x = r[..., 0] + y = r[..., 1] + z = r[..., 2] + + z = z/2.0 + y = y/2.0 + x = x/2.0 + cz = torch.cos(z) + sz = torch.sin(z) + cy = torch.cos(y) + sy = torch.sin(y) + cx = torch.cos(x) + sx = torch.sin(x) + quaternion = torch.zeros_like(r.repeat(1,2))[..., :4].to(r.device) + quaternion[..., 0] += cx*cy*cz - sx*sy*sz + quaternion[..., 1] += cx*sy*sz + cy*cz*sx + quaternion[..., 2] += cx*cz*sy - sx*cy*sz + quaternion[..., 3] += cx*cy*sz + sx*cz*sy + return quaternion + +def rotation_matrix_to_quaternion(rotation_matrix, eps=1e-6): + """Convert 3x4 rotation matrix to 4d quaternion vector + + This algorithm is based on algorithm described in + https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py#L201 + + Args: + rotation_matrix (Tensor): the rotation matrix to convert. + + Return: + Tensor: the rotation in quaternion + + Shape: + - Input: :math:`(N, 3, 4)` + - Output: :math:`(N, 4)` + + Example: + >>> input = torch.rand(4, 3, 4) # Nx3x4 + >>> output = tgm.rotation_matrix_to_quaternion(input) # Nx4 + """ + if not torch.is_tensor(rotation_matrix): + raise TypeError("Input type is not a torch.Tensor. Got {}".format( + type(rotation_matrix))) + + if len(rotation_matrix.shape) > 3: + raise ValueError( + "Input size must be a three dimensional tensor. Got {}".format( + rotation_matrix.shape)) + # if not rotation_matrix.shape[-2:] == (3, 4): + # raise ValueError( + # "Input size must be a N x 3 x 4 tensor. Got {}".format( + # rotation_matrix.shape)) + + rmat_t = torch.transpose(rotation_matrix, 1, 2) + + mask_d2 = rmat_t[:, 2, 2] < eps + + mask_d0_d1 = rmat_t[:, 0, 0] > rmat_t[:, 1, 1] + mask_d0_nd1 = rmat_t[:, 0, 0] < -rmat_t[:, 1, 1] + + t0 = 1 + rmat_t[:, 0, 0] - rmat_t[:, 1, 1] - rmat_t[:, 2, 2] + q0 = torch.stack([rmat_t[:, 1, 2] - rmat_t[:, 2, 1], + t0, rmat_t[:, 0, 1] + rmat_t[:, 1, 0], + rmat_t[:, 2, 0] + rmat_t[:, 0, 2]], -1) + t0_rep = t0.repeat(4, 1).t() + + t1 = 1 - rmat_t[:, 0, 0] + rmat_t[:, 1, 1] - rmat_t[:, 2, 2] + q1 = torch.stack([rmat_t[:, 2, 0] - rmat_t[:, 0, 2], + rmat_t[:, 0, 1] + rmat_t[:, 1, 0], + t1, rmat_t[:, 1, 2] + rmat_t[:, 2, 1]], -1) + t1_rep = t1.repeat(4, 1).t() + + t2 = 1 - rmat_t[:, 0, 0] - rmat_t[:, 1, 1] + rmat_t[:, 2, 2] + q2 = torch.stack([rmat_t[:, 0, 1] - rmat_t[:, 1, 0], + rmat_t[:, 2, 0] + rmat_t[:, 0, 2], + rmat_t[:, 1, 2] + rmat_t[:, 2, 1], t2], -1) + t2_rep = t2.repeat(4, 1).t() + + t3 = 1 + rmat_t[:, 0, 0] + rmat_t[:, 1, 1] + rmat_t[:, 2, 2] + q3 = torch.stack([t3, rmat_t[:, 1, 2] - rmat_t[:, 2, 1], + rmat_t[:, 2, 0] - rmat_t[:, 0, 2], + rmat_t[:, 0, 1] - rmat_t[:, 1, 0]], -1) + t3_rep = t3.repeat(4, 1).t() + + mask_c0 = mask_d2 * mask_d0_d1.float() + mask_c1 = mask_d2 * (1 - mask_d0_d1.float()) + mask_c2 = (1 - mask_d2.float()) * mask_d0_nd1 + mask_c3 = (1 - mask_d2.float()) * (1 - mask_d0_nd1.float()) + mask_c0 = mask_c0.view(-1, 1).type_as(q0) + mask_c1 = mask_c1.view(-1, 1).type_as(q1) + mask_c2 = mask_c2.view(-1, 1).type_as(q2) + mask_c3 = mask_c3.view(-1, 1).type_as(q3) + + q = q0 * mask_c0 + q1 * mask_c1 + q2 * mask_c2 + q3 * mask_c3 + q /= torch.sqrt(t0_rep * mask_c0 + t1_rep * mask_c1 + # noqa + t2_rep * mask_c2 + t3_rep * mask_c3) # noqa + q *= 0.5 + return q + +# def angle_axis_to_quaternion(theta): +# batch_size = theta.shape[0] +# l1norm = torch.norm(theta + 1e-8, p=2, dim=1) +# angle = torch.unsqueeze(l1norm, -1) +# normalized = torch.div(theta, angle) +# angle = angle * 0.5 +# v_cos = torch.cos(angle) +# v_sin = torch.sin(angle) +# quat = torch.cat([v_cos, v_sin * normalized], dim=1) +# return quat + +def angle_axis_to_quaternion(angle_axis: torch.Tensor) -> torch.Tensor: + """Convert an angle axis to a quaternion. + + Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h + + Args: + angle_axis (torch.Tensor): tensor with angle axis. + + Return: + torch.Tensor: tensor with quaternion. + + Shape: + - Input: :math:`(*, 3)` where `*` means, any number of dimensions + - Output: :math:`(*, 4)` + + Example: + >>> angle_axis = torch.rand(2, 4) # Nx4 + >>> quaternion = tgm.angle_axis_to_quaternion(angle_axis) # Nx3 + """ + if not torch.is_tensor(angle_axis): + raise TypeError("Input type is not a torch.Tensor. Got {}".format( + type(angle_axis))) + + if not angle_axis.shape[-1] == 3: + raise ValueError("Input must be a tensor of shape Nx3 or 3. Got {}" + .format(angle_axis.shape)) + # unpack input and compute conversion + a0: torch.Tensor = angle_axis[..., 0:1] + a1: torch.Tensor = angle_axis[..., 1:2] + a2: torch.Tensor = angle_axis[..., 2:3] + theta_squared: torch.Tensor = a0 * a0 + a1 * a1 + a2 * a2 + + theta: torch.Tensor = torch.sqrt(theta_squared) + half_theta: torch.Tensor = theta * 0.5 + + mask: torch.Tensor = theta_squared > 0.0 + ones: torch.Tensor = torch.ones_like(half_theta) + + k_neg: torch.Tensor = 0.5 * ones + k_pos: torch.Tensor = torch.sin(half_theta) / theta + k: torch.Tensor = torch.where(mask, k_pos, k_neg) + w: torch.Tensor = torch.where(mask, torch.cos(half_theta), ones) + + quaternion: torch.Tensor = torch.zeros_like(angle_axis) + quaternion[..., 0:1] += a0 * k + quaternion[..., 1:2] += a1 * k + quaternion[..., 2:3] += a2 * k + return torch.cat([w, quaternion], dim=-1) + +#### quaternion to +def quaternion_to_rotation_matrix(quat): + """Convert quaternion coefficients to rotation matrix. + Args: + quat: size = [B, 4] 4 <===>(w, x, y, z) + Returns: + Rotation matrix corresponding to the quaternion -- size = [B, 3, 3] + """ + norm_quat = quat + norm_quat = norm_quat / norm_quat.norm(p=2, dim=1, keepdim=True) + w, x, y, z = norm_quat[:, 0], norm_quat[:, 1], norm_quat[:, 2], norm_quat[:, 3] + + B = quat.size(0) + + w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2) + wx, wy, wz = w * x, w * y, w * z + xy, xz, yz = x * y, x * z, y * z + + rotMat = torch.stack([w2 + x2 - y2 - z2, 2 * xy - 2 * wz, 2 * wy + 2 * xz, + 2 * wz + 2 * xy, w2 - x2 + y2 - z2, 2 * yz - 2 * wx, + 2 * xz - 2 * wy, 2 * wx + 2 * yz, w2 - x2 - y2 + z2], dim=1).view(B, 3, 3) + return rotMat + +def quaternion_to_angle_axis(quaternion: torch.Tensor): + """Convert quaternion vector to angle axis of rotation. TODO: CORRECT + + Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h + + Args: + quaternion (torch.Tensor): tensor with quaternions. + + Return: + torch.Tensor: tensor with angle axis of rotation. + + Shape: + - Input: :math:`(*, 4)` where `*` means, any number of dimensions + - Output: :math:`(*, 3)` + + Example: + >>> quaternion = torch.rand(2, 4) # Nx4 + >>> angle_axis = tgm.quaternion_to_angle_axis(quaternion) # Nx3 + """ + if not torch.is_tensor(quaternion): + raise TypeError("Input type is not a torch.Tensor. Got {}".format( + type(quaternion))) + + if not quaternion.shape[-1] == 4: + raise ValueError("Input must be a tensor of shape Nx4 or 4. Got {}" + .format(quaternion.shape)) + # unpack input and compute conversion + q1: torch.Tensor = quaternion[..., 1] + q2: torch.Tensor = quaternion[..., 2] + q3: torch.Tensor = quaternion[..., 3] + sin_squared_theta: torch.Tensor = q1 * q1 + q2 * q2 + q3 * q3 + + sin_theta: torch.Tensor = torch.sqrt(sin_squared_theta) + cos_theta: torch.Tensor = quaternion[..., 0] + two_theta: torch.Tensor = 2.0 * torch.where( + cos_theta < 0.0, + torch.atan2(-sin_theta, -cos_theta), + torch.atan2(sin_theta, cos_theta)) + + k_pos: torch.Tensor = two_theta / sin_theta + k_neg: torch.Tensor = 2.0 * torch.ones_like(sin_theta).to(quaternion.device) + k: torch.Tensor = torch.where(sin_squared_theta > 0.0, k_pos, k_neg) + + angle_axis: torch.Tensor = torch.zeros_like(quaternion).to(quaternion.device)[..., :3] + angle_axis[..., 0] += q1 * k + angle_axis[..., 1] += q2 * k + angle_axis[..., 2] += q3 * k + return angle_axis + +#### batch converter +def batch_euler2axis(r): + return quaternion_to_angle_axis(euler_to_quaternion(r)) + +def batch_euler2matrix(r): + return quaternion_to_rotation_matrix(euler_to_quaternion(r)) + +def batch_matrix2euler(rot_mats): + # Calculates rotation matrix to euler angles + # Careful for extreme cases of eular angles like [0.0, pi, 0.0] + ### only y? + # TODO: + sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] + + rot_mats[:, 1, 0] * rot_mats[:, 1, 0]) + return torch.atan2(-rot_mats[:, 2, 0], sy) + +def batch_matrix2axis(rot_mats): + return quaternion_to_angle_axis(rotation_matrix_to_quaternion(rot_mats)) + +def batch_axis2matrix(theta): + + # angle axis to rotation matrix + # theta N x 3 + # return quat2mat(quat) + # batch_rodrigues + return quaternion_to_rotation_matrix(angle_axis_to_quaternion(theta)) + +def batch_axis2euler(theta): + return batch_matrix2euler(batch_axis2matrix(theta)) + +def batch_axis2euler(r): + return rot_mat_to_euler(batch_rodrigues(r)) + + +def batch_orth_proj(X, camera): + ''' + X is N x num_pquaternion_to_angle_axisoints x 3 + ''' + pdb.set_trace() + camera = camera.clone().view(-1, 1, 3) + X_trans = X[:, :, :2] + camera[:, :, 1:] + X_trans = torch.cat([X_trans, X[:,:,2:]], 2) + Xn = (camera[:, :, 0:1] * X_trans) + return Xn + +def batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32): + ''' same as batch_matrix2axis + Calculates the rotation matrices for a batch of rotation vectors + Parameters + ---------- + rot_vecs: torch.tensor Nx3 + array of N axis-angle vectors + Returns + ------- + R: torch.tensor Nx3x3 + The rotation matrices for the given axis-angle parameters + ''' + + batch_size = rot_vecs.shape[0] + device = rot_vecs.device + + angle = torch.norm(rot_vecs + 1e-8, dim=1, keepdim=True) + rot_dir = rot_vecs / angle + + cos = torch.unsqueeze(torch.cos(angle), dim=1) + sin = torch.unsqueeze(torch.sin(angle), dim=1) + + # Bx1 arrays + rx, ry, rz = torch.split(rot_dir, 1, dim=1) + K = torch.zeros((batch_size, 3, 3), dtype=dtype, device=device) + + zeros = torch.zeros((batch_size, 1), dtype=dtype, device=device) + K = torch.cat([zeros, -rz, ry, rz, zeros, -rx, -ry, rx, zeros], dim=1) \ + .view((batch_size, 3, 3)) + + ident = torch.eye(3, dtype=dtype, device=device).unsqueeze(dim=0) + rot_mat = ident + sin * K + (1 - cos) * torch.bmm(K, K) + return rot_mat diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/decalib/utils/util.py b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/utils/util.py new file mode 100755 index 0000000..5657fd4 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/decalib/utils/util.py @@ -0,0 +1,601 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import numpy as np +import torch +import torch.nn.functional as F +import math +from collections import OrderedDict +import os +from scipy.ndimage import morphology +from skimage.io import imsave +import cv2 + +def upsample_mesh(vertices, normals, faces, displacement_map, texture_map, dense_template): + ''' upsampling coarse mesh (with displacment map) + vertices: vertices of coarse mesh, [nv, 3] + normals: vertex normals, [nv, 3] + faces: faces of coarse mesh, [nf, 3] + texture_map: texture map, [256, 256, 3] + displacement_map: displacment map, [256, 256] + dense_template: + Returns: + dense_vertices: upsampled vertices with details, [number of dense vertices, 3] + dense_colors: vertex color, [number of dense vertices, 3] + dense_faces: [number of dense faces, 3] + ''' + img_size = dense_template['img_size'] + dense_faces = dense_template['f'] + x_coords = dense_template['x_coords'] + y_coords = dense_template['y_coords'] + valid_pixel_ids = dense_template['valid_pixel_ids'] + valid_pixel_3d_faces = dense_template['valid_pixel_3d_faces'] + valid_pixel_b_coords = dense_template['valid_pixel_b_coords'] + + pixel_3d_points = vertices[valid_pixel_3d_faces[:, 0], :] * valid_pixel_b_coords[:, 0][:, np.newaxis] + \ + vertices[valid_pixel_3d_faces[:, 1], :] * valid_pixel_b_coords[:, 1][:, np.newaxis] + \ + vertices[valid_pixel_3d_faces[:, 2], :] * valid_pixel_b_coords[:, 2][:, np.newaxis] + vertex_normals = normals + pixel_3d_normals = vertex_normals[valid_pixel_3d_faces[:, 0], :] * valid_pixel_b_coords[:, 0][:, np.newaxis] + \ + vertex_normals[valid_pixel_3d_faces[:, 1], :] * valid_pixel_b_coords[:, 1][:, np.newaxis] + \ + vertex_normals[valid_pixel_3d_faces[:, 2], :] * valid_pixel_b_coords[:, 2][:, np.newaxis] + pixel_3d_normals = pixel_3d_normals / np.linalg.norm(pixel_3d_normals, axis=-1)[:, np.newaxis] + displacements = displacement_map[y_coords[valid_pixel_ids].astype(int), x_coords[valid_pixel_ids].astype(int)] + dense_colors = texture_map[y_coords[valid_pixel_ids].astype(int), x_coords[valid_pixel_ids].astype(int)] + offsets = np.einsum('i,ij->ij', displacements, pixel_3d_normals) + dense_vertices = pixel_3d_points + offsets + return dense_vertices, dense_colors, dense_faces + +# borrowed from https://github.com/YadiraF/PRNet/blob/master/utils/write.py +def write_obj(obj_name, + vertices, + faces, + colors=None, + texture=None, + uvcoords=None, + uvfaces=None, + inverse_face_order=False, + normal_map=None, + ): + ''' Save 3D face model with texture. + Ref: https://github.com/patrikhuber/eos/blob/bd00155ebae4b1a13b08bf5a991694d682abbada/include/eos/core/Mesh.hpp + Args: + obj_name: str + vertices: shape = (nver, 3) + colors: shape = (nver, 3) + faces: shape = (ntri, 3) + texture: shape = (uv_size, uv_size, 3) + uvcoords: shape = (nver, 2) max value<=1 + ''' + if obj_name.split('.')[-1] != 'obj': + obj_name = obj_name + '.obj' + mtl_name = obj_name.replace('.obj', '.mtl') + texture_name = obj_name.replace('.obj', '.png') + material_name = 'FaceTexture' + + faces = faces.copy() + # mesh lab start with 1, python/c++ start from 0 + faces += 1 + if inverse_face_order: + faces = faces[:, [2, 1, 0]] + if uvfaces is not None: + uvfaces = uvfaces[:, [2, 1, 0]] + + # write obj + with open(obj_name, 'w') as f: + # first line: write mtlib(material library) + # f.write('# %s\n' % os.path.basename(obj_name)) + # f.write('#\n') + # f.write('\n') + if texture is not None: + f.write('mtllib %s\n\n' % os.path.basename(mtl_name)) + + # write vertices + if colors is None: + for i in range(vertices.shape[0]): + f.write('v {} {} {}\n'.format(vertices[i, 0], vertices[i, 1], vertices[i, 2])) + else: + for i in range(vertices.shape[0]): + f.write('v {} {} {} {} {} {}\n'.format(vertices[i, 0], vertices[i, 1], vertices[i, 2], colors[i, 0], colors[i, 1], colors[i, 2])) + + # write uv coords + if texture is None: + for i in range(faces.shape[0]): + f.write('f {} {} {}\n'.format(faces[i, 2], faces[i, 1], faces[i, 0])) + else: + for i in range(uvcoords.shape[0]): + f.write('vt {} {}\n'.format(uvcoords[i,0], uvcoords[i,1])) + f.write('usemtl %s\n' % material_name) + # write f: ver ind/ uv ind + uvfaces = uvfaces + 1 + for i in range(faces.shape[0]): + f.write('f {}/{} {}/{} {}/{}\n'.format( + # faces[i, 2], uvfaces[i, 2], + # faces[i, 1], uvfaces[i, 1], + # faces[i, 0], uvfaces[i, 0] + faces[i, 0], uvfaces[i, 0], + faces[i, 1], uvfaces[i, 1], + faces[i, 2], uvfaces[i, 2] + ) + ) + # write mtl + with open(mtl_name, 'w') as f: + f.write('newmtl %s\n' % material_name) + s = 'map_Kd {}\n'.format(os.path.basename(texture_name)) # map to image + f.write(s) + + if normal_map is not None: + name, _ = os.path.splitext(obj_name) + normal_name = f'{name}_normals.png' + f.write(f'disp {normal_name}') + # out_normal_map = normal_map / (np.linalg.norm( + # normal_map, axis=-1, keepdims=True) + 1e-9) + # out_normal_map = (out_normal_map + 1) * 0.5 + + cv2.imwrite( + normal_name, + # (out_normal_map * 255).astype(np.uint8)[:, :, ::-1] + normal_map + ) + cv2.imwrite(texture_name, texture) + +# ---------------------------- process/generate vertices, normals, faces +def generate_triangles(h, w, margin_x=2, margin_y=5, mask = None): + # quad layout: + # 0 1 ... w-1 + # w w+1 + #. + # w*h + triangles = [] + for x in range(margin_x, w-1-margin_x): + for y in range(margin_y, h-1-margin_y): + triangle0 = [y*w + x, y*w + x + 1, (y+1)*w + x] + triangle1 = [y*w + x + 1, (y+1)*w + x + 1, (y+1)*w + x] + triangles.append(triangle0) + triangles.append(triangle1) + triangles = np.array(triangles) + triangles = triangles[:,[0,2,1]] + return triangles + +# borrowed from https://github.com/daniilidis-group/neural_renderer/blob/master/neural_renderer/vertices_to_faces.py +def face_vertices(vertices, faces): + """ + :param vertices: [batch size, number of vertices, 3] + :param faces: [batch size, number of faces, 3] + :return: [batch size, number of faces, 3, 3] + """ + assert (vertices.ndimension() == 3) + assert (faces.ndimension() == 3) + assert (vertices.shape[0] == faces.shape[0]) + assert (vertices.shape[2] == 3) + assert (faces.shape[2] == 3) + + bs, nv = vertices.shape[:2] + bs, nf = faces.shape[:2] + device = vertices.device + faces = faces + (torch.arange(bs, dtype=torch.int32).to(device) * nv)[:, None, None] + vertices = vertices.reshape((bs * nv, 3)) + # pytorch only supports long and byte tensors for indexing + return vertices[faces.long()] + +def vertex_normals(vertices, faces): + """ + :param vertices: [batch size, number of vertices, 3] + :param faces: [batch size, number of faces, 3] + :return: [batch size, number of vertices, 3] + """ + assert (vertices.ndimension() == 3) + assert (faces.ndimension() == 3) + assert (vertices.shape[0] == faces.shape[0]) + assert (vertices.shape[2] == 3) + assert (faces.shape[2] == 3) + bs, nv = vertices.shape[:2] + bs, nf = faces.shape[:2] + device = vertices.device + normals = torch.zeros(bs * nv, 3).to(device) + + faces = faces + (torch.arange(bs, dtype=torch.int32).to(device) * nv)[:, None, None] # expanded faces + vertices_faces = vertices.reshape((bs * nv, 3))[faces.long()] + + faces = faces.reshape(-1, 3) + vertices_faces = vertices_faces.reshape(-1, 3, 3) + + normals.index_add_(0, faces[:, 1].long(), + torch.cross(vertices_faces[:, 2] - vertices_faces[:, 1], vertices_faces[:, 0] - vertices_faces[:, 1])) + normals.index_add_(0, faces[:, 2].long(), + torch.cross(vertices_faces[:, 0] - vertices_faces[:, 2], vertices_faces[:, 1] - vertices_faces[:, 2])) + normals.index_add_(0, faces[:, 0].long(), + torch.cross(vertices_faces[:, 1] - vertices_faces[:, 0], vertices_faces[:, 2] - vertices_faces[:, 0])) + + normals = F.normalize(normals, eps=1e-6, dim=1) + normals = normals.reshape((bs, nv, 3)) + # pytorch only supports long and byte tensors for indexing + return normals + +def batch_orth_proj(X, camera): + ''' orthgraphic projection + X: 3d vertices, [bz, n_point, 3] + camera: scale and translation, [bz, 3], [scale, tx, ty] + ''' + camera = camera.clone().view(-1, 1, 3) + X_trans = X[:, :, :2] + camera[:, :, 1:] + X_trans = torch.cat([X_trans, X[:,:,2:]], 2) + shape = X_trans.shape + Xn = (camera[:, :, 0:1] * X_trans) + return Xn + +# -------------------------------------- image processing +# borrowed from: https://torchgeometry.readthedocs.io/en/latest/_modules/kornia/filters +def gaussian(window_size, sigma): + def gauss_fcn(x): + return -(x - window_size // 2)**2 / float(2 * sigma**2) + gauss = torch.stack( + [torch.exp(torch.tensor(gauss_fcn(x))) for x in range(window_size)]) + return gauss / gauss.sum() + +def get_gaussian_kernel(kernel_size: int, sigma: float): + r"""Function that returns Gaussian filter coefficients. + + Args: + kernel_size (int): filter size. It should be odd and positive. + sigma (float): gaussian standard deviation. + + Returns: + Tensor: 1D tensor with gaussian filter coefficients. + + Shape: + - Output: :math:`(\text{kernel_size})` + + Examples:: + + >>> kornia.image.get_gaussian_kernel(3, 2.5) + tensor([0.3243, 0.3513, 0.3243]) + + >>> kornia.image.get_gaussian_kernel(5, 1.5) + tensor([0.1201, 0.2339, 0.2921, 0.2339, 0.1201]) + """ + if not isinstance(kernel_size, int) or kernel_size % 2 == 0 or \ + kernel_size <= 0: + raise TypeError("kernel_size must be an odd positive integer. " + "Got {}".format(kernel_size)) + window_1d = gaussian(kernel_size, sigma) + return window_1d + +def get_gaussian_kernel2d(kernel_size, sigma): + r"""Function that returns Gaussian filter matrix coefficients. + + Args: + kernel_size (Tuple[int, int]): filter sizes in the x and y direction. + Sizes should be odd and positive. + sigma (Tuple[int, int]): gaussian standard deviation in the x and y + direction. + + Returns: + Tensor: 2D tensor with gaussian filter matrix coefficients. + + Shape: + - Output: :math:`(\text{kernel_size}_x, \text{kernel_size}_y)` + + Examples:: + + >>> kornia.image.get_gaussian_kernel2d((3, 3), (1.5, 1.5)) + tensor([[0.0947, 0.1183, 0.0947], + [0.1183, 0.1478, 0.1183], + [0.0947, 0.1183, 0.0947]]) + + >>> kornia.image.get_gaussian_kernel2d((3, 5), (1.5, 1.5)) + tensor([[0.0370, 0.0720, 0.0899, 0.0720, 0.0370], + [0.0462, 0.0899, 0.1123, 0.0899, 0.0462], + [0.0370, 0.0720, 0.0899, 0.0720, 0.0370]]) + """ + if not isinstance(kernel_size, tuple) or len(kernel_size) != 2: + raise TypeError("kernel_size must be a tuple of length two. Got {}" + .format(kernel_size)) + if not isinstance(sigma, tuple) or len(sigma) != 2: + raise TypeError("sigma must be a tuple of length two. Got {}" + .format(sigma)) + ksize_x, ksize_y = kernel_size + sigma_x, sigma_y = sigma + kernel_x = get_gaussian_kernel(ksize_x, sigma_x) + kernel_y = get_gaussian_kernel(ksize_y, sigma_y) + kernel_2d = torch.matmul( + kernel_x.unsqueeze(-1), kernel_y.unsqueeze(-1).t()) + return kernel_2d + +def gaussian_blur(x, kernel_size=(3,3), sigma=(0.8,0.8)): + b, c, h, w = x.shape + kernel = get_gaussian_kernel2d(kernel_size, sigma).to(x.device).to(x.dtype) + kernel = kernel.repeat(c, 1, 1, 1) + padding = [(k - 1) // 2 for k in kernel_size] + return F.conv2d(x, kernel, padding=padding, stride=1, groups=c) + +def _compute_binary_kernel(window_size): + r"""Creates a binary kernel to extract the patches. If the window size + is HxW will create a (H*W)xHxW kernel. + """ + window_range = window_size[0] * window_size[1] + kernel: torch.Tensor = torch.zeros(window_range, window_range) + for i in range(window_range): + kernel[i, i] += 1.0 + return kernel.view(window_range, 1, window_size[0], window_size[1]) + +def median_blur(x, kernel_size=(3,3)): + b, c, h, w = x.shape + kernel = _compute_binary_kernel(kernel_size).to(x.device).to(x.dtype) + kernel = kernel.repeat(c, 1, 1, 1) + padding = [(k - 1) // 2 for k in kernel_size] + features = F.conv2d(x, kernel, padding=padding, stride=1, groups=c) + features = features.view(b,c,-1,h,w) + median = torch.median(features, dim=2)[0] + return median + +def get_laplacian_kernel2d(kernel_size: int): + r"""Function that returns Gaussian filter matrix coefficients. + + Args: + kernel_size (int): filter size should be odd. + + Returns: + Tensor: 2D tensor with laplacian filter matrix coefficients. + + Shape: + - Output: :math:`(\text{kernel_size}_x, \text{kernel_size}_y)` + + Examples:: + + >>> kornia.image.get_laplacian_kernel2d(3) + tensor([[ 1., 1., 1.], + [ 1., -8., 1.], + [ 1., 1., 1.]]) + + >>> kornia.image.get_laplacian_kernel2d(5) + tensor([[ 1., 1., 1., 1., 1.], + [ 1., 1., 1., 1., 1.], + [ 1., 1., -24., 1., 1.], + [ 1., 1., 1., 1., 1.], + [ 1., 1., 1., 1., 1.]]) + + """ + if not isinstance(kernel_size, int) or kernel_size % 2 == 0 or \ + kernel_size <= 0: + raise TypeError("ksize must be an odd positive integer. Got {}" + .format(kernel_size)) + + kernel = torch.ones((kernel_size, kernel_size)) + mid = kernel_size // 2 + kernel[mid, mid] = 1 - kernel_size ** 2 + kernel_2d: torch.Tensor = kernel + return kernel_2d + +def laplacian(x): + # https://torchgeometry.readthedocs.io/en/latest/_modules/kornia/filters/laplacian.html + b, c, h, w = x.shape + kernel_size = 3 + kernel = get_laplacian_kernel2d(kernel_size).to(x.device).to(x.dtype) + kernel = kernel.repeat(c, 1, 1, 1) + padding = (kernel_size - 1) // 2 + return F.conv2d(x, kernel, padding=padding, stride=1, groups=c) + +def angle2matrix(angles): + ''' get rotation matrix from three rotation angles(degree). right-handed. + Args: + angles: [batch_size, 3] tensor containing X, Y, and Z angles. + x: pitch. positive for looking down. + y: yaw. positive for looking left. + z: roll. positive for tilting head right. + Returns: + R: [batch_size, 3, 3]. rotation matrices. + ''' + angles = angles*(np.pi)/180. + s = torch.sin(angles) + c = torch.cos(angles) + + cx, cy, cz = (c[:, 0], c[:, 1], c[:, 2]) + sx, sy, sz = (s[:, 0], s[:, 1], s[:, 2]) + + zeros = torch.zeros_like(s[:, 0]).to(angles.device) + ones = torch.ones_like(s[:, 0]).to(angles.device) + + # Rz.dot(Ry.dot(Rx)) + R_flattened = torch.stack( + [ + cz * cy, cz * sy * sx - sz * cx, cz * sy * cx + sz * sx, + sz * cy, sz * sy * sx + cz * cx, sz * sy * cx - cz * sx, + -sy, cy * sx, cy * cx, + ], + dim=0) #[batch_size, 9] + R = torch.reshape(R_flattened, (-1, 3, 3)) #[batch_size, 3, 3] + return R + +def binary_erosion(tensor, kernel_size=5): + # tensor: [bz, 1, h, w]. + device = tensor.device + mask = tensor.cpu().numpy() + structure=np.ones((kernel_size,kernel_size)) + new_mask = mask.copy() + for i in range(mask.shape[0]): + new_mask[i,0] = morphology.binary_erosion(mask[i,0], structure) + return torch.from_numpy(new_mask.astype(np.float32)).to(device) + +def flip_image(src_image, kps): + ''' + purpose: + flip a image given by src_image and the 2d keypoints + flip_mode: + 0: horizontal flip + >0: vertical flip + <0: horizontal & vertical flip + ''' + h, w = src_image.shape[0], src_image.shape[1] + src_image = cv2.flip(src_image, 1) + if kps is not None: + kps[:, 0] = w - 1 - kps[:, 0] + kp_map = [5, 4, 3, 2, 1, 0, 11, 10, 9, 8, 7, 6, 12, 13] + kps[:, :] = kps[kp_map] + + return src_image, kps + +# -------------------------------------- io +def copy_state_dict(cur_state_dict, pre_state_dict, prefix='', load_name=None): + def _get_params(key): + key = prefix + key + if key in pre_state_dict: + return pre_state_dict[key] + return None + for k in cur_state_dict.keys(): + if load_name is not None: + if load_name not in k: + continue + v = _get_params(k) + try: + if v is None: + # print('parameter {} not found'.format(k)) + continue + cur_state_dict[k].copy_(v) + except: + # print('copy param {} failed'.format(k)) + continue + +def check_mkdir(path): + if not os.path.exists(path): + print('creating %s' % path) + os.makedirs(path) + +def check_mkdirlist(pathlist): + for path in pathlist: + if not os.path.exists(path): + print('creating %s' % path) + os.makedirs(path) + +def tensor2image(tensor): + image = tensor.detach().cpu().numpy() + image = image*255. + image = np.maximum(np.minimum(image, 255), 0) + image = image.transpose(1,2,0)[:,:,[2,1,0]] + return image.astype(np.uint8).copy() + +def dict2obj(d): + # if isinstance(d, list): + # d = [dict2obj(x) for x in d] + if not isinstance(d, dict): + return d + class C(object): + pass + o = C() + for k in d: + o.__dict__[k] = dict2obj(d[k]) + return o + +class Struct(object): + def __init__(self, **kwargs): + for key, val in kwargs.items(): + setattr(self, key, val) + +# original saved file with DataParallel +def remove_module(state_dict): +# create new OrderedDict that does not contain `module.` + new_state_dict = OrderedDict() + for k, v in state_dict.items(): + name = k[7:] # remove `module.` + new_state_dict[name] = v + return new_state_dict + +def dict_tensor2npy(tensor_dict): + npy_dict = {} + for key in tensor_dict: + npy_dict[key] = tensor_dict[key][0].cpu().numpy() + return npy_dict + +# ---------------------------------- visualization +end_list = np.array([17, 22, 27, 42, 48, 31, 36, 68], dtype = np.int32) - 1 +def plot_kpts(image, kpts, color = 'r'): + ''' Draw 68 key points + Args: + image: the input image + kpt: (68, 3). + ''' + if color == 'r': + c = (255, 0, 0) + elif color == 'g': + c = (0, 255, 0) + elif color == 'b': + c = (255, 0, 0) + image = image.copy() + kpts = kpts.copy() + + for i in range(kpts.shape[0]): + st = kpts[i, :2] + if kpts.shape[1]==4: + if kpts[i, 3] > 0.5: + c = (0, 255, 0) + else: + c = (0, 0, 255) + image = cv2.circle(image, (int(st[0]), int(st[1])), 1, c, 2) + if i in end_list: + continue + ed = kpts[i + 1, :2] + image = cv2.line(image, (int(st[0]), int(st[1])), (int(ed[0]), int(ed[1])), (255, 255, 255), 1) + + return image + +def plot_verts(image, kpts, color = 'r'): + ''' Draw 68 key points + Args: + image: the input image + kpt: (68, 3). + ''' + if color == 'r': + c = (255, 0, 0) + elif color == 'g': + c = (0, 255, 0) + elif color == 'b': + c = (0, 0, 255) + elif color == 'y': + c = (0, 255, 255) + image = image.copy() + + for i in range(kpts.shape[0]): + st = kpts[i, :2] + image = cv2.circle(image,(int(st[0]), int(st[1])), 1, c, 2) + + return image + +def tensor_vis_landmarks(images, landmarks, gt_landmarks=None, color = 'g', isScale=True): + # visualize landmarks + vis_landmarks = [] + images = images.cpu().numpy() + predicted_landmarks = landmarks.detach().cpu().numpy() + if gt_landmarks is not None: + gt_landmarks_np = gt_landmarks.detach().cpu().numpy() + for i in range(images.shape[0]): + image = images[i] + image = image.transpose(1,2,0)[:,:,[2,1,0]].copy(); image = (image*255) + if isScale: + predicted_landmark = predicted_landmarks[i]*image.shape[0]/2 + image.shape[0]/2 + else: + predicted_landmark = predicted_landmarks[i] + if predicted_landmark.shape[0] == 68: + image_landmarks = plot_kpts(image, predicted_landmark, color) + if gt_landmarks is not None: + image_landmarks = plot_verts(image_landmarks, gt_landmarks_np[i]*image.shape[0]/2 + image.shape[0]/2, 'r') + else: + image_landmarks = plot_verts(image, predicted_landmark, color) + if gt_landmarks is not None: + image_landmarks = plot_verts(image_landmarks, gt_landmarks_np[i]*image.shape[0]/2 + image.shape[0]/2, 'r') + vis_landmarks.append(image_landmarks) + + vis_landmarks = np.stack(vis_landmarks) + vis_landmarks = torch.from_numpy(vis_landmarks[:,:,:,[2,1,0]].transpose(0,3,1,2))/255.#, dtype=torch.float32) + return vis_landmarks diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/demos/__init__.py b/NeuralVoicePuppetry/neural-code/third/DECA/demos/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/demos/demo_reconstruct.py b/NeuralVoicePuppetry/neural-code/third/DECA/demos/demo_reconstruct.py new file mode 100755 index 0000000..4e5b77e --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/demos/demo_reconstruct.py @@ -0,0 +1,109 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import torch +import os, sys +import cv2 +import numpy as np +from time import time +from scipy.io import savemat +import argparse +from tqdm import tqdm + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +from decalib.deca import DECA +from decalib.datasets import datasets +from decalib.utils import util +from decalib.utils.config import cfg as deca_cfg + + +def main(args): + savefolder = args.savefolder + device = args.device + os.makedirs(savefolder, exist_ok=True) + + # load test images + testdata = datasets.TestData(args.inputpath, iscrop=args.iscrop, face_detector=args.detector) + + # run DECA + deca_cfg.model.use_tex = args.useTex + deca = DECA(config = deca_cfg, device=device) + # for i in range(len(testdata)): + for i in tqdm(range(len(testdata))): + name = testdata[i]['imagename'] + images = testdata[i]['image'].to(device)[None,...] + codedict = deca.encode(images) + opdict, visdict = deca.decode(codedict) #tensor + + print(opdict.keys()) + print(visdict.keys()) + + if args.saveDepth or args.saveKpt or args.saveObj or args.saveMat or args.saveImages: + os.makedirs(os.path.join(savefolder, name), exist_ok=True) + # -- save results + if args.saveDepth: + depth_image = deca.render.render_depth(opdict['transformed_vertices']).repeat(1,3,1,1) + visdict['depth_images'] = depth_image + cv2.imwrite(os.path.join(savefolder, name, name + '_depth.jpg'), util.tensor2image(depth_image[0])) + if args.saveKpt: + np.savetxt(os.path.join(savefolder, name, name + '_kpt2d.txt'), opdict['landmarks2d'][0].cpu().numpy()) + np.savetxt(os.path.join(savefolder, name, name + '_kpt3d.txt'), opdict['landmarks3d'][0].cpu().numpy()) + if args.saveObj: + deca.save_obj(os.path.join(savefolder, name, name + '.obj'), opdict) + if args.saveMat: + opdict = util.dict_tensor2npy(opdict) + savemat(os.path.join(savefolder, name, name + '.mat'), opdict) + if args.saveVis: + cv2.imwrite(os.path.join(savefolder, name + '_vis.jpg'), deca.visualize(visdict)) + if args.saveImages: + for vis_name in ['inputs', 'rendered_images', 'albedo_images', 'shape_images', 'shape_detail_images']: + if vis_name not in visdict.keys(): + continue + image = util.tensor2image(visdict[vis_name][0]) + cv2.imwrite(os.path.join(savefolder, name, name + '_' + vis_name +'.jpg'), util.tensor2image(visdict[vis_name][0])) + print(f'-- please check the results in {savefolder}') + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='DECA: Detailed Expression Capture and Animation') + + parser.add_argument('-i', '--inputpath', default='TestSamples/examples', type=str, + help='path to the test data, can be image folder, image path, image list, video') + parser.add_argument('-s', '--savefolder', default='TestSamples/examples/results', type=str, + help='path to the output directory, where results(obj, txt files) will be stored.') + parser.add_argument('--device', default="cuda" if torch.cuda.is_available() else "cpu", type=str, + help='set device, cpu for using cpu' ) + # process test images + parser.add_argument('--iscrop', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to crop input image, set false only when the test image are well cropped' ) + parser.add_argument('--detector', default='fan', type=str, + help='detector for cropping face, check decalib/detectors.py for details' ) + # save + parser.add_argument('--useTex', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to use FLAME texture model to generate uv texture map, \ + set it to True only if you downloaded texture model' ) + parser.add_argument('--saveVis', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save visualization of output' ) + parser.add_argument('--saveKpt', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save 2D and 3D keypoints' ) + parser.add_argument('--saveDepth', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save depth image' ) + parser.add_argument('--saveObj', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save outputs as .obj, detail mesh will end with _detail.obj. \ + Note that saving objs could be slow' ) + parser.add_argument('--saveMat', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save outputs as .mat' ) + parser.add_argument('--saveImages', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save visualization output as seperate images' ) + main(parser.parse_args()) \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/demos/demo_teaser.py b/NeuralVoicePuppetry/neural-code/third/DECA/demos/demo_teaser.py new file mode 100755 index 0000000..e7b5b2d --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/demos/demo_teaser.py @@ -0,0 +1,126 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import os, sys +import cv2 +import numpy as np +from time import time +from scipy.io import savemat +import argparse +import imageio +from skimage.transform import rescale +import torch + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +from decalib.deca import DECA +from decalib.datasets import datasets +from decalib.utils import util +from decalib.utils.rotation_converter import batch_euler2axis, deg2rad + +def main(args): + savefolder = args.savefolder + device = args.device + os.makedirs(savefolder, exist_ok=True) + + # load test images + testdata = datasets.TestData(args.inputpath, iscrop=args.iscrop, face_detector=args.detector) + expdata = datasets.TestData(args.exp_path, iscrop=args.iscrop, face_detector=args.detector) + # DECA + deca = DECA(device=device) + + visdict_list_list = [] + for i in range(len(testdata)): + name = testdata[i]['imagename'] + images = testdata[i]['image'].to(device)[None,...] + codedict = deca.encode(images) + opdict, visdict = deca.decode(codedict) #tensor + ### show shape with different views and expressions + visdict_list = [] + max_yaw = 30 + yaw_list = list(range(0,max_yaw,5)) + list(range(max_yaw,-max_yaw,-5)) + list(range(-max_yaw,0,5)) + for k in yaw_list: #jaw angle from -50 to 50 + ## yaw angle + euler_pose = torch.randn((1, 3)) + euler_pose[:,1] = k#torch.rand((self.batch_size))*160 - 80 + euler_pose[:,0] = 0#(torch.rand((self.batch_size))*60 - 30)*(2./euler_pose[:,1].abs()) + euler_pose[:,2] = 0#(torch.rand((self.batch_size))*60 - 30)*(2./euler_pose[:,1].abs()) + global_pose = batch_euler2axis(deg2rad(euler_pose[:,:3].cuda())) + codedict['pose'][:,:3] = global_pose + codedict['cam'][:,:] = 0. + codedict['cam'][:,0] = 8 + _, visdict_view = deca.decode(codedict) + visdict = {x:visdict[x] for x in ['inputs', 'shape_detail_images']} + visdict['pose'] = visdict_view['shape_detail_images'] + visdict_list.append(visdict) + + euler_pose = torch.zeros((1, 3)) + global_pose = batch_euler2axis(deg2rad(euler_pose[:,:3].cuda())) + codedict['pose'][:,:3] = global_pose + for (i,k) in enumerate(range(0,31,2)): #jaw angle from -50 to 50 + # expression: jaw pose + euler_pose = torch.randn((1, 3)) + euler_pose[:,0] = k#torch.rand((self.batch_size))*160 - 80 + euler_pose[:,1] = 0#(torch.rand((self.batch_size))*60 - 30)*(2./euler_pose[:,1].abs()) + euler_pose[:,2] = 0#(torch.rand((self.batch_size))*60 - 30)*(2./euler_pose[:,1].abs()) + jaw_pose = batch_euler2axis(deg2rad(euler_pose[:,:3].cuda())) + codedict['pose'][:,3:] = jaw_pose + _, visdict_view = deca.decode(codedict) + visdict_list[i]['exp'] = visdict_view['shape_detail_images'] + count = i + + for (i,k) in enumerate(range(len(expdata))): #jaw angle from -50 to 50 + # expression: jaw pose + exp_images = expdata[i]['image'].to(device)[None,...] + exp_codedict = deca.encode(exp_images) + # transfer exp code + codedict['pose'][:,3:] = exp_codedict['pose'][:,3:] + codedict['exp'] = exp_codedict['exp'] + _, exp_visdict = deca.decode(codedict) + visdict_list[i+count]['exp'] = exp_visdict['shape_detail_images'] + + visdict_list_list.append(visdict_list) + + ### write gif + writer = imageio.get_writer(os.path.join(savefolder, 'teaser.gif'), mode='I') + for i in range(len(yaw_list)): + grid_image_list = [] + for j in range(len(testdata)): + grid_image = deca.visualize(visdict_list_list[j][i]) + grid_image_list.append(grid_image) + grid_image_all = np.concatenate(grid_image_list, 0) + grid_image_all = rescale(grid_image_all, 0.6, multichannel=True) # resize for showing in github + writer.append_data(grid_image_all[:,:,[2,1,0]]) + + print(f'-- please check the teaser figure in {savefolder}') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='DECA: Detailed Expression Capture and Animation') + + parser.add_argument('-i', '--inputpath', default='TestSamples/teaser', type=str, + help='path to the test data, can be image folder, image path, image list, video') + parser.add_argument('-e', '--exp_path', default='TestSamples/exp', type=str, + help='path to expression') + parser.add_argument('-s', '--savefolder', default='TestSamples/teaser/results', type=str, + help='path to the output directory, where results(obj, txt files) will be stored.') + parser.add_argument('--device', default="cuda" if torch.cuda.is_available() else "cpu", type=str, + help='set device, cpu for using cpu' ) + # process test images + parser.add_argument('--iscrop', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to crop input image, set false only when the test image are well cropped' ) + parser.add_argument('--detector', default='fan', type=str, + help='detector for cropping face, check detectos.py for details' ) + + main(parser.parse_args()) \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/demos/demo_transfer.py b/NeuralVoicePuppetry/neural-code/third/DECA/demos/demo_transfer.py new file mode 100755 index 0000000..022287e --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/demos/demo_transfer.py @@ -0,0 +1,127 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import os, sys +import cv2 +import torch +import numpy as np +from time import time +import argparse + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +from decalib.deca import DECA +from decalib.datasets import datasets +from decalib.utils import util +from decalib.utils.config import cfg as deca_cfg + +def main(args): + savefolder = args.savefolder + device = args.device + os.makedirs(savefolder, exist_ok=True) + + # load test images + testdata = datasets.TestData(args.image_path, iscrop=args.iscrop, face_detector=args.detector) + expdata = datasets.TestData(args.exp_path, iscrop=args.iscrop, face_detector=args.detector) + + # run DECA + deca_cfg.model.use_tex = args.useTex + deca = DECA(config = deca_cfg, device=device) + # identity reference + i = 0 + name = testdata[i]['imagename'] + savepath = '{}/{}.jpg'.format(savefolder, name) + images = testdata[i]['image'].to(device)[None,...] + id_codedict = deca.encode(images) + id_opdict, id_visdict = deca.decode(id_codedict) + id_visdict = {x:id_visdict[x] for x in ['inputs', 'shape_detail_images']} + + # -- expression transfer + # exp code from image + exp_images = expdata[i]['image'].to(device)[None,...] + exp_codedict = deca.encode(exp_images) + # transfer exp code + id_codedict['pose'][:,3:] = exp_codedict['pose'][:,3:] + id_codedict['exp'] = exp_codedict['exp'] + transfer_opdict, transfer_visdict = deca.decode(id_codedict) + id_visdict['transferred_shape'] = transfer_visdict['shape_detail_images'] + cv2.imwrite(os.path.join(savefolder, name + '_animation.jpg'), deca.visualize(id_visdict)) + + transfer_opdict['uv_texture_gt'] = id_opdict['uv_texture_gt'] + if args.saveDepth or args.saveKpt or args.saveObj or args.saveMat or args.saveImages: + os.makedirs(os.path.join(savefolder, name, 'reconstruction'), exist_ok=True) + os.makedirs(os.path.join(savefolder, name, 'animation'), exist_ok=True) + + # -- save results + image_name = name + for save_type in ['reconstruction', 'animation']: + if save_type == 'reconstruction': + visdict = id_codedict; opdict = id_opdict + else: + visdict = transfer_visdict; opdict = transfer_opdict + if args.saveDepth: + depth_image = deca.render.render_depth(opdict['transformed_vertices']).repeat(1,3,1,1) + visdict['depth_images'] = depth_image + cv2.imwrite(os.path.join(savefolder, name, save_type, name + '_depth.jpg'), util.tensor2image(depth_image[0])) + if args.saveKpt: + np.savetxt(os.path.join(savefolder, name, save_type, name + '_kpt2d.txt'), opdict['landmarks2d'][0].cpu().numpy()) + np.savetxt(os.path.join(savefolder, name, save_type, name + '_kpt3d.txt'), opdict['landmarks3d'][0].cpu().numpy()) + if args.saveObj: + deca.save_obj(os.path.join(savefolder, name, save_type, name + '.obj'), opdict) + if args.saveMat: + opdict = util.dict_tensor2npy(opdict) + savemat(os.path.join(savefolder, name, save_type, name + '.mat'), opdict) + if args.saveImages: + for vis_name in ['inputs', 'rendered_images', 'albedo_images', 'shape_images', 'shape_detail_images']: + if vis_name not in visdict.keys(): + continue + image =util.tensor2image(visdict[vis_name][0]) + cv2.imwrite(os.path.join(savefolder, name, save_type, name + '_' + vis_name +'.jpg'), util.tensor2image(visdict[vis_name][0])) + print(f'-- please check the results in {savefolder}') + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='DECA: Detailed Expression Capture and Animation') + + parser.add_argument('-i', '--image_path', default='TestSamples/examples/IMG_0392_inputs.jpg', type=str, + help='path to input image') + parser.add_argument('-e', '--exp_path', default='TestSamples/exp/7.jpg', type=str, + help='path to expression') + parser.add_argument('-s', '--savefolder', default='TestSamples/animation_results', type=str, + help='path to the output directory, where results(obj, txt files) will be stored.') + parser.add_argument('--device', default="cuda" if torch.cuda.is_available() else "cpu", type=str, + help='set device, cpu for using cpu' ) + # process test images + parser.add_argument('--iscrop', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to crop input image, set false only when the test image are well cropped' ) + parser.add_argument('--detector', default='fan', type=str, + help='detector for cropping face, check detectos.py for details' ) + # save + parser.add_argument('--useTex', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to use FLAME texture model to generate uv texture map, \ + set it to True only if you downloaded texture model' ) + parser.add_argument('--saveVis', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save visualization of output' ) + parser.add_argument('--saveKpt', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save 2D and 3D keypoints' ) + parser.add_argument('--saveDepth', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save depth image' ) + parser.add_argument('--saveObj', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save outputs as .obj' ) + parser.add_argument('--saveMat', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save outputs as .mat' ) + parser.add_argument('--saveImages', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save visualization output as seperate images' ) + main(parser.parse_args()) + + main(parser.parse_args()) diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/demos/run_on_img.py b/NeuralVoicePuppetry/neural-code/third/DECA/demos/run_on_img.py new file mode 100644 index 0000000..e0d64f3 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/demos/run_on_img.py @@ -0,0 +1,127 @@ + +import os, sys +import cv2 +import torch +import numpy as np +from time import time +from scipy.io import savemat +import argparse +from tqdm import tqdm +import matplotlib.pyplot as plt +from skimage.transform import estimate_transform, warp, resize, rescale + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +from decalib.deca import DECA +from decalib.datasets import datasets +from decalib.utils import util +from decalib.utils.config import cfg as deca_cfg + + +def warp_back(image, oldimage, tform): + + alpha = 0.6 + + oldimage = oldimage.astype(np.float64) /255. + new_size = oldimage.shape + + dst_image = warp(image, tform, output_shape=new_size) + + # Mask of non-black pixels. + mask = np.where(np.all(dst_image == [0, 0, 0], axis=-1)) + dst_image[mask] = oldimage[mask] + + res = cv2.addWeighted(oldimage, 1 - alpha, dst_image, alpha, 0) + res = res[:, :, ::-1] + + return res + + +def main(args): + savefolder = args.savefolder + device = args.device + os.makedirs(savefolder, exist_ok=True) + + # load test images + testdata = datasets.TestData(args.inputpath, iscrop=args.iscrop, face_detector=args.detector) + + # run DECA + deca_cfg.model.use_tex = args.useTex + deca = DECA(config=deca_cfg, device=device) + # for i in range(len(testdata)): + for i in tqdm(range(len(testdata))): + + name = testdata[i]['imagename'] + images = testdata[i]['image'].to(device)[None, ...] + original_images = testdata[i]['original_image'].to(device)[None, ...] + + codedict = deca.encode(images) + opdict, visdict = deca.decode(codedict) # tensor + + images = util.tensor2image(images[0]) + original_images = util.tensor2image(original_images[0]) + image = util.tensor2image(visdict['shape_detail_images'][0]) + + # plt.imshow(image) + # plt.show() + + new = warp_back(image, original_images, testdata[i]['tform']) + plt.imshow(new) + plt.show() + + break + + + + + # if args.saveDepth or args.saveKpt or args.saveObj or args.saveMat or args.saveImages: + # os.makedirs(os.path.join(savefolder, name), exist_ok=True) + # # -- save results + # if args.saveObj: + # deca.save_obj(os.path.join(savefolder, name, name + '.obj'), opdict) + # if args.saveMat: + # opdict = util.dict_tensor2npy(opdict) + # savemat(os.path.join(savefolder, name, name + '.mat'), opdict) + # if args.saveVis: + # cv2.imwrite(os.path.join(savefolder, name + '_vis.jpg'), deca.visualize(visdict)) + # if args.saveImages: + # for vis_name in ['inputs', 'rendered_images', 'albedo_images', 'shape_images', 'shape_detail_images']: + # if vis_name not in visdict.keys(): + # continue + # image = util.tensor2image(visdict[vis_name][0]) + # cv2.imwrite(os.path.join(savefolder, name, name + '_' + vis_name + '.jpg'), + # util.tensor2image(visdict[vis_name][0])) + # print(f'-- please check the results in {savefolder}') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='DECA: Detailed Expression Capture and Animation') + + parser.add_argument('-i', '--inputpath', default='TestSamples/SRF', type=str, + help='path to the test data, can be image folder, image path, image list, video') + parser.add_argument('-s', '--savefolder', default='TestSamples/SRF/results', type=str, + help='path to the output directory, where results(obj, txt files) will be stored.') + parser.add_argument('--device', default="cuda" if torch.cuda.is_available() else "cpu", type=str, + help='set device, cpu for using cpu') + # process test images + parser.add_argument('--iscrop', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to crop input image, set false only when the test image are well cropped') + parser.add_argument('--detector', default='fan', type=str, + help='detector for cropping face, check decalib/detectors.py for details') + # save + parser.add_argument('--useTex', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to use FLAME texture model to generate uv texture map, \ + set it to True only if you downloaded texture model') + parser.add_argument('--saveVis', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save visualization of output') + parser.add_argument('--saveKpt', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save 2D and 3D keypoints') + parser.add_argument('--saveDepth', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save depth image') + parser.add_argument('--saveObj', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save outputs as .obj, detail mesh will end with _detail.obj. \ + Note that saving objs could be slow') + parser.add_argument('--saveMat', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save outputs as .mat') + parser.add_argument('--saveImages', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save visualization output as seperate images') + main(parser.parse_args()) \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/install_conda.sh b/NeuralVoicePuppetry/neural-code/third/DECA/install_conda.sh new file mode 100755 index 0000000..76c91f9 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/install_conda.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash + +export CONDA_ENV_NAME=deca-env +echo $CONDA_ENV_NAME + +conda create -n $CONDA_ENV_NAME python=3.7 + +eval "$(conda shell.bash hook)" +conda activate $CONDA_ENV_NAME +pip install -r requirements.txt \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/install_pip.sh b/NeuralVoicePuppetry/neural-code/third/DECA/install_pip.sh new file mode 100755 index 0000000..7346c85 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/install_pip.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash + +echo "Creating virtual environment" +python3.7 -m venv deca-env +echo "Activating virtual environment" + +source $PWD/deca-env/bin/activate +$PWD/deca-env/bin/pip install -r requirements.txt \ No newline at end of file diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/requirements.txt b/NeuralVoicePuppetry/neural-code/third/DECA/requirements.txt new file mode 100644 index 0000000..6539975 --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/requirements.txt @@ -0,0 +1,13 @@ +#to install all this requirements +#`pip install -r requirements.txt` +numpy>=1.18.5 +scipy>=1.4.1 +chumpy>=0.69 +scikit-image>=0.15 +opencv-python>=4.1.1 +scikit-image>=0.15 #skimage +PyYAML>=5.1.1 +torch==1.6.0 # for compatible with pytorch3d +torchvision==0.7.0 +face-alignment +# pytorch3d diff --git a/NeuralVoicePuppetry/neural-code/third/DECA/test_eye_gazing.py b/NeuralVoicePuppetry/neural-code/third/DECA/test_eye_gazing.py new file mode 100644 index 0000000..ee829ec --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DECA/test_eye_gazing.py @@ -0,0 +1,65 @@ +import sys +import os +import torch +import numpy as np +from PIL import Image +import torch.nn.functional as F + + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../third/DECA/'))) +from decalib.deca import DECA +from decalib.datasets import datasets +from decalib.utils.config import cfg as deca_cfg + +def save_img(name, image): + to_save = image[0].permute(1, 2, 0).cpu().numpy() + to_save = to_save * 255. + to_save = to_save.astype(np.uint8) + filename = name + '.jpg' + img = Image.fromarray(to_save) + img.save(filename) + +deca_cfg.model.use_tex = False +device = "cuda" if torch.cuda.is_available() else "cpu" +deca = DECA(config=deca_cfg, device=device) + +# load test images +testdata = datasets.TestData('test_expression.jpg', iscrop=True, face_detector='fan') +images = testdata[0]['image'].to(device)[None, ...] +codedict = deca.encode(images) +expr = codedict['exp'] + +# load test images +testdata = datasets.TestData('test_gaze.jpg', iscrop=True, face_detector='fan') +images = testdata[0]['image'].to(device)[None, ...] + +codedict = deca.encode(images) +opdict, visdict = deca.decode(codedict) + +save_img('shape', visdict['shape_images']) + +opdict, visdict = deca.decode(codedict) +verts_og = opdict['vertices'] +gird_og = opdict['grid'] +texture_og = opdict['uv_texture_gt'] +image = F.grid_sample(texture_og, gird_og, align_corners=False) +save_img('rendered_image_og', image) + + +# codedict['exp'] = expr +opdict, visdict = deca.decode_eyes(codedict) +verts_gaze = opdict['vertices'] +gird_gaze = opdict['grid'] +texture_gaze = opdict['uv_texture_gt'] +image = F.grid_sample(texture_og, gird_gaze, align_corners=False) +save_img('rendered_image_gaze', image) + + +euclidena_dist = sum(((verts_og - verts_gaze)**2).flatten()) +print('Mesh distance: ', euclidena_dist) + +gird_dist = sum(((gird_og - gird_gaze)**2).flatten()) +print('Grid distance: ', gird_dist) + +text_dist = sum(((texture_og - texture_gaze)**2).flatten()) +print('Texture distance: ', text_dist) diff --git a/NeuralVoicePuppetry/neural-code/third/DeepSpeech/models/output_graph.pb b/NeuralVoicePuppetry/neural-code/third/DeepSpeech/models/output_graph.pb new file mode 100644 index 0000000..da61e4e --- /dev/null +++ b/NeuralVoicePuppetry/neural-code/third/DeepSpeech/models/output_graph.pb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:309a4c24f861edca2a027e3d9ecd5ae34468d8b20ed4d9be694858e0bd6f0640 +size 490979273 diff --git a/NeuralVoicePuppetry/neural-code/third/__init__.py b/NeuralVoicePuppetry/neural-code/third/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural_voice_backend_api/__init__.py b/NeuralVoicePuppetry/neural_voice_backend_api/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural_voice_backend_api/app.py b/NeuralVoicePuppetry/neural_voice_backend_api/app.py new file mode 100644 index 0000000..81edc88 --- /dev/null +++ b/NeuralVoicePuppetry/neural_voice_backend_api/app.py @@ -0,0 +1,120 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import logging +from enum import Enum +from http import HTTPStatus +from typing import Union, List + +from fastapi import UploadFile, HTTPException, Query, Depends +from fastapi.responses import FileResponse +from mtc_api_utils.api import BaseApi +from mtc_api_utils.api_types import FirebaseUser +from mtc_api_utils.clients.firebase_client import firebase_user_auth + +from avatar_backend_api.api_types import ApiRoute, AvatarModelRequest +from avatar_backend_api.background_tools.inference_queue import InferenceQueueTask +from avatar_backend_api.clients.io_client import IoClient, IoClientException +from avatar_backend_api.models.mock_avatar_model import MockAvatarModel +from neural_voice_backend_api.config import NeuralVoiceConfig +from neural_voice_backend_api.neural_voice_inference_queue import NeuralVoiceInferenceQueue +from neural_voice_backend_api.neural_voice_model import NeuralVoiceModel + +NeuralVoiceConfig.print_config() + +user_auth = firebase_user_auth(config=NeuralVoiceConfig) + +io_client = IoClient(audio_base_path=NeuralVoiceConfig.audio_input_dir, video_base_path=NeuralVoiceConfig.video_output_dir, user_dir_mode=False) +neural_voice_model = MockAvatarModel(io_client=io_client) if NeuralVoiceConfig.mockBackend else NeuralVoiceModel(io_client=io_client) + +inference_queue = NeuralVoiceInferenceQueue( + model=neural_voice_model, + io_client=io_client, + audio_input_dir=NeuralVoiceConfig.audio_input_dir, + video_output_dir=NeuralVoiceConfig.video_output_dir, +) + +app = BaseApi(is_ready=neural_voice_model.is_ready, config=NeuralVoiceConfig) +tags: List[Union[str, Enum]] = ["Avatar Model"] + +logger = logging.Logger("App") + + +@app.get( + path=ApiRoute.video_ids.value, + response_model=List[str], + dependencies=[Depends(user_auth.with_roles(NeuralVoiceConfig.required_roles))], + tags=tags, +) +async def list_video_ids() -> List[str]: + return [path.split("/")[-1].split(".")[0] for path in io_client.video.list_videos()] + + +@app.get( + path=ApiRoute.video.value, + status_code=HTTPStatus.ACCEPTED, + response_class=FileResponse, + dependencies=[Depends(user_auth.with_roles(NeuralVoiceConfig.required_roles))], + tags=tags, +) +async def get_video(video_id: str = Query(alias="videoId")): + try: + return io_client.video.read_from_disk(video_id=video_id) + + except IoClientException: + print(f"") + + if io_client.audio.file_exists(video_id=video_id): + raise HTTPException( + status_code=HTTPStatus.PROCESSING, + detail=f"Audio with {video_id=} is currently being processed", + ) + + else: + raise HTTPException( + status_code=HTTPStatus.NOT_FOUND, + detail=f"There is no file available for {video_id=}. Either it has not yet been generated or it was removed in the meantime", + ) + + +@app.post( + path=ApiRoute.inference.value, + status_code=HTTPStatus.ACCEPTED, + response_model=AvatarModelRequest, + dependencies=[Depends(user_auth.with_roles(NeuralVoiceConfig.required_roles))], + tags=tags, +) +async def post_audio( + audio: UploadFile, + request_metadata: AvatarModelRequest = Depends(), +) -> AvatarModelRequest: + print(f"Processing {request_metadata.video_id}") + + if not request_metadata.video_id: + print(f"Creating {request_metadata.video_id=}") + request_metadata.video_id = audio.filename + + io_client.audio.save_to_disk(audio=audio, video_id=request_metadata.video_id) + + inference_queue.add_task( + task=InferenceQueueTask( + request=request_metadata, + ), + ) + + return request_metadata + + +@app.delete( + path=ApiRoute.video.value, + response_model=str, + dependencies=[Depends(user_auth)], + tags=tags, +) +async def delete_video(video_id: str = Query(alias="videoId"), user: FirebaseUser = Depends(user_auth)) -> str: + try: + io_client.video.delete_file(video_id=video_id) + except FileNotFoundError: + raise HTTPException(status_code=HTTPStatus.NOT_FOUND, detail=f"No files with {video_id=} found") + + return f"The files with {video_id=} were removed successfully" diff --git a/NeuralVoicePuppetry/neural_voice_backend_api/config.py b/NeuralVoicePuppetry/neural_voice_backend_api/config.py new file mode 100644 index 0000000..fa68ffc --- /dev/null +++ b/NeuralVoicePuppetry/neural_voice_backend_api/config.py @@ -0,0 +1,26 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os.path + +from avatar_backend_api.config import AvatarConfig +from mtc_api_utils.config import Config + + +class NeuralVoiceConfig(AvatarConfig): + mockBackend: bool = Config.parse_env_var("MOCK_BACKEND", default="False", convert_type=bool) + + # Data configs + db_filepath: str = Config.parse_env_var("DB_FILEPATH", default="/tmp/tinyDB/neuralVoices.json") + + neural_code_base_dir: str = "/app/neural-code" # Location of the neural-code dir in the execution environment + + data_base_dir: str = Config.parse_env_var("DATA_BASE_DIR", default="/tmp/neuralVoice") + audio_input_dir: str = os.path.join(data_base_dir, "input_data", "audio") + video_input_dir: str = os.path.join(data_base_dir, "input_data", "video") + + output_data_path: str = os.path.join(data_base_dir, "output_data") + features_dir: str = os.path.join(output_data_path, "features") + video_output_dir: str = os.path.join(output_data_path, "videos") + checkpoints_dir: str = os.path.join(output_data_path, "checkpoints") + mappings_dir = os.path.join(output_data_path, "mappings") diff --git a/NeuralVoicePuppetry/neural_voice_backend_api/neural_voice_inference_queue.py b/NeuralVoicePuppetry/neural_voice_backend_api/neural_voice_inference_queue.py new file mode 100644 index 0000000..1b9c1fe --- /dev/null +++ b/NeuralVoicePuppetry/neural_voice_backend_api/neural_voice_inference_queue.py @@ -0,0 +1,28 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os +import shutil + +from avatar_backend_api.background_tools.inference_queue import InferenceQueue, InferenceQueueTask +from avatar_backend_api.clients.io_client import VIDEO_EXTENSION + +from neural_voice_backend_api.config import NeuralVoiceConfig + + +class NeuralVoiceInferenceQueue(InferenceQueue): + + def post_processing(self, task: InferenceQueueTask) -> None: + # Delete input audio + self.io_client.audio.delete_file(video_id=task.request.video_id) + + # Delete features + features_path = os.path.join(NeuralVoiceConfig.features_dir, task.request.video_id) + shutil.rmtree(path=features_path, ignore_errors=True) + + # Rename file and return video path + if not NeuralVoiceConfig.mockBackend: + self.io_client.video.rename_file( + filename=f"{task.request.video_id}_to_{task.request.avatar.name}{VIDEO_EXTENSION}", + new_filename=task.request.video_id + VIDEO_EXTENSION, + ) diff --git a/NeuralVoicePuppetry/neural_voice_backend_api/neural_voice_model.py b/NeuralVoicePuppetry/neural_voice_backend_api/neural_voice_model.py new file mode 100644 index 0000000..5b2eff8 --- /dev/null +++ b/NeuralVoicePuppetry/neural_voice_backend_api/neural_voice_model.py @@ -0,0 +1,62 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details +import glob +import os +import subprocess +from typing import Dict + +from avatar_backend_api.api_types import InferenceQueueTask +from avatar_backend_api.models.avatar_base_model import AvatarBaseModel +from neural_voice_backend_api.config import NeuralVoiceConfig + + +class NeuralVoiceModel(AvatarBaseModel): + + def init_model(self): + for avatar in NeuralVoiceConfig.available_avatars.keys(): + video_input_dir = os.path.join(NeuralVoiceConfig.video_input_dir, avatar) + features_dir = os.path.join(NeuralVoiceConfig.features_dir, avatar) + checkpoints_glob = glob.glob(os.path.join(NeuralVoiceConfig.checkpoints_dir, f"*.{avatar}")) + mappings_glob = glob.glob(os.path.join(NeuralVoiceConfig.mappings_dir, "*")) + + if not os.path.isdir(video_input_dir): + raise ValueError(f"Could not find directory {video_input_dir}, which is supposed to contain the input video files for avatar {avatar}") + + # Commented for training + # if not os.path.isdir(features_dir): + # raise ValueError(f"Could not find directory {features_dir}, which is supposed to contain the feature files for avatar {avatar}") + + # if not len(checkpoints_glob) >= 1: + # raise ValueError(f"Could not find a checkpoints directory for avatar {avatar} under {NeuralVoiceConfig.checkpoints_dir}") + + # if not len(mappings_glob) >= 1: + # raise ValueError(f"Could not find a mappings directory for avatar {avatar} under {NeuralVoiceConfig.mappings_dir}") + + # Create input & output directories + os.makedirs(NeuralVoiceConfig.audio_input_dir, exist_ok=True) + os.makedirs(NeuralVoiceConfig.features_dir, exist_ok=True) + os.makedirs(NeuralVoiceConfig.video_input_dir, exist_ok=True) + os.makedirs(NeuralVoiceConfig.video_output_dir, exist_ok=True) + + print("All files were successfully downloaded, neuralVoice model is ready") + + def inference(self, task: InferenceQueueTask) -> None: + """ Takes an audio_name, which represents a filename without extension, and an avatar, runs the inference and returns the path to the resulting outpout video file""" + + # Run inference script + subprocess.run( + args=["bash", "full_pipeline_nvp.sh", task.request.video_id, task.request.avatar.name, NeuralVoiceConfig.data_base_dir], + cwd=NeuralVoiceConfig.neural_code_base_dir, # Working directory from which to run the shell command + universal_newlines=True, # Decode output + check=True, # Throw exception if return code != 0 + ) + print(f"Inference completed for {task.request.video_id} - {task.request.avatar.value}") + + @staticmethod + def available_avatars() -> Dict[str, str]: + with os.scandir(NeuralVoiceConfig.video_input_dir) as iterator: + return { + avatar_dir.name: NeuralVoiceConfig.avatar_short_name(avatar_name=avatar_dir.name) + for avatar_dir in iterator + if avatar_dir.is_dir() + } diff --git a/NeuralVoicePuppetry/neural_voice_backend_api/tests/Test-2.wav b/NeuralVoicePuppetry/neural_voice_backend_api/tests/Test-2.wav new file mode 100644 index 0000000..2debc29 Binary files /dev/null and b/NeuralVoicePuppetry/neural_voice_backend_api/tests/Test-2.wav differ diff --git a/NeuralVoicePuppetry/neural_voice_backend_api/tests/__init__.py b/NeuralVoicePuppetry/neural_voice_backend_api/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/NeuralVoicePuppetry/neural_voice_backend_api/tests/integration_test_client.py b/NeuralVoicePuppetry/neural_voice_backend_api/tests/integration_test_client.py new file mode 100644 index 0000000..0355a9b --- /dev/null +++ b/NeuralVoicePuppetry/neural_voice_backend_api/tests/integration_test_client.py @@ -0,0 +1,60 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from http import HTTPStatus +from typing import List, BinaryIO, Tuple + +from avatar_backend_api.api_types import ApiRoute, AvatarModelRequest +from mtc_api_utils.api import BaseApi +from mtc_api_utils.clients.api_client import ApiClient +from starlette.testclient import TestClient + +from avatar_backend_api.config import AvatarConfig + + +class AvatarIntegrationTestClient(ApiClient): + + def __init__(self, test_app: BaseApi): + super().__init__(backend_url="", http_client=TestClient(app=test_app)) + + def list_video_ids(self) -> List[str]: + resp = self.http_client.get( + url=ApiRoute.video_ids.value, + ) + resp.raise_for_status() + + return resp.json() + + def get_video(self, video_id: str) -> Tuple[int, bytes]: + resp = self.http_client.get( + url=ApiRoute.video.value, + params={"videoId": video_id}, + ) + + if resp.status_code >= 300: + resp.raise_for_status() + + return resp.status_code, resp.content + + def post_audio(self, audio: BinaryIO, metadata: AvatarModelRequest) -> AvatarModelRequest: + resp = self.http_client.post( + url=ApiRoute.inference.value, + params=metadata.json_dict, + files={"audio": audio}, + ) + + if resp.status_code >= 300: + print(f"{resp.reason=}") + print(f"{resp.text=}") + print(f"{resp.raw=}") + + resp.raise_for_status() + assert resp.status_code == HTTPStatus.ACCEPTED + + return AvatarModelRequest.parse_obj(resp.json()) + + def delete_video(self, video_id: str) -> str: + resp = self.http_client.delete(ApiRoute.video_url(backend_url="", video_id=video_id)) + resp.raise_for_status() + + return resp.text diff --git a/NeuralVoicePuppetry/neural_voice_backend_api/tests/ping.wav b/NeuralVoicePuppetry/neural_voice_backend_api/tests/ping.wav new file mode 100644 index 0000000..c0dc31c Binary files /dev/null and b/NeuralVoicePuppetry/neural_voice_backend_api/tests/ping.wav differ diff --git a/NeuralVoicePuppetry/neural_voice_backend_api/tests/test.mp3 b/NeuralVoicePuppetry/neural_voice_backend_api/tests/test.mp3 new file mode 100644 index 0000000..7517a57 Binary files /dev/null and b/NeuralVoicePuppetry/neural_voice_backend_api/tests/test.mp3 differ diff --git a/NeuralVoicePuppetry/neural_voice_backend_api/tests/test_integration.py b/NeuralVoicePuppetry/neural_voice_backend_api/tests/test_integration.py new file mode 100644 index 0000000..4118943 --- /dev/null +++ b/NeuralVoicePuppetry/neural_voice_backend_api/tests/test_integration.py @@ -0,0 +1,74 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os +import unittest +from enum import Enum +from http import HTTPStatus +from time import sleep + +import httpx + +from avatar_backend_api.api_types import AvatarModelRequest, BaseAvatar +from neural_voice_backend_api.app import app +from neural_voice_backend_api.tests.integration_test_client import AvatarIntegrationTestClient + + +class TestFile(Enum): + value: str + test_mp3 = "test.mp3" + ping_wav = "ping.wav" + voice_wav = "voice_test.wav" + frontend_wav = "Test-2.wav" + + +test_dir = os.path.dirname(os.path.realpath(__file__)) +test_file_name = TestFile.frontend_wav.value +test_file_path = os.path.join(test_dir, test_file_name) + +TEST_REQUEST = AvatarModelRequest( + video_id="id", + avatar=BaseAvatar.Jennifer_355_9415, +) + + +class TestIntegration(unittest.TestCase): + + def setUp(self) -> None: + self.client = AvatarIntegrationTestClient(test_app=app) + + self.client.wait_for_service_readiness() + try: + self.client.delete_video(TEST_REQUEST.video_id) + except httpx.HTTPStatusError as e: + self.assertEqual(e.response.status_code, HTTPStatus.NOT_FOUND) + + print("Integration Test Setup Completed") + + def tearDown(self) -> None: + self.setUp() + + def test_inference(self): + with open(test_file_path, "rb") as test_file: + initial_video_ids = self.client.list_video_ids() + self.assertNotIn(TEST_REQUEST.video_id, initial_video_ids) + + try: + self.client.delete_video(TEST_REQUEST.video_id) + self.fail("Expected delete to fail because video is not present") + except httpx.HTTPStatusError as e: + self.assertEqual(e.response.status_code, HTTPStatus.NOT_FOUND) + + self.assertEqual(TEST_REQUEST, self.client.post_audio(audio=test_file, metadata=TEST_REQUEST)) + + while True: + status_code, file_bytes = self.client.get_video(video_id=TEST_REQUEST.video_id) + self.assertIn(status_code, [HTTPStatus.OK, HTTPStatus.PROCESSING]) + + if status_code == HTTPStatus.OK: + break + else: + sleep(1) + + print("Test Delete Video") + self.client.delete_video(video_id=TEST_REQUEST.video_id) diff --git a/NeuralVoicePuppetry/neural_voice_backend_api/tests/voice_test.wav b/NeuralVoicePuppetry/neural_voice_backend_api/tests/voice_test.wav new file mode 100644 index 0000000..95ce3fe Binary files /dev/null and b/NeuralVoicePuppetry/neural_voice_backend_api/tests/voice_test.wav differ diff --git a/README.md b/README.md new file mode 100644 index 0000000..16e7b35 --- /dev/null +++ b/README.md @@ -0,0 +1,213 @@ +
+ +
+ +#
AvatarForge
+
Make your own digital avatars.
+ +

+ + +## Project Details +This repo contains the code of the project: Audio Driven Video Synthesis Of Personalized Moderations. + +Check out the project's scope and details on our [website](https://mtc.ethz.ch/research/image-video-processing/video-synthesis.html). + +# +## Table of Contents +1. [Demo Description](#demo-description) +2. [Available Models](#available-models) + 1. [Neural Voice Puppetry](#neural-voice-puppetry) + 2. [Motion GAN](#motion-gan) +3. [Data](#data) +4. [Usage](#usage) + 1. [Train New Avatars](#train-new-avatars) + 2. [Inference for NeuralVoicePuppertry Model](#inference-for-neuralvoicepuppertry-model) + 3. [Inference for MotionGan Model](#inference-for-motiongan-model) + 4. [Build and Run Docker Containers](#build-and-run-docker-containers) + 5. [API Usage](#api-usage) + +# +## Demo Description +This tool allows you to generate your own deepfake avatar video. + +- Provide a short video and train your own avatars. + +- Pick an avatar from our available collection, record your own audio snippet and wait for the automated process to complete. After a few minutes, your deepfake avatar video can be found in the gallery. + +# +## Available Models +You can find here the code for the two pipelines implemented during this project: +- Neural Voice Puppetry +- Motion GAN + +*More details for each pipeline can be found in the respective READMEs.* + + +### Neural Voice Puppetry +![NeuralVoicePuppetry](figures/pipeline_NVP.png "NeuralVoicePuppetry") + + +### Motion GAN +![MotionGAN](figures/pipeline_motiongan.gif "MotionGAN") + +# +## Data +If you plan on using this code with the already available and pre-trained moderators, you will only have to provide the audio data. Otherwise provide both audio and video. + +Please follow these instructions on data quality: +- Audio + - Provide a recording of a person speaking (audio of any duration is accepted). + - The cleaner the audio signal the better: audio with background noise will result in unmatching lip-sync. + - Avoid recording multiple people talking: the model is unable to distinguish between multiple voice signals. +- Video + - Provide a video of your desired avatar character talking. + - Minimum video duration: 3 minutes. + - Longer videos will results in longer training time. + - The background is irrelevant, it will be removed during preprocessing. + - Avoid hand or arms movements. Having such movements (that might cause occlusion) will interfere with the quality of the generated frames during training. + +# +## Usage +You can follow the instructions in each README to clone the respective repositories and run the python code independently. + +Following are some instructions to build both models using **Docker**. +### Train New Avatars +* Place the new video in `/data/{model-name}/input_data/video`. Where `model-name` is either `motionGan` or `neuralVoice`. + Be aware that the name of the folder determines the avatar name. + ```bash + input_data + └── video # Folder containing m video files + ├── avatar_1 + │ └── avatar_1.mp4 + ... + └── avatar_m + │ └── avatar_m.mp4 + │ + └── my_new_avatar + └── my_new_avatar.mp4 + + ``` + +* Set the `AVAILABLE_AVATARS` variable in `.env`/`debug.env` to the name of your new avatar(s) using a comma separated list +* Build and run the docker containers using instructions [here](#build-and-run-docker-containers). +* Use the API as described [here](#api-usage). + +# +### Inference for NeuralVoicePuppertry Model +* Place the checkpoints directory in `/data/neuralVoice/checkpoints`. Be aware that the name of the folder determines the avatar name. This directory must contain the following elements: + ```bash + checkpoints +    └── my_avatar +    ├── latest_inpainter.pth # Weights for the inpainter network +    ├── latest_netD.pth # Weights for the discriminator network +    ├── latest_texture_decoder.pth # Weights for the neural renderer network +    └── latest_texture.pth # Learned neural texture + ``` + *Note: all these files will be generated automatically when training an avatar on a new video.* + +* Place the checkpoints directory in `/data/neuralVoice/features`. Be aware that the name of the folder determines the avatar name. This directory must contain the following elements: + ```bash + features +    └── my_avatar +    ├── DECA_codedicts # folder containing all DECA morphable model information per frame +    ├── og_frames # folder containing all extracted video frames +    ├── my_avatar.h5 # H5 file containing all tracking information for each frame +    └── tform.npy # numpy file containing all transformation required to crop each frame + ``` + *Note: all these files will be generated automatically when training an avatar on a new video.* + +* Place the checkpoints directory in `/data/neuralVoice/mappings`. Be aware that the name of the folder determines the avatar name. This directory must contain the following elements: + ```bash + mappings + └── audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead +    └── mapping_my_avatar.npy # file containing mapping between audio expressions and person specific expressions  + ``` + *Note: all these files will be generated automatically when training an avatar on a new video.* + +* Place the input video directory in `/data/neuralVoice/input_data/video`. Be aware that the name of the folder determines the avatar name. This directory must contain the following files: + ```bash + input_data # Folder containing all input data + └── video # Folder containing video files + └── my_avatar # Folder generated by running the pipeline: contains "my_new_avatar" video processed information + ├── my_avatar.mp4 # original video of the avatar + └── my_avatar.wav # extracted audio from the original video + ``` + *Note: all these files will be generated automatically when training an avatar on a new video.* + +* Set the `AVAILABLE_AVATARS` variable in `.env`/`debug.env` to the name of your avatar(s) using a comma separated list +* Build and run the docker containers using instructions [here](#build-and-run-docker-containers). +* Use the API as described [here](#api-usage). + +# +### Inference for MotionGan Model +* Place the checkpoints directory in `/data/motionGan/checkpoints`. Be aware that the name of the folder determines the avatar name. This directory must contain the following elements: + ```bash + checkpoints # Folder containing all checkpoints of trained avatars + └── my_avatar # Folder containing checkpoints of the "my_new_avatar" Avatar + ├── GAN_config.txt # GAN configuration parameters during training + ├── head2body.pkl # checkpoint for head position to body position regression + ├── head2body_test.png # result of the regression + ├── latest_Audio2Headpose.pkl # checkpoint for audio to head-motion finetuned network + ├── latest_GAN_model.pt # checkpoint for the GAN network + ├── logs # folder containing all training logs during GAN training + └── train_opt.txt # audio to head-motion configuration parameters during finetuning + ``` + *Note: all these files will be generated automatically when training an avatar on a new video.* + + +* Place the input video directory in `/data/motionGan/input_data/video`. Be aware that the name of the folder determines the avatar name. This directory must contain the following files: + ```bash + input_data # Folder containing all input data + └── video # Folder containing video files + └── my_avatar # Folder generated by running the pipeline: contains "my_new_avatar" video processed information + ├── img_size.npy # file containing information about the frame size + ├── mapping.npy # file containing mapping between audio expressions and person specific expressions + └── track_params.pt # file containing all head poses and tracked information of the original video + ``` + *Note: all these files will be generated automatically when training an avatar on a new video.* + + +* Set the `AVAILABLE_AVATARS` variable in `.env`/`debug.env` to the name of your avatar(s) using a comma separated list +* Build and run the docker containers using instructions [here](#build-and-run-docker-containers). +* Use the API as described [here](#api-usage). + +# +### Build and Run Docker Containers +* Run the application using one of the following commands: + * In order to run a regular, full run including both models, run the following: + ```bash + docker compose up --build + ``` + This configuration makes use of the settings specified in the `docker-compose.yml` and the `.env` files + * In order to run a reduced debug setup, run the following: + ```bash + docker compose -f docker-compose.yml -f docker-compose-debug-yml up --build backend {model-name} + ``` + Where `{model-name}` is either `nerual-voice-model` or `motion-gan-model`. + + This configuration overrides the settings specified in the `docker-compose.yml` and the `.env` files with those in the `docker-compose-debug.yml` and `debug.env` files respectively. + +# +### API Usage +In order to interact with the running api server, access `http://localhost:5000/api/docs`, then navigate to the following routes: +* `POST /api/inference`: Run inference on a new input audio. If the selected avatar has not yet been trained, this will perform the training. +Inference should take roughly a minute for most short audio clips, training will take roughly 8 to 24 hours +* `GET /api/videos`: Returns the metadata for all generated videos, including ones currently being processed. Note the `inferenceCompleted` flag, indicating whether inference for this particular video has been completed. +* `GET /api/video`: Returns the video in a common web streaming format that can be displayed by most modern video players & browsers but does not work properly with the docs page. +Alternatively you can retrieve your finished videos from the folder structure the same way you added the input videos under `data/{model-name}/output_data/video`. +* `DELETE /api/video`: Deletes all data about a generated video from the database as well as the file system. + +# +### LICENSE + +This implementation AvatarForge is free and open source! All code in this repository is licensed under: + +* [MIT](LICENSE) License. + +Both pipeline rely and are inspired by the other works works. You can find further information in each the READMEs of both offered pipeline. + +# +### Contact information + +Alberto Pennino: [alberto.pennino@inf.ethz.ch](alberto.pennino@inf.ethz.ch) \ No newline at end of file diff --git a/avatar-api/.env b/avatar-api/.env new file mode 100644 index 0000000..aa6ef46 --- /dev/null +++ b/avatar-api/.env @@ -0,0 +1,18 @@ +##### GENERAL ENVS ##### +GPU_SUPPORTED=True +GPU_ENABLED=True + +##### AUTH ENVS ##### +AUTH_ENABLED=False +REQUIRED_AUTH_ROLES=video-synthesis + +##### BACKEND ENVS ##### +FFMPEG_DOCKER=True # Python will use a different version of ffmpeg, therefore we need to redirect to /usr/bin/ffmpeg + +##### Frontend ENVS ##### +FLUTTER_USE_LOCALHOST_BACKEND=true # Governs whether Flutter connects to production backend or platform dependent local backend. +FLUTTER_ENVIRONMENT=dev +FLUTTER_AUTH_ENABLED=false +FLUTTER_BACKEND_URL=/api + +BACKEND_URL=http://backend:5000 diff --git a/avatar-api/Dockerfile b/avatar-api/Dockerfile new file mode 100644 index 0000000..051488f --- /dev/null +++ b/avatar-api/Dockerfile @@ -0,0 +1,30 @@ +FROM continuumio/miniconda3 as base +ENV DEBIAN_FRONTEND noninteractive + +# Keeps Python from generating .pyc files in the container +ENV PYTHONDONTWRITEBYTECODE 1 +# Turns off buffering for easier container logging +ENV PYTHONUNBUFFERED 1 + +ENV TZ=Europe/Zurich + +SHELL ["/bin/bash", "-c"] +WORKDIR /app + +# Update & install packages +RUN apt-get -qq update --fix-missing && \ + apt-get -qq install -y bash curl wget git ca-certificates openssh-client gpg tzdata && \ + apt-get -qq clean + # Make sure google-cloud-sdk-gke-gcloud-auth-plugin is installed, because it won't be included in later releases + +FROM base as backend +WORKDIR /app + +COPY gunicorn.conf.py . +COPY requirements.txt . +RUN pip install -r requirements.txt + +COPY avatar_backend_api ./avatar_backend_api + +CMD [ "gunicorn", "-c", "gunicorn.conf.py", "--chdir", "./avatar_backend_api", "-k", "uvicorn.workers.UvicornWorker", "app:app" ] + diff --git a/avatar-api/README.md b/avatar-api/README.md new file mode 100644 index 0000000..be6a0c9 --- /dev/null +++ b/avatar-api/README.md @@ -0,0 +1,92 @@ +# Avatar API + + + +## Getting started + +To make it easy for you to get started with GitLab, here's a list of recommended next steps. + +Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)! + +## Add your files + +- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files +- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command: + +``` +cd existing_repo +git remote add origin https://gitlab.ethz.ch/mtc/video-synthesis/avatar-api.git +git branch -M main +git push -uf origin main +``` + +## Integrate with your tools + +- [ ] [Set up project integrations](https://gitlab.ethz.ch/mtc/video-synthesis/avatar-api/-/settings/integrations) + +## Collaborate with your team + +- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/) +- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html) +- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically) +- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/) +- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html) + +## Test and Deploy + +Use the built-in continuous integration in GitLab. + +- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html) +- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/) +- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html) +- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/) +- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html) + +*** + +# Editing this README + +When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template. + +## Suggestions for a good README +Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information. + +## Name +Choose a self-explaining name for your project. + +## Description +Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors. + +## Badges +On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge. + +## Visuals +Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method. + +## Installation +Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection. + +## Usage +Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README. + +## Support +Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc. + +## Roadmap +If you have ideas for releases in the future, it is a good idea to list them in the README. + +## Contributing +State if you are open to contributions and what your requirements are for accepting them. + +For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self. + +You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser. + +## Authors and acknowledgment +Show your appreciation to those who have contributed to the project. + +## License +For open source projects, say how it is licensed. + +## Project status +If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers. diff --git a/avatar-api/avatar_backend_api/__init__.py b/avatar-api/avatar_backend_api/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/avatar-api/avatar_backend_api/api_types.py b/avatar-api/avatar_backend_api/api_types.py new file mode 100644 index 0000000..6f2f9c4 --- /dev/null +++ b/avatar-api/avatar_backend_api/api_types.py @@ -0,0 +1,67 @@ +from __future__ import annotations + +from dataclasses import dataclass +from enum import Enum +from typing import Optional +from uuid import uuid4 + +from mtc_api_utils.api_types import ApiType, FirebaseUser +from pydantic import Field + +from avatar_backend_api.config import AvatarConfig + + +class ApiRoute(Enum): + value: str + + video = "/api/video" + list_videos = f"{video}s" + video_ids = f"{video}/ids" + + inference = "/api/inference" + avatars = "/api/avatars" + + @staticmethod + def video_url(backend_url: str, video_id: str) -> str: + return f"{backend_url}{ApiRoute.video.value}?videoId={video_id}" + + +class StrEnum(str, Enum): + value: str + + +class BaseAvatar(StrEnum): + Jennifer_355_9415 = "jennifer" + Arthur_A2226 = "arthur" + + +Avatar = StrEnum("Avatar", AvatarConfig.available_avatars) + + +class AvatarModel(Enum): + value: str + neural_voice = "neuralVoice" + motion_gan = "motionGan" + + +class AvatarModelRequest(ApiType): + video_id: str = Field(alias="videoId", default_factory=lambda: uuid4().hex) + avatar: Avatar = Field(example=BaseAvatar.Jennifer_355_9415) + + +class AvatarRequest(AvatarModelRequest): + audio_name: str = Field(alias="audioName", example="YourUserName") + avatar_model: AvatarModel = Field(alias="avatarModel", example=AvatarModel.neural_voice) + + def to_metadata(self, inference_completed: bool = False) -> VideoMetadata: + return VideoMetadata(**self.json_dict, inference_completed=inference_completed) + + +class VideoMetadata(AvatarRequest): + inference_completed: bool = Field(alias="inferenceCompleted", default=False) + + +@dataclass +class InferenceQueueTask: + request: AvatarModelRequest + user: Optional[FirebaseUser] = None diff --git a/avatar-api/avatar_backend_api/app.py b/avatar-api/avatar_backend_api/app.py new file mode 100644 index 0000000..3a959fc --- /dev/null +++ b/avatar-api/avatar_backend_api/app.py @@ -0,0 +1,153 @@ +from http import HTTPStatus +from typing import List, Dict +from uuid import uuid4 + +import uvicorn +from fastapi import UploadFile, Depends, Query, HTTPException +from mtc_api_utils.api import BaseApi, RequireReadinessDependency +from mtc_api_utils.api_types import FirebaseUser +from mtc_api_utils.clients.firebase_client import firebase_user_auth +from starlette.responses import FileResponse + +from avatar_backend_api.api_types import ApiRoute, VideoMetadata, AvatarRequest, AvatarModel +from avatar_backend_api.background_tools.model_result_poll_worker import ModelResultPollWorker +from avatar_backend_api.clients.avatar_client import AvatarModelClient +from avatar_backend_api.clients.db_client import AvatarDbClient +from avatar_backend_api.clients.io_client import IoClient, IoClientException +from avatar_backend_api.clients.mock_avatar_model_client import MockAvatarModelClient +from avatar_backend_api.config import AvatarConfig + +AvatarConfig.print_config() + +user_auth = firebase_user_auth(config=AvatarConfig) + +db_client = AvatarDbClient() +io_client = IoClient(user_dir_mode=True) + +model_clients: Dict[AvatarModel, AvatarModelClient] = { + AvatarModel.neural_voice: + AvatarModelClient(backend_url=AvatarConfig.neural_voice_backend_url) + if not AvatarConfig.mockBackend + else MockAvatarModelClient(backend_url=""), + AvatarModel.motion_gan: + AvatarModelClient(backend_url=AvatarConfig.motion_gan_backend_url) + if not AvatarConfig.mockBackend + else MockAvatarModelClient(backend_url=""), +} + +model_poll_worker = ModelResultPollWorker( + io_client=io_client, + db_client=db_client, + model_clients=model_clients, +) + + +def backend_is_ready() -> bool: + # Only turn ready if both models are ready + for model, client in model_clients.items(): + if model.value in AvatarConfig.avatar_models: + _, is_ready = client.get_readiness() + if not is_ready: + return False + + return True + + +app = BaseApi(is_ready=backend_is_ready, config=AvatarConfig, global_readiness_middleware_enabled=False) + + +@app.get( + path=ApiRoute.video_ids.value, + response_model=List[str], +) +async def list_video_ids(user: FirebaseUser = Depends(user_auth)) -> List[str]: + return [path.split("/")[-1].split(".")[0] for path in io_client.video.list_videos(user=user)] + + +@app.get( + path=ApiRoute.list_videos.value, + response_model=List[VideoMetadata], +) +async def list_videos(user: FirebaseUser = Depends(user_auth)) -> List[VideoMetadata]: + return db_client.list_videos(user=user) + + +@app.get( + path=ApiRoute.video.value, + response_class=FileResponse, +) +async def get_video(video_id: str = Query(alias="videoId")): # user: FirebaseUser = Depends(user_auth) + # The following line represents a workaround for the fact that the frontend video library can't send an authentication header with the video request. + # If the library should be fixed in the future, the user should be retrieved using the user_auth dependency as follows: [ user: FirebaseUser = Depends(user_auth) ] + user = FirebaseUser(email=db_client.get_user_from_id(video_id=video_id), roles=[]) + + try: + return io_client.video.read_from_disk(video_id=video_id, user=user) + except IoClientException: + if db_client.video_exists(video_id=video_id, user=user): + raise HTTPException( + status_code=HTTPStatus.PROCESSING, + detail=f"Audio with {video_id=} is currently being processed", + ) + + else: + raise HTTPException(status_code=HTTPStatus.NOT_FOUND, detail=f"A request with video_id={video_id} has not been found") + + +@app.post( + path=ApiRoute.inference.value, + status_code=HTTPStatus.ACCEPTED, + response_model=AvatarRequest, + dependencies=[Depends(RequireReadinessDependency(base_api=app))], + +) +async def inference( + audio: UploadFile, + request_metadata: AvatarRequest = Depends(), + user: FirebaseUser = Depends(user_auth), +) -> AvatarRequest: + print(f"Routing request to model={request_metadata.avatar_model.value} for audio_name={audio.filename}") + + if not request_metadata.video_id: + request_metadata.video_id = uuid4().hex + + if "." not in audio.filename: + audio.filename += ".wav" + + metadata = request_metadata.to_metadata() + filename = f"{request_metadata.video_id}.{audio.filename.split('.')[-1]}" + + model_clients[request_metadata.avatar_model].post_audio(audio=(filename, audio.file.read()), metadata=metadata) + db_client.insert_video(video_metadata=metadata, user=user) + + return metadata + + +@app.delete( + path=ApiRoute.video.value, + response_model=str, +) +async def delete_video(video_id: str = Query(alias="videoId"), user: FirebaseUser = Depends(user_auth)) -> str: + db_client.delete_video(video_id=video_id, user=user) + + try: + io_client.video.delete_file(video_id=video_id, user=user) + + except IoClientException: + raise HTTPException(status_code=HTTPStatus.NOT_FOUND, detail=f"Unable to find video with video_id={video_id}") + + return f"Successfully removed video with video_id={video_id}" + + +@app.get( + path=ApiRoute.avatars.value, + response_model=Dict[str, str], + tags=["Avatars"], + dependencies=[Depends(user_auth)], +) +async def available_avatars() -> Dict[str, str]: + return AvatarConfig.available_avatars + + +if __name__ == '__main__': + uvicorn.run(app=app, port=5000) diff --git a/avatar-api/avatar_backend_api/background_tools/__init__.py b/avatar-api/avatar_backend_api/background_tools/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/avatar-api/avatar_backend_api/background_tools/inference_queue.py b/avatar-api/avatar_backend_api/background_tools/inference_queue.py new file mode 100644 index 0000000..1fed652 --- /dev/null +++ b/avatar-api/avatar_backend_api/background_tools/inference_queue.py @@ -0,0 +1,48 @@ +import os +from queue import SimpleQueue +from threading import Thread +from time import sleep + +from avatar_backend_api.api_types import InferenceQueueTask +from avatar_backend_api.clients.io_client import IoClient +from avatar_backend_api.config import AvatarConfig +from avatar_backend_api.models.avatar_base_model import AvatarBaseModel + + +class InferenceQueue: + def __init__( + self, + model: AvatarBaseModel, + io_client: IoClient, + audio_input_dir: str = AvatarConfig.audio_input_dir, + video_output_dir: str = AvatarConfig.video_output_dir, + daemon_worker: bool = True, + ): + self.queue: SimpleQueue[InferenceQueueTask] = SimpleQueue() + + self.model = model + self.io_client = io_client + + self.audio_input_dir = audio_input_dir + self.video_output_dir = video_output_dir + self._worker_thread = Thread(target=self._worker, daemon=daemon_worker) + + os.makedirs(self.audio_input_dir, exist_ok=True) + os.makedirs(self.video_output_dir, exist_ok=True) + + self._worker_thread.start() + + def post_processing(self, task: InferenceQueueTask) -> None: + pass + + def add_task(self, task: InferenceQueueTask) -> None: + self.queue.put(task) + + def _worker(self): + while True: + if self.model.is_ready() and not self.queue.empty(): + task = self.queue.get() + self.model.inference(task=task) + self.post_processing(task=task) + else: + sleep(1) diff --git a/avatar-api/avatar_backend_api/background_tools/model_result_poll_worker.py b/avatar-api/avatar_backend_api/background_tools/model_result_poll_worker.py new file mode 100644 index 0000000..0438a6c --- /dev/null +++ b/avatar-api/avatar_backend_api/background_tools/model_result_poll_worker.py @@ -0,0 +1,69 @@ +from threading import Thread +from time import sleep +from typing import Dict, Callable + +from mtc_api_utils.api_types import FirebaseUser + +from avatar_backend_api.api_types import AvatarModel +from avatar_backend_api.clients.avatar_client import AvatarModelClient +from avatar_backend_api.clients.db_client import AvatarDbClient, AvatarDbException +from avatar_backend_api.clients.io_client import IoClient + + +class ModelResultPollWorker: + + def __init__( + self, + io_client: IoClient, + db_client: AvatarDbClient, + model_clients: Dict[AvatarModel, AvatarModelClient], + daemon_worker: bool = True, + ): + self.io_client = io_client + self.model_clients = model_clients + self.db_client = db_client + + self._worker_thread = Thread(target=self._worker, daemon=daemon_worker) + self._worker_thread.start() + + def _worker(self): + """ + Continuously polls processed videos from model backends, retrieves them and performs cleanup. + + Methods are separated mostly for testing purposes + """ + while True: + self._poll_models(perform_on_available_video=self._retrieve_video_from_model) + sleep(5) + + def _poll_models(self, perform_on_available_video: Callable[[str, AvatarModel], None]): + """ GET available videos from Model, then GET video & store video file + metadata in backend API""" + + for model, client in self.model_clients.items(): + if client.get_readiness()[1]: + video_ids = client.list_video_ids() + for video_id in video_ids: + print(f"Retrieving the following videos from model={model}: {video_ids}") + perform_on_available_video(video_id, model) + + def _retrieve_video_from_model(self, video_id: str, avatar_model: AvatarModel) -> None: + status, video_bytes = self.model_clients[avatar_model].get_video(video_id=video_id) + + try: + user = FirebaseUser( + email=self.db_client.get_user_from_id(video_id=video_id), + roles=[], + ) + + except AvatarDbException as e: + print(e.detail) + return + + video_metadata = self.db_client.get_video(video_id=video_id, user=user) + video_metadata.inference_completed = True + + self.io_client.video.save_to_disk(video_bytes=video_bytes, video_id=video_id, user=user) + self.db_client.upsert_video(video_metadata=video_metadata, user=user) + + print(f"Deleting video from model") + self.model_clients[avatar_model].delete_video(video_id=video_id) diff --git a/avatar-api/avatar_backend_api/clients/__init__.py b/avatar-api/avatar_backend_api/clients/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/avatar-api/avatar_backend_api/clients/avatar_client.py b/avatar-api/avatar_backend_api/clients/avatar_client.py new file mode 100644 index 0000000..e2d2e44 --- /dev/null +++ b/avatar-api/avatar_backend_api/clients/avatar_client.py @@ -0,0 +1,49 @@ +from typing import List, BinaryIO, Tuple, Union + +import requests +from mtc_api_utils.clients.api_client import ApiClient + +from avatar_backend_api.api_types import ApiRoute, AvatarModelRequest + + +class AvatarModelClient(ApiClient): + def list_video_ids(self) -> List[str]: + resp = requests.get( + url=self._backend_url + ApiRoute.video_ids.value, + ) + resp.raise_for_status() + + return resp.json() + + def get_video(self, video_id: str) -> Tuple[int, bytes]: + resp = requests.get( + url=self._backend_url + ApiRoute.video.value, + params={"videoId": video_id}, + ) + + if resp.status_code >= 300: + resp.raise_for_status() + + return resp.status_code, resp.content + + def post_audio(self, audio: Union[BinaryIO, Tuple[str, bytes]], metadata: AvatarModelRequest) -> AvatarModelRequest: + resp = requests.post( + url=self._backend_url + ApiRoute.inference.value, + params=metadata.json_dict, + files={"audio": audio}, + ) + + if resp.status_code >= 300: + print(f"resp.reason={resp.reason}") + print(f"resp.text={resp.text}") + print(f"resp.raw={resp.raw}") + + resp.raise_for_status() + + return metadata + + def delete_video(self, video_id: str) -> str: + resp = requests.delete(ApiRoute.video_url(backend_url=self._backend_url, video_id=video_id)) + resp.raise_for_status() + + return resp.text diff --git a/avatar-api/avatar_backend_api/clients/db_client.py b/avatar-api/avatar_backend_api/clients/db_client.py new file mode 100644 index 0000000..d36cad3 --- /dev/null +++ b/avatar-api/avatar_backend_api/clients/db_client.py @@ -0,0 +1,83 @@ +import os +from http import HTTPStatus +from typing import List + +from fastapi import HTTPException +from mtc_api_utils.api_types import FirebaseUser +from tinydb import TinyDB, where +from tinydb.table import Table + +from avatar_backend_api.api_types import VideoMetadata +from avatar_backend_api.config import AvatarConfig + + +class AvatarDbException(HTTPException): + pass + + +class AvatarDbClient: + db_filepath: str + db: TinyDB + + def __init__(self, db_filepath: str = AvatarConfig.db_filepath): + os.makedirs( + name=os.path.dirname(db_filepath), + exist_ok=True, + ) + + self.db_filepath = db_filepath + self.db = TinyDB(db_filepath) + + def _user_table(self, user: FirebaseUser) -> Table: + return self.db.table(name=user.email) + + def list_videos(self, user: FirebaseUser) -> List[VideoMetadata]: + return [VideoMetadata.parse_obj(doc) for doc in self._user_table(user=user).all()] + + def video_exists(self, video_id: str, user: FirebaseUser) -> bool: + return self._user_table(user=user).count(self.video_query(video_id=video_id)) == 1 + + def get_video(self, video_id: str, user: FirebaseUser) -> VideoMetadata: + result = self._user_table(user=user).get(self.video_query(video_id=video_id)) + + if result is None: + raise AvatarDbException(status_code=HTTPStatus.NOT_FOUND, detail=f"Video with id: {video_id} does not exist") + else: + return VideoMetadata.parse_obj(result) + + def insert_video(self, video_metadata: VideoMetadata, user: FirebaseUser) -> VideoMetadata: + if self._user_table(user=user).count(cond=self.video_query(video_id=video_metadata.video_id)) != 0: + raise AvatarDbException(HTTPStatus.CONFLICT, detail=f"An inference request with id {video_metadata.video_id} already exists") + + self._user_table(user=user).insert(document=video_metadata.json_dict) + print(f"Inserting new video_metadata for user={user} with video_id={video_metadata.video_id}") + + return video_metadata + + def upsert_video(self, video_metadata: VideoMetadata, user: FirebaseUser) -> VideoMetadata: + # Update doc if exists, else insert it + self._user_table(user=user).upsert( + document=video_metadata.json_dict, + cond=self.video_query(video_id=video_metadata.video_id), + ) + print(f"Updating video_metadata for user={user} with video_id={video_metadata.video_id}") + + return video_metadata + + def delete_video(self, video_id: str, user: FirebaseUser) -> VideoMetadata: + video_metadata = self.get_video(video_id=video_id, user=user) + self._user_table(user=user).remove(self.video_query(video_id=video_id)) + print(f"Removing video_metadata for user={user} with video_id={video_metadata.video_id}") + + return video_metadata + + def get_user_from_id(self, video_id) -> str: + for user_email in self.db.tables(): + if self.db.table(name=user_email).contains(self.video_query(video_id=video_id)): + return user_email + + raise AvatarDbException(status_code=HTTPStatus.NOT_FOUND, detail=f"Video with id: {video_id} does not exist in db. Existing users & video_ids: {[(email, self._user_table(user=FirebaseUser(email=email, roles=[])).all()) for email in self.db.tables()]}") + + @staticmethod + def video_query(video_id: str): + return where("videoId") == video_id diff --git a/avatar-api/avatar_backend_api/clients/io_client.py b/avatar-api/avatar_backend_api/clients/io_client.py new file mode 100644 index 0000000..fc8fd6b --- /dev/null +++ b/avatar-api/avatar_backend_api/clients/io_client.py @@ -0,0 +1,157 @@ +import os.path +from glob import glob +from http import HTTPStatus +from typing import BinaryIO, List, Optional, Union + +from fastapi import UploadFile, HTTPException +from mtc_api_utils.api_types import FirebaseUser +from starlette.responses import FileResponse + +from avatar_backend_api.config import AvatarConfig + +VIDEO_EXTENSION = ".mp4" + + +class IoClientException(HTTPException): + pass + + +class BaseIOClient: + def __init__(self, base_path: str, user_dir_mode: bool): + self.base_path = base_path + self.user_dir_mode = user_dir_mode + + def user_dir(self, user: Optional[FirebaseUser]) -> str: + if self.user_dir_mode: + if user is None: + raise ValueError("In user_dir_mode, a user is expected for IoClient methods") + else: + return os.path.join(self.base_path, user.email) + + if not self.user_dir_mode: + if user is not None: + raise ValueError("User is only expected in user_dir_mode. Either remove it or enable user_dir_mode in the IoClient constructor") + else: + return self.base_path + + +class AudioIoClient(BaseIOClient): + def matching_paths(self, video_id: str, user: Optional[FirebaseUser] = None) -> List[str]: + matching_paths = sorted(glob(os.path.join(self.user_dir(user=user), video_id, video_id) + ".*")) + + if len(matching_paths) == 0: + raise IoClientException(status_code=HTTPStatus.NOT_FOUND, detail=f"Unable to find file with video_id={video_id}") + + return matching_paths + + def audio_path(self, video_id: str, file_ending: str, user: Optional[FirebaseUser] = None) -> str: + return os.path.join(self.user_dir(user=user), video_id, f"{video_id}.{file_ending}") + + def file_exists(self, video_id: str, user: Optional[FirebaseUser] = None) -> bool: + try: + paths = self.matching_paths(video_id=video_id, user=user) + except IoClientException: + return False + + return len(paths) > 0 + + def save_to_disk(self, audio: UploadFile, video_id: str, user: Optional[FirebaseUser] = None) -> str: + file_ending = audio.filename.split(".")[-1] + + path = self.audio_path(video_id=video_id, file_ending=file_ending, user=user) + os.makedirs(os.path.dirname(path), exist_ok=True) + + with open(path, "wb") as disk_file: + file_bytes = audio.file.read() + disk_file.write(file_bytes) + + return path + + def read_from_disk(self, video_id: str, user: Optional[FirebaseUser] = None) -> BinaryIO: + try: + return open(self.matching_paths(video_id=video_id, user=user)[-1], "rb") + except FileNotFoundError: + raise IoClientException(status_code=HTTPStatus.NOT_FOUND, detail=f"Unable to find video with video_id={video_id}") + + def delete_file(self, video_id: str, user: Optional[FirebaseUser] = None) -> None: + for path in self.matching_paths(video_id=video_id, user=user): + try: + os.remove(path) + except FileNotFoundError: + raise IoClientException(status_code=HTTPStatus.NOT_FOUND, detail=f"Unable to find video with video_id={video_id}") + + +class VideoIoClient(BaseIOClient): + def list_videos(self, user: Optional[FirebaseUser] = None) -> List[str]: + return sorted(glob(os.path.join(self.user_dir(user=user), f"*{VIDEO_EXTENSION}"))) + + def video_path(self, video_id: str, user: Optional[FirebaseUser] = None) -> str: + return os.path.join(self.user_dir(user=user), video_id + VIDEO_EXTENSION) + + def file_exists(self, video_id: str, user: Optional[FirebaseUser] = None) -> bool: + return os.path.isfile(self.video_path(video_id=video_id, user=user)) + + def save_to_disk(self, video_bytes: Union[bytes, BinaryIO], video_id: str, user: Optional[FirebaseUser] = None) -> str: + try: + video_bytes = video_bytes.read() + except AttributeError: + pass + + path = self.video_path(video_id=video_id, user=user) + + os.makedirs(self.user_dir(user=user), exist_ok=True) + with open(path, "wb") as disk_file: + disk_file.write(video_bytes) + + return path + + def read_from_disk(self, video_id: str, user: Optional[FirebaseUser] = None) -> FileResponse: + file_path = self.video_path(video_id=video_id, user=user) + + if os.path.isfile(file_path): + return FileResponse( + path=file_path, + ) + + else: + raise IoClientException(status_code=HTTPStatus.NOT_FOUND, detail=f"Unable to find video with video_id={video_id}") + + def rename_file(self, filename: str, new_filename: str, user: Optional[FirebaseUser] = None) -> str: + input_path = os.path.join(self.user_dir(user=user), filename) + output_path = os.path.join(self.user_dir(user=user), new_filename) + + try: + os.rename( + src=input_path, + dst=output_path, + ) + except FileNotFoundError: + raise IoClientException(status_code=HTTPStatus.NOT_FOUND, detail=f"Unable to find video with filename={filename}") + + return output_path + + def delete_file(self, video_id: str, user: Optional[FirebaseUser] = None) -> str: + try: + path = self.video_path(video_id=video_id, user=user) + os.remove(path) + return path + + except FileNotFoundError: + raise IoClientException(status_code=HTTPStatus.NOT_FOUND, detail=f"Unable to find video with video_id={video_id}") + + +class IoClient: + """ Manages file input & output for Avatar audio & video files required by the Avatar project. + Can be used in two modes: If a FirebaseUser is passed to a method, the files will be read from and written to a subdirectory named after the user.email. + If the user is omitted, + """ + + def __init__( + self, + user_dir_mode: bool, + audio_base_path: str = AvatarConfig.audio_input_dir, + video_base_path: str = AvatarConfig.video_output_dir, + ): + self.user_dir_mode = user_dir_mode + self.audio = AudioIoClient(base_path=audio_base_path, user_dir_mode=user_dir_mode) + self.video = VideoIoClient(base_path=video_base_path, user_dir_mode=user_dir_mode) diff --git a/avatar-api/avatar_backend_api/clients/mock_avatar_model_client.py b/avatar-api/avatar_backend_api/clients/mock_avatar_model_client.py new file mode 100644 index 0000000..a22d470 --- /dev/null +++ b/avatar-api/avatar_backend_api/clients/mock_avatar_model_client.py @@ -0,0 +1,51 @@ +import logging +from http import HTTPStatus +from time import sleep +from typing import Dict, Union, BinaryIO, Tuple, List, Optional + +import requests +from fastapi import HTTPException +from mtc_api_utils.api_types import ApiStatus + +from avatar_backend_api.api_types import AvatarModelRequest +from avatar_backend_api.clients.avatar_client import AvatarModelClient + +log = logging.Logger("MockNeuralVoiceModel-Logger") + +INFERENCE_DELAY_SECONDS = 1 + +mock_db: Dict[str, AvatarModelRequest] = {} + + +class MockAvatarModelClient(AvatarModelClient): + + def get_liveness(self) -> Tuple[Optional[requests.Response], bool]: + return requests.Response(), True + + def get_readiness(self) -> Tuple[Optional[requests.Response], bool]: + return requests.Response(), True + + def get_status(self) -> Tuple[Optional[requests.Response], ApiStatus]: + return requests.Response(), ApiStatus(readiness=self.get_readiness()[1], gpu_supported=True, gpu_enabled=True) + + def list_video_ids(self) -> List[str]: + return [metadata.video_id for metadata in mock_db.values()] + + def get_video(self, video_id: str) -> Tuple[int, bytes]: + return HTTPStatus.OK, bytes() + + def post_audio(self, audio: Union[BinaryIO, Tuple[str, bytes]], metadata: AvatarModelRequest) -> AvatarModelRequest: + sleep(INFERENCE_DELAY_SECONDS) + + mock_db[metadata.video_id] = metadata + print(f"Processed audio: {metadata.video_id}") + + return metadata + + def delete_video(self, video_id: str) -> str: + try: + del mock_db[video_id] + except KeyError: + raise HTTPException(status_code=HTTPStatus.NOT_FOUND, detail=f"Video with video_id={video_id} was not found") + + return f"Successfully deleted video with video_id={video_id}" diff --git a/avatar-api/avatar_backend_api/config.py b/avatar-api/avatar_backend_api/config.py new file mode 100644 index 0000000..f5e5cb6 --- /dev/null +++ b/avatar-api/avatar_backend_api/config.py @@ -0,0 +1,36 @@ +from __future__ import annotations + +import os +from typing import Dict, List + +from mtc_api_utils.config import Config + + +def _avatar_short_name(avatar_name: str) -> str: + return avatar_name.split("_")[0].split("-")[0].split(".")[0].lower() + + +class AvatarConfig(Config): + # API configs + neural_voice_backend_url: str = Config.parse_env_var("NEURAL_VOICE_BACKEND_URL", default="http://neural-voice-model:5000") + motion_gan_backend_url: str = Config.parse_env_var("MOTION_GAN_BACKEND_URL", default="http://motion-gan-model:5000") + + # Debug configs + mockBackend: bool = Config.parse_env_var("MOCK_BACKEND", default="False", convert_type=bool) + + # IO configs + avatar_models: List[str] = Config.parse_env_var("AVATAR_MODELS", default="neuralVoice,motionGan", convert_type=list) + available_avatars: Dict[str, str] = { + avatar: _avatar_short_name(avatar) + for avatar in list(Config.parse_env_var("AVAILABLE_AVATARS", default="Jennifer_355_9415,Arthur_A2226", convert_type=list)) + } + + db_filepath: str = Config.parse_env_var("DB_FILEPATH", default="/tmp/tinyDB/neuralVoices.json") + + data_base_dir: str = Config.parse_env_var("DATA_BASE_DIR", default="/tmp/avatar") + audio_input_dir: str = os.path.join(data_base_dir, "input_data", "audio") + video_output_dir: str = os.path.join(data_base_dir, "output_data", "videos") + + @staticmethod + def avatar_short_name(avatar_name: str) -> str: + return _avatar_short_name(avatar_name=avatar_name) diff --git a/avatar-api/avatar_backend_api/models/__init__.py b/avatar-api/avatar_backend_api/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/avatar-api/avatar_backend_api/models/avatar_base_model.py b/avatar-api/avatar_backend_api/models/avatar_base_model.py new file mode 100644 index 0000000..f29c266 --- /dev/null +++ b/avatar-api/avatar_backend_api/models/avatar_base_model.py @@ -0,0 +1,23 @@ +from abc import ABC, abstractmethod +from typing import List + +from mtc_api_utils.base_model import MLBaseModel + +from avatar_backend_api.api_types import InferenceQueueTask +from avatar_backend_api.clients.io_client import IoClient + + +class AvatarBaseModel(MLBaseModel, ABC): + + def __init__(self, io_client: IoClient): + self.io_client = io_client + super().__init__() + + @abstractmethod + def inference(self, task: InferenceQueueTask) -> None: + raise Exception("Not implemented") + + @staticmethod + @abstractmethod + def available_avatars() -> List[str]: + raise Exception("Not implemented") diff --git a/avatar-api/avatar_backend_api/models/mock_avatar_model.py b/avatar-api/avatar_backend_api/models/mock_avatar_model.py new file mode 100644 index 0000000..f883de5 --- /dev/null +++ b/avatar-api/avatar_backend_api/models/mock_avatar_model.py @@ -0,0 +1,28 @@ +import os +from time import sleep + +from avatar_backend_api.background_tools.inference_queue import AvatarBaseModel, InferenceQueueTask + +INFERENCE_DELAY_SECONDS = 1 + + +class MockAvatarModel(AvatarBaseModel): + + def init_model(self): + pass + + def is_ready(self) -> bool: + return True + + def inference(self, task: InferenceQueueTask) -> None: + sleep(INFERENCE_DELAY_SECONDS) + print(f"Processed audio: {task.request.video_id} with avatar: {task.request.avatar.value}") + + # Create an empty file, representing the output video + os.makedirs(self.io_client.video.user_dir(user=task.user if self.io_client.user_dir_mode else None), exist_ok=True) + + file_path = self.io_client.video.video_path(video_id=task.request.video_id, user=task.user if self.io_client.user_dir_mode else None) + with open(file_path, "w") as disk_file: + disk_file.write("testFile") + + assert os.path.isfile(file_path) diff --git a/avatar-api/avatar_backend_api/py.typed b/avatar-api/avatar_backend_api/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/avatar-api/avatar_backend_api/tests/__init__.py b/avatar-api/avatar_backend_api/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/avatar-api/avatar_backend_api/tests/integration_test_client.py b/avatar-api/avatar_backend_api/tests/integration_test_client.py new file mode 100644 index 0000000..e9a8e54 --- /dev/null +++ b/avatar-api/avatar_backend_api/tests/integration_test_client.py @@ -0,0 +1,53 @@ +from typing import List, Tuple, Union, BinaryIO + +from fastapi.testclient import TestClient + +from avatar_backend_api.api_types import VideoMetadata, ApiRoute, AvatarRequest +from avatar_backend_api.app import app +from avatar_backend_api.clients.avatar_client import AvatarModelClient + + +class AvatarModelTestClient(AvatarModelClient): + def __init__(self, backend_url="", http_client=TestClient(app=app)): + super().__init__(backend_url=backend_url, http_client=http_client) + + def list_video_ids(self) -> List[str]: + resp = self.http_client.get(url=ApiRoute.video_ids.value) + resp.raise_for_status() + + return resp.json() + + def list_videos(self) -> List[VideoMetadata]: + resp = self.http_client.get(url=ApiRoute.list_videos.value) + resp.raise_for_status() + + return [VideoMetadata.parse_obj(vid) for vid in resp.json()] + + def get_video(self, video_id: str) -> Tuple[int, bytes]: + """ Returns the response code as well as the bytes representing the retrieved file""" + resp = self.http_client.get(ApiRoute.video_url(backend_url="", video_id=video_id)) + + if resp.status_code >= 300: + resp.raise_for_status() + + return resp.status_code, resp.content + + def post_audio(self, audio: Union[BinaryIO, Tuple[str, bytes]], metadata: AvatarRequest) -> AvatarRequest: + """ Overrides the AvatarBaseModel with AvatarRequest in order to send extended schema to backend for testing """ + resp = self.http_client.post( + url=ApiRoute.inference.value, + params=metadata.json_dict, + files={"audio": audio} + ) + + if resp.status_code >= 300: + print(resp.text) + resp.raise_for_status() + + return AvatarRequest.parse_obj(resp.json()) + + def delete_video(self, video_id: str) -> str: + resp = self.http_client.delete(ApiRoute.video_url(backend_url="",video_id=video_id)) + resp.raise_for_status() + + return resp.text diff --git a/avatar-api/avatar_backend_api/tests/ping.wav b/avatar-api/avatar_backend_api/tests/ping.wav new file mode 100644 index 0000000..c0dc31c Binary files /dev/null and b/avatar-api/avatar_backend_api/tests/ping.wav differ diff --git a/avatar-api/avatar_backend_api/tests/test.mp3 b/avatar-api/avatar_backend_api/tests/test.mp3 new file mode 100644 index 0000000..7517a57 Binary files /dev/null and b/avatar-api/avatar_backend_api/tests/test.mp3 differ diff --git a/avatar-api/avatar_backend_api/tests/test_api_types.py b/avatar-api/avatar_backend_api/tests/test_api_types.py new file mode 100644 index 0000000..25fd7d6 --- /dev/null +++ b/avatar-api/avatar_backend_api/tests/test_api_types.py @@ -0,0 +1,11 @@ +from unittest import TestCase + +from avatar_backend_api.api_types import AvatarModelRequest, BaseAvatar + + +class TestApiTypes(TestCase): + def test_model_request_uuid(self): + request1 = AvatarModelRequest(avatar=BaseAvatar.Jennifer_355_9415) + request2 = AvatarModelRequest(avatar=BaseAvatar.Jennifer_355_9415) + + self.assertNotEqual(request1.video_id, request2.video_id) diff --git a/avatar-api/avatar_backend_api/tests/test_db_client.py b/avatar-api/avatar_backend_api/tests/test_db_client.py new file mode 100644 index 0000000..ad3c757 --- /dev/null +++ b/avatar-api/avatar_backend_api/tests/test_db_client.py @@ -0,0 +1,62 @@ +from unittest import IsolatedAsyncioTestCase + +from mtc_api_utils.api_types import FirebaseUser + +from avatar_backend_api.api_types import VideoMetadata, AvatarModel, BaseAvatar +from avatar_backend_api.clients.db_client import AvatarDbClient, AvatarDbException + +TEST_DB_DIR = "/tmp/test-dbs/avatar-db.json" + +TEST_USER = FirebaseUser.example() + +TEST_METADATA = VideoMetadata( + video_id="test-id", + audio_name="test-video-name", + avatar=BaseAvatar.Jennifer_355_9415, + avatar_model=AvatarModel.motion_gan, +) + +TEST_METADATA_2 = VideoMetadata( + video_id=TEST_METADATA.video_id + "-2", + audio_name=TEST_METADATA.audio_name + "-2", + avatar=TEST_METADATA.avatar, + avatar_model=TEST_METADATA.avatar_model, +) + +TEST_DB_CLIENT = AvatarDbClient(db_filepath=TEST_DB_DIR) + + +class TestDbClient(IsolatedAsyncioTestCase): + + def setUp(self) -> None: + TEST_DB_CLIENT.db.drop_tables() + + def tearDown(self) -> None: + self.setUp() + + def test_db_client(self): + # Test access nonexistent entries & error handling + self.assertFalse(TEST_DB_CLIENT.video_exists(video_id=TEST_METADATA.video_id, user=TEST_USER)) + self.assertEqual(0, len(TEST_DB_CLIENT.list_videos(user=TEST_USER))) + self.assertRaises(AvatarDbException, lambda: TEST_DB_CLIENT.get_video(video_id=TEST_METADATA.video_id, user=TEST_USER)) + self.assertRaises(AvatarDbException, lambda: TEST_DB_CLIENT.delete_video(TEST_METADATA_2.video_id, user=TEST_USER)) + self.assertRaises(AvatarDbException, lambda: TEST_DB_CLIENT.get_user_from_id(video_id=TEST_METADATA.video_id)) + + # Test successful manipulation of db + TEST_DB_CLIENT.insert_video(video_metadata=TEST_METADATA, user=TEST_USER) + self.assertEqual(1, len(TEST_DB_CLIENT.list_videos(user=TEST_USER))) + self.assertEqual(TEST_METADATA, TEST_DB_CLIENT.get_video(TEST_METADATA.video_id, user=TEST_USER)) + self.assertEqual(TEST_METADATA, TEST_DB_CLIENT.get_video(video_id=TEST_METADATA.video_id, user=TEST_USER)) + + TEST_METADATA.inference_completed = True + TEST_DB_CLIENT.upsert_video(video_metadata=TEST_METADATA, user=TEST_USER) + self.assertEqual(TEST_METADATA, TEST_DB_CLIENT.get_video(video_id=TEST_METADATA.video_id, user=TEST_USER)) + + TEST_DB_CLIENT.insert_video(video_metadata=TEST_METADATA_2, user=TEST_USER) + self.assertEqual(2, len(TEST_DB_CLIENT.list_videos(user=TEST_USER))) + self.assertEqual(TEST_METADATA_2, TEST_DB_CLIENT.get_video(video_id=TEST_METADATA_2.video_id, user=TEST_USER)) + + self.assertEqual(TEST_METADATA_2, TEST_DB_CLIENT.delete_video(TEST_METADATA_2.video_id, user=TEST_USER)) + self.assertEqual(1, len(TEST_DB_CLIENT.list_videos(user=TEST_USER))) + self.assertEqual(TEST_METADATA, TEST_DB_CLIENT.get_video(TEST_METADATA.video_id, user=TEST_USER)) + self.assertEqual(TEST_METADATA, TEST_DB_CLIENT.delete_video(TEST_METADATA.video_id, user=TEST_USER)) diff --git a/avatar-api/avatar_backend_api/tests/test_inference_queue.py b/avatar-api/avatar_backend_api/tests/test_inference_queue.py new file mode 100644 index 0000000..09fb24e --- /dev/null +++ b/avatar-api/avatar_backend_api/tests/test_inference_queue.py @@ -0,0 +1,70 @@ +import shutil +import unittest +from time import sleep +from unittest import TestCase + +from fastapi import UploadFile +from mtc_api_utils.api_types import FirebaseUser + +from avatar_backend_api.api_types import AvatarModelRequest, BaseAvatar +from avatar_backend_api.app import io_client +from avatar_backend_api.background_tools.inference_queue import InferenceQueue, InferenceQueueTask +from avatar_backend_api.clients.io_client import IoClient +from avatar_backend_api.clients.mock_avatar_model_client import INFERENCE_DELAY_SECONDS +from avatar_backend_api.models.mock_avatar_model import MockAvatarModel +from avatar_backend_api.tests.test_io_client import TEST_VIDEO_BASE_PATH, TEST_AUDIO_BASE_PATH + +TEST_AUDIO_DIR = "/tmp/test-audio" + +TEST_UPLOAD_FILE = UploadFile( + filename="fest-audio-name", + file=None, +) + +TEST_TASK = InferenceQueueTask( + user=FirebaseUser.example(), + request=AvatarModelRequest( + video_id="test-id", + avatar=BaseAvatar.Jennifer_355_9415.value, + ) +) + +TEST_TASK_2 = InferenceQueueTask( + user=TEST_TASK.user, + request=AvatarModelRequest( + video_id=TEST_TASK.request.video_id + "-2", + avatar=TEST_TASK.request.avatar, + ) +) + + +class TestInferenceQueue(TestCase): + + def setUp(self) -> None: + test_io_client = IoClient(user_dir_mode=False, audio_base_path=TEST_AUDIO_BASE_PATH, video_base_path=TEST_VIDEO_BASE_PATH) + + self.inf_queue = InferenceQueue( + model=MockAvatarModel(io_client=io_client), + io_client=test_io_client, + audio_input_dir=TEST_AUDIO_DIR, + daemon_worker=True, + ) + + def tearDown(self) -> None: + shutil.rmtree(path=TEST_AUDIO_DIR) + + def test_inference_queue(self): + self.assertEqual(0, self.inf_queue.queue.qsize()) + + self.inf_queue.add_task(TEST_TASK) + self.assertEqual(1, self.inf_queue.queue.qsize()) + + self.inf_queue.add_task(TEST_TASK_2) + self.assertEqual(2, self.inf_queue.queue.qsize()) + + sleep(INFERENCE_DELAY_SECONDS * 3) + self.assertEqual(0, self.inf_queue.queue.qsize(), msg="Expected worker to process both tasks in the allotted time") + + +if __name__ == '__main__': + unittest.main() diff --git a/avatar-api/avatar_backend_api/tests/test_integration.py b/avatar-api/avatar_backend_api/tests/test_integration.py new file mode 100644 index 0000000..6d4061d --- /dev/null +++ b/avatar-api/avatar_backend_api/tests/test_integration.py @@ -0,0 +1,85 @@ +from http import HTTPStatus +from time import sleep +from unittest import TestCase + +from fastapi import HTTPException +from httpx import HTTPStatusError + +from avatar_backend_api.api_types import AvatarModel, AvatarRequest, BaseAvatar +from avatar_backend_api.clients.mock_avatar_model_client import INFERENCE_DELAY_SECONDS +from avatar_backend_api.config import AvatarConfig +from avatar_backend_api.tests.integration_test_client import AvatarModelTestClient +from avatar_backend_api.tests.test_io_client import TEST_FILE_PATH + +TEST_INFERENCE_REQUEST = AvatarRequest( + video_id="test-id", + audio_name="test-video-name", + avatar=BaseAvatar.Jennifer_355_9415.value, + avatar_model=AvatarModel.neural_voice, +) + +TEST_INFERENCE_REQUEST_2 = AvatarRequest( + video_id=TEST_INFERENCE_REQUEST.video_id + "-2", + audio_name=TEST_INFERENCE_REQUEST.audio_name + "-2", + avatar=TEST_INFERENCE_REQUEST.avatar, + avatar_model=TEST_INFERENCE_REQUEST.avatar_model, +) + + +class TestIntegration(TestCase): + client = AvatarModelTestClient() + + @classmethod + def setUpClass(cls) -> None: + cls.client.wait_for_service_readiness() + + def setUp(self) -> None: + try: + self.client.delete_video(video_id=TEST_INFERENCE_REQUEST.video_id) + except (HTTPException, HTTPStatusError): + pass + + try: + self.client.delete_video(video_id=TEST_INFERENCE_REQUEST_2.video_id) + except (HTTPException, HTTPStatusError): + pass + + def tearDown(self) -> None: + self.setUp() + + def test_api_test(self): + self.assertRaises((HTTPException, HTTPStatusError), lambda: self.client.get_video(video_id=TEST_INFERENCE_REQUEST.video_id)) + + with open(TEST_FILE_PATH, "rb") as test_audio: + created_video = self.client.post_audio(audio=test_audio, metadata=TEST_INFERENCE_REQUEST) + self.assertEqual(TEST_INFERENCE_REQUEST.video_id, created_video.video_id) + + self.assertIn(TEST_INFERENCE_REQUEST.video_id, [vid.video_id for vid in self.client.list_videos()]) + + # Wait for result to be available + while True: + status_code, file_bytes = self.client.get_video(created_video.video_id) + self.assertIn(status_code, [HTTPStatus.OK, HTTPStatus.PROCESSING]) + + if status_code == HTTPStatus.OK: + break + + if not AvatarConfig.mockBackend: + self.assertGreater(len(file_bytes), 0) + + self.client.delete_video(created_video.video_id) + self.assertNotIn(TEST_INFERENCE_REQUEST.video_id, [vid.video_id for vid in self.client.list_videos()]) + + def test_worker_test(self): + if not AvatarConfig.mockBackend: + self.skipTest("This test should only be performed if the model is mocked, otherwise it takes way too long and the delay is incorrect") + + with open(TEST_FILE_PATH, "rb") as test_audio: + self.client.post_audio(audio=test_audio, metadata=TEST_INFERENCE_REQUEST) + self.client.post_audio(audio=test_audio, metadata=TEST_INFERENCE_REQUEST_2) + + sleep(INFERENCE_DELAY_SECONDS * 3) + video_ids = self.client.list_video_ids() + + self.assertIn(TEST_INFERENCE_REQUEST.video_id, video_ids) + self.assertIn(TEST_INFERENCE_REQUEST.video_id, video_ids) diff --git a/avatar-api/avatar_backend_api/tests/test_io_client.py b/avatar-api/avatar_backend_api/tests/test_io_client.py new file mode 100644 index 0000000..3061fde --- /dev/null +++ b/avatar-api/avatar_backend_api/tests/test_io_client.py @@ -0,0 +1,169 @@ +import os.path +import shutil +from enum import Enum +from unittest import TestCase + +from avatar_backend_api.clients.io_client import IoClient, IoClientException +from avatar_backend_api.tests.test_db_client import TEST_USER +from fastapi import UploadFile +from mtc_api_utils.api_types import FirebaseUser + + +class TestFile(Enum): + value: str + test_mp3 = "test.mp3" + ping_wav = "ping.wav" + voice_wav = "voice_test.wav" + + +TEST_FILENAME = TestFile.voice_wav.value + +TEST_BASE_PATH = "/tmp/test" +TEST_AUDIO_BASE_PATH = os.path.join(TEST_BASE_PATH, "audio") +TEST_VIDEO_BASE_PATH = os.path.join(TEST_BASE_PATH, "video") + +TEST_ID = "test-id" +TEST_ID_2 = TEST_ID + "-2" + +TEST_USER_2 = FirebaseUser( + email=TEST_USER.email + "-2", + roles=TEST_USER.roles, +) + +# test_dir represents an absolute path to the test directory, independent of where the test was started from. Use this as a base dir for any path operations. +test_dir = os.path.dirname(os.path.realpath(__file__)) +TEST_FILE_PATH = os.path.join(test_dir, TEST_FILENAME) + +TEST_IO_CLIENT = IoClient(user_dir_mode=False, audio_base_path=TEST_AUDIO_BASE_PATH, video_base_path=TEST_VIDEO_BASE_PATH) +TEST_IO_CLIENT_WITH_USER_MODE = IoClient(user_dir_mode=True, audio_base_path=TEST_AUDIO_BASE_PATH, video_base_path=TEST_VIDEO_BASE_PATH) + + +class TestIoClient(TestCase): + def setUp(self) -> None: + shutil.rmtree(TEST_BASE_PATH, ignore_errors=True) + + os.makedirs(TEST_AUDIO_BASE_PATH, exist_ok=True) + os.makedirs(TEST_VIDEO_BASE_PATH, exist_ok=True) + + def tearDown(self) -> None: + shutil.rmtree(TEST_BASE_PATH, ignore_errors=True) + + def test_audio_without_user_dir_mode(self): + client = TEST_IO_CLIENT.audio + + # Expect an error to be raised for passing a user in userless mode + self.assertRaises( + ValueError, + lambda: client.file_exists(video_id=TEST_ID, user=TEST_USER), + ) + + # Test accessing nonexistent file + self.assertFalse(client.file_exists(video_id=TEST_ID)) + self.assertRaises(IoClientException, lambda: client.matching_paths(video_id=TEST_ID)) + self.assertRaises(IoClientException, lambda: client.read_from_disk(video_id=TEST_ID)) + self.assertRaises(IoClientException, lambda: client.delete_file(video_id=TEST_ID)) + + # Test save and access file successfully + with open(TEST_FILE_PATH, "rb") as test_file: + audio_file = UploadFile(filename=TEST_FILE_PATH, file=test_file) + client.save_to_disk(audio=audio_file, video_id=TEST_ID) + + self.assertTrue(client.file_exists(video_id=TEST_ID)) + self.assertEqual(1, len(client.matching_paths(video_id=TEST_ID))) + + with client.read_from_disk(video_id=TEST_ID) as read_file: + self.assertGreater(len(read_file.read()), 0) + + client.delete_file(video_id=TEST_ID) + self.assertFalse(client.file_exists(video_id=TEST_ID)) + + def test_audio_with_user_dir_mode(self): + client = TEST_IO_CLIENT_WITH_USER_MODE.audio + + # Expect an error to be raised for not passing a user in user mode + self.assertRaises( + ValueError, + lambda: client.file_exists(video_id=TEST_ID, user=None), + ) + + # Test accessing nonexistent file + self.assertFalse(client.file_exists(video_id=TEST_ID, user=TEST_USER)) + self.assertFalse(client.file_exists(video_id=TEST_ID, user=TEST_USER_2)) + self.assertRaises(IoClientException, lambda: client.matching_paths(video_id=TEST_ID, user=TEST_USER)) + self.assertRaises(IoClientException, lambda: client.read_from_disk(video_id=TEST_ID, user=TEST_USER)) + self.assertRaises(IoClientException, lambda: client.delete_file(video_id=TEST_ID, user=TEST_USER)) + + # Test save and access file successfully + with open(TEST_FILE_PATH, "rb") as test_file: + audio_file = UploadFile(filename=TEST_FILE_PATH, file=test_file) + client.save_to_disk(audio=audio_file, video_id=TEST_ID, user=TEST_USER) + + self.assertTrue(client.file_exists(video_id=TEST_ID, user=TEST_USER)) + self.assertFalse(client.file_exists(video_id=TEST_ID, user=TEST_USER_2)) + + self.assertEqual(1, len(client.matching_paths(video_id=TEST_ID, user=TEST_USER))) + + with client.read_from_disk(video_id=TEST_ID, user=TEST_USER) as read_file: + self.assertGreater(len(read_file.read()), 0) + + client.delete_file(video_id=TEST_ID, user=TEST_USER) + self.assertFalse(client.file_exists(video_id=TEST_ID, user=TEST_USER)) + + def test_video_without_user_dir_mode(self): + client = TEST_IO_CLIENT.video + + # Expect an error to be raised for passing a user in userless mode + self.assertRaises( + ValueError, + lambda: client.file_exists(video_id=TEST_ID, user=TEST_USER), + ) + + # Test accessing nonexistent file + self.assertFalse(client.file_exists(video_id=TEST_ID)) + self.assertRaises(IoClientException, lambda: client.read_from_disk(video_id=TEST_ID)) + self.assertRaises(IoClientException, lambda: client.delete_file(video_id=TEST_ID)) + + # Test save and access file successfully + with open(TEST_FILE_PATH, "rb") as test_file: + client.save_to_disk(video_bytes=test_file, video_id=TEST_ID) # Save as BinaryIO + client.save_to_disk(video_bytes=test_file.read(), video_id=TEST_ID) # Save as bytes + + self.assertTrue(client.file_exists(video_id=TEST_ID)) + + file_response = client.read_from_disk(video_id=TEST_ID) + self.assertEqual(client.video_path(video_id=TEST_ID), file_response.path) + self.assertGreater(file_response.chunk_size, 0) + self.assertIn("video", file_response.media_type) + + client.delete_file(video_id=TEST_ID) + self.assertFalse(client.file_exists(video_id=TEST_ID)) + + def test_video_with_user_dir_mode(self): + client = TEST_IO_CLIENT_WITH_USER_MODE.video + + # Expect an error to be raised for not passing a user in user mode + self.assertRaises( + ValueError, + lambda: client.file_exists(video_id=TEST_ID, user=None), + ) + + # Test accessing nonexistent file + self.assertFalse(client.file_exists(video_id=TEST_ID, user=TEST_USER)) + self.assertRaises(IoClientException, lambda: client.read_from_disk(video_id=TEST_ID, user=TEST_USER)) + self.assertRaises(IoClientException, lambda: client.delete_file(video_id=TEST_ID, user=TEST_USER)) + + # Test save and access file successfully + with open(TEST_FILE_PATH, "rb") as test_file: + client.save_to_disk(video_bytes=test_file, video_id=TEST_ID, user=TEST_USER) # Save as BinaryIO + client.save_to_disk(video_bytes=test_file.read(), video_id=TEST_ID, user=TEST_USER) # Save as bytes + + self.assertTrue(client.file_exists(video_id=TEST_ID, user=TEST_USER)) + self.assertFalse(client.file_exists(video_id=TEST_ID, user=TEST_USER_2)) + + file_response = client.read_from_disk(video_id=TEST_ID, user=TEST_USER) + self.assertEqual(client.video_path(video_id=TEST_ID, user=TEST_USER), file_response.path) + self.assertGreater(file_response.chunk_size, 0) + self.assertIn("video", file_response.media_type) + + client.delete_file(video_id=TEST_ID, user=TEST_USER) + self.assertFalse(client.file_exists(video_id=TEST_ID, user=TEST_USER)) diff --git a/avatar-api/avatar_backend_api/tests/test_model_result_poll_worker.py b/avatar-api/avatar_backend_api/tests/test_model_result_poll_worker.py new file mode 100644 index 0000000..c6f9652 --- /dev/null +++ b/avatar-api/avatar_backend_api/tests/test_model_result_poll_worker.py @@ -0,0 +1,62 @@ +import os.path +import shutil +from unittest import TestCase + +from fastapi import HTTPException + +from avatar_backend_api.api_types import AvatarModel, AvatarModelRequest +from avatar_backend_api.background_tools.model_result_poll_worker import ModelResultPollWorker +from avatar_backend_api.clients.mock_avatar_model_client import MockAvatarModelClient +from avatar_backend_api.tests.test_db_client import TEST_USER, TEST_METADATA, TEST_DB_CLIENT +from avatar_backend_api.tests.test_io_client import TEST_IO_CLIENT_WITH_USER_MODE + +TEST_AVATAR_MODEL = AvatarModel.neural_voice + +TEST_MODEL_REQUEST = AvatarModelRequest(**TEST_METADATA.json_dict) + + +class TestResultPollWorker(ModelResultPollWorker): + """ A test variation of the ModelResultPollWorker which does not call any actions in the background """ + + def _worker(self): + pass + + +class TestModelResultPollWorker(TestCase): + + def setUp(self) -> None: + self.poll_worker = TestResultPollWorker( + io_client=TEST_IO_CLIENT_WITH_USER_MODE, + db_client=TEST_DB_CLIENT, + model_clients={TEST_AVATAR_MODEL: MockAvatarModelClient(backend_url="")}, + daemon_worker=True, + ) + + self.poll_worker.db_client.db.drop_tables() + shutil.rmtree(os.path.join(self.poll_worker.io_client.audio.base_path, "/*"), ignore_errors=True) + shutil.rmtree(os.path.join(self.poll_worker.io_client.video.base_path, "/*"), ignore_errors=True) + + self.client = self.poll_worker.model_clients[TEST_AVATAR_MODEL] + + def test_no_results(self): + def fail_on_being_called(video_id: str, avatar_model: AvatarModel): + self.fail(msg=f"Did not expect any available results for video_id={video_id} and avatar_model={avatar_model.value}") + + self.poll_worker._poll_models(perform_on_available_video=fail_on_being_called) + + def test_result_available(self): + # Required for retrieving user from video_id + self.poll_worker.db_client.insert_video(video_metadata=TEST_METADATA, user=TEST_USER) + + # Simulate inference + self.client.post_audio(audio=(TEST_MODEL_REQUEST.video_id, bytes()), metadata=TEST_MODEL_REQUEST) + + # + self.poll_worker._retrieve_video_from_model(video_id=TEST_MODEL_REQUEST.video_id, avatar_model=TEST_AVATAR_MODEL) + self.assertTrue(self.poll_worker.db_client.video_exists(video_id=TEST_MODEL_REQUEST.video_id, user=TEST_USER)) + + self.test_no_results() + self.assertRaises( + HTTPException, + lambda: self.poll_worker._retrieve_video_from_model(video_id=TEST_MODEL_REQUEST.video_id, avatar_model=TEST_AVATAR_MODEL), + ) diff --git a/avatar-api/avatar_backend_api/tests/voice_test.wav b/avatar-api/avatar_backend_api/tests/voice_test.wav new file mode 100644 index 0000000..95ce3fe Binary files /dev/null and b/avatar-api/avatar_backend_api/tests/voice_test.wav differ diff --git a/avatar-api/gunicorn.conf.py b/avatar-api/gunicorn.conf.py new file mode 100644 index 0000000..b0a27bf --- /dev/null +++ b/avatar-api/gunicorn.conf.py @@ -0,0 +1,17 @@ +# https://github.com/benoitc/gunicorn/blob/master/examples/example_config.py + +# Bind & deployment + +bind = '0.0.0.0:5000' +reload = False + +# Connections +# The dashboard backend should be capable of supporting multiple workers, +# however initialization is currently an issue when running in multiple threads. +workers = 1 # if DashboardConfig.debug else 4 +threads = 4 +backlog = 64 +timeout = 300 + +# Logging +loglevel = 'info' diff --git a/avatar-api/requirements.txt b/avatar-api/requirements.txt new file mode 100644 index 0000000..5c79d8d --- /dev/null +++ b/avatar-api/requirements.txt @@ -0,0 +1,6 @@ +tinydb~=4.7.0 +python-multipart~=0.0.5 +httpx~=0.23.2 +fastapi~=0.95.0 + +git+https://github.com/mediatechnologycenter/api-utils.git@0.1.0 diff --git a/avatar-api/setup.py b/avatar-api/setup.py new file mode 100644 index 0000000..051f5d5 --- /dev/null +++ b/avatar-api/setup.py @@ -0,0 +1,33 @@ +#!/usr/bin/env python +from distutils.core import setup + +setup( + name="avatar_backend_api", + description="The Avatar backend API", + version="0.1.0", + url="https://gitlab.ethz.ch/mtc/video-synthesis/mtc-avatar-api", + author="Media Technology Center (ETH Zürich)", + author_email="mtc@ethz.ch", + package_data={ + "avatar_backend_api": ["py.typed"], + }, + packages=[ + "avatar_backend_api", + "avatar_backend_api.clients", + "avatar_backend_api.models", + "avatar_backend_api.background_tools", + ], + install_requires=[ + "mtc_api_utils @ git+https://github.com/mediatechnologycenter/api-utils.git@0.1.0", + "tinydb~=4.7.0", + "python-multipart~=0.0.5", + "httpx~=0.23.1", + ], + classifiers=[ + "Development Status :: 4 - Beta", + "Intended Audience :: Science/Research", + "Operating System :: POSIX :: Linux", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.6", + ] +) diff --git a/debug.env b/debug.env new file mode 100644 index 0000000..004cc2c --- /dev/null +++ b/debug.env @@ -0,0 +1,7 @@ +MOCK_BACKEND=False +AUTH_ENABLED=False +CORS_ALLOW_ORIGINS=localhost,localhost:80,localhost:8080,localhost:8081,http://localhost,http://localhost:80,http://localhost:8080,http://localhost:8081 + +#### AVATAR SETTINGS #### +AVAILABLE_AVATARS="" # Comma separated list of all Avatar names +AVATAR_MODELS=motionGan # All options: "neuralVoice,motionGan" diff --git a/docker-compose-debug.yml b/docker-compose-debug.yml new file mode 100644 index 0000000..e8c0088 --- /dev/null +++ b/docker-compose-debug.yml @@ -0,0 +1,27 @@ +version: "3.9" +services: + backend: + image: avatar-backend-v2-debug + container_name: avatar-backend-v2-debug + env_file: + - .env + - debug.env + + neural-voice-model: + image: neural-voice-backend-debug + container_name: neural-voice-backend-debug + env_file: + - .env + - debug.env + + command: + ["conda", "run", "--no-capture-output", "-n", "base", "gunicorn", "-c", "gunicorn.conf.py", "--log-level", "debug", "--chdir", "./neural_voice_backend_api", "-k", "uvicorn.workers.UvicornWorker", "app:app"] + + motion-gan-model: + image: motion-gan-backend-debug + container_name: motion-gan-backend-debug + env_file: + - .env + - debug.env + command: + ["conda", "run", "--no-capture-output", "-n", "base", "gunicorn", "-c", "gunicorn.conf.py", "--log-level", "debug", "--chdir", "./motion_gan_backend_api", "-k", "uvicorn.workers.UvicornWorker", "app:app"] diff --git a/docker-compose-tests.yml b/docker-compose-tests.yml new file mode 100644 index 0000000..b5dcd50 --- /dev/null +++ b/docker-compose-tests.yml @@ -0,0 +1,69 @@ +version: "3.9" +services: + backend: + image: avatar-backend-v2-debug + container_name: avatar-backend-v2-debug + environment: + - MOCK_BACKEND=False + - AUTH_ENABLED=False + - CORS_ALLOW_ORIGINS=localhost,localhost:80,localhost:8080,localhost:8081,http://localhost,http://localhost:80,http://localhost:8080,http://localhost:8081 + restart: "no" + command: + [ + "python", + "-m", + "unittest", + "discover", + "--start-directory", + "avatar_backend_api/tests" + ] + + neural-voice-model: + image: neural-voice-backend-debug + container_name: neural-voice-backend-debug + environment: + - MOCK_BACKEND=False + - AUTH_ENABLED=False + - STATIC_INPUT_FILES=Jennifer_355_9415.mp4,Jennifer_355_9415.wav # Only download the smaller of the two avatars + - FEATURE_FILES=Jennifer_355_9415.tar.gz # Only download the smaller of the two avatars + - CORS_ALLOW_ORIGINS=localhost,localhost:80,localhost:8080,localhost:8081,http://localhost,http://localhost:80,http://localhost:8080,http://localhost:8081 + restart: "no" + # Uncomment the following line in order to run the neural-voice-model integration tests instead + command: + [ + "conda", + "run", + "--no-capture-output", + "-n", + "base", + "python", + "-m", + "unittest", + "discover", + "--start-directory", + "neural_voice_backend_api/tests" + ] + + motion-gan-model: + image: motion-gan-backend-debug + container_name: motion-gan-backend-debug + environment: + - MOCK_BACKEND=False + - AUTH_ENABLED=False + - CORS_ALLOW_ORIGINS=localhost,localhost:80,localhost:8080,localhost:8081,http://localhost,http://localhost:80,http://localhost:8080,http://localhost:8081 + restart: "no" + command: + [ + "conda", + "run", + "--no-capture-output", + "-n", + "base", + "python", + "-m", + "unittest", + "discover", + "--start-directory", + "motion_gan_backend_api/tests" + ] + # command: tail -f /dev/null diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..ff3b419 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,94 @@ +version: "3.9" +services: + backend: + image: avatar-backend + container_name: avatar-backend-v2 + build: + context: avatar-api + ports: + - "5000:5000" + env_file: + - .env + volumes: + - backend-db-dir:/tmp/tinyDB + - ./data/avatar/input_data/audio:/tmp/avatar/input_data/audio + - ./data/avatar/output_data/videos:/tmp/avatar/output_data/videos + restart: unless-stopped + + neural-voice-model: + image: neural-voice-backend + container_name: neural-voice-backend + build: + context: . + dockerfile: Dockerfile-neural-voice + healthcheck: + test: [ "CMD-SHELL", "test -s `which nvidia-smi` && nvidia-smi || exit 1" ] + start_period: 1s + interval: 20s + timeout: 5s + retries: 2 + ports: + - "5001:5000" + env_file: + - .env + volumes: + - ./data/neuralVoice/input_data:/tmp/neuralVoice/input_data # Static files + - ./data/neuralVoice/output_data:/tmp/neuralVoice/output_data # Static files + - ./data/neuralVoice/input_data/audio:/tmp/neuralVoice/input_data/audio # Model input + - ./data/neuralVoice/output_data/videos:/tmp/neuralVoice/output_data/videos # Model output + - neural-voice-torch-hub:/root/.cache/torch/hub # External model cache + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: 1 + capabilities: [ gpu ] + restart: unless-stopped + + motion-gan-model: + image: motion-gan-backend + container_name: motion-gan-backend + build: + context: . + dockerfile: Dockerfile-motion-gan + healthcheck: + test: [ "CMD-SHELL", "test -s `which nvidia-smi` && nvidia-smi || exit 1" ] + start_period: 1s + interval: 20s + timeout: 5s + retries: 2 + ports: + - "5002:5000" + env_file: + - .env + volumes: + - ./data/motionGan/checkpoints:/tmp/motionGan/checkpoints # Model weights + - ./data/motionGan/input_data/video:/tmp/motionGan/input_data/video # Input data + - ./data/motionGan/input_data/audio:/tmp/motionGan/input_data/audio # Model input + - ./data/motionGan/output_data/videos:/tmp/motionGan/output_data/videos # Model output + - motion-gan-torch-hub:/root/.cache/torch/hub # External model cache + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: 1 + capabilities: [ gpu ] + restart: unless-stopped + shm_size: '2gb' + +volumes: + backend-db-dir: { } + # backend-audio-input-dir: { } + # backend-video-output-dir: { } + # neural-voice-input-data: { } + # neural-voice-output-data: { } + # neural-voice-input-audio: { } + # neural-voice-output-video: { } + neural-voice-torch-hub: { } + # motion-gan-checkpoints: { } + # motion-gan-input-video: { } + # motion-gan-input-audio: { } + # motion-gan-output-video: { } + motion-gan-torch-hub: { } diff --git a/figures/logo.png b/figures/logo.png new file mode 100644 index 0000000..793ee0a Binary files /dev/null and b/figures/logo.png differ diff --git a/figures/pipeline_NVP.png b/figures/pipeline_NVP.png new file mode 100644 index 0000000..7328917 Binary files /dev/null and b/figures/pipeline_NVP.png differ diff --git a/figures/pipeline_motiongan.gif b/figures/pipeline_motiongan.gif new file mode 100644 index 0000000..aa5a243 Binary files /dev/null and b/figures/pipeline_motiongan.gif differ diff --git a/motion-gan-pipeline/.env b/motion-gan-pipeline/.env new file mode 100644 index 0000000..2cdf2a9 --- /dev/null +++ b/motion-gan-pipeline/.env @@ -0,0 +1,18 @@ +##### GENERAL ENVS ##### +GPU_SUPPORTED=True +GPU_ENABLED=True + +##### AUTH ENVS ##### +AUTH_ENABLED=False +FIREBASE_ADMIN_CREDENTIALS_URL=*** +REQUIRED_AUTH_ROLES=video-synthesis + +##### PATHS ##### +INPUT_DATA_DOWNLOAD_TARGET="../neural-code/input_data/video/" +OUTPUT_DATA_DOWNLOAD_TARGET="../neural-code/output_data/features" + +##### BACKEND ENVS ##### +FFMPEG_DOCKER=True # Python will use a different version of ffmpeg, therefore we need to redirect to /usr/bin/ffmpeg + +##### REACT ENVS ##### +REACT_APP_ENVIRONMENT=prod diff --git a/motion-gan-pipeline/AUTHORS.txt b/motion-gan-pipeline/AUTHORS.txt new file mode 100644 index 0000000..df870c1 --- /dev/null +++ b/motion-gan-pipeline/AUTHORS.txt @@ -0,0 +1,7 @@ +Contributions: + +* Alberto Pennino : Research and backend developement. +* Thomas Steinmann : Frontend and API developement. +* Marc Willhaus : Frontend developement. +* Clara Labrador-Fernandez : Supervision. + diff --git a/motion-gan-pipeline/GFPGAN/.github/workflows/no-response.yml b/motion-gan-pipeline/GFPGAN/.github/workflows/no-response.yml new file mode 100644 index 0000000..fa702ee --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/.github/workflows/no-response.yml @@ -0,0 +1,33 @@ +name: No Response + +# TODO: it seems not to work +# Modified from: https://raw.githubusercontent.com/github/docs/main/.github/workflows/no-response.yaml + +# **What it does**: Closes issues that don't have enough information to be actionable. +# **Why we have it**: To remove the need for maintainers to remember to check back on issues periodically +# to see if contributors have responded. +# **Who does it impact**: Everyone that works on docs or docs-internal. + +on: + issue_comment: + types: [created] + + schedule: + # Schedule for five minutes after the hour every hour + - cron: '5 * * * *' + +jobs: + noResponse: + runs-on: ubuntu-latest + steps: + - uses: lee-dohm/no-response@v0.5.0 + with: + token: ${{ github.token }} + closeComment: > + This issue has been automatically closed because there has been no response + to our request for more information from the original author. With only the + information that is currently in the issue, we don't have enough information + to take action. Please reach out if you have or find the answers we need so + that we can investigate further. + If you still have questions, please improve your description and re-open it. + Thanks :-) diff --git a/motion-gan-pipeline/GFPGAN/.github/workflows/publish-pip.yml b/motion-gan-pipeline/GFPGAN/.github/workflows/publish-pip.yml new file mode 100644 index 0000000..71190fb --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/.github/workflows/publish-pip.yml @@ -0,0 +1,29 @@ +name: PyPI Publish + +on: push + +jobs: + build-n-publish: + runs-on: ubuntu-latest + if: startsWith(github.event.ref, 'refs/tags') + + steps: + - uses: actions/checkout@v2 + - name: Set up Python 3.8 + uses: actions/setup-python@v1 + with: + python-version: 3.8 + - name: Upgrade pip + run: pip install pip --upgrade + - name: Install PyTorch (cpu) + run: pip install torch==1.7.0+cpu torchvision==0.8.1+cpu -f https://download.pytorch.org/whl/torch_stable.html + - name: Install dependencies + run: pip install -r requirements.txt + - name: Build and install + run: rm -rf .eggs && pip install -e . + - name: Build for distribution + run: python setup.py sdist bdist_wheel + - name: Publish distribution to PyPI + uses: pypa/gh-action-pypi-publish@master + with: + password: ${{ secrets.PYPI_API_TOKEN }} diff --git a/motion-gan-pipeline/GFPGAN/.github/workflows/pylint.yml b/motion-gan-pipeline/GFPGAN/.github/workflows/pylint.yml new file mode 100644 index 0000000..89318e4 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/.github/workflows/pylint.yml @@ -0,0 +1,30 @@ +name: PyLint + +on: [push, pull_request] + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + matrix: + python-version: [3.8] + + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install codespell flake8 isort yapf + + - name: Lint + run: | + codespell + flake8 . + isort --check-only --diff gfpgan/ scripts/ inference_gfpgan.py setup.py + yapf -r -d gfpgan/ scripts/ inference_gfpgan.py setup.py diff --git a/motion-gan-pipeline/GFPGAN/.github/workflows/release.yml b/motion-gan-pipeline/GFPGAN/.github/workflows/release.yml new file mode 100644 index 0000000..1b8f35f --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/.github/workflows/release.yml @@ -0,0 +1,41 @@ +name: release +on: + push: + tags: + - '*' + +jobs: + build: + permissions: write-all + name: Create Release + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v2 + - name: Create Release + id: create_release + uses: actions/create-release@v1 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + tag_name: ${{ github.ref }} + release_name: GFPGAN ${{ github.ref }} Release Note + body: | + 🚀 See you again 😸 + 🚀Have a nice day 😸 and happy everyday 😃 + 🚀 Long time no see ☄️ + + ✨ **Highlights** + ✅ [Features] Support ... + + 🐛 **Bug Fixes** + + 🌴 **Improvements** + + 📢📢📢 + +

+ +

+ draft: true + prerelease: false diff --git a/motion-gan-pipeline/GFPGAN/.gitignore b/motion-gan-pipeline/GFPGAN/.gitignore new file mode 100644 index 0000000..8151890 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/.gitignore @@ -0,0 +1,139 @@ +# ignored folders +datasets/* +experiments/* +results/* +tb_logger/* +wandb/* +tmp/* + +version.py + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ diff --git a/motion-gan-pipeline/GFPGAN/.pre-commit-config.yaml b/motion-gan-pipeline/GFPGAN/.pre-commit-config.yaml new file mode 100644 index 0000000..d221d29 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/.pre-commit-config.yaml @@ -0,0 +1,46 @@ +repos: + # flake8 + - repo: https://github.com/PyCQA/flake8 + rev: 3.8.3 + hooks: + - id: flake8 + args: ["--config=setup.cfg", "--ignore=W504, W503"] + + # modify known_third_party + - repo: https://github.com/asottile/seed-isort-config + rev: v2.2.0 + hooks: + - id: seed-isort-config + + # isort + - repo: https://github.com/timothycrosley/isort + rev: 5.2.2 + hooks: + - id: isort + + # yapf + - repo: https://github.com/pre-commit/mirrors-yapf + rev: v0.30.0 + hooks: + - id: yapf + + # codespell + - repo: https://github.com/codespell-project/codespell + rev: v2.1.0 + hooks: + - id: codespell + + # pre-commit-hooks + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v3.2.0 + hooks: + - id: trailing-whitespace # Trim trailing whitespace + - id: check-yaml # Attempt to load all yaml files to verify syntax + - id: check-merge-conflict # Check for files that contain merge conflict strings + - id: double-quote-string-fixer # Replace double quoted strings with single quoted strings + - id: end-of-file-fixer # Make sure files end in a newline and only a newline + - id: requirements-txt-fixer # Sort entries in requirements.txt and remove incorrect entry for pkg-resources==0.0.0 + - id: fix-encoding-pragma # Remove the coding pragma: # -*- coding: utf-8 -*- + args: ["--remove"] + - id: mixed-line-ending # Replace or check mixed line ending + args: ["--fix=lf"] diff --git a/motion-gan-pipeline/GFPGAN/CODE_OF_CONDUCT.md b/motion-gan-pipeline/GFPGAN/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..e8cc4da --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/CODE_OF_CONDUCT.md @@ -0,0 +1,128 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, religion, or sexual identity +and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +xintao.wang@outlook.com or xintaowang@tencent.com. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or +permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. + +Community Impact Guidelines were inspired by [Mozilla's code of conduct +enforcement ladder](https://github.com/mozilla/diversity). + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see the FAQ at +https://www.contributor-covenant.org/faq. Translations are available at +https://www.contributor-covenant.org/translations. diff --git a/motion-gan-pipeline/GFPGAN/Comparisons.md b/motion-gan-pipeline/GFPGAN/Comparisons.md new file mode 100644 index 0000000..1542d4c --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/Comparisons.md @@ -0,0 +1,24 @@ +# Comparisons + +## Comparisons among different model versions + +Note that V1.3 is not always better than V1.2. You may need to try different models based on your purpose and inputs. + +| Version | Strengths | Weaknesses | +| :---: | :---: | :---: | +|V1.3 | ✓ natural outputs
✓better results on very low-quality inputs
✓ work on relatively high-quality inputs
✓ can have repeated (twice) restorations | ✗ not very sharp
✗ have a slight change on identity | +|V1.2 | ✓ sharper output
✓ with beauty makeup | ✗ some outputs are unnatural| + +For the following images, you may need to **zoom in** for comparing details, or **click the image** to see in the full size. + +| Input | V1 | V1.2 | V1.3 +| :---: | :---: | :---: | :---: | +|![019_Anne_Hathaway_01_00](https://user-images.githubusercontent.com/17445847/153762146-96b25999-4ddd-42a5-a3fe-bb90565f4c4f.png)| ![](https://user-images.githubusercontent.com/17445847/153762256-ef41e749-5a27-495c-8a9c-d8403be55869.png) | ![](https://user-images.githubusercontent.com/17445847/153762297-d41582fc-6253-4e7e-a1ce-4dc237ae3bf3.png) | ![](https://user-images.githubusercontent.com/17445847/153762215-e0535e94-b5ba-426e-97b5-35c00873604d.png) | +| ![106_Harry_Styles_00_00](https://user-images.githubusercontent.com/17445847/153789040-632c0eda-c15a-43e9-a63c-9ead64f92d4a.png) | ![](https://user-images.githubusercontent.com/17445847/153789172-93cd4980-5318-4633-a07e-1c8f8064ff89.png) | ![](https://user-images.githubusercontent.com/17445847/153789185-f7b268a7-d1db-47b0-ae4a-335e5d657a18.png) | ![](https://user-images.githubusercontent.com/17445847/153789198-7c7f3bca-0ef0-4494-92f0-20aa6f7d7464.png)| +| ![076_Paris_Hilton_00_00](https://user-images.githubusercontent.com/17445847/153789607-86387770-9db8-441f-b08a-c9679b121b85.png) | ![](https://user-images.githubusercontent.com/17445847/153789619-e56b438a-78a0-425d-8f44-ec4692a43dda.png) | ![](https://user-images.githubusercontent.com/17445847/153789633-5b28f778-3b7f-4e08-8a1d-740ca6e82d8a.png) | ![](https://user-images.githubusercontent.com/17445847/153789645-bc623f21-b32d-4fc3-bfe9-61203407a180.png)| +| ![008_George_Clooney_00_00](https://user-images.githubusercontent.com/17445847/153790017-0c3ca94d-1c9d-4a0e-b539-ab12d4da98ff.png) | ![](https://user-images.githubusercontent.com/17445847/153790028-fb0d38ab-399d-4a30-8154-2dcd72ca90e8.png) | ![](https://user-images.githubusercontent.com/17445847/153790044-1ef68e34-6120-4439-a5d9-0b6cdbe9c3d0.png) | ![](https://user-images.githubusercontent.com/17445847/153790059-a8d3cece-8989-4e9a-9ffe-903e1690cfd6.png)| +| ![057_Madonna_01_00](https://user-images.githubusercontent.com/17445847/153790624-2d0751d0-8fb4-4806-be9d-71b833c2c226.png) | ![](https://user-images.githubusercontent.com/17445847/153790639-7eb870e5-26b2-41dc-b139-b698bb40e6e6.png) | ![](https://user-images.githubusercontent.com/17445847/153790651-86899b7a-a1b6-4242-9e8a-77b462004998.png) | ![](https://user-images.githubusercontent.com/17445847/153790655-c8f6c25b-9b4e-4633-b16f-c43da86cff8f.png)| +| ![044_Amy_Schumer_01_00](https://user-images.githubusercontent.com/17445847/153790811-3fb4fc46-5b4f-45fe-8fcb-a128de2bfa60.png) | ![](https://user-images.githubusercontent.com/17445847/153790817-d45aa4ff-bfc4-4163-b462-75eef9426fab.png) | ![](https://user-images.githubusercontent.com/17445847/153790824-5f93c3a0-fe5a-42f6-8b4b-5a5de8cd0ac3.png) | ![](https://user-images.githubusercontent.com/17445847/153790835-0edf9944-05c7-41c4-8581-4dc5ffc56c9d.png)| +| ![012_Jackie_Chan_01_00](https://user-images.githubusercontent.com/17445847/153791176-737b016a-e94f-4898-8db7-43e7762141c9.png) | ![](https://user-images.githubusercontent.com/17445847/153791183-2f25a723-56bf-4cd5-aafe-a35513a6d1c5.png) | ![](https://user-images.githubusercontent.com/17445847/153791194-93416cf9-2b58-4e70-b806-27e14c58d4fd.png) | ![](https://user-images.githubusercontent.com/17445847/153791202-aa98659c-b702-4bce-9c47-a2fa5eccc5ae.png)| + + diff --git a/motion-gan-pipeline/GFPGAN/FAQ.md b/motion-gan-pipeline/GFPGAN/FAQ.md new file mode 100644 index 0000000..e4d5a49 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/FAQ.md @@ -0,0 +1,7 @@ +# FAQ + +1. **How to finetune the GFPGANCleanv1-NoCE-C2 (v1.2) model** + +**A:** 1) The GFPGANCleanv1-NoCE-C2 (v1.2) model uses the *clean* architecture, which is more friendly for deploying. +2) This model is not directly trained. Instead, it is converted from another *bilinear* model. +3) If you want to finetune the GFPGANCleanv1-NoCE-C2 (v1.2), you need to finetune its original *bilinear* model, and then do the conversion. diff --git a/motion-gan-pipeline/GFPGAN/LICENSE b/motion-gan-pipeline/GFPGAN/LICENSE new file mode 100644 index 0000000..24384c0 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/LICENSE @@ -0,0 +1,351 @@ +Tencent is pleased to support the open source community by making GFPGAN available. + +Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved. + +GFPGAN is licensed under the Apache License Version 2.0 except for the third-party components listed below. + + +Terms of the Apache License Version 2.0: +--------------------------------------------- +Apache License + +Version 2.0, January 2004 + +http://www.apache.org/licenses/ + +TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION +1. 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IN NO EVENT SHALL THE AUTHORS OR +COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/motion-gan-pipeline/GFPGAN/MANIFEST.in b/motion-gan-pipeline/GFPGAN/MANIFEST.in new file mode 100644 index 0000000..bcaa717 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/MANIFEST.in @@ -0,0 +1,8 @@ +include assets/* +include inputs/* +include scripts/*.py +include inference_gfpgan.py +include VERSION +include LICENSE +include requirements.txt +include gfpgan/weights/README.md diff --git a/motion-gan-pipeline/GFPGAN/PaperModel.md b/motion-gan-pipeline/GFPGAN/PaperModel.md new file mode 100644 index 0000000..e9c8bdc --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/PaperModel.md @@ -0,0 +1,76 @@ +# Installation + +We now provide a *clean* version of GFPGAN, which does not require customized CUDA extensions. See [here](README.md#installation) for this easier installation.
+If you want want to use the original model in our paper, please follow the instructions below. + +1. Clone repo + + ```bash + git clone https://github.com/xinntao/GFPGAN.git + cd GFPGAN + ``` + +1. Install dependent packages + + As StyleGAN2 uses customized PyTorch C++ extensions, you need to **compile them during installation** or **load them just-in-time(JIT)**. + You can refer to [BasicSR-INSTALL.md](https://github.com/xinntao/BasicSR/blob/master/INSTALL.md) for more details. + + **Option 1: Load extensions just-in-time(JIT)** (For those just want to do simple inferences, may have less issues) + + ```bash + # Install basicsr - https://github.com/xinntao/BasicSR + # We use BasicSR for both training and inference + pip install basicsr + + # Install facexlib - https://github.com/xinntao/facexlib + # We use face detection and face restoration helper in the facexlib package + pip install facexlib + + pip install -r requirements.txt + python setup.py develop + + # remember to set BASICSR_JIT=True before your running commands + ``` + + **Option 2: Compile extensions during installation** (For those need to train/inference for many times) + + ```bash + # Install basicsr - https://github.com/xinntao/BasicSR + # We use BasicSR for both training and inference + # Set BASICSR_EXT=True to compile the cuda extensions in the BasicSR - It may take several minutes to compile, please be patient + # Add -vvv for detailed log prints + BASICSR_EXT=True pip install basicsr -vvv + + # Install facexlib - https://github.com/xinntao/facexlib + # We use face detection and face restoration helper in the facexlib package + pip install facexlib + + pip install -r requirements.txt + python setup.py develop + ``` + +## :zap: Quick Inference + +Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth) + +```bash +wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models +``` + +- Option 1: Load extensions just-in-time(JIT) + + ```bash + BASICSR_JIT=True python inference_gfpgan.py --input inputs/whole_imgs --output results --version 1 + + # for aligned images + BASICSR_JIT=True python inference_gfpgan.py --input inputs/whole_imgs --output results --version 1 --aligned + ``` + +- Option 2: Have successfully compiled extensions during installation + + ```bash + python inference_gfpgan.py --input inputs/whole_imgs --output results --version 1 + + # for aligned images + python inference_gfpgan.py --input inputs/whole_imgs --output results --version 1 --aligned + ``` diff --git a/motion-gan-pipeline/GFPGAN/README.md b/motion-gan-pipeline/GFPGAN/README.md new file mode 100644 index 0000000..285956f --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/README.md @@ -0,0 +1,202 @@ +

+ +

+ +## + +
+ + +[![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases) +[![PyPI](https://img.shields.io/pypi/v/gfpgan)](https://pypi.org/project/gfpgan/) +[![Open issue](https://img.shields.io/github/issues/TencentARC/GFPGAN)](https://github.com/TencentARC/GFPGAN/issues) +[![Closed issue](https://img.shields.io/github/issues-closed/TencentARC/GFPGAN)](https://github.com/TencentARC/GFPGAN/issues) +[![LICENSE](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/TencentARC/GFPGAN/blob/master/LICENSE) +[![python lint](https://github.com/TencentARC/GFPGAN/actions/workflows/pylint.yml/badge.svg)](https://github.com/TencentARC/GFPGAN/blob/master/.github/workflows/pylint.yml) +[![Publish-pip](https://github.com/TencentARC/GFPGAN/actions/workflows/publish-pip.yml/badge.svg)](https://github.com/TencentARC/GFPGAN/blob/master/.github/workflows/publish-pip.yml) +
+ +1. :boom: **Updated** online demo: [![Replicate](https://img.shields.io/static/v1?label=Demo&message=Replicate&color=blue)](https://replicate.com/tencentarc/gfpgan). Here is the [backup](https://replicate.com/xinntao/gfpgan). +1. :boom: **Updated** online demo: [![Huggingface Gradio](https://img.shields.io/static/v1?label=Demo&message=Huggingface%20Gradio&color=orange)](https://huggingface.co/spaces/Xintao/GFPGAN) +1. [Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo) for GFPGAN google colab logo; (Another [Colab Demo](https://colab.research.google.com/drive/1Oa1WwKB4M4l1GmR7CtswDVgOCOeSLChA?usp=sharing) for the original paper model) + + + +> :rocket: **Thanks for your interest in our work. You may also want to check our new updates on the *tiny models* for *anime images and videos* in [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN/blob/master/docs/anime_video_model.md)** :blush: + +GFPGAN aims at developing a **Practical Algorithm for Real-world Face Restoration**.
+It leverages rich and diverse priors encapsulated in a pretrained face GAN (*e.g.*, StyleGAN2) for blind face restoration. + +:question: Frequently Asked Questions can be found in [FAQ.md](FAQ.md). + +:triangular_flag_on_post: **Updates** + +- :white_check_mark: Add CodeFormer ([CC BY-NC-SA 4.0 License](https://creativecommons.org/licenses/by-nc-sa/4.0/)) and RestoreFormer. +- :white_check_mark: Add [V1.4 model](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth), which produces slightly more details and better identity than V1.3. +- :white_check_mark: Add **[V1.3 model](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth)**, which produces **more natural** restoration results, and better results on *very low-quality* / *high-quality* inputs. See more in [Model zoo](#european_castle-model-zoo), [Comparisons.md](Comparisons.md) +- :white_check_mark: Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/GFPGAN). +- :white_check_mark: Support enhancing non-face regions (background) with [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN). +- :white_check_mark: We provide a *clean* version of GFPGAN, which does not require CUDA extensions. +- :white_check_mark: We provide an updated model without colorizing faces. + +--- + +If GFPGAN is helpful in your photos/projects, please help to :star: this repo or recommend it to your friends. Thanks:blush: +Other recommended projects:
+:arrow_forward: [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN): A practical algorithm for general image restoration
+:arrow_forward: [BasicSR](https://github.com/xinntao/BasicSR): An open-source image and video restoration toolbox
+:arrow_forward: [facexlib](https://github.com/xinntao/facexlib): A collection that provides useful face-relation functions
+:arrow_forward: [HandyView](https://github.com/xinntao/HandyView): A PyQt5-based image viewer that is handy for view and comparison
+ +--- + +### :book: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior + +> [[Paper](https://arxiv.org/abs/2101.04061)]   [[Project Page](https://xinntao.github.io/projects/gfpgan)]   [Demo]
+> [Xintao Wang](https://xinntao.github.io/), [Yu Li](https://yu-li.github.io/), [Honglun Zhang](https://scholar.google.com/citations?hl=en&user=KjQLROoAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en)
+> Applied Research Center (ARC), Tencent PCG + +

+ +

+ +--- + +## :wrench: Dependencies and Installation + +- Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html)) +- [PyTorch >= 1.7](https://pytorch.org/) +- Option: NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads) +- Option: Linux + +### Installation + +We now provide a *clean* version of GFPGAN, which does not require customized CUDA extensions.
+If you want to use the original model in our paper, please see [PaperModel.md](PaperModel.md) for installation. + +1. Clone repo + + ```bash + git clone https://github.com/TencentARC/GFPGAN.git + cd GFPGAN + ``` + +1. Install dependent packages + + ```bash + # Install basicsr - https://github.com/xinntao/BasicSR + # We use BasicSR for both training and inference + pip install basicsr + + # Install facexlib - https://github.com/xinntao/facexlib + # We use face detection and face restoration helper in the facexlib package + pip install facexlib + + pip install -r requirements.txt + python setup.py develop + + # If you want to enhance the background (non-face) regions with Real-ESRGAN, + # you also need to install the realesrgan package + pip install realesrgan + ``` + +## :zap: Quick Inference + +We take the v1.3 version for an example. More models can be found [here](#european_castle-model-zoo). + +Download pre-trained models: [GFPGANv1.3.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth) + +```bash +wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P experiments/pretrained_models +``` + +**Inference!** + +```bash +python inference_gfpgan.py -i inputs/whole_imgs -o results -v 1.3 -s 2 +``` + +```console +Usage: python inference_gfpgan.py -i inputs/whole_imgs -o results -v 1.3 -s 2 [options]... + + -h show this help + -i input Input image or folder. Default: inputs/whole_imgs + -o output Output folder. Default: results + -v version GFPGAN model version. Option: 1 | 1.2 | 1.3. Default: 1.3 + -s upscale The final upsampling scale of the image. Default: 2 + -bg_upsampler background upsampler. Default: realesrgan + -bg_tile Tile size for background sampler, 0 for no tile during testing. Default: 400 + -suffix Suffix of the restored faces + -only_center_face Only restore the center face + -aligned Input are aligned faces + -ext Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto +``` + +If you want to use the original model in our paper, please see [PaperModel.md](PaperModel.md) for installation and inference. + +## :european_castle: Model Zoo + +| Version | Model Name | Description | +| :---: | :---: | :---: | +| V1.3 | [GFPGANv1.3.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth) | Based on V1.2; **more natural** restoration results; better results on very low-quality / high-quality inputs. | +| V1.2 | [GFPGANCleanv1-NoCE-C2.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth) | No colorization; no CUDA extensions are required. Trained with more data with pre-processing. | +| V1 | [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth) | The paper model, with colorization. | + +The comparisons are in [Comparisons.md](Comparisons.md). + +Note that V1.3 is not always better than V1.2. You may need to select different models based on your purpose and inputs. + +| Version | Strengths | Weaknesses | +| :---: | :---: | :---: | +|V1.3 | ✓ natural outputs
✓better results on very low-quality inputs
✓ work on relatively high-quality inputs
✓ can have repeated (twice) restorations | ✗ not very sharp
✗ have a slight change on identity | +|V1.2 | ✓ sharper output
✓ with beauty makeup | ✗ some outputs are unnatural | + +You can find **more models (such as the discriminators)** here: [[Google Drive](https://drive.google.com/drive/folders/17rLiFzcUMoQuhLnptDsKolegHWwJOnHu?usp=sharing)], OR [[Tencent Cloud 腾讯微云](https://share.weiyun.com/ShYoCCoc)] + +## :computer: Training + +We provide the training codes for GFPGAN (used in our paper).
+You could improve it according to your own needs. + +**Tips** + +1. More high quality faces can improve the restoration quality. +2. You may need to perform some pre-processing, such as beauty makeup. + +**Procedures** + +(You can try a simple version ( `options/train_gfpgan_v1_simple.yml`) that does not require face component landmarks.) + +1. Dataset preparation: [FFHQ](https://github.com/NVlabs/ffhq-dataset) + +1. Download pre-trained models and other data. Put them in the `experiments/pretrained_models` folder. + 1. [Pre-trained StyleGAN2 model: StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth) + 1. [Component locations of FFHQ: FFHQ_eye_mouth_landmarks_512.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/FFHQ_eye_mouth_landmarks_512.pth) + 1. [A simple ArcFace model: arcface_resnet18.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/arcface_resnet18.pth) + +1. Modify the configuration file `options/train_gfpgan_v1.yml` accordingly. + +1. Training + +> python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 gfpgan/train.py -opt options/train_gfpgan_v1.yml --launcher pytorch + +## :scroll: License and Acknowledgement + +GFPGAN is released under Apache License Version 2.0. + +## BibTeX + + @InProceedings{wang2021gfpgan, + author = {Xintao Wang and Yu Li and Honglun Zhang and Ying Shan}, + title = {Towards Real-World Blind Face Restoration with Generative Facial Prior}, + booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + year = {2021} + } + +## :e-mail: Contact + +If you have any question, please email `xintao.wang@outlook.com` or `xintaowang@tencent.com`. diff --git a/motion-gan-pipeline/GFPGAN/README_CN.md b/motion-gan-pipeline/GFPGAN/README_CN.md new file mode 100644 index 0000000..880f206 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/README_CN.md @@ -0,0 +1,7 @@ +

+ +

+ +## + +还未完工，欢迎贡献！ diff --git a/motion-gan-pipeline/GFPGAN/VERSION b/motion-gan-pipeline/GFPGAN/VERSION new file mode 100644 index 0000000..3336003 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/VERSION @@ -0,0 +1 @@ +1.3.7 diff --git a/motion-gan-pipeline/GFPGAN/assets/gfpgan_logo.png b/motion-gan-pipeline/GFPGAN/assets/gfpgan_logo.png new file mode 100644 index 0000000..f019378 Binary files /dev/null and b/motion-gan-pipeline/GFPGAN/assets/gfpgan_logo.png differ diff --git a/motion-gan-pipeline/GFPGAN/cog.yaml b/motion-gan-pipeline/GFPGAN/cog.yaml new file mode 100644 index 0000000..f22d2c7 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/cog.yaml @@ -0,0 +1,22 @@ +# This file is used for constructing replicate env +image: "r8.im/tencentarc/gfpgan" + +build: + gpu: true + python_version: "3.8" + system_packages: + - "libgl1-mesa-glx" + - "libglib2.0-0" + python_packages: + - "torch==1.7.1" + - "torchvision==0.8.2" + - "numpy==1.21.1" + - "lmdb==1.2.1" + - "opencv-python==4.5.3.56" + - "PyYAML==5.4.1" + - "tqdm==4.62.2" + - "yapf==0.31.0" + - "basicsr==1.4.2" + - "facexlib==0.2.5" + +predict: "cog_predict.py:Predictor" diff --git a/motion-gan-pipeline/GFPGAN/cog_predict.py b/motion-gan-pipeline/GFPGAN/cog_predict.py new file mode 100644 index 0000000..ff37ae3 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/cog_predict.py @@ -0,0 +1,177 @@ +# flake8: noqa +# This file is used for deploying replicate models +# running: cog predict -i img=@inputs/whole_imgs/10045.png -i version='v1.4' -i scale=2 +# push: cog push r8.im/tencentarc/gfpgan +# push (backup): cog push r8.im/xinntao/gfpgan + +import os + +os.system('python setup.py develop') +os.system('pip install realesrgan') + +import cv2 +import shutil +import tempfile +import torch +from basicsr.archs.srvgg_arch import SRVGGNetCompact + +from gfpgan import GFPGANer + +try: + from cog import BasePredictor, Input, Path + from realesrgan.utils import RealESRGANer +except Exception: + print('please install cog and realesrgan package') + + +class Predictor(BasePredictor): + + def setup(self): + os.makedirs('output', exist_ok=True) + # download weights + if not os.path.exists('gfpgan/weights/realesr-general-x4v3.pth'): + os.system( + 'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P ./gfpgan/weights' + ) + if not os.path.exists('gfpgan/weights/GFPGANv1.2.pth'): + os.system( + 'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P ./gfpgan/weights') + if not os.path.exists('gfpgan/weights/GFPGANv1.3.pth'): + os.system( + 'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P ./gfpgan/weights') + if not os.path.exists('gfpgan/weights/GFPGANv1.4.pth'): + os.system( + 'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P ./gfpgan/weights') + if not os.path.exists('gfpgan/weights/RestoreFormer.pth'): + os.system( + 'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P ./gfpgan/weights' + ) + if not os.path.exists('gfpgan/weights/CodeFormer.pth'): + os.system( + 'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P ./gfpgan/weights') + + # background enhancer with RealESRGAN + model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu') + model_path = 'gfpgan/weights/realesr-general-x4v3.pth' + half = True if torch.cuda.is_available() else False + self.upsampler = RealESRGANer( + scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half) + + # Use GFPGAN for face enhancement + self.face_enhancer = GFPGANer( + model_path='gfpgan/weights/GFPGANv1.4.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=self.upsampler) + self.current_version = 'v1.4' + + def predict( + self, + img: Path = Input(description='Input'), + version: str = Input( + description='GFPGAN version. v1.3: better quality. v1.4: more details and better identity.', + choices=['v1.2', 'v1.3', 'v1.4', 'RestoreFormer', 'CodeFormer'], + default='v1.4'), + scale: float = Input(description='Rescaling factor', default=2), + weight: float = Input( + description='Weight, only for CodeFormer. 0 for better quality, 1 for better identity', + default=0.5, + ge=0, + le=1.0) + ) -> Path: + if not isinstance(weight, (int, float)): + weight = 0.5 + print(img, version, scale, weight) + try: + extension = os.path.splitext(os.path.basename(str(img)))[1] + img = cv2.imread(str(img), cv2.IMREAD_UNCHANGED) + if len(img.shape) == 3 and img.shape[2] == 4: + img_mode = 'RGBA' + elif len(img.shape) == 2: + img_mode = None + img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) + else: + img_mode = None + + h, w = img.shape[0:2] + if h < 300: + img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4) + + if self.current_version != version: + if version == 'v1.2': + self.face_enhancer = GFPGANer( + model_path='gfpgan/weights/GFPGANv1.2.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=self.upsampler) + self.current_version = 'v1.2' + elif version == 'v1.3': + self.face_enhancer = GFPGANer( + model_path='gfpgan/weights/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=self.upsampler) + self.current_version = 'v1.3' + elif version == 'v1.4': + self.face_enhancer = GFPGANer( + model_path='gfpgan/weights/GFPGANv1.4.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=self.upsampler) + self.current_version = 'v1.4' + elif version == 'RestoreFormer': + self.face_enhancer = GFPGANer( + model_path='gfpgan/weights/RestoreFormer.pth', + upscale=2, + arch='RestoreFormer', + channel_multiplier=2, + bg_upsampler=self.upsampler) + elif version == 'CodeFormer': + self.face_enhancer = GFPGANer( + model_path='gfpgan/weights/CodeFormer.pth', + upscale=2, + arch='CodeFormer', + channel_multiplier=2, + bg_upsampler=self.upsampler) + + try: + _, _, output = self.face_enhancer.enhance( + img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight) + except RuntimeError as error: + print('Error', error) + + try: + if scale != 2: + interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4 + h, w = img.shape[0:2] + output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation) + except Exception as error: + print('wrong scale input.', error) + + if img_mode == 'RGBA': # RGBA images should be saved in png format + extension = 'png' + # save_path = f'output/out.{extension}' + # cv2.imwrite(save_path, output) + out_path = Path(tempfile.mkdtemp()) / f'out.{extension}' + cv2.imwrite(str(out_path), output) + except Exception as error: + print('global exception: ', error) + finally: + clean_folder('output') + return out_path + + +def clean_folder(folder): + for filename in os.listdir(folder): + file_path = os.path.join(folder, filename) + try: + if os.path.isfile(file_path) or os.path.islink(file_path): + os.unlink(file_path) + elif os.path.isdir(file_path): + shutil.rmtree(file_path) + except Exception as e: + print(f'Failed to delete {file_path}. Reason: {e}') diff --git a/motion-gan-pipeline/GFPGAN/gfpgan/__init__.py b/motion-gan-pipeline/GFPGAN/gfpgan/__init__.py new file mode 100644 index 0000000..94daaee --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/gfpgan/__init__.py @@ -0,0 +1,7 @@ +# flake8: noqa +from .archs import * +from .data import * +from .models import * +from .utils import * + +# from .version import * diff --git a/motion-gan-pipeline/GFPGAN/gfpgan/archs/__init__.py b/motion-gan-pipeline/GFPGAN/gfpgan/archs/__init__.py new file mode 100644 index 0000000..bec5f17 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/gfpgan/archs/__init__.py @@ -0,0 +1,10 @@ +import importlib +from basicsr.utils import scandir +from os import path as osp + +# automatically scan and import arch modules for registry +# scan all the files that end with '_arch.py' under the archs folder +arch_folder = osp.dirname(osp.abspath(__file__)) +arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')] +# import all the arch modules +_arch_modules = [importlib.import_module(f'gfpgan.archs.{file_name}') for file_name in arch_filenames] diff --git a/motion-gan-pipeline/GFPGAN/gfpgan/archs/arcface_arch.py b/motion-gan-pipeline/GFPGAN/gfpgan/archs/arcface_arch.py new file mode 100644 index 0000000..e6d3bd9 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/gfpgan/archs/arcface_arch.py @@ -0,0 +1,245 @@ +import torch.nn as nn +from basicsr.utils.registry import ARCH_REGISTRY + + +def conv3x3(inplanes, outplanes, stride=1): + """A simple wrapper for 3x3 convolution with padding. + + Args: + inplanes (int): Channel number of inputs. + outplanes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + """ + return nn.Conv2d(inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False) + + +class BasicBlock(nn.Module): + """Basic residual block used in the ResNetArcFace architecture. + + Args: + inplanes (int): Channel number of inputs. + planes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + downsample (nn.Module): The downsample module. Default: None. + """ + expansion = 1 # output channel expansion ratio + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class IRBlock(nn.Module): + """Improved residual block (IR Block) used in the ResNetArcFace architecture. + + Args: + inplanes (int): Channel number of inputs. + planes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + downsample (nn.Module): The downsample module. Default: None. + use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True. + """ + expansion = 1 # output channel expansion ratio + + def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True): + super(IRBlock, self).__init__() + self.bn0 = nn.BatchNorm2d(inplanes) + self.conv1 = conv3x3(inplanes, inplanes) + self.bn1 = nn.BatchNorm2d(inplanes) + self.prelu = nn.PReLU() + self.conv2 = conv3x3(inplanes, planes, stride) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + self.use_se = use_se + if self.use_se: + self.se = SEBlock(planes) + + def forward(self, x): + residual = x + out = self.bn0(x) + out = self.conv1(out) + out = self.bn1(out) + out = self.prelu(out) + + out = self.conv2(out) + out = self.bn2(out) + if self.use_se: + out = self.se(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.prelu(out) + + return out + + +class Bottleneck(nn.Module): + """Bottleneck block used in the ResNetArcFace architecture. + + Args: + inplanes (int): Channel number of inputs. + planes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + downsample (nn.Module): The downsample module. Default: None. + """ + expansion = 4 # output channel expansion ratio + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class SEBlock(nn.Module): + """The squeeze-and-excitation block (SEBlock) used in the IRBlock. + + Args: + channel (int): Channel number of inputs. + reduction (int): Channel reduction ration. Default: 16. + """ + + def __init__(self, channel, reduction=16): + super(SEBlock, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) # pool to 1x1 without spatial information + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction), nn.PReLU(), nn.Linear(channel // reduction, channel), + nn.Sigmoid()) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + + +@ARCH_REGISTRY.register() +class ResNetArcFace(nn.Module): + """ArcFace with ResNet architectures. + + Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition. + + Args: + block (str): Block used in the ArcFace architecture. + layers (tuple(int)): Block numbers in each layer. + use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True. + """ + + def __init__(self, block, layers, use_se=True): + if block == 'IRBlock': + block = IRBlock + self.inplanes = 64 + self.use_se = use_se + super(ResNetArcFace, self).__init__() + + self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.prelu = nn.PReLU() + self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.bn4 = nn.BatchNorm2d(512) + self.dropout = nn.Dropout() + self.fc5 = nn.Linear(512 * 8 * 8, 512) + self.bn5 = nn.BatchNorm1d(512) + + # initialization + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.xavier_normal_(m.weight) + elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.xavier_normal_(m.weight) + nn.init.constant_(m.bias, 0) + + def _make_layer(self, block, planes, num_blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se)) + self.inplanes = planes + for _ in range(1, num_blocks): + layers.append(block(self.inplanes, planes, use_se=self.use_se)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.prelu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.bn4(x) + x = self.dropout(x) + x = x.view(x.size(0), -1) + x = self.fc5(x) + x = self.bn5(x) + + return x diff --git a/motion-gan-pipeline/GFPGAN/gfpgan/archs/codeformer_arch.py b/motion-gan-pipeline/GFPGAN/gfpgan/archs/codeformer_arch.py new file mode 100644 index 0000000..ba204a9 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/gfpgan/archs/codeformer_arch.py @@ -0,0 +1,630 @@ +""" +Modified from https://github.com/sczhou/CodeFormer +VQGAN code, adapted from the original created by the Unleashing Transformers authors: +https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py +""" +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +from basicsr.utils import get_root_logger +from basicsr.utils.registry import ARCH_REGISTRY +from torch import Tensor +from typing import Optional + + +class VectorQuantizer(nn.Module): + + def __init__(self, codebook_size, emb_dim, beta): + super(VectorQuantizer, self).__init__() + self.codebook_size = codebook_size # number of embeddings + self.emb_dim = emb_dim # dimension of embedding + self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 + self.embedding = nn.Embedding(self.codebook_size, self.emb_dim) + self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size) + + def forward(self, z): + # reshape z -> (batch, height, width, channel) and flatten + z = z.permute(0, 2, 3, 1).contiguous() + z_flattened = z.view(-1, self.emb_dim) + + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \ + 2 * torch.matmul(z_flattened, self.embedding.weight.t()) + + mean_distance = torch.mean(d) + # find closest encodings + # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) + min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False) + # [0-1], higher score, higher confidence + min_encoding_scores = torch.exp(-min_encoding_scores / 10) + + min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z) + min_encodings.scatter_(1, min_encoding_indices, 1) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) + # compute loss for embedding + loss = torch.mean((z_q.detach() - z)**2) + self.beta * torch.mean((z_q - z.detach())**2) + # preserve gradients + z_q = z + (z_q - z).detach() + + # perplexity + e_mean = torch.mean(min_encodings, dim=0) + perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q, loss, { + 'perplexity': perplexity, + 'min_encodings': min_encodings, + 'min_encoding_indices': min_encoding_indices, + 'min_encoding_scores': min_encoding_scores, + 'mean_distance': mean_distance + } + + def get_codebook_feat(self, indices, shape): + # input indices: batch*token_num -> (batch*token_num)*1 + # shape: batch, height, width, channel + indices = indices.view(-1, 1) + min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices) + min_encodings.scatter_(1, indices, 1) + # get quantized latent vectors + z_q = torch.matmul(min_encodings.float(), self.embedding.weight) + + if shape is not None: # reshape back to match original input shape + z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous() + + return z_q + + +class GumbelQuantizer(nn.Module): + + def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0): + super().__init__() + self.codebook_size = codebook_size # number of embeddings + self.emb_dim = emb_dim # dimension of embedding + self.straight_through = straight_through + self.temperature = temp_init + self.kl_weight = kl_weight + self.proj = nn.Conv2d(num_hiddens, codebook_size, 1) # projects last encoder layer to quantized logits + self.embed = nn.Embedding(codebook_size, emb_dim) + + def forward(self, z): + hard = self.straight_through if self.training else True + + logits = self.proj(z) + + soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard) + + z_q = torch.einsum('b n h w, n d -> b d h w', soft_one_hot, self.embed.weight) + + # + kl divergence to the prior loss + qy = F.softmax(logits, dim=1) + diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean() + min_encoding_indices = soft_one_hot.argmax(dim=1) + + return z_q, diff, {'min_encoding_indices': min_encoding_indices} + + +class Downsample(nn.Module): + + def __init__(self, in_channels): + super().__init__() + self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) + + def forward(self, x): + pad = (0, 1, 0, 1) + x = torch.nn.functional.pad(x, pad, mode='constant', value=0) + x = self.conv(x) + return x + + +class Upsample(nn.Module): + + def __init__(self, in_channels): + super().__init__() + self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x): + x = F.interpolate(x, scale_factor=2.0, mode='nearest') + x = self.conv(x) + + return x + + +class AttnBlock(nn.Module): + + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = q.reshape(b, c, h * w) + q = q.permute(0, 2, 1) + k = k.reshape(b, c, h * w) + w_ = torch.bmm(q, k) + w_ = w_ * (int(c)**(-0.5)) + w_ = F.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b, c, h * w) + w_ = w_.permute(0, 2, 1) + h_ = torch.bmm(v, w_) + h_ = h_.reshape(b, c, h, w) + + h_ = self.proj_out(h_) + + return x + h_ + + +class Encoder(nn.Module): + + def __init__(self, in_channels, nf, out_channels, ch_mult, num_res_blocks, resolution, attn_resolutions): + super().__init__() + self.nf = nf + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.attn_resolutions = attn_resolutions + + curr_res = self.resolution + in_ch_mult = (1, ) + tuple(ch_mult) + + blocks = [] + # initial convultion + blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1)) + + # residual and downsampling blocks, with attention on smaller res (16x16) + for i in range(self.num_resolutions): + block_in_ch = nf * in_ch_mult[i] + block_out_ch = nf * ch_mult[i] + for _ in range(self.num_res_blocks): + blocks.append(ResBlock(block_in_ch, block_out_ch)) + block_in_ch = block_out_ch + if curr_res in attn_resolutions: + blocks.append(AttnBlock(block_in_ch)) + + if i != self.num_resolutions - 1: + blocks.append(Downsample(block_in_ch)) + curr_res = curr_res // 2 + + # non-local attention block + blocks.append(ResBlock(block_in_ch, block_in_ch)) + blocks.append(AttnBlock(block_in_ch)) + blocks.append(ResBlock(block_in_ch, block_in_ch)) + + # normalise and convert to latent size + blocks.append(normalize(block_in_ch)) + blocks.append(nn.Conv2d(block_in_ch, out_channels, kernel_size=3, stride=1, padding=1)) + self.blocks = nn.ModuleList(blocks) + + def forward(self, x): + for block in self.blocks: + x = block(x) + + return x + + +class Generator(nn.Module): + + def __init__(self, nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim): + super().__init__() + self.nf = nf + self.ch_mult = ch_mult + self.num_resolutions = len(self.ch_mult) + self.num_res_blocks = res_blocks + self.resolution = img_size + self.attn_resolutions = attn_resolutions + self.in_channels = emb_dim + self.out_channels = 3 + block_in_ch = self.nf * self.ch_mult[-1] + curr_res = self.resolution // 2**(self.num_resolutions - 1) + + blocks = [] + # initial conv + blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1)) + + # non-local attention block + blocks.append(ResBlock(block_in_ch, block_in_ch)) + blocks.append(AttnBlock(block_in_ch)) + blocks.append(ResBlock(block_in_ch, block_in_ch)) + + for i in reversed(range(self.num_resolutions)): + block_out_ch = self.nf * self.ch_mult[i] + + for _ in range(self.num_res_blocks): + blocks.append(ResBlock(block_in_ch, block_out_ch)) + block_in_ch = block_out_ch + + if curr_res in self.attn_resolutions: + blocks.append(AttnBlock(block_in_ch)) + + if i != 0: + blocks.append(Upsample(block_in_ch)) + curr_res = curr_res * 2 + + blocks.append(normalize(block_in_ch)) + blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1)) + + self.blocks = nn.ModuleList(blocks) + + def forward(self, x): + for block in self.blocks: + x = block(x) + + return x + + +class VQAutoEncoder(nn.Module): + + def __init__(self, + img_size, + nf, + ch_mult, + quantizer='nearest', + res_blocks=2, + attn_resolutions=[16], + codebook_size=1024, + emb_dim=256, + beta=0.25, + gumbel_straight_through=False, + gumbel_kl_weight=1e-8, + model_path=None): + super().__init__() + logger = get_root_logger() + self.in_channels = 3 + self.nf = nf + self.n_blocks = res_blocks + self.codebook_size = codebook_size + self.embed_dim = emb_dim + self.ch_mult = ch_mult + self.resolution = img_size + self.attn_resolutions = attn_resolutions + self.quantizer_type = quantizer + self.encoder = Encoder(self.in_channels, self.nf, self.embed_dim, self.ch_mult, self.n_blocks, self.resolution, + self.attn_resolutions) + if self.quantizer_type == 'nearest': + self.beta = beta # 0.25 + self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta) + elif self.quantizer_type == 'gumbel': + self.gumbel_num_hiddens = emb_dim + self.straight_through = gumbel_straight_through + self.kl_weight = gumbel_kl_weight + self.quantize = GumbelQuantizer(self.codebook_size, self.embed_dim, self.gumbel_num_hiddens, + self.straight_through, self.kl_weight) + self.generator = Generator(nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim) + + if model_path is not None: + chkpt = torch.load(model_path, map_location='cpu') + if 'params_ema' in chkpt: + self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema']) + logger.info(f'vqgan is loaded from: {model_path} [params_ema]') + elif 'params' in chkpt: + self.load_state_dict(torch.load(model_path, map_location='cpu')['params']) + logger.info(f'vqgan is loaded from: {model_path} [params]') + else: + raise ValueError('Wrong params!') + + def forward(self, x): + x = self.encoder(x) + quant, codebook_loss, quant_stats = self.quantize(x) + x = self.generator(quant) + return x, codebook_loss, quant_stats + + +def calc_mean_std(feat, eps=1e-5): + """Calculate mean and std for adaptive_instance_normalization. + + Args: + feat (Tensor): 4D tensor. + eps (float): A small value added to the variance to avoid + divide-by-zero. Default: 1e-5. + """ + size = feat.size() + assert len(size) == 4, 'The input feature should be 4D tensor.' + b, c = size[:2] + feat_var = feat.view(b, c, -1).var(dim=2) + eps + feat_std = feat_var.sqrt().view(b, c, 1, 1) + feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1) + return feat_mean, feat_std + + +def adaptive_instance_normalization(content_feat, style_feat): + """Adaptive instance normalization. + + Adjust the reference features to have the similar color and illuminations + as those in the degradate features. + + Args: + content_feat (Tensor): The reference feature. + style_feat (Tensor): The degradate features. + """ + size = content_feat.size() + style_mean, style_std = calc_mean_std(style_feat) + content_mean, content_std = calc_mean_std(content_feat) + normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size) + return normalized_feat * style_std.expand(size) + style_mean.expand(size) + + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError('normalize should be True if scale is passed') + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, x, mask=None): + if mask is None: + mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) + not_mask = ~mask + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature**(2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == 'relu': + return F.relu + if activation == 'gelu': + return F.gelu + if activation == 'glu': + return F.glu + raise RuntimeError(F'activation should be relu/gelu, not {activation}.') + + +class TransformerSALayer(nn.Module): + + def __init__(self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation='gelu'): + super().__init__() + self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout) + # Implementation of Feedforward model - MLP + self.linear1 = nn.Linear(embed_dim, dim_mlp) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_mlp, embed_dim) + + self.norm1 = nn.LayerNorm(embed_dim) + self.norm2 = nn.LayerNorm(embed_dim) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward(self, + tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + + # self attention + tgt2 = self.norm1(tgt) + q = k = self.with_pos_embed(tgt2, query_pos) + tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout1(tgt2) + + # ffn + tgt2 = self.norm2(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout2(tgt2) + return tgt + + +def normalize(in_channels): + return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + + +@torch.jit.script +def swish(x): + return x * torch.sigmoid(x) + + +class ResBlock(nn.Module): + + def __init__(self, in_channels, out_channels=None): + super(ResBlock, self).__init__() + self.in_channels = in_channels + self.out_channels = in_channels if out_channels is None else out_channels + self.norm1 = normalize(in_channels) + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + self.norm2 = normalize(out_channels) + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + if self.in_channels != self.out_channels: + self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, x_in): + x = x_in + x = self.norm1(x) + x = swish(x) + x = self.conv1(x) + x = self.norm2(x) + x = swish(x) + x = self.conv2(x) + if self.in_channels != self.out_channels: + x_in = self.conv_out(x_in) + + return x + x_in + + +class Fuse_sft_block(nn.Module): + + def __init__(self, in_ch, out_ch): + super().__init__() + self.encode_enc = ResBlock(2 * in_ch, out_ch) + + self.scale = nn.Sequential( + nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), nn.LeakyReLU(0.2, True), + nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1)) + + self.shift = nn.Sequential( + nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), nn.LeakyReLU(0.2, True), + nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1)) + + def forward(self, enc_feat, dec_feat, w=1): + enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1)) + scale = self.scale(enc_feat) + shift = self.shift(enc_feat) + residual = w * (dec_feat * scale + shift) + out = dec_feat + residual + return out + + +@ARCH_REGISTRY.register() +class CodeFormer(VQAutoEncoder): + + def __init__(self, + dim_embd=512, + n_head=8, + n_layers=9, + codebook_size=1024, + latent_size=256, + connect_list=['32', '64', '128', '256'], + fix_modules=['quantize', 'generator']): + super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest', 2, [16], codebook_size) + + if fix_modules is not None: + for module in fix_modules: + for param in getattr(self, module).parameters(): + param.requires_grad = False + + self.connect_list = connect_list + self.n_layers = n_layers + self.dim_embd = dim_embd + self.dim_mlp = dim_embd * 2 + + self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd)) + self.feat_emb = nn.Linear(256, self.dim_embd) + + # transformer + self.ft_layers = nn.Sequential(*[ + TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0) + for _ in range(self.n_layers) + ]) + + # logits_predict head + self.idx_pred_layer = nn.Sequential(nn.LayerNorm(dim_embd), nn.Linear(dim_embd, codebook_size, bias=False)) + + self.channels = {'16': 512, '32': 256, '64': 256, '128': 128, '256': 128, '512': 64} + + # after second residual block for > 16, before attn layer for ==16 + self.fuse_encoder_block = {'512': 2, '256': 5, '128': 8, '64': 11, '32': 14, '16': 18} + # after first residual block for > 16, before attn layer for ==16 + self.fuse_generator_block = {'16': 6, '32': 9, '64': 12, '128': 15, '256': 18, '512': 21} + + # fuse_convs_dict + self.fuse_convs_dict = nn.ModuleDict() + for f_size in self.connect_list: + in_ch = self.channels[f_size] + self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch) + + def _init_weights(self, module): + if isinstance(module, (nn.Linear, nn.Embedding)): + module.weight.data.normal_(mean=0.0, std=0.02) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def forward(self, x, weight=0.5, **kwargs): + detach_16 = True + code_only = False + adain = True + # ################### Encoder ##################### + enc_feat_dict = {} + out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list] + for i, block in enumerate(self.encoder.blocks): + x = block(x) + if i in out_list: + enc_feat_dict[str(x.shape[-1])] = x.clone() + + lq_feat = x + # ################# Transformer ################### + # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat) + pos_emb = self.position_emb.unsqueeze(1).repeat(1, x.shape[0], 1) + # BCHW -> BC(HW) -> (HW)BC + feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2, 0, 1)) + query_emb = feat_emb + # Transformer encoder + for layer in self.ft_layers: + query_emb = layer(query_emb, query_pos=pos_emb) + + # output logits + logits = self.idx_pred_layer(query_emb) # (hw)bn + logits = logits.permute(1, 0, 2) # (hw)bn -> b(hw)n + + if code_only: # for training stage II + # logits doesn't need softmax before cross_entropy loss + return logits, lq_feat + + # ################# Quantization ################### + # if self.training: + # quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight]) + # # b(hw)c -> bc(hw) -> bchw + # quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape) + # ------------ + soft_one_hot = F.softmax(logits, dim=2) + _, top_idx = torch.topk(soft_one_hot, 1, dim=2) + quant_feat = self.quantize.get_codebook_feat(top_idx, shape=[x.shape[0], 16, 16, 256]) + # preserve gradients + # quant_feat = lq_feat + (quant_feat - lq_feat).detach() + + if detach_16: + quant_feat = quant_feat.detach() # for training stage III + if adain: + quant_feat = adaptive_instance_normalization(quant_feat, lq_feat) + + # ################## Generator #################### + x = quant_feat + fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list] + + for i, block in enumerate(self.generator.blocks): + x = block(x) + if i in fuse_list: # fuse after i-th block + f_size = str(x.shape[-1]) + if weight > 0: + x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, weight) + out = x + # logits doesn't need softmax before cross_entropy loss + # return out, logits, lq_feat + return out, logits diff --git a/motion-gan-pipeline/GFPGAN/gfpgan/archs/gfpgan_bilinear_arch.py b/motion-gan-pipeline/GFPGAN/gfpgan/archs/gfpgan_bilinear_arch.py new file mode 100644 index 0000000..52e0de8 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/gfpgan/archs/gfpgan_bilinear_arch.py @@ -0,0 +1,312 @@ +import math +import random +import torch +from basicsr.utils.registry import ARCH_REGISTRY +from torch import nn + +from .gfpganv1_arch import ResUpBlock +from .stylegan2_bilinear_arch import (ConvLayer, EqualConv2d, EqualLinear, ResBlock, ScaledLeakyReLU, + StyleGAN2GeneratorBilinear) + + +class StyleGAN2GeneratorBilinearSFT(StyleGAN2GeneratorBilinear): + """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). + + It is the bilinear version. It does not use the complicated UpFirDnSmooth function that is not friendly for + deployment. It can be easily converted to the clean version: StyleGAN2GeneratorCSFT. + + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__(self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + lr_mlp=0.01, + narrow=1, + sft_half=False): + super(StyleGAN2GeneratorBilinearSFT, self).__init__( + out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + lr_mlp=lr_mlp, + narrow=narrow) + self.sft_half = sft_half + + def forward(self, + styles, + conditions, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False): + """Forward function for StyleGAN2GeneratorBilinearSFT. + + Args: + styles (list[Tensor]): Sample codes of styles. + conditions (list[Tensor]): SFT conditions to generators. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], + noise[2::2], self.to_rgbs): + out = conv1(out, latent[:, i], noise=noise1) + + # the conditions may have fewer levels + if i < len(conditions): + # SFT part to combine the conditions + if self.sft_half: # only apply SFT to half of the channels + out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) + out_sft = out_sft * conditions[i - 1] + conditions[i] + out = torch.cat([out_same, out_sft], dim=1) + else: # apply SFT to all the channels + out = out * conditions[i - 1] + conditions[i] + + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +@ARCH_REGISTRY.register() +class GFPGANBilinear(nn.Module): + """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. + + It is the bilinear version and it does not use the complicated UpFirDnSmooth function that is not friendly for + deployment. It can be easily converted to the clean version: GFPGANv1Clean. + + + Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. + + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. + fix_decoder (bool): Whether to fix the decoder. Default: True. + + num_mlp (int): Layer number of MLP style layers. Default: 8. + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + input_is_latent (bool): Whether input is latent style. Default: False. + different_w (bool): Whether to use different latent w for different layers. Default: False. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + channel_multiplier=1, + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + lr_mlp=0.01, + input_is_latent=False, + different_w=False, + narrow=1, + sft_half=False): + + super(GFPGANBilinear, self).__init__() + self.input_is_latent = input_is_latent + self.different_w = different_w + self.num_style_feat = num_style_feat + + unet_narrow = narrow * 0.5 # by default, use a half of input channels + channels = { + '4': int(512 * unet_narrow), + '8': int(512 * unet_narrow), + '16': int(512 * unet_narrow), + '32': int(512 * unet_narrow), + '64': int(256 * channel_multiplier * unet_narrow), + '128': int(128 * channel_multiplier * unet_narrow), + '256': int(64 * channel_multiplier * unet_narrow), + '512': int(32 * channel_multiplier * unet_narrow), + '1024': int(16 * channel_multiplier * unet_narrow) + } + + self.log_size = int(math.log(out_size, 2)) + first_out_size = 2**(int(math.log(out_size, 2))) + + self.conv_body_first = ConvLayer(3, channels[f'{first_out_size}'], 1, bias=True, activate=True) + + # downsample + in_channels = channels[f'{first_out_size}'] + self.conv_body_down = nn.ModuleList() + for i in range(self.log_size, 2, -1): + out_channels = channels[f'{2**(i - 1)}'] + self.conv_body_down.append(ResBlock(in_channels, out_channels)) + in_channels = out_channels + + self.final_conv = ConvLayer(in_channels, channels['4'], 3, bias=True, activate=True) + + # upsample + in_channels = channels['4'] + self.conv_body_up = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f'{2**i}'] + self.conv_body_up.append(ResUpBlock(in_channels, out_channels)) + in_channels = out_channels + + # to RGB + self.toRGB = nn.ModuleList() + for i in range(3, self.log_size + 1): + self.toRGB.append(EqualConv2d(channels[f'{2**i}'], 3, 1, stride=1, padding=0, bias=True, bias_init_val=0)) + + if different_w: + linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat + else: + linear_out_channel = num_style_feat + + self.final_linear = EqualLinear( + channels['4'] * 4 * 4, linear_out_channel, bias=True, bias_init_val=0, lr_mul=1, activation=None) + + # the decoder: stylegan2 generator with SFT modulations + self.stylegan_decoder = StyleGAN2GeneratorBilinearSFT( + out_size=out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + lr_mlp=lr_mlp, + narrow=narrow, + sft_half=sft_half) + + # load pre-trained stylegan2 model if necessary + if decoder_load_path: + self.stylegan_decoder.load_state_dict( + torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema']) + # fix decoder without updating params + if fix_decoder: + for _, param in self.stylegan_decoder.named_parameters(): + param.requires_grad = False + + # for SFT modulations (scale and shift) + self.condition_scale = nn.ModuleList() + self.condition_shift = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f'{2**i}'] + if sft_half: + sft_out_channels = out_channels + else: + sft_out_channels = out_channels * 2 + self.condition_scale.append( + nn.Sequential( + EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0), + ScaledLeakyReLU(0.2), + EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=1))) + self.condition_shift.append( + nn.Sequential( + EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0), + ScaledLeakyReLU(0.2), + EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0))) + + def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True): + """Forward function for GFPGANBilinear. + + Args: + x (Tensor): Input images. + return_latents (bool): Whether to return style latents. Default: False. + return_rgb (bool): Whether return intermediate rgb images. Default: True. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + """ + conditions = [] + unet_skips = [] + out_rgbs = [] + + # encoder + feat = self.conv_body_first(x) + for i in range(self.log_size - 2): + feat = self.conv_body_down[i](feat) + unet_skips.insert(0, feat) + + feat = self.final_conv(feat) + + # style code + style_code = self.final_linear(feat.view(feat.size(0), -1)) + if self.different_w: + style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) + + # decode + for i in range(self.log_size - 2): + # add unet skip + feat = feat + unet_skips[i] + # ResUpLayer + feat = self.conv_body_up[i](feat) + # generate scale and shift for SFT layers + scale = self.condition_scale[i](feat) + conditions.append(scale.clone()) + shift = self.condition_shift[i](feat) + conditions.append(shift.clone()) + # generate rgb images + if return_rgb: + out_rgbs.append(self.toRGB[i](feat)) + + # decoder + image, _ = self.stylegan_decoder([style_code], + conditions, + return_latents=return_latents, + input_is_latent=self.input_is_latent, + randomize_noise=randomize_noise) + + return image, out_rgbs diff --git a/motion-gan-pipeline/GFPGAN/gfpgan/archs/gfpganv1_arch.py b/motion-gan-pipeline/GFPGAN/gfpgan/archs/gfpganv1_arch.py new file mode 100644 index 0000000..eaf3162 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/gfpgan/archs/gfpganv1_arch.py @@ -0,0 +1,439 @@ +import math +import random +import torch +from basicsr.archs.stylegan2_arch import (ConvLayer, EqualConv2d, EqualLinear, ResBlock, ScaledLeakyReLU, + StyleGAN2Generator) +from basicsr.ops.fused_act import FusedLeakyReLU +from basicsr.utils.registry import ARCH_REGISTRY +from torch import nn +from torch.nn import functional as F + + +class StyleGAN2GeneratorSFT(StyleGAN2Generator): + """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). + + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be + applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__(self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + resample_kernel=(1, 3, 3, 1), + lr_mlp=0.01, + narrow=1, + sft_half=False): + super(StyleGAN2GeneratorSFT, self).__init__( + out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + resample_kernel=resample_kernel, + lr_mlp=lr_mlp, + narrow=narrow) + self.sft_half = sft_half + + def forward(self, + styles, + conditions, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False): + """Forward function for StyleGAN2GeneratorSFT. + + Args: + styles (list[Tensor]): Sample codes of styles. + conditions (list[Tensor]): SFT conditions to generators. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], + noise[2::2], self.to_rgbs): + out = conv1(out, latent[:, i], noise=noise1) + + # the conditions may have fewer levels + if i < len(conditions): + # SFT part to combine the conditions + if self.sft_half: # only apply SFT to half of the channels + out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) + out_sft = out_sft * conditions[i - 1] + conditions[i] + out = torch.cat([out_same, out_sft], dim=1) + else: # apply SFT to all the channels + out = out * conditions[i - 1] + conditions[i] + + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ConvUpLayer(nn.Module): + """Convolutional upsampling layer. It uses bilinear upsampler + Conv. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + stride (int): Stride of the convolution. Default: 1 + padding (int): Zero-padding added to both sides of the input. Default: 0. + bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + activate (bool): Whether use activateion. Default: True. + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + bias=True, + bias_init_val=0, + activate=True): + super(ConvUpLayer, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + # self.scale is used to scale the convolution weights, which is related to the common initializations. + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + + self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size)) + + if bias and not activate: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter('bias', None) + + # activation + if activate: + if bias: + self.activation = FusedLeakyReLU(out_channels) + else: + self.activation = ScaledLeakyReLU(0.2) + else: + self.activation = None + + def forward(self, x): + # bilinear upsample + out = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False) + # conv + out = F.conv2d( + out, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.padding, + ) + # activation + if self.activation is not None: + out = self.activation(out) + return out + + +class ResUpBlock(nn.Module): + """Residual block with upsampling. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + """ + + def __init__(self, in_channels, out_channels): + super(ResUpBlock, self).__init__() + + self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) + self.conv2 = ConvUpLayer(in_channels, out_channels, 3, stride=1, padding=1, bias=True, activate=True) + self.skip = ConvUpLayer(in_channels, out_channels, 1, bias=False, activate=False) + + def forward(self, x): + out = self.conv1(x) + out = self.conv2(out) + skip = self.skip(x) + out = (out + skip) / math.sqrt(2) + return out + + +@ARCH_REGISTRY.register() +class GFPGANv1(nn.Module): + """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. + + Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. + + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be + applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). + decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. + fix_decoder (bool): Whether to fix the decoder. Default: True. + + num_mlp (int): Layer number of MLP style layers. Default: 8. + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + input_is_latent (bool): Whether input is latent style. Default: False. + different_w (bool): Whether to use different latent w for different layers. Default: False. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + channel_multiplier=1, + resample_kernel=(1, 3, 3, 1), + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + lr_mlp=0.01, + input_is_latent=False, + different_w=False, + narrow=1, + sft_half=False): + + super(GFPGANv1, self).__init__() + self.input_is_latent = input_is_latent + self.different_w = different_w + self.num_style_feat = num_style_feat + + unet_narrow = narrow * 0.5 # by default, use a half of input channels + channels = { + '4': int(512 * unet_narrow), + '8': int(512 * unet_narrow), + '16': int(512 * unet_narrow), + '32': int(512 * unet_narrow), + '64': int(256 * channel_multiplier * unet_narrow), + '128': int(128 * channel_multiplier * unet_narrow), + '256': int(64 * channel_multiplier * unet_narrow), + '512': int(32 * channel_multiplier * unet_narrow), + '1024': int(16 * channel_multiplier * unet_narrow) + } + + self.log_size = int(math.log(out_size, 2)) + first_out_size = 2**(int(math.log(out_size, 2))) + + self.conv_body_first = ConvLayer(3, channels[f'{first_out_size}'], 1, bias=True, activate=True) + + # downsample + in_channels = channels[f'{first_out_size}'] + self.conv_body_down = nn.ModuleList() + for i in range(self.log_size, 2, -1): + out_channels = channels[f'{2**(i - 1)}'] + self.conv_body_down.append(ResBlock(in_channels, out_channels, resample_kernel)) + in_channels = out_channels + + self.final_conv = ConvLayer(in_channels, channels['4'], 3, bias=True, activate=True) + + # upsample + in_channels = channels['4'] + self.conv_body_up = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f'{2**i}'] + self.conv_body_up.append(ResUpBlock(in_channels, out_channels)) + in_channels = out_channels + + # to RGB + self.toRGB = nn.ModuleList() + for i in range(3, self.log_size + 1): + self.toRGB.append(EqualConv2d(channels[f'{2**i}'], 3, 1, stride=1, padding=0, bias=True, bias_init_val=0)) + + if different_w: + linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat + else: + linear_out_channel = num_style_feat + + self.final_linear = EqualLinear( + channels['4'] * 4 * 4, linear_out_channel, bias=True, bias_init_val=0, lr_mul=1, activation=None) + + # the decoder: stylegan2 generator with SFT modulations + self.stylegan_decoder = StyleGAN2GeneratorSFT( + out_size=out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + resample_kernel=resample_kernel, + lr_mlp=lr_mlp, + narrow=narrow, + sft_half=sft_half) + + # load pre-trained stylegan2 model if necessary + if decoder_load_path: + self.stylegan_decoder.load_state_dict( + torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema']) + # fix decoder without updating params + if fix_decoder: + for _, param in self.stylegan_decoder.named_parameters(): + param.requires_grad = False + + # for SFT modulations (scale and shift) + self.condition_scale = nn.ModuleList() + self.condition_shift = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f'{2**i}'] + if sft_half: + sft_out_channels = out_channels + else: + sft_out_channels = out_channels * 2 + self.condition_scale.append( + nn.Sequential( + EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0), + ScaledLeakyReLU(0.2), + EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=1))) + self.condition_shift.append( + nn.Sequential( + EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0), + ScaledLeakyReLU(0.2), + EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0))) + + def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs): + """Forward function for GFPGANv1. + + Args: + x (Tensor): Input images. + return_latents (bool): Whether to return style latents. Default: False. + return_rgb (bool): Whether return intermediate rgb images. Default: True. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + """ + conditions = [] + unet_skips = [] + out_rgbs = [] + + # encoder + feat = self.conv_body_first(x) + for i in range(self.log_size - 2): + feat = self.conv_body_down[i](feat) + unet_skips.insert(0, feat) + + feat = self.final_conv(feat) + + # style code + style_code = self.final_linear(feat.view(feat.size(0), -1)) + if self.different_w: + style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) + + # decode + for i in range(self.log_size - 2): + # add unet skip + feat = feat + unet_skips[i] + # ResUpLayer + feat = self.conv_body_up[i](feat) + # generate scale and shift for SFT layers + scale = self.condition_scale[i](feat) + conditions.append(scale.clone()) + shift = self.condition_shift[i](feat) + conditions.append(shift.clone()) + # generate rgb images + if return_rgb: + out_rgbs.append(self.toRGB[i](feat)) + + # decoder + image, _ = self.stylegan_decoder([style_code], + conditions, + return_latents=return_latents, + input_is_latent=self.input_is_latent, + randomize_noise=randomize_noise) + + return image, out_rgbs + + +@ARCH_REGISTRY.register() +class FacialComponentDiscriminator(nn.Module): + """Facial component (eyes, mouth, noise) discriminator used in GFPGAN. + """ + + def __init__(self): + super(FacialComponentDiscriminator, self).__init__() + # It now uses a VGG-style architectrue with fixed model size + self.conv1 = ConvLayer(3, 64, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) + self.conv2 = ConvLayer(64, 128, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) + self.conv3 = ConvLayer(128, 128, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) + self.conv4 = ConvLayer(128, 256, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) + self.conv5 = ConvLayer(256, 256, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) + self.final_conv = ConvLayer(256, 1, 3, bias=True, activate=False) + + def forward(self, x, return_feats=False, **kwargs): + """Forward function for FacialComponentDiscriminator. + + Args: + x (Tensor): Input images. + return_feats (bool): Whether to return intermediate features. Default: False. + """ + feat = self.conv1(x) + feat = self.conv3(self.conv2(feat)) + rlt_feats = [] + if return_feats: + rlt_feats.append(feat.clone()) + feat = self.conv5(self.conv4(feat)) + if return_feats: + rlt_feats.append(feat.clone()) + out = self.final_conv(feat) + + if return_feats: + return out, rlt_feats + else: + return out, None diff --git a/motion-gan-pipeline/GFPGAN/gfpgan/archs/gfpganv1_clean_arch.py b/motion-gan-pipeline/GFPGAN/gfpgan/archs/gfpganv1_clean_arch.py new file mode 100644 index 0000000..d6c2705 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/gfpgan/archs/gfpganv1_clean_arch.py @@ -0,0 +1,324 @@ +import math +import random +import torch +from basicsr.utils.registry import ARCH_REGISTRY +from torch import nn +from torch.nn import functional as F + +from .stylegan2_clean_arch import StyleGAN2GeneratorClean + + +class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean): + """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). + + It is the clean version without custom compiled CUDA extensions used in StyleGAN2. + + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1, sft_half=False): + super(StyleGAN2GeneratorCSFT, self).__init__( + out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + narrow=narrow) + self.sft_half = sft_half + + def forward(self, + styles, + conditions, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False): + """Forward function for StyleGAN2GeneratorCSFT. + + Args: + styles (list[Tensor]): Sample codes of styles. + conditions (list[Tensor]): SFT conditions to generators. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], + noise[2::2], self.to_rgbs): + out = conv1(out, latent[:, i], noise=noise1) + + # the conditions may have fewer levels + if i < len(conditions): + # SFT part to combine the conditions + if self.sft_half: # only apply SFT to half of the channels + out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) + out_sft = out_sft * conditions[i - 1] + conditions[i] + out = torch.cat([out_same, out_sft], dim=1) + else: # apply SFT to all the channels + out = out * conditions[i - 1] + conditions[i] + + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ResBlock(nn.Module): + """Residual block with bilinear upsampling/downsampling. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + mode (str): Upsampling/downsampling mode. Options: down | up. Default: down. + """ + + def __init__(self, in_channels, out_channels, mode='down'): + super(ResBlock, self).__init__() + + self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1) + self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1) + self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False) + if mode == 'down': + self.scale_factor = 0.5 + elif mode == 'up': + self.scale_factor = 2 + + def forward(self, x): + out = F.leaky_relu_(self.conv1(x), negative_slope=0.2) + # upsample/downsample + out = F.interpolate(out, scale_factor=self.scale_factor, mode='bilinear', align_corners=False) + out = F.leaky_relu_(self.conv2(out), negative_slope=0.2) + # skip + x = F.interpolate(x, scale_factor=self.scale_factor, mode='bilinear', align_corners=False) + skip = self.skip(x) + out = out + skip + return out + + +@ARCH_REGISTRY.register() +class GFPGANv1Clean(nn.Module): + """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. + + It is the clean version without custom compiled CUDA extensions used in StyleGAN2. + + Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. + + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. + fix_decoder (bool): Whether to fix the decoder. Default: True. + + num_mlp (int): Layer number of MLP style layers. Default: 8. + input_is_latent (bool): Whether input is latent style. Default: False. + different_w (bool): Whether to use different latent w for different layers. Default: False. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + channel_multiplier=1, + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + input_is_latent=False, + different_w=False, + narrow=1, + sft_half=False): + + super(GFPGANv1Clean, self).__init__() + self.input_is_latent = input_is_latent + self.different_w = different_w + self.num_style_feat = num_style_feat + + unet_narrow = narrow * 0.5 # by default, use a half of input channels + channels = { + '4': int(512 * unet_narrow), + '8': int(512 * unet_narrow), + '16': int(512 * unet_narrow), + '32': int(512 * unet_narrow), + '64': int(256 * channel_multiplier * unet_narrow), + '128': int(128 * channel_multiplier * unet_narrow), + '256': int(64 * channel_multiplier * unet_narrow), + '512': int(32 * channel_multiplier * unet_narrow), + '1024': int(16 * channel_multiplier * unet_narrow) + } + + self.log_size = int(math.log(out_size, 2)) + first_out_size = 2**(int(math.log(out_size, 2))) + + self.conv_body_first = nn.Conv2d(3, channels[f'{first_out_size}'], 1) + + # downsample + in_channels = channels[f'{first_out_size}'] + self.conv_body_down = nn.ModuleList() + for i in range(self.log_size, 2, -1): + out_channels = channels[f'{2**(i - 1)}'] + self.conv_body_down.append(ResBlock(in_channels, out_channels, mode='down')) + in_channels = out_channels + + self.final_conv = nn.Conv2d(in_channels, channels['4'], 3, 1, 1) + + # upsample + in_channels = channels['4'] + self.conv_body_up = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f'{2**i}'] + self.conv_body_up.append(ResBlock(in_channels, out_channels, mode='up')) + in_channels = out_channels + + # to RGB + self.toRGB = nn.ModuleList() + for i in range(3, self.log_size + 1): + self.toRGB.append(nn.Conv2d(channels[f'{2**i}'], 3, 1)) + + if different_w: + linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat + else: + linear_out_channel = num_style_feat + + self.final_linear = nn.Linear(channels['4'] * 4 * 4, linear_out_channel) + + # the decoder: stylegan2 generator with SFT modulations + self.stylegan_decoder = StyleGAN2GeneratorCSFT( + out_size=out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + narrow=narrow, + sft_half=sft_half) + + # load pre-trained stylegan2 model if necessary + if decoder_load_path: + self.stylegan_decoder.load_state_dict( + torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema']) + # fix decoder without updating params + if fix_decoder: + for _, param in self.stylegan_decoder.named_parameters(): + param.requires_grad = False + + # for SFT modulations (scale and shift) + self.condition_scale = nn.ModuleList() + self.condition_shift = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f'{2**i}'] + if sft_half: + sft_out_channels = out_channels + else: + sft_out_channels = out_channels * 2 + self.condition_scale.append( + nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, 1, 1), nn.LeakyReLU(0.2, True), + nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1))) + self.condition_shift.append( + nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, 1, 1), nn.LeakyReLU(0.2, True), + nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1))) + + def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs): + """Forward function for GFPGANv1Clean. + + Args: + x (Tensor): Input images. + return_latents (bool): Whether to return style latents. Default: False. + return_rgb (bool): Whether return intermediate rgb images. Default: True. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + """ + conditions = [] + unet_skips = [] + out_rgbs = [] + + # encoder + feat = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2) + for i in range(self.log_size - 2): + feat = self.conv_body_down[i](feat) + unet_skips.insert(0, feat) + feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2) + + # style code + style_code = self.final_linear(feat.view(feat.size(0), -1)) + if self.different_w: + style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) + + # decode + for i in range(self.log_size - 2): + # add unet skip + feat = feat + unet_skips[i] + # ResUpLayer + feat = self.conv_body_up[i](feat) + # generate scale and shift for SFT layers + scale = self.condition_scale[i](feat) + conditions.append(scale.clone()) + shift = self.condition_shift[i](feat) + conditions.append(shift.clone()) + # generate rgb images + if return_rgb: + out_rgbs.append(self.toRGB[i](feat)) + + # decoder + image, _ = self.stylegan_decoder([style_code], + conditions, + return_latents=return_latents, + input_is_latent=self.input_is_latent, + randomize_noise=randomize_noise) + + return image, out_rgbs diff --git a/motion-gan-pipeline/GFPGAN/gfpgan/archs/restoreformer_arch.py b/motion-gan-pipeline/GFPGAN/gfpgan/archs/restoreformer_arch.py new file mode 100644 index 0000000..66cdff3 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/gfpgan/archs/restoreformer_arch.py @@ -0,0 +1,658 @@ +"""Modified from https://github.com/wzhouxiff/RestoreFormer +""" +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class VectorQuantizer(nn.Module): + """ + see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py + ____________________________________________ + Discretization bottleneck part of the VQ-VAE. + Inputs: + - n_e : number of embeddings + - e_dim : dimension of embedding + - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 + _____________________________________________ + """ + + def __init__(self, n_e, e_dim, beta): + super(VectorQuantizer, self).__init__() + self.n_e = n_e + self.e_dim = e_dim + self.beta = beta + + self.embedding = nn.Embedding(self.n_e, self.e_dim) + self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) + + def forward(self, z): + """ + Inputs the output of the encoder network z and maps it to a discrete + one-hot vector that is the index of the closest embedding vector e_j + z (continuous) -> z_q (discrete) + z.shape = (batch, channel, height, width) + quantization pipeline: + 1. get encoder input (B,C,H,W) + 2. flatten input to (B*H*W,C) + """ + # reshape z -> (batch, height, width, channel) and flatten + z = z.permute(0, 2, 3, 1).contiguous() + z_flattened = z.view(-1, self.e_dim) + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + + d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ + torch.sum(self.embedding.weight**2, dim=1) - 2 * \ + torch.matmul(z_flattened, self.embedding.weight.t()) + + # could possible replace this here + # #\start... + # find closest encodings + + min_value, min_encoding_indices = torch.min(d, dim=1) + + min_encoding_indices = min_encoding_indices.unsqueeze(1) + + min_encodings = torch.zeros(min_encoding_indices.shape[0], self.n_e).to(z) + min_encodings.scatter_(1, min_encoding_indices, 1) + + # dtype min encodings: torch.float32 + # min_encodings shape: torch.Size([2048, 512]) + # min_encoding_indices.shape: torch.Size([2048, 1]) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) + # .........\end + + # with: + # .........\start + # min_encoding_indices = torch.argmin(d, dim=1) + # z_q = self.embedding(min_encoding_indices) + # ......\end......... (TODO) + + # compute loss for embedding + loss = torch.mean((z_q.detach() - z)**2) + self.beta * torch.mean((z_q - z.detach())**2) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # perplexity + + e_mean = torch.mean(min_encodings, dim=0) + perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) + + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q, loss, (perplexity, min_encodings, min_encoding_indices, d) + + def get_codebook_entry(self, indices, shape): + # shape specifying (batch, height, width, channel) + # TODO: check for more easy handling with nn.Embedding + min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices) + min_encodings.scatter_(1, indices[:, None], 1) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings.float(), self.embedding.weight) + + if shape is not None: + z_q = z_q.view(shape) + + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q + + +# pytorch_diffusion + derived encoder decoder +def nonlinearity(x): + # swish + return x * torch.sigmoid(x) + + +def Normalize(in_channels): + return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + + +class Upsample(nn.Module): + + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode='nearest') + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) + + def forward(self, x): + if self.with_conv: + pad = (0, 1, 0, 1) + x = torch.nn.functional.pad(x, pad, mode='constant', value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + else: + self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x + h + + +class MultiHeadAttnBlock(nn.Module): + + def __init__(self, in_channels, head_size=1): + super().__init__() + self.in_channels = in_channels + self.head_size = head_size + self.att_size = in_channels // head_size + assert (in_channels % head_size == 0), 'The size of head should be divided by the number of channels.' + + self.norm1 = Normalize(in_channels) + self.norm2 = Normalize(in_channels) + + self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + self.num = 0 + + def forward(self, x, y=None): + h_ = x + h_ = self.norm1(h_) + if y is None: + y = h_ + else: + y = self.norm2(y) + + q = self.q(y) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = q.reshape(b, self.head_size, self.att_size, h * w) + q = q.permute(0, 3, 1, 2) # b, hw, head, att + + k = k.reshape(b, self.head_size, self.att_size, h * w) + k = k.permute(0, 3, 1, 2) + + v = v.reshape(b, self.head_size, self.att_size, h * w) + v = v.permute(0, 3, 1, 2) + + q = q.transpose(1, 2) + v = v.transpose(1, 2) + k = k.transpose(1, 2).transpose(2, 3) + + scale = int(self.att_size)**(-0.5) + q.mul_(scale) + w_ = torch.matmul(q, k) + w_ = F.softmax(w_, dim=3) + + w_ = w_.matmul(v) + + w_ = w_.transpose(1, 2).contiguous() # [b, h*w, head, att] + w_ = w_.view(b, h, w, -1) + w_ = w_.permute(0, 3, 1, 2) + + w_ = self.proj_out(w_) + + return x + w_ + + +class MultiHeadEncoder(nn.Module): + + def __init__(self, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks=2, + attn_resolutions=(16, ), + dropout=0.0, + resamp_with_conv=True, + in_channels=3, + resolution=512, + z_channels=256, + double_z=True, + enable_mid=True, + head_size=1, + **ignore_kwargs): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.enable_mid = enable_mid + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) + + curr_res = resolution + in_ch_mult = (1, ) + tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append( + ResnetBlock( + in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(MultiHeadAttnBlock(block_in, head_size)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + if self.enable_mid: + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) + self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, 2 * z_channels if double_z else z_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x): + hs = {} + # timestep embedding + temb = None + + # downsampling + h = self.conv_in(x) + hs['in'] = h + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](h, temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + + if i_level != self.num_resolutions - 1: + # hs.append(h) + hs['block_' + str(i_level)] = h + h = self.down[i_level].downsample(h) + + # middle + # h = hs[-1] + if self.enable_mid: + h = self.mid.block_1(h, temb) + hs['block_' + str(i_level) + '_atten'] = h + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + hs['mid_atten'] = h + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + # hs.append(h) + hs['out'] = h + + return hs + + +class MultiHeadDecoder(nn.Module): + + def __init__(self, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks=2, + attn_resolutions=(16, ), + dropout=0.0, + resamp_with_conv=True, + in_channels=3, + resolution=512, + z_channels=256, + give_pre_end=False, + enable_mid=True, + head_size=1, + **ignorekwargs): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.enable_mid = enable_mid + + # compute in_ch_mult, block_in and curr_res at lowest res + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2**(self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + print('Working with z of shape {} = {} dimensions.'.format(self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = torch.nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1) + + # middle + if self.enable_mid: + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) + self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append( + ResnetBlock( + in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(MultiHeadAttnBlock(block_in, head_size)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) + + def forward(self, z): + # assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + if self.enable_mid: + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class MultiHeadDecoderTransformer(nn.Module): + + def __init__(self, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks=2, + attn_resolutions=(16, ), + dropout=0.0, + resamp_with_conv=True, + in_channels=3, + resolution=512, + z_channels=256, + give_pre_end=False, + enable_mid=True, + head_size=1, + **ignorekwargs): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.enable_mid = enable_mid + + # compute in_ch_mult, block_in and curr_res at lowest res + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2**(self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + print('Working with z of shape {} = {} dimensions.'.format(self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = torch.nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1) + + # middle + if self.enable_mid: + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) + self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append( + ResnetBlock( + in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(MultiHeadAttnBlock(block_in, head_size)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) + + def forward(self, z, hs): + # assert z.shape[1:] == self.z_shape[1:] + # self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + if self.enable_mid: + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h, hs['mid_atten']) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h, hs['block_' + str(i_level) + '_atten']) + # hfeature = h.clone() + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class RestoreFormer(nn.Module): + + def __init__(self, + n_embed=1024, + embed_dim=256, + ch=64, + out_ch=3, + ch_mult=(1, 2, 2, 4, 4, 8), + num_res_blocks=2, + attn_resolutions=(16, ), + dropout=0.0, + in_channels=3, + resolution=512, + z_channels=256, + double_z=False, + enable_mid=True, + fix_decoder=False, + fix_codebook=True, + fix_encoder=False, + head_size=8): + super(RestoreFormer, self).__init__() + + self.encoder = MultiHeadEncoder( + ch=ch, + out_ch=out_ch, + ch_mult=ch_mult, + num_res_blocks=num_res_blocks, + attn_resolutions=attn_resolutions, + dropout=dropout, + in_channels=in_channels, + resolution=resolution, + z_channels=z_channels, + double_z=double_z, + enable_mid=enable_mid, + head_size=head_size) + self.decoder = MultiHeadDecoderTransformer( + ch=ch, + out_ch=out_ch, + ch_mult=ch_mult, + num_res_blocks=num_res_blocks, + attn_resolutions=attn_resolutions, + dropout=dropout, + in_channels=in_channels, + resolution=resolution, + z_channels=z_channels, + enable_mid=enable_mid, + head_size=head_size) + + self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25) + + self.quant_conv = torch.nn.Conv2d(z_channels, embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, z_channels, 1) + + if fix_decoder: + for _, param in self.decoder.named_parameters(): + param.requires_grad = False + for _, param in self.post_quant_conv.named_parameters(): + param.requires_grad = False + for _, param in self.quantize.named_parameters(): + param.requires_grad = False + elif fix_codebook: + for _, param in self.quantize.named_parameters(): + param.requires_grad = False + + if fix_encoder: + for _, param in self.encoder.named_parameters(): + param.requires_grad = False + + def encode(self, x): + + hs = self.encoder(x) + h = self.quant_conv(hs['out']) + quant, emb_loss, info = self.quantize(h) + return quant, emb_loss, info, hs + + def decode(self, quant, hs): + quant = self.post_quant_conv(quant) + dec = self.decoder(quant, hs) + + return dec + + def forward(self, input, **kwargs): + quant, diff, info, hs = self.encode(input) + dec = self.decode(quant, hs) + + return dec, None diff --git a/motion-gan-pipeline/GFPGAN/gfpgan/archs/stylegan2_bilinear_arch.py b/motion-gan-pipeline/GFPGAN/gfpgan/archs/stylegan2_bilinear_arch.py new file mode 100644 index 0000000..1342ee3 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/gfpgan/archs/stylegan2_bilinear_arch.py @@ -0,0 +1,613 @@ +import math +import random +import torch +from basicsr.ops.fused_act import FusedLeakyReLU, fused_leaky_relu +from basicsr.utils.registry import ARCH_REGISTRY +from torch import nn +from torch.nn import functional as F + + +class NormStyleCode(nn.Module): + + def forward(self, x): + """Normalize the style codes. + + Args: + x (Tensor): Style codes with shape (b, c). + + Returns: + Tensor: Normalized tensor. + """ + return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) + + +class EqualLinear(nn.Module): + """Equalized Linear as StyleGAN2. + + Args: + in_channels (int): Size of each sample. + out_channels (int): Size of each output sample. + bias (bool): If set to ``False``, the layer will not learn an additive + bias. Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + lr_mul (float): Learning rate multiplier. Default: 1. + activation (None | str): The activation after ``linear`` operation. + Supported: 'fused_lrelu', None. Default: None. + """ + + def __init__(self, in_channels, out_channels, bias=True, bias_init_val=0, lr_mul=1, activation=None): + super(EqualLinear, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.lr_mul = lr_mul + self.activation = activation + if self.activation not in ['fused_lrelu', None]: + raise ValueError(f'Wrong activation value in EqualLinear: {activation}' + "Supported ones are: ['fused_lrelu', None].") + self.scale = (1 / math.sqrt(in_channels)) * lr_mul + + self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) + if bias: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter('bias', None) + + def forward(self, x): + if self.bias is None: + bias = None + else: + bias = self.bias * self.lr_mul + if self.activation == 'fused_lrelu': + out = F.linear(x, self.weight * self.scale) + out = fused_leaky_relu(out, bias) + else: + out = F.linear(x, self.weight * self.scale, bias=bias) + return out + + def __repr__(self): + return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' + f'out_channels={self.out_channels}, bias={self.bias is not None})') + + +class ModulatedConv2d(nn.Module): + """Modulated Conv2d used in StyleGAN2. + + There is no bias in ModulatedConv2d. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether to demodulate in the conv layer. + Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. + Default: None. + eps (float): A value added to the denominator for numerical stability. + Default: 1e-8. + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + eps=1e-8, + interpolation_mode='bilinear'): + super(ModulatedConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.demodulate = demodulate + self.sample_mode = sample_mode + self.eps = eps + self.interpolation_mode = interpolation_mode + if self.interpolation_mode == 'nearest': + self.align_corners = None + else: + self.align_corners = False + + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + # modulation inside each modulated conv + self.modulation = EqualLinear( + num_style_feat, in_channels, bias=True, bias_init_val=1, lr_mul=1, activation=None) + + self.weight = nn.Parameter(torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)) + self.padding = kernel_size // 2 + + def forward(self, x, style): + """Forward function. + + Args: + x (Tensor): Tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + + Returns: + Tensor: Modulated tensor after convolution. + """ + b, c, h, w = x.shape # c = c_in + # weight modulation + style = self.modulation(style).view(b, 1, c, 1, 1) + # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) + weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) + weight = weight * demod.view(b, self.out_channels, 1, 1, 1) + + weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size) + + if self.sample_mode == 'upsample': + x = F.interpolate(x, scale_factor=2, mode=self.interpolation_mode, align_corners=self.align_corners) + elif self.sample_mode == 'downsample': + x = F.interpolate(x, scale_factor=0.5, mode=self.interpolation_mode, align_corners=self.align_corners) + + b, c, h, w = x.shape + x = x.view(1, b * c, h, w) + # weight: (b*c_out, c_in, k, k), groups=b + out = F.conv2d(x, weight, padding=self.padding, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + + return out + + def __repr__(self): + return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' + f'out_channels={self.out_channels}, ' + f'kernel_size={self.kernel_size}, ' + f'demodulate={self.demodulate}, sample_mode={self.sample_mode})') + + +class StyleConv(nn.Module): + """Style conv. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. + Default: None. + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + interpolation_mode='bilinear'): + super(StyleConv, self).__init__() + self.modulated_conv = ModulatedConv2d( + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=demodulate, + sample_mode=sample_mode, + interpolation_mode=interpolation_mode) + self.weight = nn.Parameter(torch.zeros(1)) # for noise injection + self.activate = FusedLeakyReLU(out_channels) + + def forward(self, x, style, noise=None): + # modulate + out = self.modulated_conv(x, style) + # noise injection + if noise is None: + b, _, h, w = out.shape + noise = out.new_empty(b, 1, h, w).normal_() + out = out + self.weight * noise + # activation (with bias) + out = self.activate(out) + return out + + +class ToRGB(nn.Module): + """To RGB from features. + + Args: + in_channels (int): Channel number of input. + num_style_feat (int): Channel number of style features. + upsample (bool): Whether to upsample. Default: True. + """ + + def __init__(self, in_channels, num_style_feat, upsample=True, interpolation_mode='bilinear'): + super(ToRGB, self).__init__() + self.upsample = upsample + self.interpolation_mode = interpolation_mode + if self.interpolation_mode == 'nearest': + self.align_corners = None + else: + self.align_corners = False + self.modulated_conv = ModulatedConv2d( + in_channels, + 3, + kernel_size=1, + num_style_feat=num_style_feat, + demodulate=False, + sample_mode=None, + interpolation_mode=interpolation_mode) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + def forward(self, x, style, skip=None): + """Forward function. + + Args: + x (Tensor): Feature tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + skip (Tensor): Base/skip tensor. Default: None. + + Returns: + Tensor: RGB images. + """ + out = self.modulated_conv(x, style) + out = out + self.bias + if skip is not None: + if self.upsample: + skip = F.interpolate( + skip, scale_factor=2, mode=self.interpolation_mode, align_corners=self.align_corners) + out = out + skip + return out + + +class ConstantInput(nn.Module): + """Constant input. + + Args: + num_channel (int): Channel number of constant input. + size (int): Spatial size of constant input. + """ + + def __init__(self, num_channel, size): + super(ConstantInput, self).__init__() + self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) + + def forward(self, batch): + out = self.weight.repeat(batch, 1, 1, 1) + return out + + +@ARCH_REGISTRY.register() +class StyleGAN2GeneratorBilinear(nn.Module): + """StyleGAN2 Generator. + + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of + StyleGAN2. Default: 2. + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + narrow (float): Narrow ratio for channels. Default: 1.0. + """ + + def __init__(self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + lr_mlp=0.01, + narrow=1, + interpolation_mode='bilinear'): + super(StyleGAN2GeneratorBilinear, self).__init__() + # Style MLP layers + self.num_style_feat = num_style_feat + style_mlp_layers = [NormStyleCode()] + for i in range(num_mlp): + style_mlp_layers.append( + EqualLinear( + num_style_feat, num_style_feat, bias=True, bias_init_val=0, lr_mul=lr_mlp, + activation='fused_lrelu')) + self.style_mlp = nn.Sequential(*style_mlp_layers) + + channels = { + '4': int(512 * narrow), + '8': int(512 * narrow), + '16': int(512 * narrow), + '32': int(512 * narrow), + '64': int(256 * channel_multiplier * narrow), + '128': int(128 * channel_multiplier * narrow), + '256': int(64 * channel_multiplier * narrow), + '512': int(32 * channel_multiplier * narrow), + '1024': int(16 * channel_multiplier * narrow) + } + self.channels = channels + + self.constant_input = ConstantInput(channels['4'], size=4) + self.style_conv1 = StyleConv( + channels['4'], + channels['4'], + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + interpolation_mode=interpolation_mode) + self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False, interpolation_mode=interpolation_mode) + + self.log_size = int(math.log(out_size, 2)) + self.num_layers = (self.log_size - 2) * 2 + 1 + self.num_latent = self.log_size * 2 - 2 + + self.style_convs = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + self.noises = nn.Module() + + in_channels = channels['4'] + # noise + for layer_idx in range(self.num_layers): + resolution = 2**((layer_idx + 5) // 2) + shape = [1, 1, resolution, resolution] + self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape)) + # style convs and to_rgbs + for i in range(3, self.log_size + 1): + out_channels = channels[f'{2**i}'] + self.style_convs.append( + StyleConv( + in_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode='upsample', + interpolation_mode=interpolation_mode)) + self.style_convs.append( + StyleConv( + out_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + interpolation_mode=interpolation_mode)) + self.to_rgbs.append( + ToRGB(out_channels, num_style_feat, upsample=True, interpolation_mode=interpolation_mode)) + in_channels = out_channels + + def make_noise(self): + """Make noise for noise injection.""" + device = self.constant_input.weight.device + noises = [torch.randn(1, 1, 4, 4, device=device)] + + for i in range(3, self.log_size + 1): + for _ in range(2): + noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) + + return noises + + def get_latent(self, x): + return self.style_mlp(x) + + def mean_latent(self, num_latent): + latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device) + latent = self.style_mlp(latent_in).mean(0, keepdim=True) + return latent + + def forward(self, + styles, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False): + """Forward function for StyleGAN2Generator. + + Args: + styles (list[Tensor]): Sample codes of styles. + input_is_latent (bool): Whether input is latent style. + Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is + False. Default: True. + truncation (float): TODO. Default: 1. + truncation_latent (Tensor | None): TODO. Default: None. + inject_index (int | None): The injection index for mixing noise. + Default: None. + return_latents (bool): Whether to return style latents. + Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) + styles = style_truncation + # get style latent with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], + noise[2::2], self.to_rgbs): + out = conv1(out, latent[:, i], noise=noise1) + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ScaledLeakyReLU(nn.Module): + """Scaled LeakyReLU. + + Args: + negative_slope (float): Negative slope. Default: 0.2. + """ + + def __init__(self, negative_slope=0.2): + super(ScaledLeakyReLU, self).__init__() + self.negative_slope = negative_slope + + def forward(self, x): + out = F.leaky_relu(x, negative_slope=self.negative_slope) + return out * math.sqrt(2) + + +class EqualConv2d(nn.Module): + """Equalized Linear as StyleGAN2. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + stride (int): Stride of the convolution. Default: 1 + padding (int): Zero-padding added to both sides of the input. + Default: 0. + bias (bool): If ``True``, adds a learnable bias to the output. + Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + """ + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True, bias_init_val=0): + super(EqualConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + + self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size)) + if bias: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter('bias', None) + + def forward(self, x): + out = F.conv2d( + x, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.padding, + ) + + return out + + def __repr__(self): + return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' + f'out_channels={self.out_channels}, ' + f'kernel_size={self.kernel_size},' + f' stride={self.stride}, padding={self.padding}, ' + f'bias={self.bias is not None})') + + +class ConvLayer(nn.Sequential): + """Conv Layer used in StyleGAN2 Discriminator. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Kernel size. + downsample (bool): Whether downsample by a factor of 2. + Default: False. + bias (bool): Whether with bias. Default: True. + activate (bool): Whether use activateion. Default: True. + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size, + downsample=False, + bias=True, + activate=True, + interpolation_mode='bilinear'): + layers = [] + self.interpolation_mode = interpolation_mode + # downsample + if downsample: + if self.interpolation_mode == 'nearest': + self.align_corners = None + else: + self.align_corners = False + + layers.append( + torch.nn.Upsample(scale_factor=0.5, mode=interpolation_mode, align_corners=self.align_corners)) + stride = 1 + self.padding = kernel_size // 2 + # conv + layers.append( + EqualConv2d( + in_channels, out_channels, kernel_size, stride=stride, padding=self.padding, bias=bias + and not activate)) + # activation + if activate: + if bias: + layers.append(FusedLeakyReLU(out_channels)) + else: + layers.append(ScaledLeakyReLU(0.2)) + + super(ConvLayer, self).__init__(*layers) + + +class ResBlock(nn.Module): + """Residual block used in StyleGAN2 Discriminator. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + """ + + def __init__(self, in_channels, out_channels, interpolation_mode='bilinear'): + super(ResBlock, self).__init__() + + self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) + self.conv2 = ConvLayer( + in_channels, + out_channels, + 3, + downsample=True, + interpolation_mode=interpolation_mode, + bias=True, + activate=True) + self.skip = ConvLayer( + in_channels, + out_channels, + 1, + downsample=True, + interpolation_mode=interpolation_mode, + bias=False, + activate=False) + + def forward(self, x): + out = self.conv1(x) + out = self.conv2(out) + skip = self.skip(x) + out = (out + skip) / math.sqrt(2) + return out diff --git a/motion-gan-pipeline/GFPGAN/gfpgan/archs/stylegan2_clean_arch.py b/motion-gan-pipeline/GFPGAN/gfpgan/archs/stylegan2_clean_arch.py new file mode 100644 index 0000000..9e2ee94 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/gfpgan/archs/stylegan2_clean_arch.py @@ -0,0 +1,368 @@ +import math +import random +import torch +from basicsr.archs.arch_util import default_init_weights +from basicsr.utils.registry import ARCH_REGISTRY +from torch import nn +from torch.nn import functional as F + + +class NormStyleCode(nn.Module): + + def forward(self, x): + """Normalize the style codes. + + Args: + x (Tensor): Style codes with shape (b, c). + + Returns: + Tensor: Normalized tensor. + """ + return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) + + +class ModulatedConv2d(nn.Module): + """Modulated Conv2d used in StyleGAN2. + + There is no bias in ModulatedConv2d. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether to demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. + eps (float): A value added to the denominator for numerical stability. Default: 1e-8. + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + eps=1e-8): + super(ModulatedConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.demodulate = demodulate + self.sample_mode = sample_mode + self.eps = eps + + # modulation inside each modulated conv + self.modulation = nn.Linear(num_style_feat, in_channels, bias=True) + # initialization + default_init_weights(self.modulation, scale=1, bias_fill=1, a=0, mode='fan_in', nonlinearity='linear') + + self.weight = nn.Parameter( + torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) / + math.sqrt(in_channels * kernel_size**2)) + self.padding = kernel_size // 2 + + def forward(self, x, style): + """Forward function. + + Args: + x (Tensor): Tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + + Returns: + Tensor: Modulated tensor after convolution. + """ + b, c, h, w = x.shape # c = c_in + # weight modulation + style = self.modulation(style).view(b, 1, c, 1, 1) + # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) + weight = self.weight * style # (b, c_out, c_in, k, k) + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) + weight = weight * demod.view(b, self.out_channels, 1, 1, 1) + + weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size) + + # upsample or downsample if necessary + if self.sample_mode == 'upsample': + x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False) + elif self.sample_mode == 'downsample': + x = F.interpolate(x, scale_factor=0.5, mode='bilinear', align_corners=False) + + b, c, h, w = x.shape + x = x.view(1, b * c, h, w) + # weight: (b*c_out, c_in, k, k), groups=b + out = F.conv2d(x, weight, padding=self.padding, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + + return out + + def __repr__(self): + return (f'{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, ' + f'kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})') + + +class StyleConv(nn.Module): + """Style conv used in StyleGAN2. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. + """ + + def __init__(self, in_channels, out_channels, kernel_size, num_style_feat, demodulate=True, sample_mode=None): + super(StyleConv, self).__init__() + self.modulated_conv = ModulatedConv2d( + in_channels, out_channels, kernel_size, num_style_feat, demodulate=demodulate, sample_mode=sample_mode) + self.weight = nn.Parameter(torch.zeros(1)) # for noise injection + self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1)) + self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True) + + def forward(self, x, style, noise=None): + # modulate + out = self.modulated_conv(x, style) * 2**0.5 # for conversion + # noise injection + if noise is None: + b, _, h, w = out.shape + noise = out.new_empty(b, 1, h, w).normal_() + out = out + self.weight * noise + # add bias + out = out + self.bias + # activation + out = self.activate(out) + return out + + +class ToRGB(nn.Module): + """To RGB (image space) from features. + + Args: + in_channels (int): Channel number of input. + num_style_feat (int): Channel number of style features. + upsample (bool): Whether to upsample. Default: True. + """ + + def __init__(self, in_channels, num_style_feat, upsample=True): + super(ToRGB, self).__init__() + self.upsample = upsample + self.modulated_conv = ModulatedConv2d( + in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + def forward(self, x, style, skip=None): + """Forward function. + + Args: + x (Tensor): Feature tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + skip (Tensor): Base/skip tensor. Default: None. + + Returns: + Tensor: RGB images. + """ + out = self.modulated_conv(x, style) + out = out + self.bias + if skip is not None: + if self.upsample: + skip = F.interpolate(skip, scale_factor=2, mode='bilinear', align_corners=False) + out = out + skip + return out + + +class ConstantInput(nn.Module): + """Constant input. + + Args: + num_channel (int): Channel number of constant input. + size (int): Spatial size of constant input. + """ + + def __init__(self, num_channel, size): + super(ConstantInput, self).__init__() + self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) + + def forward(self, batch): + out = self.weight.repeat(batch, 1, 1, 1) + return out + + +@ARCH_REGISTRY.register() +class StyleGAN2GeneratorClean(nn.Module): + """Clean version of StyleGAN2 Generator. + + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + narrow (float): Narrow ratio for channels. Default: 1.0. + """ + + def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1): + super(StyleGAN2GeneratorClean, self).__init__() + # Style MLP layers + self.num_style_feat = num_style_feat + style_mlp_layers = [NormStyleCode()] + for i in range(num_mlp): + style_mlp_layers.extend( + [nn.Linear(num_style_feat, num_style_feat, bias=True), + nn.LeakyReLU(negative_slope=0.2, inplace=True)]) + self.style_mlp = nn.Sequential(*style_mlp_layers) + # initialization + default_init_weights(self.style_mlp, scale=1, bias_fill=0, a=0.2, mode='fan_in', nonlinearity='leaky_relu') + + # channel list + channels = { + '4': int(512 * narrow), + '8': int(512 * narrow), + '16': int(512 * narrow), + '32': int(512 * narrow), + '64': int(256 * channel_multiplier * narrow), + '128': int(128 * channel_multiplier * narrow), + '256': int(64 * channel_multiplier * narrow), + '512': int(32 * channel_multiplier * narrow), + '1024': int(16 * channel_multiplier * narrow) + } + self.channels = channels + + self.constant_input = ConstantInput(channels['4'], size=4) + self.style_conv1 = StyleConv( + channels['4'], + channels['4'], + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None) + self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False) + + self.log_size = int(math.log(out_size, 2)) + self.num_layers = (self.log_size - 2) * 2 + 1 + self.num_latent = self.log_size * 2 - 2 + + self.style_convs = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + self.noises = nn.Module() + + in_channels = channels['4'] + # noise + for layer_idx in range(self.num_layers): + resolution = 2**((layer_idx + 5) // 2) + shape = [1, 1, resolution, resolution] + self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape)) + # style convs and to_rgbs + for i in range(3, self.log_size + 1): + out_channels = channels[f'{2**i}'] + self.style_convs.append( + StyleConv( + in_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode='upsample')) + self.style_convs.append( + StyleConv( + out_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None)) + self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True)) + in_channels = out_channels + + def make_noise(self): + """Make noise for noise injection.""" + device = self.constant_input.weight.device + noises = [torch.randn(1, 1, 4, 4, device=device)] + + for i in range(3, self.log_size + 1): + for _ in range(2): + noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) + + return noises + + def get_latent(self, x): + return self.style_mlp(x) + + def mean_latent(self, num_latent): + latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device) + latent = self.style_mlp(latent_in).mean(0, keepdim=True) + return latent + + def forward(self, + styles, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False): + """Forward function for StyleGAN2GeneratorClean. + + Args: + styles (list[Tensor]): Sample codes of styles. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], + noise[2::2], self.to_rgbs): + out = conv1(out, latent[:, i], noise=noise1) + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None diff --git a/motion-gan-pipeline/GFPGAN/gfpgan/data/__init__.py b/motion-gan-pipeline/GFPGAN/gfpgan/data/__init__.py new file mode 100644 index 0000000..69fd9f9 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/gfpgan/data/__init__.py @@ -0,0 +1,10 @@ +import importlib +from basicsr.utils import scandir +from os import path as osp + +# automatically scan and import dataset modules for registry +# scan all the files that end with '_dataset.py' under the data folder +data_folder = osp.dirname(osp.abspath(__file__)) +dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')] +# import all the dataset modules +_dataset_modules = [importlib.import_module(f'gfpgan.data.{file_name}') for file_name in dataset_filenames] diff --git a/motion-gan-pipeline/GFPGAN/gfpgan/data/ffhq_degradation_dataset.py b/motion-gan-pipeline/GFPGAN/gfpgan/data/ffhq_degradation_dataset.py new file mode 100644 index 0000000..64e5755 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/gfpgan/data/ffhq_degradation_dataset.py @@ -0,0 +1,230 @@ +import cv2 +import math +import numpy as np +import os.path as osp +import torch +import torch.utils.data as data +from basicsr.data import degradations as degradations +from basicsr.data.data_util import paths_from_folder +from basicsr.data.transforms import augment +from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor +from basicsr.utils.registry import DATASET_REGISTRY +from torchvision.transforms.functional import (adjust_brightness, adjust_contrast, adjust_hue, adjust_saturation, + normalize) + + +@DATASET_REGISTRY.register() +class FFHQDegradationDataset(data.Dataset): + """FFHQ dataset for GFPGAN. + + It reads high resolution images, and then generate low-quality (LQ) images on-the-fly. + + Args: + opt (dict): Config for train datasets. It contains the following keys: + dataroot_gt (str): Data root path for gt. + io_backend (dict): IO backend type and other kwarg. + mean (list | tuple): Image mean. + std (list | tuple): Image std. + use_hflip (bool): Whether to horizontally flip. + Please see more options in the codes. + """ + + def __init__(self, opt): + super(FFHQDegradationDataset, self).__init__() + self.opt = opt + # file client (io backend) + self.file_client = None + self.io_backend_opt = opt['io_backend'] + + self.gt_folder = opt['dataroot_gt'] + self.mean = opt['mean'] + self.std = opt['std'] + self.out_size = opt['out_size'] + + self.crop_components = opt.get('crop_components', False) # facial components + self.eye_enlarge_ratio = opt.get('eye_enlarge_ratio', 1) # whether enlarge eye regions + + if self.crop_components: + # load component list from a pre-process pth files + self.components_list = torch.load(opt.get('component_path')) + + # file client (lmdb io backend) + if self.io_backend_opt['type'] == 'lmdb': + self.io_backend_opt['db_paths'] = self.gt_folder + if not self.gt_folder.endswith('.lmdb'): + raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}") + with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin: + self.paths = [line.split('.')[0] for line in fin] + else: + # disk backend: scan file list from a folder + self.paths = paths_from_folder(self.gt_folder) + + # degradation configurations + self.blur_kernel_size = opt['blur_kernel_size'] + self.kernel_list = opt['kernel_list'] + self.kernel_prob = opt['kernel_prob'] + self.blur_sigma = opt['blur_sigma'] + self.downsample_range = opt['downsample_range'] + self.noise_range = opt['noise_range'] + self.jpeg_range = opt['jpeg_range'] + + # color jitter + self.color_jitter_prob = opt.get('color_jitter_prob') + self.color_jitter_pt_prob = opt.get('color_jitter_pt_prob') + self.color_jitter_shift = opt.get('color_jitter_shift', 20) + # to gray + self.gray_prob = opt.get('gray_prob') + + logger = get_root_logger() + logger.info(f'Blur: blur_kernel_size {self.blur_kernel_size}, sigma: [{", ".join(map(str, self.blur_sigma))}]') + logger.info(f'Downsample: downsample_range [{", ".join(map(str, self.downsample_range))}]') + logger.info(f'Noise: [{", ".join(map(str, self.noise_range))}]') + logger.info(f'JPEG compression: [{", ".join(map(str, self.jpeg_range))}]') + + if self.color_jitter_prob is not None: + logger.info(f'Use random color jitter. Prob: {self.color_jitter_prob}, shift: {self.color_jitter_shift}') + if self.gray_prob is not None: + logger.info(f'Use random gray. Prob: {self.gray_prob}') + self.color_jitter_shift /= 255. + + @staticmethod + def color_jitter(img, shift): + """jitter color: randomly jitter the RGB values, in numpy formats""" + jitter_val = np.random.uniform(-shift, shift, 3).astype(np.float32) + img = img + jitter_val + img = np.clip(img, 0, 1) + return img + + @staticmethod + def color_jitter_pt(img, brightness, contrast, saturation, hue): + """jitter color: randomly jitter the brightness, contrast, saturation, and hue, in torch Tensor formats""" + fn_idx = torch.randperm(4) + for fn_id in fn_idx: + if fn_id == 0 and brightness is not None: + brightness_factor = torch.tensor(1.0).uniform_(brightness[0], brightness[1]).item() + img = adjust_brightness(img, brightness_factor) + + if fn_id == 1 and contrast is not None: + contrast_factor = torch.tensor(1.0).uniform_(contrast[0], contrast[1]).item() + img = adjust_contrast(img, contrast_factor) + + if fn_id == 2 and saturation is not None: + saturation_factor = torch.tensor(1.0).uniform_(saturation[0], saturation[1]).item() + img = adjust_saturation(img, saturation_factor) + + if fn_id == 3 and hue is not None: + hue_factor = torch.tensor(1.0).uniform_(hue[0], hue[1]).item() + img = adjust_hue(img, hue_factor) + return img + + def get_component_coordinates(self, index, status): + """Get facial component (left_eye, right_eye, mouth) coordinates from a pre-loaded pth file""" + components_bbox = self.components_list[f'{index:08d}'] + if status[0]: # hflip + # exchange right and left eye + tmp = components_bbox['left_eye'] + components_bbox['left_eye'] = components_bbox['right_eye'] + components_bbox['right_eye'] = tmp + # modify the width coordinate + components_bbox['left_eye'][0] = self.out_size - components_bbox['left_eye'][0] + components_bbox['right_eye'][0] = self.out_size - components_bbox['right_eye'][0] + components_bbox['mouth'][0] = self.out_size - components_bbox['mouth'][0] + + # get coordinates + locations = [] + for part in ['left_eye', 'right_eye', 'mouth']: + mean = components_bbox[part][0:2] + half_len = components_bbox[part][2] + if 'eye' in part: + half_len *= self.eye_enlarge_ratio + loc = np.hstack((mean - half_len + 1, mean + half_len)) + loc = torch.from_numpy(loc).float() + locations.append(loc) + return locations + + def __getitem__(self, index): + if self.file_client is None: + self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) + + # load gt image + # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32. + gt_path = self.paths[index] + img_bytes = self.file_client.get(gt_path) + img_gt = imfrombytes(img_bytes, float32=True) + + # random horizontal flip + img_gt, status = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False, return_status=True) + h, w, _ = img_gt.shape + + # get facial component coordinates + if self.crop_components: + locations = self.get_component_coordinates(index, status) + loc_left_eye, loc_right_eye, loc_mouth = locations + + # ------------------------ generate lq image ------------------------ # + # blur + kernel = degradations.random_mixed_kernels( + self.kernel_list, + self.kernel_prob, + self.blur_kernel_size, + self.blur_sigma, + self.blur_sigma, [-math.pi, math.pi], + noise_range=None) + img_lq = cv2.filter2D(img_gt, -1, kernel) + # downsample + scale = np.random.uniform(self.downsample_range[0], self.downsample_range[1]) + img_lq = cv2.resize(img_lq, (int(w // scale), int(h // scale)), interpolation=cv2.INTER_LINEAR) + # noise + if self.noise_range is not None: + img_lq = degradations.random_add_gaussian_noise(img_lq, self.noise_range) + # jpeg compression + if self.jpeg_range is not None: + img_lq = degradations.random_add_jpg_compression(img_lq, self.jpeg_range) + + # resize to original size + img_lq = cv2.resize(img_lq, (w, h), interpolation=cv2.INTER_LINEAR) + + # random color jitter (only for lq) + if self.color_jitter_prob is not None and (np.random.uniform() < self.color_jitter_prob): + img_lq = self.color_jitter(img_lq, self.color_jitter_shift) + # random to gray (only for lq) + if self.gray_prob and np.random.uniform() < self.gray_prob: + img_lq = cv2.cvtColor(img_lq, cv2.COLOR_BGR2GRAY) + img_lq = np.tile(img_lq[:, :, None], [1, 1, 3]) + if self.opt.get('gt_gray'): # whether convert GT to gray images + img_gt = cv2.cvtColor(img_gt, cv2.COLOR_BGR2GRAY) + img_gt = np.tile(img_gt[:, :, None], [1, 1, 3]) # repeat the color channels + + # BGR to RGB, HWC to CHW, numpy to tensor + img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True) + + # random color jitter (pytorch version) (only for lq) + if self.color_jitter_pt_prob is not None and (np.random.uniform() < self.color_jitter_pt_prob): + brightness = self.opt.get('brightness', (0.5, 1.5)) + contrast = self.opt.get('contrast', (0.5, 1.5)) + saturation = self.opt.get('saturation', (0, 1.5)) + hue = self.opt.get('hue', (-0.1, 0.1)) + img_lq = self.color_jitter_pt(img_lq, brightness, contrast, saturation, hue) + + # round and clip + img_lq = torch.clamp((img_lq * 255.0).round(), 0, 255) / 255. + + # normalize + normalize(img_gt, self.mean, self.std, inplace=True) + normalize(img_lq, self.mean, self.std, inplace=True) + + if self.crop_components: + return_dict = { + 'lq': img_lq, + 'gt': img_gt, + 'gt_path': gt_path, + 'loc_left_eye': loc_left_eye, + 'loc_right_eye': loc_right_eye, + 'loc_mouth': loc_mouth + } + return return_dict + else: + return {'lq': img_lq, 'gt': img_gt, 'gt_path': gt_path} + + def __len__(self): + return len(self.paths) diff --git a/motion-gan-pipeline/GFPGAN/gfpgan/models/__init__.py b/motion-gan-pipeline/GFPGAN/gfpgan/models/__init__.py new file mode 100644 index 0000000..6afad57 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/gfpgan/models/__init__.py @@ -0,0 +1,10 @@ +import importlib +from basicsr.utils import scandir +from os import path as osp + +# automatically scan and import model modules for registry +# scan all the files that end with '_model.py' under the model folder +model_folder = osp.dirname(osp.abspath(__file__)) +model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')] +# import all the model modules +_model_modules = [importlib.import_module(f'gfpgan.models.{file_name}') for file_name in model_filenames] diff --git a/motion-gan-pipeline/GFPGAN/gfpgan/models/gfpgan_model.py b/motion-gan-pipeline/GFPGAN/gfpgan/models/gfpgan_model.py new file mode 100644 index 0000000..b5fb8c9 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/gfpgan/models/gfpgan_model.py @@ -0,0 +1,579 @@ +import math +import os.path as osp +import torch +from basicsr.archs import build_network +from basicsr.losses import build_loss +from basicsr.losses.gan_loss import r1_penalty +from basicsr.metrics import calculate_metric +from basicsr.models.base_model import BaseModel +from basicsr.utils import get_root_logger, imwrite, tensor2img +from basicsr.utils.registry import MODEL_REGISTRY +from collections import OrderedDict +from torch.nn import functional as F +from torchvision.ops import roi_align +from tqdm import tqdm + + +@MODEL_REGISTRY.register() +class GFPGANModel(BaseModel): + """The GFPGAN model for Towards real-world blind face restoratin with generative facial prior""" + + def __init__(self, opt): + super(GFPGANModel, self).__init__(opt) + self.idx = 0 # it is used for saving data for check + + # define network + self.net_g = build_network(opt['network_g']) + self.net_g = self.model_to_device(self.net_g) + self.print_network(self.net_g) + + # load pretrained model + load_path = self.opt['path'].get('pretrain_network_g', None) + if load_path is not None: + param_key = self.opt['path'].get('param_key_g', 'params') + self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key) + + self.log_size = int(math.log(self.opt['network_g']['out_size'], 2)) + + if self.is_train: + self.init_training_settings() + + def init_training_settings(self): + train_opt = self.opt['train'] + + # ----------- define net_d ----------- # + self.net_d = build_network(self.opt['network_d']) + self.net_d = self.model_to_device(self.net_d) + self.print_network(self.net_d) + # load pretrained model + load_path = self.opt['path'].get('pretrain_network_d', None) + if load_path is not None: + self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True)) + + # ----------- define net_g with Exponential Moving Average (EMA) ----------- # + # net_g_ema only used for testing on one GPU and saving. There is no need to wrap with DistributedDataParallel + self.net_g_ema = build_network(self.opt['network_g']).to(self.device) + # load pretrained model + load_path = self.opt['path'].get('pretrain_network_g', None) + if load_path is not None: + self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') + else: + self.model_ema(0) # copy net_g weight + + self.net_g.train() + self.net_d.train() + self.net_g_ema.eval() + + # ----------- facial component networks ----------- # + if ('network_d_left_eye' in self.opt and 'network_d_right_eye' in self.opt and 'network_d_mouth' in self.opt): + self.use_facial_disc = True + else: + self.use_facial_disc = False + + if self.use_facial_disc: + # left eye + self.net_d_left_eye = build_network(self.opt['network_d_left_eye']) + self.net_d_left_eye = self.model_to_device(self.net_d_left_eye) + self.print_network(self.net_d_left_eye) + load_path = self.opt['path'].get('pretrain_network_d_left_eye') + if load_path is not None: + self.load_network(self.net_d_left_eye, load_path, True, 'params') + # right eye + self.net_d_right_eye = build_network(self.opt['network_d_right_eye']) + self.net_d_right_eye = self.model_to_device(self.net_d_right_eye) + self.print_network(self.net_d_right_eye) + load_path = self.opt['path'].get('pretrain_network_d_right_eye') + if load_path is not None: + self.load_network(self.net_d_right_eye, load_path, True, 'params') + # mouth + self.net_d_mouth = build_network(self.opt['network_d_mouth']) + self.net_d_mouth = self.model_to_device(self.net_d_mouth) + self.print_network(self.net_d_mouth) + load_path = self.opt['path'].get('pretrain_network_d_mouth') + if load_path is not None: + self.load_network(self.net_d_mouth, load_path, True, 'params') + + self.net_d_left_eye.train() + self.net_d_right_eye.train() + self.net_d_mouth.train() + + # ----------- define facial component gan loss ----------- # + self.cri_component = build_loss(train_opt['gan_component_opt']).to(self.device) + + # ----------- define losses ----------- # + # pixel loss + if train_opt.get('pixel_opt'): + self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) + else: + self.cri_pix = None + + # perceptual loss + if train_opt.get('perceptual_opt'): + self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) + else: + self.cri_perceptual = None + + # L1 loss is used in pyramid loss, component style loss and identity loss + self.cri_l1 = build_loss(train_opt['L1_opt']).to(self.device) + + # gan loss (wgan) + self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) + + # ----------- define identity loss ----------- # + if 'network_identity' in self.opt: + self.use_identity = True + else: + self.use_identity = False + + if self.use_identity: + # define identity network + self.network_identity = build_network(self.opt['network_identity']) + self.network_identity = self.model_to_device(self.network_identity) + self.print_network(self.network_identity) + load_path = self.opt['path'].get('pretrain_network_identity') + if load_path is not None: + self.load_network(self.network_identity, load_path, True, None) + self.network_identity.eval() + for param in self.network_identity.parameters(): + param.requires_grad = False + + # regularization weights + self.r1_reg_weight = train_opt['r1_reg_weight'] # for discriminator + self.net_d_iters = train_opt.get('net_d_iters', 1) + self.net_d_init_iters = train_opt.get('net_d_init_iters', 0) + self.net_d_reg_every = train_opt['net_d_reg_every'] + + # set up optimizers and schedulers + self.setup_optimizers() + self.setup_schedulers() + + def setup_optimizers(self): + train_opt = self.opt['train'] + + # ----------- optimizer g ----------- # + net_g_reg_ratio = 1 + normal_params = [] + for _, param in self.net_g.named_parameters(): + normal_params.append(param) + optim_params_g = [{ # add normal params first + 'params': normal_params, + 'lr': train_opt['optim_g']['lr'] + }] + optim_type = train_opt['optim_g'].pop('type') + lr = train_opt['optim_g']['lr'] * net_g_reg_ratio + betas = (0**net_g_reg_ratio, 0.99**net_g_reg_ratio) + self.optimizer_g = self.get_optimizer(optim_type, optim_params_g, lr, betas=betas) + self.optimizers.append(self.optimizer_g) + + # ----------- optimizer d ----------- # + net_d_reg_ratio = self.net_d_reg_every / (self.net_d_reg_every + 1) + normal_params = [] + for _, param in self.net_d.named_parameters(): + normal_params.append(param) + optim_params_d = [{ # add normal params first + 'params': normal_params, + 'lr': train_opt['optim_d']['lr'] + }] + optim_type = train_opt['optim_d'].pop('type') + lr = train_opt['optim_d']['lr'] * net_d_reg_ratio + betas = (0**net_d_reg_ratio, 0.99**net_d_reg_ratio) + self.optimizer_d = self.get_optimizer(optim_type, optim_params_d, lr, betas=betas) + self.optimizers.append(self.optimizer_d) + + # ----------- optimizers for facial component networks ----------- # + if self.use_facial_disc: + # setup optimizers for facial component discriminators + optim_type = train_opt['optim_component'].pop('type') + lr = train_opt['optim_component']['lr'] + # left eye + self.optimizer_d_left_eye = self.get_optimizer( + optim_type, self.net_d_left_eye.parameters(), lr, betas=(0.9, 0.99)) + self.optimizers.append(self.optimizer_d_left_eye) + # right eye + self.optimizer_d_right_eye = self.get_optimizer( + optim_type, self.net_d_right_eye.parameters(), lr, betas=(0.9, 0.99)) + self.optimizers.append(self.optimizer_d_right_eye) + # mouth + self.optimizer_d_mouth = self.get_optimizer( + optim_type, self.net_d_mouth.parameters(), lr, betas=(0.9, 0.99)) + self.optimizers.append(self.optimizer_d_mouth) + + def feed_data(self, data): + self.lq = data['lq'].to(self.device) + if 'gt' in data: + self.gt = data['gt'].to(self.device) + + if 'loc_left_eye' in data: + # get facial component locations, shape (batch, 4) + self.loc_left_eyes = data['loc_left_eye'] + self.loc_right_eyes = data['loc_right_eye'] + self.loc_mouths = data['loc_mouth'] + + # uncomment to check data + # import torchvision + # if self.opt['rank'] == 0: + # import os + # os.makedirs('tmp/gt', exist_ok=True) + # os.makedirs('tmp/lq', exist_ok=True) + # print(self.idx) + # torchvision.utils.save_image( + # self.gt, f'tmp/gt/gt_{self.idx}.png', nrow=4, padding=2, normalize=True, range=(-1, 1)) + # torchvision.utils.save_image( + # self.lq, f'tmp/lq/lq{self.idx}.png', nrow=4, padding=2, normalize=True, range=(-1, 1)) + # self.idx = self.idx + 1 + + def construct_img_pyramid(self): + """Construct image pyramid for intermediate restoration loss""" + pyramid_gt = [self.gt] + down_img = self.gt + for _ in range(0, self.log_size - 3): + down_img = F.interpolate(down_img, scale_factor=0.5, mode='bilinear', align_corners=False) + pyramid_gt.insert(0, down_img) + return pyramid_gt + + def get_roi_regions(self, eye_out_size=80, mouth_out_size=120): + face_ratio = int(self.opt['network_g']['out_size'] / 512) + eye_out_size *= face_ratio + mouth_out_size *= face_ratio + + rois_eyes = [] + rois_mouths = [] + for b in range(self.loc_left_eyes.size(0)): # loop for batch size + # left eye and right eye + img_inds = self.loc_left_eyes.new_full((2, 1), b) + bbox = torch.stack([self.loc_left_eyes[b, :], self.loc_right_eyes[b, :]], dim=0) # shape: (2, 4) + rois = torch.cat([img_inds, bbox], dim=-1) # shape: (2, 5) + rois_eyes.append(rois) + # mouse + img_inds = self.loc_left_eyes.new_full((1, 1), b) + rois = torch.cat([img_inds, self.loc_mouths[b:b + 1, :]], dim=-1) # shape: (1, 5) + rois_mouths.append(rois) + + rois_eyes = torch.cat(rois_eyes, 0).to(self.device) + rois_mouths = torch.cat(rois_mouths, 0).to(self.device) + + # real images + all_eyes = roi_align(self.gt, boxes=rois_eyes, output_size=eye_out_size) * face_ratio + self.left_eyes_gt = all_eyes[0::2, :, :, :] + self.right_eyes_gt = all_eyes[1::2, :, :, :] + self.mouths_gt = roi_align(self.gt, boxes=rois_mouths, output_size=mouth_out_size) * face_ratio + # output + all_eyes = roi_align(self.output, boxes=rois_eyes, output_size=eye_out_size) * face_ratio + self.left_eyes = all_eyes[0::2, :, :, :] + self.right_eyes = all_eyes[1::2, :, :, :] + self.mouths = roi_align(self.output, boxes=rois_mouths, output_size=mouth_out_size) * face_ratio + + def _gram_mat(self, x): + """Calculate Gram matrix. + + Args: + x (torch.Tensor): Tensor with shape of (n, c, h, w). + + Returns: + torch.Tensor: Gram matrix. + """ + n, c, h, w = x.size() + features = x.view(n, c, w * h) + features_t = features.transpose(1, 2) + gram = features.bmm(features_t) / (c * h * w) + return gram + + def gray_resize_for_identity(self, out, size=128): + out_gray = (0.2989 * out[:, 0, :, :] + 0.5870 * out[:, 1, :, :] + 0.1140 * out[:, 2, :, :]) + out_gray = out_gray.unsqueeze(1) + out_gray = F.interpolate(out_gray, (size, size), mode='bilinear', align_corners=False) + return out_gray + + def optimize_parameters(self, current_iter): + # optimize net_g + for p in self.net_d.parameters(): + p.requires_grad = False + self.optimizer_g.zero_grad() + + # do not update facial component net_d + if self.use_facial_disc: + for p in self.net_d_left_eye.parameters(): + p.requires_grad = False + for p in self.net_d_right_eye.parameters(): + p.requires_grad = False + for p in self.net_d_mouth.parameters(): + p.requires_grad = False + + # image pyramid loss weight + pyramid_loss_weight = self.opt['train'].get('pyramid_loss_weight', 0) + if pyramid_loss_weight > 0 and current_iter > self.opt['train'].get('remove_pyramid_loss', float('inf')): + pyramid_loss_weight = 1e-12 # very small weight to avoid unused param error + if pyramid_loss_weight > 0: + self.output, out_rgbs = self.net_g(self.lq, return_rgb=True) + pyramid_gt = self.construct_img_pyramid() + else: + self.output, out_rgbs = self.net_g(self.lq, return_rgb=False) + + # get roi-align regions + if self.use_facial_disc: + self.get_roi_regions(eye_out_size=80, mouth_out_size=120) + + l_g_total = 0 + loss_dict = OrderedDict() + if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): + # pixel loss + if self.cri_pix: + l_g_pix = self.cri_pix(self.output, self.gt) + l_g_total += l_g_pix + loss_dict['l_g_pix'] = l_g_pix + + # image pyramid loss + if pyramid_loss_weight > 0: + for i in range(0, self.log_size - 2): + l_pyramid = self.cri_l1(out_rgbs[i], pyramid_gt[i]) * pyramid_loss_weight + l_g_total += l_pyramid + loss_dict[f'l_p_{2**(i+3)}'] = l_pyramid + + # perceptual loss + if self.cri_perceptual: + l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt) + if l_g_percep is not None: + l_g_total += l_g_percep + loss_dict['l_g_percep'] = l_g_percep + if l_g_style is not None: + l_g_total += l_g_style + loss_dict['l_g_style'] = l_g_style + + # gan loss + fake_g_pred = self.net_d(self.output) + l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False) + l_g_total += l_g_gan + loss_dict['l_g_gan'] = l_g_gan + + # facial component loss + if self.use_facial_disc: + # left eye + fake_left_eye, fake_left_eye_feats = self.net_d_left_eye(self.left_eyes, return_feats=True) + l_g_gan = self.cri_component(fake_left_eye, True, is_disc=False) + l_g_total += l_g_gan + loss_dict['l_g_gan_left_eye'] = l_g_gan + # right eye + fake_right_eye, fake_right_eye_feats = self.net_d_right_eye(self.right_eyes, return_feats=True) + l_g_gan = self.cri_component(fake_right_eye, True, is_disc=False) + l_g_total += l_g_gan + loss_dict['l_g_gan_right_eye'] = l_g_gan + # mouth + fake_mouth, fake_mouth_feats = self.net_d_mouth(self.mouths, return_feats=True) + l_g_gan = self.cri_component(fake_mouth, True, is_disc=False) + l_g_total += l_g_gan + loss_dict['l_g_gan_mouth'] = l_g_gan + + if self.opt['train'].get('comp_style_weight', 0) > 0: + # get gt feat + _, real_left_eye_feats = self.net_d_left_eye(self.left_eyes_gt, return_feats=True) + _, real_right_eye_feats = self.net_d_right_eye(self.right_eyes_gt, return_feats=True) + _, real_mouth_feats = self.net_d_mouth(self.mouths_gt, return_feats=True) + + def _comp_style(feat, feat_gt, criterion): + return criterion(self._gram_mat(feat[0]), self._gram_mat( + feat_gt[0].detach())) * 0.5 + criterion( + self._gram_mat(feat[1]), self._gram_mat(feat_gt[1].detach())) + + # facial component style loss + comp_style_loss = 0 + comp_style_loss += _comp_style(fake_left_eye_feats, real_left_eye_feats, self.cri_l1) + comp_style_loss += _comp_style(fake_right_eye_feats, real_right_eye_feats, self.cri_l1) + comp_style_loss += _comp_style(fake_mouth_feats, real_mouth_feats, self.cri_l1) + comp_style_loss = comp_style_loss * self.opt['train']['comp_style_weight'] + l_g_total += comp_style_loss + loss_dict['l_g_comp_style_loss'] = comp_style_loss + + # identity loss + if self.use_identity: + identity_weight = self.opt['train']['identity_weight'] + # get gray images and resize + out_gray = self.gray_resize_for_identity(self.output) + gt_gray = self.gray_resize_for_identity(self.gt) + + identity_gt = self.network_identity(gt_gray).detach() + identity_out = self.network_identity(out_gray) + l_identity = self.cri_l1(identity_out, identity_gt) * identity_weight + l_g_total += l_identity + loss_dict['l_identity'] = l_identity + + l_g_total.backward() + self.optimizer_g.step() + + # EMA + self.model_ema(decay=0.5**(32 / (10 * 1000))) + + # ----------- optimize net_d ----------- # + for p in self.net_d.parameters(): + p.requires_grad = True + self.optimizer_d.zero_grad() + if self.use_facial_disc: + for p in self.net_d_left_eye.parameters(): + p.requires_grad = True + for p in self.net_d_right_eye.parameters(): + p.requires_grad = True + for p in self.net_d_mouth.parameters(): + p.requires_grad = True + self.optimizer_d_left_eye.zero_grad() + self.optimizer_d_right_eye.zero_grad() + self.optimizer_d_mouth.zero_grad() + + fake_d_pred = self.net_d(self.output.detach()) + real_d_pred = self.net_d(self.gt) + l_d = self.cri_gan(real_d_pred, True, is_disc=True) + self.cri_gan(fake_d_pred, False, is_disc=True) + loss_dict['l_d'] = l_d + # In WGAN, real_score should be positive and fake_score should be negative + loss_dict['real_score'] = real_d_pred.detach().mean() + loss_dict['fake_score'] = fake_d_pred.detach().mean() + l_d.backward() + + # regularization loss + if current_iter % self.net_d_reg_every == 0: + self.gt.requires_grad = True + real_pred = self.net_d(self.gt) + l_d_r1 = r1_penalty(real_pred, self.gt) + l_d_r1 = (self.r1_reg_weight / 2 * l_d_r1 * self.net_d_reg_every + 0 * real_pred[0]) + loss_dict['l_d_r1'] = l_d_r1.detach().mean() + l_d_r1.backward() + + self.optimizer_d.step() + + # optimize facial component discriminators + if self.use_facial_disc: + # left eye + fake_d_pred, _ = self.net_d_left_eye(self.left_eyes.detach()) + real_d_pred, _ = self.net_d_left_eye(self.left_eyes_gt) + l_d_left_eye = self.cri_component( + real_d_pred, True, is_disc=True) + self.cri_gan( + fake_d_pred, False, is_disc=True) + loss_dict['l_d_left_eye'] = l_d_left_eye + l_d_left_eye.backward() + # right eye + fake_d_pred, _ = self.net_d_right_eye(self.right_eyes.detach()) + real_d_pred, _ = self.net_d_right_eye(self.right_eyes_gt) + l_d_right_eye = self.cri_component( + real_d_pred, True, is_disc=True) + self.cri_gan( + fake_d_pred, False, is_disc=True) + loss_dict['l_d_right_eye'] = l_d_right_eye + l_d_right_eye.backward() + # mouth + fake_d_pred, _ = self.net_d_mouth(self.mouths.detach()) + real_d_pred, _ = self.net_d_mouth(self.mouths_gt) + l_d_mouth = self.cri_component( + real_d_pred, True, is_disc=True) + self.cri_gan( + fake_d_pred, False, is_disc=True) + loss_dict['l_d_mouth'] = l_d_mouth + l_d_mouth.backward() + + self.optimizer_d_left_eye.step() + self.optimizer_d_right_eye.step() + self.optimizer_d_mouth.step() + + self.log_dict = self.reduce_loss_dict(loss_dict) + + def test(self): + with torch.no_grad(): + if hasattr(self, 'net_g_ema'): + self.net_g_ema.eval() + self.output, _ = self.net_g_ema(self.lq) + else: + logger = get_root_logger() + logger.warning('Do not have self.net_g_ema, use self.net_g.') + self.net_g.eval() + self.output, _ = self.net_g(self.lq) + self.net_g.train() + + def dist_validation(self, dataloader, current_iter, tb_logger, save_img): + if self.opt['rank'] == 0: + self.nondist_validation(dataloader, current_iter, tb_logger, save_img) + + def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): + dataset_name = dataloader.dataset.opt['name'] + with_metrics = self.opt['val'].get('metrics') is not None + use_pbar = self.opt['val'].get('pbar', False) + + if with_metrics: + if not hasattr(self, 'metric_results'): # only execute in the first run + self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()} + # initialize the best metric results for each dataset_name (supporting multiple validation datasets) + self._initialize_best_metric_results(dataset_name) + # zero self.metric_results + self.metric_results = {metric: 0 for metric in self.metric_results} + + metric_data = dict() + if use_pbar: + pbar = tqdm(total=len(dataloader), unit='image') + + for idx, val_data in enumerate(dataloader): + img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0] + self.feed_data(val_data) + self.test() + + sr_img = tensor2img(self.output.detach().cpu(), min_max=(-1, 1)) + metric_data['img'] = sr_img + if hasattr(self, 'gt'): + gt_img = tensor2img(self.gt.detach().cpu(), min_max=(-1, 1)) + metric_data['img2'] = gt_img + del self.gt + + # tentative for out of GPU memory + del self.lq + del self.output + torch.cuda.empty_cache() + + if save_img: + if self.opt['is_train']: + save_img_path = osp.join(self.opt['path']['visualization'], img_name, + f'{img_name}_{current_iter}.png') + else: + if self.opt['val']['suffix']: + save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, + f'{img_name}_{self.opt["val"]["suffix"]}.png') + else: + save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, + f'{img_name}_{self.opt["name"]}.png') + imwrite(sr_img, save_img_path) + + if with_metrics: + # calculate metrics + for name, opt_ in self.opt['val']['metrics'].items(): + self.metric_results[name] += calculate_metric(metric_data, opt_) + if use_pbar: + pbar.update(1) + pbar.set_description(f'Test {img_name}') + if use_pbar: + pbar.close() + + if with_metrics: + for metric in self.metric_results.keys(): + self.metric_results[metric] /= (idx + 1) + # update the best metric result + self._update_best_metric_result(dataset_name, metric, self.metric_results[metric], current_iter) + + self._log_validation_metric_values(current_iter, dataset_name, tb_logger) + + def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger): + log_str = f'Validation {dataset_name}\n' + for metric, value in self.metric_results.items(): + log_str += f'\t # {metric}: {value:.4f}' + if hasattr(self, 'best_metric_results'): + log_str += (f'\tBest: {self.best_metric_results[dataset_name][metric]["val"]:.4f} @ ' + f'{self.best_metric_results[dataset_name][metric]["iter"]} iter') + log_str += '\n' + + logger = get_root_logger() + logger.info(log_str) + if tb_logger: + for metric, value in self.metric_results.items(): + tb_logger.add_scalar(f'metrics/{dataset_name}/{metric}', value, current_iter) + + def save(self, epoch, current_iter): + # save net_g and net_d + self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) + self.save_network(self.net_d, 'net_d', current_iter) + # save component discriminators + if self.use_facial_disc: + self.save_network(self.net_d_left_eye, 'net_d_left_eye', current_iter) + self.save_network(self.net_d_right_eye, 'net_d_right_eye', current_iter) + self.save_network(self.net_d_mouth, 'net_d_mouth', current_iter) + # save training state + self.save_training_state(epoch, current_iter) diff --git a/motion-gan-pipeline/GFPGAN/gfpgan/train.py b/motion-gan-pipeline/GFPGAN/gfpgan/train.py new file mode 100644 index 0000000..fe5f1f9 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/gfpgan/train.py @@ -0,0 +1,11 @@ +# flake8: noqa +import os.path as osp +from basicsr.train import train_pipeline + +import gfpgan.archs +import gfpgan.data +import gfpgan.models + +if __name__ == '__main__': + root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir)) + train_pipeline(root_path) diff --git a/motion-gan-pipeline/GFPGAN/gfpgan/utils.py b/motion-gan-pipeline/GFPGAN/gfpgan/utils.py new file mode 100644 index 0000000..3dba847 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/gfpgan/utils.py @@ -0,0 +1,152 @@ +import cv2 +import os +import torch +from basicsr.utils import img2tensor, tensor2img +from basicsr.utils.download_util import load_file_from_url +from facexlib.utils.face_restoration_helper import FaceRestoreHelper +from torchvision.transforms.functional import normalize + +from gfpgan.archs.gfpgan_bilinear_arch import GFPGANBilinear +from gfpgan.archs.gfpganv1_arch import GFPGANv1 +from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean + +ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + + +class GFPGANer(): + """Helper for restoration with GFPGAN. + + It will detect and crop faces, and then resize the faces to 512x512. + GFPGAN is used to restored the resized faces. + The background is upsampled with the bg_upsampler. + Finally, the faces will be pasted back to the upsample background image. + + Args: + model_path (str): The path to the GFPGAN model. It can be urls (will first download it automatically). + upscale (float): The upscale of the final output. Default: 2. + arch (str): The GFPGAN architecture. Option: clean | original. Default: clean. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + bg_upsampler (nn.Module): The upsampler for the background. Default: None. + """ + + def __init__(self, model_path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None, device=None): + self.upscale = upscale + self.bg_upsampler = bg_upsampler + + # initialize model + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device + # initialize the GFP-GAN + if arch == 'clean': + self.gfpgan = GFPGANv1Clean( + out_size=512, + num_style_feat=512, + channel_multiplier=channel_multiplier, + decoder_load_path=None, + fix_decoder=False, + num_mlp=8, + input_is_latent=True, + different_w=True, + narrow=1, + sft_half=True) + elif arch == 'bilinear': + self.gfpgan = GFPGANBilinear( + out_size=512, + num_style_feat=512, + channel_multiplier=channel_multiplier, + decoder_load_path=None, + fix_decoder=False, + num_mlp=8, + input_is_latent=True, + different_w=True, + narrow=1, + sft_half=True) + elif arch == 'original': + self.gfpgan = GFPGANv1( + out_size=512, + num_style_feat=512, + channel_multiplier=channel_multiplier, + decoder_load_path=None, + fix_decoder=True, + num_mlp=8, + input_is_latent=True, + different_w=True, + narrow=1, + sft_half=True) + elif arch == 'RestoreFormer': + from gfpgan.archs.restoreformer_arch import RestoreFormer + self.gfpgan = RestoreFormer() + elif arch == 'CodeFormer': + from gfpgan.archs.codeformer_arch import CodeFormer + self.gfpgan = CodeFormer( + dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']) + # initialize face helper + self.face_helper = FaceRestoreHelper( + upscale, + face_size=512, + crop_ratio=(1, 1), + det_model='retinaface_resnet50', + save_ext='png', + use_parse=True, + device=self.device, + model_rootpath='gfpgan/weights') + + if model_path.startswith('https://'): + model_path = load_file_from_url( + url=model_path, model_dir=os.path.join(ROOT_DIR, 'gfpgan/weights'), progress=True, file_name=None) + loadnet = torch.load(model_path) + if 'params_ema' in loadnet: + keyname = 'params_ema' + else: + keyname = 'params' + self.gfpgan.load_state_dict(loadnet[keyname], strict=True) + self.gfpgan.eval() + self.gfpgan = self.gfpgan.to(self.device) + + @torch.no_grad() + def enhance(self, img, has_aligned=False, only_center_face=False, paste_back=True, weight=0.5): + self.face_helper.clean_all() + + if has_aligned: # the inputs are already aligned + img = cv2.resize(img, (512, 512)) + self.face_helper.cropped_faces = [img] + else: + self.face_helper.read_image(img) + # get face landmarks for each face + self.face_helper.get_face_landmarks_5(only_center_face=only_center_face, eye_dist_threshold=5) + # eye_dist_threshold=5: skip faces whose eye distance is smaller than 5 pixels + # TODO: even with eye_dist_threshold, it will still introduce wrong detections and restorations. + # align and warp each face + self.face_helper.align_warp_face() + + # face restoration + for cropped_face in self.face_helper.cropped_faces: + # prepare data + cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True) + normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) + cropped_face_t = cropped_face_t.unsqueeze(0).to(self.device) + + try: + output = self.gfpgan(cropped_face_t, return_rgb=False, weight=weight)[0] + # convert to image + restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1)) + except RuntimeError as error: + print(f'\tFailed inference for GFPGAN: {error}.') + restored_face = cropped_face + + restored_face = restored_face.astype('uint8') + self.face_helper.add_restored_face(restored_face) + + if not has_aligned and paste_back: + # upsample the background + if self.bg_upsampler is not None: + # Now only support RealESRGAN for upsampling background + bg_img = self.bg_upsampler.enhance(img, outscale=self.upscale)[0] + else: + bg_img = None + + self.face_helper.get_inverse_affine(None) + # paste each restored face to the input image + restored_img = self.face_helper.paste_faces_to_input_image(upsample_img=bg_img) + return self.face_helper.cropped_faces, self.face_helper.restored_faces, restored_img + else: + return self.face_helper.cropped_faces, self.face_helper.restored_faces, None diff --git a/motion-gan-pipeline/GFPGAN/gfpgan/weights/README.md b/motion-gan-pipeline/GFPGAN/gfpgan/weights/README.md new file mode 100644 index 0000000..4d7b7e6 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/gfpgan/weights/README.md @@ -0,0 +1,3 @@ +# Weights + +Put the downloaded weights to this folder. diff --git a/motion-gan-pipeline/GFPGAN/inference_gfpgan.py b/motion-gan-pipeline/GFPGAN/inference_gfpgan.py new file mode 100644 index 0000000..74b3a2a --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/inference_gfpgan.py @@ -0,0 +1,186 @@ +import argparse +import cv2 +import glob +import numpy as np +import os +import torch +from basicsr.utils import imwrite +from tqdm import tqdm +from gfpgan import GFPGANer +from typing import Dict + +def run_inference(args: Dict): + # ------------------------ input & output ------------------------ + if args.input.endswith('/'): + args.input = args.input[:-1] + if os.path.isfile(args.input): + img_list = [args.input] + else: + img_list = sorted(glob.glob(os.path.join(args.input, '*'))) + + os.makedirs(args.output, exist_ok=True) + + if len(os.listdir(args.output)) != 0: + print('Already done.\n\n') + return + + #------------------------ set up background upsampler ------------------------ + if args.bg_upsampler == 'realesrgan': + if not torch.cuda.is_available(): # CPU + import warnings + warnings.warn('The unoptimized RealESRGAN is slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') + bg_upsampler = None + else: + from basicsr.archs.rrdbnet_arch import RRDBNet + from realesrgan import RealESRGANer + model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) + bg_upsampler = RealESRGANer( + scale=2, + model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth', + model=model, + tile=args.bg_tile, + tile_pad=10, + pre_pad=0, + half=True) # need to set False in CPU mode + else: + bg_upsampler = None + bg_upsampler = None + + # ------------------------ set up GFPGAN restorer ------------------------ + if args.version == '1': + arch = 'original' + channel_multiplier = 1 + model_name = 'GFPGANv1' + url = 'https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth' + elif args.version == '1.2': + arch = 'clean' + channel_multiplier = 2 + model_name = 'GFPGANCleanv1-NoCE-C2' + url = 'https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth' + elif args.version == '1.3': + arch = 'clean' + channel_multiplier = 2 + model_name = 'GFPGANv1.3' + url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth' + elif args.version == '1.4': + arch = 'clean' + channel_multiplier = 2 + model_name = 'GFPGANv1.4' + url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth' + elif args.version == 'RestoreFormer': + arch = 'RestoreFormer' + channel_multiplier = 2 + model_name = 'RestoreFormer' + url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth' + elif args.version == 'CodeFormer': + arch = 'CodeFormer' + channel_multiplier = 2 + model_name = 'CodeFormer' + url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth' + else: + raise ValueError(f'Wrong model version {args.version}.') + + # determine model paths + model_path = os.path.join('experiments/pretrained_models', model_name + '.pth') + if not os.path.isfile(model_path): + model_path = os.path.join('gfpgan/weights', model_name + '.pth') + if not os.path.isfile(model_path): + # download pre-trained models from url + model_path = url + + restorer = GFPGANer( + model_path=model_path, + upscale=args.upscale, + arch=arch, + channel_multiplier=channel_multiplier, + bg_upsampler=bg_upsampler) + + # ------------------------ restore ------------------------ + for img_path in tqdm(img_list): + # read image + img_name = os.path.basename(img_path) + # print(f'Processing {img_name} ...') + basename, ext = os.path.splitext(img_name) + input_img = cv2.imread(img_path, cv2.IMREAD_COLOR) + + # restore faces and background if necessary + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, + has_aligned=args.aligned, + only_center_face=args.only_center_face, + paste_back=True, + weight=args.weight) + + # save faces + for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_faces)): + # save cropped face + save_crop_path = os.path.join(args.output, 'cropped_faces', f'{basename}_{idx:02d}.png') + imwrite(cropped_face, save_crop_path) + # save restored face + if args.suffix is not None: + save_face_name = f'{basename}_{idx:02d}_{args.suffix}.png' + else: + save_face_name = f'{basename}_{idx:02d}.png' + save_restore_path = os.path.join(args.output, 'restored_faces', save_face_name) + imwrite(restored_face, save_restore_path) + # save comparison image + cmp_img = np.concatenate((cropped_face, restored_face), axis=1) + imwrite(cmp_img, os.path.join(args.output, 'cmp', f'{basename}_{idx:02d}.png')) + + # save restored img + if restored_img is not None: + if args.ext == 'auto': + extension = ext[1:] + else: + extension = args.ext + + if args.suffix is not None: + save_restore_path = os.path.join(args.output, 'generated_frames', f'{basename}_{args.suffix}.{extension}') + else: + save_restore_path = os.path.join(args.output, 'generated_frames', f'{basename}.{extension}') + imwrite(restored_img, save_restore_path) + + print(f'Results are in the [{args.output}] folder.') + + +def main(): + """Inference demo for GFPGAN (for users). + """ + parser = argparse.ArgumentParser() + parser.add_argument( + '-i', + '--input', + type=str, + default='inputs/whole_imgs', + help='Input image or folder. Default: inputs/whole_imgs') + parser.add_argument('-o', '--output', type=str, default='results', help='Output folder. Default: results') + # we use version to select models, which is more user-friendly + parser.add_argument( + '-v', '--version', type=str, default='1.3', help='GFPGAN model version. Option: 1 | 1.2 | 1.3. Default: 1.3') + parser.add_argument( + '-s', '--upscale', type=int, default=1, help='The final upsampling scale of the image. Default: 2') + + parser.add_argument( + '--bg_upsampler', type=str, default='realesrgan', help='background upsampler. Default: realesrgan') + parser.add_argument( + '--bg_tile', + type=int, + default=400, + help='Tile size for background sampler, 0 for no tile during testing. Default: 400') + parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces') + parser.add_argument('--only_center_face', action='store_true', help='Only restore the center face') + parser.add_argument('--aligned', action='store_true', help='Input are aligned faces') + parser.add_argument( + '--ext', + type=str, + default='auto', + help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto') + parser.add_argument('-w', '--weight', type=float, default=0.5, help='Adjustable weights for CodeFormer.') + args = parser.parse_args() + + run_inference(args) + + +if __name__ == '__main__': + main() diff --git a/motion-gan-pipeline/GFPGAN/inputs/cropped_faces/Adele_crop.png b/motion-gan-pipeline/GFPGAN/inputs/cropped_faces/Adele_crop.png new file mode 100644 index 0000000..afeb555 Binary files /dev/null and b/motion-gan-pipeline/GFPGAN/inputs/cropped_faces/Adele_crop.png differ diff --git a/motion-gan-pipeline/GFPGAN/inputs/cropped_faces/Julia_Roberts_crop.png b/motion-gan-pipeline/GFPGAN/inputs/cropped_faces/Julia_Roberts_crop.png new file mode 100644 index 0000000..38c75c6 Binary files /dev/null and b/motion-gan-pipeline/GFPGAN/inputs/cropped_faces/Julia_Roberts_crop.png differ diff --git a/motion-gan-pipeline/GFPGAN/inputs/cropped_faces/Justin_Timberlake_crop.png b/motion-gan-pipeline/GFPGAN/inputs/cropped_faces/Justin_Timberlake_crop.png new file mode 100644 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-0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3fbecfabde7cdab30cb51ceaa8a19824298a8877a6895cf9dc6dbbc43ffe897d +size 15750 diff --git a/motion-gan-pipeline/GFPGAN/options/train_gfpgan_v1.yml b/motion-gan-pipeline/GFPGAN/options/train_gfpgan_v1.yml new file mode 100644 index 0000000..aa5212a --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/options/train_gfpgan_v1.yml @@ -0,0 +1,216 @@ +# general settings +name: train_GFPGANv1_512 +model_type: GFPGANModel +num_gpu: auto # officially, we use 4 GPUs +manual_seed: 0 + +# dataset and data loader settings +datasets: + train: + name: FFHQ + type: FFHQDegradationDataset + # dataroot_gt: datasets/ffhq/ffhq_512.lmdb + dataroot_gt: datasets/ffhq/ffhq_512 + io_backend: + # type: lmdb + type: disk + + use_hflip: true + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + out_size: 512 + + blur_kernel_size: 41 + kernel_list: ['iso', 'aniso'] + kernel_prob: [0.5, 0.5] + blur_sigma: [0.1, 10] + downsample_range: [0.8, 8] + noise_range: [0, 20] + jpeg_range: [60, 100] + + # color jitter and gray + color_jitter_prob: 0.3 + color_jitter_shift: 20 + color_jitter_pt_prob: 0.3 + gray_prob: 0.01 + + # If you do not want colorization, please set + # color_jitter_prob: ~ + # color_jitter_pt_prob: ~ + # gray_prob: 0.01 + # gt_gray: True + + crop_components: true + component_path: experiments/pretrained_models/FFHQ_eye_mouth_landmarks_512.pth + eye_enlarge_ratio: 1.4 + + # data loader + use_shuffle: true + num_worker_per_gpu: 6 + batch_size_per_gpu: 3 + dataset_enlarge_ratio: 1 + prefetch_mode: ~ + + val: + # Please modify accordingly to use your own validation + # Or comment the val block if do not need validation during training + name: validation + type: PairedImageDataset + dataroot_lq: datasets/faces/validation/input + dataroot_gt: datasets/faces/validation/reference + io_backend: + type: disk + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + scale: 1 + +# network structures +network_g: + type: GFPGANv1 + out_size: 512 + num_style_feat: 512 + channel_multiplier: 1 + resample_kernel: [1, 3, 3, 1] + decoder_load_path: experiments/pretrained_models/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth + fix_decoder: true + num_mlp: 8 + lr_mlp: 0.01 + input_is_latent: true + different_w: true + narrow: 1 + sft_half: true + +network_d: + type: StyleGAN2Discriminator + out_size: 512 + channel_multiplier: 1 + resample_kernel: [1, 3, 3, 1] + +network_d_left_eye: + type: FacialComponentDiscriminator + +network_d_right_eye: + type: FacialComponentDiscriminator + +network_d_mouth: + type: FacialComponentDiscriminator + +network_identity: + type: ResNetArcFace + block: IRBlock + layers: [2, 2, 2, 2] + use_se: False + +# path +path: + pretrain_network_g: ~ + param_key_g: params_ema + strict_load_g: ~ + pretrain_network_d: ~ + pretrain_network_d_left_eye: ~ + pretrain_network_d_right_eye: ~ + pretrain_network_d_mouth: ~ + pretrain_network_identity: experiments/pretrained_models/arcface_resnet18.pth + # resume + resume_state: ~ + ignore_resume_networks: ['network_identity'] + +# training settings +train: + optim_g: + type: Adam + lr: !!float 2e-3 + optim_d: + type: Adam + lr: !!float 2e-3 + optim_component: + type: Adam + lr: !!float 2e-3 + + scheduler: + type: MultiStepLR + milestones: [600000, 700000] + gamma: 0.5 + + total_iter: 800000 + warmup_iter: -1 # no warm up + + # losses + # pixel loss + pixel_opt: + type: L1Loss + loss_weight: !!float 1e-1 + reduction: mean + # L1 loss used in pyramid loss, component style loss and identity loss + L1_opt: + type: L1Loss + loss_weight: 1 + reduction: mean + + # image pyramid loss + pyramid_loss_weight: 1 + remove_pyramid_loss: 50000 + # perceptual loss (content and style losses) + perceptual_opt: + type: PerceptualLoss + layer_weights: + # before relu + 'conv1_2': 0.1 + 'conv2_2': 0.1 + 'conv3_4': 1 + 'conv4_4': 1 + 'conv5_4': 1 + vgg_type: vgg19 + use_input_norm: true + perceptual_weight: !!float 1 + style_weight: 50 + range_norm: true + criterion: l1 + # gan loss + gan_opt: + type: GANLoss + gan_type: wgan_softplus + loss_weight: !!float 1e-1 + # r1 regularization for discriminator + r1_reg_weight: 10 + # facial component loss + gan_component_opt: + type: GANLoss + gan_type: vanilla + real_label_val: 1.0 + fake_label_val: 0.0 + loss_weight: !!float 1 + comp_style_weight: 200 + # identity loss + identity_weight: 10 + + net_d_iters: 1 + net_d_init_iters: 0 + net_d_reg_every: 16 + +# validation settings +val: + val_freq: !!float 5e3 + save_img: true + + metrics: + psnr: # metric name + type: calculate_psnr + crop_border: 0 + test_y_channel: false + +# logging settings +logger: + print_freq: 100 + save_checkpoint_freq: !!float 5e3 + use_tb_logger: true + wandb: + project: ~ + resume_id: ~ + +# dist training settings +dist_params: + backend: nccl + port: 29500 + +find_unused_parameters: true diff --git a/motion-gan-pipeline/GFPGAN/options/train_gfpgan_v1_simple.yml b/motion-gan-pipeline/GFPGAN/options/train_gfpgan_v1_simple.yml new file mode 100644 index 0000000..3807575 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/options/train_gfpgan_v1_simple.yml @@ -0,0 +1,182 @@ +# general settings +name: train_GFPGANv1_512_simple +model_type: GFPGANModel +num_gpu: auto # officially, we use 4 GPUs +manual_seed: 0 + +# dataset and data loader settings +datasets: + train: + name: FFHQ + type: FFHQDegradationDataset + # dataroot_gt: datasets/ffhq/ffhq_512.lmdb + dataroot_gt: datasets/ffhq/ffhq_512 + io_backend: + # type: lmdb + type: disk + + use_hflip: true + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + out_size: 512 + + blur_kernel_size: 41 + kernel_list: ['iso', 'aniso'] + kernel_prob: [0.5, 0.5] + blur_sigma: [0.1, 10] + downsample_range: [0.8, 8] + noise_range: [0, 20] + jpeg_range: [60, 100] + + # color jitter and gray + color_jitter_prob: 0.3 + color_jitter_shift: 20 + color_jitter_pt_prob: 0.3 + gray_prob: 0.01 + + # If you do not want colorization, please set + # color_jitter_prob: ~ + # color_jitter_pt_prob: ~ + # gray_prob: 0.01 + # gt_gray: True + + # data loader + use_shuffle: true + num_worker_per_gpu: 6 + batch_size_per_gpu: 3 + dataset_enlarge_ratio: 1 + prefetch_mode: ~ + + val: + # Please modify accordingly to use your own validation + # Or comment the val block if do not need validation during training + name: validation + type: PairedImageDataset + dataroot_lq: datasets/faces/validation/input + dataroot_gt: datasets/faces/validation/reference + io_backend: + type: disk + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + scale: 1 + +# network structures +network_g: + type: GFPGANv1 + out_size: 512 + num_style_feat: 512 + channel_multiplier: 1 + resample_kernel: [1, 3, 3, 1] + decoder_load_path: experiments/pretrained_models/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth + fix_decoder: true + num_mlp: 8 + lr_mlp: 0.01 + input_is_latent: true + different_w: true + narrow: 1 + sft_half: true + +network_d: + type: StyleGAN2Discriminator + out_size: 512 + channel_multiplier: 1 + resample_kernel: [1, 3, 3, 1] + + +# path +path: + pretrain_network_g: ~ + param_key_g: params_ema + strict_load_g: ~ + pretrain_network_d: ~ + resume_state: ~ + +# training settings +train: + optim_g: + type: Adam + lr: !!float 2e-3 + optim_d: + type: Adam + lr: !!float 2e-3 + optim_component: + type: Adam + lr: !!float 2e-3 + + scheduler: + type: MultiStepLR + milestones: [600000, 700000] + gamma: 0.5 + + total_iter: 800000 + warmup_iter: -1 # no warm up + + # losses + # pixel loss + pixel_opt: + type: L1Loss + loss_weight: !!float 1e-1 + reduction: mean + # L1 loss used in pyramid loss, component style loss and identity loss + L1_opt: + type: L1Loss + loss_weight: 1 + reduction: mean + + # image pyramid loss + pyramid_loss_weight: 1 + remove_pyramid_loss: 50000 + # perceptual loss (content and style losses) + perceptual_opt: + type: PerceptualLoss + layer_weights: + # before relu + 'conv1_2': 0.1 + 'conv2_2': 0.1 + 'conv3_4': 1 + 'conv4_4': 1 + 'conv5_4': 1 + vgg_type: vgg19 + use_input_norm: true + perceptual_weight: !!float 1 + style_weight: 50 + range_norm: true + criterion: l1 + # gan loss + gan_opt: + type: GANLoss + gan_type: wgan_softplus + loss_weight: !!float 1e-1 + # r1 regularization for discriminator + r1_reg_weight: 10 + + net_d_iters: 1 + net_d_init_iters: 0 + net_d_reg_every: 16 + +# validation settings +val: + val_freq: !!float 5e3 + save_img: true + + metrics: + psnr: # metric name + type: calculate_psnr + crop_border: 0 + test_y_channel: false + +# logging settings +logger: + print_freq: 100 + save_checkpoint_freq: !!float 5e3 + use_tb_logger: true + wandb: + project: ~ + resume_id: ~ + +# dist training settings +dist_params: + backend: nccl + port: 29500 + +find_unused_parameters: true diff --git a/motion-gan-pipeline/GFPGAN/requirements.txt b/motion-gan-pipeline/GFPGAN/requirements.txt new file mode 100644 index 0000000..4f46d25 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/requirements.txt @@ -0,0 +1,12 @@ +basicsr>=1.4.2 +facexlib>=0.2.5 +lmdb +numpy +opencv-python +pyyaml +scipy +tb-nightly +torch>=1.7 +torchvision +tqdm +yapf diff --git a/motion-gan-pipeline/GFPGAN/scripts/convert_gfpganv_to_clean.py b/motion-gan-pipeline/GFPGAN/scripts/convert_gfpganv_to_clean.py new file mode 100644 index 0000000..8fdccb6 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/scripts/convert_gfpganv_to_clean.py @@ -0,0 +1,164 @@ +import argparse +import math +import torch + +from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean + + +def modify_checkpoint(checkpoint_bilinear, checkpoint_clean): + for ori_k, ori_v in checkpoint_bilinear.items(): + if 'stylegan_decoder' in ori_k: + if 'style_mlp' in ori_k: # style_mlp_layers + lr_mul = 0.01 + prefix, name, idx, var = ori_k.split('.') + idx = (int(idx) * 2) - 1 + crt_k = f'{prefix}.{name}.{idx}.{var}' + if var == 'weight': + _, c_in = ori_v.size() + scale = (1 / math.sqrt(c_in)) * lr_mul + crt_v = ori_v * scale * 2**0.5 + else: + crt_v = ori_v * lr_mul * 2**0.5 + checkpoint_clean[crt_k] = crt_v + elif 'modulation' in ori_k: # modulation in StyleConv + lr_mul = 1 + crt_k = ori_k + var = ori_k.split('.')[-1] + if var == 'weight': + _, c_in = ori_v.size() + scale = (1 / math.sqrt(c_in)) * lr_mul + crt_v = ori_v * scale + else: + crt_v = ori_v * lr_mul + checkpoint_clean[crt_k] = crt_v + elif 'style_conv' in ori_k: + # StyleConv in style_conv1 and style_convs + if 'activate' in ori_k: # FusedLeakyReLU + # eg. style_conv1.activate.bias + # eg. style_convs.13.activate.bias + split_rlt = ori_k.split('.') + if len(split_rlt) == 4: + prefix, name, _, var = split_rlt + crt_k = f'{prefix}.{name}.{var}' + elif len(split_rlt) == 5: + prefix, name, idx, _, var = split_rlt + crt_k = f'{prefix}.{name}.{idx}.{var}' + crt_v = ori_v * 2**0.5 # 2**0.5 used in FusedLeakyReLU + c = crt_v.size(0) + checkpoint_clean[crt_k] = crt_v.view(1, c, 1, 1) + elif 'modulated_conv' in ori_k: + # eg. style_conv1.modulated_conv.weight + # eg. style_convs.13.modulated_conv.weight + _, c_out, c_in, k1, k2 = ori_v.size() + scale = 1 / math.sqrt(c_in * k1 * k2) + crt_k = ori_k + checkpoint_clean[crt_k] = ori_v * scale + elif 'weight' in ori_k: + crt_k = ori_k + checkpoint_clean[crt_k] = ori_v * 2**0.5 + elif 'to_rgb' in ori_k: # StyleConv in to_rgb1 and to_rgbs + if 'modulated_conv' in ori_k: + # eg. to_rgb1.modulated_conv.weight + # eg. to_rgbs.5.modulated_conv.weight + _, c_out, c_in, k1, k2 = ori_v.size() + scale = 1 / math.sqrt(c_in * k1 * k2) + crt_k = ori_k + checkpoint_clean[crt_k] = ori_v * scale + else: + crt_k = ori_k + checkpoint_clean[crt_k] = ori_v + else: + crt_k = ori_k + checkpoint_clean[crt_k] = ori_v + # end of 'stylegan_decoder' + elif 'conv_body_first' in ori_k or 'final_conv' in ori_k: + # key name + name, _, var = ori_k.split('.') + crt_k = f'{name}.{var}' + # weight and bias + if var == 'weight': + c_out, c_in, k1, k2 = ori_v.size() + scale = 1 / math.sqrt(c_in * k1 * k2) + checkpoint_clean[crt_k] = ori_v * scale * 2**0.5 + else: + checkpoint_clean[crt_k] = ori_v * 2**0.5 + elif 'conv_body' in ori_k: + if 'conv_body_up' in ori_k: + ori_k = ori_k.replace('conv2.weight', 'conv2.1.weight') + ori_k = ori_k.replace('skip.weight', 'skip.1.weight') + name1, idx1, name2, _, var = ori_k.split('.') + crt_k = f'{name1}.{idx1}.{name2}.{var}' + if name2 == 'skip': + c_out, c_in, k1, k2 = ori_v.size() + scale = 1 / math.sqrt(c_in * k1 * k2) + checkpoint_clean[crt_k] = ori_v * scale / 2**0.5 + else: + if var == 'weight': + c_out, c_in, k1, k2 = ori_v.size() + scale = 1 / math.sqrt(c_in * k1 * k2) + checkpoint_clean[crt_k] = ori_v * scale + else: + checkpoint_clean[crt_k] = ori_v + if 'conv1' in ori_k: + checkpoint_clean[crt_k] *= 2**0.5 + elif 'toRGB' in ori_k: + crt_k = ori_k + if 'weight' in ori_k: + c_out, c_in, k1, k2 = ori_v.size() + scale = 1 / math.sqrt(c_in * k1 * k2) + checkpoint_clean[crt_k] = ori_v * scale + else: + checkpoint_clean[crt_k] = ori_v + elif 'final_linear' in ori_k: + crt_k = ori_k + if 'weight' in ori_k: + _, c_in = ori_v.size() + scale = 1 / math.sqrt(c_in) + checkpoint_clean[crt_k] = ori_v * scale + else: + checkpoint_clean[crt_k] = ori_v + elif 'condition' in ori_k: + crt_k = ori_k + if '0.weight' in ori_k: + c_out, c_in, k1, k2 = ori_v.size() + scale = 1 / math.sqrt(c_in * k1 * k2) + checkpoint_clean[crt_k] = ori_v * scale * 2**0.5 + elif '0.bias' in ori_k: + checkpoint_clean[crt_k] = ori_v * 2**0.5 + elif '2.weight' in ori_k: + c_out, c_in, k1, k2 = ori_v.size() + scale = 1 / math.sqrt(c_in * k1 * k2) + checkpoint_clean[crt_k] = ori_v * scale + elif '2.bias' in ori_k: + checkpoint_clean[crt_k] = ori_v + + return checkpoint_clean + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--ori_path', type=str, help='Path to the original model') + parser.add_argument('--narrow', type=float, default=1) + parser.add_argument('--channel_multiplier', type=float, default=2) + parser.add_argument('--save_path', type=str) + args = parser.parse_args() + + ori_ckpt = torch.load(args.ori_path)['params_ema'] + + net = GFPGANv1Clean( + 512, + num_style_feat=512, + channel_multiplier=args.channel_multiplier, + decoder_load_path=None, + fix_decoder=False, + # for stylegan decoder + num_mlp=8, + input_is_latent=True, + different_w=True, + narrow=args.narrow, + sft_half=True) + crt_ckpt = net.state_dict() + + crt_ckpt = modify_checkpoint(ori_ckpt, crt_ckpt) + print(f'Save to {args.save_path}.') + torch.save(dict(params_ema=crt_ckpt), args.save_path, _use_new_zipfile_serialization=False) diff --git a/motion-gan-pipeline/GFPGAN/scripts/parse_landmark.py b/motion-gan-pipeline/GFPGAN/scripts/parse_landmark.py new file mode 100644 index 0000000..74e2ff9 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/scripts/parse_landmark.py @@ -0,0 +1,85 @@ +import cv2 +import json +import numpy as np +import os +import torch +from basicsr.utils import FileClient, imfrombytes +from collections import OrderedDict + +# ---------------------------- This script is used to parse facial landmarks ------------------------------------- # +# Configurations +save_img = False +scale = 0.5 # 0.5 for official FFHQ (512x512), 1 for others +enlarge_ratio = 1.4 # only for eyes +json_path = 'ffhq-dataset-v2.json' +face_path = 'datasets/ffhq/ffhq_512.lmdb' +save_path = './FFHQ_eye_mouth_landmarks_512.pth' + +print('Load JSON metadata...') +# use the official json file in FFHQ dataset +with open(json_path, 'rb') as f: + json_data = json.load(f, object_pairs_hook=OrderedDict) + +print('Open LMDB file...') +# read ffhq images +file_client = FileClient('lmdb', db_paths=face_path) +with open(os.path.join(face_path, 'meta_info.txt')) as fin: + paths = [line.split('.')[0] for line in fin] + +save_dict = {} + +for item_idx, item in enumerate(json_data.values()): + print(f'\r{item_idx} / {len(json_data)}, {item["image"]["file_path"]} ', end='', flush=True) + + # parse landmarks + lm = np.array(item['image']['face_landmarks']) + lm = lm * scale + + item_dict = {} + # get image + if save_img: + img_bytes = file_client.get(paths[item_idx]) + img = imfrombytes(img_bytes, float32=True) + + # get landmarks for each component + map_left_eye = list(range(36, 42)) + map_right_eye = list(range(42, 48)) + map_mouth = list(range(48, 68)) + + # eye_left + mean_left_eye = np.mean(lm[map_left_eye], 0) # (x, y) + half_len_left_eye = np.max((np.max(np.max(lm[map_left_eye], 0) - np.min(lm[map_left_eye], 0)) / 2, 16)) + item_dict['left_eye'] = [mean_left_eye[0], mean_left_eye[1], half_len_left_eye] + # mean_left_eye[0] = 512 - mean_left_eye[0] # for testing flip + half_len_left_eye *= enlarge_ratio + loc_left_eye = np.hstack((mean_left_eye - half_len_left_eye + 1, mean_left_eye + half_len_left_eye)).astype(int) + if save_img: + eye_left_img = img[loc_left_eye[1]:loc_left_eye[3], loc_left_eye[0]:loc_left_eye[2], :] + cv2.imwrite(f'tmp/{item_idx:08d}_eye_left.png', eye_left_img * 255) + + # eye_right + mean_right_eye = np.mean(lm[map_right_eye], 0) + half_len_right_eye = np.max((np.max(np.max(lm[map_right_eye], 0) - np.min(lm[map_right_eye], 0)) / 2, 16)) + item_dict['right_eye'] = [mean_right_eye[0], mean_right_eye[1], half_len_right_eye] + # mean_right_eye[0] = 512 - mean_right_eye[0] # # for testing flip + half_len_right_eye *= enlarge_ratio + loc_right_eye = np.hstack( + (mean_right_eye - half_len_right_eye + 1, mean_right_eye + half_len_right_eye)).astype(int) + if save_img: + eye_right_img = img[loc_right_eye[1]:loc_right_eye[3], loc_right_eye[0]:loc_right_eye[2], :] + cv2.imwrite(f'tmp/{item_idx:08d}_eye_right.png', eye_right_img * 255) + + # mouth + mean_mouth = np.mean(lm[map_mouth], 0) + half_len_mouth = np.max((np.max(np.max(lm[map_mouth], 0) - np.min(lm[map_mouth], 0)) / 2, 16)) + item_dict['mouth'] = [mean_mouth[0], mean_mouth[1], half_len_mouth] + # mean_mouth[0] = 512 - mean_mouth[0] # for testing flip + loc_mouth = np.hstack((mean_mouth - half_len_mouth + 1, mean_mouth + half_len_mouth)).astype(int) + if save_img: + mouth_img = img[loc_mouth[1]:loc_mouth[3], loc_mouth[0]:loc_mouth[2], :] + cv2.imwrite(f'tmp/{item_idx:08d}_mouth.png', mouth_img * 255) + + save_dict[f'{item_idx:08d}'] = item_dict + +print('Save...') +torch.save(save_dict, save_path) diff --git a/motion-gan-pipeline/GFPGAN/setup.cfg b/motion-gan-pipeline/GFPGAN/setup.cfg new file mode 100644 index 0000000..3d90d60 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/setup.cfg @@ -0,0 +1,33 @@ +[flake8] +ignore = + # line break before binary operator (W503) + W503, + # line break after binary operator (W504) + W504, +max-line-length=120 + +[yapf] +based_on_style = pep8 +column_limit = 120 +blank_line_before_nested_class_or_def = true +split_before_expression_after_opening_paren = true + +[isort] +line_length = 120 +multi_line_output = 0 +known_standard_library = pkg_resources,setuptools +known_first_party = gfpgan +known_third_party = basicsr,cv2,facexlib,numpy,pytest,torch,torchvision,tqdm,yaml +no_lines_before = STDLIB,LOCALFOLDER +default_section = THIRDPARTY + +[codespell] +skip = .git,./docs/build +count = +quiet-level = 3 + +[aliases] +test=pytest + +[tool:pytest] +addopts=tests/ diff --git a/motion-gan-pipeline/GFPGAN/setup.py b/motion-gan-pipeline/GFPGAN/setup.py new file mode 100644 index 0000000..474e918 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/setup.py @@ -0,0 +1,107 @@ +#!/usr/bin/env python + +from setuptools import find_packages, setup + +import os +import subprocess +import time + +version_file = 'gfpgan/version.py' + + +def readme(): + with open('README.md', encoding='utf-8') as f: + content = f.read() + return content + + +def get_git_hash(): + + def _minimal_ext_cmd(cmd): + # construct minimal environment + env = {} + for k in ['SYSTEMROOT', 'PATH', 'HOME']: + v = os.environ.get(k) + if v is not None: + env[k] = v + # LANGUAGE is used on win32 + env['LANGUAGE'] = 'C' + env['LANG'] = 'C' + env['LC_ALL'] = 'C' + out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0] + return out + + try: + out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD']) + sha = out.strip().decode('ascii') + except OSError: + sha = 'unknown' + + return sha + + +def get_hash(): + if os.path.exists('.git'): + sha = get_git_hash()[:7] + else: + sha = 'unknown' + + return sha + + +def write_version_py(): + content = """# GENERATED VERSION FILE +# TIME: {} +__version__ = '{}' +__gitsha__ = '{}' +version_info = ({}) +""" + sha = get_hash() + with open('VERSION', 'r') as f: + SHORT_VERSION = f.read().strip() + VERSION_INFO = ', '.join([x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')]) + + version_file_str = content.format(time.asctime(), SHORT_VERSION, sha, VERSION_INFO) + with open(version_file, 'w') as f: + f.write(version_file_str) + + +def get_version(): + with open(version_file, 'r') as f: + exec(compile(f.read(), version_file, 'exec')) + return locals()['__version__'] + + +def get_requirements(filename='requirements.txt'): + here = os.path.dirname(os.path.realpath(__file__)) + with open(os.path.join(here, filename), 'r') as f: + requires = [line.replace('\n', '') for line in f.readlines()] + return requires + + +if __name__ == '__main__': + write_version_py() + setup( + name='gfpgan', + version=get_version(), + description='GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration', + long_description=readme(), + long_description_content_type='text/markdown', + author='Xintao Wang', + author_email='xintao.wang@outlook.com', + keywords='computer vision, pytorch, image restoration, super-resolution, face restoration, gan, gfpgan', + url='https://github.com/TencentARC/GFPGAN', + include_package_data=True, + packages=find_packages(exclude=('options', 'datasets', 'experiments', 'results', 'tb_logger', 'wandb')), + classifiers=[ + 'Development Status :: 4 - Beta', + 'License :: OSI Approved :: Apache Software License', + 'Operating System :: OS Independent', + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.7', + 'Programming Language :: Python :: 3.8', + ], + license='Apache License Version 2.0', + setup_requires=['cython', 'numpy'], + install_requires=get_requirements(), + zip_safe=False) diff --git a/motion-gan-pipeline/GFPGAN/test_GPU.py b/motion-gan-pipeline/GFPGAN/test_GPU.py new file mode 100644 index 0000000..73c2464 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/test_GPU.py @@ -0,0 +1,3 @@ +import torch + +print(torch.cuda.is_available()) \ No newline at end of file diff --git a/motion-gan-pipeline/GFPGAN/tests/data/ffhq_gt.lmdb/data.mdb b/motion-gan-pipeline/GFPGAN/tests/data/ffhq_gt.lmdb/data.mdb new file mode 100644 index 0000000..823e0a9 Binary files /dev/null and b/motion-gan-pipeline/GFPGAN/tests/data/ffhq_gt.lmdb/data.mdb differ diff --git a/motion-gan-pipeline/GFPGAN/tests/data/ffhq_gt.lmdb/lock.mdb b/motion-gan-pipeline/GFPGAN/tests/data/ffhq_gt.lmdb/lock.mdb new file mode 100644 index 0000000..c53d2e5 Binary files /dev/null and b/motion-gan-pipeline/GFPGAN/tests/data/ffhq_gt.lmdb/lock.mdb differ diff --git a/motion-gan-pipeline/GFPGAN/tests/data/ffhq_gt.lmdb/meta_info.txt b/motion-gan-pipeline/GFPGAN/tests/data/ffhq_gt.lmdb/meta_info.txt new file mode 100644 index 0000000..8f18d95 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/tests/data/ffhq_gt.lmdb/meta_info.txt @@ -0,0 +1 @@ +00000000.png (512,512,3) 1 diff --git a/motion-gan-pipeline/GFPGAN/tests/data/gt/00000000.png b/motion-gan-pipeline/GFPGAN/tests/data/gt/00000000.png new file mode 100644 index 0000000..33425aa Binary files /dev/null and b/motion-gan-pipeline/GFPGAN/tests/data/gt/00000000.png differ diff --git a/motion-gan-pipeline/GFPGAN/tests/data/test_eye_mouth_landmarks.pth b/motion-gan-pipeline/GFPGAN/tests/data/test_eye_mouth_landmarks.pth new file mode 100644 index 0000000..a27f352 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/tests/data/test_eye_mouth_landmarks.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:131583fca2cc346652f8754eb3c5a0bdeda808686039ff10ead7a26254b72358 +size 943 diff --git a/motion-gan-pipeline/GFPGAN/tests/data/test_ffhq_degradation_dataset.yml b/motion-gan-pipeline/GFPGAN/tests/data/test_ffhq_degradation_dataset.yml new file mode 100644 index 0000000..df50c4b --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/tests/data/test_ffhq_degradation_dataset.yml @@ -0,0 +1,24 @@ +name: UnitTest +type: FFHQDegradationDataset +dataroot_gt: tests/data/gt +io_backend: + type: disk + +use_hflip: true +mean: [0.5, 0.5, 0.5] +std: [0.5, 0.5, 0.5] +out_size: 512 + +blur_kernel_size: 41 +kernel_list: ['iso', 'aniso'] +kernel_prob: [0.5, 0.5] +blur_sigma: [0.1, 10] +downsample_range: [0.8, 8] +noise_range: [0, 20] +jpeg_range: [60, 100] + +# color jitter and gray +color_jitter_prob: 1 +color_jitter_shift: 20 +color_jitter_pt_prob: 1 +gray_prob: 1 diff --git a/motion-gan-pipeline/GFPGAN/tests/data/test_gfpgan_model.yml b/motion-gan-pipeline/GFPGAN/tests/data/test_gfpgan_model.yml new file mode 100644 index 0000000..bac650e --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/tests/data/test_gfpgan_model.yml @@ -0,0 +1,140 @@ +num_gpu: 1 +manual_seed: 0 +is_train: True +dist: False + +# network structures +network_g: + type: GFPGANv1 + out_size: 512 + num_style_feat: 512 + channel_multiplier: 1 + resample_kernel: [1, 3, 3, 1] + decoder_load_path: ~ + fix_decoder: true + num_mlp: 8 + lr_mlp: 0.01 + input_is_latent: true + different_w: true + narrow: 0.5 + sft_half: true + +network_d: + type: StyleGAN2Discriminator + out_size: 512 + channel_multiplier: 1 + resample_kernel: [1, 3, 3, 1] + +network_d_left_eye: + type: FacialComponentDiscriminator + +network_d_right_eye: + type: FacialComponentDiscriminator + +network_d_mouth: + type: FacialComponentDiscriminator + +network_identity: + type: ResNetArcFace + block: IRBlock + layers: [2, 2, 2, 2] + use_se: False + +# path +path: + pretrain_network_g: ~ + param_key_g: params_ema + strict_load_g: ~ + pretrain_network_d: ~ + pretrain_network_d_left_eye: ~ + pretrain_network_d_right_eye: ~ + pretrain_network_d_mouth: ~ + pretrain_network_identity: ~ + # resume + resume_state: ~ + ignore_resume_networks: ['network_identity'] + +# training settings +train: + optim_g: + type: Adam + lr: !!float 2e-3 + optim_d: + type: Adam + lr: !!float 2e-3 + optim_component: + type: Adam + lr: !!float 2e-3 + + scheduler: + type: MultiStepLR + milestones: [600000, 700000] + gamma: 0.5 + + total_iter: 800000 + warmup_iter: -1 # no warm up + + # losses + # pixel loss + pixel_opt: + type: L1Loss + loss_weight: !!float 1e-1 + reduction: mean + # L1 loss used in pyramid loss, component style loss and identity loss + L1_opt: + type: L1Loss + loss_weight: 1 + reduction: mean + + # image pyramid loss + pyramid_loss_weight: 1 + remove_pyramid_loss: 50000 + # perceptual loss (content and style losses) + perceptual_opt: + type: PerceptualLoss + layer_weights: + # before relu + 'conv1_2': 0.1 + 'conv2_2': 0.1 + 'conv3_4': 1 + 'conv4_4': 1 + 'conv5_4': 1 + vgg_type: vgg19 + use_input_norm: true + perceptual_weight: !!float 1 + style_weight: 50 + range_norm: true + criterion: l1 + # gan loss + gan_opt: + type: GANLoss + gan_type: wgan_softplus + loss_weight: !!float 1e-1 + # r1 regularization for discriminator + r1_reg_weight: 10 + # facial component loss + gan_component_opt: + type: GANLoss + gan_type: vanilla + real_label_val: 1.0 + fake_label_val: 0.0 + loss_weight: !!float 1 + comp_style_weight: 200 + # identity loss + identity_weight: 10 + + net_d_iters: 1 + net_d_init_iters: 0 + net_d_reg_every: 1 + +# validation settings +val: + val_freq: !!float 5e3 + save_img: True + use_pbar: True + + metrics: + psnr: # metric name + type: calculate_psnr + crop_border: 0 + test_y_channel: false diff --git a/motion-gan-pipeline/GFPGAN/tests/test_arcface_arch.py b/motion-gan-pipeline/GFPGAN/tests/test_arcface_arch.py new file mode 100644 index 0000000..b4b28d3 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/tests/test_arcface_arch.py @@ -0,0 +1,49 @@ +import torch + +from gfpgan.archs.arcface_arch import BasicBlock, Bottleneck, ResNetArcFace + + +def test_resnetarcface(): + """Test arch: ResNetArcFace.""" + + # model init and forward (gpu) + if torch.cuda.is_available(): + net = ResNetArcFace(block='IRBlock', layers=(2, 2, 2, 2), use_se=True).cuda().eval() + img = torch.rand((1, 1, 128, 128), dtype=torch.float32).cuda() + output = net(img) + assert output.shape == (1, 512) + + # -------------------- without SE block ----------------------- # + net = ResNetArcFace(block='IRBlock', layers=(2, 2, 2, 2), use_se=False).cuda().eval() + output = net(img) + assert output.shape == (1, 512) + + +def test_basicblock(): + """Test the BasicBlock in arcface_arch""" + block = BasicBlock(1, 3, stride=1, downsample=None).cuda() + img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda() + output = block(img) + assert output.shape == (1, 3, 12, 12) + + # ----------------- use the downsmaple module--------------- # + downsample = torch.nn.UpsamplingNearest2d(scale_factor=0.5).cuda() + block = BasicBlock(1, 3, stride=2, downsample=downsample).cuda() + img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda() + output = block(img) + assert output.shape == (1, 3, 6, 6) + + +def test_bottleneck(): + """Test the Bottleneck in arcface_arch""" + block = Bottleneck(1, 1, stride=1, downsample=None).cuda() + img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda() + output = block(img) + assert output.shape == (1, 4, 12, 12) + + # ----------------- use the downsmaple module--------------- # + downsample = torch.nn.UpsamplingNearest2d(scale_factor=0.5).cuda() + block = Bottleneck(1, 1, stride=2, downsample=downsample).cuda() + img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda() + output = block(img) + assert output.shape == (1, 4, 6, 6) diff --git a/motion-gan-pipeline/GFPGAN/tests/test_ffhq_degradation_dataset.py b/motion-gan-pipeline/GFPGAN/tests/test_ffhq_degradation_dataset.py new file mode 100644 index 0000000..fa56c03 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/tests/test_ffhq_degradation_dataset.py @@ -0,0 +1,96 @@ +import pytest +import yaml + +from gfpgan.data.ffhq_degradation_dataset import FFHQDegradationDataset + + +def test_ffhq_degradation_dataset(): + + with open('tests/data/test_ffhq_degradation_dataset.yml', mode='r') as f: + opt = yaml.load(f, Loader=yaml.FullLoader) + + dataset = FFHQDegradationDataset(opt) + assert dataset.io_backend_opt['type'] == 'disk' # io backend + assert len(dataset) == 1 # whether to read correct meta info + assert dataset.kernel_list == ['iso', 'aniso'] # correct initialization the degradation configurations + assert dataset.color_jitter_prob == 1 + + # test __getitem__ + result = dataset.__getitem__(0) + # check returned keys + expected_keys = ['gt', 'lq', 'gt_path'] + assert set(expected_keys).issubset(set(result.keys())) + # check shape and contents + assert result['gt'].shape == (3, 512, 512) + assert result['lq'].shape == (3, 512, 512) + assert result['gt_path'] == 'tests/data/gt/00000000.png' + + # ------------------ test with probability = 0 -------------------- # + opt['color_jitter_prob'] = 0 + opt['color_jitter_pt_prob'] = 0 + opt['gray_prob'] = 0 + opt['io_backend'] = dict(type='disk') + dataset = FFHQDegradationDataset(opt) + assert dataset.io_backend_opt['type'] == 'disk' # io backend + assert len(dataset) == 1 # whether to read correct meta info + assert dataset.kernel_list == ['iso', 'aniso'] # correct initialization the degradation configurations + assert dataset.color_jitter_prob == 0 + + # test __getitem__ + result = dataset.__getitem__(0) + # check returned keys + expected_keys = ['gt', 'lq', 'gt_path'] + assert set(expected_keys).issubset(set(result.keys())) + # check shape and contents + assert result['gt'].shape == (3, 512, 512) + assert result['lq'].shape == (3, 512, 512) + assert result['gt_path'] == 'tests/data/gt/00000000.png' + + # ------------------ test lmdb backend -------------------- # + opt['dataroot_gt'] = 'tests/data/ffhq_gt.lmdb' + opt['io_backend'] = dict(type='lmdb') + + dataset = FFHQDegradationDataset(opt) + assert dataset.io_backend_opt['type'] == 'lmdb' # io backend + assert len(dataset) == 1 # whether to read correct meta info + assert dataset.kernel_list == ['iso', 'aniso'] # correct initialization the degradation configurations + assert dataset.color_jitter_prob == 0 + + # test __getitem__ + result = dataset.__getitem__(0) + # check returned keys + expected_keys = ['gt', 'lq', 'gt_path'] + assert set(expected_keys).issubset(set(result.keys())) + # check shape and contents + assert result['gt'].shape == (3, 512, 512) + assert result['lq'].shape == (3, 512, 512) + assert result['gt_path'] == '00000000' + + # ------------------ test with crop_components -------------------- # + opt['crop_components'] = True + opt['component_path'] = 'tests/data/test_eye_mouth_landmarks.pth' + opt['eye_enlarge_ratio'] = 1.4 + opt['gt_gray'] = True + opt['io_backend'] = dict(type='lmdb') + + dataset = FFHQDegradationDataset(opt) + assert dataset.crop_components is True + + # test __getitem__ + result = dataset.__getitem__(0) + # check returned keys + expected_keys = ['gt', 'lq', 'gt_path', 'loc_left_eye', 'loc_right_eye', 'loc_mouth'] + assert set(expected_keys).issubset(set(result.keys())) + # check shape and contents + assert result['gt'].shape == (3, 512, 512) + assert result['lq'].shape == (3, 512, 512) + assert result['gt_path'] == '00000000' + assert result['loc_left_eye'].shape == (4, ) + assert result['loc_right_eye'].shape == (4, ) + assert result['loc_mouth'].shape == (4, ) + + # ------------------ lmdb backend should have paths ends with lmdb -------------------- # + with pytest.raises(ValueError): + opt['dataroot_gt'] = 'tests/data/gt' + opt['io_backend'] = dict(type='lmdb') + dataset = FFHQDegradationDataset(opt) diff --git a/motion-gan-pipeline/GFPGAN/tests/test_gfpgan_arch.py b/motion-gan-pipeline/GFPGAN/tests/test_gfpgan_arch.py new file mode 100644 index 0000000..cef14a4 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/tests/test_gfpgan_arch.py @@ -0,0 +1,203 @@ +import torch + +from gfpgan.archs.gfpganv1_arch import FacialComponentDiscriminator, GFPGANv1, StyleGAN2GeneratorSFT +from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean, StyleGAN2GeneratorCSFT + + +def test_stylegan2generatorsft(): + """Test arch: StyleGAN2GeneratorSFT.""" + + # model init and forward (gpu) + if torch.cuda.is_available(): + net = StyleGAN2GeneratorSFT( + out_size=32, + num_style_feat=512, + num_mlp=8, + channel_multiplier=1, + resample_kernel=(1, 3, 3, 1), + lr_mlp=0.01, + narrow=1, + sft_half=False).cuda().eval() + style = torch.rand((1, 512), dtype=torch.float32).cuda() + condition1 = torch.rand((1, 512, 8, 8), dtype=torch.float32).cuda() + condition2 = torch.rand((1, 512, 16, 16), dtype=torch.float32).cuda() + condition3 = torch.rand((1, 512, 32, 32), dtype=torch.float32).cuda() + conditions = [condition1, condition1, condition2, condition2, condition3, condition3] + output = net([style], conditions) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + # -------------------- with return_latents ----------------------- # + output = net([style], conditions, return_latents=True) + assert output[0].shape == (1, 3, 32, 32) + assert len(output[1]) == 1 + # check latent + assert output[1][0].shape == (8, 512) + + # -------------------- with randomize_noise = False ----------------------- # + output = net([style], conditions, randomize_noise=False) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + # -------------------- with truncation = 0.5 and mixing----------------------- # + output = net([style, style], conditions, truncation=0.5, truncation_latent=style) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + +def test_gfpganv1(): + """Test arch: GFPGANv1.""" + + # model init and forward (gpu) + if torch.cuda.is_available(): + net = GFPGANv1( + out_size=32, + num_style_feat=512, + channel_multiplier=1, + resample_kernel=(1, 3, 3, 1), + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + lr_mlp=0.01, + input_is_latent=False, + different_w=False, + narrow=1, + sft_half=True).cuda().eval() + img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda() + output = net(img) + assert output[0].shape == (1, 3, 32, 32) + assert len(output[1]) == 3 + # check out_rgbs for intermediate loss + assert output[1][0].shape == (1, 3, 8, 8) + assert output[1][1].shape == (1, 3, 16, 16) + assert output[1][2].shape == (1, 3, 32, 32) + + # -------------------- with different_w = True ----------------------- # + net = GFPGANv1( + out_size=32, + num_style_feat=512, + channel_multiplier=1, + resample_kernel=(1, 3, 3, 1), + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + lr_mlp=0.01, + input_is_latent=False, + different_w=True, + narrow=1, + sft_half=True).cuda().eval() + img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda() + output = net(img) + assert output[0].shape == (1, 3, 32, 32) + assert len(output[1]) == 3 + # check out_rgbs for intermediate loss + assert output[1][0].shape == (1, 3, 8, 8) + assert output[1][1].shape == (1, 3, 16, 16) + assert output[1][2].shape == (1, 3, 32, 32) + + +def test_facialcomponentdiscriminator(): + """Test arch: FacialComponentDiscriminator.""" + + # model init and forward (gpu) + if torch.cuda.is_available(): + net = FacialComponentDiscriminator().cuda().eval() + img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda() + output = net(img) + assert len(output) == 2 + assert output[0].shape == (1, 1, 8, 8) + assert output[1] is None + + # -------------------- return intermediate features ----------------------- # + output = net(img, return_feats=True) + assert len(output) == 2 + assert output[0].shape == (1, 1, 8, 8) + assert len(output[1]) == 2 + assert output[1][0].shape == (1, 128, 16, 16) + assert output[1][1].shape == (1, 256, 8, 8) + + +def test_stylegan2generatorcsft(): + """Test arch: StyleGAN2GeneratorCSFT.""" + + # model init and forward (gpu) + if torch.cuda.is_available(): + net = StyleGAN2GeneratorCSFT( + out_size=32, num_style_feat=512, num_mlp=8, channel_multiplier=1, narrow=1, sft_half=False).cuda().eval() + style = torch.rand((1, 512), dtype=torch.float32).cuda() + condition1 = torch.rand((1, 512, 8, 8), dtype=torch.float32).cuda() + condition2 = torch.rand((1, 512, 16, 16), dtype=torch.float32).cuda() + condition3 = torch.rand((1, 512, 32, 32), dtype=torch.float32).cuda() + conditions = [condition1, condition1, condition2, condition2, condition3, condition3] + output = net([style], conditions) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + # -------------------- with return_latents ----------------------- # + output = net([style], conditions, return_latents=True) + assert output[0].shape == (1, 3, 32, 32) + assert len(output[1]) == 1 + # check latent + assert output[1][0].shape == (8, 512) + + # -------------------- with randomize_noise = False ----------------------- # + output = net([style], conditions, randomize_noise=False) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + # -------------------- with truncation = 0.5 and mixing----------------------- # + output = net([style, style], conditions, truncation=0.5, truncation_latent=style) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + +def test_gfpganv1clean(): + """Test arch: GFPGANv1Clean.""" + + # model init and forward (gpu) + if torch.cuda.is_available(): + net = GFPGANv1Clean( + out_size=32, + num_style_feat=512, + channel_multiplier=1, + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + input_is_latent=False, + different_w=False, + narrow=1, + sft_half=True).cuda().eval() + + img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda() + output = net(img) + assert output[0].shape == (1, 3, 32, 32) + assert len(output[1]) == 3 + # check out_rgbs for intermediate loss + assert output[1][0].shape == (1, 3, 8, 8) + assert output[1][1].shape == (1, 3, 16, 16) + assert output[1][2].shape == (1, 3, 32, 32) + + # -------------------- with different_w = True ----------------------- # + net = GFPGANv1Clean( + out_size=32, + num_style_feat=512, + channel_multiplier=1, + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + input_is_latent=False, + different_w=True, + narrow=1, + sft_half=True).cuda().eval() + img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda() + output = net(img) + assert output[0].shape == (1, 3, 32, 32) + assert len(output[1]) == 3 + # check out_rgbs for intermediate loss + assert output[1][0].shape == (1, 3, 8, 8) + assert output[1][1].shape == (1, 3, 16, 16) + assert output[1][2].shape == (1, 3, 32, 32) diff --git a/motion-gan-pipeline/GFPGAN/tests/test_gfpgan_model.py b/motion-gan-pipeline/GFPGAN/tests/test_gfpgan_model.py new file mode 100644 index 0000000..1408ddd --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/tests/test_gfpgan_model.py @@ -0,0 +1,132 @@ +import tempfile +import torch +import yaml +from basicsr.archs.stylegan2_arch import StyleGAN2Discriminator +from basicsr.data.paired_image_dataset import PairedImageDataset +from basicsr.losses.losses import GANLoss, L1Loss, PerceptualLoss + +from gfpgan.archs.arcface_arch import ResNetArcFace +from gfpgan.archs.gfpganv1_arch import FacialComponentDiscriminator, GFPGANv1 +from gfpgan.models.gfpgan_model import GFPGANModel + + +def test_gfpgan_model(): + with open('tests/data/test_gfpgan_model.yml', mode='r') as f: + opt = yaml.load(f, Loader=yaml.FullLoader) + + # build model + model = GFPGANModel(opt) + # test attributes + assert model.__class__.__name__ == 'GFPGANModel' + assert isinstance(model.net_g, GFPGANv1) # generator + assert isinstance(model.net_d, StyleGAN2Discriminator) # discriminator + # facial component discriminators + assert isinstance(model.net_d_left_eye, FacialComponentDiscriminator) + assert isinstance(model.net_d_right_eye, FacialComponentDiscriminator) + assert isinstance(model.net_d_mouth, FacialComponentDiscriminator) + # identity network + assert isinstance(model.network_identity, ResNetArcFace) + # losses + assert isinstance(model.cri_pix, L1Loss) + assert isinstance(model.cri_perceptual, PerceptualLoss) + assert isinstance(model.cri_gan, GANLoss) + assert isinstance(model.cri_l1, L1Loss) + # optimizer + assert isinstance(model.optimizers[0], torch.optim.Adam) + assert isinstance(model.optimizers[1], torch.optim.Adam) + + # prepare data + gt = torch.rand((1, 3, 512, 512), dtype=torch.float32) + lq = torch.rand((1, 3, 512, 512), dtype=torch.float32) + loc_left_eye = torch.rand((1, 4), dtype=torch.float32) + loc_right_eye = torch.rand((1, 4), dtype=torch.float32) + loc_mouth = torch.rand((1, 4), dtype=torch.float32) + data = dict(gt=gt, lq=lq, loc_left_eye=loc_left_eye, loc_right_eye=loc_right_eye, loc_mouth=loc_mouth) + model.feed_data(data) + # check data shape + assert model.lq.shape == (1, 3, 512, 512) + assert model.gt.shape == (1, 3, 512, 512) + assert model.loc_left_eyes.shape == (1, 4) + assert model.loc_right_eyes.shape == (1, 4) + assert model.loc_mouths.shape == (1, 4) + + # ----------------- test optimize_parameters -------------------- # + model.feed_data(data) + model.optimize_parameters(1) + assert model.output.shape == (1, 3, 512, 512) + assert isinstance(model.log_dict, dict) + # check returned keys + expected_keys = [ + 'l_g_pix', 'l_g_percep', 'l_g_style', 'l_g_gan', 'l_g_gan_left_eye', 'l_g_gan_right_eye', 'l_g_gan_mouth', + 'l_g_comp_style_loss', 'l_identity', 'l_d', 'real_score', 'fake_score', 'l_d_r1', 'l_d_left_eye', + 'l_d_right_eye', 'l_d_mouth' + ] + assert set(expected_keys).issubset(set(model.log_dict.keys())) + + # ----------------- remove pyramid_loss_weight-------------------- # + model.feed_data(data) + model.optimize_parameters(100000) # large than remove_pyramid_loss = 50000 + assert model.output.shape == (1, 3, 512, 512) + assert isinstance(model.log_dict, dict) + # check returned keys + expected_keys = [ + 'l_g_pix', 'l_g_percep', 'l_g_style', 'l_g_gan', 'l_g_gan_left_eye', 'l_g_gan_right_eye', 'l_g_gan_mouth', + 'l_g_comp_style_loss', 'l_identity', 'l_d', 'real_score', 'fake_score', 'l_d_r1', 'l_d_left_eye', + 'l_d_right_eye', 'l_d_mouth' + ] + assert set(expected_keys).issubset(set(model.log_dict.keys())) + + # ----------------- test save -------------------- # + with tempfile.TemporaryDirectory() as tmpdir: + model.opt['path']['models'] = tmpdir + model.opt['path']['training_states'] = tmpdir + model.save(0, 1) + + # ----------------- test the test function -------------------- # + model.test() + assert model.output.shape == (1, 3, 512, 512) + # delete net_g_ema + model.__delattr__('net_g_ema') + model.test() + assert model.output.shape == (1, 3, 512, 512) + assert model.net_g.training is True # should back to training mode after testing + + # ----------------- test nondist_validation -------------------- # + # construct dataloader + dataset_opt = dict( + name='Demo', + dataroot_gt='tests/data/gt', + dataroot_lq='tests/data/gt', + io_backend=dict(type='disk'), + scale=4, + phase='val') + dataset = PairedImageDataset(dataset_opt) + dataloader = torch.utils.data.DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=0) + assert model.is_train is True + with tempfile.TemporaryDirectory() as tmpdir: + model.opt['path']['visualization'] = tmpdir + model.nondist_validation(dataloader, 1, None, save_img=True) + assert model.is_train is True + # check metric_results + assert 'psnr' in model.metric_results + assert isinstance(model.metric_results['psnr'], float) + + # validation + with tempfile.TemporaryDirectory() as tmpdir: + model.opt['is_train'] = False + model.opt['val']['suffix'] = 'test' + model.opt['path']['visualization'] = tmpdir + model.opt['val']['pbar'] = True + model.nondist_validation(dataloader, 1, None, save_img=True) + # check metric_results + assert 'psnr' in model.metric_results + assert isinstance(model.metric_results['psnr'], float) + + # if opt['val']['suffix'] is None + model.opt['val']['suffix'] = None + model.opt['name'] = 'demo' + model.opt['path']['visualization'] = tmpdir + model.nondist_validation(dataloader, 1, None, save_img=True) + # check metric_results + assert 'psnr' in model.metric_results + assert isinstance(model.metric_results['psnr'], float) diff --git a/motion-gan-pipeline/GFPGAN/tests/test_stylegan2_clean_arch.py b/motion-gan-pipeline/GFPGAN/tests/test_stylegan2_clean_arch.py new file mode 100644 index 0000000..78bb920 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/tests/test_stylegan2_clean_arch.py @@ -0,0 +1,52 @@ +import torch + +from gfpgan.archs.stylegan2_clean_arch import StyleGAN2GeneratorClean + + +def test_stylegan2generatorclean(): + """Test arch: StyleGAN2GeneratorClean.""" + + # model init and forward (gpu) + if torch.cuda.is_available(): + net = StyleGAN2GeneratorClean( + out_size=32, num_style_feat=512, num_mlp=8, channel_multiplier=1, narrow=0.5).cuda().eval() + style = torch.rand((1, 512), dtype=torch.float32).cuda() + output = net([style], input_is_latent=False) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + # -------------------- with return_latents ----------------------- # + output = net([style], input_is_latent=True, return_latents=True) + assert output[0].shape == (1, 3, 32, 32) + assert len(output[1]) == 1 + # check latent + assert output[1][0].shape == (8, 512) + + # -------------------- with randomize_noise = False ----------------------- # + output = net([style], randomize_noise=False) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + # -------------------- with truncation = 0.5 and mixing----------------------- # + output = net([style, style], truncation=0.5, truncation_latent=style) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + # ------------------ test make_noise ----------------------- # + out = net.make_noise() + assert len(out) == 7 + assert out[0].shape == (1, 1, 4, 4) + assert out[1].shape == (1, 1, 8, 8) + assert out[2].shape == (1, 1, 8, 8) + assert out[3].shape == (1, 1, 16, 16) + assert out[4].shape == (1, 1, 16, 16) + assert out[5].shape == (1, 1, 32, 32) + assert out[6].shape == (1, 1, 32, 32) + + # ------------------ test get_latent ----------------------- # + out = net.get_latent(style) + assert out.shape == (1, 512) + + # ------------------ test mean_latent ----------------------- # + out = net.mean_latent(2) + assert out.shape == (1, 512) diff --git a/motion-gan-pipeline/GFPGAN/tests/test_utils.py b/motion-gan-pipeline/GFPGAN/tests/test_utils.py new file mode 100644 index 0000000..a963b32 --- /dev/null +++ b/motion-gan-pipeline/GFPGAN/tests/test_utils.py @@ -0,0 +1,43 @@ +import cv2 +from facexlib.utils.face_restoration_helper import FaceRestoreHelper + +from gfpgan.archs.gfpganv1_arch import GFPGANv1 +from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean +from gfpgan.utils import GFPGANer + + +def test_gfpganer(): + # initialize with the clean model + restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANCleanv1-NoCE-C2.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=None) + # test attribute + assert isinstance(restorer.gfpgan, GFPGANv1Clean) + assert isinstance(restorer.face_helper, FaceRestoreHelper) + + # initialize with the original model + restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.pth', + upscale=2, + arch='original', + channel_multiplier=1, + bg_upsampler=None) + # test attribute + assert isinstance(restorer.gfpgan, GFPGANv1) + assert isinstance(restorer.face_helper, FaceRestoreHelper) + + # ------------------ test enhance ---------------- # + img = cv2.imread('tests/data/gt/00000000.png', cv2.IMREAD_COLOR) + result = restorer.enhance(img, has_aligned=False, paste_back=True) + assert result[0][0].shape == (512, 512, 3) + assert result[1][0].shape == (512, 512, 3) + assert result[2].shape == (1024, 1024, 3) + + # with has_aligned=True + result = restorer.enhance(img, has_aligned=True, paste_back=False) + assert result[0][0].shape == (512, 512, 3) + assert result[1][0].shape == (512, 512, 3) + assert result[2] is None diff --git a/motion-gan-pipeline/ImageToImage/README.md b/motion-gan-pipeline/ImageToImage/README.md new file mode 100644 index 0000000..52744f5 --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/README.md @@ -0,0 +1,109 @@ +# Image2Image Generation + +This repository contains and implementation of an Image-to-Image translation network used in the Audio-driven Video Synthesis project. \ + +## Requirements + +Use the package manager [pip](https://pip.pypa.io/en/stable/) to install the requirements. + +```bash +pip install -r requirements.txt +``` + +## W&B monitoring + +All training monitoring can be done on Weights & Biases . \ +You can follow here a detailed guide for [W&B](https://docs.wandb.ai/?_gl=1*1r4dwbe*_ga*OTk1MTkyMDI1LjE2NjQ4MDkyNDY.*_ga_JH1SJHJQXJ*MTY2NDg4MzM1Mi4yLjEuMTY2NDg4MzM1Ny41NS4wLjA.) to set up your own tracking environment. + +To run our code you should follow these simple instructions: +1. Sign up for a free account at https://wandb.ai/site and then login to your wandb account. + +2. Install the CLI and Python library for interacting with the Weights and Biases API: + ```bash + pip install wandb + ``` + +3. Log in your wandb account: + ```bash + wandb login + ``` +4. You can now run the code and monitor the experiments on your W&B account. + + +## Losses + +We provide training scripts to experiment with a variety of different training losses: +- L1 loss +- Perceptual VGG loss +- GAN loss +- Optical Flow warping loss for time consistency +- PatchGAN3D Video Discriminator loss for time consistency + +All losses to be used can be chosen in the config file. \ +During our experiment we found the best combination of loss functions is a mixture of: L1 + VGG Perceptual + PatchGAN3D. + +## Train +We provide 3 different scripts for network training: + +1. GAN U-Net: \ +The first proposed model is a classical Image to Image network. You can regulate training parameters such as losses used or losses weights by making your own config in the config folder. \ +You can run the training using the following command: \ + ```bash + python train.py -c $CONFIGPATH + --checkpoints_dir $CHECKPOINTPATH + --input_train_root_dir $INPUTPATH + --output_train_root_dir $OUTPUTPATH + --height $HEIGHT + --width $WIDTH + ``` + Where: + - $CONFIGPATH: path to config file (config.train.yml is given). + - $CHECKPOINTPATH: path to checkpoints directory. + - $INPUTPATH: path to input images folder. + - $OUTPUTPATH: path to target images folder. + - $HEIGHT: height of output. + - $WIDTH: width of output. +

+ +2. GAN + Optical Flow \ +In addition to the first model, you can train the Image-to-Image network usign optical flow warping loss. \ +You can run the training using the following command: + ```bash + python train_optical.py -c $CONFIGPATH + --checkpoints_dir $CHECKPOINTPATH + --input_train_root_dir $INPUTPATH + --output_train_root_dir $OUTPUTPATH + --height $HEIGHT + --width $WIDTH + ``` +

+ +3. GAN + PatchGAN3D \ +Our final model can be trained using a PatchGAN3D video discriminator. This model is shown to be the best performant in terms of time consistency. \ +You can run the training using the following command: + ```bash + python train_temporal.py -c $CONFIGPATH + --checkpoints_dir $CHECKPOINTPATH + --input_train_root_dir $INPUTPATH + --output_train_root_dir $OUTPUTPATH + --height $HEIGHT + --width $WIDTH + ``` + +## Inference +You can run inferency by running the following command: \ +```bash +python generate_images.py --dataroot $DATAROOT + --video_name $NAME_VIDEO + --input_test_root_dir $INPUTFOLDER + --out_dir $OUTPUTFOLDER + --checkpoint_dir $CHECKPOINTDIR +``` +Where: +- $INPUTDATAPATH: path to the data directory. +- $NAME_VIDEO: name of the target video. +- $INPUTFOLDER: path to input images for the network. +- $OUTPUTFOLDER: path to output folder. +- $CHECKPOINTDIR: path to the existing checkpoint folder. + +If no checkoints are found this script will autimatically start training the GAN + PatchGAN3D model. \ No newline at end of file diff --git a/motion-gan-pipeline/ImageToImage/config/sweep_train.yml b/motion-gan-pipeline/ImageToImage/config/sweep_train.yml new file mode 100644 index 0000000..a72fdb6 --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/config/sweep_train.yml @@ -0,0 +1,22 @@ +program: train.py +method: random +metric: + goal: minimize + name: metrics_combined +command: + - ${env} + - ${interpreter} + - ${program} + - "-c" + - "config/train.yml" + - ${args} + +parameters: + lr: + values: [0.000001, 0.00001, 0.0001, 0.001] + lr_disc: + values: [0.000001, 0.00001, 0.0001, 0.001] + batch_size: + values: [1, 2, 4, 8] + optimizer: + values: ['adam', 'sgd'] \ No newline at end of file diff --git a/motion-gan-pipeline/ImageToImage/config/sweep_train_temporal.yml b/motion-gan-pipeline/ImageToImage/config/sweep_train_temporal.yml new file mode 100644 index 0000000..fcb2fad --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/config/sweep_train_temporal.yml @@ -0,0 +1,42 @@ +project: video-synth/ImageToImage +entity: apennino +program: train_temporal.py +method: grid +metric: + goal: minimize + name: metrics_combined +command: + - ${env} + - ${interpreter} + - ${program} + - "-c" + - "config/train_temporal.yml" + - "--checkpoints_dir" + - "/mnt/full_pipeline/checkpoints/Alberto" + - "--input_train_root_dir" + - "/mnt/full_pipeline/input_data/video/Alberto/edges" + - "--output_train_root_dir" + - "/mnt/full_pipeline/input_data/video/Alberto/cropped" + - "--input_val_root_dir" + - "/mnt/full_pipeline/input_data/video/Alberto/edges_val" + - "--output_val_root_dir" + - "/mnt/full_pipeline/input_data/video/Alberto/cropped_val" + - "--height" + - "720" + - "--width" + - "405" + - ${args} + +parameters: + steps_train_video_discr: + values: [1, 2, 5] + + loss_gan_video_weight: + values: [0.1, 1, 10] + + lr: + values: [0.001, 0.01] + + lr_decay_steps: + values: [20, 30] + \ No newline at end of file diff --git a/motion-gan-pipeline/ImageToImage/config/test.yml b/motion-gan-pipeline/ImageToImage/config/test.yml new file mode 100644 index 0000000..e3af695 --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/config/test.yml @@ -0,0 +1,23 @@ +# mode +mode: test # val or test + +# image network input size +width: 320 +height: 320 +num_input_channels: 1 + +# dataloader parameters +num_workers: 1 +batch_size: 1 + +# metric functions +metric_names: [mean_absolute_error, mean_squared_error, structual_similarity_index_measure, peak_signal_to_noise_ratio, frechet_inception_distance] +metric_weights: [1.0, 0.0, 0.0, 0.0, 0.0] + +# data directories and dataset type +input_test_root_dir: /input_images_test +input_val_root_dir: /input_images_test +output_val_root_dir: /output_images_test +label_val_root_dir: /labels_test +dataset_type: CustomDataset + diff --git a/motion-gan-pipeline/ImageToImage/config/train.yml b/motion-gan-pipeline/ImageToImage/config/train.yml new file mode 100644 index 0000000..8d83f6e --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/config/train.yml @@ -0,0 +1,52 @@ + +# image network input size +width: 320 +height: 320 + +# model parameters +use_discriminator: false #was true +# use_video_discriminator: true +lr_disc: 0.0001 + +# training parameters +num_epochs: 50 +lr: 0.001 +lr_decay_steps: 20 +optimizer: adam +num_steps: 100000 + +# network parameters +num_input_channels: 1 + +# dataloader parameters +num_workers: 16 +batch_size: 2 + +# loss functions +loss_names: [l1_perceptual_loss, perceptual_loss_vgg] +loss_weights: [100.0, 10.0] + +# # loss +# loss_names: [l1_perceptual_loss] +# loss_weights: [100.0] +# loss_names: [perceptual_loss_vgg] +# loss_weights: [1.0] + +# metric functions +metric_names: [mean_absolute_error, mean_squared_error, structual_similarity_index_measure, peak_signal_to_noise_ratio, frechet_inception_distance] +metric_weights: [1.0, 0.0, 0.0, 0.0, 0.0] + +# data directories and dataset type +input_train_root_dir: /input_images_train +output_train_root_dir: /output_images_train +label_train_root_dir: /labels_train +input_val_root_dir: /input_images_test +output_val_root_dir: /output_images_test +label_val_root_dir: /labels_test +dataset_type: CustomDataset + +# saving training progress +checkpoints_dir: ./checkpoints/ +save_every: 10 +log_every: 200 +save_val_images: true diff --git a/motion-gan-pipeline/ImageToImage/config/train_optical.yml b/motion-gan-pipeline/ImageToImage/config/train_optical.yml new file mode 100644 index 0000000..423a8e4 --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/config/train_optical.yml @@ -0,0 +1,41 @@ + +# image network input size +width: 320 +height: 320 + +# model parameters +use_discriminator: false #was true +# use_video_discriminator: true +lr_disc: 0.0001 + +# training parameters +num_epochs: 50 +lr: 0.001 +lr_decay_steps: 20 +optimizer: adam +num_steps: 100000 + +# network parameters +num_input_channels: 1 + +# dataloader parameters +num_workers: 16 +batch_size: 3 + +# loss functions +loss_names: [l1_perceptual_loss, perceptual_loss_vgg] +loss_weights: [100.0, 10.0] + +# metric functions +metric_names: [mean_absolute_error, mean_squared_error, structual_similarity_index_measure, peak_signal_to_noise_ratio, frechet_inception_distance] +metric_weights: [1.0, 0.0, 0.0, 0.0, 0.0] + +# data directories and dataset type +dataset_type: OpticalFlowDataset +optical_steps: 5 + +# saving training progress +checkpoints_dir: ./checkpoints/ +save_every: 10 +log_every: 200 +save_val_images: true diff --git a/motion-gan-pipeline/ImageToImage/config/train_temporal.yml b/motion-gan-pipeline/ImageToImage/config/train_temporal.yml new file mode 100644 index 0000000..2d686e8 --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/config/train_temporal.yml @@ -0,0 +1,45 @@ + +# image network input size +width: 320 +height: 320 + +# model parameters +use_discriminator: false +# use_discriminator: true + +# video discriminator +use_video_discriminator: true +steps_train_video_discr: 1 +lr_disc: 0.001 +loss_gan_video_weight: 1 + +# training parameters +num_epochs: 40 +lr: 0.001 +lr_decay_steps: 20 +optimizer: adam +num_steps: 100000 + +# network parameters +num_input_channels: 1 + +# dataloader parameters +num_workers: 16 +batch_size: 8 + +# loss functions +loss_names: [l1_perceptual_loss, perceptual_loss_vgg] +loss_weights: [100.0, 10.0] + +# metric functions +metric_names: [mean_absolute_error, mean_squared_error, structual_similarity_index_measure, peak_signal_to_noise_ratio, frechet_inception_distance] +metric_weights: [1.0, 0.0, -1.0, 0.0, 0.0] + +# data directories and dataset type +dataset_type: CustomDataset + +# saving training progress +save_every: 20 +log_every: 200 +save_train_images: true +save_val_images: true diff --git a/motion-gan-pipeline/ImageToImage/datasets/__init__.py b/motion-gan-pipeline/ImageToImage/datasets/__init__.py new file mode 100644 index 0000000..4dbe0fa --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/datasets/__init__.py @@ -0,0 +1 @@ +from .base import build_dataloader, build_dataset \ No newline at end of file diff --git a/motion-gan-pipeline/ImageToImage/datasets/base.py b/motion-gan-pipeline/ImageToImage/datasets/base.py new file mode 100644 index 0000000..886e8e5 --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/datasets/base.py @@ -0,0 +1,74 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import torch +from torch.utils.data import DataLoader +from torchvision import transforms +from .custom_datasets import CustomDataset, OpticalFlowDataset +from torchvision.transforms.functional import InterpolationMode + + +def build_dataset(args, mode): + + if args.dataset_type == "CustomDataset": + transform_input = transforms.Compose([transforms.ToTensor(), transforms.Resize([args.height, args.width]),]) # transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),]) + transform_output = transforms.Compose([transforms.ToTensor(), transforms.Resize([args.height, args.width]),]) + transform_label_map = transforms.Compose([transforms.Resize([args.height, args.width], interpolation=InterpolationMode.NEAREST),]) + if mode=='train': + input_dir = args.input_train_root_dir + output_dir = args.output_train_root_dir + label_dir = args.label_train_root_dir + elif mode=='val': + input_dir = args.input_val_root_dir + output_dir = args.output_val_root_dir + label_dir = args.label_val_root_dir + elif mode=='test': + input_dir = args.input_test_root_dir + output_dir = None + label_dir = None + + dataset = CustomDataset(args = args, + input_root_dir=input_dir, + output_root_dir=output_dir, + label_root_dir=label_dir, + transform_input=transform_input, + transform_output=transform_output, + transform_label_map=transform_label_map, + mode=mode) + + elif args.dataset_type == "OpticalFlowDataset": + transform_input = transforms.Compose([transforms.ToTensor(), transforms.Resize([args.height, args.width]),]) # transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),]) + transform_output = transforms.Compose([transforms.ToTensor(), transforms.Resize([args.height, args.width]),]) + + if mode=='train': + input_dir = args.input_train_root_dir + output_dir = args.output_train_root_dir + flow_dir = args.flow_train_root_dir + elif mode=='val': + input_dir = args.input_val_root_dir + output_dir = args.output_val_root_dir + flow_dir = args.flow_val_root_dir + elif mode=='test': + input_dir = args.input_test_root_dir + output_dir = None + flow_dir = None + + dataset = OpticalFlowDataset(args = args, + input_root_dir=input_dir, + flow_dir=flow_dir, + output_root_dir=output_dir, + transform_input=transform_input, + transform_output=transform_output, + mode=mode) + else: + raise NotImplementedError() + + return dataset + + +def build_dataloader(args, mode, shuffle): + + dataset = build_dataset(args, mode) + dataloader = DataLoader(dataset, batch_size=args.batch_size, shuffle=shuffle, num_workers=args.num_workers, pin_memory=False, drop_last=True) + + return dataloader diff --git a/motion-gan-pipeline/ImageToImage/datasets/custom_datasets.py b/motion-gan-pipeline/ImageToImage/datasets/custom_datasets.py new file mode 100644 index 0000000..1510007 --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/datasets/custom_datasets.py @@ -0,0 +1,200 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from logging import raiseExceptions +import torch +from torch.utils.data import Dataset +from PIL import Image +import os +from torchvision import transforms +import numpy as np + +def readFlow(fn): + """ Read .flo file in Middlebury format""" + # Code adapted from: + # http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy + + # WARNING: this will work on little-endian architectures (eg Intel x86) only! + # print 'fn = %s'%(fn) + with open(fn, 'rb') as f: + magic = np.fromfile(f, np.float32, count=1) + if 202021.25 != magic: + print('Magic number incorrect. Invalid .flo file') + return None + else: + w = np.fromfile(f, np.int32, count=1) + h = np.fromfile(f, np.int32, count=1) + # print 'Reading %d x %d flo file\n' % (w, h) + data = np.fromfile(f, np.float32, count=2*int(w)*int(h)) + # Reshape data into 3D array (columns, rows, bands) + # The reshape here is for visualization, the original code is (w,h,2) + return np.resize(data, (int(h), int(w), 2)) + +# Example from https://pytorch.org/tutorials/beginner/data_loading_tutorial.html +class CustomDataset(Dataset): + def __init__(self, args, input_root_dir, output_root_dir, label_root_dir, transform_input=None, transform_output=None, transform_label_map=None, mode='train'): + """ + Args: + input_root_dir (string): Directory with all the input images. + output_root_dir (string): Directory with all the output images. + transform (callable, optional): Optional transform to be applied + on a sample. + """ + self.use_label_maps = args.use_label_maps + self.mode = mode + self.input_root_dir = input_root_dir + self.input_img_name_list = sorted(os.listdir(self.input_root_dir)) + self.transform_input = transform_input + if mode=='train' or mode=='val': + self.output_root_dir = output_root_dir + self.output_img_name_list = sorted(os.listdir(self.output_root_dir)) + self.transform_output = transform_output + if args.use_label_maps: + self.transform_label_map = transform_label_map + self.label_root_dir = label_root_dir + self.label_img_name_list = sorted(os.listdir(self.label_root_dir)) + + def convert_to_rgb(self, image): + if image.mode == 'RGBA': + image.load() + image_new = Image.new("RGB", image.size, (255, 255, 255)) + image_new.paste(image, mask=image.split()[3]) + elif image.mode == 'RGB' or image.mode == 'L': + image_new = image + else: + raiseExceptions('Non-compatible image format!') + return image_new + + def __len__(self): + return len(os.listdir(self.input_root_dir)) + + def __getitem__(self, idx): + + input_img_name = os.path.join(self.input_root_dir, + self.input_img_name_list[idx]) + input_image = Image.open(input_img_name) + input_image = self.convert_to_rgb(input_image) + + if self.mode == 'train' or self.mode == 'val': + output_img_name = os.path.join(self.output_root_dir, + self.output_img_name_list[idx]) + output_image = Image.open(output_img_name) + output_image = self.convert_to_rgb(output_image) + + if self.use_label_maps: + label_img_name = os.path.join(self.label_root_dir, + self.label_img_name_list[idx]) + label_image = Image.open(label_img_name) + label_image = torch.unsqueeze(torch.tensor(np.array(self.convert_to_rgb(label_image))), 0) + + + sample = {'input_image': self.transform_input(input_image), + 'output_image': self.transform_output(output_image), + 'label_image': self.transform_label_map(label_image), + 'name': self.input_img_name_list[idx]} + + else: + sample = {'input_image': self.transform_input(input_image), + 'output_image': self.transform_output(output_image), + 'name': self.input_img_name_list[idx]} + + else: + sample = {'input_image': self.transform_input(input_image), 'name': self.input_img_name_list[idx]} + + return sample + +class OpticalFlowDataset(Dataset): + def __init__(self, args, input_root_dir, flow_dir, output_root_dir, transform_input=None, transform_output=None, mode='train'): + """ + Args: + input_root_dir (string): Directory with all the input images. + flow_dir(string): Directory with all the computed optical flow. + output_root_dir (string): Directory with all the output images. + transform (callable, optional): Optional transform to be applied + on a sample. + """ + self.use_label_maps = args.use_label_maps + self.mode = mode + self.input_root_dir = input_root_dir + self.input_img_name_list = sorted(os.listdir(self.input_root_dir)) + self.transform_input = transform_input + + self.optical_steps = args.optical_steps + + if mode=='train' or mode=='val': + self.output_root_dir = output_root_dir + self.output_img_name_list = sorted(os.listdir(self.output_root_dir)) + self.transform_output = transform_output + + self.flow_dir = flow_dir + self.flow_name_list = sorted(os.listdir(self.flow_dir)) + + + def convert_to_rgb(self, image): + if image.mode == 'RGBA': + image.load() + image_new = Image.new("RGB", image.size, (255, 255, 255)) + image_new.paste(image, mask=image.split()[3]) + elif image.mode == 'RGB' or image.mode == 'L': + image_new = image + else: + raiseExceptions('Non-compatible image format!') + return image_new + + def __len__(self): + return len(os.listdir(self.input_root_dir)) + + def __getitem__(self, idx): + + input_img_name = os.path.join(self.input_root_dir, + self.input_img_name_list[idx]) + input_image = Image.open(input_img_name) + input_image = self.convert_to_rgb(input_image) + + if self.mode == 'train' or self.mode == 'val': + # Load output image + output_img_name = os.path.join(self.output_root_dir, + self.output_img_name_list[idx]) + output_image = Image.open(output_img_name) + output_image = self.convert_to_rgb(output_image) + + # Load previous images & Optical Flows + flow = np.zeros_like(readFlow(os.path.join(self.flow_dir, self.flow_name_list[0]))) + + flows = [] + prev_imgs = [] + + if idx > self.optical_steps: + + for i in range(self.optical_steps): + # Load prev image + prev_img_name = os.path.join(self.output_root_dir, + self.output_img_name_list[idx - i]) + prev_img = Image.open(prev_img_name) + prev_img = self.convert_to_rgb(prev_img) + prev_imgs.append(self.transform_output(prev_img)) + + # load flow + flow_name = os.path.join(self.flow_dir, + self.flow_name_list[idx - i]) + tmp_flow = readFlow(flow_name) + flow += tmp_flow + flows.append(self.transform_output(flow)) + + else: + prev_img_name = os.path.join(self.output_root_dir, self.output_img_name_list[idx]) + prev_img = Image.open(prev_img_name) + prev_img = self.convert_to_rgb(prev_img) + flows = [self.transform_output(flow)] * self.optical_steps + prev_imgs = [self.transform_output(prev_img)] * self.optical_steps + + sample = {'input_image': self.transform_input(input_image), + 'output_image': self.transform_output(output_image), + 'prev_images': prev_imgs, + 'flows': flows, + 'name': self.input_img_name_list[idx]} + + else: + sample = {'input_image': self.transform_input(input_image), 'name': self.input_img_name_list[idx]} + + return sample diff --git a/motion-gan-pipeline/ImageToImage/generate_images.py b/motion-gan-pipeline/ImageToImage/generate_images.py new file mode 100644 index 0000000..51bbc1e --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/generate_images.py @@ -0,0 +1,153 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from email.policy import default +from PIL import Image +import torch +import PIL +import configargparse +from pathlib import Path +import os +from tqdm import tqdm +import numpy as np + +from models.unet import UNet +from datasets.base import build_dataloader +from utils.utils import create_image_pair, save_image_list + +import warnings +warnings.filterwarnings("ignore") + +def config_parser(): + parser = configargparse.ArgumentParser() + # config file + parser.add_argument('-c', '--my-config', is_config_file=True, help='config file path', default='config/test.yml') + # dataloader options + parser.add_argument("--mode", type=str, default='test', help="test mode has if no ground truth data available, val otherwise") + parser.add_argument("--batch_size", type=int, default=16, help="size of the batches") + parser.add_argument("--num_workers", type=int, default=0, help="number of workers to use during batch generation") + parser.add_argument("--num_input_channels", type=int, default=3, help="number of input image channels") + parser.add_argument("--num_output_channels", type=int, default=3, help="number of output image channels") + parser.add_argument("--use_label_maps", action='store_true', help="choose if to use label maps for discriminator") + + # dataset options + parser.add_argument("--dataset_type", type=str, help="options: CustomDataset", default='CustomDataset') + parser.add_argument("--input_test_root_dir", type=str, help="Path to test input images", default='./data/input_images_test') + parser.add_argument("--input_val_root_dir", type=str, help="Path to val input images", default='./data/input_images_val') + parser.add_argument("--label_val_root_dir", type=str, help="Path to val label images", default='./data/label_images_val') + parser.add_argument("--output_val_root_dir", type=str, help="Path to val output images", default='./data/output_images_val') + parser.add_argument("--width", type=int, default=640, help="width") + parser.add_argument("--height", type=int, default=360, help="height") + parser.add_argument("--metric_names", nargs="*", type=str, default=['mean_absolute_error'], help="names of metrics to be logged") + parser.add_argument("--metric_weights", nargs="*", type=float, default=[1.0], help="weights assigned to metrics in the order") + # logging/saving options + parser.add_argument("--video_name", type=str, help='name of the reference video', default='Clara') + parser.add_argument("--out_dir", type=str, help='directory in which to save result images', default='./results/') + # dataroot + parser.add_argument("--dataroot", required=True, type=str, help='input data dataroot', default='') + parser.add_argument("--checkpoint_dir", required=True, type=str, help='path to checkpoint folder', default='../checkpoints/') + return parser + + +def load_model(network, args, device): + if Path(args.load_path).exists(): + checkpoint = torch.load(args.load_path, map_location=device) + network.load_state_dict(checkpoint['model_state_dict']) + try: + args.continue_from_epoch = checkpoint['epoch']+1 + print("-> loaded model %s (epoch: %d)"%(args.load_path, args.continue_from_epoch)) + + except TypeError: + args.continue_from_epoch = None + print("-> loaded model %s (epoch: final)"%(args.load_path)) + + +if __name__=='__main__': + + parser = config_parser() + args = parser.parse_args() + + # Overwrite image dimentions + edge_path = os.path.join(args.dataroot, args.video_name, 'edges') + # img_size = Image.open(os.path.join(edge_path, os.listdir(edge_path)[0])).size + try: + img_size = np.load(os.path.join(args.dataroot, args.video_name, 'img_size.npy')) + except FileNotFoundError: + img_size = Image.open(os.path.join(edge_path, os.listdir(edge_path)[0])).size + + args.width, args.height = img_size + ratio = args.height / args.width + + print('Img Ratio: ', ratio) + + # resize for training quickly + if args.width >= args.height: + args.width = 720 + args.height = int(args.width * ratio) + + else: + args.height = 720 + args.width = int(args.height / ratio) + + os.makedirs(os.path.join(args.checkpoint_dir, args.video_name), exist_ok=True) + args.load_path = os.path.join(args.checkpoint_dir, args.video_name, 'latest_GAN_model.pt') + + os.makedirs(args.out_dir, exist_ok=True) + + # Normal training + temporal = True + optical = False + + if not os.path.isfile(args.load_path): + print('Checkpoint not found!') + chkp_dir = os.path.join(args.checkpoint_dir, args.video_name) + data_input_path = os.path.join(args.dataroot, args.video_name) + + if temporal: + os.system('python train_temporal.py -c config/train_temporal.yml --checkpoints_dir ' + chkp_dir + + ' --input_train_root_dir ' + data_input_path + + '/edges --output_train_root_dir ' + data_input_path + + '/cropped --height ' + str(args.height) + ' --width ' + str(args.width) + ' --skip_log') + + elif optical: + os.system('python train_optical.py -c config/train_optical.yml --checkpoints_dir ' + chkp_dir + + ' --input_train_root_dir ' + data_input_path + + '/edges --output_train_root_dir ' + data_input_path + + '/cropped --flow_train_root_dir ' + data_input_path + + '/opticalflow --height ' + str(args.height) + ' --width ' + str(args.width)) + + else: + os.system('python train.py -c config/train.yml --checkpoints_dir ' + chkp_dir + + ' --input_train_root_dir ' + data_input_path + + '/edges --output_train_root_dir ' + data_input_path + + '/cropped' + ' --height ' + str(args.height) + ' --width ' + str(args.width)) + + + # check device + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print("Running code on", device) + + # initialize the dataloaders + test_loader = build_dataloader(args, mode=args.mode, shuffle=False) + + # build the network + network = UNet(args).to(device) + + load_model(network, args, device) + network.eval() + + # run inference + with torch.no_grad(): + for batch_idx, data in enumerate(tqdm(test_loader)): + + inputs = data['input_image'].to(device) + names = data['name'] + + prediction = network(inputs) + + images = create_image_pair([inputs, prediction]) + images_output = create_image_pair([prediction]) + + # save_image_list(images, args.results_dir, names) + save_image_list(images_output, args.out_dir, names) + diff --git a/motion-gan-pipeline/ImageToImage/losses/__init__.py b/motion-gan-pipeline/ImageToImage/losses/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/motion-gan-pipeline/ImageToImage/losses/gan_loss.py b/motion-gan-pipeline/ImageToImage/losses/gan_loss.py new file mode 100644 index 0000000..5f8f30b --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/losses/gan_loss.py @@ -0,0 +1,18 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import torch +import torch.nn as nn + + +class GANLoss(nn.Module): + def __init__(self): + super(GANLoss, self).__init__() + self.loss = nn.BCEWithLogitsLoss() + + def forward(self, y_pred, real_image_flag): + if real_image_flag: + target = torch.ones_like(y_pred) + else: + target = torch.zeros_like(y_pred) + return self.loss(y_pred, target) diff --git a/motion-gan-pipeline/ImageToImage/losses/loss.py b/motion-gan-pipeline/ImageToImage/losses/loss.py new file mode 100644 index 0000000..d2b1bce --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/losses/loss.py @@ -0,0 +1,32 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import torch +import torch.nn as nn +from .perceptual_loss_vgg import PerceptualLossVGG +from .gan_loss import GANLoss + +class Loss(nn.Module): + def __init__(self, args, device): + super().__init__() + available_losses = { + 'perceptual_loss_vgg': lambda: PerceptualLossVGG(layers_weights=[1.0/32,1.0/16,1.0/8,1.0/4,1.0]), #[1.0/32,1.0/16,1.0/8,1.0/4,1.0] + 'l1_perceptual_loss': lambda: nn.L1Loss() + } + self.loss_names = args.loss_names + self.loss_weights = args.loss_weights + self.loss_list = [] + assert(len(self.loss_names)==len(self.loss_weights)) + for loss_name in self.loss_names: + if loss_name in available_losses: + self.loss_list.append(available_losses[loss_name]().to(device)) + + def eval(self): + for loss in self.loss_list: + loss.eval() + + def forward(self, y_pred, y_gt): + loss = 0 + for k, loss_fn in enumerate(self.loss_list): + loss += self.loss_weights[k]*loss_fn(y_pred, y_gt) + return loss diff --git a/motion-gan-pipeline/ImageToImage/losses/perceptual_loss_vgg.py b/motion-gan-pipeline/ImageToImage/losses/perceptual_loss_vgg.py new file mode 100644 index 0000000..3001641 --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/losses/perceptual_loss_vgg.py @@ -0,0 +1,69 @@ +# extract perceptual features from the pre-trained Vgg16 network +# these features are used for the perceptual loss function (https://arxiv.org/abs/1603.08155) +# +# based on the code snippet of W. Falcon: +# https://gist.github.com/williamFalcon/1ee773c159ff5d76d47518653369d890 + +import torch +import torch.nn as nn +from torchvision import models + +class Vgg16Features(nn.Module): + + def __init__(self, + requires_grad=False, + layers_weights=None): + super(Vgg16Features, self).__init__() + if layers_weights is None: + self.layers_weights = [1.0, 1.0 / 16, 1.0 / 8, 1.0 / 4, 1.0] + else: + self.layers_weights = layers_weights + + vgg_pretrained_features = models.vgg16(pretrained=True).features + self.slice1 = nn.Sequential() + self.slice2 = nn.Sequential() + self.slice3 = nn.Sequential() + self.slice4 = nn.Sequential() + for x in range(4): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(4, 9): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(9, 16): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(16, 23): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, x): + + h_0 = x.flatten(start_dim=1) + h = self.slice1(x) + h_relu1_2 = h.flatten(start_dim=1) + h = self.slice2(h) + h_relu2_2 = h.flatten(start_dim=1) + h = self.slice3(h) + h_relu3_3 = h.flatten(start_dim=1) + h = self.slice4(h) + h_relu4_3 = h.flatten(start_dim=1) + + h = torch.cat([self.layers_weights[0] * h_0, + self.layers_weights[1] * h_relu1_2, + self.layers_weights[2] * h_relu2_2, + self.layers_weights[3] * h_relu3_3, + self.layers_weights[4] * h_relu4_3], 1) + + return h + +class PerceptualLossVGG(nn.Module): + + def __init__(self, layers_weights=None): + super().__init__() + self.vgg_features = Vgg16Features(layers_weights=layers_weights) + self.loss = nn.L1Loss() + + def forward(self, y_pred, y_gt): + y_pred_vgg_features = self.vgg_features(y_pred) + y_gt_vgg_features = self.vgg_features(y_gt).detach() + return self.loss(y_pred_vgg_features, y_gt_vgg_features) \ No newline at end of file diff --git a/motion-gan-pipeline/ImageToImage/metrics/__init__.py b/motion-gan-pipeline/ImageToImage/metrics/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/motion-gan-pipeline/ImageToImage/metrics/metrics.py b/motion-gan-pipeline/ImageToImage/metrics/metrics.py new file mode 100644 index 0000000..11e6f5c --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/metrics/metrics.py @@ -0,0 +1,66 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import torch +import torch.nn as nn +from ignite.metrics import MeanAbsoluteError, MeanSquaredError, SSIM, PSNR, FID + +from pytorch_fid.inception import InceptionV3 + +class Metrics(): + def __init__(self, args, device): + + # FID score model + dims = 2048 + block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] + model = InceptionV3([block_idx]).to(device) + wrapper_model = WrapperInceptionV3(model) + wrapper_model.eval() + + available_metrics = { + 'mean_absolute_error': lambda: MeanAbsoluteError(device=device), + 'mean_squared_error': lambda: MeanSquaredError(device=device), + 'structual_similarity_index_measure': lambda: SSIM(data_range=1.0, device=device), + 'peak_signal_to_noise_ratio': lambda: PSNR(data_range=1.0, device=device), + 'frechet_inception_distance': lambda: FID(num_features=dims, feature_extractor=wrapper_model, device=device) + } + self.metric_names = args.metric_names + self.metric_weights = args.metric_weights + self.metric_list = [] + for metric_name in self.metric_names: + if metric_name in available_metrics: + self.metric_list.append(available_metrics[metric_name]()) + + def update(self, y_pred, y_gt): + for metric_fn in self.metric_list: + metric_fn.update((y_pred, y_gt)) + + def reset(self): + for metric_fn in self.metric_list: + metric_fn.reset() + + def compute(self): + results = {} + metric_combined = 0 + for idx, (metric_fn, metric_name) in enumerate(zip(self.metric_list, self.metric_names)): + if metric_name == 'peak_signal_to_noise_ratio': + results[metric_name] = metric_fn.compute().item() + else: + results[metric_name] = metric_fn.compute() + metric_combined += self.metric_weights[idx]*results[metric_name] + return results, metric_combined + + +# wrapper class as feature_extractor +class WrapperInceptionV3(nn.Module): + + def __init__(self, fid_incv3): + super().__init__() + self.fid_incv3 = fid_incv3 + + @torch.no_grad() + def forward(self, x): + y = self.fid_incv3(x) + y = y[0] + y = y[:, :, 0, 0] + return y diff --git a/motion-gan-pipeline/ImageToImage/models/__init__.py b/motion-gan-pipeline/ImageToImage/models/__init__.py new file mode 100644 index 0000000..67cafc4 --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/models/__init__.py @@ -0,0 +1 @@ +from .unet import UNet diff --git a/motion-gan-pipeline/ImageToImage/models/patch_gan_discriminator.py b/motion-gan-pipeline/ImageToImage/models/patch_gan_discriminator.py new file mode 100644 index 0000000..e76953d --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/models/patch_gan_discriminator.py @@ -0,0 +1,50 @@ +import torch +import torch.nn as nn +from torchvision.utils import save_image + + +class NLayerDiscriminator(nn.Module): + """Defines a PatchGAN discriminator""" + + def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d): + """Construct a PatchGAN discriminator + Parameters: + input_nc (int) -- the number of channels in input images + ndf (int) -- the number of filters in the last conv layer + n_layers (int) -- the number of conv layers in the discriminator + norm_layer -- normalization layer + """ + super(NLayerDiscriminator, self).__init__() # no need to use bias as BatchNorm2d has affine parameters + + use_bias = norm_layer == nn.InstanceNorm2d + + kw = 4 + padw = 1 + sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] + nf_mult = 1 + nf_mult_prev = 1 + for n in range(1, n_layers): # gradually increase the number of filters + nf_mult_prev = nf_mult + nf_mult = min(2 ** n, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + nf_mult_prev = nf_mult + nf_mult = min(2 ** n_layers, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] # output 1 channel prediction map + self.model = nn.Sequential(*sequence) + + def forward(self, input_img, label_map=None): + """Standard forward.""" + if not label_map==None: + input_img = torch.where((label_map!=4) & (label_map!=5) & (label_map!=10) & (label_map!=11) & (label_map!=12) & (label_map!=1) & (label_map!=6) & (label_map!=7) & (label_map!=2), torch.ones_like(input_img), input_img) + return self.model(input_img) \ No newline at end of file diff --git a/motion-gan-pipeline/ImageToImage/models/unet.py b/motion-gan-pipeline/ImageToImage/models/unet.py new file mode 100644 index 0000000..47ce55b --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/models/unet.py @@ -0,0 +1,36 @@ +""" Full assembly of the parts to form the complete network """ + +from .unet_parts import * + + +class UNet(nn.Module): + def __init__(self, args, bilinear=False): + super().__init__() + self.n_channels_in = args.num_input_channels + self.n_channels_out = args.num_output_channels + self.bilinear = bilinear + self.inc = DoubleConv(self.n_channels_in, 64) + self.down1 = Down(64, 128) + self.down2 = Down(128, 256) + self.down3 = Down(256, 512) + factor = 2 if bilinear else 1 + self.down4 = Down(512, 1024 // factor) + self.up1 = Up(1024, 512 // factor, bilinear) + self.up2 = Up(512, 256 // factor, bilinear) + self.up3 = Up(256, 128 // factor, bilinear) + self.up4 = Up(128, 64, bilinear) + self.outc = OutConv(64, self.n_channels_out) + + def forward(self, x): + x1 = self.inc(x) + x2 = self.down1(x1) + x3 = self.down2(x2) + x4 = self.down3(x3) + x5 = self.down4(x4) + x = self.up1(x5, x4) + x = self.up2(x, x3) + x = self.up3(x, x2) + x = self.up4(x, x1) + x_out = self.outc(x) + return x_out + diff --git a/motion-gan-pipeline/ImageToImage/models/unet_parts.py b/motion-gan-pipeline/ImageToImage/models/unet_parts.py new file mode 100644 index 0000000..e7323e6 --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/models/unet_parts.py @@ -0,0 +1,80 @@ +""" Parts of the U-Net model """ + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class DoubleConv(nn.Module): + """(convolution => [BN] => ReLU) * 2""" + + def __init__(self, in_channels, out_channels, mid_channels=None): + super().__init__() + if not mid_channels: + mid_channels = out_channels + self.double_conv = nn.Sequential( + nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(mid_channels), + nn.ReLU(inplace=True), + nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True) + ) + + def forward(self, x): + return self.double_conv(x) + + +class Down(nn.Module): + """Downscaling with maxpool then double conv""" + + def __init__(self, in_channels, out_channels): + super().__init__() + self.maxpool_conv = nn.Sequential( + nn.MaxPool2d(2), + DoubleConv(in_channels, out_channels) + ) + + def forward(self, x): + return self.maxpool_conv(x) + + +class Up(nn.Module): + """Upscaling then double conv""" + + def __init__(self, in_channels, out_channels, bilinear=True): + super().__init__() + + # if bilinear, use the normal convolutions to reduce the number of channels + if bilinear: + self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) + self.conv = DoubleConv(in_channels, out_channels, in_channels // 2) + else: + self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2) + self.conv = DoubleConv(in_channels, out_channels) + + def forward(self, x1, x2): + x1 = self.up(x1) + # input is CHW + diffY = x2.size()[2] - x1.size()[2] + diffX = x2.size()[3] - x1.size()[3] + + x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, + diffY // 2, diffY - diffY // 2]) + # if you have padding issues, see + # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a + # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd + x = torch.cat([x2, x1], dim=1) + return self.conv(x) + + +class OutConv(nn.Module): + def __init__(self, in_channels, out_channels): + super().__init__() + self.conv = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1), + nn.Sigmoid(), + ) + + def forward(self, x): + return self.conv(x) diff --git a/motion-gan-pipeline/ImageToImage/models/video_discriminator.py b/motion-gan-pipeline/ImageToImage/models/video_discriminator.py new file mode 100644 index 0000000..46acfec --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/models/video_discriminator.py @@ -0,0 +1,80 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.autograd import Variable + +from torch.nn.utils import spectral_norm +from torch.nn.init import xavier_uniform_ +import torch.nn.init as init + + +def init_weights(m): + if type(m) == nn.Linear or type(m) == nn.Conv2d: + xavier_uniform_(m.weight) + m.bias.data.fill_(0.) + +def snconv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True): + return spectral_norm(nn.Conv2d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=bias)) + +def snconv3d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True): + return spectral_norm(nn.Conv3d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, + stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)) + +def snlinear(in_features, out_features, bias=True): + return spectral_norm(nn.Linear(in_features=in_features, out_features=out_features, bias=bias)) + +def sn_embedding(num_embeddings, embedding_dim): + return spectral_norm(nn.Embedding(num_embeddings=num_embeddings, embedding_dim=embedding_dim)) + + +class Discriminator_3D(nn.Module): + def __init__(self, d_conv_dim=96, T=8): + super(Discriminator_3D, self).__init__() + self.main = nn.Sequential( + # input is (nc) x T x 96 x 96 + snconv3d(3, d_conv_dim, 4, 2, 1, bias=False), + nn.LeakyReLU(0.2, inplace=True), + # state size. (ndf) x T/2 x 48 x 48 + snconv3d(d_conv_dim, d_conv_dim * 2, 4, 2, 1, bias=False), + nn.BatchNorm3d(d_conv_dim * 2), + nn.LeakyReLU(0.2, inplace=True), + # state size. (ndf*2) x T/4 x 24 x 24 + snconv3d(d_conv_dim * 2, d_conv_dim * 4, 4, 2, 1, bias=False), + nn.BatchNorm3d(d_conv_dim * 4), + nn.LeakyReLU(0.2, inplace=True), + # state size. (ndf*4) x T/8 x 12 x 12 + + #TODO: fix this because depends on batchsize for us + # snconv3d(d_conv_dim * 4, d_conv_dim * 8, 4, 2, 1, bias=False), + # nn.BatchNorm3d(d_conv_dim * 8), + # nn.LeakyReLU(0.2, inplace=True), + # # state size. (ndf*8) x T/16 x 6 x 6 + ) + self.linear = snlinear(d_conv_dim * 4, 1) # was 8 + self.final_conv = snconv2d(d_conv_dim * 4, 1, 4, 1, 1, bias=False) + # self.embed = sn_embedding(num_classes, d_conv_dim*8) + # # Weight init + # self.apply(init_weights) + # xavier_uniform_(self.embed.weight) + + def forward(self, input): + input = input.transpose(2,1) + output = self.main(input) + output = output.squeeze(2) + output_conv = self.final_conv(output) + # output = torch.sum(output, dim=[3,4]).view(-1, output.size(1)) + # output_linear = self.linear(output) + # y = class_id.long() + # embed = self.embed(y) + # prod = (output * embed).sum(1) + return output_conv diff --git a/motion-gan-pipeline/ImageToImage/train.py b/motion-gan-pipeline/ImageToImage/train.py new file mode 100644 index 0000000..2c681c7 --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/train.py @@ -0,0 +1,298 @@ +from email.policy import default +import os +import configargparse +import torch +import torch.nn as nn +from pathlib import Path +import wandb +from datasets.base import build_dataloader +from models.unet import UNet +from models.patch_gan_discriminator import NLayerDiscriminator +from models.video_discriminator import Discriminator_3D +from losses.loss import Loss +from losses.gan_loss import GANLoss +from utils.utils import create_image_pair +from metrics.metrics import Metrics + +def config_parser(): + parser = configargparse.ArgumentParser() + # config file + parser.add_argument('-c', '--my-config', required=True, is_config_file=True, help='config file path') + # training and model options + parser.add_argument("--optimizer", type=str, help='choose optimizer type', default='adam') + parser.add_argument("--num_epochs", type=int, default=61, help="number of epochs of training") + parser.add_argument("--num_steps", type=int, default=25000, help="number of steps of training") + parser.add_argument("--continue_from_epoch", type=int, default=0, help="Continue training from epoch (default=0)") + parser.add_argument("--batch_size", type=int, default=16, help="size of the batches") + parser.add_argument("--lr", type=float, default=0.001, help="learning rate") # 0.00001 + parser.add_argument("--lr_decay_steps", type=int, default=10, help="learning rate decay steps in epochs") + parser.add_argument("--lr_disc", type=float, default=0.001, help="discriminator learning rate") + parser.add_argument("--num_workers", type=int, default=0, help="number of workers to use during batch generation") + parser.add_argument("--num_input_channels", type=int, default=3, help="number of input image channels") + parser.add_argument("--num_output_channels", type=int, default=3, help="number of output image channels") + parser.add_argument("--loss_names", nargs="*", type=str, default=['perceptual_loss_vgg'], help="names of losses used in training") + parser.add_argument("--loss_weights", nargs="*", type=float, default=[1.0], help="weights assigned to losses in the order") + parser.add_argument("--metric_names", nargs="*", type=str, default=['mean_absolute_error'], help="names of metrics to be logged") + parser.add_argument("--metric_weights", nargs="*", type=float, default=[1.0], help="weights assigned to metrics in the order") + parser.add_argument("--use_discriminator", action='store_true', help="choose if to use discriminator network") + parser.add_argument("--use_label_maps", action='store_true', help="choose if to use label maps for discriminator") + # dataset options + parser.add_argument("--dataset_type", type=str, help="options: CustomDataset", default='CustomDataset') + parser.add_argument("--input_train_root_dir", type=str, help="Path to training input images", default='./data/input_images_train') + parser.add_argument("--output_train_root_dir", type=str, help="Path to training output images", default='./data/output_images_train') + parser.add_argument("--label_train_root_dir", type=str, help="Path to training label images", default='./data/label_images_train') + parser.add_argument("--input_val_root_dir", type=str, help="Path to val input images", default='./data/input_images_val') + parser.add_argument("--output_val_root_dir", type=str, help="Path to val output images", default='./data/output_images_val') + parser.add_argument("--label_val_root_dir", type=str, help="Path to val label images", default='./data/label_images_val') + parser.add_argument("--width", type=int, default=640, help="width") + parser.add_argument("--height", type=int, default=360, help="height") + # logging/saving options + parser.add_argument("--checkpoints_dir", type=str, help='specify the directory to save the model', default='./chkpts/experiment/') + parser.add_argument("--save_every", type=int, help='save model every # epochs', default=5) + parser.add_argument("--log_every", type=int, help='save logs every # batches', default=100) + parser.add_argument("--save_val_images", action='store_true', help='choose if you want to save validation images') + parser.add_argument("--wandb_dir", type=str, help="directory where to save wandb data locally", default='./wandb') + return parser + + +def save_model(network, epoch, optimizer, loss, best_val_loss, args, discriminator=None, optimizer_disc=None, best_model=False): + print('Saving model epoch: ', epoch) + if not Path(args.checkpoints_dir).exists(): + Path(args.checkpoints_dir).mkdir(exist_ok=True) + + save_dir = Path(args.checkpoints_dir, 'latest_GAN.pt') + + if args.use_discriminator: + torch.save({'epoch': epoch, + 'model_state_dict': network.state_dict(), + 'discriminator_state_dict': discriminator.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'optimizer_disc_state_dict': optimizer_disc.state_dict(), + 'loss': loss, + 'best_val_loss': best_val_loss}, + save_dir) + else: + torch.save({'epoch': epoch, + 'model_state_dict': network.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'loss': loss, + 'best_val_loss': best_val_loss}, + save_dir) + + return save_dir + + +def print_progress(loss, best_loss = None, mode='train'): + print(mode + ' loss: ', loss) + if best_loss: + print('best ' + mode + ' loss: ', best_loss) + + +def set_requires_grad(network, requires_grad_flag): + for param in network.parameters(): + param.requires_grad = requires_grad_flag + + +def init_weights(net, init_type='normal', init_gain=0.02): + """Initialize network weights. + Parameters: + net (network) -- network to be initialized + init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal + init_gain (float) -- scaling factor for normal, xavier and orthogonal. + We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might + work better for some applications. Feel free to try yourself. + """ + def init_func(m): # define the initialization function + classname = m.__class__.__name__ + if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): + if init_type == 'normal': + nn.init.normal_(m.weight.data, 0.0, init_gain) + elif init_type == 'xavier': + nn.init.xavier_normal_(m.weight.data, gain=init_gain) + elif init_type == 'kaiming': + nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') + elif init_type == 'orthogonal': + nn.init.orthogonal_(m.weight.data, gain=init_gain) + else: + raise NotImplementedError('initialization method [%s] is not implemented' % init_type) + if hasattr(m, 'bias') and m.bias is not None: + nn.init.constant_(m.bias.data, 0.0) + elif classname.find('BatchNorm2d') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies. + nn.init.normal_(m.weight.data, 1.0, init_gain) + nn.init.constant_(m.bias.data, 0.0) + + print('initialize network with %s' % init_type) + net.apply(init_func) + + +if __name__=='__main__': + + parser = config_parser() + args = parser.parse_args() + + # check device + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + print("Running code on", device) + + # prepare data + train_loader = build_dataloader(args, mode='train', shuffle=True) + + # build the network + network = UNet(args).to(device) + init_weights(network) + + # make the networks multi-gpu available + if torch.cuda.device_count() > 1: + print("There are", torch.cuda.device_count(), "gpus available.") + network = nn.DataParallel(network) + + # Build loss function + loss_fn = Loss(args, device).to(device) + # loss_fn.eval() # make sure networks used in calculating loss (e.g. VGG) are in eval mode (because of batch normalization and dropout layers) + + #Build metrics functions + metrics_fn = Metrics(args, device) + metrics_fn.reset() + + # Build an optimizer + if args.optimizer == 'adam': + optimizer = torch.optim.Adam(network.parameters(), lr=args.lr, betas=(0.5, 0.999)) + elif args.optimizer == 'sgd': + optimizer = torch.optim.SGD(network.parameters(), lr=args.lr) + else: + raise NotImplementedError() + + # Learning rate decay + lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.lr_decay_steps, gamma=0.1) + + # Print number of parameters in the generator network + pytorch_generator_params = sum(p.numel() for p in network.parameters()) + print('Number of parameters in the generator network: ', pytorch_generator_params) + + # build discriminator if it is used + if args.use_discriminator: + discriminator = NLayerDiscriminator(input_nc=args.num_input_channels+args.num_output_channels, n_layers=3).to(device) + init_weights(discriminator) + set_requires_grad(discriminator, False) + loss_gan_fn = GANLoss().to(device) + if args.optimizer == 'adam': + optimizer_discriminator = torch.optim.Adam(discriminator.parameters(), lr=args.lr_disc, betas=(0.5, 0.999)) + elif args.optimizer == 'sgd': + optimizer_discriminator = torch.optim.SGD(discriminator.parameters(), lr=args.lr_disc) + pytorch_discriminator_params = sum(p.numel() for p in discriminator.parameters()) + print('Number of parameters in the discriminator network: ', pytorch_discriminator_params) + else: + discriminator = None + optimizer_discriminator = None + + best_val_loss = None + + # initialize wandb + wandb.init(project='GAN-video-synthesis', config=args, tags=["demo"], job_type='train', dir=args.wandb_dir) + args = wandb.config + wandb.watch(network) + + # Start the training process + start_train_disc = -1 + step = 0 + for epoch in range(args.continue_from_epoch, args.num_epochs): + print('Training epoch: ', epoch) + + network.train() + if args.use_discriminator: + discriminator.train() + + for batch_idx, data in enumerate(train_loader): + + inputs = data["input_image"].to(device) + labels = data["output_image"].to(device) + if args.use_label_maps: + label_maps = data["label_image"].to(device) + else: + label_maps = None + + prediction = network(inputs) + + loss_perceptual = loss_fn(prediction, labels) + + if args.use_discriminator and epoch>start_train_disc: + + set_requires_grad(discriminator, True) + + real_pair = torch.cat((inputs, labels), 1) + pred_real = discriminator(real_pair, label_maps) + loss_real = loss_gan_fn(pred_real, real_image_flag=True) + + fake_pair = torch.cat((inputs, prediction.detach()), 1) + pred_fake = discriminator(fake_pair, label_maps) + loss_fake = loss_gan_fn(pred_fake, real_image_flag=False) + + loss_discriminator = (loss_fake + loss_real)*0.5 + + optimizer_discriminator.zero_grad() + loss_discriminator.backward() + optimizer_discriminator.step() + + set_requires_grad(discriminator, False) + + fake_pair = torch.cat((inputs, prediction), 1) + pred_fake = discriminator(fake_pair, label_maps) + + loss_gan = loss_gan_fn(pred_fake, real_image_flag=True) + + loss = loss_perceptual + loss_gan + else: + loss = loss_perceptual + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + step = step + 1 + + if batch_idx % args.log_every == 0: + if args.save_val_images: + image_concat = create_image_pair([inputs.detach(), prediction.detach(), labels.detach()]) + wandb_images = [wandb.Image(image) for image in image_concat] + if args.use_discriminator and epoch>start_train_disc: + wandb.log({'train loss perceptual': loss_perceptual, + 'train loss gan': loss_gan, + 'discriminator train loss': loss_discriminator.detach(), + 'train images predictions': wandb_images, + 'discriminator loss real': loss_real, + 'discriminator loss fake': loss_fake}, step=step) + wandb.log({'train_loss': loss.detach()}, step=step) + else: + wandb.log({'train images predictions': wandb_images}, step=step) + wandb.log({'train_loss': loss.detach()}, step=step) + else: + if args.use_discriminator and epoch>start_train_disc: + wandb.log({'train loss perceptual': loss_perceptual, + 'train loss gan': loss_gan, + 'discriminator train loss': loss_discriminator.detach(), + 'discriminator loss real': loss_real, + 'discriminator loss fake': loss_fake}, step=step) + wandb.log({'train_loss': loss.detach()}, step=step) + else: + wandb.log({'train_loss': loss.detach()}, step=step) + + if step == args.num_steps: + print(f'Breaking at {args.num_steps} steps.') + save_dir = save_model(network, epoch, optimizer, loss, best_val_loss, args, discriminator, optimizer_discriminator, best_model=False) + model_artifact = wandb.Artifact('last_model_train', type='model') + model_artifact.add_file(save_dir) + wandb.log_artifact(model_artifact) + exit() + + if epoch % args.save_every == 0: + save_dir = save_model(network, epoch, optimizer, loss, best_val_loss, args, discriminator, optimizer_discriminator, best_model=False) + model_artifact = wandb.Artifact('last_model_train', type='model') + model_artifact.add_file(save_dir) + wandb.log_artifact(model_artifact) + + lr_scheduler.step() + + save_dir = save_model(network, epoch, optimizer, loss, best_val_loss, args, discriminator, optimizer_discriminator, best_model=False) + model_artifact = wandb.Artifact('last_model_train', type='model') + model_artifact.add_file(save_dir) + wandb.log_artifact(model_artifact) \ No newline at end of file diff --git a/motion-gan-pipeline/ImageToImage/train_optical.py b/motion-gan-pipeline/ImageToImage/train_optical.py new file mode 100644 index 0000000..07d9004 --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/train_optical.py @@ -0,0 +1,324 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from email.policy import default +import os +import configargparse +import torch +import torch.nn as nn +from pathlib import Path +import wandb +from datasets.base import build_dataloader +from models.unet import UNet +from models.patch_gan_discriminator import NLayerDiscriminator +from models.video_discriminator import Discriminator_3D +from losses.loss import Loss +from losses.gan_loss import GANLoss +from utils.utils import create_image_pair +from utils.optical_flow import backwarp +from metrics.metrics import Metrics + +def config_parser(): + parser = configargparse.ArgumentParser() + # config file + parser.add_argument('-c', '--my-config', required=True, is_config_file=True, help='config file path') + # training and model options + parser.add_argument("--optimizer", type=str, help='choose optimizer type', default='adam') + parser.add_argument("--num_epochs", type=int, default=61, help="number of epochs of training") + parser.add_argument("--num_steps", type=int, default=25000, help="number of steps of training") + parser.add_argument("--continue_from_epoch", type=int, default=0, help="Continue training from epoch (default=0)") + parser.add_argument("--batch_size", type=int, default=16, help="size of the batches") + parser.add_argument("--lr", type=float, default=0.001, help="learning rate") # 0.00001 + parser.add_argument("--lr_decay_steps", type=int, default=10, help="learning rate decay steps in epochs") + parser.add_argument("--lr_disc", type=float, default=0.001, help="discriminator learning rate") + parser.add_argument("--num_workers", type=int, default=0, help="number of workers to use during batch generation") + parser.add_argument("--num_input_channels", type=int, default=3, help="number of input image channels") + parser.add_argument("--num_output_channels", type=int, default=3, help="number of output image channels") + parser.add_argument("--loss_names", nargs="*", type=str, default=['perceptual_loss_vgg'], help="names of losses used in training") + parser.add_argument("--loss_weights", nargs="*", type=float, default=[1.0], help="weights assigned to losses in the order") + parser.add_argument("--metric_names", nargs="*", type=str, default=['mean_absolute_error'], help="names of metrics to be logged") + parser.add_argument("--metric_weights", nargs="*", type=float, default=[1.0], help="weights assigned to metrics in the order") + parser.add_argument("--use_discriminator", action='store_true', help="choose if to use discriminator network") + parser.add_argument("--use_label_maps", action='store_true', help="choose if to use label maps for discriminator") + # dataset options + parser.add_argument("--dataset_type", type=str, help="options: CustomDataset", default='CustomDataset') + parser.add_argument("--input_train_root_dir", type=str, help="Path to training input images", default='./data/input_images_train') + parser.add_argument("--flow_train_root_dir", type=str, help="Path to training optical flows", default='./data/opticalflow_train') + parser.add_argument("--output_train_root_dir", type=str, help="Path to training output images", default='./data/output_images_train') + parser.add_argument("--label_train_root_dir", type=str, help="Path to training label images", default='./data/label_images_train') + parser.add_argument("--input_val_root_dir", type=str, help="Path to val input images", default='./data/input_images_val') + parser.add_argument("--flow_val_root_dir", type=str, help="Path to val optical flows", default='./data/opticalflow_val') + parser.add_argument("--output_val_root_dir", type=str, help="Path to val output images", default='./data/output_images_val') + parser.add_argument("--label_val_root_dir", type=str, help="Path to val label images", default='./data/label_images_val') + parser.add_argument("--width", type=int, default=640, help="width") + parser.add_argument("--height", type=int, default=360, help="height") + parser.add_argument("--optical_steps", type=int, default=1, help="Number of stept to go back with optical flow") + # logging/saving options + parser.add_argument("--checkpoints_dir", type=str, help='specify the directory to save the model', default='./chkpts/experiment/') + parser.add_argument("--save_every", type=int, help='save model every # epochs', default=5) + parser.add_argument("--log_every", type=int, help='save logs every # batches', default=100) + parser.add_argument("--save_val_images", action='store_true', help='choose if you want to save validation images') + parser.add_argument("--wandb_dir", type=str, help="directory where to save wandb data locally", default='./wandb') + return parser + + +def save_model(network, epoch, optimizer, loss, best_val_loss, args, discriminator=None, optimizer_disc=None, best_model=False): + print('Saving model epoch: ', epoch) + if not Path(args.checkpoints_dir).exists(): + Path(args.checkpoints_dir).mkdir(exist_ok=True) + + save_dir = Path(args.checkpoints_dir, 'latest_GAN.pt') + + if args.use_discriminator: + torch.save({'epoch': epoch, + 'model_state_dict': network.state_dict(), + 'discriminator_state_dict': discriminator.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'optimizer_disc_state_dict': optimizer_disc.state_dict(), + 'loss': loss, + 'best_val_loss': best_val_loss}, + save_dir) + else: + torch.save({'epoch': epoch, + 'model_state_dict': network.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'loss': loss, + 'best_val_loss': best_val_loss}, + save_dir) + + return save_dir + + +def print_progress(loss, best_loss = None, mode='train'): + print(mode + ' loss: ', loss) + if best_loss: + print('best ' + mode + ' loss: ', best_loss) + + +def set_requires_grad(network, requires_grad_flag): + for param in network.parameters(): + param.requires_grad = requires_grad_flag + + +def init_weights(net, init_type='normal', init_gain=0.02): + """Initialize network weights. + Parameters: + net (network) -- network to be initialized + init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal + init_gain (float) -- scaling factor for normal, xavier and orthogonal. + We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might + work better for some applications. Feel free to try yourself. + """ + def init_func(m): # define the initialization function + classname = m.__class__.__name__ + if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): + if init_type == 'normal': + nn.init.normal_(m.weight.data, 0.0, init_gain) + elif init_type == 'xavier': + nn.init.xavier_normal_(m.weight.data, gain=init_gain) + elif init_type == 'kaiming': + nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') + elif init_type == 'orthogonal': + nn.init.orthogonal_(m.weight.data, gain=init_gain) + else: + raise NotImplementedError('initialization method [%s] is not implemented' % init_type) + if hasattr(m, 'bias') and m.bias is not None: + nn.init.constant_(m.bias.data, 0.0) + elif classname.find('BatchNorm2d') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies. + nn.init.normal_(m.weight.data, 1.0, init_gain) + nn.init.constant_(m.bias.data, 0.0) + + print('initialize network with %s' % init_type) + net.apply(init_func) + + +if __name__=='__main__': + + parser = config_parser() + args = parser.parse_args() + + # check device + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + print("Running code on", device) + + # prepare data + train_loader = build_dataloader(args, mode='train', shuffle=False) + + # build the network + network = UNet(args).to(device) + init_weights(network) + + # make the networks multi-gpu available + if torch.cuda.device_count() > 1: + print("There are", torch.cuda.device_count(), "gpus available.") + network = nn.DataParallel(network) + + # Build loss function + loss_fn = Loss(args, device).to(device) + # loss_fn.eval() # make sure networks used in calculating loss (e.g. VGG) are in eval mode (because of batch normalization and dropout layers) + + #Build metrics functions + metrics_fn = Metrics(args, device) + metrics_fn.reset() + + # Build an optimizer + if args.optimizer == 'adam': + optimizer = torch.optim.Adam(network.parameters(), lr=args.lr, betas=(0.5, 0.999)) + elif args.optimizer == 'sgd': + optimizer = torch.optim.SGD(network.parameters(), lr=args.lr) + else: + raise NotImplementedError() + + # Learning rate decay + lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.lr_decay_steps, gamma=0.1) + + # Print number of parameters in the generator network + pytorch_generator_params = sum(p.numel() for p in network.parameters()) + print('Number of parameters in the generator network: ', pytorch_generator_params) + + # build discriminator if it is used + if args.use_discriminator: + discriminator = NLayerDiscriminator(input_nc=args.num_input_channels+args.num_output_channels, n_layers=3).to(device) + init_weights(discriminator) + set_requires_grad(discriminator, False) + loss_gan_fn = GANLoss().to(device) + if args.optimizer == 'adam': + optimizer_discriminator = torch.optim.Adam(discriminator.parameters(), lr=args.lr_disc, betas=(0.5, 0.999)) + elif args.optimizer == 'sgd': + optimizer_discriminator = torch.optim.SGD(discriminator.parameters(), lr=args.lr_disc) + pytorch_discriminator_params = sum(p.numel() for p in discriminator.parameters()) + print('Number of parameters in the discriminator network: ', pytorch_discriminator_params) + else: + discriminator = None + optimizer_discriminator = None + + best_val_loss = None + + # initialize wandb + wandb.init(project='GAN-video-synthesis', config=args, tags=["demo"], job_type='train', dir=args.wandb_dir) + args = wandb.config + wandb.watch(network) + + # Start the training process + start_train_disc = -1 + step = 0 + for epoch in range(args.continue_from_epoch, args.num_epochs): + print('Training epoch: ', epoch) + + network.train() + if args.use_discriminator: + discriminator.train() + + for batch_idx, data in enumerate(train_loader): + + inputs = data["input_image"].to(device) + prevs = data["prev_images"] + flows = data["flows"] + labels = data["output_image"].to(device) + + if args.use_label_maps: + label_maps = data["label_image"].to(device) + else: + label_maps = None + + prediction = network(inputs) + + # Loss on: generated VS target + loss_perceptual = loss_fn(prediction, labels) + + # Loss on: warped generated with opticalflow VS previous target frame + loss_temporal = torch.zeros_like(loss_perceptual) + + for flow, prev in zip(flows, prevs): + flow = flow.to(device) + prev = prev.to(device) + warped_prediction = backwarp(prediction, flow) + loss_temporal += loss_fn(warped_prediction, prev) + + if args.use_discriminator and epoch>start_train_disc: + + set_requires_grad(discriminator, True) + + real_pair = torch.cat((inputs, labels), 1) + pred_real = discriminator(real_pair, label_maps) + loss_real = loss_gan_fn(pred_real, real_image_flag=True) + + fake_pair = torch.cat((inputs, prediction.detach()), 1) + pred_fake = discriminator(fake_pair, label_maps) + loss_fake = loss_gan_fn(pred_fake, real_image_flag=False) + + loss_discriminator = (loss_fake + loss_real)*0.5 + + optimizer_discriminator.zero_grad() + loss_discriminator.backward() + optimizer_discriminator.step() + + set_requires_grad(discriminator, False) + + fake_pair = torch.cat((inputs, prediction), 1) + pred_fake = discriminator(fake_pair, label_maps) + + loss_gan = loss_gan_fn(pred_fake, real_image_flag=True) + + loss = loss_perceptual + loss_temporal + loss_gan + else: + loss = loss_perceptual + loss_temporal + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + step = step + 1 + + if batch_idx % args.log_every == 0: + if args.save_val_images: + image_concat = create_image_pair([inputs.detach(), prediction.detach(), labels.detach(), warped_prediction.detach(), prev.detach()]) + wandb_images = [wandb.Image(image) for image in image_concat] + if args.use_discriminator and epoch>start_train_disc: + wandb.log({'train loss perceptual': loss_perceptual, + 'train loss temporal': loss_temporal, + 'train loss gan': loss_gan, + 'discriminator train loss': loss_discriminator.detach(), + 'train images predictions': wandb_images, + 'discriminator loss real': loss_real, + 'discriminator loss fake': loss_fake}, step=step) + wandb.log({'train_loss': loss.detach()}, step=step) + else: + wandb.log({'train images predictions': wandb_images}, step=step) + wandb.log({'train loss perceptual': loss_perceptual, + 'train loss temporal': loss_temporal}, step=step) + wandb.log({'train_loss': loss.detach()}, step=step) + else: + if args.use_discriminator and epoch>start_train_disc: + wandb.log({'train loss perceptual': loss_perceptual, + 'train loss temporal': loss_temporal, + 'train loss gan': loss_gan, + 'discriminator train loss': loss_discriminator.detach(), + 'discriminator loss real': loss_real, + 'discriminator loss fake': loss_fake}, step=step) + wandb.log({'train_loss': loss.detach()}, step=step) + else: + wandb.log({'train loss perceptual': loss_perceptual, + 'train loss temporal': loss_temporal}, step=step) + wandb.log({'train_loss': loss.detach()}, step=step) + + if step == args.num_steps: + print(f'Breaking at {args.num_steps} steps.') + save_dir = save_model(network, epoch, optimizer, loss, best_val_loss, args, discriminator, optimizer_discriminator, best_model=False) + model_artifact = wandb.Artifact('last_model_train', type='model') + model_artifact.add_file(save_dir) + wandb.log_artifact(model_artifact) + exit() + + if epoch % args.save_every == 0: + save_dir = save_model(network, epoch, optimizer, loss, best_val_loss, args, discriminator, optimizer_discriminator, best_model=False) + model_artifact = wandb.Artifact('last_model_train', type='model') + model_artifact.add_file(save_dir) + wandb.log_artifact(model_artifact) + + lr_scheduler.step() + + save_dir = save_model(network, epoch, optimizer, loss, best_val_loss, args, discriminator, optimizer_discriminator, best_model=False) + model_artifact = wandb.Artifact('last_model_train', type='model') + model_artifact.add_file(save_dir) + wandb.log_artifact(model_artifact) diff --git a/motion-gan-pipeline/ImageToImage/train_temporal.py b/motion-gan-pipeline/ImageToImage/train_temporal.py new file mode 100644 index 0000000..0ec6f51 --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/train_temporal.py @@ -0,0 +1,486 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from ast import arg +from email.policy import default +import os +import configargparse +import torch +import torch.nn as nn +from pathlib import Path +import wandb +from datasets.base import build_dataloader +from models.unet import UNet +from models.patch_gan_discriminator import NLayerDiscriminator +from models.video_discriminator import Discriminator_3D +from losses.loss import Loss +from losses.gan_loss import GANLoss +from utils.utils import create_image_pair +from metrics.metrics import Metrics +import shutil +import datetime +from tqdm import tqdm +import warnings +warnings.filterwarnings("ignore") + +def print_options(opt, parser): + """Print and save options + + It will print both current options and default values(if different). + It will save options into a text file / [checkpoints_dir] / opt.txt + """ + message = '' + message += '----------------- Options ---------------\n' + for k, v in sorted(vars(opt).items()): + comment = '' + default = parser.get_default(k) + if v != default: + comment = '\t[default: %s]' % str(default) + message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment) + message += '----------------- End -------------------' + print(message) + + + file_name = os.path.join(opt.checkpoints_dir, 'GAN_config.txt') + with open(file_name, 'wt') as opt_file: + opt_file.write(message) + opt_file.write('\n') + + +def config_parser(): + parser = configargparse.ArgumentParser() + # config file + parser.add_argument('-c', '--my-config', required=True, is_config_file=True, help='config file path') + # training and model options + parser.add_argument("--optimizer", type=str, help='choose optimizer type', default='adam') + parser.add_argument("--num_epochs", type=int, default=61, help="number of epochs of training") + parser.add_argument("--num_steps", type=int, default=25000, help="number of steps of training") + parser.add_argument("--continue_from_epoch", type=int, default=0, help="Continue training from epoch (default=0)") + parser.add_argument("--batch_size", type=int, default=16, help="size of the batches") + parser.add_argument("--lr", type=float, default=0.001, help="learning rate") # 0.00001 + parser.add_argument("--lr_decay_steps", type=int, default=10, help="learning rate decay steps in epochs") + parser.add_argument("--lr_disc", type=float, default=0.001, help="discriminator learning rate") + parser.add_argument("--num_workers", type=int, default=0, help="number of workers to use during batch generation") + parser.add_argument("--num_input_channels", type=int, default=3, help="number of input image channels") + parser.add_argument("--num_output_channels", type=int, default=3, help="number of output image channels") + parser.add_argument("--loss_names", nargs="*", type=str, default=['perceptual_loss_vgg'], help="names of losses used in training") + parser.add_argument("--loss_weights", nargs="*", type=float, default=[1.0], help="weights assigned to losses in the order") + parser.add_argument("--metric_names", nargs="*", type=str, default=['mean_absolute_error'], help="names of metrics to be logged") + parser.add_argument("--metric_weights", nargs="*", type=float, default=[1.0], help="weights assigned to metrics in the order") + parser.add_argument("--use_discriminator", action='store_true', help="choose if to use discriminator network") + parser.add_argument("--use_video_discriminator", action='store_true', help="choose if to use video discriminator network") + parser.add_argument("--steps_train_video_discr", type=int, default=1, help='frequency of video discriminator training') + parser.add_argument("--loss_gan_video_weight", type=float, default=1, help='weight assigned to video discriminator loss') + parser.add_argument("--use_label_maps", action='store_true', help="choose if to use label maps for discriminator") + # dataset options + parser.add_argument("--dataset_type", type=str, help="options: CustomDataset", default='CustomDataset') + parser.add_argument("--input_train_root_dir", type=str, help="Path to training input images", default='./data/input_images_train') + parser.add_argument("--output_train_root_dir", type=str, help="Path to training output images", default='./data/output_images_train') + parser.add_argument("--label_train_root_dir", type=str, help="Path to training label images", default='./data/label_images_train') + parser.add_argument("--input_val_root_dir", type=str, help="Path to val input images", default=None) + parser.add_argument("--output_val_root_dir", type=str, help="Path to val output images", default=None) + parser.add_argument("--label_val_root_dir", type=str, help="Path to val label images", default=None) + parser.add_argument("--width", type=int, default=640, help="width") + parser.add_argument("--height", type=int, default=360, help="height") + # logging/saving options + parser.add_argument("--checkpoints_dir", type=str, help='specify the directory to save the model', default='./chkpts/experiment/') + parser.add_argument("--save_every", type=int, help='save model every # epochs', default=5) + parser.add_argument("--log_every", type=int, help='save logs every # batches', default=100) + parser.add_argument("--val_every", type=int, help='validate model every # epoch', default=0) + parser.add_argument("--save_val_images", action='store_true', help='choose if you want to save validation images') + parser.add_argument("--save_train_images", action='store_true', help='choose if you want to save train images') + parser.add_argument("--wandb_dir", type=str, help="directory where to save wandb data locally", default='./wandb') + parser.add_argument("--skip_log", action='store_true', help='choose if you want to stop wandb monitoring') + return parser + + +def save_model(network, epoch, optimizer, + loss, best_val_loss, args, + discriminator=None, optimizer_disc=None, + video_discriminator=None, optimizer_video_discriminator=None, + best_model=False): + + print('Saving model epoch: ', epoch) + if not Path(args.checkpoints_dir).exists(): + Path(args.checkpoints_dir).mkdir(exist_ok=True) + + if type(epoch) == str: + save_dir = Path(args.checkpoints_dir, epoch + '.pt') + else: + save_dir = Path(args.checkpoints_dir, 'GAN_epoch_%03d.pt' % epoch) + + if args.use_discriminator: + torch.save({'epoch': epoch, + 'model_state_dict': network.state_dict(), + 'discriminator_state_dict': discriminator.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'optimizer_disc_state_dict': optimizer_disc.state_dict(), + 'loss': loss, + 'best_val_loss': best_val_loss}, + save_dir) + + elif args.use_video_discriminator: + torch.save({'epoch': epoch, + 'model_state_dict': network.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'video_discriminator_state_dict': video_discriminator.state_dict(), + 'video_optimizer_disc_state_dict': optimizer_video_discriminator.state_dict(), + 'loss': loss, + 'best_val_loss': best_val_loss}, + save_dir) + + else: + torch.save({'epoch': epoch, + 'model_state_dict': network.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'loss': loss, + 'best_val_loss': best_val_loss}, + save_dir) + + return save_dir + + +def print_progress(loss, best_loss = None, mode='train'): + print(mode + ' loss: ', loss) + if best_loss: + print('best ' + mode + ' loss: ', best_loss) + + +def set_requires_grad(network, requires_grad_flag): + for param in network.parameters(): + param.requires_grad = requires_grad_flag + + +def init_weights(net, init_type='normal', init_gain=0.02): + """Initialize network weights. + Parameters: + net (network) -- network to be initialized + init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal + init_gain (float) -- scaling factor for normal, xavier and orthogonal. + We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might + work better for some applications. Feel free to try yourself. + """ + def init_func(m): # define the initialization function + classname = m.__class__.__name__ + if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): + if init_type == 'normal': + nn.init.normal_(m.weight.data, 0.0, init_gain) + elif init_type == 'xavier': + nn.init.xavier_normal_(m.weight.data, gain=init_gain) + elif init_type == 'kaiming': + nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') + elif init_type == 'orthogonal': + nn.init.orthogonal_(m.weight.data, gain=init_gain) + else: + raise NotImplementedError('initialization method [%s] is not implemented' % init_type) + if hasattr(m, 'bias') and m.bias is not None: + nn.init.constant_(m.bias.data, 0.0) + elif classname.find('BatchNorm2d') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies. + nn.init.normal_(m.weight.data, 1.0, init_gain) + nn.init.constant_(m.bias.data, 0.0) + + print('initialize network with %s' % init_type) + net.apply(init_func) + + +if __name__=='__main__': + + parser = config_parser() + args = parser.parse_args() + + # TODO: remove after sweep + # make new checkpoint with time + # ct = datetime.datetime.now() + # args.checkpoints_dir = args.checkpoints_dir + '_' + '%02d'%ct.day + '%02d'%ct.month + '%04d'%ct.year + '_' + '%02d'%ct.hour + '%02d'%ct.minute + '%02d'%ct.second + os.makedirs(args.checkpoints_dir, exist_ok=True) + + # copy config in checkpoint folder + print_options(args, parser) + + # check device + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + print("Running code on", device) + + # prepare data + train_loader = build_dataloader(args, mode='train', shuffle=False) + if args.val_every != 0: + val_loader = build_dataloader(args, mode='val', shuffle=False) + else: + val_loader = None + + # build the network + network = UNet(args).to(device) + init_weights(network) + + # make the networks multi-gpu available + if torch.cuda.device_count() > 1: + print("There are", torch.cuda.device_count(), "gpus available.") + network = nn.DataParallel(network) + + # Build loss function + loss_fn = Loss(args, device).to(device) + loss_fn.eval() # make sure networks used in calculating loss (e.g. VGG) are in eval mode (because of batch normalization and dropout layers) + + # Build metrics functions + metrics_fn = Metrics(args, device) + metrics_fn.reset() + + # Build an optimizer + if args.optimizer == 'adam': + optimizer = torch.optim.Adam(network.parameters(), lr=args.lr, betas=(0.5, 0.999)) + elif args.optimizer == 'sgd': + optimizer = torch.optim.SGD(network.parameters(), lr=args.lr) + else: + raise NotImplementedError() + + # Learning rate decay + lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.lr_decay_steps, gamma=0.1) + + # Print number of parameters in the generator network + pytorch_generator_params = sum(p.numel() for p in network.parameters()) + print('Number of parameters in the generator network: ', pytorch_generator_params) + + # build discriminator if it is used + if args.use_discriminator: + discriminator = NLayerDiscriminator(input_nc=args.num_input_channels+args.num_output_channels, n_layers=3).to(device) + init_weights(discriminator) + set_requires_grad(discriminator, False) + loss_gan_fn = GANLoss().to(device) + if args.optimizer == 'adam': + optimizer_discriminator = torch.optim.Adam(discriminator.parameters(), lr=args.lr_disc, betas=(0.5, 0.999)) + elif args.optimizer == 'sgd': + optimizer_discriminator = torch.optim.SGD(discriminator.parameters(), lr=args.lr_disc) + pytorch_discriminator_params = sum(p.numel() for p in discriminator.parameters()) + print('Number of parameters in the discriminator network: ', pytorch_discriminator_params) + # Learning rate decay + lr_scheduler_discr = torch.optim.lr_scheduler.StepLR(optimizer_discriminator, step_size=args.lr_decay_steps, gamma=0.1) + + else: + discriminator = None + optimizer_discriminator = None + lr_scheduler_discr = None + + # build video discriminator if it is used + if args.use_video_discriminator: + video_discriminator = Discriminator_3D(d_conv_dim=96).to(device) # TODO: test + init_weights(video_discriminator) + set_requires_grad(video_discriminator, False) + loss_gan_fn = GANLoss().to(device) + if args.optimizer == 'adam': + optimizer_video_discriminator = torch.optim.Adam(video_discriminator.parameters(), lr=args.lr_disc, betas=(0.5, 0.999)) + elif args.optimizer == 'sgd': + optimizer_video_discriminator = torch.optim.SGD(video_discriminator.parameters(), lr=args.lr_disc) + pytorch_video_discriminator_params = sum(p.numel() for p in video_discriminator.parameters()) + print('Number of parameters in the video discriminator network: ', pytorch_video_discriminator_params) + # Learning rate decay + lr_scheduler_video_discr = torch.optim.lr_scheduler.StepLR(optimizer_video_discriminator, step_size=args.lr_decay_steps, gamma=1) # gamma=1 for no updates in lr + + else: + video_discriminator = None + optimizer_video_discriminator = None + lr_scheduler_video_discr = None + + best_val_loss = None + + # initialize wandb + if not args.skip_log: + wandb.init(project='GAN-video-synthesis', config=args, tags=["demo"], job_type='train', dir=args.wandb_dir) + args = wandb.config + wandb.watch(network) + + # Start the training process + start_train_disc = -1 + step = 0 + for epoch in tqdm(range(args.continue_from_epoch, args.num_epochs), desc='Epoch: '): + # print('Training epoch: ', epoch) + network.train() + if args.use_discriminator: + discriminator.train() + + # Training loop + for batch_idx, data in enumerate(train_loader): + + inputs = data["input_image"].to(device) + labels = data["output_image"].to(device) + + if args.use_label_maps: + label_maps = data["label_image"].to(device) + else: + label_maps = None + + prediction = network(inputs) + + loss_perceptual = loss_fn(prediction, labels) + + loss = loss_perceptual + + if args.use_discriminator and epoch>start_train_disc: + + set_requires_grad(discriminator, True) + + real_pair = torch.cat((inputs, labels), 1) + pred_real = discriminator(real_pair, label_maps) + loss_real = loss_gan_fn(pred_real, real_image_flag=True) + + fake_pair = torch.cat((inputs, prediction.detach()), 1) + pred_fake = discriminator(fake_pair, label_maps) + loss_fake = loss_gan_fn(pred_fake, real_image_flag=False) + + loss_discriminator = (loss_fake + loss_real)*0.5 + + optimizer_discriminator.zero_grad() + loss_discriminator.backward() + optimizer_discriminator.step() + + set_requires_grad(discriminator, False) + + fake_pair = torch.cat((inputs, prediction), 1) + pred_fake = discriminator(fake_pair, label_maps) + + loss_gan = loss_gan_fn(pred_fake, real_image_flag=True) + loss += loss_gan + + if args.use_video_discriminator and epoch>start_train_disc: + + # Train discriminator + if (batch_idx % args.steps_train_video_discr) == 0: + set_requires_grad(video_discriminator, True) + + pred_real = video_discriminator(labels.unsqueeze(0)) + loss_video_real = loss_gan_fn(pred_real, real_image_flag=True) + + pred_fake = video_discriminator(prediction.unsqueeze(0).detach()) + loss_video_fake = loss_gan_fn(pred_fake, real_image_flag=False) + + loss_video_discriminator = (loss_video_real + loss_video_fake)*0.5 + + # print(loss_video_discriminator.item(), loss_video_fake.item(), loss_video_real.item()) + + optimizer_video_discriminator.zero_grad() + loss_video_discriminator.backward() + optimizer_video_discriminator.step() + + + # Train generator + set_requires_grad(video_discriminator, False) + + pred_fake = video_discriminator(prediction.unsqueeze(0).detach()) + loss_gan_video = loss_gan_fn(pred_fake, real_image_flag=True) + # print('Perceptual loss: ', loss_perceptual.item()) + # print('GAN video loss: ', loss_gan_video.item()) + + loss += loss_gan_video * args.loss_gan_video_weight + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + step = step + 1 + + if not args.skip_log and batch_idx % args.log_every == 0: + if args.save_train_images: + image_concat = create_image_pair([inputs.detach(), prediction.detach(), labels.detach()]) + wandb_images = [wandb.Image(image) for image in image_concat] + wandb.log({'train images predictions': wandb_images}, step=step) + + wandb.log({'train_loss': loss.detach()}, step=step) + wandb.log({'train loss perceptual': loss_perceptual.detach()}, step=step) + + if args.use_discriminator and epoch>start_train_disc: + wandb.log({ 'train loss gan': loss_gan, + 'discriminator train loss': loss_discriminator.detach(), + 'discriminator loss real': loss_real, + 'discriminator loss fake': loss_fake}, step=step) + + if args.use_video_discriminator and epoch>start_train_disc: + try: + wandb.log({ 'train loss gan video': loss_gan_video, + 'video discriminator train loss': loss_video_discriminator.detach(), + 'video discriminator loss real': loss_video_real, + 'video discriminator loss fake': loss_video_fake}, step=step) + except NameError: + wandb.log({ 'train loss gan video': loss_gan_video}, step=step) + + if step == args.num_steps: + print(f'Breaking at {args.num_steps} steps.') + save_dir = save_model(network, 'latest_GAN_model', optimizer, loss, best_val_loss, args, discriminator, optimizer_discriminator, video_discriminator, optimizer_video_discriminator, best_model=False) + # model_artifact = wandb.Artifact('last_model_train', type='model') + # model_artifact.add_file(save_dir) + # wandb.log_artifact(model_artifact) + exit() + + # Save model + if epoch % args.save_every == 0 and epoch > 0: + save_dir = save_model(network, epoch, optimizer, loss, best_val_loss, args, discriminator, optimizer_discriminator, video_discriminator, optimizer_video_discriminator, best_model=False) + # model_artifact = wandb.Artifact('last_model_train', type='model') + # model_artifact.add_file(save_dir) + # wandb.log_artifact(model_artifact) + + # Update learning rates + lr_scheduler.step() + if args.use_discriminator: + lr_scheduler_discr.step() + if args.use_video_discriminator: + lr_scheduler_video_discr.step() + + # Validation + if args.val_every != 0 and epoch % args.val_every == 0 and epoch > 0: + print('Validating epoch: ', epoch) + val_loss = 0 + count = 0 + metrics_fn.reset() + + with torch.no_grad(): + network.eval() + for batch_idx, data in enumerate(val_loader): + + inputs = data["input_image"].to(device) + labels = data["output_image"].to(device) + + prediction = network(inputs) + + # compute metrics + metrics_fn.update(prediction, labels) + + # compute losses + loss_perceptual_val = loss_fn(prediction, labels) + loss_val = loss_perceptual_val + + if args.use_video_discriminator and epoch>start_train_disc: + + pred_real_val = video_discriminator(labels.unsqueeze(0)) + loss_video_real_val = loss_gan_fn(pred_real_val, real_image_flag=True) + + pred_fake_val = video_discriminator(prediction.unsqueeze(0)) + loss_video_fake_val = loss_gan_fn(pred_fake_val, real_image_flag=False) + + loss_video_discriminator_val = (loss_video_real_val + loss_video_fake_val)*0.5 + + loss_gan_video_val = loss_gan_fn(pred_fake_val, real_image_flag=True) + + loss_val += loss_gan_video_val * args.loss_gan_video_weight + + val_loss += loss_val.detach() + count += 1 + + avg_val_loss = val_loss/count + + metrics_log, metrics_combined = metrics_fn.compute() + + if not args.skip_log: + if args.save_val_images: + image_concat = create_image_pair([inputs.detach(), prediction.detach(), labels.detach()]) + wandb_images = [wandb.Image(image) for image in image_concat] + wandb.log({'val loss': avg_val_loss, 'validation images predictions': wandb_images}, step=step) + wandb.log(metrics_log, step=step) + wandb.log({'metrics_combined': metrics_combined}, step=step) + + else: + wandb.log({'val loss': avg_val_loss}, step=step) + wandb.log(metrics_log, step=step) + wandb.log({'metrics_combined': metrics_combined}, step=step) + + save_dir = save_model(network, 'latest_GAN_model', optimizer, loss, best_val_loss, args, discriminator, optimizer_discriminator, video_discriminator, optimizer_video_discriminator, best_model=False) + # model_artifact = wandb.Artifact('last_model_train', type='model') + # model_artifact.add_file(save_dir) + # wandb.log_artifact(model_artifact) diff --git a/motion-gan-pipeline/ImageToImage/utils/__init__.py b/motion-gan-pipeline/ImageToImage/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/motion-gan-pipeline/ImageToImage/utils/optical_flow.py b/motion-gan-pipeline/ImageToImage/utils/optical_flow.py new file mode 100644 index 0000000..93628a6 --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/utils/optical_flow.py @@ -0,0 +1,18 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import torch + +backwarp_tenGrid = {} +def backwarp(tenInput, tenFlow): + if str(tenFlow.shape) not in backwarp_tenGrid: + tenHor = torch.linspace(-1.0 + (1.0 / tenFlow.shape[3]), 1.0 - (1.0 / tenFlow.shape[3]), tenFlow.shape[3]).view(1, 1, 1, -1).repeat(1, 1, tenFlow.shape[2], 1) + tenVer = torch.linspace(-1.0 + (1.0 / tenFlow.shape[2]), 1.0 - (1.0 / tenFlow.shape[2]), tenFlow.shape[2]).view(1, 1, -1, 1).repeat(1, 1, 1, tenFlow.shape[3]) + + backwarp_tenGrid[str(tenFlow.shape)] = torch.cat([ tenHor, tenVer ], 1).to(tenInput.get_device()) + # end + + tenFlow = torch.cat([ tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0), tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0) ], 1) + + return torch.nn.functional.grid_sample(input=tenInput, grid=(backwarp_tenGrid[str(tenFlow.shape)] + tenFlow).permute(0, 2, 3, 1), mode='bilinear', padding_mode='border', align_corners=False) +# end diff --git a/motion-gan-pipeline/ImageToImage/utils/utils.py b/motion-gan-pipeline/ImageToImage/utils/utils.py new file mode 100644 index 0000000..b0f914c --- /dev/null +++ b/motion-gan-pipeline/ImageToImage/utils/utils.py @@ -0,0 +1,31 @@ +import PIL +import torch +from torchvision import transforms +import wandb +import configargparse +from pathlib import Path +import os + +def create_image_pair(images_to_concat): + + num_images = images_to_concat[0].shape[0] + images = [] + for image_num in range(num_images): + image = images_to_concat[0][image_num,::] + if image.shape[0] == 1: + image = torch.cat((image,)*3, axis=0) + for img_num, img in enumerate(images_to_concat): + if not img_num==0: + image_to_concat = img[image_num,::] + if image_to_concat.shape[0] == 1: + image_to_concat = torch.cat((image_to_concat,)*3, axis=-1) + image = torch.cat((image, image_to_concat), dim=2) + image_transform = transforms.ToPILImage() + image = image_transform(image) + images.append(image) + return images + + +def save_image_list(image_list, save_path, names): + for image, name in zip(image_list, names): + image.save(save_path+name) diff --git a/motion-gan-pipeline/LICENSE b/motion-gan-pipeline/LICENSE new file mode 100644 index 0000000..352e419 --- /dev/null +++ b/motion-gan-pipeline/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Media Technology Center, ETH Zurich + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/motion-gan-pipeline/README.md b/motion-gan-pipeline/README.md new file mode 100644 index 0000000..4de7d08 --- /dev/null +++ b/motion-gan-pipeline/README.md @@ -0,0 +1,278 @@ +# Audio-Driven Video Synthesis + +This repository contains the new end-to-end "Audio Motion GAN pipeline" for the Audio-driven Video Synthesis project. + +## Requirements + + + +Please clone this repository with the following commands in order to download all the related submodules: +```bash +git clone https://gitlab.ethz.ch/mtc/video-synthesis/motion-gan-pipeline +cd motion-gan-pipeline +git submodule init +git submodule update +export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python +``` + +Create a [conda](https://www.anaconda.com) environment: +```bash +conda create -n deepspeech python=3.7 +``` + +To install the requirements follow these instructions: +```bash +conda activate deepspeech + +conda install pysoundfile -c conda-forge +# Install Pytorch and cudatoolkit +pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html +# Install the rest of the pip requirements +pip install -r requirements_deepspeech.txt + +conda deactivate +``` + +Create a [conda](https://www.anaconda.com) environment: +```bash +conda create -n pyenv python=3.9 +``` + +To install the requirements follow these instructions: +```bash +conda activate pyenv + +conda install pysoundfile -c conda-forge +# Install Pytorch and cudatoolkit +conda install pytorch=1.13.0 torchvision pytorch-cuda=11.6 -c pytorch -c nvidia +# Install ffmpeg for video editing +conda install x264=='1!152.20180717' ffmpeg=4.0.2 -c conda-forge +conda install -c fvcore -c iopath -c conda-forge fvcore iopath +# Install pytorch3d +conda install pytorch3d -c pytorch3d +# Install the rest of the pip requirements +pip install -r requirements.txt + +# Set up enhancements tool +cd GFPGAN +python setup.py develop +cd .. + +conda deactivate +``` + +## Goal + +This project is designed to generate realistic videos of human avatars. Given an audio recording and a choosen video of a target person, the code will produce a new video of that person pronouncing the given audio. + +The produced video is fully artificial: everything from the lip movement, to the head movement, to the singluar frames of the video is completely generated. \ +When an avatar is trained once, it can be used multiple times with any given audio source. + + + +[![IMAGE_ALT](https://img.youtube.com/vi/7zK_Wmpxrd0/0.jpg)](https://youtu.be/7zK_Wmpxrd0) + +## Model Structure + +You can observe the pipeline structure in the following image. + +drawing + + \ + + +- Audio2Expressions \ + The Audio2Expressions model is a network trained to generate FLAME facial expressions from the extracted audio DeepSpeech features. The Audio2Expressions model is inspired by [Neural Voice Puppetry](https://github.com/miu200521358/NeuralVoicePuppetryMMD). This network produces the corresponding mouth movements from the audio signal and is resposible for lip-sync. + +- Motion Generation \ + The Motion Generation model is a network trained to generate rotation and translation matrices from the extracted audio DeepSpeech features. This model predicts the corresponding head motion to certain audio inputs. The Motion Generation model is inspired by [Live-Speech Portraits](https://github.com/YuanxunLu/LiveSpeechPortraits). This network produces the head movements from the audio signal and is resposible for the overall head motion during the video. + +- Edges Generation \ + The Edge Generation model combines the information generated by the Audio2Expressions and the Motion Generation to render a FLAME facial mesh. Key landmarks are projected and rendered. Body landmarks (shoulders and hips) are extracted using [Mediapipe](https://github.com/google/mediapipe) during training, and predicted by a network during inference. This module prepares the input data for the Image2Image network. + +- Image2Image \ + The Image2Image model is a network trained to generate realistic images from edges information coming as input. \ + The Image2Image network transforms the images generated in the Edge Generation step to the final video frames. The image generation network is trained for both image fidelity (L1 loss, VGG loss) and temporal consistency (3D video discriminator). + +- Image Enhancement \ + Optionally the final output of the pipeline can be enhanced by using [GFPGAN](https://github.com/TencentARC/GFPGAN) and image restoration network. The enhancement only works on the facial area and produces more realistic results. + + +## Data +If you plan on using this code with the already available and pre-trained moderators, you will only have to provide the audio data. + +Please follow these instructions on data quality: +- Audio + - Provide a recording of a person speaking (audio of any duration is accepted). + - The cleaner the audio signal the better: audio with background noise will result in unmatching lip-sync. + - Avoid recording multiple people talking: the model is unable to distinguish between multiple voice signals. +- Video + - Provide a video of your desired avatar character talking. + - Minimum video duration: 3 minutes. + - Longer videos will results in longer training time. + - The background is irrelevant, it will be removed during preprocessing. + - Avoid any hand or arms movements. Having such movements will interfere with the quality of the generated frames during training. + - Provide a half-body shot recording, possibly frontal. + + + +Refer to the following image for shot instructions. + +drawing + + + +## Data structure + +Your data should be organised in the following structure: + +```bash +$DATAROOT/input_data +├── audio # Folder containing n audio files +│ ├── audio_1 +│ │ └──audio_1.wav +│ ... +│ └── audio_n +│ └──audio_n.wav +└── video # Folder containing m video files + ├── video_1 + │ └──video_1.mp4 + ... + └── video_m + └──video_m.mp4 + +``` + +## Usage +The same command can be used for both training and inference. If the trained checkpoints for the requested avatar are not found, the code will launch the training procedure. + +Use the following call to launch the code: +```bash +bash full_pipeline.sh $DATAROOT $AUDIO_NAME $VIDEO_NAME +``` +Where: +- $DATAROOT: path to the data folder. +- $AUDIO_NAME: name of the audio file (without extension). +- $VIDEO_NAME: name of the video file (without extension). This will also be the name of the Avatar. + + +If you want to launch the enhancement version please use this call instead: +```bash +bash full_pipeline_enhancement.sh $DATAROOT $AUDIO_NAME $VIDEO_NAME +``` + +## Train new Avatars +In order to train a new avatar follow this easy steps: + +1. Record a new video following the instructions above. \ + Eg: VIDEONAME = my_new_avatar +2. Move your file to your data folder inside the video subfolder. + ```bash + mv my_new_avatar.mp4 $DATAROOT/video/my_new_avatar.mp4 + ``` +3. Choose any audio file. \ + Eg: AUDIONAME = my_audio_sample + +4. Start training (and inference) with the following command: + ```bash + bash full_pipeline.sh $DATAROOT my_audio_sample my_new_avatar + ``` +5. Wait until it's finished. +6. Enjoy your new Avatar! \ + You can now use the same avatar with any other audio file. + +## Output Data +Ruinning this pipeline will generate the following data: + +```bash +$DATAROOT +├── input_data # Folder containing all input data +│ ├── audio # Folder containing audio files +│ │ └── my_audio_sample # Folder generated by running the pipeline: contains "my_audio_sample" audio processed information +│ └── video # Folder containing video files +│ └── my_new_avatar # Folder generated by running the pipeline: contains "my_new_avatar" video processed information +│ +├── checkpoints # Folder containing all checkpoints of trained avatars +│ └── my_new_avatar # Folder containing checkpoints of the "my_new_avatar" Avatar +│ ├── GAN_config.txt # GAN configuration parameters during training +│ ├── head2body.pkl # checkpoint for head position to body position regression +│ ├── head2body_test.png # result of the regression +│ ├── latest_Audio2Headpose.pkl # checkpoint for audio to head-motion finetuned network +│ ├── latest_GAN_model.pt # checkpoint for the GAN network +│ ├── logs # folder containing all training logs during GAN training +│ └── train_opt.txt # audio to head-motion configuration parameters during finetuning +│ +└── output_data # Folder containing all output data + └── my_audio_sample_to_my_new_avatar # Folder generated by running the pipeline command + ├── generated_video.mp4 # Generated video + └── generated_superes.mp4 # Generated enhanced video (only with the enhancement version) +``` + +## LICENSE + +The AMG Pipeline is free and open source! All code in this repository is licensed under: + +* [MIT](LICENSE) License. + +This pipeline relies and is inspired by the following works, and therefore refers to their individual licenses: + +- [GFPGAN](https://github.com/TencentARC/GFPGAN): [license](https://github.com/TencentARC/GFPGAN/blob/master/LICENSE), internal path `/GFPGAN`. +- [Live Speech Portraits](https://github.com/YuanxunLu/LiveSpeechPortraits): [license](https://github.com/YuanxunLu/LiveSpeechPortraits/blob/main/LICENSE), internal path `/motion-generation`. +- [Neural Voice Puppetry](https://web.archive.org/web/20201113014501/https://github.com/JustusThies/NeuralVoicePuppetry): [license](https://gitlab.ethz.ch/mtc/video-synthesis/.NeuralVoicePuppetry/-/blob/0fc75ba2edfdfd5655984f1515bfe06de880d91d/LICENSE), internal path `/preprocessing/third/Audio2ExpressionNet`. +- [DECA](https://github.com/YadiraF/DECA): [license](https://github.com/YadiraF/DECA/blob/master/LICENSE), internal path `/preprocessing/third/DECA`. +- [Robust Video Matting](https://github.com/PeterL1n/RobustVideoMatting): [license](https://github.com/PeterL1n/RobustVideoMatting/blob/master/LICENSE), internal path `/preprocessing/third/RobustVideoMatting`. +- [VOCA](https://github.com/TimoBolkart/voca): [license](https://voca.is.tue.mpg.de/license), used internally by DECA. +- [AD-NeRF](https://github.com/YudongGuo/AD-NeRF): [license](https://github.com/YudongGuo/AD-NeRF/blob/master/LICENSE), internal path `/preprocessing/face_parsing`, `/preprocessing/face_tracking` +- [DeepSpeech](https://github.com/mozilla/DeepSpeech): [license](https://github.com/mozilla/DeepSpeech/blob/master/LICENSE), used by VOCA. +- [CycleGAN and Pix2Pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix): [license](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/LICENSE), used as skeleton for GAN training. + + +## Contact information + +Alberto Pennino: [alberto.pennino@inf.ethz.ch](alberto.pennino@inf.ethz.ch) \ No newline at end of file diff --git a/motion-gan-pipeline/face2body.py b/motion-gan-pipeline/face2body.py new file mode 100644 index 0000000..54a10de --- /dev/null +++ b/motion-gan-pipeline/face2body.py @@ -0,0 +1,174 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from genericpath import isfile +from math import floor +import os +from PIL import Image +import numpy as np +import argparse +from tqdm import tqdm +import matplotlib +matplotlib.use('agg') +import matplotlib.pyplot as plt + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from torch.utils.data import Dataset +from torch.utils.data import DataLoader +from torchvision import transforms + +class LandmarkDataset(Dataset): + def __init__(self, landmarks_dir, body_dir, im_size, split): + """ + Args: + input_root_dir (string): Directory with all the input images. + output_root_dir (string): Directory with all the output images. + transform (callable, optional): Optional transform to be applied + on a sample. + """ + self.landmarks_dir = landmarks_dir + self.body_dir = body_dir + self.w, self.h = im_size + + # set split for train test + self.split_ratio = 0.8 + self.split = split + + self.total_num = len(os.listdir(self.landmarks_dir)) + + def __len__(self): + + if self.split=='Train': + return int(floor(self.total_num ) * self.split_ratio) + + else: + return int(floor(self.total_num ) * (1 - self.split_ratio)) + + def __getitem__(self, idx): + + if self.split=='Train': + base = 0 + + else: + base = int(floor(self.total_num) * (self.split_ratio)) + + idx += base + + landmark_path = os.path.join(self.landmarks_dir, '%05d.lms' % idx) + body_path = os.path.join(self.body_dir, '%05d.npy' % idx) + + input_ldk = np.loadtxt(landmark_path)[:, :-1] + output_ldk = np.load(body_path)[:, :-2] + output_ldk = np.array([output_ldk[11], output_ldk[12], output_ldk[23], output_ldk[24]]) + + # scale + input_ldk[:, 0] = input_ldk[:, 0] / self.w + input_ldk[:, 1] = input_ldk[:, 1] / self.h + + output_ldk[:, 0] = output_ldk[:, 0] / self.w + output_ldk[:, 1] = output_ldk[:, 1] / self.h + + trans = transforms.ToTensor() + sample = { + 'input_ldk': trans(input_ldk).flatten().float(), + 'output_ldk': trans(output_ldk).flatten().float(), + } + + return sample + + +def train_regression_head2body(landmarks_dir, body_dir, checkpoint_path, im_size): + + train_dataset = LandmarkDataset(landmarks_dir, body_dir, im_size, "Train") + train_dataloader = DataLoader(train_dataset, batch_size=train_dataset.__len__()) + + test_dataset = LandmarkDataset(landmarks_dir, body_dir, im_size, "Test") + test_dataloader = DataLoader(test_dataset, batch_size=test_dataset.__len__()) + + # check device + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + # print("Running on", device) + + # Make linear model + model = nn.Linear(77 * 2, 4 * 2).to(device) # 77 landmarks with 2 coordinates (x, y) -> 4 landmarks with 2 coordinates (x, y) + + # Optimizer + opt = torch.optim.SGD(model.parameters(), lr=0.1) + lr_scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=5, gamma=0.1) + + # Loss Function + loss_fn = F.mse_loss + + # Run optimisation + for epoch in tqdm(range(25)): + for batch in train_dataloader: + # Generate predictions + input_ldk = batch['input_ldk'].to(device) + output_ldk = batch['output_ldk'].to(device) + pred = model(input_ldk) + + # compute loss + loss = loss_fn(pred, output_ldk) + + # Perform gradient descent + loss.backward() + opt.step() + opt.zero_grad() + + lr_scheduler.step() + + if epoch % 10 == 0: + with torch.no_grad(): + total_loss = 0. + for batch in test_dataloader: + # Generate predictions + input_ldk = batch['input_ldk'].to(device) + output_ldk = batch['output_ldk'].to(device) + pred = model(input_ldk) + + # compute loss + loss = loss_fn(pred, output_ldk) + total_loss += loss + + input_ldk = input_ldk.cpu().detach().reshape((-1, 77, 2)).numpy() + output_ldk = output_ldk.cpu().detach().reshape((-1, 4, 2)).numpy() + pred = pred.cpu().detach().reshape((-1, 4, 2)).numpy() + plt.scatter(input_ldk[0, :, 0], input_ldk[0, :, 1], marker='o', c='red') + plt.scatter(output_ldk[0, :, 0], output_ldk[0, :, 1], marker='o', c='green') + plt.scatter(pred[0, :, 0], pred[0, :, 1], marker='^', c='blue') + + plt.savefig(os.path.splitext(checkpoint_path)[0] + '_test.png') + plt.close('all') + + print(f'Test Loss at epoch {epoch}: {total_loss.item()}') + + torch.save(model.state_dict(), checkpoint_path) + + +if __name__ == '__main__': + + # load args + parser = argparse.ArgumentParser() + parser.add_argument('--dataset_base', required=True) + parser.add_argument('--target_name', required=True) + parser.add_argument('--checkpoint_dir', required=True) + + inopt = parser.parse_args() + + landmarks_dir = os.path.join(inopt.dataset_base, 'debug/proj_landmarks') + body_dir = os.path.join(inopt.dataset_base, 'body_pose') + checkpoint_path = os.path.join(inopt.checkpoint_dir, inopt.target_name, 'head2body.pkl') + + + if os.path.isfile(checkpoint_path): + print('Head2body already trained!') + + else: + tmp_img = Image.open(os.path.join(inopt.dataset_base, 'frames', '00000.jpg')) + im_size = tmp_img.size + train_regression_head2body(landmarks_dir, body_dir, checkpoint_path, im_size) + + diff --git a/motion-gan-pipeline/full_pipeline.sh b/motion-gan-pipeline/full_pipeline.sh new file mode 100644 index 0000000..126d4bd --- /dev/null +++ b/motion-gan-pipeline/full_pipeline.sh @@ -0,0 +1,112 @@ +#!/bin/bash + +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +set -e +eval "$(conda shell.bash hook)" + +# SET GPU DEVICE +GPUID=0 + +# BASE='/mnt/full_pipeline' +BASE=$1 +echo "DATAROOT: $BASE" + +NAME_AUDIO_FILE=$2 +NAME_VIDEO_FILE=$3 +echo "NAME_AUDIO_FILE: $2" +echo "NAME_VIDEO_FILE: $3" + +# You can choose here what you want +FPS=25 +SAMPLERATE=16000 + +# DATA PATHS +INPUTDATAPATH=$BASE/input_data +OUTPUTDATAPATH=$BASE/output_data +FEATURESPATH=features + +###AUDIO### +NAME_AUDIOS_LIST=( $NAME_AUDIO_FILE ) + +### VIDEO ### +NAME_VIDEOS_LIST=( $NAME_VIDEO_FILE ) + +for NAME_AUDIO in "${NAME_AUDIOS_LIST[@]}" +do + for NAME_VIDEO in "${NAME_VIDEOS_LIST[@]}" + do + + # if [ ! -d "$INPUTDATAPATH/audio/$NAME_AUDIO" ]; then + # echo "Moving audio file.." + # mkdir -p $INPUTDATAPATH/audio/$NAME_AUDIO + # mv $INPUTDATAPATH/audio/$NAME_AUDIO.wav $INPUTDATAPATH/audio/$NAME_AUDIO/$NAME_AUDIO.wav + # fi + + # if [ ! -d "$INPUTDATAPATH/video/$NAME_VIDEO" ]; then + # echo "Moving video file.." + # mkdir -p $INPUTDATAPATH/video/$NAME_VIDEO + # mv $INPUTDATAPATH/video/$NAME_VIDEO.mp4 $INPUTDATAPATH/video/$NAME_VIDEO/$NAME_VIDEO.mp4 + # fi + + # Make output folder + tmp='_to_' + now="$(date +'_%d%m%y_%X')" + OUTPUTFOLDER=$OUTPUTDATAPATH/$NAME_AUDIO$tmp$NAME_VIDEO + # OUTPUTFOLDER=$OUTPUTDATAPATH/$NAME_AUDIO$tmp$NAME_VIDEO$now + echo $OUTPUTFOLDER + mkdir -p $OUTPUTFOLDER + + ##################### PREPROCESSING ##################### + echo -e '\n--------------PREPROCESSING---------------\n' + cd preprocessing + if [ ! -f "$INPUTDATAPATH/video/$NAME_VIDEO/track_params.pt" ] + then + bash process_video.sh $INPUTDATAPATH/video $NAME_VIDEO $FPS $SAMPLERATE + fi + + bash process_audio.sh $INPUTDATAPATH/audio $NAME_AUDIO $FPS $SAMPLERATE + cd .. + conda deactivate + + #################### AUDIO EXPRESSIONS ##################### + echo -e '\n--------------AUDIO EXPRESSIONS---------------\n' + conda activate pyenv + cd preprocessing + python third/Audio2ExpressionNet/get_audioexpr.py --name $NAME_AUDIO --dataset_base $INPUTDATAPATH/audio/$NAME_AUDIO --out_dir $OUTPUTFOLDER --mapping_path $INPUTDATAPATH/video/$NAME_VIDEO/mapping.npy + cd .. + + ##################### HEAD MOTION ##################### + echo -e '\n--------------HEAD MOTION---------------\n' + cd motion-generation + python transfer.py --dataroot $INPUTDATAPATH --dataset_names $NAME_AUDIO --target_name $NAME_VIDEO --out_dir $OUTPUTFOLDER --checkpoint_dir $BASE/checkpoints/ + cd .. + + ##################### HEAD TO SHOULDERS ##################### + echo -e '\n--------------HEAD TO BODY---------------\n' + python face2body.py --dataset_base $INPUTDATAPATH/video/$NAME_VIDEO --target_name $NAME_VIDEO --checkpoint_dir $BASE/checkpoints/ + + #################### EDGE MAP ##################### + echo -e '\n--------------EDGE MAP---------------\n' + cd preprocessing + python combine_edges.py --dataset_base $INPUTDATAPATH/video/$NAME_VIDEO --out_dir $OUTPUTFOLDER --target_name $NAME_VIDEO --checkpoint_dir $BASE/checkpoints/ + cd .. + + ##################### GAN INFERENCE ##################### + echo -e '\n--------------GAN INFERENCE---------------\n' + cd ImageToImage + python generate_images.py --dataroot $INPUTDATAPATH/video --video_name $NAME_VIDEO --input_test_root_dir $OUTPUTFOLDER/edges/ --out_dir $OUTPUTFOLDER/generated_frames/ --checkpoint_dir $BASE/checkpoints/ + cd .. + + ##################### POSTPROCESSING ##################### + echo -e '\n--------------POSTPROCESSING---------------\n' + python postprocessing.py --dataroot $INPUTDATAPATH --name_audio $NAME_AUDIO --out_dir $OUTPUTFOLDER --fps $FPS --sr $SAMPLERATE --clean --move_to_one_folder + + echo -e ' ================= ALL DONE ================= ' + conda deactivate + + + done + +done diff --git a/motion-gan-pipeline/full_pipeline_enhancement.sh b/motion-gan-pipeline/full_pipeline_enhancement.sh new file mode 100644 index 0000000..4a4701d --- /dev/null +++ b/motion-gan-pipeline/full_pipeline_enhancement.sh @@ -0,0 +1,118 @@ +#!/bin/bash + +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +set -e +eval "$(conda shell.bash hook)" + +set -e + +# eval "$(conda shell.bash hook)" +#conda activate adnerf + +# SET GPU DEVICE +GPUID=0 + +# BASE='/mnt/full_pipeline' +BASE=$1 +echo "DATAROOT: $BASE" + +NAME_AUDIO_FILE=$2 +NAME_VIDEO_FILE=$3 +echo "NAME_AUDIO_FILE: $2" +echo "NAME_VIDEO_FILE: $3" + +# You can choose here what you want +FPS=25 +SAMPLERATE=16000 + +# DATA PATHS +INPUTDATAPATH=$BASE/input_data +OUTPUTDATAPATH=$BASE/output_data +FEATURESPATH=features + +###AUDIO### +NAME_AUDIOS_LIST=( $NAME_AUDIO_FILE ) + +### VIDEO ### +NAME_VIDEOS_LIST=( $NAME_VIDEO_FILE ) + +for NAME_AUDIO in "${NAME_AUDIOS_LIST[@]}" +do + for NAME_VIDEO in "${NAME_VIDEOS_LIST[@]}" + do + + if [ ! -d "$INPUTDATAPATH/audio/$NAME_AUDIO" ]; then + echo "Moving audio file.." + mkdir -p $INPUTDATAPATH/audio/$NAME_AUDIO + mv $INPUTDATAPATH/audio/$NAME_AUDIO.wav $INPUTDATAPATH/audio/$NAME_AUDIO/$NAME_AUDIO.wav + fi + + if [ ! -d "$INPUTDATAPATH/video/$NAME_VIDEO" ]; then + echo "Moving video file.." + mkdir -p $INPUTDATAPATH/video/$NAME_VIDEO + mv $INPUTDATAPATH/video/$NAME_VIDEO.mp4 $INPUTDATAPATH/video/$NAME_VIDEO/$NAME_VIDEO.mp4 + fi + + # Make output folder + tmp='_to_' + now="$(date +'_%d%m%y_%X')" + OUTPUTFOLDER=$OUTPUTDATAPATH/$NAME_AUDIO$tmp$NAME_VIDEO + # OUTPUTFOLDER=$OUTPUTDATAPATH/$NAME_AUDIO$tmp$NAME_VIDEO$now + echo $OUTPUTFOLDER + mkdir -p $OUTPUTFOLDER + + ##################### PREPROCESSING ##################### + echo -e '\n--------------PREPROCESSING---------------\n' + conda activate deepspeech + cd preprocessing + bash process_audio.sh $INPUTDATAPATH/audio $NAME_AUDIO $FPS $SAMPLERATE + bash process_video.sh $INPUTDATAPATH/video $NAME_VIDEO $FPS $SAMPLERATE + cd .. + + #################### AUDIO EXPRESSIONS ##################### + echo -e '\n--------------AUDIO EXPRESSIONS---------------\n' + cd preprocessing/ + python third/Audio2ExpressionNet/get_audioexpr.py --name $NAME_AUDIO --dataset_base $INPUTDATAPATH/audio/$NAME_AUDIO --out_dir $OUTPUTFOLDER --mapping_path $INPUTDATAPATH/video/$NAME_VIDEO/mapping.npy + cd .. + conda deactivate + + ##################### HEAD MOTION ##################### + echo -e '\n--------------HEAD MOTION---------------\n' + conda activate pyenv + cd motion-generation + python transfer.py --dataroot $INPUTDATAPATH --dataset_names $NAME_AUDIO --target_name $NAME_VIDEO --out_dir $OUTPUTFOLDER --checkpoint_dir $BASE/checkpoints/ + cd .. + + ##################### HEAD TO SHOULDERS ##################### + echo -e '\n--------------HEAD TO BODY---------------\n' + python face2body.py --dataset_base $INPUTDATAPATH/video/$NAME_VIDEO --target_name $NAME_VIDEO --checkpoint_dir $BASE/checkpoints/ + + #################### EDGE MAP ##################### + echo -e '\n--------------EDGE MAP---------------\n' + cd preprocessing + python combine_edges.py --dataset_base $INPUTDATAPATH/video/$NAME_VIDEO --out_dir $OUTPUTFOLDER --target_name $NAME_VIDEO --checkpoint_dir $BASE/checkpoints/ + cd .. + + ##################### GAN INFERENCE ##################### + echo -e '\n--------------GAN INFERENCE---------------\n' + cd ImageToImage + python generate_images.py --dataroot $INPUTDATAPATH/video --video_name $NAME_VIDEO --input_test_root_dir $OUTPUTFOLDER/edges/ --out_dir $OUTPUTFOLDER/generated_frames/ --checkpoint_dir $BASE/checkpoints/ + cd .. + + ##################### ENHANCEMENT ##################### + echo -e '\n--------------ENHANCEMENT---------------\n' + cd GFPGAN + python inference_gfpgan.py -i $OUTPUTFOLDER/generated_frames/ -o $OUTPUTFOLDER/superes/ -v 1.4 -s 1 --bg_upsampler realesrgan + cd .. + + ##################### POSTPROCESSING ##################### + echo -e '\n--------------POSTPROCESSING---------------\n' + python postprocessing.py --dataroot $INPUTDATAPATH --name_audio $NAME_AUDIO --out_dir $OUTPUTFOLDER --fps $FPS --sr $SAMPLERATE --enhance --clean + + echo -e ' ================= ALL DONE ================= ' + + done + +done diff --git a/motion-gan-pipeline/gunicorn.conf.py b/motion-gan-pipeline/gunicorn.conf.py new file mode 100644 index 0000000..9bc8d9c --- /dev/null +++ b/motion-gan-pipeline/gunicorn.conf.py @@ -0,0 +1,19 @@ +# https://github.com/benoitc/gunicorn/blob/master/examples/example_config.py + +# Bind & deployment + +bind = '0.0.0.0:5000' +reload = False + +# Connections +# The dashboard backend should be capable of supporting multiple workers, +# however initialization is currently an issue when running in multiple threads. +workers = 1 # if DashboardConfig.debug else 4 +threads = 4 +backlog = 64 + +timeout = 300 +TIMEOUT = 300 + +# Logging +loglevel = 'info' diff --git a/motion-gan-pipeline/media/audio2video.mp4 b/motion-gan-pipeline/media/audio2video.mp4 new file mode 100644 index 0000000..4191be3 --- /dev/null +++ b/motion-gan-pipeline/media/audio2video.mp4 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0139fa480dd3c47d3c1e39bd50001f0c6a0c39444e06f03aa58d0a47a2ed4246 +size 2201602 diff --git a/motion-gan-pipeline/media/pipeline.gif b/motion-gan-pipeline/media/pipeline.gif new file mode 100644 index 0000000..aa5a243 Binary files /dev/null and b/motion-gan-pipeline/media/pipeline.gif differ diff --git a/motion-gan-pipeline/media/pose.png b/motion-gan-pipeline/media/pose.png new file mode 100644 index 0000000..b6ad696 Binary files /dev/null and b/motion-gan-pipeline/media/pose.png differ diff --git a/motion-gan-pipeline/motion-generation/README.md b/motion-gan-pipeline/motion-generation/README.md new file mode 100644 index 0000000..1b8cbe9 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/README.md @@ -0,0 +1,116 @@ +# Motion Generation + +This is a re-adaptation of the [LiveSpeechPortraits](https://github.com/YuanxunLu/LiveSpeechPortraits) repository. \ +The LSP pipeline generates realistic head motion from an input audio signal. \ +We modified the original pipeline to generate head motion from extracted Deepspeech audio features. + +## Requirements + +``` +conda create -n LSP python=3.6 +conda activate LSP +``` + +- Clone the repository: + +- FFmpeg is required to combine the audio and the silent generated videos. Please check [FFmpeg](http://ffmpeg.org/download.html) for installation. For Linux users, you can also: + +``` +sudo apt-get install ffmpeg +``` + +- Install the dependences: + +``` +pip install -r requirements.txt +``` + +## Train on large dataset + +We trained our model on a large video dataset [TED Dataset](https://snap-research.github.io/articulated-animation/) to learn generic human head motion. +You can download the dataset using the scripts in TED_dataset_utils. + +You can run the training script by using the following command: +```bash +python train.py --config config/Audio2Headpose_Ted_deepspeech.yml +``` +You can crete your own config file using the make_config.py script. + +The following data structure is expected: +```bash +TED_dataset +├── Test +│ ├── VID_0 +│ ... +│ └── VID_N +│ +└── Train + ├── VID_0 + ... + └── VID_N +``` + +Where each video folder contains processed data that was generated using our [Preprocessing pipeline](https://gitlab.ethz.ch/mtc/video-synthesis/visionPreprocessing). + + +## Transfer & Finetuning +In the Audio-driven Video Synthesis project, we finetune over the checkpoint obtained by training on the TED dataset, using the input video data. We can therefore better fit the generated motion to the person specific motion presented in the video. + +The transfer is executed using: +```bash +python transfer.py --dataroot $INPUTDATAPATH + --dataset_names $NAME_AUDIO + --target_name $NAME_VIDEO + --out_dir $OUTPUTFOLDER + --checkpoint_dir $CHECKPOINTPATH +``` +Where: +- ```$INPUTDATAPATH```: path to data folder. +- ```$NAME_AUDIO```: input audio name. +- ```$NAME_VIDEO```: target video name. +- ```$OUTPUTFOLDER```: path to the output folder. +- ```$CHECKPOINTPATH```: path to the checkpoints folder. + +If no finetuned checkpoint for the requested video exists, the finetuning script is launched automatically. + +Alternatively you can directly launch finetuning by running: +```bash +python finetune.py --name $NAME_VIDEO + --dataset_mode $DATASETMODE + --dataroot $INPUTDATAPATH + --dataset_names $NAME_AUDIO + --fps $FPS + --target_checkpoints $OUTPUTCHECKPOINT + --checkpoint_path $TUNECHECKPOINT +``` +Where: +- ```$NAME_VIDEO```: target video name. +- ```$DATASETMODE```: type of dataset, set to 'deepspeech'. +- ```$INPUTDATAPATH```: path to data folders. +- ```$NAME_AUDIO```: input audio name. +- ```$FPS```: target video's fps. +- ```$OUTPUTCHECKPOINT```: path to the output checkpoint directory. +- ```$TUNECHECKPOINT```: path to the checkpoints to finetune. + +## Correlation Visuals +We also provide some scripts to visualise audio features, and compute audio features to head motion correlation. + + +- audio_visuals: +visualise multiple audio representations, original video, tracked FLAME head and Deepspeech features. + + drawing +

+ +- emotion_correlation_visuals: +plot and save correlation between EMOCA emotions and head motion. + + drawing +

+ +- audio_correlation_visual: +plot and save correlation between audio features and head motion. + + drawing + + diff --git a/motion-gan-pipeline/motion-generation/TED_dataset_utils/load_videos.py b/motion-gan-pipeline/motion-generation/TED_dataset_utils/load_videos.py new file mode 100644 index 0000000..f909a9d --- /dev/null +++ b/motion-gan-pipeline/motion-generation/TED_dataset_utils/load_videos.py @@ -0,0 +1,127 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import numpy as np +import pandas as pd +import imageio +import os +import subprocess +from multiprocessing import Pool +from itertools import cycle +import warnings +import glob +import time +from tqdm import tqdm +from util import save +from argparse import ArgumentParser +from skimage import img_as_ubyte +from skimage.transform import resize +from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip +from moviepy.video.fx.all import crop +from moviepy.editor import * + +warnings.filterwarnings("ignore") + +DEVNULL = open(os.devnull, 'wb') + +def download(video_id, args): + video_path = os.path.join(args.video_folder, video_id + ".mp4") + subprocess.call([args.youtube, '-f', "''best/mp4''", '--write-auto-sub', '--write-sub', + '--sub-lang', 'en', '--skip-unavailable-fragments', + "https://www.youtube.com/watch?v=" + video_id, "--output", + video_path], stdout=DEVNULL, stderr=DEVNULL) + return video_path + +def run(data): + video_id, args = data + + if not os.path.exists(os.path.join(args.video_folder, video_id + '.mp4')): + download(video_id, args) + + if not os.path.exists(os.path.join(args.video_folder, video_id + '.mp4')): + print('Broken Link: ', video_id) + return + + if args.mode == 'd': + return + try: + reader = imageio.get_reader(os.path.join(args.video_folder, video_id + '.mp4')) + + except FileNotFoundError: + print(f'Could not find: {os.path.join(args.video_folder, video_id + ".mp4")}') + return + + frame = reader.get_next_data() + fps = reader.get_meta_data()['fps'] + df = pd.read_csv(args.metadata) + df = df[df['video_id'] == video_id] + + all_chunks_dict = [{'start': df['start'].iloc[j], 'end': df['end'].iloc[j], + 'bbox': list(map(int, df['bbox'].iloc[j].split('-'))), 'frames': []} for j in + range(df.shape[0])] + + ref_height = df['height'].iloc[0] + ref_width = df['width'].iloc[0] + partition = df['partition'].iloc[0] + + for entry in all_chunks_dict: + + if 'person_id' in df: + first_part = df['person_id'].iloc[0] + "#" + else: + first_part = "" + first_part = first_part + '#' + video_id + inpath = os.path.join(args.video_folder, video_id + '.mp4') + path = first_part + '#' + str(entry['start']).zfill(6) + '#' + str(entry['end']).zfill(6) + '.mp4' + outpath = os.path.join(args.out_folder, partition, path) + + clip = VideoFileClip(inpath) + print('start: ', entry['start']/fps, ', end: ', entry['end']/fps) + + try: + clip = clip.subclip(entry['start']/fps, entry['end']/fps) + + except ValueError as e: + print(e) + return + + left, top, right, bot = entry['bbox'] + + left = int(left / (ref_width / frame.shape[1])) + top = int(top / (ref_height / frame.shape[0])) + right = int(right / (ref_width / frame.shape[1])) + bot = int(bot / (ref_height / frame.shape[0])) + clip = crop(clip, left, top, right, bot) + + # saving the clip + clip.write_videofile(outpath) + + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument("--video_folder", help='Path to youtube videos') + parser.add_argument("--metadata", help='Path to metadata') + parser.add_argument("--out_folder", help='Path to output') + parser.add_argument("--format", help='Storing format') + parser.add_argument("--workers", default=1, type=int, help='Number of workers') + parser.add_argument("--youtube", default='./youtube-dl', help='Path to youtube-dl') + parser.add_argument("--mode", default='d_p', help='Mode: download (d) or download and process (d_p)') + + args = parser.parse_args() + if not os.path.exists(args.video_folder): + os.makedirs(args.video_folder) + if not os.path.exists(args.out_folder): + os.makedirs(args.out_folder) + for partition in ['test', 'train']: + if not os.path.exists(os.path.join(args.out_folder, partition)): + os.makedirs(os.path.join(args.out_folder, partition)) + + df = pd.read_csv(args.metadata) + video_ids = df['video_id'] + + pool = Pool(processes=args.workers) + args_list = cycle([args]) + for chunks_data in tqdm(pool.imap_unordered(run, zip(video_ids, args_list))): + None + + diff --git a/motion-gan-pipeline/motion-generation/TED_dataset_utils/ted384-metadata.csv b/motion-gan-pipeline/motion-generation/TED_dataset_utils/ted384-metadata.csv new file mode 100644 index 0000000..683251b --- /dev/null +++ b/motion-gan-pipeline/motion-generation/TED_dataset_utils/ted384-metadata.csv @@ -0,0 +1,1266 @@ +video_id,start,end,bbox,fps,width,height,partition +VM6HZqQKhok,16609,16737,616-112-1172-667,24.0,1920,1080,train 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a/motion-gan-pipeline/motion-generation/TED_dataset_utils/util.py b/motion-gan-pipeline/motion-generation/TED_dataset_utils/util.py new file mode 100644 index 0000000..7d693f7 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/TED_dataset_utils/util.py @@ -0,0 +1,129 @@ +import numpy as np +from skimage import img_as_ubyte +from skimage.transform import resize +import imageio + +def bb_intersection_over_union(boxA, boxB): + xA = max(boxA[0], boxB[0]) + yA = max(boxA[1], boxB[1]) + xB = min(boxA[2], boxB[2]) + yB = min(boxA[3], boxB[3]) + interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1) + boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1) + boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1) + iou = interArea / float(boxAArea + boxBArea - interArea) + return iou + + +def one_box_inside_other(boxA, boxB): + xA = boxA[0] <= boxB[0] + yA = boxA[1] <= boxB[1] + xB = boxA[2] >= boxB[2] + yB = boxA[3] >= boxB[3] + return xA and yA and xB and yB + +def join(tube_bbox, bbox): + xA = min(tube_bbox[0], bbox[0]) + yA = min(tube_bbox[1], bbox[1]) + xB = max(tube_bbox[2], bbox[2]) + yB = max(tube_bbox[3], bbox[3]) + return (xA, yA, xB, yB) + + +def compute_aspect_preserved_bbox(bbox, increase_area): + left, top, right, bot = bbox + width = right - left + height = bot - top + + width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width)) + height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height)) + + left = int(left - width_increase * width) + top = int(top - height_increase * height) + right = int(right + width_increase * width) + bot = int(bot + height_increase * height) + + return (left, top, right, bot) + +def compute_increased_bbox(bbox, increase_area): + left, top, right, bot = bbox + width = right - left + height = bot - top + + left = int(left - increase_area * width) + top = int(top - increase_area * height) + right = int(right + increase_area * width) + bot = int(bot + increase_area * height) + + return (left, top, right, bot) + + + +def crop_bbox_from_frames(frame_list, tube_bbox, min_frames=16, image_shape=(256, 256), min_size=200, + increase_area=0.1, aspect_preserving=True): + frame_shape = frame_list[0].shape + # Filter short sequences + if len(frame_list) < min_frames: + return None, None + left, top, right, bot = tube_bbox + width = right - left + height = bot - top + # Filter if it is too small + if max(width, height) < min_size: + return None, None + + if aspect_preserving: + left, top, right, bot = compute_aspect_preserved_bbox(tube_bbox, increase_area) + else: + left, top, right, bot = compute_increased_bbox(tube_bbox, increase_area) + + # Compute out of bounds + left_oob = -min(0, left) + right_oob = right - min(right, frame_shape[1]) + top_oob = -min(0, top) + bot_oob = bot - min(bot, frame_shape[0]) + + #Not use near the border + if max(left_oob / float(width), right_oob / float(width), top_oob / float(height), bot_oob / float(height)) > 0: + return [None, None] + + selected = [frame[top:bot, left:right] for frame in frame_list] + if image_shape is not None: + out = [img_as_ubyte(resize(frame, image_shape, anti_aliasing=True)) for frame in selected] + else: + out = selected + + return out, [left, top, right, bot] + +from multiprocessing import Pool +from itertools import cycle +from tqdm import tqdm +import os + +def scheduler(data_list, fn, args): + device_ids = args.device_ids.split(",") + pool = Pool(processes=args.workers) + args_list = cycle([args]) + f = open(args.chunks_metadata, 'w') + line = "{video_id},{start},{end},{bbox},{fps},{width},{height},{partition}" + print (line.replace('{', '').replace('}', ''), file=f) + for chunks_data in tqdm(pool.imap_unordered(fn, zip(data_list, cycle(device_ids), args_list))): + for data in chunks_data: + print (line.format(**data), file=f) + f.flush() + f.close() + +def save(path, frames, format): + if format == '.mp4': + imageio.mimsave(path, frames) + elif format == '.png': + if os.path.exists(path): + print ("Warning: skiping video %s" % os.path.basename(path)) + return + else: + os.makedirs(path) + for j, frame in enumerate(frames): + imageio.imsave(os.path.join(path, str(j).zfill(7) + '.png'), frames[j]) + else: + print ("Unknown format %s" % format) + exit() diff --git a/motion-gan-pipeline/motion-generation/audio_correlation_visual.py b/motion-gan-pipeline/motion-generation/audio_correlation_visual.py new file mode 100644 index 0000000..bfc1862 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/audio_correlation_visual.py @@ -0,0 +1,201 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os +from os.path import join +from shutil import move +import sys +from turtle import shape +import yaml +import argparse +import numpy as np +from options.test_audio2headpose_options import TestOptions +from datasets import create_dataset +from util.cfgnode import CfgNode +import cv2 +import subprocess +from pathlib import Path +from tqdm import tqdm +import matplotlib +matplotlib.use('agg') +import matplotlib.pyplot as plt +from matplotlib.gridspec import GridSpec +import librosa +import librosa.display +from PIL import Image +sys.path.append('../preprocessing/') +from face_tracking.FLAME.FLAME import FLAME +from face_tracking.FLAME.config import cfg as FLAME_cfg +from face_tracking.FLAME.lbs import vertices2landmarks +from face_tracking.render_3dmm import Render_FLAME +from face_tracking.util import * +import torch +from scipy.stats import pearsonr + + +def write_video_with_audio(audio_path, output_path, prefix='pred_', h=512, w=512, fps=25): + fourcc = cv2.VideoWriter_fourcc(*'DIVX') + # fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G') + video_tmp_path = join(target_dir, 'tmp.avi') + out = cv2.VideoWriter(video_tmp_path, fourcc, fps, (w, h)) + nframe = len(list(Path(target_dir).glob('*.png'))) + + for j in tqdm(range(nframe), position=0, desc='writing video'): + img = cv2.imread(join(target_dir, prefix + str(j+1) + '.png')) + out.write(img) + out.release() + cmd = 'ffmpeg -y -i "' + video_tmp_path + '" -i "' + \ + audio_path + '" -codec copy -shortest "' + output_path + '"' + subprocess.call(cmd, shell=True) + os.remove(video_tmp_path) # remove the template video + + +if __name__ == '__main__': + # Load default options + Test_parser = TestOptions() + opt = Test_parser.parse() # get training options + + # Load config + parser = argparse.ArgumentParser() + parser.add_argument('--config', default='config/Audio2Headpose_Ted.yml', + help="person name, e.g. Obama1, Obama2, May, Nadella, McStay", required=False) + inopt = parser.parse_args() + # TODO: make config + with open(inopt.config) as f: + cfg_dict = yaml.load(f, Loader=yaml.FullLoader) + cfg = CfgNode(cfg_dict) + + # Overwrite with config + opt.phase = 'Test' + opt.name = cfg.experiment.name + opt.dataset_mode = cfg.experiment.dataset_mode + opt.dataroot = cfg.experiment.dataroot + opt.dataset_names = cfg.experiment.dataset_names + opt.FPS = cfg.experiment.fps + + # save to the disk + Test_parser.print_options(opt) + + # Set device + device = torch.device('cuda:{}'.format(opt.gpu_ids[0])) if len( + opt.gpu_ids) > 0 else torch.device('cpu') + + # Load data + # create a dataset given opt.dataset_mode and other options + dataset = create_dataset(opt) + dataset = dataset.dataset + + # Target dir + target_dir= './visuals/Audio_Correlation/' + os.makedirs(target_dir, exist_ok=True) + + # Make renderer + h_fl, w_fl = 512, 512 + focal = torch.from_numpy(np.array([900.])).double() + + cxy = torch.tensor((w_fl / 2.0, h_fl / 2.0), dtype=torch.float).cpu() + + model_3dmm = FLAME(FLAME_cfg.model) + renderer = Render_FLAME(model_3dmm.faces_tensor, focal, h_fl, w_fl, 1, device) + + stop_at = 50 + + movement = [] + amplitude = [] + + for file_index in tqdm(range(len(dataset))): + + if file_index >= stop_at: + break + + test_name = dataset.clip_names[file_index] + + name, flame, frames, audio_packed, audio_features, emotions, landmarks, exps, headposes, velocity_pose, fit_data_path = dataset[file_index] + + nframes = headposes.shape[0] + + # Spectrogram of frequency + audio, sr = audio_packed + fps = 25 + audio_len = len(audio)/sr + + # load fit data + fit_data = torch.load(fit_data_path) + + # Spectogram + X = librosa.stft(audio) + # print('Amplitude shape: ', audio.shape) + # print('Num frames: ', nframes) + + # RMS + S, phase = librosa.magphase(X) + rms = librosa.feature.rms(S=S) + + ampl_delta = np.concatenate([[0], audio[1:] - audio[:-1]]) + + interval = len(audio)/nframes + + # Over teh spast second + window = fps * 2 + for frame in range(window, nframes): + mov = np.sum(np.abs(velocity_pose[frame-window:frame]), axis=0) + # mov = velocity_pose[frame] + # mov = headposes[frame] + + ampl = np.sum(np.abs(ampl_delta[int((frame-window)*interval):int(frame*interval)])) + # ampl = np.sum((ampl_delta[int((frame-window)*interval):int(frame*interval)])) + # ampl = np.mean(audio[int((frame-window)*interval):int(frame*interval)]) + movement.append(mov) + amplitude.append(ampl) + + movement = np.array(movement) + amplitude = np.array(amplitude) + + print(movement.shape) + print(amplitude.shape) + + for idx, angle in enumerate(['Pitch', 'Yaw', 'Roll']): + + # Movement per frame + r, p = pearsonr(x=amplitude, y=movement[:, idx]) + r = round(r, 3) + p = round(p, 3) + x = np.linspace(np.min(amplitude), np.max(amplitude), 1000) + + plt.title(f'{angle} Angle total movement and total change in Amplitude in the past 2 seconds \n Correlation: {int(r*100)}%, p-value: {int(p*100)}%') + plt.scatter(x=amplitude, y=movement[:, idx]) + + plt.plot(x, (x * r) + np.mean(movement[:, idx]), linestyle='solid', color='red') + plt.xlabel('Amplitude') + plt.ylabel('Movement') + plt.savefig(f'{target_dir}{angle}_movement&delta_Amplitude.png') + plt.close() + + # Talking head + R = torch.zeros(3) + R[idx] += 45 + R = R.unsqueeze(0).to(device).double() + t = torch.from_numpy(np.array([0, 0, -5])).unsqueeze(0).to(device).double() + id = torch.zeros_like(fit_data['id']).to(device).double() + expr = torch.zeros_like(exps[0]).unsqueeze(0).to(device).double() + + rott_geo = model_3dmm.forward_geo(id, expr, R, t) + landmarks3d = model_3dmm.get_3dlandmarks(id, expr, R, t, focal, cxy).cpu() + proj_geo = proj_pts(landmarks3d, focal, cxy) + + sel_pts3D = vertices2landmarks(rott_geo, + model_3dmm.faces_tensor, + model_3dmm.full_lmk_faces_idx.repeat( + 1, 1), + model_3dmm.full_lmk_bary_coords.repeat(1, 1, 1)) + + render_imgs = renderer(rott_geo.float(), model_3dmm.faces_tensor) + img_arr = render_imgs[0, :, :, :3].cpu().numpy() + img_arr *= 255 + img_arr = img_arr.astype(np.uint8) + im = Image.fromarray(img_arr) + plt.imshow(im) + plt.savefig(f'{target_dir}{angle}_45.png') + plt.close() + + print('Finish!') diff --git a/motion-gan-pipeline/motion-generation/audio_visual.py b/motion-gan-pipeline/motion-generation/audio_visual.py new file mode 100644 index 0000000..0f82fd3 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/audio_visual.py @@ -0,0 +1,245 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os +from os.path import join +import yaml +import argparse +import numpy as np +from options.test_audio2headpose_options import TestOptions +from datasets import create_dataset +from util.cfgnode import CfgNode +import cv2 +import subprocess +from pathlib import Path +from tqdm import tqdm +import matplotlib +matplotlib.use('agg') +import matplotlib.pyplot as plt +from matplotlib.gridspec import GridSpec +import librosa +import librosa.display +from PIL import Image +import sys +sys.path.append('../preprocessing/') +from face_tracking.FLAME.FLAME import FLAME +from face_tracking.FLAME.config import cfg as FLAME_cfg +from face_tracking.FLAME.lbs import vertices2landmarks +from face_tracking.render_3dmm import Render_FLAME +from face_tracking.util import * +import torch +from scipy.ndimage import gaussian_filter1d + +def write_video_with_audio(audio_path, output_path, prefix='pred_', h=512, w=512, fps=25): + fourcc = cv2.VideoWriter_fourcc(*'DIVX') + # fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G') + video_tmp_path = join(target_dir, 'tmp.avi') + out = cv2.VideoWriter(video_tmp_path, fourcc, fps, (w, h)) + nframe = len(list(Path(target_dir).glob('*.png'))) + + for j in tqdm(range(nframe), position=0, desc='writing video'): + img = cv2.imread(join(target_dir, prefix + str(j+1) + '.png')) + out.write(img) + out.release() + cmd = 'ffmpeg -y -i "' + video_tmp_path + '" -i "' + \ + audio_path + '" -codec copy -shortest "' + output_path + '"' + subprocess.call(cmd, shell=True) + os.remove(video_tmp_path) # remove the template video + + +if __name__ == '__main__': + # Load default options + Test_parser = TestOptions() + opt = Test_parser.parse() # get training options + + # Load config + parser = argparse.ArgumentParser() + parser.add_argument('--config', default='config/Audio2Headpose_Ted.yml', + help="person name, e.g. Obama1, Obama2, May, Nadella, McStay", required=False) + inopt = parser.parse_args() + # TODO: make config + with open(inopt.config) as f: + cfg_dict = yaml.load(f, Loader=yaml.FullLoader) + cfg = CfgNode(cfg_dict) + + # Overwrite with config + opt.phase = 'Test' + opt.name = cfg.experiment.name + opt.dataset_mode = cfg.experiment.dataset_mode + opt.dataroot = cfg.experiment.dataroot + opt.dataset_names = cfg.experiment.dataset_names + opt.FPS = cfg.experiment.fps + + # save to the disk + Test_parser.print_options(opt) + + # Set device + device = torch.device('cuda:{}'.format(opt.gpu_ids[0])) if len( + opt.gpu_ids) > 0 else torch.device('cpu') + + # Load data + # create a dataset given opt.dataset_mode and other options + dataset = create_dataset(opt) + dataset = dataset.dataset + + # Target dir + target_dir= './visuals/Audio_Visual/' + os.makedirs(target_dir, exist_ok=True) + + # Make renderer + h_fl, w_fl = 512, 512 + focal = torch.from_numpy(np.array([900.], dtype=np.float32)) + + cxy = torch.tensor((w_fl / 2.0, h_fl / 2.0), dtype=torch.float).cpu() + + model_3dmm = FLAME(FLAME_cfg.model) + renderer = Render_FLAME(model_3dmm.faces_tensor, focal, h_fl, w_fl, 1, device) + + stop_at = 50 + + for file_index in tqdm(range(len(dataset))): + + if file_index >= stop_at: + break + + test_name = dataset.clip_names[file_index] + + name, flame, frames, audio_packed, audio_features, emotions, landmarks, exps, headposes, velocity_pose, fit_data_path = dataset[file_index] + + # Spectrogram of frequency + audio, sr = audio_packed + fps = 25 + audio_len = len(audio)/sr + + # load fit data + fit_data = torch.load(fit_data_path) + + # Amplitude + X = audio + time_sf = np.linspace(start=0, stop=len(X)/sr, num=len(X)) + lim = np.max([np.abs(np.min(X)), np.max(X)]) + + # Spectogram + X = librosa.stft(audio) + Xdb = librosa.amplitude_to_db(abs(X)) + + # RMS + S, phase = librosa.magphase(X) + rms = librosa.feature.rms(S=S) + times = librosa.times_like(rms) + + # log Power spectrogram + lpw = librosa.amplitude_to_db(S, ref=np.max) + + # Zero Crossing Rate ZCR + zcrs = librosa.feature.zero_crossing_rate(audio) + + # Mel-Frequency Cepstral Coefficients (MFCC) + mfccs = librosa.feature.mfcc(y=audio, sr=sr) + + # Make frames for each fps + for i in tqdm(range(min(len(frames), len(audio_features)))): + + line_at = i/fps + + fig = plt.figure(figsize=(16, 15), tight_layout=True) + fontsize = 18 + gs = GridSpec(5, 2) + current_plot = 0 + column = 1 + + # Amplitude + ax1 = fig.add_subplot(gs[current_plot, column]) + ax1.set_title('Amplitude', fontsize=fontsize-2) + ax1.set_ylabel('Amplitude') + ax1.set_xlabel('Time') + ax1.plot(time_sf, audio, label=name, alpha=0.5) + ax1.set_xlim([0, audio_len]) + ax1.set_ylim([-lim, lim]) + ax1.axvline(line_at, color = 'r') + current_plot += 1 + + # Spectogram + ax2 = fig.add_subplot(gs[current_plot, column]) + ax2.set_title('Spectogram', fontsize=fontsize-2) + librosa.display.specshow(Xdb, sr=sr, x_axis='time', y_axis='hz', ax=ax2) + # plt.colorbar(orientation="horizontal", ax=ax2) + ax2.axvline(line_at, color = 'r') + current_plot += 1 + + # RMS + ax3 = fig.add_subplot(gs[current_plot, column]) + ax3.set_title('RMS - Total magnitude / energy', fontsize=fontsize) + ax3.semilogy(times, rms[0], label='RMS Energy') + ax3.set_xlim([0, audio_len]) + ax3.set_ylim([0, np.max(rms[0])]) + plt.axvline(line_at, color = 'r') + current_plot += 1 + + # log Power spectrogram + ax4 = fig.add_subplot(gs[current_plot, column]) + ax4.set_title('Log Power Spectrogram', fontsize=fontsize-2) + librosa.display.specshow(lpw, y_axis='log', x_axis='time', ax=ax4) + # plt.colorbar(orientation="horizontal", ax=ax4) + plt.axvline(line_at, color = 'r') + current_plot += 1 + + # Mel-Frequency Cepstral Coefficients (MFCC) + ax5 = fig.add_subplot(gs[current_plot, column]) + ax5.set_title('Mel-Frequency Cepstral Coefficients (MFCC)', fontsize=fontsize-2) + librosa.display.specshow(mfccs, sr=sr, x_axis='time', ax=ax5) + # plt.colorbar(orientation="horizontal", ax=ax5) + plt.axvline(line_at, color = 'r') + current_plot += 1 + + # Right column + column = 0 + + # Original frame + ax6 = fig.add_subplot(gs[0:2, column]) + ax6.set_title(name, fontsize=fontsize + 2) + ax6.imshow(Image.open(frames[i])) + plt.axis('off') + + # Talking head + R = fit_data['euler'][i] + R = R.unsqueeze(0).to(device).double() + t = torch.from_numpy(np.array([0, 0, -5])).unsqueeze(0).to(device).double() + id = fit_data['id'].to(device).double() + expr = exps[i].unsqueeze(0).to(device).double() + rott_geo = model_3dmm.forward_geo(id, expr, R, t) + landmarks3d = model_3dmm.get_3dlandmarks(id, expr, R, t, focal, cxy).cpu() + proj_geo = proj_pts(landmarks3d, focal, cxy) + render_imgs = renderer(rott_geo.float(), model_3dmm.faces_tensor) + img_arr = render_imgs[0, :, :, :3].cpu().numpy() + img_arr *= 255 + img_arr = img_arr.astype(np.uint8) + im = Image.fromarray(img_arr) + ax7 = fig.add_subplot(gs[2:4, column]) + ax7.set_title('Flame Head', fontsize=fontsize) + ax7.imshow(im) + # ax7.imshow(Image.open(flame[i])) + plt.axis('off') + + # Deepspeech visuals + ax8 = fig.add_subplot(gs[4, column]) + deepspeech = audio_features[i].reshape((16,29)) + ax8.set_title('Deepspeech Features', fontsize=fontsize-2) + ax8.imshow(deepspeech, cmap='hot', interpolation='nearest') + + # Save and close + plt.savefig(os.path.join(target_dir, f'audio_{i}.png')) + plt.close() + + # make videos + final_path = os.path.join(target_dir, test_name + '.avi') + audio_path = os.path.join(dataset.dataset_root, test_name, test_name +'.wav') + tmp = Image.open(os.path.join(target_dir, f'audio_{i}.png')) + w, h = tmp.size + write_video_with_audio(audio_path, final_path, 'audio_', h, w, fps) + + _img_paths = list(map(lambda x:str(x), list(Path(target_dir).glob('*.png')))) + for i in tqdm(range(len(_img_paths)), desc='deleting intermediate images'): + os.remove(_img_paths[i]) + + print('Finish!') diff --git a/motion-gan-pipeline/motion-generation/config/Audio2Headpose_MTC.yml b/motion-gan-pipeline/motion-generation/config/Audio2Headpose_MTC.yml new file mode 100644 index 0000000..94f11b3 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/config/Audio2Headpose_MTC.yml @@ -0,0 +1,6 @@ +experiment: + dataroot: /mnt/ + dataset_mode: deepspeech + dataset_names: MTC + fps: 25 + name: Audio2Headpose_MTC diff --git a/motion-gan-pipeline/motion-generation/config/Audio2Headpose_Ted.yml b/motion-gan-pipeline/motion-generation/config/Audio2Headpose_Ted.yml new file mode 100644 index 0000000..e982f04 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/config/Audio2Headpose_Ted.yml @@ -0,0 +1,6 @@ +experiment: + dataroot: /mnt/ + dataset_mode: deepspeech + dataset_names: MTC + fps: 25 + name: Audio2Headpose_Ted diff --git a/motion-gan-pipeline/motion-generation/config/Audio2Headpose_Ted_deepspeech.yml b/motion-gan-pipeline/motion-generation/config/Audio2Headpose_Ted_deepspeech.yml new file mode 100644 index 0000000..7df44b7 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/config/Audio2Headpose_Ted_deepspeech.yml @@ -0,0 +1,6 @@ +experiment: + dataroot: /mnt/ + dataset_mode: deepspeech + dataset_names: TED384-v2 + fps: 25 + name: Audio2Headpose_Ted_deepspeech diff --git a/motion-gan-pipeline/motion-generation/config/Audio2Headpose_Ted_emotion.yml b/motion-gan-pipeline/motion-generation/config/Audio2Headpose_Ted_emotion.yml new file mode 100644 index 0000000..89cf199 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/config/Audio2Headpose_Ted_emotion.yml @@ -0,0 +1,6 @@ +experiment: + dataroot: /mnt/ + dataset_mode: emotion + dataset_names: TED384-v2 + fps: 25 + name: Audio2Headpose_Ted_emotion diff --git a/motion-gan-pipeline/motion-generation/config/Correlation_TMP_AVSpeech.yml b/motion-gan-pipeline/motion-generation/config/Correlation_TMP_AVSpeech.yml new file mode 100644 index 0000000..6eab26d --- /dev/null +++ b/motion-gan-pipeline/motion-generation/config/Correlation_TMP_AVSpeech.yml @@ -0,0 +1,6 @@ +experiment: + dataroot: /mnt/ + dataset_mode: emotion + dataset_names: TMP_AVSpeech + fps: 25 + name: Correlation_TMP_AVSpeech diff --git a/motion-gan-pipeline/motion-generation/datasets/__init__.py b/motion-gan-pipeline/motion-generation/datasets/__init__.py new file mode 100644 index 0000000..c9e8ba5 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/datasets/__init__.py @@ -0,0 +1,93 @@ +"""This package includes all the modules related to data loading and preprocessing + + To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset. + You need to implement four functions: + -- <__init__>: initialize the class, first call BaseDataset.__init__(self, opt). + -- <__len__>: return the size of dataset. + -- <__getitem__>: get a data point from data loader. + -- : (optionally) add dataset-specific options and set default options. + +Now you can use the dataset class by specifying flag '--dataset_mode dummy'. +See our template dataset class 'template_dataset.py' for more details. +""" +import importlib +import torch.utils.data +from datasets.base_dataset import BaseDataset + + +def find_dataset_using_name(dataset_name): + """Import the module "data/[dataset_name]_dataset.py". + + In the file, the class called DatasetNameDataset() will + be instantiated. It has to be a subclass of BaseDataset, + and it is case-insensitive. + """ + dataset_filename = "datasets." + dataset_name + "_dataset" + datasetlib = importlib.import_module(dataset_filename) + + dataset = None + target_dataset_name = dataset_name.replace('_', '') + 'dataset' + for name, cls in datasetlib.__dict__.items(): + if name.lower() == target_dataset_name.lower() \ + and issubclass(cls, BaseDataset): + dataset = cls + + if dataset is None: + raise NotImplementedError("In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase." % (dataset_filename, target_dataset_name)) + + return dataset + + +def get_option_setter(dataset_name): + """Return the static method of the dataset class.""" + dataset_class = find_dataset_using_name(dataset_name) + return dataset_class.modify_commandline_options + + +def create_dataset(opt): + """Create a dataset given the option. + + This function wraps the class CustomDatasetDataLoader. + This is the main interface between this package and 'train.py'/'test.py' + + Example: + >>> from data import create_dataset + >>> dataset = create_dataset(opt) + """ + data_loader = CustomDatasetDataLoader(opt) + dataset = data_loader.load_data() + return dataset + + +class CustomDatasetDataLoader(): + """Wrapper class of Dataset class that performs multi-threaded data loading""" + + def __init__(self, opt): + """Initialize this class + + Step 1: create a dataset instance given the name [dataset_mode] + Step 2: create a multi-threaded data loader. + """ + self.opt = opt + dataset_class = find_dataset_using_name(opt.dataset_mode) + self.dataset = dataset_class(opt) + print("dataset [%s] was created" % type(self.dataset).__name__) + self.dataloader = torch.utils.data.DataLoader( + self.dataset, + batch_size=opt.batch_size, + shuffle=not opt.serial_batches, + num_workers=int(opt.num_threads)) + + def load_data(self): + return self + + def __len__(self): + """Return the number of data in the dataset""" + return min(len(self.dataset), self.opt.max_dataset_size) + + def __iter__(self): + """Return a batch of data""" + for i, data in enumerate(self.dataloader): + if i * self.opt.batch_size >= self.opt.max_dataset_size: + break + yield data diff --git a/motion-gan-pipeline/motion-generation/datasets/audio_dataset.py b/motion-gan-pipeline/motion-generation/datasets/audio_dataset.py new file mode 100644 index 0000000..0de694f --- /dev/null +++ b/motion-gan-pipeline/motion-generation/datasets/audio_dataset.py @@ -0,0 +1,150 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import sys + +sys.path.append("..") + +from datasets.base_dataset import BaseDataset +import torch +import librosa +import os +import numpy as np + + +def load_audio_expressions(clip_root): + audio_expr_path = os.path.join(clip_root, 'audio_expr') + + audio_expr = [] + for i in range(len(os.listdir(audio_expr_path))): + audio_expr.append(np.load( os.path.join(audio_expr_path, f'audio_expression_{i}.npy'))[0]) + try: + audio_expr = np.stack(audio_expr, axis=0) + return torch.from_numpy(audio_expr) + + except ValueError: + return None + + +class AudioDataset(BaseDataset): + """ + + # for wavenet: + # |----receptive_field----| + # |--output_length--| + # example: | | | | | | | | | | | | | | | | | | | | | + # target: | | | | | | | | | | + + """ + + def __init__(self, opt): + # save the option and datasets root + BaseDataset.__init__(self, opt) + self.isTrain = self.opt.isTrain + self.state = opt.phase + self.dataset_name = opt.dataset_names + self.target_length = opt.time_frame_length + self.sample_rate = opt.sample_rate + self.fps = opt.FPS + + self.audioRF_history = opt.audioRF_history + self.audioRF_future = opt.audioRF_future + # self.compute_mel_online = opt.compute_mel_online + # self.feature_name = opt.feature_name + + self.audio_samples_one_frame = self.sample_rate / self.fps + self.frame_jump_stride = opt.frame_jump_stride + self.augment = False + self.task = opt.task + self.item_length_audio = int((self.audioRF_history + self.audioRF_future) / self.fps * self.sample_rate) + + #Audio2Headpose flags + self.A2H_receptive_field = opt.A2H_receptive_field + self.A2H_item_length = self.A2H_receptive_field + self.target_length - 1 #204 + self.audio_window = opt.audio_windows + self.half_audio_win = int(self.audio_window / 2) #1 + self.frame_future = opt.frame_future #15 + self.predict_length = opt.predict_length + self.predict_len = int((self.predict_length - 1) / 2) #0 + + self.gpu_ids = opt.gpu_ids + self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu') + + self.total_len = 0 + + if opt.train_test_split: + self.dataset_root = os.path.join(self.root, self.dataset_name, self.state) + self.clip_names = sorted(os.listdir(self.dataset_root)) + + else: + self.dataset_root = self.root + self.clip_names = [self.dataset_name] + + print(self.dataset_root) + print(self.clip_names) + + # check clips + self.valid_clips = [] + for i in range(len(self.clip_names)): + # check lenght of video + name = self.clip_names[i] + clip_root = os.path.join(self.dataset_root, name) + + deepspeech_feature_path = os.path.join(clip_root, 'audio_feature') + + audio_features_len = len(os.listdir(deepspeech_feature_path)) + + # if audio_features_len > self.A2H_item_length: + # self.valid_clips.append(i) + + # else: + # print(f'Audio {name} is too short and will not be used for training.') + + self.valid_clips.append(i) + + + self.clip_nums = len(self.valid_clips) + print(f'Total clips for training: {self.clip_nums}') + + # main info + self.audio = [''] * self.clip_nums + self.audio_features = [''] * self.clip_nums + self.fit_data = [''] * self.clip_nums + + # audio expressions + self.audio_expr = [''] * self.clip_nums + + + for i in range(len(self.valid_clips)): + name = self.clip_names[i] + clip_root = os.path.join(self.dataset_root, name) + deepspeech_feature_path = os.path.join(clip_root, 'audio_feature') + + # Load audio + try: + audio_path = os.path.join(clip_root, name + '.wav') + self.audio[i], _ = librosa.load(audio_path, sr=self.sample_rate) + + except FileNotFoundError: + pass + + # load deepspeech + self.audio_features[i] = torch.from_numpy(np.stack([np.load(os.path.join(deepspeech_feature_path, ds)).flatten() for ds in os.listdir(deepspeech_feature_path)], axis=0)) + + + try: + fit_data_path = os.path.join(clip_root, 'track_params.pt') + fit_data = torch.load(fit_data_path) + self.fit_data[i] = fit_data + # load audio expr + self.audio_expr[i] = load_audio_expressions(clip_root) + + except FileNotFoundError: + pass + + def __getitem__(self, index): + return index + + def __len__(self): + return len(self.audio_features) + diff --git a/motion-gan-pipeline/motion-generation/datasets/base_dataset.py b/motion-gan-pipeline/motion-generation/datasets/base_dataset.py new file mode 100644 index 0000000..a64b758 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/datasets/base_dataset.py @@ -0,0 +1,64 @@ +"""This module implements an abstract base class (ABC) 'BaseDataset' for datasets. + +It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses. +""" + +import numpy as np +import torch.utils.data as data +from PIL import Image +import torchvision.transforms as transforms +from abc import ABC, abstractmethod + + +class BaseDataset(data.Dataset, ABC): + """This class is an abstract base class (ABC) for datasets. + + To create a subclass, you need to implement the following four functions: + -- <__init__>: initialize the class, first call BaseDataset.__init__(self, opt). + -- <__len__>: return the size of dataset. + -- <__getitem__>: get a data point. + -- : (optionally) add dataset-specific options and set default options. + """ + + def __init__(self, opt): + """Initialize the class; save the options in the class + + Parameters: + opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions + """ + self.opt = opt + self.root = opt.dataroot + + @staticmethod + def modify_commandline_options(parser, is_train): + """Add new dataset-specific options, and rewrite default values for existing options. + + Parameters: + parser -- original option parser + is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options. + + Returns: + the modified parser. + """ + return parser + + @abstractmethod + def __len__(self): + """Return the total number of images in the dataset.""" + return 0 + + @abstractmethod + def __getitem__(self, index): + """Return a data point and its metadata information. + + Parameters: + index - - a random integer for data indexing + + Returns: + a dictionary of data with their names. It ususally contains the data itself and its metadata information. + """ + pass + + + + diff --git a/motion-gan-pipeline/motion-generation/datasets/deepspeech_dataset.py b/motion-gan-pipeline/motion-generation/datasets/deepspeech_dataset.py new file mode 100644 index 0000000..62e21f1 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/datasets/deepspeech_dataset.py @@ -0,0 +1,262 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import sys +from unittest import skip + +sys.path.append("..") + +from datasets.base_dataset import BaseDataset +import scipy.io as sio +import torch +import librosa +import bisect +import os +import numpy as np + +from funcs import utils + + +class DeepspeechDataset(BaseDataset): + """ DECA datasets. currently, return 2D info and 3D tracking info. + + # for wavenet: + # |----receptive_field----| + # |--output_length--| + # example: | | | | | | | | | | | | | | | | | | | | | + # target: | | | | | | | | | | + + """ + + def __init__(self, opt): + # save the option and datasets root + BaseDataset.__init__(self, opt) + self.isTrain = self.opt.isTrain + self.state = opt.dataset_type + self.dataset_name = opt.dataset_names + self.target_length = opt.time_frame_length + self.sample_rate = opt.sample_rate + self.fps = opt.FPS + + self.audioRF_history = opt.audioRF_history + self.audioRF_future = opt.audioRF_future + + #Audio2Headpose flags + self.A2H_receptive_field = opt.A2H_receptive_field + self.A2H_item_length = self.A2H_receptive_field + self.target_length - 1 #204 + self.frame_future = opt.frame_future #15 + self.predict_length = opt.predict_length + self.predict_len = int((self.predict_length - 1) / 2) #0 + + self.gpu_ids = opt.gpu_ids + self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu') + + self.total_len = 0 + start_point = self.A2H_receptive_field + if opt.train_test_split: + self.dataset_root = os.path.join(self.root, self.dataset_name, self.state) + self.clip_names = sorted(os.listdir(self.dataset_root)) + + else: + self.dataset_root = self.root + self.clip_names = [self.dataset_name] + + + # check clips + self.valid_clips = [] + for i in range(len(self.clip_names)): + # check lenght of video + name = self.clip_names[i] + clip_root = os.path.join(self.dataset_root, name) + n_frames = len(os.listdir(os.path.join(clip_root, 'frames'))) + + if n_frames >= start_point + self.target_length: # added 25 because later they remove 25 and without it, crashes + + deepspeech_feature_path = os.path.join(clip_root, 'audio_feature') + + audio_features_len = len(os.listdir(deepspeech_feature_path)) + + if audio_features_len > self.A2H_item_length: + self.valid_clips.append(i) + + else: + print(f'Audio {name} is too short and will not be used for training.') + + else: + print(f'Clip {name} is too short and will not be used for training.') + + self.clip_nums = len(self.valid_clips) + print(f'Total clips for training: {self.clip_nums}') + + # main info + self.audio = [''] * self.clip_nums + self.audio_features = [''] * self.clip_nums + self.feats = [''] * self.clip_nums + self.exps = [''] * self.clip_nums + self.pts3d = [''] * self.clip_nums + self.rot_angles = [''] * self.clip_nums + self.trans = [''] * self.clip_nums + self.headposes = [''] * self.clip_nums + self.velocity_pose = [''] * self.clip_nums + self.acceleration_pose = [''] * self.clip_nums + self.mean_trans = [''] * self.clip_nums + if self.state == 'Test': + self.landmarks = [''] * self.clip_nums + # meta info + self.start_point = [''] * self.clip_nums + self.end_point = [''] * self.clip_nums + self.len = [''] * self.clip_nums + self.sample_start = [] + self.clip_valid = ['True'] * self.clip_nums + self.invalid_clip = [] + + self.mouth_related_indices = np.concatenate([np.arange(4, 11), np.arange(46, 64)]) + + # if opt.use_delta_pts: + # self.pts3d_mean = np.load(os.path.join(self.dataset_root, 'mean_pts3d.npy')) + + for i in range(len(self.valid_clips)): + name = self.clip_names[self.valid_clips[i]] + clip_root = os.path.join(self.dataset_root, name) + deepspeech_feature_path = os.path.join(clip_root, 'audio_feature') + + # load deepspeech + self.audio_features[i] = torch.from_numpy(np.stack([np.load(os.path.join(deepspeech_feature_path, ds)).flatten() for ds in sorted(os.listdir(deepspeech_feature_path))], axis=0)) + + # 3D landmarks & headposes + self.start_point[i] = start_point # They had 300 at 60 fps + fit_data_path = os.path.join(clip_root, 'track_params.pt') + fit_data = torch.load(fit_data_path) + self.fit_data = fit_data + + self.rot_angles[i] = fit_data['euler'] + + # # change -180~180 to 0~360 + # rot_change = self.rot_angles[i][:, 0] < 0 + # self.rot_angles[i][rot_change, 0] += 360 + # self.rot_angles[i][:, 0] -= 180 # change x axis direction + + # use delta translation + self.mean_trans[i] = fit_data['trans'].mean(axis=0) + self.trans[i] = fit_data['trans'] - self.mean_trans[i] + + self.headposes[i] = np.concatenate([self.rot_angles[i], self.trans[i]], axis=1) + self.velocity_pose[i] = np.concatenate([np.zeros(6)[None, :], self.headposes[i][1:] - self.headposes[i][:-1]]) + self.acceleration_pose[i] = np.concatenate([np.zeros(6)[None, :], self.velocity_pose[i][1:] - self.velocity_pose[i][:-1]]) + + # print(clip_root) + # print('Headposes: ', self.headposes[i].shape) + # print('Audio: ', self.audio_features[i].size()) + + total_frames = min(self.trans[i].shape[0] - 25, self.audio_features[i].shape[0] - 25) # They had -60 (1 second at 60 fps) # Crashes without 25 + # print(f'Total Frames for clip {i}: {total_frames}') + + valid_frames = total_frames - self.start_point[i] + self.len[i] = valid_frames - self.target_length # ??? They had - 400 (6,66 s at 60 fps) + if i == 0: + self.sample_start.append(0) + else: + prev_start = self.sample_start[-1] + len_prev = self.len[i - 1] + self.sample_start.append(prev_start + len_prev - 1) + self.total_len += np.int32(np.floor(self.len[i])) + + + def __getitem__(self, index): + # recover real index from compressed one + index_real = np.int32(index) + # find which audio file and the start frame index + file_index = bisect.bisect_right(self.sample_start, index_real) -1 + current_frame = index_real - self.sample_start[file_index] + self.start_point[file_index] + current_target_length = self.target_length + + # find the history info start points + A2H_history_start = current_frame - self.A2H_receptive_field + A2H_item_length = self.A2H_item_length # 204 + A2H_receptive_field = self.A2H_receptive_field + + + A2Hsamples = np.zeros([A2H_item_length, 16 * 29]) #deepspeech + for i in range(A2H_item_length - 1): + try: + A2Hsamples[i] = self.audio_features[file_index][A2H_history_start + i] + + except IndexError: + print('### ERROR AUDIO ###') + print('Name: ', self.clip_names[self.valid_clips[file_index]]) + print('Current Frame: ', current_frame) + print('Data Index: ', index_real) + print('Starts at: ', self.sample_start[file_index]) + print('Len: ', self.len[file_index]) + print(A2H_history_start + i) + print(self.audio_features[file_index].size()) + print(self.headposes[file_index].shape) + exit() + + if self.predict_len == 0: + try: + target_headpose = self.headposes[file_index][ + A2H_history_start + A2H_receptive_field: A2H_history_start + A2H_item_length + 1] # [current frame: current frame + target] + history_headpose = self.headposes[file_index][A2H_history_start: A2H_history_start + A2H_item_length] + history_headpose = history_headpose.reshape(A2H_item_length, -1) # [current_frame - self.A2H_receptive_field : current frame + target -1] + + target_velocity = self.velocity_pose[file_index][ + A2H_history_start + A2H_receptive_field: A2H_history_start + A2H_item_length + 1] + history_velocity = self.velocity_pose[file_index][ + A2H_history_start: A2H_history_start + A2H_item_length].reshape(A2H_item_length, + -1) + target_info = torch.from_numpy( + np.concatenate([target_headpose, target_velocity], axis=1).reshape(current_target_length, + -1)).float() + except ValueError: + print('### ERROR HEAD ###') + print('Name: ', self.clip_names[self.valid_clips[file_index]]) + print('Current Frame: ', current_frame) + print('Data Index: ', index_real) + print('Starts at: ', self.sample_start[file_index]) + print('Len: ', self.len[file_index]) + print(target_headpose.size) + exit() + + else: + history_headpose = self.headposes[file_index][ + A2H_history_start: A2H_history_start + A2H_item_length] + history_headpose = history_headpose.reshape(A2H_item_length, -1) #Same + + history_velocity = self.velocity_pose[file_index][ + A2H_history_start: A2H_history_start + A2H_item_length] + history_velocity = history_velocity.reshape(A2H_item_length, -1) #Same + + target_headpose_ = self.headposes[file_index][ + A2H_history_start + A2H_receptive_field - self.predict_len: A2H_history_start + A2H_item_length + 1 + self.predict_len + 1] # [current frame - predict_len: current frame + target + predict_len + 1] + target_headpose = np.zeros([current_target_length, self.predict_length, target_headpose_.shape[1]]) + for i in range(current_target_length): + target_headpose[i] = target_headpose_[i: i + self.predict_length] + target_headpose = target_headpose # .reshape(current_target_length, -1, order='F') + + target_velocity_ = self.headposes[file_index][ + A2H_history_start + A2H_receptive_field - self.predict_len: A2H_history_start + A2H_item_length + 1 + self.predict_len + 1] + target_velocity = np.zeros([current_target_length, self.predict_length, target_velocity_.shape[1]]) + for i in range(current_target_length): + target_velocity[i] = target_velocity_[i: i + self.predict_length] + target_velocity = target_velocity # .reshape(current_target_length, -1, order='F') + + target_info = torch.from_numpy( + np.concatenate([target_headpose, target_velocity], axis=2).reshape(current_target_length, + -1)).float() + + A2Hsamples = torch.from_numpy(A2Hsamples).float() + + history_info = torch.from_numpy(np.concatenate([history_headpose, history_velocity], axis=1)).float() + + # [item_length, mel_channels, mel_width], or [item_length, APC_hidden_size] + + return A2Hsamples, history_info, target_info + + + def __len__(self): + return self.total_len + # return self.total_len - 2000 # Hardcoded to make it work... does not work with short videos + + + diff --git a/motion-gan-pipeline/motion-generation/datasets/emotion_dataset.py b/motion-gan-pipeline/motion-generation/datasets/emotion_dataset.py new file mode 100644 index 0000000..55b2b76 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/datasets/emotion_dataset.py @@ -0,0 +1,136 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from email.mime import audio +import sys + +sys.path.append("..") + +from datasets.base_dataset import BaseDataset +import scipy.io as sio +import torch +import librosa +import os +import numpy as np + +from funcs import utils + + +class EmotionDataset(BaseDataset): + """ + Load Emotions and headposes. + """ + + def __init__(self, opt): + # save the option and datasets root + BaseDataset.__init__(self, opt) + self.isTrain = self.opt.isTrain + self.state = opt.dataset_type + self.dataset_name = opt.dataset_names + self.fps = opt.FPS + + self.dataset_root = os.path.join(self.root, self.dataset_name, self.state) + self.clip_names = sorted(os.listdir(self.dataset_root)) + + self.clip_nums = len(self.clip_names) + print(f'Total clips for training: {self.clip_nums}') + + # main info + self.frames = [''] * self.clip_nums + self.flame = [''] * self.clip_nums + self.audio = [''] * self.clip_nums + self.audio_features = [''] * self.clip_nums + self.emotions = [''] * self.clip_nums + self.exps = [''] * self.clip_nums + self.pts3d = [''] * self.clip_nums + self.rot_angles = [''] * self.clip_nums + self.trans = [''] * self.clip_nums + self.headposes = [''] * self.clip_nums + self.velocity_pose = [''] * self.clip_nums + self.acceleration_pose = [''] * self.clip_nums + self.mean_trans = [''] * self.clip_nums + self.landmarks = [''] * self.clip_nums + self.fit_data_path = [''] * self.clip_nums + + self.len = [''] * self.clip_nums + self.clip_valid = ['True'] * self.clip_nums + + + for i in range(len(self.clip_names)): + name = self.clip_names[i] + clip_root = os.path.join(self.dataset_root, name) + + # Paths to subfolders + frames_path = os.path.join(clip_root, 'frames') + flame_path = os.path.join(clip_root, 'debug/debug_render') + deepspeech_feature_path = os.path.join(clip_root, 'audio_feature') + emotions_path = os.path.join(clip_root, 'emotions') + expr_path = os.path.join(clip_root, 'deca_expr') + landmarks_path = os.path.join(clip_root, 'landmarks') + fit_data_path = os.path.join(clip_root, 'track_params.pt') + + # load wav audio signal + # Open wav file and read frames as bytes + x, sr = librosa.load(os.path.join(clip_root, f'{name}.wav')) + self.audio[i] = (x, sr) + + # load frames + self.frames[i] = [os.path.join(frames_path, f'{f}.jpg') for f in range(len(os.listdir(frames_path)))] + + # load debug + self.flame[i] = [os.path.join(flame_path, f'{f}.jpg') for f in range(len(os.listdir(flame_path)))] + + # load deepspeech + self.audio_features[i] = torch.from_numpy(np.stack([np.load(os.path.join(deepspeech_feature_path, f'{ds}.deepspeech.npy')).flatten() for ds in range(len(os.listdir(deepspeech_feature_path)))], axis=0)) + + # load emotions + try: + self.emotions[i] = [np.load(os.path.join(emotions_path, em), allow_pickle=True) for em in sorted(os.listdir(emotions_path))] + + except FileNotFoundError: + self.emotions[i] = None + + # load landmarks + self.landmarks[i] = torch.from_numpy(np.stack([np.loadtxt(os.path.join(landmarks_path, ldk)) for ldk in sorted(os.listdir(landmarks_path))], axis=0)) + + # load expressions + self.exps[i] = torch.from_numpy(np.stack([np.load(os.path.join(expr_path, f'{exp}.npy')) for exp in range(len(os.listdir(expr_path)))], axis=0)) + + # 3D landmarks & headposes + fit_data = torch.load(fit_data_path) + self.fit_data = fit_data + self.fit_data_path[i] = fit_data_path + self.rot_angles[i] = fit_data['euler'] + + self.mean_trans[i] = fit_data['trans'].mean(axis=0) + self.trans[i] = fit_data['trans'] - self.mean_trans[i] + self.headposes[i] = np.concatenate([self.rot_angles[i], self.trans[i]], axis=1) + self.velocity_pose[i] = np.concatenate([np.zeros(6)[None, :], self.headposes[i][1:] - self.headposes[i][:-1]]) + self.acceleration_pose[i] = np.concatenate([np.zeros(6)[None, :], self.velocity_pose[i][1:] - self.velocity_pose[i][:-1]]) + + + return + + def __getitem__(self, index): + + data = ( + self.clip_names[index], + self.flame[index], + self.frames[index], + self.audio[index], + self.audio_features[index], + self.emotions[index], + self.landmarks[index], + self.exps[index], + self.headposes[index], + self.velocity_pose[index], + self.fit_data_path[index], + ) + + return data + + def __len__(self): + return self.clip_nums + + + diff --git a/motion-gan-pipeline/motion-generation/datasets/full_dataset.py b/motion-gan-pipeline/motion-generation/datasets/full_dataset.py new file mode 100644 index 0000000..876e417 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/datasets/full_dataset.py @@ -0,0 +1,320 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import sys +from unittest import skip + +sys.path.append("..") + +from datasets.base_dataset import BaseDataset +import scipy.io as sio +import torch +import librosa +import bisect +import os +import numpy as np +from models.networks import APC_encoder + +from funcs import utils + + +class FullDataset(BaseDataset): + """ DECA datasets. currently, return 2D info and 3D tracking info. + + # for wavenet: + # |----receptive_field----| + # |--output_length--| + # example: | | | | | | | | | | | | | | | | | | | | | + # target: | | | | | | | | | | + + """ + + def __init__(self, opt): + # save the option and datasets root + BaseDataset.__init__(self, opt) + self.isTrain = self.opt.isTrain + self.state = opt.dataset_type + self.dataset_name = opt.dataset_names + self.target_length = opt.time_frame_length + self.sample_rate = opt.sample_rate + self.fps = opt.FPS + + self.audioRF_history = opt.audioRF_history + self.audioRF_future = opt.audioRF_future + # self.compute_mel_online = opt.compute_mel_online + # self.feature_name = opt.feature_name + + self.audio_samples_one_frame = self.sample_rate / self.fps + self.frame_jump_stride = opt.frame_jump_stride + self.augment = False + self.task = opt.task + self.item_length_audio = int((self.audioRF_history + self.audioRF_future) / self.fps * self.sample_rate) + + #Audio2Headpose flags + self.A2H_receptive_field = opt.A2H_receptive_field + self.A2H_item_length = self.A2H_receptive_field + self.target_length - 1 #204 + self.audio_window = opt.audio_windows + self.half_audio_win = int(self.audio_window / 2) # 1 + self.frame_future = opt.frame_future #15 + self.predict_length = opt.predict_length + self.predict_len = int((self.predict_length - 1) / 2) #0 + + self.gpu_ids = opt.gpu_ids + self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu') + + self.total_len = 0 + start_point = 105 + self.dataset_root = os.path.join(self.root, self.dataset_name, self.state) + self.clip_names = sorted(os.listdir(self.dataset_root)) + + + # check clips + self.valid_clips = [] + for i in range(len(self.clip_names)): + # check lenght of video + name = self.clip_names[i] + clip_root = os.path.join(self.dataset_root, name) + n_frames = len(os.listdir(os.path.join(clip_root, 'frames'))) + + if n_frames >= start_point + self.target_length + 25: # added 25 because later they remove 25 and without it, crashes + APC_name = os.path.split(self.opt.APC_model_path)[-1] + APC_feature_file = name + '_APC_feature_V0324_ckpt_{}.npy'.format(APC_name) + APC_feature_path = os.path.join(clip_root, APC_feature_file) + + if not os.path.exists(APC_feature_path): + self.valid_clips.append(i) + continue + + audio_features = np.load(APC_feature_path).astype(np.float32) + + if audio_features.shape[0] >= 2 * self.A2H_item_length: + self.valid_clips.append(i) + + else: + print(f'Audio {name} is too short and will not be used for training.') + + else: + print(f'Clip {name} is too short and will not be used for training.') + + self.clip_nums = len(self.valid_clips) + print(f'Total clips for training: {self.clip_nums}') + + # main info + self.audio = [''] * self.clip_nums + self.audio_features = [''] * self.clip_nums + self.feats = [''] * self.clip_nums + self.exps = [''] * self.clip_nums + self.pts3d = [''] * self.clip_nums + self.rot_angles = [''] * self.clip_nums + self.trans = [''] * self.clip_nums + self.headposes = [''] * self.clip_nums + self.velocity_pose = [''] * self.clip_nums + self.acceleration_pose = [''] * self.clip_nums + self.mean_trans = [''] * self.clip_nums + if self.state == 'Test': + self.landmarks = [''] * self.clip_nums + # meta info + self.start_point = [''] * self.clip_nums + self.end_point = [''] * self.clip_nums + self.len = [''] * self.clip_nums + self.sample_start = [] + self.clip_valid = ['True'] * self.clip_nums + self.invalid_clip = [] + + self.mouth_related_indices = np.concatenate([np.arange(4, 11), np.arange(46, 64)]) + + # if opt.use_delta_pts: + # self.pts3d_mean = np.load(os.path.join(self.dataset_root, 'mean_pts3d.npy')) + + for i in range(self.clip_nums): + name = self.clip_names[self.valid_clips[i]] + clip_root = os.path.join(self.dataset_root, name) + + # Load audio + audio_path = os.path.join(clip_root, name + '.wav') + self.audio[i], _ = librosa.load(audio_path, sr=self.sample_rate) + + if self.opt.audio_encoder == 'APC': + APC_name = os.path.split(self.opt.APC_model_path)[-1] + APC_feature_file = name + '_APC_feature_V0324_ckpt_{}.npy'.format(APC_name) + APC_feature_path = os.path.join(clip_root, APC_feature_file) + need_deepfeats = False if os.path.exists(APC_feature_path) else True + if not need_deepfeats: + self.audio_features[i] = np.load(APC_feature_path).astype(np.float32) + else: + need_deepfeats = False + + # 3D landmarks & headposes + self.start_point[i] = start_point # They had 300 at 60 fps + fit_data_path = os.path.join(clip_root, 'track_params.pt') + fit_data = torch.load(fit_data_path) + self.fit_data = fit_data + + ori_pts3d = fit_data['pts3D'] + self.pts3d[i] = ori_pts3d + + self.feats[i] = self.pts3d[i] + + # elif opt.feature_dtype == 'FW': + # self.feats[i] = fit_data['exp'] + + self.rot_angles[i] = fit_data['euler'] + # change -180~180 to 0~360 + rot_change = self.rot_angles[i][:, 0] < 0 + self.rot_angles[i][rot_change, 0] += 360 + self.rot_angles[i][:, 0] -= 180 # change x axis direction + + # use delta translation + self.mean_trans[i] = fit_data['trans'].mean(axis=0) + self.trans[i] = fit_data['trans'] - self.mean_trans[i] + + self.headposes[i] = np.concatenate([self.rot_angles[i], self.trans[i]], axis=1) + self.velocity_pose[i] = np.concatenate( + [np.zeros(6)[None, :], self.headposes[i][1:] - self.headposes[i][:-1]]) + self.acceleration_pose[i] = np.concatenate( + [np.zeros(6)[None, :], self.velocity_pose[i][1:] - self.velocity_pose[i][:-1]]) + + total_frames = self.feats[i].shape[0] - 25 # They had -60 (1 second at 60 fps) # Crashes without 25 + print(f'Total Frames for clip {i}: {total_frames}') + + if need_deepfeats: + if self.opt.audio_encoder == 'APC': + print('datasets {} need to pre-compute APC features ...'.format(name)) + print('first we compute mel spectram for datasets {} '.format(name)) + mel80 = utils.compute_mel_one_sequence(self.audio[i], sr=opt.sample_rate, fps=opt.FPS) + mel_nframe = mel80.shape[0] + print('loading pre-trained model: ', self.opt.APC_model_path) + APC_model = APC_encoder(self.opt.audiofeature_input_channels, + self.opt.APC_hidden_size, + self.opt.APC_rnn_layers, + self.opt.APC_residual) + APC_model.load_state_dict(torch.load(self.opt.APC_model_path, map_location=str(self.device)), + strict=False) + # APC_model.load_state_dict(torch.load(self.opt.APC_model_path), strict=False) + APC_model.cuda() + APC_model.eval() + with torch.no_grad(): + length = torch.Tensor([mel_nframe]) + # hidden_reps = torch.zeros([mel_nframe, self.opt.APC_hidden_size]).cuda() + mel80_torch = torch.from_numpy(mel80.astype(np.float32)).cuda().unsqueeze(0) + hidden_reps = APC_model.forward(mel80_torch, length)[0] # [mel_nframe, 512] + hidden_reps = hidden_reps.cpu().numpy() + np.save(APC_feature_path, hidden_reps) + self.audio_features[i] = hidden_reps + + valid_frames = total_frames - self.start_point[i] + self.len[i] = valid_frames - self.target_length # ??? They had - 400 (6,66 s at 60 fps) + if i == 0: + self.sample_start.append(0) + else: + self.sample_start.append(self.sample_start[-1] + self.len[i - 1] - 1) + self.total_len += np.int32(np.floor(self.len[i] / self.frame_jump_stride)) + + def __getitem__(self, index): + # recover real index from compressed one + index_real = np.int32(index * self.frame_jump_stride) + # find which audio file and the start frame index + file_index = bisect.bisect_right(self.sample_start, index_real) - 1 + current_frame = index_real - self.sample_start[file_index] + self.start_point[file_index] + current_target_length = self.target_length + + if self.task == 'Audio2Feature': + # start point is current frame + A2Lsamples = self.audio_features[file_index][current_frame * 2: (current_frame + self.seq_len) * 2] + target_pts3d = self.feats[file_index][current_frame: current_frame + self.seq_len, self.indices].reshape( + self.seq_len, -1) + + A2Lsamples = torch.from_numpy(A2Lsamples).float() + target_pts3d = torch.from_numpy(target_pts3d).float() + + # [item_length, mel_channels, mel_width], or [item_length, APC_hidden_size] + return A2Lsamples, target_pts3d + + + elif self.task == 'Audio2Headpose': + if self.opt.feature_decoder == 'WaveNet': + # find the history info start points + A2H_history_start = current_frame - self.A2H_receptive_field + A2H_item_length = self.A2H_item_length # 204 + A2H_receptive_field = self.A2H_receptive_field + + if self.half_audio_win == 1: + A2Hsamples = self.audio_features[file_index][2 * (A2H_history_start + self.frame_future): 2 * (A2H_history_start + self.frame_future + A2H_item_length)] # Why 2? + else: + A2Hsamples = np.zeros([A2H_item_length, 512]) + for i in range(A2H_item_length): + A2Hsamples[i] = self.audio_features[file_index][A2H_history_start + i] + + if self.predict_len == 0: + target_headpose = self.headposes[file_index][ + A2H_history_start + A2H_receptive_field: A2H_history_start + A2H_item_length + 1] # [current frame: current frame + target] + history_headpose = self.headposes[file_index][A2H_history_start: A2H_history_start + A2H_item_length] + history_headpose = history_headpose.reshape(A2H_item_length,-1) # [current_frame - self.A2H_receptive_field : current frame + target -1] + + target_velocity = self.velocity_pose[file_index][ + A2H_history_start + A2H_receptive_field: A2H_history_start + A2H_item_length + 1] + history_velocity = self.velocity_pose[file_index][ + A2H_history_start: A2H_history_start + A2H_item_length].reshape(A2H_item_length, + -1) + target_info = torch.from_numpy( + np.concatenate([target_headpose, target_velocity], axis=1).reshape(current_target_length, + -1)).float() + + else: + history_headpose = self.headposes[file_index][ + A2H_history_start: A2H_history_start + A2H_item_length] + history_headpose = history_headpose.reshape(A2H_item_length, -1) #Same + + history_velocity = self.velocity_pose[file_index][ + A2H_history_start: A2H_history_start + A2H_item_length] + history_velocity = history_velocity.reshape(A2H_item_length, -1) #Same + + target_headpose_ = self.headposes[file_index][ + A2H_history_start + A2H_receptive_field - self.predict_len: A2H_history_start + A2H_item_length + 1 + self.predict_len + 1] # [current frame - predict_len: current frame + target + predict_len + 1] + target_headpose = np.zeros([current_target_length, self.predict_length, target_headpose_.shape[1]]) + for i in range(current_target_length): + target_headpose[i] = target_headpose_[i: i + self.predict_length] + target_headpose = target_headpose # .reshape(current_target_length, -1, order='F') + + target_velocity_ = self.headposes[file_index][ + A2H_history_start + A2H_receptive_field - self.predict_len: A2H_history_start + A2H_item_length + 1 + self.predict_len + 1] + target_velocity = np.zeros([current_target_length, self.predict_length, target_velocity_.shape[1]]) + for i in range(current_target_length): + target_velocity[i] = target_velocity_[i: i + self.predict_length] + target_velocity = target_velocity # .reshape(current_target_length, -1, order='F') + + target_info = torch.from_numpy( + np.concatenate([target_headpose, target_velocity], axis=2).reshape(current_target_length, + -1)).float() + + A2Hsamples = torch.from_numpy(A2Hsamples).float() + + history_info = torch.from_numpy(np.concatenate([history_headpose, history_velocity], axis=1)).float() + + # [item_length, mel_channels, mel_width], or [item_length, APC_hidden_size] + + return A2Hsamples, history_info, target_info + + + elif self.opt.feature_decoder == 'LSTM': + A2Hsamples = self.audio_features[file_index][ + current_frame * 2: (current_frame + self.opt.A2H_receptive_field) * 2] + + target_headpose = self.headposes[file_index][ + current_frame: current_frame + self.opt.A2H_receptive_field] + target_velocity = self.velocity_pose[file_index][ + current_frame: current_frame + self.opt.A2H_receptive_field] + target_info = torch.from_numpy( + np.concatenate([target_headpose, target_velocity], axis=1).reshape(self.opt.A2H_receptive_field, + -1)).float() + + A2Hsamples = torch.from_numpy(A2Hsamples).float() + + # [item_length, mel_channels, mel_width], or [item_length, APC_hidden_size] + return A2Hsamples, target_info + + def __len__(self): + return self.total_len + + + diff --git a/motion-gan-pipeline/motion-generation/emotion_correlation_visual.py b/motion-gan-pipeline/motion-generation/emotion_correlation_visual.py new file mode 100644 index 0000000..c887cac --- /dev/null +++ b/motion-gan-pipeline/motion-generation/emotion_correlation_visual.py @@ -0,0 +1,173 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os +from os.path import join +from turtle import color +import yaml +import argparse +import numpy as np +from options.test_audio2headpose_options import TestOptions +from datasets import create_dataset +from util.cfgnode import CfgNode +from torch.functional import F + +from tqdm import tqdm +import matplotlib +matplotlib.use('agg') +import matplotlib.pyplot as plt +from matplotlib import colors + +import torch +from scipy.stats import pearsonr + + +if __name__ == '__main__': + # Load default options + Test_parser = TestOptions() + opt = Test_parser.parse() # get training options + + # Load config + parser = argparse.ArgumentParser() + parser.add_argument('--config', default='config/Audio2Headpose_Ted.yml', + help="person name, e.g. Obama1, Obama2, May, Nadella, McStay", required=False) + inopt = parser.parse_args() + # TODO: make config + with open(inopt.config) as f: + cfg_dict = yaml.load(f, Loader=yaml.FullLoader) + cfg = CfgNode(cfg_dict) + + # Overwrite with config + opt.phase = 'Test' + opt.name = cfg.experiment.name + opt.dataset_mode = cfg.experiment.dataset_mode + opt.dataroot = cfg.experiment.dataroot + opt.dataset_names = cfg.experiment.dataset_names + opt.FPS = cfg.experiment.fps + + # save to the disk + Test_parser.print_options(opt) + + # Set device + device = torch.device('cuda:{}'.format(opt.gpu_ids[0])) if len( + opt.gpu_ids) > 0 else torch.device('cpu') + + # Load data + # create a dataset given opt.dataset_mode and other options + dataset = create_dataset(opt) + dataset = dataset.dataset + + # Target dir + target_dir= './visuals/Correlation/' + os.makedirs(target_dir, exist_ok=True) + + total_movement = [] + total_emotions = [] + total_valence = [] + total_arousal = [] + + emotion_labels = [ + 'Neutral', + 'Happy', + 'Sad', + 'Surprise', + 'Fear', + 'Disgust', + 'Anger', + 'Contempt', + ] + + for file_index in tqdm(range(len(dataset))): + + test_name = dataset.clip_names[file_index] + + name, flame, frames, audio_packed, audio_features, emotions, landmarks, exps, headposes, velocity_pose, fit_data_path = dataset[file_index] + + try: + # Compute avarage totale movement (sum of all movement divided by number of frames) + total_i = np.sum(np.abs(velocity_pose), axis=0) / headposes.shape[0] + total_movement.append(total_i) + + avg_valence = np.mean(np.stack([frame.item()['valence'].flatten() for frame in emotions], axis=0)) + avg_arousal = np.mean(np.stack([frame.item()['arousal'].flatten() for frame in emotions], axis=0)) + avg_class = np.mean([frame.item()['expr_classification'] for frame in emotions], axis=0) + + except: + print(f'Error in: {test_name}') + pass + + avg_class = torch.tensor(avg_class) + softmax = F.softmax(avg_class) + top_expr = torch.argmax(softmax, dim=1) + + total_emotions.append(top_expr.numpy()[0]) + total_valence.append(avg_valence) + total_arousal.append(avg_arousal) + + total_emotions = np.array(total_emotions) + total_movement = np.array(total_movement) + total_arousal = np.array(total_arousal) + total_valence = np.array(total_valence) + + # Emotion distribution + plt.title(f'Emotion Distribution out of 100 videos') + xticks = np.arange(8) + 0.5 + plt.xticks(xticks, emotion_labels, rotation=0) # Set text labels and properties. + plt.yticks([]) + # Make histogram + n, bins, patches = plt.hist(total_emotions, bins=np.arange(8), label=emotion_labels) + # We'll color code by height, but you could use any scalar + fracs = n / n.max() + # we need to normalize the data to 0..1 for the full range of the colormap + norm = colors.Normalize(fracs.min(), fracs.max()) + + for idx, value in enumerate(n): + if value > 0: + plt.text(xticks[idx], value+1, int(value), ha='center') + + for thisfrac, thispatch in zip(fracs, patches): + color = plt.cm.viridis(norm(thisfrac)) + thispatch.set_facecolor(color) + + plt.savefig(f'{target_dir}Emotion_distr.png') + plt.close() + + for idx, angle in enumerate(['Pitch', 'Roll', 'Yaw']): + + # Emotion + plt.xticks(np.arange(8), emotion_labels, rotation=0) # Set text labels and properties. + plt.title(f'{angle} Angle') + plt.scatter(x=total_emotions, y=total_movement[:, idx]) + plt.ylabel('Avg total movement') + plt.savefig(f'{target_dir}{angle}_Emotions.png') + plt.close() + + # Arousal + r, p = pearsonr(x=total_arousal, y=total_movement[:, idx]) + r = round(r, 3) + p = round(p, 3) + x = np.linspace(np.min(total_arousal), np.max(total_arousal), 1000) + + plt.title(f'{angle} Angle and Arousal\n Correlation: {int(r*100)}%, p-value: {int(p*100)}%') + plt.scatter(x=total_arousal, y=total_movement[:, idx]) + plt.plot(x, (x * r) + np.mean(total_movement[:, idx]), linestyle='solid', color='red') + plt.xlabel('Arousal') + plt.ylabel('Avg total movement') + plt.savefig(f'{target_dir}{angle}_Arousal.png') + plt.close() + + # Valence + r, p = pearsonr(x=total_valence, y=total_movement[:, idx]) + r = round(r, 3) + p = round(p, 3) + x = np.linspace(np.min(total_valence), np.max(total_valence), 1000) + + plt.title(f'{angle} Angle and Valence\n Correlation: {int(r*100)}%, p-value: {int(p*100)}%') + plt.scatter(x=total_valence, y=total_movement[:, idx]) + plt.plot(x, (x * r) + np.mean(total_movement[:, idx]), linestyle='solid', color='red') + plt.xlabel('Valence') + plt.ylabel('Avg total movement') + plt.savefig(f'{target_dir}{angle}_Valence.png') + plt.close() + + print('Finish!') diff --git a/motion-gan-pipeline/motion-generation/environment.yml b/motion-gan-pipeline/motion-generation/environment.yml new file mode 100644 index 0000000..1033a91 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/environment.yml @@ -0,0 +1,179 @@ +name: adnerf +channels: + - conda-forge + - defaults +dependencies: + - _libgcc_mutex=0.1=main + - _openmp_mutex=4.5=1_gnu + - autopep8=1.5.7=pyhd3eb1b0_0 + - blas=1.0=mkl + - bzip2=1.0.8=h7b6447c_0 + - ca-certificates=2021.5.25=h06a4308_1 + - cairo=1.14.12=h8948797_3 + - certifi=2021.5.30=py37h06a4308_0 + - configargparse=1.4=pyhd3eb1b0_0 + - cudatoolkit=11.1.1=h6406543_8 + - cycler=0.10.0=py37_0 + - dbus=1.13.18=hb2f20db_0 + - expat=2.4.1=h2531618_2 + - ffmpeg=4.0=hcdf2ecd_0 + - fontconfig=2.13.1=h6c09931_0 + - freeglut=3.0.0=hf484d3e_5 + - freetype=2.10.4=h5ab3b9f_0 + - glib=2.63.1=h5a9c865_0 + - graphite2=1.3.14=h23475e2_0 + - gst-plugins-base=1.14.0=hbbd80ab_1 + - gstreamer=1.14.0=hb453b48_1 + - harfbuzz=1.8.8=hffaf4a1_0 + - hdf5=1.10.2=hba1933b_1 + - icu=58.2=he6710b0_3 + - imageio=2.9.0=pyhd3eb1b0_0 + - intel-openmp=2021.2.0=h06a4308_610 + - jasper=2.0.14=h07fcdf6_1 + - joblib=1.0.1=pyhd3eb1b0_0 + - jpeg=9b=h024ee3a_2 + - kiwisolver=1.3.1=py37h2531618_0 + - lcms2=2.12=h3be6417_0 + - libedit=3.1.20210216=h27cfd23_1 + - libffi=3.2.1=hf484d3e_1007 + - libgcc-ng=9.3.0=h5101ec6_17 + - libgfortran-ng=7.5.0=ha8ba4b0_17 + - libgfortran4=7.5.0=ha8ba4b0_17 + - libglu=9.0.0=hf484d3e_1 + - libgomp=9.3.0=h5101ec6_17 + - libllvm10=10.0.1=hbcb73fb_5 + - libopencv=3.4.2=hb342d67_1 + - libopus=1.3.1=h7b6447c_0 + - libpng=1.6.37=hbc83047_0 + - libstdcxx-ng=9.3.0=hd4cf53a_17 + - libtiff=4.2.0=h85742a9_0 + - libuuid=1.0.3=h1bed415_2 + - libuv=1.41.0=h7f98852_0 + - libvpx=1.7.0=h439df22_0 + - libwebp-base=1.2.0=h27cfd23_0 + - libxcb=1.14=h7b6447c_0 + - libxml2=2.9.10=hb55368b_3 + - llvmlite=0.36.0=py37h612dafd_4 + - lz4-c=1.9.3=h2531618_0 + - matplotlib=3.3.4=py37h06a4308_0 + - matplotlib-base=3.3.4=py37h62a2d02_0 + - mkl=2021.2.0=h06a4308_296 + - mkl-service=2.3.0=py37h27cfd23_1 + - mkl_fft=1.3.0=py37h42c9631_2 + - mkl_random=1.2.1=py37ha9443f7_2 + - natsort=7.1.1=pyhd3eb1b0_0 + - ncurses=6.2=he6710b0_1 + - numba=0.53.1=py37ha9443f7_0 + - numpy=1.20.2=py37h2d18471_0 + - numpy-base=1.20.2=py37hfae3a4d_0 + - olefile=0.46=pyh9f0ad1d_1 + - opencv=3.4.2=py37h6fd60c2_1 + - openssl=1.1.1k=h27cfd23_0 + - pandas=1.2.4=py37h2531618_0 + - pcre=8.44=he6710b0_0 + - pillow=8.2.0=py37he98fc37_0 + - pixman=0.40.0=h7b6447c_0 + - py-opencv=3.4.2=py37hb342d67_1 + - pycodestyle=2.7.0=pyhd3eb1b0_0 + - pyparsing=2.4.7=pyhd3eb1b0_0 + - pyqt=5.9.2=py37h05f1152_2 + - python=3.7.1=h0371630_7 + - python-dateutil=2.8.1=pyhd3eb1b0_0 + - python_abi=3.7=1_cp37m + - pytz=2021.1=pyhd3eb1b0_0 + - qt=5.9.7=h5867ecd_1 + - readline=7.0=h7b6447c_5 + - resampy=0.2.2=py_0 + - scikit-learn=0.24.2=py37ha9443f7_0 + - scipy=1.6.2=py37had2a1c9_1 + - sip=4.19.8=py37hf484d3e_0 + - six=1.16.0=pyhd3eb1b0_0 + - sqlite=3.33.0=h62c20be_0 + - tbb=2020.3=hfd86e86_0 + - threadpoolctl=2.1.0=pyh5ca1d4c_0 + - tk=8.6.10=hbc83047_0 + - toml=0.10.2=pyhd3eb1b0_0 + - tornado=6.1=py37h27cfd23_0 + - tqdm=4.59.0=pyhd3eb1b0_1 + - typing_extensions=3.10.0.0=pyha770c72_0 + - xz=5.2.5=h7b6447c_0 + - zlib=1.2.11=h7b6447c_3 + - zstd=1.4.9=haebb681_0 + - pip: + - absl-py==0.13.0 + - antlr4-python3-runtime==4.8 + - appdirs==1.4.4 + - astor==0.8.1 + - audioread==2.1.9 + - av==8.1.0 + - cached-property==1.5.2 + - cffi==1.15.0 + - chardet==4.0.0 + - chumpy==0.70 + - click==8.0.3 + - cloudpickle==2.0.0 + - decorator==4.4.2 + - face-alignment==1.3.4 + - filelock==3.4.2 + - future==0.18.2 + - fvcore==0.1.5.post20210617 + - gast==0.2.2 + - google-pasta==0.2.0 + - grpcio==1.38.0 + - h5py==3.2.1 + - huggingface-hub==0.4.0 + - idna==2.10 + - imageio-ffmpeg==0.4.5 + - importlib-metadata==4.5.0 + - iopath==0.1.8 + - keras-applications==1.0.8 + - keras-preprocessing==1.1.2 + - kornia==0.4.0 + - librosa==0.8.1 + - markdown==3.3.4 + - moviepy==1.0.3 + - networkx==2.5.1 + - ninja==1.10.2.3 + - omegaconf==2.1.1 + - opencv-python==4.5.2.54 + - opt-einsum==3.3.0 + - packaging==21.3 + - pims==0.5 + - pip==22.0.2 + - pooch==1.6.0 + - portalocker==2.3.0 + - proglog==0.1.9 + - protobuf==3.17.3 + - pycocotools==2.0.4 + - pycparser==2.21 + - pydub==0.25.1 + - python-speech-features==0.6 + - pywavelets==1.1.1 + - pyyaml==5.1.1 + - regex==2022.1.18 + - requests==2.25.1 + - sacremoses==0.0.47 + - scikit-image==0.18.1 + - sentencepiece==0.1.96 + - setuptools==60.6.0 + - slicerator==1.0.0 + - soundfile==0.10.3.post1 + - speechrecognition==3.8.1 + - tabulate==0.8.9 + - tensorboard==1.15.0 + - tensorflow-estimator==1.15.1 + - tensorflow-gpu==1.15.2 + - termcolor==1.1.0 + - tifffile==2021.6.14 + - tokenizers==0.11.4 + - torch==1.9.0+cu111 + - torchaudio==0.9.0 + - torchvision==0.10.0+cu111 + - transformers==4.16.2 + - urllib3==1.26.5 + - werkzeug==2.0.1 + - wheel==0.37.1 + - wrapt==1.12.1 + - yacs==0.1.8 + - zipp==3.4.1 +prefix: /home/alberto/anaconda3/envs/adnerf diff --git a/motion-gan-pipeline/motion-generation/finetune.py b/motion-gan-pipeline/motion-generation/finetune.py new file mode 100644 index 0000000..23a3663 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/finetune.py @@ -0,0 +1,109 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import imp +import os +import time +import yaml +from options.train_audio2headpose_options import TrainOptions +from datasets import create_dataset +from models import create_model +from util.visualizer import Visualizer +from tqdm import tqdm +import argparse +from util.cfgnode import CfgNode +import numpy as np + + +def finetune(name, dataroot, dataset_names, target_checkpoints, checkpoint_path, dataset_mode='deepspeech', fps=25): + print('Fine-tuning model..') + # Load default options + Train_parser = TrainOptions() + opt = Train_parser.parse() # get training options + + # Overwrite with input + opt.name = name + opt.dataset_mode = dataset_mode + opt.dataroot = os.path.join(dataroot, 'video') + opt.dataset_names = dataset_names + opt.FPS = fps + + # Finetune params + opt.n_epochs = 15 + opt.n_epochs_decay = 10 + opt.checkpoints_dir = target_checkpoints + opt.train_dataset_names = [os.path.join(opt.dataroot, opt.dataset_names)] + opt.validate_dataset_names = [] + opt.train_test_split = False + opt.lr = 1e-4 + + # save to the disk + Train_parser.print_options(opt) + + # Load data + dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options + dataset_size = len(dataset) # get the number of images in the dataset. + print('The number of training images = %d' % dataset_size) + + model = create_model(opt) # create a model given opt.model and other options + model.setup(opt) # regular setup: load and print networks; create schedulers + model.load_checkpoint(checkpoint_path) # Load checkpoint to finetune + visualizer = Visualizer(opt) # create a visualizer that display/save images and plots + total_iters = 0 # the total number of training iterations + + for epoch in tqdm(range(opt.epoch_count, opt.n_epochs + opt.n_epochs_decay + 1)): # outer loop for different epochs; we save the model by , + + epoch_start_time = time.time() # timer for entire epoch + iter_data_time = time.time() # timer for data loading per iteration + epoch_iter = 0 # the number of training iterations in current epoch, reset to 0 every epoch + # visualizer.reset() # reset the visualizer: make sure it saves the results to HTML at least once every epoch + model.update_learning_rate() # update learning rates in the beginning of every epoch. + for i, data in enumerate(dataset): # inner loop within one epoch + + iter_start_time = time.time() # timer for computation per iteration + if total_iters % opt.print_freq == 0: + t_data = iter_start_time - iter_data_time + + total_iters += opt.batch_size + epoch_iter += opt.batch_size + model.set_input(data) # unpack data from dataset and apply preprocessing + model.optimize_parameters() # calculate loss functions, get gradients, update network weights + + # if total_iters % opt.print_freq == 0: # print training losses and save logging information to the disk + # losses = model.get_current_losses() + # t_comp = (time.time() - iter_start_time) / opt.batch_size + # visualizer.print_current_errors(epoch, total_iters, losses, t_data) + # if opt.display_id > 0: + # visualizer.plot_current_errors(losses, total_iters) + + iter_data_time = time.time() + + if epoch % opt.save_epoch_freq == 0 and epoch!=0: # cache our model every epochs + print('saving the model at the end of epoch %d, iters %d' % (epoch, total_iters)) + model.save_networks('epoch_%d' % (epoch)) + + # early stopping + losses = model.get_current_losses() + if losses['GMM'] <= 0: + print('Negative loss at the end of epoch %d, total iters %d' % (epoch, total_iters)) + print('Stopping Training') + break + + print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time)) + + model.save_networks('latest') + +if __name__ == '__main__': + + # Load config + parser = argparse.ArgumentParser() + parser.add_argument('--name', default='', help="person name, e.g. Obama1, Obama2, May, Nadella, McStay") + parser.add_argument('--dataset_mode', default='deepspeech', help="type of dataset") + parser.add_argument('--dataroot', help="Path to data folders") + parser.add_argument('--dataset_names', default='', help="Nme of the dataset") + parser.add_argument('--fps', default=25, help="target fps") + parser.add_argument('--target_checkpoints', default="", help="Path to the output checkpoint directory") + parser.add_argument('--checkpoint_path', default="./checkpoints/latest_Audio2Headpose.pkl", help="Path to the checkpoints to finetune") + + inopt = parser.parse_args() + + finetune(inopt.name, inopt.dataroot, inopt.dataset_names, inopt.target_checkpoints, inopt.dataset_mode, inopt.fps, inopt.checkpoint_path) diff --git a/motion-gan-pipeline/motion-generation/funcs/audio_funcs.py b/motion-gan-pipeline/motion-generation/funcs/audio_funcs.py new file mode 100644 index 0000000..1f55930 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/funcs/audio_funcs.py @@ -0,0 +1,426 @@ +import os +import os.path +import math +# import sox +#import pyworld as pw +import torch +import torch.utils.data +import numpy as np +import librosa + + +""" +useage +fft = Audio2Mel().cuda() +# audio shape is B x 1 x T, the normalized mel shape is B x D x T +mel = fft(audio) +""" +from librosa.filters import mel as librosa_mel_fn +import torch.nn.functional as F +class Audio2Mel(torch.nn.Module): + def __init__( + self, + n_fft=512, + hop_length=256, + win_length=1024, + sampling_rate=16000, + n_mel_channels=80, + mel_fmin=90, + mel_fmax=7600.0, + ): + super(Audio2Mel, self).__init__() + ############################################## + # FFT Parameters # + ############################################## + window = torch.hann_window(win_length).float() + mel_basis = librosa_mel_fn( + sampling_rate, n_fft, n_mel_channels, mel_fmin, mel_fmax + ) + mel_basis = torch.from_numpy(mel_basis).float() + self.register_buffer("mel_basis", mel_basis) + self.register_buffer("window", window) + self.n_fft = n_fft + self.hop_length = hop_length + self.win_length = win_length + self.sampling_rate = sampling_rate + self.n_mel_channels = n_mel_channels + self.min_mel = math.log(1e-5) + self.mel_fmin = mel_fmin + self.mel_fmax = mel_fmax + + """ + input audio signal (-1,1): B x 1 x T + output mel signal: B x D x T', T' is a reduction of T + """ + + def forward(self, audio, normalize=True): + p = (self.n_fft - self.hop_length) // 2 + audio = F.pad(audio, (p, p), "reflect").squeeze(1) + fft = torch.stft( + audio, + n_fft=self.n_fft, + hop_length=self.hop_length, + win_length=self.win_length, + window=self.window, + center=False, + ) + real_part, imag_part = fft.unbind(-1) + magnitude = torch.sqrt(real_part ** 2 + imag_part ** 2) + mel_output = torch.matmul(self.mel_basis, magnitude) + log_mel_spec = torch.log(torch.clamp(mel_output, min=1e-5)) + + # normalize to the range [0,1] + if normalize: + log_mel_spec = (log_mel_spec - self.min_mel) / -self.min_mel + return log_mel_spec + + def mel_to_audio(self, mel): + mel = torch.exp(mel * (-self.min_mel) + self.min_mel) ** 2 + mel_np = mel.cpu().numpy() + audio = librosa.feature.inverse.mel_to_audio(mel_np, sr=self.sampling_rate, n_fft=self.n_fft, + hop_length=self.hop_length, win_length=self.win_length, + window='hann', center=False, + pad_mode='reflect', power=2.0, n_iter=32, fmin=self.mel_fmin, + fmax=self.mel_fmax) + return audio + + """ + here we will get per frame energy to replace mc0 in the corresponding prosody representation + the audio is already in the gpu card for accerelate the computation speed + input audio signal: B x 1 x T + output energy: B x 1 x T' + """ + + def get_energy(self, audio, normalize=True): + # B x 1 x T + p = (self.n_fft - self.hop_length) // 2 + audio_new = F.pad(audio, (p, p), "reflect").squeeze(1) + # audio_new = audio.squeeze(1) + audio_fold = audio_new.unfold(1, self.win_length, self.hop_length) + audio_energy = torch.sqrt(torch.mean(audio_fold ** 2, dim=-1)) + audio_energy = torch.log(torch.clamp(audio_energy, min=1e-5)) + if normalize: + audio_energy = (audio_energy - self.min_mel) / -self.min_mel + return audio_energy + + # we can get the energy of mels here, B*D*T + def get_energy_mel(self, mels, normalize=True): + m = mels.exp().mean(dim=1) + audio_energy = torch.log(m) + # audio_energy = torch.log(torch.clamp(m,min=1e-5)) + # if normalize: + # audio_energy = (audio_energy - self.min_mel) / -self.min_mel + return audio_energy + + + + +def mu_law_encoding(data, mu=255): + '''encode the original audio via mu-law companding and mu-bits quantization + ''' + # mu-law companding + mu_x = np.sign(data) * np.log(1 + mu * np.abs(data)) / np.log(mu + 1) + # mu-bits quantization from [-1, 1] to [0, mu] + mu_x = (mu_x + 1) / 2 * mu + 0.5 + return mu_x.astype(np.int32) + +#%timeit mu_x = mu_law_encoding(x, 255) 305 µs ± 554 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each) + + +def mu_law_decoding(data, mu=255): + '''inverse the mu-law compressed and quantized data. + ''' + # dequantization + y = 2 * (data.astype(np.float32) / mu) - 1 + # inverse mu-law companding + x = np.sign(y) * (1.0 / mu) * ((1.0 + mu)**abs(y) - 1.0) + return x + + + +## audio augmentation +def inject_gaussian_noise(data, noise_factor, use_torch=False): + ''' inject random gaussian noise (mean=0, std=1) to audio clip + In my test, a reasonable factor region could be [0, 0.01] + larger will be too large and smaller could be ignored. + Args: + data: [n,] original audio sequence + noise_factor(float): scaled factor + use_torch(bool): optional, if use_torch=True, input data and implementation will + be torch methods. + Returns: + augmented_data: [n,] noised audio clip + + ''' + if use_torch == False: + augmented_data = data + noise_factor * np.random.normal(0, 1, len(data)) + # Cast back to same data type + augmented_data = augmented_data.astype(type(data[0])) + # use torch + else: + augmented_data = data + noise_factor * torch.randn(1).cuda() + + return augmented_data + + +# pitch shifting +def pitch_shifting(data, sampling_rate=48000, factor=5): + ''' shift the audio pitch. + ''' + # Permissible factor values = -5 <= x <= 5 + pitch_factor = np.random.rand(1) * 2 * factor - factor + return librosa.effects.pitch_shift(data, sampling_rate, pitch_factor) + + +def speed_change(data, landmark=None): + ''' change the speed of input audio. Note that we return the speed_rate to + change the speed of landmarks or videos. + Args: + data: [n,] audio clip + landmark: [m, pts, 2] aligned landmarks with audio if existed. + ''' + # Permissible factor values = 0.7 <= x <= 1.3 (higher is faster) + # resulted audio length: np.round(n/rate) + speed_rate = np.random.uniform(0.7, 1.3) + # only augment audio + if landmark == None: + return librosa.effects.time_stretch(data, speed_rate), speed_rate + else: +# n_after = np.round(data.shape[0]/speed_rate) + pass + + + + +def world_augment(wav, sr, op): + f0, sp, ap = pw.wav2world(wav.astype(np.float64), sr) + op = op if op is not None else np.random.randint(0,4) + if op == 0: + base_f0 = np.random.randint(100,300) +# base_f0 = np.random.randint(100, 200) + robot_like_f0 = np.ones_like(f0) * base_f0 # 100是个适当的数字 + robot_like = pw.synthesize(robot_like_f0, sp, ap, sr) + out_wav = robot_like + elif op == 1: + ratio = 1 + np.random.rand() + female_like_sp = np.zeros_like(sp) + for f in range(female_like_sp.shape[1]): + female_like_sp[:, f] = sp[:, int(f/ratio)] + ratio_f = 0.65 + 1.4 * np.random.rand() + out_wav = pw.synthesize(f0*ratio_f, female_like_sp, ap, sr) + elif op == 2: + # change the current pitch here + ratio = 0.65 + 1.4 * np.random.rand() + out_wav = pw.synthesize(f0*ratio, sp, ap, sr) + elif op == 3: + # the random masking using the time axis + mask_len = np.random.randint(0,256 * 4) + mask_pos = np.random.randint(0, wav.shape[0] - mask_len + 1) + out_wav = np.copy(wav) + out_wav[mask_pos:mask_pos+mask_len] = 0 + else: + out_wav = np.copy(wav) + + return out_wav.astype(np.float32) + + +def sox_augment(wav, sr, tempo_ratio=1.0, op=None): + aug_choice = op if op is not None else np.random.randint(low=1, high=8) +# tempo_ratio = 1.0 + hop_length = 256 + tfm = sox.Transformer() + if aug_choice == 1: + # 1 pitch aug + param = np.random.uniform(-5.0, 5.0) + tfm.pitch(param) + elif aug_choice == 2: + # 2 tempo aug, when tempo_ratio is around 1.0, no tempo aug +# tempo_ratio = np.random.uniform(0.5, 2.0) +# if tempo_ratio >= 0.9 and tempo_ratio <= 1.1: +# tempo_ratio = 1.0 +# if tempo_ratio != 1.0: +# tfm.tempo(tempo_ratio, 's', quick=False) + pass + elif aug_choice == 3: + # 3 gain aug + param = np.random.uniform(-20, 5) + tfm.norm() + tfm.gain(param) + elif aug_choice == 4: + # 4 echo aug + # delays = np.random.uniform(5, 60) + # decays = np.random.uniform(0.2, 0.6) + # tfm.echo(delays=[delays], decays=[decays]) + pass + elif aug_choice == 5: + # 5 reverb aug + param = np.random.uniform(0, 100, size=(4,)) + tfm.reverb(param[0], param[1], param[2], param[3]) + elif aug_choice == 6: + # 6 bandreject aug + param1 = np.random.randint(200, 7500) + param2 = np.random.uniform(1.0, 4.0) + tfm.bandreject(param1, param2) + elif aug_choice == 7: + # 8 equalizer aug + param1 = np.random.randint(200, 7500) + param2 = np.random.uniform(1.0, 4.0) + param3 = np.random.uniform(-20, 5) + tfm.equalizer(param1, param2, param3) + else: + raise RuntimeError('Aug choice error!') + + wave_length = wav.shape[0] + if aug_choice == 1:# When using pitch augmentation, pad silence to keep audio length + wav = np.concatenate((wav, np.array([0.0]*(hop_length * 2))), axis=0) + + aug_wave_data = tfm.build_array(input_array=wav, sample_rate_in=sr) + + if aug_choice == 1:# Keep audio length unchanged when using pitch augmentation + aug_wave_data = aug_wave_data[:wave_length] + + return aug_wave_data + + +def sox_augment_v2(wav, sr, op=None): + aug_choice = op if op is not None else np.random.randint(low=1, high=5) + hop_length = 256 + tfm = sox.Transformer() + if aug_choice == 1: + # 1 pitch aug + param = np.random.uniform(-5.0, 5.0) + tfm.pitch(param) + elif aug_choice == 2: + # 5 reverb aug + param = np.random.uniform(0, 100, size=(4,)) + tfm.reverb(param[0], param[1], param[2], param[3]) + elif aug_choice == 3: + # 6 bandreject aug + param1 = np.random.randint(200, 7500) + param2 = np.random.uniform(1.0, 4.0) + tfm.bandreject(param1, param2) + elif aug_choice == 4: + # 8 equalizer aug + param1 = np.random.randint(200, 7500) + param2 = np.random.uniform(1.0, 4.0) + param3 = np.random.uniform(-20, 5) + tfm.equalizer(param1, param2, param3) + else: + raise RuntimeError('Aug choice error!') + + wave_length = wav.shape[0] + if aug_choice == 1:# When using pitch augmentation, pad silence to keep audio length + wav = np.concatenate((wav, np.array([0.0]*(hop_length * 2))), axis=0) + + aug_wave_data = tfm.build_array(input_array=wav, sample_rate_in=sr) + + if aug_choice == 1:# Keep audio length unchanged when using pitch augmentation + aug_wave_data = aug_wave_data[:wave_length] + + return aug_wave_data + + +def audio_output_augment(wav, sr, op=None): + aug_choice = op if op is not None else np.random.randint(low=1, high=4) + tfm = sox.Transformer() + if aug_choice == 1: + # 5 reverb aug + param = np.random.uniform(0, 100, size=(4,)) + tfm.reverb(param[0], param[1], param[2], param[3]) + elif aug_choice == 2: + # 6 bandreject aug + param1 = np.random.randint(200, 7500) + param2 = np.random.uniform(1.0, 4.0) + tfm.bandreject(param1, param2) + elif aug_choice == 3: + # 8 equalizer aug + param1 = np.random.randint(200, 7500) + param2 = np.random.uniform(1.0, 4.0) + param3 = np.random.uniform(-20, 5) + tfm.equalizer(param1, param2, param3) + else: + raise RuntimeError('Aug choice error!') + + aug_wave_data = tfm.build_array(input_array=wav, sample_rate_in=sr) + + return aug_wave_data + + +def audio_time_augment(wav, sr, time_scale): + tfm = sox.Transformer() + tfm.tempo(time_scale, 's', quick=False) + + aug_wave_data = tfm.build_array(input_array=wav, sample_rate_in=sr) + + return aug_wave_data + + +def prepare_noises(scp_file, root=None, sampline_rate=None, ignore_class=None): + noises = [] + print('Loading augmentation noises...') + with open(scp_file,'r') as fp: + for line in fp.readlines(): + line = line.rstrip('\n') + if ignore_class is not None and ignore_class in line: + continue + + noise, sr = librosa.load(os.path.join(root, line), sr=sampline_rate) + noises.append(noise) + print('Augmentation noises loaded!') + return noises, sr + + +def add_gauss_noise(wav, noise_std=0.03, max_wav_value=1.0): + if isinstance(wav, np.ndarray): + wav = torch.tensor(wav.copy()) + + real_std = np.random.random() * noise_std + wav_new = wav.float() / max_wav_value + torch.randn(wav.size()) * real_std + wav_new = wav_new * max_wav_value + wav_new = wav_new.clamp_(-max_wav_value, max_wav_value) + + return wav_new.float().numpy() + +def add_background_noise(wav, noises, min_snr=2, max_snr=15): + def mix_noise(wav, noise, scale): + x = wav + scale * noise + x = x.clip(-1, 1) + return x + + def voice_energy(wav): + wav_float = np.copy(wav) + return np.sum(wav_float ** 2) / (wav_float.shape[0] + 1e-5) + + def voice_energy_ratio(wav, noise, target_snr): + wav_eng = voice_energy(wav) + noise_eng = voice_energy(noise) + target_noise_eng = wav_eng / (10 ** (target_snr / 10.0)) + ratio = target_noise_eng / (noise_eng + 1e-5) + return ratio + + total_id = len(noises) + # 0 is no need to generate the noise + idx = np.random.choice(range(0, total_id)) + noise_wav = noises[idx] + if noise_wav.shape[0] > wav.shape[0]: + sel_range_id = np.random.choice(range(0, noise_wav.shape[0] - wav.shape[0])) + n = noise_wav[sel_range_id:sel_range_id + wav.shape[0]] + else: + n = np.zeros(wav.shape[0]) + sel_range_id = np.random.choice(range(0, wav.shape[0] - noise_wav.shape[0] + 1)) + n[sel_range_id:sel_range_id + noise_wav.shape[0]] = noise_wav + # + target_snr = np.random.random() * (max_snr - min_snr) + min_snr + scale = voice_energy_ratio(wav, n, target_snr) + wav_new = mix_noise(wav, n, scale) + return wav_new + + +def noise_augment(wav, wav_noises, gaussian_prob=0.5): + if np.random.random() > gaussian_prob:# add gauss noise + noise_std = np.random.uniform(low=0.001, high=0.02) + aug_wave_data = add_gauss_noise(wav, noise_std=noise_std) + else:# add background noise + aug_wave_data = add_background_noise(wav, wav_noises, min_snr=2, max_snr=15) + + return aug_wave_data diff --git a/motion-gan-pipeline/motion-generation/funcs/utils.py b/motion-gan-pipeline/motion-generation/funcs/utils.py new file mode 100644 index 0000000..0565af2 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/funcs/utils.py @@ -0,0 +1,422 @@ +import sys +sys.path.append("..") +from . import audio_funcs + +import numpy as np +from math import cos, sin +import torch +from numpy.linalg import solve +from scipy.ndimage import gaussian_filter1d, median_filter +from sklearn.neighbors import KDTree +import time +from tqdm import tqdm + + +class camera(object): + def __init__(self, fx=0, fy=0, cx=0, cy=0): + self.name = 'default camera' + self.fx = fx + self.fy = fy + self.cx = cx + self.cy = cy + self.relative_rotation = np.diag([1,1,1]).astype(np.float32) + self.relative_translation = np.zeros(3, dtype=np.float32) +# self.intrinsic = np.array([[self.fx, 0, self.cx], +# [0, self.fy, self.cy], +# [0, 0, 1]]) + + def intrinsic(self, trans_matrix=0): + ''' compute the intrinsic matrix + ''' + intrinsic = np.array([[self.fx, 0, self.cx], + [0, self.fy, self.cy], + [0, 0, 1]]) + + return intrinsic + + def relative(self): + ''' compute the relative transformation 4x4 matrix with respect to the + first camera kinect. specially the kinect's relative transformation + matrix is exact a identity matrix. + ''' + relative = np.eye(4, dtype=np.float32) + relative[:3, :3] = self.relative_rotation + relative[:3, 3] = self.relative_translation + + return relative + + def transform_intrinsic(self, transform_matrix): + ''' change the camera intrinsic matrix + transformed_intrinsic = transform_matrix * intrinsic + ''' + scale = transform_matrix[0,0] + self.fx *= scale + self.fy *= scale + self.cx = scale * self.cx + transform_matrix[0, 2] + self.cy = scale * self.cy + transform_matrix[1, 2] + + +# def compute_mel_one_sequence(audio, sr=16000, fps=60, device='cpu'): +# ''' compute mel for an audio sequence. +# ''' + + +# hop_length=int(sr/(fps*2)) +# win_length=1/fps +# winlen = int(sr/fps) +# winstep=0.5/fps +# device = torch.device(device) +# n_fft=512 + +# Audio2Mel_torch = audio_funcs.Audio2Mel(n_fft=n_fft, +# hop_length=hop_length, +# win_length=winlen, +# sampling_rate=sr, +# n_mel_channels=80, +# mel_fmin=90, +# mel_fmax=7600.0).to(device) + +# nframe = int(audio.shape[0] / sr * fps) +# mel_nframe = nframe +# mel_frame_len = int(sr * win_length) +# mel_frame_step = sr * winstep + +# mel80s = np.zeros([mel_nframe, 80]) +# for i in range(mel_nframe): +# st = int(i * mel_frame_step) +# audio_clip = audio[st : st + mel_frame_len] +# if len(audio_clip) < mel_frame_len: +# audio_clip = np.concatenate([audio_clip, np.zeros([mel_frame_len - len(audio_clip)])]) +# audio_clip_device = torch.from_numpy(audio_clip).unsqueeze(0).unsqueeze(0).to(device).float() +# mel_tmp = Audio2Mel_torch(audio_clip_device).cpu().numpy() +# mel80s[i] = mel_tmp[0].T # [1, 80] -> we get [2, 80] ??? +# print(mel80s[i].size) + +# return mel80s + +def compute_mel_one_sequence(audio, hop_length=int(16000/120), winlen=1/60, winstep=0.5/60, sr=16000, fps=60, device='cpu'): + ''' compute mel for an audio sequence. + ''' + device = torch.device(device) + Audio2Mel_torch = audio_funcs.Audio2Mel(n_fft=512, hop_length=int(16000/120), win_length=int(16000/60), sampling_rate=16000, + n_mel_channels=80, mel_fmin=90, mel_fmax=7600.0).to(device) + + nframe = int(audio.shape[0] / 16000 * 60) + mel_nframe = 2 * nframe + mel_frame_len = int(sr * winlen) + mel_frame_step = sr * winstep + + mel80s = np.zeros([mel_nframe, 80]) + for i in range(mel_nframe): +# for i in tqdm(range(mel_nframe)): + st = int(i * mel_frame_step) + audio_clip = audio[st : st + mel_frame_len] + if len(audio_clip) < mel_frame_len: + audio_clip = np.concatenate([audio_clip, np.zeros([mel_frame_len - len(audio_clip)])]) + audio_clip_device = torch.from_numpy(audio_clip).unsqueeze(0).unsqueeze(0).to(device).float() + mel80s[i] = Audio2Mel_torch(audio_clip_device).cpu().numpy()[0].T # [1, 80] + + return mel80s + + +def KNN(feats, feat_database, K=10): + ''' compute KNN for feat in feat base + ''' + tree = KDTree(feat_database, leaf_size=100000) + print('start computing KNN ...') + st = time.time() + dist, ind = tree.query(feats, k=K) + et = time.time() + print('Taken time: ', et-st) + + return dist, ind + + +def KNN_with_torch(feats, feat_database, K=10): + feats = torch.from_numpy(feats)#.cuda() + feat_database = torch.from_numpy(feat_database)#.cuda() + # Training + feat_base_norm = (feat_database ** 2).sum(-1) +# print('start computing KNN ...') +# st = time.time() + feats_norm = (feats ** 2).sum(-1) + diss = (feats_norm.view(-1, 1) + + feat_base_norm.view(1, -1) + - 2 * feats @ feat_database.t() # Rely on cuBLAS for better performance! + ) + ind = diss.topk(K, dim=1, largest=False).indices +# et = time.time() +# print('Taken time: ', et-st) + + return ind.cpu().numpy() + + + + +def solve_LLE_projection(feat, feat_base): + '''find LLE projection weights given feat base and target feat + Args: + feat: [ndim, ] target feat + feat_base: [K, ndim] K-nearest feat base + ======================================= + We need to solve the following function + ``` + min|| feat - \sum_0^k{w_i} * feat_base_i ||, s.t. \sum_0^k{w_i}=1 + ``` + equals to: + ft = w1*f1 + w2*f2 + ... + wk*fk, s.t. w1+w2+...+wk=1 + = (1-w2-...-wk)*f1 + w2*f2 + ... + wk*fk + ft-f1 = w2*(f2-f1) + w3*(f3-f1) + ... + wk*(fk-f1) + ft-f1 = (f2-f1, f3-f1, ..., fk-f1) dot (w2, w3, ..., wk).T + B = A dot w_, here, B: [ndim,] A: [ndim, k-1], w_: [k-1,] + Finally, + ft' = (1-w2-..wk, w2, ..., wk) dot (f1, f2, ..., fk) + ======================================= + Returns: + w: [K,] linear weights, sums to 1 + ft': [ndim,] reconstructed feats + ''' + K, ndim = feat_base.shape + if K == 1: + feat_fuse = feat_base[0] + w = np.array([1]) + else: + w = np.zeros(K) + B = feat - feat_base[0] # [ndim,] + A = (feat_base[1:] - feat_base[0]).T # [ndim, K-1] + AT = A.T + w[1:] = solve(AT.dot(A), AT.dot(B)) + w[0] = 1 - w[1:].sum() + feat_fuse = w.dot(feat_base) + + return w, feat_fuse + + + +def compute_LLE_projection_frame(feats, feat_database, ind): + nframe = feats.shape[0] + feat_fuse = np.zeros_like(feats) + w = np.zeros([nframe, ind.shape[1]]) + current_K_feats = feat_database[ind] + w, feat_fuse = solve_LLE_projection(feats, current_K_feats) + + return w, feat_fuse + + +def compute_LLE_projection_all_frame(feats, feat_database, ind, nframe): + nframe = feats.shape[0] + feat_fuse = np.zeros_like(feats) + w = np.zeros([nframe, ind.shape[1]]) + for i in tqdm(range(nframe), desc='LLE projection'): + current_K_feats = feat_database[ind[i]] + w[i], feat_fuse[i] = solve_LLE_projection(feats[i], current_K_feats) + + return w, feat_fuse + + +def angle2matrix(angles, gradient='false'): + ''' get rotation matrix from three rotation angles(degree). right-handed. + Args: + angles: [3,]. x, y, z angles + x: pitch. positive for looking down. + y: yaw. positive for looking left. + z: roll. positive for tilting head right. + gradient(str): whether to compute gradient matrix: dR/d_x,y,z + Returns: + R: [3, 3]. rotation matrix. + ''' + x, y, z = np.deg2rad(angles[0]), np.deg2rad(angles[1]), np.deg2rad(angles[2]) + # x + Rx=np.array([[1, 0, 0], + [0, cos(x), -sin(x)], + [0, sin(x), cos(x)]]) + # y + Ry=np.array([[ cos(y), 0, sin(y)], + [ 0, 1, 0], + [-sin(y), 0, cos(y)]]) + # z + Rz=np.array([[cos(z), -sin(z), 0], + [sin(z), cos(z), 0], + [ 0, 0, 1]]) + + R=Rz.dot(Ry.dot(Rx)) + #R=Rx.dot(Ry.dot(Rz)) + + if gradient != 'true': + return R.astype(np.float32) + elif gradient == 'true': + # gradident matrix + dRxdx = np.array([[0, 0, 0], + [0, -sin(x), -cos(x)], + [0, cos(x), -sin(x)]]) + dRdx = Rz.dot(Ry.dot(dRxdx)) * np.pi/180 + dRydy = np.array([[-sin(y), 0, cos(y)], + [ 0, 0, 0], + [-cos(y), 0, -sin(y)]]) + dRdy = Rz.dot(dRydy.dot(Rx)) * np.pi/180 + dRzdz = np.array([[-sin(z), -cos(z), 0], + [ cos(z), -sin(z), 0], + [ 0, 0, 0]]) + dRdz = dRzdz.dot(Ry.dot(Rx)) * np.pi/180 + + return R.astype(np.float32), [dRdx.astype(np.float32), dRdy.astype(np.float32), dRdz.astype(np.float32)] + + + +def project_landmarks(camera_intrinsic, viewpoint_R, viewpoint_T, scale, headposes, pts_3d): + ''' project 2d landmarks given predicted 3d landmarks & headposes and user-defined + camera & viewpoint parameters + ''' + rot, trans = angle2matrix(headposes[:3]), headposes[3:][:, None] + pts3d_headpose = scale * rot.dot(pts_3d.T) + trans + pts3d_viewpoint = viewpoint_R.dot(pts3d_headpose) + viewpoint_T[:, None] + pts2d_project = camera_intrinsic.dot(pts3d_viewpoint) + pts2d_project[:2, :] /= pts2d_project[2, :] # divide z + pts2d_project = pts2d_project[:2, :].T + + return pts2d_project, rot, trans + + + +def landmark_smooth_3d(pts3d, smooth_sigma=0, area='only_mouth'): + ''' smooth the input 3d landmarks using gaussian filters on each dimension. + Args: + pts3d: [N, 73, 3] + ''' + # per-landmark smooth + if not smooth_sigma == 0: + if area == 'all': + pts3d = gaussian_filter1d(pts3d.reshape(-1, 73*3), smooth_sigma, axis=0).reshape(-1, 73, 3) + elif area == 'only_mouth': + mouth_pts3d = pts3d[:, 46:64, :].copy() + mouth_pts3d = gaussian_filter1d(mouth_pts3d.reshape(-1, 18*3), smooth_sigma, axis=0).reshape(-1, 18, 3) + pts3d = gaussian_filter1d(pts3d.reshape(-1, 73*3), smooth_sigma, axis=0).reshape(-1, 73, 3) + pts3d[:, 46:64, :] = mouth_pts3d + + + + return pts3d + + + +mouth_indices = list(range(46 * 2, 64 * 2)) +upper_outer_lip = list(range(47, 52)) +upper_inner_lip = [63, 62, 61] +lower_inner_lip = [58, 59, 60] +lower_outer_lip = list(range(57, 52, -1)) +lower_mouth = [53, 54, 55, 56, 57, 58, 59, 60] +upper_mouth = [46, 47, 48, 49, 50, 51, 52, 61, 62, 63] +def mouth_pts_AMP(pts3d, is_delta=True, method='XY', paras=[1,1]): + ''' mouth region AMP to control the reaction amplitude. + method: 'XY', 'delta', 'XYZ', 'LowerMore' or 'CloseSmall' + ''' + if method == 'XY': + AMP_scale_x, AMP_scale_y = paras + if is_delta: + pts3d[:, 46:64, 0] *= AMP_scale_x + pts3d[:, 46:64, 1] *= AMP_scale_y + else: + mean_mouth3d_xy = pts3d[:, 46:64, :2].mean(axis=0) + pts3d[:, 46:64, 0] += (AMP_scale_x-1) * (pts3d[:, 46:64, 0] - mean_mouth3d_xy[:,0]) + pts3d[:, 46:64, 1] += (AMP_scale_y-1) * (pts3d[:, 46:64, 1] - mean_mouth3d_xy[:,1]) + elif method == 'delta': + AMP_scale_x, AMP_scale_y = paras + if is_delta: + diff = AMP_scale_x * (pts3d[1:, 46:64] - pts3d[:-1, 46:64]) + pts3d[1:, 46:64] += diff + + elif method == 'XYZ': + AMP_scale_x, AMP_scale_y, AMP_scale_z = paras + if is_delta: + pts3d[:, 46:64, 0] *= AMP_scale_x + pts3d[:, 46:64, 1] *= AMP_scale_y + pts3d[:, 46:64, 2] *= AMP_scale_z + + elif method == 'LowerMore': + upper_x, upper_y, upper_z, lower_x, lower_y, lower_z = paras + if is_delta: + pts3d[:, upper_mouth, 0] *= upper_x + pts3d[:, upper_mouth, 1] *= upper_y + pts3d[:, upper_mouth, 2] *= upper_z + pts3d[:, lower_mouth, 0] *= lower_x + pts3d[:, lower_mouth, 1] *= lower_y + pts3d[:, lower_mouth, 2] *= lower_z + + elif method == 'CloseSmall': + open_x, open_y, open_z, close_x, close_y, close_z = paras + nframe = pts3d.shape[0] + for i in tqdm(range(nframe), desc='AMP mouth..'): + if sum(pts3d[i, upper_mouth, 1] > 0) + sum(pts3d[i, lower_mouth, 1] < 0) > 16 * 0.3: + # open + pts3d[i, 46:64, 0] *= open_x + pts3d[i, 46:64, 1] *= open_y + pts3d[i, 46:64, 2] *= open_z + else: + # close + pts3d[:, 46:64, 0] *= close_x + pts3d[:, 46:64, 1] *= close_y + pts3d[:, 46:64, 2] *= close_z + + return pts3d + + + + +def solve_intersect_mouth(pts3d): + ''' solve the generated intersec lips, usually happens in mouth AMP usage. + Args: + pts3d: [N, 73, 3] + ''' + upper_inner = pts3d[:, upper_inner_lip] + lower_inner = pts3d[:, lower_inner_lip] + + lower_inner_y = lower_inner[:,:,1] + upper_inner_y = upper_inner[:,:,1] + # all three inner lip flip + flip = lower_inner_y > upper_inner_y + flip = np.where(flip.sum(axis=1) == 3)[0] + + # flip frames + inner_y_diff = lower_inner_y[flip] - upper_inner_y[flip] + half_inner_y_diff = inner_y_diff * 0.5 + # upper inner + pts3d[flip[:,None], upper_inner_lip, 1] += half_inner_y_diff + # lower inner + pts3d[flip[:,None], lower_inner_lip, 1] -= half_inner_y_diff + # upper outer + pts3d[flip[:,None], upper_outer_lip, 1] += half_inner_y_diff.mean() + # lower outer + pts3d[flip[:,None], lower_outer_lip, 1] -= half_inner_y_diff.mean() + + + return pts3d + + + +def headpose_smooth(headpose, smooth_sigmas=[0,0], method='gaussian'): + rot_sigma, trans_sigma = smooth_sigmas + + if method == 'gaussian': + rot = gaussian_filter1d(headpose.reshape(-1, 6)[:,:3], rot_sigma, axis=0).reshape(-1, 3) + trans = gaussian_filter1d(headpose.reshape(-1, 6)[:,3:], trans_sigma, axis=0).reshape(-1, 3) + headpose_smooth = np.concatenate([rot, trans], axis=1) + + elif method == 'median': + rot = median_filter(headpose.reshape(-1, 6)[:,:3], size=rot_sigma).reshape(-1, 3) + trans = median_filter(headpose.reshape(-1, 6)[:,3:], size=trans_sigma).reshape(-1, 3) + headpose_smooth = np.concatenate([rot, trans], axis=1) + + else: + print(f'{method} smoothing not implemented.') + return headpose + + return headpose_smooth + + + + + + + + + diff --git a/motion-gan-pipeline/motion-generation/make_config.py b/motion-gan-pipeline/motion-generation/make_config.py new file mode 100644 index 0000000..1ca3443 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/make_config.py @@ -0,0 +1,42 @@ +import yaml +import os + + +def make_config(name, dictionary): + config_path = './config' + yml_path = os.path.join(config_path, f'{name}.yml') + with open(yml_path, 'w') as outfile: + yaml.dump(dictionary, outfile, default_flow_style=False) + + +def base_config(): + + config = {} + + ## Pick a name + # name = 'MTC' + # name = 'TED384-v2' + name = 'TMP_AVSpeech' + + ## Pick a task + task = 'Audio2Headpose' + # task = 'Correlation' + + ## Pick a dataset + dataset = 'deepspeech' + # dataset = 'emotion' + + # Parameters to setup experiment. + config['experiment'] = { + 'name': f'{task}_{name}', + 'dataset_mode': dataset, + 'dataroot': '/media/apennino/', + 'dataset_names': name, + 'fps': 25, + } + + make_config(config['experiment']['name'], config) + + +if __name__ == "__main__": + base_config() diff --git a/motion-gan-pipeline/motion-generation/media/Pitch_movement&delta_RMS.png b/motion-gan-pipeline/motion-generation/media/Pitch_movement&delta_RMS.png new file mode 100644 index 0000000..21709fa Binary files /dev/null and b/motion-gan-pipeline/motion-generation/media/Pitch_movement&delta_RMS.png differ diff --git a/motion-gan-pipeline/motion-generation/media/audio_14.png b/motion-gan-pipeline/motion-generation/media/audio_14.png new file mode 100644 index 0000000..a31af89 Binary files /dev/null and b/motion-gan-pipeline/motion-generation/media/audio_14.png differ diff --git a/motion-gan-pipeline/motion-generation/media/yam_corr.png b/motion-gan-pipeline/motion-generation/media/yam_corr.png new file mode 100644 index 0000000..ccf29dc Binary files /dev/null and b/motion-gan-pipeline/motion-generation/media/yam_corr.png differ diff --git a/motion-gan-pipeline/motion-generation/models/__init__.py b/motion-gan-pipeline/motion-generation/models/__init__.py new file mode 100644 index 0000000..186aba7 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/models/__init__.py @@ -0,0 +1,142 @@ +"""This package contains modules related to objective functions, optimizations, and network architectures. + +To add a custom model class called 'dummy', you need to add a file called 'dummy_model.py' and define a subclass DummyModel inherited from BaseModel. +You need to implement the following five functions: + -- <__init__>: initialize the class; first call BaseModel.__init__(self, opt). + -- : unpack data from dataset and apply preprocessing. + -- : produce intermediate results. + -- : calculate loss, gradients, and update network weights. + -- : (optionally) add model-specific options and set default options. + +In the function <__init__>, you need to define four lists: + -- self.loss_names (str list): specify the training losses that you want to plot and save. + -- self.model_names (str list): define networks used in our training. + -- self.visual_names (str list): specify the images that you want to display and save. + -- self.optimizers (optimizer list): define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an usage. + +Now you can use the model class by specifying flag '--model dummy'. +See our template model class 'template_model.py' for more details. +""" +import os +import importlib +import numpy as np +import torch +import torch.nn as nn +from models.base_model import BaseModel + + + +def find_model_using_name(model_name): + """Import the module "models/[model_name]_model.py". + + In the file, the class called DatasetNameModel() will + be instantiated. It has to be a subclass of BaseModel, + and it is case-insensitive. + """ + model_filename = "models." + model_name + "_model" + modellib = importlib.import_module(model_filename) + model = None + target_model_name = model_name.replace('_', '') + 'model' + for name, cls in modellib.__dict__.items(): + if name.lower() == target_model_name.lower() \ + and issubclass(cls, BaseModel): + model = cls + + if model is None: + print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name)) + exit() + + return model + + +def get_option_setter(model_name): + """Return the static method of the model class.""" + model_class = find_model_using_name(model_name) + return model_class.modify_commandline_options + + +def create_model(opt): + """Create a model given the option. + + This function warps the class CustomDatasetDataLoader. + This is the main interface between this package and 'train.py'/'test.py' + + Example: + >>> from models import create_model + >>> model = create_model(opt) + """ + model = find_model_using_name(opt.model) + instance = model(opt) + print("model [%s] was created" % type(instance).__name__) + return instance + + +def save_models(opt, epoch, epoch_iter, total_steps, visualizer, iter_path, modelG, modelD, end_of_epoch=False): + if not end_of_epoch: + if total_steps % opt.save_latest_freq == 0: + visualizer.vis_print('saving the latest model (epoch %d, total_steps %d)' % (epoch, total_steps)) + modelG.module.save('latest') + modelD.module.save('latest') + np.savetxt(iter_path, (epoch, epoch_iter), delimiter=',', fmt='%d') + else: + if epoch % opt.save_epoch_freq == 0: + visualizer.vis_print('saving the model at the end of epoch %d, iters %d' % (epoch, total_steps)) + modelG.module.save('latest') + modelD.module.save('latest') + modelG.module.save(epoch) + modelD.module.save(epoch) + np.savetxt(iter_path, (epoch+1, 0), delimiter=',', fmt='%d') + + +def update_models(opt, epoch, modelG, modelD, dataset_warp): + ### linearly decay learning rate after certain iterations + if epoch > opt.niter: + modelG.module.update_learning_rate(epoch, 'G') + modelD.module.update_learning_rate(epoch, 'D') + + ### gradually grow training sequence length + if (epoch % opt.niter_step) == 0: + dataset_warp.dataset.update_training_batch(epoch//opt.niter_step) +# modelG.module.update_training_batch(epoch//opt.niter_step) + + ### finetune all scales + if (opt.n_scales_spatial > 1) and (opt.niter_fix_global != 0) and (epoch == opt.niter_fix_global): + modelG.module.update_fixed_params() + + +class myModel(nn.Module): + def __init__(self, opt, model): + super(myModel, self).__init__() + self.opt = opt + self.module = model + self.model = nn.DataParallel(model, device_ids=opt.gpu_ids) + self.bs_per_gpu = int(np.ceil(float(opt.batch_size) / len(opt.gpu_ids))) # batch size for each GPU + self.pad_bs = self.bs_per_gpu * len(opt.gpu_ids) - opt.batch_size + + def forward(self, *inputs, **kwargs): + inputs = self.add_dummy_to_tensor(inputs, self.pad_bs) + outputs = self.model(*inputs, **kwargs, dummy_bs=self.pad_bs) + if self.pad_bs == self.bs_per_gpu: # gpu 0 does 0 batch but still returns 1 batch + return self.remove_dummy_from_tensor(outputs, 1) + return outputs + + def add_dummy_to_tensor(self, tensors, add_size=0): + if add_size == 0 or tensors is None: return tensors + if type(tensors) == list or type(tensors) == tuple: + return [self.add_dummy_to_tensor(tensor, add_size) for tensor in tensors] + + if isinstance(tensors, torch.Tensor): + dummy = torch.zeros_like(tensors)[:add_size] + tensors = torch.cat([dummy, tensors]) + return tensors + + def remove_dummy_from_tensor(self, tensors, remove_size=0): + if remove_size == 0 or tensors is None: return tensors + if type(tensors) == list or type(tensors) == tuple: + return [self.remove_dummy_from_tensor(tensor, remove_size) for tensor in tensors] + + if isinstance(tensors, torch.Tensor): + tensors = tensors[remove_size:] + return tensors + + diff --git a/motion-gan-pipeline/motion-generation/models/audio2headpose.py b/motion-gan-pipeline/motion-generation/models/audio2headpose.py new file mode 100644 index 0000000..7ddc17b --- /dev/null +++ b/motion-gan-pipeline/motion-generation/models/audio2headpose.py @@ -0,0 +1,108 @@ +import torch.nn as nn + +from .networks import WaveNet + + + +class Audio2Headpose(nn.Module): + def __init__(self, opt): + super(Audio2Headpose, self).__init__() + self.opt = opt + if self.opt.loss == 'GMM': + output_size = (2 * opt.A2H_GMM_ndim + 1) * opt.A2H_GMM_ncenter + elif self.opt.loss == 'L2': + output_size = opt.A2H_GMM_ndim + # define networks + self.audio_downsample = nn.Sequential( + nn.Linear(in_features=opt.APC_hidden_size, out_features=opt.APC_hidden_size), + nn.BatchNorm1d(opt.APC_hidden_size), + nn.LeakyReLU(0.2), + nn.Linear(opt.APC_hidden_size, opt.APC_hidden_size), + ) + + self.WaveNet = WaveNet(residual_layers=opt.A2H_wavenet_residual_layers, + residual_blocks=opt.A2H_wavenet_residual_blocks, + dilation_channels=opt.A2H_wavenet_residual_channels, + residual_channels =opt.A2H_wavenet_dilation_channels, + skip_channels=opt.A2H_wavenet_skip_channels, + kernel_size=opt.A2H_wavenet_kernel_size, + output_length=opt.time_frame_length, + use_bias=opt.A2H_wavenet_use_bias, + cond=True, + input_channels=opt.A2H_wavenet_input_channels, + ncenter=opt.A2H_GMM_ncenter, + ndim=opt.A2H_GMM_ndim, + output_channels=output_size, + cond_channels=opt.A2H_wavenet_cond_channels) + self.item_length = self.WaveNet.receptive_field + opt.time_frame_length - 1 + + + def forward(self, history_info, audio_features): + ''' + Args: + history_info: [b, T, ndim] + audio_features: [b, 1, nfeas, nwins] + ''' + # APC features: [b, item_length, APC_hidden_size] ==> [b, APC_hidden_size, item_length] + bs, item_len, ndim = audio_features.shape + down_audio_feats = self.audio_downsample(audio_features.reshape(-1, ndim)).reshape(bs, item_len, -1) + pred = self.WaveNet.forward(history_info.permute(0,2,1), down_audio_feats.transpose(1,2)) + + + return pred + + + + +class Audio2Headpose_LSTM(nn.Module): + def __init__(self, opt): + super(Audio2Headpose_LSTM, self).__init__() + self.opt = opt + if self.opt.loss == 'GMM': + output_size = (2 * opt.A2H_GMM_ndim + 1) * opt.A2H_GMM_ncenter + elif self.opt.loss == 'L2': + output_size = opt.A2H_GMM_ndim + # define networks + self.audio_downsample = nn.Sequential( + nn.Linear(in_features=opt.APC_hidden_size * 2, out_features=opt.APC_hidden_size), + nn.BatchNorm1d(opt.APC_hidden_size), + nn.LeakyReLU(0.2), + nn.Linear(opt.APC_hidden_size, opt.APC_hidden_size), + ) + + self.LSTM = nn.LSTM(input_size=opt.APC_hidden_size, + hidden_size=256, + num_layers=3, + dropout=0, + bidirectional=False, + batch_first=True) + self.fc = nn.Sequential( + nn.Linear(in_features=256, out_features=512), + nn.BatchNorm1d(512), + nn.LeakyReLU(0.2), + nn.Linear(512, 512), + nn.BatchNorm1d(512), + nn.LeakyReLU(0.2), + nn.Linear(512, output_size)) + + + def forward(self, audio_features): + ''' + Args: + history_info: [b, T, ndim] + audio_features: [b, 1, nfeas, nwins] + ''' + # APC features: [b, item_length, APC_hidden_size] ==> [b, APC_hidden_size, item_length] + bs, item_len, ndim = audio_features.shape + down_audio_feats = self.audio_downsample(audio_features.reshape(-1, ndim)).reshape(bs, item_len, -1) + output, (hn, cn) = self.LSTM(down_audio_feats) + pred = self.fc(output.reshape(-1, 256)).reshape(bs, item_len, -1) + + + return pred + + + + + + \ No newline at end of file diff --git a/motion-gan-pipeline/motion-generation/models/audio2headpose_model.py b/motion-gan-pipeline/motion-generation/models/audio2headpose_model.py new file mode 100644 index 0000000..65a3f11 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/models/audio2headpose_model.py @@ -0,0 +1,197 @@ +import numpy as np +import torch +from tqdm import tqdm + +from .base_model import BaseModel +from . import networks +from . import audio2headpose +from .losses import GMMLogLoss, Sample_GMM +import torch.nn as nn + + +class Audio2HeadposeModel(BaseModel): + def __init__(self, opt): + """Initialize the Audio2Headpose class. + + Parameters: + opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions + """ + BaseModel.__init__(self, opt) + + # specify the models you want to save to the disk. The training/test scripts will call and + # define networks + self.model_names = ['Audio2Headpose'] + if opt.feature_decoder == 'WaveNet': + self.Audio2Headpose = networks.init_net(audio2headpose.Audio2Headpose(opt), init_type='normal', init_gain=0.02, gpu_ids=opt.gpu_ids) + elif opt.feature_decoder == 'LSTM': + self.Audio2Headpose = networks.init_net(audio2headpose.Audio2Headpose_LSTM(opt), init_type='normal', init_gain=0.02, gpu_ids=opt.gpu_ids) + + # define only during training time + if self.isTrain: + # losses + self.criterion_GMM = GMMLogLoss(opt.A2H_GMM_ncenter, opt.A2H_GMM_ndim, opt.A2H_GMM_sigma_min).to(self.device) + self.criterion_L2 = nn.MSELoss().cuda() + + self.loss_names = ['GMM'] + # optimizer + self.optimizer = torch.optim.Adam([{'params':self.Audio2Headpose.parameters(), + 'initial_lr': opt.lr}], lr=opt.lr, betas=(0.9, 0.99)) + + self.optimizers.append(self.optimizer) + + if opt.continue_train: + self.resume_training() + + + def resume_training(self): + opt = self.opt + ### if continue training, recover previous states + print('Resuming from epoch %s ' % (opt.load_epoch)) + # change epoch count & update schedule settings + opt.epoch_count = int(opt.load_epoch) + self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers] + # print lerning rate + lr = self.optimizers[0].param_groups[0]['lr'] + print('update learning rate: {} -> {}'.format(opt.lr, lr)) + + + + def set_input(self, data, data_info=None): + """Unpack input data from the dataloader and perform necessary pre-processing steps. + """ + if self.opt.feature_decoder == 'WaveNet': + self.headpose_audio_feats, self.history_headpose, self.target_headpose = data + self.headpose_audio_feats = self.headpose_audio_feats.to(self.device) + self.history_headpose = self.history_headpose.to(self.device) + self.target_headpose = self.target_headpose.to(self.device) + elif self.opt.feature_decoder == 'LSTM': + self.headpose_audio_feats, self.target_headpose = data + self.headpose_audio_feats = self.headpose_audio_feats.to(self.device) + self.target_headpose = self.target_headpose.to(self.device) + + + + def forward(self): + ''' + Args: + history_landmarks: [b, T, ndim] + audio_features: [b, 1, nfeas, nwins] + Returns: + preds: [b, T, output_channels] + ''' + + # if self.opt.audio_windows == 2: + # bs, item_len, ndim = self.headpose_audio_feats.shape + # self.headpose_audio_feats = self.headpose_audio_feats.reshape(bs, -1, ndim * 2) + # else: + bs, item_len, ndim = self.headpose_audio_feats.shape + if self.opt.feature_decoder == 'WaveNet': + self.preds_headpose = self.Audio2Headpose.forward(self.history_headpose, self.headpose_audio_feats) + elif self.opt.feature_decoder == 'LSTM': + self.preds_headpose = self.Audio2Headpose.forward(self.headpose_audio_feats) + + + def calculate_loss(self): + """ calculate loss in detail, only forward pass included""" + if self.opt.loss == 'GMM': + self.loss_GMM = self.criterion_GMM(self.preds_headpose, self.target_headpose) + self.loss = self.loss_GMM + elif self.opt.loss == 'L2': + self.loss_L2 = self.criterion_L2(self.preds_headpose, self.target_headpose) + self.loss = self.loss_L2 + + if not self.opt.smooth_loss == 0: + mu_gen = Sample_GMM(self.preds_headpose, + self.Audio2Headpose.module.WaveNet.ncenter, + self.Audio2Headpose.module.WaveNet.ndim, + sigma_scale=0) + self.smooth_loss = (mu_gen[:,2:] + self.target_headpose[:,:-2] - 2 * self.target_headpose[:,1:-1]).mean(dim=2).abs().mean() + self.loss += self.smooth_loss * self.opt.smooth_loss + + + + def backward(self): + """Calculate losses, gradients, and update network weights; called in every training iteration""" + self.calculate_loss() + self.loss.backward() + + + def optimize_parameters(self): + """Update network weights; it will be called in every training iteration.""" + self.optimizer.zero_grad() # clear optimizer parameters grad + self.forward() # forward pass + self.backward() # calculate loss and gradients + self.optimizer.step() # update gradients + + + def validate(self): + """ validate process """ + with torch.no_grad(): + self.forward() + self.calculate_loss() + + + def generate_sequences(self, audio_feats, pre_headpose, fill_zero=True, sigma_scale=0.0, opt=[]): + + frame_future = opt.frame_future + nframe = audio_feats.shape[0] - frame_future + pred_headpose = np.zeros([nframe, opt.A2H_GMM_ndim]) + + if opt.feature_decoder == 'WaveNet': + # fill zero or not + if fill_zero == True: + # headpose + audio_feats_insert = np.repeat(audio_feats[0], opt.A2H_receptive_field - 1) + audio_feats_insert = audio_feats_insert.reshape(-1, opt.A2H_receptive_field - 1).T + audio_feats = np.concatenate([audio_feats_insert, audio_feats]) + # history headpose + history_headpose = np.repeat(pre_headpose, opt.A2H_receptive_field) + history_headpose = history_headpose.reshape(-1, opt.A2H_receptive_field).T + history_headpose = torch.from_numpy(history_headpose).unsqueeze(0).float().to(self.device) + infer_start = 0 + else: + return None + + # evaluate mode + self.Audio2Headpose.eval() + + with torch.no_grad(): + for i in tqdm(range(infer_start, nframe), desc='generating headpose'): + history_start = i - infer_start + input_audio_feats = audio_feats[history_start + frame_future: history_start + frame_future + opt.A2H_receptive_field] + input_audio_feats = torch.from_numpy(input_audio_feats).unsqueeze(0).float().to(self.device) + + if self.opt.feature_decoder == 'WaveNet': + preds = self.Audio2Headpose.forward(history_headpose, input_audio_feats) + elif self.opt.feature_decoder == 'LSTM': + preds = self.Audio2Headpose.forward(input_audio_feats) + + if opt.loss == 'GMM': + pred_data = Sample_GMM(preds, opt.A2H_GMM_ncenter, opt.A2H_GMM_ndim, sigma_scale=sigma_scale) + elif opt.loss == 'L2': + pred_data = preds + + # get predictions + pred_headpose[i] = pred_data[0,0].cpu().detach().numpy() + history_headpose = torch.cat((history_headpose[:,1:,:], pred_data.to(self.device)), dim=1) # add in time-axis + + return pred_headpose + + elif opt.feature_decoder == 'LSTM': + self.Audio2Headpose.eval() + with torch.no_grad(): + input = torch.from_numpy(audio_feats).unsqueeze(0).float().to(self.device) + preds = self.Audio2Headpose.forward(input) + if opt.loss == 'GMM': + pred_data = Sample_GMM(preds, opt.A2H_GMM_ncenter, opt.A2H_GMM_ndim, sigma_scale=sigma_scale) + elif opt.loss == 'L2': + pred_data = preds + # get predictions + pred_headpose = pred_data[0].cpu().detach().numpy() + + return pred_headpose + + + + + \ No newline at end of file diff --git a/motion-gan-pipeline/motion-generation/models/base_model.py b/motion-gan-pipeline/motion-generation/models/base_model.py new file mode 100644 index 0000000..fe3e155 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/models/base_model.py @@ -0,0 +1,301 @@ +import os +import torch +import numpy as np +from collections import OrderedDict +from abc import ABC, abstractmethod +from . import networks + + +class BaseModel(ABC): + """This class is an abstract base class (ABC) for models. + To create a subclass, you need to implement the following five functions: + -- <__init__>: initialize the class; first call BaseModel.__init__(self, opt). + -- : unpack data from dataset and apply preprocessing. + -- : produce intermediate results. + -- : calculate losses, gradients, and update network weights. + -- : (optionally) add model-specific options and set default options. + """ + + def __init__(self, opt): + """Initialize the BaseModel class. + + Parameters: + opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions + + When creating your custom class, you need to implement your own initialization. + In this function, you should first call + Then, you need to define four lists: + -- self.loss_names (str list): specify the training losses that you want to plot and save. + -- self.model_names (str list): define networks used in our training. + -- self.visual_names (str list): specify the images that you want to display and save. + -- self.optimizers (optimizer list): define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example. + """ + self.opt = opt + self.gpu_ids = opt.gpu_ids + self.isTrain = opt.isTrain + # get device name: CPU or GPU + # if self.gpu_ids == '-1': + # self.device = torch.device('cpu') + # self.gpu_ids = opt.gpu_ids == [] + # else: + # self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) + self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if len(self.gpu_ids) > 0 else torch.device('cpu') + + self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) # save all the checkpoints to save_dir + # torch speed up training + torch.backends.cudnn.enabled = True + torch.backends.cudnn.benchmark = True + self.loss_names = [] + self.model_names = [] + self.visual_names = [] + self.optimizers = [] + self.image_paths = [] + self.metric = 0 # used for learning rate policy 'plateau' + + @staticmethod + def modify_commandline_options(parser, is_train): + """Add new model-specific options, and rewrite default values for existing options. + + Parameters: + parser -- original option parser + is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options. + + Returns: + the modified parser. + """ + return parser + + @abstractmethod + def set_input(self, input): + """Unpack input data from the dataloader and perform necessary pre-processing steps. + + Parameters: + input (dict): includes the data itself and its metadata information. + """ + pass + + @abstractmethod + def forward(self): + """Run forward pass; called by both functions and .""" + pass + + @abstractmethod + def optimize_parameters(self): + """Calculate losses, gradients, and update network weights; called in every training iteration""" + pass + + def setup(self, opt): + """Load and print networks; create schedulers + + Parameters: + opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions + """ + if self.isTrain: + self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers] + if not self.isTrain or opt.continue_train: + self.load_networks(opt.load_epoch) + + + self.print_networks(opt.verbose) + + + def train(self): + """Make models train mode during train time""" + for name in self.model_names: + if isinstance(name, str): + net = getattr(self, name) + net.train(mode=True) + + + def eval(self): + """Make models eval mode during test time""" + for name in self.model_names: + if isinstance(name, str): + net = getattr(self, name) + net.eval() + + + def test(self): + """Forward function used in test time. + + This function wraps function in no_grad() so we don't save intermediate steps for backprop + It also calls to produce additional visualization results + """ + with torch.no_grad(): + self.forward() + self.compute_visuals() + + def compute_visuals(self): + """Calculate additional output images for visdom and HTML visualization""" + pass + + def get_image_paths(self): + """ Return image paths that are used to load current data""" + return self.image_paths + + def update_learning_rate(self): + """Update learning rates for all the networks; called at the end of every epoch""" + for scheduler in self.schedulers: + if self.opt.lr_policy == 'plateau': + scheduler.step(self.metric) + else: + scheduler.step() + + lr = self.optimizers[0].param_groups[0]['lr'] + print('learning rate = %.7f' % lr) + + def get_current_visuals(self): + """Return visualization images. train.py will display these images with visdom, and save the images to a HTML""" + visual_ret = OrderedDict() + for name in self.visual_names: + if isinstance(name, str): + visual_ret[name] = getattr(self, name) + return visual_ret + + def get_current_losses(self): + """Return traning losses / errors. train.py will print out these errors on console, and save them to a file""" + errors_ret = OrderedDict() + for name in self.loss_names: + if isinstance(name, str): + errors_ret[name] = float(getattr(self, 'loss_' + name)) # float(...) works for both scalar tensor and float number + return errors_ret + + def save_networks(self, epoch, train_info=None): + """Save all the networks to the disk. + + Parameters: + epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name) + """ + for name in self.model_names: + if isinstance(name, str): + save_filename = '%s_%s.pkl' % (epoch, name) + save_path = os.path.join(self.save_dir, save_filename) + net = getattr(self, name) + torch.save(net.state_dict(), save_path) + if train_info is not None: + epoch, epoch_iter = train_info + iter_path = os.path.join(self.save_dir, 'iter.txt') + np.savetxt(iter_path, (epoch, epoch_iter), delimiter=',', fmt='%d') + + + def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0): + """Fix InstanceNorm checkpoints incompatibility (prior to 0.4)""" + key = keys[i] + if i + 1 == len(keys): # at the end, pointing to a parameter/buffer + if module.__class__.__name__.startswith('InstanceNorm') and \ + (key == 'running_mean' or key == 'running_var'): + if getattr(module, key) is None: + state_dict.pop('.'.join(keys)) + if module.__class__.__name__.startswith('InstanceNorm') and \ + (key == 'num_batches_tracked'): + state_dict.pop('.'.join(keys)) + else: + self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1) + + def load_networks(self, epoch): + """Load all the networks from the disk. + + Parameters: + epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name) + """ + + + for name in self.model_names: + if isinstance(name, str): + if epoch[-3:] == 'pkl': + load_path = epoch + else: + load_filename = '%s_%s.pkl' % (epoch, name) + load_path = os.path.join(self.save_dir, load_filename) + + net = getattr(self, name) +# if isinstance(net, torch.nn.DataParallel): +# net = net.module + if os.path.exists(load_path): + state_dict = torch.load(load_path, map_location=str(self.device)) + if self.device == torch.device('cpu'): + for key in list(state_dict.keys()): + state_dict[key[7:]] = state_dict.pop(key) + if hasattr(state_dict, '_metadata'): + del state_dict._metadata + print('loading the model from %s' % load_path) + net.load_state_dict(state_dict, strict=False) + else: + print('No model weight file:', load_path, 'initialize model without pre-trained weights.') + if self.isTrain == False: + raise ValueError('We are now in inference process, no pre-trained model found! Check the model checkpoint!') + + +# if isinstance(net, torch.nn.DataParallel): +# net = net.module + + # if you are using PyTorch newer than 0.4 (e.g., built from + # GitHub source), you can remove str() on self.device + +# state_dict = torch.load(load_path, map_location=str(self.device)) +# if hasattr(state_dict, '_metadata'): +# del state_dict._metadata +# +# # patch InstanceNorm checkpoints prior to 0.4 +# for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop +# self.__patch_instance_norm_state_dict(state_dict, net, key.split('.')) +# net.load_state_dict(state_dict) + + def load_checkpoint(self, checkpoint_path): + """ + Load all the networks from checkpoint. + """ + + + for name in self.model_names: + if isinstance(name, str): + + load_path = checkpoint_path + + net = getattr(self, name) +# if isinstance(net, torch.nn.DataParallel): +# net = net.module + if os.path.exists(load_path): + state_dict = torch.load(load_path, map_location=str(self.device)) + if self.device == torch.device('cpu'): + for key in list(state_dict.keys()): + state_dict[key[7:]] = state_dict.pop(key) + if hasattr(state_dict, '_metadata'): + del state_dict._metadata + print('loading the model from %s' % load_path) + net.load_state_dict(state_dict, strict=False) + else: + print('No model weight file:', load_path, 'initialize model without pre-trained weights.') + if self.isTrain == False: + raise ValueError('We are now in inference process, no pre-trained model found! Check the model checkpoint!') + + def print_networks(self, verbose): + """Print the total number of parameters in the network and (if verbose) network architecture + + Parameters: + verbose (bool) -- if verbose: print the network architecture + """ + print('---------- Networks initialized -------------') + for name in self.model_names: + if isinstance(name, str): + net = getattr(self, name) + num_params = 0 + for param in net.parameters(): + num_params += param.numel() + if verbose: + print(net) + print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6)) + print('-----------------------------------------------') + + def set_requires_grad(self, nets, requires_grad=False): + """Set requies_grad=Fasle for all the networks to avoid unnecessary computations + Parameters: + nets (network list) -- a list of networks + requires_grad (bool) -- whether the networks require gradients or not + """ + if not isinstance(nets, list): + nets = [nets] + for net in nets: + if net is not None: + for param in net.parameters(): + param.requires_grad = requires_grad diff --git a/motion-gan-pipeline/motion-generation/models/losses.py b/motion-gan-pipeline/motion-generation/models/losses.py new file mode 100644 index 0000000..4f38542 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/models/losses.py @@ -0,0 +1,275 @@ +import torch +import torch.nn as nn +from torch.autograd import Variable +import math +import torch.nn.functional as F + + +class GMMLogLoss(nn.Module): + ''' compute the GMM loss between model output and the groundtruth data. + Args: + ncenter: numbers of gaussian distribution + ndim: dimension of each gaussian distribution + sigma_bias: + sigma_min: current we do not use it. + ''' + def __init__(self, ncenter, ndim, sigma_min=0.03): + super(GMMLogLoss,self).__init__() + self.ncenter = ncenter + self.ndim = ndim + self.sigma_min = sigma_min + + + def forward(self, output, target): + ''' + Args: + output: [b, T, ncenter + ncenter * ndim * 2]: + [:, :, : ncenter] shows each gaussian probability + [:, :, ncenter : ncenter + ndim * ncenter] shows the average values of each dimension of each gaussian + [: ,:, ncenter + ndim * ncenter : ncenter + ndim * 2 * ncenter] show the negative log sigma of each dimension of each gaussian + target: [b, T, ndim], the ground truth target landmark data is shown here + To maximize the log-likelihood equals to minimize the negative log-likelihood. + NOTE: It is unstable to directly compute the log results of sigma, e.g. ln(-0.1) as we need to clip the sigma results + into positive. Hence here we predict the negative log sigma results to avoid numerical instablility, which mean: + `` sigma = 1/exp(predict), predict = -ln(sigma) `` + Also, it will be just the 'B' term below! + Currently we only implement single gaussian distribution, hence the first values of pred are meaningless. + For single gaussian distribution: + L(mu, sigma) = -n/2 * ln(2pi * sigma^2) - 1 / (2 x sigma^2) * sum^n (x_i - mu)^2 (n for prediction times, n=1 for one frame, x_i for gt) + = -1/2 * ln(2pi) - 1/2 * ln(sigma^2) - 1/(2 x sigma^2) * (x - mu)^2 + == min -L(mu, sgima) = 0.5 x ln(2pi) + 0.5 x ln(sigma^2) + 1/(2 x sigma^2) * (x - mu)^2 + = 0.5 x ln_2PI + ln(sigma) + 0.5 x (MU_DIFF/sigma)^2 + = A - B + C + In batch and Time sample, b and T are summed and averaged. + ''' + b, T, _ = target.shape + # read prediction paras + mus = output[:, :, self.ncenter : (self.ncenter + self.ncenter * self.ndim)].view(b, T, self.ncenter, self.ndim) # [b, T, ncenter, ndim] + + # apply min sigma + neg_log_sigmas_out = output[:, :, (self.ncenter + self.ncenter * self.ndim):].view(b, T, self.ncenter, self.ndim) # [b, T, ncenter, ndim] + inv_sigmas_min = torch.ones(neg_log_sigmas_out.size()).cuda() * (1. / self.sigma_min) + inv_sigmas_min_log = torch.log(inv_sigmas_min) + neg_log_sigmas = torch.min(neg_log_sigmas_out, inv_sigmas_min_log) + + inv_sigmas = torch.exp(neg_log_sigmas) + # replicate the target of ncenter to minus mu + target_rep = target.unsqueeze(2).expand(b, T, self.ncenter, self.ndim) # [b, T, ncenter, ndim] + MU_DIFF = target_rep - mus # [b, T, ncenter, ndim] + # sigma process + A = 0.5 * math.log(2 * math.pi) # 0.9189385332046727 + B = neg_log_sigmas # [b, T, ncenter, ndim] + C = 0.5 * (MU_DIFF * inv_sigmas)**2 # [b, T, ncenter, ndim] + negative_loglikelihood = A - B + C # [b, T, ncenter, ndim] + + return negative_loglikelihood.mean() + + +def Sample_GMM(gmm_params, ncenter, ndim, weight_smooth = 0.0, sigma_scale = 0.0): + ''' Sample values from a given a GMM distribution. + Args: + gmm_params: [b, target_length, (2 * ndim + 1) * ncenter], including the + distribution weights, average and sigma + ncenter: numbers of gaussian distribution + ndim: dimension of each gaussian distribution + weight_smooth: float, smooth the gaussian distribution weights + sigma_scale: float, adjust the gaussian scale, larger for sharper prediction, + 0 for zero sigma which always return average values + Returns: + current_sample: [] + ''' + # reshape as [b*T, (2 * ndim + 1) * ncenter] + b, T, _ = gmm_params.shape + gmm_params_cpu = gmm_params.cpu().view(-1, (2 * ndim + 1) * ncenter) + # compute each distrubution probability + prob = nn.functional.softmax(gmm_params_cpu[:, : ncenter] * (1 + weight_smooth), dim=1) + # select the gaussian distribution according to their weights + selected_idx = torch.multinomial(prob, num_samples=1, replacement=True) + + mu = gmm_params_cpu[:, ncenter : ncenter + ncenter * ndim] + # please note that we use -logsigma as output, hence here we need to take the negative + sigma = torch.exp(-gmm_params_cpu[:, ncenter + ncenter * ndim:]) * sigma_scale +# print('sigma average:', sigma.mean()) + + selected_sigma = torch.empty(b*T, ndim).float() + selected_mu = torch.empty(b*T, ndim).float() + current_sample = torch.randn(b*T, ndim).float() +# current_sample = test_sample + + for i in range(b*T): + idx = selected_idx[i, 0] + selected_sigma[i, :] = sigma[i, idx * ndim:(idx + 1) * ndim] + selected_mu[i, :] = mu[i, idx * ndim:(idx + 1) * ndim] + + # sample with sel sigma and sel mean + current_sample = current_sample * selected_sigma + selected_mu + # cur_sample = sel_mu +# return current_sample.unsqueeze(1).cuda() + return current_sample.reshape(b, T, -1).cuda() + + + + +class GANLoss(nn.Module): + def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0, + tensor=torch.FloatTensor): + super(GANLoss, self).__init__() + self.real_label = target_real_label + self.fake_label = target_fake_label + self.real_label_var = None + self.fake_label_var = None + self.Tensor = tensor + if use_lsgan: + self.loss = nn.MSELoss() + else: + self.loss = nn.BCELoss() + + def get_target_tensor(self, input, target_is_real): + target_tensor = None + gpu_id = input.get_device() + if target_is_real: + create_label = ((self.real_label_var is None) or + (self.real_label_var.numel() != input.numel())) + if create_label: + real_tensor = self.Tensor(input.size()).cuda(gpu_id).fill_(self.real_label) + self.real_label_var = Variable(real_tensor, requires_grad=False) + target_tensor = self.real_label_var + else: + create_label = ((self.fake_label_var is None) or + (self.fake_label_var.numel() != input.numel())) + if create_label: + fake_tensor = self.Tensor(input.size()).cuda(gpu_id).fill_(self.fake_label) + self.fake_label_var = Variable(fake_tensor, requires_grad=False) + target_tensor = self.fake_label_var + return target_tensor + + def __call__(self, input, target_is_real): + if isinstance(input[0], list): + loss = 0 + for input_i in input: + pred = input_i[-1] + target_tensor = self.get_target_tensor(pred, target_is_real) + loss += self.loss(pred, target_tensor) + return loss + else: + target_tensor = self.get_target_tensor(input[-1], target_is_real) + return self.loss(input[-1], target_tensor) + + + + +class VGGLoss(nn.Module): + def __init__(self, model=None): + super(VGGLoss, self).__init__() + if model is None: + self.vgg = Vgg19() + else: + self.vgg = model + + self.vgg.cuda() + # self.vgg.eval() + self.criterion = nn.L1Loss() + self.style_criterion = StyleLoss() + self.weights = [1.0, 1.0, 1.0, 1.0, 1.0] + self.style_weights = [1.0, 1.0, 1.0, 1.0, 1.0] + # self.weights = [5.0, 1.0, 0.5, 0.4, 0.8] + # self.style_weights = [10e4, 1000, 50, 15, 50] + + def forward(self, x, y, style=False): + x_vgg, y_vgg = self.vgg(x), self.vgg(y) + loss = 0 + if style: + # return both perceptual loss and style loss. + style_loss = 0 + for i in range(len(x_vgg)): + this_loss = (self.weights[i] * + self.criterion(x_vgg[i], y_vgg[i].detach())) + this_style_loss = (self.style_weights[i] * + self.style_criterion(x_vgg[i], y_vgg[i].detach())) + loss += this_loss + style_loss += this_style_loss + return loss, style_loss + + for i in range(len(x_vgg)): + this_loss = (self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach())) + loss += this_loss + return loss + + +def gram_matrix(input): + a, b, c, d = input.size() # a=batch size(=1) + # b=number of feature maps + # (c,d)=dimensions of a f. map (N=c*d) + features = input.view(a * b, c * d) # resise F_XL into \hat F_XL + G = torch.mm(features, features.t()) # compute the gram product + # we 'normalize' the values of the gram matrix + # by dividing by the number of element in each feature maps. + return G.div(a * b * c * d) + + +class StyleLoss(nn.Module): + def __init__(self): + super(StyleLoss, self).__init__() + + def forward(self, x, y): + Gx = gram_matrix(x) + Gy = gram_matrix(y) + return F.mse_loss(Gx, Gy) * 30000000 + + + +class MaskedL1Loss(nn.Module): + def __init__(self): + super(MaskedL1Loss, self).__init__() + self.criterion = nn.L1Loss() + + def forward(self, input, target, mask): + mask = mask.expand(-1, input.size()[1], -1, -1) + loss = self.criterion(input * mask, target * mask) + return loss + + + +from torchvision import models +class Vgg19(nn.Module): + def __init__(self, requires_grad=False): + super(Vgg19, self).__init__() + vgg_pretrained_features = models.vgg19(pretrained=True).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + for x in range(2): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(2, 7): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(7, 12): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(12, 21): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + for x in range(21, 30): + self.slice5.add_module(str(x), vgg_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h_relu1 = self.slice1(X) + h_relu2 = self.slice2(h_relu1) + h_relu3 = self.slice3(h_relu2) + h_relu4 = self.slice4(h_relu3) + h_relu5 = self.slice5(h_relu4) + out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5] + return out + + + + + + + + + + + diff --git a/motion-gan-pipeline/motion-generation/models/networks.py b/motion-gan-pipeline/motion-generation/models/networks.py new file mode 100644 index 0000000..17ff4e1 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/models/networks.py @@ -0,0 +1,873 @@ +import os +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.optim import lr_scheduler +from torch.nn import init +import functools + + +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence + + + +############################################################################### +# The detailed network architecture implementation for each model +############################################################################### + +class APC_encoder(nn.Module): + def __init__(self, + mel_dim, + hidden_size, + num_layers, + residual): + super(APC_encoder, self).__init__() + + input_size = mel_dim + + in_sizes = ([input_size] + [hidden_size] * (num_layers - 1)) + out_sizes = [hidden_size] * num_layers + self.rnns = nn.ModuleList( + [nn.GRU(input_size=in_size, hidden_size=out_size, batch_first=True) for (in_size, out_size) in zip(in_sizes, out_sizes)]) + + self.rnn_residual = residual + + def forward(self, inputs, lengths): + ''' + input: + inputs: (batch_size, seq_len, mel_dim) + lengths: (batch_size,) + + return: + predicted_mel: (batch_size, seq_len, mel_dim) + internal_reps: (num_layers + x, batch_size, seq_len, rnn_hidden_size), + where x is 1 if there's a prenet, otherwise 0 + ''' + with torch.no_grad(): + seq_len = inputs.size(1) + packed_rnn_inputs = pack_padded_sequence(inputs, lengths, True) + + for i, layer in enumerate(self.rnns): + packed_rnn_outputs, _ = layer(packed_rnn_inputs) + + rnn_outputs, _ = pad_packed_sequence( + packed_rnn_outputs, True, total_length=seq_len) + # outputs: (batch_size, seq_len, rnn_hidden_size) + + if i + 1 < len(self.rnns): + rnn_inputs, _ = pad_packed_sequence( + packed_rnn_inputs, True, total_length=seq_len) + # rnn_inputs: (batch_size, seq_len, rnn_hidden_size) + if self.rnn_residual and rnn_inputs.size(-1) == rnn_outputs.size(-1): + # Residual connections + rnn_outputs = rnn_outputs + rnn_inputs + packed_rnn_inputs = pack_padded_sequence(rnn_outputs, lengths, True) + + + return rnn_outputs + + + + +class WaveNet(nn.Module): + ''' This is a complete implementation of WaveNet architecture, mainly composed + of several residual blocks and some other operations. + Args: + batch_size: number of batch size + residual_layers: number of layers in each residual blocks + residual_blocks: number of residual blocks + dilation_channels: number of channels for the dilated convolution + residual_channels: number of channels for the residual connections + skip_channels: number of channels for the skip connections + end_channels: number of channels for the end convolution + classes: Number of possible values each sample can have as output + kernel_size: size of dilation convolution kernel + output_length(int): Number of samples that are generated for each input + use_bias: whether bias is used in each layer. + cond(bool): whether condition information are applied. if cond == True: + cond_channels: channel number of condition information + `` loss(str): GMM loss is adopted. `` + ''' + def __init__(self, + residual_layers = 10, + residual_blocks = 3, + dilation_channels = 32, + residual_channels = 32, + skip_channels = 256, + kernel_size = 2, + output_length = 16, + use_bias = False, + cond = True, + input_channels = 128, + ncenter = 1, + ndim = 73*2, + output_channels = 73*3, + cond_channels = 256, + activation = 'leakyrelu'): + super(WaveNet, self).__init__() + + self.layers = residual_layers + self.blocks = residual_blocks + self.dilation_channels = dilation_channels + self.residual_channels = residual_channels + self.skip_channels = skip_channels + self.input_channels = input_channels + self.ncenter = ncenter + self.ndim = ndim +# self.output_channels = (2 * self.ndim + 1) * self.ncenter + self.output_channels = output_channels + self.kernel_size = kernel_size + self.output_length = output_length + self.bias = use_bias + self.cond = cond + self.cond_channels = cond_channels + + # build modules + self.dilations = [] + self.dilation_queues = [] + residual_blocks = [] + self.receptive_field = 1 + + # 1x1 convolution to create channels + self.start_conv1 = nn.Conv1d(in_channels=self.input_channels, + out_channels=self.residual_channels, + kernel_size=1, + bias=True) + self.start_conv2 = nn.Conv1d(in_channels=self.residual_channels, + out_channels=self.residual_channels, + kernel_size=1, + bias=True) + if activation == 'relu': + self.activation = nn.ReLU(inplace = True) + elif activation == 'leakyrelu': + self.activation = nn.LeakyReLU(0.2) + self.drop_out2D = nn.Dropout2d(p=0.5) + + + # build residual blocks + for b in range(self.blocks): + new_dilation = 1 + additional_scope = kernel_size - 1 + for i in range(self.layers): + # create current residual block + residual_blocks.append(residual_block(dilation = new_dilation, + dilation_channels = self.dilation_channels, + residual_channels = self.residual_channels, + skip_channels = self.skip_channels, + kernel_size = self.kernel_size, + use_bias = self.bias, + cond = self.cond, + cond_channels = self.cond_channels)) + new_dilation *= 2 + + self.receptive_field += additional_scope + additional_scope *= 2 + + self.residual_blocks = nn.ModuleList(residual_blocks) + # end convolutions + + self.end_conv_1 = nn.Conv1d(in_channels = self.skip_channels, + out_channels = self.output_channels, + kernel_size = 1, + bias = True) + self.end_conv_2 = nn.Conv1d(in_channels = self.output_channels, + out_channels = self.output_channels, + kernel_size = 1, + bias = True) + + + def parameter_count(self): + par = list(self.parameters()) + s = sum([np.prod(list(d.size())) for d in par]) + return s + + def forward(self, input, cond=None): + ''' + Args: + input: [b, ndim, T] + cond: [b, nfeature, T] + Returns: + res: [b, T, ndim] + ''' + # dropout + x = self.drop_out2D(input) + + # preprocess + x = self.activation(self.start_conv1(x)) + x = self.activation(self.start_conv2(x)) + skip = 0 +# for i in range(self.blocks * self.layers): + for i, dilation_block in enumerate(self.residual_blocks): + x, current_skip = self.residual_blocks[i](x, cond) + skip += current_skip + + # postprocess + res = self.end_conv_1(self.activation(skip)) + res = self.end_conv_2(self.activation(res)) + + # cut the output size + res = res[:, :, -self.output_length:] # [b, ndim, T] + res = res.transpose(1, 2) # [b, T, ndim] + + return res + + + +class residual_block(nn.Module): + ''' + This is the implementation of a residual block in wavenet model. Every + residual block takes previous block's output as input. The forward pass of + each residual block can be illusatrated as below: + + ######################### Current Residual Block ########################## + # |-----------------------*residual*--------------------| # + # | | # + # | |-- dilated conv -- tanh --| | # + # -> -|-- pad--| * ---- |-- 1x1 -- + --> *input* # + # |-- dilated conv -- sigm --| | # + # 1x1 # + # | # + # ---------------------------------------------> + -------------> *skip* # + ########################################################################### + As shown above, each residual block returns two value: 'input' and 'skip': + 'input' is indeed this block's output and also is the next block's input. + 'skip' is the skip data which will be added finally to compute the prediction. + The input args own the same meaning in the WaveNet class. + + ''' + def __init__(self, + dilation, + dilation_channels = 32, + residual_channels = 32, + skip_channels = 256, + kernel_size = 2, + use_bias = False, + cond = True, + cond_channels = 128): + super(residual_block, self).__init__() + + self.dilation = dilation + self.dilation_channels = dilation_channels + self.residual_channels = residual_channels + self.skip_channels = skip_channels + self.kernel_size = kernel_size + self.bias = use_bias + self.cond = cond + self.cond_channels = cond_channels + # zero padding to the left of the sequence. + self.padding = (int((self.kernel_size - 1) * self.dilation), 0) + + # dilated convolutions + self.filter_conv= nn.Conv1d(in_channels = self.residual_channels, + out_channels = self.dilation_channels, + kernel_size = self.kernel_size, + dilation = self.dilation, + bias = self.bias) + + self.gate_conv = nn.Conv1d(in_channels = self.residual_channels, + out_channels = self.dilation_channels, + kernel_size = self.kernel_size, + dilation = self.dilation, + bias = self.bias) + + # 1x1 convolution for residual connections + self.residual_conv = nn.Conv1d(in_channels = self.dilation_channels, + out_channels = self.residual_channels, + kernel_size = 1, + bias = self.bias) + + # 1x1 convolution for skip connections + self.skip_conv = nn.Conv1d(in_channels = self.dilation_channels, + out_channels = self.skip_channels, + kernel_size = 1, + bias = self.bias) + + # condition conv, no dilation + if self.cond == True: + self.cond_filter_conv = nn.Conv1d(in_channels = self.cond_channels, + out_channels = self.dilation_channels, + kernel_size = 1, + bias = True) + self.cond_gate_conv = nn.Conv1d(in_channels = self.cond_channels, + out_channels = self.dilation_channels, + kernel_size = 1, + bias = True) + + + def forward(self, input, cond=None): + if self.cond is True and cond is None: + raise RuntimeError("set using condition to true, but no cond tensor inputed") + + x_pad = F.pad(input, self.padding) + # filter + filter = self.filter_conv(x_pad) + # gate + gate = self.gate_conv(x_pad) + + if self.cond == True and cond is not None: + filter_cond = self.cond_filter_conv(cond) + gate_cond = self.cond_gate_conv(cond) + # add cond results + filter = filter + filter_cond + gate = gate + gate_cond + + # element-wise multiple + filter = torch.tanh(filter) + gate = torch.sigmoid(gate) + x = filter * gate + + # residual and skip + residual = self.residual_conv(x) + input + skip = self.skip_conv(x) + + + return residual, skip + + + + +## 2D convolution layers +def conv2d(batch_norm, in_planes, out_planes, kernel_size=3, stride=1): + if batch_norm: + return nn.Sequential( + nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=(kernel_size-1)//2, bias=True), + nn.BatchNorm2d(out_planes), + nn.LeakyReLU(0.2, inplace=True) + ) + else: + return nn.Sequential( + nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=(kernel_size-1)//2, bias=True), + nn.LeakyReLU(0.2, inplace=True) + ) + + + +def init_weights(net, init_type='normal', init_gain=0.02): + """Initialize network weights. + + Parameters: + net (network) -- network to be initialized + init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal + init_gain (float) -- scaling factor for normal, xavier and orthogonal. + + We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might + work better for some applications. Feel free to try yourself. + """ + def init_func(m): # define the initialization function + classname = m.__class__.__name__ + if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): + if init_type == 'normal': + init.normal_(m.weight.data, 0.0, init_gain) + elif init_type == 'xavier': + init.xavier_normal_(m.weight.data, gain=init_gain) + elif init_type == 'kaiming': + init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') + elif init_type == 'orthogonal': + init.orthogonal_(m.weight.data, gain=init_gain) + else: + raise NotImplementedError('initialization method [%s] is not implemented' % init_type) + if hasattr(m, 'bias') and m.bias is not None: + init.constant_(m.bias.data, 0.0) + elif classname.find('BatchNorm2d') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies. + init.normal_(m.weight.data, 1.0, init_gain) + init.constant_(m.bias.data, 0.0) + + print('initialize network with %s' % init_type) + net.apply(init_func) # apply the initialization function + + + +def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[], useDDP=False): + """Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights + Parameters: + net (network) -- the network to be initialized + init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal + gain (float) -- scaling factor for normal, xavier and orthogonal. + gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2 + + Return an initialized network. + """ + if len(gpu_ids) > 0: + assert(torch.cuda.is_available()) + net.to(gpu_ids[0]) + if useDDP: + net = net().to(gpu_ids) + net = DDP(net, device_ids=gpu_ids) # DDP + print(f'use DDP to apply models on {gpu_ids}') + else: + net = torch.nn.DataParallel(net, gpu_ids) # multi-GPUs + init_weights(net, init_type, init_gain=init_gain) + return net + + +def get_scheduler(optimizer, opt): + """Return a learning rate scheduler + + Parameters: + optimizer -- the optimizer of the network + opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions．　 + opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine + + For 'linear', we keep the same learning rate for the first epochs + and linearly decay the rate to zero over the next epochs. + For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers. + See https://pytorch.org/docs/stable/optim.html for more details. + """ + if opt.lr_policy == 'linear': + def lambda_rule(epoch): + lr_l = 1.0 - max(0, epoch - opt.n_epochs) / float(opt.n_epochs_decay + 1) + return lr_l + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule, last_epoch=opt.epoch_count-2) + elif opt.lr_policy == 'step': + scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=opt.gamma, last_epoch=opt.epoch_count-2) + for _ in range(opt.epoch_count-2): + scheduler.step() + elif opt.lr_policy == 'plateau': + scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5) + elif opt.lr_policy == 'cosine': + scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.n_epochs, eta_min=0) + else: + return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy) + return scheduler + + + +def weights_init(m): + classname = m.__class__.__name__ + if classname.find('Conv') != -1 and hasattr(m, 'weight'): + m.weight.data.normal_(0.0, 0.02) + elif classname.find('BatchNorm2d') != -1: + m.weight.data.normal_(1.0, 0.02) + m.bias.data.fill_(0) + + +def print_network(net): + if isinstance(net, list): + net = net[0] + num_params = 0 + for param in net.parameters(): + num_params += param.numel() + print(net) + print('Total number of parameters: %d' % num_params) + + + + +class Feature2FaceGenerator_normal(nn.Module): + def __init__(self, input_nc=4, output_nc=3, num_downs=8, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(Feature2FaceGenerator_normal, self).__init__() + # construct unet structure + unet_block = ResUnetSkipConnectionBlock_small(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, + innermost=True) + + for i in range(num_downs - 5): + unet_block = ResUnetSkipConnectionBlock_small(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, + norm_layer=norm_layer, use_dropout=use_dropout) + unet_block = ResUnetSkipConnectionBlock_small(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, + norm_layer=norm_layer) + unet_block = ResUnetSkipConnectionBlock_small(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, + norm_layer=norm_layer) + unet_block = ResUnetSkipConnectionBlock_small(ngf, ngf * 2, input_nc=None, submodule=unet_block, + norm_layer=norm_layer) + unet_block = ResUnetSkipConnectionBlock_small(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, + norm_layer=norm_layer) + + self.model = unet_block + + def forward(self, input): + output = self.model(input) + output = torch.tanh(output) # scale to [-1, 1] + + return output + + +# Defines the submodule with skip connection. +# X -------------------identity---------------------- X +# |-- downsampling -- |submodule| -- upsampling --| +class ResUnetSkipConnectionBlock_small(nn.Module): + def __init__(self, outer_nc, inner_nc, input_nc=None, + submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(ResUnetSkipConnectionBlock_small, self).__init__() + self.outermost = outermost + use_bias = norm_layer == nn.InstanceNorm2d + + if input_nc is None: + input_nc = outer_nc + downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=3, + stride=2, padding=1, bias=use_bias) + # add two resblock + res_downconv = [ResidualBlock(inner_nc, norm_layer)] + res_upconv = [ResidualBlock(outer_nc, norm_layer)] + + # res_downconv = [ResidualBlock(inner_nc)] + # res_upconv = [ResidualBlock(outer_nc)] + + downrelu = nn.ReLU(True) + uprelu = nn.ReLU(True) + if norm_layer != None: + downnorm = norm_layer(inner_nc) + upnorm = norm_layer(outer_nc) + + if outermost: + upsample = nn.Upsample(scale_factor=2, mode='nearest') + upconv = nn.Conv2d(inner_nc * 2, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias) + down = [downconv, downrelu] + res_downconv + # up = [uprelu, upsample, upconv, upnorm] + up = [upsample, upconv] + model = down + [submodule] + up + elif innermost: + upsample = nn.Upsample(scale_factor=2, mode='nearest') + upconv = nn.Conv2d(inner_nc, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias) + down = [downconv, downrelu] + res_downconv + if norm_layer == None: + up = [upsample, upconv, uprelu] + res_upconv + else: + up = [upsample, upconv, upnorm, uprelu] + res_upconv + model = down + up + else: + upsample = nn.Upsample(scale_factor=2, mode='nearest') + upconv = nn.Conv2d(inner_nc * 2, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias) + if norm_layer == None: + down = [downconv, downrelu] + res_downconv + up = [upsample, upconv, uprelu] + res_upconv + else: + down = [downconv, downnorm, downrelu] + res_downconv + up = [upsample, upconv, upnorm, uprelu] + res_upconv + + if use_dropout: + model = down + [submodule] + up + [nn.Dropout(0.5)] + else: + model = down + [submodule] + up + + self.model = nn.Sequential(*model) + + def forward(self, x): + if self.outermost: + return self.model(x) + else: + return torch.cat([x, self.model(x)], 1) + + + +class Feature2FaceGenerator_large(nn.Module): + def __init__(self, input_nc=4, output_nc=3, num_downs=8, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(Feature2FaceGenerator_large, self).__init__() + # construct unet structure + unet_block = ResUnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, + innermost=True) + + for i in range(num_downs - 5): + unet_block = ResUnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, + norm_layer=norm_layer, use_dropout=use_dropout) + unet_block = ResUnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, + norm_layer=norm_layer) + unet_block = ResUnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, + norm_layer=norm_layer) + unet_block = ResUnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, + norm_layer=norm_layer) + unet_block = ResUnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, + norm_layer=norm_layer) + + self.model = unet_block + + def forward(self, input): + output = self.model(input) + output = torch.tanh(output) # scale to [-1, 1] + + return output + + +# Defines the submodule with skip connection. +# X -------------------identity---------------------- X +# |-- downsampling -- |submodule| -- upsampling --| +class ResUnetSkipConnectionBlock(nn.Module): + def __init__(self, outer_nc, inner_nc, input_nc=None, + submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(ResUnetSkipConnectionBlock, self).__init__() + self.outermost = outermost + use_bias = norm_layer == nn.InstanceNorm2d + + if input_nc is None: + input_nc = outer_nc + downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=3, + stride=2, padding=1, bias=use_bias) + # add two resblock + res_downconv = [ResidualBlock(inner_nc, norm_layer), ResidualBlock(inner_nc, norm_layer)] + res_upconv = [ResidualBlock(outer_nc, norm_layer), ResidualBlock(outer_nc, norm_layer)] + + # res_downconv = [ResidualBlock(inner_nc)] + # res_upconv = [ResidualBlock(outer_nc)] + + downrelu = nn.ReLU(True) + uprelu = nn.ReLU(True) + if norm_layer != None: + downnorm = norm_layer(inner_nc) + upnorm = norm_layer(outer_nc) + + if outermost: + upsample = nn.Upsample(scale_factor=2, mode='nearest') + upconv = nn.Conv2d(inner_nc * 2, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias) + down = [downconv, downrelu] + res_downconv + # up = [uprelu, upsample, upconv, upnorm] + up = [upsample, upconv] + model = down + [submodule] + up + elif innermost: + upsample = nn.Upsample(scale_factor=2, mode='nearest') + upconv = nn.Conv2d(inner_nc, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias) + down = [downconv, downrelu] + res_downconv + if norm_layer == None: + up = [upsample, upconv, uprelu] + res_upconv + else: + up = [upsample, upconv, upnorm, uprelu] + res_upconv + model = down + up + else: + upsample = nn.Upsample(scale_factor=2, mode='nearest') + upconv = nn.Conv2d(inner_nc * 2, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias) + if norm_layer == None: + down = [downconv, downrelu] + res_downconv + up = [upsample, upconv, uprelu] + res_upconv + else: + down = [downconv, downnorm, downrelu] + res_downconv + up = [upsample, upconv, upnorm, uprelu] + res_upconv + + if use_dropout: + model = down + [submodule] + up + [nn.Dropout(0.5)] + else: + model = down + [submodule] + up + + self.model = nn.Sequential(*model) + + def forward(self, x): + if self.outermost: + return self.model(x) + else: + return torch.cat([x, self.model(x)], 1) + + +# UNet with residual blocks +class ResidualBlock(nn.Module): + def __init__(self, in_features=64, norm_layer=nn.BatchNorm2d): + super(ResidualBlock, self).__init__() + self.relu = nn.ReLU(True) + if norm_layer == None: + # hard to converge with out batch or instance norm + self.block = nn.Sequential( + nn.Conv2d(in_features, in_features, 3, 1, 1, bias=False), + nn.ReLU(inplace=True), + nn.Conv2d(in_features, in_features, 3, 1, 1, bias=False), + ) + else: + self.block = nn.Sequential( + nn.Conv2d(in_features, in_features, 3, 1, 1, bias=False), + norm_layer(in_features), + nn.ReLU(inplace=True), + nn.Conv2d(in_features, in_features, 3, 1, 1, bias=False), + norm_layer(in_features) + ) + + def forward(self, x): + residual = x + out = self.block(x) + out += residual + out = self.relu(out) + return out + # return self.relu(x + self.block(x)) + + + +class Feature2FaceGenerator_Unet(nn.Module): + def __init__(self, input_nc=4, output_nc=3, num_downs=8, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(Feature2FaceGenerator_Unet, self).__init__() + + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) # add the innermost layer + for i in range(num_downs - 5): # add intermediate layers with ngf * 8 filters + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) + # gradually reduce the number of filters from ngf * 8 to ngf + unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + self.model = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) # add the outermost layer + + + def forward(self, input): + output = self.model(input) + + return output + + + + +class UnetSkipConnectionBlock(nn.Module): + """Defines the Unet submodule with skip connection. + X -------------------identity---------------------- + |-- downsampling -- |submodule| -- upsampling --| + """ + + def __init__(self, outer_nc, inner_nc, input_nc=None, + submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): + """Construct a Unet submodule with skip connections. + + Parameters: + outer_nc (int) -- the number of filters in the outer conv layer + inner_nc (int) -- the number of filters in the inner conv layer + input_nc (int) -- the number of channels in input images/features + submodule (UnetSkipConnectionBlock) -- previously defined submodules + outermost (bool) -- if this module is the outermost module + innermost (bool) -- if this module is the innermost module + norm_layer -- normalization layer + use_dropout (bool) -- if use dropout layers. + """ + super(UnetSkipConnectionBlock, self).__init__() + self.outermost = outermost + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + if input_nc is None: + input_nc = outer_nc + downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, + stride=2, padding=1, bias=use_bias) + downrelu = nn.LeakyReLU(0.2, True) + downnorm = norm_layer(inner_nc) + uprelu = nn.ReLU(True) + upnorm = norm_layer(outer_nc) + + if outermost: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1) + down = [downconv] + up = [uprelu, upconv, nn.Tanh()] + model = down + [submodule] + up + elif innermost: + upconv = nn.ConvTranspose2d(inner_nc, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv] + up = [uprelu, upconv, upnorm] + model = down + up + else: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv, downnorm] + up = [uprelu, upconv, upnorm] + + if use_dropout: + model = down + [submodule] + up + [nn.Dropout(0.5)] + else: + model = down + [submodule] + up + + self.model = nn.Sequential(*model) + + def forward(self, x): + if self.outermost: + return self.model(x) + else: # add skip connections + return torch.cat([x, self.model(x)], 1) + + + +class MultiscaleDiscriminator(nn.Module): + def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, + num_D=3, getIntermFeat=False): + super(MultiscaleDiscriminator, self).__init__() + self.num_D = num_D + self.n_layers = n_layers + self.getIntermFeat = getIntermFeat + ndf_max = 64 + + for i in range(num_D): + netD = NLayerDiscriminator(input_nc, min(ndf_max, ndf*(2**(num_D-1-i))), n_layers, getIntermFeat) + if getIntermFeat: + for j in range(n_layers+2): + setattr(self, 'scale'+str(i)+'_layer'+str(j), getattr(netD, 'model'+str(j))) + else: + setattr(self, 'layer'+str(i), netD.model) + + self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False) + + def singleD_forward(self, model, input): + if self.getIntermFeat: + result = [input] + for i in range(len(model)): + result.append(model[i](result[-1])) + return result[1:] + else: + return [model(input)] + + def forward(self, input): + num_D = self.num_D + result = [] + input_downsampled = input + for i in range(num_D): + if self.getIntermFeat: + model = [getattr(self, 'scale'+str(num_D-1-i)+'_layer'+str(j)) for j in range(self.n_layers+2)] + else: + model = getattr(self, 'layer'+str(num_D-1-i)) + result.append(self.singleD_forward(model, input_downsampled)) + if i != (num_D-1): + input_downsampled = self.downsample(input_downsampled) + return result + + + +# Defines the PatchGAN discriminator with the specified arguments. +class NLayerDiscriminator(nn.Module): + def __init__(self, input_nc, ndf=64, n_layers=3, getIntermFeat=False): + super(NLayerDiscriminator, self).__init__() + self.getIntermFeat = getIntermFeat + self.n_layers = n_layers + + kw = 4 + padw = int(np.ceil((kw-1.0)/2)) + sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), + nn.LeakyReLU(0.2, True)]] + + nf = ndf + for n in range(1, n_layers): + nf_prev = nf + nf = min(nf * 2, 512) + sequence += [[ + nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw), + nn.BatchNorm2d(nf), + nn.LeakyReLU(0.2, True) + ]] + + nf_prev = nf + nf = min(nf * 2, 512) + sequence += [[ + nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw), + nn.BatchNorm2d(nf), + nn.LeakyReLU(0.2, True) + ]] + + sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]] + + if getIntermFeat: + for n in range(len(sequence)): + setattr(self, 'model'+str(n), nn.Sequential(*sequence[n])) + else: + sequence_stream = [] + for n in range(len(sequence)): + sequence_stream += sequence[n] + self.model = nn.Sequential(*sequence_stream) + + + def forward(self, input): + if self.getIntermFeat: + res = [input] + for n in range(self.n_layers+2): + model = getattr(self, 'model'+str(n)) + res.append(model(res[-1])) + return res[1:] + else: + return self.model(input) + + + + + + diff --git a/motion-gan-pipeline/motion-generation/options/__init__.py b/motion-gan-pipeline/motion-generation/options/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/motion-gan-pipeline/motion-generation/options/base_options_audio2headpose.py b/motion-gan-pipeline/motion-generation/options/base_options_audio2headpose.py new file mode 100644 index 0000000..101012a --- /dev/null +++ b/motion-gan-pipeline/motion-generation/options/base_options_audio2headpose.py @@ -0,0 +1,192 @@ +import argparse +from email.policy import default +import os +from util import util +import torch +import models +import numpy as np + + +class BaseOptions(): + """This class defines options used during both training and test time. + + It also implements several helper functions such as parsing, printing, and saving the options. + It also gathers additional options defined in functions in both datasets class and model class. + """ + + def __init__(self): + """Reset the class; indicates the class hasn't been initailized""" + self.initialized = False + + def initialize(self, parser): + """Define the common options that are used in both training and test.""" + ## task + parser.add_argument('--task', type=str, default='Audio2Headpose', help='|Audio2Feature|Feature2Face|Full|') + + ## basic parameters + parser.add_argument('--model', type=str, default='audio2headpose', help='trained model') + parser.add_argument('--dataset_mode', type=str, default='full', help='chooses how datasets are loaded. [unaligned | aligned | single]') + parser.add_argument('--name', type=str, default='Audio2Headpose', help='name of the experiment. It decides where to store samples and models') + parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU') + parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints/', help='models are saved here') + + + # data parameters + parser.add_argument('--FPS', type=str, default=25, help='video fps') # they had 60 + parser.add_argument('--sample_rate', type=int, default=16000, help='audio sample rate') + parser.add_argument('--audioRF_history', type=int, default=25, help='audio history receptive field length') #TODO: whoudl I change this to 25? + parser.add_argument('--audioRF_future', type=int, default=0, help='audio future receptive field length') + parser.add_argument('--feature_decoder', type=str, default='WaveNet', help='|WaveNet|LSTM|') + parser.add_argument('--loss', type=str, default='GMM', help='|GMM|L2|') + + + # datasets parameters + parser.add_argument('--dataset_names', type=str, default='Mine', help='chooses how datasets are loaded.') + parser.add_argument('--dataroot', type=str, default='/media/apennino/') + parser.add_argument('--frame_jump_stride', type=int, default=1, help='jump index in audio datasets.') + parser.add_argument('--num_threads', default=0, type=int, help='# threads for loading data') + parser.add_argument('--batch_size', type=int, default=64, help='input batch size') #64 + parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly') + parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per datasets. If the datasets directory contains more than max_dataset_size, only a subset is loaded.') + parser.add_argument('--audio_encoder', type=str, default='APC', help='|CNN|LSTM|APC|') + parser.add_argument('--audiofeature_input_channels', type=int, default=80, help='input channels of audio features') + parser.add_argument('--frame_future', type=int, default=5, help='It also looks at frame_future in the future to predict head movents') # They had 15 + parser.add_argument('--predict_length', type=int, default=1, help='Useless parameter, keep to 1') + parser.add_argument('--audio_windows', type=int, default=2) # Was 2 + parser.add_argument('--time_frame_length', type=int, default=50, help='length of training frames in each iteration') # they used 240 because they have 60fps + parser.add_argument('--train_test_split', type=bool, default=True, help='Is dataset split into train and test?') + + # APC parameters + parser.add_argument('--APC_hidden_size', type=int, default=16 * 29) + parser.add_argument('--APC_rnn_layers', type=int, default=3) + parser.add_argument("--APC_residual", action="store_true") + parser.add_argument('--APC_frame_history', type=int, default=25) #they used 60 + parser.add_argument('--APC_model_path', default='/home/alberto/motion-generation/third/AutoregressivePredictiveCoding/bs32-rhl3-rhs512-rd0-adam-res-ts3.pt') + + + ## network parameters + parser.add_argument('--A2H_receptive_field', type=int, default=50, help='Receptive field of the Audio2headpose network') # they used 255 frames -> 4,25 seconds for them at 60 fps + # audio2headpose wavenet + parser.add_argument('--A2H_wavenet_residual_layers', type=int, default=7, help='residual layer numbers') + parser.add_argument('--A2H_wavenet_residual_blocks', type=int, default=2, help='residual block numbers') + parser.add_argument('--A2H_wavenet_dilation_channels', type=int, default=128, help='dilation convolution channels') + parser.add_argument('--A2H_wavenet_residual_channels', type=int, default=128, help='residual channels') + parser.add_argument('--A2H_wavenet_skip_channels', type=int, default=256, help='skip channels') + parser.add_argument('--A2H_wavenet_kernel_size', type=int, default=2, help='dilation convolution kernel size') + parser.add_argument('--A2H_wavenet_use_bias', type=bool, default=True, help='whether to use bias in dilation convolution') + parser.add_argument('--A2H_wavenet_cond', type=bool, default=True, help='whether use condition input') + parser.add_argument('--A2H_wavenet_cond_channels', type=int, default=16*29, help='whether use condition input') + parser.add_argument('--A2H_wavenet_input_channels', type=int, default=12, help='input channels') + parser.add_argument('--A2H_GMM_ncenter', type=int, default=1, help='gaussian distribution numbers, 1 for single gaussian distribution') + parser.add_argument('--A2H_GMM_ndim', type=int, default=12, help='dimension of each gaussian, usually number of pts') + parser.add_argument('--A2H_GMM_sigma_min', type=float, default=0.03, help='minimal gaussian sigma values') + + + # additional parameters + parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information') + parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}') + parser.add_argument('--sequence_length', type=int, default=100, help='length of training frames in each iteration') # They used 240 -> 4 seconds at 60 fps | I put 25 instead of 100 + + + self.initialized = True + return parser + + def gather_options(self): + """Initialize our parser with basic options(only once). + Add additional model-specific and datasets-specific options. + These options are defined in the function + in model and datasets classes. + """ + if not self.initialized: # check if it has been initialized + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser = self.initialize(parser) + + # get the basic options + opt, _ = parser.parse_known_args() + + # modify model-related parser options + model_name = opt.model + model_option_setter = models.get_option_setter(model_name) + parser = model_option_setter(parser, self.isTrain) + opt, _ = parser.parse_known_args() # parse again with new defaults + + + # save and return the parser + self.parser = parser + return opt + + def print_options(self, opt): + """Print and save options + + It will print both current options and default values(if different). + It will save options into a text file / [checkpoints_dir] / opt.txt + """ + message = '' + message += '----------------- Options ---------------\n' + for k, v in sorted(vars(opt).items()): + comment = '' + default = self.parser.get_default(k) + if v != default: + comment = '\t[default: %s]' % str(default) + message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment) + message += '----------------- End -------------------' + print(message) + + if opt.isTrain: + # save to the disk + expr_dir = os.path.join(opt.checkpoints_dir, opt.name) + util.mkdirs(expr_dir) + file_name = os.path.join(expr_dir, '{}_opt.txt'.format(opt.phase)) + with open(file_name, 'wt') as opt_file: + opt_file.write(message) + opt_file.write('\n') + + def parse(self): + """Parse our options, create checkpoints directory suffix, and set up gpu device.""" + opt = self.gather_options() + opt.isTrain = self.isTrain # train or test + + # process opt.suffix + if opt.suffix: + suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else '' + opt.name = opt.name + suffix + + # self.print_options(opt) + + # set gpu ids + str_ids = opt.gpu_ids.split(',') + opt.gpu_ids = [] + for str_id in str_ids: + id = int(str_id) + if id >= 0: + opt.gpu_ids.append(id) + # if len(opt.gpu_ids) > 0: + # torch.cuda.set_device(opt.gpu_ids[0]) + + # set datasets + if self.isTrain : + + # opt.train_dataset_names = [os.path.join(opt.dataroot, opt.dataset_names, 'Train', f) + # for f in os.listdir(os.path.join(opt.dataroot, opt.dataset_names, 'Train'))] + + opt.dataset_type = 'Train' + + # opt.validate_dataset_names = [os.path.join(opt.dataroot, opt.dataset_names, 'Test', f) + # for f in os.listdir(os.path.join(opt.dataroot, opt.dataset_names, 'Test'))] + + else: + opt.dataset_type = 'Test' + + self.opt = opt + return self.opt + + + + + + + + + + + diff --git a/motion-gan-pipeline/motion-generation/options/test_audio2headpose_options.py b/motion-gan-pipeline/motion-generation/options/test_audio2headpose_options.py new file mode 100644 index 0000000..b8db468 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/options/test_audio2headpose_options.py @@ -0,0 +1,24 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from .base_options_audio2headpose import BaseOptions + + +class TestOptions(BaseOptions): + """This class includes test options. + + It also includes shared options defined in BaseOptions. + """ + + def initialize(self, parser): + parser = BaseOptions.initialize(self, parser) # define shared options + parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') + parser.add_argument('--load_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') + # Dropout and Batchnorm has different behavioir during training and test. + parser.add_argument('--eval', action='store_true', help='use eval mode during test time.') + # rewrite devalue values + parser.set_defaults(time_frame_length=1) + parser.set_defaults(dataset_mode='audio') + self.isTrain = False + + return parser diff --git a/motion-gan-pipeline/motion-generation/options/train_audio2headpose_options.py b/motion-gan-pipeline/motion-generation/options/train_audio2headpose_options.py new file mode 100644 index 0000000..99af00d --- /dev/null +++ b/motion-gan-pipeline/motion-generation/options/train_audio2headpose_options.py @@ -0,0 +1,63 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from email.policy import default +from .base_options_audio2headpose import BaseOptions + + +class TrainOptions(BaseOptions): + """This class includes training options. + + It also includes shared options defined in BaseOptions. + """ + + def initialize(self, parser): + parser = BaseOptions.initialize(self, parser) + + # logging options + parser.add_argument('--tf_log', type=bool, default=True, help='Use tensorboard logs') + parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML') + + # visdom and HTML visualization parameters + parser.add_argument('--display_ncols', type=int, default=4, help='if positive, display all images in a single visdom web panel with certain number of images per row.') + parser.add_argument('--display_id', type=int, default=1, help='window id of the web display') + parser.add_argument('--display_server', type=str, default="http://localhost", help='visdom server of the web display') + parser.add_argument('--display_env', type=str, default='main', help='visdom display environment name (default is "main")') + parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display') + parser.add_argument('--update_html_freq', type=int, default=1000, help='frequency of saving training results to html') + parser.add_argument('--print_freq', type=int, default='10000', help='frequency of showing training results on console') + parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/') + + # network saving and loading parameters + parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving intermediate checkpoints during training') + parser.add_argument('--save_epoch_freq', type=int, default=25, help='frequency of saving checkpoints at the end of epochs') + parser.add_argument('--save_by_iter', action='store_true', help='whether saves model by iteration') + parser.add_argument('--continue_train', default=False, action='store_true', help='continue training: load the latest model') + parser.add_argument('--load_epoch', type=str, default='0', help='which epoch to load? set to latest to use latest cached model') + parser.add_argument('--epoch_count', type=int, default=0, help='the starting epoch count, we save the model by , +, ...') + parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') + parser.add_argument('--re_transform', type=int, default=0, help='re-transform landmarks') + + + # training parameters + parser.add_argument('--smooth_loss', type=int, default=0, help='use smooth loss weight, 0 for not use') + # parser.add_argument('--train_dataset_names', type=str, default='train_list.txt', help='chooses validation datasets.') + # parser.add_argument('--validate_dataset_names', type=str, default='val_list.txt', help='chooses validation datasets.') + parser.add_argument('--n_epochs', type=int, default=500, help='number of epochs') + parser.add_argument('--lr_policy', type=str, default='step', help='learning rate policy. [linear | step | plateau | cosine]') + parser.add_argument('--lr', type=float, default=1e-4, help='initial learning rate for adam') + parser.add_argument('--gamma', type=float, default=0.2, help='step learning rate gamma') + parser.add_argument('--lr_decay_iters', type=int, default=250, help='multiply by a gamma every lr_decay_iters iterations') + parser.add_argument('--n_epochs_decay', type=int, default=250, help='number of epochs to linearly decay learning rate to zero') + parser.add_argument('--validate_epoch', type=int, default=50, help='validate model every some epochs, 0 for not validate during training') + parser.add_argument('--loss_smooth_weight', type=float, default=0, help='smooth loss weight, 0 for not use smooth loss') + parser.add_argument('--optimizer', type=str, default='AdamW', help='Adam, AdamW, RMSprop') + + + # data augmentations + parser.add_argument('--gaussian_noise', type=int, default=1, help='whether add gaussian noise to input & groundtruth features') + parser.add_argument('--gaussian_noise_scale', type=float, default=0.01, help='gaussian noise scale') + + + self.isTrain = True + return parser diff --git a/motion-gan-pipeline/motion-generation/test.py b/motion-gan-pipeline/motion-generation/test.py new file mode 100644 index 0000000..b063e8c --- /dev/null +++ b/motion-gan-pipeline/motion-generation/test.py @@ -0,0 +1,277 @@ +import os +from os.path import join +import yaml +import argparse +import numpy as np +from options.test_audio2headpose_options import TestOptions +from datasets import create_dataset +from models import create_model +from util.cfgnode import CfgNode +import cv2 +import librosa +import soundfile as sf +from tqdm import tqdm +import matplotlib.pyplot as plt +import subprocess +from pathlib import Path +import torch +from funcs import utils +import sys +sys.path.append('../preprocessing') +from preprocessing.face_tracking.FLAME.FLAME import FLAME +from preprocessing.face_tracking.FLAME.config import cfg as FLAME_cfg +from preprocessing.face_tracking.FLAME.lbs import vertices2landmarks +from preprocessing.face_tracking.render_3dmm import Render_FLAME +from preprocessing.face_tracking.util import * + + +def write_video_with_audio(audio_path, output_path, prefix='pred_', h=512, w=512, fps=25): + fourcc = cv2.VideoWriter_fourcc(*'DIVX') + # fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G') + video_tmp_path = join(save_root, 'tmp.avi') + out = cv2.VideoWriter(video_tmp_path, fourcc, fps, (w, h)) + for j in tqdm(range(nframe), position=0, desc='writing video'): + img = cv2.imread(join(save_root, prefix + str(j+1) + '.jpg')) + out.write(img) + out.release() + cmd = 'ffmpeg -y -i "' + video_tmp_path + '" -i "' + \ + audio_path + '" -codec copy -shortest "' + output_path + '"' + subprocess.call(cmd, shell=True) + + os.remove(video_tmp_path) # remove the template video + + +def load_audio_expressions(new_opt): + + clip_names = os.listdir(self.dataset_root) + + # check clips + self.valid_clips = [] + for i in range(len(self.clip_names)): + # check lenght of video + name = self.clip_names[i] + clip_root = os.path.join(self.dataset_root, name) + n_frames = len(os.listdir(os.path.join(clip_root, 'frames'))) + if n_frames >= start_point + self.target_length + 25: # added 25 because later they remove 25 and without it, crashes + audio_path = os.path.join(clip_root, name + '.wav') + audio, _ = librosa.load(audio_path, sr=self.sample_rate) + + if len(audio) >= self.frame_future + self.A2H_item_length: + self.valid_clips.append(i) + + else: + print(f'Audio {name} is too short and will not be used for training.') + + else: + print(f'Clip {name} is too short and will not be used for training.') + + self.clip_nums = len(self.valid_clips) + print(f'Total clips for training: {self.clip_nums}') + + return + +if __name__ == '__main__': + # Load default options + Test_parser = TestOptions() + opt = Test_parser.parse() # get training options + + # Load config + parser = argparse.ArgumentParser() + parser.add_argument('--config', default='config/Audio2Headpose_Ted.yml', + help="person name, e.g. Obama1, Obama2, May, Nadella, McStay", required=False) + inopt = parser.parse_args() + # TODO: make config + with open(inopt.config) as f: + cfg_dict = yaml.load(f, Loader=yaml.FullLoader) + cfg = CfgNode(cfg_dict) + + # Overwrite with config + opt.phase = 'Test' + opt.name = cfg.experiment.name + opt.dataset_mode = 'audio' # for testing + opt.dataroot = cfg.experiment.dataroot + opt.dataset_names = cfg.experiment.dataset_names + opt.FPS = cfg.experiment.fps + opt.serial_batches = True + + # save to the disk + Test_parser.print_options(opt) + + # Set device + device = torch.device('cuda:{}'.format(opt.gpu_ids[0])) if len( + opt.gpu_ids) > 0 else torch.device('cpu') + + # Load data + # create a dataset given opt.dataset_mode and other options + dataset = create_dataset(opt) + try: + fit_data = dataset.dataset.fit_data + + except AttributeError: + focal = torch.from_numpy([900.]).to(device).double() + expr_para = torch.zeros(size=(1,53)).to(device).double() + id_para = torch.zeros(size=(1,100)).to(device).double() + + # Load Model + print('---------- Loading Model: {} -------------'.format(opt.task)) + Audio2Headpose = create_model(opt) + Audio2Headpose.setup(opt) + Audio2Headpose.eval() + + stop_at = 10 + + for iter, file_index in enumerate(dataset.dataset.valid_clips): + + if iter >= stop_at: + break + + test_name = dataset.dataset.clip_names[dataset.dataset.valid_clips[file_index]] + #____________________________________________________# + print('Generating movement for video: ', test_name) + audio = dataset.dataset.audio[file_index] + audio_features = dataset.dataset.audio_features[file_index] + try: + audio_expr = dataset.dataset.audio_expr[file_index] + except argparse.ArgumentError: + pass + + # Audio2Headpose + print('Headpose inference...') + # set history headposes as zero + pre_headpose = np.zeros(opt.A2H_wavenet_input_channels, np.float32) + pred_Head = Audio2Headpose.generate_sequences( + audio_features, pre_headpose, fill_zero=True, sigma_scale=0.3, opt=opt) + + # Build FLAME and Renderer + try: + h = fit_data[iter]['h'] + w = fit_data[iter]['w'] + focal = fit_data[iter]['focal'] + id_para = fit_data[iter]['id'] + # expr_para = fit_data[iter]['exp'] + expr_para = audio_expr + nframe = min(expr_para.size()[0], pred_Head.shape[0]) + + except KeyError: + h = 720 + w = 1280 + nframe = pred_Head.shape[0] + expr_para = expr_para.repeat(nframe) + + cxy = torch.tensor((w / 2.0, h / 2.0), dtype=torch.float).cpu() + + model_3dmm = FLAME(FLAME_cfg.model) + renderer = Render_FLAME(model_3dmm.faces_tensor, focal, h, w, 1, device) + + # Postprocessing + if fit_data: + + og_rot = fit_data[iter]['euler'].numpy() + # og_rot[:, 0] += 180 + og_trans = fit_data[iter]['trans'].numpy() + + mean_translation = og_trans.mean(axis=0) + + # headpose back to og + Head_smooth_sigma = [5, 10] + + pred_headpose = utils.headpose_smooth( + pred_Head[:, :6], Head_smooth_sigma).astype(np.float32) + + # pred_headpose = pred_Head[:, :6].astype(np.float32) + pred_headpose[:, 3:] += mean_translation + # pred_headpose[:, 0] += 180 + + else: + pred_headpose = pred_Head[:, :6].astype(np.float32) + + # Make images + save_root = os.path.join('./results/', opt.name, test_name) + if not os.path.exists(save_root): + os.makedirs(save_root) + os.makedirs(os.path.join(save_root, 'landmarks')) + + np.save(os.path.join(save_root, 'headposes.npy'), pred_headpose) + + for i in tqdm(range(nframe), desc='rendering: '): + R = torch.from_numpy(pred_headpose[i, 0:3]).unsqueeze( + 0).to(device).double() + t = torch.from_numpy(pred_headpose[i, 3:6]).unsqueeze( + 0).to(device).double() + id = id_para.to(device).double() + expr = expr_para[i].unsqueeze(0).to(device).double() + + + if 0: + og_R = torch.from_numpy(og_rot[i]).unsqueeze(0).to(device).double() + og_t = torch.from_numpy(og_trans[i]).unsqueeze( + 0).to(device).double() + print( + f'OG Rotation euler: \n{og_R.data}, \nOG trans: \n{og_t.data}') + print( + f'\nPred Rotation euler: \n{R.data},\nPred trans: \n{t.data}') + + og_rott_geo = model_3dmm.forward_geo(id, expr, og_R, og_t) + + og_landmarks3d = model_3dmm.get_3dlandmarks( + id, expr, og_R, og_t, focal, cxy).cpu() + og_proj_geo = proj_pts(og_landmarks3d, focal, cxy) + # Porj points + colormap_blue = plt.cm.Blues + for num, lin in enumerate(np.linspace(0, 0.9, len(og_proj_geo[0, :, 0]))): + plt.scatter(og_proj_geo[0, num, 0].detach().cpu(), + og_proj_geo[0, num, 1].detach().cpu(), + color=colormap_blue(lin), + s=10) + + rott_geo = model_3dmm.forward_geo(id, expr, R, t) + landmarks3d = model_3dmm.get_3dlandmarks( + id, expr, R, t, focal, cxy).cpu() + proj_geo = proj_pts(landmarks3d, focal, cxy) + np.save(os.path.join(save_root, 'landmarks', f'ldk_{i}.npy'), proj_geo[0].detach().cpu().numpy()) + + # # Porj points + # colormap_red = plt.cm.Reds + # for num, lin in enumerate(np.linspace(0, 0.9, len(proj_geo[0, :, 0]))): + # plt.scatter(proj_geo[0, num, 0].detach().cpu(), + # proj_geo[0, num, 1].detach().cpu(), + # color=colormap_red(lin), + # s=10) + + # plt.show() + + sel_pts3D = vertices2landmarks(rott_geo, + model_3dmm.faces_tensor, + model_3dmm.full_lmk_faces_idx.repeat( + 1, 1), + model_3dmm.full_lmk_bary_coords.repeat(1, 1, 1)) + + render_imgs = renderer(rott_geo.float(), model_3dmm.faces_tensor) + img_arr = render_imgs[0, :, :, :3].cpu().numpy() + # img_arr = img_arr / 255. + # img_arr = img_arr.astype(np.uint8) + # cv2.imwrite(os.path.join(save_root, f'flame_{i}.jpg'), img_arr) + + # # plt.imshow(img_arr) + # # plt.show() + from PIL import Image + img_arr *= 255 + img_arr = img_arr.astype(np.uint8) + im = Image.fromarray(img_arr) + im.save(os.path.join(save_root, f'flame_{i}.jpg')) + + # make videos + # generate corresponding audio, reused for all results + tmp_audio_path = os.path.join(save_root, 'tmp.wav') + tmp_audio_clip = audio[: np.int32(nframe * opt.sample_rate / opt.FPS)] + sf.write(tmp_audio_path, tmp_audio_clip, opt.sample_rate) + final_path = os.path.join(save_root, test_name + '.avi') + write_video_with_audio(tmp_audio_path, final_path, 'flame_', h, w) + + if os.path.exists(tmp_audio_path): + os.remove(tmp_audio_path) + _img_paths = list(map(lambda x:str(x), list(Path(save_root).glob('*.jpg')))) + for i in tqdm(range(len(_img_paths)), desc='deleting intermediate images'): + os.remove(_img_paths[i]) + + print('Finish!') diff --git a/motion-gan-pipeline/motion-generation/test_dataset.py b/motion-gan-pipeline/motion-generation/test_dataset.py new file mode 100644 index 0000000..1660b0e --- /dev/null +++ b/motion-gan-pipeline/motion-generation/test_dataset.py @@ -0,0 +1,42 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import yaml +from options.train_audio2headpose_options import TrainOptions +from datasets import create_dataset +import argparse +from util.cfgnode import CfgNode + + +if __name__ == '__main__': + # Load default options + Train_parser = TrainOptions() + opt = Train_parser.parse() # get training options + + # Load config + parser = argparse.ArgumentParser() + parser.add_argument('--config', default='config/Audio2Headpose_MTC.yml', help="person name, e.g. Obama1, Obama2, May, Nadella, McStay") + inopt = parser.parse_args() + # TODO: make config + conf_debug = 'config/Audio2Headpose_Ted.yml' + # with open(inopt.config) as f: + with open(conf_debug) as f: + cfg_dict = yaml.load(f, Loader=yaml.FullLoader) + cfg = CfgNode(cfg_dict) + + # Overwrite with config + opt.name = cfg.experiment.name + opt.dataset_mode = cfg.experiment.dataset_mode + opt.dataroot = cfg.experiment.dataroot + opt.dataset_names = cfg.experiment.dataset_names + + # Load data + dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options + dataset_size = len(dataset) # get the number of images in the dataset. + print('The number of training points = %d' % dataset_size) # 80650 + + dataset = dataset.dataset + + for i in range(len(dataset)): + A2Hsamples, history_info, target_info = dataset[i] + # print(f'Item: {i}, audio: {A2Hsamples.size()}, history: {history_info.size()}, target: {target_info.size()}') diff --git a/motion-gan-pipeline/motion-generation/train.py b/motion-gan-pipeline/motion-generation/train.py new file mode 100644 index 0000000..57236b2 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/train.py @@ -0,0 +1,88 @@ + +import imp +import os +import time +import yaml +from options.train_audio2headpose_options import TrainOptions +from datasets import create_dataset +from models import create_model +from util.visualizer import Visualizer +from tqdm import tqdm +import argparse +from util.cfgnode import CfgNode + + +if __name__ == '__main__': + # Load default options + Train_parser = TrainOptions() + opt = Train_parser.parse() # get training options + + # Load config + parser = argparse.ArgumentParser() + parser.add_argument('--config', default='config/Audio2Headpose_Mine.yml', help="person name, e.g. Obama1, Obama2, May, Nadella, McStay") + inopt = parser.parse_args() + # TODO: make config + with open(inopt.config) as f: + cfg_dict = yaml.load(f, Loader=yaml.FullLoader) + cfg = CfgNode(cfg_dict) + + # Overwrite with config + opt.name = cfg.experiment.name + opt.dataset_mode = cfg.experiment.dataset_mode + opt.dataroot = cfg.experiment.dataroot + opt.dataset_names = cfg.experiment.dataset_names + opt.FPS = cfg.experiment.fps + + # save to the disk + Train_parser.print_options(opt) + + # Load data + dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options + dataset_size = len(dataset) # get the number of images in the dataset. + print('The number of training images = %d' % dataset_size) + + model = create_model(opt) # create a model given opt.model and other options + model.setup(opt) # regular setup: load and print networks; create schedulers + visualizer = Visualizer(opt) # create a visualizer that display/save images and plots + total_iters = 0 # the total number of training iterations + + for epoch in tqdm(range(opt.epoch_count, opt.n_epochs + opt.n_epochs_decay + 1)): # outer loop for different epochs; we save the model by , + + epoch_start_time = time.time() # timer for entire epoch + iter_data_time = time.time() # timer for data loading per iteration + epoch_iter = 0 # the number of training iterations in current epoch, reset to 0 every epoch + # visualizer.reset() # reset the visualizer: make sure it saves the results to HTML at least once every epoch + model.update_learning_rate() # update learning rates in the beginning of every epoch. + for i, data in enumerate(dataset): # inner loop within one epoch + + iter_start_time = time.time() # timer for computation per iteration + if total_iters % opt.print_freq == 0: + t_data = iter_start_time - iter_data_time + + total_iters += opt.batch_size + epoch_iter += opt.batch_size + model.set_input(data) # unpack data from dataset and apply preprocessing + model.optimize_parameters() # calculate loss functions, get gradients, update network weights + + if total_iters % opt.print_freq == 0: # print training losses and save logging information to the disk + losses = model.get_current_losses() + t_comp = (time.time() - iter_start_time) / opt.batch_size + visualizer.print_current_errors(epoch, total_iters, losses, t_data) + if opt.display_id > 0: + visualizer.plot_current_errors(losses, total_iters) + + iter_data_time = time.time() + + if epoch % opt.save_epoch_freq == 0: # cache our model every epochs + print('saving the model at the end of epoch %d, iters %d' % (epoch, total_iters)) + model.save_networks('epoch_%d' % (epoch)) + + # early stopping + losses = model.get_current_losses() + if losses['GMM'] <= 0: + print('Negative loss at the end of epoch %d, total iters %d' % (epoch, total_iters)) + print('Stopping Training') + break + + print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time)) + + model.save_networks('latest') \ No newline at end of file diff --git a/motion-gan-pipeline/motion-generation/transfer.py b/motion-gan-pipeline/motion-generation/transfer.py new file mode 100644 index 0000000..61d366b --- /dev/null +++ b/motion-gan-pipeline/motion-generation/transfer.py @@ -0,0 +1,231 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from cmath import inf +import os +from os.path import join +import yaml +import argparse +import numpy as np +from options.test_audio2headpose_options import TestOptions +from datasets import create_dataset +from models import create_model +from util.cfgnode import CfgNode +import cv2 +import librosa +import soundfile as sf +from tqdm import tqdm +import matplotlib.pyplot as plt +import subprocess +from pathlib import Path +import torch +from funcs import utils +import sys +sys.path.append('../preprocessing/') +from face_tracking.FLAME.FLAME import FLAME +from face_tracking.FLAME.config import cfg as FLAME_cfg +from face_tracking.FLAME.lbs import vertices2landmarks +from face_tracking.render_3dmm import Render_FLAME +from face_tracking.util import * +from finetune import finetune +from PIL import Image + + +def write_video_with_audio(audio_path, output_path, prefix='pred_', h=512, w=512, fps=25): + fourcc = cv2.VideoWriter_fourcc(*'DIVX') + # fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G') + video_tmp_path = join(save_root, 'tmp.avi') + out = cv2.VideoWriter(video_tmp_path, fourcc, fps, (w, h)) + for j in tqdm(range(nframe), position=0, desc='writing video'): + img = cv2.imread(join(save_root, prefix + str(j+1) + '.jpg')) + out.write(img) + out.release() + cmd = 'ffmpeg -y -i "' + video_tmp_path + '" -i "' + \ + audio_path + '" -codec copy -shortest "' + output_path + '"' + subprocess.call(cmd, shell=True) + + os.remove(video_tmp_path) # remove the template video + + + +if __name__ == '__main__': + + # load args + parser = argparse.ArgumentParser() + parser.add_argument('--dataroot', required=True) + parser.add_argument('--dataset_names', required=True) + parser.add_argument('--target_name', required=True) + parser.add_argument('--checkpoint_dir', required=True) + parser.add_argument('--out_dir', required=True) + + inopt = parser.parse_args() + + # Load default options + Test_parser = TestOptions() + opt = Test_parser.parse() # get training options + + # Overwrite with config + opt.phase = '' + opt.dataset_mode = 'audio' # for testing + opt.dataroot = os.path.join(inopt.dataroot, 'audio') + opt.dataset_names = inopt.dataset_names + opt.FPS = 25 + opt.serial_batches = True + opt.train_test_split = False + + # save to the disk + Test_parser.print_options(opt) + + # Set device + device = torch.device('cuda:{}'.format(opt.gpu_ids[0])) if len( + opt.gpu_ids) > 0 else torch.device('cpu') + + # Load Model + print('---------- Loading Model: {} -------------'.format(opt.task)) + checkpoint_path = os.path.join(inopt.checkpoint_dir, inopt.target_name, 'latest_Audio2Headpose.pkl') + print(checkpoint_path) + if not os.path.isfile(checkpoint_path): + print('No fine-tuned checkpoint for headposes found..') + finetune(name=inopt.target_name, + dataroot=inopt.dataroot, + dataset_names=inopt.target_name, + target_checkpoints=inopt.checkpoint_dir, + checkpoint_path=os.path.join(inopt.checkpoint_dir, 'Audio2Headpose_TED_checkpoint.pkl'), + fps=25) + + Audio2Headpose = create_model(opt) + Audio2Headpose.load_checkpoint(checkpoint_path) + Audio2Headpose.eval() + + # Load data + dataset = create_dataset(opt) + fit_data_path = os.path.join(inopt.dataroot, 'video', inopt.target_name, 'track_params.pt') + fit_data = torch.load(fit_data_path) + + stop_at = inf + + for iter, file_index in enumerate(dataset.dataset.valid_clips): + + if iter >= stop_at: + break + + test_name = dataset.dataset.clip_names[dataset.dataset.valid_clips[file_index]] + #____________________________________________________# + print('Generating movement for video: ', test_name) + + # Get features + audio_features = dataset.dataset.audio_features[file_index] + # audio_expr = torch.from_numpy(np.stack([np.load(os.path.join(inopt.out_dir,'audio_expr', ae)).flatten() for ae in os.listdir(os.path.join(inopt.out_dir,'audio_expr'))], axis=0)) + # audio_expr = torch.from_numpy(np.stack([np.load(os.path.join(inopt.dataroot, 'video', inopt.target_name,'deca_expr', ae)).flatten() for ae in sorted(os.listdir(os.path.join(inopt.dataroot, 'video', inopt.target_name,'deca_expr')))], axis=0)) + + # Audio2Headpose + print('Headpose inference...') + + # Set history headposes as first tracked headposes + init_rot = fit_data['euler'][0].numpy() + init_trans = fit_data['trans'][0].numpy() - fit_data['trans'].mean(axis=0).numpy() + pre_headpose = np.concatenate([init_rot, init_trans], axis=0) + pre_headpose = np.concatenate([pre_headpose, np.zeros_like(pre_headpose)], axis=0) # headpose and velocity: velocity is zero: still head. + + # Set history headposes as zeros + # pre_headpose = np.zeros(opt.A2H_wavenet_input_channels, np.float32) + + print('Initial Headpose for prediction: \n', pre_headpose) + + # Generate headposes + pred_Head = Audio2Headpose.generate_sequences(audio_features, pre_headpose, fill_zero=True, sigma_scale=0.3, opt=opt) + + # Build FLAME and Renderer + h = fit_data['h'] + w = fit_data['w'] + focal = fit_data['focal'] + id_para = fit_data['id'] + # expr_para = audio_expr + # nframe = min(expr_para.size()[0], pred_Head.shape[0]) + + nframe = pred_Head.shape[0] + + + cxy = torch.tensor((w / 2.0, h / 2.0), dtype=torch.float).cpu() + + model_3dmm = FLAME(FLAME_cfg.model) + renderer = Render_FLAME(model_3dmm.faces_tensor, focal, h, w, 1, device) + + # Smooth predicted headpose + Head_smooth_sigma = [5, 10] + pred_headpose = utils.headpose_smooth(pred_Head[:, :6], Head_smooth_sigma, method='gaussian').astype(np.float32) + # Head_smooth_sigma = [10, 25] + # pred_headpose = utils.headpose_smooth(pred_Head[:, :6], Head_smooth_sigma , method='median').astype(np.float32) + + # Postprocessing + if fit_data: + + og_rot = fit_data['euler'].numpy() + og_trans = fit_data['trans'].numpy() + + mean_translation = og_trans.mean(axis=0) + + pred_headpose[:, 3:] += mean_translation + # pred_headpose[:, 0] += 180 + + # Make images + save_root = inopt.out_dir + os.makedirs(os.path.join(save_root, 'render'), exist_ok=True) + os.makedirs(os.path.join(save_root, 'landmarks'), exist_ok=True) + + np.save(os.path.join(save_root, 'headposes.npy'), pred_headpose) + + deca_expr_path = os.path.join(inopt.dataroot, 'video', inopt.target_name,'deca_expr') + audio_expr_path = os.path.join(inopt.out_dir, 'audio_expr') + + for i in tqdm(range(nframe), desc='rendering: '): + R = torch.from_numpy(pred_headpose[i, 0:3]).unsqueeze( + 0).to(device).double() + t = torch.from_numpy(pred_headpose[i, 3:6]).unsqueeze( + 0).to(device).double() + + # Zero translation + # t = torch.tensor((0, 0, -5)).unsqueeze( + # 0).to(device).double() + + id = id_para.to(device).double() + expr = torch.from_numpy(np.load(os.path.join(audio_expr_path, '%05d.npy' % i))).to(device).double() + + ''' + # Original Rotation and Translation used for debug + if 0: + og_R = torch.from_numpy(og_rot[i]).unsqueeze(0).to(device).double() + og_t = torch.from_numpy(og_trans[i]).unsqueeze( + 0).to(device).double() + print( + f'OG Rotation euler: \n{og_R.data}, \nOG trans: \n{og_t.data}') + print( + f'\nPred Rotation euler: \n{R.data},\nPred trans: \n{t.data}') + + og_rott_geo = model_3dmm.forward_geo(id, expr, og_R, og_t) + + og_landmarks3d = model_3dmm.get_3dlandmarks( + id, expr, og_R, og_t, focal, cxy).cpu() + og_proj_geo = proj_pts(og_landmarks3d, focal, cxy) + # Porj points + colormap_blue = plt.cm.Blues + for num, lin in enumerate(np.linspace(0, 0.9, len(og_proj_geo[0, :, 0]))): + plt.scatter(og_proj_geo[0, num, 0].detach().cpu(), + og_proj_geo[0, num, 1].detach().cpu(), + color=colormap_blue(lin), + s=10) + + ''' + + rott_geo = model_3dmm.forward_geo(id, expr, R, t) + landmarks3d = model_3dmm.get_3dlandmarks_forehead(id, expr, R, t, focal, cxy).cpu() + proj_geo = proj_pts(landmarks3d, focal, cxy) + np.savetxt(os.path.join(save_root, 'landmarks', '%05d.lms' % i), proj_geo[0].detach().cpu().numpy()) + render_imgs = renderer(rott_geo.float(), model_3dmm.faces_tensor) + img_arr = render_imgs[0, :, :, :3].cpu().numpy() + img_arr *= 255 + img_arr = img_arr.astype(np.uint8) + im = Image.fromarray(img_arr) + im.save(os.path.join(save_root, 'render','%05d.png' % i)) + + print('Finish!') diff --git a/motion-gan-pipeline/motion-generation/util/cfgnode.py b/motion-gan-pipeline/motion-generation/util/cfgnode.py new file mode 100644 index 0000000..8895187 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/util/cfgnode.py @@ -0,0 +1,507 @@ +""" +Define a class to hold configurations. + +Borrows and merges stuff from YACS, fvcore, and detectron2 +https://github.com/rbgirshick/yacs +https://github.com/facebookresearch/fvcore/ +https://github.com/facebookresearch/detectron2/ + +""" + +import copy +import importlib.util +import io +import logging +import os +from ast import literal_eval +from typing import Optional + +import yaml + +# File exts for yaml +_YAML_EXTS = {"", ".yml", ".yaml"} +# File exts for python +_PY_EXTS = {".py"} + +# CfgNodes can only contain a limited set of valid types +_VALID_TYPES = {tuple, list, str, int, float, bool} + +# Valid file object types +_FILE_TYPES = (io.IOBase,) + +# Logger +logger = logging.getLogger(__name__) + + +class CfgNode(dict): + r"""CfgNode is a `node` in the configuration `tree`. It's a simple wrapper around a `dict` and supports access to + `attributes` via `keys`. + """ + + IMMUTABLE = "__immutable__" + DEPRECATED_KEYS = "__deprecated_keys__" + RENAMED_KEYS = "__renamed_keys__" + NEW_ALLOWED = "__new_allowed__" + + def __init__( + self, + init_dict: Optional[dict] = None, + key_list: Optional[list] = None, + new_allowed: Optional[bool] = False, + ): + r""" + Args: + init_dict (dict): A dictionary to initialize the `CfgNode`. + key_list (list[str]): A list of names that index this `CfgNode` from the root. Currently, only used for + logging. + new_allowed (bool): Whether adding a new key is allowed when merging with other `CfgNode` objects. + + """ + + # Recursively convert nested dictionaries in `init_dict` to config tree. + init_dict = {} if init_dict is None else init_dict + key_list = [] if key_list is None else key_list + init_dict = self._create_config_tree_from_dict(init_dict, key_list) + super(CfgNode, self).__init__(init_dict) + + # Control the immutability of the `CfgNode`. + self.__dict__[CfgNode.IMMUTABLE] = False + # Support for deprecated options. + # If you choose to remove support for an option in code, but don't want to change all of the config files + # (to allow for deprecated config files to run), you can add the full config key as a string to this set. + self.__dict__[CfgNode.DEPRECATED_KEYS] = set() + # Support for renamed options. + # If you rename an option, record the mapping from the old name to the new name in this dictionary. Optionally, + # if the type also changed, you can make this value a tuple that specifies two things: the renamed key, and the + # instructions to edit the config file. + self.__dict__[CfgNode.RENAMED_KEYS] = { + # 'EXAMPLE.OLD.KEY': 'EXAMPLE.NEW.KEY', # Dummy example + # 'EXAMPLE.OLD.KEY': ( # A more complex example + # 'EXAMPLE.NEW.KEY', + # "Also convert to a tuple, eg. 'foo' -> ('foo', ) or " + # + "'foo.bar' -> ('foo', 'bar')" + # ), + } + + # Allow new attributes after initialization. + self.__dict__[CfgNode.NEW_ALLOWED] = new_allowed + + @classmethod + def _create_config_tree_from_dict(cls, init_dict: dict, key_list: list): + r"""Create a configuration tree using the input dict. Any dict-like objects inside `init_dict` will be treated + as new `CfgNode` objects. + + Args: + init_dict (dict): Input dictionary, to create config tree from. + key_list (list): A list of names that index this `CfgNode` from the root. Currently only used for logging. + + """ + + d = copy.deepcopy(init_dict) + for k, v in d.items(): + if isinstance(v, dict): + # Convert dictionary to CfgNode + d[k] = cls(v, key_list=key_list + [k]) + else: + # Check for valid leaf type or nested CfgNode + _assert_with_logging( + _valid_type(v, allow_cfg_node=False), + "Key {} with value {} is not a valid type; valid types: {}".format( + ".".join(key_list + [k]), type(v), _VALID_TYPES + ), + ) + return d + + def __getattr__(self, name: str): + if name in self: + return self[name] + else: + raise AttributeError(name) + + def __setattr__(self, name: str, value): + if self.is_frozen(): + raise AttributeError( + "Attempted to set {} to {}, but CfgNode is immutable".format( + name, value + ) + ) + + _assert_with_logging( + name not in self.__dict__, + "Invalid attempt to modify internal CfgNode state: {}".format(name), + ) + + _assert_with_logging( + _valid_type(value, allow_cfg_node=True), + "Invalid type {} for key {}; valid types = {}".format( + type(value), name, _VALID_TYPES + ), + ) + + self[name] = value + + def __str__(self): + def _indent(s_, num_spaces): + s = s_.split("\n") + if len(s) == 1: + return s_ + first = s.pop(0) + s = [(num_spaces * " ") + line for line in s] + s = "\n".join(s) + s = first + "\n" + s + return s + + r = "" + s = [] + for k, v in sorted(self.items()): + separator = "\n" if isinstance(v, CfgNode) else " " + attr_str = "{}:{}{}".format(str(k), separator, str(v)) + attr_str = _indent(attr_str, 2) + s.append(attr_str) + r += "\n".join(s) + return r + + def __repr__(self): + return "{}({})".format(self.__class__.__name__, super(CfgNode, self).__repr__()) + + def dump(self, **kwargs): + r"""Dump CfgNode to a string. + """ + + def _convert_to_dict(cfg_node, key_list): + if not isinstance(cfg_node, CfgNode): + _assert_with_logging( + _valid_type(cfg_node), + "Key {} with value {} is not a valid type; valid types: {}".format( + ".".join(key_list), type(cfg_node), _VALID_TYPES + ), + ) + return cfg_node + else: + cfg_dict = dict(cfg_node) + for k, v in cfg_dict.items(): + cfg_dict[k] = _convert_to_dict(v, key_list + [k]) + return cfg_dict + + self_as_dict = _convert_to_dict(self, []) + return yaml.safe_dump(self_as_dict, **kwargs) + + def merge_from_file(self, cfg_filename: str): + r"""Load a yaml config file and merge it with this CfgNode. + + Args: + cfg_filename (str): Config file path. + + """ + with open(cfg_filename, "r") as f: + cfg = self.load_cfg(f) + self.merge_from_other_cfg(cfg) + + def merge_from_other_cfg(self, cfg_other): + r"""Merge `cfg_other` into the current `CfgNode`. + + Args: + cfg_other + """ + _merge_a_into_b(cfg_other, self, self, []) + + def merge_from_list(self, cfg_list: list): + r"""Merge config (keys, values) in a list (eg. from commandline) into this `CfgNode`. + + Eg. `cfg_list = ['FOO.BAR', 0.5]`. + """ + _assert_with_logging( + len(cfg_list) % 2 == 0, + "Override list has odd lengths: {}; it must be a list of pairs".format( + cfg_list + ), + ) + root = self + for full_key, v in zip(cfg_list[0::2], cfg_list[1::2]): + if root.key_is_deprecated(full_key): + continue + if root.key_is_renamed(full_key): + root.raise_key_rename_error(full_key) + key_list = full_key.split(".") + d = self + for subkey in key_list[:-1]: + _assert_with_logging( + subkey in d, "Non-existent key: {}".format(full_key) + ) + d = d[subkey] + subkey = key_list[-1] + _assert_with_logging(subkey in d, "Non-existent key: {}".format(full_key)) + value = self._decode_cfg_value(v) + value = _check_and_coerce_cfg_value_type(value, d[subkey], subkey, full_key) + d[subkey] = value + + def freeze(self): + r"""Make this `CfgNode` and all of its children immutable. """ + self._immutable(True) + + def defrost(self): + r"""Make this `CfgNode` and all of its children mutable. """ + self._immutable(False) + + def is_frozen(self): + r"""Return mutability. """ + return self.__dict__[CfgNode.IMMUTABLE] + + def _immutable(self, is_immutable: bool): + r"""Set mutability and recursively apply to all nested `CfgNode` objects. + + Args: + is_immutable (bool): Whether or not the `CfgNode` and its children are immutable. + + """ + self.__dict__[CfgNode.IMMUTABLE] = is_immutable + # Recursively propagate state to all children. + for v in self.__dict__.values(): + if isinstance(v, CfgNode): + v._immutable(is_immutable) + for v in self.values(): + if isinstance(v, CfgNode): + v._immutable(is_immutable) + + def clone(self): + r"""Recursively copy this `CfgNode`. """ + return copy.deepcopy(self) + + def register_deprecated_key(self, key: str): + r"""Register key (eg. `FOO.BAR`) a deprecated option. When merging deprecated keys, a warning is generated and + the key is ignored. + """ + + _assert_with_logging( + key not in self.__dict__[CfgNode.DEPRECATED_KEYS], + "key {} is already registered as a deprecated key".format(key), + ) + self.__dict__[CfgNode.DEPRECATED_KEYS].add(key) + + def register_renamed_key( + self, old_name: str, new_name: str, message: Optional[str] = None + ): + r"""Register a key as having been renamed from `old_name` to `new_name`. When merging a renamed key, an + exception is thrown alerting the user to the fact that the key has been renamed. + """ + + _assert_with_logging( + old_name not in self.__dict__[CfgNode.RENAMED_KEYS], + "key {} is already registered as a renamed cfg key".format(old_name), + ) + value = new_name + if message: + value = (new_name, message) + self.__dict__[CfgNode.RENAMED_KEYS][old_name] = value + + def key_is_deprecated(self, full_key: str): + r"""Test if a key is deprecated. """ + if full_key in self.__dict__[CfgNode.DEPRECATED_KEYS]: + logger.warning("deprecated config key (ignoring): {}".format(full_key)) + return True + return False + + def key_is_renamed(self, full_key: str): + r"""Test if a key is renamed. """ + return full_key in self.__dict__[CfgNode.RENAMED_KEYS] + + def raise_key_rename_error(self, full_key: str): + new_key = self.__dict__[CfgNode.RENAMED_KEYS][full_key] + if isinstance(new_key, tuple): + msg = " Note: " + new_key[1] + new_key = new_key[0] + else: + msg = "" + raise KeyError( + "Key {} was renamed to {}; please update your config.{}".format( + full_key, new_key, msg + ) + ) + + def is_new_allowed(self): + return self.__dict__[CfgNode.NEW_ALLOWED] + + @classmethod + def load_cfg(cls, cfg_file_obj_or_str): + r"""Load a configuration into the `CfgNode`. + + Args: + cfg_file_obj_or_str (str or cfg compatible object): Supports loading from: + - A file object backed by a YAML file. + - A file object backed by a Python source file that exports an sttribute "cfg" (dict or `CfgNode`). + - A string that can be parsed as valid YAML. + + """ + _assert_with_logging( + isinstance(cfg_file_obj_or_str, _FILE_TYPES + (str,)), + "Expected first argument to be of type {} or {}, but got {}".format( + _FILE_TYPES, str, type(cfg_file_obj_or_str) + ), + ) + if isinstance(cfg_file_obj_or_str, str): + return cls._load_cfg_from_yaml_str(cfg_file_obj_or_str) + elif isinstance(cfg_file_obj_or_str, _FILE_TYPES): + return cls._load_cfg_from_file(cfg_file_obj_or_str) + else: + raise NotImplementedError("Impossible to reach here (unless there's a bug)") + + @classmethod + def _load_cfg_from_file(cls, file_obj): + r"""Load a config from a YAML file or a Python source file. """ + _, file_ext = os.path.splitext(file_obj.name) + if file_ext in _YAML_EXTS: + return cls._load_cfg_from_yaml_str(file_obj.read()) + elif file_ext in _PY_EXTS: + return cls._load_cfg_py_source(file_obj.name) + else: + raise Exception( + "Attempt to load from an unsupported filetype {}; only {} supported".format( + _YAML_EXTS.union(_PY_EXTS) + ) + ) + + @classmethod + def _load_cfg_from_yaml_str(cls, str_obj): + r"""Load a config from a YAML string encoding. """ + cfg_as_dict = yaml.safe_load(str_obj) + return cls(cfg_as_dict) + + @classmethod + def _load_cfg_py_source(cls, filename): + r"""Load a config from a Python source file. """ + module = _load_module_from_file("yacs.config.override", filename) + _assert_with_logging( + hasattr(module, "cfg"), + "Python module from file {} must export a 'cfg' attribute".format(filename), + ) + VALID_ATTR_TYPES = {dict, CfgNode} + _assert_with_logging( + type(module.cfg) in VALID_ATTR_TYPES, + "Import module 'cfg' attribute must be in {} but is {}".format( + VALID_ATTR_TYPES, type(module.cfg) + ), + ) + return cls(module.cfg) + + @classmethod + def _decode_cfg_value(cls, value): + r"""Decodes a raw config value (eg. from a yaml config file or commandline argument) into a Python object. + + If `value` is a dict, it will be interpreted as a new `CfgNode`. + If `value` is a str, it will be evaluated as a literal. + Otherwise, it is returned as is. + + """ + # Configs parsed from raw yaml will contain dictionary keys that need to be converted to `CfgNode` objects. + if isinstance(value, dict): + return cls(value) + # All remaining processing is only applied to strings. + if not isinstance(value, str): + return value + # Try to interpret `value` as a: string, number, tuple, list, dict, bool, or None + try: + value = literal_eval(value) + # The following two excepts allow `value` to pass through it when it represents a string. + # The type of `value` is always a string (before calling `literal_eval`), but sometimes it *represents* a + # string and other times a data structure, like a list. In the case that `value` represents a str, what we + # got back from the yaml parser is `foo` *without quotes* (so, not `"foo"`). `literal_eval` is ok with `"foo"`, + # but will raise a `ValueError` if given `foo`. In other cases, like paths (`val = 'foo/bar'`) `literal_eval` + # will raise a `SyntaxError`. + except ValueError: + pass + except SyntaxError: + pass + return value + + +# Keep this function in global scope, for backward compataibility. +load_cfg = CfgNode.load_cfg + + +def _valid_type(value, allow_cfg_node: Optional[bool] = False): + return (type(value) in _VALID_TYPES) or ( + allow_cfg_node and isinstance(value, CfgNode) + ) + + +def _merge_a_into_b(a: CfgNode, b: CfgNode, root: CfgNode, key_list: list): + r"""Merge `CfgNode` `a` into `CfgNode` `b`, clobbering the options in `b` wherever they are also specified in `a`. + """ + _assert_with_logging( + isinstance(a, CfgNode), + "`a` (cur type {}) must be an instance of {}".format(type(a), CfgNode), + ) + _assert_with_logging( + isinstance(b, CfgNode), + "`b` (cur type {}) must be an instance of {}".format(type(b), CfgNode), + ) + + for k, v_ in a.items(): + full_key = ".".join(key_list + [k]) + v = copy.deepcopy(v_) + v = b._decode_cfg_value(v) + + if k in b: + v = _check_and_coerce_cfg_value_type(v, b[k], k, full_key) + # Recursively merge dicts. + if isinstance(v, CfgNode): + try: + _merge_a_into_b(v, b[k], root, key_list + [k]) + except BaseException: + raise + else: + b[k] = v + elif b.is_new_allowed(): + b[k] = v + else: + if root.key_is_deprecated(full_key): + continue + elif root.key_is_renamed(full_key): + root.raise_key_rename_error(full_key) + else: + raise KeyError("Non-existent config key: {}".format(full_key)) + + +def _check_and_coerce_cfg_value_type(replacement, original, key, full_key): + r"""Checks that `replacement`, which is intended to replace `original` is of the right type. The type is correct if + it matches exactly or is one of a few cases in which the type can easily be coerced. + """ + + original_type = type(original) + replacement_type = type(replacement) + if replacement_type == original_type: + return replacement + + # If replacement and original types match, cast replacement from `from_type` to `to_type`. + def _conditional_cast(from_type, to_type): + if replacement_type == from_type and original_type == to_type: + return True, to_type(replacement) + else: + return False, None + + # Conditional casts. + # list <-> tuple + casts = [(tuple, list), (list, tuple)] + for (from_type, to_type) in casts: + converted, converted_value = _conditional_cast(from_type, to_type) + if converted: + return converted_value + + raise ValueError( + "Type mismatch ({} vs. {} with values ({} vs. {}) for config key: {}".format( + original_type, replacement_type, original, replacement, full_key + ) + ) + + +def _assert_with_logging(cond, msg): + if not cond: + logger.debug(msg) + assert cond, msg + + +def _load_module_from_file(name, filename): + spec = importlib.util.spec_from_file_location(name, filename) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module diff --git a/motion-gan-pipeline/motion-generation/util/flow_viz.py b/motion-gan-pipeline/motion-generation/util/flow_viz.py new file mode 100644 index 0000000..41bd013 --- /dev/null +++ b/motion-gan-pipeline/motion-generation/util/flow_viz.py @@ -0,0 +1,132 @@ +# Flow visualization code used from https://github.com/tomrunia/OpticalFlow_Visualization + + +# MIT License +# +# Copyright (c) 2018 Tom Runia +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to conditions. +# +# Author: Tom Runia +# Date Created: 2018-08-03 + +import numpy as np + +def make_colorwheel(): + """ + Generates a color wheel for optical flow visualization as presented in: + Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007) + URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf + + Code follows the original C++ source code of Daniel Scharstein. + Code follows the the Matlab source code of Deqing Sun. + + Returns: + np.ndarray: Color wheel + """ + + RY = 15 + YG = 6 + GC = 4 + CB = 11 + BM = 13 + MR = 6 + + ncols = RY + YG + GC + CB + BM + MR + colorwheel = np.zeros((ncols, 3)) + col = 0 + + # RY + colorwheel[0:RY, 0] = 255 + colorwheel[0:RY, 1] = np.floor(255*np.arange(0,RY)/RY) + col = col+RY + # YG + colorwheel[col:col+YG, 0] = 255 - np.floor(255*np.arange(0,YG)/YG) + colorwheel[col:col+YG, 1] = 255 + col = col+YG + # GC + colorwheel[col:col+GC, 1] = 255 + colorwheel[col:col+GC, 2] = np.floor(255*np.arange(0,GC)/GC) + col = col+GC + # CB + colorwheel[col:col+CB, 1] = 255 - np.floor(255*np.arange(CB)/CB) + colorwheel[col:col+CB, 2] = 255 + col = col+CB + # BM + colorwheel[col:col+BM, 2] = 255 + colorwheel[col:col+BM, 0] = np.floor(255*np.arange(0,BM)/BM) + col = col+BM + # MR + colorwheel[col:col+MR, 2] = 255 - np.floor(255*np.arange(MR)/MR) + colorwheel[col:col+MR, 0] = 255 + return colorwheel + + +def flow_uv_to_colors(u, v, convert_to_bgr=False): + """ + Applies the flow color wheel to (possibly clipped) flow components u and v. + + According to the C++ source code of Daniel Scharstein + According to the Matlab source code of Deqing Sun + + Args: + u (np.ndarray): Input horizontal flow of shape [H,W] + v (np.ndarray): Input vertical flow of shape [H,W] + convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False. + + Returns: + np.ndarray: Flow visualization image of shape [H,W,3] + """ + flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8) + colorwheel = make_colorwheel() # shape [55x3] + ncols = colorwheel.shape[0] + rad = np.sqrt(np.square(u) + np.square(v)) + a = np.arctan2(-v, -u)/np.pi + fk = (a+1) / 2*(ncols-1) + k0 = np.floor(fk).astype(np.int32) + k1 = k0 + 1 + k1[k1 == ncols] = 0 + f = fk - k0 + for i in range(colorwheel.shape[1]): + tmp = colorwheel[:,i] + col0 = tmp[k0] / 255.0 + col1 = tmp[k1] / 255.0 + col = (1-f)*col0 + f*col1 + idx = (rad <= 1) + col[idx] = 1 - rad[idx] * (1-col[idx]) + col[~idx] = col[~idx] * 0.75 # out of range + # Note the 2-i => BGR instead of RGB + ch_idx = 2-i if convert_to_bgr else i + flow_image[:,:,ch_idx] = np.floor(255 * col) + return flow_image + + +def flow_to_image(flow_uv, clip_flow=None, convert_to_bgr=False): + """ + Expects a two dimensional flow image of shape. + + Args: + flow_uv (np.ndarray): Flow UV image of shape [H,W,2] + clip_flow (float, optional): Clip maximum of flow values. Defaults to None. + convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False. + + Returns: + np.ndarray: Flow visualization image of shape [H,W,3] + """ + assert flow_uv.ndim == 3, 'input flow must have three dimensions' + assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]' + if clip_flow is not None: + flow_uv = np.clip(flow_uv, 0, clip_flow) + u = flow_uv[:,:,0] + v = flow_uv[:,:,1] + rad = np.sqrt(np.square(u) + np.square(v)) + rad_max = np.max(rad) + epsilon = 1e-5 + u = u / (rad_max + epsilon) + v = v / (rad_max + epsilon) + return flow_uv_to_colors(u, v, convert_to_bgr) \ No newline at end of file diff --git a/motion-gan-pipeline/motion-generation/util/get_data.py b/motion-gan-pipeline/motion-generation/util/get_data.py new file mode 100644 index 0000000..1efa19f --- /dev/null +++ b/motion-gan-pipeline/motion-generation/util/get_data.py @@ -0,0 +1,110 @@ +from __future__ import print_function +import os +import tarfile +import requests +from warnings import warn +from zipfile import ZipFile +from bs4 import BeautifulSoup +from os.path import abspath, isdir, join, basename + + +class GetData(object): + """A Python script for downloading CycleGAN or pix2pix datasets. + + Parameters: + technique (str) -- One of: 'cyclegan' or 'pix2pix'. + verbose (bool) -- If True, print additional information. + + Examples: + >>> from util.get_data import GetData + >>> gd = GetData(technique='cyclegan') + >>> new_data_path = gd.get(save_path='./datasets') # options will be displayed. + + Alternatively, You can use bash scripts: 'scripts/download_pix2pix_model.sh' + and 'scripts/download_cyclegan_model.sh'. + """ + + def __init__(self, technique='cyclegan', verbose=True): + url_dict = { + 'pix2pix': 'http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/', + 'cyclegan': 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets' + } + self.url = url_dict.get(technique.lower()) + self._verbose = verbose + + def _print(self, text): + if self._verbose: + print(text) + + @staticmethod + def _get_options(r): + soup = BeautifulSoup(r.text, 'lxml') + options = [h.text for h in soup.find_all('a', href=True) + if h.text.endswith(('.zip', 'tar.gz'))] + return options + + def _present_options(self): + r = requests.get(self.url) + options = self._get_options(r) + print('Options:\n') + for i, o in enumerate(options): + print("{0}: {1}".format(i, o)) + choice = input("\nPlease enter the number of the " + "dataset above you wish to download:") + return options[int(choice)] + + def _download_data(self, dataset_url, save_path): + if not isdir(save_path): + os.makedirs(save_path) + + base = basename(dataset_url) + temp_save_path = join(save_path, base) + + with open(temp_save_path, "wb") as f: + r = requests.get(dataset_url) + f.write(r.content) + + if base.endswith('.tar.gz'): + obj = tarfile.open(temp_save_path) + elif base.endswith('.zip'): + obj = ZipFile(temp_save_path, 'r') + else: + raise ValueError("Unknown File Type: {0}.".format(base)) + + self._print("Unpacking Data...") + obj.extractall(save_path) + obj.close() + os.remove(temp_save_path) + + def get(self, save_path, dataset=None): + """ + + Download a dataset. + + Parameters: + save_path (str) -- A directory to save the data to. + dataset (str) -- (optional). A specific dataset to download. + Note: this must include the file extension. + If None, options will be presented for you + to choose from. + + Returns: + save_path_full (str) -- the absolute path to the downloaded data. + + """ + if dataset is None: + selected_dataset = self._present_options() + else: + selected_dataset = dataset + + save_path_full = join(save_path, selected_dataset.split('.')[0]) + + if isdir(save_path_full): + warn("\n'{0}' already exists. Voiding Download.".format( + save_path_full)) + else: + self._print('Downloading Data...') + url = "{0}/{1}".format(self.url, selected_dataset) + self._download_data(url, save_path=save_path) + + return abspath(save_path_full) diff --git a/motion-gan-pipeline/motion-generation/util/html.py b/motion-gan-pipeline/motion-generation/util/html.py new file mode 100644 index 0000000..10f2fbd --- /dev/null +++ b/motion-gan-pipeline/motion-generation/util/html.py @@ -0,0 +1,67 @@ +import dominate +from dominate.tags import * +import os + + +class HTML: + def __init__(self, web_dir, title, reflesh=0): + self.title = title + self.web_dir = web_dir + self.img_dir = os.path.join(self.web_dir, 'images') + if not os.path.exists(self.web_dir): + os.makedirs(self.web_dir) + if not os.path.exists(self.img_dir): + os.makedirs(self.img_dir) + # print(self.img_dir) + + self.doc = dominate.document(title=title) + if reflesh > 0: + with self.doc.head: + meta(http_equiv="reflesh", content=str(reflesh)) + + def get_image_dir(self): + return self.img_dir + + def add_header(self, str): + with self.doc: + h3(str) + + def add_table(self, border=1): + self.t = table(border=border, style="table-layout: fixed;") + self.doc.add(self.t) + + def add_images(self, ims, txts, links, width=400, height=0): + self.add_table() + with self.t: + with tr(): + for im, txt, link in zip(ims, txts, links): + with td(style="word-wrap: break-word;", halign="center", valign="top"): + with p(): + with a(href=os.path.join('images', link)): + if height != 0: + img(style="width:%dpx;height:%dpx" % (width, height), src=os.path.join('images', im)) + else: + img(style="width:%dpx" % (width), src=os.path.join('images', im)) + br() + p(txt) + + def save(self): + html_file = '%s/index.html' % self.web_dir + f = open(html_file, 'wt') + f.write(self.doc.render()) + f.close() + + +if __name__ == '__main__': + html = HTML('web/', 'test_html') + html.add_header('hello world') + + ims = [] + txts = [] + links = [] + for n in range(4): + ims.append('image_%d.jpg' % n) + txts.append('text_%d' % n) + links.append('image_%d.jpg' % n) + html.add_images(ims, txts, links) + html.save() diff --git a/motion-gan-pipeline/motion-generation/util/image_pool.py b/motion-gan-pipeline/motion-generation/util/image_pool.py new file mode 100644 index 0000000..152ef5b --- /dev/null +++ b/motion-gan-pipeline/motion-generation/util/image_pool.py @@ -0,0 +1,32 @@ +import random +import numpy as np +import torch +from torch.autograd import Variable +class ImagePool(): + def __init__(self, pool_size): + self.pool_size = pool_size + if self.pool_size > 0: + self.num_imgs = 0 + self.images = [] + + def query(self, images): + if self.pool_size == 0: + return images + return_images = [] + for image in images.data: + image = torch.unsqueeze(image, 0) + if self.num_imgs < self.pool_size: + self.num_imgs = self.num_imgs + 1 + self.images.append(image) + return_images.append(image) + else: + p = random.uniform(0, 1) + if p > 0.5: + random_id = random.randint(0, self.pool_size-1) + tmp = self.images[random_id].clone() + self.images[random_id] = image + return_images.append(tmp) + else: + return_images.append(image) + return_images = Variable(torch.cat(return_images, 0)) + return return_images diff --git a/motion-gan-pipeline/motion-generation/util/util.py b/motion-gan-pipeline/motion-generation/util/util.py new file mode 100644 index 0000000..6bd1dab --- /dev/null +++ b/motion-gan-pipeline/motion-generation/util/util.py @@ -0,0 +1,93 @@ +from __future__ import print_function +import torch +import numpy as np +from PIL import Image +import inspect, re +import numpy as np +import os +import collections +from PIL import Image +import cv2 +from collections import OrderedDict + +from . import flow_viz + + + +# Converts a Tensor into a Numpy array +# |imtype|: the desired type of the converted numpy array +def tensor2im(image_tensor, imtype=np.uint8, normalize=True): + if isinstance(image_tensor, list): + image_numpy = [] + for i in range(len(image_tensor)): + image_numpy.append(tensor2im(image_tensor[i], imtype, normalize)) + return image_numpy + + if isinstance(image_tensor, torch.autograd.Variable): + image_tensor = image_tensor.data + if len(image_tensor.size()) == 5: + image_tensor = image_tensor[0, -1] + if len(image_tensor.size()) == 4: + image_tensor = image_tensor[0] + image_tensor = image_tensor[:3] + image_numpy = image_tensor.cpu().float().numpy() + if normalize: + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 + else: + image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0 + #image_numpy = (np.transpose(image_numpy, (1, 2, 0)) * std + mean) * 255.0 + image_numpy = np.clip(image_numpy, 0, 255) + if image_numpy.shape[2] == 1: + image_numpy = image_numpy[:,:,0] + return image_numpy.astype(imtype) + + +def tensor2flow(flo, imtype=np.uint8): + flo = flo[0].permute(1,2,0).cpu().detach().numpy() + flo = flow_viz.flow_to_image(flo) + return flo + + +def add_dummy_to_tensor(tensors, add_size=0): + if add_size == 0 or tensors is None: return tensors + if isinstance(tensors, list): + return [add_dummy_to_tensor(tensor, add_size) for tensor in tensors] + + if isinstance(tensors, torch.Tensor): + dummy = torch.zeros_like(tensors)[:add_size] + tensors = torch.cat([dummy, tensors]) + return tensors + +def remove_dummy_from_tensor(tensors, remove_size=0): + if remove_size == 0 or tensors is None: return tensors + if isinstance(tensors, list): + return [remove_dummy_from_tensor(tensor, remove_size) for tensor in tensors] + + if isinstance(tensors, torch.Tensor): + tensors = tensors[remove_size:] + return tensors + +def save_image(image_numpy, image_path): + image_pil = Image.fromarray(image_numpy) + image_pil.save(image_path) + +def print_numpy(x, val=True, shp=False): + x = x.astype(np.float64) + if shp: + print('shape,', x.shape) + if val: + x = x.flatten() + print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % ( + np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x))) + +def mkdirs(paths): + if isinstance(paths, list) and not isinstance(paths, str): + for path in paths: + mkdir(path) + else: + mkdir(paths) + +def mkdir(path): + if not os.path.exists(path): + os.makedirs(path) + diff --git a/motion-gan-pipeline/motion-generation/util/visualizer.py b/motion-gan-pipeline/motion-generation/util/visualizer.py new file mode 100644 index 0000000..fd25a6e --- /dev/null +++ b/motion-gan-pipeline/motion-generation/util/visualizer.py @@ -0,0 +1,149 @@ +### Copyright (C) 2017 NVIDIA Corporation. All rights reserved. +### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). +import numpy as np +import os +import time +from . import util +from . import html +import scipy.misc +try: + from StringIO import StringIO # Python 2.7 +except ImportError: + from io import BytesIO # Python 3.x + +class Visualizer(): + def __init__(self, opt): + self.opt = opt + self.tf_log = opt.tf_log + self.use_html = opt.isTrain and not opt.no_html + self.win_size = opt.display_winsize + self.name = opt.name + if opt.isTrain: + if self.tf_log: + from torch.utils.tensorboard import SummaryWriter + # import tensorflow as tf + # self.tf = tf + self.log_dir = os.path.join(opt.checkpoints_dir, opt.name, 'logs') + # self.writer = tf.summary.FileWriter(self.log_dir) + self.writer = SummaryWriter(self.log_dir, flush_secs=1) + + if self.use_html: + self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web') + self.img_dir = os.path.join(self.web_dir, 'images') + print('create web directory %s...' % self.web_dir) + util.mkdirs([self.web_dir, self.img_dir]) + + self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt') + with open(self.log_name, "a") as log_file: + now = time.strftime("%c") + log_file.write('================ Training Loss (%s) ================\n' % now) + + # |visuals|: dictionary of images to display or save + def display_current_results(self, visuals, epoch, step): +# if self.tf_log: # show images in tensorboard output +# img_summaries = [] +# for label, image_numpy in visuals.items(): +# # Write the image to a string +# try: +# s = StringIO() +# except: +# s = BytesIO() +# scipy.misc.toimage(image_numpy).save(s, format="jpeg") +# # Create an Image object +# img_sum = self.tf.Summary.Image(encoded_image_string=s.getvalue(), height=image_numpy.shape[0], width=image_numpy.shape[1]) +# # Create a Summary value +# img_summaries.append(self.tf.Summary.Value(tag=label, image=img_sum)) +# +# # Create and write Summary +# summary = self.tf.Summary(value=img_summaries) +# self.writer.add_summary(summary, step) + + if self.use_html: # save images to a html file + for label, image_numpy in visuals.items(): + if isinstance(image_numpy, list): + for i in range(len(image_numpy)): + img_path = os.path.join(self.img_dir, 'epoch%.3d_%s_%d.jpg' % (epoch, label, i)) + util.save_image(image_numpy[i], img_path) + else: + img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.jpg' % (epoch, label)) + util.save_image(image_numpy, img_path) + + # update website + webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, reflesh=1) + for n in range(epoch, 0, -1): + webpage.add_header('epoch [%d]' % n) + ims = [] + txts = [] + links = [] + + for label, image_numpy in visuals.items(): + if isinstance(image_numpy, list): + for i in range(len(image_numpy)): + img_path = 'epoch%.3d_%s_%d.jpg' % (n, label, i) + ims.append(img_path) + txts.append(label+str(i)) + links.append(img_path) + else: + img_path = 'epoch%.3d_%s.jpg' % (n, label) + ims.append(img_path) + txts.append(label) + links.append(img_path) + if len(ims) < 5: + webpage.add_images(ims, txts, links, width=self.win_size) + else: + num = int(round(len(ims)/2.0)) + webpage.add_images(ims[:num], txts[:num], links[:num], width=self.win_size) + webpage.add_images(ims[num:], txts[num:], links[num:], width=self.win_size) + webpage.save() + + # errors: dictionary of error labels and values + def plot_current_errors(self, errors, step): + if self.tf_log: + for tag, value in errors.items(): +# summary = self.tf.Summary(value=[self.tf.Summary.Value(tag=tag, simple_value=value)]) +# self.writer.add_summary(summary, step) + self.writer.add_scalar(tag, value, step) + + + # errors: same format as |errors| of plotCurrentErrors + def print_current_errors(self, epoch, i, errors, t): + message = '(epoch: %d, iters: %d, time: %.3f) ' % (epoch, i, t) + for k, v in sorted(errors.items()): + if v != 0: + message += '%s: %.3f ' % (k, v) + + print(message) + with open(self.log_name, "a") as log_file: + log_file.write('%s\n' % message) + + # save image to the disk + def save_images(self, image_dir, visuals, image_path, webpage=None): + dirname = os.path.basename(os.path.dirname(image_path[0])) + image_dir = os.path.join(image_dir, dirname) + util.mkdir(image_dir) + name = image_path +# name = os.path.basename(image_path[0]) +# name = os.path.splitext(name)[0] + + if webpage is not None: + webpage.add_header(name) + ims, txts, links = [], [], [] + + for label, image_numpy in visuals.items(): + save_ext = 'jpg' + image_name = '%s_%s.%s' % (label, name, save_ext) + save_path = os.path.join(image_dir, image_name) + util.save_image(image_numpy, save_path) + + if webpage is not None: + ims.append(image_name) + txts.append(label) + links.append(image_name) + if webpage is not None: + webpage.add_images(ims, txts, links, width=self.win_size) + + def vis_print(self, message): + print(message) + with open(self.log_name, "a") as log_file: + log_file.write('%s\n' % message) + diff --git a/motion-gan-pipeline/motion_gan_backend_api/__init__.py b/motion-gan-pipeline/motion_gan_backend_api/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/motion-gan-pipeline/motion_gan_backend_api/app.py b/motion-gan-pipeline/motion_gan_backend_api/app.py new file mode 100644 index 0000000..f99351c --- /dev/null +++ b/motion-gan-pipeline/motion_gan_backend_api/app.py @@ -0,0 +1,123 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import logging +from http import HTTPStatus +from typing import List, Dict + +from fastapi import UploadFile, Depends, Query, HTTPException +from fastapi.responses import FileResponse +from mtc_api_utils.api import BaseApi +from mtc_api_utils.clients.firebase_client import firebase_user_auth + +from avatar_backend_api.api_types import ApiRoute, InferenceQueueTask, AvatarModelRequest +from avatar_backend_api.clients.io_client import IoClient, IoClientException +from avatar_backend_api.models.avatar_base_model import AvatarBaseModel +from avatar_backend_api.models.mock_avatar_model import MockAvatarModel +from motion_gan_backend_api.config import MotionGanConfig +from motion_gan_backend_api.motion_gan_inference_queue import MotionGanInferenceQueue +from motion_gan_backend_api.motion_gan_model import MotionGANModel + +MotionGanConfig.print_config() + +user_auth = firebase_user_auth(config=MotionGanConfig) + +io_client = IoClient(audio_base_path=MotionGanConfig.audio_input_dir, video_base_path=MotionGanConfig.video_output_dir, user_dir_mode=False) +motion_gan_model: AvatarBaseModel = MockAvatarModel(io_client=io_client) if MotionGanConfig.mockBackend else MotionGANModel(io_client=io_client) + +inference_queue = MotionGanInferenceQueue( + model=motion_gan_model, + io_client=io_client, + audio_input_dir=MotionGanConfig.audio_input_dir, + video_output_dir=MotionGanConfig.video_output_dir, +) + +app = BaseApi(is_ready=motion_gan_model.is_ready, config=MotionGanConfig) +tags = ["Avatar Model"] + +logger = logging.Logger("App") + + +@app.get( + path=ApiRoute.video_ids.value, + response_model=List[str], + tags=tags, + dependencies=[Depends(user_auth)], +) +async def list_video_ids() -> List[str]: + return [path.split("/")[-1].split(".")[0] for path in io_client.video.list_videos()] + + +@app.get( + path=ApiRoute.video.value, + status_code=HTTPStatus.ACCEPTED, + response_class=FileResponse, + tags=tags, + dependencies=[Depends(user_auth)], +) +async def get_video(video_id: str = Query(alias="videoId")): # -> FileResponse: # For some reason does not seem to work with python 3.7 + try: + return io_client.video.read_from_disk(video_id=video_id) + + except IoClientException: + if io_client.audio.file_exists(video_id=video_id): + raise HTTPException( + status_code=HTTPStatus.PROCESSING, + detail=f"Audio with video_id={video_id} is currently being processed", + ) + + else: + raise HTTPException( + status_code=HTTPStatus.NOT_FOUND, + detail=f"There is no file available for video_id={video_id}. Either it has not yet been generated or it was removed in the meantime", + ) + + +@app.post( + path=ApiRoute.inference.value, + status_code=HTTPStatus.ACCEPTED, + response_model=AvatarModelRequest, + tags=tags, + dependencies=[Depends(user_auth)], +) +async def model_inference(audio: UploadFile, request_metadata: AvatarModelRequest = Depends()) -> AvatarModelRequest: + print(f"Processing request for video_id={request_metadata.video_id}") + + if not request_metadata.video_id: + print(f"Creating video_id={request_metadata.video_id}") + request_metadata.video_id = audio.filename + + io_client.audio.save_to_disk(audio=audio, video_id=request_metadata.video_id) + + inference_queue.add_task( + task=InferenceQueueTask( + request=request_metadata, + ), + ) + + return request_metadata + + +@app.delete( + path=ApiRoute.video.value, + response_model=str, + tags=tags, + dependencies=[Depends(user_auth)], +) +async def delete_video(video_id: str = Query(alias="videoId")) -> str: + try: + io_client.video.delete_file(video_id=video_id) + except FileNotFoundError: + raise HTTPException(status_code=HTTPStatus.NOT_FOUND, detail=f"No files with video_id={video_id} found") + + return f"The files with video_id={video_id} were removed successfully" + + +@app.get( + path=ApiRoute.avatars.value, + response_model=Dict[str, str], + tags=["Avatars"], + dependencies=[Depends(user_auth)], +) +async def available_avatars() -> Dict[str, str]: + return MotionGANModel.available_avatars() diff --git a/motion-gan-pipeline/motion_gan_backend_api/config.py b/motion-gan-pipeline/motion_gan_backend_api/config.py new file mode 100644 index 0000000..b9f914d --- /dev/null +++ b/motion-gan-pipeline/motion_gan_backend_api/config.py @@ -0,0 +1,25 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os.path + +from mtc_api_utils.config import Config + +from avatar_backend_api.config import AvatarConfig + + +class MotionGanConfig(AvatarConfig): + mockBackend: bool = Config.parse_env_var("MOCK_BACKEND", default="False", convert_type=bool) + + # Data configs + db_filepath: str = Config.parse_env_var("DB_FILEPATH", default="/tmp/tinyDB/neuralVoices.json") + + motion_gan_base_dir: str = "/app" # Location of the neural-code dir in the execution environment + + data_base_dir: str = Config.parse_env_var("DATA_BASE_DIR", default="/tmp/motionGan") + + audio_input_dir: str = os.path.join(data_base_dir, "input_data", "audio") + video_output_dir: str = os.path.join(data_base_dir, "output_data", "videos") + + checkpoints_dir: str = os.path.join(data_base_dir, "checkpoints") + video_input_dir: str = os.path.join(data_base_dir, "input_data", "video") diff --git a/motion-gan-pipeline/motion_gan_backend_api/motion_gan_inference_queue.py b/motion-gan-pipeline/motion_gan_backend_api/motion_gan_inference_queue.py new file mode 100644 index 0000000..0b38db4 --- /dev/null +++ b/motion-gan-pipeline/motion_gan_backend_api/motion_gan_inference_queue.py @@ -0,0 +1,24 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os +import shutil + +from avatar_backend_api.background_tools.inference_queue import InferenceQueue, InferenceQueueTask +from avatar_backend_api.clients.io_client import VIDEO_EXTENSION + +from motion_gan_backend_api.config import MotionGanConfig + + +class MotionGanInferenceQueue(InferenceQueue): + + def post_processing(self, task: InferenceQueueTask) -> None: + # Delete input audio + self.io_client.audio.delete_file(video_id=task.request.video_id) + + # Rename file and return video path + if not MotionGanConfig.mockBackend: + self.io_client.video.rename_file( + filename=f"{task.request.video_id}_to_{task.request.avatar.name}{VIDEO_EXTENSION}", + new_filename=task.request.video_id + VIDEO_EXTENSION, + ) diff --git a/motion-gan-pipeline/motion_gan_backend_api/motion_gan_model.py b/motion-gan-pipeline/motion_gan_backend_api/motion_gan_model.py new file mode 100644 index 0000000..72676e6 --- /dev/null +++ b/motion-gan-pipeline/motion_gan_backend_api/motion_gan_model.py @@ -0,0 +1,51 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details +import os +import subprocess +from enum import Enum +from typing import Dict + +from avatar_backend_api.api_types import InferenceQueueTask +from avatar_backend_api.models.avatar_base_model import AvatarBaseModel +from motion_gan_backend_api.config import MotionGanConfig + + +class Pipeline(Enum): + value: str + full_pipeline = "full_pipeline.sh" + full_pipeline_enhancement = "full_pipeline_enhancement.sh" + full_pipeline_multiview = "full_pipeline_multiview.sh" + + +class MotionGANModel(AvatarBaseModel): + def init_model(self): + # Checking if either expected files are available + for avatar in MotionGanConfig.available_avatars.keys(): + checkpoint_path = os.path.join(MotionGanConfig.checkpoints_dir, avatar) + video_path = os.path.join(MotionGanConfig.video_input_dir, avatar) + + if not os.path.isdir(checkpoint_path): + if os.path.isdir(video_path): + print(f"No checkpoints found for avatar {avatar}. Run inference on it in order to start training it") + else: + raise ValueError(f"Could not find directory {checkpoint_path}, which is supposed to contain the checkpoint files for avatar {avatar}") + + print("All files were successfully downloaded, motionGan model is ready") + + def inference(self, task: InferenceQueueTask, pipeline: Pipeline = Pipeline.full_pipeline) -> None: + subprocess.run( + args=["bash", pipeline.value, MotionGanConfig.data_base_dir, task.request.video_id, task.request.avatar.name], + cwd=MotionGanConfig.motion_gan_base_dir, # Working directory from which to run the shell command + universal_newlines=True, # Decode output + check=True, # Throw exception if return code != 0 + ) + print(f"Inference completed for {task.request.video_id} - {task.request.avatar.value}") + + @staticmethod + def available_avatars() -> Dict[str, str]: + with os.scandir(MotionGanConfig.checkpoints_dir) as dirs: + return { + avatar_dir.name: MotionGanConfig.avatar_short_name(avatar_name=avatar_dir.name) + for avatar_dir in dirs + if avatar_dir.is_dir() + } diff --git a/motion-gan-pipeline/motion_gan_backend_api/tests/Test-2.wav b/motion-gan-pipeline/motion_gan_backend_api/tests/Test-2.wav new file mode 100644 index 0000000..2debc29 Binary files /dev/null and b/motion-gan-pipeline/motion_gan_backend_api/tests/Test-2.wav differ diff --git a/motion-gan-pipeline/motion_gan_backend_api/tests/__init__.py b/motion-gan-pipeline/motion_gan_backend_api/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/motion-gan-pipeline/motion_gan_backend_api/tests/integration_test_client.py b/motion-gan-pipeline/motion_gan_backend_api/tests/integration_test_client.py new file mode 100644 index 0000000..34185de --- /dev/null +++ b/motion-gan-pipeline/motion_gan_backend_api/tests/integration_test_client.py @@ -0,0 +1,58 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from http import HTTPStatus +from typing import List, BinaryIO, Tuple + +from mtc_api_utils.api import BaseApi +from mtc_api_utils.clients.api_client import ApiClient +from starlette.testclient import TestClient + +from avatar_backend_api.api_types import ApiRoute, AvatarModelRequest + + +class AvatarIntegrationTestClient(ApiClient): + + def __init__(self, test_app: BaseApi): + super().__init__(backend_url="", http_client=TestClient(app=test_app)) + + def list_video_ids(self) -> List[str]: + resp = self.http_client.get( + url=ApiRoute.video_ids.value, + ) + resp.raise_for_status() + + return resp.json() + + def get_video(self, video_id: str) -> Tuple[int, bytes]: + resp = self.http_client.get( + url=ApiRoute.video.value, + params={"videoId": video_id}, + ) + + if resp.status_code >= 300: + resp.raise_for_status() + + return resp.status_code, resp.content + + def post_audio(self, audio: BinaryIO, metadata: AvatarModelRequest) -> AvatarModelRequest: + resp = self.http_client.post( + url=ApiRoute.inference.value, + params=metadata.json_dict, + files={"audio": audio}, + ) + + if resp.status_code >= 300: + print(f"text={resp.text}") + print(f"json={resp.json()}") + + resp.raise_for_status() + assert resp.status_code == HTTPStatus.ACCEPTED + + return AvatarModelRequest.parse_obj(resp.json()) + + def delete_video(self, video_id: str) -> str: + resp = self.http_client.delete(ApiRoute.video_url(backend_url="", video_id=video_id)) + resp.raise_for_status() + + return resp.text diff --git a/motion-gan-pipeline/motion_gan_backend_api/tests/ping.wav b/motion-gan-pipeline/motion_gan_backend_api/tests/ping.wav new file mode 100644 index 0000000..c0dc31c Binary files /dev/null and b/motion-gan-pipeline/motion_gan_backend_api/tests/ping.wav differ diff --git a/motion-gan-pipeline/motion_gan_backend_api/tests/test.mp3 b/motion-gan-pipeline/motion_gan_backend_api/tests/test.mp3 new file mode 100644 index 0000000..7517a57 Binary files /dev/null and b/motion-gan-pipeline/motion_gan_backend_api/tests/test.mp3 differ diff --git a/motion-gan-pipeline/motion_gan_backend_api/tests/test_integration.py b/motion-gan-pipeline/motion_gan_backend_api/tests/test_integration.py new file mode 100644 index 0000000..cfdef64 --- /dev/null +++ b/motion-gan-pipeline/motion_gan_backend_api/tests/test_integration.py @@ -0,0 +1,76 @@ +import os +from datetime import timedelta +from enum import Enum +from http import HTTPStatus +from time import sleep +from unittest import TestCase + +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import httpx + +from avatar_backend_api.api_types import AvatarModelRequest, BaseAvatar +from motion_gan_backend_api.app import app +from motion_gan_backend_api.tests.integration_test_client import AvatarIntegrationTestClient + + +class TestFile(Enum): + value: str + test_mp3 = "test.mp3" + ping_wav = "ping.wav" + voice_wav = "voice_test.wav" + frontend_wav = "Test-2.wav" + + +test_dir = os.path.dirname(os.path.realpath(__file__)) +test_file_name = TestFile.frontend_wav.value +test_file_path = os.path.join(test_dir, test_file_name) + +TEST_REQUEST = AvatarModelRequest( + video_id="id", + avatar=BaseAvatar.Jennifer_355_9415, +) + + +class TestIntegration(TestCase): + + def setUp(self) -> None: + self.client = AvatarIntegrationTestClient(test_app=app) + + self.client.wait_for_service_readiness(timeout=timedelta(minutes=15)) + + try: + self.client.delete_video(TEST_REQUEST.video_id) + except httpx.HTTPStatusError as e: + self.assertEqual(e.response.status_code, HTTPStatus.NOT_FOUND) + + print("Integration Test Setup Completed") + + def tearDown(self) -> None: + self.setUp() + + def test_inference(self): + with open(test_file_path, "rb") as test_file: + initial_video_ids = self.client.list_video_ids() + self.assertNotIn(TEST_REQUEST.video_id, initial_video_ids) + + try: + self.client.delete_video(TEST_REQUEST.video_id) + self.fail("Expected delete to fail because video is not present") + except httpx.HTTPStatusError as e: + self.assertEqual(e.response.status_code, HTTPStatus.NOT_FOUND) + + self.assertEqual(TEST_REQUEST, self.client.post_audio(audio=test_file, metadata=TEST_REQUEST)) + + while True: + status_code, file_bytes = self.client.get_video(video_id=TEST_REQUEST.video_id) + self.assertIn(status_code, [HTTPStatus.OK, HTTPStatus.PROCESSING]) + + if status_code == HTTPStatus.OK: + break + else: + sleep(1) + + print("Test Delete Video") + self.client.delete_video(video_id=TEST_REQUEST.video_id) diff --git a/motion-gan-pipeline/motion_gan_backend_api/tests/voice_test.wav b/motion-gan-pipeline/motion_gan_backend_api/tests/voice_test.wav new file mode 100644 index 0000000..95ce3fe Binary files /dev/null and b/motion-gan-pipeline/motion_gan_backend_api/tests/voice_test.wav differ diff --git a/motion-gan-pipeline/postprocessing.py b/motion-gan-pipeline/postprocessing.py new file mode 100644 index 0000000..b49a342 --- /dev/null +++ b/motion-gan-pipeline/postprocessing.py @@ -0,0 +1,186 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os +from os.path import join +import argparse +import numpy as np +from tqdm import tqdm +import soundfile as sf +import matplotlib.pyplot as plt +import subprocess +import librosa +from pathlib import Path +from PIL import Image +import cv2 +import shutil + +def write_video_with_audio(save_root, audio_path, output_path, h=512, w=512, fps=25): + font = cv2.FONT_HERSHEY_SIMPLEX + fontScale = 1.0 + fontColor = (255, 255, 255) + thickness = 1 + lineType = 2 + text = 'This video has been manipulated.' + + label_width, label_height = cv2.getTextSize(text, font, 1, 2)[0] + print(label_width, label_height) + bottomLeftCornerOfText = (int((w - label_width) / 2), int(h - label_height - 20)) + + fourcc = cv2.VideoWriter_fourcc(*'DIVX') + # fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G') + video_tmp_path = os.path.join(save_root, 'tmp.avi') + num_images = int(len(os.listdir(save_root))) + + out = cv2.VideoWriter(video_tmp_path, fourcc, fps, (w, h)) + for j in tqdm(range(num_images), position=0, desc='writing video'): + img = cv2.imread(os.path.join(save_root, '%05d.png' % j)) + img = cv2.putText( + img, + text, + bottomLeftCornerOfText, + font, + fontScale, + fontColor, + thickness, + lineType, + ) + out.write(img) + + out.release() + + print("ffmpeg version:") + subprocess.call("ffmpeg -version", shell=True) + + # TODO: Set proper size of video: [-s {w}x{h}] + cmd = f'ffmpeg -y -i "{video_tmp_path}" -i "{audio_path}" -vcodec libx264 -c:a aac "{output_path}"' + # "-pix_fmt yuv420p -profile:v baseline -level 3" + + print(f"ffmpeg cmd: {cmd}") + return_code = subprocess.call(cmd, shell=True) + + if return_code > 0: + raise Exception(f"An error occurred when assembling the output video: ffmpeg return_code={return_code}") + + # try: + # os.remove(video_tmp_path) # remove the template video + + # except FileNotFoundError: + # return + +def write_video_with_audio_old(save_root, audio_path, output_path, h=512, w=512, fps=25): + + cmd = 'ffmpeg -y -r ' + str(fps) + ' -s ' + f'{w}x{h}' + ' -i "' + f'{save_root}/%05d.png' + \ + '" -i "' + audio_path + '" -vcodec libx264 -c:a aac "' + output_path + '"' + subprocess.call(cmd, shell=True) + + +if __name__ == '__main__': + + # load args + parser = argparse.ArgumentParser() + parser.add_argument('--dataroot', required=True) + parser.add_argument('--name_audio', required=True) + parser.add_argument('--out_dir', required=True) + parser.add_argument('--fps', required=True, type=np.int32) + parser.add_argument('--sr', required=True, type=np.int32) + parser.add_argument('--all_vids', required=False, action='store_true', help='Generate all videos?') + parser.add_argument('--enhance', required=False, action='store_true', help='Enhance generated video. (Takes longer).') + parser.add_argument('--clean', required=False, action='store_true', help='Delete everything but the videos?') + parser.add_argument('--move_to_one_folder', required=False, action='store_true', help='Put generated video into videos folder.') + + inopt = parser.parse_args() + + # make videos + # generate corresponding audio, reused for all results + audio_path = os.path.join(inopt.dataroot, 'audio', inopt.name_audio, inopt.name_audio + '.wav') + audio, _ = librosa.load(audio_path, sr=inopt.sr) + + # make generated video + generated_frames_path = os.path.join(inopt.out_dir, 'generated_frames') + edges_path = os.path.join(inopt.out_dir, 'edges') + render_path = os.path.join(inopt.out_dir, 'render') + + try: + nframe = len(os.listdir(generated_frames_path)) + w, h = Image.open(os.path.join(generated_frames_path, os.listdir(generated_frames_path)[0])).size + + except (IndexError, FileNotFoundError) as e: + nframe = len(os.listdir(render_path)) + w, h = Image.open(os.path.join(render_path, os.listdir(render_path)[0])).size + + print(f'Image size: height {h} width {w}') + + tmp_audio_path = os.path.join(inopt.out_dir, 'tmp.wav') + tmp_audio_clip = audio[: np.int32(nframe * inopt.sr / inopt.fps)] + sf.write(tmp_audio_path, tmp_audio_clip, inopt.sr) + + # TODO: when deploying make one or the other + final_video_path = os.path.join(inopt.out_dir, 'generated_video.mp4') + write_video_with_audio(generated_frames_path, tmp_audio_path, final_video_path, h, w, inopt.fps) + + if inopt.enhance: + superes_frames_path = os.path.join(inopt.out_dir, 'superes/generated_frames') + w, h = Image.open(os.path.join(superes_frames_path, os.listdir(superes_frames_path)[0])).size + # make video of edges for comparison + video_superes_path = os.path.join(inopt.out_dir, 'generated_superes.mp4') + write_video_with_audio(superes_frames_path, tmp_audio_path, video_superes_path, h, w, inopt.fps) + + if inopt.all_vids: + # make video of edges for comparison + video_edges_path = os.path.join(inopt.out_dir, 'generated_edges.mp4') + write_video_with_audio(edges_path, tmp_audio_path, video_edges_path, h, w, inopt.fps) + + # make video of render for comparison + video_render_path = os.path.join(inopt.out_dir, 'generated_render.mp4') + write_video_with_audio(render_path, tmp_audio_path, video_render_path, h, w, inopt.fps) + + # make side to side video: render 2 edges + combined_path = os.path.join(inopt.out_dir, 'render2edges.mp4') + cmd = 'ffmpeg -y -i "' + video_render_path + '" -i "' + \ + video_edges_path + '" -filter_complex hstack -codec:v libx264 -crf 0 -preset veryslow "' + combined_path + '"' + subprocess.call(cmd, shell=True) + + # make side to side video: edges 2 video + combined_path = os.path.join(inopt.out_dir, 'edges2video.mp4') + cmd = 'ffmpeg -y -i "' + video_edges_path + '" -i "' + \ + final_video_path + '" -filter_complex hstack -codec:v libx264 -crf 0 -preset veryslow "' + combined_path + '"' + subprocess.call(cmd, shell=True) + + # make side to side video: render 2 edges 2 video + combined_path = os.path.join(inopt.out_dir, 'render2edges2video.mp4') + + cmd = 'ffmpeg -y -i "' + video_render_path + '" -i "' + \ + video_edges_path + '" -i "' + final_video_path + \ + '" -filter_complex "[0:v][1:v][2:v]hstack=inputs=3[v]" -map "[v]" -map 1:a -codec:v libx264 -crf 0 -preset veryslow "' + combined_path + '"' + + subprocess.call(cmd, shell=True) + + if os.path.exists(tmp_audio_path): + os.remove(tmp_audio_path) + + #TODO: move generated video to /videos/audio_to_video.mp4 + if inopt.move_to_one_folder: + parent, name = os.path.split(inopt.out_dir) + shutil.move(final_video_path, os.path.join(parent, 'videos', name + '.mp4')) + + if inopt.clean: + shutil.rmtree(inopt.out_dir) + + # delete subfolder + else: + if inopt.clean: + # clean all subfolders + subfolders = [f.path for f in os.scandir(inopt.out_dir) if f.is_dir()] + for sub in subfolders: + shutil.rmtree(sub) + + # remove headposes + try: + os.remove(os.path.join(inopt.out_dir, 'headposes.npy')) + + except FileNotFoundError: + pass + + + print('Finish!') diff --git a/motion-gan-pipeline/preprocessing/README.md b/motion-gan-pipeline/preprocessing/README.md new file mode 100644 index 0000000..e3f8ecf --- /dev/null +++ b/motion-gan-pipeline/preprocessing/README.md @@ -0,0 +1,158 @@ +# Video Preprocessing + +This is a Python repository for video processing. \ +This collection of techniques can be used to process any video file and extract numerous useful features. The preprocessing is project independent and can be used for any Computer Vision application where video processing is required. + +## Installation + +Use the package manager [pip](https://pip.pypa.io/en/stable/) to install the requirements. + +```bash +pip install -r requirements.txt +``` +## Available Processing Techniques +Each processing step can be accessed independently given its reference numbered: + +0. Deepspeech Features \ +Extract [Deepspeech](https://arxiv.org/abs/1412.5567) features from the original video audio signal. + +1. Frames Extraction \ +Save all frames of the input video. + +2. Landmark Detection \ +Extract [FAN](https://github.com/1adrianb/face-alignment) 2D facial landmarks from all video's frames. + +3. Head Pose Estimation \ +Run [Deca](https://github.com/YadiraF/DECA) tracker on all frames to extract [FLAME](https://flame.is.tue.mpg.de) morphable model parameters. Then fit a Rotation matrix and Translation vector to express face position at each frame. + +4. Audio Expressions \ +Generate DECA facial expressions from Deepspeech audio features. + +5. Background Matting \ +Run [RVM](https://github.com/PeterL1n/RobustVideoMatting) to remove the background from each frame. + +6. Extract Meshes \ +Create FLAME meshes for each frame of the video. + +7. Save Parameters \ +Create JSON file with all transformation parameters. Used for NeRF training. + +8. Speech To Text \ +Create text file with transcript of the audio file. + +9. Body Tracking \ +Use [Mediapipe](https://google.github.io/mediapipe/solutions/pose.html) for body pose tracking. + +10. Emotion Detection \ +Run [EMOCA](https://github.com/radekd91/emoca) for per-frame emotion detection. + +11. Edge Detection \ +Create edge images from FAN facial landmarks and mediapipe body landmarks. Used as input for generative models (GAN). + +12. Noise Reduction \ +Clean the audio file removing noise. + +13. Optical Flow \ +Use [FlowNet](https://github.com/NVIDIA/flownet2-pytorch) to extract optical flow between consecutive frames. + +## Usage + +You can run each processing step by using the following command: +```bash +python preprocessing.py --dataroot $DATAROOT + --name $NAME + --target_fps $TARGET_FPS + --preprocessing_type $TYPE + --step $STEP + --use_deca +``` + +Where: +- ```$DATAROOT```: path to data folder. +- ```$NAME```: name of the video. +- ```$TARGET_FPS```: video's frame per second (suggested: 25). +- ```$TYPE```: preprocessing type: 'audio' or 'video'. +- ```$STEP```: preprocessing step to run. + +The data should be organised in the following structure: + +```bash +Data_folder +├── audio # Folder containing n audio files +│ ├── audio_1 +│ │ └──audio_1.wav +│ ... +│ └── audio_n +│ └──audio_n.wav +└── video # Folder containing m video files + ├── video_1 + │ └──video_1.mp4 + ... + └── video_m + └──video_m.mp4 + +``` +Example: \ +To run step 0 of preprocessing on video_1 you should run the following command: +```bash +python preprocessing.py --dataroot Data_folder/video/ + --name video_1 + --target_fps 25 + --preprocessing_type video + --step 0 + --use_deca +``` + +## Audio-Driven Video Synthesis Usage + +In order to run the necessary preprocessing for the Audio-Driven Video Synthesis project, the following commands are used: + +Preprocessing for the input video: +```bash +bash process_video.sh $INPUTDATAPATH/video $NAME_VIDEO $FPS $SAMPLERATE +``` +Preprocessing for the input audio: +```bash +bash process_audio.sh $INPUTDATAPATH/audio $NAME_AUDIO $FPS $SAMPLERATE +``` + +Where: +- ```$INPUTDATAPATH```: path to input data folder. +- ```$NAME_VIDEO```: name of the video. +- ```$NAME_AUDIO```: name of the audio. +- ```$FPS```: video's frame per second (suggested: 25). +- ```$SAMPLERATE```: audio's sample rate. + +This call in automatically executed in the complete pipeline script. + + +## Output Folder Structure +By running the previous commands, the following data structure is produced. + +```bash +Video_Name +├── audio_expr # Contains Audio Expressions: .npy +├── audio_feature # Contains Deepspeech Features: .npy +├── body_pose # Contains Mediapipe Landmarks: .npy +├── cropped # Contained cropped frames for training: .png +├── debug # Debug folder +│ ├── debug_mixed # Overlay images, original + FLAME render: .jpg +│ ├── debug_render # FLAME render: .jpg +│ ├── opticalflow # Optical flow visualisation: .png +│ ├── proj_landmarks # 2D projected FAN landmarks: .lms +│ └── proj_landmarks_img # Visualization of projected landmarks: .png +├── deca_expr # DECA expressions: .npy +├── edges # Edge Images: .png +├── expr_masks # DECA mask: .jpg +├── frames # Extracted frames: .jpg +├── landmarks # FAN landmarks: .lms +├── matting # Frames without background: .png +├── opticalflow # Optical flows: .flo +├── Video_Name.mp4 # Original video file +├── Video_Name.wav # Extracted audio file +├── mapping.npy # Learned mapping deepspeech -> FLAME Expressions +└── track_params.pt # Fitted Pose information +``` + +## License +[MIT](https://choosealicense.com/licenses/mit/) \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/__init__.py b/motion-gan-pipeline/preprocessing/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/motion-gan-pipeline/preprocessing/autils/__init__.py b/motion-gan-pipeline/preprocessing/autils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/motion-gan-pipeline/preprocessing/autils/camera_pose_visualizer.py b/motion-gan-pipeline/preprocessing/autils/camera_pose_visualizer.py new file mode 100644 index 0000000..e796233 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/autils/camera_pose_visualizer.py @@ -0,0 +1,55 @@ +import numpy as np +import matplotlib as mpl +import matplotlib.pyplot as plt +from matplotlib.patches import Patch +from mpl_toolkits.mplot3d.art3d import Poly3DCollection + + +class CameraPoseVisualizer: + def __init__(self, xlim, ylim, zlim): + self.fig = plt.figure(figsize=(18, 7)) + self.ax = self.fig.gca(projection='3d') + self.ax.set_aspect("auto") + self.ax.set_xlim(xlim) + self.ax.set_ylim(ylim) + self.ax.set_zlim(zlim) + self.ax.set_xlabel('x') + self.ax.set_ylabel('y') + self.ax.set_zlabel('z') + print('initialize camera pose visualizer') + + def extrinsic2pyramid(self, extrinsic, color='r', focal_len_scaled=5, aspect_ratio=0.3): + vertex_std = np.array([[0, 0, 0, 1], + [focal_len_scaled * aspect_ratio, -focal_len_scaled * aspect_ratio, focal_len_scaled, 1], + [focal_len_scaled * aspect_ratio, focal_len_scaled * aspect_ratio, focal_len_scaled, 1], + [-focal_len_scaled * aspect_ratio, focal_len_scaled * aspect_ratio, focal_len_scaled, 1], + [-focal_len_scaled * aspect_ratio, -focal_len_scaled * aspect_ratio, focal_len_scaled, 1]]) + vertex_transformed = vertex_std @ extrinsic.T + meshes = [[vertex_transformed[0, :-1], vertex_transformed[1][:-1], vertex_transformed[2, :-1]], + [vertex_transformed[0, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1]], + [vertex_transformed[0, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]], + [vertex_transformed[0, :-1], vertex_transformed[4, :-1], vertex_transformed[1, :-1]], + [vertex_transformed[1, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]]] + self.ax.add_collection3d( + Poly3DCollection(meshes, facecolors=color, linewidths=0.3, edgecolors=color, alpha=0.35)) + + def customize_legend(self, list_label): + list_handle = [] + for idx, label in enumerate(list_label): + color = plt.cm.rainbow(idx / len(list_label)) + patch = Patch(color=color, label=label) + list_handle.append(patch) + plt.legend(loc='right', bbox_to_anchor=(1.8, 0.5), handles=list_handle) + + def colorbar(self, max_frame_length): + cmap = mpl.cm.rainbow + norm = mpl.colors.Normalize(vmin=0, vmax=max_frame_length) + self.fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), orientation='vertical', label='Frame Number') + + def show(self): + plt.title('Extrinsic Parameters') + plt.show() + + def save_img(self, name): + plt.title(f'Extrinsic Parameters {name}') + plt.savefig(fname=name) diff --git a/motion-gan-pipeline/preprocessing/autils/cfgnode.py b/motion-gan-pipeline/preprocessing/autils/cfgnode.py new file mode 100644 index 0000000..8895187 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/autils/cfgnode.py @@ -0,0 +1,507 @@ +""" +Define a class to hold configurations. + +Borrows and merges stuff from YACS, fvcore, and detectron2 +https://github.com/rbgirshick/yacs +https://github.com/facebookresearch/fvcore/ +https://github.com/facebookresearch/detectron2/ + +""" + +import copy +import importlib.util +import io +import logging +import os +from ast import literal_eval +from typing import Optional + +import yaml + +# File exts for yaml +_YAML_EXTS = {"", ".yml", ".yaml"} +# File exts for python +_PY_EXTS = {".py"} + +# CfgNodes can only contain a limited set of valid types +_VALID_TYPES = {tuple, list, str, int, float, bool} + +# Valid file object types +_FILE_TYPES = (io.IOBase,) + +# Logger +logger = logging.getLogger(__name__) + + +class CfgNode(dict): + r"""CfgNode is a `node` in the configuration `tree`. It's a simple wrapper around a `dict` and supports access to + `attributes` via `keys`. + """ + + IMMUTABLE = "__immutable__" + DEPRECATED_KEYS = "__deprecated_keys__" + RENAMED_KEYS = "__renamed_keys__" + NEW_ALLOWED = "__new_allowed__" + + def __init__( + self, + init_dict: Optional[dict] = None, + key_list: Optional[list] = None, + new_allowed: Optional[bool] = False, + ): + r""" + Args: + init_dict (dict): A dictionary to initialize the `CfgNode`. + key_list (list[str]): A list of names that index this `CfgNode` from the root. Currently, only used for + logging. + new_allowed (bool): Whether adding a new key is allowed when merging with other `CfgNode` objects. + + """ + + # Recursively convert nested dictionaries in `init_dict` to config tree. + init_dict = {} if init_dict is None else init_dict + key_list = [] if key_list is None else key_list + init_dict = self._create_config_tree_from_dict(init_dict, key_list) + super(CfgNode, self).__init__(init_dict) + + # Control the immutability of the `CfgNode`. + self.__dict__[CfgNode.IMMUTABLE] = False + # Support for deprecated options. + # If you choose to remove support for an option in code, but don't want to change all of the config files + # (to allow for deprecated config files to run), you can add the full config key as a string to this set. + self.__dict__[CfgNode.DEPRECATED_KEYS] = set() + # Support for renamed options. + # If you rename an option, record the mapping from the old name to the new name in this dictionary. Optionally, + # if the type also changed, you can make this value a tuple that specifies two things: the renamed key, and the + # instructions to edit the config file. + self.__dict__[CfgNode.RENAMED_KEYS] = { + # 'EXAMPLE.OLD.KEY': 'EXAMPLE.NEW.KEY', # Dummy example + # 'EXAMPLE.OLD.KEY': ( # A more complex example + # 'EXAMPLE.NEW.KEY', + # "Also convert to a tuple, eg. 'foo' -> ('foo', ) or " + # + "'foo.bar' -> ('foo', 'bar')" + # ), + } + + # Allow new attributes after initialization. + self.__dict__[CfgNode.NEW_ALLOWED] = new_allowed + + @classmethod + def _create_config_tree_from_dict(cls, init_dict: dict, key_list: list): + r"""Create a configuration tree using the input dict. Any dict-like objects inside `init_dict` will be treated + as new `CfgNode` objects. + + Args: + init_dict (dict): Input dictionary, to create config tree from. + key_list (list): A list of names that index this `CfgNode` from the root. Currently only used for logging. + + """ + + d = copy.deepcopy(init_dict) + for k, v in d.items(): + if isinstance(v, dict): + # Convert dictionary to CfgNode + d[k] = cls(v, key_list=key_list + [k]) + else: + # Check for valid leaf type or nested CfgNode + _assert_with_logging( + _valid_type(v, allow_cfg_node=False), + "Key {} with value {} is not a valid type; valid types: {}".format( + ".".join(key_list + [k]), type(v), _VALID_TYPES + ), + ) + return d + + def __getattr__(self, name: str): + if name in self: + return self[name] + else: + raise AttributeError(name) + + def __setattr__(self, name: str, value): + if self.is_frozen(): + raise AttributeError( + "Attempted to set {} to {}, but CfgNode is immutable".format( + name, value + ) + ) + + _assert_with_logging( + name not in self.__dict__, + "Invalid attempt to modify internal CfgNode state: {}".format(name), + ) + + _assert_with_logging( + _valid_type(value, allow_cfg_node=True), + "Invalid type {} for key {}; valid types = {}".format( + type(value), name, _VALID_TYPES + ), + ) + + self[name] = value + + def __str__(self): + def _indent(s_, num_spaces): + s = s_.split("\n") + if len(s) == 1: + return s_ + first = s.pop(0) + s = [(num_spaces * " ") + line for line in s] + s = "\n".join(s) + s = first + "\n" + s + return s + + r = "" + s = [] + for k, v in sorted(self.items()): + separator = "\n" if isinstance(v, CfgNode) else " " + attr_str = "{}:{}{}".format(str(k), separator, str(v)) + attr_str = _indent(attr_str, 2) + s.append(attr_str) + r += "\n".join(s) + return r + + def __repr__(self): + return "{}({})".format(self.__class__.__name__, super(CfgNode, self).__repr__()) + + def dump(self, **kwargs): + r"""Dump CfgNode to a string. + """ + + def _convert_to_dict(cfg_node, key_list): + if not isinstance(cfg_node, CfgNode): + _assert_with_logging( + _valid_type(cfg_node), + "Key {} with value {} is not a valid type; valid types: {}".format( + ".".join(key_list), type(cfg_node), _VALID_TYPES + ), + ) + return cfg_node + else: + cfg_dict = dict(cfg_node) + for k, v in cfg_dict.items(): + cfg_dict[k] = _convert_to_dict(v, key_list + [k]) + return cfg_dict + + self_as_dict = _convert_to_dict(self, []) + return yaml.safe_dump(self_as_dict, **kwargs) + + def merge_from_file(self, cfg_filename: str): + r"""Load a yaml config file and merge it with this CfgNode. + + Args: + cfg_filename (str): Config file path. + + """ + with open(cfg_filename, "r") as f: + cfg = self.load_cfg(f) + self.merge_from_other_cfg(cfg) + + def merge_from_other_cfg(self, cfg_other): + r"""Merge `cfg_other` into the current `CfgNode`. + + Args: + cfg_other + """ + _merge_a_into_b(cfg_other, self, self, []) + + def merge_from_list(self, cfg_list: list): + r"""Merge config (keys, values) in a list (eg. from commandline) into this `CfgNode`. + + Eg. `cfg_list = ['FOO.BAR', 0.5]`. + """ + _assert_with_logging( + len(cfg_list) % 2 == 0, + "Override list has odd lengths: {}; it must be a list of pairs".format( + cfg_list + ), + ) + root = self + for full_key, v in zip(cfg_list[0::2], cfg_list[1::2]): + if root.key_is_deprecated(full_key): + continue + if root.key_is_renamed(full_key): + root.raise_key_rename_error(full_key) + key_list = full_key.split(".") + d = self + for subkey in key_list[:-1]: + _assert_with_logging( + subkey in d, "Non-existent key: {}".format(full_key) + ) + d = d[subkey] + subkey = key_list[-1] + _assert_with_logging(subkey in d, "Non-existent key: {}".format(full_key)) + value = self._decode_cfg_value(v) + value = _check_and_coerce_cfg_value_type(value, d[subkey], subkey, full_key) + d[subkey] = value + + def freeze(self): + r"""Make this `CfgNode` and all of its children immutable. """ + self._immutable(True) + + def defrost(self): + r"""Make this `CfgNode` and all of its children mutable. """ + self._immutable(False) + + def is_frozen(self): + r"""Return mutability. """ + return self.__dict__[CfgNode.IMMUTABLE] + + def _immutable(self, is_immutable: bool): + r"""Set mutability and recursively apply to all nested `CfgNode` objects. + + Args: + is_immutable (bool): Whether or not the `CfgNode` and its children are immutable. + + """ + self.__dict__[CfgNode.IMMUTABLE] = is_immutable + # Recursively propagate state to all children. + for v in self.__dict__.values(): + if isinstance(v, CfgNode): + v._immutable(is_immutable) + for v in self.values(): + if isinstance(v, CfgNode): + v._immutable(is_immutable) + + def clone(self): + r"""Recursively copy this `CfgNode`. """ + return copy.deepcopy(self) + + def register_deprecated_key(self, key: str): + r"""Register key (eg. `FOO.BAR`) a deprecated option. When merging deprecated keys, a warning is generated and + the key is ignored. + """ + + _assert_with_logging( + key not in self.__dict__[CfgNode.DEPRECATED_KEYS], + "key {} is already registered as a deprecated key".format(key), + ) + self.__dict__[CfgNode.DEPRECATED_KEYS].add(key) + + def register_renamed_key( + self, old_name: str, new_name: str, message: Optional[str] = None + ): + r"""Register a key as having been renamed from `old_name` to `new_name`. When merging a renamed key, an + exception is thrown alerting the user to the fact that the key has been renamed. + """ + + _assert_with_logging( + old_name not in self.__dict__[CfgNode.RENAMED_KEYS], + "key {} is already registered as a renamed cfg key".format(old_name), + ) + value = new_name + if message: + value = (new_name, message) + self.__dict__[CfgNode.RENAMED_KEYS][old_name] = value + + def key_is_deprecated(self, full_key: str): + r"""Test if a key is deprecated. """ + if full_key in self.__dict__[CfgNode.DEPRECATED_KEYS]: + logger.warning("deprecated config key (ignoring): {}".format(full_key)) + return True + return False + + def key_is_renamed(self, full_key: str): + r"""Test if a key is renamed. """ + return full_key in self.__dict__[CfgNode.RENAMED_KEYS] + + def raise_key_rename_error(self, full_key: str): + new_key = self.__dict__[CfgNode.RENAMED_KEYS][full_key] + if isinstance(new_key, tuple): + msg = " Note: " + new_key[1] + new_key = new_key[0] + else: + msg = "" + raise KeyError( + "Key {} was renamed to {}; please update your config.{}".format( + full_key, new_key, msg + ) + ) + + def is_new_allowed(self): + return self.__dict__[CfgNode.NEW_ALLOWED] + + @classmethod + def load_cfg(cls, cfg_file_obj_or_str): + r"""Load a configuration into the `CfgNode`. + + Args: + cfg_file_obj_or_str (str or cfg compatible object): Supports loading from: + - A file object backed by a YAML file. + - A file object backed by a Python source file that exports an sttribute "cfg" (dict or `CfgNode`). + - A string that can be parsed as valid YAML. + + """ + _assert_with_logging( + isinstance(cfg_file_obj_or_str, _FILE_TYPES + (str,)), + "Expected first argument to be of type {} or {}, but got {}".format( + _FILE_TYPES, str, type(cfg_file_obj_or_str) + ), + ) + if isinstance(cfg_file_obj_or_str, str): + return cls._load_cfg_from_yaml_str(cfg_file_obj_or_str) + elif isinstance(cfg_file_obj_or_str, _FILE_TYPES): + return cls._load_cfg_from_file(cfg_file_obj_or_str) + else: + raise NotImplementedError("Impossible to reach here (unless there's a bug)") + + @classmethod + def _load_cfg_from_file(cls, file_obj): + r"""Load a config from a YAML file or a Python source file. """ + _, file_ext = os.path.splitext(file_obj.name) + if file_ext in _YAML_EXTS: + return cls._load_cfg_from_yaml_str(file_obj.read()) + elif file_ext in _PY_EXTS: + return cls._load_cfg_py_source(file_obj.name) + else: + raise Exception( + "Attempt to load from an unsupported filetype {}; only {} supported".format( + _YAML_EXTS.union(_PY_EXTS) + ) + ) + + @classmethod + def _load_cfg_from_yaml_str(cls, str_obj): + r"""Load a config from a YAML string encoding. """ + cfg_as_dict = yaml.safe_load(str_obj) + return cls(cfg_as_dict) + + @classmethod + def _load_cfg_py_source(cls, filename): + r"""Load a config from a Python source file. """ + module = _load_module_from_file("yacs.config.override", filename) + _assert_with_logging( + hasattr(module, "cfg"), + "Python module from file {} must export a 'cfg' attribute".format(filename), + ) + VALID_ATTR_TYPES = {dict, CfgNode} + _assert_with_logging( + type(module.cfg) in VALID_ATTR_TYPES, + "Import module 'cfg' attribute must be in {} but is {}".format( + VALID_ATTR_TYPES, type(module.cfg) + ), + ) + return cls(module.cfg) + + @classmethod + def _decode_cfg_value(cls, value): + r"""Decodes a raw config value (eg. from a yaml config file or commandline argument) into a Python object. + + If `value` is a dict, it will be interpreted as a new `CfgNode`. + If `value` is a str, it will be evaluated as a literal. + Otherwise, it is returned as is. + + """ + # Configs parsed from raw yaml will contain dictionary keys that need to be converted to `CfgNode` objects. + if isinstance(value, dict): + return cls(value) + # All remaining processing is only applied to strings. + if not isinstance(value, str): + return value + # Try to interpret `value` as a: string, number, tuple, list, dict, bool, or None + try: + value = literal_eval(value) + # The following two excepts allow `value` to pass through it when it represents a string. + # The type of `value` is always a string (before calling `literal_eval`), but sometimes it *represents* a + # string and other times a data structure, like a list. In the case that `value` represents a str, what we + # got back from the yaml parser is `foo` *without quotes* (so, not `"foo"`). `literal_eval` is ok with `"foo"`, + # but will raise a `ValueError` if given `foo`. In other cases, like paths (`val = 'foo/bar'`) `literal_eval` + # will raise a `SyntaxError`. + except ValueError: + pass + except SyntaxError: + pass + return value + + +# Keep this function in global scope, for backward compataibility. +load_cfg = CfgNode.load_cfg + + +def _valid_type(value, allow_cfg_node: Optional[bool] = False): + return (type(value) in _VALID_TYPES) or ( + allow_cfg_node and isinstance(value, CfgNode) + ) + + +def _merge_a_into_b(a: CfgNode, b: CfgNode, root: CfgNode, key_list: list): + r"""Merge `CfgNode` `a` into `CfgNode` `b`, clobbering the options in `b` wherever they are also specified in `a`. + """ + _assert_with_logging( + isinstance(a, CfgNode), + "`a` (cur type {}) must be an instance of {}".format(type(a), CfgNode), + ) + _assert_with_logging( + isinstance(b, CfgNode), + "`b` (cur type {}) must be an instance of {}".format(type(b), CfgNode), + ) + + for k, v_ in a.items(): + full_key = ".".join(key_list + [k]) + v = copy.deepcopy(v_) + v = b._decode_cfg_value(v) + + if k in b: + v = _check_and_coerce_cfg_value_type(v, b[k], k, full_key) + # Recursively merge dicts. + if isinstance(v, CfgNode): + try: + _merge_a_into_b(v, b[k], root, key_list + [k]) + except BaseException: + raise + else: + b[k] = v + elif b.is_new_allowed(): + b[k] = v + else: + if root.key_is_deprecated(full_key): + continue + elif root.key_is_renamed(full_key): + root.raise_key_rename_error(full_key) + else: + raise KeyError("Non-existent config key: {}".format(full_key)) + + +def _check_and_coerce_cfg_value_type(replacement, original, key, full_key): + r"""Checks that `replacement`, which is intended to replace `original` is of the right type. The type is correct if + it matches exactly or is one of a few cases in which the type can easily be coerced. + """ + + original_type = type(original) + replacement_type = type(replacement) + if replacement_type == original_type: + return replacement + + # If replacement and original types match, cast replacement from `from_type` to `to_type`. + def _conditional_cast(from_type, to_type): + if replacement_type == from_type and original_type == to_type: + return True, to_type(replacement) + else: + return False, None + + # Conditional casts. + # list <-> tuple + casts = [(tuple, list), (list, tuple)] + for (from_type, to_type) in casts: + converted, converted_value = _conditional_cast(from_type, to_type) + if converted: + return converted_value + + raise ValueError( + "Type mismatch ({} vs. {} with values ({} vs. {}) for config key: {}".format( + original_type, replacement_type, original, replacement, full_key + ) + ) + + +def _assert_with_logging(cond, msg): + if not cond: + logger.debug(msg) + assert cond, msg + + +def _load_module_from_file(name, filename): + spec = importlib.util.spec_from_file_location(name, filename) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module diff --git a/motion-gan-pipeline/preprocessing/autils/ops3D.py b/motion-gan-pipeline/preprocessing/autils/ops3D.py new file mode 100644 index 0000000..24cbbf1 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/autils/ops3D.py @@ -0,0 +1,147 @@ +import numpy as np +import open3d as o3d + +import argparse +import os +import numpy as np +import torch +from importlib import import_module +from plyfile import PlyData, PlyElement +from skimage import measure +from skimage.draw import ellipsoid + + +def export_obj(vertices, triangles, diffuse, normals, filename): + """ + Exports a mesh in the (.obj) format. + """ + print('Writing to obj...') + + with open(filename, "w") as fh: + + for index, v in enumerate(vertices): + fh.write("v {} {} {}".format(*v)) + if len(diffuse) > index: + fh.write(" {} {} {}".format(*diffuse[index])) + + fh.write("\n") + + for n in normals: + fh.write("vn {} {} {}\n".format(*n)) + + for f in triangles: + fh.write("f") + for index in f: + fh.write(" {}//{}".format(index + 1, index + 1)) + + fh.write("\n") + + print(f"Finished writing to {filename} with {len(vertices)} vertices") + + +def export_ply(vertices, diffuse, normals, filename): + names = 'x, y, z, nx, ny, nz, red, green, blue' + formats = 'f4, f4, f4, f4, f4, f4, u1, u1, u1' + arr = np.concatenate((vertices, normals, diffuse * 255), axis = -1) + vertices_s = np.core.records.fromarrays(arr.transpose(), names=names, formats = formats) + + # Recreate the PlyElement instance + v = PlyElement.describe(vertices_s, 'vertex') + + # Create the PlyData instance + p = PlyData([ v ], text = True) + + p.write(filename) + + +def export_point_cloud(it, ray_origins, ray_directions, depth_fine, dep_target): + vertices_output = (ray_origins + ray_directions * depth_fine[..., None]).view(-1, 3) + vertices_target = (ray_origins + ray_directions * dep_target[..., None]).view(-1, 3) + vertices = torch.cat((vertices_output, vertices_target), dim = 0) + diffuse_output = torch.zeros_like(vertices_output) + diffuse_output[:, 0] = 1.0 + diffuse_target = torch.zeros_like(vertices_target) + diffuse_target[:, 2] = 1.0 + diffuse = torch.cat((diffuse_output, diffuse_target), dim = 0) + normals = torch.cat((-ray_directions.view(-1, 3), -ray_directions.view(-1, 3)), dim = 0) + export_obj(vertices, [], diffuse, normals, f"{it:04d}.obj") + + +def create_point_cloud(ray_origins, ray_directions, depth, color, mask = None): + + if mask is not None: + ray_directions, depth = ray_directions[mask], depth[mask] + + vertices = (ray_origins + ray_directions * depth[..., None]).view(-1, 3) + diffuse = color.expand(vertices.shape) + normals = -ray_directions.view(-1, 3) + + return vertices, diffuse, normals + + +def extract_iso_level(density, iso_level): + # Density boundaries + min_a, max_a, std_a = density.min(), density.max(), density.std() + + # Adaptive iso level + iso_value = min(max(iso_level, min_a + std_a), max_a - std_a) + print(f"Min density {min_a}, Max density: {max_a}, Mean density {density.mean()}") + print(f"Querying based on iso level: {iso_value}") + + return iso_value + + +def extract_geometry(radiance, pts, iso_level, limit, res): + + # nums = (res,) * 3 + # radiance = radiance.view(*nums, 4).contiguous().numpy() + + radiance = torch.flatten(radiance, end_dim=1) + pts = torch.flatten(pts, end_dim=1) + print(radiance.size()) + print(pts.size()) + + # Density grid + density = radiance[..., 3] + + # Adaptive iso level + iso_value = extract_iso_level(density, iso_level) + + # Extracting iso-surface triangulated + # Generate a level set about zero of two identical ellipsoids in 3D + ellip_base = ellipsoid(6, 10, 16, levelset=True) + ellip_double = np.concatenate((ellip_base[:-1, ...], + ellip_base[2:, ...]), axis=0) + + print(ellip_double.shape) + results = measure.marching_cubes(density, iso_value) + + # Use contiguous tensors + vertices, triangles, normals, _ = [torch.from_numpy(np.ascontiguousarray(result)) for result in results] + + # Use contiguous tensors + normals = torch.from_numpy(np.ascontiguousarray(normals)) + vertices = torch.from_numpy(np.ascontiguousarray(vertices)) + triangles = torch.from_numpy(np.ascontiguousarray(triangles)) + + # Normalize vertices, to the (-limit, limit) + vertices = limit * (vertices / (res / 2.) - 1.) + + return vertices, triangles, normals, density + + +def export_marching_cubes(radiance, pts, iso_level, limit, res, path): + + mesh_path = os.path.join(path + 'mesh.obj') + point_cloud_path = os.path.join(path + 'pointcloud.ply') + + vertices, triangles, normals, density = extract_geometry(radiance, pts, iso_level, limit, res) + diffuse = radiance[..., :3] + + # Export obj + export_obj(vertices, triangles, diffuse, normals, mesh_path) + + # Export ply + export_ply(vertices, diffuse, normals, point_cloud_path) + + diff --git a/motion-gan-pipeline/preprocessing/autils/options.py b/motion-gan-pipeline/preprocessing/autils/options.py new file mode 100644 index 0000000..e6d8b22 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/autils/options.py @@ -0,0 +1,74 @@ +import argparse + + +class Options(): + def __init__(self): + self.initialized = False + + def initialize(self, parser): + self.initialized = True + return parser + + def gather_options(self): + # initialize parser with basic options + if not self.initialized: + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser = self.initialize(parser) + + # get the basic options + opt, _ = parser.parse_known_args() + + self.parser = parser + + return parser.parse_args() + + def print_options(self, opt): + message = '' + message += '----------------- Options ---------------\n' + for k, v in sorted(vars(opt).items()): + comment = '' + default = self.parser.get_default(k) + if v != default: + comment = '\t[default: %s]' % str(default) + message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment) + message += '----------------- End -------------------' + print(message) + + def parse(self): + + opt = self.gather_options() + + self.print_options(opt) + + self.opt = opt + return self.opt + + +class PreprocessingOptions(Options): + + def initialize(self, parser): + + parser = Options.initialize(self, parser) + parser.add_argument('--dataroot', required=True, default='/home/alberto/data/nerf-videosynth/', + help='path to data folder') + + parser.add_argument('--name', required=True, help='name of video/audio to process') + + parser.add_argument('--target_fps', type=int, default=25, help='Fps of target video') + + parser.add_argument('--preprocessing_type', type=str, default='video', help='Preprocessing for video or audio') + + parser.add_argument('--audioexpr', action='store_true', help='Extracts NVP audio-expressions') + + parser.add_argument('--step', type=str, default='0', help='Pre-processing step to take') + + parser.add_argument('--use_DECA', action='store_true', help='If true, use deca tracker for face model') + parser.add_argument('--use_FLAME', action='store_true', help='If true, use flame tracker for face model') + parser.add_argument('--use_BASEL', action='store_true', help='If true, use basel tracker for face model') + + parser.add_argument('--train_split', type=float, default=0.9, help='Percentage of data used for training') + parser.add_argument('--val_split', type=float, default=0.01, help='Percentage of data used for validation') + + return parser + diff --git a/motion-gan-pipeline/preprocessing/autils/rename_files.py b/motion-gan-pipeline/preprocessing/autils/rename_files.py new file mode 100644 index 0000000..cf748af --- /dev/null +++ b/motion-gan-pipeline/preprocessing/autils/rename_files.py @@ -0,0 +1,20 @@ +import os +from argparse import ArgumentParser +from tqdm import tqdm + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument("--folder", type=str, help='Path to folder to rename.') + + args = parser.parse_args() + + path = args.folder + file_list = os.listdir(path) + num_files = len(file_list) + print(f'Total number of files: {num_files}') + + for i, file_name in enumerate(tqdm(file_list)): + name, ext = os.path.splitext(file_name) + new_name = os.path.join(path, name, file_name) + os.makedirs(os.path.join(path, name), exist_ok=True) + os.rename(os.path.join(path, file_name), new_name) diff --git a/motion-gan-pipeline/preprocessing/autils/scheduler.py b/motion-gan-pipeline/preprocessing/autils/scheduler.py new file mode 100644 index 0000000..d2fe693 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/autils/scheduler.py @@ -0,0 +1,178 @@ +import numpy as np +import torchvision +import os +from torch.utils.tensorboard import SummaryWriter +import torch +from datetime import datetime +import imageio +from subprocess import call +import trimesh +import open3d as o3d +from .ops3D import export_marching_cubes + + +class Scheduler: + + def __init__(self, opt): + self.opt = opt + self.total_step = 0 + self.val_step = 0 + self.epoch = 0 + + def initialize(self, mode='train', num_images=None, num_images_val=None, existing_dir=None): + self.mode = mode + + if existing_dir is not None: + self.base_dir = os.path.join(existing_dir, 'test_results') + + else: + date_time = datetime.now().strftime("%m%d%Y_%H%M%S") + self.base_dir = os.path.join(self.opt.experiment.outdir, date_time) + + if self.mode == 'train': + # Setup logging and checkpoints. + self.num_images = num_images + self.num_images_val = num_images_val + self.logdir = os.path.join(self.base_dir, 'logs') + self.checkpointdir = os.path.join(self.base_dir, 'checkpoints') + os.makedirs(self.logdir, exist_ok=True) + os.makedirs(self.checkpointdir, exist_ok=True) + self.writer = SummaryWriter(self.logdir) + + # Write out config parameters. + with open(os.path.join(self.base_dir, "config.yml"), "w") as f: + f.write(self.opt.dump()) # cfg, f, default_flow_style=False) + + elif self.mode == 'test': + # Setup images directory. + self.savedir = os.path.join(self.base_dir, 'images') + os.makedirs(self.savedir, exist_ok=True) + if self.opt.experiment.save_all_images: + self.disparitydir = os.path.join(self.base_dir, "disparity") + os.makedirs(self.disparitydir, exist_ok=True) + self.depthdir = os.path.join(self.base_dir, "depth") + os.makedirs(self.depthdir, exist_ok=True) + self.targetdir = os.path.join(self.base_dir, "target") + os.makedirs(self.targetdir, exist_ok=True) + + def cast_to_image(self, img): + # Input tensor is (H, W, 3). Convert to (3, H, W). + img = img.permute(2, 0, 1) + + # Conver to PIL Image and then np.array (output shape: (H, W, 3)) + img = np.array(torchvision.transforms.ToPILImage()(img.detach().cpu())) + # # Map back to shape (3, H, W), as tensorboard needs channels first. + # img = np.moveaxis(img, [-1], [0]) + return img + + def cast_one_channel_image(self, tensor): + img = (tensor - tensor.min()) / (tensor.max() - tensor.min()) + img = img.clamp(0, 1) * 255 + return img.detach().cpu().numpy().astype(np.uint8) + + def write_losses(self, phase, losses, metrics): + # if self.total_step % self.opt.scheduler.print_every == 0 or \ + # self.total_step == (self.opt.scheduler.epochs * self.num_images) - 1 or \ + # phase == 'validation': + # + # if phase == 'validation': + # print(f"\n{phase.upper()} ==> Iter: {self.val_step}/{self.num_images_val}" + + # '\n\nLosses - ' + ''.join(f'{i}: {losses[i].item()}, ' for i in losses.keys())) + # + # else: + # print(f"\n{phase.upper()} ==> Iter: {self.current_step}/{self.num_images}" + + # '\nLosses - ' + ''.join(f'{i}: {losses[i].item()}, ' for i in losses.keys())) + # + # print('\nMetrics - ' + ''.join(f'{i}: {metrics[i]}, ' for i in metrics.keys())) + + for loss in losses.keys(): + self.writer.add_scalar(f"{phase}/losses/{loss}", losses[loss].item(), self.total_step) + + for metric in metrics.keys(): + self.writer.add_scalar(f"{phase}/metrics/{metric}", metrics[metric], self.total_step) + + def write_images(self, phase, images): + for nerf in images.keys(): + for image in images[nerf].keys(): + img = images[nerf][image].detach().cpu() + self.writer.add_image(f'{phase}/{nerf}/{image}', img, self.epoch, dataformats='HWC') + + def save_images(self, rgb, disp, depth, target, image_id): + savefile = os.path.join(self.savedir, f"{image_id:04d}.png") + rgb_image = self.cast_to_image(rgb[..., :3]) + imageio.imwrite(savefile, rgb_image) + + if self.opt.experiment.save_all_images: + # save disp + savefile = os.path.join(self.disparitydir, f"{image_id:04d}.png") + disp_image = self.cast_one_channel_image(disp) + imageio.imwrite(savefile, disp_image) + # save depth + savefile = os.path.join(self.depthdir, f"{image_id:04d}.png") + depth_image = self.cast_one_channel_image(depth) + imageio.imwrite(savefile, depth_image) + # save target + savefile = os.path.join(self.targetdir, f"{image_id:04d}.png") + target_image = self.cast_to_image(target) + imageio.imwrite(savefile, target_image) + + def save_video(self, audio_path=None): + target_fps = 25 + if audio_path is None: + cmd = f"ffmpeg -framerate {target_fps} -i {self.savedir + '/%04d.png'} -vcodec h264 -ac 2 -channel_layout stereo -pix_fmt yuv420p video.mp4".split() + else: + cmd = f"ffmpeg -framerate {target_fps} -i {self.savedir + '/%04d.png'} -i {audio_path} -vcodec h264 -ac 2 -channel_layout stereo -pix_fmt yuv420p video.mp4".split() + + call(cmd) + + def check_validation(self): + if self.epoch % self.opt.scheduler.validate_every == 0 or\ + self.epoch == self.opt.scheduler.epochs - 1: + print("[VAL] =======> Epoch: " + str(self.epoch)) + return True + + else: + return False + + def check_save(self): + if self.epoch % self.opt.scheduler.save_every == 0 or \ + self.epoch == self.opt.scheduler.epochs: + return True + + else: + return False + + def save_3d_scene(self, vertices, triangles): + + print('Saving mesh ..') + mesh = trimesh.Trimesh(vertices, triangles) + trimesh.exchange.export.export_mesh(mesh, os.path.join(self.base_dir, 'mesh.obj')) + + def save_pointcloud(self, vertices): + + print('Saving pointcloud ..') + mesh = trimesh.Trimesh(vertices) + trimesh.exchange.export.export_mesh(mesh, os.path.join(self.base_dir, 'pointcloud.ply')) + + def from_pointcloud_to_mesh(self): + print('Saving mesh from pointcloud ..') + + pcd = o3d.io.read_point_cloud(os.path.join(self.base_dir, 'pointcloud.ply')) + pcd.estimate_normals() + + # estimate radius for rolling ball + distances = pcd.compute_nearest_neighbor_distance() + avg_dist = np.mean(distances) + radius = 4.0 * avg_dist + + mesh = o3d.geometry.TriangleMesh.create_from_point_cloud_ball_pivoting( + pcd, + o3d.utility.DoubleVector([radius, radius * 2])) + + # create the triangular mesh with the vertices and faces from open3d + tri_mesh = trimesh.Trimesh(np.asarray(mesh.vertices), np.asarray(mesh.triangles), + vertex_normals=np.asarray(mesh.vertex_normals)) + + trimesh.convex.is_convex(tri_mesh) + trimesh.exchange.export.export_mesh(tri_mesh, os.path.join(self.base_dir, 'mesh.ply')) + diff --git a/motion-gan-pipeline/preprocessing/combine_edges.py b/motion-gan-pipeline/preprocessing/combine_edges.py new file mode 100644 index 0000000..ed313ec --- /dev/null +++ b/motion-gan-pipeline/preprocessing/combine_edges.py @@ -0,0 +1,132 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from ast import Num +from genericpath import isfile +import os +from tqdm import tqdm +import numpy as np +import torch +import torch.nn as nn +from torchvision import transforms +from scipy.spatial import KDTree +import matplotlib +matplotlib.use('agg') +import matplotlib.pyplot as plt + +from PIL import Image +import argparse +from edge_creation.utils import get_edge_predicted, get_edge_image_mixed, get_crop_coords, convert_to_rgb +from scipy.ndimage import gaussian_filter1d + +def combine_edges(inopt): + # load generated headposes + generated_hp_path = os.path.join(inopt.out_dir, 'headposes.npy') + generated_hp = np.load(generated_hp_path) + + # load tracked headposes from target video + track_params_path = os.path.join(inopt.dataset_base, 'track_params.pt') + tracked_params = torch.load(track_params_path) + tracked_R = tracked_params['euler'].numpy() + tracked_t = tracked_params['trans'].numpy() + tracked_hp = np.concatenate((tracked_R, tracked_t), axis=1) + + # Compute crop size + img = Image.open(os.path.join(inopt.dataset_base, 'matting', '%05d.png' % 0)) + img = convert_to_rgb(img) + + ldk_paths = os.listdir(os.path.join(inopt.out_dir, 'landmarks')) + points = np.mean([np.loadtxt(os.path.join(inopt.out_dir, 'landmarks', ldk))[:,:2] for ldk in ldk_paths], axis=0) + crop_coords = get_crop_coords(points, img.size) + + for frame_num in tqdm(range(generated_hp.shape[0])): + + ist, index = KDTree(tracked_hp).query(generated_hp[frame_num], workers=-1) + + img_path = os.path.join(inopt.dataset_base, 'matting', '%05d.png' % index) + generated_landmark_path = os.path.join(inopt.out_dir, 'landmarks', '%05d.lms' % frame_num) + tracked_landmark_path = os.path.join(inopt.dataset_base, 'landmarks', '%05d.lms' % index) + + get_edge_image_mixed(inopt.out_dir, img_path, generated_landmark_path, tracked_landmark_path, crop_coords, frame_num) + + +def predict_face2body(inopt): + + # check device + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + # load head2body model + checkpoint_path = os.path.join(inopt.checkpoint_dir, inopt.target_name, 'head2body.pkl') + if not os.path.isfile(checkpoint_path): + print('No checkpoint found.') + exit() + + model = model = nn.Linear(77 * 2, 4 * 2).to(device) + model.load_state_dict(torch.load(checkpoint_path)) + model.eval() + + # landmarks + num_ldk = len(os.listdir(os.path.join(inopt.out_dir, 'landmarks'))) + + # transforms + trans = transforms.ToTensor() + + # get img size + tmp_img = Image.open(os.path.join(inopt.out_dir, 'render', '00000.png')) + im_size = tmp_img.size + w, h = im_size + + print('Predicting body landmarks..') + output_ldks = [] + for idx in tqdm(range(num_ldk)): + landmark_name = os.path.join(inopt.out_dir, 'landmarks', '%05d.lms' % idx) + + # load and transform input + input_ldk = np.loadtxt(landmark_name)[:, :2] + input_ldk[:, 0] = input_ldk[:, 0] / w + input_ldk[:, 1] = input_ldk[:, 1] / h + input_ldk = trans(input_ldk).flatten().float().to(device).unsqueeze(0) + + # generate and scale output + output_ldk = model(input_ldk) + + # Transform + input_ldk = input_ldk.cpu().detach().reshape((77, 2)).numpy() + input_ldk[:, 0] = input_ldk[:, 0] * w + input_ldk[:, 1] = input_ldk[:, 1] * h + + output_ldk = output_ldk.cpu().detach().reshape((4, 2)).numpy() + output_ldk[:, 0] = output_ldk[:, 0] * w + output_ldk[:, 1] = output_ldk[:, 1] * h + + output_ldks.append(output_ldk) + + # Smooth predicted landmarks + smooth_sigma = 5 + output_ldks = np.array(output_ldks) + output_ldks = gaussian_filter1d(output_ldks.reshape(-1, 8), smooth_sigma, axis=0).reshape(-1, 4, 2) + + print('\nMaking edges..') + for idx in tqdm(range(num_ldk)): + landmark_name = os.path.join(inopt.out_dir, 'landmarks', '%05d.lms' % idx) + output_ldk = output_ldks[idx] + get_edge_predicted(idx, inopt.out_dir, landmark_name, output_ldk, im_size) + + + +if __name__=='__main__': + + # load args + parser = argparse.ArgumentParser() + parser.add_argument('--dataset_base', required=True) + parser.add_argument('--out_dir', required=True) + parser.add_argument('--target_name', required=True) + parser.add_argument('--checkpoint_dir', required=True) + + inopt = parser.parse_args() + + # call + # combine_edges(inopt) + + predict_face2body(inopt) + diff --git a/motion-gan-pipeline/preprocessing/deca_tracker.py b/motion-gan-pipeline/preprocessing/deca_tracker.py new file mode 100644 index 0000000..c3634b8 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/deca_tracker.py @@ -0,0 +1,43 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os, sys +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../third/DECA/'))) +from decalib.deca import DECA +from decalib.datasets import datasets +from decalib.utils import util +from decalib.utils.config import cfg as deca_cfg + + +class DECA_tracker: + def __init__(self, video_path): + + # load test images + self.testdata = datasets.TestData(video_path, iscrop=True, face_detector='fan') + self.device = 'cuda' + + # run DECA + deca_cfg.model.use_tex = False + self.deca = DECA(config=deca_cfg, device=self.device) + + def get_flame_mesh(self, codedict): + + verts, landmarks2d, landmarks3d = self.deca.flame(shape_params=codedict['shape'], + expression_params=codedict['exp'], + pose_params=codedict['pose']) + + trans_verts = util.batch_orth_proj(verts, codedict['cam']) + trans_verts[:, :, 1:] = -trans_verts[:, :, 1:] + + return verts, trans_verts + + def save_obj(self, filename, vertices): + faces = self.deca.render.faces[0].cpu().numpy() + + util.write_obj(filename, vertices, faces) + + def __call__(self, images, tform=None): + + codedict = self.deca.encode(images) + + return codedict diff --git a/motion-gan-pipeline/preprocessing/deepspeech_features.py b/motion-gan-pipeline/preprocessing/deepspeech_features.py new file mode 100644 index 0000000..627cf0a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/deepspeech_features.py @@ -0,0 +1,353 @@ +""" +20.10.14 +Code collection from VOCA: +https://github.com/TimoBolkart/voca +""" +import argparse +import re +import subprocess + +from scipy.io import wavfile +import os +import copy +import resampy +import numpy as np +import tensorflow as tf +from python_speech_features import mfcc +import moviepy.editor as mp +from pydub import AudioSegment +from subprocess import call + +class AudioHandler: + def __init__(self, config): + self.config = config + self.audio_feature_type = config['audio_feature_type'] + self.num_audio_features = config['num_audio_features'] + self.audio_window_size = config['audio_window_size'] + self.audio_window_stride = config['audio_window_stride'] + self.target_fps = config["target_fps"] + + def process(self, audio): + if self.audio_feature_type.lower() == "none": + return None + elif self.audio_feature_type.lower() == 'deepspeech': + return self.convert_to_deepspeech(audio) + else: + raise NotImplementedError("Audio features not supported") + + def convert_to_deepspeech(self, audio): + def audioToInputVector(audio, fs, numcep, numcontext): + # Get mfcc coefficients + features = mfcc(audio, samplerate=fs, numcep=numcep) + + # We only keep every second feature (BiRNN stride = 2) + features = features[::2] + + # One stride per time step in the input + num_strides = len(features) + + # Add empty initial and final contexts + empty_context = np.zeros((numcontext, numcep), dtype=features.dtype) + features = np.concatenate((empty_context, features, empty_context)) + + # Create a view into the array with overlapping strides of size + # numcontext (past) + 1 (present) + numcontext (future) + window_size = 2 * numcontext + 1 + train_inputs = np.lib.stride_tricks.as_strided( + features, + (num_strides, window_size, numcep), + (features.strides[0], features.strides[0], features.strides[1]), + writeable=False) + + # Flatten the second and third dimensions + train_inputs = np.reshape(train_inputs, [num_strides, -1]) + + train_inputs = np.copy(train_inputs) + train_inputs = (train_inputs - np.mean(train_inputs)) / np.std(train_inputs) + + # Return results + return train_inputs + + if type(audio) == dict: + pass + else: + raise ValueError('Wrong type for audio') + + # Load graph and place_holders + + with tf.io.gfile.GFile(self.config['deepspeech_graph_fname'], "rb") as f: + graph_def = tf.compat.v1.GraphDef() + graph_def.ParseFromString(f.read()) + + from tensorflow.python.framework.ops import get_default_graph + graph = get_default_graph() + tf.import_graph_def(graph_def, name="deepspeech") + input_tensor = graph.get_tensor_by_name('deepspeech/input_node:0') + # input_tensor = graph.get_tensor_by_name('input_node:0') + seq_length = graph.get_tensor_by_name('deepspeech/input_lengths:0') + layer_6 = graph.get_tensor_by_name('deepspeech/logits:0') + + n_input = 26 + n_context = 9 + + processed_audio = copy.deepcopy(audio) + with tf.compat.v1.Session(graph=graph) as sess: + for subj in audio.keys(): + for seq in audio[subj].keys(): + print('process audio: %s - %s' % (subj, seq)) + + audio_sample = audio[subj][seq]['audio'] + sample_rate = audio[subj][seq]['sample_rate'] + resampled_audio = resampy.resample(audio_sample.astype(float), sample_rate, 16000) + input_vector = audioToInputVector(resampled_audio.astype('int16'), 16000, n_input, n_context) + + network_output = sess.run(layer_6, feed_dict={input_tensor: input_vector[np.newaxis, ...], + seq_length: [input_vector.shape[0]]}) + + # Resample network output from 50 fps to 60 fps + audio_len_s = float(audio_sample.shape[0]) / sample_rate + num_frames = int(round(audio_len_s * self.target_fps)) + network_output = self.interpolate_features(network_output[:, 0], 50, self.target_fps, + output_len=num_frames) + + # Make windows + zero_pad = np.zeros((int(self.audio_window_size / 2), network_output.shape[1])) + network_output = np.concatenate((zero_pad, network_output, zero_pad), axis=0) + windows = [] + for window_index in range(0, network_output.shape[0] - self.audio_window_size, + self.audio_window_stride): + windows.append(network_output[window_index:window_index + self.audio_window_size]) + + processed_audio[subj][seq]['audio'] = np.array(windows) + return processed_audio + + def interpolate_features(self, features, input_rate, output_rate, output_len=None): + num_features = features.shape[1] + input_len = features.shape[0] + seq_len = input_len / float(input_rate) + if output_len is None: + output_len = int(seq_len * output_rate) + input_timestamps = np.arange(input_len) / float(input_rate) + output_timestamps = np.arange(output_len) / float(output_rate) + output_features = np.zeros((output_len, num_features)) + for feat in range(num_features): + output_features[:, feat] = np.interp(output_timestamps, + input_timestamps, + features[:, feat]) + return output_features + + +class AudioFeatures: + def __init__(self, config): + self.audio_handler = AudioHandler(config) + + def process_audio(self, audio, sample_rate): + tmp_audio = {'subj': {'seq': {'audio': audio, 'sample_rate': sample_rate}}} + return self.audio_handler.process(tmp_audio)['subj']['seq']['audio'] + + def inference_interpolate_styles(self, audio_fname): + # TODO: check if this works + # sample_rate, audio = wavfile.read(audio_fname) + import soundfile as sf + import librosa + import wavio + + x,_ = librosa.load(audio_fname, sr=16000) + sf.write(audio_fname, x, 16000) + print('Successfully converted.') + + wav = wavio.read(audio_fname) + audio, sample_rate = wav.data, wav.rate + + # audio, sample_rate = librosa.load(audio_fname) + + print("sample rate: ", sample_rate) + if audio.ndim != 1: + print('Audio has multiple channels, only first channel is considered') + audio = audio[:, 0] + + processed_audio = self.process_audio(audio, sample_rate) + return processed_audio + + def run(self, audio_fname): + features = self.inference_interpolate_styles(audio_fname) + return features + + +class FFMPEGMetaReader: + """ + Uses ffprobe to extract metadata from audio and video + """ + + def __init__(cls, fps): + cls.DEFAULTS = { + "samplerate": 48000, + "bitrate": 96, + "duration": "00", + "fps": fps + } + + cls.PATTERNS = { + "sample_and_bitrate": r'Audio:.*\s(\d+)\sHz.*\s(\d+)\skb/s', + "duration": r"Duration:\s(\d{2}):(\d{2}):(\d{2})\.(\d{2})", + "fps": r"Video:.*\s(\d+\.?\d*)\sfps" + } + + def _run_ffmpeg(cls, path): + process = subprocess.Popen(['ffprobe', '-i', path, '-hide_banner'], stdout=subprocess.PIPE, + stderr=subprocess.PIPE) + stdout, stderr = process.communicate() + output = stderr.decode('utf-8') + return output + + def _sample_and_bitrate(cls, path, default_samplerate=None, default_bitrate=None, output=None): + if output is None: + output = cls._run_ffmpeg(path) + match = re.search(cls.PATTERNS["sample_and_bitrate"], output) + + samplerate = default_samplerate if default_samplerate else cls.DEFAULTS["samplerate"] + bitrate = default_bitrate if default_bitrate else cls.DEFAULTS["bitrate"] + + if match: + samplerate = match.group(1) if match.group(1) else samplerate + bitrate = match.group(2) if match.group(2) else bitrate + return samplerate, bitrate + + def _duration(cls, path, output=None): + if output is None: + output = cls._run_ffmpeg(path) + + duration = cls.DEFAULTS["duration"] + + match = re.search(cls.PATTERNS["duration"], output) + + if match and match.group(4): + duration = "{}:{}:{}.{}".format(*[match.group(i) if match.group(i) else duration for i in range(1, 5)]) + return duration + + def _fps(cls, video_path, output=None): + if output is None: + output = cls._run_ffmpeg(video_path) + + fps = cls.DEFAULTS["fps"] + + match = re.search(cls.PATTERNS["fps"], output) + + if match: + fps = match.group(1) if match.group(1) else fps + else: + raise Warning("No fps found.") + return fps + + def extract_meta(cls, path): + output = cls._run_ffmpeg(path) + samplerate, bitrate = cls._sample_and_bitrate(path, output=output) + duration = cls._duration(path, output=output) + fps = cls._fps(path, output=output) + return { + "samplerate": samplerate, + "bitrate": bitrate, + "duration": duration, + "fps": fps + } + + +def get_parser(): + parser = argparse.ArgumentParser() + parser.add_argument("--datasets", type=str, required=True) + parser.add_argument("--video_id", type=str, required=True) + return parser + + +def get_config(): + config = {} + config['deepspeech_graph_fname'] = os.path.join( + "third/DeepSpeech/models/", "output_graph.pb") + config['audio_feature_type'] = 'deepspeech' + config['num_audio_features'] = 29 + + config['audio_window_size'] = 16 + config['audio_window_stride'] = 1 + # config['target_fps'] = target_fps + return config + + +def mkdir(path): + if not os.path.exists(path): + os.makedirs(path, exist_ok=True) + + +def extract_ds(dataset_base, name, file_type, sample_rate=16000, target_fps=25.0): + + config = get_config() + path_wav = '{}/{}.wav'.format(dataset_base, name) + + # First convert mp4 to wav + if file_type == 'video' and not os.path.isfile(path_wav): + + path_video_in = '{}/{}.mp4'.format(dataset_base, name) + + if not os.path.isfile(path_video_in): + path_video_in = '{}/{}.avi'.format(dataset_base, name) + + # cmd = "ffmpeg -i input-video.avi -vn -acodec copy output-audio.aac" + + cmd = ('ffmpeg' + f' -i {path_video_in} -vn -acodec copy -ar {sample_rate} {path_wav} ').split() + call(cmd) + + else: + if not os.path.isfile(path_wav): + try: + path_mp3 = '{}/{}.mp3'.format(dataset_base, name) + sound = AudioSegment.from_mp3(path_mp3) + sound.export(path_wav, format="wav") + + except FileNotFoundError: + print('Audio file not found. File format should be mp3 or wav.') + + config["target_fps"] = target_fps + + # Compute features + features = AudioFeatures(config=config).run(path_wav) + folder_out = os.path.join(dataset_base, "audio_feature") + + # Save features + mkdir(folder_out) + for i, feature in enumerate(features): + fn_out = "%05d.deepspeech.npy" % i + np.save(os.path.join(folder_out, fn_out), feature) + print("Written {} files to '{}'".format(features.shape[0], folder_out)) + + +if __name__ == "__main__": + args = get_parser().parse_args() + folder_videos = args.dataset + file_id = args.video_id + folder_nvp = "TARGETS" + + # First convert mp4 to wav + path_video_in = '{}/{}.mp4'.format(folder_videos, file_id) + path_wav = '{}/{}.wav'.format(folder_videos, file_id) + path_features = os.path.join(folder_videos, "deepspeech", "{}.npy".format(file_id)) + clip = mp.VideoFileClip(path_video_in) + clip.audio.write_audiofile(path_wav) + + # Get the meta data via ffmpeg + config = get_config() + metadata = FFMPEGMetaReader.extract_meta(path_video_in) + if int(metadata["fps"]) != float(metadata["fps"]): + raise Warning("Careful: fps is not an integer ({})".format(metadata["fps"])) + config["target_fps"] = int(metadata["fps"]) + + # Compute features + features = AudioFeatures(config=config).run(path_wav) + folder_out = os.path.join( + './NeuralVoicePuppetry/Audio2ExpressionNet/Inference/datasets/{}'.format( + folder_nvp), file_id, "audio_feature") + + # Save features + mkdir(folder_out) + for i, feature in enumerate(features): + fn_out = "%05d.deepspeech.npy" % i + np.save(os.path.join(folder_out, fn_out), feature) + print("Written {} files to '{}'".format(features.shape[0], folder_out)) diff --git a/motion-gan-pipeline/preprocessing/edge_creation/__init__.py b/motion-gan-pipeline/preprocessing/edge_creation/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/motion-gan-pipeline/preprocessing/edge_creation/keypoint2image.py b/motion-gan-pipeline/preprocessing/edge_creation/keypoint2image.py new file mode 100644 index 0000000..89e6188 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/edge_creation/keypoint2image.py @@ -0,0 +1,196 @@ +import os.path +from PIL import Image +import numpy as np +import json +import glob +from scipy.optimize import curve_fit +import warnings + +def func(x, a, b, c): + return a * x**2 + b * x + c + +def linear(x, a, b): + return a * x + b + +def setColor(im, yy, xx, color): + if len(im.shape) == 3: + if (im[yy, xx] == 0).all(): + im[yy, xx, 0], im[yy, xx, 1], im[yy, xx, 2] = color[0], color[1], color[2] + else: + im[yy, xx, 0] = ((im[yy, xx, 0].astype(float) + color[0]) / 2).astype(np.uint8) + im[yy, xx, 1] = ((im[yy, xx, 1].astype(float) + color[1]) / 2).astype(np.uint8) + im[yy, xx, 2] = ((im[yy, xx, 2].astype(float) + color[2]) / 2).astype(np.uint8) + else: + im[yy, xx] = color[0] + +def drawEdge(im, x, y, bw=1, color=(255,255,255), draw_end_points=False): + if x is not None and x.size: + h, w = im.shape[0], im.shape[1] + # edge + for i in range(-bw, bw): + for j in range(-bw, bw): + yy = np.maximum(0, np.minimum(h-1, y+i)) + xx = np.maximum(0, np.minimum(w-1, x+j)) + setColor(im, yy, xx, color) + + # edge endpoints + if draw_end_points: + for i in range(-bw*2, bw*2): + for j in range(-bw*2, bw*2): + if (i**2) + (j**2) < (4 * bw**2): + yy = np.maximum(0, np.minimum(h-1, np.array([y[0], y[-1]])+i)) + xx = np.maximum(0, np.minimum(w-1, np.array([x[0], x[-1]])+j)) + setColor(im, yy, xx, color) + +def interpPoints(x, y): + if abs(x[:-1] - x[1:]).max() < abs(y[:-1] - y[1:]).max(): + curve_y, curve_x = interpPoints(y, x) + if curve_y is None: + return None, None + else: + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + if len(x) < 3: + popt, _ = curve_fit(linear, x, y) + else: + popt, _ = curve_fit(func, x, y) + if abs(popt[0]) > 1: + return None, None + if x[0] > x[-1]: + x = list(reversed(x)) + y = list(reversed(y)) + curve_x = np.linspace(int(x[0]), int(x[-1]), int((x[-1]-x[0]))) + if len(x) < 3: + curve_y = linear(curve_x, *popt) + else: + curve_y = func(curve_x, *popt) + return curve_x.astype(int), curve_y.astype(int) + +def read_keypoints(json_input, size, random_drop_prob=0, remove_face_labels=False, basic_point_only=False): + with open(json_input, encoding='utf-8') as f: + keypoint_dicts = json.loads(f.read())["people"] + + edge_lists = define_edge_lists(basic_point_only) + w, h = size + pose_img = np.zeros((h, w, 3), np.uint8) + for keypoint_dict in keypoint_dicts: + pose_pts = np.array(keypoint_dict["pose_keypoints_2d"]).reshape(25, 3) + face_pts = np.array(keypoint_dict["face_keypoints_2d"]).reshape(70, 3) + hand_pts_l = np.array(keypoint_dict["hand_left_keypoints_2d"]).reshape(21, 3) + hand_pts_r = np.array(keypoint_dict["hand_right_keypoints_2d"]).reshape(21, 3) + pts = [extract_valid_keypoints(pts, edge_lists) for pts in [pose_pts, face_pts, hand_pts_l, hand_pts_r]] + pose_img += connect_keypoints(pts, edge_lists, size, random_drop_prob, remove_face_labels, basic_point_only) + return pose_img + +def extract_valid_keypoints(pts, edge_lists): + pose_edge_list, _, hand_edge_list, _, face_list = edge_lists + p = pts.shape[0] + thre = 0.1 if p == 70 else 0.01 + output = np.zeros((p, 2)) + + if p == 70: # face + for edge_list in face_list: + for edge in edge_list: + if (pts[edge, 2] > thre).all(): + output[edge, :] = pts[edge, :2] + elif p == 21: # hand + for edge in hand_edge_list: + if (pts[edge, 2] > thre).all(): + output[edge, :] = pts[edge, :2] + else: # pose + valid = (pts[:, 2] > thre) + output[valid, :] = pts[valid, :2] + + return output + +def connect_keypoints(pts, edge_lists, size, random_drop_prob, remove_face_labels, basic_point_only): + pose_pts, face_pts, hand_pts_l, hand_pts_r = pts + w, h = size + output_edges = np.zeros((h, w, 3), np.uint8) + pose_edge_list, pose_color_list, hand_edge_list, hand_color_list, face_list = edge_lists + + if random_drop_prob > 0 and remove_face_labels: + # add random noise to keypoints + pose_pts[[0,15,16,17,18], :] += 5 * np.random.randn(5,2) + face_pts[:,0] += 2 * np.random.randn() + face_pts[:,1] += 2 * np.random.randn() + + ### pose + for i, edge in enumerate(pose_edge_list): + x, y = pose_pts[edge, 0], pose_pts[edge, 1] + if (np.random.rand() > random_drop_prob) and (0 not in x): + curve_x, curve_y = interpPoints(x, y) + drawEdge(output_edges, curve_x, curve_y, bw=3, color=pose_color_list[i], draw_end_points=True) + + if not basic_point_only: + ### hand + for hand_pts in [hand_pts_l, hand_pts_r]: # for left and right hand + if np.random.rand() > random_drop_prob: + for i, edge in enumerate(hand_edge_list): # for each finger + for j in range(0, len(edge)-1): # for each part of the finger + sub_edge = edge[j:j+2] + x, y = hand_pts[sub_edge, 0], hand_pts[sub_edge, 1] + if 0 not in x: + line_x, line_y = interpPoints(x, y) + drawEdge(output_edges, line_x, line_y, bw=1, color=hand_color_list[i], draw_end_points=True) + + ### face + edge_len = 2 + if (np.random.rand() > random_drop_prob): + for edge_list in face_list: + for edge in edge_list: + for i in range(0, max(1, len(edge)-1), edge_len-1): + sub_edge = edge[i:i+edge_len] + x, y = face_pts[sub_edge, 0], face_pts[sub_edge, 1] + if 0 not in x: + curve_x, curve_y = interpPoints(x, y) + drawEdge(output_edges, curve_x, curve_y, draw_end_points=True) + + return output_edges + +def define_edge_lists(basic_point_only): + ### pose + pose_edge_list = [] + pose_color_list = [] + if not basic_point_only: + pose_edge_list += [[17, 15], [15, 0], [ 0, 16], [16, 18]] # head + pose_color_list += [[153, 0,153], [153, 0,102], [102, 0,153], [ 51, 0,153]] + + pose_edge_list += [ + [ 0, 1], [ 1, 8], # body + [ 1, 2], [ 2, 3], [ 3, 4], # right arm + [ 1, 5], [ 5, 6], [ 6, 7], # left arm + [ 8, 9], [ 9, 10], [10, 11], [11, 24], [11, 22], [22, 23], # right leg + [ 8, 12], [12, 13], [13, 14], [14, 21], [14, 19], [19, 20] # left leg + ] + pose_color_list += [ + [153, 0, 51], [153, 0, 0], + [153, 51, 0], [153,102, 0], [153,153, 0], + [102,153, 0], [ 51,153, 0], [ 0,153, 0], + [ 0,153, 51], [ 0,153,102], [ 0,153,153], [ 0,153,153], [ 0,153,153], [ 0,153,153], + [ 0,102,153], [ 0, 51,153], [ 0, 0,153], [ 0, 0,153], [ 0, 0,153], [ 0, 0,153] + ] + + ### hand + hand_edge_list = [ + [0, 1, 2, 3, 4], + [0, 5, 6, 7, 8], + [0, 9, 10, 11, 12], + [0, 13, 14, 15, 16], + [0, 17, 18, 19, 20] + ] + hand_color_list = [ + [204,0,0], [163,204,0], [0,204,82], [0,82,204], [163,0,204] + ] + + ### face + face_list = [ + #[range(0, 17)], # face + [range(17, 22)], # left eyebrow + [range(22, 27)], # right eyebrow + [range(27, 31), range(31, 36)], # nose + [[36,37,38,39], [39,40,41,36]], # left eye + [[42,43,44,45], [45,46,47,42]], # right eye + [range(48, 55), [54,55,56,57,58,59,48]], # mouth + ] + return pose_edge_list, pose_color_list, hand_edge_list, hand_color_list, face_list \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/edge_creation/utils.py b/motion-gan-pipeline/preprocessing/edge_creation/utils.py new file mode 100644 index 0000000..030e660 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/edge_creation/utils.py @@ -0,0 +1,455 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import numpy as np +import cv2 as cv2 +from matplotlib import pyplot as plt +from edge_creation.keypoint2image import interpPoints, drawEdge +import PIL +from PIL import Image, ImageDraw +import torch +from skimage import feature +from logging import raiseExceptions +import torchvision.transforms as transforms +import os +from scipy.ndimage import shift +from tqdm import tqdm +from math import floor + + +def read_keypoints(A_path, size): + # mapping from keypoints to face part + part_list = [[list(range(0, 17)) + list(range(68, 83)) + [0]], # face + [range(17, 22)], # right eyebrow + [range(22, 27)], # left eyebrow + [[28, 31], range(31, 36), [35, 28]], # nose + [[36,37,38,39], [39,40,41,36]], # right eye + [[42,43,44,45], [45,46,47,42]], # left eye + [range(48, 55), [54,55,56,57,58,59,48]], # mouth + [range(60, 65), [64,65,66,67,60]] # tongue + ] + label_list = [50, 100, 100, 125, 150, 150, 200, 250] # labeling for different facial parts + keypoints = np.loadtxt(A_path)[:,:2] + # keypoints = np.loadtxt(A_path, delimiter=' ') + + # add upper half face by symmetry + pts = keypoints[:17, :].astype(np.int32) + baseline_y = (pts[0,1] + pts[-1,1]) / 2 + upper_pts = pts[1:-1,:].copy() + upper_pts[:,1] = baseline_y + (baseline_y-upper_pts[:,1]) * 2 // 3 + keypoints = np.vstack((keypoints, upper_pts[::-1,:])) + + # label map for facial part + w, h = size + part_labels = np.zeros((h, w), np.uint8) + for p, edge_list in enumerate(part_list): + indices = [item for sublist in edge_list for item in sublist] + pts = keypoints[indices, :].astype(np.int32) + cv2.fillPoly(part_labels, pts=[pts], color=label_list[p]) + + return keypoints, part_list, part_labels + +def read_keypoints_forehead(A_path, size): + # mapping from keypoints to face part + part_list = [[list(range(0, 17)) + list(range(68, 77)) + [0]], # face + [range(17, 22)], # right eyebrow + [range(22, 27)], # left eyebrow + [[28, 31], range(31, 36), [35, 28]], # nose + [[36,37,38,39], [39,40,41,36]], # right eye + [[42,43,44,45], [45,46,47,42]], # left eye + [range(48, 55), [54,55,56,57,58,59,48]], # mouth + [range(60, 65), [64,65,66,67,60]] # tongue + ] + label_list = [50, 100, 100, 125, 150, 150, 200, 250] # labeling for different facial parts + keypoints = np.loadtxt(A_path)[:,:2] + + # label map for facial part + w, h = size + part_labels = np.zeros((h, w), np.uint8) + for p, edge_list in enumerate(part_list): + indices = [item for sublist in edge_list for item in sublist] + pts = keypoints[indices, :].astype(np.int32) + cv2.fillPoly(part_labels, pts=[pts], color=label_list[p]) + + return keypoints, part_list, part_labels + +def draw_face_edges(keypoints, part_list, size): + w, h = size + edge_len = 3 # interpolate 3 keypoints to form a curve when drawing edges + # edge map for face region from keypoints + im_edges = np.zeros((h, w), np.uint8) # edge map for all edges + dist_tensor = 0 + e = 1 + for edge_list in part_list: + for edge in edge_list: + for i in range(0, max(1, len(edge)-1), edge_len-1): # divide a long edge into multiple small edges when drawing + sub_edge = edge[i:i+edge_len] + x = keypoints[sub_edge, 0] + y = keypoints[sub_edge, 1] + + curve_x, curve_y = interpPoints(x, y) # interp keypoints to get the curve shape + drawEdge(im_edges, curve_x, curve_y) + + return im_edges, dist_tensor + +def draw_body_edges(keypoints, size): + w, h = size + body_edges = Image.fromarray(np.zeros((h, w), np.uint8)) # edge map for all edges + + # left to right shoulder + a = keypoints[11][:-2]# left shoulder + b = keypoints[12][:-2] # right shoulder + + # create line image + tmp1 = ImageDraw.Draw(body_edges) + shape = [a[0], a[1], b[0], b[1]] + tmp1.line(shape, fill=(255), width = 3) + + # mid to nose + c = keypoints[0][:-2] + d = (a + b) / 2 + shape = [c[0], c[1], d[0], d[1]] + tmp1.line(shape, fill=(255), width = 3) + + # left to right hips + e = keypoints[23][:-2]# left hip + f = keypoints[24][:-2] # right hip + + # create line image + shape = [a[0], a[1], e[0], e[1]] + tmp1.line(shape, fill=(255), width = 3) + shape = [b[0], b[1], f[0], f[1]] + tmp1.line(shape, fill=(255), width = 3) + + # Cross for content + shape = [a[0], a[1], f[0], f[1]] + tmp1.line(shape, fill=(255), width = 3) + shape = [b[0], b[1], e[0], e[1]] + tmp1.line(shape, fill=(255), width = 3) + + return body_edges + +def draw_keypoints(img_path, keypoints, color=(255, 0, 0)): + tmp = Image.open(img_path) + size = tmp.size + + for point in keypoints: + margin = (max(size) // 500) + 3 + ldmks = ([point[0] - margin, point[1] - margin, point[0] + margin, point[1] + margin]) + draw = ImageDraw.Draw(tmp) + draw.ellipse(ldmks, fill=color) + + return tmp + +def get_crop_coords(keypoints, size): + min_y, max_y = keypoints[:,1].min(), keypoints[:,1].max() + min_x, max_x = keypoints[:,0].min(), keypoints[:,0].max() + xc = (min_x + max_x) // 2 + yc = (min_y*3 + max_y) // 4 + h = w = (max_x - min_x) * 2.5 + xc = min(max(0, xc - w//2) + w, size[0]) - w//2 + yc = min(max(0, yc - h//2) + h, size[1]) - h//2 + min_x, max_x = xc - w//2, xc + w//2 + min_y, max_y = yc - h//2, yc + h//2 + return [int(min_y), int(max_y), int(min_x), int(max_x)] + +def crop(img, ext_values): + if isinstance(img, np.ndarray): + return img[ext_values[0]:ext_values[1], ext_values[2]:ext_values[3]] + else: + return img.crop((ext_values[2], ext_values[0], ext_values[3], ext_values[1])) + +def get_img_params(size, loadSize=320): + w, h = size + new_h, new_w = h, w + + new_w = loadSize + new_h = loadSize * h // w + + new_w = int(round(new_w / 4)) * 4 + new_h = int(round(new_h / 4)) * 4 + + return {'new_size': (new_w, new_h)} + +def get_transform(params, method=Image.BICUBIC, normalize=True, toTensor=True): + transform_list = [] + ### resize input image + transform_list.append(transforms.Lambda(lambda img: __scale_image(img, params['new_size'], method))) + + if toTensor: + transform_list += [transforms.ToTensor()] + if normalize: + transform_list += [transforms.Normalize((0.5, 0.5, 0.5), + (0.5, 0.5, 0.5))] + return transforms.Compose(transform_list) + + +def __scale_image(img, size, method=Image.BICUBIC): + w, h = size + return img.resize((w, h), method) + + +def convert_to_rgb(image): + if image.mode == 'RGBA': + image.load() + image_new = Image.new("RGB", image.size, (255, 255, 255)) + image_new.paste(image, mask=image.split()[3]) + elif image.mode == 'RGB': + image_new = image + else: + raiseExceptions('Non-compatible image format!') + return image_new + +def get_edge_predicted(img_num, out_dir, landmark_name, predicted_ldk, im_size): + # Used at inference time when combining generated landmarks and predicted shoulders + + params = get_img_params(im_size, loadSize=np.max(im_size)) + transform_scaleA = get_transform(params, method=Image.BILINEAR, normalize=False, toTensor=False) + + # Draw face + # keypoints, part_list, part_labels = read_keypoints(landmark_name, im_size) + keypoints, part_list, part_labels = read_keypoints_forehead(landmark_name, im_size) + im_edges, dist_tensor = draw_face_edges(keypoints, part_list, im_size) + + # body edges + w, h = im_size + body_edges = Image.fromarray(np.zeros((h, w), np.uint8)) # edge map for all edges + + # left to right shoulder + a = predicted_ldk[0] # left shoulder + b = predicted_ldk[1] # right shoulder + + # create line image + tmp1 = ImageDraw.Draw(body_edges) + shape = [a[0], a[1], b[0], b[1]] + tmp1.line(shape, fill=(255), width = 3) + + # mid to nose + c = keypoints[33][:2] + d = (a + b) / 2 + shape = [c[0], c[1], d[0], d[1]] + tmp1.line(shape, fill=(255), width = 3) + + # left to right hips + e = predicted_ldk[2]# left hip + f = predicted_ldk[3] # right hip + + # create line image + shape = [a[0], a[1], e[0], e[1]] + tmp1.line(shape, fill=(255), width = 3) + shape = [b[0], b[1], f[0], f[1]] + tmp1.line(shape, fill=(255), width = 3) + + # Cross for content + shape = [a[0], a[1], f[0], f[1]] + tmp1.line(shape, fill=(255), width = 3) + shape = [b[0], b[1], e[0], e[1]] + tmp1.line(shape, fill=(255), width = 3) + + # clean + body_edges = np.array(body_edges) * (part_labels == 0) # remove edges within face + im_edges += body_edges + + # Transform and save + edge_path = os.path.join(out_dir, 'edges') + os.makedirs(edge_path, exist_ok=True) + edge_image = transform_scaleA(Image.fromarray(im_edges)) + edge_image.save(os.path.join(edge_path, '%05d.png' % img_num)) + +def get_edge_image_mixed(out_dir, img_name, landmark_name, tr_landmark_name, crop_coords, img_num): + # Used at inference time when combining generated landmarks and original frame's edges + + # Flag for cropping + crop_flag = False + + # Make edges from generated landmarks and closest image + img = Image.open(img_name) + img = convert_to_rgb(img) + img_size = img.size + points = np.loadtxt(landmark_name)[:,:2] + tr_points = np.loadtxt(tr_landmark_name, delimiter=' ') + face_dist_x = np.mean(tr_points[31:36, 0]-points[31:36, 0]) + face_dist_y = np.mean(tr_points[31:36, 1]-points[31:36, 1]) + + if crop_flag: + params = get_img_params(crop(img, crop_coords).size) + + else: + params = get_img_params(img.size, loadSize=np.max(img.size)) + + transform_scaleA = get_transform(params, method=Image.BILINEAR, normalize=False, toTensor=False) + transform_label = get_transform(params, method=Image.NEAREST, normalize=False, toTensor=False) + transform_scaleB = get_transform(params, normalize=False, toTensor=False) + + # keypoints, part_list, part_labels = read_keypoints(landmark_name, img_size) + keypoints, part_list, part_labels = read_keypoints_forehead(landmark_name, img_size) + + im_edges, dist_tensor = draw_face_edges(keypoints, part_list, img_size) + + edges = feature.canny(shift(np.array(img.convert('L')), (-face_dist_y, -face_dist_x), mode='nearest')) + # edges = feature.canny(np.array(img.convert('L'))) + edges = edges * (part_labels == 0) # remove edges within face + im_edges += (edges * 255).astype(np.uint8) + + if crop_flag: + edge_image = transform_scaleA(Image.fromarray(crop(im_edges, crop_coords))) + label_image = transform_label(Image.fromarray(crop(part_labels.astype(np.uint8), crop_coords))) + img_image = transform_scaleB(crop(img, crop_coords)) + + else: + edge_image = transform_scaleA(Image.fromarray(im_edges)) + label_image = transform_label(Image.fromarray(part_labels.astype(np.uint8))) + img_image = transform_scaleB(img) + + edge_path = os.path.join(out_dir, 'edges') + os.makedirs(edge_path, exist_ok=True) + edge_image.save(os.path.join(edge_path, '%05d.png' % img_num)) + +def get_edge_image(index, img_path, tracked_landmark_path, edges_dir, cropped_dir, im_size, crop_coords): + # Used in preprocessing when combining tracked landmarks with frame's edges + + # Flag for cropping + crop_flag = False + + # Load image + img = Image.open(img_path) + img = convert_to_rgb(img) + + # Make crop parameters + if crop_flag: + params = get_img_params(crop(img, crop_coords).size) + + else: + params = get_img_params(img.size, loadSize=np.max(img.size)) + + transform_scaleA = get_transform(params, method=Image.BILINEAR, normalize=False, toTensor=False) + transform_scaleB = get_transform(params, normalize=False, toTensor=False) + + # Draw face + # keypoints, part_list, part_labels = read_keypoints(tracked_landmark_path, im_size) + keypoints, part_list, part_labels = read_keypoints_forehead(tracked_landmark_path, im_size) + im_edges, dist_tensor = draw_face_edges(keypoints, part_list, im_size) + + # Other Edges + edges = feature.canny(np.array(img.convert('L'))) + edges = edges * (part_labels == 0) # remove edges within face + im_edges += (edges * 255).astype(np.uint8) + + # Transform + if crop_flag: + edge_image = transform_scaleA(Image.fromarray(crop(im_edges, crop_coords))) + img_image = transform_scaleB(crop(img, crop_coords)) + + + else: + edge_image = transform_scaleA(Image.fromarray(im_edges)) + img_image = transform_scaleB(img) + + # Save edges and cropped target for GAN training + edge_image.save(os.path.join(edges_dir, '%05d.png' % index)) + img_image.save(os.path.join(cropped_dir, '%05d.png' % index)) + +def get_edge_image_body(index, img_path, tracked_landmark_path, edges_dir, cropped_dir, im_size, crop_coords, body_landmark_path): + # Used in preprocessing when combining tracked landmarks with tracked body edges + + # Flag for cropping + crop_flag = False + + # Load image + img = Image.open(img_path) + img = convert_to_rgb(img) + + # Make crop parameters + if crop_flag: + params = get_img_params(crop(img, crop_coords).size) + + else: + params = get_img_params(img.size, loadSize=np.max(img.size)) + + transform_scaleA = get_transform(params, method=Image.BILINEAR, normalize=False, toTensor=False) + transform_scaleB = get_transform(params, normalize=False, toTensor=False) + + # Draw face + # keypoints, part_list, part_labels = read_keypoints(tracked_landmark_path, im_size) + keypoints, part_list, part_labels = read_keypoints_forehead(tracked_landmark_path, im_size) + im_edges, dist_tensor = draw_face_edges(keypoints, part_list, im_size) + + # body edges + body_keypoints = np.load(body_landmark_path) + body_edges = np.array(draw_body_edges(body_keypoints, im_size)) + body_edges = body_edges * (part_labels == 0) # remove edges within face + im_edges += body_edges + + # Save landmarks for DEBUGGING + debug = False + if debug: + debug_dir = os.path.join(edges_dir, 'debug') + os.makedirs(debug_dir, exist_ok=True) + debug_path = os.path.join(debug_dir, '%05d.png' % index) + tmp1 = draw_keypoints(img_path, keypoints, color=(255, 0, 0)) + Image.fromarray(np.array(tmp1)).save(debug_path) + tmp2 = draw_keypoints(debug_path, body_keypoints, color=(0, 0, 255)) + Image.fromarray(np.array(tmp2)).save(debug_path) + + # Transform + if crop_flag: + edge_image = transform_scaleA(Image.fromarray(crop(im_edges, crop_coords))) + img_image = transform_scaleB(crop(img, crop_coords)) + + + else: + edge_image = transform_scaleA(Image.fromarray(im_edges)) + img_image = transform_scaleB(img) + + # Save edges and cropped target for GAN training + edge_image.save(os.path.join(edges_dir, '%05d.png' % index)) + img_image.save(os.path.join(cropped_dir, '%05d.png' % index)) + +def make_edges(mattdir, landmarkdir, edges_dir, cropped_dir): + + # Compute crop size + img = Image.open(os.path.join(mattdir, '%05d.png' % 0)) + im_size = img.size + + points = np.mean([np.loadtxt(os.path.join(landmarkdir, ldk))[:,:2] for ldk in os.listdir(landmarkdir)], axis=0) + crop_coords = get_crop_coords(points, im_size) + + for index in tqdm(range(len(os.listdir(mattdir)))): + + img_path = os.path.join(mattdir, '%05d.png' % index) + tracked_landmark_path = os.path.join(landmarkdir, '%05d.lms' % index) + + get_edge_image(index, img_path, tracked_landmark_path, edges_dir, cropped_dir, im_size, crop_coords) + +def make_edges_body(mattdir, landmarkdir, edges_dir, cropped_dir, body_dir, split_val=0.0): + # Compute crop size + img = Image.open(os.path.join(mattdir, '%05d.png' % 0)) + im_size = img.size + + points = np.mean([np.loadtxt(os.path.join(landmarkdir, ldk))[:,:2] for ldk in os.listdir(landmarkdir)], axis=0) + crop_coords = get_crop_coords(points, im_size) + + num_frames = len(os.listdir(mattdir)) + num_train = floor(num_frames * (1.0 - split_val)) + + # train + for index in tqdm(range(num_train)): + + img_path = os.path.join(mattdir, '%05d.png' % index) + tracked_landmark_path = os.path.join(landmarkdir, '%05d.lms' % index) + body_landmark_path = os.path.join(body_dir, '%05d.npy' % index) + + get_edge_image_body(index, img_path, tracked_landmark_path, edges_dir, cropped_dir, im_size, crop_coords, body_landmark_path) + + # val + for index in tqdm(range(num_train, num_frames)): + + img_path = os.path.join(mattdir, '%05d.png' % index) + tracked_landmark_path = os.path.join(landmarkdir, '%05d.lms' % index) + body_landmark_path = os.path.join(body_dir, '%05d.npy' % index) + + os.makedirs(edges_dir + '_val', exist_ok=True) + os.makedirs(cropped_dir + '_val', exist_ok=True) + + get_edge_image_body(index, img_path, tracked_landmark_path, edges_dir + '_val', cropped_dir + '_val', im_size, crop_coords, body_landmark_path) + diff --git a/motion-gan-pipeline/preprocessing/emoca_tracker.py b/motion-gan-pipeline/preprocessing/emoca_tracker.py new file mode 100644 index 0000000..17cd733 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/emoca_tracker.py @@ -0,0 +1,106 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from gdl_apps.EmotionRecognition.utils.io import load_model +from gdl.datasets.ImageTestDataset import TestData +import gdl +import numpy as np +import os +import torch +from pathlib import Path +import matplotlib.pyplot as plt +from torch.functional import F +from gdl.datasets.AffectNetDataModule import AffectNetExpressions +from gdl.utils.other import get_path_to_assets +from tqdm import tqdm + +def load_dir(lmspath, framepath, start, end): + lmss = [] + imgs_paths = [] + for i in range(start, end): + if os.path.isfile(os.path.join(lmspath, str(i) + '.lms')): + lms = np.loadtxt(os.path.join( + lmspath, str(i) + '.lms'), dtype=np.float32) + lmss.append(lms) + imgs_paths.append(os.path.join(framepath, str(i) + '.jpg')) + lmss = np.stack(lmss) + lmss = torch.as_tensor(lmss).cuda() + return imgs_paths + +class EMOCA_tracker: + def __init__(self): + + model_name = 'ResNet50' + path_to_models = get_path_to_assets() /"EmotionRecognition" + + path_to_models = path_to_models / "image_based_networks" + + self.model = load_model(Path(path_to_models) / model_name) + print(self.model) + self.model.cuda() + self.model.eval() + + def __call__(self, images, tform=None): + + codedict = self.model(images) + + return codedict + + def save_images(self, batch, predictions, output_folder): + # Save the images + + softmax = F.softmax(predictions["expr_classification"]) + top_expr = torch.argmax(softmax, dim=1) + for i in range(len(batch["image"])): + img = batch["image"][i].cpu().detach().numpy() + img = img.transpose(1, 2, 0) + img = img * 255 + img = img.astype(np.uint8) + + plt.figure() + # plot the image with matplotlib + plt.imshow(img) + # write valence and arousal to the image + expr = AffectNetExpressions(int(top_expr[i].item())) + text = "Predicted emotion:\n" + text += f'Arousal: {predictions["arousal"][i].item():.2f} \nValence: {predictions["valence"][i].item():.2f}' + text += f"\nExpression: {expr.name}, {softmax[i][expr.value].item()*100:.2f}%" + plt.title(text) + out_fname = Path(output_folder) / f"{batch['image_name'][i]}.png" + # save the image to the output folder + + # axis off + plt.axis('off') + plt.savefig(out_fname) + plt.close() + + +def emotion_detection(dataset_base, emotion_dir): + ''' + Face tracker using FLAME model. + Used to have geometry prior for nerf sampling. + ''' + + id_dir = dataset_base + debug_emotions = os.path.join(id_dir, 'debug', 'emotions_imgs') + Path(debug_emotions).mkdir(parents=True, exist_ok=True) + + emoca_tracker = EMOCA_tracker() + + # Run deca on all frames + testdata = TestData(os.path.join(id_dir, 'frames'), face_detector="fan", max_detection=20) + + for i, data in enumerate(tqdm(testdata)): + batch = testdata[i] + batch["image"] = batch["image"].cuda() + predictions = emoca_tracker(batch) + npy_pred = {k: v.cpu().detach().numpy() for k,v in predictions.items()} + np.save(os.path.join(emotion_dir, '%5d.npy' % i), npy_pred) + + emoca_tracker.save_images(batch, predictions, debug_emotions) + +if __name__ == '__main__': + + dataset_base = '/media/apennino/EmotionDetection/Test/Greta/' + emotion_dir = '/media/apennino/EmotionDetection/Test/Greta/emotions/' + emotion_detection(dataset_base, emotion_dir) diff --git a/motion-gan-pipeline/preprocessing/face_parsing/79999_iter.pth b/motion-gan-pipeline/preprocessing/face_parsing/79999_iter.pth new file mode 100644 index 0000000..ca57f32 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_parsing/79999_iter.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:468e13ca13a9b43cc0881a9f99083a430e9c0a38abd935431d1c28ee94b26567 +size 53289463 diff --git a/motion-gan-pipeline/preprocessing/face_parsing/logger.py b/motion-gan-pipeline/preprocessing/face_parsing/logger.py new file mode 100644 index 0000000..d3f9ddc --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_parsing/logger.py @@ -0,0 +1,23 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + + +import os.path as osp +import time +import sys +import logging + +import torch.distributed as dist + + +def setup_logger(logpth): + logfile = 'BiSeNet-{}.log'.format(time.strftime('%Y-%m-%d-%H-%M-%S')) + logfile = osp.join(logpth, logfile) + FORMAT = '%(levelname)s %(filename)s(%(lineno)d): %(message)s' + log_level = logging.INFO + if dist.is_initialized() and not dist.get_rank()==0: + log_level = logging.ERROR + logging.basicConfig(level=log_level, format=FORMAT, filename=logfile) + logging.root.addHandler(logging.StreamHandler()) + + diff --git a/motion-gan-pipeline/preprocessing/face_parsing/model.py b/motion-gan-pipeline/preprocessing/face_parsing/model.py new file mode 100644 index 0000000..5119e75 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_parsing/model.py @@ -0,0 +1,283 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision + +from .resnet import Resnet18 +# from modules.bn import InPlaceABNSync as BatchNorm2d + + +class ConvBNReLU(nn.Module): + def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs): + super(ConvBNReLU, self).__init__() + self.conv = nn.Conv2d(in_chan, + out_chan, + kernel_size = ks, + stride = stride, + padding = padding, + bias = False) + self.bn = nn.BatchNorm2d(out_chan) + self.init_weight() + + def forward(self, x): + x = self.conv(x) + x = F.relu(self.bn(x)) + return x + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + +class BiSeNetOutput(nn.Module): + def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs): + super(BiSeNetOutput, self).__init__() + self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1) + self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False) + self.init_weight() + + def forward(self, x): + x = self.conv(x) + x = self.conv_out(x) + return x + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +class AttentionRefinementModule(nn.Module): + def __init__(self, in_chan, out_chan, *args, **kwargs): + super(AttentionRefinementModule, self).__init__() + self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1) + self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False) + self.bn_atten = nn.BatchNorm2d(out_chan) + self.sigmoid_atten = nn.Sigmoid() + self.init_weight() + + def forward(self, x): + feat = self.conv(x) + atten = F.avg_pool2d(feat, feat.size()[2:]) + atten = self.conv_atten(atten) + atten = self.bn_atten(atten) + atten = self.sigmoid_atten(atten) + out = torch.mul(feat, atten) + return out + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + +class ContextPath(nn.Module): + def __init__(self, *args, **kwargs): + super(ContextPath, self).__init__() + self.resnet = Resnet18() + self.arm16 = AttentionRefinementModule(256, 128) + self.arm32 = AttentionRefinementModule(512, 128) + self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) + self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) + self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0) + + self.init_weight() + + def forward(self, x): + H0, W0 = x.size()[2:] + feat8, feat16, feat32 = self.resnet(x) + H8, W8 = feat8.size()[2:] + H16, W16 = feat16.size()[2:] + H32, W32 = feat32.size()[2:] + + avg = F.avg_pool2d(feat32, feat32.size()[2:]) + avg = self.conv_avg(avg) + avg_up = F.interpolate(avg, (H32, W32), mode='nearest') + + feat32_arm = self.arm32(feat32) + feat32_sum = feat32_arm + avg_up + feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest') + feat32_up = self.conv_head32(feat32_up) + + feat16_arm = self.arm16(feat16) + feat16_sum = feat16_arm + feat32_up + feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest') + feat16_up = self.conv_head16(feat16_up) + + return feat8, feat16_up, feat32_up # x8, x8, x16 + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv2d)): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +### This is not used, since I replace this with the resnet feature with the same size +class SpatialPath(nn.Module): + def __init__(self, *args, **kwargs): + super(SpatialPath, self).__init__() + self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3) + self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1) + self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1) + self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0) + self.init_weight() + + def forward(self, x): + feat = self.conv1(x) + feat = self.conv2(feat) + feat = self.conv3(feat) + feat = self.conv_out(feat) + return feat + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +class FeatureFusionModule(nn.Module): + def __init__(self, in_chan, out_chan, *args, **kwargs): + super(FeatureFusionModule, self).__init__() + self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0) + self.conv1 = nn.Conv2d(out_chan, + out_chan//4, + kernel_size = 1, + stride = 1, + padding = 0, + bias = False) + self.conv2 = nn.Conv2d(out_chan//4, + out_chan, + kernel_size = 1, + stride = 1, + padding = 0, + bias = False) + self.relu = nn.ReLU(inplace=True) + self.sigmoid = nn.Sigmoid() + self.init_weight() + + def forward(self, fsp, fcp): + fcat = torch.cat([fsp, fcp], dim=1) + feat = self.convblk(fcat) + atten = F.avg_pool2d(feat, feat.size()[2:]) + atten = self.conv1(atten) + atten = self.relu(atten) + atten = self.conv2(atten) + atten = self.sigmoid(atten) + feat_atten = torch.mul(feat, atten) + feat_out = feat_atten + feat + return feat_out + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +class BiSeNet(nn.Module): + def __init__(self, n_classes, *args, **kwargs): + super(BiSeNet, self).__init__() + self.cp = ContextPath() + ## here self.sp is deleted + self.ffm = FeatureFusionModule(256, 256) + self.conv_out = BiSeNetOutput(256, 256, n_classes) + self.conv_out16 = BiSeNetOutput(128, 64, n_classes) + self.conv_out32 = BiSeNetOutput(128, 64, n_classes) + self.init_weight() + + def forward(self, x): + H, W = x.size()[2:] + feat_res8, feat_cp8, feat_cp16 = self.cp(x) # here return res3b1 feature + feat_sp = feat_res8 # use res3b1 feature to replace spatial path feature + feat_fuse = self.ffm(feat_sp, feat_cp8) + + feat_out = self.conv_out(feat_fuse) + feat_out16 = self.conv_out16(feat_cp8) + feat_out32 = self.conv_out32(feat_cp16) + + feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True) + feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True) + feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True) + return feat_out, feat_out16, feat_out32 + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], [] + for name, child in self.named_children(): + child_wd_params, child_nowd_params = child.get_params() + if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput): + lr_mul_wd_params += child_wd_params + lr_mul_nowd_params += child_nowd_params + else: + wd_params += child_wd_params + nowd_params += child_nowd_params + return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params + + +if __name__ == "__main__": + net = BiSeNet(19) + net.cuda() + net.eval() + in_ten = torch.randn(16, 3, 640, 480).cuda() + out, out16, out32 = net(in_ten) + print(out.shape) + + net.get_params() diff --git a/motion-gan-pipeline/preprocessing/face_parsing/resnet.py b/motion-gan-pipeline/preprocessing/face_parsing/resnet.py new file mode 100644 index 0000000..aa2bf95 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_parsing/resnet.py @@ -0,0 +1,109 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.model_zoo as modelzoo + +# from modules.bn import InPlaceABNSync as BatchNorm2d + +resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth' + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + def __init__(self, in_chan, out_chan, stride=1): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(in_chan, out_chan, stride) + self.bn1 = nn.BatchNorm2d(out_chan) + self.conv2 = conv3x3(out_chan, out_chan) + self.bn2 = nn.BatchNorm2d(out_chan) + self.relu = nn.ReLU(inplace=True) + self.downsample = None + if in_chan != out_chan or stride != 1: + self.downsample = nn.Sequential( + nn.Conv2d(in_chan, out_chan, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(out_chan), + ) + + def forward(self, x): + residual = self.conv1(x) + residual = F.relu(self.bn1(residual)) + residual = self.conv2(residual) + residual = self.bn2(residual) + + shortcut = x + if self.downsample is not None: + shortcut = self.downsample(x) + + out = shortcut + residual + out = self.relu(out) + return out + + +def create_layer_basic(in_chan, out_chan, bnum, stride=1): + layers = [BasicBlock(in_chan, out_chan, stride=stride)] + for i in range(bnum-1): + layers.append(BasicBlock(out_chan, out_chan, stride=1)) + return nn.Sequential(*layers) + + +class Resnet18(nn.Module): + def __init__(self): + super(Resnet18, self).__init__() + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1) + self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2) + self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2) + self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2) + self.init_weight() + + def forward(self, x): + x = self.conv1(x) + x = F.relu(self.bn1(x)) + x = self.maxpool(x) + + x = self.layer1(x) + feat8 = self.layer2(x) # 1/8 + feat16 = self.layer3(feat8) # 1/16 + feat32 = self.layer4(feat16) # 1/32 + return feat8, feat16, feat32 + + def init_weight(self): + state_dict = modelzoo.load_url(resnet18_url) + self_state_dict = self.state_dict() + for k, v in state_dict.items(): + if 'fc' in k: continue + self_state_dict.update({k: v}) + self.load_state_dict(self_state_dict) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv2d)): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +if __name__ == "__main__": + net = Resnet18() + x = torch.randn(16, 3, 224, 224) + out = net(x) + print(out[0].size()) + print(out[1].size()) + print(out[2].size()) + net.get_params() diff --git a/motion-gan-pipeline/preprocessing/face_parsing/test.py b/motion-gan-pipeline/preprocessing/face_parsing/test.py new file mode 100644 index 0000000..de1b5d8 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_parsing/test.py @@ -0,0 +1,94 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- +import numpy as np +from .logger import setup_logger +from .model import BiSeNet + +import torch + +import os +import os.path as osp + +from PIL import Image +import torchvision.transforms as transforms +import cv2 +from pathlib import Path +from tqdm import tqdm + + +def vis_parsing_maps(im, parsing_anno, stride, save_im=False, save_path='vis_results/parsing_map_on_im.jpg', + img_size=(512, 512)): + im = np.array(im) + vis_im = im.copy().astype(np.uint8) + vis_parsing_anno = parsing_anno.copy().astype(np.uint8) + vis_parsing_anno = cv2.resize( + vis_parsing_anno, None, fx=stride, fy=stride, interpolation=cv2.INTER_NEAREST) + vis_parsing_anno_color = np.zeros( + (vis_parsing_anno.shape[0], vis_parsing_anno.shape[1], 3)) + np.array([255, 255, 255]) # + 255 + + num_of_class = np.max(vis_parsing_anno) + # print(num_of_class) + for pi in range(1, 14): + index = np.where(vis_parsing_anno == pi) + vis_parsing_anno_color[index[0], index[1], :] = np.array([255, 0, 0]) + + for pi in range(14, 16): + index = np.where(vis_parsing_anno == pi) + vis_parsing_anno_color[index[0], index[1], :] = np.array([0, 255, 0]) + for pi in range(16, 17): + index = np.where(vis_parsing_anno == pi) + vis_parsing_anno_color[index[0], index[1], :] = np.array([0, 0, 255]) + for pi in range(17, num_of_class+1): + index = np.where(vis_parsing_anno == pi) + vis_parsing_anno_color[index[0], index[1], :] = np.array([255, 0, 0]) + + vis_parsing_anno_color = vis_parsing_anno_color.astype(np.uint8) + index = np.where(vis_parsing_anno == num_of_class-1) + vis_im = cv2.resize(vis_parsing_anno_color, img_size, + interpolation=cv2.INTER_NEAREST) + if save_im: + cv2.imwrite(save_path, vis_im) + + +def evaluate(respth='./res/test_res', dspth='./data', cp='model_final_diss.pth'): + + Path(respth).mkdir(parents=True, exist_ok=True) + + n_classes = 19 + net = BiSeNet(n_classes=n_classes) + net.cuda() + net.load_state_dict(torch.load(cp)) + net.eval() + + to_tensor = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + ]) + processed_num = 0 + with torch.no_grad(): + for image_path in tqdm(os.listdir(dspth)): + if image_path.endswith('.jpg') or image_path.endswith('.png'): + img = Image.open(osp.join(dspth, image_path)) + ori_size = img.size + image = img.resize((512, 512), Image.BILINEAR) + image = image.convert("RGB") + img = to_tensor(image) + img = torch.unsqueeze(img, 0) + img = img.cuda() + out = net(img)[0] + parsing = out.squeeze(0).cpu().numpy().argmax(0) + image_path = int(image_path[:-4]) + image_path = str(image_path) + '.png' + + vis_parsing_maps(image, parsing, stride=1, save_im=True, + save_path=osp.join(respth, image_path), img_size=ori_size) + processed_num = processed_num + 1 + # if processed_num % 100 == 0: + # print('processed parsing', processed_num) + + +def parse_face(outpath, framespath): + + modelpath='preprocessing/face_parsing/79999_iter.pth' + + evaluate(respth=outpath, dspth=framespath, cp=modelpath) diff --git a/motion-gan-pipeline/preprocessing/face_tracking/3DMM/01_MorphableModel.mat b/motion-gan-pipeline/preprocessing/face_tracking/3DMM/01_MorphableModel.mat new file mode 100644 index 0000000..f251485 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_tracking/3DMM/01_MorphableModel.mat @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:37b1f0742db356a3b1568a8365a06f5b0fe0ab687ac1c3068c803666cbd4d8e2 +size 240875364 diff --git a/motion-gan-pipeline/preprocessing/face_tracking/3DMM/3DMM_info.npy b/motion-gan-pipeline/preprocessing/face_tracking/3DMM/3DMM_info.npy new file mode 100644 index 0000000..5c27d59 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_tracking/3DMM/3DMM_info.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2c360b2dc7d8cf891bd468455451b6aa10eb762615a500b02da5079791bfc9be +size 199586817 diff --git a/motion-gan-pipeline/preprocessing/face_tracking/3DMM/exp_info.npy b/motion-gan-pipeline/preprocessing/face_tracking/3DMM/exp_info.npy new file mode 100644 index 0000000..31a84bc --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_tracking/3DMM/exp_info.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c3196029ed038eb9a461df6c782125fc4d1ec1545f2e5f361891471136b6cbb6 +size 33264853 diff --git a/motion-gan-pipeline/preprocessing/face_tracking/3DMM/keys_info.npy b/motion-gan-pipeline/preprocessing/face_tracking/3DMM/keys_info.npy new file mode 100644 index 0000000..1be7e71 --- /dev/null +++ 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16396 16324 16397 +f 27265 27216 27217 +f 16324 16279 16325 +f 27313 27265 27266 +f 16279 16238 16280 +f 27358 27313 27314 +f 16280 16238 16281 +f 27358 27314 27315 +f 16445 16372 16446 +f 27192 27110 27111 +f 16446 16372 16447 +f 27192 27111 27112 diff --git a/motion-gan-pipeline/preprocessing/face_tracking/3DMM/topology_info.npy b/motion-gan-pipeline/preprocessing/face_tracking/3DMM/topology_info.npy new file mode 100644 index 0000000..854f184 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_tracking/3DMM/topology_info.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2edff6a6ad574d2dddf0d0815e0beabfea9369d7c2d6e53e0ba81f809b81e963 +size 4145201 diff --git a/motion-gan-pipeline/preprocessing/face_tracking/FLAME/FLAME.py b/motion-gan-pipeline/preprocessing/face_tracking/FLAME/FLAME.py new file mode 100755 index 0000000..4aaeda8 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_tracking/FLAME/FLAME.py @@ -0,0 +1,332 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import torch +import torch.nn as nn +import numpy as np +import pickle +import torch.nn.functional as F + +from .lbs import lbs, batch_rodrigues, vertices2landmarks, rot_mat_to_euler +from ..util import * + + +def to_tensor(array, dtype=torch.float32): + if 'torch.tensor' not in str(type(array)): + return torch.tensor(array, dtype=dtype) + + +def to_np(array, dtype=np.float32): + if 'scipy.sparse' in str(type(array)): + array = array.todense() + return np.array(array, dtype=dtype) + + +class Struct(object): + def __init__(self, **kwargs): + for key, val in kwargs.items(): + setattr(self, key, val) + + +class FLAME(nn.Module): + """ + borrowed from https://github.com/soubhiksanyal/FLAME_PyTorch/blob/master/FLAME.py + Given flame parameters this class generates a differentiable FLAME function + which outputs the a mesh and 2D/3D facial landmarks + """ + def __init__(self, config): + super(FLAME, self).__init__() + print("creating the FLAME Decoder") + with open(config.flame_model_path, 'rb') as f: + ss = pickle.load(f, encoding='latin1') + flame_model = Struct(**ss) + + self.scale_factor = torch.tensor(7.4254, dtype=torch.float).cuda() + # self.scale_factor = torch.tensor(1.0, dtype=torch.float).cuda() + + # self.dtype = torch.float32 + self.dtype = torch.double + + self.register_buffer('faces_tensor', to_tensor(to_np(flame_model.f, dtype=np.int64), dtype=torch.long).cuda()) + # The vertices of the template model + self.register_buffer('v_template', to_tensor(to_np(flame_model.v_template), dtype=self.dtype).cuda()) + # The shape components and expression + shapedirs = to_tensor(to_np(flame_model.shapedirs), dtype=self.dtype) + shapedirs = torch.cat([shapedirs[:,:,:config.n_shape], shapedirs[:,:,300:300+config.n_exp]], 2).cuda() + self.register_buffer('shapedirs', shapedirs) + # The pose components + num_pose_basis = flame_model.posedirs.shape[-1] + posedirs = np.reshape(flame_model.posedirs, [-1, num_pose_basis]).T + self.register_buffer('posedirs', to_tensor(to_np(posedirs), dtype=self.dtype).cuda()) + # + self.register_buffer('J_regressor', to_tensor(to_np(flame_model.J_regressor), dtype=self.dtype).cuda()) + parents = to_tensor(to_np(flame_model.kintree_table[0])).long(); parents[0] = -1 + self.register_buffer('parents', parents) + self.register_buffer('lbs_weights', to_tensor(to_np(flame_model.weights), dtype=self.dtype).cuda()) + + # Fixing Eyeball and neck rotation + default_eyball_pose = torch.zeros([1, 6], dtype=self.dtype, requires_grad=False).cuda() + self.register_parameter('eye_pose', nn.Parameter(default_eyball_pose, + requires_grad=False)) + default_neck_pose = torch.zeros([1, 3], dtype=self.dtype, requires_grad=False).cuda() + self.register_parameter('neck_pose', nn.Parameter(default_neck_pose, + requires_grad=False)) + + default_pose = torch.zeros([1, config.n_pose], dtype=self.dtype, requires_grad=False).cuda() + self.register_parameter('base_pose', nn.Parameter(default_pose, + requires_grad=False)) + + # Static and Dynamic Landmark embeddings for FLAME + lmk_embeddings = np.load(config.flame_lmk_embedding_path, allow_pickle=True, encoding='latin1') + lmk_embeddings = lmk_embeddings[()] + self.register_buffer('lmk_faces_idx', torch.from_numpy(lmk_embeddings['static_lmk_faces_idx']).long().cuda()) + self.register_buffer('lmk_bary_coords', torch.from_numpy(lmk_embeddings['static_lmk_bary_coords']).to(self.dtype).cuda()) + self.register_buffer('dynamic_lmk_faces_idx', lmk_embeddings['dynamic_lmk_faces_idx'].long()) + self.register_buffer('dynamic_lmk_bary_coords', lmk_embeddings['dynamic_lmk_bary_coords'].to(self.dtype)) + self.register_buffer('full_lmk_faces_idx', torch.from_numpy(lmk_embeddings['full_lmk_faces_idx']).long().cuda()) + self.register_buffer('full_lmk_bary_coords', torch.from_numpy(lmk_embeddings['full_lmk_bary_coords']).to(self.dtype).cuda()) + + neck_kin_chain = []; NECK_IDX=1 + curr_idx = torch.tensor(NECK_IDX, dtype=torch.long) + while curr_idx != -1: + neck_kin_chain.append(curr_idx) + curr_idx = self.parents[curr_idx] + self.register_buffer('neck_kin_chain', torch.stack(neck_kin_chain)) + + def _find_dynamic_lmk_idx_and_bcoords(self, pose, dynamic_lmk_faces_idx, + dynamic_lmk_b_coords, + neck_kin_chain, dtype=torch.float32): + """ + Selects the face contour depending on the reletive position of the head + Input: + vertices: N X num_of_vertices X 3 + pose: N X full pose + dynamic_lmk_faces_idx: The list of contour face indexes + dynamic_lmk_b_coords: The list of contour barycentric weights + neck_kin_chain: The tree to consider for the relative rotation + dtype: Data type + return: + The contour face indexes and the corresponding barycentric weights + """ + + batch_size = pose.shape[0] + + aa_pose = torch.index_select(pose.view(batch_size, -1, 3), 1, + neck_kin_chain) + rot_mats = batch_rodrigues( + aa_pose.view(-1, 3), dtype=dtype).view(batch_size, -1, 3, 3) + + rel_rot_mat = torch.eye(3, device=pose.device, + dtype=dtype).unsqueeze_(dim=0).expand(batch_size, -1, -1) + for idx in range(len(neck_kin_chain)): + rel_rot_mat = torch.bmm(rot_mats[:, idx], rel_rot_mat) + + y_rot_angle = torch.round( + torch.clamp(rot_mat_to_euler(rel_rot_mat) * 180.0 / np.pi, + max=39)).to(dtype=torch.long) + + neg_mask = y_rot_angle.lt(0).to(dtype=torch.long) + mask = y_rot_angle.lt(-39).to(dtype=torch.long) + neg_vals = mask * 78 + (1 - mask) * (39 - y_rot_angle) + y_rot_angle = (neg_mask * neg_vals + + (1 - neg_mask) * y_rot_angle) + + dyn_lmk_faces_idx = torch.index_select(dynamic_lmk_faces_idx, + 0, y_rot_angle) + dyn_lmk_b_coords = torch.index_select(dynamic_lmk_b_coords, + 0, y_rot_angle) + return dyn_lmk_faces_idx, dyn_lmk_b_coords + + def _vertices2landmarks(self, vertices, faces, lmk_faces_idx, lmk_bary_coords): + """ + Calculates landmarks by barycentric interpolation + Input: + vertices: torch.tensor NxVx3, dtype = torch.float32 + The tensor of input vertices + faces: torch.tensor (N*F)x3, dtype = torch.long + The faces of the mesh + lmk_faces_idx: torch.tensor N X L, dtype = torch.long + The tensor with the indices of the faces used to calculate the + landmarks. + lmk_bary_coords: torch.tensor N X L X 3, dtype = torch.float32 + The tensor of barycentric coordinates that are used to interpolate + the landmarks + + Returns: + landmarks: torch.tensor NxLx3, dtype = torch.float32 + The coordinates of the landmarks for each mesh in the batch + """ + # Extract the indices of the vertices for each face + # NxLx3 + batch_size, num_verts = vertices.shape[:dd2] + lmk_faces = torch.index_select(faces, 0, lmk_faces_idx.view(-1)).view( + 1, -1, 3).view(batch_size, lmk_faces_idx.shape[1], -1) + + lmk_faces += torch.arange(batch_size, dtype=torch.long).view(-1, 1, 1).to( + device=vertices.device) * num_verts + + lmk_vertices = vertices.view(-1, 3)[lmk_faces] + landmarks = torch.einsum('blfi,blf->bli', [lmk_vertices, lmk_bary_coords]) + return landmarks + + def seletec_3d68(self, vertices): + landmarks3d = vertices2landmarks(vertices, self.faces_tensor, + self.full_lmk_faces_idx.repeat(vertices.shape[0], 1), + self.full_lmk_bary_coords.repeat(vertices.shape[0], 1, 1)) + return landmarks3d + + def get_3dlandmarks(self, shape_params, expression_params, euler_angle, trans, focal_length, cxy): + + vertices = self.forward_geo(shape_params, expression_params, euler_angle, trans, rot=True) + + bz = vertices.shape[0] + landmarks3d = vertices2landmarks(vertices, self.faces_tensor, + self.full_lmk_faces_idx.repeat(bz, 1), + self.full_lmk_bary_coords.repeat(bz, 1, 1)) + + return landmarks3d.cuda() + + def get_3dlandmarks_special(self, shape_params, expression_params, euler_angle, trans, idx): + + vertices = self.forward_geo(shape_params, expression_params, euler_angle, trans, rot=True) + + # New vertices + new_faces = np.array([idx]) + device = shape_params.get_device() + new_faces_idx = torch.from_numpy(new_faces).unsqueeze(0).to(device) + new_bary_coord = torch.from_numpy(np.repeat([[0., 0., 1.]], new_faces.shape[0], axis=0)).unsqueeze(0).to(device) + + ldk_faces_idx = torch.cat((self.full_lmk_faces_idx, new_faces_idx), dim=1) + ldk_bary_coord = torch.cat((self.full_lmk_bary_coords, new_bary_coord), dim=1) + + bz = vertices.shape[0] + landmarks3d = vertices2landmarks(vertices, self.faces_tensor, + ldk_faces_idx.repeat(bz, 1), + ldk_bary_coord.repeat(bz, 1, 1)) + + return landmarks3d.cuda() + + def get_3dlandmarks_forehead(self, shape_params, expression_params, euler_angle, trans, arg_focal, cxy): + + vertices = self.forward_geo(shape_params, expression_params, euler_angle, trans, rot=True) + + # New vertices + # new_faces = np.array([2883, 2875, 2878, 7926, 2865, 7970, 2909, 2912, 2933, 7473, 2411, 161, 3788, 1407, 1459, 2540, 1915, 1328, 1355, 1358, 1361]) + # new_faces = np.array([2875, 7926, 7748, 1293, 1344, 3836, 1431, 1328, 1358]) # manually found in mesh + new_faces = np.array([1358, 1328, 1431, 3836, 1344, 1293, 7748, 7926, 2875]) # manually found in mesh + + device = shape_params.get_device() + new_faces_idx = torch.from_numpy(new_faces).unsqueeze(0).to(device) + new_bary_coord = torch.from_numpy(np.repeat([[0., 0., 1.]], new_faces.shape[0], axis=0)).unsqueeze(0).to(device) + + ldk_faces_idx = torch.cat((self.full_lmk_faces_idx, new_faces_idx), dim=1) + ldk_bary_coord = torch.cat((self.full_lmk_bary_coords, new_bary_coord), dim=1) + + bz = vertices.shape[0] + landmarks3d = vertices2landmarks(vertices, self.faces_tensor, + ldk_faces_idx.repeat(bz, 1), + ldk_bary_coord.repeat(bz, 1, 1)) + + return landmarks3d.cuda() + + def get_3dlandmarks_all(self, shape_params, expression_params, euler_angle, trans): + + vertices = self.forward_geo(shape_params, expression_params, euler_angle, trans, rot=True) + + # New vertices + device = shape_params.get_device() + new_faces_idx = torch.from_numpy(np.arange(0, self.faces_tensor.shape[0], step=1)).unsqueeze(0).to(device) + new_bary_coord = torch.from_numpy(np.repeat([[0., 0., 1.]], self.faces_tensor.shape[0], axis=0)).unsqueeze(0).to(device) + + bz = vertices.shape[0] + landmarks3d = vertices2landmarks(vertices, self.faces_tensor, + new_faces_idx.repeat(bz, 1), + new_bary_coord.repeat(bz, 1, 1)) + + return landmarks3d.cuda() + + def forward_geo(self, shape_params, expression_params, euler_angle, trans, rot=True): + batch_size = shape_params.shape[0] + + eye_pose_params = self.eye_pose.expand(batch_size, -1).cuda() + betas = torch.cat([shape_params, expression_params[:, :50]], dim=1) + + # TODO: fix FLAME jaw pose + full_pose = torch.cat( + [torch.zeros_like(euler_angle), + self.neck_pose.expand(batch_size, -1), + expression_params[:, 50:].expand(batch_size, -1), + eye_pose_params], dim=1) + + template_vertices = self.v_template.unsqueeze(0).expand(batch_size, -1, -1).cuda() + + vertices, _ = lbs(betas, full_pose, template_vertices, + self.shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, dtype=self.dtype) + + # Scale to Basel scale + scale = self.scale_factor.expand(1, 3) + vertices = torch.mul(vertices, scale).double() + + # Rotate and Translate mesh + if rot: + vertices = forward_rott(vertices, euler_angle, trans) + + return vertices.cuda() + + def forward(self, shape_params=None, expression_params=None, pose_params=None, eye_pose_params=None): + """ + Input: + shape_params: N X number of shape parameters + expression_params: N X number of expression parameters + pose_params: N X number of pose parameters (6) + return:d + vertices: N X V X 3 + landmarks: N X number of landmarks X 3 + """ + batch_size = shape_params.shape[0] + if eye_pose_params is None: + eye_pose_params = self.eye_pose.expand(batch_size, -1).cuda() + betas = torch.cat([shape_params, expression_params[:, :50]], dim=1) + full_pose = torch.cat([pose_params[:, :3], self.neck_pose.expand(batch_size, -1), pose_params[:, 3:], eye_pose_params], dim=1) + template_vertices = self.v_template.unsqueeze(0).expand(batch_size, -1, -1).cuda() + + vertices, _ = lbs(betas, full_pose, template_vertices, + self.shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, dtype=self.dtype) + + scale = self.scale_factor.expand(1, 3) + vertices = torch.mul(vertices, scale).double() + + lmk_faces_idx = self.lmk_faces_idx.unsqueeze(dim=0).expand(batch_size, -1) + lmk_bary_coords = self.lmk_bary_coords.unsqueeze(dim=0).expand(batch_size, -1, -1) + + dyn_lmk_faces_idx, dyn_lmk_bary_coords = self._find_dynamic_lmk_idx_and_bcoords( + full_pose, self.dynamic_lmk_faces_idx.cuda(), + self.dynamic_lmk_bary_coords.cuda(), + self.neck_kin_chain.cuda(), dtype=self.dtype) + lmk_faces_idx = torch.cat([dyn_lmk_faces_idx, lmk_faces_idx], 1) + lmk_bary_coords = torch.cat([dyn_lmk_bary_coords, lmk_bary_coords], 1) + + landmarks2d = vertices2landmarks(vertices, self.faces_tensor, + lmk_faces_idx, + lmk_bary_coords) + bz = vertices.shape[0] + landmarks3d = vertices2landmarks(vertices, self.faces_tensor, + self.full_lmk_faces_idx.repeat(bz, 1), + self.full_lmk_bary_coords.repeat(bz, 1, 1)) + return vertices, landmarks2d, landmarks3d diff --git a/motion-gan-pipeline/preprocessing/face_tracking/FLAME/__init__.py b/motion-gan-pipeline/preprocessing/face_tracking/FLAME/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/motion-gan-pipeline/preprocessing/face_tracking/FLAME/config.py b/motion-gan-pipeline/preprocessing/face_tracking/FLAME/config.py new file mode 100644 index 0000000..7bd0827 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_tracking/FLAME/config.py @@ -0,0 +1,80 @@ +''' +Default config for DECA +''' +from yacs.config import CfgNode as CN +import argparse +import yaml +import os + +cfg = CN() + +abs_deca_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) +cfg.deca_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../third/DECA/')) + +cfg.device = 'cuda' +cfg.device_id = '0' + +cfg.pretrained_modelpath = os.path.join(cfg.deca_dir, 'data', 'deca_model.tar') + +# ---------------------------------------------------------------------------- # +# Options for Face model +# ---------------------------------------------------------------------------- # +cfg.model = CN() +cfg.model.topology_path = os.path.join(cfg.deca_dir, 'data', 'head_template.obj') +# texture data original from http://files.is.tue.mpg.de/tbolkart/FLAME/FLAME_texture_data.zip +cfg.model.dense_template_path = os.path.join(cfg.deca_dir, 'data', 'texture_data_256.npy') +cfg.model.fixed_displacement_path = os.path.join(cfg.deca_dir, 'data', 'fixed_displacement_256.npy') +cfg.model.flame_model_path = os.path.join(cfg.deca_dir, 'data', 'generic_model.pkl') +cfg.model.flame_lmk_embedding_path = os.path.join(cfg.deca_dir, 'data', 'landmark_embedding.npy') +cfg.model.face_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_mask.png') +cfg.model.face_eye_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_eye_mask.png') +cfg.model.mean_tex_path = os.path.join(cfg.deca_dir, 'data', 'mean_texture.jpg') +cfg.model.tex_path = os.path.join(cfg.deca_dir, 'data', 'FLAME_albedo_from_BFM.npz') +cfg.model.tex_type = 'BFM' # BFM, FLAME, albedoMM +cfg.model.uv_size = 256 +cfg.model.param_list = ['shape', 'tex', 'exp', 'pose', 'cam', 'light'] +cfg.model.n_shape = 100 +cfg.model.n_tex = 50 +cfg.model.n_exp = 50 +cfg.model.n_cam = 3 +cfg.model.n_pose = 6 +cfg.model.n_light = 27 +cfg.model.use_tex = False +cfg.model.jaw_type = 'aa' # default use axis angle, another option: euler + +## details +cfg.model.n_detail = 128 +cfg.model.max_z = 0.01 + +# ---------------------------------------------------------------------------- # +# Options for Dataset +# ---------------------------------------------------------------------------- # +cfg.dataset = CN() +cfg.dataset.batch_size = 24 +cfg.dataset.num_workers = 2 +cfg.dataset.image_size = 224 + +def get_cfg_defaults(): + """Get a yacs CfgNode object with default values for my_project.""" + # Return a clone so that the defaults will not be altered + # This is for the "local variable" use pattern + return cfg.clone() + +def update_cfg(cfg, cfg_file): + cfg.merge_from_file(cfg_file) + return cfg.clone() + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--cfg', type=str, help='cfg file path') + + args = parser.parse_args() + print(args, end='\n\n') + + cfg = get_cfg_defaults() + if args.cfg is not None: + cfg_file = args.cfg + cfg = update_cfg(cfg, args.cfg) + cfg.cfg_file = cfg_file + + return cfg diff --git a/motion-gan-pipeline/preprocessing/face_tracking/FLAME/lbs.py b/motion-gan-pipeline/preprocessing/face_tracking/FLAME/lbs.py new file mode 100755 index 0000000..9c0fd0a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_tracking/FLAME/lbs.py @@ -0,0 +1,378 @@ +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import numpy as np + +import torch +import torch.nn.functional as F + +def rot_mat_to_euler(rot_mats): + # Calculates rotation matrix to euler angles + # Careful for extreme cases of eular angles like [0.0, pi, 0.0] + + sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] + + rot_mats[:, 1, 0] * rot_mats[:, 1, 0]) + return torch.atan2(-rot_mats[:, 2, 0], sy) + +def find_dynamic_lmk_idx_and_bcoords(vertices, pose, dynamic_lmk_faces_idx, + dynamic_lmk_b_coords, + neck_kin_chain, dtype=torch.float32): + ''' Compute the faces, barycentric coordinates for the dynamic landmarks + + + To do so, we first compute the rotation of the neck around the y-axis + and then use a pre-computed look-up table to find the faces and the + barycentric coordinates that will be used. + + Special thanks to Soubhik Sanyal (soubhik.sanyal@tuebingen.mpg.de) + for providing the original TensorFlow implementation and for the LUT. + + Parameters + ---------- + vertices: torch.tensor BxVx3, dtype = torch.float32 + The tensor of input vertices + pose: torch.tensor Bx(Jx3), dtype = torch.float32 + The current pose of the body model + dynamic_lmk_faces_idx: torch.tensor L, dtype = torch.long + The look-up table from neck rotation to faces + dynamic_lmk_b_coords: torch.tensor Lx3, dtype = torch.float32 + The look-up table from neck rotation to barycentric coordinates + neck_kin_chain: list + A python list that contains the indices of the joints that form the + kinematic chain of the neck. + dtype: torch.dtype, optional + + Returns + ------- + dyn_lmk_faces_idx: torch.tensor, dtype = torch.long + A tensor of size BxL that contains the indices of the faces that + will be used to compute the current dynamic landmarks. + dyn_lmk_b_coords: torch.tensor, dtype = torch.float32 + A tensor of size BxL that contains the indices of the faces that + will be used to compute the current dynamic landmarks. + ''' + + batch_size = vertices.shape[0] + + aa_pose = torch.index_select(pose.view(batch_size, -1, 3), 1, + neck_kin_chain) + rot_mats = batch_rodrigues( + aa_pose.view(-1, 3), dtype=dtype).view(batch_size, -1, 3, 3) + + rel_rot_mat = torch.eye(3, device=vertices.device, + dtype=dtype).unsqueeze_(dim=0) + for idx in range(len(neck_kin_chain)): + rel_rot_mat = torch.bmm(rot_mats[:, idx], rel_rot_mat) + + y_rot_angle = torch.round( + torch.clamp(-rot_mat_to_euler(rel_rot_mat) * 180.0 / np.pi, + max=39)).to(dtype=torch.long) + neg_mask = y_rot_angle.lt(0).to(dtype=torch.long) + mask = y_rot_angle.lt(-39).to(dtype=torch.long) + neg_vals = mask * 78 + (1 - mask) * (39 - y_rot_angle) + y_rot_angle = (neg_mask * neg_vals + + (1 - neg_mask) * y_rot_angle) + + dyn_lmk_faces_idx = torch.index_select(dynamic_lmk_faces_idx, + 0, y_rot_angle) + dyn_lmk_b_coords = torch.index_select(dynamic_lmk_b_coords, + 0, y_rot_angle) + + return dyn_lmk_faces_idx, dyn_lmk_b_coords + + +def vertices2landmarks(vertices, faces, lmk_faces_idx, lmk_bary_coords): + ''' Calculates landmarks by barycentric interpolation + + Parameters + ---------- + vertices: torch.tensor BxVx3, dtype = torch.float32 + The tensor of input vertices + faces: torch.tensor Fx3, dtype = torch.long + The faces of the mesh + lmk_faces_idx: torch.tensor L, dtype = torch.long + The tensor with the indices of the faces used to calculate the + landmarks. + lmk_bary_coords: torch.tensor Lx3, dtype = torch.float32 + The tensor of barycentric coordinates that are used to interpolate + the landmarks + + Returns + ------- + landmarks: torch.tensor BxLx3, dtype = torch.float32 + The coordinates of the landmarks for each mesh in the batch + ''' + # Extract the indices of the vertices for each face + # BxLx3 + batch_size, num_verts = vertices.shape[:2] + device = vertices.device + + lmk_faces = torch.index_select(faces, 0, lmk_faces_idx.view(-1)).view( + batch_size, -1, 3) + + lmk_faces += torch.arange( + batch_size, dtype=torch.long, device=device).view(-1, 1, 1) * num_verts + + lmk_vertices = vertices.contiguous().view(-1, 3)[lmk_faces].view( + batch_size, -1, 3, 3) + + landmarks = torch.einsum('blfi,blf->bli', [lmk_vertices, lmk_bary_coords]) + return landmarks + + +def lbs(betas, pose, v_template, shapedirs, posedirs, J_regressor, parents, + lbs_weights, pose2rot=True, dtype=torch.double): + ''' Performs Linear Blend Skinning with the given shape and pose parameters + + Parameters + ---------- + betas : torch.tensor BxNB + The tensor of shape parameters + pose : torch.tensor Bx(J + 1) * 3 + The pose parameters in axis-angle format + v_template torch.tensor BxVx3 + The template mesh that will be deformed + shapedirs : torch.tensor 1xNB + The tensor of PCA shape displacements + posedirs : torch.tensor Px(V * 3) + The pose PCA coefficients + J_regressor : torch.tensor JxV + The regressor array that is used to calculate the joints from + the position of the vertices + parents: torch.tensor J + The array that describes the kinematic tree for the model + lbs_weights: torch.tensor N x V x (J + 1) + The linear blend skinning weights that represent how much the + rotation matrix of each part affects each vertex + pose2rot: bool, optional + Flag on whether to convert the input pose tensor to rotation + matrices. The default value is True. If False, then the pose tensor + should already contain rotation matrices and have a size of + Bx(J + 1)x9 + dtype: torch.dtype, optional + + Returns + ------- + verts: torch.tensor BxVx3 + The vertices of the mesh after applying the shape and pose + displacements. + joints: torch.tensor BxJx3 + The joints of the model + ''' + + batch_size = max(betas.shape[0], pose.shape[0]) + device = betas.device + + # Add shape contribution + v_shaped = v_template + blend_shapes(betas, shapedirs) + + # Get the joints + # NxJx3 array + J = vertices2joints(J_regressor.type(dtype), v_shaped.type(dtype)) + + # 3. Add pose blend shapes + # N x J x 3 x 3 + ident = torch.eye(3, dtype=dtype, device=device) + if pose2rot: + rot_mats = batch_rodrigues( + pose.view(-1, 3), dtype=dtype).view([batch_size, -1, 3, 3]) + + pose_feature = (rot_mats[:, 1:, :, :] - ident).view([batch_size, -1]) + # (N x P) x (P, V * 3) -> N x V x 3 + pose_offsets = torch.matmul(pose_feature, posedirs) \ + .view(batch_size, -1, 3) + else: + pose_feature = pose[:, 1:].view(batch_size, -1, 3, 3) - ident + rot_mats = pose.view(batch_size, -1, 3, 3) + + pose_offsets = torch.matmul(pose_feature.view(batch_size, -1), + posedirs).view(batch_size, -1, 3) + + v_posed = pose_offsets + v_shaped + # 4. Get the global joint location + J_transformed, A = batch_rigid_transform(rot_mats, J, parents, dtype=dtype) + + # 5. Do skinning: + # W is N x V x (J + 1) + W = lbs_weights.unsqueeze(dim=0).expand([batch_size, -1, -1]) + # (N x V x (J + 1)) x (N x (J + 1) x 16) + num_joints = J_regressor.shape[0] + T = torch.matmul(W, A.view(batch_size, num_joints, 16)) \ + .view(batch_size, -1, 4, 4) + + homogen_coord = torch.ones([batch_size, v_posed.shape[1], 1], + dtype=dtype, device=device) + v_posed_homo = torch.cat([v_posed, homogen_coord], dim=2) + v_homo = torch.matmul(T, torch.unsqueeze(v_posed_homo, dim=-1)) + + verts = v_homo[:, :, :3, 0] + + return verts, J_transformed + + +def vertices2joints(J_regressor, vertices): + ''' Calculates the 3D joint locations from the vertices + + Parameters + ---------- + J_regressor : torch.tensor JxV + The regressor array that is used to calculate the joints from the + position of the vertices + vertices : torch.tensor BxVx3 + The tensor of mesh vertices + + Returns + ------- + torch.tensor BxJx3 + The location of the joints + ''' + + return torch.einsum('bik,ji->bjk', [vertices, J_regressor]) + + +def blend_shapes(betas, shape_disps): + ''' Calculates the per vertex displacement due to the blend shapes + + + Parameters + ---------- + betas : torch.tensor Bx(num_betas) + Blend shape coefficients + shape_disps: torch.tensor Vx3x(num_betas) + Blend shapes + + Returns + ------- + torch.tensor BxVx3 + The per-vertex displacement due to shape deformation + ''' + + # Displacement[b, m, k] = sum_{l} betas[b, l] * shape_disps[m, k, l] + # i.e. Multiply each shape displacement by its corresponding beta and + # then sum them. + blend_shape = torch.einsum('bl,mkl->bmk', [betas.double(), shape_disps.double()]) + return blend_shape + + +def batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32): + ''' Calculates the rotation matrices for a batch of rotation vectors + Parameters + ---------- + rot_vecs: torch.tensor Nx3 + array of N axis-angle vectors + Returns + ------- + R: torch.tensor Nx3x3 + The rotation matrices for the given axis-angle parameters + ''' + + batch_size = rot_vecs.shape[0] + device = rot_vecs.device + + angle = torch.norm(rot_vecs + 1e-8, dim=1, keepdim=True) + rot_dir = rot_vecs / angle + + cos = torch.unsqueeze(torch.cos(angle), dim=1) + sin = torch.unsqueeze(torch.sin(angle), dim=1) + + # Bx1 arrays + rx, ry, rz = torch.split(rot_dir, 1, dim=1) + K = torch.zeros((batch_size, 3, 3), dtype=dtype, device=device) + + zeros = torch.zeros((batch_size, 1), dtype=dtype, device=device) + K = torch.cat([zeros, -rz, ry, rz, zeros, -rx, -ry, rx, zeros], dim=1) \ + .view((batch_size, 3, 3)) + + ident = torch.eye(3, dtype=dtype, device=device).unsqueeze(dim=0) + rot_mat = ident + sin * K + (1 - cos) * torch.bmm(K, K) + return rot_mat + + +def transform_mat(R, t): + ''' Creates a batch of transformation matrices + Args: + - R: Bx3x3 array of a batch of rotation matrices + - t: Bx3x1 array of a batch of translation vectors + Returns: + - T: Bx4x4 Transformation matrix + ''' + # No padding left or right, only add an extra row + return torch.cat([F.pad(R, [0, 0, 0, 1]), + F.pad(t, [0, 0, 0, 1], value=1)], dim=2) + + +def batch_rigid_transform(rot_mats, joints, parents, dtype=torch.float32): + """ + Applies a batch of rigid transformations to the joints + + Parameters + ---------- + rot_mats : torch.tensor BxNx3x3 + Tensor of rotation matrices + joints : torch.tensor BxNx3 + Locations of joints + parents : torch.tensor BxN + The kinematic tree of each object + dtype : torch.dtype, optional: + The data type of the created tensors, the default is torch.float32 + + Returns + ------- + posed_joints : torch.tensor BxNx3 + The locations of the joints after applying the pose rotations + rel_transforms : torch.tensor BxNx4x4 + The relative (with respect to the root joint) rigid transformations + for all the joints + """ + + joints = torch.unsqueeze(joints, dim=-1) + + rel_joints = joints.clone() + rel_joints[:, 1:] -= joints[:, parents[1:]] + + # transforms_mat = transform_mat( + # rot_mats.view(-1, 3, 3), + # rel_joints.view(-1, 3, 1)).view(-1, joints.shape[1], 4, 4) + transforms_mat = transform_mat( + rot_mats.view(-1, 3, 3), + rel_joints.reshape(-1, 3, 1)).reshape(-1, joints.shape[1], 4, 4) + + transform_chain = [transforms_mat[:, 0]] + for i in range(1, parents.shape[0]): + # Subtract the joint location at the rest pose + # No need for rotation, since it's identity when at rest + curr_res = torch.matmul(transform_chain[parents[i]], + transforms_mat[:, i]) + transform_chain.append(curr_res) + + transforms = torch.stack(transform_chain, dim=1) + + # The last column of the transformations contains the posed joints + posed_joints = transforms[:, :, :3, 3] + + # The last column of the transformations contains the posed joints + posed_joints = transforms[:, :, :3, 3] + + joints_homogen = F.pad(joints, [0, 0, 0, 1]) + + rel_transforms = transforms - F.pad( + torch.matmul(transforms, joints_homogen), [3, 0, 0, 0, 0, 0, 0, 0]) + + return posed_joints, rel_transforms \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/face_tracking/__init__.py b/motion-gan-pipeline/preprocessing/face_tracking/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/motion-gan-pipeline/preprocessing/face_tracking/convert_BFM.py b/motion-gan-pipeline/preprocessing/face_tracking/convert_BFM.py new file mode 100644 index 0000000..88bc010 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_tracking/convert_BFM.py @@ -0,0 +1,61 @@ +import numpy as np +from scipy.io import loadmat + +original_BFM = loadmat('3DMM/01_MorphableModel.mat') +sub_inds = np.load('3DMM/topology_info.npy', + allow_pickle=True).item()['sub_inds'] + +print(len(sub_inds)) + +shapePC = original_BFM['shapePC'] +shapeEV = original_BFM['shapeEV'] +shapeMU = original_BFM['shapeMU'] +texPC = original_BFM['texPC'] +texEV = original_BFM['texEV'] +texMU = original_BFM['texMU'] + +print('shapePC: ', shapePC.shape) +print('shapeMU: ', shapeMU.shape) +print('texPC: ', texPC.shape) +print('texMU: ', texMU.shape) + + +b_shape = shapePC.reshape(-1, 199).transpose(1, 0).reshape(199, -1, 3) +mu_shape = shapeMU.reshape(-1, 3) +print('\nshapePC -> b_shape : ', b_shape.shape) +print('shapeMU -> mu_shape : ', mu_shape.shape) + +b_shape = b_shape[:, sub_inds, :].reshape(199, -1) +mu_shape = mu_shape[sub_inds, :].reshape(-1) +print('b_shape -> b_shape sampled : ', b_shape.shape) +print('mu_shape -> mu_shape sampled: ', mu_shape.shape) + +b_tex = texPC.reshape(-1, 199).transpose(1, 0).reshape(199, -1, 3) +mu_tex = texMU.reshape(-1, 3) +print('\ntexPC -> b_tex : ', b_tex.shape) +print('texMU -> mu_tex : ', mu_tex.shape) +b_tex = b_tex[:, sub_inds, :].reshape(199, -1) +mu_tex = mu_tex[sub_inds, :].reshape(-1) +print('b_tex -> b_tex sampled : ', b_tex.shape) +print('mu_tex -> mu_tex sampled: ', mu_tex.shape) + +exp_info = np.load('3DMM/exp_info.npy', allow_pickle=True).item() +print('\nexp_info: ') +for key in exp_info.keys(): + print(f'{key} shape: {exp_info[key].shape}') + +info = {'mu_shape': mu_shape, + 'b_shape': b_shape, + 'sig_shape': shapeEV.reshape(-1), + 'mu_exp': exp_info['mu_exp'], + 'b_exp': exp_info['base_exp'], + 'sig_exp': exp_info['sig_exp'], + 'mu_tex': mu_tex, + 'b_tex': b_tex, + 'sig_tex': texEV.reshape(-1)} + +print('\nFinal: ') +for key in info.keys(): + print(f'{key} shape: {info[key].shape}') + +np.save('3DMM/3DMM_info.npy', info) diff --git a/motion-gan-pipeline/preprocessing/face_tracking/data_loader.py b/motion-gan-pipeline/preprocessing/face_tracking/data_loader.py new file mode 100644 index 0000000..64a292e --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_tracking/data_loader.py @@ -0,0 +1,18 @@ +import torch +import cv2 +import numpy as np +import os + + +def load_dir(lmspath, framepath, start, end): + lmss = [] + imgs_paths = [] + for i in range(start, end): + if os.path.isfile(os.path.join(lmspath, '%05d.lms' % i)): + lms = np.loadtxt(os.path.join( + lmspath, '%05d.lms' % i), dtype=np.float32) + lmss.append(lms) + imgs_paths.append(os.path.join(framepath, '%05d.jpg' % i)) + lmss = np.stack(lmss) + lmss = torch.as_tensor(lmss).cuda() + return lmss, imgs_paths diff --git a/motion-gan-pipeline/preprocessing/face_tracking/face_tracker.py b/motion-gan-pipeline/preprocessing/face_tracking/face_tracker.py new file mode 100644 index 0000000..4c2dd37 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_tracking/face_tracker.py @@ -0,0 +1,663 @@ +from numpy.core.numeric import require +from numpy.lib.function_base import quantile +import torch +import numpy as np +from .facemodel import Face_3DMM +from .data_loader import load_dir +from .util import * +from .render_3dmm import Render_3DMM, Render_FLAME +from .FLAME.FLAME import FLAME +from .FLAME.config import cfg +import os +import sys +# import openmesh +import cv2 +import argparse +from pathlib import Path +from tqdm import tqdm +import matplotlib.pyplot as plt +from PIL import Image + +from pytorch3d.io import IO, save_obj + +# def np2mesh(mesh, xnp, path): +# mesh.points()[:] = xnp +# openmesh.write_mesh(path, mesh, binary=True) + + +def track_face_FLAME(dataset_base, h, w, frame_num, out_path, decaexpr_dir, expr_masks_dir): + ''' + Face tracker using FLAME model. + Used to have geometry prior for nerf sampling. + ''' + + def set_requires_grad(tensor_list): + for tensor in tensor_list: + tensor.requires_grad = True + + start_id = 0 + end_id = frame_num + + id_dir = dataset_base + + debug_ldk_dir = os.path.join(id_dir, 'debug', 'debug_landmarks') + Path(debug_ldk_dir).mkdir(parents=True, exist_ok=True) + debug_render_dir = os.path.join(id_dir, 'debug', 'debug_render') + Path(debug_render_dir).mkdir(parents=True, exist_ok=True) + debug_meshes_dir = os.path.join(id_dir, 'debug', 'debug_meshes') + Path(debug_meshes_dir).mkdir(parents=True, exist_ok=True) + + lms, img_paths = load_dir(os.path.join(id_dir, 'landmarks'), os.path.join(id_dir, 'frames'), start_id, end_id) + + num_frames = lms.shape[0] + cxy = torch.tensor((w / 2.0, h / 2.0), dtype=torch.float).cuda() + # TODO: include jaw pose flame + id_dim, exp_dim, tex_dim = 100, 53, 50 + model_3dmm = FLAME(cfg.model) + + sel_ids = np.arange(0, num_frames, 40) + sel_num = sel_ids.shape[0] + arg_focal = 1600 + arg_landis = 1e5 + + for focal in tqdm(range(600, 1500, 100)): + id_para = lms.new_zeros((1, id_dim), requires_grad=True) + exp_para = lms.new_zeros((sel_num, exp_dim), requires_grad=True) + euler_angle = lms.new_zeros((sel_num, 3), requires_grad=True) + trans = lms.new_zeros((sel_num, 3), requires_grad=True) + # trans.data[:, 2] -= 1 # DIFFERENT + trans.data[:, 2] -= 7 # ORIGINAL + focal_length = lms.new_zeros(1, requires_grad=False) + focal_length.data += focal + set_requires_grad([id_para, exp_para, euler_angle, trans]) + + optimizer_idexp = torch.optim.Adam([id_para, exp_para], lr=.1) + optimizer_frame = torch.optim.Adam([euler_angle, trans], lr=.1) # Change + + for iter in range(2000): + id_para_batch = id_para.expand(sel_num, -1) + + geometry = model_3dmm.get_3dlandmarks( + id_para_batch, exp_para, euler_angle, trans, focal_length, cxy) + + proj_geo = proj_pts(geometry, focal_length, cxy) # Different: landmarks are already rotated here + + loss_lan = cal_lan_loss( + proj_geo[:, :, :2], lms[sel_ids].detach()) + loss = loss_lan + optimizer_frame.zero_grad() + loss.backward() + optimizer_frame.step() + if iter % 100 == 0 and False: + print(focal, 'pose', iter, loss.item()) + + for iter in range(2500): + id_para_batch = id_para.expand(sel_num, -1) + + geometry = model_3dmm.get_3dlandmarks( + id_para_batch, exp_para, euler_angle, trans, focal_length, cxy) + + proj_geo = proj_pts(geometry, focal_length, cxy) + + loss_lan = cal_lan_loss( + proj_geo[:, :, :2], lms[sel_ids].detach()) + loss_regid = torch.mean(id_para * id_para) + loss_regexp = torch.mean(exp_para * exp_para) + loss = loss_lan + loss_regid * 0.5 + loss_regexp * 0.4 + optimizer_idexp.zero_grad() + optimizer_frame.zero_grad() + loss.backward() + optimizer_idexp.step() + optimizer_frame.step() + if iter % 100 == 0 and False: + print(focal, 'poseidexp', iter, loss_lan.item(), + loss_regid.item(), loss_regexp.item()) + if iter % 1500 == 0 and iter >= 1500: + for param_group in optimizer_idexp.param_groups: + param_group['lr'] *= 0.2 + for param_group in optimizer_frame.param_groups: + param_group['lr'] *= 0.2 + # print(focal, loss_lan.item(), torch.mean(trans[:, 2]).item()) + + if loss_lan.item() < arg_landis: + arg_landis = loss_lan.item() + arg_focal = focal + + print('find best focal', arg_focal) + + id_para = lms.new_zeros((1, id_dim), requires_grad=True) + exp_para = lms.new_zeros((num_frames, exp_dim), requires_grad=True) + tex_para = lms.new_zeros((1, tex_dim), requires_grad=True) + euler_angle = lms.new_zeros((num_frames, 3), requires_grad=True) + trans = lms.new_zeros((num_frames, 3), requires_grad=True) + # trans.data[:, 2] -= 1 # DIFFERENT + trans.data[:, 2] -= 7 # ORIGINAL + + light_para = lms.new_zeros((num_frames, 27), requires_grad=True) + + focal_length = lms.new_zeros(1, requires_grad=True) + focal_length.data += arg_focal + + set_requires_grad([id_para, exp_para, tex_para, + euler_angle, trans, light_para]) + + # DIFFERENT + # optimizer_idexp = torch.optim.Adam([id_para, exp_para], lr=.05) + # optimizer_frame = torch.optim.Adam([euler_angle, trans], lr=.05) + + # ORIGINAL + optimizer_idexp = torch.optim.Adam([id_para, exp_para], lr=.1) + optimizer_frame = torch.optim.Adam([euler_angle, trans], lr=1) + + for iter in tqdm(range(1500)): + id_para_batch = id_para.expand(num_frames, -1) + + geometry = model_3dmm.get_3dlandmarks( + id_para_batch, exp_para, euler_angle, trans, focal_length, cxy) + + proj_geo = proj_pts(geometry, focal_length, cxy) # DIFFERENT + + loss_lan = cal_lan_loss( + proj_geo[:, :, :2], lms.detach()) + loss = loss_lan + optimizer_frame.zero_grad() + loss.backward() + optimizer_frame.step() + if iter == 1000: + for param_group in optimizer_frame.param_groups: + param_group['lr'] = 0.1 # ORIGINAL + # param_group['lr'] = 0.01 # DIFFERENT + if iter % 100 == 0 and True: + print('pose', iter, loss.item()) + + # Added save landmarks images for debug + img = Image.open(img_paths[0]) + colormap_blue = plt.cm.Blues + colormap_red = plt.cm.Reds + + # plt.imshow(img) + for num, lin in enumerate(np.linspace(0, 0.9, len(lms[0, :, 0]))): + plt.scatter(lms[0, num, 0].detach().cpu(), + lms[0, num, 1].detach().cpu(), + color=colormap_blue(lin), + s=10) + + plt.scatter(proj_geo[0, num, 0].detach().cpu(), + proj_geo[0, num, 1].detach().cpu(), + color=colormap_red(lin), + s=10) + + plt.savefig(os.path.join(debug_ldk_dir, f'ldk_1_{iter}.png')) + plt.close() + + for param_group in optimizer_frame.param_groups: + param_group['lr'] = 0.1 # ORIGINAL + # param_group['lr'] = 0.05 # DIFFERENT + + for iter in tqdm(range(2000)): + id_para_batch = id_para.expand(num_frames, -1) + + geometry = model_3dmm.get_3dlandmarks( + id_para_batch, exp_para, euler_angle, trans, focal_length, cxy) + + proj_geo = proj_pts(geometry, focal_length, cxy) # DIFFERENT + + loss_lan = cal_lan_loss( + proj_geo[:, :, :2], lms.detach()) + loss_regid = torch.mean(id_para * id_para) + loss_regexp = torch.mean(exp_para * exp_para) + loss = loss_lan + loss_regid * 0.5 + loss_regexp * 0.4 + optimizer_idexp.zero_grad() + optimizer_frame.zero_grad() + loss.backward() + optimizer_idexp.step() + optimizer_frame.step() + if iter % 100 == 0 and True: + print('poseidexp', iter, loss_lan.item(), + loss_regid.item(), loss_regexp.item()) + + img = Image.open(img_paths[0]) + colormap_blue = plt.cm.Blues + colormap_red = plt.cm.Reds + + # plt.imshow(img) + for num, lin in enumerate(np.linspace(0, 0.9, len(lms[0, :, 0]))): + plt.scatter(lms[0, num, 0].detach().cpu(), + lms[0, num, 1].detach().cpu(), + color=colormap_blue(lin), + s=10) + + plt.scatter(proj_geo[0, num, 0].detach().cpu(), + proj_geo[0, num, 1].detach().cpu(), + color=colormap_red(lin), + s=10) + + plt.savefig(os.path.join(debug_ldk_dir, f'ldk_2_{iter}.png')) + plt.close() + + if iter % 1000 == 0 and iter >= 1000: + for param_group in optimizer_idexp.param_groups: + param_group['lr'] *= 0.2 + for param_group in optimizer_frame.param_groups: + param_group['lr'] *= 0.2 + + # THEY DO THIS + exp_para = exp_para.detach() + euler_angle = euler_angle.detach() + trans = trans.detach() + light_para = light_para.detach() + + batch_size = 10 + device_default = torch.device('cuda:0') + device_render = torch.device('cuda:0') + renderer = Render_FLAME(model_3dmm.faces_tensor, arg_focal, h, w, batch_size, device_render) + + for i in tqdm(range(int((num_frames-1)/batch_size+1))): + if (i + 1) * batch_size > num_frames: + sel_ids = np.arange(num_frames - batch_size, num_frames) + else: + sel_ids = np.arange(i * batch_size, i * batch_size + batch_size) + imgs = [] + for sel_id in sel_ids: + imgs.append(cv2.imread(img_paths[sel_id])[:, :, ::-1]) + imgs = np.stack(imgs) + sel_imgs = torch.as_tensor(imgs).cuda() + + sel_exp_para = exp_para.new_zeros( + (batch_size, exp_dim), requires_grad=False) + sel_exp_para.data = exp_para[sel_ids].clone() + sel_euler = euler_angle.new_zeros( + (batch_size, 3), requires_grad=False) + sel_euler.data = euler_angle[sel_ids].clone() + sel_trans = trans.new_zeros((batch_size, 3), requires_grad=False) + sel_trans.data = trans[sel_ids].clone() + sel_light = light_para.new_zeros( + (batch_size, 27), requires_grad=False) + sel_light.data = light_para[sel_ids].clone() + + sel_id_para = id_para.expand(batch_size, -1).detach() + + rott_geo = model_3dmm.forward_geo(sel_id_para, sel_exp_para, sel_euler, sel_trans) + + render_imgs = renderer(rott_geo.to(device_render), model_3dmm.faces_tensor.to(device_render)) + render_imgs = render_imgs.to(device_default) + + mask = (render_imgs[:, :, :, 3]).detach() > 0.0 + mask_img = mask.clone().cpu().numpy() * 255. + + render_proj = sel_imgs.clone() + render_proj[mask] = render_imgs[mask][..., :3].byte() + + for j in range(sel_ids.shape[0]): + img_arr = render_proj[j, :, :, :3].byte().detach().cpu().numpy()[ + :, :, ::-1] + cv2.imwrite(os.path.join(debug_render_dir, str(sel_ids[j]) + '.jpg'), + img_arr) + + cv2.imwrite(os.path.join(expr_masks_dir, str(sel_ids[j]) + '.jpg'), + mask_img[j]) + + # Save expr + np.save(os.path.join(decaexpr_dir, f'{sel_ids[j]}.npy'), sel_exp_para[j].cpu().numpy()) + + # renderer.get_and_save_mesh(rott_geo, os.path.join(debug_meshes_dir, str(sel_ids[j]) + '.obj')) + + print('about to save params..') + + torch.save({'id': id_para.detach().cpu(), 'exp': exp_para.detach().cpu(), + 'euler': euler_angle.detach().cpu(), 'trans': trans.detach().cpu(), + 'focal': focal_length.detach().cpu(), 'light': light_para.detach().cpu(), + 'text': tex_para.detach().cpu()}, + out_path) + + print('params saved') + + +def track_face(dataset_base, h, w, frame_num, out_path, expr_masks_dir): + ''' + Face tracker using partial Basel 2009 model with less vertices. + ''' + + dir_path = os.path.dirname(os.path.realpath(__file__)) + + def set_requires_grad(tensor_list): + for tensor in tensor_list: + tensor.requires_grad = True + + start_id = 0 + end_id = frame_num + + id_dir = dataset_base + lms, img_paths = load_dir(os.path.join(id_dir, 'landmarks'), os.path.join(id_dir, 'frames'), start_id, end_id) + num_frames = lms.shape[0] + cxy = torch.tensor((w/2.0, h/2.0), dtype=torch.float).cuda() + id_dim, exp_dim, tex_dim, point_num = 100, 79, 100, 34650 + model_3dmm = Face_3DMM(os.path.join(dir_path, '3DMM'), + id_dim, exp_dim, tex_dim, point_num) + + sel_ids = np.arange(0, num_frames, 40) + sel_num = sel_ids.shape[0] + arg_focal = 1600 + arg_landis = 1e5 + + # FINDING BEST FOCAL + for focal in tqdm(range(600, 1500, 100)): + id_para = lms.new_zeros((1, id_dim), requires_grad=True) + exp_para = lms.new_zeros((sel_num, exp_dim), requires_grad=True) + euler_angle = lms.new_zeros((sel_num, 3), requires_grad=True) + trans = lms.new_zeros((sel_num, 3), requires_grad=True) + trans.data[:, 2] -= 7 + focal_length = lms.new_zeros(1, requires_grad=False) + focal_length.data += focal + set_requires_grad([id_para, exp_para, euler_angle, trans]) + + optimizer_idexp = torch.optim.Adam([id_para, exp_para], lr=.1) + optimizer_frame = torch.optim.Adam( + [euler_angle, trans], lr=.1) + + for iter in range(2000): + id_para_batch = id_para.expand(sel_num, -1) + geometry = model_3dmm.get_3dlandmarks( + id_para_batch, exp_para, euler_angle, trans, focal_length, cxy) + proj_geo = forward_transform( + geometry, euler_angle, trans, focal_length, cxy) + loss_lan = cal_lan_loss( + proj_geo[:, :, :2], lms[sel_ids].detach()) + loss = loss_lan + optimizer_frame.zero_grad() + loss.backward() + optimizer_frame.step() + if iter % 100 == 0 and False: + print(focal, 'pose', iter, loss.item()) + + for iter in range(2500): + id_para_batch = id_para.expand(sel_num, -1) + geometry = model_3dmm.get_3dlandmarks( + id_para_batch, exp_para, euler_angle, trans, focal_length, cxy) + proj_geo = forward_transform( + geometry, euler_angle, trans, focal_length, cxy) + loss_lan = cal_lan_loss( + proj_geo[:, :, :2], lms[sel_ids].detach()) + loss_regid = torch.mean(id_para*id_para) + loss_regexp = torch.mean(exp_para*exp_para) + loss = loss_lan + loss_regid*0.5 + loss_regexp*0.4 + optimizer_idexp.zero_grad() + optimizer_frame.zero_grad() + loss.backward() + optimizer_idexp.step() + optimizer_frame.step() + if iter % 100 == 0 and False: + print(focal, 'poseidexp', iter, loss_lan.item(), + loss_regid.item(), loss_regexp.item()) + if iter % 1500 == 0 and iter >= 1500: + for param_group in optimizer_idexp.param_groups: + param_group['lr'] *= 0.2 + for param_group in optimizer_frame.param_groups: + param_group['lr'] *= 0.2 + print(focal, loss_lan.item(), torch.mean(trans[:, 2]).item()) + + if loss_lan.item() < arg_landis: + arg_landis = loss_lan.item() + arg_focal = focal + + print('find best focal', arg_focal) + + id_para = lms.new_zeros((1, id_dim), requires_grad=True) + exp_para = lms.new_zeros((num_frames, exp_dim), requires_grad=True) + tex_para = lms.new_zeros((1, tex_dim), requires_grad=True) + euler_angle = lms.new_zeros((num_frames, 3), requires_grad=True) + trans = lms.new_zeros((num_frames, 3), requires_grad=True) + light_para = lms.new_zeros((num_frames, 27), requires_grad=True) + trans.data[:, 2] -= 7 + focal_length = lms.new_zeros(1, requires_grad=True) + focal_length.data += arg_focal + + set_requires_grad([id_para, exp_para, tex_para, + euler_angle, trans, light_para]) + + optimizer_idexp = torch.optim.Adam([id_para, exp_para], lr=.1) + optimizer_frame = torch.optim.Adam([euler_angle, trans], lr=1) + + # LANDMARK BASED OPTIMIZATION + for iter in tqdm(range(1500)): + id_para_batch = id_para.expand(num_frames, -1) + geometry = model_3dmm.get_3dlandmarks( + id_para_batch, exp_para, euler_angle, trans, focal_length, cxy) + proj_geo = forward_transform( + geometry, euler_angle, trans, focal_length, cxy) + loss_lan = cal_lan_loss( + proj_geo[:, :, :2], lms.detach()) + loss = loss_lan + optimizer_frame.zero_grad() + loss.backward() + optimizer_frame.step() + if iter == 1000: + for param_group in optimizer_frame.param_groups: + param_group['lr'] = 0.1 + if iter % 100 == 0 and False: + print('pose', iter, loss.item()) + + for param_group in optimizer_frame.param_groups: + param_group['lr'] = 0.1 + + img = Image.open(img_paths[0]) + # plt.imshow(img) + plt.scatter(lms[0, :, 0].detach().cpu(), lms[0, :, 1].detach().cpu(), c='r', s=10) + plt.scatter(proj_geo[0, :, 0].detach().cpu(), proj_geo[0, :, 1].detach().cpu(), c='b', s=10) + # plt.show() + + for iter in tqdm(range(2000)): + id_para_batch = id_para.expand(num_frames, -1) + geometry = model_3dmm.get_3dlandmarks( + id_para_batch, exp_para, euler_angle, trans, focal_length, cxy) + proj_geo = forward_transform( + geometry, euler_angle, trans, focal_length, cxy) + loss_lan = cal_lan_loss( + proj_geo[:, :, :2], lms.detach()) + loss_regid = torch.mean(id_para*id_para) + loss_regexp = torch.mean(exp_para*exp_para) + loss = loss_lan + loss_regid*0.5 + loss_regexp*0.4 + optimizer_idexp.zero_grad() + optimizer_frame.zero_grad() + loss.backward() + optimizer_idexp.step() + optimizer_frame.step() + if iter % 100 == 0 and False: + print('poseidexp', iter, loss_lan.item(), + loss_regid.item(), loss_regexp.item()) + if iter % 1000 == 0 and iter >= 1000: + for param_group in optimizer_idexp.param_groups: + param_group['lr'] *= 0.2 + for param_group in optimizer_frame.param_groups: + param_group['lr'] *= 0.2 + print(loss_lan.item(), torch.mean(trans[:, 2]).item()) + + img = Image.open(img_paths[0]) + # plt.imshow(img) + plt.scatter(lms[0, :, 0].detach().cpu(), lms[0, :, 1].detach().cpu(), c='r', s=10) + plt.scatter(proj_geo[0, :, 0].detach().cpu(), proj_geo[0, :, 1].detach().cpu(), c='b', s=10) + # plt.show() + + batch_size = 50 + + device_default = torch.device('cuda:0') + device_render = torch.device('cuda:0') + renderer = Render_3DMM(arg_focal, h, w, batch_size, device_render) + + sel_ids = np.arange(0, num_frames, int(num_frames/batch_size))[:batch_size] + imgs = [] + for sel_id in sel_ids: + imgs.append(cv2.imread(img_paths[sel_id])[:, :, ::-1]) + imgs = np.stack(imgs) + sel_imgs = torch.as_tensor(imgs).cuda() + sel_lms = lms[sel_ids] + sel_light = light_para.new_zeros((batch_size, 27), requires_grad=True) + set_requires_grad([sel_light]) + optimizer_tl = torch.optim.Adam([tex_para, sel_light], lr=.1) + optimizer_id_frame = torch.optim.Adam( + [euler_angle, trans, exp_para, id_para], lr=.01) + + # RENDERING BASED OPTIMIZATION + for iter in tqdm(range(71)): + sel_exp_para, sel_euler, sel_trans = exp_para[sel_ids], euler_angle[sel_ids], trans[sel_ids] + sel_id_para = id_para.expand(batch_size, -1) + geometry = model_3dmm.get_3dlandmarks( + sel_id_para, sel_exp_para, sel_euler, sel_trans, focal_length, cxy) + proj_geo = forward_transform( + geometry, sel_euler, sel_trans, focal_length, cxy) + loss_lan = cal_lan_loss(proj_geo[:, :, :2], sel_lms.detach()) + loss_regid = torch.mean(id_para*id_para) + loss_regexp = torch.mean(sel_exp_para*sel_exp_para) + + sel_tex_para = tex_para.expand(batch_size, -1) + sel_texture = model_3dmm.forward_tex(sel_tex_para) + geometry = model_3dmm.forward_geo(sel_id_para, sel_exp_para) + rott_geo = forward_rott(geometry, sel_euler, sel_trans) + render_imgs = renderer(rott_geo.to(device_render), + sel_texture.to(device_render), + sel_light.to(device_render)) + render_imgs = render_imgs.to(device_default) + + mask = (render_imgs[:, :, :, 3]).detach() > 0.0 + render_proj = sel_imgs.clone() + render_proj[mask] = render_imgs[mask][..., :3].byte() + loss_col = cal_col_loss(render_imgs[:, :, :, :3], sel_imgs.float(), mask) + loss = loss_col + loss_lan*3 + loss_regid*2.0 + loss_regexp*1.0 + if iter > 50: + loss = loss_col + loss_lan*0.05 + loss_regid*1.0 + loss_regexp*0.8 + optimizer_tl.zero_grad() + optimizer_id_frame.zero_grad() + loss.backward() + optimizer_tl.step() + optimizer_id_frame.step() + if iter % 50 == 0 and iter >= 5: + for param_group in optimizer_id_frame.param_groups: + param_group['lr'] *= 0.2 + for param_group in optimizer_tl.param_groups: + param_group['lr'] *= 0.2 + #print(iter, loss_col.item(), loss_lan.item(), loss_regid.item(), loss_regexp.item()) + + # np2mesh(mesh, geometry[0, ...].detach().cpu().numpy( + # ), os.path.join(id_dir, 'debug', 'id.ply')) + + light_mean = torch.mean(sel_light, 0).unsqueeze(0).repeat(num_frames, 1) + light_para.data = light_mean + + exp_para = exp_para.detach() + euler_angle = euler_angle.detach() + trans = trans.detach() + light_para = light_para.detach() + + for i in range(int((num_frames-1)/batch_size+1)): + if (i+1)*batch_size > num_frames: + start_n = num_frames-batch_size + sel_ids = np.arange(num_frames-batch_size, num_frames) + else: + start_n = i*batch_size + sel_ids = np.arange(i*batch_size, i*batch_size+batch_size) + imgs = [] + for sel_id in sel_ids: + imgs.append(cv2.imread(img_paths[sel_id])[:, :, ::-1]) + imgs = np.stack(imgs) + sel_imgs = torch.as_tensor(imgs).cuda() + sel_lms = lms[sel_ids] + + sel_exp_para = exp_para.new_zeros( + (batch_size, exp_dim), requires_grad=True) + sel_exp_para.data = exp_para[sel_ids].clone() + sel_euler = euler_angle.new_zeros( + (batch_size, 3), requires_grad=True) + sel_euler.data = euler_angle[sel_ids].clone() + sel_trans = trans.new_zeros((batch_size, 3), requires_grad=True) + sel_trans.data = trans[sel_ids].clone() + sel_light = light_para.new_zeros( + (batch_size, 27), requires_grad=True) + sel_light.data = light_para[sel_ids].clone() + + set_requires_grad([sel_exp_para, sel_euler, sel_trans, sel_light]) + + optimizer_cur_batch = torch.optim.Adam( + [sel_exp_para, sel_euler, sel_trans, sel_light], lr=0.005) + + sel_id_para = id_para.expand(batch_size, -1).detach() + sel_tex_para = tex_para.expand(batch_size, -1).detach() + + pre_num = 5 + if i > 0: + pre_ids = np.arange( + start_n-pre_num, start_n) + + for iter in tqdm(range(50)): + geometry = model_3dmm.get_3dlandmarks( + sel_id_para, sel_exp_para, sel_euler, sel_trans, focal_length, cxy) + proj_geo = forward_transform( + geometry, sel_euler, sel_trans, focal_length, cxy) + loss_lan = cal_lan_loss(proj_geo[:, :, :2], sel_lms.detach()) + loss_regexp = torch.mean(sel_exp_para*sel_exp_para) + + sel_geometry = model_3dmm.forward_geo(sel_id_para, sel_exp_para) + sel_texture = model_3dmm.forward_tex(sel_tex_para) + geometry = model_3dmm.forward_geo(sel_id_para, sel_exp_para) + rott_geo = forward_rott(geometry, sel_euler, sel_trans) + render_imgs = renderer(rott_geo.to(device_render), + sel_texture.to(device_render), + sel_light.to(device_render)) + render_imgs = render_imgs.to(device_default) + + mask = (render_imgs[:, :, :, 3]).detach() > 0.0 + + loss_col = cal_col_loss( + render_imgs[:, :, :, :3], sel_imgs.float(), mask) + + if i > 0: + geometry_lap = model_3dmm.forward_geo_sub(id_para.expand( + batch_size+pre_num, -1).detach(), torch.cat((exp_para[pre_ids].detach(), sel_exp_para)), model_3dmm.rigid_ids) + rott_geo_lap = forward_rott(geometry_lap, torch.cat( + (euler_angle[pre_ids].detach(), sel_euler)), torch.cat((trans[pre_ids].detach(), sel_trans))) + + loss_lap = cal_lap_loss([rott_geo_lap.reshape(rott_geo_lap.shape[0], -1).permute(1, 0)], + [1.0]) + else: + geometry_lap = model_3dmm.forward_geo_sub( + id_para.expand(batch_size, -1).detach(), sel_exp_para, model_3dmm.rigid_ids) + rott_geo_lap = forward_rott(geometry_lap, sel_euler, sel_trans) + loss_lap = cal_lap_loss([rott_geo_lap.reshape(rott_geo_lap.shape[0], -1).permute(1, 0)], + [1.0]) + + loss = loss_col*0.5 + loss_lan*8 + loss_lap*100000 + loss_regexp*1.0 + if iter > 30: + loss = loss_col*0.5 + loss_lan*1.5 + loss_lap*100000 + loss_regexp*1.0 + optimizer_cur_batch.zero_grad() + loss.backward() + optimizer_cur_batch.step() + #print(i, iter, loss_col.item(), loss_lan.item(), loss_lap.item(), loss_regexp.item()) + print(str(i) + ' of ' + str(int((num_frames-1)/batch_size+1)) + ' done') + render_proj = sel_imgs.clone() + render_proj[mask] = render_imgs[mask][..., :3].byte() + mask_img = mask.clone().cpu().numpy() * 255. + debug_render_dir = os.path.join(id_dir, 'debug', 'debug_render') + debug_meshes_dir = os.path.join(id_dir, 'debug', 'debug_meshes') + Path(debug_render_dir).mkdir(parents=True, exist_ok=True) + # Path(debug_meshes_dir).mkdir(parents=True, exist_ok=True) + for j in range(sel_ids.shape[0]): + img_arr = render_proj[j, :, :, :3].byte().detach().cpu().numpy()[ + :, :, ::-1] + cv2.imwrite(os.path.join(debug_render_dir, str(sel_ids[j]) + '.jpg'), + img_arr) + + cv2.imwrite(os.path.join(expr_masks_dir, str(sel_ids[j]) + '.jpg'), + mask_img[j]) + + # renderer.get_and_save_mesh(rott_geo, os.path.join(debug_meshes_dir, str(sel_ids[j]) + '.obj')) + + exp_para[sel_ids] = sel_exp_para.clone() + euler_angle[sel_ids] = sel_euler.clone() + trans[sel_ids] = sel_trans.clone() + light_para[sel_ids] = sel_light.clone() + + torch.save({'id': id_para.detach().cpu(), 'exp': exp_para.detach().cpu(), + 'euler': euler_angle.detach().cpu(), 'trans': trans.detach().cpu(), + 'focal': focal_length.detach().cpu(), 'light': light_para.detach().cpu(), + 'text': tex_para.detach().cpu()} + , out_path) + print('params saved') diff --git a/motion-gan-pipeline/preprocessing/face_tracking/face_tracker_deca.py b/motion-gan-pipeline/preprocessing/face_tracking/face_tracker_deca.py new file mode 100644 index 0000000..294b456 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_tracking/face_tracker_deca.py @@ -0,0 +1,410 @@ +import os +import sys + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../third/DECA/'))) + +from decalib.deca import DECA +from decalib.datasets import datasets +from decalib.utils import util +from decalib.utils.config import cfg as deca_cfg + +from skimage.io import imread, imsave + +import numpy as np +from .data_loader import load_dir +from .util import * +from .render_3dmm import Render_FLAME +from .FLAME.FLAME import FLAME +from .FLAME.config import cfg +from .FLAME.lbs import vertices2landmarks +import cv2 +from pathlib import Path +from tqdm import tqdm +import matplotlib.pyplot as plt +from PIL import Image, ImageDraw + + +class DECA_tracker: + def __init__(self): + + # run DECA + deca_cfg.model.use_tex = False + self.deca = DECA(config=deca_cfg, device='cuda') + + def __call__(self, images, tform=None): + + codedict = self.deca.encode(images) + + return codedict + + +def track_face_DECA(dataset_base, h, w, frame_num, out_path, decaexpr_dir, expr_masks_dir): + ''' + Face tracker using FLAME model. + Used to have geometry prior for nerf sampling. + ''' + + def set_requires_grad(tensor_list): + for tensor in tensor_list: + tensor.requires_grad = True + + start_id = 0 + end_id = frame_num + + id_dir = dataset_base + + debug_ldk_dir = os.path.join(id_dir, 'debug', 'debug_landmarks') + Path(debug_ldk_dir).mkdir(parents=True, exist_ok=True) + debug_render_dir = os.path.join(id_dir, 'debug', 'debug_render') + Path(debug_render_dir).mkdir(parents=True, exist_ok=True) + debug_mix_dir = os.path.join(id_dir, 'debug', 'debug_mixed') + Path(debug_mix_dir).mkdir(parents=True, exist_ok=True) + debug_land_dir = os.path.join(id_dir, 'debug', 'proj_landmarks') + Path(debug_land_dir).mkdir(parents=True, exist_ok=True) + debug_land_img_dir = os.path.join(id_dir, 'debug', 'proj_landmarks_img') + Path(debug_land_img_dir).mkdir(parents=True, exist_ok=True) + debug_meshes_dir = os.path.join(id_dir, 'debug', 'debug_meshes') + # Path(debug_meshes_dir).mkdir(parents=True, exist_ok=True) + + lms, img_paths = load_dir(os.path.join(id_dir, 'landmarks'), os.path.join(id_dir, 'frames'), start_id, end_id) + num_frames = lms.shape[0] + cxy = torch.tensor((w / 2.0, h / 2.0), dtype=torch.float).cuda() + id_dim, exp_dim, tex_dim = 100, 53, 50 + model_3dmm = FLAME(cfg.model) + + device_default = torch.device('cuda:0') + device_render = torch.device('cuda:0') + + deca_tracker = DECA_tracker() + arg_focal = 600 + # renderer = Render_FLAME(model_3dmm.faces_tensor, arg_focal, h, w, 1, torch.device('cuda:0')) + + # id_para = lms.new_zeros((1, id_dim), requires_grad=True) + id_para = lms.new_zeros((num_frames, id_dim), requires_grad=True) + exp_para = lms.new_zeros((num_frames, exp_dim), requires_grad=False) + + # Run deca on all frames + testdata = datasets.TestData(img_paths, iscrop=True, face_detector='fan') + for i, data in enumerate(tqdm(testdata)): + images = data['image'].cuda()[None, ...] + codedict = deca_tracker(images) + to_show = codedict['images'] + shape_params = codedict['shape'] + expression_params = codedict['exp'] + pose_params = codedict['pose'] + + id_para.data[i] = shape_params + exp_para.data[i] = torch.cat((expression_params.flatten(), pose_params[:, 3:].flatten()), dim=0) + np.save(os.path.join(decaexpr_dir, '%05d.npy' % i), exp_para.data[i].unsqueeze(0).cpu().numpy()) + + '''# TO TEST DECA + pose_params = codedict['pose'] + + opdict, visdict = deca_tracker.deca.decode(codedict) + shape_detail_images = visdict['shape_detail_images'][0].permute(1, 2, 0).cpu().numpy() + shape_detail_images = shape_detail_images * 255. + shape_detail_images = shape_detail_images.astype(np.uint8) + img = Image.fromarray(shape_detail_images) + + euler_angle = torch.zeros((1, 3), dtype=torch.double).cuda() + trans = torch.from_numpy(np.array([[0., 0., -3.]])).double().cuda() + exp_para_i = torch.cat((expression_params.flatten(), pose_params[:, 3:].flatten()), dim=0).unsqueeze(0) + + rott_geo = model_3dmm.forward_geo(shape_params, exp_para_i, euler_angle, trans).float() + render_imgs = renderer(rott_geo.cuda(), model_3dmm.faces_tensor.cuda()) + render_imgs = render_imgs.cpu().detach().numpy()[0, :, :, :3] + render_imgs *= 255 + render_imgs = render_imgs.astype(np.uint8) + + # Save landmarks + im = Image.new('RGB', (w, h), (255, 255, 255)) + geometry = model_3dmm.get_3dlandmarks( + shape_params, exp_para_i, euler_angle, trans, arg_focal, cxy) + + proj_geo = proj_pts(geometry, arg_focal, cxy)[0] + for point in proj_geo: + margin = (max(h, w) // 500) + 1 + ldmks = ([point[0] - margin, point[1] - margin, point[0] + margin, point[1] + margin]) + draw = ImageDraw.Draw(im) + draw.ellipse(ldmks, fill=(255, 0, 0)) + + plt.imshow(im) + plt.show() + + if i % 500 == 0: + print(pose_params) + fig, axs = plt.subplots(1, 3) + axs[0].imshow(to_show[0].permute(1, 2, 0).cpu().numpy()) + axs[1].imshow(img) + axs[2].imshow(render_imgs) + plt.show() + # ''' + + # mean of shape + id_para = torch.mean(id_para, axis=0).unsqueeze(0) + + # Find best focal + arg_focal = 600 + arg_landis = 1e5 + sel_ids = np.arange(0, num_frames, 40) + sel_num = sel_ids.shape[0] + + for focal in tqdm(range(700, 1000, 100)): + + euler_angle = lms.new_zeros((sel_num, 3), dtype=torch.double, requires_grad=True) + trans = lms.new_zeros((sel_num, 3), dtype=torch.double, requires_grad=True) + trans.data[:, 2] -= 1 # DIFFERENT + # trans.data[:, 2] -= 7 # ORIGINAL + focal_length = lms.new_zeros(1, requires_grad=False) + focal_length.data += focal + set_requires_grad([euler_angle, trans]) + + optimizer_frame = torch.optim.Adam([euler_angle, trans], lr=.1) + + for iter in range(2000): + id_para_batch = id_para.expand(sel_num, -1) + exp_para_batch = exp_para[sel_ids] + geometry = model_3dmm.get_3dlandmarks( + id_para_batch, exp_para_batch, euler_angle, trans, focal_length, cxy) + + proj_geo = proj_pts(geometry, focal_length, cxy) + + loss_lan = cal_lan_loss( + proj_geo[:, :, :2], lms[sel_ids].detach()) + loss = loss_lan + optimizer_frame.zero_grad() + loss.backward(retain_graph=True) + optimizer_frame.step() + if iter % 100 == 0 and False: + print(focal, 'pose', iter, loss.item()) + + for iter in range(2500): + id_para_batch = id_para.expand(sel_num, -1) + exp_para_batch = exp_para[sel_ids] + + geometry = model_3dmm.get_3dlandmarks( + id_para_batch, exp_para_batch, euler_angle, trans, focal_length, cxy) + + proj_geo = proj_pts(geometry, focal_length, cxy) + + loss_lan = cal_lan_loss( + proj_geo[:, :, :2], lms[sel_ids].detach()) + loss = loss_lan + optimizer_frame.zero_grad() + loss.backward(retain_graph=True) + optimizer_frame.step() + if iter % 100 == 0 and False: + print(focal, 'poseidexp', iter, loss_lan.item(), + loss_regid.item(), loss_regexp.item()) + if iter % 1500 == 0 and iter >= 1500: + for param_group in optimizer_frame.param_groups: + param_group['lr'] *= 0.2 + # print(focal, loss_lan.item(), torch.mean(trans[:, 2]).item()) + + if loss_lan.item() < arg_landis: + arg_landis = loss_lan.item() + arg_focal = focal + #''' + print('find best focal', arg_focal) + + # Free up some memory + torch.cuda.empty_cache() + + euler_angle = lms.new_zeros((num_frames, 3), dtype=torch.double, requires_grad=True) + trans = lms.new_zeros((num_frames, 3), dtype=torch.double, requires_grad=True) + trans.data[:, 2] -= 1 # DIFFERENT + # trans.data[:, 2] -= 7 # ORIGINAL + light_para = lms.new_zeros((num_frames, 27), requires_grad=True) + + focal_length = lms.new_zeros(1, requires_grad=False) + focal_length.data += arg_focal + + set_requires_grad([euler_angle, trans, light_para]) + + # ORIGINAL + # optimizer_idexp = torch.optim.Adam([id_para, exp_para], lr=.1) + optimizer_frame = torch.optim.Adam([euler_angle, trans], lr=1) + + for iter in tqdm(range(1500)): + id_para_batch = id_para.expand(num_frames, -1) + + geometry = model_3dmm.get_3dlandmarks( + id_para_batch, exp_para, euler_angle, trans, focal_length, cxy) + + proj_geo = proj_pts(geometry, focal_length, cxy) + + loss_lan = cal_lan_loss( + proj_geo[:, :, :2], lms.detach()) + loss = loss_lan + optimizer_frame.zero_grad() + loss.backward(retain_graph=True) + optimizer_frame.step() + if iter == 1000: + for param_group in optimizer_frame.param_groups: + param_group['lr'] = 0.1 + if iter % 250 == 0 and False: + # print('pose', iter, loss.item()) + + img = Image.open(img_paths[0]) + colormap_blue = plt.cm.Blues + colormap_red = plt.cm.Reds + + plt.imshow(img) + for num, lin in enumerate(np.linspace(0, 0.9, len(lms[0, :, 0]))): + plt.scatter(lms[0, num, 0].detach().cpu(), + lms[0, num, 1].detach().cpu(), + color=colormap_blue(lin), + s=10) + + plt.scatter(proj_geo[0, num, 0].detach().cpu(), + proj_geo[0, num, 1].detach().cpu(), + color=colormap_red(lin), + s=10) + + plt.savefig(os.path.join(debug_ldk_dir, f'ldk_1_{iter}.png')) + plt.close() + + for param_group in optimizer_frame.param_groups: + param_group['lr'] = 0.1 # ORIGINAL + + for iter in tqdm(range(2000)): + id_para_batch = id_para.expand(num_frames, -1) + + geometry = model_3dmm.get_3dlandmarks( + id_para_batch, exp_para, euler_angle, trans, focal_length, cxy) + + proj_geo = proj_pts(geometry, focal_length, cxy) + + loss_lan = cal_lan_loss( + proj_geo[:, :, :2], lms.detach()) + loss = loss_lan + optimizer_frame.zero_grad() + loss.backward(retain_graph=True) + optimizer_frame.step() + + if iter % 100 == 0 and False: + # print('pose', iter, loss_lan.item()) + + img = Image.open(img_paths[0]) + colormap_blue = plt.cm.Blues + colormap_red = plt.cm.Reds + + plt.imshow(img) + for num, lin in enumerate(np.linspace(0, 0.9, len(lms[0, :, 0]))): + plt.scatter(lms[0, num, 0].detach().cpu(), + lms[0, num, 1].detach().cpu(), + color=colormap_blue(lin), + s=10) + + plt.scatter(proj_geo[0, num, 0].detach().cpu(), + proj_geo[0, num, 1].detach().cpu(), + color=colormap_red(lin), + s=10) + + plt.savefig(os.path.join(debug_ldk_dir, f'ldk_2_{iter}.png')) + plt.close() + + if iter % 1000 == 0 and iter >= 1000: + for param_group in optimizer_frame.param_groups: + param_group['lr'] *= 0.2 + + # THEY DO THIS + exp_para = exp_para.detach() + euler_angle = euler_angle.detach() + trans = trans.detach() + light_para = light_para.detach() + + batch_size = 10 + renderer = Render_FLAME(model_3dmm.faces_tensor, arg_focal, h, w, batch_size, device_render) + pts3D = [] + + for i in tqdm(range(int((num_frames - 1) / batch_size + 1))): + if (i + 1) * batch_size > num_frames: + sel_ids = np.arange(num_frames - batch_size, num_frames) + else: + sel_ids = np.arange(i * batch_size, i * batch_size + batch_size) + imgs = [] + for sel_id in sel_ids: + imgs.append(cv2.imread(img_paths[sel_id])[:, :, ::-1]) + imgs = np.stack(imgs) + sel_imgs = torch.as_tensor(imgs).cuda() + + sel_exp_para = exp_para.new_zeros( + (batch_size, exp_dim), requires_grad=False) + sel_exp_para.data = exp_para[sel_ids].clone() + sel_euler = euler_angle.new_zeros( + (batch_size, 3), requires_grad=False) + sel_euler.data = euler_angle[sel_ids].clone() + sel_trans = trans.new_zeros((batch_size, 3), requires_grad=False) + sel_trans.data = trans[sel_ids].clone() + sel_light = light_para.new_zeros( + (batch_size, 27), requires_grad=False) + sel_light.data = light_para[sel_ids].clone() + + sel_id_para = id_para.expand(batch_size, -1).detach() + + rott_geo = model_3dmm.forward_geo(sel_id_para, sel_exp_para, sel_euler, sel_trans).float() + bz = rott_geo.shape[0] + + sel_pts3D = vertices2landmarks(rott_geo, model_3dmm.faces_tensor, + model_3dmm.full_lmk_faces_idx.repeat(bz, 1), + model_3dmm.full_lmk_bary_coords.repeat(bz, 1, 1).float()) + pts3D.append(sel_pts3D) + + ldmks = model_3dmm.get_3dlandmarks_forehead( + sel_id_para, sel_exp_para, sel_euler, sel_trans, arg_focal, cxy) + proj_geos = proj_pts(ldmks, focal_length, cxy) + + render_imgs = renderer(rott_geo.to(device_render), model_3dmm.faces_tensor.to(device_render)) + render_imgs = render_imgs.to(device_default).detach() + og_imgs = sel_imgs.clone() + + for j in range(sel_ids.shape[0]): + # Save render + img_arr = render_imgs[j, :, :, :3].cpu().numpy() + img_arr *= 255 + img_arr = img_arr.astype(np.uint8) + im = Image.fromarray(img_arr) + im.save(os.path.join(debug_render_dir, '%05d.jpg' % sel_ids[j])) + + # Save mask + black_pixels_mask = np.all(img_arr != [255, 255, 255], axis=-1) + mask_img = np.zeros_like(img_arr) + mask_img[black_pixels_mask] = [255, 255, 255] + + im = Image.fromarray(mask_img) + im.save(os.path.join(expr_masks_dir, '%05d.jpg' % sel_ids[j])) + + # Save mixed + alpha_blend= 0.1 + og_img = og_imgs[j,:,:,:3].cpu().numpy() + og_img[black_pixels_mask] = og_img[black_pixels_mask] * alpha_blend + \ + img_arr[black_pixels_mask] * (1 - alpha_blend) + + og_img = og_img.astype(np.uint8) + im = Image.fromarray(og_img) + im.save(os.path.join(debug_mix_dir, '%05d.jpg' % sel_ids[j])) + + # Save landmarks + im = Image.new('RGB', (w, h), (255, 255, 255)) + proj_geo = proj_geos[j] + for point in proj_geo: + margin = (max(h, w) // 500) + 1 + ldmks = ([point[0] - margin, point[1] - margin, point[0] + margin, point[1] + margin]) + draw = ImageDraw.Draw(im) + draw.ellipse(ldmks, fill=(255, 0, 0)) + + im.save(os.path.join(debug_land_img_dir, '%05d.jpg' % sel_ids[j])) + np.savetxt(os.path.join(debug_land_dir, '%05d.lms' % sel_ids[j]), proj_geo.detach().cpu().numpy()) + + + pts3D = torch.cat(pts3D, dim=0) + + print('about to save params..') + + torch.save({'id': id_para.detach().cpu(), 'exp': exp_para.detach().cpu(), + 'euler': euler_angle.detach().cpu(), 'trans': trans.detach().cpu(), + 'focal': focal_length.detach().cpu(), 'light': light_para.detach().cpu(), + 'pts3D': pts3D.detach().cpu(), 'h': h, 'w': w}, out_path) + + print('params saved') \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/face_tracking/facemodel.py b/motion-gan-pipeline/preprocessing/face_tracking/facemodel.py new file mode 100644 index 0000000..e853748 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_tracking/facemodel.py @@ -0,0 +1,113 @@ +import torch +import torch.nn as nn +import numpy as np +import os +from .util import * + + +class Face_3DMM(nn.Module): + def __init__(self, modelpath, id_dim, exp_dim, tex_dim, point_num): + super(Face_3DMM, self).__init__() + # id_dim = 100 + # exp_dim = 79 + # tex_dim = 100 + self.point_num = point_num + DMM_info = np.load(os.path.join( + modelpath, '3DMM_info.npy'), allow_pickle=True).item() + base_id = DMM_info['b_shape'][:id_dim, :] + mu_id = DMM_info['mu_shape'] + base_exp = DMM_info['b_exp'][:exp_dim, :] + mu_exp = DMM_info['mu_exp'] + mu = mu_id + mu_exp + mu = mu.reshape(-1, 3) + for i in range(3): + mu[:, i] -= np.mean(mu[:, i]) + mu = mu.reshape(-1) + self.base_id = torch.as_tensor(base_id).cuda()/100000.0 + self.base_exp = torch.as_tensor(base_exp).cuda()/100000.0 + self.mu = torch.as_tensor(mu).cuda()/100000.0 + base_tex = DMM_info['b_tex'][:tex_dim, :] + mu_tex = DMM_info['mu_tex'] + self.base_tex = torch.as_tensor(base_tex).cuda() + self.mu_tex = torch.as_tensor(mu_tex).cuda() + sig_id = DMM_info['sig_shape'][:id_dim] + sig_tex = DMM_info['sig_tex'][:tex_dim] + sig_exp = DMM_info['sig_exp'][:exp_dim] + self.sig_id = torch.as_tensor(sig_id).cuda() + self.sig_tex = torch.as_tensor(sig_tex).cuda() + self.sig_exp = torch.as_tensor(sig_exp).cuda() + + keys_info = np.load(os.path.join( + modelpath, 'keys_info.npy'), allow_pickle=True).item() + self.keyinds = torch.as_tensor(keys_info['keyinds']).cuda() + self.left_contours = torch.as_tensor(keys_info['left_contour']).cuda() + self.right_contours = torch.as_tensor( + keys_info['right_contour']).cuda() + self.rigid_ids = torch.as_tensor(keys_info['rigid_ids']).cuda() + + def get_3dlandmarks(self, id_para, exp_para, euler_angle, trans, focal_length, cxy): + id_para = id_para*self.sig_id + exp_para = exp_para*self.sig_exp + batch_size = id_para.shape[0] + num_per_contour = self.left_contours.shape[1] + left_contours_flat = self.left_contours.reshape(-1) + right_contours_flat = self.right_contours.reshape(-1) + sel_index = torch.cat((3*left_contours_flat.unsqueeze(1), 3*left_contours_flat.unsqueeze(1)+1, + 3*left_contours_flat.unsqueeze(1)+2), dim=1).reshape(-1) + left_geometry = torch.mm(id_para, self.base_id[:, sel_index]) + \ + torch.mm(exp_para, self.base_exp[:, + sel_index]) + self.mu[sel_index] + left_geometry = left_geometry.view(batch_size, -1, 3) + proj_x = forward_transform( + left_geometry, euler_angle, trans, focal_length, cxy)[:, :, 0] + proj_x = proj_x.reshape(batch_size, 8, num_per_contour) + arg_min = proj_x.argmin(dim=2) + left_geometry = left_geometry.view(batch_size*8, num_per_contour, 3) + left_3dlands = left_geometry[torch.arange( + batch_size*8), arg_min.view(-1), :].view(batch_size, 8, 3) + + sel_index = torch.cat((3*right_contours_flat.unsqueeze(1), 3*right_contours_flat.unsqueeze(1)+1, + 3*right_contours_flat.unsqueeze(1)+2), dim=1).reshape(-1) + right_geometry = torch.mm(id_para, self.base_id[:, sel_index]) + \ + torch.mm(exp_para, self.base_exp[:, + sel_index]) + self.mu[sel_index] + right_geometry = right_geometry.view(batch_size, -1, 3) + proj_x = forward_transform( + right_geometry, euler_angle, trans, focal_length, cxy)[:, :, 0] + proj_x = proj_x.reshape(batch_size, 8, num_per_contour) + arg_max = proj_x.argmax(dim=2) + right_geometry = right_geometry.view(batch_size*8, num_per_contour, 3) + right_3dlands = right_geometry[torch.arange( + batch_size*8), arg_max.view(-1), :].view(batch_size, 8, 3) + + sel_index = torch.cat((3*self.keyinds.unsqueeze(1), 3*self.keyinds.unsqueeze(1)+1, + 3*self.keyinds.unsqueeze(1)+2), dim=1).reshape(-1) + geometry = torch.mm(id_para, self.base_id[:, sel_index]) + \ + torch.mm(exp_para, self.base_exp[:, + sel_index]) + self.mu[sel_index] + lands_3d = geometry.view(-1, self.keyinds.shape[0], 3) + lands_3d[:, :8, :] = left_3dlands + lands_3d[:, 9:17, :] = right_3dlands + return lands_3d + + def forward_geo_sub(self, id_para, exp_para, sub_index): + id_para = id_para*self.sig_id + exp_para = exp_para*self.sig_exp + sel_index = torch.cat((3*sub_index.unsqueeze(1), 3*sub_index.unsqueeze(1)+1, + 3*sub_index.unsqueeze(1)+2), dim=1).reshape(-1) + geometry = torch.mm(id_para, self.base_id[:, sel_index]) + \ + torch.mm(exp_para, self.base_exp[:, + sel_index]) + self.mu[sel_index] + return geometry.reshape(-1, sub_index.shape[0], 3) + + def forward_geo(self, id_para, exp_para): + id_para = id_para*self.sig_id + exp_para = exp_para*self.sig_exp + geometry = torch.mm(id_para, self.base_id) + \ + torch.mm(exp_para, self.base_exp) + self.mu + return geometry.reshape(-1, self.point_num, 3) + + def forward_tex(self, tex_para): + tex_para = tex_para*self.sig_tex + texture = torch.mm(tex_para, self.base_tex) + self.mu_tex + return texture.reshape(-1, self.point_num, 3) diff --git a/motion-gan-pipeline/preprocessing/face_tracking/geo_transform.py b/motion-gan-pipeline/preprocessing/face_tracking/geo_transform.py new file mode 100644 index 0000000..d9b2308 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_tracking/geo_transform.py @@ -0,0 +1,39 @@ +"""This module contains functions for geometry transform and camera projection""" +import torch +import torch.nn as nn +import numpy as np +from pytorch3d.transforms import euler_angles_to_matrix + +def euler2rot(euler_angle): + rot = torch.deg2rad(euler_angle) + rot = euler_angles_to_matrix(rot, convention='XYZ') + return rot + + +def rot_trans_geo(geometry, rot, trans): + rott_geo = torch.bmm(rot, geometry.permute(0, 2, 1)) + trans.view(-1, 3, 1) + return rott_geo.permute(0, 2, 1) + + +def euler_trans_geo(geometry, euler, trans): + rot = euler2rot(euler) + return rot_trans_geo(geometry, rot, trans) + + +def proj_geo(rott_geo, camera_para): + fx = camera_para[:, 0] + fy = camera_para[:, 0] + cx = camera_para[:, 1] + cy = camera_para[:, 2] + + X = rott_geo[:, :, 0] + Y = rott_geo[:, :, 1] + Z = rott_geo[:, :, 2] + + fxX = fx[:, None]*X + fyY = fy[:, None]*Y + + proj_x = -fxX/Z + cx[:, None] + proj_y = fyY/Z + cy[:, None] + + return torch.cat((proj_x[:, :, None], proj_y[:, :, None], Z[:, :, None]), 2) diff --git a/motion-gan-pipeline/preprocessing/face_tracking/render_3dmm.py b/motion-gan-pipeline/preprocessing/face_tracking/render_3dmm.py new file mode 100644 index 0000000..45ca741 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_tracking/render_3dmm.py @@ -0,0 +1,332 @@ +from matplotlib.pyplot import step +import torch +import torch.nn as nn +import numpy as np +import os +from pytorch3d.structures import Meshes +from pytorch3d.renderer import * + +import torch.nn.functional as F + +from pytorch3d.io import IO, save_obj + +from pytorch3d.ops import interpolate_face_attributes + +from pytorch3d.renderer.blending import ( + BlendParams, + hard_rgb_blend, + sigmoid_alpha_blend, + softmax_rgb_blend, +) + + +class SoftSimpleShader(nn.Module): + """ + Per pixel lighting - the lighting model is applied using the interpolated + coordinates and normals for each pixel. The blending function returns the + soft aggregated color using all the faces per pixel. + + To use the default values, simply initialize the shader with the desired + device e.g. + + """ + + def __init__( + self, device="cpu", cameras=None, lights=None, materials=None, blend_params=None + ): + super().__init__() + self.lights = lights if lights is not None else PointLights( + device=device) + self.materials = ( + materials if materials is not None else Materials(device=device) + ) + self.cameras = cameras + self.blend_params = blend_params if blend_params is not None else BlendParams() + + def to(self, device): + # Manually move to device modules which are not subclasses of nn.Module + self.cameras = self.cameras.to(device) + self.materials = self.materials.to(device) + self.lights = self.lights.to(device) + return self + + def forward(self, fragments, meshes, **kwargs) -> torch.Tensor: + + texels = meshes.sample_textures(fragments) + blend_params = kwargs.get("blend_params", self.blend_params) + + cameras = kwargs.get("cameras", self.cameras) + if cameras is None: + msg = "Cameras must be specified either at initialization \ + or in the forward pass of SoftPhongShader" + raise ValueError(msg) + znear = kwargs.get("znear", getattr(cameras, "znear", 1.0)) + zfar = kwargs.get("zfar", getattr(cameras, "zfar", 100.0)) + images = softmax_rgb_blend( + texels, fragments, blend_params, znear=znear, zfar=zfar + ) + return images + + +class Render_3DMM(nn.Module): + def __init__(self, focal=1015, img_h=500, img_w=500, batch_size=1, device=torch.device('cuda:0')): + super(Render_3DMM, self).__init__() + + self.focal = focal + self.img_h = img_h + self.img_w = img_w + self.device = device + self.renderer = self.get_render(batch_size) + + dir_path = os.path.dirname(os.path.realpath(__file__)) + topo_info = np.load(os.path.join( + dir_path, '3DMM', 'topology_info.npy'), allow_pickle=True).item() + + self.tris = torch.as_tensor(topo_info['tris']).to(self.device) + self.vert_tris = torch.as_tensor(topo_info['vert_tris']).to(self.device) + + def compute_normal(self, geometry): + vert_1 = torch.index_select(geometry, 1, self.tris[:, 0]) + vert_2 = torch.index_select(geometry, 1, self.tris[:, 1]) + vert_3 = torch.index_select(geometry, 1, self.tris[:, 2]) + nnorm = torch.cross(vert_2-vert_1, vert_3-vert_1, 2) + tri_normal = nn.functional.normalize(nnorm) + v_norm = tri_normal[:, self.vert_tris, :].sum(2) + vert_normal = v_norm / v_norm.norm(dim=2).unsqueeze(2) + return vert_normal + + + def get_render(self, batch_size=1): + half_s = self.img_w * 0.5 + R, T = look_at_view_transform(10, 0, 0) + R = R.repeat(batch_size, 1, 1) + T = torch.zeros((batch_size, 3), dtype=torch.float32).to(self.device) + + cameras = FoVPerspectiveCameras(device=self.device, R=R, T=T, znear=0.01, zfar=20, + fov=2*np.arctan(self.img_w//2/self.focal)*180./np.pi) + lights = PointLights( + device=self.device, + location=[[0.0, 0.0, 1e5]], + ambient_color=[[1, 1, 1]], + specular_color=[[0., 0., 0.]], + diffuse_color=[[0., 0., 0.]] + ) + sigma = 1e-4 + raster_settings = RasterizationSettings( + image_size=(self.img_h, self.img_w), + blur_radius=np.log(1. / 1e-4 - 1.)*sigma / 18.0, + faces_per_pixel=2, + perspective_correct=False, + ) + blend_params = blending.BlendParams(background_color=[0, 0, 0]) + renderer = MeshRenderer( + rasterizer=MeshRasterizer( + raster_settings=raster_settings, + cameras=cameras + ), + shader=SoftSimpleShader( + lights=lights, + blend_params=blend_params, + cameras=cameras + ), + ) + return renderer.to(self.device) + + @staticmethod + def Illumination_layer(face_texture, norm, gamma): + + n_b, num_vertex, _ = face_texture.size() + n_v_full = n_b * num_vertex + gamma = gamma.view(-1, 3, 9).clone() + gamma[:, :, 0] += 0.8 + + gamma = gamma.permute(0, 2, 1) + + a0 = np.pi + a1 = 2 * np.pi / np.sqrt(3.0) + a2 = 2 * np.pi / np.sqrt(8.0) + c0 = 1 / np.sqrt(4 * np.pi) + c1 = np.sqrt(3.0) / np.sqrt(4 * np.pi) + c2 = 3 * np.sqrt(5.0) / np.sqrt(12 * np.pi) + d0 = 0.5 / np.sqrt(3.0) + + Y0 = torch.ones(n_v_full).to(gamma.device).float() * a0 * c0 + norm = norm.view(-1, 3) + nx, ny, nz = norm[:, 0], norm[:, 1], norm[:, 2] + arrH = [] + + arrH.append(Y0) + arrH.append(-a1 * c1 * ny) + arrH.append(a1 * c1 * nz) + arrH.append(-a1 * c1 * nx) + arrH.append(a2 * c2 * nx * ny) + arrH.append(-a2 * c2 * ny * nz) + arrH.append(a2 * c2 * d0 * (3 * nz.pow(2) - 1)) + arrH.append(-a2 * c2 * nx * nz) + arrH.append(a2 * c2 * 0.5 * (nx.pow(2) - ny.pow(2))) + + H = torch.stack(arrH, 1) + Y = H.view(n_b, num_vertex, 9) + lighting = Y.bmm(gamma) + + face_color = face_texture * lighting + return face_color + + def get_mesh(self, rott_geometry): + mesh = Meshes(rott_geometry, self.tris.float().repeat( + rott_geometry.shape[0], 1, 1)) + + return mesh + + def get_and_save_mesh(self, rott_geometry, save_path): + + verts = rott_geometry.cpu()[0] + faces = self.tris.float() + + save_obj(save_path, verts=verts, faces=faces) + + def save_mesh(self, mesh, save_path): + saver = IO() + saver.save_mesh(mesh, save_path) + + def forward(self, rott_geometry, texture, diffuse_sh): + face_normal = self.compute_normal(rott_geometry) + + face_color = self.Illumination_layer(texture, face_normal, diffuse_sh) + face_color = TexturesVertex(face_color) + mesh = Meshes(rott_geometry, self.tris.float().repeat( + rott_geometry.shape[0], 1, 1), face_color) + + rendered_img = self.renderer(mesh) + rendered_img = torch.clamp(rendered_img, 0, 255) + + return rendered_img + + +class Render_FLAME(nn.Module): + def __init__(self, faces, focal=1015, img_h=500, img_w=500, batch_size=1, device=torch.device('cuda:0')): + super(Render_FLAME, self).__init__() + + self.focal = focal + self.img_h = img_h + self.img_w = img_w + self.device = device + self.renderer = self.get_render(batch_size) + + # dir_path = os.path.dirname(os.path.realpath(__file__)) + + self.tris = faces.to(self.device) + + def vertex_normals(self, vertices, faces): + """ + :param vertices: [batch size, number of vertices, 3] + :param faces: [batch size, number of faces, 3] + :return: [batch size, number of vertices, 3] + """ + assert (vertices.ndimension() == 3) + assert (faces.ndimension() == 3) + assert (vertices.shape[0] == faces.shape[0]) + assert (vertices.shape[2] == 3) + assert (faces.shape[2] == 3) + bs, nv = vertices.shape[:2] + bs, nf = faces.shape[:2] + device = vertices.device + normals = torch.zeros(bs * nv, 3).to(device) + + faces = faces + (torch.arange(bs, dtype=torch.int32).to(device) * nv)[:, None, None] # expanded faces + vertices_faces = vertices.reshape((bs * nv, 3))[faces.long()] + + faces = faces.reshape(-1, 3) + vertices_faces = vertices_faces.reshape(-1, 3, 3) + + normals.index_add_(0, faces[:, 1].long(), + torch.cross(vertices_faces[:, 2] - vertices_faces[:, 1], + vertices_faces[:, 0] - vertices_faces[:, 1])) + normals.index_add_(0, faces[:, 2].long(), + torch.cross(vertices_faces[:, 0] - vertices_faces[:, 2], + vertices_faces[:, 1] - vertices_faces[:, 2])) + normals.index_add_(0, faces[:, 0].long(), + torch.cross(vertices_faces[:, 1] - vertices_faces[:, 0], + vertices_faces[:, 2] - vertices_faces[:, 0])) + + normals = F.normalize(normals, eps=1e-6, dim=1) + normals = normals.reshape((bs, nv, 3)) + # pytorch only supports long and byte tensors for indexing + return normals + + def get_render(self, batch_size=1): + # half_s = self.img_w * 0.5 + R, T = look_at_view_transform(1, 0, 0) + R = R.repeat(batch_size, 1, 1) + T = torch.zeros((batch_size, 3), dtype=torch.float32).to(self.device) + + cxy = torch.tensor((self.img_w / 2.0, self.img_h / 2.0), dtype=torch.float).unsqueeze(0) + image_size=torch.tensor((self.img_h, self.img_w)).unsqueeze(0) + cameras = PerspectiveCameras(device=self.device, R=R, T=T, + focal_length=self.focal, + principal_point=cxy, + image_size=image_size, + in_ndc=False) + + lights = PointLights( + device=self.device, + location=[[0.0, 0.0, 0.0]], + ) + + # lights = DirectionalLights( + # device=self.device, + # direction= [[0.0, 0.0, 1.0]], + # ) + + # lights= AmbientLights( + # device=self.device + # ) + + raster_settings = RasterizationSettings( + image_size=(self.img_h, self.img_w), + blur_radius=0.0, + faces_per_pixel=1, + ) + + renderer = MeshRenderer( + rasterizer=MeshRasterizer( + raster_settings=raster_settings, + cameras=cameras + ), + shader = SoftPhongShader( + lights=lights, + cameras=cameras, + ), + ) + return renderer.to(self.device) + + def get_mesh(self, rott_geometry): + mesh = Meshes(rott_geometry, self.tris.float().repeat( + rott_geometry.shape[0], 1, 1)) + + return mesh + + def get_and_save_mesh(self, rott_geometry, save_path): + + verts = rott_geometry.cpu()[0] + faces = self.tris.float() + + save_obj(save_path, verts=verts, faces=faces) + + def save_mesh(self, mesh, save_path): + saver = IO() + saver.save_mesh(mesh, save_path) + + def forward(self, rott_geometry, faces): + + verts_rgb = (torch.ones_like(rott_geometry)) * 0.5 + + textures = Textures(verts_rgb=verts_rgb.cuda()) + + mesh = Meshes(verts=rott_geometry, + faces=faces.float().repeat(rott_geometry.shape[0], 1, 1), + textures=textures) + + rendered_img = self.renderer(mesh) + rendered_img = torch.clamp(rendered_img, 0, 255) + return rendered_img \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/face_tracking/render_land.py b/motion-gan-pipeline/preprocessing/face_tracking/render_land.py new file mode 100644 index 0000000..9dd324d --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_tracking/render_land.py @@ -0,0 +1,146 @@ +import torch +import torch.nn as nn +import render_util +import geo_transform +import numpy as np + + +def compute_tri_normal(geometry, tris): + geometry = geometry.permute(0, 2, 1) + tri_1 = tris[:, 0] + tri_2 = tris[:, 1] + tri_3 = tris[:, 2] + + vert_1 = torch.index_select(geometry, 2, tri_1) + vert_2 = torch.index_select(geometry, 2, tri_2) + vert_3 = torch.index_select(geometry, 2, tri_3) + + nnorm = torch.cross(vert_2-vert_1, vert_3-vert_1, 1) + normal = nn.functional.normalize(nnorm).permute(0, 2, 1) + return normal + + +class Compute_normal_base(torch.autograd.Function): + @staticmethod + def forward(ctx, normal): + normal_b, = render_util.normal_base_forward(normal) + ctx.save_for_backward(normal) + return normal_b + + @staticmethod + def backward(ctx, grad_normal_b): + normal, = ctx.saved_tensors + grad_normal, = render_util.normal_base_backward(grad_normal_b, normal) + return grad_normal + + +class Normal_Base(torch.nn.Module): + def __init__(self): + super(Normal_Base, self).__init__() + + def forward(self, normal): + return Compute_normal_base.apply(normal) + + +def preprocess_render(geometry, euler, trans, cam, tris, vert_tris, ori_img): + point_num = geometry.shape[1] + rott_geo = geo_transform.euler_trans_geo(geometry, euler, trans) + proj_geo = geo_transform.proj_geo(rott_geo, cam) + rot_tri_normal = compute_tri_normal(rott_geo, tris) + rot_vert_normal = torch.index_select(rot_tri_normal, 1, vert_tris) + is_visible = -torch.bmm(rot_vert_normal.reshape(-1, 1, 3), + nn.functional.normalize(rott_geo.reshape(-1, 3, 1))).reshape(-1, point_num) + is_visible[is_visible < 0.01] = -1 + pixel_valid = torch.zeros((ori_img.shape[0], ori_img.shape[1]*ori_img.shape[2]), + dtype=torch.float32, device=ori_img.device) + return rott_geo, proj_geo, rot_tri_normal, is_visible, pixel_valid + + +class Render_Face(torch.autograd.Function): + @staticmethod + def forward(ctx, proj_geo, texture, nbl, ori_img, is_visible, tri_inds, + pixel_valid): + batch_size, h, w, _ = ori_img.shape + ori_img = ori_img.view(batch_size, -1, 3) + ori_size = torch.cat((torch.ones((batch_size, 1), dtype=torch.int32, device=ori_img.device)*h, + torch.ones((batch_size, 1), dtype=torch.int32, device=ori_img.device)*w), + dim=1).view(-1) + tri_index, tri_coord, render, real = render_util.render_face_forward( + proj_geo, ori_img, ori_size, texture, nbl, is_visible, tri_inds, pixel_valid) + ctx.save_for_backward(ori_img, ori_size, proj_geo, texture, nbl, + tri_inds, tri_index, tri_coord) + return render, real + + @staticmethod + def backward(ctx, grad_render, grad_real): + ori_img, ori_size, proj_geo, texture, nbl, tri_inds, tri_index, tri_coord = \ + ctx.saved_tensors + grad_proj_geo, grad_texture, grad_nbl = render_util.render_face_backward( + grad_render, grad_real, ori_img, ori_size, proj_geo, texture, nbl, tri_inds, + tri_index, tri_coord) + return grad_proj_geo, grad_texture, grad_nbl, None, None, None, None + + +class Render_RGB(nn.Module): + def __init__(self): + super(Render_RGB, self).__init__() + + def forward(self, proj_geo, texture, nbl, ori_img, is_visible, tri_inds, pixel_valid): + return Render_Face.apply(proj_geo, texture, nbl, ori_img, is_visible, + tri_inds, pixel_valid) + + +def cal_land(proj_geo, is_visible, lands_info, land_num): + land_index, = render_util.update_contour( + lands_info, is_visible, land_num) + proj_land = torch.index_select( + proj_geo.reshape(-1, 3), 0, land_index)[:, :2].reshape(-1, land_num, 2) + return proj_land + + +class Render_Land(nn.Module): + def __init__(self): + super(Render_Land, self).__init__() + lands_info = np.loadtxt('../data/3DMM/lands_info.txt', dtype=np.int32) + self.lands_info = torch.as_tensor(lands_info).cuda() + tris = np.loadtxt('../data/3DMM/tris.txt', dtype=np.int64) + self.tris = torch.as_tensor(tris).cuda() - 1 + vert_tris = np.loadtxt('../data/3DMM/vert_tris.txt', dtype=np.int64) + self.vert_tris = torch.as_tensor(vert_tris).cuda() + self.normal_baser = Normal_Base().cuda() + self.renderer = Render_RGB().cuda() + + def render_mesh(self, geometry, euler, trans, cam, ori_img, light): + batch_size, h, w, _ = ori_img.shape + ori_img = ori_img.view(batch_size, -1, 3) + ori_size = torch.cat((torch.ones((batch_size, 1), dtype=torch.int32, device=ori_img.device)*h, + torch.ones((batch_size, 1), dtype=torch.int32, device=ori_img.device)*w), + dim=1).view(-1) + rott_geo, proj_geo, rot_tri_normal, _, _ = preprocess_render( + geometry, euler, trans, cam, self.tris, self.vert_tris, ori_img) + tri_nb = self.normal_baser(rot_tri_normal.contiguous()) + nbl = torch.bmm(tri_nb, (light.reshape(-1, 9, 3)) + [:, :, 0].unsqueeze(-1).repeat(1, 1, 3)) + texture = torch.ones_like(geometry) * 200 + render, = render_util.render_mesh( + proj_geo, ori_img, ori_size, texture, nbl, self.tris) + return render.view(batch_size, h, w, 3).byte() + + def cal_loss_rgb(self, geometry, euler, trans, cam, ori_img, light, texture, lands): + rott_geo, proj_geo, rot_tri_normal, is_visible, pixel_valid = \ + preprocess_render(geometry, euler, trans, cam, + self.tris, self.vert_tris, ori_img) + tri_nb = self.normal_baser(rot_tri_normal.contiguous()) + nbl = torch.bmm(tri_nb, light.reshape(-1, 9, 3)) + render, real = self.renderer( + proj_geo, texture, nbl, ori_img, is_visible, self.tris, pixel_valid) + proj_land = cal_land(proj_geo, is_visible, + self.lands_info, lands.shape[1]) + col_minus = torch.norm((render-real).reshape(-1, 3), + dim=1).reshape(ori_img.shape[0], -1) + col_dis = torch.mean(col_minus*pixel_valid) / \ + (torch.mean(pixel_valid)+0.00001) + land_dists = torch.norm( + (proj_land-lands).reshape(-1, 2), dim=1).reshape(ori_img.shape[0], -1) + lan_dis = torch.mean(land_dists) + return col_dis, lan_dis diff --git a/motion-gan-pipeline/preprocessing/face_tracking/util.py b/motion-gan-pipeline/preprocessing/face_tracking/util.py new file mode 100644 index 0000000..e04e226 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/face_tracking/util.py @@ -0,0 +1,66 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from .geo_transform import euler2rot + + +def compute_tri_normal(geometry, tris): + tri_1 = tris[:, 0] + tri_2 = tris[:, 1] + tri_3 = tris[:, 2] + vert_1 = torch.index_select(geometry, 1, tri_1) + vert_2 = torch.index_select(geometry, 1, tri_2) + vert_3 = torch.index_select(geometry, 1, tri_3) + nnorm = torch.cross(vert_2-vert_1, vert_3-vert_1, 2) + normal = nn.functional.normalize(nnorm) + return normal + + +def rot_trans_pts(geometry, rot, trans): + rott_geo = torch.bmm(rot, geometry.permute(0, 2, 1)) + trans[:, :, None] + return rott_geo.permute(0, 2, 1) + + +def cal_lap_loss(tensor_list, weight_list): + lap_kernel = torch.Tensor((-0.5, 1.0, -0.5)).unsqueeze(0).unsqueeze(0).float().to(tensor_list[0].device) + loss_lap = 0 + for i in range(len(tensor_list)): + in_tensor = tensor_list[i] + in_tensor = in_tensor.view(-1, 1, in_tensor.shape[-1]) + out_tensor = F.conv1d(in_tensor, lap_kernel) + loss_lap += torch.mean(out_tensor**2)*weight_list[i] + return loss_lap + + +def proj_pts(rott_geo, focal_length, cxy): + cx, cy = cxy[0], cxy[1] + X = rott_geo[:, :, 0] + Y = rott_geo[:, :, 1] + Z = rott_geo[:, :, 2] + fxX = focal_length*X + fyY = focal_length*Y + proj_x = -fxX/Z + cx + proj_y = fyY/Z + cy + return torch.cat((proj_x[:, :, None], proj_y[:, :, None], 1/Z[:, :, None]), 2) + +def forward_rott(geometry, euler_angle, trans): + rot = euler2rot(euler_angle) + rott_geo = rot_trans_pts(geometry, rot, trans) + return rott_geo + +def forward_transform(geometry, euler_angle, trans, focal_length, cxy): + rot = euler2rot(euler_angle) + rott_geo = rot_trans_pts(geometry, rot, trans) + proj_geo = proj_pts(rott_geo, focal_length, cxy) + return proj_geo + + +def cal_lan_loss(proj_lan, gt_lan): + return torch.mean((proj_lan-gt_lan)**2) + +def cal_col_loss(pred_img, gt_img, img_mask): + pred_img = pred_img.float() + loss = torch.sqrt(torch.sum(torch.square(pred_img - gt_img), 3))*img_mask/255 + loss = torch.sum(loss, dim=(1, 2)) / torch.sum(img_mask, dim=(1, 2)) + loss = torch.mean(loss) + return loss diff --git a/motion-gan-pipeline/preprocessing/facemesh_generator.py b/motion-gan-pipeline/preprocessing/facemesh_generator.py new file mode 100644 index 0000000..55efb13 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/facemesh_generator.py @@ -0,0 +1,68 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os + +from face_tracking.data_loader import load_dir +from face_tracking.FLAME.FLAME import FLAME +from face_tracking.FLAME.config import cfg +from face_tracking.render_3dmm import Render_FLAME +from face_tracking.util import * + +id_dim, exp_dim, tex_dim = 100, 50, 50 + + +class GeometryGenerator: + + def __init__(self, dataset_base, h, w, frame_num, trackparamspath, mesh_dir): + + self.dataset_base = dataset_base + self.mesh_dir = mesh_dir + + # Load and unpack tracking params + params = torch.load(trackparamspath) + self.id_para = params['id'] + self.exp_para = params['exp'] + self.euler_angle = params['euler'] + self.trans = params['trans'] + self.focal_length = params['focal'] + self.arg_focal = torch.max(self.focal_length).numpy() + + self.light_para = params['light'] + # self.text_para = params['text'] + + start_id = 0 + end_id = frame_num + id_dir = dataset_base + self.lms, self.img_paths = load_dir(os.path.join(id_dir, 'landmarks'), + os.path.join(id_dir, 'frames'), + start_id, end_id) + + self.num_frames = self.lms.shape[0] + self.batchsize = 1 + + # 3D model and Renderer + self.model_3dmm = FLAME(cfg.model) + + self.device_render = 'cuda' + self.renderer = Render_FLAME(self.model_3dmm.faces_tensor, + self.arg_focal, + h, w, + self.batchsize, + self.device_render) + + def generate_mesh(self, index): + + id_para = self.id_para.expand(self.batchsize, -1).cuda() + exp_para = self.exp_para[index].expand(self.batchsize, -1).cuda() + euler = self.euler_angle[index].expand(self.batchsize, -1).cuda() + trans = self.trans[index].expand(self.batchsize, -1).cuda() + + geometry = self.model_3dmm.forward_geo(id_para, exp_para, euler, trans, False) + + save_path = os.path.join(self.mesh_dir, '%5d.obj' % index) + + self.renderer.get_and_save_mesh(geometry, save_path) + + + diff --git a/motion-gan-pipeline/preprocessing/get_densepose.py b/motion-gan-pipeline/preprocessing/get_densepose.py new file mode 100644 index 0000000..35eabba --- /dev/null +++ b/motion-gan-pipeline/preprocessing/get_densepose.py @@ -0,0 +1,116 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os, sys +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), 'third/'))) + +import cv2 +import numpy as np +from typing import ClassVar, Dict +import matplotlib.pyplot as plt +from detectron2.config import get_cfg +from detectron2.engine.defaults import DefaultPredictor +from densepose import add_densepose_config +from densepose.vis.base import CompoundVisualizer +from densepose.vis.bounding_box import ScoredBoundingBoxVisualizer +from densepose.vis.extractor import CompoundExtractor, create_extractor +from densepose.vis.densepose_results import ( + DensePoseResultsContourVisualizer, + DensePoseResultsFineSegmentationVisualizer, + DensePoseResultsUVisualizer, + DensePoseResultsVVisualizer, +) +from pathlib import Path + + +def get_extractor_and_visualizer(): + + VISUALIZERS: ClassVar[Dict[str, object]] = { + "dp_contour": DensePoseResultsContourVisualizer, + "dp_segm": DensePoseResultsFineSegmentationVisualizer, + "dp_u": DensePoseResultsUVisualizer, + "dp_v": DensePoseResultsVVisualizer, + "bbox": ScoredBoundingBoxVisualizer, + } + + vis_specs = ['dp_contour', 'bbox'] + visualizers = [] + extractors = [] + for vis_spec in vis_specs: + vis = VISUALIZERS[vis_spec]() + visualizers.append(vis) + extractor = create_extractor(vis) + extractors.append(extractor) + visualizer = CompoundVisualizer(visualizers) + extractor = CompoundExtractor(extractors) + + context = { + "extractor": extractor, + "visualizer": visualizer + } + + visualizer = context["visualizer"] + extractor = context["extractor"] + + return extractor, visualizer + + +def predict(img, cfg): + predictor = DefaultPredictor(cfg) + outputs = predictor(img)['instances'] + + image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + image = np.tile(image[:, :, np.newaxis], [1, 1, 3]) + + extractor, visualizer = get_extractor_and_visualizer() + + data = extractor(outputs) + image_vis = visualizer.visualize(image, data) + return image_vis, data + + +def extract_denseposes(video_name, input_path, output_path): + + cfg = get_cfg() + add_densepose_config(cfg) + cfg.merge_from_file("third/densepose/densepose_rcnn_R_50_FPN_s1x.yaml") + cfg.MODEL.DEVICE = "cuda" + cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 + cfg.MODEL.WEIGHTS = "third/densepose/model_final_162be9.pkl" + + # process video + video_path = Path(input_path, video_name + '.mp4') + captura = cv2.VideoCapture(str(video_path)) + i = 0 + while(captura.isOpened()): + + ret, img = captura.read() + if ret == False: + break + + image_vis, data = predict(img, cfg) + + if data[0][0] is None: + print("error") + #cv2.imwrite("image.png", image_vis) + continue + + uv_map = data[0][0][0].uv*255.0 + uv_map = uv_map.cpu().numpy().transpose(1,2,0) + labels = data[0][0][0].labels.unsqueeze(2).cpu().numpy() + + npy_file = str(output_path) + '/uv_%5d.npy' % i + np.save(npy_file, uv_map) + i +=1 + + captura.release() + #cv2.destroyAllWindows() + + +if __name__ == "__main__": + + video_name = 'shortTED.mp4' + input_path = '../input_data/videos' + densepose_output_path = Path('../output_data', video_name, 'densepose') + extract_denseposes(video_name, input_path, densepose_output_path) + diff --git a/motion-gan-pipeline/preprocessing/get_mediapipe.py b/motion-gan-pipeline/preprocessing/get_mediapipe.py new file mode 100644 index 0000000..128cb65 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/get_mediapipe.py @@ -0,0 +1,82 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import mediapipe as mp +import os +from PIL import Image, ImageDraw +import cv2 +import numpy as np +from tqdm import tqdm + +def extract_mediapipe(input_path, output_path): + + # process video + frames_path = os.path.join(input_path, 'frames') + + mp_drawing = mp.solutions.drawing_utils + mp_drawing_styles = mp.solutions.drawing_styles + mp_pose = mp.solutions.pose + + pose = mp_pose.Pose( + static_image_mode=True, + model_complexity=2, + enable_segmentation=True, + min_detection_confidence=0.5) + + for idx, img_path in enumerate(tqdm([os.path.join(frames_path, f) for f in sorted(os.listdir(frames_path))])): + image = cv2.imread(img_path) + image_height, image_width, _ = image.shape + # Convert the BGR image to RGB before processing. + results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) + + save_path = os.path.join(output_path, '%05d.npy' % idx) + + if not results.pose_landmarks: + ldk_poses = np.zeros(shape=(33, 4)) + + else: + ldk_poses = [] + # Save as numpy + for i in range(33): + # landmarks: x, y, z, visibility + ldk = results.pose_landmarks.landmark[i] + ldk_poses.append([ldk.x * image_width , ldk.y * image_height , ldk.z, ldk.visibility]) + + np.save(save_path, ldk_poses) + + # Save landmarks and edges for DEBUGGING + debug = False + if debug: + debug_path = os.path.join(output_path, 'debug') + os.makedirs(debug_path, exist_ok=True) + + annotated_image = image.copy() + condition = np.stack((results.segmentation_mask,) * 3, axis=-1) > 0.1 + bg_image = np.zeros(image.shape, dtype=np.uint8) + bg_image[:] = (192, 192, 192) # gray + annotated_image = np.where(condition, annotated_image, bg_image) + # Draw pose landmarks on the image. + mp_drawing.draw_landmarks( + annotated_image, + results.pose_landmarks, + mp_pose.POSE_CONNECTIONS, + landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style()) + + cv2.imwrite(os.path.join(debug_path, str(idx) + '.png'), annotated_image) + + tmp = Image.open(img_path) + size = (image_height, image_width) + + for point in ldk_poses: + margin = (max(size) // 500) + 3 + ldmks = ([point[0] - margin, point[1] - margin, point[0] + margin, point[1] + margin]) + draw = ImageDraw.Draw(tmp) + draw.ellipse(ldmks, fill=(255)) + tmp.save(os.path.join(debug_path, str(idx) + '_landmarks.png')) + + +if __name__ == "__main__": + + input_path = '/home/alberto/motion-gan-pipeline/input_data/video/Clara' + output_path = '/home/alberto/motion-gan-pipeline/input_data/video/Clara/body_pose' + extract_mediapipe(input_path, output_path) diff --git a/motion-gan-pipeline/preprocessing/get_optical_flow.py b/motion-gan-pipeline/preprocessing/get_optical_flow.py new file mode 100644 index 0000000..59e56b6 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/get_optical_flow.py @@ -0,0 +1,52 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import numpy as np +from skimage.color import rgb2gray +from skimage.registration import optical_flow_tvl1 +from PIL import Image +from tqdm import tqdm +import os + +def compute_optical_flow_slow(frames, opticalflow_dir, debug_opticalflow_dir): + + for i in tqdm(range(len(frames)-1)): + out_name = os.path.join(opticalflow_dir,'%05d.npy' % i) + # --- Load the sequence + image0 = np.array(Image.open(frames[i])) + image1 = np.array(Image.open(frames[i+1])) + + # --- Convert the images to gray level: color is not supported. + image0 = rgb2gray(image0) + image1 = rgb2gray(image1) + + # --- Compute the optical flow + v, u = optical_flow_tvl1(image0, image1) + + of = np.stack((v,u),axis=0) + np.save(out_name, of) + + return + +def compute_optical_flow(frames, opticalflow_dir, debug_opticalflow_dir): + from mmflow.apis import inference_model, init_model + from mmflow.datasets import visualize_flow, write_flow + + import mmcv + + # Specify the path to model config and checkpoint file + config_file = 'third/mmflow/flownet2CS/flownet2cs_8x1_sfine_flyingthings3d_subset_384x768.py' + checkpoint_file = 'third/mmflow/flownet2CS/flownet2cs_8x1_sfine_flyingthings3d_subset_384x768.pth' + device = 'cuda:0' + + # init a model + model = init_model(config_file, checkpoint_file, device=device) + + # inference the demo image + for i in tqdm(range(len(frames)-1)): + out_name = os.path.join(opticalflow_dir,'%05d.flo' % i) + result = inference_model(model, frames[i] , frames[i+1]) + write_flow(result, flow_file=out_name) + # save the visualized flow map + debug_file = os.path.join(debug_opticalflow_dir, '%05d.png' % i) + flow_map = visualize_flow(result, save_file=debug_file) diff --git a/motion-gan-pipeline/preprocessing/get_scale_factor.py b/motion-gan-pipeline/preprocessing/get_scale_factor.py new file mode 100644 index 0000000..af2e1a2 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/get_scale_factor.py @@ -0,0 +1,118 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import numpy as np +import torch +import os +from preprocessing.face_tracking.FLAME.FLAME import FLAME, FLAMETex +from preprocessing.face_tracking.FLAME.config import cfg +from pytorch3d.io import IO, save_obj +import trimesh +from preprocessing.face_tracking.facemodel import Face_3DMM + +# Create FLAME model +sel_num = 1 +id_dim, exp_dim, tex_dim = 100, 50, 50 + +id_para = torch.zeros((1, id_dim), dtype=torch.float32).cuda() +id_para = id_para.expand(sel_num, -1) +exp_para = torch.zeros((sel_num, exp_dim), dtype=torch.float32).cuda() +euler_angle = torch.zeros((sel_num, 3), dtype=torch.float32).cuda() +trans = torch.zeros((sel_num, 3), dtype=torch.float32).cuda() +focal_length = torch.tensor([1100], dtype=torch.float32).cuda() +w, h = (512, 512) +cxy = torch.tensor((w/2.0, h/2.0), dtype=torch.float32).cuda() + +# Make model +FLAME_3dmm = FLAME(cfg.model) + +flame_landmarks = FLAME_3dmm.get_3dlandmarks(id_para, exp_para, euler_angle, trans, focal_length, cxy) +flame_geometry = FLAME_3dmm.forward_geo(id_para, exp_para, euler_angle, trans) + +print(flame_geometry.size()) +print(flame_landmarks.size()) + +# Save mesh +save_path= 'test_FLAME_mesh.obj' +verts = flame_geometry.cpu()[0] +faces = FLAME_3dmm.faces_tensor.cpu() +save_obj(save_path, verts=verts, faces=faces) + +# Save landmarks +save_path = 'test_FLAME_landmarks.obj' +flame_landmarks = flame_landmarks.cpu()[0] +pcl = trimesh.PointCloud(flame_landmarks) +pcl.export(save_path) + + +############################ + +# Create weird Basel model + +dir_path = os.path.dirname(os.path.realpath(__file__)) +sel_num = 1 +id_dim, exp_dim, tex_dim, point_num = 100, 79, 100, 34650 + +id_para = torch.zeros((1, id_dim), dtype=torch.float32).cuda() +id_para = id_para.expand(sel_num, -1) +exp_para = torch.zeros((sel_num, exp_dim), dtype=torch.float32).cuda() +euler_angle = torch.zeros((sel_num, 3), dtype=torch.float32).cuda() +trans = torch.zeros((sel_num, 3), dtype=torch.float32).cuda() +focal_length = torch.tensor([1100], dtype=torch.float32).cuda() +w, h = (512, 512) +cxy = torch.tensor((w/2.0, h/2.0), dtype=torch.float32).cuda() + +Basel_3dmm = Face_3DMM(os.path.join(dir_path, 'face_tracking/3DMM'), id_dim, exp_dim, tex_dim, point_num) + +basel_landmarks = Basel_3dmm.get_3dlandmarks(id_para, exp_para, euler_angle, trans, focal_length, cxy) +basel_geometry = Basel_3dmm.forward_geo(id_para, exp_para) + +print(basel_geometry.size()) +print(basel_landmarks.size()) + +# Save mesh +save_path= 'test_BASEL_mesh.obj' +verts = basel_geometry.cpu()[0] +topo_info = np.load(os.path.join( + dir_path, 'face_tracking/3DMM', 'topology_info.npy'), allow_pickle=True).item() +faces = torch.as_tensor(topo_info['tris']).cuda().float() + +save_obj(save_path, verts=verts, faces=faces) + +# Save landmarks +save_path= 'test_BASEL_landmarks.obj' +basel_landmarks = basel_landmarks.cpu()[0] +pcl = trimesh.PointCloud(basel_landmarks) +pcl.export(save_path) + +# find scale + +def cal_lan_loss(proj_lan, gt_lan): + return torch.mean((proj_lan-gt_lan)**2) + +scale = torch.ones((1, 1), requires_grad=True) +optimizer = torch.optim.Adam([scale], lr=.1) + +for i in range(200): + scale_batch = scale.expand(68, 3) + scaled_landmark_FLAME = torch.mul(flame_landmarks, scale_batch) + loss_lan = cal_lan_loss(scaled_landmark_FLAME, basel_landmarks) + + loss = loss_lan + optimizer.zero_grad() + loss.backward() + optimizer.step() + + if i == 100: + for param_group in optimizer.param_groups: + param_group['lr'] = 0.01 + + +print('Final scale: ', scale) + +# Save mesh +save_path= 'test_FLAME_scaled_mesh.obj' +scale = scale.expand(1, 3) +verts = torch.mul(flame_geometry[0].cpu(), scale) +faces = FLAME_3dmm.faces_tensor.cpu() +save_obj(save_path, verts=verts, faces=faces) diff --git a/motion-gan-pipeline/preprocessing/preprocessing.py b/motion-gan-pipeline/preprocessing/preprocessing.py new file mode 100644 index 0000000..a468a0a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/preprocessing.py @@ -0,0 +1,592 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +import os +import cv2 +from skimage import io +import numpy as np +from tqdm import tqdm +import torch +import json + +from autils.options import PreprocessingOptions + + + +class Preprocessor: + + def __init__(self, opt): + + self.opt = opt + + self.step = opt.step + + self.dataroot = opt.dataroot + self.name = opt.name + self.target_fps = opt.target_fps + self.type = opt.preprocessing_type + self.train_split = opt.train_split + self.val_split = opt.val_split + + self.dataset_base = os.path.join(self.dataroot, self.name) + + self.mapp = { + '0': lambda: self.deepspeech_preprocessing(), + '1': lambda: self.extract_images(), + '2': lambda: self.landmark_detection(), + '3': lambda: self.head_pose_estimation(), + '4': lambda: self.audioexpression_features(), + '5': lambda: self.face_matting(), + '6': lambda: self.extract_meshes(), + '7': lambda: self.save_params(), + '8': lambda: self.speech_to_text(), + '9': lambda: self.body_tracking(), + '10': lambda: self.emotion_detection(), + '11': lambda: self.edge_detection(), + '12': lambda: self.audio_noise_reduction(), + '13': lambda: self.optical_flow(), + } + + def initialize(self): + + self.audiofeature_dir = os.path.join(self.dataset_base, "audio_feature") + + self.audioexpr_dir = os.path.join(self.dataset_base, 'audio_expr') + + self.decaexpr_dir = os.path.join(self.dataset_base, 'deca_expr') + + self.framesdir = os.path.join(self.dataset_base, 'frames') + + self.landmarkdir = os.path.join(self.dataset_base, 'landmarks') + + self.projectedlankmarkdir = os.path.join(self.dataset_base, 'debug', 'proj_landmarks') + + self.trackparamspath = os.path.join(self.dataset_base, 'track_params.pt') + self.textpath = os.path.join(self.dataset_base, 'transcript.txt') + + self.mattdir = os.path.join(self.dataset_base, 'matting') + + self.debug_dir = os.path.join(self.dataset_base, 'debug') + + self.mesh_dir = os.path.join(self.dataset_base, 'meshes') + + self.expr_masks_dir = os.path.join(self.dataset_base, 'expr_masks') + + self.body_dir = os.path.join(self.dataset_base, 'body_pose') + + self.emotion_dir = os.path.join(self.dataset_base, 'emotions') + + self.edges_dir = os.path.join(self.dataset_base, 'edges') + + self.cropped_dir = os.path.join(self.dataset_base, 'cropped') + + self.APC_path = os.path.join(self.dataset_base, 'APC_feat.npy') + + self.opticalflow_dir = os.path.join(self.dataset_base, 'opticalflow') + self.debug_opticalflow_dir = os.path.join(self.dataset_base, 'debug', 'opticalflow') + + def get_valid_frames(self): + + max_frame_num = len(os.listdir(self.framesdir)) + + valid_img_ids = [] + for i in range(max_frame_num): + if os.path.isfile(os.path.join(self.landmarkdir, '%05d.lms' % i)): + valid_img_ids.append(i) + + valid_img_num = len(valid_img_ids) + tmp_img = cv2.imread(os.path.join(self.framesdir, '%05d.jpg' % valid_img_ids[0])) + # tmp_img = cv2.imread(os.path.join(self.framesdir, str(valid_img_ids[0]) + '.png')) + + h, w = tmp_img.shape[0], tmp_img.shape[1] + + self.max_frame_num, self.valid_img_ids, self.valid_img_num, self.h, self.w = \ + max_frame_num, valid_img_ids, valid_img_num, h, w + + def deepspeech_preprocessing(self): + from deepspeech_features import extract_ds + + print(f'\n\n--- Step 0: Extracting DeepSpeech features ---\n\n') + + os.makedirs(self.audiofeature_dir, exist_ok=True) + + if len(os.listdir(self.audiofeature_dir)) != 0: + print('Already done.\n\n') + + return + + extract_ds(self.dataset_base, self.name, self.type, self.target_fps) + + def extract_images(self): + print(f'\n\n--- Step 1: Extracting images from video ---\n\n') + + os.makedirs(self.framesdir, exist_ok=True) + + if len(os.listdir(self.framesdir)) != 0: + print('Already done.\n\n') + + return + + def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA): + # initialize the dimensions of the image to be resized and + # grab the image size + dim = None + (h, w) = image.shape[:2] + + # if both the width and height are None, then return the + # original image + if width is None and height is None: + return image + + # check to see if the width is None + if width is None: + # calculate the ratio of the height and construct the + # dimensions + r = height / float(h) + dim = (int(w * r), height) + + # otherwise, the height is None + else: + # calculate the ratio of the width and construct the + # dimensions + r = width / float(w) + dim = (width, int(h * r)) + + # resize the image + resized = cv2.resize(image, dim, interpolation = inter) + + # return the resized image + return resized + + vid_file = os.path.join(self.dataset_base, self.name + '.mp4') + + cap = cv2.VideoCapture(vid_file) + frame_num = 0 + while (True): + _, frame = cap.read() + if frame is None: + break + # resize frame to be have max dimention = 720 + (h, w) = frame.shape[:2] + if w > h: + frame = image_resize(frame, width=720) + else: + frame = image_resize(frame, height=720) + + cv2.imwrite(os.path.join(self.framesdir, '%05d.jpg' % frame_num), frame) + frame_num = frame_num + 1 + cap.release() + + def landmark_detection(self): + import face_alignment + + print('\n\n--- Step 2: Detect Landmarks ---\n\n') + + os.makedirs(self.landmarkdir, exist_ok=True) + + if len(os.listdir(self.landmarkdir)) != 0: + print('Already done.\n\n') + return + + fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False) + for image_path in tqdm(os.listdir(self.framesdir)): + if image_path.endswith('.jpg') or image_path.endswith('.png'): + input = io.imread(os.path.join(self.framesdir, image_path))[:, :, :3] + preds = fa.get_landmarks(input) + try: + if len(preds) > 0: + lands = preds[0].reshape(-1, 2)[:, :2] + np.savetxt(os.path.join(self.landmarkdir, image_path[:-3] + 'lms'), lands, '%f') + + except TypeError: + pass + + def head_pose_estimation(self): + from face_tracking.face_tracker import track_face_FLAME, track_face + from face_tracking.face_tracker_deca import track_face_DECA + + print('\n\n--- Step 3: Estimate Head Pose ---\n\n') + self.get_valid_frames() + + os.makedirs(self.debug_dir, exist_ok=True) + os.makedirs(self.expr_masks_dir, exist_ok=True) + + if os.path.isfile(self.trackparamspath): + print('Already done.\n\n') + return + + if self.opt.use_DECA: + os.makedirs(self.decaexpr_dir, exist_ok=True) + + print('Using DECA tracking..\n') + track_face_DECA(self.dataset_base, self.h, self.w, + self.max_frame_num, + self.trackparamspath, + self.decaexpr_dir, + self.expr_masks_dir) + + elif self.opt.use_FLAME: + print('Using FLAME tracking..\n') + track_face_FLAME(self.dataset_base, self.h, self.w, + self.max_frame_num, + self.trackparamspath, + self.decaexpr_dir, + self.expr_masks_dir) + + elif self.opt.use_BASEL: + print('Using BASEL tracking..\n') + track_face(self.dataset_base, + self.h, + self.w, + self.max_frame_num, + self.trackparamspath, + self.expr_masks_dir) + + def audioexpression_features(self): + from third.Audio2ExpressionNet.get_audioexpr import get_audioexpr + + print('\n\n--- Step 4: Extract Audio-Expressions ---\n\n') + + os.makedirs(self.audioexpr_dir, exist_ok=True) + + if len(os.listdir(self.audioexpr_dir)) != 0: + print('Already done.\n\n') + + return + + get_audioexpr(self.name, self.dataset_base, self.audioexpr_dir) + + def face_matting(self): + from third.RobustVideoMatting.model import MattingNetwork + from third.RobustVideoMatting.inference import convert_video + + print('\n\n--- Step 5: Face Matting ---\n\n') + + os.makedirs(self.mattdir, exist_ok=True) + + if len(os.listdir(self.mattdir)) != 0: + print('Already done.\n\n') + return + + matting_path = './third/RobustVideoMatting/' + + checkpoint_path = matting_path + 'checkpoints/' + # vid_file = os.path.join(self.dataset_base, self.name + '.mp4') + + # if not os.path.isfile(vid_file): + vid_file = self.framesdir + + model = MattingNetwork('mobilenetv3').eval().cuda() # or "resnet50" + model.load_state_dict(torch.load(checkpoint_path + 'rvm_mobilenetv3.pth')) + + convert_video( + model, # The model, can be on any device (cpu or cuda). + input_source=vid_file, # A video file or an image sequence directory. + output_type='png_sequence', # Choose "video" or "png_sequence" + output_composition=self.mattdir, # File path if video; directory path if png sequence. + output_video_mbps=4, # Output video mbps. Not needed for png sequence. + downsample_ratio=None, # A hyperparameter to adjust or use None for auto. + seq_chunk=12, # Process n frames at once for better parallelism. + ) + + def extract_meshes(self): + from facemesh_generator import GeometryGenerator + + print('\n\n--- Step 6: Extract Meshes ---\n\n') + self.get_valid_frames() + + os.makedirs(self.mesh_dir, exist_ok=True) + + if len(os.listdir(self.mesh_dir)) != 0: + print('Already done.\n\n') + return + + generator = GeometryGenerator(self.dataset_base, + self.h, self.w, + self.max_frame_num, + self.trackparamspath, + self.mesh_dir) + + for i in tqdm(range(self.valid_img_num)): + generator.generate_mesh(i) + + def save_params(self): + from face_tracking.geo_transform import euler2rot + + print('\n\n--- Step 7: Save Transform Param ---\n\n') + self.get_valid_frames() + + params_dict = torch.load(self.trackparamspath) + w, h, valid_img_ids = self.w, self.h, self.valid_img_ids + focal_len = params_dict['focal'] + euler_angle = params_dict['euler'] + trans = params_dict['trans'] / 10.0 # TODO: why? + valid_num = euler_angle.shape[0] - 1 # Last element not valid + print(valid_num, self.valid_img_num) + + train_split = int(self.train_split) + val_split = int(self.val_split) + train_split + + train_ids = torch.arange(0, train_split) + val_ids = torch.arange(train_split, val_split) + test_ids = torch.arange(val_split, valid_num) + + rot = euler2rot(euler_angle) + rot_inv = rot.permute(0, 2, 1) + trans_inv = -torch.bmm(rot_inv, trans.unsqueeze(2)) + + pose = torch.eye(4, dtype=torch.float32) + save_ids = ['train', 'val', 'test'] + train_val_ids = [train_ids, val_ids, test_ids] + mean_z = float(torch.mean(trans_inv[:, 2, 0]).item()) + for i in range(3): + transform_dict = dict() + transform_dict['focal_len'] = float(focal_len[0]) + transform_dict['camera_angle_x'] = float(w / 2.0) + transform_dict['camera_angle_y'] = float(h / 2.0) + transform_dict['frames'] = [] + ids = train_val_ids[i] + save_id = save_ids[i] + + for i in tqdm(ids): + i = i.item() + frame_dict = dict() + + # image and audio id + frame_dict['img_id'] = int(valid_img_ids[i]) + frame_dict['aud_id'] = int(valid_img_ids[i]) + + # add audio-expression + try: + # add audio_feature + frame_dict['audio_feature'] = np.load(os.path.join(self.audiofeature_dir, + '%05d.deepspeech.npy' % int(valid_img_ids[i])) + ).tolist() + + except FileNotFoundError: + print('Missing: %05d.deepspeech.npy' % int(valid_img_ids[i])) + pass + + # add audio-expression + try: + frame_dict['audio_expr'] = np.load(os.path.join(self.audioexpr_dir, + 'audio_expression_%05d.npy' % int(valid_img_ids[i])) + ).tolist() + except FileNotFoundError: + print('Missing: audio_expression_%5d.npy' % int(valid_img_ids[i])) + pass + + # add deca-expression + try: + frame_dict['deca_expr'] = np.load(os.path.join(self.decaexpr_dir, + '%05d.npy' % i) + ).tolist() + except FileNotFoundError: + print(f'Missing: %05d.npy' % i) + pass + + # add mesh path + try: + frame_dict['mesh_path'] = os.path.join(self.mesh_dir, '%05d.obj' % i) + + except FileNotFoundError: + print('Missing: ', os.path.join(self.mesh_dir, '%05d.obj' % i)) + pass + + # add expression mask + try: + frame_dict['expr_mask'] = os.path.join(self.expr_masks_dir, '%05d.jpg' % i) + + except FileNotFoundError: + print('Missing: ', os.path.join(self.expr_masks_dir, '%05d.jpg' % i)) + pass + + pose[:3, :3] = rot_inv[i] + pose[:3, 3] = trans_inv[i, :, 0] + frame_dict['transform_matrix'] = pose.numpy().tolist() + + dir_pose = torch.eye(4, dtype=torch.float32) + dir_pose[:3, :3] = rot[i] + dir_pose[:3, 3] = trans[i] + frame_dict['direct_transform'] = dir_pose.numpy().tolist() + + lms = np.loadtxt(os.path.join(self.landmarkdir, '%05d.lms' % valid_img_ids[i])) + min_x, max_x = np.min(lms, 0)[0], np.max(lms, 0)[0] + + cx = int((min_x + max_x) / 2.0) + cy = int(lms[27, 1]) + + h_w = int((max_x - cx) * 1.5) + h_h = int((lms[8, 1] - cy) * 1.15) + + rect_x = cx - h_w + rect_y = cy - h_h + if rect_x < 0: + rect_x = 0 + if rect_y < 0: + rect_y = 0 + rect_w = min(w - 1 - rect_x, 2 * h_w) + rect_h = min(h - 1 - rect_y, 2 * h_h) + + rect = np.array((rect_x, rect_y, rect_w, rect_h), dtype=np.int32) + frame_dict['bbox'] = rect.tolist() + + # add dict + transform_dict['frames'].append(frame_dict) + + with open(os.path.join(self.dataset_base, 'transforms_' + save_id + '.json'), 'w') as fp: + json.dump(transform_dict, fp, indent=2, separators=(',', ': ')) + + near_far = {'near': str(mean_z - 0.2), + 'far': str(mean_z + 0.4)} + + near_far_json = os.path.join(self.dataset_base, 'near-far.json') + with open(near_far_json, 'w') as fp: + json.dump(near_far, fp, indent=2, separators=(',', ': ')) + + def speech_to_text(self): + from preprocessing.speech_to_text import speech_to_text + print('\n\n--- Step 8: Speech to text ---\n\n') + + if os.path.isfile(self.textpath): + print('Already done.\n\n') + return + + speech_to_text(self.name, self.dataset_base, self.textpath) + + def body_tracking(self): + # from get_densepose import extract_denseposes + from get_mediapipe import extract_mediapipe + + print('\n\n--- Step 9: Body tracking ---\n\n') + + os.makedirs(self.body_dir, exist_ok=True) + + if len(os.listdir(self.body_dir)) != 0: + print('Already done.\n\n') + return + + extract_mediapipe(self.dataset_base, self.body_dir) + + def emotion_detection(self): + from emoca_tracker import emotion_detection + + print('\n\n--- Step 10: Emotion Detection ---\n\n') + self.get_valid_frames() + + os.makedirs(self.emotion_dir, exist_ok=True) + + if len(os.listdir(self.emotion_dir)) != 0: + print('Already done.\n\n') + return + + print('Using EMOCA emotion tracking..\n') + emotion_detection(self.dataset_base, + self.emotion_dir) + + return + + def edge_detection(self): + from edge_creation.utils import make_edges, make_edges_body + + print('\n\n--- Step 11: Edge Detection ---\n\n') + self.get_valid_frames() + + os.makedirs(self.edges_dir, exist_ok=True) + os.makedirs(self.cropped_dir, exist_ok=True) + + if len(os.listdir(self.edges_dir)) != 0: + print('Already done.\n\n') + return + + ## Switch here for body edges or tracked body pose edges + tracked = True + + if not tracked: + print('Making body edges using extracted edges..') + make_edges(self.mattdir, + self.projectedlankmarkdir, + self.edges_dir, + self.cropped_dir) + + else: + # TODO: REMOVE split_val + print('Making body edges using tracked body poses..') + make_edges_body(self.mattdir, + self.projectedlankmarkdir, + self.edges_dir, + self.cropped_dir, + self.body_dir, + split_val=0.1) + + return + + def audio_noise_reduction(self): + from scipy.io import wavfile + import noisereduce as nr + print('\n\n--- Step 12: Noise Reduction ---\n\n') + print('Skipping for now') + return + audio_path = os.path.join(self.dataset_base, self.name + '.wav') + # load data + rate, audio = wavfile.read(audio_path) + # perform noise reduction + reduced_noise = nr.reduce_noise(y=audio, sr=rate) + wavfile.write(audio_path, rate, reduced_noise) + return self + + def optical_flow(self): + from get_optical_flow import compute_optical_flow + + print('\n\n--- Step 13: Optical Flow ---\n\n') + self.get_valid_frames() + + os.makedirs(self.opticalflow_dir, exist_ok=True) + os.makedirs(self.debug_opticalflow_dir, exist_ok=True) + + if len(os.listdir(self.opticalflow_dir)) != 0: + print('Already done.\n\n') + return + + frames = [os.path.join(self.mattdir, f) for f in sorted(os.listdir(self.mattdir))] + + compute_optical_flow(frames, self.opticalflow_dir, self.debug_opticalflow_dir) + + return + + def preprocess_video(self): + + print('Preprocessing video: ', self.name) + + self.mapp[self.step]() + + def preprocess_audio(self): + + print('Preprocessing audio: ', self.name) + + self.mapp[self.step]() + + def __call__(self): + if self.type == 'audio': + self.preprocess_audio() + + else: + self.preprocess_video() + + +if __name__ == '__main__': + + opt = PreprocessingOptions().parse() + from pathlib import Path + opt.name = Path(opt.name).stem + preprocessor = Preprocessor(opt) + preprocessor.initialize() + preprocessor() + + + + + + + + diff --git a/motion-gan-pipeline/preprocessing/process_audio.sh b/motion-gan-pipeline/preprocessing/process_audio.sh new file mode 100644 index 0000000..a2674d9 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/process_audio.sh @@ -0,0 +1,32 @@ + +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details +eval "$(conda shell.bash hook)" +conda activate deepspeech + +DATAROOT=$1 +NAME=$2 + +TARGET_FPS=$3 +SAMPLERATE=$4 + +TYPE=audio + +tracker=--use_DECA + +STEPS=(12 0) + +for STEP in ${STEPS[@]} +do + python -W ignore preprocessing.py --dataroot $DATAROOT \ + --name $NAME \ + --target_fps $TARGET_FPS \ + --preprocessing_type $TYPE \ + --step $STEP \ + $tracker + +done + +conda deactivate + + diff --git a/motion-gan-pipeline/preprocessing/process_audio_dataset.sh b/motion-gan-pipeline/preprocessing/process_audio_dataset.sh new file mode 100644 index 0000000..f750d99 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/process_audio_dataset.sh @@ -0,0 +1,39 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +# DATAROOT=/media/apennino/TED384-v2 +# DATAROOT=/media/apennino/MTC +# DATASETS=("Test") +DATAROOT=$1 +DATASETS=$2 + +# TARGET_FPS=25 +# SAMPLERATE=16000 +TARGET_FPS=$3 +SAMPLERATE=$4 + +TYPE=audio + +tracker=--use_DECA + +STEPS=(0) + +for DATASET in ${DATASETS[@]} +do + for entry in "$DATAROOT/$DATASET"/* + do + for STEP in ${STEPS[@]} + do + + python preprocessing.py --dataroot $DATAROOT/$DATASET \ + --name $entry \ + --target_fps $TARGET_FPS \ + --preprocessing_type $TYPE \ + --step $STEP \ + $tracker + + done + done +done + + diff --git a/motion-gan-pipeline/preprocessing/process_video.sh b/motion-gan-pipeline/preprocessing/process_video.sh new file mode 100644 index 0000000..3662e63 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/process_video.sh @@ -0,0 +1,50 @@ +# ''' +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details +# ''' +eval "$(conda shell.bash hook)" +conda activate deepspeech +DATAROOT=$1 +NAME=$2 + +TARGET_FPS=$3 +SAMPLERATE=$4 + +TYPE=video + +tracker=--use_DECA + +# STEPS=(12 0 1 2 3 4 5 9 11 13) +STEPS=(12 0 1 2 3 4 5 9 11) + +process=$true + +if $process; +then + # # ffmpeg -i "$entry/$tmp.mp4" -r $TARGET_FPS -y "$entry/$tmp.tmp.mp4" + # mkvmerge --default-duration 0:${TARGET_FPS}fps --fix-bitstream-timing-information 0 "$DATAROOT/$NAME/$NAME.mp4" -o "$DATAROOT/$NAME/$NAME.tmp.mkv" + # ffmpeg -i "$DATAROOT/$NAME/$NAME.tmp.mkv" -c:v copy -y "$DATAROOT/$NAME/$NAME.mp4" + # rm "$DATAROOT/$NAME/$NAME.tmp.mkv" + + ffmpeg -i "$DATAROOT/$NAME/$NAME.mp4" -ar $SAMPLERATE -y "$DATAROOT/$NAME/$NAME.wav" + +fi + +for STEP in ${STEPS[@]} +do + + if [ $STEP = "0" ]; + then + conda activate deepspeech + else + conda activate pyenv + fi + python -W ignore preprocessing.py --dataroot $DATAROOT \ + --name $NAME \ + --target_fps $TARGET_FPS \ + --preprocessing_type $TYPE \ + --step $STEP \ + $tracker +done + +conda deactivate \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/process_video_dataset.sh b/motion-gan-pipeline/preprocessing/process_video_dataset.sh new file mode 100644 index 0000000..a582b64 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/process_video_dataset.sh @@ -0,0 +1,69 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +#!/bin/bash + +eval "$(conda shell.bash hook)" + +# DATAROOT=/media/apennino/TED384-v2 +# DATAROOT=/media/apennino/MTC +# DATAROOT=/media/apennino/EmotionDetection +# DATAROOT=/media/apennino/TMP_AVSpeech +# DATASETS=("Test") +# DATAROOT=/media/apennino/ +# DATASETS=("AI+Art") +DATAROOT=$1 +DATASETS=$2 + +# TARGET_FPS=25 +# SAMPLERATE=16000 +TARGET_FPS=$3 +SAMPLERATE=$4 + +TYPE=video +# + +tracker=--use_DECA + +STEPS=(0 2 3 4 5 1 11) +# STEPS=(0 2 3 4 1) +process=$true + +for DATASET in ${DATASETS[@]} +do + for entry in "$DATAROOT/$DATASET"/* + do + if $process; + then + tmp=$(basename "$entry") + # ffmpeg -i "$entry/$tmp.mp4" -r $TARGET_FPS -y "$entry/$tmp.tmp.mp4" + mkvmerge --default-duration 0:${TARGET_FPS}fps --fix-bitstream-timing-information 0 "$entry/$tmp.mp4" -o "$entry/$tmp.tmp.mkv" + ffmpeg -i "$entry/$tmp.tmp.mkv" -c:v copy -y "$entry/$tmp.mp4" + rm "$entry/$tmp.tmp.mkv" + + ffmpeg -i "$entry/$tmp.mp4" -ar $SAMPLERATE -y "$entry/$tmp.wav" + + fi + + tmp=$(basename "$entry") + + for STEP in ${STEPS[@]} + do + + if [ $STEP = "10" ]; + then + conda activate work36_cu11 + else + conda activate adnerf + fi + + python preprocessing.py --dataroot $DATAROOT/$DATASET \ + --name $tmp \ + --target_fps $TARGET_FPS \ + --preprocessing_type $TYPE \ + --step $STEP \ + $tracker + + done + done +done diff --git a/motion-gan-pipeline/preprocessing/speech_to_text.py b/motion-gan-pipeline/preprocessing/speech_to_text.py new file mode 100644 index 0000000..71ca159 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/speech_to_text.py @@ -0,0 +1,34 @@ +# SPDX-License-Identifier: MIT +# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details + +from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration +import soundfile as sf +import librosa +import os +import torch + + +def map_to_array(batch): + speech, _ = sf.read(batch["file"]) + batch["speech"] = speech + return batch + + +def speech_to_text(name, dataset_base, textpath): + data, samplerate = librosa.load(os.path.join(dataset_base, name +'.wav'), sr=16000) + data = torch.from_numpy(data) + + model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr") + processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr") + + inputs = processor(data, sampling_rate=samplerate, return_tensors="pt") + generated_ids = model.generate(inputs["input_features"], attention_mask=inputs["attention_mask"]) + + transcription = processor.batch_decode(generated_ids) + + textfile = open(textpath, "w") + for element in transcription: + textfile.write(element + "\n") + textfile.close() + + diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/BaselModel/__init__.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/BaselModel/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/BaselModel/basel_model.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/BaselModel/basel_model.py new file mode 100644 index 0000000..26a50c0 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/BaselModel/basel_model.py @@ -0,0 +1,94 @@ +import os +import torch +import torch.nn as nn +import torchvision.transforms as transforms +import numpy as np + +######################### +N_EXPRESSIONS=76 # <<<<<< NEEDS TO BE SPECIFIED ACCORDING TO THE USED FACE MODEL +# N_EXPRESSIONS=299 +######################### + +#import soft_renderer as sr +# +#class MorphableModel(nn.Module): +# def __init__(self, filename_average=''): +# super(MorphableModel, self).__init__() +# +# print('Load Morphable Model (Basel)') +# +# #filename_mesh = os.path.join(opt.dataroot, opt.phase + '/average_model.obj') +# filename_mesh = filename_average +# if filename_average=='': +# print('use default identity') +# filename_mesh = './BaselModel/average.obj' +# mesh = sr.Mesh.from_obj(filename_mesh, normalization=False, load_texture=True) +# self.average_vertices = mesh.vertices[0] +# self.faces = mesh.faces[0] +# self.average_vertices = self.average_vertices[None, :, :] # [num_vertices, XYZ] -> [batch_size=1, num_vertices, XYZ] +# self.faces = self.faces[None, :, :] # [num_faces, 3] -> [batch_size=1, num_faces, 3] +# self.textures = mesh.textures +# +# self.num_vertices = self.average_vertices.shape[1] +# self.num_faces = self.faces.shape[1] +# print('vertices:', self.average_vertices.shape) +# print('faces:', self.faces.shape) +# +# ## basis function +# self.expression_basis = np.memmap('./BaselModel/ExpressionBasis.matrix', dtype='float32', mode='r').__array__()[1:] # first entry is the size +# self.expression_basis = np.resize(self.expression_basis, (N_EXPRESSIONS, self.num_vertices, 4))[:,:,0:3] +# self.expression_basis = torch.tensor(self.expression_basis.astype(np.float32)).cuda() # N_EXPRESSIONS x num_vertices x 3 +# self.expression_basis = torch.transpose(self.expression_basis,0,2) # transpose for matmul +# print('expression_basis', self.expression_basis.shape) +# +# # default expression +# zeroExpr = torch.zeros(1, N_EXPRESSIONS, dtype=torch.float32).cuda() +# self.morph(zeroExpr) +# +# +# def save_model_to_obj_file(self, filename, mask=None): +# faces_cpu = self.faces.detach().cpu().numpy() +# vertices_cpu = self.vertices.detach().cpu().numpy() +# +# mask_cpu = None +# if not type(mask) == type(None): +# mask_cpu = mask.detach().cpu().numpy() +# +# f = open(filename, 'w') +# if type(mask) == type(None): +# for i in range(0, self.num_vertices): +# f.write('v ' + str(vertices_cpu[0, i, 0]) + ' ' + str(vertices_cpu[0, i, 1]) + ' ' + str(vertices_cpu[0, i, 2]) + '\n') +# else: +# for i in range(0, self.num_vertices): +# f.write('v ' + str(vertices_cpu[0, i, 0]) + ' ' + str(vertices_cpu[0, i, 1]) + ' ' + str(vertices_cpu[0, i, 2]) + ' ' + str(mask_cpu[i]) + ' ' + str(mask_cpu[i]) + ' ' + str(mask_cpu[i]) + ' 1'+ '\n') +# +# for i in range(0, self.num_faces): +# f.write('f ' + str(faces_cpu[0, i, 0]+1) + '// ' + str(faces_cpu[0, i, 1]+1) + '// ' + str(faces_cpu[0, i, 2]+1) + '//\n') +# +# f.close() +# +# def compute_expression_delta(self, expressions): +# return torch.transpose(torch.matmul(self.expression_basis, torch.transpose(expressions, 0,1)), 0, 2) # note that matmul wants to have this order: (a x b x c) x (c x m) => (a x b x m) +# +# def morph(self, expressions): +# self.vertices = self.average_vertices + self.compute_expression_delta(expressions) +# return self.vertices +# +# +# +# def LoadMask(self, filename=''): +# if filename=='': +# print('use default mask') +# filename = './BaselModel/mask/defaultMask_mouth.obj' +# +# mask = np.zeros(self.num_vertices) +# file = open(filename, 'r') +# i=0 +# for line in file: +# if line[0] == 'v': +# floats = [float(x) for x in line[1:].split()] +# if floats[3] == 1.0 and floats[4] == 0.0 and floats[5] == 0.0: +# mask[i] = 1.0 +# i += 1 +# file.close() +# return torch.tensor(mask.astype(np.float32)).cuda() \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/ReadMe.txt b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/ReadMe.txt new file mode 100644 index 0000000..9f5dd53 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/ReadMe.txt @@ -0,0 +1,21 @@ +This code runs the inference given a source audio and a target video that has been tracked. +The process is started using "transfer.sh" (where you can also specify the target sequences -> TARGET_ACTOR_LIST). +In the "transfer.py" you can specify the source sequences (search for "source_actors"). +Make sure that you specify the dimensions of your blendshape model in "BaselModel/basel_model.py" (-> N_EXPRESSIONS). + +Note that you have to extract the deepspeech features in a preprocessing step. +In the datasets folder you will find an example how the data should look like. +The deepspeech features are provided as npy files, while for the target sequence you also have to provide + the expressions (visually tracked blendshape coefficients). +If you have a different data format you need to adapt the data loader (data/face_dataset.py). + +Once you have the prepared data you can run the script. +It will optimize for the mapping from the audio-expression space to your blendshape model space. +The mapping is stored in the "mappings" folder (note: that it caches the mappings there and reuses it for the next run. + If you change something, you need to delete this cache). +The final output is stored in the "datasets/TRANSFERS" folder as a list of estimated expressions using the + source audio features. + +Given these expressions you need to generate new uv maps for the target video using the rigid pose + of the target video (only replacing the expressions). +These can then be used in the deferred neural rendering framework. \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/audio2expr_with_map.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/audio2expr_with_map.py new file mode 100644 index 0000000..fa4d4c1 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/audio2expr_with_map.py @@ -0,0 +1,107 @@ +import os +import os.path +from options.transfer_options import Audio2ExprOptions +from data import CreateDataLoader +from data.face_dataset import FaceDataset +from data.audio_dataset import AudioDataset +from models import create_model +from autil.visualizer import save_images +from autil import html +import torch +import torch.nn as nn +import torchvision.transforms as transforms +import numpy as np +from PIL import Image +import time +import random +import copy +from shutil import copyfile +from tqdm import tqdm + +from BaselModel.basel_model import * + + +def load_model(opt): + opt.output_audio_expressions = True + opt.nTrainObjects = 116 + + print('#train objects = %d' % opt.nTrainObjects) + + print('>>> create model <<<') + model = create_model(opt) + print('>>> setup model <<<') + model.setup(opt) + + return model + +def load_source_sequence(opt): + opt_source = copy.copy(opt) # create a clone + opt_source.dataroot = opt.source_actor # overwrite root directory + print(opt_source.dataroot) + opt_source.dataset_mode = 'audio' + opt_source.phase = 'train' + + dataset_source = AudioDataset() + + dataset_source.initialize(opt_source) + + dataloader = torch.utils.data.DataLoader( + dataset_source, + batch_size=opt.batch_size, + shuffle=not opt.serial_batches, + num_workers=int(opt.num_threads)) + + return dataset_source, dataloader + + +if __name__ == '__main__': + # read options + opt = Audio2ExprOptions().parse() + + # load model + model = load_model(opt) + print('model version:', opt.name) + + if opt.use_mapping: + # read mapping + mapping_fn = opt.mapping_path + + # load mapping from file + map_cpu = np.load(mapping_fn + '.npy') + mapping = torch.tensor(map_cpu.astype(np.float32)).cuda() + print('loaded mapping from file', mapping.shape) + + # make outdir + source_name = opt.source_actor.split("/")[-1] + out_dir = opt.out_dir + source_name + os.makedirs(out_dir, exist_ok=True) + audio2expr_dir = os.path.join(out_dir, 'audio_expr') + os.makedirs(audio2expr_dir, exist_ok=True) + # if opt.use_mapping: + # expr_dir = os.path.join(out_dir, 'expr') + # os.makedirs(expr_dir, exist_ok=True) + + # read source sequence + dataset_source, data_loader_source = load_source_sequence(opt) + dataset_source_size = len(dataset_source) + print('#source_actor frames = %d' % dataset_source_size) + + expression_multiplier = 1.0 # default + + # run over data + for i, data in tqdm(enumerate(data_loader_source)): + model.set_input(data) + model.test() + audio_expression = model.fake_expressions.data[0, :, 0] + + if opt.use_mapping: + expression = expression_multiplier * 10.0 * torch.matmul(mapping, audio_expression) + expression = expression[None, :] + # np.save(os.path.join(audio2expr_dir, f'expr_{i}.npy'), expression.cpu().numpy()) + np.save(os.path.join(audio2expr_dir, 'audio_expression_%05d.npy' % i), expression.cpu().numpy()) + + + # audio_expression = audio_expression[None, :] + # np.save(os.path.join(audio2expr_dir, f'audioexpr_{i}.npy'), audio_expression.cpu().numpy()) + + exit() \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/autil/__init__.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/autil/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/autil/get_data.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/autil/get_data.py new file mode 100644 index 0000000..c2da238 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/autil/get_data.py @@ -0,0 +1,115 @@ +from __future__ import print_function +import os +import tarfile +import requests +from warnings import warn +from zipfile import ZipFile +from bs4 import BeautifulSoup +from os.path import abspath, isdir, join, basename + + +class GetData(object): + """ + + Download CycleGAN or Pix2Pix Data. + + Args: + technique : str + One of: 'cyclegan' or 'pix2pix'. + verbose : bool + If True, print additional information. + + Examples: + >>> from util.get_data import GetData + >>> gd = GetData(technique='cyclegan') + >>> new_data_path = gd.get(save_path='./datasets') # options will be displayed. + + """ + + def __init__(self, technique='cyclegan', verbose=True): + url_dict = { + 'pix2pix': 'https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets', + 'cyclegan': 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets' + } + self.url = url_dict.get(technique.lower()) + self._verbose = verbose + + def _print(self, text): + if self._verbose: + print(text) + + @staticmethod + def _get_options(r): + soup = BeautifulSoup(r.text, 'lxml') + options = [h.text for h in soup.find_all('a', href=True) + if h.text.endswith(('.zip', 'tar.gz'))] + return options + + def _present_options(self): + r = requests.get(self.url) + options = self._get_options(r) + print('Options:\n') + for i, o in enumerate(options): + print("{0}: {1}".format(i, o)) + choice = input("\nPlease enter the number of the " + "datasets above you wish to download:") + return options[int(choice)] + + def _download_data(self, dataset_url, save_path): + if not isdir(save_path): + os.makedirs(save_path) + + base = basename(dataset_url) + temp_save_path = join(save_path, base) + + with open(temp_save_path, "wb") as f: + r = requests.get(dataset_url) + f.write(r.content) + + if base.endswith('.tar.gz'): + obj = tarfile.open(temp_save_path) + elif base.endswith('.zip'): + obj = ZipFile(temp_save_path, 'r') + else: + raise ValueError("Unknown File Type: {0}.".format(base)) + + self._print("Unpacking Data...") + obj.extractall(save_path) + obj.close() + os.remove(temp_save_path) + + def get(self, save_path, dataset=None): + """ + + Download a datasets. + + Args: + save_path : str + A directory to save the data to. + dataset : str, optional + A specific datasets to download. + Note: this must include the file extension. + If None, options will be presented for you + to choose from. + + Returns: + save_path_full : str + The absolute path to the downloaded data. + + """ + if dataset is None: + selected_dataset = self._present_options() + else: + selected_dataset = dataset + + save_path_full = join(save_path, selected_dataset.split('.')[0]) + + if isdir(save_path_full): + warn("\n'{0}' already exists. Voiding Download.".format( + save_path_full)) + else: + self._print('Downloading Data...') + url = "{0}/{1}".format(self.url, selected_dataset) + self._download_data(url, save_path=save_path) + + return abspath(save_path_full) diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/autil/html.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/autil/html.py new file mode 100644 index 0000000..1e7aab9 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/autil/html.py @@ -0,0 +1,64 @@ +import dominate +from dominate.tags import meta, h3, table, tr, td, p, a, img, br +import os + + +class HTML: + def __init__(self, web_dir, title, reflesh=0): + self.title = title + self.web_dir = web_dir + self.img_dir = os.path.join(self.web_dir, 'images') + if not os.path.exists(self.web_dir): + os.makedirs(self.web_dir) + if not os.path.exists(self.img_dir): + os.makedirs(self.img_dir) + # print(self.img_dir) + + self.doc = dominate.document(title=title) + if reflesh > 0: + with self.doc.head: + meta(http_equiv="reflesh", content=str(reflesh)) + + def get_image_dir(self): + return self.img_dir + + def add_header(self, str): + with self.doc: + h3(str) + + def add_table(self, border=1): + self.t = table(border=border, style="table-layout: fixed;") + self.doc.add(self.t) + + def add_images(self, ims, txts, links, width=400): + self.add_table() + with self.t: + with tr(): + for im, txt, link in zip(ims, txts, links): + with td(style="word-wrap: break-word;", halign="center", valign="top"): + with p(): + with a(href=os.path.join('images', link)): + img(style="width:%dpx" % width, src=os.path.join('images', im)) + br() + p(txt) + + def save(self): + html_file = '%s/index.html' % self.web_dir + f = open(html_file, 'wt') + f.write(self.doc.render()) + f.close() + + +if __name__ == '__main__': + html = HTML('web/', 'test_html') + html.add_header('hello world') + + ims = [] + txts = [] + links = [] + for n in range(4): + ims.append('image_%d.png' % n) + txts.append('text_%d' % n) + links.append('image_%d.png' % n) + html.add_images(ims, txts, links) + html.save() diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/autil/image_pool.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/autil/image_pool.py new file mode 100644 index 0000000..52413e0 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/autil/image_pool.py @@ -0,0 +1,32 @@ +import random +import torch + + +class ImagePool(): + def __init__(self, pool_size): + self.pool_size = pool_size + if self.pool_size > 0: + self.num_imgs = 0 + self.images = [] + + def query(self, images): + if self.pool_size == 0: + return images + return_images = [] + for image in images: + image = torch.unsqueeze(image.data, 0) + if self.num_imgs < self.pool_size: + self.num_imgs = self.num_imgs + 1 + self.images.append(image) + return_images.append(image) + else: + p = random.uniform(0, 1) + if p > 0.5: + random_id = random.randint(0, self.pool_size - 1) # randint is inclusive + tmp = self.images[random_id].clone() + self.images[random_id] = image + return_images.append(tmp) + else: + return_images.append(image) + return_images = torch.cat(return_images, 0) + return return_images diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/autil/util.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/autil/util.py new file mode 100644 index 0000000..df72b66 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/autil/util.py @@ -0,0 +1,82 @@ +from __future__ import print_function +import torch +import numpy as np +from PIL import Image +import os +import sys +import array +# import OpenEXR +# import Imath + +'''def load_exr(image_path): + # Open the input file + file = OpenEXR.InputFile(image_path) + + # Compute the size + dw = file.header()['dataWindow'] + w, h = (dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1) + + # Read the three color channels as 32-bit floats + FLOAT = Imath.PixelType(Imath.PixelType.FLOAT) + #(R,G,B) = [np.array(array.array('f', file.channel(Chan, FLOAT)).tolist()).reshape((w, h, 1)) for Chan in ("R", "G", "B") ] + + (r, g, b) = file.channels("RGB") + R = np.array(array.array('f', r).tolist()).reshape((w, h, 1)) + G = np.array(array.array('f', g).tolist()).reshape((w, h, 1)) + B = np.array(array.array('f', b).tolist()).reshape((w, h, 1)) + + return np.concatenate((R, G, B), axis=2)''' + +# Converts a Tensor into an image array (numpy) +# |imtype|: the desired type of the converted numpy array +def tensor2im(input_image, imtype=np.uint8): + if isinstance(input_image, torch.Tensor): + input_image = torch.clamp(input_image, -1.0, 1.0) + image_tensor = input_image.data + else: + return input_image + image_numpy = image_tensor[0].cpu().float().numpy() + if image_numpy.shape[0] == 1: + image_numpy = np.tile(image_numpy, (3, 1, 1)) + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 + return image_numpy.astype(imtype) + + +def diagnose_network(net, name='network'): + mean = 0.0 + count = 0 + for param in net.parameters(): + if param.grad is not None: + mean += torch.mean(torch.abs(param.grad.data)) + count += 1 + if count > 0: + mean = mean / count + print(name) + print(mean) + + +def save_image(image_numpy, image_path): + image_pil = Image.fromarray(image_numpy) + image_pil.save(image_path) + +def print_numpy(x, val=True, shp=False): + x = x.astype(np.float64) + if shp: + print('shape,', x.shape) + if val: + x = x.flatten() + print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % ( + np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x))) + + +def mkdirs(paths): + if isinstance(paths, list) and not isinstance(paths, str): + for path in paths: + mkdir(path) + else: + mkdir(paths) + + +def mkdir(path): + if not os.path.exists(path): + os.makedirs(path) diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/autil/visualizer.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/autil/visualizer.py new file mode 100644 index 0000000..7cb7750 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/autil/visualizer.py @@ -0,0 +1,207 @@ +import numpy as np +import os +import sys +import ntpath +import time +from . import util +from . import html +#from scipy.misc import imresize +import cv2 + +if sys.version_info[0] == 2: + VisdomExceptionBase = Exception +else: + VisdomExceptionBase = ConnectionError + + +# save image to the disk +def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256): + image_dir = webpage.get_image_dir() + short_path = ntpath.basename(image_path[0]) + name = os.path.splitext(short_path)[0] + + webpage.add_header(name) + ims, txts, links = [], [], [] + + for label, im_data in visuals.items(): + im = util.tensor2im(im_data) + image_name = '%s_%s.png' % (name, label) + save_path = os.path.join(image_dir, image_name) + h, w, _ = im.shape + + height = int(width * h / float(w)) + #im = imresize(im, (height,width), interp='bicubic') + im = cv2.resize(src=im, dsize=(height,width), interpolation=cv2.INTER_CUBIC) + + #im = imresize(im, (height,widht), interp='bicubic') + #if aspect_ratio > 1.0: + # im = imresize(im, (h, int(w * aspect_ratio)), interp='bicubic') + #if aspect_ratio < 1.0: + # im = imresize(im, (int(h / aspect_ratio), w), interp='bicubic') + + util.save_image(im, save_path) + + ims.append(image_name) + txts.append(label) + links.append(image_name) + webpage.add_images(ims, txts, links, width=width) + + +class Visualizer(): + def __init__(self, opt): + self.display_id = opt.display_id + self.use_html = opt.isTrain and not opt.no_html + self.win_size = opt.display_winsize + self.name = opt.name + self.opt = opt + self.saved = False + if self.display_id > 0: + import visdom + self.ncols = opt.display_ncols + self.vis = visdom.Visdom(server=opt.display_server, port=opt.display_port, env=opt.display_env, raise_exceptions=True) + + if self.use_html: + self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web') + self.img_dir = os.path.join(self.web_dir, 'images') + print('create web directory %s...' % self.web_dir) + util.mkdirs([self.web_dir, self.img_dir]) + self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt') + with open(self.log_name, "a") as log_file: + now = time.strftime("%c") + log_file.write('================ Training Loss (%s) ================\n' % now) + + def reset(self): + self.saved = False + + def throw_visdom_connection_error(self): + print('\n\nCould not connect to Visdom server (https://github.com/facebookresearch/visdom) for displaying training progress.\nYou can suppress connection to Visdom using the option --display_id -1. To install visdom, run \n$ pip install visdom\n, and start the server by \n$ python -m visdom.server.\n\n') + exit(1) + + # |visuals|: dictionary of images to display or save + def display_current_results(self, visuals, epoch, save_result, aspect_ratio=1.0, width=256): + if self.display_id > 0: # show images in the browser + ncols = self.ncols + if ncols > 0: + ncols = min(ncols, len(visuals)) + h, w = next(iter(visuals.values())).shape[2:4] + height = int(width * h / float(w)) + h = height + w = width + table_css = """""" % (w, h) + title = self.name + label_html = '' + label_html_row = '' + images = [] + idx = 0 + for label, image in visuals.items(): + # + image_numpy = util.tensor2im(image) + #image_numpy = imresize(image_numpy, (h, w), interp='bicubic') + image_numpy = cv2.resize(src=image_numpy, dsize=(h, w), interpolation=cv2.INTER_CUBIC) + image_numpy = image_numpy.transpose([2, 0, 1]) + label_html_row += '%s' % label + images.append(image_numpy) + idx += 1 + if idx % ncols == 0: + label_html += '%s' % label_html_row + label_html_row = '' + white_image = np.ones_like(image_numpy) * 255 + while idx % ncols != 0: + images.append(white_image) + label_html_row += '' + idx += 1 + if label_html_row != '': + label_html += '%s' % label_html_row + # pane col = image row + try: + self.vis.images(images, nrow=ncols, win=self.display_id + 1, padding=2, opts=dict(title=title + ' images')) + label_html = '%s
' % label_html + self.vis.text(table_css + label_html, win=self.display_id + 2, + opts=dict(title=title + ' labels')) + except VisdomExceptionBase: + self.throw_visdom_connection_error() + + else: + idx = 1 + for label, image in visuals.items(): + image_numpy = util.tensor2im(image) + self.vis.image(image_numpy.transpose([2, 0, 1]), opts=dict(title=label), + win=self.display_id + idx) + idx += 1 + + if self.use_html and (save_result or not self.saved): # save images to a html file + self.saved = True + for label, image in visuals.items(): + image_numpy = util.tensor2im(image) + img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label)) + util.save_image(image_numpy, img_path) + # update website + webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, reflesh=1) + for n in range(epoch, 0, -1): + webpage.add_header('epoch [%d]' % n) + ims, txts, links = [], [], [] + + for label, image_numpy in visuals.items(): + image_numpy = util.tensor2im(image) + img_path = 'epoch%.3d_%s.png' % (n, label) + ims.append(img_path) + txts.append(label) + links.append(img_path) + webpage.add_images(ims, txts, links, width=self.win_size) + webpage.save() + + # losses: dictionary of error labels and values + def plot_current_losses(self, epoch, counter_ratio, opt, losses): + if not hasattr(self, 'plot_data'): + self.plot_data = {'X': [], 'Y': [], 'legend': list(losses.keys())} + self.plot_data['X'].append(epoch + counter_ratio) + self.plot_data['Y'].append([losses[k] for k in self.plot_data['legend']]) + try: + self.vis.line( + X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1), + Y=np.array(self.plot_data['Y']), + opts={ + 'title': self.name + ' loss over time', + 'legend': self.plot_data['legend'], + 'xlabel': 'epoch', + 'ylabel': 'loss'}, + win=self.display_id) + except VisdomExceptionBase: + self.throw_visdom_connection_error() + + # losses: same format as |losses| of plot_current_losses + def print_current_losses(self, epoch, i, losses, t, t_data): + message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, i, t, t_data) + for k, v in losses.items(): + message += '%s: %.3f ' % (k, v) + + print(message) + with open(self.log_name, "a") as log_file: + log_file.write('%s\n' % message) + + + + + + + # losses: dictionary of error labels and values + def plot_current_validation_error(self, epoch, counter_ratio, losses): + if not hasattr(self, 'plot_validation_data'): + self.plot_validation_data = {'X': [], 'Y': [], 'legend': list(losses.keys())} + self.plot_validation_data['X'].append(epoch + counter_ratio) + self.plot_validation_data['Y'].append([losses[k] for k in self.plot_validation_data['legend']]) + try: + self.vis.line( + X=np.stack([np.array(self.plot_validation_data['X'])] * len(self.plot_validation_data['legend']), 1), + Y=np.array(self.plot_validation_data['Y']), + opts={ + 'title': self.name + ' validation error over time', + 'legend': self.plot_validation_data['legend'], + 'xlabel': 'epoch', + 'ylabel': 'error'}, + win=self.display_id+1) + except VisdomExceptionBase: + self.throw_visdom_connection_error() \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/checkpoints/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead/10_netG.pth b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/checkpoints/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead/10_netG.pth new file mode 100644 index 0000000..86f917f --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/checkpoints/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead/10_netG.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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0.000) G_L1: 22.297 G_L1_ABSOLUTE: 7.213 G_L1_RELATIVE: 15.085 G_Regularizer: 0.000 validation_error: 26.295 +(epoch: 1, iters: 6000, time: 0.523, data: 0.000) G_L1: 26.460 G_L1_ABSOLUTE: 7.193 G_L1_RELATIVE: 19.267 G_Regularizer: 0.000 validation_error: 26.111 +(epoch: 1, iters: 8000, time: 0.525, data: 0.000) G_L1: 25.699 G_L1_ABSOLUTE: 6.721 G_L1_RELATIVE: 18.978 G_Regularizer: 0.000 validation_error: 25.961 +(epoch: 1, iters: 10000, time: 0.522, data: 0.000) G_L1: 25.790 G_L1_ABSOLUTE: 6.689 G_L1_RELATIVE: 19.102 G_Regularizer: 0.000 validation_error: 26.259 +(epoch: 1, iters: 12000, time: 0.524, data: 0.000) G_L1: 21.361 G_L1_ABSOLUTE: 6.775 G_L1_RELATIVE: 14.586 G_Regularizer: 0.000 validation_error: 25.803 +(epoch: 1, iters: 14000, time: 0.524, data: 0.000) G_L1: 26.366 G_L1_ABSOLUTE: 6.800 G_L1_RELATIVE: 19.566 G_Regularizer: 0.000 validation_error: 26.035 +(epoch: 1, iters: 16000, time: 0.521, data: 0.000) G_L1: 22.563 G_L1_ABSOLUTE: 6.146 G_L1_RELATIVE: 16.416 G_Regularizer: 0.000 validation_error: 25.958 +(epoch: 1, iters: 18000, time: 0.521, data: 0.000) G_L1: 23.015 G_L1_ABSOLUTE: 6.598 G_L1_RELATIVE: 16.417 G_Regularizer: 0.000 validation_error: 26.145 +(epoch: 1, iters: 20000, time: 0.521, data: 0.000) G_L1: 27.604 G_L1_ABSOLUTE: 6.411 G_L1_RELATIVE: 21.193 G_Regularizer: 0.000 validation_error: 26.835 +(epoch: 1, iters: 22000, time: 0.524, data: 0.000) G_L1: 23.692 G_L1_ABSOLUTE: 6.645 G_L1_RELATIVE: 17.046 G_Regularizer: 0.000 validation_error: 26.400 +(epoch: 1, iters: 24000, time: 0.525, data: 0.000) G_L1: 31.754 G_L1_ABSOLUTE: 6.221 G_L1_RELATIVE: 25.533 G_Regularizer: 0.000 validation_error: 25.967 +(epoch: 1, iters: 26000, time: 0.534, data: 0.000) G_L1: 20.770 G_L1_ABSOLUTE: 6.981 G_L1_RELATIVE: 13.789 G_Regularizer: 0.000 validation_error: 26.519 +(epoch: 1, iters: 28000, time: 0.521, data: 0.000) G_L1: 21.386 G_L1_ABSOLUTE: 5.921 G_L1_RELATIVE: 15.465 G_Regularizer: 0.000 validation_error: 26.218 +(epoch: 1, iters: 30000, time: 0.527, data: 0.000) G_L1: 20.580 G_L1_ABSOLUTE: 5.905 G_L1_RELATIVE: 14.675 G_Regularizer: 0.000 validation_error: 26.542 +(epoch: 1, iters: 32000, time: 0.528, data: 0.000) G_L1: 22.306 G_L1_ABSOLUTE: 5.767 G_L1_RELATIVE: 16.538 G_Regularizer: 0.000 validation_error: 25.869 +(epoch: 1, iters: 34000, time: 0.530, data: 0.000) G_L1: 27.841 G_L1_ABSOLUTE: 6.845 G_L1_RELATIVE: 20.997 G_Regularizer: 0.000 validation_error: 25.692 +(epoch: 1, iters: 36000, time: 0.528, data: 0.000) G_L1: 24.150 G_L1_ABSOLUTE: 6.119 G_L1_RELATIVE: 18.030 G_Regularizer: 0.000 validation_error: 25.631 +(epoch: 1, iters: 38000, time: 0.530, data: 0.000) G_L1: 28.152 G_L1_ABSOLUTE: 5.918 G_L1_RELATIVE: 22.234 G_Regularizer: 0.000 validation_error: 26.156 +(epoch: 1, iters: 40000, time: 0.525, data: 0.000) G_L1: 25.895 G_L1_ABSOLUTE: 5.195 G_L1_RELATIVE: 20.700 G_Regularizer: 0.000 validation_error: 25.479 +(epoch: 1, iters: 42000, time: 0.530, data: 0.000) G_L1: 23.754 G_L1_ABSOLUTE: 5.785 G_L1_RELATIVE: 17.969 G_Regularizer: 0.000 validation_error: 25.697 +(epoch: 1, iters: 44000, time: 0.524, data: 0.000) G_L1: 23.181 G_L1_ABSOLUTE: 5.283 G_L1_RELATIVE: 17.897 G_Regularizer: 0.000 validation_error: 25.307 +(epoch: 1, iters: 46000, time: 0.528, data: 0.000) G_L1: 23.469 G_L1_ABSOLUTE: 6.536 G_L1_RELATIVE: 16.933 G_Regularizer: 0.000 validation_error: 25.563 +(epoch: 1, iters: 48000, time: 0.532, data: 0.000) G_L1: 24.980 G_L1_ABSOLUTE: 6.330 G_L1_RELATIVE: 18.649 G_Regularizer: 0.000 validation_error: 25.355 +(epoch: 1, iters: 50000, time: 0.528, data: 0.000) G_L1: 22.681 G_L1_ABSOLUTE: 5.501 G_L1_RELATIVE: 17.180 G_Regularizer: 0.000 validation_error: 25.428 +(epoch: 1, iters: 52000, time: 0.528, data: 0.000) G_L1: 18.113 G_L1_ABSOLUTE: 5.679 G_L1_RELATIVE: 12.434 G_Regularizer: 0.000 validation_error: 25.698 +(epoch: 1, iters: 54000, time: 0.527, data: 0.000) G_L1: 19.100 G_L1_ABSOLUTE: 5.162 G_L1_RELATIVE: 13.939 G_Regularizer: 0.000 validation_error: 25.779 +(epoch: 1, iters: 56000, time: 0.522, data: 0.000) G_L1: 24.206 G_L1_ABSOLUTE: 5.879 G_L1_RELATIVE: 18.327 G_Regularizer: 0.000 validation_error: 26.205 +(epoch: 1, iters: 58000, time: 0.532, data: 0.000) G_L1: 27.497 G_L1_ABSOLUTE: 5.718 G_L1_RELATIVE: 21.779 G_Regularizer: 0.000 validation_error: 25.873 +(epoch: 1, iters: 60000, time: 0.528, data: 0.000) G_L1: 23.159 G_L1_ABSOLUTE: 6.134 G_L1_RELATIVE: 17.025 G_Regularizer: 0.000 validation_error: 26.379 +(epoch: 1, iters: 62000, time: 0.533, data: 0.000) G_L1: 19.434 G_L1_ABSOLUTE: 4.600 G_L1_RELATIVE: 14.834 G_Regularizer: 0.000 validation_error: 25.891 +(epoch: 1, iters: 64000, time: 0.525, data: 0.001) G_L1: 23.563 G_L1_ABSOLUTE: 5.571 G_L1_RELATIVE: 17.992 G_Regularizer: 0.000 validation_error: 26.009 +(epoch: 1, iters: 66000, time: 0.531, data: 0.000) G_L1: 20.647 G_L1_ABSOLUTE: 5.063 G_L1_RELATIVE: 15.585 G_Regularizer: 0.000 validation_error: 25.800 +(epoch: 1, iters: 68000, time: 0.532, data: 0.000) G_L1: 24.282 G_L1_ABSOLUTE: 5.969 G_L1_RELATIVE: 18.312 G_Regularizer: 0.000 validation_error: 25.929 +(epoch: 1, iters: 70000, time: 0.530, data: 0.000) G_L1: 23.425 G_L1_ABSOLUTE: 5.420 G_L1_RELATIVE: 18.005 G_Regularizer: 0.000 validation_error: 26.189 +(epoch: 1, iters: 72000, time: 0.523, data: 0.000) G_L1: 19.160 G_L1_ABSOLUTE: 5.065 G_L1_RELATIVE: 14.095 G_Regularizer: 0.000 validation_error: 25.471 +(epoch: 1, iters: 74000, time: 0.526, data: 0.000) G_L1: 21.785 G_L1_ABSOLUTE: 5.365 G_L1_RELATIVE: 16.420 G_Regularizer: 0.000 validation_error: 25.427 +(epoch: 1, iters: 76000, time: 0.531, data: 0.000) G_L1: 24.412 G_L1_ABSOLUTE: 4.703 G_L1_RELATIVE: 19.709 G_Regularizer: 0.000 validation_error: 25.933 +(epoch: 1, iters: 78000, time: 0.523, data: 0.000) G_L1: 20.388 G_L1_ABSOLUTE: 4.958 G_L1_RELATIVE: 15.430 G_Regularizer: 0.000 validation_error: 25.459 +(epoch: 1, iters: 80000, time: 0.527, data: 0.000) G_L1: 24.715 G_L1_ABSOLUTE: 4.889 G_L1_RELATIVE: 19.826 G_Regularizer: 0.000 validation_error: 25.478 +(epoch: 1, iters: 82000, time: 0.531, data: 0.000) G_L1: 17.904 G_L1_ABSOLUTE: 4.281 G_L1_RELATIVE: 13.622 G_Regularizer: 0.000 validation_error: 25.462 +(epoch: 1, iters: 84000, time: 0.525, data: 0.000) G_L1: 23.077 G_L1_ABSOLUTE: 4.637 G_L1_RELATIVE: 18.440 G_Regularizer: 0.000 validation_error: 25.672 +(epoch: 1, iters: 86000, time: 0.538, data: 0.000) G_L1: 25.415 G_L1_ABSOLUTE: 5.170 G_L1_RELATIVE: 20.246 G_Regularizer: 0.000 validation_error: 26.016 +(epoch: 1, iters: 88000, time: 0.525, data: 0.000) G_L1: 21.505 G_L1_ABSOLUTE: 4.944 G_L1_RELATIVE: 16.561 G_Regularizer: 0.000 validation_error: 25.608 +(epoch: 1, iters: 90000, time: 0.522, data: 0.000) G_L1: 22.340 G_L1_ABSOLUTE: 4.808 G_L1_RELATIVE: 17.533 G_Regularizer: 0.000 validation_error: 25.641 +(epoch: 1, iters: 92000, time: 0.528, data: 0.000) G_L1: 17.184 G_L1_ABSOLUTE: 5.004 G_L1_RELATIVE: 12.179 G_Regularizer: 0.000 validation_error: 25.229 +(epoch: 1, iters: 94000, time: 0.526, data: 0.000) G_L1: 22.259 G_L1_ABSOLUTE: 4.649 G_L1_RELATIVE: 17.610 G_Regularizer: 0.000 validation_error: 25.663 +(epoch: 1, iters: 96000, time: 0.531, data: 0.000) G_L1: 20.482 G_L1_ABSOLUTE: 4.829 G_L1_RELATIVE: 15.653 G_Regularizer: 0.000 validation_error: 25.542 +(epoch: 1, iters: 98000, time: 0.526, data: 0.000) G_L1: 20.044 G_L1_ABSOLUTE: 4.605 G_L1_RELATIVE: 15.439 G_Regularizer: 0.000 validation_error: 25.474 +(epoch: 1, iters: 100000, time: 0.524, data: 0.000) G_L1: 24.498 G_L1_ABSOLUTE: 4.666 G_L1_RELATIVE: 19.831 G_Regularizer: 0.000 validation_error: 25.764 +(epoch: 1, iters: 102000, time: 0.529, data: 0.000) G_L1: 28.585 G_L1_ABSOLUTE: 5.306 G_L1_RELATIVE: 23.278 G_Regularizer: 0.000 validation_error: 25.472 +(epoch: 1, iters: 104000, time: 0.525, data: 0.000) G_L1: 22.490 G_L1_ABSOLUTE: 4.104 G_L1_RELATIVE: 18.385 G_Regularizer: 0.000 validation_error: 25.019 +(epoch: 1, iters: 106000, time: 0.530, data: 0.000) G_L1: 23.004 G_L1_ABSOLUTE: 5.039 G_L1_RELATIVE: 17.965 G_Regularizer: 0.000 validation_error: 25.303 +(epoch: 1, iters: 108000, time: 0.530, data: 0.000) G_L1: 27.354 G_L1_ABSOLUTE: 4.710 G_L1_RELATIVE: 22.644 G_Regularizer: 0.000 validation_error: 25.447 +(epoch: 1, iters: 110000, time: 0.523, data: 0.000) G_L1: 21.328 G_L1_ABSOLUTE: 4.957 G_L1_RELATIVE: 16.371 G_Regularizer: 0.000 validation_error: 25.714 +(epoch: 1, iters: 112000, time: 0.525, data: 0.000) G_L1: 19.985 G_L1_ABSOLUTE: 4.154 G_L1_RELATIVE: 15.831 G_Regularizer: 0.000 validation_error: 25.122 +(epoch: 1, iters: 114000, time: 0.529, data: 0.000) G_L1: 22.220 G_L1_ABSOLUTE: 4.470 G_L1_RELATIVE: 17.750 G_Regularizer: 0.000 validation_error: 25.690 +(epoch: 1, iters: 116000, time: 0.527, data: 0.000) G_L1: 20.264 G_L1_ABSOLUTE: 3.744 G_L1_RELATIVE: 16.520 G_Regularizer: 0.000 validation_error: 25.616 +(epoch: 1, iters: 118000, time: 0.524, data: 0.000) G_L1: 23.164 G_L1_ABSOLUTE: 4.250 G_L1_RELATIVE: 18.915 G_Regularizer: 0.000 validation_error: 25.461 +(epoch: 1, iters: 120000, time: 0.535, data: 0.000) G_L1: 24.064 G_L1_ABSOLUTE: 4.503 G_L1_RELATIVE: 19.561 G_Regularizer: 0.000 validation_error: 25.242 +(epoch: 1, iters: 122000, time: 0.524, data: 0.000) G_L1: 20.198 G_L1_ABSOLUTE: 3.989 G_L1_RELATIVE: 16.209 G_Regularizer: 0.000 validation_error: 25.856 +(epoch: 1, iters: 124000, time: 0.535, data: 0.000) G_L1: 22.604 G_L1_ABSOLUTE: 4.396 G_L1_RELATIVE: 18.208 G_Regularizer: 0.000 validation_error: 25.615 +(epoch: 1, iters: 126000, time: 0.532, data: 0.000) G_L1: 17.857 G_L1_ABSOLUTE: 3.804 G_L1_RELATIVE: 14.053 G_Regularizer: 0.000 validation_error: 24.820 +(epoch: 1, iters: 128000, time: 0.527, data: 0.000) G_L1: 22.076 G_L1_ABSOLUTE: 4.793 G_L1_RELATIVE: 17.284 G_Regularizer: 0.000 validation_error: 25.246 +(epoch: 1, iters: 130000, time: 0.526, data: 0.000) G_L1: 22.058 G_L1_ABSOLUTE: 4.431 G_L1_RELATIVE: 17.628 G_Regularizer: 0.000 validation_error: 24.596 +(epoch: 1, iters: 132000, time: 0.524, data: 0.000) G_L1: 25.289 G_L1_ABSOLUTE: 4.253 G_L1_RELATIVE: 21.036 G_Regularizer: 0.000 validation_error: 25.387 +(epoch: 1, iters: 134000, time: 0.527, data: 0.000) G_L1: 21.595 G_L1_ABSOLUTE: 3.987 G_L1_RELATIVE: 17.607 G_Regularizer: 0.000 validation_error: 25.077 +(epoch: 1, iters: 136000, time: 0.525, data: 0.000) G_L1: 21.235 G_L1_ABSOLUTE: 4.392 G_L1_RELATIVE: 16.843 G_Regularizer: 0.000 validation_error: 24.855 +(epoch: 1, iters: 138000, time: 0.534, data: 0.000) G_L1: 22.488 G_L1_ABSOLUTE: 4.404 G_L1_RELATIVE: 18.084 G_Regularizer: 0.000 validation_error: 25.866 +(epoch: 1, iters: 140000, time: 0.528, data: 0.000) G_L1: 23.761 G_L1_ABSOLUTE: 4.339 G_L1_RELATIVE: 19.422 G_Regularizer: 0.000 validation_error: 25.059 +(epoch: 1, iters: 142000, time: 0.527, data: 0.001) G_L1: 18.897 G_L1_ABSOLUTE: 4.298 G_L1_RELATIVE: 14.599 G_Regularizer: 0.000 validation_error: 25.060 +(epoch: 1, iters: 144000, time: 0.529, data: 0.000) G_L1: 18.984 G_L1_ABSOLUTE: 4.345 G_L1_RELATIVE: 14.639 G_Regularizer: 0.000 validation_error: 25.088 +(epoch: 1, iters: 146000, time: 0.531, data: 0.000) G_L1: 17.956 G_L1_ABSOLUTE: 4.423 G_L1_RELATIVE: 13.533 G_Regularizer: 0.000 validation_error: 25.111 +(epoch: 1, iters: 148000, time: 0.530, data: 0.000) G_L1: 19.417 G_L1_ABSOLUTE: 4.514 G_L1_RELATIVE: 14.903 G_Regularizer: 0.000 validation_error: 24.421 +(epoch: 1, iters: 150000, time: 0.525, data: 0.000) G_L1: 19.327 G_L1_ABSOLUTE: 4.618 G_L1_RELATIVE: 14.709 G_Regularizer: 0.000 validation_error: 24.444 +(epoch: 1, iters: 152000, time: 0.529, data: 0.000) G_L1: 22.617 G_L1_ABSOLUTE: 4.578 G_L1_RELATIVE: 18.040 G_Regularizer: 0.000 validation_error: 24.705 +(epoch: 1, iters: 154000, time: 0.524, data: 0.000) G_L1: 24.957 G_L1_ABSOLUTE: 3.963 G_L1_RELATIVE: 20.994 G_Regularizer: 0.000 validation_error: 24.430 +(epoch: 1, iters: 156000, time: 0.521, data: 0.000) G_L1: 20.307 G_L1_ABSOLUTE: 3.911 G_L1_RELATIVE: 16.396 G_Regularizer: 0.000 validation_error: 24.632 +(epoch: 1, iters: 158000, time: 0.527, data: 0.000) G_L1: 18.463 G_L1_ABSOLUTE: 4.156 G_L1_RELATIVE: 14.307 G_Regularizer: 0.000 validation_error: 24.343 +(epoch: 1, iters: 160000, time: 0.531, data: 0.000) G_L1: 20.475 G_L1_ABSOLUTE: 4.195 G_L1_RELATIVE: 16.280 G_Regularizer: 0.000 validation_error: 24.677 +(epoch: 1, iters: 162000, time: 0.525, data: 0.000) G_L1: 20.776 G_L1_ABSOLUTE: 4.367 G_L1_RELATIVE: 16.409 G_Regularizer: 0.000 validation_error: 24.303 +(epoch: 1, iters: 164000, time: 0.531, data: 0.000) G_L1: 18.067 G_L1_ABSOLUTE: 4.091 G_L1_RELATIVE: 13.976 G_Regularizer: 0.000 validation_error: 24.560 +(epoch: 1, iters: 166000, time: 0.526, data: 0.000) G_L1: 22.843 G_L1_ABSOLUTE: 4.290 G_L1_RELATIVE: 18.553 G_Regularizer: 0.000 validation_error: 24.721 +(epoch: 1, iters: 168000, time: 0.524, data: 0.000) G_L1: 20.924 G_L1_ABSOLUTE: 3.742 G_L1_RELATIVE: 17.182 G_Regularizer: 0.000 validation_error: 24.209 +(epoch: 1, iters: 170000, time: 0.526, data: 0.000) G_L1: 17.669 G_L1_ABSOLUTE: 4.038 G_L1_RELATIVE: 13.631 G_Regularizer: 0.000 validation_error: 24.377 +(epoch: 1, iters: 172000, time: 0.526, data: 0.000) G_L1: 20.277 G_L1_ABSOLUTE: 4.201 G_L1_RELATIVE: 16.076 G_Regularizer: 0.000 validation_error: 24.538 +(epoch: 1, iters: 174000, time: 0.527, data: 0.000) G_L1: 22.542 G_L1_ABSOLUTE: 3.949 G_L1_RELATIVE: 18.593 G_Regularizer: 0.000 validation_error: 24.260 +(epoch: 1, iters: 176000, time: 0.529, data: 0.000) G_L1: 21.529 G_L1_ABSOLUTE: 3.585 G_L1_RELATIVE: 17.944 G_Regularizer: 0.000 validation_error: 24.700 +(epoch: 1, iters: 178000, time: 0.529, data: 0.000) G_L1: 21.695 G_L1_ABSOLUTE: 4.256 G_L1_RELATIVE: 17.439 G_Regularizer: 0.000 validation_error: 24.722 +(epoch: 1, iters: 180000, time: 0.528, data: 0.000) G_L1: 18.981 G_L1_ABSOLUTE: 3.781 G_L1_RELATIVE: 15.200 G_Regularizer: 0.000 validation_error: 24.588 +(epoch: 1, iters: 182000, time: 0.531, data: 0.000) G_L1: 25.291 G_L1_ABSOLUTE: 4.210 G_L1_RELATIVE: 21.081 G_Regularizer: 0.000 validation_error: 24.406 +(epoch: 1, iters: 184000, time: 0.522, data: 0.000) G_L1: 19.536 G_L1_ABSOLUTE: 3.747 G_L1_RELATIVE: 15.789 G_Regularizer: 0.000 validation_error: 24.321 +(epoch: 1, iters: 186000, time: 0.525, data: 0.000) G_L1: 20.605 G_L1_ABSOLUTE: 4.199 G_L1_RELATIVE: 16.406 G_Regularizer: 0.000 validation_error: 24.567 +(epoch: 1, iters: 188000, time: 0.524, data: 0.000) G_L1: 18.809 G_L1_ABSOLUTE: 3.932 G_L1_RELATIVE: 14.877 G_Regularizer: 0.000 validation_error: 24.504 +(epoch: 1, iters: 190000, time: 0.527, data: 0.000) G_L1: 18.435 G_L1_ABSOLUTE: 4.072 G_L1_RELATIVE: 14.363 G_Regularizer: 0.000 validation_error: 24.471 +(epoch: 1, iters: 192000, time: 0.527, data: 0.000) G_L1: 24.383 G_L1_ABSOLUTE: 3.989 G_L1_RELATIVE: 20.394 G_Regularizer: 0.000 validation_error: 24.320 +(epoch: 1, iters: 194000, time: 0.524, data: 0.000) G_L1: 18.557 G_L1_ABSOLUTE: 4.491 G_L1_RELATIVE: 14.066 G_Regularizer: 0.000 validation_error: 24.349 +(epoch: 1, iters: 196000, time: 0.527, data: 0.000) G_L1: 22.087 G_L1_ABSOLUTE: 4.181 G_L1_RELATIVE: 17.906 G_Regularizer: 0.000 validation_error: 24.555 +(epoch: 1, iters: 198000, time: 0.532, data: 0.001) G_L1: 21.366 G_L1_ABSOLUTE: 3.752 G_L1_RELATIVE: 17.614 G_Regularizer: 0.000 validation_error: 24.559 +(epoch: 1, iters: 200000, time: 0.524, data: 0.000) G_L1: 19.948 G_L1_ABSOLUTE: 4.048 G_L1_RELATIVE: 15.900 G_Regularizer: 0.000 validation_error: 24.242 +(epoch: 1, iters: 202000, time: 0.530, data: 0.000) G_L1: 20.864 G_L1_ABSOLUTE: 3.799 G_L1_RELATIVE: 17.066 G_Regularizer: 0.000 validation_error: 24.322 +(epoch: 1, iters: 204000, time: 0.531, data: 0.000) G_L1: 20.734 G_L1_ABSOLUTE: 3.939 G_L1_RELATIVE: 16.795 G_Regularizer: 0.000 validation_error: 23.832 +(epoch: 1, iters: 206000, time: 0.527, data: 0.000) G_L1: 17.837 G_L1_ABSOLUTE: 4.024 G_L1_RELATIVE: 13.813 G_Regularizer: 0.000 validation_error: 24.326 +(epoch: 1, iters: 208000, time: 0.523, data: 0.000) G_L1: 21.147 G_L1_ABSOLUTE: 3.896 G_L1_RELATIVE: 17.251 G_Regularizer: 0.000 validation_error: 24.181 +(epoch: 1, iters: 210000, time: 0.527, data: 0.000) G_L1: 18.972 G_L1_ABSOLUTE: 3.557 G_L1_RELATIVE: 15.415 G_Regularizer: 0.000 validation_error: 24.457 +(epoch: 1, iters: 212000, time: 0.524, data: 0.000) G_L1: 21.846 G_L1_ABSOLUTE: 3.925 G_L1_RELATIVE: 17.921 G_Regularizer: 0.000 validation_error: 24.408 +(epoch: 1, iters: 214000, time: 0.528, data: 0.000) G_L1: 19.201 G_L1_ABSOLUTE: 3.623 G_L1_RELATIVE: 15.577 G_Regularizer: 0.000 validation_error: 24.239 +(epoch: 1, iters: 216000, time: 0.531, data: 0.000) G_L1: 17.034 G_L1_ABSOLUTE: 4.041 G_L1_RELATIVE: 12.993 G_Regularizer: 0.000 validation_error: 24.544 +(epoch: 1, iters: 218000, time: 0.525, data: 0.000) G_L1: 22.975 G_L1_ABSOLUTE: 3.899 G_L1_RELATIVE: 19.076 G_Regularizer: 0.000 validation_error: 23.697 +(epoch: 1, iters: 220000, time: 0.523, data: 0.000) G_L1: 18.786 G_L1_ABSOLUTE: 3.943 G_L1_RELATIVE: 14.844 G_Regularizer: 0.000 validation_error: 24.047 +(epoch: 1, iters: 222000, time: 0.527, data: 0.000) G_L1: 16.579 G_L1_ABSOLUTE: 4.034 G_L1_RELATIVE: 12.545 G_Regularizer: 0.000 validation_error: 23.986 +(epoch: 1, iters: 224000, time: 0.530, data: 0.000) G_L1: 18.551 G_L1_ABSOLUTE: 3.978 G_L1_RELATIVE: 14.573 G_Regularizer: 0.000 validation_error: 24.706 +(epoch: 1, iters: 226000, time: 0.526, data: 0.000) G_L1: 21.904 G_L1_ABSOLUTE: 3.952 G_L1_RELATIVE: 17.952 G_Regularizer: 0.000 validation_error: 24.628 +(epoch: 1, iters: 228000, time: 0.531, data: 0.000) G_L1: 18.624 G_L1_ABSOLUTE: 3.560 G_L1_RELATIVE: 15.064 G_Regularizer: 0.000 validation_error: 23.511 +(epoch: 1, iters: 230000, time: 0.524, data: 0.000) G_L1: 17.772 G_L1_ABSOLUTE: 3.938 G_L1_RELATIVE: 13.834 G_Regularizer: 0.000 validation_error: 23.734 +(epoch: 1, iters: 232000, time: 0.531, data: 0.000) G_L1: 20.067 G_L1_ABSOLUTE: 4.079 G_L1_RELATIVE: 15.988 G_Regularizer: 0.000 validation_error: 24.111 +(epoch: 1, iters: 234000, time: 0.529, data: 0.000) G_L1: 17.543 G_L1_ABSOLUTE: 4.035 G_L1_RELATIVE: 13.507 G_Regularizer: 0.000 validation_error: 24.288 +(epoch: 1, iters: 236000, time: 0.527, data: 0.000) G_L1: 23.722 G_L1_ABSOLUTE: 3.983 G_L1_RELATIVE: 19.739 G_Regularizer: 0.000 validation_error: 23.954 +(epoch: 1, iters: 238000, time: 0.526, data: 0.000) G_L1: 19.177 G_L1_ABSOLUTE: 3.632 G_L1_RELATIVE: 15.546 G_Regularizer: 0.000 validation_error: 24.831 +(epoch: 1, iters: 240000, time: 0.531, data: 0.000) G_L1: 18.756 G_L1_ABSOLUTE: 3.548 G_L1_RELATIVE: 15.207 G_Regularizer: 0.000 validation_error: 24.057 +(epoch: 1, iters: 242000, time: 0.526, data: 0.000) G_L1: 15.468 G_L1_ABSOLUTE: 3.767 G_L1_RELATIVE: 11.701 G_Regularizer: 0.000 validation_error: 24.242 +(epoch: 1, iters: 244000, time: 0.529, data: 0.000) G_L1: 18.866 G_L1_ABSOLUTE: 3.760 G_L1_RELATIVE: 15.105 G_Regularizer: 0.000 validation_error: 24.002 +(epoch: 1, iters: 246000, time: 0.522, data: 0.001) G_L1: 18.390 G_L1_ABSOLUTE: 3.824 G_L1_RELATIVE: 14.566 G_Regularizer: 0.000 validation_error: 23.662 +(epoch: 1, iters: 248000, time: 0.524, data: 0.000) G_L1: 19.327 G_L1_ABSOLUTE: 3.222 G_L1_RELATIVE: 16.105 G_Regularizer: 0.000 validation_error: 23.933 +(epoch: 1, iters: 250000, time: 0.525, data: 0.000) G_L1: 16.488 G_L1_ABSOLUTE: 4.071 G_L1_RELATIVE: 12.417 G_Regularizer: 0.000 validation_error: 23.823 +(epoch: 1, iters: 252000, time: 0.529, data: 0.001) G_L1: 19.932 G_L1_ABSOLUTE: 3.844 G_L1_RELATIVE: 16.088 G_Regularizer: 0.000 validation_error: 24.064 +(epoch: 1, iters: 254000, time: 0.528, data: 0.000) G_L1: 21.046 G_L1_ABSOLUTE: 3.934 G_L1_RELATIVE: 17.112 G_Regularizer: 0.000 validation_error: 23.750 +(epoch: 1, iters: 256000, time: 0.523, data: 0.000) G_L1: 20.878 G_L1_ABSOLUTE: 4.014 G_L1_RELATIVE: 16.864 G_Regularizer: 0.000 validation_error: 24.444 +(epoch: 1, iters: 258000, time: 0.525, data: 0.000) G_L1: 15.624 G_L1_ABSOLUTE: 3.351 G_L1_RELATIVE: 12.272 G_Regularizer: 0.000 validation_error: 23.646 +(epoch: 1, iters: 260000, time: 0.522, data: 0.000) G_L1: 21.103 G_L1_ABSOLUTE: 3.355 G_L1_RELATIVE: 17.748 G_Regularizer: 0.000 validation_error: 24.173 +(epoch: 1, iters: 262000, time: 0.528, data: 0.000) G_L1: 15.254 G_L1_ABSOLUTE: 3.802 G_L1_RELATIVE: 11.452 G_Regularizer: 0.000 validation_error: 24.097 +(epoch: 1, iters: 264000, time: 0.529, data: 0.000) G_L1: 17.711 G_L1_ABSOLUTE: 3.441 G_L1_RELATIVE: 14.270 G_Regularizer: 0.000 validation_error: 23.639 +(epoch: 1, iters: 266000, time: 0.531, data: 0.000) G_L1: 22.043 G_L1_ABSOLUTE: 3.804 G_L1_RELATIVE: 18.239 G_Regularizer: 0.000 validation_error: 24.509 +(epoch: 1, iters: 268000, time: 0.529, data: 0.000) G_L1: 15.759 G_L1_ABSOLUTE: 3.534 G_L1_RELATIVE: 12.226 G_Regularizer: 0.000 validation_error: 23.430 +(epoch: 1, iters: 270000, time: 0.527, data: 0.000) G_L1: 19.133 G_L1_ABSOLUTE: 4.115 G_L1_RELATIVE: 15.018 G_Regularizer: 0.000 validation_error: 24.018 +(epoch: 1, iters: 272000, time: 0.529, data: 0.000) G_L1: 15.650 G_L1_ABSOLUTE: 3.156 G_L1_RELATIVE: 12.494 G_Regularizer: 0.000 validation_error: 24.029 +(epoch: 1, iters: 274000, time: 0.532, data: 0.000) G_L1: 22.627 G_L1_ABSOLUTE: 3.961 G_L1_RELATIVE: 18.666 G_Regularizer: 0.000 validation_error: 24.322 +(epoch: 1, iters: 276000, time: 0.530, data: 0.000) G_L1: 21.083 G_L1_ABSOLUTE: 4.061 G_L1_RELATIVE: 17.022 G_Regularizer: 0.000 validation_error: 23.667 +(epoch: 1, iters: 278000, time: 0.531, data: 0.000) G_L1: 20.596 G_L1_ABSOLUTE: 3.878 G_L1_RELATIVE: 16.718 G_Regularizer: 0.000 validation_error: 24.293 +(epoch: 1, iters: 280000, time: 0.534, data: 0.001) G_L1: 22.605 G_L1_ABSOLUTE: 3.707 G_L1_RELATIVE: 18.898 G_Regularizer: 0.000 validation_error: 23.754 +(epoch: 1, iters: 282000, time: 0.527, data: 0.000) G_L1: 15.923 G_L1_ABSOLUTE: 3.235 G_L1_RELATIVE: 12.688 G_Regularizer: 0.000 validation_error: 23.480 +(epoch: 1, iters: 284000, time: 0.531, data: 0.000) G_L1: 20.187 G_L1_ABSOLUTE: 4.283 G_L1_RELATIVE: 15.904 G_Regularizer: 0.000 validation_error: 23.692 +(epoch: 1, iters: 286000, time: 0.528, data: 0.000) G_L1: 21.816 G_L1_ABSOLUTE: 3.407 G_L1_RELATIVE: 18.409 G_Regularizer: 0.000 validation_error: 23.527 +(epoch: 1, iters: 288000, time: 0.527, data: 0.000) G_L1: 19.219 G_L1_ABSOLUTE: 3.813 G_L1_RELATIVE: 15.405 G_Regularizer: 0.000 validation_error: 23.934 +(epoch: 1, iters: 290000, time: 0.531, data: 0.000) G_L1: 15.926 G_L1_ABSOLUTE: 3.455 G_L1_RELATIVE: 12.470 G_Regularizer: 0.000 validation_error: 23.804 +(epoch: 1, iters: 292000, time: 0.525, data: 0.000) G_L1: 23.940 G_L1_ABSOLUTE: 3.763 G_L1_RELATIVE: 20.177 G_Regularizer: 0.000 validation_error: 23.613 +(epoch: 1, iters: 294000, time: 0.532, data: 0.000) G_L1: 19.805 G_L1_ABSOLUTE: 3.906 G_L1_RELATIVE: 15.899 G_Regularizer: 0.000 validation_error: 23.512 +(epoch: 1, iters: 296000, time: 0.526, data: 0.000) G_L1: 18.564 G_L1_ABSOLUTE: 3.842 G_L1_RELATIVE: 14.722 G_Regularizer: 0.000 validation_error: 24.080 +(epoch: 1, iters: 298000, time: 0.523, data: 0.000) G_L1: 21.355 G_L1_ABSOLUTE: 4.216 G_L1_RELATIVE: 17.139 G_Regularizer: 0.000 validation_error: 23.654 +(epoch: 1, iters: 300000, time: 0.524, data: 0.000) G_L1: 16.615 G_L1_ABSOLUTE: 3.394 G_L1_RELATIVE: 13.222 G_Regularizer: 0.000 validation_error: 23.533 +(epoch: 1, iters: 302000, time: 0.531, data: 0.000) G_L1: 22.100 G_L1_ABSOLUTE: 4.024 G_L1_RELATIVE: 18.076 G_Regularizer: 0.000 validation_error: 23.328 +(epoch: 2, iters: 1248, time: 0.531, data: 0.000) G_L1: 17.071 G_L1_ABSOLUTE: 3.598 G_L1_RELATIVE: 13.473 G_Regularizer: 0.000 validation_error: 23.080 +(epoch: 2, iters: 3248, time: 0.527, data: 0.000) G_L1: 20.515 G_L1_ABSOLUTE: 3.594 G_L1_RELATIVE: 16.921 G_Regularizer: 0.000 validation_error: 23.704 +(epoch: 2, iters: 5248, time: 0.527, data: 0.000) G_L1: 16.561 G_L1_ABSOLUTE: 3.182 G_L1_RELATIVE: 13.379 G_Regularizer: 0.000 validation_error: 23.516 +(epoch: 2, iters: 7248, time: 0.531, data: 0.000) G_L1: 15.340 G_L1_ABSOLUTE: 3.116 G_L1_RELATIVE: 12.225 G_Regularizer: 0.000 validation_error: 23.368 +(epoch: 2, iters: 9248, time: 0.531, data: 0.000) G_L1: 21.704 G_L1_ABSOLUTE: 3.737 G_L1_RELATIVE: 17.967 G_Regularizer: 0.000 validation_error: 23.156 +(epoch: 2, iters: 11248, time: 0.531, data: 0.000) G_L1: 16.057 G_L1_ABSOLUTE: 3.588 G_L1_RELATIVE: 12.469 G_Regularizer: 0.000 validation_error: 23.250 +(epoch: 2, iters: 13248, time: 0.528, data: 0.000) G_L1: 21.139 G_L1_ABSOLUTE: 3.705 G_L1_RELATIVE: 17.434 G_Regularizer: 0.000 validation_error: 23.455 +(epoch: 2, iters: 15248, time: 0.527, data: 0.000) G_L1: 17.701 G_L1_ABSOLUTE: 3.750 G_L1_RELATIVE: 13.951 G_Regularizer: 0.000 validation_error: 23.150 +(epoch: 2, iters: 17248, time: 0.521, data: 0.000) G_L1: 18.372 G_L1_ABSOLUTE: 3.927 G_L1_RELATIVE: 14.445 G_Regularizer: 0.000 validation_error: 22.785 +(epoch: 2, iters: 19248, time: 0.531, data: 0.000) G_L1: 22.116 G_L1_ABSOLUTE: 3.609 G_L1_RELATIVE: 18.507 G_Regularizer: 0.000 validation_error: 23.102 +(epoch: 2, iters: 21248, time: 0.531, data: 0.000) G_L1: 21.331 G_L1_ABSOLUTE: 4.043 G_L1_RELATIVE: 17.288 G_Regularizer: 0.000 validation_error: 23.610 +(epoch: 2, iters: 23248, time: 0.532, data: 0.000) G_L1: 20.874 G_L1_ABSOLUTE: 3.990 G_L1_RELATIVE: 16.884 G_Regularizer: 0.000 validation_error: 23.080 +(epoch: 2, iters: 25248, time: 0.530, data: 0.000) G_L1: 20.500 G_L1_ABSOLUTE: 4.043 G_L1_RELATIVE: 16.458 G_Regularizer: 0.000 validation_error: 22.868 +(epoch: 2, iters: 27248, time: 0.521, data: 0.000) G_L1: 23.737 G_L1_ABSOLUTE: 3.618 G_L1_RELATIVE: 20.119 G_Regularizer: 0.000 validation_error: 23.424 +(epoch: 2, iters: 29248, time: 0.528, data: 0.000) G_L1: 16.543 G_L1_ABSOLUTE: 3.437 G_L1_RELATIVE: 13.107 G_Regularizer: 0.000 validation_error: 23.103 +(epoch: 2, iters: 31248, time: 0.532, data: 0.000) G_L1: 18.316 G_L1_ABSOLUTE: 3.081 G_L1_RELATIVE: 15.235 G_Regularizer: 0.000 validation_error: 23.809 +(epoch: 2, iters: 33248, time: 0.523, data: 0.000) G_L1: 19.593 G_L1_ABSOLUTE: 3.451 G_L1_RELATIVE: 16.142 G_Regularizer: 0.000 validation_error: 23.355 +(epoch: 2, iters: 35248, time: 0.529, data: 0.000) G_L1: 20.985 G_L1_ABSOLUTE: 3.616 G_L1_RELATIVE: 17.369 G_Regularizer: 0.000 validation_error: 23.562 +(epoch: 2, iters: 37248, time: 0.520, data: 0.000) G_L1: 19.825 G_L1_ABSOLUTE: 3.324 G_L1_RELATIVE: 16.501 G_Regularizer: 0.000 validation_error: 23.243 +(epoch: 2, iters: 39248, time: 0.524, data: 0.000) G_L1: 14.658 G_L1_ABSOLUTE: 3.161 G_L1_RELATIVE: 11.497 G_Regularizer: 0.000 validation_error: 23.368 +(epoch: 2, iters: 41248, time: 0.534, data: 0.000) G_L1: 17.936 G_L1_ABSOLUTE: 4.023 G_L1_RELATIVE: 13.913 G_Regularizer: 0.000 validation_error: 23.358 +(epoch: 2, iters: 43248, time: 0.525, data: 0.000) G_L1: 18.571 G_L1_ABSOLUTE: 3.913 G_L1_RELATIVE: 14.659 G_Regularizer: 0.000 validation_error: 23.244 +(epoch: 2, iters: 45248, time: 0.524, data: 0.000) G_L1: 21.359 G_L1_ABSOLUTE: 4.021 G_L1_RELATIVE: 17.338 G_Regularizer: 0.000 validation_error: 23.670 +(epoch: 2, iters: 47248, time: 0.523, data: 0.000) G_L1: 17.232 G_L1_ABSOLUTE: 3.526 G_L1_RELATIVE: 13.706 G_Regularizer: 0.000 validation_error: 23.741 +(epoch: 2, iters: 49248, time: 0.525, data: 0.000) G_L1: 19.561 G_L1_ABSOLUTE: 3.473 G_L1_RELATIVE: 16.088 G_Regularizer: 0.000 validation_error: 22.551 +(epoch: 2, iters: 51248, time: 0.527, data: 0.000) G_L1: 18.534 G_L1_ABSOLUTE: 3.302 G_L1_RELATIVE: 15.232 G_Regularizer: 0.000 validation_error: 23.145 +(epoch: 2, iters: 53248, time: 0.525, data: 0.000) G_L1: 16.864 G_L1_ABSOLUTE: 3.113 G_L1_RELATIVE: 13.750 G_Regularizer: 0.000 validation_error: 23.217 +(epoch: 2, iters: 55248, time: 0.528, data: 0.000) G_L1: 17.958 G_L1_ABSOLUTE: 3.384 G_L1_RELATIVE: 14.574 G_Regularizer: 0.000 validation_error: 23.627 +(epoch: 2, iters: 57248, time: 0.522, data: 0.000) G_L1: 19.117 G_L1_ABSOLUTE: 3.716 G_L1_RELATIVE: 15.401 G_Regularizer: 0.000 validation_error: 23.102 +(epoch: 2, iters: 59248, time: 0.532, data: 0.000) G_L1: 19.338 G_L1_ABSOLUTE: 3.483 G_L1_RELATIVE: 15.856 G_Regularizer: 0.000 validation_error: 23.934 +(epoch: 2, iters: 61248, time: 0.531, data: 0.000) G_L1: 20.639 G_L1_ABSOLUTE: 3.676 G_L1_RELATIVE: 16.963 G_Regularizer: 0.000 validation_error: 23.446 +(epoch: 2, iters: 63248, time: 0.528, data: 0.000) G_L1: 19.802 G_L1_ABSOLUTE: 3.931 G_L1_RELATIVE: 15.870 G_Regularizer: 0.000 validation_error: 23.258 +(epoch: 2, iters: 65248, time: 0.528, data: 0.000) G_L1: 19.555 G_L1_ABSOLUTE: 3.193 G_L1_RELATIVE: 16.362 G_Regularizer: 0.000 validation_error: 22.816 +(epoch: 2, iters: 67248, time: 0.529, data: 0.000) G_L1: 15.286 G_L1_ABSOLUTE: 3.287 G_L1_RELATIVE: 11.998 G_Regularizer: 0.000 validation_error: 23.291 +(epoch: 2, iters: 69248, time: 0.527, data: 0.000) G_L1: 16.987 G_L1_ABSOLUTE: 3.303 G_L1_RELATIVE: 13.684 G_Regularizer: 0.000 validation_error: 22.931 +(epoch: 2, iters: 71248, time: 0.522, data: 0.000) G_L1: 15.496 G_L1_ABSOLUTE: 4.119 G_L1_RELATIVE: 11.377 G_Regularizer: 0.000 validation_error: 23.433 +(epoch: 2, iters: 73248, time: 0.531, data: 0.000) G_L1: 22.068 G_L1_ABSOLUTE: 3.235 G_L1_RELATIVE: 18.833 G_Regularizer: 0.000 validation_error: 22.599 +(epoch: 2, iters: 75248, time: 0.524, data: 0.000) G_L1: 14.787 G_L1_ABSOLUTE: 3.646 G_L1_RELATIVE: 11.141 G_Regularizer: 0.000 validation_error: 23.038 +(epoch: 2, iters: 77248, time: 0.525, data: 0.000) G_L1: 19.725 G_L1_ABSOLUTE: 3.895 G_L1_RELATIVE: 15.830 G_Regularizer: 0.000 validation_error: 22.628 +(epoch: 2, iters: 79248, time: 0.524, data: 0.000) G_L1: 17.329 G_L1_ABSOLUTE: 3.965 G_L1_RELATIVE: 13.364 G_Regularizer: 0.000 validation_error: 22.406 +(epoch: 2, iters: 81248, time: 0.521, data: 0.000) G_L1: 20.568 G_L1_ABSOLUTE: 3.516 G_L1_RELATIVE: 17.052 G_Regularizer: 0.000 validation_error: 22.773 +(epoch: 2, iters: 83248, time: 0.530, data: 0.000) G_L1: 19.684 G_L1_ABSOLUTE: 3.353 G_L1_RELATIVE: 16.330 G_Regularizer: 0.000 validation_error: 22.096 +(epoch: 2, iters: 85248, time: 0.526, data: 0.000) G_L1: 18.110 G_L1_ABSOLUTE: 3.267 G_L1_RELATIVE: 14.843 G_Regularizer: 0.000 validation_error: 22.325 +(epoch: 2, iters: 87248, time: 0.523, data: 0.000) G_L1: 20.769 G_L1_ABSOLUTE: 3.615 G_L1_RELATIVE: 17.154 G_Regularizer: 0.000 validation_error: 22.466 +(epoch: 2, iters: 89248, time: 0.524, data: 0.000) G_L1: 21.016 G_L1_ABSOLUTE: 3.439 G_L1_RELATIVE: 17.577 G_Regularizer: 0.000 validation_error: 23.083 +(epoch: 2, iters: 91248, time: 0.523, data: 0.000) G_L1: 16.418 G_L1_ABSOLUTE: 3.475 G_L1_RELATIVE: 12.942 G_Regularizer: 0.000 validation_error: 22.785 +(epoch: 2, iters: 93248, time: 0.529, data: 0.000) G_L1: 16.625 G_L1_ABSOLUTE: 3.387 G_L1_RELATIVE: 13.238 G_Regularizer: 0.000 validation_error: 22.552 +(epoch: 2, iters: 95248, time: 0.524, data: 0.000) G_L1: 16.428 G_L1_ABSOLUTE: 3.582 G_L1_RELATIVE: 12.846 G_Regularizer: 0.000 validation_error: 22.430 +(epoch: 2, iters: 97248, time: 0.525, data: 0.000) G_L1: 19.261 G_L1_ABSOLUTE: 3.571 G_L1_RELATIVE: 15.690 G_Regularizer: 0.000 validation_error: 22.355 +(epoch: 2, iters: 99248, time: 0.527, data: 0.000) G_L1: 15.921 G_L1_ABSOLUTE: 3.310 G_L1_RELATIVE: 12.611 G_Regularizer: 0.000 validation_error: 22.780 +(epoch: 2, iters: 101248, time: 0.523, data: 0.000) G_L1: 18.042 G_L1_ABSOLUTE: 3.489 G_L1_RELATIVE: 14.554 G_Regularizer: 0.000 validation_error: 23.101 +(epoch: 2, iters: 103248, time: 0.529, data: 0.000) G_L1: 20.851 G_L1_ABSOLUTE: 3.513 G_L1_RELATIVE: 17.338 G_Regularizer: 0.000 validation_error: 22.728 +(epoch: 2, iters: 105248, time: 0.528, data: 0.000) G_L1: 16.144 G_L1_ABSOLUTE: 3.327 G_L1_RELATIVE: 12.817 G_Regularizer: 0.000 validation_error: 22.925 +(epoch: 2, iters: 107248, time: 0.521, data: 0.000) G_L1: 16.086 G_L1_ABSOLUTE: 3.030 G_L1_RELATIVE: 13.057 G_Regularizer: 0.000 validation_error: 22.383 +(epoch: 2, iters: 109248, time: 0.525, data: 0.000) G_L1: 19.658 G_L1_ABSOLUTE: 3.804 G_L1_RELATIVE: 15.854 G_Regularizer: 0.000 validation_error: 23.037 +(epoch: 2, iters: 111248, time: 0.524, data: 0.000) G_L1: 18.013 G_L1_ABSOLUTE: 3.070 G_L1_RELATIVE: 14.944 G_Regularizer: 0.000 validation_error: 23.241 +(epoch: 2, iters: 113248, time: 0.521, data: 0.000) G_L1: 16.377 G_L1_ABSOLUTE: 3.382 G_L1_RELATIVE: 12.995 G_Regularizer: 0.000 validation_error: 22.550 +(epoch: 2, iters: 115248, time: 0.527, data: 0.000) G_L1: 22.772 G_L1_ABSOLUTE: 3.368 G_L1_RELATIVE: 19.404 G_Regularizer: 0.000 validation_error: 22.492 +(epoch: 2, iters: 117248, time: 0.529, data: 0.000) G_L1: 17.029 G_L1_ABSOLUTE: 3.176 G_L1_RELATIVE: 13.853 G_Regularizer: 0.000 validation_error: 22.841 +(epoch: 2, iters: 119248, time: 0.533, data: 0.000) G_L1: 19.395 G_L1_ABSOLUTE: 3.149 G_L1_RELATIVE: 16.245 G_Regularizer: 0.000 validation_error: 22.454 +(epoch: 2, iters: 121248, time: 0.522, data: 0.001) G_L1: 23.174 G_L1_ABSOLUTE: 3.557 G_L1_RELATIVE: 19.618 G_Regularizer: 0.000 validation_error: 23.497 +(epoch: 2, iters: 123248, time: 0.528, data: 0.000) G_L1: 16.769 G_L1_ABSOLUTE: 3.241 G_L1_RELATIVE: 13.528 G_Regularizer: 0.000 validation_error: 22.927 +(epoch: 2, iters: 125248, time: 0.526, data: 0.000) G_L1: 19.578 G_L1_ABSOLUTE: 3.705 G_L1_RELATIVE: 15.873 G_Regularizer: 0.000 validation_error: 23.087 +(epoch: 2, iters: 127248, time: 0.529, data: 0.000) G_L1: 21.452 G_L1_ABSOLUTE: 3.541 G_L1_RELATIVE: 17.911 G_Regularizer: 0.000 validation_error: 23.492 +(epoch: 2, iters: 129248, time: 0.528, data: 0.000) G_L1: 18.801 G_L1_ABSOLUTE: 3.398 G_L1_RELATIVE: 15.404 G_Regularizer: 0.000 validation_error: 22.291 +(epoch: 2, iters: 131248, time: 0.534, data: 0.000) G_L1: 16.445 G_L1_ABSOLUTE: 3.393 G_L1_RELATIVE: 13.052 G_Regularizer: 0.000 validation_error: 21.906 +(epoch: 2, iters: 133248, time: 0.525, data: 0.000) G_L1: 18.212 G_L1_ABSOLUTE: 3.306 G_L1_RELATIVE: 14.906 G_Regularizer: 0.000 validation_error: 22.626 +(epoch: 2, iters: 135248, time: 0.525, data: 0.000) G_L1: 16.989 G_L1_ABSOLUTE: 2.998 G_L1_RELATIVE: 13.991 G_Regularizer: 0.000 validation_error: 22.980 +(epoch: 2, iters: 137248, time: 0.531, data: 0.000) G_L1: 23.062 G_L1_ABSOLUTE: 3.350 G_L1_RELATIVE: 19.711 G_Regularizer: 0.000 validation_error: 22.804 +(epoch: 2, iters: 139248, time: 0.527, data: 0.000) G_L1: 13.959 G_L1_ABSOLUTE: 3.508 G_L1_RELATIVE: 10.451 G_Regularizer: 0.000 validation_error: 22.635 +(epoch: 2, iters: 141248, time: 0.527, data: 0.001) G_L1: 15.024 G_L1_ABSOLUTE: 3.493 G_L1_RELATIVE: 11.531 G_Regularizer: 0.000 validation_error: 22.477 +(epoch: 2, iters: 143248, time: 0.530, data: 0.000) G_L1: 19.662 G_L1_ABSOLUTE: 3.676 G_L1_RELATIVE: 15.986 G_Regularizer: 0.000 validation_error: 22.946 +(epoch: 2, iters: 145248, time: 0.527, data: 0.000) G_L1: 16.094 G_L1_ABSOLUTE: 3.977 G_L1_RELATIVE: 12.117 G_Regularizer: 0.000 validation_error: 22.937 +(epoch: 2, iters: 147248, time: 0.522, data: 0.000) G_L1: 19.488 G_L1_ABSOLUTE: 3.716 G_L1_RELATIVE: 15.772 G_Regularizer: 0.000 validation_error: 22.969 +(epoch: 2, iters: 149248, time: 0.525, data: 0.000) G_L1: 16.429 G_L1_ABSOLUTE: 3.325 G_L1_RELATIVE: 13.104 G_Regularizer: 0.000 validation_error: 22.564 +(epoch: 2, iters: 151248, time: 0.525, data: 0.000) G_L1: 19.893 G_L1_ABSOLUTE: 3.890 G_L1_RELATIVE: 16.003 G_Regularizer: 0.000 validation_error: 22.257 +(epoch: 2, iters: 153248, time: 0.527, data: 0.000) G_L1: 21.929 G_L1_ABSOLUTE: 3.938 G_L1_RELATIVE: 17.991 G_Regularizer: 0.000 validation_error: 22.666 +(epoch: 2, iters: 155248, time: 0.529, data: 0.000) G_L1: 17.609 G_L1_ABSOLUTE: 3.660 G_L1_RELATIVE: 13.949 G_Regularizer: 0.000 validation_error: 22.663 +(epoch: 2, iters: 157248, time: 0.527, data: 0.000) G_L1: 19.777 G_L1_ABSOLUTE: 3.402 G_L1_RELATIVE: 16.375 G_Regularizer: 0.000 validation_error: 21.971 +(epoch: 2, iters: 159248, time: 0.523, data: 0.000) G_L1: 16.893 G_L1_ABSOLUTE: 4.129 G_L1_RELATIVE: 12.764 G_Regularizer: 0.000 validation_error: 22.675 +(epoch: 2, iters: 161248, time: 0.526, data: 0.000) G_L1: 15.298 G_L1_ABSOLUTE: 3.091 G_L1_RELATIVE: 12.207 G_Regularizer: 0.000 validation_error: 22.544 +(epoch: 2, iters: 163248, time: 0.531, data: 0.000) G_L1: 21.016 G_L1_ABSOLUTE: 3.173 G_L1_RELATIVE: 17.843 G_Regularizer: 0.000 validation_error: 22.432 +(epoch: 2, iters: 165248, time: 0.525, data: 0.000) G_L1: 23.441 G_L1_ABSOLUTE: 3.821 G_L1_RELATIVE: 19.620 G_Regularizer: 0.000 validation_error: 22.382 +(epoch: 2, iters: 167248, time: 0.528, data: 0.000) G_L1: 16.872 G_L1_ABSOLUTE: 3.352 G_L1_RELATIVE: 13.520 G_Regularizer: 0.000 validation_error: 22.550 +(epoch: 2, iters: 169248, time: 0.519, data: 0.000) G_L1: 21.007 G_L1_ABSOLUTE: 3.747 G_L1_RELATIVE: 17.260 G_Regularizer: 0.000 validation_error: 22.080 +(epoch: 2, iters: 171248, time: 0.527, data: 0.000) G_L1: 17.800 G_L1_ABSOLUTE: 3.790 G_L1_RELATIVE: 14.010 G_Regularizer: 0.000 validation_error: 22.413 +(epoch: 2, iters: 173248, time: 0.525, data: 0.001) G_L1: 19.482 G_L1_ABSOLUTE: 3.269 G_L1_RELATIVE: 16.213 G_Regularizer: 0.000 validation_error: 22.347 +(epoch: 2, iters: 175248, time: 0.527, data: 0.000) G_L1: 18.551 G_L1_ABSOLUTE: 3.339 G_L1_RELATIVE: 15.212 G_Regularizer: 0.000 validation_error: 22.609 +(epoch: 2, iters: 177248, time: 0.526, data: 0.000) G_L1: 16.063 G_L1_ABSOLUTE: 2.951 G_L1_RELATIVE: 13.112 G_Regularizer: 0.000 validation_error: 23.254 +(epoch: 2, iters: 179248, time: 0.529, data: 0.000) G_L1: 13.859 G_L1_ABSOLUTE: 3.016 G_L1_RELATIVE: 10.844 G_Regularizer: 0.000 validation_error: 22.355 +(epoch: 2, iters: 181248, time: 0.526, data: 0.000) G_L1: 17.330 G_L1_ABSOLUTE: 3.341 G_L1_RELATIVE: 13.989 G_Regularizer: 0.000 validation_error: 22.650 +(epoch: 2, iters: 183248, time: 0.524, data: 0.000) G_L1: 21.017 G_L1_ABSOLUTE: 3.245 G_L1_RELATIVE: 17.773 G_Regularizer: 0.000 validation_error: 22.689 +(epoch: 2, iters: 185248, time: 0.527, data: 0.000) G_L1: 18.863 G_L1_ABSOLUTE: 3.178 G_L1_RELATIVE: 15.685 G_Regularizer: 0.000 validation_error: 22.150 +(epoch: 2, iters: 187248, time: 0.530, data: 0.000) G_L1: 18.754 G_L1_ABSOLUTE: 3.526 G_L1_RELATIVE: 15.229 G_Regularizer: 0.000 validation_error: 21.877 +(epoch: 2, iters: 189248, time: 0.526, data: 0.000) G_L1: 16.535 G_L1_ABSOLUTE: 2.914 G_L1_RELATIVE: 13.620 G_Regularizer: 0.000 validation_error: 22.509 +(epoch: 2, iters: 191248, time: 0.531, data: 0.000) G_L1: 19.439 G_L1_ABSOLUTE: 3.230 G_L1_RELATIVE: 16.209 G_Regularizer: 0.000 validation_error: 22.330 +(epoch: 2, iters: 193248, time: 0.530, data: 0.000) G_L1: 18.030 G_L1_ABSOLUTE: 3.428 G_L1_RELATIVE: 14.602 G_Regularizer: 0.000 validation_error: 22.655 +(epoch: 2, iters: 195248, time: 0.524, data: 0.000) G_L1: 19.138 G_L1_ABSOLUTE: 3.059 G_L1_RELATIVE: 16.079 G_Regularizer: 0.000 validation_error: 22.307 +(epoch: 2, iters: 197248, time: 0.530, data: 0.000) G_L1: 14.541 G_L1_ABSOLUTE: 3.254 G_L1_RELATIVE: 11.287 G_Regularizer: 0.000 validation_error: 22.990 +(epoch: 2, iters: 199248, time: 0.536, data: 0.000) G_L1: 19.795 G_L1_ABSOLUTE: 3.369 G_L1_RELATIVE: 16.426 G_Regularizer: 0.000 validation_error: 23.097 +(epoch: 2, iters: 201248, time: 0.528, data: 0.000) G_L1: 19.176 G_L1_ABSOLUTE: 3.318 G_L1_RELATIVE: 15.858 G_Regularizer: 0.000 validation_error: 22.049 +(epoch: 2, iters: 203248, time: 0.524, data: 0.000) G_L1: 18.021 G_L1_ABSOLUTE: 3.264 G_L1_RELATIVE: 14.757 G_Regularizer: 0.000 validation_error: 22.431 +(epoch: 2, iters: 205248, time: 0.528, data: 0.000) G_L1: 16.555 G_L1_ABSOLUTE: 3.110 G_L1_RELATIVE: 13.445 G_Regularizer: 0.000 validation_error: 23.123 +(epoch: 2, iters: 207248, time: 0.529, data: 0.000) G_L1: 16.991 G_L1_ABSOLUTE: 3.376 G_L1_RELATIVE: 13.616 G_Regularizer: 0.000 validation_error: 22.364 +(epoch: 2, iters: 209248, time: 0.525, data: 0.000) G_L1: 18.126 G_L1_ABSOLUTE: 3.660 G_L1_RELATIVE: 14.466 G_Regularizer: 0.000 validation_error: 22.964 +(epoch: 2, iters: 211248, time: 0.527, data: 0.000) G_L1: 18.180 G_L1_ABSOLUTE: 3.626 G_L1_RELATIVE: 14.554 G_Regularizer: 0.000 validation_error: 22.443 +(epoch: 2, iters: 213248, time: 0.531, data: 0.000) G_L1: 18.043 G_L1_ABSOLUTE: 3.998 G_L1_RELATIVE: 14.044 G_Regularizer: 0.000 validation_error: 21.764 +(epoch: 2, iters: 215248, time: 0.523, data: 0.000) G_L1: 20.519 G_L1_ABSOLUTE: 3.863 G_L1_RELATIVE: 16.656 G_Regularizer: 0.000 validation_error: 22.037 +(epoch: 2, iters: 217248, time: 0.532, data: 0.000) G_L1: 16.253 G_L1_ABSOLUTE: 2.979 G_L1_RELATIVE: 13.274 G_Regularizer: 0.000 validation_error: 22.081 +(epoch: 2, iters: 219248, time: 0.528, data: 0.000) G_L1: 18.731 G_L1_ABSOLUTE: 3.423 G_L1_RELATIVE: 15.307 G_Regularizer: 0.000 validation_error: 21.826 +(epoch: 2, iters: 221248, time: 0.532, data: 0.000) G_L1: 17.620 G_L1_ABSOLUTE: 3.538 G_L1_RELATIVE: 14.082 G_Regularizer: 0.000 validation_error: 22.069 +(epoch: 2, iters: 223248, time: 0.529, data: 0.000) G_L1: 19.015 G_L1_ABSOLUTE: 2.803 G_L1_RELATIVE: 16.212 G_Regularizer: 0.000 validation_error: 22.037 +(epoch: 2, iters: 225248, time: 0.531, data: 0.000) G_L1: 15.481 G_L1_ABSOLUTE: 3.166 G_L1_RELATIVE: 12.315 G_Regularizer: 0.000 validation_error: 22.581 +(epoch: 2, iters: 227248, time: 0.530, data: 0.000) G_L1: 15.880 G_L1_ABSOLUTE: 3.149 G_L1_RELATIVE: 12.730 G_Regularizer: 0.000 validation_error: 22.800 +(epoch: 2, iters: 229248, time: 0.528, data: 0.001) G_L1: 14.752 G_L1_ABSOLUTE: 3.355 G_L1_RELATIVE: 11.397 G_Regularizer: 0.000 validation_error: 22.253 +(epoch: 2, iters: 231248, time: 0.527, data: 0.000) G_L1: 17.916 G_L1_ABSOLUTE: 3.297 G_L1_RELATIVE: 14.620 G_Regularizer: 0.000 validation_error: 22.567 +(epoch: 2, iters: 233248, time: 0.531, data: 0.000) G_L1: 19.253 G_L1_ABSOLUTE: 2.872 G_L1_RELATIVE: 16.381 G_Regularizer: 0.000 validation_error: 21.814 +(epoch: 2, iters: 235248, time: 0.528, data: 0.000) G_L1: 15.784 G_L1_ABSOLUTE: 2.995 G_L1_RELATIVE: 12.789 G_Regularizer: 0.000 validation_error: 22.220 +(epoch: 2, iters: 237248, time: 0.525, data: 0.000) G_L1: 18.267 G_L1_ABSOLUTE: 3.682 G_L1_RELATIVE: 14.585 G_Regularizer: 0.000 validation_error: 22.397 +(epoch: 2, iters: 239248, time: 0.531, data: 0.000) G_L1: 13.722 G_L1_ABSOLUTE: 3.123 G_L1_RELATIVE: 10.598 G_Regularizer: 0.000 validation_error: 22.534 +(epoch: 2, iters: 241248, time: 0.527, data: 0.000) G_L1: 18.989 G_L1_ABSOLUTE: 3.674 G_L1_RELATIVE: 15.315 G_Regularizer: 0.000 validation_error: 22.702 +(epoch: 2, iters: 243248, time: 0.528, data: 0.000) G_L1: 19.537 G_L1_ABSOLUTE: 3.492 G_L1_RELATIVE: 16.045 G_Regularizer: 0.000 validation_error: 22.421 +(epoch: 2, iters: 245248, time: 0.525, data: 0.000) G_L1: 16.567 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 13.790 G_Regularizer: 0.000 validation_error: 22.657 +(epoch: 2, iters: 247248, time: 0.528, data: 0.000) G_L1: 19.837 G_L1_ABSOLUTE: 3.300 G_L1_RELATIVE: 16.537 G_Regularizer: 0.000 validation_error: 22.231 +(epoch: 2, iters: 249248, time: 0.525, data: 0.000) G_L1: 17.652 G_L1_ABSOLUTE: 2.874 G_L1_RELATIVE: 14.778 G_Regularizer: 0.000 validation_error: 22.521 +(epoch: 2, iters: 251248, time: 0.536, data: 0.000) G_L1: 14.739 G_L1_ABSOLUTE: 3.446 G_L1_RELATIVE: 11.293 G_Regularizer: 0.000 validation_error: 22.760 +(epoch: 2, iters: 253248, time: 0.525, data: 0.000) G_L1: 15.555 G_L1_ABSOLUTE: 3.050 G_L1_RELATIVE: 12.505 G_Regularizer: 0.000 validation_error: 21.859 +(epoch: 2, iters: 255248, time: 0.536, data: 0.000) G_L1: 17.642 G_L1_ABSOLUTE: 2.963 G_L1_RELATIVE: 14.678 G_Regularizer: 0.000 validation_error: 22.491 +(epoch: 2, iters: 257248, time: 0.537, data: 0.000) G_L1: 15.999 G_L1_ABSOLUTE: 3.013 G_L1_RELATIVE: 12.986 G_Regularizer: 0.000 validation_error: 21.722 +(epoch: 2, iters: 259248, time: 0.561, data: 0.000) G_L1: 18.104 G_L1_ABSOLUTE: 3.312 G_L1_RELATIVE: 14.792 G_Regularizer: 0.000 validation_error: 21.919 +(epoch: 2, iters: 261248, time: 0.531, data: 0.000) G_L1: 19.184 G_L1_ABSOLUTE: 3.353 G_L1_RELATIVE: 15.831 G_Regularizer: 0.000 validation_error: 22.411 +(epoch: 2, iters: 263248, time: 0.532, data: 0.000) G_L1: 19.732 G_L1_ABSOLUTE: 3.769 G_L1_RELATIVE: 15.963 G_Regularizer: 0.000 validation_error: 22.091 +(epoch: 2, iters: 265248, time: 0.531, data: 0.000) G_L1: 17.633 G_L1_ABSOLUTE: 2.899 G_L1_RELATIVE: 14.734 G_Regularizer: 0.000 validation_error: 22.655 +(epoch: 2, iters: 267248, time: 0.529, data: 0.000) G_L1: 15.106 G_L1_ABSOLUTE: 3.098 G_L1_RELATIVE: 12.008 G_Regularizer: 0.000 validation_error: 22.111 +(epoch: 2, iters: 269248, time: 0.527, data: 0.000) G_L1: 15.622 G_L1_ABSOLUTE: 3.032 G_L1_RELATIVE: 12.589 G_Regularizer: 0.000 validation_error: 22.442 +(epoch: 2, iters: 271248, time: 0.527, data: 0.000) G_L1: 19.038 G_L1_ABSOLUTE: 3.374 G_L1_RELATIVE: 15.664 G_Regularizer: 0.000 validation_error: 21.477 +(epoch: 2, iters: 273248, time: 0.529, data: 0.000) G_L1: 15.576 G_L1_ABSOLUTE: 3.535 G_L1_RELATIVE: 12.041 G_Regularizer: 0.000 validation_error: 22.186 +(epoch: 2, iters: 275248, time: 0.530, data: 0.000) G_L1: 20.849 G_L1_ABSOLUTE: 3.242 G_L1_RELATIVE: 17.607 G_Regularizer: 0.000 validation_error: 22.657 +(epoch: 2, iters: 277248, time: 0.541, data: 0.000) G_L1: 16.140 G_L1_ABSOLUTE: 3.040 G_L1_RELATIVE: 13.099 G_Regularizer: 0.000 validation_error: 21.840 +(epoch: 2, iters: 279248, time: 0.545, data: 0.000) G_L1: 17.551 G_L1_ABSOLUTE: 3.423 G_L1_RELATIVE: 14.128 G_Regularizer: 0.000 validation_error: 22.008 +(epoch: 2, iters: 281248, time: 0.539, data: 0.000) G_L1: 18.892 G_L1_ABSOLUTE: 3.339 G_L1_RELATIVE: 15.553 G_Regularizer: 0.000 validation_error: 22.458 +(epoch: 2, iters: 283248, time: 0.535, data: 0.000) G_L1: 16.882 G_L1_ABSOLUTE: 3.635 G_L1_RELATIVE: 13.247 G_Regularizer: 0.000 validation_error: 22.282 +(epoch: 2, iters: 285248, time: 0.534, data: 0.000) G_L1: 19.265 G_L1_ABSOLUTE: 3.230 G_L1_RELATIVE: 16.036 G_Regularizer: 0.000 validation_error: 21.618 +(epoch: 2, iters: 287248, time: 0.534, data: 0.000) G_L1: 16.428 G_L1_ABSOLUTE: 2.852 G_L1_RELATIVE: 13.575 G_Regularizer: 0.000 validation_error: 21.424 +(epoch: 2, iters: 289248, time: 0.538, data: 0.000) G_L1: 17.082 G_L1_ABSOLUTE: 3.566 G_L1_RELATIVE: 13.516 G_Regularizer: 0.000 validation_error: 21.639 +(epoch: 2, iters: 291248, time: 0.534, data: 0.000) G_L1: 19.217 G_L1_ABSOLUTE: 3.050 G_L1_RELATIVE: 16.167 G_Regularizer: 0.000 validation_error: 21.731 +(epoch: 2, iters: 293248, time: 0.537, data: 0.000) G_L1: 17.839 G_L1_ABSOLUTE: 3.147 G_L1_RELATIVE: 14.692 G_Regularizer: 0.000 validation_error: 21.612 +(epoch: 2, iters: 295248, time: 0.549, data: 0.000) G_L1: 16.399 G_L1_ABSOLUTE: 2.961 G_L1_RELATIVE: 13.438 G_Regularizer: 0.000 validation_error: 22.383 +(epoch: 2, iters: 297248, time: 0.529, data: 0.000) G_L1: 18.376 G_L1_ABSOLUTE: 3.261 G_L1_RELATIVE: 15.115 G_Regularizer: 0.000 validation_error: 22.006 +(epoch: 2, iters: 299248, time: 0.539, data: 0.000) G_L1: 19.169 G_L1_ABSOLUTE: 3.104 G_L1_RELATIVE: 16.066 G_Regularizer: 0.000 validation_error: 22.928 +(epoch: 2, iters: 301248, time: 0.537, data: 0.000) G_L1: 18.682 G_L1_ABSOLUTE: 3.289 G_L1_RELATIVE: 15.393 G_Regularizer: 0.000 validation_error: 21.984 +(epoch: 3, iters: 496, time: 0.539, data: 0.000) G_L1: 16.565 G_L1_ABSOLUTE: 3.553 G_L1_RELATIVE: 13.012 G_Regularizer: 0.000 validation_error: 22.182 +(epoch: 3, iters: 2496, time: 0.870, data: 0.000) G_L1: 21.922 G_L1_ABSOLUTE: 3.485 G_L1_RELATIVE: 18.437 G_Regularizer: 0.000 validation_error: 22.160 +(epoch: 3, iters: 4496, time: 0.926, data: 0.000) G_L1: 17.519 G_L1_ABSOLUTE: 3.493 G_L1_RELATIVE: 14.026 G_Regularizer: 0.000 validation_error: 21.903 +(epoch: 3, iters: 6496, time: 0.929, data: 0.000) G_L1: 17.317 G_L1_ABSOLUTE: 2.849 G_L1_RELATIVE: 14.468 G_Regularizer: 0.000 validation_error: 22.253 +(epoch: 3, iters: 8496, time: 0.933, data: 0.000) G_L1: 16.292 G_L1_ABSOLUTE: 3.001 G_L1_RELATIVE: 13.291 G_Regularizer: 0.000 validation_error: 21.738 +(epoch: 3, iters: 10496, time: 0.928, data: 0.000) G_L1: 17.132 G_L1_ABSOLUTE: 3.135 G_L1_RELATIVE: 13.997 G_Regularizer: 0.000 validation_error: 21.739 +(epoch: 3, iters: 12496, time: 0.933, data: 0.000) G_L1: 20.222 G_L1_ABSOLUTE: 3.591 G_L1_RELATIVE: 16.631 G_Regularizer: 0.000 validation_error: 22.351 +(epoch: 3, iters: 14496, time: 0.937, data: 0.000) G_L1: 22.101 G_L1_ABSOLUTE: 4.080 G_L1_RELATIVE: 18.021 G_Regularizer: 0.000 validation_error: 21.922 +(epoch: 3, iters: 16496, time: 0.939, data: 0.000) G_L1: 20.988 G_L1_ABSOLUTE: 3.306 G_L1_RELATIVE: 17.682 G_Regularizer: 0.000 validation_error: 22.179 +(epoch: 3, iters: 18496, time: 0.941, data: 0.000) G_L1: 18.434 G_L1_ABSOLUTE: 2.928 G_L1_RELATIVE: 15.506 G_Regularizer: 0.000 validation_error: 21.850 +(epoch: 3, iters: 20496, time: 0.936, data: 0.000) G_L1: 17.564 G_L1_ABSOLUTE: 3.211 G_L1_RELATIVE: 14.353 G_Regularizer: 0.000 validation_error: 22.317 +(epoch: 3, iters: 22496, time: 0.941, data: 0.000) G_L1: 16.734 G_L1_ABSOLUTE: 3.227 G_L1_RELATIVE: 13.506 G_Regularizer: 0.000 validation_error: 21.512 +(epoch: 3, iters: 24496, time: 0.943, data: 0.000) G_L1: 15.422 G_L1_ABSOLUTE: 3.271 G_L1_RELATIVE: 12.151 G_Regularizer: 0.000 validation_error: 21.823 +(epoch: 3, iters: 26496, time: 0.943, data: 0.000) G_L1: 16.184 G_L1_ABSOLUTE: 3.400 G_L1_RELATIVE: 12.784 G_Regularizer: 0.000 validation_error: 22.031 +(epoch: 3, iters: 28496, time: 0.945, data: 0.000) G_L1: 16.703 G_L1_ABSOLUTE: 3.048 G_L1_RELATIVE: 13.654 G_Regularizer: 0.000 validation_error: 21.609 +(epoch: 3, iters: 30496, time: 0.936, data: 0.000) G_L1: 15.875 G_L1_ABSOLUTE: 3.278 G_L1_RELATIVE: 12.597 G_Regularizer: 0.000 validation_error: 22.192 +(epoch: 3, iters: 32496, time: 0.939, data: 0.000) G_L1: 17.809 G_L1_ABSOLUTE: 3.370 G_L1_RELATIVE: 14.440 G_Regularizer: 0.000 validation_error: 21.414 +(epoch: 3, iters: 34496, time: 0.945, data: 0.000) G_L1: 18.109 G_L1_ABSOLUTE: 3.659 G_L1_RELATIVE: 14.450 G_Regularizer: 0.000 validation_error: 21.787 +(epoch: 3, iters: 36496, time: 0.942, data: 0.000) G_L1: 15.798 G_L1_ABSOLUTE: 2.897 G_L1_RELATIVE: 12.901 G_Regularizer: 0.000 validation_error: 21.267 +(epoch: 3, iters: 38496, time: 0.947, data: 0.000) G_L1: 17.948 G_L1_ABSOLUTE: 3.016 G_L1_RELATIVE: 14.932 G_Regularizer: 0.000 validation_error: 21.690 +(epoch: 3, iters: 40496, time: 0.948, data: 0.000) G_L1: 15.979 G_L1_ABSOLUTE: 3.450 G_L1_RELATIVE: 12.529 G_Regularizer: 0.000 validation_error: 21.998 +(epoch: 3, iters: 42496, time: 0.942, data: 0.000) G_L1: 17.612 G_L1_ABSOLUTE: 3.498 G_L1_RELATIVE: 14.114 G_Regularizer: 0.000 validation_error: 21.841 +(epoch: 3, iters: 44496, time: 0.947, data: 0.000) G_L1: 15.908 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 13.152 G_Regularizer: 0.000 validation_error: 21.506 +(epoch: 3, iters: 46496, time: 0.941, data: 0.000) G_L1: 16.567 G_L1_ABSOLUTE: 3.162 G_L1_RELATIVE: 13.405 G_Regularizer: 0.000 validation_error: 21.237 +(epoch: 3, iters: 48496, time: 0.951, data: 0.000) G_L1: 18.775 G_L1_ABSOLUTE: 3.128 G_L1_RELATIVE: 15.647 G_Regularizer: 0.000 validation_error: 22.224 +(epoch: 3, iters: 50496, time: 0.942, data: 0.000) G_L1: 16.853 G_L1_ABSOLUTE: 3.432 G_L1_RELATIVE: 13.421 G_Regularizer: 0.000 validation_error: 21.711 +(epoch: 3, iters: 52496, time: 0.943, data: 0.000) G_L1: 15.349 G_L1_ABSOLUTE: 2.979 G_L1_RELATIVE: 12.369 G_Regularizer: 0.000 validation_error: 22.062 +(epoch: 3, iters: 54496, time: 0.950, data: 0.000) G_L1: 15.604 G_L1_ABSOLUTE: 3.300 G_L1_RELATIVE: 12.304 G_Regularizer: 0.000 validation_error: 21.327 +(epoch: 3, iters: 56496, time: 0.947, data: 0.000) G_L1: 19.685 G_L1_ABSOLUTE: 3.272 G_L1_RELATIVE: 16.413 G_Regularizer: 0.000 validation_error: 22.060 +(epoch: 3, iters: 58496, time: 0.943, data: 0.000) G_L1: 18.089 G_L1_ABSOLUTE: 3.214 G_L1_RELATIVE: 14.875 G_Regularizer: 0.000 validation_error: 21.277 +(epoch: 3, iters: 60496, time: 0.946, data: 0.000) G_L1: 17.823 G_L1_ABSOLUTE: 3.441 G_L1_RELATIVE: 14.382 G_Regularizer: 0.000 validation_error: 21.906 +(epoch: 3, iters: 62496, time: 0.943, data: 0.000) G_L1: 16.541 G_L1_ABSOLUTE: 3.503 G_L1_RELATIVE: 13.038 G_Regularizer: 0.000 validation_error: 21.393 +(epoch: 3, iters: 64496, time: 0.941, data: 0.000) G_L1: 18.917 G_L1_ABSOLUTE: 3.573 G_L1_RELATIVE: 15.344 G_Regularizer: 0.000 validation_error: 21.662 +(epoch: 3, iters: 66496, time: 0.941, data: 0.000) G_L1: 15.879 G_L1_ABSOLUTE: 2.843 G_L1_RELATIVE: 13.036 G_Regularizer: 0.000 validation_error: 22.285 +(epoch: 3, iters: 68496, time: 0.946, data: 0.000) G_L1: 19.113 G_L1_ABSOLUTE: 2.847 G_L1_RELATIVE: 16.266 G_Regularizer: 0.000 validation_error: 21.788 +(epoch: 3, iters: 70496, time: 0.941, data: 0.000) G_L1: 18.363 G_L1_ABSOLUTE: 2.796 G_L1_RELATIVE: 15.567 G_Regularizer: 0.000 validation_error: 22.055 +(epoch: 3, iters: 72496, time: 0.946, data: 0.000) G_L1: 15.999 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 13.262 G_Regularizer: 0.000 validation_error: 21.737 +(epoch: 3, iters: 74496, time: 0.946, data: 0.000) G_L1: 18.528 G_L1_ABSOLUTE: 3.078 G_L1_RELATIVE: 15.450 G_Regularizer: 0.000 validation_error: 21.563 +(epoch: 3, iters: 76496, time: 0.951, data: 0.000) G_L1: 13.771 G_L1_ABSOLUTE: 3.166 G_L1_RELATIVE: 10.605 G_Regularizer: 0.000 validation_error: 21.817 +(epoch: 3, iters: 78496, time: 0.947, data: 0.000) G_L1: 21.609 G_L1_ABSOLUTE: 3.524 G_L1_RELATIVE: 18.085 G_Regularizer: 0.000 validation_error: 21.049 +(epoch: 3, iters: 80496, time: 0.943, data: 0.000) G_L1: 19.271 G_L1_ABSOLUTE: 3.639 G_L1_RELATIVE: 15.632 G_Regularizer: 0.000 validation_error: 22.086 +(epoch: 3, iters: 82496, time: 0.576, data: 0.000) G_L1: 15.880 G_L1_ABSOLUTE: 3.172 G_L1_RELATIVE: 12.708 G_Regularizer: 0.000 validation_error: 21.125 +(epoch: 3, iters: 84496, time: 0.586, data: 0.000) G_L1: 17.768 G_L1_ABSOLUTE: 3.853 G_L1_RELATIVE: 13.915 G_Regularizer: 0.000 validation_error: 21.905 +(epoch: 3, iters: 86496, time: 0.534, data: 0.000) G_L1: 18.121 G_L1_ABSOLUTE: 3.262 G_L1_RELATIVE: 14.859 G_Regularizer: 0.000 validation_error: 21.787 +(epoch: 3, iters: 88496, time: 0.527, data: 0.000) G_L1: 18.524 G_L1_ABSOLUTE: 2.971 G_L1_RELATIVE: 15.553 G_Regularizer: 0.000 validation_error: 22.054 +(epoch: 3, iters: 90496, time: 0.528, data: 0.000) G_L1: 21.795 G_L1_ABSOLUTE: 3.187 G_L1_RELATIVE: 18.609 G_Regularizer: 0.000 validation_error: 21.429 +(epoch: 3, iters: 92496, time: 0.528, data: 0.000) G_L1: 19.608 G_L1_ABSOLUTE: 2.978 G_L1_RELATIVE: 16.630 G_Regularizer: 0.000 validation_error: 21.598 +(epoch: 3, iters: 94496, time: 0.526, data: 0.000) G_L1: 16.730 G_L1_ABSOLUTE: 3.196 G_L1_RELATIVE: 13.534 G_Regularizer: 0.000 validation_error: 22.201 +(epoch: 3, iters: 96496, time: 0.531, data: 0.000) G_L1: 16.715 G_L1_ABSOLUTE: 2.896 G_L1_RELATIVE: 13.819 G_Regularizer: 0.000 validation_error: 21.440 +(epoch: 3, iters: 98496, time: 0.521, data: 0.000) G_L1: 18.180 G_L1_ABSOLUTE: 3.171 G_L1_RELATIVE: 15.009 G_Regularizer: 0.000 validation_error: 21.957 +(epoch: 3, iters: 100496, time: 0.529, data: 0.000) G_L1: 15.614 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 12.661 G_Regularizer: 0.000 validation_error: 22.274 +(epoch: 3, iters: 102496, time: 0.527, data: 0.000) G_L1: 18.841 G_L1_ABSOLUTE: 3.175 G_L1_RELATIVE: 15.666 G_Regularizer: 0.000 validation_error: 21.431 +(epoch: 3, iters: 104496, time: 0.527, data: 0.000) G_L1: 16.661 G_L1_ABSOLUTE: 3.162 G_L1_RELATIVE: 13.500 G_Regularizer: 0.000 validation_error: 21.564 +(epoch: 3, iters: 106496, time: 0.521, data: 0.000) G_L1: 20.301 G_L1_ABSOLUTE: 3.380 G_L1_RELATIVE: 16.921 G_Regularizer: 0.000 validation_error: 21.512 +(epoch: 3, iters: 108496, time: 0.526, data: 0.000) G_L1: 18.253 G_L1_ABSOLUTE: 3.154 G_L1_RELATIVE: 15.099 G_Regularizer: 0.000 validation_error: 21.463 +(epoch: 3, iters: 110496, time: 0.525, data: 0.000) G_L1: 14.442 G_L1_ABSOLUTE: 2.799 G_L1_RELATIVE: 11.644 G_Regularizer: 0.000 validation_error: 21.249 +(epoch: 3, iters: 112496, time: 0.533, data: 0.000) G_L1: 14.927 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 12.211 G_Regularizer: 0.000 validation_error: 21.361 +(epoch: 3, iters: 114496, time: 0.526, data: 0.000) G_L1: 19.963 G_L1_ABSOLUTE: 2.703 G_L1_RELATIVE: 17.259 G_Regularizer: 0.000 validation_error: 21.335 +(epoch: 3, iters: 116496, time: 0.531, data: 0.000) G_L1: 17.456 G_L1_ABSOLUTE: 2.980 G_L1_RELATIVE: 14.477 G_Regularizer: 0.000 validation_error: 21.344 +(epoch: 3, iters: 118496, time: 0.531, data: 0.000) G_L1: 21.592 G_L1_ABSOLUTE: 2.833 G_L1_RELATIVE: 18.759 G_Regularizer: 0.000 validation_error: 21.740 +(epoch: 3, iters: 120496, time: 0.526, data: 0.000) G_L1: 16.403 G_L1_ABSOLUTE: 3.315 G_L1_RELATIVE: 13.088 G_Regularizer: 0.000 validation_error: 21.648 +(epoch: 3, iters: 122496, time: 0.524, data: 0.000) G_L1: 16.938 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 14.061 G_Regularizer: 0.000 validation_error: 21.674 +(epoch: 3, iters: 124496, time: 0.529, data: 0.000) G_L1: 22.409 G_L1_ABSOLUTE: 3.748 G_L1_RELATIVE: 18.661 G_Regularizer: 0.000 validation_error: 21.116 +(epoch: 3, iters: 126496, time: 0.534, data: 0.000) G_L1: 20.650 G_L1_ABSOLUTE: 3.539 G_L1_RELATIVE: 17.111 G_Regularizer: 0.000 validation_error: 21.293 +(epoch: 3, iters: 128496, time: 0.528, data: 0.000) G_L1: 18.110 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 15.225 G_Regularizer: 0.000 validation_error: 21.701 +(epoch: 3, iters: 130496, time: 0.524, data: 0.000) G_L1: 20.200 G_L1_ABSOLUTE: 3.020 G_L1_RELATIVE: 17.180 G_Regularizer: 0.000 validation_error: 21.355 +(epoch: 3, iters: 132496, time: 0.533, data: 0.000) G_L1: 18.152 G_L1_ABSOLUTE: 3.460 G_L1_RELATIVE: 14.692 G_Regularizer: 0.000 validation_error: 21.807 +(epoch: 3, iters: 134496, time: 0.529, data: 0.000) G_L1: 21.824 G_L1_ABSOLUTE: 3.691 G_L1_RELATIVE: 18.133 G_Regularizer: 0.000 validation_error: 21.716 +(epoch: 3, iters: 136496, time: 0.528, data: 0.000) G_L1: 16.460 G_L1_ABSOLUTE: 3.176 G_L1_RELATIVE: 13.284 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 3, iters: 138496, time: 0.531, data: 0.000) G_L1: 14.454 G_L1_ABSOLUTE: 3.432 G_L1_RELATIVE: 11.022 G_Regularizer: 0.000 validation_error: 21.599 +(epoch: 3, iters: 140496, time: 0.535, data: 0.000) G_L1: 14.606 G_L1_ABSOLUTE: 3.254 G_L1_RELATIVE: 11.352 G_Regularizer: 0.000 validation_error: 22.054 +(epoch: 3, iters: 142496, time: 0.529, data: 0.000) G_L1: 15.329 G_L1_ABSOLUTE: 3.208 G_L1_RELATIVE: 12.121 G_Regularizer: 0.000 validation_error: 21.600 +(epoch: 3, iters: 144496, time: 0.532, data: 0.000) G_L1: 15.620 G_L1_ABSOLUTE: 3.564 G_L1_RELATIVE: 12.055 G_Regularizer: 0.000 validation_error: 21.926 +(epoch: 3, iters: 146496, time: 0.529, data: 0.000) G_L1: 19.399 G_L1_ABSOLUTE: 3.785 G_L1_RELATIVE: 15.614 G_Regularizer: 0.000 validation_error: 21.624 +(epoch: 3, iters: 148496, time: 0.526, data: 0.000) G_L1: 22.538 G_L1_ABSOLUTE: 3.848 G_L1_RELATIVE: 18.689 G_Regularizer: 0.000 validation_error: 21.926 +(epoch: 3, iters: 150496, time: 0.566, data: 0.000) G_L1: 15.952 G_L1_ABSOLUTE: 2.944 G_L1_RELATIVE: 13.009 G_Regularizer: 0.000 validation_error: 21.905 +(epoch: 3, iters: 152496, time: 0.633, data: 0.000) G_L1: 20.764 G_L1_ABSOLUTE: 3.469 G_L1_RELATIVE: 17.295 G_Regularizer: 0.000 validation_error: 21.631 +(epoch: 3, iters: 154496, time: 0.621, data: 0.000) G_L1: 17.313 G_L1_ABSOLUTE: 3.185 G_L1_RELATIVE: 14.128 G_Regularizer: 0.000 validation_error: 21.827 +(epoch: 3, iters: 156496, time: 0.627, data: 0.000) G_L1: 19.455 G_L1_ABSOLUTE: 3.362 G_L1_RELATIVE: 16.094 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 3, iters: 158496, time: 0.589, data: 0.000) G_L1: 17.687 G_L1_ABSOLUTE: 2.788 G_L1_RELATIVE: 14.899 G_Regularizer: 0.000 validation_error: 21.908 +(epoch: 3, iters: 160496, time: 0.632, data: 0.000) G_L1: 16.040 G_L1_ABSOLUTE: 2.956 G_L1_RELATIVE: 13.084 G_Regularizer: 0.000 validation_error: 21.339 +(epoch: 3, iters: 162496, time: 0.630, data: 0.000) G_L1: 15.586 G_L1_ABSOLUTE: 2.893 G_L1_RELATIVE: 12.693 G_Regularizer: 0.000 validation_error: 21.502 +(epoch: 3, iters: 164496, time: 0.636, data: 0.000) G_L1: 15.950 G_L1_ABSOLUTE: 2.898 G_L1_RELATIVE: 13.052 G_Regularizer: 0.000 validation_error: 21.982 +(epoch: 3, iters: 166496, time: 0.622, data: 0.000) G_L1: 14.508 G_L1_ABSOLUTE: 2.720 G_L1_RELATIVE: 11.788 G_Regularizer: 0.000 validation_error: 20.869 +(epoch: 3, iters: 168496, time: 0.585, data: 0.000) G_L1: 15.632 G_L1_ABSOLUTE: 3.198 G_L1_RELATIVE: 12.434 G_Regularizer: 0.000 validation_error: 21.817 +(epoch: 3, iters: 170496, time: 0.630, data: 0.000) G_L1: 16.885 G_L1_ABSOLUTE: 2.935 G_L1_RELATIVE: 13.951 G_Regularizer: 0.000 validation_error: 21.575 +(epoch: 3, iters: 172496, time: 0.621, data: 0.000) G_L1: 15.546 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 12.896 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 3, iters: 174496, time: 0.600, data: 0.000) G_L1: 15.884 G_L1_ABSOLUTE: 3.753 G_L1_RELATIVE: 12.131 G_Regularizer: 0.000 validation_error: 21.523 +(epoch: 3, iters: 176496, time: 0.602, data: 0.000) G_L1: 14.790 G_L1_ABSOLUTE: 2.998 G_L1_RELATIVE: 11.792 G_Regularizer: 0.000 validation_error: 21.165 +(epoch: 3, iters: 178496, time: 0.548, data: 0.000) G_L1: 16.725 G_L1_ABSOLUTE: 2.855 G_L1_RELATIVE: 13.870 G_Regularizer: 0.000 validation_error: 21.179 +(epoch: 3, iters: 180496, time: 0.525, data: 0.000) G_L1: 17.632 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 14.856 G_Regularizer: 0.000 validation_error: 21.914 +(epoch: 3, iters: 182496, time: 0.527, data: 0.000) G_L1: 20.213 G_L1_ABSOLUTE: 3.651 G_L1_RELATIVE: 16.562 G_Regularizer: 0.000 validation_error: 21.575 +(epoch: 3, iters: 184496, time: 0.530, data: 0.000) G_L1: 17.158 G_L1_ABSOLUTE: 3.185 G_L1_RELATIVE: 13.974 G_Regularizer: 0.000 validation_error: 21.702 +(epoch: 3, iters: 186496, time: 0.528, data: 0.000) G_L1: 15.908 G_L1_ABSOLUTE: 3.113 G_L1_RELATIVE: 12.795 G_Regularizer: 0.000 validation_error: 21.021 +(epoch: 3, iters: 188496, time: 0.529, data: 0.000) G_L1: 17.625 G_L1_ABSOLUTE: 3.218 G_L1_RELATIVE: 14.407 G_Regularizer: 0.000 validation_error: 21.256 +(epoch: 3, iters: 190496, time: 0.531, data: 0.000) G_L1: 20.941 G_L1_ABSOLUTE: 3.107 G_L1_RELATIVE: 17.834 G_Regularizer: 0.000 validation_error: 21.122 +(epoch: 3, iters: 192496, time: 0.527, data: 0.000) G_L1: 18.809 G_L1_ABSOLUTE: 2.966 G_L1_RELATIVE: 15.843 G_Regularizer: 0.000 validation_error: 21.592 +(epoch: 3, iters: 194496, time: 0.524, data: 0.000) G_L1: 15.818 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 12.955 G_Regularizer: 0.000 validation_error: 21.230 +(epoch: 3, iters: 196496, time: 0.537, data: 0.000) G_L1: 16.811 G_L1_ABSOLUTE: 3.618 G_L1_RELATIVE: 13.194 G_Regularizer: 0.000 validation_error: 21.419 +(epoch: 3, iters: 198496, time: 0.529, data: 0.000) G_L1: 17.341 G_L1_ABSOLUTE: 2.854 G_L1_RELATIVE: 14.487 G_Regularizer: 0.000 validation_error: 21.866 +(epoch: 3, iters: 200496, time: 0.533, data: 0.000) G_L1: 17.436 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 14.652 G_Regularizer: 0.000 validation_error: 22.129 +(epoch: 3, iters: 202496, time: 0.531, data: 0.000) G_L1: 17.934 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 15.306 G_Regularizer: 0.000 validation_error: 22.251 +(epoch: 3, iters: 204496, time: 0.531, data: 0.000) G_L1: 16.221 G_L1_ABSOLUTE: 3.189 G_L1_RELATIVE: 13.032 G_Regularizer: 0.000 validation_error: 21.341 +(epoch: 3, iters: 206496, time: 0.527, data: 0.000) G_L1: 22.852 G_L1_ABSOLUTE: 3.629 G_L1_RELATIVE: 19.223 G_Regularizer: 0.000 validation_error: 22.227 +(epoch: 3, iters: 208496, time: 0.525, data: 0.000) G_L1: 15.474 G_L1_ABSOLUTE: 3.251 G_L1_RELATIVE: 12.223 G_Regularizer: 0.000 validation_error: 21.987 +(epoch: 3, iters: 210496, time: 0.528, data: 0.000) G_L1: 19.697 G_L1_ABSOLUTE: 3.347 G_L1_RELATIVE: 16.350 G_Regularizer: 0.000 validation_error: 21.738 +(epoch: 3, iters: 212496, time: 0.522, data: 0.000) G_L1: 15.983 G_L1_ABSOLUTE: 3.269 G_L1_RELATIVE: 12.714 G_Regularizer: 0.000 validation_error: 21.409 +(epoch: 3, iters: 214496, time: 0.532, data: 0.000) G_L1: 15.208 G_L1_ABSOLUTE: 2.860 G_L1_RELATIVE: 12.348 G_Regularizer: 0.000 validation_error: 21.843 +(epoch: 3, iters: 216496, time: 0.530, data: 0.000) G_L1: 14.705 G_L1_ABSOLUTE: 2.995 G_L1_RELATIVE: 11.710 G_Regularizer: 0.000 validation_error: 21.481 +(epoch: 3, iters: 218496, time: 0.523, data: 0.000) G_L1: 16.089 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 13.359 G_Regularizer: 0.000 validation_error: 21.103 +(epoch: 3, iters: 220496, time: 0.524, data: 0.000) G_L1: 19.863 G_L1_ABSOLUTE: 3.676 G_L1_RELATIVE: 16.187 G_Regularizer: 0.000 validation_error: 21.371 +(epoch: 3, iters: 222496, time: 0.525, data: 0.000) G_L1: 18.292 G_L1_ABSOLUTE: 3.061 G_L1_RELATIVE: 15.231 G_Regularizer: 0.000 validation_error: 21.496 +(epoch: 3, iters: 224496, time: 0.536, data: 0.000) G_L1: 16.496 G_L1_ABSOLUTE: 3.288 G_L1_RELATIVE: 13.207 G_Regularizer: 0.000 validation_error: 21.179 +(epoch: 3, iters: 226496, time: 0.534, data: 0.000) G_L1: 17.648 G_L1_ABSOLUTE: 3.121 G_L1_RELATIVE: 14.527 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 3, iters: 228496, time: 0.528, data: 0.000) G_L1: 15.308 G_L1_ABSOLUTE: 3.200 G_L1_RELATIVE: 12.108 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 3, iters: 230496, time: 0.535, data: 0.000) G_L1: 18.682 G_L1_ABSOLUTE: 3.223 G_L1_RELATIVE: 15.459 G_Regularizer: 0.000 validation_error: 21.030 +(epoch: 3, iters: 232496, time: 0.530, data: 0.000) G_L1: 18.377 G_L1_ABSOLUTE: 3.179 G_L1_RELATIVE: 15.198 G_Regularizer: 0.000 validation_error: 21.094 +(epoch: 3, iters: 234496, time: 0.526, data: 0.001) G_L1: 17.366 G_L1_ABSOLUTE: 3.299 G_L1_RELATIVE: 14.067 G_Regularizer: 0.000 validation_error: 21.876 +(epoch: 3, iters: 236496, time: 0.535, data: 0.000) G_L1: 21.660 G_L1_ABSOLUTE: 3.146 G_L1_RELATIVE: 18.513 G_Regularizer: 0.000 validation_error: 21.934 +(epoch: 3, iters: 238496, time: 0.535, data: 0.000) G_L1: 17.813 G_L1_ABSOLUTE: 2.901 G_L1_RELATIVE: 14.913 G_Regularizer: 0.000 validation_error: 21.841 +(epoch: 3, iters: 240496, time: 0.529, data: 0.000) G_L1: 16.320 G_L1_ABSOLUTE: 3.248 G_L1_RELATIVE: 13.072 G_Regularizer: 0.000 validation_error: 21.190 +(epoch: 3, iters: 242496, time: 0.530, data: 0.000) G_L1: 16.815 G_L1_ABSOLUTE: 3.004 G_L1_RELATIVE: 13.811 G_Regularizer: 0.000 validation_error: 21.124 +(epoch: 3, iters: 244496, time: 0.533, data: 0.000) G_L1: 15.978 G_L1_ABSOLUTE: 3.700 G_L1_RELATIVE: 12.278 G_Regularizer: 0.000 validation_error: 20.896 +(epoch: 3, iters: 246496, time: 0.527, data: 0.000) G_L1: 16.128 G_L1_ABSOLUTE: 3.414 G_L1_RELATIVE: 12.714 G_Regularizer: 0.000 validation_error: 21.267 +(epoch: 3, iters: 248496, time: 0.530, data: 0.000) G_L1: 14.625 G_L1_ABSOLUTE: 2.884 G_L1_RELATIVE: 11.741 G_Regularizer: 0.000 validation_error: 21.809 +(epoch: 3, iters: 250496, time: 0.530, data: 0.000) G_L1: 15.678 G_L1_ABSOLUTE: 3.089 G_L1_RELATIVE: 12.590 G_Regularizer: 0.000 validation_error: 21.367 +(epoch: 3, iters: 252496, time: 0.527, data: 0.000) G_L1: 13.569 G_L1_ABSOLUTE: 2.763 G_L1_RELATIVE: 10.806 G_Regularizer: 0.000 validation_error: 21.784 +(epoch: 3, iters: 254496, time: 0.531, data: 0.000) G_L1: 16.397 G_L1_ABSOLUTE: 2.992 G_L1_RELATIVE: 13.405 G_Regularizer: 0.000 validation_error: 21.863 +(epoch: 3, iters: 256496, time: 0.535, data: 0.000) G_L1: 15.573 G_L1_ABSOLUTE: 3.270 G_L1_RELATIVE: 12.303 G_Regularizer: 0.000 validation_error: 21.346 +(epoch: 3, iters: 258496, time: 0.530, data: 0.000) G_L1: 18.379 G_L1_ABSOLUTE: 3.364 G_L1_RELATIVE: 15.016 G_Regularizer: 0.000 validation_error: 21.315 +(epoch: 3, iters: 260496, time: 0.530, data: 0.000) G_L1: 16.494 G_L1_ABSOLUTE: 2.729 G_L1_RELATIVE: 13.766 G_Regularizer: 0.000 validation_error: 21.313 +(epoch: 3, iters: 262496, time: 0.527, data: 0.000) G_L1: 16.103 G_L1_ABSOLUTE: 3.063 G_L1_RELATIVE: 13.040 G_Regularizer: 0.000 validation_error: 22.026 +(epoch: 3, iters: 264496, time: 0.545, data: 0.000) G_L1: 16.610 G_L1_ABSOLUTE: 2.852 G_L1_RELATIVE: 13.758 G_Regularizer: 0.000 validation_error: 22.296 +(epoch: 3, iters: 266496, time: 0.633, data: 0.000) G_L1: 18.416 G_L1_ABSOLUTE: 3.152 G_L1_RELATIVE: 15.264 G_Regularizer: 0.000 validation_error: 21.743 +(epoch: 3, iters: 268496, time: 0.630, data: 0.000) G_L1: 13.812 G_L1_ABSOLUTE: 2.699 G_L1_RELATIVE: 11.113 G_Regularizer: 0.000 validation_error: 21.977 +(epoch: 3, iters: 270496, time: 0.628, data: 0.001) G_L1: 15.533 G_L1_ABSOLUTE: 3.260 G_L1_RELATIVE: 12.273 G_Regularizer: 0.000 validation_error: 22.139 +(epoch: 3, iters: 272496, time: 0.633, data: 0.000) G_L1: 16.503 G_L1_ABSOLUTE: 2.832 G_L1_RELATIVE: 13.671 G_Regularizer: 0.000 validation_error: 21.469 +(epoch: 3, iters: 274496, time: 0.627, data: 0.000) G_L1: 24.646 G_L1_ABSOLUTE: 3.712 G_L1_RELATIVE: 20.934 G_Regularizer: 0.000 validation_error: 21.617 +(epoch: 3, iters: 276496, time: 0.620, data: 0.000) G_L1: 14.582 G_L1_ABSOLUTE: 2.820 G_L1_RELATIVE: 11.762 G_Regularizer: 0.000 validation_error: 21.755 +(epoch: 3, iters: 278496, time: 0.629, data: 0.000) G_L1: 20.605 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 17.868 G_Regularizer: 0.000 validation_error: 21.140 +(epoch: 3, iters: 280496, time: 0.591, data: 0.000) G_L1: 18.820 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 16.083 G_Regularizer: 0.000 validation_error: 21.585 +(epoch: 3, iters: 282496, time: 0.627, data: 0.000) G_L1: 17.572 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 14.702 G_Regularizer: 0.000 validation_error: 21.225 +(epoch: 3, iters: 284496, time: 0.586, data: 0.000) G_L1: 16.094 G_L1_ABSOLUTE: 2.991 G_L1_RELATIVE: 13.103 G_Regularizer: 0.000 validation_error: 21.506 +(epoch: 3, iters: 286496, time: 0.528, data: 0.000) G_L1: 19.949 G_L1_ABSOLUTE: 4.025 G_L1_RELATIVE: 15.924 G_Regularizer: 0.000 validation_error: 21.667 +(epoch: 3, iters: 288496, time: 0.526, data: 0.000) G_L1: 22.480 G_L1_ABSOLUTE: 3.173 G_L1_RELATIVE: 19.307 G_Regularizer: 0.000 validation_error: 22.052 +(epoch: 3, iters: 290496, time: 0.527, data: 0.000) G_L1: 16.482 G_L1_ABSOLUTE: 2.931 G_L1_RELATIVE: 13.551 G_Regularizer: 0.000 validation_error: 21.638 +(epoch: 3, iters: 292496, time: 0.534, data: 0.000) G_L1: 16.128 G_L1_ABSOLUTE: 3.177 G_L1_RELATIVE: 12.952 G_Regularizer: 0.000 validation_error: 22.034 +(epoch: 3, iters: 294496, time: 0.522, data: 0.000) G_L1: 14.329 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 11.340 G_Regularizer: 0.000 validation_error: 21.526 +(epoch: 3, iters: 296496, time: 0.545, data: 0.000) G_L1: 19.908 G_L1_ABSOLUTE: 3.168 G_L1_RELATIVE: 16.740 G_Regularizer: 0.000 validation_error: 21.747 +(epoch: 3, iters: 298496, time: 0.624, data: 0.000) G_L1: 17.762 G_L1_ABSOLUTE: 3.407 G_L1_RELATIVE: 14.355 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 3, iters: 300496, time: 0.575, data: 0.000) G_L1: 16.927 G_L1_ABSOLUTE: 2.953 G_L1_RELATIVE: 13.974 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 3, iters: 302496, time: 0.530, data: 0.000) G_L1: 16.064 G_L1_ABSOLUTE: 2.922 G_L1_RELATIVE: 13.141 G_Regularizer: 0.000 validation_error: 21.503 +(epoch: 4, iters: 1744, time: 0.527, data: 0.000) G_L1: 17.335 G_L1_ABSOLUTE: 3.433 G_L1_RELATIVE: 13.902 G_Regularizer: 0.000 validation_error: 21.591 +(epoch: 4, iters: 3744, time: 0.530, data: 0.000) G_L1: 22.076 G_L1_ABSOLUTE: 3.282 G_L1_RELATIVE: 18.794 G_Regularizer: 0.000 validation_error: 21.497 +(epoch: 4, iters: 5744, time: 0.532, data: 0.000) G_L1: 14.521 G_L1_ABSOLUTE: 3.097 G_L1_RELATIVE: 11.425 G_Regularizer: 0.000 validation_error: 21.582 +(epoch: 4, iters: 7744, time: 0.532, data: 0.000) G_L1: 16.551 G_L1_ABSOLUTE: 2.796 G_L1_RELATIVE: 13.755 G_Regularizer: 0.000 validation_error: 21.678 +(epoch: 4, iters: 9744, time: 0.532, data: 0.000) G_L1: 16.196 G_L1_ABSOLUTE: 2.996 G_L1_RELATIVE: 13.200 G_Regularizer: 0.000 validation_error: 21.293 +(epoch: 4, iters: 11744, time: 0.524, data: 0.000) G_L1: 16.523 G_L1_ABSOLUTE: 2.955 G_L1_RELATIVE: 13.568 G_Regularizer: 0.000 validation_error: 21.899 +(epoch: 4, iters: 13744, time: 0.524, data: 0.000) G_L1: 16.354 G_L1_ABSOLUTE: 3.167 G_L1_RELATIVE: 13.187 G_Regularizer: 0.000 validation_error: 21.277 +(epoch: 4, iters: 15744, time: 0.521, data: 0.000) G_L1: 18.166 G_L1_ABSOLUTE: 2.860 G_L1_RELATIVE: 15.306 G_Regularizer: 0.000 validation_error: 21.599 +(epoch: 4, iters: 17744, time: 0.537, data: 0.000) G_L1: 13.495 G_L1_ABSOLUTE: 3.052 G_L1_RELATIVE: 10.444 G_Regularizer: 0.000 validation_error: 21.734 +(epoch: 4, iters: 19744, time: 0.525, data: 0.000) G_L1: 16.257 G_L1_ABSOLUTE: 3.035 G_L1_RELATIVE: 13.223 G_Regularizer: 0.000 validation_error: 20.847 +(epoch: 4, iters: 21744, time: 0.531, data: 0.000) G_L1: 16.905 G_L1_ABSOLUTE: 3.068 G_L1_RELATIVE: 13.837 G_Regularizer: 0.000 validation_error: 21.744 +(epoch: 4, iters: 23744, time: 0.527, data: 0.000) G_L1: 16.217 G_L1_ABSOLUTE: 3.039 G_L1_RELATIVE: 13.178 G_Regularizer: 0.000 validation_error: 21.468 +(epoch: 4, iters: 25744, time: 0.529, data: 0.000) G_L1: 19.822 G_L1_ABSOLUTE: 3.210 G_L1_RELATIVE: 16.611 G_Regularizer: 0.000 validation_error: 21.523 +(epoch: 4, iters: 27744, time: 0.528, data: 0.000) G_L1: 14.909 G_L1_ABSOLUTE: 2.909 G_L1_RELATIVE: 11.999 G_Regularizer: 0.000 validation_error: 22.234 +(epoch: 4, iters: 29744, time: 0.528, data: 0.000) G_L1: 17.506 G_L1_ABSOLUTE: 3.258 G_L1_RELATIVE: 14.248 G_Regularizer: 0.000 validation_error: 21.357 +(epoch: 4, iters: 31744, time: 0.528, data: 0.000) G_L1: 13.906 G_L1_ABSOLUTE: 2.935 G_L1_RELATIVE: 10.971 G_Regularizer: 0.000 validation_error: 21.327 +(epoch: 4, iters: 33744, time: 0.533, data: 0.000) G_L1: 15.678 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 12.886 G_Regularizer: 0.000 validation_error: 21.464 +(epoch: 4, iters: 35744, time: 0.536, data: 0.000) G_L1: 19.474 G_L1_ABSOLUTE: 2.922 G_L1_RELATIVE: 16.553 G_Regularizer: 0.000 validation_error: 21.793 +(epoch: 4, iters: 37744, time: 0.532, data: 0.001) G_L1: 16.319 G_L1_ABSOLUTE: 2.996 G_L1_RELATIVE: 13.323 G_Regularizer: 0.000 validation_error: 21.906 +(epoch: 4, iters: 39744, time: 0.529, data: 0.000) G_L1: 16.452 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 13.746 G_Regularizer: 0.000 validation_error: 22.104 +(epoch: 4, iters: 41744, time: 0.533, data: 0.000) G_L1: 18.475 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 15.583 G_Regularizer: 0.000 validation_error: 21.524 +(epoch: 4, iters: 43744, time: 0.532, data: 0.000) G_L1: 17.585 G_L1_ABSOLUTE: 3.704 G_L1_RELATIVE: 13.881 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 4, iters: 45744, time: 0.534, data: 0.000) G_L1: 15.930 G_L1_ABSOLUTE: 3.445 G_L1_RELATIVE: 12.484 G_Regularizer: 0.000 validation_error: 21.105 +(epoch: 4, iters: 47744, time: 0.529, data: 0.000) G_L1: 18.882 G_L1_ABSOLUTE: 3.354 G_L1_RELATIVE: 15.527 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 4, iters: 49744, time: 0.532, data: 0.000) G_L1: 16.443 G_L1_ABSOLUTE: 2.837 G_L1_RELATIVE: 13.606 G_Regularizer: 0.000 validation_error: 21.443 +(epoch: 4, iters: 51744, time: 0.528, data: 0.000) G_L1: 18.152 G_L1_ABSOLUTE: 2.770 G_L1_RELATIVE: 15.381 G_Regularizer: 0.000 validation_error: 21.118 +(epoch: 4, iters: 53744, time: 0.536, data: 0.000) G_L1: 18.071 G_L1_ABSOLUTE: 3.799 G_L1_RELATIVE: 14.272 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 4, iters: 55744, time: 0.534, data: 0.000) G_L1: 21.285 G_L1_ABSOLUTE: 3.057 G_L1_RELATIVE: 18.228 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 4, iters: 57744, time: 0.533, data: 0.000) G_L1: 16.574 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 13.952 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 4, iters: 59744, time: 0.533, data: 0.000) G_L1: 15.908 G_L1_ABSOLUTE: 3.138 G_L1_RELATIVE: 12.771 G_Regularizer: 0.000 validation_error: 21.746 +(epoch: 4, iters: 61744, time: 0.526, data: 0.000) G_L1: 15.686 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 13.182 G_Regularizer: 0.000 validation_error: 21.012 +(epoch: 4, iters: 63744, time: 0.533, data: 0.000) G_L1: 18.535 G_L1_ABSOLUTE: 2.883 G_L1_RELATIVE: 15.652 G_Regularizer: 0.000 validation_error: 21.244 +(epoch: 4, iters: 65744, time: 0.527, data: 0.000) G_L1: 14.268 G_L1_ABSOLUTE: 2.974 G_L1_RELATIVE: 11.294 G_Regularizer: 0.000 validation_error: 21.373 +(epoch: 4, iters: 67744, time: 0.536, data: 0.000) G_L1: 15.090 G_L1_ABSOLUTE: 2.807 G_L1_RELATIVE: 12.283 G_Regularizer: 0.000 validation_error: 20.765 +(epoch: 4, iters: 69744, time: 0.531, data: 0.000) G_L1: 16.255 G_L1_ABSOLUTE: 2.993 G_L1_RELATIVE: 13.263 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 4, iters: 71744, time: 0.532, data: 0.000) G_L1: 21.011 G_L1_ABSOLUTE: 3.050 G_L1_RELATIVE: 17.961 G_Regularizer: 0.000 validation_error: 21.989 +(epoch: 4, iters: 73744, time: 0.527, data: 0.000) G_L1: 17.072 G_L1_ABSOLUTE: 3.097 G_L1_RELATIVE: 13.975 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 4, iters: 75744, time: 0.530, data: 0.000) G_L1: 17.241 G_L1_ABSOLUTE: 2.941 G_L1_RELATIVE: 14.299 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 4, iters: 77744, time: 0.532, data: 0.000) G_L1: 19.436 G_L1_ABSOLUTE: 3.997 G_L1_RELATIVE: 15.439 G_Regularizer: 0.000 validation_error: 21.525 +(epoch: 4, iters: 79744, time: 0.533, data: 0.000) G_L1: 13.746 G_L1_ABSOLUTE: 3.092 G_L1_RELATIVE: 10.655 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 4, iters: 81744, time: 0.529, data: 0.000) G_L1: 19.946 G_L1_ABSOLUTE: 3.184 G_L1_RELATIVE: 16.762 G_Regularizer: 0.000 validation_error: 21.729 +(epoch: 4, iters: 83744, time: 0.530, data: 0.000) G_L1: 16.279 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 13.443 G_Regularizer: 0.000 validation_error: 21.631 +(epoch: 4, iters: 85744, time: 0.533, data: 0.000) G_L1: 19.037 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 16.152 G_Regularizer: 0.000 validation_error: 21.862 +(epoch: 4, iters: 87744, time: 0.529, data: 0.000) G_L1: 15.886 G_L1_ABSOLUTE: 2.925 G_L1_RELATIVE: 12.961 G_Regularizer: 0.000 validation_error: 21.782 +(epoch: 4, iters: 89744, time: 0.531, data: 0.000) G_L1: 15.377 G_L1_ABSOLUTE: 3.318 G_L1_RELATIVE: 12.059 G_Regularizer: 0.000 validation_error: 22.207 +(epoch: 4, iters: 91744, time: 0.530, data: 0.000) G_L1: 16.127 G_L1_ABSOLUTE: 3.342 G_L1_RELATIVE: 12.785 G_Regularizer: 0.000 validation_error: 21.526 +(epoch: 4, iters: 93744, time: 0.535, data: 0.000) G_L1: 18.349 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 15.460 G_Regularizer: 0.000 validation_error: 20.724 +(epoch: 4, iters: 95744, time: 0.536, data: 0.000) G_L1: 18.555 G_L1_ABSOLUTE: 3.254 G_L1_RELATIVE: 15.301 G_Regularizer: 0.000 validation_error: 21.752 +(epoch: 4, iters: 97744, time: 0.533, data: 0.000) G_L1: 17.785 G_L1_ABSOLUTE: 3.590 G_L1_RELATIVE: 14.194 G_Regularizer: 0.000 validation_error: 20.822 +(epoch: 4, iters: 99744, time: 0.529, data: 0.000) G_L1: 20.644 G_L1_ABSOLUTE: 3.022 G_L1_RELATIVE: 17.622 G_Regularizer: 0.000 validation_error: 22.042 +(epoch: 4, iters: 101744, time: 0.529, data: 0.000) G_L1: 14.574 G_L1_ABSOLUTE: 2.860 G_L1_RELATIVE: 11.714 G_Regularizer: 0.000 validation_error: 21.241 +(epoch: 4, iters: 103744, time: 0.525, data: 0.000) G_L1: 17.009 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 14.263 G_Regularizer: 0.000 validation_error: 21.882 +(epoch: 4, iters: 105744, time: 0.521, data: 0.000) G_L1: 15.014 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 12.202 G_Regularizer: 0.000 validation_error: 21.068 +(epoch: 4, iters: 107744, time: 0.536, data: 0.000) G_L1: 19.033 G_L1_ABSOLUTE: 2.993 G_L1_RELATIVE: 16.040 G_Regularizer: 0.000 validation_error: 21.504 +(epoch: 4, iters: 109744, time: 0.531, data: 0.000) G_L1: 14.486 G_L1_ABSOLUTE: 3.258 G_L1_RELATIVE: 11.228 G_Regularizer: 0.000 validation_error: 21.350 +(epoch: 4, iters: 111744, time: 0.527, data: 0.000) G_L1: 18.351 G_L1_ABSOLUTE: 3.072 G_L1_RELATIVE: 15.280 G_Regularizer: 0.000 validation_error: 21.327 +(epoch: 4, iters: 113744, time: 0.528, data: 0.000) G_L1: 20.497 G_L1_ABSOLUTE: 2.910 G_L1_RELATIVE: 17.587 G_Regularizer: 0.000 validation_error: 21.737 +(epoch: 4, iters: 115744, time: 0.532, data: 0.000) G_L1: 15.026 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 12.375 G_Regularizer: 0.000 validation_error: 21.063 +(epoch: 4, iters: 117744, time: 0.526, data: 0.000) G_L1: 14.562 G_L1_ABSOLUTE: 2.875 G_L1_RELATIVE: 11.687 G_Regularizer: 0.000 validation_error: 21.202 +(epoch: 4, iters: 119744, time: 0.530, data: 0.001) G_L1: 17.048 G_L1_ABSOLUTE: 3.127 G_L1_RELATIVE: 13.921 G_Regularizer: 0.000 validation_error: 21.372 +(epoch: 4, iters: 121744, time: 0.532, data: 0.000) G_L1: 16.221 G_L1_ABSOLUTE: 3.169 G_L1_RELATIVE: 13.052 G_Regularizer: 0.000 validation_error: 21.882 +(epoch: 4, iters: 123744, time: 0.532, data: 0.000) G_L1: 15.657 G_L1_ABSOLUTE: 3.314 G_L1_RELATIVE: 12.342 G_Regularizer: 0.000 validation_error: 21.763 +(epoch: 4, iters: 125744, time: 0.538, data: 0.000) G_L1: 19.169 G_L1_ABSOLUTE: 3.709 G_L1_RELATIVE: 15.460 G_Regularizer: 0.000 validation_error: 21.599 +(epoch: 4, iters: 127744, time: 0.521, data: 0.000) G_L1: 19.321 G_L1_ABSOLUTE: 3.477 G_L1_RELATIVE: 15.844 G_Regularizer: 0.000 validation_error: 21.398 +(epoch: 4, iters: 129744, time: 0.527, data: 0.000) G_L1: 15.468 G_L1_ABSOLUTE: 3.087 G_L1_RELATIVE: 12.382 G_Regularizer: 0.000 validation_error: 20.620 +(epoch: 4, iters: 131744, time: 0.530, data: 0.000) G_L1: 15.220 G_L1_ABSOLUTE: 2.956 G_L1_RELATIVE: 12.264 G_Regularizer: 0.000 validation_error: 21.105 +(epoch: 4, iters: 133744, time: 0.526, data: 0.000) G_L1: 17.284 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 14.530 G_Regularizer: 0.000 validation_error: 21.734 +(epoch: 4, iters: 135744, time: 0.528, data: 0.000) G_L1: 16.186 G_L1_ABSOLUTE: 2.850 G_L1_RELATIVE: 13.336 G_Regularizer: 0.000 validation_error: 21.040 +(epoch: 4, iters: 137744, time: 0.533, data: 0.000) G_L1: 19.734 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 16.974 G_Regularizer: 0.000 validation_error: 20.510 +(epoch: 4, iters: 139744, time: 0.524, data: 0.000) G_L1: 18.493 G_L1_ABSOLUTE: 2.643 G_L1_RELATIVE: 15.849 G_Regularizer: 0.000 validation_error: 21.152 +(epoch: 4, iters: 141744, time: 0.533, data: 0.000) G_L1: 16.483 G_L1_ABSOLUTE: 2.991 G_L1_RELATIVE: 13.493 G_Regularizer: 0.000 validation_error: 21.489 +(epoch: 4, iters: 143744, time: 0.533, data: 0.000) G_L1: 21.410 G_L1_ABSOLUTE: 3.390 G_L1_RELATIVE: 18.019 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 4, iters: 145744, time: 0.529, data: 0.000) G_L1: 19.219 G_L1_ABSOLUTE: 3.381 G_L1_RELATIVE: 15.838 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 4, iters: 147744, time: 0.533, data: 0.000) G_L1: 17.485 G_L1_ABSOLUTE: 3.072 G_L1_RELATIVE: 14.413 G_Regularizer: 0.000 validation_error: 21.507 +(epoch: 4, iters: 149744, time: 0.530, data: 0.000) G_L1: 17.711 G_L1_ABSOLUTE: 3.031 G_L1_RELATIVE: 14.680 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 4, iters: 151744, time: 0.529, data: 0.000) G_L1: 19.921 G_L1_ABSOLUTE: 3.418 G_L1_RELATIVE: 16.503 G_Regularizer: 0.000 validation_error: 20.691 +(epoch: 4, iters: 153744, time: 0.525, data: 0.000) G_L1: 17.161 G_L1_ABSOLUTE: 2.845 G_L1_RELATIVE: 14.316 G_Regularizer: 0.000 validation_error: 21.186 +(epoch: 4, iters: 155744, time: 0.526, data: 0.000) G_L1: 17.292 G_L1_ABSOLUTE: 3.476 G_L1_RELATIVE: 13.816 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 4, iters: 157744, time: 0.529, data: 0.000) G_L1: 14.090 G_L1_ABSOLUTE: 3.078 G_L1_RELATIVE: 11.012 G_Regularizer: 0.000 validation_error: 21.323 +(epoch: 4, iters: 159744, time: 0.527, data: 0.000) G_L1: 14.811 G_L1_ABSOLUTE: 2.711 G_L1_RELATIVE: 12.100 G_Regularizer: 0.000 validation_error: 21.647 +(epoch: 4, iters: 161744, time: 0.529, data: 0.000) G_L1: 16.950 G_L1_ABSOLUTE: 3.252 G_L1_RELATIVE: 13.699 G_Regularizer: 0.000 validation_error: 21.162 +(epoch: 4, iters: 163744, time: 0.528, data: 0.000) G_L1: 14.781 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 12.027 G_Regularizer: 0.000 validation_error: 21.249 +(epoch: 4, iters: 165744, time: 0.529, data: 0.000) G_L1: 18.643 G_L1_ABSOLUTE: 3.176 G_L1_RELATIVE: 15.467 G_Regularizer: 0.000 validation_error: 20.768 +(epoch: 4, iters: 167744, time: 0.529, data: 0.001) G_L1: 16.096 G_L1_ABSOLUTE: 2.799 G_L1_RELATIVE: 13.297 G_Regularizer: 0.000 validation_error: 20.323 +(epoch: 4, iters: 169744, time: 0.532, data: 0.000) G_L1: 23.842 G_L1_ABSOLUTE: 3.075 G_L1_RELATIVE: 20.766 G_Regularizer: 0.000 validation_error: 21.659 +(epoch: 4, iters: 171744, time: 0.527, data: 0.000) G_L1: 18.038 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 15.162 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 4, iters: 173744, time: 0.529, data: 0.000) G_L1: 17.841 G_L1_ABSOLUTE: 3.190 G_L1_RELATIVE: 14.650 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 4, iters: 175744, time: 0.531, data: 0.000) G_L1: 18.511 G_L1_ABSOLUTE: 3.043 G_L1_RELATIVE: 15.468 G_Regularizer: 0.000 validation_error: 20.358 +(epoch: 4, iters: 177744, time: 0.527, data: 0.000) G_L1: 15.785 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 13.116 G_Regularizer: 0.000 validation_error: 21.161 +(epoch: 4, iters: 179744, time: 0.526, data: 0.000) G_L1: 15.732 G_L1_ABSOLUTE: 2.769 G_L1_RELATIVE: 12.963 G_Regularizer: 0.000 validation_error: 21.321 +(epoch: 4, iters: 181744, time: 0.530, data: 0.000) G_L1: 17.038 G_L1_ABSOLUTE: 3.448 G_L1_RELATIVE: 13.590 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 4, iters: 183744, time: 0.527, data: 0.000) G_L1: 17.348 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 14.611 G_Regularizer: 0.000 validation_error: 21.898 +(epoch: 4, iters: 185744, time: 0.528, data: 0.000) G_L1: 16.733 G_L1_ABSOLUTE: 3.018 G_L1_RELATIVE: 13.715 G_Regularizer: 0.000 validation_error: 21.037 +(epoch: 4, iters: 187744, time: 0.537, data: 0.000) G_L1: 15.632 G_L1_ABSOLUTE: 3.297 G_L1_RELATIVE: 12.335 G_Regularizer: 0.000 validation_error: 21.629 +(epoch: 4, iters: 189744, time: 0.527, data: 0.000) G_L1: 15.297 G_L1_ABSOLUTE: 2.958 G_L1_RELATIVE: 12.340 G_Regularizer: 0.000 validation_error: 21.024 +(epoch: 4, iters: 191744, time: 0.529, data: 0.000) G_L1: 17.970 G_L1_ABSOLUTE: 3.312 G_L1_RELATIVE: 14.657 G_Regularizer: 0.000 validation_error: 20.910 +(epoch: 4, iters: 193744, time: 0.527, data: 0.000) G_L1: 16.879 G_L1_ABSOLUTE: 3.324 G_L1_RELATIVE: 13.555 G_Regularizer: 0.000 validation_error: 20.430 +(epoch: 4, iters: 195744, time: 0.527, data: 0.000) G_L1: 15.272 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 12.623 G_Regularizer: 0.000 validation_error: 20.946 +(epoch: 4, iters: 197744, time: 0.533, data: 0.000) G_L1: 17.341 G_L1_ABSOLUTE: 3.230 G_L1_RELATIVE: 14.112 G_Regularizer: 0.000 validation_error: 21.608 +(epoch: 4, iters: 199744, time: 0.529, data: 0.000) G_L1: 17.586 G_L1_ABSOLUTE: 2.879 G_L1_RELATIVE: 14.707 G_Regularizer: 0.000 validation_error: 21.232 +(epoch: 4, iters: 201744, time: 0.542, data: 0.000) G_L1: 14.913 G_L1_ABSOLUTE: 3.138 G_L1_RELATIVE: 11.774 G_Regularizer: 0.000 validation_error: 21.487 +(epoch: 4, iters: 203744, time: 0.530, data: 0.000) G_L1: 15.769 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 12.992 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 4, iters: 205744, time: 0.532, data: 0.000) G_L1: 19.274 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 16.516 G_Regularizer: 0.000 validation_error: 21.382 +(epoch: 4, iters: 207744, time: 0.529, data: 0.000) G_L1: 16.524 G_L1_ABSOLUTE: 3.157 G_L1_RELATIVE: 13.367 G_Regularizer: 0.000 validation_error: 21.272 +(epoch: 4, iters: 209744, time: 0.529, data: 0.000) G_L1: 14.329 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 11.671 G_Regularizer: 0.000 validation_error: 21.864 +(epoch: 4, iters: 211744, time: 0.530, data: 0.000) G_L1: 18.949 G_L1_ABSOLUTE: 2.921 G_L1_RELATIVE: 16.028 G_Regularizer: 0.000 validation_error: 21.542 +(epoch: 4, iters: 213744, time: 0.532, data: 0.000) G_L1: 15.176 G_L1_ABSOLUTE: 3.045 G_L1_RELATIVE: 12.131 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 4, iters: 215744, time: 0.529, data: 0.000) G_L1: 21.322 G_L1_ABSOLUTE: 2.912 G_L1_RELATIVE: 18.410 G_Regularizer: 0.000 validation_error: 21.861 +(epoch: 4, iters: 217744, time: 0.527, data: 0.000) G_L1: 17.395 G_L1_ABSOLUTE: 3.124 G_L1_RELATIVE: 14.271 G_Regularizer: 0.000 validation_error: 21.358 +(epoch: 4, iters: 219744, time: 0.527, data: 0.000) G_L1: 21.469 G_L1_ABSOLUTE: 2.615 G_L1_RELATIVE: 18.855 G_Regularizer: 0.000 validation_error: 21.423 +(epoch: 4, iters: 221744, time: 0.532, data: 0.000) G_L1: 14.882 G_L1_ABSOLUTE: 3.062 G_L1_RELATIVE: 11.820 G_Regularizer: 0.000 validation_error: 21.286 +(epoch: 4, iters: 223744, time: 0.527, data: 0.000) G_L1: 17.217 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 14.567 G_Regularizer: 0.000 validation_error: 21.546 +(epoch: 4, iters: 225744, time: 0.529, data: 0.000) G_L1: 19.188 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 16.644 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 4, iters: 227744, time: 0.534, data: 0.000) G_L1: 17.153 G_L1_ABSOLUTE: 3.044 G_L1_RELATIVE: 14.110 G_Regularizer: 0.000 validation_error: 21.080 +(epoch: 4, iters: 229744, time: 0.524, data: 0.000) G_L1: 17.809 G_L1_ABSOLUTE: 3.119 G_L1_RELATIVE: 14.690 G_Regularizer: 0.000 validation_error: 21.191 +(epoch: 4, iters: 231744, time: 0.528, data: 0.000) G_L1: 19.376 G_L1_ABSOLUTE: 2.941 G_L1_RELATIVE: 16.436 G_Regularizer: 0.000 validation_error: 21.467 +(epoch: 4, iters: 233744, time: 0.536, data: 0.000) G_L1: 16.162 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 13.173 G_Regularizer: 0.000 validation_error: 20.596 +(epoch: 4, iters: 235744, time: 0.529, data: 0.000) G_L1: 15.474 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 12.714 G_Regularizer: 0.000 validation_error: 21.654 +(epoch: 4, iters: 237744, time: 0.528, data: 0.000) G_L1: 14.889 G_L1_ABSOLUTE: 2.779 G_L1_RELATIVE: 12.110 G_Regularizer: 0.000 validation_error: 21.755 +(epoch: 4, iters: 239744, time: 0.527, data: 0.000) G_L1: 15.106 G_L1_ABSOLUTE: 2.891 G_L1_RELATIVE: 12.215 G_Regularizer: 0.000 validation_error: 21.126 +(epoch: 4, iters: 241744, time: 0.530, data: 0.000) G_L1: 13.297 G_L1_ABSOLUTE: 3.214 G_L1_RELATIVE: 10.083 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 4, iters: 243744, time: 0.535, data: 0.000) G_L1: 17.786 G_L1_ABSOLUTE: 3.253 G_L1_RELATIVE: 14.533 G_Regularizer: 0.000 validation_error: 21.769 +(epoch: 4, iters: 245744, time: 0.523, data: 0.000) G_L1: 17.610 G_L1_ABSOLUTE: 3.050 G_L1_RELATIVE: 14.560 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 4, iters: 247744, time: 0.532, data: 0.000) G_L1: 18.648 G_L1_ABSOLUTE: 3.270 G_L1_RELATIVE: 15.378 G_Regularizer: 0.000 validation_error: 21.160 +(epoch: 4, iters: 249744, time: 0.529, data: 0.000) G_L1: 15.973 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 13.229 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 4, iters: 251744, time: 0.527, data: 0.000) G_L1: 18.756 G_L1_ABSOLUTE: 3.282 G_L1_RELATIVE: 15.474 G_Regularizer: 0.000 validation_error: 21.351 +(epoch: 4, iters: 253744, time: 0.530, data: 0.000) G_L1: 14.392 G_L1_ABSOLUTE: 2.802 G_L1_RELATIVE: 11.590 G_Regularizer: 0.000 validation_error: 21.784 +(epoch: 4, iters: 255744, time: 0.528, data: 0.000) G_L1: 18.928 G_L1_ABSOLUTE: 3.237 G_L1_RELATIVE: 15.691 G_Regularizer: 0.000 validation_error: 21.813 +(epoch: 4, iters: 257744, time: 0.532, data: 0.000) G_L1: 15.665 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 12.840 G_Regularizer: 0.000 validation_error: 20.761 +(epoch: 4, iters: 259744, time: 0.531, data: 0.000) G_L1: 15.276 G_L1_ABSOLUTE: 2.671 G_L1_RELATIVE: 12.605 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 4, iters: 261744, time: 0.526, data: 0.000) G_L1: 16.180 G_L1_ABSOLUTE: 3.049 G_L1_RELATIVE: 13.132 G_Regularizer: 0.000 validation_error: 21.627 +(epoch: 4, iters: 263744, time: 0.527, data: 0.000) G_L1: 14.778 G_L1_ABSOLUTE: 2.682 G_L1_RELATIVE: 12.097 G_Regularizer: 0.000 validation_error: 20.331 +(epoch: 4, iters: 265744, time: 0.532, data: 0.000) G_L1: 15.793 G_L1_ABSOLUTE: 2.893 G_L1_RELATIVE: 12.900 G_Regularizer: 0.000 validation_error: 21.787 +(epoch: 4, iters: 267744, time: 0.531, data: 0.000) G_L1: 16.914 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 14.253 G_Regularizer: 0.000 validation_error: 21.357 +(epoch: 4, iters: 269744, time: 0.531, data: 0.000) G_L1: 13.687 G_L1_ABSOLUTE: 3.093 G_L1_RELATIVE: 10.594 G_Regularizer: 0.000 validation_error: 21.418 +(epoch: 4, iters: 271744, time: 0.529, data: 0.000) G_L1: 15.516 G_L1_ABSOLUTE: 3.515 G_L1_RELATIVE: 12.001 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 4, iters: 273744, time: 0.534, data: 0.000) G_L1: 16.192 G_L1_ABSOLUTE: 3.194 G_L1_RELATIVE: 12.998 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 4, iters: 275744, time: 0.531, data: 0.000) G_L1: 18.883 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 16.246 G_Regularizer: 0.000 validation_error: 20.991 +(epoch: 4, iters: 277744, time: 0.527, data: 0.000) G_L1: 15.228 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 12.642 G_Regularizer: 0.000 validation_error: 21.353 +(epoch: 4, iters: 279744, time: 0.533, data: 0.000) G_L1: 19.209 G_L1_ABSOLUTE: 3.341 G_L1_RELATIVE: 15.868 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 4, iters: 281744, time: 0.533, data: 0.000) G_L1: 17.870 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 15.218 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 4, iters: 283744, time: 0.527, data: 0.000) G_L1: 18.653 G_L1_ABSOLUTE: 3.365 G_L1_RELATIVE: 15.287 G_Regularizer: 0.000 validation_error: 21.291 +(epoch: 4, iters: 285744, time: 0.527, data: 0.000) G_L1: 12.783 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 9.926 G_Regularizer: 0.000 validation_error: 21.252 +(epoch: 4, iters: 287744, time: 0.530, data: 0.000) G_L1: 16.768 G_L1_ABSOLUTE: 3.043 G_L1_RELATIVE: 13.724 G_Regularizer: 0.000 validation_error: 21.249 +(epoch: 4, iters: 289744, time: 0.521, data: 0.000) G_L1: 18.966 G_L1_ABSOLUTE: 3.215 G_L1_RELATIVE: 15.751 G_Regularizer: 0.000 validation_error: 21.186 +(epoch: 4, iters: 291744, time: 0.531, data: 0.000) G_L1: 19.073 G_L1_ABSOLUTE: 3.205 G_L1_RELATIVE: 15.868 G_Regularizer: 0.000 validation_error: 20.856 +(epoch: 4, iters: 293744, time: 0.531, data: 0.000) G_L1: 16.995 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 14.102 G_Regularizer: 0.000 validation_error: 20.483 +(epoch: 4, iters: 295744, time: 0.527, data: 0.000) G_L1: 14.628 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 12.072 G_Regularizer: 0.000 validation_error: 21.348 +(epoch: 4, iters: 297744, time: 0.530, data: 0.000) G_L1: 16.006 G_L1_ABSOLUTE: 3.033 G_L1_RELATIVE: 12.973 G_Regularizer: 0.000 validation_error: 21.497 +(epoch: 4, iters: 299744, time: 0.526, data: 0.000) G_L1: 18.891 G_L1_ABSOLUTE: 3.243 G_L1_RELATIVE: 15.647 G_Regularizer: 0.000 validation_error: 20.483 +(epoch: 4, iters: 301744, time: 0.525, data: 0.000) G_L1: 17.897 G_L1_ABSOLUTE: 3.047 G_L1_RELATIVE: 14.850 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 5, iters: 992, time: 0.534, data: 0.000) G_L1: 14.018 G_L1_ABSOLUTE: 3.353 G_L1_RELATIVE: 10.665 G_Regularizer: 0.000 validation_error: 21.132 +(epoch: 5, iters: 2992, time: 0.537, data: 0.000) G_L1: 16.055 G_L1_ABSOLUTE: 2.983 G_L1_RELATIVE: 13.072 G_Regularizer: 0.000 validation_error: 22.075 +(epoch: 5, iters: 4992, time: 0.533, data: 0.000) G_L1: 17.554 G_L1_ABSOLUTE: 2.966 G_L1_RELATIVE: 14.588 G_Regularizer: 0.000 validation_error: 21.824 +(epoch: 5, iters: 6992, time: 0.536, data: 0.000) G_L1: 16.833 G_L1_ABSOLUTE: 2.699 G_L1_RELATIVE: 14.133 G_Regularizer: 0.000 validation_error: 21.336 +(epoch: 5, iters: 8992, time: 0.527, data: 0.000) G_L1: 17.394 G_L1_ABSOLUTE: 2.753 G_L1_RELATIVE: 14.640 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 5, iters: 10992, time: 0.528, data: 0.000) G_L1: 15.535 G_L1_ABSOLUTE: 3.328 G_L1_RELATIVE: 12.207 G_Regularizer: 0.000 validation_error: 21.223 +(epoch: 5, iters: 12992, time: 0.531, data: 0.000) G_L1: 14.813 G_L1_ABSOLUTE: 2.557 G_L1_RELATIVE: 12.256 G_Regularizer: 0.000 validation_error: 21.005 +(epoch: 5, iters: 14992, time: 0.536, data: 0.000) G_L1: 19.054 G_L1_ABSOLUTE: 3.761 G_L1_RELATIVE: 15.293 G_Regularizer: 0.000 validation_error: 21.727 +(epoch: 5, iters: 16992, time: 0.528, data: 0.000) G_L1: 16.415 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 13.823 G_Regularizer: 0.000 validation_error: 21.115 +(epoch: 5, iters: 18992, time: 0.529, data: 0.000) G_L1: 12.564 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 9.915 G_Regularizer: 0.000 validation_error: 20.410 +(epoch: 5, iters: 20992, time: 0.533, data: 0.000) G_L1: 15.131 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 12.426 G_Regularizer: 0.000 validation_error: 20.820 +(epoch: 5, iters: 22992, time: 0.533, data: 0.000) G_L1: 15.767 G_L1_ABSOLUTE: 2.759 G_L1_RELATIVE: 13.008 G_Regularizer: 0.000 validation_error: 21.057 +(epoch: 5, iters: 24992, time: 0.530, data: 0.000) G_L1: 14.083 G_L1_ABSOLUTE: 2.993 G_L1_RELATIVE: 11.090 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 5, iters: 26992, time: 0.528, data: 0.000) G_L1: 16.393 G_L1_ABSOLUTE: 2.693 G_L1_RELATIVE: 13.701 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 5, iters: 28992, time: 0.531, data: 0.000) G_L1: 14.112 G_L1_ABSOLUTE: 2.931 G_L1_RELATIVE: 11.180 G_Regularizer: 0.000 validation_error: 21.705 +(epoch: 5, iters: 30992, time: 0.531, data: 0.000) G_L1: 17.419 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 14.549 G_Regularizer: 0.000 validation_error: 21.454 +(epoch: 5, iters: 32992, time: 0.529, data: 0.000) G_L1: 17.585 G_L1_ABSOLUTE: 3.425 G_L1_RELATIVE: 14.160 G_Regularizer: 0.000 validation_error: 21.233 +(epoch: 5, iters: 34992, time: 0.530, data: 0.000) G_L1: 14.840 G_L1_ABSOLUTE: 2.803 G_L1_RELATIVE: 12.037 G_Regularizer: 0.000 validation_error: 21.131 +(epoch: 5, iters: 36992, time: 0.528, data: 0.000) G_L1: 14.474 G_L1_ABSOLUTE: 2.846 G_L1_RELATIVE: 11.628 G_Regularizer: 0.000 validation_error: 21.431 +(epoch: 5, iters: 38992, time: 0.531, data: 0.000) G_L1: 20.901 G_L1_ABSOLUTE: 3.795 G_L1_RELATIVE: 17.106 G_Regularizer: 0.000 validation_error: 21.382 +(epoch: 5, iters: 40992, time: 0.531, data: 0.000) G_L1: 13.466 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 10.982 G_Regularizer: 0.000 validation_error: 21.642 +(epoch: 5, iters: 42992, time: 0.533, data: 0.000) G_L1: 14.916 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 12.178 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 5, iters: 44992, time: 0.527, data: 0.000) G_L1: 13.906 G_L1_ABSOLUTE: 3.123 G_L1_RELATIVE: 10.784 G_Regularizer: 0.000 validation_error: 21.438 +(epoch: 5, iters: 46992, time: 0.532, data: 0.000) G_L1: 12.649 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 10.308 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 5, iters: 48992, time: 0.534, data: 0.000) G_L1: 15.013 G_L1_ABSOLUTE: 2.636 G_L1_RELATIVE: 12.377 G_Regularizer: 0.000 validation_error: 21.041 +(epoch: 5, iters: 50992, time: 0.532, data: 0.000) G_L1: 17.935 G_L1_ABSOLUTE: 3.055 G_L1_RELATIVE: 14.881 G_Regularizer: 0.000 validation_error: 21.096 +(epoch: 5, iters: 52992, time: 0.534, data: 0.000) G_L1: 18.116 G_L1_ABSOLUTE: 3.148 G_L1_RELATIVE: 14.968 G_Regularizer: 0.000 validation_error: 21.188 +(epoch: 5, iters: 54992, time: 0.537, data: 0.000) G_L1: 15.619 G_L1_ABSOLUTE: 3.478 G_L1_RELATIVE: 12.141 G_Regularizer: 0.000 validation_error: 21.112 +(epoch: 5, iters: 56992, time: 0.531, data: 0.000) G_L1: 16.092 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 13.371 G_Regularizer: 0.000 validation_error: 21.418 +(epoch: 5, iters: 58992, time: 0.520, data: 0.000) G_L1: 19.706 G_L1_ABSOLUTE: 3.103 G_L1_RELATIVE: 16.603 G_Regularizer: 0.000 validation_error: 22.348 +(epoch: 5, iters: 60992, time: 0.524, data: 0.000) G_L1: 14.858 G_L1_ABSOLUTE: 2.913 G_L1_RELATIVE: 11.945 G_Regularizer: 0.000 validation_error: 20.937 +(epoch: 5, iters: 62992, time: 0.525, data: 0.000) G_L1: 16.800 G_L1_ABSOLUTE: 3.368 G_L1_RELATIVE: 13.432 G_Regularizer: 0.000 validation_error: 21.463 +(epoch: 5, iters: 64992, time: 0.525, data: 0.000) G_L1: 15.168 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 12.483 G_Regularizer: 0.000 validation_error: 21.306 +(epoch: 5, iters: 66992, time: 0.521, data: 0.000) G_L1: 14.167 G_L1_ABSOLUTE: 3.052 G_L1_RELATIVE: 11.115 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 5, iters: 68992, time: 0.524, data: 0.000) G_L1: 16.224 G_L1_ABSOLUTE: 3.014 G_L1_RELATIVE: 13.210 G_Regularizer: 0.000 validation_error: 21.593 +(epoch: 5, iters: 70992, time: 0.525, data: 0.000) G_L1: 16.905 G_L1_ABSOLUTE: 2.849 G_L1_RELATIVE: 14.057 G_Regularizer: 0.000 validation_error: 21.296 +(epoch: 5, iters: 72992, time: 0.526, data: 0.000) G_L1: 15.040 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 12.472 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 5, iters: 74992, time: 0.526, data: 0.000) G_L1: 15.518 G_L1_ABSOLUTE: 2.873 G_L1_RELATIVE: 12.645 G_Regularizer: 0.000 validation_error: 20.984 +(epoch: 5, iters: 76992, time: 0.532, data: 0.000) G_L1: 15.827 G_L1_ABSOLUTE: 2.922 G_L1_RELATIVE: 12.904 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 5, iters: 78992, time: 0.530, data: 0.000) G_L1: 14.993 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 12.372 G_Regularizer: 0.000 validation_error: 21.610 +(epoch: 5, iters: 80992, time: 0.530, data: 0.000) G_L1: 15.138 G_L1_ABSOLUTE: 2.687 G_L1_RELATIVE: 12.451 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 5, iters: 82992, time: 0.532, data: 0.000) G_L1: 16.424 G_L1_ABSOLUTE: 3.520 G_L1_RELATIVE: 12.904 G_Regularizer: 0.000 validation_error: 21.014 +(epoch: 5, iters: 84992, time: 0.527, data: 0.000) G_L1: 13.955 G_L1_ABSOLUTE: 2.840 G_L1_RELATIVE: 11.115 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 5, iters: 86992, time: 0.534, data: 0.000) G_L1: 19.030 G_L1_ABSOLUTE: 3.118 G_L1_RELATIVE: 15.912 G_Regularizer: 0.000 validation_error: 21.587 +(epoch: 5, iters: 88992, time: 0.528, data: 0.000) G_L1: 14.833 G_L1_ABSOLUTE: 2.819 G_L1_RELATIVE: 12.014 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 5, iters: 90992, time: 0.532, data: 0.000) G_L1: 17.091 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 14.310 G_Regularizer: 0.000 validation_error: 20.106 +(epoch: 5, iters: 92992, time: 0.530, data: 0.000) G_L1: 17.745 G_L1_ABSOLUTE: 2.801 G_L1_RELATIVE: 14.944 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 5, iters: 94992, time: 0.531, data: 0.000) G_L1: 17.968 G_L1_ABSOLUTE: 3.460 G_L1_RELATIVE: 14.508 G_Regularizer: 0.000 validation_error: 21.294 +(epoch: 5, iters: 96992, time: 0.516, data: 0.000) G_L1: 18.130 G_L1_ABSOLUTE: 3.123 G_L1_RELATIVE: 15.007 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 5, iters: 98992, time: 0.520, data: 0.000) G_L1: 17.821 G_L1_ABSOLUTE: 2.968 G_L1_RELATIVE: 14.853 G_Regularizer: 0.000 validation_error: 21.699 +(epoch: 5, iters: 100992, time: 0.517, data: 0.000) G_L1: 15.793 G_L1_ABSOLUTE: 2.487 G_L1_RELATIVE: 13.306 G_Regularizer: 0.000 validation_error: 21.338 +(epoch: 5, iters: 102992, time: 0.516, data: 0.000) G_L1: 18.522 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 15.700 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 5, iters: 104992, time: 0.536, data: 0.000) G_L1: 18.343 G_L1_ABSOLUTE: 3.039 G_L1_RELATIVE: 15.304 G_Regularizer: 0.000 validation_error: 21.680 +(epoch: 5, iters: 106992, time: 0.525, data: 0.000) G_L1: 16.912 G_L1_ABSOLUTE: 2.909 G_L1_RELATIVE: 14.002 G_Regularizer: 0.000 validation_error: 21.150 +(epoch: 5, iters: 108992, time: 0.524, data: 0.000) G_L1: 19.879 G_L1_ABSOLUTE: 3.014 G_L1_RELATIVE: 16.865 G_Regularizer: 0.000 validation_error: 21.211 +(epoch: 5, iters: 110992, time: 0.521, data: 0.000) G_L1: 17.852 G_L1_ABSOLUTE: 3.022 G_L1_RELATIVE: 14.829 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 5, iters: 112992, time: 0.524, data: 0.000) G_L1: 15.363 G_L1_ABSOLUTE: 3.192 G_L1_RELATIVE: 12.171 G_Regularizer: 0.000 validation_error: 20.461 +(epoch: 5, iters: 114992, time: 0.521, data: 0.000) G_L1: 14.951 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 12.386 G_Regularizer: 0.000 validation_error: 20.140 +(epoch: 5, iters: 116992, time: 0.528, data: 0.000) G_L1: 15.852 G_L1_ABSOLUTE: 3.393 G_L1_RELATIVE: 12.459 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 5, iters: 118992, time: 0.526, data: 0.000) G_L1: 15.606 G_L1_ABSOLUTE: 2.689 G_L1_RELATIVE: 12.917 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 5, iters: 120992, time: 0.527, data: 0.000) G_L1: 17.720 G_L1_ABSOLUTE: 2.948 G_L1_RELATIVE: 14.772 G_Regularizer: 0.000 validation_error: 21.456 +(epoch: 5, iters: 122992, time: 0.537, data: 0.000) G_L1: 14.697 G_L1_ABSOLUTE: 2.932 G_L1_RELATIVE: 11.765 G_Regularizer: 0.000 validation_error: 20.899 +(epoch: 5, iters: 124992, time: 0.535, data: 0.000) G_L1: 13.932 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 10.980 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 5, iters: 126992, time: 0.522, data: 0.000) G_L1: 16.197 G_L1_ABSOLUTE: 3.104 G_L1_RELATIVE: 13.093 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 5, iters: 128992, time: 0.527, data: 0.000) G_L1: 14.652 G_L1_ABSOLUTE: 2.826 G_L1_RELATIVE: 11.826 G_Regularizer: 0.000 validation_error: 21.165 +(epoch: 5, iters: 130992, time: 0.527, data: 0.000) G_L1: 17.918 G_L1_ABSOLUTE: 3.116 G_L1_RELATIVE: 14.802 G_Regularizer: 0.000 validation_error: 20.602 +(epoch: 5, iters: 132992, time: 0.525, data: 0.000) G_L1: 17.076 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 14.167 G_Regularizer: 0.000 validation_error: 20.011 +(epoch: 5, iters: 134992, time: 0.529, data: 0.000) G_L1: 16.228 G_L1_ABSOLUTE: 2.605 G_L1_RELATIVE: 13.623 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 5, iters: 136992, time: 0.526, data: 0.000) G_L1: 15.628 G_L1_ABSOLUTE: 3.199 G_L1_RELATIVE: 12.429 G_Regularizer: 0.000 validation_error: 21.596 +(epoch: 5, iters: 138992, time: 0.528, data: 0.000) G_L1: 16.223 G_L1_ABSOLUTE: 3.144 G_L1_RELATIVE: 13.080 G_Regularizer: 0.000 validation_error: 20.500 +(epoch: 5, iters: 140992, time: 0.532, data: 0.000) G_L1: 19.719 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 17.058 G_Regularizer: 0.000 validation_error: 21.485 +(epoch: 5, iters: 142992, time: 0.532, data: 0.000) G_L1: 18.376 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 15.642 G_Regularizer: 0.000 validation_error: 21.511 +(epoch: 5, iters: 144992, time: 0.528, data: 0.000) G_L1: 16.820 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 14.171 G_Regularizer: 0.000 validation_error: 20.644 +(epoch: 5, iters: 146992, time: 0.528, data: 0.000) G_L1: 15.625 G_L1_ABSOLUTE: 3.514 G_L1_RELATIVE: 12.111 G_Regularizer: 0.000 validation_error: 20.464 +(epoch: 5, iters: 148992, time: 0.534, data: 0.000) G_L1: 15.255 G_L1_ABSOLUTE: 2.739 G_L1_RELATIVE: 12.516 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 5, iters: 150992, time: 0.525, data: 0.000) G_L1: 15.172 G_L1_ABSOLUTE: 2.842 G_L1_RELATIVE: 12.329 G_Regularizer: 0.000 validation_error: 20.742 +(epoch: 5, iters: 152992, time: 0.525, data: 0.000) G_L1: 17.636 G_L1_ABSOLUTE: 3.128 G_L1_RELATIVE: 14.509 G_Regularizer: 0.000 validation_error: 20.232 +(epoch: 5, iters: 154992, time: 0.529, data: 0.000) G_L1: 19.175 G_L1_ABSOLUTE: 3.514 G_L1_RELATIVE: 15.661 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 5, iters: 156992, time: 0.529, data: 0.000) G_L1: 16.192 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 13.521 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 5, iters: 158992, time: 0.533, data: 0.000) G_L1: 16.512 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 14.096 G_Regularizer: 0.000 validation_error: 21.003 +(epoch: 5, iters: 160992, time: 0.538, data: 0.000) G_L1: 17.621 G_L1_ABSOLUTE: 2.973 G_L1_RELATIVE: 14.649 G_Regularizer: 0.000 validation_error: 20.191 +(epoch: 5, iters: 162992, time: 0.527, data: 0.000) G_L1: 18.010 G_L1_ABSOLUTE: 3.079 G_L1_RELATIVE: 14.931 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 5, iters: 164992, time: 0.532, data: 0.000) G_L1: 12.928 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 10.315 G_Regularizer: 0.000 validation_error: 21.217 +(epoch: 5, iters: 166992, time: 0.527, data: 0.000) G_L1: 15.709 G_L1_ABSOLUTE: 3.197 G_L1_RELATIVE: 12.512 G_Regularizer: 0.000 validation_error: 21.032 +(epoch: 5, iters: 168992, time: 0.530, data: 0.000) G_L1: 14.896 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 12.200 G_Regularizer: 0.000 validation_error: 20.744 +(epoch: 5, iters: 170992, time: 0.531, data: 0.000) G_L1: 17.794 G_L1_ABSOLUTE: 2.842 G_L1_RELATIVE: 14.951 G_Regularizer: 0.000 validation_error: 20.515 +(epoch: 5, iters: 172992, time: 0.535, data: 0.000) G_L1: 16.485 G_L1_ABSOLUTE: 2.786 G_L1_RELATIVE: 13.700 G_Regularizer: 0.000 validation_error: 21.376 +(epoch: 5, iters: 174992, time: 0.530, data: 0.000) G_L1: 16.218 G_L1_ABSOLUTE: 3.070 G_L1_RELATIVE: 13.148 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 5, iters: 176992, time: 0.528, data: 0.000) G_L1: 17.142 G_L1_ABSOLUTE: 2.713 G_L1_RELATIVE: 14.428 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 5, iters: 178992, time: 0.526, data: 0.000) G_L1: 14.390 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 11.513 G_Regularizer: 0.000 validation_error: 20.437 +(epoch: 5, iters: 180992, time: 0.528, data: 0.000) G_L1: 17.505 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 14.864 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 5, iters: 182992, time: 0.530, data: 0.000) G_L1: 16.481 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 13.749 G_Regularizer: 0.000 validation_error: 21.315 +(epoch: 5, iters: 184992, time: 0.530, data: 0.000) G_L1: 15.490 G_L1_ABSOLUTE: 3.180 G_L1_RELATIVE: 12.310 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 5, iters: 186992, time: 0.523, data: 0.000) G_L1: 19.083 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 16.414 G_Regularizer: 0.000 validation_error: 21.482 +(epoch: 5, iters: 188992, time: 0.522, data: 0.000) G_L1: 12.842 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 10.120 G_Regularizer: 0.000 validation_error: 21.589 +(epoch: 5, iters: 190992, time: 0.523, data: 0.000) G_L1: 17.215 G_L1_ABSOLUTE: 3.183 G_L1_RELATIVE: 14.031 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 5, iters: 192992, time: 0.532, data: 0.000) G_L1: 17.388 G_L1_ABSOLUTE: 3.215 G_L1_RELATIVE: 14.173 G_Regularizer: 0.000 validation_error: 20.514 +(epoch: 5, iters: 194992, time: 0.523, data: 0.000) G_L1: 15.192 G_L1_ABSOLUTE: 3.067 G_L1_RELATIVE: 12.125 G_Regularizer: 0.000 validation_error: 21.642 +(epoch: 5, iters: 196992, time: 0.524, data: 0.000) G_L1: 13.670 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 11.081 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 5, iters: 198992, time: 0.531, data: 0.000) G_L1: 13.167 G_L1_ABSOLUTE: 2.790 G_L1_RELATIVE: 10.378 G_Regularizer: 0.000 validation_error: 20.930 +(epoch: 5, iters: 200992, time: 0.549, data: 0.000) G_L1: 17.505 G_L1_ABSOLUTE: 3.022 G_L1_RELATIVE: 14.482 G_Regularizer: 0.000 validation_error: 21.435 +(epoch: 5, iters: 202992, time: 0.543, data: 0.000) G_L1: 16.052 G_L1_ABSOLUTE: 2.844 G_L1_RELATIVE: 13.207 G_Regularizer: 0.000 validation_error: 19.793 +(epoch: 5, iters: 204992, time: 0.549, data: 0.000) G_L1: 16.025 G_L1_ABSOLUTE: 3.137 G_L1_RELATIVE: 12.888 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 5, iters: 206992, time: 0.545, data: 0.001) G_L1: 15.921 G_L1_ABSOLUTE: 3.198 G_L1_RELATIVE: 12.723 G_Regularizer: 0.000 validation_error: 21.422 +(epoch: 5, iters: 208992, time: 0.550, data: 0.000) G_L1: 17.209 G_L1_ABSOLUTE: 3.196 G_L1_RELATIVE: 14.013 G_Regularizer: 0.000 validation_error: 20.998 +(epoch: 5, iters: 210992, time: 0.544, data: 0.000) G_L1: 15.802 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 13.077 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 5, iters: 212992, time: 0.551, data: 0.000) G_L1: 20.727 G_L1_ABSOLUTE: 3.232 G_L1_RELATIVE: 17.495 G_Regularizer: 0.000 validation_error: 21.001 +(epoch: 5, iters: 214992, time: 0.546, data: 0.000) G_L1: 15.088 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 12.297 G_Regularizer: 0.000 validation_error: 21.220 +(epoch: 5, iters: 216992, time: 0.542, data: 0.000) G_L1: 19.945 G_L1_ABSOLUTE: 2.997 G_L1_RELATIVE: 16.947 G_Regularizer: 0.000 validation_error: 21.522 +(epoch: 5, iters: 218992, time: 0.541, data: 0.000) G_L1: 16.690 G_L1_ABSOLUTE: 3.029 G_L1_RELATIVE: 13.661 G_Regularizer: 0.000 validation_error: 20.675 +(epoch: 5, iters: 220992, time: 0.542, data: 0.000) G_L1: 15.623 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 12.866 G_Regularizer: 0.000 validation_error: 20.291 +(epoch: 5, iters: 222992, time: 0.549, data: 0.000) G_L1: 14.722 G_L1_ABSOLUTE: 3.237 G_L1_RELATIVE: 11.485 G_Regularizer: 0.000 validation_error: 21.328 +(epoch: 5, iters: 224992, time: 0.543, data: 0.000) G_L1: 15.755 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 13.361 G_Regularizer: 0.000 validation_error: 21.384 +(epoch: 5, iters: 226992, time: 0.541, data: 0.000) G_L1: 15.782 G_L1_ABSOLUTE: 3.088 G_L1_RELATIVE: 12.694 G_Regularizer: 0.000 validation_error: 21.200 +(epoch: 5, iters: 228992, time: 0.542, data: 0.000) G_L1: 14.813 G_L1_ABSOLUTE: 2.869 G_L1_RELATIVE: 11.944 G_Regularizer: 0.000 validation_error: 21.240 +(epoch: 5, iters: 230992, time: 0.545, data: 0.000) G_L1: 17.611 G_L1_ABSOLUTE: 2.796 G_L1_RELATIVE: 14.816 G_Regularizer: 0.000 validation_error: 21.975 +(epoch: 5, iters: 232992, time: 0.549, data: 0.000) G_L1: 14.335 G_L1_ABSOLUTE: 2.866 G_L1_RELATIVE: 11.470 G_Regularizer: 0.000 validation_error: 21.296 +(epoch: 5, iters: 234992, time: 0.543, data: 0.000) G_L1: 16.163 G_L1_ABSOLUTE: 3.306 G_L1_RELATIVE: 12.857 G_Regularizer: 0.000 validation_error: 21.280 +(epoch: 5, iters: 236992, time: 0.544, data: 0.000) G_L1: 13.168 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 10.658 G_Regularizer: 0.000 validation_error: 21.811 +(epoch: 5, iters: 238992, time: 0.544, data: 0.000) G_L1: 15.478 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 12.570 G_Regularizer: 0.000 validation_error: 21.095 +(epoch: 5, iters: 240992, time: 0.537, data: 0.000) G_L1: 16.343 G_L1_ABSOLUTE: 2.980 G_L1_RELATIVE: 13.363 G_Regularizer: 0.000 validation_error: 21.650 +(epoch: 5, iters: 242992, time: 0.543, data: 0.000) G_L1: 17.232 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 14.557 G_Regularizer: 0.000 validation_error: 21.806 +(epoch: 5, iters: 244992, time: 0.541, data: 0.000) G_L1: 12.887 G_L1_ABSOLUTE: 2.793 G_L1_RELATIVE: 10.095 G_Regularizer: 0.000 validation_error: 21.543 +(epoch: 5, iters: 246992, time: 0.551, data: 0.000) G_L1: 18.945 G_L1_ABSOLUTE: 2.895 G_L1_RELATIVE: 16.051 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 5, iters: 248992, time: 0.543, data: 0.000) G_L1: 17.340 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 14.518 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 5, iters: 250992, time: 0.546, data: 0.000) G_L1: 13.879 G_L1_ABSOLUTE: 2.608 G_L1_RELATIVE: 11.272 G_Regularizer: 0.000 validation_error: 21.294 +(epoch: 5, iters: 252992, time: 0.548, data: 0.000) G_L1: 16.900 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 14.029 G_Regularizer: 0.000 validation_error: 21.443 +(epoch: 5, iters: 254992, time: 0.545, data: 0.001) G_L1: 15.761 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 12.856 G_Regularizer: 0.000 validation_error: 21.263 +(epoch: 5, iters: 256992, time: 0.547, data: 0.000) G_L1: 15.379 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 12.390 G_Regularizer: 0.000 validation_error: 21.476 +(epoch: 5, iters: 258992, time: 0.549, data: 0.000) G_L1: 18.406 G_L1_ABSOLUTE: 3.294 G_L1_RELATIVE: 15.112 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 5, iters: 260992, time: 0.554, data: 0.000) G_L1: 15.002 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 12.255 G_Regularizer: 0.000 validation_error: 21.839 +(epoch: 5, iters: 262992, time: 0.541, data: 0.000) G_L1: 18.592 G_L1_ABSOLUTE: 2.828 G_L1_RELATIVE: 15.764 G_Regularizer: 0.000 validation_error: 21.559 +(epoch: 5, iters: 264992, time: 0.548, data: 0.000) G_L1: 16.400 G_L1_ABSOLUTE: 2.888 G_L1_RELATIVE: 13.512 G_Regularizer: 0.000 validation_error: 21.228 +(epoch: 5, iters: 266992, time: 0.539, data: 0.000) G_L1: 16.922 G_L1_ABSOLUTE: 3.311 G_L1_RELATIVE: 13.611 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 5, iters: 268992, time: 0.547, data: 0.000) G_L1: 16.334 G_L1_ABSOLUTE: 3.304 G_L1_RELATIVE: 13.030 G_Regularizer: 0.000 validation_error: 21.511 +(epoch: 5, iters: 270992, time: 0.545, data: 0.000) G_L1: 16.556 G_L1_ABSOLUTE: 2.924 G_L1_RELATIVE: 13.632 G_Regularizer: 0.000 validation_error: 20.518 +(epoch: 5, iters: 272992, time: 0.555, data: 0.000) G_L1: 20.760 G_L1_ABSOLUTE: 3.002 G_L1_RELATIVE: 17.758 G_Regularizer: 0.000 validation_error: 20.601 +(epoch: 5, iters: 274992, time: 0.545, data: 0.000) G_L1: 17.560 G_L1_ABSOLUTE: 3.227 G_L1_RELATIVE: 14.333 G_Regularizer: 0.000 validation_error: 21.247 +(epoch: 5, iters: 276992, time: 0.540, data: 0.000) G_L1: 14.889 G_L1_ABSOLUTE: 2.448 G_L1_RELATIVE: 12.441 G_Regularizer: 0.000 validation_error: 21.005 +(epoch: 5, iters: 278992, time: 0.544, data: 0.000) G_L1: 13.757 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 10.947 G_Regularizer: 0.000 validation_error: 21.299 +(epoch: 5, iters: 280992, time: 0.533, data: 0.000) G_L1: 18.719 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 15.828 G_Regularizer: 0.000 validation_error: 20.717 +(epoch: 5, iters: 282992, time: 0.544, data: 0.000) G_L1: 19.028 G_L1_ABSOLUTE: 3.058 G_L1_RELATIVE: 15.970 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 5, iters: 284992, time: 0.540, data: 0.000) G_L1: 16.437 G_L1_ABSOLUTE: 3.398 G_L1_RELATIVE: 13.039 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 5, iters: 286992, time: 0.544, data: 0.000) G_L1: 16.851 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 14.196 G_Regularizer: 0.000 validation_error: 20.939 +(epoch: 5, iters: 288992, time: 0.544, data: 0.000) G_L1: 17.301 G_L1_ABSOLUTE: 2.647 G_L1_RELATIVE: 14.654 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 5, iters: 290992, time: 0.538, data: 0.000) G_L1: 18.381 G_L1_ABSOLUTE: 3.082 G_L1_RELATIVE: 15.299 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 5, iters: 292992, time: 0.539, data: 0.000) G_L1: 19.436 G_L1_ABSOLUTE: 3.374 G_L1_RELATIVE: 16.062 G_Regularizer: 0.000 validation_error: 21.706 +(epoch: 5, iters: 294992, time: 0.548, data: 0.000) G_L1: 15.186 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 12.294 G_Regularizer: 0.000 validation_error: 20.577 +(epoch: 5, iters: 296992, time: 0.535, data: 0.000) G_L1: 16.207 G_L1_ABSOLUTE: 2.579 G_L1_RELATIVE: 13.628 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 5, iters: 298992, time: 0.543, data: 0.000) G_L1: 15.561 G_L1_ABSOLUTE: 3.015 G_L1_RELATIVE: 12.546 G_Regularizer: 0.000 validation_error: 20.748 +(epoch: 5, iters: 300992, time: 0.545, data: 0.000) G_L1: 17.729 G_L1_ABSOLUTE: 2.823 G_L1_RELATIVE: 14.906 G_Regularizer: 0.000 validation_error: 20.768 +(epoch: 6, iters: 240, time: 0.542, data: 0.000) G_L1: 16.193 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 13.507 G_Regularizer: 0.000 validation_error: 21.195 +(epoch: 6, iters: 2240, time: 0.549, data: 0.000) G_L1: 15.056 G_L1_ABSOLUTE: 2.957 G_L1_RELATIVE: 12.099 G_Regularizer: 0.000 validation_error: 20.636 +(epoch: 6, iters: 4240, time: 0.548, data: 0.000) G_L1: 17.886 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 15.023 G_Regularizer: 0.000 validation_error: 21.398 +(epoch: 6, iters: 6240, time: 0.543, data: 0.000) G_L1: 18.168 G_L1_ABSOLUTE: 2.751 G_L1_RELATIVE: 15.416 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 6, iters: 8240, time: 0.547, data: 0.000) G_L1: 15.935 G_L1_ABSOLUTE: 3.023 G_L1_RELATIVE: 12.912 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 6, iters: 10240, time: 0.540, data: 0.000) G_L1: 19.362 G_L1_ABSOLUTE: 3.176 G_L1_RELATIVE: 16.186 G_Regularizer: 0.000 validation_error: 22.686 +(epoch: 6, iters: 12240, time: 0.546, data: 0.000) G_L1: 14.796 G_L1_ABSOLUTE: 3.008 G_L1_RELATIVE: 11.788 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 6, iters: 14240, time: 0.540, data: 0.001) G_L1: 16.099 G_L1_ABSOLUTE: 2.815 G_L1_RELATIVE: 13.284 G_Regularizer: 0.000 validation_error: 21.051 +(epoch: 6, iters: 16240, time: 0.543, data: 0.000) G_L1: 16.948 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 14.180 G_Regularizer: 0.000 validation_error: 21.257 +(epoch: 6, iters: 18240, time: 0.550, data: 0.000) G_L1: 16.655 G_L1_ABSOLUTE: 3.055 G_L1_RELATIVE: 13.601 G_Regularizer: 0.000 validation_error: 20.445 +(epoch: 6, iters: 20240, time: 0.542, data: 0.000) G_L1: 15.364 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 12.842 G_Regularizer: 0.000 validation_error: 21.024 +(epoch: 6, iters: 22240, time: 0.543, data: 0.000) G_L1: 17.821 G_L1_ABSOLUTE: 2.992 G_L1_RELATIVE: 14.829 G_Regularizer: 0.000 validation_error: 20.789 +(epoch: 6, iters: 24240, time: 0.543, data: 0.000) G_L1: 14.774 G_L1_ABSOLUTE: 2.638 G_L1_RELATIVE: 12.135 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 6, iters: 26240, time: 0.542, data: 0.000) G_L1: 21.330 G_L1_ABSOLUTE: 3.396 G_L1_RELATIVE: 17.934 G_Regularizer: 0.000 validation_error: 21.287 +(epoch: 6, iters: 28240, time: 0.539, data: 0.000) G_L1: 17.168 G_L1_ABSOLUTE: 3.573 G_L1_RELATIVE: 13.594 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 6, iters: 30240, time: 0.534, data: 0.000) G_L1: 13.083 G_L1_ABSOLUTE: 2.807 G_L1_RELATIVE: 10.276 G_Regularizer: 0.000 validation_error: 20.281 +(epoch: 6, iters: 32240, time: 0.546, data: 0.000) G_L1: 14.334 G_L1_ABSOLUTE: 2.988 G_L1_RELATIVE: 11.347 G_Regularizer: 0.000 validation_error: 21.197 +(epoch: 6, iters: 34240, time: 0.545, data: 0.000) G_L1: 15.807 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 12.913 G_Regularizer: 0.000 validation_error: 21.105 +(epoch: 6, iters: 36240, time: 0.540, data: 0.000) G_L1: 16.104 G_L1_ABSOLUTE: 3.217 G_L1_RELATIVE: 12.887 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 6, iters: 38240, time: 0.540, data: 0.000) G_L1: 13.744 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 11.011 G_Regularizer: 0.000 validation_error: 21.162 +(epoch: 6, iters: 40240, time: 0.546, data: 0.000) G_L1: 16.080 G_L1_ABSOLUTE: 3.224 G_L1_RELATIVE: 12.856 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 6, iters: 42240, time: 0.544, data: 0.000) G_L1: 13.914 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 11.665 G_Regularizer: 0.000 validation_error: 20.292 +(epoch: 6, iters: 44240, time: 0.546, data: 0.000) G_L1: 17.629 G_L1_ABSOLUTE: 2.979 G_L1_RELATIVE: 14.650 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 6, iters: 46240, time: 0.547, data: 0.000) G_L1: 17.605 G_L1_ABSOLUTE: 2.720 G_L1_RELATIVE: 14.884 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 6, iters: 48240, time: 0.546, data: 0.000) G_L1: 15.357 G_L1_ABSOLUTE: 3.188 G_L1_RELATIVE: 12.169 G_Regularizer: 0.000 validation_error: 21.207 +(epoch: 6, iters: 50240, time: 0.549, data: 0.000) G_L1: 16.898 G_L1_ABSOLUTE: 3.118 G_L1_RELATIVE: 13.780 G_Regularizer: 0.000 validation_error: 21.300 +(epoch: 6, iters: 52240, time: 0.541, data: 0.000) G_L1: 19.373 G_L1_ABSOLUTE: 3.246 G_L1_RELATIVE: 16.127 G_Regularizer: 0.000 validation_error: 20.442 +(epoch: 6, iters: 54240, time: 0.547, data: 0.000) G_L1: 18.230 G_L1_ABSOLUTE: 2.957 G_L1_RELATIVE: 15.272 G_Regularizer: 0.000 validation_error: 21.316 +(epoch: 6, iters: 56240, time: 0.543, data: 0.000) G_L1: 14.731 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 12.177 G_Regularizer: 0.000 validation_error: 20.653 +(epoch: 6, iters: 58240, time: 0.549, data: 0.000) G_L1: 17.763 G_L1_ABSOLUTE: 3.005 G_L1_RELATIVE: 14.758 G_Regularizer: 0.000 validation_error: 20.795 +(epoch: 6, iters: 60240, time: 0.539, data: 0.000) G_L1: 14.467 G_L1_ABSOLUTE: 2.906 G_L1_RELATIVE: 11.561 G_Regularizer: 0.000 validation_error: 21.407 +(epoch: 6, iters: 62240, time: 0.547, data: 0.000) G_L1: 16.870 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 14.213 G_Regularizer: 0.000 validation_error: 20.559 +(epoch: 6, iters: 64240, time: 0.546, data: 0.000) G_L1: 18.308 G_L1_ABSOLUTE: 2.776 G_L1_RELATIVE: 15.531 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 6, iters: 66240, time: 0.541, data: 0.000) G_L1: 16.301 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 13.576 G_Regularizer: 0.000 validation_error: 21.027 +(epoch: 6, iters: 68240, time: 0.547, data: 0.000) G_L1: 17.460 G_L1_ABSOLUTE: 2.747 G_L1_RELATIVE: 14.712 G_Regularizer: 0.000 validation_error: 21.079 +(epoch: 6, iters: 70240, time: 0.549, data: 0.000) G_L1: 20.230 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 17.353 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 6, iters: 72240, time: 0.547, data: 0.000) G_L1: 18.004 G_L1_ABSOLUTE: 3.023 G_L1_RELATIVE: 14.981 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 6, iters: 74240, time: 0.548, data: 0.000) G_L1: 15.911 G_L1_ABSOLUTE: 2.925 G_L1_RELATIVE: 12.986 G_Regularizer: 0.000 validation_error: 20.854 +(epoch: 6, iters: 76240, time: 0.542, data: 0.000) G_L1: 16.619 G_L1_ABSOLUTE: 2.946 G_L1_RELATIVE: 13.673 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 6, iters: 78240, time: 0.546, data: 0.000) G_L1: 15.331 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 12.743 G_Regularizer: 0.000 validation_error: 21.296 +(epoch: 6, iters: 80240, time: 0.542, data: 0.000) G_L1: 14.614 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 12.046 G_Regularizer: 0.000 validation_error: 21.117 +(epoch: 6, iters: 82240, time: 0.547, data: 0.000) G_L1: 15.960 G_L1_ABSOLUTE: 2.543 G_L1_RELATIVE: 13.417 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 6, iters: 84240, time: 0.550, data: 0.000) G_L1: 15.596 G_L1_ABSOLUTE: 2.662 G_L1_RELATIVE: 12.934 G_Regularizer: 0.000 validation_error: 21.694 +(epoch: 6, iters: 86240, time: 0.542, data: 0.000) G_L1: 16.010 G_L1_ABSOLUTE: 2.647 G_L1_RELATIVE: 13.362 G_Regularizer: 0.000 validation_error: 21.115 +(epoch: 6, iters: 88240, time: 0.544, data: 0.000) G_L1: 18.031 G_L1_ABSOLUTE: 3.014 G_L1_RELATIVE: 15.016 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 6, iters: 90240, time: 0.546, data: 0.001) G_L1: 17.389 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 14.739 G_Regularizer: 0.000 validation_error: 20.744 +(epoch: 6, iters: 92240, time: 0.542, data: 0.000) G_L1: 14.932 G_L1_ABSOLUTE: 2.692 G_L1_RELATIVE: 12.240 G_Regularizer: 0.000 validation_error: 20.896 +(epoch: 6, iters: 94240, time: 0.548, data: 0.000) G_L1: 15.241 G_L1_ABSOLUTE: 3.093 G_L1_RELATIVE: 12.147 G_Regularizer: 0.000 validation_error: 20.566 +(epoch: 6, iters: 96240, time: 0.547, data: 0.000) G_L1: 18.588 G_L1_ABSOLUTE: 2.930 G_L1_RELATIVE: 15.658 G_Regularizer: 0.000 validation_error: 21.328 +(epoch: 6, iters: 98240, time: 0.544, data: 0.000) G_L1: 18.605 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 15.748 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 6, iters: 100240, time: 0.543, data: 0.000) G_L1: 15.938 G_L1_ABSOLUTE: 2.886 G_L1_RELATIVE: 13.053 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 6, iters: 102240, time: 0.542, data: 0.000) G_L1: 15.017 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 12.326 G_Regularizer: 0.000 validation_error: 21.923 +(epoch: 6, iters: 104240, time: 0.555, data: 0.000) G_L1: 17.065 G_L1_ABSOLUTE: 3.598 G_L1_RELATIVE: 13.467 G_Regularizer: 0.000 validation_error: 21.277 +(epoch: 6, iters: 106240, time: 0.542, data: 0.000) G_L1: 16.373 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 13.639 G_Regularizer: 0.000 validation_error: 20.512 +(epoch: 6, iters: 108240, time: 0.547, data: 0.000) G_L1: 17.522 G_L1_ABSOLUTE: 2.490 G_L1_RELATIVE: 15.031 G_Regularizer: 0.000 validation_error: 20.971 +(epoch: 6, iters: 110240, time: 0.548, data: 0.000) G_L1: 16.326 G_L1_ABSOLUTE: 3.191 G_L1_RELATIVE: 13.135 G_Regularizer: 0.000 validation_error: 21.113 +(epoch: 6, iters: 112240, time: 0.542, data: 0.000) G_L1: 15.629 G_L1_ABSOLUTE: 3.486 G_L1_RELATIVE: 12.143 G_Regularizer: 0.000 validation_error: 21.058 +(epoch: 6, iters: 114240, time: 0.544, data: 0.000) G_L1: 18.839 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 16.026 G_Regularizer: 0.000 validation_error: 21.161 +(epoch: 6, iters: 116240, time: 0.541, data: 0.000) G_L1: 13.580 G_L1_ABSOLUTE: 2.425 G_L1_RELATIVE: 11.155 G_Regularizer: 0.000 validation_error: 21.138 +(epoch: 6, iters: 118240, time: 0.546, data: 0.000) G_L1: 16.520 G_L1_ABSOLUTE: 2.534 G_L1_RELATIVE: 13.986 G_Regularizer: 0.000 validation_error: 21.329 +(epoch: 6, iters: 120240, time: 0.549, data: 0.000) G_L1: 16.249 G_L1_ABSOLUTE: 2.978 G_L1_RELATIVE: 13.270 G_Regularizer: 0.000 validation_error: 20.401 +(epoch: 6, iters: 122240, time: 0.544, data: 0.000) G_L1: 14.663 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 12.039 G_Regularizer: 0.000 validation_error: 20.699 +(epoch: 6, iters: 124240, time: 0.536, data: 0.000) G_L1: 15.925 G_L1_ABSOLUTE: 3.021 G_L1_RELATIVE: 12.903 G_Regularizer: 0.000 validation_error: 21.166 +(epoch: 6, iters: 126240, time: 0.539, data: 0.000) G_L1: 21.046 G_L1_ABSOLUTE: 2.758 G_L1_RELATIVE: 18.287 G_Regularizer: 0.000 validation_error: 21.553 +(epoch: 6, iters: 128240, time: 0.547, data: 0.000) G_L1: 16.882 G_L1_ABSOLUTE: 3.099 G_L1_RELATIVE: 13.783 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 6, iters: 130240, time: 0.555, data: 0.000) G_L1: 16.164 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 13.256 G_Regularizer: 0.000 validation_error: 20.737 +(epoch: 6, iters: 132240, time: 0.549, data: 0.000) G_L1: 14.700 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 12.128 G_Regularizer: 0.000 validation_error: 21.349 +(epoch: 6, iters: 134240, time: 0.541, data: 0.000) G_L1: 15.702 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 13.032 G_Regularizer: 0.000 validation_error: 21.009 +(epoch: 6, iters: 136240, time: 0.553, data: 0.000) G_L1: 21.830 G_L1_ABSOLUTE: 3.493 G_L1_RELATIVE: 18.337 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 6, iters: 138240, time: 0.549, data: 0.000) G_L1: 16.293 G_L1_ABSOLUTE: 3.088 G_L1_RELATIVE: 13.205 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 6, iters: 140240, time: 0.542, data: 0.000) G_L1: 13.667 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 11.381 G_Regularizer: 0.000 validation_error: 21.096 +(epoch: 6, iters: 142240, time: 0.552, data: 0.000) G_L1: 21.931 G_L1_ABSOLUTE: 2.770 G_L1_RELATIVE: 19.161 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 6, iters: 144240, time: 0.543, data: 0.000) G_L1: 16.510 G_L1_ABSOLUTE: 2.922 G_L1_RELATIVE: 13.588 G_Regularizer: 0.000 validation_error: 20.791 +(epoch: 6, iters: 146240, time: 0.550, data: 0.000) G_L1: 15.841 G_L1_ABSOLUTE: 2.599 G_L1_RELATIVE: 13.241 G_Regularizer: 0.000 validation_error: 21.473 +(epoch: 6, iters: 148240, time: 0.541, data: 0.000) G_L1: 16.553 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 13.816 G_Regularizer: 0.000 validation_error: 21.066 +(epoch: 6, iters: 150240, time: 0.542, data: 0.000) G_L1: 15.483 G_L1_ABSOLUTE: 2.921 G_L1_RELATIVE: 12.562 G_Regularizer: 0.000 validation_error: 22.378 +(epoch: 6, iters: 152240, time: 0.547, data: 0.000) G_L1: 16.069 G_L1_ABSOLUTE: 3.265 G_L1_RELATIVE: 12.803 G_Regularizer: 0.000 validation_error: 21.070 +(epoch: 6, iters: 154240, time: 0.539, data: 0.000) G_L1: 16.541 G_L1_ABSOLUTE: 2.372 G_L1_RELATIVE: 14.169 G_Regularizer: 0.000 validation_error: 21.590 +(epoch: 6, iters: 156240, time: 0.548, data: 0.000) G_L1: 17.000 G_L1_ABSOLUTE: 2.788 G_L1_RELATIVE: 14.212 G_Regularizer: 0.000 validation_error: 21.530 +(epoch: 6, iters: 158240, time: 0.535, data: 0.000) G_L1: 13.460 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 11.087 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 6, iters: 160240, time: 0.543, data: 0.001) G_L1: 14.255 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 11.808 G_Regularizer: 0.000 validation_error: 21.350 +(epoch: 6, iters: 162240, time: 0.545, data: 0.000) G_L1: 17.160 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 14.436 G_Regularizer: 0.000 validation_error: 21.780 +(epoch: 6, iters: 164240, time: 0.540, data: 0.000) G_L1: 17.760 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 15.114 G_Regularizer: 0.000 validation_error: 20.557 +(epoch: 6, iters: 166240, time: 0.542, data: 0.000) G_L1: 15.203 G_L1_ABSOLUTE: 2.727 G_L1_RELATIVE: 12.476 G_Regularizer: 0.000 validation_error: 20.299 +(epoch: 6, iters: 168240, time: 0.542, data: 0.000) G_L1: 17.053 G_L1_ABSOLUTE: 3.043 G_L1_RELATIVE: 14.010 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 6, iters: 170240, time: 0.548, data: 0.000) G_L1: 17.217 G_L1_ABSOLUTE: 2.785 G_L1_RELATIVE: 14.432 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 6, iters: 172240, time: 0.537, data: 0.000) G_L1: 25.567 G_L1_ABSOLUTE: 2.867 G_L1_RELATIVE: 22.699 G_Regularizer: 0.000 validation_error: 20.464 +(epoch: 6, iters: 174240, time: 0.547, data: 0.000) G_L1: 14.403 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 11.817 G_Regularizer: 0.000 validation_error: 20.336 +(epoch: 6, iters: 176240, time: 0.549, data: 0.000) G_L1: 15.679 G_L1_ABSOLUTE: 2.913 G_L1_RELATIVE: 12.766 G_Regularizer: 0.000 validation_error: 20.993 +(epoch: 6, iters: 178240, time: 0.551, data: 0.000) G_L1: 15.552 G_L1_ABSOLUTE: 2.854 G_L1_RELATIVE: 12.698 G_Regularizer: 0.000 validation_error: 21.157 +(epoch: 6, iters: 180240, time: 0.550, data: 0.000) G_L1: 17.262 G_L1_ABSOLUTE: 3.042 G_L1_RELATIVE: 14.220 G_Regularizer: 0.000 validation_error: 20.596 +(epoch: 6, iters: 182240, time: 0.546, data: 0.000) G_L1: 16.092 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 13.478 G_Regularizer: 0.000 validation_error: 21.095 +(epoch: 6, iters: 184240, time: 0.553, data: 0.000) G_L1: 16.253 G_L1_ABSOLUTE: 3.051 G_L1_RELATIVE: 13.202 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 6, iters: 186240, time: 0.548, data: 0.000) G_L1: 17.756 G_L1_ABSOLUTE: 2.643 G_L1_RELATIVE: 15.113 G_Regularizer: 0.000 validation_error: 21.175 +(epoch: 6, iters: 188240, time: 0.536, data: 0.000) G_L1: 15.022 G_L1_ABSOLUTE: 2.514 G_L1_RELATIVE: 12.509 G_Regularizer: 0.000 validation_error: 21.060 +(epoch: 6, iters: 190240, time: 0.544, data: 0.000) G_L1: 15.218 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 12.655 G_Regularizer: 0.000 validation_error: 21.270 +(epoch: 6, iters: 192240, time: 0.546, data: 0.000) G_L1: 16.688 G_L1_ABSOLUTE: 2.937 G_L1_RELATIVE: 13.751 G_Regularizer: 0.000 validation_error: 20.366 +(epoch: 6, iters: 194240, time: 0.546, data: 0.000) G_L1: 16.584 G_L1_ABSOLUTE: 2.699 G_L1_RELATIVE: 13.886 G_Regularizer: 0.000 validation_error: 21.354 +(epoch: 6, iters: 196240, time: 0.545, data: 0.000) G_L1: 15.798 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 13.059 G_Regularizer: 0.000 validation_error: 21.148 +(epoch: 6, iters: 198240, time: 0.541, data: 0.000) G_L1: 16.513 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 13.994 G_Regularizer: 0.000 validation_error: 21.365 +(epoch: 6, iters: 200240, time: 0.548, data: 0.000) G_L1: 16.807 G_L1_ABSOLUTE: 3.509 G_L1_RELATIVE: 13.298 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 6, iters: 202240, time: 0.544, data: 0.000) G_L1: 16.552 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 14.004 G_Regularizer: 0.000 validation_error: 21.044 +(epoch: 6, iters: 204240, time: 0.544, data: 0.000) G_L1: 14.405 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 11.581 G_Regularizer: 0.000 validation_error: 21.549 +(epoch: 6, iters: 206240, time: 0.540, data: 0.000) G_L1: 16.689 G_L1_ABSOLUTE: 2.982 G_L1_RELATIVE: 13.707 G_Regularizer: 0.000 validation_error: 20.910 +(epoch: 6, iters: 208240, time: 0.544, data: 0.001) G_L1: 15.751 G_L1_ABSOLUTE: 2.970 G_L1_RELATIVE: 12.781 G_Regularizer: 0.000 validation_error: 20.422 +(epoch: 6, iters: 210240, time: 0.544, data: 0.000) G_L1: 13.601 G_L1_ABSOLUTE: 2.441 G_L1_RELATIVE: 11.160 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 6, iters: 212240, time: 0.549, data: 0.000) G_L1: 15.976 G_L1_ABSOLUTE: 2.613 G_L1_RELATIVE: 13.363 G_Regularizer: 0.000 validation_error: 20.545 +(epoch: 6, iters: 214240, time: 0.544, data: 0.000) G_L1: 15.391 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 12.683 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 6, iters: 216240, time: 0.546, data: 0.000) G_L1: 17.680 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 14.884 G_Regularizer: 0.000 validation_error: 20.636 +(epoch: 6, iters: 218240, time: 0.543, data: 0.000) G_L1: 11.809 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 9.031 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 6, iters: 220240, time: 0.544, data: 0.000) G_L1: 15.956 G_L1_ABSOLUTE: 3.297 G_L1_RELATIVE: 12.659 G_Regularizer: 0.000 validation_error: 21.823 +(epoch: 6, iters: 222240, time: 0.542, data: 0.000) G_L1: 16.555 G_L1_ABSOLUTE: 3.246 G_L1_RELATIVE: 13.308 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 6, iters: 224240, time: 0.548, data: 0.000) G_L1: 14.384 G_L1_ABSOLUTE: 2.859 G_L1_RELATIVE: 11.525 G_Regularizer: 0.000 validation_error: 21.103 +(epoch: 6, iters: 226240, time: 0.546, data: 0.000) G_L1: 14.149 G_L1_ABSOLUTE: 2.693 G_L1_RELATIVE: 11.456 G_Regularizer: 0.000 validation_error: 20.217 +(epoch: 6, iters: 228240, time: 0.542, data: 0.000) G_L1: 17.708 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 15.118 G_Regularizer: 0.000 validation_error: 20.540 +(epoch: 6, iters: 230240, time: 0.544, data: 0.000) G_L1: 19.234 G_L1_ABSOLUTE: 2.976 G_L1_RELATIVE: 16.258 G_Regularizer: 0.000 validation_error: 21.519 +(epoch: 6, iters: 232240, time: 0.542, data: 0.000) G_L1: 15.313 G_L1_ABSOLUTE: 2.770 G_L1_RELATIVE: 12.543 G_Regularizer: 0.000 validation_error: 20.979 +(epoch: 6, iters: 234240, time: 0.540, data: 0.000) G_L1: 16.593 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 13.832 G_Regularizer: 0.000 validation_error: 21.124 +(epoch: 6, iters: 236240, time: 0.546, data: 0.000) G_L1: 15.655 G_L1_ABSOLUTE: 3.120 G_L1_RELATIVE: 12.535 G_Regularizer: 0.000 validation_error: 20.913 +(epoch: 6, iters: 238240, time: 0.549, data: 0.000) G_L1: 23.141 G_L1_ABSOLUTE: 3.225 G_L1_RELATIVE: 19.915 G_Regularizer: 0.000 validation_error: 21.092 +(epoch: 6, iters: 240240, time: 0.546, data: 0.000) G_L1: 16.912 G_L1_ABSOLUTE: 2.991 G_L1_RELATIVE: 13.921 G_Regularizer: 0.000 validation_error: 20.274 +(epoch: 6, iters: 242240, time: 0.547, data: 0.000) G_L1: 15.885 G_L1_ABSOLUTE: 2.699 G_L1_RELATIVE: 13.186 G_Regularizer: 0.000 validation_error: 21.735 +(epoch: 6, iters: 244240, time: 0.548, data: 0.000) G_L1: 17.707 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 14.907 G_Regularizer: 0.000 validation_error: 20.557 +(epoch: 6, iters: 246240, time: 0.543, data: 0.000) G_L1: 19.591 G_L1_ABSOLUTE: 2.865 G_L1_RELATIVE: 16.726 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 6, iters: 248240, time: 0.552, data: 0.000) G_L1: 18.637 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 16.097 G_Regularizer: 0.000 validation_error: 21.345 +(epoch: 6, iters: 250240, time: 0.547, data: 0.000) G_L1: 16.012 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 13.235 G_Regularizer: 0.000 validation_error: 20.373 +(epoch: 6, iters: 252240, time: 0.545, data: 0.000) G_L1: 17.392 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 14.661 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 6, iters: 254240, time: 0.544, data: 0.000) G_L1: 15.459 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 12.648 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 6, iters: 256240, time: 0.548, data: 0.000) G_L1: 15.094 G_L1_ABSOLUTE: 2.984 G_L1_RELATIVE: 12.110 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 6, iters: 258240, time: 0.548, data: 0.000) G_L1: 16.904 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 14.378 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 6, iters: 260240, time: 0.544, data: 0.001) G_L1: 13.811 G_L1_ABSOLUTE: 2.348 G_L1_RELATIVE: 11.463 G_Regularizer: 0.000 validation_error: 20.306 +(epoch: 6, iters: 262240, time: 0.535, data: 0.000) G_L1: 12.540 G_L1_ABSOLUTE: 2.617 G_L1_RELATIVE: 9.923 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 6, iters: 264240, time: 0.546, data: 0.000) G_L1: 14.167 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 11.457 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 6, iters: 266240, time: 0.544, data: 0.000) G_L1: 18.652 G_L1_ABSOLUTE: 2.944 G_L1_RELATIVE: 15.707 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 6, iters: 268240, time: 0.540, data: 0.000) G_L1: 19.862 G_L1_ABSOLUTE: 2.538 G_L1_RELATIVE: 17.324 G_Regularizer: 0.000 validation_error: 21.056 +(epoch: 6, iters: 270240, time: 0.540, data: 0.000) G_L1: 13.911 G_L1_ABSOLUTE: 3.011 G_L1_RELATIVE: 10.901 G_Regularizer: 0.000 validation_error: 20.904 +(epoch: 6, iters: 272240, time: 0.543, data: 0.000) G_L1: 15.678 G_L1_ABSOLUTE: 2.334 G_L1_RELATIVE: 13.343 G_Regularizer: 0.000 validation_error: 21.303 +(epoch: 6, iters: 274240, time: 0.541, data: 0.000) G_L1: 15.646 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 13.046 G_Regularizer: 0.000 validation_error: 20.612 +(epoch: 6, iters: 276240, time: 0.547, data: 0.000) G_L1: 15.791 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 12.928 G_Regularizer: 0.000 validation_error: 21.014 +(epoch: 6, iters: 278240, time: 0.542, data: 0.000) G_L1: 16.174 G_L1_ABSOLUTE: 3.083 G_L1_RELATIVE: 13.091 G_Regularizer: 0.000 validation_error: 20.386 +(epoch: 6, iters: 280240, time: 0.549, data: 0.000) G_L1: 19.565 G_L1_ABSOLUTE: 3.290 G_L1_RELATIVE: 16.276 G_Regularizer: 0.000 validation_error: 20.907 +(epoch: 6, iters: 282240, time: 0.543, data: 0.000) G_L1: 12.985 G_L1_ABSOLUTE: 3.002 G_L1_RELATIVE: 9.982 G_Regularizer: 0.000 validation_error: 21.824 +(epoch: 6, iters: 284240, time: 0.542, data: 0.000) G_L1: 13.479 G_L1_ABSOLUTE: 2.392 G_L1_RELATIVE: 11.087 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 6, iters: 286240, time: 0.548, data: 0.000) G_L1: 14.722 G_L1_ABSOLUTE: 2.695 G_L1_RELATIVE: 12.026 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 6, iters: 288240, time: 0.544, data: 0.000) G_L1: 15.923 G_L1_ABSOLUTE: 2.603 G_L1_RELATIVE: 13.320 G_Regularizer: 0.000 validation_error: 20.170 +(epoch: 6, iters: 290240, time: 0.544, data: 0.000) G_L1: 14.956 G_L1_ABSOLUTE: 3.179 G_L1_RELATIVE: 11.777 G_Regularizer: 0.000 validation_error: 20.532 +(epoch: 6, iters: 292240, time: 0.546, data: 0.000) G_L1: 19.723 G_L1_ABSOLUTE: 3.184 G_L1_RELATIVE: 16.539 G_Regularizer: 0.000 validation_error: 20.377 +(epoch: 6, iters: 294240, time: 0.549, data: 0.000) G_L1: 16.031 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 13.151 G_Regularizer: 0.000 validation_error: 21.341 +(epoch: 6, iters: 296240, time: 0.542, data: 0.000) G_L1: 18.362 G_L1_ABSOLUTE: 3.022 G_L1_RELATIVE: 15.340 G_Regularizer: 0.000 validation_error: 21.096 +(epoch: 6, iters: 298240, time: 0.546, data: 0.000) G_L1: 20.405 G_L1_ABSOLUTE: 3.140 G_L1_RELATIVE: 17.264 G_Regularizer: 0.000 validation_error: 21.096 +(epoch: 6, iters: 300240, time: 0.544, data: 0.000) G_L1: 19.462 G_L1_ABSOLUTE: 3.233 G_L1_RELATIVE: 16.229 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 6, iters: 302240, time: 0.545, data: 0.000) G_L1: 18.690 G_L1_ABSOLUTE: 3.216 G_L1_RELATIVE: 15.474 G_Regularizer: 0.000 validation_error: 21.379 +(epoch: 7, iters: 1488, time: 0.539, data: 0.000) G_L1: 15.882 G_L1_ABSOLUTE: 2.817 G_L1_RELATIVE: 13.064 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 7, iters: 3488, time: 0.543, data: 0.000) G_L1: 16.438 G_L1_ABSOLUTE: 2.997 G_L1_RELATIVE: 13.440 G_Regularizer: 0.000 validation_error: 20.629 +(epoch: 7, iters: 5488, time: 0.549, data: 0.000) G_L1: 15.449 G_L1_ABSOLUTE: 2.615 G_L1_RELATIVE: 12.833 G_Regularizer: 0.000 validation_error: 20.333 +(epoch: 7, iters: 7488, time: 0.542, data: 0.000) G_L1: 16.196 G_L1_ABSOLUTE: 2.796 G_L1_RELATIVE: 13.400 G_Regularizer: 0.000 validation_error: 20.108 +(epoch: 7, iters: 9488, time: 0.540, data: 0.000) G_L1: 14.910 G_L1_ABSOLUTE: 2.872 G_L1_RELATIVE: 12.038 G_Regularizer: 0.000 validation_error: 20.521 +(epoch: 7, iters: 11488, time: 0.547, data: 0.000) G_L1: 17.306 G_L1_ABSOLUTE: 2.950 G_L1_RELATIVE: 14.356 G_Regularizer: 0.000 validation_error: 21.329 +(epoch: 7, iters: 13488, time: 0.540, data: 0.000) G_L1: 16.909 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 14.289 G_Regularizer: 0.000 validation_error: 21.121 +(epoch: 7, iters: 15488, time: 0.546, data: 0.000) G_L1: 15.133 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 12.525 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 7, iters: 17488, time: 0.546, data: 0.000) G_L1: 16.227 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 13.846 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 7, iters: 19488, time: 0.548, data: 0.000) G_L1: 13.446 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 11.031 G_Regularizer: 0.000 validation_error: 20.445 +(epoch: 7, iters: 21488, time: 0.546, data: 0.000) G_L1: 16.821 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 14.043 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 7, iters: 23488, time: 0.547, data: 0.000) G_L1: 16.599 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 14.136 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 7, iters: 25488, time: 0.547, data: 0.000) G_L1: 13.995 G_L1_ABSOLUTE: 2.537 G_L1_RELATIVE: 11.458 G_Regularizer: 0.000 validation_error: 20.340 +(epoch: 7, iters: 27488, time: 0.542, data: 0.000) G_L1: 18.691 G_L1_ABSOLUTE: 2.950 G_L1_RELATIVE: 15.741 G_Regularizer: 0.000 validation_error: 20.634 +(epoch: 7, iters: 29488, time: 0.543, data: 0.000) G_L1: 17.314 G_L1_ABSOLUTE: 2.854 G_L1_RELATIVE: 14.460 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 7, iters: 31488, time: 0.552, data: 0.000) G_L1: 15.181 G_L1_ABSOLUTE: 2.743 G_L1_RELATIVE: 12.438 G_Regularizer: 0.000 validation_error: 21.165 +(epoch: 7, iters: 33488, time: 0.543, data: 0.000) G_L1: 15.187 G_L1_ABSOLUTE: 2.926 G_L1_RELATIVE: 12.261 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 7, iters: 35488, time: 0.545, data: 0.001) G_L1: 14.766 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 11.885 G_Regularizer: 0.000 validation_error: 21.656 +(epoch: 7, iters: 37488, time: 0.547, data: 0.000) G_L1: 15.962 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 13.278 G_Regularizer: 0.000 validation_error: 20.946 +(epoch: 7, iters: 39488, time: 0.547, data: 0.000) G_L1: 18.470 G_L1_ABSOLUTE: 3.467 G_L1_RELATIVE: 15.002 G_Regularizer: 0.000 validation_error: 21.208 +(epoch: 7, iters: 41488, time: 0.545, data: 0.000) G_L1: 14.587 G_L1_ABSOLUTE: 3.014 G_L1_RELATIVE: 11.572 G_Regularizer: 0.000 validation_error: 21.597 +(epoch: 7, iters: 43488, time: 0.541, data: 0.000) G_L1: 18.191 G_L1_ABSOLUTE: 3.081 G_L1_RELATIVE: 15.110 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 7, iters: 45488, time: 0.550, data: 0.000) G_L1: 16.190 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 13.646 G_Regularizer: 0.000 validation_error: 21.036 +(epoch: 7, iters: 47488, time: 0.545, data: 0.000) G_L1: 19.757 G_L1_ABSOLUTE: 3.506 G_L1_RELATIVE: 16.251 G_Regularizer: 0.000 validation_error: 21.215 +(epoch: 7, iters: 49488, time: 0.550, data: 0.000) G_L1: 16.511 G_L1_ABSOLUTE: 2.932 G_L1_RELATIVE: 13.579 G_Regularizer: 0.000 validation_error: 20.791 +(epoch: 7, iters: 51488, time: 0.545, data: 0.000) G_L1: 16.738 G_L1_ABSOLUTE: 3.006 G_L1_RELATIVE: 13.733 G_Regularizer: 0.000 validation_error: 20.469 +(epoch: 7, iters: 53488, time: 0.545, data: 0.000) G_L1: 18.144 G_L1_ABSOLUTE: 3.038 G_L1_RELATIVE: 15.106 G_Regularizer: 0.000 validation_error: 21.288 +(epoch: 7, iters: 55488, time: 0.549, data: 0.000) G_L1: 15.120 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 12.625 G_Regularizer: 0.000 validation_error: 21.472 +(epoch: 7, iters: 57488, time: 0.552, data: 0.000) G_L1: 14.521 G_L1_ABSOLUTE: 2.823 G_L1_RELATIVE: 11.698 G_Regularizer: 0.000 validation_error: 20.431 +(epoch: 7, iters: 59488, time: 0.545, data: 0.000) G_L1: 16.929 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 14.115 G_Regularizer: 0.000 validation_error: 21.118 +(epoch: 7, iters: 61488, time: 0.559, data: 0.000) G_L1: 15.309 G_L1_ABSOLUTE: 2.562 G_L1_RELATIVE: 12.746 G_Regularizer: 0.000 validation_error: 21.073 +(epoch: 7, iters: 63488, time: 0.551, data: 0.000) G_L1: 17.289 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 14.841 G_Regularizer: 0.000 validation_error: 20.844 +(epoch: 7, iters: 65488, time: 0.554, data: 0.000) G_L1: 16.505 G_L1_ABSOLUTE: 2.874 G_L1_RELATIVE: 13.630 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 7, iters: 67488, time: 0.552, data: 0.000) G_L1: 13.462 G_L1_ABSOLUTE: 2.853 G_L1_RELATIVE: 10.609 G_Regularizer: 0.000 validation_error: 21.350 +(epoch: 7, iters: 69488, time: 0.550, data: 0.000) G_L1: 19.528 G_L1_ABSOLUTE: 3.199 G_L1_RELATIVE: 16.329 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 7, iters: 71488, time: 0.546, data: 0.000) G_L1: 17.029 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 14.281 G_Regularizer: 0.000 validation_error: 21.144 +(epoch: 7, iters: 73488, time: 0.546, data: 0.000) G_L1: 16.107 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 13.296 G_Regularizer: 0.000 validation_error: 21.319 +(epoch: 7, iters: 75488, time: 0.554, data: 0.000) G_L1: 15.360 G_L1_ABSOLUTE: 2.868 G_L1_RELATIVE: 12.492 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 7, iters: 77488, time: 0.549, data: 0.001) G_L1: 17.641 G_L1_ABSOLUTE: 2.821 G_L1_RELATIVE: 14.820 G_Regularizer: 0.000 validation_error: 21.300 +(epoch: 7, iters: 79488, time: 0.550, data: 0.000) G_L1: 16.862 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 14.099 G_Regularizer: 0.000 validation_error: 20.632 +(epoch: 7, iters: 81488, time: 0.549, data: 0.000) G_L1: 15.642 G_L1_ABSOLUTE: 2.794 G_L1_RELATIVE: 12.848 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 7, iters: 83488, time: 0.556, data: 0.001) G_L1: 16.978 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 14.315 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 7, iters: 85488, time: 0.549, data: 0.000) G_L1: 17.241 G_L1_ABSOLUTE: 2.966 G_L1_RELATIVE: 14.274 G_Regularizer: 0.000 validation_error: 20.216 +(epoch: 7, iters: 87488, time: 0.561, data: 0.000) G_L1: 17.939 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 15.125 G_Regularizer: 0.000 validation_error: 20.442 +(epoch: 7, iters: 89488, time: 0.543, data: 0.000) G_L1: 12.685 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 10.216 G_Regularizer: 0.000 validation_error: 21.291 +(epoch: 7, iters: 91488, time: 0.548, data: 0.000) G_L1: 15.271 G_L1_ABSOLUTE: 2.602 G_L1_RELATIVE: 12.669 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 7, iters: 93488, time: 0.545, data: 0.000) G_L1: 16.596 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 14.102 G_Regularizer: 0.000 validation_error: 20.430 +(epoch: 7, iters: 95488, time: 0.542, data: 0.000) G_L1: 15.808 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 13.331 G_Regularizer: 0.000 validation_error: 21.248 +(epoch: 7, iters: 97488, time: 0.543, data: 0.000) G_L1: 17.778 G_L1_ABSOLUTE: 3.207 G_L1_RELATIVE: 14.571 G_Regularizer: 0.000 validation_error: 21.453 +(epoch: 7, iters: 99488, time: 0.541, data: 0.000) G_L1: 17.883 G_L1_ABSOLUTE: 3.667 G_L1_RELATIVE: 14.216 G_Regularizer: 0.000 validation_error: 21.235 +(epoch: 7, iters: 101488, time: 0.546, data: 0.000) G_L1: 15.022 G_L1_ABSOLUTE: 2.801 G_L1_RELATIVE: 12.221 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 7, iters: 103488, time: 0.543, data: 0.000) G_L1: 16.312 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 13.567 G_Regularizer: 0.000 validation_error: 21.468 +(epoch: 7, iters: 105488, time: 0.545, data: 0.000) G_L1: 15.513 G_L1_ABSOLUTE: 3.156 G_L1_RELATIVE: 12.356 G_Regularizer: 0.000 validation_error: 20.670 +(epoch: 7, iters: 107488, time: 0.541, data: 0.000) G_L1: 16.795 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 13.984 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 7, iters: 109488, time: 0.540, data: 0.000) G_L1: 12.669 G_L1_ABSOLUTE: 2.980 G_L1_RELATIVE: 9.689 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 7, iters: 111488, time: 0.550, data: 0.000) G_L1: 22.348 G_L1_ABSOLUTE: 2.791 G_L1_RELATIVE: 19.557 G_Regularizer: 0.000 validation_error: 20.577 +(epoch: 7, iters: 113488, time: 0.542, data: 0.000) G_L1: 13.866 G_L1_ABSOLUTE: 3.062 G_L1_RELATIVE: 10.804 G_Regularizer: 0.000 validation_error: 20.384 +(epoch: 7, iters: 115488, time: 0.542, data: 0.000) G_L1: 15.793 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 13.257 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 7, iters: 117488, time: 0.543, data: 0.000) G_L1: 14.482 G_L1_ABSOLUTE: 2.719 G_L1_RELATIVE: 11.763 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 7, iters: 119488, time: 0.539, data: 0.000) G_L1: 14.201 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 11.578 G_Regularizer: 0.000 validation_error: 20.291 +(epoch: 7, iters: 121488, time: 0.540, data: 0.000) G_L1: 15.631 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 13.023 G_Regularizer: 0.000 validation_error: 21.198 +(epoch: 7, iters: 123488, time: 0.539, data: 0.000) G_L1: 15.074 G_L1_ABSOLUTE: 2.869 G_L1_RELATIVE: 12.205 G_Regularizer: 0.000 validation_error: 19.916 +(epoch: 7, iters: 125488, time: 0.547, data: 0.000) G_L1: 16.335 G_L1_ABSOLUTE: 3.257 G_L1_RELATIVE: 13.077 G_Regularizer: 0.000 validation_error: 21.910 +(epoch: 7, iters: 127488, time: 0.536, data: 0.000) G_L1: 16.518 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 14.253 G_Regularizer: 0.000 validation_error: 21.432 +(epoch: 7, iters: 129488, time: 0.541, data: 0.000) G_L1: 16.800 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 14.158 G_Regularizer: 0.000 validation_error: 20.313 +(epoch: 7, iters: 131488, time: 0.543, data: 0.000) G_L1: 14.252 G_L1_ABSOLUTE: 2.902 G_L1_RELATIVE: 11.350 G_Regularizer: 0.000 validation_error: 21.259 +(epoch: 7, iters: 133488, time: 0.540, data: 0.000) G_L1: 14.494 G_L1_ABSOLUTE: 2.831 G_L1_RELATIVE: 11.663 G_Regularizer: 0.000 validation_error: 21.392 +(epoch: 7, iters: 135488, time: 0.546, data: 0.000) G_L1: 15.983 G_L1_ABSOLUTE: 2.700 G_L1_RELATIVE: 13.283 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 7, iters: 137488, time: 0.543, data: 0.000) G_L1: 13.936 G_L1_ABSOLUTE: 2.297 G_L1_RELATIVE: 11.639 G_Regularizer: 0.000 validation_error: 20.782 +(epoch: 7, iters: 139488, time: 0.541, data: 0.000) G_L1: 16.582 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 14.199 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 7, iters: 141488, time: 0.547, data: 0.000) G_L1: 14.801 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 12.183 G_Regularizer: 0.000 validation_error: 20.370 +(epoch: 7, iters: 143488, time: 0.543, data: 0.000) G_L1: 13.359 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 10.722 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 7, iters: 145488, time: 0.540, data: 0.000) G_L1: 14.873 G_L1_ABSOLUTE: 2.772 G_L1_RELATIVE: 12.102 G_Regularizer: 0.000 validation_error: 20.768 +(epoch: 7, iters: 147488, time: 0.552, data: 0.000) G_L1: 15.189 G_L1_ABSOLUTE: 2.775 G_L1_RELATIVE: 12.414 G_Regularizer: 0.000 validation_error: 20.601 +(epoch: 7, iters: 149488, time: 0.541, data: 0.000) G_L1: 16.811 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 14.392 G_Regularizer: 0.000 validation_error: 21.655 +(epoch: 7, iters: 151488, time: 0.543, data: 0.000) G_L1: 13.832 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 11.147 G_Regularizer: 0.000 validation_error: 21.180 +(epoch: 7, iters: 153488, time: 0.543, data: 0.000) G_L1: 16.855 G_L1_ABSOLUTE: 3.334 G_L1_RELATIVE: 13.521 G_Regularizer: 0.000 validation_error: 20.895 +(epoch: 7, iters: 155488, time: 0.543, data: 0.000) G_L1: 13.690 G_L1_ABSOLUTE: 2.351 G_L1_RELATIVE: 11.339 G_Regularizer: 0.000 validation_error: 21.328 +(epoch: 7, iters: 157488, time: 0.545, data: 0.000) G_L1: 13.795 G_L1_ABSOLUTE: 2.808 G_L1_RELATIVE: 10.986 G_Regularizer: 0.000 validation_error: 20.917 +(epoch: 7, iters: 159488, time: 0.544, data: 0.000) G_L1: 12.846 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 10.214 G_Regularizer: 0.000 validation_error: 21.252 +(epoch: 7, iters: 161488, time: 0.538, data: 0.000) G_L1: 20.032 G_L1_ABSOLUTE: 3.413 G_L1_RELATIVE: 16.619 G_Regularizer: 0.000 validation_error: 21.123 +(epoch: 7, iters: 163488, time: 0.546, data: 0.000) G_L1: 13.905 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 11.444 G_Regularizer: 0.000 validation_error: 21.584 +(epoch: 7, iters: 165488, time: 0.540, data: 0.000) G_L1: 15.271 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 12.822 G_Regularizer: 0.000 validation_error: 20.738 +(epoch: 7, iters: 167488, time: 0.537, data: 0.000) G_L1: 17.307 G_L1_ABSOLUTE: 2.514 G_L1_RELATIVE: 14.793 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 7, iters: 169488, time: 0.535, data: 0.000) G_L1: 17.404 G_L1_ABSOLUTE: 3.677 G_L1_RELATIVE: 13.727 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 7, iters: 171488, time: 0.544, data: 0.000) G_L1: 14.203 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 11.446 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 7, iters: 173488, time: 0.538, data: 0.000) G_L1: 14.211 G_L1_ABSOLUTE: 2.241 G_L1_RELATIVE: 11.970 G_Regularizer: 0.000 validation_error: 21.401 +(epoch: 7, iters: 175488, time: 0.543, data: 0.000) G_L1: 15.308 G_L1_ABSOLUTE: 2.838 G_L1_RELATIVE: 12.471 G_Regularizer: 0.000 validation_error: 21.791 +(epoch: 7, iters: 177488, time: 0.548, data: 0.000) G_L1: 18.719 G_L1_ABSOLUTE: 3.481 G_L1_RELATIVE: 15.238 G_Regularizer: 0.000 validation_error: 21.239 +(epoch: 7, iters: 179488, time: 0.539, data: 0.000) G_L1: 16.404 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 13.637 G_Regularizer: 0.000 validation_error: 20.776 +(epoch: 7, iters: 181488, time: 0.557, data: 0.000) G_L1: 14.715 G_L1_ABSOLUTE: 2.865 G_L1_RELATIVE: 11.850 G_Regularizer: 0.000 validation_error: 20.639 +(epoch: 7, iters: 183488, time: 0.555, data: 0.000) G_L1: 13.604 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 11.151 G_Regularizer: 0.000 validation_error: 21.630 +(epoch: 7, iters: 185488, time: 0.539, data: 0.000) G_L1: 15.135 G_L1_ABSOLUTE: 2.842 G_L1_RELATIVE: 12.293 G_Regularizer: 0.000 validation_error: 20.922 +(epoch: 7, iters: 187488, time: 0.546, data: 0.000) G_L1: 16.448 G_L1_ABSOLUTE: 2.936 G_L1_RELATIVE: 13.512 G_Regularizer: 0.000 validation_error: 20.867 +(epoch: 7, iters: 189488, time: 0.542, data: 0.000) G_L1: 16.333 G_L1_ABSOLUTE: 2.936 G_L1_RELATIVE: 13.397 G_Regularizer: 0.000 validation_error: 20.362 +(epoch: 7, iters: 191488, time: 0.547, data: 0.000) G_L1: 16.083 G_L1_ABSOLUTE: 2.358 G_L1_RELATIVE: 13.725 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 7, iters: 193488, time: 0.547, data: 0.000) G_L1: 15.457 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 12.549 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 7, iters: 195488, time: 0.543, data: 0.000) G_L1: 15.379 G_L1_ABSOLUTE: 2.272 G_L1_RELATIVE: 13.108 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 7, iters: 197488, time: 0.543, data: 0.000) G_L1: 13.654 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 10.819 G_Regularizer: 0.000 validation_error: 21.407 +(epoch: 7, iters: 199488, time: 0.548, data: 0.000) G_L1: 17.973 G_L1_ABSOLUTE: 3.258 G_L1_RELATIVE: 14.716 G_Regularizer: 0.000 validation_error: 19.863 +(epoch: 7, iters: 201488, time: 0.540, data: 0.000) G_L1: 15.333 G_L1_ABSOLUTE: 2.806 G_L1_RELATIVE: 12.528 G_Regularizer: 0.000 validation_error: 21.430 +(epoch: 7, iters: 203488, time: 0.534, data: 0.000) G_L1: 15.083 G_L1_ABSOLUTE: 2.636 G_L1_RELATIVE: 12.447 G_Regularizer: 0.000 validation_error: 21.256 +(epoch: 7, iters: 205488, time: 0.534, data: 0.000) G_L1: 14.415 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 11.507 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 7, iters: 207488, time: 0.551, data: 0.000) G_L1: 15.934 G_L1_ABSOLUTE: 2.718 G_L1_RELATIVE: 13.216 G_Regularizer: 0.000 validation_error: 20.611 +(epoch: 7, iters: 209488, time: 0.536, data: 0.000) G_L1: 17.534 G_L1_ABSOLUTE: 2.711 G_L1_RELATIVE: 14.822 G_Regularizer: 0.000 validation_error: 19.974 +(epoch: 7, iters: 211488, time: 0.542, data: 0.000) G_L1: 15.730 G_L1_ABSOLUTE: 2.859 G_L1_RELATIVE: 12.872 G_Regularizer: 0.000 validation_error: 20.745 +(epoch: 7, iters: 213488, time: 0.952, data: 0.000) G_L1: 15.402 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 12.784 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 7, iters: 215488, time: 0.950, data: 0.000) G_L1: 17.083 G_L1_ABSOLUTE: 2.329 G_L1_RELATIVE: 14.754 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 7, iters: 217488, time: 0.952, data: 0.000) G_L1: 17.356 G_L1_ABSOLUTE: 3.062 G_L1_RELATIVE: 14.293 G_Regularizer: 0.000 validation_error: 20.979 +(epoch: 7, iters: 219488, time: 0.945, data: 0.000) G_L1: 18.794 G_L1_ABSOLUTE: 3.116 G_L1_RELATIVE: 15.678 G_Regularizer: 0.000 validation_error: 21.487 +(epoch: 7, iters: 221488, time: 0.946, data: 0.000) G_L1: 16.169 G_L1_ABSOLUTE: 2.854 G_L1_RELATIVE: 13.315 G_Regularizer: 0.000 validation_error: 20.513 +(epoch: 7, iters: 223488, time: 0.952, data: 0.000) G_L1: 16.591 G_L1_ABSOLUTE: 2.643 G_L1_RELATIVE: 13.949 G_Regularizer: 0.000 validation_error: 21.398 +(epoch: 7, iters: 225488, time: 0.950, data: 0.000) G_L1: 13.806 G_L1_ABSOLUTE: 2.444 G_L1_RELATIVE: 11.361 G_Regularizer: 0.000 validation_error: 21.101 +(epoch: 7, iters: 227488, time: 0.950, data: 0.000) G_L1: 15.240 G_L1_ABSOLUTE: 3.177 G_L1_RELATIVE: 12.062 G_Regularizer: 0.000 validation_error: 21.106 +(epoch: 7, iters: 229488, time: 0.959, data: 0.000) G_L1: 16.177 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 13.365 G_Regularizer: 0.000 validation_error: 20.564 +(epoch: 7, iters: 231488, time: 0.956, data: 0.000) G_L1: 21.500 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 18.707 G_Regularizer: 0.000 validation_error: 21.081 +(epoch: 7, iters: 233488, time: 0.960, data: 0.000) G_L1: 16.471 G_L1_ABSOLUTE: 3.149 G_L1_RELATIVE: 13.322 G_Regularizer: 0.000 validation_error: 20.582 +(epoch: 7, iters: 235488, time: 0.948, data: 0.000) G_L1: 14.604 G_L1_ABSOLUTE: 2.768 G_L1_RELATIVE: 11.835 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 7, iters: 237488, time: 0.962, data: 0.000) G_L1: 17.029 G_L1_ABSOLUTE: 3.118 G_L1_RELATIVE: 13.911 G_Regularizer: 0.000 validation_error: 20.459 +(epoch: 7, iters: 239488, time: 0.957, data: 0.000) G_L1: 14.547 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 11.905 G_Regularizer: 0.000 validation_error: 20.421 +(epoch: 7, iters: 241488, time: 0.966, data: 0.000) G_L1: 16.705 G_L1_ABSOLUTE: 2.906 G_L1_RELATIVE: 13.800 G_Regularizer: 0.000 validation_error: 21.885 +(epoch: 7, iters: 243488, time: 0.954, data: 0.000) G_L1: 14.955 G_L1_ABSOLUTE: 2.868 G_L1_RELATIVE: 12.088 G_Regularizer: 0.000 validation_error: 20.867 +(epoch: 7, iters: 245488, time: 0.964, data: 0.000) G_L1: 17.937 G_L1_ABSOLUTE: 3.135 G_L1_RELATIVE: 14.803 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 7, iters: 247488, time: 0.961, data: 0.000) G_L1: 14.924 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 12.354 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 7, iters: 249488, time: 0.961, data: 0.000) G_L1: 16.443 G_L1_ABSOLUTE: 3.196 G_L1_RELATIVE: 13.246 G_Regularizer: 0.000 validation_error: 20.158 +(epoch: 7, iters: 251488, time: 0.964, data: 0.004) G_L1: 13.191 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 10.580 G_Regularizer: 0.000 validation_error: 21.441 +(epoch: 7, iters: 253488, time: 0.967, data: 0.000) G_L1: 16.307 G_L1_ABSOLUTE: 3.398 G_L1_RELATIVE: 12.909 G_Regularizer: 0.000 validation_error: 19.433 +(epoch: 7, iters: 255488, time: 0.960, data: 0.000) G_L1: 13.085 G_L1_ABSOLUTE: 3.145 G_L1_RELATIVE: 9.940 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 7, iters: 257488, time: 0.964, data: 0.000) G_L1: 15.551 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 12.759 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 7, iters: 259488, time: 0.962, data: 0.000) G_L1: 13.729 G_L1_ABSOLUTE: 3.238 G_L1_RELATIVE: 10.491 G_Regularizer: 0.000 validation_error: 20.112 +(epoch: 7, iters: 261488, time: 0.961, data: 0.000) G_L1: 14.181 G_L1_ABSOLUTE: 2.983 G_L1_RELATIVE: 11.198 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 7, iters: 263488, time: 0.964, data: 0.000) G_L1: 16.138 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 13.459 G_Regularizer: 0.000 validation_error: 20.066 +(epoch: 7, iters: 265488, time: 0.963, data: 0.000) G_L1: 18.784 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 15.974 G_Regularizer: 0.000 validation_error: 21.068 +(epoch: 7, iters: 267488, time: 0.960, data: 0.000) G_L1: 14.018 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 11.220 G_Regularizer: 0.000 validation_error: 20.661 +(epoch: 7, iters: 269488, time: 0.962, data: 0.000) G_L1: 20.672 G_L1_ABSOLUTE: 3.066 G_L1_RELATIVE: 17.606 G_Regularizer: 0.000 validation_error: 21.336 +(epoch: 7, iters: 271488, time: 0.966, data: 0.000) G_L1: 16.259 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 13.370 G_Regularizer: 0.000 validation_error: 20.412 +(epoch: 7, iters: 273488, time: 0.957, data: 0.000) G_L1: 15.082 G_L1_ABSOLUTE: 3.035 G_L1_RELATIVE: 12.047 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 7, iters: 275488, time: 0.966, data: 0.000) G_L1: 14.281 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 11.689 G_Regularizer: 0.000 validation_error: 21.335 +(epoch: 7, iters: 277488, time: 0.968, data: 0.000) G_L1: 16.842 G_L1_ABSOLUTE: 3.163 G_L1_RELATIVE: 13.679 G_Regularizer: 0.000 validation_error: 20.936 +(epoch: 7, iters: 279488, time: 0.962, data: 0.000) G_L1: 17.019 G_L1_ABSOLUTE: 2.404 G_L1_RELATIVE: 14.615 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 7, iters: 281488, time: 0.969, data: 0.000) G_L1: 19.542 G_L1_ABSOLUTE: 2.802 G_L1_RELATIVE: 16.740 G_Regularizer: 0.000 validation_error: 20.332 +(epoch: 7, iters: 283488, time: 0.963, data: 0.000) G_L1: 13.776 G_L1_ABSOLUTE: 2.583 G_L1_RELATIVE: 11.193 G_Regularizer: 0.000 validation_error: 20.893 +(epoch: 7, iters: 285488, time: 0.967, data: 0.000) G_L1: 13.699 G_L1_ABSOLUTE: 2.873 G_L1_RELATIVE: 10.826 G_Regularizer: 0.000 validation_error: 20.716 +(epoch: 7, iters: 287488, time: 0.970, data: 0.000) G_L1: 14.708 G_L1_ABSOLUTE: 3.108 G_L1_RELATIVE: 11.600 G_Regularizer: 0.000 validation_error: 20.441 +(epoch: 7, iters: 289488, time: 0.969, data: 0.000) G_L1: 15.487 G_L1_ABSOLUTE: 3.355 G_L1_RELATIVE: 12.132 G_Regularizer: 0.000 validation_error: 21.359 +(epoch: 7, iters: 291488, time: 0.960, data: 0.000) G_L1: 16.891 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 14.379 G_Regularizer: 0.000 validation_error: 20.388 +(epoch: 7, iters: 293488, time: 0.960, data: 0.000) G_L1: 15.866 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 13.009 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 7, iters: 295488, time: 0.968, data: 0.000) G_L1: 14.271 G_L1_ABSOLUTE: 2.465 G_L1_RELATIVE: 11.807 G_Regularizer: 0.000 validation_error: 20.226 +(epoch: 7, iters: 297488, time: 0.969, data: 0.000) G_L1: 16.518 G_L1_ABSOLUTE: 3.038 G_L1_RELATIVE: 13.480 G_Regularizer: 0.000 validation_error: 20.260 +(epoch: 7, iters: 299488, time: 0.968, data: 0.000) G_L1: 18.973 G_L1_ABSOLUTE: 3.642 G_L1_RELATIVE: 15.332 G_Regularizer: 0.000 validation_error: 20.323 +(epoch: 7, iters: 301488, time: 0.965, data: 0.000) G_L1: 16.323 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 13.565 G_Regularizer: 0.000 validation_error: 20.480 +(epoch: 8, iters: 736, time: 0.967, data: 0.000) G_L1: 13.612 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 11.073 G_Regularizer: 0.000 validation_error: 20.894 +(epoch: 8, iters: 2736, time: 0.966, data: 0.000) G_L1: 14.812 G_L1_ABSOLUTE: 2.783 G_L1_RELATIVE: 12.029 G_Regularizer: 0.000 validation_error: 20.003 +(epoch: 8, iters: 4736, time: 0.962, data: 0.000) G_L1: 13.494 G_L1_ABSOLUTE: 2.641 G_L1_RELATIVE: 10.853 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 8, iters: 6736, time: 0.968, data: 0.000) G_L1: 13.571 G_L1_ABSOLUTE: 2.840 G_L1_RELATIVE: 10.731 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 8, iters: 8736, time: 0.963, data: 0.000) G_L1: 17.155 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 14.275 G_Regularizer: 0.000 validation_error: 19.969 +(epoch: 8, iters: 10736, time: 0.967, data: 0.000) G_L1: 17.185 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 14.659 G_Regularizer: 0.000 validation_error: 20.665 +(epoch: 8, iters: 12736, time: 0.963, data: 0.000) G_L1: 15.634 G_L1_ABSOLUTE: 3.076 G_L1_RELATIVE: 12.558 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 8, iters: 14736, time: 0.965, data: 0.000) G_L1: 17.600 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 14.744 G_Regularizer: 0.000 validation_error: 21.009 +(epoch: 8, iters: 16736, time: 0.964, data: 0.000) G_L1: 14.371 G_L1_ABSOLUTE: 2.717 G_L1_RELATIVE: 11.654 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 8, iters: 18736, time: 0.968, data: 0.000) G_L1: 15.998 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 13.551 G_Regularizer: 0.000 validation_error: 22.041 +(epoch: 8, iters: 20736, time: 0.967, data: 0.000) G_L1: 16.580 G_L1_ABSOLUTE: 3.075 G_L1_RELATIVE: 13.505 G_Regularizer: 0.000 validation_error: 20.944 +(epoch: 8, iters: 22736, time: 0.967, data: 0.000) G_L1: 15.155 G_L1_ABSOLUTE: 2.605 G_L1_RELATIVE: 12.550 G_Regularizer: 0.000 validation_error: 20.506 +(epoch: 8, iters: 24736, time: 0.966, data: 0.001) G_L1: 19.403 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 16.629 G_Regularizer: 0.000 validation_error: 20.771 +(epoch: 8, iters: 26736, time: 0.969, data: 0.000) G_L1: 12.731 G_L1_ABSOLUTE: 2.154 G_L1_RELATIVE: 10.578 G_Regularizer: 0.000 validation_error: 21.552 +(epoch: 8, iters: 28736, time: 0.965, data: 0.002) G_L1: 14.734 G_L1_ABSOLUTE: 3.341 G_L1_RELATIVE: 11.393 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 8, iters: 30736, time: 0.961, data: 0.000) G_L1: 15.440 G_L1_ABSOLUTE: 3.515 G_L1_RELATIVE: 11.925 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 8, iters: 32736, time: 0.965, data: 0.000) G_L1: 14.674 G_L1_ABSOLUTE: 2.771 G_L1_RELATIVE: 11.903 G_Regularizer: 0.000 validation_error: 21.198 +(epoch: 8, iters: 34736, time: 0.955, data: 0.000) G_L1: 16.497 G_L1_ABSOLUTE: 2.688 G_L1_RELATIVE: 13.809 G_Regularizer: 0.000 validation_error: 21.251 +(epoch: 8, iters: 36736, time: 0.957, data: 0.000) G_L1: 13.661 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 11.315 G_Regularizer: 0.000 validation_error: 20.202 +(epoch: 8, iters: 38736, time: 0.966, data: 0.000) G_L1: 17.127 G_L1_ABSOLUTE: 3.039 G_L1_RELATIVE: 14.088 G_Regularizer: 0.000 validation_error: 20.592 +(epoch: 8, iters: 40736, time: 0.960, data: 0.000) G_L1: 13.988 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 11.569 G_Regularizer: 0.000 validation_error: 20.324 +(epoch: 8, iters: 42736, time: 0.967, data: 0.000) G_L1: 14.757 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 12.184 G_Regularizer: 0.000 validation_error: 20.436 +(epoch: 8, iters: 44736, time: 0.962, data: 0.000) G_L1: 15.931 G_L1_ABSOLUTE: 3.102 G_L1_RELATIVE: 12.828 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 8, iters: 46736, time: 0.968, data: 0.000) G_L1: 15.296 G_L1_ABSOLUTE: 3.449 G_L1_RELATIVE: 11.846 G_Regularizer: 0.000 validation_error: 20.290 +(epoch: 8, iters: 48736, time: 0.962, data: 0.000) G_L1: 15.891 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 13.231 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 8, iters: 50736, time: 0.966, data: 0.000) G_L1: 16.759 G_L1_ABSOLUTE: 2.961 G_L1_RELATIVE: 13.798 G_Regularizer: 0.000 validation_error: 20.594 +(epoch: 8, iters: 52736, time: 0.964, data: 0.000) G_L1: 14.306 G_L1_ABSOLUTE: 2.745 G_L1_RELATIVE: 11.561 G_Regularizer: 0.000 validation_error: 20.297 +(epoch: 8, iters: 54736, time: 0.963, data: 0.000) G_L1: 16.300 G_L1_ABSOLUTE: 3.089 G_L1_RELATIVE: 13.211 G_Regularizer: 0.000 validation_error: 20.478 +(epoch: 8, iters: 56736, time: 0.969, data: 0.000) G_L1: 12.454 G_L1_ABSOLUTE: 2.156 G_L1_RELATIVE: 10.299 G_Regularizer: 0.000 validation_error: 20.183 +(epoch: 8, iters: 58736, time: 0.963, data: 0.000) G_L1: 18.585 G_L1_ABSOLUTE: 2.876 G_L1_RELATIVE: 15.710 G_Regularizer: 0.000 validation_error: 21.060 +(epoch: 8, iters: 60736, time: 0.970, data: 0.000) G_L1: 13.725 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 10.969 G_Regularizer: 0.000 validation_error: 21.520 +(epoch: 8, iters: 62736, time: 0.965, data: 0.000) G_L1: 17.687 G_L1_ABSOLUTE: 3.004 G_L1_RELATIVE: 14.683 G_Regularizer: 0.000 validation_error: 20.616 +(epoch: 8, iters: 64736, time: 0.964, data: 0.000) G_L1: 15.836 G_L1_ABSOLUTE: 3.160 G_L1_RELATIVE: 12.676 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 8, iters: 66736, time: 0.972, data: 0.000) G_L1: 15.817 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 13.297 G_Regularizer: 0.000 validation_error: 20.188 +(epoch: 8, iters: 68736, time: 0.966, data: 0.000) G_L1: 13.620 G_L1_ABSOLUTE: 2.895 G_L1_RELATIVE: 10.725 G_Regularizer: 0.000 validation_error: 20.218 +(epoch: 8, iters: 70736, time: 0.967, data: 0.000) G_L1: 16.255 G_L1_ABSOLUTE: 3.095 G_L1_RELATIVE: 13.160 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 8, iters: 72736, time: 0.966, data: 0.000) G_L1: 19.138 G_L1_ABSOLUTE: 2.639 G_L1_RELATIVE: 16.500 G_Regularizer: 0.000 validation_error: 20.409 +(epoch: 8, iters: 74736, time: 0.965, data: 0.000) G_L1: 15.723 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 13.448 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 8, iters: 76736, time: 0.965, data: 0.000) G_L1: 13.382 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 10.736 G_Regularizer: 0.000 validation_error: 21.307 +(epoch: 8, iters: 78736, time: 0.964, data: 0.000) G_L1: 14.600 G_L1_ABSOLUTE: 2.252 G_L1_RELATIVE: 12.348 G_Regularizer: 0.000 validation_error: 21.214 +(epoch: 8, iters: 80736, time: 0.965, data: 0.000) G_L1: 17.443 G_L1_ABSOLUTE: 3.210 G_L1_RELATIVE: 14.232 G_Regularizer: 0.000 validation_error: 20.518 +(epoch: 8, iters: 82736, time: 0.957, data: 0.000) G_L1: 17.354 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 14.491 G_Regularizer: 0.000 validation_error: 21.371 +(epoch: 8, iters: 84736, time: 0.972, data: 0.000) G_L1: 14.949 G_L1_ABSOLUTE: 3.037 G_L1_RELATIVE: 11.912 G_Regularizer: 0.000 validation_error: 20.509 +(epoch: 8, iters: 86736, time: 0.963, data: 0.000) G_L1: 15.421 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 12.898 G_Regularizer: 0.000 validation_error: 20.156 +(epoch: 8, iters: 88736, time: 0.966, data: 0.000) G_L1: 16.252 G_L1_ABSOLUTE: 2.960 G_L1_RELATIVE: 13.292 G_Regularizer: 0.000 validation_error: 20.066 +(epoch: 8, iters: 90736, time: 0.711, data: 0.000) G_L1: 15.504 G_L1_ABSOLUTE: 2.514 G_L1_RELATIVE: 12.989 G_Regularizer: 0.000 validation_error: 19.965 +(epoch: 8, iters: 92736, time: 0.557, data: 0.000) G_L1: 16.518 G_L1_ABSOLUTE: 2.833 G_L1_RELATIVE: 13.686 G_Regularizer: 0.000 validation_error: 19.995 +(epoch: 8, iters: 94736, time: 0.573, data: 0.000) G_L1: 18.142 G_L1_ABSOLUTE: 2.728 G_L1_RELATIVE: 15.414 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 8, iters: 96736, time: 0.542, data: 0.000) G_L1: 14.603 G_L1_ABSOLUTE: 2.965 G_L1_RELATIVE: 11.637 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 8, iters: 98736, time: 0.551, data: 0.000) G_L1: 17.956 G_L1_ABSOLUTE: 2.862 G_L1_RELATIVE: 15.095 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 8, iters: 100736, time: 0.543, data: 0.000) G_L1: 17.222 G_L1_ABSOLUTE: 2.848 G_L1_RELATIVE: 14.375 G_Regularizer: 0.000 validation_error: 21.441 +(epoch: 8, iters: 102736, time: 0.547, data: 0.000) G_L1: 15.981 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 13.409 G_Regularizer: 0.000 validation_error: 21.885 +(epoch: 8, iters: 104736, time: 0.542, data: 0.000) G_L1: 14.450 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 11.900 G_Regularizer: 0.000 validation_error: 20.391 +(epoch: 8, iters: 106736, time: 0.540, data: 0.000) G_L1: 13.132 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 10.591 G_Regularizer: 0.000 validation_error: 20.402 +(epoch: 8, iters: 108736, time: 0.544, data: 0.000) G_L1: 16.296 G_L1_ABSOLUTE: 3.268 G_L1_RELATIVE: 13.028 G_Regularizer: 0.000 validation_error: 20.400 +(epoch: 8, iters: 110736, time: 0.548, data: 0.000) G_L1: 17.367 G_L1_ABSOLUTE: 3.012 G_L1_RELATIVE: 14.355 G_Regularizer: 0.000 validation_error: 21.134 +(epoch: 8, iters: 112736, time: 0.550, data: 0.000) G_L1: 14.403 G_L1_ABSOLUTE: 2.986 G_L1_RELATIVE: 11.416 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 8, iters: 114736, time: 0.540, data: 0.000) G_L1: 15.408 G_L1_ABSOLUTE: 3.190 G_L1_RELATIVE: 12.218 G_Regularizer: 0.000 validation_error: 20.336 +(epoch: 8, iters: 116736, time: 0.551, data: 0.000) G_L1: 17.879 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 15.158 G_Regularizer: 0.000 validation_error: 20.932 +(epoch: 8, iters: 118736, time: 0.542, data: 0.000) G_L1: 15.972 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 13.382 G_Regularizer: 0.000 validation_error: 20.459 +(epoch: 8, iters: 120736, time: 0.548, data: 0.000) G_L1: 14.268 G_L1_ABSOLUTE: 2.862 G_L1_RELATIVE: 11.406 G_Regularizer: 0.000 validation_error: 20.406 +(epoch: 8, iters: 122736, time: 0.542, data: 0.000) G_L1: 14.050 G_L1_ABSOLUTE: 2.529 G_L1_RELATIVE: 11.521 G_Regularizer: 0.000 validation_error: 20.415 +(epoch: 8, iters: 124736, time: 0.547, data: 0.000) G_L1: 15.425 G_L1_ABSOLUTE: 2.988 G_L1_RELATIVE: 12.437 G_Regularizer: 0.000 validation_error: 20.573 +(epoch: 8, iters: 126736, time: 0.553, data: 0.000) G_L1: 16.021 G_L1_ABSOLUTE: 3.221 G_L1_RELATIVE: 12.800 G_Regularizer: 0.000 validation_error: 20.475 +(epoch: 8, iters: 128736, time: 0.549, data: 0.000) G_L1: 16.918 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 14.239 G_Regularizer: 0.000 validation_error: 20.598 +(epoch: 8, iters: 130736, time: 0.552, data: 0.000) G_L1: 16.067 G_L1_ABSOLUTE: 2.993 G_L1_RELATIVE: 13.075 G_Regularizer: 0.000 validation_error: 20.427 +(epoch: 8, iters: 132736, time: 0.547, data: 0.000) G_L1: 14.398 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 11.508 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 8, iters: 134736, time: 0.545, data: 0.000) G_L1: 16.672 G_L1_ABSOLUTE: 3.076 G_L1_RELATIVE: 13.596 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 8, iters: 136736, time: 0.546, data: 0.000) G_L1: 16.482 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 13.605 G_Regularizer: 0.000 validation_error: 20.708 +(epoch: 8, iters: 138736, time: 0.550, data: 0.000) G_L1: 17.175 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 14.604 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 8, iters: 140736, time: 0.546, data: 0.000) G_L1: 13.729 G_L1_ABSOLUTE: 2.359 G_L1_RELATIVE: 11.371 G_Regularizer: 0.000 validation_error: 21.048 +(epoch: 8, iters: 142736, time: 0.545, data: 0.000) G_L1: 18.831 G_L1_ABSOLUTE: 3.262 G_L1_RELATIVE: 15.570 G_Regularizer: 0.000 validation_error: 19.818 +(epoch: 8, iters: 144736, time: 0.544, data: 0.000) G_L1: 16.613 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 13.978 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 8, iters: 146736, time: 0.545, data: 0.000) G_L1: 15.680 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 13.235 G_Regularizer: 0.000 validation_error: 20.455 +(epoch: 8, iters: 148736, time: 0.549, data: 0.000) G_L1: 16.197 G_L1_ABSOLUTE: 3.263 G_L1_RELATIVE: 12.934 G_Regularizer: 0.000 validation_error: 20.628 +(epoch: 8, iters: 150736, time: 0.544, data: 0.000) G_L1: 19.209 G_L1_ABSOLUTE: 3.083 G_L1_RELATIVE: 16.126 G_Regularizer: 0.000 validation_error: 20.394 +(epoch: 8, iters: 152736, time: 0.536, data: 0.000) G_L1: 14.369 G_L1_ABSOLUTE: 2.768 G_L1_RELATIVE: 11.601 G_Regularizer: 0.000 validation_error: 20.639 +(epoch: 8, iters: 154736, time: 0.547, data: 0.000) G_L1: 15.404 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 12.741 G_Regularizer: 0.000 validation_error: 20.092 +(epoch: 8, iters: 156736, time: 0.551, data: 0.000) G_L1: 15.602 G_L1_ABSOLUTE: 3.300 G_L1_RELATIVE: 12.302 G_Regularizer: 0.000 validation_error: 20.547 +(epoch: 8, iters: 158736, time: 0.543, data: 0.000) G_L1: 16.330 G_L1_ABSOLUTE: 2.772 G_L1_RELATIVE: 13.559 G_Regularizer: 0.000 validation_error: 21.238 +(epoch: 8, iters: 160736, time: 0.545, data: 0.000) G_L1: 19.179 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 16.437 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 8, iters: 162736, time: 0.551, data: 0.000) G_L1: 15.590 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 13.191 G_Regularizer: 0.000 validation_error: 20.494 +(epoch: 8, iters: 164736, time: 0.542, data: 0.000) G_L1: 14.680 G_L1_ABSOLUTE: 2.391 G_L1_RELATIVE: 12.289 G_Regularizer: 0.000 validation_error: 21.076 +(epoch: 8, iters: 166736, time: 0.539, data: 0.000) G_L1: 18.449 G_L1_ABSOLUTE: 3.356 G_L1_RELATIVE: 15.092 G_Regularizer: 0.000 validation_error: 20.249 +(epoch: 8, iters: 168736, time: 0.543, data: 0.000) G_L1: 17.683 G_L1_ABSOLUTE: 3.361 G_L1_RELATIVE: 14.322 G_Regularizer: 0.000 validation_error: 20.285 +(epoch: 8, iters: 170736, time: 0.554, data: 0.000) G_L1: 13.039 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 10.485 G_Regularizer: 0.000 validation_error: 19.896 +(epoch: 8, iters: 172736, time: 0.555, data: 0.000) G_L1: 13.817 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 11.313 G_Regularizer: 0.000 validation_error: 21.357 +(epoch: 8, iters: 174736, time: 0.555, data: 0.001) G_L1: 12.515 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 10.103 G_Regularizer: 0.000 validation_error: 20.604 +(epoch: 8, iters: 176736, time: 0.545, data: 0.000) G_L1: 15.166 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 12.509 G_Regularizer: 0.000 validation_error: 21.538 +(epoch: 8, iters: 178736, time: 0.549, data: 0.000) G_L1: 18.090 G_L1_ABSOLUTE: 2.695 G_L1_RELATIVE: 15.395 G_Regularizer: 0.000 validation_error: 21.251 +(epoch: 8, iters: 180736, time: 0.544, data: 0.000) G_L1: 16.903 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 14.285 G_Regularizer: 0.000 validation_error: 21.493 +(epoch: 8, iters: 182736, time: 0.537, data: 0.000) G_L1: 13.108 G_L1_ABSOLUTE: 2.411 G_L1_RELATIVE: 10.696 G_Regularizer: 0.000 validation_error: 19.816 +(epoch: 8, iters: 184736, time: 0.539, data: 0.000) G_L1: 18.393 G_L1_ABSOLUTE: 3.445 G_L1_RELATIVE: 14.949 G_Regularizer: 0.000 validation_error: 20.791 +(epoch: 8, iters: 186736, time: 0.549, data: 0.000) G_L1: 17.608 G_L1_ABSOLUTE: 2.939 G_L1_RELATIVE: 14.669 G_Regularizer: 0.000 validation_error: 20.057 +(epoch: 8, iters: 188736, time: 0.549, data: 0.000) G_L1: 15.536 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 12.967 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 8, iters: 190736, time: 0.543, data: 0.000) G_L1: 15.097 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 12.843 G_Regularizer: 0.000 validation_error: 21.409 +(epoch: 8, iters: 192736, time: 0.543, data: 0.000) G_L1: 15.143 G_L1_ABSOLUTE: 2.852 G_L1_RELATIVE: 12.292 G_Regularizer: 0.000 validation_error: 20.420 +(epoch: 8, iters: 194736, time: 0.546, data: 0.000) G_L1: 15.580 G_L1_ABSOLUTE: 2.916 G_L1_RELATIVE: 12.664 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 8, iters: 196736, time: 0.546, data: 0.000) G_L1: 12.428 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 9.913 G_Regularizer: 0.000 validation_error: 21.258 +(epoch: 8, iters: 198736, time: 0.545, data: 0.000) G_L1: 13.673 G_L1_ABSOLUTE: 3.224 G_L1_RELATIVE: 10.450 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 8, iters: 200736, time: 0.553, data: 0.000) G_L1: 15.773 G_L1_ABSOLUTE: 4.077 G_L1_RELATIVE: 11.695 G_Regularizer: 0.000 validation_error: 20.588 +(epoch: 8, iters: 202736, time: 0.541, data: 0.000) G_L1: 15.781 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 13.218 G_Regularizer: 0.000 validation_error: 20.315 +(epoch: 8, iters: 204736, time: 0.543, data: 0.000) G_L1: 15.969 G_L1_ABSOLUTE: 2.423 G_L1_RELATIVE: 13.545 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 8, iters: 206736, time: 0.547, data: 0.000) G_L1: 14.095 G_L1_ABSOLUTE: 2.471 G_L1_RELATIVE: 11.624 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 8, iters: 208736, time: 0.553, data: 0.000) G_L1: 17.238 G_L1_ABSOLUTE: 3.357 G_L1_RELATIVE: 13.881 G_Regularizer: 0.000 validation_error: 20.240 +(epoch: 8, iters: 210736, time: 0.542, data: 0.000) G_L1: 13.716 G_L1_ABSOLUTE: 2.307 G_L1_RELATIVE: 11.408 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 8, iters: 212736, time: 0.544, data: 0.000) G_L1: 15.258 G_L1_ABSOLUTE: 2.969 G_L1_RELATIVE: 12.289 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 8, iters: 214736, time: 0.537, data: 0.000) G_L1: 15.738 G_L1_ABSOLUTE: 2.545 G_L1_RELATIVE: 13.193 G_Regularizer: 0.000 validation_error: 20.583 +(epoch: 8, iters: 216736, time: 0.546, data: 0.000) G_L1: 13.430 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 11.028 G_Regularizer: 0.000 validation_error: 20.533 +(epoch: 8, iters: 218736, time: 0.950, data: 0.000) G_L1: 18.152 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 15.502 G_Regularizer: 0.000 validation_error: 20.148 +(epoch: 8, iters: 220736, time: 0.955, data: 0.000) G_L1: 15.518 G_L1_ABSOLUTE: 2.898 G_L1_RELATIVE: 12.620 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 8, iters: 222736, time: 0.950, data: 0.000) G_L1: 10.679 G_L1_ABSOLUTE: 2.643 G_L1_RELATIVE: 8.035 G_Regularizer: 0.000 validation_error: 21.310 +(epoch: 8, iters: 224736, time: 0.955, data: 0.000) G_L1: 18.038 G_L1_ABSOLUTE: 2.934 G_L1_RELATIVE: 15.104 G_Regularizer: 0.000 validation_error: 21.303 +(epoch: 8, iters: 226736, time: 0.954, data: 0.000) G_L1: 17.917 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 14.966 G_Regularizer: 0.000 validation_error: 21.494 +(epoch: 8, iters: 228736, time: 0.953, data: 0.000) G_L1: 16.140 G_L1_ABSOLUTE: 2.838 G_L1_RELATIVE: 13.302 G_Regularizer: 0.000 validation_error: 20.266 +(epoch: 8, iters: 230736, time: 0.962, data: 0.000) G_L1: 14.914 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 12.236 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 8, iters: 232736, time: 0.965, data: 0.000) G_L1: 13.346 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 10.817 G_Regularizer: 0.000 validation_error: 21.059 +(epoch: 8, iters: 234736, time: 0.962, data: 0.000) G_L1: 17.994 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 15.359 G_Regularizer: 0.000 validation_error: 21.063 +(epoch: 8, iters: 236736, time: 0.962, data: 0.000) G_L1: 15.085 G_L1_ABSOLUTE: 3.021 G_L1_RELATIVE: 12.064 G_Regularizer: 0.000 validation_error: 19.728 +(epoch: 8, iters: 238736, time: 0.962, data: 0.000) G_L1: 15.655 G_L1_ABSOLUTE: 3.387 G_L1_RELATIVE: 12.268 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 8, iters: 240736, time: 0.964, data: 0.000) G_L1: 18.510 G_L1_ABSOLUTE: 3.149 G_L1_RELATIVE: 15.361 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 8, iters: 242736, time: 0.960, data: 0.000) G_L1: 13.298 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 10.779 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 8, iters: 244736, time: 0.958, data: 0.000) G_L1: 14.388 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 11.678 G_Regularizer: 0.000 validation_error: 20.631 +(epoch: 8, iters: 246736, time: 0.966, data: 0.000) G_L1: 14.198 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 11.488 G_Regularizer: 0.000 validation_error: 20.568 +(epoch: 8, iters: 248736, time: 0.965, data: 0.000) G_L1: 16.713 G_L1_ABSOLUTE: 3.385 G_L1_RELATIVE: 13.328 G_Regularizer: 0.000 validation_error: 20.020 +(epoch: 8, iters: 250736, time: 0.963, data: 0.000) G_L1: 14.918 G_L1_ABSOLUTE: 2.751 G_L1_RELATIVE: 12.167 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 8, iters: 252736, time: 0.962, data: 0.000) G_L1: 19.178 G_L1_ABSOLUTE: 2.752 G_L1_RELATIVE: 16.426 G_Regularizer: 0.000 validation_error: 20.505 +(epoch: 8, iters: 254736, time: 0.962, data: 0.000) G_L1: 12.395 G_L1_ABSOLUTE: 2.560 G_L1_RELATIVE: 9.835 G_Regularizer: 0.000 validation_error: 20.434 +(epoch: 8, iters: 256736, time: 0.964, data: 0.000) G_L1: 18.127 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 15.497 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 8, iters: 258736, time: 0.966, data: 0.000) G_L1: 13.785 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 11.148 G_Regularizer: 0.000 validation_error: 20.716 +(epoch: 8, iters: 260736, time: 0.960, data: 0.000) G_L1: 14.304 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 11.780 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 8, iters: 262736, time: 0.965, data: 0.000) G_L1: 14.036 G_L1_ABSOLUTE: 2.692 G_L1_RELATIVE: 11.344 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 8, iters: 264736, time: 0.965, data: 0.000) G_L1: 16.708 G_L1_ABSOLUTE: 2.776 G_L1_RELATIVE: 13.932 G_Regularizer: 0.000 validation_error: 20.542 +(epoch: 8, iters: 266736, time: 0.963, data: 0.000) G_L1: 15.014 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 12.426 G_Regularizer: 0.000 validation_error: 20.406 +(epoch: 8, iters: 268736, time: 0.968, data: 0.000) G_L1: 15.856 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 13.021 G_Regularizer: 0.000 validation_error: 21.142 +(epoch: 8, iters: 270736, time: 0.969, data: 0.000) G_L1: 17.382 G_L1_ABSOLUTE: 3.273 G_L1_RELATIVE: 14.110 G_Regularizer: 0.000 validation_error: 21.355 +(epoch: 8, iters: 272736, time: 0.963, data: 0.000) G_L1: 16.861 G_L1_ABSOLUTE: 3.114 G_L1_RELATIVE: 13.747 G_Regularizer: 0.000 validation_error: 20.128 +(epoch: 8, iters: 274736, time: 0.966, data: 0.000) G_L1: 15.006 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 12.360 G_Regularizer: 0.000 validation_error: 20.285 +(epoch: 8, iters: 276736, time: 0.958, data: 0.001) G_L1: 16.904 G_L1_ABSOLUTE: 2.578 G_L1_RELATIVE: 14.325 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 8, iters: 278736, time: 0.964, data: 0.000) G_L1: 18.011 G_L1_ABSOLUTE: 3.172 G_L1_RELATIVE: 14.839 G_Regularizer: 0.000 validation_error: 20.392 +(epoch: 8, iters: 280736, time: 0.970, data: 0.000) G_L1: 17.662 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 15.165 G_Regularizer: 0.000 validation_error: 20.215 +(epoch: 8, iters: 282736, time: 0.968, data: 0.000) G_L1: 14.436 G_L1_ABSOLUTE: 2.816 G_L1_RELATIVE: 11.621 G_Regularizer: 0.000 validation_error: 21.121 +(epoch: 8, iters: 284736, time: 0.971, data: 0.000) G_L1: 15.203 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 12.740 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 8, iters: 286736, time: 0.962, data: 0.000) G_L1: 16.904 G_L1_ABSOLUTE: 2.726 G_L1_RELATIVE: 14.178 G_Regularizer: 0.000 validation_error: 19.925 +(epoch: 8, iters: 288736, time: 0.964, data: 0.000) G_L1: 15.568 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 13.180 G_Regularizer: 0.000 validation_error: 20.339 +(epoch: 8, iters: 290736, time: 0.962, data: 0.000) G_L1: 17.917 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 15.082 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 8, iters: 292736, time: 0.973, data: 0.000) G_L1: 16.667 G_L1_ABSOLUTE: 3.062 G_L1_RELATIVE: 13.605 G_Regularizer: 0.000 validation_error: 20.607 +(epoch: 8, iters: 294736, time: 0.966, data: 0.000) G_L1: 18.075 G_L1_ABSOLUTE: 2.771 G_L1_RELATIVE: 15.304 G_Regularizer: 0.000 validation_error: 20.076 +(epoch: 8, iters: 296736, time: 0.968, data: 0.000) G_L1: 16.862 G_L1_ABSOLUTE: 2.793 G_L1_RELATIVE: 14.069 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 8, iters: 298736, time: 0.967, data: 0.000) G_L1: 17.603 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 14.895 G_Regularizer: 0.000 validation_error: 20.511 +(epoch: 8, iters: 300736, time: 0.967, data: 0.000) G_L1: 15.650 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 12.745 G_Regularizer: 0.000 validation_error: 20.432 +(epoch: 8, iters: 302736, time: 0.966, data: 0.000) G_L1: 15.968 G_L1_ABSOLUTE: 2.739 G_L1_RELATIVE: 13.229 G_Regularizer: 0.000 validation_error: 20.544 +(epoch: 9, iters: 1984, time: 0.960, data: 0.000) G_L1: 15.391 G_L1_ABSOLUTE: 3.043 G_L1_RELATIVE: 12.348 G_Regularizer: 0.000 validation_error: 20.588 +(epoch: 9, iters: 3984, time: 0.964, data: 0.000) G_L1: 15.321 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 12.854 G_Regularizer: 0.000 validation_error: 21.430 +(epoch: 9, iters: 5984, time: 0.969, data: 0.000) G_L1: 12.995 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 10.235 G_Regularizer: 0.000 validation_error: 20.382 +(epoch: 9, iters: 7984, time: 0.970, data: 0.000) G_L1: 17.092 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 14.458 G_Regularizer: 0.000 validation_error: 20.504 +(epoch: 9, iters: 9984, time: 0.970, data: 0.000) G_L1: 16.882 G_L1_ABSOLUTE: 2.956 G_L1_RELATIVE: 13.926 G_Regularizer: 0.000 validation_error: 20.484 +(epoch: 9, iters: 11984, time: 0.965, data: 0.000) G_L1: 22.417 G_L1_ABSOLUTE: 3.428 G_L1_RELATIVE: 18.989 G_Regularizer: 0.000 validation_error: 20.157 +(epoch: 9, iters: 13984, time: 0.967, data: 0.000) G_L1: 18.115 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 15.531 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 9, iters: 15984, time: 0.968, data: 0.000) G_L1: 17.238 G_L1_ABSOLUTE: 3.226 G_L1_RELATIVE: 14.013 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 9, iters: 17984, time: 0.966, data: 0.000) G_L1: 12.049 G_L1_ABSOLUTE: 2.225 G_L1_RELATIVE: 9.824 G_Regularizer: 0.000 validation_error: 20.956 +(epoch: 9, iters: 19984, time: 0.967, data: 0.000) G_L1: 14.266 G_L1_ABSOLUTE: 2.346 G_L1_RELATIVE: 11.920 G_Regularizer: 0.000 validation_error: 20.479 +(epoch: 9, iters: 21984, time: 0.969, data: 0.000) G_L1: 15.944 G_L1_ABSOLUTE: 3.077 G_L1_RELATIVE: 12.867 G_Regularizer: 0.000 validation_error: 20.566 +(epoch: 9, iters: 23984, time: 0.971, data: 0.000) G_L1: 19.269 G_L1_ABSOLUTE: 3.584 G_L1_RELATIVE: 15.684 G_Regularizer: 0.000 validation_error: 20.180 +(epoch: 9, iters: 25984, time: 0.968, data: 0.000) G_L1: 17.748 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 15.041 G_Regularizer: 0.000 validation_error: 21.080 +(epoch: 9, iters: 27984, time: 0.967, data: 0.000) G_L1: 16.678 G_L1_ABSOLUTE: 2.747 G_L1_RELATIVE: 13.931 G_Regularizer: 0.000 validation_error: 20.578 +(epoch: 9, iters: 29984, time: 0.958, data: 0.000) G_L1: 16.547 G_L1_ABSOLUTE: 3.203 G_L1_RELATIVE: 13.344 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 9, iters: 31984, time: 0.964, data: 0.000) G_L1: 13.849 G_L1_ABSOLUTE: 2.232 G_L1_RELATIVE: 11.617 G_Regularizer: 0.000 validation_error: 20.741 +(epoch: 9, iters: 33984, time: 0.968, data: 0.000) G_L1: 18.135 G_L1_ABSOLUTE: 3.049 G_L1_RELATIVE: 15.086 G_Regularizer: 0.000 validation_error: 20.022 +(epoch: 9, iters: 35984, time: 0.965, data: 0.000) G_L1: 12.726 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 10.320 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 9, iters: 37984, time: 0.964, data: 0.000) G_L1: 18.169 G_L1_ABSOLUTE: 3.230 G_L1_RELATIVE: 14.939 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 9, iters: 39984, time: 0.963, data: 0.000) G_L1: 17.010 G_L1_ABSOLUTE: 3.178 G_L1_RELATIVE: 13.832 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 9, iters: 41984, time: 0.969, data: 0.000) G_L1: 15.596 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 13.227 G_Regularizer: 0.000 validation_error: 19.750 +(epoch: 9, iters: 43984, time: 0.965, data: 0.000) G_L1: 17.123 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 14.542 G_Regularizer: 0.000 validation_error: 20.237 +(epoch: 9, iters: 45984, time: 0.965, data: 0.000) G_L1: 17.597 G_L1_ABSOLUTE: 3.007 G_L1_RELATIVE: 14.590 G_Regularizer: 0.000 validation_error: 20.175 +(epoch: 9, iters: 47984, time: 0.966, data: 0.000) G_L1: 14.714 G_L1_ABSOLUTE: 2.771 G_L1_RELATIVE: 11.943 G_Regularizer: 0.000 validation_error: 20.571 +(epoch: 9, iters: 49984, time: 0.963, data: 0.000) G_L1: 14.604 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 12.053 G_Regularizer: 0.000 validation_error: 21.180 +(epoch: 9, iters: 51984, time: 0.963, data: 0.000) G_L1: 14.205 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 11.536 G_Regularizer: 0.000 validation_error: 20.072 +(epoch: 9, iters: 53984, time: 0.965, data: 0.000) G_L1: 15.603 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 12.718 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 9, iters: 55984, time: 0.961, data: 0.000) G_L1: 18.314 G_L1_ABSOLUTE: 3.172 G_L1_RELATIVE: 15.142 G_Regularizer: 0.000 validation_error: 20.462 +(epoch: 9, iters: 57984, time: 0.965, data: 0.000) G_L1: 17.680 G_L1_ABSOLUTE: 2.689 G_L1_RELATIVE: 14.991 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 9, iters: 59984, time: 0.969, data: 0.000) G_L1: 15.235 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 12.552 G_Regularizer: 0.000 validation_error: 20.628 +(epoch: 9, iters: 61984, time: 0.967, data: 0.000) G_L1: 14.881 G_L1_ABSOLUTE: 2.555 G_L1_RELATIVE: 12.326 G_Regularizer: 0.000 validation_error: 20.616 +(epoch: 9, iters: 63984, time: 0.969, data: 0.000) G_L1: 14.579 G_L1_ABSOLUTE: 2.831 G_L1_RELATIVE: 11.748 G_Regularizer: 0.000 validation_error: 20.340 +(epoch: 9, iters: 65984, time: 0.969, data: 0.000) G_L1: 14.290 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 11.664 G_Regularizer: 0.000 validation_error: 20.917 +(epoch: 9, iters: 67984, time: 0.963, data: 0.000) G_L1: 15.652 G_L1_ABSOLUTE: 2.865 G_L1_RELATIVE: 12.787 G_Regularizer: 0.000 validation_error: 21.268 +(epoch: 9, iters: 69984, time: 0.962, data: 0.000) G_L1: 17.347 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 14.776 G_Regularizer: 0.000 validation_error: 20.386 +(epoch: 9, iters: 71984, time: 0.968, data: 0.000) G_L1: 17.116 G_L1_ABSOLUTE: 2.727 G_L1_RELATIVE: 14.389 G_Regularizer: 0.000 validation_error: 20.591 +(epoch: 9, iters: 73984, time: 0.967, data: 0.000) G_L1: 14.543 G_L1_ABSOLUTE: 3.130 G_L1_RELATIVE: 11.413 G_Regularizer: 0.000 validation_error: 20.029 +(epoch: 9, iters: 75984, time: 0.968, data: 0.000) G_L1: 17.660 G_L1_ABSOLUTE: 2.923 G_L1_RELATIVE: 14.737 G_Regularizer: 0.000 validation_error: 21.179 +(epoch: 9, iters: 77984, time: 0.966, data: 0.000) G_L1: 13.225 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 10.547 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 9, iters: 79984, time: 0.967, data: 0.000) G_L1: 15.474 G_L1_ABSOLUTE: 2.785 G_L1_RELATIVE: 12.689 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 9, iters: 81984, time: 0.964, data: 0.000) G_L1: 18.693 G_L1_ABSOLUTE: 2.860 G_L1_RELATIVE: 15.834 G_Regularizer: 0.000 validation_error: 20.523 +(epoch: 9, iters: 83984, time: 0.960, data: 0.000) G_L1: 17.049 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 14.408 G_Regularizer: 0.000 validation_error: 20.922 +(epoch: 9, iters: 85984, time: 0.959, data: 0.001) G_L1: 14.629 G_L1_ABSOLUTE: 2.826 G_L1_RELATIVE: 11.803 G_Regularizer: 0.000 validation_error: 20.564 +(epoch: 9, iters: 87984, time: 0.968, data: 0.001) G_L1: 16.568 G_L1_ABSOLUTE: 2.440 G_L1_RELATIVE: 14.128 G_Regularizer: 0.000 validation_error: 20.076 +(epoch: 9, iters: 89984, time: 0.969, data: 0.000) G_L1: 17.583 G_L1_ABSOLUTE: 2.981 G_L1_RELATIVE: 14.602 G_Regularizer: 0.000 validation_error: 21.361 +(epoch: 9, iters: 91984, time: 0.966, data: 0.000) G_L1: 17.062 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 14.321 G_Regularizer: 0.000 validation_error: 20.198 +(epoch: 9, iters: 93984, time: 0.965, data: 0.000) G_L1: 16.944 G_L1_ABSOLUTE: 2.690 G_L1_RELATIVE: 14.254 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 9, iters: 95984, time: 0.969, data: 0.000) G_L1: 15.196 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 12.622 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 9, iters: 97984, time: 0.968, data: 0.000) G_L1: 19.242 G_L1_ABSOLUTE: 3.369 G_L1_RELATIVE: 15.872 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 9, iters: 99984, time: 0.968, data: 0.000) G_L1: 17.298 G_L1_ABSOLUTE: 3.143 G_L1_RELATIVE: 14.155 G_Regularizer: 0.000 validation_error: 21.179 +(epoch: 9, iters: 101984, time: 0.972, data: 0.000) G_L1: 18.865 G_L1_ABSOLUTE: 3.097 G_L1_RELATIVE: 15.768 G_Regularizer: 0.000 validation_error: 20.525 +(epoch: 9, iters: 103984, time: 0.962, data: 0.000) G_L1: 15.893 G_L1_ABSOLUTE: 3.196 G_L1_RELATIVE: 12.697 G_Regularizer: 0.000 validation_error: 21.222 +(epoch: 9, iters: 105984, time: 0.969, data: 0.000) G_L1: 16.690 G_L1_ABSOLUTE: 2.795 G_L1_RELATIVE: 13.895 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 9, iters: 107984, time: 0.970, data: 0.000) G_L1: 15.500 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 12.794 G_Regularizer: 0.000 validation_error: 20.929 +(epoch: 9, iters: 109984, time: 0.965, data: 0.000) G_L1: 16.343 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 13.959 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 9, iters: 111984, time: 0.963, data: 0.000) G_L1: 15.450 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 12.658 G_Regularizer: 0.000 validation_error: 20.946 +(epoch: 9, iters: 113984, time: 0.966, data: 0.000) G_L1: 18.040 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 15.201 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 9, iters: 115984, time: 0.685, data: 0.000) G_L1: 17.283 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 14.413 G_Regularizer: 0.000 validation_error: 21.065 +(epoch: 9, iters: 117984, time: 0.546, data: 0.000) G_L1: 13.555 G_L1_ABSOLUTE: 2.613 G_L1_RELATIVE: 10.942 G_Regularizer: 0.000 validation_error: 20.506 +(epoch: 9, iters: 119984, time: 0.539, data: 0.000) G_L1: 16.151 G_L1_ABSOLUTE: 3.106 G_L1_RELATIVE: 13.045 G_Regularizer: 0.000 validation_error: 20.723 +(epoch: 9, iters: 121984, time: 0.553, data: 0.000) G_L1: 15.498 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 12.962 G_Regularizer: 0.000 validation_error: 20.471 +(epoch: 9, iters: 123984, time: 0.554, data: 0.000) G_L1: 15.203 G_L1_ABSOLUTE: 2.441 G_L1_RELATIVE: 12.763 G_Regularizer: 0.000 validation_error: 20.070 +(epoch: 9, iters: 125984, time: 0.555, data: 0.000) G_L1: 15.588 G_L1_ABSOLUTE: 2.986 G_L1_RELATIVE: 12.602 G_Regularizer: 0.000 validation_error: 20.765 +(epoch: 9, iters: 127984, time: 0.549, data: 0.000) G_L1: 15.423 G_L1_ABSOLUTE: 2.604 G_L1_RELATIVE: 12.819 G_Regularizer: 0.000 validation_error: 20.575 +(epoch: 9, iters: 129984, time: 0.553, data: 0.000) G_L1: 14.916 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 12.381 G_Regularizer: 0.000 validation_error: 20.572 +(epoch: 9, iters: 131984, time: 0.546, data: 0.000) G_L1: 16.137 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 13.593 G_Regularizer: 0.000 validation_error: 20.159 +(epoch: 9, iters: 133984, time: 0.545, data: 0.000) G_L1: 15.612 G_L1_ABSOLUTE: 2.543 G_L1_RELATIVE: 13.070 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 9, iters: 135984, time: 0.545, data: 0.000) G_L1: 14.613 G_L1_ABSOLUTE: 2.440 G_L1_RELATIVE: 12.174 G_Regularizer: 0.000 validation_error: 21.282 +(epoch: 9, iters: 137984, time: 0.548, data: 0.000) G_L1: 15.895 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 13.160 G_Regularizer: 0.000 validation_error: 21.194 +(epoch: 9, iters: 139984, time: 0.544, data: 0.000) G_L1: 13.625 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 11.038 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 9, iters: 141984, time: 0.549, data: 0.000) G_L1: 15.495 G_L1_ABSOLUTE: 2.868 G_L1_RELATIVE: 12.627 G_Regularizer: 0.000 validation_error: 21.056 +(epoch: 9, iters: 143984, time: 0.546, data: 0.000) G_L1: 13.926 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 11.560 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 9, iters: 145984, time: 0.548, data: 0.000) G_L1: 15.235 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 12.665 G_Regularizer: 0.000 validation_error: 20.537 +(epoch: 9, iters: 147984, time: 0.550, data: 0.000) G_L1: 14.352 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 11.937 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 9, iters: 149984, time: 0.544, data: 0.000) G_L1: 18.128 G_L1_ABSOLUTE: 2.832 G_L1_RELATIVE: 15.297 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 9, iters: 151984, time: 0.546, data: 0.000) G_L1: 16.792 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 14.253 G_Regularizer: 0.000 validation_error: 21.160 +(epoch: 9, iters: 153984, time: 0.553, data: 0.000) G_L1: 16.713 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 14.148 G_Regularizer: 0.000 validation_error: 20.294 +(epoch: 9, iters: 155984, time: 0.550, data: 0.000) G_L1: 17.000 G_L1_ABSOLUTE: 3.398 G_L1_RELATIVE: 13.602 G_Regularizer: 0.000 validation_error: 21.202 +(epoch: 9, iters: 157984, time: 0.546, data: 0.000) G_L1: 14.628 G_L1_ABSOLUTE: 3.023 G_L1_RELATIVE: 11.605 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 9, iters: 159984, time: 0.549, data: 0.000) G_L1: 15.870 G_L1_ABSOLUTE: 2.558 G_L1_RELATIVE: 13.312 G_Regularizer: 0.000 validation_error: 21.116 +(epoch: 9, iters: 161984, time: 0.542, data: 0.000) G_L1: 13.638 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 10.980 G_Regularizer: 0.000 validation_error: 20.561 +(epoch: 9, iters: 163984, time: 0.549, data: 0.000) G_L1: 13.394 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 10.992 G_Regularizer: 0.000 validation_error: 20.508 +(epoch: 9, iters: 165984, time: 0.547, data: 0.000) G_L1: 14.899 G_L1_ABSOLUTE: 2.992 G_L1_RELATIVE: 11.907 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 9, iters: 167984, time: 0.550, data: 0.000) G_L1: 17.892 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 15.036 G_Regularizer: 0.000 validation_error: 21.693 +(epoch: 9, iters: 169984, time: 0.543, data: 0.000) G_L1: 14.069 G_L1_ABSOLUTE: 2.545 G_L1_RELATIVE: 11.524 G_Regularizer: 0.000 validation_error: 20.057 +(epoch: 9, iters: 171984, time: 0.542, data: 0.000) G_L1: 16.640 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 14.013 G_Regularizer: 0.000 validation_error: 21.367 +(epoch: 9, iters: 173984, time: 0.542, data: 0.000) G_L1: 14.436 G_L1_ABSOLUTE: 2.393 G_L1_RELATIVE: 12.044 G_Regularizer: 0.000 validation_error: 20.504 +(epoch: 9, iters: 175984, time: 0.551, data: 0.000) G_L1: 13.468 G_L1_ABSOLUTE: 2.392 G_L1_RELATIVE: 11.075 G_Regularizer: 0.000 validation_error: 20.037 +(epoch: 9, iters: 177984, time: 0.554, data: 0.000) G_L1: 15.018 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 12.556 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 9, iters: 179984, time: 0.541, data: 0.000) G_L1: 18.841 G_L1_ABSOLUTE: 3.417 G_L1_RELATIVE: 15.424 G_Regularizer: 0.000 validation_error: 21.483 +(epoch: 9, iters: 181984, time: 0.542, data: 0.000) G_L1: 13.313 G_L1_ABSOLUTE: 2.827 G_L1_RELATIVE: 10.486 G_Regularizer: 0.000 validation_error: 20.972 +(epoch: 9, iters: 183984, time: 0.543, data: 0.000) G_L1: 14.184 G_L1_ABSOLUTE: 2.875 G_L1_RELATIVE: 11.309 G_Regularizer: 0.000 validation_error: 20.609 +(epoch: 9, iters: 185984, time: 0.548, data: 0.000) G_L1: 15.670 G_L1_ABSOLUTE: 2.330 G_L1_RELATIVE: 13.341 G_Regularizer: 0.000 validation_error: 21.245 +(epoch: 9, iters: 187984, time: 0.542, data: 0.000) G_L1: 14.869 G_L1_ABSOLUTE: 2.346 G_L1_RELATIVE: 12.524 G_Regularizer: 0.000 validation_error: 20.964 +(epoch: 9, iters: 189984, time: 0.542, data: 0.000) G_L1: 17.299 G_L1_ABSOLUTE: 3.104 G_L1_RELATIVE: 14.195 G_Regularizer: 0.000 validation_error: 20.745 +(epoch: 9, iters: 191984, time: 0.552, data: 0.000) G_L1: 14.024 G_L1_ABSOLUTE: 2.350 G_L1_RELATIVE: 11.674 G_Regularizer: 0.000 validation_error: 20.832 +(epoch: 9, iters: 193984, time: 0.543, data: 0.000) G_L1: 16.882 G_L1_ABSOLUTE: 2.353 G_L1_RELATIVE: 14.529 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 9, iters: 195984, time: 0.546, data: 0.000) G_L1: 13.199 G_L1_ABSOLUTE: 2.644 G_L1_RELATIVE: 10.555 G_Regularizer: 0.000 validation_error: 20.653 +(epoch: 9, iters: 197984, time: 0.548, data: 0.000) G_L1: 14.760 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 12.227 G_Regularizer: 0.000 validation_error: 21.057 +(epoch: 9, iters: 199984, time: 0.545, data: 0.000) G_L1: 18.370 G_L1_ABSOLUTE: 2.959 G_L1_RELATIVE: 15.411 G_Regularizer: 0.000 validation_error: 20.337 +(epoch: 9, iters: 201984, time: 0.548, data: 0.000) G_L1: 11.193 G_L1_ABSOLUTE: 2.337 G_L1_RELATIVE: 8.856 G_Regularizer: 0.000 validation_error: 21.236 +(epoch: 9, iters: 203984, time: 0.544, data: 0.000) G_L1: 15.613 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 12.976 G_Regularizer: 0.000 validation_error: 20.735 +(epoch: 9, iters: 205984, time: 0.554, data: 0.000) G_L1: 18.866 G_L1_ABSOLUTE: 3.138 G_L1_RELATIVE: 15.728 G_Regularizer: 0.000 validation_error: 21.234 +(epoch: 9, iters: 207984, time: 0.544, data: 0.000) G_L1: 15.386 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 12.884 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 9, iters: 209984, time: 0.549, data: 0.000) G_L1: 17.763 G_L1_ABSOLUTE: 2.775 G_L1_RELATIVE: 14.988 G_Regularizer: 0.000 validation_error: 21.195 +(epoch: 9, iters: 211984, time: 0.543, data: 0.000) G_L1: 15.748 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 13.037 G_Regularizer: 0.000 validation_error: 20.436 +(epoch: 9, iters: 213984, time: 0.549, data: 0.000) G_L1: 14.852 G_L1_ABSOLUTE: 3.001 G_L1_RELATIVE: 11.852 G_Regularizer: 0.000 validation_error: 21.267 +(epoch: 9, iters: 215984, time: 0.548, data: 0.000) G_L1: 17.066 G_L1_ABSOLUTE: 2.567 G_L1_RELATIVE: 14.500 G_Regularizer: 0.000 validation_error: 20.058 +(epoch: 9, iters: 217984, time: 0.549, data: 0.000) G_L1: 15.206 G_L1_ABSOLUTE: 2.472 G_L1_RELATIVE: 12.735 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 9, iters: 219984, time: 0.539, data: 0.000) G_L1: 18.198 G_L1_ABSOLUTE: 2.922 G_L1_RELATIVE: 15.276 G_Regularizer: 0.000 validation_error: 20.740 +(epoch: 9, iters: 221984, time: 0.541, data: 0.000) G_L1: 16.940 G_L1_ABSOLUTE: 3.160 G_L1_RELATIVE: 13.781 G_Regularizer: 0.000 validation_error: 20.947 +(epoch: 9, iters: 223984, time: 0.552, data: 0.000) G_L1: 13.963 G_L1_ABSOLUTE: 2.356 G_L1_RELATIVE: 11.606 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 9, iters: 225984, time: 0.555, data: 0.000) G_L1: 13.250 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 10.915 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 9, iters: 227984, time: 0.545, data: 0.001) G_L1: 13.509 G_L1_ABSOLUTE: 3.088 G_L1_RELATIVE: 10.421 G_Regularizer: 0.000 validation_error: 20.153 +(epoch: 9, iters: 229984, time: 0.546, data: 0.000) G_L1: 15.049 G_L1_ABSOLUTE: 2.466 G_L1_RELATIVE: 12.584 G_Regularizer: 0.000 validation_error: 20.307 +(epoch: 9, iters: 231984, time: 0.630, data: 0.000) G_L1: 13.619 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 10.949 G_Regularizer: 0.000 validation_error: 20.469 +(epoch: 9, iters: 233984, time: 0.624, data: 0.000) G_L1: 15.607 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 13.017 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 9, iters: 235984, time: 0.617, data: 0.000) G_L1: 14.109 G_L1_ABSOLUTE: 3.144 G_L1_RELATIVE: 10.965 G_Regularizer: 0.000 validation_error: 20.604 +(epoch: 9, iters: 237984, time: 0.548, data: 0.000) G_L1: 19.180 G_L1_ABSOLUTE: 2.959 G_L1_RELATIVE: 16.222 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 9, iters: 239984, time: 0.545, data: 0.000) G_L1: 13.808 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 11.168 G_Regularizer: 0.000 validation_error: 20.471 +(epoch: 9, iters: 241984, time: 0.550, data: 0.000) G_L1: 12.543 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 9.923 G_Regularizer: 0.000 validation_error: 21.082 +(epoch: 9, iters: 243984, time: 0.551, data: 0.000) G_L1: 15.811 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 13.225 G_Regularizer: 0.000 validation_error: 20.474 +(epoch: 9, iters: 245984, time: 0.545, data: 0.000) G_L1: 14.515 G_L1_ABSOLUTE: 3.198 G_L1_RELATIVE: 11.317 G_Regularizer: 0.000 validation_error: 20.509 +(epoch: 9, iters: 247984, time: 0.538, data: 0.000) G_L1: 12.108 G_L1_ABSOLUTE: 3.111 G_L1_RELATIVE: 8.996 G_Regularizer: 0.000 validation_error: 20.513 +(epoch: 9, iters: 249984, time: 0.540, data: 0.000) G_L1: 15.355 G_L1_ABSOLUTE: 2.145 G_L1_RELATIVE: 13.210 G_Regularizer: 0.000 validation_error: 20.125 +(epoch: 9, iters: 251984, time: 0.543, data: 0.000) G_L1: 16.205 G_L1_ABSOLUTE: 3.125 G_L1_RELATIVE: 13.081 G_Regularizer: 0.000 validation_error: 20.205 +(epoch: 9, iters: 253984, time: 0.548, data: 0.000) G_L1: 13.934 G_L1_ABSOLUTE: 2.552 G_L1_RELATIVE: 11.382 G_Regularizer: 0.000 validation_error: 20.574 +(epoch: 9, iters: 255984, time: 0.548, data: 0.000) G_L1: 12.349 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 9.647 G_Regularizer: 0.000 validation_error: 21.705 +(epoch: 9, iters: 257984, time: 0.545, data: 0.001) G_L1: 14.679 G_L1_ABSOLUTE: 2.703 G_L1_RELATIVE: 11.976 G_Regularizer: 0.000 validation_error: 20.335 +(epoch: 9, iters: 259984, time: 0.545, data: 0.000) G_L1: 13.881 G_L1_ABSOLUTE: 2.299 G_L1_RELATIVE: 11.582 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 9, iters: 261984, time: 0.543, data: 0.000) G_L1: 15.650 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 13.040 G_Regularizer: 0.000 validation_error: 20.567 +(epoch: 9, iters: 263984, time: 0.546, data: 0.000) G_L1: 15.620 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 12.998 G_Regularizer: 0.000 validation_error: 20.940 +(epoch: 9, iters: 265984, time: 0.549, data: 0.000) G_L1: 15.056 G_L1_ABSOLUTE: 2.268 G_L1_RELATIVE: 12.788 G_Regularizer: 0.000 validation_error: 20.753 +(epoch: 9, iters: 267984, time: 0.546, data: 0.000) G_L1: 15.834 G_L1_ABSOLUTE: 2.958 G_L1_RELATIVE: 12.875 G_Regularizer: 0.000 validation_error: 19.715 +(epoch: 9, iters: 269984, time: 0.546, data: 0.000) G_L1: 16.128 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 13.233 G_Regularizer: 0.000 validation_error: 20.263 +(epoch: 9, iters: 271984, time: 0.549, data: 0.000) G_L1: 15.477 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 13.058 G_Regularizer: 0.000 validation_error: 21.156 +(epoch: 9, iters: 273984, time: 0.548, data: 0.000) G_L1: 14.259 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 11.553 G_Regularizer: 0.000 validation_error: 21.190 +(epoch: 9, iters: 275984, time: 0.545, data: 0.000) G_L1: 14.822 G_L1_ABSOLUTE: 2.796 G_L1_RELATIVE: 12.026 G_Regularizer: 0.000 validation_error: 20.103 +(epoch: 9, iters: 277984, time: 0.545, data: 0.000) G_L1: 14.809 G_L1_ABSOLUTE: 2.932 G_L1_RELATIVE: 11.877 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 9, iters: 279984, time: 0.546, data: 0.000) G_L1: 15.399 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 12.871 G_Regularizer: 0.000 validation_error: 20.763 +(epoch: 9, iters: 281984, time: 0.547, data: 0.000) G_L1: 15.017 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 12.403 G_Regularizer: 0.000 validation_error: 20.419 +(epoch: 9, iters: 283984, time: 0.542, data: 0.000) G_L1: 22.202 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 19.572 G_Regularizer: 0.000 validation_error: 20.691 +(epoch: 9, iters: 285984, time: 0.551, data: 0.000) G_L1: 15.115 G_L1_ABSOLUTE: 2.395 G_L1_RELATIVE: 12.721 G_Regularizer: 0.000 validation_error: 19.677 +(epoch: 9, iters: 287984, time: 0.546, data: 0.000) G_L1: 14.265 G_L1_ABSOLUTE: 3.070 G_L1_RELATIVE: 11.195 G_Regularizer: 0.000 validation_error: 20.434 +(epoch: 9, iters: 289984, time: 0.542, data: 0.000) G_L1: 15.141 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 12.616 G_Regularizer: 0.000 validation_error: 20.186 +(epoch: 9, iters: 291984, time: 0.547, data: 0.000) G_L1: 15.511 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 12.781 G_Regularizer: 0.000 validation_error: 20.067 +(epoch: 9, iters: 293984, time: 0.550, data: 0.000) G_L1: 15.141 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 12.674 G_Regularizer: 0.000 validation_error: 21.343 +(epoch: 9, iters: 295984, time: 0.548, data: 0.000) G_L1: 15.472 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 12.789 G_Regularizer: 0.000 validation_error: 20.261 +(epoch: 9, iters: 297984, time: 0.549, data: 0.000) G_L1: 17.730 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 14.953 G_Regularizer: 0.000 validation_error: 20.460 +(epoch: 9, iters: 299984, time: 0.548, data: 0.000) G_L1: 17.515 G_L1_ABSOLUTE: 2.872 G_L1_RELATIVE: 14.643 G_Regularizer: 0.000 validation_error: 20.756 +(epoch: 9, iters: 301984, time: 0.542, data: 0.000) G_L1: 17.828 G_L1_ABSOLUTE: 3.263 G_L1_RELATIVE: 14.565 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 10, iters: 1232, time: 0.544, data: 0.000) G_L1: 15.572 G_L1_ABSOLUTE: 2.912 G_L1_RELATIVE: 12.661 G_Regularizer: 0.000 validation_error: 20.653 +(epoch: 10, iters: 3232, time: 0.551, data: 0.000) G_L1: 17.238 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 14.611 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 10, iters: 5232, time: 0.545, data: 0.000) G_L1: 15.144 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 12.453 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 10, iters: 7232, time: 0.547, data: 0.001) G_L1: 15.155 G_L1_ABSOLUTE: 3.141 G_L1_RELATIVE: 12.014 G_Regularizer: 0.000 validation_error: 20.582 +(epoch: 10, iters: 9232, time: 0.548, data: 0.000) G_L1: 18.825 G_L1_ABSOLUTE: 2.671 G_L1_RELATIVE: 16.154 G_Regularizer: 0.000 validation_error: 20.148 +(epoch: 10, iters: 11232, time: 0.548, data: 0.000) G_L1: 14.238 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 11.716 G_Regularizer: 0.000 validation_error: 20.464 +(epoch: 10, iters: 13232, time: 0.539, data: 0.000) G_L1: 19.797 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 17.278 G_Regularizer: 0.000 validation_error: 20.471 +(epoch: 10, iters: 15232, time: 0.545, data: 0.001) G_L1: 15.785 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 13.122 G_Regularizer: 0.000 validation_error: 20.022 +(epoch: 10, iters: 17232, time: 0.547, data: 0.000) G_L1: 14.396 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 11.719 G_Regularizer: 0.000 validation_error: 20.340 +(epoch: 10, iters: 19232, time: 0.550, data: 0.000) G_L1: 16.141 G_L1_ABSOLUTE: 3.529 G_L1_RELATIVE: 12.612 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 10, iters: 21232, time: 0.547, data: 0.000) G_L1: 17.689 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 15.227 G_Regularizer: 0.000 validation_error: 21.075 +(epoch: 10, iters: 23232, time: 0.540, data: 0.000) G_L1: 17.677 G_L1_ABSOLUTE: 2.750 G_L1_RELATIVE: 14.928 G_Regularizer: 0.000 validation_error: 21.038 +(epoch: 10, iters: 25232, time: 0.545, data: 0.000) G_L1: 15.482 G_L1_ABSOLUTE: 2.788 G_L1_RELATIVE: 12.693 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 10, iters: 27232, time: 0.542, data: 0.000) G_L1: 16.848 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 14.320 G_Regularizer: 0.000 validation_error: 20.402 +(epoch: 10, iters: 29232, time: 0.542, data: 0.000) G_L1: 18.471 G_L1_ABSOLUTE: 3.160 G_L1_RELATIVE: 15.311 G_Regularizer: 0.000 validation_error: 20.398 +(epoch: 10, iters: 31232, time: 0.550, data: 0.000) G_L1: 13.583 G_L1_ABSOLUTE: 2.638 G_L1_RELATIVE: 10.945 G_Regularizer: 0.000 validation_error: 21.071 +(epoch: 10, iters: 33232, time: 0.545, data: 0.000) G_L1: 18.439 G_L1_ABSOLUTE: 2.983 G_L1_RELATIVE: 15.456 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 10, iters: 35232, time: 0.547, data: 0.000) G_L1: 14.395 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 11.942 G_Regularizer: 0.000 validation_error: 21.390 +(epoch: 10, iters: 37232, time: 0.552, data: 0.000) G_L1: 15.150 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 12.388 G_Regularizer: 0.000 validation_error: 20.524 +(epoch: 10, iters: 39232, time: 0.538, data: 0.000) G_L1: 15.819 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 12.881 G_Regularizer: 0.000 validation_error: 21.298 +(epoch: 10, iters: 41232, time: 0.543, data: 0.000) G_L1: 14.680 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 12.264 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 10, iters: 43232, time: 0.553, data: 0.000) G_L1: 15.394 G_L1_ABSOLUTE: 2.847 G_L1_RELATIVE: 12.547 G_Regularizer: 0.000 validation_error: 20.938 +(epoch: 10, iters: 45232, time: 0.542, data: 0.000) G_L1: 17.995 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 15.449 G_Regularizer: 0.000 validation_error: 20.217 +(epoch: 10, iters: 47232, time: 0.547, data: 0.000) G_L1: 14.848 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 12.247 G_Regularizer: 0.000 validation_error: 20.282 +(epoch: 10, iters: 49232, time: 0.545, data: 0.000) G_L1: 15.107 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 12.708 G_Regularizer: 0.000 validation_error: 21.832 +(epoch: 10, iters: 51232, time: 0.541, data: 0.000) G_L1: 16.661 G_L1_ABSOLUTE: 2.961 G_L1_RELATIVE: 13.700 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 10, iters: 53232, time: 0.546, data: 0.000) G_L1: 15.706 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 13.008 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 10, iters: 55232, time: 0.546, data: 0.000) G_L1: 16.812 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 14.243 G_Regularizer: 0.000 validation_error: 21.406 +(epoch: 10, iters: 57232, time: 0.548, data: 0.000) G_L1: 14.104 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 11.426 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 10, iters: 59232, time: 0.548, data: 0.000) G_L1: 14.951 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 12.362 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 10, iters: 61232, time: 0.549, data: 0.000) G_L1: 14.276 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 11.560 G_Regularizer: 0.000 validation_error: 20.417 +(epoch: 10, iters: 63232, time: 0.544, data: 0.000) G_L1: 16.909 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 14.203 G_Regularizer: 0.000 validation_error: 20.492 +(epoch: 10, iters: 65232, time: 0.543, data: 0.000) G_L1: 17.253 G_L1_ABSOLUTE: 2.288 G_L1_RELATIVE: 14.965 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 10, iters: 67232, time: 0.554, data: 0.000) G_L1: 14.807 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 12.144 G_Regularizer: 0.000 validation_error: 21.235 +(epoch: 10, iters: 69232, time: 0.542, data: 0.000) G_L1: 14.487 G_L1_ABSOLUTE: 2.508 G_L1_RELATIVE: 11.979 G_Regularizer: 0.000 validation_error: 20.401 +(epoch: 10, iters: 71232, time: 0.549, data: 0.000) G_L1: 14.774 G_L1_ABSOLUTE: 2.907 G_L1_RELATIVE: 11.868 G_Regularizer: 0.000 validation_error: 20.380 +(epoch: 10, iters: 73232, time: 0.542, data: 0.000) G_L1: 13.222 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 10.658 G_Regularizer: 0.000 validation_error: 21.290 +(epoch: 10, iters: 75232, time: 0.542, data: 0.001) G_L1: 14.659 G_L1_ABSOLUTE: 2.263 G_L1_RELATIVE: 12.395 G_Regularizer: 0.000 validation_error: 19.907 +(epoch: 10, iters: 77232, time: 0.550, data: 0.000) G_L1: 21.355 G_L1_ABSOLUTE: 3.249 G_L1_RELATIVE: 18.107 G_Regularizer: 0.000 validation_error: 20.249 +(epoch: 10, iters: 79232, time: 0.556, data: 0.000) G_L1: 15.954 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 13.326 G_Regularizer: 0.000 validation_error: 21.347 +(epoch: 10, iters: 81232, time: 0.547, data: 0.000) G_L1: 17.125 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 14.448 G_Regularizer: 0.000 validation_error: 20.140 +(epoch: 10, iters: 83232, time: 0.539, data: 0.000) G_L1: 16.122 G_L1_ABSOLUTE: 2.933 G_L1_RELATIVE: 13.189 G_Regularizer: 0.000 validation_error: 20.130 +(epoch: 10, iters: 85232, time: 0.544, data: 0.000) G_L1: 14.880 G_L1_ABSOLUTE: 3.152 G_L1_RELATIVE: 11.728 G_Regularizer: 0.000 validation_error: 20.672 +(epoch: 10, iters: 87232, time: 0.545, data: 0.000) G_L1: 18.042 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 15.459 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 10, iters: 89232, time: 0.543, data: 0.000) G_L1: 15.052 G_L1_ABSOLUTE: 2.379 G_L1_RELATIVE: 12.674 G_Regularizer: 0.000 validation_error: 22.012 +(epoch: 10, iters: 91232, time: 0.541, data: 0.000) G_L1: 14.189 G_L1_ABSOLUTE: 3.181 G_L1_RELATIVE: 11.009 G_Regularizer: 0.000 validation_error: 20.145 +(epoch: 10, iters: 93232, time: 0.545, data: 0.000) G_L1: 15.443 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 12.824 G_Regularizer: 0.000 validation_error: 20.939 +(epoch: 10, iters: 95232, time: 0.545, data: 0.000) G_L1: 16.188 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 13.685 G_Regularizer: 0.000 validation_error: 20.505 +(epoch: 10, iters: 97232, time: 0.544, data: 0.000) G_L1: 18.122 G_L1_ABSOLUTE: 3.304 G_L1_RELATIVE: 14.818 G_Regularizer: 0.000 validation_error: 20.263 +(epoch: 10, iters: 99232, time: 0.544, data: 0.000) G_L1: 19.433 G_L1_ABSOLUTE: 2.788 G_L1_RELATIVE: 16.645 G_Regularizer: 0.000 validation_error: 21.333 +(epoch: 10, iters: 101232, time: 0.539, data: 0.000) G_L1: 16.613 G_L1_ABSOLUTE: 2.299 G_L1_RELATIVE: 14.314 G_Regularizer: 0.000 validation_error: 20.554 +(epoch: 10, iters: 103232, time: 0.539, data: 0.000) G_L1: 22.337 G_L1_ABSOLUTE: 2.918 G_L1_RELATIVE: 19.419 G_Regularizer: 0.000 validation_error: 20.227 +(epoch: 10, iters: 105232, time: 0.548, data: 0.000) G_L1: 13.335 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 10.987 G_Regularizer: 0.000 validation_error: 20.627 +(epoch: 10, iters: 107232, time: 0.551, data: 0.000) G_L1: 17.967 G_L1_ABSOLUTE: 3.361 G_L1_RELATIVE: 14.606 G_Regularizer: 0.000 validation_error: 21.174 +(epoch: 10, iters: 109232, time: 0.550, data: 0.000) G_L1: 18.124 G_L1_ABSOLUTE: 3.704 G_L1_RELATIVE: 14.420 G_Regularizer: 0.000 validation_error: 20.492 +(epoch: 10, iters: 111232, time: 0.544, data: 0.000) G_L1: 13.830 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 11.052 G_Regularizer: 0.000 validation_error: 19.846 +(epoch: 10, iters: 113232, time: 0.542, data: 0.000) G_L1: 15.722 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 12.828 G_Regularizer: 0.000 validation_error: 20.707 +(epoch: 10, iters: 115232, time: 0.542, data: 0.000) G_L1: 14.628 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 12.163 G_Regularizer: 0.000 validation_error: 20.289 +(epoch: 10, iters: 117232, time: 0.549, data: 0.000) G_L1: 16.276 G_L1_ABSOLUTE: 2.963 G_L1_RELATIVE: 13.313 G_Regularizer: 0.000 validation_error: 20.735 +(epoch: 10, iters: 119232, time: 0.545, data: 0.000) G_L1: 16.165 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 13.360 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 10, iters: 121232, time: 0.541, data: 0.000) G_L1: 15.414 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 12.617 G_Regularizer: 0.000 validation_error: 20.455 +(epoch: 10, iters: 123232, time: 0.541, data: 0.000) G_L1: 17.959 G_L1_ABSOLUTE: 3.439 G_L1_RELATIVE: 14.519 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 10, iters: 125232, time: 0.543, data: 0.000) G_L1: 16.408 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 13.750 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 10, iters: 127232, time: 0.549, data: 0.000) G_L1: 15.866 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 13.322 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 10, iters: 129232, time: 0.545, data: 0.000) G_L1: 14.576 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 11.699 G_Regularizer: 0.000 validation_error: 20.377 +(epoch: 10, iters: 131232, time: 0.552, data: 0.000) G_L1: 18.244 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 15.726 G_Regularizer: 0.000 validation_error: 21.169 +(epoch: 10, iters: 133232, time: 0.543, data: 0.000) G_L1: 14.349 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 11.711 G_Regularizer: 0.000 validation_error: 20.050 +(epoch: 10, iters: 135232, time: 0.546, data: 0.000) G_L1: 14.903 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 12.092 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 10, iters: 137232, time: 0.554, data: 0.000) G_L1: 17.455 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 14.836 G_Regularizer: 0.000 validation_error: 20.345 +(epoch: 10, iters: 139232, time: 0.547, data: 0.000) G_L1: 14.852 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 12.339 G_Regularizer: 0.000 validation_error: 20.586 +(epoch: 10, iters: 141232, time: 0.545, data: 0.000) G_L1: 14.591 G_L1_ABSOLUTE: 2.423 G_L1_RELATIVE: 12.168 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 10, iters: 143232, time: 0.542, data: 0.001) G_L1: 12.710 G_L1_ABSOLUTE: 2.120 G_L1_RELATIVE: 10.590 G_Regularizer: 0.000 validation_error: 20.434 +(epoch: 10, iters: 145232, time: 0.546, data: 0.000) G_L1: 12.996 G_L1_ABSOLUTE: 2.380 G_L1_RELATIVE: 10.617 G_Regularizer: 0.000 validation_error: 21.059 +(epoch: 10, iters: 147232, time: 0.550, data: 0.000) G_L1: 16.103 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 13.443 G_Regularizer: 0.000 validation_error: 20.179 +(epoch: 10, iters: 149232, time: 0.545, data: 0.000) G_L1: 15.736 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 13.277 G_Regularizer: 0.000 validation_error: 20.741 +(epoch: 10, iters: 151232, time: 0.548, data: 0.000) G_L1: 15.790 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 13.176 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 10, iters: 153232, time: 0.548, data: 0.000) G_L1: 19.871 G_L1_ABSOLUTE: 3.389 G_L1_RELATIVE: 16.481 G_Regularizer: 0.000 validation_error: 20.670 +(epoch: 10, iters: 155232, time: 0.546, data: 0.000) G_L1: 14.880 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 12.421 G_Regularizer: 0.000 validation_error: 20.503 +(epoch: 10, iters: 157232, time: 0.544, data: 0.000) G_L1: 16.626 G_L1_ABSOLUTE: 2.906 G_L1_RELATIVE: 13.720 G_Regularizer: 0.000 validation_error: 20.956 +(epoch: 10, iters: 159232, time: 0.547, data: 0.000) G_L1: 14.700 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 11.864 G_Regularizer: 0.000 validation_error: 21.093 +(epoch: 10, iters: 161232, time: 0.541, data: 0.000) G_L1: 16.012 G_L1_ABSOLUTE: 2.472 G_L1_RELATIVE: 13.540 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 10, iters: 163232, time: 0.540, data: 0.000) G_L1: 16.534 G_L1_ABSOLUTE: 3.063 G_L1_RELATIVE: 13.471 G_Regularizer: 0.000 validation_error: 20.047 +(epoch: 10, iters: 165232, time: 0.555, data: 0.001) G_L1: 17.346 G_L1_ABSOLUTE: 3.091 G_L1_RELATIVE: 14.255 G_Regularizer: 0.000 validation_error: 20.836 +(epoch: 10, iters: 167232, time: 0.553, data: 0.000) G_L1: 14.109 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 11.556 G_Regularizer: 0.000 validation_error: 20.547 +(epoch: 10, iters: 169232, time: 0.544, data: 0.000) G_L1: 14.122 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 11.391 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 10, iters: 171232, time: 0.545, data: 0.000) G_L1: 12.755 G_L1_ABSOLUTE: 2.328 G_L1_RELATIVE: 10.427 G_Regularizer: 0.000 validation_error: 20.380 +(epoch: 10, iters: 173232, time: 0.549, data: 0.000) G_L1: 17.591 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 14.918 G_Regularizer: 0.000 validation_error: 20.791 +(epoch: 10, iters: 175232, time: 0.545, data: 0.000) G_L1: 16.113 G_L1_ABSOLUTE: 2.831 G_L1_RELATIVE: 13.282 G_Regularizer: 0.000 validation_error: 20.408 +(epoch: 10, iters: 177232, time: 0.549, data: 0.000) G_L1: 13.938 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 11.182 G_Regularizer: 0.000 validation_error: 21.442 +(epoch: 10, iters: 179232, time: 0.544, data: 0.000) G_L1: 12.992 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 10.590 G_Regularizer: 0.000 validation_error: 20.125 +(epoch: 10, iters: 181232, time: 0.544, data: 0.000) G_L1: 14.906 G_L1_ABSOLUTE: 3.289 G_L1_RELATIVE: 11.617 G_Regularizer: 0.000 validation_error: 21.198 +(epoch: 10, iters: 183232, time: 0.543, data: 0.000) G_L1: 14.732 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 11.970 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 10, iters: 185232, time: 0.547, data: 0.000) G_L1: 14.420 G_L1_ABSOLUTE: 3.417 G_L1_RELATIVE: 11.002 G_Regularizer: 0.000 validation_error: 20.052 +(epoch: 10, iters: 187232, time: 0.547, data: 0.000) G_L1: 14.213 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 11.768 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 10, iters: 189232, time: 0.549, data: 0.000) G_L1: 13.537 G_L1_ABSOLUTE: 2.356 G_L1_RELATIVE: 11.181 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 10, iters: 191232, time: 0.545, data: 0.000) G_L1: 18.029 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 15.523 G_Regularizer: 0.000 validation_error: 20.999 +(epoch: 10, iters: 193232, time: 0.546, data: 0.000) G_L1: 12.655 G_L1_ABSOLUTE: 2.248 G_L1_RELATIVE: 10.407 G_Regularizer: 0.000 validation_error: 21.423 +(epoch: 10, iters: 195232, time: 0.549, data: 0.000) G_L1: 14.996 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 12.236 G_Regularizer: 0.000 validation_error: 20.178 +(epoch: 10, iters: 197232, time: 0.548, data: 0.000) G_L1: 14.274 G_L1_ABSOLUTE: 3.032 G_L1_RELATIVE: 11.242 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 10, iters: 199232, time: 0.545, data: 0.000) G_L1: 13.755 G_L1_ABSOLUTE: 2.391 G_L1_RELATIVE: 11.365 G_Regularizer: 0.000 validation_error: 20.429 +(epoch: 10, iters: 201232, time: 0.551, data: 0.000) G_L1: 14.799 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 12.258 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 10, iters: 203232, time: 0.545, data: 0.000) G_L1: 13.486 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 10.871 G_Regularizer: 0.000 validation_error: 20.114 +(epoch: 10, iters: 205232, time: 0.546, data: 0.000) G_L1: 15.124 G_L1_ABSOLUTE: 2.929 G_L1_RELATIVE: 12.194 G_Regularizer: 0.000 validation_error: 20.145 +(epoch: 10, iters: 207232, time: 0.553, data: 0.000) G_L1: 18.527 G_L1_ABSOLUTE: 3.160 G_L1_RELATIVE: 15.367 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 10, iters: 209232, time: 0.547, data: 0.000) G_L1: 14.778 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 12.479 G_Regularizer: 0.000 validation_error: 20.730 +(epoch: 10, iters: 211232, time: 0.547, data: 0.001) G_L1: 15.017 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 12.508 G_Regularizer: 0.000 validation_error: 20.552 +(epoch: 10, iters: 213232, time: 0.554, data: 0.000) G_L1: 17.296 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 14.661 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 10, iters: 215232, time: 0.552, data: 0.000) G_L1: 15.949 G_L1_ABSOLUTE: 2.191 G_L1_RELATIVE: 13.758 G_Regularizer: 0.000 validation_error: 20.483 +(epoch: 10, iters: 217232, time: 0.551, data: 0.000) G_L1: 14.761 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 12.112 G_Regularizer: 0.000 validation_error: 21.312 +(epoch: 10, iters: 219232, time: 0.553, data: 0.001) G_L1: 12.814 G_L1_ABSOLUTE: 2.382 G_L1_RELATIVE: 10.433 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 10, iters: 221232, time: 0.556, data: 0.000) G_L1: 15.400 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 12.916 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 10, iters: 223232, time: 0.549, data: 0.000) G_L1: 15.091 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 12.752 G_Regularizer: 0.000 validation_error: 21.217 +(epoch: 10, iters: 225232, time: 0.565, data: 0.000) G_L1: 17.512 G_L1_ABSOLUTE: 2.936 G_L1_RELATIVE: 14.576 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 10, iters: 227232, time: 0.553, data: 0.000) G_L1: 16.970 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 14.232 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 10, iters: 229232, time: 0.554, data: 0.000) G_L1: 17.336 G_L1_ABSOLUTE: 3.443 G_L1_RELATIVE: 13.893 G_Regularizer: 0.000 validation_error: 20.342 +(epoch: 10, iters: 231232, time: 0.554, data: 0.000) G_L1: 14.998 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 12.562 G_Regularizer: 0.000 validation_error: 20.045 +(epoch: 10, iters: 233232, time: 0.551, data: 0.000) G_L1: 16.179 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 13.381 G_Regularizer: 0.000 validation_error: 20.145 +(epoch: 10, iters: 235232, time: 0.552, data: 0.000) G_L1: 14.076 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 11.503 G_Regularizer: 0.000 validation_error: 20.510 +(epoch: 10, iters: 237232, time: 0.545, data: 0.000) G_L1: 16.731 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 13.920 G_Regularizer: 0.000 validation_error: 20.528 +(epoch: 10, iters: 239232, time: 0.553, data: 0.000) G_L1: 14.125 G_L1_ABSOLUTE: 2.992 G_L1_RELATIVE: 11.134 G_Regularizer: 0.000 validation_error: 20.737 +(epoch: 10, iters: 241232, time: 0.549, data: 0.000) G_L1: 15.174 G_L1_ABSOLUTE: 3.535 G_L1_RELATIVE: 11.639 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 10, iters: 243232, time: 0.545, data: 0.000) G_L1: 16.767 G_L1_ABSOLUTE: 2.897 G_L1_RELATIVE: 13.869 G_Regularizer: 0.000 validation_error: 20.551 +(epoch: 10, iters: 245232, time: 0.553, data: 0.000) G_L1: 23.020 G_L1_ABSOLUTE: 2.900 G_L1_RELATIVE: 20.120 G_Regularizer: 0.000 validation_error: 19.363 +(epoch: 10, iters: 247232, time: 0.552, data: 0.000) G_L1: 17.410 G_L1_ABSOLUTE: 2.860 G_L1_RELATIVE: 14.550 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 10, iters: 249232, time: 0.545, data: 0.000) G_L1: 14.205 G_L1_ABSOLUTE: 2.802 G_L1_RELATIVE: 11.403 G_Regularizer: 0.000 validation_error: 19.963 +(epoch: 10, iters: 251232, time: 0.548, data: 0.000) G_L1: 17.507 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 14.753 G_Regularizer: 0.000 validation_error: 19.682 +(epoch: 10, iters: 253232, time: 0.555, data: 0.000) G_L1: 14.642 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 11.974 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 10, iters: 255232, time: 0.552, data: 0.000) G_L1: 14.878 G_L1_ABSOLUTE: 3.030 G_L1_RELATIVE: 11.847 G_Regularizer: 0.000 validation_error: 20.699 +(epoch: 10, iters: 257232, time: 0.547, data: 0.000) G_L1: 13.846 G_L1_ABSOLUTE: 2.307 G_L1_RELATIVE: 11.538 G_Regularizer: 0.000 validation_error: 21.463 +(epoch: 10, iters: 259232, time: 0.545, data: 0.000) G_L1: 11.461 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 8.911 G_Regularizer: 0.000 validation_error: 20.281 +(epoch: 10, iters: 261232, time: 0.545, data: 0.000) G_L1: 16.568 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 14.028 G_Regularizer: 0.000 validation_error: 19.805 +(epoch: 10, iters: 263232, time: 0.550, data: 0.000) G_L1: 14.315 G_L1_ABSOLUTE: 2.639 G_L1_RELATIVE: 11.676 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 10, iters: 265232, time: 0.541, data: 0.000) G_L1: 15.470 G_L1_ABSOLUTE: 2.918 G_L1_RELATIVE: 12.551 G_Regularizer: 0.000 validation_error: 20.637 +(epoch: 10, iters: 267232, time: 0.551, data: 0.000) G_L1: 15.038 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 12.278 G_Regularizer: 0.000 validation_error: 21.138 +(epoch: 10, iters: 269232, time: 0.549, data: 0.000) G_L1: 15.097 G_L1_ABSOLUTE: 2.815 G_L1_RELATIVE: 12.282 G_Regularizer: 0.000 validation_error: 20.063 +(epoch: 10, iters: 271232, time: 0.547, data: 0.000) G_L1: 16.487 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 13.820 G_Regularizer: 0.000 validation_error: 20.431 +(epoch: 10, iters: 273232, time: 0.545, data: 0.000) G_L1: 13.480 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 10.773 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 10, iters: 275232, time: 0.545, data: 0.000) G_L1: 17.855 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 15.399 G_Regularizer: 0.000 validation_error: 21.204 +(epoch: 10, iters: 277232, time: 0.547, data: 0.000) G_L1: 13.515 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 11.099 G_Regularizer: 0.000 validation_error: 20.197 +(epoch: 10, iters: 279232, time: 0.538, data: 0.000) G_L1: 18.028 G_L1_ABSOLUTE: 2.765 G_L1_RELATIVE: 15.263 G_Regularizer: 0.000 validation_error: 20.173 +(epoch: 10, iters: 281232, time: 0.553, data: 0.000) G_L1: 15.266 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 12.561 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 10, iters: 283232, time: 0.551, data: 0.000) G_L1: 15.726 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 13.080 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 10, iters: 285232, time: 0.549, data: 0.000) G_L1: 17.751 G_L1_ABSOLUTE: 3.109 G_L1_RELATIVE: 14.642 G_Regularizer: 0.000 validation_error: 20.437 +(epoch: 10, iters: 287232, time: 0.552, data: 0.001) G_L1: 14.509 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 11.840 G_Regularizer: 0.000 validation_error: 20.600 +(epoch: 10, iters: 289232, time: 0.536, data: 0.000) G_L1: 17.136 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 14.279 G_Regularizer: 0.000 validation_error: 21.193 +(epoch: 10, iters: 291232, time: 0.545, data: 0.000) G_L1: 17.186 G_L1_ABSOLUTE: 2.701 G_L1_RELATIVE: 14.485 G_Regularizer: 0.000 validation_error: 21.151 +(epoch: 10, iters: 293232, time: 0.547, data: 0.000) G_L1: 14.177 G_L1_ABSOLUTE: 3.148 G_L1_RELATIVE: 11.029 G_Regularizer: 0.000 validation_error: 20.417 +(epoch: 10, iters: 295232, time: 0.550, data: 0.000) G_L1: 13.360 G_L1_ABSOLUTE: 2.162 G_L1_RELATIVE: 11.198 G_Regularizer: 0.000 validation_error: 19.833 +(epoch: 10, iters: 297232, time: 0.545, data: 0.000) G_L1: 13.799 G_L1_ABSOLUTE: 2.319 G_L1_RELATIVE: 11.479 G_Regularizer: 0.000 validation_error: 20.525 +(epoch: 10, iters: 299232, time: 0.546, data: 0.000) G_L1: 14.907 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 12.217 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 10, iters: 301232, time: 0.544, data: 0.000) G_L1: 13.607 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 10.997 G_Regularizer: 0.000 validation_error: 20.512 +(epoch: 11, iters: 480, time: 0.549, data: 0.000) G_L1: 16.376 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 13.707 G_Regularizer: 0.000 validation_error: 20.742 +(epoch: 11, iters: 2480, time: 0.546, data: 0.000) G_L1: 13.902 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 11.401 G_Regularizer: 0.000 validation_error: 21.817 +(epoch: 11, iters: 4480, time: 0.542, data: 0.000) G_L1: 18.020 G_L1_ABSOLUTE: 3.025 G_L1_RELATIVE: 14.994 G_Regularizer: 0.000 validation_error: 20.607 +(epoch: 11, iters: 6480, time: 0.547, data: 0.000) G_L1: 13.064 G_L1_ABSOLUTE: 2.252 G_L1_RELATIVE: 10.812 G_Regularizer: 0.000 validation_error: 20.303 +(epoch: 11, iters: 8480, time: 0.548, data: 0.000) G_L1: 17.276 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 14.656 G_Regularizer: 0.000 validation_error: 20.267 +(epoch: 11, iters: 10480, time: 0.549, data: 0.000) G_L1: 17.090 G_L1_ABSOLUTE: 2.688 G_L1_RELATIVE: 14.402 G_Regularizer: 0.000 validation_error: 21.273 +(epoch: 11, iters: 12480, time: 0.555, data: 0.000) G_L1: 14.087 G_L1_ABSOLUTE: 2.709 G_L1_RELATIVE: 11.378 G_Regularizer: 0.000 validation_error: 21.315 +(epoch: 11, iters: 14480, time: 0.554, data: 0.000) G_L1: 14.267 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 11.792 G_Regularizer: 0.000 validation_error: 20.320 +(epoch: 11, iters: 16480, time: 0.546, data: 0.001) G_L1: 15.473 G_L1_ABSOLUTE: 2.939 G_L1_RELATIVE: 12.534 G_Regularizer: 0.000 validation_error: 19.640 +(epoch: 11, iters: 18480, time: 0.540, data: 0.000) G_L1: 17.398 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 14.765 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 11, iters: 20480, time: 0.543, data: 0.000) G_L1: 14.396 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 11.765 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 11, iters: 22480, time: 0.546, data: 0.000) G_L1: 14.633 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 12.010 G_Regularizer: 0.000 validation_error: 22.118 +(epoch: 11, iters: 24480, time: 0.546, data: 0.000) G_L1: 13.481 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 10.681 G_Regularizer: 0.000 validation_error: 21.266 +(epoch: 11, iters: 26480, time: 0.553, data: 0.000) G_L1: 13.165 G_L1_ABSOLUTE: 2.745 G_L1_RELATIVE: 10.420 G_Regularizer: 0.000 validation_error: 20.287 +(epoch: 11, iters: 28480, time: 0.547, data: 0.001) G_L1: 14.899 G_L1_ABSOLUTE: 2.477 G_L1_RELATIVE: 12.422 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 11, iters: 30480, time: 0.543, data: 0.000) G_L1: 18.560 G_L1_ABSOLUTE: 3.533 G_L1_RELATIVE: 15.027 G_Regularizer: 0.000 validation_error: 20.532 +(epoch: 11, iters: 32480, time: 0.542, data: 0.000) G_L1: 15.031 G_L1_ABSOLUTE: 2.764 G_L1_RELATIVE: 12.267 G_Regularizer: 0.000 validation_error: 20.572 +(epoch: 11, iters: 34480, time: 0.545, data: 0.000) G_L1: 17.692 G_L1_ABSOLUTE: 2.758 G_L1_RELATIVE: 14.934 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 11, iters: 36480, time: 0.549, data: 0.000) G_L1: 16.194 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 13.679 G_Regularizer: 0.000 validation_error: 20.206 +(epoch: 11, iters: 38480, time: 0.546, data: 0.000) G_L1: 19.133 G_L1_ABSOLUTE: 2.913 G_L1_RELATIVE: 16.220 G_Regularizer: 0.000 validation_error: 20.541 +(epoch: 11, iters: 40480, time: 0.543, data: 0.000) G_L1: 16.560 G_L1_ABSOLUTE: 2.455 G_L1_RELATIVE: 14.104 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 11, iters: 42480, time: 0.550, data: 0.000) G_L1: 15.410 G_L1_ABSOLUTE: 2.205 G_L1_RELATIVE: 13.205 G_Regularizer: 0.000 validation_error: 20.208 +(epoch: 11, iters: 44480, time: 0.543, data: 0.000) G_L1: 18.799 G_L1_ABSOLUTE: 3.002 G_L1_RELATIVE: 15.797 G_Regularizer: 0.000 validation_error: 20.021 +(epoch: 11, iters: 46480, time: 0.549, data: 0.000) G_L1: 16.024 G_L1_ABSOLUTE: 2.487 G_L1_RELATIVE: 13.537 G_Regularizer: 0.000 validation_error: 21.482 +(epoch: 11, iters: 48480, time: 0.546, data: 0.000) G_L1: 16.181 G_L1_ABSOLUTE: 2.806 G_L1_RELATIVE: 13.375 G_Regularizer: 0.000 validation_error: 20.675 +(epoch: 11, iters: 50480, time: 0.547, data: 0.000) G_L1: 15.226 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 12.823 G_Regularizer: 0.000 validation_error: 20.059 +(epoch: 11, iters: 52480, time: 0.551, data: 0.000) G_L1: 14.939 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 12.485 G_Regularizer: 0.000 validation_error: 20.214 +(epoch: 11, iters: 54480, time: 0.547, data: 0.000) G_L1: 18.968 G_L1_ABSOLUTE: 2.351 G_L1_RELATIVE: 16.616 G_Regularizer: 0.000 validation_error: 20.430 +(epoch: 11, iters: 56480, time: 0.543, data: 0.000) G_L1: 16.062 G_L1_ABSOLUTE: 2.405 G_L1_RELATIVE: 13.657 G_Regularizer: 0.000 validation_error: 20.357 +(epoch: 11, iters: 58480, time: 0.547, data: 0.001) G_L1: 14.969 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 12.172 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 11, iters: 60480, time: 0.547, data: 0.000) G_L1: 16.544 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 13.848 G_Regularizer: 0.000 validation_error: 20.776 +(epoch: 11, iters: 62480, time: 0.544, data: 0.000) G_L1: 15.426 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 12.932 G_Regularizer: 0.000 validation_error: 20.785 +(epoch: 11, iters: 64480, time: 0.549, data: 0.000) G_L1: 13.320 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 10.769 G_Regularizer: 0.000 validation_error: 20.560 +(epoch: 11, iters: 66480, time: 0.548, data: 0.000) G_L1: 15.972 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 13.115 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 11, iters: 68480, time: 0.545, data: 0.000) G_L1: 13.796 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 11.354 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 11, iters: 70480, time: 0.550, data: 0.000) G_L1: 14.291 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 11.909 G_Regularizer: 0.000 validation_error: 21.171 +(epoch: 11, iters: 72480, time: 0.542, data: 0.000) G_L1: 15.998 G_L1_ABSOLUTE: 2.923 G_L1_RELATIVE: 13.075 G_Regularizer: 0.000 validation_error: 21.129 +(epoch: 11, iters: 74480, time: 0.560, data: 0.000) G_L1: 13.909 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 11.284 G_Regularizer: 0.000 validation_error: 20.157 +(epoch: 11, iters: 76480, time: 0.547, data: 0.000) G_L1: 13.925 G_L1_ABSOLUTE: 2.875 G_L1_RELATIVE: 11.050 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 11, iters: 78480, time: 0.550, data: 0.000) G_L1: 16.667 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 13.935 G_Regularizer: 0.000 validation_error: 20.937 +(epoch: 11, iters: 80480, time: 0.548, data: 0.000) G_L1: 18.635 G_L1_ABSOLUTE: 3.035 G_L1_RELATIVE: 15.600 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 11, iters: 82480, time: 0.539, data: 0.000) G_L1: 14.653 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 12.306 G_Regularizer: 0.000 validation_error: 21.124 +(epoch: 11, iters: 84480, time: 0.536, data: 0.000) G_L1: 15.658 G_L1_ABSOLUTE: 2.220 G_L1_RELATIVE: 13.438 G_Regularizer: 0.000 validation_error: 20.509 +(epoch: 11, iters: 86480, time: 0.548, data: 0.000) G_L1: 14.230 G_L1_ABSOLUTE: 2.821 G_L1_RELATIVE: 11.409 G_Regularizer: 0.000 validation_error: 20.718 +(epoch: 11, iters: 88480, time: 0.544, data: 0.000) G_L1: 15.662 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 13.112 G_Regularizer: 0.000 validation_error: 20.400 +(epoch: 11, iters: 90480, time: 0.544, data: 0.000) G_L1: 13.886 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 11.087 G_Regularizer: 0.000 validation_error: 19.992 +(epoch: 11, iters: 92480, time: 0.533, data: 0.000) G_L1: 12.838 G_L1_ABSOLUTE: 2.654 G_L1_RELATIVE: 10.183 G_Regularizer: 0.000 validation_error: 20.913 +(epoch: 11, iters: 94480, time: 0.546, data: 0.000) G_L1: 17.600 G_L1_ABSOLUTE: 3.057 G_L1_RELATIVE: 14.543 G_Regularizer: 0.000 validation_error: 20.485 +(epoch: 11, iters: 96480, time: 0.535, data: 0.000) G_L1: 14.893 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 12.139 G_Regularizer: 0.000 validation_error: 21.407 +(epoch: 11, iters: 98480, time: 0.530, data: 0.000) G_L1: 14.051 G_L1_ABSOLUTE: 3.233 G_L1_RELATIVE: 10.818 G_Regularizer: 0.000 validation_error: 21.251 +(epoch: 11, iters: 100480, time: 0.534, data: 0.000) G_L1: 13.616 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 11.019 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 11, iters: 102480, time: 0.536, data: 0.000) G_L1: 15.465 G_L1_ABSOLUTE: 2.761 G_L1_RELATIVE: 12.704 G_Regularizer: 0.000 validation_error: 20.061 +(epoch: 11, iters: 104480, time: 0.529, data: 0.000) G_L1: 16.854 G_L1_ABSOLUTE: 2.210 G_L1_RELATIVE: 14.644 G_Regularizer: 0.000 validation_error: 20.299 +(epoch: 11, iters: 106480, time: 0.544, data: 0.000) G_L1: 14.665 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 12.064 G_Regularizer: 0.000 validation_error: 20.782 +(epoch: 11, iters: 108480, time: 0.534, data: 0.000) G_L1: 14.821 G_L1_ABSOLUTE: 3.057 G_L1_RELATIVE: 11.764 G_Regularizer: 0.000 validation_error: 20.255 +(epoch: 11, iters: 110480, time: 0.526, data: 0.000) G_L1: 15.236 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 12.803 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 11, iters: 112480, time: 0.548, data: 0.000) G_L1: 14.774 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 12.212 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 11, iters: 114480, time: 0.539, data: 0.000) G_L1: 13.771 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 11.293 G_Regularizer: 0.000 validation_error: 19.755 +(epoch: 11, iters: 116480, time: 0.535, data: 0.000) G_L1: 16.205 G_L1_ABSOLUTE: 2.886 G_L1_RELATIVE: 13.318 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 11, iters: 118480, time: 0.522, data: 0.000) G_L1: 12.835 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 10.559 G_Regularizer: 0.000 validation_error: 20.497 +(epoch: 11, iters: 120480, time: 0.544, data: 0.000) G_L1: 16.972 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 14.508 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 11, iters: 122480, time: 0.542, data: 0.000) G_L1: 13.825 G_L1_ABSOLUTE: 3.029 G_L1_RELATIVE: 10.796 G_Regularizer: 0.000 validation_error: 21.800 +(epoch: 11, iters: 124480, time: 0.531, data: 0.000) G_L1: 16.790 G_L1_ABSOLUTE: 2.346 G_L1_RELATIVE: 14.443 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 11, iters: 126480, time: 0.536, data: 0.000) G_L1: 15.606 G_L1_ABSOLUTE: 2.775 G_L1_RELATIVE: 12.831 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 11, iters: 128480, time: 0.544, data: 0.000) G_L1: 13.453 G_L1_ABSOLUTE: 2.277 G_L1_RELATIVE: 11.176 G_Regularizer: 0.000 validation_error: 20.572 +(epoch: 11, iters: 130480, time: 0.540, data: 0.000) G_L1: 16.980 G_L1_ABSOLUTE: 2.972 G_L1_RELATIVE: 14.008 G_Regularizer: 0.000 validation_error: 21.207 +(epoch: 11, iters: 132480, time: 0.905, data: 0.000) G_L1: 13.569 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 11.194 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 11, iters: 134480, time: 0.918, data: 0.000) G_L1: 16.084 G_L1_ABSOLUTE: 2.718 G_L1_RELATIVE: 13.366 G_Regularizer: 0.000 validation_error: 20.218 +(epoch: 11, iters: 136480, time: 0.920, data: 0.000) G_L1: 14.828 G_L1_ABSOLUTE: 3.328 G_L1_RELATIVE: 11.500 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 11, iters: 138480, time: 0.920, data: 0.000) G_L1: 20.499 G_L1_ABSOLUTE: 2.876 G_L1_RELATIVE: 17.623 G_Regularizer: 0.000 validation_error: 20.491 +(epoch: 11, iters: 140480, time: 0.920, data: 0.000) G_L1: 14.632 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 12.000 G_Regularizer: 0.000 validation_error: 20.408 +(epoch: 11, iters: 142480, time: 0.919, data: 0.000) G_L1: 16.573 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 13.679 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 11, iters: 144480, time: 0.918, data: 0.000) G_L1: 16.041 G_L1_ABSOLUTE: 2.957 G_L1_RELATIVE: 13.084 G_Regularizer: 0.000 validation_error: 20.929 +(epoch: 11, iters: 146480, time: 0.925, data: 0.001) G_L1: 14.816 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 12.533 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 11, iters: 148480, time: 0.918, data: 0.000) G_L1: 17.811 G_L1_ABSOLUTE: 2.959 G_L1_RELATIVE: 14.853 G_Regularizer: 0.000 validation_error: 21.003 +(epoch: 11, iters: 150480, time: 0.919, data: 0.000) G_L1: 13.809 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 11.355 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 11, iters: 152480, time: 0.919, data: 0.000) G_L1: 13.772 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 11.270 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 11, iters: 154480, time: 0.923, data: 0.000) G_L1: 15.495 G_L1_ABSOLUTE: 2.855 G_L1_RELATIVE: 12.640 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 11, iters: 156480, time: 0.927, data: 0.000) G_L1: 14.256 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 11.881 G_Regularizer: 0.000 validation_error: 19.980 +(epoch: 11, iters: 158480, time: 0.920, data: 0.000) G_L1: 16.756 G_L1_ABSOLUTE: 2.562 G_L1_RELATIVE: 14.194 G_Regularizer: 0.000 validation_error: 20.596 +(epoch: 11, iters: 160480, time: 0.925, data: 0.000) G_L1: 17.864 G_L1_ABSOLUTE: 2.916 G_L1_RELATIVE: 14.947 G_Regularizer: 0.000 validation_error: 20.221 +(epoch: 11, iters: 162480, time: 0.917, data: 0.000) G_L1: 15.868 G_L1_ABSOLUTE: 2.927 G_L1_RELATIVE: 12.941 G_Regularizer: 0.000 validation_error: 20.288 +(epoch: 11, iters: 164480, time: 0.919, data: 0.000) G_L1: 12.488 G_L1_ABSOLUTE: 2.594 G_L1_RELATIVE: 9.894 G_Regularizer: 0.000 validation_error: 20.631 +(epoch: 11, iters: 166480, time: 0.925, data: 0.000) G_L1: 18.878 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 16.249 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 11, iters: 168480, time: 0.923, data: 0.000) G_L1: 15.654 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 13.055 G_Regularizer: 0.000 validation_error: 20.369 +(epoch: 11, iters: 170480, time: 0.923, data: 0.000) G_L1: 15.099 G_L1_ABSOLUTE: 2.821 G_L1_RELATIVE: 12.279 G_Regularizer: 0.000 validation_error: 20.030 +(epoch: 11, iters: 172480, time: 0.922, data: 0.000) G_L1: 16.376 G_L1_ABSOLUTE: 2.560 G_L1_RELATIVE: 13.815 G_Regularizer: 0.000 validation_error: 20.749 +(epoch: 11, iters: 174480, time: 0.924, data: 0.000) G_L1: 13.603 G_L1_ABSOLUTE: 2.692 G_L1_RELATIVE: 10.911 G_Regularizer: 0.000 validation_error: 19.996 +(epoch: 11, iters: 176480, time: 0.930, data: 0.000) G_L1: 16.873 G_L1_ABSOLUTE: 2.692 G_L1_RELATIVE: 14.181 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 11, iters: 178480, time: 0.923, data: 0.000) G_L1: 19.851 G_L1_ABSOLUTE: 3.333 G_L1_RELATIVE: 16.518 G_Regularizer: 0.000 validation_error: 20.615 +(epoch: 11, iters: 180480, time: 0.928, data: 0.000) G_L1: 17.791 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 15.049 G_Regularizer: 0.000 validation_error: 20.285 +(epoch: 11, iters: 182480, time: 0.919, data: 0.000) G_L1: 19.061 G_L1_ABSOLUTE: 3.048 G_L1_RELATIVE: 16.013 G_Regularizer: 0.000 validation_error: 21.129 +(epoch: 11, iters: 184480, time: 0.915, data: 0.000) G_L1: 15.264 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 12.638 G_Regularizer: 0.000 validation_error: 20.644 +(epoch: 11, iters: 186480, time: 0.920, data: 0.000) G_L1: 13.892 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 11.555 G_Regularizer: 0.000 validation_error: 21.263 +(epoch: 11, iters: 188480, time: 0.921, data: 0.000) G_L1: 15.035 G_L1_ABSOLUTE: 2.827 G_L1_RELATIVE: 12.209 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 11, iters: 190480, time: 0.925, data: 0.000) G_L1: 15.177 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 12.713 G_Regularizer: 0.000 validation_error: 21.315 +(epoch: 11, iters: 192480, time: 0.924, data: 0.000) G_L1: 15.585 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 13.320 G_Regularizer: 0.000 validation_error: 21.604 +(epoch: 11, iters: 194480, time: 0.926, data: 0.000) G_L1: 12.338 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 9.768 G_Regularizer: 0.000 validation_error: 21.072 +(epoch: 11, iters: 196480, time: 0.921, data: 0.000) G_L1: 18.636 G_L1_ABSOLUTE: 2.819 G_L1_RELATIVE: 15.817 G_Regularizer: 0.000 validation_error: 20.552 +(epoch: 11, iters: 198480, time: 0.925, data: 0.000) G_L1: 14.545 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 12.021 G_Regularizer: 0.000 validation_error: 20.528 +(epoch: 11, iters: 200480, time: 0.920, data: 0.000) G_L1: 20.975 G_L1_ABSOLUTE: 3.117 G_L1_RELATIVE: 17.858 G_Regularizer: 0.000 validation_error: 20.569 +(epoch: 11, iters: 202480, time: 0.928, data: 0.000) G_L1: 13.589 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 11.132 G_Regularizer: 0.000 validation_error: 21.420 +(epoch: 11, iters: 204480, time: 0.921, data: 0.000) G_L1: 13.505 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 11.010 G_Regularizer: 0.000 validation_error: 20.684 +(epoch: 11, iters: 206480, time: 0.926, data: 0.000) G_L1: 16.471 G_L1_ABSOLUTE: 2.922 G_L1_RELATIVE: 13.549 G_Regularizer: 0.000 validation_error: 20.504 +(epoch: 11, iters: 208480, time: 0.921, data: 0.000) G_L1: 16.531 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 13.865 G_Regularizer: 0.000 validation_error: 20.447 +(epoch: 11, iters: 210480, time: 0.886, data: 0.000) G_L1: 15.495 G_L1_ABSOLUTE: 2.795 G_L1_RELATIVE: 12.700 G_Regularizer: 0.000 validation_error: 21.027 +(epoch: 11, iters: 212480, time: 0.557, data: 0.000) G_L1: 15.242 G_L1_ABSOLUTE: 3.111 G_L1_RELATIVE: 12.131 G_Regularizer: 0.000 validation_error: 21.078 +(epoch: 11, iters: 214480, time: 0.533, data: 0.000) G_L1: 17.768 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 15.428 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 11, iters: 216480, time: 0.532, data: 0.001) G_L1: 17.826 G_L1_ABSOLUTE: 2.813 G_L1_RELATIVE: 15.012 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 11, iters: 218480, time: 0.529, data: 0.001) G_L1: 26.968 G_L1_ABSOLUTE: 3.524 G_L1_RELATIVE: 23.444 G_Regularizer: 0.000 validation_error: 20.216 +(epoch: 11, iters: 220480, time: 0.526, data: 0.000) G_L1: 16.722 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 14.228 G_Regularizer: 0.000 validation_error: 19.986 +(epoch: 11, iters: 222480, time: 0.529, data: 0.000) G_L1: 15.934 G_L1_ABSOLUTE: 2.429 G_L1_RELATIVE: 13.505 G_Regularizer: 0.000 validation_error: 20.684 +(epoch: 11, iters: 224480, time: 0.528, data: 0.000) G_L1: 16.382 G_L1_ABSOLUTE: 2.853 G_L1_RELATIVE: 13.529 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 11, iters: 226480, time: 0.530, data: 0.001) G_L1: 15.439 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 12.813 G_Regularizer: 0.000 validation_error: 21.076 +(epoch: 11, iters: 228480, time: 0.533, data: 0.000) G_L1: 14.611 G_L1_ABSOLUTE: 2.051 G_L1_RELATIVE: 12.560 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 11, iters: 230480, time: 0.528, data: 0.001) G_L1: 15.223 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 12.546 G_Regularizer: 0.000 validation_error: 20.168 +(epoch: 11, iters: 232480, time: 0.521, data: 0.000) G_L1: 18.622 G_L1_ABSOLUTE: 2.998 G_L1_RELATIVE: 15.624 G_Regularizer: 0.000 validation_error: 20.415 +(epoch: 11, iters: 234480, time: 0.530, data: 0.000) G_L1: 17.019 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 14.343 G_Regularizer: 0.000 validation_error: 19.730 +(epoch: 11, iters: 236480, time: 0.525, data: 0.000) G_L1: 11.582 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 8.746 G_Regularizer: 0.000 validation_error: 20.749 +(epoch: 11, iters: 238480, time: 0.528, data: 0.000) G_L1: 17.524 G_L1_ABSOLUTE: 3.012 G_L1_RELATIVE: 14.511 G_Regularizer: 0.000 validation_error: 20.177 +(epoch: 11, iters: 240480, time: 0.530, data: 0.000) G_L1: 16.383 G_L1_ABSOLUTE: 2.977 G_L1_RELATIVE: 13.406 G_Regularizer: 0.000 validation_error: 21.028 +(epoch: 11, iters: 242480, time: 0.531, data: 0.000) G_L1: 12.936 G_L1_ABSOLUTE: 3.339 G_L1_RELATIVE: 9.596 G_Regularizer: 0.000 validation_error: 20.097 +(epoch: 11, iters: 244480, time: 0.526, data: 0.000) G_L1: 16.490 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 14.145 G_Regularizer: 0.000 validation_error: 21.038 +(epoch: 11, iters: 246480, time: 0.526, data: 0.000) G_L1: 19.234 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 16.453 G_Regularizer: 0.000 validation_error: 21.165 +(epoch: 11, iters: 248480, time: 0.528, data: 0.000) G_L1: 13.478 G_L1_ABSOLUTE: 2.899 G_L1_RELATIVE: 10.579 G_Regularizer: 0.000 validation_error: 19.934 +(epoch: 11, iters: 250480, time: 0.530, data: 0.000) G_L1: 14.532 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 11.949 G_Regularizer: 0.000 validation_error: 20.152 +(epoch: 11, iters: 252480, time: 0.528, data: 0.000) G_L1: 12.507 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 10.015 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 11, iters: 254480, time: 0.526, data: 0.000) G_L1: 13.786 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 11.160 G_Regularizer: 0.000 validation_error: 20.327 +(epoch: 11, iters: 256480, time: 0.527, data: 0.000) G_L1: 14.052 G_L1_ABSOLUTE: 2.538 G_L1_RELATIVE: 11.514 G_Regularizer: 0.000 validation_error: 20.498 +(epoch: 11, iters: 258480, time: 0.530, data: 0.000) G_L1: 14.081 G_L1_ABSOLUTE: 2.356 G_L1_RELATIVE: 11.725 G_Regularizer: 0.000 validation_error: 20.433 +(epoch: 11, iters: 260480, time: 0.524, data: 0.000) G_L1: 15.754 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 12.932 G_Regularizer: 0.000 validation_error: 20.470 +(epoch: 11, iters: 262480, time: 0.526, data: 0.000) G_L1: 16.718 G_L1_ABSOLUTE: 3.036 G_L1_RELATIVE: 13.682 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 11, iters: 264480, time: 0.530, data: 0.000) G_L1: 13.801 G_L1_ABSOLUTE: 2.472 G_L1_RELATIVE: 11.329 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 11, iters: 266480, time: 0.527, data: 0.000) G_L1: 14.400 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 11.864 G_Regularizer: 0.000 validation_error: 20.235 +(epoch: 11, iters: 268480, time: 0.533, data: 0.000) G_L1: 13.516 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 11.005 G_Regularizer: 0.000 validation_error: 20.992 +(epoch: 11, iters: 270480, time: 0.528, data: 0.000) G_L1: 16.431 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 13.550 G_Regularizer: 0.000 validation_error: 20.257 +(epoch: 11, iters: 272480, time: 0.526, data: 0.000) G_L1: 16.999 G_L1_ABSOLUTE: 3.061 G_L1_RELATIVE: 13.938 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 11, iters: 274480, time: 0.527, data: 0.000) G_L1: 14.973 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 12.235 G_Regularizer: 0.000 validation_error: 20.146 +(epoch: 11, iters: 276480, time: 0.528, data: 0.000) G_L1: 20.296 G_L1_ABSOLUTE: 2.959 G_L1_RELATIVE: 17.337 G_Regularizer: 0.000 validation_error: 21.501 +(epoch: 11, iters: 278480, time: 0.522, data: 0.000) G_L1: 13.399 G_L1_ABSOLUTE: 3.155 G_L1_RELATIVE: 10.243 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 11, iters: 280480, time: 0.529, data: 0.000) G_L1: 15.377 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 12.610 G_Regularizer: 0.000 validation_error: 21.688 +(epoch: 11, iters: 282480, time: 0.530, data: 0.000) G_L1: 17.750 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 15.281 G_Regularizer: 0.000 validation_error: 20.327 +(epoch: 11, iters: 284480, time: 0.531, data: 0.000) G_L1: 17.927 G_L1_ABSOLUTE: 2.972 G_L1_RELATIVE: 14.955 G_Regularizer: 0.000 validation_error: 20.068 +(epoch: 11, iters: 286480, time: 0.523, data: 0.000) G_L1: 13.003 G_L1_ABSOLUTE: 2.220 G_L1_RELATIVE: 10.782 G_Regularizer: 0.000 validation_error: 20.509 +(epoch: 11, iters: 288480, time: 0.530, data: 0.000) G_L1: 14.933 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 12.226 G_Regularizer: 0.000 validation_error: 21.162 +(epoch: 11, iters: 290480, time: 0.536, data: 0.000) G_L1: 17.147 G_L1_ABSOLUTE: 3.229 G_L1_RELATIVE: 13.917 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 11, iters: 292480, time: 0.525, data: 0.000) G_L1: 13.382 G_L1_ABSOLUTE: 2.441 G_L1_RELATIVE: 10.942 G_Regularizer: 0.000 validation_error: 20.436 +(epoch: 11, iters: 294480, time: 0.524, data: 0.000) G_L1: 19.117 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 16.372 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 11, iters: 296480, time: 0.529, data: 0.000) G_L1: 14.440 G_L1_ABSOLUTE: 3.084 G_L1_RELATIVE: 11.356 G_Regularizer: 0.000 validation_error: 20.279 +(epoch: 11, iters: 298480, time: 0.531, data: 0.000) G_L1: 14.145 G_L1_ABSOLUTE: 2.807 G_L1_RELATIVE: 11.338 G_Regularizer: 0.000 validation_error: 21.221 +(epoch: 11, iters: 300480, time: 0.530, data: 0.000) G_L1: 16.273 G_L1_ABSOLUTE: 2.911 G_L1_RELATIVE: 13.362 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 11, iters: 302480, time: 0.534, data: 0.000) G_L1: 15.915 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 13.282 G_Regularizer: 0.000 validation_error: 20.481 +(epoch: 12, iters: 1728, time: 0.522, data: 0.000) G_L1: 15.724 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 13.210 G_Regularizer: 0.000 validation_error: 21.135 +(epoch: 12, iters: 3728, time: 0.532, data: 0.000) G_L1: 17.741 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 15.019 G_Regularizer: 0.000 validation_error: 19.812 +(epoch: 12, iters: 5728, time: 0.535, data: 0.000) G_L1: 12.924 G_L1_ABSOLUTE: 2.351 G_L1_RELATIVE: 10.573 G_Regularizer: 0.000 validation_error: 20.099 +(epoch: 12, iters: 7728, time: 0.525, data: 0.000) G_L1: 14.210 G_L1_ABSOLUTE: 2.361 G_L1_RELATIVE: 11.850 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 12, iters: 9728, time: 0.527, data: 0.000) G_L1: 16.350 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 13.870 G_Regularizer: 0.000 validation_error: 21.565 +(epoch: 12, iters: 11728, time: 0.529, data: 0.000) G_L1: 13.257 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 10.620 G_Regularizer: 0.000 validation_error: 20.657 +(epoch: 12, iters: 13728, time: 0.533, data: 0.000) G_L1: 15.167 G_L1_ABSOLUTE: 2.471 G_L1_RELATIVE: 12.696 G_Regularizer: 0.000 validation_error: 22.384 +(epoch: 12, iters: 15728, time: 0.530, data: 0.000) G_L1: 13.293 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 10.660 G_Regularizer: 0.000 validation_error: 20.582 +(epoch: 12, iters: 17728, time: 0.526, data: 0.000) G_L1: 14.108 G_L1_ABSOLUTE: 2.909 G_L1_RELATIVE: 11.200 G_Regularizer: 0.000 validation_error: 20.206 +(epoch: 12, iters: 19728, time: 0.521, data: 0.000) G_L1: 15.960 G_L1_ABSOLUTE: 2.685 G_L1_RELATIVE: 13.274 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 12, iters: 21728, time: 0.523, data: 0.000) G_L1: 13.859 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 11.471 G_Regularizer: 0.000 validation_error: 20.943 +(epoch: 12, iters: 23728, time: 0.527, data: 0.000) G_L1: 14.965 G_L1_ABSOLUTE: 2.251 G_L1_RELATIVE: 12.714 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 12, iters: 25728, time: 0.532, data: 0.000) G_L1: 13.731 G_L1_ABSOLUTE: 2.560 G_L1_RELATIVE: 11.171 G_Regularizer: 0.000 validation_error: 20.535 +(epoch: 12, iters: 27728, time: 0.522, data: 0.000) G_L1: 15.238 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 12.575 G_Regularizer: 0.000 validation_error: 20.522 +(epoch: 12, iters: 29728, time: 0.523, data: 0.000) G_L1: 16.831 G_L1_ABSOLUTE: 2.886 G_L1_RELATIVE: 13.945 G_Regularizer: 0.000 validation_error: 20.735 +(epoch: 12, iters: 31728, time: 0.529, data: 0.000) G_L1: 14.177 G_L1_ABSOLUTE: 2.996 G_L1_RELATIVE: 11.181 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 12, iters: 33728, time: 0.527, data: 0.000) G_L1: 18.937 G_L1_ABSOLUTE: 2.977 G_L1_RELATIVE: 15.960 G_Regularizer: 0.000 validation_error: 21.125 +(epoch: 12, iters: 35728, time: 0.527, data: 0.000) G_L1: 14.674 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 12.141 G_Regularizer: 0.000 validation_error: 21.017 +(epoch: 12, iters: 37728, time: 0.522, data: 0.000) G_L1: 17.610 G_L1_ABSOLUTE: 2.907 G_L1_RELATIVE: 14.703 G_Regularizer: 0.000 validation_error: 20.417 +(epoch: 12, iters: 39728, time: 0.525, data: 0.001) G_L1: 15.945 G_L1_ABSOLUTE: 2.330 G_L1_RELATIVE: 13.615 G_Regularizer: 0.000 validation_error: 19.909 +(epoch: 12, iters: 41728, time: 0.523, data: 0.000) G_L1: 17.535 G_L1_ABSOLUTE: 2.929 G_L1_RELATIVE: 14.606 G_Regularizer: 0.000 validation_error: 20.520 +(epoch: 12, iters: 43728, time: 0.529, data: 0.000) G_L1: 16.762 G_L1_ABSOLUTE: 2.672 G_L1_RELATIVE: 14.090 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 12, iters: 45728, time: 0.533, data: 0.000) G_L1: 16.834 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 14.252 G_Regularizer: 0.000 validation_error: 20.778 +(epoch: 12, iters: 47728, time: 0.530, data: 0.000) G_L1: 17.534 G_L1_ABSOLUTE: 2.719 G_L1_RELATIVE: 14.815 G_Regularizer: 0.000 validation_error: 20.067 +(epoch: 12, iters: 49728, time: 0.529, data: 0.000) G_L1: 15.151 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 12.765 G_Regularizer: 0.000 validation_error: 20.490 +(epoch: 12, iters: 51728, time: 0.526, data: 0.000) G_L1: 14.129 G_L1_ABSOLUTE: 2.848 G_L1_RELATIVE: 11.281 G_Regularizer: 0.000 validation_error: 20.452 +(epoch: 12, iters: 53728, time: 0.528, data: 0.000) G_L1: 14.174 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 11.572 G_Regularizer: 0.000 validation_error: 20.471 +(epoch: 12, iters: 55728, time: 0.528, data: 0.000) G_L1: 17.472 G_L1_ABSOLUTE: 2.875 G_L1_RELATIVE: 14.597 G_Regularizer: 0.000 validation_error: 21.149 +(epoch: 12, iters: 57728, time: 0.533, data: 0.000) G_L1: 16.179 G_L1_ABSOLUTE: 2.851 G_L1_RELATIVE: 13.328 G_Regularizer: 0.000 validation_error: 21.284 +(epoch: 12, iters: 59728, time: 0.534, data: 0.000) G_L1: 18.631 G_L1_ABSOLUTE: 2.775 G_L1_RELATIVE: 15.856 G_Regularizer: 0.000 validation_error: 20.998 +(epoch: 12, iters: 61728, time: 0.528, data: 0.000) G_L1: 14.799 G_L1_ABSOLUTE: 3.099 G_L1_RELATIVE: 11.700 G_Regularizer: 0.000 validation_error: 19.772 +(epoch: 12, iters: 63728, time: 0.525, data: 0.000) G_L1: 14.629 G_L1_ABSOLUTE: 2.606 G_L1_RELATIVE: 12.023 G_Regularizer: 0.000 validation_error: 21.674 +(epoch: 12, iters: 65728, time: 0.526, data: 0.001) G_L1: 15.409 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 12.797 G_Regularizer: 0.000 validation_error: 20.369 +(epoch: 12, iters: 67728, time: 0.528, data: 0.000) G_L1: 13.566 G_L1_ABSOLUTE: 2.931 G_L1_RELATIVE: 10.635 G_Regularizer: 0.000 validation_error: 20.993 +(epoch: 12, iters: 69728, time: 0.530, data: 0.000) G_L1: 15.074 G_L1_ABSOLUTE: 3.131 G_L1_RELATIVE: 11.943 G_Regularizer: 0.000 validation_error: 20.041 +(epoch: 12, iters: 71728, time: 0.526, data: 0.000) G_L1: 13.059 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 10.395 G_Regularizer: 0.000 validation_error: 20.370 +(epoch: 12, iters: 73728, time: 0.524, data: 0.000) G_L1: 12.749 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 10.337 G_Regularizer: 0.000 validation_error: 20.185 +(epoch: 12, iters: 75728, time: 0.527, data: 0.000) G_L1: 15.351 G_L1_ABSOLUTE: 2.779 G_L1_RELATIVE: 12.572 G_Regularizer: 0.000 validation_error: 20.555 +(epoch: 12, iters: 77728, time: 0.527, data: 0.000) G_L1: 13.469 G_L1_ABSOLUTE: 2.444 G_L1_RELATIVE: 11.025 G_Regularizer: 0.000 validation_error: 20.747 +(epoch: 12, iters: 79728, time: 0.530, data: 0.001) G_L1: 17.070 G_L1_ABSOLUTE: 2.727 G_L1_RELATIVE: 14.342 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 12, iters: 81728, time: 0.527, data: 0.000) G_L1: 17.607 G_L1_ABSOLUTE: 2.362 G_L1_RELATIVE: 15.244 G_Regularizer: 0.000 validation_error: 20.839 +(epoch: 12, iters: 83728, time: 0.527, data: 0.000) G_L1: 16.949 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 14.365 G_Regularizer: 0.000 validation_error: 20.403 +(epoch: 12, iters: 85728, time: 0.531, data: 0.000) G_L1: 15.851 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 13.227 G_Regularizer: 0.000 validation_error: 21.052 +(epoch: 12, iters: 87728, time: 0.527, data: 0.000) G_L1: 16.581 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 13.835 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 12, iters: 89728, time: 0.529, data: 0.000) G_L1: 13.167 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 10.461 G_Regularizer: 0.000 validation_error: 20.096 +(epoch: 12, iters: 91728, time: 0.532, data: 0.000) G_L1: 15.076 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 12.662 G_Regularizer: 0.000 validation_error: 21.315 +(epoch: 12, iters: 93728, time: 0.526, data: 0.000) G_L1: 17.040 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 14.394 G_Regularizer: 0.000 validation_error: 21.336 +(epoch: 12, iters: 95728, time: 0.526, data: 0.000) G_L1: 12.777 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 10.295 G_Regularizer: 0.000 validation_error: 20.283 +(epoch: 12, iters: 97728, time: 0.529, data: 0.000) G_L1: 19.675 G_L1_ABSOLUTE: 3.133 G_L1_RELATIVE: 16.542 G_Regularizer: 0.000 validation_error: 20.522 +(epoch: 12, iters: 99728, time: 0.531, data: 0.000) G_L1: 18.210 G_L1_ABSOLUTE: 3.050 G_L1_RELATIVE: 15.160 G_Regularizer: 0.000 validation_error: 21.378 +(epoch: 12, iters: 101728, time: 0.528, data: 0.000) G_L1: 13.621 G_L1_ABSOLUTE: 2.500 G_L1_RELATIVE: 11.121 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 12, iters: 103728, time: 0.527, data: 0.000) G_L1: 14.532 G_L1_ABSOLUTE: 2.985 G_L1_RELATIVE: 11.547 G_Regularizer: 0.000 validation_error: 20.692 +(epoch: 12, iters: 105728, time: 0.526, data: 0.000) G_L1: 16.584 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 13.958 G_Regularizer: 0.000 validation_error: 21.020 +(epoch: 12, iters: 107728, time: 0.526, data: 0.000) G_L1: 15.125 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 12.497 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 12, iters: 109728, time: 0.526, data: 0.000) G_L1: 13.830 G_L1_ABSOLUTE: 2.643 G_L1_RELATIVE: 11.187 G_Regularizer: 0.000 validation_error: 20.117 +(epoch: 12, iters: 111728, time: 0.528, data: 0.000) G_L1: 14.338 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 11.765 G_Regularizer: 0.000 validation_error: 19.777 +(epoch: 12, iters: 113728, time: 0.529, data: 0.000) G_L1: 15.024 G_L1_ABSOLUTE: 2.434 G_L1_RELATIVE: 12.590 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 12, iters: 115728, time: 0.523, data: 0.000) G_L1: 14.143 G_L1_ABSOLUTE: 2.887 G_L1_RELATIVE: 11.256 G_Regularizer: 0.000 validation_error: 20.357 +(epoch: 12, iters: 117728, time: 0.531, data: 0.000) G_L1: 13.189 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 10.531 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 12, iters: 119728, time: 0.527, data: 0.000) G_L1: 15.810 G_L1_ABSOLUTE: 2.939 G_L1_RELATIVE: 12.871 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 12, iters: 121728, time: 0.527, data: 0.000) G_L1: 14.398 G_L1_ABSOLUTE: 2.257 G_L1_RELATIVE: 12.141 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 12, iters: 123728, time: 0.536, data: 0.000) G_L1: 18.415 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 15.760 G_Regularizer: 0.000 validation_error: 20.453 +(epoch: 12, iters: 125728, time: 0.525, data: 0.000) G_L1: 16.323 G_L1_ABSOLUTE: 2.793 G_L1_RELATIVE: 13.530 G_Regularizer: 0.000 validation_error: 20.003 +(epoch: 12, iters: 127728, time: 0.524, data: 0.000) G_L1: 17.923 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 15.289 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 12, iters: 129728, time: 0.529, data: 0.000) G_L1: 13.750 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 10.928 G_Regularizer: 0.000 validation_error: 20.490 +(epoch: 12, iters: 131728, time: 0.523, data: 0.000) G_L1: 12.818 G_L1_ABSOLUTE: 2.312 G_L1_RELATIVE: 10.506 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 12, iters: 133728, time: 0.527, data: 0.000) G_L1: 18.953 G_L1_ABSOLUTE: 3.169 G_L1_RELATIVE: 15.784 G_Regularizer: 0.000 validation_error: 20.982 +(epoch: 12, iters: 135728, time: 0.528, data: 0.000) G_L1: 16.005 G_L1_ABSOLUTE: 2.552 G_L1_RELATIVE: 13.453 G_Regularizer: 0.000 validation_error: 20.369 +(epoch: 12, iters: 137728, time: 0.526, data: 0.000) G_L1: 14.852 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 12.188 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 12, iters: 139728, time: 0.527, data: 0.000) G_L1: 14.041 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 11.367 G_Regularizer: 0.000 validation_error: 20.450 +(epoch: 12, iters: 141728, time: 0.524, data: 0.000) G_L1: 15.628 G_L1_ABSOLUTE: 3.054 G_L1_RELATIVE: 12.574 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 12, iters: 143728, time: 0.532, data: 0.000) G_L1: 16.777 G_L1_ABSOLUTE: 2.605 G_L1_RELATIVE: 14.172 G_Regularizer: 0.000 validation_error: 19.954 +(epoch: 12, iters: 145728, time: 0.526, data: 0.000) G_L1: 16.162 G_L1_ABSOLUTE: 2.557 G_L1_RELATIVE: 13.605 G_Regularizer: 0.000 validation_error: 20.296 +(epoch: 12, iters: 147728, time: 0.531, data: 0.000) G_L1: 16.454 G_L1_ABSOLUTE: 2.851 G_L1_RELATIVE: 13.603 G_Regularizer: 0.000 validation_error: 20.313 +(epoch: 12, iters: 149728, time: 0.529, data: 0.000) G_L1: 15.254 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 12.265 G_Regularizer: 0.000 validation_error: 19.959 +(epoch: 12, iters: 151728, time: 0.529, data: 0.000) G_L1: 16.641 G_L1_ABSOLUTE: 3.272 G_L1_RELATIVE: 13.370 G_Regularizer: 0.000 validation_error: 21.006 +(epoch: 12, iters: 153728, time: 0.529, data: 0.000) G_L1: 15.600 G_L1_ABSOLUTE: 2.813 G_L1_RELATIVE: 12.787 G_Regularizer: 0.000 validation_error: 19.959 +(epoch: 12, iters: 155728, time: 0.528, data: 0.000) G_L1: 15.437 G_L1_ABSOLUTE: 3.343 G_L1_RELATIVE: 12.094 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 12, iters: 157728, time: 0.525, data: 0.000) G_L1: 20.543 G_L1_ABSOLUTE: 2.935 G_L1_RELATIVE: 17.608 G_Regularizer: 0.000 validation_error: 20.566 +(epoch: 12, iters: 159728, time: 0.530, data: 0.000) G_L1: 14.562 G_L1_ABSOLUTE: 2.534 G_L1_RELATIVE: 12.028 G_Regularizer: 0.000 validation_error: 20.600 +(epoch: 12, iters: 161728, time: 0.528, data: 0.000) G_L1: 12.041 G_L1_ABSOLUTE: 2.456 G_L1_RELATIVE: 9.585 G_Regularizer: 0.000 validation_error: 20.802 +(epoch: 12, iters: 163728, time: 0.529, data: 0.000) G_L1: 14.755 G_L1_ABSOLUTE: 3.024 G_L1_RELATIVE: 11.731 G_Regularizer: 0.000 validation_error: 20.393 +(epoch: 12, iters: 165728, time: 0.542, data: 0.000) G_L1: 12.314 G_L1_ABSOLUTE: 2.372 G_L1_RELATIVE: 9.943 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 12, iters: 167728, time: 0.578, data: 0.000) G_L1: 16.751 G_L1_ABSOLUTE: 2.829 G_L1_RELATIVE: 13.922 G_Regularizer: 0.000 validation_error: 21.163 +(epoch: 12, iters: 169728, time: 0.586, data: 0.000) G_L1: 15.225 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 12.697 G_Regularizer: 0.000 validation_error: 20.814 +(epoch: 12, iters: 171728, time: 0.577, data: 0.000) G_L1: 13.867 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 11.210 G_Regularizer: 0.000 validation_error: 20.297 +(epoch: 12, iters: 173728, time: 0.571, data: 0.000) G_L1: 15.300 G_L1_ABSOLUTE: 2.825 G_L1_RELATIVE: 12.475 G_Regularizer: 0.000 validation_error: 20.375 +(epoch: 12, iters: 175728, time: 0.570, data: 0.000) G_L1: 21.425 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 18.890 G_Regularizer: 0.000 validation_error: 20.098 +(epoch: 12, iters: 177728, time: 0.584, data: 0.000) G_L1: 13.583 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 10.974 G_Regularizer: 0.000 validation_error: 20.568 +(epoch: 12, iters: 179728, time: 0.580, data: 0.000) G_L1: 14.473 G_L1_ABSOLUTE: 2.358 G_L1_RELATIVE: 12.115 G_Regularizer: 0.000 validation_error: 20.723 +(epoch: 12, iters: 181728, time: 0.587, data: 0.000) G_L1: 16.857 G_L1_ABSOLUTE: 2.949 G_L1_RELATIVE: 13.908 G_Regularizer: 0.000 validation_error: 20.352 +(epoch: 12, iters: 183728, time: 0.586, data: 0.000) G_L1: 11.880 G_L1_ABSOLUTE: 2.180 G_L1_RELATIVE: 9.700 G_Regularizer: 0.000 validation_error: 20.911 +(epoch: 12, iters: 185728, time: 0.527, data: 0.000) G_L1: 16.344 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 13.850 G_Regularizer: 0.000 validation_error: 20.724 +(epoch: 12, iters: 187728, time: 0.526, data: 0.000) G_L1: 13.986 G_L1_ABSOLUTE: 2.465 G_L1_RELATIVE: 11.522 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 12, iters: 189728, time: 0.525, data: 0.000) G_L1: 17.501 G_L1_ABSOLUTE: 3.148 G_L1_RELATIVE: 14.353 G_Regularizer: 0.000 validation_error: 21.521 +(epoch: 12, iters: 191728, time: 0.529, data: 0.000) G_L1: 13.334 G_L1_ABSOLUTE: 2.828 G_L1_RELATIVE: 10.505 G_Regularizer: 0.000 validation_error: 21.024 +(epoch: 12, iters: 193728, time: 0.526, data: 0.000) G_L1: 13.050 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 10.522 G_Regularizer: 0.000 validation_error: 20.246 +(epoch: 12, iters: 195728, time: 0.527, data: 0.000) G_L1: 15.878 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 13.538 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 12, iters: 197728, time: 0.527, data: 0.000) G_L1: 14.032 G_L1_ABSOLUTE: 2.179 G_L1_RELATIVE: 11.852 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 12, iters: 199728, time: 0.528, data: 0.000) G_L1: 15.836 G_L1_ABSOLUTE: 2.838 G_L1_RELATIVE: 12.998 G_Regularizer: 0.000 validation_error: 21.012 +(epoch: 12, iters: 201728, time: 0.526, data: 0.000) G_L1: 16.718 G_L1_ABSOLUTE: 2.281 G_L1_RELATIVE: 14.438 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 12, iters: 203728, time: 0.533, data: 0.000) G_L1: 17.392 G_L1_ABSOLUTE: 2.752 G_L1_RELATIVE: 14.640 G_Regularizer: 0.000 validation_error: 20.563 +(epoch: 12, iters: 205728, time: 0.529, data: 0.001) G_L1: 15.247 G_L1_ABSOLUTE: 2.769 G_L1_RELATIVE: 12.478 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 12, iters: 207728, time: 0.529, data: 0.000) G_L1: 15.448 G_L1_ABSOLUTE: 2.872 G_L1_RELATIVE: 12.576 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 12, iters: 209728, time: 0.537, data: 0.000) G_L1: 14.785 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 12.171 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 12, iters: 211728, time: 0.524, data: 0.000) G_L1: 17.222 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 14.498 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 12, iters: 213728, time: 0.532, data: 0.000) G_L1: 14.576 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 11.980 G_Regularizer: 0.000 validation_error: 19.973 +(epoch: 12, iters: 215728, time: 0.533, data: 0.000) G_L1: 16.782 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 14.147 G_Regularizer: 0.000 validation_error: 19.712 +(epoch: 12, iters: 217728, time: 0.528, data: 0.000) G_L1: 13.155 G_L1_ABSOLUTE: 2.285 G_L1_RELATIVE: 10.869 G_Regularizer: 0.000 validation_error: 20.357 +(epoch: 12, iters: 219728, time: 0.528, data: 0.000) G_L1: 14.675 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 12.107 G_Regularizer: 0.000 validation_error: 20.791 +(epoch: 12, iters: 221728, time: 0.527, data: 0.000) G_L1: 15.247 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 12.463 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 12, iters: 223728, time: 0.541, data: 0.000) G_L1: 15.439 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 13.085 G_Regularizer: 0.000 validation_error: 20.293 +(epoch: 12, iters: 225728, time: 0.544, data: 0.000) G_L1: 15.914 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 13.582 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 12, iters: 227728, time: 0.547, data: 0.000) G_L1: 14.425 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 11.766 G_Regularizer: 0.000 validation_error: 21.276 +(epoch: 12, iters: 229728, time: 0.555, data: 0.000) G_L1: 15.796 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 12.919 G_Regularizer: 0.000 validation_error: 20.359 +(epoch: 12, iters: 231728, time: 0.527, data: 0.000) G_L1: 13.090 G_L1_ABSOLUTE: 2.452 G_L1_RELATIVE: 10.638 G_Regularizer: 0.000 validation_error: 19.841 +(epoch: 12, iters: 233728, time: 0.535, data: 0.000) G_L1: 14.464 G_L1_ABSOLUTE: 2.808 G_L1_RELATIVE: 11.656 G_Regularizer: 0.000 validation_error: 21.254 +(epoch: 12, iters: 235728, time: 0.554, data: 0.000) G_L1: 16.927 G_L1_ABSOLUTE: 2.803 G_L1_RELATIVE: 14.124 G_Regularizer: 0.000 validation_error: 20.020 +(epoch: 12, iters: 237728, time: 0.551, data: 0.000) G_L1: 18.946 G_L1_ABSOLUTE: 3.049 G_L1_RELATIVE: 15.897 G_Regularizer: 0.000 validation_error: 19.887 +(epoch: 12, iters: 239728, time: 0.544, data: 0.000) G_L1: 14.554 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 12.121 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 12, iters: 241728, time: 0.550, data: 0.000) G_L1: 14.955 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 12.482 G_Regularizer: 0.000 validation_error: 20.197 +(epoch: 12, iters: 243728, time: 0.556, data: 0.000) G_L1: 15.971 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 12.982 G_Regularizer: 0.000 validation_error: 20.488 +(epoch: 12, iters: 245728, time: 0.554, data: 0.000) G_L1: 17.786 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 14.951 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 12, iters: 247728, time: 0.559, data: 0.000) G_L1: 15.590 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 12.875 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 12, iters: 249728, time: 0.530, data: 0.001) G_L1: 15.817 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 13.144 G_Regularizer: 0.000 validation_error: 20.531 +(epoch: 12, iters: 251728, time: 0.545, data: 0.000) G_L1: 17.129 G_L1_ABSOLUTE: 2.779 G_L1_RELATIVE: 14.351 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 12, iters: 253728, time: 0.562, data: 0.000) G_L1: 15.595 G_L1_ABSOLUTE: 3.490 G_L1_RELATIVE: 12.105 G_Regularizer: 0.000 validation_error: 20.769 +(epoch: 12, iters: 255728, time: 0.554, data: 0.000) G_L1: 16.554 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 13.888 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 12, iters: 257728, time: 0.530, data: 0.000) G_L1: 14.065 G_L1_ABSOLUTE: 2.874 G_L1_RELATIVE: 11.191 G_Regularizer: 0.000 validation_error: 20.533 +(epoch: 12, iters: 259728, time: 0.559, data: 0.000) G_L1: 14.618 G_L1_ABSOLUTE: 2.899 G_L1_RELATIVE: 11.719 G_Regularizer: 0.000 validation_error: 21.422 +(epoch: 12, iters: 261728, time: 0.565, data: 0.000) G_L1: 14.307 G_L1_ABSOLUTE: 3.278 G_L1_RELATIVE: 11.029 G_Regularizer: 0.000 validation_error: 21.353 +(epoch: 12, iters: 263728, time: 0.556, data: 0.000) G_L1: 12.485 G_L1_ABSOLUTE: 2.318 G_L1_RELATIVE: 10.167 G_Regularizer: 0.000 validation_error: 20.369 +(epoch: 12, iters: 265728, time: 0.526, data: 0.000) G_L1: 14.852 G_L1_ABSOLUTE: 2.833 G_L1_RELATIVE: 12.019 G_Regularizer: 0.000 validation_error: 20.661 +(epoch: 12, iters: 267728, time: 0.532, data: 0.000) G_L1: 14.449 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 11.773 G_Regularizer: 0.000 validation_error: 20.399 +(epoch: 12, iters: 269728, time: 0.538, data: 0.000) G_L1: 14.787 G_L1_ABSOLUTE: 2.422 G_L1_RELATIVE: 12.365 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 12, iters: 271728, time: 0.550, data: 0.000) G_L1: 14.876 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 12.140 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 12, iters: 273728, time: 0.543, data: 0.001) G_L1: 14.125 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 11.704 G_Regularizer: 0.000 validation_error: 20.573 +(epoch: 12, iters: 275728, time: 0.542, data: 0.000) G_L1: 16.684 G_L1_ABSOLUTE: 3.195 G_L1_RELATIVE: 13.489 G_Regularizer: 0.000 validation_error: 20.960 +(epoch: 12, iters: 277728, time: 0.561, data: 0.000) G_L1: 16.461 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 13.737 G_Regularizer: 0.000 validation_error: 21.175 +(epoch: 12, iters: 279728, time: 0.537, data: 0.000) G_L1: 17.806 G_L1_ABSOLUTE: 2.990 G_L1_RELATIVE: 14.816 G_Regularizer: 0.000 validation_error: 21.170 +(epoch: 12, iters: 281728, time: 0.537, data: 0.000) G_L1: 14.342 G_L1_ABSOLUTE: 2.961 G_L1_RELATIVE: 11.381 G_Regularizer: 0.000 validation_error: 20.239 +(epoch: 12, iters: 283728, time: 0.532, data: 0.000) G_L1: 18.945 G_L1_ABSOLUTE: 2.579 G_L1_RELATIVE: 16.366 G_Regularizer: 0.000 validation_error: 20.480 +(epoch: 12, iters: 285728, time: 0.531, data: 0.000) G_L1: 12.938 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 10.281 G_Regularizer: 0.000 validation_error: 21.294 +(epoch: 12, iters: 287728, time: 0.547, data: 0.000) G_L1: 17.512 G_L1_ABSOLUTE: 2.974 G_L1_RELATIVE: 14.537 G_Regularizer: 0.000 validation_error: 20.894 +(epoch: 12, iters: 289728, time: 0.545, data: 0.000) G_L1: 16.963 G_L1_ABSOLUTE: 3.036 G_L1_RELATIVE: 13.927 G_Regularizer: 0.000 validation_error: 22.002 +(epoch: 12, iters: 291728, time: 0.540, data: 0.000) G_L1: 15.221 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 12.696 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 12, iters: 293728, time: 0.555, data: 0.001) G_L1: 14.782 G_L1_ABSOLUTE: 3.108 G_L1_RELATIVE: 11.674 G_Regularizer: 0.000 validation_error: 20.818 +(epoch: 12, iters: 295728, time: 0.554, data: 0.000) G_L1: 14.291 G_L1_ABSOLUTE: 2.499 G_L1_RELATIVE: 11.792 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 12, iters: 297728, time: 0.558, data: 0.000) G_L1: 17.145 G_L1_ABSOLUTE: 2.511 G_L1_RELATIVE: 14.634 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 12, iters: 299728, time: 0.549, data: 0.000) G_L1: 17.186 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 14.316 G_Regularizer: 0.000 validation_error: 20.579 +(epoch: 12, iters: 301728, time: 0.561, data: 0.000) G_L1: 13.919 G_L1_ABSOLUTE: 2.747 G_L1_RELATIVE: 11.172 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 13, iters: 976, time: 0.544, data: 0.000) G_L1: 16.858 G_L1_ABSOLUTE: 3.082 G_L1_RELATIVE: 13.777 G_Regularizer: 0.000 validation_error: 20.114 +(epoch: 13, iters: 2976, time: 0.562, data: 0.000) G_L1: 15.040 G_L1_ABSOLUTE: 2.553 G_L1_RELATIVE: 12.487 G_Regularizer: 0.000 validation_error: 20.042 +(epoch: 13, iters: 4976, time: 0.540, data: 0.000) G_L1: 15.250 G_L1_ABSOLUTE: 3.119 G_L1_RELATIVE: 12.131 G_Regularizer: 0.000 validation_error: 21.293 +(epoch: 13, iters: 6976, time: 0.554, data: 0.000) G_L1: 16.756 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 14.099 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 13, iters: 8976, time: 0.549, data: 0.000) G_L1: 15.237 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 12.647 G_Regularizer: 0.000 validation_error: 20.301 +(epoch: 13, iters: 10976, time: 0.550, data: 0.000) G_L1: 19.038 G_L1_ABSOLUTE: 3.167 G_L1_RELATIVE: 15.871 G_Regularizer: 0.000 validation_error: 20.470 +(epoch: 13, iters: 12976, time: 0.541, data: 0.000) G_L1: 17.155 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 14.604 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 13, iters: 14976, time: 0.549, data: 0.000) G_L1: 13.155 G_L1_ABSOLUTE: 2.163 G_L1_RELATIVE: 10.992 G_Regularizer: 0.000 validation_error: 20.383 +(epoch: 13, iters: 16976, time: 0.554, data: 0.000) G_L1: 12.324 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 9.879 G_Regularizer: 0.000 validation_error: 20.944 +(epoch: 13, iters: 18976, time: 0.555, data: 0.000) G_L1: 13.218 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 10.766 G_Regularizer: 0.000 validation_error: 21.233 +(epoch: 13, iters: 20976, time: 0.548, data: 0.000) G_L1: 15.478 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 12.588 G_Regularizer: 0.000 validation_error: 20.501 +(epoch: 13, iters: 22976, time: 0.556, data: 0.000) G_L1: 15.290 G_L1_ABSOLUTE: 3.475 G_L1_RELATIVE: 11.815 G_Regularizer: 0.000 validation_error: 21.195 +(epoch: 13, iters: 24976, time: 0.561, data: 0.000) G_L1: 13.541 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 10.744 G_Regularizer: 0.000 validation_error: 20.109 +(epoch: 13, iters: 26976, time: 0.558, data: 0.000) G_L1: 13.602 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 11.129 G_Regularizer: 0.000 validation_error: 20.068 +(epoch: 13, iters: 28976, time: 0.526, data: 0.000) G_L1: 17.087 G_L1_ABSOLUTE: 2.764 G_L1_RELATIVE: 14.323 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 13, iters: 30976, time: 0.538, data: 0.000) G_L1: 15.520 G_L1_ABSOLUTE: 3.129 G_L1_RELATIVE: 12.391 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 13, iters: 32976, time: 0.556, data: 0.000) G_L1: 17.732 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 14.986 G_Regularizer: 0.000 validation_error: 21.526 +(epoch: 13, iters: 34976, time: 0.554, data: 0.000) G_L1: 14.897 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 12.477 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 13, iters: 36976, time: 0.564, data: 0.000) G_L1: 16.279 G_L1_ABSOLUTE: 2.770 G_L1_RELATIVE: 13.509 G_Regularizer: 0.000 validation_error: 21.424 +(epoch: 13, iters: 38976, time: 0.550, data: 0.001) G_L1: 16.573 G_L1_ABSOLUTE: 3.176 G_L1_RELATIVE: 13.397 G_Regularizer: 0.000 validation_error: 20.671 +(epoch: 13, iters: 40976, time: 0.549, data: 0.000) G_L1: 15.057 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 12.468 G_Regularizer: 0.000 validation_error: 20.276 +(epoch: 13, iters: 42976, time: 0.556, data: 0.000) G_L1: 15.123 G_L1_ABSOLUTE: 2.745 G_L1_RELATIVE: 12.377 G_Regularizer: 0.000 validation_error: 20.620 +(epoch: 13, iters: 44976, time: 0.556, data: 0.000) G_L1: 16.213 G_L1_ABSOLUTE: 2.662 G_L1_RELATIVE: 13.551 G_Regularizer: 0.000 validation_error: 20.859 +(epoch: 13, iters: 46976, time: 0.538, data: 0.000) G_L1: 12.090 G_L1_ABSOLUTE: 2.532 G_L1_RELATIVE: 9.558 G_Regularizer: 0.000 validation_error: 20.435 +(epoch: 13, iters: 48976, time: 0.531, data: 0.000) G_L1: 15.107 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 12.526 G_Regularizer: 0.000 validation_error: 20.920 +(epoch: 13, iters: 50976, time: 0.547, data: 0.000) G_L1: 16.637 G_L1_ABSOLUTE: 2.963 G_L1_RELATIVE: 13.675 G_Regularizer: 0.000 validation_error: 20.904 +(epoch: 13, iters: 52976, time: 0.551, data: 0.000) G_L1: 14.541 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 12.243 G_Regularizer: 0.000 validation_error: 20.662 +(epoch: 13, iters: 54976, time: 0.544, data: 0.001) G_L1: 16.875 G_L1_ABSOLUTE: 2.448 G_L1_RELATIVE: 14.427 G_Regularizer: 0.000 validation_error: 20.852 +(epoch: 13, iters: 56976, time: 0.537, data: 0.000) G_L1: 18.710 G_L1_ABSOLUTE: 3.124 G_L1_RELATIVE: 15.586 G_Regularizer: 0.000 validation_error: 20.432 +(epoch: 13, iters: 58976, time: 0.552, data: 0.000) G_L1: 17.448 G_L1_ABSOLUTE: 3.101 G_L1_RELATIVE: 14.347 G_Regularizer: 0.000 validation_error: 21.337 +(epoch: 13, iters: 60976, time: 0.555, data: 0.000) G_L1: 13.407 G_L1_ABSOLUTE: 2.411 G_L1_RELATIVE: 10.996 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 13, iters: 62976, time: 0.550, data: 0.000) G_L1: 16.745 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 14.161 G_Regularizer: 0.000 validation_error: 20.230 +(epoch: 13, iters: 64976, time: 0.540, data: 0.000) G_L1: 14.258 G_L1_ABSOLUTE: 2.364 G_L1_RELATIVE: 11.894 G_Regularizer: 0.000 validation_error: 20.502 +(epoch: 13, iters: 66976, time: 0.530, data: 0.000) G_L1: 15.699 G_L1_ABSOLUTE: 2.821 G_L1_RELATIVE: 12.878 G_Regularizer: 0.000 validation_error: 20.006 +(epoch: 13, iters: 68976, time: 0.545, data: 0.000) G_L1: 13.681 G_L1_ABSOLUTE: 2.440 G_L1_RELATIVE: 11.241 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 13, iters: 70976, time: 0.539, data: 0.000) G_L1: 14.305 G_L1_ABSOLUTE: 2.851 G_L1_RELATIVE: 11.454 G_Regularizer: 0.000 validation_error: 20.435 +(epoch: 13, iters: 72976, time: 0.542, data: 0.000) G_L1: 13.469 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 11.050 G_Regularizer: 0.000 validation_error: 20.148 +(epoch: 13, iters: 74976, time: 0.543, data: 0.000) G_L1: 16.010 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 13.675 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 13, iters: 76976, time: 0.549, data: 0.000) G_L1: 14.021 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 11.366 G_Regularizer: 0.000 validation_error: 19.921 +(epoch: 13, iters: 78976, time: 0.554, data: 0.000) G_L1: 16.178 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 13.814 G_Regularizer: 0.000 validation_error: 20.473 +(epoch: 13, iters: 80976, time: 0.540, data: 0.000) G_L1: 14.161 G_L1_ABSOLUTE: 2.135 G_L1_RELATIVE: 12.027 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 13, iters: 82976, time: 0.539, data: 0.000) G_L1: 14.157 G_L1_ABSOLUTE: 2.843 G_L1_RELATIVE: 11.314 G_Regularizer: 0.000 validation_error: 20.412 +(epoch: 13, iters: 84976, time: 0.558, data: 0.000) G_L1: 16.261 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 13.602 G_Regularizer: 0.000 validation_error: 20.473 +(epoch: 13, iters: 86976, time: 0.555, data: 0.001) G_L1: 18.562 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 16.230 G_Regularizer: 0.000 validation_error: 21.262 +(epoch: 13, iters: 88976, time: 0.531, data: 0.000) G_L1: 13.676 G_L1_ABSOLUTE: 2.299 G_L1_RELATIVE: 11.377 G_Regularizer: 0.000 validation_error: 20.505 +(epoch: 13, iters: 90976, time: 0.552, data: 0.000) G_L1: 13.200 G_L1_ABSOLUTE: 2.987 G_L1_RELATIVE: 10.213 G_Regularizer: 0.000 validation_error: 20.622 +(epoch: 13, iters: 92976, time: 0.553, data: 0.000) G_L1: 14.376 G_L1_ABSOLUTE: 2.775 G_L1_RELATIVE: 11.601 G_Regularizer: 0.000 validation_error: 21.009 +(epoch: 13, iters: 94976, time: 0.554, data: 0.000) G_L1: 14.770 G_L1_ABSOLUTE: 2.562 G_L1_RELATIVE: 12.208 G_Regularizer: 0.000 validation_error: 20.313 +(epoch: 13, iters: 96976, time: 0.540, data: 0.000) G_L1: 15.290 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 12.922 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 13, iters: 98976, time: 0.535, data: 0.000) G_L1: 14.612 G_L1_ABSOLUTE: 2.939 G_L1_RELATIVE: 11.673 G_Regularizer: 0.000 validation_error: 20.456 +(epoch: 13, iters: 100976, time: 0.553, data: 0.000) G_L1: 17.365 G_L1_ABSOLUTE: 3.105 G_L1_RELATIVE: 14.260 G_Regularizer: 0.000 validation_error: 20.457 +(epoch: 13, iters: 102976, time: 0.551, data: 0.000) G_L1: 15.983 G_L1_ABSOLUTE: 3.114 G_L1_RELATIVE: 12.868 G_Regularizer: 0.000 validation_error: 20.552 +(epoch: 13, iters: 104976, time: 0.540, data: 0.000) G_L1: 12.562 G_L1_ABSOLUTE: 2.594 G_L1_RELATIVE: 9.968 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 13, iters: 106976, time: 0.550, data: 0.000) G_L1: 14.977 G_L1_ABSOLUTE: 2.884 G_L1_RELATIVE: 12.093 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 13, iters: 108976, time: 0.561, data: 0.001) G_L1: 15.194 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 12.597 G_Regularizer: 0.000 validation_error: 20.220 +(epoch: 13, iters: 110976, time: 0.556, data: 0.000) G_L1: 15.224 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 12.698 G_Regularizer: 0.000 validation_error: 20.510 +(epoch: 13, iters: 112976, time: 0.554, data: 0.000) G_L1: 12.690 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 10.277 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 13, iters: 114976, time: 0.542, data: 0.000) G_L1: 19.616 G_L1_ABSOLUTE: 3.087 G_L1_RELATIVE: 16.529 G_Regularizer: 0.000 validation_error: 21.171 +(epoch: 13, iters: 116976, time: 0.553, data: 0.001) G_L1: 12.822 G_L1_ABSOLUTE: 2.315 G_L1_RELATIVE: 10.508 G_Regularizer: 0.000 validation_error: 20.274 +(epoch: 13, iters: 118976, time: 0.563, data: 0.000) G_L1: 17.822 G_L1_ABSOLUTE: 2.681 G_L1_RELATIVE: 15.141 G_Regularizer: 0.000 validation_error: 22.026 +(epoch: 13, iters: 120976, time: 0.562, data: 0.000) G_L1: 13.213 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 10.664 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 13, iters: 122976, time: 0.529, data: 0.000) G_L1: 15.571 G_L1_ABSOLUTE: 3.059 G_L1_RELATIVE: 12.512 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 13, iters: 124976, time: 0.533, data: 0.000) G_L1: 18.208 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 15.800 G_Regularizer: 0.000 validation_error: 21.545 +(epoch: 13, iters: 126976, time: 0.544, data: 0.000) G_L1: 14.760 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 12.172 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 13, iters: 128976, time: 0.562, data: 0.000) G_L1: 15.249 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 12.738 G_Regularizer: 0.000 validation_error: 20.782 +(epoch: 13, iters: 130976, time: 0.531, data: 0.000) G_L1: 15.656 G_L1_ABSOLUTE: 2.593 G_L1_RELATIVE: 13.063 G_Regularizer: 0.000 validation_error: 19.508 +(epoch: 13, iters: 132976, time: 0.543, data: 0.000) G_L1: 13.916 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 11.407 G_Regularizer: 0.000 validation_error: 20.651 +(epoch: 13, iters: 134976, time: 0.555, data: 0.000) G_L1: 13.957 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 11.460 G_Regularizer: 0.000 validation_error: 20.404 +(epoch: 13, iters: 136976, time: 0.554, data: 0.000) G_L1: 15.795 G_L1_ABSOLUTE: 2.290 G_L1_RELATIVE: 13.505 G_Regularizer: 0.000 validation_error: 20.251 +(epoch: 13, iters: 138976, time: 0.547, data: 0.000) G_L1: 14.870 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 12.327 G_Regularizer: 0.000 validation_error: 20.366 +(epoch: 13, iters: 140976, time: 0.550, data: 0.000) G_L1: 18.950 G_L1_ABSOLUTE: 3.091 G_L1_RELATIVE: 15.859 G_Regularizer: 0.000 validation_error: 21.057 +(epoch: 13, iters: 142976, time: 0.561, data: 0.000) G_L1: 15.685 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 13.168 G_Regularizer: 0.000 validation_error: 20.146 +(epoch: 13, iters: 144976, time: 0.552, data: 0.000) G_L1: 12.027 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 9.689 G_Regularizer: 0.000 validation_error: 20.240 +(epoch: 13, iters: 146976, time: 0.542, data: 0.000) G_L1: 15.488 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 12.819 G_Regularizer: 0.000 validation_error: 20.413 +(epoch: 13, iters: 148976, time: 0.560, data: 0.001) G_L1: 17.268 G_L1_ABSOLUTE: 2.956 G_L1_RELATIVE: 14.312 G_Regularizer: 0.000 validation_error: 20.271 +(epoch: 13, iters: 150976, time: 0.558, data: 0.000) G_L1: 16.290 G_L1_ABSOLUTE: 2.804 G_L1_RELATIVE: 13.486 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 13, iters: 152976, time: 0.563, data: 0.000) G_L1: 15.585 G_L1_ABSOLUTE: 2.929 G_L1_RELATIVE: 12.657 G_Regularizer: 0.000 validation_error: 20.342 +(epoch: 13, iters: 154976, time: 0.548, data: 0.000) G_L1: 15.288 G_L1_ABSOLUTE: 3.083 G_L1_RELATIVE: 12.205 G_Regularizer: 0.000 validation_error: 21.066 +(epoch: 13, iters: 156976, time: 0.535, data: 0.000) G_L1: 15.031 G_L1_ABSOLUTE: 2.307 G_L1_RELATIVE: 12.724 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 13, iters: 158976, time: 0.560, data: 0.000) G_L1: 13.922 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 11.220 G_Regularizer: 0.000 validation_error: 20.862 +(epoch: 13, iters: 160976, time: 0.558, data: 0.000) G_L1: 14.386 G_L1_ABSOLUTE: 2.712 G_L1_RELATIVE: 11.674 G_Regularizer: 0.000 validation_error: 20.471 +(epoch: 13, iters: 162976, time: 0.553, data: 0.000) G_L1: 15.780 G_L1_ABSOLUTE: 2.932 G_L1_RELATIVE: 12.849 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 13, iters: 164976, time: 0.541, data: 0.000) G_L1: 17.217 G_L1_ABSOLUTE: 3.031 G_L1_RELATIVE: 14.186 G_Regularizer: 0.000 validation_error: 21.197 +(epoch: 13, iters: 166976, time: 0.532, data: 0.000) G_L1: 14.763 G_L1_ABSOLUTE: 2.820 G_L1_RELATIVE: 11.943 G_Regularizer: 0.000 validation_error: 20.441 +(epoch: 13, iters: 168976, time: 0.542, data: 0.000) G_L1: 12.840 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 10.523 G_Regularizer: 0.000 validation_error: 21.131 +(epoch: 13, iters: 170976, time: 0.532, data: 0.000) G_L1: 12.160 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 9.483 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 13, iters: 172976, time: 0.527, data: 0.000) G_L1: 15.002 G_L1_ABSOLUTE: 2.959 G_L1_RELATIVE: 12.044 G_Regularizer: 0.000 validation_error: 20.702 +(epoch: 13, iters: 174976, time: 0.544, data: 0.000) G_L1: 17.234 G_L1_ABSOLUTE: 2.703 G_L1_RELATIVE: 14.532 G_Regularizer: 0.000 validation_error: 20.904 +(epoch: 13, iters: 176976, time: 0.553, data: 0.000) G_L1: 15.465 G_L1_ABSOLUTE: 3.074 G_L1_RELATIVE: 12.391 G_Regularizer: 0.000 validation_error: 20.205 +(epoch: 13, iters: 178976, time: 0.558, data: 0.000) G_L1: 20.354 G_L1_ABSOLUTE: 3.295 G_L1_RELATIVE: 17.059 G_Regularizer: 0.000 validation_error: 20.416 +(epoch: 13, iters: 180976, time: 0.540, data: 0.000) G_L1: 13.678 G_L1_ABSOLUTE: 2.213 G_L1_RELATIVE: 11.465 G_Regularizer: 0.000 validation_error: 20.651 +(epoch: 13, iters: 182976, time: 0.548, data: 0.000) G_L1: 14.003 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 11.377 G_Regularizer: 0.000 validation_error: 20.396 +(epoch: 13, iters: 184976, time: 0.560, data: 0.000) G_L1: 13.682 G_L1_ABSOLUTE: 2.300 G_L1_RELATIVE: 11.382 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 13, iters: 186976, time: 0.558, data: 0.000) G_L1: 15.921 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 13.459 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 13, iters: 188976, time: 0.533, data: 0.000) G_L1: 14.549 G_L1_ABSOLUTE: 3.106 G_L1_RELATIVE: 11.443 G_Regularizer: 0.000 validation_error: 21.312 +(epoch: 13, iters: 190976, time: 0.549, data: 0.000) G_L1: 19.071 G_L1_ABSOLUTE: 3.293 G_L1_RELATIVE: 15.778 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 13, iters: 192976, time: 0.558, data: 0.000) G_L1: 12.730 G_L1_ABSOLUTE: 2.217 G_L1_RELATIVE: 10.513 G_Regularizer: 0.000 validation_error: 20.683 +(epoch: 13, iters: 194976, time: 0.563, data: 0.000) G_L1: 15.409 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 13.040 G_Regularizer: 0.000 validation_error: 20.395 +(epoch: 13, iters: 196976, time: 0.551, data: 0.000) G_L1: 11.787 G_L1_ABSOLUTE: 2.161 G_L1_RELATIVE: 9.626 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 13, iters: 198976, time: 0.554, data: 0.000) G_L1: 15.777 G_L1_ABSOLUTE: 3.023 G_L1_RELATIVE: 12.754 G_Regularizer: 0.000 validation_error: 20.496 +(epoch: 13, iters: 200976, time: 0.558, data: 0.000) G_L1: 15.247 G_L1_ABSOLUTE: 2.817 G_L1_RELATIVE: 12.430 G_Regularizer: 0.000 validation_error: 21.662 +(epoch: 13, iters: 202976, time: 0.555, data: 0.000) G_L1: 19.820 G_L1_ABSOLUTE: 3.537 G_L1_RELATIVE: 16.283 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 13, iters: 204976, time: 0.552, data: 0.001) G_L1: 16.276 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 13.570 G_Regularizer: 0.000 validation_error: 20.453 +(epoch: 13, iters: 206976, time: 0.535, data: 0.000) G_L1: 14.404 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 11.974 G_Regularizer: 0.000 validation_error: 21.069 +(epoch: 13, iters: 208976, time: 0.528, data: 0.000) G_L1: 15.424 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 12.644 G_Regularizer: 0.000 validation_error: 20.246 +(epoch: 13, iters: 210976, time: 0.537, data: 0.001) G_L1: 13.346 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 10.489 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 13, iters: 212976, time: 0.559, data: 0.000) G_L1: 12.006 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 9.497 G_Regularizer: 0.000 validation_error: 20.972 +(epoch: 13, iters: 214976, time: 0.530, data: 0.000) G_L1: 14.671 G_L1_ABSOLUTE: 2.196 G_L1_RELATIVE: 12.475 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 13, iters: 216976, time: 0.553, data: 0.000) G_L1: 17.917 G_L1_ABSOLUTE: 3.110 G_L1_RELATIVE: 14.807 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 13, iters: 218976, time: 0.547, data: 0.000) G_L1: 15.271 G_L1_ABSOLUTE: 3.704 G_L1_RELATIVE: 11.568 G_Regularizer: 0.000 validation_error: 20.354 +(epoch: 13, iters: 220976, time: 0.561, data: 0.000) G_L1: 14.334 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 11.999 G_Regularizer: 0.000 validation_error: 21.103 +(epoch: 13, iters: 222976, time: 0.535, data: 0.000) G_L1: 15.187 G_L1_ABSOLUTE: 3.125 G_L1_RELATIVE: 12.062 G_Regularizer: 0.000 validation_error: 20.728 +(epoch: 13, iters: 224976, time: 0.559, data: 0.000) G_L1: 16.925 G_L1_ABSOLUTE: 3.393 G_L1_RELATIVE: 13.532 G_Regularizer: 0.000 validation_error: 20.367 +(epoch: 13, iters: 226976, time: 0.553, data: 0.000) G_L1: 15.152 G_L1_ABSOLUTE: 2.709 G_L1_RELATIVE: 12.443 G_Regularizer: 0.000 validation_error: 20.564 +(epoch: 13, iters: 228976, time: 0.551, data: 0.000) G_L1: 14.851 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 12.040 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 13, iters: 230976, time: 0.540, data: 0.000) G_L1: 19.207 G_L1_ABSOLUTE: 3.127 G_L1_RELATIVE: 16.080 G_Regularizer: 0.000 validation_error: 20.479 +(epoch: 13, iters: 232976, time: 0.544, data: 0.000) G_L1: 15.016 G_L1_ABSOLUTE: 2.878 G_L1_RELATIVE: 12.138 G_Regularizer: 0.000 validation_error: 20.984 +(epoch: 13, iters: 234976, time: 0.552, data: 0.001) G_L1: 14.377 G_L1_ABSOLUTE: 2.834 G_L1_RELATIVE: 11.543 G_Regularizer: 0.000 validation_error: 21.381 +(epoch: 13, iters: 236976, time: 0.552, data: 0.000) G_L1: 14.846 G_L1_ABSOLUTE: 2.729 G_L1_RELATIVE: 12.117 G_Regularizer: 0.000 validation_error: 20.464 +(epoch: 13, iters: 238976, time: 0.559, data: 0.000) G_L1: 15.752 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 13.303 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 13, iters: 240976, time: 0.552, data: 0.000) G_L1: 16.469 G_L1_ABSOLUTE: 3.031 G_L1_RELATIVE: 13.438 G_Regularizer: 0.000 validation_error: 20.025 +(epoch: 13, iters: 242976, time: 0.550, data: 0.000) G_L1: 15.704 G_L1_ABSOLUTE: 2.309 G_L1_RELATIVE: 13.395 G_Regularizer: 0.000 validation_error: 20.190 +(epoch: 13, iters: 244976, time: 0.555, data: 0.000) G_L1: 14.215 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 11.757 G_Regularizer: 0.000 validation_error: 20.452 +(epoch: 13, iters: 246976, time: 0.560, data: 0.000) G_L1: 14.174 G_L1_ABSOLUTE: 3.193 G_L1_RELATIVE: 10.982 G_Regularizer: 0.000 validation_error: 20.322 +(epoch: 13, iters: 248976, time: 0.542, data: 0.000) G_L1: 14.630 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 11.641 G_Regularizer: 0.000 validation_error: 20.240 +(epoch: 13, iters: 250976, time: 0.547, data: 0.001) G_L1: 16.934 G_L1_ABSOLUTE: 2.794 G_L1_RELATIVE: 14.139 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 13, iters: 252976, time: 0.555, data: 0.000) G_L1: 17.732 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 15.401 G_Regularizer: 0.000 validation_error: 20.559 +(epoch: 13, iters: 254976, time: 0.565, data: 0.000) G_L1: 16.282 G_L1_ABSOLUTE: 2.964 G_L1_RELATIVE: 13.317 G_Regularizer: 0.000 validation_error: 20.568 +(epoch: 13, iters: 256976, time: 0.535, data: 0.000) G_L1: 15.360 G_L1_ABSOLUTE: 3.134 G_L1_RELATIVE: 12.226 G_Regularizer: 0.000 validation_error: 20.449 +(epoch: 13, iters: 258976, time: 0.531, data: 0.000) G_L1: 16.790 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 14.153 G_Regularizer: 0.000 validation_error: 20.742 +(epoch: 13, iters: 260976, time: 0.558, data: 0.001) G_L1: 13.683 G_L1_ABSOLUTE: 2.579 G_L1_RELATIVE: 11.105 G_Regularizer: 0.000 validation_error: 20.139 +(epoch: 13, iters: 262976, time: 0.557, data: 0.000) G_L1: 17.135 G_L1_ABSOLUTE: 2.128 G_L1_RELATIVE: 15.007 G_Regularizer: 0.000 validation_error: 20.347 +(epoch: 13, iters: 264976, time: 0.543, data: 0.000) G_L1: 18.879 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 16.203 G_Regularizer: 0.000 validation_error: 21.254 +(epoch: 13, iters: 266976, time: 0.534, data: 0.000) G_L1: 14.312 G_L1_ABSOLUTE: 2.873 G_L1_RELATIVE: 11.438 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 13, iters: 268976, time: 0.547, data: 0.001) G_L1: 17.528 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 14.792 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 13, iters: 270976, time: 0.557, data: 0.000) G_L1: 15.771 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 13.170 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 13, iters: 272976, time: 0.548, data: 0.000) G_L1: 17.449 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 14.829 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 13, iters: 274976, time: 0.552, data: 0.000) G_L1: 16.237 G_L1_ABSOLUTE: 2.641 G_L1_RELATIVE: 13.596 G_Regularizer: 0.000 validation_error: 20.163 +(epoch: 13, iters: 276976, time: 0.562, data: 0.000) G_L1: 16.604 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 13.963 G_Regularizer: 0.000 validation_error: 20.237 +(epoch: 13, iters: 278976, time: 0.549, data: 0.000) G_L1: 16.955 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 14.341 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 13, iters: 280976, time: 0.542, data: 0.000) G_L1: 16.656 G_L1_ABSOLUTE: 2.816 G_L1_RELATIVE: 13.839 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 13, iters: 282976, time: 0.533, data: 0.000) G_L1: 15.946 G_L1_ABSOLUTE: 2.557 G_L1_RELATIVE: 13.389 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 13, iters: 284976, time: 0.557, data: 0.000) G_L1: 16.758 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 14.129 G_Regularizer: 0.000 validation_error: 20.746 +(epoch: 13, iters: 286976, time: 0.549, data: 0.000) G_L1: 15.261 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 12.630 G_Regularizer: 0.000 validation_error: 21.089 +(epoch: 13, iters: 288976, time: 0.563, data: 0.000) G_L1: 13.659 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 10.980 G_Regularizer: 0.000 validation_error: 20.469 +(epoch: 13, iters: 290976, time: 0.531, data: 0.000) G_L1: 13.772 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 11.171 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 13, iters: 292976, time: 0.549, data: 0.000) G_L1: 12.840 G_L1_ABSOLUTE: 2.326 G_L1_RELATIVE: 10.514 G_Regularizer: 0.000 validation_error: 20.628 +(epoch: 13, iters: 294976, time: 0.554, data: 0.000) G_L1: 15.227 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 12.543 G_Regularizer: 0.000 validation_error: 21.275 +(epoch: 13, iters: 296976, time: 0.553, data: 0.000) G_L1: 13.760 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 11.132 G_Regularizer: 0.000 validation_error: 20.595 +(epoch: 13, iters: 298976, time: 0.532, data: 0.001) G_L1: 13.696 G_L1_ABSOLUTE: 2.295 G_L1_RELATIVE: 11.401 G_Regularizer: 0.000 validation_error: 20.447 +(epoch: 13, iters: 300976, time: 0.538, data: 0.000) G_L1: 17.344 G_L1_ABSOLUTE: 3.016 G_L1_RELATIVE: 14.327 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 14, iters: 224, time: 0.532, data: 0.000) G_L1: 13.684 G_L1_ABSOLUTE: 2.511 G_L1_RELATIVE: 11.173 G_Regularizer: 0.000 validation_error: 20.417 +(epoch: 14, iters: 2224, time: 0.561, data: 0.000) G_L1: 14.627 G_L1_ABSOLUTE: 2.888 G_L1_RELATIVE: 11.739 G_Regularizer: 0.000 validation_error: 21.105 +(epoch: 14, iters: 4224, time: 0.542, data: 0.000) G_L1: 13.497 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 11.154 G_Regularizer: 0.000 validation_error: 20.458 +(epoch: 14, iters: 6224, time: 0.535, data: 0.000) G_L1: 13.816 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 11.157 G_Regularizer: 0.000 validation_error: 20.100 +(epoch: 14, iters: 8224, time: 0.558, data: 0.000) G_L1: 15.192 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 12.542 G_Regularizer: 0.000 validation_error: 21.564 +(epoch: 14, iters: 10224, time: 0.551, data: 0.000) G_L1: 15.258 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 12.640 G_Regularizer: 0.000 validation_error: 20.359 +(epoch: 14, iters: 12224, time: 0.551, data: 0.000) G_L1: 14.358 G_L1_ABSOLUTE: 2.842 G_L1_RELATIVE: 11.516 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 14, iters: 14224, time: 0.536, data: 0.000) G_L1: 13.070 G_L1_ABSOLUTE: 2.475 G_L1_RELATIVE: 10.595 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 14, iters: 16224, time: 0.561, data: 0.001) G_L1: 16.479 G_L1_ABSOLUTE: 2.830 G_L1_RELATIVE: 13.649 G_Regularizer: 0.000 validation_error: 20.582 +(epoch: 14, iters: 18224, time: 0.565, data: 0.000) G_L1: 16.760 G_L1_ABSOLUTE: 3.008 G_L1_RELATIVE: 13.753 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 14, iters: 20224, time: 0.548, data: 0.000) G_L1: 18.038 G_L1_ABSOLUTE: 3.077 G_L1_RELATIVE: 14.961 G_Regularizer: 0.000 validation_error: 20.699 +(epoch: 14, iters: 22224, time: 0.543, data: 0.000) G_L1: 15.946 G_L1_ABSOLUTE: 2.244 G_L1_RELATIVE: 13.703 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 14, iters: 24224, time: 0.554, data: 0.001) G_L1: 15.020 G_L1_ABSOLUTE: 2.939 G_L1_RELATIVE: 12.081 G_Regularizer: 0.000 validation_error: 21.037 +(epoch: 14, iters: 26224, time: 0.534, data: 0.000) G_L1: 14.694 G_L1_ABSOLUTE: 2.695 G_L1_RELATIVE: 12.000 G_Regularizer: 0.000 validation_error: 20.377 +(epoch: 14, iters: 28224, time: 0.561, data: 0.000) G_L1: 19.784 G_L1_ABSOLUTE: 3.016 G_L1_RELATIVE: 16.768 G_Regularizer: 0.000 validation_error: 20.063 +(epoch: 14, iters: 30224, time: 0.530, data: 0.000) G_L1: 14.794 G_L1_ABSOLUTE: 2.327 G_L1_RELATIVE: 12.467 G_Regularizer: 0.000 validation_error: 20.854 +(epoch: 14, iters: 32224, time: 0.549, data: 0.000) G_L1: 15.858 G_L1_ABSOLUTE: 2.976 G_L1_RELATIVE: 12.881 G_Regularizer: 0.000 validation_error: 20.735 +(epoch: 14, iters: 34224, time: 0.556, data: 0.000) G_L1: 14.560 G_L1_ABSOLUTE: 2.815 G_L1_RELATIVE: 11.745 G_Regularizer: 0.000 validation_error: 20.597 +(epoch: 14, iters: 36224, time: 0.559, data: 0.000) G_L1: 14.073 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 11.628 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 14, iters: 38224, time: 0.541, data: 0.000) G_L1: 18.371 G_L1_ABSOLUTE: 3.390 G_L1_RELATIVE: 14.981 G_Regularizer: 0.000 validation_error: 20.252 +(epoch: 14, iters: 40224, time: 0.562, data: 0.000) G_L1: 16.922 G_L1_ABSOLUTE: 2.779 G_L1_RELATIVE: 14.143 G_Regularizer: 0.000 validation_error: 21.084 +(epoch: 14, iters: 42224, time: 0.557, data: 0.000) G_L1: 16.187 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 13.634 G_Regularizer: 0.000 validation_error: 20.217 +(epoch: 14, iters: 44224, time: 0.560, data: 0.000) G_L1: 14.905 G_L1_ABSOLUTE: 3.128 G_L1_RELATIVE: 11.777 G_Regularizer: 0.000 validation_error: 21.246 +(epoch: 14, iters: 46224, time: 0.537, data: 0.000) G_L1: 30.416 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 27.830 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 14, iters: 48224, time: 0.558, data: 0.000) G_L1: 14.523 G_L1_ABSOLUTE: 2.675 G_L1_RELATIVE: 11.848 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 14, iters: 50224, time: 0.556, data: 0.000) G_L1: 13.315 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 10.974 G_Regularizer: 0.000 validation_error: 20.577 +(epoch: 14, iters: 52224, time: 0.559, data: 0.000) G_L1: 15.785 G_L1_ABSOLUTE: 2.425 G_L1_RELATIVE: 13.360 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 14, iters: 54224, time: 0.544, data: 0.000) G_L1: 17.110 G_L1_ABSOLUTE: 3.062 G_L1_RELATIVE: 14.048 G_Regularizer: 0.000 validation_error: 21.070 +(epoch: 14, iters: 56224, time: 0.552, data: 0.000) G_L1: 14.542 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 12.032 G_Regularizer: 0.000 validation_error: 21.195 +(epoch: 14, iters: 58224, time: 0.562, data: 0.000) G_L1: 15.618 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 12.885 G_Regularizer: 0.000 validation_error: 21.401 +(epoch: 14, iters: 60224, time: 0.552, data: 0.000) G_L1: 14.941 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 12.131 G_Regularizer: 0.000 validation_error: 20.571 +(epoch: 14, iters: 62224, time: 0.549, data: 0.001) G_L1: 15.085 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 12.516 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 14, iters: 64224, time: 0.532, data: 0.000) G_L1: 12.951 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 10.490 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 14, iters: 66224, time: 0.546, data: 0.000) G_L1: 15.685 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 13.121 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 14, iters: 68224, time: 0.557, data: 0.000) G_L1: 14.376 G_L1_ABSOLUTE: 2.423 G_L1_RELATIVE: 11.954 G_Regularizer: 0.000 validation_error: 21.306 +(epoch: 14, iters: 70224, time: 0.567, data: 0.000) G_L1: 14.034 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 11.328 G_Regularizer: 0.000 validation_error: 20.702 +(epoch: 14, iters: 72224, time: 0.528, data: 0.000) G_L1: 12.247 G_L1_ABSOLUTE: 2.203 G_L1_RELATIVE: 10.043 G_Regularizer: 0.000 validation_error: 20.385 +(epoch: 14, iters: 74224, time: 0.531, data: 0.000) G_L1: 15.744 G_L1_ABSOLUTE: 2.821 G_L1_RELATIVE: 12.923 G_Regularizer: 0.000 validation_error: 21.137 +(epoch: 14, iters: 76224, time: 0.554, data: 0.000) G_L1: 16.642 G_L1_ABSOLUTE: 3.066 G_L1_RELATIVE: 13.575 G_Regularizer: 0.000 validation_error: 20.893 +(epoch: 14, iters: 78224, time: 0.558, data: 0.000) G_L1: 15.446 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 12.712 G_Regularizer: 0.000 validation_error: 21.316 +(epoch: 14, iters: 80224, time: 0.533, data: 0.000) G_L1: 11.685 G_L1_ABSOLUTE: 2.253 G_L1_RELATIVE: 9.432 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 14, iters: 82224, time: 0.536, data: 0.000) G_L1: 14.890 G_L1_ABSOLUTE: 2.411 G_L1_RELATIVE: 12.479 G_Regularizer: 0.000 validation_error: 20.565 +(epoch: 14, iters: 84224, time: 0.548, data: 0.000) G_L1: 13.770 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 11.377 G_Regularizer: 0.000 validation_error: 20.631 +(epoch: 14, iters: 86224, time: 0.556, data: 0.000) G_L1: 17.046 G_L1_ABSOLUTE: 3.220 G_L1_RELATIVE: 13.827 G_Regularizer: 0.000 validation_error: 20.874 +(epoch: 14, iters: 88224, time: 0.541, data: 0.000) G_L1: 14.803 G_L1_ABSOLUTE: 2.441 G_L1_RELATIVE: 12.362 G_Regularizer: 0.000 validation_error: 21.492 +(epoch: 14, iters: 90224, time: 0.549, data: 0.000) G_L1: 16.064 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 13.283 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 14, iters: 92224, time: 0.545, data: 0.000) G_L1: 15.302 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 12.860 G_Regularizer: 0.000 validation_error: 20.288 +(epoch: 14, iters: 94224, time: 0.535, data: 0.000) G_L1: 15.026 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 12.252 G_Regularizer: 0.000 validation_error: 20.693 +(epoch: 14, iters: 96224, time: 0.550, data: 0.000) G_L1: 16.932 G_L1_ABSOLUTE: 2.935 G_L1_RELATIVE: 13.996 G_Regularizer: 0.000 validation_error: 20.729 +(epoch: 14, iters: 98224, time: 0.541, data: 0.000) G_L1: 16.517 G_L1_ABSOLUTE: 2.263 G_L1_RELATIVE: 14.254 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 14, iters: 100224, time: 0.558, data: 0.000) G_L1: 14.135 G_L1_ABSOLUTE: 2.953 G_L1_RELATIVE: 11.182 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 14, iters: 102224, time: 0.554, data: 0.001) G_L1: 14.996 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 12.174 G_Regularizer: 0.000 validation_error: 19.977 +(epoch: 14, iters: 104224, time: 0.562, data: 0.000) G_L1: 14.915 G_L1_ABSOLUTE: 2.490 G_L1_RELATIVE: 12.426 G_Regularizer: 0.000 validation_error: 20.161 +(epoch: 14, iters: 106224, time: 0.532, data: 0.000) G_L1: 16.644 G_L1_ABSOLUTE: 2.306 G_L1_RELATIVE: 14.338 G_Regularizer: 0.000 validation_error: 20.285 +(epoch: 14, iters: 108224, time: 0.528, data: 0.000) G_L1: 15.151 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 12.580 G_Regularizer: 0.000 validation_error: 20.085 +(epoch: 14, iters: 110224, time: 0.546, data: 0.000) G_L1: 14.887 G_L1_ABSOLUTE: 2.783 G_L1_RELATIVE: 12.104 G_Regularizer: 0.000 validation_error: 20.357 +(epoch: 14, iters: 112224, time: 0.547, data: 0.001) G_L1: 15.276 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 12.800 G_Regularizer: 0.000 validation_error: 20.650 +(epoch: 14, iters: 114224, time: 0.535, data: 0.000) G_L1: 13.229 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 10.711 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 14, iters: 116224, time: 0.559, data: 0.000) G_L1: 15.714 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 13.221 G_Regularizer: 0.000 validation_error: 20.663 +(epoch: 14, iters: 118224, time: 0.560, data: 0.000) G_L1: 16.992 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 14.603 G_Regularizer: 0.000 validation_error: 21.071 +(epoch: 14, iters: 120224, time: 0.556, data: 0.000) G_L1: 15.779 G_L1_ABSOLUTE: 2.608 G_L1_RELATIVE: 13.171 G_Regularizer: 0.000 validation_error: 20.491 +(epoch: 14, iters: 122224, time: 0.533, data: 0.001) G_L1: 14.952 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 12.320 G_Regularizer: 0.000 validation_error: 20.212 +(epoch: 14, iters: 124224, time: 0.552, data: 0.000) G_L1: 22.967 G_L1_ABSOLUTE: 2.943 G_L1_RELATIVE: 20.024 G_Regularizer: 0.000 validation_error: 21.244 +(epoch: 14, iters: 126224, time: 0.563, data: 0.000) G_L1: 18.744 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 16.011 G_Regularizer: 0.000 validation_error: 20.537 +(epoch: 14, iters: 128224, time: 0.551, data: 0.001) G_L1: 15.218 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 12.627 G_Regularizer: 0.000 validation_error: 20.076 +(epoch: 14, iters: 130224, time: 0.549, data: 0.001) G_L1: 14.472 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 11.766 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 14, iters: 132224, time: 0.551, data: 0.000) G_L1: 16.892 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 14.324 G_Regularizer: 0.000 validation_error: 20.415 +(epoch: 14, iters: 134224, time: 0.537, data: 0.000) G_L1: 16.180 G_L1_ABSOLUTE: 2.825 G_L1_RELATIVE: 13.356 G_Regularizer: 0.000 validation_error: 20.552 +(epoch: 14, iters: 136224, time: 0.560, data: 0.000) G_L1: 12.685 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 10.114 G_Regularizer: 0.000 validation_error: 20.090 +(epoch: 14, iters: 138224, time: 0.547, data: 0.000) G_L1: 18.553 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 16.033 G_Regularizer: 0.000 validation_error: 19.844 +(epoch: 14, iters: 140224, time: 0.533, data: 0.000) G_L1: 13.461 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 10.940 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 14, iters: 142224, time: 0.553, data: 0.000) G_L1: 14.492 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 12.032 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 14, iters: 144224, time: 0.561, data: 0.000) G_L1: 15.609 G_L1_ABSOLUTE: 2.818 G_L1_RELATIVE: 12.791 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 14, iters: 146224, time: 0.556, data: 0.000) G_L1: 30.928 G_L1_ABSOLUTE: 2.967 G_L1_RELATIVE: 27.962 G_Regularizer: 0.000 validation_error: 21.021 +(epoch: 14, iters: 148224, time: 0.555, data: 0.000) G_L1: 12.491 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 10.093 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 14, iters: 150224, time: 0.559, data: 0.000) G_L1: 15.139 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 12.656 G_Regularizer: 0.000 validation_error: 20.500 +(epoch: 14, iters: 152224, time: 0.560, data: 0.000) G_L1: 16.724 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 14.177 G_Regularizer: 0.000 validation_error: 20.225 +(epoch: 14, iters: 154224, time: 0.553, data: 0.000) G_L1: 15.024 G_L1_ABSOLUTE: 2.277 G_L1_RELATIVE: 12.746 G_Regularizer: 0.000 validation_error: 21.184 +(epoch: 14, iters: 156224, time: 0.534, data: 0.000) G_L1: 16.824 G_L1_ABSOLUTE: 2.901 G_L1_RELATIVE: 13.923 G_Regularizer: 0.000 validation_error: 20.680 +(epoch: 14, iters: 158224, time: 0.541, data: 0.000) G_L1: 12.779 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 10.297 G_Regularizer: 0.000 validation_error: 20.394 +(epoch: 14, iters: 160224, time: 0.535, data: 0.000) G_L1: 14.315 G_L1_ABSOLUTE: 3.194 G_L1_RELATIVE: 11.121 G_Regularizer: 0.000 validation_error: 21.799 +(epoch: 14, iters: 162224, time: 0.555, data: 0.000) G_L1: 14.730 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 12.247 G_Regularizer: 0.000 validation_error: 20.036 +(epoch: 14, iters: 164224, time: 0.541, data: 0.000) G_L1: 16.525 G_L1_ABSOLUTE: 2.807 G_L1_RELATIVE: 13.718 G_Regularizer: 0.000 validation_error: 20.573 +(epoch: 14, iters: 166224, time: 0.556, data: 0.000) G_L1: 12.857 G_L1_ABSOLUTE: 3.065 G_L1_RELATIVE: 9.792 G_Regularizer: 0.000 validation_error: 20.506 +(epoch: 14, iters: 168224, time: 0.533, data: 0.000) G_L1: 16.778 G_L1_ABSOLUTE: 2.534 G_L1_RELATIVE: 14.244 G_Regularizer: 0.000 validation_error: 20.100 +(epoch: 14, iters: 170224, time: 0.533, data: 0.000) G_L1: 14.420 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 11.606 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 14, iters: 172224, time: 0.539, data: 0.000) G_L1: 17.305 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 14.570 G_Regularizer: 0.000 validation_error: 21.149 +(epoch: 14, iters: 174224, time: 0.550, data: 0.000) G_L1: 15.405 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 12.844 G_Regularizer: 0.000 validation_error: 21.260 +(epoch: 14, iters: 176224, time: 0.548, data: 0.000) G_L1: 13.658 G_L1_ABSOLUTE: 2.602 G_L1_RELATIVE: 11.056 G_Regularizer: 0.000 validation_error: 21.290 +(epoch: 14, iters: 178224, time: 0.562, data: 0.000) G_L1: 13.634 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 11.277 G_Regularizer: 0.000 validation_error: 20.202 +(epoch: 14, iters: 180224, time: 0.543, data: 0.000) G_L1: 13.252 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 10.824 G_Regularizer: 0.000 validation_error: 20.100 +(epoch: 14, iters: 182224, time: 0.547, data: 0.000) G_L1: 15.642 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 12.908 G_Regularizer: 0.000 validation_error: 20.310 +(epoch: 14, iters: 184224, time: 0.557, data: 0.000) G_L1: 13.468 G_L1_ABSOLUTE: 2.949 G_L1_RELATIVE: 10.518 G_Regularizer: 0.000 validation_error: 21.233 +(epoch: 14, iters: 186224, time: 0.542, data: 0.001) G_L1: 16.542 G_L1_ABSOLUTE: 2.826 G_L1_RELATIVE: 13.716 G_Regularizer: 0.000 validation_error: 21.418 +(epoch: 14, iters: 188224, time: 0.557, data: 0.000) G_L1: 17.850 G_L1_ABSOLUTE: 2.817 G_L1_RELATIVE: 15.033 G_Regularizer: 0.000 validation_error: 20.894 +(epoch: 14, iters: 190224, time: 0.531, data: 0.000) G_L1: 12.831 G_L1_ABSOLUTE: 2.313 G_L1_RELATIVE: 10.518 G_Regularizer: 0.000 validation_error: 21.340 +(epoch: 14, iters: 192224, time: 0.557, data: 0.000) G_L1: 15.403 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 12.719 G_Regularizer: 0.000 validation_error: 20.593 +(epoch: 14, iters: 194224, time: 0.560, data: 0.000) G_L1: 17.601 G_L1_ABSOLUTE: 2.794 G_L1_RELATIVE: 14.807 G_Regularizer: 0.000 validation_error: 20.466 +(epoch: 14, iters: 196224, time: 0.556, data: 0.000) G_L1: 15.566 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 12.804 G_Regularizer: 0.000 validation_error: 20.077 +(epoch: 14, iters: 198224, time: 0.530, data: 0.000) G_L1: 15.816 G_L1_ABSOLUTE: 2.575 G_L1_RELATIVE: 13.241 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 14, iters: 200224, time: 0.551, data: 0.000) G_L1: 15.988 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 13.418 G_Regularizer: 0.000 validation_error: 20.469 +(epoch: 14, iters: 202224, time: 0.544, data: 0.000) G_L1: 16.594 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 13.656 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 14, iters: 204224, time: 0.533, data: 0.001) G_L1: 15.443 G_L1_ABSOLUTE: 2.807 G_L1_RELATIVE: 12.636 G_Regularizer: 0.000 validation_error: 21.076 +(epoch: 14, iters: 206224, time: 0.540, data: 0.000) G_L1: 14.741 G_L1_ABSOLUTE: 2.751 G_L1_RELATIVE: 11.991 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 14, iters: 208224, time: 0.531, data: 0.000) G_L1: 16.643 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 13.763 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 14, iters: 210224, time: 0.534, data: 0.000) G_L1: 13.667 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 10.983 G_Regularizer: 0.000 validation_error: 20.226 +(epoch: 14, iters: 212224, time: 0.548, data: 0.000) G_L1: 13.905 G_L1_ABSOLUTE: 2.507 G_L1_RELATIVE: 11.398 G_Regularizer: 0.000 validation_error: 20.373 +(epoch: 14, iters: 214224, time: 0.553, data: 0.000) G_L1: 13.585 G_L1_ABSOLUTE: 2.374 G_L1_RELATIVE: 11.211 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 14, iters: 216224, time: 0.547, data: 0.000) G_L1: 15.356 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 12.955 G_Regularizer: 0.000 validation_error: 21.687 +(epoch: 14, iters: 218224, time: 0.556, data: 0.000) G_L1: 14.291 G_L1_ABSOLUTE: 2.376 G_L1_RELATIVE: 11.915 G_Regularizer: 0.000 validation_error: 21.087 +(epoch: 14, iters: 220224, time: 0.549, data: 0.000) G_L1: 17.474 G_L1_ABSOLUTE: 3.008 G_L1_RELATIVE: 14.466 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 14, iters: 222224, time: 0.548, data: 0.000) G_L1: 14.063 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 11.413 G_Regularizer: 0.000 validation_error: 20.204 +(epoch: 14, iters: 224224, time: 0.532, data: 0.000) G_L1: 13.770 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 11.423 G_Regularizer: 0.000 validation_error: 21.275 +(epoch: 14, iters: 226224, time: 0.565, data: 0.000) G_L1: 18.916 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 16.296 G_Regularizer: 0.000 validation_error: 20.632 +(epoch: 14, iters: 228224, time: 0.562, data: 0.000) G_L1: 16.020 G_L1_ABSOLUTE: 2.913 G_L1_RELATIVE: 13.106 G_Regularizer: 0.000 validation_error: 20.003 +(epoch: 14, iters: 230224, time: 0.542, data: 0.000) G_L1: 14.386 G_L1_ABSOLUTE: 3.006 G_L1_RELATIVE: 11.380 G_Regularizer: 0.000 validation_error: 20.365 +(epoch: 14, iters: 232224, time: 0.537, data: 0.000) G_L1: 17.274 G_L1_ABSOLUTE: 3.228 G_L1_RELATIVE: 14.045 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 14, iters: 234224, time: 0.551, data: 0.000) G_L1: 14.806 G_L1_ABSOLUTE: 2.438 G_L1_RELATIVE: 12.368 G_Regularizer: 0.000 validation_error: 20.425 +(epoch: 14, iters: 236224, time: 0.537, data: 0.000) G_L1: 13.294 G_L1_ABSOLUTE: 2.250 G_L1_RELATIVE: 11.044 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 14, iters: 238224, time: 0.533, data: 0.000) G_L1: 14.870 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 12.065 G_Regularizer: 0.000 validation_error: 20.802 +(epoch: 14, iters: 240224, time: 0.529, data: 0.000) G_L1: 15.175 G_L1_ABSOLUTE: 2.376 G_L1_RELATIVE: 12.799 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 14, iters: 242224, time: 0.560, data: 0.000) G_L1: 11.894 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 9.488 G_Regularizer: 0.000 validation_error: 21.107 +(epoch: 14, iters: 244224, time: 0.557, data: 0.000) G_L1: 15.985 G_L1_ABSOLUTE: 2.472 G_L1_RELATIVE: 13.513 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 14, iters: 246224, time: 0.558, data: 0.000) G_L1: 14.717 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 12.125 G_Regularizer: 0.000 validation_error: 20.559 +(epoch: 14, iters: 248224, time: 0.544, data: 0.000) G_L1: 12.281 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 10.032 G_Regularizer: 0.000 validation_error: 20.250 +(epoch: 14, iters: 250224, time: 0.537, data: 0.000) G_L1: 16.302 G_L1_ABSOLUTE: 3.177 G_L1_RELATIVE: 13.124 G_Regularizer: 0.000 validation_error: 20.457 +(epoch: 14, iters: 252224, time: 0.548, data: 0.000) G_L1: 14.173 G_L1_ABSOLUTE: 3.021 G_L1_RELATIVE: 11.152 G_Regularizer: 0.000 validation_error: 20.010 +(epoch: 14, iters: 254224, time: 0.559, data: 0.000) G_L1: 15.591 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 13.098 G_Regularizer: 0.000 validation_error: 20.111 +(epoch: 14, iters: 256224, time: 0.554, data: 0.000) G_L1: 14.896 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 12.298 G_Regularizer: 0.000 validation_error: 20.202 +(epoch: 14, iters: 258224, time: 0.530, data: 0.000) G_L1: 15.855 G_L1_ABSOLUTE: 2.605 G_L1_RELATIVE: 13.249 G_Regularizer: 0.000 validation_error: 20.258 +(epoch: 14, iters: 260224, time: 0.547, data: 0.000) G_L1: 16.619 G_L1_ABSOLUTE: 2.965 G_L1_RELATIVE: 13.654 G_Regularizer: 0.000 validation_error: 20.707 +(epoch: 14, iters: 262224, time: 0.551, data: 0.000) G_L1: 14.176 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 11.462 G_Regularizer: 0.000 validation_error: 20.537 +(epoch: 14, iters: 264224, time: 0.545, data: 0.000) G_L1: 15.293 G_L1_ABSOLUTE: 2.771 G_L1_RELATIVE: 12.523 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 14, iters: 266224, time: 0.535, data: 0.000) G_L1: 16.441 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 13.791 G_Regularizer: 0.000 validation_error: 21.230 +(epoch: 14, iters: 268224, time: 0.558, data: 0.000) G_L1: 16.647 G_L1_ABSOLUTE: 3.582 G_L1_RELATIVE: 13.064 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 14, iters: 270224, time: 0.524, data: 0.000) G_L1: 14.827 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 12.060 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 14, iters: 272224, time: 0.554, data: 0.000) G_L1: 14.202 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 11.345 G_Regularizer: 0.000 validation_error: 20.892 +(epoch: 14, iters: 274224, time: 0.534, data: 0.001) G_L1: 17.657 G_L1_ABSOLUTE: 3.009 G_L1_RELATIVE: 14.648 G_Regularizer: 0.000 validation_error: 21.150 +(epoch: 14, iters: 276224, time: 0.534, data: 0.000) G_L1: 15.536 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 12.758 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 14, iters: 278224, time: 0.542, data: 0.000) G_L1: 15.977 G_L1_ABSOLUTE: 3.250 G_L1_RELATIVE: 12.727 G_Regularizer: 0.000 validation_error: 21.009 +(epoch: 14, iters: 280224, time: 0.544, data: 0.001) G_L1: 18.644 G_L1_ABSOLUTE: 2.893 G_L1_RELATIVE: 15.751 G_Regularizer: 0.000 validation_error: 20.105 +(epoch: 14, iters: 282224, time: 0.545, data: 0.000) G_L1: 13.348 G_L1_ABSOLUTE: 2.318 G_L1_RELATIVE: 11.030 G_Regularizer: 0.000 validation_error: 20.538 +(epoch: 14, iters: 284224, time: 0.553, data: 0.001) G_L1: 14.323 G_L1_ABSOLUTE: 2.296 G_L1_RELATIVE: 12.027 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 14, iters: 286224, time: 0.554, data: 0.000) G_L1: 13.136 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 10.527 G_Regularizer: 0.000 validation_error: 21.321 +(epoch: 14, iters: 288224, time: 0.556, data: 0.000) G_L1: 12.455 G_L1_ABSOLUTE: 2.156 G_L1_RELATIVE: 10.299 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 14, iters: 290224, time: 0.551, data: 0.001) G_L1: 15.343 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 12.753 G_Regularizer: 0.000 validation_error: 20.200 +(epoch: 14, iters: 292224, time: 0.534, data: 0.000) G_L1: 16.609 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 14.120 G_Regularizer: 0.000 validation_error: 21.462 +(epoch: 14, iters: 294224, time: 0.533, data: 0.000) G_L1: 13.999 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 11.668 G_Regularizer: 0.000 validation_error: 20.362 +(epoch: 14, iters: 296224, time: 0.551, data: 0.000) G_L1: 14.865 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 12.311 G_Regularizer: 0.000 validation_error: 20.525 +(epoch: 14, iters: 298224, time: 0.555, data: 0.000) G_L1: 17.743 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 14.907 G_Regularizer: 0.000 validation_error: 20.222 +(epoch: 14, iters: 300224, time: 0.535, data: 0.000) G_L1: 15.517 G_L1_ABSOLUTE: 2.126 G_L1_RELATIVE: 13.392 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 14, iters: 302224, time: 0.553, data: 0.000) G_L1: 14.543 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 11.923 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 15, iters: 1472, time: 0.553, data: 0.000) G_L1: 15.186 G_L1_ABSOLUTE: 2.562 G_L1_RELATIVE: 12.624 G_Regularizer: 0.000 validation_error: 20.566 +(epoch: 15, iters: 3472, time: 0.548, data: 0.001) G_L1: 13.347 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 10.721 G_Regularizer: 0.000 validation_error: 20.254 +(epoch: 15, iters: 5472, time: 0.539, data: 0.000) G_L1: 13.893 G_L1_ABSOLUTE: 2.246 G_L1_RELATIVE: 11.647 G_Regularizer: 0.000 validation_error: 20.409 +(epoch: 15, iters: 7472, time: 0.537, data: 0.000) G_L1: 12.412 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 9.815 G_Regularizer: 0.000 validation_error: 20.101 +(epoch: 15, iters: 9472, time: 0.535, data: 0.000) G_L1: 14.820 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 12.484 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 15, iters: 11472, time: 0.543, data: 0.000) G_L1: 15.621 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 12.847 G_Regularizer: 0.000 validation_error: 21.129 +(epoch: 15, iters: 13472, time: 0.549, data: 0.000) G_L1: 16.442 G_L1_ABSOLUTE: 2.834 G_L1_RELATIVE: 13.608 G_Regularizer: 0.000 validation_error: 20.632 +(epoch: 15, iters: 15472, time: 0.538, data: 0.000) G_L1: 14.048 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 11.425 G_Regularizer: 0.000 validation_error: 21.495 +(epoch: 15, iters: 17472, time: 0.559, data: 0.000) G_L1: 17.324 G_L1_ABSOLUTE: 3.275 G_L1_RELATIVE: 14.049 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 15, iters: 19472, time: 0.562, data: 0.000) G_L1: 16.229 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 13.572 G_Regularizer: 0.000 validation_error: 21.073 +(epoch: 15, iters: 21472, time: 0.541, data: 0.000) G_L1: 14.771 G_L1_ABSOLUTE: 2.803 G_L1_RELATIVE: 11.969 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 15, iters: 23472, time: 0.560, data: 0.000) G_L1: 14.570 G_L1_ABSOLUTE: 2.900 G_L1_RELATIVE: 11.670 G_Regularizer: 0.000 validation_error: 20.954 +(epoch: 15, iters: 25472, time: 0.552, data: 0.000) G_L1: 17.073 G_L1_ABSOLUTE: 2.873 G_L1_RELATIVE: 14.200 G_Regularizer: 0.000 validation_error: 21.467 +(epoch: 15, iters: 27472, time: 0.552, data: 0.000) G_L1: 15.421 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 12.754 G_Regularizer: 0.000 validation_error: 20.599 +(epoch: 15, iters: 29472, time: 0.551, data: 0.000) G_L1: 14.491 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 11.793 G_Regularizer: 0.000 validation_error: 20.260 +(epoch: 15, iters: 31472, time: 0.531, data: 0.000) G_L1: 13.707 G_L1_ABSOLUTE: 2.404 G_L1_RELATIVE: 11.304 G_Regularizer: 0.000 validation_error: 19.965 +(epoch: 15, iters: 33472, time: 0.556, data: 0.000) G_L1: 14.878 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 11.984 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 15, iters: 35472, time: 0.557, data: 0.000) G_L1: 14.907 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 12.374 G_Regularizer: 0.000 validation_error: 20.938 +(epoch: 15, iters: 37472, time: 0.556, data: 0.001) G_L1: 15.687 G_L1_ABSOLUTE: 2.929 G_L1_RELATIVE: 12.758 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 15, iters: 39472, time: 0.558, data: 0.000) G_L1: 13.030 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 10.462 G_Regularizer: 0.000 validation_error: 21.352 +(epoch: 15, iters: 41472, time: 0.551, data: 0.000) G_L1: 18.711 G_L1_ABSOLUTE: 2.718 G_L1_RELATIVE: 15.993 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 15, iters: 43472, time: 0.540, data: 0.000) G_L1: 16.082 G_L1_ABSOLUTE: 2.321 G_L1_RELATIVE: 13.761 G_Regularizer: 0.000 validation_error: 20.229 +(epoch: 15, iters: 45472, time: 0.558, data: 0.000) G_L1: 13.439 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 10.628 G_Regularizer: 0.000 validation_error: 20.856 +(epoch: 15, iters: 47472, time: 0.532, data: 0.000) G_L1: 14.323 G_L1_ABSOLUTE: 3.060 G_L1_RELATIVE: 11.263 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 15, iters: 49472, time: 0.530, data: 0.000) G_L1: 14.825 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 12.234 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 15, iters: 51472, time: 0.563, data: 0.000) G_L1: 16.566 G_L1_ABSOLUTE: 3.555 G_L1_RELATIVE: 13.011 G_Regularizer: 0.000 validation_error: 21.219 +(epoch: 15, iters: 53472, time: 0.537, data: 0.000) G_L1: 15.074 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 12.454 G_Regularizer: 0.000 validation_error: 21.111 +(epoch: 15, iters: 55472, time: 0.537, data: 0.000) G_L1: 15.761 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 13.302 G_Regularizer: 0.000 validation_error: 20.423 +(epoch: 15, iters: 57472, time: 0.532, data: 0.000) G_L1: 17.178 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 14.364 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 15, iters: 59472, time: 0.556, data: 0.000) G_L1: 14.585 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 12.013 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 15, iters: 61472, time: 0.535, data: 0.000) G_L1: 12.946 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 10.354 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 15, iters: 63472, time: 0.546, data: 0.000) G_L1: 16.376 G_L1_ABSOLUTE: 3.204 G_L1_RELATIVE: 13.172 G_Regularizer: 0.000 validation_error: 20.435 +(epoch: 15, iters: 65472, time: 0.539, data: 0.000) G_L1: 13.115 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 10.784 G_Regularizer: 0.000 validation_error: 21.298 +(epoch: 15, iters: 67472, time: 0.532, data: 0.000) G_L1: 13.339 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 10.607 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 15, iters: 69472, time: 0.560, data: 0.000) G_L1: 16.132 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 13.409 G_Regularizer: 0.000 validation_error: 20.346 +(epoch: 15, iters: 71472, time: 0.541, data: 0.001) G_L1: 15.479 G_L1_ABSOLUTE: 2.704 G_L1_RELATIVE: 12.775 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 15, iters: 73472, time: 0.545, data: 0.000) G_L1: 15.688 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 12.957 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 15, iters: 75472, time: 0.560, data: 0.000) G_L1: 16.235 G_L1_ABSOLUTE: 3.098 G_L1_RELATIVE: 13.137 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 15, iters: 77472, time: 0.548, data: 0.000) G_L1: 16.214 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 13.536 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 15, iters: 79472, time: 0.559, data: 0.000) G_L1: 16.152 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 13.521 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 15, iters: 81472, time: 0.541, data: 0.000) G_L1: 13.228 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 10.755 G_Regularizer: 0.000 validation_error: 21.006 +(epoch: 15, iters: 83472, time: 0.547, data: 0.000) G_L1: 17.289 G_L1_ABSOLUTE: 2.813 G_L1_RELATIVE: 14.476 G_Regularizer: 0.000 validation_error: 20.583 +(epoch: 15, iters: 85472, time: 0.565, data: 0.000) G_L1: 14.868 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 12.480 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 15, iters: 87472, time: 0.559, data: 0.000) G_L1: 15.980 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 13.648 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 15, iters: 89472, time: 0.544, data: 0.000) G_L1: 13.769 G_L1_ABSOLUTE: 2.308 G_L1_RELATIVE: 11.461 G_Regularizer: 0.000 validation_error: 20.474 +(epoch: 15, iters: 91472, time: 0.539, data: 0.000) G_L1: 14.901 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 12.125 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 15, iters: 93472, time: 0.553, data: 0.000) G_L1: 14.701 G_L1_ABSOLUTE: 2.352 G_L1_RELATIVE: 12.349 G_Regularizer: 0.000 validation_error: 20.351 +(epoch: 15, iters: 95472, time: 0.547, data: 0.000) G_L1: 16.253 G_L1_ABSOLUTE: 2.931 G_L1_RELATIVE: 13.322 G_Regularizer: 0.000 validation_error: 20.452 +(epoch: 15, iters: 97472, time: 0.543, data: 0.000) G_L1: 13.488 G_L1_ABSOLUTE: 2.337 G_L1_RELATIVE: 11.152 G_Regularizer: 0.000 validation_error: 20.618 +(epoch: 15, iters: 99472, time: 0.550, data: 0.000) G_L1: 15.606 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 12.795 G_Regularizer: 0.000 validation_error: 20.326 +(epoch: 15, iters: 101472, time: 0.551, data: 0.000) G_L1: 14.492 G_L1_ABSOLUTE: 2.654 G_L1_RELATIVE: 11.838 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 15, iters: 103472, time: 0.559, data: 0.000) G_L1: 13.631 G_L1_ABSOLUTE: 3.128 G_L1_RELATIVE: 10.503 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 15, iters: 105472, time: 0.554, data: 0.000) G_L1: 14.674 G_L1_ABSOLUTE: 3.176 G_L1_RELATIVE: 11.498 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 15, iters: 107472, time: 0.535, data: 0.000) G_L1: 15.979 G_L1_ABSOLUTE: 3.190 G_L1_RELATIVE: 12.789 G_Regularizer: 0.000 validation_error: 20.396 +(epoch: 15, iters: 109472, time: 0.547, data: 0.000) G_L1: 15.654 G_L1_ABSOLUTE: 2.904 G_L1_RELATIVE: 12.750 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 15, iters: 111472, time: 0.544, data: 0.000) G_L1: 14.869 G_L1_ABSOLUTE: 2.645 G_L1_RELATIVE: 12.224 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 15, iters: 113472, time: 0.546, data: 0.000) G_L1: 15.339 G_L1_ABSOLUTE: 2.876 G_L1_RELATIVE: 12.463 G_Regularizer: 0.000 validation_error: 20.944 +(epoch: 15, iters: 115472, time: 0.531, data: 0.000) G_L1: 13.937 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 11.474 G_Regularizer: 0.000 validation_error: 19.658 +(epoch: 15, iters: 117472, time: 0.557, data: 0.000) G_L1: 17.793 G_L1_ABSOLUTE: 3.152 G_L1_RELATIVE: 14.641 G_Regularizer: 0.000 validation_error: 20.536 +(epoch: 15, iters: 119472, time: 0.555, data: 0.000) G_L1: 15.269 G_L1_ABSOLUTE: 2.487 G_L1_RELATIVE: 12.781 G_Regularizer: 0.000 validation_error: 21.558 +(epoch: 15, iters: 121472, time: 0.542, data: 0.000) G_L1: 15.236 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 12.599 G_Regularizer: 0.000 validation_error: 19.879 +(epoch: 15, iters: 123472, time: 0.549, data: 0.000) G_L1: 15.470 G_L1_ABSOLUTE: 2.739 G_L1_RELATIVE: 12.732 G_Regularizer: 0.000 validation_error: 20.528 +(epoch: 15, iters: 125472, time: 0.563, data: 0.000) G_L1: 14.923 G_L1_ABSOLUTE: 3.223 G_L1_RELATIVE: 11.701 G_Regularizer: 0.000 validation_error: 20.444 +(epoch: 15, iters: 127472, time: 0.546, data: 0.000) G_L1: 26.215 G_L1_ABSOLUTE: 2.356 G_L1_RELATIVE: 23.859 G_Regularizer: 0.000 validation_error: 20.420 +(epoch: 15, iters: 129472, time: 0.558, data: 0.000) G_L1: 17.561 G_L1_ABSOLUTE: 3.160 G_L1_RELATIVE: 14.401 G_Regularizer: 0.000 validation_error: 20.373 +(epoch: 15, iters: 131472, time: 0.542, data: 0.000) G_L1: 14.723 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 12.093 G_Regularizer: 0.000 validation_error: 19.950 +(epoch: 15, iters: 133472, time: 0.560, data: 0.000) G_L1: 12.727 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 10.092 G_Regularizer: 0.000 validation_error: 19.751 +(epoch: 15, iters: 135472, time: 0.560, data: 0.000) G_L1: 15.908 G_L1_ABSOLUTE: 2.844 G_L1_RELATIVE: 13.065 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 15, iters: 137472, time: 0.558, data: 0.000) G_L1: 14.417 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 11.764 G_Regularizer: 0.000 validation_error: 20.797 +(epoch: 15, iters: 139472, time: 0.542, data: 0.000) G_L1: 13.710 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 11.184 G_Regularizer: 0.000 validation_error: 19.684 +(epoch: 15, iters: 141472, time: 0.562, data: 0.000) G_L1: 15.427 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 12.992 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 15, iters: 143472, time: 0.559, data: 0.000) G_L1: 14.828 G_L1_ABSOLUTE: 2.765 G_L1_RELATIVE: 12.063 G_Regularizer: 0.000 validation_error: 19.963 +(epoch: 15, iters: 145472, time: 0.559, data: 0.000) G_L1: 15.792 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 13.192 G_Regularizer: 0.000 validation_error: 20.550 +(epoch: 15, iters: 147472, time: 0.545, data: 0.000) G_L1: 17.714 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 14.966 G_Regularizer: 0.000 validation_error: 20.238 +(epoch: 15, iters: 149472, time: 0.534, data: 0.000) G_L1: 15.828 G_L1_ABSOLUTE: 2.770 G_L1_RELATIVE: 13.058 G_Regularizer: 0.000 validation_error: 20.618 +(epoch: 15, iters: 151472, time: 0.533, data: 0.000) G_L1: 14.684 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 12.027 G_Regularizer: 0.000 validation_error: 20.303 +(epoch: 15, iters: 153472, time: 0.556, data: 0.000) G_L1: 17.120 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 14.284 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 15, iters: 155472, time: 0.541, data: 0.000) G_L1: 15.430 G_L1_ABSOLUTE: 2.479 G_L1_RELATIVE: 12.951 G_Regularizer: 0.000 validation_error: 20.744 +(epoch: 15, iters: 157472, time: 0.534, data: 0.000) G_L1: 15.051 G_L1_ABSOLUTE: 2.990 G_L1_RELATIVE: 12.061 G_Regularizer: 0.000 validation_error: 20.867 +(epoch: 15, iters: 159472, time: 0.560, data: 0.000) G_L1: 14.118 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 11.255 G_Regularizer: 0.000 validation_error: 20.757 +(epoch: 15, iters: 161472, time: 0.557, data: 0.000) G_L1: 15.586 G_L1_ABSOLUTE: 2.910 G_L1_RELATIVE: 12.675 G_Regularizer: 0.000 validation_error: 20.439 +(epoch: 15, iters: 163472, time: 0.566, data: 0.000) G_L1: 18.545 G_L1_ABSOLUTE: 3.261 G_L1_RELATIVE: 15.285 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 15, iters: 165472, time: 0.535, data: 0.000) G_L1: 14.515 G_L1_ABSOLUTE: 2.846 G_L1_RELATIVE: 11.669 G_Regularizer: 0.000 validation_error: 20.972 +(epoch: 15, iters: 167472, time: 0.539, data: 0.000) G_L1: 13.231 G_L1_ABSOLUTE: 2.396 G_L1_RELATIVE: 10.834 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 15, iters: 169472, time: 0.554, data: 0.000) G_L1: 14.182 G_L1_ABSOLUTE: 2.567 G_L1_RELATIVE: 11.615 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 15, iters: 171472, time: 0.564, data: 0.000) G_L1: 17.483 G_L1_ABSOLUTE: 3.067 G_L1_RELATIVE: 14.417 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 15, iters: 173472, time: 0.533, data: 0.001) G_L1: 16.094 G_L1_ABSOLUTE: 2.831 G_L1_RELATIVE: 13.263 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 15, iters: 175472, time: 0.550, data: 0.000) G_L1: 13.102 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 10.772 G_Regularizer: 0.000 validation_error: 20.769 +(epoch: 15, iters: 177472, time: 0.554, data: 0.000) G_L1: 16.650 G_L1_ABSOLUTE: 2.933 G_L1_RELATIVE: 13.716 G_Regularizer: 0.000 validation_error: 20.180 +(epoch: 15, iters: 179472, time: 0.550, data: 0.000) G_L1: 16.171 G_L1_ABSOLUTE: 2.977 G_L1_RELATIVE: 13.194 G_Regularizer: 0.000 validation_error: 20.300 +(epoch: 15, iters: 181472, time: 0.542, data: 0.000) G_L1: 14.661 G_L1_ABSOLUTE: 2.769 G_L1_RELATIVE: 11.892 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 15, iters: 183472, time: 0.555, data: 0.000) G_L1: 18.622 G_L1_ABSOLUTE: 2.817 G_L1_RELATIVE: 15.805 G_Regularizer: 0.000 validation_error: 20.513 +(epoch: 15, iters: 185472, time: 0.543, data: 0.000) G_L1: 13.532 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 11.270 G_Regularizer: 0.000 validation_error: 20.405 +(epoch: 15, iters: 187472, time: 0.559, data: 0.000) G_L1: 14.766 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 11.988 G_Regularizer: 0.000 validation_error: 20.122 +(epoch: 15, iters: 189472, time: 0.552, data: 0.000) G_L1: 16.124 G_L1_ABSOLUTE: 2.826 G_L1_RELATIVE: 13.297 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 15, iters: 191472, time: 0.559, data: 0.000) G_L1: 15.698 G_L1_ABSOLUTE: 2.067 G_L1_RELATIVE: 13.631 G_Regularizer: 0.000 validation_error: 20.789 +(epoch: 15, iters: 193472, time: 0.557, data: 0.000) G_L1: 12.478 G_L1_ABSOLUTE: 2.662 G_L1_RELATIVE: 9.817 G_Regularizer: 0.000 validation_error: 20.756 +(epoch: 15, iters: 195472, time: 0.558, data: 0.000) G_L1: 14.350 G_L1_ABSOLUTE: 2.147 G_L1_RELATIVE: 12.203 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 15, iters: 197472, time: 0.550, data: 0.000) G_L1: 17.161 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 14.749 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 15, iters: 199472, time: 0.536, data: 0.000) G_L1: 17.677 G_L1_ABSOLUTE: 2.654 G_L1_RELATIVE: 15.023 G_Regularizer: 0.000 validation_error: 21.571 +(epoch: 15, iters: 201472, time: 0.560, data: 0.000) G_L1: 14.986 G_L1_ABSOLUTE: 3.197 G_L1_RELATIVE: 11.789 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 15, iters: 203472, time: 0.559, data: 0.000) G_L1: 15.386 G_L1_ABSOLUTE: 3.146 G_L1_RELATIVE: 12.241 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 15, iters: 205472, time: 0.556, data: 0.000) G_L1: 15.276 G_L1_ABSOLUTE: 2.823 G_L1_RELATIVE: 12.453 G_Regularizer: 0.000 validation_error: 20.298 +(epoch: 15, iters: 207472, time: 0.549, data: 0.000) G_L1: 14.406 G_L1_ABSOLUTE: 2.617 G_L1_RELATIVE: 11.789 G_Regularizer: 0.000 validation_error: 20.629 +(epoch: 15, iters: 209472, time: 0.556, data: 0.000) G_L1: 14.142 G_L1_ABSOLUTE: 2.166 G_L1_RELATIVE: 11.976 G_Regularizer: 0.000 validation_error: 21.290 +(epoch: 15, iters: 211472, time: 0.541, data: 0.000) G_L1: 14.233 G_L1_ABSOLUTE: 2.392 G_L1_RELATIVE: 11.841 G_Regularizer: 0.000 validation_error: 21.752 +(epoch: 15, iters: 213472, time: 0.550, data: 0.001) G_L1: 14.178 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 11.298 G_Regularizer: 0.000 validation_error: 20.306 +(epoch: 15, iters: 215472, time: 0.547, data: 0.000) G_L1: 16.368 G_L1_ABSOLUTE: 2.580 G_L1_RELATIVE: 13.787 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 15, iters: 217472, time: 0.535, data: 0.000) G_L1: 12.199 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 9.766 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 15, iters: 219472, time: 0.532, data: 0.001) G_L1: 14.260 G_L1_ABSOLUTE: 2.973 G_L1_RELATIVE: 11.286 G_Regularizer: 0.000 validation_error: 21.014 +(epoch: 15, iters: 221472, time: 0.555, data: 0.000) G_L1: 14.878 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 12.124 G_Regularizer: 0.000 validation_error: 20.326 +(epoch: 15, iters: 223472, time: 0.545, data: 0.001) G_L1: 17.159 G_L1_ABSOLUTE: 3.035 G_L1_RELATIVE: 14.124 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 15, iters: 225472, time: 0.560, data: 0.000) G_L1: 15.612 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 13.001 G_Regularizer: 0.000 validation_error: 20.145 +(epoch: 15, iters: 227472, time: 0.561, data: 0.000) G_L1: 14.660 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 12.104 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 15, iters: 229472, time: 0.538, data: 0.000) G_L1: 14.824 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 12.459 G_Regularizer: 0.000 validation_error: 19.850 +(epoch: 15, iters: 231472, time: 0.534, data: 0.000) G_L1: 17.321 G_L1_ABSOLUTE: 3.581 G_L1_RELATIVE: 13.740 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 15, iters: 233472, time: 0.532, data: 0.000) G_L1: 14.237 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 11.822 G_Regularizer: 0.000 validation_error: 21.171 +(epoch: 15, iters: 235472, time: 0.541, data: 0.000) G_L1: 16.495 G_L1_ABSOLUTE: 2.913 G_L1_RELATIVE: 13.582 G_Regularizer: 0.000 validation_error: 20.304 +(epoch: 15, iters: 237472, time: 0.537, data: 0.000) G_L1: 13.676 G_L1_ABSOLUTE: 2.441 G_L1_RELATIVE: 11.235 G_Regularizer: 0.000 validation_error: 20.770 +(epoch: 15, iters: 239472, time: 0.556, data: 0.000) G_L1: 13.043 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 10.827 G_Regularizer: 0.000 validation_error: 20.675 +(epoch: 15, iters: 241472, time: 0.550, data: 0.000) G_L1: 14.686 G_L1_ABSOLUTE: 2.617 G_L1_RELATIVE: 12.069 G_Regularizer: 0.000 validation_error: 20.284 +(epoch: 15, iters: 243472, time: 0.565, data: 0.000) G_L1: 15.374 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 13.004 G_Regularizer: 0.000 validation_error: 20.375 +(epoch: 15, iters: 245472, time: 0.549, data: 0.000) G_L1: 13.655 G_L1_ABSOLUTE: 2.772 G_L1_RELATIVE: 10.883 G_Regularizer: 0.000 validation_error: 20.598 +(epoch: 15, iters: 247472, time: 0.564, data: 0.000) G_L1: 12.804 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 10.457 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 15, iters: 249472, time: 0.537, data: 0.000) G_L1: 13.787 G_L1_ABSOLUTE: 2.727 G_L1_RELATIVE: 11.060 G_Regularizer: 0.000 validation_error: 20.352 +(epoch: 15, iters: 251472, time: 0.559, data: 0.000) G_L1: 17.749 G_L1_ABSOLUTE: 2.786 G_L1_RELATIVE: 14.963 G_Regularizer: 0.000 validation_error: 21.260 +(epoch: 15, iters: 253472, time: 0.566, data: 0.000) G_L1: 15.926 G_L1_ABSOLUTE: 2.411 G_L1_RELATIVE: 13.515 G_Regularizer: 0.000 validation_error: 19.741 +(epoch: 15, iters: 255472, time: 0.558, data: 0.000) G_L1: 13.374 G_L1_ABSOLUTE: 2.396 G_L1_RELATIVE: 10.978 G_Regularizer: 0.000 validation_error: 20.073 +(epoch: 15, iters: 257472, time: 0.543, data: 0.000) G_L1: 16.039 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 13.513 G_Regularizer: 0.000 validation_error: 20.683 +(epoch: 15, iters: 259472, time: 0.560, data: 0.000) G_L1: 15.081 G_L1_ABSOLUTE: 3.131 G_L1_RELATIVE: 11.950 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 15, iters: 261472, time: 0.556, data: 0.000) G_L1: 12.990 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 10.707 G_Regularizer: 0.000 validation_error: 20.537 +(epoch: 15, iters: 263472, time: 0.540, data: 0.000) G_L1: 13.021 G_L1_ABSOLUTE: 2.313 G_L1_RELATIVE: 10.708 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 15, iters: 265472, time: 0.543, data: 0.001) G_L1: 14.745 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 12.428 G_Regularizer: 0.000 validation_error: 20.096 +(epoch: 15, iters: 267472, time: 0.559, data: 0.000) G_L1: 15.903 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 13.327 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 15, iters: 269472, time: 0.549, data: 0.000) G_L1: 16.743 G_L1_ABSOLUTE: 3.025 G_L1_RELATIVE: 13.718 G_Regularizer: 0.000 validation_error: 20.451 +(epoch: 15, iters: 271472, time: 0.541, data: 0.000) G_L1: 20.410 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 17.752 G_Regularizer: 0.000 validation_error: 20.430 +(epoch: 15, iters: 273472, time: 0.536, data: 0.000) G_L1: 13.005 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 10.555 G_Regularizer: 0.000 validation_error: 20.445 +(epoch: 15, iters: 275472, time: 0.553, data: 0.000) G_L1: 13.376 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 11.032 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 15, iters: 277472, time: 0.544, data: 0.000) G_L1: 15.083 G_L1_ABSOLUTE: 3.236 G_L1_RELATIVE: 11.846 G_Regularizer: 0.000 validation_error: 19.986 +(epoch: 15, iters: 279472, time: 0.560, data: 0.000) G_L1: 17.279 G_L1_ABSOLUTE: 2.514 G_L1_RELATIVE: 14.764 G_Regularizer: 0.000 validation_error: 20.893 +(epoch: 15, iters: 281472, time: 0.550, data: 0.000) G_L1: 17.204 G_L1_ABSOLUTE: 3.145 G_L1_RELATIVE: 14.059 G_Regularizer: 0.000 validation_error: 19.612 +(epoch: 15, iters: 283472, time: 0.554, data: 0.001) G_L1: 15.606 G_L1_ABSOLUTE: 2.927 G_L1_RELATIVE: 12.679 G_Regularizer: 0.000 validation_error: 20.637 +(epoch: 15, iters: 285472, time: 0.558, data: 0.000) G_L1: 14.598 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 11.925 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 15, iters: 287472, time: 0.549, data: 0.000) G_L1: 17.151 G_L1_ABSOLUTE: 3.517 G_L1_RELATIVE: 13.634 G_Regularizer: 0.000 validation_error: 20.366 +(epoch: 15, iters: 289472, time: 0.558, data: 0.001) G_L1: 14.643 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 12.182 G_Regularizer: 0.000 validation_error: 20.107 +(epoch: 15, iters: 291472, time: 0.533, data: 0.000) G_L1: 15.612 G_L1_ABSOLUTE: 2.245 G_L1_RELATIVE: 13.367 G_Regularizer: 0.000 validation_error: 20.902 +(epoch: 15, iters: 293472, time: 0.549, data: 0.000) G_L1: 15.987 G_L1_ABSOLUTE: 2.311 G_L1_RELATIVE: 13.675 G_Regularizer: 0.000 validation_error: 19.834 +(epoch: 15, iters: 295472, time: 0.547, data: 0.000) G_L1: 16.113 G_L1_ABSOLUTE: 2.685 G_L1_RELATIVE: 13.428 G_Regularizer: 0.000 validation_error: 20.233 +(epoch: 15, iters: 297472, time: 0.554, data: 0.000) G_L1: 14.056 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 11.675 G_Regularizer: 0.000 validation_error: 20.371 +(epoch: 15, iters: 299472, time: 0.539, data: 0.000) G_L1: 16.233 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 13.765 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 15, iters: 301472, time: 0.559, data: 0.000) G_L1: 12.279 G_L1_ABSOLUTE: 3.032 G_L1_RELATIVE: 9.247 G_Regularizer: 0.000 validation_error: 20.330 +(epoch: 16, iters: 720, time: 0.529, data: 0.000) G_L1: 16.885 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 14.273 G_Regularizer: 0.000 validation_error: 20.362 +(epoch: 16, iters: 2720, time: 0.560, data: 0.000) G_L1: 11.384 G_L1_ABSOLUTE: 2.176 G_L1_RELATIVE: 9.208 G_Regularizer: 0.000 validation_error: 20.468 +(epoch: 16, iters: 4720, time: 0.543, data: 0.000) G_L1: 17.008 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 14.563 G_Regularizer: 0.000 validation_error: 21.485 +(epoch: 16, iters: 6720, time: 0.554, data: 0.000) G_L1: 14.577 G_L1_ABSOLUTE: 3.018 G_L1_RELATIVE: 11.559 G_Regularizer: 0.000 validation_error: 20.296 +(epoch: 16, iters: 8720, time: 0.558, data: 0.000) G_L1: 17.480 G_L1_ABSOLUTE: 2.384 G_L1_RELATIVE: 15.096 G_Regularizer: 0.000 validation_error: 21.332 +(epoch: 16, iters: 10720, time: 0.554, data: 0.000) G_L1: 15.768 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 13.249 G_Regularizer: 0.000 validation_error: 20.545 +(epoch: 16, iters: 12720, time: 0.538, data: 0.000) G_L1: 15.892 G_L1_ABSOLUTE: 2.488 G_L1_RELATIVE: 13.403 G_Regularizer: 0.000 validation_error: 21.166 +(epoch: 16, iters: 14720, time: 0.555, data: 0.000) G_L1: 19.379 G_L1_ABSOLUTE: 2.667 G_L1_RELATIVE: 16.712 G_Regularizer: 0.000 validation_error: 20.631 +(epoch: 16, iters: 16720, time: 0.550, data: 0.000) G_L1: 17.897 G_L1_ABSOLUTE: 3.307 G_L1_RELATIVE: 14.590 G_Regularizer: 0.000 validation_error: 21.294 +(epoch: 16, iters: 18720, time: 0.551, data: 0.000) G_L1: 15.584 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 12.595 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 16, iters: 20720, time: 0.553, data: 0.000) G_L1: 14.203 G_L1_ABSOLUTE: 2.741 G_L1_RELATIVE: 11.462 G_Regularizer: 0.000 validation_error: 21.029 +(epoch: 16, iters: 22720, time: 0.539, data: 0.000) G_L1: 14.078 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 11.347 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 16, iters: 24720, time: 0.558, data: 0.000) G_L1: 16.643 G_L1_ABSOLUTE: 3.107 G_L1_RELATIVE: 13.536 G_Regularizer: 0.000 validation_error: 20.228 +(epoch: 16, iters: 26720, time: 0.563, data: 0.000) G_L1: 13.955 G_L1_ABSOLUTE: 2.099 G_L1_RELATIVE: 11.856 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 16, iters: 28720, time: 0.557, data: 0.000) G_L1: 14.943 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 12.594 G_Regularizer: 0.000 validation_error: 21.253 +(epoch: 16, iters: 30720, time: 0.559, data: 0.000) G_L1: 15.303 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 12.728 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 16, iters: 32720, time: 0.568, data: 0.000) G_L1: 16.298 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 13.725 G_Regularizer: 0.000 validation_error: 20.816 +(epoch: 16, iters: 34720, time: 0.556, data: 0.000) G_L1: 21.052 G_L1_ABSOLUTE: 3.165 G_L1_RELATIVE: 17.887 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 16, iters: 36720, time: 0.562, data: 0.000) G_L1: 15.074 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 12.260 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 16, iters: 38720, time: 0.535, data: 0.000) G_L1: 14.326 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 11.658 G_Regularizer: 0.000 validation_error: 20.344 +(epoch: 16, iters: 40720, time: 0.564, data: 0.000) G_L1: 17.524 G_L1_ABSOLUTE: 3.234 G_L1_RELATIVE: 14.290 G_Regularizer: 0.000 validation_error: 21.422 +(epoch: 16, iters: 42720, time: 0.564, data: 0.000) G_L1: 14.633 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 12.225 G_Regularizer: 0.000 validation_error: 20.699 +(epoch: 16, iters: 44720, time: 0.559, data: 0.000) G_L1: 17.483 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 14.620 G_Regularizer: 0.000 validation_error: 19.984 +(epoch: 16, iters: 46720, time: 0.531, data: 0.000) G_L1: 15.632 G_L1_ABSOLUTE: 2.662 G_L1_RELATIVE: 12.970 G_Regularizer: 0.000 validation_error: 20.311 +(epoch: 16, iters: 48720, time: 0.536, data: 0.000) G_L1: 16.441 G_L1_ABSOLUTE: 2.882 G_L1_RELATIVE: 13.559 G_Regularizer: 0.000 validation_error: 20.579 +(epoch: 16, iters: 50720, time: 0.551, data: 0.000) G_L1: 14.654 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 12.191 G_Regularizer: 0.000 validation_error: 20.602 +(epoch: 16, iters: 52720, time: 0.551, data: 0.000) G_L1: 13.702 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 11.218 G_Regularizer: 0.000 validation_error: 20.571 +(epoch: 16, iters: 54720, time: 0.544, data: 0.000) G_L1: 15.200 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 12.811 G_Regularizer: 0.000 validation_error: 20.317 +(epoch: 16, iters: 56720, time: 0.542, data: 0.000) G_L1: 13.088 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 10.725 G_Regularizer: 0.000 validation_error: 20.112 +(epoch: 16, iters: 58720, time: 0.567, data: 0.000) G_L1: 15.605 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 13.101 G_Regularizer: 0.000 validation_error: 20.572 +(epoch: 16, iters: 60720, time: 0.558, data: 0.000) G_L1: 15.511 G_L1_ABSOLUTE: 2.431 G_L1_RELATIVE: 13.080 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 16, iters: 62720, time: 0.548, data: 0.000) G_L1: 16.407 G_L1_ABSOLUTE: 2.917 G_L1_RELATIVE: 13.489 G_Regularizer: 0.000 validation_error: 20.244 +(epoch: 16, iters: 64720, time: 0.553, data: 0.000) G_L1: 15.366 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 12.907 G_Regularizer: 0.000 validation_error: 20.309 +(epoch: 16, iters: 66720, time: 0.557, data: 0.000) G_L1: 18.665 G_L1_ABSOLUTE: 2.695 G_L1_RELATIVE: 15.969 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 16, iters: 68720, time: 0.557, data: 0.000) G_L1: 13.910 G_L1_ABSOLUTE: 2.293 G_L1_RELATIVE: 11.617 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 16, iters: 70720, time: 0.553, data: 0.000) G_L1: 14.873 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 12.449 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 16, iters: 72720, time: 0.543, data: 0.000) G_L1: 13.579 G_L1_ABSOLUTE: 2.212 G_L1_RELATIVE: 11.367 G_Regularizer: 0.000 validation_error: 20.463 +(epoch: 16, iters: 74720, time: 0.537, data: 0.000) G_L1: 15.235 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 12.611 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 16, iters: 76720, time: 0.550, data: 0.000) G_L1: 14.233 G_L1_ABSOLUTE: 2.180 G_L1_RELATIVE: 12.053 G_Regularizer: 0.000 validation_error: 19.776 +(epoch: 16, iters: 78720, time: 0.564, data: 0.000) G_L1: 13.733 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 11.241 G_Regularizer: 0.000 validation_error: 20.120 +(epoch: 16, iters: 80720, time: 0.533, data: 0.000) G_L1: 14.295 G_L1_ABSOLUTE: 2.713 G_L1_RELATIVE: 11.582 G_Regularizer: 0.000 validation_error: 21.179 +(epoch: 16, iters: 82720, time: 0.551, data: 0.000) G_L1: 14.630 G_L1_ABSOLUTE: 2.799 G_L1_RELATIVE: 11.831 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 16, iters: 84720, time: 0.559, data: 0.000) G_L1: 23.292 G_L1_ABSOLUTE: 2.741 G_L1_RELATIVE: 20.550 G_Regularizer: 0.000 validation_error: 19.963 +(epoch: 16, iters: 86720, time: 0.557, data: 0.000) G_L1: 16.564 G_L1_ABSOLUTE: 2.852 G_L1_RELATIVE: 13.713 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 16, iters: 88720, time: 0.532, data: 0.000) G_L1: 17.371 G_L1_ABSOLUTE: 2.845 G_L1_RELATIVE: 14.526 G_Regularizer: 0.000 validation_error: 20.331 +(epoch: 16, iters: 90720, time: 0.561, data: 0.001) G_L1: 16.049 G_L1_ABSOLUTE: 2.719 G_L1_RELATIVE: 13.330 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 16, iters: 92720, time: 0.558, data: 0.003) G_L1: 13.792 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 11.457 G_Regularizer: 0.000 validation_error: 20.620 +(epoch: 16, iters: 94720, time: 0.558, data: 0.000) G_L1: 16.870 G_L1_ABSOLUTE: 2.993 G_L1_RELATIVE: 13.877 G_Regularizer: 0.000 validation_error: 21.130 +(epoch: 16, iters: 96720, time: 0.547, data: 0.000) G_L1: 14.726 G_L1_ABSOLUTE: 2.751 G_L1_RELATIVE: 11.975 G_Regularizer: 0.000 validation_error: 20.240 +(epoch: 16, iters: 98720, time: 0.558, data: 0.000) G_L1: 17.494 G_L1_ABSOLUTE: 3.166 G_L1_RELATIVE: 14.327 G_Regularizer: 0.000 validation_error: 20.665 +(epoch: 16, iters: 100720, time: 0.561, data: 0.000) G_L1: 18.075 G_L1_ABSOLUTE: 3.031 G_L1_RELATIVE: 15.044 G_Regularizer: 0.000 validation_error: 20.292 +(epoch: 16, iters: 102720, time: 0.558, data: 0.000) G_L1: 16.573 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 13.983 G_Regularizer: 0.000 validation_error: 20.221 +(epoch: 16, iters: 104720, time: 0.541, data: 0.000) G_L1: 15.126 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 12.690 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 16, iters: 106720, time: 0.535, data: 0.000) G_L1: 15.484 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 13.099 G_Regularizer: 0.000 validation_error: 20.765 +(epoch: 16, iters: 108720, time: 0.548, data: 0.000) G_L1: 17.463 G_L1_ABSOLUTE: 2.813 G_L1_RELATIVE: 14.650 G_Regularizer: 0.000 validation_error: 21.038 +(epoch: 16, iters: 110720, time: 0.556, data: 0.000) G_L1: 13.720 G_L1_ABSOLUTE: 3.238 G_L1_RELATIVE: 10.482 G_Regularizer: 0.000 validation_error: 20.202 +(epoch: 16, iters: 112720, time: 0.544, data: 0.000) G_L1: 15.258 G_L1_ABSOLUTE: 2.496 G_L1_RELATIVE: 12.762 G_Regularizer: 0.000 validation_error: 20.320 +(epoch: 16, iters: 114720, time: 0.544, data: 0.000) G_L1: 15.533 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 13.028 G_Regularizer: 0.000 validation_error: 21.620 +(epoch: 16, iters: 116720, time: 0.545, data: 0.000) G_L1: 14.462 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 11.830 G_Regularizer: 0.000 validation_error: 20.455 +(epoch: 16, iters: 118720, time: 0.560, data: 0.000) G_L1: 13.131 G_L1_ABSOLUTE: 2.765 G_L1_RELATIVE: 10.367 G_Regularizer: 0.000 validation_error: 20.187 +(epoch: 16, iters: 120720, time: 0.548, data: 0.000) G_L1: 15.572 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 13.118 G_Regularizer: 0.000 validation_error: 20.012 +(epoch: 16, iters: 122720, time: 0.534, data: 0.000) G_L1: 16.460 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 13.993 G_Regularizer: 0.000 validation_error: 20.167 +(epoch: 16, iters: 124720, time: 0.550, data: 0.000) G_L1: 14.460 G_L1_ABSOLUTE: 2.809 G_L1_RELATIVE: 11.651 G_Regularizer: 0.000 validation_error: 19.905 +(epoch: 16, iters: 126720, time: 0.555, data: 0.001) G_L1: 13.489 G_L1_ABSOLUTE: 2.465 G_L1_RELATIVE: 11.024 G_Regularizer: 0.000 validation_error: 20.026 +(epoch: 16, iters: 128720, time: 0.559, data: 0.000) G_L1: 14.814 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 11.909 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 16, iters: 130720, time: 0.553, data: 0.000) G_L1: 16.321 G_L1_ABSOLUTE: 2.321 G_L1_RELATIVE: 13.999 G_Regularizer: 0.000 validation_error: 20.522 +(epoch: 16, iters: 132720, time: 0.554, data: 0.000) G_L1: 15.255 G_L1_ABSOLUTE: 2.898 G_L1_RELATIVE: 12.357 G_Regularizer: 0.000 validation_error: 20.939 +(epoch: 16, iters: 134720, time: 0.537, data: 0.000) G_L1: 17.156 G_L1_ABSOLUTE: 3.070 G_L1_RELATIVE: 14.086 G_Regularizer: 0.000 validation_error: 20.517 +(epoch: 16, iters: 136720, time: 0.544, data: 0.000) G_L1: 12.925 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 10.536 G_Regularizer: 0.000 validation_error: 20.453 +(epoch: 16, iters: 138720, time: 0.536, data: 0.000) G_L1: 16.760 G_L1_ABSOLUTE: 3.422 G_L1_RELATIVE: 13.338 G_Regularizer: 0.000 validation_error: 20.411 +(epoch: 16, iters: 140720, time: 0.556, data: 0.000) G_L1: 13.775 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 11.392 G_Regularizer: 0.000 validation_error: 20.108 +(epoch: 16, iters: 142720, time: 0.529, data: 0.000) G_L1: 15.551 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 13.114 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 16, iters: 144720, time: 0.537, data: 0.000) G_L1: 13.227 G_L1_ABSOLUTE: 2.934 G_L1_RELATIVE: 10.293 G_Regularizer: 0.000 validation_error: 21.079 +(epoch: 16, iters: 146720, time: 0.555, data: 0.000) G_L1: 14.019 G_L1_ABSOLUTE: 2.980 G_L1_RELATIVE: 11.039 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 16, iters: 148720, time: 0.553, data: 0.000) G_L1: 17.428 G_L1_ABSOLUTE: 3.080 G_L1_RELATIVE: 14.348 G_Regularizer: 0.000 validation_error: 21.541 +(epoch: 16, iters: 150720, time: 0.536, data: 0.000) G_L1: 11.972 G_L1_ABSOLUTE: 2.114 G_L1_RELATIVE: 9.858 G_Regularizer: 0.000 validation_error: 20.913 +(epoch: 16, iters: 152720, time: 0.548, data: 0.000) G_L1: 15.307 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 12.560 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 16, iters: 154720, time: 0.543, data: 0.000) G_L1: 14.417 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 11.788 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 16, iters: 156720, time: 0.546, data: 0.000) G_L1: 16.045 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 13.343 G_Regularizer: 0.000 validation_error: 20.342 +(epoch: 16, iters: 158720, time: 0.558, data: 0.000) G_L1: 16.841 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 14.213 G_Regularizer: 0.000 validation_error: 20.771 +(epoch: 16, iters: 160720, time: 0.547, data: 0.000) G_L1: 15.947 G_L1_ABSOLUTE: 2.038 G_L1_RELATIVE: 13.909 G_Regularizer: 0.000 validation_error: 19.876 +(epoch: 16, iters: 162720, time: 0.537, data: 0.000) G_L1: 16.784 G_L1_ABSOLUTE: 2.912 G_L1_RELATIVE: 13.872 G_Regularizer: 0.000 validation_error: 20.312 +(epoch: 16, iters: 164720, time: 0.539, data: 0.000) G_L1: 18.464 G_L1_ABSOLUTE: 2.804 G_L1_RELATIVE: 15.660 G_Regularizer: 0.000 validation_error: 21.184 +(epoch: 16, iters: 166720, time: 0.560, data: 0.000) G_L1: 15.393 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 12.999 G_Regularizer: 0.000 validation_error: 21.163 +(epoch: 16, iters: 168720, time: 0.558, data: 0.000) G_L1: 14.255 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 11.694 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 16, iters: 170720, time: 0.553, data: 0.000) G_L1: 17.315 G_L1_ABSOLUTE: 2.553 G_L1_RELATIVE: 14.761 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 16, iters: 172720, time: 0.532, data: 0.000) G_L1: 15.116 G_L1_ABSOLUTE: 2.186 G_L1_RELATIVE: 12.930 G_Regularizer: 0.000 validation_error: 21.568 +(epoch: 16, iters: 174720, time: 0.533, data: 0.000) G_L1: 16.115 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 13.384 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 16, iters: 176720, time: 0.537, data: 0.000) G_L1: 14.471 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 11.647 G_Regularizer: 0.000 validation_error: 20.380 +(epoch: 16, iters: 178720, time: 0.556, data: 0.000) G_L1: 14.840 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 12.327 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 16, iters: 180720, time: 0.548, data: 0.000) G_L1: 15.483 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 12.944 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 16, iters: 182720, time: 0.561, data: 0.000) G_L1: 13.437 G_L1_ABSOLUTE: 2.957 G_L1_RELATIVE: 10.480 G_Regularizer: 0.000 validation_error: 20.717 +(epoch: 16, iters: 184720, time: 0.557, data: 0.000) G_L1: 13.706 G_L1_ABSOLUTE: 2.864 G_L1_RELATIVE: 10.842 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 16, iters: 186720, time: 0.543, data: 0.000) G_L1: 13.478 G_L1_ABSOLUTE: 3.309 G_L1_RELATIVE: 10.168 G_Regularizer: 0.000 validation_error: 20.468 +(epoch: 16, iters: 188720, time: 0.546, data: 0.000) G_L1: 14.468 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 11.921 G_Regularizer: 0.000 validation_error: 20.320 +(epoch: 16, iters: 190720, time: 0.560, data: 0.000) G_L1: 13.578 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 10.905 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 16, iters: 192720, time: 0.555, data: 0.000) G_L1: 15.430 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 12.818 G_Regularizer: 0.000 validation_error: 20.691 +(epoch: 16, iters: 194720, time: 0.552, data: 0.000) G_L1: 17.654 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 15.089 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 16, iters: 196720, time: 0.546, data: 0.000) G_L1: 12.817 G_L1_ABSOLUTE: 2.246 G_L1_RELATIVE: 10.571 G_Regularizer: 0.000 validation_error: 19.939 +(epoch: 16, iters: 198720, time: 0.536, data: 0.000) G_L1: 16.361 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 13.561 G_Regularizer: 0.000 validation_error: 21.146 +(epoch: 16, iters: 200720, time: 0.532, data: 0.000) G_L1: 13.658 G_L1_ABSOLUTE: 2.606 G_L1_RELATIVE: 11.052 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 16, iters: 202720, time: 0.552, data: 0.000) G_L1: 16.023 G_L1_ABSOLUTE: 2.360 G_L1_RELATIVE: 13.663 G_Regularizer: 0.000 validation_error: 21.128 +(epoch: 16, iters: 204720, time: 0.561, data: 0.000) G_L1: 15.265 G_L1_ABSOLUTE: 3.236 G_L1_RELATIVE: 12.028 G_Regularizer: 0.000 validation_error: 20.401 +(epoch: 16, iters: 206720, time: 0.528, data: 0.000) G_L1: 16.003 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 13.511 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 16, iters: 208720, time: 0.539, data: 0.000) G_L1: 14.003 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 11.550 G_Regularizer: 0.000 validation_error: 19.942 +(epoch: 16, iters: 210720, time: 0.548, data: 0.000) G_L1: 16.765 G_L1_ABSOLUTE: 3.080 G_L1_RELATIVE: 13.686 G_Regularizer: 0.000 validation_error: 20.114 +(epoch: 16, iters: 212720, time: 0.536, data: 0.000) G_L1: 13.262 G_L1_ABSOLUTE: 2.813 G_L1_RELATIVE: 10.449 G_Regularizer: 0.000 validation_error: 20.501 +(epoch: 16, iters: 214720, time: 0.533, data: 0.000) G_L1: 16.756 G_L1_ABSOLUTE: 2.891 G_L1_RELATIVE: 13.865 G_Regularizer: 0.000 validation_error: 20.385 +(epoch: 16, iters: 216720, time: 0.555, data: 0.000) G_L1: 15.796 G_L1_ABSOLUTE: 2.802 G_L1_RELATIVE: 12.994 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 16, iters: 218720, time: 0.552, data: 0.000) G_L1: 14.234 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 11.538 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 16, iters: 220720, time: 0.559, data: 0.000) G_L1: 19.184 G_L1_ABSOLUTE: 3.437 G_L1_RELATIVE: 15.747 G_Regularizer: 0.000 validation_error: 20.367 +(epoch: 16, iters: 222720, time: 0.545, data: 0.000) G_L1: 14.050 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 11.424 G_Regularizer: 0.000 validation_error: 20.554 +(epoch: 16, iters: 224720, time: 0.559, data: 0.000) G_L1: 14.392 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 11.850 G_Regularizer: 0.000 validation_error: 20.261 +(epoch: 16, iters: 226720, time: 0.560, data: 0.000) G_L1: 16.596 G_L1_ABSOLUTE: 2.899 G_L1_RELATIVE: 13.697 G_Regularizer: 0.000 validation_error: 20.415 +(epoch: 16, iters: 228720, time: 0.563, data: 0.000) G_L1: 13.605 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 11.075 G_Regularizer: 0.000 validation_error: 20.021 +(epoch: 16, iters: 230720, time: 0.550, data: 0.000) G_L1: 15.686 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 13.278 G_Regularizer: 0.000 validation_error: 21.021 +(epoch: 16, iters: 232720, time: 0.558, data: 0.000) G_L1: 14.688 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 12.199 G_Regularizer: 0.000 validation_error: 19.775 +(epoch: 16, iters: 234720, time: 0.563, data: 0.002) G_L1: 15.675 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 13.160 G_Regularizer: 0.000 validation_error: 20.201 +(epoch: 16, iters: 236720, time: 0.544, data: 0.000) G_L1: 14.422 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 11.882 G_Regularizer: 0.000 validation_error: 20.182 +(epoch: 16, iters: 238720, time: 0.554, data: 0.000) G_L1: 15.008 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 12.515 G_Regularizer: 0.000 validation_error: 20.468 +(epoch: 16, iters: 240720, time: 0.540, data: 0.000) G_L1: 14.674 G_L1_ABSOLUTE: 2.837 G_L1_RELATIVE: 11.837 G_Regularizer: 0.000 validation_error: 20.745 +(epoch: 16, iters: 242720, time: 0.548, data: 0.000) G_L1: 14.527 G_L1_ABSOLUTE: 2.804 G_L1_RELATIVE: 11.723 G_Regularizer: 0.000 validation_error: 21.132 +(epoch: 16, iters: 244720, time: 0.545, data: 0.000) G_L1: 12.441 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 10.033 G_Regularizer: 0.000 validation_error: 20.225 +(epoch: 16, iters: 246720, time: 0.560, data: 0.001) G_L1: 13.330 G_L1_ABSOLUTE: 2.256 G_L1_RELATIVE: 11.074 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 16, iters: 248720, time: 0.559, data: 0.000) G_L1: 19.203 G_L1_ABSOLUTE: 3.099 G_L1_RELATIVE: 16.104 G_Regularizer: 0.000 validation_error: 20.244 +(epoch: 16, iters: 250720, time: 0.553, data: 0.000) G_L1: 15.022 G_L1_ABSOLUTE: 2.514 G_L1_RELATIVE: 12.508 G_Regularizer: 0.000 validation_error: 20.288 +(epoch: 16, iters: 252720, time: 0.529, data: 0.000) G_L1: 16.017 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 13.261 G_Regularizer: 0.000 validation_error: 20.707 +(epoch: 16, iters: 254720, time: 0.548, data: 0.000) G_L1: 13.318 G_L1_ABSOLUTE: 2.232 G_L1_RELATIVE: 11.086 G_Regularizer: 0.000 validation_error: 20.183 +(epoch: 16, iters: 256720, time: 0.541, data: 0.000) G_L1: 14.801 G_L1_ABSOLUTE: 3.037 G_L1_RELATIVE: 11.764 G_Regularizer: 0.000 validation_error: 20.579 +(epoch: 16, iters: 258720, time: 0.563, data: 0.000) G_L1: 13.756 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 11.174 G_Regularizer: 0.000 validation_error: 19.838 +(epoch: 16, iters: 260720, time: 0.558, data: 0.000) G_L1: 17.510 G_L1_ABSOLUTE: 2.605 G_L1_RELATIVE: 14.905 G_Regularizer: 0.000 validation_error: 19.520 +(epoch: 16, iters: 262720, time: 0.555, data: 0.001) G_L1: 13.722 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 10.898 G_Regularizer: 0.000 validation_error: 20.350 +(epoch: 16, iters: 264720, time: 0.534, data: 0.000) G_L1: 15.876 G_L1_ABSOLUTE: 2.638 G_L1_RELATIVE: 13.237 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 16, iters: 266720, time: 0.559, data: 0.000) G_L1: 15.286 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 12.513 G_Regularizer: 0.000 validation_error: 19.742 +(epoch: 16, iters: 268720, time: 0.545, data: 0.000) G_L1: 14.128 G_L1_ABSOLUTE: 2.776 G_L1_RELATIVE: 11.353 G_Regularizer: 0.000 validation_error: 21.087 +(epoch: 16, iters: 270720, time: 0.555, data: 0.000) G_L1: 17.456 G_L1_ABSOLUTE: 3.059 G_L1_RELATIVE: 14.397 G_Regularizer: 0.000 validation_error: 19.679 +(epoch: 16, iters: 272720, time: 0.540, data: 0.000) G_L1: 15.453 G_L1_ABSOLUTE: 2.924 G_L1_RELATIVE: 12.529 G_Regularizer: 0.000 validation_error: 20.869 +(epoch: 16, iters: 274720, time: 0.555, data: 0.000) G_L1: 17.890 G_L1_ABSOLUTE: 3.044 G_L1_RELATIVE: 14.845 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 16, iters: 276720, time: 0.549, data: 0.000) G_L1: 16.135 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 13.254 G_Regularizer: 0.000 validation_error: 20.979 +(epoch: 16, iters: 278720, time: 0.556, data: 0.000) G_L1: 15.921 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 13.107 G_Regularizer: 0.000 validation_error: 20.116 +(epoch: 16, iters: 280720, time: 0.553, data: 0.000) G_L1: 13.633 G_L1_ABSOLUTE: 3.046 G_L1_RELATIVE: 10.587 G_Regularizer: 0.000 validation_error: 20.333 +(epoch: 16, iters: 282720, time: 0.564, data: 0.000) G_L1: 13.566 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 11.164 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 16, iters: 284720, time: 0.552, data: 0.000) G_L1: 15.336 G_L1_ABSOLUTE: 2.825 G_L1_RELATIVE: 12.511 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 16, iters: 286720, time: 0.560, data: 0.000) G_L1: 13.536 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 10.680 G_Regularizer: 0.000 validation_error: 20.155 +(epoch: 16, iters: 288720, time: 0.538, data: 0.000) G_L1: 23.352 G_L1_ABSOLUTE: 2.919 G_L1_RELATIVE: 20.433 G_Regularizer: 0.000 validation_error: 20.100 +(epoch: 16, iters: 290720, time: 0.537, data: 0.000) G_L1: 13.752 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 11.133 G_Regularizer: 0.000 validation_error: 20.518 +(epoch: 16, iters: 292720, time: 0.546, data: 0.000) G_L1: 14.433 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 11.859 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 16, iters: 294720, time: 0.563, data: 0.000) G_L1: 15.785 G_L1_ABSOLUTE: 2.950 G_L1_RELATIVE: 12.834 G_Regularizer: 0.000 validation_error: 20.279 +(epoch: 16, iters: 296720, time: 0.559, data: 0.001) G_L1: 14.784 G_L1_ABSOLUTE: 2.977 G_L1_RELATIVE: 11.807 G_Regularizer: 0.000 validation_error: 20.326 +(epoch: 16, iters: 298720, time: 0.528, data: 0.000) G_L1: 12.828 G_L1_ABSOLUTE: 2.411 G_L1_RELATIVE: 10.417 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 16, iters: 300720, time: 0.553, data: 0.000) G_L1: 17.238 G_L1_ABSOLUTE: 3.026 G_L1_RELATIVE: 14.212 G_Regularizer: 0.000 validation_error: 20.386 +(epoch: 16, iters: 302720, time: 0.557, data: 0.000) G_L1: 15.511 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 12.603 G_Regularizer: 0.000 validation_error: 20.427 +(epoch: 17, iters: 1968, time: 0.557, data: 0.000) G_L1: 11.920 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 9.096 G_Regularizer: 0.000 validation_error: 19.892 +(epoch: 17, iters: 3968, time: 0.546, data: 0.000) G_L1: 19.525 G_L1_ABSOLUTE: 2.653 G_L1_RELATIVE: 16.872 G_Regularizer: 0.000 validation_error: 19.894 +(epoch: 17, iters: 5968, time: 0.560, data: 0.000) G_L1: 16.621 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 13.989 G_Regularizer: 0.000 validation_error: 20.372 +(epoch: 17, iters: 7968, time: 0.554, data: 0.000) G_L1: 15.289 G_L1_ABSOLUTE: 2.769 G_L1_RELATIVE: 12.519 G_Regularizer: 0.000 validation_error: 20.667 +(epoch: 17, iters: 9968, time: 0.558, data: 0.000) G_L1: 15.042 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 12.783 G_Regularizer: 0.000 validation_error: 20.298 +(epoch: 17, iters: 11968, time: 0.537, data: 0.000) G_L1: 14.694 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 12.043 G_Regularizer: 0.000 validation_error: 20.377 +(epoch: 17, iters: 13968, time: 0.563, data: 0.000) G_L1: 15.037 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 12.722 G_Regularizer: 0.000 validation_error: 20.276 +(epoch: 17, iters: 15968, time: 0.552, data: 0.000) G_L1: 13.194 G_L1_ABSOLUTE: 2.869 G_L1_RELATIVE: 10.325 G_Regularizer: 0.000 validation_error: 21.020 +(epoch: 17, iters: 17968, time: 0.559, data: 0.000) G_L1: 18.673 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 15.892 G_Regularizer: 0.000 validation_error: 20.261 +(epoch: 17, iters: 19968, time: 0.549, data: 0.000) G_L1: 15.147 G_L1_ABSOLUTE: 3.294 G_L1_RELATIVE: 11.852 G_Regularizer: 0.000 validation_error: 20.494 +(epoch: 17, iters: 21968, time: 0.534, data: 0.000) G_L1: 12.430 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 10.090 G_Regularizer: 0.000 validation_error: 20.157 +(epoch: 17, iters: 23968, time: 0.538, data: 0.000) G_L1: 15.988 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 13.576 G_Regularizer: 0.000 validation_error: 20.552 +(epoch: 17, iters: 25968, time: 0.540, data: 0.000) G_L1: 16.052 G_L1_ABSOLUTE: 3.089 G_L1_RELATIVE: 12.962 G_Regularizer: 0.000 validation_error: 20.218 +(epoch: 17, iters: 27968, time: 0.554, data: 0.000) G_L1: 15.197 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 12.532 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 17, iters: 29968, time: 0.541, data: 0.000) G_L1: 13.195 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 10.946 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 17, iters: 31968, time: 0.543, data: 0.000) G_L1: 16.461 G_L1_ABSOLUTE: 2.689 G_L1_RELATIVE: 13.772 G_Regularizer: 0.000 validation_error: 20.102 +(epoch: 17, iters: 33968, time: 0.540, data: 0.000) G_L1: 14.395 G_L1_ABSOLUTE: 2.799 G_L1_RELATIVE: 11.596 G_Regularizer: 0.000 validation_error: 20.578 +(epoch: 17, iters: 35968, time: 0.548, data: 0.000) G_L1: 12.853 G_L1_ABSOLUTE: 2.238 G_L1_RELATIVE: 10.615 G_Regularizer: 0.000 validation_error: 20.708 +(epoch: 17, iters: 37968, time: 0.538, data: 0.000) G_L1: 14.160 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 11.252 G_Regularizer: 0.000 validation_error: 20.349 +(epoch: 17, iters: 39968, time: 0.556, data: 0.000) G_L1: 12.741 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 10.459 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 17, iters: 41968, time: 0.540, data: 0.000) G_L1: 15.529 G_L1_ABSOLUTE: 3.307 G_L1_RELATIVE: 12.222 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 17, iters: 43968, time: 0.554, data: 0.000) G_L1: 18.339 G_L1_ABSOLUTE: 3.094 G_L1_RELATIVE: 15.245 G_Regularizer: 0.000 validation_error: 19.930 +(epoch: 17, iters: 45968, time: 0.537, data: 0.000) G_L1: 17.984 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 15.150 G_Regularizer: 0.000 validation_error: 20.168 +(epoch: 17, iters: 47968, time: 0.531, data: 0.000) G_L1: 18.809 G_L1_ABSOLUTE: 2.300 G_L1_RELATIVE: 16.509 G_Regularizer: 0.000 validation_error: 21.123 +(epoch: 17, iters: 49968, time: 0.552, data: 0.000) G_L1: 16.118 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 13.596 G_Regularizer: 0.000 validation_error: 21.257 +(epoch: 17, iters: 51968, time: 0.553, data: 0.000) G_L1: 17.296 G_L1_ABSOLUTE: 2.688 G_L1_RELATIVE: 14.608 G_Regularizer: 0.000 validation_error: 20.086 +(epoch: 17, iters: 53968, time: 0.539, data: 0.000) G_L1: 13.852 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 11.374 G_Regularizer: 0.000 validation_error: 20.639 +(epoch: 17, iters: 55968, time: 0.560, data: 0.000) G_L1: 13.626 G_L1_ABSOLUTE: 2.204 G_L1_RELATIVE: 11.421 G_Regularizer: 0.000 validation_error: 20.791 +(epoch: 17, iters: 57968, time: 0.561, data: 0.000) G_L1: 15.592 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 12.792 G_Regularizer: 0.000 validation_error: 20.227 +(epoch: 17, iters: 59968, time: 0.541, data: 0.000) G_L1: 13.263 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 10.909 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 17, iters: 61968, time: 0.537, data: 0.000) G_L1: 14.913 G_L1_ABSOLUTE: 3.380 G_L1_RELATIVE: 11.533 G_Regularizer: 0.000 validation_error: 20.181 +(epoch: 17, iters: 63968, time: 0.544, data: 0.000) G_L1: 14.263 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 11.622 G_Regularizer: 0.000 validation_error: 20.114 +(epoch: 17, iters: 65968, time: 0.555, data: 0.000) G_L1: 14.417 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 11.853 G_Regularizer: 0.000 validation_error: 19.570 +(epoch: 17, iters: 67968, time: 0.557, data: 0.000) G_L1: 14.157 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 11.523 G_Regularizer: 0.000 validation_error: 20.097 +(epoch: 17, iters: 69968, time: 0.553, data: 0.000) G_L1: 17.874 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 15.201 G_Regularizer: 0.000 validation_error: 20.745 +(epoch: 17, iters: 71968, time: 0.551, data: 0.000) G_L1: 14.719 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 11.919 G_Regularizer: 0.000 validation_error: 20.586 +(epoch: 17, iters: 73968, time: 0.552, data: 0.000) G_L1: 15.799 G_L1_ABSOLUTE: 3.008 G_L1_RELATIVE: 12.791 G_Regularizer: 0.000 validation_error: 19.934 +(epoch: 17, iters: 75968, time: 0.559, data: 0.000) G_L1: 13.372 G_L1_ABSOLUTE: 2.763 G_L1_RELATIVE: 10.609 G_Regularizer: 0.000 validation_error: 20.629 +(epoch: 17, iters: 77968, time: 0.566, data: 0.000) G_L1: 15.469 G_L1_ABSOLUTE: 3.039 G_L1_RELATIVE: 12.430 G_Regularizer: 0.000 validation_error: 20.494 +(epoch: 17, iters: 79968, time: 0.540, data: 0.001) G_L1: 11.991 G_L1_ABSOLUTE: 1.882 G_L1_RELATIVE: 10.109 G_Regularizer: 0.000 validation_error: 20.113 +(epoch: 17, iters: 81968, time: 0.555, data: 0.000) G_L1: 14.806 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 12.220 G_Regularizer: 0.000 validation_error: 20.285 +(epoch: 17, iters: 83968, time: 0.540, data: 0.000) G_L1: 14.778 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 12.336 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 17, iters: 85968, time: 0.563, data: 0.000) G_L1: 17.656 G_L1_ABSOLUTE: 2.452 G_L1_RELATIVE: 15.204 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 17, iters: 87968, time: 0.535, data: 0.000) G_L1: 14.340 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 11.722 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 17, iters: 89968, time: 0.536, data: 0.000) G_L1: 15.230 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 12.910 G_Regularizer: 0.000 validation_error: 20.491 +(epoch: 17, iters: 91968, time: 0.553, data: 0.000) G_L1: 12.511 G_L1_ABSOLUTE: 2.207 G_L1_RELATIVE: 10.304 G_Regularizer: 0.000 validation_error: 20.627 +(epoch: 17, iters: 93968, time: 0.559, data: 0.001) G_L1: 12.857 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 10.334 G_Regularizer: 0.000 validation_error: 19.803 +(epoch: 17, iters: 95968, time: 0.538, data: 0.000) G_L1: 14.705 G_L1_ABSOLUTE: 2.834 G_L1_RELATIVE: 11.870 G_Regularizer: 0.000 validation_error: 20.515 +(epoch: 17, iters: 97968, time: 0.532, data: 0.000) G_L1: 12.738 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 10.330 G_Regularizer: 0.000 validation_error: 19.981 +(epoch: 17, iters: 99968, time: 0.531, data: 0.000) G_L1: 16.750 G_L1_ABSOLUTE: 3.009 G_L1_RELATIVE: 13.741 G_Regularizer: 0.000 validation_error: 19.988 +(epoch: 17, iters: 101968, time: 0.534, data: 0.000) G_L1: 18.399 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 15.676 G_Regularizer: 0.000 validation_error: 20.515 +(epoch: 17, iters: 103968, time: 0.529, data: 0.000) G_L1: 15.688 G_L1_ABSOLUTE: 3.399 G_L1_RELATIVE: 12.289 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 17, iters: 105968, time: 0.534, data: 0.000) G_L1: 16.067 G_L1_ABSOLUTE: 2.895 G_L1_RELATIVE: 13.172 G_Regularizer: 0.000 validation_error: 19.969 +(epoch: 17, iters: 107968, time: 0.532, data: 0.001) G_L1: 15.416 G_L1_ABSOLUTE: 2.585 G_L1_RELATIVE: 12.831 G_Regularizer: 0.000 validation_error: 21.027 +(epoch: 17, iters: 109968, time: 0.556, data: 0.000) G_L1: 16.782 G_L1_ABSOLUTE: 2.972 G_L1_RELATIVE: 13.810 G_Regularizer: 0.000 validation_error: 20.361 +(epoch: 17, iters: 111968, time: 0.563, data: 0.000) G_L1: 13.588 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 11.019 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 17, iters: 113968, time: 0.534, data: 0.000) G_L1: 14.154 G_L1_ABSOLUTE: 3.342 G_L1_RELATIVE: 10.812 G_Regularizer: 0.000 validation_error: 20.355 +(epoch: 17, iters: 115968, time: 0.543, data: 0.000) G_L1: 15.585 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 13.036 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 17, iters: 117968, time: 0.566, data: 0.000) G_L1: 13.967 G_L1_ABSOLUTE: 2.611 G_L1_RELATIVE: 11.356 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 17, iters: 119968, time: 0.551, data: 0.000) G_L1: 16.432 G_L1_ABSOLUTE: 2.507 G_L1_RELATIVE: 13.926 G_Regularizer: 0.000 validation_error: 20.480 +(epoch: 17, iters: 121968, time: 0.542, data: 0.000) G_L1: 16.600 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 14.180 G_Regularizer: 0.000 validation_error: 20.882 +(epoch: 17, iters: 123968, time: 0.559, data: 0.000) G_L1: 13.620 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 10.904 G_Regularizer: 0.000 validation_error: 20.785 +(epoch: 17, iters: 125968, time: 0.547, data: 0.000) G_L1: 13.021 G_L1_ABSOLUTE: 2.077 G_L1_RELATIVE: 10.944 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 17, iters: 127968, time: 0.554, data: 0.000) G_L1: 13.997 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 11.323 G_Regularizer: 0.000 validation_error: 21.351 +(epoch: 17, iters: 129968, time: 0.550, data: 0.000) G_L1: 15.293 G_L1_ABSOLUTE: 2.119 G_L1_RELATIVE: 13.174 G_Regularizer: 0.000 validation_error: 20.310 +(epoch: 17, iters: 131968, time: 0.556, data: 0.000) G_L1: 15.260 G_L1_ABSOLUTE: 3.054 G_L1_RELATIVE: 12.206 G_Regularizer: 0.000 validation_error: 20.449 +(epoch: 17, iters: 133968, time: 0.565, data: 0.000) G_L1: 16.629 G_L1_ABSOLUTE: 2.715 G_L1_RELATIVE: 13.914 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 17, iters: 135968, time: 0.554, data: 0.001) G_L1: 15.744 G_L1_ABSOLUTE: 2.753 G_L1_RELATIVE: 12.991 G_Regularizer: 0.000 validation_error: 21.411 +(epoch: 17, iters: 137968, time: 0.545, data: 0.001) G_L1: 15.926 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 13.121 G_Regularizer: 0.000 validation_error: 20.982 +(epoch: 17, iters: 139968, time: 0.552, data: 0.000) G_L1: 15.891 G_L1_ABSOLUTE: 2.971 G_L1_RELATIVE: 12.919 G_Regularizer: 0.000 validation_error: 20.543 +(epoch: 17, iters: 141968, time: 0.562, data: 0.000) G_L1: 13.408 G_L1_ABSOLUTE: 2.770 G_L1_RELATIVE: 10.638 G_Regularizer: 0.000 validation_error: 20.457 +(epoch: 17, iters: 143968, time: 0.564, data: 0.000) G_L1: 14.026 G_L1_ABSOLUTE: 2.425 G_L1_RELATIVE: 11.601 G_Regularizer: 0.000 validation_error: 20.320 +(epoch: 17, iters: 145968, time: 0.548, data: 0.000) G_L1: 16.352 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 13.951 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 17, iters: 147968, time: 0.555, data: 0.000) G_L1: 13.706 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 11.165 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 17, iters: 149968, time: 0.548, data: 0.000) G_L1: 14.768 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 12.194 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 17, iters: 151968, time: 0.551, data: 0.000) G_L1: 14.084 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 11.405 G_Regularizer: 0.000 validation_error: 20.028 +(epoch: 17, iters: 153968, time: 0.560, data: 0.000) G_L1: 13.537 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 11.076 G_Regularizer: 0.000 validation_error: 20.499 +(epoch: 17, iters: 155968, time: 0.538, data: 0.000) G_L1: 15.545 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 13.061 G_Regularizer: 0.000 validation_error: 20.798 +(epoch: 17, iters: 157968, time: 0.555, data: 0.000) G_L1: 14.579 G_L1_ABSOLUTE: 2.288 G_L1_RELATIVE: 12.291 G_Regularizer: 0.000 validation_error: 21.051 +(epoch: 17, iters: 159968, time: 0.557, data: 0.000) G_L1: 15.402 G_L1_ABSOLUTE: 3.150 G_L1_RELATIVE: 12.252 G_Regularizer: 0.000 validation_error: 20.325 +(epoch: 17, iters: 161968, time: 0.558, data: 0.000) G_L1: 16.114 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 13.468 G_Regularizer: 0.000 validation_error: 20.220 +(epoch: 17, iters: 163968, time: 0.536, data: 0.001) G_L1: 16.297 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 13.486 G_Regularizer: 0.000 validation_error: 20.723 +(epoch: 17, iters: 165968, time: 0.540, data: 0.000) G_L1: 12.516 G_L1_ABSOLUTE: 2.671 G_L1_RELATIVE: 9.845 G_Regularizer: 0.000 validation_error: 22.236 +(epoch: 17, iters: 167968, time: 0.553, data: 0.000) G_L1: 14.838 G_L1_ABSOLUTE: 2.850 G_L1_RELATIVE: 11.989 G_Regularizer: 0.000 validation_error: 20.175 +(epoch: 17, iters: 169968, time: 0.557, data: 0.000) G_L1: 16.682 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 13.948 G_Regularizer: 0.000 validation_error: 20.221 +(epoch: 17, iters: 171968, time: 0.543, data: 0.000) G_L1: 13.896 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 11.286 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 17, iters: 173968, time: 0.553, data: 0.000) G_L1: 13.196 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 10.646 G_Regularizer: 0.000 validation_error: 21.175 +(epoch: 17, iters: 175968, time: 0.551, data: 0.000) G_L1: 17.553 G_L1_ABSOLUTE: 2.859 G_L1_RELATIVE: 14.695 G_Regularizer: 0.000 validation_error: 20.714 +(epoch: 17, iters: 177968, time: 0.540, data: 0.000) G_L1: 16.289 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 13.603 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 17, iters: 179968, time: 0.540, data: 0.001) G_L1: 13.996 G_L1_ABSOLUTE: 2.446 G_L1_RELATIVE: 11.551 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 17, iters: 181968, time: 0.546, data: 0.000) G_L1: 12.996 G_L1_ABSOLUTE: 2.567 G_L1_RELATIVE: 10.430 G_Regularizer: 0.000 validation_error: 20.439 +(epoch: 17, iters: 183968, time: 0.545, data: 0.000) G_L1: 14.641 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 12.064 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 17, iters: 185968, time: 0.565, data: 0.000) G_L1: 15.966 G_L1_ABSOLUTE: 2.979 G_L1_RELATIVE: 12.987 G_Regularizer: 0.000 validation_error: 20.597 +(epoch: 17, iters: 187968, time: 0.540, data: 0.000) G_L1: 16.219 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 13.446 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 17, iters: 189968, time: 0.550, data: 0.000) G_L1: 13.807 G_L1_ABSOLUTE: 2.921 G_L1_RELATIVE: 10.887 G_Regularizer: 0.000 validation_error: 21.272 +(epoch: 17, iters: 191968, time: 0.557, data: 0.000) G_L1: 14.659 G_L1_ABSOLUTE: 2.465 G_L1_RELATIVE: 12.194 G_Regularizer: 0.000 validation_error: 20.120 +(epoch: 17, iters: 193968, time: 0.560, data: 0.000) G_L1: 14.254 G_L1_ABSOLUTE: 2.490 G_L1_RELATIVE: 11.764 G_Regularizer: 0.000 validation_error: 20.234 +(epoch: 17, iters: 195968, time: 0.555, data: 0.000) G_L1: 15.314 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 12.957 G_Regularizer: 0.000 validation_error: 20.490 +(epoch: 17, iters: 197968, time: 0.532, data: 0.000) G_L1: 16.058 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 13.562 G_Regularizer: 0.000 validation_error: 20.564 +(epoch: 17, iters: 199968, time: 0.552, data: 0.000) G_L1: 19.091 G_L1_ABSOLUTE: 3.121 G_L1_RELATIVE: 15.970 G_Regularizer: 0.000 validation_error: 20.167 +(epoch: 17, iters: 201968, time: 0.560, data: 0.000) G_L1: 13.293 G_L1_ABSOLUTE: 2.840 G_L1_RELATIVE: 10.453 G_Regularizer: 0.000 validation_error: 20.874 +(epoch: 17, iters: 203968, time: 0.560, data: 0.000) G_L1: 13.949 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 11.367 G_Regularizer: 0.000 validation_error: 20.357 +(epoch: 17, iters: 205968, time: 0.530, data: 0.000) G_L1: 16.915 G_L1_ABSOLUTE: 3.088 G_L1_RELATIVE: 13.826 G_Regularizer: 0.000 validation_error: 20.849 +(epoch: 17, iters: 207968, time: 0.555, data: 0.000) G_L1: 16.828 G_L1_ABSOLUTE: 3.042 G_L1_RELATIVE: 13.785 G_Regularizer: 0.000 validation_error: 20.137 +(epoch: 17, iters: 209968, time: 0.560, data: 0.000) G_L1: 14.972 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 12.695 G_Regularizer: 0.000 validation_error: 20.365 +(epoch: 17, iters: 211968, time: 0.557, data: 0.000) G_L1: 14.074 G_L1_ABSOLUTE: 2.511 G_L1_RELATIVE: 11.563 G_Regularizer: 0.000 validation_error: 19.955 +(epoch: 17, iters: 213968, time: 0.528, data: 0.000) G_L1: 15.085 G_L1_ABSOLUTE: 2.703 G_L1_RELATIVE: 12.382 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 17, iters: 215968, time: 0.554, data: 0.000) G_L1: 14.957 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 12.357 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 17, iters: 217968, time: 0.562, data: 0.000) G_L1: 16.228 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 13.494 G_Regularizer: 0.000 validation_error: 19.508 +(epoch: 17, iters: 219968, time: 0.541, data: 0.000) G_L1: 26.527 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 23.819 G_Regularizer: 0.000 validation_error: 20.338 +(epoch: 17, iters: 221968, time: 0.546, data: 0.000) G_L1: 15.703 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 12.995 G_Regularizer: 0.000 validation_error: 20.376 +(epoch: 17, iters: 223968, time: 0.531, data: 0.000) G_L1: 14.644 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 12.083 G_Regularizer: 0.000 validation_error: 20.094 +(epoch: 17, iters: 225968, time: 0.535, data: 0.000) G_L1: 16.403 G_L1_ABSOLUTE: 2.984 G_L1_RELATIVE: 13.419 G_Regularizer: 0.000 validation_error: 20.410 +(epoch: 17, iters: 227968, time: 0.539, data: 0.000) G_L1: 13.928 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 11.389 G_Regularizer: 0.000 validation_error: 20.420 +(epoch: 17, iters: 229968, time: 0.537, data: 0.000) G_L1: 17.750 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 14.952 G_Regularizer: 0.000 validation_error: 20.428 +(epoch: 17, iters: 231968, time: 0.543, data: 0.000) G_L1: 12.163 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 9.831 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 17, iters: 233968, time: 0.558, data: 0.000) G_L1: 13.506 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 11.082 G_Regularizer: 0.000 validation_error: 20.072 +(epoch: 17, iters: 235968, time: 0.557, data: 0.001) G_L1: 12.007 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 9.632 G_Regularizer: 0.000 validation_error: 21.491 +(epoch: 17, iters: 237968, time: 0.562, data: 0.000) G_L1: 15.590 G_L1_ABSOLUTE: 2.750 G_L1_RELATIVE: 12.839 G_Regularizer: 0.000 validation_error: 20.424 +(epoch: 17, iters: 239968, time: 0.540, data: 0.000) G_L1: 17.080 G_L1_ABSOLUTE: 2.790 G_L1_RELATIVE: 14.290 G_Regularizer: 0.000 validation_error: 21.168 +(epoch: 17, iters: 241968, time: 0.543, data: 0.000) G_L1: 13.154 G_L1_ABSOLUTE: 2.204 G_L1_RELATIVE: 10.950 G_Regularizer: 0.000 validation_error: 21.441 +(epoch: 17, iters: 243968, time: 0.542, data: 0.000) G_L1: 15.872 G_L1_ABSOLUTE: 3.244 G_L1_RELATIVE: 12.628 G_Regularizer: 0.000 validation_error: 20.419 +(epoch: 17, iters: 245968, time: 0.549, data: 0.000) G_L1: 14.376 G_L1_ABSOLUTE: 2.427 G_L1_RELATIVE: 11.949 G_Regularizer: 0.000 validation_error: 20.284 +(epoch: 17, iters: 247968, time: 0.540, data: 0.000) G_L1: 15.255 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 12.643 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 17, iters: 249968, time: 0.529, data: 0.000) G_L1: 17.225 G_L1_ABSOLUTE: 3.111 G_L1_RELATIVE: 14.114 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 17, iters: 251968, time: 0.563, data: 0.000) G_L1: 15.128 G_L1_ABSOLUTE: 3.274 G_L1_RELATIVE: 11.854 G_Regularizer: 0.000 validation_error: 19.942 +(epoch: 17, iters: 253968, time: 0.537, data: 0.000) G_L1: 14.670 G_L1_ABSOLUTE: 2.979 G_L1_RELATIVE: 11.692 G_Regularizer: 0.000 validation_error: 20.444 +(epoch: 17, iters: 255968, time: 0.553, data: 0.000) G_L1: 15.665 G_L1_ABSOLUTE: 2.648 G_L1_RELATIVE: 13.016 G_Regularizer: 0.000 validation_error: 20.738 +(epoch: 17, iters: 257968, time: 0.558, data: 0.000) G_L1: 16.477 G_L1_ABSOLUTE: 2.783 G_L1_RELATIVE: 13.694 G_Regularizer: 0.000 validation_error: 20.482 +(epoch: 17, iters: 259968, time: 0.563, data: 0.000) G_L1: 13.218 G_L1_ABSOLUTE: 2.695 G_L1_RELATIVE: 10.524 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 17, iters: 261968, time: 0.552, data: 0.001) G_L1: 16.081 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 13.429 G_Regularizer: 0.000 validation_error: 20.404 +(epoch: 17, iters: 263968, time: 0.548, data: 0.000) G_L1: 14.530 G_L1_ABSOLUTE: 2.636 G_L1_RELATIVE: 11.894 G_Regularizer: 0.000 validation_error: 21.071 +(epoch: 17, iters: 265968, time: 0.544, data: 0.000) G_L1: 14.587 G_L1_ABSOLUTE: 2.779 G_L1_RELATIVE: 11.809 G_Regularizer: 0.000 validation_error: 20.227 +(epoch: 17, iters: 267968, time: 0.562, data: 0.000) G_L1: 12.878 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 10.257 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 17, iters: 269968, time: 0.562, data: 0.000) G_L1: 15.606 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 13.009 G_Regularizer: 0.000 validation_error: 20.416 +(epoch: 17, iters: 271968, time: 0.556, data: 0.000) G_L1: 15.309 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 12.712 G_Regularizer: 0.000 validation_error: 20.253 +(epoch: 17, iters: 273968, time: 0.554, data: 0.000) G_L1: 15.118 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 12.440 G_Regularizer: 0.000 validation_error: 20.747 +(epoch: 17, iters: 275968, time: 0.558, data: 0.000) G_L1: 16.377 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 13.623 G_Regularizer: 0.000 validation_error: 20.456 +(epoch: 17, iters: 277968, time: 0.558, data: 0.001) G_L1: 13.261 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 10.586 G_Regularizer: 0.000 validation_error: 19.896 +(epoch: 17, iters: 279968, time: 0.560, data: 0.000) G_L1: 14.669 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 12.129 G_Regularizer: 0.000 validation_error: 20.038 +(epoch: 17, iters: 281968, time: 0.545, data: 0.000) G_L1: 13.214 G_L1_ABSOLUTE: 2.384 G_L1_RELATIVE: 10.830 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 17, iters: 283968, time: 0.559, data: 0.000) G_L1: 16.798 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 14.242 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 17, iters: 285968, time: 0.560, data: 0.000) G_L1: 15.719 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 13.051 G_Regularizer: 0.000 validation_error: 20.663 +(epoch: 17, iters: 287968, time: 0.559, data: 0.000) G_L1: 16.138 G_L1_ABSOLUTE: 2.985 G_L1_RELATIVE: 13.153 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 17, iters: 289968, time: 0.531, data: 0.000) G_L1: 14.275 G_L1_ABSOLUTE: 2.264 G_L1_RELATIVE: 12.011 G_Regularizer: 0.000 validation_error: 20.113 +(epoch: 17, iters: 291968, time: 0.553, data: 0.000) G_L1: 15.133 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 12.519 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 17, iters: 293968, time: 0.555, data: 0.000) G_L1: 15.072 G_L1_ABSOLUTE: 3.032 G_L1_RELATIVE: 12.040 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 17, iters: 295968, time: 0.564, data: 0.000) G_L1: 16.255 G_L1_ABSOLUTE: 2.704 G_L1_RELATIVE: 13.551 G_Regularizer: 0.000 validation_error: 20.987 +(epoch: 17, iters: 297968, time: 0.549, data: 0.000) G_L1: 14.665 G_L1_ABSOLUTE: 2.529 G_L1_RELATIVE: 12.136 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 17, iters: 299968, time: 0.559, data: 0.000) G_L1: 14.294 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 11.928 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 17, iters: 301968, time: 0.560, data: 0.001) G_L1: 17.263 G_L1_ABSOLUTE: 3.306 G_L1_RELATIVE: 13.958 G_Regularizer: 0.000 validation_error: 20.609 +(epoch: 18, iters: 1216, time: 0.558, data: 0.000) G_L1: 16.176 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 13.337 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 18, iters: 3216, time: 0.543, data: 0.000) G_L1: 14.646 G_L1_ABSOLUTE: 2.604 G_L1_RELATIVE: 12.042 G_Regularizer: 0.000 validation_error: 21.444 +(epoch: 18, iters: 5216, time: 0.563, data: 0.000) G_L1: 14.041 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 11.635 G_Regularizer: 0.000 validation_error: 20.387 +(epoch: 18, iters: 7216, time: 0.559, data: 0.000) G_L1: 13.743 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 11.142 G_Regularizer: 0.000 validation_error: 20.406 +(epoch: 18, iters: 9216, time: 0.558, data: 0.000) G_L1: 12.023 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 9.603 G_Regularizer: 0.000 validation_error: 20.997 +(epoch: 18, iters: 11216, time: 0.543, data: 0.000) G_L1: 14.247 G_L1_ABSOLUTE: 2.898 G_L1_RELATIVE: 11.349 G_Regularizer: 0.000 validation_error: 20.785 +(epoch: 18, iters: 13216, time: 0.545, data: 0.000) G_L1: 12.613 G_L1_ABSOLUTE: 2.496 G_L1_RELATIVE: 10.117 G_Regularizer: 0.000 validation_error: 20.375 +(epoch: 18, iters: 15216, time: 0.555, data: 0.000) G_L1: 13.729 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 11.139 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 18, iters: 17216, time: 0.568, data: 0.001) G_L1: 14.390 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 11.842 G_Regularizer: 0.000 validation_error: 20.540 +(epoch: 18, iters: 19216, time: 0.553, data: 0.000) G_L1: 12.291 G_L1_ABSOLUTE: 2.082 G_L1_RELATIVE: 10.208 G_Regularizer: 0.000 validation_error: 20.345 +(epoch: 18, iters: 21216, time: 0.534, data: 0.000) G_L1: 16.179 G_L1_ABSOLUTE: 3.110 G_L1_RELATIVE: 13.069 G_Regularizer: 0.000 validation_error: 21.002 +(epoch: 18, iters: 23216, time: 0.557, data: 0.000) G_L1: 12.535 G_L1_ABSOLUTE: 2.210 G_L1_RELATIVE: 10.325 G_Regularizer: 0.000 validation_error: 20.621 +(epoch: 18, iters: 25216, time: 0.564, data: 0.000) G_L1: 15.132 G_L1_ABSOLUTE: 2.215 G_L1_RELATIVE: 12.917 G_Regularizer: 0.000 validation_error: 20.598 +(epoch: 18, iters: 27216, time: 0.541, data: 0.001) G_L1: 13.412 G_L1_ABSOLUTE: 2.727 G_L1_RELATIVE: 10.684 G_Regularizer: 0.000 validation_error: 21.135 +(epoch: 18, iters: 29216, time: 0.533, data: 0.000) G_L1: 14.811 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 12.272 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 18, iters: 31216, time: 0.556, data: 0.000) G_L1: 14.716 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 12.119 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 18, iters: 33216, time: 0.556, data: 0.000) G_L1: 16.690 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 14.114 G_Regularizer: 0.000 validation_error: 20.367 +(epoch: 18, iters: 35216, time: 0.561, data: 0.000) G_L1: 14.212 G_L1_ABSOLUTE: 3.041 G_L1_RELATIVE: 11.171 G_Regularizer: 0.000 validation_error: 20.480 +(epoch: 18, iters: 37216, time: 0.538, data: 0.000) G_L1: 15.275 G_L1_ABSOLUTE: 2.991 G_L1_RELATIVE: 12.284 G_Regularizer: 0.000 validation_error: 21.030 +(epoch: 18, iters: 39216, time: 0.561, data: 0.000) G_L1: 12.672 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 10.331 G_Regularizer: 0.000 validation_error: 21.241 +(epoch: 18, iters: 41216, time: 0.555, data: 0.000) G_L1: 13.011 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 10.671 G_Regularizer: 0.000 validation_error: 20.723 +(epoch: 18, iters: 43216, time: 0.557, data: 0.000) G_L1: 15.643 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 13.101 G_Regularizer: 0.000 validation_error: 21.163 +(epoch: 18, iters: 45216, time: 0.530, data: 0.000) G_L1: 13.586 G_L1_ABSOLUTE: 2.984 G_L1_RELATIVE: 10.603 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 18, iters: 47216, time: 0.556, data: 0.000) G_L1: 15.771 G_L1_ABSOLUTE: 2.789 G_L1_RELATIVE: 12.982 G_Regularizer: 0.000 validation_error: 20.293 +(epoch: 18, iters: 49216, time: 0.562, data: 0.000) G_L1: 14.453 G_L1_ABSOLUTE: 2.726 G_L1_RELATIVE: 11.727 G_Regularizer: 0.000 validation_error: 20.249 +(epoch: 18, iters: 51216, time: 0.564, data: 0.000) G_L1: 11.702 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 9.160 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 18, iters: 53216, time: 0.546, data: 0.000) G_L1: 15.676 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 13.234 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 18, iters: 55216, time: 0.546, data: 0.000) G_L1: 17.108 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 14.231 G_Regularizer: 0.000 validation_error: 21.528 +(epoch: 18, iters: 57216, time: 0.562, data: 0.001) G_L1: 16.457 G_L1_ABSOLUTE: 2.358 G_L1_RELATIVE: 14.099 G_Regularizer: 0.000 validation_error: 21.925 +(epoch: 18, iters: 59216, time: 0.561, data: 0.000) G_L1: 17.877 G_L1_ABSOLUTE: 2.496 G_L1_RELATIVE: 15.381 G_Regularizer: 0.000 validation_error: 20.492 +(epoch: 18, iters: 61216, time: 0.549, data: 0.000) G_L1: 15.558 G_L1_ABSOLUTE: 2.826 G_L1_RELATIVE: 12.732 G_Regularizer: 0.000 validation_error: 20.249 +(epoch: 18, iters: 63216, time: 0.533, data: 0.000) G_L1: 14.077 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 11.640 G_Regularizer: 0.000 validation_error: 20.995 +(epoch: 18, iters: 65216, time: 0.553, data: 0.000) G_L1: 15.755 G_L1_ABSOLUTE: 3.205 G_L1_RELATIVE: 12.550 G_Regularizer: 0.000 validation_error: 20.008 +(epoch: 18, iters: 67216, time: 0.546, data: 0.000) G_L1: 11.631 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 9.148 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 18, iters: 69216, time: 0.540, data: 0.000) G_L1: 14.422 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 11.612 G_Regularizer: 0.000 validation_error: 20.674 +(epoch: 18, iters: 71216, time: 0.542, data: 0.000) G_L1: 15.464 G_L1_ABSOLUTE: 3.263 G_L1_RELATIVE: 12.201 G_Regularizer: 0.000 validation_error: 20.669 +(epoch: 18, iters: 73216, time: 0.549, data: 0.000) G_L1: 14.880 G_L1_ABSOLUTE: 3.110 G_L1_RELATIVE: 11.770 G_Regularizer: 0.000 validation_error: 21.127 +(epoch: 18, iters: 75216, time: 0.563, data: 0.000) G_L1: 17.239 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 14.555 G_Regularizer: 0.000 validation_error: 20.222 +(epoch: 18, iters: 77216, time: 0.558, data: 0.000) G_L1: 13.780 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 11.046 G_Regularizer: 0.000 validation_error: 20.310 +(epoch: 18, iters: 79216, time: 0.552, data: 0.000) G_L1: 15.787 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 13.190 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 18, iters: 81216, time: 0.554, data: 0.000) G_L1: 14.247 G_L1_ABSOLUTE: 2.417 G_L1_RELATIVE: 11.830 G_Regularizer: 0.000 validation_error: 21.327 +(epoch: 18, iters: 83216, time: 0.531, data: 0.000) G_L1: 15.223 G_L1_ABSOLUTE: 3.059 G_L1_RELATIVE: 12.164 G_Regularizer: 0.000 validation_error: 20.062 +(epoch: 18, iters: 85216, time: 0.559, data: 0.000) G_L1: 18.024 G_L1_ABSOLUTE: 3.053 G_L1_RELATIVE: 14.971 G_Regularizer: 0.000 validation_error: 20.288 +(epoch: 18, iters: 87216, time: 0.532, data: 0.001) G_L1: 13.467 G_L1_ABSOLUTE: 2.174 G_L1_RELATIVE: 11.293 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 18, iters: 89216, time: 0.531, data: 0.000) G_L1: 17.445 G_L1_ABSOLUTE: 2.942 G_L1_RELATIVE: 14.503 G_Regularizer: 0.000 validation_error: 20.483 +(epoch: 18, iters: 91216, time: 0.573, data: 0.000) G_L1: 13.877 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 11.268 G_Regularizer: 0.000 validation_error: 20.346 +(epoch: 18, iters: 93216, time: 0.557, data: 0.000) G_L1: 13.989 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 11.559 G_Regularizer: 0.000 validation_error: 21.003 +(epoch: 18, iters: 95216, time: 0.546, data: 0.000) G_L1: 15.038 G_L1_ABSOLUTE: 2.583 G_L1_RELATIVE: 12.455 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 18, iters: 97216, time: 0.566, data: 0.000) G_L1: 14.544 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 12.130 G_Regularizer: 0.000 validation_error: 21.269 +(epoch: 18, iters: 99216, time: 0.546, data: 0.000) G_L1: 17.851 G_L1_ABSOLUTE: 2.675 G_L1_RELATIVE: 15.176 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 18, iters: 101216, time: 0.547, data: 0.000) G_L1: 15.127 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 12.237 G_Regularizer: 0.000 validation_error: 20.463 +(epoch: 18, iters: 103216, time: 0.552, data: 0.000) G_L1: 14.622 G_L1_ABSOLUTE: 2.396 G_L1_RELATIVE: 12.226 G_Regularizer: 0.000 validation_error: 20.927 +(epoch: 18, iters: 105216, time: 0.549, data: 0.000) G_L1: 16.605 G_L1_ABSOLUTE: 2.787 G_L1_RELATIVE: 13.818 G_Regularizer: 0.000 validation_error: 20.429 +(epoch: 18, iters: 107216, time: 0.560, data: 0.001) G_L1: 17.746 G_L1_ABSOLUTE: 3.226 G_L1_RELATIVE: 14.520 G_Regularizer: 0.000 validation_error: 21.239 +(epoch: 18, iters: 109216, time: 0.556, data: 0.000) G_L1: 16.168 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 13.546 G_Regularizer: 0.000 validation_error: 20.418 +(epoch: 18, iters: 111216, time: 0.541, data: 0.000) G_L1: 15.366 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 12.912 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 18, iters: 113216, time: 0.536, data: 0.000) G_L1: 13.260 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 10.995 G_Regularizer: 0.000 validation_error: 20.334 +(epoch: 18, iters: 115216, time: 0.560, data: 0.000) G_L1: 16.590 G_L1_ABSOLUTE: 2.903 G_L1_RELATIVE: 13.686 G_Regularizer: 0.000 validation_error: 20.425 +(epoch: 18, iters: 117216, time: 0.560, data: 0.000) G_L1: 14.155 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 11.664 G_Regularizer: 0.000 validation_error: 21.314 +(epoch: 18, iters: 119216, time: 0.566, data: 0.000) G_L1: 14.299 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 11.671 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 18, iters: 121216, time: 0.537, data: 0.000) G_L1: 14.783 G_L1_ABSOLUTE: 2.787 G_L1_RELATIVE: 11.995 G_Regularizer: 0.000 validation_error: 19.758 +(epoch: 18, iters: 123216, time: 0.558, data: 0.000) G_L1: 14.921 G_L1_ABSOLUTE: 3.135 G_L1_RELATIVE: 11.787 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 18, iters: 125216, time: 0.564, data: 0.000) G_L1: 17.450 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 14.905 G_Regularizer: 0.000 validation_error: 20.258 +(epoch: 18, iters: 127216, time: 0.554, data: 0.000) G_L1: 13.985 G_L1_ABSOLUTE: 2.928 G_L1_RELATIVE: 11.058 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 18, iters: 129216, time: 0.531, data: 0.000) G_L1: 15.816 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 13.220 G_Regularizer: 0.000 validation_error: 20.407 +(epoch: 18, iters: 131216, time: 0.543, data: 0.000) G_L1: 16.696 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 13.989 G_Regularizer: 0.000 validation_error: 20.761 +(epoch: 18, iters: 133216, time: 0.565, data: 0.000) G_L1: 18.754 G_L1_ABSOLUTE: 3.401 G_L1_RELATIVE: 15.354 G_Regularizer: 0.000 validation_error: 21.752 +(epoch: 18, iters: 135216, time: 0.561, data: 0.001) G_L1: 14.709 G_L1_ABSOLUTE: 2.240 G_L1_RELATIVE: 12.470 G_Regularizer: 0.000 validation_error: 20.276 +(epoch: 18, iters: 137216, time: 0.536, data: 0.001) G_L1: 13.303 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 10.706 G_Regularizer: 0.000 validation_error: 20.342 +(epoch: 18, iters: 139216, time: 0.559, data: 0.000) G_L1: 15.003 G_L1_ABSOLUTE: 3.174 G_L1_RELATIVE: 11.829 G_Regularizer: 0.000 validation_error: 20.982 +(epoch: 18, iters: 141216, time: 0.559, data: 0.001) G_L1: 17.664 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 15.301 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 18, iters: 143216, time: 0.556, data: 0.000) G_L1: 12.305 G_L1_ABSOLUTE: 2.076 G_L1_RELATIVE: 10.228 G_Regularizer: 0.000 validation_error: 20.056 +(epoch: 18, iters: 145216, time: 0.553, data: 0.000) G_L1: 15.035 G_L1_ABSOLUTE: 2.376 G_L1_RELATIVE: 12.659 G_Regularizer: 0.000 validation_error: 21.290 +(epoch: 18, iters: 147216, time: 0.559, data: 0.000) G_L1: 20.920 G_L1_ABSOLUTE: 2.934 G_L1_RELATIVE: 17.987 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 18, iters: 149216, time: 0.558, data: 0.000) G_L1: 14.125 G_L1_ABSOLUTE: 2.350 G_L1_RELATIVE: 11.775 G_Regularizer: 0.000 validation_error: 21.245 +(epoch: 18, iters: 151216, time: 0.553, data: 0.000) G_L1: 15.524 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 13.041 G_Regularizer: 0.000 validation_error: 21.081 +(epoch: 18, iters: 153216, time: 0.554, data: 0.000) G_L1: 23.109 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 20.298 G_Regularizer: 0.000 validation_error: 20.291 +(epoch: 18, iters: 155216, time: 0.545, data: 0.001) G_L1: 16.714 G_L1_ABSOLUTE: 2.723 G_L1_RELATIVE: 13.991 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 18, iters: 157216, time: 0.560, data: 0.000) G_L1: 15.369 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 12.488 G_Regularizer: 0.000 validation_error: 20.535 +(epoch: 18, iters: 159216, time: 0.549, data: 0.000) G_L1: 14.507 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 12.013 G_Regularizer: 0.000 validation_error: 20.067 +(epoch: 18, iters: 161216, time: 0.552, data: 0.001) G_L1: 17.323 G_L1_ABSOLUTE: 3.133 G_L1_RELATIVE: 14.190 G_Regularizer: 0.000 validation_error: 20.533 +(epoch: 18, iters: 163216, time: 0.561, data: 0.000) G_L1: 11.816 G_L1_ABSOLUTE: 2.844 G_L1_RELATIVE: 8.972 G_Regularizer: 0.000 validation_error: 20.425 +(epoch: 18, iters: 165216, time: 0.558, data: 0.000) G_L1: 14.764 G_L1_ABSOLUTE: 2.602 G_L1_RELATIVE: 12.162 G_Regularizer: 0.000 validation_error: 20.955 +(epoch: 18, iters: 167216, time: 0.549, data: 0.000) G_L1: 11.201 G_L1_ABSOLUTE: 2.072 G_L1_RELATIVE: 9.129 G_Regularizer: 0.000 validation_error: 21.024 +(epoch: 18, iters: 169216, time: 0.553, data: 0.000) G_L1: 17.063 G_L1_ABSOLUTE: 2.799 G_L1_RELATIVE: 14.264 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 18, iters: 171216, time: 0.551, data: 0.001) G_L1: 17.420 G_L1_ABSOLUTE: 2.971 G_L1_RELATIVE: 14.449 G_Regularizer: 0.000 validation_error: 20.160 +(epoch: 18, iters: 173216, time: 0.539, data: 0.000) G_L1: 17.074 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 14.453 G_Regularizer: 0.000 validation_error: 20.329 +(epoch: 18, iters: 175216, time: 0.555, data: 0.000) G_L1: 14.226 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 11.465 G_Regularizer: 0.000 validation_error: 21.001 +(epoch: 18, iters: 177216, time: 0.553, data: 0.000) G_L1: 16.457 G_L1_ABSOLUTE: 3.215 G_L1_RELATIVE: 13.242 G_Regularizer: 0.000 validation_error: 20.255 +(epoch: 18, iters: 179216, time: 0.550, data: 0.000) G_L1: 17.378 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 14.580 G_Regularizer: 0.000 validation_error: 20.267 +(epoch: 18, iters: 181216, time: 0.560, data: 0.001) G_L1: 15.439 G_L1_ABSOLUTE: 2.861 G_L1_RELATIVE: 12.578 G_Regularizer: 0.000 validation_error: 20.372 +(epoch: 18, iters: 183216, time: 0.561, data: 0.000) G_L1: 12.987 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 10.647 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 18, iters: 185216, time: 0.541, data: 0.000) G_L1: 17.012 G_L1_ABSOLUTE: 2.427 G_L1_RELATIVE: 14.585 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 18, iters: 187216, time: 0.553, data: 0.000) G_L1: 15.516 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 12.898 G_Regularizer: 0.000 validation_error: 20.450 +(epoch: 18, iters: 189216, time: 0.559, data: 0.001) G_L1: 16.233 G_L1_ABSOLUTE: 2.288 G_L1_RELATIVE: 13.945 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 18, iters: 191216, time: 0.557, data: 0.000) G_L1: 14.026 G_L1_ABSOLUTE: 2.815 G_L1_RELATIVE: 11.212 G_Regularizer: 0.000 validation_error: 20.025 +(epoch: 18, iters: 193216, time: 0.558, data: 0.000) G_L1: 15.920 G_L1_ABSOLUTE: 2.838 G_L1_RELATIVE: 13.082 G_Regularizer: 0.000 validation_error: 20.295 +(epoch: 18, iters: 195216, time: 0.540, data: 0.000) G_L1: 15.112 G_L1_ABSOLUTE: 2.263 G_L1_RELATIVE: 12.849 G_Regularizer: 0.000 validation_error: 20.416 +(epoch: 18, iters: 197216, time: 0.539, data: 0.000) G_L1: 11.663 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 9.295 G_Regularizer: 0.000 validation_error: 19.968 +(epoch: 18, iters: 199216, time: 0.556, data: 0.001) G_L1: 17.890 G_L1_ABSOLUTE: 2.643 G_L1_RELATIVE: 15.247 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 18, iters: 201216, time: 0.554, data: 0.000) G_L1: 11.252 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 8.950 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 18, iters: 203216, time: 0.552, data: 0.000) G_L1: 14.577 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 11.683 G_Regularizer: 0.000 validation_error: 20.569 +(epoch: 18, iters: 205216, time: 0.543, data: 0.000) G_L1: 15.436 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 12.692 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 18, iters: 207216, time: 0.559, data: 0.000) G_L1: 12.824 G_L1_ABSOLUTE: 2.281 G_L1_RELATIVE: 10.544 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 18, iters: 209216, time: 0.564, data: 0.000) G_L1: 13.086 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 10.305 G_Regularizer: 0.000 validation_error: 19.755 +(epoch: 18, iters: 211216, time: 0.548, data: 0.000) G_L1: 14.105 G_L1_ABSOLUTE: 2.559 G_L1_RELATIVE: 11.545 G_Regularizer: 0.000 validation_error: 21.111 +(epoch: 18, iters: 213216, time: 0.538, data: 0.000) G_L1: 13.234 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 10.819 G_Regularizer: 0.000 validation_error: 20.550 +(epoch: 18, iters: 215216, time: 0.549, data: 0.000) G_L1: 13.325 G_L1_ABSOLUTE: 2.155 G_L1_RELATIVE: 11.170 G_Regularizer: 0.000 validation_error: 21.060 +(epoch: 18, iters: 217216, time: 0.538, data: 0.000) G_L1: 15.643 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 12.960 G_Regularizer: 0.000 validation_error: 20.599 +(epoch: 18, iters: 219216, time: 0.550, data: 0.000) G_L1: 12.902 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 10.397 G_Regularizer: 0.000 validation_error: 20.178 +(epoch: 18, iters: 221216, time: 0.538, data: 0.000) G_L1: 12.255 G_L1_ABSOLUTE: 2.327 G_L1_RELATIVE: 9.928 G_Regularizer: 0.000 validation_error: 20.984 +(epoch: 18, iters: 223216, time: 0.548, data: 0.001) G_L1: 16.002 G_L1_ABSOLUTE: 3.029 G_L1_RELATIVE: 12.973 G_Regularizer: 0.000 validation_error: 20.392 +(epoch: 18, iters: 225216, time: 0.563, data: 0.000) G_L1: 17.544 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 14.664 G_Regularizer: 0.000 validation_error: 20.049 +(epoch: 18, iters: 227216, time: 0.553, data: 0.000) G_L1: 13.862 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 11.358 G_Regularizer: 0.000 validation_error: 20.255 +(epoch: 18, iters: 229216, time: 0.538, data: 0.000) G_L1: 14.428 G_L1_ABSOLUTE: 2.832 G_L1_RELATIVE: 11.596 G_Regularizer: 0.000 validation_error: 20.107 +(epoch: 18, iters: 231216, time: 0.559, data: 0.000) G_L1: 17.832 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 15.172 G_Regularizer: 0.000 validation_error: 21.467 +(epoch: 18, iters: 233216, time: 0.556, data: 0.000) G_L1: 13.670 G_L1_ABSOLUTE: 2.761 G_L1_RELATIVE: 10.909 G_Regularizer: 0.000 validation_error: 20.486 +(epoch: 18, iters: 235216, time: 0.538, data: 0.000) G_L1: 16.803 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 14.006 G_Regularizer: 0.000 validation_error: 20.393 +(epoch: 18, iters: 237216, time: 0.561, data: 0.000) G_L1: 13.685 G_L1_ABSOLUTE: 2.064 G_L1_RELATIVE: 11.620 G_Regularizer: 0.000 validation_error: 21.200 +(epoch: 18, iters: 239216, time: 0.531, data: 0.000) G_L1: 13.574 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 11.003 G_Regularizer: 0.000 validation_error: 20.065 +(epoch: 18, iters: 241216, time: 0.556, data: 0.000) G_L1: 16.014 G_L1_ABSOLUTE: 3.533 G_L1_RELATIVE: 12.481 G_Regularizer: 0.000 validation_error: 20.607 +(epoch: 18, iters: 243216, time: 0.546, data: 0.000) G_L1: 13.984 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 11.645 G_Regularizer: 0.000 validation_error: 20.289 +(epoch: 18, iters: 245216, time: 0.562, data: 0.000) G_L1: 18.028 G_L1_ABSOLUTE: 2.604 G_L1_RELATIVE: 15.424 G_Regularizer: 0.000 validation_error: 21.240 +(epoch: 18, iters: 247216, time: 0.537, data: 0.000) G_L1: 13.711 G_L1_ABSOLUTE: 2.177 G_L1_RELATIVE: 11.534 G_Regularizer: 0.000 validation_error: 21.157 +(epoch: 18, iters: 249216, time: 0.559, data: 0.000) G_L1: 16.200 G_L1_ABSOLUTE: 2.771 G_L1_RELATIVE: 13.429 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 18, iters: 251216, time: 0.553, data: 0.000) G_L1: 15.787 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 13.201 G_Regularizer: 0.000 validation_error: 20.544 +(epoch: 18, iters: 253216, time: 0.553, data: 0.000) G_L1: 15.760 G_L1_ABSOLUTE: 2.779 G_L1_RELATIVE: 12.981 G_Regularizer: 0.000 validation_error: 21.431 +(epoch: 18, iters: 255216, time: 0.542, data: 0.000) G_L1: 30.076 G_L1_ABSOLUTE: 2.713 G_L1_RELATIVE: 27.364 G_Regularizer: 0.000 validation_error: 21.041 +(epoch: 18, iters: 257216, time: 0.551, data: 0.000) G_L1: 18.217 G_L1_ABSOLUTE: 2.901 G_L1_RELATIVE: 15.316 G_Regularizer: 0.000 validation_error: 20.549 +(epoch: 18, iters: 259216, time: 0.547, data: 0.000) G_L1: 21.037 G_L1_ABSOLUTE: 3.265 G_L1_RELATIVE: 17.772 G_Regularizer: 0.000 validation_error: 20.288 +(epoch: 18, iters: 261216, time: 0.559, data: 0.000) G_L1: 14.459 G_L1_ABSOLUTE: 2.270 G_L1_RELATIVE: 12.190 G_Regularizer: 0.000 validation_error: 20.007 +(epoch: 18, iters: 263216, time: 0.539, data: 0.000) G_L1: 15.024 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 12.478 G_Regularizer: 0.000 validation_error: 20.560 +(epoch: 18, iters: 265216, time: 0.556, data: 0.002) G_L1: 15.508 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 12.844 G_Regularizer: 0.000 validation_error: 20.646 +(epoch: 18, iters: 267216, time: 0.559, data: 0.000) G_L1: 15.475 G_L1_ABSOLUTE: 2.701 G_L1_RELATIVE: 12.774 G_Regularizer: 0.000 validation_error: 20.175 +(epoch: 18, iters: 269216, time: 0.559, data: 0.000) G_L1: 14.271 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 11.916 G_Regularizer: 0.000 validation_error: 21.079 +(epoch: 18, iters: 271216, time: 0.540, data: 0.000) G_L1: 18.815 G_L1_ABSOLUTE: 2.441 G_L1_RELATIVE: 16.374 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 18, iters: 273216, time: 0.553, data: 0.000) G_L1: 18.615 G_L1_ABSOLUTE: 2.833 G_L1_RELATIVE: 15.782 G_Regularizer: 0.000 validation_error: 20.525 +(epoch: 18, iters: 275216, time: 0.537, data: 0.000) G_L1: 15.630 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 12.985 G_Regularizer: 0.000 validation_error: 21.069 +(epoch: 18, iters: 277216, time: 0.540, data: 0.000) G_L1: 14.254 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 11.667 G_Regularizer: 0.000 validation_error: 20.609 +(epoch: 18, iters: 279216, time: 0.548, data: 0.000) G_L1: 16.233 G_L1_ABSOLUTE: 2.405 G_L1_RELATIVE: 13.828 G_Regularizer: 0.000 validation_error: 20.647 +(epoch: 18, iters: 281216, time: 0.555, data: 0.000) G_L1: 15.125 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 12.628 G_Regularizer: 0.000 validation_error: 20.525 +(epoch: 18, iters: 283216, time: 0.561, data: 0.000) G_L1: 16.377 G_L1_ABSOLUTE: 2.289 G_L1_RELATIVE: 14.088 G_Regularizer: 0.000 validation_error: 21.426 +(epoch: 18, iters: 285216, time: 0.561, data: 0.000) G_L1: 14.843 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 12.095 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 18, iters: 287216, time: 0.560, data: 0.000) G_L1: 15.489 G_L1_ABSOLUTE: 2.866 G_L1_RELATIVE: 12.623 G_Regularizer: 0.000 validation_error: 21.113 +(epoch: 18, iters: 289216, time: 0.537, data: 0.000) G_L1: 18.288 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 15.604 G_Regularizer: 0.000 validation_error: 20.504 +(epoch: 18, iters: 291216, time: 0.565, data: 0.000) G_L1: 17.055 G_L1_ABSOLUTE: 3.277 G_L1_RELATIVE: 13.779 G_Regularizer: 0.000 validation_error: 21.628 +(epoch: 18, iters: 293216, time: 0.552, data: 0.000) G_L1: 15.469 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 12.913 G_Regularizer: 0.000 validation_error: 20.241 +(epoch: 18, iters: 295216, time: 0.534, data: 0.000) G_L1: 18.294 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 15.752 G_Regularizer: 0.000 validation_error: 20.582 +(epoch: 18, iters: 297216, time: 0.537, data: 0.000) G_L1: 13.243 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 10.940 G_Regularizer: 0.000 validation_error: 20.117 +(epoch: 18, iters: 299216, time: 0.553, data: 0.000) G_L1: 14.747 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 12.232 G_Regularizer: 0.000 validation_error: 20.449 +(epoch: 18, iters: 301216, time: 0.556, data: 0.000) G_L1: 16.174 G_L1_ABSOLUTE: 2.869 G_L1_RELATIVE: 13.305 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 19, iters: 464, time: 0.551, data: 0.000) G_L1: 15.367 G_L1_ABSOLUTE: 3.116 G_L1_RELATIVE: 12.251 G_Regularizer: 0.000 validation_error: 20.358 +(epoch: 19, iters: 2464, time: 0.540, data: 0.000) G_L1: 15.152 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 12.720 G_Regularizer: 0.000 validation_error: 20.692 +(epoch: 19, iters: 4464, time: 0.556, data: 0.000) G_L1: 12.386 G_L1_ABSOLUTE: 2.500 G_L1_RELATIVE: 9.886 G_Regularizer: 0.000 validation_error: 20.871 +(epoch: 19, iters: 6464, time: 0.549, data: 0.000) G_L1: 14.758 G_L1_ABSOLUTE: 2.293 G_L1_RELATIVE: 12.465 G_Regularizer: 0.000 validation_error: 20.409 +(epoch: 19, iters: 8464, time: 0.550, data: 0.000) G_L1: 16.797 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 14.225 G_Regularizer: 0.000 validation_error: 21.302 +(epoch: 19, iters: 10464, time: 0.547, data: 0.000) G_L1: 14.510 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 12.137 G_Regularizer: 0.000 validation_error: 20.913 +(epoch: 19, iters: 12464, time: 0.533, data: 0.000) G_L1: 22.304 G_L1_ABSOLUTE: 3.062 G_L1_RELATIVE: 19.242 G_Regularizer: 0.000 validation_error: 20.399 +(epoch: 19, iters: 14464, time: 0.546, data: 0.000) G_L1: 14.920 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 12.302 G_Regularizer: 0.000 validation_error: 20.402 +(epoch: 19, iters: 16464, time: 0.558, data: 0.000) G_L1: 14.168 G_L1_ABSOLUTE: 2.758 G_L1_RELATIVE: 11.410 G_Regularizer: 0.000 validation_error: 20.291 +(epoch: 19, iters: 18464, time: 0.545, data: 0.000) G_L1: 13.241 G_L1_ABSOLUTE: 2.970 G_L1_RELATIVE: 10.271 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 19, iters: 20464, time: 0.547, data: 0.000) G_L1: 13.955 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 11.291 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 19, iters: 22464, time: 0.553, data: 0.000) G_L1: 16.244 G_L1_ABSOLUTE: 3.079 G_L1_RELATIVE: 13.165 G_Regularizer: 0.000 validation_error: 20.496 +(epoch: 19, iters: 24464, time: 0.553, data: 0.000) G_L1: 14.189 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 11.765 G_Regularizer: 0.000 validation_error: 21.137 +(epoch: 19, iters: 26464, time: 0.554, data: 0.000) G_L1: 17.305 G_L1_ABSOLUTE: 2.921 G_L1_RELATIVE: 14.384 G_Regularizer: 0.000 validation_error: 20.158 +(epoch: 19, iters: 28464, time: 0.539, data: 0.000) G_L1: 14.232 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 11.778 G_Regularizer: 0.000 validation_error: 20.618 +(epoch: 19, iters: 30464, time: 0.561, data: 0.000) G_L1: 18.771 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 16.199 G_Regularizer: 0.000 validation_error: 20.516 +(epoch: 19, iters: 32464, time: 0.565, data: 0.000) G_L1: 16.101 G_L1_ABSOLUTE: 3.052 G_L1_RELATIVE: 13.049 G_Regularizer: 0.000 validation_error: 20.404 +(epoch: 19, iters: 34464, time: 0.552, data: 0.001) G_L1: 15.848 G_L1_ABSOLUTE: 2.790 G_L1_RELATIVE: 13.057 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 19, iters: 36464, time: 0.541, data: 0.000) G_L1: 12.889 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 10.212 G_Regularizer: 0.000 validation_error: 21.344 +(epoch: 19, iters: 38464, time: 0.557, data: 0.000) G_L1: 12.939 G_L1_ABSOLUTE: 2.697 G_L1_RELATIVE: 10.241 G_Regularizer: 0.000 validation_error: 20.546 +(epoch: 19, iters: 40464, time: 0.562, data: 0.000) G_L1: 12.421 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 9.967 G_Regularizer: 0.000 validation_error: 20.007 +(epoch: 19, iters: 42464, time: 0.546, data: 0.000) G_L1: 14.580 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 11.818 G_Regularizer: 0.000 validation_error: 20.814 +(epoch: 19, iters: 44464, time: 0.537, data: 0.000) G_L1: 14.803 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 12.135 G_Regularizer: 0.000 validation_error: 20.194 +(epoch: 19, iters: 46464, time: 0.560, data: 0.000) G_L1: 15.727 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 13.256 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 19, iters: 48464, time: 0.558, data: 0.000) G_L1: 16.829 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 14.238 G_Regularizer: 0.000 validation_error: 20.325 +(epoch: 19, iters: 50464, time: 0.553, data: 0.000) G_L1: 14.140 G_L1_ABSOLUTE: 2.648 G_L1_RELATIVE: 11.492 G_Regularizer: 0.000 validation_error: 20.430 +(epoch: 19, iters: 52464, time: 0.530, data: 0.000) G_L1: 16.743 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 13.986 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 19, iters: 54464, time: 0.560, data: 0.000) G_L1: 16.116 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 13.277 G_Regularizer: 0.000 validation_error: 20.520 +(epoch: 19, iters: 56464, time: 0.552, data: 0.000) G_L1: 13.345 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 10.758 G_Regularizer: 0.000 validation_error: 21.104 +(epoch: 19, iters: 58464, time: 0.558, data: 0.000) G_L1: 15.797 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 13.521 G_Regularizer: 0.000 validation_error: 20.150 +(epoch: 19, iters: 60464, time: 0.533, data: 0.000) G_L1: 16.582 G_L1_ABSOLUTE: 2.884 G_L1_RELATIVE: 13.698 G_Regularizer: 0.000 validation_error: 20.565 +(epoch: 19, iters: 62464, time: 0.531, data: 0.000) G_L1: 14.153 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 11.871 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 19, iters: 64464, time: 0.551, data: 0.000) G_L1: 12.850 G_L1_ABSOLUTE: 2.180 G_L1_RELATIVE: 10.670 G_Regularizer: 0.000 validation_error: 20.508 +(epoch: 19, iters: 66464, time: 0.558, data: 0.000) G_L1: 14.965 G_L1_ABSOLUTE: 2.978 G_L1_RELATIVE: 11.987 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 19, iters: 68464, time: 0.537, data: 0.000) G_L1: 15.289 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 12.692 G_Regularizer: 0.000 validation_error: 20.697 +(epoch: 19, iters: 70464, time: 0.546, data: 0.001) G_L1: 12.357 G_L1_ABSOLUTE: 2.241 G_L1_RELATIVE: 10.116 G_Regularizer: 0.000 validation_error: 20.994 +(epoch: 19, iters: 72464, time: 0.563, data: 0.001) G_L1: 14.091 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 11.752 G_Regularizer: 0.000 validation_error: 20.434 +(epoch: 19, iters: 74464, time: 0.557, data: 0.000) G_L1: 14.433 G_L1_ABSOLUTE: 2.324 G_L1_RELATIVE: 12.109 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 19, iters: 76464, time: 0.563, data: 0.000) G_L1: 17.195 G_L1_ABSOLUTE: 2.205 G_L1_RELATIVE: 14.990 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 19, iters: 78464, time: 0.552, data: 0.000) G_L1: 15.788 G_L1_ABSOLUTE: 2.661 G_L1_RELATIVE: 13.126 G_Regularizer: 0.000 validation_error: 21.358 +(epoch: 19, iters: 80464, time: 0.564, data: 0.000) G_L1: 14.927 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 12.329 G_Regularizer: 0.000 validation_error: 20.387 +(epoch: 19, iters: 82464, time: 0.561, data: 0.000) G_L1: 19.138 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 16.718 G_Regularizer: 0.000 validation_error: 20.237 +(epoch: 19, iters: 84464, time: 0.557, data: 0.000) G_L1: 16.363 G_L1_ABSOLUTE: 2.794 G_L1_RELATIVE: 13.569 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 19, iters: 86464, time: 0.548, data: 0.000) G_L1: 15.052 G_L1_ABSOLUTE: 2.933 G_L1_RELATIVE: 12.119 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 19, iters: 88464, time: 0.554, data: 0.000) G_L1: 16.366 G_L1_ABSOLUTE: 2.758 G_L1_RELATIVE: 13.608 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 19, iters: 90464, time: 0.561, data: 0.000) G_L1: 16.883 G_L1_ABSOLUTE: 2.534 G_L1_RELATIVE: 14.348 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 19, iters: 92464, time: 0.534, data: 0.000) G_L1: 13.485 G_L1_ABSOLUTE: 2.834 G_L1_RELATIVE: 10.651 G_Regularizer: 0.000 validation_error: 20.450 +(epoch: 19, iters: 94464, time: 0.536, data: 0.000) G_L1: 14.322 G_L1_ABSOLUTE: 3.047 G_L1_RELATIVE: 11.275 G_Regularizer: 0.000 validation_error: 21.266 +(epoch: 19, iters: 96464, time: 0.538, data: 0.000) G_L1: 13.121 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 10.785 G_Regularizer: 0.000 validation_error: 20.367 +(epoch: 19, iters: 98464, time: 0.551, data: 0.000) G_L1: 16.684 G_L1_ABSOLUTE: 2.853 G_L1_RELATIVE: 13.831 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 19, iters: 100464, time: 0.563, data: 0.000) G_L1: 13.774 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 10.977 G_Regularizer: 0.000 validation_error: 19.794 +(epoch: 19, iters: 102464, time: 0.547, data: 0.000) G_L1: 18.478 G_L1_ABSOLUTE: 3.217 G_L1_RELATIVE: 15.261 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 19, iters: 104464, time: 0.546, data: 0.001) G_L1: 15.116 G_L1_ABSOLUTE: 3.028 G_L1_RELATIVE: 12.088 G_Regularizer: 0.000 validation_error: 20.502 +(epoch: 19, iters: 106464, time: 0.560, data: 0.000) G_L1: 16.216 G_L1_ABSOLUTE: 3.163 G_L1_RELATIVE: 13.053 G_Regularizer: 0.000 validation_error: 20.441 +(epoch: 19, iters: 108464, time: 0.559, data: 0.000) G_L1: 15.052 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 12.529 G_Regularizer: 0.000 validation_error: 20.667 +(epoch: 19, iters: 110464, time: 0.543, data: 0.000) G_L1: 18.565 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 15.991 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 19, iters: 112464, time: 0.562, data: 0.000) G_L1: 14.327 G_L1_ABSOLUTE: 2.583 G_L1_RELATIVE: 11.744 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 19, iters: 114464, time: 0.561, data: 0.000) G_L1: 13.605 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 11.152 G_Regularizer: 0.000 validation_error: 20.575 +(epoch: 19, iters: 116464, time: 0.557, data: 0.000) G_L1: 14.028 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 11.562 G_Regularizer: 0.000 validation_error: 20.554 +(epoch: 19, iters: 118464, time: 0.552, data: 0.000) G_L1: 16.283 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 13.734 G_Regularizer: 0.000 validation_error: 20.895 +(epoch: 19, iters: 120464, time: 0.532, data: 0.000) G_L1: 14.463 G_L1_ABSOLUTE: 3.139 G_L1_RELATIVE: 11.324 G_Regularizer: 0.000 validation_error: 20.627 +(epoch: 19, iters: 122464, time: 0.561, data: 0.000) G_L1: 16.128 G_L1_ABSOLUTE: 2.975 G_L1_RELATIVE: 13.154 G_Regularizer: 0.000 validation_error: 20.032 +(epoch: 19, iters: 124464, time: 0.562, data: 0.000) G_L1: 13.943 G_L1_ABSOLUTE: 2.222 G_L1_RELATIVE: 11.721 G_Regularizer: 0.000 validation_error: 20.681 +(epoch: 19, iters: 126464, time: 0.552, data: 0.000) G_L1: 11.543 G_L1_ABSOLUTE: 2.207 G_L1_RELATIVE: 9.336 G_Regularizer: 0.000 validation_error: 20.372 +(epoch: 19, iters: 128464, time: 0.539, data: 0.000) G_L1: 13.510 G_L1_ABSOLUTE: 2.898 G_L1_RELATIVE: 10.611 G_Regularizer: 0.000 validation_error: 21.280 +(epoch: 19, iters: 130464, time: 0.558, data: 0.000) G_L1: 14.039 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 11.554 G_Regularizer: 0.000 validation_error: 20.204 +(epoch: 19, iters: 132464, time: 0.566, data: 0.000) G_L1: 15.535 G_L1_ABSOLUTE: 2.833 G_L1_RELATIVE: 12.702 G_Regularizer: 0.000 validation_error: 21.550 +(epoch: 19, iters: 134464, time: 0.563, data: 0.000) G_L1: 19.217 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 16.768 G_Regularizer: 0.000 validation_error: 21.616 +(epoch: 19, iters: 136464, time: 0.534, data: 0.000) G_L1: 14.811 G_L1_ABSOLUTE: 2.361 G_L1_RELATIVE: 12.450 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 19, iters: 138464, time: 0.537, data: 0.000) G_L1: 15.159 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 12.816 G_Regularizer: 0.000 validation_error: 20.502 +(epoch: 19, iters: 140464, time: 0.558, data: 0.000) G_L1: 16.230 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 13.598 G_Regularizer: 0.000 validation_error: 20.622 +(epoch: 19, iters: 142464, time: 0.556, data: 0.001) G_L1: 13.698 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 11.086 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 19, iters: 144464, time: 0.551, data: 0.000) G_L1: 14.963 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 12.544 G_Regularizer: 0.000 validation_error: 20.305 +(epoch: 19, iters: 146464, time: 0.533, data: 0.000) G_L1: 11.375 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 8.962 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 19, iters: 148464, time: 0.538, data: 0.000) G_L1: 14.273 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 11.738 G_Regularizer: 0.000 validation_error: 20.407 +(epoch: 19, iters: 150464, time: 0.557, data: 0.000) G_L1: 14.813 G_L1_ABSOLUTE: 2.247 G_L1_RELATIVE: 12.565 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 19, iters: 152464, time: 0.547, data: 0.000) G_L1: 16.240 G_L1_ABSOLUTE: 2.896 G_L1_RELATIVE: 13.343 G_Regularizer: 0.000 validation_error: 20.470 +(epoch: 19, iters: 154464, time: 0.551, data: 0.000) G_L1: 13.387 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 10.929 G_Regularizer: 0.000 validation_error: 20.117 +(epoch: 19, iters: 156464, time: 0.559, data: 0.000) G_L1: 15.117 G_L1_ABSOLUTE: 2.969 G_L1_RELATIVE: 12.148 G_Regularizer: 0.000 validation_error: 21.090 +(epoch: 19, iters: 158464, time: 0.556, data: 0.000) G_L1: 13.023 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 10.325 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 19, iters: 160464, time: 0.528, data: 0.000) G_L1: 14.413 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 12.006 G_Regularizer: 0.000 validation_error: 20.160 +(epoch: 19, iters: 162464, time: 0.549, data: 0.000) G_L1: 15.285 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 12.886 G_Regularizer: 0.000 validation_error: 20.410 +(epoch: 19, iters: 164464, time: 0.553, data: 0.000) G_L1: 15.926 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 13.353 G_Regularizer: 0.000 validation_error: 21.419 +(epoch: 19, iters: 166464, time: 0.558, data: 0.000) G_L1: 15.601 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 13.107 G_Regularizer: 0.000 validation_error: 20.482 +(epoch: 19, iters: 168464, time: 0.555, data: 0.000) G_L1: 11.379 G_L1_ABSOLUTE: 2.123 G_L1_RELATIVE: 9.256 G_Regularizer: 0.000 validation_error: 20.094 +(epoch: 19, iters: 170464, time: 0.561, data: 0.000) G_L1: 15.207 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 12.677 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 19, iters: 172464, time: 0.556, data: 0.000) G_L1: 13.897 G_L1_ABSOLUTE: 2.799 G_L1_RELATIVE: 11.098 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 19, iters: 174464, time: 0.553, data: 0.000) G_L1: 15.087 G_L1_ABSOLUTE: 2.585 G_L1_RELATIVE: 12.501 G_Regularizer: 0.000 validation_error: 21.157 +(epoch: 19, iters: 176464, time: 0.561, data: 0.000) G_L1: 16.418 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 14.004 G_Regularizer: 0.000 validation_error: 21.435 +(epoch: 19, iters: 178464, time: 0.549, data: 0.000) G_L1: 16.224 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 13.389 G_Regularizer: 0.000 validation_error: 21.063 +(epoch: 19, iters: 180464, time: 0.556, data: 0.000) G_L1: 18.171 G_L1_ABSOLUTE: 3.185 G_L1_RELATIVE: 14.986 G_Regularizer: 0.000 validation_error: 20.400 +(epoch: 19, iters: 182464, time: 0.562, data: 0.000) G_L1: 19.761 G_L1_ABSOLUTE: 3.100 G_L1_RELATIVE: 16.661 G_Regularizer: 0.000 validation_error: 20.464 +(epoch: 19, iters: 184464, time: 0.569, data: 0.000) G_L1: 15.198 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 12.694 G_Regularizer: 0.000 validation_error: 21.203 +(epoch: 19, iters: 186464, time: 0.549, data: 0.000) G_L1: 13.735 G_L1_ABSOLUTE: 2.395 G_L1_RELATIVE: 11.340 G_Regularizer: 0.000 validation_error: 20.449 +(epoch: 19, iters: 188464, time: 0.542, data: 0.000) G_L1: 16.593 G_L1_ABSOLUTE: 2.967 G_L1_RELATIVE: 13.626 G_Regularizer: 0.000 validation_error: 20.775 +(epoch: 19, iters: 190464, time: 0.558, data: 0.000) G_L1: 13.408 G_L1_ABSOLUTE: 2.644 G_L1_RELATIVE: 10.765 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 19, iters: 192464, time: 0.536, data: 0.001) G_L1: 13.301 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 10.895 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 19, iters: 194464, time: 0.553, data: 0.000) G_L1: 16.433 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 14.044 G_Regularizer: 0.000 validation_error: 20.224 +(epoch: 19, iters: 196464, time: 0.552, data: 0.000) G_L1: 15.682 G_L1_ABSOLUTE: 2.661 G_L1_RELATIVE: 13.021 G_Regularizer: 0.000 validation_error: 20.469 +(epoch: 19, iters: 198464, time: 0.540, data: 0.000) G_L1: 14.566 G_L1_ABSOLUTE: 2.808 G_L1_RELATIVE: 11.758 G_Regularizer: 0.000 validation_error: 21.055 +(epoch: 19, iters: 200464, time: 0.543, data: 0.000) G_L1: 12.281 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 9.573 G_Regularizer: 0.000 validation_error: 20.428 +(epoch: 19, iters: 202464, time: 0.556, data: 0.000) G_L1: 14.448 G_L1_ABSOLUTE: 2.534 G_L1_RELATIVE: 11.914 G_Regularizer: 0.000 validation_error: 20.551 +(epoch: 19, iters: 204464, time: 0.543, data: 0.000) G_L1: 13.210 G_L1_ABSOLUTE: 2.585 G_L1_RELATIVE: 10.625 G_Regularizer: 0.000 validation_error: 20.378 +(epoch: 19, iters: 206464, time: 0.560, data: 0.000) G_L1: 16.016 G_L1_ABSOLUTE: 2.749 G_L1_RELATIVE: 13.267 G_Regularizer: 0.000 validation_error: 20.729 +(epoch: 19, iters: 208464, time: 0.555, data: 0.001) G_L1: 15.054 G_L1_ABSOLUTE: 2.675 G_L1_RELATIVE: 12.380 G_Regularizer: 0.000 validation_error: 20.596 +(epoch: 19, iters: 210464, time: 0.561, data: 0.000) G_L1: 14.048 G_L1_ABSOLUTE: 2.024 G_L1_RELATIVE: 12.024 G_Regularizer: 0.000 validation_error: 20.126 +(epoch: 19, iters: 212464, time: 0.530, data: 0.000) G_L1: 12.941 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 10.586 G_Regularizer: 0.000 validation_error: 21.458 +(epoch: 19, iters: 214464, time: 0.562, data: 0.000) G_L1: 14.947 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 12.351 G_Regularizer: 0.000 validation_error: 20.632 +(epoch: 19, iters: 216464, time: 0.556, data: 0.000) G_L1: 11.938 G_L1_ABSOLUTE: 2.244 G_L1_RELATIVE: 9.694 G_Regularizer: 0.000 validation_error: 20.312 +(epoch: 19, iters: 218464, time: 0.560, data: 0.000) G_L1: 15.287 G_L1_ABSOLUTE: 2.996 G_L1_RELATIVE: 12.291 G_Regularizer: 0.000 validation_error: 21.428 +(epoch: 19, iters: 220464, time: 0.531, data: 0.000) G_L1: 16.462 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 13.944 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 19, iters: 222464, time: 0.548, data: 0.001) G_L1: 16.299 G_L1_ABSOLUTE: 2.862 G_L1_RELATIVE: 13.437 G_Regularizer: 0.000 validation_error: 21.230 +(epoch: 19, iters: 224464, time: 0.566, data: 0.000) G_L1: 14.828 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 12.205 G_Regularizer: 0.000 validation_error: 20.512 +(epoch: 19, iters: 226464, time: 0.562, data: 0.000) G_L1: 15.328 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 12.698 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 19, iters: 228464, time: 0.540, data: 0.000) G_L1: 14.298 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 11.709 G_Regularizer: 0.000 validation_error: 21.248 +(epoch: 19, iters: 230464, time: 0.550, data: 0.000) G_L1: 16.469 G_L1_ABSOLUTE: 2.062 G_L1_RELATIVE: 14.407 G_Regularizer: 0.000 validation_error: 20.657 +(epoch: 19, iters: 232464, time: 0.541, data: 0.000) G_L1: 18.113 G_L1_ABSOLUTE: 3.111 G_L1_RELATIVE: 15.003 G_Regularizer: 0.000 validation_error: 21.186 +(epoch: 19, iters: 234464, time: 0.554, data: 0.000) G_L1: 19.876 G_L1_ABSOLUTE: 3.223 G_L1_RELATIVE: 16.653 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 19, iters: 236464, time: 0.553, data: 0.000) G_L1: 14.758 G_L1_ABSOLUTE: 2.980 G_L1_RELATIVE: 11.778 G_Regularizer: 0.000 validation_error: 20.672 +(epoch: 19, iters: 238464, time: 0.559, data: 0.000) G_L1: 13.041 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 10.444 G_Regularizer: 0.000 validation_error: 21.080 +(epoch: 19, iters: 240464, time: 0.559, data: 0.001) G_L1: 16.267 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 13.861 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 19, iters: 242464, time: 0.552, data: 0.000) G_L1: 14.442 G_L1_ABSOLUTE: 2.444 G_L1_RELATIVE: 11.998 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 19, iters: 244464, time: 0.552, data: 0.000) G_L1: 13.572 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 11.314 G_Regularizer: 0.000 validation_error: 20.002 +(epoch: 19, iters: 246464, time: 0.543, data: 0.000) G_L1: 16.713 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 13.997 G_Regularizer: 0.000 validation_error: 19.997 +(epoch: 19, iters: 248464, time: 0.559, data: 0.001) G_L1: 16.037 G_L1_ABSOLUTE: 3.002 G_L1_RELATIVE: 13.035 G_Regularizer: 0.000 validation_error: 20.414 +(epoch: 19, iters: 250464, time: 0.561, data: 0.000) G_L1: 14.102 G_L1_ABSOLUTE: 3.022 G_L1_RELATIVE: 11.080 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 19, iters: 252464, time: 0.559, data: 0.000) G_L1: 11.413 G_L1_ABSOLUTE: 2.161 G_L1_RELATIVE: 9.252 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 19, iters: 254464, time: 0.564, data: 0.000) G_L1: 13.805 G_L1_ABSOLUTE: 2.940 G_L1_RELATIVE: 10.865 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 19, iters: 256464, time: 0.562, data: 0.000) G_L1: 15.724 G_L1_ABSOLUTE: 3.154 G_L1_RELATIVE: 12.571 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 19, iters: 258464, time: 0.556, data: 0.001) G_L1: 13.393 G_L1_ABSOLUTE: 2.828 G_L1_RELATIVE: 10.564 G_Regularizer: 0.000 validation_error: 21.063 +(epoch: 19, iters: 260464, time: 0.570, data: 0.000) G_L1: 12.988 G_L1_ABSOLUTE: 2.703 G_L1_RELATIVE: 10.285 G_Regularizer: 0.000 validation_error: 21.557 +(epoch: 19, iters: 262464, time: 0.539, data: 0.000) G_L1: 15.013 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 12.236 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 19, iters: 264464, time: 0.555, data: 0.000) G_L1: 14.775 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 12.515 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 19, iters: 266464, time: 0.566, data: 0.000) G_L1: 15.779 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 13.127 G_Regularizer: 0.000 validation_error: 21.028 +(epoch: 19, iters: 268464, time: 0.563, data: 0.000) G_L1: 14.394 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 11.699 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 19, iters: 270464, time: 0.550, data: 0.001) G_L1: 12.722 G_L1_ABSOLUTE: 2.203 G_L1_RELATIVE: 10.519 G_Regularizer: 0.000 validation_error: 21.519 +(epoch: 19, iters: 272464, time: 0.562, data: 0.000) G_L1: 12.909 G_L1_ABSOLUTE: 2.322 G_L1_RELATIVE: 10.587 G_Regularizer: 0.000 validation_error: 20.732 +(epoch: 19, iters: 274464, time: 0.561, data: 0.000) G_L1: 15.075 G_L1_ABSOLUTE: 2.382 G_L1_RELATIVE: 12.693 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 19, iters: 276464, time: 0.552, data: 0.000) G_L1: 16.129 G_L1_ABSOLUTE: 3.120 G_L1_RELATIVE: 13.009 G_Regularizer: 0.000 validation_error: 21.236 +(epoch: 19, iters: 278464, time: 0.553, data: 0.000) G_L1: 15.697 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 13.303 G_Regularizer: 0.000 validation_error: 20.442 +(epoch: 19, iters: 280464, time: 0.558, data: 0.000) G_L1: 12.489 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 10.075 G_Regularizer: 0.000 validation_error: 20.594 +(epoch: 19, iters: 282464, time: 0.560, data: 0.000) G_L1: 15.038 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 12.240 G_Regularizer: 0.000 validation_error: 20.316 +(epoch: 19, iters: 284464, time: 0.561, data: 0.000) G_L1: 14.210 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 11.728 G_Regularizer: 0.000 validation_error: 19.859 +(epoch: 19, iters: 286464, time: 0.540, data: 0.001) G_L1: 14.200 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 11.705 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 19, iters: 288464, time: 0.547, data: 0.000) G_L1: 17.944 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 15.144 G_Regularizer: 0.000 validation_error: 20.043 +(epoch: 19, iters: 290464, time: 0.565, data: 0.000) G_L1: 15.249 G_L1_ABSOLUTE: 2.804 G_L1_RELATIVE: 12.445 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 19, iters: 292464, time: 0.544, data: 0.000) G_L1: 13.994 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 11.578 G_Regularizer: 0.000 validation_error: 20.051 +(epoch: 19, iters: 294464, time: 0.542, data: 0.000) G_L1: 14.150 G_L1_ABSOLUTE: 2.173 G_L1_RELATIVE: 11.977 G_Regularizer: 0.000 validation_error: 20.356 +(epoch: 19, iters: 296464, time: 0.553, data: 0.000) G_L1: 14.967 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 12.482 G_Regularizer: 0.000 validation_error: 20.412 +(epoch: 19, iters: 298464, time: 0.556, data: 0.000) G_L1: 13.791 G_L1_ABSOLUTE: 2.580 G_L1_RELATIVE: 11.210 G_Regularizer: 0.000 validation_error: 20.647 +(epoch: 19, iters: 300464, time: 0.557, data: 0.000) G_L1: 16.250 G_L1_ABSOLUTE: 2.929 G_L1_RELATIVE: 13.321 G_Regularizer: 0.000 validation_error: 20.461 +(epoch: 19, iters: 302464, time: 0.558, data: 0.001) G_L1: 16.661 G_L1_ABSOLUTE: 2.951 G_L1_RELATIVE: 13.710 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 20, iters: 1712, time: 0.542, data: 0.000) G_L1: 13.294 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 10.718 G_Regularizer: 0.000 validation_error: 19.992 +(epoch: 20, iters: 3712, time: 0.550, data: 0.000) G_L1: 14.482 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 11.910 G_Regularizer: 0.000 validation_error: 20.263 +(epoch: 20, iters: 5712, time: 0.548, data: 0.000) G_L1: 15.929 G_L1_ABSOLUTE: 2.295 G_L1_RELATIVE: 13.634 G_Regularizer: 0.000 validation_error: 20.357 +(epoch: 20, iters: 7712, time: 0.570, data: 0.001) G_L1: 12.130 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 9.693 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 20, iters: 9712, time: 0.541, data: 0.000) G_L1: 13.645 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 11.239 G_Regularizer: 0.000 validation_error: 20.322 +(epoch: 20, iters: 11712, time: 0.545, data: 0.000) G_L1: 15.685 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 12.941 G_Regularizer: 0.000 validation_error: 19.595 +(epoch: 20, iters: 13712, time: 0.561, data: 0.000) G_L1: 16.539 G_L1_ABSOLUTE: 3.046 G_L1_RELATIVE: 13.492 G_Regularizer: 0.000 validation_error: 20.558 +(epoch: 20, iters: 15712, time: 0.566, data: 0.000) G_L1: 13.760 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 11.318 G_Regularizer: 0.000 validation_error: 20.357 +(epoch: 20, iters: 17712, time: 0.528, data: 0.000) G_L1: 17.161 G_L1_ABSOLUTE: 2.641 G_L1_RELATIVE: 14.520 G_Regularizer: 0.000 validation_error: 20.256 +(epoch: 20, iters: 19712, time: 0.556, data: 0.000) G_L1: 13.413 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 10.787 G_Regularizer: 0.000 validation_error: 20.276 +(epoch: 20, iters: 21712, time: 0.557, data: 0.000) G_L1: 14.865 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 12.318 G_Regularizer: 0.000 validation_error: 20.280 +(epoch: 20, iters: 23712, time: 0.538, data: 0.000) G_L1: 12.584 G_L1_ABSOLUTE: 2.203 G_L1_RELATIVE: 10.380 G_Regularizer: 0.000 validation_error: 20.168 +(epoch: 20, iters: 25712, time: 0.549, data: 0.000) G_L1: 14.139 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 11.650 G_Regularizer: 0.000 validation_error: 20.220 +(epoch: 20, iters: 27712, time: 0.545, data: 0.000) G_L1: 14.669 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 12.038 G_Regularizer: 0.000 validation_error: 21.688 +(epoch: 20, iters: 29712, time: 0.562, data: 0.000) G_L1: 14.389 G_L1_ABSOLUTE: 1.899 G_L1_RELATIVE: 12.490 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 20, iters: 31712, time: 0.548, data: 0.000) G_L1: 17.641 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 14.801 G_Regularizer: 0.000 validation_error: 20.637 +(epoch: 20, iters: 33712, time: 0.551, data: 0.000) G_L1: 16.171 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 13.653 G_Regularizer: 0.000 validation_error: 19.895 +(epoch: 20, iters: 35712, time: 0.559, data: 0.000) G_L1: 14.244 G_L1_ABSOLUTE: 2.405 G_L1_RELATIVE: 11.839 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 20, iters: 37712, time: 0.564, data: 0.000) G_L1: 16.852 G_L1_ABSOLUTE: 2.913 G_L1_RELATIVE: 13.938 G_Regularizer: 0.000 validation_error: 21.262 +(epoch: 20, iters: 39712, time: 0.560, data: 0.000) G_L1: 13.470 G_L1_ABSOLUTE: 3.007 G_L1_RELATIVE: 10.463 G_Regularizer: 0.000 validation_error: 20.346 +(epoch: 20, iters: 41712, time: 0.545, data: 0.000) G_L1: 13.327 G_L1_ABSOLUTE: 2.386 G_L1_RELATIVE: 10.941 G_Regularizer: 0.000 validation_error: 20.286 +(epoch: 20, iters: 43712, time: 0.541, data: 0.000) G_L1: 14.136 G_L1_ABSOLUTE: 2.795 G_L1_RELATIVE: 11.341 G_Regularizer: 0.000 validation_error: 19.731 +(epoch: 20, iters: 45712, time: 0.538, data: 0.001) G_L1: 14.796 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 12.317 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 20, iters: 47712, time: 0.538, data: 0.000) G_L1: 16.569 G_L1_ABSOLUTE: 3.045 G_L1_RELATIVE: 13.523 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 20, iters: 49712, time: 0.543, data: 0.000) G_L1: 14.120 G_L1_ABSOLUTE: 3.059 G_L1_RELATIVE: 11.060 G_Regularizer: 0.000 validation_error: 20.500 +(epoch: 20, iters: 51712, time: 0.548, data: 0.000) G_L1: 19.758 G_L1_ABSOLUTE: 2.875 G_L1_RELATIVE: 16.883 G_Regularizer: 0.000 validation_error: 21.265 +(epoch: 20, iters: 53712, time: 0.561, data: 0.000) G_L1: 16.173 G_L1_ABSOLUTE: 2.902 G_L1_RELATIVE: 13.272 G_Regularizer: 0.000 validation_error: 21.558 +(epoch: 20, iters: 55712, time: 0.558, data: 0.000) G_L1: 18.107 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 15.216 G_Regularizer: 0.000 validation_error: 21.047 +(epoch: 20, iters: 57712, time: 0.559, data: 0.000) G_L1: 16.029 G_L1_ABSOLUTE: 2.270 G_L1_RELATIVE: 13.759 G_Regularizer: 0.000 validation_error: 20.738 +(epoch: 20, iters: 59712, time: 0.546, data: 0.000) G_L1: 15.108 G_L1_ABSOLUTE: 2.850 G_L1_RELATIVE: 12.259 G_Regularizer: 0.000 validation_error: 20.466 +(epoch: 20, iters: 61712, time: 0.562, data: 0.001) G_L1: 13.365 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 10.620 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 20, iters: 63712, time: 0.555, data: 0.000) G_L1: 12.254 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 9.841 G_Regularizer: 0.000 validation_error: 20.498 +(epoch: 20, iters: 65712, time: 0.557, data: 0.001) G_L1: 12.039 G_L1_ABSOLUTE: 2.681 G_L1_RELATIVE: 9.358 G_Regularizer: 0.000 validation_error: 20.496 +(epoch: 20, iters: 67712, time: 0.546, data: 0.001) G_L1: 12.054 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 9.370 G_Regularizer: 0.000 validation_error: 20.070 +(epoch: 20, iters: 69712, time: 0.553, data: 0.000) G_L1: 15.241 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 12.790 G_Regularizer: 0.000 validation_error: 21.048 +(epoch: 20, iters: 71712, time: 0.556, data: 0.000) G_L1: 13.573 G_L1_ABSOLUTE: 2.532 G_L1_RELATIVE: 11.041 G_Regularizer: 0.000 validation_error: 20.464 +(epoch: 20, iters: 73712, time: 0.558, data: 0.000) G_L1: 14.532 G_L1_ABSOLUTE: 2.763 G_L1_RELATIVE: 11.769 G_Regularizer: 0.000 validation_error: 20.206 +(epoch: 20, iters: 75712, time: 0.545, data: 0.001) G_L1: 14.106 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 11.735 G_Regularizer: 0.000 validation_error: 20.494 +(epoch: 20, iters: 77712, time: 0.534, data: 0.000) G_L1: 12.649 G_L1_ABSOLUTE: 2.599 G_L1_RELATIVE: 10.050 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 20, iters: 79712, time: 0.557, data: 0.000) G_L1: 16.299 G_L1_ABSOLUTE: 2.700 G_L1_RELATIVE: 13.599 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 20, iters: 81712, time: 0.561, data: 0.000) G_L1: 14.904 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 12.374 G_Regularizer: 0.000 validation_error: 20.959 +(epoch: 20, iters: 83712, time: 0.547, data: 0.000) G_L1: 14.523 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 11.942 G_Regularizer: 0.000 validation_error: 20.551 +(epoch: 20, iters: 85712, time: 0.561, data: 0.000) G_L1: 13.507 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 11.139 G_Regularizer: 0.000 validation_error: 20.573 +(epoch: 20, iters: 87712, time: 0.554, data: 0.000) G_L1: 13.606 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 10.871 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 20, iters: 89712, time: 0.559, data: 0.000) G_L1: 17.434 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 15.069 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 20, iters: 91712, time: 0.545, data: 0.001) G_L1: 12.433 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 9.930 G_Regularizer: 0.000 validation_error: 20.372 +(epoch: 20, iters: 93712, time: 0.537, data: 0.000) G_L1: 15.699 G_L1_ABSOLUTE: 3.113 G_L1_RELATIVE: 12.585 G_Regularizer: 0.000 validation_error: 20.575 +(epoch: 20, iters: 95712, time: 0.535, data: 0.000) G_L1: 14.181 G_L1_ABSOLUTE: 2.431 G_L1_RELATIVE: 11.750 G_Regularizer: 0.000 validation_error: 20.028 +(epoch: 20, iters: 97712, time: 0.566, data: 0.000) G_L1: 17.770 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 15.297 G_Regularizer: 0.000 validation_error: 20.944 +(epoch: 20, iters: 99712, time: 0.547, data: 0.000) G_L1: 12.943 G_L1_ABSOLUTE: 2.398 G_L1_RELATIVE: 10.545 G_Regularizer: 0.000 validation_error: 20.304 +(epoch: 20, iters: 101712, time: 0.531, data: 0.000) G_L1: 14.338 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 11.672 G_Regularizer: 0.000 validation_error: 20.970 +(epoch: 20, iters: 103712, time: 0.541, data: 0.000) G_L1: 14.233 G_L1_ABSOLUTE: 2.808 G_L1_RELATIVE: 11.426 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 20, iters: 105712, time: 0.559, data: 0.000) G_L1: 13.696 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 11.155 G_Regularizer: 0.000 validation_error: 21.181 +(epoch: 20, iters: 107712, time: 0.558, data: 0.000) G_L1: 15.298 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 12.836 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 20, iters: 109712, time: 0.533, data: 0.000) G_L1: 15.229 G_L1_ABSOLUTE: 2.532 G_L1_RELATIVE: 12.697 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 20, iters: 111712, time: 0.536, data: 0.000) G_L1: 15.135 G_L1_ABSOLUTE: 2.906 G_L1_RELATIVE: 12.229 G_Regularizer: 0.000 validation_error: 20.312 +(epoch: 20, iters: 113712, time: 0.559, data: 0.000) G_L1: 14.096 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 11.282 G_Regularizer: 0.000 validation_error: 19.982 +(epoch: 20, iters: 115712, time: 0.549, data: 0.001) G_L1: 16.537 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 13.879 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 20, iters: 117712, time: 0.560, data: 0.000) G_L1: 14.532 G_L1_ABSOLUTE: 2.917 G_L1_RELATIVE: 11.615 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 20, iters: 119712, time: 0.560, data: 0.000) G_L1: 16.893 G_L1_ABSOLUTE: 2.337 G_L1_RELATIVE: 14.556 G_Regularizer: 0.000 validation_error: 20.462 +(epoch: 20, iters: 121712, time: 0.558, data: 0.000) G_L1: 12.785 G_L1_ABSOLUTE: 2.127 G_L1_RELATIVE: 10.658 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 20, iters: 123712, time: 0.553, data: 0.000) G_L1: 13.511 G_L1_ABSOLUTE: 2.900 G_L1_RELATIVE: 10.612 G_Regularizer: 0.000 validation_error: 20.595 +(epoch: 20, iters: 125712, time: 0.554, data: 0.000) G_L1: 15.083 G_L1_ABSOLUTE: 2.184 G_L1_RELATIVE: 12.899 G_Regularizer: 0.000 validation_error: 20.305 +(epoch: 20, iters: 127712, time: 0.551, data: 0.000) G_L1: 15.096 G_L1_ABSOLUTE: 2.740 G_L1_RELATIVE: 12.356 G_Regularizer: 0.000 validation_error: 20.015 +(epoch: 20, iters: 129712, time: 0.553, data: 0.000) G_L1: 13.688 G_L1_ABSOLUTE: 2.068 G_L1_RELATIVE: 11.620 G_Regularizer: 0.000 validation_error: 20.409 +(epoch: 20, iters: 131712, time: 0.551, data: 0.000) G_L1: 15.090 G_L1_ABSOLUTE: 2.946 G_L1_RELATIVE: 12.144 G_Regularizer: 0.000 validation_error: 20.453 +(epoch: 20, iters: 133712, time: 0.547, data: 0.000) G_L1: 13.897 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 11.230 G_Regularizer: 0.000 validation_error: 21.005 +(epoch: 20, iters: 135712, time: 0.550, data: 0.000) G_L1: 14.331 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 12.017 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 20, iters: 137712, time: 0.562, data: 0.000) G_L1: 13.897 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 11.357 G_Regularizer: 0.000 validation_error: 20.795 +(epoch: 20, iters: 139712, time: 0.559, data: 0.000) G_L1: 15.935 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 13.415 G_Regularizer: 0.000 validation_error: 20.971 +(epoch: 20, iters: 141712, time: 0.558, data: 0.000) G_L1: 13.656 G_L1_ABSOLUTE: 2.052 G_L1_RELATIVE: 11.603 G_Regularizer: 0.000 validation_error: 20.500 +(epoch: 20, iters: 143712, time: 0.530, data: 0.000) G_L1: 15.467 G_L1_ABSOLUTE: 2.578 G_L1_RELATIVE: 12.888 G_Regularizer: 0.000 validation_error: 20.269 +(epoch: 20, iters: 145712, time: 0.562, data: 0.000) G_L1: 17.814 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 15.272 G_Regularizer: 0.000 validation_error: 19.935 +(epoch: 20, iters: 147712, time: 0.551, data: 0.000) G_L1: 13.837 G_L1_ABSOLUTE: 2.275 G_L1_RELATIVE: 11.561 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 20, iters: 149712, time: 0.550, data: 0.000) G_L1: 14.251 G_L1_ABSOLUTE: 2.223 G_L1_RELATIVE: 12.028 G_Regularizer: 0.000 validation_error: 21.090 +(epoch: 20, iters: 151712, time: 0.554, data: 0.000) G_L1: 13.915 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 11.288 G_Regularizer: 0.000 validation_error: 20.133 +(epoch: 20, iters: 153712, time: 0.548, data: 0.000) G_L1: 12.948 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 10.431 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 20, iters: 155712, time: 0.561, data: 0.000) G_L1: 14.393 G_L1_ABSOLUTE: 2.690 G_L1_RELATIVE: 11.703 G_Regularizer: 0.000 validation_error: 20.046 +(epoch: 20, iters: 157712, time: 0.560, data: 0.000) G_L1: 15.802 G_L1_ABSOLUTE: 2.878 G_L1_RELATIVE: 12.924 G_Regularizer: 0.000 validation_error: 20.309 +(epoch: 20, iters: 159712, time: 0.551, data: 0.000) G_L1: 15.897 G_L1_ABSOLUTE: 3.229 G_L1_RELATIVE: 12.668 G_Regularizer: 0.000 validation_error: 20.640 +(epoch: 20, iters: 161712, time: 0.560, data: 0.000) G_L1: 12.938 G_L1_ABSOLUTE: 2.181 G_L1_RELATIVE: 10.758 G_Regularizer: 0.000 validation_error: 20.378 +(epoch: 20, iters: 163712, time: 0.557, data: 0.000) G_L1: 13.757 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 11.168 G_Regularizer: 0.000 validation_error: 20.204 +(epoch: 20, iters: 165712, time: 0.559, data: 0.000) G_L1: 14.497 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 11.776 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 20, iters: 167712, time: 0.549, data: 0.000) G_L1: 13.481 G_L1_ABSOLUTE: 2.201 G_L1_RELATIVE: 11.280 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 20, iters: 169712, time: 0.548, data: 0.000) G_L1: 12.913 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 10.416 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 20, iters: 171712, time: 0.557, data: 0.000) G_L1: 15.840 G_L1_ABSOLUTE: 2.889 G_L1_RELATIVE: 12.951 G_Regularizer: 0.000 validation_error: 20.615 +(epoch: 20, iters: 173712, time: 0.559, data: 0.000) G_L1: 15.864 G_L1_ABSOLUTE: 2.680 G_L1_RELATIVE: 13.184 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 20, iters: 175712, time: 0.547, data: 0.001) G_L1: 16.734 G_L1_ABSOLUTE: 2.675 G_L1_RELATIVE: 14.059 G_Regularizer: 0.000 validation_error: 20.284 +(epoch: 20, iters: 177712, time: 0.549, data: 0.000) G_L1: 13.263 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 10.779 G_Regularizer: 0.000 validation_error: 19.910 +(epoch: 20, iters: 179712, time: 0.564, data: 0.000) G_L1: 16.104 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 13.517 G_Regularizer: 0.000 validation_error: 20.350 +(epoch: 20, iters: 181712, time: 0.548, data: 0.000) G_L1: 16.790 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 14.300 G_Regularizer: 0.000 validation_error: 19.638 +(epoch: 20, iters: 183712, time: 0.558, data: 0.000) G_L1: 14.402 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 11.741 G_Regularizer: 0.000 validation_error: 20.432 +(epoch: 20, iters: 185712, time: 0.539, data: 0.000) G_L1: 14.274 G_L1_ABSOLUTE: 2.920 G_L1_RELATIVE: 11.354 G_Regularizer: 0.000 validation_error: 20.143 +(epoch: 20, iters: 187712, time: 0.561, data: 0.000) G_L1: 15.507 G_L1_ABSOLUTE: 2.806 G_L1_RELATIVE: 12.701 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 20, iters: 189712, time: 0.556, data: 0.000) G_L1: 15.384 G_L1_ABSOLUTE: 2.253 G_L1_RELATIVE: 13.131 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 20, iters: 191712, time: 0.548, data: 0.000) G_L1: 13.230 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 10.477 G_Regularizer: 0.000 validation_error: 20.061 +(epoch: 20, iters: 193712, time: 0.530, data: 0.000) G_L1: 11.959 G_L1_ABSOLUTE: 2.685 G_L1_RELATIVE: 9.273 G_Regularizer: 0.000 validation_error: 20.280 +(epoch: 20, iters: 195712, time: 0.538, data: 0.000) G_L1: 15.844 G_L1_ABSOLUTE: 2.852 G_L1_RELATIVE: 12.992 G_Regularizer: 0.000 validation_error: 20.519 +(epoch: 20, iters: 197712, time: 0.551, data: 0.000) G_L1: 12.650 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 10.303 G_Regularizer: 0.000 validation_error: 20.993 +(epoch: 20, iters: 199712, time: 0.561, data: 0.000) G_L1: 12.087 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 9.667 G_Regularizer: 0.000 validation_error: 20.099 +(epoch: 20, iters: 201712, time: 0.549, data: 0.001) G_L1: 13.922 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 11.325 G_Regularizer: 0.000 validation_error: 19.985 +(epoch: 20, iters: 203712, time: 0.538, data: 0.000) G_L1: 14.822 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 12.184 G_Regularizer: 0.000 validation_error: 21.059 +(epoch: 20, iters: 205712, time: 0.561, data: 0.000) G_L1: 15.803 G_L1_ABSOLUTE: 2.672 G_L1_RELATIVE: 13.131 G_Regularizer: 0.000 validation_error: 20.279 +(epoch: 20, iters: 207712, time: 0.538, data: 0.000) G_L1: 12.743 G_L1_ABSOLUTE: 2.417 G_L1_RELATIVE: 10.325 G_Regularizer: 0.000 validation_error: 20.137 +(epoch: 20, iters: 209712, time: 0.537, data: 0.000) G_L1: 14.386 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 11.928 G_Regularizer: 0.000 validation_error: 20.241 +(epoch: 20, iters: 211712, time: 0.559, data: 0.001) G_L1: 17.438 G_L1_ABSOLUTE: 3.186 G_L1_RELATIVE: 14.252 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 20, iters: 213712, time: 0.546, data: 0.001) G_L1: 13.378 G_L1_ABSOLUTE: 2.334 G_L1_RELATIVE: 11.044 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 20, iters: 215712, time: 0.552, data: 0.001) G_L1: 15.050 G_L1_ABSOLUTE: 3.258 G_L1_RELATIVE: 11.792 G_Regularizer: 0.000 validation_error: 20.281 +(epoch: 20, iters: 217712, time: 0.551, data: 0.000) G_L1: 15.921 G_L1_ABSOLUTE: 2.715 G_L1_RELATIVE: 13.205 G_Regularizer: 0.000 validation_error: 21.289 +(epoch: 20, iters: 219712, time: 0.556, data: 0.000) G_L1: 14.646 G_L1_ABSOLUTE: 2.983 G_L1_RELATIVE: 11.663 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 20, iters: 221712, time: 0.549, data: 0.000) G_L1: 15.619 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 13.224 G_Regularizer: 0.000 validation_error: 20.747 +(epoch: 20, iters: 223712, time: 0.536, data: 0.000) G_L1: 14.936 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 12.231 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 20, iters: 225712, time: 0.559, data: 0.000) G_L1: 13.936 G_L1_ABSOLUTE: 2.552 G_L1_RELATIVE: 11.384 G_Regularizer: 0.000 validation_error: 20.641 +(epoch: 20, iters: 227712, time: 0.537, data: 0.000) G_L1: 14.771 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 12.176 G_Regularizer: 0.000 validation_error: 20.533 +(epoch: 20, iters: 229712, time: 0.549, data: 0.000) G_L1: 16.245 G_L1_ABSOLUTE: 2.682 G_L1_RELATIVE: 13.563 G_Regularizer: 0.000 validation_error: 21.293 +(epoch: 20, iters: 231712, time: 0.548, data: 0.000) G_L1: 13.700 G_L1_ABSOLUTE: 2.238 G_L1_RELATIVE: 11.462 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 20, iters: 233712, time: 0.566, data: 0.000) G_L1: 13.444 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 10.608 G_Regularizer: 0.000 validation_error: 20.183 +(epoch: 20, iters: 235712, time: 0.533, data: 0.000) G_L1: 16.799 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 14.148 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 20, iters: 237712, time: 0.557, data: 0.001) G_L1: 14.784 G_L1_ABSOLUTE: 2.300 G_L1_RELATIVE: 12.484 G_Regularizer: 0.000 validation_error: 20.871 +(epoch: 20, iters: 239712, time: 0.560, data: 0.000) G_L1: 12.940 G_L1_ABSOLUTE: 2.768 G_L1_RELATIVE: 10.172 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 20, iters: 241712, time: 0.557, data: 0.001) G_L1: 14.575 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 12.151 G_Regularizer: 0.000 validation_error: 21.450 +(epoch: 20, iters: 243712, time: 0.546, data: 0.000) G_L1: 13.789 G_L1_ABSOLUTE: 2.593 G_L1_RELATIVE: 11.196 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 20, iters: 245712, time: 0.561, data: 0.000) G_L1: 15.898 G_L1_ABSOLUTE: 2.166 G_L1_RELATIVE: 13.732 G_Regularizer: 0.000 validation_error: 20.343 +(epoch: 20, iters: 247712, time: 0.560, data: 0.000) G_L1: 15.946 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 13.320 G_Regularizer: 0.000 validation_error: 20.712 +(epoch: 20, iters: 249712, time: 0.556, data: 0.000) G_L1: 16.146 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 13.545 G_Regularizer: 0.000 validation_error: 20.516 +(epoch: 20, iters: 251712, time: 0.544, data: 0.001) G_L1: 15.664 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 13.040 G_Regularizer: 0.000 validation_error: 20.531 +(epoch: 20, iters: 253712, time: 0.565, data: 0.001) G_L1: 15.903 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 13.444 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 20, iters: 255712, time: 0.561, data: 0.000) G_L1: 16.753 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 14.036 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 20, iters: 257712, time: 0.561, data: 0.000) G_L1: 14.228 G_L1_ABSOLUTE: 2.848 G_L1_RELATIVE: 11.380 G_Regularizer: 0.000 validation_error: 20.257 +(epoch: 20, iters: 259712, time: 0.546, data: 0.000) G_L1: 15.523 G_L1_ABSOLUTE: 2.558 G_L1_RELATIVE: 12.964 G_Regularizer: 0.000 validation_error: 20.487 +(epoch: 20, iters: 261712, time: 0.535, data: 0.000) G_L1: 12.375 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 9.789 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 20, iters: 263712, time: 0.563, data: 0.000) G_L1: 16.328 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 13.928 G_Regularizer: 0.000 validation_error: 20.196 +(epoch: 20, iters: 265712, time: 0.564, data: 0.000) G_L1: 19.714 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 17.160 G_Regularizer: 0.000 validation_error: 20.498 +(epoch: 20, iters: 267712, time: 0.551, data: 0.000) G_L1: 17.411 G_L1_ABSOLUTE: 2.267 G_L1_RELATIVE: 15.144 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 20, iters: 269712, time: 0.554, data: 0.000) G_L1: 18.151 G_L1_ABSOLUTE: 2.980 G_L1_RELATIVE: 15.172 G_Regularizer: 0.000 validation_error: 20.222 +(epoch: 20, iters: 271712, time: 0.559, data: 0.000) G_L1: 15.165 G_L1_ABSOLUTE: 2.232 G_L1_RELATIVE: 12.934 G_Regularizer: 0.000 validation_error: 20.573 +(epoch: 20, iters: 273712, time: 0.556, data: 0.001) G_L1: 13.962 G_L1_ABSOLUTE: 2.608 G_L1_RELATIVE: 11.354 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 20, iters: 275712, time: 0.560, data: 0.000) G_L1: 14.854 G_L1_ABSOLUTE: 2.655 G_L1_RELATIVE: 12.199 G_Regularizer: 0.000 validation_error: 19.767 +(epoch: 20, iters: 277712, time: 0.537, data: 0.000) G_L1: 14.725 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 11.988 G_Regularizer: 0.000 validation_error: 21.058 +(epoch: 20, iters: 279712, time: 0.556, data: 0.000) G_L1: 12.802 G_L1_ABSOLUTE: 2.879 G_L1_RELATIVE: 9.923 G_Regularizer: 0.000 validation_error: 20.366 +(epoch: 20, iters: 281712, time: 0.549, data: 0.000) G_L1: 15.423 G_L1_ABSOLUTE: 2.904 G_L1_RELATIVE: 12.519 G_Regularizer: 0.000 validation_error: 20.397 +(epoch: 20, iters: 283712, time: 0.560, data: 0.000) G_L1: 15.268 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 12.893 G_Regularizer: 0.000 validation_error: 19.939 +(epoch: 20, iters: 285712, time: 0.553, data: 0.000) G_L1: 14.921 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 12.272 G_Regularizer: 0.000 validation_error: 20.387 +(epoch: 20, iters: 287712, time: 0.556, data: 0.000) G_L1: 13.322 G_L1_ABSOLUTE: 2.287 G_L1_RELATIVE: 11.035 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 20, iters: 289712, time: 0.548, data: 0.000) G_L1: 15.256 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 12.699 G_Regularizer: 0.000 validation_error: 20.276 +(epoch: 20, iters: 291712, time: 0.557, data: 0.000) G_L1: 12.531 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 9.891 G_Regularizer: 0.000 validation_error: 21.067 +(epoch: 20, iters: 293712, time: 0.560, data: 0.000) G_L1: 14.645 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 12.153 G_Regularizer: 0.000 validation_error: 20.753 +(epoch: 20, iters: 295712, time: 0.551, data: 0.000) G_L1: 14.970 G_L1_ABSOLUTE: 2.644 G_L1_RELATIVE: 12.326 G_Regularizer: 0.000 validation_error: 20.363 +(epoch: 20, iters: 297712, time: 0.559, data: 0.000) G_L1: 13.845 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 11.424 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 20, iters: 299712, time: 0.555, data: 0.000) G_L1: 15.202 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 12.458 G_Regularizer: 0.000 validation_error: 20.319 +(epoch: 20, iters: 301712, time: 0.559, data: 0.000) G_L1: 14.606 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 12.091 G_Regularizer: 0.000 validation_error: 20.307 +(epoch: 21, iters: 960, time: 0.549, data: 0.000) G_L1: 14.657 G_L1_ABSOLUTE: 2.723 G_L1_RELATIVE: 11.933 G_Regularizer: 0.000 validation_error: 20.526 +(epoch: 21, iters: 2960, time: 0.561, data: 0.000) G_L1: 16.028 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 13.372 G_Regularizer: 0.000 validation_error: 21.096 +(epoch: 21, iters: 4960, time: 0.554, data: 0.000) G_L1: 15.241 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 12.562 G_Regularizer: 0.000 validation_error: 20.206 +(epoch: 21, iters: 6960, time: 0.566, data: 0.000) G_L1: 15.466 G_L1_ABSOLUTE: 3.025 G_L1_RELATIVE: 12.441 G_Regularizer: 0.000 validation_error: 21.060 +(epoch: 21, iters: 8960, time: 0.554, data: 0.000) G_L1: 14.840 G_L1_ABSOLUTE: 2.728 G_L1_RELATIVE: 12.113 G_Regularizer: 0.000 validation_error: 21.051 +(epoch: 21, iters: 10960, time: 0.560, data: 0.000) G_L1: 14.623 G_L1_ABSOLUTE: 3.103 G_L1_RELATIVE: 11.520 G_Regularizer: 0.000 validation_error: 20.194 +(epoch: 21, iters: 12960, time: 0.552, data: 0.000) G_L1: 14.090 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 11.681 G_Regularizer: 0.000 validation_error: 20.493 +(epoch: 21, iters: 14960, time: 0.568, data: 0.000) G_L1: 13.330 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 10.911 G_Regularizer: 0.000 validation_error: 20.316 +(epoch: 21, iters: 16960, time: 0.553, data: 0.000) G_L1: 16.091 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 13.359 G_Regularizer: 0.000 validation_error: 20.184 +(epoch: 21, iters: 18960, time: 0.552, data: 0.000) G_L1: 17.419 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 14.430 G_Regularizer: 0.000 validation_error: 20.262 +(epoch: 21, iters: 20960, time: 0.553, data: 0.000) G_L1: 13.049 G_L1_ABSOLUTE: 2.079 G_L1_RELATIVE: 10.970 G_Regularizer: 0.000 validation_error: 20.441 +(epoch: 21, iters: 22960, time: 0.555, data: 0.000) G_L1: 14.094 G_L1_ABSOLUTE: 2.250 G_L1_RELATIVE: 11.843 G_Regularizer: 0.000 validation_error: 20.347 +(epoch: 21, iters: 24960, time: 0.556, data: 0.000) G_L1: 15.238 G_L1_ABSOLUTE: 3.027 G_L1_RELATIVE: 12.211 G_Regularizer: 0.000 validation_error: 20.641 +(epoch: 21, iters: 26960, time: 0.550, data: 0.000) G_L1: 14.844 G_L1_ABSOLUTE: 2.532 G_L1_RELATIVE: 12.312 G_Regularizer: 0.000 validation_error: 20.778 +(epoch: 21, iters: 28960, time: 0.556, data: 0.000) G_L1: 18.415 G_L1_ABSOLUTE: 3.177 G_L1_RELATIVE: 15.238 G_Regularizer: 0.000 validation_error: 20.818 +(epoch: 21, iters: 30960, time: 0.561, data: 0.000) G_L1: 16.895 G_L1_ABSOLUTE: 3.039 G_L1_RELATIVE: 13.856 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 21, iters: 32960, time: 0.565, data: 0.000) G_L1: 14.095 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 11.436 G_Regularizer: 0.000 validation_error: 20.609 +(epoch: 21, iters: 34960, time: 0.573, data: 0.000) G_L1: 14.357 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 11.975 G_Regularizer: 0.000 validation_error: 20.307 +(epoch: 21, iters: 36960, time: 0.572, data: 0.000) G_L1: 15.365 G_L1_ABSOLUTE: 2.819 G_L1_RELATIVE: 12.545 G_Regularizer: 0.000 validation_error: 20.405 +(epoch: 21, iters: 38960, time: 0.584, data: 0.000) G_L1: 11.983 G_L1_ABSOLUTE: 2.173 G_L1_RELATIVE: 9.810 G_Regularizer: 0.000 validation_error: 20.400 +(epoch: 21, iters: 40960, time: 0.584, data: 0.000) G_L1: 15.344 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 12.831 G_Regularizer: 0.000 validation_error: 20.407 +(epoch: 21, iters: 42960, time: 0.574, data: 0.000) G_L1: 16.427 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 13.967 G_Regularizer: 0.000 validation_error: 20.146 +(epoch: 21, iters: 44960, time: 0.574, data: 0.000) G_L1: 14.184 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 11.452 G_Regularizer: 0.000 validation_error: 20.276 +(epoch: 21, iters: 46960, time: 0.581, data: 0.000) G_L1: 15.763 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 12.939 G_Regularizer: 0.000 validation_error: 20.420 +(epoch: 21, iters: 48960, time: 0.575, data: 0.000) G_L1: 15.683 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 13.000 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 21, iters: 50960, time: 0.575, data: 0.000) G_L1: 15.563 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 13.182 G_Regularizer: 0.000 validation_error: 20.892 +(epoch: 21, iters: 52960, time: 0.583, data: 0.000) G_L1: 16.392 G_L1_ABSOLUTE: 2.700 G_L1_RELATIVE: 13.692 G_Regularizer: 0.000 validation_error: 19.954 +(epoch: 21, iters: 54960, time: 0.575, data: 0.000) G_L1: 14.211 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 11.701 G_Regularizer: 0.000 validation_error: 19.679 +(epoch: 21, iters: 56960, time: 0.573, data: 0.000) G_L1: 14.834 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 12.098 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 21, iters: 58960, time: 0.573, data: 0.000) G_L1: 14.887 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 12.258 G_Regularizer: 0.000 validation_error: 20.214 +(epoch: 21, iters: 60960, time: 0.573, data: 0.001) G_L1: 15.122 G_L1_ABSOLUTE: 2.972 G_L1_RELATIVE: 12.150 G_Regularizer: 0.000 validation_error: 21.343 +(epoch: 21, iters: 62960, time: 0.593, data: 0.000) G_L1: 11.192 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 8.827 G_Regularizer: 0.000 validation_error: 20.569 +(epoch: 21, iters: 64960, time: 0.574, data: 0.000) G_L1: 14.716 G_L1_ABSOLUTE: 2.740 G_L1_RELATIVE: 11.975 G_Regularizer: 0.000 validation_error: 19.854 +(epoch: 21, iters: 66960, time: 0.580, data: 0.000) G_L1: 13.322 G_L1_ABSOLUTE: 2.639 G_L1_RELATIVE: 10.683 G_Regularizer: 0.000 validation_error: 20.418 +(epoch: 21, iters: 68960, time: 0.569, data: 0.000) G_L1: 13.937 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 11.417 G_Regularizer: 0.000 validation_error: 20.374 +(epoch: 21, iters: 70960, time: 0.573, data: 0.000) G_L1: 16.676 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 14.111 G_Regularizer: 0.000 validation_error: 20.457 +(epoch: 21, iters: 72960, time: 0.572, data: 0.000) G_L1: 18.013 G_L1_ABSOLUTE: 2.655 G_L1_RELATIVE: 15.358 G_Regularizer: 0.000 validation_error: 20.543 +(epoch: 21, iters: 74960, time: 0.576, data: 0.000) G_L1: 18.551 G_L1_ABSOLUTE: 2.508 G_L1_RELATIVE: 16.043 G_Regularizer: 0.000 validation_error: 20.871 +(epoch: 21, iters: 76960, time: 0.581, data: 0.000) G_L1: 15.418 G_L1_ABSOLUTE: 2.324 G_L1_RELATIVE: 13.093 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 21, iters: 78960, time: 0.583, data: 0.000) G_L1: 15.758 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 13.168 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 21, iters: 80960, time: 0.570, data: 0.000) G_L1: 12.358 G_L1_ABSOLUTE: 2.305 G_L1_RELATIVE: 10.054 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 21, iters: 82960, time: 0.573, data: 0.000) G_L1: 17.076 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 14.352 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 21, iters: 84960, time: 0.576, data: 0.000) G_L1: 15.612 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 13.200 G_Regularizer: 0.000 validation_error: 19.960 +(epoch: 21, iters: 86960, time: 0.567, data: 0.000) G_L1: 16.900 G_L1_ABSOLUTE: 2.802 G_L1_RELATIVE: 14.098 G_Regularizer: 0.000 validation_error: 19.428 +(epoch: 21, iters: 88960, time: 0.577, data: 0.000) G_L1: 14.592 G_L1_ABSOLUTE: 2.972 G_L1_RELATIVE: 11.620 G_Regularizer: 0.000 validation_error: 20.434 +(epoch: 21, iters: 90960, time: 0.571, data: 0.000) G_L1: 16.350 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 13.701 G_Regularizer: 0.000 validation_error: 20.541 +(epoch: 21, iters: 92960, time: 0.569, data: 0.000) G_L1: 17.157 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 14.616 G_Regularizer: 0.000 validation_error: 20.439 +(epoch: 21, iters: 94960, time: 0.585, data: 0.000) G_L1: 12.284 G_L1_ABSOLUTE: 2.191 G_L1_RELATIVE: 10.094 G_Regularizer: 0.000 validation_error: 20.511 +(epoch: 21, iters: 96960, time: 0.576, data: 0.000) G_L1: 14.595 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 11.858 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 21, iters: 98960, time: 0.567, data: 0.000) G_L1: 14.613 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 12.339 G_Regularizer: 0.000 validation_error: 19.728 +(epoch: 21, iters: 100960, time: 0.573, data: 0.000) G_L1: 14.365 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 11.837 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 21, iters: 102960, time: 0.581, data: 0.000) G_L1: 12.860 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 10.401 G_Regularizer: 0.000 validation_error: 19.775 +(epoch: 21, iters: 104960, time: 0.573, data: 0.001) G_L1: 15.804 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 13.330 G_Regularizer: 0.000 validation_error: 20.370 +(epoch: 21, iters: 106960, time: 0.573, data: 0.000) G_L1: 14.789 G_L1_ABSOLUTE: 2.869 G_L1_RELATIVE: 11.920 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 21, iters: 108960, time: 0.576, data: 0.000) G_L1: 14.283 G_L1_ABSOLUTE: 2.393 G_L1_RELATIVE: 11.890 G_Regularizer: 0.000 validation_error: 20.018 +(epoch: 21, iters: 110960, time: 0.574, data: 0.000) G_L1: 14.779 G_L1_ABSOLUTE: 2.448 G_L1_RELATIVE: 12.331 G_Regularizer: 0.000 validation_error: 20.261 +(epoch: 21, iters: 112960, time: 0.583, data: 0.000) G_L1: 12.205 G_L1_ABSOLUTE: 2.304 G_L1_RELATIVE: 9.900 G_Regularizer: 0.000 validation_error: 20.604 +(epoch: 21, iters: 114960, time: 0.570, data: 0.000) G_L1: 15.022 G_L1_ABSOLUTE: 3.463 G_L1_RELATIVE: 11.560 G_Regularizer: 0.000 validation_error: 19.131 +(epoch: 21, iters: 116960, time: 0.571, data: 0.000) G_L1: 14.601 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 11.910 G_Regularizer: 0.000 validation_error: 20.317 +(epoch: 21, iters: 118960, time: 0.572, data: 0.000) G_L1: 16.187 G_L1_ABSOLUTE: 3.353 G_L1_RELATIVE: 12.834 G_Regularizer: 0.000 validation_error: 20.050 +(epoch: 21, iters: 120960, time: 0.578, data: 0.000) G_L1: 15.491 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 12.839 G_Regularizer: 0.000 validation_error: 20.246 +(epoch: 21, iters: 122960, time: 0.571, data: 0.000) G_L1: 15.616 G_L1_ABSOLUTE: 2.739 G_L1_RELATIVE: 12.878 G_Regularizer: 0.000 validation_error: 20.259 +(epoch: 21, iters: 124960, time: 0.577, data: 0.000) G_L1: 15.792 G_L1_ABSOLUTE: 2.527 G_L1_RELATIVE: 13.265 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 21, iters: 126960, time: 0.574, data: 0.000) G_L1: 15.560 G_L1_ABSOLUTE: 2.924 G_L1_RELATIVE: 12.637 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 21, iters: 128960, time: 0.585, data: 0.000) G_L1: 13.490 G_L1_ABSOLUTE: 2.575 G_L1_RELATIVE: 10.916 G_Regularizer: 0.000 validation_error: 20.185 +(epoch: 21, iters: 130960, time: 0.568, data: 0.000) G_L1: 15.531 G_L1_ABSOLUTE: 2.616 G_L1_RELATIVE: 12.915 G_Regularizer: 0.000 validation_error: 20.054 +(epoch: 21, iters: 132960, time: 0.573, data: 0.000) G_L1: 15.307 G_L1_ABSOLUTE: 3.090 G_L1_RELATIVE: 12.217 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 21, iters: 134960, time: 0.576, data: 0.000) G_L1: 15.118 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 12.381 G_Regularizer: 0.000 validation_error: 20.300 +(epoch: 21, iters: 136960, time: 0.570, data: 0.000) G_L1: 14.705 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 12.350 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 21, iters: 138960, time: 0.580, data: 0.000) G_L1: 14.849 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 12.229 G_Regularizer: 0.000 validation_error: 20.674 +(epoch: 21, iters: 140960, time: 0.575, data: 0.000) G_L1: 16.150 G_L1_ABSOLUTE: 2.689 G_L1_RELATIVE: 13.461 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 21, iters: 142960, time: 0.578, data: 0.000) G_L1: 13.754 G_L1_ABSOLUTE: 2.607 G_L1_RELATIVE: 11.147 G_Regularizer: 0.000 validation_error: 20.607 +(epoch: 21, iters: 144960, time: 0.575, data: 0.000) G_L1: 14.477 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 12.063 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 21, iters: 146960, time: 0.570, data: 0.000) G_L1: 14.357 G_L1_ABSOLUTE: 3.352 G_L1_RELATIVE: 11.005 G_Regularizer: 0.000 validation_error: 20.315 +(epoch: 21, iters: 148960, time: 0.576, data: 0.000) G_L1: 15.390 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 12.905 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 21, iters: 150960, time: 0.585, data: 0.000) G_L1: 14.044 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 11.233 G_Regularizer: 0.000 validation_error: 20.618 +(epoch: 21, iters: 152960, time: 0.572, data: 0.000) G_L1: 16.000 G_L1_ABSOLUTE: 2.786 G_L1_RELATIVE: 13.214 G_Regularizer: 0.000 validation_error: 20.680 +(epoch: 21, iters: 154960, time: 0.569, data: 0.000) G_L1: 16.367 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 13.429 G_Regularizer: 0.000 validation_error: 20.716 +(epoch: 21, iters: 156960, time: 0.574, data: 0.000) G_L1: 16.136 G_L1_ABSOLUTE: 3.180 G_L1_RELATIVE: 12.956 G_Regularizer: 0.000 validation_error: 20.413 +(epoch: 21, iters: 158960, time: 0.569, data: 0.000) G_L1: 13.863 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 11.161 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 21, iters: 160960, time: 0.579, data: 0.000) G_L1: 16.223 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 13.700 G_Regularizer: 0.000 validation_error: 20.490 +(epoch: 21, iters: 162960, time: 0.582, data: 0.000) G_L1: 12.864 G_L1_ABSOLUTE: 2.487 G_L1_RELATIVE: 10.377 G_Regularizer: 0.000 validation_error: 20.460 +(epoch: 21, iters: 164960, time: 0.583, data: 0.000) G_L1: 16.661 G_L1_ABSOLUTE: 2.348 G_L1_RELATIVE: 14.313 G_Regularizer: 0.000 validation_error: 20.349 +(epoch: 21, iters: 166960, time: 0.572, data: 0.000) G_L1: 16.378 G_L1_ABSOLUTE: 2.653 G_L1_RELATIVE: 13.725 G_Regularizer: 0.000 validation_error: 20.399 +(epoch: 21, iters: 168960, time: 0.574, data: 0.000) G_L1: 16.458 G_L1_ABSOLUTE: 3.036 G_L1_RELATIVE: 13.422 G_Regularizer: 0.000 validation_error: 20.674 +(epoch: 21, iters: 170960, time: 0.576, data: 0.000) G_L1: 19.124 G_L1_ABSOLUTE: 2.769 G_L1_RELATIVE: 16.355 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 21, iters: 172960, time: 0.572, data: 0.000) G_L1: 16.306 G_L1_ABSOLUTE: 3.212 G_L1_RELATIVE: 13.094 G_Regularizer: 0.000 validation_error: 20.693 +(epoch: 21, iters: 174960, time: 0.577, data: 0.000) G_L1: 14.540 G_L1_ABSOLUTE: 2.986 G_L1_RELATIVE: 11.554 G_Regularizer: 0.000 validation_error: 20.260 +(epoch: 21, iters: 176960, time: 0.578, data: 0.000) G_L1: 13.875 G_L1_ABSOLUTE: 2.653 G_L1_RELATIVE: 11.222 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 21, iters: 178960, time: 0.589, data: 0.000) G_L1: 16.822 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 14.090 G_Regularizer: 0.000 validation_error: 20.178 +(epoch: 21, iters: 180960, time: 0.576, data: 0.000) G_L1: 12.724 G_L1_ABSOLUTE: 2.287 G_L1_RELATIVE: 10.437 G_Regularizer: 0.000 validation_error: 20.268 +(epoch: 21, iters: 182960, time: 0.578, data: 0.000) G_L1: 13.672 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 11.128 G_Regularizer: 0.000 validation_error: 20.251 +(epoch: 21, iters: 184960, time: 0.571, data: 0.000) G_L1: 15.223 G_L1_ABSOLUTE: 2.945 G_L1_RELATIVE: 12.278 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 21, iters: 186960, time: 0.575, data: 0.000) G_L1: 17.398 G_L1_ABSOLUTE: 2.994 G_L1_RELATIVE: 14.404 G_Regularizer: 0.000 validation_error: 20.079 +(epoch: 21, iters: 188960, time: 0.574, data: 0.000) G_L1: 15.853 G_L1_ABSOLUTE: 2.808 G_L1_RELATIVE: 13.044 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 21, iters: 190960, time: 0.574, data: 0.000) G_L1: 18.669 G_L1_ABSOLUTE: 3.504 G_L1_RELATIVE: 15.165 G_Regularizer: 0.000 validation_error: 20.719 +(epoch: 21, iters: 192960, time: 0.579, data: 0.000) G_L1: 14.501 G_L1_ABSOLUTE: 2.889 G_L1_RELATIVE: 11.612 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 21, iters: 194960, time: 0.598, data: 0.000) G_L1: 16.251 G_L1_ABSOLUTE: 2.990 G_L1_RELATIVE: 13.261 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 21, iters: 196960, time: 0.585, data: 0.000) G_L1: 18.594 G_L1_ABSOLUTE: 2.790 G_L1_RELATIVE: 15.804 G_Regularizer: 0.000 validation_error: 20.412 +(epoch: 21, iters: 198960, time: 0.576, data: 0.000) G_L1: 17.582 G_L1_ABSOLUTE: 2.653 G_L1_RELATIVE: 14.930 G_Regularizer: 0.000 validation_error: 21.273 +(epoch: 21, iters: 200960, time: 0.570, data: 0.000) G_L1: 12.575 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 9.958 G_Regularizer: 0.000 validation_error: 21.299 +(epoch: 21, iters: 202960, time: 0.572, data: 0.000) G_L1: 19.067 G_L1_ABSOLUTE: 3.030 G_L1_RELATIVE: 16.037 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 21, iters: 204960, time: 0.577, data: 0.000) G_L1: 16.324 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 13.759 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 21, iters: 206960, time: 0.573, data: 0.001) G_L1: 16.264 G_L1_ABSOLUTE: 2.816 G_L1_RELATIVE: 13.448 G_Regularizer: 0.000 validation_error: 20.947 +(epoch: 21, iters: 208960, time: 0.566, data: 0.000) G_L1: 16.862 G_L1_ABSOLUTE: 2.275 G_L1_RELATIVE: 14.588 G_Regularizer: 0.000 validation_error: 21.278 +(epoch: 21, iters: 210960, time: 0.567, data: 0.000) G_L1: 14.952 G_L1_ABSOLUTE: 2.829 G_L1_RELATIVE: 12.122 G_Regularizer: 0.000 validation_error: 20.732 +(epoch: 21, iters: 212960, time: 0.600, data: 0.000) G_L1: 15.201 G_L1_ABSOLUTE: 2.709 G_L1_RELATIVE: 12.492 G_Regularizer: 0.000 validation_error: 20.765 +(epoch: 21, iters: 214960, time: 0.579, data: 0.001) G_L1: 16.019 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 13.349 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 21, iters: 216960, time: 0.570, data: 0.000) G_L1: 12.810 G_L1_ABSOLUTE: 2.681 G_L1_RELATIVE: 10.129 G_Regularizer: 0.000 validation_error: 20.490 +(epoch: 21, iters: 218960, time: 0.577, data: 0.000) G_L1: 14.482 G_L1_ABSOLUTE: 1.966 G_L1_RELATIVE: 12.515 G_Regularizer: 0.000 validation_error: 20.016 +(epoch: 21, iters: 220960, time: 0.586, data: 0.000) G_L1: 13.491 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 10.694 G_Regularizer: 0.000 validation_error: 20.304 +(epoch: 21, iters: 222960, time: 0.570, data: 0.000) G_L1: 16.252 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 13.536 G_Regularizer: 0.000 validation_error: 20.205 +(epoch: 21, iters: 224960, time: 0.568, data: 0.000) G_L1: 14.137 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 11.252 G_Regularizer: 0.000 validation_error: 20.273 +(epoch: 21, iters: 226960, time: 0.568, data: 0.000) G_L1: 12.263 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 9.754 G_Regularizer: 0.000 validation_error: 21.229 +(epoch: 21, iters: 228960, time: 0.593, data: 0.000) G_L1: 14.767 G_L1_ABSOLUTE: 2.902 G_L1_RELATIVE: 11.865 G_Regularizer: 0.000 validation_error: 21.277 +(epoch: 21, iters: 230960, time: 0.579, data: 0.000) G_L1: 14.950 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 12.321 G_Regularizer: 0.000 validation_error: 20.281 +(epoch: 21, iters: 232960, time: 0.565, data: 0.000) G_L1: 17.562 G_L1_ABSOLUTE: 2.786 G_L1_RELATIVE: 14.776 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 21, iters: 234960, time: 0.577, data: 0.000) G_L1: 17.497 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 14.864 G_Regularizer: 0.000 validation_error: 19.722 +(epoch: 21, iters: 236960, time: 0.576, data: 0.000) G_L1: 15.994 G_L1_ABSOLUTE: 2.723 G_L1_RELATIVE: 13.271 G_Regularizer: 0.000 validation_error: 20.190 +(epoch: 21, iters: 238960, time: 0.572, data: 0.001) G_L1: 16.431 G_L1_ABSOLUTE: 3.000 G_L1_RELATIVE: 13.431 G_Regularizer: 0.000 validation_error: 20.234 +(epoch: 21, iters: 240960, time: 0.569, data: 0.000) G_L1: 13.958 G_L1_ABSOLUTE: 2.183 G_L1_RELATIVE: 11.775 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 21, iters: 242960, time: 0.569, data: 0.000) G_L1: 13.054 G_L1_ABSOLUTE: 2.392 G_L1_RELATIVE: 10.662 G_Regularizer: 0.000 validation_error: 21.270 +(epoch: 21, iters: 244960, time: 0.841, data: 0.000) G_L1: 14.084 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 11.348 G_Regularizer: 0.000 validation_error: 20.360 +(epoch: 21, iters: 246960, time: 0.570, data: 0.000) G_L1: 12.456 G_L1_ABSOLUTE: 2.193 G_L1_RELATIVE: 10.264 G_Regularizer: 0.000 validation_error: 20.157 +(epoch: 21, iters: 248960, time: 0.575, data: 0.000) G_L1: 13.866 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 11.344 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 21, iters: 250960, time: 0.575, data: 0.000) G_L1: 16.931 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 14.224 G_Regularizer: 0.000 validation_error: 20.745 +(epoch: 21, iters: 252960, time: 0.568, data: 0.000) G_L1: 21.860 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 19.425 G_Regularizer: 0.000 validation_error: 20.662 +(epoch: 21, iters: 254960, time: 0.573, data: 0.000) G_L1: 17.362 G_L1_ABSOLUTE: 2.973 G_L1_RELATIVE: 14.389 G_Regularizer: 0.000 validation_error: 20.383 +(epoch: 21, iters: 256960, time: 0.570, data: 0.000) G_L1: 12.647 G_L1_ABSOLUTE: 2.076 G_L1_RELATIVE: 10.570 G_Regularizer: 0.000 validation_error: 20.578 +(epoch: 21, iters: 258960, time: 0.577, data: 0.000) G_L1: 15.212 G_L1_ABSOLUTE: 2.941 G_L1_RELATIVE: 12.271 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 21, iters: 260960, time: 0.579, data: 0.000) G_L1: 13.113 G_L1_ABSOLUTE: 2.308 G_L1_RELATIVE: 10.805 G_Regularizer: 0.000 validation_error: 20.557 +(epoch: 21, iters: 262960, time: 0.567, data: 0.000) G_L1: 14.773 G_L1_ABSOLUTE: 2.379 G_L1_RELATIVE: 12.394 G_Regularizer: 0.000 validation_error: 21.484 +(epoch: 21, iters: 264960, time: 0.573, data: 0.000) G_L1: 13.611 G_L1_ABSOLUTE: 3.133 G_L1_RELATIVE: 10.478 G_Regularizer: 0.000 validation_error: 21.320 +(epoch: 21, iters: 266960, time: 0.568, data: 0.000) G_L1: 15.283 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 12.859 G_Regularizer: 0.000 validation_error: 20.300 +(epoch: 21, iters: 268960, time: 0.566, data: 0.000) G_L1: 14.007 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 11.553 G_Regularizer: 0.000 validation_error: 20.614 +(epoch: 21, iters: 270960, time: 0.571, data: 0.000) G_L1: 12.995 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 10.513 G_Regularizer: 0.000 validation_error: 20.728 +(epoch: 21, iters: 272960, time: 0.588, data: 0.000) G_L1: 12.453 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 9.876 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 21, iters: 274960, time: 0.569, data: 0.000) G_L1: 12.002 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 9.671 G_Regularizer: 0.000 validation_error: 20.515 +(epoch: 21, iters: 276960, time: 0.568, data: 0.000) G_L1: 13.820 G_L1_ABSOLUTE: 2.197 G_L1_RELATIVE: 11.623 G_Regularizer: 0.000 validation_error: 20.532 +(epoch: 21, iters: 278960, time: 0.597, data: 0.000) G_L1: 14.299 G_L1_ABSOLUTE: 2.903 G_L1_RELATIVE: 11.396 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 21, iters: 280960, time: 0.580, data: 0.000) G_L1: 16.800 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 14.205 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 21, iters: 282960, time: 0.567, data: 0.000) G_L1: 16.754 G_L1_ABSOLUTE: 3.119 G_L1_RELATIVE: 13.635 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 21, iters: 284960, time: 0.573, data: 0.000) G_L1: 17.310 G_L1_ABSOLUTE: 2.849 G_L1_RELATIVE: 14.461 G_Regularizer: 0.000 validation_error: 21.149 +(epoch: 21, iters: 286960, time: 0.575, data: 0.000) G_L1: 16.583 G_L1_ABSOLUTE: 3.059 G_L1_RELATIVE: 13.524 G_Regularizer: 0.000 validation_error: 20.423 +(epoch: 21, iters: 288960, time: 0.569, data: 0.000) G_L1: 16.779 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 14.438 G_Regularizer: 0.000 validation_error: 20.482 +(epoch: 21, iters: 290960, time: 0.570, data: 0.000) G_L1: 22.240 G_L1_ABSOLUTE: 3.778 G_L1_RELATIVE: 18.461 G_Regularizer: 0.000 validation_error: 20.423 +(epoch: 21, iters: 292960, time: 0.574, data: 0.000) G_L1: 14.951 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 12.660 G_Regularizer: 0.000 validation_error: 20.524 +(epoch: 21, iters: 294960, time: 0.597, data: 0.000) G_L1: 13.017 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 10.347 G_Regularizer: 0.000 validation_error: 21.043 +(epoch: 21, iters: 296960, time: 0.578, data: 0.000) G_L1: 15.471 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 12.940 G_Regularizer: 0.000 validation_error: 21.172 +(epoch: 21, iters: 298960, time: 0.570, data: 0.000) G_L1: 17.117 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 14.457 G_Regularizer: 0.000 validation_error: 20.197 +(epoch: 21, iters: 300960, time: 0.572, data: 0.000) G_L1: 15.888 G_L1_ABSOLUTE: 2.776 G_L1_RELATIVE: 13.111 G_Regularizer: 0.000 validation_error: 20.555 +(epoch: 22, iters: 208, time: 0.581, data: 0.000) G_L1: 13.767 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 11.084 G_Regularizer: 0.000 validation_error: 20.404 +(epoch: 22, iters: 2208, time: 0.578, data: 0.000) G_L1: 11.624 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 9.206 G_Regularizer: 0.000 validation_error: 20.377 +(epoch: 22, iters: 4208, time: 0.570, data: 0.000) G_L1: 13.907 G_L1_ABSOLUTE: 2.508 G_L1_RELATIVE: 11.399 G_Regularizer: 0.000 validation_error: 20.513 +(epoch: 22, iters: 6208, time: 0.577, data: 0.000) G_L1: 15.842 G_L1_ABSOLUTE: 2.559 G_L1_RELATIVE: 13.282 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 22, iters: 8208, time: 0.586, data: 0.001) G_L1: 13.831 G_L1_ABSOLUTE: 2.578 G_L1_RELATIVE: 11.253 G_Regularizer: 0.000 validation_error: 20.089 +(epoch: 22, iters: 10208, time: 0.569, data: 0.000) G_L1: 13.905 G_L1_ABSOLUTE: 2.771 G_L1_RELATIVE: 11.134 G_Regularizer: 0.000 validation_error: 20.664 +(epoch: 22, iters: 12208, time: 0.571, data: 0.000) G_L1: 14.782 G_L1_ABSOLUTE: 2.690 G_L1_RELATIVE: 12.092 G_Regularizer: 0.000 validation_error: 20.198 +(epoch: 22, iters: 14208, time: 0.574, data: 0.000) G_L1: 17.022 G_L1_ABSOLUTE: 2.935 G_L1_RELATIVE: 14.087 G_Regularizer: 0.000 validation_error: 20.714 +(epoch: 22, iters: 16208, time: 0.574, data: 0.000) G_L1: 12.987 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 10.448 G_Regularizer: 0.000 validation_error: 20.663 +(epoch: 22, iters: 18208, time: 0.570, data: 0.000) G_L1: 15.535 G_L1_ABSOLUTE: 3.347 G_L1_RELATIVE: 12.188 G_Regularizer: 0.000 validation_error: 20.937 +(epoch: 22, iters: 20208, time: 0.568, data: 0.000) G_L1: 14.662 G_L1_ABSOLUTE: 2.718 G_L1_RELATIVE: 11.944 G_Regularizer: 0.000 validation_error: 20.849 +(epoch: 22, iters: 22208, time: 0.571, data: 0.000) G_L1: 14.410 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 11.740 G_Regularizer: 0.000 validation_error: 20.125 +(epoch: 22, iters: 24208, time: 0.571, data: 0.000) G_L1: 14.574 G_L1_ABSOLUTE: 2.081 G_L1_RELATIVE: 12.493 G_Regularizer: 0.000 validation_error: 20.535 +(epoch: 22, iters: 26208, time: 0.572, data: 0.000) G_L1: 13.919 G_L1_ABSOLUTE: 2.250 G_L1_RELATIVE: 11.669 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 22, iters: 28208, time: 0.579, data: 0.000) G_L1: 16.660 G_L1_ABSOLUTE: 2.847 G_L1_RELATIVE: 13.813 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 22, iters: 30208, time: 0.581, data: 0.000) G_L1: 13.343 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 10.929 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 22, iters: 32208, time: 0.569, data: 0.000) G_L1: 16.373 G_L1_ABSOLUTE: 2.701 G_L1_RELATIVE: 13.673 G_Regularizer: 0.000 validation_error: 20.521 +(epoch: 22, iters: 34208, time: 0.572, data: 0.000) G_L1: 15.778 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 13.132 G_Regularizer: 0.000 validation_error: 20.795 +(epoch: 22, iters: 36208, time: 0.573, data: 0.000) G_L1: 18.115 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 15.378 G_Regularizer: 0.000 validation_error: 20.577 +(epoch: 22, iters: 38208, time: 0.577, data: 0.000) G_L1: 13.812 G_L1_ABSOLUTE: 2.750 G_L1_RELATIVE: 11.062 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 22, iters: 40208, time: 0.578, data: 0.000) G_L1: 15.650 G_L1_ABSOLUTE: 2.636 G_L1_RELATIVE: 13.014 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 22, iters: 42208, time: 0.596, data: 0.000) G_L1: 15.177 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 12.393 G_Regularizer: 0.000 validation_error: 20.303 +(epoch: 22, iters: 44208, time: 0.577, data: 0.000) G_L1: 13.245 G_L1_ABSOLUTE: 2.937 G_L1_RELATIVE: 10.308 G_Regularizer: 0.000 validation_error: 20.586 +(epoch: 22, iters: 46208, time: 0.565, data: 0.000) G_L1: 17.448 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 14.712 G_Regularizer: 0.000 validation_error: 20.407 +(epoch: 22, iters: 48208, time: 0.575, data: 0.000) G_L1: 14.781 G_L1_ABSOLUTE: 2.410 G_L1_RELATIVE: 12.371 G_Regularizer: 0.000 validation_error: 20.546 +(epoch: 22, iters: 50208, time: 0.570, data: 0.000) G_L1: 17.763 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 15.102 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 22, iters: 52208, time: 0.571, data: 0.000) G_L1: 14.533 G_L1_ABSOLUTE: 2.934 G_L1_RELATIVE: 11.599 G_Regularizer: 0.000 validation_error: 20.583 +(epoch: 22, iters: 54208, time: 0.569, data: 0.000) G_L1: 16.015 G_L1_ABSOLUTE: 2.146 G_L1_RELATIVE: 13.869 G_Regularizer: 0.000 validation_error: 21.094 +(epoch: 22, iters: 56208, time: 0.585, data: 0.000) G_L1: 15.299 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 12.566 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 22, iters: 58208, time: 0.596, data: 0.000) G_L1: 16.195 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 13.669 G_Regularizer: 0.000 validation_error: 21.326 +(epoch: 22, iters: 60208, time: 0.578, data: 0.000) G_L1: 13.140 G_L1_ABSOLUTE: 2.685 G_L1_RELATIVE: 10.454 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 22, iters: 62208, time: 0.578, data: 0.000) G_L1: 15.618 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 12.885 G_Regularizer: 0.000 validation_error: 20.423 +(epoch: 22, iters: 64208, time: 0.564, data: 0.000) G_L1: 16.610 G_L1_ABSOLUTE: 2.599 G_L1_RELATIVE: 14.011 G_Regularizer: 0.000 validation_error: 20.450 +(epoch: 22, iters: 66208, time: 0.581, data: 0.000) G_L1: 13.698 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 11.032 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 22, iters: 68208, time: 0.579, data: 0.000) G_L1: 14.884 G_L1_ABSOLUTE: 3.019 G_L1_RELATIVE: 11.865 G_Regularizer: 0.000 validation_error: 20.385 +(epoch: 22, iters: 70208, time: 0.573, data: 0.000) G_L1: 16.106 G_L1_ABSOLUTE: 2.789 G_L1_RELATIVE: 13.316 G_Regularizer: 0.000 validation_error: 20.435 +(epoch: 22, iters: 72208, time: 0.580, data: 0.000) G_L1: 13.456 G_L1_ABSOLUTE: 2.715 G_L1_RELATIVE: 10.741 G_Regularizer: 0.000 validation_error: 21.499 +(epoch: 22, iters: 74208, time: 0.569, data: 0.000) G_L1: 14.068 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 11.754 G_Regularizer: 0.000 validation_error: 20.775 +(epoch: 22, iters: 76208, time: 0.571, data: 0.000) G_L1: 17.691 G_L1_ABSOLUTE: 2.982 G_L1_RELATIVE: 14.709 G_Regularizer: 0.000 validation_error: 21.433 +(epoch: 22, iters: 78208, time: 0.568, data: 0.000) G_L1: 14.402 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 11.901 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 22, iters: 80208, time: 0.576, data: 0.000) G_L1: 21.391 G_L1_ABSOLUTE: 3.044 G_L1_RELATIVE: 18.347 G_Regularizer: 0.000 validation_error: 21.178 +(epoch: 22, iters: 82208, time: 0.573, data: 0.000) G_L1: 15.181 G_L1_ABSOLUTE: 2.443 G_L1_RELATIVE: 12.738 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 22, iters: 84208, time: 0.568, data: 0.000) G_L1: 13.031 G_L1_ABSOLUTE: 2.240 G_L1_RELATIVE: 10.791 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 22, iters: 86208, time: 0.575, data: 0.000) G_L1: 15.170 G_L1_ABSOLUTE: 2.789 G_L1_RELATIVE: 12.381 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 22, iters: 88208, time: 0.569, data: 0.000) G_L1: 19.579 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 16.641 G_Regularizer: 0.000 validation_error: 21.381 +(epoch: 22, iters: 90208, time: 0.571, data: 0.000) G_L1: 16.322 G_L1_ABSOLUTE: 2.562 G_L1_RELATIVE: 13.760 G_Regularizer: 0.000 validation_error: 20.183 +(epoch: 22, iters: 92208, time: 0.598, data: 0.000) G_L1: 14.492 G_L1_ABSOLUTE: 2.829 G_L1_RELATIVE: 11.663 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 22, iters: 94208, time: 0.574, data: 0.000) G_L1: 16.454 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 13.866 G_Regularizer: 0.000 validation_error: 20.422 +(epoch: 22, iters: 96208, time: 0.563, data: 0.000) G_L1: 16.045 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 13.338 G_Regularizer: 0.000 validation_error: 21.268 +(epoch: 22, iters: 98208, time: 0.580, data: 0.000) G_L1: 15.223 G_L1_ABSOLUTE: 2.745 G_L1_RELATIVE: 12.478 G_Regularizer: 0.000 validation_error: 21.412 +(epoch: 22, iters: 100208, time: 0.570, data: 0.000) G_L1: 16.753 G_L1_ABSOLUTE: 2.861 G_L1_RELATIVE: 13.892 G_Regularizer: 0.000 validation_error: 21.237 +(epoch: 22, iters: 102208, time: 0.571, data: 0.000) G_L1: 15.389 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 12.519 G_Regularizer: 0.000 validation_error: 21.167 +(epoch: 22, iters: 104208, time: 0.573, data: 0.000) G_L1: 14.735 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 12.225 G_Regularizer: 0.000 validation_error: 20.261 +(epoch: 22, iters: 106208, time: 0.576, data: 0.001) G_L1: 17.569 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 14.861 G_Regularizer: 0.000 validation_error: 20.466 +(epoch: 22, iters: 108208, time: 0.590, data: 0.000) G_L1: 14.956 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 12.004 G_Regularizer: 0.000 validation_error: 21.253 +(epoch: 22, iters: 110208, time: 0.563, data: 0.000) G_L1: 13.005 G_L1_ABSOLUTE: 2.647 G_L1_RELATIVE: 10.358 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 22, iters: 112208, time: 0.572, data: 0.000) G_L1: 14.331 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 11.722 G_Regularizer: 0.000 validation_error: 20.977 +(epoch: 22, iters: 114208, time: 0.568, data: 0.000) G_L1: 12.098 G_L1_ABSOLUTE: 2.984 G_L1_RELATIVE: 9.114 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 22, iters: 116208, time: 0.581, data: 0.000) G_L1: 14.154 G_L1_ABSOLUTE: 2.712 G_L1_RELATIVE: 11.442 G_Regularizer: 0.000 validation_error: 20.237 +(epoch: 22, iters: 118208, time: 0.576, data: 0.000) G_L1: 14.685 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 12.116 G_Regularizer: 0.000 validation_error: 20.396 +(epoch: 22, iters: 120208, time: 0.583, data: 0.000) G_L1: 15.159 G_L1_ABSOLUTE: 2.500 G_L1_RELATIVE: 12.659 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 22, iters: 122208, time: 0.570, data: 0.000) G_L1: 14.941 G_L1_ABSOLUTE: 2.271 G_L1_RELATIVE: 12.671 G_Regularizer: 0.000 validation_error: 20.482 +(epoch: 22, iters: 124208, time: 0.585, data: 0.001) G_L1: 14.165 G_L1_ABSOLUTE: 2.507 G_L1_RELATIVE: 11.658 G_Regularizer: 0.000 validation_error: 20.360 +(epoch: 22, iters: 126208, time: 0.570, data: 0.000) G_L1: 15.853 G_L1_ABSOLUTE: 3.241 G_L1_RELATIVE: 12.612 G_Regularizer: 0.000 validation_error: 20.387 +(epoch: 22, iters: 128208, time: 0.567, data: 0.000) G_L1: 15.820 G_L1_ABSOLUTE: 3.012 G_L1_RELATIVE: 12.808 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 22, iters: 130208, time: 0.579, data: 0.000) G_L1: 14.438 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 11.854 G_Regularizer: 0.000 validation_error: 20.220 +(epoch: 22, iters: 132208, time: 0.576, data: 0.000) G_L1: 15.887 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 13.252 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 22, iters: 134208, time: 0.570, data: 0.000) G_L1: 17.251 G_L1_ABSOLUTE: 2.603 G_L1_RELATIVE: 14.648 G_Regularizer: 0.000 validation_error: 20.650 +(epoch: 22, iters: 136208, time: 0.577, data: 0.000) G_L1: 15.337 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 12.788 G_Regularizer: 0.000 validation_error: 20.529 +(epoch: 22, iters: 138208, time: 0.575, data: 0.000) G_L1: 13.904 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 11.234 G_Regularizer: 0.000 validation_error: 21.091 +(epoch: 22, iters: 140208, time: 0.569, data: 0.000) G_L1: 13.335 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 10.898 G_Regularizer: 0.000 validation_error: 21.438 +(epoch: 22, iters: 142208, time: 0.574, data: 0.000) G_L1: 14.563 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 11.849 G_Regularizer: 0.000 validation_error: 20.052 +(epoch: 22, iters: 144208, time: 0.573, data: 0.000) G_L1: 17.985 G_L1_ABSOLUTE: 2.514 G_L1_RELATIVE: 15.471 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 22, iters: 146208, time: 0.573, data: 0.000) G_L1: 13.857 G_L1_ABSOLUTE: 2.500 G_L1_RELATIVE: 11.357 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 22, iters: 148208, time: 0.570, data: 0.000) G_L1: 15.106 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 12.432 G_Regularizer: 0.000 validation_error: 19.957 +(epoch: 22, iters: 150208, time: 0.573, data: 0.000) G_L1: 14.317 G_L1_ABSOLUTE: 3.207 G_L1_RELATIVE: 11.110 G_Regularizer: 0.000 validation_error: 20.544 +(epoch: 22, iters: 152208, time: 0.572, data: 0.000) G_L1: 16.559 G_L1_ABSOLUTE: 3.074 G_L1_RELATIVE: 13.485 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 22, iters: 154208, time: 0.574, data: 0.000) G_L1: 15.326 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 12.605 G_Regularizer: 0.000 validation_error: 20.054 +(epoch: 22, iters: 156208, time: 0.569, data: 0.000) G_L1: 13.580 G_L1_ABSOLUTE: 2.671 G_L1_RELATIVE: 10.909 G_Regularizer: 0.000 validation_error: 20.284 +(epoch: 22, iters: 158208, time: 0.599, data: 0.000) G_L1: 15.024 G_L1_ABSOLUTE: 2.529 G_L1_RELATIVE: 12.495 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 22, iters: 160208, time: 0.576, data: 0.000) G_L1: 17.291 G_L1_ABSOLUTE: 2.872 G_L1_RELATIVE: 14.419 G_Regularizer: 0.000 validation_error: 20.231 +(epoch: 22, iters: 162208, time: 0.571, data: 0.000) G_L1: 14.548 G_L1_ABSOLUTE: 2.500 G_L1_RELATIVE: 12.047 G_Regularizer: 0.000 validation_error: 21.089 +(epoch: 22, iters: 164208, time: 0.567, data: 0.001) G_L1: 14.304 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 11.845 G_Regularizer: 0.000 validation_error: 20.469 +(epoch: 22, iters: 166208, time: 0.585, data: 0.000) G_L1: 13.681 G_L1_ABSOLUTE: 2.648 G_L1_RELATIVE: 11.033 G_Regularizer: 0.000 validation_error: 20.275 +(epoch: 22, iters: 168208, time: 0.578, data: 0.000) G_L1: 17.491 G_L1_ABSOLUTE: 3.211 G_L1_RELATIVE: 14.280 G_Regularizer: 0.000 validation_error: 20.382 +(epoch: 22, iters: 170208, time: 0.568, data: 0.000) G_L1: 16.265 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 13.528 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 22, iters: 172208, time: 0.572, data: 0.000) G_L1: 15.112 G_L1_ABSOLUTE: 2.228 G_L1_RELATIVE: 12.884 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 22, iters: 174208, time: 0.595, data: 0.000) G_L1: 15.892 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 13.619 G_Regularizer: 0.000 validation_error: 20.315 +(epoch: 22, iters: 176208, time: 0.571, data: 0.000) G_L1: 15.324 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 12.807 G_Regularizer: 0.000 validation_error: 20.197 +(epoch: 22, iters: 178208, time: 0.575, data: 0.000) G_L1: 14.730 G_L1_ABSOLUTE: 2.616 G_L1_RELATIVE: 12.115 G_Regularizer: 0.000 validation_error: 21.161 +(epoch: 22, iters: 180208, time: 0.566, data: 0.000) G_L1: 14.645 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 12.135 G_Regularizer: 0.000 validation_error: 21.628 +(epoch: 22, iters: 182208, time: 0.585, data: 0.000) G_L1: 14.469 G_L1_ABSOLUTE: 2.841 G_L1_RELATIVE: 11.628 G_Regularizer: 0.000 validation_error: 20.338 +(epoch: 22, iters: 184208, time: 0.573, data: 0.000) G_L1: 15.051 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 12.455 G_Regularizer: 0.000 validation_error: 20.761 +(epoch: 22, iters: 186208, time: 0.577, data: 0.001) G_L1: 14.330 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 11.729 G_Regularizer: 0.000 validation_error: 21.162 +(epoch: 22, iters: 188208, time: 0.571, data: 0.000) G_L1: 16.372 G_L1_ABSOLUTE: 3.071 G_L1_RELATIVE: 13.301 G_Regularizer: 0.000 validation_error: 20.983 +(epoch: 22, iters: 190208, time: 0.585, data: 0.000) G_L1: 17.225 G_L1_ABSOLUTE: 2.806 G_L1_RELATIVE: 14.418 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 22, iters: 192208, time: 0.574, data: 0.000) G_L1: 12.951 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 10.081 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 22, iters: 194208, time: 0.571, data: 0.000) G_L1: 13.193 G_L1_ABSOLUTE: 2.964 G_L1_RELATIVE: 10.229 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 22, iters: 196208, time: 0.579, data: 0.000) G_L1: 13.304 G_L1_ABSOLUTE: 2.997 G_L1_RELATIVE: 10.307 G_Regularizer: 0.000 validation_error: 20.415 +(epoch: 22, iters: 198208, time: 0.576, data: 0.000) G_L1: 16.164 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 13.308 G_Regularizer: 0.000 validation_error: 20.463 +(epoch: 22, iters: 200208, time: 0.568, data: 0.000) G_L1: 13.908 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 11.545 G_Regularizer: 0.000 validation_error: 20.277 +(epoch: 22, iters: 202208, time: 0.579, data: 0.000) G_L1: 16.757 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 14.239 G_Regularizer: 0.000 validation_error: 20.318 +(epoch: 22, iters: 204208, time: 0.572, data: 0.000) G_L1: 14.372 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 11.960 G_Regularizer: 0.000 validation_error: 20.490 +(epoch: 22, iters: 206208, time: 0.566, data: 0.000) G_L1: 16.698 G_L1_ABSOLUTE: 2.770 G_L1_RELATIVE: 13.927 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 22, iters: 208208, time: 0.585, data: 0.000) G_L1: 14.706 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 11.816 G_Regularizer: 0.000 validation_error: 21.336 +(epoch: 22, iters: 210208, time: 0.574, data: 0.000) G_L1: 15.137 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 12.655 G_Regularizer: 0.000 validation_error: 21.339 +(epoch: 22, iters: 212208, time: 0.573, data: 0.000) G_L1: 15.206 G_L1_ABSOLUTE: 2.720 G_L1_RELATIVE: 12.485 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 22, iters: 214208, time: 0.578, data: 0.000) G_L1: 19.794 G_L1_ABSOLUTE: 2.301 G_L1_RELATIVE: 17.494 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 22, iters: 216208, time: 0.574, data: 0.000) G_L1: 14.741 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 12.098 G_Regularizer: 0.000 validation_error: 21.149 +(epoch: 22, iters: 218208, time: 0.578, data: 0.000) G_L1: 15.228 G_L1_ABSOLUTE: 2.157 G_L1_RELATIVE: 13.071 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 22, iters: 220208, time: 0.562, data: 0.000) G_L1: 14.777 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 12.189 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 22, iters: 222208, time: 0.566, data: 0.000) G_L1: 16.897 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 14.347 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 22, iters: 224208, time: 0.584, data: 0.000) G_L1: 13.679 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 11.128 G_Regularizer: 0.000 validation_error: 20.558 +(epoch: 22, iters: 226208, time: 0.575, data: 0.000) G_L1: 12.343 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 9.936 G_Regularizer: 0.000 validation_error: 20.332 +(epoch: 22, iters: 228208, time: 0.569, data: 0.000) G_L1: 15.391 G_L1_ABSOLUTE: 3.176 G_L1_RELATIVE: 12.214 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 22, iters: 230208, time: 0.578, data: 0.000) G_L1: 13.300 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 10.827 G_Regularizer: 0.000 validation_error: 20.924 +(epoch: 22, iters: 232208, time: 0.574, data: 0.000) G_L1: 13.914 G_L1_ABSOLUTE: 2.269 G_L1_RELATIVE: 11.645 G_Regularizer: 0.000 validation_error: 20.849 +(epoch: 22, iters: 234208, time: 0.565, data: 0.000) G_L1: 15.530 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 12.911 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 22, iters: 236208, time: 0.575, data: 0.000) G_L1: 16.802 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 14.277 G_Regularizer: 0.000 validation_error: 21.191 +(epoch: 22, iters: 238208, time: 0.564, data: 0.001) G_L1: 15.094 G_L1_ABSOLUTE: 3.021 G_L1_RELATIVE: 12.073 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 22, iters: 240208, time: 0.576, data: 0.000) G_L1: 17.860 G_L1_ABSOLUTE: 2.807 G_L1_RELATIVE: 15.053 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 22, iters: 242208, time: 0.576, data: 0.000) G_L1: 14.657 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 11.884 G_Regularizer: 0.000 validation_error: 20.423 +(epoch: 22, iters: 244208, time: 0.568, data: 0.000) G_L1: 14.489 G_L1_ABSOLUTE: 2.718 G_L1_RELATIVE: 11.771 G_Regularizer: 0.000 validation_error: 20.614 +(epoch: 22, iters: 246208, time: 0.572, data: 0.000) G_L1: 15.719 G_L1_ABSOLUTE: 3.348 G_L1_RELATIVE: 12.371 G_Regularizer: 0.000 validation_error: 20.354 +(epoch: 22, iters: 248208, time: 0.572, data: 0.000) G_L1: 13.449 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 11.147 G_Regularizer: 0.000 validation_error: 20.401 +(epoch: 22, iters: 250208, time: 0.572, data: 0.000) G_L1: 19.439 G_L1_ABSOLUTE: 2.923 G_L1_RELATIVE: 16.516 G_Regularizer: 0.000 validation_error: 20.596 +(epoch: 22, iters: 252208, time: 0.576, data: 0.000) G_L1: 20.473 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 17.929 G_Regularizer: 0.000 validation_error: 20.345 +(epoch: 22, iters: 254208, time: 0.578, data: 0.000) G_L1: 13.250 G_L1_ABSOLUTE: 2.361 G_L1_RELATIVE: 10.889 G_Regularizer: 0.000 validation_error: 19.922 +(epoch: 22, iters: 256208, time: 0.578, data: 0.000) G_L1: 14.300 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 11.919 G_Regularizer: 0.000 validation_error: 21.463 +(epoch: 22, iters: 258208, time: 0.594, data: 0.000) G_L1: 16.455 G_L1_ABSOLUTE: 2.439 G_L1_RELATIVE: 14.016 G_Regularizer: 0.000 validation_error: 21.107 +(epoch: 22, iters: 260208, time: 0.574, data: 0.000) G_L1: 17.511 G_L1_ABSOLUTE: 3.236 G_L1_RELATIVE: 14.275 G_Regularizer: 0.000 validation_error: 20.033 +(epoch: 22, iters: 262208, time: 0.580, data: 0.000) G_L1: 16.892 G_L1_ABSOLUTE: 3.088 G_L1_RELATIVE: 13.803 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 22, iters: 264208, time: 0.576, data: 0.000) G_L1: 16.247 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 13.515 G_Regularizer: 0.000 validation_error: 20.622 +(epoch: 22, iters: 266208, time: 0.575, data: 0.000) G_L1: 11.013 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 8.562 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 22, iters: 268208, time: 0.579, data: 0.000) G_L1: 12.126 G_L1_ABSOLUTE: 2.097 G_L1_RELATIVE: 10.029 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 22, iters: 270208, time: 0.559, data: 0.000) G_L1: 13.119 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 10.628 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 22, iters: 272208, time: 0.554, data: 0.000) G_L1: 16.110 G_L1_ABSOLUTE: 2.555 G_L1_RELATIVE: 13.555 G_Regularizer: 0.000 validation_error: 20.491 +(epoch: 22, iters: 274208, time: 0.578, data: 0.000) G_L1: 14.058 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 11.284 G_Regularizer: 0.000 validation_error: 20.390 +(epoch: 22, iters: 276208, time: 0.564, data: 0.000) G_L1: 13.771 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 11.480 G_Regularizer: 0.000 validation_error: 21.177 +(epoch: 22, iters: 278208, time: 0.551, data: 0.000) G_L1: 15.467 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 12.827 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 22, iters: 280208, time: 0.560, data: 0.000) G_L1: 12.856 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 10.591 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 22, iters: 282208, time: 0.566, data: 0.000) G_L1: 15.769 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 13.227 G_Regularizer: 0.000 validation_error: 20.404 +(epoch: 22, iters: 284208, time: 0.555, data: 0.000) G_L1: 14.615 G_L1_ABSOLUTE: 2.806 G_L1_RELATIVE: 11.809 G_Regularizer: 0.000 validation_error: 20.317 +(epoch: 22, iters: 286208, time: 0.559, data: 0.000) G_L1: 13.354 G_L1_ABSOLUTE: 2.439 G_L1_RELATIVE: 10.915 G_Regularizer: 0.000 validation_error: 20.728 +(epoch: 22, iters: 288208, time: 0.556, data: 0.000) G_L1: 14.620 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 12.047 G_Regularizer: 0.000 validation_error: 20.308 +(epoch: 22, iters: 290208, time: 0.560, data: 0.000) G_L1: 14.149 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 11.601 G_Regularizer: 0.000 validation_error: 20.183 +(epoch: 22, iters: 292208, time: 0.565, data: 0.001) G_L1: 14.548 G_L1_ABSOLUTE: 2.387 G_L1_RELATIVE: 12.161 G_Regularizer: 0.000 validation_error: 21.161 +(epoch: 22, iters: 294208, time: 0.565, data: 0.000) G_L1: 17.412 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 14.653 G_Regularizer: 0.000 validation_error: 21.128 +(epoch: 22, iters: 296208, time: 0.568, data: 0.000) G_L1: 16.536 G_L1_ABSOLUTE: 2.874 G_L1_RELATIVE: 13.662 G_Regularizer: 0.000 validation_error: 21.395 +(epoch: 22, iters: 298208, time: 0.559, data: 0.000) G_L1: 13.809 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 11.281 G_Regularizer: 0.000 validation_error: 21.273 +(epoch: 22, iters: 300208, time: 0.556, data: 0.000) G_L1: 17.023 G_L1_ABSOLUTE: 2.865 G_L1_RELATIVE: 14.158 G_Regularizer: 0.000 validation_error: 20.576 +(epoch: 22, iters: 302208, time: 0.554, data: 0.000) G_L1: 13.653 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 11.173 G_Regularizer: 0.000 validation_error: 20.612 +(epoch: 23, iters: 1456, time: 0.553, data: 0.000) G_L1: 14.903 G_L1_ABSOLUTE: 2.769 G_L1_RELATIVE: 12.133 G_Regularizer: 0.000 validation_error: 20.371 +(epoch: 23, iters: 3456, time: 0.556, data: 0.000) G_L1: 15.112 G_L1_ABSOLUTE: 3.050 G_L1_RELATIVE: 12.061 G_Regularizer: 0.000 validation_error: 21.096 +(epoch: 23, iters: 5456, time: 0.564, data: 0.000) G_L1: 15.330 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 12.802 G_Regularizer: 0.000 validation_error: 21.416 +(epoch: 23, iters: 7456, time: 0.562, data: 0.000) G_L1: 12.981 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 10.399 G_Regularizer: 0.000 validation_error: 20.816 +(epoch: 23, iters: 9456, time: 0.558, data: 0.000) G_L1: 15.927 G_L1_ABSOLUTE: 2.717 G_L1_RELATIVE: 13.210 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 23, iters: 11456, time: 0.549, data: 0.000) G_L1: 14.318 G_L1_ABSOLUTE: 2.150 G_L1_RELATIVE: 12.168 G_Regularizer: 0.000 validation_error: 20.687 +(epoch: 23, iters: 13456, time: 0.556, data: 0.000) G_L1: 11.802 G_L1_ABSOLUTE: 2.034 G_L1_RELATIVE: 9.767 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 23, iters: 15456, time: 0.564, data: 0.000) G_L1: 16.303 G_L1_ABSOLUTE: 2.599 G_L1_RELATIVE: 13.704 G_Regularizer: 0.000 validation_error: 20.654 +(epoch: 23, iters: 17456, time: 0.561, data: 0.000) G_L1: 14.655 G_L1_ABSOLUTE: 2.718 G_L1_RELATIVE: 11.937 G_Regularizer: 0.000 validation_error: 21.117 +(epoch: 23, iters: 19456, time: 0.563, data: 0.000) G_L1: 14.235 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 11.478 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 23, iters: 21456, time: 0.562, data: 0.000) G_L1: 13.999 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 11.514 G_Regularizer: 0.000 validation_error: 20.555 +(epoch: 23, iters: 23456, time: 0.569, data: 0.000) G_L1: 15.028 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 12.534 G_Regularizer: 0.000 validation_error: 20.498 +(epoch: 23, iters: 25456, time: 0.557, data: 0.000) G_L1: 15.079 G_L1_ABSOLUTE: 2.439 G_L1_RELATIVE: 12.639 G_Regularizer: 0.000 validation_error: 20.540 +(epoch: 23, iters: 27456, time: 0.554, data: 0.000) G_L1: 17.180 G_L1_ABSOLUTE: 2.900 G_L1_RELATIVE: 14.281 G_Regularizer: 0.000 validation_error: 21.392 +(epoch: 23, iters: 29456, time: 0.568, data: 0.000) G_L1: 16.802 G_L1_ABSOLUTE: 2.933 G_L1_RELATIVE: 13.869 G_Regularizer: 0.000 validation_error: 20.398 +(epoch: 23, iters: 31456, time: 0.558, data: 0.000) G_L1: 16.203 G_L1_ABSOLUTE: 2.560 G_L1_RELATIVE: 13.643 G_Regularizer: 0.000 validation_error: 20.441 +(epoch: 23, iters: 33456, time: 0.553, data: 0.000) G_L1: 13.631 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 11.033 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 23, iters: 35456, time: 0.559, data: 0.000) G_L1: 11.032 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 8.699 G_Regularizer: 0.000 validation_error: 21.133 +(epoch: 23, iters: 37456, time: 0.555, data: 0.000) G_L1: 12.836 G_L1_ABSOLUTE: 2.362 G_L1_RELATIVE: 10.473 G_Regularizer: 0.000 validation_error: 20.687 +(epoch: 23, iters: 39456, time: 0.566, data: 0.000) G_L1: 13.384 G_L1_ABSOLUTE: 2.917 G_L1_RELATIVE: 10.467 G_Regularizer: 0.000 validation_error: 20.393 +(epoch: 23, iters: 41456, time: 0.547, data: 0.000) G_L1: 14.499 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 12.023 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 23, iters: 43456, time: 0.555, data: 0.000) G_L1: 14.552 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 11.681 G_Regularizer: 0.000 validation_error: 20.330 +(epoch: 23, iters: 45456, time: 0.552, data: 0.000) G_L1: 15.767 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 13.259 G_Regularizer: 0.000 validation_error: 20.593 +(epoch: 23, iters: 47456, time: 0.562, data: 0.000) G_L1: 20.930 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 18.565 G_Regularizer: 0.000 validation_error: 20.489 +(epoch: 23, iters: 49456, time: 0.555, data: 0.000) G_L1: 19.710 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 17.014 G_Regularizer: 0.000 validation_error: 19.938 +(epoch: 23, iters: 51456, time: 0.555, data: 0.000) G_L1: 13.471 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 10.993 G_Regularizer: 0.000 validation_error: 20.495 +(epoch: 23, iters: 53456, time: 0.560, data: 0.000) G_L1: 15.839 G_L1_ABSOLUTE: 3.018 G_L1_RELATIVE: 12.820 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 23, iters: 55456, time: 0.566, data: 0.000) G_L1: 13.349 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 11.018 G_Regularizer: 0.000 validation_error: 21.056 +(epoch: 23, iters: 57456, time: 0.571, data: 0.000) G_L1: 14.110 G_L1_ABSOLUTE: 2.279 G_L1_RELATIVE: 11.831 G_Regularizer: 0.000 validation_error: 20.386 +(epoch: 23, iters: 59456, time: 0.554, data: 0.000) G_L1: 15.851 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 13.233 G_Regularizer: 0.000 validation_error: 20.577 +(epoch: 23, iters: 61456, time: 0.556, data: 0.001) G_L1: 13.561 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 10.788 G_Regularizer: 0.000 validation_error: 20.307 +(epoch: 23, iters: 63456, time: 0.561, data: 0.000) G_L1: 17.033 G_L1_ABSOLUTE: 2.769 G_L1_RELATIVE: 14.264 G_Regularizer: 0.000 validation_error: 20.742 +(epoch: 23, iters: 65456, time: 0.559, data: 0.000) G_L1: 16.582 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 13.878 G_Regularizer: 0.000 validation_error: 20.233 +(epoch: 23, iters: 67456, time: 0.558, data: 0.000) G_L1: 15.147 G_L1_ABSOLUTE: 2.896 G_L1_RELATIVE: 12.250 G_Regularizer: 0.000 validation_error: 20.009 +(epoch: 23, iters: 69456, time: 0.557, data: 0.000) G_L1: 14.285 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 11.471 G_Regularizer: 0.000 validation_error: 20.364 +(epoch: 23, iters: 71456, time: 0.564, data: 0.000) G_L1: 13.552 G_L1_ABSOLUTE: 2.203 G_L1_RELATIVE: 11.349 G_Regularizer: 0.000 validation_error: 21.166 +(epoch: 23, iters: 73456, time: 0.578, data: 0.000) G_L1: 15.057 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 12.716 G_Regularizer: 0.000 validation_error: 20.451 +(epoch: 23, iters: 75456, time: 0.558, data: 0.000) G_L1: 13.736 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 11.078 G_Regularizer: 0.000 validation_error: 20.577 +(epoch: 23, iters: 77456, time: 0.557, data: 0.000) G_L1: 14.478 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 11.901 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 23, iters: 79456, time: 0.559, data: 0.000) G_L1: 15.038 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 12.303 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 23, iters: 81456, time: 0.552, data: 0.000) G_L1: 13.107 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 10.551 G_Regularizer: 0.000 validation_error: 20.512 +(epoch: 23, iters: 83456, time: 0.547, data: 0.000) G_L1: 16.529 G_L1_ABSOLUTE: 2.367 G_L1_RELATIVE: 14.161 G_Regularizer: 0.000 validation_error: 20.509 +(epoch: 23, iters: 85456, time: 0.561, data: 0.000) G_L1: 15.137 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 12.701 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 23, iters: 87456, time: 0.559, data: 0.000) G_L1: 12.414 G_L1_ABSOLUTE: 2.475 G_L1_RELATIVE: 9.939 G_Regularizer: 0.000 validation_error: 20.514 +(epoch: 23, iters: 89456, time: 0.558, data: 0.000) G_L1: 15.780 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 13.184 G_Regularizer: 0.000 validation_error: 20.927 +(epoch: 23, iters: 91456, time: 0.560, data: 0.000) G_L1: 14.742 G_L1_ABSOLUTE: 2.749 G_L1_RELATIVE: 11.993 G_Regularizer: 0.000 validation_error: 20.667 +(epoch: 23, iters: 93456, time: 0.557, data: 0.000) G_L1: 13.654 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 10.791 G_Regularizer: 0.000 validation_error: 20.079 +(epoch: 23, iters: 95456, time: 0.551, data: 0.000) G_L1: 14.073 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 11.740 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 23, iters: 97456, time: 0.558, data: 0.000) G_L1: 15.919 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 13.402 G_Regularizer: 0.000 validation_error: 20.174 +(epoch: 23, iters: 99456, time: 0.557, data: 0.000) G_L1: 12.953 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 10.503 G_Regularizer: 0.000 validation_error: 20.749 +(epoch: 23, iters: 101456, time: 0.553, data: 0.000) G_L1: 15.671 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 12.973 G_Regularizer: 0.000 validation_error: 20.712 +(epoch: 23, iters: 103456, time: 0.557, data: 0.000) G_L1: 14.997 G_L1_ABSOLUTE: 2.321 G_L1_RELATIVE: 12.676 G_Regularizer: 0.000 validation_error: 20.744 +(epoch: 23, iters: 105456, time: 0.561, data: 0.000) G_L1: 13.304 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 11.106 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 23, iters: 107456, time: 0.582, data: 0.000) G_L1: 13.757 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 11.253 G_Regularizer: 0.000 validation_error: 21.773 +(epoch: 23, iters: 109456, time: 0.552, data: 0.000) G_L1: 14.261 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 11.632 G_Regularizer: 0.000 validation_error: 20.621 +(epoch: 23, iters: 111456, time: 0.563, data: 0.000) G_L1: 15.512 G_L1_ABSOLUTE: 2.704 G_L1_RELATIVE: 12.808 G_Regularizer: 0.000 validation_error: 20.513 +(epoch: 23, iters: 113456, time: 0.561, data: 0.000) G_L1: 17.521 G_L1_ABSOLUTE: 2.831 G_L1_RELATIVE: 14.690 G_Regularizer: 0.000 validation_error: 20.707 +(epoch: 23, iters: 115456, time: 0.550, data: 0.000) G_L1: 13.892 G_L1_ABSOLUTE: 2.260 G_L1_RELATIVE: 11.632 G_Regularizer: 0.000 validation_error: 20.582 +(epoch: 23, iters: 117456, time: 0.556, data: 0.000) G_L1: 13.963 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 11.151 G_Regularizer: 0.000 validation_error: 20.216 +(epoch: 23, iters: 119456, time: 0.547, data: 0.000) G_L1: 13.331 G_L1_ABSOLUTE: 2.329 G_L1_RELATIVE: 11.002 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 23, iters: 121456, time: 0.557, data: 0.000) G_L1: 14.324 G_L1_ABSOLUTE: 3.214 G_L1_RELATIVE: 11.110 G_Regularizer: 0.000 validation_error: 20.091 +(epoch: 23, iters: 123456, time: 0.565, data: 0.000) G_L1: 15.333 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 12.743 G_Regularizer: 0.000 validation_error: 20.622 +(epoch: 23, iters: 125456, time: 0.578, data: 0.000) G_L1: 14.077 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 11.630 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 23, iters: 127456, time: 0.557, data: 0.000) G_L1: 14.390 G_L1_ABSOLUTE: 2.422 G_L1_RELATIVE: 11.969 G_Regularizer: 0.000 validation_error: 20.859 +(epoch: 23, iters: 129456, time: 0.559, data: 0.000) G_L1: 17.098 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 14.241 G_Regularizer: 0.000 validation_error: 20.732 +(epoch: 23, iters: 131456, time: 0.562, data: 0.000) G_L1: 18.305 G_L1_ABSOLUTE: 3.021 G_L1_RELATIVE: 15.283 G_Regularizer: 0.000 validation_error: 20.391 +(epoch: 23, iters: 133456, time: 0.556, data: 0.000) G_L1: 14.170 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 11.593 G_Regularizer: 0.000 validation_error: 20.354 +(epoch: 23, iters: 135456, time: 0.555, data: 0.000) G_L1: 13.717 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 11.250 G_Regularizer: 0.000 validation_error: 20.454 +(epoch: 23, iters: 137456, time: 0.553, data: 0.000) G_L1: 14.577 G_L1_ABSOLUTE: 2.543 G_L1_RELATIVE: 12.034 G_Regularizer: 0.000 validation_error: 20.170 +(epoch: 23, iters: 139456, time: 0.564, data: 0.000) G_L1: 13.808 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 11.262 G_Regularizer: 0.000 validation_error: 21.105 +(epoch: 23, iters: 141456, time: 0.574, data: 0.000) G_L1: 15.080 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 12.651 G_Regularizer: 0.000 validation_error: 20.779 +(epoch: 23, iters: 143456, time: 0.557, data: 0.000) G_L1: 14.016 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 11.131 G_Regularizer: 0.000 validation_error: 21.800 +(epoch: 23, iters: 145456, time: 0.555, data: 0.000) G_L1: 17.481 G_L1_ABSOLUTE: 3.489 G_L1_RELATIVE: 13.992 G_Regularizer: 0.000 validation_error: 20.616 +(epoch: 23, iters: 147456, time: 0.561, data: 0.000) G_L1: 13.330 G_L1_ABSOLUTE: 2.602 G_L1_RELATIVE: 10.727 G_Regularizer: 0.000 validation_error: 20.591 +(epoch: 23, iters: 149456, time: 0.563, data: 0.000) G_L1: 23.808 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 21.158 G_Regularizer: 0.000 validation_error: 19.820 +(epoch: 23, iters: 151456, time: 0.561, data: 0.000) G_L1: 14.457 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 11.774 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 23, iters: 153456, time: 0.559, data: 0.000) G_L1: 13.211 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 10.516 G_Regularizer: 0.000 validation_error: 21.164 +(epoch: 23, iters: 155456, time: 0.559, data: 0.000) G_L1: 16.166 G_L1_ABSOLUTE: 2.289 G_L1_RELATIVE: 13.876 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 23, iters: 157456, time: 0.552, data: 0.000) G_L1: 12.921 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 10.406 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 23, iters: 159456, time: 0.578, data: 0.000) G_L1: 16.799 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 14.279 G_Regularizer: 0.000 validation_error: 21.566 +(epoch: 23, iters: 161456, time: 0.552, data: 0.000) G_L1: 13.658 G_L1_ABSOLUTE: 2.384 G_L1_RELATIVE: 11.274 G_Regularizer: 0.000 validation_error: 21.284 +(epoch: 23, iters: 163456, time: 0.558, data: 0.000) G_L1: 16.607 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 14.036 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 23, iters: 165456, time: 0.563, data: 0.000) G_L1: 15.910 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 13.567 G_Regularizer: 0.000 validation_error: 22.094 +(epoch: 23, iters: 167456, time: 0.555, data: 0.000) G_L1: 14.702 G_L1_ABSOLUTE: 2.472 G_L1_RELATIVE: 12.230 G_Regularizer: 0.000 validation_error: 20.756 +(epoch: 23, iters: 169456, time: 0.561, data: 0.000) G_L1: 13.708 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 11.227 G_Regularizer: 0.000 validation_error: 21.033 +(epoch: 23, iters: 171456, time: 0.552, data: 0.001) G_L1: 14.870 G_L1_ABSOLUTE: 2.187 G_L1_RELATIVE: 12.682 G_Regularizer: 0.000 validation_error: 20.329 +(epoch: 23, iters: 173456, time: 0.566, data: 0.000) G_L1: 15.420 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 12.760 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 23, iters: 175456, time: 0.574, data: 0.000) G_L1: 16.386 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 13.664 G_Regularizer: 0.000 validation_error: 20.932 +(epoch: 23, iters: 177456, time: 0.554, data: 0.000) G_L1: 15.572 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 12.835 G_Regularizer: 0.000 validation_error: 20.771 +(epoch: 23, iters: 179456, time: 0.558, data: 0.000) G_L1: 16.214 G_L1_ABSOLUTE: 2.304 G_L1_RELATIVE: 13.910 G_Regularizer: 0.000 validation_error: 21.309 +(epoch: 23, iters: 181456, time: 0.557, data: 0.000) G_L1: 12.737 G_L1_ABSOLUTE: 2.292 G_L1_RELATIVE: 10.446 G_Regularizer: 0.000 validation_error: 21.051 +(epoch: 23, iters: 183456, time: 0.559, data: 0.000) G_L1: 17.185 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 14.393 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 23, iters: 185456, time: 0.561, data: 0.000) G_L1: 14.596 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 12.149 G_Regularizer: 0.000 validation_error: 21.241 +(epoch: 23, iters: 187456, time: 0.561, data: 0.000) G_L1: 12.859 G_L1_ABSOLUTE: 2.998 G_L1_RELATIVE: 9.861 G_Regularizer: 0.000 validation_error: 20.328 +(epoch: 23, iters: 189456, time: 0.563, data: 0.000) G_L1: 17.788 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 15.258 G_Regularizer: 0.000 validation_error: 20.757 +(epoch: 23, iters: 191456, time: 0.558, data: 0.000) G_L1: 12.839 G_L1_ABSOLUTE: 2.311 G_L1_RELATIVE: 10.529 G_Regularizer: 0.000 validation_error: 20.343 +(epoch: 23, iters: 193456, time: 0.574, data: 0.000) G_L1: 17.296 G_L1_ABSOLUTE: 3.072 G_L1_RELATIVE: 14.224 G_Regularizer: 0.000 validation_error: 21.222 +(epoch: 23, iters: 195456, time: 0.561, data: 0.000) G_L1: 13.738 G_L1_ABSOLUTE: 2.210 G_L1_RELATIVE: 11.528 G_Regularizer: 0.000 validation_error: 21.024 +(epoch: 23, iters: 197456, time: 0.561, data: 0.000) G_L1: 13.004 G_L1_ABSOLUTE: 2.232 G_L1_RELATIVE: 10.772 G_Regularizer: 0.000 validation_error: 21.131 +(epoch: 23, iters: 199456, time: 0.559, data: 0.000) G_L1: 13.265 G_L1_ABSOLUTE: 2.392 G_L1_RELATIVE: 10.872 G_Regularizer: 0.000 validation_error: 20.959 +(epoch: 23, iters: 201456, time: 0.561, data: 0.000) G_L1: 13.755 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 11.235 G_Regularizer: 0.000 validation_error: 20.346 +(epoch: 23, iters: 203456, time: 0.577, data: 0.000) G_L1: 15.805 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 13.458 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 23, iters: 205456, time: 0.551, data: 0.000) G_L1: 14.326 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 11.848 G_Regularizer: 0.000 validation_error: 19.950 +(epoch: 23, iters: 207456, time: 0.557, data: 0.000) G_L1: 20.621 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 17.891 G_Regularizer: 0.000 validation_error: 20.447 +(epoch: 23, iters: 209456, time: 0.570, data: 0.000) G_L1: 18.799 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 16.120 G_Regularizer: 0.000 validation_error: 20.761 +(epoch: 23, iters: 211456, time: 0.557, data: 0.000) G_L1: 16.145 G_L1_ABSOLUTE: 3.031 G_L1_RELATIVE: 13.114 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 23, iters: 213456, time: 0.560, data: 0.000) G_L1: 14.570 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 11.748 G_Regularizer: 0.000 validation_error: 20.910 +(epoch: 23, iters: 215456, time: 0.565, data: 0.000) G_L1: 15.458 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 13.050 G_Regularizer: 0.000 validation_error: 20.251 +(epoch: 23, iters: 217456, time: 0.562, data: 0.000) G_L1: 14.459 G_L1_ABSOLUTE: 2.562 G_L1_RELATIVE: 11.897 G_Regularizer: 0.000 validation_error: 20.465 +(epoch: 23, iters: 219456, time: 0.559, data: 0.000) G_L1: 14.497 G_L1_ABSOLUTE: 2.884 G_L1_RELATIVE: 11.614 G_Regularizer: 0.000 validation_error: 21.918 +(epoch: 23, iters: 221456, time: 0.561, data: 0.000) G_L1: 13.540 G_L1_ABSOLUTE: 2.752 G_L1_RELATIVE: 10.788 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 23, iters: 223456, time: 0.577, data: 0.000) G_L1: 11.678 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 9.215 G_Regularizer: 0.000 validation_error: 21.082 +(epoch: 23, iters: 225456, time: 0.556, data: 0.000) G_L1: 16.451 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 13.925 G_Regularizer: 0.000 validation_error: 20.154 +(epoch: 23, iters: 227456, time: 0.574, data: 0.000) G_L1: 15.848 G_L1_ABSOLUTE: 2.786 G_L1_RELATIVE: 13.063 G_Regularizer: 0.000 validation_error: 20.640 +(epoch: 23, iters: 229456, time: 0.559, data: 0.000) G_L1: 16.631 G_L1_ABSOLUTE: 3.117 G_L1_RELATIVE: 13.514 G_Regularizer: 0.000 validation_error: 20.021 +(epoch: 23, iters: 231456, time: 0.566, data: 0.000) G_L1: 15.471 G_L1_ABSOLUTE: 2.906 G_L1_RELATIVE: 12.565 G_Regularizer: 0.000 validation_error: 20.376 +(epoch: 23, iters: 233456, time: 0.563, data: 0.000) G_L1: 13.764 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 11.394 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 23, iters: 235456, time: 0.556, data: 0.000) G_L1: 16.741 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 13.986 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 23, iters: 237456, time: 0.557, data: 0.000) G_L1: 13.736 G_L1_ABSOLUTE: 2.152 G_L1_RELATIVE: 11.584 G_Regularizer: 0.000 validation_error: 20.593 +(epoch: 23, iters: 239456, time: 0.564, data: 0.000) G_L1: 16.643 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 14.182 G_Regularizer: 0.000 validation_error: 20.247 +(epoch: 23, iters: 241456, time: 0.556, data: 0.000) G_L1: 14.922 G_L1_ABSOLUTE: 2.171 G_L1_RELATIVE: 12.751 G_Regularizer: 0.000 validation_error: 20.554 +(epoch: 23, iters: 243456, time: 0.567, data: 0.000) G_L1: 14.584 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 11.823 G_Regularizer: 0.000 validation_error: 20.128 +(epoch: 23, iters: 245456, time: 0.556, data: 0.000) G_L1: 12.958 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 10.478 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 23, iters: 247456, time: 0.562, data: 0.000) G_L1: 16.286 G_L1_ABSOLUTE: 2.927 G_L1_RELATIVE: 13.359 G_Regularizer: 0.000 validation_error: 20.105 +(epoch: 23, iters: 249456, time: 0.548, data: 0.000) G_L1: 16.571 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 13.861 G_Regularizer: 0.000 validation_error: 20.146 +(epoch: 23, iters: 251456, time: 0.554, data: 0.000) G_L1: 15.254 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 12.602 G_Regularizer: 0.000 validation_error: 20.185 +(epoch: 23, iters: 253456, time: 0.563, data: 0.000) G_L1: 14.676 G_L1_ABSOLUTE: 2.965 G_L1_RELATIVE: 11.711 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 23, iters: 255456, time: 0.565, data: 0.000) G_L1: 16.715 G_L1_ABSOLUTE: 2.566 G_L1_RELATIVE: 14.149 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 23, iters: 257456, time: 0.564, data: 0.000) G_L1: 13.777 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 11.515 G_Regularizer: 0.000 validation_error: 20.578 +(epoch: 23, iters: 259456, time: 0.551, data: 0.000) G_L1: 15.047 G_L1_ABSOLUTE: 2.455 G_L1_RELATIVE: 12.592 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 23, iters: 261456, time: 0.577, data: 0.000) G_L1: 15.325 G_L1_ABSOLUTE: 2.617 G_L1_RELATIVE: 12.709 G_Regularizer: 0.000 validation_error: 20.329 +(epoch: 23, iters: 263456, time: 0.559, data: 0.000) G_L1: 12.947 G_L1_ABSOLUTE: 2.281 G_L1_RELATIVE: 10.666 G_Regularizer: 0.000 validation_error: 20.885 +(epoch: 23, iters: 265456, time: 0.563, data: 0.000) G_L1: 15.869 G_L1_ABSOLUTE: 2.801 G_L1_RELATIVE: 13.068 G_Regularizer: 0.000 validation_error: 20.419 +(epoch: 23, iters: 267456, time: 0.554, data: 0.000) G_L1: 20.493 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 17.929 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 23, iters: 269456, time: 0.562, data: 0.000) G_L1: 12.860 G_L1_ABSOLUTE: 2.218 G_L1_RELATIVE: 10.642 G_Regularizer: 0.000 validation_error: 20.666 +(epoch: 23, iters: 271456, time: 0.557, data: 0.000) G_L1: 16.405 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 13.729 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 23, iters: 273456, time: 0.564, data: 0.000) G_L1: 14.032 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 11.398 G_Regularizer: 0.000 validation_error: 21.418 +(epoch: 23, iters: 275456, time: 0.553, data: 0.000) G_L1: 13.872 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 11.240 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 23, iters: 277456, time: 0.572, data: 0.000) G_L1: 16.094 G_L1_ABSOLUTE: 2.359 G_L1_RELATIVE: 13.735 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 23, iters: 279456, time: 0.555, data: 0.000) G_L1: 16.458 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 14.059 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 23, iters: 281456, time: 0.556, data: 0.000) G_L1: 15.113 G_L1_ABSOLUTE: 2.594 G_L1_RELATIVE: 12.519 G_Regularizer: 0.000 validation_error: 20.267 +(epoch: 23, iters: 283456, time: 0.562, data: 0.000) G_L1: 16.488 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 13.908 G_Regularizer: 0.000 validation_error: 20.325 +(epoch: 23, iters: 285456, time: 0.559, data: 0.000) G_L1: 13.824 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 11.314 G_Regularizer: 0.000 validation_error: 20.373 +(epoch: 23, iters: 287456, time: 0.560, data: 0.000) G_L1: 13.868 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 11.246 G_Regularizer: 0.000 validation_error: 20.758 +(epoch: 23, iters: 289456, time: 0.558, data: 0.000) G_L1: 14.106 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 11.594 G_Regularizer: 0.000 validation_error: 21.119 +(epoch: 23, iters: 291456, time: 0.558, data: 0.000) G_L1: 15.317 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 13.058 G_Regularizer: 0.000 validation_error: 20.786 +(epoch: 23, iters: 293456, time: 0.557, data: 0.000) G_L1: 14.033 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 11.657 G_Regularizer: 0.000 validation_error: 21.267 +(epoch: 23, iters: 295456, time: 0.581, data: 0.000) G_L1: 13.531 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 11.013 G_Regularizer: 0.000 validation_error: 20.395 +(epoch: 23, iters: 297456, time: 0.558, data: 0.000) G_L1: 14.668 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 12.047 G_Regularizer: 0.000 validation_error: 20.163 +(epoch: 23, iters: 299456, time: 0.562, data: 0.000) G_L1: 18.088 G_L1_ABSOLUTE: 2.745 G_L1_RELATIVE: 15.343 G_Regularizer: 0.000 validation_error: 20.351 +(epoch: 23, iters: 301456, time: 0.561, data: 0.000) G_L1: 14.324 G_L1_ABSOLUTE: 2.444 G_L1_RELATIVE: 11.879 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 24, iters: 704, time: 0.559, data: 0.000) G_L1: 16.026 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 13.300 G_Regularizer: 0.000 validation_error: 21.425 +(epoch: 24, iters: 2704, time: 0.566, data: 0.000) G_L1: 15.383 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 12.848 G_Regularizer: 0.000 validation_error: 20.120 +(epoch: 24, iters: 4704, time: 0.564, data: 0.000) G_L1: 14.147 G_L1_ABSOLUTE: 2.973 G_L1_RELATIVE: 11.173 G_Regularizer: 0.000 validation_error: 21.167 +(epoch: 24, iters: 6704, time: 0.560, data: 0.000) G_L1: 15.940 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 13.350 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 24, iters: 8704, time: 0.574, data: 0.001) G_L1: 12.715 G_L1_ABSOLUTE: 2.611 G_L1_RELATIVE: 10.104 G_Regularizer: 0.000 validation_error: 21.032 +(epoch: 24, iters: 10704, time: 0.561, data: 0.000) G_L1: 15.848 G_L1_ABSOLUTE: 2.455 G_L1_RELATIVE: 13.393 G_Regularizer: 0.000 validation_error: 20.456 +(epoch: 24, iters: 12704, time: 0.558, data: 0.000) G_L1: 15.634 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 13.111 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 24, iters: 14704, time: 0.557, data: 0.000) G_L1: 16.633 G_L1_ABSOLUTE: 2.690 G_L1_RELATIVE: 13.942 G_Regularizer: 0.000 validation_error: 21.277 +(epoch: 24, iters: 16704, time: 0.559, data: 0.000) G_L1: 14.336 G_L1_ABSOLUTE: 2.237 G_L1_RELATIVE: 12.099 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 24, iters: 18704, time: 0.555, data: 0.000) G_L1: 19.221 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 16.386 G_Regularizer: 0.000 validation_error: 20.361 +(epoch: 24, iters: 20704, time: 0.555, data: 0.000) G_L1: 14.520 G_L1_ABSOLUTE: 2.267 G_L1_RELATIVE: 12.254 G_Regularizer: 0.000 validation_error: 21.255 +(epoch: 24, iters: 22704, time: 0.565, data: 0.000) G_L1: 12.642 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 9.760 G_Regularizer: 0.000 validation_error: 20.331 +(epoch: 24, iters: 24704, time: 0.555, data: 0.000) G_L1: 13.993 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 11.543 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 24, iters: 26704, time: 0.584, data: 0.000) G_L1: 15.209 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 12.614 G_Regularizer: 0.000 validation_error: 21.081 +(epoch: 24, iters: 28704, time: 0.556, data: 0.000) G_L1: 12.063 G_L1_ABSOLUTE: 3.180 G_L1_RELATIVE: 8.883 G_Regularizer: 0.000 validation_error: 20.429 +(epoch: 24, iters: 30704, time: 0.555, data: 0.000) G_L1: 14.699 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 12.113 G_Regularizer: 0.000 validation_error: 20.719 +(epoch: 24, iters: 32704, time: 0.554, data: 0.000) G_L1: 15.142 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 12.757 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 24, iters: 34704, time: 0.556, data: 0.000) G_L1: 13.133 G_L1_ABSOLUTE: 2.471 G_L1_RELATIVE: 10.662 G_Regularizer: 0.000 validation_error: 21.045 +(epoch: 24, iters: 36704, time: 0.551, data: 0.000) G_L1: 13.107 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 10.734 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 24, iters: 38704, time: 0.562, data: 0.000) G_L1: 15.375 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 12.878 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 24, iters: 40704, time: 0.559, data: 0.000) G_L1: 14.742 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 12.142 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 24, iters: 42704, time: 0.577, data: 0.000) G_L1: 13.693 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 11.081 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 24, iters: 44704, time: 0.555, data: 0.000) G_L1: 14.181 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 11.627 G_Regularizer: 0.000 validation_error: 20.984 +(epoch: 24, iters: 46704, time: 0.567, data: 0.000) G_L1: 15.434 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 12.895 G_Regularizer: 0.000 validation_error: 20.114 +(epoch: 24, iters: 48704, time: 0.559, data: 0.000) G_L1: 13.183 G_L1_ABSOLUTE: 2.606 G_L1_RELATIVE: 10.577 G_Regularizer: 0.000 validation_error: 20.422 +(epoch: 24, iters: 50704, time: 0.561, data: 0.002) G_L1: 14.344 G_L1_ABSOLUTE: 2.557 G_L1_RELATIVE: 11.788 G_Regularizer: 0.000 validation_error: 20.565 +(epoch: 24, iters: 52704, time: 0.561, data: 0.000) G_L1: 13.094 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 10.820 G_Regularizer: 0.000 validation_error: 20.629 +(epoch: 24, iters: 54704, time: 0.561, data: 0.000) G_L1: 12.628 G_L1_ABSOLUTE: 1.923 G_L1_RELATIVE: 10.705 G_Regularizer: 0.000 validation_error: 20.560 +(epoch: 24, iters: 56704, time: 0.554, data: 0.000) G_L1: 15.932 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 13.355 G_Regularizer: 0.000 validation_error: 20.712 +(epoch: 24, iters: 58704, time: 0.558, data: 0.000) G_L1: 15.586 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 13.050 G_Regularizer: 0.000 validation_error: 20.571 +(epoch: 24, iters: 60704, time: 0.581, data: 0.000) G_L1: 14.326 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 11.784 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 24, iters: 62704, time: 0.564, data: 0.000) G_L1: 12.218 G_L1_ABSOLUTE: 2.064 G_L1_RELATIVE: 10.154 G_Regularizer: 0.000 validation_error: 20.559 +(epoch: 24, iters: 64704, time: 0.557, data: 0.000) G_L1: 13.757 G_L1_ABSOLUTE: 2.603 G_L1_RELATIVE: 11.154 G_Regularizer: 0.000 validation_error: 20.413 +(epoch: 24, iters: 66704, time: 0.562, data: 0.000) G_L1: 15.032 G_L1_ABSOLUTE: 2.466 G_L1_RELATIVE: 12.566 G_Regularizer: 0.000 validation_error: 20.705 +(epoch: 24, iters: 68704, time: 0.567, data: 0.000) G_L1: 13.397 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 10.878 G_Regularizer: 0.000 validation_error: 20.308 +(epoch: 24, iters: 70704, time: 0.556, data: 0.000) G_L1: 15.283 G_L1_ABSOLUTE: 3.241 G_L1_RELATIVE: 12.042 G_Regularizer: 0.000 validation_error: 20.116 +(epoch: 24, iters: 72704, time: 0.554, data: 0.000) G_L1: 12.996 G_L1_ABSOLUTE: 2.060 G_L1_RELATIVE: 10.935 G_Regularizer: 0.000 validation_error: 20.231 +(epoch: 24, iters: 74704, time: 0.558, data: 0.000) G_L1: 15.953 G_L1_ABSOLUTE: 3.092 G_L1_RELATIVE: 12.861 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 24, iters: 76704, time: 0.563, data: 0.000) G_L1: 17.268 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 14.755 G_Regularizer: 0.000 validation_error: 20.748 +(epoch: 24, iters: 78704, time: 0.559, data: 0.000) G_L1: 19.120 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 16.456 G_Regularizer: 0.000 validation_error: 20.496 +(epoch: 24, iters: 80704, time: 0.559, data: 0.000) G_L1: 14.164 G_L1_ABSOLUTE: 2.212 G_L1_RELATIVE: 11.953 G_Regularizer: 0.000 validation_error: 20.508 +(epoch: 24, iters: 82704, time: 0.553, data: 0.000) G_L1: 16.816 G_L1_ABSOLUTE: 2.918 G_L1_RELATIVE: 13.898 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 24, iters: 84704, time: 0.572, data: 0.000) G_L1: 15.151 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 12.246 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 24, iters: 86704, time: 0.557, data: 0.000) G_L1: 13.900 G_L1_ABSOLUTE: 2.553 G_L1_RELATIVE: 11.347 G_Regularizer: 0.000 validation_error: 21.016 +(epoch: 24, iters: 88704, time: 0.566, data: 0.000) G_L1: 13.983 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 11.480 G_Regularizer: 0.000 validation_error: 20.134 +(epoch: 24, iters: 90704, time: 0.549, data: 0.000) G_L1: 14.020 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 11.658 G_Regularizer: 0.000 validation_error: 20.553 +(epoch: 24, iters: 92704, time: 0.560, data: 0.000) G_L1: 13.211 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 10.793 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 24, iters: 94704, time: 0.574, data: 0.000) G_L1: 17.891 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 15.149 G_Regularizer: 0.000 validation_error: 20.398 +(epoch: 24, iters: 96704, time: 0.572, data: 0.000) G_L1: 13.850 G_L1_ABSOLUTE: 2.585 G_L1_RELATIVE: 11.265 G_Regularizer: 0.000 validation_error: 20.770 +(epoch: 24, iters: 98704, time: 0.558, data: 0.000) G_L1: 16.148 G_L1_ABSOLUTE: 3.617 G_L1_RELATIVE: 12.531 G_Regularizer: 0.000 validation_error: 20.564 +(epoch: 24, iters: 100704, time: 0.559, data: 0.000) G_L1: 17.273 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 14.810 G_Regularizer: 0.000 validation_error: 19.873 +(epoch: 24, iters: 102704, time: 0.570, data: 0.000) G_L1: 12.051 G_L1_ABSOLUTE: 2.248 G_L1_RELATIVE: 9.803 G_Regularizer: 0.000 validation_error: 21.108 +(epoch: 24, iters: 104704, time: 0.562, data: 0.000) G_L1: 14.975 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 12.343 G_Regularizer: 0.000 validation_error: 20.948 +(epoch: 24, iters: 106704, time: 0.563, data: 0.000) G_L1: 15.431 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 12.998 G_Regularizer: 0.000 validation_error: 21.456 +(epoch: 24, iters: 108704, time: 0.551, data: 0.000) G_L1: 15.512 G_L1_ABSOLUTE: 1.910 G_L1_RELATIVE: 13.602 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 24, iters: 110704, time: 0.571, data: 0.000) G_L1: 16.515 G_L1_ABSOLUTE: 2.681 G_L1_RELATIVE: 13.834 G_Regularizer: 0.000 validation_error: 20.112 +(epoch: 24, iters: 112704, time: 0.563, data: 0.000) G_L1: 14.358 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 11.807 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 24, iters: 114704, time: 0.560, data: 0.000) G_L1: 13.907 G_L1_ABSOLUTE: 2.308 G_L1_RELATIVE: 11.599 G_Regularizer: 0.000 validation_error: 20.020 +(epoch: 24, iters: 116704, time: 0.563, data: 0.000) G_L1: 19.225 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 16.711 G_Regularizer: 0.000 validation_error: 20.375 +(epoch: 24, iters: 118704, time: 0.569, data: 0.000) G_L1: 15.135 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 12.452 G_Regularizer: 0.000 validation_error: 21.361 +(epoch: 24, iters: 120704, time: 0.561, data: 0.000) G_L1: 15.855 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 13.118 G_Regularizer: 0.000 validation_error: 20.683 +(epoch: 24, iters: 122704, time: 0.571, data: 0.000) G_L1: 16.073 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 13.589 G_Regularizer: 0.000 validation_error: 20.551 +(epoch: 24, iters: 124704, time: 0.554, data: 0.000) G_L1: 12.422 G_L1_ABSOLUTE: 2.508 G_L1_RELATIVE: 9.913 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 24, iters: 126704, time: 0.566, data: 0.000) G_L1: 12.164 G_L1_ABSOLUTE: 2.316 G_L1_RELATIVE: 9.848 G_Regularizer: 0.000 validation_error: 20.869 +(epoch: 24, iters: 128704, time: 0.576, data: 0.000) G_L1: 12.460 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 9.992 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 24, iters: 130704, time: 0.573, data: 0.000) G_L1: 14.777 G_L1_ABSOLUTE: 2.229 G_L1_RELATIVE: 12.548 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 24, iters: 132704, time: 0.563, data: 0.000) G_L1: 15.096 G_L1_ABSOLUTE: 2.949 G_L1_RELATIVE: 12.147 G_Regularizer: 0.000 validation_error: 20.202 +(epoch: 24, iters: 134704, time: 0.563, data: 0.000) G_L1: 15.063 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 12.660 G_Regularizer: 0.000 validation_error: 20.100 +(epoch: 24, iters: 136704, time: 0.558, data: 0.000) G_L1: 13.086 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 10.594 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 24, iters: 138704, time: 0.570, data: 0.000) G_L1: 14.226 G_L1_ABSOLUTE: 2.808 G_L1_RELATIVE: 11.418 G_Regularizer: 0.000 validation_error: 20.070 +(epoch: 24, iters: 140704, time: 0.561, data: 0.000) G_L1: 13.786 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 11.163 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 24, iters: 142704, time: 0.566, data: 0.000) G_L1: 18.055 G_L1_ABSOLUTE: 3.263 G_L1_RELATIVE: 14.792 G_Regularizer: 0.000 validation_error: 21.038 +(epoch: 24, iters: 144704, time: 0.576, data: 0.000) G_L1: 14.387 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 11.799 G_Regularizer: 0.000 validation_error: 20.728 +(epoch: 24, iters: 146704, time: 0.557, data: 0.000) G_L1: 15.297 G_L1_ABSOLUTE: 2.255 G_L1_RELATIVE: 13.042 G_Regularizer: 0.000 validation_error: 20.861 +(epoch: 24, iters: 148704, time: 0.555, data: 0.000) G_L1: 16.317 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 13.766 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 24, iters: 150704, time: 0.573, data: 0.000) G_L1: 14.623 G_L1_ABSOLUTE: 3.099 G_L1_RELATIVE: 11.524 G_Regularizer: 0.000 validation_error: 20.980 +(epoch: 24, iters: 152704, time: 0.557, data: 0.000) G_L1: 13.387 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 10.985 G_Regularizer: 0.000 validation_error: 20.129 +(epoch: 24, iters: 154704, time: 0.563, data: 0.000) G_L1: 15.086 G_L1_ABSOLUTE: 2.477 G_L1_RELATIVE: 12.609 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 24, iters: 156704, time: 0.555, data: 0.000) G_L1: 15.020 G_L1_ABSOLUTE: 2.575 G_L1_RELATIVE: 12.446 G_Regularizer: 0.000 validation_error: 20.750 +(epoch: 24, iters: 158704, time: 0.558, data: 0.001) G_L1: 15.231 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 12.655 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 24, iters: 160704, time: 0.562, data: 0.000) G_L1: 12.787 G_L1_ABSOLUTE: 2.452 G_L1_RELATIVE: 10.335 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 24, iters: 162704, time: 0.565, data: 0.000) G_L1: 12.796 G_L1_ABSOLUTE: 2.802 G_L1_RELATIVE: 9.995 G_Regularizer: 0.000 validation_error: 20.340 +(epoch: 24, iters: 164704, time: 0.564, data: 0.000) G_L1: 14.526 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 12.035 G_Regularizer: 0.000 validation_error: 20.397 +(epoch: 24, iters: 166704, time: 0.565, data: 0.000) G_L1: 15.657 G_L1_ABSOLUTE: 2.206 G_L1_RELATIVE: 13.452 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 24, iters: 168704, time: 0.563, data: 0.000) G_L1: 15.639 G_L1_ABSOLUTE: 2.897 G_L1_RELATIVE: 12.742 G_Regularizer: 0.000 validation_error: 20.230 +(epoch: 24, iters: 170704, time: 0.562, data: 0.000) G_L1: 14.529 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 12.037 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 24, iters: 172704, time: 0.567, data: 0.000) G_L1: 15.020 G_L1_ABSOLUTE: 2.475 G_L1_RELATIVE: 12.545 G_Regularizer: 0.000 validation_error: 20.921 +(epoch: 24, iters: 174704, time: 0.563, data: 0.000) G_L1: 16.078 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 13.505 G_Regularizer: 0.000 validation_error: 21.276 +(epoch: 24, iters: 176704, time: 0.561, data: 0.000) G_L1: 12.635 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 10.221 G_Regularizer: 0.000 validation_error: 21.314 +(epoch: 24, iters: 178704, time: 0.573, data: 0.000) G_L1: 14.076 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 11.657 G_Regularizer: 0.000 validation_error: 20.482 +(epoch: 24, iters: 180704, time: 0.564, data: 0.000) G_L1: 16.631 G_L1_ABSOLUTE: 2.764 G_L1_RELATIVE: 13.866 G_Regularizer: 0.000 validation_error: 21.043 +(epoch: 24, iters: 182704, time: 0.555, data: 0.000) G_L1: 17.755 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 15.366 G_Regularizer: 0.000 validation_error: 20.874 +(epoch: 24, iters: 184704, time: 0.557, data: 0.000) G_L1: 15.627 G_L1_ABSOLUTE: 2.723 G_L1_RELATIVE: 12.904 G_Regularizer: 0.000 validation_error: 21.361 +(epoch: 24, iters: 186704, time: 0.563, data: 0.001) G_L1: 15.070 G_L1_ABSOLUTE: 2.316 G_L1_RELATIVE: 12.754 G_Regularizer: 0.000 validation_error: 20.093 +(epoch: 24, iters: 188704, time: 0.565, data: 0.000) G_L1: 14.186 G_L1_ABSOLUTE: 3.432 G_L1_RELATIVE: 10.754 G_Regularizer: 0.000 validation_error: 20.337 +(epoch: 24, iters: 190704, time: 0.563, data: 0.000) G_L1: 13.941 G_L1_ABSOLUTE: 2.362 G_L1_RELATIVE: 11.579 G_Regularizer: 0.000 validation_error: 21.670 +(epoch: 24, iters: 192704, time: 0.560, data: 0.000) G_L1: 13.593 G_L1_ABSOLUTE: 2.372 G_L1_RELATIVE: 11.221 G_Regularizer: 0.000 validation_error: 20.559 +(epoch: 24, iters: 194704, time: 0.565, data: 0.000) G_L1: 14.358 G_L1_ABSOLUTE: 2.752 G_L1_RELATIVE: 11.605 G_Regularizer: 0.000 validation_error: 20.538 +(epoch: 24, iters: 196704, time: 0.559, data: 0.000) G_L1: 13.918 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 11.054 G_Regularizer: 0.000 validation_error: 20.202 +(epoch: 24, iters: 198704, time: 0.551, data: 0.000) G_L1: 14.000 G_L1_ABSOLUTE: 3.021 G_L1_RELATIVE: 10.978 G_Regularizer: 0.000 validation_error: 20.332 +(epoch: 24, iters: 200704, time: 0.570, data: 0.000) G_L1: 13.345 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 10.799 G_Regularizer: 0.000 validation_error: 20.839 +(epoch: 24, iters: 202704, time: 0.559, data: 0.000) G_L1: 15.501 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 13.000 G_Regularizer: 0.000 validation_error: 20.992 +(epoch: 24, iters: 204704, time: 0.561, data: 0.000) G_L1: 13.382 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 10.963 G_Regularizer: 0.000 validation_error: 20.500 +(epoch: 24, iters: 206704, time: 0.563, data: 0.000) G_L1: 13.188 G_L1_ABSOLUTE: 2.149 G_L1_RELATIVE: 11.040 G_Regularizer: 0.000 validation_error: 21.278 +(epoch: 24, iters: 208704, time: 0.561, data: 0.000) G_L1: 18.588 G_L1_ABSOLUTE: 2.821 G_L1_RELATIVE: 15.767 G_Regularizer: 0.000 validation_error: 20.485 +(epoch: 24, iters: 210704, time: 0.561, data: 0.000) G_L1: 12.726 G_L1_ABSOLUTE: 2.907 G_L1_RELATIVE: 9.820 G_Regularizer: 0.000 validation_error: 20.207 +(epoch: 24, iters: 212704, time: 0.580, data: 0.000) G_L1: 14.909 G_L1_ABSOLUTE: 2.223 G_L1_RELATIVE: 12.687 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 24, iters: 214704, time: 0.559, data: 0.000) G_L1: 14.428 G_L1_ABSOLUTE: 2.309 G_L1_RELATIVE: 12.119 G_Regularizer: 0.000 validation_error: 21.191 +(epoch: 24, iters: 216704, time: 0.570, data: 0.000) G_L1: 14.761 G_L1_ABSOLUTE: 2.268 G_L1_RELATIVE: 12.493 G_Regularizer: 0.000 validation_error: 20.453 +(epoch: 24, iters: 218704, time: 0.562, data: 0.000) G_L1: 16.550 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 13.940 G_Regularizer: 0.000 validation_error: 21.610 +(epoch: 24, iters: 220704, time: 0.559, data: 0.000) G_L1: 15.994 G_L1_ABSOLUTE: 2.891 G_L1_RELATIVE: 13.103 G_Regularizer: 0.000 validation_error: 20.462 +(epoch: 24, iters: 222704, time: 0.570, data: 0.000) G_L1: 14.513 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 12.125 G_Regularizer: 0.000 validation_error: 21.009 +(epoch: 24, iters: 224704, time: 0.558, data: 0.000) G_L1: 17.063 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 14.612 G_Regularizer: 0.000 validation_error: 20.523 +(epoch: 24, iters: 226704, time: 0.561, data: 0.001) G_L1: 12.676 G_L1_ABSOLUTE: 2.328 G_L1_RELATIVE: 10.348 G_Regularizer: 0.000 validation_error: 21.187 +(epoch: 24, iters: 228704, time: 0.577, data: 0.000) G_L1: 13.067 G_L1_ABSOLUTE: 2.197 G_L1_RELATIVE: 10.870 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 24, iters: 230704, time: 0.565, data: 0.000) G_L1: 16.636 G_L1_ABSOLUTE: 2.471 G_L1_RELATIVE: 14.166 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 24, iters: 232704, time: 0.575, data: 0.000) G_L1: 16.097 G_L1_ABSOLUTE: 3.122 G_L1_RELATIVE: 12.975 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 24, iters: 234704, time: 0.562, data: 0.000) G_L1: 17.661 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 14.998 G_Regularizer: 0.000 validation_error: 20.313 +(epoch: 24, iters: 236704, time: 0.559, data: 0.000) G_L1: 11.944 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 9.746 G_Regularizer: 0.000 validation_error: 20.937 +(epoch: 24, iters: 238704, time: 0.558, data: 0.000) G_L1: 16.410 G_L1_ABSOLUTE: 2.719 G_L1_RELATIVE: 13.691 G_Regularizer: 0.000 validation_error: 20.449 +(epoch: 24, iters: 240704, time: 0.558, data: 0.000) G_L1: 13.702 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 11.224 G_Regularizer: 0.000 validation_error: 20.564 +(epoch: 24, iters: 242704, time: 0.563, data: 0.000) G_L1: 17.343 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 14.769 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 24, iters: 244704, time: 0.565, data: 0.000) G_L1: 12.764 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 10.133 G_Regularizer: 0.000 validation_error: 20.303 +(epoch: 24, iters: 246704, time: 0.576, data: 0.000) G_L1: 14.188 G_L1_ABSOLUTE: 2.785 G_L1_RELATIVE: 11.403 G_Regularizer: 0.000 validation_error: 20.797 +(epoch: 24, iters: 248704, time: 0.575, data: 0.000) G_L1: 14.787 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 12.129 G_Regularizer: 0.000 validation_error: 20.737 +(epoch: 24, iters: 250704, time: 0.553, data: 0.000) G_L1: 14.373 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 12.157 G_Regularizer: 0.000 validation_error: 21.296 +(epoch: 24, iters: 252704, time: 0.544, data: 0.000) G_L1: 15.521 G_L1_ABSOLUTE: 2.488 G_L1_RELATIVE: 13.033 G_Regularizer: 0.000 validation_error: 20.509 +(epoch: 24, iters: 254704, time: 0.543, data: 0.000) G_L1: 14.207 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 11.715 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 24, iters: 256704, time: 0.549, data: 0.000) G_L1: 16.004 G_L1_ABSOLUTE: 2.763 G_L1_RELATIVE: 13.240 G_Regularizer: 0.000 validation_error: 21.286 +(epoch: 24, iters: 258704, time: 0.548, data: 0.000) G_L1: 16.068 G_L1_ABSOLUTE: 2.847 G_L1_RELATIVE: 13.222 G_Regularizer: 0.000 validation_error: 20.416 +(epoch: 24, iters: 260704, time: 0.539, data: 0.000) G_L1: 16.207 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 13.704 G_Regularizer: 0.000 validation_error: 20.565 +(epoch: 24, iters: 262704, time: 0.543, data: 0.000) G_L1: 15.212 G_L1_ABSOLUTE: 2.956 G_L1_RELATIVE: 12.256 G_Regularizer: 0.000 validation_error: 20.585 +(epoch: 24, iters: 264704, time: 0.547, data: 0.000) G_L1: 14.536 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 11.965 G_Regularizer: 0.000 validation_error: 20.461 +(epoch: 24, iters: 266704, time: 0.548, data: 0.000) G_L1: 12.771 G_L1_ABSOLUTE: 2.011 G_L1_RELATIVE: 10.760 G_Regularizer: 0.000 validation_error: 20.092 +(epoch: 24, iters: 268704, time: 0.549, data: 0.000) G_L1: 15.456 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 12.632 G_Regularizer: 0.000 validation_error: 20.382 +(epoch: 24, iters: 270704, time: 0.545, data: 0.000) G_L1: 12.213 G_L1_ABSOLUTE: 2.030 G_L1_RELATIVE: 10.184 G_Regularizer: 0.000 validation_error: 20.902 +(epoch: 24, iters: 272704, time: 0.536, data: 0.000) G_L1: 13.201 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 10.487 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 24, iters: 274704, time: 0.548, data: 0.000) G_L1: 13.934 G_L1_ABSOLUTE: 2.864 G_L1_RELATIVE: 11.070 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 24, iters: 276704, time: 0.543, data: 0.000) G_L1: 13.380 G_L1_ABSOLUTE: 2.749 G_L1_RELATIVE: 10.631 G_Regularizer: 0.000 validation_error: 20.089 +(epoch: 24, iters: 278704, time: 0.550, data: 0.000) G_L1: 14.844 G_L1_ABSOLUTE: 2.782 G_L1_RELATIVE: 12.062 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 24, iters: 280704, time: 0.552, data: 0.000) G_L1: 15.046 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 12.683 G_Regularizer: 0.000 validation_error: 20.708 +(epoch: 24, iters: 282704, time: 0.548, data: 0.001) G_L1: 12.310 G_L1_ABSOLUTE: 2.030 G_L1_RELATIVE: 10.279 G_Regularizer: 0.000 validation_error: 20.618 +(epoch: 24, iters: 284704, time: 0.555, data: 0.000) G_L1: 15.632 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 13.292 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 24, iters: 286704, time: 0.546, data: 0.000) G_L1: 14.568 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 12.102 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 24, iters: 288704, time: 0.547, data: 0.000) G_L1: 13.261 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 10.886 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 24, iters: 290704, time: 0.548, data: 0.000) G_L1: 12.579 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 10.043 G_Regularizer: 0.000 validation_error: 20.595 +(epoch: 24, iters: 292704, time: 0.549, data: 0.000) G_L1: 16.031 G_L1_ABSOLUTE: 2.997 G_L1_RELATIVE: 13.034 G_Regularizer: 0.000 validation_error: 21.705 +(epoch: 24, iters: 294704, time: 0.546, data: 0.000) G_L1: 13.506 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 10.966 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 24, iters: 296704, time: 0.545, data: 0.000) G_L1: 12.504 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 10.069 G_Regularizer: 0.000 validation_error: 20.456 +(epoch: 24, iters: 298704, time: 0.547, data: 0.001) G_L1: 14.962 G_L1_ABSOLUTE: 2.579 G_L1_RELATIVE: 12.383 G_Regularizer: 0.000 validation_error: 20.574 +(epoch: 24, iters: 300704, time: 0.550, data: 0.000) G_L1: 14.775 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 12.361 G_Regularizer: 0.000 validation_error: 20.667 +(epoch: 24, iters: 302704, time: 0.547, data: 0.000) G_L1: 13.865 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 11.197 G_Regularizer: 0.000 validation_error: 20.438 +(epoch: 25, iters: 1952, time: 0.546, data: 0.000) G_L1: 16.715 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 14.064 G_Regularizer: 0.000 validation_error: 20.354 +(epoch: 25, iters: 3952, time: 0.549, data: 0.000) G_L1: 18.925 G_L1_ABSOLUTE: 2.796 G_L1_RELATIVE: 16.129 G_Regularizer: 0.000 validation_error: 20.198 +(epoch: 25, iters: 5952, time: 0.550, data: 0.000) G_L1: 14.260 G_L1_ABSOLUTE: 3.075 G_L1_RELATIVE: 11.185 G_Regularizer: 0.000 validation_error: 20.948 +(epoch: 25, iters: 7952, time: 0.546, data: 0.001) G_L1: 13.572 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 11.169 G_Regularizer: 0.000 validation_error: 20.370 +(epoch: 25, iters: 9952, time: 0.545, data: 0.000) G_L1: 17.079 G_L1_ABSOLUTE: 3.210 G_L1_RELATIVE: 13.869 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 25, iters: 11952, time: 0.550, data: 0.000) G_L1: 14.941 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 12.379 G_Regularizer: 0.000 validation_error: 20.099 +(epoch: 25, iters: 13952, time: 0.547, data: 0.000) G_L1: 12.610 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 10.104 G_Regularizer: 0.000 validation_error: 20.946 +(epoch: 25, iters: 15952, time: 0.549, data: 0.000) G_L1: 23.800 G_L1_ABSOLUTE: 2.837 G_L1_RELATIVE: 20.963 G_Regularizer: 0.000 validation_error: 20.311 +(epoch: 25, iters: 17952, time: 0.552, data: 0.000) G_L1: 13.479 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 10.939 G_Regularizer: 0.000 validation_error: 20.415 +(epoch: 25, iters: 19952, time: 0.550, data: 0.000) G_L1: 13.971 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 11.019 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 25, iters: 21952, time: 0.552, data: 0.000) G_L1: 15.496 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 12.771 G_Regularizer: 0.000 validation_error: 19.754 +(epoch: 25, iters: 23952, time: 0.544, data: 0.000) G_L1: 14.165 G_L1_ABSOLUTE: 2.807 G_L1_RELATIVE: 11.358 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 25, iters: 25952, time: 0.548, data: 0.000) G_L1: 16.510 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 13.964 G_Regularizer: 0.000 validation_error: 20.315 +(epoch: 25, iters: 27952, time: 0.544, data: 0.000) G_L1: 13.271 G_L1_ABSOLUTE: 2.396 G_L1_RELATIVE: 10.875 G_Regularizer: 0.000 validation_error: 20.520 +(epoch: 25, iters: 29952, time: 0.543, data: 0.000) G_L1: 15.635 G_L1_ABSOLUTE: 2.176 G_L1_RELATIVE: 13.459 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 25, iters: 31952, time: 0.541, data: 0.001) G_L1: 15.312 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 12.847 G_Regularizer: 0.000 validation_error: 20.651 +(epoch: 25, iters: 33952, time: 0.546, data: 0.000) G_L1: 11.945 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 9.435 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 25, iters: 35952, time: 0.551, data: 0.000) G_L1: 15.506 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 12.762 G_Regularizer: 0.000 validation_error: 20.555 +(epoch: 25, iters: 37952, time: 0.542, data: 0.000) G_L1: 12.867 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 10.525 G_Regularizer: 0.000 validation_error: 20.484 +(epoch: 25, iters: 39952, time: 0.547, data: 0.001) G_L1: 15.521 G_L1_ABSOLUTE: 2.769 G_L1_RELATIVE: 12.752 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 25, iters: 41952, time: 0.549, data: 0.000) G_L1: 13.969 G_L1_ABSOLUTE: 2.199 G_L1_RELATIVE: 11.770 G_Regularizer: 0.000 validation_error: 20.564 +(epoch: 25, iters: 43952, time: 0.543, data: 0.000) G_L1: 16.354 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 13.935 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 25, iters: 45952, time: 0.548, data: 0.000) G_L1: 16.821 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 14.143 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 25, iters: 47952, time: 0.548, data: 0.000) G_L1: 13.068 G_L1_ABSOLUTE: 2.047 G_L1_RELATIVE: 11.022 G_Regularizer: 0.000 validation_error: 20.238 +(epoch: 25, iters: 49952, time: 0.542, data: 0.000) G_L1: 14.608 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 11.884 G_Regularizer: 0.000 validation_error: 20.591 +(epoch: 25, iters: 51952, time: 0.549, data: 0.000) G_L1: 13.869 G_L1_ABSOLUTE: 2.499 G_L1_RELATIVE: 11.370 G_Regularizer: 0.000 validation_error: 20.288 +(epoch: 25, iters: 53952, time: 0.547, data: 0.000) G_L1: 11.907 G_L1_ABSOLUTE: 2.311 G_L1_RELATIVE: 9.596 G_Regularizer: 0.000 validation_error: 20.600 +(epoch: 25, iters: 55952, time: 0.550, data: 0.000) G_L1: 16.315 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 13.693 G_Regularizer: 0.000 validation_error: 20.261 +(epoch: 25, iters: 57952, time: 0.542, data: 0.000) G_L1: 12.949 G_L1_ABSOLUTE: 2.325 G_L1_RELATIVE: 10.624 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 25, iters: 59952, time: 0.542, data: 0.000) G_L1: 13.782 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 11.281 G_Regularizer: 0.000 validation_error: 20.241 +(epoch: 25, iters: 61952, time: 0.547, data: 0.000) G_L1: 15.093 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 12.636 G_Regularizer: 0.000 validation_error: 20.158 +(epoch: 25, iters: 63952, time: 0.547, data: 0.000) G_L1: 12.885 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 10.180 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 25, iters: 65952, time: 0.551, data: 0.000) G_L1: 16.276 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 13.759 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 25, iters: 67952, time: 0.555, data: 0.000) G_L1: 14.900 G_L1_ABSOLUTE: 2.689 G_L1_RELATIVE: 12.211 G_Regularizer: 0.000 validation_error: 20.139 +(epoch: 25, iters: 69952, time: 0.546, data: 0.000) G_L1: 19.067 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 16.610 G_Regularizer: 0.000 validation_error: 20.381 +(epoch: 25, iters: 71952, time: 0.545, data: 0.000) G_L1: 13.194 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 10.492 G_Regularizer: 0.000 validation_error: 21.068 +(epoch: 25, iters: 73952, time: 0.556, data: 0.000) G_L1: 13.975 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 11.265 G_Regularizer: 0.000 validation_error: 20.512 +(epoch: 25, iters: 75952, time: 0.548, data: 0.000) G_L1: 16.101 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 13.466 G_Regularizer: 0.000 validation_error: 20.379 +(epoch: 25, iters: 77952, time: 0.556, data: 0.000) G_L1: 14.420 G_L1_ABSOLUTE: 2.322 G_L1_RELATIVE: 12.098 G_Regularizer: 0.000 validation_error: 20.497 +(epoch: 25, iters: 79952, time: 0.552, data: 0.000) G_L1: 16.715 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 14.248 G_Regularizer: 0.000 validation_error: 20.640 +(epoch: 25, iters: 81952, time: 0.544, data: 0.000) G_L1: 17.056 G_L1_ABSOLUTE: 2.919 G_L1_RELATIVE: 14.137 G_Regularizer: 0.000 validation_error: 20.750 +(epoch: 25, iters: 83952, time: 0.546, data: 0.000) G_L1: 20.427 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 17.622 G_Regularizer: 0.000 validation_error: 20.378 +(epoch: 25, iters: 85952, time: 0.547, data: 0.000) G_L1: 14.589 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 11.998 G_Regularizer: 0.000 validation_error: 20.499 +(epoch: 25, iters: 87952, time: 0.546, data: 0.001) G_L1: 14.603 G_L1_ABSOLUTE: 2.438 G_L1_RELATIVE: 12.166 G_Regularizer: 0.000 validation_error: 20.041 +(epoch: 25, iters: 89952, time: 0.550, data: 0.000) G_L1: 13.411 G_L1_ABSOLUTE: 2.285 G_L1_RELATIVE: 11.126 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 25, iters: 91952, time: 0.554, data: 0.000) G_L1: 15.037 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 12.702 G_Regularizer: 0.000 validation_error: 19.758 +(epoch: 25, iters: 93952, time: 0.545, data: 0.000) G_L1: 15.665 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 13.006 G_Regularizer: 0.000 validation_error: 20.452 +(epoch: 25, iters: 95952, time: 0.546, data: 0.000) G_L1: 16.046 G_L1_ABSOLUTE: 2.849 G_L1_RELATIVE: 13.197 G_Regularizer: 0.000 validation_error: 21.242 +(epoch: 25, iters: 97952, time: 0.549, data: 0.000) G_L1: 12.299 G_L1_ABSOLUTE: 2.189 G_L1_RELATIVE: 10.110 G_Regularizer: 0.000 validation_error: 20.861 +(epoch: 25, iters: 99952, time: 0.546, data: 0.000) G_L1: 15.122 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 12.643 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 25, iters: 101952, time: 0.545, data: 0.000) G_L1: 12.570 G_L1_ABSOLUTE: 2.580 G_L1_RELATIVE: 9.990 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 25, iters: 103952, time: 0.546, data: 0.000) G_L1: 14.672 G_L1_ABSOLUTE: 2.829 G_L1_RELATIVE: 11.843 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 25, iters: 105952, time: 0.552, data: 0.000) G_L1: 13.057 G_L1_ABSOLUTE: 2.398 G_L1_RELATIVE: 10.659 G_Regularizer: 0.000 validation_error: 21.183 +(epoch: 25, iters: 107952, time: 0.544, data: 0.000) G_L1: 13.316 G_L1_ABSOLUTE: 1.974 G_L1_RELATIVE: 11.342 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 25, iters: 109952, time: 0.543, data: 0.000) G_L1: 14.259 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 11.945 G_Regularizer: 0.000 validation_error: 20.457 +(epoch: 25, iters: 111952, time: 0.541, data: 0.000) G_L1: 14.341 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 11.665 G_Regularizer: 0.000 validation_error: 20.737 +(epoch: 25, iters: 113952, time: 0.546, data: 0.000) G_L1: 14.547 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 12.080 G_Regularizer: 0.000 validation_error: 21.192 +(epoch: 25, iters: 115952, time: 0.543, data: 0.000) G_L1: 15.822 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 13.333 G_Regularizer: 0.000 validation_error: 20.771 +(epoch: 25, iters: 117952, time: 0.542, data: 0.000) G_L1: 12.656 G_L1_ABSOLUTE: 2.102 G_L1_RELATIVE: 10.554 G_Regularizer: 0.000 validation_error: 20.859 +(epoch: 25, iters: 119952, time: 0.538, data: 0.000) G_L1: 15.247 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 12.989 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 25, iters: 121952, time: 0.548, data: 0.000) G_L1: 12.808 G_L1_ABSOLUTE: 2.179 G_L1_RELATIVE: 10.629 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 25, iters: 123952, time: 0.545, data: 0.000) G_L1: 11.810 G_L1_ABSOLUTE: 2.166 G_L1_RELATIVE: 9.644 G_Regularizer: 0.000 validation_error: 19.865 +(epoch: 25, iters: 125952, time: 0.548, data: 0.000) G_L1: 16.592 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 14.179 G_Regularizer: 0.000 validation_error: 20.483 +(epoch: 25, iters: 127952, time: 0.551, data: 0.001) G_L1: 15.898 G_L1_ABSOLUTE: 3.202 G_L1_RELATIVE: 12.696 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 25, iters: 129952, time: 0.542, data: 0.000) G_L1: 14.299 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 11.463 G_Regularizer: 0.000 validation_error: 20.181 +(epoch: 25, iters: 131952, time: 0.537, data: 0.000) G_L1: 13.003 G_L1_ABSOLUTE: 2.134 G_L1_RELATIVE: 10.870 G_Regularizer: 0.000 validation_error: 20.569 +(epoch: 25, iters: 133952, time: 0.546, data: 0.000) G_L1: 12.881 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 10.491 G_Regularizer: 0.000 validation_error: 20.601 +(epoch: 25, iters: 135952, time: 0.544, data: 0.000) G_L1: 17.695 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 15.064 G_Regularizer: 0.000 validation_error: 20.486 +(epoch: 25, iters: 137952, time: 0.549, data: 0.000) G_L1: 15.002 G_L1_ABSOLUTE: 2.545 G_L1_RELATIVE: 12.457 G_Regularizer: 0.000 validation_error: 20.314 +(epoch: 25, iters: 139952, time: 0.545, data: 0.000) G_L1: 12.675 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 10.255 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 25, iters: 141952, time: 0.556, data: 0.000) G_L1: 17.942 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 15.141 G_Regularizer: 0.000 validation_error: 20.489 +(epoch: 25, iters: 143952, time: 0.540, data: 0.000) G_L1: 16.373 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 13.636 G_Regularizer: 0.000 validation_error: 20.541 +(epoch: 25, iters: 145952, time: 0.539, data: 0.000) G_L1: 11.442 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 8.932 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 25, iters: 147952, time: 0.547, data: 0.001) G_L1: 13.761 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 11.161 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 25, iters: 149952, time: 0.550, data: 0.000) G_L1: 14.855 G_L1_ABSOLUTE: 2.846 G_L1_RELATIVE: 12.009 G_Regularizer: 0.000 validation_error: 20.789 +(epoch: 25, iters: 151952, time: 0.551, data: 0.000) G_L1: 16.074 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 13.553 G_Regularizer: 0.000 validation_error: 20.418 +(epoch: 25, iters: 153952, time: 0.556, data: 0.000) G_L1: 14.377 G_L1_ABSOLUTE: 2.575 G_L1_RELATIVE: 11.802 G_Regularizer: 0.000 validation_error: 20.404 +(epoch: 25, iters: 155952, time: 0.546, data: 0.000) G_L1: 12.834 G_L1_ABSOLUTE: 2.059 G_L1_RELATIVE: 10.775 G_Regularizer: 0.000 validation_error: 20.009 +(epoch: 25, iters: 157952, time: 0.548, data: 0.000) G_L1: 15.957 G_L1_ABSOLUTE: 2.883 G_L1_RELATIVE: 13.074 G_Regularizer: 0.000 validation_error: 20.911 +(epoch: 25, iters: 159952, time: 0.546, data: 0.001) G_L1: 14.605 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 12.329 G_Regularizer: 0.000 validation_error: 21.007 +(epoch: 25, iters: 161952, time: 0.545, data: 0.000) G_L1: 15.289 G_L1_ABSOLUTE: 2.936 G_L1_RELATIVE: 12.353 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 25, iters: 163952, time: 0.550, data: 0.000) G_L1: 13.254 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 10.750 G_Regularizer: 0.000 validation_error: 20.414 +(epoch: 25, iters: 165952, time: 0.544, data: 0.000) G_L1: 15.712 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 12.982 G_Regularizer: 0.000 validation_error: 20.207 +(epoch: 25, iters: 167952, time: 0.545, data: 0.000) G_L1: 16.877 G_L1_ABSOLUTE: 2.917 G_L1_RELATIVE: 13.960 G_Regularizer: 0.000 validation_error: 21.030 +(epoch: 25, iters: 169952, time: 0.542, data: 0.000) G_L1: 15.824 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 13.295 G_Regularizer: 0.000 validation_error: 21.383 +(epoch: 25, iters: 171952, time: 0.548, data: 0.000) G_L1: 13.776 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 11.355 G_Regularizer: 0.000 validation_error: 21.416 +(epoch: 25, iters: 173952, time: 0.546, data: 0.000) G_L1: 13.602 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 11.041 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 25, iters: 175952, time: 0.546, data: 0.000) G_L1: 15.279 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 12.877 G_Regularizer: 0.000 validation_error: 21.287 +(epoch: 25, iters: 177952, time: 0.550, data: 0.000) G_L1: 16.209 G_L1_ABSOLUTE: 2.965 G_L1_RELATIVE: 13.244 G_Regularizer: 0.000 validation_error: 21.056 +(epoch: 25, iters: 179952, time: 0.539, data: 0.000) G_L1: 14.468 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 12.095 G_Regularizer: 0.000 validation_error: 20.992 +(epoch: 25, iters: 181952, time: 0.539, data: 0.000) G_L1: 14.712 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 11.877 G_Regularizer: 0.000 validation_error: 20.561 +(epoch: 25, iters: 183952, time: 0.542, data: 0.000) G_L1: 13.878 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 11.255 G_Regularizer: 0.000 validation_error: 20.940 +(epoch: 25, iters: 185952, time: 0.548, data: 0.000) G_L1: 14.138 G_L1_ABSOLUTE: 2.146 G_L1_RELATIVE: 11.991 G_Regularizer: 0.000 validation_error: 20.104 +(epoch: 25, iters: 187952, time: 0.545, data: 0.000) G_L1: 15.302 G_L1_ABSOLUTE: 2.322 G_L1_RELATIVE: 12.980 G_Regularizer: 0.000 validation_error: 20.142 +(epoch: 25, iters: 189952, time: 0.553, data: 0.001) G_L1: 14.616 G_L1_ABSOLUTE: 2.639 G_L1_RELATIVE: 11.977 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 25, iters: 191952, time: 0.545, data: 0.000) G_L1: 16.370 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 13.831 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 25, iters: 193952, time: 0.544, data: 0.000) G_L1: 12.403 G_L1_ABSOLUTE: 2.183 G_L1_RELATIVE: 10.220 G_Regularizer: 0.000 validation_error: 20.006 +(epoch: 25, iters: 195952, time: 0.544, data: 0.000) G_L1: 12.550 G_L1_ABSOLUTE: 2.285 G_L1_RELATIVE: 10.265 G_Regularizer: 0.000 validation_error: 20.427 +(epoch: 25, iters: 197952, time: 0.541, data: 0.000) G_L1: 15.400 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 12.810 G_Regularizer: 0.000 validation_error: 20.943 +(epoch: 25, iters: 199952, time: 0.548, data: 0.000) G_L1: 13.333 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 10.647 G_Regularizer: 0.000 validation_error: 20.598 +(epoch: 25, iters: 201952, time: 0.545, data: 0.000) G_L1: 12.835 G_L1_ABSOLUTE: 2.763 G_L1_RELATIVE: 10.072 G_Regularizer: 0.000 validation_error: 21.254 +(epoch: 25, iters: 203952, time: 0.543, data: 0.000) G_L1: 15.029 G_L1_ABSOLUTE: 2.823 G_L1_RELATIVE: 12.206 G_Regularizer: 0.000 validation_error: 20.956 +(epoch: 25, iters: 205952, time: 0.546, data: 0.000) G_L1: 15.837 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 13.356 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 25, iters: 207952, time: 0.549, data: 0.000) G_L1: 19.813 G_L1_ABSOLUTE: 2.868 G_L1_RELATIVE: 16.946 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 25, iters: 209952, time: 0.545, data: 0.000) G_L1: 15.073 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 12.178 G_Regularizer: 0.000 validation_error: 20.342 +(epoch: 25, iters: 211952, time: 0.553, data: 0.000) G_L1: 16.697 G_L1_ABSOLUTE: 2.776 G_L1_RELATIVE: 13.921 G_Regularizer: 0.000 validation_error: 21.111 +(epoch: 25, iters: 213952, time: 0.553, data: 0.000) G_L1: 12.403 G_L1_ABSOLUTE: 2.352 G_L1_RELATIVE: 10.051 G_Regularizer: 0.000 validation_error: 20.069 +(epoch: 25, iters: 215952, time: 0.550, data: 0.000) G_L1: 13.453 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 10.994 G_Regularizer: 0.000 validation_error: 21.216 +(epoch: 25, iters: 217952, time: 0.546, data: 0.000) G_L1: 13.543 G_L1_ABSOLUTE: 2.566 G_L1_RELATIVE: 10.977 G_Regularizer: 0.000 validation_error: 20.594 +(epoch: 25, iters: 219952, time: 0.541, data: 0.000) G_L1: 14.260 G_L1_ABSOLUTE: 2.128 G_L1_RELATIVE: 12.132 G_Regularizer: 0.000 validation_error: 21.248 +(epoch: 25, iters: 221952, time: 0.543, data: 0.000) G_L1: 15.988 G_L1_ABSOLUTE: 2.613 G_L1_RELATIVE: 13.375 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 25, iters: 223952, time: 0.539, data: 0.000) G_L1: 13.488 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 10.863 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 25, iters: 225952, time: 0.548, data: 0.000) G_L1: 14.501 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 11.723 G_Regularizer: 0.000 validation_error: 20.549 +(epoch: 25, iters: 227952, time: 0.542, data: 0.000) G_L1: 14.266 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 11.567 G_Regularizer: 0.000 validation_error: 20.583 +(epoch: 25, iters: 229952, time: 0.544, data: 0.000) G_L1: 15.227 G_L1_ABSOLUTE: 2.186 G_L1_RELATIVE: 13.041 G_Regularizer: 0.000 validation_error: 20.238 +(epoch: 25, iters: 231952, time: 0.550, data: 0.000) G_L1: 14.301 G_L1_ABSOLUTE: 2.693 G_L1_RELATIVE: 11.608 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 25, iters: 233952, time: 0.537, data: 0.000) G_L1: 19.735 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 16.987 G_Regularizer: 0.000 validation_error: 21.341 +(epoch: 25, iters: 235952, time: 0.542, data: 0.000) G_L1: 12.368 G_L1_ABSOLUTE: 2.306 G_L1_RELATIVE: 10.062 G_Regularizer: 0.000 validation_error: 20.566 +(epoch: 25, iters: 237952, time: 0.548, data: 0.001) G_L1: 15.184 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 12.589 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 25, iters: 239952, time: 0.541, data: 0.000) G_L1: 14.809 G_L1_ABSOLUTE: 2.765 G_L1_RELATIVE: 12.044 G_Regularizer: 0.000 validation_error: 21.258 +(epoch: 25, iters: 241952, time: 0.539, data: 0.000) G_L1: 13.245 G_L1_ABSOLUTE: 2.532 G_L1_RELATIVE: 10.713 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 25, iters: 243952, time: 0.548, data: 0.000) G_L1: 14.554 G_L1_ABSOLUTE: 2.661 G_L1_RELATIVE: 11.893 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 25, iters: 245952, time: 0.548, data: 0.000) G_L1: 12.945 G_L1_ABSOLUTE: 2.155 G_L1_RELATIVE: 10.790 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 25, iters: 247952, time: 0.541, data: 0.000) G_L1: 10.626 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 8.344 G_Regularizer: 0.000 validation_error: 20.536 +(epoch: 25, iters: 249952, time: 0.548, data: 0.000) G_L1: 13.284 G_L1_ABSOLUTE: 2.019 G_L1_RELATIVE: 11.265 G_Regularizer: 0.000 validation_error: 20.339 +(epoch: 25, iters: 251952, time: 0.540, data: 0.000) G_L1: 14.619 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 11.814 G_Regularizer: 0.000 validation_error: 21.218 +(epoch: 25, iters: 253952, time: 0.548, data: 0.000) G_L1: 11.994 G_L1_ABSOLUTE: 2.052 G_L1_RELATIVE: 9.942 G_Regularizer: 0.000 validation_error: 20.401 +(epoch: 25, iters: 255952, time: 0.545, data: 0.000) G_L1: 15.365 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 12.643 G_Regularizer: 0.000 validation_error: 20.283 +(epoch: 25, iters: 257952, time: 0.537, data: 0.000) G_L1: 14.425 G_L1_ABSOLUTE: 3.009 G_L1_RELATIVE: 11.416 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 25, iters: 259952, time: 0.546, data: 0.000) G_L1: 13.229 G_L1_ABSOLUTE: 2.098 G_L1_RELATIVE: 11.131 G_Regularizer: 0.000 validation_error: 20.186 +(epoch: 25, iters: 261952, time: 0.543, data: 0.000) G_L1: 13.845 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 11.364 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 25, iters: 263952, time: 0.547, data: 0.000) G_L1: 14.886 G_L1_ABSOLUTE: 2.498 G_L1_RELATIVE: 12.388 G_Regularizer: 0.000 validation_error: 20.093 +(epoch: 25, iters: 265952, time: 0.549, data: 0.000) G_L1: 14.481 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 12.078 G_Regularizer: 0.000 validation_error: 20.432 +(epoch: 25, iters: 267952, time: 0.537, data: 0.000) G_L1: 16.035 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 13.340 G_Regularizer: 0.000 validation_error: 20.377 +(epoch: 25, iters: 269952, time: 0.537, data: 0.000) G_L1: 17.378 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 14.539 G_Regularizer: 0.000 validation_error: 19.787 +(epoch: 25, iters: 271952, time: 0.545, data: 0.000) G_L1: 14.124 G_L1_ABSOLUTE: 2.466 G_L1_RELATIVE: 11.658 G_Regularizer: 0.000 validation_error: 20.638 +(epoch: 25, iters: 273952, time: 0.557, data: 0.000) G_L1: 13.374 G_L1_ABSOLUTE: 2.975 G_L1_RELATIVE: 10.399 G_Regularizer: 0.000 validation_error: 21.318 +(epoch: 25, iters: 275952, time: 0.550, data: 0.000) G_L1: 12.762 G_L1_ABSOLUTE: 2.244 G_L1_RELATIVE: 10.518 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 25, iters: 277952, time: 0.547, data: 0.000) G_L1: 16.992 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 14.367 G_Regularizer: 0.000 validation_error: 20.382 +(epoch: 25, iters: 279952, time: 0.545, data: 0.000) G_L1: 13.341 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 10.835 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 25, iters: 281952, time: 0.543, data: 0.000) G_L1: 14.834 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 12.366 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 25, iters: 283952, time: 0.543, data: 0.000) G_L1: 17.367 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 14.876 G_Regularizer: 0.000 validation_error: 20.432 +(epoch: 25, iters: 285952, time: 0.546, data: 0.000) G_L1: 17.617 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 15.071 G_Regularizer: 0.000 validation_error: 20.355 +(epoch: 25, iters: 287952, time: 0.550, data: 0.000) G_L1: 14.524 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 11.950 G_Regularizer: 0.000 validation_error: 21.399 +(epoch: 25, iters: 289952, time: 0.538, data: 0.000) G_L1: 16.944 G_L1_ABSOLUTE: 2.900 G_L1_RELATIVE: 14.044 G_Regularizer: 0.000 validation_error: 21.237 +(epoch: 25, iters: 291952, time: 0.551, data: 0.000) G_L1: 13.965 G_L1_ABSOLUTE: 2.981 G_L1_RELATIVE: 10.984 G_Regularizer: 0.000 validation_error: 20.345 +(epoch: 25, iters: 293952, time: 0.546, data: 0.000) G_L1: 16.071 G_L1_ABSOLUTE: 2.879 G_L1_RELATIVE: 13.193 G_Regularizer: 0.000 validation_error: 21.338 +(epoch: 25, iters: 295952, time: 0.538, data: 0.000) G_L1: 14.207 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 11.302 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 25, iters: 297952, time: 0.550, data: 0.000) G_L1: 14.575 G_L1_ABSOLUTE: 2.334 G_L1_RELATIVE: 12.241 G_Regularizer: 0.000 validation_error: 21.365 +(epoch: 25, iters: 299952, time: 0.542, data: 0.000) G_L1: 15.937 G_L1_ABSOLUTE: 3.362 G_L1_RELATIVE: 12.575 G_Regularizer: 0.000 validation_error: 20.360 +(epoch: 25, iters: 301952, time: 0.541, data: 0.000) G_L1: 11.934 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 9.718 G_Regularizer: 0.000 validation_error: 20.271 +(epoch: 26, iters: 1200, time: 0.547, data: 0.001) G_L1: 17.731 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 15.272 G_Regularizer: 0.000 validation_error: 20.519 +(epoch: 26, iters: 3200, time: 0.543, data: 0.000) G_L1: 13.515 G_L1_ABSOLUTE: 2.788 G_L1_RELATIVE: 10.728 G_Regularizer: 0.000 validation_error: 20.033 +(epoch: 26, iters: 5200, time: 0.551, data: 0.000) G_L1: 15.812 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 13.369 G_Regularizer: 0.000 validation_error: 20.323 +(epoch: 26, iters: 7200, time: 0.546, data: 0.000) G_L1: 11.655 G_L1_ABSOLUTE: 2.727 G_L1_RELATIVE: 8.928 G_Regularizer: 0.000 validation_error: 20.559 +(epoch: 26, iters: 9200, time: 0.546, data: 0.000) G_L1: 12.563 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 10.279 G_Regularizer: 0.000 validation_error: 20.309 +(epoch: 26, iters: 11200, time: 0.542, data: 0.000) G_L1: 14.576 G_L1_ABSOLUTE: 2.379 G_L1_RELATIVE: 12.197 G_Regularizer: 0.000 validation_error: 20.699 +(epoch: 26, iters: 13200, time: 0.545, data: 0.000) G_L1: 14.302 G_L1_ABSOLUTE: 2.661 G_L1_RELATIVE: 11.641 G_Regularizer: 0.000 validation_error: 20.191 +(epoch: 26, iters: 15200, time: 0.541, data: 0.000) G_L1: 14.633 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 11.747 G_Regularizer: 0.000 validation_error: 20.184 +(epoch: 26, iters: 17200, time: 0.540, data: 0.000) G_L1: 13.829 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 11.245 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 26, iters: 19200, time: 0.548, data: 0.000) G_L1: 16.028 G_L1_ABSOLUTE: 3.242 G_L1_RELATIVE: 12.785 G_Regularizer: 0.000 validation_error: 20.394 +(epoch: 26, iters: 21200, time: 0.544, data: 0.000) G_L1: 11.484 G_L1_ABSOLUTE: 2.245 G_L1_RELATIVE: 9.239 G_Regularizer: 0.000 validation_error: 20.894 +(epoch: 26, iters: 23200, time: 0.552, data: 0.000) G_L1: 11.320 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 8.698 G_Regularizer: 0.000 validation_error: 21.237 +(epoch: 26, iters: 25200, time: 0.541, data: 0.000) G_L1: 15.500 G_L1_ABSOLUTE: 2.915 G_L1_RELATIVE: 12.586 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 26, iters: 27200, time: 0.541, data: 0.000) G_L1: 13.222 G_L1_ABSOLUTE: 2.308 G_L1_RELATIVE: 10.914 G_Regularizer: 0.000 validation_error: 21.151 +(epoch: 26, iters: 29200, time: 0.546, data: 0.000) G_L1: 14.034 G_L1_ABSOLUTE: 2.018 G_L1_RELATIVE: 12.016 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 26, iters: 31200, time: 0.555, data: 0.000) G_L1: 15.462 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 12.912 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 26, iters: 33200, time: 0.541, data: 0.000) G_L1: 16.336 G_L1_ABSOLUTE: 2.834 G_L1_RELATIVE: 13.502 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 26, iters: 35200, time: 0.546, data: 0.000) G_L1: 15.006 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 12.250 G_Regularizer: 0.000 validation_error: 20.639 +(epoch: 26, iters: 37200, time: 0.551, data: 0.000) G_L1: 15.101 G_L1_ABSOLUTE: 2.329 G_L1_RELATIVE: 12.773 G_Regularizer: 0.000 validation_error: 20.779 +(epoch: 26, iters: 39200, time: 0.550, data: 0.000) G_L1: 12.957 G_L1_ABSOLUTE: 2.266 G_L1_RELATIVE: 10.691 G_Regularizer: 0.000 validation_error: 19.895 +(epoch: 26, iters: 41200, time: 0.545, data: 0.000) G_L1: 14.701 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 12.176 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 26, iters: 43200, time: 0.540, data: 0.000) G_L1: 18.566 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 15.902 G_Regularizer: 0.000 validation_error: 21.293 +(epoch: 26, iters: 45200, time: 0.547, data: 0.000) G_L1: 12.798 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 10.506 G_Regularizer: 0.000 validation_error: 20.735 +(epoch: 26, iters: 47200, time: 0.538, data: 0.000) G_L1: 14.607 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 12.126 G_Regularizer: 0.000 validation_error: 21.059 +(epoch: 26, iters: 49200, time: 0.550, data: 0.000) G_L1: 15.208 G_L1_ABSOLUTE: 2.167 G_L1_RELATIVE: 13.041 G_Regularizer: 0.000 validation_error: 21.204 +(epoch: 26, iters: 51200, time: 0.542, data: 0.000) G_L1: 16.370 G_L1_ABSOLUTE: 2.374 G_L1_RELATIVE: 13.996 G_Regularizer: 0.000 validation_error: 20.775 +(epoch: 26, iters: 53200, time: 0.550, data: 0.000) G_L1: 15.528 G_L1_ABSOLUTE: 2.841 G_L1_RELATIVE: 12.687 G_Regularizer: 0.000 validation_error: 20.594 +(epoch: 26, iters: 55200, time: 0.555, data: 0.000) G_L1: 14.653 G_L1_ABSOLUTE: 2.296 G_L1_RELATIVE: 12.357 G_Regularizer: 0.000 validation_error: 20.162 +(epoch: 26, iters: 57200, time: 0.547, data: 0.000) G_L1: 15.623 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 13.193 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 26, iters: 59200, time: 0.544, data: 0.000) G_L1: 12.558 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 10.271 G_Regularizer: 0.000 validation_error: 20.936 +(epoch: 26, iters: 61200, time: 0.540, data: 0.000) G_L1: 15.960 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 13.469 G_Regularizer: 0.000 validation_error: 20.636 +(epoch: 26, iters: 63200, time: 0.547, data: 0.000) G_L1: 15.280 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 13.026 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 26, iters: 65200, time: 0.540, data: 0.000) G_L1: 12.284 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 9.655 G_Regularizer: 0.000 validation_error: 20.516 +(epoch: 26, iters: 67200, time: 0.540, data: 0.000) G_L1: 13.738 G_L1_ABSOLUTE: 2.277 G_L1_RELATIVE: 11.461 G_Regularizer: 0.000 validation_error: 21.355 +(epoch: 26, iters: 69200, time: 0.545, data: 0.000) G_L1: 14.205 G_L1_ABSOLUTE: 2.992 G_L1_RELATIVE: 11.213 G_Regularizer: 0.000 validation_error: 20.600 +(epoch: 26, iters: 71200, time: 0.550, data: 0.000) G_L1: 14.195 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 11.706 G_Regularizer: 0.000 validation_error: 20.331 +(epoch: 26, iters: 73200, time: 0.542, data: 0.000) G_L1: 15.752 G_L1_ABSOLUTE: 2.697 G_L1_RELATIVE: 13.055 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 26, iters: 75200, time: 0.556, data: 0.000) G_L1: 16.121 G_L1_ABSOLUTE: 2.603 G_L1_RELATIVE: 13.518 G_Regularizer: 0.000 validation_error: 20.241 +(epoch: 26, iters: 77200, time: 0.538, data: 0.000) G_L1: 14.030 G_L1_ABSOLUTE: 2.775 G_L1_RELATIVE: 11.256 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 26, iters: 79200, time: 0.540, data: 0.000) G_L1: 16.021 G_L1_ABSOLUTE: 2.665 G_L1_RELATIVE: 13.357 G_Regularizer: 0.000 validation_error: 20.457 +(epoch: 26, iters: 81200, time: 0.551, data: 0.000) G_L1: 14.119 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 11.440 G_Regularizer: 0.000 validation_error: 20.575 +(epoch: 26, iters: 83200, time: 0.550, data: 0.000) G_L1: 15.805 G_L1_ABSOLUTE: 2.720 G_L1_RELATIVE: 13.086 G_Regularizer: 0.000 validation_error: 20.421 +(epoch: 26, iters: 85200, time: 0.546, data: 0.000) G_L1: 11.093 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 8.537 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 26, iters: 87200, time: 0.542, data: 0.000) G_L1: 15.693 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 13.224 G_Regularizer: 0.000 validation_error: 20.338 +(epoch: 26, iters: 89200, time: 0.548, data: 0.000) G_L1: 17.452 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 14.742 G_Regularizer: 0.000 validation_error: 20.403 +(epoch: 26, iters: 91200, time: 0.550, data: 0.000) G_L1: 15.264 G_L1_ABSOLUTE: 2.215 G_L1_RELATIVE: 13.049 G_Regularizer: 0.000 validation_error: 20.443 +(epoch: 26, iters: 93200, time: 0.543, data: 0.000) G_L1: 14.834 G_L1_ABSOLUTE: 2.507 G_L1_RELATIVE: 12.327 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 26, iters: 95200, time: 0.549, data: 0.001) G_L1: 14.425 G_L1_ABSOLUTE: 2.700 G_L1_RELATIVE: 11.725 G_Regularizer: 0.000 validation_error: 21.058 +(epoch: 26, iters: 97200, time: 0.543, data: 0.000) G_L1: 16.007 G_L1_ABSOLUTE: 2.644 G_L1_RELATIVE: 13.363 G_Regularizer: 0.000 validation_error: 20.438 +(epoch: 26, iters: 99200, time: 0.546, data: 0.000) G_L1: 14.259 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 11.978 G_Regularizer: 0.000 validation_error: 20.168 +(epoch: 26, iters: 101200, time: 0.555, data: 0.001) G_L1: 14.692 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 12.290 G_Regularizer: 0.000 validation_error: 20.213 +(epoch: 26, iters: 103200, time: 0.544, data: 0.000) G_L1: 17.673 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 15.119 G_Regularizer: 0.000 validation_error: 20.747 +(epoch: 26, iters: 105200, time: 0.545, data: 0.000) G_L1: 14.042 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 11.585 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 26, iters: 107200, time: 0.544, data: 0.000) G_L1: 13.302 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 10.558 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 26, iters: 109200, time: 0.551, data: 0.000) G_L1: 14.729 G_L1_ABSOLUTE: 2.422 G_L1_RELATIVE: 12.307 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 26, iters: 111200, time: 0.544, data: 0.000) G_L1: 15.444 G_L1_ABSOLUTE: 2.850 G_L1_RELATIVE: 12.594 G_Regularizer: 0.000 validation_error: 20.637 +(epoch: 26, iters: 113200, time: 0.547, data: 0.000) G_L1: 14.586 G_L1_ABSOLUTE: 3.393 G_L1_RELATIVE: 11.193 G_Regularizer: 0.000 validation_error: 20.563 +(epoch: 26, iters: 115200, time: 0.556, data: 0.000) G_L1: 14.339 G_L1_ABSOLUTE: 2.791 G_L1_RELATIVE: 11.549 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 26, iters: 117200, time: 0.539, data: 0.000) G_L1: 12.470 G_L1_ABSOLUTE: 2.096 G_L1_RELATIVE: 10.374 G_Regularizer: 0.000 validation_error: 20.576 +(epoch: 26, iters: 119200, time: 0.539, data: 0.000) G_L1: 28.228 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 25.394 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 26, iters: 121200, time: 0.546, data: 0.000) G_L1: 14.364 G_L1_ABSOLUTE: 2.700 G_L1_RELATIVE: 11.664 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 26, iters: 123200, time: 0.553, data: 0.000) G_L1: 13.052 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 10.559 G_Regularizer: 0.000 validation_error: 20.460 +(epoch: 26, iters: 125200, time: 0.548, data: 0.000) G_L1: 15.790 G_L1_ABSOLUTE: 2.815 G_L1_RELATIVE: 12.975 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 26, iters: 127200, time: 0.541, data: 0.000) G_L1: 26.302 G_L1_ABSOLUTE: 2.775 G_L1_RELATIVE: 23.527 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 26, iters: 129200, time: 0.542, data: 0.000) G_L1: 15.641 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 13.029 G_Regularizer: 0.000 validation_error: 21.323 +(epoch: 26, iters: 131200, time: 0.545, data: 0.000) G_L1: 15.445 G_L1_ABSOLUTE: 2.667 G_L1_RELATIVE: 12.778 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 26, iters: 133200, time: 0.541, data: 0.000) G_L1: 15.923 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 13.166 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 26, iters: 135200, time: 0.543, data: 0.000) G_L1: 13.783 G_L1_ABSOLUTE: 2.726 G_L1_RELATIVE: 11.057 G_Regularizer: 0.000 validation_error: 20.563 +(epoch: 26, iters: 137200, time: 0.543, data: 0.000) G_L1: 12.564 G_L1_ABSOLUTE: 2.559 G_L1_RELATIVE: 10.005 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 26, iters: 139200, time: 0.546, data: 0.000) G_L1: 14.751 G_L1_ABSOLUTE: 2.817 G_L1_RELATIVE: 11.934 G_Regularizer: 0.000 validation_error: 21.395 +(epoch: 26, iters: 141200, time: 0.563, data: 0.001) G_L1: 16.133 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 13.417 G_Regularizer: 0.000 validation_error: 20.400 +(epoch: 26, iters: 143200, time: 0.548, data: 0.000) G_L1: 12.502 G_L1_ABSOLUTE: 2.246 G_L1_RELATIVE: 10.256 G_Regularizer: 0.000 validation_error: 20.304 +(epoch: 26, iters: 145200, time: 0.546, data: 0.000) G_L1: 15.244 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 12.565 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 26, iters: 147200, time: 0.543, data: 0.000) G_L1: 16.972 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 14.494 G_Regularizer: 0.000 validation_error: 21.227 +(epoch: 26, iters: 149200, time: 0.543, data: 0.000) G_L1: 15.682 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 12.923 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 26, iters: 151200, time: 0.546, data: 0.000) G_L1: 14.424 G_L1_ABSOLUTE: 2.538 G_L1_RELATIVE: 11.886 G_Regularizer: 0.000 validation_error: 20.537 +(epoch: 26, iters: 153200, time: 0.546, data: 0.000) G_L1: 14.488 G_L1_ABSOLUTE: 2.496 G_L1_RELATIVE: 11.992 G_Regularizer: 0.000 validation_error: 20.279 +(epoch: 26, iters: 155200, time: 0.541, data: 0.000) G_L1: 13.382 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 10.928 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 26, iters: 157200, time: 0.551, data: 0.000) G_L1: 13.709 G_L1_ABSOLUTE: 2.100 G_L1_RELATIVE: 11.609 G_Regularizer: 0.000 validation_error: 21.339 +(epoch: 26, iters: 159200, time: 0.544, data: 0.000) G_L1: 14.290 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 11.793 G_Regularizer: 0.000 validation_error: 20.520 +(epoch: 26, iters: 161200, time: 0.546, data: 0.000) G_L1: 13.713 G_L1_ABSOLUTE: 2.239 G_L1_RELATIVE: 11.474 G_Regularizer: 0.000 validation_error: 20.503 +(epoch: 26, iters: 163200, time: 0.543, data: 0.000) G_L1: 15.597 G_L1_ABSOLUTE: 2.141 G_L1_RELATIVE: 13.455 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 26, iters: 165200, time: 0.540, data: 0.000) G_L1: 15.178 G_L1_ABSOLUTE: 2.988 G_L1_RELATIVE: 12.190 G_Regularizer: 0.000 validation_error: 21.046 +(epoch: 26, iters: 167200, time: 0.544, data: 0.000) G_L1: 16.894 G_L1_ABSOLUTE: 2.779 G_L1_RELATIVE: 14.115 G_Regularizer: 0.000 validation_error: 20.355 +(epoch: 26, iters: 169200, time: 0.542, data: 0.000) G_L1: 13.856 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 11.280 G_Regularizer: 0.000 validation_error: 20.572 +(epoch: 26, iters: 171200, time: 0.554, data: 0.000) G_L1: 14.541 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 12.090 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 26, iters: 173200, time: 0.540, data: 0.000) G_L1: 13.012 G_L1_ABSOLUTE: 2.233 G_L1_RELATIVE: 10.779 G_Regularizer: 0.000 validation_error: 20.748 +(epoch: 26, iters: 175200, time: 0.550, data: 0.000) G_L1: 11.372 G_L1_ABSOLUTE: 2.093 G_L1_RELATIVE: 9.279 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 26, iters: 177200, time: 0.550, data: 0.000) G_L1: 13.548 G_L1_ABSOLUTE: 2.230 G_L1_RELATIVE: 11.318 G_Regularizer: 0.000 validation_error: 20.579 +(epoch: 26, iters: 179200, time: 0.545, data: 0.000) G_L1: 16.508 G_L1_ABSOLUTE: 2.417 G_L1_RELATIVE: 14.091 G_Regularizer: 0.000 validation_error: 20.758 +(epoch: 26, iters: 181200, time: 0.545, data: 0.000) G_L1: 13.808 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 11.273 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 26, iters: 183200, time: 0.548, data: 0.000) G_L1: 16.096 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 13.520 G_Regularizer: 0.000 validation_error: 20.480 +(epoch: 26, iters: 185200, time: 0.545, data: 0.001) G_L1: 16.210 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 13.726 G_Regularizer: 0.000 validation_error: 20.782 +(epoch: 26, iters: 187200, time: 0.543, data: 0.001) G_L1: 15.265 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 12.723 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 26, iters: 189200, time: 0.548, data: 0.000) G_L1: 16.285 G_L1_ABSOLUTE: 2.953 G_L1_RELATIVE: 13.332 G_Regularizer: 0.000 validation_error: 21.345 +(epoch: 26, iters: 191200, time: 0.554, data: 0.000) G_L1: 17.032 G_L1_ABSOLUTE: 2.709 G_L1_RELATIVE: 14.323 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 26, iters: 193200, time: 0.545, data: 0.000) G_L1: 15.819 G_L1_ABSOLUTE: 2.477 G_L1_RELATIVE: 13.342 G_Regularizer: 0.000 validation_error: 21.332 +(epoch: 26, iters: 195200, time: 0.542, data: 0.000) G_L1: 14.127 G_L1_ABSOLUTE: 2.973 G_L1_RELATIVE: 11.154 G_Regularizer: 0.000 validation_error: 20.365 +(epoch: 26, iters: 197200, time: 0.545, data: 0.000) G_L1: 15.976 G_L1_ABSOLUTE: 2.557 G_L1_RELATIVE: 13.419 G_Regularizer: 0.000 validation_error: 20.785 +(epoch: 26, iters: 199200, time: 0.540, data: 0.000) G_L1: 15.561 G_L1_ABSOLUTE: 2.324 G_L1_RELATIVE: 13.237 G_Regularizer: 0.000 validation_error: 20.559 +(epoch: 26, iters: 201200, time: 0.557, data: 0.000) G_L1: 15.706 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 13.142 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 26, iters: 203200, time: 0.546, data: 0.000) G_L1: 15.360 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 13.021 G_Regularizer: 0.000 validation_error: 21.071 +(epoch: 26, iters: 205200, time: 0.545, data: 0.000) G_L1: 14.373 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 11.625 G_Regularizer: 0.000 validation_error: 20.449 +(epoch: 26, iters: 207200, time: 0.536, data: 0.000) G_L1: 13.921 G_L1_ABSOLUTE: 2.426 G_L1_RELATIVE: 11.495 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 26, iters: 209200, time: 0.547, data: 0.000) G_L1: 12.059 G_L1_ABSOLUTE: 2.074 G_L1_RELATIVE: 9.985 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 26, iters: 211200, time: 0.544, data: 0.000) G_L1: 16.363 G_L1_ABSOLUTE: 2.923 G_L1_RELATIVE: 13.440 G_Regularizer: 0.000 validation_error: 20.607 +(epoch: 26, iters: 213200, time: 0.545, data: 0.000) G_L1: 15.442 G_L1_ABSOLUTE: 2.813 G_L1_RELATIVE: 12.629 G_Regularizer: 0.000 validation_error: 21.784 +(epoch: 26, iters: 215200, time: 0.544, data: 0.000) G_L1: 16.341 G_L1_ABSOLUTE: 2.946 G_L1_RELATIVE: 13.395 G_Regularizer: 0.000 validation_error: 20.121 +(epoch: 26, iters: 217200, time: 0.542, data: 0.000) G_L1: 16.771 G_L1_ABSOLUTE: 2.759 G_L1_RELATIVE: 14.012 G_Regularizer: 0.000 validation_error: 20.322 +(epoch: 26, iters: 219200, time: 0.549, data: 0.000) G_L1: 15.739 G_L1_ABSOLUTE: 2.793 G_L1_RELATIVE: 12.947 G_Regularizer: 0.000 validation_error: 20.436 +(epoch: 26, iters: 221200, time: 0.539, data: 0.000) G_L1: 13.654 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 11.255 G_Regularizer: 0.000 validation_error: 21.284 +(epoch: 26, iters: 223200, time: 0.543, data: 0.001) G_L1: 14.085 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 11.567 G_Regularizer: 0.000 validation_error: 21.489 +(epoch: 26, iters: 225200, time: 0.547, data: 0.000) G_L1: 15.109 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 12.605 G_Regularizer: 0.000 validation_error: 21.141 +(epoch: 26, iters: 227200, time: 0.544, data: 0.000) G_L1: 14.358 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 12.060 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 26, iters: 229200, time: 0.542, data: 0.000) G_L1: 13.022 G_L1_ABSOLUTE: 2.776 G_L1_RELATIVE: 10.246 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 26, iters: 231200, time: 0.546, data: 0.000) G_L1: 14.675 G_L1_ABSOLUTE: 3.004 G_L1_RELATIVE: 11.671 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 26, iters: 233200, time: 0.549, data: 0.001) G_L1: 12.705 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 10.200 G_Regularizer: 0.000 validation_error: 20.456 +(epoch: 26, iters: 235200, time: 0.609, data: 0.000) G_L1: 11.844 G_L1_ABSOLUTE: 2.426 G_L1_RELATIVE: 9.418 G_Regularizer: 0.000 validation_error: 21.056 +(epoch: 26, iters: 237200, time: 0.532, data: 0.000) G_L1: 16.622 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 13.973 G_Regularizer: 0.000 validation_error: 20.418 +(epoch: 26, iters: 239200, time: 0.540, data: 0.000) G_L1: 14.724 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 12.129 G_Regularizer: 0.000 validation_error: 21.137 +(epoch: 26, iters: 241200, time: 0.545, data: 0.000) G_L1: 14.158 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 11.661 G_Regularizer: 0.000 validation_error: 21.256 +(epoch: 26, iters: 243200, time: 0.548, data: 0.000) G_L1: 13.462 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 11.027 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 26, iters: 245200, time: 0.542, data: 0.000) G_L1: 11.069 G_L1_ABSOLUTE: 2.338 G_L1_RELATIVE: 8.731 G_Regularizer: 0.000 validation_error: 20.263 +(epoch: 26, iters: 247200, time: 0.547, data: 0.000) G_L1: 15.064 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 12.677 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 26, iters: 249200, time: 0.541, data: 0.000) G_L1: 15.840 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 13.310 G_Regularizer: 0.000 validation_error: 21.022 +(epoch: 26, iters: 251200, time: 0.545, data: 0.000) G_L1: 12.541 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 9.977 G_Regularizer: 0.000 validation_error: 20.947 +(epoch: 26, iters: 253200, time: 0.544, data: 0.000) G_L1: 16.257 G_L1_ABSOLUTE: 2.701 G_L1_RELATIVE: 13.556 G_Regularizer: 0.000 validation_error: 20.731 +(epoch: 26, iters: 255200, time: 0.545, data: 0.000) G_L1: 15.291 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 12.822 G_Regularizer: 0.000 validation_error: 20.431 +(epoch: 26, iters: 257200, time: 0.535, data: 0.000) G_L1: 15.446 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 12.852 G_Regularizer: 0.000 validation_error: 20.284 +(epoch: 26, iters: 259200, time: 0.541, data: 0.000) G_L1: 14.829 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 12.365 G_Regularizer: 0.000 validation_error: 20.654 +(epoch: 26, iters: 261200, time: 0.549, data: 0.000) G_L1: 13.339 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 10.853 G_Regularizer: 0.000 validation_error: 20.519 +(epoch: 26, iters: 263200, time: 0.544, data: 0.000) G_L1: 14.754 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 12.400 G_Regularizer: 0.000 validation_error: 20.775 +(epoch: 26, iters: 265200, time: 0.549, data: 0.000) G_L1: 14.671 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 12.051 G_Regularizer: 0.000 validation_error: 20.693 +(epoch: 26, iters: 267200, time: 0.548, data: 0.000) G_L1: 12.848 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 10.479 G_Regularizer: 0.000 validation_error: 20.415 +(epoch: 26, iters: 269200, time: 0.549, data: 0.000) G_L1: 16.247 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 13.391 G_Regularizer: 0.000 validation_error: 20.542 +(epoch: 26, iters: 271200, time: 0.538, data: 0.000) G_L1: 12.614 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 10.186 G_Regularizer: 0.000 validation_error: 20.286 +(epoch: 26, iters: 273200, time: 0.551, data: 0.000) G_L1: 14.505 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 11.868 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 26, iters: 275200, time: 0.544, data: 0.000) G_L1: 14.805 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 12.170 G_Regularizer: 0.000 validation_error: 20.176 +(epoch: 26, iters: 277200, time: 0.546, data: 0.000) G_L1: 16.320 G_L1_ABSOLUTE: 2.851 G_L1_RELATIVE: 13.469 G_Regularizer: 0.000 validation_error: 20.379 +(epoch: 26, iters: 279200, time: 0.543, data: 0.000) G_L1: 15.443 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 13.025 G_Regularizer: 0.000 validation_error: 20.417 +(epoch: 26, iters: 281200, time: 0.548, data: 0.000) G_L1: 16.069 G_L1_ABSOLUTE: 2.728 G_L1_RELATIVE: 13.340 G_Regularizer: 0.000 validation_error: 21.047 +(epoch: 26, iters: 283200, time: 0.542, data: 0.001) G_L1: 13.604 G_L1_ABSOLUTE: 2.060 G_L1_RELATIVE: 11.544 G_Regularizer: 0.000 validation_error: 20.666 +(epoch: 26, iters: 285200, time: 0.551, data: 0.000) G_L1: 15.449 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 13.106 G_Regularizer: 0.000 validation_error: 20.523 +(epoch: 26, iters: 287200, time: 0.555, data: 0.000) G_L1: 13.753 G_L1_ABSOLUTE: 2.181 G_L1_RELATIVE: 11.572 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 26, iters: 289200, time: 0.539, data: 0.000) G_L1: 14.601 G_L1_ABSOLUTE: 2.907 G_L1_RELATIVE: 11.694 G_Regularizer: 0.000 validation_error: 20.216 +(epoch: 26, iters: 291200, time: 0.544, data: 0.000) G_L1: 14.594 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 12.130 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 26, iters: 293200, time: 0.543, data: 0.000) G_L1: 14.140 G_L1_ABSOLUTE: 2.543 G_L1_RELATIVE: 11.597 G_Regularizer: 0.000 validation_error: 20.466 +(epoch: 26, iters: 295200, time: 0.546, data: 0.000) G_L1: 13.739 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 11.476 G_Regularizer: 0.000 validation_error: 21.508 +(epoch: 26, iters: 297200, time: 0.542, data: 0.000) G_L1: 17.267 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 14.719 G_Regularizer: 0.000 validation_error: 21.108 +(epoch: 26, iters: 299200, time: 0.549, data: 0.000) G_L1: 11.134 G_L1_ABSOLUTE: 1.970 G_L1_RELATIVE: 9.164 G_Regularizer: 0.000 validation_error: 20.826 +(epoch: 26, iters: 301200, time: 0.541, data: 0.000) G_L1: 13.578 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 10.843 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 27, iters: 448, time: 0.539, data: 0.000) G_L1: 16.217 G_L1_ABSOLUTE: 2.543 G_L1_RELATIVE: 13.674 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 27, iters: 2448, time: 0.546, data: 0.000) G_L1: 14.076 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 11.562 G_Regularizer: 0.000 validation_error: 20.490 +(epoch: 27, iters: 4448, time: 0.546, data: 0.000) G_L1: 16.323 G_L1_ABSOLUTE: 2.947 G_L1_RELATIVE: 13.376 G_Regularizer: 0.000 validation_error: 19.992 +(epoch: 27, iters: 6448, time: 0.544, data: 0.000) G_L1: 13.348 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 10.865 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 27, iters: 8448, time: 0.551, data: 0.001) G_L1: 12.721 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 10.244 G_Regularizer: 0.000 validation_error: 20.151 +(epoch: 27, iters: 10448, time: 0.540, data: 0.000) G_L1: 12.463 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 9.966 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 27, iters: 12448, time: 0.545, data: 0.000) G_L1: 15.999 G_L1_ABSOLUTE: 2.828 G_L1_RELATIVE: 13.171 G_Regularizer: 0.000 validation_error: 21.436 +(epoch: 27, iters: 14448, time: 0.545, data: 0.000) G_L1: 11.687 G_L1_ABSOLUTE: 1.926 G_L1_RELATIVE: 9.761 G_Regularizer: 0.000 validation_error: 20.122 +(epoch: 27, iters: 16448, time: 0.535, data: 0.000) G_L1: 16.158 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 13.942 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 27, iters: 18448, time: 0.550, data: 0.000) G_L1: 15.476 G_L1_ABSOLUTE: 2.868 G_L1_RELATIVE: 12.608 G_Regularizer: 0.000 validation_error: 20.794 +(epoch: 27, iters: 20448, time: 0.549, data: 0.000) G_L1: 11.684 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 9.315 G_Regularizer: 0.000 validation_error: 20.621 +(epoch: 27, iters: 22448, time: 0.542, data: 0.000) G_L1: 13.060 G_L1_ABSOLUTE: 2.110 G_L1_RELATIVE: 10.950 G_Regularizer: 0.000 validation_error: 20.548 +(epoch: 27, iters: 24448, time: 0.546, data: 0.000) G_L1: 12.763 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 10.254 G_Regularizer: 0.000 validation_error: 20.136 +(epoch: 27, iters: 26448, time: 0.551, data: 0.000) G_L1: 14.683 G_L1_ABSOLUTE: 2.455 G_L1_RELATIVE: 12.229 G_Regularizer: 0.000 validation_error: 20.745 +(epoch: 27, iters: 28448, time: 0.539, data: 0.000) G_L1: 13.817 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 11.192 G_Regularizer: 0.000 validation_error: 19.994 +(epoch: 27, iters: 30448, time: 0.544, data: 0.000) G_L1: 15.856 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 13.449 G_Regularizer: 0.000 validation_error: 20.194 +(epoch: 27, iters: 32448, time: 0.542, data: 0.000) G_L1: 14.978 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 12.605 G_Regularizer: 0.000 validation_error: 21.392 +(epoch: 27, iters: 34448, time: 0.541, data: 0.000) G_L1: 15.978 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 13.458 G_Regularizer: 0.000 validation_error: 20.195 +(epoch: 27, iters: 36448, time: 0.543, data: 0.000) G_L1: 14.425 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 12.043 G_Regularizer: 0.000 validation_error: 20.527 +(epoch: 27, iters: 38448, time: 0.547, data: 0.000) G_L1: 14.608 G_L1_ABSOLUTE: 2.194 G_L1_RELATIVE: 12.414 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 27, iters: 40448, time: 0.543, data: 0.000) G_L1: 14.977 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 12.430 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 27, iters: 42448, time: 0.543, data: 0.000) G_L1: 17.086 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 14.437 G_Regularizer: 0.000 validation_error: 21.025 +(epoch: 27, iters: 44448, time: 0.548, data: 0.001) G_L1: 13.298 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 10.674 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 27, iters: 46448, time: 0.551, data: 0.000) G_L1: 15.198 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 12.475 G_Regularizer: 0.000 validation_error: 20.078 +(epoch: 27, iters: 48448, time: 0.544, data: 0.000) G_L1: 14.075 G_L1_ABSOLUTE: 2.719 G_L1_RELATIVE: 11.355 G_Regularizer: 0.000 validation_error: 20.344 +(epoch: 27, iters: 50448, time: 0.542, data: 0.000) G_L1: 15.117 G_L1_ABSOLUTE: 2.380 G_L1_RELATIVE: 12.737 G_Regularizer: 0.000 validation_error: 20.406 +(epoch: 27, iters: 52448, time: 0.546, data: 0.000) G_L1: 14.462 G_L1_ABSOLUTE: 2.358 G_L1_RELATIVE: 12.105 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 27, iters: 54448, time: 0.551, data: 0.000) G_L1: 13.081 G_L1_ABSOLUTE: 2.272 G_L1_RELATIVE: 10.809 G_Regularizer: 0.000 validation_error: 20.461 +(epoch: 27, iters: 56448, time: 0.546, data: 0.000) G_L1: 13.450 G_L1_ABSOLUTE: 2.303 G_L1_RELATIVE: 11.148 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 27, iters: 58448, time: 0.540, data: 0.000) G_L1: 15.751 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 13.130 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 27, iters: 60448, time: 0.547, data: 0.000) G_L1: 12.128 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 9.716 G_Regularizer: 0.000 validation_error: 21.781 +(epoch: 27, iters: 62448, time: 0.541, data: 0.000) G_L1: 13.082 G_L1_ABSOLUTE: 2.055 G_L1_RELATIVE: 11.027 G_Regularizer: 0.000 validation_error: 21.784 +(epoch: 27, iters: 64448, time: 0.542, data: 0.000) G_L1: 13.702 G_L1_ABSOLUTE: 2.717 G_L1_RELATIVE: 10.985 G_Regularizer: 0.000 validation_error: 20.671 +(epoch: 27, iters: 66448, time: 0.537, data: 0.000) G_L1: 15.008 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 12.641 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 27, iters: 68448, time: 0.542, data: 0.001) G_L1: 14.308 G_L1_ABSOLUTE: 2.567 G_L1_RELATIVE: 11.741 G_Regularizer: 0.000 validation_error: 21.469 +(epoch: 27, iters: 70448, time: 0.557, data: 0.000) G_L1: 13.099 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 10.616 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 27, iters: 72448, time: 0.536, data: 0.000) G_L1: 13.134 G_L1_ABSOLUTE: 3.055 G_L1_RELATIVE: 10.079 G_Regularizer: 0.000 validation_error: 20.421 +(epoch: 27, iters: 74448, time: 0.542, data: 0.000) G_L1: 16.029 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 13.519 G_Regularizer: 0.000 validation_error: 21.231 +(epoch: 27, iters: 76448, time: 0.541, data: 0.000) G_L1: 16.189 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 13.309 G_Regularizer: 0.000 validation_error: 20.681 +(epoch: 27, iters: 78448, time: 0.548, data: 0.000) G_L1: 12.902 G_L1_ABSOLUTE: 2.636 G_L1_RELATIVE: 10.265 G_Regularizer: 0.000 validation_error: 20.964 +(epoch: 27, iters: 80448, time: 0.547, data: 0.000) G_L1: 13.733 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 11.296 G_Regularizer: 0.000 validation_error: 20.359 +(epoch: 27, iters: 82448, time: 0.544, data: 0.000) G_L1: 13.420 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 10.785 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 27, iters: 84448, time: 0.543, data: 0.000) G_L1: 13.639 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 11.366 G_Regularizer: 0.000 validation_error: 20.247 +(epoch: 27, iters: 86448, time: 0.548, data: 0.000) G_L1: 15.903 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 13.332 G_Regularizer: 0.000 validation_error: 20.195 +(epoch: 27, iters: 88448, time: 0.546, data: 0.000) G_L1: 13.779 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 11.492 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 27, iters: 90448, time: 0.548, data: 0.000) G_L1: 18.633 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 16.172 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 27, iters: 92448, time: 0.546, data: 0.000) G_L1: 14.887 G_L1_ABSOLUTE: 2.575 G_L1_RELATIVE: 12.311 G_Regularizer: 0.000 validation_error: 21.125 +(epoch: 27, iters: 94448, time: 0.539, data: 0.000) G_L1: 13.500 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 10.964 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 27, iters: 96448, time: 0.539, data: 0.000) G_L1: 12.768 G_L1_ABSOLUTE: 2.055 G_L1_RELATIVE: 10.713 G_Regularizer: 0.000 validation_error: 21.382 +(epoch: 27, iters: 98448, time: 0.544, data: 0.000) G_L1: 13.590 G_L1_ABSOLUTE: 2.278 G_L1_RELATIVE: 11.312 G_Regularizer: 0.000 validation_error: 21.358 +(epoch: 27, iters: 100448, time: 0.541, data: 0.000) G_L1: 13.560 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 11.229 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 27, iters: 102448, time: 0.550, data: 0.000) G_L1: 13.789 G_L1_ABSOLUTE: 2.655 G_L1_RELATIVE: 11.134 G_Regularizer: 0.000 validation_error: 20.481 +(epoch: 27, iters: 104448, time: 0.546, data: 0.000) G_L1: 13.524 G_L1_ABSOLUTE: 2.303 G_L1_RELATIVE: 11.221 G_Regularizer: 0.000 validation_error: 20.538 +(epoch: 27, iters: 106448, time: 0.546, data: 0.000) G_L1: 15.241 G_L1_ABSOLUTE: 2.222 G_L1_RELATIVE: 13.019 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 27, iters: 108448, time: 0.551, data: 0.000) G_L1: 12.794 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 10.385 G_Regularizer: 0.000 validation_error: 20.714 +(epoch: 27, iters: 110448, time: 0.551, data: 0.000) G_L1: 14.867 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 12.237 G_Regularizer: 0.000 validation_error: 20.429 +(epoch: 27, iters: 112448, time: 0.547, data: 0.000) G_L1: 14.473 G_L1_ABSOLUTE: 2.843 G_L1_RELATIVE: 11.630 G_Regularizer: 0.000 validation_error: 20.548 +(epoch: 27, iters: 114448, time: 0.550, data: 0.000) G_L1: 14.742 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 11.791 G_Regularizer: 0.000 validation_error: 20.549 +(epoch: 27, iters: 116448, time: 0.547, data: 0.000) G_L1: 14.700 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 12.264 G_Regularizer: 0.000 validation_error: 19.920 +(epoch: 27, iters: 118448, time: 0.544, data: 0.000) G_L1: 13.651 G_L1_ABSOLUTE: 2.607 G_L1_RELATIVE: 11.044 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 27, iters: 120448, time: 0.539, data: 0.000) G_L1: 16.959 G_L1_ABSOLUTE: 2.534 G_L1_RELATIVE: 14.425 G_Regularizer: 0.000 validation_error: 20.741 +(epoch: 27, iters: 122448, time: 0.542, data: 0.000) G_L1: 15.101 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 12.452 G_Regularizer: 0.000 validation_error: 20.263 +(epoch: 27, iters: 124448, time: 0.542, data: 0.000) G_L1: 16.394 G_L1_ABSOLUTE: 2.804 G_L1_RELATIVE: 13.590 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 27, iters: 126448, time: 0.543, data: 0.001) G_L1: 14.281 G_L1_ABSOLUTE: 2.653 G_L1_RELATIVE: 11.628 G_Regularizer: 0.000 validation_error: 21.049 +(epoch: 27, iters: 128448, time: 0.542, data: 0.000) G_L1: 13.161 G_L1_ABSOLUTE: 2.202 G_L1_RELATIVE: 10.959 G_Regularizer: 0.000 validation_error: 20.634 +(epoch: 27, iters: 130448, time: 0.552, data: 0.000) G_L1: 15.239 G_L1_ABSOLUTE: 3.082 G_L1_RELATIVE: 12.157 G_Regularizer: 0.000 validation_error: 21.066 +(epoch: 27, iters: 132448, time: 0.543, data: 0.000) G_L1: 14.367 G_L1_ABSOLUTE: 2.712 G_L1_RELATIVE: 11.654 G_Regularizer: 0.000 validation_error: 21.046 +(epoch: 27, iters: 134448, time: 0.547, data: 0.000) G_L1: 13.851 G_L1_ABSOLUTE: 2.921 G_L1_RELATIVE: 10.930 G_Regularizer: 0.000 validation_error: 21.254 +(epoch: 27, iters: 136448, time: 0.547, data: 0.000) G_L1: 14.202 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 11.689 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 27, iters: 138448, time: 0.548, data: 0.000) G_L1: 13.871 G_L1_ABSOLUTE: 1.988 G_L1_RELATIVE: 11.883 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 27, iters: 140448, time: 0.542, data: 0.000) G_L1: 14.385 G_L1_ABSOLUTE: 3.218 G_L1_RELATIVE: 11.166 G_Regularizer: 0.000 validation_error: 20.273 +(epoch: 27, iters: 142448, time: 0.548, data: 0.000) G_L1: 13.903 G_L1_ABSOLUTE: 2.239 G_L1_RELATIVE: 11.664 G_Regularizer: 0.000 validation_error: 20.251 +(epoch: 27, iters: 144448, time: 0.546, data: 0.000) G_L1: 14.992 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 12.282 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 27, iters: 146448, time: 0.541, data: 0.000) G_L1: 15.290 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 12.920 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 27, iters: 148448, time: 0.544, data: 0.000) G_L1: 14.233 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 11.847 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 27, iters: 150448, time: 0.554, data: 0.000) G_L1: 13.004 G_L1_ABSOLUTE: 2.207 G_L1_RELATIVE: 10.797 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 27, iters: 152448, time: 0.549, data: 0.000) G_L1: 13.517 G_L1_ABSOLUTE: 1.930 G_L1_RELATIVE: 11.588 G_Regularizer: 0.000 validation_error: 21.092 +(epoch: 27, iters: 154448, time: 0.543, data: 0.000) G_L1: 14.781 G_L1_ABSOLUTE: 2.882 G_L1_RELATIVE: 11.899 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 27, iters: 156448, time: 0.543, data: 0.000) G_L1: 13.633 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 11.187 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 27, iters: 158448, time: 0.550, data: 0.000) G_L1: 14.732 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 12.251 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 27, iters: 160448, time: 0.538, data: 0.000) G_L1: 13.907 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 11.424 G_Regularizer: 0.000 validation_error: 20.616 +(epoch: 27, iters: 162448, time: 0.544, data: 0.000) G_L1: 14.725 G_L1_ABSOLUTE: 2.118 G_L1_RELATIVE: 12.607 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 27, iters: 164448, time: 0.545, data: 0.000) G_L1: 13.966 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 11.374 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 27, iters: 166448, time: 0.545, data: 0.000) G_L1: 15.247 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 12.443 G_Regularizer: 0.000 validation_error: 20.427 +(epoch: 27, iters: 168448, time: 0.547, data: 0.001) G_L1: 14.234 G_L1_ABSOLUTE: 2.726 G_L1_RELATIVE: 11.509 G_Regularizer: 0.000 validation_error: 21.210 +(epoch: 27, iters: 170448, time: 0.545, data: 0.000) G_L1: 14.903 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 12.355 G_Regularizer: 0.000 validation_error: 21.277 +(epoch: 27, iters: 172448, time: 0.548, data: 0.000) G_L1: 15.826 G_L1_ABSOLUTE: 3.004 G_L1_RELATIVE: 12.823 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 27, iters: 174448, time: 0.544, data: 0.000) G_L1: 13.223 G_L1_ABSOLUTE: 2.516 G_L1_RELATIVE: 10.707 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 27, iters: 176448, time: 0.546, data: 0.000) G_L1: 13.849 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 11.277 G_Regularizer: 0.000 validation_error: 20.594 +(epoch: 27, iters: 178448, time: 0.545, data: 0.000) G_L1: 13.122 G_L1_ABSOLUTE: 2.342 G_L1_RELATIVE: 10.780 G_Regularizer: 0.000 validation_error: 20.940 +(epoch: 27, iters: 180448, time: 0.542, data: 0.000) G_L1: 14.672 G_L1_ABSOLUTE: 2.358 G_L1_RELATIVE: 12.315 G_Regularizer: 0.000 validation_error: 20.516 +(epoch: 27, iters: 182448, time: 0.538, data: 0.000) G_L1: 15.489 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 12.869 G_Regularizer: 0.000 validation_error: 20.424 +(epoch: 27, iters: 184448, time: 0.549, data: 0.000) G_L1: 12.518 G_L1_ABSOLUTE: 2.310 G_L1_RELATIVE: 10.208 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 27, iters: 186448, time: 0.538, data: 0.000) G_L1: 16.759 G_L1_ABSOLUTE: 3.110 G_L1_RELATIVE: 13.649 G_Regularizer: 0.000 validation_error: 20.663 +(epoch: 27, iters: 188448, time: 0.548, data: 0.000) G_L1: 13.250 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 10.752 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 27, iters: 190448, time: 0.546, data: 0.000) G_L1: 11.783 G_L1_ABSOLUTE: 2.440 G_L1_RELATIVE: 9.343 G_Regularizer: 0.000 validation_error: 21.755 +(epoch: 27, iters: 192448, time: 0.543, data: 0.000) G_L1: 14.930 G_L1_ABSOLUTE: 2.977 G_L1_RELATIVE: 11.953 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 27, iters: 194448, time: 0.546, data: 0.000) G_L1: 14.583 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 11.778 G_Regularizer: 0.000 validation_error: 21.003 +(epoch: 27, iters: 196448, time: 0.550, data: 0.001) G_L1: 12.034 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 9.522 G_Regularizer: 0.000 validation_error: 20.044 +(epoch: 27, iters: 198448, time: 0.549, data: 0.000) G_L1: 15.646 G_L1_ABSOLUTE: 2.915 G_L1_RELATIVE: 12.731 G_Regularizer: 0.000 validation_error: 20.383 +(epoch: 27, iters: 200448, time: 0.539, data: 0.000) G_L1: 13.578 G_L1_ABSOLUTE: 2.793 G_L1_RELATIVE: 10.785 G_Regularizer: 0.000 validation_error: 20.301 +(epoch: 27, iters: 202448, time: 0.542, data: 0.000) G_L1: 13.811 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 10.872 G_Regularizer: 0.000 validation_error: 20.993 +(epoch: 27, iters: 204448, time: 0.545, data: 0.000) G_L1: 13.987 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 11.565 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 27, iters: 206448, time: 0.543, data: 0.000) G_L1: 14.170 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 11.835 G_Regularizer: 0.000 validation_error: 20.462 +(epoch: 27, iters: 208448, time: 0.548, data: 0.000) G_L1: 14.103 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 11.594 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 27, iters: 210448, time: 0.547, data: 0.000) G_L1: 13.652 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 11.284 G_Regularizer: 0.000 validation_error: 20.731 +(epoch: 27, iters: 212448, time: 0.550, data: 0.000) G_L1: 13.366 G_L1_ABSOLUTE: 3.042 G_L1_RELATIVE: 10.324 G_Regularizer: 0.000 validation_error: 20.398 +(epoch: 27, iters: 214448, time: 0.543, data: 0.000) G_L1: 14.637 G_L1_ABSOLUTE: 2.191 G_L1_RELATIVE: 12.447 G_Regularizer: 0.000 validation_error: 20.476 +(epoch: 27, iters: 216448, time: 0.547, data: 0.000) G_L1: 17.143 G_L1_ABSOLUTE: 2.806 G_L1_RELATIVE: 14.337 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 27, iters: 218448, time: 0.544, data: 0.000) G_L1: 15.904 G_L1_ABSOLUTE: 3.098 G_L1_RELATIVE: 12.806 G_Regularizer: 0.000 validation_error: 20.495 +(epoch: 27, iters: 220448, time: 0.550, data: 0.000) G_L1: 12.901 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 10.539 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 27, iters: 222448, time: 0.551, data: 0.000) G_L1: 14.060 G_L1_ABSOLUTE: 2.507 G_L1_RELATIVE: 11.553 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 27, iters: 224448, time: 0.540, data: 0.000) G_L1: 12.161 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 9.540 G_Regularizer: 0.000 validation_error: 20.206 +(epoch: 27, iters: 226448, time: 0.540, data: 0.000) G_L1: 15.672 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 13.423 G_Regularizer: 0.000 validation_error: 20.362 +(epoch: 27, iters: 228448, time: 0.546, data: 0.000) G_L1: 11.906 G_L1_ABSOLUTE: 2.193 G_L1_RELATIVE: 9.713 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 27, iters: 230448, time: 0.537, data: 0.000) G_L1: 16.354 G_L1_ABSOLUTE: 2.884 G_L1_RELATIVE: 13.470 G_Regularizer: 0.000 validation_error: 20.611 +(epoch: 27, iters: 232448, time: 0.547, data: 0.000) G_L1: 14.434 G_L1_ABSOLUTE: 2.295 G_L1_RELATIVE: 12.138 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 27, iters: 234448, time: 0.541, data: 0.000) G_L1: 15.380 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 12.649 G_Regularizer: 0.000 validation_error: 21.126 +(epoch: 27, iters: 236448, time: 0.545, data: 0.000) G_L1: 12.197 G_L1_ABSOLUTE: 2.084 G_L1_RELATIVE: 10.113 G_Regularizer: 0.000 validation_error: 20.922 +(epoch: 27, iters: 238448, time: 0.538, data: 0.000) G_L1: 11.949 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 9.379 G_Regularizer: 0.000 validation_error: 20.277 +(epoch: 27, iters: 240448, time: 0.541, data: 0.000) G_L1: 14.986 G_L1_ABSOLUTE: 2.196 G_L1_RELATIVE: 12.791 G_Regularizer: 0.000 validation_error: 21.303 +(epoch: 27, iters: 242448, time: 0.549, data: 0.001) G_L1: 13.793 G_L1_ABSOLUTE: 2.422 G_L1_RELATIVE: 11.371 G_Regularizer: 0.000 validation_error: 20.495 +(epoch: 27, iters: 244448, time: 0.542, data: 0.000) G_L1: 16.133 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 13.557 G_Regularizer: 0.000 validation_error: 22.036 +(epoch: 27, iters: 246448, time: 0.543, data: 0.000) G_L1: 13.984 G_L1_ABSOLUTE: 2.799 G_L1_RELATIVE: 11.184 G_Regularizer: 0.000 validation_error: 20.576 +(epoch: 27, iters: 248448, time: 0.544, data: 0.000) G_L1: 12.124 G_L1_ABSOLUTE: 2.311 G_L1_RELATIVE: 9.813 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 27, iters: 250448, time: 0.549, data: 0.000) G_L1: 13.900 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 11.586 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 27, iters: 252448, time: 0.548, data: 0.000) G_L1: 16.025 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 13.295 G_Regularizer: 0.000 validation_error: 20.446 +(epoch: 27, iters: 254448, time: 0.541, data: 0.000) G_L1: 16.162 G_L1_ABSOLUTE: 2.884 G_L1_RELATIVE: 13.277 G_Regularizer: 0.000 validation_error: 20.478 +(epoch: 27, iters: 256448, time: 0.546, data: 0.000) G_L1: 17.459 G_L1_ABSOLUTE: 3.209 G_L1_RELATIVE: 14.250 G_Regularizer: 0.000 validation_error: 21.225 +(epoch: 27, iters: 258448, time: 0.547, data: 0.000) G_L1: 13.863 G_L1_ABSOLUTE: 2.740 G_L1_RELATIVE: 11.123 G_Regularizer: 0.000 validation_error: 20.138 +(epoch: 27, iters: 260448, time: 0.540, data: 0.000) G_L1: 15.102 G_L1_ABSOLUTE: 2.726 G_L1_RELATIVE: 12.376 G_Regularizer: 0.000 validation_error: 21.196 +(epoch: 27, iters: 262448, time: 0.539, data: 0.000) G_L1: 14.970 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 12.273 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 27, iters: 264448, time: 0.547, data: 0.000) G_L1: 16.592 G_L1_ABSOLUTE: 2.861 G_L1_RELATIVE: 13.731 G_Regularizer: 0.000 validation_error: 20.563 +(epoch: 27, iters: 266448, time: 0.546, data: 0.000) G_L1: 14.695 G_L1_ABSOLUTE: 2.580 G_L1_RELATIVE: 12.115 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 27, iters: 268448, time: 0.537, data: 0.000) G_L1: 11.287 G_L1_ABSOLUTE: 2.338 G_L1_RELATIVE: 8.950 G_Regularizer: 0.000 validation_error: 20.159 +(epoch: 27, iters: 270448, time: 0.539, data: 0.000) G_L1: 11.582 G_L1_ABSOLUTE: 2.382 G_L1_RELATIVE: 9.200 G_Regularizer: 0.000 validation_error: 21.043 +(epoch: 27, iters: 272448, time: 0.547, data: 0.000) G_L1: 13.537 G_L1_ABSOLUTE: 3.089 G_L1_RELATIVE: 10.447 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 27, iters: 274448, time: 0.555, data: 0.000) G_L1: 16.325 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 13.468 G_Regularizer: 0.000 validation_error: 21.605 +(epoch: 27, iters: 276448, time: 0.551, data: 0.001) G_L1: 14.123 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 11.450 G_Regularizer: 0.000 validation_error: 21.040 +(epoch: 27, iters: 278448, time: 0.541, data: 0.000) G_L1: 14.788 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 12.086 G_Regularizer: 0.000 validation_error: 20.084 +(epoch: 27, iters: 280448, time: 0.547, data: 0.000) G_L1: 15.491 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 13.038 G_Regularizer: 0.000 validation_error: 21.260 +(epoch: 27, iters: 282448, time: 0.552, data: 0.000) G_L1: 15.821 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 13.327 G_Regularizer: 0.000 validation_error: 20.699 +(epoch: 27, iters: 284448, time: 0.537, data: 0.000) G_L1: 15.459 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 12.982 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 27, iters: 286448, time: 0.543, data: 0.000) G_L1: 16.494 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 13.696 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 27, iters: 288448, time: 0.550, data: 0.000) G_L1: 15.917 G_L1_ABSOLUTE: 2.648 G_L1_RELATIVE: 13.268 G_Regularizer: 0.000 validation_error: 20.794 +(epoch: 27, iters: 290448, time: 0.545, data: 0.000) G_L1: 12.894 G_L1_ABSOLUTE: 2.172 G_L1_RELATIVE: 10.722 G_Regularizer: 0.000 validation_error: 20.757 +(epoch: 27, iters: 292448, time: 0.542, data: 0.000) G_L1: 16.555 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 14.110 G_Regularizer: 0.000 validation_error: 20.301 +(epoch: 27, iters: 294448, time: 0.541, data: 0.000) G_L1: 14.297 G_L1_ABSOLUTE: 2.728 G_L1_RELATIVE: 11.569 G_Regularizer: 0.000 validation_error: 21.617 +(epoch: 27, iters: 296448, time: 0.540, data: 0.000) G_L1: 12.925 G_L1_ABSOLUTE: 2.315 G_L1_RELATIVE: 10.610 G_Regularizer: 0.000 validation_error: 21.087 +(epoch: 27, iters: 298448, time: 0.550, data: 0.000) G_L1: 12.797 G_L1_ABSOLUTE: 2.247 G_L1_RELATIVE: 10.551 G_Regularizer: 0.000 validation_error: 20.300 +(epoch: 27, iters: 300448, time: 0.548, data: 0.000) G_L1: 13.827 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 11.357 G_Regularizer: 0.000 validation_error: 20.479 +(epoch: 27, iters: 302448, time: 0.547, data: 0.000) G_L1: 14.407 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 11.615 G_Regularizer: 0.000 validation_error: 21.435 +(epoch: 28, iters: 1696, time: 0.541, data: 0.000) G_L1: 16.847 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 14.099 G_Regularizer: 0.000 validation_error: 20.432 +(epoch: 28, iters: 3696, time: 0.553, data: 0.000) G_L1: 14.083 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 11.472 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 28, iters: 5696, time: 0.546, data: 0.000) G_L1: 14.381 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 11.769 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 28, iters: 7696, time: 0.544, data: 0.000) G_L1: 15.810 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 13.402 G_Regularizer: 0.000 validation_error: 21.154 +(epoch: 28, iters: 9696, time: 0.546, data: 0.000) G_L1: 16.450 G_L1_ABSOLUTE: 2.709 G_L1_RELATIVE: 13.741 G_Regularizer: 0.000 validation_error: 21.007 +(epoch: 28, iters: 11696, time: 0.548, data: 0.000) G_L1: 13.376 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 11.141 G_Regularizer: 0.000 validation_error: 21.241 +(epoch: 28, iters: 13696, time: 0.542, data: 0.000) G_L1: 13.447 G_L1_ABSOLUTE: 2.324 G_L1_RELATIVE: 11.123 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 28, iters: 15696, time: 0.549, data: 0.000) G_L1: 14.224 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 11.771 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 28, iters: 17696, time: 0.543, data: 0.000) G_L1: 15.751 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 13.043 G_Regularizer: 0.000 validation_error: 21.133 +(epoch: 28, iters: 19696, time: 0.549, data: 0.000) G_L1: 12.690 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 10.275 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 28, iters: 21696, time: 0.544, data: 0.000) G_L1: 17.179 G_L1_ABSOLUTE: 2.701 G_L1_RELATIVE: 14.478 G_Regularizer: 0.000 validation_error: 20.453 +(epoch: 28, iters: 23696, time: 0.544, data: 0.000) G_L1: 16.326 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 13.888 G_Regularizer: 0.000 validation_error: 20.654 +(epoch: 28, iters: 25696, time: 0.541, data: 0.000) G_L1: 15.768 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 13.194 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 28, iters: 27696, time: 0.538, data: 0.000) G_L1: 14.396 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 11.728 G_Regularizer: 0.000 validation_error: 20.641 +(epoch: 28, iters: 29696, time: 0.544, data: 0.000) G_L1: 15.326 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 12.611 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 28, iters: 31696, time: 0.540, data: 0.000) G_L1: 15.457 G_L1_ABSOLUTE: 2.717 G_L1_RELATIVE: 12.740 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 28, iters: 33696, time: 0.544, data: 0.000) G_L1: 11.802 G_L1_ABSOLUTE: 2.405 G_L1_RELATIVE: 9.398 G_Regularizer: 0.000 validation_error: 20.653 +(epoch: 28, iters: 35696, time: 0.544, data: 0.000) G_L1: 17.464 G_L1_ABSOLUTE: 2.888 G_L1_RELATIVE: 14.576 G_Regularizer: 0.000 validation_error: 20.222 +(epoch: 28, iters: 37696, time: 0.545, data: 0.000) G_L1: 15.017 G_L1_ABSOLUTE: 2.888 G_L1_RELATIVE: 12.128 G_Regularizer: 0.000 validation_error: 20.323 +(epoch: 28, iters: 39696, time: 0.543, data: 0.001) G_L1: 13.474 G_L1_ABSOLUTE: 2.671 G_L1_RELATIVE: 10.803 G_Regularizer: 0.000 validation_error: 21.069 +(epoch: 28, iters: 41696, time: 0.545, data: 0.000) G_L1: 15.910 G_L1_ABSOLUTE: 2.959 G_L1_RELATIVE: 12.951 G_Regularizer: 0.000 validation_error: 21.087 +(epoch: 28, iters: 43696, time: 0.537, data: 0.000) G_L1: 14.471 G_L1_ABSOLUTE: 2.743 G_L1_RELATIVE: 11.728 G_Regularizer: 0.000 validation_error: 20.553 +(epoch: 28, iters: 45696, time: 0.551, data: 0.001) G_L1: 13.407 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 10.607 G_Regularizer: 0.000 validation_error: 20.382 +(epoch: 28, iters: 47696, time: 0.548, data: 0.000) G_L1: 13.171 G_L1_ABSOLUTE: 2.423 G_L1_RELATIVE: 10.748 G_Regularizer: 0.000 validation_error: 20.489 +(epoch: 28, iters: 49696, time: 0.536, data: 0.000) G_L1: 14.030 G_L1_ABSOLUTE: 2.342 G_L1_RELATIVE: 11.688 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 28, iters: 51696, time: 0.546, data: 0.000) G_L1: 13.750 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 11.154 G_Regularizer: 0.000 validation_error: 21.091 +(epoch: 28, iters: 53696, time: 0.540, data: 0.000) G_L1: 15.999 G_L1_ABSOLUTE: 2.796 G_L1_RELATIVE: 13.202 G_Regularizer: 0.000 validation_error: 21.351 +(epoch: 28, iters: 55696, time: 0.544, data: 0.000) G_L1: 13.970 G_L1_ABSOLUTE: 2.330 G_L1_RELATIVE: 11.640 G_Regularizer: 0.000 validation_error: 20.650 +(epoch: 28, iters: 57696, time: 0.545, data: 0.000) G_L1: 15.411 G_L1_ABSOLUTE: 2.917 G_L1_RELATIVE: 12.494 G_Regularizer: 0.000 validation_error: 20.712 +(epoch: 28, iters: 59696, time: 0.547, data: 0.000) G_L1: 12.091 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 9.756 G_Regularizer: 0.000 validation_error: 20.388 +(epoch: 28, iters: 61696, time: 0.541, data: 0.000) G_L1: 19.008 G_L1_ABSOLUTE: 2.717 G_L1_RELATIVE: 16.291 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 28, iters: 63696, time: 0.548, data: 0.000) G_L1: 12.466 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 10.021 G_Regularizer: 0.000 validation_error: 21.262 +(epoch: 28, iters: 65696, time: 0.543, data: 0.000) G_L1: 14.919 G_L1_ABSOLUTE: 2.692 G_L1_RELATIVE: 12.227 G_Regularizer: 0.000 validation_error: 20.309 +(epoch: 28, iters: 67696, time: 0.557, data: 0.000) G_L1: 15.407 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 13.013 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 28, iters: 69696, time: 0.536, data: 0.000) G_L1: 13.233 G_L1_ABSOLUTE: 3.164 G_L1_RELATIVE: 10.069 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 28, iters: 71696, time: 0.539, data: 0.000) G_L1: 15.579 G_L1_ABSOLUTE: 2.808 G_L1_RELATIVE: 12.771 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 28, iters: 73696, time: 0.544, data: 0.000) G_L1: 13.295 G_L1_ABSOLUTE: 2.108 G_L1_RELATIVE: 11.187 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 28, iters: 75696, time: 0.552, data: 0.000) G_L1: 14.383 G_L1_ABSOLUTE: 2.348 G_L1_RELATIVE: 12.035 G_Regularizer: 0.000 validation_error: 20.303 +(epoch: 28, iters: 77696, time: 0.539, data: 0.000) G_L1: 14.285 G_L1_ABSOLUTE: 3.004 G_L1_RELATIVE: 11.281 G_Regularizer: 0.000 validation_error: 21.360 +(epoch: 28, iters: 79696, time: 0.551, data: 0.000) G_L1: 13.302 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 10.679 G_Regularizer: 0.000 validation_error: 20.893 +(epoch: 28, iters: 81696, time: 0.546, data: 0.000) G_L1: 16.662 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 14.179 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 28, iters: 83696, time: 0.545, data: 0.000) G_L1: 11.143 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 8.660 G_Regularizer: 0.000 validation_error: 21.236 +(epoch: 28, iters: 85696, time: 0.551, data: 0.000) G_L1: 15.131 G_L1_ABSOLUTE: 3.040 G_L1_RELATIVE: 12.091 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 28, iters: 87696, time: 0.550, data: 0.000) G_L1: 15.833 G_L1_ABSOLUTE: 2.318 G_L1_RELATIVE: 13.515 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 28, iters: 89696, time: 0.548, data: 0.000) G_L1: 13.583 G_L1_ABSOLUTE: 2.531 G_L1_RELATIVE: 11.053 G_Regularizer: 0.000 validation_error: 20.189 +(epoch: 28, iters: 91696, time: 0.547, data: 0.000) G_L1: 15.882 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 13.148 G_Regularizer: 0.000 validation_error: 20.874 +(epoch: 28, iters: 93696, time: 0.540, data: 0.000) G_L1: 14.932 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 12.384 G_Regularizer: 0.000 validation_error: 21.236 +(epoch: 28, iters: 95696, time: 0.548, data: 0.000) G_L1: 14.063 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 11.723 G_Regularizer: 0.000 validation_error: 21.194 +(epoch: 28, iters: 97696, time: 0.544, data: 0.000) G_L1: 16.807 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 14.241 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 28, iters: 99696, time: 0.547, data: 0.000) G_L1: 13.905 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 11.275 G_Regularizer: 0.000 validation_error: 20.733 +(epoch: 28, iters: 101696, time: 0.546, data: 0.001) G_L1: 15.005 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 12.531 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 28, iters: 103696, time: 0.543, data: 0.000) G_L1: 12.282 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 9.695 G_Regularizer: 0.000 validation_error: 20.544 +(epoch: 28, iters: 105696, time: 0.543, data: 0.001) G_L1: 16.014 G_L1_ABSOLUTE: 3.374 G_L1_RELATIVE: 12.640 G_Regularizer: 0.000 validation_error: 20.079 +(epoch: 28, iters: 107696, time: 0.552, data: 0.000) G_L1: 14.152 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 11.703 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 28, iters: 109696, time: 0.544, data: 0.000) G_L1: 12.903 G_L1_ABSOLUTE: 2.608 G_L1_RELATIVE: 10.295 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 28, iters: 111696, time: 0.555, data: 0.000) G_L1: 16.142 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 13.489 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 28, iters: 113696, time: 0.543, data: 0.000) G_L1: 15.277 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 12.828 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 28, iters: 115696, time: 0.546, data: 0.000) G_L1: 16.657 G_L1_ABSOLUTE: 2.753 G_L1_RELATIVE: 13.904 G_Regularizer: 0.000 validation_error: 21.056 +(epoch: 28, iters: 117696, time: 0.543, data: 0.000) G_L1: 16.223 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 13.728 G_Regularizer: 0.000 validation_error: 20.284 +(epoch: 28, iters: 119696, time: 0.546, data: 0.000) G_L1: 16.767 G_L1_ABSOLUTE: 2.844 G_L1_RELATIVE: 13.923 G_Regularizer: 0.000 validation_error: 21.318 +(epoch: 28, iters: 121696, time: 0.545, data: 0.000) G_L1: 18.802 G_L1_ABSOLUTE: 3.270 G_L1_RELATIVE: 15.531 G_Regularizer: 0.000 validation_error: 20.844 +(epoch: 28, iters: 123696, time: 0.550, data: 0.001) G_L1: 12.158 G_L1_ABSOLUTE: 2.338 G_L1_RELATIVE: 9.820 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 28, iters: 125696, time: 0.543, data: 0.000) G_L1: 14.648 G_L1_ABSOLUTE: 2.831 G_L1_RELATIVE: 11.817 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 28, iters: 127696, time: 0.543, data: 0.000) G_L1: 13.391 G_L1_ABSOLUTE: 2.109 G_L1_RELATIVE: 11.282 G_Regularizer: 0.000 validation_error: 20.107 +(epoch: 28, iters: 129696, time: 0.544, data: 0.000) G_L1: 12.770 G_L1_ABSOLUTE: 2.372 G_L1_RELATIVE: 10.397 G_Regularizer: 0.000 validation_error: 21.369 +(epoch: 28, iters: 131696, time: 0.540, data: 0.000) G_L1: 13.142 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 10.662 G_Regularizer: 0.000 validation_error: 20.485 +(epoch: 28, iters: 133696, time: 0.541, data: 0.000) G_L1: 16.728 G_L1_ABSOLUTE: 2.791 G_L1_RELATIVE: 13.937 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 28, iters: 135696, time: 0.542, data: 0.000) G_L1: 16.139 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 13.571 G_Regularizer: 0.000 validation_error: 21.212 +(epoch: 28, iters: 137696, time: 0.547, data: 0.000) G_L1: 14.723 G_L1_ABSOLUTE: 2.853 G_L1_RELATIVE: 11.869 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 28, iters: 139696, time: 0.543, data: 0.000) G_L1: 15.737 G_L1_ABSOLUTE: 2.973 G_L1_RELATIVE: 12.763 G_Regularizer: 0.000 validation_error: 20.598 +(epoch: 28, iters: 141696, time: 0.547, data: 0.000) G_L1: 13.670 G_L1_ABSOLUTE: 2.783 G_L1_RELATIVE: 10.887 G_Regularizer: 0.000 validation_error: 20.596 +(epoch: 28, iters: 143696, time: 0.537, data: 0.000) G_L1: 15.923 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 13.290 G_Regularizer: 0.000 validation_error: 20.572 +(epoch: 28, iters: 145696, time: 0.546, data: 0.000) G_L1: 12.602 G_L1_ABSOLUTE: 2.260 G_L1_RELATIVE: 10.342 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 28, iters: 147696, time: 0.538, data: 0.000) G_L1: 18.570 G_L1_ABSOLUTE: 3.407 G_L1_RELATIVE: 15.163 G_Regularizer: 0.000 validation_error: 20.364 +(epoch: 28, iters: 149696, time: 0.539, data: 0.000) G_L1: 13.627 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 11.178 G_Regularizer: 0.000 validation_error: 20.043 +(epoch: 28, iters: 151696, time: 0.547, data: 0.000) G_L1: 14.241 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 11.612 G_Regularizer: 0.000 validation_error: 19.906 +(epoch: 28, iters: 153696, time: 0.549, data: 0.000) G_L1: 14.662 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 12.188 G_Regularizer: 0.000 validation_error: 21.253 +(epoch: 28, iters: 155696, time: 0.541, data: 0.001) G_L1: 13.812 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 11.339 G_Regularizer: 0.000 validation_error: 21.256 +(epoch: 28, iters: 157696, time: 0.541, data: 0.000) G_L1: 15.984 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 13.437 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 28, iters: 159696, time: 0.542, data: 0.001) G_L1: 15.607 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 12.930 G_Regularizer: 0.000 validation_error: 21.162 +(epoch: 28, iters: 161696, time: 0.546, data: 0.000) G_L1: 16.120 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 13.543 G_Regularizer: 0.000 validation_error: 20.749 +(epoch: 28, iters: 163696, time: 0.549, data: 0.001) G_L1: 15.788 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 13.160 G_Regularizer: 0.000 validation_error: 20.257 +(epoch: 28, iters: 165696, time: 0.544, data: 0.000) G_L1: 13.577 G_L1_ABSOLUTE: 2.396 G_L1_RELATIVE: 11.180 G_Regularizer: 0.000 validation_error: 21.215 +(epoch: 28, iters: 167696, time: 0.546, data: 0.000) G_L1: 17.069 G_L1_ABSOLUTE: 3.256 G_L1_RELATIVE: 13.813 G_Regularizer: 0.000 validation_error: 20.437 +(epoch: 28, iters: 169696, time: 0.542, data: 0.000) G_L1: 16.515 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 13.857 G_Regularizer: 0.000 validation_error: 21.392 +(epoch: 28, iters: 171696, time: 0.549, data: 0.000) G_L1: 14.691 G_L1_ABSOLUTE: 2.545 G_L1_RELATIVE: 12.146 G_Regularizer: 0.000 validation_error: 21.302 +(epoch: 28, iters: 173696, time: 0.543, data: 0.000) G_L1: 12.286 G_L1_ABSOLUTE: 2.025 G_L1_RELATIVE: 10.260 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 28, iters: 175696, time: 0.545, data: 0.000) G_L1: 13.660 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 10.906 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 28, iters: 177696, time: 0.540, data: 0.000) G_L1: 15.050 G_L1_ABSOLUTE: 2.944 G_L1_RELATIVE: 12.106 G_Regularizer: 0.000 validation_error: 21.179 +(epoch: 28, iters: 179696, time: 0.539, data: 0.000) G_L1: 10.556 G_L1_ABSOLUTE: 1.943 G_L1_RELATIVE: 8.614 G_Regularizer: 0.000 validation_error: 20.422 +(epoch: 28, iters: 181696, time: 0.539, data: 0.000) G_L1: 13.187 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 10.667 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 28, iters: 183696, time: 0.540, data: 0.000) G_L1: 12.725 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 10.266 G_Regularizer: 0.000 validation_error: 19.901 +(epoch: 28, iters: 185696, time: 0.545, data: 0.000) G_L1: 15.240 G_L1_ABSOLUTE: 2.315 G_L1_RELATIVE: 12.925 G_Regularizer: 0.000 validation_error: 20.545 +(epoch: 28, iters: 187696, time: 0.547, data: 0.000) G_L1: 14.390 G_L1_ABSOLUTE: 2.604 G_L1_RELATIVE: 11.787 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 28, iters: 189696, time: 0.543, data: 0.000) G_L1: 18.476 G_L1_ABSOLUTE: 2.205 G_L1_RELATIVE: 16.271 G_Regularizer: 0.000 validation_error: 20.297 +(epoch: 28, iters: 191696, time: 0.539, data: 0.000) G_L1: 12.897 G_L1_ABSOLUTE: 2.257 G_L1_RELATIVE: 10.640 G_Regularizer: 0.000 validation_error: 20.538 +(epoch: 28, iters: 193696, time: 0.548, data: 0.000) G_L1: 12.322 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 9.892 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 28, iters: 195696, time: 0.537, data: 0.000) G_L1: 16.990 G_L1_ABSOLUTE: 2.434 G_L1_RELATIVE: 14.556 G_Regularizer: 0.000 validation_error: 20.455 +(epoch: 28, iters: 197696, time: 0.552, data: 0.000) G_L1: 16.008 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 13.513 G_Regularizer: 0.000 validation_error: 20.299 +(epoch: 28, iters: 199696, time: 0.547, data: 0.000) G_L1: 14.336 G_L1_ABSOLUTE: 2.393 G_L1_RELATIVE: 11.943 G_Regularizer: 0.000 validation_error: 20.653 +(epoch: 28, iters: 201696, time: 0.549, data: 0.000) G_L1: 12.775 G_L1_ABSOLUTE: 2.163 G_L1_RELATIVE: 10.612 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 28, iters: 203696, time: 0.536, data: 0.000) G_L1: 17.169 G_L1_ABSOLUTE: 3.224 G_L1_RELATIVE: 13.945 G_Regularizer: 0.000 validation_error: 20.959 +(epoch: 28, iters: 205696, time: 0.548, data: 0.000) G_L1: 13.948 G_L1_ABSOLUTE: 2.376 G_L1_RELATIVE: 11.571 G_Regularizer: 0.000 validation_error: 20.287 +(epoch: 28, iters: 207696, time: 0.542, data: 0.000) G_L1: 13.001 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 10.585 G_Regularizer: 0.000 validation_error: 20.037 +(epoch: 28, iters: 209696, time: 0.541, data: 0.000) G_L1: 15.556 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 13.187 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 28, iters: 211696, time: 0.545, data: 0.000) G_L1: 16.728 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 14.231 G_Regularizer: 0.000 validation_error: 20.442 +(epoch: 28, iters: 213696, time: 0.540, data: 0.000) G_L1: 17.605 G_L1_ABSOLUTE: 2.516 G_L1_RELATIVE: 15.090 G_Regularizer: 0.000 validation_error: 20.871 +(epoch: 28, iters: 215696, time: 0.537, data: 0.000) G_L1: 14.490 G_L1_ABSOLUTE: 2.338 G_L1_RELATIVE: 12.152 G_Regularizer: 0.000 validation_error: 20.379 +(epoch: 28, iters: 217696, time: 0.547, data: 0.000) G_L1: 16.151 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 13.426 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 28, iters: 219696, time: 0.553, data: 0.000) G_L1: 14.544 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 11.639 G_Regularizer: 0.000 validation_error: 20.343 +(epoch: 28, iters: 221696, time: 0.548, data: 0.000) G_L1: 15.586 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 12.788 G_Regularizer: 0.000 validation_error: 20.168 +(epoch: 28, iters: 223696, time: 0.550, data: 0.000) G_L1: 13.016 G_L1_ABSOLUTE: 2.277 G_L1_RELATIVE: 10.738 G_Regularizer: 0.000 validation_error: 21.314 +(epoch: 28, iters: 225696, time: 0.547, data: 0.000) G_L1: 16.028 G_L1_ABSOLUTE: 2.266 G_L1_RELATIVE: 13.763 G_Regularizer: 0.000 validation_error: 20.992 +(epoch: 28, iters: 227696, time: 0.550, data: 0.000) G_L1: 13.567 G_L1_ABSOLUTE: 2.718 G_L1_RELATIVE: 10.849 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 28, iters: 229696, time: 0.541, data: 0.000) G_L1: 16.846 G_L1_ABSOLUTE: 3.201 G_L1_RELATIVE: 13.645 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 28, iters: 231696, time: 0.554, data: 0.000) G_L1: 14.361 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 11.730 G_Regularizer: 0.000 validation_error: 20.692 +(epoch: 28, iters: 233696, time: 0.552, data: 0.000) G_L1: 14.936 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 12.426 G_Regularizer: 0.000 validation_error: 20.567 +(epoch: 28, iters: 235696, time: 0.540, data: 0.000) G_L1: 17.507 G_L1_ABSOLUTE: 2.825 G_L1_RELATIVE: 14.682 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 28, iters: 237696, time: 0.540, data: 0.000) G_L1: 14.675 G_L1_ABSOLUTE: 2.500 G_L1_RELATIVE: 12.175 G_Regularizer: 0.000 validation_error: 20.389 +(epoch: 28, iters: 239696, time: 0.548, data: 0.000) G_L1: 14.594 G_L1_ABSOLUTE: 2.638 G_L1_RELATIVE: 11.957 G_Regularizer: 0.000 validation_error: 20.475 +(epoch: 28, iters: 241696, time: 0.542, data: 0.000) G_L1: 12.876 G_L1_ABSOLUTE: 2.159 G_L1_RELATIVE: 10.718 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 28, iters: 243696, time: 0.545, data: 0.000) G_L1: 16.207 G_L1_ABSOLUTE: 2.997 G_L1_RELATIVE: 13.210 G_Regularizer: 0.000 validation_error: 20.389 +(epoch: 28, iters: 245696, time: 0.559, data: 0.000) G_L1: 15.270 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 12.614 G_Regularizer: 0.000 validation_error: 20.234 +(epoch: 28, iters: 247696, time: 0.541, data: 0.000) G_L1: 13.788 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 11.082 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 28, iters: 249696, time: 0.546, data: 0.000) G_L1: 14.391 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 11.806 G_Regularizer: 0.000 validation_error: 20.532 +(epoch: 28, iters: 251696, time: 0.548, data: 0.000) G_L1: 15.266 G_L1_ABSOLUTE: 2.308 G_L1_RELATIVE: 12.958 G_Regularizer: 0.000 validation_error: 20.312 +(epoch: 28, iters: 253696, time: 0.556, data: 0.000) G_L1: 13.227 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 10.861 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 28, iters: 255696, time: 0.546, data: 0.000) G_L1: 13.636 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 11.062 G_Regularizer: 0.000 validation_error: 20.653 +(epoch: 28, iters: 257696, time: 0.547, data: 0.001) G_L1: 16.406 G_L1_ABSOLUTE: 2.750 G_L1_RELATIVE: 13.656 G_Regularizer: 0.000 validation_error: 20.421 +(epoch: 28, iters: 259696, time: 0.547, data: 0.000) G_L1: 12.322 G_L1_ABSOLUTE: 2.228 G_L1_RELATIVE: 10.094 G_Regularizer: 0.000 validation_error: 20.397 +(epoch: 28, iters: 261696, time: 0.543, data: 0.000) G_L1: 14.052 G_L1_ABSOLUTE: 2.234 G_L1_RELATIVE: 11.818 G_Regularizer: 0.000 validation_error: 21.172 +(epoch: 28, iters: 263696, time: 0.544, data: 0.000) G_L1: 15.915 G_L1_ABSOLUTE: 2.879 G_L1_RELATIVE: 13.036 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 28, iters: 265696, time: 0.559, data: 0.000) G_L1: 14.742 G_L1_ABSOLUTE: 3.041 G_L1_RELATIVE: 11.701 G_Regularizer: 0.000 validation_error: 21.300 +(epoch: 28, iters: 267696, time: 0.542, data: 0.000) G_L1: 17.566 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 15.112 G_Regularizer: 0.000 validation_error: 20.634 +(epoch: 28, iters: 269696, time: 0.541, data: 0.000) G_L1: 16.793 G_L1_ABSOLUTE: 2.987 G_L1_RELATIVE: 13.806 G_Regularizer: 0.000 validation_error: 20.529 +(epoch: 28, iters: 271696, time: 0.540, data: 0.000) G_L1: 11.008 G_L1_ABSOLUTE: 2.054 G_L1_RELATIVE: 8.955 G_Regularizer: 0.000 validation_error: 20.748 +(epoch: 28, iters: 273696, time: 0.550, data: 0.001) G_L1: 14.266 G_L1_ABSOLUTE: 2.293 G_L1_RELATIVE: 11.974 G_Regularizer: 0.000 validation_error: 21.194 +(epoch: 28, iters: 275696, time: 0.547, data: 0.000) G_L1: 11.475 G_L1_ABSOLUTE: 2.141 G_L1_RELATIVE: 9.334 G_Regularizer: 0.000 validation_error: 20.854 +(epoch: 28, iters: 277696, time: 0.539, data: 0.000) G_L1: 14.498 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 12.153 G_Regularizer: 0.000 validation_error: 20.576 +(epoch: 28, iters: 279696, time: 0.558, data: 0.000) G_L1: 15.444 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 12.919 G_Regularizer: 0.000 validation_error: 21.060 +(epoch: 28, iters: 281696, time: 0.538, data: 0.001) G_L1: 18.425 G_L1_ABSOLUTE: 2.313 G_L1_RELATIVE: 16.113 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 28, iters: 283696, time: 0.549, data: 0.000) G_L1: 15.748 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 13.264 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 28, iters: 285696, time: 0.541, data: 0.000) G_L1: 13.062 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 10.463 G_Regularizer: 0.000 validation_error: 20.927 +(epoch: 28, iters: 287696, time: 0.552, data: 0.001) G_L1: 13.333 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 10.596 G_Regularizer: 0.000 validation_error: 20.768 +(epoch: 28, iters: 289696, time: 0.542, data: 0.000) G_L1: 14.396 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 11.813 G_Regularizer: 0.000 validation_error: 20.292 +(epoch: 28, iters: 291696, time: 0.548, data: 0.000) G_L1: 15.197 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 12.616 G_Regularizer: 0.000 validation_error: 20.672 +(epoch: 28, iters: 293696, time: 0.542, data: 0.000) G_L1: 15.412 G_L1_ABSOLUTE: 2.270 G_L1_RELATIVE: 13.142 G_Regularizer: 0.000 validation_error: 20.288 +(epoch: 28, iters: 295696, time: 0.546, data: 0.000) G_L1: 17.772 G_L1_ABSOLUTE: 3.231 G_L1_RELATIVE: 14.542 G_Regularizer: 0.000 validation_error: 20.325 +(epoch: 28, iters: 297696, time: 0.545, data: 0.000) G_L1: 15.716 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 13.160 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 28, iters: 299696, time: 0.549, data: 0.000) G_L1: 14.640 G_L1_ABSOLUTE: 2.205 G_L1_RELATIVE: 12.435 G_Regularizer: 0.000 validation_error: 20.207 +(epoch: 28, iters: 301696, time: 0.538, data: 0.000) G_L1: 15.122 G_L1_ABSOLUTE: 3.334 G_L1_RELATIVE: 11.788 G_Regularizer: 0.000 validation_error: 20.309 +(epoch: 29, iters: 944, time: 0.538, data: 0.000) G_L1: 15.783 G_L1_ABSOLUTE: 3.073 G_L1_RELATIVE: 12.710 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 29, iters: 2944, time: 0.544, data: 0.000) G_L1: 13.689 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 11.153 G_Regularizer: 0.000 validation_error: 21.223 +(epoch: 29, iters: 4944, time: 0.548, data: 0.000) G_L1: 14.162 G_L1_ABSOLUTE: 2.917 G_L1_RELATIVE: 11.245 G_Regularizer: 0.000 validation_error: 19.976 +(epoch: 29, iters: 6944, time: 0.545, data: 0.001) G_L1: 14.831 G_L1_ABSOLUTE: 3.379 G_L1_RELATIVE: 11.452 G_Regularizer: 0.000 validation_error: 21.496 +(epoch: 29, iters: 8944, time: 0.543, data: 0.000) G_L1: 14.772 G_L1_ABSOLUTE: 2.529 G_L1_RELATIVE: 12.243 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 29, iters: 10944, time: 0.549, data: 0.000) G_L1: 14.717 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 12.146 G_Regularizer: 0.000 validation_error: 20.939 +(epoch: 29, iters: 12944, time: 0.541, data: 0.000) G_L1: 12.274 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 9.614 G_Regularizer: 0.000 validation_error: 20.661 +(epoch: 29, iters: 14944, time: 0.551, data: 0.000) G_L1: 13.604 G_L1_ABSOLUTE: 2.359 G_L1_RELATIVE: 11.245 G_Regularizer: 0.000 validation_error: 20.429 +(epoch: 29, iters: 16944, time: 0.541, data: 0.000) G_L1: 12.651 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 10.110 G_Regularizer: 0.000 validation_error: 21.190 +(epoch: 29, iters: 18944, time: 0.546, data: 0.001) G_L1: 14.415 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 11.789 G_Regularizer: 0.000 validation_error: 20.413 +(epoch: 29, iters: 20944, time: 0.537, data: 0.000) G_L1: 14.138 G_L1_ABSOLUTE: 2.465 G_L1_RELATIVE: 11.673 G_Regularizer: 0.000 validation_error: 20.475 +(epoch: 29, iters: 22944, time: 0.545, data: 0.000) G_L1: 17.081 G_L1_ABSOLUTE: 2.906 G_L1_RELATIVE: 14.175 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 29, iters: 24944, time: 0.551, data: 0.000) G_L1: 15.952 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 13.230 G_Regularizer: 0.000 validation_error: 21.090 +(epoch: 29, iters: 26944, time: 0.546, data: 0.000) G_L1: 24.085 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 21.588 G_Regularizer: 0.000 validation_error: 20.798 +(epoch: 29, iters: 28944, time: 0.544, data: 0.000) G_L1: 14.598 G_L1_ABSOLUTE: 2.324 G_L1_RELATIVE: 12.274 G_Regularizer: 0.000 validation_error: 20.920 +(epoch: 29, iters: 30944, time: 0.534, data: 0.000) G_L1: 16.065 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 13.253 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 29, iters: 32944, time: 0.559, data: 0.000) G_L1: 13.906 G_L1_ABSOLUTE: 2.862 G_L1_RELATIVE: 11.044 G_Regularizer: 0.000 validation_error: 21.089 +(epoch: 29, iters: 34944, time: 0.541, data: 0.001) G_L1: 12.031 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 9.684 G_Regularizer: 0.000 validation_error: 21.318 +(epoch: 29, iters: 36944, time: 0.539, data: 0.000) G_L1: 12.281 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 9.658 G_Regularizer: 0.000 validation_error: 20.602 +(epoch: 29, iters: 38944, time: 0.549, data: 0.000) G_L1: 14.355 G_L1_ABSOLUTE: 2.330 G_L1_RELATIVE: 12.025 G_Regularizer: 0.000 validation_error: 20.459 +(epoch: 29, iters: 40944, time: 0.547, data: 0.000) G_L1: 15.190 G_L1_ABSOLUTE: 2.479 G_L1_RELATIVE: 12.711 G_Regularizer: 0.000 validation_error: 20.674 +(epoch: 29, iters: 42944, time: 0.544, data: 0.000) G_L1: 17.793 G_L1_ABSOLUTE: 3.034 G_L1_RELATIVE: 14.759 G_Regularizer: 0.000 validation_error: 20.339 +(epoch: 29, iters: 44944, time: 0.548, data: 0.000) G_L1: 13.982 G_L1_ABSOLUTE: 2.896 G_L1_RELATIVE: 11.086 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 29, iters: 46944, time: 0.551, data: 0.000) G_L1: 16.979 G_L1_ABSOLUTE: 2.934 G_L1_RELATIVE: 14.046 G_Regularizer: 0.000 validation_error: 20.854 +(epoch: 29, iters: 48944, time: 0.547, data: 0.000) G_L1: 14.975 G_L1_ABSOLUTE: 3.475 G_L1_RELATIVE: 11.500 G_Regularizer: 0.000 validation_error: 20.206 +(epoch: 29, iters: 50944, time: 0.544, data: 0.000) G_L1: 14.249 G_L1_ABSOLUTE: 2.163 G_L1_RELATIVE: 12.086 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 29, iters: 52944, time: 0.548, data: 0.000) G_L1: 14.180 G_L1_ABSOLUTE: 2.477 G_L1_RELATIVE: 11.703 G_Regularizer: 0.000 validation_error: 20.597 +(epoch: 29, iters: 54944, time: 0.547, data: 0.000) G_L1: 15.496 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 13.001 G_Regularizer: 0.000 validation_error: 21.246 +(epoch: 29, iters: 56944, time: 0.543, data: 0.000) G_L1: 14.402 G_L1_ABSOLUTE: 2.654 G_L1_RELATIVE: 11.748 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 29, iters: 58944, time: 0.543, data: 0.000) G_L1: 16.604 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 13.806 G_Regularizer: 0.000 validation_error: 20.258 +(epoch: 29, iters: 60944, time: 0.544, data: 0.000) G_L1: 13.965 G_L1_ABSOLUTE: 2.766 G_L1_RELATIVE: 11.199 G_Regularizer: 0.000 validation_error: 21.063 +(epoch: 29, iters: 62944, time: 0.549, data: 0.000) G_L1: 13.309 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 10.674 G_Regularizer: 0.000 validation_error: 20.424 +(epoch: 29, iters: 64944, time: 0.539, data: 0.000) G_L1: 11.475 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 9.221 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 29, iters: 66944, time: 0.538, data: 0.000) G_L1: 18.099 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 15.606 G_Regularizer: 0.000 validation_error: 20.943 +(epoch: 29, iters: 68944, time: 0.541, data: 0.000) G_L1: 16.482 G_L1_ABSOLUTE: 2.690 G_L1_RELATIVE: 13.793 G_Regularizer: 0.000 validation_error: 20.681 +(epoch: 29, iters: 70944, time: 0.546, data: 0.000) G_L1: 13.784 G_L1_ABSOLUTE: 2.608 G_L1_RELATIVE: 11.176 G_Regularizer: 0.000 validation_error: 20.380 +(epoch: 29, iters: 72944, time: 0.541, data: 0.000) G_L1: 13.097 G_L1_ABSOLUTE: 2.109 G_L1_RELATIVE: 10.988 G_Regularizer: 0.000 validation_error: 21.043 +(epoch: 29, iters: 74944, time: 0.559, data: 0.001) G_L1: 17.087 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 14.669 G_Regularizer: 0.000 validation_error: 20.383 +(epoch: 29, iters: 76944, time: 0.539, data: 0.000) G_L1: 14.200 G_L1_ABSOLUTE: 2.532 G_L1_RELATIVE: 11.667 G_Regularizer: 0.000 validation_error: 20.374 +(epoch: 29, iters: 78944, time: 0.541, data: 0.000) G_L1: 14.180 G_L1_ABSOLUTE: 3.126 G_L1_RELATIVE: 11.053 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 29, iters: 80944, time: 0.553, data: 0.000) G_L1: 15.635 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 13.014 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 29, iters: 82944, time: 0.543, data: 0.000) G_L1: 14.037 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 11.388 G_Regularizer: 0.000 validation_error: 20.553 +(epoch: 29, iters: 84944, time: 0.539, data: 0.000) G_L1: 11.372 G_L1_ABSOLUTE: 2.244 G_L1_RELATIVE: 9.129 G_Regularizer: 0.000 validation_error: 20.674 +(epoch: 29, iters: 86944, time: 0.542, data: 0.000) G_L1: 12.872 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 10.316 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 29, iters: 88944, time: 0.551, data: 0.000) G_L1: 12.699 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 10.110 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 29, iters: 90944, time: 0.548, data: 0.000) G_L1: 13.365 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 10.621 G_Regularizer: 0.000 validation_error: 20.527 +(epoch: 29, iters: 92944, time: 0.545, data: 0.000) G_L1: 14.232 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 11.595 G_Regularizer: 0.000 validation_error: 20.229 +(epoch: 29, iters: 94944, time: 0.545, data: 0.000) G_L1: 20.259 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 17.590 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 29, iters: 96944, time: 0.544, data: 0.000) G_L1: 15.416 G_L1_ABSOLUTE: 2.761 G_L1_RELATIVE: 12.655 G_Regularizer: 0.000 validation_error: 20.038 +(epoch: 29, iters: 98944, time: 0.537, data: 0.000) G_L1: 13.431 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 10.723 G_Regularizer: 0.000 validation_error: 21.228 +(epoch: 29, iters: 100944, time: 0.545, data: 0.000) G_L1: 14.055 G_L1_ABSOLUTE: 2.427 G_L1_RELATIVE: 11.627 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 29, iters: 102944, time: 0.547, data: 0.001) G_L1: 15.922 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 13.396 G_Regularizer: 0.000 validation_error: 20.323 +(epoch: 29, iters: 104944, time: 0.548, data: 0.000) G_L1: 16.599 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 14.093 G_Regularizer: 0.000 validation_error: 20.027 +(epoch: 29, iters: 106944, time: 0.542, data: 0.000) G_L1: 16.517 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 13.763 G_Regularizer: 0.000 validation_error: 20.421 +(epoch: 29, iters: 108944, time: 0.551, data: 0.000) G_L1: 14.025 G_L1_ABSOLUTE: 2.267 G_L1_RELATIVE: 11.758 G_Regularizer: 0.000 validation_error: 21.238 +(epoch: 29, iters: 110944, time: 0.540, data: 0.000) G_L1: 16.654 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 14.114 G_Regularizer: 0.000 validation_error: 19.846 +(epoch: 29, iters: 112944, time: 0.549, data: 0.000) G_L1: 12.593 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 10.239 G_Regularizer: 0.000 validation_error: 20.408 +(epoch: 29, iters: 114944, time: 0.549, data: 0.000) G_L1: 12.488 G_L1_ABSOLUTE: 2.353 G_L1_RELATIVE: 10.135 G_Regularizer: 0.000 validation_error: 21.152 +(epoch: 29, iters: 116944, time: 0.545, data: 0.000) G_L1: 16.239 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 13.625 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 29, iters: 118944, time: 0.547, data: 0.000) G_L1: 13.338 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 10.629 G_Regularizer: 0.000 validation_error: 21.249 +(epoch: 29, iters: 120944, time: 0.548, data: 0.001) G_L1: 13.866 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 11.131 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 29, iters: 122944, time: 0.554, data: 0.000) G_L1: 13.125 G_L1_ABSOLUTE: 2.204 G_L1_RELATIVE: 10.921 G_Regularizer: 0.000 validation_error: 20.622 +(epoch: 29, iters: 124944, time: 0.542, data: 0.000) G_L1: 12.550 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 9.864 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 29, iters: 126944, time: 0.554, data: 0.000) G_L1: 15.320 G_L1_ABSOLUTE: 2.575 G_L1_RELATIVE: 12.745 G_Regularizer: 0.000 validation_error: 20.972 +(epoch: 29, iters: 128944, time: 0.544, data: 0.000) G_L1: 11.659 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 8.847 G_Regularizer: 0.000 validation_error: 21.224 +(epoch: 29, iters: 130944, time: 0.545, data: 0.000) G_L1: 13.934 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 11.534 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 29, iters: 132944, time: 0.538, data: 0.000) G_L1: 14.750 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 12.349 G_Regularizer: 0.000 validation_error: 20.697 +(epoch: 29, iters: 134944, time: 0.539, data: 0.000) G_L1: 14.704 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 11.990 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 29, iters: 136944, time: 0.544, data: 0.000) G_L1: 14.151 G_L1_ABSOLUTE: 2.241 G_L1_RELATIVE: 11.910 G_Regularizer: 0.000 validation_error: 20.357 +(epoch: 29, iters: 138944, time: 0.549, data: 0.000) G_L1: 16.096 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 13.467 G_Regularizer: 0.000 validation_error: 20.364 +(epoch: 29, iters: 140944, time: 0.549, data: 0.000) G_L1: 13.122 G_L1_ABSOLUTE: 2.155 G_L1_RELATIVE: 10.967 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 29, iters: 142944, time: 0.543, data: 0.000) G_L1: 13.117 G_L1_ABSOLUTE: 2.667 G_L1_RELATIVE: 10.450 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 29, iters: 144944, time: 0.543, data: 0.000) G_L1: 13.839 G_L1_ABSOLUTE: 2.704 G_L1_RELATIVE: 11.135 G_Regularizer: 0.000 validation_error: 20.747 +(epoch: 29, iters: 146944, time: 0.542, data: 0.000) G_L1: 14.990 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 12.522 G_Regularizer: 0.000 validation_error: 21.075 +(epoch: 29, iters: 148944, time: 0.552, data: 0.000) G_L1: 15.735 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 12.993 G_Regularizer: 0.000 validation_error: 20.867 +(epoch: 29, iters: 150944, time: 0.538, data: 0.000) G_L1: 15.098 G_L1_ABSOLUTE: 2.711 G_L1_RELATIVE: 12.387 G_Regularizer: 0.000 validation_error: 21.192 +(epoch: 29, iters: 152944, time: 0.542, data: 0.000) G_L1: 14.173 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 11.915 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 29, iters: 154944, time: 0.538, data: 0.000) G_L1: 14.265 G_L1_ABSOLUTE: 2.617 G_L1_RELATIVE: 11.649 G_Regularizer: 0.000 validation_error: 20.730 +(epoch: 29, iters: 156944, time: 0.569, data: 0.000) G_L1: 13.822 G_L1_ABSOLUTE: 2.170 G_L1_RELATIVE: 11.651 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 29, iters: 158944, time: 0.539, data: 0.000) G_L1: 12.739 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 10.272 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 29, iters: 160944, time: 0.549, data: 0.001) G_L1: 17.458 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 14.833 G_Regularizer: 0.000 validation_error: 21.370 +(epoch: 29, iters: 162944, time: 0.541, data: 0.000) G_L1: 12.440 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 10.182 G_Regularizer: 0.000 validation_error: 20.377 +(epoch: 29, iters: 164944, time: 0.547, data: 0.000) G_L1: 16.039 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 13.356 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 29, iters: 166944, time: 0.553, data: 0.000) G_L1: 14.918 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 12.286 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 29, iters: 168944, time: 0.546, data: 0.000) G_L1: 14.001 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 11.612 G_Regularizer: 0.000 validation_error: 21.360 +(epoch: 29, iters: 170944, time: 0.546, data: 0.000) G_L1: 12.809 G_L1_ABSOLUTE: 2.608 G_L1_RELATIVE: 10.202 G_Regularizer: 0.000 validation_error: 20.626 +(epoch: 29, iters: 172944, time: 0.547, data: 0.000) G_L1: 14.471 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 12.136 G_Regularizer: 0.000 validation_error: 20.508 +(epoch: 29, iters: 174944, time: 0.549, data: 0.000) G_L1: 15.480 G_L1_ABSOLUTE: 3.167 G_L1_RELATIVE: 12.313 G_Regularizer: 0.000 validation_error: 20.181 +(epoch: 29, iters: 176944, time: 0.552, data: 0.000) G_L1: 13.173 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 10.832 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 29, iters: 178944, time: 0.540, data: 0.000) G_L1: 14.595 G_L1_ABSOLUTE: 2.693 G_L1_RELATIVE: 11.902 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 29, iters: 180944, time: 0.555, data: 0.000) G_L1: 13.217 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 10.757 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 29, iters: 182944, time: 0.546, data: 0.000) G_L1: 13.267 G_L1_ABSOLUTE: 2.334 G_L1_RELATIVE: 10.932 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 29, iters: 184944, time: 0.550, data: 0.000) G_L1: 13.577 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 11.006 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 29, iters: 186944, time: 0.541, data: 0.000) G_L1: 13.685 G_L1_ABSOLUTE: 2.218 G_L1_RELATIVE: 11.467 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 29, iters: 188944, time: 0.540, data: 0.000) G_L1: 16.126 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 13.396 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 29, iters: 190944, time: 0.543, data: 0.000) G_L1: 15.471 G_L1_ABSOLUTE: 2.942 G_L1_RELATIVE: 12.530 G_Regularizer: 0.000 validation_error: 20.708 +(epoch: 29, iters: 192944, time: 0.538, data: 0.000) G_L1: 14.545 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 12.091 G_Regularizer: 0.000 validation_error: 20.161 +(epoch: 29, iters: 194944, time: 0.555, data: 0.000) G_L1: 12.990 G_L1_ABSOLUTE: 2.089 G_L1_RELATIVE: 10.901 G_Regularizer: 0.000 validation_error: 20.503 +(epoch: 29, iters: 196944, time: 0.545, data: 0.000) G_L1: 13.842 G_L1_ABSOLUTE: 2.788 G_L1_RELATIVE: 11.054 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 29, iters: 198944, time: 0.539, data: 0.000) G_L1: 16.051 G_L1_ABSOLUTE: 2.507 G_L1_RELATIVE: 13.544 G_Regularizer: 0.000 validation_error: 20.455 +(epoch: 29, iters: 200944, time: 0.545, data: 0.000) G_L1: 14.118 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 11.408 G_Regularizer: 0.000 validation_error: 21.144 +(epoch: 29, iters: 202944, time: 0.546, data: 0.000) G_L1: 13.683 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 10.937 G_Regularizer: 0.000 validation_error: 20.515 +(epoch: 29, iters: 204944, time: 0.538, data: 0.001) G_L1: 14.252 G_L1_ABSOLUTE: 2.943 G_L1_RELATIVE: 11.310 G_Regularizer: 0.000 validation_error: 21.027 +(epoch: 29, iters: 206944, time: 0.542, data: 0.000) G_L1: 14.931 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 12.657 G_Regularizer: 0.000 validation_error: 20.502 +(epoch: 29, iters: 208944, time: 0.547, data: 0.000) G_L1: 13.522 G_L1_ABSOLUTE: 2.211 G_L1_RELATIVE: 11.310 G_Regularizer: 0.000 validation_error: 20.513 +(epoch: 29, iters: 210944, time: 0.535, data: 0.001) G_L1: 27.837 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 25.083 G_Regularizer: 0.000 validation_error: 20.395 +(epoch: 29, iters: 212944, time: 0.554, data: 0.000) G_L1: 13.080 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 10.645 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 29, iters: 214944, time: 0.545, data: 0.000) G_L1: 13.408 G_L1_ABSOLUTE: 2.653 G_L1_RELATIVE: 10.754 G_Regularizer: 0.000 validation_error: 19.890 +(epoch: 29, iters: 216944, time: 0.538, data: 0.001) G_L1: 13.051 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 10.400 G_Regularizer: 0.000 validation_error: 20.712 +(epoch: 29, iters: 218944, time: 0.538, data: 0.000) G_L1: 14.443 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 11.826 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 29, iters: 220944, time: 0.555, data: 0.000) G_L1: 12.196 G_L1_ABSOLUTE: 2.671 G_L1_RELATIVE: 9.524 G_Regularizer: 0.000 validation_error: 21.364 +(epoch: 29, iters: 222944, time: 0.542, data: 0.000) G_L1: 17.012 G_L1_ABSOLUTE: 2.552 G_L1_RELATIVE: 14.460 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 29, iters: 224944, time: 0.547, data: 0.000) G_L1: 13.753 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 11.470 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 29, iters: 226944, time: 0.549, data: 0.000) G_L1: 14.797 G_L1_ABSOLUTE: 2.993 G_L1_RELATIVE: 11.804 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 29, iters: 228944, time: 0.547, data: 0.000) G_L1: 14.709 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 12.090 G_Regularizer: 0.000 validation_error: 20.716 +(epoch: 29, iters: 230944, time: 0.543, data: 0.000) G_L1: 15.298 G_L1_ABSOLUTE: 2.323 G_L1_RELATIVE: 12.975 G_Regularizer: 0.000 validation_error: 20.867 +(epoch: 29, iters: 232944, time: 0.542, data: 0.000) G_L1: 10.488 G_L1_ABSOLUTE: 2.380 G_L1_RELATIVE: 8.108 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 29, iters: 234944, time: 0.554, data: 0.000) G_L1: 15.791 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 13.203 G_Regularizer: 0.000 validation_error: 20.675 +(epoch: 29, iters: 236944, time: 0.540, data: 0.000) G_L1: 15.349 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 12.709 G_Regularizer: 0.000 validation_error: 20.478 +(epoch: 29, iters: 238944, time: 0.544, data: 0.000) G_L1: 13.044 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 10.778 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 29, iters: 240944, time: 0.539, data: 0.000) G_L1: 15.243 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 12.840 G_Regularizer: 0.000 validation_error: 21.164 +(epoch: 29, iters: 242944, time: 0.553, data: 0.000) G_L1: 14.975 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 12.439 G_Regularizer: 0.000 validation_error: 20.564 +(epoch: 29, iters: 244944, time: 0.544, data: 0.000) G_L1: 15.308 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 12.937 G_Regularizer: 0.000 validation_error: 20.533 +(epoch: 29, iters: 246944, time: 0.557, data: 0.000) G_L1: 15.104 G_L1_ABSOLUTE: 2.848 G_L1_RELATIVE: 12.256 G_Regularizer: 0.000 validation_error: 20.611 +(epoch: 29, iters: 248944, time: 0.546, data: 0.000) G_L1: 14.972 G_L1_ABSOLUTE: 2.338 G_L1_RELATIVE: 12.634 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 29, iters: 250944, time: 0.546, data: 0.000) G_L1: 11.544 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 8.800 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 29, iters: 252944, time: 0.542, data: 0.000) G_L1: 15.329 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 12.951 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 29, iters: 254944, time: 0.551, data: 0.000) G_L1: 13.078 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 10.671 G_Regularizer: 0.000 validation_error: 21.055 +(epoch: 29, iters: 256944, time: 0.539, data: 0.000) G_L1: 13.231 G_L1_ABSOLUTE: 2.655 G_L1_RELATIVE: 10.577 G_Regularizer: 0.000 validation_error: 20.305 +(epoch: 29, iters: 258944, time: 0.543, data: 0.000) G_L1: 13.720 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 10.972 G_Regularizer: 0.000 validation_error: 21.093 +(epoch: 29, iters: 260944, time: 0.553, data: 0.000) G_L1: 15.331 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 12.692 G_Regularizer: 0.000 validation_error: 20.538 +(epoch: 29, iters: 262944, time: 0.541, data: 0.000) G_L1: 14.216 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 11.833 G_Regularizer: 0.000 validation_error: 21.278 +(epoch: 29, iters: 264944, time: 0.545, data: 0.000) G_L1: 13.856 G_L1_ABSOLUTE: 2.511 G_L1_RELATIVE: 11.345 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 29, iters: 266944, time: 0.542, data: 0.000) G_L1: 15.001 G_L1_ABSOLUTE: 2.962 G_L1_RELATIVE: 12.039 G_Regularizer: 0.000 validation_error: 20.971 +(epoch: 29, iters: 268944, time: 0.548, data: 0.000) G_L1: 14.325 G_L1_ABSOLUTE: 2.700 G_L1_RELATIVE: 11.625 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 29, iters: 270944, time: 0.534, data: 0.000) G_L1: 18.325 G_L1_ABSOLUTE: 3.427 G_L1_RELATIVE: 14.899 G_Regularizer: 0.000 validation_error: 21.179 +(epoch: 29, iters: 272944, time: 0.547, data: 0.000) G_L1: 12.054 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 9.541 G_Regularizer: 0.000 validation_error: 20.529 +(epoch: 29, iters: 274944, time: 0.547, data: 0.000) G_L1: 13.416 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 11.046 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 29, iters: 276944, time: 0.545, data: 0.000) G_L1: 15.214 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 12.594 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 29, iters: 278944, time: 0.549, data: 0.000) G_L1: 14.631 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 12.107 G_Regularizer: 0.000 validation_error: 21.150 +(epoch: 29, iters: 280944, time: 0.548, data: 0.000) G_L1: 14.953 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 12.169 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 29, iters: 282944, time: 0.547, data: 0.000) G_L1: 11.592 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 9.172 G_Regularizer: 0.000 validation_error: 20.692 +(epoch: 29, iters: 284944, time: 0.546, data: 0.000) G_L1: 15.213 G_L1_ABSOLUTE: 2.173 G_L1_RELATIVE: 13.039 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 29, iters: 286944, time: 0.550, data: 0.000) G_L1: 13.939 G_L1_ABSOLUTE: 2.279 G_L1_RELATIVE: 11.660 G_Regularizer: 0.000 validation_error: 20.259 +(epoch: 29, iters: 288944, time: 0.547, data: 0.000) G_L1: 15.641 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 12.837 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 29, iters: 290944, time: 0.548, data: 0.000) G_L1: 13.848 G_L1_ABSOLUTE: 3.074 G_L1_RELATIVE: 10.774 G_Regularizer: 0.000 validation_error: 20.316 +(epoch: 29, iters: 292944, time: 0.548, data: 0.000) G_L1: 14.348 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 11.698 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 29, iters: 294944, time: 0.544, data: 0.000) G_L1: 13.913 G_L1_ABSOLUTE: 2.411 G_L1_RELATIVE: 11.502 G_Regularizer: 0.000 validation_error: 20.531 +(epoch: 29, iters: 296944, time: 0.545, data: 0.000) G_L1: 12.746 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 10.491 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 29, iters: 298944, time: 0.547, data: 0.000) G_L1: 17.247 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 14.583 G_Regularizer: 0.000 validation_error: 20.342 +(epoch: 29, iters: 300944, time: 0.545, data: 0.000) G_L1: 15.133 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 12.647 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 30, iters: 192, time: 0.544, data: 0.000) G_L1: 15.310 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 12.828 G_Regularizer: 0.000 validation_error: 20.912 +(epoch: 30, iters: 2192, time: 0.544, data: 0.000) G_L1: 14.362 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 11.867 G_Regularizer: 0.000 validation_error: 20.445 +(epoch: 30, iters: 4192, time: 0.550, data: 0.000) G_L1: 11.628 G_L1_ABSOLUTE: 2.353 G_L1_RELATIVE: 9.276 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 30, iters: 6192, time: 0.541, data: 0.000) G_L1: 23.910 G_L1_ABSOLUTE: 2.935 G_L1_RELATIVE: 20.975 G_Regularizer: 0.000 validation_error: 20.758 +(epoch: 30, iters: 8192, time: 0.542, data: 0.000) G_L1: 16.729 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 13.983 G_Regularizer: 0.000 validation_error: 21.262 +(epoch: 30, iters: 10192, time: 0.543, data: 0.000) G_L1: 15.147 G_L1_ABSOLUTE: 3.024 G_L1_RELATIVE: 12.124 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 30, iters: 12192, time: 0.558, data: 0.000) G_L1: 12.274 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 9.853 G_Regularizer: 0.000 validation_error: 20.861 +(epoch: 30, iters: 14192, time: 0.549, data: 0.000) G_L1: 14.458 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 11.975 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 30, iters: 16192, time: 0.548, data: 0.000) G_L1: 14.216 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 11.874 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 30, iters: 18192, time: 0.547, data: 0.000) G_L1: 14.198 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 11.728 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 30, iters: 20192, time: 0.548, data: 0.000) G_L1: 13.245 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 10.549 G_Regularizer: 0.000 validation_error: 21.215 +(epoch: 30, iters: 22192, time: 0.544, data: 0.000) G_L1: 13.640 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 10.963 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 30, iters: 24192, time: 0.542, data: 0.000) G_L1: 15.972 G_L1_ABSOLUTE: 3.341 G_L1_RELATIVE: 12.630 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 30, iters: 26192, time: 0.545, data: 0.000) G_L1: 11.734 G_L1_ABSOLUTE: 1.850 G_L1_RELATIVE: 9.884 G_Regularizer: 0.000 validation_error: 20.687 +(epoch: 30, iters: 28192, time: 0.545, data: 0.000) G_L1: 17.166 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 14.743 G_Regularizer: 0.000 validation_error: 21.116 +(epoch: 30, iters: 30192, time: 0.546, data: 0.000) G_L1: 12.942 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 10.345 G_Regularizer: 0.000 validation_error: 20.595 +(epoch: 30, iters: 32192, time: 0.549, data: 0.000) G_L1: 15.928 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 13.049 G_Regularizer: 0.000 validation_error: 20.463 +(epoch: 30, iters: 34192, time: 0.544, data: 0.000) G_L1: 14.299 G_L1_ABSOLUTE: 2.695 G_L1_RELATIVE: 11.604 G_Regularizer: 0.000 validation_error: 20.027 +(epoch: 30, iters: 36192, time: 0.541, data: 0.000) G_L1: 13.821 G_L1_ABSOLUTE: 2.255 G_L1_RELATIVE: 11.566 G_Regularizer: 0.000 validation_error: 20.491 +(epoch: 30, iters: 38192, time: 0.547, data: 0.000) G_L1: 14.256 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 11.715 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 30, iters: 40192, time: 0.550, data: 0.000) G_L1: 12.104 G_L1_ABSOLUTE: 2.325 G_L1_RELATIVE: 9.779 G_Regularizer: 0.000 validation_error: 21.229 +(epoch: 30, iters: 42192, time: 0.547, data: 0.000) G_L1: 12.979 G_L1_ABSOLUTE: 2.156 G_L1_RELATIVE: 10.822 G_Regularizer: 0.000 validation_error: 21.224 +(epoch: 30, iters: 44192, time: 0.547, data: 0.000) G_L1: 14.828 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 12.395 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 30, iters: 46192, time: 0.546, data: 0.000) G_L1: 14.724 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 12.147 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 30, iters: 48192, time: 0.539, data: 0.000) G_L1: 15.215 G_L1_ABSOLUTE: 2.243 G_L1_RELATIVE: 12.972 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 30, iters: 50192, time: 0.540, data: 0.001) G_L1: 13.391 G_L1_ABSOLUTE: 2.980 G_L1_RELATIVE: 10.411 G_Regularizer: 0.000 validation_error: 20.684 +(epoch: 30, iters: 52192, time: 0.549, data: 0.000) G_L1: 13.780 G_L1_ABSOLUTE: 2.566 G_L1_RELATIVE: 11.214 G_Regularizer: 0.000 validation_error: 20.659 +(epoch: 30, iters: 54192, time: 0.538, data: 0.000) G_L1: 16.405 G_L1_ABSOLUTE: 3.036 G_L1_RELATIVE: 13.369 G_Regularizer: 0.000 validation_error: 21.271 +(epoch: 30, iters: 56192, time: 0.548, data: 0.000) G_L1: 14.091 G_L1_ABSOLUTE: 3.002 G_L1_RELATIVE: 11.090 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 30, iters: 58192, time: 0.547, data: 0.000) G_L1: 14.600 G_L1_ABSOLUTE: 2.448 G_L1_RELATIVE: 12.152 G_Regularizer: 0.000 validation_error: 20.169 +(epoch: 30, iters: 60192, time: 0.542, data: 0.000) G_L1: 14.532 G_L1_ABSOLUTE: 2.130 G_L1_RELATIVE: 12.402 G_Regularizer: 0.000 validation_error: 21.194 +(epoch: 30, iters: 62192, time: 0.550, data: 0.001) G_L1: 15.906 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 13.025 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 30, iters: 64192, time: 0.549, data: 0.000) G_L1: 14.577 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 11.841 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 30, iters: 66192, time: 0.540, data: 0.001) G_L1: 13.675 G_L1_ABSOLUTE: 2.776 G_L1_RELATIVE: 10.898 G_Regularizer: 0.000 validation_error: 20.957 +(epoch: 30, iters: 68192, time: 0.545, data: 0.001) G_L1: 15.177 G_L1_ABSOLUTE: 2.242 G_L1_RELATIVE: 12.935 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 30, iters: 70192, time: 0.564, data: 0.001) G_L1: 18.073 G_L1_ABSOLUTE: 2.750 G_L1_RELATIVE: 15.323 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 30, iters: 72192, time: 0.546, data: 0.000) G_L1: 14.435 G_L1_ABSOLUTE: 3.039 G_L1_RELATIVE: 11.396 G_Regularizer: 0.000 validation_error: 20.659 +(epoch: 30, iters: 74192, time: 0.544, data: 0.000) G_L1: 15.635 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 13.220 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 30, iters: 76192, time: 0.544, data: 0.000) G_L1: 11.577 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 9.091 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 30, iters: 78192, time: 0.541, data: 0.000) G_L1: 17.850 G_L1_ABSOLUTE: 2.776 G_L1_RELATIVE: 15.074 G_Regularizer: 0.000 validation_error: 21.188 +(epoch: 30, iters: 80192, time: 0.545, data: 0.000) G_L1: 29.229 G_L1_ABSOLUTE: 3.360 G_L1_RELATIVE: 25.869 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 30, iters: 82192, time: 0.546, data: 0.000) G_L1: 17.498 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 14.862 G_Regularizer: 0.000 validation_error: 20.145 +(epoch: 30, iters: 84192, time: 0.540, data: 0.000) G_L1: 14.515 G_L1_ABSOLUTE: 3.195 G_L1_RELATIVE: 11.319 G_Regularizer: 0.000 validation_error: 20.327 +(epoch: 30, iters: 86192, time: 0.551, data: 0.000) G_L1: 13.770 G_L1_ABSOLUTE: 2.391 G_L1_RELATIVE: 11.379 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 30, iters: 88192, time: 0.543, data: 0.000) G_L1: 12.906 G_L1_ABSOLUTE: 2.146 G_L1_RELATIVE: 10.760 G_Regularizer: 0.000 validation_error: 20.541 +(epoch: 30, iters: 90192, time: 0.547, data: 0.000) G_L1: 13.634 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 11.046 G_Regularizer: 0.000 validation_error: 20.699 +(epoch: 30, iters: 92192, time: 0.550, data: 0.000) G_L1: 16.852 G_L1_ABSOLUTE: 2.975 G_L1_RELATIVE: 13.877 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 30, iters: 94192, time: 0.550, data: 0.000) G_L1: 13.199 G_L1_ABSOLUTE: 2.342 G_L1_RELATIVE: 10.857 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 30, iters: 96192, time: 0.542, data: 0.000) G_L1: 13.920 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 11.600 G_Regularizer: 0.000 validation_error: 20.977 +(epoch: 30, iters: 98192, time: 0.553, data: 0.000) G_L1: 14.092 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 11.671 G_Regularizer: 0.000 validation_error: 20.600 +(epoch: 30, iters: 100192, time: 0.548, data: 0.000) G_L1: 11.938 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 9.298 G_Regularizer: 0.000 validation_error: 20.681 +(epoch: 30, iters: 102192, time: 0.542, data: 0.000) G_L1: 14.652 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 12.131 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 30, iters: 104192, time: 0.550, data: 0.000) G_L1: 14.278 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 12.019 G_Regularizer: 0.000 validation_error: 20.982 +(epoch: 30, iters: 106192, time: 0.541, data: 0.000) G_L1: 13.384 G_L1_ABSOLUTE: 2.311 G_L1_RELATIVE: 11.073 G_Regularizer: 0.000 validation_error: 21.059 +(epoch: 30, iters: 108192, time: 0.538, data: 0.001) G_L1: 13.742 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 10.975 G_Regularizer: 0.000 validation_error: 20.550 +(epoch: 30, iters: 110192, time: 0.546, data: 0.000) G_L1: 14.492 G_L1_ABSOLUTE: 2.681 G_L1_RELATIVE: 11.811 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 30, iters: 112192, time: 0.548, data: 0.000) G_L1: 15.726 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 13.139 G_Regularizer: 0.000 validation_error: 20.560 +(epoch: 30, iters: 114192, time: 0.542, data: 0.000) G_L1: 13.824 G_L1_ABSOLUTE: 2.682 G_L1_RELATIVE: 11.142 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 30, iters: 116192, time: 0.547, data: 0.000) G_L1: 14.053 G_L1_ABSOLUTE: 2.374 G_L1_RELATIVE: 11.679 G_Regularizer: 0.000 validation_error: 20.653 +(epoch: 30, iters: 118192, time: 0.543, data: 0.000) G_L1: 13.528 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 11.064 G_Regularizer: 0.000 validation_error: 20.509 +(epoch: 30, iters: 120192, time: 0.546, data: 0.000) G_L1: 12.066 G_L1_ABSOLUTE: 2.384 G_L1_RELATIVE: 9.682 G_Regularizer: 0.000 validation_error: 21.084 +(epoch: 30, iters: 122192, time: 0.550, data: 0.000) G_L1: 14.032 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 11.557 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 30, iters: 124192, time: 0.544, data: 0.000) G_L1: 12.804 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 10.368 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 30, iters: 126192, time: 0.547, data: 0.000) G_L1: 15.183 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 12.732 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 30, iters: 128192, time: 0.536, data: 0.000) G_L1: 16.607 G_L1_ABSOLUTE: 2.832 G_L1_RELATIVE: 13.776 G_Regularizer: 0.000 validation_error: 20.960 +(epoch: 30, iters: 130192, time: 0.548, data: 0.000) G_L1: 13.462 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 10.820 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 30, iters: 132192, time: 0.542, data: 0.000) G_L1: 12.991 G_L1_ABSOLUTE: 2.154 G_L1_RELATIVE: 10.837 G_Regularizer: 0.000 validation_error: 20.496 +(epoch: 30, iters: 134192, time: 0.545, data: 0.000) G_L1: 12.391 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 10.010 G_Regularizer: 0.000 validation_error: 20.431 +(epoch: 30, iters: 136192, time: 0.548, data: 0.000) G_L1: 15.861 G_L1_ABSOLUTE: 2.715 G_L1_RELATIVE: 13.146 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 30, iters: 138192, time: 0.552, data: 0.000) G_L1: 14.146 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 11.683 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 30, iters: 140192, time: 0.542, data: 0.000) G_L1: 13.303 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 10.601 G_Regularizer: 0.000 validation_error: 21.349 +(epoch: 30, iters: 142192, time: 0.549, data: 0.001) G_L1: 14.247 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 11.703 G_Regularizer: 0.000 validation_error: 21.032 +(epoch: 30, iters: 144192, time: 0.547, data: 0.000) G_L1: 11.657 G_L1_ABSOLUTE: 2.312 G_L1_RELATIVE: 9.345 G_Regularizer: 0.000 validation_error: 21.345 +(epoch: 30, iters: 146192, time: 0.542, data: 0.000) G_L1: 15.758 G_L1_ABSOLUTE: 2.295 G_L1_RELATIVE: 13.463 G_Regularizer: 0.000 validation_error: 20.412 +(epoch: 30, iters: 148192, time: 0.543, data: 0.000) G_L1: 15.643 G_L1_ABSOLUTE: 2.901 G_L1_RELATIVE: 12.742 G_Regularizer: 0.000 validation_error: 21.291 +(epoch: 30, iters: 150192, time: 0.544, data: 0.000) G_L1: 12.995 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 10.398 G_Regularizer: 0.000 validation_error: 21.241 +(epoch: 30, iters: 152192, time: 0.541, data: 0.000) G_L1: 14.608 G_L1_ABSOLUTE: 2.648 G_L1_RELATIVE: 11.960 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 30, iters: 154192, time: 0.554, data: 0.000) G_L1: 24.356 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 21.693 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 30, iters: 156192, time: 0.544, data: 0.000) G_L1: 15.083 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 12.461 G_Regularizer: 0.000 validation_error: 20.724 +(epoch: 30, iters: 158192, time: 0.544, data: 0.001) G_L1: 14.519 G_L1_ABSOLUTE: 2.156 G_L1_RELATIVE: 12.363 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 30, iters: 160192, time: 0.540, data: 0.001) G_L1: 13.068 G_L1_ABSOLUTE: 2.825 G_L1_RELATIVE: 10.243 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 30, iters: 162192, time: 0.542, data: 0.000) G_L1: 15.199 G_L1_ABSOLUTE: 2.965 G_L1_RELATIVE: 12.234 G_Regularizer: 0.000 validation_error: 20.321 +(epoch: 30, iters: 164192, time: 0.541, data: 0.000) G_L1: 17.267 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 14.698 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 30, iters: 166192, time: 0.541, data: 0.000) G_L1: 13.261 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 10.694 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 30, iters: 168192, time: 0.544, data: 0.000) G_L1: 13.988 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 11.579 G_Regularizer: 0.000 validation_error: 20.620 +(epoch: 30, iters: 170192, time: 0.545, data: 0.000) G_L1: 15.456 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 12.631 G_Regularizer: 0.000 validation_error: 21.144 +(epoch: 30, iters: 172192, time: 0.548, data: 0.000) G_L1: 12.206 G_L1_ABSOLUTE: 2.307 G_L1_RELATIVE: 9.899 G_Regularizer: 0.000 validation_error: 20.612 +(epoch: 30, iters: 174192, time: 0.547, data: 0.000) G_L1: 13.960 G_L1_ABSOLUTE: 2.417 G_L1_RELATIVE: 11.544 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 30, iters: 176192, time: 0.543, data: 0.000) G_L1: 16.451 G_L1_ABSOLUTE: 2.393 G_L1_RELATIVE: 14.057 G_Regularizer: 0.000 validation_error: 21.047 +(epoch: 30, iters: 178192, time: 0.545, data: 0.000) G_L1: 14.124 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 11.390 G_Regularizer: 0.000 validation_error: 20.538 +(epoch: 30, iters: 180192, time: 0.549, data: 0.000) G_L1: 15.308 G_L1_ABSOLUTE: 2.930 G_L1_RELATIVE: 12.378 G_Regularizer: 0.000 validation_error: 20.644 +(epoch: 30, iters: 182192, time: 0.548, data: 0.001) G_L1: 16.418 G_L1_ABSOLUTE: 2.946 G_L1_RELATIVE: 13.472 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 30, iters: 184192, time: 0.550, data: 0.001) G_L1: 14.245 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 11.725 G_Regularizer: 0.000 validation_error: 20.332 +(epoch: 30, iters: 186192, time: 0.546, data: 0.000) G_L1: 14.132 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 11.747 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 30, iters: 188192, time: 0.552, data: 0.000) G_L1: 17.100 G_L1_ABSOLUTE: 3.022 G_L1_RELATIVE: 14.078 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 30, iters: 190192, time: 0.542, data: 0.000) G_L1: 13.068 G_L1_ABSOLUTE: 2.231 G_L1_RELATIVE: 10.837 G_Regularizer: 0.000 validation_error: 20.932 +(epoch: 30, iters: 192192, time: 0.550, data: 0.000) G_L1: 12.071 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 9.769 G_Regularizer: 0.000 validation_error: 20.702 +(epoch: 30, iters: 194192, time: 0.549, data: 0.000) G_L1: 15.276 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 12.600 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 30, iters: 196192, time: 0.546, data: 0.000) G_L1: 15.834 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 13.558 G_Regularizer: 0.000 validation_error: 20.344 +(epoch: 30, iters: 198192, time: 0.548, data: 0.000) G_L1: 14.914 G_L1_ABSOLUTE: 2.189 G_L1_RELATIVE: 12.725 G_Regularizer: 0.000 validation_error: 20.560 +(epoch: 30, iters: 200192, time: 0.538, data: 0.000) G_L1: 14.316 G_L1_ABSOLUTE: 2.786 G_L1_RELATIVE: 11.531 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 30, iters: 202192, time: 0.548, data: 0.000) G_L1: 14.288 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 11.872 G_Regularizer: 0.000 validation_error: 21.105 +(epoch: 30, iters: 204192, time: 0.541, data: 0.001) G_L1: 16.212 G_L1_ABSOLUTE: 2.215 G_L1_RELATIVE: 13.997 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 30, iters: 206192, time: 0.541, data: 0.000) G_L1: 14.991 G_L1_ABSOLUTE: 2.636 G_L1_RELATIVE: 12.355 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 30, iters: 208192, time: 0.544, data: 0.000) G_L1: 15.000 G_L1_ABSOLUTE: 2.177 G_L1_RELATIVE: 12.823 G_Regularizer: 0.000 validation_error: 20.567 +(epoch: 30, iters: 210192, time: 0.546, data: 0.000) G_L1: 13.916 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 11.374 G_Regularizer: 0.000 validation_error: 20.452 +(epoch: 30, iters: 212192, time: 0.543, data: 0.001) G_L1: 13.664 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 10.978 G_Regularizer: 0.000 validation_error: 20.899 +(epoch: 30, iters: 214192, time: 0.549, data: 0.000) G_L1: 14.181 G_L1_ABSOLUTE: 2.361 G_L1_RELATIVE: 11.820 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 30, iters: 216192, time: 0.542, data: 0.000) G_L1: 13.448 G_L1_ABSOLUTE: 2.329 G_L1_RELATIVE: 11.119 G_Regularizer: 0.000 validation_error: 20.528 +(epoch: 30, iters: 218192, time: 0.554, data: 0.000) G_L1: 15.203 G_L1_ABSOLUTE: 2.783 G_L1_RELATIVE: 12.421 G_Regularizer: 0.000 validation_error: 21.239 +(epoch: 30, iters: 220192, time: 0.542, data: 0.000) G_L1: 12.132 G_L1_ABSOLUTE: 2.869 G_L1_RELATIVE: 9.264 G_Regularizer: 0.000 validation_error: 21.286 +(epoch: 30, iters: 222192, time: 0.545, data: 0.000) G_L1: 16.070 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 13.498 G_Regularizer: 0.000 validation_error: 21.268 +(epoch: 30, iters: 224192, time: 0.555, data: 0.000) G_L1: 16.334 G_L1_ABSOLUTE: 2.970 G_L1_RELATIVE: 13.365 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 30, iters: 226192, time: 0.539, data: 0.000) G_L1: 12.728 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 10.203 G_Regularizer: 0.000 validation_error: 20.991 +(epoch: 30, iters: 228192, time: 0.558, data: 0.000) G_L1: 14.609 G_L1_ABSOLUTE: 2.271 G_L1_RELATIVE: 12.338 G_Regularizer: 0.000 validation_error: 21.066 +(epoch: 30, iters: 230192, time: 0.551, data: 0.000) G_L1: 13.980 G_L1_ABSOLUTE: 3.189 G_L1_RELATIVE: 10.791 G_Regularizer: 0.000 validation_error: 20.977 +(epoch: 30, iters: 232192, time: 0.553, data: 0.000) G_L1: 13.544 G_L1_ABSOLUTE: 2.247 G_L1_RELATIVE: 11.297 G_Regularizer: 0.000 validation_error: 20.285 +(epoch: 30, iters: 234192, time: 0.539, data: 0.001) G_L1: 13.285 G_L1_ABSOLUTE: 2.228 G_L1_RELATIVE: 11.057 G_Regularizer: 0.000 validation_error: 20.276 +(epoch: 30, iters: 236192, time: 0.548, data: 0.000) G_L1: 14.005 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 11.375 G_Regularizer: 0.000 validation_error: 20.623 +(epoch: 30, iters: 238192, time: 0.544, data: 0.000) G_L1: 13.833 G_L1_ABSOLUTE: 2.208 G_L1_RELATIVE: 11.625 G_Regularizer: 0.000 validation_error: 20.741 +(epoch: 30, iters: 240192, time: 0.540, data: 0.000) G_L1: 14.473 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 11.837 G_Regularizer: 0.000 validation_error: 20.483 +(epoch: 30, iters: 242192, time: 0.545, data: 0.000) G_L1: 13.537 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 10.965 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 30, iters: 244192, time: 0.549, data: 0.000) G_L1: 15.545 G_L1_ABSOLUTE: 2.417 G_L1_RELATIVE: 13.128 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 30, iters: 246192, time: 0.539, data: 0.000) G_L1: 14.572 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 11.960 G_Regularizer: 0.000 validation_error: 20.585 +(epoch: 30, iters: 248192, time: 0.546, data: 0.000) G_L1: 15.845 G_L1_ABSOLUTE: 2.579 G_L1_RELATIVE: 13.267 G_Regularizer: 0.000 validation_error: 20.987 +(epoch: 30, iters: 250192, time: 0.544, data: 0.000) G_L1: 15.287 G_L1_ABSOLUTE: 2.680 G_L1_RELATIVE: 12.607 G_Regularizer: 0.000 validation_error: 20.768 +(epoch: 30, iters: 252192, time: 0.545, data: 0.000) G_L1: 13.651 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 11.158 G_Regularizer: 0.000 validation_error: 20.363 +(epoch: 30, iters: 254192, time: 0.540, data: 0.000) G_L1: 12.626 G_L1_ABSOLUTE: 2.204 G_L1_RELATIVE: 10.422 G_Regularizer: 0.000 validation_error: 20.203 +(epoch: 30, iters: 256192, time: 0.550, data: 0.000) G_L1: 16.060 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 13.743 G_Regularizer: 0.000 validation_error: 20.820 +(epoch: 30, iters: 258192, time: 0.540, data: 0.000) G_L1: 13.435 G_L1_ABSOLUTE: 2.157 G_L1_RELATIVE: 11.278 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 30, iters: 260192, time: 0.539, data: 0.000) G_L1: 14.946 G_L1_ABSOLUTE: 2.712 G_L1_RELATIVE: 12.234 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 30, iters: 262192, time: 0.547, data: 0.001) G_L1: 14.251 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 11.577 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 30, iters: 264192, time: 0.542, data: 0.000) G_L1: 13.953 G_L1_ABSOLUTE: 2.346 G_L1_RELATIVE: 11.607 G_Regularizer: 0.000 validation_error: 20.214 +(epoch: 30, iters: 266192, time: 0.544, data: 0.000) G_L1: 15.665 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 13.172 G_Regularizer: 0.000 validation_error: 20.245 +(epoch: 30, iters: 268192, time: 0.551, data: 0.000) G_L1: 14.015 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 11.580 G_Regularizer: 0.000 validation_error: 20.156 +(epoch: 30, iters: 270192, time: 0.560, data: 0.000) G_L1: 13.780 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 11.097 G_Regularizer: 0.000 validation_error: 19.974 +(epoch: 30, iters: 272192, time: 0.540, data: 0.000) G_L1: 13.332 G_L1_ABSOLUTE: 2.342 G_L1_RELATIVE: 10.990 G_Regularizer: 0.000 validation_error: 20.414 +(epoch: 30, iters: 274192, time: 0.546, data: 0.000) G_L1: 12.581 G_L1_ABSOLUTE: 2.061 G_L1_RELATIVE: 10.520 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 30, iters: 276192, time: 0.552, data: 0.000) G_L1: 13.195 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 10.561 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 30, iters: 278192, time: 0.540, data: 0.000) G_L1: 13.686 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 11.354 G_Regularizer: 0.000 validation_error: 20.805 +(epoch: 30, iters: 280192, time: 0.540, data: 0.000) G_L1: 12.855 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 10.157 G_Regularizer: 0.000 validation_error: 21.163 +(epoch: 30, iters: 282192, time: 0.552, data: 0.001) G_L1: 14.369 G_L1_ABSOLUTE: 2.496 G_L1_RELATIVE: 11.873 G_Regularizer: 0.000 validation_error: 20.367 +(epoch: 30, iters: 284192, time: 0.556, data: 0.001) G_L1: 13.699 G_L1_ABSOLUTE: 2.264 G_L1_RELATIVE: 11.435 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 30, iters: 286192, time: 0.540, data: 0.000) G_L1: 14.483 G_L1_ABSOLUTE: 3.027 G_L1_RELATIVE: 11.456 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 30, iters: 288192, time: 0.546, data: 0.000) G_L1: 16.015 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 13.260 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 30, iters: 290192, time: 0.546, data: 0.000) G_L1: 14.025 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 11.477 G_Regularizer: 0.000 validation_error: 21.372 +(epoch: 30, iters: 292192, time: 0.546, data: 0.000) G_L1: 17.001 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 14.375 G_Regularizer: 0.000 validation_error: 20.575 +(epoch: 30, iters: 294192, time: 0.545, data: 0.000) G_L1: 15.014 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 12.258 G_Regularizer: 0.000 validation_error: 20.430 +(epoch: 30, iters: 296192, time: 0.553, data: 0.000) G_L1: 13.765 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 11.433 G_Regularizer: 0.000 validation_error: 20.697 +(epoch: 30, iters: 298192, time: 0.543, data: 0.000) G_L1: 13.902 G_L1_ABSOLUTE: 2.321 G_L1_RELATIVE: 11.581 G_Regularizer: 0.000 validation_error: 20.219 +(epoch: 30, iters: 300192, time: 0.548, data: 0.000) G_L1: 13.175 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 10.893 G_Regularizer: 0.000 validation_error: 21.028 +(epoch: 30, iters: 302192, time: 0.542, data: 0.000) G_L1: 12.268 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 9.890 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 31, iters: 1440, time: 0.553, data: 0.000) G_L1: 15.434 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 12.865 G_Regularizer: 0.000 validation_error: 21.134 +(epoch: 31, iters: 3440, time: 0.546, data: 0.000) G_L1: 16.577 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 14.065 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 31, iters: 5440, time: 0.551, data: 0.001) G_L1: 19.288 G_L1_ABSOLUTE: 3.330 G_L1_RELATIVE: 15.958 G_Regularizer: 0.000 validation_error: 20.264 +(epoch: 31, iters: 7440, time: 0.545, data: 0.000) G_L1: 13.993 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 11.653 G_Regularizer: 0.000 validation_error: 20.330 +(epoch: 31, iters: 9440, time: 0.542, data: 0.000) G_L1: 12.465 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 10.130 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 31, iters: 11440, time: 0.547, data: 0.000) G_L1: 13.523 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 10.684 G_Regularizer: 0.000 validation_error: 21.027 +(epoch: 31, iters: 13440, time: 0.541, data: 0.000) G_L1: 17.036 G_L1_ABSOLUTE: 2.886 G_L1_RELATIVE: 14.150 G_Regularizer: 0.000 validation_error: 20.242 +(epoch: 31, iters: 15440, time: 0.546, data: 0.000) G_L1: 14.027 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 11.768 G_Regularizer: 0.000 validation_error: 20.603 +(epoch: 31, iters: 17440, time: 0.540, data: 0.000) G_L1: 18.270 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 15.633 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 31, iters: 19440, time: 0.548, data: 0.000) G_L1: 22.899 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 20.640 G_Regularizer: 0.000 validation_error: 20.400 +(epoch: 31, iters: 21440, time: 0.548, data: 0.000) G_L1: 12.428 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 10.092 G_Regularizer: 0.000 validation_error: 20.429 +(epoch: 31, iters: 23440, time: 0.555, data: 0.000) G_L1: 14.527 G_L1_ABSOLUTE: 2.924 G_L1_RELATIVE: 11.603 G_Regularizer: 0.000 validation_error: 20.473 +(epoch: 31, iters: 25440, time: 0.546, data: 0.000) G_L1: 15.899 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 13.022 G_Regularizer: 0.000 validation_error: 20.548 +(epoch: 31, iters: 27440, time: 0.543, data: 0.000) G_L1: 15.554 G_L1_ABSOLUTE: 2.689 G_L1_RELATIVE: 12.865 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 31, iters: 29440, time: 0.537, data: 0.000) G_L1: 18.762 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 16.025 G_Regularizer: 0.000 validation_error: 20.354 +(epoch: 31, iters: 31440, time: 0.542, data: 0.000) G_L1: 21.914 G_L1_ABSOLUTE: 2.936 G_L1_RELATIVE: 18.978 G_Regularizer: 0.000 validation_error: 20.271 +(epoch: 31, iters: 33440, time: 0.541, data: 0.001) G_L1: 14.733 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 12.320 G_Regularizer: 0.000 validation_error: 20.574 +(epoch: 31, iters: 35440, time: 0.545, data: 0.000) G_L1: 15.080 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 12.616 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 31, iters: 37440, time: 0.556, data: 0.000) G_L1: 14.983 G_L1_ABSOLUTE: 2.559 G_L1_RELATIVE: 12.424 G_Regularizer: 0.000 validation_error: 20.948 +(epoch: 31, iters: 39440, time: 0.544, data: 0.000) G_L1: 16.416 G_L1_ABSOLUTE: 2.578 G_L1_RELATIVE: 13.838 G_Regularizer: 0.000 validation_error: 20.311 +(epoch: 31, iters: 41440, time: 0.545, data: 0.000) G_L1: 16.003 G_L1_ABSOLUTE: 3.008 G_L1_RELATIVE: 12.995 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 31, iters: 43440, time: 0.535, data: 0.000) G_L1: 14.084 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 11.310 G_Regularizer: 0.000 validation_error: 20.508 +(epoch: 31, iters: 45440, time: 0.543, data: 0.000) G_L1: 14.728 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 12.258 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 31, iters: 47440, time: 0.549, data: 0.000) G_L1: 15.850 G_L1_ABSOLUTE: 2.645 G_L1_RELATIVE: 13.205 G_Regularizer: 0.000 validation_error: 20.983 +(epoch: 31, iters: 49440, time: 0.542, data: 0.001) G_L1: 13.453 G_L1_ABSOLUTE: 2.059 G_L1_RELATIVE: 11.393 G_Regularizer: 0.000 validation_error: 20.757 +(epoch: 31, iters: 51440, time: 0.545, data: 0.000) G_L1: 14.042 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 11.296 G_Regularizer: 0.000 validation_error: 20.920 +(epoch: 31, iters: 53440, time: 0.547, data: 0.000) G_L1: 14.859 G_L1_ABSOLUTE: 2.786 G_L1_RELATIVE: 12.072 G_Regularizer: 0.000 validation_error: 21.458 +(epoch: 31, iters: 55440, time: 0.545, data: 0.000) G_L1: 13.159 G_L1_ABSOLUTE: 2.479 G_L1_RELATIVE: 10.680 G_Regularizer: 0.000 validation_error: 20.672 +(epoch: 31, iters: 57440, time: 0.540, data: 0.000) G_L1: 14.141 G_L1_ABSOLUTE: 2.037 G_L1_RELATIVE: 12.104 G_Regularizer: 0.000 validation_error: 20.708 +(epoch: 31, iters: 59440, time: 0.551, data: 0.000) G_L1: 15.777 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 12.921 G_Regularizer: 0.000 validation_error: 21.281 +(epoch: 31, iters: 61440, time: 0.540, data: 0.000) G_L1: 13.814 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 11.528 G_Regularizer: 0.000 validation_error: 20.463 +(epoch: 31, iters: 63440, time: 0.543, data: 0.000) G_L1: 15.590 G_L1_ABSOLUTE: 2.699 G_L1_RELATIVE: 12.891 G_Regularizer: 0.000 validation_error: 21.075 +(epoch: 31, iters: 65440, time: 0.546, data: 0.000) G_L1: 14.573 G_L1_ABSOLUTE: 2.827 G_L1_RELATIVE: 11.746 G_Regularizer: 0.000 validation_error: 21.196 +(epoch: 31, iters: 67440, time: 0.548, data: 0.000) G_L1: 15.223 G_L1_ABSOLUTE: 2.638 G_L1_RELATIVE: 12.585 G_Regularizer: 0.000 validation_error: 20.310 +(epoch: 31, iters: 69440, time: 0.540, data: 0.000) G_L1: 24.533 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 21.991 G_Regularizer: 0.000 validation_error: 20.324 +(epoch: 31, iters: 71440, time: 0.549, data: 0.000) G_L1: 13.004 G_L1_ABSOLUTE: 2.143 G_L1_RELATIVE: 10.861 G_Regularizer: 0.000 validation_error: 20.616 +(epoch: 31, iters: 73440, time: 0.541, data: 0.000) G_L1: 14.813 G_L1_ABSOLUTE: 2.426 G_L1_RELATIVE: 12.386 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 31, iters: 75440, time: 0.543, data: 0.001) G_L1: 11.469 G_L1_ABSOLUTE: 2.281 G_L1_RELATIVE: 9.188 G_Regularizer: 0.000 validation_error: 20.450 +(epoch: 31, iters: 77440, time: 0.537, data: 0.000) G_L1: 15.981 G_L1_ABSOLUTE: 3.171 G_L1_RELATIVE: 12.810 G_Regularizer: 0.000 validation_error: 21.421 +(epoch: 31, iters: 79440, time: 0.549, data: 0.000) G_L1: 15.608 G_L1_ABSOLUTE: 2.995 G_L1_RELATIVE: 12.613 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 31, iters: 81440, time: 0.540, data: 0.000) G_L1: 15.966 G_L1_ABSOLUTE: 3.113 G_L1_RELATIVE: 12.853 G_Regularizer: 0.000 validation_error: 20.746 +(epoch: 31, iters: 83440, time: 0.542, data: 0.000) G_L1: 14.199 G_L1_ABSOLUTE: 2.359 G_L1_RELATIVE: 11.840 G_Regularizer: 0.000 validation_error: 20.980 +(epoch: 31, iters: 85440, time: 0.545, data: 0.000) G_L1: 11.848 G_L1_ABSOLUTE: 2.477 G_L1_RELATIVE: 9.371 G_Regularizer: 0.000 validation_error: 20.646 +(epoch: 31, iters: 87440, time: 0.546, data: 0.001) G_L1: 15.115 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 12.540 G_Regularizer: 0.000 validation_error: 20.885 +(epoch: 31, iters: 89440, time: 0.549, data: 0.000) G_L1: 14.289 G_L1_ABSOLUTE: 2.438 G_L1_RELATIVE: 11.851 G_Regularizer: 0.000 validation_error: 20.749 +(epoch: 31, iters: 91440, time: 0.553, data: 0.000) G_L1: 13.378 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 10.768 G_Regularizer: 0.000 validation_error: 20.640 +(epoch: 31, iters: 93440, time: 0.546, data: 0.000) G_L1: 13.935 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 11.594 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 31, iters: 95440, time: 0.538, data: 0.001) G_L1: 13.643 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 11.445 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 31, iters: 97440, time: 0.546, data: 0.000) G_L1: 14.737 G_L1_ABSOLUTE: 2.440 G_L1_RELATIVE: 12.296 G_Regularizer: 0.000 validation_error: 20.899 +(epoch: 31, iters: 99440, time: 0.558, data: 0.000) G_L1: 13.818 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 11.402 G_Regularizer: 0.000 validation_error: 21.150 +(epoch: 31, iters: 101440, time: 0.543, data: 0.000) G_L1: 14.930 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 12.273 G_Regularizer: 0.000 validation_error: 21.045 +(epoch: 31, iters: 103440, time: 0.544, data: 0.000) G_L1: 16.166 G_L1_ABSOLUTE: 2.889 G_L1_RELATIVE: 13.278 G_Regularizer: 0.000 validation_error: 20.248 +(epoch: 31, iters: 105440, time: 0.555, data: 0.000) G_L1: 13.641 G_L1_ABSOLUTE: 2.683 G_L1_RELATIVE: 10.959 G_Regularizer: 0.000 validation_error: 20.787 +(epoch: 31, iters: 107440, time: 0.541, data: 0.000) G_L1: 16.349 G_L1_ABSOLUTE: 2.113 G_L1_RELATIVE: 14.236 G_Regularizer: 0.000 validation_error: 20.651 +(epoch: 31, iters: 109440, time: 0.543, data: 0.000) G_L1: 14.956 G_L1_ABSOLUTE: 2.443 G_L1_RELATIVE: 12.513 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 31, iters: 111440, time: 0.547, data: 0.000) G_L1: 14.041 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 11.508 G_Regularizer: 0.000 validation_error: 21.146 +(epoch: 31, iters: 113440, time: 0.547, data: 0.000) G_L1: 14.198 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 11.822 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 31, iters: 115440, time: 0.543, data: 0.000) G_L1: 12.266 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 9.900 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 31, iters: 117440, time: 0.546, data: 0.001) G_L1: 14.075 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 11.453 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 31, iters: 119440, time: 0.537, data: 0.000) G_L1: 13.390 G_L1_ABSOLUTE: 2.410 G_L1_RELATIVE: 10.980 G_Regularizer: 0.000 validation_error: 21.235 +(epoch: 31, iters: 121440, time: 0.541, data: 0.000) G_L1: 13.559 G_L1_ABSOLUTE: 2.615 G_L1_RELATIVE: 10.944 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 31, iters: 123440, time: 0.556, data: 0.000) G_L1: 16.467 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 13.839 G_Regularizer: 0.000 validation_error: 21.761 +(epoch: 31, iters: 125440, time: 0.542, data: 0.000) G_L1: 16.186 G_L1_ABSOLUTE: 2.771 G_L1_RELATIVE: 13.416 G_Regularizer: 0.000 validation_error: 20.315 +(epoch: 31, iters: 127440, time: 0.542, data: 0.001) G_L1: 15.462 G_L1_ABSOLUTE: 2.292 G_L1_RELATIVE: 13.169 G_Regularizer: 0.000 validation_error: 20.475 +(epoch: 31, iters: 129440, time: 0.548, data: 0.000) G_L1: 15.405 G_L1_ABSOLUTE: 2.987 G_L1_RELATIVE: 12.418 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 31, iters: 131440, time: 0.554, data: 0.000) G_L1: 14.183 G_L1_ABSOLUTE: 3.031 G_L1_RELATIVE: 11.151 G_Regularizer: 0.000 validation_error: 20.661 +(epoch: 31, iters: 133440, time: 0.548, data: 0.000) G_L1: 13.904 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 11.236 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 31, iters: 135440, time: 0.542, data: 0.001) G_L1: 13.359 G_L1_ABSOLUTE: 2.166 G_L1_RELATIVE: 11.193 G_Regularizer: 0.000 validation_error: 21.101 +(epoch: 31, iters: 137440, time: 0.556, data: 0.000) G_L1: 17.182 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 14.427 G_Regularizer: 0.000 validation_error: 20.327 +(epoch: 31, iters: 139440, time: 0.551, data: 0.001) G_L1: 13.251 G_L1_ABSOLUTE: 2.982 G_L1_RELATIVE: 10.268 G_Regularizer: 0.000 validation_error: 21.214 +(epoch: 31, iters: 141440, time: 0.539, data: 0.000) G_L1: 14.122 G_L1_ABSOLUTE: 3.042 G_L1_RELATIVE: 11.080 G_Regularizer: 0.000 validation_error: 20.270 +(epoch: 31, iters: 143440, time: 0.547, data: 0.000) G_L1: 15.564 G_L1_ABSOLUTE: 2.857 G_L1_RELATIVE: 12.707 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 31, iters: 145440, time: 0.541, data: 0.000) G_L1: 13.296 G_L1_ABSOLUTE: 2.644 G_L1_RELATIVE: 10.652 G_Regularizer: 0.000 validation_error: 20.546 +(epoch: 31, iters: 147440, time: 0.547, data: 0.001) G_L1: 16.899 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 14.120 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 31, iters: 149440, time: 0.541, data: 0.000) G_L1: 12.817 G_L1_ABSOLUTE: 2.260 G_L1_RELATIVE: 10.557 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 31, iters: 151440, time: 0.550, data: 0.000) G_L1: 13.392 G_L1_ABSOLUTE: 2.559 G_L1_RELATIVE: 10.833 G_Regularizer: 0.000 validation_error: 20.748 +(epoch: 31, iters: 153440, time: 0.550, data: 0.000) G_L1: 15.198 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 12.633 G_Regularizer: 0.000 validation_error: 20.963 +(epoch: 31, iters: 155440, time: 0.544, data: 0.000) G_L1: 13.105 G_L1_ABSOLUTE: 2.252 G_L1_RELATIVE: 10.853 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 31, iters: 157440, time: 0.545, data: 0.000) G_L1: 15.714 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 13.244 G_Regularizer: 0.000 validation_error: 20.867 +(epoch: 31, iters: 159440, time: 0.549, data: 0.000) G_L1: 13.129 G_L1_ABSOLUTE: 2.201 G_L1_RELATIVE: 10.928 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 31, iters: 161440, time: 0.540, data: 0.000) G_L1: 14.800 G_L1_ABSOLUTE: 2.977 G_L1_RELATIVE: 11.823 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 31, iters: 163440, time: 0.544, data: 0.000) G_L1: 12.636 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 10.010 G_Regularizer: 0.000 validation_error: 21.075 +(epoch: 31, iters: 165440, time: 0.545, data: 0.000) G_L1: 13.435 G_L1_ABSOLUTE: 2.490 G_L1_RELATIVE: 10.945 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 31, iters: 167440, time: 0.548, data: 0.000) G_L1: 17.262 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 14.734 G_Regularizer: 0.000 validation_error: 21.028 +(epoch: 31, iters: 169440, time: 0.546, data: 0.001) G_L1: 13.210 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 10.827 G_Regularizer: 0.000 validation_error: 20.729 +(epoch: 31, iters: 171440, time: 0.548, data: 0.000) G_L1: 14.519 G_L1_ABSOLUTE: 2.188 G_L1_RELATIVE: 12.331 G_Regularizer: 0.000 validation_error: 21.162 +(epoch: 31, iters: 173440, time: 0.548, data: 0.000) G_L1: 15.137 G_L1_ABSOLUTE: 2.477 G_L1_RELATIVE: 12.660 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 31, iters: 175440, time: 0.538, data: 0.000) G_L1: 14.146 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 11.436 G_Regularizer: 0.000 validation_error: 21.497 +(epoch: 31, iters: 177440, time: 0.554, data: 0.000) G_L1: 12.884 G_L1_ABSOLUTE: 2.176 G_L1_RELATIVE: 10.708 G_Regularizer: 0.000 validation_error: 20.789 +(epoch: 31, iters: 179440, time: 0.568, data: 0.000) G_L1: 12.651 G_L1_ABSOLUTE: 2.380 G_L1_RELATIVE: 10.271 G_Regularizer: 0.000 validation_error: 21.057 +(epoch: 31, iters: 181440, time: 0.542, data: 0.000) G_L1: 18.021 G_L1_ABSOLUTE: 2.844 G_L1_RELATIVE: 15.177 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 31, iters: 183440, time: 0.550, data: 0.000) G_L1: 14.459 G_L1_ABSOLUTE: 3.212 G_L1_RELATIVE: 11.246 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 31, iters: 185440, time: 0.548, data: 0.000) G_L1: 15.211 G_L1_ABSOLUTE: 2.266 G_L1_RELATIVE: 12.945 G_Regularizer: 0.000 validation_error: 21.377 +(epoch: 31, iters: 187440, time: 0.544, data: 0.000) G_L1: 12.814 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 10.338 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 31, iters: 189440, time: 0.540, data: 0.000) G_L1: 13.989 G_L1_ABSOLUTE: 2.297 G_L1_RELATIVE: 11.692 G_Regularizer: 0.000 validation_error: 20.994 +(epoch: 31, iters: 191440, time: 0.547, data: 0.000) G_L1: 15.546 G_L1_ABSOLUTE: 3.495 G_L1_RELATIVE: 12.051 G_Regularizer: 0.000 validation_error: 21.090 +(epoch: 31, iters: 193440, time: 0.539, data: 0.000) G_L1: 14.442 G_L1_ABSOLUTE: 2.172 G_L1_RELATIVE: 12.270 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 31, iters: 195440, time: 0.544, data: 0.000) G_L1: 14.517 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 11.956 G_Regularizer: 0.000 validation_error: 20.997 +(epoch: 31, iters: 197440, time: 0.553, data: 0.000) G_L1: 10.857 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 8.565 G_Regularizer: 0.000 validation_error: 20.224 +(epoch: 31, iters: 199440, time: 0.546, data: 0.000) G_L1: 14.373 G_L1_ABSOLUTE: 2.287 G_L1_RELATIVE: 12.086 G_Regularizer: 0.000 validation_error: 20.794 +(epoch: 31, iters: 201440, time: 0.551, data: 0.000) G_L1: 14.260 G_L1_ABSOLUTE: 2.904 G_L1_RELATIVE: 11.356 G_Regularizer: 0.000 validation_error: 20.927 +(epoch: 31, iters: 203440, time: 0.537, data: 0.001) G_L1: 14.945 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 12.529 G_Regularizer: 0.000 validation_error: 21.655 +(epoch: 31, iters: 205440, time: 0.546, data: 0.001) G_L1: 15.469 G_L1_ABSOLUTE: 2.319 G_L1_RELATIVE: 13.150 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 31, iters: 207440, time: 0.535, data: 0.000) G_L1: 15.561 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 13.326 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 31, iters: 209440, time: 0.550, data: 0.001) G_L1: 15.538 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 12.906 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 31, iters: 211440, time: 0.547, data: 0.000) G_L1: 13.534 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 10.966 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 31, iters: 213440, time: 0.547, data: 0.000) G_L1: 15.084 G_L1_ABSOLUTE: 2.163 G_L1_RELATIVE: 12.920 G_Regularizer: 0.000 validation_error: 20.763 +(epoch: 31, iters: 215440, time: 0.542, data: 0.000) G_L1: 14.579 G_L1_ABSOLUTE: 2.845 G_L1_RELATIVE: 11.734 G_Regularizer: 0.000 validation_error: 20.028 +(epoch: 31, iters: 217440, time: 0.547, data: 0.000) G_L1: 14.830 G_L1_ABSOLUTE: 2.272 G_L1_RELATIVE: 12.558 G_Regularizer: 0.000 validation_error: 20.591 +(epoch: 31, iters: 219440, time: 0.539, data: 0.000) G_L1: 13.070 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 10.542 G_Regularizer: 0.000 validation_error: 20.143 +(epoch: 31, iters: 221440, time: 0.541, data: 0.000) G_L1: 15.771 G_L1_ABSOLUTE: 3.115 G_L1_RELATIVE: 12.656 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 31, iters: 223440, time: 0.543, data: 0.000) G_L1: 13.083 G_L1_ABSOLUTE: 2.425 G_L1_RELATIVE: 10.659 G_Regularizer: 0.000 validation_error: 20.312 +(epoch: 31, iters: 225440, time: 0.546, data: 0.000) G_L1: 12.079 G_L1_ABSOLUTE: 2.498 G_L1_RELATIVE: 9.581 G_Regularizer: 0.000 validation_error: 20.226 +(epoch: 31, iters: 227440, time: 0.537, data: 0.001) G_L1: 11.687 G_L1_ABSOLUTE: 2.061 G_L1_RELATIVE: 9.627 G_Regularizer: 0.000 validation_error: 21.388 +(epoch: 31, iters: 229440, time: 0.552, data: 0.000) G_L1: 12.275 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 9.735 G_Regularizer: 0.000 validation_error: 20.608 +(epoch: 31, iters: 231440, time: 0.548, data: 0.001) G_L1: 15.704 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 12.982 G_Regularizer: 0.000 validation_error: 21.033 +(epoch: 31, iters: 233440, time: 0.543, data: 0.000) G_L1: 11.945 G_L1_ABSOLUTE: 2.431 G_L1_RELATIVE: 9.514 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 31, iters: 235440, time: 0.537, data: 0.000) G_L1: 17.086 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 14.517 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 31, iters: 237440, time: 0.550, data: 0.000) G_L1: 14.511 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 11.886 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 31, iters: 239440, time: 0.544, data: 0.000) G_L1: 12.547 G_L1_ABSOLUTE: 2.638 G_L1_RELATIVE: 9.909 G_Regularizer: 0.000 validation_error: 20.339 +(epoch: 31, iters: 241440, time: 0.546, data: 0.000) G_L1: 17.247 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 15.031 G_Regularizer: 0.000 validation_error: 20.079 +(epoch: 31, iters: 243440, time: 0.553, data: 0.000) G_L1: 15.518 G_L1_ABSOLUTE: 2.994 G_L1_RELATIVE: 12.524 G_Regularizer: 0.000 validation_error: 20.546 +(epoch: 31, iters: 245440, time: 0.549, data: 0.000) G_L1: 13.652 G_L1_ABSOLUTE: 2.328 G_L1_RELATIVE: 11.324 G_Regularizer: 0.000 validation_error: 20.553 +(epoch: 31, iters: 247440, time: 0.548, data: 0.000) G_L1: 23.853 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 21.129 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 31, iters: 249440, time: 0.538, data: 0.000) G_L1: 14.342 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 11.545 G_Regularizer: 0.000 validation_error: 20.405 +(epoch: 31, iters: 251440, time: 0.555, data: 0.001) G_L1: 13.841 G_L1_ABSOLUTE: 2.279 G_L1_RELATIVE: 11.562 G_Regularizer: 0.000 validation_error: 21.196 +(epoch: 31, iters: 253440, time: 0.542, data: 0.000) G_L1: 13.652 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 11.082 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 31, iters: 255440, time: 0.542, data: 0.000) G_L1: 25.598 G_L1_ABSOLUTE: 2.970 G_L1_RELATIVE: 22.629 G_Regularizer: 0.000 validation_error: 21.666 +(epoch: 31, iters: 257440, time: 0.559, data: 0.000) G_L1: 12.904 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 10.173 G_Regularizer: 0.000 validation_error: 20.641 +(epoch: 31, iters: 259440, time: 0.552, data: 0.000) G_L1: 13.977 G_L1_ABSOLUTE: 2.188 G_L1_RELATIVE: 11.790 G_Regularizer: 0.000 validation_error: 20.500 +(epoch: 31, iters: 261440, time: 0.546, data: 0.000) G_L1: 13.721 G_L1_ABSOLUTE: 2.772 G_L1_RELATIVE: 10.949 G_Regularizer: 0.000 validation_error: 20.716 +(epoch: 31, iters: 263440, time: 0.537, data: 0.000) G_L1: 13.652 G_L1_ABSOLUTE: 2.488 G_L1_RELATIVE: 11.165 G_Regularizer: 0.000 validation_error: 21.073 +(epoch: 31, iters: 265440, time: 0.547, data: 0.000) G_L1: 11.899 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 9.533 G_Regularizer: 0.000 validation_error: 20.620 +(epoch: 31, iters: 267440, time: 0.544, data: 0.000) G_L1: 14.270 G_L1_ABSOLUTE: 2.599 G_L1_RELATIVE: 11.671 G_Regularizer: 0.000 validation_error: 21.065 +(epoch: 31, iters: 269440, time: 0.550, data: 0.000) G_L1: 14.495 G_L1_ABSOLUTE: 2.292 G_L1_RELATIVE: 12.202 G_Regularizer: 0.000 validation_error: 21.185 +(epoch: 31, iters: 271440, time: 0.545, data: 0.000) G_L1: 15.408 G_L1_ABSOLUTE: 2.682 G_L1_RELATIVE: 12.726 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 31, iters: 273440, time: 0.554, data: 0.000) G_L1: 12.439 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 10.096 G_Regularizer: 0.000 validation_error: 20.175 +(epoch: 31, iters: 275440, time: 0.542, data: 0.000) G_L1: 12.176 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 9.894 G_Regularizer: 0.000 validation_error: 20.119 +(epoch: 31, iters: 277440, time: 0.541, data: 0.000) G_L1: 15.300 G_L1_ABSOLUTE: 2.989 G_L1_RELATIVE: 12.311 G_Regularizer: 0.000 validation_error: 20.907 +(epoch: 31, iters: 279440, time: 0.541, data: 0.000) G_L1: 15.100 G_L1_ABSOLUTE: 2.878 G_L1_RELATIVE: 12.222 G_Regularizer: 0.000 validation_error: 20.320 +(epoch: 31, iters: 281440, time: 0.549, data: 0.000) G_L1: 15.950 G_L1_ABSOLUTE: 2.802 G_L1_RELATIVE: 13.148 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 31, iters: 283440, time: 0.551, data: 0.000) G_L1: 14.677 G_L1_ABSOLUTE: 2.186 G_L1_RELATIVE: 12.491 G_Regularizer: 0.000 validation_error: 20.302 +(epoch: 31, iters: 285440, time: 0.550, data: 0.000) G_L1: 13.018 G_L1_ABSOLUTE: 2.593 G_L1_RELATIVE: 10.425 G_Regularizer: 0.000 validation_error: 20.572 +(epoch: 31, iters: 287440, time: 0.547, data: 0.000) G_L1: 14.301 G_L1_ABSOLUTE: 2.817 G_L1_RELATIVE: 11.484 G_Regularizer: 0.000 validation_error: 20.856 +(epoch: 31, iters: 289440, time: 0.545, data: 0.000) G_L1: 15.117 G_L1_ABSOLUTE: 2.465 G_L1_RELATIVE: 12.652 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 31, iters: 291440, time: 0.548, data: 0.000) G_L1: 16.981 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 14.273 G_Regularizer: 0.000 validation_error: 20.592 +(epoch: 31, iters: 293440, time: 0.545, data: 0.001) G_L1: 17.295 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 14.719 G_Regularizer: 0.000 validation_error: 20.565 +(epoch: 31, iters: 295440, time: 0.542, data: 0.001) G_L1: 13.315 G_L1_ABSOLUTE: 2.137 G_L1_RELATIVE: 11.178 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 31, iters: 297440, time: 0.552, data: 0.000) G_L1: 13.286 G_L1_ABSOLUTE: 2.069 G_L1_RELATIVE: 11.217 G_Regularizer: 0.000 validation_error: 20.517 +(epoch: 31, iters: 299440, time: 0.559, data: 0.000) G_L1: 12.168 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 9.582 G_Regularizer: 0.000 validation_error: 21.457 +(epoch: 31, iters: 301440, time: 0.545, data: 0.000) G_L1: 14.315 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 11.983 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 32, iters: 688, time: 0.559, data: 0.000) G_L1: 13.400 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 11.146 G_Regularizer: 0.000 validation_error: 20.670 +(epoch: 32, iters: 2688, time: 0.548, data: 0.000) G_L1: 14.849 G_L1_ABSOLUTE: 2.871 G_L1_RELATIVE: 11.978 G_Regularizer: 0.000 validation_error: 20.638 +(epoch: 32, iters: 4688, time: 0.545, data: 0.001) G_L1: 13.882 G_L1_ABSOLUTE: 2.422 G_L1_RELATIVE: 11.461 G_Regularizer: 0.000 validation_error: 20.134 +(epoch: 32, iters: 6688, time: 0.542, data: 0.000) G_L1: 14.857 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 12.407 G_Regularizer: 0.000 validation_error: 20.553 +(epoch: 32, iters: 8688, time: 0.550, data: 0.000) G_L1: 14.870 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 12.007 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 32, iters: 10688, time: 0.550, data: 0.000) G_L1: 12.436 G_L1_ABSOLUTE: 2.261 G_L1_RELATIVE: 10.175 G_Regularizer: 0.000 validation_error: 20.972 +(epoch: 32, iters: 12688, time: 0.540, data: 0.000) G_L1: 15.911 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 13.248 G_Regularizer: 0.000 validation_error: 20.687 +(epoch: 32, iters: 14688, time: 0.542, data: 0.000) G_L1: 14.270 G_L1_ABSOLUTE: 2.219 G_L1_RELATIVE: 12.051 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 32, iters: 16688, time: 0.553, data: 0.000) G_L1: 13.647 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 10.791 G_Regularizer: 0.000 validation_error: 20.535 +(epoch: 32, iters: 18688, time: 0.552, data: 0.000) G_L1: 14.976 G_L1_ABSOLUTE: 2.911 G_L1_RELATIVE: 12.065 G_Regularizer: 0.000 validation_error: 20.320 +(epoch: 32, iters: 20688, time: 0.551, data: 0.000) G_L1: 15.050 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 12.376 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 32, iters: 22688, time: 0.545, data: 0.000) G_L1: 15.020 G_L1_ABSOLUTE: 2.709 G_L1_RELATIVE: 12.312 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 32, iters: 24688, time: 0.545, data: 0.000) G_L1: 12.586 G_L1_ABSOLUTE: 2.531 G_L1_RELATIVE: 10.054 G_Regularizer: 0.000 validation_error: 20.946 +(epoch: 32, iters: 26688, time: 0.540, data: 0.000) G_L1: 14.275 G_L1_ABSOLUTE: 2.783 G_L1_RELATIVE: 11.492 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 32, iters: 28688, time: 0.539, data: 0.000) G_L1: 14.102 G_L1_ABSOLUTE: 2.299 G_L1_RELATIVE: 11.804 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 32, iters: 30688, time: 0.550, data: 0.000) G_L1: 13.777 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 11.350 G_Regularizer: 0.000 validation_error: 20.560 +(epoch: 32, iters: 32688, time: 0.537, data: 0.000) G_L1: 16.939 G_L1_ABSOLUTE: 2.934 G_L1_RELATIVE: 14.006 G_Regularizer: 0.000 validation_error: 20.899 +(epoch: 32, iters: 34688, time: 0.549, data: 0.000) G_L1: 15.492 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 12.988 G_Regularizer: 0.000 validation_error: 21.394 +(epoch: 32, iters: 36688, time: 0.537, data: 0.000) G_L1: 14.049 G_L1_ABSOLUTE: 3.010 G_L1_RELATIVE: 11.039 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 32, iters: 38688, time: 0.546, data: 0.000) G_L1: 16.644 G_L1_ABSOLUTE: 3.173 G_L1_RELATIVE: 13.472 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 32, iters: 40688, time: 0.545, data: 0.000) G_L1: 18.708 G_L1_ABSOLUTE: 3.011 G_L1_RELATIVE: 15.697 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 32, iters: 42688, time: 0.547, data: 0.000) G_L1: 12.636 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 10.139 G_Regularizer: 0.000 validation_error: 20.666 +(epoch: 32, iters: 44688, time: 0.544, data: 0.000) G_L1: 14.435 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 11.721 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 32, iters: 46688, time: 0.545, data: 0.000) G_L1: 14.676 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 12.275 G_Regularizer: 0.000 validation_error: 20.797 +(epoch: 32, iters: 48688, time: 0.549, data: 0.000) G_L1: 16.271 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 13.377 G_Regularizer: 0.000 validation_error: 20.746 +(epoch: 32, iters: 50688, time: 0.545, data: 0.000) G_L1: 18.568 G_L1_ABSOLUTE: 3.174 G_L1_RELATIVE: 15.394 G_Regularizer: 0.000 validation_error: 20.523 +(epoch: 32, iters: 52688, time: 0.547, data: 0.000) G_L1: 13.803 G_L1_ABSOLUTE: 2.751 G_L1_RELATIVE: 11.052 G_Regularizer: 0.000 validation_error: 21.169 +(epoch: 32, iters: 54688, time: 0.545, data: 0.000) G_L1: 14.388 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 11.710 G_Regularizer: 0.000 validation_error: 20.542 +(epoch: 32, iters: 56688, time: 0.550, data: 0.000) G_L1: 13.372 G_L1_ABSOLUTE: 2.801 G_L1_RELATIVE: 10.572 G_Regularizer: 0.000 validation_error: 21.048 +(epoch: 32, iters: 58688, time: 0.547, data: 0.000) G_L1: 15.227 G_L1_ABSOLUTE: 2.189 G_L1_RELATIVE: 13.038 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 32, iters: 60688, time: 0.550, data: 0.000) G_L1: 12.307 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 9.860 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 32, iters: 62688, time: 0.547, data: 0.000) G_L1: 14.540 G_L1_ABSOLUTE: 2.787 G_L1_RELATIVE: 11.753 G_Regularizer: 0.000 validation_error: 21.120 +(epoch: 32, iters: 64688, time: 0.544, data: 0.000) G_L1: 13.082 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 10.552 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 32, iters: 66688, time: 0.547, data: 0.000) G_L1: 16.768 G_L1_ABSOLUTE: 2.991 G_L1_RELATIVE: 13.777 G_Regularizer: 0.000 validation_error: 20.972 +(epoch: 32, iters: 68688, time: 0.548, data: 0.000) G_L1: 16.751 G_L1_ABSOLUTE: 3.600 G_L1_RELATIVE: 13.151 G_Regularizer: 0.000 validation_error: 20.077 +(epoch: 32, iters: 70688, time: 0.539, data: 0.000) G_L1: 13.511 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 10.962 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 32, iters: 72688, time: 0.549, data: 0.000) G_L1: 11.979 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 9.567 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 32, iters: 74688, time: 0.542, data: 0.000) G_L1: 12.890 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 10.342 G_Regularizer: 0.000 validation_error: 20.369 +(epoch: 32, iters: 76688, time: 0.539, data: 0.000) G_L1: 12.758 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 10.482 G_Regularizer: 0.000 validation_error: 20.691 +(epoch: 32, iters: 78688, time: 0.542, data: 0.000) G_L1: 12.683 G_L1_ABSOLUTE: 1.921 G_L1_RELATIVE: 10.761 G_Regularizer: 0.000 validation_error: 20.423 +(epoch: 32, iters: 80688, time: 0.551, data: 0.000) G_L1: 13.188 G_L1_ABSOLUTE: 2.390 G_L1_RELATIVE: 10.799 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 32, iters: 82688, time: 0.543, data: 0.000) G_L1: 14.750 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 12.204 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 32, iters: 84688, time: 0.538, data: 0.000) G_L1: 14.953 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 12.362 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 32, iters: 86688, time: 0.551, data: 0.000) G_L1: 12.258 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 10.042 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 32, iters: 88688, time: 0.543, data: 0.001) G_L1: 13.914 G_L1_ABSOLUTE: 2.144 G_L1_RELATIVE: 11.770 G_Regularizer: 0.000 validation_error: 21.074 +(epoch: 32, iters: 90688, time: 0.548, data: 0.000) G_L1: 13.129 G_L1_ABSOLUTE: 1.969 G_L1_RELATIVE: 11.159 G_Regularizer: 0.000 validation_error: 21.156 +(epoch: 32, iters: 92688, time: 0.546, data: 0.000) G_L1: 13.964 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 11.440 G_Regularizer: 0.000 validation_error: 20.936 +(epoch: 32, iters: 94688, time: 0.551, data: 0.000) G_L1: 16.338 G_L1_ABSOLUTE: 3.060 G_L1_RELATIVE: 13.278 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 32, iters: 96688, time: 0.541, data: 0.001) G_L1: 15.931 G_L1_ABSOLUTE: 2.959 G_L1_RELATIVE: 12.972 G_Regularizer: 0.000 validation_error: 21.036 +(epoch: 32, iters: 98688, time: 0.545, data: 0.001) G_L1: 14.770 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 12.248 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 32, iters: 100688, time: 0.558, data: 0.000) G_L1: 11.763 G_L1_ABSOLUTE: 2.242 G_L1_RELATIVE: 9.521 G_Regularizer: 0.000 validation_error: 20.115 +(epoch: 32, iters: 102688, time: 0.550, data: 0.000) G_L1: 12.021 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 9.547 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 32, iters: 104688, time: 0.538, data: 0.000) G_L1: 13.468 G_L1_ABSOLUTE: 2.095 G_L1_RELATIVE: 11.373 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 32, iters: 106688, time: 0.541, data: 0.000) G_L1: 14.633 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 11.751 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 32, iters: 108688, time: 0.552, data: 0.000) G_L1: 15.795 G_L1_ABSOLUTE: 2.866 G_L1_RELATIVE: 12.928 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 32, iters: 110688, time: 0.546, data: 0.000) G_L1: 13.843 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 11.392 G_Regularizer: 0.000 validation_error: 20.463 +(epoch: 32, iters: 112688, time: 0.538, data: 0.000) G_L1: 13.016 G_L1_ABSOLUTE: 2.444 G_L1_RELATIVE: 10.572 G_Regularizer: 0.000 validation_error: 20.744 +(epoch: 32, iters: 114688, time: 0.550, data: 0.000) G_L1: 14.111 G_L1_ABSOLUTE: 2.098 G_L1_RELATIVE: 12.013 G_Regularizer: 0.000 validation_error: 20.604 +(epoch: 32, iters: 116688, time: 0.555, data: 0.000) G_L1: 15.896 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 13.189 G_Regularizer: 0.000 validation_error: 20.374 +(epoch: 32, iters: 118688, time: 0.542, data: 0.000) G_L1: 15.725 G_L1_ABSOLUTE: 2.832 G_L1_RELATIVE: 12.893 G_Regularizer: 0.000 validation_error: 20.384 +(epoch: 32, iters: 120688, time: 0.544, data: 0.000) G_L1: 16.500 G_L1_ABSOLUTE: 3.475 G_L1_RELATIVE: 13.025 G_Regularizer: 0.000 validation_error: 20.210 +(epoch: 32, iters: 122688, time: 0.547, data: 0.000) G_L1: 16.913 G_L1_ABSOLUTE: 2.604 G_L1_RELATIVE: 14.309 G_Regularizer: 0.000 validation_error: 21.131 +(epoch: 32, iters: 124688, time: 0.542, data: 0.000) G_L1: 13.215 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 10.559 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 32, iters: 126688, time: 0.540, data: 0.000) G_L1: 14.580 G_L1_ABSOLUTE: 2.750 G_L1_RELATIVE: 11.830 G_Regularizer: 0.000 validation_error: 20.310 +(epoch: 32, iters: 128688, time: 0.546, data: 0.000) G_L1: 16.992 G_L1_ABSOLUTE: 3.004 G_L1_RELATIVE: 13.989 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 32, iters: 130688, time: 0.545, data: 0.000) G_L1: 13.562 G_L1_ABSOLUTE: 2.239 G_L1_RELATIVE: 11.323 G_Regularizer: 0.000 validation_error: 20.318 +(epoch: 32, iters: 132688, time: 0.544, data: 0.000) G_L1: 15.200 G_L1_ABSOLUTE: 2.238 G_L1_RELATIVE: 12.962 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 32, iters: 134688, time: 0.552, data: 0.001) G_L1: 15.393 G_L1_ABSOLUTE: 3.627 G_L1_RELATIVE: 11.766 G_Regularizer: 0.000 validation_error: 20.595 +(epoch: 32, iters: 136688, time: 0.543, data: 0.000) G_L1: 13.503 G_L1_ABSOLUTE: 2.301 G_L1_RELATIVE: 11.201 G_Regularizer: 0.000 validation_error: 20.340 +(epoch: 32, iters: 138688, time: 0.549, data: 0.000) G_L1: 13.169 G_L1_ABSOLUTE: 2.844 G_L1_RELATIVE: 10.325 G_Regularizer: 0.000 validation_error: 21.112 +(epoch: 32, iters: 140688, time: 0.544, data: 0.000) G_L1: 14.620 G_L1_ABSOLUTE: 2.606 G_L1_RELATIVE: 12.014 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 32, iters: 142688, time: 0.549, data: 0.000) G_L1: 15.324 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 12.854 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 32, iters: 144688, time: 0.545, data: 0.000) G_L1: 15.071 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 12.387 G_Regularizer: 0.000 validation_error: 20.593 +(epoch: 32, iters: 146688, time: 0.542, data: 0.001) G_L1: 15.299 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 12.839 G_Regularizer: 0.000 validation_error: 20.597 +(epoch: 32, iters: 148688, time: 0.548, data: 0.000) G_L1: 14.960 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 12.605 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 32, iters: 150688, time: 0.545, data: 0.000) G_L1: 13.038 G_L1_ABSOLUTE: 2.268 G_L1_RELATIVE: 10.770 G_Regularizer: 0.000 validation_error: 20.662 +(epoch: 32, iters: 152688, time: 0.544, data: 0.000) G_L1: 15.606 G_L1_ABSOLUTE: 3.091 G_L1_RELATIVE: 12.514 G_Regularizer: 0.000 validation_error: 20.346 +(epoch: 32, iters: 154688, time: 0.548, data: 0.000) G_L1: 13.577 G_L1_ABSOLUTE: 2.753 G_L1_RELATIVE: 10.824 G_Regularizer: 0.000 validation_error: 20.490 +(epoch: 32, iters: 156688, time: 0.546, data: 0.000) G_L1: 14.621 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 12.119 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 32, iters: 158688, time: 0.558, data: 0.000) G_L1: 16.494 G_L1_ABSOLUTE: 2.203 G_L1_RELATIVE: 14.292 G_Regularizer: 0.000 validation_error: 20.234 +(epoch: 32, iters: 160688, time: 0.544, data: 0.000) G_L1: 15.431 G_L1_ABSOLUTE: 2.854 G_L1_RELATIVE: 12.577 G_Regularizer: 0.000 validation_error: 20.337 +(epoch: 32, iters: 162688, time: 0.546, data: 0.000) G_L1: 13.904 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 11.603 G_Regularizer: 0.000 validation_error: 20.626 +(epoch: 32, iters: 164688, time: 0.541, data: 0.001) G_L1: 13.935 G_L1_ABSOLUTE: 2.104 G_L1_RELATIVE: 11.831 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 32, iters: 166688, time: 0.544, data: 0.000) G_L1: 17.123 G_L1_ABSOLUTE: 3.043 G_L1_RELATIVE: 14.080 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 32, iters: 168688, time: 0.546, data: 0.000) G_L1: 12.744 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 10.330 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 32, iters: 170688, time: 0.541, data: 0.000) G_L1: 15.111 G_L1_ABSOLUTE: 2.790 G_L1_RELATIVE: 12.320 G_Regularizer: 0.000 validation_error: 20.465 +(epoch: 32, iters: 172688, time: 0.550, data: 0.000) G_L1: 15.355 G_L1_ABSOLUTE: 2.719 G_L1_RELATIVE: 12.636 G_Regularizer: 0.000 validation_error: 20.280 +(epoch: 32, iters: 174688, time: 0.546, data: 0.000) G_L1: 12.118 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 9.786 G_Regularizer: 0.000 validation_error: 20.684 +(epoch: 32, iters: 176688, time: 0.551, data: 0.000) G_L1: 15.378 G_L1_ABSOLUTE: 2.964 G_L1_RELATIVE: 12.415 G_Regularizer: 0.000 validation_error: 21.288 +(epoch: 32, iters: 178688, time: 0.543, data: 0.000) G_L1: 15.049 G_L1_ABSOLUTE: 2.241 G_L1_RELATIVE: 12.808 G_Regularizer: 0.000 validation_error: 20.892 +(epoch: 32, iters: 180688, time: 0.544, data: 0.000) G_L1: 16.692 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 14.216 G_Regularizer: 0.000 validation_error: 20.440 +(epoch: 32, iters: 182688, time: 0.549, data: 0.000) G_L1: 13.916 G_L1_ABSOLUTE: 3.170 G_L1_RELATIVE: 10.746 G_Regularizer: 0.000 validation_error: 20.662 +(epoch: 32, iters: 184688, time: 0.549, data: 0.000) G_L1: 13.190 G_L1_ABSOLUTE: 2.387 G_L1_RELATIVE: 10.803 G_Regularizer: 0.000 validation_error: 20.149 +(epoch: 32, iters: 186688, time: 0.551, data: 0.000) G_L1: 12.744 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 10.208 G_Regularizer: 0.000 validation_error: 20.457 +(epoch: 32, iters: 188688, time: 0.552, data: 0.001) G_L1: 15.539 G_L1_ABSOLUTE: 2.898 G_L1_RELATIVE: 12.641 G_Regularizer: 0.000 validation_error: 20.392 +(epoch: 32, iters: 190688, time: 0.545, data: 0.000) G_L1: 15.924 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 13.265 G_Regularizer: 0.000 validation_error: 20.080 +(epoch: 32, iters: 192688, time: 0.545, data: 0.000) G_L1: 16.039 G_L1_ABSOLUTE: 3.031 G_L1_RELATIVE: 13.008 G_Regularizer: 0.000 validation_error: 21.080 +(epoch: 32, iters: 194688, time: 0.540, data: 0.000) G_L1: 13.209 G_L1_ABSOLUTE: 2.309 G_L1_RELATIVE: 10.900 G_Regularizer: 0.000 validation_error: 19.919 +(epoch: 32, iters: 196688, time: 0.552, data: 0.000) G_L1: 14.111 G_L1_ABSOLUTE: 2.037 G_L1_RELATIVE: 12.074 G_Regularizer: 0.000 validation_error: 20.351 +(epoch: 32, iters: 198688, time: 0.540, data: 0.001) G_L1: 13.787 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 11.485 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 32, iters: 200688, time: 0.540, data: 0.000) G_L1: 16.914 G_L1_ABSOLUTE: 3.042 G_L1_RELATIVE: 13.872 G_Regularizer: 0.000 validation_error: 20.594 +(epoch: 32, iters: 202688, time: 0.557, data: 0.000) G_L1: 13.284 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 10.806 G_Regularizer: 0.000 validation_error: 20.559 +(epoch: 32, iters: 204688, time: 0.544, data: 0.000) G_L1: 14.461 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 11.968 G_Regularizer: 0.000 validation_error: 20.536 +(epoch: 32, iters: 206688, time: 0.547, data: 0.000) G_L1: 12.262 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 9.734 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 32, iters: 208688, time: 0.544, data: 0.000) G_L1: 13.697 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 11.365 G_Regularizer: 0.000 validation_error: 21.244 +(epoch: 32, iters: 210688, time: 0.544, data: 0.000) G_L1: 15.230 G_L1_ABSOLUTE: 3.028 G_L1_RELATIVE: 12.202 G_Regularizer: 0.000 validation_error: 20.524 +(epoch: 32, iters: 212688, time: 0.544, data: 0.000) G_L1: 16.565 G_L1_ABSOLUTE: 2.479 G_L1_RELATIVE: 14.086 G_Regularizer: 0.000 validation_error: 20.650 +(epoch: 32, iters: 214688, time: 0.544, data: 0.001) G_L1: 13.447 G_L1_ABSOLUTE: 2.104 G_L1_RELATIVE: 11.343 G_Regularizer: 0.000 validation_error: 20.896 +(epoch: 32, iters: 216688, time: 0.544, data: 0.000) G_L1: 16.394 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 13.818 G_Regularizer: 0.000 validation_error: 20.612 +(epoch: 32, iters: 218688, time: 0.542, data: 0.000) G_L1: 15.679 G_L1_ABSOLUTE: 2.361 G_L1_RELATIVE: 13.318 G_Regularizer: 0.000 validation_error: 20.983 +(epoch: 32, iters: 220688, time: 0.545, data: 0.000) G_L1: 13.574 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 11.320 G_Regularizer: 0.000 validation_error: 20.820 +(epoch: 32, iters: 222688, time: 0.550, data: 0.000) G_L1: 15.023 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 12.538 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 32, iters: 224688, time: 0.544, data: 0.000) G_L1: 14.424 G_L1_ABSOLUTE: 2.545 G_L1_RELATIVE: 11.878 G_Regularizer: 0.000 validation_error: 20.659 +(epoch: 32, iters: 226688, time: 0.544, data: 0.000) G_L1: 12.131 G_L1_ABSOLUTE: 2.021 G_L1_RELATIVE: 10.110 G_Regularizer: 0.000 validation_error: 21.043 +(epoch: 32, iters: 228688, time: 0.540, data: 0.000) G_L1: 13.827 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 11.470 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 32, iters: 230688, time: 0.549, data: 0.001) G_L1: 15.114 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 12.560 G_Regularizer: 0.000 validation_error: 21.387 +(epoch: 32, iters: 232688, time: 0.555, data: 0.000) G_L1: 13.571 G_L1_ABSOLUTE: 2.296 G_L1_RELATIVE: 11.275 G_Regularizer: 0.000 validation_error: 21.091 +(epoch: 32, iters: 234688, time: 0.546, data: 0.000) G_L1: 12.361 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 9.842 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 32, iters: 236688, time: 0.550, data: 0.001) G_L1: 14.613 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 12.245 G_Regularizer: 0.000 validation_error: 21.153 +(epoch: 32, iters: 238688, time: 0.542, data: 0.000) G_L1: 17.433 G_L1_ABSOLUTE: 2.306 G_L1_RELATIVE: 15.126 G_Regularizer: 0.000 validation_error: 21.147 +(epoch: 32, iters: 240688, time: 0.554, data: 0.000) G_L1: 13.187 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 10.462 G_Regularizer: 0.000 validation_error: 21.063 +(epoch: 32, iters: 242688, time: 0.541, data: 0.000) G_L1: 12.326 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 9.773 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 32, iters: 244688, time: 0.544, data: 0.000) G_L1: 13.364 G_L1_ABSOLUTE: 2.566 G_L1_RELATIVE: 10.798 G_Regularizer: 0.000 validation_error: 21.338 +(epoch: 32, iters: 246688, time: 0.547, data: 0.000) G_L1: 11.285 G_L1_ABSOLUTE: 2.042 G_L1_RELATIVE: 9.243 G_Regularizer: 0.000 validation_error: 20.714 +(epoch: 32, iters: 248688, time: 0.555, data: 0.000) G_L1: 17.801 G_L1_ABSOLUTE: 2.968 G_L1_RELATIVE: 14.833 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 32, iters: 250688, time: 0.539, data: 0.000) G_L1: 17.351 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 14.931 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 32, iters: 252688, time: 0.547, data: 0.000) G_L1: 13.652 G_L1_ABSOLUTE: 2.135 G_L1_RELATIVE: 11.517 G_Regularizer: 0.000 validation_error: 20.911 +(epoch: 32, iters: 254688, time: 0.547, data: 0.000) G_L1: 15.311 G_L1_ABSOLUTE: 2.953 G_L1_RELATIVE: 12.358 G_Regularizer: 0.000 validation_error: 21.229 +(epoch: 32, iters: 256688, time: 0.541, data: 0.000) G_L1: 15.517 G_L1_ABSOLUTE: 2.499 G_L1_RELATIVE: 13.018 G_Regularizer: 0.000 validation_error: 20.504 +(epoch: 32, iters: 258688, time: 0.539, data: 0.000) G_L1: 16.318 G_L1_ABSOLUTE: 2.645 G_L1_RELATIVE: 13.673 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 32, iters: 260688, time: 0.542, data: 0.000) G_L1: 12.333 G_L1_ABSOLUTE: 2.387 G_L1_RELATIVE: 9.946 G_Regularizer: 0.000 validation_error: 20.305 +(epoch: 32, iters: 262688, time: 0.536, data: 0.000) G_L1: 11.829 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 9.452 G_Regularizer: 0.000 validation_error: 20.405 +(epoch: 32, iters: 264688, time: 0.543, data: 0.000) G_L1: 12.642 G_L1_ABSOLUTE: 2.167 G_L1_RELATIVE: 10.474 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 32, iters: 266688, time: 0.548, data: 0.000) G_L1: 13.674 G_L1_ABSOLUTE: 2.241 G_L1_RELATIVE: 11.433 G_Regularizer: 0.000 validation_error: 20.621 +(epoch: 32, iters: 268688, time: 0.553, data: 0.000) G_L1: 15.524 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 13.021 G_Regularizer: 0.000 validation_error: 20.496 +(epoch: 32, iters: 270688, time: 0.540, data: 0.000) G_L1: 13.685 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 10.971 G_Regularizer: 0.000 validation_error: 20.748 +(epoch: 32, iters: 272688, time: 0.549, data: 0.000) G_L1: 16.412 G_L1_ABSOLUTE: 3.004 G_L1_RELATIVE: 13.408 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 32, iters: 274688, time: 0.552, data: 0.000) G_L1: 17.846 G_L1_ABSOLUTE: 3.076 G_L1_RELATIVE: 14.770 G_Regularizer: 0.000 validation_error: 20.763 +(epoch: 32, iters: 276688, time: 0.542, data: 0.000) G_L1: 15.899 G_L1_ABSOLUTE: 2.741 G_L1_RELATIVE: 13.158 G_Regularizer: 0.000 validation_error: 21.252 +(epoch: 32, iters: 278688, time: 0.542, data: 0.000) G_L1: 12.479 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 10.106 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 32, iters: 280688, time: 0.549, data: 0.000) G_L1: 14.473 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 12.042 G_Regularizer: 0.000 validation_error: 20.848 +(epoch: 32, iters: 282688, time: 0.549, data: 0.000) G_L1: 16.495 G_L1_ABSOLUTE: 2.655 G_L1_RELATIVE: 13.840 G_Regularizer: 0.000 validation_error: 20.730 +(epoch: 32, iters: 284688, time: 0.545, data: 0.000) G_L1: 15.645 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 13.025 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 32, iters: 286688, time: 0.551, data: 0.000) G_L1: 14.766 G_L1_ABSOLUTE: 2.251 G_L1_RELATIVE: 12.515 G_Regularizer: 0.000 validation_error: 20.485 +(epoch: 32, iters: 288688, time: 0.553, data: 0.000) G_L1: 14.682 G_L1_ABSOLUTE: 2.593 G_L1_RELATIVE: 12.090 G_Regularizer: 0.000 validation_error: 21.310 +(epoch: 32, iters: 290688, time: 0.544, data: 0.000) G_L1: 14.097 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 11.595 G_Regularizer: 0.000 validation_error: 21.100 +(epoch: 32, iters: 292688, time: 0.550, data: 0.000) G_L1: 15.115 G_L1_ABSOLUTE: 2.806 G_L1_RELATIVE: 12.309 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 32, iters: 294688, time: 0.548, data: 0.000) G_L1: 14.533 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 11.776 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 32, iters: 296688, time: 0.545, data: 0.000) G_L1: 12.034 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 9.635 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 32, iters: 298688, time: 0.550, data: 0.000) G_L1: 14.842 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 12.395 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 32, iters: 300688, time: 0.554, data: 0.000) G_L1: 18.641 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 15.867 G_Regularizer: 0.000 validation_error: 21.033 +(epoch: 32, iters: 302688, time: 0.542, data: 0.001) G_L1: 13.871 G_L1_ABSOLUTE: 2.088 G_L1_RELATIVE: 11.783 G_Regularizer: 0.000 validation_error: 20.503 +(epoch: 33, iters: 1936, time: 0.546, data: 0.000) G_L1: 13.409 G_L1_ABSOLUTE: 2.122 G_L1_RELATIVE: 11.287 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 33, iters: 3936, time: 0.547, data: 0.000) G_L1: 15.309 G_L1_ABSOLUTE: 2.929 G_L1_RELATIVE: 12.379 G_Regularizer: 0.000 validation_error: 20.475 +(epoch: 33, iters: 5936, time: 0.551, data: 0.000) G_L1: 13.525 G_L1_ABSOLUTE: 2.148 G_L1_RELATIVE: 11.377 G_Regularizer: 0.000 validation_error: 20.178 +(epoch: 33, iters: 7936, time: 0.541, data: 0.000) G_L1: 14.559 G_L1_ABSOLUTE: 2.296 G_L1_RELATIVE: 12.263 G_Regularizer: 0.000 validation_error: 21.116 +(epoch: 33, iters: 9936, time: 0.545, data: 0.000) G_L1: 15.649 G_L1_ABSOLUTE: 2.904 G_L1_RELATIVE: 12.745 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 33, iters: 11936, time: 0.545, data: 0.000) G_L1: 12.280 G_L1_ABSOLUTE: 2.120 G_L1_RELATIVE: 10.159 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 33, iters: 13936, time: 0.543, data: 0.000) G_L1: 14.211 G_L1_ABSOLUTE: 2.932 G_L1_RELATIVE: 11.279 G_Regularizer: 0.000 validation_error: 20.371 +(epoch: 33, iters: 15936, time: 0.552, data: 0.000) G_L1: 14.902 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 12.396 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 33, iters: 17936, time: 0.544, data: 0.000) G_L1: 15.253 G_L1_ABSOLUTE: 2.207 G_L1_RELATIVE: 13.046 G_Regularizer: 0.000 validation_error: 20.782 +(epoch: 33, iters: 19936, time: 0.552, data: 0.000) G_L1: 16.224 G_L1_ABSOLUTE: 3.257 G_L1_RELATIVE: 12.967 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 33, iters: 21936, time: 0.547, data: 0.000) G_L1: 13.255 G_L1_ABSOLUTE: 2.884 G_L1_RELATIVE: 10.371 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 33, iters: 23936, time: 0.553, data: 0.000) G_L1: 15.811 G_L1_ABSOLUTE: 2.209 G_L1_RELATIVE: 13.601 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 33, iters: 25936, time: 0.544, data: 0.000) G_L1: 17.588 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 15.189 G_Regularizer: 0.000 validation_error: 21.231 +(epoch: 33, iters: 27936, time: 0.553, data: 0.000) G_L1: 12.939 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 10.218 G_Regularizer: 0.000 validation_error: 20.705 +(epoch: 33, iters: 29936, time: 0.556, data: 0.000) G_L1: 24.473 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 21.968 G_Regularizer: 0.000 validation_error: 20.922 +(epoch: 33, iters: 31936, time: 0.554, data: 0.000) G_L1: 19.335 G_L1_ABSOLUTE: 3.288 G_L1_RELATIVE: 16.048 G_Regularizer: 0.000 validation_error: 21.261 +(epoch: 33, iters: 33936, time: 0.541, data: 0.000) G_L1: 15.943 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 13.474 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 33, iters: 35936, time: 0.545, data: 0.000) G_L1: 17.470 G_L1_ABSOLUTE: 2.192 G_L1_RELATIVE: 15.279 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 33, iters: 37936, time: 0.546, data: 0.000) G_L1: 14.971 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 12.385 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 33, iters: 39936, time: 0.544, data: 0.000) G_L1: 17.201 G_L1_ABSOLUTE: 2.417 G_L1_RELATIVE: 14.784 G_Regularizer: 0.000 validation_error: 20.776 +(epoch: 33, iters: 41936, time: 0.550, data: 0.000) G_L1: 12.462 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 10.092 G_Regularizer: 0.000 validation_error: 21.161 +(epoch: 33, iters: 43936, time: 0.543, data: 0.000) G_L1: 15.364 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 12.773 G_Regularizer: 0.000 validation_error: 21.223 +(epoch: 33, iters: 45936, time: 0.547, data: 0.000) G_L1: 16.958 G_L1_ABSOLUTE: 3.378 G_L1_RELATIVE: 13.580 G_Regularizer: 0.000 validation_error: 21.549 +(epoch: 33, iters: 47936, time: 0.546, data: 0.000) G_L1: 14.058 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 11.461 G_Regularizer: 0.000 validation_error: 20.539 +(epoch: 33, iters: 49936, time: 0.546, data: 0.000) G_L1: 18.969 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 16.350 G_Regularizer: 0.000 validation_error: 20.480 +(epoch: 33, iters: 51936, time: 0.546, data: 0.000) G_L1: 16.446 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 13.664 G_Regularizer: 0.000 validation_error: 20.637 +(epoch: 33, iters: 53936, time: 0.546, data: 0.000) G_L1: 16.173 G_L1_ABSOLUTE: 2.419 G_L1_RELATIVE: 13.754 G_Regularizer: 0.000 validation_error: 20.461 +(epoch: 33, iters: 55936, time: 0.545, data: 0.000) G_L1: 12.979 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 10.446 G_Regularizer: 0.000 validation_error: 21.299 +(epoch: 33, iters: 57936, time: 0.552, data: 0.000) G_L1: 15.505 G_L1_ABSOLUTE: 2.717 G_L1_RELATIVE: 12.788 G_Regularizer: 0.000 validation_error: 21.066 +(epoch: 33, iters: 59936, time: 0.544, data: 0.000) G_L1: 11.402 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 9.069 G_Regularizer: 0.000 validation_error: 21.067 +(epoch: 33, iters: 61936, time: 0.537, data: 0.000) G_L1: 15.358 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 12.768 G_Regularizer: 0.000 validation_error: 20.327 +(epoch: 33, iters: 63936, time: 0.553, data: 0.000) G_L1: 13.175 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 10.607 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 33, iters: 65936, time: 0.550, data: 0.000) G_L1: 13.051 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 10.616 G_Regularizer: 0.000 validation_error: 20.650 +(epoch: 33, iters: 67936, time: 0.543, data: 0.000) G_L1: 14.593 G_L1_ABSOLUTE: 2.239 G_L1_RELATIVE: 12.354 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 33, iters: 69936, time: 0.546, data: 0.000) G_L1: 14.735 G_L1_ABSOLUTE: 2.039 G_L1_RELATIVE: 12.696 G_Regularizer: 0.000 validation_error: 20.400 +(epoch: 33, iters: 71936, time: 0.551, data: 0.000) G_L1: 13.781 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 11.255 G_Regularizer: 0.000 validation_error: 21.063 +(epoch: 33, iters: 73936, time: 0.543, data: 0.000) G_L1: 13.518 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 11.227 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 33, iters: 75936, time: 0.554, data: 0.000) G_L1: 14.428 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 12.098 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 33, iters: 77936, time: 0.541, data: 0.000) G_L1: 14.429 G_L1_ABSOLUTE: 2.239 G_L1_RELATIVE: 12.190 G_Regularizer: 0.000 validation_error: 20.545 +(epoch: 33, iters: 79936, time: 0.549, data: 0.000) G_L1: 15.632 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 12.752 G_Regularizer: 0.000 validation_error: 21.076 +(epoch: 33, iters: 81936, time: 0.550, data: 0.000) G_L1: 15.446 G_L1_ABSOLUTE: 3.144 G_L1_RELATIVE: 12.302 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 33, iters: 83936, time: 0.546, data: 0.000) G_L1: 15.278 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 12.863 G_Regularizer: 0.000 validation_error: 20.957 +(epoch: 33, iters: 85936, time: 0.539, data: 0.000) G_L1: 13.841 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 11.576 G_Regularizer: 0.000 validation_error: 21.012 +(epoch: 33, iters: 87936, time: 0.545, data: 0.000) G_L1: 17.099 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 14.318 G_Regularizer: 0.000 validation_error: 21.172 +(epoch: 33, iters: 89936, time: 0.560, data: 0.000) G_L1: 13.707 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 11.282 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 33, iters: 91936, time: 0.558, data: 0.000) G_L1: 14.734 G_L1_ABSOLUTE: 2.415 G_L1_RELATIVE: 12.319 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 33, iters: 93936, time: 0.547, data: 0.000) G_L1: 13.566 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 11.041 G_Regularizer: 0.000 validation_error: 21.175 +(epoch: 33, iters: 95936, time: 0.547, data: 0.000) G_L1: 13.512 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 10.877 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 33, iters: 97936, time: 0.547, data: 0.000) G_L1: 12.153 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 9.691 G_Regularizer: 0.000 validation_error: 20.497 +(epoch: 33, iters: 99936, time: 0.539, data: 0.000) G_L1: 15.071 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 12.420 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 33, iters: 101936, time: 0.542, data: 0.000) G_L1: 15.074 G_L1_ABSOLUTE: 3.011 G_L1_RELATIVE: 12.063 G_Regularizer: 0.000 validation_error: 20.456 +(epoch: 33, iters: 103936, time: 0.549, data: 0.000) G_L1: 14.412 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 11.784 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 33, iters: 105936, time: 0.547, data: 0.000) G_L1: 14.999 G_L1_ABSOLUTE: 2.567 G_L1_RELATIVE: 12.431 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 33, iters: 107936, time: 0.544, data: 0.000) G_L1: 15.014 G_L1_ABSOLUTE: 2.379 G_L1_RELATIVE: 12.635 G_Regularizer: 0.000 validation_error: 20.601 +(epoch: 33, iters: 109936, time: 0.550, data: 0.000) G_L1: 14.181 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 11.564 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 33, iters: 111936, time: 0.546, data: 0.001) G_L1: 13.038 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 10.629 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 33, iters: 113936, time: 0.542, data: 0.000) G_L1: 14.570 G_L1_ABSOLUTE: 2.834 G_L1_RELATIVE: 11.736 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 33, iters: 115936, time: 0.546, data: 0.000) G_L1: 13.842 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 11.502 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 33, iters: 117936, time: 0.556, data: 0.000) G_L1: 13.724 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 11.228 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 33, iters: 119936, time: 0.547, data: 0.000) G_L1: 14.401 G_L1_ABSOLUTE: 2.771 G_L1_RELATIVE: 11.630 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 33, iters: 121936, time: 0.539, data: 0.000) G_L1: 15.212 G_L1_ABSOLUTE: 2.654 G_L1_RELATIVE: 12.558 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 33, iters: 123936, time: 0.545, data: 0.000) G_L1: 13.879 G_L1_ABSOLUTE: 2.723 G_L1_RELATIVE: 11.156 G_Regularizer: 0.000 validation_error: 20.503 +(epoch: 33, iters: 125936, time: 0.550, data: 0.000) G_L1: 14.953 G_L1_ABSOLUTE: 2.741 G_L1_RELATIVE: 12.212 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 33, iters: 127936, time: 0.545, data: 0.000) G_L1: 16.142 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 13.757 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 33, iters: 129936, time: 0.547, data: 0.000) G_L1: 14.769 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 12.055 G_Regularizer: 0.000 validation_error: 20.349 +(epoch: 33, iters: 131936, time: 0.549, data: 0.000) G_L1: 16.240 G_L1_ABSOLUTE: 2.823 G_L1_RELATIVE: 13.417 G_Regularizer: 0.000 validation_error: 21.006 +(epoch: 33, iters: 133936, time: 0.542, data: 0.000) G_L1: 12.864 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 10.605 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 33, iters: 135936, time: 0.548, data: 0.000) G_L1: 13.053 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 10.768 G_Regularizer: 0.000 validation_error: 20.183 +(epoch: 33, iters: 137936, time: 0.546, data: 0.001) G_L1: 11.928 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 9.730 G_Regularizer: 0.000 validation_error: 20.681 +(epoch: 33, iters: 139936, time: 0.541, data: 0.000) G_L1: 15.216 G_L1_ABSOLUTE: 2.871 G_L1_RELATIVE: 12.345 G_Regularizer: 0.000 validation_error: 20.998 +(epoch: 33, iters: 141936, time: 0.537, data: 0.000) G_L1: 13.808 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 11.156 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 33, iters: 143936, time: 0.545, data: 0.000) G_L1: 13.653 G_L1_ABSOLUTE: 2.846 G_L1_RELATIVE: 10.808 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 33, iters: 145936, time: 0.542, data: 0.000) G_L1: 14.972 G_L1_ABSOLUTE: 2.465 G_L1_RELATIVE: 12.506 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 33, iters: 147936, time: 0.553, data: 0.000) G_L1: 10.777 G_L1_ABSOLUTE: 1.930 G_L1_RELATIVE: 8.848 G_Regularizer: 0.000 validation_error: 20.377 +(epoch: 33, iters: 149936, time: 0.544, data: 0.000) G_L1: 14.503 G_L1_ABSOLUTE: 2.182 G_L1_RELATIVE: 12.321 G_Regularizer: 0.000 validation_error: 20.391 +(epoch: 33, iters: 151936, time: 0.544, data: 0.000) G_L1: 16.129 G_L1_ABSOLUTE: 2.560 G_L1_RELATIVE: 13.569 G_Regularizer: 0.000 validation_error: 21.069 +(epoch: 33, iters: 153936, time: 0.547, data: 0.001) G_L1: 16.473 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 14.024 G_Regularizer: 0.000 validation_error: 20.519 +(epoch: 33, iters: 155936, time: 0.541, data: 0.000) G_L1: 15.486 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 13.075 G_Regularizer: 0.000 validation_error: 21.350 +(epoch: 33, iters: 157936, time: 0.550, data: 0.000) G_L1: 12.938 G_L1_ABSOLUTE: 2.128 G_L1_RELATIVE: 10.810 G_Regularizer: 0.000 validation_error: 20.957 +(epoch: 33, iters: 159936, time: 0.547, data: 0.000) G_L1: 13.856 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 11.454 G_Regularizer: 0.000 validation_error: 20.527 +(epoch: 33, iters: 161936, time: 0.563, data: 0.000) G_L1: 13.801 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 11.110 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 33, iters: 163936, time: 0.545, data: 0.000) G_L1: 15.292 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 12.978 G_Regularizer: 0.000 validation_error: 20.854 +(epoch: 33, iters: 165936, time: 0.546, data: 0.000) G_L1: 12.393 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 10.073 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 33, iters: 167936, time: 0.543, data: 0.000) G_L1: 14.948 G_L1_ABSOLUTE: 2.944 G_L1_RELATIVE: 12.004 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 33, iters: 169936, time: 0.547, data: 0.000) G_L1: 16.082 G_L1_ABSOLUTE: 3.403 G_L1_RELATIVE: 12.679 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 33, iters: 171936, time: 0.544, data: 0.000) G_L1: 13.112 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 10.744 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 33, iters: 173936, time: 0.546, data: 0.000) G_L1: 14.250 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 11.883 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 33, iters: 175936, time: 0.547, data: 0.000) G_L1: 15.091 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 12.553 G_Regularizer: 0.000 validation_error: 20.785 +(epoch: 33, iters: 177936, time: 0.554, data: 0.000) G_L1: 15.981 G_L1_ABSOLUTE: 2.838 G_L1_RELATIVE: 13.143 G_Regularizer: 0.000 validation_error: 21.251 +(epoch: 33, iters: 179936, time: 0.541, data: 0.000) G_L1: 15.346 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 12.812 G_Regularizer: 0.000 validation_error: 21.156 +(epoch: 33, iters: 181936, time: 0.546, data: 0.000) G_L1: 15.805 G_L1_ABSOLUTE: 2.912 G_L1_RELATIVE: 12.893 G_Regularizer: 0.000 validation_error: 20.565 +(epoch: 33, iters: 183936, time: 0.546, data: 0.000) G_L1: 13.610 G_L1_ABSOLUTE: 2.287 G_L1_RELATIVE: 11.323 G_Regularizer: 0.000 validation_error: 21.227 +(epoch: 33, iters: 185936, time: 0.544, data: 0.000) G_L1: 17.934 G_L1_ABSOLUTE: 2.626 G_L1_RELATIVE: 15.308 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 33, iters: 187936, time: 0.549, data: 0.000) G_L1: 14.328 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 12.051 G_Regularizer: 0.000 validation_error: 20.671 +(epoch: 33, iters: 189936, time: 0.548, data: 0.001) G_L1: 15.319 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 12.641 G_Regularizer: 0.000 validation_error: 20.847 +(epoch: 33, iters: 191936, time: 0.547, data: 0.000) G_L1: 12.611 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 10.040 G_Regularizer: 0.000 validation_error: 20.551 +(epoch: 33, iters: 193936, time: 0.549, data: 0.000) G_L1: 9.905 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 7.558 G_Regularizer: 0.000 validation_error: 21.098 +(epoch: 33, iters: 195936, time: 0.544, data: 0.000) G_L1: 15.420 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 12.902 G_Regularizer: 0.000 validation_error: 20.569 +(epoch: 33, iters: 197936, time: 0.545, data: 0.000) G_L1: 14.987 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 12.256 G_Regularizer: 0.000 validation_error: 20.461 +(epoch: 33, iters: 199936, time: 0.540, data: 0.000) G_L1: 15.013 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 12.530 G_Regularizer: 0.000 validation_error: 21.404 +(epoch: 33, iters: 201936, time: 0.550, data: 0.000) G_L1: 12.909 G_L1_ABSOLUTE: 2.763 G_L1_RELATIVE: 10.145 G_Regularizer: 0.000 validation_error: 20.532 +(epoch: 33, iters: 203936, time: 0.546, data: 0.000) G_L1: 14.679 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 12.430 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 33, iters: 205936, time: 0.551, data: 0.000) G_L1: 14.088 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 11.596 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 33, iters: 207936, time: 0.549, data: 0.000) G_L1: 14.133 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 11.816 G_Regularizer: 0.000 validation_error: 20.555 +(epoch: 33, iters: 209936, time: 0.550, data: 0.000) G_L1: 14.676 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 12.336 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 33, iters: 211936, time: 0.546, data: 0.000) G_L1: 17.943 G_L1_ABSOLUTE: 2.398 G_L1_RELATIVE: 15.544 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 33, iters: 213936, time: 0.538, data: 0.000) G_L1: 14.725 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 12.186 G_Regularizer: 0.000 validation_error: 21.211 +(epoch: 33, iters: 215936, time: 0.545, data: 0.000) G_L1: 13.449 G_L1_ABSOLUTE: 2.239 G_L1_RELATIVE: 11.210 G_Regularizer: 0.000 validation_error: 20.598 +(epoch: 33, iters: 217936, time: 0.540, data: 0.000) G_L1: 14.112 G_L1_ABSOLUTE: 2.272 G_L1_RELATIVE: 11.839 G_Regularizer: 0.000 validation_error: 21.074 +(epoch: 33, iters: 219936, time: 0.547, data: 0.000) G_L1: 12.961 G_L1_ABSOLUTE: 2.393 G_L1_RELATIVE: 10.568 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 33, iters: 221936, time: 0.553, data: 0.000) G_L1: 12.151 G_L1_ABSOLUTE: 1.813 G_L1_RELATIVE: 10.338 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 33, iters: 223936, time: 0.548, data: 0.000) G_L1: 14.776 G_L1_ABSOLUTE: 2.899 G_L1_RELATIVE: 11.877 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 33, iters: 225936, time: 0.540, data: 0.000) G_L1: 13.661 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 11.212 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 33, iters: 227936, time: 0.549, data: 0.000) G_L1: 17.337 G_L1_ABSOLUTE: 2.937 G_L1_RELATIVE: 14.400 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 33, iters: 229936, time: 0.549, data: 0.001) G_L1: 14.612 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 11.980 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 33, iters: 231936, time: 0.543, data: 0.000) G_L1: 13.629 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 11.222 G_Regularizer: 0.000 validation_error: 20.381 +(epoch: 33, iters: 233936, time: 0.543, data: 0.000) G_L1: 16.069 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 13.264 G_Regularizer: 0.000 validation_error: 20.574 +(epoch: 33, iters: 235936, time: 0.543, data: 0.000) G_L1: 14.173 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 11.743 G_Regularizer: 0.000 validation_error: 19.987 +(epoch: 33, iters: 237936, time: 0.547, data: 0.000) G_L1: 14.772 G_L1_ABSOLUTE: 2.175 G_L1_RELATIVE: 12.597 G_Regularizer: 0.000 validation_error: 20.548 +(epoch: 33, iters: 239936, time: 0.543, data: 0.001) G_L1: 12.691 G_L1_ABSOLUTE: 2.241 G_L1_RELATIVE: 10.450 G_Regularizer: 0.000 validation_error: 20.628 +(epoch: 33, iters: 241936, time: 0.549, data: 0.000) G_L1: 16.423 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 13.824 G_Regularizer: 0.000 validation_error: 20.484 +(epoch: 33, iters: 243936, time: 0.542, data: 0.000) G_L1: 15.230 G_L1_ABSOLUTE: 2.197 G_L1_RELATIVE: 13.033 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 33, iters: 245936, time: 0.548, data: 0.000) G_L1: 12.223 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 9.969 G_Regularizer: 0.000 validation_error: 21.022 +(epoch: 33, iters: 247936, time: 0.546, data: 0.000) G_L1: 13.160 G_L1_ABSOLUTE: 2.338 G_L1_RELATIVE: 10.822 G_Regularizer: 0.000 validation_error: 19.880 +(epoch: 33, iters: 249936, time: 0.543, data: 0.000) G_L1: 15.686 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 13.258 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 33, iters: 251936, time: 0.548, data: 0.000) G_L1: 14.987 G_L1_ABSOLUTE: 2.749 G_L1_RELATIVE: 12.239 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 33, iters: 253936, time: 0.545, data: 0.000) G_L1: 16.745 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 14.249 G_Regularizer: 0.000 validation_error: 20.515 +(epoch: 33, iters: 255936, time: 0.546, data: 0.000) G_L1: 12.479 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 10.094 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 33, iters: 257936, time: 0.550, data: 0.000) G_L1: 14.069 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 11.442 G_Regularizer: 0.000 validation_error: 20.298 +(epoch: 33, iters: 259936, time: 0.540, data: 0.001) G_L1: 14.659 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 12.217 G_Regularizer: 0.000 validation_error: 20.893 +(epoch: 33, iters: 261936, time: 0.559, data: 0.001) G_L1: 14.342 G_L1_ABSOLUTE: 3.172 G_L1_RELATIVE: 11.170 G_Regularizer: 0.000 validation_error: 20.637 +(epoch: 33, iters: 263936, time: 0.546, data: 0.000) G_L1: 13.462 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 10.730 G_Regularizer: 0.000 validation_error: 21.515 +(epoch: 33, iters: 265936, time: 0.544, data: 0.000) G_L1: 14.168 G_L1_ABSOLUTE: 2.290 G_L1_RELATIVE: 11.877 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 33, iters: 267936, time: 0.553, data: 0.000) G_L1: 15.194 G_L1_ABSOLUTE: 2.806 G_L1_RELATIVE: 12.389 G_Regularizer: 0.000 validation_error: 20.408 +(epoch: 33, iters: 269936, time: 0.551, data: 0.000) G_L1: 15.737 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 13.095 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 33, iters: 271936, time: 0.543, data: 0.000) G_L1: 13.815 G_L1_ABSOLUTE: 2.195 G_L1_RELATIVE: 11.620 G_Regularizer: 0.000 validation_error: 20.741 +(epoch: 33, iters: 273936, time: 0.538, data: 0.000) G_L1: 16.555 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 14.033 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 33, iters: 275936, time: 0.548, data: 0.000) G_L1: 15.634 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 13.060 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 33, iters: 277936, time: 0.542, data: 0.000) G_L1: 13.981 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 11.490 G_Regularizer: 0.000 validation_error: 21.032 +(epoch: 33, iters: 279936, time: 0.540, data: 0.000) G_L1: 16.058 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 13.372 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 33, iters: 281936, time: 0.552, data: 0.000) G_L1: 14.263 G_L1_ABSOLUTE: 2.555 G_L1_RELATIVE: 11.707 G_Regularizer: 0.000 validation_error: 20.355 +(epoch: 33, iters: 283936, time: 0.545, data: 0.000) G_L1: 17.923 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 15.443 G_Regularizer: 0.000 validation_error: 20.141 +(epoch: 33, iters: 285936, time: 0.551, data: 0.001) G_L1: 11.887 G_L1_ABSOLUTE: 1.917 G_L1_RELATIVE: 9.970 G_Regularizer: 0.000 validation_error: 20.588 +(epoch: 33, iters: 287936, time: 0.550, data: 0.000) G_L1: 12.751 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 10.431 G_Regularizer: 0.000 validation_error: 20.350 +(epoch: 33, iters: 289936, time: 0.553, data: 0.000) G_L1: 13.549 G_L1_ABSOLUTE: 2.508 G_L1_RELATIVE: 11.041 G_Regularizer: 0.000 validation_error: 20.995 +(epoch: 33, iters: 291936, time: 0.542, data: 0.000) G_L1: 14.457 G_L1_ABSOLUTE: 2.215 G_L1_RELATIVE: 12.242 G_Regularizer: 0.000 validation_error: 20.367 +(epoch: 33, iters: 293936, time: 0.544, data: 0.000) G_L1: 16.487 G_L1_ABSOLUTE: 2.092 G_L1_RELATIVE: 14.394 G_Regularizer: 0.000 validation_error: 20.445 +(epoch: 33, iters: 295936, time: 0.548, data: 0.001) G_L1: 13.280 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 10.646 G_Regularizer: 0.000 validation_error: 20.554 +(epoch: 33, iters: 297936, time: 0.546, data: 0.000) G_L1: 15.739 G_L1_ABSOLUTE: 2.655 G_L1_RELATIVE: 13.083 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 33, iters: 299936, time: 0.546, data: 0.000) G_L1: 14.956 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 12.422 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 33, iters: 301936, time: 0.546, data: 0.000) G_L1: 12.875 G_L1_ABSOLUTE: 2.111 G_L1_RELATIVE: 10.764 G_Regularizer: 0.000 validation_error: 21.353 +(epoch: 34, iters: 1184, time: 0.548, data: 0.000) G_L1: 15.059 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 12.549 G_Regularizer: 0.000 validation_error: 20.452 +(epoch: 34, iters: 3184, time: 0.548, data: 0.000) G_L1: 15.093 G_L1_ABSOLUTE: 2.269 G_L1_RELATIVE: 12.825 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 34, iters: 5184, time: 0.546, data: 0.000) G_L1: 15.700 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 12.969 G_Regularizer: 0.000 validation_error: 20.665 +(epoch: 34, iters: 7184, time: 0.544, data: 0.000) G_L1: 14.791 G_L1_ABSOLUTE: 2.761 G_L1_RELATIVE: 12.030 G_Regularizer: 0.000 validation_error: 20.731 +(epoch: 34, iters: 9184, time: 0.548, data: 0.000) G_L1: 17.464 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 14.739 G_Regularizer: 0.000 validation_error: 20.687 +(epoch: 34, iters: 11184, time: 0.545, data: 0.000) G_L1: 16.011 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 13.554 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 34, iters: 13184, time: 0.549, data: 0.000) G_L1: 17.306 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 14.675 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 34, iters: 15184, time: 0.547, data: 0.000) G_L1: 16.258 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 13.662 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 34, iters: 17184, time: 0.542, data: 0.000) G_L1: 15.093 G_L1_ABSOLUTE: 2.516 G_L1_RELATIVE: 12.576 G_Regularizer: 0.000 validation_error: 21.060 +(epoch: 34, iters: 19184, time: 0.547, data: 0.000) G_L1: 12.898 G_L1_ABSOLUTE: 2.379 G_L1_RELATIVE: 10.519 G_Regularizer: 0.000 validation_error: 20.657 +(epoch: 34, iters: 21184, time: 0.547, data: 0.000) G_L1: 14.618 G_L1_ABSOLUTE: 2.439 G_L1_RELATIVE: 12.179 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 34, iters: 23184, time: 0.551, data: 0.000) G_L1: 14.822 G_L1_ABSOLUTE: 2.358 G_L1_RELATIVE: 12.464 G_Regularizer: 0.000 validation_error: 21.133 +(epoch: 34, iters: 25184, time: 0.544, data: 0.000) G_L1: 15.176 G_L1_ABSOLUTE: 2.995 G_L1_RELATIVE: 12.181 G_Regularizer: 0.000 validation_error: 20.549 +(epoch: 34, iters: 27184, time: 0.547, data: 0.000) G_L1: 16.341 G_L1_ABSOLUTE: 2.851 G_L1_RELATIVE: 13.490 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 34, iters: 29184, time: 0.552, data: 0.000) G_L1: 12.301 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 9.981 G_Regularizer: 0.000 validation_error: 20.261 +(epoch: 34, iters: 31184, time: 0.547, data: 0.000) G_L1: 14.375 G_L1_ABSOLUTE: 2.729 G_L1_RELATIVE: 11.646 G_Regularizer: 0.000 validation_error: 20.669 +(epoch: 34, iters: 33184, time: 0.547, data: 0.000) G_L1: 15.799 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 13.346 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 34, iters: 35184, time: 0.546, data: 0.000) G_L1: 14.437 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 11.806 G_Regularizer: 0.000 validation_error: 20.594 +(epoch: 34, iters: 37184, time: 0.536, data: 0.000) G_L1: 11.891 G_L1_ABSOLUTE: 2.225 G_L1_RELATIVE: 9.666 G_Regularizer: 0.000 validation_error: 20.839 +(epoch: 34, iters: 39184, time: 0.550, data: 0.000) G_L1: 14.314 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 11.840 G_Regularizer: 0.000 validation_error: 20.521 +(epoch: 34, iters: 41184, time: 0.537, data: 0.000) G_L1: 15.686 G_L1_ABSOLUTE: 2.901 G_L1_RELATIVE: 12.785 G_Regularizer: 0.000 validation_error: 20.693 +(epoch: 34, iters: 43184, time: 0.551, data: 0.000) G_L1: 15.393 G_L1_ABSOLUTE: 2.878 G_L1_RELATIVE: 12.514 G_Regularizer: 0.000 validation_error: 20.347 +(epoch: 34, iters: 45184, time: 0.544, data: 0.000) G_L1: 15.652 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 13.148 G_Regularizer: 0.000 validation_error: 20.462 +(epoch: 34, iters: 47184, time: 0.546, data: 0.000) G_L1: 13.669 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 11.097 G_Regularizer: 0.000 validation_error: 20.546 +(epoch: 34, iters: 49184, time: 0.548, data: 0.000) G_L1: 11.164 G_L1_ABSOLUTE: 2.088 G_L1_RELATIVE: 9.076 G_Regularizer: 0.000 validation_error: 20.449 +(epoch: 34, iters: 51184, time: 0.538, data: 0.000) G_L1: 12.824 G_L1_ABSOLUTE: 2.247 G_L1_RELATIVE: 10.577 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 34, iters: 53184, time: 0.551, data: 0.000) G_L1: 18.164 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 15.634 G_Regularizer: 0.000 validation_error: 20.475 +(epoch: 34, iters: 55184, time: 0.544, data: 0.000) G_L1: 16.480 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 13.706 G_Regularizer: 0.000 validation_error: 20.626 +(epoch: 34, iters: 57184, time: 0.550, data: 0.000) G_L1: 17.145 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 14.619 G_Regularizer: 0.000 validation_error: 20.431 +(epoch: 34, iters: 59184, time: 0.547, data: 0.000) G_L1: 13.581 G_L1_ABSOLUTE: 2.575 G_L1_RELATIVE: 11.006 G_Regularizer: 0.000 validation_error: 20.176 +(epoch: 34, iters: 61184, time: 0.543, data: 0.000) G_L1: 13.479 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 11.061 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 34, iters: 63184, time: 0.543, data: 0.001) G_L1: 14.007 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 11.420 G_Regularizer: 0.000 validation_error: 20.566 +(epoch: 34, iters: 65184, time: 0.545, data: 0.000) G_L1: 11.177 G_L1_ABSOLUTE: 1.836 G_L1_RELATIVE: 9.341 G_Regularizer: 0.000 validation_error: 20.820 +(epoch: 34, iters: 67184, time: 0.549, data: 0.000) G_L1: 13.192 G_L1_ABSOLUTE: 2.545 G_L1_RELATIVE: 10.647 G_Regularizer: 0.000 validation_error: 20.567 +(epoch: 34, iters: 69184, time: 0.552, data: 0.000) G_L1: 13.467 G_L1_ABSOLUTE: 2.301 G_L1_RELATIVE: 11.166 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 34, iters: 71184, time: 0.538, data: 0.000) G_L1: 22.214 G_L1_ABSOLUTE: 2.170 G_L1_RELATIVE: 20.045 G_Regularizer: 0.000 validation_error: 20.506 +(epoch: 34, iters: 73184, time: 0.551, data: 0.000) G_L1: 15.430 G_L1_ABSOLUTE: 2.222 G_L1_RELATIVE: 13.208 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 34, iters: 75184, time: 0.543, data: 0.001) G_L1: 11.367 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 8.973 G_Regularizer: 0.000 validation_error: 19.999 +(epoch: 34, iters: 77184, time: 0.543, data: 0.001) G_L1: 13.287 G_L1_ABSOLUTE: 2.277 G_L1_RELATIVE: 11.010 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 34, iters: 79184, time: 0.556, data: 0.000) G_L1: 15.083 G_L1_ABSOLUTE: 2.538 G_L1_RELATIVE: 12.545 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 34, iters: 81184, time: 0.549, data: 0.000) G_L1: 14.232 G_L1_ABSOLUTE: 2.496 G_L1_RELATIVE: 11.736 G_Regularizer: 0.000 validation_error: 20.913 +(epoch: 34, iters: 83184, time: 0.545, data: 0.000) G_L1: 12.045 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 9.688 G_Regularizer: 0.000 validation_error: 20.740 +(epoch: 34, iters: 85184, time: 0.543, data: 0.000) G_L1: 11.533 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 8.874 G_Regularizer: 0.000 validation_error: 20.309 +(epoch: 34, iters: 87184, time: 0.550, data: 0.000) G_L1: 16.227 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 13.467 G_Regularizer: 0.000 validation_error: 20.386 +(epoch: 34, iters: 89184, time: 0.550, data: 0.000) G_L1: 13.429 G_L1_ABSOLUTE: 2.688 G_L1_RELATIVE: 10.741 G_Regularizer: 0.000 validation_error: 20.672 +(epoch: 34, iters: 91184, time: 0.556, data: 0.000) G_L1: 16.072 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 13.657 G_Regularizer: 0.000 validation_error: 20.612 +(epoch: 34, iters: 93184, time: 0.542, data: 0.000) G_L1: 14.348 G_L1_ABSOLUTE: 2.814 G_L1_RELATIVE: 11.534 G_Regularizer: 0.000 validation_error: 20.359 +(epoch: 34, iters: 95184, time: 0.545, data: 0.000) G_L1: 13.312 G_L1_ABSOLUTE: 2.171 G_L1_RELATIVE: 11.140 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 34, iters: 97184, time: 0.545, data: 0.000) G_L1: 16.660 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 14.000 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 34, iters: 99184, time: 0.559, data: 0.000) G_L1: 13.822 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 11.282 G_Regularizer: 0.000 validation_error: 20.460 +(epoch: 34, iters: 101184, time: 0.550, data: 0.000) G_L1: 13.258 G_L1_ABSOLUTE: 2.350 G_L1_RELATIVE: 10.908 G_Regularizer: 0.000 validation_error: 20.412 +(epoch: 34, iters: 103184, time: 0.553, data: 0.000) G_L1: 13.720 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 11.214 G_Regularizer: 0.000 validation_error: 21.352 +(epoch: 34, iters: 105184, time: 0.539, data: 0.000) G_L1: 14.298 G_L1_ABSOLUTE: 2.367 G_L1_RELATIVE: 11.931 G_Regularizer: 0.000 validation_error: 20.267 +(epoch: 34, iters: 107184, time: 0.563, data: 0.000) G_L1: 17.242 G_L1_ABSOLUTE: 3.000 G_L1_RELATIVE: 14.242 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 34, iters: 109184, time: 0.547, data: 0.001) G_L1: 14.901 G_L1_ABSOLUTE: 3.010 G_L1_RELATIVE: 11.891 G_Regularizer: 0.000 validation_error: 20.586 +(epoch: 34, iters: 111184, time: 0.539, data: 0.000) G_L1: 13.142 G_L1_ABSOLUTE: 2.692 G_L1_RELATIVE: 10.449 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 34, iters: 113184, time: 0.545, data: 0.000) G_L1: 11.338 G_L1_ABSOLUTE: 2.367 G_L1_RELATIVE: 8.971 G_Regularizer: 0.000 validation_error: 20.786 +(epoch: 34, iters: 115184, time: 0.553, data: 0.000) G_L1: 13.905 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 11.622 G_Regularizer: 0.000 validation_error: 20.718 +(epoch: 34, iters: 117184, time: 0.540, data: 0.000) G_L1: 15.692 G_L1_ABSOLUTE: 2.255 G_L1_RELATIVE: 13.437 G_Regularizer: 0.000 validation_error: 20.455 +(epoch: 34, iters: 119184, time: 0.548, data: 0.000) G_L1: 12.918 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 10.395 G_Regularizer: 0.000 validation_error: 20.601 +(epoch: 34, iters: 121184, time: 0.547, data: 0.000) G_L1: 15.171 G_L1_ABSOLUTE: 2.926 G_L1_RELATIVE: 12.245 G_Regularizer: 0.000 validation_error: 20.844 +(epoch: 34, iters: 123184, time: 0.541, data: 0.000) G_L1: 10.889 G_L1_ABSOLUTE: 1.943 G_L1_RELATIVE: 8.945 G_Regularizer: 0.000 validation_error: 20.569 +(epoch: 34, iters: 125184, time: 0.541, data: 0.000) G_L1: 15.342 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 12.603 G_Regularizer: 0.000 validation_error: 20.602 +(epoch: 34, iters: 127184, time: 0.543, data: 0.000) G_L1: 14.748 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 12.128 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 34, iters: 129184, time: 0.548, data: 0.000) G_L1: 15.600 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 12.867 G_Regularizer: 0.000 validation_error: 21.193 +(epoch: 34, iters: 131184, time: 0.548, data: 0.000) G_L1: 15.209 G_L1_ABSOLUTE: 2.145 G_L1_RELATIVE: 13.064 G_Regularizer: 0.000 validation_error: 20.852 +(epoch: 34, iters: 133184, time: 0.553, data: 0.000) G_L1: 14.867 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 12.199 G_Regularizer: 0.000 validation_error: 20.539 +(epoch: 34, iters: 135184, time: 0.542, data: 0.000) G_L1: 13.998 G_L1_ABSOLUTE: 2.219 G_L1_RELATIVE: 11.779 G_Regularizer: 0.000 validation_error: 20.718 +(epoch: 34, iters: 137184, time: 0.543, data: 0.000) G_L1: 15.199 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 12.830 G_Regularizer: 0.000 validation_error: 20.399 +(epoch: 34, iters: 139184, time: 0.549, data: 0.000) G_L1: 15.387 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 12.920 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 34, iters: 141184, time: 0.554, data: 0.000) G_L1: 12.215 G_L1_ABSOLUTE: 2.471 G_L1_RELATIVE: 9.744 G_Regularizer: 0.000 validation_error: 20.514 +(epoch: 34, iters: 143184, time: 0.544, data: 0.000) G_L1: 13.411 G_L1_ABSOLUTE: 2.694 G_L1_RELATIVE: 10.717 G_Regularizer: 0.000 validation_error: 20.515 +(epoch: 34, iters: 145184, time: 0.545, data: 0.000) G_L1: 11.331 G_L1_ABSOLUTE: 2.312 G_L1_RELATIVE: 9.018 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 34, iters: 147184, time: 0.547, data: 0.000) G_L1: 14.001 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 11.499 G_Regularizer: 0.000 validation_error: 20.455 +(epoch: 34, iters: 149184, time: 0.541, data: 0.000) G_L1: 15.105 G_L1_ABSOLUTE: 2.318 G_L1_RELATIVE: 12.787 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 34, iters: 151184, time: 0.544, data: 0.000) G_L1: 12.803 G_L1_ABSOLUTE: 2.196 G_L1_RELATIVE: 10.607 G_Regularizer: 0.000 validation_error: 20.567 +(epoch: 34, iters: 153184, time: 0.548, data: 0.000) G_L1: 15.405 G_L1_ABSOLUTE: 2.761 G_L1_RELATIVE: 12.644 G_Regularizer: 0.000 validation_error: 21.112 +(epoch: 34, iters: 155184, time: 0.547, data: 0.000) G_L1: 13.326 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 10.798 G_Regularizer: 0.000 validation_error: 21.130 +(epoch: 34, iters: 157184, time: 0.547, data: 0.000) G_L1: 13.622 G_L1_ABSOLUTE: 2.266 G_L1_RELATIVE: 11.356 G_Regularizer: 0.000 validation_error: 20.214 +(epoch: 34, iters: 159184, time: 0.552, data: 0.000) G_L1: 12.488 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 10.027 G_Regularizer: 0.000 validation_error: 20.626 +(epoch: 34, iters: 161184, time: 0.544, data: 0.001) G_L1: 13.222 G_L1_ABSOLUTE: 2.221 G_L1_RELATIVE: 11.000 G_Regularizer: 0.000 validation_error: 20.814 +(epoch: 34, iters: 163184, time: 0.549, data: 0.000) G_L1: 14.559 G_L1_ABSOLUTE: 2.209 G_L1_RELATIVE: 12.350 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 34, iters: 165184, time: 0.544, data: 0.001) G_L1: 15.095 G_L1_ABSOLUTE: 2.360 G_L1_RELATIVE: 12.735 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 34, iters: 167184, time: 0.550, data: 0.001) G_L1: 16.368 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 13.611 G_Regularizer: 0.000 validation_error: 21.242 +(epoch: 34, iters: 169184, time: 0.545, data: 0.000) G_L1: 11.490 G_L1_ABSOLUTE: 2.431 G_L1_RELATIVE: 9.059 G_Regularizer: 0.000 validation_error: 20.427 +(epoch: 34, iters: 171184, time: 0.550, data: 0.000) G_L1: 14.567 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 11.865 G_Regularizer: 0.000 validation_error: 20.485 +(epoch: 34, iters: 173184, time: 0.559, data: 0.000) G_L1: 16.130 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 13.604 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 34, iters: 175184, time: 0.544, data: 0.000) G_L1: 12.206 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 9.803 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 34, iters: 177184, time: 0.554, data: 0.000) G_L1: 14.025 G_L1_ABSOLUTE: 2.808 G_L1_RELATIVE: 11.217 G_Regularizer: 0.000 validation_error: 21.048 +(epoch: 34, iters: 179184, time: 0.541, data: 0.000) G_L1: 12.317 G_L1_ABSOLUTE: 2.238 G_L1_RELATIVE: 10.079 G_Regularizer: 0.000 validation_error: 20.844 +(epoch: 34, iters: 181184, time: 0.549, data: 0.000) G_L1: 12.714 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 10.346 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 34, iters: 183184, time: 0.543, data: 0.000) G_L1: 14.825 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 12.216 G_Regularizer: 0.000 validation_error: 21.106 +(epoch: 34, iters: 185184, time: 0.539, data: 0.000) G_L1: 17.890 G_L1_ABSOLUTE: 2.836 G_L1_RELATIVE: 15.053 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 34, iters: 187184, time: 0.543, data: 0.000) G_L1: 14.988 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 12.366 G_Regularizer: 0.000 validation_error: 20.665 +(epoch: 34, iters: 189184, time: 0.550, data: 0.000) G_L1: 14.471 G_L1_ABSOLUTE: 2.711 G_L1_RELATIVE: 11.760 G_Regularizer: 0.000 validation_error: 20.363 +(epoch: 34, iters: 191184, time: 0.544, data: 0.000) G_L1: 13.362 G_L1_ABSOLUTE: 3.077 G_L1_RELATIVE: 10.284 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 34, iters: 193184, time: 0.547, data: 0.001) G_L1: 14.773 G_L1_ABSOLUTE: 3.029 G_L1_RELATIVE: 11.744 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 34, iters: 195184, time: 0.554, data: 0.000) G_L1: 16.561 G_L1_ABSOLUTE: 2.692 G_L1_RELATIVE: 13.869 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 34, iters: 197184, time: 0.542, data: 0.000) G_L1: 14.922 G_L1_ABSOLUTE: 2.223 G_L1_RELATIVE: 12.699 G_Regularizer: 0.000 validation_error: 20.464 +(epoch: 34, iters: 199184, time: 0.550, data: 0.000) G_L1: 15.048 G_L1_ABSOLUTE: 3.136 G_L1_RELATIVE: 11.912 G_Regularizer: 0.000 validation_error: 21.065 +(epoch: 34, iters: 201184, time: 0.546, data: 0.000) G_L1: 16.401 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 14.058 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 34, iters: 203184, time: 0.543, data: 0.000) G_L1: 15.534 G_L1_ABSOLUTE: 3.041 G_L1_RELATIVE: 12.493 G_Regularizer: 0.000 validation_error: 20.836 +(epoch: 34, iters: 205184, time: 0.551, data: 0.000) G_L1: 13.815 G_L1_ABSOLUTE: 2.003 G_L1_RELATIVE: 11.812 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 34, iters: 207184, time: 0.556, data: 0.000) G_L1: 15.092 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 12.542 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 34, iters: 209184, time: 0.543, data: 0.000) G_L1: 13.630 G_L1_ABSOLUTE: 2.329 G_L1_RELATIVE: 11.301 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 34, iters: 211184, time: 0.542, data: 0.000) G_L1: 14.107 G_L1_ABSOLUTE: 2.200 G_L1_RELATIVE: 11.907 G_Regularizer: 0.000 validation_error: 20.936 +(epoch: 34, iters: 213184, time: 0.541, data: 0.000) G_L1: 16.203 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 13.426 G_Regularizer: 0.000 validation_error: 20.789 +(epoch: 34, iters: 215184, time: 0.555, data: 0.000) G_L1: 12.858 G_L1_ABSOLUTE: 1.922 G_L1_RELATIVE: 10.935 G_Regularizer: 0.000 validation_error: 20.816 +(epoch: 34, iters: 217184, time: 0.547, data: 0.000) G_L1: 15.288 G_L1_ABSOLUTE: 3.170 G_L1_RELATIVE: 12.118 G_Regularizer: 0.000 validation_error: 21.492 +(epoch: 34, iters: 219184, time: 0.560, data: 0.000) G_L1: 13.499 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 11.167 G_Regularizer: 0.000 validation_error: 20.552 +(epoch: 34, iters: 221184, time: 0.541, data: 0.000) G_L1: 13.337 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 10.900 G_Regularizer: 0.000 validation_error: 21.290 +(epoch: 34, iters: 223184, time: 0.550, data: 0.000) G_L1: 15.404 G_L1_ABSOLUTE: 2.694 G_L1_RELATIVE: 12.710 G_Regularizer: 0.000 validation_error: 20.922 +(epoch: 34, iters: 225184, time: 0.558, data: 0.000) G_L1: 14.200 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 11.737 G_Regularizer: 0.000 validation_error: 20.498 +(epoch: 34, iters: 227184, time: 0.556, data: 0.000) G_L1: 14.652 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 12.201 G_Regularizer: 0.000 validation_error: 20.614 +(epoch: 34, iters: 229184, time: 0.546, data: 0.000) G_L1: 12.858 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 9.995 G_Regularizer: 0.000 validation_error: 21.277 +(epoch: 34, iters: 231184, time: 0.543, data: 0.000) G_L1: 13.539 G_L1_ABSOLUTE: 2.303 G_L1_RELATIVE: 11.236 G_Regularizer: 0.000 validation_error: 20.814 +(epoch: 34, iters: 233184, time: 0.547, data: 0.000) G_L1: 14.691 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 12.158 G_Regularizer: 0.000 validation_error: 20.492 +(epoch: 34, iters: 235184, time: 0.555, data: 0.000) G_L1: 13.630 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 11.194 G_Regularizer: 0.000 validation_error: 20.702 +(epoch: 34, iters: 237184, time: 0.546, data: 0.000) G_L1: 16.811 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 14.285 G_Regularizer: 0.000 validation_error: 19.916 +(epoch: 34, iters: 239184, time: 0.548, data: 0.000) G_L1: 14.660 G_L1_ABSOLUTE: 2.886 G_L1_RELATIVE: 11.774 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 34, iters: 241184, time: 0.554, data: 0.000) G_L1: 13.469 G_L1_ABSOLUTE: 2.448 G_L1_RELATIVE: 11.020 G_Regularizer: 0.000 validation_error: 20.342 +(epoch: 34, iters: 243184, time: 0.547, data: 0.000) G_L1: 15.489 G_L1_ABSOLUTE: 2.095 G_L1_RELATIVE: 13.393 G_Regularizer: 0.000 validation_error: 20.657 +(epoch: 34, iters: 245184, time: 0.550, data: 0.000) G_L1: 16.412 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 13.691 G_Regularizer: 0.000 validation_error: 20.266 +(epoch: 34, iters: 247184, time: 0.543, data: 0.000) G_L1: 14.881 G_L1_ABSOLUTE: 2.560 G_L1_RELATIVE: 12.321 G_Regularizer: 0.000 validation_error: 20.669 +(epoch: 34, iters: 249184, time: 0.542, data: 0.000) G_L1: 14.717 G_L1_ABSOLUTE: 2.585 G_L1_RELATIVE: 12.132 G_Regularizer: 0.000 validation_error: 20.744 +(epoch: 34, iters: 251184, time: 0.549, data: 0.000) G_L1: 13.605 G_L1_ABSOLUTE: 2.846 G_L1_RELATIVE: 10.759 G_Regularizer: 0.000 validation_error: 20.798 +(epoch: 34, iters: 253184, time: 0.554, data: 0.001) G_L1: 10.945 G_L1_ABSOLUTE: 2.246 G_L1_RELATIVE: 8.699 G_Regularizer: 0.000 validation_error: 21.468 +(epoch: 34, iters: 255184, time: 0.546, data: 0.000) G_L1: 15.271 G_L1_ABSOLUTE: 2.281 G_L1_RELATIVE: 12.990 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 34, iters: 257184, time: 0.552, data: 0.000) G_L1: 23.669 G_L1_ABSOLUTE: 2.943 G_L1_RELATIVE: 20.726 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 34, iters: 259184, time: 0.543, data: 0.000) G_L1: 13.679 G_L1_ABSOLUTE: 2.386 G_L1_RELATIVE: 11.293 G_Regularizer: 0.000 validation_error: 21.213 +(epoch: 34, iters: 261184, time: 0.548, data: 0.000) G_L1: 12.633 G_L1_ABSOLUTE: 2.180 G_L1_RELATIVE: 10.453 G_Regularizer: 0.000 validation_error: 20.658 +(epoch: 34, iters: 263184, time: 0.556, data: 0.000) G_L1: 14.253 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 11.633 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 34, iters: 265184, time: 0.543, data: 0.000) G_L1: 13.934 G_L1_ABSOLUTE: 2.947 G_L1_RELATIVE: 10.987 G_Regularizer: 0.000 validation_error: 20.575 +(epoch: 34, iters: 267184, time: 0.553, data: 0.000) G_L1: 13.701 G_L1_ABSOLUTE: 2.384 G_L1_RELATIVE: 11.316 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 34, iters: 269184, time: 0.551, data: 0.000) G_L1: 16.795 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 14.158 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 34, iters: 271184, time: 0.556, data: 0.000) G_L1: 15.317 G_L1_ABSOLUTE: 2.699 G_L1_RELATIVE: 12.618 G_Regularizer: 0.000 validation_error: 21.102 +(epoch: 34, iters: 273184, time: 0.543, data: 0.000) G_L1: 13.312 G_L1_ABSOLUTE: 2.158 G_L1_RELATIVE: 11.155 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 34, iters: 275184, time: 0.555, data: 0.000) G_L1: 12.181 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 9.811 G_Regularizer: 0.000 validation_error: 20.963 +(epoch: 34, iters: 277184, time: 0.541, data: 0.000) G_L1: 13.430 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 11.195 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 34, iters: 279184, time: 0.549, data: 0.000) G_L1: 15.151 G_L1_ABSOLUTE: 2.319 G_L1_RELATIVE: 12.831 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 34, iters: 281184, time: 0.550, data: 0.000) G_L1: 14.077 G_L1_ABSOLUTE: 2.580 G_L1_RELATIVE: 11.497 G_Regularizer: 0.000 validation_error: 20.948 +(epoch: 34, iters: 283184, time: 0.550, data: 0.000) G_L1: 13.556 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 10.718 G_Regularizer: 0.000 validation_error: 20.394 +(epoch: 34, iters: 285184, time: 0.540, data: 0.000) G_L1: 16.008 G_L1_ABSOLUTE: 3.011 G_L1_RELATIVE: 12.997 G_Regularizer: 0.000 validation_error: 20.404 +(epoch: 34, iters: 287184, time: 0.540, data: 0.000) G_L1: 13.901 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 11.441 G_Regularizer: 0.000 validation_error: 20.586 +(epoch: 34, iters: 289184, time: 0.545, data: 0.000) G_L1: 16.710 G_L1_ABSOLUTE: 3.333 G_L1_RELATIVE: 13.377 G_Regularizer: 0.000 validation_error: 20.471 +(epoch: 34, iters: 291184, time: 0.544, data: 0.000) G_L1: 14.125 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 11.631 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 34, iters: 293184, time: 0.550, data: 0.000) G_L1: 16.259 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 13.874 G_Regularizer: 0.000 validation_error: 20.761 +(epoch: 34, iters: 295184, time: 0.548, data: 0.000) G_L1: 13.856 G_L1_ABSOLUTE: 2.487 G_L1_RELATIVE: 11.368 G_Regularizer: 0.000 validation_error: 21.087 +(epoch: 34, iters: 297184, time: 0.539, data: 0.000) G_L1: 16.735 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 14.138 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 34, iters: 299184, time: 0.551, data: 0.000) G_L1: 14.549 G_L1_ABSOLUTE: 2.741 G_L1_RELATIVE: 11.808 G_Regularizer: 0.000 validation_error: 20.287 +(epoch: 34, iters: 301184, time: 0.546, data: 0.000) G_L1: 14.377 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 11.599 G_Regularizer: 0.000 validation_error: 20.341 +(epoch: 35, iters: 432, time: 0.561, data: 0.000) G_L1: 15.108 G_L1_ABSOLUTE: 2.605 G_L1_RELATIVE: 12.504 G_Regularizer: 0.000 validation_error: 21.294 +(epoch: 35, iters: 2432, time: 0.544, data: 0.000) G_L1: 13.208 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 10.732 G_Regularizer: 0.000 validation_error: 21.066 +(epoch: 35, iters: 4432, time: 0.547, data: 0.000) G_L1: 13.142 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 10.785 G_Regularizer: 0.000 validation_error: 20.497 +(epoch: 35, iters: 6432, time: 0.547, data: 0.000) G_L1: 13.918 G_L1_ABSOLUTE: 2.319 G_L1_RELATIVE: 11.599 G_Regularizer: 0.000 validation_error: 20.547 +(epoch: 35, iters: 8432, time: 0.543, data: 0.000) G_L1: 14.711 G_L1_ABSOLUTE: 2.553 G_L1_RELATIVE: 12.158 G_Regularizer: 0.000 validation_error: 20.705 +(epoch: 35, iters: 10432, time: 0.540, data: 0.000) G_L1: 12.538 G_L1_ABSOLUTE: 2.367 G_L1_RELATIVE: 10.172 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 35, iters: 12432, time: 0.550, data: 0.000) G_L1: 14.281 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 11.681 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 35, iters: 14432, time: 0.553, data: 0.000) G_L1: 15.156 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 12.505 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 35, iters: 16432, time: 0.541, data: 0.000) G_L1: 14.692 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 11.971 G_Regularizer: 0.000 validation_error: 20.552 +(epoch: 35, iters: 18432, time: 0.548, data: 0.000) G_L1: 14.659 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 11.848 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 35, iters: 20432, time: 0.546, data: 0.000) G_L1: 27.341 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 24.860 G_Regularizer: 0.000 validation_error: 21.140 +(epoch: 35, iters: 22432, time: 0.544, data: 0.000) G_L1: 17.350 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 14.880 G_Regularizer: 0.000 validation_error: 21.065 +(epoch: 35, iters: 24432, time: 0.551, data: 0.000) G_L1: 10.966 G_L1_ABSOLUTE: 2.247 G_L1_RELATIVE: 8.719 G_Regularizer: 0.000 validation_error: 21.127 +(epoch: 35, iters: 26432, time: 0.547, data: 0.000) G_L1: 13.850 G_L1_ABSOLUTE: 2.404 G_L1_RELATIVE: 11.446 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 35, iters: 28432, time: 0.545, data: 0.000) G_L1: 13.254 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 10.841 G_Regularizer: 0.000 validation_error: 20.601 +(epoch: 35, iters: 30432, time: 0.546, data: 0.000) G_L1: 14.489 G_L1_ABSOLUTE: 2.955 G_L1_RELATIVE: 11.534 G_Regularizer: 0.000 validation_error: 20.693 +(epoch: 35, iters: 32432, time: 0.556, data: 0.000) G_L1: 17.096 G_L1_ABSOLUTE: 2.813 G_L1_RELATIVE: 14.283 G_Regularizer: 0.000 validation_error: 21.144 +(epoch: 35, iters: 34432, time: 0.553, data: 0.000) G_L1: 13.948 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 11.579 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 35, iters: 36432, time: 0.547, data: 0.001) G_L1: 13.091 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 10.633 G_Regularizer: 0.000 validation_error: 20.591 +(epoch: 35, iters: 38432, time: 0.546, data: 0.000) G_L1: 14.237 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 11.892 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 35, iters: 40432, time: 0.548, data: 0.000) G_L1: 12.291 G_L1_ABSOLUTE: 2.087 G_L1_RELATIVE: 10.205 G_Regularizer: 0.000 validation_error: 21.088 +(epoch: 35, iters: 42432, time: 0.538, data: 0.000) G_L1: 14.414 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 11.961 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 35, iters: 44432, time: 0.547, data: 0.000) G_L1: 15.494 G_L1_ABSOLUTE: 2.312 G_L1_RELATIVE: 13.182 G_Regularizer: 0.000 validation_error: 21.045 +(epoch: 35, iters: 46432, time: 0.543, data: 0.000) G_L1: 18.387 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 15.713 G_Regularizer: 0.000 validation_error: 20.824 +(epoch: 35, iters: 48432, time: 0.546, data: 0.001) G_L1: 12.920 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 10.324 G_Regularizer: 0.000 validation_error: 20.383 +(epoch: 35, iters: 50432, time: 0.546, data: 0.000) G_L1: 13.810 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 11.284 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 35, iters: 52432, time: 0.548, data: 0.000) G_L1: 13.354 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 10.592 G_Regularizer: 0.000 validation_error: 21.333 +(epoch: 35, iters: 54432, time: 0.547, data: 0.001) G_L1: 10.727 G_L1_ABSOLUTE: 2.441 G_L1_RELATIVE: 8.286 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 35, iters: 56432, time: 0.547, data: 0.000) G_L1: 14.973 G_L1_ABSOLUTE: 2.604 G_L1_RELATIVE: 12.369 G_Regularizer: 0.000 validation_error: 21.081 +(epoch: 35, iters: 58432, time: 0.546, data: 0.000) G_L1: 15.700 G_L1_ABSOLUTE: 3.142 G_L1_RELATIVE: 12.557 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 35, iters: 60432, time: 0.550, data: 0.000) G_L1: 12.650 G_L1_ABSOLUTE: 2.194 G_L1_RELATIVE: 10.456 G_Regularizer: 0.000 validation_error: 21.641 +(epoch: 35, iters: 62432, time: 0.545, data: 0.000) G_L1: 14.885 G_L1_ABSOLUTE: 2.809 G_L1_RELATIVE: 12.076 G_Regularizer: 0.000 validation_error: 21.093 +(epoch: 35, iters: 64432, time: 0.539, data: 0.000) G_L1: 12.725 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 10.115 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 35, iters: 66432, time: 0.548, data: 0.000) G_L1: 15.069 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 12.706 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 35, iters: 68432, time: 0.544, data: 0.000) G_L1: 12.469 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 10.012 G_Regularizer: 0.000 validation_error: 20.547 +(epoch: 35, iters: 70432, time: 0.544, data: 0.001) G_L1: 13.441 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 10.845 G_Regularizer: 0.000 validation_error: 20.430 +(epoch: 35, iters: 72432, time: 0.542, data: 0.001) G_L1: 14.156 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 11.676 G_Regularizer: 0.000 validation_error: 20.778 +(epoch: 35, iters: 74432, time: 0.548, data: 0.000) G_L1: 14.899 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 12.535 G_Regularizer: 0.000 validation_error: 20.467 +(epoch: 35, iters: 76432, time: 0.550, data: 0.000) G_L1: 11.970 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 9.688 G_Regularizer: 0.000 validation_error: 21.268 +(epoch: 35, iters: 78432, time: 0.550, data: 0.000) G_L1: 12.159 G_L1_ABSOLUTE: 2.187 G_L1_RELATIVE: 9.972 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 35, iters: 80432, time: 0.546, data: 0.001) G_L1: 14.338 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 11.854 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 35, iters: 82432, time: 0.543, data: 0.000) G_L1: 14.446 G_L1_ABSOLUTE: 2.981 G_L1_RELATIVE: 11.465 G_Regularizer: 0.000 validation_error: 21.268 +(epoch: 35, iters: 84432, time: 0.541, data: 0.000) G_L1: 14.229 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 11.657 G_Regularizer: 0.000 validation_error: 21.146 +(epoch: 35, iters: 86432, time: 0.545, data: 0.000) G_L1: 14.126 G_L1_ABSOLUTE: 2.386 G_L1_RELATIVE: 11.739 G_Regularizer: 0.000 validation_error: 20.595 +(epoch: 35, iters: 88432, time: 0.546, data: 0.000) G_L1: 13.250 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 10.837 G_Regularizer: 0.000 validation_error: 21.387 +(epoch: 35, iters: 90432, time: 0.542, data: 0.000) G_L1: 13.639 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 10.829 G_Regularizer: 0.000 validation_error: 20.779 +(epoch: 35, iters: 92432, time: 0.542, data: 0.000) G_L1: 14.748 G_L1_ABSOLUTE: 3.043 G_L1_RELATIVE: 11.705 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 35, iters: 94432, time: 0.541, data: 0.000) G_L1: 12.252 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 9.828 G_Regularizer: 0.000 validation_error: 20.540 +(epoch: 35, iters: 96432, time: 0.547, data: 0.000) G_L1: 15.842 G_L1_ABSOLUTE: 2.269 G_L1_RELATIVE: 13.573 G_Regularizer: 0.000 validation_error: 20.732 +(epoch: 35, iters: 98432, time: 0.546, data: 0.000) G_L1: 15.805 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 13.176 G_Regularizer: 0.000 validation_error: 20.674 +(epoch: 35, iters: 100432, time: 0.546, data: 0.000) G_L1: 13.318 G_L1_ABSOLUTE: 2.207 G_L1_RELATIVE: 11.111 G_Regularizer: 0.000 validation_error: 20.403 +(epoch: 35, iters: 102432, time: 0.547, data: 0.000) G_L1: 17.161 G_L1_ABSOLUTE: 2.749 G_L1_RELATIVE: 14.412 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 35, iters: 104432, time: 0.546, data: 0.000) G_L1: 13.643 G_L1_ABSOLUTE: 2.815 G_L1_RELATIVE: 10.828 G_Regularizer: 0.000 validation_error: 21.214 +(epoch: 35, iters: 106432, time: 0.552, data: 0.000) G_L1: 16.303 G_L1_ABSOLUTE: 2.220 G_L1_RELATIVE: 14.083 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 35, iters: 108432, time: 0.549, data: 0.000) G_L1: 14.148 G_L1_ABSOLUTE: 1.935 G_L1_RELATIVE: 12.214 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 35, iters: 110432, time: 0.548, data: 0.000) G_L1: 14.823 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 12.354 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 35, iters: 112432, time: 0.546, data: 0.000) G_L1: 14.131 G_L1_ABSOLUTE: 2.472 G_L1_RELATIVE: 11.659 G_Regularizer: 0.000 validation_error: 20.744 +(epoch: 35, iters: 114432, time: 0.544, data: 0.000) G_L1: 13.438 G_L1_ABSOLUTE: 2.529 G_L1_RELATIVE: 10.909 G_Regularizer: 0.000 validation_error: 20.763 +(epoch: 35, iters: 116432, time: 0.545, data: 0.000) G_L1: 13.549 G_L1_ABSOLUTE: 2.186 G_L1_RELATIVE: 11.362 G_Regularizer: 0.000 validation_error: 20.763 +(epoch: 35, iters: 118432, time: 0.549, data: 0.001) G_L1: 14.729 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 11.930 G_Regularizer: 0.000 validation_error: 20.456 +(epoch: 35, iters: 120432, time: 0.544, data: 0.000) G_L1: 13.506 G_L1_ABSOLUTE: 2.479 G_L1_RELATIVE: 11.027 G_Regularizer: 0.000 validation_error: 21.247 +(epoch: 35, iters: 122432, time: 0.542, data: 0.000) G_L1: 15.021 G_L1_ABSOLUTE: 2.555 G_L1_RELATIVE: 12.467 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 35, iters: 124432, time: 0.547, data: 0.000) G_L1: 12.166 G_L1_ABSOLUTE: 2.228 G_L1_RELATIVE: 9.938 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 35, iters: 126432, time: 0.543, data: 0.000) G_L1: 13.504 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 11.251 G_Regularizer: 0.000 validation_error: 21.091 +(epoch: 35, iters: 128432, time: 0.561, data: 0.000) G_L1: 13.455 G_L1_ABSOLUTE: 2.271 G_L1_RELATIVE: 11.184 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 35, iters: 130432, time: 0.550, data: 0.000) G_L1: 15.314 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 12.651 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 35, iters: 132432, time: 0.545, data: 0.000) G_L1: 15.193 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 12.625 G_Regularizer: 0.000 validation_error: 20.638 +(epoch: 35, iters: 134432, time: 0.544, data: 0.000) G_L1: 14.937 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 12.504 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 35, iters: 136432, time: 0.544, data: 0.000) G_L1: 12.729 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 10.132 G_Regularizer: 0.000 validation_error: 21.278 +(epoch: 35, iters: 138432, time: 0.547, data: 0.000) G_L1: 13.802 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 10.967 G_Regularizer: 0.000 validation_error: 21.155 +(epoch: 35, iters: 140432, time: 0.547, data: 0.000) G_L1: 15.749 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 13.367 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 35, iters: 142432, time: 0.549, data: 0.001) G_L1: 12.536 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 9.788 G_Regularizer: 0.000 validation_error: 21.081 +(epoch: 35, iters: 144432, time: 0.547, data: 0.000) G_L1: 15.147 G_L1_ABSOLUTE: 2.797 G_L1_RELATIVE: 12.350 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 35, iters: 146432, time: 0.538, data: 0.000) G_L1: 14.023 G_L1_ABSOLUTE: 2.583 G_L1_RELATIVE: 11.439 G_Regularizer: 0.000 validation_error: 20.396 +(epoch: 35, iters: 148432, time: 0.546, data: 0.000) G_L1: 14.647 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 12.135 G_Regularizer: 0.000 validation_error: 20.425 +(epoch: 35, iters: 150432, time: 0.549, data: 0.000) G_L1: 14.835 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 12.344 G_Regularizer: 0.000 validation_error: 20.492 +(epoch: 35, iters: 152432, time: 0.552, data: 0.000) G_L1: 13.585 G_L1_ABSOLUTE: 2.391 G_L1_RELATIVE: 11.195 G_Regularizer: 0.000 validation_error: 20.750 +(epoch: 35, iters: 154432, time: 0.543, data: 0.001) G_L1: 15.351 G_L1_ABSOLUTE: 2.325 G_L1_RELATIVE: 13.025 G_Regularizer: 0.000 validation_error: 20.782 +(epoch: 35, iters: 156432, time: 0.548, data: 0.001) G_L1: 11.860 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 9.577 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 35, iters: 158432, time: 0.547, data: 0.000) G_L1: 17.214 G_L1_ABSOLUTE: 2.974 G_L1_RELATIVE: 14.240 G_Regularizer: 0.000 validation_error: 21.128 +(epoch: 35, iters: 160432, time: 0.551, data: 0.000) G_L1: 13.221 G_L1_ABSOLUTE: 2.431 G_L1_RELATIVE: 10.790 G_Regularizer: 0.000 validation_error: 21.157 +(epoch: 35, iters: 162432, time: 0.550, data: 0.000) G_L1: 14.750 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 12.026 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 35, iters: 164432, time: 0.548, data: 0.000) G_L1: 12.771 G_L1_ABSOLUTE: 2.456 G_L1_RELATIVE: 10.315 G_Regularizer: 0.000 validation_error: 20.885 +(epoch: 35, iters: 166432, time: 0.550, data: 0.000) G_L1: 13.837 G_L1_ABSOLUTE: 2.242 G_L1_RELATIVE: 11.595 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 35, iters: 168432, time: 0.543, data: 0.000) G_L1: 15.765 G_L1_ABSOLUTE: 2.086 G_L1_RELATIVE: 13.678 G_Regularizer: 0.000 validation_error: 20.937 +(epoch: 35, iters: 170432, time: 0.543, data: 0.000) G_L1: 12.664 G_L1_ABSOLUTE: 2.761 G_L1_RELATIVE: 9.903 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 35, iters: 172432, time: 0.547, data: 0.000) G_L1: 16.045 G_L1_ABSOLUTE: 2.823 G_L1_RELATIVE: 13.222 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 35, iters: 174432, time: 0.546, data: 0.000) G_L1: 13.900 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 11.580 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 35, iters: 176432, time: 0.540, data: 0.000) G_L1: 15.201 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 12.749 G_Regularizer: 0.000 validation_error: 21.176 +(epoch: 35, iters: 178432, time: 0.549, data: 0.000) G_L1: 15.717 G_L1_ABSOLUTE: 3.291 G_L1_RELATIVE: 12.427 G_Regularizer: 0.000 validation_error: 21.272 +(epoch: 35, iters: 180432, time: 0.545, data: 0.000) G_L1: 15.519 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 12.974 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 35, iters: 182432, time: 0.551, data: 0.000) G_L1: 12.836 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 10.185 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 35, iters: 184432, time: 0.540, data: 0.000) G_L1: 13.647 G_L1_ABSOLUTE: 2.426 G_L1_RELATIVE: 11.220 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 35, iters: 186432, time: 0.546, data: 0.000) G_L1: 14.176 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 11.885 G_Regularizer: 0.000 validation_error: 21.331 +(epoch: 35, iters: 188432, time: 0.548, data: 0.000) G_L1: 14.045 G_L1_ABSOLUTE: 2.199 G_L1_RELATIVE: 11.845 G_Regularizer: 0.000 validation_error: 20.779 +(epoch: 35, iters: 190432, time: 0.547, data: 0.000) G_L1: 13.398 G_L1_ABSOLUTE: 2.356 G_L1_RELATIVE: 11.043 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 35, iters: 192432, time: 0.548, data: 0.000) G_L1: 13.529 G_L1_ABSOLUTE: 2.187 G_L1_RELATIVE: 11.342 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 35, iters: 194432, time: 0.541, data: 0.000) G_L1: 12.448 G_L1_ABSOLUTE: 2.611 G_L1_RELATIVE: 9.838 G_Regularizer: 0.000 validation_error: 20.575 +(epoch: 35, iters: 196432, time: 0.557, data: 0.000) G_L1: 14.495 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 12.178 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 35, iters: 198432, time: 0.557, data: 0.000) G_L1: 16.566 G_L1_ABSOLUTE: 2.296 G_L1_RELATIVE: 14.270 G_Regularizer: 0.000 validation_error: 20.729 +(epoch: 35, iters: 200432, time: 0.539, data: 0.000) G_L1: 13.388 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 11.017 G_Regularizer: 0.000 validation_error: 21.287 +(epoch: 35, iters: 202432, time: 0.553, data: 0.000) G_L1: 14.485 G_L1_ABSOLUTE: 2.783 G_L1_RELATIVE: 11.702 G_Regularizer: 0.000 validation_error: 20.253 +(epoch: 35, iters: 204432, time: 0.547, data: 0.001) G_L1: 14.549 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 11.693 G_Regularizer: 0.000 validation_error: 20.936 +(epoch: 35, iters: 206432, time: 0.547, data: 0.000) G_L1: 11.949 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 9.684 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 35, iters: 208432, time: 0.551, data: 0.000) G_L1: 16.691 G_L1_ABSOLUTE: 3.128 G_L1_RELATIVE: 13.563 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 35, iters: 210432, time: 0.539, data: 0.000) G_L1: 16.314 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 13.727 G_Regularizer: 0.000 validation_error: 20.896 +(epoch: 35, iters: 212432, time: 0.552, data: 0.000) G_L1: 14.663 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 12.024 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 35, iters: 214432, time: 0.544, data: 0.000) G_L1: 20.001 G_L1_ABSOLUTE: 2.579 G_L1_RELATIVE: 17.422 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 35, iters: 216432, time: 0.560, data: 0.000) G_L1: 15.776 G_L1_ABSOLUTE: 2.735 G_L1_RELATIVE: 13.042 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 35, iters: 218432, time: 0.540, data: 0.000) G_L1: 12.126 G_L1_ABSOLUTE: 2.248 G_L1_RELATIVE: 9.878 G_Regularizer: 0.000 validation_error: 21.323 +(epoch: 35, iters: 220432, time: 0.544, data: 0.000) G_L1: 14.225 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 11.817 G_Regularizer: 0.000 validation_error: 20.522 +(epoch: 35, iters: 222432, time: 0.549, data: 0.000) G_L1: 13.570 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 10.885 G_Regularizer: 0.000 validation_error: 21.216 +(epoch: 35, iters: 224432, time: 0.551, data: 0.000) G_L1: 15.088 G_L1_ABSOLUTE: 2.928 G_L1_RELATIVE: 12.160 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 35, iters: 226432, time: 0.539, data: 0.000) G_L1: 14.787 G_L1_ABSOLUTE: 2.555 G_L1_RELATIVE: 12.232 G_Regularizer: 0.000 validation_error: 20.852 +(epoch: 35, iters: 228432, time: 0.546, data: 0.000) G_L1: 14.327 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 12.065 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 35, iters: 230432, time: 0.547, data: 0.000) G_L1: 11.794 G_L1_ABSOLUTE: 2.165 G_L1_RELATIVE: 9.629 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 35, iters: 232432, time: 0.551, data: 0.000) G_L1: 12.711 G_L1_ABSOLUTE: 2.122 G_L1_RELATIVE: 10.590 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 35, iters: 234432, time: 0.539, data: 0.000) G_L1: 26.445 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 23.896 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 35, iters: 236432, time: 0.542, data: 0.000) G_L1: 11.979 G_L1_ABSOLUTE: 2.328 G_L1_RELATIVE: 9.651 G_Regularizer: 0.000 validation_error: 20.604 +(epoch: 35, iters: 238432, time: 0.545, data: 0.000) G_L1: 15.302 G_L1_ABSOLUTE: 2.988 G_L1_RELATIVE: 12.314 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 35, iters: 240432, time: 0.543, data: 0.001) G_L1: 12.377 G_L1_ABSOLUTE: 2.353 G_L1_RELATIVE: 10.024 G_Regularizer: 0.000 validation_error: 20.599 +(epoch: 35, iters: 242432, time: 0.546, data: 0.000) G_L1: 17.676 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 15.175 G_Regularizer: 0.000 validation_error: 20.416 +(epoch: 35, iters: 244432, time: 0.544, data: 0.000) G_L1: 13.397 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 10.740 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 35, iters: 246432, time: 0.540, data: 0.001) G_L1: 12.560 G_L1_ABSOLUTE: 2.261 G_L1_RELATIVE: 10.299 G_Regularizer: 0.000 validation_error: 20.770 +(epoch: 35, iters: 248432, time: 0.549, data: 0.000) G_L1: 17.413 G_L1_ABSOLUTE: 3.502 G_L1_RELATIVE: 13.911 G_Regularizer: 0.000 validation_error: 20.671 +(epoch: 35, iters: 250432, time: 0.555, data: 0.000) G_L1: 14.278 G_L1_ABSOLUTE: 3.166 G_L1_RELATIVE: 11.112 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 35, iters: 252432, time: 0.547, data: 0.000) G_L1: 13.589 G_L1_ABSOLUTE: 2.197 G_L1_RELATIVE: 11.392 G_Regularizer: 0.000 validation_error: 21.255 +(epoch: 35, iters: 254432, time: 0.551, data: 0.001) G_L1: 13.261 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 10.768 G_Regularizer: 0.000 validation_error: 20.692 +(epoch: 35, iters: 256432, time: 0.543, data: 0.000) G_L1: 14.996 G_L1_ABSOLUTE: 2.553 G_L1_RELATIVE: 12.443 G_Regularizer: 0.000 validation_error: 20.629 +(epoch: 35, iters: 258432, time: 0.548, data: 0.000) G_L1: 15.326 G_L1_ABSOLUTE: 3.128 G_L1_RELATIVE: 12.198 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 35, iters: 260432, time: 0.541, data: 0.001) G_L1: 13.579 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 11.178 G_Regularizer: 0.000 validation_error: 20.536 +(epoch: 35, iters: 262432, time: 0.542, data: 0.000) G_L1: 14.027 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 11.696 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 35, iters: 264432, time: 0.542, data: 0.002) G_L1: 14.784 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 12.316 G_Regularizer: 0.000 validation_error: 21.156 +(epoch: 35, iters: 266432, time: 0.545, data: 0.000) G_L1: 14.524 G_L1_ABSOLUTE: 3.165 G_L1_RELATIVE: 11.359 G_Regularizer: 0.000 validation_error: 20.524 +(epoch: 35, iters: 268432, time: 0.544, data: 0.000) G_L1: 13.273 G_L1_ABSOLUTE: 2.830 G_L1_RELATIVE: 10.443 G_Regularizer: 0.000 validation_error: 21.072 +(epoch: 35, iters: 270432, time: 0.550, data: 0.001) G_L1: 13.977 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 11.575 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 35, iters: 272432, time: 0.549, data: 0.000) G_L1: 12.767 G_L1_ABSOLUTE: 2.025 G_L1_RELATIVE: 10.741 G_Regularizer: 0.000 validation_error: 20.592 +(epoch: 35, iters: 274432, time: 0.545, data: 0.000) G_L1: 18.408 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 15.523 G_Regularizer: 0.000 validation_error: 20.719 +(epoch: 35, iters: 276432, time: 0.552, data: 0.001) G_L1: 14.640 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 11.909 G_Regularizer: 0.000 validation_error: 20.599 +(epoch: 35, iters: 278432, time: 0.547, data: 0.001) G_L1: 16.662 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 14.136 G_Regularizer: 0.000 validation_error: 20.444 +(epoch: 35, iters: 280432, time: 0.545, data: 0.000) G_L1: 15.315 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 12.979 G_Regularizer: 0.000 validation_error: 20.472 +(epoch: 35, iters: 282432, time: 0.557, data: 0.000) G_L1: 13.674 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 11.111 G_Regularizer: 0.000 validation_error: 20.186 +(epoch: 35, iters: 284432, time: 0.547, data: 0.000) G_L1: 16.963 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 14.219 G_Regularizer: 0.000 validation_error: 20.406 +(epoch: 35, iters: 286432, time: 0.541, data: 0.000) G_L1: 19.652 G_L1_ABSOLUTE: 2.860 G_L1_RELATIVE: 16.791 G_Regularizer: 0.000 validation_error: 20.647 +(epoch: 35, iters: 288432, time: 0.552, data: 0.000) G_L1: 16.436 G_L1_ABSOLUTE: 2.953 G_L1_RELATIVE: 13.482 G_Regularizer: 0.000 validation_error: 20.203 +(epoch: 35, iters: 290432, time: 0.552, data: 0.000) G_L1: 14.637 G_L1_ABSOLUTE: 1.958 G_L1_RELATIVE: 12.679 G_Regularizer: 0.000 validation_error: 20.546 +(epoch: 35, iters: 292432, time: 0.540, data: 0.000) G_L1: 13.801 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 11.080 G_Regularizer: 0.000 validation_error: 20.385 +(epoch: 35, iters: 294432, time: 0.541, data: 0.000) G_L1: 13.243 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 10.601 G_Regularizer: 0.000 validation_error: 20.691 +(epoch: 35, iters: 296432, time: 0.541, data: 0.001) G_L1: 15.631 G_L1_ABSOLUTE: 2.567 G_L1_RELATIVE: 13.063 G_Regularizer: 0.000 validation_error: 20.786 +(epoch: 35, iters: 298432, time: 0.547, data: 0.000) G_L1: 12.644 G_L1_ABSOLUTE: 2.559 G_L1_RELATIVE: 10.084 G_Regularizer: 0.000 validation_error: 20.325 +(epoch: 35, iters: 300432, time: 0.542, data: 0.001) G_L1: 14.638 G_L1_ABSOLUTE: 2.322 G_L1_RELATIVE: 12.316 G_Regularizer: 0.000 validation_error: 20.907 +(epoch: 35, iters: 302432, time: 0.556, data: 0.000) G_L1: 13.227 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 10.706 G_Regularizer: 0.000 validation_error: 20.582 +(epoch: 36, iters: 1680, time: 0.549, data: 0.000) G_L1: 15.592 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 13.139 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 36, iters: 3680, time: 0.547, data: 0.000) G_L1: 14.185 G_L1_ABSOLUTE: 2.957 G_L1_RELATIVE: 11.228 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 36, iters: 5680, time: 0.552, data: 0.000) G_L1: 11.535 G_L1_ABSOLUTE: 2.173 G_L1_RELATIVE: 9.361 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 36, iters: 7680, time: 0.545, data: 0.000) G_L1: 13.719 G_L1_ABSOLUTE: 2.199 G_L1_RELATIVE: 11.520 G_Regularizer: 0.000 validation_error: 20.454 +(epoch: 36, iters: 9680, time: 0.546, data: 0.000) G_L1: 14.041 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 11.368 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 36, iters: 11680, time: 0.549, data: 0.000) G_L1: 16.669 G_L1_ABSOLUTE: 2.825 G_L1_RELATIVE: 13.844 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 36, iters: 13680, time: 0.548, data: 0.000) G_L1: 12.521 G_L1_ABSOLUTE: 2.064 G_L1_RELATIVE: 10.457 G_Regularizer: 0.000 validation_error: 21.065 +(epoch: 36, iters: 15680, time: 0.552, data: 0.000) G_L1: 14.798 G_L1_ABSOLUTE: 2.611 G_L1_RELATIVE: 12.187 G_Regularizer: 0.000 validation_error: 20.731 +(epoch: 36, iters: 17680, time: 0.549, data: 0.000) G_L1: 15.035 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 12.515 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 36, iters: 19680, time: 0.546, data: 0.000) G_L1: 16.540 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 13.819 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 36, iters: 21680, time: 0.544, data: 0.000) G_L1: 12.153 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 9.735 G_Regularizer: 0.000 validation_error: 20.548 +(epoch: 36, iters: 23680, time: 0.547, data: 0.000) G_L1: 15.721 G_L1_ABSOLUTE: 2.909 G_L1_RELATIVE: 12.812 G_Regularizer: 0.000 validation_error: 20.599 +(epoch: 36, iters: 25680, time: 0.538, data: 0.000) G_L1: 13.707 G_L1_ABSOLUTE: 2.295 G_L1_RELATIVE: 11.412 G_Regularizer: 0.000 validation_error: 20.595 +(epoch: 36, iters: 27680, time: 0.542, data: 0.000) G_L1: 12.990 G_L1_ABSOLUTE: 2.151 G_L1_RELATIVE: 10.839 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 36, iters: 29680, time: 0.539, data: 0.000) G_L1: 14.621 G_L1_ABSOLUTE: 2.740 G_L1_RELATIVE: 11.881 G_Regularizer: 0.000 validation_error: 20.596 +(epoch: 36, iters: 31680, time: 0.547, data: 0.000) G_L1: 14.413 G_L1_ABSOLUTE: 2.201 G_L1_RELATIVE: 12.212 G_Regularizer: 0.000 validation_error: 20.592 +(epoch: 36, iters: 33680, time: 0.538, data: 0.000) G_L1: 13.441 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 10.817 G_Regularizer: 0.000 validation_error: 20.578 +(epoch: 36, iters: 35680, time: 0.544, data: 0.000) G_L1: 14.226 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 11.964 G_Regularizer: 0.000 validation_error: 20.956 +(epoch: 36, iters: 37680, time: 0.539, data: 0.000) G_L1: 14.391 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 11.659 G_Regularizer: 0.000 validation_error: 20.576 +(epoch: 36, iters: 39680, time: 0.551, data: 0.000) G_L1: 16.365 G_L1_ABSOLUTE: 2.390 G_L1_RELATIVE: 13.975 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 36, iters: 41680, time: 0.552, data: 0.000) G_L1: 12.881 G_L1_ABSOLUTE: 2.148 G_L1_RELATIVE: 10.733 G_Regularizer: 0.000 validation_error: 20.924 +(epoch: 36, iters: 43680, time: 0.544, data: 0.000) G_L1: 12.676 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 10.252 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 36, iters: 45680, time: 0.548, data: 0.000) G_L1: 13.897 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 11.446 G_Regularizer: 0.000 validation_error: 21.078 +(epoch: 36, iters: 47680, time: 0.551, data: 0.000) G_L1: 12.347 G_L1_ABSOLUTE: 2.290 G_L1_RELATIVE: 10.057 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 36, iters: 49680, time: 0.551, data: 0.000) G_L1: 12.085 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 9.681 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 36, iters: 51680, time: 0.552, data: 0.000) G_L1: 14.464 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 11.704 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 36, iters: 53680, time: 0.542, data: 0.000) G_L1: 12.095 G_L1_ABSOLUTE: 2.315 G_L1_RELATIVE: 9.779 G_Regularizer: 0.000 validation_error: 20.607 +(epoch: 36, iters: 55680, time: 0.549, data: 0.000) G_L1: 13.488 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 11.052 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 36, iters: 57680, time: 0.541, data: 0.000) G_L1: 14.588 G_L1_ABSOLUTE: 2.129 G_L1_RELATIVE: 12.460 G_Regularizer: 0.000 validation_error: 20.920 +(epoch: 36, iters: 59680, time: 0.558, data: 0.000) G_L1: 12.554 G_L1_ABSOLUTE: 2.593 G_L1_RELATIVE: 9.961 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 36, iters: 61680, time: 0.545, data: 0.000) G_L1: 14.162 G_L1_ABSOLUTE: 2.913 G_L1_RELATIVE: 11.249 G_Regularizer: 0.000 validation_error: 21.139 +(epoch: 36, iters: 63680, time: 0.550, data: 0.000) G_L1: 14.355 G_L1_ABSOLUTE: 2.499 G_L1_RELATIVE: 11.857 G_Regularizer: 0.000 validation_error: 20.749 +(epoch: 36, iters: 65680, time: 0.545, data: 0.000) G_L1: 16.490 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 13.944 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 36, iters: 67680, time: 0.548, data: 0.000) G_L1: 13.661 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 11.072 G_Regularizer: 0.000 validation_error: 20.515 +(epoch: 36, iters: 69680, time: 0.549, data: 0.000) G_L1: 14.511 G_L1_ABSOLUTE: 2.033 G_L1_RELATIVE: 12.478 G_Regularizer: 0.000 validation_error: 21.267 +(epoch: 36, iters: 71680, time: 0.544, data: 0.000) G_L1: 15.471 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 13.189 G_Regularizer: 0.000 validation_error: 21.280 +(epoch: 36, iters: 73680, time: 0.552, data: 0.000) G_L1: 17.074 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 14.732 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 36, iters: 75680, time: 0.547, data: 0.000) G_L1: 14.070 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 11.697 G_Regularizer: 0.000 validation_error: 20.707 +(epoch: 36, iters: 77680, time: 0.543, data: 0.000) G_L1: 14.237 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 11.961 G_Regularizer: 0.000 validation_error: 20.991 +(epoch: 36, iters: 79680, time: 0.543, data: 0.000) G_L1: 14.036 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 11.558 G_Regularizer: 0.000 validation_error: 20.912 +(epoch: 36, iters: 81680, time: 0.547, data: 0.001) G_L1: 13.799 G_L1_ABSOLUTE: 2.537 G_L1_RELATIVE: 11.262 G_Regularizer: 0.000 validation_error: 21.281 +(epoch: 36, iters: 83680, time: 0.543, data: 0.000) G_L1: 12.351 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 9.620 G_Regularizer: 0.000 validation_error: 21.517 +(epoch: 36, iters: 85680, time: 0.546, data: 0.000) G_L1: 12.763 G_L1_ABSOLUTE: 2.828 G_L1_RELATIVE: 9.935 G_Regularizer: 0.000 validation_error: 20.545 +(epoch: 36, iters: 87680, time: 0.553, data: 0.000) G_L1: 14.524 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 12.009 G_Regularizer: 0.000 validation_error: 20.644 +(epoch: 36, iters: 89680, time: 0.542, data: 0.000) G_L1: 16.921 G_L1_ABSOLUTE: 2.699 G_L1_RELATIVE: 14.222 G_Regularizer: 0.000 validation_error: 20.573 +(epoch: 36, iters: 91680, time: 0.541, data: 0.000) G_L1: 13.842 G_L1_ABSOLUTE: 2.034 G_L1_RELATIVE: 11.808 G_Regularizer: 0.000 validation_error: 21.049 +(epoch: 36, iters: 93680, time: 0.555, data: 0.000) G_L1: 14.418 G_L1_ABSOLUTE: 3.457 G_L1_RELATIVE: 10.961 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 36, iters: 95680, time: 0.549, data: 0.000) G_L1: 14.342 G_L1_ABSOLUTE: 2.182 G_L1_RELATIVE: 12.161 G_Regularizer: 0.000 validation_error: 21.313 +(epoch: 36, iters: 97680, time: 0.541, data: 0.000) G_L1: 15.866 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 13.129 G_Regularizer: 0.000 validation_error: 20.938 +(epoch: 36, iters: 99680, time: 0.544, data: 0.000) G_L1: 14.401 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 11.916 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 36, iters: 101680, time: 0.551, data: 0.001) G_L1: 13.188 G_L1_ABSOLUTE: 2.545 G_L1_RELATIVE: 10.644 G_Regularizer: 0.000 validation_error: 20.563 +(epoch: 36, iters: 103680, time: 0.547, data: 0.000) G_L1: 14.381 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 11.901 G_Regularizer: 0.000 validation_error: 20.920 +(epoch: 36, iters: 105680, time: 0.544, data: 0.000) G_L1: 14.364 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 11.841 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 36, iters: 107680, time: 0.547, data: 0.000) G_L1: 13.436 G_L1_ABSOLUTE: 2.327 G_L1_RELATIVE: 11.109 G_Regularizer: 0.000 validation_error: 20.795 +(epoch: 36, iters: 109680, time: 0.542, data: 0.000) G_L1: 12.987 G_L1_ABSOLUTE: 2.154 G_L1_RELATIVE: 10.833 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 36, iters: 111680, time: 0.541, data: 0.000) G_L1: 16.860 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 14.204 G_Regularizer: 0.000 validation_error: 20.646 +(epoch: 36, iters: 113680, time: 0.547, data: 0.001) G_L1: 14.083 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 11.615 G_Regularizer: 0.000 validation_error: 21.161 +(epoch: 36, iters: 115680, time: 0.548, data: 0.000) G_L1: 14.176 G_L1_ABSOLUTE: 2.479 G_L1_RELATIVE: 11.697 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 36, iters: 117680, time: 0.538, data: 0.000) G_L1: 14.236 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 11.373 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 36, iters: 119680, time: 0.551, data: 0.000) G_L1: 13.760 G_L1_ABSOLUTE: 2.127 G_L1_RELATIVE: 11.634 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 36, iters: 121680, time: 0.553, data: 0.000) G_L1: 12.979 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 10.370 G_Regularizer: 0.000 validation_error: 21.073 +(epoch: 36, iters: 123680, time: 0.552, data: 0.000) G_L1: 14.568 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 12.228 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 36, iters: 125680, time: 0.549, data: 0.000) G_L1: 16.296 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 13.814 G_Regularizer: 0.000 validation_error: 20.301 +(epoch: 36, iters: 127680, time: 0.549, data: 0.000) G_L1: 13.548 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 11.047 G_Regularizer: 0.000 validation_error: 20.733 +(epoch: 36, iters: 129680, time: 0.550, data: 0.001) G_L1: 12.949 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 10.535 G_Regularizer: 0.000 validation_error: 20.297 +(epoch: 36, iters: 131680, time: 0.543, data: 0.000) G_L1: 13.813 G_L1_ABSOLUTE: 2.456 G_L1_RELATIVE: 11.358 G_Regularizer: 0.000 validation_error: 20.543 +(epoch: 36, iters: 133680, time: 0.545, data: 0.000) G_L1: 12.497 G_L1_ABSOLUTE: 2.272 G_L1_RELATIVE: 10.224 G_Regularizer: 0.000 validation_error: 21.380 +(epoch: 36, iters: 135680, time: 0.551, data: 0.000) G_L1: 13.479 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 11.022 G_Regularizer: 0.000 validation_error: 21.230 +(epoch: 36, iters: 137680, time: 0.542, data: 0.000) G_L1: 13.454 G_L1_ABSOLUTE: 3.182 G_L1_RELATIVE: 10.271 G_Regularizer: 0.000 validation_error: 21.007 +(epoch: 36, iters: 139680, time: 0.543, data: 0.000) G_L1: 16.195 G_L1_ABSOLUTE: 3.217 G_L1_RELATIVE: 12.978 G_Regularizer: 0.000 validation_error: 21.069 +(epoch: 36, iters: 141680, time: 0.545, data: 0.000) G_L1: 12.818 G_L1_ABSOLUTE: 2.346 G_L1_RELATIVE: 10.471 G_Regularizer: 0.000 validation_error: 20.735 +(epoch: 36, iters: 143680, time: 0.544, data: 0.000) G_L1: 15.261 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 12.524 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 36, iters: 145680, time: 0.548, data: 0.000) G_L1: 14.384 G_L1_ABSOLUTE: 2.897 G_L1_RELATIVE: 11.486 G_Regularizer: 0.000 validation_error: 21.248 +(epoch: 36, iters: 147680, time: 0.547, data: 0.000) G_L1: 16.200 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 13.602 G_Regularizer: 0.000 validation_error: 20.355 +(epoch: 36, iters: 149680, time: 0.547, data: 0.000) G_L1: 12.884 G_L1_ABSOLUTE: 2.266 G_L1_RELATIVE: 10.618 G_Regularizer: 0.000 validation_error: 20.611 +(epoch: 36, iters: 151680, time: 0.549, data: 0.000) G_L1: 16.105 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 13.581 G_Regularizer: 0.000 validation_error: 21.071 +(epoch: 36, iters: 153680, time: 0.547, data: 0.001) G_L1: 12.350 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 9.606 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 36, iters: 155680, time: 0.552, data: 0.000) G_L1: 13.861 G_L1_ABSOLUTE: 2.789 G_L1_RELATIVE: 11.071 G_Regularizer: 0.000 validation_error: 21.484 +(epoch: 36, iters: 157680, time: 0.553, data: 0.000) G_L1: 12.906 G_L1_ABSOLUTE: 2.393 G_L1_RELATIVE: 10.513 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 36, iters: 159680, time: 0.552, data: 0.000) G_L1: 15.220 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 12.550 G_Regularizer: 0.000 validation_error: 20.847 +(epoch: 36, iters: 161680, time: 0.554, data: 0.000) G_L1: 13.658 G_L1_ABSOLUTE: 2.557 G_L1_RELATIVE: 11.100 G_Regularizer: 0.000 validation_error: 20.993 +(epoch: 36, iters: 163680, time: 0.548, data: 0.000) G_L1: 16.049 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 13.678 G_Regularizer: 0.000 validation_error: 20.683 +(epoch: 36, iters: 165680, time: 0.545, data: 0.000) G_L1: 11.625 G_L1_ABSOLUTE: 2.297 G_L1_RELATIVE: 9.328 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 36, iters: 167680, time: 0.546, data: 0.000) G_L1: 14.289 G_L1_ABSOLUTE: 2.261 G_L1_RELATIVE: 12.027 G_Regularizer: 0.000 validation_error: 20.591 +(epoch: 36, iters: 169680, time: 0.540, data: 0.000) G_L1: 14.768 G_L1_ABSOLUTE: 2.361 G_L1_RELATIVE: 12.407 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 36, iters: 171680, time: 0.548, data: 0.000) G_L1: 20.242 G_L1_ABSOLUTE: 3.288 G_L1_RELATIVE: 16.954 G_Regularizer: 0.000 validation_error: 20.946 +(epoch: 36, iters: 173680, time: 0.544, data: 0.000) G_L1: 13.411 G_L1_ABSOLUTE: 2.250 G_L1_RELATIVE: 11.161 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 36, iters: 175680, time: 0.554, data: 0.000) G_L1: 16.492 G_L1_ABSOLUTE: 2.639 G_L1_RELATIVE: 13.854 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 36, iters: 177680, time: 0.558, data: 0.000) G_L1: 14.755 G_L1_ABSOLUTE: 2.533 G_L1_RELATIVE: 12.221 G_Regularizer: 0.000 validation_error: 20.046 +(epoch: 36, iters: 179680, time: 0.542, data: 0.000) G_L1: 16.950 G_L1_ABSOLUTE: 3.028 G_L1_RELATIVE: 13.922 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 36, iters: 181680, time: 0.544, data: 0.000) G_L1: 10.602 G_L1_ABSOLUTE: 2.179 G_L1_RELATIVE: 8.422 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 36, iters: 183680, time: 0.546, data: 0.000) G_L1: 14.370 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 11.897 G_Regularizer: 0.000 validation_error: 20.732 +(epoch: 36, iters: 185680, time: 0.551, data: 0.000) G_L1: 13.858 G_L1_ABSOLUTE: 2.309 G_L1_RELATIVE: 11.549 G_Regularizer: 0.000 validation_error: 20.418 +(epoch: 36, iters: 187680, time: 0.555, data: 0.001) G_L1: 16.004 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 13.529 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 36, iters: 189680, time: 0.544, data: 0.000) G_L1: 15.760 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 13.235 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 36, iters: 191680, time: 0.549, data: 0.000) G_L1: 13.238 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 10.643 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 36, iters: 193680, time: 0.556, data: 0.000) G_L1: 14.353 G_L1_ABSOLUTE: 2.975 G_L1_RELATIVE: 11.378 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 36, iters: 195680, time: 0.547, data: 0.000) G_L1: 14.086 G_L1_ABSOLUTE: 2.288 G_L1_RELATIVE: 11.798 G_Regularizer: 0.000 validation_error: 20.520 +(epoch: 36, iters: 197680, time: 0.536, data: 0.000) G_L1: 13.319 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 10.912 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 36, iters: 199680, time: 0.550, data: 0.000) G_L1: 11.218 G_L1_ABSOLUTE: 2.128 G_L1_RELATIVE: 9.090 G_Regularizer: 0.000 validation_error: 20.954 +(epoch: 36, iters: 201680, time: 0.541, data: 0.001) G_L1: 13.131 G_L1_ABSOLUTE: 2.210 G_L1_RELATIVE: 10.921 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 36, iters: 203680, time: 0.549, data: 0.000) G_L1: 15.191 G_L1_ABSOLUTE: 2.356 G_L1_RELATIVE: 12.835 G_Regularizer: 0.000 validation_error: 20.493 +(epoch: 36, iters: 205680, time: 0.555, data: 0.000) G_L1: 16.060 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 13.268 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 36, iters: 207680, time: 0.546, data: 0.000) G_L1: 14.089 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 11.621 G_Regularizer: 0.000 validation_error: 20.669 +(epoch: 36, iters: 209680, time: 0.547, data: 0.000) G_L1: 15.208 G_L1_ABSOLUTE: 2.537 G_L1_RELATIVE: 12.671 G_Regularizer: 0.000 validation_error: 21.075 +(epoch: 36, iters: 211680, time: 0.548, data: 0.000) G_L1: 16.146 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 13.682 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 36, iters: 213680, time: 0.555, data: 0.000) G_L1: 14.347 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 11.799 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 36, iters: 215680, time: 0.536, data: 0.000) G_L1: 13.851 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 11.463 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 36, iters: 217680, time: 0.549, data: 0.000) G_L1: 15.306 G_L1_ABSOLUTE: 2.768 G_L1_RELATIVE: 12.537 G_Regularizer: 0.000 validation_error: 20.554 +(epoch: 36, iters: 219680, time: 0.550, data: 0.000) G_L1: 17.994 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 15.365 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 36, iters: 221680, time: 0.547, data: 0.000) G_L1: 15.728 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 13.304 G_Regularizer: 0.000 validation_error: 20.954 +(epoch: 36, iters: 223680, time: 0.545, data: 0.000) G_L1: 13.260 G_L1_ABSOLUTE: 2.074 G_L1_RELATIVE: 11.186 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 36, iters: 225680, time: 0.544, data: 0.000) G_L1: 15.579 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 13.009 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 36, iters: 227680, time: 0.552, data: 0.000) G_L1: 15.285 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 12.580 G_Regularizer: 0.000 validation_error: 20.797 +(epoch: 36, iters: 229680, time: 0.548, data: 0.001) G_L1: 13.415 G_L1_ABSOLUTE: 2.883 G_L1_RELATIVE: 10.532 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 36, iters: 231680, time: 0.547, data: 0.000) G_L1: 13.090 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 10.655 G_Regularizer: 0.000 validation_error: 21.211 +(epoch: 36, iters: 233680, time: 0.545, data: 0.000) G_L1: 15.319 G_L1_ABSOLUTE: 2.555 G_L1_RELATIVE: 12.764 G_Regularizer: 0.000 validation_error: 20.480 +(epoch: 36, iters: 235680, time: 0.542, data: 0.000) G_L1: 14.656 G_L1_ABSOLUTE: 2.021 G_L1_RELATIVE: 12.634 G_Regularizer: 0.000 validation_error: 20.956 +(epoch: 36, iters: 237680, time: 0.548, data: 0.000) G_L1: 15.900 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 13.126 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 36, iters: 239680, time: 0.545, data: 0.000) G_L1: 13.728 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 11.334 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 36, iters: 241680, time: 0.542, data: 0.000) G_L1: 13.463 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 10.889 G_Regularizer: 0.000 validation_error: 21.165 +(epoch: 36, iters: 243680, time: 0.546, data: 0.001) G_L1: 12.777 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 10.495 G_Regularizer: 0.000 validation_error: 21.306 +(epoch: 36, iters: 245680, time: 0.551, data: 0.000) G_L1: 15.934 G_L1_ABSOLUTE: 2.920 G_L1_RELATIVE: 13.014 G_Regularizer: 0.000 validation_error: 20.979 +(epoch: 36, iters: 247680, time: 0.563, data: 0.000) G_L1: 15.549 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 12.897 G_Regularizer: 0.000 validation_error: 20.761 +(epoch: 36, iters: 249680, time: 0.546, data: 0.000) G_L1: 11.263 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 8.761 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 36, iters: 251680, time: 0.544, data: 0.000) G_L1: 13.584 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 11.248 G_Regularizer: 0.000 validation_error: 20.802 +(epoch: 36, iters: 253680, time: 0.555, data: 0.000) G_L1: 14.620 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 12.200 G_Regularizer: 0.000 validation_error: 20.455 +(epoch: 36, iters: 255680, time: 0.550, data: 0.000) G_L1: 11.185 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 8.923 G_Regularizer: 0.000 validation_error: 20.536 +(epoch: 36, iters: 257680, time: 0.547, data: 0.000) G_L1: 13.534 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 11.140 G_Regularizer: 0.000 validation_error: 21.278 +(epoch: 36, iters: 259680, time: 0.549, data: 0.000) G_L1: 14.603 G_L1_ABSOLUTE: 2.963 G_L1_RELATIVE: 11.640 G_Regularizer: 0.000 validation_error: 21.027 +(epoch: 36, iters: 261680, time: 0.555, data: 0.000) G_L1: 17.107 G_L1_ABSOLUTE: 3.222 G_L1_RELATIVE: 13.885 G_Regularizer: 0.000 validation_error: 21.161 +(epoch: 36, iters: 263680, time: 0.544, data: 0.000) G_L1: 13.101 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 10.644 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 36, iters: 265680, time: 0.557, data: 0.000) G_L1: 14.716 G_L1_ABSOLUTE: 2.616 G_L1_RELATIVE: 12.100 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 36, iters: 267680, time: 0.539, data: 0.000) G_L1: 13.044 G_L1_ABSOLUTE: 2.148 G_L1_RELATIVE: 10.896 G_Regularizer: 0.000 validation_error: 20.816 +(epoch: 36, iters: 269680, time: 0.543, data: 0.000) G_L1: 15.949 G_L1_ABSOLUTE: 2.912 G_L1_RELATIVE: 13.037 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 36, iters: 271680, time: 0.539, data: 0.000) G_L1: 12.048 G_L1_ABSOLUTE: 2.270 G_L1_RELATIVE: 9.778 G_Regularizer: 0.000 validation_error: 20.525 +(epoch: 36, iters: 273680, time: 0.545, data: 0.000) G_L1: 13.558 G_L1_ABSOLUTE: 2.665 G_L1_RELATIVE: 10.893 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 36, iters: 275680, time: 0.554, data: 0.000) G_L1: 17.435 G_L1_ABSOLUTE: 3.108 G_L1_RELATIVE: 14.327 G_Regularizer: 0.000 validation_error: 20.848 +(epoch: 36, iters: 277680, time: 0.546, data: 0.000) G_L1: 12.425 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 9.862 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 36, iters: 279680, time: 0.547, data: 0.000) G_L1: 14.006 G_L1_ABSOLUTE: 2.280 G_L1_RELATIVE: 11.726 G_Regularizer: 0.000 validation_error: 21.126 +(epoch: 36, iters: 281680, time: 0.552, data: 0.000) G_L1: 12.728 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 10.453 G_Regularizer: 0.000 validation_error: 20.552 +(epoch: 36, iters: 283680, time: 0.548, data: 0.000) G_L1: 13.367 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 10.827 G_Regularizer: 0.000 validation_error: 20.603 +(epoch: 36, iters: 285680, time: 0.545, data: 0.000) G_L1: 14.505 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 11.908 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 36, iters: 287680, time: 0.547, data: 0.000) G_L1: 12.516 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 9.999 G_Regularizer: 0.000 validation_error: 20.862 +(epoch: 36, iters: 289680, time: 0.545, data: 0.000) G_L1: 11.822 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 9.386 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 36, iters: 291680, time: 0.545, data: 0.000) G_L1: 13.045 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 10.630 G_Regularizer: 0.000 validation_error: 21.254 +(epoch: 36, iters: 293680, time: 0.545, data: 0.000) G_L1: 13.524 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 11.051 G_Regularizer: 0.000 validation_error: 21.239 +(epoch: 36, iters: 295680, time: 0.552, data: 0.001) G_L1: 13.582 G_L1_ABSOLUTE: 2.359 G_L1_RELATIVE: 11.223 G_Regularizer: 0.000 validation_error: 20.874 +(epoch: 36, iters: 297680, time: 0.545, data: 0.000) G_L1: 16.238 G_L1_ABSOLUTE: 2.656 G_L1_RELATIVE: 13.582 G_Regularizer: 0.000 validation_error: 20.269 +(epoch: 36, iters: 299680, time: 0.547, data: 0.000) G_L1: 11.939 G_L1_ABSOLUTE: 2.280 G_L1_RELATIVE: 9.659 G_Regularizer: 0.000 validation_error: 21.025 +(epoch: 36, iters: 301680, time: 0.543, data: 0.000) G_L1: 13.939 G_L1_ABSOLUTE: 2.157 G_L1_RELATIVE: 11.783 G_Regularizer: 0.000 validation_error: 20.408 +(epoch: 37, iters: 928, time: 0.551, data: 0.000) G_L1: 12.967 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 10.732 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 37, iters: 2928, time: 0.550, data: 0.000) G_L1: 17.113 G_L1_ABSOLUTE: 3.076 G_L1_RELATIVE: 14.037 G_Regularizer: 0.000 validation_error: 20.344 +(epoch: 37, iters: 4928, time: 0.555, data: 0.000) G_L1: 11.039 G_L1_ABSOLUTE: 2.358 G_L1_RELATIVE: 8.681 G_Regularizer: 0.000 validation_error: 21.347 +(epoch: 37, iters: 6928, time: 0.550, data: 0.000) G_L1: 13.640 G_L1_ABSOLUTE: 2.029 G_L1_RELATIVE: 11.611 G_Regularizer: 0.000 validation_error: 21.055 +(epoch: 37, iters: 8928, time: 0.559, data: 0.000) G_L1: 14.808 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 12.465 G_Regularizer: 0.000 validation_error: 20.571 +(epoch: 37, iters: 10928, time: 0.541, data: 0.000) G_L1: 13.467 G_L1_ABSOLUTE: 2.882 G_L1_RELATIVE: 10.585 G_Regularizer: 0.000 validation_error: 20.446 +(epoch: 37, iters: 12928, time: 0.556, data: 0.000) G_L1: 14.200 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 11.946 G_Regularizer: 0.000 validation_error: 20.397 +(epoch: 37, iters: 14928, time: 0.543, data: 0.000) G_L1: 13.905 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 11.556 G_Regularizer: 0.000 validation_error: 21.206 +(epoch: 37, iters: 16928, time: 0.555, data: 0.000) G_L1: 13.272 G_L1_ABSOLUTE: 2.256 G_L1_RELATIVE: 11.016 G_Regularizer: 0.000 validation_error: 21.051 +(epoch: 37, iters: 18928, time: 0.546, data: 0.000) G_L1: 16.778 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 14.275 G_Regularizer: 0.000 validation_error: 21.199 +(epoch: 37, iters: 20928, time: 0.544, data: 0.000) G_L1: 12.605 G_L1_ABSOLUTE: 2.257 G_L1_RELATIVE: 10.348 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 37, iters: 22928, time: 0.546, data: 0.000) G_L1: 14.604 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 12.133 G_Regularizer: 0.000 validation_error: 20.611 +(epoch: 37, iters: 24928, time: 0.545, data: 0.000) G_L1: 13.154 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 10.651 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 37, iters: 26928, time: 0.546, data: 0.001) G_L1: 12.146 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 9.811 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 37, iters: 28928, time: 0.544, data: 0.000) G_L1: 13.214 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 10.857 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 37, iters: 30928, time: 0.549, data: 0.000) G_L1: 14.946 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 12.337 G_Regularizer: 0.000 validation_error: 20.669 +(epoch: 37, iters: 32928, time: 0.543, data: 0.000) G_L1: 15.114 G_L1_ABSOLUTE: 2.508 G_L1_RELATIVE: 12.606 G_Regularizer: 0.000 validation_error: 20.740 +(epoch: 37, iters: 34928, time: 0.550, data: 0.000) G_L1: 11.943 G_L1_ABSOLUTE: 2.050 G_L1_RELATIVE: 9.893 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 37, iters: 36928, time: 0.546, data: 0.000) G_L1: 14.628 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 11.994 G_Regularizer: 0.000 validation_error: 21.566 +(epoch: 37, iters: 38928, time: 0.551, data: 0.000) G_L1: 14.147 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 11.774 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 37, iters: 40928, time: 0.545, data: 0.000) G_L1: 15.369 G_L1_ABSOLUTE: 2.552 G_L1_RELATIVE: 12.816 G_Regularizer: 0.000 validation_error: 20.639 +(epoch: 37, iters: 42928, time: 0.546, data: 0.000) G_L1: 11.599 G_L1_ABSOLUTE: 2.236 G_L1_RELATIVE: 9.364 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 37, iters: 44928, time: 0.550, data: 0.000) G_L1: 13.443 G_L1_ABSOLUTE: 2.950 G_L1_RELATIVE: 10.492 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 37, iters: 46928, time: 0.539, data: 0.000) G_L1: 12.944 G_L1_ABSOLUTE: 2.374 G_L1_RELATIVE: 10.569 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 37, iters: 48928, time: 0.538, data: 0.000) G_L1: 13.358 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 10.898 G_Regularizer: 0.000 validation_error: 20.691 +(epoch: 37, iters: 50928, time: 0.549, data: 0.000) G_L1: 16.174 G_L1_ABSOLUTE: 3.209 G_L1_RELATIVE: 12.965 G_Regularizer: 0.000 validation_error: 21.353 +(epoch: 37, iters: 52928, time: 0.548, data: 0.000) G_L1: 11.681 G_L1_ABSOLUTE: 1.838 G_L1_RELATIVE: 9.842 G_Regularizer: 0.000 validation_error: 20.585 +(epoch: 37, iters: 54928, time: 0.544, data: 0.000) G_L1: 14.113 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 11.643 G_Regularizer: 0.000 validation_error: 21.262 +(epoch: 37, iters: 56928, time: 0.548, data: 0.000) G_L1: 13.541 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 11.043 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 37, iters: 58928, time: 0.548, data: 0.000) G_L1: 16.033 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 13.573 G_Regularizer: 0.000 validation_error: 20.702 +(epoch: 37, iters: 60928, time: 0.540, data: 0.000) G_L1: 13.711 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 11.099 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 37, iters: 62928, time: 0.560, data: 0.000) G_L1: 12.815 G_L1_ABSOLUTE: 2.231 G_L1_RELATIVE: 10.584 G_Regularizer: 0.000 validation_error: 21.328 +(epoch: 37, iters: 64928, time: 0.550, data: 0.000) G_L1: 14.106 G_L1_ABSOLUTE: 2.212 G_L1_RELATIVE: 11.894 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 37, iters: 66928, time: 0.552, data: 0.000) G_L1: 14.485 G_L1_ABSOLUTE: 2.267 G_L1_RELATIVE: 12.218 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 37, iters: 68928, time: 0.550, data: 0.000) G_L1: 14.771 G_L1_ABSOLUTE: 2.236 G_L1_RELATIVE: 12.535 G_Regularizer: 0.000 validation_error: 21.139 +(epoch: 37, iters: 70928, time: 0.547, data: 0.000) G_L1: 15.445 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 12.811 G_Regularizer: 0.000 validation_error: 20.553 +(epoch: 37, iters: 72928, time: 0.546, data: 0.000) G_L1: 13.060 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 10.652 G_Regularizer: 0.000 validation_error: 21.058 +(epoch: 37, iters: 74928, time: 0.544, data: 0.000) G_L1: 14.045 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 11.701 G_Regularizer: 0.000 validation_error: 21.095 +(epoch: 37, iters: 76928, time: 0.547, data: 0.000) G_L1: 15.547 G_L1_ABSOLUTE: 2.053 G_L1_RELATIVE: 13.493 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 37, iters: 78928, time: 0.550, data: 0.000) G_L1: 16.004 G_L1_ABSOLUTE: 2.129 G_L1_RELATIVE: 13.875 G_Regularizer: 0.000 validation_error: 20.153 +(epoch: 37, iters: 80928, time: 0.543, data: 0.001) G_L1: 17.452 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 14.774 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 37, iters: 82928, time: 0.544, data: 0.000) G_L1: 14.551 G_L1_ABSOLUTE: 2.915 G_L1_RELATIVE: 11.637 G_Regularizer: 0.000 validation_error: 20.684 +(epoch: 37, iters: 84928, time: 0.552, data: 0.000) G_L1: 17.284 G_L1_ABSOLUTE: 3.144 G_L1_RELATIVE: 14.140 G_Regularizer: 0.000 validation_error: 21.184 +(epoch: 37, iters: 86928, time: 0.542, data: 0.000) G_L1: 14.708 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 12.359 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 37, iters: 88928, time: 0.542, data: 0.000) G_L1: 14.674 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 11.796 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 37, iters: 90928, time: 0.546, data: 0.000) G_L1: 12.175 G_L1_ABSOLUTE: 2.181 G_L1_RELATIVE: 9.994 G_Regularizer: 0.000 validation_error: 20.980 +(epoch: 37, iters: 92928, time: 0.540, data: 0.000) G_L1: 13.612 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 10.907 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 37, iters: 94928, time: 0.545, data: 0.000) G_L1: 11.470 G_L1_ABSOLUTE: 2.064 G_L1_RELATIVE: 9.406 G_Regularizer: 0.000 validation_error: 21.327 +(epoch: 37, iters: 96928, time: 0.547, data: 0.000) G_L1: 16.009 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 13.444 G_Regularizer: 0.000 validation_error: 20.627 +(epoch: 37, iters: 98928, time: 0.555, data: 0.000) G_L1: 16.093 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 13.516 G_Regularizer: 0.000 validation_error: 21.059 +(epoch: 37, iters: 100928, time: 0.541, data: 0.000) G_L1: 14.228 G_L1_ABSOLUTE: 3.043 G_L1_RELATIVE: 11.184 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 37, iters: 102928, time: 0.548, data: 0.000) G_L1: 16.499 G_L1_ABSOLUTE: 3.352 G_L1_RELATIVE: 13.147 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 37, iters: 104928, time: 0.556, data: 0.000) G_L1: 13.859 G_L1_ABSOLUTE: 2.185 G_L1_RELATIVE: 11.674 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 37, iters: 106928, time: 0.542, data: 0.000) G_L1: 13.762 G_L1_ABSOLUTE: 2.197 G_L1_RELATIVE: 11.565 G_Regularizer: 0.000 validation_error: 21.361 +(epoch: 37, iters: 108928, time: 0.538, data: 0.000) G_L1: 14.099 G_L1_ABSOLUTE: 2.334 G_L1_RELATIVE: 11.765 G_Regularizer: 0.000 validation_error: 20.505 +(epoch: 37, iters: 110928, time: 0.544, data: 0.000) G_L1: 12.755 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 10.282 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 37, iters: 112928, time: 0.551, data: 0.000) G_L1: 15.362 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 12.793 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 37, iters: 114928, time: 0.547, data: 0.000) G_L1: 14.409 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 11.631 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 37, iters: 116928, time: 0.546, data: 0.001) G_L1: 13.312 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 10.661 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 37, iters: 118928, time: 0.548, data: 0.000) G_L1: 17.696 G_L1_ABSOLUTE: 2.558 G_L1_RELATIVE: 15.138 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 37, iters: 120928, time: 0.553, data: 0.000) G_L1: 15.186 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 12.780 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 37, iters: 122928, time: 0.558, data: 0.000) G_L1: 12.544 G_L1_ABSOLUTE: 2.644 G_L1_RELATIVE: 9.900 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 37, iters: 124928, time: 0.556, data: 0.000) G_L1: 12.290 G_L1_ABSOLUTE: 1.966 G_L1_RELATIVE: 10.323 G_Regularizer: 0.000 validation_error: 20.507 +(epoch: 37, iters: 126928, time: 0.544, data: 0.000) G_L1: 12.523 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 10.264 G_Regularizer: 0.000 validation_error: 21.110 +(epoch: 37, iters: 128928, time: 0.547, data: 0.000) G_L1: 15.895 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 13.296 G_Regularizer: 0.000 validation_error: 21.340 +(epoch: 37, iters: 130928, time: 0.553, data: 0.000) G_L1: 14.848 G_L1_ABSOLUTE: 2.638 G_L1_RELATIVE: 12.209 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 37, iters: 132928, time: 0.541, data: 0.000) G_L1: 16.628 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 13.914 G_Regularizer: 0.000 validation_error: 21.169 +(epoch: 37, iters: 134928, time: 0.549, data: 0.000) G_L1: 12.203 G_L1_ABSOLUTE: 2.241 G_L1_RELATIVE: 9.962 G_Regularizer: 0.000 validation_error: 21.029 +(epoch: 37, iters: 136928, time: 0.547, data: 0.001) G_L1: 15.411 G_L1_ABSOLUTE: 2.665 G_L1_RELATIVE: 12.745 G_Regularizer: 0.000 validation_error: 20.535 +(epoch: 37, iters: 138928, time: 0.548, data: 0.000) G_L1: 14.649 G_L1_ABSOLUTE: 2.580 G_L1_RELATIVE: 12.069 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 37, iters: 140928, time: 0.542, data: 0.001) G_L1: 12.124 G_L1_ABSOLUTE: 1.944 G_L1_RELATIVE: 10.180 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 37, iters: 142928, time: 0.554, data: 0.000) G_L1: 13.249 G_L1_ABSOLUTE: 2.956 G_L1_RELATIVE: 10.292 G_Regularizer: 0.000 validation_error: 21.278 +(epoch: 37, iters: 144928, time: 0.547, data: 0.001) G_L1: 14.035 G_L1_ABSOLUTE: 2.747 G_L1_RELATIVE: 11.288 G_Regularizer: 0.000 validation_error: 20.548 +(epoch: 37, iters: 146928, time: 0.547, data: 0.000) G_L1: 13.470 G_L1_ABSOLUTE: 2.384 G_L1_RELATIVE: 11.086 G_Regularizer: 0.000 validation_error: 20.947 +(epoch: 37, iters: 148928, time: 0.550, data: 0.000) G_L1: 15.007 G_L1_ABSOLUTE: 2.602 G_L1_RELATIVE: 12.405 G_Regularizer: 0.000 validation_error: 21.312 +(epoch: 37, iters: 150928, time: 0.553, data: 0.000) G_L1: 13.359 G_L1_ABSOLUTE: 2.236 G_L1_RELATIVE: 11.123 G_Regularizer: 0.000 validation_error: 21.126 +(epoch: 37, iters: 152928, time: 0.548, data: 0.001) G_L1: 12.775 G_L1_ABSOLUTE: 2.322 G_L1_RELATIVE: 10.453 G_Regularizer: 0.000 validation_error: 20.560 +(epoch: 37, iters: 154928, time: 0.559, data: 0.000) G_L1: 13.748 G_L1_ABSOLUTE: 2.219 G_L1_RELATIVE: 11.529 G_Regularizer: 0.000 validation_error: 20.714 +(epoch: 37, iters: 156928, time: 0.544, data: 0.000) G_L1: 15.873 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 13.275 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 37, iters: 158928, time: 0.553, data: 0.000) G_L1: 14.122 G_L1_ABSOLUTE: 2.073 G_L1_RELATIVE: 12.050 G_Regularizer: 0.000 validation_error: 20.844 +(epoch: 37, iters: 160928, time: 0.551, data: 0.000) G_L1: 14.958 G_L1_ABSOLUTE: 3.084 G_L1_RELATIVE: 11.874 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 37, iters: 162928, time: 0.548, data: 0.001) G_L1: 16.528 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 13.716 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 37, iters: 164928, time: 0.550, data: 0.000) G_L1: 17.369 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 14.746 G_Regularizer: 0.000 validation_error: 20.690 +(epoch: 37, iters: 166928, time: 0.543, data: 0.000) G_L1: 14.611 G_L1_ABSOLUTE: 2.969 G_L1_RELATIVE: 11.642 G_Regularizer: 0.000 validation_error: 20.576 +(epoch: 37, iters: 168928, time: 0.536, data: 0.000) G_L1: 14.118 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 11.609 G_Regularizer: 0.000 validation_error: 20.621 +(epoch: 37, iters: 170928, time: 0.544, data: 0.000) G_L1: 15.144 G_L1_ABSOLUTE: 2.790 G_L1_RELATIVE: 12.354 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 37, iters: 172928, time: 0.557, data: 0.000) G_L1: 15.802 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 13.138 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 37, iters: 174928, time: 0.547, data: 0.000) G_L1: 14.387 G_L1_ABSOLUTE: 2.798 G_L1_RELATIVE: 11.589 G_Regularizer: 0.000 validation_error: 20.930 +(epoch: 37, iters: 176928, time: 0.549, data: 0.001) G_L1: 14.969 G_L1_ABSOLUTE: 2.840 G_L1_RELATIVE: 12.128 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 37, iters: 178928, time: 0.548, data: 0.000) G_L1: 11.070 G_L1_ABSOLUTE: 2.271 G_L1_RELATIVE: 8.799 G_Regularizer: 0.000 validation_error: 20.588 +(epoch: 37, iters: 180928, time: 0.545, data: 0.000) G_L1: 13.724 G_L1_ABSOLUTE: 2.456 G_L1_RELATIVE: 11.268 G_Regularizer: 0.000 validation_error: 20.960 +(epoch: 37, iters: 182928, time: 0.547, data: 0.000) G_L1: 14.327 G_L1_ABSOLUTE: 2.103 G_L1_RELATIVE: 12.224 G_Regularizer: 0.000 validation_error: 21.053 +(epoch: 37, iters: 184928, time: 0.555, data: 0.000) G_L1: 13.489 G_L1_ABSOLUTE: 2.288 G_L1_RELATIVE: 11.202 G_Regularizer: 0.000 validation_error: 20.346 +(epoch: 37, iters: 186928, time: 0.551, data: 0.000) G_L1: 12.048 G_L1_ABSOLUTE: 2.223 G_L1_RELATIVE: 9.825 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 37, iters: 188928, time: 0.550, data: 0.000) G_L1: 12.823 G_L1_ABSOLUTE: 2.325 G_L1_RELATIVE: 10.498 G_Regularizer: 0.000 validation_error: 21.005 +(epoch: 37, iters: 190928, time: 0.546, data: 0.000) G_L1: 16.678 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 13.976 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 37, iters: 192928, time: 0.554, data: 0.000) G_L1: 14.917 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 12.287 G_Regularizer: 0.000 validation_error: 20.693 +(epoch: 37, iters: 194928, time: 0.548, data: 0.000) G_L1: 22.297 G_L1_ABSOLUTE: 3.000 G_L1_RELATIVE: 19.296 G_Regularizer: 0.000 validation_error: 20.921 +(epoch: 37, iters: 196928, time: 0.541, data: 0.000) G_L1: 14.453 G_L1_ABSOLUTE: 2.310 G_L1_RELATIVE: 12.143 G_Regularizer: 0.000 validation_error: 20.621 +(epoch: 37, iters: 198928, time: 0.547, data: 0.000) G_L1: 13.898 G_L1_ABSOLUTE: 2.529 G_L1_RELATIVE: 11.369 G_Regularizer: 0.000 validation_error: 20.228 +(epoch: 37, iters: 200928, time: 0.546, data: 0.000) G_L1: 14.344 G_L1_ABSOLUTE: 2.219 G_L1_RELATIVE: 12.125 G_Regularizer: 0.000 validation_error: 20.466 +(epoch: 37, iters: 202928, time: 0.538, data: 0.000) G_L1: 12.804 G_L1_ABSOLUTE: 2.050 G_L1_RELATIVE: 10.754 G_Regularizer: 0.000 validation_error: 21.129 +(epoch: 37, iters: 204928, time: 0.545, data: 0.000) G_L1: 14.758 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 12.475 G_Regularizer: 0.000 validation_error: 20.744 +(epoch: 37, iters: 206928, time: 0.553, data: 0.000) G_L1: 14.930 G_L1_ABSOLUTE: 2.211 G_L1_RELATIVE: 12.719 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 37, iters: 208928, time: 0.542, data: 0.000) G_L1: 13.251 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 10.994 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 37, iters: 210928, time: 0.549, data: 0.000) G_L1: 12.444 G_L1_ABSOLUTE: 2.825 G_L1_RELATIVE: 9.619 G_Regularizer: 0.000 validation_error: 20.533 +(epoch: 37, iters: 212928, time: 0.537, data: 0.000) G_L1: 14.678 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 12.058 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 37, iters: 214928, time: 0.549, data: 0.000) G_L1: 13.076 G_L1_ABSOLUTE: 2.353 G_L1_RELATIVE: 10.722 G_Regularizer: 0.000 validation_error: 21.088 +(epoch: 37, iters: 216928, time: 0.546, data: 0.000) G_L1: 13.338 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 10.764 G_Regularizer: 0.000 validation_error: 20.621 +(epoch: 37, iters: 218928, time: 0.554, data: 0.001) G_L1: 16.410 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 13.936 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 37, iters: 220928, time: 0.546, data: 0.000) G_L1: 10.908 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 8.610 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 37, iters: 222928, time: 0.546, data: 0.000) G_L1: 12.444 G_L1_ABSOLUTE: 2.614 G_L1_RELATIVE: 9.830 G_Regularizer: 0.000 validation_error: 21.130 +(epoch: 37, iters: 224928, time: 0.550, data: 0.000) G_L1: 14.714 G_L1_ABSOLUTE: 2.709 G_L1_RELATIVE: 12.005 G_Regularizer: 0.000 validation_error: 20.944 +(epoch: 37, iters: 226928, time: 0.538, data: 0.000) G_L1: 16.877 G_L1_ABSOLUTE: 2.917 G_L1_RELATIVE: 13.960 G_Regularizer: 0.000 validation_error: 20.307 +(epoch: 37, iters: 228928, time: 0.542, data: 0.000) G_L1: 12.911 G_L1_ABSOLUTE: 2.854 G_L1_RELATIVE: 10.057 G_Regularizer: 0.000 validation_error: 21.094 +(epoch: 37, iters: 230928, time: 0.548, data: 0.000) G_L1: 12.581 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 9.991 G_Regularizer: 0.000 validation_error: 20.368 +(epoch: 37, iters: 232928, time: 0.550, data: 0.000) G_L1: 15.459 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 12.955 G_Regularizer: 0.000 validation_error: 21.189 +(epoch: 37, iters: 234928, time: 0.543, data: 0.000) G_L1: 16.508 G_L1_ABSOLUTE: 3.179 G_L1_RELATIVE: 13.329 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 37, iters: 236928, time: 0.554, data: 0.000) G_L1: 13.954 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 11.541 G_Regularizer: 0.000 validation_error: 20.421 +(epoch: 37, iters: 238928, time: 0.550, data: 0.000) G_L1: 12.614 G_L1_ABSOLUTE: 2.393 G_L1_RELATIVE: 10.222 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 37, iters: 240928, time: 0.546, data: 0.000) G_L1: 14.030 G_L1_ABSOLUTE: 2.063 G_L1_RELATIVE: 11.968 G_Regularizer: 0.000 validation_error: 21.043 +(epoch: 37, iters: 242928, time: 0.540, data: 0.000) G_L1: 15.094 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 12.531 G_Regularizer: 0.000 validation_error: 20.525 +(epoch: 37, iters: 244928, time: 0.555, data: 0.000) G_L1: 16.561 G_L1_ABSOLUTE: 2.426 G_L1_RELATIVE: 14.135 G_Regularizer: 0.000 validation_error: 20.441 +(epoch: 37, iters: 246928, time: 0.544, data: 0.000) G_L1: 15.541 G_L1_ABSOLUTE: 2.034 G_L1_RELATIVE: 13.507 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 37, iters: 248928, time: 0.545, data: 0.000) G_L1: 14.270 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 11.809 G_Regularizer: 0.000 validation_error: 20.497 +(epoch: 37, iters: 250928, time: 0.548, data: 0.000) G_L1: 14.143 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 11.685 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 37, iters: 252928, time: 0.544, data: 0.000) G_L1: 15.520 G_L1_ABSOLUTE: 2.813 G_L1_RELATIVE: 12.707 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 37, iters: 254928, time: 0.545, data: 0.000) G_L1: 13.282 G_L1_ABSOLUTE: 2.834 G_L1_RELATIVE: 10.447 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 37, iters: 256928, time: 0.546, data: 0.000) G_L1: 15.505 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 12.916 G_Regularizer: 0.000 validation_error: 20.636 +(epoch: 37, iters: 258928, time: 0.551, data: 0.000) G_L1: 14.054 G_L1_ABSOLUTE: 2.714 G_L1_RELATIVE: 11.340 G_Regularizer: 0.000 validation_error: 20.599 +(epoch: 37, iters: 260928, time: 0.544, data: 0.000) G_L1: 12.391 G_L1_ABSOLUTE: 2.236 G_L1_RELATIVE: 10.155 G_Regularizer: 0.000 validation_error: 20.300 +(epoch: 37, iters: 262928, time: 0.558, data: 0.000) G_L1: 13.390 G_L1_ABSOLUTE: 2.745 G_L1_RELATIVE: 10.645 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 37, iters: 264928, time: 0.546, data: 0.000) G_L1: 14.348 G_L1_ABSOLUTE: 2.883 G_L1_RELATIVE: 11.465 G_Regularizer: 0.000 validation_error: 20.193 +(epoch: 37, iters: 266928, time: 0.540, data: 0.000) G_L1: 15.885 G_L1_ABSOLUTE: 2.616 G_L1_RELATIVE: 13.268 G_Regularizer: 0.000 validation_error: 20.484 +(epoch: 37, iters: 268928, time: 0.544, data: 0.000) G_L1: 13.083 G_L1_ABSOLUTE: 2.602 G_L1_RELATIVE: 10.480 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 37, iters: 270928, time: 0.550, data: 0.000) G_L1: 14.129 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 11.697 G_Regularizer: 0.000 validation_error: 19.990 +(epoch: 37, iters: 272928, time: 0.541, data: 0.000) G_L1: 20.214 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 17.509 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 37, iters: 274928, time: 0.543, data: 0.000) G_L1: 13.674 G_L1_ABSOLUTE: 2.544 G_L1_RELATIVE: 11.131 G_Regularizer: 0.000 validation_error: 21.386 +(epoch: 37, iters: 276928, time: 0.546, data: 0.000) G_L1: 11.363 G_L1_ABSOLUTE: 2.348 G_L1_RELATIVE: 9.015 G_Regularizer: 0.000 validation_error: 21.065 +(epoch: 37, iters: 278928, time: 0.552, data: 0.000) G_L1: 14.836 G_L1_ABSOLUTE: 2.220 G_L1_RELATIVE: 12.616 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 37, iters: 280928, time: 0.543, data: 0.000) G_L1: 14.157 G_L1_ABSOLUTE: 2.750 G_L1_RELATIVE: 11.407 G_Regularizer: 0.000 validation_error: 20.513 +(epoch: 37, iters: 282928, time: 0.542, data: 0.000) G_L1: 13.533 G_L1_ABSOLUTE: 2.872 G_L1_RELATIVE: 10.661 G_Regularizer: 0.000 validation_error: 20.480 +(epoch: 37, iters: 284928, time: 0.547, data: 0.000) G_L1: 13.805 G_L1_ABSOLUTE: 2.621 G_L1_RELATIVE: 11.185 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 37, iters: 286928, time: 0.540, data: 0.000) G_L1: 13.148 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 10.899 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 37, iters: 288928, time: 0.545, data: 0.001) G_L1: 14.926 G_L1_ABSOLUTE: 2.410 G_L1_RELATIVE: 12.516 G_Regularizer: 0.000 validation_error: 21.186 +(epoch: 37, iters: 290928, time: 0.541, data: 0.000) G_L1: 12.482 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 10.134 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 37, iters: 292928, time: 0.548, data: 0.000) G_L1: 10.319 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 7.866 G_Regularizer: 0.000 validation_error: 20.395 +(epoch: 37, iters: 294928, time: 0.546, data: 0.000) G_L1: 12.993 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 10.575 G_Regularizer: 0.000 validation_error: 20.502 +(epoch: 37, iters: 296928, time: 0.549, data: 0.001) G_L1: 13.037 G_L1_ABSOLUTE: 2.141 G_L1_RELATIVE: 10.896 G_Regularizer: 0.000 validation_error: 20.658 +(epoch: 37, iters: 298928, time: 0.548, data: 0.000) G_L1: 12.847 G_L1_ABSOLUTE: 2.641 G_L1_RELATIVE: 10.207 G_Regularizer: 0.000 validation_error: 21.357 +(epoch: 37, iters: 300928, time: 0.548, data: 0.000) G_L1: 14.922 G_L1_ABSOLUTE: 2.531 G_L1_RELATIVE: 12.391 G_Regularizer: 0.000 validation_error: 20.658 +(epoch: 38, iters: 176, time: 0.547, data: 0.000) G_L1: 15.220 G_L1_ABSOLUTE: 2.898 G_L1_RELATIVE: 12.323 G_Regularizer: 0.000 validation_error: 21.216 +(epoch: 38, iters: 2176, time: 0.552, data: 0.001) G_L1: 13.925 G_L1_ABSOLUTE: 2.545 G_L1_RELATIVE: 11.380 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 38, iters: 4176, time: 0.546, data: 0.000) G_L1: 14.061 G_L1_ABSOLUTE: 2.466 G_L1_RELATIVE: 11.595 G_Regularizer: 0.000 validation_error: 21.230 +(epoch: 38, iters: 6176, time: 0.544, data: 0.000) G_L1: 12.640 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 10.307 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 38, iters: 8176, time: 0.548, data: 0.000) G_L1: 15.937 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 13.221 G_Regularizer: 0.000 validation_error: 20.719 +(epoch: 38, iters: 10176, time: 0.547, data: 0.000) G_L1: 12.983 G_L1_ABSOLUTE: 2.647 G_L1_RELATIVE: 10.336 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 38, iters: 12176, time: 0.543, data: 0.000) G_L1: 13.620 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 11.221 G_Regularizer: 0.000 validation_error: 20.323 +(epoch: 38, iters: 14176, time: 0.549, data: 0.000) G_L1: 11.918 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 9.582 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 38, iters: 16176, time: 0.555, data: 0.000) G_L1: 14.488 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 12.173 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 38, iters: 18176, time: 0.541, data: 0.000) G_L1: 17.008 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 14.693 G_Regularizer: 0.000 validation_error: 20.512 +(epoch: 38, iters: 20176, time: 0.552, data: 0.000) G_L1: 15.017 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 12.287 G_Regularizer: 0.000 validation_error: 20.705 +(epoch: 38, iters: 22176, time: 0.540, data: 0.000) G_L1: 12.597 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 10.096 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 38, iters: 24176, time: 0.548, data: 0.000) G_L1: 14.606 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 12.015 G_Regularizer: 0.000 validation_error: 20.457 +(epoch: 38, iters: 26176, time: 0.551, data: 0.001) G_L1: 14.208 G_L1_ABSOLUTE: 2.188 G_L1_RELATIVE: 12.020 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 38, iters: 28176, time: 0.552, data: 0.000) G_L1: 16.070 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 13.519 G_Regularizer: 0.000 validation_error: 20.982 +(epoch: 38, iters: 30176, time: 0.547, data: 0.000) G_L1: 15.815 G_L1_ABSOLUTE: 2.842 G_L1_RELATIVE: 12.972 G_Regularizer: 0.000 validation_error: 21.146 +(epoch: 38, iters: 32176, time: 0.550, data: 0.000) G_L1: 13.450 G_L1_ABSOLUTE: 2.386 G_L1_RELATIVE: 11.064 G_Regularizer: 0.000 validation_error: 20.603 +(epoch: 38, iters: 34176, time: 0.564, data: 0.000) G_L1: 15.928 G_L1_ABSOLUTE: 2.372 G_L1_RELATIVE: 13.556 G_Regularizer: 0.000 validation_error: 20.770 +(epoch: 38, iters: 36176, time: 0.551, data: 0.000) G_L1: 14.883 G_L1_ABSOLUTE: 2.444 G_L1_RELATIVE: 12.439 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 38, iters: 38176, time: 0.550, data: 0.000) G_L1: 18.228 G_L1_ABSOLUTE: 3.090 G_L1_RELATIVE: 15.138 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 38, iters: 40176, time: 0.549, data: 0.000) G_L1: 18.201 G_L1_ABSOLUTE: 2.257 G_L1_RELATIVE: 15.943 G_Regularizer: 0.000 validation_error: 21.069 +(epoch: 38, iters: 42176, time: 0.552, data: 0.000) G_L1: 12.995 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 10.625 G_Regularizer: 0.000 validation_error: 20.852 +(epoch: 38, iters: 44176, time: 0.544, data: 0.000) G_L1: 11.206 G_L1_ABSOLUTE: 2.240 G_L1_RELATIVE: 8.966 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 38, iters: 46176, time: 0.546, data: 0.000) G_L1: 15.798 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 13.025 G_Regularizer: 0.000 validation_error: 20.374 +(epoch: 38, iters: 48176, time: 0.550, data: 0.001) G_L1: 16.572 G_L1_ABSOLUTE: 2.351 G_L1_RELATIVE: 14.221 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 38, iters: 50176, time: 0.548, data: 0.000) G_L1: 14.366 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 11.917 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 38, iters: 52176, time: 0.546, data: 0.000) G_L1: 15.267 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 12.711 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 38, iters: 54176, time: 0.555, data: 0.001) G_L1: 11.563 G_L1_ABSOLUTE: 2.029 G_L1_RELATIVE: 9.534 G_Regularizer: 0.000 validation_error: 20.532 +(epoch: 38, iters: 56176, time: 0.550, data: 0.000) G_L1: 13.524 G_L1_ABSOLUTE: 2.307 G_L1_RELATIVE: 11.218 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 38, iters: 58176, time: 0.549, data: 0.000) G_L1: 15.926 G_L1_ABSOLUTE: 2.998 G_L1_RELATIVE: 12.928 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 38, iters: 60176, time: 0.539, data: 0.000) G_L1: 13.947 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 11.431 G_Regularizer: 0.000 validation_error: 21.002 +(epoch: 38, iters: 62176, time: 0.554, data: 0.000) G_L1: 14.023 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 11.688 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 38, iters: 64176, time: 0.543, data: 0.000) G_L1: 14.440 G_L1_ABSOLUTE: 1.935 G_L1_RELATIVE: 12.505 G_Regularizer: 0.000 validation_error: 21.267 +(epoch: 38, iters: 66176, time: 0.544, data: 0.001) G_L1: 14.853 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 12.068 G_Regularizer: 0.000 validation_error: 21.002 +(epoch: 38, iters: 68176, time: 0.557, data: 0.000) G_L1: 14.449 G_L1_ABSOLUTE: 2.832 G_L1_RELATIVE: 11.617 G_Regularizer: 0.000 validation_error: 20.816 +(epoch: 38, iters: 70176, time: 0.547, data: 0.000) G_L1: 13.842 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 11.213 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 38, iters: 72176, time: 0.550, data: 0.000) G_L1: 14.803 G_L1_ABSOLUTE: 3.007 G_L1_RELATIVE: 11.796 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 38, iters: 74176, time: 0.542, data: 0.000) G_L1: 12.441 G_L1_ABSOLUTE: 2.293 G_L1_RELATIVE: 10.147 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 38, iters: 76176, time: 0.552, data: 0.000) G_L1: 14.937 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 12.530 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 38, iters: 78176, time: 0.552, data: 0.000) G_L1: 12.647 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 10.097 G_Regularizer: 0.000 validation_error: 21.110 +(epoch: 38, iters: 80176, time: 0.558, data: 0.000) G_L1: 14.093 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 11.424 G_Regularizer: 0.000 validation_error: 20.789 +(epoch: 38, iters: 82176, time: 0.541, data: 0.000) G_L1: 13.347 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 10.862 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 38, iters: 84176, time: 0.588, data: 0.000) G_L1: 17.222 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 14.544 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 38, iters: 86176, time: 0.606, data: 0.000) G_L1: 19.185 G_L1_ABSOLUTE: 2.863 G_L1_RELATIVE: 16.322 G_Regularizer: 0.000 validation_error: 20.995 +(epoch: 38, iters: 88176, time: 0.588, data: 0.000) G_L1: 13.624 G_L1_ABSOLUTE: 2.164 G_L1_RELATIVE: 11.460 G_Regularizer: 0.000 validation_error: 20.514 +(epoch: 38, iters: 90176, time: 0.611, data: 0.000) G_L1: 10.437 G_L1_ABSOLUTE: 2.200 G_L1_RELATIVE: 8.237 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 38, iters: 92176, time: 0.608, data: 0.000) G_L1: 12.581 G_L1_ABSOLUTE: 2.290 G_L1_RELATIVE: 10.291 G_Regularizer: 0.000 validation_error: 21.365 +(epoch: 38, iters: 94176, time: 0.604, data: 0.000) G_L1: 12.218 G_L1_ABSOLUTE: 2.196 G_L1_RELATIVE: 10.021 G_Regularizer: 0.000 validation_error: 21.034 +(epoch: 38, iters: 96176, time: 0.610, data: 0.000) G_L1: 15.028 G_L1_ABSOLUTE: 2.139 G_L1_RELATIVE: 12.889 G_Regularizer: 0.000 validation_error: 21.223 +(epoch: 38, iters: 98176, time: 0.611, data: 0.000) G_L1: 16.481 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 13.761 G_Regularizer: 0.000 validation_error: 20.594 +(epoch: 38, iters: 100176, time: 0.567, data: 0.000) G_L1: 14.994 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 12.619 G_Regularizer: 0.000 validation_error: 20.650 +(epoch: 38, iters: 102176, time: 0.614, data: 0.000) G_L1: 16.942 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 14.217 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 38, iters: 104176, time: 0.608, data: 0.000) G_L1: 13.322 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 10.989 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 38, iters: 106176, time: 0.610, data: 0.000) G_L1: 14.850 G_L1_ABSOLUTE: 3.199 G_L1_RELATIVE: 11.652 G_Regularizer: 0.000 validation_error: 20.902 +(epoch: 38, iters: 108176, time: 0.594, data: 0.000) G_L1: 12.929 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 10.516 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 38, iters: 110176, time: 0.601, data: 0.000) G_L1: 14.459 G_L1_ABSOLUTE: 2.967 G_L1_RELATIVE: 11.493 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 38, iters: 112176, time: 0.606, data: 0.000) G_L1: 11.923 G_L1_ABSOLUTE: 2.062 G_L1_RELATIVE: 9.861 G_Regularizer: 0.000 validation_error: 20.849 +(epoch: 38, iters: 114176, time: 0.607, data: 0.000) G_L1: 15.638 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 13.184 G_Regularizer: 0.000 validation_error: 20.907 +(epoch: 38, iters: 116176, time: 0.608, data: 0.000) G_L1: 13.054 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 10.819 G_Regularizer: 0.000 validation_error: 20.546 +(epoch: 38, iters: 118176, time: 0.612, data: 0.000) G_L1: 13.862 G_L1_ABSOLUTE: 2.720 G_L1_RELATIVE: 11.142 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 38, iters: 120176, time: 0.561, data: 0.000) G_L1: 12.793 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 9.971 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 38, iters: 122176, time: 0.604, data: 0.000) G_L1: 16.206 G_L1_ABSOLUTE: 2.309 G_L1_RELATIVE: 13.897 G_Regularizer: 0.000 validation_error: 21.165 +(epoch: 38, iters: 124176, time: 0.578, data: 0.000) G_L1: 14.435 G_L1_ABSOLUTE: 3.003 G_L1_RELATIVE: 11.432 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 38, iters: 126176, time: 0.607, data: 0.001) G_L1: 14.751 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 12.045 G_Regularizer: 0.000 validation_error: 20.532 +(epoch: 38, iters: 128176, time: 0.608, data: 0.000) G_L1: 15.299 G_L1_ABSOLUTE: 2.661 G_L1_RELATIVE: 12.638 G_Regularizer: 0.000 validation_error: 20.691 +(epoch: 38, iters: 130176, time: 0.608, data: 0.000) G_L1: 14.575 G_L1_ABSOLUTE: 2.953 G_L1_RELATIVE: 11.623 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 38, iters: 132176, time: 0.613, data: 0.000) G_L1: 14.889 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 12.591 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 38, iters: 134176, time: 0.609, data: 0.000) G_L1: 13.422 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 10.648 G_Regularizer: 0.000 validation_error: 20.563 +(epoch: 38, iters: 136176, time: 0.611, data: 0.000) G_L1: 16.604 G_L1_ABSOLUTE: 2.991 G_L1_RELATIVE: 13.613 G_Regularizer: 0.000 validation_error: 21.018 +(epoch: 38, iters: 138176, time: 0.613, data: 0.000) G_L1: 12.279 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 9.732 G_Regularizer: 0.000 validation_error: 21.006 +(epoch: 38, iters: 140176, time: 0.589, data: 0.000) G_L1: 12.235 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 9.895 G_Regularizer: 0.000 validation_error: 20.599 +(epoch: 38, iters: 142176, time: 0.603, data: 0.000) G_L1: 12.970 G_L1_ABSOLUTE: 2.398 G_L1_RELATIVE: 10.572 G_Regularizer: 0.000 validation_error: 21.033 +(epoch: 38, iters: 144176, time: 0.610, data: 0.000) G_L1: 15.953 G_L1_ABSOLUTE: 2.832 G_L1_RELATIVE: 13.121 G_Regularizer: 0.000 validation_error: 20.615 +(epoch: 38, iters: 146176, time: 0.599, data: 0.000) G_L1: 13.399 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 10.990 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 38, iters: 148176, time: 0.546, data: 0.000) G_L1: 18.064 G_L1_ABSOLUTE: 3.088 G_L1_RELATIVE: 14.976 G_Regularizer: 0.000 validation_error: 20.892 +(epoch: 38, iters: 150176, time: 0.541, data: 0.000) G_L1: 12.437 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 9.840 G_Regularizer: 0.000 validation_error: 20.738 +(epoch: 38, iters: 152176, time: 0.541, data: 0.000) G_L1: 15.600 G_L1_ABSOLUTE: 3.037 G_L1_RELATIVE: 12.564 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 38, iters: 154176, time: 0.540, data: 0.000) G_L1: 17.176 G_L1_ABSOLUTE: 3.004 G_L1_RELATIVE: 14.172 G_Regularizer: 0.000 validation_error: 21.127 +(epoch: 38, iters: 156176, time: 0.546, data: 0.000) G_L1: 14.082 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 11.716 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 38, iters: 158176, time: 0.534, data: 0.000) G_L1: 15.048 G_L1_ABSOLUTE: 2.661 G_L1_RELATIVE: 12.387 G_Regularizer: 0.000 validation_error: 21.406 +(epoch: 38, iters: 160176, time: 0.543, data: 0.000) G_L1: 16.212 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 13.475 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 38, iters: 162176, time: 0.544, data: 0.000) G_L1: 14.247 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 11.758 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 38, iters: 164176, time: 0.538, data: 0.000) G_L1: 14.005 G_L1_ABSOLUTE: 2.313 G_L1_RELATIVE: 11.692 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 38, iters: 166176, time: 0.533, data: 0.000) G_L1: 15.798 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 13.074 G_Regularizer: 0.000 validation_error: 20.482 +(epoch: 38, iters: 168176, time: 0.540, data: 0.000) G_L1: 15.040 G_L1_ABSOLUTE: 2.310 G_L1_RELATIVE: 12.730 G_Regularizer: 0.000 validation_error: 21.048 +(epoch: 38, iters: 170176, time: 0.544, data: 0.000) G_L1: 13.183 G_L1_ABSOLUTE: 2.228 G_L1_RELATIVE: 10.955 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 38, iters: 172176, time: 0.549, data: 0.000) G_L1: 14.113 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 11.484 G_Regularizer: 0.000 validation_error: 21.258 +(epoch: 38, iters: 174176, time: 0.532, data: 0.000) G_L1: 13.811 G_L1_ABSOLUTE: 2.924 G_L1_RELATIVE: 10.887 G_Regularizer: 0.000 validation_error: 20.970 +(epoch: 38, iters: 176176, time: 0.540, data: 0.000) G_L1: 14.389 G_L1_ABSOLUTE: 2.496 G_L1_RELATIVE: 11.893 G_Regularizer: 0.000 validation_error: 20.716 +(epoch: 38, iters: 178176, time: 0.547, data: 0.000) G_L1: 14.586 G_L1_ABSOLUTE: 2.687 G_L1_RELATIVE: 11.898 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 38, iters: 180176, time: 0.542, data: 0.000) G_L1: 13.691 G_L1_ABSOLUTE: 2.300 G_L1_RELATIVE: 11.391 G_Regularizer: 0.000 validation_error: 20.580 +(epoch: 38, iters: 182176, time: 0.543, data: 0.000) G_L1: 14.191 G_L1_ABSOLUTE: 2.073 G_L1_RELATIVE: 12.119 G_Regularizer: 0.000 validation_error: 21.025 +(epoch: 38, iters: 184176, time: 0.532, data: 0.000) G_L1: 13.878 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 11.277 G_Regularizer: 0.000 validation_error: 20.737 +(epoch: 38, iters: 186176, time: 0.539, data: 0.000) G_L1: 12.997 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 10.739 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 38, iters: 188176, time: 0.538, data: 0.000) G_L1: 14.409 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 12.125 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 38, iters: 190176, time: 0.544, data: 0.000) G_L1: 14.019 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 11.444 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 38, iters: 192176, time: 0.536, data: 0.001) G_L1: 13.911 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 11.653 G_Regularizer: 0.000 validation_error: 21.366 +(epoch: 38, iters: 194176, time: 0.542, data: 0.000) G_L1: 15.196 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 12.288 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 38, iters: 196176, time: 0.537, data: 0.000) G_L1: 20.692 G_L1_ABSOLUTE: 2.904 G_L1_RELATIVE: 17.788 G_Regularizer: 0.000 validation_error: 20.936 +(epoch: 38, iters: 198176, time: 0.547, data: 0.000) G_L1: 12.780 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 10.256 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 38, iters: 200176, time: 0.537, data: 0.000) G_L1: 13.877 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 11.364 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 38, iters: 202176, time: 0.541, data: 0.000) G_L1: 14.712 G_L1_ABSOLUTE: 2.688 G_L1_RELATIVE: 12.024 G_Regularizer: 0.000 validation_error: 21.329 +(epoch: 38, iters: 204176, time: 0.540, data: 0.000) G_L1: 14.898 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 12.456 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 38, iters: 206176, time: 0.543, data: 0.000) G_L1: 12.426 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 9.924 G_Regularizer: 0.000 validation_error: 20.964 +(epoch: 38, iters: 208176, time: 0.539, data: 0.000) G_L1: 13.448 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 10.946 G_Regularizer: 0.000 validation_error: 20.979 +(epoch: 38, iters: 210176, time: 0.530, data: 0.000) G_L1: 13.728 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 11.361 G_Regularizer: 0.000 validation_error: 20.651 +(epoch: 38, iters: 212176, time: 0.543, data: 0.000) G_L1: 13.261 G_L1_ABSOLUTE: 2.193 G_L1_RELATIVE: 11.068 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 38, iters: 214176, time: 0.538, data: 0.001) G_L1: 16.541 G_L1_ABSOLUTE: 2.826 G_L1_RELATIVE: 13.715 G_Regularizer: 0.000 validation_error: 21.139 +(epoch: 38, iters: 216176, time: 0.539, data: 0.000) G_L1: 16.186 G_L1_ABSOLUTE: 2.818 G_L1_RELATIVE: 13.367 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 38, iters: 218176, time: 0.538, data: 0.000) G_L1: 14.750 G_L1_ABSOLUTE: 2.226 G_L1_RELATIVE: 12.523 G_Regularizer: 0.000 validation_error: 20.657 +(epoch: 38, iters: 220176, time: 0.539, data: 0.000) G_L1: 14.231 G_L1_ABSOLUTE: 2.224 G_L1_RELATIVE: 12.008 G_Regularizer: 0.000 validation_error: 20.469 +(epoch: 38, iters: 222176, time: 0.539, data: 0.000) G_L1: 13.746 G_L1_ABSOLUTE: 3.117 G_L1_RELATIVE: 10.629 G_Regularizer: 0.000 validation_error: 21.262 +(epoch: 38, iters: 224176, time: 0.542, data: 0.000) G_L1: 15.530 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 13.057 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 38, iters: 226176, time: 0.535, data: 0.000) G_L1: 15.602 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 13.155 G_Regularizer: 0.000 validation_error: 20.651 +(epoch: 38, iters: 228176, time: 0.541, data: 0.000) G_L1: 20.649 G_L1_ABSOLUTE: 2.326 G_L1_RELATIVE: 18.323 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 38, iters: 230176, time: 0.542, data: 0.000) G_L1: 15.236 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 12.649 G_Regularizer: 0.000 validation_error: 20.386 +(epoch: 38, iters: 232176, time: 0.539, data: 0.000) G_L1: 15.066 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 12.592 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 38, iters: 234176, time: 0.539, data: 0.001) G_L1: 14.797 G_L1_ABSOLUTE: 2.434 G_L1_RELATIVE: 12.363 G_Regularizer: 0.000 validation_error: 21.187 +(epoch: 38, iters: 236176, time: 0.545, data: 0.000) G_L1: 12.323 G_L1_ABSOLUTE: 2.594 G_L1_RELATIVE: 9.729 G_Regularizer: 0.000 validation_error: 20.940 +(epoch: 38, iters: 238176, time: 0.541, data: 0.000) G_L1: 13.334 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 10.898 G_Regularizer: 0.000 validation_error: 20.373 +(epoch: 38, iters: 240176, time: 0.546, data: 0.000) G_L1: 13.343 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 10.935 G_Regularizer: 0.000 validation_error: 20.674 +(epoch: 38, iters: 242176, time: 0.541, data: 0.000) G_L1: 14.764 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 12.196 G_Regularizer: 0.000 validation_error: 21.050 +(epoch: 38, iters: 244176, time: 0.547, data: 0.000) G_L1: 15.758 G_L1_ABSOLUTE: 2.386 G_L1_RELATIVE: 13.371 G_Regularizer: 0.000 validation_error: 20.384 +(epoch: 38, iters: 246176, time: 0.546, data: 0.000) G_L1: 13.677 G_L1_ABSOLUTE: 2.165 G_L1_RELATIVE: 11.512 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 38, iters: 248176, time: 0.541, data: 0.000) G_L1: 12.901 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 10.603 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 38, iters: 250176, time: 0.547, data: 0.000) G_L1: 16.396 G_L1_ABSOLUTE: 2.352 G_L1_RELATIVE: 14.044 G_Regularizer: 0.000 validation_error: 20.816 +(epoch: 38, iters: 252176, time: 0.542, data: 0.000) G_L1: 14.126 G_L1_ABSOLUTE: 2.738 G_L1_RELATIVE: 11.388 G_Regularizer: 0.000 validation_error: 20.640 +(epoch: 38, iters: 254176, time: 0.539, data: 0.000) G_L1: 15.742 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 13.220 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 38, iters: 256176, time: 0.539, data: 0.000) G_L1: 14.959 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 12.385 G_Regularizer: 0.000 validation_error: 20.692 +(epoch: 38, iters: 258176, time: 0.542, data: 0.000) G_L1: 14.760 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 12.027 G_Regularizer: 0.000 validation_error: 20.957 +(epoch: 38, iters: 260176, time: 0.543, data: 0.000) G_L1: 14.052 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 11.468 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 38, iters: 262176, time: 0.541, data: 0.000) G_L1: 15.193 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 12.382 G_Regularizer: 0.000 validation_error: 20.782 +(epoch: 38, iters: 264176, time: 0.541, data: 0.000) G_L1: 13.122 G_L1_ABSOLUTE: 2.188 G_L1_RELATIVE: 10.934 G_Regularizer: 0.000 validation_error: 20.849 +(epoch: 38, iters: 266176, time: 0.538, data: 0.000) G_L1: 13.409 G_L1_ABSOLUTE: 2.139 G_L1_RELATIVE: 11.270 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 38, iters: 268176, time: 0.546, data: 0.001) G_L1: 13.196 G_L1_ABSOLUTE: 2.338 G_L1_RELATIVE: 10.858 G_Regularizer: 0.000 validation_error: 20.970 +(epoch: 38, iters: 270176, time: 0.534, data: 0.000) G_L1: 15.325 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 12.800 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 38, iters: 272176, time: 0.543, data: 0.000) G_L1: 15.162 G_L1_ABSOLUTE: 2.823 G_L1_RELATIVE: 12.339 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 38, iters: 274176, time: 0.546, data: 0.000) G_L1: 11.841 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 9.581 G_Regularizer: 0.000 validation_error: 21.324 +(epoch: 38, iters: 276176, time: 0.549, data: 0.000) G_L1: 13.307 G_L1_ABSOLUTE: 2.098 G_L1_RELATIVE: 11.208 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 38, iters: 278176, time: 0.524, data: 0.000) G_L1: 13.719 G_L1_ABSOLUTE: 2.780 G_L1_RELATIVE: 10.939 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 38, iters: 280176, time: 0.534, data: 0.000) G_L1: 14.010 G_L1_ABSOLUTE: 3.018 G_L1_RELATIVE: 10.992 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 38, iters: 282176, time: 0.538, data: 0.000) G_L1: 15.020 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 12.606 G_Regularizer: 0.000 validation_error: 20.707 +(epoch: 38, iters: 284176, time: 0.541, data: 0.000) G_L1: 13.529 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 11.026 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 38, iters: 286176, time: 0.542, data: 0.000) G_L1: 15.800 G_L1_ABSOLUTE: 2.616 G_L1_RELATIVE: 13.184 G_Regularizer: 0.000 validation_error: 20.520 +(epoch: 38, iters: 288176, time: 0.544, data: 0.000) G_L1: 12.757 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 10.304 G_Regularizer: 0.000 validation_error: 21.168 +(epoch: 38, iters: 290176, time: 0.551, data: 0.000) G_L1: 13.296 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 10.925 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 38, iters: 292176, time: 0.536, data: 0.000) G_L1: 14.027 G_L1_ABSOLUTE: 2.939 G_L1_RELATIVE: 11.089 G_Regularizer: 0.000 validation_error: 20.893 +(epoch: 38, iters: 294176, time: 0.534, data: 0.000) G_L1: 11.779 G_L1_ABSOLUTE: 1.975 G_L1_RELATIVE: 9.804 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 38, iters: 296176, time: 0.541, data: 0.000) G_L1: 15.051 G_L1_ABSOLUTE: 2.427 G_L1_RELATIVE: 12.624 G_Regularizer: 0.000 validation_error: 20.644 +(epoch: 38, iters: 298176, time: 0.544, data: 0.000) G_L1: 12.588 G_L1_ABSOLUTE: 2.134 G_L1_RELATIVE: 10.455 G_Regularizer: 0.000 validation_error: 20.460 +(epoch: 38, iters: 300176, time: 0.545, data: 0.000) G_L1: 14.091 G_L1_ABSOLUTE: 2.384 G_L1_RELATIVE: 11.708 G_Regularizer: 0.000 validation_error: 21.418 +(epoch: 38, iters: 302176, time: 0.545, data: 0.000) G_L1: 14.047 G_L1_ABSOLUTE: 2.150 G_L1_RELATIVE: 11.897 G_Regularizer: 0.000 validation_error: 20.683 +(epoch: 39, iters: 1424, time: 0.532, data: 0.000) G_L1: 14.844 G_L1_ABSOLUTE: 2.183 G_L1_RELATIVE: 12.661 G_Regularizer: 0.000 validation_error: 20.911 +(epoch: 39, iters: 3424, time: 0.548, data: 0.000) G_L1: 12.174 G_L1_ABSOLUTE: 2.487 G_L1_RELATIVE: 9.687 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 39, iters: 5424, time: 0.542, data: 0.000) G_L1: 11.743 G_L1_ABSOLUTE: 1.980 G_L1_RELATIVE: 9.763 G_Regularizer: 0.000 validation_error: 20.802 +(epoch: 39, iters: 7424, time: 0.548, data: 0.000) G_L1: 14.694 G_L1_ABSOLUTE: 2.125 G_L1_RELATIVE: 12.569 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 39, iters: 9424, time: 0.538, data: 0.001) G_L1: 14.174 G_L1_ABSOLUTE: 2.285 G_L1_RELATIVE: 11.889 G_Regularizer: 0.000 validation_error: 21.087 +(epoch: 39, iters: 11424, time: 0.543, data: 0.001) G_L1: 14.830 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 12.352 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 39, iters: 13424, time: 0.533, data: 0.000) G_L1: 13.771 G_L1_ABSOLUTE: 2.865 G_L1_RELATIVE: 10.906 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 39, iters: 15424, time: 0.546, data: 0.000) G_L1: 14.888 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 12.499 G_Regularizer: 0.000 validation_error: 20.576 +(epoch: 39, iters: 17424, time: 0.547, data: 0.000) G_L1: 13.508 G_L1_ABSOLUTE: 2.982 G_L1_RELATIVE: 10.526 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 39, iters: 19424, time: 0.537, data: 0.000) G_L1: 13.126 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 10.613 G_Regularizer: 0.000 validation_error: 20.712 +(epoch: 39, iters: 21424, time: 0.545, data: 0.000) G_L1: 14.570 G_L1_ABSOLUTE: 3.111 G_L1_RELATIVE: 11.459 G_Regularizer: 0.000 validation_error: 20.592 +(epoch: 39, iters: 23424, time: 0.540, data: 0.000) G_L1: 13.792 G_L1_ABSOLUTE: 2.380 G_L1_RELATIVE: 11.412 G_Regularizer: 0.000 validation_error: 21.383 +(epoch: 39, iters: 25424, time: 0.546, data: 0.000) G_L1: 14.204 G_L1_ABSOLUTE: 2.680 G_L1_RELATIVE: 11.524 G_Regularizer: 0.000 validation_error: 20.432 +(epoch: 39, iters: 27424, time: 0.541, data: 0.000) G_L1: 13.481 G_L1_ABSOLUTE: 2.737 G_L1_RELATIVE: 10.744 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 39, iters: 29424, time: 0.541, data: 0.000) G_L1: 14.124 G_L1_ABSOLUTE: 2.770 G_L1_RELATIVE: 11.354 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 39, iters: 31424, time: 0.541, data: 0.000) G_L1: 14.669 G_L1_ABSOLUTE: 2.580 G_L1_RELATIVE: 12.089 G_Regularizer: 0.000 validation_error: 20.729 +(epoch: 39, iters: 33424, time: 0.542, data: 0.000) G_L1: 15.470 G_L1_ABSOLUTE: 2.955 G_L1_RELATIVE: 12.515 G_Regularizer: 0.000 validation_error: 21.068 +(epoch: 39, iters: 35424, time: 0.541, data: 0.000) G_L1: 13.683 G_L1_ABSOLUTE: 2.937 G_L1_RELATIVE: 10.746 G_Regularizer: 0.000 validation_error: 20.987 +(epoch: 39, iters: 37424, time: 0.544, data: 0.000) G_L1: 13.217 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 10.815 G_Regularizer: 0.000 validation_error: 20.899 +(epoch: 39, iters: 39424, time: 0.538, data: 0.000) G_L1: 24.063 G_L1_ABSOLUTE: 2.740 G_L1_RELATIVE: 21.323 G_Regularizer: 0.000 validation_error: 20.666 +(epoch: 39, iters: 41424, time: 0.541, data: 0.000) G_L1: 16.581 G_L1_ABSOLUTE: 2.452 G_L1_RELATIVE: 14.129 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 39, iters: 43424, time: 0.542, data: 0.001) G_L1: 14.113 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 11.666 G_Regularizer: 0.000 validation_error: 21.416 +(epoch: 39, iters: 45424, time: 0.545, data: 0.000) G_L1: 11.419 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 9.046 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 39, iters: 47424, time: 0.538, data: 0.001) G_L1: 12.732 G_L1_ABSOLUTE: 2.255 G_L1_RELATIVE: 10.476 G_Regularizer: 0.000 validation_error: 21.097 +(epoch: 39, iters: 49424, time: 0.541, data: 0.000) G_L1: 15.876 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 13.302 G_Regularizer: 0.000 validation_error: 21.171 +(epoch: 39, iters: 51424, time: 0.541, data: 0.000) G_L1: 14.098 G_L1_ABSOLUTE: 2.911 G_L1_RELATIVE: 11.186 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 39, iters: 53424, time: 0.544, data: 0.000) G_L1: 13.286 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 10.746 G_Regularizer: 0.000 validation_error: 20.466 +(epoch: 39, iters: 55424, time: 0.542, data: 0.000) G_L1: 12.995 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 10.336 G_Regularizer: 0.000 validation_error: 21.227 +(epoch: 39, iters: 57424, time: 0.542, data: 0.001) G_L1: 17.724 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 15.354 G_Regularizer: 0.000 validation_error: 20.386 +(epoch: 39, iters: 59424, time: 0.540, data: 0.000) G_L1: 15.137 G_L1_ABSOLUTE: 2.376 G_L1_RELATIVE: 12.761 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 39, iters: 61424, time: 0.537, data: 0.000) G_L1: 14.749 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 12.311 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 39, iters: 63424, time: 0.543, data: 0.001) G_L1: 13.935 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 11.676 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 39, iters: 65424, time: 0.543, data: 0.000) G_L1: 12.096 G_L1_ABSOLUTE: 2.153 G_L1_RELATIVE: 9.943 G_Regularizer: 0.000 validation_error: 20.509 +(epoch: 39, iters: 67424, time: 0.545, data: 0.000) G_L1: 14.398 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 12.065 G_Regularizer: 0.000 validation_error: 20.826 +(epoch: 39, iters: 69424, time: 0.537, data: 0.001) G_L1: 14.968 G_L1_ABSOLUTE: 3.076 G_L1_RELATIVE: 11.892 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 39, iters: 71424, time: 0.536, data: 0.001) G_L1: 14.318 G_L1_ABSOLUTE: 2.327 G_L1_RELATIVE: 11.991 G_Regularizer: 0.000 validation_error: 21.104 +(epoch: 39, iters: 73424, time: 0.542, data: 0.000) G_L1: 13.797 G_L1_ABSOLUTE: 2.821 G_L1_RELATIVE: 10.976 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 39, iters: 75424, time: 0.544, data: 0.001) G_L1: 15.121 G_L1_ABSOLUTE: 3.140 G_L1_RELATIVE: 11.981 G_Regularizer: 0.000 validation_error: 21.007 +(epoch: 39, iters: 77424, time: 0.540, data: 0.000) G_L1: 13.510 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 10.991 G_Regularizer: 0.000 validation_error: 20.778 +(epoch: 39, iters: 79424, time: 0.544, data: 0.000) G_L1: 14.367 G_L1_ABSOLUTE: 2.323 G_L1_RELATIVE: 12.044 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 39, iters: 81424, time: 0.540, data: 0.000) G_L1: 15.942 G_L1_ABSOLUTE: 2.936 G_L1_RELATIVE: 13.006 G_Regularizer: 0.000 validation_error: 20.634 +(epoch: 39, iters: 83424, time: 0.542, data: 0.000) G_L1: 14.078 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 11.624 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 39, iters: 85424, time: 0.542, data: 0.000) G_L1: 14.920 G_L1_ABSOLUTE: 2.122 G_L1_RELATIVE: 12.797 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 39, iters: 87424, time: 0.540, data: 0.000) G_L1: 14.167 G_L1_ABSOLUTE: 2.752 G_L1_RELATIVE: 11.415 G_Regularizer: 0.000 validation_error: 20.539 +(epoch: 39, iters: 89424, time: 0.545, data: 0.000) G_L1: 11.949 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 9.446 G_Regularizer: 0.000 validation_error: 20.946 +(epoch: 39, iters: 91424, time: 0.537, data: 0.000) G_L1: 11.065 G_L1_ABSOLUTE: 2.293 G_L1_RELATIVE: 8.772 G_Regularizer: 0.000 validation_error: 21.169 +(epoch: 39, iters: 93424, time: 0.545, data: 0.000) G_L1: 11.189 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 8.620 G_Regularizer: 0.000 validation_error: 21.112 +(epoch: 39, iters: 95424, time: 0.549, data: 0.001) G_L1: 15.107 G_L1_ABSOLUTE: 2.877 G_L1_RELATIVE: 12.230 G_Regularizer: 0.000 validation_error: 20.553 +(epoch: 39, iters: 97424, time: 0.542, data: 0.000) G_L1: 15.318 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 12.800 G_Regularizer: 0.000 validation_error: 20.927 +(epoch: 39, iters: 99424, time: 0.543, data: 0.000) G_L1: 13.345 G_L1_ABSOLUTE: 2.138 G_L1_RELATIVE: 11.207 G_Regularizer: 0.000 validation_error: 21.212 +(epoch: 39, iters: 101424, time: 0.543, data: 0.001) G_L1: 13.989 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 11.247 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 39, iters: 103424, time: 0.540, data: 0.000) G_L1: 14.894 G_L1_ABSOLUTE: 3.144 G_L1_RELATIVE: 11.750 G_Regularizer: 0.000 validation_error: 20.530 +(epoch: 39, iters: 105424, time: 0.539, data: 0.000) G_L1: 13.646 G_L1_ABSOLUTE: 2.516 G_L1_RELATIVE: 11.131 G_Regularizer: 0.000 validation_error: 20.566 +(epoch: 39, iters: 107424, time: 0.542, data: 0.000) G_L1: 12.621 G_L1_ABSOLUTE: 2.228 G_L1_RELATIVE: 10.393 G_Regularizer: 0.000 validation_error: 21.103 +(epoch: 39, iters: 109424, time: 0.538, data: 0.000) G_L1: 14.827 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 12.107 G_Regularizer: 0.000 validation_error: 20.616 +(epoch: 39, iters: 111424, time: 0.542, data: 0.000) G_L1: 14.047 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 11.237 G_Regularizer: 0.000 validation_error: 21.190 +(epoch: 39, iters: 113424, time: 0.524, data: 0.001) G_L1: 14.037 G_L1_ABSOLUTE: 2.321 G_L1_RELATIVE: 11.716 G_Regularizer: 0.000 validation_error: 20.892 +(epoch: 39, iters: 115424, time: 0.540, data: 0.000) G_L1: 15.471 G_L1_ABSOLUTE: 2.827 G_L1_RELATIVE: 12.644 G_Regularizer: 0.000 validation_error: 21.159 +(epoch: 39, iters: 117424, time: 0.544, data: 0.000) G_L1: 15.030 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 12.525 G_Regularizer: 0.000 validation_error: 20.924 +(epoch: 39, iters: 119424, time: 0.542, data: 0.000) G_L1: 15.885 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 13.516 G_Regularizer: 0.000 validation_error: 21.244 +(epoch: 39, iters: 121424, time: 0.535, data: 0.000) G_L1: 15.028 G_L1_ABSOLUTE: 2.390 G_L1_RELATIVE: 12.639 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 39, iters: 123424, time: 0.541, data: 0.000) G_L1: 14.175 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 11.754 G_Regularizer: 0.000 validation_error: 21.165 +(epoch: 39, iters: 125424, time: 0.541, data: 0.000) G_L1: 12.162 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 9.870 G_Regularizer: 0.000 validation_error: 21.172 +(epoch: 39, iters: 127424, time: 0.543, data: 0.000) G_L1: 16.847 G_L1_ABSOLUTE: 3.025 G_L1_RELATIVE: 13.822 G_Regularizer: 0.000 validation_error: 20.511 +(epoch: 39, iters: 129424, time: 0.542, data: 0.000) G_L1: 12.637 G_L1_ABSOLUTE: 2.076 G_L1_RELATIVE: 10.561 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 39, iters: 131424, time: 0.536, data: 0.000) G_L1: 13.061 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 10.543 G_Regularizer: 0.000 validation_error: 21.331 +(epoch: 39, iters: 133424, time: 0.543, data: 0.000) G_L1: 14.440 G_L1_ABSOLUTE: 2.567 G_L1_RELATIVE: 11.873 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 39, iters: 135424, time: 0.541, data: 0.000) G_L1: 15.554 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 13.186 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 39, iters: 137424, time: 0.543, data: 0.000) G_L1: 13.646 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 11.062 G_Regularizer: 0.000 validation_error: 20.576 +(epoch: 39, iters: 139424, time: 0.535, data: 0.000) G_L1: 12.292 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 9.890 G_Regularizer: 0.000 validation_error: 21.135 +(epoch: 39, iters: 141424, time: 0.538, data: 0.000) G_L1: 13.164 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 10.505 G_Regularizer: 0.000 validation_error: 20.527 +(epoch: 39, iters: 143424, time: 0.542, data: 0.000) G_L1: 14.892 G_L1_ABSOLUTE: 2.322 G_L1_RELATIVE: 12.571 G_Regularizer: 0.000 validation_error: 21.454 +(epoch: 39, iters: 145424, time: 0.541, data: 0.000) G_L1: 12.512 G_L1_ABSOLUTE: 2.360 G_L1_RELATIVE: 10.151 G_Regularizer: 0.000 validation_error: 20.518 +(epoch: 39, iters: 147424, time: 0.537, data: 0.000) G_L1: 15.870 G_L1_ABSOLUTE: 2.426 G_L1_RELATIVE: 13.444 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 39, iters: 149424, time: 0.540, data: 0.000) G_L1: 15.958 G_L1_ABSOLUTE: 2.425 G_L1_RELATIVE: 13.533 G_Regularizer: 0.000 validation_error: 20.188 +(epoch: 39, iters: 151424, time: 0.544, data: 0.000) G_L1: 16.256 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 13.614 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 39, iters: 153424, time: 0.542, data: 0.000) G_L1: 14.178 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 11.442 G_Regularizer: 0.000 validation_error: 21.381 +(epoch: 39, iters: 155424, time: 0.539, data: 0.000) G_L1: 12.463 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 9.916 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 39, iters: 157424, time: 0.530, data: 0.000) G_L1: 13.798 G_L1_ABSOLUTE: 2.397 G_L1_RELATIVE: 11.401 G_Regularizer: 0.000 validation_error: 20.627 +(epoch: 39, iters: 159424, time: 0.538, data: 0.000) G_L1: 14.811 G_L1_ABSOLUTE: 2.334 G_L1_RELATIVE: 12.477 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 39, iters: 161424, time: 0.539, data: 0.000) G_L1: 14.122 G_L1_ABSOLUTE: 2.425 G_L1_RELATIVE: 11.697 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 39, iters: 163424, time: 0.541, data: 0.000) G_L1: 13.110 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 10.486 G_Regularizer: 0.000 validation_error: 20.894 +(epoch: 39, iters: 165424, time: 0.537, data: 0.000) G_L1: 14.945 G_L1_ABSOLUTE: 2.279 G_L1_RELATIVE: 12.666 G_Regularizer: 0.000 validation_error: 21.020 +(epoch: 39, iters: 167424, time: 0.544, data: 0.000) G_L1: 12.862 G_L1_ABSOLUTE: 2.391 G_L1_RELATIVE: 10.472 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 39, iters: 169424, time: 0.543, data: 0.000) G_L1: 11.935 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 9.300 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 39, iters: 171424, time: 0.544, data: 0.000) G_L1: 14.485 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 12.008 G_Regularizer: 0.000 validation_error: 20.601 +(epoch: 39, iters: 173424, time: 0.539, data: 0.001) G_L1: 14.638 G_L1_ABSOLUTE: 2.559 G_L1_RELATIVE: 12.079 G_Regularizer: 0.000 validation_error: 20.932 +(epoch: 39, iters: 175424, time: 0.537, data: 0.000) G_L1: 15.872 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 13.311 G_Regularizer: 0.000 validation_error: 20.554 +(epoch: 39, iters: 177424, time: 0.549, data: 0.000) G_L1: 11.961 G_L1_ABSOLUTE: 2.120 G_L1_RELATIVE: 9.841 G_Regularizer: 0.000 validation_error: 20.623 +(epoch: 39, iters: 179424, time: 0.538, data: 0.000) G_L1: 15.014 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 12.453 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 39, iters: 181424, time: 0.535, data: 0.000) G_L1: 15.708 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 12.935 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 39, iters: 183424, time: 0.537, data: 0.001) G_L1: 12.920 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 10.458 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 39, iters: 185424, time: 0.539, data: 0.000) G_L1: 13.314 G_L1_ABSOLUTE: 2.636 G_L1_RELATIVE: 10.678 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 39, iters: 187424, time: 0.540, data: 0.001) G_L1: 12.708 G_L1_ABSOLUTE: 2.051 G_L1_RELATIVE: 10.657 G_Regularizer: 0.000 validation_error: 20.636 +(epoch: 39, iters: 189424, time: 0.546, data: 0.000) G_L1: 16.990 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 14.304 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 39, iters: 191424, time: 0.536, data: 0.000) G_L1: 13.874 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 11.093 G_Regularizer: 0.000 validation_error: 20.304 +(epoch: 39, iters: 193424, time: 0.539, data: 0.000) G_L1: 14.722 G_L1_ABSOLUTE: 2.543 G_L1_RELATIVE: 12.179 G_Regularizer: 0.000 validation_error: 20.487 +(epoch: 39, iters: 195424, time: 0.543, data: 0.000) G_L1: 15.065 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 12.515 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 39, iters: 197424, time: 0.547, data: 0.000) G_L1: 11.881 G_L1_ABSOLUTE: 2.171 G_L1_RELATIVE: 9.711 G_Regularizer: 0.000 validation_error: 20.955 +(epoch: 39, iters: 199424, time: 0.539, data: 0.000) G_L1: 13.134 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 10.359 G_Regularizer: 0.000 validation_error: 21.057 +(epoch: 39, iters: 201424, time: 0.544, data: 0.000) G_L1: 15.879 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 13.473 G_Regularizer: 0.000 validation_error: 20.651 +(epoch: 39, iters: 203424, time: 0.545, data: 0.000) G_L1: 14.215 G_L1_ABSOLUTE: 2.685 G_L1_RELATIVE: 11.530 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 39, iters: 205424, time: 0.539, data: 0.000) G_L1: 15.453 G_L1_ABSOLUTE: 2.174 G_L1_RELATIVE: 13.279 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 39, iters: 207424, time: 0.538, data: 0.000) G_L1: 15.155 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 12.705 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 39, iters: 209424, time: 0.538, data: 0.000) G_L1: 14.244 G_L1_ABSOLUTE: 2.443 G_L1_RELATIVE: 11.801 G_Regularizer: 0.000 validation_error: 20.584 +(epoch: 39, iters: 211424, time: 0.547, data: 0.001) G_L1: 13.553 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 11.073 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 39, iters: 213424, time: 0.546, data: 0.000) G_L1: 12.236 G_L1_ABSOLUTE: 2.089 G_L1_RELATIVE: 10.148 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 39, iters: 215424, time: 0.544, data: 0.000) G_L1: 14.512 G_L1_ABSOLUTE: 2.095 G_L1_RELATIVE: 12.417 G_Regularizer: 0.000 validation_error: 20.438 +(epoch: 39, iters: 217424, time: 0.537, data: 0.000) G_L1: 12.221 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 9.649 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 39, iters: 219424, time: 0.550, data: 0.000) G_L1: 14.180 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 11.275 G_Regularizer: 0.000 validation_error: 20.983 +(epoch: 39, iters: 221424, time: 0.558, data: 0.000) G_L1: 14.195 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 11.453 G_Regularizer: 0.000 validation_error: 20.826 +(epoch: 39, iters: 223424, time: 0.556, data: 0.001) G_L1: 13.123 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 10.745 G_Regularizer: 0.000 validation_error: 20.433 +(epoch: 39, iters: 225424, time: 0.538, data: 0.000) G_L1: 15.493 G_L1_ABSOLUTE: 2.954 G_L1_RELATIVE: 12.539 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 39, iters: 227424, time: 0.554, data: 0.000) G_L1: 14.385 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 12.004 G_Regularizer: 0.000 validation_error: 20.602 +(epoch: 39, iters: 229424, time: 0.543, data: 0.000) G_L1: 14.897 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 12.490 G_Regularizer: 0.000 validation_error: 21.317 +(epoch: 39, iters: 231424, time: 0.556, data: 0.000) G_L1: 13.115 G_L1_ABSOLUTE: 2.210 G_L1_RELATIVE: 10.905 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 39, iters: 233424, time: 0.548, data: 0.000) G_L1: 13.395 G_L1_ABSOLUTE: 2.605 G_L1_RELATIVE: 10.790 G_Regularizer: 0.000 validation_error: 20.487 +(epoch: 39, iters: 235424, time: 0.551, data: 0.001) G_L1: 13.899 G_L1_ABSOLUTE: 2.452 G_L1_RELATIVE: 11.446 G_Regularizer: 0.000 validation_error: 20.531 +(epoch: 39, iters: 237424, time: 0.551, data: 0.000) G_L1: 13.591 G_L1_ABSOLUTE: 2.766 G_L1_RELATIVE: 10.825 G_Regularizer: 0.000 validation_error: 20.647 +(epoch: 39, iters: 239424, time: 0.551, data: 0.000) G_L1: 14.379 G_L1_ABSOLUTE: 2.382 G_L1_RELATIVE: 11.997 G_Regularizer: 0.000 validation_error: 20.565 +(epoch: 39, iters: 241424, time: 0.547, data: 0.000) G_L1: 15.929 G_L1_ABSOLUTE: 2.936 G_L1_RELATIVE: 12.993 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 39, iters: 243424, time: 0.556, data: 0.000) G_L1: 12.747 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 10.005 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 39, iters: 245424, time: 0.555, data: 0.000) G_L1: 12.735 G_L1_ABSOLUTE: 2.261 G_L1_RELATIVE: 10.474 G_Regularizer: 0.000 validation_error: 21.088 +(epoch: 39, iters: 247424, time: 0.554, data: 0.000) G_L1: 13.421 G_L1_ABSOLUTE: 2.934 G_L1_RELATIVE: 10.488 G_Regularizer: 0.000 validation_error: 21.091 +(epoch: 39, iters: 249424, time: 0.555, data: 0.000) G_L1: 14.009 G_L1_ABSOLUTE: 2.316 G_L1_RELATIVE: 11.693 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 39, iters: 251424, time: 0.563, data: 0.000) G_L1: 17.074 G_L1_ABSOLUTE: 2.921 G_L1_RELATIVE: 14.154 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 39, iters: 253424, time: 0.551, data: 0.000) G_L1: 15.411 G_L1_ABSOLUTE: 2.546 G_L1_RELATIVE: 12.864 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 39, iters: 255424, time: 0.555, data: 0.000) G_L1: 13.196 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 10.796 G_Regularizer: 0.000 validation_error: 20.738 +(epoch: 39, iters: 257424, time: 0.554, data: 0.000) G_L1: 14.129 G_L1_ABSOLUTE: 2.794 G_L1_RELATIVE: 11.335 G_Regularizer: 0.000 validation_error: 21.329 +(epoch: 39, iters: 259424, time: 0.548, data: 0.000) G_L1: 14.082 G_L1_ABSOLUTE: 2.616 G_L1_RELATIVE: 11.466 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 39, iters: 261424, time: 0.548, data: 0.000) G_L1: 15.934 G_L1_ABSOLUTE: 2.841 G_L1_RELATIVE: 13.093 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 39, iters: 263424, time: 0.550, data: 0.000) G_L1: 15.937 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 12.999 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 39, iters: 265424, time: 0.555, data: 0.000) G_L1: 15.392 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 12.723 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 39, iters: 267424, time: 0.550, data: 0.000) G_L1: 12.420 G_L1_ABSOLUTE: 2.322 G_L1_RELATIVE: 10.098 G_Regularizer: 0.000 validation_error: 20.970 +(epoch: 39, iters: 269424, time: 0.553, data: 0.000) G_L1: 15.722 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 13.194 G_Regularizer: 0.000 validation_error: 20.483 +(epoch: 39, iters: 271424, time: 0.556, data: 0.000) G_L1: 13.586 G_L1_ABSOLUTE: 2.402 G_L1_RELATIVE: 11.184 G_Regularizer: 0.000 validation_error: 20.636 +(epoch: 39, iters: 273424, time: 0.541, data: 0.000) G_L1: 14.064 G_L1_ABSOLUTE: 2.439 G_L1_RELATIVE: 11.625 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 39, iters: 275424, time: 0.556, data: 0.000) G_L1: 15.616 G_L1_ABSOLUTE: 2.130 G_L1_RELATIVE: 13.486 G_Regularizer: 0.000 validation_error: 20.670 +(epoch: 39, iters: 277424, time: 0.549, data: 0.000) G_L1: 12.700 G_L1_ABSOLUTE: 2.359 G_L1_RELATIVE: 10.342 G_Regularizer: 0.000 validation_error: 20.523 +(epoch: 39, iters: 279424, time: 0.555, data: 0.000) G_L1: 13.904 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 11.303 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 39, iters: 281424, time: 0.558, data: 0.000) G_L1: 12.649 G_L1_ABSOLUTE: 2.434 G_L1_RELATIVE: 10.214 G_Regularizer: 0.000 validation_error: 20.998 +(epoch: 39, iters: 283424, time: 0.547, data: 0.000) G_L1: 13.580 G_L1_ABSOLUTE: 2.217 G_L1_RELATIVE: 11.362 G_Regularizer: 0.000 validation_error: 20.658 +(epoch: 39, iters: 285424, time: 0.549, data: 0.000) G_L1: 13.243 G_L1_ABSOLUTE: 2.195 G_L1_RELATIVE: 11.048 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 39, iters: 287424, time: 0.561, data: 0.001) G_L1: 17.081 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 14.599 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 39, iters: 289424, time: 0.652, data: 0.000) G_L1: 14.443 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 11.727 G_Regularizer: 0.000 validation_error: 20.579 +(epoch: 39, iters: 291424, time: 0.647, data: 0.000) G_L1: 12.757 G_L1_ABSOLUTE: 2.921 G_L1_RELATIVE: 9.836 G_Regularizer: 0.000 validation_error: 21.050 +(epoch: 39, iters: 293424, time: 0.592, data: 0.000) G_L1: 14.040 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 11.286 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 39, iters: 295424, time: 0.614, data: 0.000) G_L1: 14.847 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 12.332 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 39, iters: 297424, time: 0.563, data: 0.000) G_L1: 12.949 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 10.413 G_Regularizer: 0.000 validation_error: 20.735 +(epoch: 39, iters: 299424, time: 0.619, data: 0.000) G_L1: 13.104 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 10.214 G_Regularizer: 0.000 validation_error: 20.746 +(epoch: 39, iters: 301424, time: 0.606, data: 0.000) G_L1: 11.411 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 8.928 G_Regularizer: 0.000 validation_error: 20.500 +(epoch: 40, iters: 672, time: 0.619, data: 0.000) G_L1: 13.078 G_L1_ABSOLUTE: 2.410 G_L1_RELATIVE: 10.667 G_Regularizer: 0.000 validation_error: 20.737 +(epoch: 40, iters: 2672, time: 0.603, data: 0.000) G_L1: 16.802 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 14.047 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 40, iters: 4672, time: 0.614, data: 0.000) G_L1: 13.812 G_L1_ABSOLUTE: 2.429 G_L1_RELATIVE: 11.383 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 40, iters: 6672, time: 0.612, data: 0.000) G_L1: 14.037 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 11.233 G_Regularizer: 0.000 validation_error: 20.957 +(epoch: 40, iters: 8672, time: 0.611, data: 0.000) G_L1: 12.319 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 9.685 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 40, iters: 10672, time: 0.575, data: 0.000) G_L1: 12.714 G_L1_ABSOLUTE: 2.422 G_L1_RELATIVE: 10.292 G_Regularizer: 0.000 validation_error: 20.798 +(epoch: 40, iters: 12672, time: 0.605, data: 0.000) G_L1: 13.719 G_L1_ABSOLUTE: 2.304 G_L1_RELATIVE: 11.415 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 40, iters: 14672, time: 0.610, data: 0.000) G_L1: 13.007 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 10.444 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 40, iters: 16672, time: 0.610, data: 0.000) G_L1: 13.234 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 10.980 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 40, iters: 18672, time: 0.600, data: 0.000) G_L1: 13.697 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 11.354 G_Regularizer: 0.000 validation_error: 21.176 +(epoch: 40, iters: 20672, time: 0.581, data: 0.000) G_L1: 13.538 G_L1_ABSOLUTE: 2.391 G_L1_RELATIVE: 11.147 G_Regularizer: 0.000 validation_error: 20.493 +(epoch: 40, iters: 22672, time: 0.601, data: 0.000) G_L1: 12.109 G_L1_ABSOLUTE: 2.192 G_L1_RELATIVE: 9.917 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 40, iters: 24672, time: 0.576, data: 0.000) G_L1: 14.281 G_L1_ABSOLUTE: 2.115 G_L1_RELATIVE: 12.166 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 40, iters: 26672, time: 0.614, data: 0.000) G_L1: 13.699 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 11.047 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 40, iters: 28672, time: 0.618, data: 0.000) G_L1: 16.170 G_L1_ABSOLUTE: 3.070 G_L1_RELATIVE: 13.100 G_Regularizer: 0.000 validation_error: 20.391 +(epoch: 40, iters: 30672, time: 0.611, data: 0.000) G_L1: 15.237 G_L1_ABSOLUTE: 2.194 G_L1_RELATIVE: 13.043 G_Regularizer: 0.000 validation_error: 21.147 +(epoch: 40, iters: 32672, time: 0.603, data: 0.000) G_L1: 14.975 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 12.606 G_Regularizer: 0.000 validation_error: 21.032 +(epoch: 40, iters: 34672, time: 0.579, data: 0.000) G_L1: 14.283 G_L1_ABSOLUTE: 2.943 G_L1_RELATIVE: 11.340 G_Regularizer: 0.000 validation_error: 20.688 +(epoch: 40, iters: 36672, time: 0.619, data: 0.000) G_L1: 14.336 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 11.718 G_Regularizer: 0.000 validation_error: 21.384 +(epoch: 40, iters: 38672, time: 0.604, data: 0.000) G_L1: 11.601 G_L1_ABSOLUTE: 2.046 G_L1_RELATIVE: 9.555 G_Regularizer: 0.000 validation_error: 20.885 +(epoch: 40, iters: 40672, time: 0.620, data: 0.000) G_L1: 14.937 G_L1_ABSOLUTE: 2.649 G_L1_RELATIVE: 12.288 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 40, iters: 42672, time: 0.593, data: 0.000) G_L1: 14.351 G_L1_ABSOLUTE: 2.741 G_L1_RELATIVE: 11.610 G_Regularizer: 0.000 validation_error: 21.124 +(epoch: 40, iters: 44672, time: 0.615, data: 0.000) G_L1: 17.192 G_L1_ABSOLUTE: 2.689 G_L1_RELATIVE: 14.503 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 40, iters: 46672, time: 0.611, data: 0.000) G_L1: 14.012 G_L1_ABSOLUTE: 3.186 G_L1_RELATIVE: 10.826 G_Regularizer: 0.000 validation_error: 20.939 +(epoch: 40, iters: 48672, time: 0.597, data: 0.000) G_L1: 15.074 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 12.341 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 40, iters: 50672, time: 0.529, data: 0.000) G_L1: 14.695 G_L1_ABSOLUTE: 2.835 G_L1_RELATIVE: 11.859 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 40, iters: 52672, time: 0.537, data: 0.000) G_L1: 12.661 G_L1_ABSOLUTE: 2.759 G_L1_RELATIVE: 9.902 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 40, iters: 54672, time: 0.545, data: 0.000) G_L1: 12.950 G_L1_ABSOLUTE: 2.466 G_L1_RELATIVE: 10.484 G_Regularizer: 0.000 validation_error: 21.123 +(epoch: 40, iters: 56672, time: 0.539, data: 0.000) G_L1: 15.742 G_L1_ABSOLUTE: 2.654 G_L1_RELATIVE: 13.089 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 40, iters: 58672, time: 0.540, data: 0.001) G_L1: 15.573 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 12.789 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 40, iters: 60672, time: 0.540, data: 0.000) G_L1: 13.603 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 10.968 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 40, iters: 62672, time: 0.531, data: 0.000) G_L1: 14.146 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 11.829 G_Regularizer: 0.000 validation_error: 20.672 +(epoch: 40, iters: 64672, time: 0.538, data: 0.000) G_L1: 16.657 G_L1_ABSOLUTE: 2.562 G_L1_RELATIVE: 14.094 G_Regularizer: 0.000 validation_error: 20.753 +(epoch: 40, iters: 66672, time: 0.550, data: 0.000) G_L1: 15.610 G_L1_ABSOLUTE: 2.224 G_L1_RELATIVE: 13.386 G_Regularizer: 0.000 validation_error: 21.139 +(epoch: 40, iters: 68672, time: 0.543, data: 0.001) G_L1: 13.831 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 11.082 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 40, iters: 70672, time: 0.528, data: 0.000) G_L1: 12.770 G_L1_ABSOLUTE: 2.141 G_L1_RELATIVE: 10.630 G_Regularizer: 0.000 validation_error: 20.957 +(epoch: 40, iters: 72672, time: 0.545, data: 0.000) G_L1: 13.850 G_L1_ABSOLUTE: 2.395 G_L1_RELATIVE: 11.454 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 40, iters: 74672, time: 0.542, data: 0.000) G_L1: 13.191 G_L1_ABSOLUTE: 2.313 G_L1_RELATIVE: 10.878 G_Regularizer: 0.000 validation_error: 20.920 +(epoch: 40, iters: 76672, time: 0.538, data: 0.000) G_L1: 15.712 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 12.957 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 40, iters: 78672, time: 0.537, data: 0.000) G_L1: 14.055 G_L1_ABSOLUTE: 2.440 G_L1_RELATIVE: 11.615 G_Regularizer: 0.000 validation_error: 20.979 +(epoch: 40, iters: 80672, time: 0.542, data: 0.000) G_L1: 15.825 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 13.355 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 40, iters: 82672, time: 0.539, data: 0.000) G_L1: 14.311 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 11.825 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 40, iters: 84672, time: 0.543, data: 0.000) G_L1: 12.777 G_L1_ABSOLUTE: 2.236 G_L1_RELATIVE: 10.541 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 40, iters: 86672, time: 0.546, data: 0.000) G_L1: 12.341 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 9.574 G_Regularizer: 0.000 validation_error: 20.757 +(epoch: 40, iters: 88672, time: 0.548, data: 0.000) G_L1: 13.642 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 10.969 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 40, iters: 90672, time: 0.538, data: 0.000) G_L1: 16.614 G_L1_ABSOLUTE: 2.944 G_L1_RELATIVE: 13.670 G_Regularizer: 0.000 validation_error: 21.009 +(epoch: 40, iters: 92672, time: 0.539, data: 0.000) G_L1: 15.413 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 13.014 G_Regularizer: 0.000 validation_error: 21.162 +(epoch: 40, iters: 94672, time: 0.538, data: 0.000) G_L1: 14.949 G_L1_ABSOLUTE: 2.417 G_L1_RELATIVE: 12.532 G_Regularizer: 0.000 validation_error: 21.158 +(epoch: 40, iters: 96672, time: 0.549, data: 0.000) G_L1: 14.418 G_L1_ABSOLUTE: 2.789 G_L1_RELATIVE: 11.629 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 40, iters: 98672, time: 0.548, data: 0.000) G_L1: 15.354 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 12.842 G_Regularizer: 0.000 validation_error: 21.181 +(epoch: 40, iters: 100672, time: 0.542, data: 0.000) G_L1: 13.249 G_L1_ABSOLUTE: 2.329 G_L1_RELATIVE: 10.920 G_Regularizer: 0.000 validation_error: 21.020 +(epoch: 40, iters: 102672, time: 0.548, data: 0.000) G_L1: 14.609 G_L1_ABSOLUTE: 2.167 G_L1_RELATIVE: 12.442 G_Regularizer: 0.000 validation_error: 20.653 +(epoch: 40, iters: 104672, time: 0.544, data: 0.001) G_L1: 12.020 G_L1_ABSOLUTE: 2.183 G_L1_RELATIVE: 9.837 G_Regularizer: 0.000 validation_error: 21.367 +(epoch: 40, iters: 106672, time: 0.541, data: 0.000) G_L1: 14.735 G_L1_ABSOLUTE: 3.030 G_L1_RELATIVE: 11.705 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 40, iters: 108672, time: 0.542, data: 0.000) G_L1: 17.364 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 14.964 G_Regularizer: 0.000 validation_error: 20.902 +(epoch: 40, iters: 110672, time: 0.548, data: 0.000) G_L1: 13.443 G_L1_ABSOLUTE: 2.898 G_L1_RELATIVE: 10.545 G_Regularizer: 0.000 validation_error: 21.269 +(epoch: 40, iters: 112672, time: 0.543, data: 0.000) G_L1: 13.810 G_L1_ABSOLUTE: 2.238 G_L1_RELATIVE: 11.572 G_Regularizer: 0.000 validation_error: 21.411 +(epoch: 40, iters: 114672, time: 0.552, data: 0.000) G_L1: 12.922 G_L1_ABSOLUTE: 2.191 G_L1_RELATIVE: 10.731 G_Regularizer: 0.000 validation_error: 21.044 +(epoch: 40, iters: 116672, time: 0.545, data: 0.000) G_L1: 15.168 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 12.837 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 40, iters: 118672, time: 0.545, data: 0.001) G_L1: 14.672 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 11.861 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 40, iters: 120672, time: 0.536, data: 0.000) G_L1: 13.127 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 10.929 G_Regularizer: 0.000 validation_error: 20.922 +(epoch: 40, iters: 122672, time: 0.534, data: 0.000) G_L1: 12.248 G_L1_ABSOLUTE: 2.144 G_L1_RELATIVE: 10.105 G_Regularizer: 0.000 validation_error: 20.431 +(epoch: 40, iters: 124672, time: 0.547, data: 0.000) G_L1: 11.208 G_L1_ABSOLUTE: 2.328 G_L1_RELATIVE: 8.881 G_Regularizer: 0.000 validation_error: 21.215 +(epoch: 40, iters: 126672, time: 0.542, data: 0.000) G_L1: 17.507 G_L1_ABSOLUTE: 2.695 G_L1_RELATIVE: 14.812 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 40, iters: 128672, time: 0.549, data: 0.000) G_L1: 14.393 G_L1_ABSOLUTE: 2.539 G_L1_RELATIVE: 11.854 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 40, iters: 130672, time: 0.550, data: 0.001) G_L1: 14.427 G_L1_ABSOLUTE: 2.316 G_L1_RELATIVE: 12.111 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 40, iters: 132672, time: 0.539, data: 0.000) G_L1: 13.453 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 10.864 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 40, iters: 134672, time: 0.544, data: 0.000) G_L1: 13.472 G_L1_ABSOLUTE: 2.077 G_L1_RELATIVE: 11.394 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 40, iters: 136672, time: 0.542, data: 0.001) G_L1: 13.380 G_L1_ABSOLUTE: 2.859 G_L1_RELATIVE: 10.521 G_Regularizer: 0.000 validation_error: 21.014 +(epoch: 40, iters: 138672, time: 0.537, data: 0.000) G_L1: 11.557 G_L1_ABSOLUTE: 2.163 G_L1_RELATIVE: 9.394 G_Regularizer: 0.000 validation_error: 21.187 +(epoch: 40, iters: 140672, time: 0.538, data: 0.000) G_L1: 12.548 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 10.137 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 40, iters: 142672, time: 0.541, data: 0.000) G_L1: 12.271 G_L1_ABSOLUTE: 2.212 G_L1_RELATIVE: 10.058 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 40, iters: 144672, time: 0.547, data: 0.000) G_L1: 13.786 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 11.223 G_Regularizer: 0.000 validation_error: 20.367 +(epoch: 40, iters: 146672, time: 0.543, data: 0.000) G_L1: 13.371 G_L1_ABSOLUTE: 2.360 G_L1_RELATIVE: 11.011 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 40, iters: 148672, time: 0.541, data: 0.000) G_L1: 14.804 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 12.177 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 40, iters: 150672, time: 0.539, data: 0.000) G_L1: 17.266 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 14.624 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 40, iters: 152672, time: 0.544, data: 0.000) G_L1: 13.602 G_L1_ABSOLUTE: 1.813 G_L1_RELATIVE: 11.789 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 40, iters: 154672, time: 0.544, data: 0.000) G_L1: 14.020 G_L1_ABSOLUTE: 2.707 G_L1_RELATIVE: 11.313 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 40, iters: 156672, time: 0.530, data: 0.001) G_L1: 13.597 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 10.921 G_Regularizer: 0.000 validation_error: 20.748 +(epoch: 40, iters: 158672, time: 0.540, data: 0.000) G_L1: 14.663 G_L1_ABSOLUTE: 2.511 G_L1_RELATIVE: 12.152 G_Regularizer: 0.000 validation_error: 20.959 +(epoch: 40, iters: 160672, time: 0.537, data: 0.000) G_L1: 18.136 G_L1_ABSOLUTE: 3.774 G_L1_RELATIVE: 14.362 G_Regularizer: 0.000 validation_error: 20.522 +(epoch: 40, iters: 162672, time: 0.543, data: 0.000) G_L1: 11.588 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 9.083 G_Regularizer: 0.000 validation_error: 20.674 +(epoch: 40, iters: 164672, time: 0.538, data: 0.000) G_L1: 12.127 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 9.668 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 40, iters: 166672, time: 0.543, data: 0.000) G_L1: 13.542 G_L1_ABSOLUTE: 2.139 G_L1_RELATIVE: 11.403 G_Regularizer: 0.000 validation_error: 21.041 +(epoch: 40, iters: 168672, time: 0.545, data: 0.000) G_L1: 15.490 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 13.089 G_Regularizer: 0.000 validation_error: 20.516 +(epoch: 40, iters: 170672, time: 0.538, data: 0.000) G_L1: 15.693 G_L1_ABSOLUTE: 2.652 G_L1_RELATIVE: 13.041 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 40, iters: 172672, time: 0.543, data: 0.000) G_L1: 14.590 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 11.992 G_Regularizer: 0.000 validation_error: 20.740 +(epoch: 40, iters: 174672, time: 0.539, data: 0.000) G_L1: 13.389 G_L1_ABSOLUTE: 2.208 G_L1_RELATIVE: 11.180 G_Regularizer: 0.000 validation_error: 20.573 +(epoch: 40, iters: 176672, time: 0.546, data: 0.000) G_L1: 13.479 G_L1_ABSOLUTE: 2.329 G_L1_RELATIVE: 11.150 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 40, iters: 178672, time: 0.544, data: 0.000) G_L1: 14.187 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 11.752 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 40, iters: 180672, time: 0.543, data: 0.000) G_L1: 14.545 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 11.653 G_Regularizer: 0.000 validation_error: 21.057 +(epoch: 40, iters: 182672, time: 0.541, data: 0.000) G_L1: 12.671 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 10.095 G_Regularizer: 0.000 validation_error: 20.176 +(epoch: 40, iters: 184672, time: 0.537, data: 0.000) G_L1: 13.153 G_L1_ABSOLUTE: 2.064 G_L1_RELATIVE: 11.088 G_Regularizer: 0.000 validation_error: 21.239 +(epoch: 40, iters: 186672, time: 0.537, data: 0.000) G_L1: 14.732 G_L1_ABSOLUTE: 2.470 G_L1_RELATIVE: 12.262 G_Regularizer: 0.000 validation_error: 20.284 +(epoch: 40, iters: 188672, time: 0.550, data: 0.000) G_L1: 15.211 G_L1_ABSOLUTE: 2.514 G_L1_RELATIVE: 12.697 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 40, iters: 190672, time: 0.548, data: 0.000) G_L1: 11.419 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 9.128 G_Regularizer: 0.000 validation_error: 21.177 +(epoch: 40, iters: 192672, time: 0.547, data: 0.000) G_L1: 13.893 G_L1_ABSOLUTE: 2.324 G_L1_RELATIVE: 11.569 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 40, iters: 194672, time: 0.544, data: 0.000) G_L1: 12.214 G_L1_ABSOLUTE: 2.067 G_L1_RELATIVE: 10.147 G_Regularizer: 0.000 validation_error: 20.994 +(epoch: 40, iters: 196672, time: 0.539, data: 0.001) G_L1: 15.905 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 13.496 G_Regularizer: 0.000 validation_error: 21.317 +(epoch: 40, iters: 198672, time: 0.544, data: 0.001) G_L1: 11.825 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 9.471 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 40, iters: 200672, time: 0.545, data: 0.000) G_L1: 13.308 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 11.007 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 40, iters: 202672, time: 0.540, data: 0.000) G_L1: 16.156 G_L1_ABSOLUTE: 2.920 G_L1_RELATIVE: 13.236 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 40, iters: 204672, time: 0.539, data: 0.000) G_L1: 14.554 G_L1_ABSOLUTE: 2.446 G_L1_RELATIVE: 12.108 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 40, iters: 206672, time: 0.554, data: 0.001) G_L1: 14.706 G_L1_ABSOLUTE: 2.231 G_L1_RELATIVE: 12.475 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 40, iters: 208672, time: 0.538, data: 0.000) G_L1: 11.998 G_L1_ABSOLUTE: 2.351 G_L1_RELATIVE: 9.646 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 40, iters: 210672, time: 0.540, data: 0.000) G_L1: 13.910 G_L1_ABSOLUTE: 2.301 G_L1_RELATIVE: 11.609 G_Regularizer: 0.000 validation_error: 20.627 +(epoch: 40, iters: 212672, time: 0.533, data: 0.000) G_L1: 12.840 G_L1_ABSOLUTE: 2.173 G_L1_RELATIVE: 10.667 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 40, iters: 214672, time: 0.542, data: 0.000) G_L1: 15.175 G_L1_ABSOLUTE: 2.748 G_L1_RELATIVE: 12.427 G_Regularizer: 0.000 validation_error: 20.575 +(epoch: 40, iters: 216672, time: 0.537, data: 0.000) G_L1: 12.762 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 10.232 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 40, iters: 218672, time: 0.544, data: 0.000) G_L1: 13.906 G_L1_ABSOLUTE: 2.252 G_L1_RELATIVE: 11.655 G_Regularizer: 0.000 validation_error: 21.040 +(epoch: 40, iters: 220672, time: 0.544, data: 0.000) G_L1: 14.043 G_L1_ABSOLUTE: 2.255 G_L1_RELATIVE: 11.788 G_Regularizer: 0.000 validation_error: 20.538 +(epoch: 40, iters: 222672, time: 0.541, data: 0.000) G_L1: 11.845 G_L1_ABSOLUTE: 1.986 G_L1_RELATIVE: 9.859 G_Regularizer: 0.000 validation_error: 20.758 +(epoch: 40, iters: 224672, time: 0.532, data: 0.000) G_L1: 13.444 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 11.050 G_Regularizer: 0.000 validation_error: 20.874 +(epoch: 40, iters: 226672, time: 0.546, data: 0.000) G_L1: 12.270 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 9.633 G_Regularizer: 0.000 validation_error: 20.882 +(epoch: 40, iters: 228672, time: 0.541, data: 0.001) G_L1: 11.422 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 8.964 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 40, iters: 230672, time: 0.541, data: 0.000) G_L1: 16.320 G_L1_ABSOLUTE: 2.982 G_L1_RELATIVE: 13.339 G_Regularizer: 0.000 validation_error: 21.021 +(epoch: 40, iters: 232672, time: 0.550, data: 0.001) G_L1: 13.957 G_L1_ABSOLUTE: 2.147 G_L1_RELATIVE: 11.810 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 40, iters: 234672, time: 0.539, data: 0.000) G_L1: 14.591 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 11.951 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 40, iters: 236672, time: 0.546, data: 0.000) G_L1: 12.607 G_L1_ABSOLUTE: 2.228 G_L1_RELATIVE: 10.379 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 40, iters: 238672, time: 0.537, data: 0.000) G_L1: 13.054 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 10.856 G_Regularizer: 0.000 validation_error: 20.992 +(epoch: 40, iters: 240672, time: 0.545, data: 0.000) G_L1: 17.009 G_L1_ABSOLUTE: 2.141 G_L1_RELATIVE: 14.868 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 40, iters: 242672, time: 0.528, data: 0.000) G_L1: 13.186 G_L1_ABSOLUTE: 2.741 G_L1_RELATIVE: 10.445 G_Regularizer: 0.000 validation_error: 21.145 +(epoch: 40, iters: 244672, time: 0.539, data: 0.000) G_L1: 14.491 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 11.882 G_Regularizer: 0.000 validation_error: 21.205 +(epoch: 40, iters: 246672, time: 0.543, data: 0.001) G_L1: 15.506 G_L1_ABSOLUTE: 2.285 G_L1_RELATIVE: 13.220 G_Regularizer: 0.000 validation_error: 20.753 +(epoch: 40, iters: 248672, time: 0.542, data: 0.000) G_L1: 15.389 G_L1_ABSOLUTE: 2.875 G_L1_RELATIVE: 12.515 G_Regularizer: 0.000 validation_error: 21.071 +(epoch: 40, iters: 250672, time: 0.542, data: 0.000) G_L1: 14.497 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 11.831 G_Regularizer: 0.000 validation_error: 20.545 +(epoch: 40, iters: 252672, time: 0.538, data: 0.000) G_L1: 13.287 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 10.910 G_Regularizer: 0.000 validation_error: 20.472 +(epoch: 40, iters: 254672, time: 0.543, data: 0.000) G_L1: 12.912 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 10.395 G_Regularizer: 0.000 validation_error: 20.498 +(epoch: 40, iters: 256672, time: 0.547, data: 0.000) G_L1: 12.527 G_L1_ABSOLUTE: 2.082 G_L1_RELATIVE: 10.445 G_Regularizer: 0.000 validation_error: 20.510 +(epoch: 40, iters: 258672, time: 0.544, data: 0.000) G_L1: 14.253 G_L1_ABSOLUTE: 3.104 G_L1_RELATIVE: 11.148 G_Regularizer: 0.000 validation_error: 21.270 +(epoch: 40, iters: 260672, time: 0.543, data: 0.000) G_L1: 14.204 G_L1_ABSOLUTE: 2.434 G_L1_RELATIVE: 11.769 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 40, iters: 262672, time: 0.545, data: 0.001) G_L1: 12.295 G_L1_ABSOLUTE: 2.261 G_L1_RELATIVE: 10.034 G_Regularizer: 0.000 validation_error: 20.487 +(epoch: 40, iters: 264672, time: 0.542, data: 0.000) G_L1: 12.326 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 9.928 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 40, iters: 266672, time: 0.543, data: 0.000) G_L1: 16.281 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 13.784 G_Regularizer: 0.000 validation_error: 20.484 +(epoch: 40, iters: 268672, time: 0.526, data: 0.000) G_L1: 15.234 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 12.709 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 40, iters: 270672, time: 0.546, data: 0.000) G_L1: 13.575 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 11.299 G_Regularizer: 0.000 validation_error: 20.425 +(epoch: 40, iters: 272672, time: 0.544, data: 0.000) G_L1: 12.616 G_L1_ABSOLUTE: 2.759 G_L1_RELATIVE: 9.858 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 40, iters: 274672, time: 0.547, data: 0.000) G_L1: 15.339 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 12.741 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 40, iters: 276672, time: 0.537, data: 0.000) G_L1: 15.659 G_L1_ABSOLUTE: 2.887 G_L1_RELATIVE: 12.772 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 40, iters: 278672, time: 0.547, data: 0.000) G_L1: 11.873 G_L1_ABSOLUTE: 2.636 G_L1_RELATIVE: 9.237 G_Regularizer: 0.000 validation_error: 20.778 +(epoch: 40, iters: 280672, time: 0.547, data: 0.000) G_L1: 14.515 G_L1_ABSOLUTE: 2.237 G_L1_RELATIVE: 12.278 G_Regularizer: 0.000 validation_error: 20.531 +(epoch: 40, iters: 282672, time: 0.545, data: 0.000) G_L1: 13.618 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 10.985 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 40, iters: 284672, time: 0.544, data: 0.000) G_L1: 13.044 G_L1_ABSOLUTE: 2.959 G_L1_RELATIVE: 10.085 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 40, iters: 286672, time: 0.540, data: 0.000) G_L1: 15.020 G_L1_ABSOLUTE: 2.983 G_L1_RELATIVE: 12.037 G_Regularizer: 0.000 validation_error: 20.667 +(epoch: 40, iters: 288672, time: 0.543, data: 0.000) G_L1: 15.352 G_L1_ABSOLUTE: 2.187 G_L1_RELATIVE: 13.165 G_Regularizer: 0.000 validation_error: 21.306 +(epoch: 40, iters: 290672, time: 0.543, data: 0.000) G_L1: 15.308 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 12.802 G_Regularizer: 0.000 validation_error: 20.716 +(epoch: 40, iters: 292672, time: 0.541, data: 0.000) G_L1: 13.123 G_L1_ABSOLUTE: 2.323 G_L1_RELATIVE: 10.800 G_Regularizer: 0.000 validation_error: 20.827 +(epoch: 40, iters: 294672, time: 0.540, data: 0.000) G_L1: 15.378 G_L1_ABSOLUTE: 2.410 G_L1_RELATIVE: 12.967 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 40, iters: 296672, time: 0.548, data: 0.000) G_L1: 15.537 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 13.017 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 40, iters: 298672, time: 0.540, data: 0.000) G_L1: 13.935 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 11.452 G_Regularizer: 0.000 validation_error: 20.384 +(epoch: 40, iters: 300672, time: 0.546, data: 0.000) G_L1: 14.812 G_L1_ABSOLUTE: 2.608 G_L1_RELATIVE: 12.204 G_Regularizer: 0.000 validation_error: 20.659 +(epoch: 40, iters: 302672, time: 0.531, data: 0.000) G_L1: 13.715 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 11.174 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 41, iters: 1920, time: 0.546, data: 0.000) G_L1: 15.368 G_L1_ABSOLUTE: 2.668 G_L1_RELATIVE: 12.701 G_Regularizer: 0.000 validation_error: 20.638 +(epoch: 41, iters: 3920, time: 0.539, data: 0.000) G_L1: 14.953 G_L1_ABSOLUTE: 2.552 G_L1_RELATIVE: 12.402 G_Regularizer: 0.000 validation_error: 20.904 +(epoch: 41, iters: 5920, time: 0.545, data: 0.000) G_L1: 14.879 G_L1_ABSOLUTE: 2.790 G_L1_RELATIVE: 12.090 G_Regularizer: 0.000 validation_error: 20.423 +(epoch: 41, iters: 7920, time: 0.539, data: 0.000) G_L1: 13.578 G_L1_ABSOLUTE: 2.475 G_L1_RELATIVE: 11.103 G_Regularizer: 0.000 validation_error: 20.697 +(epoch: 41, iters: 9920, time: 0.545, data: 0.000) G_L1: 12.208 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 9.864 G_Regularizer: 0.000 validation_error: 20.894 +(epoch: 41, iters: 11920, time: 0.540, data: 0.000) G_L1: 15.097 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 12.496 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 41, iters: 13920, time: 0.542, data: 0.000) G_L1: 13.298 G_L1_ABSOLUTE: 2.372 G_L1_RELATIVE: 10.927 G_Regularizer: 0.000 validation_error: 20.474 +(epoch: 41, iters: 15920, time: 0.546, data: 0.000) G_L1: 12.808 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 10.285 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 41, iters: 17920, time: 0.539, data: 0.000) G_L1: 13.554 G_L1_ABSOLUTE: 2.361 G_L1_RELATIVE: 11.192 G_Regularizer: 0.000 validation_error: 20.824 +(epoch: 41, iters: 19920, time: 0.541, data: 0.000) G_L1: 13.445 G_L1_ABSOLUTE: 2.018 G_L1_RELATIVE: 11.428 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 41, iters: 21920, time: 0.540, data: 0.000) G_L1: 14.210 G_L1_ABSOLUTE: 2.352 G_L1_RELATIVE: 11.858 G_Regularizer: 0.000 validation_error: 21.121 +(epoch: 41, iters: 23920, time: 0.539, data: 0.000) G_L1: 13.030 G_L1_ABSOLUTE: 1.957 G_L1_RELATIVE: 11.073 G_Regularizer: 0.000 validation_error: 20.562 +(epoch: 41, iters: 25920, time: 0.536, data: 0.000) G_L1: 13.076 G_L1_ABSOLUTE: 2.304 G_L1_RELATIVE: 10.773 G_Regularizer: 0.000 validation_error: 20.987 +(epoch: 41, iters: 27920, time: 0.545, data: 0.000) G_L1: 13.921 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 11.638 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 41, iters: 29920, time: 0.547, data: 0.001) G_L1: 15.518 G_L1_ABSOLUTE: 2.599 G_L1_RELATIVE: 12.920 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 41, iters: 31920, time: 0.538, data: 0.001) G_L1: 11.288 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 8.955 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 41, iters: 33920, time: 0.534, data: 0.000) G_L1: 13.922 G_L1_ABSOLUTE: 2.318 G_L1_RELATIVE: 11.604 G_Regularizer: 0.000 validation_error: 20.954 +(epoch: 41, iters: 35920, time: 0.544, data: 0.000) G_L1: 14.840 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 12.352 G_Regularizer: 0.000 validation_error: 20.980 +(epoch: 41, iters: 37920, time: 0.542, data: 0.001) G_L1: 12.792 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 10.256 G_Regularizer: 0.000 validation_error: 21.192 +(epoch: 41, iters: 39920, time: 0.545, data: 0.000) G_L1: 12.906 G_L1_ABSOLUTE: 2.685 G_L1_RELATIVE: 10.220 G_Regularizer: 0.000 validation_error: 20.895 +(epoch: 41, iters: 41920, time: 0.537, data: 0.000) G_L1: 14.645 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 12.049 G_Regularizer: 0.000 validation_error: 20.856 +(epoch: 41, iters: 43920, time: 0.546, data: 0.000) G_L1: 24.490 G_L1_ABSOLUTE: 2.920 G_L1_RELATIVE: 21.570 G_Regularizer: 0.000 validation_error: 20.166 +(epoch: 41, iters: 45920, time: 0.541, data: 0.000) G_L1: 13.116 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 10.699 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 41, iters: 47920, time: 0.546, data: 0.000) G_L1: 14.506 G_L1_ABSOLUTE: 3.201 G_L1_RELATIVE: 11.305 G_Regularizer: 0.000 validation_error: 21.069 +(epoch: 41, iters: 49920, time: 0.542, data: 0.001) G_L1: 13.297 G_L1_ABSOLUTE: 2.431 G_L1_RELATIVE: 10.866 G_Regularizer: 0.000 validation_error: 20.848 +(epoch: 41, iters: 51920, time: 0.528, data: 0.000) G_L1: 13.154 G_L1_ABSOLUTE: 2.795 G_L1_RELATIVE: 10.359 G_Regularizer: 0.000 validation_error: 20.620 +(epoch: 41, iters: 53920, time: 0.543, data: 0.000) G_L1: 12.910 G_L1_ABSOLUTE: 2.192 G_L1_RELATIVE: 10.719 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 41, iters: 55920, time: 0.546, data: 0.000) G_L1: 14.887 G_L1_ABSOLUTE: 2.379 G_L1_RELATIVE: 12.508 G_Regularizer: 0.000 validation_error: 21.139 +(epoch: 41, iters: 57920, time: 0.535, data: 0.000) G_L1: 13.194 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 10.945 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 41, iters: 59920, time: 0.532, data: 0.000) G_L1: 14.837 G_L1_ABSOLUTE: 3.009 G_L1_RELATIVE: 11.828 G_Regularizer: 0.000 validation_error: 21.175 +(epoch: 41, iters: 61920, time: 0.540, data: 0.000) G_L1: 14.714 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 12.223 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 41, iters: 63920, time: 0.532, data: 0.000) G_L1: 13.255 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 10.739 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 41, iters: 65920, time: 0.546, data: 0.000) G_L1: 14.609 G_L1_ABSOLUTE: 2.171 G_L1_RELATIVE: 12.439 G_Regularizer: 0.000 validation_error: 20.621 +(epoch: 41, iters: 67920, time: 0.535, data: 0.000) G_L1: 14.591 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 12.124 G_Regularizer: 0.000 validation_error: 20.740 +(epoch: 41, iters: 69920, time: 0.542, data: 0.000) G_L1: 13.949 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 11.606 G_Regularizer: 0.000 validation_error: 20.591 +(epoch: 41, iters: 71920, time: 0.547, data: 0.001) G_L1: 12.103 G_L1_ABSOLUTE: 2.557 G_L1_RELATIVE: 9.546 G_Regularizer: 0.000 validation_error: 20.997 +(epoch: 41, iters: 73920, time: 0.545, data: 0.000) G_L1: 12.799 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 10.331 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 41, iters: 75920, time: 0.544, data: 0.000) G_L1: 16.727 G_L1_ABSOLUTE: 2.726 G_L1_RELATIVE: 14.001 G_Regularizer: 0.000 validation_error: 20.832 +(epoch: 41, iters: 77920, time: 0.538, data: 0.000) G_L1: 13.721 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 11.180 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 41, iters: 79920, time: 0.551, data: 0.000) G_L1: 13.400 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 10.968 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 41, iters: 81920, time: 0.541, data: 0.000) G_L1: 12.837 G_L1_ABSOLUTE: 2.221 G_L1_RELATIVE: 10.616 G_Regularizer: 0.000 validation_error: 21.051 +(epoch: 41, iters: 83920, time: 0.547, data: 0.000) G_L1: 13.586 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 11.237 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 41, iters: 85920, time: 0.540, data: 0.000) G_L1: 12.719 G_L1_ABSOLUTE: 2.080 G_L1_RELATIVE: 10.639 G_Regularizer: 0.000 validation_error: 21.063 +(epoch: 41, iters: 87920, time: 0.545, data: 0.000) G_L1: 13.034 G_L1_ABSOLUTE: 2.061 G_L1_RELATIVE: 10.973 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 41, iters: 89920, time: 0.542, data: 0.000) G_L1: 13.021 G_L1_ABSOLUTE: 2.285 G_L1_RELATIVE: 10.736 G_Regularizer: 0.000 validation_error: 20.670 +(epoch: 41, iters: 91920, time: 0.550, data: 0.000) G_L1: 12.626 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 10.256 G_Regularizer: 0.000 validation_error: 21.345 +(epoch: 41, iters: 93920, time: 0.543, data: 0.000) G_L1: 13.590 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 10.894 G_Regularizer: 0.000 validation_error: 21.147 +(epoch: 41, iters: 95920, time: 0.541, data: 0.000) G_L1: 13.292 G_L1_ABSOLUTE: 2.247 G_L1_RELATIVE: 11.045 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 41, iters: 97920, time: 0.546, data: 0.000) G_L1: 14.344 G_L1_ABSOLUTE: 2.431 G_L1_RELATIVE: 11.913 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 41, iters: 99920, time: 0.542, data: 0.000) G_L1: 16.477 G_L1_ABSOLUTE: 2.156 G_L1_RELATIVE: 14.321 G_Regularizer: 0.000 validation_error: 20.626 +(epoch: 41, iters: 101920, time: 0.544, data: 0.000) G_L1: 13.518 G_L1_ABSOLUTE: 2.693 G_L1_RELATIVE: 10.825 G_Regularizer: 0.000 validation_error: 20.939 +(epoch: 41, iters: 103920, time: 0.538, data: 0.001) G_L1: 14.605 G_L1_ABSOLUTE: 1.966 G_L1_RELATIVE: 12.639 G_Regularizer: 0.000 validation_error: 21.306 +(epoch: 41, iters: 105920, time: 0.546, data: 0.000) G_L1: 13.908 G_L1_ABSOLUTE: 2.015 G_L1_RELATIVE: 11.893 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 41, iters: 107920, time: 0.547, data: 0.001) G_L1: 14.094 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 11.643 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 41, iters: 109920, time: 0.536, data: 0.000) G_L1: 16.367 G_L1_ABSOLUTE: 3.152 G_L1_RELATIVE: 13.216 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 41, iters: 111920, time: 0.541, data: 0.000) G_L1: 12.847 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 10.498 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 41, iters: 113920, time: 0.542, data: 0.000) G_L1: 14.352 G_L1_ABSOLUTE: 2.395 G_L1_RELATIVE: 11.957 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 41, iters: 115920, time: 0.541, data: 0.000) G_L1: 15.487 G_L1_ABSOLUTE: 2.558 G_L1_RELATIVE: 12.929 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 41, iters: 117920, time: 0.542, data: 0.001) G_L1: 14.679 G_L1_ABSOLUTE: 2.434 G_L1_RELATIVE: 12.245 G_Regularizer: 0.000 validation_error: 21.258 +(epoch: 41, iters: 119920, time: 0.534, data: 0.000) G_L1: 14.116 G_L1_ABSOLUTE: 2.723 G_L1_RELATIVE: 11.394 G_Regularizer: 0.000 validation_error: 21.093 +(epoch: 41, iters: 121920, time: 0.550, data: 0.001) G_L1: 13.570 G_L1_ABSOLUTE: 2.396 G_L1_RELATIVE: 11.174 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 41, iters: 123920, time: 0.549, data: 0.000) G_L1: 12.472 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 10.093 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 41, iters: 125920, time: 0.545, data: 0.000) G_L1: 12.734 G_L1_ABSOLUTE: 2.725 G_L1_RELATIVE: 10.010 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 41, iters: 127920, time: 0.538, data: 0.001) G_L1: 14.107 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 11.498 G_Regularizer: 0.000 validation_error: 21.108 +(epoch: 41, iters: 129920, time: 0.533, data: 0.001) G_L1: 13.977 G_L1_ABSOLUTE: 2.252 G_L1_RELATIVE: 11.725 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 41, iters: 131920, time: 0.537, data: 0.000) G_L1: 14.809 G_L1_ABSOLUTE: 2.817 G_L1_RELATIVE: 11.993 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 41, iters: 133920, time: 0.544, data: 0.001) G_L1: 13.871 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 11.517 G_Regularizer: 0.000 validation_error: 21.073 +(epoch: 41, iters: 135920, time: 0.541, data: 0.000) G_L1: 15.046 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 12.483 G_Regularizer: 0.000 validation_error: 20.761 +(epoch: 41, iters: 137920, time: 0.539, data: 0.000) G_L1: 13.278 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 10.683 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 41, iters: 139920, time: 0.541, data: 0.000) G_L1: 14.789 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 12.281 G_Regularizer: 0.000 validation_error: 21.143 +(epoch: 41, iters: 141920, time: 0.546, data: 0.000) G_L1: 14.524 G_L1_ABSOLUTE: 2.325 G_L1_RELATIVE: 12.199 G_Regularizer: 0.000 validation_error: 20.468 +(epoch: 41, iters: 143920, time: 0.539, data: 0.000) G_L1: 14.327 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 11.522 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 41, iters: 145920, time: 0.541, data: 0.001) G_L1: 12.166 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 9.653 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 41, iters: 147920, time: 0.540, data: 0.001) G_L1: 13.152 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 10.646 G_Regularizer: 0.000 validation_error: 20.662 +(epoch: 41, iters: 149920, time: 0.539, data: 0.000) G_L1: 13.092 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 10.603 G_Regularizer: 0.000 validation_error: 20.882 +(epoch: 41, iters: 151920, time: 0.544, data: 0.000) G_L1: 14.673 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 12.228 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 41, iters: 153920, time: 0.538, data: 0.000) G_L1: 13.313 G_L1_ABSOLUTE: 2.423 G_L1_RELATIVE: 10.891 G_Regularizer: 0.000 validation_error: 20.577 +(epoch: 41, iters: 155920, time: 0.548, data: 0.000) G_L1: 12.737 G_L1_ABSOLUTE: 2.336 G_L1_RELATIVE: 10.402 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 41, iters: 157920, time: 0.537, data: 0.000) G_L1: 13.158 G_L1_ABSOLUTE: 2.168 G_L1_RELATIVE: 10.990 G_Regularizer: 0.000 validation_error: 20.602 +(epoch: 41, iters: 159920, time: 0.541, data: 0.000) G_L1: 10.480 G_L1_ABSOLUTE: 2.154 G_L1_RELATIVE: 8.326 G_Regularizer: 0.000 validation_error: 20.770 +(epoch: 41, iters: 161920, time: 0.540, data: 0.000) G_L1: 15.453 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 12.568 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 41, iters: 163920, time: 0.543, data: 0.000) G_L1: 14.635 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 12.386 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 41, iters: 165920, time: 0.540, data: 0.000) G_L1: 16.296 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 13.907 G_Regularizer: 0.000 validation_error: 20.604 +(epoch: 41, iters: 167920, time: 0.544, data: 0.000) G_L1: 12.955 G_L1_ABSOLUTE: 2.604 G_L1_RELATIVE: 10.351 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 41, iters: 169920, time: 0.542, data: 0.000) G_L1: 14.811 G_L1_ABSOLUTE: 2.987 G_L1_RELATIVE: 11.824 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 41, iters: 171920, time: 0.532, data: 0.001) G_L1: 13.791 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 11.018 G_Regularizer: 0.000 validation_error: 20.930 +(epoch: 41, iters: 173920, time: 0.546, data: 0.000) G_L1: 14.836 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 11.944 G_Regularizer: 0.000 validation_error: 21.214 +(epoch: 41, iters: 175920, time: 0.546, data: 0.000) G_L1: 14.719 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 12.184 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 41, iters: 177920, time: 0.543, data: 0.000) G_L1: 15.604 G_L1_ABSOLUTE: 2.841 G_L1_RELATIVE: 12.763 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 41, iters: 179920, time: 0.539, data: 0.000) G_L1: 15.723 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 13.291 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 41, iters: 181920, time: 0.543, data: 0.000) G_L1: 14.724 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 12.339 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 41, iters: 183920, time: 0.541, data: 0.000) G_L1: 14.425 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 12.151 G_Regularizer: 0.000 validation_error: 20.587 +(epoch: 41, iters: 185920, time: 0.547, data: 0.000) G_L1: 15.123 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 12.608 G_Regularizer: 0.000 validation_error: 20.639 +(epoch: 41, iters: 187920, time: 0.541, data: 0.000) G_L1: 16.249 G_L1_ABSOLUTE: 2.143 G_L1_RELATIVE: 14.106 G_Regularizer: 0.000 validation_error: 21.005 +(epoch: 41, iters: 189920, time: 0.545, data: 0.000) G_L1: 14.550 G_L1_ABSOLUTE: 2.359 G_L1_RELATIVE: 12.192 G_Regularizer: 0.000 validation_error: 21.313 +(epoch: 41, iters: 191920, time: 0.545, data: 0.000) G_L1: 13.311 G_L1_ABSOLUTE: 2.207 G_L1_RELATIVE: 11.104 G_Regularizer: 0.000 validation_error: 21.030 +(epoch: 41, iters: 193920, time: 0.538, data: 0.000) G_L1: 16.444 G_L1_ABSOLUTE: 2.292 G_L1_RELATIVE: 14.153 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 41, iters: 195920, time: 0.541, data: 0.000) G_L1: 13.255 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 10.877 G_Regularizer: 0.000 validation_error: 20.568 +(epoch: 41, iters: 197920, time: 0.544, data: 0.000) G_L1: 16.000 G_L1_ABSOLUTE: 2.616 G_L1_RELATIVE: 13.385 G_Regularizer: 0.000 validation_error: 20.832 +(epoch: 41, iters: 199920, time: 0.543, data: 0.001) G_L1: 12.830 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 10.194 G_Regularizer: 0.000 validation_error: 20.808 +(epoch: 41, iters: 201920, time: 0.537, data: 0.000) G_L1: 11.620 G_L1_ABSOLUTE: 2.116 G_L1_RELATIVE: 9.504 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 41, iters: 203920, time: 0.545, data: 0.000) G_L1: 14.841 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 12.074 G_Regularizer: 0.000 validation_error: 20.740 +(epoch: 41, iters: 205920, time: 0.538, data: 0.001) G_L1: 15.624 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 13.305 G_Regularizer: 0.000 validation_error: 21.363 +(epoch: 41, iters: 207920, time: 0.538, data: 0.000) G_L1: 16.028 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 13.542 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 41, iters: 209920, time: 0.543, data: 0.000) G_L1: 13.495 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 11.002 G_Regularizer: 0.000 validation_error: 21.293 +(epoch: 41, iters: 211920, time: 0.548, data: 0.000) G_L1: 15.630 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 13.162 G_Regularizer: 0.000 validation_error: 20.874 +(epoch: 41, iters: 213920, time: 0.536, data: 0.000) G_L1: 14.036 G_L1_ABSOLUTE: 2.114 G_L1_RELATIVE: 11.922 G_Regularizer: 0.000 validation_error: 20.402 +(epoch: 41, iters: 215920, time: 0.538, data: 0.001) G_L1: 14.125 G_L1_ABSOLUTE: 2.827 G_L1_RELATIVE: 11.298 G_Regularizer: 0.000 validation_error: 21.380 +(epoch: 41, iters: 217920, time: 0.537, data: 0.000) G_L1: 11.927 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 9.610 G_Regularizer: 0.000 validation_error: 20.697 +(epoch: 41, iters: 219920, time: 0.540, data: 0.000) G_L1: 14.071 G_L1_ABSOLUTE: 2.184 G_L1_RELATIVE: 11.887 G_Regularizer: 0.000 validation_error: 20.640 +(epoch: 41, iters: 221920, time: 0.547, data: 0.000) G_L1: 16.713 G_L1_ABSOLUTE: 3.001 G_L1_RELATIVE: 13.712 G_Regularizer: 0.000 validation_error: 20.948 +(epoch: 41, iters: 223920, time: 0.538, data: 0.000) G_L1: 14.712 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 11.956 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 41, iters: 225920, time: 0.539, data: 0.000) G_L1: 15.197 G_L1_ABSOLUTE: 2.752 G_L1_RELATIVE: 12.445 G_Regularizer: 0.000 validation_error: 21.130 +(epoch: 41, iters: 227920, time: 0.533, data: 0.000) G_L1: 13.016 G_L1_ABSOLUTE: 2.374 G_L1_RELATIVE: 10.642 G_Regularizer: 0.000 validation_error: 20.963 +(epoch: 41, iters: 229920, time: 0.535, data: 0.000) G_L1: 14.113 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 11.637 G_Regularizer: 0.000 validation_error: 21.294 +(epoch: 41, iters: 231920, time: 0.532, data: 0.000) G_L1: 12.419 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 9.824 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 41, iters: 233920, time: 0.547, data: 0.000) G_L1: 12.612 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 10.017 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 41, iters: 235920, time: 0.542, data: 0.000) G_L1: 12.278 G_L1_ABSOLUTE: 2.330 G_L1_RELATIVE: 9.948 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 41, iters: 237920, time: 0.548, data: 0.000) G_L1: 16.593 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 13.895 G_Regularizer: 0.000 validation_error: 20.644 +(epoch: 41, iters: 239920, time: 0.543, data: 0.000) G_L1: 15.663 G_L1_ABSOLUTE: 2.962 G_L1_RELATIVE: 12.701 G_Regularizer: 0.000 validation_error: 20.776 +(epoch: 41, iters: 241920, time: 0.541, data: 0.000) G_L1: 13.155 G_L1_ABSOLUTE: 2.092 G_L1_RELATIVE: 11.063 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 41, iters: 243920, time: 0.544, data: 0.000) G_L1: 17.743 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 14.999 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 41, iters: 245920, time: 0.540, data: 0.000) G_L1: 14.676 G_L1_ABSOLUTE: 3.065 G_L1_RELATIVE: 11.610 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 41, iters: 247920, time: 0.531, data: 0.000) G_L1: 15.601 G_L1_ABSOLUTE: 2.466 G_L1_RELATIVE: 13.135 G_Regularizer: 0.000 validation_error: 20.779 +(epoch: 41, iters: 249920, time: 0.535, data: 0.001) G_L1: 11.738 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 9.325 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 41, iters: 251920, time: 0.546, data: 0.000) G_L1: 13.309 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 10.906 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 41, iters: 253920, time: 0.547, data: 0.000) G_L1: 13.060 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 10.774 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 41, iters: 255920, time: 0.547, data: 0.000) G_L1: 12.577 G_L1_ABSOLUTE: 2.263 G_L1_RELATIVE: 10.313 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 41, iters: 257920, time: 0.538, data: 0.000) G_L1: 12.586 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 9.994 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 41, iters: 259920, time: 0.545, data: 0.000) G_L1: 14.196 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 11.606 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 41, iters: 261920, time: 0.546, data: 0.001) G_L1: 13.981 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 11.494 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 41, iters: 263920, time: 0.541, data: 0.000) G_L1: 15.267 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 13.050 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 41, iters: 265920, time: 0.535, data: 0.000) G_L1: 13.087 G_L1_ABSOLUTE: 2.185 G_L1_RELATIVE: 10.902 G_Regularizer: 0.000 validation_error: 20.737 +(epoch: 41, iters: 267920, time: 0.548, data: 0.000) G_L1: 15.162 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 12.652 G_Regularizer: 0.000 validation_error: 20.601 +(epoch: 41, iters: 269920, time: 0.545, data: 0.000) G_L1: 14.791 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 12.424 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 41, iters: 271920, time: 0.543, data: 0.000) G_L1: 14.261 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 11.773 G_Regularizer: 0.000 validation_error: 20.947 +(epoch: 41, iters: 273920, time: 0.540, data: 0.000) G_L1: 13.169 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 10.915 G_Regularizer: 0.000 validation_error: 20.943 +(epoch: 41, iters: 275920, time: 0.546, data: 0.000) G_L1: 15.720 G_L1_ABSOLUTE: 2.861 G_L1_RELATIVE: 12.859 G_Regularizer: 0.000 validation_error: 20.641 +(epoch: 41, iters: 277920, time: 0.544, data: 0.001) G_L1: 13.598 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 11.265 G_Regularizer: 0.000 validation_error: 21.320 +(epoch: 41, iters: 279920, time: 0.546, data: 0.000) G_L1: 13.220 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 10.738 G_Regularizer: 0.000 validation_error: 21.036 +(epoch: 41, iters: 281920, time: 0.553, data: 0.000) G_L1: 11.818 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 9.391 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 41, iters: 283920, time: 0.543, data: 0.000) G_L1: 13.965 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 11.529 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 41, iters: 285920, time: 0.543, data: 0.000) G_L1: 14.259 G_L1_ABSOLUTE: 2.816 G_L1_RELATIVE: 11.443 G_Regularizer: 0.000 validation_error: 20.210 +(epoch: 41, iters: 287920, time: 0.542, data: 0.001) G_L1: 15.088 G_L1_ABSOLUTE: 2.583 G_L1_RELATIVE: 12.505 G_Regularizer: 0.000 validation_error: 20.523 +(epoch: 41, iters: 289920, time: 0.538, data: 0.001) G_L1: 14.560 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 12.010 G_Regularizer: 0.000 validation_error: 20.776 +(epoch: 41, iters: 291920, time: 0.530, data: 0.000) G_L1: 12.680 G_L1_ABSOLUTE: 2.665 G_L1_RELATIVE: 10.015 G_Regularizer: 0.000 validation_error: 21.195 +(epoch: 41, iters: 293920, time: 0.529, data: 0.000) G_L1: 14.786 G_L1_ABSOLUTE: 2.143 G_L1_RELATIVE: 12.643 G_Regularizer: 0.000 validation_error: 20.592 +(epoch: 41, iters: 295920, time: 0.528, data: 0.000) G_L1: 12.972 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 10.342 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 41, iters: 297920, time: 0.538, data: 0.001) G_L1: 12.231 G_L1_ABSOLUTE: 2.615 G_L1_RELATIVE: 9.616 G_Regularizer: 0.000 validation_error: 20.778 +(epoch: 41, iters: 299920, time: 0.531, data: 0.001) G_L1: 15.068 G_L1_ABSOLUTE: 3.068 G_L1_RELATIVE: 12.000 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 41, iters: 301920, time: 0.538, data: 0.000) G_L1: 12.908 G_L1_ABSOLUTE: 2.222 G_L1_RELATIVE: 10.686 G_Regularizer: 0.000 validation_error: 21.135 +(epoch: 42, iters: 1168, time: 0.531, data: 0.000) G_L1: 14.898 G_L1_ABSOLUTE: 3.260 G_L1_RELATIVE: 11.639 G_Regularizer: 0.000 validation_error: 21.053 +(epoch: 42, iters: 3168, time: 0.531, data: 0.001) G_L1: 15.221 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 12.459 G_Regularizer: 0.000 validation_error: 20.907 +(epoch: 42, iters: 5168, time: 0.533, data: 0.001) G_L1: 13.587 G_L1_ABSOLUTE: 2.573 G_L1_RELATIVE: 11.015 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 42, iters: 7168, time: 0.531, data: 0.001) G_L1: 13.430 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 10.976 G_Regularizer: 0.000 validation_error: 20.492 +(epoch: 42, iters: 9168, time: 0.537, data: 0.000) G_L1: 13.689 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 11.277 G_Regularizer: 0.000 validation_error: 21.145 +(epoch: 42, iters: 11168, time: 0.531, data: 0.000) G_L1: 13.307 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 10.811 G_Regularizer: 0.000 validation_error: 20.508 +(epoch: 42, iters: 13168, time: 0.531, data: 0.000) G_L1: 14.809 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 12.135 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 42, iters: 15168, time: 0.544, data: 0.000) G_L1: 12.463 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 10.116 G_Regularizer: 0.000 validation_error: 21.055 +(epoch: 42, iters: 17168, time: 0.532, data: 0.001) G_L1: 16.653 G_L1_ABSOLUTE: 2.919 G_L1_RELATIVE: 13.734 G_Regularizer: 0.000 validation_error: 20.716 +(epoch: 42, iters: 19168, time: 0.531, data: 0.001) G_L1: 13.070 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 10.565 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 42, iters: 21168, time: 0.529, data: 0.000) G_L1: 14.784 G_L1_ABSOLUTE: 2.793 G_L1_RELATIVE: 11.991 G_Regularizer: 0.000 validation_error: 21.078 +(epoch: 42, iters: 23168, time: 0.527, data: 0.000) G_L1: 14.689 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 11.905 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 42, iters: 25168, time: 0.540, data: 0.000) G_L1: 15.421 G_L1_ABSOLUTE: 2.785 G_L1_RELATIVE: 12.636 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 42, iters: 27168, time: 0.533, data: 0.000) G_L1: 12.748 G_L1_ABSOLUTE: 2.213 G_L1_RELATIVE: 10.535 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 42, iters: 29168, time: 0.532, data: 0.000) G_L1: 12.323 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 9.861 G_Regularizer: 0.000 validation_error: 20.561 +(epoch: 42, iters: 31168, time: 0.541, data: 0.000) G_L1: 16.265 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 13.632 G_Regularizer: 0.000 validation_error: 20.547 +(epoch: 42, iters: 33168, time: 0.536, data: 0.000) G_L1: 15.132 G_L1_ABSOLUTE: 2.547 G_L1_RELATIVE: 12.585 G_Regularizer: 0.000 validation_error: 20.740 +(epoch: 42, iters: 35168, time: 0.535, data: 0.000) G_L1: 13.779 G_L1_ABSOLUTE: 2.440 G_L1_RELATIVE: 11.339 G_Regularizer: 0.000 validation_error: 21.196 +(epoch: 42, iters: 37168, time: 0.540, data: 0.001) G_L1: 14.525 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 11.879 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 42, iters: 39168, time: 0.526, data: 0.000) G_L1: 13.796 G_L1_ABSOLUTE: 2.499 G_L1_RELATIVE: 11.297 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 42, iters: 41168, time: 0.534, data: 0.000) G_L1: 14.898 G_L1_ABSOLUTE: 2.029 G_L1_RELATIVE: 12.869 G_Regularizer: 0.000 validation_error: 20.560 +(epoch: 42, iters: 43168, time: 0.532, data: 0.001) G_L1: 11.622 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 9.024 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 42, iters: 45168, time: 0.534, data: 0.000) G_L1: 12.213 G_L1_ABSOLUTE: 2.163 G_L1_RELATIVE: 10.050 G_Regularizer: 0.000 validation_error: 21.198 +(epoch: 42, iters: 47168, time: 0.529, data: 0.000) G_L1: 15.624 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 13.142 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 42, iters: 49168, time: 0.531, data: 0.000) G_L1: 16.454 G_L1_ABSOLUTE: 2.238 G_L1_RELATIVE: 14.216 G_Regularizer: 0.000 validation_error: 20.464 +(epoch: 42, iters: 51168, time: 0.537, data: 0.000) G_L1: 24.276 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 21.384 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 42, iters: 53168, time: 0.532, data: 0.000) G_L1: 15.067 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 12.371 G_Regularizer: 0.000 validation_error: 20.690 +(epoch: 42, iters: 55168, time: 0.536, data: 0.000) G_L1: 16.208 G_L1_ABSOLUTE: 2.264 G_L1_RELATIVE: 13.944 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 42, iters: 57168, time: 0.532, data: 0.000) G_L1: 14.603 G_L1_ABSOLUTE: 2.669 G_L1_RELATIVE: 11.933 G_Regularizer: 0.000 validation_error: 21.144 +(epoch: 42, iters: 59168, time: 0.531, data: 0.000) G_L1: 17.747 G_L1_ABSOLUTE: 2.259 G_L1_RELATIVE: 15.488 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 42, iters: 61168, time: 0.534, data: 0.001) G_L1: 12.610 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 10.232 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 42, iters: 63168, time: 0.531, data: 0.000) G_L1: 15.618 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 13.205 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 42, iters: 65168, time: 0.534, data: 0.000) G_L1: 13.746 G_L1_ABSOLUTE: 2.204 G_L1_RELATIVE: 11.542 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 42, iters: 67168, time: 0.533, data: 0.000) G_L1: 13.690 G_L1_ABSOLUTE: 2.490 G_L1_RELATIVE: 11.201 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 42, iters: 69168, time: 0.532, data: 0.000) G_L1: 14.227 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 11.764 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 42, iters: 71168, time: 0.530, data: 0.000) G_L1: 11.916 G_L1_ABSOLUTE: 2.613 G_L1_RELATIVE: 9.303 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 42, iters: 73168, time: 0.543, data: 0.000) G_L1: 14.582 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 11.962 G_Regularizer: 0.000 validation_error: 21.435 +(epoch: 42, iters: 75168, time: 0.535, data: 0.000) G_L1: 11.818 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 9.277 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 42, iters: 77168, time: 0.530, data: 0.000) G_L1: 13.539 G_L1_ABSOLUTE: 2.305 G_L1_RELATIVE: 11.234 G_Regularizer: 0.000 validation_error: 20.995 +(epoch: 42, iters: 79168, time: 0.533, data: 0.000) G_L1: 13.848 G_L1_ABSOLUTE: 2.305 G_L1_RELATIVE: 11.543 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 42, iters: 81168, time: 0.530, data: 0.000) G_L1: 13.983 G_L1_ABSOLUTE: 2.908 G_L1_RELATIVE: 11.074 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 42, iters: 83168, time: 0.531, data: 0.000) G_L1: 14.572 G_L1_ABSOLUTE: 2.507 G_L1_RELATIVE: 12.066 G_Regularizer: 0.000 validation_error: 20.920 +(epoch: 42, iters: 85168, time: 0.534, data: 0.000) G_L1: 12.953 G_L1_ABSOLUTE: 2.362 G_L1_RELATIVE: 10.591 G_Regularizer: 0.000 validation_error: 20.667 +(epoch: 42, iters: 87168, time: 0.537, data: 0.001) G_L1: 17.711 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 15.119 G_Regularizer: 0.000 validation_error: 20.995 +(epoch: 42, iters: 89168, time: 0.532, data: 0.000) G_L1: 13.802 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 11.192 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 42, iters: 91168, time: 0.535, data: 0.000) G_L1: 11.532 G_L1_ABSOLUTE: 2.116 G_L1_RELATIVE: 9.416 G_Regularizer: 0.000 validation_error: 21.163 +(epoch: 42, iters: 93168, time: 0.539, data: 0.000) G_L1: 15.567 G_L1_ABSOLUTE: 2.933 G_L1_RELATIVE: 12.634 G_Regularizer: 0.000 validation_error: 20.611 +(epoch: 42, iters: 95168, time: 0.531, data: 0.001) G_L1: 15.912 G_L1_ABSOLUTE: 2.703 G_L1_RELATIVE: 13.210 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 42, iters: 97168, time: 0.535, data: 0.000) G_L1: 11.685 G_L1_ABSOLUTE: 2.273 G_L1_RELATIVE: 9.412 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 42, iters: 99168, time: 0.531, data: 0.000) G_L1: 15.493 G_L1_ABSOLUTE: 3.470 G_L1_RELATIVE: 12.023 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 42, iters: 101168, time: 0.528, data: 0.000) G_L1: 14.381 G_L1_ABSOLUTE: 2.266 G_L1_RELATIVE: 12.115 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 42, iters: 103168, time: 0.537, data: 0.000) G_L1: 11.017 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 8.752 G_Regularizer: 0.000 validation_error: 20.761 +(epoch: 42, iters: 105168, time: 0.532, data: 0.000) G_L1: 12.457 G_L1_ABSOLUTE: 2.651 G_L1_RELATIVE: 9.805 G_Regularizer: 0.000 validation_error: 21.221 +(epoch: 42, iters: 107168, time: 0.530, data: 0.000) G_L1: 13.843 G_L1_ABSOLUTE: 2.045 G_L1_RELATIVE: 11.798 G_Regularizer: 0.000 validation_error: 21.109 +(epoch: 42, iters: 109168, time: 0.529, data: 0.000) G_L1: 12.759 G_L1_ABSOLUTE: 2.396 G_L1_RELATIVE: 10.363 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 42, iters: 111168, time: 0.536, data: 0.001) G_L1: 13.539 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 11.035 G_Regularizer: 0.000 validation_error: 21.256 +(epoch: 42, iters: 113168, time: 0.543, data: 0.001) G_L1: 16.596 G_L1_ABSOLUTE: 2.692 G_L1_RELATIVE: 13.904 G_Regularizer: 0.000 validation_error: 20.728 +(epoch: 42, iters: 115168, time: 0.535, data: 0.000) G_L1: 13.268 G_L1_ABSOLUTE: 2.834 G_L1_RELATIVE: 10.434 G_Regularizer: 0.000 validation_error: 21.127 +(epoch: 42, iters: 117168, time: 0.534, data: 0.000) G_L1: 15.633 G_L1_ABSOLUTE: 2.717 G_L1_RELATIVE: 12.916 G_Regularizer: 0.000 validation_error: 21.236 +(epoch: 42, iters: 119168, time: 0.536, data: 0.000) G_L1: 14.495 G_L1_ABSOLUTE: 2.701 G_L1_RELATIVE: 11.794 G_Regularizer: 0.000 validation_error: 20.746 +(epoch: 42, iters: 121168, time: 0.539, data: 0.000) G_L1: 14.744 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 12.287 G_Regularizer: 0.000 validation_error: 20.786 +(epoch: 42, iters: 123168, time: 0.536, data: 0.000) G_L1: 13.618 G_L1_ABSOLUTE: 2.255 G_L1_RELATIVE: 11.363 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 42, iters: 125168, time: 0.537, data: 0.000) G_L1: 15.296 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 12.846 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 42, iters: 127168, time: 0.539, data: 0.000) G_L1: 14.226 G_L1_ABSOLUTE: 3.105 G_L1_RELATIVE: 11.121 G_Regularizer: 0.000 validation_error: 21.070 +(epoch: 42, iters: 129168, time: 0.535, data: 0.000) G_L1: 12.424 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 9.873 G_Regularizer: 0.000 validation_error: 21.070 +(epoch: 42, iters: 131168, time: 0.535, data: 0.000) G_L1: 15.546 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 13.347 G_Regularizer: 0.000 validation_error: 20.971 +(epoch: 42, iters: 133168, time: 0.534, data: 0.000) G_L1: 11.841 G_L1_ABSOLUTE: 2.184 G_L1_RELATIVE: 9.657 G_Regularizer: 0.000 validation_error: 20.994 +(epoch: 42, iters: 135168, time: 0.541, data: 0.000) G_L1: 14.553 G_L1_ABSOLUTE: 2.785 G_L1_RELATIVE: 11.768 G_Regularizer: 0.000 validation_error: 20.985 +(epoch: 42, iters: 137168, time: 0.532, data: 0.000) G_L1: 12.794 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 10.265 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 42, iters: 139168, time: 0.532, data: 0.000) G_L1: 12.897 G_L1_ABSOLUTE: 2.615 G_L1_RELATIVE: 10.282 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 42, iters: 141168, time: 0.551, data: 0.000) G_L1: 16.315 G_L1_ABSOLUTE: 2.897 G_L1_RELATIVE: 13.418 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 42, iters: 143168, time: 0.580, data: 0.000) G_L1: 14.513 G_L1_ABSOLUTE: 2.161 G_L1_RELATIVE: 12.353 G_Regularizer: 0.000 validation_error: 21.194 +(epoch: 42, iters: 145168, time: 0.595, data: 0.000) G_L1: 14.164 G_L1_ABSOLUTE: 2.879 G_L1_RELATIVE: 11.285 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 42, iters: 147168, time: 0.580, data: 0.000) G_L1: 13.181 G_L1_ABSOLUTE: 2.047 G_L1_RELATIVE: 11.133 G_Regularizer: 0.000 validation_error: 20.861 +(epoch: 42, iters: 149168, time: 0.551, data: 0.000) G_L1: 15.500 G_L1_ABSOLUTE: 2.612 G_L1_RELATIVE: 12.888 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 42, iters: 151168, time: 0.565, data: 0.000) G_L1: 13.768 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 11.250 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 42, iters: 153168, time: 0.589, data: 0.000) G_L1: 14.585 G_L1_ABSOLUTE: 2.925 G_L1_RELATIVE: 11.660 G_Regularizer: 0.000 validation_error: 21.302 +(epoch: 42, iters: 155168, time: 0.567, data: 0.000) G_L1: 14.202 G_L1_ABSOLUTE: 2.384 G_L1_RELATIVE: 11.818 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 42, iters: 157168, time: 0.581, data: 0.000) G_L1: 14.411 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 12.195 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 42, iters: 159168, time: 0.531, data: 0.000) G_L1: 14.604 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 11.974 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 42, iters: 161168, time: 0.527, data: 0.000) G_L1: 16.121 G_L1_ABSOLUTE: 2.871 G_L1_RELATIVE: 13.250 G_Regularizer: 0.000 validation_error: 20.929 +(epoch: 42, iters: 163168, time: 0.539, data: 0.000) G_L1: 15.832 G_L1_ABSOLUTE: 2.862 G_L1_RELATIVE: 12.969 G_Regularizer: 0.000 validation_error: 20.508 +(epoch: 42, iters: 165168, time: 0.534, data: 0.000) G_L1: 14.615 G_L1_ABSOLUTE: 2.085 G_L1_RELATIVE: 12.530 G_Regularizer: 0.000 validation_error: 21.125 +(epoch: 42, iters: 167168, time: 0.540, data: 0.000) G_L1: 13.989 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 11.546 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 42, iters: 169168, time: 0.533, data: 0.000) G_L1: 15.605 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 13.120 G_Regularizer: 0.000 validation_error: 20.446 +(epoch: 42, iters: 171168, time: 0.534, data: 0.000) G_L1: 13.519 G_L1_ABSOLUTE: 2.569 G_L1_RELATIVE: 10.950 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 42, iters: 173168, time: 0.536, data: 0.001) G_L1: 15.572 G_L1_ABSOLUTE: 2.829 G_L1_RELATIVE: 12.744 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 42, iters: 175168, time: 0.528, data: 0.000) G_L1: 12.501 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 10.073 G_Regularizer: 0.000 validation_error: 20.999 +(epoch: 42, iters: 177168, time: 0.539, data: 0.000) G_L1: 14.300 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 11.831 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 42, iters: 179168, time: 0.527, data: 0.001) G_L1: 14.476 G_L1_ABSOLUTE: 2.175 G_L1_RELATIVE: 12.301 G_Regularizer: 0.000 validation_error: 21.133 +(epoch: 42, iters: 181168, time: 0.539, data: 0.000) G_L1: 13.121 G_L1_ABSOLUTE: 2.039 G_L1_RELATIVE: 11.082 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 42, iters: 183168, time: 0.533, data: 0.001) G_L1: 12.156 G_L1_ABSOLUTE: 2.192 G_L1_RELATIVE: 9.964 G_Regularizer: 0.000 validation_error: 21.007 +(epoch: 42, iters: 185168, time: 0.537, data: 0.001) G_L1: 13.700 G_L1_ABSOLUTE: 2.700 G_L1_RELATIVE: 11.000 G_Regularizer: 0.000 validation_error: 21.193 +(epoch: 42, iters: 187168, time: 0.538, data: 0.000) G_L1: 13.698 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 11.115 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 42, iters: 189168, time: 0.535, data: 0.000) G_L1: 13.041 G_L1_ABSOLUTE: 2.527 G_L1_RELATIVE: 10.513 G_Regularizer: 0.000 validation_error: 21.148 +(epoch: 42, iters: 191168, time: 0.534, data: 0.000) G_L1: 14.259 G_L1_ABSOLUTE: 2.240 G_L1_RELATIVE: 12.018 G_Regularizer: 0.000 validation_error: 20.808 +(epoch: 42, iters: 193168, time: 0.530, data: 0.000) G_L1: 13.404 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 11.121 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 42, iters: 195168, time: 0.531, data: 0.000) G_L1: 15.108 G_L1_ABSOLUTE: 3.216 G_L1_RELATIVE: 11.892 G_Regularizer: 0.000 validation_error: 20.852 +(epoch: 42, iters: 197168, time: 0.532, data: 0.001) G_L1: 17.183 G_L1_ABSOLUTE: 2.516 G_L1_RELATIVE: 14.668 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 42, iters: 199168, time: 0.536, data: 0.000) G_L1: 13.602 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 10.945 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 42, iters: 201168, time: 0.531, data: 0.000) G_L1: 13.978 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 11.595 G_Regularizer: 0.000 validation_error: 20.651 +(epoch: 42, iters: 203168, time: 0.530, data: 0.000) G_L1: 17.911 G_L1_ABSOLUTE: 2.929 G_L1_RELATIVE: 14.982 G_Regularizer: 0.000 validation_error: 20.984 +(epoch: 42, iters: 205168, time: 0.533, data: 0.000) G_L1: 14.804 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 12.459 G_Regularizer: 0.000 validation_error: 21.128 +(epoch: 42, iters: 207168, time: 0.538, data: 0.000) G_L1: 13.170 G_L1_ABSOLUTE: 2.279 G_L1_RELATIVE: 10.891 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 42, iters: 209168, time: 0.537, data: 0.000) G_L1: 13.825 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 11.003 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 42, iters: 211168, time: 0.533, data: 0.000) G_L1: 15.966 G_L1_ABSOLUTE: 2.185 G_L1_RELATIVE: 13.781 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 42, iters: 213168, time: 0.531, data: 0.000) G_L1: 14.473 G_L1_ABSOLUTE: 3.027 G_L1_RELATIVE: 11.446 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 42, iters: 215168, time: 0.536, data: 0.001) G_L1: 14.682 G_L1_ABSOLUTE: 2.321 G_L1_RELATIVE: 12.360 G_Regularizer: 0.000 validation_error: 20.570 +(epoch: 42, iters: 217168, time: 0.531, data: 0.001) G_L1: 14.106 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 11.556 G_Regularizer: 0.000 validation_error: 21.158 +(epoch: 42, iters: 219168, time: 0.538, data: 0.001) G_L1: 13.832 G_L1_ABSOLUTE: 2.192 G_L1_RELATIVE: 11.641 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 42, iters: 221168, time: 0.539, data: 0.000) G_L1: 13.313 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 10.999 G_Regularizer: 0.000 validation_error: 20.904 +(epoch: 42, iters: 223168, time: 0.530, data: 0.001) G_L1: 15.922 G_L1_ABSOLUTE: 2.615 G_L1_RELATIVE: 13.308 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 42, iters: 225168, time: 0.536, data: 0.000) G_L1: 14.113 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 11.639 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 42, iters: 227168, time: 0.528, data: 0.001) G_L1: 12.425 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 9.958 G_Regularizer: 0.000 validation_error: 21.524 +(epoch: 42, iters: 229168, time: 0.542, data: 0.000) G_L1: 12.262 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 9.372 G_Regularizer: 0.000 validation_error: 21.311 +(epoch: 42, iters: 231168, time: 0.530, data: 0.000) G_L1: 13.269 G_L1_ABSOLUTE: 2.230 G_L1_RELATIVE: 11.039 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 42, iters: 233168, time: 0.542, data: 0.000) G_L1: 12.916 G_L1_ABSOLUTE: 1.891 G_L1_RELATIVE: 11.025 G_Regularizer: 0.000 validation_error: 20.719 +(epoch: 42, iters: 235168, time: 0.536, data: 0.000) G_L1: 11.145 G_L1_ABSOLUTE: 2.540 G_L1_RELATIVE: 8.606 G_Regularizer: 0.000 validation_error: 21.202 +(epoch: 42, iters: 237168, time: 0.539, data: 0.001) G_L1: 14.368 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 12.134 G_Regularizer: 0.000 validation_error: 21.124 +(epoch: 42, iters: 239168, time: 0.536, data: 0.000) G_L1: 18.056 G_L1_ABSOLUTE: 2.500 G_L1_RELATIVE: 15.556 G_Regularizer: 0.000 validation_error: 21.165 +(epoch: 42, iters: 241168, time: 0.534, data: 0.001) G_L1: 15.834 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 13.292 G_Regularizer: 0.000 validation_error: 20.954 +(epoch: 42, iters: 243168, time: 0.550, data: 0.000) G_L1: 12.678 G_L1_ABSOLUTE: 2.139 G_L1_RELATIVE: 10.538 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 42, iters: 245168, time: 0.553, data: 0.000) G_L1: 11.597 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 9.093 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 42, iters: 247168, time: 0.556, data: 0.000) G_L1: 13.666 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 10.999 G_Regularizer: 0.000 validation_error: 21.202 +(epoch: 42, iters: 249168, time: 0.536, data: 0.000) G_L1: 12.560 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 10.298 G_Regularizer: 0.000 validation_error: 20.747 +(epoch: 42, iters: 251168, time: 0.552, data: 0.000) G_L1: 17.271 G_L1_ABSOLUTE: 2.838 G_L1_RELATIVE: 14.433 G_Regularizer: 0.000 validation_error: 21.006 +(epoch: 42, iters: 253168, time: 0.555, data: 0.000) G_L1: 13.828 G_L1_ABSOLUTE: 2.237 G_L1_RELATIVE: 11.591 G_Regularizer: 0.000 validation_error: 21.268 +(epoch: 42, iters: 255168, time: 0.552, data: 0.000) G_L1: 14.657 G_L1_ABSOLUTE: 2.264 G_L1_RELATIVE: 12.393 G_Regularizer: 0.000 validation_error: 21.155 +(epoch: 42, iters: 257168, time: 0.558, data: 0.000) G_L1: 12.840 G_L1_ABSOLUTE: 2.229 G_L1_RELATIVE: 10.611 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 42, iters: 259168, time: 0.550, data: 0.000) G_L1: 14.897 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 12.239 G_Regularizer: 0.000 validation_error: 21.428 +(epoch: 42, iters: 261168, time: 0.547, data: 0.000) G_L1: 12.898 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 10.415 G_Regularizer: 0.000 validation_error: 21.192 +(epoch: 42, iters: 263168, time: 0.539, data: 0.000) G_L1: 17.170 G_L1_ABSOLUTE: 3.259 G_L1_RELATIVE: 13.911 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 42, iters: 265168, time: 0.540, data: 0.000) G_L1: 16.443 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 14.088 G_Regularizer: 0.000 validation_error: 21.057 +(epoch: 42, iters: 267168, time: 0.546, data: 0.000) G_L1: 12.907 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 10.230 G_Regularizer: 0.000 validation_error: 20.960 +(epoch: 42, iters: 269168, time: 0.547, data: 0.000) G_L1: 12.705 G_L1_ABSOLUTE: 2.260 G_L1_RELATIVE: 10.446 G_Regularizer: 0.000 validation_error: 20.736 +(epoch: 42, iters: 271168, time: 0.551, data: 0.000) G_L1: 15.396 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 12.819 G_Regularizer: 0.000 validation_error: 21.166 +(epoch: 42, iters: 273168, time: 0.545, data: 0.000) G_L1: 14.342 G_L1_ABSOLUTE: 2.358 G_L1_RELATIVE: 11.984 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 42, iters: 275168, time: 0.540, data: 0.000) G_L1: 14.749 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 12.092 G_Regularizer: 0.000 validation_error: 21.155 +(epoch: 42, iters: 277168, time: 0.547, data: 0.000) G_L1: 11.636 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 8.945 G_Regularizer: 0.000 validation_error: 20.805 +(epoch: 42, iters: 279168, time: 0.553, data: 0.000) G_L1: 12.489 G_L1_ABSOLUTE: 2.148 G_L1_RELATIVE: 10.341 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 42, iters: 281168, time: 0.555, data: 0.000) G_L1: 11.346 G_L1_ABSOLUTE: 2.217 G_L1_RELATIVE: 9.129 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 42, iters: 283168, time: 0.542, data: 0.000) G_L1: 13.733 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 11.498 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 42, iters: 285168, time: 0.534, data: 0.000) G_L1: 13.730 G_L1_ABSOLUTE: 2.563 G_L1_RELATIVE: 11.167 G_Regularizer: 0.000 validation_error: 20.671 +(epoch: 42, iters: 287168, time: 0.552, data: 0.000) G_L1: 12.243 G_L1_ABSOLUTE: 2.290 G_L1_RELATIVE: 9.954 G_Regularizer: 0.000 validation_error: 21.153 +(epoch: 42, iters: 289168, time: 0.561, data: 0.001) G_L1: 16.455 G_L1_ABSOLUTE: 3.102 G_L1_RELATIVE: 13.352 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 42, iters: 291168, time: 0.548, data: 0.000) G_L1: 14.505 G_L1_ABSOLUTE: 2.083 G_L1_RELATIVE: 12.422 G_Regularizer: 0.000 validation_error: 20.984 +(epoch: 42, iters: 293168, time: 0.548, data: 0.000) G_L1: 12.925 G_L1_ABSOLUTE: 1.966 G_L1_RELATIVE: 10.960 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 42, iters: 295168, time: 0.553, data: 0.000) G_L1: 14.089 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 11.488 G_Regularizer: 0.000 validation_error: 21.310 +(epoch: 42, iters: 297168, time: 0.550, data: 0.000) G_L1: 14.222 G_L1_ABSOLUTE: 2.442 G_L1_RELATIVE: 11.780 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 42, iters: 299168, time: 0.536, data: 0.000) G_L1: 16.529 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 13.937 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 42, iters: 301168, time: 0.521, data: 0.000) G_L1: 14.893 G_L1_ABSOLUTE: 3.190 G_L1_RELATIVE: 11.703 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 43, iters: 416, time: 0.552, data: 0.000) G_L1: 15.790 G_L1_ABSOLUTE: 2.907 G_L1_RELATIVE: 12.883 G_Regularizer: 0.000 validation_error: 21.137 +(epoch: 43, iters: 2416, time: 0.550, data: 0.000) G_L1: 13.883 G_L1_ABSOLUTE: 2.260 G_L1_RELATIVE: 11.623 G_Regularizer: 0.000 validation_error: 21.040 +(epoch: 43, iters: 4416, time: 0.557, data: 0.000) G_L1: 14.368 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 11.691 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 43, iters: 6416, time: 0.536, data: 0.001) G_L1: 15.613 G_L1_ABSOLUTE: 2.744 G_L1_RELATIVE: 12.869 G_Regularizer: 0.000 validation_error: 21.251 +(epoch: 43, iters: 8416, time: 0.544, data: 0.000) G_L1: 12.993 G_L1_ABSOLUTE: 2.180 G_L1_RELATIVE: 10.812 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 43, iters: 10416, time: 0.552, data: 0.000) G_L1: 15.451 G_L1_ABSOLUTE: 2.749 G_L1_RELATIVE: 12.702 G_Regularizer: 0.000 validation_error: 21.103 +(epoch: 43, iters: 12416, time: 0.546, data: 0.001) G_L1: 13.129 G_L1_ABSOLUTE: 2.516 G_L1_RELATIVE: 10.612 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 43, iters: 14416, time: 0.557, data: 0.000) G_L1: 12.447 G_L1_ABSOLUTE: 2.219 G_L1_RELATIVE: 10.227 G_Regularizer: 0.000 validation_error: 20.818 +(epoch: 43, iters: 16416, time: 0.545, data: 0.000) G_L1: 14.548 G_L1_ABSOLUTE: 2.508 G_L1_RELATIVE: 12.040 G_Regularizer: 0.000 validation_error: 21.148 +(epoch: 43, iters: 18416, time: 0.551, data: 0.000) G_L1: 14.733 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 12.390 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 43, iters: 20416, time: 0.551, data: 0.000) G_L1: 12.578 G_L1_ABSOLUTE: 2.553 G_L1_RELATIVE: 10.025 G_Regularizer: 0.000 validation_error: 20.785 +(epoch: 43, iters: 22416, time: 0.560, data: 0.000) G_L1: 13.393 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 10.986 G_Regularizer: 0.000 validation_error: 20.797 +(epoch: 43, iters: 24416, time: 0.553, data: 0.000) G_L1: 14.071 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 11.610 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 43, iters: 26416, time: 0.551, data: 0.000) G_L1: 14.614 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 12.379 G_Regularizer: 0.000 validation_error: 20.756 +(epoch: 43, iters: 28416, time: 0.544, data: 0.000) G_L1: 11.688 G_L1_ABSOLUTE: 2.794 G_L1_RELATIVE: 8.894 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 43, iters: 30416, time: 0.554, data: 0.000) G_L1: 13.910 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 10.972 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 43, iters: 32416, time: 0.557, data: 0.000) G_L1: 11.773 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 9.252 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 43, iters: 34416, time: 0.549, data: 0.000) G_L1: 14.544 G_L1_ABSOLUTE: 2.487 G_L1_RELATIVE: 12.057 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 43, iters: 36416, time: 0.553, data: 0.000) G_L1: 13.890 G_L1_ABSOLUTE: 2.285 G_L1_RELATIVE: 11.605 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 43, iters: 38416, time: 0.556, data: 0.000) G_L1: 13.093 G_L1_ABSOLUTE: 2.136 G_L1_RELATIVE: 10.957 G_Regularizer: 0.000 validation_error: 20.991 +(epoch: 43, iters: 40416, time: 0.567, data: 0.001) G_L1: 10.620 G_L1_ABSOLUTE: 2.076 G_L1_RELATIVE: 8.545 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 43, iters: 42416, time: 0.548, data: 0.000) G_L1: 11.518 G_L1_ABSOLUTE: 2.117 G_L1_RELATIVE: 9.401 G_Regularizer: 0.000 validation_error: 20.633 +(epoch: 43, iters: 44416, time: 0.549, data: 0.000) G_L1: 15.369 G_L1_ABSOLUTE: 2.811 G_L1_RELATIVE: 12.558 G_Regularizer: 0.000 validation_error: 20.964 +(epoch: 43, iters: 46416, time: 0.553, data: 0.000) G_L1: 11.842 G_L1_ABSOLUTE: 2.211 G_L1_RELATIVE: 9.631 G_Regularizer: 0.000 validation_error: 21.389 +(epoch: 43, iters: 48416, time: 0.565, data: 0.000) G_L1: 19.041 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 16.469 G_Regularizer: 0.000 validation_error: 20.498 +(epoch: 43, iters: 50416, time: 0.552, data: 0.000) G_L1: 15.270 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 12.925 G_Regularizer: 0.000 validation_error: 20.683 +(epoch: 43, iters: 52416, time: 0.550, data: 0.000) G_L1: 13.026 G_L1_ABSOLUTE: 2.055 G_L1_RELATIVE: 10.971 G_Regularizer: 0.000 validation_error: 20.644 +(epoch: 43, iters: 54416, time: 0.551, data: 0.000) G_L1: 12.111 G_L1_ABSOLUTE: 2.397 G_L1_RELATIVE: 9.714 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 43, iters: 56416, time: 0.554, data: 0.000) G_L1: 12.857 G_L1_ABSOLUTE: 2.255 G_L1_RELATIVE: 10.603 G_Regularizer: 0.000 validation_error: 20.571 +(epoch: 43, iters: 58416, time: 0.549, data: 0.000) G_L1: 15.180 G_L1_ABSOLUTE: 2.244 G_L1_RELATIVE: 12.935 G_Regularizer: 0.000 validation_error: 21.345 +(epoch: 43, iters: 60416, time: 0.541, data: 0.000) G_L1: 13.603 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 11.254 G_Regularizer: 0.000 validation_error: 21.151 +(epoch: 43, iters: 62416, time: 0.541, data: 0.000) G_L1: 12.914 G_L1_ABSOLUTE: 2.824 G_L1_RELATIVE: 10.089 G_Regularizer: 0.000 validation_error: 21.068 +(epoch: 43, iters: 64416, time: 0.545, data: 0.000) G_L1: 14.504 G_L1_ABSOLUTE: 2.713 G_L1_RELATIVE: 11.791 G_Regularizer: 0.000 validation_error: 21.090 +(epoch: 43, iters: 66416, time: 0.537, data: 0.000) G_L1: 13.085 G_L1_ABSOLUTE: 2.727 G_L1_RELATIVE: 10.359 G_Regularizer: 0.000 validation_error: 20.964 +(epoch: 43, iters: 68416, time: 0.559, data: 0.000) G_L1: 15.026 G_L1_ABSOLUTE: 2.297 G_L1_RELATIVE: 12.729 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 43, iters: 70416, time: 0.563, data: 0.000) G_L1: 12.017 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 9.309 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 43, iters: 72416, time: 0.558, data: 0.000) G_L1: 16.321 G_L1_ABSOLUTE: 2.968 G_L1_RELATIVE: 13.353 G_Regularizer: 0.000 validation_error: 20.992 +(epoch: 43, iters: 74416, time: 0.554, data: 0.000) G_L1: 13.189 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 10.730 G_Regularizer: 0.000 validation_error: 21.219 +(epoch: 43, iters: 76416, time: 0.553, data: 0.000) G_L1: 13.080 G_L1_ABSOLUTE: 2.137 G_L1_RELATIVE: 10.943 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 43, iters: 78416, time: 0.561, data: 0.000) G_L1: 12.084 G_L1_ABSOLUTE: 2.191 G_L1_RELATIVE: 9.893 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 43, iters: 80416, time: 0.556, data: 0.000) G_L1: 13.273 G_L1_ABSOLUTE: 2.293 G_L1_RELATIVE: 10.980 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 43, iters: 82416, time: 0.544, data: 0.000) G_L1: 12.943 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 10.241 G_Regularizer: 0.000 validation_error: 20.673 +(epoch: 43, iters: 84416, time: 0.562, data: 0.000) G_L1: 16.056 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 13.171 G_Regularizer: 0.000 validation_error: 20.980 +(epoch: 43, iters: 86416, time: 0.539, data: 0.000) G_L1: 14.271 G_L1_ABSOLUTE: 2.492 G_L1_RELATIVE: 11.778 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 43, iters: 88416, time: 0.564, data: 0.000) G_L1: 12.287 G_L1_ABSOLUTE: 2.452 G_L1_RELATIVE: 9.835 G_Regularizer: 0.000 validation_error: 20.588 +(epoch: 43, iters: 90416, time: 0.552, data: 0.001) G_L1: 14.875 G_L1_ABSOLUTE: 2.335 G_L1_RELATIVE: 12.540 G_Regularizer: 0.000 validation_error: 20.921 +(epoch: 43, iters: 92416, time: 0.544, data: 0.000) G_L1: 13.125 G_L1_ABSOLUTE: 3.086 G_L1_RELATIVE: 10.040 G_Regularizer: 0.000 validation_error: 21.058 +(epoch: 43, iters: 94416, time: 0.559, data: 0.000) G_L1: 14.287 G_L1_ABSOLUTE: 2.791 G_L1_RELATIVE: 11.496 G_Regularizer: 0.000 validation_error: 21.029 +(epoch: 43, iters: 96416, time: 0.565, data: 0.001) G_L1: 14.310 G_L1_ABSOLUTE: 2.790 G_L1_RELATIVE: 11.520 G_Regularizer: 0.000 validation_error: 21.033 +(epoch: 43, iters: 98416, time: 0.569, data: 0.000) G_L1: 12.250 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 9.714 G_Regularizer: 0.000 validation_error: 21.041 +(epoch: 43, iters: 100416, time: 0.533, data: 0.000) G_L1: 16.172 G_L1_ABSOLUTE: 3.402 G_L1_RELATIVE: 12.770 G_Regularizer: 0.000 validation_error: 20.808 +(epoch: 43, iters: 102416, time: 0.556, data: 0.000) G_L1: 12.235 G_L1_ABSOLUTE: 2.153 G_L1_RELATIVE: 10.082 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 43, iters: 104416, time: 0.543, data: 0.000) G_L1: 14.404 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 11.713 G_Regularizer: 0.000 validation_error: 20.984 +(epoch: 43, iters: 106416, time: 0.543, data: 0.000) G_L1: 11.852 G_L1_ABSOLUTE: 2.326 G_L1_RELATIVE: 9.525 G_Regularizer: 0.000 validation_error: 20.929 +(epoch: 43, iters: 108416, time: 0.541, data: 0.000) G_L1: 14.888 G_L1_ABSOLUTE: 2.910 G_L1_RELATIVE: 11.977 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 43, iters: 110416, time: 0.550, data: 0.000) G_L1: 16.410 G_L1_ABSOLUTE: 3.048 G_L1_RELATIVE: 13.362 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 43, iters: 112416, time: 0.555, data: 0.000) G_L1: 14.724 G_L1_ABSOLUTE: 2.294 G_L1_RELATIVE: 12.431 G_Regularizer: 0.000 validation_error: 20.681 +(epoch: 43, iters: 114416, time: 0.555, data: 0.000) G_L1: 16.235 G_L1_ABSOLUTE: 2.478 G_L1_RELATIVE: 13.758 G_Regularizer: 0.000 validation_error: 20.458 +(epoch: 43, iters: 116416, time: 0.552, data: 0.000) G_L1: 13.759 G_L1_ABSOLUTE: 2.541 G_L1_RELATIVE: 11.218 G_Regularizer: 0.000 validation_error: 20.622 +(epoch: 43, iters: 118416, time: 0.558, data: 0.000) G_L1: 15.833 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 13.298 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 43, iters: 120416, time: 0.533, data: 0.000) G_L1: 13.304 G_L1_ABSOLUTE: 2.747 G_L1_RELATIVE: 10.557 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 43, iters: 122416, time: 0.537, data: 0.000) G_L1: 12.839 G_L1_ABSOLUTE: 2.341 G_L1_RELATIVE: 10.498 G_Regularizer: 0.000 validation_error: 21.108 +(epoch: 43, iters: 124416, time: 0.543, data: 0.001) G_L1: 16.667 G_L1_ABSOLUTE: 2.819 G_L1_RELATIVE: 13.849 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 43, iters: 126416, time: 0.557, data: 0.000) G_L1: 14.240 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 11.790 G_Regularizer: 0.000 validation_error: 21.075 +(epoch: 43, iters: 128416, time: 0.549, data: 0.000) G_L1: 13.758 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 11.460 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 43, iters: 130416, time: 0.563, data: 0.000) G_L1: 15.255 G_L1_ABSOLUTE: 2.949 G_L1_RELATIVE: 12.306 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 43, iters: 132416, time: 0.559, data: 0.000) G_L1: 13.908 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 11.451 G_Regularizer: 0.000 validation_error: 20.869 +(epoch: 43, iters: 134416, time: 0.540, data: 0.000) G_L1: 13.171 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 10.266 G_Regularizer: 0.000 validation_error: 21.120 +(epoch: 43, iters: 136416, time: 0.537, data: 0.000) G_L1: 14.653 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 12.028 G_Regularizer: 0.000 validation_error: 21.074 +(epoch: 43, iters: 138416, time: 0.561, data: 0.001) G_L1: 12.042 G_L1_ABSOLUTE: 3.019 G_L1_RELATIVE: 9.024 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 43, iters: 140416, time: 0.556, data: 0.000) G_L1: 13.130 G_L1_ABSOLUTE: 2.571 G_L1_RELATIVE: 10.559 G_Regularizer: 0.000 validation_error: 21.137 +(epoch: 43, iters: 142416, time: 0.540, data: 0.002) G_L1: 12.754 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 10.024 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 43, iters: 144416, time: 0.560, data: 0.000) G_L1: 15.613 G_L1_ABSOLUTE: 2.687 G_L1_RELATIVE: 12.926 G_Regularizer: 0.000 validation_error: 21.459 +(epoch: 43, iters: 146416, time: 0.553, data: 0.000) G_L1: 13.586 G_L1_ABSOLUTE: 2.374 G_L1_RELATIVE: 11.212 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 43, iters: 148416, time: 0.536, data: 0.000) G_L1: 14.672 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 12.328 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 43, iters: 150416, time: 0.544, data: 0.000) G_L1: 12.208 G_L1_ABSOLUTE: 2.268 G_L1_RELATIVE: 9.940 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 43, iters: 152416, time: 0.548, data: 0.000) G_L1: 13.934 G_L1_ABSOLUTE: 2.629 G_L1_RELATIVE: 11.305 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 43, iters: 154416, time: 0.556, data: 0.000) G_L1: 12.636 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 10.055 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 43, iters: 156416, time: 0.547, data: 0.001) G_L1: 14.918 G_L1_ABSOLUTE: 2.337 G_L1_RELATIVE: 12.581 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 43, iters: 158416, time: 0.536, data: 0.000) G_L1: 14.234 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 11.610 G_Regularizer: 0.000 validation_error: 20.819 +(epoch: 43, iters: 160416, time: 0.553, data: 0.000) G_L1: 16.868 G_L1_ABSOLUTE: 2.972 G_L1_RELATIVE: 13.897 G_Regularizer: 0.000 validation_error: 20.535 +(epoch: 43, iters: 162416, time: 0.565, data: 0.000) G_L1: 14.377 G_L1_ABSOLUTE: 2.263 G_L1_RELATIVE: 12.114 G_Regularizer: 0.000 validation_error: 20.753 +(epoch: 43, iters: 164416, time: 0.562, data: 0.000) G_L1: 12.621 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 10.246 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 43, iters: 166416, time: 0.559, data: 0.000) G_L1: 11.947 G_L1_ABSOLUTE: 2.235 G_L1_RELATIVE: 9.711 G_Regularizer: 0.000 validation_error: 20.972 +(epoch: 43, iters: 168416, time: 0.540, data: 0.000) G_L1: 15.267 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 12.883 G_Regularizer: 0.000 validation_error: 20.902 +(epoch: 43, iters: 170416, time: 0.543, data: 0.000) G_L1: 12.812 G_L1_ABSOLUTE: 2.280 G_L1_RELATIVE: 10.531 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 43, iters: 172416, time: 0.550, data: 0.000) G_L1: 12.279 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 9.858 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 43, iters: 174416, time: 0.556, data: 0.000) G_L1: 11.145 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 8.731 G_Regularizer: 0.000 validation_error: 21.222 +(epoch: 43, iters: 176416, time: 0.556, data: 0.000) G_L1: 16.240 G_L1_ABSOLUTE: 2.583 G_L1_RELATIVE: 13.658 G_Regularizer: 0.000 validation_error: 21.119 +(epoch: 43, iters: 178416, time: 0.555, data: 0.000) G_L1: 12.569 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 10.109 G_Regularizer: 0.000 validation_error: 20.795 +(epoch: 43, iters: 180416, time: 0.559, data: 0.001) G_L1: 13.105 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 10.695 G_Regularizer: 0.000 validation_error: 21.095 +(epoch: 43, iters: 182416, time: 0.556, data: 0.001) G_L1: 12.657 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 10.202 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 43, iters: 184416, time: 0.544, data: 0.000) G_L1: 14.547 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 12.021 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 43, iters: 186416, time: 0.558, data: 0.000) G_L1: 12.185 G_L1_ABSOLUTE: 2.096 G_L1_RELATIVE: 10.089 G_Regularizer: 0.000 validation_error: 20.836 +(epoch: 43, iters: 188416, time: 0.564, data: 0.000) G_L1: 15.334 G_L1_ABSOLUTE: 2.528 G_L1_RELATIVE: 12.806 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 43, iters: 190416, time: 0.564, data: 0.001) G_L1: 15.090 G_L1_ABSOLUTE: 2.277 G_L1_RELATIVE: 12.813 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 43, iters: 192416, time: 0.559, data: 0.000) G_L1: 11.732 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 9.449 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 43, iters: 194416, time: 0.555, data: 0.001) G_L1: 21.905 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 19.024 G_Regularizer: 0.000 validation_error: 20.628 +(epoch: 43, iters: 196416, time: 0.549, data: 0.000) G_L1: 16.290 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 13.640 G_Regularizer: 0.000 validation_error: 20.893 +(epoch: 43, iters: 198416, time: 0.540, data: 0.000) G_L1: 13.504 G_L1_ABSOLUTE: 2.438 G_L1_RELATIVE: 11.066 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 43, iters: 200416, time: 0.570, data: 0.000) G_L1: 15.594 G_L1_ABSOLUTE: 2.200 G_L1_RELATIVE: 13.394 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 43, iters: 202416, time: 0.545, data: 0.000) G_L1: 15.267 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 12.897 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 43, iters: 204416, time: 0.556, data: 0.000) G_L1: 14.213 G_L1_ABSOLUTE: 2.280 G_L1_RELATIVE: 11.933 G_Regularizer: 0.000 validation_error: 20.892 +(epoch: 43, iters: 206416, time: 0.562, data: 0.000) G_L1: 13.420 G_L1_ABSOLUTE: 2.453 G_L1_RELATIVE: 10.968 G_Regularizer: 0.000 validation_error: 21.029 +(epoch: 43, iters: 208416, time: 0.563, data: 0.000) G_L1: 13.414 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 10.723 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 43, iters: 210416, time: 0.541, data: 0.000) G_L1: 15.183 G_L1_ABSOLUTE: 2.092 G_L1_RELATIVE: 13.091 G_Regularizer: 0.000 validation_error: 21.076 +(epoch: 43, iters: 212416, time: 0.545, data: 0.000) G_L1: 11.589 G_L1_ABSOLUTE: 2.292 G_L1_RELATIVE: 9.297 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 43, iters: 214416, time: 0.553, data: 0.000) G_L1: 13.032 G_L1_ABSOLUTE: 2.138 G_L1_RELATIVE: 10.893 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 43, iters: 216416, time: 0.556, data: 0.000) G_L1: 16.056 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 13.539 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 43, iters: 218416, time: 0.549, data: 0.000) G_L1: 13.361 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 10.739 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 43, iters: 220416, time: 0.548, data: 0.000) G_L1: 13.177 G_L1_ABSOLUTE: 3.134 G_L1_RELATIVE: 10.043 G_Regularizer: 0.000 validation_error: 20.938 +(epoch: 43, iters: 222416, time: 0.565, data: 0.000) G_L1: 12.793 G_L1_ABSOLUTE: 2.032 G_L1_RELATIVE: 10.761 G_Regularizer: 0.000 validation_error: 20.805 +(epoch: 43, iters: 224416, time: 0.561, data: 0.000) G_L1: 13.973 G_L1_ABSOLUTE: 2.743 G_L1_RELATIVE: 11.230 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 43, iters: 226416, time: 0.540, data: 0.000) G_L1: 14.540 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 12.157 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 43, iters: 228416, time: 0.545, data: 0.000) G_L1: 13.696 G_L1_ABSOLUTE: 2.294 G_L1_RELATIVE: 11.402 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 43, iters: 230416, time: 0.555, data: 0.000) G_L1: 12.669 G_L1_ABSOLUTE: 2.303 G_L1_RELATIVE: 10.366 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 43, iters: 232416, time: 0.553, data: 0.000) G_L1: 14.723 G_L1_ABSOLUTE: 2.316 G_L1_RELATIVE: 12.407 G_Regularizer: 0.000 validation_error: 20.542 +(epoch: 43, iters: 234416, time: 0.562, data: 0.000) G_L1: 17.991 G_L1_ABSOLUTE: 2.915 G_L1_RELATIVE: 15.076 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 43, iters: 236416, time: 0.534, data: 0.000) G_L1: 15.218 G_L1_ABSOLUTE: 2.193 G_L1_RELATIVE: 13.026 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 43, iters: 238416, time: 0.559, data: 0.000) G_L1: 15.558 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 13.111 G_Regularizer: 0.000 validation_error: 21.423 +(epoch: 43, iters: 240416, time: 0.564, data: 0.000) G_L1: 13.123 G_L1_ABSOLUTE: 2.257 G_L1_RELATIVE: 10.865 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 43, iters: 242416, time: 0.553, data: 0.000) G_L1: 15.231 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 12.645 G_Regularizer: 0.000 validation_error: 21.022 +(epoch: 43, iters: 244416, time: 0.558, data: 0.000) G_L1: 16.928 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 14.446 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 43, iters: 246416, time: 0.562, data: 0.000) G_L1: 12.564 G_L1_ABSOLUTE: 2.142 G_L1_RELATIVE: 10.422 G_Regularizer: 0.000 validation_error: 20.485 +(epoch: 43, iters: 248416, time: 0.562, data: 0.000) G_L1: 14.410 G_L1_ABSOLUTE: 2.613 G_L1_RELATIVE: 11.797 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 43, iters: 250416, time: 0.550, data: 0.000) G_L1: 14.934 G_L1_ABSOLUTE: 2.980 G_L1_RELATIVE: 11.954 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 43, iters: 252416, time: 0.563, data: 0.000) G_L1: 14.875 G_L1_ABSOLUTE: 2.685 G_L1_RELATIVE: 12.190 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 43, iters: 254416, time: 0.535, data: 0.000) G_L1: 13.767 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 11.394 G_Regularizer: 0.000 validation_error: 20.729 +(epoch: 43, iters: 256416, time: 0.558, data: 0.000) G_L1: 11.073 G_L1_ABSOLUTE: 1.991 G_L1_RELATIVE: 9.082 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 43, iters: 258416, time: 0.553, data: 0.000) G_L1: 13.420 G_L1_ABSOLUTE: 2.234 G_L1_RELATIVE: 11.185 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 43, iters: 260416, time: 0.561, data: 0.000) G_L1: 15.477 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 12.745 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 43, iters: 262416, time: 0.544, data: 0.000) G_L1: 13.963 G_L1_ABSOLUTE: 2.443 G_L1_RELATIVE: 11.520 G_Regularizer: 0.000 validation_error: 20.848 +(epoch: 43, iters: 264416, time: 0.535, data: 0.000) G_L1: 13.710 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 11.199 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 43, iters: 266416, time: 0.557, data: 0.000) G_L1: 14.061 G_L1_ABSOLUTE: 2.742 G_L1_RELATIVE: 11.319 G_Regularizer: 0.000 validation_error: 21.073 +(epoch: 43, iters: 268416, time: 0.551, data: 0.000) G_L1: 14.393 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 11.994 G_Regularizer: 0.000 validation_error: 20.533 +(epoch: 43, iters: 270416, time: 0.548, data: 0.000) G_L1: 13.754 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 11.434 G_Regularizer: 0.000 validation_error: 20.410 +(epoch: 43, iters: 272416, time: 0.551, data: 0.000) G_L1: 16.454 G_L1_ABSOLUTE: 2.499 G_L1_RELATIVE: 13.954 G_Regularizer: 0.000 validation_error: 21.116 +(epoch: 43, iters: 274416, time: 0.549, data: 0.000) G_L1: 15.649 G_L1_ABSOLUTE: 2.878 G_L1_RELATIVE: 12.771 G_Regularizer: 0.000 validation_error: 20.669 +(epoch: 43, iters: 276416, time: 0.541, data: 0.000) G_L1: 14.685 G_L1_ABSOLUTE: 2.655 G_L1_RELATIVE: 12.030 G_Regularizer: 0.000 validation_error: 20.994 +(epoch: 43, iters: 278416, time: 0.533, data: 0.001) G_L1: 17.176 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 14.492 G_Regularizer: 0.000 validation_error: 21.146 +(epoch: 43, iters: 280416, time: 0.558, data: 0.000) G_L1: 13.515 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 10.710 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 43, iters: 282416, time: 0.557, data: 0.000) G_L1: 15.595 G_L1_ABSOLUTE: 2.313 G_L1_RELATIVE: 13.282 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 43, iters: 284416, time: 0.562, data: 0.000) G_L1: 15.134 G_L1_ABSOLUTE: 2.568 G_L1_RELATIVE: 12.566 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 43, iters: 286416, time: 0.544, data: 0.000) G_L1: 15.731 G_L1_ABSOLUTE: 2.597 G_L1_RELATIVE: 13.134 G_Regularizer: 0.000 validation_error: 20.452 +(epoch: 43, iters: 288416, time: 0.529, data: 0.000) G_L1: 16.617 G_L1_ABSOLUTE: 2.234 G_L1_RELATIVE: 14.383 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 43, iters: 290416, time: 0.561, data: 0.000) G_L1: 13.604 G_L1_ABSOLUTE: 3.127 G_L1_RELATIVE: 10.477 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 43, iters: 292416, time: 0.547, data: 0.000) G_L1: 14.011 G_L1_ABSOLUTE: 2.224 G_L1_RELATIVE: 11.786 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 43, iters: 294416, time: 0.542, data: 0.000) G_L1: 17.116 G_L1_ABSOLUTE: 2.988 G_L1_RELATIVE: 14.128 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 43, iters: 296416, time: 0.563, data: 0.000) G_L1: 14.414 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 12.026 G_Regularizer: 0.000 validation_error: 20.645 +(epoch: 43, iters: 298416, time: 0.553, data: 0.000) G_L1: 13.677 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 10.998 G_Regularizer: 0.000 validation_error: 21.028 +(epoch: 43, iters: 300416, time: 0.551, data: 0.000) G_L1: 13.808 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 11.305 G_Regularizer: 0.000 validation_error: 20.631 +(epoch: 43, iters: 302416, time: 0.545, data: 0.000) G_L1: 18.610 G_L1_ABSOLUTE: 2.981 G_L1_RELATIVE: 15.629 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 44, iters: 1664, time: 0.556, data: 0.000) G_L1: 12.910 G_L1_ABSOLUTE: 2.291 G_L1_RELATIVE: 10.619 G_Regularizer: 0.000 validation_error: 20.470 +(epoch: 44, iters: 3664, time: 0.543, data: 0.000) G_L1: 14.913 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 12.536 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 44, iters: 5664, time: 0.553, data: 0.000) G_L1: 11.004 G_L1_ABSOLUTE: 2.122 G_L1_RELATIVE: 8.883 G_Regularizer: 0.000 validation_error: 20.738 +(epoch: 44, iters: 7664, time: 0.560, data: 0.000) G_L1: 13.712 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 11.324 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 44, iters: 9664, time: 0.531, data: 0.000) G_L1: 13.678 G_L1_ABSOLUTE: 3.108 G_L1_RELATIVE: 10.570 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 44, iters: 11664, time: 0.563, data: 0.000) G_L1: 15.823 G_L1_ABSOLUTE: 2.953 G_L1_RELATIVE: 12.870 G_Regularizer: 0.000 validation_error: 20.853 +(epoch: 44, iters: 13664, time: 0.549, data: 0.000) G_L1: 13.809 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 11.176 G_Regularizer: 0.000 validation_error: 20.690 +(epoch: 44, iters: 15664, time: 0.559, data: 0.000) G_L1: 14.197 G_L1_ABSOLUTE: 2.301 G_L1_RELATIVE: 11.896 G_Regularizer: 0.000 validation_error: 20.705 +(epoch: 44, iters: 17664, time: 0.540, data: 0.000) G_L1: 13.534 G_L1_ABSOLUTE: 2.659 G_L1_RELATIVE: 10.875 G_Regularizer: 0.000 validation_error: 20.818 +(epoch: 44, iters: 19664, time: 0.556, data: 0.000) G_L1: 14.900 G_L1_ABSOLUTE: 2.473 G_L1_RELATIVE: 12.427 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 44, iters: 21664, time: 0.553, data: 0.000) G_L1: 13.066 G_L1_ABSOLUTE: 2.359 G_L1_RELATIVE: 10.707 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 44, iters: 23664, time: 0.560, data: 0.000) G_L1: 14.756 G_L1_ABSOLUTE: 2.604 G_L1_RELATIVE: 12.152 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 44, iters: 25664, time: 0.555, data: 0.000) G_L1: 16.557 G_L1_ABSOLUTE: 2.578 G_L1_RELATIVE: 13.979 G_Regularizer: 0.000 validation_error: 21.288 +(epoch: 44, iters: 27664, time: 0.550, data: 0.000) G_L1: 16.587 G_L1_ABSOLUTE: 2.599 G_L1_RELATIVE: 13.987 G_Regularizer: 0.000 validation_error: 20.775 +(epoch: 44, iters: 29664, time: 0.555, data: 0.000) G_L1: 14.444 G_L1_ABSOLUTE: 3.279 G_L1_RELATIVE: 11.165 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 44, iters: 31664, time: 0.556, data: 0.000) G_L1: 15.145 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 12.774 G_Regularizer: 0.000 validation_error: 21.114 +(epoch: 44, iters: 33664, time: 0.535, data: 0.000) G_L1: 14.363 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 11.992 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 44, iters: 35664, time: 0.553, data: 0.000) G_L1: 14.682 G_L1_ABSOLUTE: 2.288 G_L1_RELATIVE: 12.394 G_Regularizer: 0.000 validation_error: 20.416 +(epoch: 44, iters: 37664, time: 0.537, data: 0.000) G_L1: 13.645 G_L1_ABSOLUTE: 2.280 G_L1_RELATIVE: 11.365 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 44, iters: 39664, time: 0.539, data: 0.000) G_L1: 13.659 G_L1_ABSOLUTE: 2.466 G_L1_RELATIVE: 11.193 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 44, iters: 41664, time: 0.544, data: 0.000) G_L1: 13.728 G_L1_ABSOLUTE: 2.347 G_L1_RELATIVE: 11.382 G_Regularizer: 0.000 validation_error: 21.115 +(epoch: 44, iters: 43664, time: 0.534, data: 0.000) G_L1: 15.018 G_L1_ABSOLUTE: 2.593 G_L1_RELATIVE: 12.425 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 44, iters: 45664, time: 0.538, data: 0.000) G_L1: 13.819 G_L1_ABSOLUTE: 2.854 G_L1_RELATIVE: 10.965 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 44, iters: 47664, time: 0.557, data: 0.000) G_L1: 12.634 G_L1_ABSOLUTE: 2.184 G_L1_RELATIVE: 10.450 G_Regularizer: 0.000 validation_error: 21.017 +(epoch: 44, iters: 49664, time: 0.538, data: 0.000) G_L1: 15.692 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 13.246 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 44, iters: 51664, time: 0.539, data: 0.000) G_L1: 13.981 G_L1_ABSOLUTE: 3.000 G_L1_RELATIVE: 10.981 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 44, iters: 53664, time: 0.531, data: 0.000) G_L1: 12.315 G_L1_ABSOLUTE: 2.066 G_L1_RELATIVE: 10.249 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 44, iters: 55664, time: 0.563, data: 0.000) G_L1: 11.918 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 9.413 G_Regularizer: 0.000 validation_error: 20.765 +(epoch: 44, iters: 57664, time: 0.545, data: 0.000) G_L1: 13.255 G_L1_ABSOLUTE: 2.537 G_L1_RELATIVE: 10.718 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 44, iters: 59664, time: 0.531, data: 0.000) G_L1: 12.950 G_L1_ABSOLUTE: 2.207 G_L1_RELATIVE: 10.743 G_Regularizer: 0.000 validation_error: 20.465 +(epoch: 44, iters: 61664, time: 0.551, data: 0.000) G_L1: 15.299 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 12.575 G_Regularizer: 0.000 validation_error: 20.513 +(epoch: 44, iters: 63664, time: 0.556, data: 0.000) G_L1: 15.545 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 12.919 G_Regularizer: 0.000 validation_error: 20.824 +(epoch: 44, iters: 65664, time: 0.557, data: 0.000) G_L1: 11.876 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 9.504 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 44, iters: 67664, time: 0.539, data: 0.000) G_L1: 16.032 G_L1_ABSOLUTE: 2.653 G_L1_RELATIVE: 13.379 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 44, iters: 69664, time: 0.540, data: 0.000) G_L1: 14.275 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 11.904 G_Regularizer: 0.000 validation_error: 20.680 +(epoch: 44, iters: 71664, time: 0.551, data: 0.000) G_L1: 13.500 G_L1_ABSOLUTE: 2.878 G_L1_RELATIVE: 10.622 G_Regularizer: 0.000 validation_error: 20.714 +(epoch: 44, iters: 73664, time: 0.549, data: 0.000) G_L1: 14.199 G_L1_ABSOLUTE: 2.793 G_L1_RELATIVE: 11.406 G_Regularizer: 0.000 validation_error: 20.520 +(epoch: 44, iters: 75664, time: 0.525, data: 0.000) G_L1: 14.983 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 12.548 G_Regularizer: 0.000 validation_error: 21.081 +(epoch: 44, iters: 77664, time: 0.535, data: 0.000) G_L1: 13.512 G_L1_ABSOLUTE: 2.605 G_L1_RELATIVE: 10.907 G_Regularizer: 0.000 validation_error: 20.728 +(epoch: 44, iters: 79664, time: 0.550, data: 0.000) G_L1: 13.438 G_L1_ABSOLUTE: 2.100 G_L1_RELATIVE: 11.338 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 44, iters: 81664, time: 0.553, data: 0.000) G_L1: 14.166 G_L1_ABSOLUTE: 2.460 G_L1_RELATIVE: 11.706 G_Regularizer: 0.000 validation_error: 20.348 +(epoch: 44, iters: 83664, time: 0.538, data: 0.000) G_L1: 13.733 G_L1_ABSOLUTE: 2.475 G_L1_RELATIVE: 11.258 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 44, iters: 85664, time: 0.668, data: 0.000) G_L1: 14.807 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 12.045 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 44, iters: 87664, time: 0.680, data: 0.000) G_L1: 10.322 G_L1_ABSOLUTE: 2.159 G_L1_RELATIVE: 8.163 G_Regularizer: 0.000 validation_error: 21.139 +(epoch: 44, iters: 89664, time: 0.668, data: 0.000) G_L1: 11.637 G_L1_ABSOLUTE: 2.560 G_L1_RELATIVE: 9.077 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 44, iters: 91664, time: 0.650, data: 0.000) G_L1: 13.373 G_L1_ABSOLUTE: 2.381 G_L1_RELATIVE: 10.992 G_Regularizer: 0.000 validation_error: 20.794 +(epoch: 44, iters: 93664, time: 0.638, data: 0.000) G_L1: 15.732 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 12.951 G_Regularizer: 0.000 validation_error: 20.856 +(epoch: 44, iters: 95664, time: 0.658, data: 0.000) G_L1: 14.252 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 11.965 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 44, iters: 97664, time: 0.687, data: 0.000) G_L1: 12.953 G_L1_ABSOLUTE: 2.538 G_L1_RELATIVE: 10.416 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 44, iters: 99664, time: 0.688, data: 0.000) G_L1: 12.121 G_L1_ABSOLUTE: 2.465 G_L1_RELATIVE: 9.657 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 44, iters: 101664, time: 0.672, data: 0.000) G_L1: 12.312 G_L1_ABSOLUTE: 2.031 G_L1_RELATIVE: 10.281 G_Regularizer: 0.000 validation_error: 20.578 +(epoch: 44, iters: 103664, time: 0.643, data: 0.000) G_L1: 12.856 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 10.481 G_Regularizer: 0.000 validation_error: 20.701 +(epoch: 44, iters: 105664, time: 0.673, data: 0.000) G_L1: 16.290 G_L1_ABSOLUTE: 2.743 G_L1_RELATIVE: 13.548 G_Regularizer: 0.000 validation_error: 21.179 +(epoch: 44, iters: 107664, time: 0.679, data: 0.000) G_L1: 17.360 G_L1_ABSOLUTE: 2.903 G_L1_RELATIVE: 14.457 G_Regularizer: 0.000 validation_error: 20.733 +(epoch: 44, iters: 109664, time: 0.675, data: 0.000) G_L1: 13.873 G_L1_ABSOLUTE: 2.912 G_L1_RELATIVE: 10.962 G_Regularizer: 0.000 validation_error: 21.051 +(epoch: 44, iters: 111664, time: 0.663, data: 0.001) G_L1: 15.268 G_L1_ABSOLUTE: 2.633 G_L1_RELATIVE: 12.635 G_Regularizer: 0.000 validation_error: 20.677 +(epoch: 44, iters: 113664, time: 0.686, data: 0.000) G_L1: 14.383 G_L1_ABSOLUTE: 2.593 G_L1_RELATIVE: 11.790 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 44, iters: 115664, time: 0.671, data: 0.000) G_L1: 12.953 G_L1_ABSOLUTE: 2.065 G_L1_RELATIVE: 10.888 G_Regularizer: 0.000 validation_error: 20.799 +(epoch: 44, iters: 117664, time: 0.674, data: 0.000) G_L1: 15.540 G_L1_ABSOLUTE: 2.948 G_L1_RELATIVE: 12.592 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 44, iters: 119664, time: 0.565, data: 0.000) G_L1: 15.911 G_L1_ABSOLUTE: 2.321 G_L1_RELATIVE: 13.590 G_Regularizer: 0.000 validation_error: 21.038 +(epoch: 44, iters: 121664, time: 0.554, data: 0.000) G_L1: 18.023 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 15.288 G_Regularizer: 0.000 validation_error: 20.626 +(epoch: 44, iters: 123664, time: 0.531, data: 0.000) G_L1: 14.718 G_L1_ABSOLUTE: 2.477 G_L1_RELATIVE: 12.241 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 44, iters: 125664, time: 0.559, data: 0.000) G_L1: 12.288 G_L1_ABSOLUTE: 2.603 G_L1_RELATIVE: 9.684 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 44, iters: 127664, time: 0.553, data: 0.001) G_L1: 14.185 G_L1_ABSOLUTE: 2.243 G_L1_RELATIVE: 11.942 G_Regularizer: 0.000 validation_error: 20.274 +(epoch: 44, iters: 129664, time: 0.552, data: 0.001) G_L1: 12.771 G_L1_ABSOLUTE: 2.596 G_L1_RELATIVE: 10.175 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 44, iters: 131664, time: 0.539, data: 0.000) G_L1: 13.106 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 10.741 G_Regularizer: 0.000 validation_error: 21.270 +(epoch: 44, iters: 133664, time: 0.547, data: 0.000) G_L1: 14.906 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 12.476 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 44, iters: 135664, time: 0.543, data: 0.001) G_L1: 13.279 G_L1_ABSOLUTE: 2.483 G_L1_RELATIVE: 10.796 G_Regularizer: 0.000 validation_error: 21.028 +(epoch: 44, iters: 137664, time: 0.548, data: 0.000) G_L1: 15.095 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 12.658 G_Regularizer: 0.000 validation_error: 21.094 +(epoch: 44, iters: 139664, time: 0.555, data: 0.000) G_L1: 13.736 G_L1_ABSOLUTE: 2.825 G_L1_RELATIVE: 10.911 G_Regularizer: 0.000 validation_error: 20.907 +(epoch: 44, iters: 141664, time: 0.546, data: 0.000) G_L1: 12.980 G_L1_ABSOLUTE: 2.701 G_L1_RELATIVE: 10.279 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 44, iters: 143664, time: 0.553, data: 0.000) G_L1: 13.982 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 11.261 G_Regularizer: 0.000 validation_error: 21.071 +(epoch: 44, iters: 145664, time: 0.556, data: 0.000) G_L1: 12.555 G_L1_ABSOLUTE: 2.098 G_L1_RELATIVE: 10.457 G_Regularizer: 0.000 validation_error: 21.142 +(epoch: 44, iters: 147664, time: 0.557, data: 0.000) G_L1: 13.550 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 11.115 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 44, iters: 149664, time: 0.530, data: 0.001) G_L1: 15.160 G_L1_ABSOLUTE: 3.024 G_L1_RELATIVE: 12.135 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 44, iters: 151664, time: 0.557, data: 0.000) G_L1: 13.735 G_L1_ABSOLUTE: 2.411 G_L1_RELATIVE: 11.325 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 44, iters: 153664, time: 0.540, data: 0.000) G_L1: 11.744 G_L1_ABSOLUTE: 1.894 G_L1_RELATIVE: 9.850 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 44, iters: 155664, time: 0.539, data: 0.000) G_L1: 14.952 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 12.554 G_Regularizer: 0.000 validation_error: 21.082 +(epoch: 44, iters: 157664, time: 0.547, data: 0.001) G_L1: 14.598 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 12.161 G_Regularizer: 0.000 validation_error: 20.722 +(epoch: 44, iters: 159664, time: 0.549, data: 0.001) G_L1: 14.374 G_L1_ABSOLUTE: 2.318 G_L1_RELATIVE: 12.056 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 44, iters: 161664, time: 0.533, data: 0.000) G_L1: 14.563 G_L1_ABSOLUTE: 2.682 G_L1_RELATIVE: 11.881 G_Regularizer: 0.000 validation_error: 21.153 +(epoch: 44, iters: 163664, time: 0.550, data: 0.002) G_L1: 14.036 G_L1_ABSOLUTE: 2.142 G_L1_RELATIVE: 11.895 G_Regularizer: 0.000 validation_error: 20.697 +(epoch: 44, iters: 165664, time: 0.539, data: 0.001) G_L1: 14.653 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 11.886 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 44, iters: 167664, time: 0.530, data: 0.000) G_L1: 24.341 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 21.668 G_Regularizer: 0.000 validation_error: 21.029 +(epoch: 44, iters: 169664, time: 0.532, data: 0.000) G_L1: 14.539 G_L1_ABSOLUTE: 2.498 G_L1_RELATIVE: 12.041 G_Regularizer: 0.000 validation_error: 21.235 +(epoch: 44, iters: 171664, time: 0.539, data: 0.000) G_L1: 12.021 G_L1_ABSOLUTE: 2.353 G_L1_RELATIVE: 9.668 G_Regularizer: 0.000 validation_error: 20.547 +(epoch: 44, iters: 173664, time: 0.545, data: 0.000) G_L1: 13.551 G_L1_ABSOLUTE: 2.697 G_L1_RELATIVE: 10.854 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 44, iters: 175664, time: 0.548, data: 0.000) G_L1: 13.379 G_L1_ABSOLUTE: 2.390 G_L1_RELATIVE: 10.989 G_Regularizer: 0.000 validation_error: 20.913 +(epoch: 44, iters: 177664, time: 0.545, data: 0.000) G_L1: 12.556 G_L1_ABSOLUTE: 2.232 G_L1_RELATIVE: 10.323 G_Regularizer: 0.000 validation_error: 21.121 +(epoch: 44, iters: 179664, time: 0.538, data: 0.000) G_L1: 12.779 G_L1_ABSOLUTE: 2.558 G_L1_RELATIVE: 10.221 G_Regularizer: 0.000 validation_error: 21.146 +(epoch: 44, iters: 181664, time: 0.525, data: 0.001) G_L1: 14.219 G_L1_ABSOLUTE: 2.516 G_L1_RELATIVE: 11.704 G_Regularizer: 0.000 validation_error: 21.173 +(epoch: 44, iters: 183664, time: 0.549, data: 0.000) G_L1: 15.731 G_L1_ABSOLUTE: 2.722 G_L1_RELATIVE: 13.009 G_Regularizer: 0.000 validation_error: 21.138 +(epoch: 44, iters: 185664, time: 0.558, data: 0.000) G_L1: 15.280 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 12.710 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 44, iters: 187664, time: 0.559, data: 0.000) G_L1: 14.145 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 11.423 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 44, iters: 189664, time: 0.552, data: 0.000) G_L1: 15.451 G_L1_ABSOLUTE: 2.625 G_L1_RELATIVE: 12.826 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 44, iters: 191664, time: 0.547, data: 0.000) G_L1: 15.158 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 12.357 G_Regularizer: 0.000 validation_error: 20.566 +(epoch: 44, iters: 193664, time: 0.559, data: 0.000) G_L1: 14.323 G_L1_ABSOLUTE: 2.616 G_L1_RELATIVE: 11.706 G_Regularizer: 0.000 validation_error: 20.992 +(epoch: 44, iters: 195664, time: 0.555, data: 0.000) G_L1: 13.411 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 11.002 G_Regularizer: 0.000 validation_error: 20.619 +(epoch: 44, iters: 197664, time: 0.562, data: 0.000) G_L1: 13.375 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 10.861 G_Regularizer: 0.000 validation_error: 21.196 +(epoch: 44, iters: 199664, time: 0.545, data: 0.000) G_L1: 13.807 G_L1_ABSOLUTE: 2.677 G_L1_RELATIVE: 11.130 G_Regularizer: 0.000 validation_error: 21.006 +(epoch: 44, iters: 201664, time: 0.546, data: 0.000) G_L1: 15.030 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 12.510 G_Regularizer: 0.000 validation_error: 20.689 +(epoch: 44, iters: 203664, time: 0.531, data: 0.000) G_L1: 12.952 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 10.456 G_Regularizer: 0.000 validation_error: 21.170 +(epoch: 44, iters: 205664, time: 0.546, data: 0.000) G_L1: 13.989 G_L1_ABSOLUTE: 2.260 G_L1_RELATIVE: 11.729 G_Regularizer: 0.000 validation_error: 21.033 +(epoch: 44, iters: 207664, time: 0.538, data: 0.000) G_L1: 14.157 G_L1_ABSOLUTE: 2.654 G_L1_RELATIVE: 11.503 G_Regularizer: 0.000 validation_error: 20.822 +(epoch: 44, iters: 209664, time: 0.549, data: 0.000) G_L1: 13.158 G_L1_ABSOLUTE: 2.278 G_L1_RELATIVE: 10.880 G_Regularizer: 0.000 validation_error: 21.007 +(epoch: 44, iters: 211664, time: 0.525, data: 0.000) G_L1: 14.107 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 11.702 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 44, iters: 213664, time: 0.542, data: 0.001) G_L1: 12.167 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 9.881 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 44, iters: 215664, time: 0.550, data: 0.000) G_L1: 15.549 G_L1_ABSOLUTE: 2.455 G_L1_RELATIVE: 13.094 G_Regularizer: 0.000 validation_error: 21.116 +(epoch: 44, iters: 217664, time: 0.538, data: 0.000) G_L1: 12.672 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 10.315 G_Regularizer: 0.000 validation_error: 21.268 +(epoch: 44, iters: 219664, time: 0.553, data: 0.000) G_L1: 13.563 G_L1_ABSOLUTE: 2.775 G_L1_RELATIVE: 10.788 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 44, iters: 221664, time: 0.552, data: 0.000) G_L1: 13.774 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 11.386 G_Regularizer: 0.000 validation_error: 21.132 +(epoch: 44, iters: 223664, time: 0.553, data: 0.000) G_L1: 12.195 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 9.672 G_Regularizer: 0.000 validation_error: 20.629 +(epoch: 44, iters: 225664, time: 0.552, data: 0.000) G_L1: 14.490 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 11.967 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 44, iters: 227664, time: 0.554, data: 0.000) G_L1: 13.406 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 11.121 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 44, iters: 229664, time: 0.553, data: 0.000) G_L1: 14.638 G_L1_ABSOLUTE: 2.662 G_L1_RELATIVE: 11.977 G_Regularizer: 0.000 validation_error: 21.064 +(epoch: 44, iters: 231664, time: 0.532, data: 0.000) G_L1: 13.355 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 11.053 G_Regularizer: 0.000 validation_error: 20.694 +(epoch: 44, iters: 233664, time: 0.551, data: 0.000) G_L1: 12.180 G_L1_ABSOLUTE: 1.990 G_L1_RELATIVE: 10.190 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 44, iters: 235664, time: 0.548, data: 0.000) G_L1: 14.377 G_L1_ABSOLUTE: 2.772 G_L1_RELATIVE: 11.605 G_Regularizer: 0.000 validation_error: 20.683 +(epoch: 44, iters: 237664, time: 0.539, data: 0.000) G_L1: 16.523 G_L1_ABSOLUTE: 2.152 G_L1_RELATIVE: 14.371 G_Regularizer: 0.000 validation_error: 20.938 +(epoch: 44, iters: 239664, time: 0.558, data: 0.000) G_L1: 16.653 G_L1_ABSOLUTE: 2.648 G_L1_RELATIVE: 14.004 G_Regularizer: 0.000 validation_error: 21.054 +(epoch: 44, iters: 241664, time: 0.558, data: 0.000) G_L1: 11.480 G_L1_ABSOLUTE: 2.412 G_L1_RELATIVE: 9.068 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 44, iters: 243664, time: 0.532, data: 0.000) G_L1: 20.372 G_L1_ABSOLUTE: 2.788 G_L1_RELATIVE: 17.583 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 44, iters: 245664, time: 0.533, data: 0.000) G_L1: 11.691 G_L1_ABSOLUTE: 2.137 G_L1_RELATIVE: 9.554 G_Regularizer: 0.000 validation_error: 20.818 +(epoch: 44, iters: 247664, time: 0.551, data: 0.000) G_L1: 12.576 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 10.071 G_Regularizer: 0.000 validation_error: 20.704 +(epoch: 44, iters: 249664, time: 0.559, data: 0.000) G_L1: 13.963 G_L1_ABSOLUTE: 2.514 G_L1_RELATIVE: 11.449 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 44, iters: 251664, time: 0.545, data: 0.001) G_L1: 11.396 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 8.967 G_Regularizer: 0.000 validation_error: 20.606 +(epoch: 44, iters: 253664, time: 0.538, data: 0.000) G_L1: 13.734 G_L1_ABSOLUTE: 2.646 G_L1_RELATIVE: 11.088 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 44, iters: 255664, time: 0.557, data: 0.000) G_L1: 17.421 G_L1_ABSOLUTE: 2.253 G_L1_RELATIVE: 15.169 G_Regularizer: 0.000 validation_error: 20.778 +(epoch: 44, iters: 257664, time: 0.561, data: 0.000) G_L1: 13.732 G_L1_ABSOLUTE: 2.471 G_L1_RELATIVE: 11.261 G_Regularizer: 0.000 validation_error: 20.874 +(epoch: 44, iters: 259664, time: 0.554, data: 0.000) G_L1: 12.591 G_L1_ABSOLUTE: 2.148 G_L1_RELATIVE: 10.443 G_Regularizer: 0.000 validation_error: 20.911 +(epoch: 44, iters: 261664, time: 0.543, data: 0.000) G_L1: 12.428 G_L1_ABSOLUTE: 2.257 G_L1_RELATIVE: 10.170 G_Regularizer: 0.000 validation_error: 20.460 +(epoch: 44, iters: 263664, time: 0.534, data: 0.000) G_L1: 14.386 G_L1_ABSOLUTE: 2.515 G_L1_RELATIVE: 11.871 G_Regularizer: 0.000 validation_error: 21.080 +(epoch: 44, iters: 265664, time: 0.552, data: 0.001) G_L1: 10.812 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 8.326 G_Regularizer: 0.000 validation_error: 20.970 +(epoch: 44, iters: 267664, time: 0.539, data: 0.000) G_L1: 13.236 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 10.974 G_Regularizer: 0.000 validation_error: 20.567 +(epoch: 44, iters: 269664, time: 0.536, data: 0.000) G_L1: 14.057 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 11.610 G_Regularizer: 0.000 validation_error: 21.106 +(epoch: 44, iters: 271664, time: 0.554, data: 0.000) G_L1: 14.849 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 12.651 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 44, iters: 273664, time: 0.562, data: 0.001) G_L1: 14.149 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 11.579 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 44, iters: 275664, time: 0.557, data: 0.000) G_L1: 13.989 G_L1_ABSOLUTE: 2.746 G_L1_RELATIVE: 11.243 G_Regularizer: 0.000 validation_error: 21.130 +(epoch: 44, iters: 277664, time: 0.539, data: 0.000) G_L1: 14.709 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 12.161 G_Regularizer: 0.000 validation_error: 20.728 +(epoch: 44, iters: 279664, time: 0.550, data: 0.000) G_L1: 14.411 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 11.820 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 44, iters: 281664, time: 0.551, data: 0.001) G_L1: 14.643 G_L1_ABSOLUTE: 2.032 G_L1_RELATIVE: 12.611 G_Regularizer: 0.000 validation_error: 20.769 +(epoch: 44, iters: 283664, time: 0.540, data: 0.000) G_L1: 15.154 G_L1_ABSOLUTE: 2.661 G_L1_RELATIVE: 12.494 G_Regularizer: 0.000 validation_error: 20.791 +(epoch: 44, iters: 285664, time: 0.542, data: 0.000) G_L1: 15.561 G_L1_ABSOLUTE: 3.076 G_L1_RELATIVE: 12.484 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 44, iters: 287664, time: 0.531, data: 0.000) G_L1: 14.634 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 12.165 G_Regularizer: 0.000 validation_error: 20.815 +(epoch: 44, iters: 289664, time: 0.536, data: 0.000) G_L1: 16.457 G_L1_ABSOLUTE: 3.252 G_L1_RELATIVE: 13.206 G_Regularizer: 0.000 validation_error: 20.778 +(epoch: 44, iters: 291664, time: 0.550, data: 0.000) G_L1: 14.604 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 12.248 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 44, iters: 293664, time: 0.538, data: 0.000) G_L1: 16.174 G_L1_ABSOLUTE: 2.493 G_L1_RELATIVE: 13.681 G_Regularizer: 0.000 validation_error: 21.114 +(epoch: 44, iters: 295664, time: 0.527, data: 0.000) G_L1: 13.417 G_L1_ABSOLUTE: 2.434 G_L1_RELATIVE: 10.983 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 44, iters: 297664, time: 0.544, data: 0.000) G_L1: 11.399 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 8.942 G_Regularizer: 0.000 validation_error: 20.991 +(epoch: 44, iters: 299664, time: 0.535, data: 0.000) G_L1: 15.657 G_L1_ABSOLUTE: 2.371 G_L1_RELATIVE: 13.287 G_Regularizer: 0.000 validation_error: 20.648 +(epoch: 44, iters: 301664, time: 0.538, data: 0.000) G_L1: 15.529 G_L1_ABSOLUTE: 2.408 G_L1_RELATIVE: 13.121 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 45, iters: 912, time: 0.531, data: 0.000) G_L1: 12.325 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 10.051 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 45, iters: 2912, time: 0.556, data: 0.000) G_L1: 11.568 G_L1_ABSOLUTE: 2.163 G_L1_RELATIVE: 9.405 G_Regularizer: 0.000 validation_error: 20.577 +(epoch: 45, iters: 4912, time: 0.530, data: 0.000) G_L1: 14.774 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 12.420 G_Regularizer: 0.000 validation_error: 20.964 +(epoch: 45, iters: 6912, time: 0.549, data: 0.000) G_L1: 19.406 G_L1_ABSOLUTE: 2.531 G_L1_RELATIVE: 16.875 G_Regularizer: 0.000 validation_error: 20.987 +(epoch: 45, iters: 8912, time: 0.554, data: 0.000) G_L1: 13.827 G_L1_ABSOLUTE: 1.972 G_L1_RELATIVE: 11.855 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 45, iters: 10912, time: 0.554, data: 0.000) G_L1: 13.889 G_L1_ABSOLUTE: 2.963 G_L1_RELATIVE: 10.925 G_Regularizer: 0.000 validation_error: 20.657 +(epoch: 45, iters: 12912, time: 0.546, data: 0.000) G_L1: 13.482 G_L1_ABSOLUTE: 2.667 G_L1_RELATIVE: 10.815 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 45, iters: 14912, time: 0.561, data: 0.000) G_L1: 13.473 G_L1_ABSOLUTE: 2.786 G_L1_RELATIVE: 10.687 G_Regularizer: 0.000 validation_error: 20.685 +(epoch: 45, iters: 16912, time: 0.554, data: 0.000) G_L1: 13.540 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 10.992 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 45, iters: 18912, time: 0.560, data: 0.000) G_L1: 14.248 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 11.827 G_Regularizer: 0.000 validation_error: 20.614 +(epoch: 45, iters: 20912, time: 0.554, data: 0.000) G_L1: 13.981 G_L1_ABSOLUTE: 2.785 G_L1_RELATIVE: 11.196 G_Regularizer: 0.000 validation_error: 21.024 +(epoch: 45, iters: 22912, time: 0.541, data: 0.000) G_L1: 11.274 G_L1_ABSOLUTE: 2.188 G_L1_RELATIVE: 9.085 G_Regularizer: 0.000 validation_error: 20.856 +(epoch: 45, iters: 24912, time: 0.545, data: 0.000) G_L1: 14.602 G_L1_ABSOLUTE: 2.906 G_L1_RELATIVE: 11.696 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 45, iters: 26912, time: 0.544, data: 0.000) G_L1: 12.710 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 10.122 G_Regularizer: 0.000 validation_error: 20.753 +(epoch: 45, iters: 28912, time: 0.549, data: 0.000) G_L1: 13.804 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 11.298 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 45, iters: 30912, time: 0.543, data: 0.001) G_L1: 13.549 G_L1_ABSOLUTE: 2.305 G_L1_RELATIVE: 11.245 G_Regularizer: 0.000 validation_error: 20.798 +(epoch: 45, iters: 32912, time: 0.547, data: 0.000) G_L1: 13.908 G_L1_ABSOLUTE: 2.382 G_L1_RELATIVE: 11.527 G_Regularizer: 0.000 validation_error: 20.593 +(epoch: 45, iters: 34912, time: 0.544, data: 0.000) G_L1: 15.413 G_L1_ABSOLUTE: 2.145 G_L1_RELATIVE: 13.268 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 45, iters: 36912, time: 0.555, data: 0.000) G_L1: 12.771 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 10.387 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 45, iters: 38912, time: 0.555, data: 0.000) G_L1: 12.948 G_L1_ABSOLUTE: 2.337 G_L1_RELATIVE: 10.611 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 45, iters: 40912, time: 0.547, data: 0.000) G_L1: 12.964 G_L1_ABSOLUTE: 2.937 G_L1_RELATIVE: 10.027 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 45, iters: 42912, time: 0.546, data: 0.000) G_L1: 16.626 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 14.220 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 45, iters: 44912, time: 0.553, data: 0.000) G_L1: 12.110 G_L1_ABSOLUTE: 2.104 G_L1_RELATIVE: 10.006 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 45, iters: 46912, time: 0.555, data: 0.000) G_L1: 12.978 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 10.578 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 45, iters: 48912, time: 0.561, data: 0.000) G_L1: 16.011 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 13.244 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 45, iters: 50912, time: 0.533, data: 0.000) G_L1: 12.741 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 10.244 G_Regularizer: 0.000 validation_error: 20.859 +(epoch: 45, iters: 52912, time: 0.561, data: 0.000) G_L1: 14.835 G_L1_ABSOLUTE: 2.635 G_L1_RELATIVE: 12.200 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 45, iters: 54912, time: 0.552, data: 0.000) G_L1: 14.650 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 12.176 G_Regularizer: 0.000 validation_error: 20.682 +(epoch: 45, iters: 56912, time: 0.560, data: 0.000) G_L1: 11.943 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 9.626 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 45, iters: 58912, time: 0.551, data: 0.000) G_L1: 16.132 G_L1_ABSOLUTE: 2.781 G_L1_RELATIVE: 13.352 G_Regularizer: 0.000 validation_error: 20.994 +(epoch: 45, iters: 60912, time: 0.548, data: 0.000) G_L1: 11.620 G_L1_ABSOLUTE: 2.331 G_L1_RELATIVE: 9.289 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 45, iters: 62912, time: 0.558, data: 0.000) G_L1: 15.430 G_L1_ABSOLUTE: 2.139 G_L1_RELATIVE: 13.292 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 45, iters: 64912, time: 0.555, data: 0.000) G_L1: 14.492 G_L1_ABSOLUTE: 2.475 G_L1_RELATIVE: 12.018 G_Regularizer: 0.000 validation_error: 20.725 +(epoch: 45, iters: 66912, time: 0.555, data: 0.000) G_L1: 12.844 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 10.188 G_Regularizer: 0.000 validation_error: 20.675 +(epoch: 45, iters: 68912, time: 0.530, data: 0.000) G_L1: 14.981 G_L1_ABSOLUTE: 2.326 G_L1_RELATIVE: 12.654 G_Regularizer: 0.000 validation_error: 21.107 +(epoch: 45, iters: 70912, time: 0.548, data: 0.000) G_L1: 14.608 G_L1_ABSOLUTE: 2.257 G_L1_RELATIVE: 12.351 G_Regularizer: 0.000 validation_error: 20.592 +(epoch: 45, iters: 72912, time: 0.559, data: 0.000) G_L1: 15.087 G_L1_ABSOLUTE: 3.155 G_L1_RELATIVE: 11.932 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 45, iters: 74912, time: 0.532, data: 0.000) G_L1: 14.655 G_L1_ABSOLUTE: 2.620 G_L1_RELATIVE: 12.035 G_Regularizer: 0.000 validation_error: 20.534 +(epoch: 45, iters: 76912, time: 0.535, data: 0.001) G_L1: 13.630 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 11.180 G_Regularizer: 0.000 validation_error: 20.680 +(epoch: 45, iters: 78912, time: 0.558, data: 0.000) G_L1: 12.824 G_L1_ABSOLUTE: 2.123 G_L1_RELATIVE: 10.701 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 45, iters: 80912, time: 0.551, data: 0.000) G_L1: 12.364 G_L1_ABSOLUTE: 2.199 G_L1_RELATIVE: 10.165 G_Regularizer: 0.000 validation_error: 20.617 +(epoch: 45, iters: 82912, time: 0.544, data: 0.001) G_L1: 15.620 G_L1_ABSOLUTE: 2.698 G_L1_RELATIVE: 12.922 G_Regularizer: 0.000 validation_error: 20.693 +(epoch: 45, iters: 84912, time: 0.537, data: 0.000) G_L1: 13.472 G_L1_ABSOLUTE: 2.099 G_L1_RELATIVE: 11.373 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 45, iters: 86912, time: 0.561, data: 0.000) G_L1: 12.495 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 10.211 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 45, iters: 88912, time: 0.559, data: 0.001) G_L1: 13.591 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 11.214 G_Regularizer: 0.000 validation_error: 20.669 +(epoch: 45, iters: 90912, time: 0.547, data: 0.000) G_L1: 13.515 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 10.831 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 45, iters: 92912, time: 0.548, data: 0.000) G_L1: 15.287 G_L1_ABSOLUTE: 2.778 G_L1_RELATIVE: 12.509 G_Regularizer: 0.000 validation_error: 20.913 +(epoch: 45, iters: 94912, time: 0.530, data: 0.000) G_L1: 12.667 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 10.045 G_Regularizer: 0.000 validation_error: 20.826 +(epoch: 45, iters: 96912, time: 0.533, data: 0.000) G_L1: 14.379 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 11.737 G_Regularizer: 0.000 validation_error: 20.982 +(epoch: 45, iters: 98912, time: 0.551, data: 0.000) G_L1: 13.414 G_L1_ABSOLUTE: 2.805 G_L1_RELATIVE: 10.609 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 45, iters: 100912, time: 0.553, data: 0.000) G_L1: 12.269 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 9.863 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 45, iters: 102912, time: 0.542, data: 0.000) G_L1: 15.669 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 13.210 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 45, iters: 104912, time: 0.558, data: 0.000) G_L1: 13.533 G_L1_ABSOLUTE: 2.749 G_L1_RELATIVE: 10.784 G_Regularizer: 0.000 validation_error: 20.756 +(epoch: 45, iters: 106912, time: 0.553, data: 0.000) G_L1: 14.889 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 12.487 G_Regularizer: 0.000 validation_error: 20.472 +(epoch: 45, iters: 108912, time: 0.539, data: 0.000) G_L1: 13.449 G_L1_ABSOLUTE: 2.688 G_L1_RELATIVE: 10.761 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 45, iters: 110912, time: 0.547, data: 0.000) G_L1: 14.344 G_L1_ABSOLUTE: 2.374 G_L1_RELATIVE: 11.970 G_Regularizer: 0.000 validation_error: 20.590 +(epoch: 45, iters: 112912, time: 0.558, data: 0.000) G_L1: 13.522 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 11.109 G_Regularizer: 0.000 validation_error: 20.691 +(epoch: 45, iters: 114912, time: 0.559, data: 0.000) G_L1: 12.916 G_L1_ABSOLUTE: 2.146 G_L1_RELATIVE: 10.771 G_Regularizer: 0.000 validation_error: 21.095 +(epoch: 45, iters: 116912, time: 0.536, data: 0.000) G_L1: 13.311 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 10.638 G_Regularizer: 0.000 validation_error: 20.857 +(epoch: 45, iters: 118912, time: 0.548, data: 0.000) G_L1: 12.679 G_L1_ABSOLUTE: 2.025 G_L1_RELATIVE: 10.654 G_Regularizer: 0.000 validation_error: 20.635 +(epoch: 45, iters: 120912, time: 0.553, data: 0.000) G_L1: 15.030 G_L1_ABSOLUTE: 2.394 G_L1_RELATIVE: 12.636 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 45, iters: 122912, time: 0.539, data: 0.000) G_L1: 13.833 G_L1_ABSOLUTE: 2.588 G_L1_RELATIVE: 11.245 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 45, iters: 124912, time: 0.543, data: 0.000) G_L1: 16.347 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 13.747 G_Regularizer: 0.000 validation_error: 20.610 +(epoch: 45, iters: 126912, time: 0.562, data: 0.000) G_L1: 13.368 G_L1_ABSOLUTE: 2.613 G_L1_RELATIVE: 10.755 G_Regularizer: 0.000 validation_error: 20.693 +(epoch: 45, iters: 128912, time: 0.539, data: 0.000) G_L1: 13.526 G_L1_ABSOLUTE: 2.205 G_L1_RELATIVE: 11.321 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 45, iters: 130912, time: 0.556, data: 0.000) G_L1: 13.877 G_L1_ABSOLUTE: 2.410 G_L1_RELATIVE: 11.467 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 45, iters: 132912, time: 0.540, data: 0.000) G_L1: 13.944 G_L1_ABSOLUTE: 2.277 G_L1_RELATIVE: 11.667 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 45, iters: 134912, time: 0.547, data: 0.000) G_L1: 13.832 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 11.283 G_Regularizer: 0.000 validation_error: 20.598 +(epoch: 45, iters: 136912, time: 0.528, data: 0.000) G_L1: 15.013 G_L1_ABSOLUTE: 2.090 G_L1_RELATIVE: 12.923 G_Regularizer: 0.000 validation_error: 20.462 +(epoch: 45, iters: 138912, time: 0.538, data: 0.000) G_L1: 18.113 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 15.590 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 45, iters: 140912, time: 0.536, data: 0.000) G_L1: 14.581 G_L1_ABSOLUTE: 2.593 G_L1_RELATIVE: 11.988 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 45, iters: 142912, time: 0.551, data: 0.000) G_L1: 12.635 G_L1_ABSOLUTE: 2.174 G_L1_RELATIVE: 10.461 G_Regularizer: 0.000 validation_error: 20.862 +(epoch: 45, iters: 144912, time: 0.540, data: 0.000) G_L1: 15.563 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 13.265 G_Regularizer: 0.000 validation_error: 20.602 +(epoch: 45, iters: 146912, time: 0.544, data: 0.000) G_L1: 14.898 G_L1_ABSOLUTE: 2.199 G_L1_RELATIVE: 12.699 G_Regularizer: 0.000 validation_error: 21.024 +(epoch: 45, iters: 148912, time: 0.549, data: 0.000) G_L1: 14.120 G_L1_ABSOLUTE: 2.265 G_L1_RELATIVE: 11.854 G_Regularizer: 0.000 validation_error: 20.747 +(epoch: 45, iters: 150912, time: 0.552, data: 0.000) G_L1: 15.065 G_L1_ABSOLUTE: 2.779 G_L1_RELATIVE: 12.287 G_Regularizer: 0.000 validation_error: 20.665 +(epoch: 45, iters: 152912, time: 0.550, data: 0.000) G_L1: 14.803 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 12.319 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 45, iters: 154912, time: 0.533, data: 0.000) G_L1: 14.263 G_L1_ABSOLUTE: 2.186 G_L1_RELATIVE: 12.077 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 45, iters: 156912, time: 0.537, data: 0.000) G_L1: 13.356 G_L1_ABSOLUTE: 2.812 G_L1_RELATIVE: 10.543 G_Regularizer: 0.000 validation_error: 20.686 +(epoch: 45, iters: 158912, time: 0.531, data: 0.000) G_L1: 13.681 G_L1_ABSOLUTE: 2.130 G_L1_RELATIVE: 11.551 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 45, iters: 160912, time: 0.558, data: 0.000) G_L1: 13.346 G_L1_ABSOLUTE: 2.693 G_L1_RELATIVE: 10.653 G_Regularizer: 0.000 validation_error: 20.642 +(epoch: 45, iters: 162912, time: 0.547, data: 0.000) G_L1: 11.734 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 9.476 G_Regularizer: 0.000 validation_error: 21.017 +(epoch: 45, iters: 164912, time: 0.551, data: 0.001) G_L1: 14.742 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 12.367 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 45, iters: 166912, time: 0.541, data: 0.000) G_L1: 12.975 G_L1_ABSOLUTE: 2.362 G_L1_RELATIVE: 10.614 G_Regularizer: 0.000 validation_error: 20.971 +(epoch: 45, iters: 168912, time: 0.560, data: 0.000) G_L1: 14.345 G_L1_ABSOLUTE: 2.890 G_L1_RELATIVE: 11.455 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 45, iters: 170912, time: 0.551, data: 0.000) G_L1: 15.889 G_L1_ABSOLUTE: 2.743 G_L1_RELATIVE: 13.146 G_Regularizer: 0.000 validation_error: 20.757 +(epoch: 45, iters: 172912, time: 0.543, data: 0.000) G_L1: 13.414 G_L1_ABSOLUTE: 2.691 G_L1_RELATIVE: 10.723 G_Regularizer: 0.000 validation_error: 20.743 +(epoch: 45, iters: 174912, time: 0.564, data: 0.000) G_L1: 13.832 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 11.331 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 45, iters: 176912, time: 0.557, data: 0.001) G_L1: 13.623 G_L1_ABSOLUTE: 1.989 G_L1_RELATIVE: 11.633 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 45, iters: 178912, time: 0.547, data: 0.000) G_L1: 12.384 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 10.101 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 45, iters: 180912, time: 0.554, data: 0.000) G_L1: 12.701 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 10.129 G_Regularizer: 0.000 validation_error: 20.859 +(epoch: 45, iters: 182912, time: 0.545, data: 0.000) G_L1: 14.088 G_L1_ABSOLUTE: 2.388 G_L1_RELATIVE: 11.700 G_Regularizer: 0.000 validation_error: 21.223 +(epoch: 45, iters: 184912, time: 0.541, data: 0.000) G_L1: 13.657 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 11.188 G_Regularizer: 0.000 validation_error: 21.324 +(epoch: 45, iters: 186912, time: 0.554, data: 0.000) G_L1: 11.856 G_L1_ABSOLUTE: 2.314 G_L1_RELATIVE: 9.541 G_Regularizer: 0.000 validation_error: 21.297 +(epoch: 45, iters: 188912, time: 0.545, data: 0.000) G_L1: 14.098 G_L1_ABSOLUTE: 2.920 G_L1_RELATIVE: 11.178 G_Regularizer: 0.000 validation_error: 20.911 +(epoch: 45, iters: 190912, time: 0.529, data: 0.000) G_L1: 14.718 G_L1_ABSOLUTE: 2.954 G_L1_RELATIVE: 11.764 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 45, iters: 192912, time: 0.541, data: 0.000) G_L1: 15.457 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 12.741 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 45, iters: 194912, time: 0.559, data: 0.000) G_L1: 13.891 G_L1_ABSOLUTE: 2.592 G_L1_RELATIVE: 11.298 G_Regularizer: 0.000 validation_error: 20.662 +(epoch: 45, iters: 196912, time: 0.544, data: 0.000) G_L1: 12.764 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 10.213 G_Regularizer: 0.000 validation_error: 21.146 +(epoch: 45, iters: 198912, time: 0.552, data: 0.000) G_L1: 13.252 G_L1_ABSOLUTE: 2.681 G_L1_RELATIVE: 10.571 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 45, iters: 200912, time: 0.555, data: 0.000) G_L1: 13.043 G_L1_ABSOLUTE: 2.666 G_L1_RELATIVE: 10.376 G_Regularizer: 0.000 validation_error: 21.076 +(epoch: 45, iters: 202912, time: 0.565, data: 0.000) G_L1: 14.355 G_L1_ABSOLUTE: 2.178 G_L1_RELATIVE: 12.177 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 45, iters: 204912, time: 0.537, data: 0.000) G_L1: 14.718 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 12.256 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 45, iters: 206912, time: 0.558, data: 0.000) G_L1: 12.417 G_L1_ABSOLUTE: 2.207 G_L1_RELATIVE: 10.210 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 45, iters: 208912, time: 0.535, data: 0.000) G_L1: 13.597 G_L1_ABSOLUTE: 2.475 G_L1_RELATIVE: 11.122 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 45, iters: 210912, time: 0.543, data: 0.000) G_L1: 13.592 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 11.247 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 45, iters: 212912, time: 0.538, data: 0.000) G_L1: 13.934 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 11.559 G_Regularizer: 0.000 validation_error: 21.025 +(epoch: 45, iters: 214912, time: 0.560, data: 0.000) G_L1: 14.406 G_L1_ABSOLUTE: 2.197 G_L1_RELATIVE: 12.209 G_Regularizer: 0.000 validation_error: 20.963 +(epoch: 45, iters: 216912, time: 0.546, data: 0.000) G_L1: 14.902 G_L1_ABSOLUTE: 2.740 G_L1_RELATIVE: 12.162 G_Regularizer: 0.000 validation_error: 21.038 +(epoch: 45, iters: 218912, time: 0.552, data: 0.000) G_L1: 13.231 G_L1_ABSOLUTE: 2.238 G_L1_RELATIVE: 10.993 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 45, iters: 220912, time: 0.555, data: 0.000) G_L1: 14.574 G_L1_ABSOLUTE: 2.387 G_L1_RELATIVE: 12.188 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 45, iters: 222912, time: 0.558, data: 0.000) G_L1: 13.484 G_L1_ABSOLUTE: 2.647 G_L1_RELATIVE: 10.837 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 45, iters: 224912, time: 0.538, data: 0.001) G_L1: 13.402 G_L1_ABSOLUTE: 2.285 G_L1_RELATIVE: 11.116 G_Regularizer: 0.000 validation_error: 21.026 +(epoch: 45, iters: 226912, time: 0.536, data: 0.000) G_L1: 13.634 G_L1_ABSOLUTE: 2.454 G_L1_RELATIVE: 11.179 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 45, iters: 228912, time: 0.558, data: 0.000) G_L1: 14.295 G_L1_ABSOLUTE: 2.634 G_L1_RELATIVE: 11.661 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 45, iters: 230912, time: 0.542, data: 0.000) G_L1: 15.519 G_L1_ABSOLUTE: 2.535 G_L1_RELATIVE: 12.984 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 45, iters: 232912, time: 0.544, data: 0.000) G_L1: 13.650 G_L1_ABSOLUTE: 2.282 G_L1_RELATIVE: 11.368 G_Regularizer: 0.000 validation_error: 21.155 +(epoch: 45, iters: 234912, time: 0.549, data: 0.000) G_L1: 16.477 G_L1_ABSOLUTE: 3.072 G_L1_RELATIVE: 13.405 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 45, iters: 236912, time: 0.542, data: 0.000) G_L1: 15.186 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 12.614 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 45, iters: 238912, time: 0.540, data: 0.000) G_L1: 12.063 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 9.800 G_Regularizer: 0.000 validation_error: 21.243 +(epoch: 45, iters: 240912, time: 0.544, data: 0.000) G_L1: 13.855 G_L1_ABSOLUTE: 2.261 G_L1_RELATIVE: 11.594 G_Regularizer: 0.000 validation_error: 20.679 +(epoch: 45, iters: 242912, time: 0.549, data: 0.000) G_L1: 14.809 G_L1_ABSOLUTE: 2.801 G_L1_RELATIVE: 12.008 G_Regularizer: 0.000 validation_error: 20.826 +(epoch: 45, iters: 244912, time: 0.544, data: 0.000) G_L1: 15.088 G_L1_ABSOLUTE: 2.434 G_L1_RELATIVE: 12.654 G_Regularizer: 0.000 validation_error: 20.604 +(epoch: 45, iters: 246912, time: 0.544, data: 0.000) G_L1: 13.458 G_L1_ABSOLUTE: 2.312 G_L1_RELATIVE: 11.146 G_Regularizer: 0.000 validation_error: 20.959 +(epoch: 45, iters: 248912, time: 0.527, data: 0.000) G_L1: 12.984 G_L1_ABSOLUTE: 2.859 G_L1_RELATIVE: 10.126 G_Regularizer: 0.000 validation_error: 20.954 +(epoch: 45, iters: 250912, time: 0.541, data: 0.000) G_L1: 14.836 G_L1_ABSOLUTE: 2.780 G_L1_RELATIVE: 12.056 G_Regularizer: 0.000 validation_error: 20.647 +(epoch: 45, iters: 252912, time: 0.526, data: 0.000) G_L1: 15.372 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 12.599 G_Regularizer: 0.000 validation_error: 21.062 +(epoch: 45, iters: 254912, time: 0.551, data: 0.000) G_L1: 15.427 G_L1_ABSOLUTE: 3.341 G_L1_RELATIVE: 12.086 G_Regularizer: 0.000 validation_error: 20.957 +(epoch: 45, iters: 256912, time: 0.552, data: 0.000) G_L1: 14.768 G_L1_ABSOLUTE: 2.920 G_L1_RELATIVE: 11.848 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 45, iters: 258912, time: 0.536, data: 0.000) G_L1: 15.252 G_L1_ABSOLUTE: 2.477 G_L1_RELATIVE: 12.775 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 45, iters: 260912, time: 0.553, data: 0.000) G_L1: 11.715 G_L1_ABSOLUTE: 2.337 G_L1_RELATIVE: 9.379 G_Regularizer: 0.000 validation_error: 20.698 +(epoch: 45, iters: 262912, time: 0.556, data: 0.000) G_L1: 14.512 G_L1_ABSOLUTE: 3.017 G_L1_RELATIVE: 11.495 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 45, iters: 264912, time: 0.535, data: 0.000) G_L1: 13.746 G_L1_ABSOLUTE: 2.006 G_L1_RELATIVE: 11.740 G_Regularizer: 0.000 validation_error: 20.944 +(epoch: 45, iters: 266912, time: 0.548, data: 0.000) G_L1: 16.370 G_L1_ABSOLUTE: 2.886 G_L1_RELATIVE: 13.484 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 45, iters: 268912, time: 0.555, data: 0.001) G_L1: 14.656 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 11.954 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 45, iters: 270912, time: 0.549, data: 0.000) G_L1: 10.988 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 8.705 G_Regularizer: 0.000 validation_error: 21.072 +(epoch: 45, iters: 272912, time: 0.538, data: 0.000) G_L1: 14.963 G_L1_ABSOLUTE: 2.398 G_L1_RELATIVE: 12.566 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 45, iters: 274912, time: 0.545, data: 0.000) G_L1: 16.807 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 14.295 G_Regularizer: 0.000 validation_error: 21.297 +(epoch: 45, iters: 276912, time: 0.538, data: 0.000) G_L1: 13.994 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 11.372 G_Regularizer: 0.000 validation_error: 21.251 +(epoch: 45, iters: 278912, time: 0.559, data: 0.000) G_L1: 17.085 G_L1_ABSOLUTE: 2.708 G_L1_RELATIVE: 14.377 G_Regularizer: 0.000 validation_error: 21.353 +(epoch: 45, iters: 280912, time: 0.555, data: 0.001) G_L1: 14.177 G_L1_ABSOLUTE: 2.142 G_L1_RELATIVE: 12.034 G_Regularizer: 0.000 validation_error: 20.624 +(epoch: 45, iters: 282912, time: 0.541, data: 0.000) G_L1: 12.332 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 9.823 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 45, iters: 284912, time: 0.534, data: 0.000) G_L1: 14.091 G_L1_ABSOLUTE: 2.139 G_L1_RELATIVE: 11.951 G_Regularizer: 0.000 validation_error: 21.042 +(epoch: 45, iters: 286912, time: 0.548, data: 0.000) G_L1: 14.436 G_L1_ABSOLUTE: 2.676 G_L1_RELATIVE: 11.761 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 45, iters: 288912, time: 0.559, data: 0.001) G_L1: 14.049 G_L1_ABSOLUTE: 2.724 G_L1_RELATIVE: 11.325 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 45, iters: 290912, time: 0.536, data: 0.000) G_L1: 14.785 G_L1_ABSOLUTE: 2.681 G_L1_RELATIVE: 12.104 G_Regularizer: 0.000 validation_error: 21.153 +(epoch: 45, iters: 292912, time: 0.560, data: 0.000) G_L1: 14.719 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 12.364 G_Regularizer: 0.000 validation_error: 20.808 +(epoch: 45, iters: 294912, time: 0.552, data: 0.000) G_L1: 13.824 G_L1_ABSOLUTE: 2.706 G_L1_RELATIVE: 11.118 G_Regularizer: 0.000 validation_error: 21.014 +(epoch: 45, iters: 296912, time: 0.557, data: 0.000) G_L1: 13.322 G_L1_ABSOLUTE: 2.711 G_L1_RELATIVE: 10.610 G_Regularizer: 0.000 validation_error: 20.630 +(epoch: 45, iters: 298912, time: 0.533, data: 0.000) G_L1: 12.308 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 9.935 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 45, iters: 300912, time: 0.548, data: 0.000) G_L1: 13.233 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 10.950 G_Regularizer: 0.000 validation_error: 20.959 +(epoch: 46, iters: 160, time: 0.546, data: 0.000) G_L1: 12.205 G_L1_ABSOLUTE: 2.495 G_L1_RELATIVE: 9.709 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 46, iters: 2160, time: 0.553, data: 0.000) G_L1: 15.929 G_L1_ABSOLUTE: 2.362 G_L1_RELATIVE: 13.567 G_Regularizer: 0.000 validation_error: 20.650 +(epoch: 46, iters: 4160, time: 0.535, data: 0.000) G_L1: 11.617 G_L1_ABSOLUTE: 2.253 G_L1_RELATIVE: 9.364 G_Regularizer: 0.000 validation_error: 20.588 +(epoch: 46, iters: 6160, time: 0.556, data: 0.001) G_L1: 14.480 G_L1_ABSOLUTE: 3.309 G_L1_RELATIVE: 11.171 G_Regularizer: 0.000 validation_error: 21.056 +(epoch: 46, iters: 8160, time: 0.545, data: 0.000) G_L1: 10.192 G_L1_ABSOLUTE: 2.131 G_L1_RELATIVE: 8.061 G_Regularizer: 0.000 validation_error: 21.174 +(epoch: 46, iters: 10160, time: 0.558, data: 0.000) G_L1: 14.256 G_L1_ABSOLUTE: 2.127 G_L1_RELATIVE: 12.130 G_Regularizer: 0.000 validation_error: 20.474 +(epoch: 46, iters: 12160, time: 0.547, data: 0.000) G_L1: 14.516 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 11.743 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 46, iters: 14160, time: 0.548, data: 0.000) G_L1: 15.049 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 12.684 G_Regularizer: 0.000 validation_error: 20.625 +(epoch: 46, iters: 16160, time: 0.547, data: 0.001) G_L1: 13.996 G_L1_ABSOLUTE: 2.256 G_L1_RELATIVE: 11.739 G_Regularizer: 0.000 validation_error: 21.088 +(epoch: 46, iters: 18160, time: 0.555, data: 0.000) G_L1: 12.049 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 9.513 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 46, iters: 20160, time: 0.565, data: 0.000) G_L1: 14.241 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 11.875 G_Regularizer: 0.000 validation_error: 21.029 +(epoch: 46, iters: 22160, time: 0.533, data: 0.000) G_L1: 14.137 G_L1_ABSOLUTE: 2.444 G_L1_RELATIVE: 11.692 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 46, iters: 24160, time: 0.556, data: 0.000) G_L1: 14.732 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 12.318 G_Regularizer: 0.000 validation_error: 20.957 +(epoch: 46, iters: 26160, time: 0.556, data: 0.000) G_L1: 13.747 G_L1_ABSOLUTE: 2.566 G_L1_RELATIVE: 11.180 G_Regularizer: 0.000 validation_error: 20.637 +(epoch: 46, iters: 28160, time: 0.566, data: 0.000) G_L1: 15.750 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 13.317 G_Regularizer: 0.000 validation_error: 20.781 +(epoch: 46, iters: 30160, time: 0.532, data: 0.000) G_L1: 25.271 G_L1_ABSOLUTE: 2.881 G_L1_RELATIVE: 22.390 G_Regularizer: 0.000 validation_error: 20.710 +(epoch: 46, iters: 32160, time: 0.556, data: 0.000) G_L1: 14.843 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 12.413 G_Regularizer: 0.000 validation_error: 21.285 +(epoch: 46, iters: 34160, time: 0.555, data: 0.000) G_L1: 13.749 G_L1_ABSOLUTE: 2.953 G_L1_RELATIVE: 10.797 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 46, iters: 36160, time: 0.544, data: 0.000) G_L1: 14.151 G_L1_ABSOLUTE: 2.894 G_L1_RELATIVE: 11.258 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 46, iters: 38160, time: 0.552, data: 0.000) G_L1: 14.118 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 11.735 G_Regularizer: 0.000 validation_error: 20.854 +(epoch: 46, iters: 40160, time: 0.559, data: 0.000) G_L1: 16.428 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 14.075 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 46, iters: 42160, time: 0.550, data: 0.000) G_L1: 14.367 G_L1_ABSOLUTE: 2.628 G_L1_RELATIVE: 11.739 G_Regularizer: 0.000 validation_error: 20.955 +(epoch: 46, iters: 44160, time: 0.534, data: 0.000) G_L1: 14.633 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 12.220 G_Regularizer: 0.000 validation_error: 20.939 +(epoch: 46, iters: 46160, time: 0.534, data: 0.000) G_L1: 14.090 G_L1_ABSOLUTE: 2.537 G_L1_RELATIVE: 11.553 G_Regularizer: 0.000 validation_error: 20.788 +(epoch: 46, iters: 48160, time: 0.544, data: 0.000) G_L1: 11.956 G_L1_ABSOLUTE: 2.245 G_L1_RELATIVE: 9.711 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 46, iters: 50160, time: 0.552, data: 0.000) G_L1: 13.873 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 11.470 G_Regularizer: 0.000 validation_error: 20.763 +(epoch: 46, iters: 52160, time: 0.542, data: 0.000) G_L1: 11.227 G_L1_ABSOLUTE: 2.296 G_L1_RELATIVE: 8.930 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 46, iters: 54160, time: 0.549, data: 0.000) G_L1: 13.779 G_L1_ABSOLUTE: 2.202 G_L1_RELATIVE: 11.577 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 46, iters: 56160, time: 0.554, data: 0.000) G_L1: 13.107 G_L1_ABSOLUTE: 2.196 G_L1_RELATIVE: 10.911 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 46, iters: 58160, time: 0.553, data: 0.000) G_L1: 14.125 G_L1_ABSOLUTE: 2.971 G_L1_RELATIVE: 11.153 G_Regularizer: 0.000 validation_error: 20.899 +(epoch: 46, iters: 60160, time: 0.532, data: 0.000) G_L1: 17.177 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 14.559 G_Regularizer: 0.000 validation_error: 20.812 +(epoch: 46, iters: 62160, time: 0.550, data: 0.000) G_L1: 13.997 G_L1_ABSOLUTE: 2.146 G_L1_RELATIVE: 11.852 G_Regularizer: 0.000 validation_error: 20.569 +(epoch: 46, iters: 64160, time: 0.541, data: 0.000) G_L1: 13.882 G_L1_ABSOLUTE: 2.179 G_L1_RELATIVE: 11.703 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 46, iters: 66160, time: 0.553, data: 0.001) G_L1: 15.299 G_L1_ABSOLUTE: 2.868 G_L1_RELATIVE: 12.431 G_Regularizer: 0.000 validation_error: 20.820 +(epoch: 46, iters: 68160, time: 0.551, data: 0.000) G_L1: 12.648 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 10.147 G_Regularizer: 0.000 validation_error: 20.932 +(epoch: 46, iters: 70160, time: 0.554, data: 0.000) G_L1: 14.140 G_L1_ABSOLUTE: 2.184 G_L1_RELATIVE: 11.956 G_Regularizer: 0.000 validation_error: 20.836 +(epoch: 46, iters: 72160, time: 0.550, data: 0.000) G_L1: 15.911 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 12.960 G_Regularizer: 0.000 validation_error: 20.775 +(epoch: 46, iters: 74160, time: 0.558, data: 0.000) G_L1: 14.518 G_L1_ABSOLUTE: 2.647 G_L1_RELATIVE: 11.871 G_Regularizer: 0.000 validation_error: 21.132 +(epoch: 46, iters: 76160, time: 0.555, data: 0.000) G_L1: 14.460 G_L1_ABSOLUTE: 2.728 G_L1_RELATIVE: 11.733 G_Regularizer: 0.000 validation_error: 20.789 +(epoch: 46, iters: 78160, time: 0.549, data: 0.000) G_L1: 13.665 G_L1_ABSOLUTE: 2.350 G_L1_RELATIVE: 11.314 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 46, iters: 80160, time: 0.534, data: 0.000) G_L1: 14.599 G_L1_ABSOLUTE: 2.837 G_L1_RELATIVE: 11.762 G_Regularizer: 0.000 validation_error: 20.742 +(epoch: 46, iters: 82160, time: 0.562, data: 0.000) G_L1: 13.851 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 11.370 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 46, iters: 84160, time: 0.554, data: 0.000) G_L1: 16.545 G_L1_ABSOLUTE: 2.398 G_L1_RELATIVE: 14.147 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 46, iters: 86160, time: 0.540, data: 0.000) G_L1: 14.633 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 12.294 G_Regularizer: 0.000 validation_error: 20.669 +(epoch: 46, iters: 88160, time: 0.541, data: 0.000) G_L1: 17.945 G_L1_ABSOLUTE: 3.102 G_L1_RELATIVE: 14.843 G_Regularizer: 0.000 validation_error: 20.847 +(epoch: 46, iters: 90160, time: 0.549, data: 0.000) G_L1: 14.786 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 12.318 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 46, iters: 92160, time: 0.553, data: 0.000) G_L1: 13.286 G_L1_ABSOLUTE: 2.315 G_L1_RELATIVE: 10.971 G_Regularizer: 0.000 validation_error: 21.193 +(epoch: 46, iters: 94160, time: 0.552, data: 0.001) G_L1: 15.157 G_L1_ABSOLUTE: 2.667 G_L1_RELATIVE: 12.490 G_Regularizer: 0.000 validation_error: 21.021 +(epoch: 46, iters: 96160, time: 0.534, data: 0.000) G_L1: 13.560 G_L1_ABSOLUTE: 2.905 G_L1_RELATIVE: 10.655 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 46, iters: 98160, time: 0.546, data: 0.000) G_L1: 13.281 G_L1_ABSOLUTE: 1.972 G_L1_RELATIVE: 11.309 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 46, iters: 100160, time: 0.562, data: 0.000) G_L1: 14.769 G_L1_ABSOLUTE: 2.134 G_L1_RELATIVE: 12.635 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 46, iters: 102160, time: 0.546, data: 0.000) G_L1: 14.546 G_L1_ABSOLUTE: 2.841 G_L1_RELATIVE: 11.705 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 46, iters: 104160, time: 0.537, data: 0.000) G_L1: 13.381 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 10.820 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 46, iters: 106160, time: 0.534, data: 0.000) G_L1: 14.442 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 12.010 G_Regularizer: 0.000 validation_error: 21.104 +(epoch: 46, iters: 108160, time: 0.557, data: 0.000) G_L1: 14.541 G_L1_ABSOLUTE: 2.403 G_L1_RELATIVE: 12.138 G_Regularizer: 0.000 validation_error: 21.040 +(epoch: 46, iters: 110160, time: 0.558, data: 0.000) G_L1: 13.826 G_L1_ABSOLUTE: 2.876 G_L1_RELATIVE: 10.949 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 46, iters: 112160, time: 0.549, data: 0.001) G_L1: 13.467 G_L1_ABSOLUTE: 2.501 G_L1_RELATIVE: 10.966 G_Regularizer: 0.000 validation_error: 21.108 +(epoch: 46, iters: 114160, time: 0.545, data: 0.000) G_L1: 15.713 G_L1_ABSOLUTE: 2.229 G_L1_RELATIVE: 13.484 G_Regularizer: 0.000 validation_error: 20.711 +(epoch: 46, iters: 116160, time: 0.531, data: 0.000) G_L1: 14.656 G_L1_ABSOLUTE: 2.739 G_L1_RELATIVE: 11.917 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 46, iters: 118160, time: 0.548, data: 0.000) G_L1: 14.223 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 11.518 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 46, iters: 120160, time: 0.556, data: 0.000) G_L1: 13.219 G_L1_ABSOLUTE: 2.332 G_L1_RELATIVE: 10.887 G_Regularizer: 0.000 validation_error: 20.862 +(epoch: 46, iters: 122160, time: 0.555, data: 0.000) G_L1: 14.154 G_L1_ABSOLUTE: 2.537 G_L1_RELATIVE: 11.617 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 46, iters: 124160, time: 0.543, data: 0.000) G_L1: 16.266 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 13.889 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 46, iters: 126160, time: 0.561, data: 0.000) G_L1: 12.310 G_L1_ABSOLUTE: 2.092 G_L1_RELATIVE: 10.219 G_Regularizer: 0.000 validation_error: 20.818 +(epoch: 46, iters: 128160, time: 0.551, data: 0.000) G_L1: 13.216 G_L1_ABSOLUTE: 2.218 G_L1_RELATIVE: 10.998 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 46, iters: 130160, time: 0.541, data: 0.001) G_L1: 14.190 G_L1_ABSOLUTE: 3.018 G_L1_RELATIVE: 11.171 G_Regularizer: 0.000 validation_error: 20.936 +(epoch: 46, iters: 132160, time: 0.528, data: 0.001) G_L1: 13.241 G_L1_ABSOLUTE: 2.424 G_L1_RELATIVE: 10.817 G_Regularizer: 0.000 validation_error: 20.885 +(epoch: 46, iters: 134160, time: 0.565, data: 0.001) G_L1: 15.186 G_L1_ABSOLUTE: 2.175 G_L1_RELATIVE: 13.011 G_Regularizer: 0.000 validation_error: 21.184 +(epoch: 46, iters: 136160, time: 0.562, data: 0.000) G_L1: 13.719 G_L1_ABSOLUTE: 2.700 G_L1_RELATIVE: 11.019 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 46, iters: 138160, time: 0.541, data: 0.000) G_L1: 12.734 G_L1_ABSOLUTE: 2.716 G_L1_RELATIVE: 10.019 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 46, iters: 140160, time: 0.544, data: 0.000) G_L1: 15.116 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 12.753 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 46, iters: 142160, time: 0.539, data: 0.000) G_L1: 13.739 G_L1_ABSOLUTE: 2.695 G_L1_RELATIVE: 11.044 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 46, iters: 144160, time: 0.548, data: 0.001) G_L1: 15.581 G_L1_ABSOLUTE: 2.391 G_L1_RELATIVE: 13.190 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 46, iters: 146160, time: 0.539, data: 0.000) G_L1: 14.450 G_L1_ABSOLUTE: 2.364 G_L1_RELATIVE: 12.086 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 46, iters: 148160, time: 0.542, data: 0.000) G_L1: 15.531 G_L1_ABSOLUTE: 2.241 G_L1_RELATIVE: 13.290 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 46, iters: 150160, time: 0.538, data: 0.000) G_L1: 14.299 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 11.719 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 46, iters: 152160, time: 0.537, data: 0.000) G_L1: 14.310 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 11.816 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 46, iters: 154160, time: 0.538, data: 0.000) G_L1: 16.373 G_L1_ABSOLUTE: 3.089 G_L1_RELATIVE: 13.284 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 46, iters: 156160, time: 0.554, data: 0.000) G_L1: 13.353 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 10.922 G_Regularizer: 0.000 validation_error: 21.252 +(epoch: 46, iters: 158160, time: 0.543, data: 0.001) G_L1: 14.793 G_L1_ABSOLUTE: 2.087 G_L1_RELATIVE: 12.706 G_Regularizer: 0.000 validation_error: 20.867 +(epoch: 46, iters: 160160, time: 0.549, data: 0.000) G_L1: 14.896 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 12.529 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 46, iters: 162160, time: 0.546, data: 0.000) G_L1: 12.189 G_L1_ABSOLUTE: 2.496 G_L1_RELATIVE: 9.693 G_Regularizer: 0.000 validation_error: 21.266 +(epoch: 46, iters: 164160, time: 0.551, data: 0.000) G_L1: 17.667 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 15.181 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 46, iters: 166160, time: 0.537, data: 0.000) G_L1: 14.345 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 11.825 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 46, iters: 168160, time: 0.555, data: 0.000) G_L1: 14.146 G_L1_ABSOLUTE: 2.367 G_L1_RELATIVE: 11.779 G_Regularizer: 0.000 validation_error: 21.224 +(epoch: 46, iters: 170160, time: 0.558, data: 0.000) G_L1: 16.307 G_L1_ABSOLUTE: 2.694 G_L1_RELATIVE: 13.613 G_Regularizer: 0.000 validation_error: 20.980 +(epoch: 46, iters: 172160, time: 0.549, data: 0.001) G_L1: 16.617 G_L1_ABSOLUTE: 2.468 G_L1_RELATIVE: 14.148 G_Regularizer: 0.000 validation_error: 21.002 +(epoch: 46, iters: 174160, time: 0.541, data: 0.001) G_L1: 14.651 G_L1_ABSOLUTE: 2.484 G_L1_RELATIVE: 12.167 G_Regularizer: 0.000 validation_error: 20.741 +(epoch: 46, iters: 176160, time: 0.562, data: 0.001) G_L1: 14.125 G_L1_ABSOLUTE: 2.553 G_L1_RELATIVE: 11.573 G_Regularizer: 0.000 validation_error: 20.583 +(epoch: 46, iters: 178160, time: 0.558, data: 0.000) G_L1: 13.338 G_L1_ABSOLUTE: 2.928 G_L1_RELATIVE: 10.410 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 46, iters: 180160, time: 0.535, data: 0.000) G_L1: 17.832 G_L1_ABSOLUTE: 2.859 G_L1_RELATIVE: 14.973 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 46, iters: 182160, time: 0.537, data: 0.000) G_L1: 12.465 G_L1_ABSOLUTE: 1.986 G_L1_RELATIVE: 10.479 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 46, iters: 184160, time: 0.551, data: 0.000) G_L1: 14.832 G_L1_ABSOLUTE: 3.165 G_L1_RELATIVE: 11.667 G_Regularizer: 0.000 validation_error: 20.987 +(epoch: 46, iters: 186160, time: 0.561, data: 0.000) G_L1: 15.628 G_L1_ABSOLUTE: 2.987 G_L1_RELATIVE: 12.640 G_Regularizer: 0.000 validation_error: 20.814 +(epoch: 46, iters: 188160, time: 0.530, data: 0.000) G_L1: 16.298 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 13.418 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 46, iters: 190160, time: 0.543, data: 0.000) G_L1: 13.556 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 11.074 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 46, iters: 192160, time: 0.533, data: 0.000) G_L1: 15.481 G_L1_ABSOLUTE: 2.325 G_L1_RELATIVE: 13.156 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 46, iters: 194160, time: 0.552, data: 0.000) G_L1: 11.629 G_L1_ABSOLUTE: 2.094 G_L1_RELATIVE: 9.535 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 46, iters: 196160, time: 0.535, data: 0.000) G_L1: 13.976 G_L1_ABSOLUTE: 2.792 G_L1_RELATIVE: 11.184 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 46, iters: 198160, time: 0.557, data: 0.000) G_L1: 13.653 G_L1_ABSOLUTE: 2.084 G_L1_RELATIVE: 11.569 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 46, iters: 200160, time: 0.535, data: 0.000) G_L1: 17.706 G_L1_ABSOLUTE: 3.464 G_L1_RELATIVE: 14.242 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 46, iters: 202160, time: 0.550, data: 0.000) G_L1: 14.049 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 11.850 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 46, iters: 204160, time: 0.557, data: 0.001) G_L1: 11.970 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 9.772 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 46, iters: 206160, time: 0.561, data: 0.000) G_L1: 11.703 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 9.318 G_Regularizer: 0.000 validation_error: 21.151 +(epoch: 46, iters: 208160, time: 0.553, data: 0.000) G_L1: 12.781 G_L1_ABSOLUTE: 2.301 G_L1_RELATIVE: 10.480 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 46, iters: 210160, time: 0.532, data: 0.000) G_L1: 13.413 G_L1_ABSOLUTE: 2.385 G_L1_RELATIVE: 11.029 G_Regularizer: 0.000 validation_error: 21.278 +(epoch: 46, iters: 212160, time: 0.548, data: 0.000) G_L1: 11.529 G_L1_ABSOLUTE: 2.538 G_L1_RELATIVE: 8.991 G_Regularizer: 0.000 validation_error: 20.657 +(epoch: 46, iters: 214160, time: 0.554, data: 0.000) G_L1: 13.389 G_L1_ABSOLUTE: 2.427 G_L1_RELATIVE: 10.962 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 46, iters: 216160, time: 0.560, data: 0.000) G_L1: 13.063 G_L1_ABSOLUTE: 2.418 G_L1_RELATIVE: 10.646 G_Regularizer: 0.000 validation_error: 20.571 +(epoch: 46, iters: 218160, time: 0.547, data: 0.000) G_L1: 15.455 G_L1_ABSOLUTE: 2.289 G_L1_RELATIVE: 13.167 G_Regularizer: 0.000 validation_error: 21.003 +(epoch: 46, iters: 220160, time: 0.555, data: 0.000) G_L1: 14.340 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 11.984 G_Regularizer: 0.000 validation_error: 20.940 +(epoch: 46, iters: 222160, time: 0.557, data: 0.000) G_L1: 13.434 G_L1_ABSOLUTE: 2.537 G_L1_RELATIVE: 10.896 G_Regularizer: 0.000 validation_error: 20.832 +(epoch: 46, iters: 224160, time: 0.551, data: 0.000) G_L1: 12.900 G_L1_ABSOLUTE: 2.189 G_L1_RELATIVE: 10.711 G_Regularizer: 0.000 validation_error: 20.905 +(epoch: 46, iters: 226160, time: 0.541, data: 0.000) G_L1: 16.123 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 13.599 G_Regularizer: 0.000 validation_error: 21.003 +(epoch: 46, iters: 228160, time: 0.557, data: 0.001) G_L1: 13.802 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 10.864 G_Regularizer: 0.000 validation_error: 21.177 +(epoch: 46, iters: 230160, time: 0.532, data: 0.000) G_L1: 17.382 G_L1_ABSOLUTE: 2.641 G_L1_RELATIVE: 14.741 G_Regularizer: 0.000 validation_error: 20.930 +(epoch: 46, iters: 232160, time: 0.555, data: 0.001) G_L1: 13.207 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 10.620 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 46, iters: 234160, time: 0.542, data: 0.000) G_L1: 12.219 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 10.003 G_Regularizer: 0.000 validation_error: 20.818 +(epoch: 46, iters: 236160, time: 0.545, data: 0.000) G_L1: 13.487 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 10.649 G_Regularizer: 0.000 validation_error: 21.078 +(epoch: 46, iters: 238160, time: 0.533, data: 0.001) G_L1: 12.732 G_L1_ABSOLUTE: 2.509 G_L1_RELATIVE: 10.224 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 46, iters: 240160, time: 0.548, data: 0.000) G_L1: 14.951 G_L1_ABSOLUTE: 3.240 G_L1_RELATIVE: 11.710 G_Regularizer: 0.000 validation_error: 20.503 +(epoch: 46, iters: 242160, time: 0.557, data: 0.000) G_L1: 14.910 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 12.309 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 46, iters: 244160, time: 0.560, data: 0.000) G_L1: 16.740 G_L1_ABSOLUTE: 2.667 G_L1_RELATIVE: 14.073 G_Regularizer: 0.000 validation_error: 21.231 +(epoch: 46, iters: 246160, time: 0.539, data: 0.000) G_L1: 12.884 G_L1_ABSOLUTE: 2.280 G_L1_RELATIVE: 10.604 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 46, iters: 248160, time: 0.563, data: 0.000) G_L1: 12.877 G_L1_ABSOLUTE: 2.182 G_L1_RELATIVE: 10.696 G_Regularizer: 0.000 validation_error: 20.676 +(epoch: 46, iters: 250160, time: 0.557, data: 0.000) G_L1: 11.678 G_L1_ABSOLUTE: 2.133 G_L1_RELATIVE: 9.545 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 46, iters: 252160, time: 0.533, data: 0.000) G_L1: 15.731 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 13.376 G_Regularizer: 0.000 validation_error: 20.932 +(epoch: 46, iters: 254160, time: 0.541, data: 0.000) G_L1: 14.257 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 11.837 G_Regularizer: 0.000 validation_error: 21.001 +(epoch: 46, iters: 256160, time: 0.561, data: 0.000) G_L1: 13.311 G_L1_ABSOLUTE: 2.333 G_L1_RELATIVE: 10.978 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 46, iters: 258160, time: 0.556, data: 0.000) G_L1: 12.909 G_L1_ABSOLUTE: 2.710 G_L1_RELATIVE: 10.199 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 46, iters: 260160, time: 0.545, data: 0.001) G_L1: 14.195 G_L1_ABSOLUTE: 2.399 G_L1_RELATIVE: 11.797 G_Regularizer: 0.000 validation_error: 20.604 +(epoch: 46, iters: 262160, time: 0.543, data: 0.000) G_L1: 19.543 G_L1_ABSOLUTE: 2.997 G_L1_RELATIVE: 16.546 G_Regularizer: 0.000 validation_error: 20.672 +(epoch: 46, iters: 264160, time: 0.526, data: 0.000) G_L1: 12.245 G_L1_ABSOLUTE: 2.226 G_L1_RELATIVE: 10.019 G_Regularizer: 0.000 validation_error: 21.087 +(epoch: 46, iters: 266160, time: 0.533, data: 0.000) G_L1: 13.813 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 11.448 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 46, iters: 268160, time: 0.547, data: 0.001) G_L1: 12.655 G_L1_ABSOLUTE: 2.151 G_L1_RELATIVE: 10.504 G_Regularizer: 0.000 validation_error: 20.675 +(epoch: 46, iters: 270160, time: 0.525, data: 0.000) G_L1: 14.083 G_L1_ABSOLUTE: 2.580 G_L1_RELATIVE: 11.503 G_Regularizer: 0.000 validation_error: 21.072 +(epoch: 46, iters: 272160, time: 0.554, data: 0.000) G_L1: 11.637 G_L1_ABSOLUTE: 2.529 G_L1_RELATIVE: 9.109 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 46, iters: 274160, time: 0.552, data: 0.000) G_L1: 14.829 G_L1_ABSOLUTE: 2.682 G_L1_RELATIVE: 12.147 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 46, iters: 276160, time: 0.540, data: 0.000) G_L1: 14.370 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 11.890 G_Regularizer: 0.000 validation_error: 20.979 +(epoch: 46, iters: 278160, time: 0.531, data: 0.000) G_L1: 15.222 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 12.638 G_Regularizer: 0.000 validation_error: 20.747 +(epoch: 46, iters: 280160, time: 0.535, data: 0.000) G_L1: 12.378 G_L1_ABSOLUTE: 2.154 G_L1_RELATIVE: 10.223 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 46, iters: 282160, time: 0.554, data: 0.000) G_L1: 16.971 G_L1_ABSOLUTE: 2.186 G_L1_RELATIVE: 14.784 G_Regularizer: 0.000 validation_error: 21.177 +(epoch: 46, iters: 284160, time: 0.547, data: 0.001) G_L1: 11.308 G_L1_ABSOLUTE: 2.242 G_L1_RELATIVE: 9.066 G_Regularizer: 0.000 validation_error: 20.738 +(epoch: 46, iters: 286160, time: 0.545, data: 0.000) G_L1: 14.867 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 12.267 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 46, iters: 288160, time: 0.543, data: 0.000) G_L1: 15.869 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 13.505 G_Regularizer: 0.000 validation_error: 21.118 +(epoch: 46, iters: 290160, time: 0.552, data: 0.000) G_L1: 13.980 G_L1_ABSOLUTE: 2.664 G_L1_RELATIVE: 11.315 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 46, iters: 292160, time: 0.558, data: 0.000) G_L1: 14.178 G_L1_ABSOLUTE: 2.456 G_L1_RELATIVE: 11.722 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 46, iters: 294160, time: 0.555, data: 0.000) G_L1: 13.050 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 10.633 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 46, iters: 296160, time: 0.542, data: 0.000) G_L1: 13.002 G_L1_ABSOLUTE: 2.295 G_L1_RELATIVE: 10.707 G_Regularizer: 0.000 validation_error: 20.811 +(epoch: 46, iters: 298160, time: 0.557, data: 0.000) G_L1: 13.162 G_L1_ABSOLUTE: 2.383 G_L1_RELATIVE: 10.778 G_Regularizer: 0.000 validation_error: 20.795 +(epoch: 46, iters: 300160, time: 0.557, data: 0.000) G_L1: 17.280 G_L1_ABSOLUTE: 2.757 G_L1_RELATIVE: 14.523 G_Regularizer: 0.000 validation_error: 20.776 +(epoch: 46, iters: 302160, time: 0.538, data: 0.001) G_L1: 13.382 G_L1_ABSOLUTE: 2.413 G_L1_RELATIVE: 10.968 G_Regularizer: 0.000 validation_error: 21.015 +(epoch: 47, iters: 1408, time: 0.532, data: 0.000) G_L1: 14.104 G_L1_ABSOLUTE: 2.306 G_L1_RELATIVE: 11.798 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 47, iters: 3408, time: 0.559, data: 0.001) G_L1: 16.565 G_L1_ABSOLUTE: 2.892 G_L1_RELATIVE: 13.672 G_Regularizer: 0.000 validation_error: 21.022 +(epoch: 47, iters: 5408, time: 0.552, data: 0.000) G_L1: 11.763 G_L1_ABSOLUTE: 1.965 G_L1_RELATIVE: 9.797 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 47, iters: 7408, time: 0.545, data: 0.000) G_L1: 14.375 G_L1_ABSOLUTE: 2.851 G_L1_RELATIVE: 11.524 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 47, iters: 9408, time: 0.545, data: 0.000) G_L1: 17.191 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 14.489 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 47, iters: 11408, time: 0.532, data: 0.000) G_L1: 13.082 G_L1_ABSOLUTE: 2.263 G_L1_RELATIVE: 10.819 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 47, iters: 13408, time: 0.533, data: 0.000) G_L1: 14.603 G_L1_ABSOLUTE: 2.560 G_L1_RELATIVE: 12.043 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 47, iters: 15408, time: 0.553, data: 0.000) G_L1: 12.431 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 10.182 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 47, iters: 17408, time: 0.553, data: 0.000) G_L1: 13.540 G_L1_ABSOLUTE: 2.430 G_L1_RELATIVE: 11.110 G_Regularizer: 0.000 validation_error: 20.836 +(epoch: 47, iters: 19408, time: 0.535, data: 0.000) G_L1: 13.446 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 10.970 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 47, iters: 21408, time: 0.548, data: 0.000) G_L1: 16.318 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 13.688 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 47, iters: 23408, time: 0.555, data: 0.000) G_L1: 14.793 G_L1_ABSOLUTE: 2.883 G_L1_RELATIVE: 11.909 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 47, iters: 25408, time: 0.535, data: 0.000) G_L1: 15.210 G_L1_ABSOLUTE: 2.455 G_L1_RELATIVE: 12.755 G_Regularizer: 0.000 validation_error: 20.927 +(epoch: 47, iters: 27408, time: 0.534, data: 0.000) G_L1: 12.666 G_L1_ABSOLUTE: 2.704 G_L1_RELATIVE: 9.962 G_Regularizer: 0.000 validation_error: 21.033 +(epoch: 47, iters: 29408, time: 0.548, data: 0.000) G_L1: 12.741 G_L1_ABSOLUTE: 2.327 G_L1_RELATIVE: 10.414 G_Regularizer: 0.000 validation_error: 20.667 +(epoch: 47, iters: 31408, time: 0.555, data: 0.000) G_L1: 14.120 G_L1_ABSOLUTE: 2.753 G_L1_RELATIVE: 11.367 G_Regularizer: 0.000 validation_error: 20.983 +(epoch: 47, iters: 33408, time: 0.548, data: 0.000) G_L1: 14.030 G_L1_ABSOLUTE: 2.397 G_L1_RELATIVE: 11.633 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 47, iters: 35408, time: 0.571, data: 0.000) G_L1: 11.744 G_L1_ABSOLUTE: 2.369 G_L1_RELATIVE: 9.375 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 47, iters: 37408, time: 0.557, data: 0.000) G_L1: 14.338 G_L1_ABSOLUTE: 3.048 G_L1_RELATIVE: 11.290 G_Regularizer: 0.000 validation_error: 20.922 +(epoch: 47, iters: 39408, time: 0.551, data: 0.000) G_L1: 14.526 G_L1_ABSOLUTE: 2.318 G_L1_RELATIVE: 12.208 G_Regularizer: 0.000 validation_error: 21.129 +(epoch: 47, iters: 41408, time: 0.559, data: 0.000) G_L1: 12.454 G_L1_ABSOLUTE: 2.343 G_L1_RELATIVE: 10.111 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 47, iters: 43408, time: 0.538, data: 0.001) G_L1: 12.883 G_L1_ABSOLUTE: 1.771 G_L1_RELATIVE: 11.111 G_Regularizer: 0.000 validation_error: 20.852 +(epoch: 47, iters: 45408, time: 0.551, data: 0.000) G_L1: 15.913 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 13.281 G_Regularizer: 0.000 validation_error: 21.107 +(epoch: 47, iters: 47408, time: 0.555, data: 0.000) G_L1: 15.051 G_L1_ABSOLUTE: 2.609 G_L1_RELATIVE: 12.442 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 47, iters: 49408, time: 0.546, data: 0.000) G_L1: 14.884 G_L1_ABSOLUTE: 2.665 G_L1_RELATIVE: 12.219 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 47, iters: 51408, time: 0.550, data: 0.000) G_L1: 13.978 G_L1_ABSOLUTE: 2.826 G_L1_RELATIVE: 11.151 G_Regularizer: 0.000 validation_error: 20.844 +(epoch: 47, iters: 53408, time: 0.536, data: 0.000) G_L1: 12.330 G_L1_ABSOLUTE: 2.164 G_L1_RELATIVE: 10.166 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 47, iters: 55408, time: 0.559, data: 0.000) G_L1: 12.410 G_L1_ABSOLUTE: 1.940 G_L1_RELATIVE: 10.470 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 47, iters: 57408, time: 0.534, data: 0.000) G_L1: 13.164 G_L1_ABSOLUTE: 2.632 G_L1_RELATIVE: 10.532 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 47, iters: 59408, time: 0.539, data: 0.001) G_L1: 15.502 G_L1_ABSOLUTE: 3.134 G_L1_RELATIVE: 12.368 G_Regularizer: 0.000 validation_error: 21.133 +(epoch: 47, iters: 61408, time: 0.547, data: 0.000) G_L1: 12.568 G_L1_ABSOLUTE: 2.426 G_L1_RELATIVE: 10.142 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 47, iters: 63408, time: 0.553, data: 0.000) G_L1: 11.820 G_L1_ABSOLUTE: 2.179 G_L1_RELATIVE: 9.641 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 47, iters: 65408, time: 0.556, data: 0.000) G_L1: 12.960 G_L1_ABSOLUTE: 2.482 G_L1_RELATIVE: 10.478 G_Regularizer: 0.000 validation_error: 20.723 +(epoch: 47, iters: 67408, time: 0.550, data: 0.000) G_L1: 12.496 G_L1_ABSOLUTE: 2.078 G_L1_RELATIVE: 10.418 G_Regularizer: 0.000 validation_error: 20.696 +(epoch: 47, iters: 69408, time: 0.554, data: 0.001) G_L1: 13.477 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 11.044 G_Regularizer: 0.000 validation_error: 20.746 +(epoch: 47, iters: 71408, time: 0.541, data: 0.001) G_L1: 13.205 G_L1_ABSOLUTE: 2.069 G_L1_RELATIVE: 11.136 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 47, iters: 73408, time: 0.557, data: 0.000) G_L1: 15.519 G_L1_ABSOLUTE: 2.488 G_L1_RELATIVE: 13.032 G_Regularizer: 0.000 validation_error: 20.805 +(epoch: 47, iters: 75408, time: 0.574, data: 0.001) G_L1: 16.120 G_L1_ABSOLUTE: 2.933 G_L1_RELATIVE: 13.187 G_Regularizer: 0.000 validation_error: 21.053 +(epoch: 47, iters: 77408, time: 0.550, data: 0.000) G_L1: 13.096 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 10.812 G_Regularizer: 0.000 validation_error: 20.917 +(epoch: 47, iters: 79408, time: 0.551, data: 0.001) G_L1: 13.960 G_L1_ABSOLUTE: 2.556 G_L1_RELATIVE: 11.405 G_Regularizer: 0.000 validation_error: 20.940 +(epoch: 47, iters: 81408, time: 0.548, data: 0.000) G_L1: 14.104 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 11.517 G_Regularizer: 0.000 validation_error: 20.754 +(epoch: 47, iters: 83408, time: 0.551, data: 0.000) G_L1: 14.406 G_L1_ABSOLUTE: 2.400 G_L1_RELATIVE: 12.006 G_Regularizer: 0.000 validation_error: 20.836 +(epoch: 47, iters: 85408, time: 0.537, data: 0.001) G_L1: 13.791 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 11.226 G_Regularizer: 0.000 validation_error: 21.014 +(epoch: 47, iters: 87408, time: 0.554, data: 0.000) G_L1: 13.983 G_L1_ABSOLUTE: 2.356 G_L1_RELATIVE: 11.628 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 47, iters: 89408, time: 0.550, data: 0.000) G_L1: 12.670 G_L1_ABSOLUTE: 2.426 G_L1_RELATIVE: 10.244 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 47, iters: 91408, time: 0.553, data: 0.000) G_L1: 11.894 G_L1_ABSOLUTE: 2.374 G_L1_RELATIVE: 9.520 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 47, iters: 93408, time: 0.533, data: 0.000) G_L1: 16.572 G_L1_ABSOLUTE: 2.815 G_L1_RELATIVE: 13.757 G_Regularizer: 0.000 validation_error: 20.982 +(epoch: 47, iters: 95408, time: 0.541, data: 0.000) G_L1: 13.587 G_L1_ABSOLUTE: 2.822 G_L1_RELATIVE: 10.766 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 47, iters: 97408, time: 0.538, data: 0.000) G_L1: 14.780 G_L1_ABSOLUTE: 2.896 G_L1_RELATIVE: 11.883 G_Regularizer: 0.000 validation_error: 20.678 +(epoch: 47, iters: 99408, time: 0.548, data: 0.000) G_L1: 14.149 G_L1_ABSOLUTE: 2.896 G_L1_RELATIVE: 11.252 G_Regularizer: 0.000 validation_error: 20.726 +(epoch: 47, iters: 101408, time: 0.540, data: 0.001) G_L1: 12.224 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 9.809 G_Regularizer: 0.000 validation_error: 20.555 +(epoch: 47, iters: 103408, time: 0.543, data: 0.000) G_L1: 12.564 G_L1_ABSOLUTE: 2.255 G_L1_RELATIVE: 10.309 G_Regularizer: 0.000 validation_error: 21.009 +(epoch: 47, iters: 105408, time: 0.545, data: 0.000) G_L1: 13.255 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 10.898 G_Regularizer: 0.000 validation_error: 20.789 +(epoch: 47, iters: 107408, time: 0.553, data: 0.000) G_L1: 15.414 G_L1_ABSOLUTE: 2.688 G_L1_RELATIVE: 12.727 G_Regularizer: 0.000 validation_error: 20.649 +(epoch: 47, iters: 109408, time: 0.552, data: 0.000) G_L1: 12.213 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 9.843 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 47, iters: 111408, time: 0.529, data: 0.000) G_L1: 14.098 G_L1_ABSOLUTE: 2.876 G_L1_RELATIVE: 11.222 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 47, iters: 113408, time: 0.546, data: 0.000) G_L1: 13.347 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 10.645 G_Regularizer: 0.000 validation_error: 20.376 +(epoch: 47, iters: 115408, time: 0.557, data: 0.000) G_L1: 15.296 G_L1_ABSOLUTE: 2.452 G_L1_RELATIVE: 12.844 G_Regularizer: 0.000 validation_error: 20.962 +(epoch: 47, iters: 117408, time: 0.549, data: 0.000) G_L1: 12.658 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 10.172 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 47, iters: 119408, time: 0.540, data: 0.000) G_L1: 13.671 G_L1_ABSOLUTE: 2.249 G_L1_RELATIVE: 11.422 G_Regularizer: 0.000 validation_error: 20.993 +(epoch: 47, iters: 121408, time: 0.556, data: 0.000) G_L1: 14.940 G_L1_ABSOLUTE: 2.508 G_L1_RELATIVE: 12.431 G_Regularizer: 0.000 validation_error: 21.183 +(epoch: 47, iters: 123408, time: 0.558, data: 0.000) G_L1: 12.359 G_L1_ABSOLUTE: 2.606 G_L1_RELATIVE: 9.752 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 47, iters: 125408, time: 0.549, data: 0.000) G_L1: 11.674 G_L1_ABSOLUTE: 2.459 G_L1_RELATIVE: 9.216 G_Regularizer: 0.000 validation_error: 20.695 +(epoch: 47, iters: 127408, time: 0.540, data: 0.000) G_L1: 13.552 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 11.132 G_Regularizer: 0.000 validation_error: 20.981 +(epoch: 47, iters: 129408, time: 0.563, data: 0.000) G_L1: 14.697 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 12.247 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 47, iters: 131408, time: 0.558, data: 0.000) G_L1: 13.827 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 11.228 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 47, iters: 133408, time: 0.562, data: 0.000) G_L1: 11.654 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 9.455 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 47, iters: 135408, time: 0.534, data: 0.000) G_L1: 14.003 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 11.705 G_Regularizer: 0.000 validation_error: 21.049 +(epoch: 47, iters: 137408, time: 0.571, data: 0.000) G_L1: 12.175 G_L1_ABSOLUTE: 2.192 G_L1_RELATIVE: 9.984 G_Regularizer: 0.000 validation_error: 20.626 +(epoch: 47, iters: 139408, time: 0.537, data: 0.000) G_L1: 16.023 G_L1_ABSOLUTE: 2.206 G_L1_RELATIVE: 13.817 G_Regularizer: 0.000 validation_error: 20.589 +(epoch: 47, iters: 141408, time: 0.550, data: 0.000) G_L1: 14.130 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 11.931 G_Regularizer: 0.000 validation_error: 21.031 +(epoch: 47, iters: 143408, time: 0.546, data: 0.000) G_L1: 12.628 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 10.104 G_Regularizer: 0.000 validation_error: 20.943 +(epoch: 47, iters: 145408, time: 0.542, data: 0.000) G_L1: 14.340 G_L1_ABSOLUTE: 2.133 G_L1_RELATIVE: 12.208 G_Regularizer: 0.000 validation_error: 20.536 +(epoch: 47, iters: 147408, time: 0.556, data: 0.000) G_L1: 12.750 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 10.120 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 47, iters: 149408, time: 0.560, data: 0.000) G_L1: 15.616 G_L1_ABSOLUTE: 2.586 G_L1_RELATIVE: 13.030 G_Regularizer: 0.000 validation_error: 21.173 +(epoch: 47, iters: 151408, time: 0.532, data: 0.001) G_L1: 13.719 G_L1_ABSOLUTE: 2.321 G_L1_RELATIVE: 11.398 G_Regularizer: 0.000 validation_error: 21.127 +(epoch: 47, iters: 153408, time: 0.536, data: 0.000) G_L1: 15.255 G_L1_ABSOLUTE: 2.518 G_L1_RELATIVE: 12.737 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 47, iters: 155408, time: 0.548, data: 0.000) G_L1: 12.808 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 10.278 G_Regularizer: 0.000 validation_error: 20.800 +(epoch: 47, iters: 157408, time: 0.549, data: 0.001) G_L1: 15.417 G_L1_ABSOLUTE: 2.334 G_L1_RELATIVE: 13.083 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 47, iters: 159408, time: 0.545, data: 0.001) G_L1: 20.813 G_L1_ABSOLUTE: 2.850 G_L1_RELATIVE: 17.963 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 47, iters: 161408, time: 0.541, data: 0.001) G_L1: 13.836 G_L1_ABSOLUTE: 2.956 G_L1_RELATIVE: 10.880 G_Regularizer: 0.000 validation_error: 21.045 +(epoch: 47, iters: 163408, time: 0.555, data: 0.000) G_L1: 12.773 G_L1_ABSOLUTE: 2.361 G_L1_RELATIVE: 10.413 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 47, iters: 165408, time: 0.554, data: 0.000) G_L1: 13.057 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 10.439 G_Regularizer: 0.000 validation_error: 21.013 +(epoch: 47, iters: 167408, time: 0.547, data: 0.000) G_L1: 12.377 G_L1_ABSOLUTE: 2.324 G_L1_RELATIVE: 10.052 G_Regularizer: 0.000 validation_error: 20.937 +(epoch: 47, iters: 169408, time: 0.551, data: 0.000) G_L1: 11.914 G_L1_ABSOLUTE: 2.110 G_L1_RELATIVE: 9.804 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 47, iters: 171408, time: 0.553, data: 0.000) G_L1: 13.415 G_L1_ABSOLUTE: 2.844 G_L1_RELATIVE: 10.572 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 47, iters: 173408, time: 0.551, data: 0.000) G_L1: 14.099 G_L1_ABSOLUTE: 2.115 G_L1_RELATIVE: 11.984 G_Regularizer: 0.000 validation_error: 21.054 +(epoch: 47, iters: 175408, time: 0.549, data: 0.000) G_L1: 14.728 G_L1_ABSOLUTE: 2.429 G_L1_RELATIVE: 12.299 G_Regularizer: 0.000 validation_error: 20.989 +(epoch: 47, iters: 177408, time: 0.558, data: 0.000) G_L1: 15.051 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 12.274 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 47, iters: 179408, time: 0.532, data: 0.000) G_L1: 14.385 G_L1_ABSOLUTE: 2.437 G_L1_RELATIVE: 11.948 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 47, iters: 181408, time: 0.533, data: 0.000) G_L1: 13.918 G_L1_ABSOLUTE: 2.723 G_L1_RELATIVE: 11.195 G_Regularizer: 0.000 validation_error: 20.779 +(epoch: 47, iters: 183408, time: 0.548, data: 0.000) G_L1: 13.733 G_L1_ABSOLUTE: 2.030 G_L1_RELATIVE: 11.703 G_Regularizer: 0.000 validation_error: 20.960 +(epoch: 47, iters: 185408, time: 0.557, data: 0.000) G_L1: 14.363 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 11.786 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 47, iters: 187408, time: 0.529, data: 0.000) G_L1: 15.495 G_L1_ABSOLUTE: 2.452 G_L1_RELATIVE: 13.043 G_Regularizer: 0.000 validation_error: 20.924 +(epoch: 47, iters: 189408, time: 0.556, data: 0.000) G_L1: 14.321 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 11.791 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 47, iters: 191408, time: 0.559, data: 0.000) G_L1: 15.200 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 12.822 G_Regularizer: 0.000 validation_error: 21.050 +(epoch: 47, iters: 193408, time: 0.547, data: 0.000) G_L1: 12.572 G_L1_ABSOLUTE: 2.004 G_L1_RELATIVE: 10.568 G_Regularizer: 0.000 validation_error: 21.073 +(epoch: 47, iters: 195408, time: 0.540, data: 0.000) G_L1: 14.585 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 12.099 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 47, iters: 197408, time: 0.560, data: 0.000) G_L1: 13.173 G_L1_ABSOLUTE: 2.349 G_L1_RELATIVE: 10.823 G_Regularizer: 0.000 validation_error: 20.892 +(epoch: 47, iters: 199408, time: 0.539, data: 0.000) G_L1: 13.287 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 10.752 G_Regularizer: 0.000 validation_error: 20.982 +(epoch: 47, iters: 201408, time: 0.542, data: 0.000) G_L1: 14.310 G_L1_ABSOLUTE: 2.767 G_L1_RELATIVE: 11.544 G_Regularizer: 0.000 validation_error: 20.911 +(epoch: 47, iters: 203408, time: 0.556, data: 0.000) G_L1: 13.023 G_L1_ABSOLUTE: 2.206 G_L1_RELATIVE: 10.817 G_Regularizer: 0.000 validation_error: 20.956 +(epoch: 47, iters: 205408, time: 0.545, data: 0.000) G_L1: 15.485 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 12.701 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 47, iters: 207408, time: 0.531, data: 0.000) G_L1: 12.024 G_L1_ABSOLUTE: 2.406 G_L1_RELATIVE: 9.618 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 47, iters: 209408, time: 0.537, data: 0.000) G_L1: 14.493 G_L1_ABSOLUTE: 2.610 G_L1_RELATIVE: 11.883 G_Regularizer: 0.000 validation_error: 21.189 +(epoch: 47, iters: 211408, time: 0.548, data: 0.000) G_L1: 11.333 G_L1_ABSOLUTE: 2.074 G_L1_RELATIVE: 9.259 G_Regularizer: 0.000 validation_error: 21.061 +(epoch: 47, iters: 213408, time: 0.541, data: 0.000) G_L1: 13.648 G_L1_ABSOLUTE: 2.559 G_L1_RELATIVE: 11.089 G_Regularizer: 0.000 validation_error: 20.660 +(epoch: 47, iters: 215408, time: 0.556, data: 0.000) G_L1: 13.757 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 11.126 G_Regularizer: 0.000 validation_error: 20.700 +(epoch: 47, iters: 217408, time: 0.555, data: 0.000) G_L1: 14.165 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 11.641 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 47, iters: 219408, time: 0.558, data: 0.000) G_L1: 15.206 G_L1_ABSOLUTE: 2.777 G_L1_RELATIVE: 12.429 G_Regularizer: 0.000 validation_error: 21.037 +(epoch: 47, iters: 221408, time: 0.539, data: 0.000) G_L1: 15.954 G_L1_ABSOLUTE: 2.395 G_L1_RELATIVE: 13.559 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 47, iters: 223408, time: 0.531, data: 0.000) G_L1: 13.023 G_L1_ABSOLUTE: 2.195 G_L1_RELATIVE: 10.828 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 47, iters: 225408, time: 0.556, data: 0.000) G_L1: 15.072 G_L1_ABSOLUTE: 2.780 G_L1_RELATIVE: 12.292 G_Regularizer: 0.000 validation_error: 21.050 +(epoch: 47, iters: 227408, time: 0.538, data: 0.000) G_L1: 13.501 G_L1_ABSOLUTE: 2.213 G_L1_RELATIVE: 11.288 G_Regularizer: 0.000 validation_error: 20.796 +(epoch: 47, iters: 229408, time: 0.545, data: 0.000) G_L1: 14.215 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 11.826 G_Regularizer: 0.000 validation_error: 21.189 +(epoch: 47, iters: 231408, time: 0.537, data: 0.000) G_L1: 13.624 G_L1_ABSOLUTE: 2.542 G_L1_RELATIVE: 11.082 G_Regularizer: 0.000 validation_error: 20.709 +(epoch: 47, iters: 233408, time: 0.534, data: 0.000) G_L1: 14.455 G_L1_ABSOLUTE: 2.457 G_L1_RELATIVE: 11.997 G_Regularizer: 0.000 validation_error: 21.004 +(epoch: 47, iters: 235408, time: 0.540, data: 0.000) G_L1: 15.438 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 13.029 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 47, iters: 237408, time: 0.546, data: 0.000) G_L1: 23.324 G_L1_ABSOLUTE: 2.531 G_L1_RELATIVE: 20.792 G_Regularizer: 0.000 validation_error: 20.930 +(epoch: 47, iters: 239408, time: 0.547, data: 0.000) G_L1: 15.218 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 12.554 G_Regularizer: 0.000 validation_error: 20.713 +(epoch: 47, iters: 241408, time: 0.559, data: 0.000) G_L1: 14.062 G_L1_ABSOLUTE: 1.943 G_L1_RELATIVE: 12.118 G_Regularizer: 0.000 validation_error: 20.983 +(epoch: 47, iters: 243408, time: 0.536, data: 0.000) G_L1: 16.495 G_L1_ABSOLUTE: 2.600 G_L1_RELATIVE: 13.894 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 47, iters: 245408, time: 0.554, data: 0.001) G_L1: 12.721 G_L1_ABSOLUTE: 2.538 G_L1_RELATIVE: 10.184 G_Regularizer: 0.000 validation_error: 21.130 +(epoch: 47, iters: 247408, time: 0.558, data: 0.000) G_L1: 13.595 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 11.311 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 47, iters: 249408, time: 0.559, data: 0.000) G_L1: 15.289 G_L1_ABSOLUTE: 2.756 G_L1_RELATIVE: 12.533 G_Regularizer: 0.000 validation_error: 20.871 +(epoch: 47, iters: 251408, time: 0.551, data: 0.000) G_L1: 12.865 G_L1_ABSOLUTE: 2.209 G_L1_RELATIVE: 10.656 G_Regularizer: 0.000 validation_error: 20.786 +(epoch: 47, iters: 253408, time: 0.555, data: 0.000) G_L1: 18.581 G_L1_ABSOLUTE: 2.139 G_L1_RELATIVE: 16.442 G_Regularizer: 0.000 validation_error: 20.783 +(epoch: 47, iters: 255408, time: 0.539, data: 0.000) G_L1: 13.438 G_L1_ABSOLUTE: 2.606 G_L1_RELATIVE: 10.832 G_Regularizer: 0.000 validation_error: 20.835 +(epoch: 47, iters: 257408, time: 0.552, data: 0.000) G_L1: 14.660 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 11.900 G_Regularizer: 0.000 validation_error: 20.613 +(epoch: 47, iters: 259408, time: 0.548, data: 0.000) G_L1: 11.916 G_L1_ABSOLUTE: 2.167 G_L1_RELATIVE: 9.748 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 47, iters: 261408, time: 0.556, data: 0.000) G_L1: 13.628 G_L1_ABSOLUTE: 2.704 G_L1_RELATIVE: 10.924 G_Regularizer: 0.000 validation_error: 20.659 +(epoch: 47, iters: 263408, time: 0.554, data: 0.000) G_L1: 14.608 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 12.310 G_Regularizer: 0.000 validation_error: 20.756 +(epoch: 47, iters: 265408, time: 0.544, data: 0.000) G_L1: 15.293 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 12.662 G_Regularizer: 0.000 validation_error: 21.075 +(epoch: 47, iters: 267408, time: 0.554, data: 0.000) G_L1: 14.477 G_L1_ABSOLUTE: 2.585 G_L1_RELATIVE: 11.892 G_Regularizer: 0.000 validation_error: 21.014 +(epoch: 47, iters: 269408, time: 0.550, data: 0.000) G_L1: 11.432 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 9.004 G_Regularizer: 0.000 validation_error: 20.715 +(epoch: 47, iters: 271408, time: 0.565, data: 0.000) G_L1: 12.547 G_L1_ABSOLUTE: 2.026 G_L1_RELATIVE: 10.522 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 47, iters: 273408, time: 0.552, data: 0.000) G_L1: 13.879 G_L1_ABSOLUTE: 2.474 G_L1_RELATIVE: 11.405 G_Regularizer: 0.000 validation_error: 21.027 +(epoch: 47, iters: 275408, time: 0.554, data: 0.000) G_L1: 16.228 G_L1_ABSOLUTE: 2.467 G_L1_RELATIVE: 13.761 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 47, iters: 277408, time: 0.543, data: 0.000) G_L1: 16.246 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 13.812 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 47, iters: 279408, time: 0.557, data: 0.000) G_L1: 13.866 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 11.235 G_Regularizer: 0.000 validation_error: 20.581 +(epoch: 47, iters: 281408, time: 0.530, data: 0.000) G_L1: 12.102 G_L1_ABSOLUTE: 2.365 G_L1_RELATIVE: 9.737 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 47, iters: 283408, time: 0.563, data: 0.000) G_L1: 13.211 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 10.727 G_Regularizer: 0.000 validation_error: 21.058 +(epoch: 47, iters: 285408, time: 0.541, data: 0.000) G_L1: 15.288 G_L1_ABSOLUTE: 2.219 G_L1_RELATIVE: 13.069 G_Regularizer: 0.000 validation_error: 20.467 +(epoch: 47, iters: 287408, time: 0.553, data: 0.000) G_L1: 12.115 G_L1_ABSOLUTE: 2.367 G_L1_RELATIVE: 9.748 G_Regularizer: 0.000 validation_error: 20.849 +(epoch: 47, iters: 289408, time: 0.542, data: 0.000) G_L1: 15.578 G_L1_ABSOLUTE: 2.404 G_L1_RELATIVE: 13.174 G_Regularizer: 0.000 validation_error: 20.720 +(epoch: 47, iters: 291408, time: 0.562, data: 0.000) G_L1: 13.177 G_L1_ABSOLUTE: 2.438 G_L1_RELATIVE: 10.739 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 47, iters: 293408, time: 0.530, data: 0.000) G_L1: 14.891 G_L1_ABSOLUTE: 2.414 G_L1_RELATIVE: 12.477 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 47, iters: 295408, time: 0.559, data: 0.000) G_L1: 15.205 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 12.432 G_Regularizer: 0.000 validation_error: 20.740 +(epoch: 47, iters: 297408, time: 0.557, data: 0.000) G_L1: 13.349 G_L1_ABSOLUTE: 2.425 G_L1_RELATIVE: 10.924 G_Regularizer: 0.000 validation_error: 20.847 +(epoch: 47, iters: 299408, time: 0.560, data: 0.000) G_L1: 13.582 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 11.243 G_Regularizer: 0.000 validation_error: 20.836 +(epoch: 47, iters: 301408, time: 0.539, data: 0.000) G_L1: 15.124 G_L1_ABSOLUTE: 2.984 G_L1_RELATIVE: 12.140 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 48, iters: 656, time: 0.542, data: 0.000) G_L1: 13.665 G_L1_ABSOLUTE: 2.657 G_L1_RELATIVE: 11.008 G_Regularizer: 0.000 validation_error: 21.029 +(epoch: 48, iters: 2656, time: 0.552, data: 0.000) G_L1: 14.486 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 11.891 G_Regularizer: 0.000 validation_error: 20.826 +(epoch: 48, iters: 4656, time: 0.535, data: 0.000) G_L1: 12.929 G_L1_ABSOLUTE: 2.516 G_L1_RELATIVE: 10.413 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 48, iters: 6656, time: 0.539, data: 0.000) G_L1: 14.075 G_L1_ABSOLUTE: 2.552 G_L1_RELATIVE: 11.523 G_Regularizer: 0.000 validation_error: 20.820 +(epoch: 48, iters: 8656, time: 0.552, data: 0.000) G_L1: 15.876 G_L1_ABSOLUTE: 2.564 G_L1_RELATIVE: 13.312 G_Regularizer: 0.000 validation_error: 21.023 +(epoch: 48, iters: 10656, time: 0.569, data: 0.000) G_L1: 14.472 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 11.966 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 48, iters: 12656, time: 0.535, data: 0.000) G_L1: 13.844 G_L1_ABSOLUTE: 3.055 G_L1_RELATIVE: 10.790 G_Regularizer: 0.000 validation_error: 20.643 +(epoch: 48, iters: 14656, time: 0.536, data: 0.000) G_L1: 14.081 G_L1_ABSOLUTE: 3.025 G_L1_RELATIVE: 11.057 G_Regularizer: 0.000 validation_error: 21.133 +(epoch: 48, iters: 16656, time: 0.536, data: 0.000) G_L1: 13.959 G_L1_ABSOLUTE: 2.289 G_L1_RELATIVE: 11.670 G_Regularizer: 0.000 validation_error: 20.814 +(epoch: 48, iters: 18656, time: 0.540, data: 0.000) G_L1: 16.811 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 14.233 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 48, iters: 20656, time: 0.541, data: 0.000) G_L1: 12.896 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 10.556 G_Regularizer: 0.000 validation_error: 21.105 +(epoch: 48, iters: 22656, time: 0.543, data: 0.000) G_L1: 12.357 G_L1_ABSOLUTE: 2.450 G_L1_RELATIVE: 9.907 G_Regularizer: 0.000 validation_error: 20.786 +(epoch: 48, iters: 24656, time: 0.548, data: 0.001) G_L1: 12.884 G_L1_ABSOLUTE: 2.435 G_L1_RELATIVE: 10.449 G_Regularizer: 0.000 validation_error: 20.656 +(epoch: 48, iters: 26656, time: 0.556, data: 0.001) G_L1: 14.310 G_L1_ABSOLUTE: 2.870 G_L1_RELATIVE: 11.440 G_Regularizer: 0.000 validation_error: 21.019 +(epoch: 48, iters: 28656, time: 0.550, data: 0.000) G_L1: 16.712 G_L1_ABSOLUTE: 2.623 G_L1_RELATIVE: 14.089 G_Regularizer: 0.000 validation_error: 20.851 +(epoch: 48, iters: 30656, time: 0.534, data: 0.000) G_L1: 15.419 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 12.686 G_Regularizer: 0.000 validation_error: 20.655 +(epoch: 48, iters: 32656, time: 0.528, data: 0.000) G_L1: 16.417 G_L1_ABSOLUTE: 2.491 G_L1_RELATIVE: 13.926 G_Regularizer: 0.000 validation_error: 20.797 +(epoch: 48, iters: 34656, time: 0.565, data: 0.000) G_L1: 15.450 G_L1_ABSOLUTE: 2.731 G_L1_RELATIVE: 12.720 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 48, iters: 36656, time: 0.541, data: 0.000) G_L1: 15.602 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 13.224 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 48, iters: 38656, time: 0.532, data: 0.000) G_L1: 14.467 G_L1_ABSOLUTE: 2.750 G_L1_RELATIVE: 11.717 G_Regularizer: 0.000 validation_error: 21.106 +(epoch: 48, iters: 40656, time: 0.539, data: 0.000) G_L1: 14.569 G_L1_ABSOLUTE: 2.705 G_L1_RELATIVE: 11.864 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 48, iters: 42656, time: 0.559, data: 0.000) G_L1: 15.759 G_L1_ABSOLUTE: 2.733 G_L1_RELATIVE: 13.026 G_Regularizer: 0.000 validation_error: 20.866 +(epoch: 48, iters: 44656, time: 0.561, data: 0.000) G_L1: 14.941 G_L1_ABSOLUTE: 3.087 G_L1_RELATIVE: 11.854 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 48, iters: 46656, time: 0.548, data: 0.001) G_L1: 15.772 G_L1_ABSOLUTE: 2.622 G_L1_RELATIVE: 13.150 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 48, iters: 48656, time: 0.537, data: 0.000) G_L1: 13.331 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 10.576 G_Regularizer: 0.000 validation_error: 20.769 +(epoch: 48, iters: 50656, time: 0.533, data: 0.001) G_L1: 12.280 G_L1_ABSOLUTE: 2.521 G_L1_RELATIVE: 9.759 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 48, iters: 52656, time: 0.545, data: 0.000) G_L1: 12.602 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 10.240 G_Regularizer: 0.000 validation_error: 20.895 +(epoch: 48, iters: 54656, time: 0.549, data: 0.000) G_L1: 14.524 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 11.964 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 48, iters: 56656, time: 0.535, data: 0.000) G_L1: 13.512 G_L1_ABSOLUTE: 2.404 G_L1_RELATIVE: 11.108 G_Regularizer: 0.000 validation_error: 21.071 +(epoch: 48, iters: 58656, time: 0.535, data: 0.000) G_L1: 11.933 G_L1_ABSOLUTE: 2.247 G_L1_RELATIVE: 9.686 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 48, iters: 60656, time: 0.553, data: 0.000) G_L1: 12.891 G_L1_ABSOLUTE: 3.088 G_L1_RELATIVE: 9.803 G_Regularizer: 0.000 validation_error: 20.749 +(epoch: 48, iters: 62656, time: 0.559, data: 0.000) G_L1: 13.460 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 11.106 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 48, iters: 64656, time: 0.557, data: 0.000) G_L1: 15.884 G_L1_ABSOLUTE: 2.293 G_L1_RELATIVE: 13.591 G_Regularizer: 0.000 validation_error: 21.039 +(epoch: 48, iters: 66656, time: 0.527, data: 0.000) G_L1: 12.567 G_L1_ABSOLUTE: 2.310 G_L1_RELATIVE: 10.258 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 48, iters: 68656, time: 0.557, data: 0.000) G_L1: 13.751 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 11.306 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 48, iters: 70656, time: 0.556, data: 0.000) G_L1: 14.940 G_L1_ABSOLUTE: 2.364 G_L1_RELATIVE: 12.576 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 48, iters: 72656, time: 0.542, data: 0.000) G_L1: 15.313 G_L1_ABSOLUTE: 2.768 G_L1_RELATIVE: 12.545 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 48, iters: 74656, time: 0.554, data: 0.000) G_L1: 13.672 G_L1_ABSOLUTE: 2.387 G_L1_RELATIVE: 11.284 G_Regularizer: 0.000 validation_error: 20.764 +(epoch: 48, iters: 76656, time: 0.556, data: 0.000) G_L1: 16.601 G_L1_ABSOLUTE: 2.398 G_L1_RELATIVE: 14.203 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 48, iters: 78656, time: 0.558, data: 0.000) G_L1: 14.090 G_L1_ABSOLUTE: 2.160 G_L1_RELATIVE: 11.930 G_Regularizer: 0.000 validation_error: 20.861 +(epoch: 48, iters: 80656, time: 0.543, data: 0.000) G_L1: 12.661 G_L1_ABSOLUTE: 2.379 G_L1_RELATIVE: 10.283 G_Regularizer: 0.000 validation_error: 20.687 +(epoch: 48, iters: 82656, time: 0.535, data: 0.000) G_L1: 10.948 G_L1_ABSOLUTE: 2.080 G_L1_RELATIVE: 8.868 G_Regularizer: 0.000 validation_error: 20.862 +(epoch: 48, iters: 84656, time: 0.550, data: 0.000) G_L1: 12.662 G_L1_ABSOLUTE: 2.551 G_L1_RELATIVE: 10.111 G_Regularizer: 0.000 validation_error: 20.923 +(epoch: 48, iters: 86656, time: 0.561, data: 0.000) G_L1: 13.155 G_L1_ABSOLUTE: 2.261 G_L1_RELATIVE: 10.894 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 48, iters: 88656, time: 0.554, data: 0.000) G_L1: 14.460 G_L1_ABSOLUTE: 2.195 G_L1_RELATIVE: 12.265 G_Regularizer: 0.000 validation_error: 21.046 +(epoch: 48, iters: 90656, time: 0.537, data: 0.000) G_L1: 12.292 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 9.597 G_Regularizer: 0.000 validation_error: 20.895 +(epoch: 48, iters: 92656, time: 0.561, data: 0.000) G_L1: 14.466 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 11.946 G_Regularizer: 0.000 validation_error: 20.912 +(epoch: 48, iters: 94656, time: 0.552, data: 0.000) G_L1: 12.726 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 10.201 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 48, iters: 96656, time: 0.561, data: 0.000) G_L1: 17.071 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 14.635 G_Regularizer: 0.000 validation_error: 20.671 +(epoch: 48, iters: 98656, time: 0.536, data: 0.000) G_L1: 12.858 G_L1_ABSOLUTE: 2.062 G_L1_RELATIVE: 10.796 G_Regularizer: 0.000 validation_error: 21.102 +(epoch: 48, iters: 100656, time: 0.559, data: 0.000) G_L1: 13.393 G_L1_ABSOLUTE: 2.279 G_L1_RELATIVE: 11.114 G_Regularizer: 0.000 validation_error: 20.810 +(epoch: 48, iters: 102656, time: 0.560, data: 0.000) G_L1: 12.866 G_L1_ABSOLUTE: 2.519 G_L1_RELATIVE: 10.347 G_Regularizer: 0.000 validation_error: 20.816 +(epoch: 48, iters: 104656, time: 0.559, data: 0.000) G_L1: 13.026 G_L1_ABSOLUTE: 2.167 G_L1_RELATIVE: 10.859 G_Regularizer: 0.000 validation_error: 20.924 +(epoch: 48, iters: 106656, time: 0.542, data: 0.001) G_L1: 14.272 G_L1_ABSOLUTE: 2.351 G_L1_RELATIVE: 11.921 G_Regularizer: 0.000 validation_error: 20.760 +(epoch: 48, iters: 108656, time: 0.555, data: 0.000) G_L1: 16.128 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 13.580 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 48, iters: 110656, time: 0.552, data: 0.001) G_L1: 12.689 G_L1_ABSOLUTE: 2.464 G_L1_RELATIVE: 10.226 G_Regularizer: 0.000 validation_error: 20.692 +(epoch: 48, iters: 112656, time: 0.563, data: 0.000) G_L1: 13.948 G_L1_ABSOLUTE: 2.439 G_L1_RELATIVE: 11.508 G_Regularizer: 0.000 validation_error: 21.054 +(epoch: 48, iters: 114656, time: 0.544, data: 0.000) G_L1: 13.888 G_L1_ABSOLUTE: 2.451 G_L1_RELATIVE: 11.437 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 48, iters: 116656, time: 0.563, data: 0.000) G_L1: 14.214 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 11.564 G_Regularizer: 0.000 validation_error: 20.702 +(epoch: 48, iters: 118656, time: 0.552, data: 0.000) G_L1: 19.357 G_L1_ABSOLUTE: 3.049 G_L1_RELATIVE: 16.308 G_Regularizer: 0.000 validation_error: 20.756 +(epoch: 48, iters: 120656, time: 0.555, data: 0.000) G_L1: 14.225 G_L1_ABSOLUTE: 2.503 G_L1_RELATIVE: 11.723 G_Regularizer: 0.000 validation_error: 20.804 +(epoch: 48, iters: 122656, time: 0.545, data: 0.000) G_L1: 14.400 G_L1_ABSOLUTE: 2.562 G_L1_RELATIVE: 11.838 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 48, iters: 124656, time: 0.558, data: 0.000) G_L1: 14.816 G_L1_ABSOLUTE: 2.590 G_L1_RELATIVE: 12.226 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 48, iters: 126656, time: 0.559, data: 0.000) G_L1: 13.514 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 11.010 G_Regularizer: 0.000 validation_error: 20.639 +(epoch: 48, iters: 128656, time: 0.557, data: 0.000) G_L1: 13.873 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 11.466 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 48, iters: 130656, time: 0.545, data: 0.000) G_L1: 15.862 G_L1_ABSOLUTE: 3.140 G_L1_RELATIVE: 12.723 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 48, iters: 132656, time: 0.527, data: 0.000) G_L1: 13.523 G_L1_ABSOLUTE: 2.213 G_L1_RELATIVE: 11.309 G_Regularizer: 0.000 validation_error: 20.907 +(epoch: 48, iters: 134656, time: 0.541, data: 0.000) G_L1: 14.828 G_L1_ABSOLUTE: 2.696 G_L1_RELATIVE: 12.132 G_Regularizer: 0.000 validation_error: 20.912 +(epoch: 48, iters: 136656, time: 0.531, data: 0.000) G_L1: 14.793 G_L1_ABSOLUTE: 2.704 G_L1_RELATIVE: 12.090 G_Regularizer: 0.000 validation_error: 20.768 +(epoch: 48, iters: 138656, time: 0.543, data: 0.000) G_L1: 14.514 G_L1_ABSOLUTE: 2.752 G_L1_RELATIVE: 11.762 G_Regularizer: 0.000 validation_error: 20.888 +(epoch: 48, iters: 140656, time: 0.539, data: 0.000) G_L1: 15.677 G_L1_ABSOLUTE: 2.502 G_L1_RELATIVE: 13.174 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 48, iters: 142656, time: 0.535, data: 0.000) G_L1: 13.754 G_L1_ABSOLUTE: 2.227 G_L1_RELATIVE: 11.527 G_Regularizer: 0.000 validation_error: 20.588 +(epoch: 48, iters: 144656, time: 0.555, data: 0.000) G_L1: 26.933 G_L1_ABSOLUTE: 2.548 G_L1_RELATIVE: 24.384 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 48, iters: 146656, time: 0.560, data: 0.002) G_L1: 12.350 G_L1_ABSOLUTE: 2.301 G_L1_RELATIVE: 10.049 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 48, iters: 148656, time: 0.541, data: 0.000) G_L1: 14.605 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 12.146 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 48, iters: 150656, time: 0.531, data: 0.000) G_L1: 14.682 G_L1_ABSOLUTE: 2.147 G_L1_RELATIVE: 12.535 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 48, iters: 152656, time: 0.541, data: 0.000) G_L1: 14.205 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 11.683 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 48, iters: 154656, time: 0.551, data: 0.000) G_L1: 16.240 G_L1_ABSOLUTE: 2.276 G_L1_RELATIVE: 13.964 G_Regularizer: 0.000 validation_error: 21.048 +(epoch: 48, iters: 156656, time: 0.547, data: 0.000) G_L1: 27.442 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 24.802 G_Regularizer: 0.000 validation_error: 21.055 +(epoch: 48, iters: 158656, time: 0.541, data: 0.000) G_L1: 15.127 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 12.622 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 48, iters: 160656, time: 0.543, data: 0.001) G_L1: 14.967 G_L1_ABSOLUTE: 2.796 G_L1_RELATIVE: 12.171 G_Regularizer: 0.000 validation_error: 20.912 +(epoch: 48, iters: 162656, time: 0.558, data: 0.000) G_L1: 14.070 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 11.717 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 48, iters: 164656, time: 0.552, data: 0.001) G_L1: 13.150 G_L1_ABSOLUTE: 2.334 G_L1_RELATIVE: 10.817 G_Regularizer: 0.000 validation_error: 21.126 +(epoch: 48, iters: 166656, time: 0.554, data: 0.000) G_L1: 14.276 G_L1_ABSOLUTE: 2.315 G_L1_RELATIVE: 11.961 G_Regularizer: 0.000 validation_error: 20.776 +(epoch: 48, iters: 168656, time: 0.546, data: 0.000) G_L1: 12.856 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 10.372 G_Regularizer: 0.000 validation_error: 20.977 +(epoch: 48, iters: 170656, time: 0.556, data: 0.000) G_L1: 12.938 G_L1_ABSOLUTE: 2.144 G_L1_RELATIVE: 10.794 G_Regularizer: 0.000 validation_error: 20.742 +(epoch: 48, iters: 172656, time: 0.553, data: 0.000) G_L1: 13.878 G_L1_ABSOLUTE: 2.386 G_L1_RELATIVE: 11.492 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 48, iters: 174656, time: 0.543, data: 0.000) G_L1: 12.340 G_L1_ABSOLUTE: 2.185 G_L1_RELATIVE: 10.155 G_Regularizer: 0.000 validation_error: 20.879 +(epoch: 48, iters: 176656, time: 0.545, data: 0.001) G_L1: 17.896 G_L1_ABSOLUTE: 2.619 G_L1_RELATIVE: 15.277 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 48, iters: 178656, time: 0.547, data: 0.000) G_L1: 15.512 G_L1_ABSOLUTE: 2.747 G_L1_RELATIVE: 12.765 G_Regularizer: 0.000 validation_error: 21.044 +(epoch: 48, iters: 180656, time: 0.538, data: 0.000) G_L1: 14.918 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 12.486 G_Regularizer: 0.000 validation_error: 21.058 +(epoch: 48, iters: 182656, time: 0.546, data: 0.000) G_L1: 14.545 G_L1_ABSOLUTE: 2.064 G_L1_RELATIVE: 12.481 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 48, iters: 184656, time: 0.557, data: 0.000) G_L1: 13.449 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 10.715 G_Regularizer: 0.000 validation_error: 20.840 +(epoch: 48, iters: 186656, time: 0.554, data: 0.000) G_L1: 15.108 G_L1_ABSOLUTE: 2.768 G_L1_RELATIVE: 12.340 G_Regularizer: 0.000 validation_error: 20.807 +(epoch: 48, iters: 188656, time: 0.558, data: 0.000) G_L1: 12.296 G_L1_ABSOLUTE: 2.511 G_L1_RELATIVE: 9.785 G_Regularizer: 0.000 validation_error: 20.850 +(epoch: 48, iters: 190656, time: 0.527, data: 0.000) G_L1: 13.134 G_L1_ABSOLUTE: 2.713 G_L1_RELATIVE: 10.421 G_Regularizer: 0.000 validation_error: 21.120 +(epoch: 48, iters: 192656, time: 0.563, data: 0.000) G_L1: 14.674 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 12.306 G_Regularizer: 0.000 validation_error: 20.787 +(epoch: 48, iters: 194656, time: 0.553, data: 0.000) G_L1: 13.659 G_L1_ABSOLUTE: 2.386 G_L1_RELATIVE: 11.274 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 48, iters: 196656, time: 0.556, data: 0.000) G_L1: 12.328 G_L1_ABSOLUTE: 2.307 G_L1_RELATIVE: 10.022 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 48, iters: 198656, time: 0.548, data: 0.000) G_L1: 14.231 G_L1_ABSOLUTE: 2.407 G_L1_RELATIVE: 11.824 G_Regularizer: 0.000 validation_error: 21.116 +(epoch: 48, iters: 200656, time: 0.533, data: 0.000) G_L1: 13.626 G_L1_ABSOLUTE: 2.572 G_L1_RELATIVE: 11.054 G_Regularizer: 0.000 validation_error: 21.045 +(epoch: 48, iters: 202656, time: 0.539, data: 0.000) G_L1: 11.540 G_L1_ABSOLUTE: 2.254 G_L1_RELATIVE: 9.286 G_Regularizer: 0.000 validation_error: 20.820 +(epoch: 48, iters: 204656, time: 0.554, data: 0.000) G_L1: 12.710 G_L1_ABSOLUTE: 2.693 G_L1_RELATIVE: 10.018 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 48, iters: 206656, time: 0.559, data: 0.000) G_L1: 12.429 G_L1_ABSOLUTE: 2.117 G_L1_RELATIVE: 10.312 G_Regularizer: 0.000 validation_error: 20.639 +(epoch: 48, iters: 208656, time: 0.531, data: 0.000) G_L1: 13.704 G_L1_ABSOLUTE: 2.239 G_L1_RELATIVE: 11.465 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 48, iters: 210656, time: 0.562, data: 0.000) G_L1: 13.644 G_L1_ABSOLUTE: 3.132 G_L1_RELATIVE: 10.512 G_Regularizer: 0.000 validation_error: 20.991 +(epoch: 48, iters: 212656, time: 0.551, data: 0.001) G_L1: 12.194 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 9.731 G_Regularizer: 0.000 validation_error: 21.077 +(epoch: 48, iters: 214656, time: 0.547, data: 0.001) G_L1: 16.337 G_L1_ABSOLUTE: 1.998 G_L1_RELATIVE: 14.339 G_Regularizer: 0.000 validation_error: 21.080 +(epoch: 48, iters: 216656, time: 0.546, data: 0.000) G_L1: 14.736 G_L1_ABSOLUTE: 2.561 G_L1_RELATIVE: 12.175 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 48, iters: 218656, time: 0.534, data: 0.000) G_L1: 14.381 G_L1_ABSOLUTE: 2.479 G_L1_RELATIVE: 11.902 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 48, iters: 220656, time: 0.542, data: 0.000) G_L1: 15.921 G_L1_ABSOLUTE: 2.745 G_L1_RELATIVE: 13.175 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 48, iters: 222656, time: 0.562, data: 0.000) G_L1: 11.972 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 9.390 G_Regularizer: 0.000 validation_error: 20.785 +(epoch: 48, iters: 224656, time: 0.534, data: 0.000) G_L1: 13.156 G_L1_ABSOLUTE: 2.270 G_L1_RELATIVE: 10.886 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 48, iters: 226656, time: 0.553, data: 0.000) G_L1: 13.792 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 11.437 G_Regularizer: 0.000 validation_error: 21.020 +(epoch: 48, iters: 228656, time: 0.561, data: 0.000) G_L1: 13.376 G_L1_ABSOLUTE: 2.856 G_L1_RELATIVE: 10.520 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 48, iters: 230656, time: 0.557, data: 0.000) G_L1: 14.795 G_L1_ABSOLUTE: 2.273 G_L1_RELATIVE: 12.522 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 48, iters: 232656, time: 0.542, data: 0.000) G_L1: 13.533 G_L1_ABSOLUTE: 2.427 G_L1_RELATIVE: 11.106 G_Regularizer: 0.000 validation_error: 20.565 +(epoch: 48, iters: 234656, time: 0.553, data: 0.000) G_L1: 13.277 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 10.676 G_Regularizer: 0.000 validation_error: 21.014 +(epoch: 48, iters: 236656, time: 0.554, data: 0.000) G_L1: 11.555 G_L1_ABSOLUTE: 2.211 G_L1_RELATIVE: 9.344 G_Regularizer: 0.000 validation_error: 20.890 +(epoch: 48, iters: 238656, time: 0.544, data: 0.001) G_L1: 13.956 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 11.524 G_Regularizer: 0.000 validation_error: 20.782 +(epoch: 48, iters: 240656, time: 0.560, data: 0.000) G_L1: 13.908 G_L1_ABSOLUTE: 2.587 G_L1_RELATIVE: 11.321 G_Regularizer: 0.000 validation_error: 20.868 +(epoch: 48, iters: 242656, time: 0.551, data: 0.000) G_L1: 13.177 G_L1_ABSOLUTE: 2.223 G_L1_RELATIVE: 10.953 G_Regularizer: 0.000 validation_error: 20.871 +(epoch: 48, iters: 244656, time: 0.566, data: 0.000) G_L1: 14.607 G_L1_ABSOLUTE: 2.730 G_L1_RELATIVE: 11.878 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 48, iters: 246656, time: 0.558, data: 0.000) G_L1: 13.182 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 10.842 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 48, iters: 248656, time: 0.547, data: 0.000) G_L1: 14.058 G_L1_ABSOLUTE: 2.498 G_L1_RELATIVE: 11.561 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 48, iters: 250656, time: 0.548, data: 0.000) G_L1: 13.218 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 10.712 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 48, iters: 252656, time: 0.544, data: 0.000) G_L1: 12.762 G_L1_ABSOLUTE: 1.940 G_L1_RELATIVE: 10.822 G_Regularizer: 0.000 validation_error: 20.790 +(epoch: 48, iters: 254656, time: 0.572, data: 0.000) G_L1: 13.513 G_L1_ABSOLUTE: 2.268 G_L1_RELATIVE: 11.245 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 48, iters: 256656, time: 0.545, data: 0.000) G_L1: 13.616 G_L1_ABSOLUTE: 2.129 G_L1_RELATIVE: 11.487 G_Regularizer: 0.000 validation_error: 20.922 +(epoch: 48, iters: 258656, time: 0.541, data: 0.000) G_L1: 14.539 G_L1_ABSOLUTE: 2.885 G_L1_RELATIVE: 11.654 G_Regularizer: 0.000 validation_error: 20.844 +(epoch: 48, iters: 260656, time: 0.543, data: 0.000) G_L1: 15.157 G_L1_ABSOLUTE: 2.721 G_L1_RELATIVE: 12.435 G_Regularizer: 0.000 validation_error: 20.858 +(epoch: 48, iters: 262656, time: 0.544, data: 0.000) G_L1: 14.287 G_L1_ABSOLUTE: 2.591 G_L1_RELATIVE: 11.696 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 48, iters: 264656, time: 0.550, data: 0.000) G_L1: 11.775 G_L1_ABSOLUTE: 2.237 G_L1_RELATIVE: 9.538 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 48, iters: 266656, time: 0.557, data: 0.000) G_L1: 13.859 G_L1_ABSOLUTE: 2.338 G_L1_RELATIVE: 11.521 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 48, iters: 268656, time: 0.550, data: 0.000) G_L1: 12.632 G_L1_ABSOLUTE: 2.640 G_L1_RELATIVE: 9.992 G_Regularizer: 0.000 validation_error: 20.861 +(epoch: 48, iters: 270656, time: 0.555, data: 0.000) G_L1: 14.029 G_L1_ABSOLUTE: 3.007 G_L1_RELATIVE: 11.022 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 48, iters: 272656, time: 0.552, data: 0.000) G_L1: 14.219 G_L1_ABSOLUTE: 2.298 G_L1_RELATIVE: 11.921 G_Regularizer: 0.000 validation_error: 20.767 +(epoch: 48, iters: 274656, time: 0.553, data: 0.000) G_L1: 14.481 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 12.111 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 48, iters: 276656, time: 0.534, data: 0.000) G_L1: 16.469 G_L1_ABSOLUTE: 2.754 G_L1_RELATIVE: 13.715 G_Regularizer: 0.000 validation_error: 20.786 +(epoch: 48, iters: 278656, time: 0.537, data: 0.001) G_L1: 15.058 G_L1_ABSOLUTE: 2.736 G_L1_RELATIVE: 12.323 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 48, iters: 280656, time: 0.558, data: 0.000) G_L1: 13.037 G_L1_ABSOLUTE: 2.448 G_L1_RELATIVE: 10.589 G_Regularizer: 0.000 validation_error: 20.875 +(epoch: 48, iters: 282656, time: 0.560, data: 0.000) G_L1: 15.770 G_L1_ABSOLUTE: 2.686 G_L1_RELATIVE: 13.084 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 48, iters: 284656, time: 0.532, data: 0.000) G_L1: 15.240 G_L1_ABSOLUTE: 2.153 G_L1_RELATIVE: 13.088 G_Regularizer: 0.000 validation_error: 20.707 +(epoch: 48, iters: 286656, time: 0.559, data: 0.001) G_L1: 13.817 G_L1_ABSOLUTE: 2.637 G_L1_RELATIVE: 11.180 G_Regularizer: 0.000 validation_error: 20.802 +(epoch: 48, iters: 288656, time: 0.554, data: 0.000) G_L1: 12.126 G_L1_ABSOLUTE: 2.630 G_L1_RELATIVE: 9.496 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 48, iters: 290656, time: 0.538, data: 0.000) G_L1: 15.244 G_L1_ABSOLUTE: 2.578 G_L1_RELATIVE: 12.666 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 48, iters: 292656, time: 0.548, data: 0.000) G_L1: 11.808 G_L1_ABSOLUTE: 2.449 G_L1_RELATIVE: 9.359 G_Regularizer: 0.000 validation_error: 20.830 +(epoch: 48, iters: 294656, time: 0.532, data: 0.000) G_L1: 11.594 G_L1_ABSOLUTE: 2.230 G_L1_RELATIVE: 9.364 G_Regularizer: 0.000 validation_error: 21.083 +(epoch: 48, iters: 296656, time: 0.564, data: 0.000) G_L1: 13.473 G_L1_ABSOLUTE: 2.354 G_L1_RELATIVE: 11.119 G_Regularizer: 0.000 validation_error: 20.893 +(epoch: 48, iters: 298656, time: 0.534, data: 0.001) G_L1: 13.508 G_L1_ABSOLUTE: 2.148 G_L1_RELATIVE: 11.360 G_Regularizer: 0.000 validation_error: 20.833 +(epoch: 48, iters: 300656, time: 0.569, data: 0.000) G_L1: 12.742 G_L1_ABSOLUTE: 2.152 G_L1_RELATIVE: 10.590 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 48, iters: 302656, time: 0.544, data: 0.001) G_L1: 16.292 G_L1_ABSOLUTE: 2.617 G_L1_RELATIVE: 13.675 G_Regularizer: 0.000 validation_error: 20.605 +(epoch: 49, iters: 1904, time: 0.549, data: 0.000) G_L1: 13.613 G_L1_ABSOLUTE: 2.340 G_L1_RELATIVE: 11.273 G_Regularizer: 0.000 validation_error: 20.808 +(epoch: 49, iters: 3904, time: 0.553, data: 0.000) G_L1: 12.797 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 10.365 G_Regularizer: 0.000 validation_error: 20.902 +(epoch: 49, iters: 5904, time: 0.561, data: 0.000) G_L1: 13.107 G_L1_ABSOLUTE: 2.183 G_L1_RELATIVE: 10.924 G_Regularizer: 0.000 validation_error: 20.932 +(epoch: 49, iters: 7904, time: 0.537, data: 0.000) G_L1: 14.259 G_L1_ABSOLUTE: 2.506 G_L1_RELATIVE: 11.753 G_Regularizer: 0.000 validation_error: 20.822 +(epoch: 49, iters: 9904, time: 0.557, data: 0.000) G_L1: 17.067 G_L1_ABSOLUTE: 2.274 G_L1_RELATIVE: 14.793 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 49, iters: 11904, time: 0.546, data: 0.000) G_L1: 12.834 G_L1_ABSOLUTE: 2.147 G_L1_RELATIVE: 10.687 G_Regularizer: 0.000 validation_error: 21.168 +(epoch: 49, iters: 13904, time: 0.541, data: 0.000) G_L1: 17.336 G_L1_ABSOLUTE: 2.488 G_L1_RELATIVE: 14.848 G_Regularizer: 0.000 validation_error: 20.745 +(epoch: 49, iters: 15904, time: 0.534, data: 0.000) G_L1: 14.037 G_L1_ABSOLUTE: 2.773 G_L1_RELATIVE: 11.265 G_Regularizer: 0.000 validation_error: 20.829 +(epoch: 49, iters: 17904, time: 0.549, data: 0.000) G_L1: 17.021 G_L1_ABSOLUTE: 2.538 G_L1_RELATIVE: 14.482 G_Regularizer: 0.000 validation_error: 20.892 +(epoch: 49, iters: 19904, time: 0.546, data: 0.000) G_L1: 13.703 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 11.181 G_Regularizer: 0.000 validation_error: 21.002 +(epoch: 49, iters: 21904, time: 0.559, data: 0.000) G_L1: 13.633 G_L1_ABSOLUTE: 2.981 G_L1_RELATIVE: 10.652 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 49, iters: 23904, time: 0.537, data: 0.001) G_L1: 15.637 G_L1_ABSOLUTE: 2.367 G_L1_RELATIVE: 13.271 G_Regularizer: 0.000 validation_error: 20.751 +(epoch: 49, iters: 25904, time: 0.530, data: 0.000) G_L1: 14.873 G_L1_ABSOLUTE: 2.552 G_L1_RELATIVE: 12.321 G_Regularizer: 0.000 validation_error: 20.749 +(epoch: 49, iters: 27904, time: 0.550, data: 0.000) G_L1: 12.277 G_L1_ABSOLUTE: 2.203 G_L1_RELATIVE: 10.074 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 49, iters: 29904, time: 0.552, data: 0.000) G_L1: 14.633 G_L1_ABSOLUTE: 2.525 G_L1_RELATIVE: 12.108 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 49, iters: 31904, time: 0.550, data: 0.000) G_L1: 15.170 G_L1_ABSOLUTE: 2.663 G_L1_RELATIVE: 12.507 G_Regularizer: 0.000 validation_error: 20.834 +(epoch: 49, iters: 33904, time: 0.535, data: 0.000) G_L1: 14.249 G_L1_ABSOLUTE: 2.712 G_L1_RELATIVE: 11.537 G_Regularizer: 0.000 validation_error: 21.047 +(epoch: 49, iters: 35904, time: 0.557, data: 0.000) G_L1: 13.938 G_L1_ABSOLUTE: 2.048 G_L1_RELATIVE: 11.890 G_Regularizer: 0.000 validation_error: 20.600 +(epoch: 49, iters: 37904, time: 0.549, data: 0.000) G_L1: 13.543 G_L1_ABSOLUTE: 2.101 G_L1_RELATIVE: 11.442 G_Regularizer: 0.000 validation_error: 20.750 +(epoch: 49, iters: 39904, time: 0.550, data: 0.000) G_L1: 13.573 G_L1_ABSOLUTE: 2.123 G_L1_RELATIVE: 11.450 G_Regularizer: 0.000 validation_error: 21.045 +(epoch: 49, iters: 41904, time: 0.534, data: 0.000) G_L1: 16.738 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 14.234 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 49, iters: 43904, time: 0.558, data: 0.000) G_L1: 15.040 G_L1_ABSOLUTE: 2.318 G_L1_RELATIVE: 12.722 G_Regularizer: 0.000 validation_error: 20.880 +(epoch: 49, iters: 45904, time: 0.537, data: 0.000) G_L1: 14.812 G_L1_ABSOLUTE: 2.679 G_L1_RELATIVE: 12.133 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 49, iters: 47904, time: 0.569, data: 0.000) G_L1: 13.980 G_L1_ABSOLUTE: 2.302 G_L1_RELATIVE: 11.677 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 49, iters: 49904, time: 0.540, data: 0.000) G_L1: 12.633 G_L1_ABSOLUTE: 2.228 G_L1_RELATIVE: 10.405 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 49, iters: 51904, time: 0.559, data: 0.000) G_L1: 12.430 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 10.052 G_Regularizer: 0.000 validation_error: 20.739 +(epoch: 49, iters: 53904, time: 0.547, data: 0.000) G_L1: 13.977 G_L1_ABSOLUTE: 2.681 G_L1_RELATIVE: 11.296 G_Regularizer: 0.000 validation_error: 20.793 +(epoch: 49, iters: 55904, time: 0.546, data: 0.001) G_L1: 11.549 G_L1_ABSOLUTE: 2.475 G_L1_RELATIVE: 9.074 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 49, iters: 57904, time: 0.546, data: 0.001) G_L1: 12.847 G_L1_ABSOLUTE: 2.784 G_L1_RELATIVE: 10.063 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 49, iters: 59904, time: 0.558, data: 0.000) G_L1: 13.375 G_L1_ABSOLUTE: 2.131 G_L1_RELATIVE: 11.243 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 49, iters: 61904, time: 0.553, data: 0.000) G_L1: 13.330 G_L1_ABSOLUTE: 2.125 G_L1_RELATIVE: 11.205 G_Regularizer: 0.000 validation_error: 20.809 +(epoch: 49, iters: 63904, time: 0.554, data: 0.000) G_L1: 11.689 G_L1_ABSOLUTE: 2.368 G_L1_RELATIVE: 9.321 G_Regularizer: 0.000 validation_error: 20.964 +(epoch: 49, iters: 65904, time: 0.553, data: 0.000) G_L1: 15.371 G_L1_ABSOLUTE: 2.034 G_L1_RELATIVE: 13.337 G_Regularizer: 0.000 validation_error: 20.920 +(epoch: 49, iters: 67904, time: 0.536, data: 0.000) G_L1: 17.632 G_L1_ABSOLUTE: 3.010 G_L1_RELATIVE: 14.622 G_Regularizer: 0.000 validation_error: 20.779 +(epoch: 49, iters: 69904, time: 0.548, data: 0.000) G_L1: 13.731 G_L1_ABSOLUTE: 2.379 G_L1_RELATIVE: 11.352 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 49, iters: 71904, time: 0.557, data: 0.000) G_L1: 15.937 G_L1_ABSOLUTE: 2.234 G_L1_RELATIVE: 13.702 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 49, iters: 73904, time: 0.560, data: 0.000) G_L1: 11.998 G_L1_ABSOLUTE: 2.069 G_L1_RELATIVE: 9.929 G_Regularizer: 0.000 validation_error: 20.703 +(epoch: 49, iters: 75904, time: 0.549, data: 0.000) G_L1: 12.983 G_L1_ABSOLUTE: 2.205 G_L1_RELATIVE: 10.778 G_Regularizer: 0.000 validation_error: 20.826 +(epoch: 49, iters: 77904, time: 0.535, data: 0.000) G_L1: 14.605 G_L1_ABSOLUTE: 2.624 G_L1_RELATIVE: 11.981 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 49, iters: 79904, time: 0.541, data: 0.000) G_L1: 12.581 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 10.319 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 49, iters: 81904, time: 0.534, data: 0.000) G_L1: 13.376 G_L1_ABSOLUTE: 2.223 G_L1_RELATIVE: 11.153 G_Regularizer: 0.000 validation_error: 21.084 +(epoch: 49, iters: 83904, time: 0.530, data: 0.001) G_L1: 13.694 G_L1_ABSOLUTE: 1.927 G_L1_RELATIVE: 11.767 G_Regularizer: 0.000 validation_error: 20.650 +(epoch: 49, iters: 85904, time: 0.559, data: 0.000) G_L1: 14.352 G_L1_ABSOLUTE: 2.584 G_L1_RELATIVE: 11.767 G_Regularizer: 0.000 validation_error: 20.987 +(epoch: 49, iters: 87904, time: 0.541, data: 0.000) G_L1: 14.997 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 12.596 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 49, iters: 89904, time: 0.535, data: 0.000) G_L1: 13.576 G_L1_ABSOLUTE: 2.880 G_L1_RELATIVE: 10.696 G_Regularizer: 0.000 validation_error: 20.884 +(epoch: 49, iters: 91904, time: 0.544, data: 0.000) G_L1: 11.584 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 9.002 G_Regularizer: 0.000 validation_error: 20.846 +(epoch: 49, iters: 93904, time: 0.551, data: 0.000) G_L1: 13.397 G_L1_ABSOLUTE: 2.618 G_L1_RELATIVE: 10.778 G_Regularizer: 0.000 validation_error: 20.941 +(epoch: 49, iters: 95904, time: 0.540, data: 0.000) G_L1: 14.491 G_L1_ABSOLUTE: 2.611 G_L1_RELATIVE: 11.880 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 49, iters: 97904, time: 0.558, data: 0.001) G_L1: 15.263 G_L1_ABSOLUTE: 2.938 G_L1_RELATIVE: 12.325 G_Regularizer: 0.000 validation_error: 20.684 +(epoch: 49, iters: 99904, time: 0.552, data: 0.000) G_L1: 13.163 G_L1_ABSOLUTE: 3.027 G_L1_RELATIVE: 10.136 G_Regularizer: 0.000 validation_error: 20.721 +(epoch: 49, iters: 101904, time: 0.538, data: 0.000) G_L1: 10.810 G_L1_ABSOLUTE: 2.130 G_L1_RELATIVE: 8.680 G_Regularizer: 0.000 validation_error: 20.919 +(epoch: 49, iters: 103904, time: 0.542, data: 0.001) G_L1: 17.370 G_L1_ABSOLUTE: 2.861 G_L1_RELATIVE: 14.509 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 49, iters: 105904, time: 0.565, data: 0.001) G_L1: 11.656 G_L1_ABSOLUTE: 2.312 G_L1_RELATIVE: 9.344 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 49, iters: 107904, time: 0.564, data: 0.000) G_L1: 14.769 G_L1_ABSOLUTE: 2.602 G_L1_RELATIVE: 12.167 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 49, iters: 109904, time: 0.545, data: 0.000) G_L1: 12.786 G_L1_ABSOLUTE: 2.234 G_L1_RELATIVE: 10.552 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 49, iters: 111904, time: 0.543, data: 0.000) G_L1: 15.174 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 12.774 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 49, iters: 113904, time: 0.552, data: 0.000) G_L1: 13.885 G_L1_ABSOLUTE: 2.458 G_L1_RELATIVE: 11.427 G_Regularizer: 0.000 validation_error: 20.963 +(epoch: 49, iters: 115904, time: 0.555, data: 0.000) G_L1: 14.428 G_L1_ABSOLUTE: 2.387 G_L1_RELATIVE: 12.040 G_Regularizer: 0.000 validation_error: 21.016 +(epoch: 49, iters: 117904, time: 0.542, data: 0.001) G_L1: 14.676 G_L1_ABSOLUTE: 2.497 G_L1_RELATIVE: 12.179 G_Regularizer: 0.000 validation_error: 20.996 +(epoch: 49, iters: 119904, time: 0.546, data: 0.000) G_L1: 13.709 G_L1_ABSOLUTE: 2.507 G_L1_RELATIVE: 11.202 G_Regularizer: 0.000 validation_error: 20.841 +(epoch: 49, iters: 121904, time: 0.550, data: 0.000) G_L1: 13.924 G_L1_ABSOLUTE: 2.129 G_L1_RELATIVE: 11.795 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 49, iters: 123904, time: 0.551, data: 0.000) G_L1: 12.596 G_L1_ABSOLUTE: 1.983 G_L1_RELATIVE: 10.612 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 49, iters: 125904, time: 0.538, data: 0.000) G_L1: 11.128 G_L1_ABSOLUTE: 2.289 G_L1_RELATIVE: 8.839 G_Regularizer: 0.000 validation_error: 20.820 +(epoch: 49, iters: 127904, time: 0.551, data: 0.000) G_L1: 14.323 G_L1_ABSOLUTE: 2.827 G_L1_RELATIVE: 11.496 G_Regularizer: 0.000 validation_error: 21.104 +(epoch: 49, iters: 129904, time: 0.532, data: 0.000) G_L1: 15.281 G_L1_ABSOLUTE: 2.510 G_L1_RELATIVE: 12.772 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 49, iters: 131904, time: 0.551, data: 0.000) G_L1: 13.254 G_L1_ABSOLUTE: 2.075 G_L1_RELATIVE: 11.179 G_Regularizer: 0.000 validation_error: 20.891 +(epoch: 49, iters: 133904, time: 0.556, data: 0.001) G_L1: 13.731 G_L1_ABSOLUTE: 2.447 G_L1_RELATIVE: 11.285 G_Regularizer: 0.000 validation_error: 21.002 +(epoch: 49, iters: 135904, time: 0.552, data: 0.000) G_L1: 14.572 G_L1_ABSOLUTE: 2.280 G_L1_RELATIVE: 12.292 G_Regularizer: 0.000 validation_error: 21.034 +(epoch: 49, iters: 137904, time: 0.542, data: 0.000) G_L1: 12.657 G_L1_ABSOLUTE: 2.576 G_L1_RELATIVE: 10.080 G_Regularizer: 0.000 validation_error: 20.855 +(epoch: 49, iters: 139904, time: 0.557, data: 0.001) G_L1: 12.236 G_L1_ABSOLUTE: 2.427 G_L1_RELATIVE: 9.809 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 49, iters: 141904, time: 0.557, data: 0.000) G_L1: 15.094 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 12.717 G_Regularizer: 0.000 validation_error: 20.946 +(epoch: 49, iters: 143904, time: 0.532, data: 0.000) G_L1: 15.125 G_L1_ABSOLUTE: 3.032 G_L1_RELATIVE: 12.093 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 49, iters: 145904, time: 0.536, data: 0.000) G_L1: 14.029 G_L1_ABSOLUTE: 2.615 G_L1_RELATIVE: 11.414 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 49, iters: 147904, time: 0.551, data: 0.000) G_L1: 13.184 G_L1_ABSOLUTE: 2.204 G_L1_RELATIVE: 10.980 G_Regularizer: 0.000 validation_error: 20.734 +(epoch: 49, iters: 149904, time: 0.563, data: 0.000) G_L1: 11.877 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 9.502 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 49, iters: 151904, time: 0.555, data: 0.000) G_L1: 13.322 G_L1_ABSOLUTE: 2.505 G_L1_RELATIVE: 10.817 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 49, iters: 153904, time: 0.541, data: 0.000) G_L1: 14.177 G_L1_ABSOLUTE: 2.554 G_L1_RELATIVE: 11.623 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 49, iters: 155904, time: 0.558, data: 0.000) G_L1: 13.299 G_L1_ABSOLUTE: 2.198 G_L1_RELATIVE: 11.101 G_Regularizer: 0.000 validation_error: 20.706 +(epoch: 49, iters: 157904, time: 0.558, data: 0.000) G_L1: 16.129 G_L1_ABSOLUTE: 2.658 G_L1_RELATIVE: 13.470 G_Regularizer: 0.000 validation_error: 20.773 +(epoch: 49, iters: 159904, time: 0.542, data: 0.000) G_L1: 13.056 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 10.530 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 49, iters: 161904, time: 0.531, data: 0.000) G_L1: 13.267 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 10.744 G_Regularizer: 0.000 validation_error: 21.113 +(epoch: 49, iters: 163904, time: 0.541, data: 0.000) G_L1: 14.652 G_L1_ABSOLUTE: 2.175 G_L1_RELATIVE: 12.477 G_Regularizer: 0.000 validation_error: 20.780 +(epoch: 49, iters: 165904, time: 0.539, data: 0.000) G_L1: 14.093 G_L1_ABSOLUTE: 2.766 G_L1_RELATIVE: 11.328 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 49, iters: 167904, time: 0.566, data: 0.000) G_L1: 13.120 G_L1_ABSOLUTE: 2.893 G_L1_RELATIVE: 10.227 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 49, iters: 169904, time: 0.535, data: 0.000) G_L1: 11.731 G_L1_ABSOLUTE: 2.526 G_L1_RELATIVE: 9.205 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 49, iters: 171904, time: 0.546, data: 0.000) G_L1: 16.165 G_L1_ABSOLUTE: 2.570 G_L1_RELATIVE: 13.595 G_Regularizer: 0.000 validation_error: 20.970 +(epoch: 49, iters: 173904, time: 0.557, data: 0.001) G_L1: 15.285 G_L1_ABSOLUTE: 2.642 G_L1_RELATIVE: 12.643 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 49, iters: 175904, time: 0.552, data: 0.000) G_L1: 14.278 G_L1_ABSOLUTE: 2.145 G_L1_RELATIVE: 12.133 G_Regularizer: 0.000 validation_error: 20.900 +(epoch: 49, iters: 177904, time: 0.539, data: 0.000) G_L1: 14.081 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 11.595 G_Regularizer: 0.000 validation_error: 20.872 +(epoch: 49, iters: 179904, time: 0.542, data: 0.000) G_L1: 13.539 G_L1_ABSOLUTE: 2.436 G_L1_RELATIVE: 11.102 G_Regularizer: 0.000 validation_error: 20.867 +(epoch: 49, iters: 181904, time: 0.530, data: 0.000) G_L1: 13.544 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 10.979 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 49, iters: 183904, time: 0.531, data: 0.000) G_L1: 14.716 G_L1_ABSOLUTE: 2.718 G_L1_RELATIVE: 11.998 G_Regularizer: 0.000 validation_error: 20.958 +(epoch: 49, iters: 185904, time: 0.538, data: 0.000) G_L1: 13.016 G_L1_ABSOLUTE: 2.309 G_L1_RELATIVE: 10.707 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 49, iters: 187904, time: 0.540, data: 0.000) G_L1: 15.010 G_L1_ABSOLUTE: 2.762 G_L1_RELATIVE: 12.248 G_Regularizer: 0.000 validation_error: 20.988 +(epoch: 49, iters: 189904, time: 0.535, data: 0.000) G_L1: 14.609 G_L1_ABSOLUTE: 2.673 G_L1_RELATIVE: 11.936 G_Regularizer: 0.000 validation_error: 20.634 +(epoch: 49, iters: 191904, time: 0.552, data: 0.001) G_L1: 13.307 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 10.932 G_Regularizer: 0.000 validation_error: 20.950 +(epoch: 49, iters: 193904, time: 0.554, data: 0.001) G_L1: 12.827 G_L1_ABSOLUTE: 2.432 G_L1_RELATIVE: 10.395 G_Regularizer: 0.000 validation_error: 20.837 +(epoch: 49, iters: 195904, time: 0.542, data: 0.000) G_L1: 13.582 G_L1_ABSOLUTE: 2.208 G_L1_RELATIVE: 11.373 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 49, iters: 197904, time: 0.558, data: 0.000) G_L1: 15.217 G_L1_ABSOLUTE: 2.312 G_L1_RELATIVE: 12.905 G_Regularizer: 0.000 validation_error: 20.927 +(epoch: 49, iters: 199904, time: 0.555, data: 0.000) G_L1: 15.170 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 12.702 G_Regularizer: 0.000 validation_error: 21.103 +(epoch: 49, iters: 201904, time: 0.567, data: 0.000) G_L1: 14.614 G_L1_ABSOLUTE: 2.372 G_L1_RELATIVE: 12.242 G_Regularizer: 0.000 validation_error: 21.134 +(epoch: 49, iters: 203904, time: 0.540, data: 0.000) G_L1: 12.198 G_L1_ABSOLUTE: 2.263 G_L1_RELATIVE: 9.935 G_Regularizer: 0.000 validation_error: 20.995 +(epoch: 49, iters: 205904, time: 0.560, data: 0.000) G_L1: 12.741 G_L1_ABSOLUTE: 2.219 G_L1_RELATIVE: 10.522 G_Regularizer: 0.000 validation_error: 21.091 +(epoch: 49, iters: 207904, time: 0.538, data: 0.000) G_L1: 15.057 G_L1_ABSOLUTE: 2.433 G_L1_RELATIVE: 12.624 G_Regularizer: 0.000 validation_error: 21.248 +(epoch: 49, iters: 209904, time: 0.546, data: 0.000) G_L1: 12.408 G_L1_ABSOLUTE: 2.480 G_L1_RELATIVE: 9.927 G_Regularizer: 0.000 validation_error: 20.871 +(epoch: 49, iters: 211904, time: 0.542, data: 0.000) G_L1: 14.120 G_L1_ABSOLUTE: 2.339 G_L1_RELATIVE: 11.781 G_Regularizer: 0.000 validation_error: 21.099 +(epoch: 49, iters: 213904, time: 0.560, data: 0.000) G_L1: 14.100 G_L1_ABSOLUTE: 2.398 G_L1_RELATIVE: 11.702 G_Regularizer: 0.000 validation_error: 21.002 +(epoch: 49, iters: 215904, time: 0.560, data: 0.000) G_L1: 12.077 G_L1_ABSOLUTE: 2.063 G_L1_RELATIVE: 10.014 G_Regularizer: 0.000 validation_error: 21.082 +(epoch: 49, iters: 217904, time: 0.558, data: 0.000) G_L1: 13.099 G_L1_ABSOLUTE: 2.461 G_L1_RELATIVE: 10.638 G_Regularizer: 0.000 validation_error: 21.170 +(epoch: 49, iters: 219904, time: 0.540, data: 0.000) G_L1: 13.035 G_L1_ABSOLUTE: 2.017 G_L1_RELATIVE: 11.018 G_Regularizer: 0.000 validation_error: 20.902 +(epoch: 49, iters: 221904, time: 0.542, data: 0.000) G_L1: 14.600 G_L1_ABSOLUTE: 2.310 G_L1_RELATIVE: 12.290 G_Regularizer: 0.000 validation_error: 20.755 +(epoch: 49, iters: 223904, time: 0.554, data: 0.000) G_L1: 13.688 G_L1_ABSOLUTE: 2.828 G_L1_RELATIVE: 10.860 G_Regularizer: 0.000 validation_error: 21.045 +(epoch: 49, iters: 225904, time: 0.556, data: 0.001) G_L1: 11.710 G_L1_ABSOLUTE: 1.925 G_L1_RELATIVE: 9.785 G_Regularizer: 0.000 validation_error: 20.877 +(epoch: 49, iters: 227904, time: 0.555, data: 0.000) G_L1: 13.748 G_L1_ABSOLUTE: 2.987 G_L1_RELATIVE: 10.761 G_Regularizer: 0.000 validation_error: 21.104 +(epoch: 49, iters: 229904, time: 0.544, data: 0.000) G_L1: 11.599 G_L1_ABSOLUTE: 2.162 G_L1_RELATIVE: 9.437 G_Regularizer: 0.000 validation_error: 21.085 +(epoch: 49, iters: 231904, time: 0.553, data: 0.000) G_L1: 13.653 G_L1_ABSOLUTE: 2.602 G_L1_RELATIVE: 11.051 G_Regularizer: 0.000 validation_error: 21.082 +(epoch: 49, iters: 233904, time: 0.559, data: 0.000) G_L1: 13.177 G_L1_ABSOLUTE: 2.357 G_L1_RELATIVE: 10.820 G_Regularizer: 0.000 validation_error: 20.779 +(epoch: 49, iters: 235904, time: 0.562, data: 0.001) G_L1: 14.612 G_L1_ABSOLUTE: 2.243 G_L1_RELATIVE: 12.368 G_Regularizer: 0.000 validation_error: 21.001 +(epoch: 49, iters: 237904, time: 0.530, data: 0.000) G_L1: 13.244 G_L1_ABSOLUTE: 2.296 G_L1_RELATIVE: 10.948 G_Regularizer: 0.000 validation_error: 20.978 +(epoch: 49, iters: 239904, time: 0.537, data: 0.000) G_L1: 13.886 G_L1_ABSOLUTE: 2.205 G_L1_RELATIVE: 11.681 G_Regularizer: 0.000 validation_error: 20.897 +(epoch: 49, iters: 241904, time: 0.556, data: 0.000) G_L1: 13.006 G_L1_ABSOLUTE: 2.389 G_L1_RELATIVE: 10.617 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 49, iters: 243904, time: 0.555, data: 0.000) G_L1: 14.110 G_L1_ABSOLUTE: 2.946 G_L1_RELATIVE: 11.163 G_Regularizer: 0.000 validation_error: 20.951 +(epoch: 49, iters: 245904, time: 0.535, data: 0.000) G_L1: 11.671 G_L1_ABSOLUTE: 1.877 G_L1_RELATIVE: 9.794 G_Regularizer: 0.000 validation_error: 21.156 +(epoch: 49, iters: 247904, time: 0.554, data: 0.000) G_L1: 13.502 G_L1_ABSOLUTE: 2.372 G_L1_RELATIVE: 11.130 G_Regularizer: 0.000 validation_error: 20.971 +(epoch: 49, iters: 249904, time: 0.549, data: 0.000) G_L1: 12.961 G_L1_ABSOLUTE: 2.774 G_L1_RELATIVE: 10.187 G_Regularizer: 0.000 validation_error: 20.930 +(epoch: 49, iters: 251904, time: 0.555, data: 0.000) G_L1: 13.851 G_L1_ABSOLUTE: 2.421 G_L1_RELATIVE: 11.430 G_Regularizer: 0.000 validation_error: 20.915 +(epoch: 49, iters: 253904, time: 0.548, data: 0.000) G_L1: 15.253 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 12.550 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 49, iters: 255904, time: 0.560, data: 0.000) G_L1: 11.771 G_L1_ABSOLUTE: 2.297 G_L1_RELATIVE: 9.474 G_Regularizer: 0.000 validation_error: 20.896 +(epoch: 49, iters: 257904, time: 0.551, data: 0.000) G_L1: 12.998 G_L1_ABSOLUTE: 2.874 G_L1_RELATIVE: 10.123 G_Regularizer: 0.000 validation_error: 20.795 +(epoch: 49, iters: 259904, time: 0.552, data: 0.001) G_L1: 17.093 G_L1_ABSOLUTE: 2.448 G_L1_RELATIVE: 14.646 G_Regularizer: 0.000 validation_error: 20.931 +(epoch: 49, iters: 261904, time: 0.549, data: 0.000) G_L1: 13.355 G_L1_ABSOLUTE: 2.807 G_L1_RELATIVE: 10.548 G_Regularizer: 0.000 validation_error: 20.762 +(epoch: 49, iters: 263904, time: 0.555, data: 0.000) G_L1: 14.607 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 12.085 G_Regularizer: 0.000 validation_error: 20.714 +(epoch: 49, iters: 265904, time: 0.563, data: 0.000) G_L1: 14.565 G_L1_ABSOLUTE: 2.517 G_L1_RELATIVE: 12.048 G_Regularizer: 0.000 validation_error: 20.907 +(epoch: 49, iters: 267904, time: 0.559, data: 0.000) G_L1: 14.950 G_L1_ABSOLUTE: 2.655 G_L1_RELATIVE: 12.295 G_Regularizer: 0.000 validation_error: 21.087 +(epoch: 49, iters: 269904, time: 0.554, data: 0.001) G_L1: 14.625 G_L1_ABSOLUTE: 2.832 G_L1_RELATIVE: 11.793 G_Regularizer: 0.000 validation_error: 20.873 +(epoch: 49, iters: 271904, time: 0.549, data: 0.000) G_L1: 17.761 G_L1_ABSOLUTE: 2.755 G_L1_RELATIVE: 15.006 G_Regularizer: 0.000 validation_error: 20.969 +(epoch: 49, iters: 273904, time: 0.556, data: 0.000) G_L1: 14.041 G_L1_ABSOLUTE: 2.405 G_L1_RELATIVE: 11.636 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 49, iters: 275904, time: 0.559, data: 0.000) G_L1: 13.499 G_L1_ABSOLUTE: 2.445 G_L1_RELATIVE: 11.054 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 49, iters: 277904, time: 0.561, data: 0.000) G_L1: 14.529 G_L1_ABSOLUTE: 2.401 G_L1_RELATIVE: 12.128 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 49, iters: 279904, time: 0.540, data: 0.000) G_L1: 14.537 G_L1_ABSOLUTE: 2.382 G_L1_RELATIVE: 12.155 G_Regularizer: 0.000 validation_error: 20.797 +(epoch: 49, iters: 281904, time: 0.563, data: 0.000) G_L1: 13.450 G_L1_ABSOLUTE: 2.248 G_L1_RELATIVE: 11.202 G_Regularizer: 0.000 validation_error: 20.759 +(epoch: 49, iters: 283904, time: 0.558, data: 0.000) G_L1: 14.795 G_L1_ABSOLUTE: 2.373 G_L1_RELATIVE: 12.422 G_Regularizer: 0.000 validation_error: 20.899 +(epoch: 49, iters: 285904, time: 0.679, data: 0.000) G_L1: 11.269 G_L1_ABSOLUTE: 2.105 G_L1_RELATIVE: 9.164 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 49, iters: 287904, time: 0.530, data: 0.000) G_L1: 17.003 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 14.438 G_Regularizer: 0.000 validation_error: 20.727 +(epoch: 49, iters: 289904, time: 0.535, data: 0.000) G_L1: 12.346 G_L1_ABSOLUTE: 2.606 G_L1_RELATIVE: 9.741 G_Regularizer: 0.000 validation_error: 20.652 +(epoch: 49, iters: 291904, time: 0.547, data: 0.000) G_L1: 12.442 G_L1_ABSOLUTE: 2.689 G_L1_RELATIVE: 9.752 G_Regularizer: 0.000 validation_error: 20.741 +(epoch: 49, iters: 293904, time: 0.566, data: 0.000) G_L1: 14.000 G_L1_ABSOLUTE: 3.031 G_L1_RELATIVE: 10.969 G_Regularizer: 0.000 validation_error: 20.752 +(epoch: 49, iters: 295904, time: 0.551, data: 0.000) G_L1: 13.322 G_L1_ABSOLUTE: 2.566 G_L1_RELATIVE: 10.756 G_Regularizer: 0.000 validation_error: 20.771 +(epoch: 49, iters: 297904, time: 0.553, data: 0.000) G_L1: 13.791 G_L1_ABSOLUTE: 2.462 G_L1_RELATIVE: 11.328 G_Regularizer: 0.000 validation_error: 20.936 +(epoch: 49, iters: 299904, time: 0.564, data: 0.000) G_L1: 14.164 G_L1_ABSOLUTE: 2.216 G_L1_RELATIVE: 11.948 G_Regularizer: 0.000 validation_error: 20.909 +(epoch: 49, iters: 301904, time: 0.557, data: 0.000) G_L1: 15.067 G_L1_ABSOLUTE: 3.471 G_L1_RELATIVE: 11.597 G_Regularizer: 0.000 validation_error: 21.283 +(epoch: 50, iters: 1152, time: 0.533, data: 0.000) G_L1: 14.371 G_L1_ABSOLUTE: 2.522 G_L1_RELATIVE: 11.849 G_Regularizer: 0.000 validation_error: 20.933 +(epoch: 50, iters: 3152, time: 0.554, data: 0.000) G_L1: 13.130 G_L1_ABSOLUTE: 2.278 G_L1_RELATIVE: 10.853 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 50, iters: 5152, time: 0.554, data: 0.000) G_L1: 14.526 G_L1_ABSOLUTE: 2.749 G_L1_RELATIVE: 11.777 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 50, iters: 7152, time: 0.556, data: 0.000) G_L1: 14.835 G_L1_ABSOLUTE: 2.280 G_L1_RELATIVE: 12.555 G_Regularizer: 0.000 validation_error: 20.898 +(epoch: 50, iters: 9152, time: 0.535, data: 0.000) G_L1: 11.811 G_L1_ABSOLUTE: 2.200 G_L1_RELATIVE: 9.611 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 50, iters: 11152, time: 0.543, data: 0.001) G_L1: 12.810 G_L1_ABSOLUTE: 2.041 G_L1_RELATIVE: 10.769 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 50, iters: 13152, time: 0.556, data: 0.000) G_L1: 15.019 G_L1_ABSOLUTE: 2.815 G_L1_RELATIVE: 12.204 G_Regularizer: 0.000 validation_error: 20.838 +(epoch: 50, iters: 15152, time: 0.561, data: 0.000) G_L1: 18.882 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 15.930 G_Regularizer: 0.000 validation_error: 20.903 +(epoch: 50, iters: 17152, time: 0.540, data: 0.000) G_L1: 13.298 G_L1_ABSOLUTE: 2.134 G_L1_RELATIVE: 11.164 G_Regularizer: 0.000 validation_error: 21.011 +(epoch: 50, iters: 19152, time: 0.536, data: 0.000) G_L1: 15.481 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 12.821 G_Regularizer: 0.000 validation_error: 20.972 +(epoch: 50, iters: 21152, time: 0.548, data: 0.000) G_L1: 14.504 G_L1_ABSOLUTE: 2.530 G_L1_RELATIVE: 11.974 G_Regularizer: 0.000 validation_error: 20.794 +(epoch: 50, iters: 23152, time: 0.554, data: 0.000) G_L1: 13.488 G_L1_ABSOLUTE: 2.795 G_L1_RELATIVE: 10.693 G_Regularizer: 0.000 validation_error: 20.828 +(epoch: 50, iters: 25152, time: 0.549, data: 0.000) G_L1: 13.609 G_L1_ABSOLUTE: 2.325 G_L1_RELATIVE: 11.283 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 50, iters: 27152, time: 0.534, data: 0.000) G_L1: 15.034 G_L1_ABSOLUTE: 2.993 G_L1_RELATIVE: 12.041 G_Regularizer: 0.000 validation_error: 20.881 +(epoch: 50, iters: 29152, time: 0.558, data: 0.000) G_L1: 12.685 G_L1_ABSOLUTE: 2.345 G_L1_RELATIVE: 10.340 G_Regularizer: 0.000 validation_error: 20.738 +(epoch: 50, iters: 31152, time: 0.540, data: 0.000) G_L1: 14.289 G_L1_ABSOLUTE: 2.687 G_L1_RELATIVE: 11.602 G_Regularizer: 0.000 validation_error: 20.774 +(epoch: 50, iters: 33152, time: 0.563, data: 0.000) G_L1: 12.960 G_L1_ABSOLUTE: 2.189 G_L1_RELATIVE: 10.771 G_Regularizer: 0.000 validation_error: 20.859 +(epoch: 50, iters: 35152, time: 0.541, data: 0.000) G_L1: 10.776 G_L1_ABSOLUTE: 2.262 G_L1_RELATIVE: 8.514 G_Regularizer: 0.000 validation_error: 20.803 +(epoch: 50, iters: 37152, time: 0.547, data: 0.000) G_L1: 11.814 G_L1_ABSOLUTE: 2.732 G_L1_RELATIVE: 9.081 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 50, iters: 39152, time: 0.552, data: 0.000) G_L1: 13.315 G_L1_ABSOLUTE: 2.082 G_L1_RELATIVE: 11.233 G_Regularizer: 0.000 validation_error: 21.025 +(epoch: 50, iters: 41152, time: 0.558, data: 0.000) G_L1: 12.997 G_L1_ABSOLUTE: 2.941 G_L1_RELATIVE: 10.056 G_Regularizer: 0.000 validation_error: 21.070 +(epoch: 50, iters: 43152, time: 0.551, data: 0.000) G_L1: 13.694 G_L1_ABSOLUTE: 2.486 G_L1_RELATIVE: 11.208 G_Regularizer: 0.000 validation_error: 20.831 +(epoch: 50, iters: 45152, time: 0.544, data: 0.000) G_L1: 14.561 G_L1_ABSOLUTE: 2.582 G_L1_RELATIVE: 11.979 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 50, iters: 47152, time: 0.533, data: 0.001) G_L1: 12.045 G_L1_ABSOLUTE: 2.292 G_L1_RELATIVE: 9.752 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 50, iters: 49152, time: 0.539, data: 0.000) G_L1: 14.838 G_L1_ABSOLUTE: 2.765 G_L1_RELATIVE: 12.073 G_Regularizer: 0.000 validation_error: 20.948 +(epoch: 50, iters: 51152, time: 0.542, data: 0.000) G_L1: 13.778 G_L1_ABSOLUTE: 2.233 G_L1_RELATIVE: 11.545 G_Regularizer: 0.000 validation_error: 20.849 +(epoch: 50, iters: 53152, time: 0.535, data: 0.000) G_L1: 14.896 G_L1_ABSOLUTE: 2.222 G_L1_RELATIVE: 12.674 G_Regularizer: 0.000 validation_error: 20.847 +(epoch: 50, iters: 55152, time: 0.535, data: 0.000) G_L1: 13.896 G_L1_ABSOLUTE: 2.595 G_L1_RELATIVE: 11.300 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 50, iters: 57152, time: 0.534, data: 0.001) G_L1: 14.520 G_L1_ABSOLUTE: 2.549 G_L1_RELATIVE: 11.971 G_Regularizer: 0.000 validation_error: 20.914 +(epoch: 50, iters: 59152, time: 0.550, data: 0.000) G_L1: 14.624 G_L1_ABSOLUTE: 2.440 G_L1_RELATIVE: 12.185 G_Regularizer: 0.000 validation_error: 20.798 +(epoch: 50, iters: 61152, time: 0.541, data: 0.000) G_L1: 12.540 G_L1_ABSOLUTE: 2.209 G_L1_RELATIVE: 10.331 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 50, iters: 63152, time: 0.552, data: 0.000) G_L1: 12.599 G_L1_ABSOLUTE: 2.245 G_L1_RELATIVE: 10.354 G_Regularizer: 0.000 validation_error: 20.777 +(epoch: 50, iters: 65152, time: 0.552, data: 0.000) G_L1: 14.447 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 11.935 G_Regularizer: 0.000 validation_error: 20.805 +(epoch: 50, iters: 67152, time: 0.553, data: 0.000) G_L1: 12.909 G_L1_ABSOLUTE: 2.350 G_L1_RELATIVE: 10.559 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 50, iters: 69152, time: 0.562, data: 0.001) G_L1: 14.041 G_L1_ABSOLUTE: 2.601 G_L1_RELATIVE: 11.440 G_Regularizer: 0.000 validation_error: 20.776 +(epoch: 50, iters: 71152, time: 0.545, data: 0.000) G_L1: 13.394 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 11.032 G_Regularizer: 0.000 validation_error: 20.887 +(epoch: 50, iters: 73152, time: 0.541, data: 0.000) G_L1: 12.909 G_L1_ABSOLUTE: 2.520 G_L1_RELATIVE: 10.389 G_Regularizer: 0.000 validation_error: 20.860 +(epoch: 50, iters: 75152, time: 0.554, data: 0.000) G_L1: 16.826 G_L1_ABSOLUTE: 2.631 G_L1_RELATIVE: 14.196 G_Regularizer: 0.000 validation_error: 20.836 +(epoch: 50, iters: 77152, time: 0.557, data: 0.000) G_L1: 13.076 G_L1_ABSOLUTE: 2.187 G_L1_RELATIVE: 10.889 G_Regularizer: 0.000 validation_error: 20.847 +(epoch: 50, iters: 79152, time: 0.533, data: 0.000) G_L1: 12.667 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 10.069 G_Regularizer: 0.000 validation_error: 21.086 +(epoch: 50, iters: 81152, time: 0.562, data: 0.000) G_L1: 16.399 G_L1_ABSOLUTE: 3.145 G_L1_RELATIVE: 13.254 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 50, iters: 83152, time: 0.555, data: 0.000) G_L1: 17.972 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 15.421 G_Regularizer: 0.000 validation_error: 21.033 +(epoch: 50, iters: 85152, time: 0.538, data: 0.001) G_L1: 14.157 G_L1_ABSOLUTE: 2.513 G_L1_RELATIVE: 11.644 G_Regularizer: 0.000 validation_error: 21.007 +(epoch: 50, iters: 87152, time: 0.531, data: 0.001) G_L1: 14.550 G_L1_ABSOLUTE: 2.574 G_L1_RELATIVE: 11.976 G_Regularizer: 0.000 validation_error: 20.895 +(epoch: 50, iters: 89152, time: 0.564, data: 0.000) G_L1: 12.514 G_L1_ABSOLUTE: 2.362 G_L1_RELATIVE: 10.152 G_Regularizer: 0.000 validation_error: 20.824 +(epoch: 50, iters: 91152, time: 0.551, data: 0.000) G_L1: 12.283 G_L1_ABSOLUTE: 2.494 G_L1_RELATIVE: 9.789 G_Regularizer: 0.000 validation_error: 20.945 +(epoch: 50, iters: 93152, time: 0.552, data: 0.001) G_L1: 11.937 G_L1_ABSOLUTE: 2.155 G_L1_RELATIVE: 9.781 G_Regularizer: 0.000 validation_error: 20.768 +(epoch: 50, iters: 95152, time: 0.548, data: 0.000) G_L1: 11.778 G_L1_ABSOLUTE: 2.242 G_L1_RELATIVE: 9.536 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 50, iters: 97152, time: 0.558, data: 0.000) G_L1: 14.091 G_L1_ABSOLUTE: 2.370 G_L1_RELATIVE: 11.721 G_Regularizer: 0.000 validation_error: 20.878 +(epoch: 50, iters: 99152, time: 0.552, data: 0.000) G_L1: 12.872 G_L1_ABSOLUTE: 2.511 G_L1_RELATIVE: 10.361 G_Regularizer: 0.000 validation_error: 20.817 +(epoch: 50, iters: 101152, time: 0.531, data: 0.001) G_L1: 15.522 G_L1_ABSOLUTE: 2.422 G_L1_RELATIVE: 13.100 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 50, iters: 103152, time: 0.551, data: 0.001) G_L1: 12.428 G_L1_ABSOLUTE: 2.258 G_L1_RELATIVE: 10.169 G_Regularizer: 0.000 validation_error: 20.910 +(epoch: 50, iters: 105152, time: 0.545, data: 0.000) G_L1: 15.354 G_L1_ABSOLUTE: 2.550 G_L1_RELATIVE: 12.804 G_Regularizer: 0.000 validation_error: 20.929 +(epoch: 50, iters: 107152, time: 0.559, data: 0.000) G_L1: 13.092 G_L1_ABSOLUTE: 2.171 G_L1_RELATIVE: 10.922 G_Regularizer: 0.000 validation_error: 20.795 +(epoch: 50, iters: 109152, time: 0.559, data: 0.000) G_L1: 16.573 G_L1_ABSOLUTE: 3.057 G_L1_RELATIVE: 13.517 G_Regularizer: 0.000 validation_error: 20.895 +(epoch: 50, iters: 111152, time: 0.563, data: 0.000) G_L1: 13.745 G_L1_ABSOLUTE: 2.581 G_L1_RELATIVE: 11.165 G_Regularizer: 0.000 validation_error: 21.008 +(epoch: 50, iters: 113152, time: 0.542, data: 0.000) G_L1: 14.991 G_L1_ABSOLUTE: 2.684 G_L1_RELATIVE: 12.307 G_Regularizer: 0.000 validation_error: 20.917 +(epoch: 50, iters: 115152, time: 0.533, data: 0.000) G_L1: 14.602 G_L1_ABSOLUTE: 2.578 G_L1_RELATIVE: 12.024 G_Regularizer: 0.000 validation_error: 20.966 +(epoch: 50, iters: 117152, time: 0.540, data: 0.001) G_L1: 12.585 G_L1_ABSOLUTE: 2.405 G_L1_RELATIVE: 10.180 G_Regularizer: 0.000 validation_error: 21.089 +(epoch: 50, iters: 119152, time: 0.546, data: 0.000) G_L1: 13.344 G_L1_ABSOLUTE: 2.589 G_L1_RELATIVE: 10.755 G_Regularizer: 0.000 validation_error: 20.864 +(epoch: 50, iters: 121152, time: 0.539, data: 0.000) G_L1: 14.067 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 11.690 G_Regularizer: 0.000 validation_error: 20.974 +(epoch: 50, iters: 123152, time: 0.529, data: 0.000) G_L1: 12.631 G_L1_ABSOLUTE: 2.286 G_L1_RELATIVE: 10.345 G_Regularizer: 0.000 validation_error: 21.035 +(epoch: 50, iters: 125152, time: 0.543, data: 0.000) G_L1: 12.825 G_L1_ABSOLUTE: 2.702 G_L1_RELATIVE: 10.124 G_Regularizer: 0.000 validation_error: 21.034 +(epoch: 50, iters: 127152, time: 0.563, data: 0.000) G_L1: 14.406 G_L1_ABSOLUTE: 2.613 G_L1_RELATIVE: 11.793 G_Regularizer: 0.000 validation_error: 21.003 +(epoch: 50, iters: 129152, time: 0.542, data: 0.002) G_L1: 13.469 G_L1_ABSOLUTE: 2.165 G_L1_RELATIVE: 11.304 G_Regularizer: 0.000 validation_error: 20.970 +(epoch: 50, iters: 131152, time: 0.559, data: 0.000) G_L1: 13.021 G_L1_ABSOLUTE: 3.026 G_L1_RELATIVE: 9.995 G_Regularizer: 0.000 validation_error: 21.050 +(epoch: 50, iters: 133152, time: 0.552, data: 0.000) G_L1: 12.392 G_L1_ABSOLUTE: 2.456 G_L1_RELATIVE: 9.936 G_Regularizer: 0.000 validation_error: 20.942 +(epoch: 50, iters: 135152, time: 0.553, data: 0.000) G_L1: 14.762 G_L1_ABSOLUTE: 2.860 G_L1_RELATIVE: 11.902 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 50, iters: 137152, time: 0.555, data: 0.000) G_L1: 12.442 G_L1_ABSOLUTE: 2.155 G_L1_RELATIVE: 10.287 G_Regularizer: 0.000 validation_error: 21.010 +(epoch: 50, iters: 139152, time: 0.544, data: 0.000) G_L1: 13.416 G_L1_ABSOLUTE: 2.524 G_L1_RELATIVE: 10.892 G_Regularizer: 0.000 validation_error: 20.973 +(epoch: 50, iters: 141152, time: 0.559, data: 0.000) G_L1: 11.459 G_L1_ABSOLUTE: 2.348 G_L1_RELATIVE: 9.111 G_Regularizer: 0.000 validation_error: 21.022 +(epoch: 50, iters: 143152, time: 0.537, data: 0.000) G_L1: 13.826 G_L1_ABSOLUTE: 2.156 G_L1_RELATIVE: 11.669 G_Regularizer: 0.000 validation_error: 20.990 +(epoch: 50, iters: 145152, time: 0.559, data: 0.000) G_L1: 17.179 G_L1_ABSOLUTE: 2.485 G_L1_RELATIVE: 14.694 G_Regularizer: 0.000 validation_error: 20.813 +(epoch: 50, iters: 147152, time: 0.530, data: 0.000) G_L1: 11.547 G_L1_ABSOLUTE: 2.557 G_L1_RELATIVE: 8.990 G_Regularizer: 0.000 validation_error: 20.983 +(epoch: 50, iters: 149152, time: 0.539, data: 0.001) G_L1: 14.324 G_L1_ABSOLUTE: 2.536 G_L1_RELATIVE: 11.788 G_Regularizer: 0.000 validation_error: 21.112 +(epoch: 50, iters: 151152, time: 0.541, data: 0.000) G_L1: 14.865 G_L1_ABSOLUTE: 2.404 G_L1_RELATIVE: 12.461 G_Regularizer: 0.000 validation_error: 20.908 +(epoch: 50, iters: 153152, time: 0.559, data: 0.000) G_L1: 14.809 G_L1_ABSOLUTE: 2.531 G_L1_RELATIVE: 12.278 G_Regularizer: 0.000 validation_error: 20.870 +(epoch: 50, iters: 155152, time: 0.549, data: 0.000) G_L1: 12.128 G_L1_ABSOLUTE: 2.213 G_L1_RELATIVE: 9.915 G_Regularizer: 0.000 validation_error: 20.961 +(epoch: 50, iters: 157152, time: 0.545, data: 0.000) G_L1: 13.782 G_L1_ABSOLUTE: 2.800 G_L1_RELATIVE: 10.983 G_Regularizer: 0.000 validation_error: 20.750 +(epoch: 50, iters: 159152, time: 0.555, data: 0.000) G_L1: 13.700 G_L1_ABSOLUTE: 2.423 G_L1_RELATIVE: 11.276 G_Regularizer: 0.000 validation_error: 20.862 +(epoch: 50, iters: 161152, time: 0.559, data: 0.000) G_L1: 16.878 G_L1_ABSOLUTE: 2.810 G_L1_RELATIVE: 14.068 G_Regularizer: 0.000 validation_error: 21.028 +(epoch: 50, iters: 163152, time: 0.545, data: 0.000) G_L1: 14.249 G_L1_ABSOLUTE: 2.355 G_L1_RELATIVE: 11.894 G_Regularizer: 0.000 validation_error: 20.968 +(epoch: 50, iters: 165152, time: 0.546, data: 0.000) G_L1: 13.474 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 11.064 G_Regularizer: 0.000 validation_error: 20.949 +(epoch: 50, iters: 167152, time: 0.541, data: 0.000) G_L1: 14.579 G_L1_ABSOLUTE: 2.638 G_L1_RELATIVE: 11.941 G_Regularizer: 0.000 validation_error: 21.046 +(epoch: 50, iters: 169152, time: 0.552, data: 0.000) G_L1: 13.612 G_L1_ABSOLUTE: 2.317 G_L1_RELATIVE: 11.295 G_Regularizer: 0.000 validation_error: 20.797 +(epoch: 50, iters: 171152, time: 0.550, data: 0.000) G_L1: 13.918 G_L1_ABSOLUTE: 2.350 G_L1_RELATIVE: 11.568 G_Regularizer: 0.000 validation_error: 21.055 +(epoch: 50, iters: 173152, time: 0.559, data: 0.000) G_L1: 14.002 G_L1_ABSOLUTE: 2.420 G_L1_RELATIVE: 11.582 G_Regularizer: 0.000 validation_error: 21.025 +(epoch: 50, iters: 175152, time: 0.544, data: 0.000) G_L1: 12.535 G_L1_ABSOLUTE: 3.139 G_L1_RELATIVE: 9.397 G_Regularizer: 0.000 validation_error: 20.916 +(epoch: 50, iters: 177152, time: 0.540, data: 0.000) G_L1: 15.029 G_L1_ABSOLUTE: 3.106 G_L1_RELATIVE: 11.923 G_Regularizer: 0.000 validation_error: 20.865 +(epoch: 50, iters: 179152, time: 0.562, data: 0.001) G_L1: 13.282 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 10.908 G_Regularizer: 0.000 validation_error: 20.845 +(epoch: 50, iters: 181152, time: 0.538, data: 0.001) G_L1: 12.506 G_L1_ABSOLUTE: 2.523 G_L1_RELATIVE: 9.982 G_Regularizer: 0.000 validation_error: 20.818 +(epoch: 50, iters: 183152, time: 0.549, data: 0.000) G_L1: 13.664 G_L1_ABSOLUTE: 2.346 G_L1_RELATIVE: 11.318 G_Regularizer: 0.000 validation_error: 20.921 +(epoch: 50, iters: 185152, time: 0.554, data: 0.000) G_L1: 12.013 G_L1_ABSOLUTE: 2.378 G_L1_RELATIVE: 9.635 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 50, iters: 187152, time: 0.541, data: 0.000) G_L1: 13.810 G_L1_ABSOLUTE: 2.155 G_L1_RELATIVE: 11.655 G_Regularizer: 0.000 validation_error: 20.929 +(epoch: 50, iters: 189152, time: 0.546, data: 0.000) G_L1: 12.634 G_L1_ABSOLUTE: 2.278 G_L1_RELATIVE: 10.356 G_Regularizer: 0.000 validation_error: 20.871 +(epoch: 50, iters: 191152, time: 0.549, data: 0.000) G_L1: 12.997 G_L1_ABSOLUTE: 2.296 G_L1_RELATIVE: 10.701 G_Regularizer: 0.000 validation_error: 20.886 +(epoch: 50, iters: 193152, time: 0.535, data: 0.000) G_L1: 11.561 G_L1_ABSOLUTE: 2.284 G_L1_RELATIVE: 9.277 G_Regularizer: 0.000 validation_error: 20.876 +(epoch: 50, iters: 195152, time: 0.557, data: 0.001) G_L1: 9.892 G_L1_ABSOLUTE: 2.195 G_L1_RELATIVE: 7.697 G_Regularizer: 0.000 validation_error: 20.967 +(epoch: 50, iters: 197152, time: 0.540, data: 0.000) G_L1: 14.692 G_L1_ABSOLUTE: 2.481 G_L1_RELATIVE: 12.211 G_Regularizer: 0.000 validation_error: 20.668 +(epoch: 50, iters: 199152, time: 0.558, data: 0.000) G_L1: 13.356 G_L1_ABSOLUTE: 2.219 G_L1_RELATIVE: 11.137 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 50, iters: 201152, time: 0.539, data: 0.000) G_L1: 10.934 G_L1_ABSOLUTE: 2.351 G_L1_RELATIVE: 8.584 G_Regularizer: 0.000 validation_error: 20.843 +(epoch: 50, iters: 203152, time: 0.532, data: 0.000) G_L1: 14.364 G_L1_ABSOLUTE: 2.226 G_L1_RELATIVE: 12.138 G_Regularizer: 0.000 validation_error: 20.766 +(epoch: 50, iters: 205152, time: 0.544, data: 0.000) G_L1: 13.461 G_L1_ABSOLUTE: 2.390 G_L1_RELATIVE: 11.071 G_Regularizer: 0.000 validation_error: 20.925 +(epoch: 50, iters: 207152, time: 0.547, data: 0.000) G_L1: 16.535 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 13.865 G_Regularizer: 0.000 validation_error: 20.801 +(epoch: 50, iters: 209152, time: 0.543, data: 0.001) G_L1: 14.066 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 11.393 G_Regularizer: 0.000 validation_error: 20.806 +(epoch: 50, iters: 211152, time: 0.545, data: 0.000) G_L1: 14.960 G_L1_ABSOLUTE: 2.678 G_L1_RELATIVE: 12.282 G_Regularizer: 0.000 validation_error: 21.038 +(epoch: 50, iters: 213152, time: 0.552, data: 0.000) G_L1: 13.359 G_L1_ABSOLUTE: 2.577 G_L1_RELATIVE: 10.782 G_Regularizer: 0.000 validation_error: 21.114 +(epoch: 50, iters: 215152, time: 0.550, data: 0.000) G_L1: 13.462 G_L1_ABSOLUTE: 2.839 G_L1_RELATIVE: 10.623 G_Regularizer: 0.000 validation_error: 21.005 +(epoch: 50, iters: 217152, time: 0.534, data: 0.001) G_L1: 14.286 G_L1_ABSOLUTE: 2.416 G_L1_RELATIVE: 11.870 G_Regularizer: 0.000 validation_error: 21.007 +(epoch: 50, iters: 219152, time: 0.549, data: 0.001) G_L1: 13.220 G_L1_ABSOLUTE: 2.181 G_L1_RELATIVE: 11.039 G_Regularizer: 0.000 validation_error: 20.965 +(epoch: 50, iters: 221152, time: 0.549, data: 0.000) G_L1: 14.465 G_L1_ABSOLUTE: 2.674 G_L1_RELATIVE: 11.791 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 50, iters: 223152, time: 0.543, data: 0.000) G_L1: 15.709 G_L1_ABSOLUTE: 2.489 G_L1_RELATIVE: 13.220 G_Regularizer: 0.000 validation_error: 20.687 +(epoch: 50, iters: 225152, time: 0.552, data: 0.000) G_L1: 14.548 G_L1_ABSOLUTE: 2.997 G_L1_RELATIVE: 11.551 G_Regularizer: 0.000 validation_error: 20.821 +(epoch: 50, iters: 227152, time: 0.551, data: 0.000) G_L1: 16.420 G_L1_ABSOLUTE: 2.512 G_L1_RELATIVE: 13.907 G_Regularizer: 0.000 validation_error: 21.000 +(epoch: 50, iters: 229152, time: 0.553, data: 0.000) G_L1: 14.365 G_L1_ABSOLUTE: 2.567 G_L1_RELATIVE: 11.798 G_Regularizer: 0.000 validation_error: 20.863 +(epoch: 50, iters: 231152, time: 0.554, data: 0.000) G_L1: 13.529 G_L1_ABSOLUTE: 2.409 G_L1_RELATIVE: 11.120 G_Regularizer: 0.000 validation_error: 20.823 +(epoch: 50, iters: 233152, time: 0.541, data: 0.000) G_L1: 13.512 G_L1_ABSOLUTE: 2.270 G_L1_RELATIVE: 11.242 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 50, iters: 235152, time: 0.546, data: 0.000) G_L1: 14.784 G_L1_ABSOLUTE: 2.499 G_L1_RELATIVE: 12.284 G_Regularizer: 0.000 validation_error: 20.963 +(epoch: 50, iters: 237152, time: 0.540, data: 0.001) G_L1: 17.443 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 14.966 G_Regularizer: 0.000 validation_error: 20.952 +(epoch: 50, iters: 239152, time: 0.536, data: 0.000) G_L1: 14.884 G_L1_ABSOLUTE: 2.760 G_L1_RELATIVE: 12.125 G_Regularizer: 0.000 validation_error: 20.934 +(epoch: 50, iters: 241152, time: 0.546, data: 0.000) G_L1: 13.389 G_L1_ABSOLUTE: 2.895 G_L1_RELATIVE: 10.495 G_Regularizer: 0.000 validation_error: 20.825 +(epoch: 50, iters: 243152, time: 0.544, data: 0.000) G_L1: 17.264 G_L1_ABSOLUTE: 2.650 G_L1_RELATIVE: 14.614 G_Regularizer: 0.000 validation_error: 20.975 +(epoch: 50, iters: 245152, time: 0.563, data: 0.000) G_L1: 13.888 G_L1_ABSOLUTE: 2.428 G_L1_RELATIVE: 11.461 G_Regularizer: 0.000 validation_error: 20.784 +(epoch: 50, iters: 247152, time: 0.559, data: 0.000) G_L1: 12.697 G_L1_ABSOLUTE: 2.565 G_L1_RELATIVE: 10.132 G_Regularizer: 0.000 validation_error: 21.009 +(epoch: 50, iters: 249152, time: 0.539, data: 0.000) G_L1: 14.719 G_L1_ABSOLUTE: 2.404 G_L1_RELATIVE: 12.315 G_Regularizer: 0.000 validation_error: 20.901 +(epoch: 50, iters: 251152, time: 0.540, data: 0.000) G_L1: 14.773 G_L1_ABSOLUTE: 2.320 G_L1_RELATIVE: 12.453 G_Regularizer: 0.000 validation_error: 20.998 +(epoch: 50, iters: 253152, time: 0.541, data: 0.000) G_L1: 14.338 G_L1_ABSOLUTE: 2.660 G_L1_RELATIVE: 11.679 G_Regularizer: 0.000 validation_error: 20.889 +(epoch: 50, iters: 255152, time: 0.562, data: 0.000) G_L1: 15.671 G_L1_ABSOLUTE: 2.504 G_L1_RELATIVE: 13.166 G_Regularizer: 0.000 validation_error: 20.839 +(epoch: 50, iters: 257152, time: 0.546, data: 0.001) G_L1: 14.734 G_L1_ABSOLUTE: 2.952 G_L1_RELATIVE: 11.783 G_Regularizer: 0.000 validation_error: 20.976 +(epoch: 50, iters: 259152, time: 0.546, data: 0.000) G_L1: 14.127 G_L1_ABSOLUTE: 2.734 G_L1_RELATIVE: 11.393 G_Regularizer: 0.000 validation_error: 20.826 +(epoch: 50, iters: 261152, time: 0.560, data: 0.000) G_L1: 21.629 G_L1_ABSOLUTE: 2.627 G_L1_RELATIVE: 19.002 G_Regularizer: 0.000 validation_error: 20.832 +(epoch: 50, iters: 263152, time: 0.561, data: 0.000) G_L1: 11.584 G_L1_ABSOLUTE: 2.200 G_L1_RELATIVE: 9.384 G_Regularizer: 0.000 validation_error: 20.842 +(epoch: 50, iters: 265152, time: 0.547, data: 0.000) G_L1: 10.555 G_L1_ABSOLUTE: 2.069 G_L1_RELATIVE: 8.486 G_Regularizer: 0.000 validation_error: 20.883 +(epoch: 50, iters: 267152, time: 0.545, data: 0.000) G_L1: 11.750 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 9.152 G_Regularizer: 0.000 validation_error: 20.791 +(epoch: 50, iters: 269152, time: 0.542, data: 0.000) G_L1: 13.131 G_L1_ABSOLUTE: 2.469 G_L1_RELATIVE: 10.662 G_Regularizer: 0.000 validation_error: 20.953 +(epoch: 50, iters: 271152, time: 0.551, data: 0.000) G_L1: 15.054 G_L1_ABSOLUTE: 2.363 G_L1_RELATIVE: 12.691 G_Regularizer: 0.000 validation_error: 20.928 +(epoch: 50, iters: 273152, time: 0.553, data: 0.000) G_L1: 14.395 G_L1_ABSOLUTE: 2.375 G_L1_RELATIVE: 12.021 G_Regularizer: 0.000 validation_error: 20.935 +(epoch: 50, iters: 275152, time: 0.531, data: 0.000) G_L1: 16.199 G_L1_ABSOLUTE: 2.816 G_L1_RELATIVE: 13.383 G_Regularizer: 0.000 validation_error: 21.037 +(epoch: 50, iters: 277152, time: 0.537, data: 0.000) G_L1: 13.642 G_L1_ABSOLUTE: 2.463 G_L1_RELATIVE: 11.180 G_Regularizer: 0.000 validation_error: 20.772 +(epoch: 50, iters: 279152, time: 0.554, data: 0.000) G_L1: 15.279 G_L1_ABSOLUTE: 2.476 G_L1_RELATIVE: 12.803 G_Regularizer: 0.000 validation_error: 20.792 +(epoch: 50, iters: 281152, time: 0.552, data: 0.000) G_L1: 13.569 G_L1_ABSOLUTE: 2.366 G_L1_RELATIVE: 11.202 G_Regularizer: 0.000 validation_error: 20.918 +(epoch: 50, iters: 283152, time: 0.550, data: 0.001) G_L1: 14.213 G_L1_ABSOLUTE: 2.670 G_L1_RELATIVE: 11.543 G_Regularizer: 0.000 validation_error: 20.986 +(epoch: 50, iters: 285152, time: 0.555, data: 0.000) G_L1: 15.793 G_L1_ABSOLUTE: 2.404 G_L1_RELATIVE: 13.390 G_Regularizer: 0.000 validation_error: 20.636 +(epoch: 50, iters: 287152, time: 0.553, data: 0.000) G_L1: 12.423 G_L1_ABSOLUTE: 2.377 G_L1_RELATIVE: 10.046 G_Regularizer: 0.000 validation_error: 20.906 +(epoch: 50, iters: 289152, time: 0.563, data: 0.000) G_L1: 12.481 G_L1_ABSOLUTE: 2.242 G_L1_RELATIVE: 10.239 G_Regularizer: 0.000 validation_error: 20.723 +(epoch: 50, iters: 291152, time: 0.554, data: 0.000) G_L1: 14.564 G_L1_ABSOLUTE: 2.944 G_L1_RELATIVE: 11.619 G_Regularizer: 0.000 validation_error: 20.637 +(epoch: 50, iters: 293152, time: 0.540, data: 0.000) G_L1: 13.002 G_L1_ABSOLUTE: 2.353 G_L1_RELATIVE: 10.648 G_Regularizer: 0.000 validation_error: 20.926 +(epoch: 50, iters: 295152, time: 0.550, data: 0.001) G_L1: 15.001 G_L1_ABSOLUTE: 2.648 G_L1_RELATIVE: 12.353 G_Regularizer: 0.000 validation_error: 20.943 +(epoch: 50, iters: 297152, time: 0.560, data: 0.001) G_L1: 13.265 G_L1_ABSOLUTE: 2.233 G_L1_RELATIVE: 11.032 G_Regularizer: 0.000 validation_error: 21.106 +(epoch: 50, iters: 299152, time: 0.539, data: 0.000) G_L1: 14.484 G_L1_ABSOLUTE: 2.598 G_L1_RELATIVE: 11.886 G_Regularizer: 0.000 validation_error: 20.849 +(epoch: 50, iters: 301152, time: 0.535, data: 0.000) G_L1: 15.200 G_L1_ABSOLUTE: 2.283 G_L1_RELATIVE: 12.917 G_Regularizer: 0.000 validation_error: 20.902 diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/checkpoints/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead/opt.txt b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/checkpoints/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead/opt.txt new file mode 100644 index 0000000..d05388c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/checkpoints/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead/opt.txt @@ -0,0 +1,62 @@ +----------------- Options --------------- + aspect_ratio: 1.0 + audio_window_size: 16 + batch_size: 1 + cached_images: False + checkpoints_dir: ./checkpoints + dataroot: /ARD_ZDF [default: None] + dataset_mode: multi_face_audio_eq_tmp_cached [default: aligned] + direction: AtoB + display_winsize: 512 [default: 256] + epoch: latest + erosionFactor: 1.0 + eval: False + fineSize: 512 + fix_renderer: False + gpu_ids: 0 + hierarchicalTex: False + init_gain: 0.02 + init_type: xavier + input_nc: 3 + isTrain: False [default: None] + loadSize: 512 + load_iter: 0 [default: 0] + look_ahead: True [default: False] + lossType: RMS [default: L1] + mapping_path: /home/alberto/NeuralVoicePuppetry/mappings/audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead/mapping_Severin_videos_SC [default: ] + max_dataset_size: inf + model: audio2ExpressionsAttentionTMP4 [default: test] + n_layers_D: 3 + name: audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead [default: experiment_name] + ndf: 64 + netD: basic + netG: unet_256 + ngf: 64 + no_augmentation: False + no_dropout: False + norm: instance + ntest: inf + num_test: 50 + num_threads: 4 + out_dir: /home/alberto/data/dave_fxfy/audio2exprNVP/ [default: ] + output_audio_expressions: False + output_nc: 3 + phase: test + renderer: no_renderer + rendererType: estimatorAttention [default: UNET_5_level] + resize_or_crop: resize_and_crop + results_dir: ./results/ + seq_len: 8 [default: 1] + serial_batches: False + source_actor: /home/alberto/NeuralVoicePuppetry/datasets/External/Severin_videos_transformers_lecture [default: ] + source_dir: ./datasets/ + suffix: + target_actor: + tex_dim: 256 + tex_features: 16 +tex_features_intermediate: 16 + textureModel: DynamicNeuralTextureAudio + use_mapping: True [default: False] + verbose: False + write_no_images: True [default: False] +----------------- End ------------------- diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/checkpoints/audio2ExpressionsAttentionTMP4/opt.txt b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/checkpoints/audio2ExpressionsAttentionTMP4/opt.txt new file mode 100644 index 0000000..caf26b3 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/checkpoints/audio2ExpressionsAttentionTMP4/opt.txt @@ -0,0 +1,59 @@ +----------------- Options --------------- + aspect_ratio: 1.0 + audio_window_size: 16 + batch_size: 1 + cached_images: False + checkpoints_dir: ./checkpoints + dataroot: /ARD_ZDF [default: None] + dataset_mode: multi_face_audio_eq_tmp_cached [default: aligned] + direction: AtoB + display_winsize: 512 [default: 256] + epoch: latest + erosionFactor: 1.0 + eval: False + fineSize: 512 + fix_renderer: False + gpu_ids: 0 + hierarchicalTex: False + init_gain: 0.02 + init_type: xavier + input_nc: 3 + isTrain: False [default: None] + loadSize: 512 + load_iter: 0 [default: 0] + look_ahead: True [default: False] + lossType: RMS [default: L1] + max_dataset_size: inf + model: audio2ExpressionsAttentionTMP4 [default: test] + n_layers_D: 3 + name: audio2ExpressionsAttentionTMP4 [default: experiment_name] + ndf: 64 + netD: basic + netG: unet_256 + ngf: 64 + no_augmentation: False + no_dropout: False + norm: instance + ntest: inf + num_test: 50 + num_threads: 4 + output_audio_expressions: False + output_nc: 3 + phase: test + renderer: no_renderer + rendererType: estimatorAttention [default: UNET_5_level] + resize_or_crop: resize_and_crop + results_dir: ./results/ + seq_len: 8 [default: 1] + serial_batches: False + source_actor: /home/alberto/NeuralVoicePuppetry/datasets//External/Sekunden_Wissen_01 [default: ] + source_dir: ./datasets/ + suffix: + target_actor: /home/alberto/NeuralVoicePuppetry/datasets//External/Russian_guy [default: ] + tex_dim: 256 + tex_features: 16 +tex_features_intermediate: 16 + textureModel: DynamicNeuralTextureAudio + verbose: False + write_no_images: True [default: False] +----------------- End ------------------- diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/__init__.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/__init__.py new file mode 100644 index 0000000..a42be17 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/__init__.py @@ -0,0 +1,90 @@ +import importlib +import torch.utils.data +from .base_data_loader import BaseDataLoader +from .base_dataset import BaseDataset + + +def find_dataset_using_name(dataset_name): + # Given the option --dataset_mode [datasetname], + # the file "data/datasetname_dataset.py" + # will be imported. + dataset_filename = "data." + dataset_name + "_dataset" + datasetlib = importlib.import_module(dataset_filename) + + # In the file, the class called DatasetNameDataset() will + # be instantiated. It has to be a subclass of BaseDataset, + # and it is case-insensitive. + dataset = None + target_dataset_name = dataset_name.replace('_', '') + 'datasets' + for name, cls in datasetlib.__dict__.items(): + if name.lower() == target_dataset_name.lower() \ + and issubclass(cls, BaseDataset): + dataset = cls + + if dataset is None: + print("In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase." % (dataset_filename, target_dataset_name)) + exit(0) + + return dataset + + +def get_option_setter(dataset_name): + dataset_class = find_dataset_using_name(dataset_name) + return dataset_class.modify_commandline_options + + +def create_dataset(opt): + dataset = find_dataset_using_name(opt.dataset_mode) + instance = dataset() + instance.initialize(opt) + print("datasets [%s] was created" % (instance.name())) + return instance + + +def CreateDataLoader(opt): + data_loader = CustomDatasetDataLoader() + data_loader.initialize(opt) + return data_loader + + +# Wrapper class of Dataset class that performs +# multi-threaded data loading +class CustomDatasetDataLoader(BaseDataLoader): + def name(self): + return 'CustomDatasetDataLoader' + + def initialize(self, opt): + BaseDataLoader.initialize(self, opt) + self.dataset = create_dataset(opt) + if opt.serial_batches: + self.dataloader = torch.utils.data.DataLoader( + self.dataset, + batch_size=opt.batch_size, + shuffle=not opt.serial_batches, + num_workers=int(opt.num_threads)) + else: + #weights = make_weights_for_balanced_classes(dataset_train.imgs, len(dataset_train.classes)) + weights = self.dataset.getSampleWeights() + weights = torch.DoubleTensor(weights) + sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights)) + + self.dataloader = torch.utils.data.DataLoader( + self.dataset, + batch_size=opt.batch_size, + #shuffle=True, + sampler=sampler, + pin_memory=True, + num_workers=int(opt.num_threads)) + + + def load_data(self): + return self + + def __len__(self): + return min(len(self.dataset), self.opt.max_dataset_size) + + def __iter__(self): + for i, data in enumerate(self.dataloader): + if i * self.opt.batch_size >= self.opt.max_dataset_size: + break + yield data diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/audio.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/audio.py new file mode 100644 index 0000000..4932c94 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/audio.py @@ -0,0 +1,77 @@ +import time +import random +import math + +import numpy as np + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.transforms as transforms + +import torchaudio +import torchaudio.transforms + +import librosa +import scipy.signal +import librosa.display +import matplotlib.pyplot as plt + + +class Audio(): + def name(self): + return 'Audio' + + def __init__(self, filename, write_mel_spectogram = False): + self.n_mels=128 + self.fmax=8000 + self.hop_length_ms = 20 + + sound, sample_rate = librosa.load(filename)#torchaudio.load(filename) + self.raw_audio = sound + self.sample_rate = sample_rate + print('sample_rate = %d' % self.sample_rate) + self.n_samples = sound.shape[0] + self.time_total = self.n_samples / self.sample_rate + print('length = %ds' % self.time_total) + + print('compute mel spectrogram...') + self.hop_length = int(sample_rate / 1000.0 * self.hop_length_ms) + print('hop_length: ', self.hop_length) + self.mel_spectrogram = librosa.feature.melspectrogram(y=self.raw_audio, sr=self.sample_rate, hop_length=self.hop_length, n_mels=self.n_mels, fmax=self.fmax) + + + if write_mel_spectogram: + print('write spectrogram to file') + plt.figure(figsize=(100, 15)) + librosa.display.specshow(librosa.power_to_db(self.mel_spectrogram, ref=np.max), y_axis='mel', fmax=self.fmax, x_axis='time') + plt.colorbar(format='%+2.0f dB') + plt.title('Mel spectrogram') + plt.tight_layout() + plt.savefig('mel_features.png', dpi=None, facecolor='w', edgecolor='w', orientation='portrait', papertype=None, format=None, transparent=False, bbox_inches=None, pad_inches=0.1, frameon=None, metadata=None) + + print('mel: ', self.mel_spectrogram.shape) # (128, 18441) + self.n_mel_frames = self.mel_spectrogram.shape[1] + self.mel_sample_rate = self.mel_spectrogram.shape[1] / self.time_total + print('n_mel_frames: ', self.n_mel_frames) + print('mel_sample_rate: ', self.mel_sample_rate) + + # convert to torch + self.mel_spectrogram = torch.FloatTensor(self.mel_spectrogram) + + def getWindow(self, mel_frame_idx, window_size): + # get audio mel sample window + audio_start = mel_frame_idx - (window_size//2) + audio_end = mel_frame_idx + (window_size//2) + if audio_start < 0: + audio_input = self.mel_spectrogram[0:self.n_mels, 0:audio_end] + zeros = torch.zeros((self.n_mels,-audio_start)) + audio_input = torch.cat([zeros, audio_input], 1) + elif audio_end >= self.n_mel_frames: + audio_input = self.mel_spectrogram[:, audio_start:-1] + zeros = torch.zeros((self.n_mels,audio_end-self.n_mel_frames + 1)) + audio_input = torch.cat([audio_input, zeros], 1) + else: + audio_input = self.mel_spectrogram[:, audio_start:audio_end] + + return torch.reshape(audio_input, (1, 1, self.n_mels, window_size)) \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/audio_dataset.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/audio_dataset.py new file mode 100644 index 0000000..519cbe1 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/audio_dataset.py @@ -0,0 +1,150 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from .base_dataset import BaseDataset +from .audio import Audio +#from data.image_folder import make_dataset +from PIL import Image +import h5py + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.deepspeech.npy']): + id_str = fname[:-15] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.deepspeech.npy' + path = os.path.join(root, fname) + images.append(path) + return images + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + #id_str = fname[l+1:-4] + id_str = fname[l+1:-15] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + +class AudioDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + self.root = opt.dataroot + self.audiofeat_path = os.path.join(opt.dataroot, 'audio_feature') + + self.audiofeat_list = sorted(os.listdir(self.audiofeat_path)) + + print('\taudio_feat_path:', self.audiofeat_path) + + opt.nObjects = 1 + opt.nTrainObjects = 116 # TODO + opt.nTestObjects = 1 + opt.test_sequence_names = [[opt.dataroot.split("/")[-1], 'test']] + assert(opt.resize_or_crop == 'resize_and_crop') + + if opt.isTrain: + print('ERROR: audio_dataset only allowed for test') + exit() + + def getSampleWeights(self): + weights = np.ones((len(self.frame_paths))) + return weights + + def getAudioFilename(self): + return os.path.join(self.root, 'audio.wav') + + def getAudioFeatureFilename(self, idx): + return self.frame_paths[idx % len(self.frame_paths)] + + def load_npy(self, index): + + file_path = os.path.join(self.audiofeat_path, "%05d.deepspeech.npy" % index) + + feature_array = np.load(file_path) + + return feature_array + + def __getitem__(self, index): + + # load deepspeech feature + feature_array = self.load_npy(index) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf = transforms.ToTensor()(dsf_np.astype(np.float32)) + + # load sequence data if necessary + if self.opt.look_ahead:# use prev and following frame infos + r = self.opt.seq_len//2 + for i in range(1,r): # prev frames + index_seq = index - i + if index_seq < 0: index_seq = 0 + + feature_array = self.load_npy(index_seq) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + for i in range(1,self.opt.seq_len - r + 1): # following frames + index_seq = index + i + max_idx = len(self.audiofeat_list)-1 + if index_seq > max_idx: index_seq = max_idx + + feature_array = self.load_npy(index_seq) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf, dsf_seq], 0) # seq_len x 16 x 29 + # note the ordering [old ... current ... future] + else: + for i in range(1, self.opt.seq_len): + index_seq = index - i + if index_seq < 0: index_seq = 0 + + feature_array = self.load_npy(index_seq) + + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + + ################################# + zeroIdentity = torch.zeros(100) + zeroExpressions = torch.zeros(76) + + target_id = -1 + internal_sequence_id = 0 + + weight = 1.0 / self.__len__() + + return {'paths': '', + 'expressions': zeroExpressions, + 'identity': zeroIdentity, + 'intrinsics': np.zeros((4)), + 'extrinsics': np.zeros((4,4)), + 'audio_deepspeech': dsf, # deepspeech feature + 'target_id':target_id, + 'internal_id':internal_sequence_id, + 'weight': np.array([weight]).astype(np.float32)} + + def __len__(self): + return len(self.audiofeat_list) + + def name(self): + return 'AudioDataset' diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/base_data_loader.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/base_data_loader.py new file mode 100644 index 0000000..ae5a168 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/base_data_loader.py @@ -0,0 +1,10 @@ +class BaseDataLoader(): + def __init__(self): + pass + + def initialize(self, opt): + self.opt = opt + pass + + def load_data(): + return None diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/base_dataset.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/base_dataset.py new file mode 100644 index 0000000..25c7c8c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/base_dataset.py @@ -0,0 +1,105 @@ +import torch.utils.data as data +from PIL import Image +import torchvision.transforms as transforms + + +class BaseDataset(data.Dataset): + def __init__(self): + super(BaseDataset, self).__init__() + + def name(self): + return 'BaseDataset' + + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + pass + + def getSampleWeights(self): + return torch.ones((len(self))) + + def __len__(self): + return 0 + + +def get_transform(opt): + transform_list = [] + if opt.resize_or_crop == 'resize_and_crop': + osize = [opt.loadSize, opt.loadSize] + transform_list.append(transforms.Resize(osize, Image.BICUBIC)) + transform_list.append(transforms.RandomCrop(opt.fineSize)) + elif opt.resize_or_crop == 'crop': + transform_list.append(transforms.RandomCrop(opt.fineSize)) + elif opt.resize_or_crop == 'scale_width': + transform_list.append(transforms.Lambda( + lambda img: __scale_width(img, opt.fineSize))) + elif opt.resize_or_crop == 'scale_width_and_crop': + transform_list.append(transforms.Lambda( + lambda img: __scale_width(img, opt.loadSize))) + transform_list.append(transforms.RandomCrop(opt.fineSize)) + elif opt.resize_or_crop == 'none': + transform_list.append(transforms.Lambda( + lambda img: __adjust(img))) + else: + raise ValueError('--resize_or_crop %s is not a valid option.' % opt.resize_or_crop) + + if opt.isTrain and not opt.no_flip: + transform_list.append(transforms.RandomHorizontalFlip()) + + transform_list += [transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), + (0.5, 0.5, 0.5))] + return transforms.Compose(transform_list) + + +# just modify the width and height to be multiple of 4 +def __adjust(img): + ow, oh = img.size + + # the size needs to be a multiple of this number, + # because going through generator network may change img size + # and eventually cause size mismatch error + mult = 4 + if ow % mult == 0 and oh % mult == 0: + return img + w = (ow - 1) // mult + w = (w + 1) * mult + h = (oh - 1) // mult + h = (h + 1) * mult + + if ow != w or oh != h: + __print_size_warning(ow, oh, w, h) + + return img.resize((w, h), Image.BICUBIC) + + +def __scale_width(img, target_width): + ow, oh = img.size + + # the size needs to be a multiple of this number, + # because going through generator network may change img size + # and eventually cause size mismatch error + mult = 4 + assert target_width % mult == 0, "the target width needs to be multiple of %d." % mult + if (ow == target_width and oh % mult == 0): + return img + w = target_width + target_height = int(target_width * oh / ow) + m = (target_height - 1) // mult + h = (m + 1) * mult + + if target_height != h: + __print_size_warning(target_width, target_height, w, h) + + return img.resize((w, h), Image.BICUBIC) + + +def __print_size_warning(ow, oh, w, h): + if not hasattr(__print_size_warning, 'has_printed'): + print("The image size needs to be a multiple of 4. " + "The loaded image size was (%d, %d), so it was adjusted to " + "(%d, %d). This adjustment will be done to all images " + "whose sizes are not multiples of 4" % (ow, oh, w, h)) + __print_size_warning.has_printed = True diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/face_dataset.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/face_dataset.py new file mode 100644 index 0000000..6e46108 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/face_dataset.py @@ -0,0 +1,178 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from .base_dataset import BaseDataset +from .audio import Audio +from PIL import Image +import h5py + + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.npy', '.NPY']): + #.deepspeech.npy + id_str = fname[:-15] #4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.deepspeech.npy' + path = os.path.join(root, fname) + images.append(path) + return images + +def make_expr_dataset(dir): + expressions = [] + num_expr = len([name for name in os.listdir(dir) if os.path.isfile(name)]) + for i in range(num_expr): + fname = f'expr_{i}.npy' + path = os.path.join(dir, fname) + expressions.append(path) + return expressions + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + id_str = fname[l+1:-15]#4] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + + +# def load_expressions(input_dir): +# file = open(input_dir+"/expressions/", "r") +# expressions = [[float(x) for x in line.split()] for line in file] +# file.close() +# return expressions + +class FaceDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + + # directories + self.dataroot = opt.dataroot + self.h5py_path = os.path.join(opt.dataroot, opt.dataroot.split("/")[-1]+'.h5') + + # debug print + print('load sequence:', self.dataroot) + print('\th5py_path:', self.h5py_path) + + self.data = h5py.File(self.h5py_path, 'r') + + # set data + self.n_frames_total = min(len(self.data["dsf"]), len(self.data["ep"])) + + print('\tnum frames:', self.n_frames_total) + + + opt.nTrainObjects = 1 + opt.nValObjects = 1 + opt.nTestObjects = 1 + + opt.test_sequence_names = [[opt.dataroot.split("/")[-1], 'train']] + assert(opt.resize_or_crop == 'resize_and_crop') + + def getSampleWeights(self): + weights = np.ones((self.n_frames_total)) + return weights + + + def getAudioFilename(self): + return os.path.join(self.dataroot, 'audio.wav') + + def getAudioFeatureFilename(self, idx): + #return self.frame_paths[idx % len(self.frame_paths)] + audio_id = self.audio_ids[idx] + return os.path.join(self.audio_feature_dir, str(audio_id) + '.deepspeech.npy') + + + def __getitem__(self, global_index): + # select frame from sequence + index = global_index + + # intrinsics and extrinsics + intrinsics = np.zeros((4)) # not used + extrinsics = np.zeros((4,4))# not used + + # expressions + expressions = self.data["ep"][index] + expressions = torch.tensor(expressions) + + # identity + identity = torch.zeros(100) # not used + + # load deepspeech feature + feature_array = self.data["dsf"][index] + dsf_np = np.resize(feature_array, (16,29,1)) + dsf = transforms.ToTensor()(dsf_np.astype(np.float32)) + + + # load sequence data if necessary + if self.opt.look_ahead:# use prev and following frame infos + r = self.opt.seq_len//2 + for i in range(1,r): # prev frames + index_seq = index - i + if index_seq < 0: index_seq = 0 + + feature_array = self.data["dsf"][index_seq] + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + for i in range(1,self.opt.seq_len - r + 1): # following frames + index_seq = index + i + max_idx = len(self.data["dsf"])-1 + if index_seq > max_idx: index_seq = max_idx + + feature_array = self.data["dsf"][index_seq] + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf, dsf_seq], 0) # seq_len x 16 x 29 + # note the ordering [old ... current ... future] + else: + last_valid_idx = audio_id + for i in range(1,self.opt.seq_len): + index_seq = index - i + if index_seq < 0: index_seq = 0 + + feature_array = self.data["dsf"][index_seq] + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + + ################################# + weight = 1.0 / self.n_frames_total + + return {#'TARGET': TARGET, 'UV': UV, + 'paths': '', #img_path, + 'intrinsics': np.array(intrinsics), + 'extrinsics': np.array(extrinsics), + 'expressions': expressions, + 'identity': identity, + 'audio_deepspeech': dsf, # deepspeech feature + 'target_id': -1, + 'internal_id': 0, + 'weight': np.array([weight]).astype(np.float32)} + + def __len__(self): + return self.n_frames_total + + def name(self): + return 'FaceDataset' diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/multi_face_audio_eq_tmp_cached_dataset.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/multi_face_audio_eq_tmp_cached_dataset.py new file mode 100644 index 0000000..99535be --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/data/multi_face_audio_eq_tmp_cached_dataset.py @@ -0,0 +1,458 @@ +import os.path +import random +import torchvision.transforms as transforms +import torch +import numpy as np +from data.base_dataset import BaseDataset +from data.audio import Audio +#from data.image_folder import make_dataset +from PIL import Image +import progressbar + +#def make_dataset(dir): +# images = [] +# assert os.path.isdir(dir), '%s is not a valid directory' % dir +# for root, _, fnames in sorted(os.walk(dir)): +# for fname in fnames: +# if any(fname.endswith(extension) for extension in ['.bin', '.BIN']): +# path = os.path.join(root, fname) +# images.append(path) +# return sorted(images) + +def make_dataset(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.npy', '.NPY']): + #.deepspeech.npy + id_str = fname[:-15] #4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + + for id in ids: + fname=str(id)+'.deepspeech.npy' + path = os.path.join(root, fname) + images.append(path) + return images + +def make_ids(paths, root_dir): + ids = [] + + for fname in paths: + l = fname.rfind('/') + id_str = fname[l+1:-15]#4] + i = int(id_str) + #print(fname, ': ', i) + ids.append(i) + return ids + +def make_dataset_ids_png(dir): + images = [] + ids = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if any(fname.endswith(extension) for extension in ['.png', '.png']): + id_str = fname[:-4] + i = int(id_str) + ids.append(i) + ids = sorted(ids) + return ids + +def load_intrinsics(input_dir): + file = open(input_dir+"/intrinsics.txt", "r") + intrinsics = [[(float(x) for x in line.split())] for line in file] + file.close() + intrinsics = list(intrinsics[0][0]) + return intrinsics + +def load_rigids(input_dir): + file = open(input_dir+"/rigid.txt", "r") + rigid_floats = [[float(x) for x in line.split()] for line in file] # note that it stores 5 lines per matrix (blank line) + file.close() + all_rigids = [ [rigid_floats[4*idx + 0],rigid_floats[4*idx + 1],rigid_floats[4*idx + 2],rigid_floats[4*idx + 3]] for idx in range(0, len(rigid_floats)//4) ] + return all_rigids + +def load_expressions(input_dir): + file = open(input_dir+"/expression.txt", "r") + expressions = [[float(x) for x in line.split()] for line in file] + file.close() + return expressions + +def load_identity(input_dir): + file = open(input_dir+"/identities.txt", "r") + identities = [[float(x) for x in line.split()] for line in file] + file.close() + return identities + + +class MultiFaceAudioEQTmpCachedDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + self.root = opt.dataroot + + # read datasets file that contains the filenames for the train, val and test lists + file = open(self.root+"/datasets.txt", "r") + self.filename_train_list, self.filename_val_list, self.filename_test_list = [str(line) for line in file] + file.close() + if self.filename_train_list[-1] == '\n': self.filename_train_list = self.filename_train_list[:-1] + if self.filename_val_list[-1] == '\n': self.filename_val_list = self.filename_val_list[:-1] + if self.filename_test_list[-1] == '\n': self.filename_test_list = self.filename_test_list[:-1] + + + + # get list of train sequences + file = open(self.root+"/" + self.filename_train_list, "r") + self.train_sequence_names = [str(line) for line in file] + file.close() + for i in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[i][-1] == '\n': + self.train_sequence_names[i] = self.train_sequence_names[i][:-1] + + # get list of val sequences + file = open(self.root+"/" + self.filename_val_list, "r") + self.val_sequence_names = [[str(w) for w in line.split()] for line in file] + file.close() + for i in range(0,len(self.val_sequence_names)): + if self.val_sequence_names[i][0][-1] == '\n': self.val_sequence_names[i][0] = self.val_sequence_names[i][0][:-1] + if self.val_sequence_names[i][1][-1] == '\n': self.val_sequence_names[i][1] = self.val_sequence_names[i][1][:-1] + + # get list of test sequences + file = open(self.root+"/" + self.filename_test_list, "r") + self.test_sequence_names = [[str(w) for w in line.split()] for line in file] + if opt.output_audio_expressions: self.test_sequence_names = self.test_sequence_names[0:1] + file.close() + for i in range(0,len(self.test_sequence_names)): + if self.test_sequence_names[i][0][-1] == '\n': self.test_sequence_names[i][0] = self.test_sequence_names[i][0][:-1] + if self.test_sequence_names[i][1][-1] == '\n': self.test_sequence_names[i][1] = self.test_sequence_names[i][1][:-1] + + # print some stats + print('filename_train_list:', self.filename_train_list) + print('\tnum_seq:', len(self.train_sequence_names)) + print('filename_val_list: ', self.filename_val_list) + print('\tnum_seq:', len(self.val_sequence_names)) + print('filename_test_list: ', self.filename_test_list) + print('\tnum_seq:', len(self.test_sequence_names)) + + opt.train_sequence_names = self.train_sequence_names + opt.val_sequence_names = self.val_sequence_names + opt.test_sequence_names = self.test_sequence_names + + # search mapping from val, test to train sequences that are used as targets + self.val_sequence_targets = [] + for i in range(0,len(self.val_sequence_names)): + target_name = self.val_sequence_names[i][1] + target_id = -1 + for j in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[j] == target_name: + target_id = j + break + if target_id == -1: + print('Target sequence not in train set! ', target_name) + exit() + self.val_sequence_targets.append(target_id) + + self.test_sequence_targets = [] + for i in range(0,len(self.test_sequence_names)): + target_name = self.test_sequence_names[i][1] + target_id = -1 + for j in range(0,len(self.train_sequence_names)): + if self.train_sequence_names[j] == target_name: + target_id = j + break + if target_id == -1: + print('Target sequence not in train set! ', target_name) + exit() + self.test_sequence_targets.append(target_id) + print('test: ', self.test_sequence_names[i]) + print('\t target:', target_id) + + # store len values + opt.nTrainObjects = len(self.train_sequence_names) + opt.nValObjects = len(self.val_sequence_names) + opt.nTestObjects = len(self.test_sequence_names) + + ################################################ + ################################################ + ################################################ + + # prepare dataloader paths / data + self.audio_feature_dir = [] + self.image_dir = [] + self.uvs_dir = [] + self.audio_ids = [] + self.image_ids = [] + self.intrinsics = [] + self.extrinsics = [] + self.expressions = [] + self.identities = [] + self.target_id = [] + self.n_frames_total = 0 + + if opt.phase == 'train': + self.sequence_names = self.train_sequence_names + for i in range(0,len(self.train_sequence_names)): + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[i]) + audio_feature_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[i], 'uvs') + print('load train sequence:', self.train_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + + audio_ids = make_ids(make_dataset(audio_feature_dir), dataroot) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(dataroot) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(i) + + self.n_frames_total += min_len + elif opt.phase == 'val': + for i in range(0,len(self.val_sequence_names)): + target_id = self.val_sequence_targets[i] + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[target_id]) + audio_feature_dir = os.path.join(opt.dataroot, self.val_sequence_names[i][0], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'uvs') + print('load val sequence:', self.val_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + audio_ids = make_ids(make_dataset(audio_feature_dir), os.path.join(opt.dataroot, self.val_sequence_names[i][0])) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(os.path.join(opt.dataroot, self.val_sequence_names[i][0])) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(target_id) + + self.n_frames_total += min_len + else: # test + for i in range(0,len(self.test_sequence_names)): + target_id = self.test_sequence_targets[i] + dataroot = os.path.join(opt.dataroot, self.train_sequence_names[target_id]) + audio_feature_dir = os.path.join(opt.dataroot, self.test_sequence_names[i][0], 'audio_feature') + image_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'images') + uvs_dir = os.path.join(opt.dataroot, self.train_sequence_names[target_id], 'uvs') + print('load test sequence:', self.test_sequence_names[i]) + print('\tidentity_dir:', dataroot) + print('\taudio_feature_dir:', audio_feature_dir) + print('\timage_dir:', image_dir) + print('\tuvs_dir:', uvs_dir) + audio_ids = make_ids(make_dataset(audio_feature_dir), os.path.join(opt.dataroot, self.test_sequence_names[i][0])) + image_ids = make_dataset_ids_png(image_dir) # [-1] * len(audio_ids) #make_ids(make_dataset(image_dir), dataroot) + intrinsics = load_intrinsics(dataroot) + extrinsics = load_rigids(dataroot) + expressions = load_expressions(os.path.join(opt.dataroot, self.test_sequence_names[i][0])) + identity = load_identity(dataroot) + + min_len = min(len(audio_ids), len(image_ids), len(extrinsics), len(expressions)) + + self.audio_feature_dir.append(audio_feature_dir) + self.image_dir.append(image_dir) + self.uvs_dir.append(uvs_dir) + self.audio_ids.append(audio_ids[:min_len]) + self.image_ids.append(image_ids[:min_len]) + self.intrinsics.append(intrinsics) + self.extrinsics.append(extrinsics[:min_len]) + self.expressions.append(expressions[:min_len]) + self.identities.append(identity[:min_len]) + self.target_id.append(target_id) + + self.n_frames_total += min_len + + + print('frames_total:', self.n_frames_total) + + + #global_target_ids = [] + #for i in range(0,len(self.audio_ids)): + # for j in range(0,len(self.audio_ids[i])): + # global_target_ids.append(self.target_id[i]) + #global_target_ids=np.array(global_target_ids) + #self.weights = np.where(global_target_ids==2, 1.0 * np.ones((self.n_frames_total)), 0.01 * np.ones((self.n_frames_total)) ) + self.weights = [] + for i in range(0,len(self.audio_ids)): + l = len(self.audio_ids[i]) + for j in range(0,l): + self.weights.append(1.0 / l) + self.weights = np.array(self.weights) + + assert(opt.resize_or_crop == 'resize_and_crop') + + # mapping global to internal + self.mapping_global2internal = [] + self.mapping_global2internal_offset = [] + self.dsf = [] + offset = 0 + with progressbar.ProgressBar(max_value=self.n_frames_total) as bar: + for i in range(0,len(self.audio_ids)): + l = len(self.audio_ids[i]) + dsf_seq = [] + for k in range(0,l): + self.mapping_global2internal.append(i) + self.mapping_global2internal_offset.append(offset) + dsf_fname = os.path.join(self.audio_feature_dir[i], str(self.audio_ids[i][k]) + '.deepspeech.npy') + feature_array = np.load(dsf_fname) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq.append(dsf_np.astype(np.float32)) + bar.update(offset + k) + self.dsf.append(dsf_seq) + offset += l + + + def getSampleWeights(self): + return self.weights + + def getitem(self, global_index): + + # select sequence + internal_sequence_id = self.mapping_global2internal[global_index] + sum_frames = self.mapping_global2internal_offset[global_index] + + # select frame from sequence + index = (global_index-sum_frames) % len(self.audio_ids[internal_sequence_id]) + + # get data ids + audio_id = self.audio_ids[internal_sequence_id][index] + image_id = self.image_ids[internal_sequence_id][index] + + #print('GET ITEM: ', index) + #img_path = self.frame_paths[sequence_id][index] + + # intrinsics and extrinsics + intrinsics = self.intrinsics[internal_sequence_id] + extrinsics = self.extrinsics[internal_sequence_id][image_id] + + # expressions + expressions = np.asarray(self.expressions[internal_sequence_id][audio_id], dtype=np.float32) + #print('expressions:', expressions.shape) + expressions[32] *= 0.0 # remove eye brow movements + expressions[41] *= 0.0 # remove eye brow movements + expressions[71:75] *= 0.0 # remove eye brow movements + expressions = torch.tensor(expressions) + + # identity + identity = torch.tensor(self.identities[internal_sequence_id][image_id]) + target_id = self.target_id[internal_sequence_id] # sequence id refers to the target sequence (of the training corpus) + + # load deepspeech feature + #print('audio_id', audio_id) + dsf_fname = os.path.join(self.audio_feature_dir[internal_sequence_id], str(audio_id) + '.deepspeech.npy') + + dsf_np = self.dsf[internal_sequence_id][index] + dsf = transforms.ToTensor()(dsf_np) + + # load sequence data if necessary + if self.opt.look_ahead:# use prev and following frame infos + r = self.opt.seq_len//2 + for i in range(1,r): # prev frames + index_seq = index - i + if index_seq < 0: index_seq = 0 + dsf_np = self.dsf[internal_sequence_id][index_seq] + dsf_seq = transforms.ToTensor()(dsf_np) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + for i in range(1,self.opt.seq_len - r + 1): # following frames + index_seq = index + i + max_idx = len(self.audio_ids[internal_sequence_id])-1 + if index_seq > max_idx: index_seq = max_idx + dsf_np = self.dsf[internal_sequence_id][index_seq] + dsf_seq = transforms.ToTensor()(dsf_np) # 1 x 16 x 29 + dsf = torch.cat([dsf, dsf_seq], 0) # seq_len x 16 x 29 + # note the ordering [old ... current ... future] + else: + for i in range(1,self.opt.seq_len): + index_seq = index - i + if index_seq < 0: index_seq = 0 + dsf_np = self.dsf[internal_sequence_id][index_seq] + dsf_seq = transforms.ToTensor()(dsf_np) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + #weight = 1.0 / len(self.audio_feature_dir[internal_sequence_id]) + weight = self.weights[global_index] + + return {'paths': dsf_fname, #img_path, + 'intrinsics': np.array(intrinsics), + 'extrinsics': np.array(extrinsics), + 'expressions': expressions, + 'identity': identity, + 'audio_deepspeech': dsf, # deepspeech feature + 'target_id':target_id, + 'internal_id':internal_sequence_id, + + 'weight': np.array([weight]).astype(np.float32)} + + + def __getitem__(self, global_index): + # select frame from sequence + index = global_index + current = self.getitem(index) + prv = self.getitem(max(index-1, 0)) + nxt = self.getitem(min(index+1, self.n_frames_total-1)) + + return { + 'paths': current['paths'], #img_path, + 'target_id': current['target_id'], + 'internal_id': current['internal_id'], + 'weight': current['weight'], + 'identity': current['identity'], + 'intrinsics': current['intrinsics'], + + 'extrinsics': current['extrinsics'], + 'expressions': current['expressions'], + 'audio_deepspeech': current['audio_deepspeech'], + + 'extrinsics_prv': prv['extrinsics'], + 'expressions_prv': prv['expressions'], + 'audio_deepspeech_prv': prv['audio_deepspeech'], + + 'extrinsics_nxt': nxt['extrinsics'], + 'expressions_nxt': nxt['expressions'], + 'audio_deepspeech_nxt': nxt['audio_deepspeech'], + } + + + def __len__(self): + return self.n_frames_total #len(self.frame_paths[0]) + + def name(self): + return 'MultiFaceAudioEQTmpCachedDataset' diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/get_audioexpr.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/get_audioexpr.py new file mode 100644 index 0000000..4fb7501 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/get_audioexpr.py @@ -0,0 +1,383 @@ +import sys +import os +import random +import torchvision.transforms as transforms +import argparse + + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))) + +from data.audio_dataset import AudioDataset +from data.base_dataset import BaseDataset + +from models import create_model +import torch +import numpy as np +import copy +from tqdm import tqdm + +class FaceDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + self.opt = opt + + # directories + self.dataroot = opt.dataroot + self.expr_path = os.path.join(opt.dataroot, 'deca_expr') + self.audio_feature_path = os.path.join(opt.dataroot, 'audio_feature') + + # debug print + print('load sequence:', self.dataroot) + + self.n_frames_total = len(os.listdir(self.expr_path)) - 1 + + opt.nTrainObjects = 1 + opt.nValObjects = 1 + opt.nTestObjects = 1 + + opt.test_sequence_names = [[opt.dataroot.split("/")[-1], 'train']] + assert(opt.resize_or_crop == 'resize_and_crop') + + def getSampleWeights(self): + weights = np.ones((self.n_frames_total)) + return weights + + + def getAudioFilename(self): + return os.path.join(self.dataroot, 'audio.wav') + + def getAudioFeatureFilename(self, idx): + return os.path.join(self.audio_feature_path, str(idx) + '.deepspeech.npy') + + + def __getitem__(self, global_index): + # select frame from sequence + index = global_index + + # expressions + expressions = np.load(os.path.join(self.expr_path, '%05d.npy' % index)) + expressions = torch.from_numpy(expressions)[0] + + # identity + identity = torch.zeros(100) # not used + + # load deepspeech feature + feature_array = np.load(os.path.join(self.audio_feature_path, '%05d.deepspeech.npy' % index)) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf = transforms.ToTensor()(dsf_np.astype(np.float32)) + + + # load sequence data if necessary + if self.opt.look_ahead:# use prev and following frame infos + r = self.opt.seq_len//2 + for i in range(1,r): # prev frames + index_seq = index - i + if index_seq < 0: index_seq = 0 + + feature_array = np.load(os.path.join(self.audio_feature_path, '%05d.deepspeech.npy' % index_seq)) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + for i in range(1,self.opt.seq_len - r + 1): # following frames + index_seq = index + i + max_idx = self.n_frames_total-1 + if index_seq > max_idx: index_seq = max_idx + + feature_array = np.load(os.path.join(self.audio_feature_path, '%05d.deepspeech.npy' % index_seq)) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf, dsf_seq], 0) # seq_len x 16 x 29 + # note the ordering [old ... current ... future] + else: + last_valid_idx = audio_id + for i in range(1,self.opt.seq_len): + index_seq = index - i + if index_seq < 0: index_seq = 0 + + feature_array = np.load(os.path.join(self.audio_feature_path, '%05d.deepspeech.npy' % index_seq)) + dsf_np = np.resize(feature_array, (16,29,1)) + dsf_seq = transforms.ToTensor()(dsf_np.astype(np.float32)) # 1 x 16 x 29 + dsf = torch.cat([dsf_seq, dsf], 0) # seq_len x 16 x 29 + # note the ordering [old ... current] + + + ################################# + weight = 1.0 / self.n_frames_total + + return {#'TARGET': TARGET, 'UV': UV, + 'paths': '', #img_path, + 'expressions': expressions, + 'identity': identity, + 'audio_deepspeech': dsf, # deepspeech feature + 'target_id': -1, + 'internal_id': 0, + 'weight': np.array([weight]).astype(np.float32)} + + def __len__(self): + return self.n_frames_total + + def name(self): + return 'FaceDataset' + + + + self.expressions = input['expressions'].cuda() + self.audio_features = input['audio_deepspeech'].cuda() # b x seq_len x 16 x 29 + self.target_id = input['target_id'].cuda() + + +def make_opts(dataset_base): + class Struct: + def __init__(self, **entries): + self.__dict__.update(entries) + + opt = {} + opt['dataroot'] = dataset_base + opt['batch_size'] = 1 + opt['seq_len'] = 8 + opt['fineSize'] = 512 + opt['display_winsize'] = 512 + opt['input_nc'] = 3 + opt['output_nc'] = 3 + opt['ngf'] = 64 + opt['ndf'] = 64 + opt['netD'] = 'basic' + opt['netG'] = 'unet_256' + opt['n_layers_D'] = 3 + opt['gpu_ids'] = '0' + opt['fineSize'] = 256 + + opt['name'] = 'audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead' + opt['renderer'] = 'no_renderer' + opt['fix_renderer'] = True + opt['dataset_mode']='multi_face_audio_eq_tmp_cached' + opt['model'] = 'audio2ExpressionsAttentionTMP4' + opt['direction'] = 'AtoB' + opt['epoch'] = 'latest' + opt['load_iter'] = 0 + opt['num_threads'] = 4 + opt['checkpoints_dir'] = os.getcwd() + '/third/Audio2ExpressionNet/checkpoints' + opt['norm'] = 'instance' + opt['serial_batches'] = False + opt['no_dropout'] = False + opt['max_dataset_size'] = float("inf") + + opt['resize_or_crop'] = 'resize_and_crop' + opt['no_augmentation'] = False + opt['init_type'] = 'xavier' + opt['init_gain'] = 0.02 + + opt['verbose'] = False + opt['suffix'] = '' + opt['tex_dim'] = 256 + opt['tex_features_intermediate'] = 16 + opt['tex_features'] = 16 + opt['textureModel'] = 'DynamicNeuralTextureAudio' + opt['rendererType'] = 'estimatorAttention' + opt['lossType'] = 'RMS' + + opt['hierarchicalTex'] = False + opt['output_audio_expressions'] = False + opt['erosionFactor'] = 1.0 + opt['audio_window_size'] = 16 + opt['look_ahead'] = True + opt['cached_images'] = False + opt['ntest'] = float("inf") + opt['results_dir'] = './results/' + opt['aspect_ratio'] = 1.0, + opt['phase'] = 'test' + opt['eval'] = False + opt['num_test'] = 50 + opt['write_no_images'] = True + + # Important + opt['source_dir'] = './datasets/' + opt['source_actor'] = dataset_base + + # extra + opt['isTrain'] = False + + s = Struct(**opt) + + return s + + +def load_model(opt): + opt.output_audio_expressions = True + opt.nTrainObjects = 116 + + print('#train objects = %d' % opt.nTrainObjects) + + print('>>> create model <<<') + model = create_model(opt) + print('>>> setup model <<<') + model.setup(opt) + + return model + + +def load_source_sequence(opt): + opt_source = copy.copy(opt) # create a clone + opt_source.dataroot = opt.source_actor # overwrite root directory + print(opt_source.dataroot) + opt_source.dataset_mode = 'audio' + opt_source.phase = 'train' + + dataset_source = AudioDataset() + + dataset_source.initialize(opt_source) + + dataloader = torch.utils.data.DataLoader( + dataset_source, + batch_size=opt.batch_size, + shuffle=not opt.serial_batches, + num_workers=int(opt.num_threads)) + + return dataset_source, dataloader + + +def load_target_sequence(opt): + opt_target = copy.copy(opt) # create a clone + # opt_target.dataroot = opt.target_actor # overwrite root directory + opt_target.dataset_mode = 'face' + opt_target.phase = 'train' + dataset_target = FaceDataset() + dataset_target.initialize(opt_target) + + dataloader = torch.utils.data.DataLoader( + dataset_target, + batch_size=opt.batch_size, + shuffle=not opt.serial_batches, + num_workers=int(opt.num_threads)) + + return dataset_target, dataloader + + +def get_audioexpr(name, dataset_base, out_dir, mapping_path=None): + + # read options + print('Base: ', dataset_base) + opt = make_opts(dataset_base) + + # hard-code some parameters for test + opt.num_threads = 1 # test code only supports num_threads = 1 + opt.batch_size = 1 # test code only supports batch_size = 1 + opt.serial_batches = True # no shuffle + opt.no_augmentation = True # no flip + opt.display_id = -1 # no visdom display + opt.source_actor = dataset_base + + # load model + model = load_model(opt) + + # Make mapping + if mapping_path is None: + mapping_fn = os.path.join(dataset_base, 'mapping.npy') + + else: + mapping_fn = mapping_path + + not_exists = not os.path.exists(mapping_fn) + + if not_exists: + # read target sequence + dataset_target, data_loader_target = load_target_sequence(opt) + + # collect data + print('collect data') + audio_expressions = None + gt_expressions = None + for i, data in tqdm(enumerate(data_loader_target)): + model.set_input(data) + model.test() + + ae = model.fake_expressions.data[:,:,0] + if type(audio_expressions) == type(None): + audio_expressions = ae + e = model.expressions.data + gt_expressions = e + else: + audio_expressions = torch.cat([audio_expressions,ae],dim=0) + e = model.expressions.data + gt_expressions = torch.cat([gt_expressions,e],dim=0) + + # solve for mapping + print('solve for mapping') + optimize_in_parameter_space = True #False + if optimize_in_parameter_space: +# A = audio_expressions +# B = gt_expressions +# # solve lstsq ||AX - B|| +# X, _ = torch.gels(B, A, out=None) +# #X, _ = torch.lstsq(B, A) # requires pytorch 1.2 +# X = X[0:A.shape[1],:] +# mapping = X.t() + + # use gradient descent method + n = audio_expressions.shape[0] + subspace_dim = 32 + + # TODO: patch + n_expr = 53 + + X = torch.nn.Parameter(torch.randn(n_expr, subspace_dim, requires_grad=True).cuda()) + optimizer = torch.optim.Adam([X], lr=0.01) + lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1) + num_epochs = 90 + random_range = [k for k in range(0,n)] + + for ep in tqdm(range(0,num_epochs)): + random.shuffle(random_range) + for j in random_range: + expressions = gt_expressions[j] + fake_expressions = torch.matmul(X, audio_expressions[j]) + diff_expression = fake_expressions - expressions + loss = torch.mean(diff_expression * diff_expression) # L2 + optimizer.zero_grad() + loss.backward() + optimizer.step() + lr_scheduler.step() + + mapping = X.data + + map_cpu = mapping.data.cpu().numpy() + np.save(mapping_fn, map_cpu) + + else: + # load mapping from file + map_cpu = np.load(mapping_fn) + mapping = torch.tensor(map_cpu.astype(np.float32)).cuda() + print('loaded mapping from file', mapping.shape) + + + # read source sequence + dataset_source, data_loader_source = load_source_sequence(opt) + + expression_multiplier = 1.0 + + print(f'Extracting audio-features of {name}..') + for i, data in enumerate(tqdm(data_loader_source)): + model.set_input(data) + model.test() + audio_expression = model.fake_expressions.data[0, :, 0] + expression = expression_multiplier * torch.matmul(mapping, audio_expression) + expression = expression[None, :] + np.save(os.path.join(out_dir, '%05d.npy' % i), expression.cpu().numpy()) + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--name', required=True) + parser.add_argument('--dataset_base', required=True) + parser.add_argument('--out_dir', required=True) + parser.add_argument('--mapping_path', required=True) + inopt = parser.parse_args() + + out_dir = os.path.join(inopt.out_dir, 'audio_expr') + os.makedirs(out_dir, exist_ok=True) + + get_audioexpr(inopt.name, inopt.dataset_base, out_dir, inopt.mapping_path) diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/models/__init__.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/models/__init__.py new file mode 100644 index 0000000..eeddb5a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/models/__init__.py @@ -0,0 +1,42 @@ +import importlib +from .base_model import BaseModel + + +def find_model_using_name(model_name): + # Given the option --model [modelname], + # the file "models/modelname_model.py" + # will be imported. + model_filename = "." + model_name + "_model" + print(model_filename) + modellib = importlib.import_module(model_filename, package="models") + print(modellib) + + # In the file, the class called ModelNameModel() will + # be instantiated. It has to be a subclass of BaseModel, + # and it is case-insensitive. + model = None + target_model_name = model_name.replace('_', '') + 'model' + print(target_model_name) + for name, cls in modellib.__dict__.items(): + if name.lower() == target_model_name.lower() \ + and issubclass(cls, BaseModel): + model = cls + + if model is None: + print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name)) + exit(0) + + return model + + +def get_option_setter(model_name): + model_class = find_model_using_name(model_name) + return model_class.modify_commandline_options + + +def create_model(opt): + model = find_model_using_name(opt.model) + instance = model() + instance.initialize(opt) + print("model [%s] was created" % (instance.name())) + return instance diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/models/audio2ExpressionsAttentionTMP4_model.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/models/audio2ExpressionsAttentionTMP4_model.py new file mode 100644 index 0000000..654d99e --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/models/audio2ExpressionsAttentionTMP4_model.py @@ -0,0 +1,348 @@ +import os +import torch +import torch.nn as nn +import torchvision.transforms as transforms +from autil.image_pool import ImagePool +from .base_model import BaseModel +from . import networks +import numpy as np +import functools + +INVALID_UV = -1.0 +N_EXPRESSIONS=76 + +from torchvision import models +from collections import namedtuple + + +class ExpressionEstimator_Attention(nn.Module): + def __init__(self, n_output_expressions, nIdentities, seq_len, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(ExpressionEstimator_Attention, self).__init__() + print('Estimator Attention') + ################################# + ######## audio net ########## + ################################# + self.seq_len = seq_len + + dropout_rate = 0.0 + if use_dropout == True: + #dropout_rate = 0.5 + dropout_rate = 0.25 + + self.convNet = nn.Sequential( + nn.Conv2d(29, 32, kernel_size=(3,1), stride=(2,1), padding=(1,0), bias=True), # 29 x 16 x 1 => 32 x 8 x 1 + nn.LeakyReLU(0.02, True), + nn.Conv2d(32, 32, kernel_size=(3,1), stride=(2,1), padding=(1,0), bias=True), # 32 x 8 x 1 => 32 x 4 x 1 + nn.LeakyReLU(0.02, True), + nn.Conv2d(32, 64, kernel_size=(3,1), stride=(2,1), padding=(1,0), bias=True), # 32 x 4 x 1 => 64 x 2 x 1 + nn.LeakyReLU(0.2, True), + nn.Conv2d(64, 64, kernel_size=(3,1), stride=(2,1), padding=(1,0), bias=True), # 64 x 2 x 1 => 64 x 1 x 1 + nn.LeakyReLU(0.2, True), + ) + + fullNet_input_size = 64 + + self.subspace_dim = 32 # number of audio expressions + print('fullNet_input_size: ', fullNet_input_size) + self.fullNet = nn.Sequential( + nn.Linear(in_features = fullNet_input_size, out_features=128, bias = True), + nn.LeakyReLU(0.02), + + nn.Linear(in_features = 128, out_features=64, bias = True), + nn.LeakyReLU(0.02), + + nn.Linear(in_features = 64, out_features=self.subspace_dim, bias = True), + nn.Tanh() + ) + + + # # mapping from subspace to full expression space + self.register_parameter('mapping', torch.nn.Parameter(torch.randn(1, nIdentities, N_EXPRESSIONS, self.subspace_dim, requires_grad=True))) + + # attention + self.attentionConvNet = nn.Sequential( # b x subspace_dim x seq_len + nn.Conv1d(self.subspace_dim, 16, kernel_size=3, stride=1, padding=1, bias=True), + nn.LeakyReLU(0.02, True), + nn.Conv1d(16, 8, kernel_size=3, stride=1, padding=1, bias=True), + nn.LeakyReLU(0.02, True), + nn.Conv1d(8, 4, kernel_size=3, stride=1, padding=1, bias=True), + nn.LeakyReLU(0.02, True), + nn.Conv1d(4, 2, kernel_size=3, stride=1, padding=1, bias=True), + nn.LeakyReLU(0.02, True), + nn.Conv1d(2, 1, kernel_size=3, stride=1, padding=1, bias=True), + nn.LeakyReLU(0.02, True) + ) + self.attentionNet = nn.Sequential( + nn.Linear(in_features = self.seq_len, out_features=self.seq_len, bias = True), + nn.Softmax(dim=1) + ) + #self.hidden2subspace = nn.Linear(self.subspace_dim,self.subspace_dim) + + def forward_internal(self, audio_features_sequence, identity_id): + result_subspace, intermediate_expression = self.getAudioExpressions_internal(audio_features_sequence) + mapping = torch.index_select(self.mapping[0], dim = 0, index = identity_id) + result = 10.0 * torch.bmm(mapping, result_subspace)[:,:,0] + result_intermediate = 10.0 * torch.bmm(mapping, intermediate_expression)[:,:,0] + #exit(-1) + return result, result_intermediate + + def forward(self, audio_features_sequence, identity_id): + result_subspace = self.getAudioExpressions(audio_features_sequence) + mapping = torch.index_select(self.mapping[0], dim = 0, index = identity_id) + result = torch.bmm(mapping, result_subspace)[:,:,0] + #exit(-1) + return 10.0 * result + + def getAudioExpressions_internal(self, audio_features_sequence): + # audio_features_sequence: b x seq_len x 16 x 29 + b = audio_features_sequence.shape[0] # batchsize + audio_features_sequence = audio_features_sequence.view(b * self.seq_len, 1, 16, 29) # b * seq_len x 1 x 16 x 29 + audio_features_sequence = torch.transpose(audio_features_sequence, 1, 3) # b* seq_len x 29 x 16 x 1 + conv_res = self.convNet( audio_features_sequence ) + conv_res = torch.reshape( conv_res, (b * self.seq_len, 1, -1)) + result_subspace = self.fullNet(conv_res)[:,0,:] # b * seq_len x subspace_dim + result_subspace = result_subspace.view(b, self.seq_len, self.subspace_dim)# b x seq_len x subspace_dim + + ################# + ### attention ### + ################# + result_subspace_T = torch.transpose(result_subspace, 1, 2) # b x subspace_dim x seq_len + intermediate_expression = result_subspace_T[:,:,(self.seq_len // 2):(self.seq_len // 2) + 1] + att_conv_res = self.attentionConvNet(result_subspace_T) + #print('att_conv_res', att_conv_res.shape) + attention = self.attentionNet(att_conv_res.view(b, self.seq_len)).view(b, self.seq_len, 1) # b x seq_len x 1 + #print('attention', attention.shape) + # pooling along the sequence dimension + result_subspace = torch.bmm(result_subspace_T, attention) + #print('result_subspace', result_subspace.shape) + ### + + return result_subspace.view(b, self.subspace_dim, 1), intermediate_expression + + def getAudioExpressions(self, audio_features_sequence): + expr, _ = self.getAudioExpressions_internal(audio_features_sequence) + return expr + + def regularizer(self): + #reg = torch.norm(self.mapping) + reg_mapping = torch.mean(torch.abs(self.mapping)) + + # one could also enforce orthogonality here + + # s_browExpressions[] = { 32, 41, 71, 72, 73, 74, 75 }; + reg_eye_brow = torch.mean(torch.abs( self.mapping[0,:,[32, 41, 71, 72, 73, 74, 75],:] )) + #return 0.01 * reg_mapping + 1.0 * reg_eye_brow + return 0.0 * reg_mapping + + + +def define_ExpressionEstimator(estimatorType='estimatorDefault', nIdentities=1, seq_len=1, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]): + net = None + print('EstimatorType: ', estimatorType) + if estimatorType=='estimatorAttention': net = ExpressionEstimator_Attention(N_EXPRESSIONS,nIdentities, seq_len) + + return networks.init_net(net, init_type, init_gain, gpu_ids) + + +class Audio2ExpressionsAttentionTMP4Model(BaseModel): + def name(self): + return 'Audio2ExpressionsAttentionTMP4Model' + + @staticmethod + def modify_commandline_options(parser, is_train=True): + + # changing the default values to match the pix2pix paper + # (https://phillipi.github.io/pix2pix/) + #parser.set_defaults(norm='batch', netG='unet_256') + parser.set_defaults(norm='instance', netG='unet_256') + parser.set_defaults(dataset_mode='aligned') + if is_train: + parser.set_defaults(pool_size=0, no_lsgan=True) + parser.add_argument('--lambda_L1', type=float, default=100.0, help='weight for L1 loss') + + return parser + + def initialize(self, opt): + BaseModel.initialize(self, opt) + self.isTrain = opt.isTrain + + self.trainRenderer = not opt.fix_renderer + + # specify the training losses you want to print out. The program will call base_model.get_current_losses + self.loss_names = ['G_L1','G_L1_ABSOLUTE','G_L1_RELATIVE', 'G_Regularizer'] + + # specify the images you want to save/display. The program will call base_model.get_current_visuals + #self.visual_names = ['input_uv', 'fake', 'target'] + self.visual_names = ['zeros'] + self.zeros = torch.zeros(1,3,2,2) + + # specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks + if self.isTrain: + self.model_names = ['netG'] + else: # during test time, only load Gs + self.model_names = ['netG'] + + self.fake_expressions = None + self.fake_expressions_prv = None + self.fake_expressions_nxt = None + + if self.isTrain: + self.morphable_model = MorphableModel() + self.mask = self.morphable_model.LoadMask() + + nIdentities=opt.nTrainObjects + + # load/define networks + self.netG = define_ExpressionEstimator(estimatorType=opt.rendererType, nIdentities=nIdentities, seq_len=opt.seq_len, gpu_ids=self.gpu_ids) + + if self.isTrain: + use_sigmoid = opt.no_lsgan + self.fake_AB_pool = ImagePool(opt.pool_size) + + # define loss functions + self.criterionL1 = torch.nn.L1Loss() + self.criterionL1Smooth = torch.nn.SmoothL1Loss() + self.criterionL2 = torch.nn.MSELoss() + + # initialize optimizers + self.optimizers = [] + #self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999), weight_decay=0.1 )#10.0) + self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999), weight_decay=0.0 )#10.0) + self.optimizers.append(self.optimizer_G) + + + def set_input(self, input): + #self.image_paths = input['paths'] + + self.expressions = input['expressions'].cuda() + self.audio_features = input['audio_deepspeech'].cuda() # b x seq_len x 16 x 29 + + if self.isTrain: + self.expressions_prv = input['expressions_prv'].cuda() + self.audio_features_prv = input['audio_deepspeech_prv'].cuda() # b x seq_len x 16 x 29 + + self.expressions_nxt = input['expressions_nxt'].cuda() + self.audio_features_nxt = input['audio_deepspeech_nxt'].cuda() # b x seq_len x 16 x 29 + + self.target_id = input['target_id'].cuda() + + + def forward(self): + # estimate expressions + if self.opt.output_audio_expressions: #self.opt.dataset_mode=='audio': + self.fake_expressions = self.netG.getAudioExpressions(self.audio_features) + if self.isTrain: + self.fake_expressions_prv = self.netG.getAudioExpressions(self.audio_features_prv) + self.fake_expressions_nxt = self.netG.getAudioExpressions(self.audio_features_nxt) + else: + self.fake_expressions, self.fake_expressions_intermediate = self.netG.forward_internal(self.audio_features, self.target_id) + if self.isTrain: + self.fake_expressions_prv = self.netG(self.audio_features_prv, self.target_id) + self.fake_expressions_nxt = self.netG(self.audio_features_nxt, self.target_id) + + + + def backward_G(self, epoch): + + # Second, G(A) = B + #self.loss_G_L1 = self.criterionL1(self.fake_expressions, self.expressions) + + # difference in vertex space + mask = torch.cat([self.mask[:,None],self.mask[:,None],self.mask[:,None]], 1) + mask = mask + 0.1 * torch.ones_like(mask) # priority for the mask region, but other region should also be constrained + + # absolute (single timesteps) + diff_expression = self.fake_expressions - self.expressions + diff_vertices = self.morphable_model.compute_expression_delta(diff_expression) + + + diff_expression_intermediate = self.fake_expressions_intermediate - self.expressions + diff_vertices_intermediate = self.morphable_model.compute_expression_delta(diff_expression_intermediate) + + + diff_expression_prv = self.fake_expressions_prv - self.expressions_prv + diff_vertices_prv = self.morphable_model.compute_expression_delta(diff_expression_prv) + + diff_expression_nxt = self.fake_expressions_nxt - self.expressions_nxt + diff_vertices_nxt = self.morphable_model.compute_expression_delta(diff_expression_nxt) + + # relative (temporal 1 timestep) cur - nxt and prv - cur + diff_expression_tmp_cur_nxt = (self.fake_expressions - self.fake_expressions_nxt) - (self.expressions - self.expressions_nxt) + diff_vertices_tmp_cur_nxt = self.morphable_model.compute_expression_delta(diff_expression_tmp_cur_nxt) + diff_expression_tmp_prv_cur = (self.fake_expressions_prv - self.fake_expressions) - (self.expressions_prv - self.expressions) + diff_vertices_tmp_prv_cur = self.morphable_model.compute_expression_delta(diff_expression_tmp_prv_cur) + + # relative (temporal 2 timesteps) nxt - prv + diff_expression_tmp_nxt_prv = (self.fake_expressions_nxt - self.fake_expressions_prv) - (self.expressions_nxt - self.expressions_prv) + diff_vertices_tmp_nxt_prv = self.morphable_model.compute_expression_delta(diff_expression_tmp_nxt_prv) + + #print('mask: ', mask.shape) + #print('diff_vertices: ', diff_vertices.shape) + + self.loss_G_L1_ABSOLUTE = 0.0 + self.loss_G_L1_RELATIVE = 0.0 + if self.opt.lossType == 'L1': + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices)) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices_prv)) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices_nxt)) + + self.loss_G_L1_ABSOLUTE += 3000.0 * torch.mean(mask * torch.abs(diff_vertices_intermediate)) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_cur_nxt)) + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_prv_cur)) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_nxt_prv)) + + elif self.opt.lossType == 'L2': + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * diff_vertices * diff_vertices) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * diff_vertices_prv * diff_vertices_prv) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * diff_vertices_nxt * diff_vertices_nxt) + + self.loss_G_L1_ABSOLUTE += 3000.0 * torch.mean(mask * diff_vertices_intermediate * diff_vertices_intermediate) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * diff_vertices_tmp_cur_nxt * diff_vertices_tmp_cur_nxt) + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * diff_vertices_tmp_prv_cur * diff_vertices_tmp_prv_cur) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * diff_vertices_tmp_nxt_prv * diff_vertices_tmp_nxt_prv) + + elif self.opt.lossType == 'RMS': + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.sqrt(torch.mean(mask * diff_vertices * diff_vertices)) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.sqrt(torch.mean(mask * diff_vertices_prv * diff_vertices_prv)) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.sqrt(torch.mean(mask * diff_vertices_nxt * diff_vertices_nxt)) + + self.loss_G_L1_ABSOLUTE += 3000.0 * torch.sqrt(torch.mean(mask * diff_vertices_intermediate * diff_vertices_intermediate)) + + + self.loss_G_L1_RELATIVE += 20000.0 * torch.sqrt(torch.mean(mask * diff_vertices_tmp_cur_nxt * diff_vertices_tmp_cur_nxt)) + self.loss_G_L1_RELATIVE += 20000.0 * torch.sqrt(torch.mean(mask * diff_vertices_tmp_prv_cur * diff_vertices_tmp_prv_cur)) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.sqrt(torch.mean(mask * diff_vertices_tmp_nxt_prv * diff_vertices_tmp_nxt_prv)) + + else: # L1 + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices)) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices_prv)) + self.loss_G_L1_ABSOLUTE += 1000.0 * torch.mean(mask * torch.abs(diff_vertices_nxt)) + + self.loss_G_L1_ABSOLUTE += 3000.0 * torch.mean(mask * torch.abs(diff_vertices_intermediate)) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_cur_nxt)) + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_prv_cur)) + + self.loss_G_L1_RELATIVE += 20000.0 * torch.mean(mask * torch.abs(diff_vertices_tmp_nxt_prv)) + + self.loss_G_L1 = self.loss_G_L1_ABSOLUTE + self.loss_G_L1_RELATIVE + self.loss_G_Regularizer = self.netG.regularizer() + + self.loss_G = self.loss_G_L1 + self.loss_G_Regularizer #self.loss_G_GAN + self.loss_G_L1 + self.regularizerTex + + + self.loss_G.backward() + + def optimize_parameters(self, epoch_iter): + self.forward() + + # update Generator + self.optimizer_G.zero_grad() + self.backward_G(epoch_iter) + self.optimizer_G.step() + diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/models/base_model.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/models/base_model.py new file mode 100644 index 0000000..2a76496 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/models/base_model.py @@ -0,0 +1,223 @@ +import os +import torch +import torch.nn as nn +from collections import OrderedDict +from . import networks +import numpy as np +from PIL import Image + +def save_tensor_image(input_image, image_path): + if isinstance(input_image, torch.Tensor): + image_tensor = input_image.data + image_numpy = image_tensor[0].cpu().float().numpy() + if image_numpy.shape[0] == 1: + image_numpy = np.tile(image_numpy, (3, 1, 1)) + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 + else: + image_numpy = input_image + image_numpy = image_numpy.astype(np.uint8) + image_pil = Image.fromarray(image_numpy) + image_pil.save(image_path) + +class BaseModel(): + + # modify parser to add command line options, + # and also change the default values if needed + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def name(self): + return 'BaseModel' + + def initialize(self, opt): + self.opt = opt + self.gpu_ids = opt.gpu_ids + self.isTrain = opt.isTrain + self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu') + self.load_dir = os.path.join(opt.checkpoints_dir, opt.name) + self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) + if opt.resize_or_crop != 'scale_width': + torch.backends.cudnn.benchmark = True + self.loss_names = [] + self.model_names = [] + self.visual_names = [] + self.image_paths = [] + + def set_input(self, input): + pass + + def forward(self): + pass + + # load and print networks; create schedulers + def setup(self, opt, parser=None): + if self.isTrain: + self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers] + if not self.isTrain or opt.continue_train: + load_suffix = 'iter_%d' % opt.load_iter if opt.load_iter > 0 else opt.epoch + self.load_networks(load_suffix) + self.print_networks(opt.verbose) + + + + # load specific moudles + def loadModules(self, opt, model_name, module_names): + for name in module_names: + if isinstance(name, str): + load_dir = os.path.join(opt.checkpoints_dir, model_name) + load_filename = 'latest_%s.pth' % (name) + load_path = os.path.join(load_dir, load_filename) + net = getattr(self, name) + if isinstance(net, torch.Tensor): + print('loading the tensor from %s' % load_path) + net_loaded = torch.load(load_path, map_location=str(self.device)) + net.copy_(net_loaded) + else: + # if isinstance(net, torch.nn.DataParallel): + # net = net.module + print('loading the module from %s' % load_path) + # if you are using PyTorch newer than 0.4 (e.g., built from + # GitHub source), you can remove str() on self.device + state_dict = torch.load(load_path, map_location=str(self.device)) + if hasattr(state_dict, '_metadata'): + del state_dict._metadata + + # patch InstanceNorm checkpoints prior to 0.4 + for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop + self.__patch_instance_norm_state_dict(state_dict, net, key.split('.')) + net.load_state_dict(state_dict) + + + + + # make models eval mode during test time + def eval(self): + for name in self.model_names: + if isinstance(name, str): + net = getattr(self, name) + net.eval() + + # used in test time, wrapping `forward` in no_grad() so we don't save + # intermediate steps for backprop + def test(self): + with torch.no_grad(): + self.forward() + + # get image paths + def get_image_paths(self): + return self.image_paths + + def optimize_parameters(self): + pass + + # update learning rate (called once every epoch) + def update_learning_rate(self): + for scheduler in self.schedulers: + scheduler.step() + lr = self.optimizers[0].param_groups[0]['lr'] + print('learning rate = %.7f' % lr) + + # return visualization images. train.py will display these images, and save the images to a html + def get_current_visuals(self): + visual_ret = OrderedDict() + for name in self.visual_names: + if isinstance(name, str): + visual_ret[name] = getattr(self, name) + return visual_ret + + # return traning losses/errors. train.py will print out these errors as debugging information + def get_current_losses(self): + errors_ret = OrderedDict() + for name in self.loss_names: + if isinstance(name, str): + # float(...) works for both scalar tensor and float number + errors_ret[name] = float(getattr(self, 'loss_' + name)) + return errors_ret + + # save models to the disk + def save_networks(self, epoch): + for name in self.model_names: + if isinstance(name, str): + save_filename = '%s_%s.pth' % (epoch, name) + save_path = os.path.join(self.save_dir, save_filename) + net = getattr(self, name) + + if isinstance(net, torch.Tensor): + #torch.save(net.state_dict(), save_path) + torch.save(net, save_path) + for i in range(0, list(net.size())[0]): + save_tensor_image(net[i:i+1,0:3,:,:], save_path+str(i)+'.png') + else: + if len(self.gpu_ids) > 0 and torch.cuda.is_available(): + #torch.save(net.module.cpu().state_dict(), save_path) # << original + torch.save(net.cpu().state_dict(), save_path) + net.cuda(self.gpu_ids[0]) + else: + torch.save(net.cpu().state_dict(), save_path) + + def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0): + key = keys[i] + if i + 1 == len(keys): # at the end, pointing to a parameter/buffer + if module.__class__.__name__.startswith('InstanceNorm') and \ + (key == 'running_mean' or key == 'running_var'): + if getattr(module, key) is None: + state_dict.pop('.'.join(keys)) + if module.__class__.__name__.startswith('InstanceNorm') and \ + (key == 'num_batches_tracked'): + state_dict.pop('.'.join(keys)) + else: + self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1) + + # load models from the disk + def load_networks(self, epoch): + for name in self.model_names: + if isinstance(name, str): + load_filename = '%s_%s.pth' % (epoch, name) + load_path = os.path.join(self.load_dir, load_filename) + net = getattr(self, name) + if isinstance(net, torch.Tensor): + print('loading the tensor from %s' % load_path) + net_loaded = torch.load(load_path, map_location=str(self.device)) + net.copy_(net_loaded) + else: + # if isinstance(net, torch.nn.DataParallel): + # net = net.module + print('loading the module from %s' % load_path) + # if you are using PyTorch newer than 0.4 (e.g., built from + # GitHub source), you can remove str() on self.device + state_dict = torch.load(load_path, map_location=str(self.device)) + if hasattr(state_dict, '_metadata'): + del state_dict._metadata + + # patch InstanceNorm checkpoints prior to 0.4 + for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop + self.__patch_instance_norm_state_dict(state_dict, net, key.split('.')) + net.load_state_dict(state_dict) + + # print network information + def print_networks(self, verbose): + print('---------- Networks initialized -------------') + for name in self.model_names: + if isinstance(name, str): + net = getattr(self, name) + if isinstance(net, torch.Tensor): + num_params = net.numel() + print('[Tensor %s] Total number of parameters : %.3f M' % (name, num_params / 1e6)) + else: + num_params = 0 + for param in net.parameters(): + num_params += param.numel() + if verbose: + print(net) + print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6)) + print('-----------------------------------------------') + + # set requies_grad=False to avoid computation + def set_requires_grad(self, nets, requires_grad=False): + if not isinstance(nets, list): + nets = [nets] + for net in nets: + if net is not None: + for param in net.parameters(): + param.requires_grad = requires_grad diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/models/networks.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/models/networks.py new file mode 100644 index 0000000..f5495e1 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/models/networks.py @@ -0,0 +1,383 @@ +import torch +import torch.nn as nn +from torch.nn import init +import functools +from torch.optim import lr_scheduler + +############################################################################### +# Helper Functions +############################################################################### + + +def get_norm_layer(norm_type='instance'): + if norm_type == 'batch': + norm_layer = functools.partial(nn.BatchNorm2d, affine=True) + elif norm_type == 'instance': + norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False) + elif norm_type == 'none': + norm_layer = None + else: + raise NotImplementedError('normalization layer [%s] is not found' % norm_type) + return norm_layer + + +def get_scheduler(optimizer, opt): + if opt.lr_policy == 'lambda': + def lambda_rule(epoch): + lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.niter) / float(opt.niter_decay + 1) + return lr_l + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule) + elif opt.lr_policy == 'step': + scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1) + elif opt.lr_policy == 'plateau': + scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5) + elif opt.lr_policy == 'cosine': + scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.niter, eta_min=0) + else: + return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy) + return scheduler + + +def init_weights(net, init_type='normal', gain=0.02): + def init_func(m): + classname = m.__class__.__name__ + if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): + if init_type == 'normal': + init.normal_(m.weight.data, 0.0, gain) + elif init_type == 'xavier': + init.xavier_normal_(m.weight.data, gain=gain) + elif init_type == 'kaiming': + init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') + elif init_type == 'orthogonal': + init.orthogonal_(m.weight.data, gain=gain) + else: + raise NotImplementedError('initialization method [%s] is not implemented' % init_type) + if hasattr(m, 'bias') and m.bias is not None: + init.constant_(m.bias.data, 0.0) + elif classname.find('BatchNorm2d') != -1: + init.normal_(m.weight.data, 1.0, gain) + init.constant_(m.bias.data, 0.0) + + print('initialize network with %s' % init_type) + net.apply(init_func) + + +def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]): + if len(gpu_ids) > 0: + assert(torch.cuda.is_available()) + net.to(int(gpu_ids[0])) + #net = torch.nn.DataParallel(net, gpu_ids) + init_weights(net, init_type, gain=init_gain) + return net + + +def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]): + net = None + norm_layer = get_norm_layer(norm_type=norm) + + if netG == 'resnet_9blocks': + net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9) + elif netG == 'resnet_6blocks': + net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6) + elif netG == 'unet_128': + net = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout) + elif netG == 'unet_256': + net = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout) + else: + raise NotImplementedError('Generator model name [%s] is not recognized' % netG) + return init_net(net, init_type, init_gain, gpu_ids) + + +def define_D(input_nc, ndf, netD, + n_layers_D=3, norm='batch', use_sigmoid=False, init_type='normal', init_gain=0.02, gpu_ids=[]): + net = None + norm_layer = get_norm_layer(norm_type=norm) + + if netD == 'basic': + net = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer, use_sigmoid=use_sigmoid) + elif netD == 'n_layers': + net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer, use_sigmoid=use_sigmoid) + elif netD == 'pixel': + net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer, use_sigmoid=use_sigmoid) + else: + raise NotImplementedError('Discriminator model name [%s] is not recognized' % net) + return init_net(net, init_type, init_gain, gpu_ids) + + +############################################################################## +# Classes +############################################################################## + + +# Defines the GAN loss which uses either LSGAN or the regular GAN. +# When LSGAN is used, it is basically same as MSELoss, +# but it abstracts away the need to create the target label tensor +# that has the same size as the input +class GANLoss(nn.Module): + def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0): + super(GANLoss, self).__init__() + self.register_buffer('real_label', torch.tensor(target_real_label)) + self.register_buffer('fake_label', torch.tensor(target_fake_label)) + if use_lsgan: + self.loss = nn.MSELoss() + else: + self.loss = nn.BCELoss() + + def get_target_tensor(self, input, target_is_real): + if target_is_real: + target_tensor = self.real_label + else: + target_tensor = self.fake_label + return target_tensor.expand_as(input) + + def __call__(self, input, target_is_real): + target_tensor = self.get_target_tensor(input, target_is_real) + return self.loss(input, target_tensor) + + +# Defines the generator that consists of Resnet blocks between a few +# downsampling/upsampling operations. +# Code and idea originally from Justin Johnson's architecture. +# https://github.com/jcjohnson/fast-neural-style/ +class ResnetGenerator(nn.Module): + def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'): + assert(n_blocks >= 0) + super(ResnetGenerator, self).__init__() + self.input_nc = input_nc + self.output_nc = output_nc + self.ngf = ngf + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + + model = [nn.ReflectionPad2d(3), + nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, + bias=use_bias), + norm_layer(ngf), + nn.ReLU(True)] + + n_downsampling = 2 + for i in range(n_downsampling): + mult = 2**i + model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, + stride=2, padding=1, bias=use_bias), + norm_layer(ngf * mult * 2), + nn.ReLU(True)] + + mult = 2**n_downsampling + for i in range(n_blocks): + model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)] + + for i in range(n_downsampling): + mult = 2**(n_downsampling - i) + model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), + kernel_size=3, stride=2, + padding=1, output_padding=1, + bias=use_bias), + norm_layer(int(ngf * mult / 2)), + nn.ReLU(True)] + model += [nn.ReflectionPad2d(3)] + model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] + model += [nn.Tanh()] + + self.model = nn.Sequential(*model) + + def forward(self, input): + return self.model(input) + + +# Define a resnet block +class ResnetBlock(nn.Module): + def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias): + super(ResnetBlock, self).__init__() + self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias) + + def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias): + conv_block = [] + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(1)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(1)] + elif padding_type == 'zero': + p = 1 + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + + conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), + norm_layer(dim), + nn.ReLU(True)] + if use_dropout: + conv_block += [nn.Dropout(0.5)] + + p = 0 + if padding_type == 'reflect': + conv_block += [nn.ReflectionPad2d(1)] + elif padding_type == 'replicate': + conv_block += [nn.ReplicationPad2d(1)] + elif padding_type == 'zero': + p = 1 + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), + norm_layer(dim)] + + return nn.Sequential(*conv_block) + + def forward(self, x): + out = x + self.conv_block(x) + return out + + +# Defines the Unet generator. +# |num_downs|: number of downsamplings in UNet. For example, +# if |num_downs| == 7, image of size 128x128 will become of size 1x1 +# at the bottleneck +class UnetGenerator(nn.Module): + def __init__(self, input_nc, output_nc, num_downs, ngf=64, + norm_layer=nn.BatchNorm2d, use_dropout=False): + super(UnetGenerator, self).__init__() + + # construct unet structure + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) + for i in range(num_downs - 5): + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) + unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) + + self.model = unet_block + + def forward(self, input): + return self.model(input) + + +# Defines the submodule with skip connection. +# X -------------------identity---------------------- X +# |-- downsampling -- |submodule| -- upsampling --| +class UnetSkipConnectionBlock(nn.Module): + def __init__(self, outer_nc, inner_nc, input_nc=None, + submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(UnetSkipConnectionBlock, self).__init__() + self.outermost = outermost + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + if input_nc is None: + input_nc = outer_nc + downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, + stride=2, padding=1, bias=use_bias) + downrelu = nn.LeakyReLU(0.2, True) + downnorm = norm_layer(inner_nc) + uprelu = nn.ReLU(True) + upnorm = norm_layer(outer_nc) + + if outermost: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1) + down = [downconv] + up = [uprelu, upconv, nn.Tanh()] + model = down + [submodule] + up + elif innermost: + upconv = nn.ConvTranspose2d(inner_nc, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv] + up = [uprelu, upconv, upnorm] + model = down + up + else: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv, downnorm] + up = [uprelu, upconv, upnorm] + + if use_dropout: + model = down + [submodule] + up + [nn.Dropout(0.5)] + else: + model = down + [submodule] + up + + self.model = nn.Sequential(*model) + + def forward(self, x): + if self.outermost: + return self.model(x) + else: + return torch.cat([x, self.model(x)], 1) + + +# Defines the PatchGAN discriminator with the specified arguments. +class NLayerDiscriminator(nn.Module): + def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False): + super(NLayerDiscriminator, self).__init__() + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + + kw = 4 + padw = 1 + sequence = [ + nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), + nn.LeakyReLU(0.2, True) + ] + + nf_mult = 1 + nf_mult_prev = 1 + for n in range(1, n_layers): + nf_mult_prev = nf_mult + nf_mult = min(2**n, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, + kernel_size=kw, stride=2, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + nf_mult_prev = nf_mult + nf_mult = min(2**n_layers, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, + kernel_size=kw, stride=1, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] + + if use_sigmoid: + sequence += [nn.Sigmoid()] + + self.model = nn.Sequential(*sequence) + + def forward(self, input): + return self.model(input) + + +class PixelDiscriminator(nn.Module): + def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d, use_sigmoid=False): + super(PixelDiscriminator, self).__init__() + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + + self.net = [ + nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0), + nn.LeakyReLU(0.2, True), + nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias), + norm_layer(ndf * 2), + nn.LeakyReLU(0.2, True), + nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)] + + if use_sigmoid: + self.net.append(nn.Sigmoid()) + + self.net = nn.Sequential(*self.net) + + def forward(self, input): + return self.net(input) diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/options/__init__.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/options/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/options/base_options.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/options/base_options.py new file mode 100644 index 0000000..94801a6 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/options/base_options.py @@ -0,0 +1,138 @@ +import argparse +import os +from autil import util +import torch +import models +import data + + +class BaseOptions(): + def __init__(self): + self.initialized = False + + def initialize(self, parser): + parser.add_argument('--dataroot', required=True, help='path to images (should have subfolders trainA, trainB, valA, valB, etc)') + parser.add_argument('--batch_size', type=int, default=1, help='input batch size') + parser.add_argument('--seq_len', type=int, default=1, help='sequence length (if applicable)') + parser.add_argument('--fineSize', type=int, default=512, help='then crop to this size') + parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML') + parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels') + parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels') + parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer') + parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer') + parser.add_argument('--netD', type=str, default='basic', help='selects model to use for netD') + parser.add_argument('--netG', type=str, default='resnet_9blocks', help='selects model to use for netG') + parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers') + parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU') + parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models') + parser.add_argument('--renderer', type=str, default='no_renderer', help='name of the renderer to load the models from') + parser.add_argument('--fix_renderer', action='store_true', help='renderer is fixed') + parser.add_argument('--dataset_mode', type=str, default='aligned', help='chooses how datasets are loaded. [aligned | multi]') + parser.add_argument('--model', type=str, default='cycle_gan', help='chooses which model to use. cycle_gan, pix2pix, test') + parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA') + parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') + parser.add_argument('--load_iter', type=int, default='0', help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]') + parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data') + parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here') + parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization') + parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly') + parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator') + parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per datasets. If the datasets directory contains more than max_dataset_size, only a subset is loaded.') + parser.add_argument('--resize_or_crop', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop|none]') + parser.add_argument('--no_augmentation', action='store_true', help='if specified, no data augmentation') + #parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal|xavier|kaiming|orthogonal]') + parser.add_argument('--init_type', type=str, default='xavier', help='network initialization [normal|xavier|kaiming|orthogonal]') + parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.') + parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information') + parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{loadSize}') + parser.add_argument('--tex_dim', type=int, default=256, help='neural texture dimensions') + parser.add_argument('--tex_features_intermediate', type=int, default=16, help='# intermediate neural texture features when using dynamic textures') + parser.add_argument('--tex_features', type=int, default=16, help='# neural texture features') + parser.add_argument('--textureModel', type=str, default='DynamicNeuralTextureAudio', help='texture model') + parser.add_argument('--rendererType', type=str, default='UNET_5_level', help='neural renderer network') + parser.add_argument('--lossType', type=str, default='L1', help='loss type for the final output') + + parser.add_argument('--hierarchicalTex', action='store_true', help='if specified, hierachical neural textures are used') + + parser.add_argument('--output_audio_expressions', action='store_true', help='if specified, no sh layers are used') + + parser.add_argument('--erosionFactor', type=float, default=1.0, help='scaling factor for erosion of the background.') + + parser.add_argument('--audio_window_size', type=float, default=16, help='audio window size = #mel feature bins') + + parser.add_argument('--look_ahead', action='store_true', help='cache images in numpy format') + + parser.add_argument('--cached_images', action='store_true', help='cache images in numpy format') + + self.initialized = True + return parser + + def gather_options(self): + # initialize parser with basic options + if not self.initialized: + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser = self.initialize(parser) + + # get the basic options + opt, _ = parser.parse_known_args() + + # modify model-related parser options + model_name = opt.model + model_option_setter = models.get_option_setter(model_name) + parser = model_option_setter(parser, self.isTrain) + opt, _ = parser.parse_known_args() # parse again with the new defaults + + # modify datasets-related parser options + dataset_name = opt.dataset_mode + dataset_option_setter = data.get_option_setter(dataset_name) + parser = dataset_option_setter(parser, self.isTrain) + + self.parser = parser + + return parser.parse_args() + + def print_options(self, opt): + message = '' + message += '----------------- Options ---------------\n' + for k, v in sorted(vars(opt).items()): + comment = '' + default = self.parser.get_default(k) + if v != default: + comment = '\t[default: %s]' % str(default) + message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment) + message += '----------------- End -------------------' + print(message) + + # save to the disk + expr_dir = os.path.join(opt.checkpoints_dir, opt.name) + util.mkdirs(expr_dir) + file_name = os.path.join(expr_dir, 'opt.txt') + with open(file_name, 'wt') as opt_file: + opt_file.write(message) + opt_file.write('\n') + + def parse(self): + + opt = self.gather_options() + opt.isTrain = self.isTrain # train or test + + # process opt.suffix + if opt.suffix: + suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else '' + opt.name = opt.name + suffix + + self.print_options(opt) + + # set gpu ids + str_ids = opt.gpu_ids.split(',') + opt.gpu_ids = [] + for str_id in str_ids: + id = int(str_id) + if id >= 0: + opt.gpu_ids.append(id) + if len(opt.gpu_ids) > 0: + torch.cuda.set_device(opt.gpu_ids[0]) + + self.opt = opt + return self.opt diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/options/test_options.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/options/test_options.py new file mode 100644 index 0000000..d21da35 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/options/test_options.py @@ -0,0 +1,27 @@ +from .base_options import BaseOptions + + +class TestOptions(BaseOptions): + def initialize(self, parser): + parser = BaseOptions.initialize(self, parser) + parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.') + parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.') + parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images') + parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') + # Dropout and Batchnorm has different behavioir during training and test. + parser.add_argument('--eval', action='store_true', help='use eval mode during test time.') + parser.add_argument('--num_test', type=int, default=50, help='how many test images to run') + + parser.add_argument('--write_no_images', action='store_true', help='compute validation') + + parser.add_argument('--write_video', action='store_true', help='write video') + parser.add_argument('--video_fps', type=float, default=25.0, help='video fps') + + + parser.add_argument('--source_dir', type=str, default='./datasets/', help='loads source files (expressions, audio, uvs).') + + parser.set_defaults(model='test') + # To avoid cropping, the loadSize should be the same as fineSize + parser.set_defaults(loadSize=parser.get_default('fineSize')) + self.isTrain = False + return parser diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/options/train_options.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/options/train_options.py new file mode 100644 index 0000000..c91d50d --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/options/train_options.py @@ -0,0 +1,37 @@ +from .base_options import BaseOptions + + +class TrainOptions(BaseOptions): + def initialize(self, parser): + parser = BaseOptions.initialize(self, parser) + parser.add_argument('--display_freq', type=int, default=400, help='frequency of showing training results on screen') + parser.add_argument('--display_ncols', type=int, default=4, help='if positive, display all images in a single visdom web panel with certain number of images per row.') + parser.add_argument('--display_id', type=int, default=1, help='window id of the web display') + parser.add_argument('--display_server', type=str, default="http://localhost", help='visdom server of the web display') + parser.add_argument('--display_env', type=str, default='main', help='visdom display environment name (default is "main")') + parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display') + + parser.add_argument('--compute_val', action='store_true', help='compute validation') + parser.add_argument('--input_noise_augmentation', action='store_true', help='add input noise') + + parser.add_argument('--update_html_freq', type=int, default=1000, help='frequency of saving training results to html') + parser.add_argument('--print_freq', type=int, default=500, help='frequency of showing training results on console') + parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results') + parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs') + parser.add_argument('--save_by_iter', action='store_true', help='whether saves model by iteration') + + parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model') + parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by , +, ...') + parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') + parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate') + parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero') + parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam') + parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam') + parser.add_argument('--no_lsgan', action='store_true', help='do *not* use least square GAN, if false, use vanilla GAN') + parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images') + parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/') + parser.add_argument('--lr_policy', type=str, default='lambda', help='learning rate policy: lambda|step|plateau|cosine') + parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations') + + self.isTrain = True + return parser diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/options/transfer_options.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/options/transfer_options.py new file mode 100644 index 0000000..13b78a0 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/options/transfer_options.py @@ -0,0 +1,42 @@ +from .base_options import BaseOptions + + +class TransferOptions(BaseOptions): + def initialize(self, parser): + parser = BaseOptions.initialize(self, parser) + parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.') + parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.') + parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images') + parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') + # Dropout and Batchnorm has different behavioir during training and test. + parser.add_argument('--eval', action='store_true', help='use eval mode during test time.') + parser.add_argument('--num_test', type=int, default=50, help='how many test images to run') + + parser.add_argument('--write_no_images', action='store_true', help='compute validation') + + + parser.add_argument('--source_dir', type=str, default='./datasets/', help='loads source files (expressions, audio, uvs).') + + + + parser.add_argument('--source_actor', type=str, default='', help='source actor directory') + parser.add_argument('--target_actor', type=str, default='', help='target actor directory') + + + parser.set_defaults(model='test') + # To avoid cropping, the loadSize should be the same as fineSize + parser.set_defaults(loadSize=parser.get_default('fineSize')) + self.isTrain = False + return parser + +class Audio2ExprOptions(TransferOptions): + def initialize(self, parser): + parser = TransferOptions.initialize(self, parser) + parser.add_argument('--use_mapping', action='store_true', help='use mapping function.') + parser.add_argument('--mapping_path', type=str, default='', help='path to mapping function.') + parser.add_argument('--out_dir', type=str, default='', help='path to output directory.') + parser.set_defaults(model='test') + # To avoid cropping, the loadSize should be the same as fineSize + parser.set_defaults(loadSize=parser.get_default('fineSize')) + self.isTrain = False + return parser \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/transfer.py b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/transfer.py new file mode 100644 index 0000000..99c3711 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/transfer.py @@ -0,0 +1,257 @@ +import os +import os.path +from options.transfer_options import TransferOptions +from data import CreateDataLoader +from data.face_dataset import FaceDataset +from data.audio_dataset import AudioDataset +from models import create_model +from util.visualizer import save_images +from util import html +import torch +import torch.nn as nn +import torchvision.transforms as transforms +import numpy as np +from PIL import Image +import time +import random +import progressbar +import copy +from shutil import copyfile + +from BaselModel.basel_model import * + + +def load_model(opt): + opt.output_audio_expressions = True + opt.nTrainObjects = 116 + + print('#train objects = %d' % opt.nTrainObjects) + + print('>>> create model <<<') + model = create_model(opt) + print('>>> setup model <<<') + model.setup(opt) + + return model + +def load_target_sequence(opt): + opt_target = copy.copy(opt) # create a clone + opt_target.dataroot = opt.target_actor # overwrite root directory + opt_target.dataset_mode = 'face' + opt_target.phase = 'train' + dataset_target = FaceDataset() + dataset_target.initialize(opt_target) + + dataloader = torch.utils.data.DataLoader( + dataset_target, + batch_size=opt.batch_size, + shuffle=not opt.serial_batches, + num_workers=int(opt.num_threads)) + + return dataset_target, dataloader + + +def load_source_sequence(opt): + opt_source = copy.copy(opt) # create a clone + opt_source.dataroot = opt.source_actor # overwrite root directory + print(opt_source.dataroot) + opt_source.dataset_mode = 'audio' + opt_source.phase = 'train' + + dataset_source = AudioDataset() + + dataset_source.initialize(opt_source) + + dataloader = torch.utils.data.DataLoader( + dataset_source, + batch_size=opt.batch_size, + shuffle=not opt.serial_batches, + num_workers=int(opt.num_threads)) + + return dataset_source, dataloader + + +if __name__ == '__main__': + # read options + opt = TransferOptions().parse() + + target_name = opt.target_actor.split("/")[-1] + + # hard-code some parameters for test + opt.num_threads = 1 # test code only supports num_threads = 1 + opt.batch_size = 1 # test code only supports batch_size = 1 + opt.serial_batches = True # no shuffle + opt.no_augmentation = True # no flip + opt.display_id = -1 # no visdom display + + + # load model + model = load_model(opt) + print('model version:', opt.name) + + + # # load face model + # morphable_model = MorphableModel() + # mask = morphable_model.LoadMask() + # mask = mask + 0.1 * torch.ones_like(mask) + + + # read target sequence + dataset_target, data_loader_target = load_target_sequence(opt) + dataset_target_size = len(dataset_target) + print('#target_actor frames = %d' % dataset_target_size) + + ################################## + ####### create mapping ####### + ################################## + base_path = '../..' #'/home/alberto/NeuralVoicePuppetry' + dataset_path = '../../datasets' + #dataset_path = '/home/alberto/NeuralVoicePuppetry/datasets' + os.makedirs(base_path+'/mappings/'+opt.name, exist_ok=True) + mapping_fn = base_path+'/mappings/'+opt.name+'/'+'mapping_'+target_name + print(mapping_fn) + #not_exists = True + not_exists = not os.path.exists(mapping_fn+'.npy') + if not_exists: + # collect data + print('collect data') + audio_expressions = None + gt_expressions = None + with progressbar.ProgressBar(max_value=len(dataset_target)) as bar: + for i, data in enumerate(data_loader_target): + bar.update(i) + model.set_input(data) + model.test() + + ae = model.fake_expressions.data[:,:,0] + if type(audio_expressions) == type(None): + audio_expressions = ae + e = model.expressions.data + gt_expressions = e + else: + audio_expressions = torch.cat([audio_expressions,ae],dim=0) + e = model.expressions.data + gt_expressions = torch.cat([gt_expressions,e],dim=0) + + # solve for mapping + print('solve for mapping') + optimize_in_parameter_space = True #False + if optimize_in_parameter_space: +# A = audio_expressions +# B = gt_expressions +# # solve lstsq ||AX - B|| +# X, _ = torch.gels(B, A, out=None) +# #X, _ = torch.lstsq(B, A) # requires pytorch 1.2 +# X = X[0:A.shape[1],:] +# mapping = X.t() + + # use gradient descent method + n = audio_expressions.shape[0] + print(n) + subspace_dim = 32 + + # TODO: patch + n_expr = 53 + + X = torch.nn.Parameter(torch.randn(n_expr, subspace_dim, requires_grad=True).cuda()) + optimizer = torch.optim.Adam([X], lr=0.01) + lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1) + num_epochs = 90 + random_range = [k for k in range(0,n)] + with progressbar.ProgressBar(max_value=num_epochs) as bar: + for ep in range(0,num_epochs): + bar.update(ep) + random.shuffle(random_range) + for j in random_range: + expressions = gt_expressions[j] + fake_expressions = 10.0 * torch.matmul(X, audio_expressions[j]) + diff_expression = fake_expressions - expressions + loss = torch.mean(diff_expression * diff_expression) # L2 + optimizer.zero_grad() + loss.backward() + optimizer.step() + lr_scheduler.step() + mapping = X.data + +# else: # optimize in vertex space +# # use gradient descent method +# n = audio_expressions.shape[0] +# subspace_dim = 32 +# X = torch.nn.Parameter(torch.randn(N_EXPRESSIONS, subspace_dim, requires_grad=True).cuda()) +# optimizer = torch.optim.Adam([X], lr=0.01) +# lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1) +# num_epochs = 90 +# random_range = [k for k in range(0,n)] +# with progressbar.ProgressBar(max_value=num_epochs) as bar: +# for ep in range(0,num_epochs): +# bar.update(ep) +# random.shuffle(random_range) +# for j in random_range: +# expressions = gt_expressions[j] +# fake_expressions = 10.0 * torch.matmul(X, audio_expressions[j]) +# diff_expression = fake_expressions - expressions +# diff_vertices = torch.matmul(morphable_model.expression_basis, diff_expression) +# #loss = torch.sqrt(torch.mean(mask * diff_vertices * diff_vertices)) # RMS +# loss = torch.mean(mask * diff_vertices * diff_vertices) # L2 +# # +# optimizer.zero_grad() +# loss.backward() +# optimizer.step() +# lr_scheduler.step() +# +# mapping = X.data + + map_cpu = mapping.data.cpu().numpy() + print(map_cpu.shape) + file_out=open(mapping_fn+'.txt', 'w') + np.savetxt(file_out, map_cpu, delimiter=' ') + file_out.close() + np.save(mapping_fn+'.npy', map_cpu) + else: + # load mapping from file + map_cpu = np.load(mapping_fn+'.npy') + mapping = torch.tensor(map_cpu.astype(np.float32)).cuda() + print('loaded mapping from file', mapping.shape) + + # ############# + # map_cpu = np.load(base_path + '/mappings/' + opt.name + '/' + 'mapping_Close_355_9105.npy') + # mapping = torch.tensor(map_cpu.astype(np.float32)).cuda() + # print('loaded mapping from file', mapping.shape) + # ############# + + # process source sequence (opt.source_actor) + source_actors = [opt.source_actor] + + os.makedirs(dataset_path+'/TRANSFERS/'+opt.name, exist_ok=True) + list_transfer = open(dataset_path+'/TRANSFERS/'+opt.name+'/list_transfer.txt', "a") + + target_actor_offset = 0 # default + expression_multiplier = 1.0 # default + + if target_actor_offset != 0.0: + target_name = target_name + '--offset' + if expression_multiplier != 1.0: + target_name = target_name + '-X' + + for source_actor in source_actors: + opt.source_actor = source_actor + source_name = opt.source_actor.split("/")[-1] + # read source sequence + dataset_source, data_loader_source = load_source_sequence(opt) + dataset_source_size = len(dataset_source) + print('#source_actor frames = %d' % dataset_source_size) + list_transfer.write(source_name+'--'+target_name+'\n') + out_dir = dataset_path+'/TRANSFERS/'+opt.name+'/'+source_name+'--'+target_name+'/expressions' + os.makedirs(out_dir, exist_ok=True) + with progressbar.ProgressBar(max_value=len(dataset_source)) as bar: + for i, data in enumerate(data_loader_source): + bar.update(i) + model.set_input(data) + model.test() + audio_expression = model.fake_expressions.data[0,:,0] + expression = expression_multiplier * 10.0 * torch.matmul(mapping, audio_expression) + expression = expression[None,:] + np.save(os.path.join(out_dir, f'expr_{i}.npy'), expression.cpu().numpy()) + + list_transfer.close() + exit() diff --git a/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/transfer.sh b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/transfer.sh new file mode 100644 index 0000000..2726e84 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/Audio2ExpressionNet/transfer.sh @@ -0,0 +1,57 @@ +set -ex +# . transfer.sh & +GPUID=0 + +###################################################### +################## SPECIFY MODEL ################## +###################################################### + +## audio2ExpressionsAttentionTMP4-estimatorAttention-SL8-BS16-ARD_ZDF-multi_face_audio_eq_tmp_cached-RMS-20191105-115332-look_ahead # <<<<<<<<<<<<< +DATASETS_DIR=/home/alberto/NeuralVoicePuppetry/datasets +OBJECT=ARD_ZDF +LR=0.00001 +N_ITER=150 +N_ITER_LR_DECAY=50 +RENDERER=$OBJECT +EROSION=1.0 +BATCH_SIZE=16 +MODEL=audio2ExpressionsAttentionTMP4 +RENDERER_TYPE=estimatorAttention +DATASET_MODE=multi_face_audio_eq_tmp_cached +LOSS=RMS +SEQ_LEN=8 +DATE_WITH_TIME=20191105-115332 +NAME=$MODEL-$RENDERER_TYPE-SL$SEQ_LEN-BS$BATCH_SIZE-$OBJECT-$DATASET_MODE-$LOSS-$DATE_WITH_TIME-look_ahead + +# --look_ahead +EPOCH=latest + +############################################################### +###################### SPECIFY TARGET ###################### +############################################################### + +# target actors +#SOURCE_ACTOR_LIST=(/home/alberto/NeuralVoicePuppetry/datasets/External/Alberto_videos_uno /home/alberto/NeuralVoicePuppetry/datasets/External/Alberto_videos_due /home/alberto/NeuralVoicePuppetry/datasets/External/Alberto_videos_tre) +SOURCE_ACTOR_LIST=(/home/alberto/NeuralVoicePuppetry/datasets/External/Clara_audios_one /home/alberto/NeuralVoicePuppetry/datasets/External/Clara_audios_two /home/alberto/NeuralVoicePuppetry/datasets/External/Clara_audios_three) + +#TARGET_ACTOR_LIST[1]=/home/alberto/NeuralVoicePuppetry/datasets/External/Youtube_Russian_guy +TARGET_ACTOR_LIST[1]=/home/alberto/NeuralVoicePuppetry/datasets/SRF_anchor_short/Halbtotale_355_9415 + + +rm -f ./datasets/TRANSFERS/$NAME/list_transfer.txt +for TARGET_ACTOR in "${TARGET_ACTOR_LIST[@]}" +do + echo $TARGET_ACTOR + + for SOURCE_ACTOR in "${SOURCE_ACTOR_LIST[@]}" + do + echo $SOURCE_ACTOR + # --look_ahead + python transfer.py --look_ahead --seq_len $SEQ_LEN --source_actor $SOURCE_ACTOR --target_actor $TARGET_ACTOR --write_no_images --name $NAME --erosionFactor $EROSION --epoch $EPOCH --display_winsize 512 --rendererType $RENDERER_TYPE --lossType $LOSS --dataroot $DATASETS_DIR/$OBJECT --model $MODEL --netG unet_256 --dataset_mode $DATASET_MODE --norm instance --gpu_ids $GPUID + done +done + + +############################################################### +############################################################### +############################################################### \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/AutoregressivePredictiveCoding/bs32-rhl3-rhs512-rd0-adam-res-ts3.pt b/motion-gan-pipeline/preprocessing/third/AutoregressivePredictiveCoding/bs32-rhl3-rhs512-rd0-adam-res-ts3.pt new file mode 100644 index 0000000..244d060 Binary files /dev/null and b/motion-gan-pipeline/preprocessing/third/AutoregressivePredictiveCoding/bs32-rhl3-rhs512-rd0-adam-res-ts3.pt differ diff --git a/motion-gan-pipeline/preprocessing/third/DECA/Doc/images/DECA_evaluation_github.png b/motion-gan-pipeline/preprocessing/third/DECA/Doc/images/DECA_evaluation_github.png new file mode 100644 index 0000000..a690d88 Binary files /dev/null and b/motion-gan-pipeline/preprocessing/third/DECA/Doc/images/DECA_evaluation_github.png differ diff --git a/motion-gan-pipeline/preprocessing/third/DECA/Doc/images/DECA_performance.png b/motion-gan-pipeline/preprocessing/third/DECA/Doc/images/DECA_performance.png new file mode 100755 index 0000000..1d483c6 Binary files /dev/null and b/motion-gan-pipeline/preprocessing/third/DECA/Doc/images/DECA_performance.png differ diff --git a/motion-gan-pipeline/preprocessing/third/DECA/Doc/images/soubhik.gif b/motion-gan-pipeline/preprocessing/third/DECA/Doc/images/soubhik.gif new file mode 100644 index 0000000..9cee13f Binary files /dev/null and b/motion-gan-pipeline/preprocessing/third/DECA/Doc/images/soubhik.gif differ diff --git a/motion-gan-pipeline/preprocessing/third/DECA/README.md b/motion-gan-pipeline/preprocessing/third/DECA/README.md new file mode 100755 index 0000000..359dce6 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/README.md @@ -0,0 +1,125 @@ +# DECA: Detailed Expression Capture and Animation + +

+ +

+

input image, aligned reconstruction, animation with various poses & expressions

+ +This is the official Pytorch implementation of DECA. + +DECA reconstructs a 3D head model with detailed facial geometry from a single input image. The resulting 3D head model can be easily animated. Please refer to the [arXiv paper](https://arxiv.org/abs/2012.04012) for more details. + +The main features: + +* **Reconstruction:** produces head pose, shape, detailed face geometry, and lighting information from a single image. +* **Animation:** animate the face with realistic wrinkle deformations. +* **Robustness:** tested on facial images in unconstrained conditions. Our method is robust to various poses, illuminations and occlusions. +* **Accurate:** state-of-the-art 3D face shape reconstruction on the [NoW Challenge](https://ringnet.is.tue.mpg.de/challenge) benchmark dataset. + +## Getting Started +Clone the repo: + ```bash + git clone https://github.com/YadiraF/DECA + cd DECA + ``` + +### Requirements +* Python 3.7 (numpy, skimage, scipy, opencv) +* PyTorch >= 1.6 (pytorch3d) +* face-alignment (Optional for detecting face) + You can run + ```bash + pip install -r requirements.txt + ``` + Or use virtual environment by runing + ```bash + bash install_pip.sh + ``` + Then follow the instruction to install [pytorch3d](https://github.com/facebookresearch/pytorch3d/blob/master/INSTALL.md). + +### Usage +1. Prepare data + a. download [FLAME model](https://flame.is.tue.mpg.de/downloads), choose **FLAME 2020** and unzip it, copy 'generic_model.pkl' into ./data + b. download [DECA trained model](https://drive.google.com/file/d/1rp8kdyLPvErw2dTmqtjISRVvQLj6Yzje/view?usp=sharing), and put it in ./data (**no unzip required**) + c. (Optional) follow the instructions for the [Albedo model](https://github.com/TimoBolkart/BFM_to_FLAME) to get 'FLAME_albedo_from_BFM.npz', put it into ./data + +2. Run demos + a. **reconstruction** + ```bash + python demos/demo_reconstruct.py -i TestSamples/examples --saveDepth True --saveObj True + ``` + to visualize the predicted 2D landmanks, 3D landmarks (red means non-visible points), coarse geometry, detailed geometry, and depth. +

+ +

+

+ +

+ You can also generate an obj file (which can be opened with Meshlab) that includes extracted texture from the input image. + + Please run `python demos/demo_reconstruct.py --help` for more details. + + b. **expression transfer** + ```bash + python demos/demo_transfer.py + ``` + Given an image, you can reconstruct its 3D face, then animate it by tranfering expressions from other images. + Using Meshlab to open the detailed mesh obj file, you can see something like that: +

+ +

+ (Thank Soubhik for allowing me to use his face ^_^) + + Note that, you need to set '--useTex True' to get full texture. + + c. for the [teaser gif](https://github.com/YadiraF/DECA/results/teaser.gif) (**reposing** and **animation**) + ```bash + python demos/demo_teaser.py + ``` + + More demos and training code coming soon. + +## Evaluation +DECA (ours) achieves 9% lower mean shape reconstruction error on the [NoW Challenge](https://ringnet.is.tue.mpg.de/challenge) dataset compared to the previous state-of-the-art method. +The left figure compares the cumulative error of our approach and other recent methods (RingNet and Deng et al. have nearly identitical performance, so their curves overlap each other). Here we use point-to-surface distance as the error metric, following the NoW Challenge. +

+ +

+ +For more details of the evaluation, please check our [arXiv paper](https://arxiv.org/abs/2012.04012). + +## Citation +If you find our work useful to your research, please consider citing: +``` +@inproceedings{deca2020, + title={Learning an Animatable Detailed {3D} Face Model from In-The-Wild Images}, + author={Feng, Yao and Feng, Haiwen and Black, Michael J. and Bolkart, Timo}, + booktitle = {arxiv}, + month = {Dec}, + year = {2020} +} +``` + + + +## License +This code and model are available for non-commercial scientific research purposes as defined in the [LICENSE](https://github.com/YadiraF/DECA/blob/master/LICENSE) file. +By downloading and using the code and model you agree to the terms in the [LICENSE](https://github.com/YadiraF/DECA/blob/master/LICENSE). + +## Acknowledgements +For functions or scripts that are based on external sources, we acknowledge the origin individually in each file. +Here are some great resources we benefit: +- [FLAME_PyTorch](https://github.com/soubhiksanyal/FLAME_PyTorch) and [TF_FLAME](https://github.com/TimoBolkart/TF_FLAME) for the FLAME model +- [Pytorch3D](https://pytorch3d.org/), [neural_renderer](https://github.com/daniilidis-group/neural_renderer), [SoftRas](https://github.com/ShichenLiu/SoftRas) for rendering +- [kornia](https://github.com/kornia/kornia) for image/rotation processing +- [face-alignment](https://github.com/1adrianb/face-alignment) for cropping + +We would also like to thank other recent public 3D face reconstruction works that allow us to easily perform quantitative and qualitative comparisons :) +[RingNet](https://github.com/soubhiksanyal/RingNet), +[Deep3DFaceReconstruction](https://github.com/microsoft/Deep3DFaceReconstruction/blob/master/renderer/rasterize_triangles.py), +[Nonlinear_Face_3DMM](https://github.com/tranluan/Nonlinear_Face_3DMM), +[3DDFA-v2](https://github.com/cleardusk/3DDFA_V2), +[extreme_3d_faces](https://github.com/anhttran/extreme_3d_faces), +[facescape](https://github.com/zhuhao-nju/facescape) + diff --git a/motion-gan-pipeline/preprocessing/third/DECA/data/FLAME_albedo_from_BFM.npz b/motion-gan-pipeline/preprocessing/third/DECA/data/FLAME_albedo_from_BFM.npz new file mode 100644 index 0000000..72066bf --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/data/FLAME_albedo_from_BFM.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:07cefceafab36150b458f138dd9fc001488454be889fa78ce08df5de7f5753b4 +size 1258291694 diff --git a/motion-gan-pipeline/preprocessing/third/DECA/data/deca_model.tar b/motion-gan-pipeline/preprocessing/third/DECA/data/deca_model.tar new file mode 100644 index 0000000..c04160f --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/data/deca_model.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e714ed293054cba5eea9c96bd3b6b57880074cd84b3fd00d606cbaf0bee7c5c2 +size 434142943 diff --git 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differ diff --git a/motion-gan-pipeline/preprocessing/third/DECA/data/uv_face_mask.png b/motion-gan-pipeline/preprocessing/third/DECA/data/uv_face_mask.png new file mode 100755 index 0000000..7baaceb Binary files /dev/null and b/motion-gan-pipeline/preprocessing/third/DECA/data/uv_face_mask.png differ diff --git a/motion-gan-pipeline/preprocessing/third/DECA/decalib/__init__.py b/motion-gan-pipeline/preprocessing/third/DECA/decalib/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/motion-gan-pipeline/preprocessing/third/DECA/decalib/datasets/datasets.py b/motion-gan-pipeline/preprocessing/third/DECA/decalib/datasets/datasets.py new file mode 100755 index 0000000..c949524 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/decalib/datasets/datasets.py @@ -0,0 +1,169 @@ +#-*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import os, sys +import torch +from torch.utils.data import Dataset, DataLoader +import torchvision.transforms as transforms +import numpy as np +import cv2 +import scipy +from skimage.io import imread, imsave +from skimage.transform import estimate_transform, warp, resize, rescale +from glob import glob +import scipy.io +import matplotlib.pyplot as plt +from subprocess import call + +from . import detectors + + +def check_mkdir(path): + if not os.path.exists(path): + print('creating %s' % path) + os.makedirs(path) + +def video2sequence(video_path): + videofolder = video_path.split('.')[0] + check_mkdir(videofolder) + video_name = video_path.split('/')[-1].split('.')[0] + vidcap = cv2.VideoCapture(video_path) + success,image = vidcap.read() + count = 0 + imagepath_list = [] + while success: + imagepath = '{}/{}_frame{:04d}.png'.format(videofolder, video_name, count) + cv2.imwrite(imagepath, image) # save frame as PNG file + success,image = vidcap.read() + count += 1 + imagepath_list.append(imagepath) + print('video frames are stored in {}'.format(videofolder)) + return imagepath_list + +def video2sequence_lossless(video_path): + videofolder = video_path.split('.')[0] + check_mkdir(videofolder) + video_name = video_path.split('/')[-1].split('.')[0] + cmd = (f'ffmpeg -i {video_path} -vf fps=25 {videofolder}/{video_name}_frame%04d.png').split() + call(cmd) + imagepath_list = [os.path.join(videofolder, f) for f in os.listdir(videofolder)] + print('video frames are stored in {}'.format(videofolder)) + return imagepath_list + +class TestData(Dataset): + def __init__(self, testpath, iscrop=True, crop_size=224, scale=1.25, face_detector='mtcnn'): + ''' + testpath: folder, imagepath_list, image path, video path + ''' + # print('testpath: ', testpath) + if isinstance(testpath, list): + self.imagepath_list = testpath + elif os.path.isdir(testpath): + self.imagepath_list = glob(testpath + '/*.jpg') + glob(testpath + '/*.png') + glob(testpath + '/*.bmp') + elif os.path.isfile(testpath) and (testpath[-3:] in ['jpg', 'png', 'bmp']): + self.imagepath_list = [testpath] + elif os.path.isfile(testpath) and (testpath[-3:] in ['mp4', 'csv', 'vid', 'ebm']): + self.imagepath_list = video2sequence_lossless(testpath) + else: + print(f'please check the test path: {testpath}') + exit() + print('total {} images'.format(len(self.imagepath_list))) + self.imagepath_list = sorted(self.imagepath_list) + self.crop_size = crop_size + self.scale = scale + self.iscrop = iscrop + self.resolution_inp = crop_size + if face_detector == 'fan': + self.face_detector = detectors.FAN() + # elif face_detector == 'mtcnn': + # self.face_detector = detectors.MTCNN() + else: + print(f'please check the detector: {face_detector}') + exit() + + def __len__(self): + return len(self.imagepath_list) + + def bbox2point(self, left, right, top, bottom, type='bbox'): + ''' bbox from detector and landmarks are different + ''' + if type=='kpt68': + old_size = (right - left + bottom - top)/2*1.1 + center = np.array([right - (right - left) / 2.0, bottom - (bottom - top) / 2.0 ]) + elif type=='bbox': + old_size = (right - left + bottom - top)/2 + center = np.array([right - (right - left) / 2.0, bottom - (bottom - top) / 2.0 + old_size*0.12]) + else: + raise NotImplementedError + return old_size, center + + def __getitem__(self, index): + imagepath = self.imagepath_list[index] + imagename = imagepath.split('/')[-1].split('.')[0] + + image = np.array(imread(imagepath)) + if len(image.shape) == 2: + image = image[:,:,None].repeat(1,1,3) + if len(image.shape) == 3 and image.shape[2] > 3: + image = image[:,:,:3] + + h, w, _ = image.shape + if self.iscrop: + # provide kpt as txt file, or mat file (for AFLW2000) + kpt_matpath = imagepath.replace('.jpg', '.mat').replace('.png', '.mat') + kpt_txtpath = imagepath.replace('.jpg', '.txt').replace('.png', '.txt') + if os.path.exists(kpt_matpath): + kpt = scipy.io.loadmat(kpt_matpath)['pt3d_68'].T + left = np.min(kpt[:,0]); right = np.max(kpt[:,0]); + top = np.min(kpt[:,1]); bottom = np.max(kpt[:,1]) + old_size, center = self.bbox2point(left, right, top, bottom, type='kpt68') + elif os.path.exists(kpt_txtpath): + kpt = np.loadtxt(kptpath) + left = np.min(kpt[:,0]); right = np.max(kpt[:,0]); + top = np.min(kpt[:,1]); bottom = np.max(kpt[:,1]) + old_size, center = self.bbox2point(left, right, top, bottom, type='kpt68') + else: + try: + bbox, bbox_type = self.face_detector.run(image) + + + except ValueError: + bbox = self.face_detector.run(image) + bbox_type = 'kpt68' + + if len(bbox) < 4: + print('no face detected! run original image') + left = 0; right = h-1; top=0; bottom=w-1 + else: + left = bbox[0]; right=bbox[2] + top = bbox[1]; bottom=bbox[3] + old_size, center = self.bbox2point(left, right, top, bottom, type=bbox_type) + + size = int(old_size*self.scale) + src_pts = np.array([[center[0]-size/2, center[1]-size/2], [center[0] - size/2, center[1]+size/2], [center[0]+size/2, center[1]-size/2]]) + else: + src_pts = np.array([[0, 0], [0, h-1], [w-1, 0]]) + + DST_PTS = np.array([[0,0], [0,self.resolution_inp - 1], [self.resolution_inp - 1, 0]]) + tform = estimate_transform('similarity', src_pts, DST_PTS) + + image = image/255. + dst_image = warp(image, tform.inverse, output_shape=(self.resolution_inp, self.resolution_inp)) + dst_image = dst_image.transpose(2,0,1) + return {'image': torch.tensor(dst_image).float(), + 'imagename': imagename, + 'tform': tform, + 'original_image': torch.tensor(image.transpose(2,0,1)).float(), + } \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/DECA/decalib/datasets/detectors.py b/motion-gan-pipeline/preprocessing/third/DECA/decalib/datasets/detectors.py new file mode 100755 index 0000000..09d6688 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/decalib/datasets/detectors.py @@ -0,0 +1,60 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import numpy as np +import torch + +class FAN(object): + def __init__(self): + import face_alignment + self.model = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False) + + def run(self, image): + ''' + image: 0-255, uint8, rgb, [h, w, 3] + return: detected box list + ''' + out = self.model.get_landmarks(image) + if out is None: + return [0] + else: + kpt = out[0].squeeze() + left = np.min(kpt[:,0]); right = np.max(kpt[:,0]); + top = np.min(kpt[:,1]); bottom = np.max(kpt[:,1]) + bbox = [left,top, right, bottom] + return bbox, 'kpt68' + +class MTCNN(object): + def __init__(self, device = 'cpu'): + ''' + https://github.com/timesler/facenet-pytorch/blob/master/examples/infer.ipynb + ''' + from facenet_pytorch import MTCNN as mtcnn + self.device = device + self.model = mtcnn(keep_all=True) + def run(self, input): + ''' + image: 0-255, uint8, rgb, [h, w, 3] + return: detected box + ''' + out = self.model.detect(input[None,...]) + if out[0][0] is None: + return [0] + else: + bbox = out[0][0].squeeze() + return bbox, 'bbox' + + + diff --git a/motion-gan-pipeline/preprocessing/third/DECA/decalib/deca.py b/motion-gan-pipeline/preprocessing/third/DECA/decalib/deca.py new file mode 100644 index 0000000..8eec61c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/decalib/deca.py @@ -0,0 +1,473 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import os, sys +import torch +import torchvision +from torchvision import transforms +from PIL import Image +import torch.nn.functional as F +import torch.nn as nn +import pdb +import matplotlib.pyplot as plt +from skimage.transform import warp +import numpy as np +from time import time +from skimage.io import imread + +import cv2 +import pickle +from .utils.renderer import SRenderY +from .models.encoders import ResnetEncoder +from .models.FLAME import FLAME, FLAMETex +from .models.decoders import Generator +from .utils import util +from .utils.rotation_converter import batch_euler2axis +from .datasets import datasets +from .utils.config import cfg +torch.backends.cudnn.benchmark = True + +class DECA(object): + def __init__(self, config=None, device='cuda'): + if config is None: + self.cfg = cfg + else: + self.cfg = config + self.device = device + self.image_size = self.cfg.dataset.image_size + self.uv_size = self.cfg.model.uv_size + + self._create_model(self.cfg.model) + self._setup_renderer(self.cfg.model) + + def _setup_renderer(self, model_cfg): + self.render = SRenderY(self.image_size, obj_filename=model_cfg.topology_path, uv_size=model_cfg.uv_size).to(self.device) + # face mask for rendering details + mask = imread(model_cfg.face_eye_mask_path).astype(np.float32)/255.; mask = torch.from_numpy(mask[:,:,0])[None,None,:,:].contiguous() + self.uv_face_eye_mask = F.interpolate(mask, [model_cfg.uv_size, model_cfg.uv_size]).to(self.device) + mask = imread(model_cfg.face_mask_path).astype(np.float32)/255.; mask = torch.from_numpy(mask[:,:,0])[None,None,:,:].contiguous() + self.uv_face_mask = F.interpolate(mask, [model_cfg.uv_size, model_cfg.uv_size]).to(self.device) + # displacement correction + fixed_dis = np.load(model_cfg.fixed_displacement_path) + self.fixed_uv_dis = torch.tensor(fixed_dis).float().to(self.device) + # mean texture + mean_texture = imread(model_cfg.mean_tex_path).astype(np.float32)/255.; mean_texture = torch.from_numpy(mean_texture.transpose(2,0,1))[None,:,:,:].contiguous() + self.mean_texture = F.interpolate(mean_texture, [model_cfg.uv_size, model_cfg.uv_size]).to(self.device) + # dense mesh template, for save detail mesh + self.dense_template = np.load(model_cfg.dense_template_path, allow_pickle=True, encoding='latin1').item() + + def _create_model(self, model_cfg): + # set up parameters + self.n_param = model_cfg.n_shape+model_cfg.n_tex+model_cfg.n_exp+model_cfg.n_pose+model_cfg.n_cam+model_cfg.n_light + self.n_detail = model_cfg.n_detail + self.n_cond = model_cfg.n_exp + 3 # exp + jaw pose + self.num_list = [model_cfg.n_shape, model_cfg.n_tex, model_cfg.n_exp, model_cfg.n_pose, model_cfg.n_cam, model_cfg.n_light] + self.param_dict = {i:model_cfg.get('n_' + i) for i in model_cfg.param_list} + + # encoders + self.E_flame = ResnetEncoder(outsize=self.n_param).to(self.device) + self.E_detail = ResnetEncoder(outsize=self.n_detail).to(self.device) + # decoders + self.flame = FLAME(model_cfg).to(self.device) + if model_cfg.use_tex: + self.flametex = FLAMETex(model_cfg).to(self.device) + self.D_detail = Generator(latent_dim=self.n_detail+self.n_cond, out_channels=1, out_scale=model_cfg.max_z, sample_mode = 'bilinear').to(self.device) + # resume model + model_path = self.cfg.pretrained_modelpath + if os.path.exists(model_path): + print(f'trained model found. load {model_path}') + checkpoint = torch.load(model_path) + self.checkpoint = checkpoint + util.copy_state_dict(self.E_flame.state_dict(), checkpoint['E_flame']) + util.copy_state_dict(self.E_detail.state_dict(), checkpoint['E_detail']) + util.copy_state_dict(self.D_detail.state_dict(), checkpoint['D_detail']) + else: + print(f'please check model path: {model_path}') + exit() + # eval mode + self.E_flame.eval() + self.E_detail.eval() + self.D_detail.eval() + + def decompose_code(self, code, num_dict): + ''' Convert a flattened parameter vector to a dictionary of parameters + code_dict.keys() = ['shape', 'tex', 'exp', 'pose', 'cam', 'light'] + ''' + code_dict = {} + start = 0 + for key in num_dict: + end = start+int(num_dict[key]) + code_dict[key] = code[:, start:end] + start = end + if key == 'light': + code_dict[key] = code_dict[key].reshape(code_dict[key].shape[0], 9, 3) + return code_dict + + def displacement2normal(self, uv_z, coarse_verts, coarse_normals): + ''' Convert displacement map into detail normal map + ''' + batch_size = uv_z.shape[0] + uv_coarse_vertices = self.render.world2uv(coarse_verts).detach() + uv_coarse_normals = self.render.world2uv(coarse_normals).detach() + + uv_z = uv_z*self.uv_face_eye_mask + uv_detail_vertices = uv_coarse_vertices + uv_z*uv_coarse_normals + self.fixed_uv_dis[None,None,:,:]*uv_coarse_normals.detach() + dense_vertices = uv_detail_vertices.permute(0,2,3,1).reshape([batch_size, -1, 3]) + uv_detail_normals = util.vertex_normals(dense_vertices, self.render.dense_faces.expand(batch_size, -1, -1)) + uv_detail_normals = uv_detail_normals.reshape([batch_size, uv_coarse_vertices.shape[2], uv_coarse_vertices.shape[3], 3]).permute(0,3,1,2) + return uv_detail_normals + + def displacement2vertex(self, uv_z, coarse_verts, coarse_normals): + ''' Convert displacement map into detail vertices + ''' + batch_size = uv_z.shape[0] + uv_coarse_vertices = self.render.world2uv(coarse_verts).detach() + uv_coarse_normals = self.render.world2uv(coarse_normals).detach() + + uv_z = uv_z*self.uv_face_eye_mask + uv_detail_vertices = uv_coarse_vertices + uv_z*uv_coarse_normals + self.fixed_uv_dis[None,None,:,:]*uv_coarse_normals.detach() + dense_vertices = uv_detail_vertices.permute(0,2,3,1).reshape([batch_size, -1, 3]) + # uv_detail_normals = util.vertex_normals(dense_vertices, self.render.dense_faces.expand(batch_size, -1, -1)) + # uv_detail_normals = uv_detail_normals.reshape([batch_size, uv_coarse_vertices.shape[2], uv_coarse_vertices.shape[3], 3]).permute(0,3,1,2) + detail_faces = self.render.dense_faces + return dense_vertices, detail_faces + + def visofp(self, normals): + ''' visibility of keypoints, based on the normal direction + ''' + normals68 = self.flame.seletec_3d68(normals) + vis68 = (normals68[:,:,2:] < 0.1).float() + return vis68 + + @torch.no_grad() + def encode(self, images): + batch_size = images.shape[0] + parameters = self.E_flame(images) + detailcode = self.E_detail(images) + codedict = self.decompose_code(parameters, self.param_dict) + codedict['detail'] = detailcode + codedict['images'] = images + return codedict + + @torch.no_grad() + def decode(self, codedict, tform=None): + images = codedict['images'] + batch_size = images.shape[0] + + # pose = codedict['pose'] + # print(f'Pose: {pose}') + # camera = codedict['cam'] + # print(f'Camera: {camera}') + + ## decode + verts, landmarks2d, landmarks3d = self.flame(shape_params=codedict['shape'], expression_params=codedict['exp'], pose_params=codedict['pose']) + uv_z = self.D_detail(torch.cat([codedict['pose'][:,3:], codedict['exp'], codedict['detail']], dim=1)) + if self.cfg.model.use_tex: + albedo = self.flametex(codedict['tex']) + else: + albedo = torch.zeros([batch_size, 3, self.uv_size, self.uv_size], device=images.device) + + # pdb.set_trace() + + ## projection + landmarks2d = util.batch_orth_proj(landmarks2d, codedict['cam'])[:,:,:2]; landmarks2d[:,:,1:] = -landmarks2d[:,:,1:]; landmarks2d = landmarks2d*self.image_size/2 + self.image_size/2 + landmarks3d = util.batch_orth_proj(landmarks3d, codedict['cam']); landmarks3d[:,:,1:] = -landmarks3d[:,:,1:]; landmarks3d = landmarks3d*self.image_size/2 + self.image_size/2 + trans_verts = util.batch_orth_proj(verts, codedict['cam']); trans_verts[:,:,1:] = -trans_verts[:,:,1:] + + if tform is not None: + tform_tensor = torch.tensor(tform.params, dtype=torch.float32).cuda() + dst_image = warp(trans_verts[0,:,1:].cpu().numpy(), tform) + trans_verts = torch.cat((trans_verts[0,:,:1], torch.tensor(dst_image, dtype=torch.float32).cuda()), dim = 1) + trans_verts = torch.unsqueeze(trans_verts, dim=0) + + ## rendering + ops = self.render(verts, trans_verts, albedo, codedict['light']) + uv_detail_normals = self.displacement2normal(uv_z, verts, ops['normals']) + uv_shading = self.render.add_SHlight(uv_detail_normals, codedict['light']) + uv_texture = albedo*uv_shading + + landmarks3d_vis = self.visofp(ops['transformed_normals']) + landmarks3d = torch.cat([landmarks3d, landmarks3d_vis], dim=2) + + ## render shape + shape_images = self.render.render_shape(verts, trans_verts) + + # new_shape = shape_images[0].permute(1, 2, 0).cpu().numpy() + # plt.imshow(new_shape) + # plt.show() + + detail_normal_images = F.grid_sample(uv_detail_normals, ops['grid'], align_corners=False)*ops['alpha_images'] + shape_detail_images = self.render.render_shape(verts, trans_verts, detail_normal_images=detail_normal_images) + + ## extract texture + ## TODO: current resolution 256x256, support higher resolution, and add visibility + uv_pverts = self.render.world2uv(trans_verts) + uv_gt = F.grid_sample(images, uv_pverts.permute(0,2,3,1)[:,:,:,:2], mode='bilinear') + if self.cfg.model.use_tex: + ## TODO: poisson blending should give better-looking results + uv_texture_gt = uv_gt[:,:3,:,:]*self.uv_face_eye_mask + (uv_texture[:,:3,:,:]*(1-self.uv_face_eye_mask)*0.7) + else: + uv_texture_gt = uv_gt[:,:3,:,:]*self.uv_face_eye_mask + (torch.ones_like(uv_gt[:,:3,:,:])*(1-self.uv_face_eye_mask)*0.7) + + ## output + opdict = { + 'vertices': verts, + 'normals': ops['normals'], + 'grid': ops['grid'], + 'transformed_vertices': trans_verts, + 'landmarks2d': landmarks2d, + 'landmarks3d': landmarks3d, + 'uv_detail_normals': uv_detail_normals, + 'uv_texture_gt': uv_texture_gt, + 'displacement_map': uv_z+self.fixed_uv_dis[None,None,:,:], + 'detail_normal_images': detail_normal_images, + } + + if self.cfg.model.use_tex: + opdict['albedo'] = albedo + opdict['uv_texture'] = uv_texture + + visdict = { + 'inputs': images, + 'landmarks2d': util.tensor_vis_landmarks(images, landmarks2d, isScale=False), + 'landmarks3d': util.tensor_vis_landmarks(images, landmarks3d, isScale=False), + 'shape_images': shape_images, + 'shape_detail_images': shape_detail_images, + } + + if self.cfg.model.use_tex: + visdict['rendered_images'] = ops['images'] + + return opdict, visdict + + @torch.no_grad() + def decode_eyes(self, codedict, tform=None): + + images = codedict['images'] + batch_size = images.shape[0] + + pose = codedict['pose'] + print(f'Pose: {pose}') + camera = codedict['cam'] + print(f'Camera: {camera}') + + eye_pose_params = torch.tensor([[0, 0, 0, 0, 0, 0]], device='cuda') + eye_pose_params = -20 * pose + print(f'eye_pose_params: {eye_pose_params}') + + ## decode + verts, landmarks2d, landmarks3d = self.flame(shape_params=codedict['shape'], expression_params=codedict['exp'], + pose_params=codedict['pose'], eye_pose_params=eye_pose_params) + + uv_z = self.D_detail(torch.cat([codedict['pose'][:, 3:], codedict['exp'], codedict['detail']], dim=1)) + if self.cfg.model.use_tex: + albedo = self.flametex(codedict['tex']) + else: + albedo = torch.zeros([batch_size, 3, self.uv_size, self.uv_size], device=images.device) + + # pdb.set_trace() + + ## projection + landmarks2d = util.batch_orth_proj(landmarks2d, codedict['cam'])[:, :, :2]; + landmarks2d[:, :, 1:] = -landmarks2d[:, :, 1:]; + landmarks2d = landmarks2d * self.image_size / 2 + self.image_size / 2 + landmarks3d = util.batch_orth_proj(landmarks3d, codedict['cam']); + landmarks3d[:, :, 1:] = -landmarks3d[:, :, 1:]; + landmarks3d = landmarks3d * self.image_size / 2 + self.image_size / 2 + trans_verts = util.batch_orth_proj(verts, codedict['cam']); + trans_verts[:, :, 1:] = -trans_verts[:, :, 1:] + + if tform is not None: + tform_tensor = torch.tensor(tform.params, dtype=torch.float32).cuda() + dst_image = warp(trans_verts[0, :, 1:].cpu().numpy(), tform) + trans_verts = torch.cat((trans_verts[0, :, :1], torch.tensor(dst_image, dtype=torch.float32).cuda()), dim=1) + trans_verts = torch.unsqueeze(trans_verts, dim=0) + + ## rendering + ops = self.render(verts, trans_verts, albedo, codedict['light']) + uv_detail_normals = self.displacement2normal(uv_z, verts, ops['normals']) + uv_shading = self.render.add_SHlight(uv_detail_normals, codedict['light']) + uv_texture = albedo * uv_shading + + landmarks3d_vis = self.visofp(ops['transformed_normals']) + landmarks3d = torch.cat([landmarks3d, landmarks3d_vis], dim=2) + + ## render shape + shape_images = self.render.render_shape(verts, trans_verts) + + # new_shape = shape_images[0].permute(1, 2, 0).cpu().numpy() + # plt.imshow(new_shape) + # plt.show() + + detail_normal_images = F.grid_sample(uv_detail_normals, ops['grid'], align_corners=False) * ops['alpha_images'] + shape_detail_images = self.render.render_shape(verts, trans_verts, detail_normal_images=detail_normal_images) + + ## extract texture + ## TODO: current resolution 256x256, support higher resolution, and add visibility + uv_pverts = self.render.world2uv(trans_verts) + uv_gt = F.grid_sample(images, uv_pverts.permute(0, 2, 3, 1)[:, :, :, :2], mode='bilinear') + if self.cfg.model.use_tex: + ## TODO: poisson blending should give better-looking results + uv_texture_gt = uv_gt[:, :3, :, :] * self.uv_face_eye_mask + ( + uv_texture[:, :3, :, :] * (1 - self.uv_face_eye_mask) * 0.7) + else: + uv_texture_gt = uv_gt[:, :3, :, :] * self.uv_face_eye_mask + ( + torch.ones_like(uv_gt[:, :3, :, :]) * (1 - self.uv_face_eye_mask) * 0.7) + + ## output + opdict = { + 'vertices': verts, + 'normals': ops['normals'], + 'grid': ops['grid'], + 'transformed_vertices': trans_verts, + 'landmarks2d': landmarks2d, + 'landmarks3d': landmarks3d, + 'uv_detail_normals': uv_detail_normals, + 'uv_texture_gt': uv_texture_gt, + 'displacement_map': uv_z + self.fixed_uv_dis[None, None, :, :], + 'detail_normal_images': detail_normal_images, + + } + + if self.cfg.model.use_tex: + opdict['albedo'] = albedo + opdict['uv_texture'] = uv_texture + + visdict = { + 'inputs': images, + 'landmarks2d': util.tensor_vis_landmarks(images, landmarks2d, isScale=False), + 'landmarks3d': util.tensor_vis_landmarks(images, landmarks3d, isScale=False), + 'shape_images': shape_images, + 'shape_detail_images': shape_detail_images, + } + + if self.cfg.model.use_tex: + visdict['rendered_images'] = ops['images'] + + return opdict, visdict + + def render_uv(self, codedict, exp, pose): + + images = codedict['images'] + batch_size = images.shape[0] + + ## decode + verts, landmarks2d, landmarks3d = self.flame(shape_params=codedict['shape'], expression_params=exp, + pose_params=pose) + + if self.cfg.model.use_tex: + albedo = self.flametex(codedict['tex']) + else: + albedo = torch.zeros([batch_size, 3, self.uv_size, self.uv_size], device=images.device) + + ## projection + trans_verts = util.batch_orth_proj(verts, codedict['cam']) + trans_verts[:, :, 1:] = -trans_verts[:, :, 1:] + + ## rendering + ops = self.render(verts, trans_verts, albedo, codedict['light']) + + return ops['grid'] + + def render_uv_details(self, codedict, exp, pose): + + images = codedict['images'] + batch_size = images.shape[0] + + ## decode + verts, landmarks2d, landmarks3d = self.flame(shape_params=codedict['shape'], expression_params=exp, + pose_params=pose) + uv_z = self.D_detail(torch.cat([pose[:, 3:], exp, codedict['detail']], dim=1)) + + if self.cfg.model.use_tex: + albedo = self.flametex(codedict['tex']) + else: + albedo = torch.zeros([batch_size, 3, self.uv_size, self.uv_size], device=images.device) + + ## projection + trans_verts = util.batch_orth_proj(verts, codedict['cam']); + trans_verts[:, :, 1:] = -trans_verts[:, :, 1:] + + ## rendering + ops = self.render(verts, trans_verts, albedo, codedict['light']) + uv_detail_normals = self.displacement2normal(uv_z, verts, ops['normals']) + + detail_normal_images = F.grid_sample(uv_detail_normals, ops['grid'], align_corners=False) * ops['alpha_images'] + + return ops['grid'], detail_normal_images + + def render_mask(self, grid): + + # image = Image.open('./third/DECA/data/mask_face.png') + # image = Image.open('./third/DECA/data/mask_3.png') + current_path = os.getcwd() + try : + head_tail = os.path.split(current_path) + image = Image.open(head_tail[0] + '/third/DECA/data/mask_mouth_2.png') + + except FileNotFoundError: + image = Image.open(current_path + '/third/DECA/data/mask_mouth_2.png') + + trans = transforms.ToTensor() + tmp = trans(image) + image_tensor = trans(image).cuda().unsqueeze(0) + mask_1 = (grid[:, :, :, 0:1] != 0.0) & (grid[:, :, :, 1:2] != 0.0) + mask_1 = mask_1.permute(0, 3, 1, 2) + + mask = F.grid_sample(image_tensor, grid, align_corners=False, padding_mode='zeros') + mask = mask_1 * mask + + return mask[0,0,:,:] + + def visualize(self, visdict, size=None): + grids = {} + if size is None: + size = self.image_size + for key in visdict: + grids[key] = torchvision.utils.make_grid(F.interpolate(visdict[key], [size, size])).detach().cpu() + grid = torch.cat(list(grids.values()), 2) + grid_image = (grid.numpy().transpose(1,2,0).copy()*255)[:,:,[2,1,0]] + grid_image = np.minimum(np.maximum(grid_image, 0), 255).astype(np.uint8) + return grid_image + + def save_obj(self, filename, opdict): + ''' + vertices: [nv, 3], tensor + texture: [3, h, w], tensor + ''' + i = 0 + vertices = opdict['vertices'][i].cpu().numpy() + faces = self.render.faces[0].cpu().numpy() + texture = util.tensor2image(opdict['uv_texture_gt'][i]) + uvcoords = self.render.raw_uvcoords[0].cpu().numpy() + uvfaces = self.render.uvfaces[0].cpu().numpy() + # save coarse mesh, with texture and normal map + normal_map = util.tensor2image(opdict['uv_detail_normals'][i]*0.5 + 0.5) + util.write_obj(filename, vertices, faces, + texture=texture, + uvcoords=uvcoords, + uvfaces=uvfaces, + normal_map=normal_map) + # upsample mesh, save detailed mesh + texture = texture[:,:,[2,1,0]] + normals = opdict['normals'][i].cpu().numpy() + displacement_map = opdict['displacement_map'][i].cpu().numpy().squeeze() + dense_vertices, dense_colors, dense_faces = util.upsample_mesh(vertices, normals, faces, displacement_map, + texture, self.dense_template) + util.write_obj(filename.replace('.obj', '_detail.obj'), + dense_vertices, + dense_faces, + colors=dense_colors, + inverse_face_order=True) \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/DECA/decalib/models/FLAME.py b/motion-gan-pipeline/preprocessing/third/DECA/decalib/models/FLAME.py new file mode 100755 index 0000000..7e2821a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/decalib/models/FLAME.py @@ -0,0 +1,262 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import torch +import torch.nn as nn +import numpy as np +import pickle +import torch.nn.functional as F + +from .lbs import lbs, batch_rodrigues, vertices2landmarks, rot_mat_to_euler + +def to_tensor(array, dtype=torch.float32): + if 'torch.tensor' not in str(type(array)): + return torch.tensor(array, dtype=dtype) +def to_np(array, dtype=np.float32): + if 'scipy.sparse' in str(type(array)): + array = array.todense() + return np.array(array, dtype=dtype) + +class Struct(object): + def __init__(self, **kwargs): + for key, val in kwargs.items(): + setattr(self, key, val) + +class FLAME(nn.Module): + """ + borrowed from https://github.com/soubhiksanyal/FLAME_PyTorch/blob/master/FLAME.py + Given flame parameters this class generates a differentiable FLAME function + which outputs the a mesh and 2D/3D facial landmarks + """ + def __init__(self, config): + super(FLAME, self).__init__() + print("creating the FLAME Decoder") + with open(config.flame_model_path, 'rb') as f: + ss = pickle.load(f, encoding='latin1') + flame_model = Struct(**ss) + + self.dtype = torch.float32 + self.register_buffer('faces_tensor', to_tensor(to_np(flame_model.f, dtype=np.int64), dtype=torch.long)) + # The vertices of the template model + self.register_buffer('v_template', to_tensor(to_np(flame_model.v_template), dtype=self.dtype)) + # The shape components and expression + shapedirs = to_tensor(to_np(flame_model.shapedirs), dtype=self.dtype) + shapedirs = torch.cat([shapedirs[:,:,:config.n_shape], shapedirs[:,:,300:300+config.n_exp]], 2) + self.register_buffer('shapedirs', shapedirs) + # The pose components + num_pose_basis = flame_model.posedirs.shape[-1] + posedirs = np.reshape(flame_model.posedirs, [-1, num_pose_basis]).T + self.register_buffer('posedirs', to_tensor(to_np(posedirs), dtype=self.dtype)) + # + self.register_buffer('J_regressor', to_tensor(to_np(flame_model.J_regressor), dtype=self.dtype)) + parents = to_tensor(to_np(flame_model.kintree_table[0])).long(); parents[0] = -1 + self.register_buffer('parents', parents) + self.register_buffer('lbs_weights', to_tensor(to_np(flame_model.weights), dtype=self.dtype)) + + # Fixing Eyeball and neck rotation + default_eyball_pose = torch.zeros([1, 6], dtype=self.dtype, requires_grad=False) + self.register_parameter('eye_pose', nn.Parameter(default_eyball_pose, + requires_grad=False)) + default_neck_pose = torch.zeros([1, 3], dtype=self.dtype, requires_grad=False) + self.register_parameter('neck_pose', nn.Parameter(default_neck_pose, + requires_grad=False)) + + # Static and Dynamic Landmark embeddings for FLAME + lmk_embeddings = np.load(config.flame_lmk_embedding_path, allow_pickle=True, encoding='latin1') + lmk_embeddings = lmk_embeddings[()] + self.register_buffer('lmk_faces_idx', torch.from_numpy(lmk_embeddings['static_lmk_faces_idx']).long()) + self.register_buffer('lmk_bary_coords', torch.from_numpy(lmk_embeddings['static_lmk_bary_coords']).to(self.dtype)) + self.register_buffer('dynamic_lmk_faces_idx', lmk_embeddings['dynamic_lmk_faces_idx'].long()) + self.register_buffer('dynamic_lmk_bary_coords', lmk_embeddings['dynamic_lmk_bary_coords'].to(self.dtype)) + self.register_buffer('full_lmk_faces_idx', torch.from_numpy(lmk_embeddings['full_lmk_faces_idx']).long()) + self.register_buffer('full_lmk_bary_coords', torch.from_numpy(lmk_embeddings['full_lmk_bary_coords']).to(self.dtype)) + + neck_kin_chain = []; NECK_IDX=1 + curr_idx = torch.tensor(NECK_IDX, dtype=torch.long) + while curr_idx != -1: + neck_kin_chain.append(curr_idx) + curr_idx = self.parents[curr_idx] + self.register_buffer('neck_kin_chain', torch.stack(neck_kin_chain)) + + def _find_dynamic_lmk_idx_and_bcoords(self, pose, dynamic_lmk_faces_idx, + dynamic_lmk_b_coords, + neck_kin_chain, dtype=torch.float32): + """ + Selects the face contour depending on the reletive position of the head + Input: + vertices: N X num_of_vertices X 3 + pose: N X full pose + dynamic_lmk_faces_idx: The list of contour face indexes + dynamic_lmk_b_coords: The list of contour barycentric weights + neck_kin_chain: The tree to consider for the relative rotation + dtype: Data type + return: + The contour face indexes and the corresponding barycentric weights + """ + + batch_size = pose.shape[0] + + aa_pose = torch.index_select(pose.view(batch_size, -1, 3), 1, + neck_kin_chain) + rot_mats = batch_rodrigues( + aa_pose.view(-1, 3), dtype=dtype).view(batch_size, -1, 3, 3) + + rel_rot_mat = torch.eye(3, device=pose.device, + dtype=dtype).unsqueeze_(dim=0).expand(batch_size, -1, -1) + for idx in range(len(neck_kin_chain)): + rel_rot_mat = torch.bmm(rot_mats[:, idx], rel_rot_mat) + + y_rot_angle = torch.round( + torch.clamp(rot_mat_to_euler(rel_rot_mat) * 180.0 / np.pi, + max=39)).to(dtype=torch.long) + + neg_mask = y_rot_angle.lt(0).to(dtype=torch.long) + mask = y_rot_angle.lt(-39).to(dtype=torch.long) + neg_vals = mask * 78 + (1 - mask) * (39 - y_rot_angle) + y_rot_angle = (neg_mask * neg_vals + + (1 - neg_mask) * y_rot_angle) + + dyn_lmk_faces_idx = torch.index_select(dynamic_lmk_faces_idx, + 0, y_rot_angle) + dyn_lmk_b_coords = torch.index_select(dynamic_lmk_b_coords, + 0, y_rot_angle) + return dyn_lmk_faces_idx, dyn_lmk_b_coords + + def _vertices2landmarks(self, vertices, faces, lmk_faces_idx, lmk_bary_coords): + """ + Calculates landmarks by barycentric interpolation + Input: + vertices: torch.tensor NxVx3, dtype = torch.float32 + The tensor of input vertices + faces: torch.tensor (N*F)x3, dtype = torch.long + The faces of the mesh + lmk_faces_idx: torch.tensor N X L, dtype = torch.long + The tensor with the indices of the faces used to calculate the + landmarks. + lmk_bary_coords: torch.tensor N X L X 3, dtype = torch.float32 + The tensor of barycentric coordinates that are used to interpolate + the landmarks + + Returns: + landmarks: torch.tensor NxLx3, dtype = torch.float32 + The coordinates of the landmarks for each mesh in the batch + """ + # Extract the indices of the vertices for each face + # NxLx3 + batch_size, num_verts = vertices.shape[:dd2] + lmk_faces = torch.index_select(faces, 0, lmk_faces_idx.view(-1)).view( + 1, -1, 3).view(batch_size, lmk_faces_idx.shape[1], -1) + + lmk_faces += torch.arange(batch_size, dtype=torch.long).view(-1, 1, 1).to( + device=vertices.device) * num_verts + + lmk_vertices = vertices.view(-1, 3)[lmk_faces] + landmarks = torch.einsum('blfi,blf->bli', [lmk_vertices, lmk_bary_coords]) + return landmarks + + def seletec_3d68(self, vertices): + landmarks3d = vertices2landmarks(vertices, self.faces_tensor, + self.full_lmk_faces_idx.repeat(vertices.shape[0], 1), + self.full_lmk_bary_coords.repeat(vertices.shape[0], 1, 1)) + return landmarks3d + + def forward(self, shape_params=None, expression_params=None, pose_params=None, eye_pose_params=None): + """ + Input: + shape_params: N X number of shape parameters + expression_params: N X number of expression parameters + pose_params: N X number of pose parameters (6) + return:d + vertices: N X V X 3 + landmarks: N X number of landmarks X 3 + """ + batch_size = shape_params.shape[0] + if eye_pose_params is None: + eye_pose_params = self.eye_pose.expand(batch_size, -1) + betas = torch.cat([shape_params, expression_params], dim=1) + full_pose = torch.cat([pose_params[:, :3], self.neck_pose.expand(batch_size, -1), pose_params[:, 3:], eye_pose_params], dim=1) + template_vertices = self.v_template.unsqueeze(0).expand(batch_size, -1, -1) + + vertices, _ = lbs(betas, full_pose, template_vertices, + self.shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, dtype=self.dtype) + + lmk_faces_idx = self.lmk_faces_idx.unsqueeze(dim=0).expand(batch_size, -1) + lmk_bary_coords = self.lmk_bary_coords.unsqueeze(dim=0).expand(batch_size, -1, -1) + + dyn_lmk_faces_idx, dyn_lmk_bary_coords = self._find_dynamic_lmk_idx_and_bcoords( + full_pose, self.dynamic_lmk_faces_idx, + self.dynamic_lmk_bary_coords, + self.neck_kin_chain, dtype=self.dtype) + lmk_faces_idx = torch.cat([dyn_lmk_faces_idx, lmk_faces_idx], 1) + lmk_bary_coords = torch.cat([dyn_lmk_bary_coords, lmk_bary_coords], 1) + + landmarks2d = vertices2landmarks(vertices, self.faces_tensor, + lmk_faces_idx, + lmk_bary_coords) + bz = vertices.shape[0] + landmarks3d = vertices2landmarks(vertices, self.faces_tensor, + self.full_lmk_faces_idx.repeat(bz, 1), + self.full_lmk_bary_coords.repeat(bz, 1, 1)) + return vertices, landmarks2d, landmarks3d + +class FLAMETex(nn.Module): + """ + FLAME texture: + https://github.com/TimoBolkart/TF_FLAME/blob/ade0ab152300ec5f0e8555d6765411555c5ed43d/sample_texture.py#L64 + FLAME texture converted from BFM: + https://github.com/TimoBolkart/BFM_to_FLAME + """ + def __init__(self, config): + super(FLAMETex, self).__init__() + if config.tex_type == 'BFM': + mu_key = 'MU' + pc_key = 'PC' + n_pc = 199 + tex_path = config.tex_path + tex_space = np.load(tex_path) + texture_mean = tex_space[mu_key].reshape(1, -1) + texture_basis = tex_space[pc_key].reshape(-1, n_pc) + + elif config.tex_type == 'FLAME': + mu_key = 'mean' + pc_key = 'tex_dir' + n_pc = 200 + tex_path = config.flame_tex_path + tex_space = np.load(tex_path) + texture_mean = tex_space[mu_key].reshape(1, -1)/255. + texture_basis = tex_space[pc_key].reshape(-1, n_pc)/255. + else: + print('texture type ', config.tex_type, 'not exist!') + raise NotImplementedError + + n_tex = config.n_tex + num_components = texture_basis.shape[1] + texture_mean = torch.from_numpy(texture_mean).float()[None,...] + texture_basis = torch.from_numpy(texture_basis[:,:n_tex]).float()[None,...] + self.register_buffer('texture_mean', texture_mean) + self.register_buffer('texture_basis', texture_basis) + + def forward(self, texcode): + ''' + texcode: [batchsize, n_tex] + texture: [bz, 3, 256, 256], range: 0-1 + ''' + texture = self.texture_mean + (self.texture_basis*texcode[:,None,:]).sum(-1) + texture = texture.reshape(texcode.shape[0], 512, 512, 3).permute(0,3,1,2) + texture = F.interpolate(texture, [256, 256]) + texture = texture[:,[2,1,0], :,:] + return texture \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/DECA/decalib/models/decoders.py b/motion-gan-pipeline/preprocessing/third/DECA/decalib/models/decoders.py new file mode 100755 index 0000000..b9af7e2 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/decalib/models/decoders.py @@ -0,0 +1,56 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import torch +import torch.nn as nn + +class Generator(nn.Module): + def __init__(self, latent_dim=100, out_channels=1, out_scale=0.01, sample_mode = 'bilinear'): + super(Generator, self).__init__() + self.out_scale = out_scale + + self.init_size = 32 // 4 # Initial size before upsampling + self.l1 = nn.Sequential(nn.Linear(latent_dim, 128 * self.init_size ** 2)) + self.conv_blocks = nn.Sequential( + nn.BatchNorm2d(128), + nn.Upsample(scale_factor=2, mode=sample_mode), #16 + nn.Conv2d(128, 128, 3, stride=1, padding=1), + nn.BatchNorm2d(128, 0.8), + nn.LeakyReLU(0.2, inplace=True), + nn.Upsample(scale_factor=2, mode=sample_mode), #32 + nn.Conv2d(128, 64, 3, stride=1, padding=1), + nn.BatchNorm2d(64, 0.8), + nn.LeakyReLU(0.2, inplace=True), + nn.Upsample(scale_factor=2, mode=sample_mode), #64 + nn.Conv2d(64, 64, 3, stride=1, padding=1), + nn.BatchNorm2d(64, 0.8), + nn.LeakyReLU(0.2, inplace=True), + nn.Upsample(scale_factor=2, mode=sample_mode), #128 + nn.Conv2d(64, 32, 3, stride=1, padding=1), + nn.BatchNorm2d(32, 0.8), + nn.LeakyReLU(0.2, inplace=True), + nn.Upsample(scale_factor=2, mode=sample_mode), #256 + nn.Conv2d(32, 16, 3, stride=1, padding=1), + nn.BatchNorm2d(16, 0.8), + nn.LeakyReLU(0.2, inplace=True), + nn.Conv2d(16, out_channels, 3, stride=1, padding=1), + nn.Tanh(), + ) + + def forward(self, noise): + out = self.l1(noise) + out = out.view(out.shape[0], 128, self.init_size, self.init_size) + img = self.conv_blocks(out) + return img*self.out_scale \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/DECA/decalib/models/encoders.py b/motion-gan-pipeline/preprocessing/third/DECA/decalib/models/encoders.py new file mode 100755 index 0000000..48937ad --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/decalib/models/encoders.py @@ -0,0 +1,40 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import numpy as np +import torch.nn as nn +import torch +import torch.nn.functional as F +from . import resnet + +class ResnetEncoder(nn.Module): + def __init__(self, outsize, last_op=None): + super(ResnetEncoder, self).__init__() + feature_size = 2048 + self.encoder = resnet.load_ResNet50Model() #out: 2048 + ### regressor + self.layers = nn.Sequential( + nn.Linear(feature_size, 1024), + nn.ReLU(), + nn.Linear(1024, outsize) + ) + self.last_op = last_op + + def forward(self, inputs): + features = self.encoder(inputs) + parameters = self.layers(features) + if self.last_op: + parameters = self.last_op(parameters) + return parameters diff --git a/motion-gan-pipeline/preprocessing/third/DECA/decalib/models/lbs.py b/motion-gan-pipeline/preprocessing/third/DECA/decalib/models/lbs.py new file mode 100755 index 0000000..df55ab3 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/decalib/models/lbs.py @@ -0,0 +1,378 @@ +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import numpy as np + +import torch +import torch.nn.functional as F + +def rot_mat_to_euler(rot_mats): + # Calculates rotation matrix to euler angles + # Careful for extreme cases of eular angles like [0.0, pi, 0.0] + + sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] + + rot_mats[:, 1, 0] * rot_mats[:, 1, 0]) + return torch.atan2(-rot_mats[:, 2, 0], sy) + +def find_dynamic_lmk_idx_and_bcoords(vertices, pose, dynamic_lmk_faces_idx, + dynamic_lmk_b_coords, + neck_kin_chain, dtype=torch.float32): + ''' Compute the faces, barycentric coordinates for the dynamic landmarks + + + To do so, we first compute the rotation of the neck around the y-axis + and then use a pre-computed look-up table to find the faces and the + barycentric coordinates that will be used. + + Special thanks to Soubhik Sanyal (soubhik.sanyal@tuebingen.mpg.de) + for providing the original TensorFlow implementation and for the LUT. + + Parameters + ---------- + vertices: torch.tensor BxVx3, dtype = torch.float32 + The tensor of input vertices + pose: torch.tensor Bx(Jx3), dtype = torch.float32 + The current pose of the body model + dynamic_lmk_faces_idx: torch.tensor L, dtype = torch.long + The look-up table from neck rotation to faces + dynamic_lmk_b_coords: torch.tensor Lx3, dtype = torch.float32 + The look-up table from neck rotation to barycentric coordinates + neck_kin_chain: list + A python list that contains the indices of the joints that form the + kinematic chain of the neck. + dtype: torch.dtype, optional + + Returns + ------- + dyn_lmk_faces_idx: torch.tensor, dtype = torch.long + A tensor of size BxL that contains the indices of the faces that + will be used to compute the current dynamic landmarks. + dyn_lmk_b_coords: torch.tensor, dtype = torch.float32 + A tensor of size BxL that contains the indices of the faces that + will be used to compute the current dynamic landmarks. + ''' + + batch_size = vertices.shape[0] + + aa_pose = torch.index_select(pose.view(batch_size, -1, 3), 1, + neck_kin_chain) + rot_mats = batch_rodrigues( + aa_pose.view(-1, 3), dtype=dtype).view(batch_size, -1, 3, 3) + + rel_rot_mat = torch.eye(3, device=vertices.device, + dtype=dtype).unsqueeze_(dim=0) + for idx in range(len(neck_kin_chain)): + rel_rot_mat = torch.bmm(rot_mats[:, idx], rel_rot_mat) + + y_rot_angle = torch.round( + torch.clamp(-rot_mat_to_euler(rel_rot_mat) * 180.0 / np.pi, + max=39)).to(dtype=torch.long) + neg_mask = y_rot_angle.lt(0).to(dtype=torch.long) + mask = y_rot_angle.lt(-39).to(dtype=torch.long) + neg_vals = mask * 78 + (1 - mask) * (39 - y_rot_angle) + y_rot_angle = (neg_mask * neg_vals + + (1 - neg_mask) * y_rot_angle) + + dyn_lmk_faces_idx = torch.index_select(dynamic_lmk_faces_idx, + 0, y_rot_angle) + dyn_lmk_b_coords = torch.index_select(dynamic_lmk_b_coords, + 0, y_rot_angle) + + return dyn_lmk_faces_idx, dyn_lmk_b_coords + + +def vertices2landmarks(vertices, faces, lmk_faces_idx, lmk_bary_coords): + ''' Calculates landmarks by barycentric interpolation + + Parameters + ---------- + vertices: torch.tensor BxVx3, dtype = torch.float32 + The tensor of input vertices + faces: torch.tensor Fx3, dtype = torch.long + The faces of the mesh + lmk_faces_idx: torch.tensor L, dtype = torch.long + The tensor with the indices of the faces used to calculate the + landmarks. + lmk_bary_coords: torch.tensor Lx3, dtype = torch.float32 + The tensor of barycentric coordinates that are used to interpolate + the landmarks + + Returns + ------- + landmarks: torch.tensor BxLx3, dtype = torch.float32 + The coordinates of the landmarks for each mesh in the batch + ''' + # Extract the indices of the vertices for each face + # BxLx3 + batch_size, num_verts = vertices.shape[:2] + device = vertices.device + + lmk_faces = torch.index_select(faces, 0, lmk_faces_idx.view(-1)).view( + batch_size, -1, 3) + + lmk_faces += torch.arange( + batch_size, dtype=torch.long, device=device).view(-1, 1, 1) * num_verts + + lmk_vertices = vertices.view(-1, 3)[lmk_faces].view( + batch_size, -1, 3, 3) + + landmarks = torch.einsum('blfi,blf->bli', [lmk_vertices, lmk_bary_coords]) + return landmarks + + +def lbs(betas, pose, v_template, shapedirs, posedirs, J_regressor, parents, + lbs_weights, pose2rot=True, dtype=torch.float32): + ''' Performs Linear Blend Skinning with the given shape and pose parameters + + Parameters + ---------- + betas : torch.tensor BxNB + The tensor of shape parameters + pose : torch.tensor Bx(J + 1) * 3 + The pose parameters in axis-angle format + v_template torch.tensor BxVx3 + The template mesh that will be deformed + shapedirs : torch.tensor 1xNB + The tensor of PCA shape displacements + posedirs : torch.tensor Px(V * 3) + The pose PCA coefficients + J_regressor : torch.tensor JxV + The regressor array that is used to calculate the joints from + the position of the vertices + parents: torch.tensor J + The array that describes the kinematic tree for the model + lbs_weights: torch.tensor N x V x (J + 1) + The linear blend skinning weights that represent how much the + rotation matrix of each part affects each vertex + pose2rot: bool, optional + Flag on whether to convert the input pose tensor to rotation + matrices. The default value is True. If False, then the pose tensor + should already contain rotation matrices and have a size of + Bx(J + 1)x9 + dtype: torch.dtype, optional + + Returns + ------- + verts: torch.tensor BxVx3 + The vertices of the mesh after applying the shape and pose + displacements. + joints: torch.tensor BxJx3 + The joints of the model + ''' + + batch_size = max(betas.shape[0], pose.shape[0]) + device = betas.device + + # Add shape contribution + v_shaped = v_template + blend_shapes(betas, shapedirs) + + # Get the joints + # NxJx3 array + J = vertices2joints(J_regressor, v_shaped) + + # 3. Add pose blend shapes + # N x J x 3 x 3 + ident = torch.eye(3, dtype=dtype, device=device) + if pose2rot: + rot_mats = batch_rodrigues( + pose.view(-1, 3), dtype=dtype).view([batch_size, -1, 3, 3]) + + pose_feature = (rot_mats[:, 1:, :, :] - ident).view([batch_size, -1]) + # (N x P) x (P, V * 3) -> N x V x 3 + pose_offsets = torch.matmul(pose_feature, posedirs) \ + .view(batch_size, -1, 3) + else: + pose_feature = pose[:, 1:].view(batch_size, -1, 3, 3) - ident + rot_mats = pose.view(batch_size, -1, 3, 3) + + pose_offsets = torch.matmul(pose_feature.view(batch_size, -1), + posedirs).view(batch_size, -1, 3) + + v_posed = pose_offsets + v_shaped + # 4. Get the global joint location + J_transformed, A = batch_rigid_transform(rot_mats, J, parents, dtype=dtype) + + # 5. Do skinning: + # W is N x V x (J + 1) + W = lbs_weights.unsqueeze(dim=0).expand([batch_size, -1, -1]) + # (N x V x (J + 1)) x (N x (J + 1) x 16) + num_joints = J_regressor.shape[0] + T = torch.matmul(W, A.view(batch_size, num_joints, 16)) \ + .view(batch_size, -1, 4, 4) + + homogen_coord = torch.ones([batch_size, v_posed.shape[1], 1], + dtype=dtype, device=device) + v_posed_homo = torch.cat([v_posed, homogen_coord], dim=2) + v_homo = torch.matmul(T, torch.unsqueeze(v_posed_homo, dim=-1)) + + verts = v_homo[:, :, :3, 0] + + return verts, J_transformed + + +def vertices2joints(J_regressor, vertices): + ''' Calculates the 3D joint locations from the vertices + + Parameters + ---------- + J_regressor : torch.tensor JxV + The regressor array that is used to calculate the joints from the + position of the vertices + vertices : torch.tensor BxVx3 + The tensor of mesh vertices + + Returns + ------- + torch.tensor BxJx3 + The location of the joints + ''' + + return torch.einsum('bik,ji->bjk', [vertices, J_regressor]) + + +def blend_shapes(betas, shape_disps): + ''' Calculates the per vertex displacement due to the blend shapes + + + Parameters + ---------- + betas : torch.tensor Bx(num_betas) + Blend shape coefficients + shape_disps: torch.tensor Vx3x(num_betas) + Blend shapes + + Returns + ------- + torch.tensor BxVx3 + The per-vertex displacement due to shape deformation + ''' + + # Displacement[b, m, k] = sum_{l} betas[b, l] * shape_disps[m, k, l] + # i.e. Multiply each shape displacement by its corresponding beta and + # then sum them. + blend_shape = torch.einsum('bl,mkl->bmk', [betas, shape_disps]) + return blend_shape + + +def batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32): + ''' Calculates the rotation matrices for a batch of rotation vectors + Parameters + ---------- + rot_vecs: torch.tensor Nx3 + array of N axis-angle vectors + Returns + ------- + R: torch.tensor Nx3x3 + The rotation matrices for the given axis-angle parameters + ''' + + batch_size = rot_vecs.shape[0] + device = rot_vecs.device + + angle = torch.norm(rot_vecs + 1e-8, dim=1, keepdim=True) + rot_dir = rot_vecs / angle + + cos = torch.unsqueeze(torch.cos(angle), dim=1) + sin = torch.unsqueeze(torch.sin(angle), dim=1) + + # Bx1 arrays + rx, ry, rz = torch.split(rot_dir, 1, dim=1) + K = torch.zeros((batch_size, 3, 3), dtype=dtype, device=device) + + zeros = torch.zeros((batch_size, 1), dtype=dtype, device=device) + K = torch.cat([zeros, -rz, ry, rz, zeros, -rx, -ry, rx, zeros], dim=1) \ + .view((batch_size, 3, 3)) + + ident = torch.eye(3, dtype=dtype, device=device).unsqueeze(dim=0) + rot_mat = ident + sin * K + (1 - cos) * torch.bmm(K, K) + return rot_mat + + +def transform_mat(R, t): + ''' Creates a batch of transformation matrices + Args: + - R: Bx3x3 array of a batch of rotation matrices + - t: Bx3x1 array of a batch of translation vectors + Returns: + - T: Bx4x4 Transformation matrix + ''' + # No padding left or right, only add an extra row + return torch.cat([F.pad(R, [0, 0, 0, 1]), + F.pad(t, [0, 0, 0, 1], value=1)], dim=2) + + +def batch_rigid_transform(rot_mats, joints, parents, dtype=torch.float32): + """ + Applies a batch of rigid transformations to the joints + + Parameters + ---------- + rot_mats : torch.tensor BxNx3x3 + Tensor of rotation matrices + joints : torch.tensor BxNx3 + Locations of joints + parents : torch.tensor BxN + The kinematic tree of each object + dtype : torch.dtype, optional: + The data type of the created tensors, the default is torch.float32 + + Returns + ------- + posed_joints : torch.tensor BxNx3 + The locations of the joints after applying the pose rotations + rel_transforms : torch.tensor BxNx4x4 + The relative (with respect to the root joint) rigid transformations + for all the joints + """ + + joints = torch.unsqueeze(joints, dim=-1) + + rel_joints = joints.clone() + rel_joints[:, 1:] -= joints[:, parents[1:]] + + # transforms_mat = transform_mat( + # rot_mats.view(-1, 3, 3), + # rel_joints.view(-1, 3, 1)).view(-1, joints.shape[1], 4, 4) + transforms_mat = transform_mat( + rot_mats.view(-1, 3, 3), + rel_joints.reshape(-1, 3, 1)).reshape(-1, joints.shape[1], 4, 4) + + transform_chain = [transforms_mat[:, 0]] + for i in range(1, parents.shape[0]): + # Subtract the joint location at the rest pose + # No need for rotation, since it's identity when at rest + curr_res = torch.matmul(transform_chain[parents[i]], + transforms_mat[:, i]) + transform_chain.append(curr_res) + + transforms = torch.stack(transform_chain, dim=1) + + # The last column of the transformations contains the posed joints + posed_joints = transforms[:, :, :3, 3] + + # The last column of the transformations contains the posed joints + posed_joints = transforms[:, :, :3, 3] + + joints_homogen = F.pad(joints, [0, 0, 0, 1]) + + rel_transforms = transforms - F.pad( + torch.matmul(transforms, joints_homogen), [3, 0, 0, 0, 0, 0, 0, 0]) + + return posed_joints, rel_transforms \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/DECA/decalib/models/resnet.py b/motion-gan-pipeline/preprocessing/third/DECA/decalib/models/resnet.py new file mode 100755 index 0000000..039bab0 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/decalib/models/resnet.py @@ -0,0 +1,291 @@ +""" +Author: Soubhik Sanyal +Copyright (c) 2019, Soubhik Sanyal +All rights reserved. +Loads different resnet models +""" +''' + file: Resnet.py + date: 2018_05_02 + author: zhangxiong(1025679612@qq.com) + mark: copied from pytorch source code +''' + +import torch.nn as nn +import torch.nn.functional as F +import torch +from torch.nn.parameter import Parameter +import torch.optim as optim +import numpy as np +import math +import torchvision + +class ResNet(nn.Module): + def __init__(self, block, layers, num_classes=1000): + self.inplanes = 64 + super(ResNet, self).__init__() + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AvgPool2d(7, stride=1) + # self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x1 = self.layer4(x) + + x2 = self.avgpool(x1) + x2 = x2.view(x2.size(0), -1) + # x = self.fc(x) + ## x2: [bz, 2048] for shape + ## x1: [bz, 2048, 7, 7] for texture + return x2 + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + +def copy_parameter_from_resnet(model, resnet_dict): + cur_state_dict = model.state_dict() + # import ipdb; ipdb.set_trace() + for name, param in list(resnet_dict.items())[0:None]: + if name not in cur_state_dict: + print(name, ' not available in reconstructed resnet') + continue + if isinstance(param, Parameter): + param = param.data + try: + cur_state_dict[name].copy_(param) + except: + print(name, ' is inconsistent!') + continue + # print('copy resnet state dict finished!') + # import ipdb; ipdb.set_trace() + +def load_ResNet50Model(): + model = ResNet(Bottleneck, [3, 4, 6, 3]) + copy_parameter_from_resnet(model, torchvision.models.resnet50(pretrained = True).state_dict()) + return model + +def load_ResNet101Model(): + model = ResNet(Bottleneck, [3, 4, 23, 3]) + copy_parameter_from_resnet(model, torchvision.models.resnet101(pretrained = True).state_dict()) + return model + +def load_ResNet152Model(): + model = ResNet(Bottleneck, [3, 8, 36, 3]) + copy_parameter_from_resnet(model, torchvision.models.resnet152(pretrained = True).state_dict()) + return model + +# model.load_state_dict(checkpoint['model_state_dict']) + + +######## Unet + +class DoubleConv(nn.Module): + """(convolution => [BN] => ReLU) * 2""" + + def __init__(self, in_channels, out_channels): + super().__init__() + self.double_conv = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True) + ) + + def forward(self, x): + return self.double_conv(x) + + +class Down(nn.Module): + """Downscaling with maxpool then double conv""" + + def __init__(self, in_channels, out_channels): + super().__init__() + self.maxpool_conv = nn.Sequential( + nn.MaxPool2d(2), + DoubleConv(in_channels, out_channels) + ) + + def forward(self, x): + return self.maxpool_conv(x) + + +class Up(nn.Module): + """Upscaling then double conv""" + + def __init__(self, in_channels, out_channels, bilinear=True): + super().__init__() + + # if bilinear, use the normal convolutions to reduce the number of channels + if bilinear: + self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) + else: + self.up = nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2) + + self.conv = DoubleConv(in_channels, out_channels) + + def forward(self, x1, x2): + x1 = self.up(x1) + # input is CHW + diffY = x2.size()[2] - x1.size()[2] + diffX = x2.size()[3] - x1.size()[3] + + x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, + diffY // 2, diffY - diffY // 2]) + # if you have padding issues, see + # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a + # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd + x = torch.cat([x2, x1], dim=1) + return self.conv(x) + + +class OutConv(nn.Module): + def __init__(self, in_channels, out_channels): + super(OutConv, self).__init__() + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1) + + def forward(self, x): + return self.conv(x) + +class UNet(nn.Module): + def __init__(self, n_channels, n_classes, bilinear=True): + super(UNet, self).__init__() + self.n_channels = n_channels + self.n_classes = n_classes + self.bilinear = bilinear + + self.inc = DoubleConv(n_channels, 64) + self.down1 = Down(64, 128) + self.down2 = Down(128, 256) + self.down3 = Down(256, 512) + self.down4 = Down(512, 512) + self.up1 = Up(1024, 256, bilinear) + self.up2 = Up(512, 128, bilinear) + self.up3 = Up(256, 64, bilinear) + self.up4 = Up(128, 64, bilinear) + self.outc = OutConv(64, n_classes) + + def forward(self, x): + x1 = self.inc(x) + x2 = self.down1(x1) + x3 = self.down2(x2) + x4 = self.down3(x3) + x5 = self.down4(x4) + x = self.up1(x5, x4) + x = self.up2(x, x3) + x = self.up3(x, x2) + x = self.up4(x, x1) + x = F.normalize(x) + return x \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/DECA/decalib/utils/config.py b/motion-gan-pipeline/preprocessing/third/DECA/decalib/utils/config.py new file mode 100644 index 0000000..a44d1e2 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/decalib/utils/config.py @@ -0,0 +1,79 @@ +''' +Default config for DECA +''' +from yacs.config import CfgNode as CN +import argparse +import yaml +import os + +cfg = CN() + +abs_deca_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) +cfg.deca_dir = abs_deca_dir +cfg.device = 'cuda' +cfg.device_id = '0' + +cfg.pretrained_modelpath = os.path.join(cfg.deca_dir, 'data', 'deca_model.tar') + +# ---------------------------------------------------------------------------- # +# Options for Face model +# ---------------------------------------------------------------------------- # +cfg.model = CN() +cfg.model.topology_path = os.path.join(cfg.deca_dir, 'data', 'head_template.obj') +# texture data original from http://files.is.tue.mpg.de/tbolkart/FLAME/FLAME_texture_data.zip +cfg.model.dense_template_path = os.path.join(cfg.deca_dir, 'data', 'texture_data_256.npy') +cfg.model.fixed_displacement_path = os.path.join(cfg.deca_dir, 'data', 'fixed_displacement_256.npy') +cfg.model.flame_model_path = os.path.join(cfg.deca_dir, 'data', 'generic_model.pkl') +cfg.model.flame_lmk_embedding_path = os.path.join(cfg.deca_dir, 'data', 'landmark_embedding.npy') +cfg.model.face_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_mask.png') +cfg.model.face_eye_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_eye_mask.png') +cfg.model.mean_tex_path = os.path.join(cfg.deca_dir, 'data', 'mean_texture.jpg') +cfg.model.tex_path = os.path.join(cfg.deca_dir, 'data', 'FLAME_albedo_from_BFM.npz') +cfg.model.tex_type = 'BFM' # BFM, FLAME, albedoMM +cfg.model.uv_size = 256 +cfg.model.param_list = ['shape', 'tex', 'exp', 'pose', 'cam', 'light'] +cfg.model.n_shape = 100 +cfg.model.n_tex = 50 +cfg.model.n_exp = 50 +cfg.model.n_cam = 3 +cfg.model.n_pose = 6 +cfg.model.n_light = 27 +cfg.model.use_tex = False +cfg.model.jaw_type = 'aa' # default use axis angle, another option: euler + +## details +cfg.model.n_detail = 128 +cfg.model.max_z = 0.01 + +# ---------------------------------------------------------------------------- # +# Options for Dataset +# ---------------------------------------------------------------------------- # +cfg.dataset = CN() +cfg.dataset.batch_size = 24 +cfg.dataset.num_workers = 2 +cfg.dataset.image_size = 224 + +def get_cfg_defaults(): + """Get a yacs CfgNode object with default values for my_project.""" + # Return a clone so that the defaults will not be altered + # This is for the "local variable" use pattern + return cfg.clone() + +def update_cfg(cfg, cfg_file): + cfg.merge_from_file(cfg_file) + return cfg.clone() + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--cfg', type=str, help='cfg file path') + + args = parser.parse_args() + print(args, end='\n\n') + + cfg = get_cfg_defaults() + if args.cfg is not None: + cfg_file = args.cfg + cfg = update_cfg(cfg, args.cfg) + cfg.cfg_file = cfg_file + + return cfg diff --git a/motion-gan-pipeline/preprocessing/third/DECA/decalib/utils/renderer.py b/motion-gan-pipeline/preprocessing/third/DECA/decalib/utils/renderer.py new file mode 100755 index 0000000..e7a2155 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/decalib/utils/renderer.py @@ -0,0 +1,342 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de +import pdb +import matplotlib.pyplot as plt +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from skimage.io import imread +import imageio +from pytorch3d.structures import Meshes +from pytorch3d.io import load_obj +from pytorch3d.renderer.mesh import rasterize_meshes +from . import util +# from .rasterizer.standard_rasterize_cuda import standard_rasterize + +class Pytorch3dRasterizer(nn.Module): + """ Borrowed from https://github.com/facebookresearch/pytorch3d + Notice: + x,y,z are in image space, normalized + can only render squared image now + """ + + def __init__(self, image_size=224): + """ + use fixed raster_settings for rendering faces + """ + super().__init__() + raster_settings = { + 'image_size': image_size, + 'blur_radius': 0.0, + 'faces_per_pixel': 1, + 'bin_size': None, + 'max_faces_per_bin': None, + 'perspective_correct': False, + } + raster_settings = util.dict2obj(raster_settings) + self.raster_settings = raster_settings + + def forward(self, vertices, faces, attributes=None): + fixed_vertices = vertices.clone() + fixed_vertices[...,:2] = -fixed_vertices[...,:2] + meshes_screen = Meshes(verts=fixed_vertices.float(), faces=faces.long()) + raster_settings = self.raster_settings + pix_to_face, zbuf, bary_coords, dists = rasterize_meshes( + meshes_screen, + image_size=raster_settings.image_size, + blur_radius=raster_settings.blur_radius, + faces_per_pixel=raster_settings.faces_per_pixel, + bin_size=raster_settings.bin_size, + max_faces_per_bin=raster_settings.max_faces_per_bin, + perspective_correct=raster_settings.perspective_correct, + ) + vismask = (pix_to_face > -1).float() + D = attributes.shape[-1] + attributes = attributes.clone(); attributes = attributes.view(attributes.shape[0]*attributes.shape[1], 3, attributes.shape[-1]) + N, H, W, K, _ = bary_coords.shape + mask = pix_to_face == -1 + pix_to_face = pix_to_face.clone() + pix_to_face[mask] = 0 + idx = pix_to_face.view(N * H * W * K, 1, 1).expand(N * H * W * K, 3, D) + pixel_face_vals = attributes.gather(0, idx).view(N, H, W, K, 3, D) + pixel_vals = (bary_coords[..., None] * pixel_face_vals).sum(dim=-2) + pixel_vals[mask] = 0 # Replace masked values in output. + pixel_vals = pixel_vals[:,:,:,0].permute(0,3,1,2) + pixel_vals = torch.cat([pixel_vals, vismask[:,:,:,0][:,None,:,:]], dim=1) + return pixel_vals + +class SRenderY(nn.Module): + def __init__(self, image_size, obj_filename, uv_size=256, rasterizer_type='pytorch3d'): + super(SRenderY, self).__init__() + self.image_size = image_size + self.uv_size = uv_size + + verts, faces, aux = load_obj(obj_filename) + uvcoords = aux.verts_uvs[None, ...] # (N, V, 2) + uvfaces = faces.textures_idx[None, ...] # (N, F, 3) + faces = faces.verts_idx[None,...] + + if rasterizer_type == 'pytorch3d': + self.rasterizer = Pytorch3dRasterizer(image_size) + self.uv_rasterizer = Pytorch3dRasterizer(uv_size) + + # faces + dense_triangles = util.generate_triangles(uv_size, uv_size) + self.register_buffer('dense_faces', torch.from_numpy(dense_triangles).long()[None,:,:]) + self.register_buffer('faces', faces) + self.register_buffer('raw_uvcoords', uvcoords) + + # uv coords + uvcoords = torch.cat([uvcoords, uvcoords[:,:,0:1]*0.+1.], -1) #[bz, ntv, 3] + uvcoords = uvcoords*2 - 1; uvcoords[...,1] = -uvcoords[...,1] + face_uvcoords = util.face_vertices(uvcoords, uvfaces) + self.register_buffer('uvcoords', uvcoords) + self.register_buffer('uvfaces', uvfaces) + self.register_buffer('face_uvcoords', face_uvcoords) + + # shape colors, for rendering shape overlay + colors = torch.tensor([180, 180, 180])[None, None, :].repeat(1, faces.max()+1, 1).float()/255. + face_colors = util.face_vertices(colors, faces) + self.register_buffer('face_colors', face_colors) + + ## SH factors for lighting + pi = np.pi + constant_factor = torch.tensor([1/np.sqrt(4*pi), ((2*pi)/3)*(np.sqrt(3/(4*pi))), ((2*pi)/3)*(np.sqrt(3/(4*pi))),\ + ((2*pi)/3)*(np.sqrt(3/(4*pi))), (pi/4)*(3)*(np.sqrt(5/(12*pi))), (pi/4)*(3)*(np.sqrt(5/(12*pi))),\ + (pi/4)*(3)*(np.sqrt(5/(12*pi))), (pi/4)*(3/2)*(np.sqrt(5/(12*pi))), (pi/4)*(1/2)*(np.sqrt(5/(4*pi)))]).float() + self.register_buffer('constant_factor', constant_factor) + + def forward(self, vertices, transformed_vertices, albedos, lights=None, light_type='point'): + ''' + -- Texture Rendering + vertices: [batch_size, V, 3], vertices in world space, for calculating normals, then shading + transformed_vertices: [batch_size, V, 3], range:normalized to [-1,1], projected vertices in image space (that is aligned to the iamge pixel), for rasterization + albedos: [batch_size, 3, h, w], uv map + lights: + spherical homarnic: [N, 9(shcoeff), 3(rgb)] + points/directional lighting: [N, n_lights, 6(xyzrgb)] + light_type: + point or directional + ''' + batch_size = vertices.shape[0] + ## rasterizer near 0 far 100. move mesh so minz larger than 0 + transformed_vertices[:,:,2] = transformed_vertices[:,:,2] + 10 + # attributes + face_vertices = util.face_vertices(vertices, self.faces.expand(batch_size, -1, -1)) + normals = util.vertex_normals(vertices, self.faces.expand(batch_size, -1, -1)); face_normals = util.face_vertices(normals, self.faces.expand(batch_size, -1, -1)) + transformed_normals = util.vertex_normals(transformed_vertices, self.faces.expand(batch_size, -1, -1)); transformed_face_normals = util.face_vertices(transformed_normals, self.faces.expand(batch_size, -1, -1)) + + attributes = torch.cat([self.face_uvcoords.expand(batch_size, -1, -1, -1), + transformed_face_normals.detach(), + face_vertices.detach(), + face_normals], + -1) + # rasterize + rendering = self.rasterizer(transformed_vertices, self.faces.expand(batch_size, -1, -1), attributes) + + #### + # vis mask + alpha_images = rendering[:, -1, :, :][:, None, :, :].detach() + + # albedo + #pdb.set_trace() + uvcoords_images = rendering[:, :3, :, :]; grid = (uvcoords_images).permute(0, 2, 3, 1)[:, :, :, :2] + albedo_images = F.grid_sample(albedos, grid, align_corners=False) + + # visible mask for pixels with positive normal direction + transformed_normal_map = rendering[:, 3:6, :, :].detach() + pos_mask = (transformed_normal_map[:, 2:, :, :] < -0.05).float() + + # shading + normal_images = rendering[:, 9:12, :, :] + if lights is not None: + if lights.shape[1] == 9: + shading_images = self.add_SHlight(normal_images, lights) + else: + if light_type=='point': + vertice_images = rendering[:, 6:9, :, :].detach() + shading = self.add_pointlight(vertice_images.permute(0,2,3,1).reshape([batch_size, -1, 3]), normal_images.permute(0,2,3,1).reshape([batch_size, -1, 3]), lights) + shading_images = shading.reshape([batch_size, albedo_images.shape[2], albedo_images.shape[3], 3]).permute(0,3,1,2) + else: + shading = self.add_directionlight(normal_images.permute(0,2,3,1).reshape([batch_size, -1, 3]), lights) + shading_images = shading.reshape([batch_size, albedo_images.shape[2], albedo_images.shape[3], 3]).permute(0,3,1,2) + images = albedo_images*shading_images + else: + images = albedo_images + shading_images = images.detach()*0. + + outputs = { + 'images': images*alpha_images, + 'albedo_images': albedo_images*alpha_images, + 'alpha_images': alpha_images, + 'pos_mask': pos_mask, + 'shading_images': shading_images, + 'grid': grid, + 'normals': normals, + 'normal_images': normal_images*alpha_images, + 'transformed_normals': transformed_normals, + } + + return outputs + + def add_SHlight(self, normal_images, sh_coeff): + ''' + sh_coeff: [bz, 9, 3] + ''' + N = normal_images + sh = torch.stack([ + N[:,0]*0.+1., N[:,0], N[:,1], \ + N[:,2], N[:,0]*N[:,1], N[:,0]*N[:,2], + N[:,1]*N[:,2], N[:,0]**2 - N[:,1]**2, 3*(N[:,2]**2) - 1 + ], + 1) # [bz, 9, h, w] + sh = sh*self.constant_factor[None,:,None,None] + shading = torch.sum(sh_coeff[:,:,:,None,None]*sh[:,:,None,:,:], 1) # [bz, 9, 3, h, w] + return shading + + def add_pointlight(self, vertices, normals, lights): + ''' + vertices: [bz, nv, 3] + lights: [bz, nlight, 6] + returns: + shading: [bz, nv, 3] + ''' + light_positions = lights[:,:,:3]; light_intensities = lights[:,:,3:] + directions_to_lights = F.normalize(light_positions[:,:,None,:] - vertices[:,None,:,:], dim=3) + # normals_dot_lights = torch.clamp((normals[:,None,:,:]*directions_to_lights).sum(dim=3), 0., 1.) + normals_dot_lights = (normals[:,None,:,:]*directions_to_lights).sum(dim=3) + shading = normals_dot_lights[:,:,:,None]*light_intensities[:,:,None,:] + return shading.mean(1) + + def add_directionlight(self, normals, lights): + ''' + normals: [bz, nv, 3] + lights: [bz, nlight, 6] + returns: + shading: [bz, nv, 3] + ''' + light_direction = lights[:,:,:3]; light_intensities = lights[:,:,3:] + directions_to_lights = F.normalize(light_direction[:,:,None,:].expand(-1,-1,normals.shape[1],-1), dim=3) + # normals_dot_lights = torch.clamp((normals[:,None,:,:]*directions_to_lights).sum(dim=3), 0., 1.) + # normals_dot_lights = (normals[:,None,:,:]*directions_to_lights).sum(dim=3) + normals_dot_lights = torch.clamp((normals[:,None,:,:]*directions_to_lights).sum(dim=3), 0., 1.) + shading = normals_dot_lights[:,:,:,None]*light_intensities[:,:,None,:] + return shading.mean(1) + + def render_shape(self, vertices, transformed_vertices, images=None, detail_normal_images=None, lights=None): + ''' + -- rendering shape with detail normal map + ''' + batch_size = vertices.shape[0] + # set lighting + if lights is None: + light_positions = torch.tensor( + [ + [-1,1,1], + [1,1,1], + [-1,-1,1], + [1,-1,1], + [0,0,1] + ] + )[None,:,:].expand(batch_size, -1, -1).float() + light_intensities = torch.ones_like(light_positions).float()*1.7 + lights = torch.cat((light_positions, light_intensities), 2).to(vertices.device) + transformed_vertices[:,:,2] = transformed_vertices[:,:,2] + 10 + + # Attributes + face_vertices = util.face_vertices(vertices, self.faces.expand(batch_size, -1, -1)) + normals = util.vertex_normals(vertices, self.faces.expand(batch_size, -1, -1)); face_normals = util.face_vertices(normals, self.faces.expand(batch_size, -1, -1)) + transformed_normals = util.vertex_normals(transformed_vertices, self.faces.expand(batch_size, -1, -1)); transformed_face_normals = util.face_vertices(transformed_normals, self.faces.expand(batch_size, -1, -1)) + attributes = torch.cat([self.face_colors.expand(batch_size, -1, -1, -1), + transformed_face_normals.detach(), + face_vertices.detach(), + face_normals], + -1) + # rasterize + rendering = self.rasterizer(transformed_vertices, self.faces.expand(batch_size, -1, -1), attributes) + + #### + alpha_images = rendering[:, -1, :, :][:, None, :, :].detach() + + # albedo + albedo_images = rendering[:, :3, :, :] + # mask + transformed_normal_map = rendering[:, 3:6, :, :].detach() + pos_mask = (transformed_normal_map[:, 2:, :, :] < 0.15).float() + + # shading + normal_images = rendering[:, 9:12, :, :].detach() + vertice_images = rendering[:, 6:9, :, :].detach() + if detail_normal_images is not None: + normal_images = detail_normal_images + + shading = self.add_directionlight(normal_images.permute(0,2,3,1).reshape([batch_size, -1, 3]), lights) + shading_images = shading.reshape([batch_size, albedo_images.shape[2], albedo_images.shape[3], 3]).permute(0,3,1,2).contiguous() + shaded_images = albedo_images*shading_images + + alpha_images = alpha_images*pos_mask + if images is None: + shape_images = shaded_images*alpha_images + torch.zeros_like(shaded_images).to(vertices.device)*(1-alpha_images) + else: + shape_images = shaded_images*alpha_images + images*(1-alpha_images) + return shape_images + + def render_depth(self, transformed_vertices): + ''' + -- rendering depth + ''' + batch_size = transformed_vertices.shape[0] + + transformed_vertices[:,:,2] = transformed_vertices[:,:,2] - transformed_vertices[:,:,2].min() + z = -transformed_vertices[:,:,2:].repeat(1,1,3).clone() + z = z-z.min() + z = z/z.max() + # Attributes + attributes = util.face_vertices(z, self.faces.expand(batch_size, -1, -1)) + # rasterize + transformed_vertices[:,:,2] = transformed_vertices[:,:,2] + 10 + rendering = self.rasterizer(transformed_vertices, self.faces.expand(batch_size, -1, -1), attributes) + + #### + alpha_images = rendering[:, -1, :, :][:, None, :, :].detach() + depth_images = rendering[:, :1, :, :] + return depth_images + + def render_normal(self, transformed_vertices, normals): + ''' + -- rendering normal + ''' + batch_size = normals.shape[0] + + # Attributes + attributes = util.face_vertices(normals, self.faces.expand(batch_size, -1, -1)) + # rasterize + rendering = self.rasterizer(transformed_vertices, self.faces.expand(batch_size, -1, -1), attributes) + #### + alpha_images = rendering[:, -1, :, :][:, None, :, :].detach() + normal_images = rendering[:, :3, :, :] + return normal_images + + def world2uv(self, vertices): + ''' + warp vertices from world space to uv space + vertices: [bz, V, 3] + uv_vertices: [bz, 3, h, w] + ''' + batch_size = vertices.shape[0] + face_vertices = util.face_vertices(vertices, self.faces.expand(batch_size, -1, -1)) + uv_vertices = self.uv_rasterizer(self.uvcoords.expand(batch_size, -1, -1), self.uvfaces.expand(batch_size, -1, -1), face_vertices)[:, :3] + return uv_vertices \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/DECA/decalib/utils/rotation_converter.py b/motion-gan-pipeline/preprocessing/third/DECA/decalib/utils/rotation_converter.py new file mode 100644 index 0000000..89756d9 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/decalib/utils/rotation_converter.py @@ -0,0 +1,381 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import torch +import pdb + +''' Rotation Converter +Representations: + euler angle(3), angle axis(3), rotation matrix(3x3), quaternion(4), continous repre +Ref: + https://kornia.readthedocs.io/en/v0.1.2/_modules/torchgeometry/core/conversions.html# + smplx/lbs +''' + +pi = torch.Tensor([3.14159265358979323846]) +def rad2deg(tensor): + """Function that converts angles from radians to degrees. + + See :class:`~torchgeometry.RadToDeg` for details. + + Args: + tensor (Tensor): Tensor of arbitrary shape. + + Returns: + Tensor: Tensor with same shape as input. + + Example: + >>> input = tgm.pi * torch.rand(1, 3, 3) + >>> output = tgm.rad2deg(input) + """ + if not torch.is_tensor(tensor): + raise TypeError("Input type is not a torch.Tensor. Got {}" + .format(type(tensor))) + + return 180. * tensor / pi.to(tensor.device).type(tensor.dtype) + +def deg2rad(tensor): + """Function that converts angles from degrees to radians. + + See :class:`~torchgeometry.DegToRad` for details. + + Args: + tensor (Tensor): Tensor of arbitrary shape. + + Returns: + Tensor: Tensor with same shape as input. + + Examples:: + + >>> input = 360. * torch.rand(1, 3, 3) + >>> output = tgm.deg2rad(input) + """ + if not torch.is_tensor(tensor): + raise TypeError("Input type is not a torch.Tensor. Got {}" + .format(type(tensor))) + + return tensor * pi.to(tensor.device).type(tensor.dtype) / 180. + +######### to quaternion +def euler_to_quaternion(r): + x = r[..., 0] + y = r[..., 1] + z = r[..., 2] + + z = z/2.0 + y = y/2.0 + x = x/2.0 + cz = torch.cos(z) + sz = torch.sin(z) + cy = torch.cos(y) + sy = torch.sin(y) + cx = torch.cos(x) + sx = torch.sin(x) + quaternion = torch.zeros_like(r.repeat(1,2))[..., :4].to(r.device) + quaternion[..., 0] += cx*cy*cz - sx*sy*sz + quaternion[..., 1] += cx*sy*sz + cy*cz*sx + quaternion[..., 2] += cx*cz*sy - sx*cy*sz + quaternion[..., 3] += cx*cy*sz + sx*cz*sy + return quaternion + +def rotation_matrix_to_quaternion(rotation_matrix, eps=1e-6): + """Convert 3x4 rotation matrix to 4d quaternion vector + + This algorithm is based on algorithm described in + https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py#L201 + + Args: + rotation_matrix (Tensor): the rotation matrix to convert. + + Return: + Tensor: the rotation in quaternion + + Shape: + - Input: :math:`(N, 3, 4)` + - Output: :math:`(N, 4)` + + Example: + >>> input = torch.rand(4, 3, 4) # Nx3x4 + >>> output = tgm.rotation_matrix_to_quaternion(input) # Nx4 + """ + if not torch.is_tensor(rotation_matrix): + raise TypeError("Input type is not a torch.Tensor. Got {}".format( + type(rotation_matrix))) + + if len(rotation_matrix.shape) > 3: + raise ValueError( + "Input size must be a three dimensional tensor. Got {}".format( + rotation_matrix.shape)) + # if not rotation_matrix.shape[-2:] == (3, 4): + # raise ValueError( + # "Input size must be a N x 3 x 4 tensor. Got {}".format( + # rotation_matrix.shape)) + + rmat_t = torch.transpose(rotation_matrix, 1, 2) + + mask_d2 = rmat_t[:, 2, 2] < eps + + mask_d0_d1 = rmat_t[:, 0, 0] > rmat_t[:, 1, 1] + mask_d0_nd1 = rmat_t[:, 0, 0] < -rmat_t[:, 1, 1] + + t0 = 1 + rmat_t[:, 0, 0] - rmat_t[:, 1, 1] - rmat_t[:, 2, 2] + q0 = torch.stack([rmat_t[:, 1, 2] - rmat_t[:, 2, 1], + t0, rmat_t[:, 0, 1] + rmat_t[:, 1, 0], + rmat_t[:, 2, 0] + rmat_t[:, 0, 2]], -1) + t0_rep = t0.repeat(4, 1).t() + + t1 = 1 - rmat_t[:, 0, 0] + rmat_t[:, 1, 1] - rmat_t[:, 2, 2] + q1 = torch.stack([rmat_t[:, 2, 0] - rmat_t[:, 0, 2], + rmat_t[:, 0, 1] + rmat_t[:, 1, 0], + t1, rmat_t[:, 1, 2] + rmat_t[:, 2, 1]], -1) + t1_rep = t1.repeat(4, 1).t() + + t2 = 1 - rmat_t[:, 0, 0] - rmat_t[:, 1, 1] + rmat_t[:, 2, 2] + q2 = torch.stack([rmat_t[:, 0, 1] - rmat_t[:, 1, 0], + rmat_t[:, 2, 0] + rmat_t[:, 0, 2], + rmat_t[:, 1, 2] + rmat_t[:, 2, 1], t2], -1) + t2_rep = t2.repeat(4, 1).t() + + t3 = 1 + rmat_t[:, 0, 0] + rmat_t[:, 1, 1] + rmat_t[:, 2, 2] + q3 = torch.stack([t3, rmat_t[:, 1, 2] - rmat_t[:, 2, 1], + rmat_t[:, 2, 0] - rmat_t[:, 0, 2], + rmat_t[:, 0, 1] - rmat_t[:, 1, 0]], -1) + t3_rep = t3.repeat(4, 1).t() + + mask_c0 = mask_d2 * mask_d0_d1.float() + mask_c1 = mask_d2 * (1 - mask_d0_d1.float()) + mask_c2 = (1 - mask_d2.float()) * mask_d0_nd1 + mask_c3 = (1 - mask_d2.float()) * (1 - mask_d0_nd1.float()) + mask_c0 = mask_c0.view(-1, 1).type_as(q0) + mask_c1 = mask_c1.view(-1, 1).type_as(q1) + mask_c2 = mask_c2.view(-1, 1).type_as(q2) + mask_c3 = mask_c3.view(-1, 1).type_as(q3) + + q = q0 * mask_c0 + q1 * mask_c1 + q2 * mask_c2 + q3 * mask_c3 + q /= torch.sqrt(t0_rep * mask_c0 + t1_rep * mask_c1 + # noqa + t2_rep * mask_c2 + t3_rep * mask_c3) # noqa + q *= 0.5 + return q + +# def angle_axis_to_quaternion(theta): +# batch_size = theta.shape[0] +# l1norm = torch.norm(theta + 1e-8, p=2, dim=1) +# angle = torch.unsqueeze(l1norm, -1) +# normalized = torch.div(theta, angle) +# angle = angle * 0.5 +# v_cos = torch.cos(angle) +# v_sin = torch.sin(angle) +# quat = torch.cat([v_cos, v_sin * normalized], dim=1) +# return quat + +def angle_axis_to_quaternion(angle_axis: torch.Tensor) -> torch.Tensor: + """Convert an angle axis to a quaternion. + + Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h + + Args: + angle_axis (torch.Tensor): tensor with angle axis. + + Return: + torch.Tensor: tensor with quaternion. + + Shape: + - Input: :math:`(*, 3)` where `*` means, any number of dimensions + - Output: :math:`(*, 4)` + + Example: + >>> angle_axis = torch.rand(2, 4) # Nx4 + >>> quaternion = tgm.angle_axis_to_quaternion(angle_axis) # Nx3 + """ + if not torch.is_tensor(angle_axis): + raise TypeError("Input type is not a torch.Tensor. Got {}".format( + type(angle_axis))) + + if not angle_axis.shape[-1] == 3: + raise ValueError("Input must be a tensor of shape Nx3 or 3. Got {}" + .format(angle_axis.shape)) + # unpack input and compute conversion + a0: torch.Tensor = angle_axis[..., 0:1] + a1: torch.Tensor = angle_axis[..., 1:2] + a2: torch.Tensor = angle_axis[..., 2:3] + theta_squared: torch.Tensor = a0 * a0 + a1 * a1 + a2 * a2 + + theta: torch.Tensor = torch.sqrt(theta_squared) + half_theta: torch.Tensor = theta * 0.5 + + mask: torch.Tensor = theta_squared > 0.0 + ones: torch.Tensor = torch.ones_like(half_theta) + + k_neg: torch.Tensor = 0.5 * ones + k_pos: torch.Tensor = torch.sin(half_theta) / theta + k: torch.Tensor = torch.where(mask, k_pos, k_neg) + w: torch.Tensor = torch.where(mask, torch.cos(half_theta), ones) + + quaternion: torch.Tensor = torch.zeros_like(angle_axis) + quaternion[..., 0:1] += a0 * k + quaternion[..., 1:2] += a1 * k + quaternion[..., 2:3] += a2 * k + return torch.cat([w, quaternion], dim=-1) + +#### quaternion to +def quaternion_to_rotation_matrix(quat): + """Convert quaternion coefficients to rotation matrix. + Args: + quat: size = [B, 4] 4 <===>(w, x, y, z) + Returns: + Rotation matrix corresponding to the quaternion -- size = [B, 3, 3] + """ + norm_quat = quat + norm_quat = norm_quat / norm_quat.norm(p=2, dim=1, keepdim=True) + w, x, y, z = norm_quat[:, 0], norm_quat[:, 1], norm_quat[:, 2], norm_quat[:, 3] + + B = quat.size(0) + + w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2) + wx, wy, wz = w * x, w * y, w * z + xy, xz, yz = x * y, x * z, y * z + + rotMat = torch.stack([w2 + x2 - y2 - z2, 2 * xy - 2 * wz, 2 * wy + 2 * xz, + 2 * wz + 2 * xy, w2 - x2 + y2 - z2, 2 * yz - 2 * wx, + 2 * xz - 2 * wy, 2 * wx + 2 * yz, w2 - x2 - y2 + z2], dim=1).view(B, 3, 3) + return rotMat + +def quaternion_to_angle_axis(quaternion: torch.Tensor): + """Convert quaternion vector to angle axis of rotation. TODO: CORRECT + + Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h + + Args: + quaternion (torch.Tensor): tensor with quaternions. + + Return: + torch.Tensor: tensor with angle axis of rotation. + + Shape: + - Input: :math:`(*, 4)` where `*` means, any number of dimensions + - Output: :math:`(*, 3)` + + Example: + >>> quaternion = torch.rand(2, 4) # Nx4 + >>> angle_axis = tgm.quaternion_to_angle_axis(quaternion) # Nx3 + """ + if not torch.is_tensor(quaternion): + raise TypeError("Input type is not a torch.Tensor. Got {}".format( + type(quaternion))) + + if not quaternion.shape[-1] == 4: + raise ValueError("Input must be a tensor of shape Nx4 or 4. Got {}" + .format(quaternion.shape)) + # unpack input and compute conversion + q1: torch.Tensor = quaternion[..., 1] + q2: torch.Tensor = quaternion[..., 2] + q3: torch.Tensor = quaternion[..., 3] + sin_squared_theta: torch.Tensor = q1 * q1 + q2 * q2 + q3 * q3 + + sin_theta: torch.Tensor = torch.sqrt(sin_squared_theta) + cos_theta: torch.Tensor = quaternion[..., 0] + two_theta: torch.Tensor = 2.0 * torch.where( + cos_theta < 0.0, + torch.atan2(-sin_theta, -cos_theta), + torch.atan2(sin_theta, cos_theta)) + + k_pos: torch.Tensor = two_theta / sin_theta + k_neg: torch.Tensor = 2.0 * torch.ones_like(sin_theta).to(quaternion.device) + k: torch.Tensor = torch.where(sin_squared_theta > 0.0, k_pos, k_neg) + + angle_axis: torch.Tensor = torch.zeros_like(quaternion).to(quaternion.device)[..., :3] + angle_axis[..., 0] += q1 * k + angle_axis[..., 1] += q2 * k + angle_axis[..., 2] += q3 * k + return angle_axis + +#### batch converter +def batch_euler2axis(r): + return quaternion_to_angle_axis(euler_to_quaternion(r)) + +def batch_euler2matrix(r): + return quaternion_to_rotation_matrix(euler_to_quaternion(r)) + +def batch_matrix2euler(rot_mats): + # Calculates rotation matrix to euler angles + # Careful for extreme cases of eular angles like [0.0, pi, 0.0] + ### only y? + # TODO: + sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] + + rot_mats[:, 1, 0] * rot_mats[:, 1, 0]) + return torch.atan2(-rot_mats[:, 2, 0], sy) + +def batch_matrix2axis(rot_mats): + return quaternion_to_angle_axis(rotation_matrix_to_quaternion(rot_mats)) + +def batch_axis2matrix(theta): + + # angle axis to rotation matrix + # theta N x 3 + # return quat2mat(quat) + # batch_rodrigues + return quaternion_to_rotation_matrix(angle_axis_to_quaternion(theta)) + +def batch_axis2euler(theta): + return batch_matrix2euler(batch_axis2matrix(theta)) + +def batch_axis2euler(r): + return rot_mat_to_euler(batch_rodrigues(r)) + + +def batch_orth_proj(X, camera): + ''' + X is N x num_pquaternion_to_angle_axisoints x 3 + ''' + pdb.set_trace() + camera = camera.clone().view(-1, 1, 3) + X_trans = X[:, :, :2] + camera[:, :, 1:] + X_trans = torch.cat([X_trans, X[:,:,2:]], 2) + Xn = (camera[:, :, 0:1] * X_trans) + return Xn + +def batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32): + ''' same as batch_matrix2axis + Calculates the rotation matrices for a batch of rotation vectors + Parameters + ---------- + rot_vecs: torch.tensor Nx3 + array of N axis-angle vectors + Returns + ------- + R: torch.tensor Nx3x3 + The rotation matrices for the given axis-angle parameters + ''' + + batch_size = rot_vecs.shape[0] + device = rot_vecs.device + + angle = torch.norm(rot_vecs + 1e-8, dim=1, keepdim=True) + rot_dir = rot_vecs / angle + + cos = torch.unsqueeze(torch.cos(angle), dim=1) + sin = torch.unsqueeze(torch.sin(angle), dim=1) + + # Bx1 arrays + rx, ry, rz = torch.split(rot_dir, 1, dim=1) + K = torch.zeros((batch_size, 3, 3), dtype=dtype, device=device) + + zeros = torch.zeros((batch_size, 1), dtype=dtype, device=device) + K = torch.cat([zeros, -rz, ry, rz, zeros, -rx, -ry, rx, zeros], dim=1) \ + .view((batch_size, 3, 3)) + + ident = torch.eye(3, dtype=dtype, device=device).unsqueeze(dim=0) + rot_mat = ident + sin * K + (1 - cos) * torch.bmm(K, K) + return rot_mat diff --git a/motion-gan-pipeline/preprocessing/third/DECA/decalib/utils/util.py b/motion-gan-pipeline/preprocessing/third/DECA/decalib/utils/util.py new file mode 100755 index 0000000..42e3172 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/decalib/utils/util.py @@ -0,0 +1,601 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import numpy as np +import torch +import torch.nn.functional as F +import math +from collections import OrderedDict +import os +from scipy.ndimage import morphology +from skimage.io import imsave +import cv2 + +def upsample_mesh(vertices, normals, faces, displacement_map, texture_map, dense_template): + ''' upsampling coarse mesh (with displacment map) + vertices: vertices of coarse mesh, [nv, 3] + normals: vertex normals, [nv, 3] + faces: faces of coarse mesh, [nf, 3] + texture_map: texture map, [256, 256, 3] + displacement_map: displacment map, [256, 256] + dense_template: + Returns: + dense_vertices: upsampled vertices with details, [number of dense vertices, 3] + dense_colors: vertex color, [number of dense vertices, 3] + dense_faces: [number of dense faces, 3] + ''' + img_size = dense_template['img_size'] + dense_faces = dense_template['f'] + x_coords = dense_template['x_coords'] + y_coords = dense_template['y_coords'] + valid_pixel_ids = dense_template['valid_pixel_ids'] + valid_pixel_3d_faces = dense_template['valid_pixel_3d_faces'] + valid_pixel_b_coords = dense_template['valid_pixel_b_coords'] + + pixel_3d_points = vertices[valid_pixel_3d_faces[:, 0], :] * valid_pixel_b_coords[:, 0][:, np.newaxis] + \ + vertices[valid_pixel_3d_faces[:, 1], :] * valid_pixel_b_coords[:, 1][:, np.newaxis] + \ + vertices[valid_pixel_3d_faces[:, 2], :] * valid_pixel_b_coords[:, 2][:, np.newaxis] + vertex_normals = normals + pixel_3d_normals = vertex_normals[valid_pixel_3d_faces[:, 0], :] * valid_pixel_b_coords[:, 0][:, np.newaxis] + \ + vertex_normals[valid_pixel_3d_faces[:, 1], :] * valid_pixel_b_coords[:, 1][:, np.newaxis] + \ + vertex_normals[valid_pixel_3d_faces[:, 2], :] * valid_pixel_b_coords[:, 2][:, np.newaxis] + pixel_3d_normals = pixel_3d_normals / np.linalg.norm(pixel_3d_normals, axis=-1)[:, np.newaxis] + displacements = displacement_map[y_coords[valid_pixel_ids].astype(int), x_coords[valid_pixel_ids].astype(int)] + dense_colors = texture_map[y_coords[valid_pixel_ids].astype(int), x_coords[valid_pixel_ids].astype(int)] + offsets = np.einsum('i,ij->ij', displacements, pixel_3d_normals) + dense_vertices = pixel_3d_points + offsets + return dense_vertices, dense_colors, dense_faces + +# borrowed from https://github.com/YadiraF/PRNet/blob/master/utils/write.py +def write_obj(obj_name, + vertices, + faces, + colors=None, + texture=None, + uvcoords=None, + uvfaces=None, + inverse_face_order=False, + normal_map=None, + ): + ''' Save 3D face model with texture. + Ref: https://github.com/patrikhuber/eos/blob/bd00155ebae4b1a13b08bf5a991694d682abbada/include/eos/core/Mesh.hpp + Args: + obj_name: str + vertices: shape = (nver, 3) + colors: shape = (nver, 3) + faces: shape = (ntri, 3) + texture: shape = (uv_size, uv_size, 3) + uvcoords: shape = (nver, 2) max value<=1 + ''' + if obj_name.split('.')[-1] != 'obj': + obj_name = obj_name + '.obj' + mtl_name = obj_name.replace('.obj', '.mtl') + texture_name = obj_name.replace('.obj', '.png') + material_name = 'FaceTexture' + + faces = faces.copy() + # mesh lab start with 1, python/c++ start from 0 + faces += 1 + if inverse_face_order: + faces = faces[:, [2, 1, 0]] + if uvfaces is not None: + uvfaces = uvfaces[:, [2, 1, 0]] + + # write obj + with open(obj_name, 'w') as f: + # first line: write mtlib(material library) + # f.write('# %s\n' % os.path.basename(obj_name)) + # f.write('#\n') + # f.write('\n') + if texture is not None: + f.write('mtllib %s\n\n' % os.path.basename(mtl_name)) + + # write vertices + if colors is None: + for i in range(vertices.shape[0]): + f.write('v {} {} {}\n'.format(vertices[i, 0], vertices[i, 1], vertices[i, 2])) + else: + for i in range(vertices.shape[0]): + f.write('v {} {} {} {} {} {}\n'.format(vertices[i, 0], vertices[i, 1], vertices[i, 2], colors[i, 0], colors[i, 1], colors[i, 2])) + + # write uv coords + if texture is None: + for i in range(faces.shape[0]): + f.write('f {} {} {}\n'.format(faces[i, 2], faces[i, 1], faces[i, 0])) + else: + for i in range(uvcoords.shape[0]): + f.write('vt {} {}\n'.format(uvcoords[i,0], uvcoords[i,1])) + f.write('usemtl %s\n' % material_name) + # write f: ver ind/ uv ind + uvfaces = uvfaces + 1 + for i in range(faces.shape[0]): + f.write('f {}/{} {}/{} {}/{}\n'.format( + # faces[i, 2], uvfaces[i, 2], + # faces[i, 1], uvfaces[i, 1], + # faces[i, 0], uvfaces[i, 0] + faces[i, 0], uvfaces[i, 0], + faces[i, 1], uvfaces[i, 1], + faces[i, 2], uvfaces[i, 2] + ) + ) + # write mtl + with open(mtl_name, 'w') as f: + f.write('newmtl %s\n' % material_name) + s = 'map_Kd {}\n'.format(os.path.basename(texture_name)) # map to image + f.write(s) + + if normal_map is not None: + name, _ = os.path.splitext(obj_name) + normal_name = f'{name}_normals.png' + f.write(f'disp {normal_name}') + # out_normal_map = normal_map / (np.linalg.norm( + # normal_map, axis=-1, keepdims=True) + 1e-9) + # out_normal_map = (out_normal_map + 1) * 0.5 + + cv2.imwrite( + normal_name, + # (out_normal_map * 255).astype(np.uint8)[:, :, ::-1] + normal_map + ) + cv2.imwrite(texture_name, texture) + +# ---------------------------- process/generate vertices, normals, faces +def generate_triangles(h, w, margin_x=2, margin_y=5, mask = None): + # quad layout: + # 0 1 ... w-1 + # w w+1 + #. + # w*h + triangles = [] + for x in range(margin_x, w-1-margin_x): + for y in range(margin_y, h-1-margin_y): + triangle0 = [y*w + x, y*w + x + 1, (y+1)*w + x] + triangle1 = [y*w + x + 1, (y+1)*w + x + 1, (y+1)*w + x] + triangles.append(triangle0) + triangles.append(triangle1) + triangles = np.array(triangles) + triangles = triangles[:,[0,2,1]] + return triangles + +# borrowed from https://github.com/daniilidis-group/neural_renderer/blob/master/neural_renderer/vertices_to_faces.py +def face_vertices(vertices, faces): + """ + :param vertices: [batch size, number of vertices, 3] + :param faces: [batch size, number of faces, 3] + :return: [batch size, number of faces, 3, 3] + """ + assert (vertices.ndimension() == 3) + assert (faces.ndimension() == 3) + assert (vertices.shape[0] == faces.shape[0]) + assert (vertices.shape[2] == 3) + assert (faces.shape[2] == 3) + + bs, nv = vertices.shape[:2] + bs, nf = faces.shape[:2] + device = vertices.device + faces = faces + (torch.arange(bs, dtype=torch.int32).to(device) * nv)[:, None, None] + vertices = vertices.reshape((bs * nv, 3)) + # pytorch only supports long and byte tensors for indexing + return vertices[faces.long()] + +def vertex_normals(vertices, faces): + """ + :param vertices: [batch size, number of vertices, 3] + :param faces: [batch size, number of faces, 3] + :return: [batch size, number of vertices, 3] + """ + assert (vertices.ndimension() == 3) + assert (faces.ndimension() == 3) + assert (vertices.shape[0] == faces.shape[0]) + assert (vertices.shape[2] == 3) + assert (faces.shape[2] == 3) + bs, nv = vertices.shape[:2] + bs, nf = faces.shape[:2] + device = vertices.device + normals = torch.zeros(bs * nv, 3).to(device) + + faces = faces + (torch.arange(bs, dtype=torch.int32).to(device) * nv)[:, None, None] # expanded faces + vertices_faces = vertices.reshape((bs * nv, 3))[faces.long()] + + faces = faces.reshape(-1, 3) + vertices_faces = vertices_faces.reshape(-1, 3, 3) + + normals.index_add_(0, faces[:, 1].long(), + torch.cross(vertices_faces[:, 2] - vertices_faces[:, 1], vertices_faces[:, 0] - vertices_faces[:, 1])) + normals.index_add_(0, faces[:, 2].long(), + torch.cross(vertices_faces[:, 0] - vertices_faces[:, 2], vertices_faces[:, 1] - vertices_faces[:, 2])) + normals.index_add_(0, faces[:, 0].long(), + torch.cross(vertices_faces[:, 1] - vertices_faces[:, 0], vertices_faces[:, 2] - vertices_faces[:, 0])) + + normals = F.normalize(normals, eps=1e-6, dim=1) + normals = normals.reshape((bs, nv, 3)) + # pytorch only supports long and byte tensors for indexing + return normals + +def batch_orth_proj(X, camera): + ''' orthgraphic projection + X: 3d vertices, [bz, n_point, 3] + camera: scale and translation, [bz, 3], [scale, tx, ty] + ''' + camera = camera.clone().view(-1, 1, 3) + X_trans = X[:, :, :2] + camera[:, :, 1:] + X_trans = torch.cat([X_trans, X[:,:,2:]], 2) + shape = X_trans.shape + Xn = (camera[:, :, 0:1] * X_trans) + return Xn + +# -------------------------------------- image processing +# borrowed from: https://torchgeometry.readthedocs.io/en/latest/_modules/kornia/filters +def gaussian(window_size, sigma): + def gauss_fcn(x): + return -(x - window_size // 2)**2 / float(2 * sigma**2) + gauss = torch.stack( + [torch.exp(torch.tensor(gauss_fcn(x))) for x in range(window_size)]) + return gauss / gauss.sum() + +def get_gaussian_kernel(kernel_size: int, sigma: float): + r"""Function that returns Gaussian filter coefficients. + + Args: + kernel_size (int): filter size. It should be odd and positive. + sigma (float): gaussian standard deviation. + + Returns: + Tensor: 1D tensor with gaussian filter coefficients. + + Shape: + - Output: :math:`(\text{kernel_size})` + + Examples:: + + >>> kornia.image.get_gaussian_kernel(3, 2.5) + tensor([0.3243, 0.3513, 0.3243]) + + >>> kornia.image.get_gaussian_kernel(5, 1.5) + tensor([0.1201, 0.2339, 0.2921, 0.2339, 0.1201]) + """ + if not isinstance(kernel_size, int) or kernel_size % 2 == 0 or \ + kernel_size <= 0: + raise TypeError("kernel_size must be an odd positive integer. " + "Got {}".format(kernel_size)) + window_1d = gaussian(kernel_size, sigma) + return window_1d + +def get_gaussian_kernel2d(kernel_size, sigma): + r"""Function that returns Gaussian filter matrix coefficients. + + Args: + kernel_size (Tuple[int, int]): filter sizes in the x and y direction. + Sizes should be odd and positive. + sigma (Tuple[int, int]): gaussian standard deviation in the x and y + direction. + + Returns: + Tensor: 2D tensor with gaussian filter matrix coefficients. + + Shape: + - Output: :math:`(\text{kernel_size}_x, \text{kernel_size}_y)` + + Examples:: + + >>> kornia.image.get_gaussian_kernel2d((3, 3), (1.5, 1.5)) + tensor([[0.0947, 0.1183, 0.0947], + [0.1183, 0.1478, 0.1183], + [0.0947, 0.1183, 0.0947]]) + + >>> kornia.image.get_gaussian_kernel2d((3, 5), (1.5, 1.5)) + tensor([[0.0370, 0.0720, 0.0899, 0.0720, 0.0370], + [0.0462, 0.0899, 0.1123, 0.0899, 0.0462], + [0.0370, 0.0720, 0.0899, 0.0720, 0.0370]]) + """ + if not isinstance(kernel_size, tuple) or len(kernel_size) != 2: + raise TypeError("kernel_size must be a tuple of length two. Got {}" + .format(kernel_size)) + if not isinstance(sigma, tuple) or len(sigma) != 2: + raise TypeError("sigma must be a tuple of length two. Got {}" + .format(sigma)) + ksize_x, ksize_y = kernel_size + sigma_x, sigma_y = sigma + kernel_x = get_gaussian_kernel(ksize_x, sigma_x) + kernel_y = get_gaussian_kernel(ksize_y, sigma_y) + kernel_2d = torch.matmul( + kernel_x.unsqueeze(-1), kernel_y.unsqueeze(-1).t()) + return kernel_2d + +def gaussian_blur(x, kernel_size=(3,3), sigma=(0.8,0.8)): + b, c, h, w = x.shape + kernel = get_gaussian_kernel2d(kernel_size, sigma).to(x.device).to(x.dtype) + kernel = kernel.repeat(c, 1, 1, 1) + padding = [(k - 1) // 2 for k in kernel_size] + return F.conv2d(x, kernel, padding=padding, stride=1, groups=c) + +def _compute_binary_kernel(window_size): + r"""Creates a binary kernel to extract the patches. If the window size + is HxW will create a (H*W)xHxW kernel. + """ + window_range = window_size[0] * window_size[1] + kernel: torch.Tensor = torch.zeros(window_range, window_range) + for i in range(window_range): + kernel[i, i] += 1.0 + return kernel.view(window_range, 1, window_size[0], window_size[1]) + +def median_blur(x, kernel_size=(3,3)): + b, c, h, w = x.shape + kernel = _compute_binary_kernel(kernel_size).to(x.device).to(x.dtype) + kernel = kernel.repeat(c, 1, 1, 1) + padding = [(k - 1) // 2 for k in kernel_size] + features = F.conv2d(x, kernel, padding=padding, stride=1, groups=c) + features = features.view(b,c,-1,h,w) + median = torch.median(features, dim=2)[0] + return median + +def get_laplacian_kernel2d(kernel_size: int): + r"""Function that returns Gaussian filter matrix coefficients. + + Args: + kernel_size (int): filter size should be odd. + + Returns: + Tensor: 2D tensor with laplacian filter matrix coefficients. + + Shape: + - Output: :math:`(\text{kernel_size}_x, \text{kernel_size}_y)` + + Examples:: + + >>> kornia.image.get_laplacian_kernel2d(3) + tensor([[ 1., 1., 1.], + [ 1., -8., 1.], + [ 1., 1., 1.]]) + + >>> kornia.image.get_laplacian_kernel2d(5) + tensor([[ 1., 1., 1., 1., 1.], + [ 1., 1., 1., 1., 1.], + [ 1., 1., -24., 1., 1.], + [ 1., 1., 1., 1., 1.], + [ 1., 1., 1., 1., 1.]]) + + """ + if not isinstance(kernel_size, int) or kernel_size % 2 == 0 or \ + kernel_size <= 0: + raise TypeError("ksize must be an odd positive integer. Got {}" + .format(kernel_size)) + + kernel = torch.ones((kernel_size, kernel_size)) + mid = kernel_size // 2 + kernel[mid, mid] = 1 - kernel_size ** 2 + kernel_2d: torch.Tensor = kernel + return kernel_2d + +def laplacian(x): + # https://torchgeometry.readthedocs.io/en/latest/_modules/kornia/filters/laplacian.html + b, c, h, w = x.shape + kernel_size = 3 + kernel = get_laplacian_kernel2d(kernel_size).to(x.device).to(x.dtype) + kernel = kernel.repeat(c, 1, 1, 1) + padding = (kernel_size - 1) // 2 + return F.conv2d(x, kernel, padding=padding, stride=1, groups=c) + +def angle2matrix(angles): + ''' get rotation matrix from three rotation angles(degree). right-handed. + Args: + angles: [batch_size, 3] tensor containing X, Y, and Z angles. + x: pitch. positive for looking down. + y: yaw. positive for looking left. + z: roll. positive for tilting head right. + Returns: + R: [batch_size, 3, 3]. rotation matrices. + ''' + angles = angles*(np.pi)/180. + s = torch.sin(angles) + c = torch.cos(angles) + + cx, cy, cz = (c[:, 0], c[:, 1], c[:, 2]) + sx, sy, sz = (s[:, 0], s[:, 1], s[:, 2]) + + zeros = torch.zeros_like(s[:, 0]).to(angles.device) + ones = torch.ones_like(s[:, 0]).to(angles.device) + + # Rz.dot(Ry.dot(Rx)) + R_flattened = torch.stack( + [ + cz * cy, cz * sy * sx - sz * cx, cz * sy * cx + sz * sx, + sz * cy, sz * sy * sx + cz * cx, sz * sy * cx - cz * sx, + -sy, cy * sx, cy * cx, + ], + dim=0) #[batch_size, 9] + R = torch.reshape(R_flattened, (-1, 3, 3)) #[batch_size, 3, 3] + return R + +def binary_erosion(tensor, kernel_size=5): + # tensor: [bz, 1, h, w]. + device = tensor.device + mask = tensor.cpu().numpy() + structure=np.ones((kernel_size,kernel_size)) + new_mask = mask.copy() + for i in range(mask.shape[0]): + new_mask[i,0] = morphology.binary_erosion(mask[i,0], structure) + return torch.from_numpy(new_mask.astype(np.float32)).to(device) + +def flip_image(src_image, kps): + ''' + purpose: + flip a image given by src_image and the 2d keypoints + flip_mode: + 0: horizontal flip + >0: vertical flip + <0: horizontal & vertical flip + ''' + h, w = src_image.shape[0], src_image.shape[1] + src_image = cv2.flip(src_image, 1) + if kps is not None: + kps[:, 0] = w - 1 - kps[:, 0] + kp_map = [5, 4, 3, 2, 1, 0, 11, 10, 9, 8, 7, 6, 12, 13] + kps[:, :] = kps[kp_map] + + return src_image, kps + +# -------------------------------------- io +def copy_state_dict(cur_state_dict, pre_state_dict, prefix='', load_name=None): + def _get_params(key): + key = prefix + key + if key in pre_state_dict: + return pre_state_dict[key] + return None + for k in cur_state_dict.keys(): + if load_name is not None: + if load_name not in k: + continue + v = _get_params(k) + try: + if v is None: + # print('parameter {} not found'.format(k)) + continue + cur_state_dict[k].copy_(v) + except: + # print('copy param {} failed'.format(k)) + continue + +def check_mkdir(path): + if not os.path.exists(path): + print('creating %s' % path) + os.makedirs(path) + +def check_mkdirlist(pathlist): + for path in pathlist: + if not os.path.exists(path): + print('creating %s' % path) + os.makedirs(path) + +def tensor2image(tensor): + image = tensor.detach().cpu().numpy() + image = image*255. + image = np.maximum(np.minimum(image, 255), 0) + image = image.transpose(1,2,0)[:,:,[2,1,0]] + return image.astype(np.uint8).copy() + +def dict2obj(d): + # if isinstance(d, list): + # d = [dict2obj(x) for x in d] + if not isinstance(d, dict): + return d + class C(object): + pass + o = C() + for k in d: + o.__dict__[k] = dict2obj(d[k]) + return o + +class Struct(object): + def __init__(self, **kwargs): + for key, val in kwargs.items(): + setattr(self, key, val) + +# original saved file with DataParallel +def remove_module(state_dict): +# create new OrderedDict that does not contain `module.` + new_state_dict = OrderedDict() + for k, v in state_dict.items(): + name = k[7:] # remove `module.` + new_state_dict[name] = v + return new_state_dict + +def dict_tensor2npy(tensor_dict): + npy_dict = {} + for key in tensor_dict: + npy_dict[key] = tensor_dict[key][0].cpu().numpy() + return npy_dict + +# ---------------------------------- visualization +end_list = np.array([17, 22, 27, 42, 48, 31, 36, 68], dtype = np.int32) - 1 +def plot_kpts(image, kpts, color = 'r'): + ''' Draw 68 key points + Args: + image: the input image + kpt: (68, 3). + ''' + if color == 'r': + c = (255, 0, 0) + elif color == 'g': + c = (0, 255, 0) + elif color == 'b': + c = (255, 0, 0) + image = image.copy() + kpts = kpts.copy() + + for i in range(kpts.shape[0]): + st = kpts[i, :2] + if kpts.shape[1]==4: + if kpts[i, 3] > 0.5: + c = (0, 255, 0) + else: + c = (0, 0, 255) + image = cv2.circle(image,(st[0], st[1]), 1, c, 2) + if i in end_list: + continue + ed = kpts[i + 1, :2] + image = cv2.line(image, (st[0], st[1]), (ed[0], ed[1]), (255, 255, 255), 1) + + return image + +def plot_verts(image, kpts, color = 'r'): + ''' Draw 68 key points + Args: + image: the input image + kpt: (68, 3). + ''' + if color == 'r': + c = (255, 0, 0) + elif color == 'g': + c = (0, 255, 0) + elif color == 'b': + c = (0, 0, 255) + elif color == 'y': + c = (0, 255, 255) + image = image.copy() + + for i in range(kpts.shape[0]): + st = kpts[i, :2] + image = cv2.circle(image,(st[0], st[1]), 1, c, 2) + + return image + +def tensor_vis_landmarks(images, landmarks, gt_landmarks=None, color = 'g', isScale=True): + # visualize landmarks + vis_landmarks = [] + images = images.cpu().numpy() + predicted_landmarks = landmarks.detach().cpu().numpy() + if gt_landmarks is not None: + gt_landmarks_np = gt_landmarks.detach().cpu().numpy() + for i in range(images.shape[0]): + image = images[i] + image = image.transpose(1,2,0)[:,:,[2,1,0]].copy(); image = (image*255) + if isScale: + predicted_landmark = predicted_landmarks[i]*image.shape[0]/2 + image.shape[0]/2 + else: + predicted_landmark = predicted_landmarks[i] + if predicted_landmark.shape[0] == 68: + image_landmarks = plot_kpts(image, predicted_landmark, color) + if gt_landmarks is not None: + image_landmarks = plot_verts(image_landmarks, gt_landmarks_np[i]*image.shape[0]/2 + image.shape[0]/2, 'r') + else: + image_landmarks = plot_verts(image, predicted_landmark, color) + if gt_landmarks is not None: + image_landmarks = plot_verts(image_landmarks, gt_landmarks_np[i]*image.shape[0]/2 + image.shape[0]/2, 'r') + vis_landmarks.append(image_landmarks) + + vis_landmarks = np.stack(vis_landmarks) + vis_landmarks = torch.from_numpy(vis_landmarks[:,:,:,[2,1,0]].transpose(0,3,1,2))/255.#, dtype=torch.float32) + return vis_landmarks \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/DECA/demos/__init__.py b/motion-gan-pipeline/preprocessing/third/DECA/demos/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/motion-gan-pipeline/preprocessing/third/DECA/demos/demo_reconstruct.py b/motion-gan-pipeline/preprocessing/third/DECA/demos/demo_reconstruct.py new file mode 100755 index 0000000..16310f8 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/demos/demo_reconstruct.py @@ -0,0 +1,108 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import os, sys +import cv2 +import numpy as np +from time import time +from scipy.io import savemat +import argparse +from tqdm import tqdm + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +from decalib.deca import DECA +from decalib.datasets import datasets +from decalib.utils import util +from decalib.utils.config import cfg as deca_cfg + + +def main(args): + savefolder = args.savefolder + device = args.device + os.makedirs(savefolder, exist_ok=True) + + # load test images + testdata = datasets.TestData(args.inputpath, iscrop=args.iscrop, face_detector=args.detector) + + # run DECA + deca_cfg.model.use_tex = args.useTex + deca = DECA(config = deca_cfg, device=device) + # for i in range(len(testdata)): + for i in tqdm(range(len(testdata))): + name = testdata[i]['imagename'] + images = testdata[i]['image'].to(device)[None,...] + codedict = deca.encode(images) + opdict, visdict = deca.decode(codedict) #tensor + + print(opdict.keys()) + print(visdict.keys()) + + if args.saveDepth or args.saveKpt or args.saveObj or args.saveMat or args.saveImages: + os.makedirs(os.path.join(savefolder, name), exist_ok=True) + # -- save results + if args.saveDepth: + depth_image = deca.render.render_depth(opdict['transformed_vertices']).repeat(1,3,1,1) + visdict['depth_images'] = depth_image + cv2.imwrite(os.path.join(savefolder, name, name + '_depth.jpg'), util.tensor2image(depth_image[0])) + if args.saveKpt: + np.savetxt(os.path.join(savefolder, name, name + '_kpt2d.txt'), opdict['landmarks2d'][0].cpu().numpy()) + np.savetxt(os.path.join(savefolder, name, name + '_kpt3d.txt'), opdict['landmarks3d'][0].cpu().numpy()) + if args.saveObj: + deca.save_obj(os.path.join(savefolder, name, name + '.obj'), opdict) + if args.saveMat: + opdict = util.dict_tensor2npy(opdict) + savemat(os.path.join(savefolder, name, name + '.mat'), opdict) + if args.saveVis: + cv2.imwrite(os.path.join(savefolder, name + '_vis.jpg'), deca.visualize(visdict)) + if args.saveImages: + for vis_name in ['inputs', 'rendered_images', 'albedo_images', 'shape_images', 'shape_detail_images']: + if vis_name not in visdict.keys(): + continue + image = util.tensor2image(visdict[vis_name][0]) + cv2.imwrite(os.path.join(savefolder, name, name + '_' + vis_name +'.jpg'), util.tensor2image(visdict[vis_name][0])) + print(f'-- please check the results in {savefolder}') + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='DECA: Detailed Expression Capture and Animation') + + parser.add_argument('-i', '--inputpath', default='TestSamples/examples', type=str, + help='path to the test data, can be image folder, image path, image list, video') + parser.add_argument('-s', '--savefolder', default='TestSamples/examples/results', type=str, + help='path to the output directory, where results(obj, txt files) will be stored.') + parser.add_argument('--device', default='cuda', type=str, + help='set device, cpu for using cpu' ) + # process test images + parser.add_argument('--iscrop', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to crop input image, set false only when the test image are well cropped' ) + parser.add_argument('--detector', default='fan', type=str, + help='detector for cropping face, check decalib/detectors.py for details' ) + # save + parser.add_argument('--useTex', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to use FLAME texture model to generate uv texture map, \ + set it to True only if you downloaded texture model' ) + parser.add_argument('--saveVis', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save visualization of output' ) + parser.add_argument('--saveKpt', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save 2D and 3D keypoints' ) + parser.add_argument('--saveDepth', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save depth image' ) + parser.add_argument('--saveObj', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save outputs as .obj, detail mesh will end with _detail.obj. \ + Note that saving objs could be slow' ) + parser.add_argument('--saveMat', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save outputs as .mat' ) + parser.add_argument('--saveImages', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save visualization output as seperate images' ) + main(parser.parse_args()) \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/DECA/demos/demo_teaser.py b/motion-gan-pipeline/preprocessing/third/DECA/demos/demo_teaser.py new file mode 100755 index 0000000..bc0e630 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/demos/demo_teaser.py @@ -0,0 +1,126 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import os, sys +import cv2 +import numpy as np +from time import time +from scipy.io import savemat +import argparse +import imageio +from skimage.transform import rescale +import torch + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +from decalib.deca import DECA +from decalib.datasets import datasets +from decalib.utils import util +from decalib.utils.rotation_converter import batch_euler2axis, deg2rad + +def main(args): + savefolder = args.savefolder + device = args.device + os.makedirs(savefolder, exist_ok=True) + + # load test images + testdata = datasets.TestData(args.inputpath, iscrop=args.iscrop, face_detector=args.detector) + expdata = datasets.TestData(args.exp_path, iscrop=args.iscrop, face_detector=args.detector) + # DECA + deca = DECA(device=device) + + visdict_list_list = [] + for i in range(len(testdata)): + name = testdata[i]['imagename'] + images = testdata[i]['image'].to(device)[None,...] + codedict = deca.encode(images) + opdict, visdict = deca.decode(codedict) #tensor + ### show shape with different views and expressions + visdict_list = [] + max_yaw = 30 + yaw_list = list(range(0,max_yaw,5)) + list(range(max_yaw,-max_yaw,-5)) + list(range(-max_yaw,0,5)) + for k in yaw_list: #jaw angle from -50 to 50 + ## yaw angle + euler_pose = torch.randn((1, 3)) + euler_pose[:,1] = k#torch.rand((self.batch_size))*160 - 80 + euler_pose[:,0] = 0#(torch.rand((self.batch_size))*60 - 30)*(2./euler_pose[:,1].abs()) + euler_pose[:,2] = 0#(torch.rand((self.batch_size))*60 - 30)*(2./euler_pose[:,1].abs()) + global_pose = batch_euler2axis(deg2rad(euler_pose[:,:3].cuda())) + codedict['pose'][:,:3] = global_pose + codedict['cam'][:,:] = 0. + codedict['cam'][:,0] = 8 + _, visdict_view = deca.decode(codedict) + visdict = {x:visdict[x] for x in ['inputs', 'shape_detail_images']} + visdict['pose'] = visdict_view['shape_detail_images'] + visdict_list.append(visdict) + + euler_pose = torch.zeros((1, 3)) + global_pose = batch_euler2axis(deg2rad(euler_pose[:,:3].cuda())) + codedict['pose'][:,:3] = global_pose + for (i,k) in enumerate(range(0,31,2)): #jaw angle from -50 to 50 + # expression: jaw pose + euler_pose = torch.randn((1, 3)) + euler_pose[:,0] = k#torch.rand((self.batch_size))*160 - 80 + euler_pose[:,1] = 0#(torch.rand((self.batch_size))*60 - 30)*(2./euler_pose[:,1].abs()) + euler_pose[:,2] = 0#(torch.rand((self.batch_size))*60 - 30)*(2./euler_pose[:,1].abs()) + jaw_pose = batch_euler2axis(deg2rad(euler_pose[:,:3].cuda())) + codedict['pose'][:,3:] = jaw_pose + _, visdict_view = deca.decode(codedict) + visdict_list[i]['exp'] = visdict_view['shape_detail_images'] + count = i + + for (i,k) in enumerate(range(len(expdata))): #jaw angle from -50 to 50 + # expression: jaw pose + exp_images = expdata[i]['image'].to(device)[None,...] + exp_codedict = deca.encode(exp_images) + # transfer exp code + codedict['pose'][:,3:] = exp_codedict['pose'][:,3:] + codedict['exp'] = exp_codedict['exp'] + _, exp_visdict = deca.decode(codedict) + visdict_list[i+count]['exp'] = exp_visdict['shape_detail_images'] + + visdict_list_list.append(visdict_list) + + ### write gif + writer = imageio.get_writer(os.path.join(savefolder, 'teaser.gif'), mode='I') + for i in range(len(yaw_list)): + grid_image_list = [] + for j in range(len(testdata)): + grid_image = deca.visualize(visdict_list_list[j][i]) + grid_image_list.append(grid_image) + grid_image_all = np.concatenate(grid_image_list, 0) + grid_image_all = rescale(grid_image_all, 0.6, multichannel=True) # resize for showing in github + writer.append_data(grid_image_all[:,:,[2,1,0]]) + + print(f'-- please check the teaser figure in {savefolder}') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='DECA: Detailed Expression Capture and Animation') + + parser.add_argument('-i', '--inputpath', default='TestSamples/teaser', type=str, + help='path to the test data, can be image folder, image path, image list, video') + parser.add_argument('-e', '--exp_path', default='TestSamples/exp', type=str, + help='path to expression') + parser.add_argument('-s', '--savefolder', default='TestSamples/teaser/results', type=str, + help='path to the output directory, where results(obj, txt files) will be stored.') + parser.add_argument('--device', default='cuda', type=str, + help='set device, cpu for using cpu' ) + # process test images + parser.add_argument('--iscrop', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to crop input image, set false only when the test image are well cropped' ) + parser.add_argument('--detector', default='fan', type=str, + help='detector for cropping face, check detectos.py for details' ) + + main(parser.parse_args()) \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/DECA/demos/demo_transfer.py b/motion-gan-pipeline/preprocessing/third/DECA/demos/demo_transfer.py new file mode 100755 index 0000000..4ea3037 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/demos/demo_transfer.py @@ -0,0 +1,126 @@ +# -*- coding: utf-8 -*- +# +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# Using this computer program means that you agree to the terms +# in the LICENSE file included with this software distribution. +# Any use not explicitly granted by the LICENSE is prohibited. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# For comments or questions, please email us at deca@tue.mpg.de +# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de + +import os, sys +import cv2 +import numpy as np +from time import time +import argparse + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +from decalib.deca import DECA +from decalib.datasets import datasets +from decalib.utils import util +from decalib.utils.config import cfg as deca_cfg + +def main(args): + savefolder = args.savefolder + device = args.device + os.makedirs(savefolder, exist_ok=True) + + # load test images + testdata = datasets.TestData(args.image_path, iscrop=args.iscrop, face_detector=args.detector) + expdata = datasets.TestData(args.exp_path, iscrop=args.iscrop, face_detector=args.detector) + + # run DECA + deca_cfg.model.use_tex = args.useTex + deca = DECA(config = deca_cfg, device=device) + # identity reference + i = 0 + name = testdata[i]['imagename'] + savepath = '{}/{}.jpg'.format(savefolder, name) + images = testdata[i]['image'].to(device)[None,...] + id_codedict = deca.encode(images) + id_opdict, id_visdict = deca.decode(id_codedict) + id_visdict = {x:id_visdict[x] for x in ['inputs', 'shape_detail_images']} + + # -- expression transfer + # exp code from image + exp_images = expdata[i]['image'].to(device)[None,...] + exp_codedict = deca.encode(exp_images) + # transfer exp code + id_codedict['pose'][:,3:] = exp_codedict['pose'][:,3:] + id_codedict['exp'] = exp_codedict['exp'] + transfer_opdict, transfer_visdict = deca.decode(id_codedict) + id_visdict['transferred_shape'] = transfer_visdict['shape_detail_images'] + cv2.imwrite(os.path.join(savefolder, name + '_animation.jpg'), deca.visualize(id_visdict)) + + transfer_opdict['uv_texture_gt'] = id_opdict['uv_texture_gt'] + if args.saveDepth or args.saveKpt or args.saveObj or args.saveMat or args.saveImages: + os.makedirs(os.path.join(savefolder, name, 'reconstruction'), exist_ok=True) + os.makedirs(os.path.join(savefolder, name, 'animation'), exist_ok=True) + + # -- save results + image_name = name + for save_type in ['reconstruction', 'animation']: + if save_type == 'reconstruction': + visdict = id_codedict; opdict = id_opdict + else: + visdict = transfer_visdict; opdict = transfer_opdict + if args.saveDepth: + depth_image = deca.render.render_depth(opdict['transformed_vertices']).repeat(1,3,1,1) + visdict['depth_images'] = depth_image + cv2.imwrite(os.path.join(savefolder, name, save_type, name + '_depth.jpg'), util.tensor2image(depth_image[0])) + if args.saveKpt: + np.savetxt(os.path.join(savefolder, name, save_type, name + '_kpt2d.txt'), opdict['landmarks2d'][0].cpu().numpy()) + np.savetxt(os.path.join(savefolder, name, save_type, name + '_kpt3d.txt'), opdict['landmarks3d'][0].cpu().numpy()) + if args.saveObj: + deca.save_obj(os.path.join(savefolder, name, save_type, name + '.obj'), opdict) + if args.saveMat: + opdict = util.dict_tensor2npy(opdict) + savemat(os.path.join(savefolder, name, save_type, name + '.mat'), opdict) + if args.saveImages: + for vis_name in ['inputs', 'rendered_images', 'albedo_images', 'shape_images', 'shape_detail_images']: + if vis_name not in visdict.keys(): + continue + image =util.tensor2image(visdict[vis_name][0]) + cv2.imwrite(os.path.join(savefolder, name, save_type, name + '_' + vis_name +'.jpg'), util.tensor2image(visdict[vis_name][0])) + print(f'-- please check the results in {savefolder}') + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='DECA: Detailed Expression Capture and Animation') + + parser.add_argument('-i', '--image_path', default='TestSamples/examples/IMG_0392_inputs.jpg', type=str, + help='path to input image') + parser.add_argument('-e', '--exp_path', default='TestSamples/exp/7.jpg', type=str, + help='path to expression') + parser.add_argument('-s', '--savefolder', default='TestSamples/animation_results', type=str, + help='path to the output directory, where results(obj, txt files) will be stored.') + parser.add_argument('--device', default='cuda', type=str, + help='set device, cpu for using cpu' ) + # process test images + parser.add_argument('--iscrop', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to crop input image, set false only when the test image are well cropped' ) + parser.add_argument('--detector', default='fan', type=str, + help='detector for cropping face, check detectos.py for details' ) + # save + parser.add_argument('--useTex', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to use FLAME texture model to generate uv texture map, \ + set it to True only if you downloaded texture model' ) + parser.add_argument('--saveVis', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save visualization of output' ) + parser.add_argument('--saveKpt', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save 2D and 3D keypoints' ) + parser.add_argument('--saveDepth', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save depth image' ) + parser.add_argument('--saveObj', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save outputs as .obj' ) + parser.add_argument('--saveMat', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save outputs as .mat' ) + parser.add_argument('--saveImages', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save visualization output as seperate images' ) + main(parser.parse_args()) + + main(parser.parse_args()) diff --git a/motion-gan-pipeline/preprocessing/third/DECA/demos/run_on_img.py b/motion-gan-pipeline/preprocessing/third/DECA/demos/run_on_img.py new file mode 100644 index 0000000..d75969a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/demos/run_on_img.py @@ -0,0 +1,126 @@ + +import os, sys +import cv2 +import numpy as np +from time import time +from scipy.io import savemat +import argparse +from tqdm import tqdm +import matplotlib.pyplot as plt +from skimage.transform import estimate_transform, warp, resize, rescale + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) +from decalib.deca import DECA +from decalib.datasets import datasets +from decalib.utils import util +from decalib.utils.config import cfg as deca_cfg + + +def warp_back(image, oldimage, tform): + + alpha = 0.6 + + oldimage = oldimage.astype(np.float64) /255. + new_size = oldimage.shape + + dst_image = warp(image, tform, output_shape=new_size) + + # Mask of non-black pixels. + mask = np.where(np.all(dst_image == [0, 0, 0], axis=-1)) + dst_image[mask] = oldimage[mask] + + res = cv2.addWeighted(oldimage, 1 - alpha, dst_image, alpha, 0) + res = res[:, :, ::-1] + + return res + + +def main(args): + savefolder = args.savefolder + device = args.device + os.makedirs(savefolder, exist_ok=True) + + # load test images + testdata = datasets.TestData(args.inputpath, iscrop=args.iscrop, face_detector=args.detector) + + # run DECA + deca_cfg.model.use_tex = args.useTex + deca = DECA(config=deca_cfg, device=device) + # for i in range(len(testdata)): + for i in tqdm(range(len(testdata))): + + name = testdata[i]['imagename'] + images = testdata[i]['image'].to(device)[None, ...] + original_images = testdata[i]['original_image'].to(device)[None, ...] + + codedict = deca.encode(images) + opdict, visdict = deca.decode(codedict) # tensor + + images = util.tensor2image(images[0]) + original_images = util.tensor2image(original_images[0]) + image = util.tensor2image(visdict['shape_detail_images'][0]) + + # plt.imshow(image) + # plt.show() + + new = warp_back(image, original_images, testdata[i]['tform']) + plt.imshow(new) + plt.show() + + break + + + + + # if args.saveDepth or args.saveKpt or args.saveObj or args.saveMat or args.saveImages: + # os.makedirs(os.path.join(savefolder, name), exist_ok=True) + # # -- save results + # if args.saveObj: + # deca.save_obj(os.path.join(savefolder, name, name + '.obj'), opdict) + # if args.saveMat: + # opdict = util.dict_tensor2npy(opdict) + # savemat(os.path.join(savefolder, name, name + '.mat'), opdict) + # if args.saveVis: + # cv2.imwrite(os.path.join(savefolder, name + '_vis.jpg'), deca.visualize(visdict)) + # if args.saveImages: + # for vis_name in ['inputs', 'rendered_images', 'albedo_images', 'shape_images', 'shape_detail_images']: + # if vis_name not in visdict.keys(): + # continue + # image = util.tensor2image(visdict[vis_name][0]) + # cv2.imwrite(os.path.join(savefolder, name, name + '_' + vis_name + '.jpg'), + # util.tensor2image(visdict[vis_name][0])) + # print(f'-- please check the results in {savefolder}') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='DECA: Detailed Expression Capture and Animation') + + parser.add_argument('-i', '--inputpath', default='TestSamples/SRF', type=str, + help='path to the test data, can be image folder, image path, image list, video') + parser.add_argument('-s', '--savefolder', default='TestSamples/SRF/results', type=str, + help='path to the output directory, where results(obj, txt files) will be stored.') + parser.add_argument('--device', default='cuda', type=str, + help='set device, cpu for using cpu') + # process test images + parser.add_argument('--iscrop', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to crop input image, set false only when the test image are well cropped') + parser.add_argument('--detector', default='fan', type=str, + help='detector for cropping face, check decalib/detectors.py for details') + # save + parser.add_argument('--useTex', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to use FLAME texture model to generate uv texture map, \ + set it to True only if you downloaded texture model') + parser.add_argument('--saveVis', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save visualization of output') + parser.add_argument('--saveKpt', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save 2D and 3D keypoints') + parser.add_argument('--saveDepth', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save depth image') + parser.add_argument('--saveObj', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save outputs as .obj, detail mesh will end with _detail.obj. \ + Note that saving objs could be slow') + parser.add_argument('--saveMat', default=False, type=lambda x: x.lower() in ['true', '1'], + help='whether to save outputs as .mat') + parser.add_argument('--saveImages', default=True, type=lambda x: x.lower() in ['true', '1'], + help='whether to save visualization output as seperate images') + main(parser.parse_args()) \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/DECA/install_conda.sh b/motion-gan-pipeline/preprocessing/third/DECA/install_conda.sh new file mode 100755 index 0000000..76c91f9 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/install_conda.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash + +export CONDA_ENV_NAME=deca-env +echo $CONDA_ENV_NAME + +conda create -n $CONDA_ENV_NAME python=3.7 + +eval "$(conda shell.bash hook)" +conda activate $CONDA_ENV_NAME +pip install -r requirements.txt \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/DECA/install_pip.sh b/motion-gan-pipeline/preprocessing/third/DECA/install_pip.sh new file mode 100755 index 0000000..7346c85 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/install_pip.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash + +echo "Creating virtual environment" +python3.7 -m venv deca-env +echo "Activating virtual environment" + +source $PWD/deca-env/bin/activate +$PWD/deca-env/bin/pip install -r requirements.txt \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/DECA/rendered_image_gaze.jpg b/motion-gan-pipeline/preprocessing/third/DECA/rendered_image_gaze.jpg new file mode 100644 index 0000000..1af4837 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/rendered_image_gaze.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:84dbf5d84fc14f56baad37ed3a607f30110d79a8170ffb807273552b2fb2dfe2 +size 6204 diff --git a/motion-gan-pipeline/preprocessing/third/DECA/rendered_image_og.jpg b/motion-gan-pipeline/preprocessing/third/DECA/rendered_image_og.jpg new file mode 100644 index 0000000..7451f00 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/rendered_image_og.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3cfad6a31c2a684887fc17092bca6037a398bddd25e672cffb2bd754284fc47f +size 6176 diff --git a/motion-gan-pipeline/preprocessing/third/DECA/requirements.txt b/motion-gan-pipeline/preprocessing/third/DECA/requirements.txt new file mode 100644 index 0000000..e931eb7 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/requirements.txt @@ -0,0 +1,13 @@ +#to install all this requirements +#`pip install -r requirements.txt` +numpy>=1.18.5 +scipy>=1.4.1 +chumpy>=0.69 +scikit-image>=0.15 +opencv-python>=4.1.1 +scikit-image>=0.15 #skimage +PyYAML>=5.1.1 +torch==1.6.0 # for compatible with pytorch3d +torchvision==0.7.0 +face-alignment +# pytorch3d \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/DECA/shape.jpg b/motion-gan-pipeline/preprocessing/third/DECA/shape.jpg new file mode 100644 index 0000000..8ddc9f8 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/shape.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8a4c504331e5c6c93d3b62d79e30c8c2daf4821b49f5adc5f8dc41e339d3899 +size 5138 diff --git a/motion-gan-pipeline/preprocessing/third/DECA/test_expression.jpg b/motion-gan-pipeline/preprocessing/third/DECA/test_expression.jpg new file mode 100644 index 0000000..99d17c8 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/test_expression.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:584255a499aa5bfa4a3511e98acb63af703b2bb2524ffb1d3aa8b11a9f99e547 +size 142515 diff --git a/motion-gan-pipeline/preprocessing/third/DECA/test_eye_gazing.py b/motion-gan-pipeline/preprocessing/third/DECA/test_eye_gazing.py new file mode 100644 index 0000000..4efacc3 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/test_eye_gazing.py @@ -0,0 +1,67 @@ +import sys +import os +from tqdm import tqdm +import matplotlib.pyplot as plt +import numpy as np +from PIL import Image +import torch.nn.functional as F + + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../third/DECA/'))) +from decalib.deca import DECA +from decalib.datasets import datasets +from decalib.utils import util +from decalib.utils.config import cfg as deca_cfg + +def save_img(name, image): + to_save = image[0].permute(1, 2, 0).cpu().numpy() + to_save = to_save * 255. + to_save = to_save.astype(np.uint8) + filename = name + '.jpg' + img = Image.fromarray(to_save) + img.save(filename) + +deca_cfg.model.use_tex = False +device = 'cuda' +deca = DECA(config=deca_cfg, device=device) + +# load test images +testdata = datasets.TestData('test_expression.jpg', iscrop=True, face_detector='fan') +images = testdata[0]['image'].to(device)[None, ...] +codedict = deca.encode(images) +expr = codedict['exp'] + +# load test images +testdata = datasets.TestData('test_gaze.jpg', iscrop=True, face_detector='fan') +images = testdata[0]['image'].to(device)[None, ...] + +codedict = deca.encode(images) +opdict, visdict = deca.decode(codedict) + +save_img('shape', visdict['shape_images']) + +opdict, visdict = deca.decode(codedict) +verts_og = opdict['vertices'] +gird_og = opdict['grid'] +texture_og = opdict['uv_texture_gt'] +image = F.grid_sample(texture_og, gird_og, align_corners=False) +save_img('rendered_image_og', image) + + +# codedict['exp'] = expr +opdict, visdict = deca.decode_eyes(codedict) +verts_gaze = opdict['vertices'] +gird_gaze = opdict['grid'] +texture_gaze = opdict['uv_texture_gt'] +image = F.grid_sample(texture_og, gird_gaze, align_corners=False) +save_img('rendered_image_gaze', image) + + +euclidena_dist = sum(((verts_og - verts_gaze)**2).flatten()) +print('Mesh distance: ', euclidena_dist) + +gird_dist = sum(((gird_og - gird_gaze)**2).flatten()) +print('Grid distance: ', gird_dist) + +text_dist = sum(((texture_og - texture_gaze)**2).flatten()) +print('Texture distance: ', text_dist) \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/DECA/test_gaze.jpg b/motion-gan-pipeline/preprocessing/third/DECA/test_gaze.jpg new file mode 100644 index 0000000..a324dd6 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DECA/test_gaze.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:181ff4fe3433b81cf338e31b9ec7b6ab603fc57dd4bc1c46d9f909dbcf85c33e +size 121963 diff --git a/motion-gan-pipeline/preprocessing/third/DeepSpeech/models/output_graph.pb b/motion-gan-pipeline/preprocessing/third/DeepSpeech/models/output_graph.pb new file mode 100644 index 0000000..da61e4e --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/DeepSpeech/models/output_graph.pb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:309a4c24f861edca2a027e3d9ecd5ae34468d8b20ed4d9be694858e0bd6f0640 +size 490979273 diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/LICENSE b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/LICENSE new file mode 100644 index 0000000..e72bfdd --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/README.md b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/README.md new file mode 100644 index 0000000..262d439 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/README.md @@ -0,0 +1,235 @@ +# Robust Video Matting (RVM) + +![Teaser](/documentation/image/teaser.gif) + +

English | 中文

+ +Official repository for the paper [Robust High-Resolution Video Matting with Temporal Guidance](https://peterl1n.github.io/RobustVideoMatting/). RVM is specifically designed for robust human video matting. Unlike existing neural models that process frames as independent images, RVM uses a recurrent neural network to process videos with temporal memory. RVM can perform matting in real-time on any videos without additional inputs. It achieves **4K 76FPS** and **HD 104FPS** on an Nvidia GTX 1080 Ti GPU. The project was developed at [ByteDance Inc.](https://www.bytedance.com/) + +
+ +## News + +* [Sep 16 2021] Code is re-released under GPL-3.0 license. +* [Aug 25 2021] Source code and pretrained models are published. +* [Jul 27 2021] Paper is accepted by WACV 2022. + +
+ +## Showreel +Watch the showreel video ([YouTube](https://youtu.be/Jvzltozpbpk), [Bilibili](https://www.bilibili.com/video/BV1Z3411B7g7/)) to see the model's performance. + +

+ + + +

+ +All footage in the video are available in [Google Drive](https://drive.google.com/drive/folders/1VFnWwuu-YXDKG-N6vcjK_nL7YZMFapMU?usp=sharing) and [Baidu Pan](https://pan.baidu.com/s/1igMteDwN5rO1Sn7YIhBlvQ) (code: tb3w). + +
+ + +## Demo +* [Webcam Demo](https://peterl1n.github.io/RobustVideoMatting/#/demo): Run the model live in your browser. Visualize recurrent states. +* [Colab Demo](https://colab.research.google.com/drive/10z-pNKRnVNsp0Lq9tH1J_XPZ7CBC_uHm?usp=sharing): Test our model on your own videos with free GPU. + +
+ +## Download + +We recommend MobileNetv3 models for most use cases. ResNet50 models are the larger variant with small performance improvements. Our model is available on various inference frameworks. See [inference documentation](documentation/inference.md) for more instructions. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
FrameworkDownloadNotes
PyTorch + rvm_mobilenetv3.pth
+ rvm_resnet50.pth + 		+ Official weights for PyTorch. Doc +
TorchHub + Nothing to Download. + + Easiest way to use our model in your PyTorch project. Doc +
TorchScript + rvm_mobilenetv3_fp32.torchscript
+ rvm_mobilenetv3_fp16.torchscript
+ rvm_resnet50_fp32.torchscript
+ rvm_resnet50_fp16.torchscript + 		+ If inference on mobile, consider export int8 quantized models yourself. Doc +
ONNX + rvm_mobilenetv3_fp32.onnx
+ rvm_mobilenetv3_fp16.onnx
+ rvm_resnet50_fp32.onnx
+ rvm_resnet50_fp16.onnx + 		+ Tested on ONNX Runtime with CPU and CUDA backends. Provided models use opset 12. Doc, Exporter. +
TensorFlow + rvm_mobilenetv3_tf.zip
+ rvm_resnet50_tf.zip + 		+ TensorFlow 2 SavedModel. Doc +
TensorFlow.js + rvm_mobilenetv3_tfjs_int8.zip
+ 		+ Run the model on the web. Demo, Starter Code +
CoreML + rvm_mobilenetv3_1280x720_s0.375_fp16.mlmodel
+ rvm_mobilenetv3_1280x720_s0.375_int8.mlmodel
+ rvm_mobilenetv3_1920x1080_s0.25_fp16.mlmodel
+ rvm_mobilenetv3_1920x1080_s0.25_int8.mlmodel
+ 		+ CoreML does not support dynamic resolution. Other resolutions can be exported yourself. Models require iOS 13+. s denotes downsample_ratio. Doc, Exporter +
+ +All models are available in [Google Drive](https://drive.google.com/drive/folders/1pBsG-SCTatv-95SnEuxmnvvlRx208VKj?usp=sharing) and [Baidu Pan](https://pan.baidu.com/s/1puPSxQqgBFOVpW4W7AolkA) (code: gym7). + +
+ +## PyTorch Example + +1. Install dependencies: +```sh +pip install -r requirements_inference.txt +``` + +2. Load the model: + +```python +import torch +from model import MattingNetwork + +model = MattingNetwork('mobilenetv3').eval().cuda() # or "resnet50" +model.load_state_dict(torch.load('rvm_mobilenetv3.pth')) +``` + +3. To convert videos, we provide a simple conversion API: + +```python +from inference import convert_video + +convert_video( + model, # The model, can be on any device (cpu or cuda). + input_source='input.mp4', # A video file or an image sequence directory. + output_type='video', # Choose "video" or "png_sequence" + output_composition='output.mp4', # File path if video; directory path if png sequence. + output_video_mbps=4, # Output video mbps. Not needed for png sequence. + downsample_ratio=None, # A hyperparameter to adjust or use None for auto. + seq_chunk=12, # Process n frames at once for better parallelism. +) +``` + +4. Or write your own inference code: +```python +from torch.utils.data import DataLoader +from torchvision.transforms import ToTensor +from inference_utils import VideoReader, VideoWriter + +reader = VideoReader('input.mp4', transform=ToTensor()) +writer = VideoWriter('output.mp4', frame_rate=30) + +bgr = torch.tensor([.47, 1, .6]).view(3, 1, 1).cuda() # Green background. +rec = [None] * 4 # Initial recurrent states. +downsample_ratio = 0.25 # Adjust based on your video. + +with torch.no_grad(): + for src in DataLoader(reader): # RGB tensor normalized to 0 ~ 1. + fgr, pha, *rec = model(src.cuda(), *rec, downsample_ratio) # Cycle the recurrent states. + com = fgr * pha + bgr * (1 - pha) # Composite to green background. + writer.write(com) # Write frame. +``` + +5. The models and converter API are also available through TorchHub. + +```python +# Load the model. +model = torch.hub.load("PeterL1n/RobustVideoMatting", "mobilenetv3") # or "resnet50" + +# Converter API. +convert_video = torch.hub.load("PeterL1n/RobustVideoMatting", "converter") +``` + +Please see [inference documentation](documentation/inference.md) for details on `downsample_ratio` hyperparameter, more converter arguments, and more advanced usage. + +
+ +## Training and Evaluation + +Please refer to the [training documentation](documentation/training.md) to train and evaluate your own model. + +
+ +## Speed + +Speed is measured with `inference_speed_test.py` for reference. + +| GPU | dType | HD (1920x1080) | 4K (3840x2160) | +| -------------- | ----- | -------------- |----------------| +| RTX 3090 | FP16 | 172 FPS | 154 FPS | +| RTX 2060 Super | FP16 | 134 FPS | 108 FPS | +| GTX 1080 Ti | FP32 | 104 FPS | 74 FPS | + +* Note 1: HD uses `downsample_ratio=0.25`, 4K uses `downsample_ratio=0.125`. All tests use batch size 1 and frame chunk 1. +* Note 2: GPUs before Turing architecture does not support FP16 inference, so GTX 1080 Ti uses FP32. +* Note 3: We only measure tensor throughput. The provided video conversion script in this repo is expected to be much slower, because it does not utilize hardware video encoding/decoding and does not have the tensor transfer done on parallel threads. If you are interested in implementing hardware video encoding/decoding in Python, please refer to [PyNvCodec](https://github.com/NVIDIA/VideoProcessingFramework). + +
+ +## Project Members +* [Shanchuan Lin](https://www.linkedin.com/in/shanchuanlin/) +* [Linjie Yang](https://sites.google.com/site/linjieyang89/) +* [Imran Saleemi](https://www.linkedin.com/in/imran-saleemi/) +* [Soumyadip Sengupta](https://homes.cs.washington.edu/~soumya91/) + +
+ +## Third-Party Projects + +* [NCNN C++ Android](https://github.com/FeiGeChuanShu/ncnn_Android_RobustVideoMatting) ([@FeiGeChuanShu](https://github.com/FeiGeChuanShu)) +* [lite.ai.toolkit](https://github.com/DefTruth/RobustVideoMatting.lite.ai.toolkit) ([@DefTruth](https://github.com/DefTruth)) +* [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/Robust-Video-Matting) ([@AK391](https://github.com/AK391)) +* [Unity Engine demo with NatML](https://hub.natml.ai/@natsuite/robust-video-matting) ([@natsuite](https://github.com/natsuite)) \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/README_zh_Hans.md b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/README_zh_Hans.md new file mode 100644 index 0000000..f6a8886 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/README_zh_Hans.md @@ -0,0 +1,234 @@ +# 稳定视频抠像 (RVM) + +![Teaser](/documentation/image/teaser.gif) + +

English | 中文

+ +论文 [Robust High-Resolution Video Matting with Temporal Guidance](https://peterl1n.github.io/RobustVideoMatting/) 的官方 GitHub 库。RVM 专为稳定人物视频抠像设计。不同于现有神经网络将每一帧作为单独图片处理，RVM 使用循环神经网络，在处理视频流时有时间记忆。RVM 可在任意视频上做实时高清抠像。在 Nvidia GTX 1080Ti 上实现 **4K 76FPS** 和 **HD 104FPS**。此研究项目来自[字节跳动](https://www.bytedance.com/)。 + +
+ +## 更新 + +* [2021年9月16日] 代码重新以 GPL-3.0 许可发布。 +* [2021年8月25日] 公开代码和模型。 +* [2021年7月27日] 论文被 WACV 2022 收录。 + +
+ +## 展示视频 +观看展示视频 ([YouTube](https://youtu.be/Jvzltozpbpk), [Bilibili](https://www.bilibili.com/video/BV1Z3411B7g7/))，了解模型能力。 +

+ + + +

+ +视频中的所有素材都提供下载，可用于测试模型：[Google Drive](https://drive.google.com/drive/folders/1VFnWwuu-YXDKG-N6vcjK_nL7YZMFapMU?usp=sharing) 或[百度网盘](https://pan.baidu.com/s/1igMteDwN5rO1Sn7YIhBlvQ)（密码: tb3w） + +
+ + +## Demo +* [网页](https://peterl1n.github.io/RobustVideoMatting/#/demo): 在浏览器里看摄像头抠像效果，展示模型内部循环记忆值。 +* [Colab](https://colab.research.google.com/drive/10z-pNKRnVNsp0Lq9tH1J_XPZ7CBC_uHm?usp=sharing): 用我们的模型转换你的视频。 + +
+ +## 下载 + +推荐在通常情况下使用 MobileNetV3 的模型。ResNet50 的模型大很多，效果稍有提高。我们的模型支持很多框架。详情请阅读[推断文档](documentation/inference_zh_Hans.md)。 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
框架下载备注
PyTorch + rvm_mobilenetv3.pth
+ rvm_resnet50.pth + 		+ 官方 PyTorch 模型权值。文档 +
TorchHub + 无需手动下载。 + + 更方便地在你的 PyTorch 项目里使用此模型。文档 +
TorchScript + rvm_mobilenetv3_fp32.torchscript
+ rvm_mobilenetv3_fp16.torchscript
+ rvm_resnet50_fp32.torchscript
+ rvm_resnet50_fp16.torchscript + 		+ 若需在移动端推断，可以考虑自行导出 int8 量化的模型。文档 +
ONNX + rvm_mobilenetv3_fp32.onnx
+ rvm_mobilenetv3_fp16.onnx
+ rvm_resnet50_fp32.onnx
+ rvm_resnet50_fp16.onnx + 		+ 在 ONNX Runtime 的 CPU 和 CUDA backend 上测试过。提供的模型用 opset 12。文档，导出 +
TensorFlow + rvm_mobilenetv3_tf.zip
+ rvm_resnet50_tf.zip + 		+ TensorFlow 2 SavedModel 格式。文档 +
TensorFlow.js + rvm_mobilenetv3_tfjs_int8.zip
+ 		+ 在网页上跑模型。展示，示范代码 +
CoreML + rvm_mobilenetv3_1280x720_s0.375_fp16.mlmodel
+ rvm_mobilenetv3_1280x720_s0.375_int8.mlmodel
+ rvm_mobilenetv3_1920x1080_s0.25_fp16.mlmodel
+ rvm_mobilenetv3_1920x1080_s0.25_int8.mlmodel
+ 		+ CoreML 只能导出固定分辨率，其他分辨率可自行导出。支持 iOS 13+。s 代表下采样比。文档，导出 +
+ +所有模型可在 [Google Drive](https://drive.google.com/drive/folders/1pBsG-SCTatv-95SnEuxmnvvlRx208VKj?usp=sharing) 或[百度网盘](https://pan.baidu.com/s/1puPSxQqgBFOVpW4W7AolkA)（密码: gym7）上下载。 + +
+ +## PyTorch 范例 + +1. 安装 Python 库: +```sh +pip install -r requirements_inference.txt +``` + +2. 加载模型: + +```python +import torch +from model import MattingNetwork + +model = MattingNetwork('mobilenetv3').eval().cuda() # 或 "resnet50" +model.load_state_dict(torch.load('rvm_mobilenetv3.pth')) +``` + +3. 若只需要做视频抠像处理，我们提供简单的 API: + +```python +from inference import convert_video + +convert_video( + model, # 模型，可以加载到任何设备（cpu 或 cuda） + input_source='input.mp4', # 视频文件，或图片序列文件夹 + output_type='video', # 可选 "video"（视频）或 "png_sequence"（PNG 序列） + output_composition='output.mp4', # 若导出视频，提供文件路径。若导出 PNG 序列，提供文件夹路径 + output_video_mbps=4, # 若导出视频，提供视频码率 + downsample_ratio=None, # 下采样比，可根据具体视频调节，或 None 选择自动 + seq_chunk=12, # 设置多帧并行计算 +) +``` + +4. 或自己写推断逻辑: +```python +from torch.utils.data import DataLoader +from torchvision.transforms import ToTensor +from inference_utils import VideoReader, VideoWriter + +reader = VideoReader('input.mp4', transform=ToTensor()) +writer = VideoWriter('output.mp4', frame_rate=30) + +bgr = torch.tensor([.47, 1, .6]).view(3, 1, 1).cuda() # 绿背景 +rec = [None] * 4 # 初始循环记忆（Recurrent States） +downsample_ratio = 0.25 # 下采样比，根据视频调节 + +with torch.no_grad(): + for src in DataLoader(reader): # 输入张量，RGB通道，范围为 0～1 + fgr, pha, *rec = model(src.cuda(), *rec, downsample_ratio) # 将上一帧的记忆给下一帧 + com = fgr * pha + bgr * (1 - pha) # 将前景合成到绿色背景 + writer.write(com) # 输出帧 +``` + +5. 模型和 API 也可通过 TorchHub 快速载入。 + +```python +# 加载模型 +model = torch.hub.load("PeterL1n/RobustVideoMatting", "mobilenetv3") # 或 "resnet50" + +# 转换 API +convert_video = torch.hub.load("PeterL1n/RobustVideoMatting", "converter") +``` + +[推断文档](documentation/inference_zh_Hans.md)里有对 `downsample_ratio` 参数，API 使用，和高阶使用的讲解。 + +
+ +## 训练和评估 + +请参照[训练文档（英文）](documentation/training.md)。 + +
+ +## 速度 + +速度用 `inference_speed_test.py` 测量以供参考。 + +| GPU | dType | HD (1920x1080) | 4K (3840x2160) | +| -------------- | ----- | -------------- |----------------| +| RTX 3090 | FP16 | 172 FPS | 154 FPS | +| RTX 2060 Super | FP16 | 134 FPS | 108 FPS | +| GTX 1080 Ti | FP32 | 104 FPS | 74 FPS | + +* 注释1：HD 使用 `downsample_ratio=0.25`，4K 使用 `downsample_ratio=0.125`。 所有测试都使用 batch size 1 和 frame chunk 1。 +* 注释2：图灵架构之前的 GPU 不支持 FP16 推理，所以 GTX 1080 Ti 使用 FP32。 +* 注释3：我们只测量张量吞吐量（tensor throughput）。 提供的视频转换脚本会慢得多，因为它不使用硬件视频编码/解码，也没有在并行线程上完成张量传输。如果您有兴趣在 Python 中实现硬件视频编码/解码，请参考 [PyNvCodec](https://github.com/NVIDIA/VideoProcessingFramework)。 + +
+ +## 项目成员 +* [Shanchuan Lin](https://www.linkedin.com/in/shanchuanlin/) +* [Linjie Yang](https://sites.google.com/site/linjieyang89/) +* [Imran Saleemi](https://www.linkedin.com/in/imran-saleemi/) +* [Soumyadip Sengupta](https://homes.cs.washington.edu/~soumya91/) + +
+ +## 第三方资源 + +* [NCNN C++ Android](https://github.com/FeiGeChuanShu/ncnn_Android_RobustVideoMatting) ([@FeiGeChuanShu](https://github.com/FeiGeChuanShu)) +* [lite.ai.toolkit](https://github.com/DefTruth/RobustVideoMatting.lite.ai.toolkit) ([@DefTruth](https://github.com/DefTruth)) +* [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/Robust-Video-Matting) ([@AK391](https://github.com/AK391)) +* [带有 NatML 的 Unity 引擎](https://hub.natml.ai/@natsuite/robust-video-matting) ([@natsuite](https://github.com/natsuite)) \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/__init__.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/checkpoints/rvm_mobilenetv3.pth b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/checkpoints/rvm_mobilenetv3.pth new file mode 100644 index 0000000..bb67632 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/checkpoints/rvm_mobilenetv3.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3c7c1d92033f7c38d6577c481d13a195d7d80a159b960f4f3119ac7b534cf4f8 +size 15217721 diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/checkpoints/rvm_resnet50.pth b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/checkpoints/rvm_resnet50.pth new file mode 100644 index 0000000..a94490c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/checkpoints/rvm_resnet50.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c191a807251164c073dce5fa408e7a816070d539b882b2a3150330a9fec112ce +size 107905875 diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/augmentation.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/augmentation.py new file mode 100644 index 0000000..4a12869 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/augmentation.py @@ -0,0 +1,260 @@ +import easing_functions as ef +import random +import torch +from torchvision import transforms +from torchvision.transforms import functional as F + + +class MotionAugmentation: + def __init__(self, + size, + prob_fgr_affine, + prob_bgr_affine, + prob_noise, + prob_color_jitter, + prob_grayscale, + prob_sharpness, + prob_blur, + prob_hflip, + prob_pause, + static_affine=True, + aspect_ratio_range=(0.9, 1.1)): + self.size = size + self.prob_fgr_affine = prob_fgr_affine + self.prob_bgr_affine = prob_bgr_affine + self.prob_noise = prob_noise + self.prob_color_jitter = prob_color_jitter + self.prob_grayscale = prob_grayscale + self.prob_sharpness = prob_sharpness + self.prob_blur = prob_blur + self.prob_hflip = prob_hflip + self.prob_pause = prob_pause + self.static_affine = static_affine + self.aspect_ratio_range = aspect_ratio_range + + def __call__(self, fgrs, phas, bgrs): + # Foreground affine + if random.random() < self.prob_fgr_affine: + fgrs, phas = self._motion_affine(fgrs, phas) + + # Background affine + if random.random() < self.prob_bgr_affine / 2: + bgrs = self._motion_affine(bgrs) + if random.random() < self.prob_bgr_affine / 2: + fgrs, phas, bgrs = self._motion_affine(fgrs, phas, bgrs) + + # Still Affine + if self.static_affine: + fgrs, phas = self._static_affine(fgrs, phas, scale_ranges=(0.5, 1)) + bgrs = self._static_affine(bgrs, scale_ranges=(1, 1.5)) + + # To tensor + fgrs = torch.stack([F.to_tensor(fgr) for fgr in fgrs]) + phas = torch.stack([F.to_tensor(pha) for pha in phas]) + bgrs = torch.stack([F.to_tensor(bgr) for bgr in bgrs]) + + # Resize + params = transforms.RandomResizedCrop.get_params(fgrs, scale=(1, 1), ratio=self.aspect_ratio_range) + fgrs = F.resized_crop(fgrs, *params, self.size, interpolation=F.InterpolationMode.BILINEAR) + phas = F.resized_crop(phas, *params, self.size, interpolation=F.InterpolationMode.BILINEAR) + params = transforms.RandomResizedCrop.get_params(bgrs, scale=(1, 1), ratio=self.aspect_ratio_range) + bgrs = F.resized_crop(bgrs, *params, self.size, interpolation=F.InterpolationMode.BILINEAR) + + # Horizontal flip + if random.random() < self.prob_hflip: + fgrs = F.hflip(fgrs) + phas = F.hflip(phas) + if random.random() < self.prob_hflip: + bgrs = F.hflip(bgrs) + + # Noise + if random.random() < self.prob_noise: + fgrs, bgrs = self._motion_noise(fgrs, bgrs) + + # Color jitter + if random.random() < self.prob_color_jitter: + fgrs = self._motion_color_jitter(fgrs) + if random.random() < self.prob_color_jitter: + bgrs = self._motion_color_jitter(bgrs) + + # Grayscale + if random.random() < self.prob_grayscale: + fgrs = F.rgb_to_grayscale(fgrs, num_output_channels=3).contiguous() + bgrs = F.rgb_to_grayscale(bgrs, num_output_channels=3).contiguous() + + # Sharpen + if random.random() < self.prob_sharpness: + sharpness = random.random() * 8 + fgrs = F.adjust_sharpness(fgrs, sharpness) + phas = F.adjust_sharpness(phas, sharpness) + bgrs = F.adjust_sharpness(bgrs, sharpness) + + # Blur + if random.random() < self.prob_blur / 3: + fgrs, phas = self._motion_blur(fgrs, phas) + if random.random() < self.prob_blur / 3: + bgrs = self._motion_blur(bgrs) + if random.random() < self.prob_blur / 3: + fgrs, phas, bgrs = self._motion_blur(fgrs, phas, bgrs) + + # Pause + if random.random() < self.prob_pause: + fgrs, phas, bgrs = self._motion_pause(fgrs, phas, bgrs) + + return fgrs, phas, bgrs + + def _static_affine(self, *imgs, scale_ranges): + params = transforms.RandomAffine.get_params( + degrees=(-10, 10), translate=(0.1, 0.1), scale_ranges=scale_ranges, + shears=(-5, 5), img_size=imgs[0][0].size) + imgs = [[F.affine(t, *params, F.InterpolationMode.BILINEAR) for t in img] for img in imgs] + return imgs if len(imgs) > 1 else imgs[0] + + def _motion_affine(self, *imgs): + config = dict(degrees=(-10, 10), translate=(0.1, 0.1), + scale_ranges=(0.9, 1.1), shears=(-5, 5), img_size=imgs[0][0].size) + angleA, (transXA, transYA), scaleA, (shearXA, shearYA) = transforms.RandomAffine.get_params(**config) + angleB, (transXB, transYB), scaleB, (shearXB, shearYB) = transforms.RandomAffine.get_params(**config) + + T = len(imgs[0]) + easing = random_easing_fn() + for t in range(T): + percentage = easing(t / (T - 1)) + angle = lerp(angleA, angleB, percentage) + transX = lerp(transXA, transXB, percentage) + transY = lerp(transYA, transYB, percentage) + scale = lerp(scaleA, scaleB, percentage) + shearX = lerp(shearXA, shearXB, percentage) + shearY = lerp(shearYA, shearYB, percentage) + for img in imgs: + img[t] = F.affine(img[t], angle, (transX, transY), scale, (shearX, shearY), F.InterpolationMode.BILINEAR) + return imgs if len(imgs) > 1 else imgs[0] + + def _motion_noise(self, *imgs): + grain_size = random.random() * 3 + 1 # range 1 ~ 4 + monochrome = random.random() < 0.5 + for img in imgs: + T, C, H, W = img.shape + noise = torch.randn((T, 1 if monochrome else C, round(H / grain_size), round(W / grain_size))) + noise.mul_(random.random() * 0.2 / grain_size) + if grain_size != 1: + noise = F.resize(noise, (H, W)) + img.add_(noise).clamp_(0, 1) + return imgs if len(imgs) > 1 else imgs[0] + + def _motion_color_jitter(self, *imgs): + brightnessA, brightnessB, contrastA, contrastB, saturationA, saturationB, hueA, hueB \ + = torch.randn(8).mul(0.1).tolist() + strength = random.random() * 0.2 + easing = random_easing_fn() + T = len(imgs[0]) + for t in range(T): + percentage = easing(t / (T - 1)) * strength + for img in imgs: + img[t] = F.adjust_brightness(img[t], max(1 + lerp(brightnessA, brightnessB, percentage), 0.1)) + img[t] = F.adjust_contrast(img[t], max(1 + lerp(contrastA, contrastB, percentage), 0.1)) + img[t] = F.adjust_saturation(img[t], max(1 + lerp(brightnessA, brightnessB, percentage), 0.1)) + img[t] = F.adjust_hue(img[t], min(0.5, max(-0.5, lerp(hueA, hueB, percentage) * 0.1))) + return imgs if len(imgs) > 1 else imgs[0] + + def _motion_blur(self, *imgs): + blurA = random.random() * 10 + blurB = random.random() * 10 + + T = len(imgs[0]) + easing = random_easing_fn() + for t in range(T): + percentage = easing(t / (T - 1)) + blur = max(lerp(blurA, blurB, percentage), 0) + if blur != 0: + kernel_size = int(blur * 2) + if kernel_size % 2 == 0: + kernel_size += 1 # Make kernel_size odd + for img in imgs: + img[t] = F.gaussian_blur(img[t], kernel_size, sigma=blur) + + return imgs if len(imgs) > 1 else imgs[0] + + def _motion_pause(self, *imgs): + T = len(imgs[0]) + pause_frame = random.choice(range(T - 1)) + pause_length = random.choice(range(T - pause_frame)) + for img in imgs: + img[pause_frame + 1 : pause_frame + pause_length] = img[pause_frame] + return imgs if len(imgs) > 1 else imgs[0] + + +def lerp(a, b, percentage): + return a * (1 - percentage) + b * percentage + + +def random_easing_fn(): + if random.random() < 0.2: + return ef.LinearInOut() + else: + return random.choice([ + ef.BackEaseIn, + ef.BackEaseOut, + ef.BackEaseInOut, + ef.BounceEaseIn, + ef.BounceEaseOut, + ef.BounceEaseInOut, + ef.CircularEaseIn, + ef.CircularEaseOut, + ef.CircularEaseInOut, + ef.CubicEaseIn, + ef.CubicEaseOut, + ef.CubicEaseInOut, + ef.ExponentialEaseIn, + ef.ExponentialEaseOut, + ef.ExponentialEaseInOut, + ef.ElasticEaseIn, + ef.ElasticEaseOut, + ef.ElasticEaseInOut, + ef.QuadEaseIn, + ef.QuadEaseOut, + ef.QuadEaseInOut, + ef.QuarticEaseIn, + ef.QuarticEaseOut, + ef.QuarticEaseInOut, + ef.QuinticEaseIn, + ef.QuinticEaseOut, + ef.QuinticEaseInOut, + ef.SineEaseIn, + ef.SineEaseOut, + ef.SineEaseInOut, + Step, + ])() + +class Step: # Custom easing function for sudden change. + def __call__(self, value): + return 0 if value < 0.5 else 1 + + +# ---------------------------- Frame Sampler ---------------------------- + + +class TrainFrameSampler: + def __init__(self, speed=[0.5, 1, 2, 3, 4, 5]): + self.speed = speed + + def __call__(self, seq_length): + frames = list(range(seq_length)) + + # Speed up + speed = random.choice(self.speed) + frames = [int(f * speed) for f in frames] + + # Shift + shift = random.choice(range(seq_length)) + frames = [f + shift for f in frames] + + # Reverse + if random.random() < 0.5: + frames = frames[::-1] + + return frames + +class ValidFrameSampler: + def __call__(self, seq_length): + return range(seq_length) diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/coco.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/coco.py new file mode 100644 index 0000000..3c183cb --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/coco.py @@ -0,0 +1,103 @@ +import os +import numpy as np +import random +import json +import os +from torch.utils.data import Dataset +from torchvision import transforms +from torchvision.transforms import functional as F +from PIL import Image + + +class CocoPanopticDataset(Dataset): + def __init__(self, + imgdir: str, + anndir: str, + annfile: str, + transform=None): + with open(annfile) as f: + self.data = json.load(f)['annotations'] + self.data = list(filter(lambda data: any(info['category_id'] == 1 for info in data['segments_info']), self.data)) + self.imgdir = imgdir + self.anndir = anndir + self.transform = transform + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + data = self.data[idx] + img = self._load_img(data) + seg = self._load_seg(data) + + if self.transform is not None: + img, seg = self.transform(img, seg) + + return img, seg + + def _load_img(self, data): + with Image.open(os.path.join(self.imgdir, data['file_name'].replace('.png', '.jpg'))) as img: + return img.convert('RGB') + + def _load_seg(self, data): + with Image.open(os.path.join(self.anndir, data['file_name'])) as ann: + ann.load() + + ann = np.array(ann, copy=False).astype(np.int32) + ann = ann[:, :, 0] + 256 * ann[:, :, 1] + 256 * 256 * ann[:, :, 2] + seg = np.zeros(ann.shape, np.uint8) + + for segments_info in data['segments_info']: + if segments_info['category_id'] in [1, 27, 32]: # person, backpack, tie + seg[ann == segments_info['id']] = 255 + + return Image.fromarray(seg) + + +class CocoPanopticTrainAugmentation: + def __init__(self, size): + self.size = size + self.jitter = transforms.ColorJitter(0.1, 0.1, 0.1, 0.1) + + def __call__(self, img, seg): + # Affine + params = transforms.RandomAffine.get_params(degrees=(-20, 20), translate=(0.1, 0.1), + scale_ranges=(1, 1), shears=(-10, 10), img_size=img.size) + img = F.affine(img, *params, interpolation=F.InterpolationMode.BILINEAR) + seg = F.affine(seg, *params, interpolation=F.InterpolationMode.NEAREST) + + # Resize + params = transforms.RandomResizedCrop.get_params(img, scale=(0.5, 1), ratio=(0.7, 1.3)) + img = F.resized_crop(img, *params, self.size, interpolation=F.InterpolationMode.BILINEAR) + seg = F.resized_crop(seg, *params, self.size, interpolation=F.InterpolationMode.NEAREST) + + # Horizontal flip + if random.random() < 0.5: + img = F.hflip(img) + seg = F.hflip(seg) + + # Color jitter + img = self.jitter(img) + + # To tensor + img = F.to_tensor(img) + seg = F.to_tensor(seg) + + return img, seg + + +class CocoPanopticValidAugmentation: + def __init__(self, size): + self.size = size + + def __call__(self, img, seg): + # Resize + params = transforms.RandomResizedCrop.get_params(img, scale=(1, 1), ratio=(1., 1.)) + img = F.resized_crop(img, *params, self.size, interpolation=F.InterpolationMode.BILINEAR) + seg = F.resized_crop(seg, *params, self.size, interpolation=F.InterpolationMode.NEAREST) + + # To tensor + img = F.to_tensor(img) + seg = F.to_tensor(seg) + + return img, seg \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/imagematte.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/imagematte.py new file mode 100644 index 0000000..124faca --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/imagematte.py @@ -0,0 +1,98 @@ +import os +import random +from torch.utils.data import Dataset +from PIL import Image + +from .augmentation import MotionAugmentation + + +class ImageMatteDataset(Dataset): + def __init__(self, + imagematte_dir, + background_image_dir, + background_video_dir, + size, + seq_length, + seq_sampler, + transform): + self.imagematte_dir = imagematte_dir + self.imagematte_files = os.listdir(os.path.join(imagematte_dir, 'fgr')) + self.background_image_dir = background_image_dir + self.background_image_files = os.listdir(background_image_dir) + self.background_video_dir = background_video_dir + self.background_video_clips = os.listdir(background_video_dir) + self.background_video_frames = [sorted(os.listdir(os.path.join(background_video_dir, clip))) + for clip in self.background_video_clips] + self.seq_length = seq_length + self.seq_sampler = seq_sampler + self.size = size + self.transform = transform + + def __len__(self): + return max(len(self.imagematte_files), len(self.background_image_files) + len(self.background_video_clips)) + + def __getitem__(self, idx): + if random.random() < 0.5: + bgrs = self._get_random_image_background() + else: + bgrs = self._get_random_video_background() + + fgrs, phas = self._get_imagematte(idx) + + if self.transform is not None: + return self.transform(fgrs, phas, bgrs) + + return fgrs, phas, bgrs + + def _get_imagematte(self, idx): + with Image.open(os.path.join(self.imagematte_dir, 'fgr', self.imagematte_files[idx % len(self.imagematte_files)])) as fgr, \ + Image.open(os.path.join(self.imagematte_dir, 'pha', self.imagematte_files[idx % len(self.imagematte_files)])) as pha: + fgr = self._downsample_if_needed(fgr.convert('RGB')) + pha = self._downsample_if_needed(pha.convert('L')) + fgrs = [fgr] * self.seq_length + phas = [pha] * self.seq_length + return fgrs, phas + + def _get_random_image_background(self): + with Image.open(os.path.join(self.background_image_dir, self.background_image_files[random.choice(range(len(self.background_image_files)))])) as bgr: + bgr = self._downsample_if_needed(bgr.convert('RGB')) + bgrs = [bgr] * self.seq_length + return bgrs + + def _get_random_video_background(self): + clip_idx = random.choice(range(len(self.background_video_clips))) + frame_count = len(self.background_video_frames[clip_idx]) + frame_idx = random.choice(range(max(1, frame_count - self.seq_length))) + clip = self.background_video_clips[clip_idx] + bgrs = [] + for i in self.seq_sampler(self.seq_length): + frame_idx_t = frame_idx + i + frame = self.background_video_frames[clip_idx][frame_idx_t % frame_count] + with Image.open(os.path.join(self.background_video_dir, clip, frame)) as bgr: + bgr = self._downsample_if_needed(bgr.convert('RGB')) + bgrs.append(bgr) + return bgrs + + def _downsample_if_needed(self, img): + w, h = img.size + if min(w, h) > self.size: + scale = self.size / min(w, h) + w = int(scale * w) + h = int(scale * h) + img = img.resize((w, h)) + return img + +class ImageMatteAugmentation(MotionAugmentation): + def __init__(self, size): + super().__init__( + size=size, + prob_fgr_affine=0.95, + prob_bgr_affine=0.3, + prob_noise=0.05, + prob_color_jitter=0.3, + prob_grayscale=0.03, + prob_sharpness=0.05, + prob_blur=0.02, + prob_hflip=0.5, + prob_pause=0.03, + ) diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/spd.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/spd.py new file mode 100644 index 0000000..7f76b82 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/spd.py @@ -0,0 +1,27 @@ +import os +from torch.utils.data import Dataset +from PIL import Image + + +class SuperviselyPersonDataset(Dataset): + def __init__(self, imgdir, segdir, transform=None): + self.img_dir = imgdir + self.img_files = sorted(os.listdir(imgdir)) + self.seg_dir = segdir + self.seg_files = sorted(os.listdir(segdir)) + assert len(self.img_files) == len(self.seg_files) + self.transform = transform + + def __len__(self): + return len(self.img_files) + + def __getitem__(self, idx): + with Image.open(os.path.join(self.img_dir, self.img_files[idx])) as img, \ + Image.open(os.path.join(self.seg_dir, self.seg_files[idx])) as seg: + img = img.convert('RGB') + seg = seg.convert('L') + + if self.transform is not None: + img, seg = self.transform(img, seg) + + return img, seg diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/videomatte.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/videomatte.py new file mode 100644 index 0000000..555911b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/videomatte.py @@ -0,0 +1,125 @@ +import os +import random +from torch.utils.data import Dataset +from PIL import Image + +from .augmentation import MotionAugmentation + + +class VideoMatteDataset(Dataset): + def __init__(self, + videomatte_dir, + background_image_dir, + background_video_dir, + size, + seq_length, + seq_sampler, + transform=None): + self.background_image_dir = background_image_dir + self.background_image_files = os.listdir(background_image_dir) + self.background_video_dir = background_video_dir + self.background_video_clips = sorted(os.listdir(background_video_dir)) + self.background_video_frames = [sorted(os.listdir(os.path.join(background_video_dir, clip))) + for clip in self.background_video_clips] + + self.videomatte_dir = videomatte_dir + self.videomatte_clips = sorted(os.listdir(os.path.join(videomatte_dir, 'fgr'))) + self.videomatte_frames = [sorted(os.listdir(os.path.join(videomatte_dir, 'fgr', clip))) + for clip in self.videomatte_clips] + self.videomatte_idx = [(clip_idx, frame_idx) + for clip_idx in range(len(self.videomatte_clips)) + for frame_idx in range(0, len(self.videomatte_frames[clip_idx]), seq_length)] + self.size = size + self.seq_length = seq_length + self.seq_sampler = seq_sampler + self.transform = transform + + def __len__(self): + return len(self.videomatte_idx) + + def __getitem__(self, idx): + if random.random() < 0.5: + bgrs = self._get_random_image_background() + else: + bgrs = self._get_random_video_background() + + fgrs, phas = self._get_videomatte(idx) + + if self.transform is not None: + return self.transform(fgrs, phas, bgrs) + + return fgrs, phas, bgrs + + def _get_random_image_background(self): + with Image.open(os.path.join(self.background_image_dir, random.choice(self.background_image_files))) as bgr: + bgr = self._downsample_if_needed(bgr.convert('RGB')) + bgrs = [bgr] * self.seq_length + return bgrs + + def _get_random_video_background(self): + clip_idx = random.choice(range(len(self.background_video_clips))) + frame_count = len(self.background_video_frames[clip_idx]) + frame_idx = random.choice(range(max(1, frame_count - self.seq_length))) + clip = self.background_video_clips[clip_idx] + bgrs = [] + for i in self.seq_sampler(self.seq_length): + frame_idx_t = frame_idx + i + frame = self.background_video_frames[clip_idx][frame_idx_t % frame_count] + with Image.open(os.path.join(self.background_video_dir, clip, frame)) as bgr: + bgr = self._downsample_if_needed(bgr.convert('RGB')) + bgrs.append(bgr) + return bgrs + + def _get_videomatte(self, idx): + clip_idx, frame_idx = self.videomatte_idx[idx] + clip = self.videomatte_clips[clip_idx] + frame_count = len(self.videomatte_frames[clip_idx]) + fgrs, phas = [], [] + for i in self.seq_sampler(self.seq_length): + frame = self.videomatte_frames[clip_idx][(frame_idx + i) % frame_count] + with Image.open(os.path.join(self.videomatte_dir, 'fgr', clip, frame)) as fgr, \ + Image.open(os.path.join(self.videomatte_dir, 'pha', clip, frame)) as pha: + fgr = self._downsample_if_needed(fgr.convert('RGB')) + pha = self._downsample_if_needed(pha.convert('L')) + fgrs.append(fgr) + phas.append(pha) + return fgrs, phas + + def _downsample_if_needed(self, img): + w, h = img.size + if min(w, h) > self.size: + scale = self.size / min(w, h) + w = int(scale * w) + h = int(scale * h) + img = img.resize((w, h)) + return img + +class VideoMatteTrainAugmentation(MotionAugmentation): + def __init__(self, size): + super().__init__( + size=size, + prob_fgr_affine=0.3, + prob_bgr_affine=0.3, + prob_noise=0.1, + prob_color_jitter=0.3, + prob_grayscale=0.02, + prob_sharpness=0.1, + prob_blur=0.02, + prob_hflip=0.5, + prob_pause=0.03, + ) + +class VideoMatteValidAugmentation(MotionAugmentation): + def __init__(self, size): + super().__init__( + size=size, + prob_fgr_affine=0, + prob_bgr_affine=0, + prob_noise=0, + prob_color_jitter=0, + prob_grayscale=0, + prob_sharpness=0, + prob_blur=0, + prob_hflip=0, + prob_pause=0, + ) diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/youtubevis.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/youtubevis.py new file mode 100644 index 0000000..babbf0e --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/dataset/youtubevis.py @@ -0,0 +1,123 @@ +import torch +import os +import json +import numpy as np +import random +from torch.utils.data import Dataset +from PIL import Image +from torchvision import transforms +from torchvision.transforms import functional as F + + +class YouTubeVISDataset(Dataset): + def __init__(self, videodir, annfile, size, seq_length, seq_sampler, transform=None): + self.videodir = videodir + self.size = size + self.seq_length = seq_length + self.seq_sampler = seq_sampler + self.transform = transform + + with open(annfile) as f: + data = json.load(f) + + self.masks = {} + for ann in data['annotations']: + if ann['category_id'] == 26: # person + video_id = ann['video_id'] + if video_id not in self.masks: + self.masks[video_id] = [[] for _ in range(len(ann['segmentations']))] + for frame, mask in zip(self.masks[video_id], ann['segmentations']): + if mask is not None: + frame.append(mask) + + self.videos = {} + for video in data['videos']: + video_id = video['id'] + if video_id in self.masks: + self.videos[video_id] = video + + self.index = [] + for video_id in self.videos.keys(): + for frame in range(len(self.videos[video_id])): + self.index.append((video_id, frame)) + + def __len__(self): + return len(self.index) + + def __getitem__(self, idx): + video_id, frame_id = self.index[idx] + video = self.videos[video_id] + frame_count = len(self.videos[video_id]['file_names']) + H, W = video['height'], video['width'] + + imgs, segs = [], [] + for t in self.seq_sampler(self.seq_length): + frame = (frame_id + t) % frame_count + + filename = video['file_names'][frame] + masks = self.masks[video_id][frame] + + with Image.open(os.path.join(self.videodir, filename)) as img: + imgs.append(self._downsample_if_needed(img.convert('RGB'), Image.BILINEAR)) + + seg = np.zeros((H, W), dtype=np.uint8) + for mask in masks: + seg |= self._decode_rle(mask) + segs.append(self._downsample_if_needed(Image.fromarray(seg), Image.NEAREST)) + + if self.transform is not None: + imgs, segs = self.transform(imgs, segs) + + return imgs, segs + + def _decode_rle(self, rle): + H, W = rle['size'] + msk = np.zeros(H * W, dtype=np.uint8) + encoding = rle['counts'] + skip = 0 + for i in range(0, len(encoding) - 1, 2): + skip += encoding[i] + draw = encoding[i + 1] + msk[skip : skip + draw] = 255 + skip += draw + return msk.reshape(W, H).transpose() + + def _downsample_if_needed(self, img, resample): + w, h = img.size + if min(w, h) > self.size: + scale = self.size / min(w, h) + w = int(scale * w) + h = int(scale * h) + img = img.resize((w, h), resample) + return img + + +class YouTubeVISAugmentation: + def __init__(self, size): + self.size = size + self.jitter = transforms.ColorJitter(0.3, 0.3, 0.3, 0.15) + + def __call__(self, imgs, segs): + + # To tensor + imgs = torch.stack([F.to_tensor(img) for img in imgs]) + segs = torch.stack([F.to_tensor(seg) for seg in segs]) + + # Resize + params = transforms.RandomResizedCrop.get_params(imgs, scale=(0.8, 1), ratio=(0.9, 1.1)) + imgs = F.resized_crop(imgs, *params, self.size, interpolation=F.InterpolationMode.BILINEAR) + segs = F.resized_crop(segs, *params, self.size, interpolation=F.InterpolationMode.BILINEAR) + + # Color jitter + imgs = self.jitter(imgs) + + # Grayscale + if random.random() < 0.05: + imgs = F.rgb_to_grayscale(imgs, num_output_channels=3) + + # Horizontal flip + if random.random() < 0.5: + imgs = F.hflip(imgs) + segs = F.hflip(segs) + + return imgs, segs \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/image/showreel.gif b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/image/showreel.gif new file mode 100644 index 0000000..5af4c45 Binary files /dev/null and b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/image/showreel.gif differ diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/image/teaser.gif b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/image/teaser.gif new file mode 100644 index 0000000..26c9ec2 Binary files /dev/null and b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/image/teaser.gif differ diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/inference.md b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/inference.md new file mode 100644 index 0000000..413d720 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/inference.md @@ -0,0 +1,352 @@ +# Inference + +

English | 中文

+ +## Content + +* [Concepts](#concepts) + * [Downsample Ratio](#downsample-ratio) + * [Recurrent States](#recurrent-states) +* [PyTorch](#pytorch) +* [TorchHub](#torchhub) +* [TorchScript](#torchscript) +* [ONNX](#onnx) +* [TensorFlow](#tensorflow) +* [TensorFlow.js](#tensorflowjs) +* [CoreML](#coreml) + +
+ + +## Concepts + +### Downsample Ratio + +The table provides a general guideline. Please adjust based on your video content. + +| Resolution | Portrait | Full-Body | +| ------------- | ------------- | -------------- | +| <= 512x512 | 1 | 1 | +| 1280x720 | 0.375 | 0.6 | +| 1920x1080 | 0.25 | 0.4 | +| 3840x2160 | 0.125 | 0.2 | + +Internally, the model resizes down the input for stage 1. Then, it refines at high-resolution for stage 2. + +Set `downsample_ratio` so that the downsampled resolution is between 256 and 512. For example, for `1920x1080` input with `downsample_ratio=0.25`, the resized resolution `480x270` is between 256 and 512. + +Adjust `downsample_ratio` base on the video content. If the shot is portrait, a lower `downsample_ratio` is sufficient. If the shot contains the full human body, use high `downsample_ratio`. Note that higher `downsample_ratio` is not always better. + + +
+ +### Recurrent States +The model is a recurrent neural network. You must process frames sequentially and recycle its recurrent states. + +**Correct Way** + +The recurrent outputs are recycled back as input when processing the next frame. The states are essentially the model's memory. + +```python +rec = [None] * 4 # Initial recurrent states are None + +for frame in YOUR_VIDEO: + fgr, pha, *rec = model(frame, *rec, downsample_ratio) +``` + +**Wrong Way** + +The model does not utilize the recurrent states. Only use it to process independent images. + +```python +for frame in YOUR_VIDEO: + fgr, pha = model(frame, downsample_ratio)[:2] +``` + +More technical details are in the [paper](https://peterl1n.github.io/RobustVideoMatting/). + +


+ + +## PyTorch + +Model loading: + +```python +import torch +from model import MattingNetwork + +model = MattingNetwork(variant='mobilenetv3').eval().cuda() # Or variant="resnet50" +model.load_state_dict(torch.load('rvm_mobilenetv3.pth')) +``` + +Example inference loop: +```python +rec = [None] * 4 # Set initial recurrent states to None + +for src in YOUR_VIDEO: # src can be [B, C, H, W] or [B, T, C, H, W] + fgr, pha, *rec = model(src, *rec, downsample_ratio=0.25) +``` + +* `src`: Input frame. + * Can be of shape `[B, C, H, W]` or `[B, T, C, H, W]`. + * If `[B, T, C, H, W]`, a chunk of `T` frames can be given at once for better parallelism. + * RGB input is normalized to `0~1` range. + +* `fgr, pha`: Foreground and alpha predictions. + * Can be of shape `[B, C, H, W]` or `[B, T, C, H, W]` depends on `src`. + * `fgr` has `C=3` for RGB, `pha` has `C=1`. + * Outputs normalized to `0~1` range. +* `rec`: Recurrent states. + * Type of `List[Tensor, Tensor, Tensor, Tensor]`. + * Initial `rec` can be `List[None, None, None, None]`. + * It has 4 recurrent states because the model has 4 ConvGRU layers. + * All tensors are rank 4 regardless of `src` rank. + * If a chunk of `T` frames is given, only the last frame's recurrent states will be returned. + +To inference on video, here is a complete example: + +```python +from torch.utils.data import DataLoader +from torchvision.transforms import ToTensor +from inference_utils import VideoReader, VideoWriter + +reader = VideoReader('input.mp4', transform=ToTensor()) +writer = VideoWriter('output.mp4', frame_rate=30) + +bgr = torch.tensor([.47, 1, .6]).view(3, 1, 1).cuda() # Green background. +rec = [None] * 4 # Initial recurrent states. + +with torch.no_grad(): + for src in DataLoader(reader): + fgr, pha, *rec = model(src.cuda(), *rec, downsample_ratio=0.25) # Cycle the recurrent states. + writer.write(fgr * pha + bgr * (1 - pha)) +``` + +Or you can use the provided video converter: + +```python +from inference import convert_video + +convert_video( + model, # The loaded model, can be on any device (cpu or cuda). + input_source='input.mp4', # A video file or an image sequence directory. + input_resize=(1920, 1080), # [Optional] Resize the input (also the output). + downsample_ratio=0.25, # [Optional] If None, make downsampled max size be 512px. + output_type='video', # Choose "video" or "png_sequence" + output_composition='com.mp4', # File path if video; directory path if png sequence. + output_alpha="pha.mp4", # [Optional] Output the raw alpha prediction. + output_foreground="fgr.mp4", # [Optional] Output the raw foreground prediction. + output_video_mbps=4, # Output video mbps. Not needed for png sequence. + seq_chunk=12, # Process n frames at once for better parallelism. + num_workers=1, # Only for image sequence input. Reader threads. + progress=True # Print conversion progress. +) +``` + +The converter can also be invoked in command line: + +```sh +python inference.py \ + --variant mobilenetv3 \ + --checkpoint "CHECKPOINT" \ + --device cuda \ + --input-source "input.mp4" \ + --downsample-ratio 0.25 \ + --output-type video \ + --output-composition "composition.mp4" \ + --output-alpha "alpha.mp4" \ + --output-foreground "foreground.mp4" \ + --output-video-mbps 4 \ + --seq-chunk 12 +``` + +


+ +## TorchHub + +Model loading: + +```python +model = torch.hub.load("PeterL1n/RobustVideoMatting", "mobilenetv3") # or "resnet50" +``` + +Use the conversion function. Refer to the documentation for `convert_video` function above. + +```python +convert_video = torch.hub.load("PeterL1n/RobustVideoMatting", "converter") + +convert_video(model, ...args...) +``` + +


+ +## TorchScript + +Model loading: + +```python +import torch +model = torch.jit.load('rvm_mobilenetv3.torchscript') +``` + +Optionally, freeze the model. This will trigger graph optimization, such as BatchNorm fusion etc. Frozen models are faster. + +```python +model = torch.jit.freeze(model) +``` + +Then, you can use the `model` exactly the same as a PyTorch model, with the exception that you must manually provide `device` and `dtype` to the converter API for frozen model. For example: + +```python +convert_video(frozen_model, ...args..., device='cuda', dtype=torch.float32) +``` + +


+ +## ONNX + +Model spec: +* Inputs: [`src`, `r1i`, `r2i`, `r3i`, `r4i`, `downsample_ratio`]. + * `src` is the RGB input frame of shape `[B, C, H, W]` normalized to `0~1` range. + * `rXi` are the recurrent state inputs. Initial recurrent states are zero value tensors of shape `[1, 1, 1, 1]`. + * `downsample_ratio` is a tensor of shape `[1]`. + * Only `downsample_ratio` must have `dtype=FP32`. Other inputs must have `dtype` matching the loaded model's precision. +* Outputs: [`fgr`, `pha`, `r1o`, `r2o`, `r3o`, `r4o`] + * `fgr, pha` are the foreground and alpha prediction. Normalized to `0~1` range. + * `rXo` are the recurrent state outputs. + +We only show examples of using onnxruntime CUDA backend in Python. + +Model loading + +```python +import onnxruntime as ort + +sess = ort.InferenceSession('rvm_mobilenetv3_fp16.onnx') +``` + +Naive inference loop + +```python +import numpy as np + +rec = [ np.zeros([1, 1, 1, 1], dtype=np.float16) ] * 4 # Must match dtype of the model. +downsample_ratio = np.array([0.25], dtype=np.float32) # dtype always FP32 + +for src in YOUR_VIDEO: # src is of [B, C, H, W] with dtype of the model. + fgr, pha, *rec = sess.run([], { + 'src': src, + 'r1i': rec[0], + 'r2i': rec[1], + 'r3i': rec[2], + 'r4i': rec[3], + 'downsample_ratio': downsample_ratio + }) +``` + +If you use GPU version of ONNX Runtime, the above naive implementation has recurrent states transferred between CPU and GPU on every frame. They could have just stayed on the GPU for better performance. Below is an example using `iobinding` to eliminate useless transfers. + +```python +import onnxruntime as ort +import numpy as np + +# Load model. +sess = ort.InferenceSession('rvm_mobilenetv3_fp16.onnx') + +# Create an io binding. +io = sess.io_binding() + +# Create tensors on CUDA. +rec = [ ort.OrtValue.ortvalue_from_numpy(np.zeros([1, 1, 1, 1], dtype=np.float16), 'cuda') ] * 4 +downsample_ratio = ort.OrtValue.ortvalue_from_numpy(np.asarray([0.25], dtype=np.float32), 'cuda') + +# Set output binding. +for name in ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o']: + io.bind_output(name, 'cuda') + +# Inference loop +for src in YOUR_VIDEO: + io.bind_cpu_input('src', src) + io.bind_ortvalue_input('r1i', rec[0]) + io.bind_ortvalue_input('r2i', rec[1]) + io.bind_ortvalue_input('r3i', rec[2]) + io.bind_ortvalue_input('r4i', rec[3]) + io.bind_ortvalue_input('downsample_ratio', downsample_ratio) + + sess.run_with_iobinding(io) + + fgr, pha, *rec = io.get_outputs() + + # Only transfer `fgr` and `pha` to CPU. + fgr = fgr.numpy() + pha = pha.numpy() +``` + +Note: depending on the inference tool you choose, it may not support all the operations in our official ONNX model. You are responsible for modifying the model code and exporting your own ONNX model. You can refer to our exporter code in the [onnx branch](https://github.com/PeterL1n/RobustVideoMatting/tree/onnx). + +


+ +### TensorFlow + +An example usage: + +```python +import tensorflow as tf + +model = tf.keras.models.load_model('rvm_mobilenetv3_tf') +model = tf.function(model) + +rec = [ tf.constant(0.) ] * 4 # Initial recurrent states. +downsample_ratio = tf.constant(0.25) # Adjust based on your video. + +for src in YOUR_VIDEO: # src is of shape [B, H, W, C], not [B, C, H, W]! + out = model([src, *rec, downsample_ratio]) + fgr, pha, *rec = out['fgr'], out['pha'], out['r1o'], out['r2o'], out['r3o'], out['r4o'] +``` + +Note the the tensors are all channel last. Otherwise, the inputs and outputs are exactly the same as PyTorch. + +We also provide the raw TensorFlow model code in the [tensorflow branch](https://github.com/PeterL1n/RobustVideoMatting/tree/tensorflow). You can transfer PyTorch checkpoint weights to TensorFlow models. + +


+ +### TensorFlow.js + +We provide a starter code in the [tfjs branch](https://github.com/PeterL1n/RobustVideoMatting/tree/tfjs). The example is very self-explanatory. It shows how to properly use the model. + +


+ +### CoreML + +We only show example usage of the CoreML models in Python API using `coremltools`. In production, the same logic can be applied in Swift. When processing the first frame, do not provide recurrent states. CoreML will internally construct zero tensors of the correct shapes as the initial recurrent states. + +```python +import coremltools as ct + +model = ct.models.model.MLModel('rvm_mobilenetv3_1920x1080_s0.25_int8.mlmodel') + +r1, r2, r3, r4 = None, None, None, None + +for src in YOUR_VIDEO: # src is PIL.Image. + + if r1 is None: + # Initial frame, do not provide recurrent states. + inputs = {'src': src} + else: + # Subsequent frames, provide recurrent states. + inputs = {'src': src, 'r1i': r1, 'r2i': r2, 'r3i': r3, 'r4i': r4} + + outputs = model.predict(inputs) + + fgr = outputs['fgr'] # PIL.Image. + pha = outputs['pha'] # PIL.Image. + + r1 = outputs['r1o'] # Numpy array. + r2 = outputs['r2o'] # Numpy array. + r3 = outputs['r3o'] # Numpy array. + r4 = outputs['r4o'] # Numpy array. + +``` + +Our CoreML models only support fixed resolutions. If you need other resolutions, you can export them yourself. See [coreml branch](https://github.com/PeterL1n/RobustVideoMatting/tree/coreml) for model export. \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/inference_zh_Hans.md b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/inference_zh_Hans.md new file mode 100644 index 0000000..7995f40 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/inference_zh_Hans.md @@ -0,0 +1,353 @@ +# 推断文档 + +

English | 中文

+ +## 目录 + +* [概念](#概念) + * [下采样比](#下采样比) + * [循环记忆](#循环记忆) +* [PyTorch](#pytorch) +* [TorchHub](#torchhub) +* [TorchScript](#torchscript) +* [ONNX](#onnx) +* [TensorFlow](#tensorflow) +* [TensorFlow.js](#tensorflowjs) +* [CoreML](#coreml) + +
+ + +## 概念 + +### 下采样比 + +该表仅供参考。可根据视频内容进行调节。 + +| 分辨率 | 人像 | 全身 | +| ------------- | ------------- | -------------- | +| <= 512x512 | 1 | 1 | +| 1280x720 | 0.375 | 0.6 | +| 1920x1080 | 0.25 | 0.4 | +| 3840x2160 | 0.125 | 0.2 | + +模型在内部将高分辨率输入缩小做初步的处理，然后再放大做细分处理。 + +建议设置 `downsample_ratio` 使缩小后的分辨率维持在 256 到 512 像素之间. 例如，`1920x1080` 的输入用 `downsample_ratio=0.25`，缩小后的分辨率 `480x270` 在 256 到 512 像素之间。 + +根据视频内容调整 `downsample_ratio`。若视频是上身人像，低 `downsample_ratio` 足矣。若视频是全身像，建议尝试更高的 `downsample_ratio`。但注意，过高的 `downsample_ratio` 反而会降低效果。 + + +
+ +### 循环记忆 +此模型是循环神经网络（Recurrent Neural Network）。必须按顺序处理视频每帧，并提供网络循环记忆。 + +**正确用法** + +循环记忆输出被传递到下一帧做输入。 + +```python +rec = [None] * 4 # 初始值设置为 None + +for frame in YOUR_VIDEO: + fgr, pha, *rec = model(frame, *rec, downsample_ratio) +``` + +**错误用法** + +没有使用循环记忆。此方法仅可用于处理单独的图片。 + +```python +for frame in YOUR_VIDEO: + fgr, pha = model(frame, downsample_ratio)[:2] +``` + +更多技术细节见[论文](https://peterl1n.github.io/RobustVideoMatting/)。 + +


+ + +## PyTorch + +载入模型： + +```python +import torch +from model import MattingNetwork + +model = MattingNetwork(variant='mobilenetv3').eval().cuda() # 或 variant="resnet50" +model.load_state_dict(torch.load('rvm_mobilenetv3.pth')) +``` + +推断循环： +```python +rec = [None] * 4 # 初始值设置为 None + +for src in YOUR_VIDEO: # src 可以是 [B, C, H, W] 或 [B, T, C, H, W] + fgr, pha, *rec = model(src, *rec, downsample_ratio=0.25) +``` + +* `src`: 输入帧（Source）。 + * 可以是 `[B, C, H, W]` 或 `[B, T, C, H, W]` 的张量。 + * 若是 `[B, T, C, H, W]`，可给模型一次 `T` 帧，做一小段一小段地处理，用于更好的并行计算。 + * RGB 通道输入，范围为 `0~1`。 + +* `fgr, pha`: 前景（Foreground）和透明度通道（Alpha）的预测。 + * 根据`src`，可为 `[B, C, H, W]` 或 `[B, T, C, H, W]` 的输出。 + * `fgr` 是 RGB 三通道，`pha` 为一通道。 + * 输出范围为 `0~1`。 +* `rec`: 循环记忆（Recurrent States）。 + * `List[Tensor, Tensor, Tensor, Tensor]` 类型。 + * 初始 `rec` 为 `List[None, None, None, None]`。 + * 有四个记忆，因为网络使用四个 `ConvGRU` 层。 + * 无论 `src` 的 Rank，所有记忆张量的 Rank 为 4。 + * 若一次给予 `T` 帧，只返回处理完最后一帧后的记忆。 + +完整的推断例子： + +```python +from torch.utils.data import DataLoader +from torchvision.transforms import ToTensor +from inference_utils import VideoReader, VideoWriter + +reader = VideoReader('input.mp4', transform=ToTensor()) +writer = VideoWriter('output.mp4', frame_rate=30) + +bgr = torch.tensor([.47, 1, .6]).view(3, 1, 1).cuda() # 绿背景 +rec = [None] * 4 # 初始记忆 + +with torch.no_grad(): + for src in DataLoader(reader): + fgr, pha, *rec = model(src.cuda(), *rec, downsample_ratio=0.25) # 将上一帧的记忆给下一帧 + writer.write(fgr * pha + bgr * (1 - pha)) +``` + +或者使用提供的视频转换 API： + +```python +from inference import convert_video + +convert_video( + model, # 模型，可以加载到任何设备（cpu 或 cuda） + input_source='input.mp4', # 视频文件，或图片序列文件夹 + input_resize=(1920, 1080), # [可选项] 缩放视频大小 + downsample_ratio=0.25, # [可选项] 下采样比，若 None，自动下采样至 512px + output_type='video', # 可选 "video"（视频）或 "png_sequence"（PNG 序列） + output_composition='com.mp4', # 若导出视频，提供文件路径。若导出 PNG 序列，提供文件夹路径 + output_alpha="pha.mp4", # [可选项] 输出透明度预测 + output_foreground="fgr.mp4", # [可选项] 输出前景预测 + output_video_mbps=4, # 若导出视频，提供视频码率 + seq_chunk=12, # 设置多帧并行计算 + num_workers=1, # 只适用于图片序列输入，读取线程 + progress=True # 显示进度条 +) +``` + +也可通过命令行调用转换 API： + +```sh +python inference.py \ + --variant mobilenetv3 \ + --checkpoint "CHECKPOINT" \ + --device cuda \ + --input-source "input.mp4" \ + --downsample-ratio 0.25 \ + --output-type video \ + --output-composition "composition.mp4" \ + --output-alpha "alpha.mp4" \ + --output-foreground "foreground.mp4" \ + --output-video-mbps 4 \ + --seq-chunk 12 +``` + +


+ +## TorchHub + +载入模型： + +```python +model = torch.hub.load("PeterL1n/RobustVideoMatting", "mobilenetv3") # or "resnet50" +``` + +使用转换 API，具体请参考之前对 `convert_video` 的文档。 + +```python +convert_video = torch.hub.load("PeterL1n/RobustVideoMatting", "converter") + +convert_video(model, ...args...) +``` + +


+ +## TorchScript + +载入模型： + +```python +import torch +model = torch.jit.load('rvm_mobilenetv3.torchscript') +``` + +也可以可选的将模型固化（Freeze）。这会对模型进行优化，例如 BatchNorm Fusion 等。固化的模型更快。 + +```python +model = torch.jit.freeze(model) +``` + +然后，可以将 `model` 作为普通的 PyTorch 模型使用。但注意，若用固化模型调用转换 API，必须手动提供 `device` 和 `dtype`: + +```python +convert_video(frozen_model, ...args..., device='cuda', dtype=torch.float32) +``` + +


+ +## ONNX + +模型规格: +* 输入: [`src`, `r1i`, `r2i`, `r3i`, `r4i`, `downsample_ratio`]. + * `src`：输入帧，RGB 通道，形状为 `[B, C, H, W]`，范围为`0~1`。 + * `rXi`：记忆输入，初始值是是形状为 `[1, 1, 1, 1]` 的零张量。 + * `downsample_ratio` 下采样比，张量形状为 `[1]`。 + * 只有 `downsample_ratio` 必须是 `FP32`，其他输入必须和加载的模型使用一样的 `dtype`。 +* 输出: [`fgr`, `pha`, `r1o`, `r2o`, `r3o`, `r4o`] + * `fgr, pha`：前景和透明度通道输出，范围为 `0~1`。 + * `rXo`：记忆输出。 + +我们只展示用 ONNX Runtime CUDA Backend 在 Python 上的使用范例。 + +载入模型： + +```python +import onnxruntime as ort + +sess = ort.InferenceSession('rvm_mobilenetv3_fp16.onnx') +``` + +简单推断循环，但此方法不是最优化的： + +```python +import numpy as np + +rec = [ np.zeros([1, 1, 1, 1], dtype=np.float16) ] * 4 # 必须用模型一样的 dtype +downsample_ratio = np.array([0.25], dtype=np.float32) # 必须是 FP32 + +for src in YOUR_VIDEO: # src 张量是 [B, C, H, W] 形状，必须用模型一样的 dtype + fgr, pha, *rec = sess.run([], { + 'src': src, + 'r1i': rec[0], + 'r2i': rec[1], + 'r3i': rec[2], + 'r4i': rec[3], + 'downsample_ratio': downsample_ratio + }) +``` + +若使用 GPU，上例会将记忆输出传回到 CPU，再在下一帧时传回到 GPU。这种传输是无意义的，因为记忆值可以留在 GPU 上。下例使用 `iobinding` 来杜绝无用的传输。 + +```python +import onnxruntime as ort +import numpy as np + +# 载入模型 +sess = ort.InferenceSession('rvm_mobilenetv3_fp16.onnx') + +# 创建 io binding. +io = sess.io_binding() + +# 在 CUDA 上创建张量 +rec = [ ort.OrtValue.ortvalue_from_numpy(np.zeros([1, 1, 1, 1], dtype=np.float16), 'cuda') ] * 4 +downsample_ratio = ort.OrtValue.ortvalue_from_numpy(np.asarray([0.25], dtype=np.float32), 'cuda') + +# 设置输出项 +for name in ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o']: + io.bind_output(name, 'cuda') + +# 推断 +for src in YOUR_VIDEO: + io.bind_cpu_input('src', src) + io.bind_ortvalue_input('r1i', rec[0]) + io.bind_ortvalue_input('r2i', rec[1]) + io.bind_ortvalue_input('r3i', rec[2]) + io.bind_ortvalue_input('r4i', rec[3]) + io.bind_ortvalue_input('downsample_ratio', downsample_ratio) + + sess.run_with_iobinding(io) + + fgr, pha, *rec = io.get_outputs() + + # 只将 `fgr` 和 `pha` 回传到 CPU + fgr = fgr.numpy() + pha = pha.numpy() +``` + +注：若你使用其他推断框架，可能有些 ONNX ops 不被支持，需被替换。可以参考 [onnx](https://github.com/PeterL1n/RobustVideoMatting/tree/onnx) 分支的代码做自行导出。 + +


+ +### TensorFlow + +范例: + +```python +import tensorflow as tf + +model = tf.keras.models.load_model('rvm_mobilenetv3_tf') +model = tf.function(model) + +rec = [ tf.constant(0.) ] * 4 # 初始记忆 +downsample_ratio = tf.constant(0.25) # 下采样率，根据视频调整 + +for src in YOUR_VIDEO: # src 张量是 [B, H, W, C] 的形状，而不是 [B, C, H, W]! + out = model([src, *rec, downsample_ratio]) + fgr, pha, *rec = out['fgr'], out['pha'], out['r1o'], out['r2o'], out['r3o'], out['r4o'] +``` + +注意，在 TensorFlow 上，所有张量都是 Channal Last 的格式。 + +我们提供 TensorFlow 的原始模型代码，请参考 [tensorflow](https://github.com/PeterL1n/RobustVideoMatting/tree/tensorflow) 分支。您可自行将 PyTorch 的权值转到 TensorFlow 模型上。 + + +


+ +### TensorFlow.js + +我们在 [tfjs](https://github.com/PeterL1n/RobustVideoMatting/tree/tfjs) 分支提供范例代码。代码简单易懂，解释如何正确使用模型。 + +


+ +### CoreML + +我们只展示在 Python 下通过 `coremltools` 使用 CoreML 模型。在部署时，同样逻辑可用于 Swift。模型的循环记忆输入不需要在处理第一帧时提供。CoreML 内部会自动创建零张量作为初始记忆。 + +```python +import coremltools as ct + +model = ct.models.model.MLModel('rvm_mobilenetv3_1920x1080_s0.25_int8.mlmodel') + +r1, r2, r3, r4 = None, None, None, None + +for src in YOUR_VIDEO: # src 是 PIL.Image. + + if r1 is None: + # 初始帧, 不用提供循环记忆 + inputs = {'src': src} + else: + # 剩余帧，提供循环记忆 + inputs = {'src': src, 'r1i': r1, 'r2i': r2, 'r3i': r3, 'r4i': r4} + + outputs = model.predict(inputs) + + fgr = outputs['fgr'] # PIL.Image + pha = outputs['pha'] # PIL.Image + + r1 = outputs['r1o'] # Numpy array + r2 = outputs['r2o'] # Numpy array + r3 = outputs['r3o'] # Numpy array + r4 = outputs['r4o'] # Numpy array + +``` + +我们的 CoreML 模型只支持固定分辨率。如果你需要其他分辨率，可自行导出。导出代码见 [coreml](https://github.com/PeterL1n/RobustVideoMatting/tree/coreml) 分支。 \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/misc/aim_test.txt b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/misc/aim_test.txt new file mode 100644 index 0000000..088006b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/misc/aim_test.txt @@ -0,0 +1,11 @@ +boy-1518482_1920.png +girl-1219339_1920.png +girl-1467820_1280.png +girl-beautiful-young-face-53000.png +long-1245787_1920.png +model-600238_1920.png +pexels-photo-58463.png +sea-sunny-person-beach.png +wedding-dresses-1486260_1280.png +woman-952506_1920 (1).png +woman-morning-bathrobe-bathroom.png diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/misc/d646_test.txt b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/misc/d646_test.txt new file mode 100644 index 0000000..b5f02d5 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/misc/d646_test.txt @@ -0,0 +1,11 @@ +test_13.png +test_16.png +test_18.png +test_22.png +test_32.png +test_35.png +test_39.png +test_42.png +test_46.png +test_4.png +test_6.png diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/misc/dvm_background_test_clips.txt b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/misc/dvm_background_test_clips.txt new file mode 100644 index 0000000..a821c86 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/misc/dvm_background_test_clips.txt @@ -0,0 +1,162 @@ +0000 +0001 +0002 +0004 +0005 +0007 +0008 +0009 +0010 +0012 +0013 +0014 +0015 +0016 +0017 +0018 +0019 +0021 +0022 +0023 +0024 +0025 +0027 +0029 +0030 +0032 +0033 +0034 +0035 +0037 +0038 +0039 +0040 +0041 +0042 +0043 +0045 +0046 +0047 +0048 +0050 +0051 +0052 +0054 +0055 +0057 +0058 +0059 +0060 +0061 +0062 +0063 +0064 +0065 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@@ +13564741125_753939e9ce_o.jpg +3858897226_cae5b75963_o.jpg +538724499685900405.jpg +ballerina-855652_1920.jpg +boy-454633_1920.jpg +h_110.jpg +h_150.jpg +h_16.jpg +h_246.jpg +h_267.jpg +h_275.jpg +h_306.jpg +h_328.jpg +model-610352_960_720.jpg +t_66.jpg diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/misc/spd_preprocess.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/misc/spd_preprocess.py new file mode 100644 index 0000000..b9a309a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/misc/spd_preprocess.py @@ -0,0 +1,45 @@ +# pip install supervisely +import supervisely_lib as sly +import numpy as np +import os +from PIL import Image +from tqdm import tqdm + +# Download dataset from +project_root = 'PATH_TO/Supervisely Person Dataset' # <-- Configure input +project = sly.Project(project_root, sly.OpenMode.READ) + +output_path = 'OUTPUT_DIR' # <-- Configure output +os.makedirs(os.path.join(output_path, 'train', 'src')) +os.makedirs(os.path.join(output_path, 'train', 'msk')) +os.makedirs(os.path.join(output_path, 'valid', 'src')) +os.makedirs(os.path.join(output_path, 'valid', 'msk')) + +max_size = 2048 # <-- Configure max size + +for dataset in project.datasets: + for item in tqdm(dataset): + ann = sly.Annotation.load_json_file(dataset.get_ann_path(item), project.meta) + msk = np.zeros(ann.img_size, dtype=np.uint8) + for label in ann.labels: + label.geometry.draw(msk, color=[255]) + msk = Image.fromarray(msk) + + img = Image.open(dataset.get_img_path(item)).convert('RGB') + if img.size[0] > max_size or img.size[1] > max_size: + scale = max_size / max(img.size) + img = img.resize((int(img.size[0] * scale), int(img.size[1] * scale)), Image.BILINEAR) + msk = msk.resize((int(msk.size[0] * scale), int(msk.size[1] * scale)), Image.NEAREST) + + img.save(os.path.join(output_path, 'train', 'src', item.replace('.png', '.jpg'))) + msk.save(os.path.join(output_path, 'train', 'msk', item.replace('.png', '.jpg'))) + +# Move first 100 to validation set +names = os.listdir(os.path.join(output_path, 'train', 'src')) +for name in tqdm(names[:100]): + os.rename( + os.path.join(output_path, 'train', 'src', name), + os.path.join(output_path, 'valid', 'src', name)) + os.rename( + os.path.join(output_path, 'train', 'msk', name), + os.path.join(output_path, 'valid', 'msk', name)) \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/training.md b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/training.md new file mode 100644 index 0000000..a66ee8c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/documentation/training.md @@ -0,0 +1,158 @@ +# Training Documentation + +This documentation only shows the way to re-produce our [paper](https://peterl1n.github.io/RobustVideoMatting/). If you would like to remove or add a dataset to the training, you are responsible for adapting the training code yourself. + +## Datasets + +The following datasets are used during our training. + +**IMPORTANT: If you choose to download our preprocessed versions. Please avoid repeated downloads and cache the data locally. All traffics cost our expense. Please be responsible. We may only provide the preprocessed version of a limited time.** + +### Matting Datasets +* [VideoMatte240K](https://grail.cs.washington.edu/projects/background-matting-v2/#/datasets) + * Download JPEG SD version (6G) for stage 1 and 2. + * Download JPEG HD version (60G) for stage 3 and 4. + * Manually move clips `0000`, `0100`, `0200`, `0300` from the training set to a validation set. +* ImageMatte + * ImageMatte consists of [Distinctions-646](https://wukaoliu.github.io/HAttMatting/) and [Adobe Image Matting](https://sites.google.com/view/deepimagematting) datasets. + * Only needed for stage 4. + * You need to contact their authors to acquire. + * After downloading both datasets, merge their samples together to form ImageMatte dataset. + * Only keep samples of humans. + * Full list of images we used in ImageMatte for training: + * [imagematte_train.txt](/documentation/misc/imagematte_train.txt) + * [imagematte_valid.txt](/documentation/misc/imagematte_train.txt) + * Full list of images we used for evaluation. + * [aim_test.txt](/documentation/misc/aim_test.txt) + * [d646_test.txt](/documentation/misc/d646_test.txt) +### Background Datasets +* Video Backgrounds + * We process from [DVM Background Set](https://github.com/nowsyn/DVM) by selecting clips without humans and extract only the first 100 frames as JPEG sequence. + * Full list of clips we used: + * [dvm_background_train_clips.txt](/documentation/misc/dvm_background_train_clips.txt) + * [dvm_background_test_clips.txt](/documentation/misc/dvm_background_test_clips.txt) + * You can download our preprocessed versions: + * [Train set (14.6G)](https://robustvideomatting.blob.core.windows.net/data/BackgroundVideosTrain.tar) (Manually move some clips to validation set) + * [Test set (936M)](https://robustvideomatting.blob.core.windows.net/data/BackgroundVideosTest.tar) (Not needed for training. Only used for making synthetic test samples for evaluation) +* Image Backgrounds + * Train set: + * We crawled 8000 suitable images from Google and Flicker. + * We will not publish these images. + * [Test set](https://grail.cs.washington.edu/projects/background-matting-v2/#/datasets) + * We use the validation background set from [BGMv2](https://grail.cs.washington.edu/projects/background-matting-v2/) project. + * It contains about 200 images. + * It is not used in our training. Only used for making synthetic test samples for evaluation. + * But if you just want to quickly tryout training, you may use this as a temporary subsitute for the train set. + +### Segmentation Datasets + +* [COCO](https://cocodataset.org/#download) + * Download [train2017.zip (18G)](http://images.cocodataset.org/zips/train2017.zip) + * Download [panoptic_annotations_trainval2017.zip (821M)](http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip) + * Note that our train script expects the panopitc version. +* [YouTubeVIS 2021](https://youtube-vos.org/dataset/vis/) + * Download the train set. No preprocessing needed. +* [Supervisely Person Dataset](https://supervise.ly/explore/projects/supervisely-person-dataset-23304/datasets) + * We used the supervisedly library to convert their encoding to bitmaps masks before using our script. We also resized down some of the large images to avoid disk loading bottleneck. + * You can refer to [spd_preprocess.py](/documentation/misc/spd_preprocess.py) + * Or, you can download our [preprocessed version (800M)](https://robustvideomatting.blob.core.windows.net/data/SuperviselyPersonDataset.tar) + +## Training + +For reference, our training was done on data center machines with 48 CPU cores, 300G CPU memory, and 4 Nvidia V100 32G GPUs. + +During our official training, the code contains custom logics for our infrastructure. For release, the script has been cleaned up. There may be bugs existing in this version of the code but not in our official training. If you find problems, please file an issue. + +After you have downloaded the datasets. Please configure `train_config.py` to provide paths to your datasets. + +The training consists of 4 stages. For detail, please refer to the [paper](https://peterl1n.github.io/RobustVideoMatting/). + +### Stage 1 +```sh +python train.py \ + --model-variant mobilenetv3 \ + --dataset videomatte \ + --resolution-lr 512 \ + --seq-length-lr 15 \ + --learning-rate-backbone 0.0001 \ + --learning-rate-aspp 0.0002 \ + --learning-rate-decoder 0.0002 \ + --learning-rate-refiner 0 \ + --checkpoint-dir checkpoint/stage1 \ + --log-dir log/stage1 \ + --epoch-start 0 \ + --epoch-end 20 +``` + +### Stage 2 +```sh +python train.py \ + --model-variant mobilenetv3 \ + --dataset videomatte \ + --resolution-lr 512 \ + --seq-length-lr 50 \ + --learning-rate-backbone 0.00005 \ + --learning-rate-aspp 0.0001 \ + --learning-rate-decoder 0.0001 \ + --learning-rate-refiner 0 \ + --checkpoint checkpoint/stage1/epoch-19.pth \ + --checkpoint-dir checkpoint/stage2 \ + --log-dir log/stage2 \ + --epoch-start 20 \ + --epoch-end 22 +``` + +### Stage 3 +```sh +python train.py \ + --model-variant mobilenetv3 \ + --dataset videomatte \ + --train-hr \ + --resolution-lr 512 \ + --resolution-hr 2048 \ + --seq-length-lr 40 \ + --seq-length-hr 6 \ + --learning-rate-backbone 0.00001 \ + --learning-rate-aspp 0.00001 \ + --learning-rate-decoder 0.00001 \ + --learning-rate-refiner 0.0002 \ + --checkpoint checkpoint/stage2/epoch-21.pth \ + --checkpoint-dir checkpoint/stage3 \ + --log-dir log/stage3 \ + --epoch-start 22 \ + --epoch-end 23 +``` + +### Stage 4 +```sh +python train.py \ + --model-variant mobilenetv3 \ + --dataset imagematte \ + --train-hr \ + --resolution-lr 512 \ + --resolution-hr 2048 \ + --seq-length-lr 40 \ + --seq-length-hr 6 \ + --learning-rate-backbone 0.00001 \ + --learning-rate-aspp 0.00001 \ + --learning-rate-decoder 0.00005 \ + --learning-rate-refiner 0.0002 \ + --checkpoint checkpoint/stage3/epoch-22.pth \ + --checkpoint-dir checkpoint/stage4 \ + --log-dir log/stage4 \ + --epoch-start 23 \ + --epoch-end 28 +``` + +


+ +## Evaluation + +We synthetically composite test samples to both image and video backgrounds. Image samples (from D646, AIM) are augmented with synthetic motion. + +We only provide the composited VideoMatte240K test set. They are used in our paper evaluation. For D646 and AIM, you need to acquire the data from their authors and composite them yourself. The composition scripts we used are saved in `/evaluation` folder as reference backup. You need to modify them based on your setup. + +* [videomatte_512x512.tar (PNG 1.8G)](https://robustvideomatting.blob.core.windows.net/eval/videomatte_512x288.tar) +* [videomatte_1920x1080.tar (JPG 2.2G)](https://robustvideomatting.blob.core.windows.net/eval/videomatte_1920x1080.tar) + +Evaluation scripts are provided in `/evaluation` folder. \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/evaluate_hr.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/evaluate_hr.py new file mode 100644 index 0000000..cb5d146 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/evaluate_hr.py @@ -0,0 +1,216 @@ +""" +HR (High-Resolution) evaluation. We found using numpy is very slow for high resolution, so we moved it to PyTorch using CUDA. + +Note, the script only does evaluation. You will need to first inference yourself and save the results to disk +Expected directory format for both prediction and ground-truth is: + + videomatte_1920x1080 + ├── videomatte_motion + ├── pha + ├── 0000 + ├── 0000.png + ├── fgr + ├── 0000 + ├── 0000.png + ├── videomatte_static + ├── pha + ├── 0000 + ├── 0000.png + ├── fgr + ├── 0000 + ├── 0000.png + +Prediction must have the exact file structure and file name as the ground-truth, +meaning that if the ground-truth is png/jpg, prediction should be png/jpg. + +Example usage: + +python evaluate.py \ + --pred-dir pred/videomatte_1920x1080 \ + --true-dir true/videomatte_1920x1080 + +An excel sheet with evaluation results will be written to "pred/videomatte_1920x1080/videomatte_1920x1080.xlsx" +""" + + +import argparse +import os +import cv2 +import kornia +import numpy as np +import xlsxwriter +import torch +from concurrent.futures import ThreadPoolExecutor +from tqdm import tqdm + + +class Evaluator: + def __init__(self): + self.parse_args() + self.init_metrics() + self.evaluate() + self.write_excel() + + def parse_args(self): + parser = argparse.ArgumentParser() + parser.add_argument('--pred-dir', type=str, required=True) + parser.add_argument('--true-dir', type=str, required=True) + parser.add_argument('--num-workers', type=int, default=48) + parser.add_argument('--metrics', type=str, nargs='+', default=[ + 'pha_mad', 'pha_mse', 'pha_grad', 'pha_dtssd', 'fgr_mse']) + self.args = parser.parse_args() + + def init_metrics(self): + self.mad = MetricMAD() + self.mse = MetricMSE() + self.grad = MetricGRAD() + self.dtssd = MetricDTSSD() + + def evaluate(self): + tasks = [] + position = 0 + + with ThreadPoolExecutor(max_workers=self.args.num_workers) as executor: + for dataset in sorted(os.listdir(self.args.pred_dir)): + if os.path.isdir(os.path.join(self.args.pred_dir, dataset)): + for clip in sorted(os.listdir(os.path.join(self.args.pred_dir, dataset))): + future = executor.submit(self.evaluate_worker, dataset, clip, position) + tasks.append((dataset, clip, future)) + position += 1 + + self.results = [(dataset, clip, future.result()) for dataset, clip, future in tasks] + + def write_excel(self): + workbook = xlsxwriter.Workbook(os.path.join(self.args.pred_dir, f'{os.path.basename(self.args.pred_dir)}.xlsx')) + summarysheet = workbook.add_worksheet('summary') + metricsheets = [workbook.add_worksheet(metric) for metric in self.results[0][2].keys()] + + for i, metric in enumerate(self.results[0][2].keys()): + summarysheet.write(i, 0, metric) + summarysheet.write(i, 1, f'={metric}!B2') + + for row, (dataset, clip, metrics) in enumerate(self.results): + for metricsheet, metric in zip(metricsheets, metrics.values()): + # Write the header + if row == 0: + metricsheet.write(1, 0, 'Average') + metricsheet.write(1, 1, f'=AVERAGE(C2:ZZ2)') + for col in range(len(metric)): + metricsheet.write(0, col + 2, col) + colname = xlsxwriter.utility.xl_col_to_name(col + 2) + metricsheet.write(1, col + 2, f'=AVERAGE({colname}3:{colname}9999)') + + metricsheet.write(row + 2, 0, dataset) + metricsheet.write(row + 2, 1, clip) + metricsheet.write_row(row + 2, 2, metric) + + workbook.close() + + def evaluate_worker(self, dataset, clip, position): + framenames = sorted(os.listdir(os.path.join(self.args.pred_dir, dataset, clip, 'pha'))) + metrics = {metric_name : [] for metric_name in self.args.metrics} + + pred_pha_tm1 = None + true_pha_tm1 = None + + for i, framename in enumerate(tqdm(framenames, desc=f'{dataset} {clip}', position=position, dynamic_ncols=True)): + true_pha = cv2.imread(os.path.join(self.args.true_dir, dataset, clip, 'pha', framename), cv2.IMREAD_GRAYSCALE) + pred_pha = cv2.imread(os.path.join(self.args.pred_dir, dataset, clip, 'pha', framename), cv2.IMREAD_GRAYSCALE) + + true_pha = torch.from_numpy(true_pha).cuda(non_blocking=True).float().div_(255) + pred_pha = torch.from_numpy(pred_pha).cuda(non_blocking=True).float().div_(255) + + if 'pha_mad' in self.args.metrics: + metrics['pha_mad'].append(self.mad(pred_pha, true_pha)) + if 'pha_mse' in self.args.metrics: + metrics['pha_mse'].append(self.mse(pred_pha, true_pha)) + if 'pha_grad' in self.args.metrics: + metrics['pha_grad'].append(self.grad(pred_pha, true_pha)) + if 'pha_conn' in self.args.metrics: + metrics['pha_conn'].append(self.conn(pred_pha, true_pha)) + if 'pha_dtssd' in self.args.metrics: + if i == 0: + metrics['pha_dtssd'].append(0) + else: + metrics['pha_dtssd'].append(self.dtssd(pred_pha, pred_pha_tm1, true_pha, true_pha_tm1)) + + pred_pha_tm1 = pred_pha + true_pha_tm1 = true_pha + + if 'fgr_mse' in self.args.metrics: + true_fgr = cv2.imread(os.path.join(self.args.true_dir, dataset, clip, 'fgr', framename), cv2.IMREAD_COLOR) + pred_fgr = cv2.imread(os.path.join(self.args.pred_dir, dataset, clip, 'fgr', framename), cv2.IMREAD_COLOR) + + true_fgr = torch.from_numpy(true_fgr).float().div_(255) + pred_fgr = torch.from_numpy(pred_fgr).float().div_(255) + + true_msk = true_pha > 0 + metrics['fgr_mse'].append(self.mse(pred_fgr[true_msk], true_fgr[true_msk])) + + return metrics + + +class MetricMAD: + def __call__(self, pred, true): + return (pred - true).abs_().mean() * 1e3 + + +class MetricMSE: + def __call__(self, pred, true): + return ((pred - true) ** 2).mean() * 1e3 + + +class MetricGRAD: + def __init__(self, sigma=1.4): + self.filter_x, self.filter_y = self.gauss_filter(sigma) + self.filter_x = torch.from_numpy(self.filter_x).unsqueeze(0).cuda() + self.filter_y = torch.from_numpy(self.filter_y).unsqueeze(0).cuda() + + def __call__(self, pred, true): + true_grad = self.gauss_gradient(true) + pred_grad = self.gauss_gradient(pred) + return ((true_grad - pred_grad) ** 2).sum() / 1000 + + def gauss_gradient(self, img): + img_filtered_x = kornia.filters.filter2D(img[None, None, :, :], self.filter_x, border_type='replicate')[0, 0] + img_filtered_y = kornia.filters.filter2D(img[None, None, :, :], self.filter_y, border_type='replicate')[0, 0] + return (img_filtered_x**2 + img_filtered_y**2).sqrt() + + @staticmethod + def gauss_filter(sigma, epsilon=1e-2): + half_size = np.ceil(sigma * np.sqrt(-2 * np.log(np.sqrt(2 * np.pi) * sigma * epsilon))) + size = np.int(2 * half_size + 1) + + # create filter in x axis + filter_x = np.zeros((size, size)) + for i in range(size): + for j in range(size): + filter_x[i, j] = MetricGRAD.gaussian(i - half_size, sigma) * MetricGRAD.dgaussian( + j - half_size, sigma) + + # normalize filter + norm = np.sqrt((filter_x**2).sum()) + filter_x = filter_x / norm + filter_y = np.transpose(filter_x) + + return filter_x, filter_y + + @staticmethod + def gaussian(x, sigma): + return np.exp(-x**2 / (2 * sigma**2)) / (sigma * np.sqrt(2 * np.pi)) + + @staticmethod + def dgaussian(x, sigma): + return -x * MetricGRAD.gaussian(x, sigma) / sigma**2 + + +class MetricDTSSD: + def __call__(self, pred_t, pred_tm1, true_t, true_tm1): + dtSSD = ((pred_t - pred_tm1) - (true_t - true_tm1)) ** 2 + dtSSD = dtSSD.sum() / true_t.numel() + dtSSD = dtSSD.sqrt() + return dtSSD * 1e2 + + +if __name__ == '__main__': + Evaluator() \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/evaluate_lr.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/evaluate_lr.py new file mode 100644 index 0000000..92d671f --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/evaluate_lr.py @@ -0,0 +1,254 @@ +""" +LR (Low-Resolution) evaluation. + +Note, the script only does evaluation. You will need to first inference yourself and save the results to disk +Expected directory format for both prediction and ground-truth is: + + videomatte_512x288 + ├── videomatte_motion + ├── pha + ├── 0000 + ├── 0000.png + ├── fgr + ├── 0000 + ├── 0000.png + ├── videomatte_static + ├── pha + ├── 0000 + ├── 0000.png + ├── fgr + ├── 0000 + ├── 0000.png + +Prediction must have the exact file structure and file name as the ground-truth, +meaning that if the ground-truth is png/jpg, prediction should be png/jpg. + +Example usage: + +python evaluate.py \ + --pred-dir PATH_TO_PREDICTIONS/videomatte_512x288 \ + --true-dir PATH_TO_GROUNDTURTH/videomatte_512x288 + +An excel sheet with evaluation results will be written to "PATH_TO_PREDICTIONS/videomatte_512x288/videomatte_512x288.xlsx" +""" + + +import argparse +import os +import cv2 +import numpy as np +import xlsxwriter +from concurrent.futures import ThreadPoolExecutor +from tqdm import tqdm + + +class Evaluator: + def __init__(self): + self.parse_args() + self.init_metrics() + self.evaluate() + self.write_excel() + + def parse_args(self): + parser = argparse.ArgumentParser() + parser.add_argument('--pred-dir', type=str, required=True) + parser.add_argument('--true-dir', type=str, required=True) + parser.add_argument('--num-workers', type=int, default=48) + parser.add_argument('--metrics', type=str, nargs='+', default=[ + 'pha_mad', 'pha_mse', 'pha_grad', 'pha_conn', 'pha_dtssd', 'fgr_mad', 'fgr_mse']) + self.args = parser.parse_args() + + def init_metrics(self): + self.mad = MetricMAD() + self.mse = MetricMSE() + self.grad = MetricGRAD() + self.conn = MetricCONN() + self.dtssd = MetricDTSSD() + + def evaluate(self): + tasks = [] + position = 0 + + with ThreadPoolExecutor(max_workers=self.args.num_workers) as executor: + for dataset in sorted(os.listdir(self.args.pred_dir)): + if os.path.isdir(os.path.join(self.args.pred_dir, dataset)): + for clip in sorted(os.listdir(os.path.join(self.args.pred_dir, dataset))): + future = executor.submit(self.evaluate_worker, dataset, clip, position) + tasks.append((dataset, clip, future)) + position += 1 + + self.results = [(dataset, clip, future.result()) for dataset, clip, future in tasks] + + def write_excel(self): + workbook = xlsxwriter.Workbook(os.path.join(self.args.pred_dir, f'{os.path.basename(self.args.pred_dir)}.xlsx')) + summarysheet = workbook.add_worksheet('summary') + metricsheets = [workbook.add_worksheet(metric) for metric in self.results[0][2].keys()] + + for i, metric in enumerate(self.results[0][2].keys()): + summarysheet.write(i, 0, metric) + summarysheet.write(i, 1, f'={metric}!B2') + + for row, (dataset, clip, metrics) in enumerate(self.results): + for metricsheet, metric in zip(metricsheets, metrics.values()): + # Write the header + if row == 0: + metricsheet.write(1, 0, 'Average') + metricsheet.write(1, 1, f'=AVERAGE(C2:ZZ2)') + for col in range(len(metric)): + metricsheet.write(0, col + 2, col) + colname = xlsxwriter.utility.xl_col_to_name(col + 2) + metricsheet.write(1, col + 2, f'=AVERAGE({colname}3:{colname}9999)') + + metricsheet.write(row + 2, 0, dataset) + metricsheet.write(row + 2, 1, clip) + metricsheet.write_row(row + 2, 2, metric) + + workbook.close() + + def evaluate_worker(self, dataset, clip, position): + framenames = sorted(os.listdir(os.path.join(self.args.pred_dir, dataset, clip, 'pha'))) + metrics = {metric_name : [] for metric_name in self.args.metrics} + + pred_pha_tm1 = None + true_pha_tm1 = None + + for i, framename in enumerate(tqdm(framenames, desc=f'{dataset} {clip}', position=position, dynamic_ncols=True)): + true_pha = cv2.imread(os.path.join(self.args.true_dir, dataset, clip, 'pha', framename), cv2.IMREAD_GRAYSCALE).astype(np.float32) / 255 + pred_pha = cv2.imread(os.path.join(self.args.pred_dir, dataset, clip, 'pha', framename), cv2.IMREAD_GRAYSCALE).astype(np.float32) / 255 + if 'pha_mad' in self.args.metrics: + metrics['pha_mad'].append(self.mad(pred_pha, true_pha)) + if 'pha_mse' in self.args.metrics: + metrics['pha_mse'].append(self.mse(pred_pha, true_pha)) + if 'pha_grad' in self.args.metrics: + metrics['pha_grad'].append(self.grad(pred_pha, true_pha)) + if 'pha_conn' in self.args.metrics: + metrics['pha_conn'].append(self.conn(pred_pha, true_pha)) + if 'pha_dtssd' in self.args.metrics: + if i == 0: + metrics['pha_dtssd'].append(0) + else: + metrics['pha_dtssd'].append(self.dtssd(pred_pha, pred_pha_tm1, true_pha, true_pha_tm1)) + + pred_pha_tm1 = pred_pha + true_pha_tm1 = true_pha + + if 'fgr_mse' in self.args.metrics or 'fgr_mad' in self.args.metrics: + true_fgr = cv2.imread(os.path.join(self.args.true_dir, dataset, clip, 'fgr', framename), cv2.IMREAD_COLOR).astype(np.float32) / 255 + pred_fgr = cv2.imread(os.path.join(self.args.pred_dir, dataset, clip, 'fgr', framename), cv2.IMREAD_COLOR).astype(np.float32) / 255 + true_msk = true_pha > 0 + + if 'fgr_mse' in self.args.metrics: + metrics['fgr_mse'].append(self.mse(pred_fgr[true_msk], true_fgr[true_msk])) + if 'fgr_mad' in self.args.metrics: + metrics['fgr_mad'].append(self.mad(pred_fgr[true_msk], true_fgr[true_msk])) + + return metrics + + +class MetricMAD: + def __call__(self, pred, true): + return np.abs(pred - true).mean() * 1e3 + + +class MetricMSE: + def __call__(self, pred, true): + return ((pred - true) ** 2).mean() * 1e3 + + +class MetricGRAD: + def __init__(self, sigma=1.4): + self.filter_x, self.filter_y = self.gauss_filter(sigma) + + def __call__(self, pred, true): + pred_normed = np.zeros_like(pred) + true_normed = np.zeros_like(true) + cv2.normalize(pred, pred_normed, 1., 0., cv2.NORM_MINMAX) + cv2.normalize(true, true_normed, 1., 0., cv2.NORM_MINMAX) + + true_grad = self.gauss_gradient(true_normed).astype(np.float32) + pred_grad = self.gauss_gradient(pred_normed).astype(np.float32) + + grad_loss = ((true_grad - pred_grad) ** 2).sum() + return grad_loss / 1000 + + def gauss_gradient(self, img): + img_filtered_x = cv2.filter2D(img, -1, self.filter_x, borderType=cv2.BORDER_REPLICATE) + img_filtered_y = cv2.filter2D(img, -1, self.filter_y, borderType=cv2.BORDER_REPLICATE) + return np.sqrt(img_filtered_x**2 + img_filtered_y**2) + + @staticmethod + def gauss_filter(sigma, epsilon=1e-2): + half_size = np.ceil(sigma * np.sqrt(-2 * np.log(np.sqrt(2 * np.pi) * sigma * epsilon))) + size = np.int(2 * half_size + 1) + + # create filter in x axis + filter_x = np.zeros((size, size)) + for i in range(size): + for j in range(size): + filter_x[i, j] = MetricGRAD.gaussian(i - half_size, sigma) * MetricGRAD.dgaussian( + j - half_size, sigma) + + # normalize filter + norm = np.sqrt((filter_x**2).sum()) + filter_x = filter_x / norm + filter_y = np.transpose(filter_x) + + return filter_x, filter_y + + @staticmethod + def gaussian(x, sigma): + return np.exp(-x**2 / (2 * sigma**2)) / (sigma * np.sqrt(2 * np.pi)) + + @staticmethod + def dgaussian(x, sigma): + return -x * MetricGRAD.gaussian(x, sigma) / sigma**2 + + +class MetricCONN: + def __call__(self, pred, true): + step=0.1 + thresh_steps = np.arange(0, 1 + step, step) + round_down_map = -np.ones_like(true) + for i in range(1, len(thresh_steps)): + true_thresh = true >= thresh_steps[i] + pred_thresh = pred >= thresh_steps[i] + intersection = (true_thresh & pred_thresh).astype(np.uint8) + + # connected components + _, output, stats, _ = cv2.connectedComponentsWithStats( + intersection, connectivity=4) + # start from 1 in dim 0 to exclude background + size = stats[1:, -1] + + # largest connected component of the intersection + omega = np.zeros_like(true) + if len(size) != 0: + max_id = np.argmax(size) + # plus one to include background + omega[output == max_id + 1] = 1 + + mask = (round_down_map == -1) & (omega == 0) + round_down_map[mask] = thresh_steps[i - 1] + round_down_map[round_down_map == -1] = 1 + + true_diff = true - round_down_map + pred_diff = pred - round_down_map + # only calculate difference larger than or equal to 0.15 + true_phi = 1 - true_diff * (true_diff >= 0.15) + pred_phi = 1 - pred_diff * (pred_diff >= 0.15) + + connectivity_error = np.sum(np.abs(true_phi - pred_phi)) + return connectivity_error / 1000 + + +class MetricDTSSD: + def __call__(self, pred_t, pred_tm1, true_t, true_tm1): + dtSSD = ((pred_t - pred_tm1) - (true_t - true_tm1)) ** 2 + dtSSD = np.sum(dtSSD) / true_t.size + dtSSD = np.sqrt(dtSSD) + return dtSSD * 1e2 + + + +if __name__ == '__main__': + Evaluator() \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/generate_imagematte_with_background_image.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/generate_imagematte_with_background_image.py new file mode 100644 index 0000000..9d836f1 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/generate_imagematte_with_background_image.py @@ -0,0 +1,146 @@ +""" +python generate_imagematte_with_background_image.py \ + --imagematte-dir ../matting-data/Distinctions/test \ + --background-dir ../matting-data/Backgrounds/valid \ + --resolution 512 \ + --out-dir ../matting-data/evaluation/distinction_static_sd/ \ + --random-seed 10 + +Seed: + 10 - distinction-static + 11 - distinction-motion + 12 - adobe-static + 13 - adobe-motion + +""" + +import argparse +import os +import pims +import numpy as np +import random +from PIL import Image +from tqdm import tqdm +from tqdm.contrib.concurrent import process_map +from torchvision import transforms +from torchvision.transforms import functional as F + +parser = argparse.ArgumentParser() +parser.add_argument('--imagematte-dir', type=str, required=True) +parser.add_argument('--background-dir', type=str, required=True) +parser.add_argument('--num-samples', type=int, default=20) +parser.add_argument('--num-frames', type=int, default=100) +parser.add_argument('--resolution', type=int, required=True) +parser.add_argument('--out-dir', type=str, required=True) +parser.add_argument('--random-seed', type=int) +parser.add_argument('--extension', type=str, default='.png') +args = parser.parse_args() + +random.seed(args.random_seed) + +imagematte_filenames = os.listdir(os.path.join(args.imagematte_dir, 'fgr')) +background_filenames = os.listdir(args.background_dir) +random.shuffle(imagematte_filenames) +random.shuffle(background_filenames) + + +def lerp(a, b, percentage): + return a * (1 - percentage) + b * percentage + +def motion_affine(*imgs): + config = dict(degrees=(-10, 10), translate=(0.1, 0.1), + scale_ranges=(0.9, 1.1), shears=(-5, 5), img_size=imgs[0][0].size) + angleA, (transXA, transYA), scaleA, (shearXA, shearYA) = transforms.RandomAffine.get_params(**config) + angleB, (transXB, transYB), scaleB, (shearXB, shearYB) = transforms.RandomAffine.get_params(**config) + + T = len(imgs[0]) + variation_over_time = random.random() + for t in range(T): + percentage = (t / (T - 1)) * variation_over_time + angle = lerp(angleA, angleB, percentage) + transX = lerp(transXA, transXB, percentage) + transY = lerp(transYA, transYB, percentage) + scale = lerp(scaleA, scaleB, percentage) + shearX = lerp(shearXA, shearXB, percentage) + shearY = lerp(shearYA, shearYB, percentage) + for img in imgs: + img[t] = F.affine(img[t], angle, (transX, transY), scale, (shearX, shearY), F.InterpolationMode.BILINEAR) + return imgs + + + +def process(i): + imagematte_filename = imagematte_filenames[i % len(imagematte_filenames)] + background_filename = background_filenames[i % len(background_filenames)] + + out_path = os.path.join(args.out_dir, str(i).zfill(4)) + os.makedirs(os.path.join(out_path, 'fgr'), exist_ok=True) + os.makedirs(os.path.join(out_path, 'pha'), exist_ok=True) + os.makedirs(os.path.join(out_path, 'com'), exist_ok=True) + os.makedirs(os.path.join(out_path, 'bgr'), exist_ok=True) + + with Image.open(os.path.join(args.background_dir, background_filename)) as bgr: + bgr = bgr.convert('RGB') + + w, h = bgr.size + scale = args.resolution / min(h, w) + w, h = int(w * scale), int(h * scale) + bgr = bgr.resize((w, h)) + bgr = F.center_crop(bgr, (args.resolution, args.resolution)) + + with Image.open(os.path.join(args.imagematte_dir, 'fgr', imagematte_filename)) as fgr, \ + Image.open(os.path.join(args.imagematte_dir, 'pha', imagematte_filename)) as pha: + fgr = fgr.convert('RGB') + pha = pha.convert('L') + + fgrs = [fgr] * args.num_frames + phas = [pha] * args.num_frames + fgrs, phas = motion_affine(fgrs, phas) + + for t in tqdm(range(args.num_frames), desc=str(i).zfill(4)): + fgr = fgrs[t] + pha = phas[t] + + w, h = fgr.size + scale = args.resolution / max(h, w) + w, h = int(w * scale), int(h * scale) + + fgr = fgr.resize((w, h)) + pha = pha.resize((w, h)) + + if h < args.resolution: + pt = (args.resolution - h) // 2 + pb = args.resolution - h - pt + else: + pt = 0 + pb = 0 + + if w < args.resolution: + pl = (args.resolution - w) // 2 + pr = args.resolution - w - pl + else: + pl = 0 + pr = 0 + + fgr = F.pad(fgr, [pl, pt, pr, pb]) + pha = F.pad(pha, [pl, pt, pr, pb]) + + if i // len(imagematte_filenames) % 2 == 1: + fgr = fgr.transpose(Image.FLIP_LEFT_RIGHT) + pha = pha.transpose(Image.FLIP_LEFT_RIGHT) + + fgr.save(os.path.join(out_path, 'fgr', str(t).zfill(4) + args.extension)) + pha.save(os.path.join(out_path, 'pha', str(t).zfill(4) + args.extension)) + + if t == 0: + bgr.save(os.path.join(out_path, 'bgr', str(t).zfill(4) + args.extension)) + else: + os.symlink(str(0).zfill(4) + args.extension, os.path.join(out_path, 'bgr', str(t).zfill(4) + args.extension)) + + pha = np.asarray(pha).astype(float)[:, :, None] / 255 + com = Image.fromarray(np.uint8(np.asarray(fgr) * pha + np.asarray(bgr) * (1 - pha))) + com.save(os.path.join(out_path, 'com', str(t).zfill(4) + args.extension)) + + +if __name__ == '__main__': + r = process_map(process, range(args.num_samples), max_workers=32) \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/generate_imagematte_with_background_video.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/generate_imagematte_with_background_video.py new file mode 100644 index 0000000..6c67c7b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/generate_imagematte_with_background_video.py @@ -0,0 +1,189 @@ +""" +python generate_imagematte_with_background_video.py \ + --imagematte-dir ../matting-data/Distinctions/test \ + --background-dir ../matting-data/BackgroundVideos_mp4/test \ + --resolution 512 \ + --out-dir ../matting-data/evaluation/distinction_motion_sd/ \ + --random-seed 11 + +Seed: + 10 - distinction-static + 11 - distinction-motion + 12 - adobe-static + 13 - adobe-motion + +""" + +import argparse +import os +import pims +import numpy as np +import random +from multiprocessing import Pool +from PIL import Image +# from tqdm import tqdm +from tqdm.contrib.concurrent import process_map +from torchvision import transforms +from torchvision.transforms import functional as F + +parser = argparse.ArgumentParser() +parser.add_argument('--imagematte-dir', type=str, required=True) +parser.add_argument('--background-dir', type=str, required=True) +parser.add_argument('--num-samples', type=int, default=20) +parser.add_argument('--num-frames', type=int, default=100) +parser.add_argument('--resolution', type=int, required=True) +parser.add_argument('--out-dir', type=str, required=True) +parser.add_argument('--random-seed', type=int) +parser.add_argument('--extension', type=str, default='.png') +args = parser.parse_args() + +random.seed(args.random_seed) + +imagematte_filenames = os.listdir(os.path.join(args.imagematte_dir, 'fgr')) +random.shuffle(imagematte_filenames) + +background_filenames = [ + "0000.mp4", + "0007.mp4", + "0008.mp4", + "0010.mp4", + "0013.mp4", + "0015.mp4", + "0016.mp4", + "0018.mp4", + "0021.mp4", + "0029.mp4", + "0033.mp4", + "0035.mp4", + "0039.mp4", + "0050.mp4", + "0052.mp4", + "0055.mp4", + "0060.mp4", + "0063.mp4", + "0087.mp4", + "0086.mp4", + "0090.mp4", + "0101.mp4", + "0110.mp4", + "0117.mp4", + "0120.mp4", + "0122.mp4", + "0123.mp4", + "0125.mp4", + "0128.mp4", + "0131.mp4", + "0172.mp4", + "0176.mp4", + "0181.mp4", + "0187.mp4", + "0193.mp4", + "0198.mp4", + "0220.mp4", + "0221.mp4", + "0224.mp4", + "0229.mp4", + "0233.mp4", + "0238.mp4", + "0241.mp4", + "0245.mp4", + "0246.mp4" +] + +random.shuffle(background_filenames) + +def lerp(a, b, percentage): + return a * (1 - percentage) + b * percentage + +def motion_affine(*imgs): + config = dict(degrees=(-10, 10), translate=(0.1, 0.1), + scale_ranges=(0.9, 1.1), shears=(-5, 5), img_size=imgs[0][0].size) + angleA, (transXA, transYA), scaleA, (shearXA, shearYA) = transforms.RandomAffine.get_params(**config) + angleB, (transXB, transYB), scaleB, (shearXB, shearYB) = transforms.RandomAffine.get_params(**config) + + T = len(imgs[0]) + variation_over_time = random.random() + for t in range(T): + percentage = (t / (T - 1)) * variation_over_time + angle = lerp(angleA, angleB, percentage) + transX = lerp(transXA, transXB, percentage) + transY = lerp(transYA, transYB, percentage) + scale = lerp(scaleA, scaleB, percentage) + shearX = lerp(shearXA, shearXB, percentage) + shearY = lerp(shearYA, shearYB, percentage) + for img in imgs: + img[t] = F.affine(img[t], angle, (transX, transY), scale, (shearX, shearY), F.InterpolationMode.BILINEAR) + return imgs + + +def process(i): + imagematte_filename = imagematte_filenames[i % len(imagematte_filenames)] + background_filename = background_filenames[i % len(background_filenames)] + + bgrs = pims.PyAVVideoReader(os.path.join(args.background_dir, background_filename)) + + out_path = os.path.join(args.out_dir, str(i).zfill(4)) + os.makedirs(os.path.join(out_path, 'fgr'), exist_ok=True) + os.makedirs(os.path.join(out_path, 'pha'), exist_ok=True) + os.makedirs(os.path.join(out_path, 'com'), exist_ok=True) + os.makedirs(os.path.join(out_path, 'bgr'), exist_ok=True) + + with Image.open(os.path.join(args.imagematte_dir, 'fgr', imagematte_filename)) as fgr, \ + Image.open(os.path.join(args.imagematte_dir, 'pha', imagematte_filename)) as pha: + fgr = fgr.convert('RGB') + pha = pha.convert('L') + + fgrs = [fgr] * args.num_frames + phas = [pha] * args.num_frames + fgrs, phas = motion_affine(fgrs, phas) + + for t in range(args.num_frames): + fgr = fgrs[t] + pha = phas[t] + + w, h = fgr.size + scale = args.resolution / max(h, w) + w, h = int(w * scale), int(h * scale) + + fgr = fgr.resize((w, h)) + pha = pha.resize((w, h)) + + if h < args.resolution: + pt = (args.resolution - h) // 2 + pb = args.resolution - h - pt + else: + pt = 0 + pb = 0 + + if w < args.resolution: + pl = (args.resolution - w) // 2 + pr = args.resolution - w - pl + else: + pl = 0 + pr = 0 + + fgr = F.pad(fgr, [pl, pt, pr, pb]) + pha = F.pad(pha, [pl, pt, pr, pb]) + + if i // len(imagematte_filenames) % 2 == 1: + fgr = fgr.transpose(Image.FLIP_LEFT_RIGHT) + pha = pha.transpose(Image.FLIP_LEFT_RIGHT) + + fgr.save(os.path.join(out_path, 'fgr', str(t).zfill(4) + args.extension)) + pha.save(os.path.join(out_path, 'pha', str(t).zfill(4) + args.extension)) + + bgr = Image.fromarray(bgrs[t]).convert('RGB') + w, h = bgr.size + scale = args.resolution / min(h, w) + w, h = int(w * scale), int(h * scale) + bgr = bgr.resize((w, h)) + bgr = F.center_crop(bgr, (args.resolution, args.resolution)) + bgr.save(os.path.join(out_path, 'bgr', str(t).zfill(4) + args.extension)) + + pha = np.asarray(pha).astype(float)[:, :, None] / 255 + com = Image.fromarray(np.uint8(np.asarray(fgr) * pha + np.asarray(bgr) * (1 - pha))) + com.save(os.path.join(out_path, 'com', str(t).zfill(4) + args.extension)) + +if __name__ == '__main__': + r = process_map(process, range(args.num_samples), max_workers=10) + diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/generate_videomatte_with_background_image.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/generate_videomatte_with_background_image.py new file mode 100644 index 0000000..1c9f349 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/generate_videomatte_with_background_image.py @@ -0,0 +1,78 @@ +""" +python generate_videomatte_with_background_image.py \ + --videomatte-dir ../matting-data/VideoMatte240K_JPEG_HD/test \ + --background-dir ../matting-data/Backgrounds/valid \ + --num-samples 25 \ + --resize 512 288 \ + --out-dir ../matting-data/evaluation/vidematte_static_sd/ +""" + +import argparse +import os +import pims +import numpy as np +import random +from PIL import Image +from tqdm import tqdm + +parser = argparse.ArgumentParser() +parser.add_argument('--videomatte-dir', type=str, required=True) +parser.add_argument('--background-dir', type=str, required=True) +parser.add_argument('--num-samples', type=int, default=20) +parser.add_argument('--num-frames', type=int, default=100) +parser.add_argument('--resize', type=int, default=None, nargs=2) +parser.add_argument('--out-dir', type=str, required=True) +parser.add_argument('--extension', type=str, default='.png') +args = parser.parse_args() + +random.seed(10) + +videomatte_filenames = [(clipname, sorted(os.listdir(os.path.join(args.videomatte_dir, 'fgr', clipname)))) + for clipname in sorted(os.listdir(os.path.join(args.videomatte_dir, 'fgr')))] + +background_filenames = os.listdir(args.background_dir) +random.shuffle(background_filenames) + +for i in range(args.num_samples): + + clipname, framenames = videomatte_filenames[i % len(videomatte_filenames)] + + out_path = os.path.join(args.out_dir, str(i).zfill(4)) + os.makedirs(os.path.join(out_path, 'fgr'), exist_ok=True) + os.makedirs(os.path.join(out_path, 'pha'), exist_ok=True) + os.makedirs(os.path.join(out_path, 'com'), exist_ok=True) + os.makedirs(os.path.join(out_path, 'bgr'), exist_ok=True) + + with Image.open(os.path.join(args.background_dir, background_filenames[i])) as bgr: + bgr = bgr.convert('RGB') + + + base_t = random.choice(range(len(framenames) - args.num_frames)) + + for t in tqdm(range(args.num_frames), desc=str(i).zfill(4)): + with Image.open(os.path.join(args.videomatte_dir, 'fgr', clipname, framenames[base_t + t])) as fgr, \ + Image.open(os.path.join(args.videomatte_dir, 'pha', clipname, framenames[base_t + t])) as pha: + fgr = fgr.convert('RGB') + pha = pha.convert('L') + + if args.resize is not None: + fgr = fgr.resize(args.resize, Image.BILINEAR) + pha = pha.resize(args.resize, Image.BILINEAR) + + + if i // len(videomatte_filenames) % 2 == 1: + fgr = fgr.transpose(Image.FLIP_LEFT_RIGHT) + pha = pha.transpose(Image.FLIP_LEFT_RIGHT) + + fgr.save(os.path.join(out_path, 'fgr', str(t).zfill(4) + args.extension)) + pha.save(os.path.join(out_path, 'pha', str(t).zfill(4) + args.extension)) + + if t == 0: + bgr = bgr.resize(fgr.size, Image.BILINEAR) + bgr.save(os.path.join(out_path, 'bgr', str(t).zfill(4) + args.extension)) + else: + os.symlink(str(0).zfill(4) + args.extension, os.path.join(out_path, 'bgr', str(t).zfill(4) + args.extension)) + + pha = np.asarray(pha).astype(float)[:, :, None] / 255 + com = Image.fromarray(np.uint8(np.asarray(fgr) * pha + np.asarray(bgr) * (1 - pha))) + com.save(os.path.join(out_path, 'com', str(t).zfill(4) + args.extension)) diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/generate_videomatte_with_background_video.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/generate_videomatte_with_background_video.py new file mode 100644 index 0000000..63c24ce --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/evaluation/generate_videomatte_with_background_video.py @@ -0,0 +1,118 @@ +""" +python generate_videomatte_with_background_video.py \ + --videomatte-dir ../matting-data/VideoMatte240K_JPEG_HD/test \ + --background-dir ../matting-data/BackgroundVideos_mp4/test \ + --resize 512 288 \ + --out-dir ../matting-data/evaluation/vidematte_motion_sd/ +""" + +import argparse +import os +import pims +import numpy as np +import random +from PIL import Image +from tqdm import tqdm + +parser = argparse.ArgumentParser() +parser.add_argument('--videomatte-dir', type=str, required=True) +parser.add_argument('--background-dir', type=str, required=True) +parser.add_argument('--num-samples', type=int, default=20) +parser.add_argument('--num-frames', type=int, default=100) +parser.add_argument('--resize', type=int, default=None, nargs=2) +parser.add_argument('--out-dir', type=str, required=True) +args = parser.parse_args() + +# Hand selected a list of videos +background_filenames = [ + "0000.mp4", + "0007.mp4", + "0008.mp4", + "0010.mp4", + "0013.mp4", + "0015.mp4", + "0016.mp4", + "0018.mp4", + "0021.mp4", + "0029.mp4", + "0033.mp4", + "0035.mp4", + "0039.mp4", + "0050.mp4", + "0052.mp4", + "0055.mp4", + "0060.mp4", + "0063.mp4", + "0087.mp4", + "0086.mp4", + "0090.mp4", + "0101.mp4", + "0110.mp4", + "0117.mp4", + "0120.mp4", + "0122.mp4", + "0123.mp4", + "0125.mp4", + "0128.mp4", + "0131.mp4", + "0172.mp4", + "0176.mp4", + "0181.mp4", + "0187.mp4", + "0193.mp4", + "0198.mp4", + "0220.mp4", + "0221.mp4", + "0224.mp4", + "0229.mp4", + "0233.mp4", + "0238.mp4", + "0241.mp4", + "0245.mp4", + "0246.mp4" +] + +random.seed(10) + +videomatte_filenames = [(clipname, sorted(os.listdir(os.path.join(args.videomatte_dir, 'fgr', clipname)))) + for clipname in sorted(os.listdir(os.path.join(args.videomatte_dir, 'fgr')))] + +random.shuffle(background_filenames) + +for i in range(args.num_samples): + bgrs = pims.PyAVVideoReader(os.path.join(args.background_dir, background_filenames[i % len(background_filenames)])) + clipname, framenames = videomatte_filenames[i % len(videomatte_filenames)] + + out_path = os.path.join(args.out_dir, str(i).zfill(4)) + os.makedirs(os.path.join(out_path, 'fgr'), exist_ok=True) + os.makedirs(os.path.join(out_path, 'pha'), exist_ok=True) + os.makedirs(os.path.join(out_path, 'com'), exist_ok=True) + os.makedirs(os.path.join(out_path, 'bgr'), exist_ok=True) + + base_t = random.choice(range(len(framenames) - args.num_frames)) + + for t in tqdm(range(args.num_frames), desc=str(i).zfill(4)): + with Image.open(os.path.join(args.videomatte_dir, 'fgr', clipname, framenames[base_t + t])) as fgr, \ + Image.open(os.path.join(args.videomatte_dir, 'pha', clipname, framenames[base_t + t])) as pha: + fgr = fgr.convert('RGB') + pha = pha.convert('L') + + if args.resize is not None: + fgr = fgr.resize(args.resize, Image.BILINEAR) + pha = pha.resize(args.resize, Image.BILINEAR) + + + if i // len(videomatte_filenames) % 2 == 1: + fgr = fgr.transpose(Image.FLIP_LEFT_RIGHT) + pha = pha.transpose(Image.FLIP_LEFT_RIGHT) + + fgr.save(os.path.join(out_path, 'fgr', str(t).zfill(4) + '.png')) + pha.save(os.path.join(out_path, 'pha', str(t).zfill(4) + '.png')) + + bgr = Image.fromarray(bgrs[t]) + bgr = bgr.resize(fgr.size, Image.BILINEAR) + bgr.save(os.path.join(out_path, 'bgr', str(t).zfill(4) + '.png')) + + pha = np.asarray(pha).astype(float)[:, :, None] / 255 + com = Image.fromarray(np.uint8(np.asarray(fgr) * pha + np.asarray(bgr) * (1 - pha))) + com.save(os.path.join(out_path, 'com', str(t).zfill(4) + '.png')) diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/hubconf.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/hubconf.py new file mode 100644 index 0000000..9b1ceba --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/hubconf.py @@ -0,0 +1,39 @@ +""" +Loading model + model = torch.hub.load("PeterL1n/RobustVideoMatting", "mobilenetv3") + model = torch.hub.load("PeterL1n/RobustVideoMatting", "resnet50") + +Converter API + convert_video = torch.hub.load("PeterL1n/RobustVideoMatting", "converter") +""" + + +dependencies = ['torch', 'torchvision'] + +import torch +from model import MattingNetwork + + +def mobilenetv3(pretrained: bool = True, progress: bool = True): + model = MattingNetwork('mobilenetv3') + if pretrained: + url = 'https://github.com/PeterL1n/RobustVideoMatting/releases/download/v1.0.0/rvm_mobilenetv3.pth' + model.load_state_dict(torch.hub.load_state_dict_from_url(url, map_location='cpu', progress=progress)) + return model + + +def resnet50(pretrained: bool = True, progress: bool = True): + model = MattingNetwork('resnet50') + if pretrained: + url = 'https://github.com/PeterL1n/RobustVideoMatting/releases/download/v1.0.0/rvm_resnet50.pth' + model.load_state_dict(torch.hub.load_state_dict_from_url(url, map_location='cpu', progress=progress)) + return model + + +def converter(): + try: + from inference import convert_video + return convert_video + except ModuleNotFoundError as error: + print(error) + print('Please run "pip install av tqdm pims"') diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/inference.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/inference.py new file mode 100644 index 0000000..1e5471c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/inference.py @@ -0,0 +1,207 @@ +""" +python inference.py \ + --variant mobilenetv3 \ + --checkpoint "CHECKPOINT" \ + --device cuda \ + --input-source "input.mp4" \ + --output-type video \ + --output-composition "composition.mp4" \ + --output-alpha "alpha.mp4" \ + --output-foreground "foreground.mp4" \ + --output-video-mbps 4 \ + --seq-chunk 1 +""" + +import torch +import os +from torch.utils.data import DataLoader +from torchvision import transforms +from typing import Optional, Tuple +from tqdm.auto import tqdm + +from .inference_utils import * + +def convert_video(model, + input_source: str, + input_resize: Optional[Tuple[int, int]] = None, + downsample_ratio: Optional[float] = None, + output_type: str = 'video', + output_composition: Optional[str] = None, + output_alpha: Optional[str] = None, + output_foreground: Optional[str] = None, + output_video_mbps: Optional[float] = None, + seq_chunk: int = 1, + num_workers: int = 0, + progress: bool = True, + device: Optional[str] = None, + dtype: Optional[torch.dtype] = None): + + """ + Args: + input_source:A video file, or an image sequence directory. Images must be sorted in accending order, support png and jpg. + input_resize: If provided, the input are first resized to (w, h). + downsample_ratio: The model's downsample_ratio hyperparameter. If not provided, model automatically set one. + output_type: Options: ["video", "png_sequence"]. + output_composition: + The composition output path. File path if output_type == 'video'. Directory path if output_type == 'png_sequence'. + If output_type == 'video', the composition has green screen background. + If output_type == 'png_sequence'. the composition is RGBA png images. + output_alpha: The alpha output from the model. + output_foreground: The foreground output from the model. + seq_chunk: Number of frames to process at once. Increase it for better parallelism. + num_workers: PyTorch's DataLoader workers. Only use >0 for image input. + progress: Show progress bar. + device: Only need to manually provide if model is a TorchScript freezed model. + dtype: Only need to manually provide if model is a TorchScript freezed model. + """ + + assert downsample_ratio is None or (downsample_ratio > 0 and downsample_ratio <= 1), 'Downsample ratio must be between 0 (exclusive) and 1 (inclusive).' + assert any([output_composition, output_alpha, output_foreground]), 'Must provide at least one output.' + assert output_type in ['video', 'png_sequence'], 'Only support "video" and "png_sequence" output modes.' + assert seq_chunk >= 1, 'Sequence chunk must be >= 1' + assert num_workers >= 0, 'Number of workers must be >= 0' + + # Initialize transform + if input_resize is not None: + transform = transforms.Compose([ + transforms.Resize(input_resize[::-1]), + transforms.ToTensor() + ]) + else: + transform = transforms.ToTensor() + + # Initialize reader + if os.path.isfile(input_source): + source = VideoReader(input_source, transform) + else: + source = ImageSequenceReader(input_source, transform) + reader = DataLoader(source, batch_size=seq_chunk, pin_memory=True, num_workers=num_workers) + + # Initialize writers + if output_type == 'video': + frame_rate = source.frame_rate if isinstance(source, VideoReader) else 30 + output_video_mbps = 1 if output_video_mbps is None else output_video_mbps + if output_composition is not None: + writer_com = VideoWriter( + path=output_composition, + frame_rate=frame_rate, + bit_rate=int(output_video_mbps * 1000000)) + if output_alpha is not None: + writer_pha = VideoWriter( + path=output_alpha, + frame_rate=frame_rate, + bit_rate=int(output_video_mbps * 1000000)) + if output_foreground is not None: + writer_fgr = VideoWriter( + path=output_foreground, + frame_rate=frame_rate, + bit_rate=int(output_video_mbps * 1000000)) + else: + if output_composition is not None: + writer_com = ImageSequenceWriter(output_composition, 'png') + if output_alpha is not None: + writer_pha = ImageSequenceWriter(output_alpha, 'png') + if output_foreground is not None: + writer_fgr = ImageSequenceWriter(output_foreground, 'png') + + # Inference + model = model.eval() + if device is None or dtype is None: + param = next(model.parameters()) + dtype = param.dtype + device = param.device + + if (output_composition is not None) and (output_type == 'video'): + bgr = torch.tensor([120, 255, 155], device=device, dtype=dtype).div(255).view(1, 1, 3, 1, 1) + + try: + with torch.no_grad(): + bar = tqdm(total=len(source), disable=not progress, dynamic_ncols=True) + rec = [None] * 4 + for src in reader: + + if downsample_ratio is None: + downsample_ratio = auto_downsample_ratio(*src.shape[2:]) + + src = src.to(device, dtype, non_blocking=True).unsqueeze(0) # [B, T, C, H, W] + fgr, pha, *rec = model(src, *rec, downsample_ratio) + + if output_foreground is not None: + writer_fgr.write(fgr[0]) + if output_alpha is not None: + writer_pha.write(pha[0]) + if output_composition is not None: + if output_type == 'video': + com = fgr * pha + bgr * (1 - pha) + else: + fgr = fgr * pha.gt(0) + com = torch.cat([fgr, pha], dim=-3) + writer_com.write(com[0]) + + bar.update(src.size(1)) + + finally: + # Clean up + if output_composition is not None: + writer_com.close() + if output_alpha is not None: + writer_pha.close() + if output_foreground is not None: + writer_fgr.close() + + +def auto_downsample_ratio(h, w): + """ + Automatically find a downsample ratio so that the largest side of the resolution be 512px. + """ + return min(512 / max(h, w), 1) + + +class Converter: + def __init__(self, variant: str, checkpoint: str, device: str): + self.model = MattingNetwork(variant).eval().to(device) + self.model.load_state_dict(torch.load(checkpoint, map_location=device)) + self.model = torch.jit.script(self.model) + self.model = torch.jit.freeze(self.model) + self.device = device + + def convert(self, *args, **kwargs): + convert_video(self.model, device=self.device, dtype=torch.float32, *args, **kwargs) + +if __name__ == '__main__': + import argparse + from model import MattingNetwork + + parser = argparse.ArgumentParser() + parser.add_argument('--variant', type=str, required=True, choices=['mobilenetv3', 'resnet50']) + parser.add_argument('--checkpoint', type=str, required=True) + parser.add_argument('--device', type=str, required=True) + parser.add_argument('--input-source', type=str, required=True) + parser.add_argument('--input-resize', type=int, default=None, nargs=2) + parser.add_argument('--downsample-ratio', type=float) + parser.add_argument('--output-composition', type=str) + parser.add_argument('--output-alpha', type=str) + parser.add_argument('--output-foreground', type=str) + parser.add_argument('--output-type', type=str, required=True, choices=['video', 'png_sequence']) + parser.add_argument('--output-video-mbps', type=int, default=1) + parser.add_argument('--seq-chunk', type=int, default=1) + parser.add_argument('--num-workers', type=int, default=0) + parser.add_argument('--disable-progress', action='store_true') + args = parser.parse_args() + + converter = Converter(args.variant, args.checkpoint, args.device) + converter.convert( + input_source=args.input_source, + input_resize=args.input_resize, + downsample_ratio=args.downsample_ratio, + output_type=args.output_type, + output_composition=args.output_composition, + output_alpha=args.output_alpha, + output_foreground=args.output_foreground, + output_video_mbps=args.output_video_mbps, + seq_chunk=args.seq_chunk, + num_workers=args.num_workers, + progress=not args.disable_progress + ) + + diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/inference_speed_test.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/inference_speed_test.py new file mode 100644 index 0000000..2f6808f --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/inference_speed_test.py @@ -0,0 +1,50 @@ +""" +python inference_speed_test.py \ + --model-variant mobilenetv3 \ + --resolution 1920 1080 \ + --downsample-ratio 0.25 \ + --precision float32 +""" + +import argparse +import torch +from tqdm import tqdm + +from model.model import MattingNetwork + +torch.backends.cudnn.benchmark = True + +class InferenceSpeedTest: + def __init__(self): + self.parse_args() + self.init_model() + self.loop() + + def parse_args(self): + parser = argparse.ArgumentParser() + parser.add_argument('--model-variant', type=str, required=True) + parser.add_argument('--resolution', type=int, required=True, nargs=2) + parser.add_argument('--downsample-ratio', type=float, required=True) + parser.add_argument('--precision', type=str, default='float32') + parser.add_argument('--disable-refiner', action='store_true') + self.args = parser.parse_args() + + def init_model(self): + self.device = 'cuda' + self.precision = {'float32': torch.float32, 'float16': torch.float16}[self.args.precision] + self.model = MattingNetwork(self.args.model_variant) + self.model = self.model.to(device=self.device, dtype=self.precision).eval() + self.model = torch.jit.script(self.model) + self.model = torch.jit.freeze(self.model) + + def loop(self): + w, h = self.args.resolution + src = torch.randn((1, 3, h, w), device=self.device, dtype=self.precision) + with torch.no_grad(): + rec = None, None, None, None + for _ in tqdm(range(1000)): + fgr, pha, *rec = self.model(src, *rec, self.args.downsample_ratio) + torch.cuda.synchronize() + +if __name__ == '__main__': + InferenceSpeedTest() \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/inference_utils.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/inference_utils.py new file mode 100644 index 0000000..ceada29 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/inference_utils.py @@ -0,0 +1,88 @@ +import av +import os +import pims +import numpy as np +from torch.utils.data import Dataset +from torchvision.transforms.functional import to_pil_image +from PIL import Image + + +class VideoReader(Dataset): + def __init__(self, path, transform=None): + self.video = pims.PyAVVideoReader(path) + self.rate = self.video.frame_rate + self.transform = transform + + @property + def frame_rate(self): + return self.rate + + def __len__(self): + return len(self.video) + + def __getitem__(self, idx): + frame = self.video[idx] + frame = Image.fromarray(np.asarray(frame)) + if self.transform is not None: + frame = self.transform(frame) + return frame + + +class VideoWriter: + def __init__(self, path, frame_rate, bit_rate=1000000): + self.container = av.open(path, mode='w') + self.stream = self.container.add_stream('h264', rate=round(frame_rate)) + self.stream.pix_fmt = 'yuv420p' + self.stream.bit_rate = bit_rate + + def write(self, frames): + # frames: [T, C, H, W] + self.stream.width = frames.size(3) + self.stream.height = frames.size(2) + if frames.size(1) == 1: + frames = frames.repeat(1, 3, 1, 1) # convert grayscale to RGB + frames = frames.mul(255).byte().cpu().permute(0, 2, 3, 1).numpy() + for t in range(frames.shape[0]): + frame = frames[t] + frame = av.VideoFrame.from_ndarray(frame, format='rgb24') + self.container.mux(self.stream.encode(frame)) + + def close(self): + self.container.mux(self.stream.encode()) + self.container.close() + + +class ImageSequenceReader(Dataset): + def __init__(self, path, transform=None): + self.path = path + self.files = sorted(os.listdir(path)) + self.transform = transform + + def __len__(self): + return len(self.files) + + def __getitem__(self, idx): + with Image.open(os.path.join(self.path, self.files[idx])) as img: + img.load() + if self.transform is not None: + return self.transform(img) + return img + + +class ImageSequenceWriter: + def __init__(self, path, extension='jpg'): + self.path = path + self.extension = extension + self.counter = 0 + os.makedirs(path, exist_ok=True) + + def write(self, frames): + # frames: [T, C, H, W] + for t in range(frames.shape[0]): + to_pil_image(frames[t]).save(os.path.join( + self.path, '%05d' % self.counter + '.' + self.extension)) + self.counter += 1 + + def close(self): + pass + \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/__init__.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/__init__.py new file mode 100644 index 0000000..ac047a1 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/__init__.py @@ -0,0 +1 @@ +from .model import MattingNetwork \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/decoder.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/decoder.py new file mode 100644 index 0000000..7307435 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/decoder.py @@ -0,0 +1,210 @@ +import torch +from torch import Tensor +from torch import nn +from torch.nn import functional as F +from typing import Tuple, Optional + +class RecurrentDecoder(nn.Module): + def __init__(self, feature_channels, decoder_channels): + super().__init__() + self.avgpool = AvgPool() + self.decode4 = BottleneckBlock(feature_channels[3]) + self.decode3 = UpsamplingBlock(feature_channels[3], feature_channels[2], 3, decoder_channels[0]) + self.decode2 = UpsamplingBlock(decoder_channels[0], feature_channels[1], 3, decoder_channels[1]) + self.decode1 = UpsamplingBlock(decoder_channels[1], feature_channels[0], 3, decoder_channels[2]) + self.decode0 = OutputBlock(decoder_channels[2], 3, decoder_channels[3]) + + def forward(self, + s0: Tensor, f1: Tensor, f2: Tensor, f3: Tensor, f4: Tensor, + r1: Optional[Tensor], r2: Optional[Tensor], + r3: Optional[Tensor], r4: Optional[Tensor]): + s1, s2, s3 = self.avgpool(s0) + x4, r4 = self.decode4(f4, r4) + x3, r3 = self.decode3(x4, f3, s3, r3) + x2, r2 = self.decode2(x3, f2, s2, r2) + x1, r1 = self.decode1(x2, f1, s1, r1) + x0 = self.decode0(x1, s0) + return x0, r1, r2, r3, r4 + + +class AvgPool(nn.Module): + def __init__(self): + super().__init__() + self.avgpool = nn.AvgPool2d(2, 2, count_include_pad=False, ceil_mode=True) + + def forward_single_frame(self, s0): + s1 = self.avgpool(s0) + s2 = self.avgpool(s1) + s3 = self.avgpool(s2) + return s1, s2, s3 + + def forward_time_series(self, s0): + B, T = s0.shape[:2] + s0 = s0.flatten(0, 1) + s1, s2, s3 = self.forward_single_frame(s0) + s1 = s1.unflatten(0, (B, T)) + s2 = s2.unflatten(0, (B, T)) + s3 = s3.unflatten(0, (B, T)) + return s1, s2, s3 + + def forward(self, s0): + if s0.ndim == 5: + return self.forward_time_series(s0) + else: + return self.forward_single_frame(s0) + + +class BottleneckBlock(nn.Module): + def __init__(self, channels): + super().__init__() + self.channels = channels + self.gru = ConvGRU(channels // 2) + + def forward(self, x, r: Optional[Tensor]): + a, b = x.split(self.channels // 2, dim=-3) + b, r = self.gru(b, r) + x = torch.cat([a, b], dim=-3) + return x, r + + +class UpsamplingBlock(nn.Module): + def __init__(self, in_channels, skip_channels, src_channels, out_channels): + super().__init__() + self.out_channels = out_channels + self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False) + self.conv = nn.Sequential( + nn.Conv2d(in_channels + skip_channels + src_channels, out_channels, 3, 1, 1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(True), + ) + self.gru = ConvGRU(out_channels // 2) + + def forward_single_frame(self, x, f, s, r: Optional[Tensor]): + x = self.upsample(x) + x = x[:, :, :s.size(2), :s.size(3)] + x = torch.cat([x, f, s], dim=1) + x = self.conv(x) + a, b = x.split(self.out_channels // 2, dim=1) + b, r = self.gru(b, r) + x = torch.cat([a, b], dim=1) + return x, r + + def forward_time_series(self, x, f, s, r: Optional[Tensor]): + B, T, _, H, W = s.shape + x = x.flatten(0, 1) + f = f.flatten(0, 1) + s = s.flatten(0, 1) + x = self.upsample(x) + x = x[:, :, :H, :W] + x = torch.cat([x, f, s], dim=1) + x = self.conv(x) + x = x.unflatten(0, (B, T)) + a, b = x.split(self.out_channels // 2, dim=2) + b, r = self.gru(b, r) + x = torch.cat([a, b], dim=2) + return x, r + + def forward(self, x, f, s, r: Optional[Tensor]): + if x.ndim == 5: + return self.forward_time_series(x, f, s, r) + else: + return self.forward_single_frame(x, f, s, r) + + +class OutputBlock(nn.Module): + def __init__(self, in_channels, src_channels, out_channels): + super().__init__() + self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False) + self.conv = nn.Sequential( + nn.Conv2d(in_channels + src_channels, out_channels, 3, 1, 1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(True), + nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(True), + ) + + def forward_single_frame(self, x, s): + x = self.upsample(x) + x = x[:, :, :s.size(2), :s.size(3)] + x = torch.cat([x, s], dim=1) + x = self.conv(x) + return x + + def forward_time_series(self, x, s): + B, T, _, H, W = s.shape + x = x.flatten(0, 1) + s = s.flatten(0, 1) + x = self.upsample(x) + x = x[:, :, :H, :W] + x = torch.cat([x, s], dim=1) + x = self.conv(x) + x = x.unflatten(0, (B, T)) + return x + + def forward(self, x, s): + if x.ndim == 5: + return self.forward_time_series(x, s) + else: + return self.forward_single_frame(x, s) + + +class ConvGRU(nn.Module): + def __init__(self, + channels: int, + kernel_size: int = 3, + padding: int = 1): + super().__init__() + self.channels = channels + self.ih = nn.Sequential( + nn.Conv2d(channels * 2, channels * 2, kernel_size, padding=padding), + nn.Sigmoid() + ) + self.hh = nn.Sequential( + nn.Conv2d(channels * 2, channels, kernel_size, padding=padding), + nn.Tanh() + ) + + def forward_single_frame(self, x, h): + r, z = self.ih(torch.cat([x, h], dim=1)).split(self.channels, dim=1) + c = self.hh(torch.cat([x, r * h], dim=1)) + h = (1 - z) * h + z * c + return h, h + + def forward_time_series(self, x, h): + o = [] + for xt in x.unbind(dim=1): + ot, h = self.forward_single_frame(xt, h) + o.append(ot) + o = torch.stack(o, dim=1) + return o, h + + def forward(self, x, h: Optional[Tensor]): + if h is None: + h = torch.zeros((x.size(0), x.size(-3), x.size(-2), x.size(-1)), + device=x.device, dtype=x.dtype) + + if x.ndim == 5: + return self.forward_time_series(x, h) + else: + return self.forward_single_frame(x, h) + + +class Projection(nn.Module): + def __init__(self, in_channels, out_channels): + super().__init__() + self.conv = nn.Conv2d(in_channels, out_channels, 1) + + def forward_single_frame(self, x): + return self.conv(x) + + def forward_time_series(self, x): + B, T = x.shape[:2] + return self.conv(x.flatten(0, 1)).unflatten(0, (B, T)) + + def forward(self, x): + if x.ndim == 5: + return self.forward_time_series(x) + else: + return self.forward_single_frame(x) + \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/deep_guided_filter.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/deep_guided_filter.py new file mode 100644 index 0000000..a24b8c5 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/deep_guided_filter.py @@ -0,0 +1,61 @@ +import torch +from torch import nn +from torch.nn import functional as F + +""" +Adopted from +""" + +class DeepGuidedFilterRefiner(nn.Module): + def __init__(self, hid_channels=16): + super().__init__() + self.box_filter = nn.Conv2d(4, 4, kernel_size=3, padding=1, bias=False, groups=4) + self.box_filter.weight.data[...] = 1 / 9 + self.conv = nn.Sequential( + nn.Conv2d(4 * 2 + hid_channels, hid_channels, kernel_size=1, bias=False), + nn.BatchNorm2d(hid_channels), + nn.ReLU(True), + nn.Conv2d(hid_channels, hid_channels, kernel_size=1, bias=False), + nn.BatchNorm2d(hid_channels), + nn.ReLU(True), + nn.Conv2d(hid_channels, 4, kernel_size=1, bias=True) + ) + + def forward_single_frame(self, fine_src, base_src, base_fgr, base_pha, base_hid): + fine_x = torch.cat([fine_src, fine_src.mean(1, keepdim=True)], dim=1) + base_x = torch.cat([base_src, base_src.mean(1, keepdim=True)], dim=1) + base_y = torch.cat([base_fgr, base_pha], dim=1) + + mean_x = self.box_filter(base_x) + mean_y = self.box_filter(base_y) + cov_xy = self.box_filter(base_x * base_y) - mean_x * mean_y + var_x = self.box_filter(base_x * base_x) - mean_x * mean_x + + A = self.conv(torch.cat([cov_xy, var_x, base_hid], dim=1)) + b = mean_y - A * mean_x + + H, W = fine_src.shape[2:] + A = F.interpolate(A, (H, W), mode='bilinear', align_corners=False) + b = F.interpolate(b, (H, W), mode='bilinear', align_corners=False) + + out = A * fine_x + b + fgr, pha = out.split([3, 1], dim=1) + return fgr, pha + + def forward_time_series(self, fine_src, base_src, base_fgr, base_pha, base_hid): + B, T = fine_src.shape[:2] + fgr, pha = self.forward_single_frame( + fine_src.flatten(0, 1), + base_src.flatten(0, 1), + base_fgr.flatten(0, 1), + base_pha.flatten(0, 1), + base_hid.flatten(0, 1)) + fgr = fgr.unflatten(0, (B, T)) + pha = pha.unflatten(0, (B, T)) + return fgr, pha + + def forward(self, fine_src, base_src, base_fgr, base_pha, base_hid): + if fine_src.ndim == 5: + return self.forward_time_series(fine_src, base_src, base_fgr, base_pha, base_hid) + else: + return self.forward_single_frame(fine_src, base_src, base_fgr, base_pha, base_hid) diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/fast_guided_filter.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/fast_guided_filter.py new file mode 100644 index 0000000..df9b4b2 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/fast_guided_filter.py @@ -0,0 +1,76 @@ +import torch +from torch import nn +from torch.nn import functional as F + +""" +Adopted from +""" + +class FastGuidedFilterRefiner(nn.Module): + def __init__(self, *args, **kwargs): + super().__init__() + self.guilded_filter = FastGuidedFilter(1) + + def forward_single_frame(self, fine_src, base_src, base_fgr, base_pha): + fine_src_gray = fine_src.mean(1, keepdim=True) + base_src_gray = base_src.mean(1, keepdim=True) + + fgr, pha = self.guilded_filter( + torch.cat([base_src, base_src_gray], dim=1), + torch.cat([base_fgr, base_pha], dim=1), + torch.cat([fine_src, fine_src_gray], dim=1)).split([3, 1], dim=1) + + return fgr, pha + + def forward_time_series(self, fine_src, base_src, base_fgr, base_pha): + B, T = fine_src.shape[:2] + fgr, pha = self.forward_single_frame( + fine_src.flatten(0, 1), + base_src.flatten(0, 1), + base_fgr.flatten(0, 1), + base_pha.flatten(0, 1)) + fgr = fgr.unflatten(0, (B, T)) + pha = pha.unflatten(0, (B, T)) + return fgr, pha + + def forward(self, fine_src, base_src, base_fgr, base_pha, base_hid): + if fine_src.ndim == 5: + return self.forward_time_series(fine_src, base_src, base_fgr, base_pha) + else: + return self.forward_single_frame(fine_src, base_src, base_fgr, base_pha) + + +class FastGuidedFilter(nn.Module): + def __init__(self, r: int, eps: float = 1e-5): + super().__init__() + self.r = r + self.eps = eps + self.boxfilter = BoxFilter(r) + + def forward(self, lr_x, lr_y, hr_x): + mean_x = self.boxfilter(lr_x) + mean_y = self.boxfilter(lr_y) + cov_xy = self.boxfilter(lr_x * lr_y) - mean_x * mean_y + var_x = self.boxfilter(lr_x * lr_x) - mean_x * mean_x + A = cov_xy / (var_x + self.eps) + b = mean_y - A * mean_x + A = F.interpolate(A, hr_x.shape[2:], mode='bilinear', align_corners=False) + b = F.interpolate(b, hr_x.shape[2:], mode='bilinear', align_corners=False) + return A * hr_x + b + + +class BoxFilter(nn.Module): + def __init__(self, r): + super(BoxFilter, self).__init__() + self.r = r + + def forward(self, x): + # Note: The original implementation at + # uses faster box blur. However, it may not be friendly for ONNX export. + # We are switching to use simple convolution for box blur. + kernel_size = 2 * self.r + 1 + kernel_x = torch.full((x.data.shape[1], 1, 1, kernel_size), 1 / kernel_size, device=x.device, dtype=x.dtype) + kernel_y = torch.full((x.data.shape[1], 1, kernel_size, 1), 1 / kernel_size, device=x.device, dtype=x.dtype) + x = F.conv2d(x, kernel_x, padding=(0, self.r), groups=x.data.shape[1]) + x = F.conv2d(x, kernel_y, padding=(self.r, 0), groups=x.data.shape[1]) + return x \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/lraspp.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/lraspp.py new file mode 100644 index 0000000..5fc7079 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/lraspp.py @@ -0,0 +1,29 @@ +from torch import nn + +class LRASPP(nn.Module): + def __init__(self, in_channels, out_channels): + super().__init__() + self.aspp1 = nn.Sequential( + nn.Conv2d(in_channels, out_channels, 1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(True) + ) + self.aspp2 = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(in_channels, out_channels, 1, bias=False), + nn.Sigmoid() + ) + + def forward_single_frame(self, x): + return self.aspp1(x) * self.aspp2(x) + + def forward_time_series(self, x): + B, T = x.shape[:2] + x = self.forward_single_frame(x.flatten(0, 1)).unflatten(0, (B, T)) + return x + + def forward(self, x): + if x.ndim == 5: + return self.forward_time_series(x) + else: + return self.forward_single_frame(x) \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/mobilenetv3.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/mobilenetv3.py new file mode 100644 index 0000000..9e863d3 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/mobilenetv3.py @@ -0,0 +1,73 @@ +from torch import nn +from torchvision.models.mobilenetv3 import MobileNetV3, InvertedResidualConfig +# from torchvision.models.utils import load_state_dict_from_url +from torch.utils.model_zoo import load_url as load_state_dict_from_url +from torchvision.transforms.functional import normalize + +class MobileNetV3LargeEncoder(MobileNetV3): + def __init__(self, pretrained: bool = False): + super().__init__( + inverted_residual_setting=[ + InvertedResidualConfig( 16, 3, 16, 16, False, "RE", 1, 1, 1), + InvertedResidualConfig( 16, 3, 64, 24, False, "RE", 2, 1, 1), # C1 + InvertedResidualConfig( 24, 3, 72, 24, False, "RE", 1, 1, 1), + InvertedResidualConfig( 24, 5, 72, 40, True, "RE", 2, 1, 1), # C2 + InvertedResidualConfig( 40, 5, 120, 40, True, "RE", 1, 1, 1), + InvertedResidualConfig( 40, 5, 120, 40, True, "RE", 1, 1, 1), + InvertedResidualConfig( 40, 3, 240, 80, False, "HS", 2, 1, 1), # C3 + InvertedResidualConfig( 80, 3, 200, 80, False, "HS", 1, 1, 1), + InvertedResidualConfig( 80, 3, 184, 80, False, "HS", 1, 1, 1), + InvertedResidualConfig( 80, 3, 184, 80, False, "HS", 1, 1, 1), + InvertedResidualConfig( 80, 3, 480, 112, True, "HS", 1, 1, 1), + InvertedResidualConfig(112, 3, 672, 112, True, "HS", 1, 1, 1), + InvertedResidualConfig(112, 5, 672, 160, True, "HS", 2, 2, 1), # C4 + InvertedResidualConfig(160, 5, 960, 160, True, "HS", 1, 2, 1), + InvertedResidualConfig(160, 5, 960, 160, True, "HS", 1, 2, 1), + ], + last_channel=1280 + ) + + if pretrained: + self.load_state_dict(load_state_dict_from_url( + 'https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth')) + + del self.avgpool + del self.classifier + + def forward_single_frame(self, x): + x = normalize(x, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + + x = self.features[0](x) + x = self.features[1](x) + f1 = x + x = self.features[2](x) + x = self.features[3](x) + f2 = x + x = self.features[4](x) + x = self.features[5](x) + x = self.features[6](x) + f3 = x + x = self.features[7](x) + x = self.features[8](x) + x = self.features[9](x) + x = self.features[10](x) + x = self.features[11](x) + x = self.features[12](x) + x = self.features[13](x) + x = self.features[14](x) + x = self.features[15](x) + x = self.features[16](x) + f4 = x + return [f1, f2, f3, f4] + + def forward_time_series(self, x): + B, T = x.shape[:2] + features = self.forward_single_frame(x.flatten(0, 1)) + features = [f.unflatten(0, (B, T)) for f in features] + return features + + def forward(self, x): + if x.ndim == 5: + return self.forward_time_series(x) + else: + return self.forward_single_frame(x) diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/model.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/model.py new file mode 100644 index 0000000..71fc684 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/model.py @@ -0,0 +1,79 @@ +import torch +from torch import Tensor +from torch import nn +from torch.nn import functional as F +from typing import Optional, List + +from .mobilenetv3 import MobileNetV3LargeEncoder +from .resnet import ResNet50Encoder +from .lraspp import LRASPP +from .decoder import RecurrentDecoder, Projection +from .fast_guided_filter import FastGuidedFilterRefiner +from .deep_guided_filter import DeepGuidedFilterRefiner + +class MattingNetwork(nn.Module): + def __init__(self, + variant: str = 'mobilenetv3', + refiner: str = 'deep_guided_filter', + pretrained_backbone: bool = False): + super().__init__() + assert variant in ['mobilenetv3', 'resnet50'] + assert refiner in ['fast_guided_filter', 'deep_guided_filter'] + + if variant == 'mobilenetv3': + self.backbone = MobileNetV3LargeEncoder(pretrained_backbone) + self.aspp = LRASPP(960, 128) + self.decoder = RecurrentDecoder([16, 24, 40, 128], [80, 40, 32, 16]) + else: + self.backbone = ResNet50Encoder(pretrained_backbone) + self.aspp = LRASPP(2048, 256) + self.decoder = RecurrentDecoder([64, 256, 512, 256], [128, 64, 32, 16]) + + self.project_mat = Projection(16, 4) + self.project_seg = Projection(16, 1) + + if refiner == 'deep_guided_filter': + self.refiner = DeepGuidedFilterRefiner() + else: + self.refiner = FastGuidedFilterRefiner() + + def forward(self, + src: Tensor, + r1: Optional[Tensor] = None, + r2: Optional[Tensor] = None, + r3: Optional[Tensor] = None, + r4: Optional[Tensor] = None, + downsample_ratio: float = 1, + segmentation_pass: bool = False): + + if downsample_ratio != 1: + src_sm = self._interpolate(src, scale_factor=downsample_ratio) + else: + src_sm = src + + f1, f2, f3, f4 = self.backbone(src_sm) + f4 = self.aspp(f4) + hid, *rec = self.decoder(src_sm, f1, f2, f3, f4, r1, r2, r3, r4) + + if not segmentation_pass: + fgr_residual, pha = self.project_mat(hid).split([3, 1], dim=-3) + if downsample_ratio != 1: + fgr_residual, pha = self.refiner(src, src_sm, fgr_residual, pha, hid) + fgr = fgr_residual + src + fgr = fgr.clamp(0., 1.) + pha = pha.clamp(0., 1.) + return [fgr, pha, *rec] + else: + seg = self.project_seg(hid) + return [seg, *rec] + + def _interpolate(self, x: Tensor, scale_factor: float): + if x.ndim == 5: + B, T = x.shape[:2] + x = F.interpolate(x.flatten(0, 1), scale_factor=scale_factor, + mode='bilinear', align_corners=False, recompute_scale_factor=False) + x = x.unflatten(0, (B, T)) + else: + x = F.interpolate(x, scale_factor=scale_factor, + mode='bilinear', align_corners=False, recompute_scale_factor=False) + return x diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/resnet.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/resnet.py new file mode 100644 index 0000000..ecb3304 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/model/resnet.py @@ -0,0 +1,45 @@ +from torch import nn +from torchvision.models.resnet import ResNet, Bottleneck +from torch.utils.model_zoo import load_url as load_state_dict_from_url + +class ResNet50Encoder(ResNet): + def __init__(self, pretrained: bool = False): + super().__init__( + block=Bottleneck, + layers=[3, 4, 6, 3], + replace_stride_with_dilation=[False, False, True], + norm_layer=None) + + if pretrained: + self.load_state_dict(load_state_dict_from_url( + 'https://download.pytorch.org/models/resnet50-0676ba61.pth')) + + del self.avgpool + del self.fc + + def forward_single_frame(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + f1 = x # 1/2 + x = self.maxpool(x) + x = self.layer1(x) + f2 = x # 1/4 + x = self.layer2(x) + f3 = x # 1/8 + x = self.layer3(x) + x = self.layer4(x) + f4 = x # 1/16 + return [f1, f2, f3, f4] + + def forward_time_series(self, x): + B, T = x.shape[:2] + features = self.forward_single_frame(x.flatten(0, 1)) + features = [f.unflatten(0, (B, T)) for f in features] + return features + + def forward(self, x): + if x.ndim == 5: + return self.forward_time_series(x) + else: + return self.forward_single_frame(x) diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/requirements_inference.txt b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/requirements_inference.txt new file mode 100644 index 0000000..4b24a22 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/requirements_inference.txt @@ -0,0 +1,5 @@ +av==8.0.3 +torch==1.9.0 +torchvision==0.10.0 +tqdm==4.61.1 +pims==0.5 \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/requirements_training.txt b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/requirements_training.txt new file mode 100644 index 0000000..70fd4b1 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/requirements_training.txt @@ -0,0 +1,5 @@ +easing_functions==1.0.4 +tensorboard==2.5.0 +torch==1.9.0 +torchvision==0.10.0 +tqdm==4.61.1 \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/test_matting.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/test_matting.py new file mode 100644 index 0000000..f5711d7 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/test_matting.py @@ -0,0 +1,24 @@ +import torch +import os +from model import MattingNetwork +from inference import convert_video + +input_video_path = '/home/alberto/data/nerf-videosynth/MTC_data/Clara/Clara.mp4' +checkpoint_path = 'checkpoints/' +results_path = '/home/alberto/data/nerf-videosynth/MTC_data/Clara/matting/' +os.makedirs(results_path, exist_ok=True) + +# load model +model = MattingNetwork('mobilenetv3').eval().cuda() # or "resnet50" +model.load_state_dict(torch.load(checkpoint_path + 'rvm_mobilenetv3.pth')) + + +convert_video( + model, # The model, can be on any device (cpu or cuda). + input_source=input_video_path, # A video file or an image sequence directory. + output_type='png_sequence', # Choose "video" or "png_sequence" + output_composition=results_path, # File path if video; directory path if png sequence. + output_video_mbps=4, # Output video mbps. Not needed for png sequence. + downsample_ratio=None, # A hyperparameter to adjust or use None for auto. + seq_chunk=12, # Process n frames at once for better parallelism. +) \ No newline at end of file diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/train.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/train.py new file mode 100644 index 0000000..e38918d --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/train.py @@ -0,0 +1,504 @@ +""" +# First update `train_config.py` to set paths to your dataset locations. + +# You may want to change `--num-workers` according to your machine's memory. +# The default num-workers=8 may cause dataloader to exit unexpectedly when +# machine is out of memory. + +# Stage 1 +python train.py \ + --model-variant mobilenetv3 \ + --dataset videomatte \ + --resolution-lr 512 \ + --seq-length-lr 15 \ + --learning-rate-backbone 0.0001 \ + --learning-rate-aspp 0.0002 \ + --learning-rate-decoder 0.0002 \ + --learning-rate-refiner 0 \ + --checkpoint-dir checkpoint/stage1 \ + --log-dir log/stage1 \ + --epoch-start 0 \ + --epoch-end 20 + +# Stage 2 +python train.py \ + --model-variant mobilenetv3 \ + --dataset videomatte \ + --resolution-lr 512 \ + --seq-length-lr 50 \ + --learning-rate-backbone 0.00005 \ + --learning-rate-aspp 0.0001 \ + --learning-rate-decoder 0.0001 \ + --learning-rate-refiner 0 \ + --checkpoint checkpoint/stage1/epoch-19.pth \ + --checkpoint-dir checkpoint/stage2 \ + --log-dir log/stage2 \ + --epoch-start 20 \ + --epoch-end 22 + +# Stage 3 +python train.py \ + --model-variant mobilenetv3 \ + --dataset videomatte \ + --train-hr \ + --resolution-lr 512 \ + --resolution-hr 2048 \ + --seq-length-lr 40 \ + --seq-length-hr 6 \ + --learning-rate-backbone 0.00001 \ + --learning-rate-aspp 0.00001 \ + --learning-rate-decoder 0.00001 \ + --learning-rate-refiner 0.0002 \ + --checkpoint checkpoint/stage2/epoch-21.pth \ + --checkpoint-dir checkpoint/stage3 \ + --log-dir log/stage3 \ + --epoch-start 22 \ + --epoch-end 23 + +# Stage 4 +python train.py \ + --model-variant mobilenetv3 \ + --dataset imagematte \ + --train-hr \ + --resolution-lr 512 \ + --resolution-hr 2048 \ + --seq-length-lr 40 \ + --seq-length-hr 6 \ + --learning-rate-backbone 0.00001 \ + --learning-rate-aspp 0.00001 \ + --learning-rate-decoder 0.00005 \ + --learning-rate-refiner 0.0002 \ + --checkpoint checkpoint/stage3/epoch-22.pth \ + --checkpoint-dir checkpoint/stage4 \ + --log-dir log/stage4 \ + --epoch-start 23 \ + --epoch-end 28 +""" + + +import argparse +import torch +import random +import os +from torch import nn +from torch import distributed as dist +from torch import multiprocessing as mp +from torch.nn import functional as F +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.optim import Adam +from torch.cuda.amp import autocast, GradScaler +from torch.utils.data import DataLoader, ConcatDataset +from torch.utils.data.distributed import DistributedSampler +from torch.utils.tensorboard import SummaryWriter +from torchvision.utils import make_grid +from torchvision.transforms.functional import center_crop +from tqdm import tqdm + +from dataset.videomatte import ( + VideoMatteDataset, + VideoMatteTrainAugmentation, + VideoMatteValidAugmentation, +) +from dataset.imagematte import ( + ImageMatteDataset, + ImageMatteAugmentation +) +from dataset.coco import ( + CocoPanopticDataset, + CocoPanopticTrainAugmentation, +) +from dataset.spd import ( + SuperviselyPersonDataset +) +from dataset.youtubevis import ( + YouTubeVISDataset, + YouTubeVISAugmentation +) +from dataset.augmentation import ( + TrainFrameSampler, + ValidFrameSampler +) +from model import MattingNetwork +from train_config import DATA_PATHS +from train_loss import matting_loss, segmentation_loss + + +class Trainer: + def __init__(self, rank, world_size): + self.parse_args() + self.init_distributed(rank, world_size) + self.init_datasets() + self.init_model() + self.init_writer() + self.train() + self.cleanup() + + def parse_args(self): + parser = argparse.ArgumentParser() + # Model + parser.add_argument('--model-variant', type=str, required=True, choices=['mobilenetv3', 'resnet50']) + # Matting dataset + parser.add_argument('--dataset', type=str, required=True, choices=['videomatte', 'imagematte']) + # Learning rate + parser.add_argument('--learning-rate-backbone', type=float, required=True) + parser.add_argument('--learning-rate-aspp', type=float, required=True) + parser.add_argument('--learning-rate-decoder', type=float, required=True) + parser.add_argument('--learning-rate-refiner', type=float, required=True) + # Training setting + parser.add_argument('--train-hr', action='store_true') + parser.add_argument('--resolution-lr', type=int, default=512) + parser.add_argument('--resolution-hr', type=int, default=2048) + parser.add_argument('--seq-length-lr', type=int, required=True) + parser.add_argument('--seq-length-hr', type=int, default=6) + parser.add_argument('--downsample-ratio', type=float, default=0.25) + parser.add_argument('--batch-size-per-gpu', type=int, default=1) + parser.add_argument('--num-workers', type=int, default=8) + parser.add_argument('--epoch-start', type=int, default=0) + parser.add_argument('--epoch-end', type=int, default=16) + # Tensorboard logging + parser.add_argument('--log-dir', type=str, required=True) + parser.add_argument('--log-train-loss-interval', type=int, default=20) + parser.add_argument('--log-train-images-interval', type=int, default=500) + # Checkpoint loading and saving + parser.add_argument('--checkpoint', type=str) + parser.add_argument('--checkpoint-dir', type=str, required=True) + parser.add_argument('--checkpoint-save-interval', type=int, default=500) + # Distributed + parser.add_argument('--distributed-addr', type=str, default='localhost') + parser.add_argument('--distributed-port', type=str, default='12355') + # Debugging + parser.add_argument('--disable-progress-bar', action='store_true') + parser.add_argument('--disable-validation', action='store_true') + parser.add_argument('--disable-mixed-precision', action='store_true') + self.args = parser.parse_args() + + def init_distributed(self, rank, world_size): + self.rank = rank + self.world_size = world_size + self.log('Initializing distributed') + os.environ['MASTER_ADDR'] = self.args.distributed_addr + os.environ['MASTER_PORT'] = self.args.distributed_port + dist.init_process_group("nccl", rank=rank, world_size=world_size) + + def init_datasets(self): + self.log('Initializing matting datasets') + size_hr = (self.args.resolution_hr, self.args.resolution_hr) + size_lr = (self.args.resolution_lr, self.args.resolution_lr) + + # Matting datasets: + if self.args.dataset == 'videomatte': + self.dataset_lr_train = VideoMatteDataset( + videomatte_dir=DATA_PATHS['videomatte']['train'], + background_image_dir=DATA_PATHS['background_images']['train'], + background_video_dir=DATA_PATHS['background_videos']['train'], + size=self.args.resolution_lr, + seq_length=self.args.seq_length_lr, + seq_sampler=TrainFrameSampler(), + transform=VideoMatteTrainAugmentation(size_lr)) + if self.args.train_hr: + self.dataset_hr_train = VideoMatteDataset( + videomatte_dir=DATA_PATHS['videomatte']['train'], + background_image_dir=DATA_PATHS['background_images']['train'], + background_video_dir=DATA_PATHS['background_videos']['train'], + size=self.args.resolution_hr, + seq_length=self.args.seq_length_hr, + seq_sampler=TrainFrameSampler(), + transform=VideoMatteTrainAugmentation(size_hr)) + self.dataset_valid = VideoMatteDataset( + videomatte_dir=DATA_PATHS['videomatte']['valid'], + background_image_dir=DATA_PATHS['background_images']['valid'], + background_video_dir=DATA_PATHS['background_videos']['valid'], + size=self.args.resolution_hr if self.args.train_hr else self.args.resolution_lr, + seq_length=self.args.seq_length_hr if self.args.train_hr else self.args.seq_length_lr, + seq_sampler=ValidFrameSampler(), + transform=VideoMatteValidAugmentation(size_hr if self.args.train_hr else size_lr)) + else: + self.dataset_lr_train = ImageMatteDataset( + imagematte_dir=DATA_PATHS['imagematte']['train'], + background_image_dir=DATA_PATHS['background_images']['train'], + background_video_dir=DATA_PATHS['background_videos']['train'], + size=self.args.resolution_lr, + seq_length=self.args.seq_length_lr, + seq_sampler=TrainFrameSampler(), + transform=ImageMatteAugmentation(size_lr)) + if self.args.train_hr: + self.dataset_hr_train = ImageMatteDataset( + imagematte_dir=DATA_PATHS['imagematte']['train'], + background_image_dir=DATA_PATHS['background_images']['train'], + background_video_dir=DATA_PATHS['background_videos']['train'], + size=self.args.resolution_hr, + seq_length=self.args.seq_length_hr, + seq_sampler=TrainFrameSampler(), + transform=ImageMatteAugmentation(size_hr)) + self.dataset_valid = ImageMatteDataset( + imagematte_dir=DATA_PATHS['imagematte']['valid'], + background_image_dir=DATA_PATHS['background_images']['valid'], + background_video_dir=DATA_PATHS['background_videos']['valid'], + size=self.args.resolution_hr if self.args.train_hr else self.args.resolution_lr, + seq_length=self.args.seq_length_hr if self.args.train_hr else self.args.seq_length_lr, + seq_sampler=ValidFrameSampler(), + transform=ImageMatteAugmentation(size_hr if self.args.train_hr else size_lr)) + + # Matting dataloaders: + self.datasampler_lr_train = DistributedSampler( + dataset=self.dataset_lr_train, + rank=self.rank, + num_replicas=self.world_size, + shuffle=True) + self.dataloader_lr_train = DataLoader( + dataset=self.dataset_lr_train, + batch_size=self.args.batch_size_per_gpu, + num_workers=self.args.num_workers, + sampler=self.datasampler_lr_train, + pin_memory=True) + if self.args.train_hr: + self.datasampler_hr_train = DistributedSampler( + dataset=self.dataset_hr_train, + rank=self.rank, + num_replicas=self.world_size, + shuffle=True) + self.dataloader_hr_train = DataLoader( + dataset=self.dataset_hr_train, + batch_size=self.args.batch_size_per_gpu, + num_workers=self.args.num_workers, + sampler=self.datasampler_hr_train, + pin_memory=True) + self.dataloader_valid = DataLoader( + dataset=self.dataset_valid, + batch_size=self.args.batch_size_per_gpu, + num_workers=self.args.num_workers, + pin_memory=True) + + # Segementation datasets + self.log('Initializing image segmentation datasets') + self.dataset_seg_image = ConcatDataset([ + CocoPanopticDataset( + imgdir=DATA_PATHS['coco_panoptic']['imgdir'], + anndir=DATA_PATHS['coco_panoptic']['anndir'], + annfile=DATA_PATHS['coco_panoptic']['annfile'], + transform=CocoPanopticTrainAugmentation(size_lr)), + SuperviselyPersonDataset( + imgdir=DATA_PATHS['spd']['imgdir'], + segdir=DATA_PATHS['spd']['segdir'], + transform=CocoPanopticTrainAugmentation(size_lr)) + ]) + self.datasampler_seg_image = DistributedSampler( + dataset=self.dataset_seg_image, + rank=self.rank, + num_replicas=self.world_size, + shuffle=True) + self.dataloader_seg_image = DataLoader( + dataset=self.dataset_seg_image, + batch_size=self.args.batch_size_per_gpu * self.args.seq_length_lr, + num_workers=self.args.num_workers, + sampler=self.datasampler_seg_image, + pin_memory=True) + + self.log('Initializing video segmentation datasets') + self.dataset_seg_video = YouTubeVISDataset( + videodir=DATA_PATHS['youtubevis']['videodir'], + annfile=DATA_PATHS['youtubevis']['annfile'], + size=self.args.resolution_lr, + seq_length=self.args.seq_length_lr, + seq_sampler=TrainFrameSampler(speed=[1]), + transform=YouTubeVISAugmentation(size_lr)) + self.datasampler_seg_video = DistributedSampler( + dataset=self.dataset_seg_video, + rank=self.rank, + num_replicas=self.world_size, + shuffle=True) + self.dataloader_seg_video = DataLoader( + dataset=self.dataset_seg_video, + batch_size=self.args.batch_size_per_gpu, + num_workers=self.args.num_workers, + sampler=self.datasampler_seg_video, + pin_memory=True) + + def init_model(self): + self.log('Initializing model') + self.model = MattingNetwork(self.args.model_variant, pretrained_backbone=True).to(self.rank) + + if self.args.checkpoint: + self.log(f'Restoring from checkpoint: {self.args.checkpoint}') + self.log(self.model.load_state_dict( + torch.load(self.args.checkpoint, map_location=f'cuda:{self.rank}'))) + + self.model = nn.SyncBatchNorm.convert_sync_batchnorm(self.model) + self.model_ddp = DDP(self.model, device_ids=[self.rank], broadcast_buffers=False, find_unused_parameters=True) + self.optimizer = Adam([ + {'params': self.model.backbone.parameters(), 'lr': self.args.learning_rate_backbone}, + {'params': self.model.aspp.parameters(), 'lr': self.args.learning_rate_aspp}, + {'params': self.model.decoder.parameters(), 'lr': self.args.learning_rate_decoder}, + {'params': self.model.refiner.parameters(), 'lr': self.args.learning_rate_refiner}, + ]) + self.scaler = GradScaler() + + def init_writer(self): + if self.rank == 0: + self.log('Initializing writer') + self.writer = SummaryWriter(self.args.log_dir) + + def train(self): + for epoch in range(self.args.epoch_start, self.args.epoch_end): + self.epoch = epoch + self.step = epoch * len(self.dataloader_lr_train) + + if not self.args.disable_validation: + self.validate() + + self.log(f'Training epoch: {epoch}') + for true_fgr, true_pha, true_bgr in tqdm(self.dataloader_lr_train, disable=self.args.disable_progress_bar, dynamic_ncols=True): + # Low resolution pass + self.train_mat(true_fgr, true_pha, true_bgr, downsample_ratio=1, tag='lr') + + # High resolution pass + if self.args.train_hr: + true_fgr, true_pha, true_bgr = self.load_next_mat_hr_sample() + self.train_mat(true_fgr, true_pha, true_bgr, downsample_ratio=self.args.downsample_ratio, tag='hr') + + # Segmentation pass + if self.step % 2 == 0: + true_img, true_seg = self.load_next_seg_video_sample() + self.train_seg(true_img, true_seg, log_label='seg_video') + else: + true_img, true_seg = self.load_next_seg_image_sample() + self.train_seg(true_img.unsqueeze(1), true_seg.unsqueeze(1), log_label='seg_image') + + if self.step % self.args.checkpoint_save_interval == 0: + self.save() + + self.step += 1 + + def train_mat(self, true_fgr, true_pha, true_bgr, downsample_ratio, tag): + true_fgr = true_fgr.to(self.rank, non_blocking=True) + true_pha = true_pha.to(self.rank, non_blocking=True) + true_bgr = true_bgr.to(self.rank, non_blocking=True) + true_fgr, true_pha, true_bgr = self.random_crop(true_fgr, true_pha, true_bgr) + true_src = true_fgr * true_pha + true_bgr * (1 - true_pha) + + with autocast(enabled=not self.args.disable_mixed_precision): + pred_fgr, pred_pha = self.model_ddp(true_src, downsample_ratio=downsample_ratio)[:2] + loss = matting_loss(pred_fgr, pred_pha, true_fgr, true_pha) + + self.scaler.scale(loss['total']).backward() + self.scaler.step(self.optimizer) + self.scaler.update() + self.optimizer.zero_grad() + + if self.rank == 0 and self.step % self.args.log_train_loss_interval == 0: + for loss_name, loss_value in loss.items(): + self.writer.add_scalar(f'train_{tag}_{loss_name}', loss_value, self.step) + + if self.rank == 0 and self.step % self.args.log_train_images_interval == 0: + self.writer.add_image(f'train_{tag}_pred_fgr', make_grid(pred_fgr.flatten(0, 1), nrow=pred_fgr.size(1)), self.step) + self.writer.add_image(f'train_{tag}_pred_pha', make_grid(pred_pha.flatten(0, 1), nrow=pred_pha.size(1)), self.step) + self.writer.add_image(f'train_{tag}_true_fgr', make_grid(true_fgr.flatten(0, 1), nrow=true_fgr.size(1)), self.step) + self.writer.add_image(f'train_{tag}_true_pha', make_grid(true_pha.flatten(0, 1), nrow=true_pha.size(1)), self.step) + self.writer.add_image(f'train_{tag}_true_src', make_grid(true_src.flatten(0, 1), nrow=true_src.size(1)), self.step) + + def train_seg(self, true_img, true_seg, log_label): + true_img = true_img.to(self.rank, non_blocking=True) + true_seg = true_seg.to(self.rank, non_blocking=True) + + true_img, true_seg = self.random_crop(true_img, true_seg) + + with autocast(enabled=not self.args.disable_mixed_precision): + pred_seg = self.model_ddp(true_img, segmentation_pass=True)[0] + loss = segmentation_loss(pred_seg, true_seg) + + self.scaler.scale(loss).backward() + self.scaler.step(self.optimizer) + self.scaler.update() + self.optimizer.zero_grad() + + if self.rank == 0 and (self.step - self.step % 2) % self.args.log_train_loss_interval == 0: + self.writer.add_scalar(f'{log_label}_loss', loss, self.step) + + if self.rank == 0 and (self.step - self.step % 2) % self.args.log_train_images_interval == 0: + self.writer.add_image(f'{log_label}_pred_seg', make_grid(pred_seg.flatten(0, 1).float().sigmoid(), nrow=self.args.seq_length_lr), self.step) + self.writer.add_image(f'{log_label}_true_seg', make_grid(true_seg.flatten(0, 1), nrow=self.args.seq_length_lr), self.step) + self.writer.add_image(f'{log_label}_true_img', make_grid(true_img.flatten(0, 1), nrow=self.args.seq_length_lr), self.step) + + def load_next_mat_hr_sample(self): + try: + sample = next(self.dataiterator_mat_hr) + except: + self.datasampler_hr_train.set_epoch(self.datasampler_hr_train.epoch + 1) + self.dataiterator_mat_hr = iter(self.dataloader_hr_train) + sample = next(self.dataiterator_mat_hr) + return sample + + def load_next_seg_video_sample(self): + try: + sample = next(self.dataiterator_seg_video) + except: + self.datasampler_seg_video.set_epoch(self.datasampler_seg_video.epoch + 1) + self.dataiterator_seg_video = iter(self.dataloader_seg_video) + sample = next(self.dataiterator_seg_video) + return sample + + def load_next_seg_image_sample(self): + try: + sample = next(self.dataiterator_seg_image) + except: + self.datasampler_seg_image.set_epoch(self.datasampler_seg_image.epoch + 1) + self.dataiterator_seg_image = iter(self.dataloader_seg_image) + sample = next(self.dataiterator_seg_image) + return sample + + def validate(self): + if self.rank == 0: + self.log(f'Validating at the start of epoch: {self.epoch}') + self.model_ddp.eval() + total_loss, total_count = 0, 0 + with torch.no_grad(): + with autocast(enabled=not self.args.disable_mixed_precision): + for true_fgr, true_pha, true_bgr in tqdm(self.dataloader_valid, disable=self.args.disable_progress_bar, dynamic_ncols=True): + true_fgr = true_fgr.to(self.rank, non_blocking=True) + true_pha = true_pha.to(self.rank, non_blocking=True) + true_bgr = true_bgr.to(self.rank, non_blocking=True) + true_src = true_fgr * true_pha + true_bgr * (1 - true_pha) + batch_size = true_src.size(0) + pred_fgr, pred_pha = self.model(true_src)[:2] + total_loss += matting_loss(pred_fgr, pred_pha, true_fgr, true_pha)['total'].item() * batch_size + total_count += batch_size + avg_loss = total_loss / total_count + self.log(f'Validation set average loss: {avg_loss}') + self.writer.add_scalar('valid_loss', avg_loss, self.step) + self.model_ddp.train() + dist.barrier() + + def random_crop(self, *imgs): + h, w = imgs[0].shape[-2:] + w = random.choice(range(w // 2, w)) + h = random.choice(range(w // 2, h)) + results = [] + for img in imgs: + B, T = img.shape[:2] + img = img.flatten(0, 1) + img = F.interpolate(img, (max(h, w), max(h, w)), mode='bilinear', align_corners=False) + img = center_crop(img, (h, w)) + img = img.reshape(B, T, *img.shape[1:]) + results.append(img) + return results + + def save(self): + if self.rank == 0: + os.makedirs(self.args.checkpoint_dir, exist_ok=True) + torch.save(self.model.state_dict(), os.path.join(self.args.checkpoint_dir, f'epoch-{self.epoch}.pth')) + self.log('Model saved') + dist.barrier() + + def cleanup(self): + dist.destroy_process_group() + + def log(self, msg): + print(f'[GPU{self.rank}] {msg}') + +if __name__ == '__main__': + world_size = torch.cuda.device_count() + mp.spawn( + Trainer, + nprocs=world_size, + args=(world_size,), + join=True) diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/train_config.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/train_config.py new file mode 100644 index 0000000..0792696 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/train_config.py @@ -0,0 +1,68 @@ +""" +Expected directory format: + +VideoMatte Train/Valid: + ├──fgr/ + ├── 0001/ + ├── 00000.jpg + ├── 00001.jpg + ├── pha/ + ├── 0001/ + ├── 00000.jpg + ├── 00001.jpg + +ImageMatte Train/Valid: + ├── fgr/ + ├── sample1.jpg + ├── sample2.jpg + ├── pha/ + ├── sample1.jpg + ├── sample2.jpg + +Background Image Train/Valid + ├── sample1.png + ├── sample2.png + +Background Video Train/Valid + ├── 0000/ + ├── 0000.jpg/ + ├── 0001.jpg/ + +""" + + +DATA_PATHS = { + + 'videomatte': { + 'train': '../matting-data/VideoMatte240K_JPEG_SD/train', + 'valid': '../matting-data/VideoMatte240K_JPEG_SD/valid', + }, + 'imagematte': { + 'train': '../matting-data/ImageMatte/train', + 'valid': '../matting-data/ImageMatte/valid', + }, + 'background_images': { + 'train': '../matting-data/Backgrounds/train', + 'valid': '../matting-data/Backgrounds/valid', + }, + 'background_videos': { + 'train': '../matting-data/BackgroundVideos/train', + 'valid': '../matting-data/BackgroundVideos/valid', + }, + + + 'coco_panoptic': { + 'imgdir': '../matting-data/coco/train2017/', + 'anndir': '../matting-data/coco/panoptic_train2017/', + 'annfile': '../matting-data/coco/annotations/panoptic_train2017.json', + }, + 'spd': { + 'imgdir': '../matting-data/SuperviselyPersonDataset/img', + 'segdir': '../matting-data/SuperviselyPersonDataset/seg', + }, + 'youtubevis': { + 'videodir': '../matting-data/YouTubeVIS/train/JPEGImages', + 'annfile': '../matting-data/YouTubeVIS/train/instances.json', + } + +} diff --git a/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/train_loss.py b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/train_loss.py new file mode 100644 index 0000000..01d8f1d --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/RobustVideoMatting/train_loss.py @@ -0,0 +1,101 @@ +import torch +from torch.nn import functional as F + +# --------------------------------------------------------------------------------- Train Loss + + +def matting_loss(pred_fgr, pred_pha, true_fgr, true_pha): + """ + Args: + pred_fgr: Shape(B, T, 3, H, W) + pred_pha: Shape(B, T, 1, H, W) + true_fgr: Shape(B, T, 3, H, W) + true_pha: Shape(B, T, 1, H, W) + """ + loss = dict() + # Alpha losses + loss['pha_l1'] = F.l1_loss(pred_pha, true_pha) + loss['pha_laplacian'] = laplacian_loss(pred_pha.flatten(0, 1), true_pha.flatten(0, 1)) + loss['pha_coherence'] = F.mse_loss(pred_pha[:, 1:] - pred_pha[:, :-1], + true_pha[:, 1:] - true_pha[:, :-1]) * 5 + # Foreground losses + true_msk = true_pha.gt(0) + pred_fgr = pred_fgr * true_msk + true_fgr = true_fgr * true_msk + loss['fgr_l1'] = F.l1_loss(pred_fgr, true_fgr) + loss['fgr_coherence'] = F.mse_loss(pred_fgr[:, 1:] - pred_fgr[:, :-1], + true_fgr[:, 1:] - true_fgr[:, :-1]) * 5 + # Total + loss['total'] = loss['pha_l1'] + loss['pha_coherence'] + loss['pha_laplacian'] \ + + loss['fgr_l1'] + loss['fgr_coherence'] + return loss + +def segmentation_loss(pred_seg, true_seg): + """ + Args: + pred_seg: Shape(B, T, 1, H, W) + true_seg: Shape(B, T, 1, H, W) + """ + return F.binary_cross_entropy_with_logits(pred_seg, true_seg) + + +# ----------------------------------------------------------------------------- Laplacian Loss + + +def laplacian_loss(pred, true, max_levels=5): + kernel = gauss_kernel(device=pred.device, dtype=pred.dtype) + pred_pyramid = laplacian_pyramid(pred, kernel, max_levels) + true_pyramid = laplacian_pyramid(true, kernel, max_levels) + loss = 0 + for level in range(max_levels): + loss += (2 ** level) * F.l1_loss(pred_pyramid[level], true_pyramid[level]) + return loss / max_levels + +def laplacian_pyramid(img, kernel, max_levels): + current = img + pyramid = [] + for _ in range(max_levels): + current = crop_to_even_size(current) + down = downsample(current, kernel) + up = upsample(down, kernel) + diff = current - up + pyramid.append(diff) + current = down + return pyramid + +def gauss_kernel(device='cpu', dtype=torch.float32): + kernel = torch.tensor([[1, 4, 6, 4, 1], + [4, 16, 24, 16, 4], + [6, 24, 36, 24, 6], + [4, 16, 24, 16, 4], + [1, 4, 6, 4, 1]], device=device, dtype=dtype) + kernel /= 256 + kernel = kernel[None, None, :, :] + return kernel + +def gauss_convolution(img, kernel): + B, C, H, W = img.shape + img = img.reshape(B * C, 1, H, W) + img = F.pad(img, (2, 2, 2, 2), mode='reflect') + img = F.conv2d(img, kernel) + img = img.reshape(B, C, H, W) + return img + +def downsample(img, kernel): + img = gauss_convolution(img, kernel) + img = img[:, :, ::2, ::2] + return img + +def upsample(img, kernel): + B, C, H, W = img.shape + out = torch.zeros((B, C, H * 2, W * 2), device=img.device, dtype=img.dtype) + out[:, :, ::2, ::2] = img * 4 + out = gauss_convolution(out, kernel) + return out + +def crop_to_even_size(img): + H, W = img.shape[2:] + H = H - H % 2 + W = W - W % 2 + return img[:, :, :H, :W] + diff --git a/motion-gan-pipeline/preprocessing/third/densepose/Base-DensePose-RCNN-FPN.yaml b/motion-gan-pipeline/preprocessing/third/densepose/Base-DensePose-RCNN-FPN.yaml new file mode 100644 index 0000000..1579187 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/Base-DensePose-RCNN-FPN.yaml @@ -0,0 +1,48 @@ +VERSION: 2 +MODEL: + META_ARCHITECTURE: "GeneralizedRCNN" + BACKBONE: + NAME: "build_resnet_fpn_backbone" + RESNETS: + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + FPN: + IN_FEATURES: ["res2", "res3", "res4", "res5"] + ANCHOR_GENERATOR: + SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map + ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps) + RPN: + IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"] + PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level + PRE_NMS_TOPK_TEST: 1000 # Per FPN level + # Detectron1 uses 2000 proposals per-batch, + # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue) + # which is approximately 1000 proposals per-image since the default batch size for FPN is 2. + POST_NMS_TOPK_TRAIN: 1000 + POST_NMS_TOPK_TEST: 1000 + + DENSEPOSE_ON: True + ROI_HEADS: + NAME: "DensePoseROIHeads" + IN_FEATURES: ["p2", "p3", "p4", "p5"] + NUM_CLASSES: 1 + ROI_BOX_HEAD: + NAME: "FastRCNNConvFCHead" + NUM_FC: 2 + POOLER_RESOLUTION: 7 + POOLER_SAMPLING_RATIO: 2 + POOLER_TYPE: "ROIAlign" + ROI_DENSEPOSE_HEAD: + NAME: "DensePoseV1ConvXHead" + POOLER_TYPE: "ROIAlign" + NUM_COARSE_SEGM_CHANNELS: 2 +DATASETS: + TRAIN: ("densepose_coco_2014_train", "densepose_coco_2014_valminusminival") + TEST: ("densepose_coco_2014_minival",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.01 + STEPS: (60000, 80000) + MAX_ITER: 90000 + WARMUP_FACTOR: 0.1 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/__init__.py new file mode 100644 index 0000000..b50a3da --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .data.datasets import builtin # just to register data +from .converters import builtin as builtin_converters # register converters +from .config import ( + add_densepose_config, + add_densepose_head_config, + add_hrnet_config, + add_dataset_category_config, + add_bootstrap_config, + load_bootstrap_config, +) +from .structures import DensePoseDataRelative, DensePoseList, DensePoseTransformData +from .evaluation import DensePoseCOCOEvaluator +from .modeling.roi_heads import DensePoseROIHeads +from .modeling.test_time_augmentation import ( + DensePoseGeneralizedRCNNWithTTA, + DensePoseDatasetMapperTTA, +) +from .utils.transform import load_from_cfg +from .modeling.hrfpn import build_hrfpn_backbone diff --git a/motion-gan-pipeline/preprocessing/third/densepose/config.py b/motion-gan-pipeline/preprocessing/third/densepose/config.py new file mode 100644 index 0000000..2a06a09 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/config.py @@ -0,0 +1,277 @@ +# -*- coding = utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +# pyre-ignore-all-errors + +from detectron2.config import CfgNode as CN + + +def add_dataset_category_config(cfg: CN) -> None: + """ + Add config for additional category-related dataset options + - category whitelisting + - category mapping + """ + _C = cfg + _C.DATASETS.CATEGORY_MAPS = CN(new_allowed=True) + _C.DATASETS.WHITELISTED_CATEGORIES = CN(new_allowed=True) + # class to mesh mapping + _C.DATASETS.CLASS_TO_MESH_NAME_MAPPING = CN(new_allowed=True) + + +def add_evaluation_config(cfg: CN) -> None: + _C = cfg + _C.DENSEPOSE_EVALUATION = CN() + # evaluator type, possible values: + # - "iou": evaluator for models that produce iou data + # - "cse": evaluator for models that produce cse data + _C.DENSEPOSE_EVALUATION.TYPE = "iou" + # storage for DensePose results, possible values: + # - "none": no explicit storage, all the results are stored in the + # dictionary with predictions, memory intensive; + # historically the default storage type + # - "ram": RAM storage, uses per-process RAM storage, which is + # reduced to a single process storage on later stages, + # less memory intensive + # - "file": file storage, uses per-process file-based storage, + # the least memory intensive, but may create bottlenecks + # on file system accesses + _C.DENSEPOSE_EVALUATION.STORAGE = "none" + # minimum threshold for IOU values: the lower its values is, + # the more matches are produced (and the higher the AP score) + _C.DENSEPOSE_EVALUATION.MIN_IOU_THRESHOLD = 0.5 + # Non-distributed inference is slower (at inference time) but can avoid RAM OOM + _C.DENSEPOSE_EVALUATION.DISTRIBUTED_INFERENCE = True + # evaluate mesh alignment based on vertex embeddings, only makes sense in CSE context + _C.DENSEPOSE_EVALUATION.EVALUATE_MESH_ALIGNMENT = False + # meshes to compute mesh alignment for + _C.DENSEPOSE_EVALUATION.MESH_ALIGNMENT_MESH_NAMES = [] + + +def add_bootstrap_config(cfg: CN) -> None: + """ """ + _C = cfg + _C.BOOTSTRAP_DATASETS = [] + _C.BOOTSTRAP_MODEL = CN() + _C.BOOTSTRAP_MODEL.WEIGHTS = "" + _C.BOOTSTRAP_MODEL.DEVICE = "cuda" + + +def get_bootstrap_dataset_config() -> CN: + _C = CN() + _C.DATASET = "" + # ratio used to mix data loaders + _C.RATIO = 0.1 + # image loader + _C.IMAGE_LOADER = CN(new_allowed=True) + _C.IMAGE_LOADER.TYPE = "" + _C.IMAGE_LOADER.BATCH_SIZE = 4 + _C.IMAGE_LOADER.NUM_WORKERS = 4 + _C.IMAGE_LOADER.CATEGORIES = [] + _C.IMAGE_LOADER.MAX_COUNT_PER_CATEGORY = 1_000_000 + _C.IMAGE_LOADER.CATEGORY_TO_CLASS_MAPPING = CN(new_allowed=True) + # inference + _C.INFERENCE = CN() + # batch size for model inputs + _C.INFERENCE.INPUT_BATCH_SIZE = 4 + # batch size to group model outputs + _C.INFERENCE.OUTPUT_BATCH_SIZE = 2 + # sampled data + _C.DATA_SAMPLER = CN(new_allowed=True) + _C.DATA_SAMPLER.TYPE = "" + _C.DATA_SAMPLER.USE_GROUND_TRUTH_CATEGORIES = False + # filter + _C.FILTER = CN(new_allowed=True) + _C.FILTER.TYPE = "" + return _C + + +def load_bootstrap_config(cfg: CN) -> None: + """ + Bootstrap datasets are given as a list of `dict` that are not automatically + converted into CfgNode. This method processes all bootstrap dataset entries + and ensures that they are in CfgNode format and comply with the specification + """ + if not cfg.BOOTSTRAP_DATASETS: + return + + bootstrap_datasets_cfgnodes = [] + for dataset_cfg in cfg.BOOTSTRAP_DATASETS: + _C = get_bootstrap_dataset_config().clone() + _C.merge_from_other_cfg(CN(dataset_cfg)) + bootstrap_datasets_cfgnodes.append(_C) + cfg.BOOTSTRAP_DATASETS = bootstrap_datasets_cfgnodes + + +def add_densepose_head_cse_config(cfg: CN) -> None: + """ + Add configuration options for Continuous Surface Embeddings (CSE) + """ + _C = cfg + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE = CN() + # Dimensionality D of the embedding space + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE = 16 + # Embedder specifications for various mesh IDs + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDERS = CN(new_allowed=True) + # normalization coefficient for embedding distances + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDING_DIST_GAUSS_SIGMA = 0.01 + # normalization coefficient for geodesic distances + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.GEODESIC_DIST_GAUSS_SIGMA = 0.01 + # embedding loss weight + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_LOSS_WEIGHT = 0.6 + # embedding loss name, currently the following options are supported: + # - EmbeddingLoss: cross-entropy on vertex labels + # - SoftEmbeddingLoss: cross-entropy on vertex label combined with + # Gaussian penalty on distance between vertices + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_LOSS_NAME = "EmbeddingLoss" + # optimizer hyperparameters + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.FEATURES_LR_FACTOR = 1.0 + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDING_LR_FACTOR = 1.0 + # Shape to shape cycle consistency loss parameters: + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS = CN({"ENABLED": False}) + # shape to shape cycle consistency loss weight + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.WEIGHT = 0.025 + # norm type used for loss computation + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.NORM_P = 2 + # normalization term for embedding similarity matrices + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.TEMPERATURE = 0.05 + # maximum number of vertices to include into shape to shape cycle loss + # if negative or zero, all vertices are considered + # if positive, random subset of vertices of given size is considered + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.MAX_NUM_VERTICES = 4936 + # Pixel to shape cycle consistency loss parameters: + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS = CN({"ENABLED": False}) + # pixel to shape cycle consistency loss weight + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.WEIGHT = 0.0001 + # norm type used for loss computation + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.NORM_P = 2 + # map images to all meshes and back (if false, use only gt meshes from the batch) + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.USE_ALL_MESHES_NOT_GT_ONLY = False + # Randomly select at most this number of pixels from every instance + # if negative or zero, all vertices are considered + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.NUM_PIXELS_TO_SAMPLE = 100 + # normalization factor for pixel to pixel distances (higher value = smoother distribution) + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.PIXEL_SIGMA = 5.0 + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.TEMPERATURE_PIXEL_TO_VERTEX = 0.05 + _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.TEMPERATURE_VERTEX_TO_PIXEL = 0.05 + + +def add_densepose_head_config(cfg: CN) -> None: + """ + Add config for densepose head. + """ + _C = cfg + + _C.MODEL.DENSEPOSE_ON = True + + _C.MODEL.ROI_DENSEPOSE_HEAD = CN() + _C.MODEL.ROI_DENSEPOSE_HEAD.NAME = "" + _C.MODEL.ROI_DENSEPOSE_HEAD.NUM_STACKED_CONVS = 8 + # Number of parts used for point labels + _C.MODEL.ROI_DENSEPOSE_HEAD.NUM_PATCHES = 24 + _C.MODEL.ROI_DENSEPOSE_HEAD.DECONV_KERNEL = 4 + _C.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_DIM = 512 + _C.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_KERNEL = 3 + _C.MODEL.ROI_DENSEPOSE_HEAD.UP_SCALE = 2 + _C.MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE = 112 + _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_TYPE = "ROIAlignV2" + _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_RESOLUTION = 28 + _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_SAMPLING_RATIO = 2 + _C.MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS = 2 # 15 or 2 + # Overlap threshold for an RoI to be considered foreground (if >= FG_IOU_THRESHOLD) + _C.MODEL.ROI_DENSEPOSE_HEAD.FG_IOU_THRESHOLD = 0.7 + # Loss weights for annotation masks.(14 Parts) + _C.MODEL.ROI_DENSEPOSE_HEAD.INDEX_WEIGHTS = 5.0 + # Loss weights for surface parts. (24 Parts) + _C.MODEL.ROI_DENSEPOSE_HEAD.PART_WEIGHTS = 1.0 + # Loss weights for UV regression. + _C.MODEL.ROI_DENSEPOSE_HEAD.POINT_REGRESSION_WEIGHTS = 0.01 + # Coarse segmentation is trained using instance segmentation task data + _C.MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS = False + # For Decoder + _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_ON = True + _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_NUM_CLASSES = 256 + _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_CONV_DIMS = 256 + _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_NORM = "" + _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_COMMON_STRIDE = 4 + # For DeepLab head + _C.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB = CN() + _C.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB.NORM = "GN" + _C.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB.NONLOCAL_ON = 0 + # Predictor class name, must be registered in DENSEPOSE_PREDICTOR_REGISTRY + # Some registered predictors: + # "DensePoseChartPredictor": predicts segmentation and UV coordinates for predefined charts + # "DensePoseChartWithConfidencePredictor": predicts segmentation, UV coordinates + # and associated confidences for predefined charts (default) + # "DensePoseEmbeddingWithConfidencePredictor": predicts segmentation, embeddings + # and associated confidences for CSE + _C.MODEL.ROI_DENSEPOSE_HEAD.PREDICTOR_NAME = "DensePoseChartWithConfidencePredictor" + # Loss class name, must be registered in DENSEPOSE_LOSS_REGISTRY + # Some registered losses: + # "DensePoseChartLoss": loss for chart-based models that estimate + # segmentation and UV coordinates + # "DensePoseChartWithConfidenceLoss": loss for chart-based models that estimate + # segmentation, UV coordinates and the corresponding confidences (default) + _C.MODEL.ROI_DENSEPOSE_HEAD.LOSS_NAME = "DensePoseChartWithConfidenceLoss" + # Confidences + # Enable learning UV confidences (variances) along with the actual values + _C.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE = CN({"ENABLED": False}) + # UV confidence lower bound + _C.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.EPSILON = 0.01 + # Enable learning segmentation confidences (variances) along with the actual values + _C.MODEL.ROI_DENSEPOSE_HEAD.SEGM_CONFIDENCE = CN({"ENABLED": False}) + # Segmentation confidence lower bound + _C.MODEL.ROI_DENSEPOSE_HEAD.SEGM_CONFIDENCE.EPSILON = 0.01 + # Statistical model type for confidence learning, possible values: + # - "iid_iso": statistically independent identically distributed residuals + # with isotropic covariance + # - "indep_aniso": statistically independent residuals with anisotropic + # covariances + _C.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.TYPE = "iid_iso" + # List of angles for rotation in data augmentation during training + _C.INPUT.ROTATION_ANGLES = [0] + _C.TEST.AUG.ROTATION_ANGLES = () # Rotation TTA + + add_densepose_head_cse_config(cfg) + + +def add_hrnet_config(cfg: CN) -> None: + """ + Add config for HRNet backbone. + """ + _C = cfg + + # For HigherHRNet w32 + _C.MODEL.HRNET = CN() + _C.MODEL.HRNET.STEM_INPLANES = 64 + _C.MODEL.HRNET.STAGE2 = CN() + _C.MODEL.HRNET.STAGE2.NUM_MODULES = 1 + _C.MODEL.HRNET.STAGE2.NUM_BRANCHES = 2 + _C.MODEL.HRNET.STAGE2.BLOCK = "BASIC" + _C.MODEL.HRNET.STAGE2.NUM_BLOCKS = [4, 4] + _C.MODEL.HRNET.STAGE2.NUM_CHANNELS = [32, 64] + _C.MODEL.HRNET.STAGE2.FUSE_METHOD = "SUM" + _C.MODEL.HRNET.STAGE3 = CN() + _C.MODEL.HRNET.STAGE3.NUM_MODULES = 4 + _C.MODEL.HRNET.STAGE3.NUM_BRANCHES = 3 + _C.MODEL.HRNET.STAGE3.BLOCK = "BASIC" + _C.MODEL.HRNET.STAGE3.NUM_BLOCKS = [4, 4, 4] + _C.MODEL.HRNET.STAGE3.NUM_CHANNELS = [32, 64, 128] + _C.MODEL.HRNET.STAGE3.FUSE_METHOD = "SUM" + _C.MODEL.HRNET.STAGE4 = CN() + _C.MODEL.HRNET.STAGE4.NUM_MODULES = 3 + _C.MODEL.HRNET.STAGE4.NUM_BRANCHES = 4 + _C.MODEL.HRNET.STAGE4.BLOCK = "BASIC" + _C.MODEL.HRNET.STAGE4.NUM_BLOCKS = [4, 4, 4, 4] + _C.MODEL.HRNET.STAGE4.NUM_CHANNELS = [32, 64, 128, 256] + _C.MODEL.HRNET.STAGE4.FUSE_METHOD = "SUM" + + _C.MODEL.HRNET.HRFPN = CN() + _C.MODEL.HRNET.HRFPN.OUT_CHANNELS = 256 + + +def add_densepose_config(cfg: CN) -> None: + add_densepose_head_config(cfg) + add_hrnet_config(cfg) + add_bootstrap_config(cfg) + add_dataset_category_config(cfg) + add_evaluation_config(cfg) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/converters/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/converters/__init__.py new file mode 100644 index 0000000..930339e --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/converters/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from .hflip import HFlipConverter +from .to_mask import ToMaskConverter +from .to_chart_result import ToChartResultConverter, ToChartResultConverterWithConfidences +from .segm_to_mask import ( + predictor_output_with_fine_and_coarse_segm_to_mask, + predictor_output_with_coarse_segm_to_mask, + resample_fine_and_coarse_segm_to_bbox, +) +from .chart_output_to_chart_result import ( + densepose_chart_predictor_output_to_result, + densepose_chart_predictor_output_to_result_with_confidences, +) +from .chart_output_hflip import densepose_chart_predictor_output_hflip diff --git a/motion-gan-pipeline/preprocessing/third/densepose/converters/base.py b/motion-gan-pipeline/preprocessing/third/densepose/converters/base.py new file mode 100644 index 0000000..c9dbe56 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/converters/base.py @@ -0,0 +1,93 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from typing import Any, Tuple, Type +import torch + + +class BaseConverter: + """ + Converter base class to be reused by various converters. + Converter allows one to convert data from various source types to a particular + destination type. Each source type needs to register its converter. The + registration for each source type is valid for all descendants of that type. + """ + + @classmethod + def register(cls, from_type: Type, converter: Any = None): + """ + Registers a converter for the specified type. + Can be used as a decorator (if converter is None), or called as a method. + + Args: + from_type (type): type to register the converter for; + all instances of this type will use the same converter + converter (callable): converter to be registered for the given + type; if None, this method is assumed to be a decorator for the converter + """ + + if converter is not None: + cls._do_register(from_type, converter) + + def wrapper(converter: Any) -> Any: + cls._do_register(from_type, converter) + return converter + + return wrapper + + @classmethod + def _do_register(cls, from_type: Type, converter: Any): + cls.registry[from_type] = converter # pyre-ignore[16] + + @classmethod + def _lookup_converter(cls, from_type: Type) -> Any: + """ + Perform recursive lookup for the given type + to find registered converter. If a converter was found for some base + class, it gets registered for this class to save on further lookups. + + Args: + from_type: type for which to find a converter + Return: + callable or None - registered converter or None + if no suitable entry was found in the registry + """ + if from_type in cls.registry: # pyre-ignore[16] + return cls.registry[from_type] + for base in from_type.__bases__: + converter = cls._lookup_converter(base) + if converter is not None: + cls._do_register(from_type, converter) + return converter + return None + + @classmethod + def convert(cls, instance: Any, *args, **kwargs): + """ + Convert an instance to the destination type using some registered + converter. Does recursive lookup for base classes, so there's no need + for explicit registration for derived classes. + + Args: + instance: source instance to convert to the destination type + Return: + An instance of the destination type obtained from the source instance + Raises KeyError, if no suitable converter found + """ + instance_type = type(instance) + converter = cls._lookup_converter(instance_type) + if converter is None: + if cls.dst_type is None: # pyre-ignore[16] + output_type_str = "itself" + else: + output_type_str = cls.dst_type + raise KeyError(f"Could not find converter from {instance_type} to {output_type_str}") + return converter(instance, *args, **kwargs) + + +IntTupleBox = Tuple[int, int, int, int] + + +def make_int_box(box: torch.Tensor) -> IntTupleBox: + int_box = [0, 0, 0, 0] + int_box[0], int_box[1], int_box[2], int_box[3] = tuple(box.long().tolist()) + return int_box[0], int_box[1], int_box[2], int_box[3] diff --git a/motion-gan-pipeline/preprocessing/third/densepose/converters/builtin.py b/motion-gan-pipeline/preprocessing/third/densepose/converters/builtin.py new file mode 100644 index 0000000..3bd48f8 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/converters/builtin.py @@ -0,0 +1,31 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from ..structures import DensePoseChartPredictorOutput, DensePoseEmbeddingPredictorOutput +from . import ( + HFlipConverter, + ToChartResultConverter, + ToChartResultConverterWithConfidences, + ToMaskConverter, + densepose_chart_predictor_output_hflip, + densepose_chart_predictor_output_to_result, + densepose_chart_predictor_output_to_result_with_confidences, + predictor_output_with_coarse_segm_to_mask, + predictor_output_with_fine_and_coarse_segm_to_mask, +) + +ToMaskConverter.register( + DensePoseChartPredictorOutput, predictor_output_with_fine_and_coarse_segm_to_mask +) +ToMaskConverter.register( + DensePoseEmbeddingPredictorOutput, predictor_output_with_coarse_segm_to_mask +) + +ToChartResultConverter.register( + DensePoseChartPredictorOutput, densepose_chart_predictor_output_to_result +) + +ToChartResultConverterWithConfidences.register( + DensePoseChartPredictorOutput, densepose_chart_predictor_output_to_result_with_confidences +) + +HFlipConverter.register(DensePoseChartPredictorOutput, densepose_chart_predictor_output_hflip) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/converters/chart_output_hflip.py b/motion-gan-pipeline/preprocessing/third/densepose/converters/chart_output_hflip.py new file mode 100644 index 0000000..17d2948 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/converters/chart_output_hflip.py @@ -0,0 +1,71 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from dataclasses import fields +import torch + +from densepose.structures import DensePoseChartPredictorOutput, DensePoseTransformData + + +def densepose_chart_predictor_output_hflip( + densepose_predictor_output: DensePoseChartPredictorOutput, + transform_data: DensePoseTransformData, +) -> DensePoseChartPredictorOutput: + """ + Change to take into account a Horizontal flip. + """ + if len(densepose_predictor_output) > 0: + + PredictorOutput = type(densepose_predictor_output) + output_dict = {} + + for field in fields(densepose_predictor_output): + field_value = getattr(densepose_predictor_output, field.name) + # flip tensors + if isinstance(field_value, torch.Tensor): + setattr(densepose_predictor_output, field.name, torch.flip(field_value, [3])) + + densepose_predictor_output = _flip_iuv_semantics_tensor( + densepose_predictor_output, transform_data + ) + densepose_predictor_output = _flip_segm_semantics_tensor( + densepose_predictor_output, transform_data + ) + + for field in fields(densepose_predictor_output): + output_dict[field.name] = getattr(densepose_predictor_output, field.name) + + return PredictorOutput(**output_dict) + else: + return densepose_predictor_output + + +def _flip_iuv_semantics_tensor( + densepose_predictor_output: DensePoseChartPredictorOutput, + dp_transform_data: DensePoseTransformData, +) -> DensePoseChartPredictorOutput: + point_label_symmetries = dp_transform_data.point_label_symmetries + uv_symmetries = dp_transform_data.uv_symmetries + + N, C, H, W = densepose_predictor_output.u.shape + u_loc = (densepose_predictor_output.u[:, 1:, :, :].clamp(0, 1) * 255).long() + v_loc = (densepose_predictor_output.v[:, 1:, :, :].clamp(0, 1) * 255).long() + Iindex = torch.arange(C - 1, device=densepose_predictor_output.u.device)[ + None, :, None, None + ].expand(N, C - 1, H, W) + densepose_predictor_output.u[:, 1:, :, :] = uv_symmetries["U_transforms"][Iindex, v_loc, u_loc] + densepose_predictor_output.v[:, 1:, :, :] = uv_symmetries["V_transforms"][Iindex, v_loc, u_loc] + + for el in ["fine_segm", "u", "v"]: + densepose_predictor_output.__dict__[el] = densepose_predictor_output.__dict__[el][ + :, point_label_symmetries, :, : + ] + return densepose_predictor_output + + +def _flip_segm_semantics_tensor( + densepose_predictor_output: DensePoseChartPredictorOutput, dp_transform_data +): + if densepose_predictor_output.coarse_segm.shape[1] > 2: + densepose_predictor_output.coarse_segm = densepose_predictor_output.coarse_segm[ + :, dp_transform_data.mask_label_symmetries, :, : + ] + return densepose_predictor_output diff --git a/motion-gan-pipeline/preprocessing/third/densepose/converters/chart_output_to_chart_result.py b/motion-gan-pipeline/preprocessing/third/densepose/converters/chart_output_to_chart_result.py new file mode 100644 index 0000000..b589519 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/converters/chart_output_to_chart_result.py @@ -0,0 +1,189 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from typing import Dict +import torch +from torch.nn import functional as F + +from detectron2.structures.boxes import Boxes, BoxMode + +from ..structures import ( + DensePoseChartPredictorOutput, + DensePoseChartResult, + DensePoseChartResultWithConfidences, +) +from . import resample_fine_and_coarse_segm_to_bbox +from .base import IntTupleBox, make_int_box + + +def resample_uv_tensors_to_bbox( + u: torch.Tensor, + v: torch.Tensor, + labels: torch.Tensor, + box_xywh_abs: IntTupleBox, +) -> torch.Tensor: + """ + Resamples U and V coordinate estimates for the given bounding box + + Args: + u (tensor [1, C, H, W] of float): U coordinates + v (tensor [1, C, H, W] of float): V coordinates + labels (tensor [H, W] of long): labels obtained by resampling segmentation + outputs for the given bounding box + box_xywh_abs (tuple of 4 int): bounding box that corresponds to predictor outputs + Return: + Resampled U and V coordinates - a tensor [2, H, W] of float + """ + x, y, w, h = box_xywh_abs + w = max(int(w), 1) + h = max(int(h), 1) + # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got `Tuple[int, int]`. + u_bbox = F.interpolate(u, (h, w), mode="bilinear", align_corners=False) + # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got `Tuple[int, int]`. + v_bbox = F.interpolate(v, (h, w), mode="bilinear", align_corners=False) + uv = torch.zeros([2, h, w], dtype=torch.float32, device=u.device) + for part_id in range(1, u_bbox.size(1)): + uv[0][labels == part_id] = u_bbox[0, part_id][labels == part_id] + uv[1][labels == part_id] = v_bbox[0, part_id][labels == part_id] + return uv + + +def resample_uv_to_bbox( + predictor_output: DensePoseChartPredictorOutput, + labels: torch.Tensor, + box_xywh_abs: IntTupleBox, +) -> torch.Tensor: + """ + Resamples U and V coordinate estimates for the given bounding box + + Args: + predictor_output (DensePoseChartPredictorOutput): DensePose predictor + output to be resampled + labels (tensor [H, W] of long): labels obtained by resampling segmentation + outputs for the given bounding box + box_xywh_abs (tuple of 4 int): bounding box that corresponds to predictor outputs + Return: + Resampled U and V coordinates - a tensor [2, H, W] of float + """ + return resample_uv_tensors_to_bbox( + predictor_output.u, + predictor_output.v, + labels, + box_xywh_abs, + ) + + +def densepose_chart_predictor_output_to_result( + predictor_output: DensePoseChartPredictorOutput, boxes: Boxes +) -> DensePoseChartResult: + """ + Convert densepose chart predictor outputs to results + + Args: + predictor_output (DensePoseChartPredictorOutput): DensePose predictor + output to be converted to results, must contain only 1 output + boxes (Boxes): bounding box that corresponds to the predictor output, + must contain only 1 bounding box + Return: + DensePose chart-based result (DensePoseChartResult) + """ + assert len(predictor_output) == 1 and len(boxes) == 1, ( + f"Predictor output to result conversion can operate only single outputs" + f", got {len(predictor_output)} predictor outputs and {len(boxes)} boxes" + ) + + boxes_xyxy_abs = boxes.tensor.clone() + boxes_xywh_abs = BoxMode.convert(boxes_xyxy_abs, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + box_xywh = make_int_box(boxes_xywh_abs[0]) + + labels = resample_fine_and_coarse_segm_to_bbox(predictor_output, box_xywh).squeeze(0) + uv = resample_uv_to_bbox(predictor_output, labels, box_xywh) + return DensePoseChartResult(labels=labels, uv=uv) + + +def resample_confidences_to_bbox( + predictor_output: DensePoseChartPredictorOutput, + labels: torch.Tensor, + box_xywh_abs: IntTupleBox, +) -> Dict[str, torch.Tensor]: + """ + Resamples confidences for the given bounding box + + Args: + predictor_output (DensePoseChartPredictorOutput): DensePose predictor + output to be resampled + labels (tensor [H, W] of long): labels obtained by resampling segmentation + outputs for the given bounding box + box_xywh_abs (tuple of 4 int): bounding box that corresponds to predictor outputs + Return: + Resampled confidences - a dict of [H, W] tensors of float + """ + + x, y, w, h = box_xywh_abs + w = max(int(w), 1) + h = max(int(h), 1) + + confidence_names = [ + "sigma_1", + "sigma_2", + "kappa_u", + "kappa_v", + "fine_segm_confidence", + "coarse_segm_confidence", + ] + confidence_results = {key: None for key in confidence_names} + confidence_names = [ + key for key in confidence_names if getattr(predictor_output, key) is not None + ] + confidence_base = torch.zeros([h, w], dtype=torch.float32, device=predictor_output.u.device) + + # assign data from channels that correspond to the labels + for key in confidence_names: + resampled_confidence = F.interpolate( + # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got + # `Tuple[int, int]`. + getattr(predictor_output, key), (h, w), mode="bilinear", align_corners=False + ) + result = confidence_base.clone() + for part_id in range(1, predictor_output.u.size(1)): + if resampled_confidence.size(1) != predictor_output.u.size(1): + # confidence is not part-based, don't try to fill it part by part + continue + result[labels == part_id] = resampled_confidence[0, part_id][labels == part_id] + + if resampled_confidence.size(1) != predictor_output.u.size(1): + # confidence is not part-based, fill the data with the first channel + # (targeted for segmentation confidences that have only 1 channel) + result = resampled_confidence[0, 0] + + confidence_results[key] = result + + return confidence_results # pyre-ignore[7] + + +def densepose_chart_predictor_output_to_result_with_confidences( + predictor_output: DensePoseChartPredictorOutput, boxes: Boxes +) -> DensePoseChartResultWithConfidences: + """ + Convert densepose chart predictor outputs to results + + Args: + predictor_output (DensePoseChartPredictorOutput): DensePose predictor + output with confidences to be converted to results, must contain only 1 output + boxes (Boxes): bounding box that corresponds to the predictor output, + must contain only 1 bounding box + Return: + DensePose chart-based result with confidences (DensePoseChartResultWithConfidences) + """ + assert len(predictor_output) == 1 and len(boxes) == 1, ( + f"Predictor output to result conversion can operate only single outputs" + f", got {len(predictor_output)} predictor outputs and {len(boxes)} boxes" + ) + + boxes_xyxy_abs = boxes.tensor.clone() + boxes_xywh_abs = BoxMode.convert(boxes_xyxy_abs, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + box_xywh = make_int_box(boxes_xywh_abs[0]) + + labels = resample_fine_and_coarse_segm_to_bbox(predictor_output, box_xywh).squeeze(0) + uv = resample_uv_to_bbox(predictor_output, labels, box_xywh) + confidences = resample_confidences_to_bbox(predictor_output, labels, box_xywh) + return DensePoseChartResultWithConfidences(labels=labels, uv=uv, **confidences) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/converters/hflip.py b/motion-gan-pipeline/preprocessing/third/densepose/converters/hflip.py new file mode 100644 index 0000000..092c50c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/converters/hflip.py @@ -0,0 +1,32 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from typing import Any + +from .base import BaseConverter + + +class HFlipConverter(BaseConverter): + """ + Converts various DensePose predictor outputs to DensePose results. + Each DensePose predictor output type has to register its convertion strategy. + """ + + registry = {} + dst_type = None + + @classmethod + def convert(cls, predictor_outputs: Any, transform_data: Any, *args, **kwargs): + """ + Performs an horizontal flip on DensePose predictor outputs. + Does recursive lookup for base classes, so there's no need + for explicit registration for derived classes. + + Args: + predictor_outputs: DensePose predictor output to be converted to BitMasks + transform_data: Anything useful for the flip + Return: + An instance of the same type as predictor_outputs + """ + return super(HFlipConverter, cls).convert( + predictor_outputs, transform_data, *args, **kwargs + ) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/converters/segm_to_mask.py b/motion-gan-pipeline/preprocessing/third/densepose/converters/segm_to_mask.py new file mode 100644 index 0000000..e667b7f --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/converters/segm_to_mask.py @@ -0,0 +1,154 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from typing import Any +import torch +from torch.nn import functional as F + +from detectron2.structures import BitMasks, Boxes, BoxMode + +from .base import IntTupleBox, make_int_box +from .to_mask import ImageSizeType + + +def resample_coarse_segm_tensor_to_bbox(coarse_segm: torch.Tensor, box_xywh_abs: IntTupleBox): + """ + Resample coarse segmentation tensor to the given + bounding box and derive labels for each pixel of the bounding box + + Args: + coarse_segm: float tensor of shape [1, K, Hout, Wout] + box_xywh_abs (tuple of 4 int): bounding box given by its upper-left + corner coordinates, width (W) and height (H) + Return: + Labels for each pixel of the bounding box, a long tensor of size [1, H, W] + """ + x, y, w, h = box_xywh_abs + w = max(int(w), 1) + h = max(int(h), 1) + # pyre-fixme[16]: `Tensor` has no attribute `argmax`. + # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got `Tuple[int, int]`. + labels = F.interpolate(coarse_segm, (h, w), mode="bilinear", align_corners=False).argmax(dim=1) + return labels + + +def resample_fine_and_coarse_segm_tensors_to_bbox( + fine_segm: torch.Tensor, coarse_segm: torch.Tensor, box_xywh_abs: IntTupleBox +): + """ + Resample fine and coarse segmentation tensors to the given + bounding box and derive labels for each pixel of the bounding box + + Args: + fine_segm: float tensor of shape [1, C, Hout, Wout] + coarse_segm: float tensor of shape [1, K, Hout, Wout] + box_xywh_abs (tuple of 4 int): bounding box given by its upper-left + corner coordinates, width (W) and height (H) + Return: + Labels for each pixel of the bounding box, a long tensor of size [1, H, W] + """ + x, y, w, h = box_xywh_abs + w = max(int(w), 1) + h = max(int(h), 1) + # coarse segmentation + # pyre-fixme[16]: `Tensor` has no attribute `argmax`. + coarse_segm_bbox = F.interpolate( + # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got `Tuple[int, + # int]`. + coarse_segm, (h, w), mode="bilinear", align_corners=False + ).argmax(dim=1) + # combined coarse and fine segmentation + labels = ( + # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got `Tuple[int, + # int]`. + F.interpolate(fine_segm, (h, w), mode="bilinear", align_corners=False).argmax(dim=1) + * (coarse_segm_bbox > 0).long() + ) + return labels + + +def resample_fine_and_coarse_segm_to_bbox(predictor_output: Any, box_xywh_abs: IntTupleBox): + """ + Resample fine and coarse segmentation outputs from a predictor to the given + bounding box and derive labels for each pixel of the bounding box + + Args: + predictor_output: DensePose predictor output that contains segmentation + results to be resampled + box_xywh_abs (tuple of 4 int): bounding box given by its upper-left + corner coordinates, width (W) and height (H) + Return: + Labels for each pixel of the bounding box, a long tensor of size [1, H, W] + """ + return resample_fine_and_coarse_segm_tensors_to_bbox( + predictor_output.fine_segm, + predictor_output.coarse_segm, + box_xywh_abs, + ) + + +def predictor_output_with_coarse_segm_to_mask( + predictor_output: Any, boxes: Boxes, image_size_hw: ImageSizeType +) -> BitMasks: + """ + Convert predictor output with coarse and fine segmentation to a mask. + Assumes that predictor output has the following attributes: + - coarse_segm (tensor of size [N, D, H, W]): coarse segmentation + unnormalized scores for N instances; D is the number of coarse + segmentation labels, H and W is the resolution of the estimate + + Args: + predictor_output: DensePose predictor output to be converted to mask + boxes (Boxes): bounding boxes that correspond to the DensePose + predictor outputs + image_size_hw (tuple [int, int]): image height Himg and width Wimg + Return: + BitMasks that contain a bool tensor of size [N, Himg, Wimg] with + a mask of the size of the image for each instance + """ + H, W = image_size_hw + boxes_xyxy_abs = boxes.tensor.clone() + boxes_xywh_abs = BoxMode.convert(boxes_xyxy_abs, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + N = len(boxes_xywh_abs) + masks = torch.zeros((N, H, W), dtype=torch.bool, device=boxes.tensor.device) + for i in range(len(boxes_xywh_abs)): + box_xywh = make_int_box(boxes_xywh_abs[i]) + box_mask = resample_coarse_segm_tensor_to_bbox(predictor_output[i].coarse_segm, box_xywh) + x, y, w, h = box_xywh + masks[i, y : y + h, x : x + w] = box_mask + + return BitMasks(masks) + + +def predictor_output_with_fine_and_coarse_segm_to_mask( + predictor_output: Any, boxes: Boxes, image_size_hw: ImageSizeType +) -> BitMasks: + """ + Convert predictor output with coarse and fine segmentation to a mask. + Assumes that predictor output has the following attributes: + - coarse_segm (tensor of size [N, D, H, W]): coarse segmentation + unnormalized scores for N instances; D is the number of coarse + segmentation labels, H and W is the resolution of the estimate + - fine_segm (tensor of size [N, C, H, W]): fine segmentation + unnormalized scores for N instances; C is the number of fine + segmentation labels, H and W is the resolution of the estimate + + Args: + predictor_output: DensePose predictor output to be converted to mask + boxes (Boxes): bounding boxes that correspond to the DensePose + predictor outputs + image_size_hw (tuple [int, int]): image height Himg and width Wimg + Return: + BitMasks that contain a bool tensor of size [N, Himg, Wimg] with + a mask of the size of the image for each instance + """ + H, W = image_size_hw + boxes_xyxy_abs = boxes.tensor.clone() + boxes_xywh_abs = BoxMode.convert(boxes_xyxy_abs, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + N = len(boxes_xywh_abs) + masks = torch.zeros((N, H, W), dtype=torch.bool, device=boxes.tensor.device) + for i in range(len(boxes_xywh_abs)): + box_xywh = make_int_box(boxes_xywh_abs[i]) + labels_i = resample_fine_and_coarse_segm_to_bbox(predictor_output[i], box_xywh) + x, y, w, h = box_xywh + masks[i, y : y + h, x : x + w] = labels_i > 0 + return BitMasks(masks) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/converters/to_chart_result.py b/motion-gan-pipeline/preprocessing/third/densepose/converters/to_chart_result.py new file mode 100644 index 0000000..f96da3c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/converters/to_chart_result.py @@ -0,0 +1,66 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from typing import Any + +from detectron2.structures import Boxes + +from ..structures import DensePoseChartResult, DensePoseChartResultWithConfidences +from .base import BaseConverter + + +class ToChartResultConverter(BaseConverter): + """ + Converts various DensePose predictor outputs to DensePose results. + Each DensePose predictor output type has to register its convertion strategy. + """ + + registry = {} + dst_type = DensePoseChartResult + + @classmethod + def convert(cls, predictor_outputs: Any, boxes: Boxes, *args, **kwargs) -> DensePoseChartResult: + """ + Convert DensePose predictor outputs to DensePoseResult using some registered + converter. Does recursive lookup for base classes, so there's no need + for explicit registration for derived classes. + + Args: + densepose_predictor_outputs: DensePose predictor output to be + converted to BitMasks + boxes (Boxes): bounding boxes that correspond to the DensePose + predictor outputs + Return: + An instance of DensePoseResult. If no suitable converter was found, raises KeyError + """ + return super(ToChartResultConverter, cls).convert(predictor_outputs, boxes, *args, **kwargs) + + +class ToChartResultConverterWithConfidences(BaseConverter): + """ + Converts various DensePose predictor outputs to DensePose results. + Each DensePose predictor output type has to register its convertion strategy. + """ + + registry = {} + dst_type = DensePoseChartResultWithConfidences + + @classmethod + def convert( + cls, predictor_outputs: Any, boxes: Boxes, *args, **kwargs + ) -> DensePoseChartResultWithConfidences: + """ + Convert DensePose predictor outputs to DensePoseResult with confidences + using some registered converter. Does recursive lookup for base classes, + so there's no need for explicit registration for derived classes. + + Args: + densepose_predictor_outputs: DensePose predictor output with confidences + to be converted to BitMasks + boxes (Boxes): bounding boxes that correspond to the DensePose + predictor outputs + Return: + An instance of DensePoseResult. If no suitable converter was found, raises KeyError + """ + return super(ToChartResultConverterWithConfidences, cls).convert( + predictor_outputs, boxes, *args, **kwargs + ) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/converters/to_mask.py b/motion-gan-pipeline/preprocessing/third/densepose/converters/to_mask.py new file mode 100644 index 0000000..9d8152b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/converters/to_mask.py @@ -0,0 +1,47 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from typing import Any, Tuple + +from detectron2.structures import BitMasks, Boxes + +from .base import BaseConverter + +ImageSizeType = Tuple[int, int] + + +class ToMaskConverter(BaseConverter): + """ + Converts various DensePose predictor outputs to masks + in bit mask format (see `BitMasks`). Each DensePose predictor output type + has to register its convertion strategy. + """ + + registry = {} + dst_type = BitMasks + + @classmethod + def convert( + cls, + densepose_predictor_outputs: Any, + boxes: Boxes, + image_size_hw: ImageSizeType, + *args, + **kwargs + ) -> BitMasks: + """ + Convert DensePose predictor outputs to BitMasks using some registered + converter. Does recursive lookup for base classes, so there's no need + for explicit registration for derived classes. + + Args: + densepose_predictor_outputs: DensePose predictor output to be + converted to BitMasks + boxes (Boxes): bounding boxes that correspond to the DensePose + predictor outputs + image_size_hw (tuple [int, int]): image height and width + Return: + An instance of `BitMasks`. If no suitable converter was found, raises KeyError + """ + return super(ToMaskConverter, cls).convert( + densepose_predictor_outputs, boxes, image_size_hw, *args, **kwargs + ) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/data/__init__.py new file mode 100644 index 0000000..bf21ba7 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/__init__.py @@ -0,0 +1,25 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from .meshes import builtin +from .build import ( + build_detection_test_loader, + build_detection_train_loader, + build_combined_loader, + build_frame_selector, + build_inference_based_loaders, + has_inference_based_loaders, + BootstrapDatasetFactoryCatalog, +) +from .combined_loader import CombinedDataLoader +from .dataset_mapper import DatasetMapper +from .inference_based_loader import InferenceBasedLoader, ScoreBasedFilter +from .image_list_dataset import ImageListDataset +from .utils import is_relative_local_path, maybe_prepend_base_path + +# ensure the builtin datasets are registered +from . import datasets + +# ensure the bootstrap datasets builders are registered +from . import build + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/build.py b/motion-gan-pipeline/preprocessing/third/densepose/data/build.py new file mode 100644 index 0000000..db709d5 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/build.py @@ -0,0 +1,734 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import itertools +import logging +import numpy as np +from collections import UserDict, defaultdict +from dataclasses import dataclass +from typing import Any, Callable, Collection, Dict, Iterable, List, Optional, Sequence, Tuple +import torch +from torch.utils.data.dataset import Dataset + +from detectron2.config import CfgNode +from detectron2.data.build import build_detection_test_loader as d2_build_detection_test_loader +from detectron2.data.build import build_detection_train_loader as d2_build_detection_train_loader +from detectron2.data.build import ( + load_proposals_into_dataset, + print_instances_class_histogram, + trivial_batch_collator, + worker_init_reset_seed, +) +from detectron2.data.catalog import DatasetCatalog, Metadata, MetadataCatalog +from detectron2.data.samplers import TrainingSampler +from detectron2.utils.comm import get_world_size + +from densepose.config import get_bootstrap_dataset_config +from densepose.modeling import build_densepose_embedder + +from .combined_loader import CombinedDataLoader, Loader +from .dataset_mapper import DatasetMapper +from .datasets.coco import DENSEPOSE_CSE_KEYS_WITHOUT_MASK, DENSEPOSE_IUV_KEYS_WITHOUT_MASK +from .datasets.dataset_type import DatasetType +from .inference_based_loader import InferenceBasedLoader, ScoreBasedFilter +from .samplers import ( + DensePoseConfidenceBasedSampler, + DensePoseCSEConfidenceBasedSampler, + DensePoseCSEUniformSampler, + DensePoseUniformSampler, + MaskFromDensePoseSampler, + PredictionToGroundTruthSampler, +) +from .transform import ImageResizeTransform +from .utils import get_category_to_class_mapping, get_class_to_mesh_name_mapping +from .video import ( + FirstKFramesSelector, + FrameSelectionStrategy, + LastKFramesSelector, + RandomKFramesSelector, + VideoKeyframeDataset, + video_list_from_file, +) + +__all__ = ["build_detection_train_loader", "build_detection_test_loader"] + + +Instance = Dict[str, Any] +InstancePredicate = Callable[[Instance], bool] + + +def _compute_num_images_per_worker(cfg: CfgNode) -> int: + num_workers = get_world_size() + images_per_batch = cfg.SOLVER.IMS_PER_BATCH + assert ( + images_per_batch % num_workers == 0 + ), "SOLVER.IMS_PER_BATCH ({}) must be divisible by the number of workers ({}).".format( + images_per_batch, num_workers + ) + assert ( + images_per_batch >= num_workers + ), "SOLVER.IMS_PER_BATCH ({}) must be larger than the number of workers ({}).".format( + images_per_batch, num_workers + ) + images_per_worker = images_per_batch // num_workers + return images_per_worker + + +def _map_category_id_to_contiguous_id(dataset_name: str, dataset_dicts: Iterable[Instance]) -> None: + meta = MetadataCatalog.get(dataset_name) + for dataset_dict in dataset_dicts: + for ann in dataset_dict["annotations"]: + ann["category_id"] = meta.thing_dataset_id_to_contiguous_id[ann["category_id"]] + + +@dataclass +class _DatasetCategory: + """ + Class representing category data in a dataset: + - id: category ID, as specified in the dataset annotations file + - name: category name, as specified in the dataset annotations file + - mapped_id: category ID after applying category maps (DATASETS.CATEGORY_MAPS config option) + - mapped_name: category name after applying category maps + - dataset_name: dataset in which the category is defined + + For example, when training models in a class-agnostic manner, one could take LVIS 1.0 + dataset and map the animal categories to the same category as human data from COCO: + id = 225 + name = "cat" + mapped_id = 1 + mapped_name = "person" + dataset_name = "lvis_v1_animals_dp_train" + """ + + id: int + name: str + mapped_id: int + mapped_name: str + dataset_name: str + + +_MergedCategoriesT = Dict[int, List[_DatasetCategory]] + + +def _add_category_id_to_contiguous_id_maps_to_metadata(merged_categories: _MergedCategoriesT) -> None: + merged_categories_per_dataset = {} + for contiguous_cat_id, cat_id in enumerate(sorted(merged_categories.keys())): + for cat in merged_categories[cat_id]: + if cat.dataset_name not in merged_categories_per_dataset: + merged_categories_per_dataset[cat.dataset_name] = defaultdict(list) + merged_categories_per_dataset[cat.dataset_name][cat_id].append( + ( + contiguous_cat_id, + cat, + ) + ) + + logger = logging.getLogger(__name__) + for dataset_name, merged_categories in merged_categories_per_dataset.items(): + meta = MetadataCatalog.get(dataset_name) + if not hasattr(meta, "thing_classes"): + meta.thing_classes = [] + meta.thing_dataset_id_to_contiguous_id = {} + meta.thing_dataset_id_to_merged_id = {} + else: + meta.thing_classes.clear() + meta.thing_dataset_id_to_contiguous_id.clear() + meta.thing_dataset_id_to_merged_id.clear() + logger.info(f"Dataset {dataset_name}: category ID to contiguous ID mapping:") + for _cat_id, categories in sorted(merged_categories.items()): + added_to_thing_classes = False + for contiguous_cat_id, cat in categories: + if not added_to_thing_classes: + meta.thing_classes.append(cat.mapped_name) + added_to_thing_classes = True + meta.thing_dataset_id_to_contiguous_id[cat.id] = contiguous_cat_id + meta.thing_dataset_id_to_merged_id[cat.id] = cat.mapped_id + logger.info(f"{cat.id} ({cat.name}) -> {contiguous_cat_id}") + + +def _maybe_create_general_keep_instance_predicate(cfg: CfgNode) -> Optional[InstancePredicate]: + def has_annotations(instance: Instance) -> bool: + return "annotations" in instance + + def has_only_crowd_anotations(instance: Instance) -> bool: + for ann in instance["annotations"]: + if ann.get("is_crowd", 0) == 0: + return False + return True + + def general_keep_instance_predicate(instance: Instance) -> bool: + return has_annotations(instance) and not has_only_crowd_anotations(instance) + + if not cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS: + return None + return general_keep_instance_predicate + + +def _maybe_create_keypoints_keep_instance_predicate(cfg: CfgNode) -> Optional[InstancePredicate]: + + min_num_keypoints = cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE + + def has_sufficient_num_keypoints(instance: Instance) -> bool: + num_kpts = sum( + (np.array(ann["keypoints"][2::3]) > 0).sum() + for ann in instance["annotations"] + if "keypoints" in ann + ) + return num_kpts >= min_num_keypoints + + if cfg.MODEL.KEYPOINT_ON and (min_num_keypoints > 0): + return has_sufficient_num_keypoints + return None + + +def _maybe_create_mask_keep_instance_predicate(cfg: CfgNode) -> Optional[InstancePredicate]: + if not cfg.MODEL.MASK_ON: + return None + + def has_mask_annotations(instance: Instance) -> bool: + return any("segmentation" in ann for ann in instance["annotations"]) + + return has_mask_annotations + + +def _maybe_create_densepose_keep_instance_predicate(cfg: CfgNode) -> Optional[InstancePredicate]: + if not cfg.MODEL.DENSEPOSE_ON: + return None + + use_masks = cfg.MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS + + def has_densepose_annotations(instance: Instance) -> bool: + for ann in instance["annotations"]: + if all(key in ann for key in DENSEPOSE_IUV_KEYS_WITHOUT_MASK) or all( + key in ann for key in DENSEPOSE_CSE_KEYS_WITHOUT_MASK + ): + return True + if use_masks and "segmentation" in ann: + return True + return False + + return has_densepose_annotations + + +def _maybe_create_specific_keep_instance_predicate(cfg: CfgNode) -> Optional[InstancePredicate]: + specific_predicate_creators = [ + _maybe_create_keypoints_keep_instance_predicate, + _maybe_create_mask_keep_instance_predicate, + _maybe_create_densepose_keep_instance_predicate, + ] + predicates = [creator(cfg) for creator in specific_predicate_creators] + predicates = [p for p in predicates if p is not None] + if not predicates: + return None + + def combined_predicate(instance: Instance) -> bool: + return any(p(instance) for p in predicates) + + return combined_predicate + + +def _get_train_keep_instance_predicate(cfg: CfgNode): + general_keep_predicate = _maybe_create_general_keep_instance_predicate(cfg) + combined_specific_keep_predicate = _maybe_create_specific_keep_instance_predicate(cfg) + + def combined_general_specific_keep_predicate(instance: Instance) -> bool: + return general_keep_predicate(instance) and combined_specific_keep_predicate(instance) + + if (general_keep_predicate is None) and (combined_specific_keep_predicate is None): + return None + if general_keep_predicate is None: + return combined_specific_keep_predicate + if combined_specific_keep_predicate is None: + return general_keep_predicate + return combined_general_specific_keep_predicate + + +def _get_test_keep_instance_predicate(cfg: CfgNode): + general_keep_predicate = _maybe_create_general_keep_instance_predicate(cfg) + return general_keep_predicate + + +def _maybe_filter_and_map_categories( + dataset_name: str, dataset_dicts: List[Instance] +) -> List[Instance]: + meta = MetadataCatalog.get(dataset_name) + category_id_map = meta.thing_dataset_id_to_contiguous_id + filtered_dataset_dicts = [] + for dataset_dict in dataset_dicts: + anns = [] + for ann in dataset_dict["annotations"]: + cat_id = ann["category_id"] + if cat_id not in category_id_map: + continue + ann["category_id"] = category_id_map[cat_id] + anns.append(ann) + dataset_dict["annotations"] = anns + filtered_dataset_dicts.append(dataset_dict) + return filtered_dataset_dicts + + +def _add_category_whitelists_to_metadata(cfg: CfgNode) -> None: + for dataset_name, whitelisted_cat_ids in cfg.DATASETS.WHITELISTED_CATEGORIES.items(): + meta = MetadataCatalog.get(dataset_name) + meta.whitelisted_categories = whitelisted_cat_ids + logger = logging.getLogger(__name__) + logger.info( + "Whitelisted categories for dataset {}: {}".format( + dataset_name, meta.whitelisted_categories + ) + ) + + +def _add_category_maps_to_metadata(cfg: CfgNode) -> None: + for dataset_name, category_map in cfg.DATASETS.CATEGORY_MAPS.items(): + category_map = { + int(cat_id_src): int(cat_id_dst) for cat_id_src, cat_id_dst in category_map.items() + } + meta = MetadataCatalog.get(dataset_name) + meta.category_map = category_map + logger = logging.getLogger(__name__) + logger.info("Category maps for dataset {}: {}".format(dataset_name, meta.category_map)) + + +def _add_category_info_to_bootstrapping_metadata(dataset_name: str, dataset_cfg: CfgNode) -> None: + meta = MetadataCatalog.get(dataset_name) + meta.category_to_class_mapping = get_category_to_class_mapping(dataset_cfg) + meta.categories = dataset_cfg.CATEGORIES + meta.max_count_per_category = dataset_cfg.MAX_COUNT_PER_CATEGORY + logger = logging.getLogger(__name__) + logger.info( + "Category to class mapping for dataset {}: {}".format( + dataset_name, meta.category_to_class_mapping + ) + ) + + +def _maybe_add_class_to_mesh_name_map_to_metadata(dataset_names: List[str], cfg: CfgNode) -> None: + for dataset_name in dataset_names: + meta = MetadataCatalog.get(dataset_name) + if not hasattr(meta, "class_to_mesh_name"): + meta.class_to_mesh_name = get_class_to_mesh_name_mapping(cfg) + + +def _merge_categories(dataset_names: Collection[str]) -> _MergedCategoriesT: + merged_categories = defaultdict(list) + category_names = {} + for dataset_name in dataset_names: + meta = MetadataCatalog.get(dataset_name) + whitelisted_categories = meta.get("whitelisted_categories") + category_map = meta.get("category_map", {}) + cat_ids = ( + whitelisted_categories if whitelisted_categories is not None else meta.categories.keys() + ) + for cat_id in cat_ids: + cat_name = meta.categories[cat_id] + cat_id_mapped = category_map.get(cat_id, cat_id) + if cat_id_mapped == cat_id or cat_id_mapped in cat_ids: + category_names[cat_id] = cat_name + else: + category_names[cat_id] = str(cat_id_mapped) + # assign temporary mapped category name, this name can be changed + # during the second pass, since mapped ID can correspond to a category + # from a different dataset + cat_name_mapped = meta.categories[cat_id_mapped] + merged_categories[cat_id_mapped].append( + _DatasetCategory( + id=cat_id, + name=cat_name, + mapped_id=cat_id_mapped, + mapped_name=cat_name_mapped, + dataset_name=dataset_name, + ) + ) + # second pass to assign proper mapped category names + for cat_id, categories in merged_categories.items(): + for cat in categories: + if cat_id in category_names and cat.mapped_name != category_names[cat_id]: + cat.mapped_name = category_names[cat_id] + + return merged_categories + + +def _warn_if_merged_different_categories(merged_categories: _MergedCategoriesT) -> None: + logger = logging.getLogger(__name__) + for cat_id in merged_categories: + merged_categories_i = merged_categories[cat_id] + first_cat_name = merged_categories_i[0].name + if len(merged_categories_i) > 1 and not all( + cat.name == first_cat_name for cat in merged_categories_i[1:] + ): + cat_summary_str = ", ".join( + [f"{cat.id} ({cat.name}) from {cat.dataset_name}" for cat in merged_categories_i] + ) + logger.warning( + f"Merged category {cat_id} corresponds to the following categories: " + f"{cat_summary_str}" + ) + + +def combine_detection_dataset_dicts( + dataset_names: Collection[str], + keep_instance_predicate: Optional[InstancePredicate] = None, + proposal_files: Optional[Collection[str]] = None, +) -> List[Instance]: + """ + Load and prepare dataset dicts for training / testing + + Args: + dataset_names (Collection[str]): a list of dataset names + keep_instance_predicate (Callable: Dict[str, Any] -> bool): predicate + applied to instance dicts which defines whether to keep the instance + proposal_files (Collection[str]): if given, a list of object proposal files + that match each dataset in `dataset_names`. + """ + assert len(dataset_names) + if proposal_files is None: + proposal_files = [None] * len(dataset_names) + assert len(dataset_names) == len(proposal_files) + # load datasets and metadata + dataset_name_to_dicts = {} + for dataset_name in dataset_names: + dataset_name_to_dicts[dataset_name] = DatasetCatalog.get(dataset_name) + assert len(dataset_name_to_dicts), f"Dataset '{dataset_name}' is empty!" + # merge categories, requires category metadata to be loaded + # cat_id -> [(orig_cat_id, cat_name, dataset_name)] + merged_categories = _merge_categories(dataset_names) + _warn_if_merged_different_categories(merged_categories) + merged_category_names = [ + merged_categories[cat_id][0].mapped_name for cat_id in sorted(merged_categories) + ] + # map to contiguous category IDs + _add_category_id_to_contiguous_id_maps_to_metadata(merged_categories) + # load annotations and dataset metadata + for dataset_name, proposal_file in zip(dataset_names, proposal_files): + dataset_dicts = dataset_name_to_dicts[dataset_name] + assert len(dataset_dicts), f"Dataset '{dataset_name}' is empty!" + if proposal_file is not None: + dataset_dicts = load_proposals_into_dataset(dataset_dicts, proposal_file) + dataset_dicts = _maybe_filter_and_map_categories(dataset_name, dataset_dicts) + print_instances_class_histogram(dataset_dicts, merged_category_names) + dataset_name_to_dicts[dataset_name] = dataset_dicts + + if keep_instance_predicate is not None: + all_datasets_dicts_plain = [ + d + for d in itertools.chain.from_iterable(dataset_name_to_dicts.values()) + if keep_instance_predicate(d) + ] + else: + all_datasets_dicts_plain = list( + itertools.chain.from_iterable(dataset_name_to_dicts.values()) + ) + return all_datasets_dicts_plain + + +def build_detection_train_loader(cfg: CfgNode, mapper=None): + """ + A data loader is created in a way similar to that of Detectron2. + The main differences are: + - it allows to combine datasets with different but compatible object category sets + + The data loader is created by the following steps: + 1. Use the dataset names in config to query :class:`DatasetCatalog`, and obtain a list of dicts. + 2. Start workers to work on the dicts. Each worker will: + * Map each metadata dict into another format to be consumed by the model. + * Batch them by simply putting dicts into a list. + The batched ``list[mapped_dict]`` is what this dataloader will return. + + Args: + cfg (CfgNode): the config + mapper (callable): a callable which takes a sample (dict) from dataset and + returns the format to be consumed by the model. + By default it will be `DatasetMapper(cfg, True)`. + + Returns: + an infinite iterator of training data + """ + + _add_category_whitelists_to_metadata(cfg) + _add_category_maps_to_metadata(cfg) + _maybe_add_class_to_mesh_name_map_to_metadata(cfg.DATASETS.TRAIN, cfg) + dataset_dicts = combine_detection_dataset_dicts( + cfg.DATASETS.TRAIN, + keep_instance_predicate=_get_train_keep_instance_predicate(cfg), + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + ) + if mapper is None: + mapper = DatasetMapper(cfg, True) + return d2_build_detection_train_loader(cfg, dataset=dataset_dicts, mapper=mapper) + + +def build_detection_test_loader(cfg, dataset_name, mapper=None): + """ + Similar to `build_detection_train_loader`. + But this function uses the given `dataset_name` argument (instead of the names in cfg), + and uses batch size 1. + + Args: + cfg: a detectron2 CfgNode + dataset_name (str): a name of the dataset that's available in the DatasetCatalog + mapper (callable): a callable which takes a sample (dict) from dataset + and returns the format to be consumed by the model. + By default it will be `DatasetMapper(cfg, False)`. + + Returns: + DataLoader: a torch DataLoader, that loads the given detection + dataset, with test-time transformation and batching. + """ + _add_category_whitelists_to_metadata(cfg) + _add_category_maps_to_metadata(cfg) + _maybe_add_class_to_mesh_name_map_to_metadata([dataset_name], cfg) + dataset_dicts = combine_detection_dataset_dicts( + [dataset_name], + keep_instance_predicate=_get_test_keep_instance_predicate(cfg), + proposal_files=[ + cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(dataset_name)] + ] + if cfg.MODEL.LOAD_PROPOSALS + else None, + ) + sampler = None + if not cfg.DENSEPOSE_EVALUATION.DISTRIBUTED_INFERENCE: + sampler = torch.utils.data.SequentialSampler(dataset_dicts) + if mapper is None: + mapper = DatasetMapper(cfg, False) + return d2_build_detection_test_loader( + dataset_dicts, mapper=mapper, num_workers=cfg.DATALOADER.NUM_WORKERS, sampler=sampler + ) + + +def build_frame_selector(cfg: CfgNode): + strategy = FrameSelectionStrategy(cfg.STRATEGY) + if strategy == FrameSelectionStrategy.RANDOM_K: + frame_selector = RandomKFramesSelector(cfg.NUM_IMAGES) + elif strategy == FrameSelectionStrategy.FIRST_K: + frame_selector = FirstKFramesSelector(cfg.NUM_IMAGES) + elif strategy == FrameSelectionStrategy.LAST_K: + frame_selector = LastKFramesSelector(cfg.NUM_IMAGES) + elif strategy == FrameSelectionStrategy.ALL: + frame_selector = None + # pyre-fixme[61]: `frame_selector` may not be initialized here. + return frame_selector + + +def build_transform(cfg: CfgNode, data_type: str): + if cfg.TYPE == "resize": + if data_type == "image": + return ImageResizeTransform(cfg.MIN_SIZE, cfg.MAX_SIZE) + raise ValueError(f"Unknown transform {cfg.TYPE} for data type {data_type}") + + +def build_combined_loader(cfg: CfgNode, loaders: Collection[Loader], ratios: Sequence[float]): + images_per_worker = _compute_num_images_per_worker(cfg) + return CombinedDataLoader(loaders, images_per_worker, ratios) + + +def build_bootstrap_dataset(dataset_name: str, cfg: CfgNode) -> Sequence[torch.Tensor]: + """ + Build dataset that provides data to bootstrap on + + Args: + dataset_name (str): Name of the dataset, needs to have associated metadata + to load the data + cfg (CfgNode): bootstrapping config + Returns: + Sequence[Tensor] - dataset that provides image batches, Tensors of size + [N, C, H, W] of type float32 + """ + logger = logging.getLogger(__name__) + _add_category_info_to_bootstrapping_metadata(dataset_name, cfg) + meta = MetadataCatalog.get(dataset_name) + factory = BootstrapDatasetFactoryCatalog.get(meta.dataset_type) + dataset = None + if factory is not None: + dataset = factory(meta, cfg) + if dataset is None: + logger.warning(f"Failed to create dataset {dataset_name} of type {meta.dataset_type}") + return dataset + + +def build_data_sampler(cfg: CfgNode, sampler_cfg: CfgNode, embedder: Optional[torch.nn.Module]): + if sampler_cfg.TYPE == "densepose_uniform": + data_sampler = PredictionToGroundTruthSampler() + # transform densepose pred -> gt + data_sampler.register_sampler( + "pred_densepose", + "gt_densepose", + DensePoseUniformSampler(count_per_class=sampler_cfg.COUNT_PER_CLASS), + ) + data_sampler.register_sampler("pred_densepose", "gt_masks", MaskFromDensePoseSampler()) + return data_sampler + elif sampler_cfg.TYPE == "densepose_UV_confidence": + data_sampler = PredictionToGroundTruthSampler() + # transform densepose pred -> gt + data_sampler.register_sampler( + "pred_densepose", + "gt_densepose", + DensePoseConfidenceBasedSampler( + confidence_channel="sigma_2", + count_per_class=sampler_cfg.COUNT_PER_CLASS, + search_proportion=0.5, + ), + ) + data_sampler.register_sampler("pred_densepose", "gt_masks", MaskFromDensePoseSampler()) + return data_sampler + elif sampler_cfg.TYPE == "densepose_fine_segm_confidence": + data_sampler = PredictionToGroundTruthSampler() + # transform densepose pred -> gt + data_sampler.register_sampler( + "pred_densepose", + "gt_densepose", + DensePoseConfidenceBasedSampler( + confidence_channel="fine_segm_confidence", + count_per_class=sampler_cfg.COUNT_PER_CLASS, + search_proportion=0.5, + ), + ) + data_sampler.register_sampler("pred_densepose", "gt_masks", MaskFromDensePoseSampler()) + return data_sampler + elif sampler_cfg.TYPE == "densepose_coarse_segm_confidence": + data_sampler = PredictionToGroundTruthSampler() + # transform densepose pred -> gt + data_sampler.register_sampler( + "pred_densepose", + "gt_densepose", + DensePoseConfidenceBasedSampler( + confidence_channel="coarse_segm_confidence", + count_per_class=sampler_cfg.COUNT_PER_CLASS, + search_proportion=0.5, + ), + ) + data_sampler.register_sampler("pred_densepose", "gt_masks", MaskFromDensePoseSampler()) + return data_sampler + elif sampler_cfg.TYPE == "densepose_cse_uniform": + assert embedder is not None + data_sampler = PredictionToGroundTruthSampler() + # transform densepose pred -> gt + data_sampler.register_sampler( + "pred_densepose", + "gt_densepose", + DensePoseCSEUniformSampler( + cfg=cfg, + use_gt_categories=sampler_cfg.USE_GROUND_TRUTH_CATEGORIES, + embedder=embedder, + count_per_class=sampler_cfg.COUNT_PER_CLASS, + ), + ) + data_sampler.register_sampler("pred_densepose", "gt_masks", MaskFromDensePoseSampler()) + return data_sampler + elif sampler_cfg.TYPE == "densepose_cse_coarse_segm_confidence": + assert embedder is not None + data_sampler = PredictionToGroundTruthSampler() + # transform densepose pred -> gt + data_sampler.register_sampler( + "pred_densepose", + "gt_densepose", + DensePoseCSEConfidenceBasedSampler( + cfg=cfg, + use_gt_categories=sampler_cfg.USE_GROUND_TRUTH_CATEGORIES, + embedder=embedder, + confidence_channel="coarse_segm_confidence", + count_per_class=sampler_cfg.COUNT_PER_CLASS, + search_proportion=0.5, + ), + ) + data_sampler.register_sampler("pred_densepose", "gt_masks", MaskFromDensePoseSampler()) + return data_sampler + + raise ValueError(f"Unknown data sampler type {sampler_cfg.TYPE}") + + +def build_data_filter(cfg: CfgNode): + if cfg.TYPE == "detection_score": + min_score = cfg.MIN_VALUE + return ScoreBasedFilter(min_score=min_score) + raise ValueError(f"Unknown data filter type {cfg.TYPE}") + + +def build_inference_based_loader( + cfg: CfgNode, + dataset_cfg: CfgNode, + model: torch.nn.Module, + embedder: Optional[torch.nn.Module] = None, +) -> InferenceBasedLoader: + """ + Constructs data loader based on inference results of a model. + """ + dataset = build_bootstrap_dataset(dataset_cfg.DATASET, dataset_cfg.IMAGE_LOADER) + meta = MetadataCatalog.get(dataset_cfg.DATASET) + training_sampler = TrainingSampler(len(dataset)) + data_loader = torch.utils.data.DataLoader( + dataset, # pyre-ignore[6] + batch_size=dataset_cfg.IMAGE_LOADER.BATCH_SIZE, + sampler=training_sampler, + num_workers=dataset_cfg.IMAGE_LOADER.NUM_WORKERS, + collate_fn=trivial_batch_collator, + worker_init_fn=worker_init_reset_seed, + ) + return InferenceBasedLoader( + model, + data_loader=data_loader, + data_sampler=build_data_sampler(cfg, dataset_cfg.DATA_SAMPLER, embedder), + data_filter=build_data_filter(dataset_cfg.FILTER), + shuffle=True, + batch_size=dataset_cfg.INFERENCE.OUTPUT_BATCH_SIZE, + inference_batch_size=dataset_cfg.INFERENCE.INPUT_BATCH_SIZE, + category_to_class_mapping=meta.category_to_class_mapping, + ) + + +def has_inference_based_loaders(cfg: CfgNode) -> bool: + """ + Returns True, if at least one inferense-based loader must + be instantiated for training + """ + return len(cfg.BOOTSTRAP_DATASETS) > 0 + + +def build_inference_based_loaders( + cfg: CfgNode, model: torch.nn.Module +) -> Tuple[List[InferenceBasedLoader], List[float]]: + loaders = [] + ratios = [] + embedder = build_densepose_embedder(cfg).to(device=model.device) # pyre-ignore[16] + for dataset_spec in cfg.BOOTSTRAP_DATASETS: + dataset_cfg = get_bootstrap_dataset_config().clone() + dataset_cfg.merge_from_other_cfg(CfgNode(dataset_spec)) + loader = build_inference_based_loader(cfg, dataset_cfg, model, embedder) + loaders.append(loader) + ratios.append(dataset_cfg.RATIO) + return loaders, ratios + + +def build_video_list_dataset(meta: Metadata, cfg: CfgNode): + video_list_fpath = meta.video_list_fpath + video_base_path = meta.video_base_path + category = meta.category + if cfg.TYPE == "video_keyframe": + frame_selector = build_frame_selector(cfg.SELECT) + transform = build_transform(cfg.TRANSFORM, data_type="image") + video_list = video_list_from_file(video_list_fpath, video_base_path) + keyframe_helper_fpath = cfg.KEYFRAME_HELPER if hasattr(cfg, "KEYFRAME_HELPER") else None + return VideoKeyframeDataset( + video_list, category, frame_selector, transform, keyframe_helper_fpath + ) + + +class _BootstrapDatasetFactoryCatalog(UserDict): + """ + A global dictionary that stores information about bootstrapped datasets creation functions + from metadata and config, for diverse DatasetType + """ + + def register(self, dataset_type: DatasetType, factory: Callable[[Metadata, CfgNode], Dataset]): + """ + Args: + dataset_type (DatasetType): a DatasetType e.g. DatasetType.VIDEO_LIST + factory (Callable[Metadata, CfgNode]): a callable which takes Metadata and cfg + arguments and returns a dataset object. + """ + assert dataset_type not in self, "Dataset '{}' is already registered!".format(dataset_type) + self[dataset_type] = factory + + +BootstrapDatasetFactoryCatalog = _BootstrapDatasetFactoryCatalog() +BootstrapDatasetFactoryCatalog.register(DatasetType.VIDEO_LIST, build_video_list_dataset) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/combined_loader.py b/motion-gan-pipeline/preprocessing/third/densepose/data/combined_loader.py new file mode 100644 index 0000000..5bfbbde --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/combined_loader.py @@ -0,0 +1,44 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import random +from collections import deque +from typing import Any, Collection, Deque, Iterable, Iterator, List, Sequence + +Loader = Iterable[Any] + + +def _pooled_next(iterator: Iterator[Any], pool: Deque[Any]): + if not pool: + pool.extend(next(iterator)) + return pool.popleft() + + +class CombinedDataLoader: + """ + Combines data loaders using the provided sampling ratios + """ + + BATCH_COUNT = 100 + + def __init__(self, loaders: Collection[Loader], batch_size: int, ratios: Sequence[float]): + self.loaders = loaders + self.batch_size = batch_size + self.ratios = ratios + + def __iter__(self) -> Iterator[List[Any]]: + iters = [iter(loader) for loader in self.loaders] + indices = [] + pool = [deque()] * len(iters) + # infinite iterator, as in D2 + while True: + if not indices: + # just a buffer of indices, its size doesn't matter + # as long as it's a multiple of batch_size + k = self.batch_size * self.BATCH_COUNT + indices = random.choices(range(len(self.loaders)), self.ratios, k=k) + try: + batch = [_pooled_next(iters[i], pool[i]) for i in indices[: self.batch_size]] + except StopIteration: + break + indices = indices[self.batch_size :] + yield batch diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/dataset_mapper.py b/motion-gan-pipeline/preprocessing/third/densepose/data/dataset_mapper.py new file mode 100644 index 0000000..4f1c289 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/dataset_mapper.py @@ -0,0 +1,168 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import copy +import logging +from typing import Any, Dict, List, Tuple +import torch + +from detectron2.data import MetadataCatalog +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.layers import ROIAlign +from detectron2.structures import BoxMode +from detectron2.utils.file_io import PathManager + +from densepose.structures import DensePoseDataRelative, DensePoseList, DensePoseTransformData + + +def build_augmentation(cfg, is_train): + logger = logging.getLogger(__name__) + result = utils.build_augmentation(cfg, is_train) + if is_train: + random_rotation = T.RandomRotation( + cfg.INPUT.ROTATION_ANGLES, expand=False, sample_style="choice" + ) + result.append(random_rotation) + logger.info("DensePose-specific augmentation used in training: " + str(random_rotation)) + return result + + +class DatasetMapper: + """ + A customized version of `detectron2.data.DatasetMapper` + """ + + def __init__(self, cfg, is_train=True): + self.augmentation = build_augmentation(cfg, is_train) + + # fmt: off + self.img_format = cfg.INPUT.FORMAT + self.mask_on = ( + cfg.MODEL.MASK_ON or ( + cfg.MODEL.DENSEPOSE_ON + and cfg.MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS) + ) + self.keypoint_on = cfg.MODEL.KEYPOINT_ON + self.densepose_on = cfg.MODEL.DENSEPOSE_ON + assert not cfg.MODEL.LOAD_PROPOSALS, "not supported yet" + # fmt: on + if self.keypoint_on and is_train: + # Flip only makes sense in training + self.keypoint_hflip_indices = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN) + else: + self.keypoint_hflip_indices = None + + if self.densepose_on: + densepose_transform_srcs = [ + MetadataCatalog.get(ds).densepose_transform_src + for ds in cfg.DATASETS.TRAIN + cfg.DATASETS.TEST + ] + assert len(densepose_transform_srcs) > 0 + # TODO: check that DensePose transformation data is the same for + # all the datasets. Otherwise one would have to pass DB ID with + # each entry to select proper transformation data. For now, since + # all DensePose annotated data uses the same data semantics, we + # omit this check. + densepose_transform_data_fpath = PathManager.get_local_path(densepose_transform_srcs[0]) + self.densepose_transform_data = DensePoseTransformData.load( + densepose_transform_data_fpath + ) + + self.is_train = is_train + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + image = utils.read_image(dataset_dict["file_name"], format=self.img_format) + utils.check_image_size(dataset_dict, image) + + image, transforms = T.apply_transform_gens(self.augmentation, image) + image_shape = image.shape[:2] # h, w + dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32")) + + if not self.is_train: + dataset_dict.pop("annotations", None) + return dataset_dict + + for anno in dataset_dict["annotations"]: + if not self.mask_on: + anno.pop("segmentation", None) + if not self.keypoint_on: + anno.pop("keypoints", None) + + # USER: Implement additional transformations if you have other types of data + # USER: Don't call transpose_densepose if you don't need + annos = [ + self._transform_densepose( + utils.transform_instance_annotations( + obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices + ), + transforms, + ) + for obj in dataset_dict.pop("annotations") + if obj.get("iscrowd", 0) == 0 + ] + + if self.mask_on: + self._add_densepose_masks_as_segmentation(annos, image_shape) + + instances = utils.annotations_to_instances(annos, image_shape, mask_format="bitmask") + densepose_annotations = [obj.get("densepose") for obj in annos] + if densepose_annotations and not all(v is None for v in densepose_annotations): + instances.gt_densepose = DensePoseList( + densepose_annotations, instances.gt_boxes, image_shape + ) + + dataset_dict["instances"] = instances[instances.gt_boxes.nonempty()] + return dataset_dict + + def _transform_densepose(self, annotation, transforms): + if not self.densepose_on: + return annotation + + # Handle densepose annotations + is_valid, reason_not_valid = DensePoseDataRelative.validate_annotation(annotation) + if is_valid: + densepose_data = DensePoseDataRelative(annotation, cleanup=True) + densepose_data.apply_transform(transforms, self.densepose_transform_data) + annotation["densepose"] = densepose_data + else: + # logger = logging.getLogger(__name__) + # logger.debug("Could not load DensePose annotation: {}".format(reason_not_valid)) + DensePoseDataRelative.cleanup_annotation(annotation) + # NOTE: annotations for certain instances may be unavailable. + # 'None' is accepted by the DensePostList data structure. + annotation["densepose"] = None + return annotation + + def _add_densepose_masks_as_segmentation( + self, annotations: List[Dict[str, Any]], image_shape_hw: Tuple[int, int] + ): + for obj in annotations: + if ("densepose" not in obj) or ("segmentation" in obj): + continue + # DP segmentation: torch.Tensor [S, S] of float32, S=256 + segm_dp = torch.zeros_like(obj["densepose"].segm) + segm_dp[obj["densepose"].segm > 0] = 1 + segm_h, segm_w = segm_dp.shape + bbox_segm_dp = torch.tensor((0, 0, segm_h - 1, segm_w - 1), dtype=torch.float32) + # image bbox + x0, y0, x1, y1 = ( + v.item() for v in BoxMode.convert(obj["bbox"], obj["bbox_mode"], BoxMode.XYXY_ABS) + ) + segm_aligned = ( + ROIAlign((y1 - y0, x1 - x0), 1.0, 0, aligned=True) + .forward(segm_dp.view(1, 1, *segm_dp.shape), bbox_segm_dp) + .squeeze() + ) + image_mask = torch.zeros(*image_shape_hw, dtype=torch.float32) + image_mask[y0:y1, x0:x1] = segm_aligned + # segmentation for BitMask: np.array [H, W] of np.bool + obj["segmentation"] = image_mask >= 0.5 diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/__init__.py new file mode 100644 index 0000000..260ccb9 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from . import builtin # ensure the builtin datasets are registered + +__all__ = [k for k in globals().keys() if "builtin" not in k and not k.startswith("_")] diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/builtin.py b/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/builtin.py new file mode 100644 index 0000000..7572cd6 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/builtin.py @@ -0,0 +1,16 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .chimpnsee import register_dataset as register_chimpnsee_dataset +from .coco import BASE_DATASETS as BASE_COCO_DATASETS +from .coco import DATASETS as COCO_DATASETS +from .coco import register_datasets as register_coco_datasets +from .lvis import DATASETS as LVIS_DATASETS +from .lvis import register_datasets as register_lvis_datasets + +DEFAULT_DATASETS_ROOT = "datasets" + + +register_coco_datasets(COCO_DATASETS, DEFAULT_DATASETS_ROOT) +register_coco_datasets(BASE_COCO_DATASETS, DEFAULT_DATASETS_ROOT) +register_lvis_datasets(LVIS_DATASETS, DEFAULT_DATASETS_ROOT) + +register_chimpnsee_dataset(DEFAULT_DATASETS_ROOT) # pyre-ignore[19] diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/chimpnsee.py b/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/chimpnsee.py new file mode 100644 index 0000000..61e0b50 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/chimpnsee.py @@ -0,0 +1,29 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from typing import Optional + +from detectron2.data import DatasetCatalog, MetadataCatalog + +from ..utils import maybe_prepend_base_path +from .dataset_type import DatasetType + +CHIMPNSEE_DATASET_NAME = "chimpnsee" + + +def register_dataset(datasets_root: Optional[str] = None) -> None: + def empty_load_callback(): + pass + + video_list_fpath = maybe_prepend_base_path( + datasets_root, + "chimpnsee/cdna.eva.mpg.de/video_list.txt", + ) + video_base_path = maybe_prepend_base_path(datasets_root, "chimpnsee/cdna.eva.mpg.de") + + DatasetCatalog.register(CHIMPNSEE_DATASET_NAME, empty_load_callback) + MetadataCatalog.get(CHIMPNSEE_DATASET_NAME).set( + dataset_type=DatasetType.VIDEO_LIST, + video_list_fpath=video_list_fpath, + video_base_path=video_base_path, + category="chimpanzee", + ) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/coco.py b/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/coco.py new file mode 100644 index 0000000..c19f7b0 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/coco.py @@ -0,0 +1,432 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import contextlib +import io +import logging +import os +from collections import defaultdict +from dataclasses import dataclass +from typing import Any, Dict, Iterable, List, Optional +from fvcore.common.timer import Timer + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.structures import BoxMode +from detectron2.utils.file_io import PathManager + +from ..utils import maybe_prepend_base_path + +DENSEPOSE_MASK_KEY = "dp_masks" +DENSEPOSE_IUV_KEYS_WITHOUT_MASK = ["dp_x", "dp_y", "dp_I", "dp_U", "dp_V"] +DENSEPOSE_CSE_KEYS_WITHOUT_MASK = ["dp_x", "dp_y", "dp_vertex", "ref_model"] +DENSEPOSE_ALL_POSSIBLE_KEYS = set( + DENSEPOSE_IUV_KEYS_WITHOUT_MASK + DENSEPOSE_CSE_KEYS_WITHOUT_MASK + [DENSEPOSE_MASK_KEY] +) +DENSEPOSE_METADATA_URL_PREFIX = "https://dl.fbaipublicfiles.com/densepose/data/" + + +@dataclass +class CocoDatasetInfo: + name: str + images_root: str + annotations_fpath: str + + +DATASETS = [ + CocoDatasetInfo( + name="densepose_coco_2014_train", + images_root="coco/train2014", + annotations_fpath="coco/annotations/densepose_train2014.json", + ), + CocoDatasetInfo( + name="densepose_coco_2014_minival", + images_root="coco/val2014", + annotations_fpath="coco/annotations/densepose_minival2014.json", + ), + CocoDatasetInfo( + name="densepose_coco_2014_minival_100", + images_root="coco/val2014", + annotations_fpath="coco/annotations/densepose_minival2014_100.json", + ), + CocoDatasetInfo( + name="densepose_coco_2014_valminusminival", + images_root="coco/val2014", + annotations_fpath="coco/annotations/densepose_valminusminival2014.json", + ), + CocoDatasetInfo( + name="densepose_coco_2014_train_cse", + images_root="coco/train2014", + annotations_fpath="coco_cse/densepose_train2014_cse.json", + ), + CocoDatasetInfo( + name="densepose_coco_2014_minival_cse", + images_root="coco/val2014", + annotations_fpath="coco_cse/densepose_minival2014_cse.json", + ), + CocoDatasetInfo( + name="densepose_coco_2014_minival_100_cse", + images_root="coco/val2014", + annotations_fpath="coco_cse/densepose_minival2014_100_cse.json", + ), + CocoDatasetInfo( + name="densepose_coco_2014_valminusminival_cse", + images_root="coco/val2014", + annotations_fpath="coco_cse/densepose_valminusminival2014_cse.json", + ), + CocoDatasetInfo( + name="densepose_chimps", + images_root="densepose_chimps/images", + annotations_fpath="densepose_chimps/densepose_chimps_densepose.json", + ), + CocoDatasetInfo( + name="densepose_chimps_cse_train", + images_root="densepose_chimps/images", + annotations_fpath="densepose_chimps/densepose_chimps_cse_train.json", + ), + CocoDatasetInfo( + name="densepose_chimps_cse_val", + images_root="densepose_chimps/images", + annotations_fpath="densepose_chimps/densepose_chimps_cse_val.json", + ), + CocoDatasetInfo( + name="posetrack2017_train", + images_root="posetrack2017/posetrack_data_2017", + annotations_fpath="posetrack2017/densepose_posetrack_train2017.json", + ), + CocoDatasetInfo( + name="posetrack2017_val", + images_root="posetrack2017/posetrack_data_2017", + annotations_fpath="posetrack2017/densepose_posetrack_val2017.json", + ), + CocoDatasetInfo( + name="lvis_v05_train", + images_root="coco/train2017", + annotations_fpath="lvis/lvis_v0.5_plus_dp_train.json", + ), + CocoDatasetInfo( + name="lvis_v05_val", + images_root="coco/val2017", + annotations_fpath="lvis/lvis_v0.5_plus_dp_val.json", + ), +] + + +BASE_DATASETS = [ + CocoDatasetInfo( + name="base_coco_2017_train", + images_root="coco/train2017", + annotations_fpath="coco/annotations/instances_train2017.json", + ), + CocoDatasetInfo( + name="base_coco_2017_val", + images_root="coco/val2017", + annotations_fpath="coco/annotations/instances_val2017.json", + ), + CocoDatasetInfo( + name="base_coco_2017_val_100", + images_root="coco/val2017", + annotations_fpath="coco/annotations/instances_val2017_100.json", + ), +] + + +def get_metadata(base_path: Optional[str]) -> Dict[str, Any]: + """ + Returns metadata associated with COCO DensePose datasets + + Args: + base_path: Optional[str] + Base path used to load metadata from + + Returns: + Dict[str, Any] + Metadata in the form of a dictionary + """ + meta = { + "densepose_transform_src": maybe_prepend_base_path(base_path, "UV_symmetry_transforms.mat"), + "densepose_smpl_subdiv": maybe_prepend_base_path(base_path, "SMPL_subdiv.mat"), + "densepose_smpl_subdiv_transform": maybe_prepend_base_path( + base_path, + "SMPL_SUBDIV_TRANSFORM.mat", + ), + } + return meta + + +def _load_coco_annotations(json_file: str): + """ + Load COCO annotations from a JSON file + + Args: + json_file: str + Path to the file to load annotations from + Returns: + Instance of `pycocotools.coco.COCO` that provides access to annotations + data + """ + from pycocotools.coco import COCO + + logger = logging.getLogger(__name__) + timer = Timer() + with contextlib.redirect_stdout(io.StringIO()): + coco_api = COCO(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) + return coco_api + + +def _add_categories_metadata(dataset_name: str, categories: List[Dict[str, Any]]): + meta = MetadataCatalog.get(dataset_name) + meta.categories = {c["id"]: c["name"] for c in categories} + logger = logging.getLogger(__name__) + logger.info("Dataset {} categories: {}".format(dataset_name, meta.categories)) + + +def _verify_annotations_have_unique_ids(json_file: str, anns: List[List[Dict[str, Any]]]): + if "minival" in json_file: + # Skip validation on COCO2014 valminusminival and minival annotations + # The ratio of buggy annotations there is tiny and does not affect accuracy + # Therefore we explicitly white-list them + return + ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] + assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format( + json_file + ) + + +def _maybe_add_bbox(obj: Dict[str, Any], ann_dict: Dict[str, Any]): + if "bbox" not in ann_dict: + return + obj["bbox"] = ann_dict["bbox"] + obj["bbox_mode"] = BoxMode.XYWH_ABS + + +def _maybe_add_segm(obj: Dict[str, Any], ann_dict: Dict[str, Any]): + if "segmentation" not in ann_dict: + return + segm = ann_dict["segmentation"] + if not isinstance(segm, dict): + # filter out invalid polygons (< 3 points) + segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + if len(segm) == 0: + return + obj["segmentation"] = segm + + +def _maybe_add_keypoints(obj: Dict[str, Any], ann_dict: Dict[str, Any]): + if "keypoints" not in ann_dict: + return + keypts = ann_dict["keypoints"] # list[int] + for idx, v in enumerate(keypts): + if idx % 3 != 2: + # COCO's segmentation coordinates are floating points in [0, H or W], + # but keypoint coordinates are integers in [0, H-1 or W-1] + # Therefore we assume the coordinates are "pixel indices" and + # add 0.5 to convert to floating point coordinates. + keypts[idx] = v + 0.5 + obj["keypoints"] = keypts + + +def _maybe_add_densepose(obj: Dict[str, Any], ann_dict: Dict[str, Any]): + for key in DENSEPOSE_ALL_POSSIBLE_KEYS: + if key in ann_dict: + obj[key] = ann_dict[key] + + +def _combine_images_with_annotations( + dataset_name: str, + image_root: str, + img_datas: Iterable[Dict[str, Any]], + ann_datas: Iterable[Iterable[Dict[str, Any]]], +): + + ann_keys = ["iscrowd", "category_id"] + dataset_dicts = [] + contains_video_frame_info = False + + for img_dict, ann_dicts in zip(img_datas, ann_datas): + record = {} + record["file_name"] = os.path.join(image_root, img_dict["file_name"]) + record["height"] = img_dict["height"] + record["width"] = img_dict["width"] + record["image_id"] = img_dict["id"] + record["dataset"] = dataset_name + if "frame_id" in img_dict: + record["frame_id"] = img_dict["frame_id"] + record["video_id"] = img_dict.get("vid_id", None) + contains_video_frame_info = True + objs = [] + for ann_dict in ann_dicts: + assert ann_dict["image_id"] == record["image_id"] + assert ann_dict.get("ignore", 0) == 0 + obj = {key: ann_dict[key] for key in ann_keys if key in ann_dict} + _maybe_add_bbox(obj, ann_dict) + _maybe_add_segm(obj, ann_dict) + _maybe_add_keypoints(obj, ann_dict) + _maybe_add_densepose(obj, ann_dict) + objs.append(obj) + record["annotations"] = objs + dataset_dicts.append(record) + if contains_video_frame_info: + create_video_frame_mapping(dataset_name, dataset_dicts) + return dataset_dicts + + +def get_contiguous_id_to_category_id_map(metadata): + cat_id_2_cont_id = metadata.thing_dataset_id_to_contiguous_id + cont_id_2_cat_id = {} + for cat_id, cont_id in cat_id_2_cont_id.items(): + if cont_id in cont_id_2_cat_id: + continue + cont_id_2_cat_id[cont_id] = cat_id + return cont_id_2_cat_id + + +def maybe_filter_categories_cocoapi(dataset_name, coco_api): + meta = MetadataCatalog.get(dataset_name) + cont_id_2_cat_id = get_contiguous_id_to_category_id_map(meta) + cat_id_2_cont_id = meta.thing_dataset_id_to_contiguous_id + # filter categories + cats = [] + for cat in coco_api.dataset["categories"]: + cat_id = cat["id"] + if cat_id not in cat_id_2_cont_id: + continue + cont_id = cat_id_2_cont_id[cat_id] + if (cont_id in cont_id_2_cat_id) and (cont_id_2_cat_id[cont_id] == cat_id): + cats.append(cat) + coco_api.dataset["categories"] = cats + # filter annotations, if multiple categories are mapped to a single + # contiguous ID, use only one category ID and map all annotations to that category ID + anns = [] + for ann in coco_api.dataset["annotations"]: + cat_id = ann["category_id"] + if cat_id not in cat_id_2_cont_id: + continue + cont_id = cat_id_2_cont_id[cat_id] + ann["category_id"] = cont_id_2_cat_id[cont_id] + anns.append(ann) + coco_api.dataset["annotations"] = anns + # recreate index + coco_api.createIndex() + + +def maybe_filter_and_map_categories_cocoapi(dataset_name, coco_api): + meta = MetadataCatalog.get(dataset_name) + category_id_map = meta.thing_dataset_id_to_contiguous_id + # map categories + cats = [] + for cat in coco_api.dataset["categories"]: + cat_id = cat["id"] + if cat_id not in category_id_map: + continue + cat["id"] = category_id_map[cat_id] + cats.append(cat) + coco_api.dataset["categories"] = cats + # map annotation categories + anns = [] + for ann in coco_api.dataset["annotations"]: + cat_id = ann["category_id"] + if cat_id not in category_id_map: + continue + ann["category_id"] = category_id_map[cat_id] + anns.append(ann) + coco_api.dataset["annotations"] = anns + # recreate index + coco_api.createIndex() + + +def create_video_frame_mapping(dataset_name, dataset_dicts): + mapping = defaultdict(dict) + for d in dataset_dicts: + video_id = d.get("video_id") + if video_id is None: + continue + mapping[video_id].update({d["frame_id"]: d["file_name"]}) + MetadataCatalog.get(dataset_name).set(video_frame_mapping=mapping) + + +def load_coco_json(annotations_json_file: str, image_root: str, dataset_name: str): + """ + Loads a JSON file with annotations in COCO instances format. + Replaces `detectron2.data.datasets.coco.load_coco_json` to handle metadata + in a more flexible way. Postpones category mapping to a later stage to be + able to combine several datasets with different (but coherent) sets of + categories. + + Args: + + annotations_json_file: str + Path to the JSON file with annotations in COCO instances format. + image_root: str + directory that contains all the images + dataset_name: str + the name that identifies a dataset, e.g. "densepose_coco_2014_train" + extra_annotation_keys: Optional[List[str]] + If provided, these keys are used to extract additional data from + the annotations. + """ + coco_api = _load_coco_annotations(PathManager.get_local_path(annotations_json_file)) + _add_categories_metadata(dataset_name, coco_api.loadCats(coco_api.getCatIds())) + # sort indices for reproducible results + img_ids = sorted(coco_api.imgs.keys()) + # imgs is a list of dicts, each looks something like: + # {'license': 4, + # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', + # 'file_name': 'COCO_val2014_000000001268.jpg', + # 'height': 427, + # 'width': 640, + # 'date_captured': '2013-11-17 05:57:24', + # 'id': 1268} + imgs = coco_api.loadImgs(img_ids) + logger = logging.getLogger(__name__) + logger.info("Loaded {} images in COCO format from {}".format(len(imgs), annotations_json_file)) + # anns is a list[list[dict]], where each dict is an annotation + # record for an object. The inner list enumerates the objects in an image + # and the outer list enumerates over images. + anns = [coco_api.imgToAnns[img_id] for img_id in img_ids] + _verify_annotations_have_unique_ids(annotations_json_file, anns) + dataset_records = _combine_images_with_annotations(dataset_name, image_root, imgs, anns) + return dataset_records + + +def register_dataset(dataset_data: CocoDatasetInfo, datasets_root: Optional[str] = None): + """ + Registers provided COCO DensePose dataset + + Args: + dataset_data: CocoDatasetInfo + Dataset data + datasets_root: Optional[str] + Datasets root folder (default: None) + """ + annotations_fpath = maybe_prepend_base_path(datasets_root, dataset_data.annotations_fpath) + images_root = maybe_prepend_base_path(datasets_root, dataset_data.images_root) + + def load_annotations(): + return load_coco_json( + annotations_json_file=annotations_fpath, + image_root=images_root, + dataset_name=dataset_data.name, + ) + + DatasetCatalog.register(dataset_data.name, load_annotations) + MetadataCatalog.get(dataset_data.name).set( + json_file=annotations_fpath, + image_root=images_root, + **get_metadata(DENSEPOSE_METADATA_URL_PREFIX) + ) + + +def register_datasets( + datasets_data: Iterable[CocoDatasetInfo], datasets_root: Optional[str] = None +): + """ + Registers provided COCO DensePose datasets + + Args: + datasets_data: Iterable[CocoDatasetInfo] + An iterable of dataset datas + datasets_root: Optional[str] + Datasets root folder (default: None) + """ + for dataset_data in datasets_data: + register_dataset(dataset_data, datasets_root) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/dataset_type.py b/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/dataset_type.py new file mode 100644 index 0000000..ed8f8f2 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/dataset_type.py @@ -0,0 +1,11 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from enum import Enum + + +class DatasetType(Enum): + """ + Dataset type, mostly used for datasets that contain data to bootstrap models on + """ + + VIDEO_LIST = "video_list" diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/lvis.py b/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/lvis.py new file mode 100644 index 0000000..b4af9fa --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/datasets/lvis.py @@ -0,0 +1,257 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import os +from typing import Any, Dict, Iterable, List, Optional +from fvcore.common.timer import Timer + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets.lvis import get_lvis_instances_meta +from detectron2.structures import BoxMode +from detectron2.utils.file_io import PathManager + +from ..utils import maybe_prepend_base_path +from .coco import ( + DENSEPOSE_ALL_POSSIBLE_KEYS, + DENSEPOSE_METADATA_URL_PREFIX, + CocoDatasetInfo, + get_metadata, +) + +DATASETS = [ + CocoDatasetInfo( + name="densepose_lvis_v1_ds1_train_v1", + images_root="coco_", + annotations_fpath="lvis/densepose_lvis_v1_ds1_train_v1.json", + ), + CocoDatasetInfo( + name="densepose_lvis_v1_ds1_val_v1", + images_root="coco_", + annotations_fpath="lvis/densepose_lvis_v1_ds1_val_v1.json", + ), + CocoDatasetInfo( + name="densepose_lvis_v1_ds2_train_v1", + images_root="coco_", + annotations_fpath="lvis/densepose_lvis_v1_ds2_train_v1.json", + ), + CocoDatasetInfo( + name="densepose_lvis_v1_ds2_val_v1", + images_root="coco_", + annotations_fpath="lvis/densepose_lvis_v1_ds2_val_v1.json", + ), + CocoDatasetInfo( + name="densepose_lvis_v1_ds1_val_animals_100", + images_root="coco_", + annotations_fpath="lvis/densepose_lvis_v1_val_animals_100_v2.json", + ), +] + + +def _load_lvis_annotations(json_file: str): + """ + Load COCO annotations from a JSON file + + Args: + json_file: str + Path to the file to load annotations from + Returns: + Instance of `pycocotools.coco.COCO` that provides access to annotations + data + """ + from lvis import LVIS + + json_file = PathManager.get_local_path(json_file) + logger = logging.getLogger(__name__) + timer = Timer() + lvis_api = LVIS(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) + return lvis_api + + +def _add_categories_metadata(dataset_name: str) -> None: + metadict = get_lvis_instances_meta(dataset_name) + categories = metadict["thing_classes"] + metadata = MetadataCatalog.get(dataset_name) + metadata.categories = {i + 1: categories[i] for i in range(len(categories))} + logger = logging.getLogger(__name__) + logger.info(f"Dataset {dataset_name} has {len(categories)} categories") + + +def _verify_annotations_have_unique_ids(json_file: str, anns: List[List[Dict[str, Any]]]) -> None: + ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] + assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format( + json_file + ) + + +def _maybe_add_bbox(obj: Dict[str, Any], ann_dict: Dict[str, Any]) -> None: + if "bbox" not in ann_dict: + return + obj["bbox"] = ann_dict["bbox"] + obj["bbox_mode"] = BoxMode.XYWH_ABS + + +def _maybe_add_segm(obj: Dict[str, Any], ann_dict: Dict[str, Any]) -> None: + if "segmentation" not in ann_dict: + return + segm = ann_dict["segmentation"] + if not isinstance(segm, dict): + # filter out invalid polygons (< 3 points) + segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + if len(segm) == 0: + return + obj["segmentation"] = segm + + +def _maybe_add_keypoints(obj: Dict[str, Any], ann_dict: Dict[str, Any]) -> None: + if "keypoints" not in ann_dict: + return + keypts = ann_dict["keypoints"] # list[int] + for idx, v in enumerate(keypts): + if idx % 3 != 2: + # COCO's segmentation coordinates are floating points in [0, H or W], + # but keypoint coordinates are integers in [0, H-1 or W-1] + # Therefore we assume the coordinates are "pixel indices" and + # add 0.5 to convert to floating point coordinates. + keypts[idx] = v + 0.5 + obj["keypoints"] = keypts + + +def _maybe_add_densepose(obj: Dict[str, Any], ann_dict: Dict[str, Any]) -> None: + for key in DENSEPOSE_ALL_POSSIBLE_KEYS: + if key in ann_dict: + obj[key] = ann_dict[key] + + +def _combine_images_with_annotations( + dataset_name: str, + image_root: str, + img_datas: Iterable[Dict[str, Any]], + ann_datas: Iterable[Iterable[Dict[str, Any]]], +): + + dataset_dicts = [] + + def get_file_name(img_root, img_dict): + # Determine the path including the split folder ("train2017", "val2017", "test2017") from + # the coco_url field. Example: + # 'coco_url': 'http://images.cocodataset.org/train2017/000000155379.jpg' + split_folder, file_name = img_dict["coco_url"].split("/")[-2:] + return os.path.join(img_root + split_folder, file_name) + + for img_dict, ann_dicts in zip(img_datas, ann_datas): + record = {} + record["file_name"] = get_file_name(image_root, img_dict) + record["height"] = img_dict["height"] + record["width"] = img_dict["width"] + record["not_exhaustive_category_ids"] = img_dict.get("not_exhaustive_category_ids", []) + record["neg_category_ids"] = img_dict.get("neg_category_ids", []) + record["image_id"] = img_dict["id"] + record["dataset"] = dataset_name + + objs = [] + for ann_dict in ann_dicts: + assert ann_dict["image_id"] == record["image_id"] + obj = {} + _maybe_add_bbox(obj, ann_dict) + obj["iscrowd"] = ann_dict.get("iscrowd", 0) + obj["category_id"] = ann_dict["category_id"] + _maybe_add_segm(obj, ann_dict) + _maybe_add_keypoints(obj, ann_dict) + _maybe_add_densepose(obj, ann_dict) + objs.append(obj) + record["annotations"] = objs + dataset_dicts.append(record) + return dataset_dicts + + +def load_lvis_json(annotations_json_file: str, image_root: str, dataset_name: str): + """ + Loads a JSON file with annotations in LVIS instances format. + Replaces `detectron2.data.datasets.coco.load_lvis_json` to handle metadata + in a more flexible way. Postpones category mapping to a later stage to be + able to combine several datasets with different (but coherent) sets of + categories. + + Args: + + annotations_json_file: str + Path to the JSON file with annotations in COCO instances format. + image_root: str + directory that contains all the images + dataset_name: str + the name that identifies a dataset, e.g. "densepose_coco_2014_train" + extra_annotation_keys: Optional[List[str]] + If provided, these keys are used to extract additional data from + the annotations. + """ + lvis_api = _load_lvis_annotations(PathManager.get_local_path(annotations_json_file)) + + _add_categories_metadata(dataset_name) + + # sort indices for reproducible results + img_ids = sorted(lvis_api.imgs.keys()) + # imgs is a list of dicts, each looks something like: + # {'license': 4, + # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', + # 'file_name': 'COCO_val2014_000000001268.jpg', + # 'height': 427, + # 'width': 640, + # 'date_captured': '2013-11-17 05:57:24', + # 'id': 1268} + imgs = lvis_api.load_imgs(img_ids) + logger = logging.getLogger(__name__) + logger.info("Loaded {} images in LVIS format from {}".format(len(imgs), annotations_json_file)) + # anns is a list[list[dict]], where each dict is an annotation + # record for an object. The inner list enumerates the objects in an image + # and the outer list enumerates over images. + anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids] + + _verify_annotations_have_unique_ids(annotations_json_file, anns) + dataset_records = _combine_images_with_annotations(dataset_name, image_root, imgs, anns) + return dataset_records + + +def register_dataset(dataset_data: CocoDatasetInfo, datasets_root: Optional[str] = None) -> None: + """ + Registers provided LVIS DensePose dataset + + Args: + dataset_data: CocoDatasetInfo + Dataset data + datasets_root: Optional[str] + Datasets root folder (default: None) + """ + annotations_fpath = maybe_prepend_base_path(datasets_root, dataset_data.annotations_fpath) + images_root = maybe_prepend_base_path(datasets_root, dataset_data.images_root) + + def load_annotations(): + return load_lvis_json( + annotations_json_file=annotations_fpath, + image_root=images_root, + dataset_name=dataset_data.name, + ) + + DatasetCatalog.register(dataset_data.name, load_annotations) + MetadataCatalog.get(dataset_data.name).set( + json_file=annotations_fpath, + image_root=images_root, + evaluator_type="lvis", + **get_metadata(DENSEPOSE_METADATA_URL_PREFIX), + ) + + +def register_datasets( + datasets_data: Iterable[CocoDatasetInfo], datasets_root: Optional[str] = None +) -> None: + """ + Registers provided LVIS DensePose datasets + + Args: + datasets_data: Iterable[CocoDatasetInfo] + An iterable of dataset datas + datasets_root: Optional[str] + Datasets root folder (default: None) + """ + for dataset_data in datasets_data: + register_dataset(dataset_data, datasets_root) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/image_list_dataset.py b/motion-gan-pipeline/preprocessing/third/densepose/data/image_list_dataset.py new file mode 100644 index 0000000..92a95d3 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/image_list_dataset.py @@ -0,0 +1,72 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import logging +import numpy as np +from typing import Any, Callable, Dict, List, Optional, Union +import torch +from torch.utils.data.dataset import Dataset + +from detectron2.data.detection_utils import read_image + +ImageTransform = Callable[[torch.Tensor], torch.Tensor] + + +class ImageListDataset(Dataset): + """ + Dataset that provides images from a list. + """ + + _EMPTY_IMAGE = torch.empty((0, 3, 1, 1)) + + def __init__( + self, + image_list: List[str], + category_list: Union[str, List[str], None] = None, + transform: Optional[ImageTransform] = None, + ): + """ + Args: + image_list (List[str]): list of paths to image files + category_list (Union[str, List[str], None]): list of animal categories for + each image. If it is a string, or None, this applies to all images + """ + if type(category_list) == list: + self.category_list = category_list + else: + self.category_list = [category_list] * len(image_list) + assert len(image_list) == len( + self.category_list + ), "length of image and category lists must be equal" + self.image_list = image_list + self.transform = transform + + def __getitem__(self, idx: int) -> Dict[str, Any]: + """ + Gets selected images from the list + + Args: + idx (int): video index in the video list file + Returns: + A dictionary containing two keys: + images (torch.Tensor): tensor of size [N, 3, H, W] (N = 1, or 0 for _EMPTY_IMAGE) + categories (List[str]): categories of the frames + """ + categories = [self.category_list[idx]] + fpath = self.image_list[idx] + transform = self.transform + + try: + image = torch.from_numpy(np.ascontiguousarray(read_image(fpath, format="BGR"))) + image = image.permute(2, 0, 1).unsqueeze(0).float() # HWC -> NCHW + if transform is not None: + image = transform(image) + return {"images": image, "categories": categories} + except (OSError, RuntimeError) as e: + logger = logging.getLogger(__name__) + logger.warning(f"Error opening image file container {fpath}: {e}") + + return {"images": self._EMPTY_IMAGE, "categories": []} + + def __len__(self): + return len(self.image_list) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/inference_based_loader.py b/motion-gan-pipeline/preprocessing/third/densepose/data/inference_based_loader.py new file mode 100644 index 0000000..cb89544 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/inference_based_loader.py @@ -0,0 +1,172 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import random +from typing import Any, Callable, Dict, Iterable, Iterator, List, Optional, Tuple +import torch +from torch import nn + +SampledData = Any +ModelOutput = Any + + +def _grouper(iterable: Iterable[Any], n: int, fillvalue=None) -> Iterator[Tuple[Any]]: + """ + Group elements of an iterable by chunks of size `n`, e.g. + grouper(range(9), 4) -> + (0, 1, 2, 3), (4, 5, 6, 7), (8, None, None, None) + """ + it = iter(iterable) + while True: + values = [] + for _ in range(n): + try: + value = next(it) + except StopIteration: + if values: + values.extend([fillvalue] * (n - len(values))) + yield tuple(values) + return + values.append(value) + yield tuple(values) + + +class ScoreBasedFilter: + """ + Filters entries in model output based on their scores + Discards all entries with score less than the specified minimum + """ + + def __init__(self, min_score: float = 0.8): + self.min_score = min_score + + def __call__(self, model_output: ModelOutput) -> ModelOutput: + for model_output_i in model_output: + instances = model_output_i["instances"] + if not instances.has("scores"): + continue + instances_filtered = instances[instances.scores >= self.min_score] + model_output_i["instances"] = instances_filtered + return model_output + + +class InferenceBasedLoader: + """ + Data loader based on results inferred by a model. Consists of: + - a data loader that provides batches of images + - a model that is used to infer the results + - a data sampler that converts inferred results to annotations + """ + + def __init__( + self, + model: nn.Module, + data_loader: Iterable[List[Dict[str, Any]]], + data_sampler: Optional[Callable[[ModelOutput], List[SampledData]]] = None, + data_filter: Optional[Callable[[ModelOutput], ModelOutput]] = None, + shuffle: bool = True, + batch_size: int = 4, + inference_batch_size: int = 4, + drop_last: bool = False, + category_to_class_mapping: Optional[dict] = None, + ): + """ + Constructor + + Args: + model (torch.nn.Module): model used to produce data + data_loader (Iterable[List[Dict[str, Any]]]): iterable that provides + dictionaries with "images" and "categories" fields to perform inference on + data_sampler (Callable: ModelOutput -> SampledData): functor + that produces annotation data from inference results; + (optional, default: None) + data_filter (Callable: ModelOutput -> ModelOutput): filter + that selects model outputs for further processing + (optional, default: None) + shuffle (bool): if True, the input images get shuffled + batch_size (int): batch size for the produced annotation data + inference_batch_size (int): batch size for input images + drop_last (bool): if True, drop the last batch if it is undersized + category_to_class_mapping (dict): category to class mapping + """ + self.model = model + self.model.eval() + self.data_loader = data_loader + self.data_sampler = data_sampler + self.data_filter = data_filter + self.shuffle = shuffle + self.batch_size = batch_size + self.inference_batch_size = inference_batch_size + self.drop_last = drop_last + if category_to_class_mapping is not None: + self.category_to_class_mapping = category_to_class_mapping + else: + self.category_to_class_mapping = {} + + def __iter__(self) -> Iterator[List[SampledData]]: + for batch in self.data_loader: + # batch : List[Dict[str: Tensor[N, C, H, W], str: Optional[str]]] + # images_batch : Tensor[N, C, H, W] + # image : Tensor[C, H, W] + images_and_categories = [ + {"image": image, "category": category} + for element in batch + for image, category in zip(element["images"], element["categories"]) + ] + if not images_and_categories: + continue + if self.shuffle: + random.shuffle(images_and_categories) + yield from self._produce_data(images_and_categories) # pyre-ignore[6] + + def _produce_data( + self, images_and_categories: List[Tuple[torch.Tensor, Optional[str]]] + ) -> Iterator[List[SampledData]]: + """ + Produce batches of data from images + + Args: + images_and_categories (List[Tuple[torch.Tensor, Optional[str]]]): + list of images and corresponding categories to process + + Returns: + Iterator over batches of data sampled from model outputs + """ + data_batches: List[SampledData] = [] + category_to_class_mapping = self.category_to_class_mapping + batched_images_and_categories = _grouper(images_and_categories, self.inference_batch_size) + for batch in batched_images_and_categories: + batch = [ + { + "image": image_and_category["image"].to(self.model.device), + "category": image_and_category["category"], + } + for image_and_category in batch + if image_and_category is not None + ] + if not batch: + continue + with torch.no_grad(): + model_output = self.model(batch) + for model_output_i, batch_i in zip(model_output, batch): + assert len(batch_i["image"].shape) == 3 + model_output_i["image"] = batch_i["image"] + instance_class = category_to_class_mapping.get(batch_i["category"], 0) + model_output_i["instances"].dataset_classes = torch.tensor( + [instance_class] * len(model_output_i["instances"]) + ) + model_output_filtered = ( + model_output if self.data_filter is None else self.data_filter(model_output) + ) + data = ( + model_output_filtered + if self.data_sampler is None + else self.data_sampler(model_output_filtered) + ) + for data_i in data: + if len(data_i["instances"]): + data_batches.append(data_i) + if len(data_batches) >= self.batch_size: + yield data_batches[: self.batch_size] + data_batches = data_batches[self.batch_size :] + if not self.drop_last and data_batches: + yield data_batches diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/meshes/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/data/meshes/__init__.py new file mode 100644 index 0000000..1e1f0d5 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/meshes/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +from . import builtin + +__all__ = [k for k in globals().keys() if "builtin" not in k and not k.startswith("_")] diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/meshes/builtin.py b/motion-gan-pipeline/preprocessing/third/densepose/data/meshes/builtin.py new file mode 100644 index 0000000..c0b2376 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/meshes/builtin.py @@ -0,0 +1,101 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +from .catalog import MeshInfo, register_meshes + +DENSEPOSE_MESHES_DIR = "https://dl.fbaipublicfiles.com/densepose/meshes/" + +MESHES = [ + MeshInfo( + name="smpl_27554", + data="smpl_27554.pkl", + geodists="geodists/geodists_smpl_27554.pkl", + symmetry="symmetry/symmetry_smpl_27554.pkl", + texcoords="texcoords/texcoords_smpl_27554.pkl", + ), + MeshInfo( + name="chimp_5029", + data="chimp_5029.pkl", + geodists="geodists/geodists_chimp_5029.pkl", + symmetry="symmetry/symmetry_chimp_5029.pkl", + texcoords="texcoords/texcoords_chimp_5029.pkl", + ), + MeshInfo( + name="cat_5001", + data="cat_5001.pkl", + geodists="geodists/geodists_cat_5001.pkl", + symmetry="symmetry/symmetry_cat_5001.pkl", + texcoords="texcoords/texcoords_cat_5001.pkl", + ), + MeshInfo( + name="cat_7466", + data="cat_7466.pkl", + geodists="geodists/geodists_cat_7466.pkl", + symmetry="symmetry/symmetry_cat_7466.pkl", + texcoords="texcoords/texcoords_cat_7466.pkl", + ), + MeshInfo( + name="sheep_5004", + data="sheep_5004.pkl", + geodists="geodists/geodists_sheep_5004.pkl", + symmetry="symmetry/symmetry_sheep_5004.pkl", + texcoords="texcoords/texcoords_sheep_5004.pkl", + ), + MeshInfo( + name="zebra_5002", + data="zebra_5002.pkl", + geodists="geodists/geodists_zebra_5002.pkl", + symmetry="symmetry/symmetry_zebra_5002.pkl", + texcoords="texcoords/texcoords_zebra_5002.pkl", + ), + MeshInfo( + name="horse_5004", + data="horse_5004.pkl", + geodists="geodists/geodists_horse_5004.pkl", + symmetry="symmetry/symmetry_horse_5004.pkl", + texcoords="texcoords/texcoords_zebra_5002.pkl", + ), + MeshInfo( + name="giraffe_5002", + data="giraffe_5002.pkl", + geodists="geodists/geodists_giraffe_5002.pkl", + symmetry="symmetry/symmetry_giraffe_5002.pkl", + texcoords="texcoords/texcoords_giraffe_5002.pkl", + ), + MeshInfo( + name="elephant_5002", + data="elephant_5002.pkl", + geodists="geodists/geodists_elephant_5002.pkl", + symmetry="symmetry/symmetry_elephant_5002.pkl", + texcoords="texcoords/texcoords_elephant_5002.pkl", + ), + MeshInfo( + name="dog_5002", + data="dog_5002.pkl", + geodists="geodists/geodists_dog_5002.pkl", + symmetry="symmetry/symmetry_dog_5002.pkl", + texcoords="texcoords/texcoords_dog_5002.pkl", + ), + MeshInfo( + name="dog_7466", + data="dog_7466.pkl", + geodists="geodists/geodists_dog_7466.pkl", + symmetry="symmetry/symmetry_dog_7466.pkl", + texcoords="texcoords/texcoords_dog_7466.pkl", + ), + MeshInfo( + name="cow_5002", + data="cow_5002.pkl", + geodists="geodists/geodists_cow_5002.pkl", + symmetry="symmetry/symmetry_cow_5002.pkl", + texcoords="texcoords/texcoords_cow_5002.pkl", + ), + MeshInfo( + name="bear_4936", + data="bear_4936.pkl", + geodists="geodists/geodists_bear_4936.pkl", + symmetry="symmetry/symmetry_bear_4936.pkl", + texcoords="texcoords/texcoords_bear_4936.pkl", + ), +] + +register_meshes(MESHES, DENSEPOSE_MESHES_DIR) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/meshes/catalog.py b/motion-gan-pipeline/preprocessing/third/densepose/data/meshes/catalog.py new file mode 100644 index 0000000..b258f3c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/meshes/catalog.py @@ -0,0 +1,71 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import logging +from collections import UserDict +from dataclasses import dataclass +from typing import Iterable, Optional + +from ..utils import maybe_prepend_base_path + + +@dataclass +class MeshInfo: + name: str + data: str + geodists: Optional[str] = None + symmetry: Optional[str] = None + texcoords: Optional[str] = None + + +class _MeshCatalog(UserDict): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.mesh_ids = {} + self.mesh_names = {} + self.max_mesh_id = -1 + + def __setitem__(self, key, value): + if key in self: + logger = logging.getLogger(__name__) + logger.warning( + f"Overwriting mesh catalog entry '{key}': old value {self[key]}" + f", new value {value}" + ) + mesh_id = self.mesh_ids[key] + else: + self.max_mesh_id += 1 + mesh_id = self.max_mesh_id + super().__setitem__(key, value) + self.mesh_ids[key] = mesh_id + self.mesh_names[mesh_id] = key + + def get_mesh_id(self, shape_name: str) -> int: + return self.mesh_ids[shape_name] + + def get_mesh_name(self, mesh_id: int) -> str: + return self.mesh_names[mesh_id] + + +MeshCatalog = _MeshCatalog() + + +def register_mesh(mesh_info: MeshInfo, base_path: Optional[str]) -> None: + geodists, symmetry, texcoords = mesh_info.geodists, mesh_info.symmetry, mesh_info.texcoords + if geodists: + geodists = maybe_prepend_base_path(base_path, geodists) + if symmetry: + symmetry = maybe_prepend_base_path(base_path, symmetry) + if texcoords: + texcoords = maybe_prepend_base_path(base_path, texcoords) + MeshCatalog[mesh_info.name] = MeshInfo( + name=mesh_info.name, + data=maybe_prepend_base_path(base_path, mesh_info.data), + geodists=geodists, + symmetry=symmetry, + texcoords=texcoords, + ) + + +def register_meshes(mesh_infos: Iterable[MeshInfo], base_path: Optional[str]) -> None: + for mesh_info in mesh_infos: + register_mesh(mesh_info, base_path) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/__init__.py new file mode 100644 index 0000000..7dba87e --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from .densepose_uniform import DensePoseUniformSampler +from .densepose_confidence_based import DensePoseConfidenceBasedSampler +from .densepose_cse_uniform import DensePoseCSEUniformSampler +from .densepose_cse_confidence_based import DensePoseCSEConfidenceBasedSampler +from .mask_from_densepose import MaskFromDensePoseSampler +from .prediction_to_gt import PredictionToGroundTruthSampler diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_base.py b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_base.py new file mode 100644 index 0000000..eaf4780 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_base.py @@ -0,0 +1,205 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from typing import Any, Dict, List, Tuple +import torch +from torch.nn import functional as F + +from detectron2.structures import BoxMode, Instances + +from densepose.converters import ToChartResultConverter +from densepose.converters.base import IntTupleBox, make_int_box +from densepose.structures import DensePoseDataRelative, DensePoseList + + +class DensePoseBaseSampler: + """ + Base DensePose sampler to produce DensePose data from DensePose predictions. + Samples for each class are drawn according to some distribution over all pixels estimated + to belong to that class. + """ + + def __init__(self, count_per_class: int = 8): + """ + Constructor + + Args: + count_per_class (int): the sampler produces at most `count_per_class` + samples for each category + """ + self.count_per_class = count_per_class + + def __call__(self, instances: Instances) -> DensePoseList: + """ + Convert DensePose predictions (an instance of `DensePoseChartPredictorOutput`) + into DensePose annotations data (an instance of `DensePoseList`) + """ + boxes_xyxy_abs = instances.pred_boxes.tensor.clone().cpu() + boxes_xywh_abs = BoxMode.convert(boxes_xyxy_abs, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + dp_datas = [] + for i in range(len(boxes_xywh_abs)): + annotation_i = self._sample(instances[i], make_int_box(boxes_xywh_abs[i])) + annotation_i[DensePoseDataRelative.S_KEY] = self._resample_mask( # pyre-ignore[6] + instances[i].pred_densepose + ) + dp_datas.append(DensePoseDataRelative(annotation_i)) + # create densepose annotations on CPU + dp_list = DensePoseList(dp_datas, boxes_xyxy_abs, instances.image_size) + return dp_list + + def _sample(self, instance: Instances, bbox_xywh: IntTupleBox) -> Dict[str, List[Any]]: + """ + Sample DensPoseDataRelative from estimation results + """ + labels, dp_result = self._produce_labels_and_results(instance) + annotation = { + DensePoseDataRelative.X_KEY: [], + DensePoseDataRelative.Y_KEY: [], + DensePoseDataRelative.U_KEY: [], + DensePoseDataRelative.V_KEY: [], + DensePoseDataRelative.I_KEY: [], + } + n, h, w = dp_result.shape + for part_id in range(1, DensePoseDataRelative.N_PART_LABELS + 1): + # indices - tuple of 3 1D tensors of size k + # 0: index along the first dimension N + # 1: index along H dimension + # 2: index along W dimension + indices = torch.nonzero(labels.expand(n, h, w) == part_id, as_tuple=True) + # values - an array of size [n, k] + # n: number of channels (U, V, confidences) + # k: number of points labeled with part_id + values = dp_result[indices].view(n, -1) + k = values.shape[1] + count = min(self.count_per_class, k) + if count <= 0: + continue + index_sample = self._produce_index_sample(values, count) + sampled_values = values[:, index_sample] + sampled_y = indices[1][index_sample] + 0.5 + sampled_x = indices[2][index_sample] + 0.5 + # prepare / normalize data + x = (sampled_x / w * 256.0).cpu().tolist() + y = (sampled_y / h * 256.0).cpu().tolist() + u = sampled_values[0].clamp(0, 1).cpu().tolist() + v = sampled_values[1].clamp(0, 1).cpu().tolist() + fine_segm_labels = [part_id] * count + # extend annotations + annotation[DensePoseDataRelative.X_KEY].extend(x) + annotation[DensePoseDataRelative.Y_KEY].extend(y) + annotation[DensePoseDataRelative.U_KEY].extend(u) + annotation[DensePoseDataRelative.V_KEY].extend(v) + annotation[DensePoseDataRelative.I_KEY].extend(fine_segm_labels) + return annotation + + def _produce_index_sample(self, values: torch.Tensor, count: int): + """ + Abstract method to produce a sample of indices to select data + To be implemented in descendants + + Args: + values (torch.Tensor): an array of size [n, k] that contains + estimated values (U, V, confidences); + n: number of channels (U, V, confidences) + k: number of points labeled with part_id + count (int): number of samples to produce, should be positive and <= k + + Return: + list(int): indices of values (along axis 1) selected as a sample + """ + raise NotImplementedError + + def _produce_labels_and_results(self, instance: Instances) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Method to get labels and DensePose results from an instance + + Args: + instance (Instances): an instance of `DensePoseChartPredictorOutput` + + Return: + labels (torch.Tensor): shape [H, W], DensePose segmentation labels + dp_result (torch.Tensor): shape [2, H, W], stacked DensePose results u and v + """ + converter = ToChartResultConverter + chart_result = converter.convert(instance.pred_densepose, instance.pred_boxes) + labels, dp_result = chart_result.labels.cpu(), chart_result.uv.cpu() + return labels, dp_result + + def _resample_mask(self, output: Any) -> torch.Tensor: + """ + Convert DensePose predictor output to segmentation annotation - tensors of size + (256, 256) and type `int64`. + + Args: + output: DensePose predictor output with the following attributes: + - coarse_segm: tensor of size [N, D, H, W] with unnormalized coarse + segmentation scores + - fine_segm: tensor of size [N, C, H, W] with unnormalized fine + segmentation scores + Return: + Tensor of size (S, S) and type `int64` with coarse segmentation annotations, + where S = DensePoseDataRelative.MASK_SIZE + """ + sz = DensePoseDataRelative.MASK_SIZE + S = ( + # pyre-fixme[16]: `Tensor` has no attribute `argmax`. + # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got + # `Tuple[int, int]`. + F.interpolate(output.coarse_segm, (sz, sz), mode="bilinear", align_corners=False) + .argmax(dim=1) + .long() + ) + I = ( + ( + F.interpolate( + # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got + # `Tuple[int, int]`. + output.fine_segm, (sz, sz), mode="bilinear", align_corners=False + ).argmax(dim=1) + * (S > 0).long() + ) + .squeeze() + .cpu() + ) + # Map fine segmentation results to coarse segmentation ground truth + # TODO: extract this into separate classes + # coarse segmentation: 1 = Torso, 2 = Right Hand, 3 = Left Hand, + # 4 = Left Foot, 5 = Right Foot, 6 = Upper Leg Right, 7 = Upper Leg Left, + # 8 = Lower Leg Right, 9 = Lower Leg Left, 10 = Upper Arm Left, + # 11 = Upper Arm Right, 12 = Lower Arm Left, 13 = Lower Arm Right, + # 14 = Head + # fine segmentation: 1, 2 = Torso, 3 = Right Hand, 4 = Left Hand, + # 5 = Left Foot, 6 = Right Foot, 7, 9 = Upper Leg Right, + # 8, 10 = Upper Leg Left, 11, 13 = Lower Leg Right, + # 12, 14 = Lower Leg Left, 15, 17 = Upper Arm Left, + # 16, 18 = Upper Arm Right, 19, 21 = Lower Arm Left, + # 20, 22 = Lower Arm Right, 23, 24 = Head + FINE_TO_COARSE_SEGMENTATION = { + 1: 1, + 2: 1, + 3: 2, + 4: 3, + 5: 4, + 6: 5, + 7: 6, + 8: 7, + 9: 6, + 10: 7, + 11: 8, + 12: 9, + 13: 8, + 14: 9, + 15: 10, + 16: 11, + 17: 10, + 18: 11, + 19: 12, + 20: 13, + 21: 12, + 22: 13, + 23: 14, + 24: 14, + } + mask = torch.zeros((sz, sz), dtype=torch.int64, device=torch.device("cpu")) + for i in range(DensePoseDataRelative.N_PART_LABELS): + mask[I == i + 1] = FINE_TO_COARSE_SEGMENTATION[i + 1] + return mask diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_confidence_based.py b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_confidence_based.py new file mode 100644 index 0000000..48e325b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_confidence_based.py @@ -0,0 +1,108 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import random +from typing import Optional, Tuple +import torch + +from densepose.converters import ToChartResultConverterWithConfidences + +from .densepose_base import DensePoseBaseSampler + + +class DensePoseConfidenceBasedSampler(DensePoseBaseSampler): + """ + Samples DensePose data from DensePose predictions. + Samples for each class are drawn using confidence value estimates. + """ + + def __init__( + self, + confidence_channel: str, + count_per_class: int = 8, + search_count_multiplier: Optional[float] = None, + search_proportion: Optional[float] = None, + ): + """ + Constructor + + Args: + confidence_channel (str): confidence channel to use for sampling; + possible values: + "sigma_2": confidences for UV values + "fine_segm_confidence": confidences for fine segmentation + "coarse_segm_confidence": confidences for coarse segmentation + (default: "sigma_2") + count_per_class (int): the sampler produces at most `count_per_class` + samples for each category (default: 8) + search_count_multiplier (float or None): if not None, the total number + of the most confident estimates of a given class to consider is + defined as `min(search_count_multiplier * count_per_class, N)`, + where `N` is the total number of estimates of the class; cannot be + specified together with `search_proportion` (default: None) + search_proportion (float or None): if not None, the total number of the + of the most confident estimates of a given class to consider is + defined as `min(max(search_proportion * N, count_per_class), N)`, + where `N` is the total number of estimates of the class; cannot be + specified together with `search_count_multiplier` (default: None) + """ + super().__init__(count_per_class) + self.confidence_channel = confidence_channel + self.search_count_multiplier = search_count_multiplier + self.search_proportion = search_proportion + assert (search_count_multiplier is None) or (search_proportion is None), ( + f"Cannot specify both search_count_multiplier (={search_count_multiplier})" + f"and search_proportion (={search_proportion})" + ) + + def _produce_index_sample(self, values: torch.Tensor, count: int): + """ + Produce a sample of indices to select data based on confidences + + Args: + values (torch.Tensor): an array of size [n, k] that contains + estimated values (U, V, confidences); + n: number of channels (U, V, confidences) + k: number of points labeled with part_id + count (int): number of samples to produce, should be positive and <= k + + Return: + list(int): indices of values (along axis 1) selected as a sample + """ + k = values.shape[1] + if k == count: + index_sample = list(range(k)) + else: + # take the best count * search_count_multiplier pixels, + # sample from them uniformly + # (here best = smallest variance) + _, sorted_confidence_indices = torch.sort(values[2]) + if self.search_count_multiplier is not None: + search_count = min(int(count * self.search_count_multiplier), k) + elif self.search_proportion is not None: + search_count = min(max(int(k * self.search_proportion), count), k) + else: + search_count = min(count, k) + sample_from_top = random.sample(range(search_count), count) + index_sample = sorted_confidence_indices[:search_count][sample_from_top] + return index_sample + + def _produce_labels_and_results(self, instance) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Method to get labels and DensePose results from an instance, with confidences + + Args: + instance (Instances): an instance of `DensePoseChartPredictorOutputWithConfidences` + + Return: + labels (torch.Tensor): shape [H, W], DensePose segmentation labels + dp_result (torch.Tensor): shape [3, H, W], DensePose results u and v + stacked with the confidence channel + """ + converter = ToChartResultConverterWithConfidences + chart_result = converter.convert(instance.pred_densepose, instance.pred_boxes) + labels, dp_result = chart_result.labels.cpu(), chart_result.uv.cpu() + dp_result = torch.cat( + (dp_result, getattr(chart_result, self.confidence_channel)[None].cpu()) + ) + + return labels, dp_result diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_cse_base.py b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_cse_base.py new file mode 100644 index 0000000..ca2b1b2 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_cse_base.py @@ -0,0 +1,146 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from typing import Any, Dict, List, Tuple +import torch +from torch.nn import functional as F + +from detectron2.config import CfgNode +from detectron2.structures import Instances + +from densepose.converters.base import IntTupleBox +from densepose.data.utils import get_class_to_mesh_name_mapping +from densepose.modeling.cse.utils import squared_euclidean_distance_matrix +from densepose.structures import DensePoseDataRelative + +from .densepose_base import DensePoseBaseSampler + + +class DensePoseCSEBaseSampler(DensePoseBaseSampler): + """ + Base DensePose sampler to produce DensePose data from DensePose predictions. + Samples for each class are drawn according to some distribution over all pixels estimated + to belong to that class. + """ + + def __init__( + self, + cfg: CfgNode, + use_gt_categories: bool, + embedder: torch.nn.Module, + count_per_class: int = 8, + ): + """ + Constructor + + Args: + cfg (CfgNode): the config of the model + embedder (torch.nn.Module): necessary to compute mesh vertex embeddings + count_per_class (int): the sampler produces at most `count_per_class` + samples for each category + """ + super().__init__(count_per_class) + self.embedder = embedder + self.class_to_mesh_name = get_class_to_mesh_name_mapping(cfg) + self.use_gt_categories = use_gt_categories + + def _sample(self, instance: Instances, bbox_xywh: IntTupleBox) -> Dict[str, List[Any]]: + """ + Sample DensPoseDataRelative from estimation results + """ + if self.use_gt_categories: + instance_class = instance.dataset_classes.tolist()[0] + else: + instance_class = instance.pred_classes.tolist()[0] + mesh_name = self.class_to_mesh_name[instance_class] + + annotation = { + DensePoseDataRelative.X_KEY: [], + DensePoseDataRelative.Y_KEY: [], + DensePoseDataRelative.VERTEX_IDS_KEY: [], + DensePoseDataRelative.MESH_NAME_KEY: mesh_name, + } + + mask, embeddings, other_values = self._produce_mask_and_results(instance, bbox_xywh) + indices = torch.nonzero(mask, as_tuple=True) + selected_embeddings = embeddings.permute(1, 2, 0)[indices].cpu() + values = other_values[:, indices[0], indices[1]] + k = values.shape[1] + + count = min(self.count_per_class, k) + if count <= 0: + return annotation + + index_sample = self._produce_index_sample(values, count) + closest_vertices = squared_euclidean_distance_matrix( + selected_embeddings[index_sample], self.embedder(mesh_name) + ) + closest_vertices = torch.argmin(closest_vertices, dim=1) + + sampled_y = indices[0][index_sample] + 0.5 + sampled_x = indices[1][index_sample] + 0.5 + # prepare / normalize data + _, _, w, h = bbox_xywh + x = (sampled_x / w * 256.0).cpu().tolist() + y = (sampled_y / h * 256.0).cpu().tolist() + # extend annotations + annotation[DensePoseDataRelative.X_KEY].extend(x) + annotation[DensePoseDataRelative.Y_KEY].extend(y) + annotation[DensePoseDataRelative.VERTEX_IDS_KEY].extend(closest_vertices.cpu().tolist()) + return annotation + + def _produce_mask_and_results( + self, instance: Instances, bbox_xywh: IntTupleBox + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Method to get labels and DensePose results from an instance + + Args: + instance (Instances): an instance of `DensePoseEmbeddingPredictorOutput` + bbox_xywh (IntTupleBox): the corresponding bounding box + + Return: + mask (torch.Tensor): shape [H, W], DensePose segmentation mask + embeddings (Tuple[torch.Tensor]): a tensor of shape [D, H, W], + DensePose CSE Embeddings + other_values (Tuple[torch.Tensor]): a tensor of shape [0, H, W], + for potential other values + """ + densepose_output = instance.pred_densepose + S = densepose_output.coarse_segm + E = densepose_output.embedding + _, _, w, h = bbox_xywh + # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got `Tuple[int, + # int]`. + embeddings = F.interpolate(E, size=(h, w), mode="bilinear")[0] + # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got `Tuple[int, + # int]`. + coarse_segm_resized = F.interpolate(S, size=(h, w), mode="bilinear")[0] + mask = coarse_segm_resized.argmax(0) > 0 + other_values = torch.empty((0, h, w), device=E.device) + return mask, embeddings, other_values + + def _resample_mask(self, output: Any) -> torch.Tensor: + """ + Convert DensePose predictor output to segmentation annotation - tensors of size + (256, 256) and type `int64`. + + Args: + output: DensePose predictor output with the following attributes: + - coarse_segm: tensor of size [N, D, H, W] with unnormalized coarse + segmentation scores + Return: + Tensor of size (S, S) and type `int64` with coarse segmentation annotations, + where S = DensePoseDataRelative.MASK_SIZE + """ + sz = DensePoseDataRelative.MASK_SIZE + mask = ( + # pyre-fixme[16]: `Tensor` has no attribute `argmax`. + # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got + # `Tuple[int, int]`. + F.interpolate(output.coarse_segm, (sz, sz), mode="bilinear", align_corners=False) + .argmax(dim=1) + .long() + .squeeze() + .cpu() + ) + return mask diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_cse_confidence_based.py b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_cse_confidence_based.py new file mode 100644 index 0000000..d664fd4 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_cse_confidence_based.py @@ -0,0 +1,119 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import random +from typing import Optional, Tuple +import torch +from torch.nn import functional as F + +from detectron2.config import CfgNode +from detectron2.structures import Instances + +from densepose.converters.base import IntTupleBox + +from .densepose_cse_base import DensePoseCSEBaseSampler + + +class DensePoseCSEConfidenceBasedSampler(DensePoseCSEBaseSampler): + """ + Samples DensePose data from DensePose predictions. + Samples for each class are drawn using confidence value estimates. + """ + + def __init__( + self, + cfg: CfgNode, + use_gt_categories: bool, + embedder: torch.nn.Module, + confidence_channel: str, + count_per_class: int = 8, + search_count_multiplier: Optional[float] = None, + search_proportion: Optional[float] = None, + ): + """ + Constructor + + Args: + cfg (CfgNode): the config of the model + embedder (torch.nn.Module): necessary to compute mesh vertex embeddings + confidence_channel (str): confidence channel to use for sampling; + possible values: + "coarse_segm_confidence": confidences for coarse segmentation + (default: "coarse_segm_confidence") + count_per_class (int): the sampler produces at most `count_per_class` + samples for each category (default: 8) + search_count_multiplier (float or None): if not None, the total number + of the most confident estimates of a given class to consider is + defined as `min(search_count_multiplier * count_per_class, N)`, + where `N` is the total number of estimates of the class; cannot be + specified together with `search_proportion` (default: None) + search_proportion (float or None): if not None, the total number of the + of the most confident estimates of a given class to consider is + defined as `min(max(search_proportion * N, count_per_class), N)`, + where `N` is the total number of estimates of the class; cannot be + specified together with `search_count_multiplier` (default: None) + """ + super().__init__(cfg, use_gt_categories, embedder, count_per_class) + self.confidence_channel = confidence_channel + self.search_count_multiplier = search_count_multiplier + self.search_proportion = search_proportion + assert (search_count_multiplier is None) or (search_proportion is None), ( + f"Cannot specify both search_count_multiplier (={search_count_multiplier})" + f"and search_proportion (={search_proportion})" + ) + + def _produce_index_sample(self, values: torch.Tensor, count: int): + """ + Produce a sample of indices to select data based on confidences + + Args: + values (torch.Tensor): a tensor of length k that contains confidences + k: number of points labeled with part_id + count (int): number of samples to produce, should be positive and <= k + + Return: + list(int): indices of values (along axis 1) selected as a sample + """ + k = values.shape[1] + if k == count: + index_sample = list(range(k)) + else: + # take the best count * search_count_multiplier pixels, + # sample from them uniformly + # (here best = smallest variance) + _, sorted_confidence_indices = torch.sort(values[0]) + if self.search_count_multiplier is not None: + search_count = min(int(count * self.search_count_multiplier), k) + elif self.search_proportion is not None: + search_count = min(max(int(k * self.search_proportion), count), k) + else: + search_count = min(count, k) + sample_from_top = random.sample(range(search_count), count) + index_sample = sorted_confidence_indices[-search_count:][sample_from_top] + return index_sample + + def _produce_mask_and_results( + self, instance: Instances, bbox_xywh: IntTupleBox + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Method to get labels and DensePose results from an instance + + Args: + instance (Instances): an instance of + `DensePoseEmbeddingPredictorOutputWithConfidences` + bbox_xywh (IntTupleBox): the corresponding bounding box + + Return: + mask (torch.Tensor): shape [H, W], DensePose segmentation mask + embeddings (Tuple[torch.Tensor]): a tensor of shape [D, H, W] + DensePose CSE Embeddings + other_values: a tensor of shape [1, H, W], DensePose CSE confidence + """ + _, _, w, h = bbox_xywh + densepose_output = instance.pred_densepose + mask, embeddings, _ = super()._produce_mask_and_results(instance, bbox_xywh) + other_values = F.interpolate( + # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got + # `Tuple[int, int]`. + getattr(densepose_output, self.confidence_channel), size=(h, w), mode="bilinear" + )[0].cpu() + return mask, embeddings, other_values diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_cse_uniform.py b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_cse_uniform.py new file mode 100644 index 0000000..567636c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_cse_uniform.py @@ -0,0 +1,12 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from .densepose_cse_base import DensePoseCSEBaseSampler +from .densepose_uniform import DensePoseUniformSampler + + +class DensePoseCSEUniformSampler(DensePoseCSEBaseSampler, DensePoseUniformSampler): + """ + Uniform Sampler for CSE + """ + + pass diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_uniform.py b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_uniform.py new file mode 100644 index 0000000..0d72cc3 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/densepose_uniform.py @@ -0,0 +1,41 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import random +import torch + +from .densepose_base import DensePoseBaseSampler + + +class DensePoseUniformSampler(DensePoseBaseSampler): + """ + Samples DensePose data from DensePose predictions. + Samples for each class are drawn uniformly over all pixels estimated + to belong to that class. + """ + + def __init__(self, count_per_class: int = 8): + """ + Constructor + + Args: + count_per_class (int): the sampler produces at most `count_per_class` + samples for each category + """ + super().__init__(count_per_class) + + def _produce_index_sample(self, values: torch.Tensor, count: int): + """ + Produce a uniform sample of indices to select data + + Args: + values (torch.Tensor): an array of size [n, k] that contains + estimated values (U, V, confidences); + n: number of channels (U, V, confidences) + k: number of points labeled with part_id + count (int): number of samples to produce, should be positive and <= k + + Return: + list(int): indices of values (along axis 1) selected as a sample + """ + k = values.shape[1] + return random.sample(range(k), count) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/mask_from_densepose.py b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/mask_from_densepose.py new file mode 100644 index 0000000..0e6e812 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/mask_from_densepose.py @@ -0,0 +1,28 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from detectron2.structures import BitMasks, Instances + +from densepose.converters import ToMaskConverter + + +class MaskFromDensePoseSampler: + """ + Produce mask GT from DensePose predictions + This sampler simply converts DensePose predictions to BitMasks + that a contain a bool tensor of the size of the input image + """ + + def __call__(self, instances: Instances) -> BitMasks: + """ + Converts predicted data from `instances` into the GT mask data + + Args: + instances (Instances): predicted results, expected to have `pred_densepose` field + + Returns: + Boolean Tensor of the size of the input image that has non-zero + values at pixels that are estimated to belong to the detected object + """ + return ToMaskConverter.convert( + instances.pred_densepose, instances.pred_boxes, instances.image_size + ) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/prediction_to_gt.py b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/prediction_to_gt.py new file mode 100644 index 0000000..3881fa5 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/samplers/prediction_to_gt.py @@ -0,0 +1,98 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from dataclasses import dataclass +from typing import Any, Callable, Dict, List, Optional + +from detectron2.structures import Instances + +ModelOutput = Dict[str, Any] +SampledData = Dict[str, Any] + + +@dataclass +class _Sampler: + """ + Sampler registry entry that contains: + - src (str): source field to sample from (deleted after sampling) + - dst (Optional[str]): destination field to sample to, if not None + - func (Optional[Callable: Any -> Any]): function that performs sampling, + if None, reference copy is performed + """ + + src: str + dst: Optional[str] + func: Optional[Callable[[Any], Any]] + + +class PredictionToGroundTruthSampler: + """ + Sampler implementation that converts predictions to GT using registered + samplers for different fields of `Instances`. + """ + + def __init__(self, dataset_name: str = ""): + self.dataset_name = dataset_name + self._samplers = {} + self.register_sampler("pred_boxes", "gt_boxes", None) + self.register_sampler("pred_classes", "gt_classes", None) + # delete scores + self.register_sampler("scores") + + def __call__(self, model_output: List[ModelOutput]) -> List[SampledData]: + """ + Transform model output into ground truth data through sampling + + Args: + model_output (Dict[str, Any]): model output + Returns: + Dict[str, Any]: sampled data + """ + for model_output_i in model_output: + instances: Instances = model_output_i["instances"] + # transform data in each field + for _, sampler in self._samplers.items(): + if not instances.has(sampler.src) or sampler.dst is None: + continue + if sampler.func is None: + instances.set(sampler.dst, instances.get(sampler.src)) + else: + instances.set(sampler.dst, sampler.func(instances)) + # delete model output data that was transformed + for _, sampler in self._samplers.items(): + if sampler.src != sampler.dst and instances.has(sampler.src): + instances.remove(sampler.src) + model_output_i["dataset"] = self.dataset_name + return model_output + + def register_sampler( + self, + prediction_attr: str, + gt_attr: Optional[str] = None, + func: Optional[Callable[[Any], Any]] = None, + ): + """ + Register sampler for a field + + Args: + prediction_attr (str): field to replace with a sampled value + gt_attr (Optional[str]): field to store the sampled value to, if not None + func (Optional[Callable: Any -> Any]): sampler function + """ + self._samplers[(prediction_attr, gt_attr)] = _Sampler( + src=prediction_attr, dst=gt_attr, func=func + ) + + def remove_sampler( + self, + prediction_attr: str, + gt_attr: Optional[str] = None, + ): + """ + Remove sampler for a field + + Args: + prediction_attr (str): field to replace with a sampled value + gt_attr (Optional[str]): field to store the sampled value to, if not None + """ + assert (prediction_attr, gt_attr) in self._samplers + del self._samplers[(prediction_attr, gt_attr)] diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/transform/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/data/transform/__init__.py new file mode 100644 index 0000000..369e1b2 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/transform/__init__.py @@ -0,0 +1,3 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from .image import ImageResizeTransform diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/transform/image.py b/motion-gan-pipeline/preprocessing/third/densepose/data/transform/image.py new file mode 100644 index 0000000..842865f --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/transform/image.py @@ -0,0 +1,38 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import torch + + +class ImageResizeTransform: + """ + Transform that resizes images loaded from a dataset + (BGR data in NCHW channel order, typically uint8) to a format ready to be + consumed by DensePose training (BGR float32 data in NCHW channel order) + """ + + def __init__(self, min_size: int = 800, max_size: int = 1333): + self.min_size = min_size + self.max_size = max_size + + def __call__(self, images: torch.Tensor) -> torch.Tensor: + """ + Args: + images (torch.Tensor): tensor of size [N, 3, H, W] that contains + BGR data (typically in uint8) + Returns: + images (torch.Tensor): tensor of size [N, 3, H1, W1] where + H1 and W1 are chosen to respect the specified min and max sizes + and preserve the original aspect ratio, the data channels + follow BGR order and the data type is `torch.float32` + """ + # resize with min size + images = images.float() + min_size = min(images.shape[-2:]) + max_size = max(images.shape[-2:]) + scale = min(self.min_size / min_size, self.max_size / max_size) + images = torch.nn.functional.interpolate( + # pyre-fixme[6]: Expected `Optional[typing.List[float]]` for 2nd param + # but got `float`. + images, scale_factor=scale, mode="bilinear", align_corners=False + ) + return images diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/utils.py b/motion-gan-pipeline/preprocessing/third/densepose/data/utils.py new file mode 100644 index 0000000..9878c31 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/utils.py @@ -0,0 +1,38 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import os +from typing import Dict, Optional + +from detectron2.config import CfgNode + + +def is_relative_local_path(path: str) -> bool: + path_str = os.fsdecode(path) + return ("://" not in path_str) and not os.path.isabs(path) + + +def maybe_prepend_base_path(base_path: Optional[str], path: str): + """ + Prepends the provided path with a base path prefix if: + 1) base path is not None; + 2) path is a local path + """ + if base_path is None: + return path + if is_relative_local_path(path): + return os.path.join(base_path, path) + return path + + +def get_class_to_mesh_name_mapping(cfg: CfgNode) -> Dict[int, str]: + return { + int(class_id): mesh_name + for class_id, mesh_name in cfg.DATASETS.CLASS_TO_MESH_NAME_MAPPING.items() + } + + +def get_category_to_class_mapping(dataset_cfg: CfgNode) -> Dict[str, int]: + return { + category: int(class_id) + for category, class_id in dataset_cfg.CATEGORY_TO_CLASS_MAPPING.items() + } diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/video/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/data/video/__init__.py new file mode 100644 index 0000000..72406e1 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/video/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from .frame_selector import ( + FrameSelectionStrategy, + RandomKFramesSelector, + FirstKFramesSelector, + LastKFramesSelector, + FrameTsList, + FrameSelector, +) + +from .video_keyframe_dataset import ( + VideoKeyframeDataset, + video_list_from_file, + list_keyframes, + read_keyframes, +) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/video/frame_selector.py b/motion-gan-pipeline/preprocessing/third/densepose/data/video/frame_selector.py new file mode 100644 index 0000000..c28f0e9 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/video/frame_selector.py @@ -0,0 +1,87 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import random +from collections.abc import Callable +from enum import Enum +from typing import Callable as TCallable +from typing import List + +FrameTsList = List[int] +FrameSelector = TCallable[[FrameTsList], FrameTsList] + + +class FrameSelectionStrategy(Enum): + """ + Frame selection strategy used with videos: + - "random_k": select k random frames + - "first_k": select k first frames + - "last_k": select k last frames + - "all": select all frames + """ + + # fmt: off + RANDOM_K = "random_k" + FIRST_K = "first_k" + LAST_K = "last_k" + ALL = "all" + # fmt: on + + +class RandomKFramesSelector(Callable): # pyre-ignore[39] + """ + Selector that retains at most `k` random frames + """ + + def __init__(self, k: int): + self.k = k + + def __call__(self, frame_tss: FrameTsList) -> FrameTsList: + """ + Select `k` random frames + + Args: + frames_tss (List[int]): timestamps of input frames + Returns: + List[int]: timestamps of selected frames + """ + return random.sample(frame_tss, min(self.k, len(frame_tss))) + + +class FirstKFramesSelector(Callable): # pyre-ignore[39] + """ + Selector that retains at most `k` first frames + """ + + def __init__(self, k: int): + self.k = k + + def __call__(self, frame_tss: FrameTsList) -> FrameTsList: + """ + Select `k` first frames + + Args: + frames_tss (List[int]): timestamps of input frames + Returns: + List[int]: timestamps of selected frames + """ + return frame_tss[: self.k] + + +class LastKFramesSelector(Callable): # pyre-ignore[39] + """ + Selector that retains at most `k` last frames from video data + """ + + def __init__(self, k: int): + self.k = k + + def __call__(self, frame_tss: FrameTsList) -> FrameTsList: + """ + Select `k` last frames + + Args: + frames_tss (List[int]): timestamps of input frames + Returns: + List[int]: timestamps of selected frames + """ + return frame_tss[-self.k :] diff --git a/motion-gan-pipeline/preprocessing/third/densepose/data/video/video_keyframe_dataset.py b/motion-gan-pipeline/preprocessing/third/densepose/data/video/video_keyframe_dataset.py new file mode 100644 index 0000000..214365c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/data/video/video_keyframe_dataset.py @@ -0,0 +1,300 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import csv +import logging +import numpy as np +from typing import Any, Callable, Dict, List, Optional, Union +import av +import torch +from torch.utils.data.dataset import Dataset + +from detectron2.utils.file_io import PathManager + +from ..utils import maybe_prepend_base_path +from .frame_selector import FrameSelector, FrameTsList + +FrameList = List[av.frame.Frame] # pyre-ignore[16] +FrameTransform = Callable[[torch.Tensor], torch.Tensor] + + +def list_keyframes(video_fpath: str, video_stream_idx: int = 0) -> FrameTsList: + """ + Traverses all keyframes of a video file. Returns a list of keyframe + timestamps. Timestamps are counts in timebase units. + + Args: + video_fpath (str): Video file path + video_stream_idx (int): Video stream index (default: 0) + Returns: + List[int]: list of keyframe timestaps (timestamp is a count in timebase + units) + """ + try: + with PathManager.open(video_fpath, "rb") as io: + container = av.open(io, mode="r") + stream = container.streams.video[video_stream_idx] + keyframes = [] + pts = -1 + # Note: even though we request forward seeks for keyframes, sometimes + # a keyframe in backwards direction is returned. We introduce tolerance + # as a max count of ignored backward seeks + tolerance_backward_seeks = 2 + while True: + try: + container.seek(pts + 1, backward=False, any_frame=False, stream=stream) + except av.AVError as e: + # the exception occurs when the video length is exceeded, + # we then return whatever data we've already collected + logger = logging.getLogger(__name__) + logger.debug( + f"List keyframes: Error seeking video file {video_fpath}, " + f"video stream {video_stream_idx}, pts {pts + 1}, AV error: {e}" + ) + return keyframes + except OSError as e: + logger = logging.getLogger(__name__) + logger.warning( + f"List keyframes: Error seeking video file {video_fpath}, " + f"video stream {video_stream_idx}, pts {pts + 1}, OS error: {e}" + ) + return [] + packet = next(container.demux(video=video_stream_idx)) + if packet.pts is not None and packet.pts <= pts: + logger = logging.getLogger(__name__) + logger.warning( + f"Video file {video_fpath}, stream {video_stream_idx}: " + f"bad seek for packet {pts + 1} (got packet {packet.pts}), " + f"tolerance {tolerance_backward_seeks}." + ) + tolerance_backward_seeks -= 1 + if tolerance_backward_seeks == 0: + return [] + pts += 1 + continue + tolerance_backward_seeks = 2 + pts = packet.pts + if pts is None: + return keyframes + if packet.is_keyframe: + keyframes.append(pts) + return keyframes + except OSError as e: + logger = logging.getLogger(__name__) + logger.warning( + f"List keyframes: Error opening video file container {video_fpath}, " f"OS error: {e}" + ) + except RuntimeError as e: + logger = logging.getLogger(__name__) + logger.warning( + f"List keyframes: Error opening video file container {video_fpath}, " + f"Runtime error: {e}" + ) + return [] + + +def read_keyframes( + video_fpath: str, keyframes: FrameTsList, video_stream_idx: int = 0 +) -> FrameList: # pyre-ignore[11] + """ + Reads keyframe data from a video file. + + Args: + video_fpath (str): Video file path + keyframes (List[int]): List of keyframe timestamps (as counts in + timebase units to be used in container seek operations) + video_stream_idx (int): Video stream index (default: 0) + Returns: + List[Frame]: list of frames that correspond to the specified timestamps + """ + try: + with PathManager.open(video_fpath, "rb") as io: + container = av.open(io) + stream = container.streams.video[video_stream_idx] + frames = [] + for pts in keyframes: + try: + container.seek(pts, any_frame=False, stream=stream) + frame = next(container.decode(video=0)) + frames.append(frame) + except av.AVError as e: + logger = logging.getLogger(__name__) + logger.warning( + f"Read keyframes: Error seeking video file {video_fpath}, " + f"video stream {video_stream_idx}, pts {pts}, AV error: {e}" + ) + container.close() + return frames + except OSError as e: + logger = logging.getLogger(__name__) + logger.warning( + f"Read keyframes: Error seeking video file {video_fpath}, " + f"video stream {video_stream_idx}, pts {pts}, OS error: {e}" + ) + container.close() + return frames + except StopIteration: + logger = logging.getLogger(__name__) + logger.warning( + f"Read keyframes: Error decoding frame from {video_fpath}, " + f"video stream {video_stream_idx}, pts {pts}" + ) + container.close() + return frames + + container.close() + return frames + except OSError as e: + logger = logging.getLogger(__name__) + logger.warning( + f"Read keyframes: Error opening video file container {video_fpath}, OS error: {e}" + ) + except RuntimeError as e: + logger = logging.getLogger(__name__) + logger.warning( + f"Read keyframes: Error opening video file container {video_fpath}, Runtime error: {e}" + ) + return [] + + +def video_list_from_file(video_list_fpath: str, base_path: Optional[str] = None): + """ + Create a list of paths to video files from a text file. + + Args: + video_list_fpath (str): path to a plain text file with the list of videos + base_path (str): base path for entries from the video list (default: None) + """ + video_list = [] + with PathManager.open(video_list_fpath, "r") as io: + for line in io: + video_list.append(maybe_prepend_base_path(base_path, str(line.strip()))) + return video_list + + +def read_keyframe_helper_data(fpath: str): + """ + Read keyframe data from a file in CSV format: the header should contain + "video_id" and "keyframes" fields. Value specifications are: + video_id: int + keyframes: list(int) + Example of contents: + video_id,keyframes + 2,"[1,11,21,31,41,51,61,71,81]" + + Args: + fpath (str): File containing keyframe data + + Return: + video_id_to_keyframes (dict: int -> list(int)): for a given video ID it + contains a list of keyframes for that video + """ + video_id_to_keyframes = {} + try: + with PathManager.open(fpath, "r") as io: + csv_reader = csv.reader(io) # pyre-ignore[6] + header = next(csv_reader) + video_id_idx = header.index("video_id") + keyframes_idx = header.index("keyframes") + for row in csv_reader: + video_id = int(row[video_id_idx]) + assert ( + video_id not in video_id_to_keyframes + ), f"Duplicate keyframes entry for video {fpath}" + video_id_to_keyframes[video_id] = ( + [int(v) for v in row[keyframes_idx][1:-1].split(",")] + if len(row[keyframes_idx]) > 2 + else [] + ) + except Exception as e: + logger = logging.getLogger(__name__) + logger.warning(f"Error reading keyframe helper data from {fpath}: {e}") + return video_id_to_keyframes + + +class VideoKeyframeDataset(Dataset): + """ + Dataset that provides keyframes for a set of videos. + """ + + _EMPTY_FRAMES = torch.empty((0, 3, 1, 1)) + + def __init__( + self, + video_list: List[str], + category_list: Union[str, List[str], None] = None, + frame_selector: Optional[FrameSelector] = None, + transform: Optional[FrameTransform] = None, + keyframe_helper_fpath: Optional[str] = None, + ): + """ + Dataset constructor + + Args: + video_list (List[str]): list of paths to video files + category_list (Union[str, List[str], None]): list of animal categories for each + video file. If it is a string, or None, this applies to all videos + frame_selector (Callable: KeyFrameList -> KeyFrameList): + selects keyframes to process, keyframes are given by + packet timestamps in timebase counts. If None, all keyframes + are selected (default: None) + transform (Callable: torch.Tensor -> torch.Tensor): + transforms a batch of RGB images (tensors of size [B, 3, H, W]), + returns a tensor of the same size. If None, no transform is + applied (default: None) + + """ + if type(category_list) == list: + self.category_list = category_list + else: + self.category_list = [category_list] * len(video_list) + assert len(video_list) == len( + self.category_list + ), "length of video and category lists must be equal" + self.video_list = video_list + self.frame_selector = frame_selector + self.transform = transform + self.keyframe_helper_data = ( + read_keyframe_helper_data(keyframe_helper_fpath) + if keyframe_helper_fpath is not None + else None + ) + + def __getitem__(self, idx: int) -> Dict[str, Any]: + """ + Gets selected keyframes from a given video + + Args: + idx (int): video index in the video list file + Returns: + A dictionary containing two keys: + images (torch.Tensor): tensor of size [N, H, W, 3] or of size + defined by the transform that contains keyframes data + categories (List[str]): categories of the frames + """ + categories = [self.category_list[idx]] + fpath = self.video_list[idx] + keyframes = ( + list_keyframes(fpath) + if self.keyframe_helper_data is None or idx not in self.keyframe_helper_data + else self.keyframe_helper_data[idx] + ) + transform = self.transform + frame_selector = self.frame_selector + if not keyframes: + return {"images": self._EMPTY_FRAMES, "categories": []} + if frame_selector is not None: + keyframes = frame_selector(keyframes) + frames = read_keyframes(fpath, keyframes) + if not frames: + return {"images": self._EMPTY_FRAMES, "categories": []} + frames = np.stack([frame.to_rgb().to_ndarray() for frame in frames]) + frames = torch.as_tensor(frames, device=torch.device("cpu")) + frames = frames[..., [2, 1, 0]] # RGB -> BGR + frames = frames.permute(0, 3, 1, 2).float() # NHWC -> NCHW + if transform is not None: + frames = transform(frames) + return {"images": frames, "categories": categories} + + def __len__(self): + return len(self.video_list) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/densepose_rcnn_R_50_FPN_s1x.yaml b/motion-gan-pipeline/preprocessing/third/densepose/densepose_rcnn_R_50_FPN_s1x.yaml new file mode 100644 index 0000000..d2dd14c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/densepose_rcnn_R_50_FPN_s1x.yaml @@ -0,0 +1,8 @@ +_BASE_: "Base-DensePose-RCNN-FPN.yaml" +MODEL: + WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl" + RESNETS: + DEPTH: 50 +SOLVER: + MAX_ITER: 130000 + STEPS: (100000, 120000) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/engine/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/engine/__init__.py new file mode 100644 index 0000000..539b93a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/engine/__init__.py @@ -0,0 +1,3 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from .trainer import Trainer diff --git a/motion-gan-pipeline/preprocessing/third/densepose/engine/trainer.py b/motion-gan-pipeline/preprocessing/third/densepose/engine/trainer.py new file mode 100644 index 0000000..1c25131 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/engine/trainer.py @@ -0,0 +1,257 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import logging +import os +from collections import OrderedDict +from typing import List, Optional, Union +import torch +from torch import nn + +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import CfgNode +from detectron2.engine import DefaultTrainer +from detectron2.evaluation import ( + DatasetEvaluator, + DatasetEvaluators, + inference_on_dataset, + print_csv_format, +) +from detectron2.solver.build import get_default_optimizer_params, maybe_add_gradient_clipping +from detectron2.utils import comm +from detectron2.utils.events import EventWriter, get_event_storage + +from densepose import DensePoseDatasetMapperTTA, DensePoseGeneralizedRCNNWithTTA, load_from_cfg +from densepose.data import ( + DatasetMapper, + build_combined_loader, + build_detection_test_loader, + build_detection_train_loader, + build_inference_based_loaders, + has_inference_based_loaders, +) +from densepose.evaluation.d2_evaluator_adapter import Detectron2COCOEvaluatorAdapter +from densepose.evaluation.evaluator import DensePoseCOCOEvaluator, build_densepose_evaluator_storage +from densepose.modeling.cse import Embedder + + +class SampleCountingLoader: + def __init__(self, loader): + self.loader = loader + + def __iter__(self): + it = iter(self.loader) + storage = get_event_storage() + while True: + try: + batch = next(it) + num_inst_per_dataset = {} + for data in batch: + dataset_name = data["dataset"] + if dataset_name not in num_inst_per_dataset: + num_inst_per_dataset[dataset_name] = 0 + num_inst = len(data["instances"]) + num_inst_per_dataset[dataset_name] += num_inst + for dataset_name in num_inst_per_dataset: + storage.put_scalar(f"batch/{dataset_name}", num_inst_per_dataset[dataset_name]) + yield batch + except StopIteration: + break + + +class SampleCountMetricPrinter(EventWriter): + def __init__(self): + self.logger = logging.getLogger(__name__) + + def write(self): + storage = get_event_storage() + batch_stats_strs = [] + for key, buf in storage.histories().items(): + if key.startswith("batch/"): + batch_stats_strs.append(f"{key} {buf.avg(20)}") + self.logger.info(", ".join(batch_stats_strs)) + + +class Trainer(DefaultTrainer): + @classmethod + def extract_embedder_from_model(cls, model: nn.Module) -> Optional[Embedder]: + if isinstance(model, nn.parallel.DistributedDataParallel): + model = model.module + if hasattr(model, "roi_heads") and hasattr(model.roi_heads, "embedder"): + return model.roi_heads.embedder + return None + + # TODO: the only reason to copy the base class code here is to pass the embedder from + # the model to the evaluator; that should be refactored to avoid unnecessary copy-pasting + @classmethod + def test( + cls, + cfg: CfgNode, + model: nn.Module, + evaluators: Optional[Union[DatasetEvaluator, List[DatasetEvaluator]]] = None, + ): + """ + Args: + cfg (CfgNode): + model (nn.Module): + evaluators (DatasetEvaluator, list[DatasetEvaluator] or None): if None, will call + :meth:`build_evaluator`. Otherwise, must have the same length as + ``cfg.DATASETS.TEST``. + + Returns: + dict: a dict of result metrics + """ + logger = logging.getLogger(__name__) + if isinstance(evaluators, DatasetEvaluator): + evaluators = [evaluators] + if evaluators is not None: + assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format( + len(cfg.DATASETS.TEST), len(evaluators) + ) + + results = OrderedDict() + for idx, dataset_name in enumerate(cfg.DATASETS.TEST): + data_loader = cls.build_test_loader(cfg, dataset_name) + # When evaluators are passed in as arguments, + # implicitly assume that evaluators can be created before data_loader. + if evaluators is not None: + evaluator = evaluators[idx] + else: + try: + embedder = cls.extract_embedder_from_model(model) + evaluator = cls.build_evaluator(cfg, dataset_name, embedder=embedder) + except NotImplementedError: + logger.warn( + "No evaluator found. Use `DefaultTrainer.test(evaluators=)`, " + "or implement its `build_evaluator` method." + ) + results[dataset_name] = {} + continue + if cfg.DENSEPOSE_EVALUATION.DISTRIBUTED_INFERENCE or comm.is_main_process(): + results_i = inference_on_dataset(model, data_loader, evaluator) + else: + results_i = {} + results[dataset_name] = results_i + if comm.is_main_process(): + assert isinstance( + results_i, dict + ), "Evaluator must return a dict on the main process. Got {} instead.".format( + results_i + ) + logger.info("Evaluation results for {} in csv format:".format(dataset_name)) + print_csv_format(results_i) + + if len(results) == 1: + results = list(results.values())[0] + return results + + @classmethod + def build_evaluator( + cls, + cfg: CfgNode, + dataset_name: str, + output_folder: Optional[str] = None, + embedder: Optional[Embedder] = None, + ) -> DatasetEvaluators: + if output_folder is None: + output_folder = os.path.join(cfg.OUTPUT_DIR, "inference") + evaluators = [] + distributed = cfg.DENSEPOSE_EVALUATION.DISTRIBUTED_INFERENCE + # Note: we currently use COCO evaluator for both COCO and LVIS datasets + # to have compatible metrics. LVIS bbox evaluator could also be used + # with an adapter to properly handle filtered / mapped categories + # evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type + # if evaluator_type == "coco": + # evaluators.append(COCOEvaluator(dataset_name, output_dir=output_folder)) + # elif evaluator_type == "lvis": + # evaluators.append(LVISEvaluator(dataset_name, output_dir=output_folder)) + evaluators.append( + Detectron2COCOEvaluatorAdapter( + dataset_name, output_dir=output_folder, distributed=distributed + ) + ) + if cfg.MODEL.DENSEPOSE_ON: + storage = build_densepose_evaluator_storage(cfg, output_folder) + evaluators.append( + DensePoseCOCOEvaluator( + dataset_name, + distributed, + output_folder, + evaluator_type=cfg.DENSEPOSE_EVALUATION.TYPE, + min_iou_threshold=cfg.DENSEPOSE_EVALUATION.MIN_IOU_THRESHOLD, + storage=storage, + embedder=embedder, + should_evaluate_mesh_alignment=cfg.DENSEPOSE_EVALUATION.EVALUATE_MESH_ALIGNMENT, + mesh_alignment_mesh_names=cfg.DENSEPOSE_EVALUATION.MESH_ALIGNMENT_MESH_NAMES, + ) + ) + return DatasetEvaluators(evaluators) + + @classmethod + def build_optimizer(cls, cfg: CfgNode, model: nn.Module): + params = get_default_optimizer_params( + model, + base_lr=cfg.SOLVER.BASE_LR, + weight_decay_norm=cfg.SOLVER.WEIGHT_DECAY_NORM, + bias_lr_factor=cfg.SOLVER.BIAS_LR_FACTOR, + weight_decay_bias=cfg.SOLVER.WEIGHT_DECAY_BIAS, + overrides={ + "features": { + "lr": cfg.SOLVER.BASE_LR * cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.FEATURES_LR_FACTOR, + }, + "embeddings": { + "lr": cfg.SOLVER.BASE_LR * cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDING_LR_FACTOR, + }, + }, + ) + optimizer = torch.optim.SGD( + params, + cfg.SOLVER.BASE_LR, + momentum=cfg.SOLVER.MOMENTUM, + nesterov=cfg.SOLVER.NESTEROV, + weight_decay=cfg.SOLVER.WEIGHT_DECAY, + ) + return maybe_add_gradient_clipping(cfg, optimizer) + + @classmethod + def build_test_loader(cls, cfg: CfgNode, dataset_name): + return build_detection_test_loader(cfg, dataset_name, mapper=DatasetMapper(cfg, False)) + + @classmethod + def build_train_loader(cls, cfg: CfgNode): + data_loader = build_detection_train_loader(cfg, mapper=DatasetMapper(cfg, True)) + if not has_inference_based_loaders(cfg): + return data_loader + model = cls.build_model(cfg) + model.to(cfg.BOOTSTRAP_MODEL.DEVICE) + DetectionCheckpointer(model).resume_or_load(cfg.BOOTSTRAP_MODEL.WEIGHTS, resume=False) + inference_based_loaders, ratios = build_inference_based_loaders(cfg, model) + loaders = [data_loader] + inference_based_loaders + ratios = [1.0] + ratios + combined_data_loader = build_combined_loader(cfg, loaders, ratios) + sample_counting_loader = SampleCountingLoader(combined_data_loader) + return sample_counting_loader + + def build_writers(self): + writers = super().build_writers() + writers.append(SampleCountMetricPrinter()) + return writers + + @classmethod + def test_with_TTA(cls, cfg: CfgNode, model): + logger = logging.getLogger("detectron2.trainer") + # In the end of training, run an evaluation with TTA + # Only support some R-CNN models. + logger.info("Running inference with test-time augmentation ...") + transform_data = load_from_cfg(cfg) + model = DensePoseGeneralizedRCNNWithTTA( + cfg, model, transform_data, DensePoseDatasetMapperTTA(cfg) + ) + evaluators = [ + cls.build_evaluator( + cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA") + ) + for name in cfg.DATASETS.TEST + ] + res = cls.test(cfg, model, evaluators) # pyre-ignore[6] + res = OrderedDict({k + "_TTA": v for k, v in res.items()}) + return res diff --git a/motion-gan-pipeline/preprocessing/third/densepose/evaluation/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/evaluation/__init__.py new file mode 100644 index 0000000..e5ae1f2 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/evaluation/__init__.py @@ -0,0 +1,3 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from .evaluator import DensePoseCOCOEvaluator diff --git a/motion-gan-pipeline/preprocessing/third/densepose/evaluation/d2_evaluator_adapter.py b/motion-gan-pipeline/preprocessing/third/densepose/evaluation/d2_evaluator_adapter.py new file mode 100644 index 0000000..1fbc526 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/evaluation/d2_evaluator_adapter.py @@ -0,0 +1,50 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from detectron2.data.catalog import Metadata +from detectron2.evaluation import COCOEvaluator + +from densepose.data.datasets.coco import ( + get_contiguous_id_to_category_id_map, + maybe_filter_categories_cocoapi, +) + + +def _maybe_add_iscrowd_annotations(cocoapi) -> None: + for ann in cocoapi.dataset["annotations"]: + if "iscrowd" not in ann: + ann["iscrowd"] = 0 + + +class Detectron2COCOEvaluatorAdapter(COCOEvaluator): + def __init__( + self, + dataset_name, + output_dir=None, + distributed=True, + ): + super().__init__(dataset_name, output_dir=output_dir, distributed=distributed) + maybe_filter_categories_cocoapi(dataset_name, self._coco_api) + _maybe_add_iscrowd_annotations(self._coco_api) + # substitute category metadata to account for categories + # that are mapped to the same contiguous id + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + self._maybe_substitute_metadata() + + def _maybe_substitute_metadata(self): + cont_id_2_cat_id = get_contiguous_id_to_category_id_map(self._metadata) + cat_id_2_cont_id = self._metadata.thing_dataset_id_to_contiguous_id + if len(cont_id_2_cat_id) == len(cat_id_2_cont_id): + return + + cat_id_2_cont_id_injective = {} + for cat_id, cont_id in cat_id_2_cont_id.items(): + if (cont_id in cont_id_2_cat_id) and (cont_id_2_cat_id[cont_id] == cat_id): + cat_id_2_cont_id_injective[cat_id] = cont_id + + metadata_new = Metadata(name=self._metadata.name) + for key, value in self._metadata.__dict__.items(): + if key == "thing_dataset_id_to_contiguous_id": + setattr(metadata_new, key, cat_id_2_cont_id_injective) + else: + setattr(metadata_new, key, value) + self._metadata = metadata_new diff --git a/motion-gan-pipeline/preprocessing/third/densepose/evaluation/densepose_coco_evaluation.py b/motion-gan-pipeline/preprocessing/third/densepose/evaluation/densepose_coco_evaluation.py new file mode 100644 index 0000000..5cd507f --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/evaluation/densepose_coco_evaluation.py @@ -0,0 +1,1302 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# This is a modified version of cocoeval.py where we also have the densepose evaluation. + +__author__ = "tsungyi" + +import copy +import datetime +import logging +import numpy as np +import pickle +import time +from collections import defaultdict +from enum import Enum +from typing import Any, Dict, Tuple +import scipy.spatial.distance as ssd +import torch +import torch.nn.functional as F +from pycocotools import mask as maskUtils +from scipy.io import loadmat +from scipy.ndimage import zoom as spzoom + +from detectron2.utils.file_io import PathManager + +from densepose.converters.chart_output_to_chart_result import resample_uv_tensors_to_bbox +from densepose.converters.segm_to_mask import ( + resample_coarse_segm_tensor_to_bbox, + resample_fine_and_coarse_segm_tensors_to_bbox, +) +from densepose.modeling.cse.utils import squared_euclidean_distance_matrix +from densepose.structures import DensePoseDataRelative +from densepose.structures.mesh import create_mesh + +logger = logging.getLogger(__name__) + + +class DensePoseEvalMode(str, Enum): + # use both masks and geodesic distances (GPS * IOU) to compute scores + GPSM = "gpsm" + # use only geodesic distances (GPS) to compute scores + GPS = "gps" + # use only masks (IOU) to compute scores + IOU = "iou" + + +class DensePoseDataMode(str, Enum): + # use estimated IUV data (default mode) + IUV_DT = "iuvdt" + # use ground truth IUV data + IUV_GT = "iuvgt" + # use ground truth labels I and set UV to 0 + I_GT_UV_0 = "igtuv0" + # use ground truth labels I and estimated UV coordinates + I_GT_UV_DT = "igtuvdt" + # use estimated labels I and set UV to 0 + I_DT_UV_0 = "idtuv0" + + +class DensePoseCocoEval(object): + # Interface for evaluating detection on the Microsoft COCO dataset. + # + # The usage for CocoEval is as follows: + # cocoGt=..., cocoDt=... # load dataset and results + # E = CocoEval(cocoGt,cocoDt); # initialize CocoEval object + # E.params.recThrs = ...; # set parameters as desired + # E.evaluate(); # run per image evaluation + # E.accumulate(); # accumulate per image results + # E.summarize(); # display summary metrics of results + # For example usage see evalDemo.m and http://mscoco.org/. + # + # The evaluation parameters are as follows (defaults in brackets): + # imgIds - [all] N img ids to use for evaluation + # catIds - [all] K cat ids to use for evaluation + # iouThrs - [.5:.05:.95] T=10 IoU thresholds for evaluation + # recThrs - [0:.01:1] R=101 recall thresholds for evaluation + # areaRng - [...] A=4 object area ranges for evaluation + # maxDets - [1 10 100] M=3 thresholds on max detections per image + # iouType - ['segm'] set iouType to 'segm', 'bbox', 'keypoints' or 'densepose' + # iouType replaced the now DEPRECATED useSegm parameter. + # useCats - [1] if true use category labels for evaluation + # Note: if useCats=0 category labels are ignored as in proposal scoring. + # Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified. + # + # evaluate(): evaluates detections on every image and every category and + # concats the results into the "evalImgs" with fields: + # dtIds - [1xD] id for each of the D detections (dt) + # gtIds - [1xG] id for each of the G ground truths (gt) + # dtMatches - [TxD] matching gt id at each IoU or 0 + # gtMatches - [TxG] matching dt id at each IoU or 0 + # dtScores - [1xD] confidence of each dt + # gtIgnore - [1xG] ignore flag for each gt + # dtIgnore - [TxD] ignore flag for each dt at each IoU + # + # accumulate(): accumulates the per-image, per-category evaluation + # results in "evalImgs" into the dictionary "eval" with fields: + # params - parameters used for evaluation + # date - date evaluation was performed + # counts - [T,R,K,A,M] parameter dimensions (see above) + # precision - [TxRxKxAxM] precision for every evaluation setting + # recall - [TxKxAxM] max recall for every evaluation setting + # Note: precision and recall==-1 for settings with no gt objects. + # + # See also coco, mask, pycocoDemo, pycocoEvalDemo + # + # Microsoft COCO Toolbox. version 2.0 + # Data, paper, and tutorials available at: http://mscoco.org/ + # Code written by Piotr Dollar and Tsung-Yi Lin, 2015. + # Licensed under the Simplified BSD License [see coco/license.txt] + def __init__( + self, + cocoGt=None, + cocoDt=None, + iouType: str = "densepose", + multi_storage=None, + embedder=None, + dpEvalMode: DensePoseEvalMode = DensePoseEvalMode.GPS, + dpDataMode: DensePoseDataMode = DensePoseDataMode.IUV_DT, + ): + """ + Initialize CocoEval using coco APIs for gt and dt + :param cocoGt: coco object with ground truth annotations + :param cocoDt: coco object with detection results + :return: None + """ + self.cocoGt = cocoGt # ground truth COCO API + self.cocoDt = cocoDt # detections COCO API + self.multi_storage = multi_storage + self.embedder = embedder + self._dpEvalMode = dpEvalMode + self._dpDataMode = dpDataMode + self.evalImgs = defaultdict(list) # per-image per-category eval results [KxAxI] + self.eval = {} # accumulated evaluation results + self._gts = defaultdict(list) # gt for evaluation + self._dts = defaultdict(list) # dt for evaluation + self.params = Params(iouType=iouType) # parameters + self._paramsEval = {} # parameters for evaluation + self.stats = [] # result summarization + self.ious = {} # ious between all gts and dts + if cocoGt is not None: + self.params.imgIds = sorted(cocoGt.getImgIds()) + self.params.catIds = sorted(cocoGt.getCatIds()) + self.ignoreThrBB = 0.7 + self.ignoreThrUV = 0.9 + + def _loadGEval(self): + smpl_subdiv_fpath = PathManager.get_local_path( + "https://dl.fbaipublicfiles.com/densepose/data/SMPL_subdiv.mat" + ) + pdist_transform_fpath = PathManager.get_local_path( + "https://dl.fbaipublicfiles.com/densepose/data/SMPL_SUBDIV_TRANSFORM.mat" + ) + pdist_matrix_fpath = PathManager.get_local_path( + "https://dl.fbaipublicfiles.com/densepose/data/Pdist_matrix.pkl", timeout_sec=120 + ) + SMPL_subdiv = loadmat(smpl_subdiv_fpath) + self.PDIST_transform = loadmat(pdist_transform_fpath) + self.PDIST_transform = self.PDIST_transform["index"].squeeze() + UV = np.array([SMPL_subdiv["U_subdiv"], SMPL_subdiv["V_subdiv"]]).squeeze() + ClosestVertInds = np.arange(UV.shape[1]) + 1 + self.Part_UVs = [] + self.Part_ClosestVertInds = [] + for i in np.arange(24): + self.Part_UVs.append(UV[:, SMPL_subdiv["Part_ID_subdiv"].squeeze() == (i + 1)]) + self.Part_ClosestVertInds.append( + ClosestVertInds[SMPL_subdiv["Part_ID_subdiv"].squeeze() == (i + 1)] + ) + + with open(pdist_matrix_fpath, "rb") as hFile: + arrays = pickle.load(hFile, encoding="latin1") + self.Pdist_matrix = arrays["Pdist_matrix"] + self.Part_ids = np.array(SMPL_subdiv["Part_ID_subdiv"].squeeze()) + # Mean geodesic distances for parts. + self.Mean_Distances = np.array([0, 0.351, 0.107, 0.126, 0.237, 0.173, 0.142, 0.128, 0.150]) + # Coarse Part labels. + self.CoarseParts = np.array( + [0, 1, 1, 2, 2, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8] + ) + + def _prepare(self): + """ + Prepare ._gts and ._dts for evaluation based on params + :return: None + """ + + def _toMask(anns, coco): + # modify ann['segmentation'] by reference + for ann in anns: + # safeguard for invalid segmentation annotation; + # annotations containing empty lists exist in the posetrack + # dataset. This is not a correct segmentation annotation + # in terms of COCO format; we need to deal with it somehow + segm = ann["segmentation"] + if type(segm) == list and len(segm) == 0: + ann["segmentation"] = None + continue + rle = coco.annToRLE(ann) + ann["segmentation"] = rle + + def _getIgnoreRegion(iid, coco): + img = coco.imgs[iid] + + if "ignore_regions_x" not in img.keys(): + return None + + if len(img["ignore_regions_x"]) == 0: + return None + + rgns_merged = [ + [v for xy in zip(region_x, region_y) for v in xy] + for region_x, region_y in zip(img["ignore_regions_x"], img["ignore_regions_y"]) + ] + rles = maskUtils.frPyObjects(rgns_merged, img["height"], img["width"]) + rle = maskUtils.merge(rles) + return maskUtils.decode(rle) + + def _checkIgnore(dt, iregion): + if iregion is None: + return True + + bb = np.array(dt["bbox"]).astype(np.int) + x1, y1, x2, y2 = bb[0], bb[1], bb[0] + bb[2], bb[1] + bb[3] + x2 = min([x2, iregion.shape[1]]) + y2 = min([y2, iregion.shape[0]]) + + if bb[2] * bb[3] == 0: + return False + + crop_iregion = iregion[y1:y2, x1:x2] + + if crop_iregion.sum() == 0: + return True + + if "densepose" not in dt.keys(): # filtering boxes + return crop_iregion.sum() / bb[2] / bb[3] < self.ignoreThrBB + + # filtering UVs + ignoremask = np.require(crop_iregion, requirements=["F"]) + mask = self._extract_mask(dt) + uvmask = np.require(np.asarray(mask > 0), dtype=np.uint8, requirements=["F"]) + uvmask_ = maskUtils.encode(uvmask) + ignoremask_ = maskUtils.encode(ignoremask) + uviou = maskUtils.iou([uvmask_], [ignoremask_], [1])[0] + return uviou < self.ignoreThrUV + + p = self.params + + if p.useCats: + gts = self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) + dts = self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) + else: + gts = self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds)) + dts = self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds)) + + imns = self.cocoGt.loadImgs(p.imgIds) + self.size_mapping = {} + for im in imns: + self.size_mapping[im["id"]] = [im["height"], im["width"]] + + # if iouType == 'uv', add point gt annotations + if p.iouType == "densepose": + self._loadGEval() + + # convert ground truth to mask if iouType == 'segm' + if p.iouType == "segm": + _toMask(gts, self.cocoGt) + _toMask(dts, self.cocoDt) + + # set ignore flag + for gt in gts: + gt["ignore"] = gt["ignore"] if "ignore" in gt else 0 + gt["ignore"] = "iscrowd" in gt and gt["iscrowd"] + if p.iouType == "keypoints": + gt["ignore"] = (gt["num_keypoints"] == 0) or gt["ignore"] + if p.iouType == "densepose": + gt["ignore"] = ("dp_x" in gt) == 0 + if p.iouType == "segm": + gt["ignore"] = gt["segmentation"] is None + + self._gts = defaultdict(list) # gt for evaluation + self._dts = defaultdict(list) # dt for evaluation + self._igrgns = defaultdict(list) + + for gt in gts: + iid = gt["image_id"] + if iid not in self._igrgns.keys(): + self._igrgns[iid] = _getIgnoreRegion(iid, self.cocoGt) + if _checkIgnore(gt, self._igrgns[iid]): + self._gts[iid, gt["category_id"]].append(gt) + for dt in dts: + iid = dt["image_id"] + if (iid not in self._igrgns) or _checkIgnore(dt, self._igrgns[iid]): + self._dts[iid, dt["category_id"]].append(dt) + + self.evalImgs = defaultdict(list) # per-image per-category evaluation results + self.eval = {} # accumulated evaluation results + + def evaluate(self): + """ + Run per image evaluation on given images and store results (a list of dict) in self.evalImgs + :return: None + """ + tic = time.time() + logger.info("Running per image DensePose evaluation... {}".format(self.params.iouType)) + p = self.params + # add backward compatibility if useSegm is specified in params + if p.useSegm is not None: + p.iouType = "segm" if p.useSegm == 1 else "bbox" + logger.info("useSegm (deprecated) is not None. Running DensePose evaluation") + p.imgIds = list(np.unique(p.imgIds)) + if p.useCats: + p.catIds = list(np.unique(p.catIds)) + p.maxDets = sorted(p.maxDets) + self.params = p + + self._prepare() + # loop through images, area range, max detection number + catIds = p.catIds if p.useCats else [-1] + + if p.iouType in ["segm", "bbox"]: + computeIoU = self.computeIoU + elif p.iouType == "keypoints": + computeIoU = self.computeOks + elif p.iouType == "densepose": + computeIoU = self.computeOgps + if self._dpEvalMode in {DensePoseEvalMode.GPSM, DensePoseEvalMode.IOU}: + self.real_ious = { + (imgId, catId): self.computeDPIoU(imgId, catId) + for imgId in p.imgIds + for catId in catIds + } + + self.ious = { + (imgId, catId): computeIoU(imgId, catId) for imgId in p.imgIds for catId in catIds + } + + evaluateImg = self.evaluateImg + maxDet = p.maxDets[-1] + self.evalImgs = [ + evaluateImg(imgId, catId, areaRng, maxDet) + for catId in catIds + for areaRng in p.areaRng + for imgId in p.imgIds + ] + self._paramsEval = copy.deepcopy(self.params) + toc = time.time() + logger.info("DensePose evaluation DONE (t={:0.2f}s).".format(toc - tic)) + + def getDensePoseMask(self, polys): + maskGen = np.zeros([256, 256]) + stop = min(len(polys) + 1, 15) + for i in range(1, stop): + if polys[i - 1]: + currentMask = maskUtils.decode(polys[i - 1]) + maskGen[currentMask > 0] = i + return maskGen + + def _generate_rlemask_on_image(self, mask, imgId, data): + bbox_xywh = np.array(data["bbox"]) + x, y, w, h = bbox_xywh + im_h, im_w = self.size_mapping[imgId] + im_mask = np.zeros((im_h, im_w), dtype=np.uint8) + if mask is not None: + x0 = max(int(x), 0) + x1 = min(int(x + w), im_w, int(x) + mask.shape[1]) + y0 = max(int(y), 0) + y1 = min(int(y + h), im_h, int(y) + mask.shape[0]) + y = int(y) + x = int(x) + im_mask[y0:y1, x0:x1] = mask[y0 - y : y1 - y, x0 - x : x1 - x] + im_mask = np.require(np.asarray(im_mask > 0), dtype=np.uint8, requirements=["F"]) + rle_mask = maskUtils.encode(np.array(im_mask[:, :, np.newaxis], order="F"))[0] + return rle_mask + + def computeDPIoU(self, imgId, catId): + p = self.params + if p.useCats: + gt = self._gts[imgId, catId] + dt = self._dts[imgId, catId] + else: + gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]] + dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]] + if len(gt) == 0 and len(dt) == 0: + return [] + inds = np.argsort([-d["score"] for d in dt], kind="mergesort") + dt = [dt[i] for i in inds] + if len(dt) > p.maxDets[-1]: + dt = dt[0 : p.maxDets[-1]] + + gtmasks = [] + for g in gt: + if DensePoseDataRelative.S_KEY in g: + # convert DensePose mask to a binary mask + mask = np.minimum(self.getDensePoseMask(g[DensePoseDataRelative.S_KEY]), 1.0) + _, _, w, h = g["bbox"] + scale_x = float(max(w, 1)) / mask.shape[1] + scale_y = float(max(h, 1)) / mask.shape[0] + mask = spzoom(mask, (scale_y, scale_x), order=1, prefilter=False) + mask = np.array(mask > 0.5, dtype=np.uint8) + rle_mask = self._generate_rlemask_on_image(mask, imgId, g) + elif "segmentation" in g: + segmentation = g["segmentation"] + if isinstance(segmentation, list) and segmentation: + # polygons + im_h, im_w = self.size_mapping[imgId] + rles = maskUtils.frPyObjects(segmentation, im_h, im_w) + rle_mask = maskUtils.merge(rles) + elif isinstance(segmentation, dict): + if isinstance(segmentation["counts"], list): + # uncompressed RLE + im_h, im_w = self.size_mapping[imgId] + rle_mask = maskUtils.frPyObjects(segmentation, im_h, im_w) + else: + # compressed RLE + rle_mask = segmentation + else: + rle_mask = self._generate_rlemask_on_image(None, imgId, g) + else: + rle_mask = self._generate_rlemask_on_image(None, imgId, g) + gtmasks.append(rle_mask) + + dtmasks = [] + for d in dt: + mask = self._extract_mask(d) + mask = np.require(np.asarray(mask > 0), dtype=np.uint8, requirements=["F"]) + rle_mask = self._generate_rlemask_on_image(mask, imgId, d) + dtmasks.append(rle_mask) + + # compute iou between each dt and gt region + iscrowd = [int(o.get("iscrowd", 0)) for o in gt] + iousDP = maskUtils.iou(dtmasks, gtmasks, iscrowd) + return iousDP + + def computeIoU(self, imgId, catId): + p = self.params + if p.useCats: + gt = self._gts[imgId, catId] + dt = self._dts[imgId, catId] + else: + gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]] + dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]] + if len(gt) == 0 and len(dt) == 0: + return [] + inds = np.argsort([-d["score"] for d in dt], kind="mergesort") + dt = [dt[i] for i in inds] + if len(dt) > p.maxDets[-1]: + dt = dt[0 : p.maxDets[-1]] + + if p.iouType == "segm": + g = [g["segmentation"] for g in gt if g["segmentation"] is not None] + d = [d["segmentation"] for d in dt if d["segmentation"] is not None] + elif p.iouType == "bbox": + g = [g["bbox"] for g in gt] + d = [d["bbox"] for d in dt] + else: + raise Exception("unknown iouType for iou computation") + + # compute iou between each dt and gt region + iscrowd = [int(o.get("iscrowd", 0)) for o in gt] + ious = maskUtils.iou(d, g, iscrowd) + return ious + + def computeOks(self, imgId, catId): + p = self.params + # dimension here should be Nxm + gts = self._gts[imgId, catId] + dts = self._dts[imgId, catId] + inds = np.argsort([-d["score"] for d in dts], kind="mergesort") + dts = [dts[i] for i in inds] + if len(dts) > p.maxDets[-1]: + dts = dts[0 : p.maxDets[-1]] + # if len(gts) == 0 and len(dts) == 0: + if len(gts) == 0 or len(dts) == 0: + return [] + ious = np.zeros((len(dts), len(gts))) + sigmas = ( + np.array( + [ + 0.26, + 0.25, + 0.25, + 0.35, + 0.35, + 0.79, + 0.79, + 0.72, + 0.72, + 0.62, + 0.62, + 1.07, + 1.07, + 0.87, + 0.87, + 0.89, + 0.89, + ] + ) + / 10.0 + ) + vars = (sigmas * 2) ** 2 + k = len(sigmas) + # compute oks between each detection and ground truth object + for j, gt in enumerate(gts): + # create bounds for ignore regions(double the gt bbox) + g = np.array(gt["keypoints"]) + xg = g[0::3] + yg = g[1::3] + vg = g[2::3] + k1 = np.count_nonzero(vg > 0) + bb = gt["bbox"] + x0 = bb[0] - bb[2] + x1 = bb[0] + bb[2] * 2 + y0 = bb[1] - bb[3] + y1 = bb[1] + bb[3] * 2 + for i, dt in enumerate(dts): + d = np.array(dt["keypoints"]) + xd = d[0::3] + yd = d[1::3] + if k1 > 0: + # measure the per-keypoint distance if keypoints visible + dx = xd - xg + dy = yd - yg + else: + # measure minimum distance to keypoints in (x0,y0) & (x1,y1) + z = np.zeros(k) + dx = np.max((z, x0 - xd), axis=0) + np.max((z, xd - x1), axis=0) + dy = np.max((z, y0 - yd), axis=0) + np.max((z, yd - y1), axis=0) + e = (dx ** 2 + dy ** 2) / vars / (gt["area"] + np.spacing(1)) / 2 + if k1 > 0: + e = e[vg > 0] + ious[i, j] = np.sum(np.exp(-e)) / e.shape[0] + return ious + + def _extract_mask(self, dt: Dict[str, Any]) -> np.ndarray: + if "densepose" in dt: + densepose_results_quantized = dt["densepose"] + return densepose_results_quantized.labels_uv_uint8[0].numpy() + elif "cse_mask" in dt: + return dt["cse_mask"] + elif "coarse_segm" in dt: + dy = max(int(dt["bbox"][3]), 1) + dx = max(int(dt["bbox"][2]), 1) + return ( + # pyre-fixme[16]: `Tensor` has no attribute `argmax`. + F.interpolate( + # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got + # `Tuple[int, int]`. + dt["coarse_segm"].unsqueeze(0), (dy, dx), mode="bilinear", align_corners=False + ) + .squeeze(0) + .argmax(0) + .numpy() + .astype(np.uint8) + ) + elif "record_id" in dt: + assert ( + self.multi_storage is not None + ), f"Storage record id encountered in a detection {dt}, but no storage provided!" + record = self.multi_storage.get(dt["rank"], dt["record_id"]) + coarse_segm = record["coarse_segm"] + dy = max(int(dt["bbox"][3]), 1) + dx = max(int(dt["bbox"][2]), 1) + return ( + F.interpolate( + # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got + # `Tuple[int, int]`. + coarse_segm.unsqueeze(0), (dy, dx), mode="bilinear", align_corners=False + ) + .squeeze(0) + .argmax(0) + .numpy() + .astype(np.uint8) + ) + else: + raise Exception(f"No mask data in the detection: {dt}") + raise ValueError('The prediction dict needs to contain either "densepose" or "cse_mask"') + + def _extract_iuv( + self, densepose_data: np.ndarray, py: np.ndarray, px: np.ndarray, gt: Dict[str, Any] + ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + Extract arrays of I, U and V values at given points as numpy arrays + given the data mode stored in self._dpDataMode + """ + if self._dpDataMode == DensePoseDataMode.IUV_DT: + # estimated labels and UV (default) + ipoints = densepose_data[0, py, px] + upoints = densepose_data[1, py, px] / 255.0 # convert from uint8 by /255. + vpoints = densepose_data[2, py, px] / 255.0 + elif self._dpDataMode == DensePoseDataMode.IUV_GT: + # ground truth + ipoints = np.array(gt["dp_I"]) + upoints = np.array(gt["dp_U"]) + vpoints = np.array(gt["dp_V"]) + elif self._dpDataMode == DensePoseDataMode.I_GT_UV_0: + # ground truth labels, UV = 0 + ipoints = np.array(gt["dp_I"]) + upoints = upoints * 0.0 + vpoints = vpoints * 0.0 + elif self._dpDataMode == DensePoseDataMode.I_GT_UV_DT: + # ground truth labels, estimated UV + ipoints = np.array(gt["dp_I"]) + upoints = densepose_data[1, py, px] / 255.0 # convert from uint8 by /255. + vpoints = densepose_data[2, py, px] / 255.0 + elif self._dpDataMode == DensePoseDataMode.I_DT_UV_0: + # estimated labels, UV = 0 + ipoints = densepose_data[0, py, px] + upoints = upoints * 0.0 + vpoints = vpoints * 0.0 + else: + raise ValueError(f"Unknown data mode: {self._dpDataMode}") + return ipoints, upoints, vpoints + + def computeOgps_single_pair(self, dt, gt, py, px, pt_mask): + if "densepose" in dt: + ipoints, upoints, vpoints = self.extract_iuv_from_quantized(dt, gt, py, px, pt_mask) + return self.computeOgps_single_pair_iuv(dt, gt, ipoints, upoints, vpoints) + elif "u" in dt: + ipoints, upoints, vpoints = self.extract_iuv_from_raw(dt, gt, py, px, pt_mask) + return self.computeOgps_single_pair_iuv(dt, gt, ipoints, upoints, vpoints) + elif "record_id" in dt: + assert ( + self.multi_storage is not None + ), f"Storage record id encountered in detection {dt}, but no storage provided!" + record = self.multi_storage.get(dt["rank"], dt["record_id"]) + record["bbox"] = dt["bbox"] + if "u" in record: + ipoints, upoints, vpoints = self.extract_iuv_from_raw(record, gt, py, px, pt_mask) + return self.computeOgps_single_pair_iuv(dt, gt, ipoints, upoints, vpoints) + elif "embedding" in record: + return self.computeOgps_single_pair_cse( + dt, + gt, + py, + px, + pt_mask, + record["coarse_segm"], + record["embedding"], + record["bbox"], + ) + else: + raise Exception(f"Unknown record format: {record}") + elif "embedding" in dt: + return self.computeOgps_single_pair_cse( + dt, gt, py, px, pt_mask, dt["coarse_segm"], dt["embedding"], dt["bbox"] + ) + raise Exception(f"Unknown detection format: {dt}") + + def extract_iuv_from_quantized(self, dt, gt, py, px, pt_mask): + densepose_results_quantized = dt["densepose"] + ipoints, upoints, vpoints = self._extract_iuv( + densepose_results_quantized.labels_uv_uint8.numpy(), py, px, gt + ) + ipoints[pt_mask == -1] = 0 + return ipoints, upoints, vpoints + + def extract_iuv_from_raw(self, dt, gt, py, px, pt_mask): + labels_dt = resample_fine_and_coarse_segm_tensors_to_bbox( + dt["fine_segm"].unsqueeze(0), + dt["coarse_segm"].unsqueeze(0), + dt["bbox"], + ) + uv = resample_uv_tensors_to_bbox( + dt["u"].unsqueeze(0), dt["v"].unsqueeze(0), labels_dt.squeeze(0), dt["bbox"] + ) + labels_uv_uint8 = torch.cat((labels_dt.byte(), (uv * 255).clamp(0, 255).byte())) + ipoints, upoints, vpoints = self._extract_iuv(labels_uv_uint8.numpy(), py, px, gt) + ipoints[pt_mask == -1] = 0 + return ipoints, upoints, vpoints + + def computeOgps_single_pair_iuv(self, dt, gt, ipoints, upoints, vpoints): + cVertsGT, ClosestVertsGTTransformed = self.findAllClosestVertsGT(gt) + cVerts = self.findAllClosestVertsUV(upoints, vpoints, ipoints) + # Get pairwise geodesic distances between gt and estimated mesh points. + dist = self.getDistancesUV(ClosestVertsGTTransformed, cVerts) + # Compute the Ogps measure. + # Find the mean geodesic normalization distance for + # each GT point, based on which part it is on. + Current_Mean_Distances = self.Mean_Distances[ + self.CoarseParts[self.Part_ids[cVertsGT[cVertsGT > 0].astype(int) - 1]] + ] + return dist, Current_Mean_Distances + + def computeOgps_single_pair_cse( + self, dt, gt, py, px, pt_mask, coarse_segm, embedding, bbox_xywh_abs + ): + # 0-based mesh vertex indices + cVertsGT = torch.as_tensor(gt["dp_vertex"], dtype=torch.int64) + # label for each pixel of the bbox, [H, W] tensor of long + labels_dt = resample_coarse_segm_tensor_to_bbox( + coarse_segm.unsqueeze(0), bbox_xywh_abs + ).squeeze(0) + x, y, w, h = bbox_xywh_abs + # embedding for each pixel of the bbox, [D, H, W] tensor of float32 + embedding = F.interpolate( + embedding.unsqueeze(0), (int(h), int(w)), mode="bilinear", align_corners=False + ).squeeze(0) + # valid locations py, px + py_pt = torch.from_numpy(py[pt_mask > -1]) + px_pt = torch.from_numpy(px[pt_mask > -1]) + cVerts = torch.ones_like(cVertsGT) * -1 + cVerts[pt_mask > -1] = self.findClosestVertsCse( + embedding, py_pt, px_pt, labels_dt, gt["ref_model"] + ) + # Get pairwise geodesic distances between gt and estimated mesh points. + dist = self.getDistancesCse(cVertsGT, cVerts, gt["ref_model"]) + # normalize distances + if (gt["ref_model"] == "smpl_27554") and ("dp_I" in gt): + Current_Mean_Distances = self.Mean_Distances[ + self.CoarseParts[np.array(gt["dp_I"], dtype=int)] + ] + else: + Current_Mean_Distances = 0.255 + return dist, Current_Mean_Distances + + def computeOgps(self, imgId, catId): + p = self.params + # dimension here should be Nxm + g = self._gts[imgId, catId] + d = self._dts[imgId, catId] + inds = np.argsort([-d_["score"] for d_ in d], kind="mergesort") + d = [d[i] for i in inds] + if len(d) > p.maxDets[-1]: + d = d[0 : p.maxDets[-1]] + # if len(gts) == 0 and len(dts) == 0: + if len(g) == 0 or len(d) == 0: + return [] + ious = np.zeros((len(d), len(g))) + # compute opgs between each detection and ground truth object + # sigma = self.sigma #0.255 # dist = 0.3m corresponds to ogps = 0.5 + # 1 # dist = 0.3m corresponds to ogps = 0.96 + # 1.45 # dist = 1.7m (person height) corresponds to ogps = 0.5) + for j, gt in enumerate(g): + if not gt["ignore"]: + g_ = gt["bbox"] + for i, dt in enumerate(d): + # + dy = int(dt["bbox"][3]) + dx = int(dt["bbox"][2]) + dp_x = np.array(gt["dp_x"]) * g_[2] / 255.0 + dp_y = np.array(gt["dp_y"]) * g_[3] / 255.0 + py = (dp_y + g_[1] - dt["bbox"][1]).astype(np.int) + px = (dp_x + g_[0] - dt["bbox"][0]).astype(np.int) + # + pts = np.zeros(len(px)) + pts[px >= dx] = -1 + pts[py >= dy] = -1 + pts[px < 0] = -1 + pts[py < 0] = -1 + if len(pts) < 1: + ogps = 0.0 + elif np.max(pts) == -1: + ogps = 0.0 + else: + px[pts == -1] = 0 + py[pts == -1] = 0 + dists_between_matches, dist_norm_coeffs = self.computeOgps_single_pair( + dt, gt, py, px, pts + ) + # Compute gps + ogps_values = np.exp( + -(dists_between_matches ** 2) / (2 * (dist_norm_coeffs ** 2)) + ) + # + ogps = np.mean(ogps_values) if len(ogps_values) > 0 else 0.0 + ious[i, j] = ogps + + gbb = [gt["bbox"] for gt in g] + dbb = [dt["bbox"] for dt in d] + + # compute iou between each dt and gt region + iscrowd = [int(o.get("iscrowd", 0)) for o in g] + ious_bb = maskUtils.iou(dbb, gbb, iscrowd) + return ious, ious_bb + + def evaluateImg(self, imgId, catId, aRng, maxDet): + """ + perform evaluation for single category and image + :return: dict (single image results) + """ + + p = self.params + if p.useCats: + gt = self._gts[imgId, catId] + dt = self._dts[imgId, catId] + else: + gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]] + dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]] + if len(gt) == 0 and len(dt) == 0: + return None + + for g in gt: + # g['_ignore'] = g['ignore'] + if g["ignore"] or (g["area"] < aRng[0] or g["area"] > aRng[1]): + g["_ignore"] = True + else: + g["_ignore"] = False + + # sort dt highest score first, sort gt ignore last + gtind = np.argsort([g["_ignore"] for g in gt], kind="mergesort") + gt = [gt[i] for i in gtind] + dtind = np.argsort([-d["score"] for d in dt], kind="mergesort") + dt = [dt[i] for i in dtind[0:maxDet]] + iscrowd = [int(o.get("iscrowd", 0)) for o in gt] + # load computed ious + if p.iouType == "densepose": + # print('Checking the length', len(self.ious[imgId, catId])) + # if len(self.ious[imgId, catId]) == 0: + # print(self.ious[imgId, catId]) + ious = ( + self.ious[imgId, catId][0][:, gtind] + if len(self.ious[imgId, catId]) > 0 + else self.ious[imgId, catId] + ) + ioubs = ( + self.ious[imgId, catId][1][:, gtind] + if len(self.ious[imgId, catId]) > 0 + else self.ious[imgId, catId] + ) + if self._dpEvalMode in {DensePoseEvalMode.GPSM, DensePoseEvalMode.IOU}: + iousM = ( + self.real_ious[imgId, catId][:, gtind] + if len(self.real_ious[imgId, catId]) > 0 + else self.real_ious[imgId, catId] + ) + else: + ious = ( + self.ious[imgId, catId][:, gtind] + if len(self.ious[imgId, catId]) > 0 + else self.ious[imgId, catId] + ) + + T = len(p.iouThrs) + G = len(gt) + D = len(dt) + gtm = np.zeros((T, G)) + dtm = np.zeros((T, D)) + gtIg = np.array([g["_ignore"] for g in gt]) + dtIg = np.zeros((T, D)) + if np.all(gtIg) and p.iouType == "densepose": + dtIg = np.logical_or(dtIg, True) + + if len(ious) > 0: # and not p.iouType == 'densepose': + for tind, t in enumerate(p.iouThrs): + for dind, d in enumerate(dt): + # information about best match so far (m=-1 -> unmatched) + iou = min([t, 1 - 1e-10]) + m = -1 + for gind, _g in enumerate(gt): + # if this gt already matched, and not a crowd, continue + if gtm[tind, gind] > 0 and not iscrowd[gind]: + continue + # if dt matched to reg gt, and on ignore gt, stop + if m > -1 and gtIg[m] == 0 and gtIg[gind] == 1: + break + if p.iouType == "densepose": + if self._dpEvalMode == DensePoseEvalMode.GPSM: + new_iou = np.sqrt(iousM[dind, gind] * ious[dind, gind]) + elif self._dpEvalMode == DensePoseEvalMode.IOU: + new_iou = iousM[dind, gind] + elif self._dpEvalMode == DensePoseEvalMode.GPS: + new_iou = ious[dind, gind] + else: + new_iou = ious[dind, gind] + if new_iou < iou: + continue + if new_iou == 0.0: + continue + # if match successful and best so far, store appropriately + iou = new_iou + m = gind + # if match made store id of match for both dt and gt + if m == -1: + continue + dtIg[tind, dind] = gtIg[m] + dtm[tind, dind] = gt[m]["id"] + gtm[tind, m] = d["id"] + + if p.iouType == "densepose": + if not len(ioubs) == 0: + for dind, d in enumerate(dt): + # information about best match so far (m=-1 -> unmatched) + if dtm[tind, dind] == 0: + ioub = 0.8 + m = -1 + for gind, _g in enumerate(gt): + # if this gt already matched, and not a crowd, continue + if gtm[tind, gind] > 0 and not iscrowd[gind]: + continue + # continue to next gt unless better match made + if ioubs[dind, gind] < ioub: + continue + # if match successful and best so far, store appropriately + ioub = ioubs[dind, gind] + m = gind + # if match made store id of match for both dt and gt + if m > -1: + dtIg[:, dind] = gtIg[m] + if gtIg[m]: + dtm[tind, dind] = gt[m]["id"] + gtm[tind, m] = d["id"] + # set unmatched detections outside of area range to ignore + a = np.array([d["area"] < aRng[0] or d["area"] > aRng[1] for d in dt]).reshape((1, len(dt))) + dtIg = np.logical_or(dtIg, np.logical_and(dtm == 0, np.repeat(a, T, 0))) + # store results for given image and category + # print('Done with the function', len(self.ious[imgId, catId])) + return { + "image_id": imgId, + "category_id": catId, + "aRng": aRng, + "maxDet": maxDet, + "dtIds": [d["id"] for d in dt], + "gtIds": [g["id"] for g in gt], + "dtMatches": dtm, + "gtMatches": gtm, + "dtScores": [d["score"] for d in dt], + "gtIgnore": gtIg, + "dtIgnore": dtIg, + } + + def accumulate(self, p=None): + """ + Accumulate per image evaluation results and store the result in self.eval + :param p: input params for evaluation + :return: None + """ + logger.info("Accumulating evaluation results...") + tic = time.time() + if not self.evalImgs: + logger.info("Please run evaluate() first") + # allows input customized parameters + if p is None: + p = self.params + p.catIds = p.catIds if p.useCats == 1 else [-1] + T = len(p.iouThrs) + R = len(p.recThrs) + K = len(p.catIds) if p.useCats else 1 + A = len(p.areaRng) + M = len(p.maxDets) + precision = -(np.ones((T, R, K, A, M))) # -1 for the precision of absent categories + recall = -(np.ones((T, K, A, M))) + + # create dictionary for future indexing + logger.info("Categories: {}".format(p.catIds)) + _pe = self._paramsEval + catIds = _pe.catIds if _pe.useCats else [-1] + setK = set(catIds) + setA = set(map(tuple, _pe.areaRng)) + setM = set(_pe.maxDets) + setI = set(_pe.imgIds) + # get inds to evaluate + k_list = [n for n, k in enumerate(p.catIds) if k in setK] + m_list = [m for n, m in enumerate(p.maxDets) if m in setM] + a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA] + i_list = [n for n, i in enumerate(p.imgIds) if i in setI] + I0 = len(_pe.imgIds) + A0 = len(_pe.areaRng) + # retrieve E at each category, area range, and max number of detections + for k, k0 in enumerate(k_list): + Nk = k0 * A0 * I0 + for a, a0 in enumerate(a_list): + Na = a0 * I0 + for m, maxDet in enumerate(m_list): + E = [self.evalImgs[Nk + Na + i] for i in i_list] + E = [e for e in E if e is not None] + if len(E) == 0: + continue + dtScores = np.concatenate([e["dtScores"][0:maxDet] for e in E]) + + # different sorting method generates slightly different results. + # mergesort is used to be consistent as Matlab implementation. + inds = np.argsort(-dtScores, kind="mergesort") + + dtm = np.concatenate([e["dtMatches"][:, 0:maxDet] for e in E], axis=1)[:, inds] + dtIg = np.concatenate([e["dtIgnore"][:, 0:maxDet] for e in E], axis=1)[:, inds] + gtIg = np.concatenate([e["gtIgnore"] for e in E]) + npig = np.count_nonzero(gtIg == 0) + if npig == 0: + continue + tps = np.logical_and(dtm, np.logical_not(dtIg)) + fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg)) + tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) + fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float) + for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): + tp = np.array(tp) + fp = np.array(fp) + nd = len(tp) + rc = tp / npig + pr = tp / (fp + tp + np.spacing(1)) + q = np.zeros((R,)) + + if nd: + recall[t, k, a, m] = rc[-1] + else: + recall[t, k, a, m] = 0 + + # numpy is slow without cython optimization for accessing elements + # use python array gets significant speed improvement + pr = pr.tolist() + q = q.tolist() + + for i in range(nd - 1, 0, -1): + if pr[i] > pr[i - 1]: + pr[i - 1] = pr[i] + + inds = np.searchsorted(rc, p.recThrs, side="left") + try: + for ri, pi in enumerate(inds): + q[ri] = pr[pi] + except Exception: + pass + precision[t, :, k, a, m] = np.array(q) + logger.info( + "Final: max precision {}, min precision {}".format(np.max(precision), np.min(precision)) + ) + self.eval = { + "params": p, + "counts": [T, R, K, A, M], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + } + toc = time.time() + logger.info("DONE (t={:0.2f}s).".format(toc - tic)) + + def summarize(self): + """ + Compute and display summary metrics for evaluation results. + Note this function can *only* be applied on the default parameter setting + """ + + def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100): + p = self.params + iStr = " {:<18} {} @[ {}={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}" + titleStr = "Average Precision" if ap == 1 else "Average Recall" + typeStr = "(AP)" if ap == 1 else "(AR)" + measure = "IoU" + if self.params.iouType == "keypoints": + measure = "OKS" + elif self.params.iouType == "densepose": + measure = "OGPS" + iouStr = ( + "{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1]) + if iouThr is None + else "{:0.2f}".format(iouThr) + ) + + aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] + mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] + if ap == 1: + # dimension of precision: [TxRxKxAxM] + s = self.eval["precision"] + # IoU + if iouThr is not None: + t = np.where(np.abs(iouThr - p.iouThrs) < 0.001)[0] + s = s[t] + s = s[:, :, :, aind, mind] + else: + # dimension of recall: [TxKxAxM] + s = self.eval["recall"] + if iouThr is not None: + t = np.where(np.abs(iouThr - p.iouThrs) < 0.001)[0] + s = s[t] + s = s[:, :, aind, mind] + if len(s[s > -1]) == 0: + mean_s = -1 + else: + mean_s = np.mean(s[s > -1]) + logger.info(iStr.format(titleStr, typeStr, measure, iouStr, areaRng, maxDets, mean_s)) + return mean_s + + def _summarizeDets(): + stats = np.zeros((12,)) + stats[0] = _summarize(1) + stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2]) + stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2]) + stats[3] = _summarize(1, areaRng="small", maxDets=self.params.maxDets[2]) + stats[4] = _summarize(1, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[5] = _summarize(1, areaRng="large", maxDets=self.params.maxDets[2]) + stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) + stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) + stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) + stats[9] = _summarize(0, areaRng="small", maxDets=self.params.maxDets[2]) + stats[10] = _summarize(0, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[11] = _summarize(0, areaRng="large", maxDets=self.params.maxDets[2]) + return stats + + def _summarizeKps(): + stats = np.zeros((10,)) + stats[0] = _summarize(1, maxDets=20) + stats[1] = _summarize(1, maxDets=20, iouThr=0.5) + stats[2] = _summarize(1, maxDets=20, iouThr=0.75) + stats[3] = _summarize(1, maxDets=20, areaRng="medium") + stats[4] = _summarize(1, maxDets=20, areaRng="large") + stats[5] = _summarize(0, maxDets=20) + stats[6] = _summarize(0, maxDets=20, iouThr=0.5) + stats[7] = _summarize(0, maxDets=20, iouThr=0.75) + stats[8] = _summarize(0, maxDets=20, areaRng="medium") + stats[9] = _summarize(0, maxDets=20, areaRng="large") + return stats + + def _summarizeUvs(): + stats = [_summarize(1, maxDets=self.params.maxDets[0])] + min_threshold = self.params.iouThrs.min() + if min_threshold <= 0.201: + stats += [_summarize(1, maxDets=self.params.maxDets[0], iouThr=0.2)] + if min_threshold <= 0.301: + stats += [_summarize(1, maxDets=self.params.maxDets[0], iouThr=0.3)] + if min_threshold <= 0.401: + stats += [_summarize(1, maxDets=self.params.maxDets[0], iouThr=0.4)] + stats += [ + _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.5), + _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.75), + _summarize(1, maxDets=self.params.maxDets[0], areaRng="medium"), + _summarize(1, maxDets=self.params.maxDets[0], areaRng="large"), + _summarize(0, maxDets=self.params.maxDets[0]), + _summarize(0, maxDets=self.params.maxDets[0], iouThr=0.5), + _summarize(0, maxDets=self.params.maxDets[0], iouThr=0.75), + _summarize(0, maxDets=self.params.maxDets[0], areaRng="medium"), + _summarize(0, maxDets=self.params.maxDets[0], areaRng="large"), + ] + return np.array(stats) + + def _summarizeUvsOld(): + stats = np.zeros((18,)) + stats[0] = _summarize(1, maxDets=self.params.maxDets[0]) + stats[1] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.5) + stats[2] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.55) + stats[3] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.60) + stats[4] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.65) + stats[5] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.70) + stats[6] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.75) + stats[7] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.80) + stats[8] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.85) + stats[9] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.90) + stats[10] = _summarize(1, maxDets=self.params.maxDets[0], iouThr=0.95) + stats[11] = _summarize(1, maxDets=self.params.maxDets[0], areaRng="medium") + stats[12] = _summarize(1, maxDets=self.params.maxDets[0], areaRng="large") + stats[13] = _summarize(0, maxDets=self.params.maxDets[0]) + stats[14] = _summarize(0, maxDets=self.params.maxDets[0], iouThr=0.5) + stats[15] = _summarize(0, maxDets=self.params.maxDets[0], iouThr=0.75) + stats[16] = _summarize(0, maxDets=self.params.maxDets[0], areaRng="medium") + stats[17] = _summarize(0, maxDets=self.params.maxDets[0], areaRng="large") + return stats + + if not self.eval: + raise Exception("Please run accumulate() first") + iouType = self.params.iouType + if iouType in ["segm", "bbox"]: + summarize = _summarizeDets + elif iouType in ["keypoints"]: + summarize = _summarizeKps + elif iouType in ["densepose"]: + summarize = _summarizeUvs + self.stats = summarize() + + def __str__(self): + self.summarize() + + # ================ functions for dense pose ============================== + def findAllClosestVertsUV(self, U_points, V_points, Index_points): + ClosestVerts = np.ones(Index_points.shape) * -1 + for i in np.arange(24): + # + if (i + 1) in Index_points: + UVs = np.array( + [U_points[Index_points == (i + 1)], V_points[Index_points == (i + 1)]] + ) + Current_Part_UVs = self.Part_UVs[i] + Current_Part_ClosestVertInds = self.Part_ClosestVertInds[i] + D = ssd.cdist(Current_Part_UVs.transpose(), UVs.transpose()).squeeze() + ClosestVerts[Index_points == (i + 1)] = Current_Part_ClosestVertInds[ + np.argmin(D, axis=0) + ] + ClosestVertsTransformed = self.PDIST_transform[ClosestVerts.astype(int) - 1] + ClosestVertsTransformed[ClosestVerts < 0] = 0 + return ClosestVertsTransformed + + def findClosestVertsCse(self, embedding, py, px, mask, mesh_name): + mesh_vertex_embeddings = self.embedder(mesh_name) + pixel_embeddings = embedding[:, py, px].t().to(device="cuda") + mask_vals = mask[py, px] + edm = squared_euclidean_distance_matrix(pixel_embeddings, mesh_vertex_embeddings) + vertex_indices = edm.argmin(dim=1).cpu() + vertex_indices[mask_vals <= 0] = -1 + return vertex_indices + + def findAllClosestVertsGT(self, gt): + # + I_gt = np.array(gt["dp_I"]) + U_gt = np.array(gt["dp_U"]) + V_gt = np.array(gt["dp_V"]) + # + # print(I_gt) + # + ClosestVertsGT = np.ones(I_gt.shape) * -1 + for i in np.arange(24): + if (i + 1) in I_gt: + UVs = np.array([U_gt[I_gt == (i + 1)], V_gt[I_gt == (i + 1)]]) + Current_Part_UVs = self.Part_UVs[i] + Current_Part_ClosestVertInds = self.Part_ClosestVertInds[i] + D = ssd.cdist(Current_Part_UVs.transpose(), UVs.transpose()).squeeze() + ClosestVertsGT[I_gt == (i + 1)] = Current_Part_ClosestVertInds[np.argmin(D, axis=0)] + # + ClosestVertsGTTransformed = self.PDIST_transform[ClosestVertsGT.astype(int) - 1] + ClosestVertsGTTransformed[ClosestVertsGT < 0] = 0 + return ClosestVertsGT, ClosestVertsGTTransformed + + def getDistancesCse(self, cVertsGT, cVerts, mesh_name): + geodists_vertices = torch.ones_like(cVertsGT) * float("inf") + selected = (cVertsGT >= 0) * (cVerts >= 0) + mesh = create_mesh(mesh_name, "cpu") + geodists_vertices[selected] = mesh.geodists[cVertsGT[selected], cVerts[selected]] + return geodists_vertices.numpy() + + def getDistancesUV(self, cVertsGT, cVerts): + # + n = 27554 + dists = [] + for d in range(len(cVertsGT)): + if cVertsGT[d] > 0: + if cVerts[d] > 0: + i = cVertsGT[d] - 1 + j = cVerts[d] - 1 + if j == i: + dists.append(0) + elif j > i: + ccc = i + i = j + j = ccc + i = n - i - 1 + j = n - j - 1 + k = (n * (n - 1) / 2) - (n - i) * ((n - i) - 1) / 2 + j - i - 1 + k = (n * n - n) / 2 - k - 1 + dists.append(self.Pdist_matrix[int(k)][0]) + else: + i = n - i - 1 + j = n - j - 1 + k = (n * (n - 1) / 2) - (n - i) * ((n - i) - 1) / 2 + j - i - 1 + k = (n * n - n) / 2 - k - 1 + dists.append(self.Pdist_matrix[int(k)][0]) + else: + dists.append(np.inf) + return np.atleast_1d(np.array(dists).squeeze()) + + +class Params: + """ + Params for coco evaluation api + """ + + def setDetParams(self): + self.imgIds = [] + self.catIds = [] + # np.arange causes trouble. the data point on arange is slightly larger than the true value + self.iouThrs = np.linspace(0.5, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True) + self.recThrs = np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01)) + 1, endpoint=True) + self.maxDets = [1, 10, 100] + self.areaRng = [ + [0 ** 2, 1e5 ** 2], + [0 ** 2, 32 ** 2], + [32 ** 2, 96 ** 2], + [96 ** 2, 1e5 ** 2], + ] + self.areaRngLbl = ["all", "small", "medium", "large"] + self.useCats = 1 + + def setKpParams(self): + self.imgIds = [] + self.catIds = [] + # np.arange causes trouble. the data point on arange is slightly larger than the true value + self.iouThrs = np.linspace(0.5, 0.95, np.round((0.95 - 0.5) / 0.05) + 1, endpoint=True) + self.recThrs = np.linspace(0.0, 1.00, np.round((1.00 - 0.0) / 0.01) + 1, endpoint=True) + self.maxDets = [20] + self.areaRng = [[0 ** 2, 1e5 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]] + self.areaRngLbl = ["all", "medium", "large"] + self.useCats = 1 + + def setUvParams(self): + self.imgIds = [] + self.catIds = [] + self.iouThrs = np.linspace(0.5, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True) + self.recThrs = np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01)) + 1, endpoint=True) + self.maxDets = [20] + self.areaRng = [[0 ** 2, 1e5 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]] + self.areaRngLbl = ["all", "medium", "large"] + self.useCats = 1 + + def __init__(self, iouType="segm"): + if iouType == "segm" or iouType == "bbox": + self.setDetParams() + elif iouType == "keypoints": + self.setKpParams() + elif iouType == "densepose": + self.setUvParams() + else: + raise Exception("iouType not supported") + self.iouType = iouType + # useSegm is deprecated + self.useSegm = None diff --git a/motion-gan-pipeline/preprocessing/third/densepose/evaluation/evaluator.py b/motion-gan-pipeline/preprocessing/third/densepose/evaluation/evaluator.py new file mode 100644 index 0000000..0e0e9e3 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/evaluation/evaluator.py @@ -0,0 +1,421 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import contextlib +import copy +import io +import itertools +import logging +import numpy as np +import os +from collections import OrderedDict +from typing import Dict, Iterable, List, Optional +import pycocotools.mask as mask_utils +import torch +from pycocotools.coco import COCO +from tabulate import tabulate + +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.evaluation import DatasetEvaluator +from detectron2.structures import BoxMode +from detectron2.utils.comm import gather, get_rank, is_main_process, synchronize +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import create_small_table + +from densepose.converters import ToChartResultConverter, ToMaskConverter +from densepose.data.datasets.coco import maybe_filter_and_map_categories_cocoapi +from densepose.structures import ( + DensePoseChartPredictorOutput, + DensePoseEmbeddingPredictorOutput, + quantize_densepose_chart_result, +) + +from .densepose_coco_evaluation import DensePoseCocoEval, DensePoseEvalMode +from .mesh_alignment_evaluator import MeshAlignmentEvaluator +from .tensor_storage import ( + SingleProcessFileTensorStorage, + SingleProcessRamTensorStorage, + SingleProcessTensorStorage, + SizeData, + storage_gather, +) + + +class DensePoseCOCOEvaluator(DatasetEvaluator): + def __init__( + self, + dataset_name, + distributed, + output_dir=None, + evaluator_type: str = "iuv", + min_iou_threshold: float = 0.5, + storage: Optional[SingleProcessTensorStorage] = None, + embedder=None, + should_evaluate_mesh_alignment: bool = False, + mesh_alignment_mesh_names: Optional[List[str]] = None, + ): + self._embedder = embedder + self._distributed = distributed + self._output_dir = output_dir + self._evaluator_type = evaluator_type + self._storage = storage + self._should_evaluate_mesh_alignment = should_evaluate_mesh_alignment + + assert not ( + should_evaluate_mesh_alignment and embedder is None + ), "Mesh alignment evaluation is activated, but no vertex embedder provided!" + if should_evaluate_mesh_alignment: + self._mesh_alignment_evaluator = MeshAlignmentEvaluator( + embedder, + mesh_alignment_mesh_names, + ) + + self._cpu_device = torch.device("cpu") + self._logger = logging.getLogger(__name__) + + self._metadata = MetadataCatalog.get(dataset_name) + self._min_threshold = min_iou_threshold + json_file = PathManager.get_local_path(self._metadata.json_file) + with contextlib.redirect_stdout(io.StringIO()): + self._coco_api = COCO(json_file) + maybe_filter_and_map_categories_cocoapi(dataset_name, self._coco_api) + + def reset(self): + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a COCO model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + The :class:`Instances` object needs to have `densepose` field. + """ + for input, output in zip(inputs, outputs): + instances = output["instances"].to(self._cpu_device) + if not instances.has("pred_densepose"): + continue + prediction_list = prediction_to_dict( + instances, + input["image_id"], + self._embedder, + self._metadata.class_to_mesh_name, + self._storage is not None, + ) + if self._storage is not None: + for prediction_dict in prediction_list: + dict_to_store = {} + for field_name in self._storage.data_schema: + dict_to_store[field_name] = prediction_dict[field_name] + record_id = self._storage.put(dict_to_store) + prediction_dict["record_id"] = record_id + prediction_dict["rank"] = get_rank() + for field_name in self._storage.data_schema: + del prediction_dict[field_name] + self._predictions.extend(prediction_list) + + def evaluate(self, img_ids=None): + if self._distributed: + synchronize() + predictions = gather(self._predictions) + predictions = list(itertools.chain(*predictions)) + else: + predictions = self._predictions + + multi_storage = storage_gather(self._storage) if self._storage is not None else None + + if not is_main_process(): + return + return copy.deepcopy(self._eval_predictions(predictions, multi_storage, img_ids)) + + def _eval_predictions(self, predictions, multi_storage=None, img_ids=None): + """ + Evaluate predictions on densepose. + Return results with the metrics of the tasks. + """ + self._logger.info("Preparing results for COCO format ...") + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "coco_densepose_predictions.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(predictions, f) + + self._logger.info("Evaluating predictions ...") + res = OrderedDict() + results_gps, results_gpsm, results_segm = _evaluate_predictions_on_coco( + self._coco_api, + predictions, + multi_storage, + self._embedder, + class_names=self._metadata.get("thing_classes"), + min_threshold=self._min_threshold, + img_ids=img_ids, + ) + res["densepose_gps"] = results_gps + res["densepose_gpsm"] = results_gpsm + res["densepose_segm"] = results_segm + if self._should_evaluate_mesh_alignment: + res["densepose_mesh_alignment"] = self._evaluate_mesh_alignment() + return res + + def _evaluate_mesh_alignment(self): + self._logger.info("Mesh alignment evaluation ...") + mean_ge, mean_gps, per_mesh_metrics = self._mesh_alignment_evaluator.evaluate() + results = { + "GE": mean_ge * 100, + "GPS": mean_gps * 100, + } + mesh_names = set() + for metric_name in per_mesh_metrics: + for mesh_name, value in per_mesh_metrics[metric_name].items(): + results[f"{metric_name}-{mesh_name}"] = value * 100 + mesh_names.add(mesh_name) + self._print_mesh_alignment_results(results, mesh_names) + return results + + def _print_mesh_alignment_results(self, results: Dict[str, float], mesh_names: Iterable[str]): + self._logger.info("Evaluation results for densepose, mesh alignment:") + self._logger.info(f'| {"Mesh":13s} | {"GErr":7s} | {"GPS":7s} |') + self._logger.info("| :-----------: | :-----: | :-----: |") + for mesh_name in mesh_names: + ge_key = f"GE-{mesh_name}" + ge_str = f"{results[ge_key]:.4f}" if ge_key in results else " " + gps_key = f"GPS-{mesh_name}" + gps_str = f"{results[gps_key]:.4f}" if gps_key in results else " " + self._logger.info(f"| {mesh_name:13s} | {ge_str:7s} | {gps_str:7s} |") + self._logger.info("| :-------------------------------: |") + ge_key = "GE" + ge_str = f"{results[ge_key]:.4f}" if ge_key in results else " " + gps_key = "GPS" + gps_str = f"{results[gps_key]:.4f}" if gps_key in results else " " + self._logger.info(f'| {"MEAN":13s} | {ge_str:7s} | {gps_str:7s} |') + + +def prediction_to_dict(instances, img_id, embedder, class_to_mesh_name, use_storage): + """ + Args: + instances (Instances): the output of the model + img_id (str): the image id in COCO + + Returns: + list[dict]: the results in densepose evaluation format + """ + scores = instances.scores.tolist() + classes = instances.pred_classes.tolist() + raw_boxes_xywh = BoxMode.convert( + instances.pred_boxes.tensor.clone(), BoxMode.XYXY_ABS, BoxMode.XYWH_ABS + ) + + if isinstance(instances.pred_densepose, DensePoseEmbeddingPredictorOutput): + results_densepose = densepose_cse_predictions_to_dict( + instances, embedder, class_to_mesh_name, use_storage + ) + elif isinstance(instances.pred_densepose, DensePoseChartPredictorOutput): + if not use_storage: + results_densepose = densepose_chart_predictions_to_dict(instances) + else: + results_densepose = densepose_chart_predictions_to_storage_dict(instances) + + results = [] + for k in range(len(instances)): + result = { + "image_id": img_id, + "category_id": classes[k], + "bbox": raw_boxes_xywh[k].tolist(), + "score": scores[k], + } + results.append({**result, **results_densepose[k]}) + return results + + +def densepose_chart_predictions_to_dict(instances): + segmentations = ToMaskConverter.convert( + instances.pred_densepose, instances.pred_boxes, instances.image_size + ) + + results = [] + for k in range(len(instances)): + densepose_results_quantized = quantize_densepose_chart_result( + ToChartResultConverter.convert(instances.pred_densepose[k], instances.pred_boxes[k]) + ) + densepose_results_quantized.labels_uv_uint8 = ( + densepose_results_quantized.labels_uv_uint8.cpu() + ) + segmentation = segmentations.tensor[k] + segmentation_encoded = mask_utils.encode( + np.require(segmentation.numpy(), dtype=np.uint8, requirements=["F"]) + ) + segmentation_encoded["counts"] = segmentation_encoded["counts"].decode("utf-8") + result = { + "densepose": densepose_results_quantized, + "segmentation": segmentation_encoded, + } + results.append(result) + return results + + +def densepose_chart_predictions_to_storage_dict(instances): + results = [] + for k in range(len(instances)): + densepose_predictor_output = instances.pred_densepose[k] + result = { + "coarse_segm": densepose_predictor_output.coarse_segm.squeeze(0).cpu(), + "fine_segm": densepose_predictor_output.fine_segm.squeeze(0).cpu(), + "u": densepose_predictor_output.u.squeeze(0).cpu(), + "v": densepose_predictor_output.v.squeeze(0).cpu(), + } + results.append(result) + return results + + +def densepose_cse_predictions_to_dict(instances, embedder, class_to_mesh_name, use_storage): + results = [] + for k in range(len(instances)): + cse = instances.pred_densepose[k] + results.append( + { + "coarse_segm": cse.coarse_segm[0].cpu(), + "embedding": cse.embedding[0].cpu(), + } + ) + return results + + +def _evaluate_predictions_on_coco( + coco_gt, + coco_results, + multi_storage=None, + embedder=None, + class_names=None, + min_threshold: float=0.5, + img_ids=None, +): + logger = logging.getLogger(__name__) + + densepose_metrics = _get_densepose_metrics(min_threshold) + if len(coco_results) == 0: # cocoapi does not handle empty results very well + logger.warn("No predictions from the model! Set scores to -1") + results_gps = {metric: -1 for metric in densepose_metrics} + results_gpsm = {metric: -1 for metric in densepose_metrics} + results_segm = {metric: -1 for metric in densepose_metrics} + return results_gps, results_gpsm, results_segm + + coco_dt = coco_gt.loadRes(coco_results) + + results = [] + for eval_mode_name in ["GPS", "GPSM", "IOU"]: + eval_mode = getattr(DensePoseEvalMode, eval_mode_name) + coco_eval = DensePoseCocoEval( + coco_gt, coco_dt, "densepose", multi_storage, embedder, dpEvalMode=eval_mode + ) + result = _derive_results_from_coco_eval( + coco_eval, eval_mode_name, densepose_metrics, class_names, min_threshold, img_ids + ) + results.append(result) + return results + + +def _get_densepose_metrics(min_threshold: float=0.5): + metrics = ["AP"] + if min_threshold <= 0.201: + metrics += ["AP20"] + if min_threshold <= 0.301: + metrics += ["AP30"] + if min_threshold <= 0.401: + metrics += ["AP40"] + metrics.extend(["AP50", "AP75", "APm", "APl", "AR", "AR50", "AR75", "ARm", "ARl"]) + return metrics + + +def _derive_results_from_coco_eval( + coco_eval, eval_mode_name, metrics, class_names, min_threshold: float, img_ids +): + if img_ids is not None: + coco_eval.params.imgIds = img_ids + coco_eval.params.iouThrs = np.linspace( + min_threshold, 0.95, int(np.round((0.95 - min_threshold) / 0.05)) + 1, endpoint=True + ) + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + results = {metric: float(coco_eval.stats[idx] * 100) for idx, metric in enumerate(metrics)} + logger = logging.getLogger(__name__) + logger.info( + f"Evaluation results for densepose, {eval_mode_name} metric: \n" + + create_small_table(results) + ) + if class_names is None or len(class_names) <= 1: + return results + + # Compute per-category AP, the same way as it is done in D2 + # (see detectron2/evaluation/coco_evaluation.py): + precisions = coco_eval.eval["precision"] + # precision has dims (iou, recall, cls, area range, max dets) + assert len(class_names) == precisions.shape[2] + + results_per_category = [] + for idx, name in enumerate(class_names): + # area range index 0: all area ranges + # max dets index -1: typically 100 per image + precision = precisions[:, :, idx, 0, -1] + precision = precision[precision > -1] + ap = np.mean(precision) if precision.size else float("nan") + results_per_category.append((f"{name}", float(ap * 100))) + + # tabulate it + n_cols = min(6, len(results_per_category) * 2) + results_flatten = list(itertools.chain(*results_per_category)) + results_2d = itertools.zip_longest(*[results_flatten[i::n_cols] for i in range(n_cols)]) + table = tabulate( + results_2d, + tablefmt="pipe", + floatfmt=".3f", + headers=["category", "AP"] * (n_cols // 2), + numalign="left", + ) + logger.info(f"Per-category {eval_mode_name} AP: \n" + table) + + results.update({"AP-" + name: ap for name, ap in results_per_category}) + return results + + +def build_densepose_evaluator_storage(cfg: CfgNode, output_folder: str): + storage_spec = cfg.DENSEPOSE_EVALUATION.STORAGE + if storage_spec == "none": + return None + evaluator_type = cfg.DENSEPOSE_EVALUATION.TYPE + # common output tensor sizes + hout = cfg.MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE + wout = cfg.MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE + n_csc = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS + # specific output tensors + if evaluator_type == "iuv": + n_fsc = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_PATCHES + 1 + schema = { + "coarse_segm": SizeData(dtype="float32", shape=(n_csc, hout, wout)), + "fine_segm": SizeData(dtype="float32", shape=(n_fsc, hout, wout)), + "u": SizeData(dtype="float32", shape=(n_fsc, hout, wout)), + "v": SizeData(dtype="float32", shape=(n_fsc, hout, wout)), + } + elif evaluator_type == "cse": + embed_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE + schema = { + "coarse_segm": SizeData(dtype="float32", shape=(n_csc, hout, wout)), + "embedding": SizeData(dtype="float32", shape=(embed_size, hout, wout)), + } + else: + raise ValueError(f"Unknown evaluator type: {evaluator_type}") + # storage types + if storage_spec == "ram": + storage = SingleProcessRamTensorStorage(schema, io.BytesIO()) + elif storage_spec == "file": + fpath = os.path.join(output_folder, f"DensePoseEvaluatorStorage.{get_rank()}.bin") + PathManager.mkdirs(output_folder) + storage = SingleProcessFileTensorStorage(schema, fpath, "wb") + else: + raise ValueError(f"Unknown storage specification: {storage_spec}") + return storage diff --git a/motion-gan-pipeline/preprocessing/third/densepose/evaluation/mesh_alignment_evaluator.py b/motion-gan-pipeline/preprocessing/third/densepose/evaluation/mesh_alignment_evaluator.py new file mode 100644 index 0000000..c5ef290 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/evaluation/mesh_alignment_evaluator.py @@ -0,0 +1,66 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import json +import logging +from typing import List, Optional +import torch +from torch import nn + +from detectron2.utils.file_io import PathManager + +from densepose.structures.mesh import create_mesh + + +class MeshAlignmentEvaluator: + """ + Class for evaluation of 3D mesh alignment based on the learned vertex embeddings + """ + + def __init__(self, embedder: nn.Module, mesh_names: Optional[List[str]]): + self.embedder = embedder + # use the provided mesh names if not None and not an empty list + self.mesh_names = mesh_names if mesh_names else embedder.mesh_names + self.logger = logging.getLogger(__name__) + with PathManager.open( + "https://dl.fbaipublicfiles.com/densepose/data/cse/mesh_keyvertices_v0.json", "r" + ) as f: + self.mesh_keyvertices = json.load(f) + + def evaluate(self): + ge_per_mesh = {} + gps_per_mesh = {} + for mesh_name_1 in self.mesh_names: + avg_errors = [] + avg_gps = [] + embeddings_1 = self.embedder(mesh_name_1) + keyvertices_1 = self.mesh_keyvertices[mesh_name_1] + keyvertex_names_1 = list(keyvertices_1.keys()) + keyvertex_indices_1 = [keyvertices_1[name] for name in keyvertex_names_1] + for mesh_name_2 in self.mesh_names: + if mesh_name_1 == mesh_name_2: + continue + embeddings_2 = self.embedder(mesh_name_2) + keyvertices_2 = self.mesh_keyvertices[mesh_name_2] + sim_matrix_12 = embeddings_1[keyvertex_indices_1].mm(embeddings_2.T) + vertices_2_matching_keyvertices_1 = sim_matrix_12.argmax(axis=1) + mesh_2 = create_mesh(mesh_name_2, embeddings_2.device) + geodists = mesh_2.geodists[ + vertices_2_matching_keyvertices_1, + [keyvertices_2[name] for name in keyvertex_names_1], + ] + Current_Mean_Distances = 0.255 + gps = (-(geodists ** 2) / (2 * (Current_Mean_Distances ** 2))).exp() + avg_errors.append(geodists.mean().item()) + avg_gps.append(gps.mean().item()) + + ge_mean = torch.as_tensor(avg_errors).mean().item() + gps_mean = torch.as_tensor(avg_gps).mean().item() + ge_per_mesh[mesh_name_1] = ge_mean + gps_per_mesh[mesh_name_1] = gps_mean + ge_mean_global = torch.as_tensor(list(ge_per_mesh.values())).mean().item() + gps_mean_global = torch.as_tensor(list(gps_per_mesh.values())).mean().item() + per_mesh_metrics = { + "GE": ge_per_mesh, + "GPS": gps_per_mesh, + } + return ge_mean_global, gps_mean_global, per_mesh_metrics diff --git a/motion-gan-pipeline/preprocessing/third/densepose/evaluation/tensor_storage.py b/motion-gan-pipeline/preprocessing/third/densepose/evaluation/tensor_storage.py new file mode 100644 index 0000000..ea0ece2 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/evaluation/tensor_storage.py @@ -0,0 +1,238 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import io +import numpy as np +import os +from dataclasses import dataclass +from functools import reduce +from operator import mul +from typing import BinaryIO, Dict, Optional, Tuple +import torch + +from detectron2.utils.comm import gather, get_rank +from detectron2.utils.file_io import PathManager + + +@dataclass +class SizeData: + dtype: str + shape: Tuple[int] + + +def _calculate_record_field_size_b(data_schema: Dict[str, SizeData], field_name: str) -> int: + schema = data_schema[field_name] + element_size_b = np.dtype(schema.dtype).itemsize + record_field_size_b = reduce(mul, schema.shape) * element_size_b + return record_field_size_b + + +def _calculate_record_size_b(data_schema: Dict[str, SizeData]) -> int: + record_size_b = 0 + for field_name in data_schema: + record_field_size_b = _calculate_record_field_size_b(data_schema, field_name) + record_size_b += record_field_size_b + return record_size_b + + +def _calculate_record_field_sizes_b(data_schema: Dict[str, SizeData]) -> Dict[str, int]: + field_sizes_b = {} + for field_name in data_schema: + field_sizes_b[field_name] = _calculate_record_field_size_b(data_schema, field_name) + return field_sizes_b + + +class SingleProcessTensorStorage: + """ + Compact tensor storage to keep tensor data of predefined size and type. + """ + + def __init__(self, data_schema: Dict[str, SizeData], storage_impl: BinaryIO): + """ + Construct tensor storage based on information on data shape and size. + Internally uses numpy to interpret the type specification. + The storage must support operations `seek(offset, whence=os.SEEK_SET)` and + `read(size)` to be able to perform the `get` operation. + The storage must support operation `write(bytes)` to be able to perform + the `put` operation. + + Args: + data_schema (dict: str -> SizeData): dictionary which maps tensor name + to its size data (shape and data type), e.g. + ``` + { + "coarse_segm": SizeData(dtype="float32", shape=(112, 112)), + "embedding": SizeData(dtype="float32", shape=(16, 112, 112)), + } + ``` + storage_impl (BinaryIO): io instance that handles file-like seek, read + and write operations, e.g. a file handle or a memory buffer like io.BytesIO + """ + self.data_schema = data_schema + self.record_size_b = _calculate_record_size_b(data_schema) + self.record_field_sizes_b = _calculate_record_field_sizes_b(data_schema) + self.storage_impl = storage_impl + self.next_record_id = 0 + + def get(self, record_id: int) -> Dict[str, torch.Tensor]: + """ + Load tensors from the storage by record ID + + Args: + record_id (int): Record ID, for which to load the data + + Return: + dict: str -> tensor: tensor name mapped to tensor data, recorded under the provided ID + """ + self.storage_impl.seek(record_id * self.record_size_b, os.SEEK_SET) + data_bytes = self.storage_impl.read(self.record_size_b) + assert len(data_bytes) == self.record_size_b, ( + f"Expected data size {self.record_size_b} B could not be read: " + f"got {len(data_bytes)} B" + ) + record = {} + cur_idx = 0 + # it's important to read and write in the same order + for field_name in sorted(self.data_schema): + schema = self.data_schema[field_name] + field_size_b = self.record_field_sizes_b[field_name] + chunk = data_bytes[cur_idx : cur_idx + field_size_b] + data_np = np.frombuffer( + chunk, dtype=schema.dtype, count=reduce(mul, schema.shape) + ).reshape(schema.shape) + record[field_name] = torch.from_numpy(data_np) + cur_idx += field_size_b + return record + + def put(self, data: Dict[str, torch.Tensor]) -> int: + """ + Store tensors in the storage + + Args: + data (dict: str -> tensor): data to store, a dictionary which maps + tensor names into tensors; tensor shapes must match those specified + in data schema. + Return: + int: record ID, under which the data is stored + """ + # it's important to read and write in the same order + for field_name in sorted(self.data_schema): + assert ( + field_name in data + ), f"Field '{field_name}' not present in data: data keys are {data.keys()}" + value = data[field_name] + assert value.shape == self.data_schema[field_name].shape, ( + f"Mismatched tensor shapes for field '{field_name}': " + f"expected {self.data_schema[field_name].shape}, got {value.shape}" + ) + data_bytes = value.cpu().numpy().tobytes() + assert len(data_bytes) == self.record_field_sizes_b[field_name], ( + f"Expected field {field_name} to be of size " + f"{self.record_field_sizes_b[field_name]} B, got {len(data_bytes)} B" + ) + self.storage_impl.write(data_bytes) + record_id = self.next_record_id + self.next_record_id += 1 + return record_id + + +class SingleProcessFileTensorStorage(SingleProcessTensorStorage): + """ + Implementation of a single process tensor storage which stores data in a file + """ + + def __init__(self, data_schema: Dict[str, SizeData], fpath: str, mode: str): + self.fpath = fpath + assert "b" in mode, f"Tensor storage should be opened in binary mode, got '{mode}'" + if "w" in mode: + file_h = PathManager.open(fpath, mode) + elif "r" in mode: + local_fpath = PathManager.get_local_path(fpath) + file_h = open(local_fpath, mode) + else: + raise ValueError(f"Unsupported file mode {mode}, supported modes: rb, wb") + super().__init__(data_schema, file_h) # pyre-ignore[6] + + +class SingleProcessRamTensorStorage(SingleProcessTensorStorage): + """ + Implementation of a single process tensor storage which stores data in RAM + """ + + def __init__(self, data_schema: Dict[str, SizeData], buf: io.BytesIO): + super().__init__(data_schema, buf) + + +class MultiProcessTensorStorage: + """ + Representation of a set of tensor storages created by individual processes, + allows to access those storages from a single owner process. The storages + should either be shared or broadcasted to the owner process. + The processes are identified by their rank, data is uniquely defined by + the rank of the process and the record ID. + """ + + def __init__(self, rank_to_storage: Dict[int, SingleProcessTensorStorage]): + self.rank_to_storage = rank_to_storage + + def get(self, rank: int, record_id: int) -> Dict[str, torch.Tensor]: + storage = self.rank_to_storage[rank] + return storage.get(record_id) + + def put(self, rank: int, data: Dict[str, torch.Tensor]) -> int: + storage = self.rank_to_storage[rank] + return storage.put(data) + + +class MultiProcessFileTensorStorage(MultiProcessTensorStorage): + def __init__(self, data_schema: Dict[str, SizeData], rank_to_fpath: Dict[int, str], mode: str): + rank_to_storage = { + rank: SingleProcessFileTensorStorage(data_schema, fpath, mode) + for rank, fpath in rank_to_fpath.items() + } + super().__init__(rank_to_storage) # pyre-ignore[6] + + +class MultiProcessRamTensorStorage(MultiProcessTensorStorage): + def __init__(self, data_schema: Dict[str, SizeData], rank_to_buffer: Dict[int, io.BytesIO]): + rank_to_storage = { + rank: SingleProcessRamTensorStorage(data_schema, buf) + for rank, buf in rank_to_buffer.items() + } + super().__init__(rank_to_storage) # pyre-ignore[6] + + +def _ram_storage_gather( + storage: SingleProcessRamTensorStorage, dst_rank: int = 0 +) -> Optional[MultiProcessRamTensorStorage]: + storage.storage_impl.seek(0, os.SEEK_SET) + # TODO: overhead, pickling a bytes object, can just pass bytes in a tensor directly + # see detectron2/autils.comm.py + data_list = gather(storage.storage_impl.read(), dst=dst_rank) + if get_rank() != dst_rank: + return None + rank_to_buffer = {i: io.BytesIO(data_list[i]) for i in range(len(data_list))} + multiprocess_storage = MultiProcessRamTensorStorage(storage.data_schema, rank_to_buffer) + return multiprocess_storage + + +def _file_storage_gather( + storage: SingleProcessFileTensorStorage, + dst_rank: int = 0, + mode: str = "rb", +) -> Optional[MultiProcessFileTensorStorage]: + storage.storage_impl.close() + fpath_list = gather(storage.fpath, dst=dst_rank) + if get_rank() != dst_rank: + return None + rank_to_fpath = {i: fpath_list[i] for i in range(len(fpath_list))} + return MultiProcessFileTensorStorage(storage.data_schema, rank_to_fpath, mode) + + +def storage_gather( + storage: SingleProcessTensorStorage, dst_rank: int = 0 +) -> Optional[MultiProcessTensorStorage]: + if isinstance(storage, SingleProcessRamTensorStorage): + return _ram_storage_gather(storage, dst_rank) + elif isinstance(storage, SingleProcessFileTensorStorage): + return _file_storage_gather(storage, dst_rank) + raise Exception(f"Unsupported storage for gather operation: {storage}") diff --git a/motion-gan-pipeline/preprocessing/third/densepose/model_final_162be9.pkl b/motion-gan-pipeline/preprocessing/third/densepose/model_final_162be9.pkl new file mode 100644 index 0000000..1556c53 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/model_final_162be9.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b8a7382001b16e453bad95ca9dbc68ae8f2b839b304cf90eaf5c27fbdb4dae91 +size 255757821 diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/__init__.py new file mode 100644 index 0000000..4c49f6d --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from .confidence import DensePoseConfidenceModelConfig, DensePoseUVConfidenceType +from .filter import DensePoseDataFilter +from .inference import densepose_inference +from .utils import initialize_module_params +from .build import ( + build_densepose_data_filter, + build_densepose_embedder, + build_densepose_head, + build_densepose_losses, + build_densepose_predictor, +) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/build.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/build.py new file mode 100644 index 0000000..bb7f54b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/build.py @@ -0,0 +1,87 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from typing import Optional +from torch import nn + +from detectron2.config import CfgNode + +from .cse.embedder import Embedder +from .filter import DensePoseDataFilter + + +def build_densepose_predictor(cfg: CfgNode, input_channels: int): + """ + Create an instance of DensePose predictor based on configuration options. + + Args: + cfg (CfgNode): configuration options + input_channels (int): input tensor size along the channel dimension + Return: + An instance of DensePose predictor + """ + from .predictors import DENSEPOSE_PREDICTOR_REGISTRY + + predictor_name = cfg.MODEL.ROI_DENSEPOSE_HEAD.PREDICTOR_NAME + return DENSEPOSE_PREDICTOR_REGISTRY.get(predictor_name)(cfg, input_channels) + + +def build_densepose_data_filter(cfg: CfgNode): + """ + Build DensePose data filter which selects data for training + + Args: + cfg (CfgNode): configuration options + + Return: + Callable: list(Tensor), list(Instances) -> list(Tensor), list(Instances) + An instance of DensePose filter, which takes feature tensors and proposals + as an input and returns filtered features and proposals + """ + dp_filter = DensePoseDataFilter(cfg) + return dp_filter + + +def build_densepose_head(cfg: CfgNode, input_channels: int): + """ + Build DensePose head based on configurations options + + Args: + cfg (CfgNode): configuration options + input_channels (int): input tensor size along the channel dimension + Return: + An instance of DensePose head + """ + from .roi_heads.registry import ROI_DENSEPOSE_HEAD_REGISTRY + + head_name = cfg.MODEL.ROI_DENSEPOSE_HEAD.NAME + return ROI_DENSEPOSE_HEAD_REGISTRY.get(head_name)(cfg, input_channels) + + +def build_densepose_losses(cfg: CfgNode): + """ + Build DensePose loss based on configurations options + + Args: + cfg (CfgNode): configuration options + Return: + An instance of DensePose loss + """ + from .losses import DENSEPOSE_LOSS_REGISTRY + + loss_name = cfg.MODEL.ROI_DENSEPOSE_HEAD.LOSS_NAME + return DENSEPOSE_LOSS_REGISTRY.get(loss_name)(cfg) + + +def build_densepose_embedder(cfg: CfgNode) -> Optional[nn.Module]: + """ + Build embedder used to embed mesh vertices into an embedding space. + Embedder contains sub-embedders, one for each mesh ID. + + Args: + cfg (cfgNode): configuration options + Return: + Embedding module + """ + if cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDERS: + return Embedder(cfg) + return None diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/confidence.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/confidence.py new file mode 100644 index 0000000..6f4a72e --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/confidence.py @@ -0,0 +1,73 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from dataclasses import dataclass +from enum import Enum + +from detectron2.config import CfgNode + + +class DensePoseUVConfidenceType(Enum): + """ + Statistical model type for confidence learning, possible values: + - "iid_iso": statistically independent identically distributed residuals + with anisotropic covariance + - "indep_aniso": statistically independent residuals with anisotropic + covariances + For details, see: + N. Neverova, D. Novotny, A. Vedaldi "Correlated Uncertainty for Learning + Dense Correspondences from Noisy Labels", p. 918--926, in Proc. NIPS 2019 + """ + + # fmt: off + IID_ISO = "iid_iso" + INDEP_ANISO = "indep_aniso" + # fmt: on + + +@dataclass +class DensePoseUVConfidenceConfig: + """ + Configuration options for confidence on UV data + """ + + enabled: bool = False + # lower bound on UV confidences + epsilon: float = 0.01 + type: DensePoseUVConfidenceType = DensePoseUVConfidenceType.IID_ISO + + +@dataclass +class DensePoseSegmConfidenceConfig: + """ + Configuration options for confidence on segmentation + """ + + enabled: bool = False + # lower bound on confidence values + epsilon: float = 0.01 + + +@dataclass +class DensePoseConfidenceModelConfig: + """ + Configuration options for confidence models + """ + + # confidence for U and V values + uv_confidence: DensePoseUVConfidenceConfig + # segmentation confidence + segm_confidence: DensePoseSegmConfidenceConfig + + @staticmethod + def from_cfg(cfg: CfgNode) -> "DensePoseConfidenceModelConfig": + return DensePoseConfidenceModelConfig( + uv_confidence=DensePoseUVConfidenceConfig( + enabled=cfg.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.ENABLED, + epsilon=cfg.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.EPSILON, + type=DensePoseUVConfidenceType(cfg.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.TYPE), + ), + segm_confidence=DensePoseSegmConfidenceConfig( + enabled=cfg.MODEL.ROI_DENSEPOSE_HEAD.SEGM_CONFIDENCE.ENABLED, + epsilon=cfg.MODEL.ROI_DENSEPOSE_HEAD.SEGM_CONFIDENCE.EPSILON, + ), + ) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/cse/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/cse/__init__.py new file mode 100644 index 0000000..a227360 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/cse/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +from .vertex_direct_embedder import VertexDirectEmbedder +from .vertex_feature_embedder import VertexFeatureEmbedder +from .embedder import Embedder diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/cse/embedder.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/cse/embedder.py new file mode 100644 index 0000000..e326953 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/cse/embedder.py @@ -0,0 +1,130 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import logging +import numpy as np +import pickle +from enum import Enum +from typing import Optional +import torch +from torch import nn + +from detectron2.config import CfgNode +from detectron2.utils.file_io import PathManager + +from .vertex_direct_embedder import VertexDirectEmbedder +from .vertex_feature_embedder import VertexFeatureEmbedder + + +class EmbedderType(Enum): + """ + Embedder type which defines how vertices are mapped into the embedding space: + - "vertex_direct": direct vertex embedding + - "vertex_feature": embedding vertex features + """ + + VERTEX_DIRECT = "vertex_direct" + VERTEX_FEATURE = "vertex_feature" + + +def create_embedder(embedder_spec: CfgNode, embedder_dim: int) -> nn.Module: + """ + Create an embedder based on the provided configuration + + Args: + embedder_spec (CfgNode): embedder configuration + embedder_dim (int): embedding space dimensionality + Return: + An embedder instance for the specified configuration + Raises ValueError, in case of unexpected embedder type + """ + embedder_type = EmbedderType(embedder_spec.TYPE) + if embedder_type == EmbedderType.VERTEX_DIRECT: + embedder = VertexDirectEmbedder( + num_vertices=embedder_spec.NUM_VERTICES, + embed_dim=embedder_dim, + ) + if embedder_spec.INIT_FILE != "": + embedder.load(embedder_spec.INIT_FILE) + elif embedder_type == EmbedderType.VERTEX_FEATURE: + embedder = VertexFeatureEmbedder( + num_vertices=embedder_spec.NUM_VERTICES, + feature_dim=embedder_spec.FEATURE_DIM, + embed_dim=embedder_dim, + train_features=embedder_spec.FEATURES_TRAINABLE, + ) + if embedder_spec.INIT_FILE != "": + embedder.load(embedder_spec.INIT_FILE) + else: + raise ValueError(f"Unexpected embedder type {embedder_type}") + + if not embedder_spec.IS_TRAINABLE: + embedder.requires_grad_(False) + + return embedder + + +class Embedder(nn.Module): + """ + Embedder module that serves as a container for embedders to use with different + meshes. Extends Module to automatically save / load state dict. + """ + + DEFAULT_MODEL_CHECKPOINT_PREFIX = "roi_heads.embedder." + + def __init__(self, cfg: CfgNode): + """ + Initialize mesh embedders. An embedder for mesh `i` is stored in a submodule + "embedder_{i}". + + Args: + cfg (CfgNode): configuration options + """ + super(Embedder, self).__init__() + self.mesh_names = set() + embedder_dim = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE + logger = logging.getLogger(__name__) + for mesh_name, embedder_spec in cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDERS.items(): + logger.info(f"Adding embedder embedder_{mesh_name} with spec {embedder_spec}") + self.add_module(f"embedder_{mesh_name}", create_embedder(embedder_spec, embedder_dim)) + self.mesh_names.add(mesh_name) + if cfg.MODEL.WEIGHTS != "": + self.load_from_model_checkpoint(cfg.MODEL.WEIGHTS) + + def load_from_model_checkpoint(self, fpath: str, prefix: Optional[str] = None): + if prefix is None: + prefix = Embedder.DEFAULT_MODEL_CHECKPOINT_PREFIX + state_dict = None + if fpath.endswith(".pkl"): + with PathManager.open(fpath, "rb") as hFile: + state_dict = pickle.load(hFile, encoding="latin1") # pyre-ignore[6] + else: + with PathManager.open(fpath, "rb") as hFile: + state_dict = torch.load(hFile, map_location=torch.device("cpu")) + if state_dict is not None and "model" in state_dict: + state_dict_local = {} + for key in state_dict["model"]: + if key.startswith(prefix): + v_key = state_dict["model"][key] + if isinstance(v_key, np.ndarray): + v_key = torch.from_numpy(v_key) + state_dict_local[key[len(prefix) :]] = v_key + # non-strict loading to finetune on different meshes + # pyre-fixme[6]: Expected `OrderedDict[typing.Any, typing.Any]` for 1st + # param but got `Dict[typing.Any, typing.Any]`. + self.load_state_dict(state_dict_local, strict=False) + + def forward(self, mesh_name: str) -> torch.Tensor: + """ + Produce vertex embeddings for the specific mesh; vertex embeddings are + a tensor of shape [N, D] where: + N = number of vertices + D = number of dimensions in the embedding space + Args: + mesh_name (str): name of a mesh for which to obtain vertex embeddings + Return: + Vertex embeddings, a tensor of shape [N, D] + """ + return getattr(self, f"embedder_{mesh_name}")() + + def has_embeddings(self, mesh_name: str) -> bool: + return hasattr(self, f"embedder_{mesh_name}") diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/cse/utils.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/cse/utils.py new file mode 100644 index 0000000..18480db --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/cse/utils.py @@ -0,0 +1,85 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import torch +from torch.nn import functional as F + + +def squared_euclidean_distance_matrix(pts1: torch.Tensor, pts2: torch.Tensor) -> torch.Tensor: + """ + Get squared Euclidean Distance Matrix + Computes pairwise squared Euclidean distances between points + + Args: + pts1: Tensor [M x D], M is the number of points, D is feature dimensionality + pts2: Tensor [N x D], N is the number of points, D is feature dimensionality + + Return: + Tensor [M, N]: matrix of squared Euclidean distances; at index (m, n) + it contains || pts1[m] - pts2[n] ||^2 + """ + edm = torch.mm(-2 * pts1, pts2.t()) + edm += (pts1 * pts1).sum(1, keepdim=True) + (pts2 * pts2).sum(1, keepdim=True).t() + return edm.contiguous() + + +def normalize_embeddings(embeddings: torch.Tensor, epsilon: float = 1e-6) -> torch.Tensor: + """ + Normalize N D-dimensional embedding vectors arranged in a tensor [N, D] + + Args: + embeddings (tensor [N, D]): N D-dimensional embedding vectors + epsilon (float): minimum value for a vector norm + Return: + Normalized embeddings (tensor [N, D]), such that L2 vector norms are all equal to 1. + """ + return embeddings / torch.clamp( + embeddings.norm(p=None, dim=1, keepdim=True), min=epsilon # pyre-ignore[6] + ) + + +def get_closest_vertices_mask_from_ES( + E: torch.Tensor, + S: torch.Tensor, + h: int, + w: int, + mesh_vertex_embeddings: torch.Tensor, + device: torch.device, +): + """ + Interpolate Embeddings and Segmentations to the size of a given bounding box, + and compute closest vertices and the segmentation mask + + Args: + E (tensor [1, D, H, W]): D-dimensional embedding vectors for every point of the + default-sized box + S (tensor [1, 2, H, W]): 2-dimensional segmentation mask for every point of the + default-sized box + h (int): height of the target bounding box + w (int): width of the target bounding box + mesh_vertex_embeddings (tensor [N, D]): vertex embeddings for a chosen mesh + N is the number of vertices in the mesh, D is feature dimensionality + device (torch.device): device to move the tensors to + Return: + Closest Vertices (tensor [h, w]), int, for every point of the resulting box + Segmentation mask (tensor [h, w]), boolean, for every point of the resulting box + """ + # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got `Tuple[int, int]`. + embedding_resized = F.interpolate(E, size=(h, w), mode="bilinear")[0].to(device) + # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got `Tuple[int, int]`. + coarse_segm_resized = F.interpolate(S, size=(h, w), mode="bilinear")[0].to(device) + mask = coarse_segm_resized.argmax(0) > 0 + closest_vertices = torch.zeros(mask.shape, dtype=torch.long, device=device) + all_embeddings = embedding_resized[:, mask].t() + size_chunk = 10_000 # Chunking to avoid possible OOM + edm = [] + if len(all_embeddings) == 0: + return closest_vertices, mask + for chunk in range((len(all_embeddings) - 1) // size_chunk + 1): + chunk_embeddings = all_embeddings[size_chunk * chunk : size_chunk * (chunk + 1)] + edm.append( + torch.argmin( + squared_euclidean_distance_matrix(chunk_embeddings, mesh_vertex_embeddings), dim=1 + ) + ) + closest_vertices[mask] = torch.cat(edm) + return closest_vertices, mask diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/cse/vertex_direct_embedder.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/cse/vertex_direct_embedder.py new file mode 100644 index 0000000..60fba27 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/cse/vertex_direct_embedder.py @@ -0,0 +1,64 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import pickle +import torch +from torch import nn + +from detectron2.utils.file_io import PathManager + +from .utils import normalize_embeddings + + +class VertexDirectEmbedder(nn.Module): + """ + Class responsible for embedding vertices. Vertex embeddings take + the form of a tensor of size [N, D], where + N = number of vertices + D = number of dimensions in the embedding space + """ + + def __init__(self, num_vertices: int, embed_dim: int): + """ + Initialize embedder, set random embeddings + + Args: + num_vertices (int): number of vertices to embed + embed_dim (int): number of dimensions in the embedding space + """ + super(VertexDirectEmbedder, self).__init__() + self.embeddings = nn.Parameter(torch.Tensor(num_vertices, embed_dim)) + self.reset_parameters() + + @torch.no_grad() + def reset_parameters(self): + """ + Reset embeddings to random values + """ + self.embeddings.zero_() + + def forward(self) -> torch.Tensor: + """ + Produce vertex embeddings, a tensor of shape [N, D] where: + N = number of vertices + D = number of dimensions in the embedding space + + Return: + Full vertex embeddings, a tensor of shape [N, D] + """ + return normalize_embeddings(self.embeddings) + + @torch.no_grad() + def load(self, fpath: str): + """ + Load data from a file + + Args: + fpath (str): file path to load data from + """ + with PathManager.open(fpath, "rb") as hFile: + data = pickle.load(hFile) # pyre-ignore[6] + for name in ["embeddings"]: + if name in data: + getattr(self, name).copy_( + torch.tensor(data[name]).float().to(device=getattr(self, name).device) + ) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/cse/vertex_feature_embedder.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/cse/vertex_feature_embedder.py new file mode 100644 index 0000000..dcb2f20 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/cse/vertex_feature_embedder.py @@ -0,0 +1,75 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import pickle +import torch +from torch import nn + +from detectron2.utils.file_io import PathManager + +from .utils import normalize_embeddings + + +class VertexFeatureEmbedder(nn.Module): + """ + Class responsible for embedding vertex features. Mapping from + feature space to the embedding space is a tensor of size [K, D], where + K = number of dimensions in the feature space + D = number of dimensions in the embedding space + Vertex features is a tensor of size [N, K], where + N = number of vertices + K = number of dimensions in the feature space + Vertex embeddings are computed as F * E = tensor of size [N, D] + """ + + def __init__( + self, num_vertices: int, feature_dim: int, embed_dim: int, train_features: bool = False + ): + """ + Initialize embedder, set random embeddings + + Args: + num_vertices (int): number of vertices to embed + feature_dim (int): number of dimensions in the feature space + embed_dim (int): number of dimensions in the embedding space + train_features (bool): determines whether vertex features should + be trained (default: False) + """ + super(VertexFeatureEmbedder, self).__init__() + if train_features: + self.features = nn.Parameter(torch.Tensor(num_vertices, feature_dim)) + else: + self.register_buffer("features", torch.Tensor(num_vertices, feature_dim)) + self.embeddings = nn.Parameter(torch.Tensor(feature_dim, embed_dim)) + self.reset_parameters() + + @torch.no_grad() + def reset_parameters(self): + self.features.zero_() + self.embeddings.zero_() + + def forward(self) -> torch.Tensor: + """ + Produce vertex embeddings, a tensor of shape [N, D] where: + N = number of vertices + D = number of dimensions in the embedding space + + Return: + Full vertex embeddings, a tensor of shape [N, D] + """ + return normalize_embeddings(torch.mm(self.features, self.embeddings)) + + @torch.no_grad() + def load(self, fpath: str): + """ + Load data from a file + + Args: + fpath (str): file path to load data from + """ + with PathManager.open(fpath, "rb") as hFile: + data = pickle.load(hFile) # pyre-ignore[6] + for name in ["features", "embeddings"]: + if name in data: + getattr(self, name).copy_( + torch.tensor(data[name]).float().to(device=getattr(self, name).device) + ) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/densepose_checkpoint.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/densepose_checkpoint.py new file mode 100644 index 0000000..8c2b4f2 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/densepose_checkpoint.py @@ -0,0 +1,35 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from collections import OrderedDict + +from detectron2.checkpoint import DetectionCheckpointer + + +def _rename_HRNet_weights(weights): + # We detect and rename HRNet weights for DensePose. 1956 and 1716 are values that are + # common to all HRNet pretrained weights, and should be enough to accurately identify them + if ( + len(weights["model"].keys()) == 1956 + and len([k for k in weights["model"].keys() if k.startswith("stage")]) == 1716 + ): + hrnet_weights = OrderedDict() + for k in weights["model"].keys(): + hrnet_weights["backbone.bottom_up." + str(k)] = weights["model"][k] + return {"model": hrnet_weights} + else: + return weights + + +class DensePoseCheckpointer(DetectionCheckpointer): + """ + Same as :class:`DetectionCheckpointer`, but is able to handle HRNet weights + """ + + def __init__(self, model, save_dir="", *, save_to_disk=None, **checkpointables): + super().__init__(model, save_dir, save_to_disk=save_to_disk, **checkpointables) + + def _load_file(self, filename: str) -> object: + """ + Adding hrnet support + """ + weights = super()._load_file(filename) + return _rename_HRNet_weights(weights) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/filter.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/filter.py new file mode 100644 index 0000000..18a8567 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/filter.py @@ -0,0 +1,94 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from typing import List +import torch + +from detectron2.config import CfgNode +from detectron2.structures import Instances +from detectron2.structures.boxes import matched_pairwise_iou + + +class DensePoseDataFilter(object): + def __init__(self, cfg: CfgNode): + self.iou_threshold = cfg.MODEL.ROI_DENSEPOSE_HEAD.FG_IOU_THRESHOLD + self.keep_masks = cfg.MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS + + @torch.no_grad() + def __call__(self, features: List[torch.Tensor], proposals_with_targets: List[Instances]): + """ + Filters proposals with targets to keep only the ones relevant for + DensePose training + + Args: + features (list[Tensor]): input data as a list of features, + each feature is a tensor. Axis 0 represents the number of + images `N` in the input data; axes 1-3 are channels, + height, and width, which may vary between features + (e.g., if a feature pyramid is used). + proposals_with_targets (list[Instances]): length `N` list of + `Instances`. The i-th `Instances` contains instances + (proposals, GT) for the i-th input image, + Returns: + list[Tensor]: filtered features + list[Instances]: filtered proposals + """ + proposals_filtered = [] + # TODO: the commented out code was supposed to correctly deal with situations + # where no valid DensePose GT is available for certain images. The corresponding + # image features were sliced and proposals were filtered. This led to performance + # deterioration, both in terms of runtime and in terms of evaluation results. + # + # feature_mask = torch.ones( + # len(proposals_with_targets), + # dtype=torch.bool, + # device=features[0].device if len(features) > 0 else torch.device("cpu"), + # ) + for i, proposals_per_image in enumerate(proposals_with_targets): + if not proposals_per_image.has("gt_densepose") and ( + not proposals_per_image.has("gt_masks") or not self.keep_masks + ): + # feature_mask[i] = 0 + continue + gt_boxes = proposals_per_image.gt_boxes + est_boxes = proposals_per_image.proposal_boxes + # apply match threshold for densepose head + iou = matched_pairwise_iou(gt_boxes, est_boxes) + iou_select = iou > self.iou_threshold + proposals_per_image = proposals_per_image[iou_select] # pyre-ignore[6] + + N_gt_boxes = len(proposals_per_image.gt_boxes) + assert N_gt_boxes == len(proposals_per_image.proposal_boxes), ( + f"The number of GT boxes {N_gt_boxes} is different from the " + f"number of proposal boxes {len(proposals_per_image.proposal_boxes)}" + ) + # filter out any target without suitable annotation + if self.keep_masks: + gt_masks = ( + proposals_per_image.gt_masks + if hasattr(proposals_per_image, "gt_masks") + else [None] * N_gt_boxes + ) + else: + gt_masks = [None] * N_gt_boxes + gt_densepose = ( + proposals_per_image.gt_densepose + if hasattr(proposals_per_image, "gt_densepose") + else [None] * N_gt_boxes + ) + assert len(gt_masks) == N_gt_boxes + assert len(gt_densepose) == N_gt_boxes + selected_indices = [ + i + for i, (dp_target, mask_target) in enumerate(zip(gt_densepose, gt_masks)) + if (dp_target is not None) or (mask_target is not None) + ] + # if not len(selected_indices): + # feature_mask[i] = 0 + # continue + if len(selected_indices) != N_gt_boxes: + proposals_per_image = proposals_per_image[selected_indices] # pyre-ignore[6] + assert len(proposals_per_image.gt_boxes) == len(proposals_per_image.proposal_boxes) + proposals_filtered.append(proposals_per_image) + # features_filtered = [feature[feature_mask] for feature in features] + # return features_filtered, proposals_filtered + return features, proposals_filtered diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/hrfpn.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/hrfpn.py new file mode 100644 index 0000000..6e3c222 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/hrfpn.py @@ -0,0 +1,182 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +""" +MIT License +Copyright (c) 2019 Microsoft +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone import BACKBONE_REGISTRY +from detectron2.modeling.backbone.backbone import Backbone + +from .hrnet import build_pose_hrnet_backbone + + +class HRFPN(Backbone): + """HRFPN (High Resolution Feature Pyramids) + Transforms outputs of HRNet backbone so they are suitable for the ROI_heads + arXiv: https://arxiv.org/abs/1904.04514 + Adapted from https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/necks/hrfpn.py + Args: + bottom_up: (list) output of HRNet + in_features (list): names of the input features (output of HRNet) + in_channels (list): number of channels for each branch + out_channels (int): output channels of feature pyramids + n_out_features (int): number of output stages + pooling (str): pooling for generating feature pyramids (from {MAX, AVG}) + share_conv (bool): Have one conv per output, or share one with all the outputs + """ + + def __init__( + self, + bottom_up, + in_features, + n_out_features, + in_channels, + out_channels, + pooling="AVG", + share_conv=False, + ): + super(HRFPN, self).__init__() + assert isinstance(in_channels, list) + self.bottom_up = bottom_up + self.in_features = in_features + self.n_out_features = n_out_features + self.in_channels = in_channels + self.out_channels = out_channels + self.num_ins = len(in_channels) + self.share_conv = share_conv + + if self.share_conv: + self.fpn_conv = nn.Conv2d( + in_channels=out_channels, out_channels=out_channels, kernel_size=3, padding=1 + ) + else: + self.fpn_conv = nn.ModuleList() + for _ in range(self.n_out_features): + self.fpn_conv.append( + nn.Conv2d( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=3, + padding=1, + ) + ) + + # Custom change: Replaces a simple bilinear interpolation + self.interp_conv = nn.ModuleList() + for i in range(len(self.in_features)): + self.interp_conv.append( + nn.Sequential( + nn.ConvTranspose2d( + in_channels=in_channels[i], + out_channels=in_channels[i], + kernel_size=4, + stride=2 ** i, + padding=0, + output_padding=0, + bias=False, + ), + nn.BatchNorm2d(in_channels[i], momentum=0.1), + nn.ReLU(inplace=True), + ) + ) + + # Custom change: Replaces a couple (reduction conv + pooling) by one conv + self.reduction_pooling_conv = nn.ModuleList() + for i in range(self.n_out_features): + self.reduction_pooling_conv.append( + nn.Sequential( + nn.Conv2d(sum(in_channels), out_channels, kernel_size=2 ** i, stride=2 ** i), + nn.BatchNorm2d(out_channels, momentum=0.1), + nn.ReLU(inplace=True), + ) + ) + + if pooling == "MAX": + self.pooling = F.max_pool2d + else: + self.pooling = F.avg_pool2d + + self._out_features = [] + self._out_feature_channels = {} + self._out_feature_strides = {} + + for i in range(self.n_out_features): + self._out_features.append("p%d" % (i + 1)) + self._out_feature_channels.update({self._out_features[-1]: self.out_channels}) + self._out_feature_strides.update({self._out_features[-1]: 2 ** (i + 2)}) + + # default init_weights for conv(msra) and norm in ConvModule + def init_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, a=1) + nn.init.constant_(m.bias, 0) + + def forward(self, inputs): + bottom_up_features = self.bottom_up(inputs) + assert len(bottom_up_features) == len(self.in_features) + inputs = [bottom_up_features[f] for f in self.in_features] + + outs = [] + for i in range(len(inputs)): + outs.append(self.interp_conv[i](inputs[i])) + shape_2 = min(o.shape[2] for o in outs) + shape_3 = min(o.shape[3] for o in outs) + out = torch.cat([o[:, :, :shape_2, :shape_3] for o in outs], dim=1) + outs = [] + for i in range(self.n_out_features): + outs.append(self.reduction_pooling_conv[i](out)) + for i in range(len(outs)): # Make shapes consistent + outs[-1 - i] = outs[-1 - i][ + :, :, : outs[-1].shape[2] * 2 ** i, : outs[-1].shape[3] * 2 ** i + ] + outputs = [] + for i in range(len(outs)): + if self.share_conv: + outputs.append(self.fpn_conv(outs[i])) + else: + outputs.append(self.fpn_conv[i](outs[i])) + + assert len(self._out_features) == len(outputs) + return dict(zip(self._out_features, outputs)) + + +@BACKBONE_REGISTRY.register() +def build_hrfpn_backbone(cfg, input_shape: ShapeSpec) -> HRFPN: + + in_channels = cfg.MODEL.HRNET.STAGE4.NUM_CHANNELS + in_features = ["p%d" % (i + 1) for i in range(cfg.MODEL.HRNET.STAGE4.NUM_BRANCHES)] + n_out_features = len(cfg.MODEL.ROI_HEADS.IN_FEATURES) + out_channels = cfg.MODEL.HRNET.HRFPN.OUT_CHANNELS + hrnet = build_pose_hrnet_backbone(cfg, input_shape) + hrfpn = HRFPN( + hrnet, + in_features, + n_out_features, + in_channels, + out_channels, + pooling="AVG", + share_conv=False, + ) + + return hrfpn diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/hrnet.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/hrnet.py new file mode 100644 index 0000000..ca24671 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/hrnet.py @@ -0,0 +1,474 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (leoxiaobin@gmail.com) +# Modified by Bowen Cheng (bcheng9@illinois.edu) +# Adapted from https://github.com/HRNet/Higher-HRNet-Human-Pose-Estimation/blob/master/lib/models/pose_higher_hrnet.py # noqa +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import, division, print_function +import logging +import torch.nn as nn + +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone import BACKBONE_REGISTRY +from detectron2.modeling.backbone.backbone import Backbone + +BN_MOMENTUM = 0.1 +logger = logging.getLogger(__name__) + +__all__ = ["build_pose_hrnet_backbone", "PoseHigherResolutionNet"] + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class HighResolutionModule(nn.Module): + """HighResolutionModule + Building block of the PoseHigherResolutionNet (see lower) + arXiv: https://arxiv.org/abs/1908.10357 + Args: + num_branches (int): number of branches of the modyle + blocks (str): type of block of the module + num_blocks (int): number of blocks of the module + num_inchannels (int): number of input channels of the module + num_channels (list): number of channels of each branch + multi_scale_output (bool): only used by the last module of PoseHigherResolutionNet + """ + + def __init__( + self, + num_branches, + blocks, + num_blocks, + num_inchannels, + num_channels, + multi_scale_output=True, + ): + super(HighResolutionModule, self).__init__() + self._check_branches(num_branches, blocks, num_blocks, num_inchannels, num_channels) + + self.num_inchannels = num_inchannels + self.num_branches = num_branches + + self.multi_scale_output = multi_scale_output + + self.branches = self._make_branches(num_branches, blocks, num_blocks, num_channels) + self.fuse_layers = self._make_fuse_layers() + self.relu = nn.ReLU(True) + + def _check_branches(self, num_branches, blocks, num_blocks, num_inchannels, num_channels): + if num_branches != len(num_blocks): + error_msg = "NUM_BRANCHES({}) <> NUM_BLOCKS({})".format(num_branches, len(num_blocks)) + logger.error(error_msg) + raise ValueError(error_msg) + + if num_branches != len(num_channels): + error_msg = "NUM_BRANCHES({}) <> NUM_CHANNELS({})".format( + num_branches, len(num_channels) + ) + logger.error(error_msg) + raise ValueError(error_msg) + + if num_branches != len(num_inchannels): + error_msg = "NUM_BRANCHES({}) <> NUM_INCHANNELS({})".format( + num_branches, len(num_inchannels) + ) + logger.error(error_msg) + raise ValueError(error_msg) + + def _make_one_branch(self, branch_index, block, num_blocks, num_channels, stride=1): + downsample = None + if ( + stride != 1 + or self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion + ): + downsample = nn.Sequential( + nn.Conv2d( + self.num_inchannels[branch_index], + num_channels[branch_index] * block.expansion, + kernel_size=1, + stride=stride, + bias=False, + ), + nn.BatchNorm2d(num_channels[branch_index] * block.expansion, momentum=BN_MOMENTUM), + ) + + layers = [] + layers.append( + block(self.num_inchannels[branch_index], num_channels[branch_index], stride, downsample) + ) + self.num_inchannels[branch_index] = num_channels[branch_index] * block.expansion + for _ in range(1, num_blocks[branch_index]): + layers.append(block(self.num_inchannels[branch_index], num_channels[branch_index])) + + return nn.Sequential(*layers) + + def _make_branches(self, num_branches, block, num_blocks, num_channels): + branches = [] + + for i in range(num_branches): + branches.append(self._make_one_branch(i, block, num_blocks, num_channels)) + + return nn.ModuleList(branches) + + def _make_fuse_layers(self): + if self.num_branches == 1: + return None + + num_branches = self.num_branches + num_inchannels = self.num_inchannels + fuse_layers = [] + for i in range(num_branches if self.multi_scale_output else 1): + fuse_layer = [] + for j in range(num_branches): + if j > i: + fuse_layer.append( + nn.Sequential( + nn.Conv2d(num_inchannels[j], num_inchannels[i], 1, 1, 0, bias=False), + nn.BatchNorm2d(num_inchannels[i]), + nn.Upsample(scale_factor=2 ** (j - i), mode="nearest"), + ) + ) + elif j == i: + fuse_layer.append(None) + else: + conv3x3s = [] + for k in range(i - j): + if k == i - j - 1: + num_outchannels_conv3x3 = num_inchannels[i] + conv3x3s.append( + nn.Sequential( + nn.Conv2d( + num_inchannels[j], + num_outchannels_conv3x3, + 3, + 2, + 1, + bias=False, + ), + nn.BatchNorm2d(num_outchannels_conv3x3), + ) + ) + else: + num_outchannels_conv3x3 = num_inchannels[j] + conv3x3s.append( + nn.Sequential( + nn.Conv2d( + num_inchannels[j], + num_outchannels_conv3x3, + 3, + 2, + 1, + bias=False, + ), + nn.BatchNorm2d(num_outchannels_conv3x3), + nn.ReLU(True), + ) + ) + fuse_layer.append(nn.Sequential(*conv3x3s)) + fuse_layers.append(nn.ModuleList(fuse_layer)) + + return nn.ModuleList(fuse_layers) + + def get_num_inchannels(self): + return self.num_inchannels + + def forward(self, x): + if self.num_branches == 1: + return [self.branches[0](x[0])] + + for i in range(self.num_branches): + x[i] = self.branches[i](x[i]) + + x_fuse = [] + + for i in range(len(self.fuse_layers)): + y = x[0] if i == 0 else self.fuse_layers[i][0](x[0]) + for j in range(1, self.num_branches): + if i == j: + y = y + x[j] + else: + z = self.fuse_layers[i][j](x[j])[:, :, : y.shape[2], : y.shape[3]] + y = y + z + x_fuse.append(self.relu(y)) + + return x_fuse + + +blocks_dict = {"BASIC": BasicBlock, "BOTTLENECK": Bottleneck} + + +class PoseHigherResolutionNet(Backbone): + """PoseHigherResolutionNet + Composed of several HighResolutionModule tied together with ConvNets + Adapted from the GitHub version to fit with HRFPN and the Detectron2 infrastructure + arXiv: https://arxiv.org/abs/1908.10357 + """ + + def __init__(self, cfg, **kwargs): + self.inplanes = cfg.MODEL.HRNET.STEM_INPLANES + super(PoseHigherResolutionNet, self).__init__() + + # stem net + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) + self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.layer1 = self._make_layer(Bottleneck, 64, 4) + + self.stage2_cfg = cfg.MODEL.HRNET.STAGE2 + num_channels = self.stage2_cfg.NUM_CHANNELS + block = blocks_dict[self.stage2_cfg.BLOCK] + num_channels = [num_channels[i] * block.expansion for i in range(len(num_channels))] + self.transition1 = self._make_transition_layer([256], num_channels) + self.stage2, pre_stage_channels = self._make_stage(self.stage2_cfg, num_channels) + + self.stage3_cfg = cfg.MODEL.HRNET.STAGE3 + num_channels = self.stage3_cfg.NUM_CHANNELS + block = blocks_dict[self.stage3_cfg.BLOCK] + num_channels = [num_channels[i] * block.expansion for i in range(len(num_channels))] + self.transition2 = self._make_transition_layer(pre_stage_channels, num_channels) + self.stage3, pre_stage_channels = self._make_stage(self.stage3_cfg, num_channels) + + self.stage4_cfg = cfg.MODEL.HRNET.STAGE4 + num_channels = self.stage4_cfg.NUM_CHANNELS + block = blocks_dict[self.stage4_cfg.BLOCK] + num_channels = [num_channels[i] * block.expansion for i in range(len(num_channels))] + self.transition3 = self._make_transition_layer(pre_stage_channels, num_channels) + self.stage4, pre_stage_channels = self._make_stage( + self.stage4_cfg, num_channels, multi_scale_output=True + ) + + self._out_features = [] + self._out_feature_channels = {} + self._out_feature_strides = {} + + for i in range(cfg.MODEL.HRNET.STAGE4.NUM_BRANCHES): + self._out_features.append("p%d" % (i + 1)) + self._out_feature_channels.update( + {self._out_features[-1]: cfg.MODEL.HRNET.STAGE4.NUM_CHANNELS[i]} + ) + self._out_feature_strides.update({self._out_features[-1]: 1}) + + def _get_deconv_cfg(self, deconv_kernel): + if deconv_kernel == 4: + padding = 1 + output_padding = 0 + elif deconv_kernel == 3: + padding = 1 + output_padding = 1 + elif deconv_kernel == 2: + padding = 0 + output_padding = 0 + + return deconv_kernel, padding, output_padding + + def _make_transition_layer(self, num_channels_pre_layer, num_channels_cur_layer): + num_branches_cur = len(num_channels_cur_layer) + num_branches_pre = len(num_channels_pre_layer) + + transition_layers = [] + for i in range(num_branches_cur): + if i < num_branches_pre: + if num_channels_cur_layer[i] != num_channels_pre_layer[i]: + transition_layers.append( + nn.Sequential( + nn.Conv2d( + num_channels_pre_layer[i], + num_channels_cur_layer[i], + 3, + 1, + 1, + bias=False, + ), + nn.BatchNorm2d(num_channels_cur_layer[i]), + nn.ReLU(inplace=True), + ) + ) + else: + transition_layers.append(None) + else: + conv3x3s = [] + for j in range(i + 1 - num_branches_pre): + inchannels = num_channels_pre_layer[-1] + outchannels = ( + num_channels_cur_layer[i] if j == i - num_branches_pre else inchannels + ) + conv3x3s.append( + nn.Sequential( + nn.Conv2d(inchannels, outchannels, 3, 2, 1, bias=False), + nn.BatchNorm2d(outchannels), + nn.ReLU(inplace=True), + ) + ) + transition_layers.append(nn.Sequential(*conv3x3s)) + + return nn.ModuleList(transition_layers) + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d( + self.inplanes, + planes * block.expansion, + kernel_size=1, + stride=stride, + bias=False, + ), + nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def _make_stage(self, layer_config, num_inchannels, multi_scale_output=True): + num_modules = layer_config["NUM_MODULES"] + num_branches = layer_config["NUM_BRANCHES"] + num_blocks = layer_config["NUM_BLOCKS"] + num_channels = layer_config["NUM_CHANNELS"] + block = blocks_dict[layer_config["BLOCK"]] + + modules = [] + for i in range(num_modules): + # multi_scale_output is only used last module + if not multi_scale_output and i == num_modules - 1: + reset_multi_scale_output = False + else: + reset_multi_scale_output = True + + modules.append( + HighResolutionModule( + num_branches, + block, + num_blocks, + num_inchannels, + num_channels, + reset_multi_scale_output, + ) + ) + num_inchannels = modules[-1].get_num_inchannels() + + return nn.Sequential(*modules), num_inchannels + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.conv2(x) + x = self.bn2(x) + x = self.relu(x) + x = self.layer1(x) + + x_list = [] + for i in range(self.stage2_cfg.NUM_BRANCHES): + if self.transition1[i] is not None: + x_list.append(self.transition1[i](x)) + else: + x_list.append(x) + y_list = self.stage2(x_list) + + x_list = [] + for i in range(self.stage3_cfg.NUM_BRANCHES): + if self.transition2[i] is not None: + x_list.append(self.transition2[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage3(x_list) + + x_list = [] + for i in range(self.stage4_cfg.NUM_BRANCHES): + if self.transition3[i] is not None: + x_list.append(self.transition3[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage4(x_list) + + assert len(self._out_features) == len(y_list) + return dict(zip(self._out_features, y_list)) # final_outputs + + +@BACKBONE_REGISTRY.register() +def build_pose_hrnet_backbone(cfg, input_shape: ShapeSpec): + model = PoseHigherResolutionNet(cfg) + return model diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/inference.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/inference.py new file mode 100644 index 0000000..de486d5 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/inference.py @@ -0,0 +1,43 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from dataclasses import fields +from typing import Any, List +import torch + +from detectron2.structures import Instances + + +def densepose_inference(densepose_predictor_output: Any, detections: List[Instances]) -> None: + """ + Splits DensePose predictor outputs into chunks, each chunk corresponds to + detections on one image. Predictor output chunks are stored in `pred_densepose` + attribute of the corresponding `Instances` object. + + Args: + densepose_predictor_output: a dataclass instance (can be of different types, + depending on predictor used for inference). Each field can be `None` + (if the corresponding output was not inferred) or a tensor of size + [N, ...], where N = N_1 + N_2 + .. + N_k is a total number of + detections on all images, N_1 is the number of detections on image 1, + N_2 is the number of detections on image 2, etc. + detections: a list of objects of type `Instance`, k-th object corresponds + to detections on k-th image. + """ + k = 0 + for detection_i in detections: + if densepose_predictor_output is None: + # don't add `pred_densepose` attribute + continue + n_i = len(detection_i) + PredictorOutput = type(densepose_predictor_output) + output_i_dict = {} + # we assume here that `densepose_predictor_output` is a dataclass object + for field in fields(densepose_predictor_output): + field_value = getattr(densepose_predictor_output, field.name) + # slice tensors + if isinstance(field_value, torch.Tensor): + output_i_dict[field.name] = field_value[k : k + n_i] + # leave others as is + else: + output_i_dict[field.name] = field_value + detection_i.pred_densepose = PredictorOutput(**output_i_dict) + k += n_i diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/__init__.py new file mode 100644 index 0000000..e5c5937 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from .chart import DensePoseChartLoss +from .chart_with_confidences import DensePoseChartWithConfidenceLoss +from .cse import DensePoseCseLoss +from .registry import DENSEPOSE_LOSS_REGISTRY + + +__all__ = [ + "DensePoseChartLoss", + "DensePoseChartWithConfidenceLoss", + "DensePoseCseLoss", + "DENSEPOSE_LOSS_REGISTRY", +] diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/chart.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/chart.py new file mode 100644 index 0000000..a2007af --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/chart.py @@ -0,0 +1,290 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from typing import Any, List +import torch +from torch.nn import functional as F + +from detectron2.config import CfgNode +from detectron2.structures import Instances + +from .mask_or_segm import MaskOrSegmentationLoss +from .registry import DENSEPOSE_LOSS_REGISTRY +from .utils import ( + BilinearInterpolationHelper, + ChartBasedAnnotationsAccumulator, + LossDict, + extract_packed_annotations_from_matches, +) + + +@DENSEPOSE_LOSS_REGISTRY.register() +class DensePoseChartLoss: + """ + DensePose loss for chart-based training. A mesh is split into charts, + each chart is given a label (I) and parametrized by 2 coordinates referred to + as U and V. Ground truth consists of a number of points annotated with + I, U and V values and coarse segmentation S defined for all pixels of the + object bounding box. In some cases (see `COARSE_SEGM_TRAINED_BY_MASKS`), + semantic segmentation annotations can be used as ground truth inputs as well. + + Estimated values are tensors: + * U coordinates, tensor of shape [N, C, S, S] + * V coordinates, tensor of shape [N, C, S, S] + * fine segmentation estimates, tensor of shape [N, C, S, S] with raw unnormalized + scores for each fine segmentation label at each location + * coarse segmentation estimates, tensor of shape [N, D, S, S] with raw unnormalized + scores for each coarse segmentation label at each location + where N is the number of detections, C is the number of fine segmentation + labels, S is the estimate size ( = width = height) and D is the number of + coarse segmentation channels. + + The losses are: + * regression (smooth L1) loss for U and V coordinates + * cross entropy loss for fine (I) and coarse (S) segmentations + Each loss has an associated weight + """ + + def __init__(self, cfg: CfgNode): + """ + Initialize chart-based loss from configuration options + + Args: + cfg (CfgNode): configuration options + """ + # fmt: off + self.heatmap_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE + self.w_points = cfg.MODEL.ROI_DENSEPOSE_HEAD.POINT_REGRESSION_WEIGHTS + self.w_part = cfg.MODEL.ROI_DENSEPOSE_HEAD.PART_WEIGHTS + self.w_segm = cfg.MODEL.ROI_DENSEPOSE_HEAD.INDEX_WEIGHTS + self.n_segm_chan = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS + # fmt: on + self.segm_trained_by_masks = cfg.MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS + self.segm_loss = MaskOrSegmentationLoss(cfg) + + def __call__( + self, proposals_with_gt: List[Instances], densepose_predictor_outputs: Any, **kwargs + ) -> LossDict: + """ + Produce chart-based DensePose losses + + Args: + proposals_with_gt (list of Instances): detections with associated ground truth data + densepose_predictor_outputs: an object of a dataclass that contains predictor outputs + with estimated values; assumed to have the following attributes: + * coarse_segm - coarse segmentation estimates, tensor of shape [N, D, S, S] + * fine_segm - fine segmentation estimates, tensor of shape [N, C, S, S] + * u - U coordinate estimates per fine labels, tensor of shape [N, C, S, S] + * v - V coordinate estimates per fine labels, tensor of shape [N, C, S, S] + where N is the number of detections, C is the number of fine segmentation + labels, S is the estimate size ( = width = height) and D is the number of + coarse segmentation channels. + + Return: + dict: str -> tensor: dict of losses with the following entries: + * `loss_densepose_U`: smooth L1 loss for U coordinate estimates + * `loss_densepose_V`: smooth L1 loss for V coordinate estimates + * `loss_densepose_I`: cross entropy for raw unnormalized scores for fine + segmentation estimates given ground truth labels; + * `loss_densepose_S`: cross entropy for raw unnormalized scores for coarse + segmentation estimates given ground truth labels; + """ + # densepose outputs are computed for all images and all bounding boxes; + # i.e. if a batch has 4 images with (3, 1, 2, 1) proposals respectively, + # the outputs will have size(0) == 3+1+2+1 == 7 + + if not len(proposals_with_gt): + return self.produce_fake_densepose_losses(densepose_predictor_outputs) + + accumulator = ChartBasedAnnotationsAccumulator() + packed_annotations = extract_packed_annotations_from_matches(proposals_with_gt, accumulator) + + # NOTE: we need to keep the same computation graph on all the GPUs to + # perform reduction properly. Hence even if we have no data on one + # of the GPUs, we still need to generate the computation graph. + # Add fake (zero) loss in the form Tensor.sum() * 0 + if packed_annotations is None: + return self.produce_fake_densepose_losses(densepose_predictor_outputs) + + h, w = densepose_predictor_outputs.u.shape[2:] + interpolator = BilinearInterpolationHelper.from_matches( + packed_annotations, + (h, w), + ) + + j_valid_fg = interpolator.j_valid * ( # pyre-ignore[16] + packed_annotations.fine_segm_labels_gt > 0 + ) + if not torch.any(j_valid_fg): + return self.produce_fake_densepose_losses(densepose_predictor_outputs) + + losses_uv = self.produce_densepose_losses_uv( + proposals_with_gt, + densepose_predictor_outputs, + packed_annotations, + interpolator, + j_valid_fg, # pyre-ignore[6] + ) + + losses_segm = self.produce_densepose_losses_segm( + proposals_with_gt, + densepose_predictor_outputs, + packed_annotations, + interpolator, + j_valid_fg, # pyre-ignore[6] + ) + + return {**losses_uv, **losses_segm} + + def produce_fake_densepose_losses(self, densepose_predictor_outputs: Any) -> LossDict: + """ + Fake losses for fine segmentation and U/V coordinates. These are used when + no suitable ground truth data was found in a batch. The loss has a value 0 + and is primarily used to construct the computation graph, so that + `DistributedDataParallel` has similar graphs on all GPUs and can perform + reduction properly. + + Args: + densepose_predictor_outputs: DensePose predictor outputs, an object + of a dataclass that is assumed to have the following attributes: + * fine_segm - fine segmentation estimates, tensor of shape [N, C, S, S] + * u - U coordinate estimates per fine labels, tensor of shape [N, C, S, S] + * v - V coordinate estimates per fine labels, tensor of shape [N, C, S, S] + Return: + dict: str -> tensor: dict of losses with the following entries: + * `loss_densepose_U`: has value 0 + * `loss_densepose_V`: has value 0 + * `loss_densepose_I`: has value 0 + * `loss_densepose_S`: has value 0 + """ + losses_uv = self.produce_fake_densepose_losses_uv(densepose_predictor_outputs) + losses_segm = self.produce_fake_densepose_losses_segm(densepose_predictor_outputs) + return {**losses_uv, **losses_segm} + + def produce_fake_densepose_losses_uv(self, densepose_predictor_outputs: Any) -> LossDict: + """ + Fake losses for U/V coordinates. These are used when no suitable ground + truth data was found in a batch. The loss has a value 0 + and is primarily used to construct the computation graph, so that + `DistributedDataParallel` has similar graphs on all GPUs and can perform + reduction properly. + + Args: + densepose_predictor_outputs: DensePose predictor outputs, an object + of a dataclass that is assumed to have the following attributes: + * u - U coordinate estimates per fine labels, tensor of shape [N, C, S, S] + * v - V coordinate estimates per fine labels, tensor of shape [N, C, S, S] + Return: + dict: str -> tensor: dict of losses with the following entries: + * `loss_densepose_U`: has value 0 + * `loss_densepose_V`: has value 0 + """ + return { + "loss_densepose_U": densepose_predictor_outputs.u.sum() * 0, + "loss_densepose_V": densepose_predictor_outputs.v.sum() * 0, + } + + def produce_fake_densepose_losses_segm(self, densepose_predictor_outputs: Any) -> LossDict: + """ + Fake losses for fine / coarse segmentation. These are used when + no suitable ground truth data was found in a batch. The loss has a value 0 + and is primarily used to construct the computation graph, so that + `DistributedDataParallel` has similar graphs on all GPUs and can perform + reduction properly. + + Args: + densepose_predictor_outputs: DensePose predictor outputs, an object + of a dataclass that is assumed to have the following attributes: + * fine_segm - fine segmentation estimates, tensor of shape [N, C, S, S] + * coarse_segm - coarse segmentation estimates, tensor of shape [N, D, S, S] + Return: + dict: str -> tensor: dict of losses with the following entries: + * `loss_densepose_I`: has value 0 + * `loss_densepose_S`: has value 0, added only if `segm_trained_by_masks` is False + """ + losses = { + "loss_densepose_I": densepose_predictor_outputs.fine_segm.sum() * 0, + "loss_densepose_S": self.segm_loss.fake_value(densepose_predictor_outputs), + } + return losses + + def produce_densepose_losses_uv( + self, + proposals_with_gt: List[Instances], + densepose_predictor_outputs: Any, + packed_annotations: Any, + interpolator: BilinearInterpolationHelper, + j_valid_fg: torch.Tensor, + ) -> LossDict: + """ + Compute losses for U/V coordinates: smooth L1 loss between + estimated coordinates and the ground truth. + + Args: + proposals_with_gt (list of Instances): detections with associated ground truth data + densepose_predictor_outputs: DensePose predictor outputs, an object + of a dataclass that is assumed to have the following attributes: + * u - U coordinate estimates per fine labels, tensor of shape [N, C, S, S] + * v - V coordinate estimates per fine labels, tensor of shape [N, C, S, S] + Return: + dict: str -> tensor: dict of losses with the following entries: + * `loss_densepose_U`: smooth L1 loss for U coordinate estimates + * `loss_densepose_V`: smooth L1 loss for V coordinate estimates + """ + u_gt = packed_annotations.u_gt[j_valid_fg] + u_est = interpolator.extract_at_points(densepose_predictor_outputs.u)[j_valid_fg] + v_gt = packed_annotations.v_gt[j_valid_fg] + v_est = interpolator.extract_at_points(densepose_predictor_outputs.v)[j_valid_fg] + return { + "loss_densepose_U": F.smooth_l1_loss(u_est, u_gt, reduction="sum") * self.w_points, + "loss_densepose_V": F.smooth_l1_loss(v_est, v_gt, reduction="sum") * self.w_points, + } + + def produce_densepose_losses_segm( + self, + proposals_with_gt: List[Instances], + densepose_predictor_outputs: Any, + packed_annotations: Any, + interpolator: BilinearInterpolationHelper, + j_valid_fg: torch.Tensor, + ) -> LossDict: + """ + Losses for fine / coarse segmentation: cross-entropy + for segmentation unnormalized scores given ground truth labels at + annotated points for fine segmentation and dense mask annotations + for coarse segmentation. + + Args: + proposals_with_gt (list of Instances): detections with associated ground truth data + densepose_predictor_outputs: DensePose predictor outputs, an object + of a dataclass that is assumed to have the following attributes: + * fine_segm - fine segmentation estimates, tensor of shape [N, C, S, S] + * coarse_segm - coarse segmentation estimates, tensor of shape [N, D, S, S] + Return: + dict: str -> tensor: dict of losses with the following entries: + * `loss_densepose_I`: cross entropy for raw unnormalized scores for fine + segmentation estimates given ground truth labels + * `loss_densepose_S`: cross entropy for raw unnormalized scores for coarse + segmentation estimates given ground truth labels; + may be included if coarse segmentation is only trained + using DensePose ground truth; if additional supervision through + instance segmentation data is performed (`segm_trained_by_masks` is True), + this loss is handled by `produce_mask_losses` instead + """ + fine_segm_gt = packed_annotations.fine_segm_labels_gt[ + interpolator.j_valid # pyre-ignore[16] + ] + fine_segm_est = interpolator.extract_at_points( + densepose_predictor_outputs.fine_segm, + slice_fine_segm=slice(None), + w_ylo_xlo=interpolator.w_ylo_xlo[:, None], # pyre-ignore[16] + w_ylo_xhi=interpolator.w_ylo_xhi[:, None], # pyre-ignore[16] + w_yhi_xlo=interpolator.w_yhi_xlo[:, None], # pyre-ignore[16] + w_yhi_xhi=interpolator.w_yhi_xhi[:, None], # pyre-ignore[16] + )[interpolator.j_valid, :] + return { + "loss_densepose_I": F.cross_entropy(fine_segm_est, fine_segm_gt.long()) * self.w_part, + "loss_densepose_S": self.segm_loss( + proposals_with_gt, densepose_predictor_outputs, packed_annotations + ) + * self.w_segm, + } diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/chart_with_confidences.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/chart_with_confidences.py new file mode 100644 index 0000000..39f76b4 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/chart_with_confidences.py @@ -0,0 +1,206 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import math +from typing import Any, List +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import CfgNode +from detectron2.structures import Instances + +from .. import DensePoseConfidenceModelConfig, DensePoseUVConfidenceType +from .chart import DensePoseChartLoss +from .registry import DENSEPOSE_LOSS_REGISTRY +from .utils import BilinearInterpolationHelper, LossDict + + +@DENSEPOSE_LOSS_REGISTRY.register() +class DensePoseChartWithConfidenceLoss(DensePoseChartLoss): + """ """ + + def __init__(self, cfg: CfgNode): + super().__init__(cfg) + self.confidence_model_cfg = DensePoseConfidenceModelConfig.from_cfg(cfg) + if self.confidence_model_cfg.uv_confidence.type == DensePoseUVConfidenceType.IID_ISO: + self.uv_loss_with_confidences = IIDIsotropicGaussianUVLoss( + self.confidence_model_cfg.uv_confidence.epsilon + ) + elif self.confidence_model_cfg.uv_confidence.type == DensePoseUVConfidenceType.INDEP_ANISO: + self.uv_loss_with_confidences = IndepAnisotropicGaussianUVLoss( + self.confidence_model_cfg.uv_confidence.epsilon + ) + + def produce_fake_densepose_losses_uv(self, densepose_predictor_outputs: Any) -> LossDict: + """ + Overrides fake losses for fine segmentation and U/V coordinates to + include computation graphs for additional confidence parameters. + These are used when no suitable ground truth data was found in a batch. + The loss has a value 0 and is primarily used to construct the computation graph, + so that `DistributedDataParallel` has similar graphs on all GPUs and can + perform reduction properly. + + Args: + densepose_predictor_outputs: DensePose predictor outputs, an object + of a dataclass that is assumed to have the following attributes: + * fine_segm - fine segmentation estimates, tensor of shape [N, C, S, S] + * u - U coordinate estimates per fine labels, tensor of shape [N, C, S, S] + * v - V coordinate estimates per fine labels, tensor of shape [N, C, S, S] + Return: + dict: str -> tensor: dict of losses with the following entries: + * `loss_densepose_U`: has value 0 + * `loss_densepose_V`: has value 0 + * `loss_densepose_I`: has value 0 + """ + conf_type = self.confidence_model_cfg.uv_confidence.type + if self.confidence_model_cfg.uv_confidence.enabled: + loss_uv = ( + densepose_predictor_outputs.u.sum() + densepose_predictor_outputs.v.sum() + ) * 0 + if conf_type == DensePoseUVConfidenceType.IID_ISO: + loss_uv += densepose_predictor_outputs.sigma_2.sum() * 0 + elif conf_type == DensePoseUVConfidenceType.INDEP_ANISO: + loss_uv += ( + densepose_predictor_outputs.sigma_2.sum() + + densepose_predictor_outputs.kappa_u.sum() + + densepose_predictor_outputs.kappa_v.sum() + ) * 0 + return {"loss_densepose_UV": loss_uv} + else: + return super().produce_fake_densepose_losses_uv(densepose_predictor_outputs) + + def produce_densepose_losses_uv( + self, + proposals_with_gt: List[Instances], + densepose_predictor_outputs: Any, + packed_annotations: Any, + interpolator: BilinearInterpolationHelper, + j_valid_fg: torch.Tensor, + ) -> LossDict: + conf_type = self.confidence_model_cfg.uv_confidence.type + if self.confidence_model_cfg.uv_confidence.enabled: + u_gt = packed_annotations.u_gt[j_valid_fg] + u_est = interpolator.extract_at_points(densepose_predictor_outputs.u)[j_valid_fg] + v_gt = packed_annotations.v_gt[j_valid_fg] + v_est = interpolator.extract_at_points(densepose_predictor_outputs.v)[j_valid_fg] + sigma_2_est = interpolator.extract_at_points(densepose_predictor_outputs.sigma_2)[ + j_valid_fg + ] + if conf_type == DensePoseUVConfidenceType.IID_ISO: + return { + "loss_densepose_UV": ( + self.uv_loss_with_confidences(u_est, v_est, sigma_2_est, u_gt, v_gt) + * self.w_points + ) + } + elif conf_type in [DensePoseUVConfidenceType.INDEP_ANISO]: + kappa_u_est = interpolator.extract_at_points(densepose_predictor_outputs.kappa_u)[ + j_valid_fg + ] + kappa_v_est = interpolator.extract_at_points(densepose_predictor_outputs.kappa_v)[ + j_valid_fg + ] + return { + "loss_densepose_UV": ( + self.uv_loss_with_confidences( + u_est, v_est, sigma_2_est, kappa_u_est, kappa_v_est, u_gt, v_gt + ) + * self.w_points + ) + } + return super().produce_densepose_losses_uv( + proposals_with_gt, + densepose_predictor_outputs, + packed_annotations, + interpolator, + j_valid_fg, + ) + + +class IIDIsotropicGaussianUVLoss(nn.Module): + """ + Loss for the case of iid residuals with isotropic covariance: + $Sigma_i = sigma_i^2 I$ + The loss (negative log likelihood) is then: + $1/2 sum_{i=1}^n (log(2 pi) + 2 log sigma_i^2 + ||delta_i||^2 / sigma_i^2)$, + where $delta_i=(u - u', v - v')$ is a 2D vector containing UV coordinates + difference between estimated and ground truth UV values + For details, see: + N. Neverova, D. Novotny, A. Vedaldi "Correlated Uncertainty for Learning + Dense Correspondences from Noisy Labels", p. 918--926, in Proc. NIPS 2019 + """ + + def __init__(self, sigma_lower_bound: float): + super(IIDIsotropicGaussianUVLoss, self).__init__() + self.sigma_lower_bound = sigma_lower_bound + self.log2pi = math.log(2 * math.pi) + + def forward( + self, + u: torch.Tensor, + v: torch.Tensor, + sigma_u: torch.Tensor, + target_u: torch.Tensor, + target_v: torch.Tensor, + ): + # compute $\sigma_i^2$ + # use sigma_lower_bound to avoid degenerate solution for variance + # (sigma -> 0) + sigma2 = F.softplus(sigma_u) + self.sigma_lower_bound + # compute \|delta_i\|^2 + delta_t_delta = (u - target_u) ** 2 + (v - target_v) ** 2 + # the total loss from the formula above: + loss = 0.5 * (self.log2pi + 2 * torch.log(sigma2) + delta_t_delta / sigma2) + # pyre-fixme[16]: `float` has no attribute `sum`. + return loss.sum() + + +class IndepAnisotropicGaussianUVLoss(nn.Module): + """ + Loss for the case of independent residuals with anisotropic covariances: + $Sigma_i = sigma_i^2 I + r_i r_i^T$ + The loss (negative log likelihood) is then: + $1/2 sum_{i=1}^n (log(2 pi) + + log sigma_i^2 (sigma_i^2 + ||r_i||^2) + + ||delta_i||^2 / sigma_i^2 + - ^2 / (sigma_i^2 * (sigma_i^2 + ||r_i||^2)))$, + where $delta_i=(u - u', v - v')$ is a 2D vector containing UV coordinates + difference between estimated and ground truth UV values + For details, see: + N. Neverova, D. Novotny, A. Vedaldi "Correlated Uncertainty for Learning + Dense Correspondences from Noisy Labels", p. 918--926, in Proc. NIPS 2019 + """ + + def __init__(self, sigma_lower_bound: float): + super(IndepAnisotropicGaussianUVLoss, self).__init__() + self.sigma_lower_bound = sigma_lower_bound + self.log2pi = math.log(2 * math.pi) + + def forward( + self, + u: torch.Tensor, + v: torch.Tensor, + sigma_u: torch.Tensor, + kappa_u_est: torch.Tensor, + kappa_v_est: torch.Tensor, + target_u: torch.Tensor, + target_v: torch.Tensor, + ): + # compute $\sigma_i^2$ + sigma2 = F.softplus(sigma_u) + self.sigma_lower_bound + # compute \|r_i\|^2 + r_sqnorm2 = kappa_u_est ** 2 + kappa_v_est ** 2 + delta_u = u - target_u + delta_v = v - target_v + # compute \|delta_i\|^2 + delta_sqnorm = delta_u ** 2 + delta_v ** 2 + delta_u_r_u = delta_u * kappa_u_est + delta_v_r_v = delta_v * kappa_v_est + # compute the scalar product + delta_r = delta_u_r_u + delta_v_r_v + # compute squared scalar product ^2 + delta_r_sqnorm = delta_r ** 2 + denom2 = sigma2 * (sigma2 + r_sqnorm2) + loss = 0.5 * ( + self.log2pi + torch.log(denom2) + delta_sqnorm / sigma2 - delta_r_sqnorm / denom2 + ) + return loss.sum() # pyre-ignore[16] diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/cse.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/cse.py new file mode 100644 index 0000000..dd561ad --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/cse.py @@ -0,0 +1,115 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +from typing import Any, List +from torch import nn + +from detectron2.config import CfgNode +from detectron2.structures import Instances + +from .cycle_pix2shape import PixToShapeCycleLoss +from .cycle_shape2shape import ShapeToShapeCycleLoss +from .embed import EmbeddingLoss +from .embed_utils import CseAnnotationsAccumulator +from .mask_or_segm import MaskOrSegmentationLoss +from .registry import DENSEPOSE_LOSS_REGISTRY +from .soft_embed import SoftEmbeddingLoss +from .utils import BilinearInterpolationHelper, LossDict, extract_packed_annotations_from_matches + + +@DENSEPOSE_LOSS_REGISTRY.register() +class DensePoseCseLoss: + """ """ + + _EMBED_LOSS_REGISTRY = { + EmbeddingLoss.__name__: EmbeddingLoss, + SoftEmbeddingLoss.__name__: SoftEmbeddingLoss, + } + + def __init__(self, cfg: CfgNode): + """ + Initialize CSE loss from configuration options + + Args: + cfg (CfgNode): configuration options + """ + self.w_segm = cfg.MODEL.ROI_DENSEPOSE_HEAD.INDEX_WEIGHTS + self.w_embed = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_LOSS_WEIGHT + self.segm_loss = MaskOrSegmentationLoss(cfg) + self.embed_loss = DensePoseCseLoss.create_embed_loss(cfg) + self.do_shape2shape = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.ENABLED + if self.do_shape2shape: + self.w_shape2shape = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.WEIGHT + self.shape2shape_loss = ShapeToShapeCycleLoss(cfg) + self.do_pix2shape = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.ENABLED + if self.do_pix2shape: + self.w_pix2shape = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.WEIGHT + self.pix2shape_loss = PixToShapeCycleLoss(cfg) + + @classmethod + def create_embed_loss(cls, cfg: CfgNode): + # registry not used here, since embedding losses are currently local + # and are not used anywhere else + return cls._EMBED_LOSS_REGISTRY[cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_LOSS_NAME](cfg) + + def __call__( + self, + proposals_with_gt: List[Instances], + densepose_predictor_outputs: Any, + embedder: nn.Module, + ) -> LossDict: + if not len(proposals_with_gt): + return self.produce_fake_losses(densepose_predictor_outputs, embedder) + accumulator = CseAnnotationsAccumulator() + packed_annotations = extract_packed_annotations_from_matches(proposals_with_gt, accumulator) + if packed_annotations is None: + return self.produce_fake_losses(densepose_predictor_outputs, embedder) + h, w = densepose_predictor_outputs.embedding.shape[2:] + interpolator = BilinearInterpolationHelper.from_matches( + packed_annotations, + (h, w), + ) + meshid_to_embed_losses = self.embed_loss( + proposals_with_gt, + densepose_predictor_outputs, + packed_annotations, + interpolator, + embedder, + ) + embed_loss_dict = { + f"loss_densepose_E{meshid}": self.w_embed * meshid_to_embed_losses[meshid] + for meshid in meshid_to_embed_losses + } + all_loss_dict = { + "loss_densepose_S": self.w_segm + * self.segm_loss(proposals_with_gt, densepose_predictor_outputs, packed_annotations), + **embed_loss_dict, + } + if self.do_shape2shape: + all_loss_dict["loss_shape2shape"] = self.w_shape2shape * self.shape2shape_loss(embedder) + if self.do_pix2shape: + all_loss_dict["loss_pix2shape"] = self.w_pix2shape * self.pix2shape_loss( + proposals_with_gt, densepose_predictor_outputs, packed_annotations, embedder + ) + return all_loss_dict + + def produce_fake_losses( + self, densepose_predictor_outputs: Any, embedder: nn.Module + ) -> LossDict: + meshname_to_embed_losses = self.embed_loss.fake_values( + densepose_predictor_outputs, embedder=embedder + ) + embed_loss_dict = { + f"loss_densepose_E{mesh_name}": meshname_to_embed_losses[mesh_name] + for mesh_name in meshname_to_embed_losses + } + all_loss_dict = { + "loss_densepose_S": self.segm_loss.fake_value(densepose_predictor_outputs), + **embed_loss_dict, + } + if self.do_shape2shape: + all_loss_dict["loss_shape2shape"] = self.shape2shape_loss.fake_value(embedder) + if self.do_pix2shape: + all_loss_dict["loss_pix2shape"] = self.pix2shape_loss.fake_value( + densepose_predictor_outputs, embedder + ) + return all_loss_dict diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/cycle_pix2shape.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/cycle_pix2shape.py new file mode 100644 index 0000000..4447ff4 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/cycle_pix2shape.py @@ -0,0 +1,156 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +from typing import Any, List +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import CfgNode +from detectron2.structures import Instances + +from densepose.data.meshes.catalog import MeshCatalog +from densepose.modeling.cse.utils import normalize_embeddings, squared_euclidean_distance_matrix + +from .embed_utils import PackedCseAnnotations +from .mask import extract_data_for_mask_loss_from_matches + + +def _create_pixel_dist_matrix(grid_size: int) -> torch.Tensor: + rows = torch.arange(grid_size) + cols = torch.arange(grid_size) + # at index `i` contains [row, col], where + # row = i // grid_size + # col = i % grid_size + pix_coords = ( + torch.stack(torch.meshgrid(rows, cols), -1).reshape((grid_size * grid_size, 2)).float() + ) + return squared_euclidean_distance_matrix(pix_coords, pix_coords) + + +def _sample_fg_pixels_randperm(fg_mask: torch.Tensor, sample_size: int) -> torch.Tensor: + fg_mask_flattened = fg_mask.reshape((-1,)) + num_pixels = int(fg_mask_flattened.sum().item()) + fg_pixel_indices = fg_mask_flattened.nonzero(as_tuple=True)[0] + if (sample_size <= 0) or (num_pixels <= sample_size): + return fg_pixel_indices + sample_indices = torch.randperm(num_pixels, device=fg_mask.device)[:sample_size] + return fg_pixel_indices[sample_indices] + + +def _sample_fg_pixels_multinomial(fg_mask: torch.Tensor, sample_size: int) -> torch.Tensor: + fg_mask_flattened = fg_mask.reshape((-1,)) + num_pixels = int(fg_mask_flattened.sum().item()) + if (sample_size <= 0) or (num_pixels <= sample_size): + return fg_mask_flattened.nonzero(as_tuple=True)[0] + return fg_mask_flattened.float().multinomial(sample_size, replacement=False) # pyre-ignore[16] + + +class PixToShapeCycleLoss(nn.Module): + """ + Cycle loss for pixel-vertex correspondence + """ + + def __init__(self, cfg: CfgNode): + super().__init__() + self.shape_names = list(cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDERS.keys()) + self.embed_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE + self.norm_p = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.NORM_P + self.use_all_meshes_not_gt_only = ( + cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.USE_ALL_MESHES_NOT_GT_ONLY + ) + self.num_pixels_to_sample = ( + cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.NUM_PIXELS_TO_SAMPLE + ) + self.pix_sigma = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.PIXEL_SIGMA + self.temperature_pix_to_vertex = ( + cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.TEMPERATURE_PIXEL_TO_VERTEX + ) + self.temperature_vertex_to_pix = ( + cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.TEMPERATURE_VERTEX_TO_PIXEL + ) + self.pixel_dists = _create_pixel_dist_matrix(cfg.MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE) + + def forward( + self, + proposals_with_gt: List[Instances], + densepose_predictor_outputs: Any, + packed_annotations: PackedCseAnnotations, + embedder: nn.Module, + ): + """ + Args: + proposals_with_gt (list of Instances): detections with associated + ground truth data; each item corresponds to instances detected + on 1 image; the number of items corresponds to the number of + images in a batch + densepose_predictor_outputs: an object of a dataclass that contains predictor + outputs with estimated values; assumed to have the following attributes: + * embedding - embedding estimates, tensor of shape [N, D, S, S], where + N = number of instances (= sum N_i, where N_i is the number of + instances on image i) + D = embedding space dimensionality (MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE) + S = output size (width and height) + packed_annotations (PackedCseAnnotations): contains various data useful + for loss computation, each data is packed into a single tensor + embedder (nn.Module): module that computes vertex embeddings for different meshes + """ + pix_embeds = densepose_predictor_outputs.embedding + if self.pixel_dists.device != pix_embeds.device: + # should normally be done only once + self.pixel_dists = self.pixel_dists.to(device=pix_embeds.device) + with torch.no_grad(): + mask_loss_data = extract_data_for_mask_loss_from_matches( + proposals_with_gt, densepose_predictor_outputs.coarse_segm + ) + # GT masks - tensor of shape [N, S, S] of int64 + masks_gt = mask_loss_data.masks_gt.long() # pyre-ignore[16] + assert len(pix_embeds) == len(masks_gt), ( + f"Number of instances with embeddings {len(pix_embeds)} != " + f"number of instances with GT masks {len(masks_gt)}" + ) + losses = [] + mesh_names = ( + self.shape_names + if self.use_all_meshes_not_gt_only + else [ + MeshCatalog.get_mesh_name(mesh_id.item()) + for mesh_id in packed_annotations.vertex_mesh_ids_gt.unique() # pyre-ignore[16] + ] + ) + for pixel_embeddings, mask_gt in zip(pix_embeds, masks_gt): + # pixel_embeddings [D, S, S] + # mask_gt [S, S] + for mesh_name in mesh_names: + mesh_vertex_embeddings = embedder(mesh_name) + # pixel indices [M] + pixel_indices_flattened = _sample_fg_pixels_randperm( + mask_gt, self.num_pixels_to_sample + ) + # pixel distances [M, M] + pixel_dists = self.pixel_dists.to(pixel_embeddings.device)[ + torch.meshgrid(pixel_indices_flattened, pixel_indices_flattened) + ] + # pixel embeddings [M, D] + pixel_embeddings_sampled = normalize_embeddings( + pixel_embeddings.reshape((self.embed_size, -1))[:, pixel_indices_flattened].T + ) + # pixel-vertex similarity [M, K] + sim_matrix = pixel_embeddings_sampled.mm( # pyre-ignore[16] + mesh_vertex_embeddings.T + ) + c_pix_vertex = F.softmax(sim_matrix / self.temperature_pix_to_vertex, dim=1) + c_vertex_pix = F.softmax(sim_matrix.T / self.temperature_vertex_to_pix, dim=1) + c_cycle = c_pix_vertex.mm(c_vertex_pix) + loss_cycle = torch.norm(pixel_dists * c_cycle, p=self.norm_p) + losses.append(loss_cycle) + + if len(losses) == 0: + return pix_embeds.sum() * 0 + return torch.stack(losses, dim=0).mean() + + def fake_value(self, densepose_predictor_outputs: Any, embedder: nn.Module): + losses = [ + embedder(mesh_name).sum() * 0 for mesh_name in embedder.mesh_names # pyre-ignore[29] + ] + losses.append(densepose_predictor_outputs.embedding.sum() * 0) + return torch.mean(torch.stack(losses)) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/cycle_shape2shape.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/cycle_shape2shape.py new file mode 100644 index 0000000..d2adc13 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/cycle_shape2shape.py @@ -0,0 +1,117 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import random +from typing import Tuple +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import CfgNode + +from densepose.structures.mesh import create_mesh + +from .utils import sample_random_indices + + +class ShapeToShapeCycleLoss(nn.Module): + """ + Cycle Loss for Shapes. + Inspired by: + "Mapping in a Cycle: Sinkhorn Regularized Unsupervised Learning for Point Cloud Shapes". + """ + + def __init__(self, cfg: CfgNode): + super().__init__() + self.shape_names = list(cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDERS.keys()) + self.all_shape_pairs = [ + (x, y) for i, x in enumerate(self.shape_names) for y in self.shape_names[i + 1 :] + ] + random.shuffle(self.all_shape_pairs) + self.cur_pos = 0 + self.norm_p = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.NORM_P + self.temperature = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.TEMPERATURE + self.max_num_vertices = ( + cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.MAX_NUM_VERTICES + ) + + def _sample_random_pair(self) -> Tuple[str, str]: + """ + Produce a random pair of different mesh names + + Return: + tuple(str, str): a pair of different mesh names + """ + if self.cur_pos >= len(self.all_shape_pairs): + random.shuffle(self.all_shape_pairs) + self.cur_pos = 0 + shape_pair = self.all_shape_pairs[self.cur_pos] + self.cur_pos += 1 + return shape_pair + + def forward(self, embedder: nn.Module): + """ + Do a forward pass with a random pair (src, dst) pair of shapes + Args: + embedder (nn.Module): module that computes vertex embeddings for different meshes + """ + src_mesh_name, dst_mesh_name = self._sample_random_pair() + return self._forward_one_pair(embedder, src_mesh_name, dst_mesh_name) + + def fake_value(self, embedder: nn.Module): + losses = [] + for mesh_name in embedder.mesh_names: # pyre-ignore[29] + losses.append(embedder(mesh_name).sum() * 0) + return torch.mean(torch.stack(losses)) + + def _get_embeddings_and_geodists_for_mesh( + self, embedder: nn.Module, mesh_name: str + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Produces embeddings and geodesic distance tensors for a given mesh. May subsample + the mesh, if it contains too many vertices (controlled by + SHAPE_CYCLE_LOSS_MAX_NUM_VERTICES parameter). + Args: + embedder (nn.Module): module that computes embeddings for mesh vertices + mesh_name (str): mesh name + Return: + embeddings (torch.Tensor of size [N, D]): embeddings for selected mesh + vertices (N = number of selected vertices, D = embedding space dim) + geodists (torch.Tensor of size [N, N]): geodesic distances for the selected + mesh vertices (N = number of selected vertices) + """ + embeddings = embedder(mesh_name) + indices = sample_random_indices( + embeddings.shape[0], self.max_num_vertices, embeddings.device + ) + mesh = create_mesh(mesh_name, embeddings.device) + geodists = mesh.geodists + if indices is not None: + embeddings = embeddings[indices] + geodists = geodists[torch.meshgrid(indices, indices)] + return embeddings, geodists + + def _forward_one_pair( + self, embedder: nn.Module, mesh_name_1: str, mesh_name_2: str + ) -> torch.Tensor: + """ + Do a forward pass with a selected pair of meshes + Args: + embedder (nn.Module): module that computes vertex embeddings for different meshes + mesh_name_1 (str): first mesh name + mesh_name_2 (str): second mesh name + Return: + Tensor containing the loss value + """ + embeddings_1, geodists_1 = self._get_embeddings_and_geodists_for_mesh(embedder, mesh_name_1) + embeddings_2, geodists_2 = self._get_embeddings_and_geodists_for_mesh(embedder, mesh_name_2) + sim_matrix_12 = embeddings_1.mm(embeddings_2.T) # pyre-ignore[16] + + c_12 = F.softmax(sim_matrix_12 / self.temperature, dim=1) + c_21 = F.softmax(sim_matrix_12.T / self.temperature, dim=1) + c_11 = c_12.mm(c_21) + c_22 = c_21.mm(c_12) + + loss_cycle_11 = torch.norm(geodists_1 * c_11, p=self.norm_p) + loss_cycle_22 = torch.norm(geodists_2 * c_22, p=self.norm_p) + + return loss_cycle_11 + loss_cycle_22 diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/embed.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/embed.py new file mode 100644 index 0000000..4feff03 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/embed.py @@ -0,0 +1,127 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +from typing import Any, Dict, List +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import CfgNode +from detectron2.structures import Instances + +from densepose.data.meshes.catalog import MeshCatalog +from densepose.modeling.cse.utils import normalize_embeddings, squared_euclidean_distance_matrix + +from .embed_utils import PackedCseAnnotations +from .utils import BilinearInterpolationHelper + + +class EmbeddingLoss: + """ + Computes losses for estimated embeddings given annotated vertices. + Instances in a minibatch that correspond to the same mesh are grouped + together. For each group, loss is computed as cross-entropy for + unnormalized scores given ground truth mesh vertex ids. + Scores are based on squared distances between estimated vertex embeddings + and mesh vertex embeddings. + """ + + def __init__(self, cfg: CfgNode): + """ + Initialize embedding loss from config + """ + self.embdist_gauss_sigma = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDING_DIST_GAUSS_SIGMA + + def __call__( + self, + proposals_with_gt: List[Instances], + densepose_predictor_outputs: Any, + packed_annotations: PackedCseAnnotations, + interpolator: BilinearInterpolationHelper, + embedder: nn.Module, + ) -> Dict[int, torch.Tensor]: + """ + Produces losses for estimated embeddings given annotated vertices. + Embeddings for all the vertices of a mesh are computed by the embedder. + Embeddings for observed pixels are estimated by a predictor. + Losses are computed as cross-entropy for squared distances between + observed vertex embeddings and all mesh vertex embeddings given + ground truth vertex IDs. + + Args: + proposals_with_gt (list of Instances): detections with associated + ground truth data; each item corresponds to instances detected + on 1 image; the number of items corresponds to the number of + images in a batch + densepose_predictor_outputs: an object of a dataclass that contains predictor + outputs with estimated values; assumed to have the following attributes: + * embedding - embedding estimates, tensor of shape [N, D, S, S], where + N = number of instances (= sum N_i, where N_i is the number of + instances on image i) + D = embedding space dimensionality (MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE) + S = output size (width and height) + packed_annotations (PackedCseAnnotations): contains various data useful + for loss computation, each data is packed into a single tensor + interpolator (BilinearInterpolationHelper): bilinear interpolation helper + embedder (nn.Module): module that computes vertex embeddings for different meshes + Return: + dict(int -> tensor): losses for different mesh IDs + """ + losses = {} + for mesh_id_tensor in packed_annotations.vertex_mesh_ids_gt.unique(): # pyre-ignore[16] + mesh_id = mesh_id_tensor.item() + mesh_name = MeshCatalog.get_mesh_name(mesh_id) + # valid points are those that fall into estimated bbox + # and correspond to the current mesh + j_valid = interpolator.j_valid * ( # pyre-ignore[16] + packed_annotations.vertex_mesh_ids_gt == mesh_id + ) + if not torch.any(j_valid): + continue + # extract estimated embeddings for valid points + # -> tensor [J, D] + vertex_embeddings_i = normalize_embeddings( + interpolator.extract_at_points( + densepose_predictor_outputs.embedding, + slice_fine_segm=slice(None), + w_ylo_xlo=interpolator.w_ylo_xlo[:, None], # pyre-ignore[16] + w_ylo_xhi=interpolator.w_ylo_xhi[:, None], # pyre-ignore[16] + w_yhi_xlo=interpolator.w_yhi_xlo[:, None], # pyre-ignore[16] + w_yhi_xhi=interpolator.w_yhi_xhi[:, None], # pyre-ignore[16] + )[j_valid, :] + ) + # extract vertex ids for valid points + # -> tensor [J] + vertex_indices_i = packed_annotations.vertex_ids_gt[j_valid] + # embeddings for all mesh vertices + # -> tensor [K, D] + mesh_vertex_embeddings = embedder(mesh_name) + # unnormalized scores for valid points + # -> tensor [J, K] + scores = squared_euclidean_distance_matrix( + vertex_embeddings_i, mesh_vertex_embeddings + ) / (-self.embdist_gauss_sigma) + losses[mesh_name] = F.cross_entropy(scores, vertex_indices_i, ignore_index=-1) + + # pyre-fixme[29]: + # `Union[BoundMethod[typing.Callable(torch.Tensor.__iter__)[[Named(self, + # torch.Tensor)], typing.Iterator[typing.Any]], torch.Tensor], nn.Module, + # torch.Tensor]` is not a function. + for mesh_name in embedder.mesh_names: + if mesh_name not in losses: + losses[mesh_name] = self.fake_value( + densepose_predictor_outputs, embedder, mesh_name + ) + return losses + + def fake_values(self, densepose_predictor_outputs: Any, embedder: nn.Module): + losses = {} + # pyre-fixme[29]: + # `Union[BoundMethod[typing.Callable(torch.Tensor.__iter__)[[Named(self, + # torch.Tensor)], typing.Iterator[typing.Any]], torch.Tensor], nn.Module, + # torch.Tensor]` is not a function. + for mesh_name in embedder.mesh_names: + losses[mesh_name] = self.fake_value(densepose_predictor_outputs, embedder, mesh_name) + return losses + + def fake_value(self, densepose_predictor_outputs: Any, embedder: nn.Module, mesh_name: str): + return densepose_predictor_outputs.embedding.sum() * 0 + embedder(mesh_name).sum() * 0 diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/embed_utils.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/embed_utils.py new file mode 100644 index 0000000..eb9492f --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/embed_utils.py @@ -0,0 +1,135 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +from dataclasses import dataclass +from typing import Any, Optional +import torch + +from detectron2.structures import BoxMode, Instances + +from .utils import AnnotationsAccumulator + + +@dataclass +class PackedCseAnnotations: + x_gt: torch.Tensor + y_gt: torch.Tensor + coarse_segm_gt: Optional[torch.Tensor] + vertex_mesh_ids_gt: torch.Tensor + vertex_ids_gt: torch.Tensor + bbox_xywh_gt: torch.Tensor + bbox_xywh_est: torch.Tensor + point_bbox_with_dp_indices: torch.Tensor + point_bbox_indices: torch.Tensor + bbox_indices: torch.Tensor + + +class CseAnnotationsAccumulator(AnnotationsAccumulator): + """ + Accumulates annotations by batches that correspond to objects detected on + individual images. Can pack them together into single tensors. + """ + + def __init__(self): + self.x_gt = [] + self.y_gt = [] + self.s_gt = [] + self.vertex_mesh_ids_gt = [] + self.vertex_ids_gt = [] + self.bbox_xywh_gt = [] + self.bbox_xywh_est = [] + self.point_bbox_with_dp_indices = [] + self.point_bbox_indices = [] + self.bbox_indices = [] + self.nxt_bbox_with_dp_index = 0 + self.nxt_bbox_index = 0 + + def accumulate(self, instances_one_image: Instances): + """ + Accumulate instances data for one image + + Args: + instances_one_image (Instances): instances data to accumulate + """ + boxes_xywh_est = BoxMode.convert( + instances_one_image.proposal_boxes.tensor.clone(), BoxMode.XYXY_ABS, BoxMode.XYWH_ABS + ) + boxes_xywh_gt = BoxMode.convert( + instances_one_image.gt_boxes.tensor.clone(), BoxMode.XYXY_ABS, BoxMode.XYWH_ABS + ) + n_matches = len(boxes_xywh_gt) + assert n_matches == len( + boxes_xywh_est + ), f"Got {len(boxes_xywh_est)} proposal boxes and {len(boxes_xywh_gt)} GT boxes" + if not n_matches: + # no detection - GT matches + return + if ( + not hasattr(instances_one_image, "gt_densepose") + or instances_one_image.gt_densepose is None + ): + # no densepose GT for the detections, just increase the bbox index + self.nxt_bbox_index += n_matches + return + for box_xywh_est, box_xywh_gt, dp_gt in zip( + boxes_xywh_est, boxes_xywh_gt, instances_one_image.gt_densepose + ): + if (dp_gt is not None) and (len(dp_gt.x) > 0): + self._do_accumulate(box_xywh_gt, box_xywh_est, dp_gt) + self.nxt_bbox_index += 1 + + def _do_accumulate(self, box_xywh_gt: torch.Tensor, box_xywh_est: torch.Tensor, dp_gt: Any): + """ + Accumulate instances data for one image, given that the data is not empty + + Args: + box_xywh_gt (tensor): GT bounding box + box_xywh_est (tensor): estimated bounding box + dp_gt: GT densepose data with the following attributes: + - x: normalized X coordinates + - y: normalized Y coordinates + - segm: tensor of size [S, S] with coarse segmentation + - + """ + self.x_gt.append(dp_gt.x) + self.y_gt.append(dp_gt.y) + if hasattr(dp_gt, "segm"): + self.s_gt.append(dp_gt.segm.unsqueeze(0)) + self.vertex_ids_gt.append(dp_gt.vertex_ids) + self.vertex_mesh_ids_gt.append(torch.full_like(dp_gt.vertex_ids, dp_gt.mesh_id)) + self.bbox_xywh_gt.append(box_xywh_gt.view(-1, 4)) + self.bbox_xywh_est.append(box_xywh_est.view(-1, 4)) + self.point_bbox_with_dp_indices.append( + torch.full_like(dp_gt.vertex_ids, self.nxt_bbox_with_dp_index) + ) + self.point_bbox_indices.append(torch.full_like(dp_gt.vertex_ids, self.nxt_bbox_index)) + self.bbox_indices.append(self.nxt_bbox_index) + self.nxt_bbox_with_dp_index += 1 + + def pack(self) -> Optional[PackedCseAnnotations]: + """ + Pack data into tensors + """ + if not len(self.x_gt): + # TODO: + # returning proper empty annotations would require + # creating empty tensors of appropriate shape and + # type on an appropriate device; + # we return None so far to indicate empty annotations + return None + return PackedCseAnnotations( + x_gt=torch.cat(self.x_gt, 0), + y_gt=torch.cat(self.y_gt, 0), + vertex_mesh_ids_gt=torch.cat(self.vertex_mesh_ids_gt, 0), + vertex_ids_gt=torch.cat(self.vertex_ids_gt, 0), + # ignore segmentation annotations, if not all the instances contain those + coarse_segm_gt=torch.cat(self.s_gt, 0) + if len(self.s_gt) == len(self.bbox_xywh_gt) + else None, + bbox_xywh_gt=torch.cat(self.bbox_xywh_gt, 0), + bbox_xywh_est=torch.cat(self.bbox_xywh_est, 0), + point_bbox_with_dp_indices=torch.cat(self.point_bbox_with_dp_indices, 0), + point_bbox_indices=torch.cat(self.point_bbox_indices, 0), + bbox_indices=torch.as_tensor( + self.bbox_indices, dtype=torch.long, device=self.x_gt[0].device + ), + ) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/mask.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/mask.py new file mode 100644 index 0000000..c16b15c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/mask.py @@ -0,0 +1,125 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +from dataclasses import dataclass +from typing import Any, Iterable, List, Optional +import torch +from torch.nn import functional as F + +from detectron2.structures import Instances + + +@dataclass +class DataForMaskLoss: + """ + Contains mask GT and estimated data for proposals from multiple images: + """ + + # tensor of size (K, H, W) containing GT labels + masks_gt: Optional[torch.Tensor] = None + # tensor of size (K, C, H, W) containing estimated scores + masks_est: Optional[torch.Tensor] = None + + +def extract_data_for_mask_loss_from_matches( + proposals_targets: Iterable[Instances], estimated_segm: torch.Tensor +) -> DataForMaskLoss: + """ + Extract data for mask loss from instances that contain matched GT and + estimated bounding boxes. + Args: + proposals_targets: Iterable[Instances] + matched GT and estimated results, each item in the iterable + corresponds to data in 1 image + estimated_segm: tensor(K, C, S, S) of float - raw unnormalized + segmentation scores, here S is the size to which GT masks are + to be resized + Return: + masks_est: tensor(K, C, S, S) of float - class scores + masks_gt: tensor(K, S, S) of int64 - labels + """ + data = DataForMaskLoss() + masks_gt = [] + offset = 0 + assert estimated_segm.shape[2] == estimated_segm.shape[3], ( + f"Expected estimated segmentation to have a square shape, " + f"but the actual shape is {estimated_segm.shape[2:]}" + ) + mask_size = estimated_segm.shape[2] + num_proposals = sum(inst.proposal_boxes.tensor.size(0) for inst in proposals_targets) + num_estimated = estimated_segm.shape[0] + assert ( + num_proposals == num_estimated + ), "The number of proposals {} must be equal to the number of estimates {}".format( + num_proposals, num_estimated + ) + + for proposals_targets_per_image in proposals_targets: + n_i = proposals_targets_per_image.proposal_boxes.tensor.size(0) + if not n_i: + continue + gt_masks_per_image = proposals_targets_per_image.gt_masks.crop_and_resize( + proposals_targets_per_image.proposal_boxes.tensor, mask_size + ).to(device=estimated_segm.device) + masks_gt.append(gt_masks_per_image) + offset += n_i + if masks_gt: + data.masks_est = estimated_segm + data.masks_gt = torch.cat(masks_gt, dim=0) + return data + + +class MaskLoss: + """ + Mask loss as cross-entropy for raw unnormalized scores given ground truth labels. + Mask ground truth labels are defined for the whole image and not only the + bounding box of interest. They are stored as objects that are assumed to implement + the `crop_and_resize` interface (e.g. BitMasks, PolygonMasks). + """ + + def __call__( + self, proposals_with_gt: List[Instances], densepose_predictor_outputs: Any + ) -> torch.Tensor: + """ + Computes segmentation loss as cross-entropy for raw unnormalized + scores given ground truth labels. + + Args: + proposals_with_gt (list of Instances): detections with associated ground truth data + densepose_predictor_outputs: an object of a dataclass that contains predictor outputs + with estimated values; assumed to have the following attribute: + * coarse_segm (tensor of shape [N, D, S, S]): coarse segmentation estimates + as raw unnormalized scores + where N is the number of detections, S is the estimate size ( = width = height) + and D is the number of coarse segmentation channels. + Return: + Cross entropy for raw unnormalized scores for coarse segmentation given + ground truth labels from masks + """ + if not len(proposals_with_gt): + return self.fake_value(densepose_predictor_outputs) + # densepose outputs are computed for all images and all bounding boxes; + # i.e. if a batch has 4 images with (3, 1, 2, 1) proposals respectively, + # the outputs will have size(0) == 3+1+2+1 == 7 + with torch.no_grad(): + mask_loss_data = extract_data_for_mask_loss_from_matches( + proposals_with_gt, densepose_predictor_outputs.coarse_segm + ) + if (mask_loss_data.masks_gt is None) or (mask_loss_data.masks_est is None): + return self.fake_value(densepose_predictor_outputs) + return F.cross_entropy(mask_loss_data.masks_est, mask_loss_data.masks_gt.long()) + + def fake_value(self, densepose_predictor_outputs: Any) -> torch.Tensor: + """ + Fake segmentation loss used when no suitable ground truth data + was found in a batch. The loss has a value 0 and is primarily used to + construct the computation graph, so that `DistributedDataParallel` + has similar graphs on all GPUs and can perform reduction properly. + + Args: + densepose_predictor_outputs: DensePose predictor outputs, an object + of a dataclass that is assumed to have `coarse_segm` + attribute + Return: + Zero value loss with proper computation graph + """ + return densepose_predictor_outputs.coarse_segm.sum() * 0 diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/mask_or_segm.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/mask_or_segm.py new file mode 100644 index 0000000..98b773d --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/mask_or_segm.py @@ -0,0 +1,72 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +from typing import Any, List +import torch + +from detectron2.config import CfgNode +from detectron2.structures import Instances + +from .mask import MaskLoss +from .segm import SegmentationLoss + + +class MaskOrSegmentationLoss: + """ + Mask or segmentation loss as cross-entropy for raw unnormalized scores + given ground truth labels. Ground truth labels are either defined by coarse + segmentation annotation, or by mask annotation, depending on the config + value MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS + """ + + def __init__(self, cfg: CfgNode): + """ + Initialize segmentation loss from configuration options + + Args: + cfg (CfgNode): configuration options + """ + self.segm_trained_by_masks = cfg.MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS + if self.segm_trained_by_masks: + self.mask_loss = MaskLoss() + self.segm_loss = SegmentationLoss(cfg) + + def __call__( + self, + proposals_with_gt: List[Instances], + densepose_predictor_outputs: Any, + packed_annotations: Any, + ) -> torch.Tensor: + """ + Compute segmentation loss as cross-entropy between aligned unnormalized + score estimates and ground truth; with ground truth given + either by masks, or by coarse segmentation annotations. + + Args: + proposals_with_gt (list of Instances): detections with associated ground truth data + densepose_predictor_outputs: an object of a dataclass that contains predictor outputs + with estimated values; assumed to have the following attributes: + * coarse_segm - coarse segmentation estimates, tensor of shape [N, D, S, S] + packed_annotations: packed annotations for efficient loss computation + Return: + tensor: loss value as cross-entropy for raw unnormalized scores + given ground truth labels + """ + if self.segm_trained_by_masks: + return self.mask_loss(proposals_with_gt, densepose_predictor_outputs) + return self.segm_loss(proposals_with_gt, densepose_predictor_outputs, packed_annotations) + + def fake_value(self, densepose_predictor_outputs: Any) -> torch.Tensor: + """ + Fake segmentation loss used when no suitable ground truth data + was found in a batch. The loss has a value 0 and is primarily used to + construct the computation graph, so that `DistributedDataParallel` + has similar graphs on all GPUs and can perform reduction properly. + + Args: + densepose_predictor_outputs: DensePose predictor outputs, an object + of a dataclass that is assumed to have `coarse_segm` + attribute + Return: + Zero value loss with proper computation graph + """ + return densepose_predictor_outputs.coarse_segm.sum() * 0 diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/registry.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/registry.py new file mode 100644 index 0000000..d9c8817 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/registry.py @@ -0,0 +1,5 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from detectron2.utils.registry import Registry + +DENSEPOSE_LOSS_REGISTRY = Registry("DENSEPOSE_LOSS") diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/segm.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/segm.py new file mode 100644 index 0000000..1962b88 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/segm.py @@ -0,0 +1,83 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +from typing import Any, List +import torch +from torch.nn import functional as F + +from detectron2.config import CfgNode +from detectron2.structures import Instances + +from .utils import resample_data + + +class SegmentationLoss: + """ + Segmentation loss as cross-entropy for raw unnormalized scores given ground truth + labels. Segmentation ground truth labels are defined for the bounding box of + interest at some fixed resolution [S, S], where + S = MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE. + """ + + def __init__(self, cfg: CfgNode): + """ + Initialize segmentation loss from configuration options + + Args: + cfg (CfgNode): configuration options + """ + self.heatmap_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE + self.n_segm_chan = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS + + def __call__( + self, + proposals_with_gt: List[Instances], + densepose_predictor_outputs: Any, + packed_annotations: Any, + ) -> torch.Tensor: + """ + Compute segmentation loss as cross-entropy on aligned segmentation + ground truth and estimated scores. + + Args: + proposals_with_gt (list of Instances): detections with associated ground truth data + densepose_predictor_outputs: an object of a dataclass that contains predictor outputs + with estimated values; assumed to have the following attributes: + * coarse_segm - coarse segmentation estimates, tensor of shape [N, D, S, S] + packed_annotations: packed annotations for efficient loss computation; + the following attributes are used: + - coarse_segm_gt + - bbox_xywh_gt + - bbox_xywh_est + """ + if packed_annotations.coarse_segm_gt is None: + return self.fake_value(densepose_predictor_outputs) + coarse_segm_est = densepose_predictor_outputs.coarse_segm[packed_annotations.bbox_indices] + with torch.no_grad(): + coarse_segm_gt = resample_data( + packed_annotations.coarse_segm_gt.unsqueeze(1), + packed_annotations.bbox_xywh_gt, + packed_annotations.bbox_xywh_est, + self.heatmap_size, + self.heatmap_size, + mode="nearest", + padding_mode="zeros", + ).squeeze(1) + if self.n_segm_chan == 2: + coarse_segm_gt = coarse_segm_gt > 0 + return F.cross_entropy(coarse_segm_est, coarse_segm_gt.long()) + + def fake_value(self, densepose_predictor_outputs: Any) -> torch.Tensor: + """ + Fake segmentation loss used when no suitable ground truth data + was found in a batch. The loss has a value 0 and is primarily used to + construct the computation graph, so that `DistributedDataParallel` + has similar graphs on all GPUs and can perform reduction properly. + + Args: + densepose_predictor_outputs: DensePose predictor outputs, an object + of a dataclass that is assumed to have `coarse_segm` + attribute + Return: + Zero value loss with proper computation graph + """ + return densepose_predictor_outputs.coarse_segm.sum() * 0 diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/soft_embed.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/soft_embed.py new file mode 100644 index 0000000..7b055c4 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/soft_embed.py @@ -0,0 +1,141 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +from typing import Any, Dict, List +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import CfgNode +from detectron2.structures import Instances + +from densepose.data.meshes.catalog import MeshCatalog +from densepose.modeling.cse.utils import normalize_embeddings, squared_euclidean_distance_matrix +from densepose.structures.mesh import create_mesh + +from .embed_utils import PackedCseAnnotations +from .utils import BilinearInterpolationHelper + + +class SoftEmbeddingLoss: + """ + Computes losses for estimated embeddings given annotated vertices. + Instances in a minibatch that correspond to the same mesh are grouped + together. For each group, loss is computed as cross-entropy for + unnormalized scores given ground truth mesh vertex ids. + Scores are based on: + 1) squared distances between estimated vertex embeddings + and mesh vertex embeddings; + 2) geodesic distances between vertices of a mesh + """ + + def __init__(self, cfg: CfgNode): + """ + Initialize embedding loss from config + """ + self.embdist_gauss_sigma = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDING_DIST_GAUSS_SIGMA + self.geodist_gauss_sigma = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.GEODESIC_DIST_GAUSS_SIGMA + + def __call__( + self, + proposals_with_gt: List[Instances], + densepose_predictor_outputs: Any, + packed_annotations: PackedCseAnnotations, + interpolator: BilinearInterpolationHelper, + embedder: nn.Module, + ) -> Dict[int, torch.Tensor]: + """ + Produces losses for estimated embeddings given annotated vertices. + Embeddings for all the vertices of a mesh are computed by the embedder. + Embeddings for observed pixels are estimated by a predictor. + Losses are computed as cross-entropy for unnormalized scores given + ground truth vertex IDs. + 1) squared distances between estimated vertex embeddings + and mesh vertex embeddings; + 2) geodesic distances between vertices of a mesh + + Args: + proposals_with_gt (list of Instances): detections with associated + ground truth data; each item corresponds to instances detected + on 1 image; the number of items corresponds to the number of + images in a batch + densepose_predictor_outputs: an object of a dataclass that contains predictor + outputs with estimated values; assumed to have the following attributes: + * embedding - embedding estimates, tensor of shape [N, D, S, S], where + N = number of instances (= sum N_i, where N_i is the number of + instances on image i) + D = embedding space dimensionality (MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE) + S = output size (width and height) + packed_annotations (PackedCseAnnotations): contains various data useful + for loss computation, each data is packed into a single tensor + interpolator (BilinearInterpolationHelper): bilinear interpolation helper + embedder (nn.Module): module that computes vertex embeddings for different meshes + Return: + dict(int -> tensor): losses for different mesh IDs + """ + losses = {} + for mesh_id_tensor in packed_annotations.vertex_mesh_ids_gt.unique(): # pyre-ignore[16] + mesh_id = mesh_id_tensor.item() + mesh_name = MeshCatalog.get_mesh_name(mesh_id) + # valid points are those that fall into estimated bbox + # and correspond to the current mesh + j_valid = interpolator.j_valid * ( # pyre-ignore[16] + packed_annotations.vertex_mesh_ids_gt == mesh_id + ) + if not torch.any(j_valid): + continue + # extract estimated embeddings for valid points + # -> tensor [J, D] + vertex_embeddings_i = normalize_embeddings( + interpolator.extract_at_points( + densepose_predictor_outputs.embedding, + slice_fine_segm=slice(None), + w_ylo_xlo=interpolator.w_ylo_xlo[:, None], # pyre-ignore[16] + w_ylo_xhi=interpolator.w_ylo_xhi[:, None], # pyre-ignore[16] + w_yhi_xlo=interpolator.w_yhi_xlo[:, None], # pyre-ignore[16] + w_yhi_xhi=interpolator.w_yhi_xhi[:, None], # pyre-ignore[16] + )[j_valid, :] + ) + # extract vertex ids for valid points + # -> tensor [J] + vertex_indices_i = packed_annotations.vertex_ids_gt[j_valid] + # embeddings for all mesh vertices + # -> tensor [K, D] + mesh_vertex_embeddings = embedder(mesh_name) + # softmax values of geodesic distances for GT mesh vertices + # -> tensor [J, K] + mesh = create_mesh(mesh_name, mesh_vertex_embeddings.device) + geodist_softmax_values = F.softmax( + mesh.geodists[vertex_indices_i] / (-self.geodist_gauss_sigma), dim=1 + ) + # logsoftmax values for valid points + # -> tensor [J, K] + embdist_logsoftmax_values = F.log_softmax( + squared_euclidean_distance_matrix(vertex_embeddings_i, mesh_vertex_embeddings) + / (-self.embdist_gauss_sigma), + dim=1, + ) + losses[mesh_name] = (-geodist_softmax_values * embdist_logsoftmax_values).sum(1).mean() + + # pyre-fixme[29]: + # `Union[BoundMethod[typing.Callable(torch.Tensor.__iter__)[[Named(self, + # torch.Tensor)], typing.Iterator[typing.Any]], torch.Tensor], nn.Module, + # torch.Tensor]` is not a function. + for mesh_name in embedder.mesh_names: + if mesh_name not in losses: + losses[mesh_name] = self.fake_value( + densepose_predictor_outputs, embedder, mesh_name + ) + return losses + + def fake_values(self, densepose_predictor_outputs: Any, embedder: nn.Module): + losses = {} + # pyre-fixme[29]: + # `Union[BoundMethod[typing.Callable(torch.Tensor.__iter__)[[Named(self, + # torch.Tensor)], typing.Iterator[typing.Any]], torch.Tensor], nn.Module, + # torch.Tensor]` is not a function. + for mesh_name in embedder.mesh_names: + losses[mesh_name] = self.fake_value(densepose_predictor_outputs, embedder, mesh_name) + return losses + + def fake_value(self, densepose_predictor_outputs: Any, embedder: nn.Module, mesh_name: str): + return densepose_predictor_outputs.embedding.sum() * 0 + embedder(mesh_name).sum() * 0 diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/utils.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/utils.py new file mode 100644 index 0000000..346a2e0 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/losses/utils.py @@ -0,0 +1,441 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple +import torch +from torch.nn import functional as F + +from detectron2.structures import BoxMode, Instances + +from densepose import DensePoseDataRelative + +LossDict = Dict[str, torch.Tensor] + + +def _linear_interpolation_utilities(v_norm, v0_src, size_src, v0_dst, size_dst, size_z): + """ + Computes utility values for linear interpolation at points v. + The points are given as normalized offsets in the source interval + (v0_src, v0_src + size_src), more precisely: + v = v0_src + v_norm * size_src / 256.0 + The computed utilities include lower points v_lo, upper points v_hi, + interpolation weights v_w and flags j_valid indicating whether the + points falls into the destination interval (v0_dst, v0_dst + size_dst). + + Args: + v_norm (:obj: `torch.Tensor`): tensor of size N containing + normalized point offsets + v0_src (:obj: `torch.Tensor`): tensor of size N containing + left bounds of source intervals for normalized points + size_src (:obj: `torch.Tensor`): tensor of size N containing + source interval sizes for normalized points + v0_dst (:obj: `torch.Tensor`): tensor of size N containing + left bounds of destination intervals + size_dst (:obj: `torch.Tensor`): tensor of size N containing + destination interval sizes + size_z (int): interval size for data to be interpolated + + Returns: + v_lo (:obj: `torch.Tensor`): int tensor of size N containing + indices of lower values used for interpolation, all values are + integers from [0, size_z - 1] + v_hi (:obj: `torch.Tensor`): int tensor of size N containing + indices of upper values used for interpolation, all values are + integers from [0, size_z - 1] + v_w (:obj: `torch.Tensor`): float tensor of size N containing + interpolation weights + j_valid (:obj: `torch.Tensor`): uint8 tensor of size N containing + 0 for points outside the estimation interval + (v0_est, v0_est + size_est) and 1 otherwise + """ + v = v0_src + v_norm * size_src / 256.0 + j_valid = (v - v0_dst >= 0) * (v - v0_dst < size_dst) + v_grid = (v - v0_dst) * size_z / size_dst + v_lo = v_grid.floor().long().clamp(min=0, max=size_z - 1) + v_hi = (v_lo + 1).clamp(max=size_z - 1) + v_grid = torch.min(v_hi.float(), v_grid) + v_w = v_grid - v_lo.float() + return v_lo, v_hi, v_w, j_valid + + +class BilinearInterpolationHelper: + """ + Args: + packed_annotations: object that contains packed annotations + j_valid (:obj: `torch.Tensor`): uint8 tensor of size M containing + 0 for points to be discarded and 1 for points to be selected + y_lo (:obj: `torch.Tensor`): int tensor of indices of upper values + in z_est for each point + y_hi (:obj: `torch.Tensor`): int tensor of indices of lower values + in z_est for each point + x_lo (:obj: `torch.Tensor`): int tensor of indices of left values + in z_est for each point + x_hi (:obj: `torch.Tensor`): int tensor of indices of right values + in z_est for each point + w_ylo_xlo (:obj: `torch.Tensor`): float tensor of size M; + contains upper-left value weight for each point + w_ylo_xhi (:obj: `torch.Tensor`): float tensor of size M; + contains upper-right value weight for each point + w_yhi_xlo (:obj: `torch.Tensor`): float tensor of size M; + contains lower-left value weight for each point + w_yhi_xhi (:obj: `torch.Tensor`): float tensor of size M; + contains lower-right value weight for each point + """ + + def __init__( + self, + packed_annotations: Any, + j_valid: torch.Tensor, + y_lo: torch.Tensor, + y_hi: torch.Tensor, + x_lo: torch.Tensor, + x_hi: torch.Tensor, + w_ylo_xlo: torch.Tensor, + w_ylo_xhi: torch.Tensor, + w_yhi_xlo: torch.Tensor, + w_yhi_xhi: torch.Tensor, + ): + for k, v in locals().items(): + if k != "self": + setattr(self, k, v) + + @staticmethod + def from_matches( + packed_annotations: Any, densepose_outputs_size_hw: Tuple[int, int] + ) -> "BilinearInterpolationHelper": + """ + Args: + packed_annotations: annotations packed into tensors, the following + attributes are required: + - bbox_xywh_gt + - bbox_xywh_est + - x_gt + - y_gt + - point_bbox_with_dp_indices + - point_bbox_indices + densepose_outputs_size_hw (tuple [int, int]): resolution of + DensePose predictor outputs (H, W) + Return: + An instance of `BilinearInterpolationHelper` used to perform + interpolation for the given annotation points and output resolution + """ + + zh, zw = densepose_outputs_size_hw + x0_gt, y0_gt, w_gt, h_gt = packed_annotations.bbox_xywh_gt[ + packed_annotations.point_bbox_with_dp_indices + ].unbind(dim=1) + x0_est, y0_est, w_est, h_est = packed_annotations.bbox_xywh_est[ + packed_annotations.point_bbox_with_dp_indices + ].unbind(dim=1) + x_lo, x_hi, x_w, jx_valid = _linear_interpolation_utilities( + packed_annotations.x_gt, x0_gt, w_gt, x0_est, w_est, zw + ) + y_lo, y_hi, y_w, jy_valid = _linear_interpolation_utilities( + packed_annotations.y_gt, y0_gt, h_gt, y0_est, h_est, zh + ) + j_valid = jx_valid * jy_valid + + w_ylo_xlo = (1.0 - x_w) * (1.0 - y_w) + w_ylo_xhi = x_w * (1.0 - y_w) + w_yhi_xlo = (1.0 - x_w) * y_w + w_yhi_xhi = x_w * y_w + + return BilinearInterpolationHelper( + packed_annotations, + j_valid, + y_lo, + y_hi, + x_lo, + x_hi, + w_ylo_xlo, # pyre-ignore[6] + w_ylo_xhi, + # pyre-fixme[6]: Expected `Tensor` for 9th param but got `float`. + w_yhi_xlo, + w_yhi_xhi, + ) + + def extract_at_points( + self, + z_est, + slice_fine_segm=None, + w_ylo_xlo=None, + w_ylo_xhi=None, + w_yhi_xlo=None, + w_yhi_xhi=None, + ): + """ + Extract ground truth values z_gt for valid point indices and estimated + values z_est using bilinear interpolation over top-left (y_lo, x_lo), + top-right (y_lo, x_hi), bottom-left (y_hi, x_lo) and bottom-right + (y_hi, x_hi) values in z_est with corresponding weights: + w_ylo_xlo, w_ylo_xhi, w_yhi_xlo and w_yhi_xhi. + Use slice_fine_segm to slice dim=1 in z_est + """ + slice_fine_segm = ( + self.packed_annotations.fine_segm_labels_gt + if slice_fine_segm is None + else slice_fine_segm + ) + w_ylo_xlo = self.w_ylo_xlo if w_ylo_xlo is None else w_ylo_xlo + w_ylo_xhi = self.w_ylo_xhi if w_ylo_xhi is None else w_ylo_xhi + w_yhi_xlo = self.w_yhi_xlo if w_yhi_xlo is None else w_yhi_xlo + w_yhi_xhi = self.w_yhi_xhi if w_yhi_xhi is None else w_yhi_xhi + + index_bbox = self.packed_annotations.point_bbox_indices + z_est_sampled = ( + z_est[index_bbox, slice_fine_segm, self.y_lo, self.x_lo] * w_ylo_xlo + + z_est[index_bbox, slice_fine_segm, self.y_lo, self.x_hi] * w_ylo_xhi + + z_est[index_bbox, slice_fine_segm, self.y_hi, self.x_lo] * w_yhi_xlo + + z_est[index_bbox, slice_fine_segm, self.y_hi, self.x_hi] * w_yhi_xhi + ) + return z_est_sampled + + +def resample_data( + z, bbox_xywh_src, bbox_xywh_dst, wout, hout, mode: str="nearest", padding_mode: str="zeros" +): + """ + Args: + z (:obj: `torch.Tensor`): tensor of size (N,C,H,W) with data to be + resampled + bbox_xywh_src (:obj: `torch.Tensor`): tensor of size (N,4) containing + source bounding boxes in format XYWH + bbox_xywh_dst (:obj: `torch.Tensor`): tensor of size (N,4) containing + destination bounding boxes in format XYWH + Return: + zresampled (:obj: `torch.Tensor`): tensor of size (N, C, Hout, Wout) + with resampled values of z, where D is the discretization size + """ + n = bbox_xywh_src.size(0) + assert n == bbox_xywh_dst.size(0), ( + "The number of " + "source ROIs for resampling ({}) should be equal to the number " + "of destination ROIs ({})".format(bbox_xywh_src.size(0), bbox_xywh_dst.size(0)) + ) + x0src, y0src, wsrc, hsrc = bbox_xywh_src.unbind(dim=1) + x0dst, y0dst, wdst, hdst = bbox_xywh_dst.unbind(dim=1) + x0dst_norm = 2 * (x0dst - x0src) / wsrc - 1 + y0dst_norm = 2 * (y0dst - y0src) / hsrc - 1 + x1dst_norm = 2 * (x0dst + wdst - x0src) / wsrc - 1 + y1dst_norm = 2 * (y0dst + hdst - y0src) / hsrc - 1 + grid_w = torch.arange(wout, device=z.device, dtype=torch.float) / wout + grid_h = torch.arange(hout, device=z.device, dtype=torch.float) / hout + grid_w_expanded = grid_w[None, None, :].expand(n, hout, wout) + grid_h_expanded = grid_h[None, :, None].expand(n, hout, wout) + dx_expanded = (x1dst_norm - x0dst_norm)[:, None, None].expand(n, hout, wout) + dy_expanded = (y1dst_norm - y0dst_norm)[:, None, None].expand(n, hout, wout) + x0_expanded = x0dst_norm[:, None, None].expand(n, hout, wout) + y0_expanded = y0dst_norm[:, None, None].expand(n, hout, wout) + grid_x = grid_w_expanded * dx_expanded + x0_expanded + grid_y = grid_h_expanded * dy_expanded + y0_expanded + grid = torch.stack((grid_x, grid_y), dim=3) + # resample Z from (N, C, H, W) into (N, C, Hout, Wout) + zresampled = F.grid_sample(z, grid, mode=mode, padding_mode=padding_mode, align_corners=True) + return zresampled + + +class AnnotationsAccumulator(ABC): + """ + Abstract class for an accumulator for annotations that can produce + dense annotations packed into tensors. + """ + + @abstractmethod + def accumulate(self, instances_one_image: Instances): + """ + Accumulate instances data for one image + + Args: + instances_one_image (Instances): instances data to accumulate + """ + pass + + @abstractmethod + def pack(self) -> Any: + """ + Pack data into tensors + """ + pass + + +@dataclass +class PackedChartBasedAnnotations: + """ + Packed annotations for chart-based model training. The following attributes + are defined: + - fine_segm_labels_gt (tensor [K] of `int64`): GT fine segmentation point labels + - x_gt (tensor [K] of `float32`): GT normalized X point coordinates + - y_gt (tensor [K] of `float32`): GT normalized Y point coordinates + - u_gt (tensor [K] of `float32`): GT point U values + - v_gt (tensor [K] of `float32`): GT point V values + - coarse_segm_gt (tensor [N, S, S] of `float32`): GT segmentation for bounding boxes + - bbox_xywh_gt (tensor [N, 4] of `float32`): selected GT bounding boxes in + XYWH format + - bbox_xywh_est (tensor [N, 4] of `float32`): selected matching estimated + bounding boxes in XYWH format + - point_bbox_with_dp_indices (tensor [K] of `int64`): indices of bounding boxes + with DensePose annotations that correspond to the point data + - point_bbox_indices (tensor [K] of `int64`): indices of bounding boxes + (not necessarily the selected ones with DensePose data) that correspond + to the point data + - bbox_indices (tensor [N] of `int64`): global indices of selected bounding + boxes with DensePose annotations; these indices could be used to access + features that are computed for all bounding boxes, not only the ones with + DensePose annotations. + Here K is the total number of points and N is the total number of instances + with DensePose annotations. + """ + + fine_segm_labels_gt: torch.Tensor + x_gt: torch.Tensor + y_gt: torch.Tensor + u_gt: torch.Tensor + v_gt: torch.Tensor + coarse_segm_gt: Optional[torch.Tensor] + bbox_xywh_gt: torch.Tensor + bbox_xywh_est: torch.Tensor + point_bbox_with_dp_indices: torch.Tensor + point_bbox_indices: torch.Tensor + bbox_indices: torch.Tensor + + +class ChartBasedAnnotationsAccumulator(AnnotationsAccumulator): + """ + Accumulates annotations by batches that correspond to objects detected on + individual images. Can pack them together into single tensors. + """ + + def __init__(self): + self.i_gt = [] + self.x_gt = [] + self.y_gt = [] + self.u_gt = [] + self.v_gt = [] + self.s_gt = [] + self.bbox_xywh_gt = [] + self.bbox_xywh_est = [] + self.point_bbox_with_dp_indices = [] + self.point_bbox_indices = [] + self.bbox_indices = [] + self.nxt_bbox_with_dp_index = 0 + self.nxt_bbox_index = 0 + + def accumulate(self, instances_one_image: Instances): + """ + Accumulate instances data for one image + + Args: + instances_one_image (Instances): instances data to accumulate + """ + boxes_xywh_est = BoxMode.convert( + instances_one_image.proposal_boxes.tensor.clone(), BoxMode.XYXY_ABS, BoxMode.XYWH_ABS + ) + boxes_xywh_gt = BoxMode.convert( + instances_one_image.gt_boxes.tensor.clone(), BoxMode.XYXY_ABS, BoxMode.XYWH_ABS + ) + n_matches = len(boxes_xywh_gt) + assert n_matches == len( + boxes_xywh_est + ), f"Got {len(boxes_xywh_est)} proposal boxes and {len(boxes_xywh_gt)} GT boxes" + if not n_matches: + # no detection - GT matches + return + if ( + not hasattr(instances_one_image, "gt_densepose") + or instances_one_image.gt_densepose is None + ): + # no densepose GT for the detections, just increase the bbox index + self.nxt_bbox_index += n_matches + return + for box_xywh_est, box_xywh_gt, dp_gt in zip( + boxes_xywh_est, boxes_xywh_gt, instances_one_image.gt_densepose + ): + if (dp_gt is not None) and (len(dp_gt.x) > 0): + self._do_accumulate(box_xywh_gt, box_xywh_est, dp_gt) + self.nxt_bbox_index += 1 + + def _do_accumulate( + self, box_xywh_gt: torch.Tensor, box_xywh_est: torch.Tensor, dp_gt: DensePoseDataRelative + ): + """ + Accumulate instances data for one image, given that the data is not empty + + Args: + box_xywh_gt (tensor): GT bounding box + box_xywh_est (tensor): estimated bounding box + dp_gt (DensePoseDataRelative): GT densepose data + """ + self.i_gt.append(dp_gt.i) + self.x_gt.append(dp_gt.x) + self.y_gt.append(dp_gt.y) + self.u_gt.append(dp_gt.u) + self.v_gt.append(dp_gt.v) + if hasattr(dp_gt, "segm"): + self.s_gt.append(dp_gt.segm.unsqueeze(0)) + self.bbox_xywh_gt.append(box_xywh_gt.view(-1, 4)) + self.bbox_xywh_est.append(box_xywh_est.view(-1, 4)) + self.point_bbox_with_dp_indices.append( + torch.full_like(dp_gt.i, self.nxt_bbox_with_dp_index) + ) + self.point_bbox_indices.append(torch.full_like(dp_gt.i, self.nxt_bbox_index)) + self.bbox_indices.append(self.nxt_bbox_index) + self.nxt_bbox_with_dp_index += 1 + + def pack(self) -> Optional[PackedChartBasedAnnotations]: + """ + Pack data into tensors + """ + if not len(self.i_gt): + # TODO: + # returning proper empty annotations would require + # creating empty tensors of appropriate shape and + # type on an appropriate device; + # we return None so far to indicate empty annotations + return None + return PackedChartBasedAnnotations( + fine_segm_labels_gt=torch.cat(self.i_gt, 0).long(), + x_gt=torch.cat(self.x_gt, 0), + y_gt=torch.cat(self.y_gt, 0), + u_gt=torch.cat(self.u_gt, 0), + v_gt=torch.cat(self.v_gt, 0), + # ignore segmentation annotations, if not all the instances contain those + coarse_segm_gt=torch.cat(self.s_gt, 0) + if len(self.s_gt) == len(self.bbox_xywh_gt) + else None, + bbox_xywh_gt=torch.cat(self.bbox_xywh_gt, 0), + bbox_xywh_est=torch.cat(self.bbox_xywh_est, 0), + point_bbox_with_dp_indices=torch.cat(self.point_bbox_with_dp_indices, 0).long(), + point_bbox_indices=torch.cat(self.point_bbox_indices, 0).long(), + bbox_indices=torch.as_tensor( + self.bbox_indices, dtype=torch.long, device=self.x_gt[0].device + ).long(), + ) + + +def extract_packed_annotations_from_matches( + proposals_with_targets: List[Instances], accumulator: AnnotationsAccumulator +) -> Any: + for proposals_targets_per_image in proposals_with_targets: + accumulator.accumulate(proposals_targets_per_image) + return accumulator.pack() + + +def sample_random_indices( + n_indices: int, n_samples: int, device: Optional[torch.device] = None +) -> Optional[torch.Tensor]: + """ + Samples `n_samples` random indices from range `[0..n_indices - 1]`. + If `n_indices` is smaller than `n_samples`, returns `None` meaning that all indices + are selected. + Args: + n_indices (int): total number of indices + n_samples (int): number of indices to sample + device (torch.device): the desired device of returned tensor + Return: + Tensor of selected vertex indices, or `None`, if all vertices are selected + """ + if (n_samples <= 0) or (n_indices <= n_samples): + return None + indices = torch.randperm(n_indices, device=device)[:n_samples] + return indices diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/__init__.py new file mode 100644 index 0000000..1ece075 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from .chart import DensePoseChartPredictor +from .chart_confidence import DensePoseChartConfidencePredictorMixin +from .chart_with_confidence import DensePoseChartWithConfidencePredictor +from .cse import DensePoseEmbeddingPredictor +from .cse_confidence import DensePoseEmbeddingConfidencePredictorMixin +from .cse_with_confidence import DensePoseEmbeddingWithConfidencePredictor +from .registry import DENSEPOSE_PREDICTOR_REGISTRY diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/chart.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/chart.py new file mode 100644 index 0000000..3bcd13f --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/chart.py @@ -0,0 +1,94 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import torch +from torch import nn + +from detectron2.config import CfgNode +from detectron2.layers import ConvTranspose2d, interpolate + +from ...structures import DensePoseChartPredictorOutput +from ..utils import initialize_module_params +from .registry import DENSEPOSE_PREDICTOR_REGISTRY + + +@DENSEPOSE_PREDICTOR_REGISTRY.register() +class DensePoseChartPredictor(nn.Module): + """ + Predictor (last layers of a DensePose model) that takes DensePose head outputs as an input + and produces 4 tensors which represent DensePose results for predefined body parts + (patches / charts): + * coarse segmentation, a tensor of shape [N, K, Hout, Wout] + * fine segmentation, a tensor of shape [N, C, Hout, Wout] + * U coordinates, a tensor of shape [N, C, Hout, Wout] + * V coordinates, a tensor of shape [N, C, Hout, Wout] + where + - N is the number of instances + - K is the number of coarse segmentation channels ( + 2 = foreground / background, + 15 = one of 14 body parts / background) + - C is the number of fine segmentation channels ( + 24 fine body parts / background) + - Hout and Wout are height and width of predictions + """ + + def __init__(self, cfg: CfgNode, input_channels: int): + """ + Initialize predictor using configuration options + + Args: + cfg (CfgNode): configuration options + input_channels (int): input tensor size along the channel dimension + """ + super().__init__() + dim_in = input_channels + n_segm_chan = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS + dim_out_patches = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_PATCHES + 1 + kernel_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECONV_KERNEL + # coarse segmentation + self.ann_index_lowres = ConvTranspose2d( + dim_in, n_segm_chan, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) + ) + # fine segmentation + self.index_uv_lowres = ConvTranspose2d( + dim_in, dim_out_patches, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) + ) + # U + self.u_lowres = ConvTranspose2d( + dim_in, dim_out_patches, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) + ) + # V + self.v_lowres = ConvTranspose2d( + dim_in, dim_out_patches, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) + ) + self.scale_factor = cfg.MODEL.ROI_DENSEPOSE_HEAD.UP_SCALE + initialize_module_params(self) + + def interp2d(self, tensor_nchw: torch.Tensor): + """ + Bilinear interpolation method to be used for upscaling + + Args: + tensor_nchw (tensor): tensor of shape (N, C, H, W) + Return: + tensor of shape (N, C, Hout, Wout), where Hout and Wout are computed + by applying the scale factor to H and W + """ + return interpolate( + tensor_nchw, scale_factor=self.scale_factor, mode="bilinear", align_corners=False + ) + + def forward(self, head_outputs: torch.Tensor): + """ + Perform forward step on DensePose head outputs + + Args: + head_outputs (tensor): DensePose head outputs, tensor of shape [N, D, H, W] + Return: + An instance of DensePoseChartPredictorOutput + """ + return DensePoseChartPredictorOutput( + coarse_segm=self.interp2d(self.ann_index_lowres(head_outputs)), + fine_segm=self.interp2d(self.index_uv_lowres(head_outputs)), + u=self.interp2d(self.u_lowres(head_outputs)), + v=self.interp2d(self.v_lowres(head_outputs)), + ) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/chart_confidence.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/chart_confidence.py new file mode 100644 index 0000000..0c00999 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/chart_confidence.py @@ -0,0 +1,174 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from typing import Any +import torch +from torch.nn import functional as F + +from detectron2.config import CfgNode +from detectron2.layers import ConvTranspose2d + +from ...structures import decorate_predictor_output_class_with_confidences +from ..confidence import DensePoseConfidenceModelConfig, DensePoseUVConfidenceType +from ..utils import initialize_module_params + + +class DensePoseChartConfidencePredictorMixin: + """ + Predictor contains the last layers of a DensePose model that take DensePose head + outputs as an input and produce model outputs. Confidence predictor mixin is used + to generate confidences for segmentation and UV tensors estimated by some + base predictor. Several assumptions need to hold for the base predictor: + 1) the `forward` method must return SIUV tuple as the first result ( + S = coarse segmentation, I = fine segmentation, U and V are intrinsic + chart coordinates) + 2) `interp2d` method must be defined to perform bilinear interpolation; + the same method is typically used for SIUV and confidences + Confidence predictor mixin provides confidence estimates, as described in: + N. Neverova et al., Correlated Uncertainty for Learning Dense Correspondences + from Noisy Labels, NeurIPS 2019 + A. Sanakoyeu et al., Transferring Dense Pose to Proximal Animal Classes, CVPR 2020 + """ + + def __init__(self, cfg: CfgNode, input_channels: int): + """ + Initialize confidence predictor using configuration options. + + Args: + cfg (CfgNode): configuration options + input_channels (int): number of input channels + """ + # we rely on base predictor to call nn.Module.__init__ + super().__init__(cfg, input_channels) # pyre-ignore[19] + self.confidence_model_cfg = DensePoseConfidenceModelConfig.from_cfg(cfg) + self._initialize_confidence_estimation_layers(cfg, input_channels) + self._registry = {} + initialize_module_params(self) # pyre-ignore[6] + + def _initialize_confidence_estimation_layers(self, cfg: CfgNode, dim_in: int): + """ + Initialize confidence estimation layers based on configuration options + + Args: + cfg (CfgNode): configuration options + dim_in (int): number of input channels + """ + dim_out_patches = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_PATCHES + 1 + kernel_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECONV_KERNEL + if self.confidence_model_cfg.uv_confidence.enabled: + if self.confidence_model_cfg.uv_confidence.type == DensePoseUVConfidenceType.IID_ISO: + self.sigma_2_lowres = ConvTranspose2d( # pyre-ignore[16] + dim_in, dim_out_patches, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) + ) + elif ( + self.confidence_model_cfg.uv_confidence.type + == DensePoseUVConfidenceType.INDEP_ANISO + ): + self.sigma_2_lowres = ConvTranspose2d( + dim_in, dim_out_patches, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) + ) + self.kappa_u_lowres = ConvTranspose2d( # pyre-ignore[16] + dim_in, dim_out_patches, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) + ) + self.kappa_v_lowres = ConvTranspose2d( # pyre-ignore[16] + dim_in, dim_out_patches, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) + ) + else: + raise ValueError( + f"Unknown confidence model type: " + f"{self.confidence_model_cfg.confidence_model_type}" + ) + if self.confidence_model_cfg.segm_confidence.enabled: + self.fine_segm_confidence_lowres = ConvTranspose2d( # pyre-ignore[16] + dim_in, 1, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) + ) + self.coarse_segm_confidence_lowres = ConvTranspose2d( # pyre-ignore[16] + dim_in, 1, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) + ) + + def forward(self, head_outputs: torch.Tensor): + """ + Perform forward operation on head outputs used as inputs for the predictor. + Calls forward method from the base predictor and uses its outputs to compute + confidences. + + Args: + head_outputs (Tensor): head outputs used as predictor inputs + Return: + An instance of outputs with confidences, + see `decorate_predictor_output_class_with_confidences` + """ + # assuming base class returns SIUV estimates in its first result + base_predictor_outputs = super().forward(head_outputs) # pyre-ignore[16] + + # create output instance by extending base predictor outputs: + output = self._create_output_instance(base_predictor_outputs) + + if self.confidence_model_cfg.uv_confidence.enabled: + if self.confidence_model_cfg.uv_confidence.type == DensePoseUVConfidenceType.IID_ISO: + # assuming base class defines interp2d method for bilinear interpolation + output.sigma_2 = self.interp2d(self.sigma_2_lowres(head_outputs)) # pyre-ignore[16] + elif ( + self.confidence_model_cfg.uv_confidence.type + == DensePoseUVConfidenceType.INDEP_ANISO + ): + # assuming base class defines interp2d method for bilinear interpolation + output.sigma_2 = self.interp2d(self.sigma_2_lowres(head_outputs)) + output.kappa_u = self.interp2d(self.kappa_u_lowres(head_outputs)) # pyre-ignore[16] + output.kappa_v = self.interp2d(self.kappa_v_lowres(head_outputs)) # pyre-ignore[16] + else: + raise ValueError( + f"Unknown confidence model type: " + f"{self.confidence_model_cfg.confidence_model_type}" + ) + if self.confidence_model_cfg.segm_confidence.enabled: + # base predictor outputs are assumed to have `fine_segm` and `coarse_segm` attributes + # base predictor is assumed to define `interp2d` method for bilinear interpolation + output.fine_segm_confidence = ( + F.softplus( + self.interp2d(self.fine_segm_confidence_lowres(head_outputs)) # pyre-ignore[16] + ) + + self.confidence_model_cfg.segm_confidence.epsilon + ) + output.fine_segm = base_predictor_outputs.fine_segm * torch.repeat_interleave( + output.fine_segm_confidence, base_predictor_outputs.fine_segm.shape[1], dim=1 + ) + output.coarse_segm_confidence = ( + F.softplus( + self.interp2d( + self.coarse_segm_confidence_lowres(head_outputs) # pyre-ignore[16] + ) + ) + + self.confidence_model_cfg.segm_confidence.epsilon + ) + output.coarse_segm = base_predictor_outputs.coarse_segm * torch.repeat_interleave( + output.coarse_segm_confidence, base_predictor_outputs.coarse_segm.shape[1], dim=1 + ) + + return output + + def _create_output_instance(self, base_predictor_outputs: Any): + """ + Create an instance of predictor outputs by copying the outputs from the + base predictor and initializing confidence + + Args: + base_predictor_outputs: an instance of base predictor outputs + (the outputs type is assumed to be a dataclass) + Return: + An instance of outputs with confidences + """ + PredictorOutput = decorate_predictor_output_class_with_confidences( + type(base_predictor_outputs) # pyre-ignore[6] + ) + # base_predictor_outputs is assumed to be a dataclass + # reassign all the fields from base_predictor_outputs (no deep copy!), add new fields + output = PredictorOutput( + **base_predictor_outputs.__dict__, + coarse_segm_confidence=None, + fine_segm_confidence=None, + sigma_1=None, + sigma_2=None, + kappa_u=None, + kappa_v=None, + ) + return output diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/chart_with_confidence.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/chart_with_confidence.py new file mode 100644 index 0000000..9c1cd6c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/chart_with_confidence.py @@ -0,0 +1,15 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from . import DensePoseChartConfidencePredictorMixin, DensePoseChartPredictor +from .registry import DENSEPOSE_PREDICTOR_REGISTRY + + +@DENSEPOSE_PREDICTOR_REGISTRY.register() +class DensePoseChartWithConfidencePredictor( + DensePoseChartConfidencePredictorMixin, DensePoseChartPredictor +): + """ + Predictor that combines chart and chart confidence estimation + """ + + pass diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/cse.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/cse.py new file mode 100644 index 0000000..466a5ec --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/cse.py @@ -0,0 +1,70 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import torch +from torch import nn + +from detectron2.config import CfgNode +from detectron2.layers import ConvTranspose2d, interpolate + +from ...structures import DensePoseEmbeddingPredictorOutput +from ..utils import initialize_module_params +from .registry import DENSEPOSE_PREDICTOR_REGISTRY + + +@DENSEPOSE_PREDICTOR_REGISTRY.register() +class DensePoseEmbeddingPredictor(nn.Module): + """ + Last layers of a DensePose model that take DensePose head outputs as an input + and produce model outputs for continuous surface embeddings (CSE). + """ + + def __init__(self, cfg: CfgNode, input_channels: int): + """ + Initialize predictor using configuration options + + Args: + cfg (CfgNode): configuration options + input_channels (int): input tensor size along the channel dimension + """ + super().__init__() + dim_in = input_channels + n_segm_chan = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS + embed_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE + kernel_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECONV_KERNEL + # coarse segmentation + self.coarse_segm_lowres = ConvTranspose2d( + dim_in, n_segm_chan, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) + ) + # embedding + self.embed_lowres = ConvTranspose2d( + dim_in, embed_size, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) + ) + self.scale_factor = cfg.MODEL.ROI_DENSEPOSE_HEAD.UP_SCALE + initialize_module_params(self) + + def interp2d(self, tensor_nchw: torch.Tensor): + """ + Bilinear interpolation method to be used for upscaling + + Args: + tensor_nchw (tensor): tensor of shape (N, C, H, W) + Return: + tensor of shape (N, C, Hout, Wout), where Hout and Wout are computed + by applying the scale factor to H and W + """ + return interpolate( + tensor_nchw, scale_factor=self.scale_factor, mode="bilinear", align_corners=False + ) + + def forward(self, head_outputs): + """ + Perform forward step on DensePose head outputs + + Args: + head_outputs (tensor): DensePose head outputs, tensor of shape [N, D, H, W] + """ + embed_lowres = self.embed_lowres(head_outputs) + coarse_segm_lowres = self.coarse_segm_lowres(head_outputs) + embed = self.interp2d(embed_lowres) + coarse_segm = self.interp2d(coarse_segm_lowres) + return DensePoseEmbeddingPredictorOutput(embedding=embed, coarse_segm=coarse_segm) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/cse_confidence.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/cse_confidence.py new file mode 100644 index 0000000..8220337 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/cse_confidence.py @@ -0,0 +1,115 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from typing import Any +import torch +from torch.nn import functional as F + +from detectron2.config import CfgNode +from detectron2.layers import ConvTranspose2d + +from densepose.modeling.confidence import DensePoseConfidenceModelConfig +from densepose.modeling.utils import initialize_module_params +from densepose.structures import decorate_cse_predictor_output_class_with_confidences + + +class DensePoseEmbeddingConfidencePredictorMixin: + """ + Predictor contains the last layers of a DensePose model that take DensePose head + outputs as an input and produce model outputs. Confidence predictor mixin is used + to generate confidences for coarse segmentation estimated by some + base predictor. Several assumptions need to hold for the base predictor: + 1) the `forward` method must return CSE DensePose head outputs, + tensor of shape [N, D, H, W] + 2) `interp2d` method must be defined to perform bilinear interpolation; + the same method is typically used for masks and confidences + Confidence predictor mixin provides confidence estimates, as described in: + N. Neverova et al., Correlated Uncertainty for Learning Dense Correspondences + from Noisy Labels, NeurIPS 2019 + A. Sanakoyeu et al., Transferring Dense Pose to Proximal Animal Classes, CVPR 2020 + """ + + def __init__(self, cfg: CfgNode, input_channels: int): + """ + Initialize confidence predictor using configuration options. + + Args: + cfg (CfgNode): configuration options + input_channels (int): number of input channels + """ + # we rely on base predictor to call nn.Module.__init__ + super().__init__(cfg, input_channels) # pyre-ignore[19] + self.confidence_model_cfg = DensePoseConfidenceModelConfig.from_cfg(cfg) + self._initialize_confidence_estimation_layers(cfg, input_channels) + self._registry = {} + initialize_module_params(self) # pyre-ignore[6] + + def _initialize_confidence_estimation_layers(self, cfg: CfgNode, dim_in: int): + """ + Initialize confidence estimation layers based on configuration options + + Args: + cfg (CfgNode): configuration options + dim_in (int): number of input channels + """ + kernel_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECONV_KERNEL + if self.confidence_model_cfg.segm_confidence.enabled: + self.coarse_segm_confidence_lowres = ConvTranspose2d( # pyre-ignore[16] + dim_in, 1, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) + ) + + def forward(self, head_outputs: torch.Tensor): + """ + Perform forward operation on head outputs used as inputs for the predictor. + Calls forward method from the base predictor and uses its outputs to compute + confidences. + + Args: + head_outputs (Tensor): head outputs used as predictor inputs + Return: + An instance of outputs with confidences, + see `decorate_cse_predictor_output_class_with_confidences` + """ + # assuming base class returns SIUV estimates in its first result + base_predictor_outputs = super().forward(head_outputs) # pyre-ignore[16] + + # create output instance by extending base predictor outputs: + output = self._create_output_instance(base_predictor_outputs) + + if self.confidence_model_cfg.segm_confidence.enabled: + # base predictor outputs are assumed to have `coarse_segm` attribute + # base predictor is assumed to define `interp2d` method for bilinear interpolation + output.coarse_segm_confidence = ( + F.softplus( + self.interp2d( # pyre-ignore[16] + self.coarse_segm_confidence_lowres(head_outputs) # pyre-ignore[16] + ) + ) + + self.confidence_model_cfg.segm_confidence.epsilon + ) + output.coarse_segm = base_predictor_outputs.coarse_segm * torch.repeat_interleave( + output.coarse_segm_confidence, base_predictor_outputs.coarse_segm.shape[1], dim=1 + ) + + return output + + def _create_output_instance(self, base_predictor_outputs: Any): + """ + Create an instance of predictor outputs by copying the outputs from the + base predictor and initializing confidence + + Args: + base_predictor_outputs: an instance of base predictor outputs + (the outputs type is assumed to be a dataclass) + Return: + An instance of outputs with confidences + """ + PredictorOutput = decorate_cse_predictor_output_class_with_confidences( + type(base_predictor_outputs) # pyre-ignore[6] + ) + # base_predictor_outputs is assumed to be a dataclass + # reassign all the fields from base_predictor_outputs (no deep copy!), add new fields + output = PredictorOutput( + **base_predictor_outputs.__dict__, + coarse_segm_confidence=None, + ) + return output diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/cse_with_confidence.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/cse_with_confidence.py new file mode 100644 index 0000000..17ecef6 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/cse_with_confidence.py @@ -0,0 +1,15 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from . import DensePoseEmbeddingConfidencePredictorMixin, DensePoseEmbeddingPredictor +from .registry import DENSEPOSE_PREDICTOR_REGISTRY + + +@DENSEPOSE_PREDICTOR_REGISTRY.register() +class DensePoseEmbeddingWithConfidencePredictor( + DensePoseEmbeddingConfidencePredictorMixin, DensePoseEmbeddingPredictor +): + """ + Predictor that combines CSE and CSE confidence estimation + """ + + pass diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/registry.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/registry.py new file mode 100644 index 0000000..f96901d --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/predictors/registry.py @@ -0,0 +1,5 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from detectron2.utils.registry import Registry + +DENSEPOSE_PREDICTOR_REGISTRY = Registry("DENSEPOSE_PREDICTOR") diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/roi_heads/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/roi_heads/__init__.py new file mode 100644 index 0000000..8403589 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/roi_heads/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from .v1convx import DensePoseV1ConvXHead +from .deeplab import DensePoseDeepLabHead +from .registry import ROI_DENSEPOSE_HEAD_REGISTRY +from .roi_head import Decoder, DensePoseROIHeads diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/roi_heads/deeplab.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/roi_heads/deeplab.py new file mode 100644 index 0000000..4e5cb48 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/roi_heads/deeplab.py @@ -0,0 +1,263 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import CfgNode +from detectron2.layers import Conv2d + +from .registry import ROI_DENSEPOSE_HEAD_REGISTRY + + +@ROI_DENSEPOSE_HEAD_REGISTRY.register() +class DensePoseDeepLabHead(nn.Module): + """ + DensePose head using DeepLabV3 model from + "Rethinking Atrous Convolution for Semantic Image Segmentation" + . + """ + + def __init__(self, cfg: CfgNode, input_channels: int): + super(DensePoseDeepLabHead, self).__init__() + # fmt: off + hidden_dim = cfg.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_DIM + kernel_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_KERNEL + norm = cfg.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB.NORM + self.n_stacked_convs = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_STACKED_CONVS + self.use_nonlocal = cfg.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB.NONLOCAL_ON + # fmt: on + pad_size = kernel_size // 2 + n_channels = input_channels + + self.ASPP = ASPP(input_channels, [6, 12, 56], n_channels) # 6, 12, 56 + self.add_module("ASPP", self.ASPP) + + if self.use_nonlocal: + self.NLBlock = NONLocalBlock2D(input_channels, bn_layer=True) + self.add_module("NLBlock", self.NLBlock) + # weight_init.c2_msra_fill(self.ASPP) + + for i in range(self.n_stacked_convs): + norm_module = nn.GroupNorm(32, hidden_dim) if norm == "GN" else None + layer = Conv2d( + n_channels, + hidden_dim, + kernel_size, + stride=1, + padding=pad_size, + bias=not norm, + norm=norm_module, + ) + weight_init.c2_msra_fill(layer) + n_channels = hidden_dim + layer_name = self._get_layer_name(i) + self.add_module(layer_name, layer) + self.n_out_channels = hidden_dim + # initialize_module_params(self) + + def forward(self, features): + x0 = features + x = self.ASPP(x0) + if self.use_nonlocal: + x = self.NLBlock(x) + output = x + for i in range(self.n_stacked_convs): + layer_name = self._get_layer_name(i) + x = getattr(self, layer_name)(x) + x = F.relu(x) + output = x + return output + + def _get_layer_name(self, i: int): + layer_name = "body_conv_fcn{}".format(i + 1) + return layer_name + + +# Copied from +# https://github.com/pytorch/vision/blob/master/torchvision/models/segmentation/deeplabv3.py +# See https://arxiv.org/pdf/1706.05587.pdf for details +class ASPPConv(nn.Sequential): + def __init__(self, in_channels, out_channels, dilation): + modules = [ + nn.Conv2d( + in_channels, out_channels, 3, padding=dilation, dilation=dilation, bias=False + ), + nn.GroupNorm(32, out_channels), + nn.ReLU(), + ] + super(ASPPConv, self).__init__(*modules) + + +class ASPPPooling(nn.Sequential): + def __init__(self, in_channels, out_channels): + super(ASPPPooling, self).__init__( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(in_channels, out_channels, 1, bias=False), + nn.GroupNorm(32, out_channels), + nn.ReLU(), + ) + + def forward(self, x): + size = x.shape[-2:] + x = super(ASPPPooling, self).forward(x) + return F.interpolate(x, size=size, mode="bilinear", align_corners=False) + + +class ASPP(nn.Module): + def __init__(self, in_channels, atrous_rates, out_channels): + super(ASPP, self).__init__() + modules = [] + modules.append( + nn.Sequential( + nn.Conv2d(in_channels, out_channels, 1, bias=False), + nn.GroupNorm(32, out_channels), + nn.ReLU(), + ) + ) + + rate1, rate2, rate3 = tuple(atrous_rates) + modules.append(ASPPConv(in_channels, out_channels, rate1)) + modules.append(ASPPConv(in_channels, out_channels, rate2)) + modules.append(ASPPConv(in_channels, out_channels, rate3)) + modules.append(ASPPPooling(in_channels, out_channels)) + + self.convs = nn.ModuleList(modules) + + self.project = nn.Sequential( + nn.Conv2d(5 * out_channels, out_channels, 1, bias=False), + # nn.BatchNorm2d(out_channels), + nn.ReLU() + # nn.Dropout(0.5) + ) + + def forward(self, x): + res = [] + for conv in self.convs: + res.append(conv(x)) + res = torch.cat(res, dim=1) + return self.project(res) + + +# copied from +# https://github.com/AlexHex7/Non-local_pytorch/blob/master/lib/non_local_embedded_gaussian.py +# See https://arxiv.org/abs/1711.07971 for details +class _NonLocalBlockND(nn.Module): + def __init__( + self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True + ): + super(_NonLocalBlockND, self).__init__() + + assert dimension in [1, 2, 3] + + self.dimension = dimension + self.sub_sample = sub_sample + + self.in_channels = in_channels + self.inter_channels = inter_channels + + if self.inter_channels is None: + self.inter_channels = in_channels // 2 + if self.inter_channels == 0: + self.inter_channels = 1 + + if dimension == 3: + conv_nd = nn.Conv3d + max_pool_layer = nn.MaxPool3d(kernel_size=(1, 2, 2)) + bn = nn.GroupNorm # (32, hidden_dim) #nn.BatchNorm3d + elif dimension == 2: + conv_nd = nn.Conv2d + max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2)) + bn = nn.GroupNorm # (32, hidden_dim)nn.BatchNorm2d + else: + conv_nd = nn.Conv1d + max_pool_layer = nn.MaxPool1d(kernel_size=2) + bn = nn.GroupNorm # (32, hidden_dim)nn.BatchNorm1d + + self.g = conv_nd( + in_channels=self.in_channels, + out_channels=self.inter_channels, + kernel_size=1, + stride=1, + padding=0, + ) + + if bn_layer: + self.W = nn.Sequential( + conv_nd( + in_channels=self.inter_channels, + out_channels=self.in_channels, + kernel_size=1, + stride=1, + padding=0, + ), + bn(32, self.in_channels), + ) + nn.init.constant_(self.W[1].weight, 0) + nn.init.constant_(self.W[1].bias, 0) + else: + self.W = conv_nd( + in_channels=self.inter_channels, + out_channels=self.in_channels, + kernel_size=1, + stride=1, + padding=0, + ) + nn.init.constant_(self.W.weight, 0) + nn.init.constant_(self.W.bias, 0) + + self.theta = conv_nd( + in_channels=self.in_channels, + out_channels=self.inter_channels, + kernel_size=1, + stride=1, + padding=0, + ) + self.phi = conv_nd( + in_channels=self.in_channels, + out_channels=self.inter_channels, + kernel_size=1, + stride=1, + padding=0, + ) + + if sub_sample: + self.g = nn.Sequential(self.g, max_pool_layer) + self.phi = nn.Sequential(self.phi, max_pool_layer) + + def forward(self, x): + """ + :param x: (b, c, t, h, w) + :return: + """ + + batch_size = x.size(0) + + g_x = self.g(x).view(batch_size, self.inter_channels, -1) + g_x = g_x.permute(0, 2, 1) + + theta_x = self.theta(x).view(batch_size, self.inter_channels, -1) + theta_x = theta_x.permute(0, 2, 1) + phi_x = self.phi(x).view(batch_size, self.inter_channels, -1) + f = torch.matmul(theta_x, phi_x) + f_div_C = F.softmax(f, dim=-1) + + y = torch.matmul(f_div_C, g_x) + y = y.permute(0, 2, 1).contiguous() + y = y.view(batch_size, self.inter_channels, *x.size()[2:]) + W_y = self.W(y) + z = W_y + x + + return z + + +class NONLocalBlock2D(_NonLocalBlockND): + def __init__(self, in_channels, inter_channels=None, sub_sample=True, bn_layer=True): + super(NONLocalBlock2D, self).__init__( + in_channels, + inter_channels=inter_channels, + dimension=2, + sub_sample=sub_sample, + bn_layer=bn_layer, + ) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/roi_heads/registry.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/roi_heads/registry.py new file mode 100644 index 0000000..e1cea43 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/roi_heads/registry.py @@ -0,0 +1,5 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from detectron2.utils.registry import Registry + +ROI_DENSEPOSE_HEAD_REGISTRY = Registry("ROI_DENSEPOSE_HEAD") diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/roi_heads/roi_head.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/roi_heads/roi_head.py new file mode 100644 index 0000000..8f9d9a6 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/roi_heads/roi_head.py @@ -0,0 +1,221 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import numpy as np +from typing import Dict, List, Optional +import fvcore.nn.weight_init as weight_init +import torch +import torch.nn as nn +from torch.nn import functional as F + +from detectron2.layers import Conv2d, ShapeSpec, get_norm +from detectron2.modeling import ROI_HEADS_REGISTRY, StandardROIHeads +from detectron2.modeling.poolers import ROIPooler +from detectron2.modeling.roi_heads import select_foreground_proposals +from detectron2.structures import ImageList, Instances + +from .. import ( + build_densepose_data_filter, + build_densepose_embedder, + build_densepose_head, + build_densepose_losses, + build_densepose_predictor, + densepose_inference, +) + + +class Decoder(nn.Module): + """ + A semantic segmentation head described in detail in the Panoptic Feature Pyramid Networks paper + (https://arxiv.org/abs/1901.02446). It takes FPN features as input and merges information from + all levels of the FPN into single output. + """ + + def __init__(self, cfg, input_shape: Dict[str, ShapeSpec], in_features): + super(Decoder, self).__init__() + + # fmt: off + self.in_features = in_features + feature_strides = {k: v.stride for k, v in input_shape.items()} + feature_channels = {k: v.channels for k, v in input_shape.items()} + num_classes = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECODER_NUM_CLASSES + conv_dims = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECODER_CONV_DIMS + self.common_stride = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECODER_COMMON_STRIDE + norm = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECODER_NORM + # fmt: on + + self.scale_heads = [] + for in_feature in self.in_features: + head_ops = [] + head_length = max( + 1, int(np.log2(feature_strides[in_feature]) - np.log2(self.common_stride)) + ) + for k in range(head_length): + conv = Conv2d( + feature_channels[in_feature] if k == 0 else conv_dims, + conv_dims, + kernel_size=3, + stride=1, + padding=1, + bias=not norm, + norm=get_norm(norm, conv_dims), + activation=F.relu, + ) + weight_init.c2_msra_fill(conv) + head_ops.append(conv) + if feature_strides[in_feature] != self.common_stride: + head_ops.append( + nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False) + ) + self.scale_heads.append(nn.Sequential(*head_ops)) + self.add_module(in_feature, self.scale_heads[-1]) + self.predictor = Conv2d(conv_dims, num_classes, kernel_size=1, stride=1, padding=0) + weight_init.c2_msra_fill(self.predictor) + + def forward(self, features: List[torch.Tensor]): + for i, _ in enumerate(self.in_features): + if i == 0: + x = self.scale_heads[i](features[i]) + else: + x = x + self.scale_heads[i](features[i]) + x = self.predictor(x) + return x + + +@ROI_HEADS_REGISTRY.register() +class DensePoseROIHeads(StandardROIHeads): + """ + A Standard ROIHeads which contains an addition of DensePose head. + """ + + def __init__(self, cfg, input_shape): + super().__init__(cfg, input_shape) + self._init_densepose_head(cfg, input_shape) + + def _init_densepose_head(self, cfg, input_shape): + # fmt: off + self.densepose_on = cfg.MODEL.DENSEPOSE_ON + if not self.densepose_on: + return + self.densepose_data_filter = build_densepose_data_filter(cfg) + dp_pooler_resolution = cfg.MODEL.ROI_DENSEPOSE_HEAD.POOLER_RESOLUTION + dp_pooler_sampling_ratio = cfg.MODEL.ROI_DENSEPOSE_HEAD.POOLER_SAMPLING_RATIO + dp_pooler_type = cfg.MODEL.ROI_DENSEPOSE_HEAD.POOLER_TYPE + self.use_decoder = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECODER_ON + # fmt: on + if self.use_decoder: + dp_pooler_scales = (1.0 / input_shape[self.in_features[0]].stride,) + else: + dp_pooler_scales = tuple(1.0 / input_shape[k].stride for k in self.in_features) + in_channels = [input_shape[f].channels for f in self.in_features][0] + + if self.use_decoder: + self.decoder = Decoder(cfg, input_shape, self.in_features) + + self.densepose_pooler = ROIPooler( + output_size=dp_pooler_resolution, + scales=dp_pooler_scales, + sampling_ratio=dp_pooler_sampling_ratio, + pooler_type=dp_pooler_type, + ) + self.densepose_head = build_densepose_head(cfg, in_channels) + self.densepose_predictor = build_densepose_predictor( + cfg, self.densepose_head.n_out_channels + ) + self.densepose_losses = build_densepose_losses(cfg) + self.embedder = build_densepose_embedder(cfg) + + def _forward_densepose(self, features: Dict[str, torch.Tensor], instances: List[Instances]): + """ + Forward logic of the densepose prediction branch. + + Args: + features (dict[str, Tensor]): input data as a mapping from feature + map name to tensor. Axis 0 represents the number of images `N` in + the input data; axes 1-3 are channels, height, and width, which may + vary between feature maps (e.g., if a feature pyramid is used). + instances (list[Instances]): length `N` list of `Instances`. The i-th + `Instances` contains instances for the i-th input image, + In training, they can be the proposals. + In inference, they can be the predicted boxes. + + Returns: + In training, a dict of losses. + In inference, update `instances` with new fields "densepose" and return it. + """ + if not self.densepose_on: + return {} if self.training else instances + + features_list = [features[f] for f in self.in_features] + if self.training: + proposals, _ = select_foreground_proposals(instances, self.num_classes) + features_list, proposals = self.densepose_data_filter(features_list, proposals) + if len(proposals) > 0: + proposal_boxes = [x.proposal_boxes for x in proposals] + + if self.use_decoder: + # pyre-fixme[29]: `Union[nn.Module, torch.Tensor]` is not a + # function. + features_list = [self.decoder(features_list)] + + features_dp = self.densepose_pooler(features_list, proposal_boxes) + densepose_head_outputs = self.densepose_head(features_dp) + densepose_predictor_outputs = self.densepose_predictor(densepose_head_outputs) + densepose_loss_dict = self.densepose_losses( + proposals, densepose_predictor_outputs, embedder=self.embedder + ) + return densepose_loss_dict + else: + pred_boxes = [x.pred_boxes for x in instances] + + if self.use_decoder: + # pyre-fixme[29]: `Union[nn.Module, torch.Tensor]` is not a function. + features_list = [self.decoder(features_list)] + + features_dp = self.densepose_pooler(features_list, pred_boxes) + if len(features_dp) > 0: + densepose_head_outputs = self.densepose_head(features_dp) + densepose_predictor_outputs = self.densepose_predictor(densepose_head_outputs) + else: + densepose_predictor_outputs = None + + densepose_inference(densepose_predictor_outputs, instances) + return instances + + def forward( + self, + images: ImageList, + features: Dict[str, torch.Tensor], + proposals: List[Instances], + targets: Optional[List[Instances]] = None, + ): + instances, losses = super().forward(images, features, proposals, targets) + del targets, images + + if self.training: + losses.update(self._forward_densepose(features, instances)) + return instances, losses + + def forward_with_given_boxes( + self, features: Dict[str, torch.Tensor], instances: List[Instances] + ): + """ + Use the given boxes in `instances` to produce other (non-box) per-ROI outputs. + + This is useful for downstream tasks where a box is known, but need to obtain + other attributes (outputs of other heads). + Test-time augmentation also uses this. + + Args: + features: same as in `forward()` + instances (list[Instances]): instances to predict other outputs. Expect the keys + "pred_boxes" and "pred_classes" to exist. + + Returns: + instances (list[Instances]): + the same `Instances` objects, with extra + fields such as `pred_masks` or `pred_keypoints`. + """ + + instances = super().forward_with_given_boxes(features, instances) + instances = self._forward_densepose(features, instances) + return instances diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/roi_heads/v1convx.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/roi_heads/v1convx.py new file mode 100644 index 0000000..df79f65 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/roi_heads/v1convx.py @@ -0,0 +1,64 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import CfgNode +from detectron2.layers import Conv2d + +from ..utils import initialize_module_params +from .registry import ROI_DENSEPOSE_HEAD_REGISTRY + + +@ROI_DENSEPOSE_HEAD_REGISTRY.register() +class DensePoseV1ConvXHead(nn.Module): + """ + Fully convolutional DensePose head. + """ + + def __init__(self, cfg: CfgNode, input_channels: int): + """ + Initialize DensePose fully convolutional head + + Args: + cfg (CfgNode): configuration options + input_channels (int): number of input channels + """ + super(DensePoseV1ConvXHead, self).__init__() + # fmt: off + hidden_dim = cfg.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_DIM + kernel_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_KERNEL + self.n_stacked_convs = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_STACKED_CONVS + # fmt: on + pad_size = kernel_size // 2 + n_channels = input_channels + for i in range(self.n_stacked_convs): + layer = Conv2d(n_channels, hidden_dim, kernel_size, stride=1, padding=pad_size) + layer_name = self._get_layer_name(i) + self.add_module(layer_name, layer) + n_channels = hidden_dim + self.n_out_channels = n_channels + initialize_module_params(self) + + def forward(self, features: torch.Tensor): + """ + Apply DensePose fully convolutional head to the input features + + Args: + features (tensor): input features + Result: + A tensor of DensePose head outputs + """ + x = features + output = x + for i in range(self.n_stacked_convs): + layer_name = self._get_layer_name(i) + x = getattr(self, layer_name)(x) + x = F.relu(x) + output = x + return output + + def _get_layer_name(self, i: int): + layer_name = "body_conv_fcn{}".format(i + 1) + return layer_name diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/test_time_augmentation.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/test_time_augmentation.py new file mode 100644 index 0000000..bf36022 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/test_time_augmentation.py @@ -0,0 +1,207 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import numpy as np +import torch +from fvcore.transforms import HFlipTransform, TransformList +from torch.nn import functional as F + +from detectron2.data.transforms import RandomRotation, RotationTransform, apply_transform_gens +from detectron2.modeling.postprocessing import detector_postprocess +from detectron2.modeling.test_time_augmentation import DatasetMapperTTA, GeneralizedRCNNWithTTA + +from ..converters import HFlipConverter + + +class DensePoseDatasetMapperTTA(DatasetMapperTTA): + def __init__(self, cfg): + super().__init__(cfg=cfg) + self.angles = cfg.TEST.AUG.ROTATION_ANGLES + + def __call__(self, dataset_dict): + ret = super().__call__(dataset_dict=dataset_dict) + numpy_image = dataset_dict["image"].permute(1, 2, 0).numpy() + for angle in self.angles: + rotate = RandomRotation(angle=angle, expand=True) + new_numpy_image, tfms = apply_transform_gens([rotate], np.copy(numpy_image)) + torch_image = torch.from_numpy(np.ascontiguousarray(new_numpy_image.transpose(2, 0, 1))) + dic = copy.deepcopy(dataset_dict) + # In DatasetMapperTTA, there is a pre_tfm transform (resize or no-op) that is + # added at the beginning of each TransformList. That's '.transforms[0]'. + dic["transforms"] = TransformList( + [ret[-1]["transforms"].transforms[0]] + tfms.transforms + ) + dic["image"] = torch_image + ret.append(dic) + return ret + + +class DensePoseGeneralizedRCNNWithTTA(GeneralizedRCNNWithTTA): + def __init__(self, cfg, model, transform_data, tta_mapper=None, batch_size=1): + """ + Args: + cfg (CfgNode): + model (GeneralizedRCNN): a GeneralizedRCNN to apply TTA on. + transform_data (DensePoseTransformData): contains symmetry label + transforms used for horizontal flip + tta_mapper (callable): takes a dataset dict and returns a list of + augmented versions of the dataset dict. Defaults to + `DatasetMapperTTA(cfg)`. + batch_size (int): batch the augmented images into this batch size for inference. + """ + self._transform_data = transform_data.to(model.device) + super().__init__(cfg=cfg, model=model, tta_mapper=tta_mapper, batch_size=batch_size) + + # the implementation follows closely the one from detectron2/modeling + def _inference_one_image(self, input): + """ + Args: + input (dict): one dataset dict with "image" field being a CHW tensor + + Returns: + dict: one output dict + """ + orig_shape = (input["height"], input["width"]) + # For some reason, resize with uint8 slightly increases box AP but decreases densepose AP + input["image"] = input["image"].to(torch.uint8) + augmented_inputs, tfms = self._get_augmented_inputs(input) + # Detect boxes from all augmented versions + with self._turn_off_roi_heads(["mask_on", "keypoint_on", "densepose_on"]): + # temporarily disable roi heads + all_boxes, all_scores, all_classes = self._get_augmented_boxes(augmented_inputs, tfms) + merged_instances = self._merge_detections(all_boxes, all_scores, all_classes, orig_shape) + + if self.cfg.MODEL.MASK_ON or self.cfg.MODEL.DENSEPOSE_ON: + # Use the detected boxes to obtain new fields + augmented_instances = self._rescale_detected_boxes( + augmented_inputs, merged_instances, tfms + ) + # run forward on the detected boxes + outputs = self._batch_inference(augmented_inputs, augmented_instances) + # Delete now useless variables to avoid being out of memory + del augmented_inputs, augmented_instances + # average the predictions + if self.cfg.MODEL.MASK_ON: + merged_instances.pred_masks = self._reduce_pred_masks(outputs, tfms) + if self.cfg.MODEL.DENSEPOSE_ON: + merged_instances.pred_densepose = self._reduce_pred_densepose(outputs, tfms) + # postprocess + merged_instances = detector_postprocess(merged_instances, *orig_shape) + return {"instances": merged_instances} + else: + return {"instances": merged_instances} + + def _get_augmented_boxes(self, augmented_inputs, tfms): + # Heavily based on detectron2/modeling/test_time_augmentation.py + # Only difference is that RotationTransform is excluded from bbox computation + # 1: forward with all augmented images + outputs = self._batch_inference(augmented_inputs) + # 2: union the results + all_boxes = [] + all_scores = [] + all_classes = [] + for output, tfm in zip(outputs, tfms): + # Need to inverse the transforms on boxes, to obtain results on original image + if not any(isinstance(t, RotationTransform) for t in tfm.transforms): + # Some transforms can't compute bbox correctly + pred_boxes = output.pred_boxes.tensor + original_pred_boxes = tfm.inverse().apply_box(pred_boxes.cpu().numpy()) + all_boxes.append(torch.from_numpy(original_pred_boxes).to(pred_boxes.device)) + all_scores.extend(output.scores) + all_classes.extend(output.pred_classes) + all_boxes = torch.cat(all_boxes, dim=0) + return all_boxes, all_scores, all_classes + + def _reduce_pred_densepose(self, outputs, tfms): + # Should apply inverse transforms on densepose preds. + # We assume only rotation, resize & flip are used. pred_masks is a scale-invariant + # representation, so we handle the other ones specially + for idx, (output, tfm) in enumerate(zip(outputs, tfms)): + for t in tfm.transforms: + for attr in ["coarse_segm", "fine_segm", "u", "v"]: + setattr( + output.pred_densepose, + attr, + _inverse_rotation( + getattr(output.pred_densepose, attr), output.pred_boxes.tensor, t + ), + ) + if any(isinstance(t, HFlipTransform) for t in tfm.transforms): + output.pred_densepose = HFlipConverter.convert( + output.pred_densepose, self._transform_data + ) + self._incremental_avg_dp(outputs[0].pred_densepose, output.pred_densepose, idx) + return outputs[0].pred_densepose + + # incrementally computed average: u_(n + 1) = u_n + (x_(n+1) - u_n) / (n + 1). + def _incremental_avg_dp(self, avg, new_el, idx): + for attr in ["coarse_segm", "fine_segm", "u", "v"]: + setattr(avg, attr, (getattr(avg, attr) * idx + getattr(new_el, attr)) / (idx + 1)) + if idx: + # Deletion of the > 0 index intermediary values to prevent GPU OOM + setattr(new_el, attr, None) + return avg + + +def _inverse_rotation(densepose_attrs, boxes, transform): + # resample outputs to image size and rotate back the densepose preds + # on the rotated images to the space of the original image + if len(boxes) == 0 or not isinstance(transform, RotationTransform): + return densepose_attrs + boxes = boxes.int().cpu().numpy() + wh_boxes = boxes[:, 2:] - boxes[:, :2] # bboxes in the rotated space + inv_boxes = rotate_box_inverse(transform, boxes).astype(int) # bboxes in original image + wh_diff = (inv_boxes[:, 2:] - inv_boxes[:, :2] - wh_boxes) // 2 # diff between new/old bboxes + rotation_matrix = torch.tensor([transform.rm_image]).to(device=densepose_attrs.device).float() + rotation_matrix[:, :, -1] = 0 + # To apply grid_sample for rotation, we need to have enough space to fit the original and + # rotated bboxes. l_bds and r_bds are the left/right bounds that will be used to + # crop the difference once the rotation is done + l_bds = np.maximum(0, -wh_diff) + for i in range(len(densepose_attrs)): + if min(wh_boxes[i]) <= 0: + continue + densepose_attr = densepose_attrs[[i]].clone() + # 1. Interpolate densepose attribute to size of the rotated bbox + densepose_attr = F.interpolate(densepose_attr, wh_boxes[i].tolist()[::-1], mode="bilinear") + # 2. Pad the interpolated attribute so it has room for the original + rotated bbox + densepose_attr = F.pad(densepose_attr, tuple(np.repeat(np.maximum(0, wh_diff[i]), 2))) + # 3. Compute rotation grid and transform + grid = F.affine_grid(rotation_matrix, size=densepose_attr.shape) + densepose_attr = F.grid_sample(densepose_attr, grid) + # 4. Compute right bounds and crop the densepose_attr to the size of the original bbox + r_bds = densepose_attr.shape[2:][::-1] - l_bds[i] + densepose_attr = densepose_attr[:, :, l_bds[i][1] : r_bds[1], l_bds[i][0] : r_bds[0]] + if min(densepose_attr.shape) > 0: + # Interpolate back to the original size of the densepose attribute + densepose_attr = F.interpolate( + densepose_attr, densepose_attrs.shape[-2:], mode="bilinear" + ) + # Adding a very small probability to the background class to fill padded zones + densepose_attr[:, 0] += 1e-10 + densepose_attrs[i] = densepose_attr + return densepose_attrs + + +def rotate_box_inverse(rot_tfm, rotated_box): + """ + rotated_box is a N * 4 array of [x0, y0, x1, y1] boxes + When a bbox is rotated, it gets bigger, because we need to surround the tilted bbox + So when a bbox is rotated then inverse-rotated, it is much bigger than the original + This function aims to invert the rotation on the box, but also resize it to its original size + """ + # 1. Compute the inverse rotation of the rotated bboxes (bigger than it ) + invrot_box = rot_tfm.inverse().apply_box(rotated_box) + h, w = rotated_box[:, 3] - rotated_box[:, 1], rotated_box[:, 2] - rotated_box[:, 0] + ih, iw = invrot_box[:, 3] - invrot_box[:, 1], invrot_box[:, 2] - invrot_box[:, 0] + assert 2 * rot_tfm.abs_sin ** 2 != 1, "45 degrees angle can't be inverted" + # 2. Inverse the corresponding computation in the rotation transform + # to get the original height/width of the rotated boxes + orig_h = (h * rot_tfm.abs_cos - w * rot_tfm.abs_sin) / (1 - 2 * rot_tfm.abs_sin ** 2) + orig_w = (w * rot_tfm.abs_cos - h * rot_tfm.abs_sin) / (1 - 2 * rot_tfm.abs_sin ** 2) + # 3. Resize the inverse-rotated bboxes to their original size + invrot_box[:, 0] += (iw - orig_w) / 2 + invrot_box[:, 1] += (ih - orig_h) / 2 + invrot_box[:, 2] -= (iw - orig_w) / 2 + invrot_box[:, 3] -= (ih - orig_h) / 2 + + return invrot_box diff --git a/motion-gan-pipeline/preprocessing/third/densepose/modeling/utils.py b/motion-gan-pipeline/preprocessing/third/densepose/modeling/utils.py new file mode 100644 index 0000000..2e76eb9 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/modeling/utils.py @@ -0,0 +1,11 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from torch import nn + + +def initialize_module_params(module: nn.Module) -> None: + for name, param in module.named_parameters(): + if "bias" in name: + nn.init.constant_(param, 0) + elif "weight" in name: + nn.init.kaiming_normal_(param, mode="fan_out", nonlinearity="relu") diff --git a/motion-gan-pipeline/preprocessing/third/densepose/structures/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/structures/__init__.py new file mode 100644 index 0000000..ed32c5e --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/structures/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from .chart import DensePoseChartPredictorOutput +from .chart_confidence import decorate_predictor_output_class_with_confidences +from .cse_confidence import decorate_cse_predictor_output_class_with_confidences +from .chart_result import ( + DensePoseChartResult, + DensePoseChartResultWithConfidences, + quantize_densepose_chart_result, + compress_quantized_densepose_chart_result, + decompress_compressed_densepose_chart_result, +) +from .cse import DensePoseEmbeddingPredictorOutput +from .data_relative import DensePoseDataRelative +from .list import DensePoseList +from .mesh import Mesh, create_mesh +from .transform_data import DensePoseTransformData, normalized_coords_transform diff --git a/motion-gan-pipeline/preprocessing/third/densepose/structures/chart.py b/motion-gan-pipeline/preprocessing/third/densepose/structures/chart.py new file mode 100644 index 0000000..115cc08 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/structures/chart.py @@ -0,0 +1,70 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from dataclasses import dataclass +from typing import Union +import torch + + +@dataclass +class DensePoseChartPredictorOutput: + """ + Predictor output that contains segmentation and inner coordinates predictions for predefined + body parts: + * coarse segmentation, a tensor of shape [N, K, Hout, Wout] + * fine segmentation, a tensor of shape [N, C, Hout, Wout] + * U coordinates, a tensor of shape [N, C, Hout, Wout] + * V coordinates, a tensor of shape [N, C, Hout, Wout] + where + - N is the number of instances + - K is the number of coarse segmentation channels ( + 2 = foreground / background, + 15 = one of 14 body parts / background) + - C is the number of fine segmentation channels ( + 24 fine body parts / background) + - Hout and Wout are height and width of predictions + """ + + coarse_segm: torch.Tensor + fine_segm: torch.Tensor + u: torch.Tensor + v: torch.Tensor + + def __len__(self): + """ + Number of instances (N) in the output + """ + return self.coarse_segm.size(0) + + def __getitem__( + self, item: Union[int, slice, torch.BoolTensor] + ) -> "DensePoseChartPredictorOutput": + """ + Get outputs for the selected instance(s) + + Args: + item (int or slice or tensor): selected items + """ + if isinstance(item, int): + return DensePoseChartPredictorOutput( + coarse_segm=self.coarse_segm[item].unsqueeze(0), + fine_segm=self.fine_segm[item].unsqueeze(0), + u=self.u[item].unsqueeze(0), + v=self.v[item].unsqueeze(0), + ) + else: + return DensePoseChartPredictorOutput( + coarse_segm=self.coarse_segm[item], + fine_segm=self.fine_segm[item], + u=self.u[item], + v=self.v[item], + ) + + def to(self, device: torch.device): + """ + Transfers all tensors to the given device + """ + coarse_segm = self.coarse_segm.to(device) + fine_segm = self.fine_segm.to(device) + u = self.u.to(device) + v = self.v.to(device) + return DensePoseChartPredictorOutput(coarse_segm=coarse_segm, fine_segm=fine_segm, u=u, v=v) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/structures/chart_confidence.py b/motion-gan-pipeline/preprocessing/third/densepose/structures/chart_confidence.py new file mode 100644 index 0000000..01b54d7 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/structures/chart_confidence.py @@ -0,0 +1,98 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from dataclasses import make_dataclass +from functools import lru_cache +from typing import Any, Optional +import torch + + +@lru_cache(maxsize=None) +def decorate_predictor_output_class_with_confidences(BasePredictorOutput: type) -> type: + """ + Create a new output class from an existing one by adding new attributes + related to confidence estimation: + - sigma_1 (tensor) + - sigma_2 (tensor) + - kappa_u (tensor) + - kappa_v (tensor) + - fine_segm_confidence (tensor) + - coarse_segm_confidence (tensor) + + Details on confidence estimation parameters can be found in: + N. Neverova, D. Novotny, A. Vedaldi "Correlated Uncertainty for Learning + Dense Correspondences from Noisy Labels", p. 918--926, in Proc. NIPS 2019 + A. Sanakoyeu et al., Transferring Dense Pose to Proximal Animal Classes, CVPR 2020 + + The new class inherits the provided `BasePredictorOutput` class, + it's name is composed of the name of the provided class and + "WithConfidences" suffix. + + Args: + BasePredictorOutput (type): output type to which confidence data + is to be added, assumed to be a dataclass + Return: + New dataclass derived from the provided one that has attributes + for confidence estimation + """ + + PredictorOutput = make_dataclass( + BasePredictorOutput.__name__ + "WithConfidences", + fields=[ # pyre-ignore[6] + ("sigma_1", Optional[torch.Tensor], None), + ("sigma_2", Optional[torch.Tensor], None), + ("kappa_u", Optional[torch.Tensor], None), + ("kappa_v", Optional[torch.Tensor], None), + ("fine_segm_confidence", Optional[torch.Tensor], None), + ("coarse_segm_confidence", Optional[torch.Tensor], None), + ], + bases=(BasePredictorOutput,), + ) + + # add possibility to index PredictorOutput + + def slice_if_not_none(data, item): + if data is None: + return None + if isinstance(item, int): + return data[item].unsqueeze(0) + return data[item] + + def PredictorOutput_getitem(self, item): + PredictorOutput = type(self) + base_predictor_output_sliced = super(PredictorOutput, self).__getitem__(item) + return PredictorOutput( + **base_predictor_output_sliced.__dict__, + coarse_segm_confidence=slice_if_not_none(self.coarse_segm_confidence, item), + fine_segm_confidence=slice_if_not_none(self.fine_segm_confidence, item), + sigma_1=slice_if_not_none(self.sigma_1, item), + sigma_2=slice_if_not_none(self.sigma_2, item), + kappa_u=slice_if_not_none(self.kappa_u, item), + kappa_v=slice_if_not_none(self.kappa_v, item), + ) + + PredictorOutput.__getitem__ = PredictorOutput_getitem + + def PredictorOutput_to(self, device: torch.device): + """ + Transfers all tensors to the given device + """ + PredictorOutput = type(self) + base_predictor_output_to = super(PredictorOutput, self).to(device) # pyre-ignore[16] + + def to_device_if_tensor(var: Any): + if isinstance(var, torch.Tensor): + return var.to(device) + return var + + return PredictorOutput( + **base_predictor_output_to.__dict__, + sigma_1=to_device_if_tensor(self.sigma_1), + sigma_2=to_device_if_tensor(self.sigma_2), + kappa_u=to_device_if_tensor(self.kappa_u), + kappa_v=to_device_if_tensor(self.kappa_v), + fine_segm_confidence=to_device_if_tensor(self.fine_segm_confidence), + coarse_segm_confidence=to_device_if_tensor(self.coarse_segm_confidence), + ) + + PredictorOutput.to = PredictorOutput_to + return PredictorOutput diff --git a/motion-gan-pipeline/preprocessing/third/densepose/structures/chart_result.py b/motion-gan-pipeline/preprocessing/third/densepose/structures/chart_result.py new file mode 100644 index 0000000..003933d --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/structures/chart_result.py @@ -0,0 +1,183 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from dataclasses import dataclass +from typing import Any, Optional, Tuple +import torch + + +@dataclass +class DensePoseChartResult: + """ + DensePose results for chart-based methods represented by labels and inner + coordinates (U, V) of individual charts. Each chart is a 2D manifold + that has an associated label and is parameterized by two coordinates U and V. + Both U and V take values in [0, 1]. + Thus the results are represented by two tensors: + - labels (tensor [H, W] of long): contains estimated label for each pixel of + the detection bounding box of size (H, W) + - uv (tensor [2, H, W] of float): contains estimated U and V coordinates + for each pixel of the detection bounding box of size (H, W) + """ + + labels: torch.Tensor + uv: torch.Tensor + + def to(self, device: torch.device): + """ + Transfers all tensors to the given device + """ + labels = self.labels.to(device) + uv = self.uv.to(device) + return DensePoseChartResult(labels=labels, uv=uv) + + +@dataclass +class DensePoseChartResultWithConfidences: + """ + We add confidence values to DensePoseChartResult + Thus the results are represented by two tensors: + - labels (tensor [H, W] of long): contains estimated label for each pixel of + the detection bounding box of size (H, W) + - uv (tensor [2, H, W] of float): contains estimated U and V coordinates + for each pixel of the detection bounding box of size (H, W) + Plus one [H, W] tensor of float for each confidence type + """ + + labels: torch.Tensor + uv: torch.Tensor + sigma_1: Optional[torch.Tensor] = None + sigma_2: Optional[torch.Tensor] = None + kappa_u: Optional[torch.Tensor] = None + kappa_v: Optional[torch.Tensor] = None + fine_segm_confidence: Optional[torch.Tensor] = None + coarse_segm_confidence: Optional[torch.Tensor] = None + + def to(self, device: torch.device): + """ + Transfers all tensors to the given device, except if their value is None + """ + + def to_device_if_tensor(var: Any): + if isinstance(var, torch.Tensor): + return var.to(device) + return var + + return DensePoseChartResultWithConfidences( + labels=self.labels.to(device), + uv=self.uv.to(device), + sigma_1=to_device_if_tensor(self.sigma_1), + sigma_2=to_device_if_tensor(self.sigma_2), + kappa_u=to_device_if_tensor(self.kappa_u), + kappa_v=to_device_if_tensor(self.kappa_v), + fine_segm_confidence=to_device_if_tensor(self.fine_segm_confidence), + coarse_segm_confidence=to_device_if_tensor(self.coarse_segm_confidence), + ) + + +@dataclass +class DensePoseChartResultQuantized: + """ + DensePose results for chart-based methods represented by labels and quantized + inner coordinates (U, V) of individual charts. Each chart is a 2D manifold + that has an associated label and is parameterized by two coordinates U and V. + Both U and V take values in [0, 1]. + Quantized coordinates Uq and Vq have uint8 values which are obtained as: + Uq = U * 255 (hence 0 <= Uq <= 255) + Vq = V * 255 (hence 0 <= Vq <= 255) + Thus the results are represented by one tensor: + - labels_uv_uint8 (tensor [3, H, W] of uint8): contains estimated label + and quantized coordinates Uq and Vq for each pixel of the detection + bounding box of size (H, W) + """ + + labels_uv_uint8: torch.Tensor + + def to(self, device: torch.device): + """ + Transfers all tensors to the given device + """ + labels_uv_uint8 = self.labels_uv_uint8.to(device) + return DensePoseChartResultQuantized(labels_uv_uint8=labels_uv_uint8) + + +@dataclass +class DensePoseChartResultCompressed: + """ + DensePose results for chart-based methods represented by a PNG-encoded string. + The tensor of quantized DensePose results of size [3, H, W] is considered + as an image with 3 color channels. PNG compression is applied and the result + is stored as a Base64-encoded string. The following attributes are defined: + - shape_chw (tuple of 3 int): contains shape of the result tensor + (number of channels, height, width) + - labels_uv_str (str): contains Base64-encoded results tensor of size + [3, H, W] compressed with PNG compression methods + """ + + shape_chw: Tuple[int, int, int] + labels_uv_str: str + + +def quantize_densepose_chart_result(result: DensePoseChartResult) -> DensePoseChartResultQuantized: + """ + Applies quantization to DensePose chart-based result. + + Args: + result (DensePoseChartResult): DensePose chart-based result + Return: + Quantized DensePose chart-based result (DensePoseChartResultQuantized) + """ + h, w = result.labels.shape + labels_uv_uint8 = torch.zeros([3, h, w], dtype=torch.uint8, device=result.labels.device) + labels_uv_uint8[0] = result.labels + labels_uv_uint8[1:] = (result.uv * 255).clamp(0, 255).byte() + return DensePoseChartResultQuantized(labels_uv_uint8=labels_uv_uint8) + + +def compress_quantized_densepose_chart_result( + result: DensePoseChartResultQuantized, +) -> DensePoseChartResultCompressed: + """ + Compresses quantized DensePose chart-based result + + Args: + result (DensePoseChartResultQuantized): quantized DensePose chart-based result + Return: + Compressed DensePose chart-based result (DensePoseChartResultCompressed) + """ + import base64 + import numpy as np + from io import BytesIO + from PIL import Image + + labels_uv_uint8_np_chw = result.labels_uv_uint8.cpu().numpy() + labels_uv_uint8_np_hwc = np.moveaxis(labels_uv_uint8_np_chw, 0, -1) + im = Image.fromarray(labels_uv_uint8_np_hwc) + fstream = BytesIO() + im.save(fstream, format="png", optimize=True) + labels_uv_str = base64.encodebytes(fstream.getvalue()).decode() + shape_chw = labels_uv_uint8_np_chw.shape + return DensePoseChartResultCompressed(labels_uv_str=labels_uv_str, shape_chw=shape_chw) + + +def decompress_compressed_densepose_chart_result( + result: DensePoseChartResultCompressed, +) -> DensePoseChartResultQuantized: + """ + Decompresses DensePose chart-based result encoded into a base64 string + + Args: + result (DensePoseChartResultCompressed): compressed DensePose chart result + Return: + Quantized DensePose chart-based result (DensePoseChartResultQuantized) + """ + import base64 + import numpy as np + from io import BytesIO + from PIL import Image + + fstream = BytesIO(base64.decodebytes(result.labels_uv_str.encode())) + im = Image.open(fstream) + labels_uv_uint8_np_chw = np.moveaxis(np.array(im, dtype=np.uint8), -1, 0) + return DensePoseChartResultQuantized( + labels_uv_uint8=torch.from_numpy(labels_uv_uint8_np_chw.reshape(result.shape_chw)) + ) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/structures/cse.py b/motion-gan-pipeline/preprocessing/third/densepose/structures/cse.py new file mode 100644 index 0000000..9cd65da --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/structures/cse.py @@ -0,0 +1,52 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +from dataclasses import dataclass +from typing import Union +import torch + + +@dataclass +class DensePoseEmbeddingPredictorOutput: + """ + Predictor output that contains embedding and coarse segmentation data: + * embedding: float tensor of size [N, D, H, W], contains estimated embeddings + * coarse_segm: float tensor of size [N, K, H, W] + Here D = MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE + K = MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS + """ + + embedding: torch.Tensor + coarse_segm: torch.Tensor + + def __len__(self): + """ + Number of instances (N) in the output + """ + return self.coarse_segm.size(0) + + def __getitem__( + self, item: Union[int, slice, torch.BoolTensor] + ) -> "DensePoseEmbeddingPredictorOutput": + """ + Get outputs for the selected instance(s) + + Args: + item (int or slice or tensor): selected items + """ + if isinstance(item, int): + return DensePoseEmbeddingPredictorOutput( + coarse_segm=self.coarse_segm[item].unsqueeze(0), + embedding=self.embedding[item].unsqueeze(0), + ) + else: + return DensePoseEmbeddingPredictorOutput( + coarse_segm=self.coarse_segm[item], embedding=self.embedding[item] + ) + + def to(self, device: torch.device): + """ + Transfers all tensors to the given device + """ + coarse_segm = self.coarse_segm.to(device) + embedding = self.embedding.to(device) + return DensePoseEmbeddingPredictorOutput(coarse_segm=coarse_segm, embedding=embedding) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/structures/cse_confidence.py b/motion-gan-pipeline/preprocessing/third/densepose/structures/cse_confidence.py new file mode 100644 index 0000000..9a1319c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/structures/cse_confidence.py @@ -0,0 +1,78 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from dataclasses import make_dataclass +from functools import lru_cache +from typing import Any, Optional +import torch + + +@lru_cache(maxsize=None) +def decorate_cse_predictor_output_class_with_confidences(BasePredictorOutput: type) -> type: + """ + Create a new output class from an existing one by adding new attributes + related to confidence estimation: + - coarse_segm_confidence (tensor) + + Details on confidence estimation parameters can be found in: + N. Neverova, D. Novotny, A. Vedaldi "Correlated Uncertainty for Learning + Dense Correspondences from Noisy Labels", p. 918--926, in Proc. NIPS 2019 + A. Sanakoyeu et al., Transferring Dense Pose to Proximal Animal Classes, CVPR 2020 + + The new class inherits the provided `BasePredictorOutput` class, + it's name is composed of the name of the provided class and + "WithConfidences" suffix. + + Args: + BasePredictorOutput (type): output type to which confidence data + is to be added, assumed to be a dataclass + Return: + New dataclass derived from the provided one that has attributes + for confidence estimation + """ + + PredictorOutput = make_dataclass( + BasePredictorOutput.__name__ + "WithConfidences", + fields=[ # pyre-ignore[6] + ("coarse_segm_confidence", Optional[torch.Tensor], None), + ], + bases=(BasePredictorOutput,), + ) + + # add possibility to index PredictorOutput + + def slice_if_not_none(data, item): + if data is None: + return None + if isinstance(item, int): + return data[item].unsqueeze(0) + return data[item] + + def PredictorOutput_getitem(self, item): + PredictorOutput = type(self) + base_predictor_output_sliced = super(PredictorOutput, self).__getitem__(item) + return PredictorOutput( + **base_predictor_output_sliced.__dict__, + coarse_segm_confidence=slice_if_not_none(self.coarse_segm_confidence, item), + ) + + PredictorOutput.__getitem__ = PredictorOutput_getitem + + def PredictorOutput_to(self, device: torch.device): + """ + Transfers all tensors to the given device + """ + PredictorOutput = type(self) + base_predictor_output_to = super(PredictorOutput, self).to(device) # pyre-ignore[16] + + def to_device_if_tensor(var: Any): + if isinstance(var, torch.Tensor): + return var.to(device) + return var + + return PredictorOutput( + **base_predictor_output_to.__dict__, + coarse_segm_confidence=to_device_if_tensor(self.coarse_segm_confidence), + ) + + PredictorOutput.to = PredictorOutput_to + return PredictorOutput diff --git a/motion-gan-pipeline/preprocessing/third/densepose/structures/data_relative.py b/motion-gan-pipeline/preprocessing/third/densepose/structures/data_relative.py new file mode 100644 index 0000000..a148fa7 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/structures/data_relative.py @@ -0,0 +1,243 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +import torch +from torch.nn import functional as F + +from densepose.data.meshes.catalog import MeshCatalog +from densepose.structures.mesh import load_mesh_symmetry +from densepose.structures.transform_data import DensePoseTransformData + + +class DensePoseDataRelative(object): + """ + Dense pose relative annotations that can be applied to any bounding box: + x - normalized X coordinates [0, 255] of annotated points + y - normalized Y coordinates [0, 255] of annotated points + i - body part labels 0,...,24 for annotated points + u - body part U coordinates [0, 1] for annotated points + v - body part V coordinates [0, 1] for annotated points + segm - 256x256 segmentation mask with values 0,...,14 + To obtain absolute x and y data wrt some bounding box one needs to first + divide the data by 256, multiply by the respective bounding box size + and add bounding box offset: + x_img = x0 + x_norm * w / 256.0 + y_img = y0 + y_norm * h / 256.0 + Segmentation masks are typically sampled to get image-based masks. + """ + + # Key for normalized X coordinates in annotation dict + X_KEY = "dp_x" + # Key for normalized Y coordinates in annotation dict + Y_KEY = "dp_y" + # Key for U part coordinates in annotation dict (used in chart-based annotations) + U_KEY = "dp_U" + # Key for V part coordinates in annotation dict (used in chart-based annotations) + V_KEY = "dp_V" + # Key for I point labels in annotation dict (used in chart-based annotations) + I_KEY = "dp_I" + # Key for segmentation mask in annotation dict + S_KEY = "dp_masks" + # Key for vertex ids (used in continuous surface embeddings annotations) + VERTEX_IDS_KEY = "dp_vertex" + # Key for mesh id (used in continuous surface embeddings annotations) + MESH_NAME_KEY = "ref_model" + # Number of body parts in segmentation masks + N_BODY_PARTS = 14 + # Number of parts in point labels + N_PART_LABELS = 24 + MASK_SIZE = 256 + + def __init__(self, annotation, cleanup=False): + self.x = torch.as_tensor(annotation[DensePoseDataRelative.X_KEY]) + self.y = torch.as_tensor(annotation[DensePoseDataRelative.Y_KEY]) + if ( + DensePoseDataRelative.I_KEY in annotation + and DensePoseDataRelative.U_KEY in annotation + and DensePoseDataRelative.V_KEY in annotation + ): + self.i = torch.as_tensor(annotation[DensePoseDataRelative.I_KEY]) + self.u = torch.as_tensor(annotation[DensePoseDataRelative.U_KEY]) + self.v = torch.as_tensor(annotation[DensePoseDataRelative.V_KEY]) + if ( + DensePoseDataRelative.VERTEX_IDS_KEY in annotation + and DensePoseDataRelative.MESH_NAME_KEY in annotation + ): + self.vertex_ids = torch.as_tensor( + annotation[DensePoseDataRelative.VERTEX_IDS_KEY], dtype=torch.long + ) + self.mesh_id = MeshCatalog.get_mesh_id(annotation[DensePoseDataRelative.MESH_NAME_KEY]) + if DensePoseDataRelative.S_KEY in annotation: + self.segm = DensePoseDataRelative.extract_segmentation_mask(annotation) + self.device = torch.device("cpu") + if cleanup: + DensePoseDataRelative.cleanup_annotation(annotation) + + def to(self, device): + if self.device == device: + return self + new_data = DensePoseDataRelative.__new__(DensePoseDataRelative) + new_data.x = self.x.to(device) + new_data.y = self.y.to(device) + for attr in ["i", "u", "v", "vertex_ids", "segm"]: + if hasattr(self, attr): + setattr(new_data, attr, getattr(self, attr).to(device)) + if hasattr(self, "mesh_id"): + new_data.mesh_id = self.mesh_id + new_data.device = device + return new_data + + @staticmethod + def extract_segmentation_mask(annotation): + import pycocotools.mask as mask_utils + + # TODO: annotation instance is accepted if it contains either + # DensePose segmentation or instance segmentation. However, here we + # only rely on DensePose segmentation + poly_specs = annotation[DensePoseDataRelative.S_KEY] + if isinstance(poly_specs, torch.Tensor): + # data is already given as mask tensors, no need to decode + return poly_specs + segm = torch.zeros((DensePoseDataRelative.MASK_SIZE,) * 2, dtype=torch.float32) + if isinstance(poly_specs, dict): + if poly_specs: + mask = mask_utils.decode(poly_specs) + segm[mask > 0] = 1 + else: + for i in range(len(poly_specs)): + poly_i = poly_specs[i] + if poly_i: + mask_i = mask_utils.decode(poly_i) + segm[mask_i > 0] = i + 1 + return segm + + @staticmethod + def validate_annotation(annotation): + for key in [ + DensePoseDataRelative.X_KEY, + DensePoseDataRelative.Y_KEY, + ]: + if key not in annotation: + return False, "no {key} data in the annotation".format(key=key) + valid_for_iuv_setting = all( + key in annotation + for key in [ + DensePoseDataRelative.I_KEY, + DensePoseDataRelative.U_KEY, + DensePoseDataRelative.V_KEY, + ] + ) + valid_for_cse_setting = all( + key in annotation + for key in [ + DensePoseDataRelative.VERTEX_IDS_KEY, + DensePoseDataRelative.MESH_NAME_KEY, + ] + ) + if not valid_for_iuv_setting and not valid_for_cse_setting: + return ( + False, + "expected either {} (IUV setting) or {} (CSE setting) annotations".format( + ", ".join( + [ + DensePoseDataRelative.I_KEY, + DensePoseDataRelative.U_KEY, + DensePoseDataRelative.V_KEY, + ] + ), + ", ".join( + [ + DensePoseDataRelative.VERTEX_IDS_KEY, + DensePoseDataRelative.MESH_NAME_KEY, + ] + ), + ), + ) + return True, None + + @staticmethod + def cleanup_annotation(annotation): + for key in [ + DensePoseDataRelative.X_KEY, + DensePoseDataRelative.Y_KEY, + DensePoseDataRelative.I_KEY, + DensePoseDataRelative.U_KEY, + DensePoseDataRelative.V_KEY, + DensePoseDataRelative.S_KEY, + DensePoseDataRelative.VERTEX_IDS_KEY, + DensePoseDataRelative.MESH_NAME_KEY, + ]: + if key in annotation: + del annotation[key] + + def apply_transform(self, transforms, densepose_transform_data): + self._transform_pts(transforms, densepose_transform_data) + if hasattr(self, "segm"): + self._transform_segm(transforms, densepose_transform_data) + + def _transform_pts(self, transforms, dp_transform_data): + import detectron2.data.transforms as T + + # NOTE: This assumes that HorizFlipTransform is the only one that does flip + do_hflip = sum(isinstance(t, T.HFlipTransform) for t in transforms.transforms) % 2 == 1 + if do_hflip: + self.x = self.MASK_SIZE - self.x + if hasattr(self, "i"): + self._flip_iuv_semantics(dp_transform_data) + if hasattr(self, "vertex_ids"): + self._flip_vertices() + + for t in transforms.transforms: + if isinstance(t, T.RotationTransform): + xy_scale = np.array((t.w, t.h)) / DensePoseDataRelative.MASK_SIZE + xy = t.apply_coords(np.stack((self.x, self.y), axis=1) * xy_scale) + self.x, self.y = torch.tensor(xy / xy_scale, dtype=self.x.dtype).T + + def _flip_iuv_semantics(self, dp_transform_data: DensePoseTransformData) -> None: + i_old = self.i.clone() + uv_symmetries = dp_transform_data.uv_symmetries + pt_label_symmetries = dp_transform_data.point_label_symmetries + for i in range(self.N_PART_LABELS): + if i + 1 in i_old: + annot_indices_i = i_old == i + 1 + if pt_label_symmetries[i + 1] != i + 1: + self.i[annot_indices_i] = pt_label_symmetries[i + 1] + u_loc = (self.u[annot_indices_i] * 255).long() + v_loc = (self.v[annot_indices_i] * 255).long() + self.u[annot_indices_i] = uv_symmetries["U_transforms"][i][v_loc, u_loc].to( + device=self.u.device + ) + self.v[annot_indices_i] = uv_symmetries["V_transforms"][i][v_loc, u_loc].to( + device=self.v.device + ) + + def _flip_vertices(self): + mesh_info = MeshCatalog[MeshCatalog.get_mesh_name(self.mesh_id)] + mesh_symmetry = ( + load_mesh_symmetry(mesh_info.symmetry) if mesh_info.symmetry is not None else None + ) + self.vertex_ids = mesh_symmetry["vertex_transforms"][self.vertex_ids] + + def _transform_segm(self, transforms, dp_transform_data): + import detectron2.data.transforms as T + + # NOTE: This assumes that HorizFlipTransform is the only one that does flip + do_hflip = sum(isinstance(t, T.HFlipTransform) for t in transforms.transforms) % 2 == 1 + if do_hflip: + self.segm = torch.flip(self.segm, [1]) + self._flip_segm_semantics(dp_transform_data) + + for t in transforms.transforms: + if isinstance(t, T.RotationTransform): + self._transform_segm_rotation(t) + + def _flip_segm_semantics(self, dp_transform_data): + old_segm = self.segm.clone() + mask_label_symmetries = dp_transform_data.mask_label_symmetries + for i in range(self.N_BODY_PARTS): + if mask_label_symmetries[i + 1] != i + 1: + self.segm[old_segm == i + 1] = mask_label_symmetries[i + 1] + + def _transform_segm_rotation(self, rotation): + self.segm = F.interpolate(self.segm[None, None, :], (rotation.h, rotation.w)).numpy() + self.segm = torch.tensor(rotation.apply_segmentation(self.segm[0, 0]))[None, None, :] + self.segm = F.interpolate(self.segm, [DensePoseDataRelative.MASK_SIZE] * 2)[0, 0] diff --git a/motion-gan-pipeline/preprocessing/third/densepose/structures/list.py b/motion-gan-pipeline/preprocessing/third/densepose/structures/list.py new file mode 100644 index 0000000..3dc40b0 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/structures/list.py @@ -0,0 +1,70 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import torch + +from densepose.structures.data_relative import DensePoseDataRelative + + +class DensePoseList(object): + + _TORCH_DEVICE_CPU = torch.device("cpu") + + def __init__(self, densepose_datas, boxes_xyxy_abs, image_size_hw, device=_TORCH_DEVICE_CPU): + assert len(densepose_datas) == len( + boxes_xyxy_abs + ), "Attempt to initialize DensePoseList with {} DensePose datas " "and {} boxes".format( + len(densepose_datas), len(boxes_xyxy_abs) + ) + self.densepose_datas = [] + for densepose_data in densepose_datas: + assert isinstance(densepose_data, DensePoseDataRelative) or densepose_data is None, ( + "Attempt to initialize DensePoseList with DensePose datas " + "of type {}, expected DensePoseDataRelative".format(type(densepose_data)) + ) + densepose_data_ondevice = ( + densepose_data.to(device) if densepose_data is not None else None + ) + self.densepose_datas.append(densepose_data_ondevice) + self.boxes_xyxy_abs = boxes_xyxy_abs.to(device) + self.image_size_hw = image_size_hw + self.device = device + + def to(self, device): + if self.device == device: + return self + return DensePoseList(self.densepose_datas, self.boxes_xyxy_abs, self.image_size_hw, device) + + def __iter__(self): + return iter(self.densepose_datas) + + def __len__(self): + return len(self.densepose_datas) + + def __repr__(self): + s = self.__class__.__name__ + "(" + s += "num_instances={}, ".format(len(self.densepose_datas)) + s += "image_width={}, ".format(self.image_size_hw[1]) + s += "image_height={})".format(self.image_size_hw[0]) + return s + + def __getitem__(self, item): + if isinstance(item, int): + densepose_data_rel = self.densepose_datas[item] + return densepose_data_rel + elif isinstance(item, slice): + densepose_datas_rel = self.densepose_datas[item] + boxes_xyxy_abs = self.boxes_xyxy_abs[item] + return DensePoseList( + densepose_datas_rel, boxes_xyxy_abs, self.image_size_hw, self.device + ) + elif isinstance(item, torch.Tensor) and (item.dtype == torch.bool): + densepose_datas_rel = [self.densepose_datas[i] for i, x in enumerate(item) if x > 0] + boxes_xyxy_abs = self.boxes_xyxy_abs[item] + return DensePoseList( + densepose_datas_rel, boxes_xyxy_abs, self.image_size_hw, self.device + ) + else: + densepose_datas_rel = [self.densepose_datas[i] for i in item] + boxes_xyxy_abs = self.boxes_xyxy_abs[item] + return DensePoseList( + densepose_datas_rel, boxes_xyxy_abs, self.image_size_hw, self.device + ) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/structures/mesh.py b/motion-gan-pipeline/preprocessing/third/densepose/structures/mesh.py new file mode 100644 index 0000000..e3304e7 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/structures/mesh.py @@ -0,0 +1,170 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import pickle +from functools import lru_cache +from typing import Dict, Optional, Tuple +import torch + +from detectron2.utils.file_io import PathManager + +from densepose.data.meshes.catalog import MeshCatalog, MeshInfo + + +def _maybe_copy_to_device( + attribute: Optional[torch.Tensor], device: torch.device +) -> Optional[torch.Tensor]: + if attribute is None: + return None + return attribute.to(device) + + +class Mesh: + def __init__( + self, + vertices: Optional[torch.Tensor] = None, + faces: Optional[torch.Tensor] = None, + geodists: Optional[torch.Tensor] = None, + symmetry: Optional[Dict[str, torch.Tensor]] = None, + texcoords: Optional[torch.Tensor] = None, + mesh_info: Optional[MeshInfo] = None, + device: Optional[torch.device] = None, + ): + """ + Args: + vertices (tensor [N, 3] of float32): vertex coordinates in 3D + faces (tensor [M, 3] of long): triangular face represented as 3 + vertex indices + geodists (tensor [N, N] of float32): geodesic distances from + vertex `i` to vertex `j` (optional, default: None) + symmetry (dict: str -> tensor): various mesh symmetry data: + - "vertex_transforms": vertex mapping under horizontal flip, + tensor of size [N] of type long; vertex `i` is mapped to + vertex `tensor[i]` (optional, default: None) + texcoords (tensor [N, 2] of float32): texture coordinates, i.e. global + and normalized mesh UVs (optional, default: None) + mesh_info (MeshInfo type): necessary to load the attributes on-the-go, + can be used instead of passing all the variables one by one + device (torch.device): device of the Mesh. If not provided, will use + the device of the vertices + """ + self._vertices = vertices + self._faces = faces + self._geodists = geodists + self._symmetry = symmetry + self._texcoords = texcoords + self.mesh_info = mesh_info + self.device = device + + assert self._vertices is not None or self.mesh_info is not None + + all_fields = [self._vertices, self._faces, self._geodists, self._texcoords] + + if self.device is None: + for field in all_fields: + if field is not None: + self.device = field.device + break + if self.device is None and symmetry is not None: + for key in symmetry: + self.device = symmetry[key].device + break + self.device = torch.device("cpu") if self.device is None else self.device + + assert all([var.device == self.device for var in all_fields if var is not None]) + if symmetry: + assert all(symmetry[key].device == self.device for key in symmetry) + if texcoords and vertices: + assert len(vertices) == len(texcoords) + + def to(self, device: torch.device): + device_symmetry = self._symmetry + if device_symmetry: + device_symmetry = {key: value.to(device) for key, value in device_symmetry.items()} + return Mesh( + _maybe_copy_to_device(self._vertices, device), + _maybe_copy_to_device(self._faces, device), + _maybe_copy_to_device(self._geodists, device), + device_symmetry, + _maybe_copy_to_device(self._texcoords, device), + self.mesh_info, + device, + ) + + @property + def vertices(self): + if self._vertices is None and self.mesh_info is not None: + self._vertices = load_mesh_data(self.mesh_info.data, "vertices", self.device) + return self._vertices + + @property + def faces(self): + if self._faces is None and self.mesh_info is not None: + self._faces = load_mesh_data(self.mesh_info.data, "faces", self.device) + return self._faces + + @property + def geodists(self): + if self._geodists is None and self.mesh_info is not None: + self._geodists = load_mesh_auxiliary_data(self.mesh_info.geodists, self.device) + return self._geodists + + @property + def symmetry(self): + if self._symmetry is None and self.mesh_info is not None: + self._symmetry = load_mesh_symmetry(self.mesh_info.symmetry, self.device) + return self._symmetry + + @property + def texcoords(self): + if self._texcoords is None and self.mesh_info is not None: + self._texcoords = load_mesh_auxiliary_data(self.mesh_info.texcoords, self.device) + return self._texcoords + + def get_geodists(self): + if self.geodists is None: + self.geodists = self._compute_geodists() + return self.geodists + + def _compute_geodists(self): + # TODO: compute using Laplace-Beltrami + geodists = None + return geodists + + +def load_mesh_data( + mesh_fpath: str, field: str, device: Optional[torch.device] = None +) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]: + with PathManager.open(mesh_fpath, "rb") as hFile: + return torch.as_tensor(pickle.load(hFile)[field], dtype=torch.float).to( # pyre-ignore[6] + device + ) + return None + + +def load_mesh_auxiliary_data( + fpath: str, device: Optional[torch.device] = None +) -> Optional[torch.Tensor]: + fpath_local = PathManager.get_local_path(fpath) + with PathManager.open(fpath_local, "rb") as hFile: + return torch.as_tensor(pickle.load(hFile), dtype=torch.float).to(device) # pyre-ignore[6] + return None + + +@lru_cache() +def load_mesh_symmetry( + symmetry_fpath: str, device: Optional[torch.device] = None +) -> Optional[Dict[str, torch.Tensor]]: + with PathManager.open(symmetry_fpath, "rb") as hFile: + symmetry_loaded = pickle.load(hFile) # pyre-ignore[6] + symmetry = { + "vertex_transforms": torch.as_tensor( + symmetry_loaded["vertex_transforms"], dtype=torch.long + ).to(device), + } + return symmetry + return None + + +@lru_cache() +def create_mesh(mesh_name: str, device: Optional[torch.device] = None) -> Mesh: + return Mesh(mesh_info=MeshCatalog[mesh_name], device=device) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/structures/transform_data.py b/motion-gan-pipeline/preprocessing/third/densepose/structures/transform_data.py new file mode 100644 index 0000000..7cac1bb --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/structures/transform_data.py @@ -0,0 +1,71 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from typing import BinaryIO, Dict, Union +import torch + + +def normalized_coords_transform(x0, y0, w, h): + """ + Coordinates transform that maps top left corner to (-1, -1) and bottom + right corner to (1, 1). Used for torch.grid_sample to initialize the + grid + """ + + def f(p): + return (2 * (p[0] - x0) / w - 1, 2 * (p[1] - y0) / h - 1) + + return f + + +class DensePoseTransformData(object): + + # Horizontal symmetry label transforms used for horizontal flip + MASK_LABEL_SYMMETRIES = [0, 1, 3, 2, 5, 4, 7, 6, 9, 8, 11, 10, 13, 12, 14] + # fmt: off + POINT_LABEL_SYMMETRIES = [ 0, 1, 2, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15, 18, 17, 20, 19, 22, 21, 24, 23] # noqa + # fmt: on + + def __init__(self, uv_symmetries: Dict[str, torch.Tensor], device: torch.device): + self.mask_label_symmetries = DensePoseTransformData.MASK_LABEL_SYMMETRIES + self.point_label_symmetries = DensePoseTransformData.POINT_LABEL_SYMMETRIES + self.uv_symmetries = uv_symmetries + self.device = torch.device("cpu") + + def to(self, device: torch.device, copy: bool = False) -> "DensePoseTransformData": + """ + Convert transform data to the specified device + + Args: + device (torch.device): device to convert the data to + copy (bool): flag that specifies whether to copy or to reference the data + in case the device is the same + Return: + An instance of `DensePoseTransformData` with data stored on the specified device + """ + if self.device == device and not copy: + return self + uv_symmetry_map = {} + for key in self.uv_symmetries: + uv_symmetry_map[key] = self.uv_symmetries[key].to(device=device, copy=copy) + return DensePoseTransformData(uv_symmetry_map, device) + + @staticmethod + def load(io: Union[str, BinaryIO]): + """ + Args: + io: (str or binary file-like object): input file to load data from + Returns: + An instance of `DensePoseTransformData` with transforms loaded from the file + """ + import scipy.io + + uv_symmetry_map = scipy.io.loadmat(io) + uv_symmetry_map_torch = {} + for key in ["U_transforms", "V_transforms"]: + uv_symmetry_map_torch[key] = [] + map_src = uv_symmetry_map[key] + map_dst = uv_symmetry_map_torch[key] + for i in range(map_src.shape[1]): + map_dst.append(torch.from_numpy(map_src[0, i]).to(dtype=torch.float)) + uv_symmetry_map_torch[key] = torch.stack(map_dst, dim=0) + transform_data = DensePoseTransformData(uv_symmetry_map_torch, device=torch.device("cpu")) + return transform_data diff --git a/motion-gan-pipeline/preprocessing/third/densepose/utils/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/motion-gan-pipeline/preprocessing/third/densepose/utils/dbhelper.py b/motion-gan-pipeline/preprocessing/third/densepose/utils/dbhelper.py new file mode 100644 index 0000000..65b6157 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/utils/dbhelper.py @@ -0,0 +1,147 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from typing import Any, Dict, Optional, Tuple + + +class EntrySelector(object): + """ + Base class for entry selectors + """ + + @staticmethod + def from_string(spec: str) -> "EntrySelector": + if spec == "*": + return AllEntrySelector() + return FieldEntrySelector(spec) + + +class AllEntrySelector(EntrySelector): + """ + Selector that accepts all entries + """ + + SPECIFIER = "*" + + def __call__(self, entry): + return True + + +class FieldEntrySelector(EntrySelector): + """ + Selector that accepts only entries that match provided field + specifier(s). Only a limited set of specifiers is supported for now: + ::=[] + ::=[] + is a valid identifier + ::= "int" | "str" + ::= "=" + ::= "," + ::= ":" + ::= | + ::= + ::= "-" + is a string without spaces and special symbols + (e.g. , , , ) + """ + + _SPEC_DELIM = "," + _TYPE_DELIM = ":" + _RANGE_DELIM = "-" + _EQUAL = "=" + _ERROR_PREFIX = "Invalid field selector specifier" + + class _FieldEntryValuePredicate(object): + """ + Predicate that checks strict equality for the specified entry field + """ + + def __init__(self, name: str, typespec: Optional[str], value: str): + import builtins + + self.name = name + self.type = getattr(builtins, typespec) if typespec is not None else str + self.value = value + + def __call__(self, entry): + return entry[self.name] == self.type(self.value) + + class _FieldEntryRangePredicate(object): + """ + Predicate that checks whether an entry field falls into the specified range + """ + + def __init__(self, name: str, typespec: Optional[str], vmin: str, vmax: str): + import builtins + + self.name = name + self.type = getattr(builtins, typespec) if typespec is not None else str + self.vmin = vmin + self.vmax = vmax + + def __call__(self, entry): + return (entry[self.name] >= self.type(self.vmin)) and ( + entry[self.name] <= self.type(self.vmax) + ) + + def __init__(self, spec: str): + self._predicates = self._parse_specifier_into_predicates(spec) + + def __call__(self, entry: Dict[str, Any]): + for predicate in self._predicates: + if not predicate(entry): + return False + return True + + def _parse_specifier_into_predicates(self, spec: str): + predicates = [] + specs = spec.split(self._SPEC_DELIM) + for subspec in specs: + eq_idx = subspec.find(self._EQUAL) + if eq_idx > 0: + field_name_with_type = subspec[:eq_idx] + field_name, field_type = self._parse_field_name_type(field_name_with_type) + field_value_or_range = subspec[eq_idx + 1 :] + if self._is_range_spec(field_value_or_range): + vmin, vmax = self._get_range_spec(field_value_or_range) + predicate = FieldEntrySelector._FieldEntryRangePredicate( + field_name, field_type, vmin, vmax + ) + else: + predicate = FieldEntrySelector._FieldEntryValuePredicate( + field_name, field_type, field_value_or_range + ) + predicates.append(predicate) + elif eq_idx == 0: + self._parse_error(f'"{subspec}", field name is empty!') + else: + self._parse_error(f'"{subspec}", should have format ' "=!") + return predicates + + def _parse_field_name_type(self, field_name_with_type: str) -> Tuple[str, Optional[str]]: + type_delim_idx = field_name_with_type.find(self._TYPE_DELIM) + if type_delim_idx > 0: + field_name = field_name_with_type[:type_delim_idx] + field_type = field_name_with_type[type_delim_idx + 1 :] + elif type_delim_idx == 0: + self._parse_error(f'"{field_name_with_type}", field name is empty!') + else: + field_name = field_name_with_type + field_type = None + # pyre-fixme[61]: `field_name` may not be initialized here. + # pyre-fixme[61]: `field_type` may not be initialized here. + return field_name, field_type + + def _is_range_spec(self, field_value_or_range): + delim_idx = field_value_or_range.find(self._RANGE_DELIM) + return delim_idx > 0 + + def _get_range_spec(self, field_value_or_range): + if self._is_range_spec(field_value_or_range): + delim_idx = field_value_or_range.find(self._RANGE_DELIM) + vmin = field_value_or_range[:delim_idx] + vmax = field_value_or_range[delim_idx + 1 :] + return vmin, vmax + else: + self._parse_error('"field_value_or_range", range of values expected!') + + def _parse_error(self, msg): + raise ValueError(f"{self._ERROR_PREFIX}: {msg}") diff --git a/motion-gan-pipeline/preprocessing/third/densepose/utils/logger.py b/motion-gan-pipeline/preprocessing/third/densepose/utils/logger.py new file mode 100644 index 0000000..70cd3cb --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/utils/logger.py @@ -0,0 +1,13 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging + + +def verbosity_to_level(verbosity) -> int: + if verbosity is not None: + if verbosity == 0: + return logging.WARNING + elif verbosity == 1: + return logging.INFO + elif verbosity >= 2: + return logging.DEBUG + return logging.WARNING diff --git a/motion-gan-pipeline/preprocessing/third/densepose/utils/transform.py b/motion-gan-pipeline/preprocessing/third/densepose/utils/transform.py new file mode 100644 index 0000000..8dc4ae7 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/utils/transform.py @@ -0,0 +1,15 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from detectron2.data import MetadataCatalog +from detectron2.utils.file_io import PathManager + +from densepose import DensePoseTransformData + + +def load_for_dataset(dataset_name): + path = MetadataCatalog.get(dataset_name).densepose_transform_src + densepose_transform_data_fpath = PathManager.get_local_path(path) + return DensePoseTransformData.load(densepose_transform_data_fpath) + + +def load_from_cfg(cfg): + return load_for_dataset(cfg.DATASETS.TEST[0]) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/vis/__init__.py b/motion-gan-pipeline/preprocessing/third/densepose/vis/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/motion-gan-pipeline/preprocessing/third/densepose/vis/base.py b/motion-gan-pipeline/preprocessing/third/densepose/vis/base.py new file mode 100644 index 0000000..7b35397 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/vis/base.py @@ -0,0 +1,191 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import numpy as np +import cv2 +import torch + +Image = np.ndarray +Boxes = torch.Tensor + + +class MatrixVisualizer(object): + """ + Base visualizer for matrix data + """ + + def __init__( + self, + inplace=True, + cmap=cv2.COLORMAP_PARULA, + val_scale=1.0, + alpha=0.7, + interp_method_matrix=cv2.INTER_LINEAR, + interp_method_mask=cv2.INTER_NEAREST, + ): + self.inplace = inplace + self.cmap = cmap + self.val_scale = val_scale + self.alpha = alpha + self.interp_method_matrix = interp_method_matrix + self.interp_method_mask = interp_method_mask + + def visualize(self, image_bgr, mask, matrix, bbox_xywh): + self._check_image(image_bgr) + self._check_mask_matrix(mask, matrix) + if self.inplace: + image_target_bgr = image_bgr + else: + image_target_bgr = image_bgr * 0 + x, y, w, h = [int(v) for v in bbox_xywh] + if w <= 0 or h <= 0: + return image_bgr + mask, matrix = self._resize(mask, matrix, w, h) + mask_bg = np.tile((mask == 0)[:, :, np.newaxis], [1, 1, 3]) + matrix_scaled = matrix.astype(np.float32) * self.val_scale + _EPSILON = 1e-6 + if np.any(matrix_scaled > 255 + _EPSILON): + logger = logging.getLogger(__name__) + logger.warning( + f"Matrix has values > {255 + _EPSILON} after " f"scaling, clipping to [0..255]" + ) + matrix_scaled_8u = matrix_scaled.clip(0, 255).astype(np.uint8) + matrix_vis = cv2.applyColorMap(matrix_scaled_8u, self.cmap) + matrix_vis[mask_bg] = image_target_bgr[y : y + h, x : x + w, :][mask_bg] + image_target_bgr[y : y + h, x : x + w, :] = ( + image_target_bgr[y : y + h, x : x + w, :] * (1.0 - self.alpha) + matrix_vis * self.alpha + ) + return image_target_bgr.astype(np.uint8) + + def _resize(self, mask, matrix, w, h): + if (w != mask.shape[1]) or (h != mask.shape[0]): + mask = cv2.resize(mask, (w, h), self.interp_method_mask) + if (w != matrix.shape[1]) or (h != matrix.shape[0]): + matrix = cv2.resize(matrix, (w, h), self.interp_method_matrix) + return mask, matrix + + def _check_image(self, image_rgb): + assert len(image_rgb.shape) == 3 + assert image_rgb.shape[2] == 3 + assert image_rgb.dtype == np.uint8 + + def _check_mask_matrix(self, mask, matrix): + assert len(matrix.shape) == 2 + assert len(mask.shape) == 2 + assert mask.dtype == np.uint8 + + +class RectangleVisualizer(object): + + _COLOR_GREEN = (18, 127, 15) + + def __init__(self, color=_COLOR_GREEN, thickness=1): + self.color = color + self.thickness = thickness + + def visualize(self, image_bgr, bbox_xywh, color=None, thickness=None): + x, y, w, h = bbox_xywh + color = color or self.color + thickness = thickness or self.thickness + cv2.rectangle(image_bgr, (int(x), int(y)), (int(x + w), int(y + h)), color, thickness) + return image_bgr + + +class PointsVisualizer(object): + + _COLOR_GREEN = (18, 127, 15) + + def __init__(self, color_bgr=_COLOR_GREEN, r=5): + self.color_bgr = color_bgr + self.r = r + + def visualize(self, image_bgr, pts_xy, colors_bgr=None, rs=None): + for j, pt_xy in enumerate(pts_xy): + x, y = pt_xy + color_bgr = colors_bgr[j] if colors_bgr is not None else self.color_bgr + r = rs[j] if rs is not None else self.r + cv2.circle(image_bgr, (x, y), r, color_bgr, -1) + return image_bgr + + +class TextVisualizer(object): + + _COLOR_GRAY = (218, 227, 218) + _COLOR_WHITE = (255, 255, 255) + + def __init__( + self, + font_face=cv2.FONT_HERSHEY_SIMPLEX, + font_color_bgr=_COLOR_GRAY, + font_scale=0.35, + font_line_type=cv2.LINE_AA, + font_line_thickness=1, + fill_color_bgr=_COLOR_WHITE, + fill_color_transparency=1.0, + frame_color_bgr=_COLOR_WHITE, + frame_color_transparency=1.0, + frame_thickness=1, + ): + self.font_face = font_face + self.font_color_bgr = font_color_bgr + self.font_scale = font_scale + self.font_line_type = font_line_type + self.font_line_thickness = font_line_thickness + self.fill_color_bgr = fill_color_bgr + self.fill_color_transparency = fill_color_transparency + self.frame_color_bgr = frame_color_bgr + self.frame_color_transparency = frame_color_transparency + self.frame_thickness = frame_thickness + + def visualize(self, image_bgr, txt, topleft_xy): + txt_w, txt_h = self.get_text_size_wh(txt) + topleft_xy = tuple(map(int, topleft_xy)) + x, y = topleft_xy + if self.frame_color_transparency < 1.0: + t = self.frame_thickness + image_bgr[y - t : y + txt_h + t, x - t : x + txt_w + t, :] = ( + image_bgr[y - t : y + txt_h + t, x - t : x + txt_w + t, :] + * self.frame_color_transparency + + np.array(self.frame_color_bgr) * (1.0 - self.frame_color_transparency) + ).astype(np.float) + if self.fill_color_transparency < 1.0: + image_bgr[y : y + txt_h, x : x + txt_w, :] = ( + image_bgr[y : y + txt_h, x : x + txt_w, :] * self.fill_color_transparency + + np.array(self.fill_color_bgr) * (1.0 - self.fill_color_transparency) + ).astype(np.float) + cv2.putText( + image_bgr, + txt, + topleft_xy, + self.font_face, + self.font_scale, + self.font_color_bgr, + self.font_line_thickness, + self.font_line_type, + ) + return image_bgr + + def get_text_size_wh(self, txt): + ((txt_w, txt_h), _) = cv2.getTextSize( + txt, self.font_face, self.font_scale, self.font_line_thickness + ) + return txt_w, txt_h + + +class CompoundVisualizer(object): + def __init__(self, visualizers): + self.visualizers = visualizers + + def visualize(self, image_bgr, data): + assert len(data) == len( + self.visualizers + ), "The number of datas {} should match the number of visualizers" " {}".format( + len(data), len(self.visualizers) + ) + image = image_bgr + for i, visualizer in enumerate(self.visualizers): + image = visualizer.visualize(image, data[i]) + return image + + def __str__(self): + visualizer_str = ", ".join([str(v) for v in self.visualizers]) + return "Compound Visualizer [{}]".format(visualizer_str) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/vis/bounding_box.py b/motion-gan-pipeline/preprocessing/third/densepose/vis/bounding_box.py new file mode 100644 index 0000000..4f83957 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/vis/bounding_box.py @@ -0,0 +1,37 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .base import RectangleVisualizer, TextVisualizer + + +class BoundingBoxVisualizer(object): + def __init__(self): + self.rectangle_visualizer = RectangleVisualizer() + + def visualize(self, image_bgr, boxes_xywh): + for bbox_xywh in boxes_xywh: + image_bgr = self.rectangle_visualizer.visualize(image_bgr, bbox_xywh) + return image_bgr + + +class ScoredBoundingBoxVisualizer(object): + def __init__(self, bbox_visualizer_params=None, score_visualizer_params=None, **kwargs): + if bbox_visualizer_params is None: + bbox_visualizer_params = {} + if score_visualizer_params is None: + score_visualizer_params = {} + self.visualizer_bbox = RectangleVisualizer(**bbox_visualizer_params) + self.visualizer_score = TextVisualizer(**score_visualizer_params) + + def visualize(self, image_bgr, scored_bboxes): + boxes_xywh, box_scores = scored_bboxes + assert len(boxes_xywh) == len( + box_scores + ), "Number of bounding boxes {} should be equal to the number of scores {}".format( + len(boxes_xywh), len(box_scores) + ) + for i, box_xywh in enumerate(boxes_xywh): + score_i = box_scores[i] + image_bgr = self.visualizer_bbox.visualize(image_bgr, box_xywh) + score_txt = "{0:6.4f}".format(score_i) + topleft_xy = box_xywh[0], box_xywh[1] + image_bgr = self.visualizer_score.visualize(image_bgr, score_txt, topleft_xy) + return image_bgr diff --git a/motion-gan-pipeline/preprocessing/third/densepose/vis/densepose_data_points.py b/motion-gan-pipeline/preprocessing/third/densepose/vis/densepose_data_points.py new file mode 100644 index 0000000..b6839a9 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/vis/densepose_data_points.py @@ -0,0 +1,106 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +from typing import Iterable, Optional, Tuple +import cv2 + +from densepose.structures import DensePoseDataRelative + +from .base import Boxes, Image, MatrixVisualizer, PointsVisualizer + + +class DensePoseDataCoarseSegmentationVisualizer(object): + """ + Visualizer for ground truth segmentation + """ + + def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, **kwargs): + self.mask_visualizer = MatrixVisualizer( + inplace=inplace, + cmap=cmap, + val_scale=255.0 / DensePoseDataRelative.N_BODY_PARTS, + alpha=alpha, + ) + + def visualize( + self, + image_bgr: Image, + bbox_densepose_datas: Optional[Tuple[Iterable[Boxes], Iterable[DensePoseDataRelative]]], + ) -> Image: + if bbox_densepose_datas is None: + return image_bgr + for bbox_xywh, densepose_data in zip(*bbox_densepose_datas): + matrix = densepose_data.segm.numpy() + mask = np.zeros(matrix.shape, dtype=np.uint8) + mask[matrix > 0] = 1 + image_bgr = self.mask_visualizer.visualize(image_bgr, mask, matrix, bbox_xywh.numpy()) + return image_bgr + + +class DensePoseDataPointsVisualizer(object): + def __init__(self, densepose_data_to_value_fn=None, cmap=cv2.COLORMAP_PARULA, **kwargs): + self.points_visualizer = PointsVisualizer() + self.densepose_data_to_value_fn = densepose_data_to_value_fn + self.cmap = cmap + + def visualize( + self, + image_bgr: Image, + bbox_densepose_datas: Optional[Tuple[Iterable[Boxes], Iterable[DensePoseDataRelative]]], + ) -> Image: + if bbox_densepose_datas is None: + return image_bgr + for bbox_xywh, densepose_data in zip(*bbox_densepose_datas): + x0, y0, w, h = bbox_xywh.numpy() + x = densepose_data.x.numpy() * w / 255.0 + x0 + y = densepose_data.y.numpy() * h / 255.0 + y0 + pts_xy = zip(x, y) + if self.densepose_data_to_value_fn is None: + image_bgr = self.points_visualizer.visualize(image_bgr, pts_xy) + else: + v = self.densepose_data_to_value_fn(densepose_data) + img_colors_bgr = cv2.applyColorMap(v, self.cmap) + colors_bgr = [ + [int(v) for v in img_color_bgr.ravel()] for img_color_bgr in img_colors_bgr + ] + image_bgr = self.points_visualizer.visualize(image_bgr, pts_xy, colors_bgr) + return image_bgr + + +def _densepose_data_u_for_cmap(densepose_data): + u = np.clip(densepose_data.u.numpy(), 0, 1) * 255.0 + return u.astype(np.uint8) + + +def _densepose_data_v_for_cmap(densepose_data): + v = np.clip(densepose_data.v.numpy(), 0, 1) * 255.0 + return v.astype(np.uint8) + + +def _densepose_data_i_for_cmap(densepose_data): + i = ( + np.clip(densepose_data.i.numpy(), 0.0, DensePoseDataRelative.N_PART_LABELS) + * 255.0 + / DensePoseDataRelative.N_PART_LABELS + ) + return i.astype(np.uint8) + + +class DensePoseDataPointsUVisualizer(DensePoseDataPointsVisualizer): + def __init__(self, **kwargs): + super(DensePoseDataPointsUVisualizer, self).__init__( + densepose_data_to_value_fn=_densepose_data_u_for_cmap, **kwargs + ) + + +class DensePoseDataPointsVVisualizer(DensePoseDataPointsVisualizer): + def __init__(self, **kwargs): + super(DensePoseDataPointsVVisualizer, self).__init__( + densepose_data_to_value_fn=_densepose_data_v_for_cmap, **kwargs + ) + + +class DensePoseDataPointsIVisualizer(DensePoseDataPointsVisualizer): + def __init__(self, **kwargs): + super(DensePoseDataPointsIVisualizer, self).__init__( + densepose_data_to_value_fn=_densepose_data_i_for_cmap, **kwargs + ) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/vis/densepose_outputs_iuv.py b/motion-gan-pipeline/preprocessing/third/densepose/vis/densepose_outputs_iuv.py new file mode 100644 index 0000000..a32a418 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/vis/densepose_outputs_iuv.py @@ -0,0 +1,101 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +from typing import Optional, Tuple +import cv2 + +from densepose.structures import DensePoseDataRelative + +from ..structures import DensePoseChartPredictorOutput +from .base import Boxes, Image, MatrixVisualizer + + +class DensePoseOutputsVisualizer(object): + def __init__( + self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, to_visualize=None, **kwargs + ): + assert to_visualize in "IUV", "can only visualize IUV" + self.to_visualize = to_visualize + + if self.to_visualize == "I": + val_scale = 255.0 / DensePoseDataRelative.N_PART_LABELS + else: + val_scale = 1.0 + self.mask_visualizer = MatrixVisualizer( + inplace=inplace, cmap=cmap, val_scale=val_scale, alpha=alpha + ) + + def visualize( + self, + image_bgr: Image, + dp_output_with_bboxes: Tuple[Optional[DensePoseChartPredictorOutput], Optional[Boxes]], + ) -> Image: + densepose_output, bboxes_xywh = dp_output_with_bboxes + if densepose_output is None or bboxes_xywh is None: + return image_bgr + + assert isinstance( + densepose_output, DensePoseChartPredictorOutput + ), "DensePoseChartPredictorOutput expected, {} encountered".format(type(densepose_output)) + + S = densepose_output.coarse_segm + I = densepose_output.fine_segm # noqa + U = densepose_output.u + V = densepose_output.v + N = S.size(0) + assert N == I.size( + 0 + ), "densepose outputs S {} and I {}" " should have equal first dim size".format( + S.size(), I.size() + ) + assert N == U.size( + 0 + ), "densepose outputs S {} and U {}" " should have equal first dim size".format( + S.size(), U.size() + ) + assert N == V.size( + 0 + ), "densepose outputs S {} and V {}" " should have equal first dim size".format( + S.size(), V.size() + ) + assert N == len( + bboxes_xywh + ), "number of bounding boxes {}" " should be equal to first dim size of outputs {}".format( + len(bboxes_xywh), N + ) + for n in range(N): + Sn = S[n].argmax(dim=0) + In = I[n].argmax(dim=0) * (Sn > 0).long() + segmentation = In.cpu().numpy().astype(np.uint8) + mask = np.zeros(segmentation.shape, dtype=np.uint8) + mask[segmentation > 0] = 1 + bbox_xywh = bboxes_xywh[n] + + if self.to_visualize == "I": + vis = segmentation + elif self.to_visualize in "UV": + U_or_Vn = {"U": U, "V": V}[self.to_visualize][n].cpu().numpy().astype(np.float32) + vis = np.zeros(segmentation.shape, dtype=np.float32) + for partId in range(U_or_Vn.shape[0]): + vis[segmentation == partId] = ( + U_or_Vn[partId][segmentation == partId].clip(0, 1) * 255 + ) + + # pyre-fixme[61]: `vis` may not be initialized here. + image_bgr = self.mask_visualizer.visualize(image_bgr, mask, vis, bbox_xywh) + + return image_bgr + + +class DensePoseOutputsUVisualizer(DensePoseOutputsVisualizer): + def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, **kwargs): + super().__init__(inplace=inplace, cmap=cmap, alpha=alpha, to_visualize="U", **kwargs) + + +class DensePoseOutputsVVisualizer(DensePoseOutputsVisualizer): + def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, **kwargs): + super().__init__(inplace=inplace, cmap=cmap, alpha=alpha, to_visualize="V", **kwargs) + + +class DensePoseOutputsFineSegmentationVisualizer(DensePoseOutputsVisualizer): + def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, **kwargs): + super().__init__(inplace=inplace, cmap=cmap, alpha=alpha, to_visualize="I", **kwargs) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/vis/densepose_outputs_vertex.py b/motion-gan-pipeline/preprocessing/third/densepose/vis/densepose_outputs_vertex.py new file mode 100644 index 0000000..a9c0c56 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/vis/densepose_outputs_vertex.py @@ -0,0 +1,229 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import json +import numpy as np +from functools import lru_cache +from typing import Dict, List, Optional, Tuple +import cv2 +import torch + +from detectron2.utils.file_io import PathManager + +from densepose.modeling import build_densepose_embedder +from densepose.modeling.cse.utils import get_closest_vertices_mask_from_ES + +from ..data.utils import get_class_to_mesh_name_mapping +from ..structures import DensePoseEmbeddingPredictorOutput +from ..structures.mesh import create_mesh +from .base import Boxes, Image, MatrixVisualizer +from .densepose_results_textures import get_texture_atlas + + +@lru_cache() +def get_xyz_vertex_embedding(mesh_name: str, device: torch.device): + if mesh_name == "smpl_27554": + embed_path = PathManager.get_local_path( + "https://dl.fbaipublicfiles.com/densepose/data/cse/mds_d=256.npy" + ) + embed_map, _ = np.load(embed_path, allow_pickle=True) + embed_map = torch.tensor(embed_map).float()[:, 0] + embed_map -= embed_map.min() + embed_map /= embed_map.max() + else: + mesh = create_mesh(mesh_name, device) + embed_map = mesh.vertices.sum(dim=1) + embed_map -= embed_map.min() + embed_map /= embed_map.max() + embed_map = embed_map ** 2 + return embed_map + + +class DensePoseOutputsVertexVisualizer(object): + def __init__( + self, + cfg, + inplace=True, + cmap=cv2.COLORMAP_JET, + alpha=0.7, + device="cuda", + default_class=0, + **kwargs, + ): + self.mask_visualizer = MatrixVisualizer( + inplace=inplace, cmap=cmap, val_scale=1.0, alpha=alpha + ) + self.class_to_mesh_name = get_class_to_mesh_name_mapping(cfg) + self.embedder = build_densepose_embedder(cfg) + self.device = torch.device(device) + self.default_class = default_class + + self.mesh_vertex_embeddings = { + mesh_name: self.embedder(mesh_name).to(self.device) + for mesh_name in self.class_to_mesh_name.values() + if self.embedder.has_embeddings(mesh_name) + } + + def visualize( + self, + image_bgr: Image, + outputs_boxes_xywh_classes: Tuple[ + Optional[DensePoseEmbeddingPredictorOutput], Optional[Boxes], Optional[List[int]] + ], + ) -> Image: + if outputs_boxes_xywh_classes[0] is None: + return image_bgr + + S, E, N, bboxes_xywh, pred_classes = self.extract_and_check_outputs_and_boxes( + outputs_boxes_xywh_classes + ) + + for n in range(N): + x, y, w, h = bboxes_xywh[n].int().tolist() + mesh_name = self.class_to_mesh_name[pred_classes[n]] + closest_vertices, mask = get_closest_vertices_mask_from_ES( + E[[n]], + S[[n]], + h, + w, + self.mesh_vertex_embeddings[mesh_name], + self.device, + ) + embed_map = get_xyz_vertex_embedding(mesh_name, self.device) + vis = (embed_map[closest_vertices].clip(0, 1) * 255.0).cpu().numpy() + mask_numpy = mask.cpu().numpy().astype(dtype=np.uint8) + image_bgr = self.mask_visualizer.visualize(image_bgr, mask_numpy, vis, [x, y, w, h]) + + return image_bgr + + def extract_and_check_outputs_and_boxes(self, outputs_boxes_xywh_classes): + + densepose_output, bboxes_xywh, pred_classes = outputs_boxes_xywh_classes + + if pred_classes is None: + pred_classes = [self.default_class] * len(bboxes_xywh) + + assert isinstance( + densepose_output, DensePoseEmbeddingPredictorOutput + ), "DensePoseEmbeddingPredictorOutput expected, {} encountered".format( + type(densepose_output) + ) + + S = densepose_output.coarse_segm + E = densepose_output.embedding + N = S.size(0) + assert N == E.size( + 0 + ), "CSE coarse_segm {} and embeddings {}" " should have equal first dim size".format( + S.size(), E.size() + ) + assert N == len( + bboxes_xywh + ), "number of bounding boxes {}" " should be equal to first dim size of outputs {}".format( + len(bboxes_xywh), N + ) + assert N == len(pred_classes), ( + "number of predicted classes {}" + " should be equal to first dim size of outputs {}".format(len(bboxes_xywh), N) + ) + + return S, E, N, bboxes_xywh, pred_classes + + +def get_texture_atlases(json_str: Optional[str]) -> Optional[Dict[str, Optional[np.ndarray]]]: + """ + json_str is a JSON string representing a mesh_name -> texture_atlas_path dictionary + """ + if json_str is None: + return None + + paths = json.loads(json_str) + return {mesh_name: get_texture_atlas(path) for mesh_name, path in paths.items()} + + +class DensePoseOutputsTextureVisualizer(DensePoseOutputsVertexVisualizer): + def __init__( + self, + cfg, + texture_atlases_dict, + device="cuda", + default_class=0, + **kwargs, + ): + self.embedder = build_densepose_embedder(cfg) + + self.texture_image_dict = {} + self.alpha_dict = {} + + for mesh_name in texture_atlases_dict.keys(): + if texture_atlases_dict[mesh_name].shape[-1] == 4: # Image with alpha channel + self.alpha_dict[mesh_name] = texture_atlases_dict[mesh_name][:, :, -1] / 255.0 + self.texture_image_dict[mesh_name] = texture_atlases_dict[mesh_name][:, :, :3] + else: + self.alpha_dict[mesh_name] = texture_atlases_dict[mesh_name].sum(axis=-1) > 0 + self.texture_image_dict[mesh_name] = texture_atlases_dict[mesh_name] + + self.device = torch.device(device) + self.class_to_mesh_name = get_class_to_mesh_name_mapping(cfg) + self.default_class = default_class + + self.mesh_vertex_embeddings = { + mesh_name: self.embedder(mesh_name).to(self.device) + for mesh_name in self.class_to_mesh_name.values() + } + + def visualize( + self, + image_bgr: Image, + outputs_boxes_xywh_classes: Tuple[ + Optional[DensePoseEmbeddingPredictorOutput], Optional[Boxes], Optional[List[int]] + ], + ) -> Image: + image_target_bgr = image_bgr.copy() + if outputs_boxes_xywh_classes[0] is None: + return image_target_bgr + + S, E, N, bboxes_xywh, pred_classes = self.extract_and_check_outputs_and_boxes( + outputs_boxes_xywh_classes + ) + + meshes = { + p: create_mesh(self.class_to_mesh_name[p], self.device) for p in np.unique(pred_classes) + } + + for n in range(N): + x, y, w, h = bboxes_xywh[n].int().cpu().numpy() + mesh_name = self.class_to_mesh_name[pred_classes[n]] + closest_vertices, mask = get_closest_vertices_mask_from_ES( + E[[n]], + S[[n]], + h, + w, + self.mesh_vertex_embeddings[mesh_name], + self.device, + ) + uv_array = meshes[pred_classes[n]].texcoords[closest_vertices].permute((2, 0, 1)) + uv_array = uv_array.cpu().numpy().clip(0, 1) + textured_image = self.generate_image_with_texture( + image_target_bgr[y : y + h, x : x + w], + uv_array, + mask.cpu().numpy(), + self.class_to_mesh_name[pred_classes[n]], + ) + if textured_image is None: + continue + image_target_bgr[y : y + h, x : x + w] = textured_image + + return image_target_bgr + + def generate_image_with_texture(self, bbox_image_bgr, uv_array, mask, mesh_name): + alpha = self.alpha_dict.get(mesh_name) + texture_image = self.texture_image_dict.get(mesh_name) + if alpha is None or texture_image is None: + return None + U, V = uv_array + x_index = (U * texture_image.shape[1]).astype(int) + y_index = (V * texture_image.shape[0]).astype(int) + local_texture = texture_image[y_index, x_index][mask] + local_alpha = np.expand_dims(alpha[y_index, x_index][mask], -1) + output_image = bbox_image_bgr.copy() + output_image[mask] = output_image[mask] * (1 - local_alpha) + local_texture * local_alpha + return output_image.astype(np.uint8) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/vis/densepose_results.py b/motion-gan-pipeline/preprocessing/third/densepose/vis/densepose_results.py new file mode 100644 index 0000000..f8382f8 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/vis/densepose_results.py @@ -0,0 +1,355 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import numpy as np +from typing import List, Optional, Tuple +import cv2 +import torch + +from densepose.structures import DensePoseDataRelative + +from ..structures import DensePoseChartResult +from .base import Boxes, Image, MatrixVisualizer + + +class DensePoseResultsVisualizer(object): + def visualize( + self, + image_bgr: Image, + results_and_boxes_xywh: Tuple[Optional[List[DensePoseChartResult]], Optional[Boxes]], + ) -> Image: + densepose_result, boxes_xywh = results_and_boxes_xywh + if densepose_result is None or boxes_xywh is None: + return image_bgr + + boxes_xywh = boxes_xywh.cpu().numpy() + context = self.create_visualization_context(image_bgr) + for i, result in enumerate(densepose_result): + iuv_array = torch.cat( + (result.labels[None].type(torch.float32), result.uv * 255.0) + ).type(torch.uint8) + self.visualize_iuv_arr(context, iuv_array.cpu().numpy(), boxes_xywh[i]) + image_bgr = self.context_to_image_bgr(context) + return image_bgr + + def create_visualization_context(self, image_bgr: Image): + return image_bgr + + def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh) -> None: + pass + + def context_to_image_bgr(self, context): + return context + + def get_image_bgr_from_context(self, context): + return context + + +class DensePoseMaskedColormapResultsVisualizer(DensePoseResultsVisualizer): + def __init__( + self, + data_extractor, + segm_extractor, + inplace=True, + cmap=cv2.COLORMAP_PARULA, + alpha=0.7, + val_scale=1.0, + **kwargs, + ): + self.mask_visualizer = MatrixVisualizer( + inplace=inplace, cmap=cmap, val_scale=val_scale, alpha=alpha + ) + self.data_extractor = data_extractor + self.segm_extractor = segm_extractor + + def context_to_image_bgr(self, context): + return context + + def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh) -> None: + image_bgr = self.get_image_bgr_from_context(context) + matrix = self.data_extractor(iuv_arr) + segm = self.segm_extractor(iuv_arr) + mask = np.zeros(matrix.shape, dtype=np.uint8) + mask[segm > 0] = 1 + image_bgr = self.mask_visualizer.visualize(image_bgr, mask, matrix, bbox_xywh) + + +def _extract_i_from_iuvarr(iuv_arr): + return iuv_arr[0, :, :] + + +def _extract_u_from_iuvarr(iuv_arr): + return iuv_arr[1, :, :] + + +def _extract_v_from_iuvarr(iuv_arr): + return iuv_arr[2, :, :] + + +class DensePoseResultsMplContourVisualizer(DensePoseResultsVisualizer): + def __init__(self, levels=10, **kwargs): + self.levels = levels + self.plot_args = kwargs + + def create_visualization_context(self, image_bgr: Image): + import matplotlib.pyplot as plt + from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas + + context = {} + context["image_bgr"] = image_bgr + dpi = 100 + height_inches = float(image_bgr.shape[0]) / dpi + width_inches = float(image_bgr.shape[1]) / dpi + fig = plt.figure(figsize=(width_inches, height_inches), dpi=dpi) + plt.axes([0, 0, 1, 1]) + plt.axis("off") + context["fig"] = fig + canvas = FigureCanvas(fig) + context["canvas"] = canvas + extent = (0, image_bgr.shape[1], image_bgr.shape[0], 0) + plt.imshow(image_bgr[:, :, ::-1], extent=extent) + return context + + def context_to_image_bgr(self, context): + fig = context["fig"] + w, h = map(int, fig.get_size_inches() * fig.get_dpi()) + canvas = context["canvas"] + canvas.draw() + image_1d = np.fromstring(canvas.tostring_rgb(), dtype="uint8") + image_rgb = image_1d.reshape(h, w, 3) + image_bgr = image_rgb[:, :, ::-1].copy() + return image_bgr + + def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh: Boxes) -> None: + import matplotlib.pyplot as plt + + u = _extract_u_from_iuvarr(iuv_arr).astype(float) / 255.0 + v = _extract_v_from_iuvarr(iuv_arr).astype(float) / 255.0 + extent = ( + bbox_xywh[0], + bbox_xywh[0] + bbox_xywh[2], + bbox_xywh[1], + bbox_xywh[1] + bbox_xywh[3], + ) + plt.contour(u, self.levels, extent=extent, **self.plot_args) + plt.contour(v, self.levels, extent=extent, **self.plot_args) + + +class DensePoseResultsCustomContourVisualizer(DensePoseResultsVisualizer): + """ + Contour visualization using marching squares + """ + + def __init__(self, levels=10, **kwargs): + # TODO: colormap is hardcoded + cmap = cv2.COLORMAP_PARULA + if isinstance(levels, int): + self.levels = np.linspace(0, 1, levels) + else: + self.levels = levels + if "linewidths" in kwargs: + self.linewidths = kwargs["linewidths"] + else: + self.linewidths = [1] * len(self.levels) + self.plot_args = kwargs + img_colors_bgr = cv2.applyColorMap((self.levels * 255).astype(np.uint8), cmap) + self.level_colors_bgr = [ + [int(v) for v in img_color_bgr.ravel()] for img_color_bgr in img_colors_bgr + ] + + def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh: Boxes) -> None: + image_bgr = self.get_image_bgr_from_context(context) + segm = _extract_i_from_iuvarr(iuv_arr) + u = _extract_u_from_iuvarr(iuv_arr).astype(float) / 255.0 + v = _extract_v_from_iuvarr(iuv_arr).astype(float) / 255.0 + self._contours(image_bgr, u, segm, bbox_xywh) + self._contours(image_bgr, v, segm, bbox_xywh) + + def _contours(self, image_bgr, arr, segm, bbox_xywh): + for part_idx in range(1, DensePoseDataRelative.N_PART_LABELS + 1): + mask = segm == part_idx + if not np.any(mask): + continue + arr_min = np.amin(arr[mask]) + arr_max = np.amax(arr[mask]) + I, J = np.nonzero(mask) + i0 = np.amin(I) + i1 = np.amax(I) + 1 + j0 = np.amin(J) + j1 = np.amax(J) + 1 + if (j1 == j0 + 1) or (i1 == i0 + 1): + continue + Nw = arr.shape[1] - 1 + Nh = arr.shape[0] - 1 + for level_idx, level in enumerate(self.levels): + if (level < arr_min) or (level > arr_max): + continue + vp = arr[i0:i1, j0:j1] >= level + bin_codes = vp[:-1, :-1] + vp[1:, :-1] * 2 + vp[1:, 1:] * 4 + vp[:-1, 1:] * 8 + mp = mask[i0:i1, j0:j1] + bin_mask_codes = mp[:-1, :-1] + mp[1:, :-1] * 2 + mp[1:, 1:] * 4 + mp[:-1, 1:] * 8 + it = np.nditer(bin_codes, flags=["multi_index"]) + color_bgr = self.level_colors_bgr[level_idx] + linewidth = self.linewidths[level_idx] + while not it.finished: + if (it[0] != 0) and (it[0] != 15): + i, j = it.multi_index + if bin_mask_codes[i, j] != 0: + self._draw_line( + image_bgr, + arr, + mask, + level, + color_bgr, + linewidth, + it[0], + it.multi_index, + bbox_xywh, + Nw, + Nh, + (i0, j0), + ) + it.iternext() + + def _draw_line( + self, + image_bgr, + arr, + mask, + v, + color_bgr, + linewidth, + bin_code, + multi_idx, + bbox_xywh, + Nw, + Nh, + offset, + ): + lines = self._bin_code_2_lines(arr, v, bin_code, multi_idx, Nw, Nh, offset) + x0, y0, w, h = bbox_xywh + x1 = x0 + w + y1 = y0 + h + for line in lines: + x0r, y0r = line[0] + x1r, y1r = line[1] + pt0 = (int(x0 + x0r * (x1 - x0)), int(y0 + y0r * (y1 - y0))) + pt1 = (int(x0 + x1r * (x1 - x0)), int(y0 + y1r * (y1 - y0))) + cv2.line(image_bgr, pt0, pt1, color_bgr, linewidth) + + def _bin_code_2_lines(self, arr, v, bin_code, multi_idx, Nw, Nh, offset): + i0, j0 = offset + i, j = multi_idx + i += i0 + j += j0 + v0, v1, v2, v3 = arr[i, j], arr[i + 1, j], arr[i + 1, j + 1], arr[i, j + 1] + x0i = float(j) / Nw + y0j = float(i) / Nh + He = 1.0 / Nh + We = 1.0 / Nw + if (bin_code == 1) or (bin_code == 14): + a = (v - v0) / (v1 - v0) + b = (v - v0) / (v3 - v0) + pt1 = (x0i, y0j + a * He) + pt2 = (x0i + b * We, y0j) + return [(pt1, pt2)] + elif (bin_code == 2) or (bin_code == 13): + a = (v - v0) / (v1 - v0) + b = (v - v1) / (v2 - v1) + pt1 = (x0i, y0j + a * He) + pt2 = (x0i + b * We, y0j + He) + return [(pt1, pt2)] + elif (bin_code == 3) or (bin_code == 12): + a = (v - v0) / (v3 - v0) + b = (v - v1) / (v2 - v1) + pt1 = (x0i + a * We, y0j) + pt2 = (x0i + b * We, y0j + He) + return [(pt1, pt2)] + elif (bin_code == 4) or (bin_code == 11): + a = (v - v1) / (v2 - v1) + b = (v - v3) / (v2 - v3) + pt1 = (x0i + a * We, y0j + He) + pt2 = (x0i + We, y0j + b * He) + return [(pt1, pt2)] + elif (bin_code == 6) or (bin_code == 9): + a = (v - v0) / (v1 - v0) + b = (v - v3) / (v2 - v3) + pt1 = (x0i, y0j + a * He) + pt2 = (x0i + We, y0j + b * He) + return [(pt1, pt2)] + elif (bin_code == 7) or (bin_code == 8): + a = (v - v0) / (v3 - v0) + b = (v - v3) / (v2 - v3) + pt1 = (x0i + a * We, y0j) + pt2 = (x0i + We, y0j + b * He) + return [(pt1, pt2)] + elif bin_code == 5: + a1 = (v - v0) / (v1 - v0) + b1 = (v - v1) / (v2 - v1) + pt11 = (x0i, y0j + a1 * He) + pt12 = (x0i + b1 * We, y0j + He) + a2 = (v - v0) / (v3 - v0) + b2 = (v - v3) / (v2 - v3) + pt21 = (x0i + a2 * We, y0j) + pt22 = (x0i + We, y0j + b2 * He) + return [(pt11, pt12), (pt21, pt22)] + elif bin_code == 10: + a1 = (v - v0) / (v3 - v0) + b1 = (v - v0) / (v1 - v0) + pt11 = (x0i + a1 * We, y0j) + pt12 = (x0i, y0j + b1 * He) + a2 = (v - v1) / (v2 - v1) + b2 = (v - v3) / (v2 - v3) + pt21 = (x0i + a2 * We, y0j + He) + pt22 = (x0i + We, y0j + b2 * He) + return [(pt11, pt12), (pt21, pt22)] + return [] + + +try: + import matplotlib + + # matplotlib.use("Agg") + DensePoseResultsContourVisualizer = DensePoseResultsMplContourVisualizer +except ModuleNotFoundError: + logger = logging.getLogger(__name__) + logger.warning("Could not import matplotlib, using custom contour visualizer") + DensePoseResultsContourVisualizer = DensePoseResultsCustomContourVisualizer + + +class DensePoseResultsFineSegmentationVisualizer(DensePoseMaskedColormapResultsVisualizer): + def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, **kwargs): + super(DensePoseResultsFineSegmentationVisualizer, self).__init__( + _extract_i_from_iuvarr, + _extract_i_from_iuvarr, + inplace, + cmap, + alpha, + val_scale=255.0 / DensePoseDataRelative.N_PART_LABELS, + **kwargs, + ) + + +class DensePoseResultsUVisualizer(DensePoseMaskedColormapResultsVisualizer): + def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, **kwargs): + super(DensePoseResultsUVisualizer, self).__init__( + _extract_u_from_iuvarr, + _extract_i_from_iuvarr, + inplace, + cmap, + alpha, + val_scale=1.0, + **kwargs, + ) + + +class DensePoseResultsVVisualizer(DensePoseMaskedColormapResultsVisualizer): + def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, **kwargs): + super(DensePoseResultsVVisualizer, self).__init__( + _extract_v_from_iuvarr, + _extract_i_from_iuvarr, + inplace, + cmap, + alpha, + val_scale=1.0, + **kwargs, + ) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/vis/densepose_results_textures.py b/motion-gan-pipeline/preprocessing/third/densepose/vis/densepose_results_textures.py new file mode 100644 index 0000000..8b02f2b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/vis/densepose_results_textures.py @@ -0,0 +1,91 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +from typing import List, Optional, Tuple +import torch + +from detectron2.data.detection_utils import read_image + +from ..structures import DensePoseChartResult +from .base import Boxes, Image +from .densepose_results import DensePoseResultsVisualizer + + +def get_texture_atlas(path: Optional[str]) -> Optional[np.ndarray]: + if path is None: + return None + + # Reading images like that downsamples 16-bit images to 8-bit + # If 16-bit images are needed, we can replace that by cv2.imread with the + # cv2.IMREAD_UNCHANGED flag (with cv2 we also need it to keep alpha channels) + # The rest of the pipeline would need to be adapted to 16-bit images too + bgr_image = read_image(path) + rgb_image = np.copy(bgr_image) # Convert BGR -> RGB + rgb_image[:, :, :3] = rgb_image[:, :, 2::-1] # Works with alpha channel + return rgb_image + + +class DensePoseResultsVisualizerWithTexture(DensePoseResultsVisualizer): + """ + texture_atlas: An image, size 6N * 4N, with N * N squares for each of the 24 body parts. + It must follow the grid found at https://github.com/facebookresearch/DensePose/blob/master/DensePoseData/demo_data/texture_atlas_200.png # noqa + For each body part, U is proportional to the x coordinate, and (1 - V) to y + """ + + def __init__(self, texture_atlas, **kwargs): + self.texture_atlas = texture_atlas + self.body_part_size = texture_atlas.shape[0] // 6 + assert self.body_part_size == texture_atlas.shape[1] // 4 + + def visualize( + self, + image_bgr: Image, + results_and_boxes_xywh: Tuple[Optional[List[DensePoseChartResult]], Optional[Boxes]], + ) -> Image: + densepose_result, boxes_xywh = results_and_boxes_xywh + if densepose_result is None or boxes_xywh is None: + return image_bgr + + boxes_xywh = boxes_xywh.int().cpu().numpy() + texture_image, alpha = self.get_texture() + for i, result in enumerate(densepose_result): + iuv_array = torch.cat((result.labels[None], result.uv.clamp(0, 1))) + x, y, w, h = boxes_xywh[i] + bbox_image = image_bgr[y : y + h, x : x + w] + image_bgr[y : y + h, x : x + w] = self.generate_image_with_texture( + texture_image, alpha, bbox_image, iuv_array.cpu().numpy() + ) + return image_bgr + + def get_texture(self): + N = self.body_part_size + texture_image = np.zeros([24, N, N, self.texture_atlas.shape[-1]]) + for i in range(4): + for j in range(6): + texture_image[(6 * i + j), :, :, :] = self.texture_atlas[ + N * j : N * (j + 1), N * i : N * (i + 1), : + ] + + if texture_image.shape[-1] == 4: # Image with alpha channel + alpha = texture_image[:, :, :, -1] / 255.0 + texture_image = texture_image[:, :, :, :3] + else: + alpha = texture_image.sum(axis=-1) > 0 + + return texture_image, alpha + + def generate_image_with_texture(self, texture_image, alpha, bbox_image_bgr, iuv_array): + + I, U, V = iuv_array + generated_image_bgr = bbox_image_bgr.copy() + + for PartInd in range(1, 25): + x, y = np.where(I == PartInd) + x_index = (U[x, y] * (self.body_part_size - 1)).astype(int) + y_index = ((1 - V[x, y]) * (self.body_part_size - 1)).astype(int) + part_alpha = np.expand_dims(alpha[PartInd - 1, y_index, x_index], -1) + generated_image_bgr[I == PartInd] = ( + generated_image_bgr[I == PartInd] * (1 - part_alpha) + + texture_image[PartInd - 1, y_index, x_index] * part_alpha + ) + + return generated_image_bgr.astype(np.uint8) diff --git a/motion-gan-pipeline/preprocessing/third/densepose/vis/extractor.py b/motion-gan-pipeline/preprocessing/third/densepose/vis/extractor.py new file mode 100644 index 0000000..bfb2bdf --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/densepose/vis/extractor.py @@ -0,0 +1,199 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +from typing import List, Optional, Sequence, Tuple +import torch + +from detectron2.layers.nms import batched_nms +from detectron2.structures.instances import Instances + +from densepose.converters import ToChartResultConverterWithConfidences +from densepose.structures import ( + DensePoseChartResultWithConfidences, + DensePoseEmbeddingPredictorOutput, +) +from densepose.vis.bounding_box import BoundingBoxVisualizer, ScoredBoundingBoxVisualizer +from densepose.vis.densepose_outputs_vertex import DensePoseOutputsVertexVisualizer +from densepose.vis.densepose_results import DensePoseResultsVisualizer + +from .base import CompoundVisualizer + +Scores = Sequence[float] +DensePoseChartResultsWithConfidences = List[DensePoseChartResultWithConfidences] + + +def extract_scores_from_instances(instances: Instances, select=None): + if instances.has("scores"): + return instances.scores if select is None else instances.scores[select] + return None + + +def extract_boxes_xywh_from_instances(instances: Instances, select=None): + if instances.has("pred_boxes"): + boxes_xywh = instances.pred_boxes.tensor.clone() + boxes_xywh[:, 2] -= boxes_xywh[:, 0] + boxes_xywh[:, 3] -= boxes_xywh[:, 1] + return boxes_xywh if select is None else boxes_xywh[select] + return None + + +def create_extractor(visualizer: object): + """ + Create an extractor for the provided visualizer + """ + if isinstance(visualizer, CompoundVisualizer): + extractors = [create_extractor(v) for v in visualizer.visualizers] + return CompoundExtractor(extractors) + elif isinstance(visualizer, DensePoseResultsVisualizer): + return DensePoseResultExtractor() + elif isinstance(visualizer, ScoredBoundingBoxVisualizer): + return CompoundExtractor([extract_boxes_xywh_from_instances, extract_scores_from_instances]) + elif isinstance(visualizer, BoundingBoxVisualizer): + return extract_boxes_xywh_from_instances + elif isinstance(visualizer, DensePoseOutputsVertexVisualizer): + return DensePoseOutputsExtractor() + else: + logger = logging.getLogger(__name__) + logger.error(f"Could not create extractor for {visualizer}") + return None + + +class BoundingBoxExtractor(object): + """ + Extracts bounding boxes from instances + """ + + def __call__(self, instances: Instances): + boxes_xywh = extract_boxes_xywh_from_instances(instances) + return boxes_xywh + + +class ScoredBoundingBoxExtractor(object): + """ + Extracts bounding boxes from instances + """ + + def __call__(self, instances: Instances, select=None): + scores = extract_scores_from_instances(instances) + boxes_xywh = extract_boxes_xywh_from_instances(instances) + if (scores is None) or (boxes_xywh is None): + return (boxes_xywh, scores) + if select is not None: + scores = scores[select] + boxes_xywh = boxes_xywh[select] + return (boxes_xywh, scores) + + +class DensePoseResultExtractor(object): + """ + Extracts DensePose chart result with confidences from instances + """ + + def __call__( + self, instances: Instances, select=None + ) -> Tuple[Optional[DensePoseChartResultsWithConfidences], Optional[torch.Tensor]]: + if instances.has("pred_densepose") and instances.has("pred_boxes"): + dpout = instances.pred_densepose + boxes_xyxy = instances.pred_boxes + boxes_xywh = extract_boxes_xywh_from_instances(instances) + if select is not None: + dpout = dpout[select] + boxes_xyxy = boxes_xyxy[select] + converter = ToChartResultConverterWithConfidences() + results = [converter.convert(dpout[i], boxes_xyxy[[i]]) for i in range(len(dpout))] + return results, boxes_xywh + else: + return None, None + + +class DensePoseOutputsExtractor(object): + """ + Extracts DensePose result from instances + """ + + def __call__( + self, + instances: Instances, + select=None, + ) -> Tuple[ + Optional[DensePoseEmbeddingPredictorOutput], Optional[torch.Tensor], Optional[List[int]] + ]: + if not (instances.has("pred_densepose") and instances.has("pred_boxes")): + return None, None, None + + dpout = instances.pred_densepose + boxes_xyxy = instances.pred_boxes + boxes_xywh = extract_boxes_xywh_from_instances(instances) + + if instances.has("pred_classes"): + classes = instances.pred_classes.tolist() + else: + classes = None + + if select is not None: + dpout = dpout[select] + boxes_xyxy = boxes_xyxy[select] + if classes is not None: + classes = classes[select] + + return dpout, boxes_xywh, classes + + +class CompoundExtractor(object): + """ + Extracts data for CompoundVisualizer + """ + + def __init__(self, extractors): + self.extractors = extractors + + def __call__(self, instances: Instances, select=None): + datas = [] + for extractor in self.extractors: + data = extractor(instances, select) + datas.append(data) + return datas + + +class NmsFilteredExtractor(object): + """ + Extracts data in the format accepted by NmsFilteredVisualizer + """ + + def __init__(self, extractor, iou_threshold): + self.extractor = extractor + self.iou_threshold = iou_threshold + + def __call__(self, instances: Instances, select=None): + scores = extract_scores_from_instances(instances) + boxes_xywh = extract_boxes_xywh_from_instances(instances) + if boxes_xywh is None: + return None + select_local_idx = batched_nms( + boxes_xywh, + scores, + torch.zeros(len(scores), dtype=torch.int32), + iou_threshold=self.iou_threshold, + ).squeeze() + select_local = torch.zeros(len(boxes_xywh), dtype=torch.bool, device=boxes_xywh.device) + select_local[select_local_idx] = True + select = select_local if select is None else (select & select_local) + return self.extractor(instances, select=select) + + +class ScoreThresholdedExtractor(object): + """ + Extracts data in the format accepted by ScoreThresholdedVisualizer + """ + + def __init__(self, extractor, min_score): + self.extractor = extractor + self.min_score = min_score + + def __call__(self, instances: Instances, select=None): + scores = extract_scores_from_instances(instances) + if scores is None: + return None + select_local = scores > self.min_score + select = select_local if select is None else (select & select_local) + data = self.extractor(instances, select=select) + return data diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/__init__.py new file mode 100644 index 0000000..bdd994b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from .utils.env import setup_environment + +setup_environment() + + +# This line will be programatically read/write by setup.py. +# Leave them at the bottom of this file and don't touch them. +__version__ = "0.6" diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/checkpoint/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/checkpoint/__init__.py new file mode 100644 index 0000000..99da046 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/checkpoint/__init__.py @@ -0,0 +1,10 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +# File: + + +from . import catalog as _UNUSED # register the handler +from .detection_checkpoint import DetectionCheckpointer +from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer + +__all__ = ["Checkpointer", "PeriodicCheckpointer", "DetectionCheckpointer"] diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/checkpoint/c2_model_loading.py b/motion-gan-pipeline/preprocessing/third/detectron2/checkpoint/c2_model_loading.py new file mode 100644 index 0000000..8c8d181 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/checkpoint/c2_model_loading.py @@ -0,0 +1,407 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import logging +import re +from typing import Dict, List +import torch +from tabulate import tabulate + + +def convert_basic_c2_names(original_keys): + """ + Apply some basic name conversion to names in C2 weights. + It only deals with typical backbone models. + + Args: + original_keys (list[str]): + Returns: + list[str]: The same number of strings matching those in original_keys. + """ + layer_keys = copy.deepcopy(original_keys) + layer_keys = [ + {"pred_b": "linear_b", "pred_w": "linear_w"}.get(k, k) for k in layer_keys + ] # some hard-coded mappings + + layer_keys = [k.replace("_", ".") for k in layer_keys] + layer_keys = [re.sub("\\.b$", ".bias", k) for k in layer_keys] + layer_keys = [re.sub("\\.w$", ".weight", k) for k in layer_keys] + # Uniform both bn and gn names to "norm" + layer_keys = [re.sub("bn\\.s$", "norm.weight", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.bias$", "norm.bias", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.rm", "norm.running_mean", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.running.mean$", "norm.running_mean", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.riv$", "norm.running_var", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.running.var$", "norm.running_var", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.gamma$", "norm.weight", k) for k in layer_keys] + layer_keys = [re.sub("bn\\.beta$", "norm.bias", k) for k in layer_keys] + layer_keys = [re.sub("gn\\.s$", "norm.weight", k) for k in layer_keys] + layer_keys = [re.sub("gn\\.bias$", "norm.bias", k) for k in layer_keys] + + # stem + layer_keys = [re.sub("^res\\.conv1\\.norm\\.", "conv1.norm.", k) for k in layer_keys] + # to avoid mis-matching with "conv1" in other components (e.g. detection head) + layer_keys = [re.sub("^conv1\\.", "stem.conv1.", k) for k in layer_keys] + + # layer1-4 is used by torchvision, however we follow the C2 naming strategy (res2-5) + # layer_keys = [re.sub("^res2.", "layer1.", k) for k in layer_keys] + # layer_keys = [re.sub("^res3.", "layer2.", k) for k in layer_keys] + # layer_keys = [re.sub("^res4.", "layer3.", k) for k in layer_keys] + # layer_keys = [re.sub("^res5.", "layer4.", k) for k in layer_keys] + + # blocks + layer_keys = [k.replace(".branch1.", ".shortcut.") for k in layer_keys] + layer_keys = [k.replace(".branch2a.", ".conv1.") for k in layer_keys] + layer_keys = [k.replace(".branch2b.", ".conv2.") for k in layer_keys] + layer_keys = [k.replace(".branch2c.", ".conv3.") for k in layer_keys] + + # DensePose substitutions + layer_keys = [re.sub("^body.conv.fcn", "body_conv_fcn", k) for k in layer_keys] + layer_keys = [k.replace("AnnIndex.lowres", "ann_index_lowres") for k in layer_keys] + layer_keys = [k.replace("Index.UV.lowres", "index_uv_lowres") for k in layer_keys] + layer_keys = [k.replace("U.lowres", "u_lowres") for k in layer_keys] + layer_keys = [k.replace("V.lowres", "v_lowres") for k in layer_keys] + return layer_keys + + +def convert_c2_detectron_names(weights): + """ + Map Caffe2 Detectron weight names to Detectron2 names. + + Args: + weights (dict): name -> tensor + + Returns: + dict: detectron2 names -> tensor + dict: detectron2 names -> C2 names + """ + logger = logging.getLogger(__name__) + logger.info("Renaming Caffe2 weights ......") + original_keys = sorted(weights.keys()) + layer_keys = copy.deepcopy(original_keys) + + layer_keys = convert_basic_c2_names(layer_keys) + + # -------------------------------------------------------------------------- + # RPN hidden representation conv + # -------------------------------------------------------------------------- + # FPN case + # In the C2 model, the RPN hidden layer conv is defined for FPN level 2 and then + # shared for all other levels, hence the appearance of "fpn2" + layer_keys = [ + k.replace("conv.rpn.fpn2", "proposal_generator.rpn_head.conv") for k in layer_keys + ] + # Non-FPN case + layer_keys = [k.replace("conv.rpn", "proposal_generator.rpn_head.conv") for k in layer_keys] + + # -------------------------------------------------------------------------- + # RPN box transformation conv + # -------------------------------------------------------------------------- + # FPN case (see note above about "fpn2") + layer_keys = [ + k.replace("rpn.bbox.pred.fpn2", "proposal_generator.rpn_head.anchor_deltas") + for k in layer_keys + ] + layer_keys = [ + k.replace("rpn.cls.logits.fpn2", "proposal_generator.rpn_head.objectness_logits") + for k in layer_keys + ] + # Non-FPN case + layer_keys = [ + k.replace("rpn.bbox.pred", "proposal_generator.rpn_head.anchor_deltas") for k in layer_keys + ] + layer_keys = [ + k.replace("rpn.cls.logits", "proposal_generator.rpn_head.objectness_logits") + for k in layer_keys + ] + + # -------------------------------------------------------------------------- + # Fast R-CNN box head + # -------------------------------------------------------------------------- + layer_keys = [re.sub("^bbox\\.pred", "bbox_pred", k) for k in layer_keys] + layer_keys = [re.sub("^cls\\.score", "cls_score", k) for k in layer_keys] + layer_keys = [re.sub("^fc6\\.", "box_head.fc1.", k) for k in layer_keys] + layer_keys = [re.sub("^fc7\\.", "box_head.fc2.", k) for k in layer_keys] + # 4conv1fc head tensor names: head_conv1_w, head_conv1_gn_s + layer_keys = [re.sub("^head\\.conv", "box_head.conv", k) for k in layer_keys] + + # -------------------------------------------------------------------------- + # FPN lateral and output convolutions + # -------------------------------------------------------------------------- + def fpn_map(name): + """ + Look for keys with the following patterns: + 1) Starts with "fpn.inner." + Example: "fpn.inner.res2.2.sum.lateral.weight" + Meaning: These are lateral pathway convolutions + 2) Starts with "fpn.res" + Example: "fpn.res2.2.sum.weight" + Meaning: These are FPN output convolutions + """ + splits = name.split(".") + norm = ".norm" if "norm" in splits else "" + if name.startswith("fpn.inner."): + # splits example: ['fpn', 'inner', 'res2', '2', 'sum', 'lateral', 'weight'] + stage = int(splits[2][len("res") :]) + return "fpn_lateral{}{}.{}".format(stage, norm, splits[-1]) + elif name.startswith("fpn.res"): + # splits example: ['fpn', 'res2', '2', 'sum', 'weight'] + stage = int(splits[1][len("res") :]) + return "fpn_output{}{}.{}".format(stage, norm, splits[-1]) + return name + + layer_keys = [fpn_map(k) for k in layer_keys] + + # -------------------------------------------------------------------------- + # Mask R-CNN mask head + # -------------------------------------------------------------------------- + # roi_heads.StandardROIHeads case + layer_keys = [k.replace(".[mask].fcn", "mask_head.mask_fcn") for k in layer_keys] + layer_keys = [re.sub("^\\.mask\\.fcn", "mask_head.mask_fcn", k) for k in layer_keys] + layer_keys = [k.replace("mask.fcn.logits", "mask_head.predictor") for k in layer_keys] + # roi_heads.Res5ROIHeads case + layer_keys = [k.replace("conv5.mask", "mask_head.deconv") for k in layer_keys] + + # -------------------------------------------------------------------------- + # Keypoint R-CNN head + # -------------------------------------------------------------------------- + # interestingly, the keypoint head convs have blob names that are simply "conv_fcnX" + layer_keys = [k.replace("conv.fcn", "roi_heads.keypoint_head.conv_fcn") for k in layer_keys] + layer_keys = [ + k.replace("kps.score.lowres", "roi_heads.keypoint_head.score_lowres") for k in layer_keys + ] + layer_keys = [k.replace("kps.score.", "roi_heads.keypoint_head.score.") for k in layer_keys] + + # -------------------------------------------------------------------------- + # Done with replacements + # -------------------------------------------------------------------------- + assert len(set(layer_keys)) == len(layer_keys) + assert len(original_keys) == len(layer_keys) + + new_weights = {} + new_keys_to_original_keys = {} + for orig, renamed in zip(original_keys, layer_keys): + new_keys_to_original_keys[renamed] = orig + if renamed.startswith("bbox_pred.") or renamed.startswith("mask_head.predictor."): + # remove the meaningless prediction weight for background class + new_start_idx = 4 if renamed.startswith("bbox_pred.") else 1 + new_weights[renamed] = weights[orig][new_start_idx:] + logger.info( + "Remove prediction weight for background class in {}. The shape changes from " + "{} to {}.".format( + renamed, tuple(weights[orig].shape), tuple(new_weights[renamed].shape) + ) + ) + elif renamed.startswith("cls_score."): + # move weights of bg class from original index 0 to last index + logger.info( + "Move classification weights for background class in {} from index 0 to " + "index {}.".format(renamed, weights[orig].shape[0] - 1) + ) + new_weights[renamed] = torch.cat([weights[orig][1:], weights[orig][:1]]) + else: + new_weights[renamed] = weights[orig] + + return new_weights, new_keys_to_original_keys + + +# Note the current matching is not symmetric. +# it assumes model_state_dict will have longer names. +def align_and_update_state_dicts(model_state_dict, ckpt_state_dict, c2_conversion=True): + """ + Match names between the two state-dict, and returns a new chkpt_state_dict with names + converted to match model_state_dict with heuristics. The returned dict can be later + loaded with fvcore checkpointer. + If `c2_conversion==True`, `ckpt_state_dict` is assumed to be a Caffe2 + model and will be renamed at first. + + Strategy: suppose that the models that we will create will have prefixes appended + to each of its keys, for example due to an extra level of nesting that the original + pre-trained weights from ImageNet won't contain. For example, model.state_dict() + might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains + res2.conv1.weight. We thus want to match both parameters together. + For that, we look for each model weight, look among all loaded keys if there is one + that is a suffix of the current weight name, and use it if that's the case. + If multiple matches exist, take the one with longest size + of the corresponding name. For example, for the same model as before, the pretrained + weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case, + we want to match backbone[0].body.conv1.weight to conv1.weight, and + backbone[0].body.res2.conv1.weight to res2.conv1.weight. + """ + model_keys = sorted(model_state_dict.keys()) + if c2_conversion: + ckpt_state_dict, original_keys = convert_c2_detectron_names(ckpt_state_dict) + # original_keys: the name in the original dict (before renaming) + else: + original_keys = {x: x for x in ckpt_state_dict.keys()} + ckpt_keys = sorted(ckpt_state_dict.keys()) + + def match(a, b): + # Matched ckpt_key should be a complete (starts with '.') suffix. + # For example, roi_heads.mesh_head.whatever_conv1 does not match conv1, + # but matches whatever_conv1 or mesh_head.whatever_conv1. + return a == b or a.endswith("." + b) + + # get a matrix of string matches, where each (i, j) entry correspond to the size of the + # ckpt_key string, if it matches + match_matrix = [len(j) if match(i, j) else 0 for i in model_keys for j in ckpt_keys] + match_matrix = torch.as_tensor(match_matrix).view(len(model_keys), len(ckpt_keys)) + # use the matched one with longest size in case of multiple matches + max_match_size, idxs = match_matrix.max(1) + # remove indices that correspond to no-match + idxs[max_match_size == 0] = -1 + + logger = logging.getLogger(__name__) + # matched_pairs (matched checkpoint key --> matched model key) + matched_keys = {} + result_state_dict = {} + for idx_model, idx_ckpt in enumerate(idxs.tolist()): + if idx_ckpt == -1: + continue + key_model = model_keys[idx_model] + key_ckpt = ckpt_keys[idx_ckpt] + value_ckpt = ckpt_state_dict[key_ckpt] + shape_in_model = model_state_dict[key_model].shape + + if shape_in_model != value_ckpt.shape: + logger.warning( + "Shape of {} in checkpoint is {}, while shape of {} in model is {}.".format( + key_ckpt, value_ckpt.shape, key_model, shape_in_model + ) + ) + logger.warning( + "{} will not be loaded. Please double check and see if this is desired.".format( + key_ckpt + ) + ) + continue + + assert key_model not in result_state_dict + result_state_dict[key_model] = value_ckpt + if key_ckpt in matched_keys: # already added to matched_keys + logger.error( + "Ambiguity found for {} in checkpoint!" + "It matches at least two keys in the model ({} and {}).".format( + key_ckpt, key_model, matched_keys[key_ckpt] + ) + ) + raise ValueError("Cannot match one checkpoint key to multiple keys in the model.") + + matched_keys[key_ckpt] = key_model + + # logging: + matched_model_keys = sorted(matched_keys.values()) + if len(matched_model_keys) == 0: + logger.warning("No weights in checkpoint matched with model.") + return ckpt_state_dict + common_prefix = _longest_common_prefix(matched_model_keys) + rev_matched_keys = {v: k for k, v in matched_keys.items()} + original_keys = {k: original_keys[rev_matched_keys[k]] for k in matched_model_keys} + + model_key_groups = _group_keys_by_module(matched_model_keys, original_keys) + table = [] + memo = set() + for key_model in matched_model_keys: + if key_model in memo: + continue + if key_model in model_key_groups: + group = model_key_groups[key_model] + memo |= set(group) + shapes = [tuple(model_state_dict[k].shape) for k in group] + table.append( + ( + _longest_common_prefix([k[len(common_prefix) :] for k in group]) + "*", + _group_str([original_keys[k] for k in group]), + " ".join([str(x).replace(" ", "") for x in shapes]), + ) + ) + else: + key_checkpoint = original_keys[key_model] + shape = str(tuple(model_state_dict[key_model].shape)) + table.append((key_model[len(common_prefix) :], key_checkpoint, shape)) + table_str = tabulate( + table, tablefmt="pipe", headers=["Names in Model", "Names in Checkpoint", "Shapes"] + ) + logger.info( + "Following weights matched with " + + (f"submodule {common_prefix[:-1]}" if common_prefix else "model") + + ":\n" + + table_str + ) + + unmatched_ckpt_keys = [k for k in ckpt_keys if k not in set(matched_keys.keys())] + for k in unmatched_ckpt_keys: + result_state_dict[k] = ckpt_state_dict[k] + return result_state_dict + + +def _group_keys_by_module(keys: List[str], original_names: Dict[str, str]): + """ + Params in the same submodule are grouped together. + + Args: + keys: names of all parameters + original_names: mapping from parameter name to their name in the checkpoint + + Returns: + dict[name -> all other names in the same group] + """ + + def _submodule_name(key): + pos = key.rfind(".") + if pos < 0: + return None + prefix = key[: pos + 1] + return prefix + + all_submodules = [_submodule_name(k) for k in keys] + all_submodules = [x for x in all_submodules if x] + all_submodules = sorted(all_submodules, key=len) + + ret = {} + for prefix in all_submodules: + group = [k for k in keys if k.startswith(prefix)] + if len(group) <= 1: + continue + original_name_lcp = _longest_common_prefix_str([original_names[k] for k in group]) + if len(original_name_lcp) == 0: + # don't group weights if original names don't share prefix + continue + + for k in group: + if k in ret: + continue + ret[k] = group + return ret + + +def _longest_common_prefix(names: List[str]) -> str: + """ + ["abc.zfg", "abc.zef"] -> "abc." + """ + names = [n.split(".") for n in names] + m1, m2 = min(names), max(names) + ret = [a for a, b in zip(m1, m2) if a == b] + ret = ".".join(ret) + "." if len(ret) else "" + return ret + + +def _longest_common_prefix_str(names: List[str]) -> str: + m1, m2 = min(names), max(names) + lcp = [a for a, b in zip(m1, m2) if a == b] + lcp = "".join(lcp) + return lcp + + +def _group_str(names: List[str]) -> str: + """ + Turn "common1", "common2", "common3" into "common{1,2,3}" + """ + lcp = _longest_common_prefix_str(names) + rest = [x[len(lcp) :] for x in names] + rest = "{" + ",".join(rest) + "}" + ret = lcp + rest + + # add some simplification for BN specifically + ret = ret.replace("bn_{beta,running_mean,running_var,gamma}", "bn_*") + ret = ret.replace("bn_beta,bn_running_mean,bn_running_var,bn_gamma", "bn_*") + return ret diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/checkpoint/catalog.py b/motion-gan-pipeline/preprocessing/third/detectron2/checkpoint/catalog.py new file mode 100644 index 0000000..9a85736 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/checkpoint/catalog.py @@ -0,0 +1,115 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging + +from detectron2.utils.file_io import PathHandler, PathManager + + +class ModelCatalog(object): + """ + Store mappings from names to third-party models. + """ + + S3_C2_DETECTRON_PREFIX = "https://dl.fbaipublicfiles.com/detectron" + + # MSRA models have STRIDE_IN_1X1=True. False otherwise. + # NOTE: all BN models here have fused BN into an affine layer. + # As a result, you should only load them to a model with "FrozenBN". + # Loading them to a model with regular BN or SyncBN is wrong. + # Even when loaded to FrozenBN, it is still different from affine by an epsilon, + # which should be negligible for training. + # NOTE: all models here uses PIXEL_STD=[1,1,1] + # NOTE: Most of the BN models here are no longer used. We use the + # re-converted pre-trained models under detectron2 model zoo instead. + C2_IMAGENET_MODELS = { + "MSRA/R-50": "ImageNetPretrained/MSRA/R-50.pkl", + "MSRA/R-101": "ImageNetPretrained/MSRA/R-101.pkl", + "FAIR/R-50-GN": "ImageNetPretrained/47261647/R-50-GN.pkl", + "FAIR/R-101-GN": "ImageNetPretrained/47592356/R-101-GN.pkl", + "FAIR/X-101-32x8d": "ImageNetPretrained/20171220/X-101-32x8d.pkl", + "FAIR/X-101-64x4d": "ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl", + "FAIR/X-152-32x8d-IN5k": "ImageNetPretrained/25093814/X-152-32x8d-IN5k.pkl", + } + + C2_DETECTRON_PATH_FORMAT = ( + "{prefix}/{url}/output/train/{dataset}/{type}/model_final.pkl" # noqa B950 + ) + + C2_DATASET_COCO = "coco_2014_train%3Acoco_2014_valminusminival" + C2_DATASET_COCO_KEYPOINTS = "keypoints_coco_2014_train%3Akeypoints_coco_2014_valminusminival" + + # format: {model_name} -> part of the url + C2_DETECTRON_MODELS = { + "35857197/e2e_faster_rcnn_R-50-C4_1x": "35857197/12_2017_baselines/e2e_faster_rcnn_R-50-C4_1x.yaml.01_33_49.iAX0mXvW", # noqa B950 + "35857345/e2e_faster_rcnn_R-50-FPN_1x": "35857345/12_2017_baselines/e2e_faster_rcnn_R-50-FPN_1x.yaml.01_36_30.cUF7QR7I", # noqa B950 + "35857890/e2e_faster_rcnn_R-101-FPN_1x": "35857890/12_2017_baselines/e2e_faster_rcnn_R-101-FPN_1x.yaml.01_38_50.sNxI7sX7", # noqa B950 + "36761737/e2e_faster_rcnn_X-101-32x8d-FPN_1x": "36761737/12_2017_baselines/e2e_faster_rcnn_X-101-32x8d-FPN_1x.yaml.06_31_39.5MIHi1fZ", # noqa B950 + "35858791/e2e_mask_rcnn_R-50-C4_1x": "35858791/12_2017_baselines/e2e_mask_rcnn_R-50-C4_1x.yaml.01_45_57.ZgkA7hPB", # noqa B950 + "35858933/e2e_mask_rcnn_R-50-FPN_1x": "35858933/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml.01_48_14.DzEQe4wC", # noqa B950 + "35861795/e2e_mask_rcnn_R-101-FPN_1x": "35861795/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_1x.yaml.02_31_37.KqyEK4tT", # noqa B950 + "36761843/e2e_mask_rcnn_X-101-32x8d-FPN_1x": "36761843/12_2017_baselines/e2e_mask_rcnn_X-101-32x8d-FPN_1x.yaml.06_35_59.RZotkLKI", # noqa B950 + "48616381/e2e_mask_rcnn_R-50-FPN_2x_gn": "GN/48616381/04_2018_gn_baselines/e2e_mask_rcnn_R-50-FPN_2x_gn_0416.13_23_38.bTlTI97Q", # noqa B950 + "37697547/e2e_keypoint_rcnn_R-50-FPN_1x": "37697547/12_2017_baselines/e2e_keypoint_rcnn_R-50-FPN_1x.yaml.08_42_54.kdzV35ao", # noqa B950 + "35998355/rpn_R-50-C4_1x": "35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L", # noqa B950 + "35998814/rpn_R-50-FPN_1x": "35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179", # noqa B950 + "36225147/fast_R-50-FPN_1x": "36225147/12_2017_baselines/fast_rcnn_R-50-FPN_1x.yaml.08_39_09.L3obSdQ2", # noqa B950 + } + + @staticmethod + def get(name): + if name.startswith("Caffe2Detectron/COCO"): + return ModelCatalog._get_c2_detectron_baseline(name) + if name.startswith("ImageNetPretrained/"): + return ModelCatalog._get_c2_imagenet_pretrained(name) + raise RuntimeError("model not present in the catalog: {}".format(name)) + + @staticmethod + def _get_c2_imagenet_pretrained(name): + prefix = ModelCatalog.S3_C2_DETECTRON_PREFIX + name = name[len("ImageNetPretrained/") :] + name = ModelCatalog.C2_IMAGENET_MODELS[name] + url = "/".join([prefix, name]) + return url + + @staticmethod + def _get_c2_detectron_baseline(name): + name = name[len("Caffe2Detectron/COCO/") :] + url = ModelCatalog.C2_DETECTRON_MODELS[name] + if "keypoint_rcnn" in name: + dataset = ModelCatalog.C2_DATASET_COCO_KEYPOINTS + else: + dataset = ModelCatalog.C2_DATASET_COCO + + if "35998355/rpn_R-50-C4_1x" in name: + # this one model is somehow different from others .. + type = "rpn" + else: + type = "generalized_rcnn" + + # Detectron C2 models are stored in the structure defined in `C2_DETECTRON_PATH_FORMAT`. + url = ModelCatalog.C2_DETECTRON_PATH_FORMAT.format( + prefix=ModelCatalog.S3_C2_DETECTRON_PREFIX, url=url, type=type, dataset=dataset + ) + return url + + +class ModelCatalogHandler(PathHandler): + """ + Resolve URL like catalog://. + """ + + PREFIX = "catalog://" + + def _get_supported_prefixes(self): + return [self.PREFIX] + + def _get_local_path(self, path, **kwargs): + logger = logging.getLogger(__name__) + catalog_path = ModelCatalog.get(path[len(self.PREFIX) :]) + logger.info("Catalog entry {} points to {}".format(path, catalog_path)) + return PathManager.get_local_path(catalog_path, **kwargs) + + def _open(self, path, mode="r", **kwargs): + return PathManager.open(self._get_local_path(path), mode, **kwargs) + + +PathManager.register_handler(ModelCatalogHandler()) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/checkpoint/detection_checkpoint.py b/motion-gan-pipeline/preprocessing/third/detectron2/checkpoint/detection_checkpoint.py new file mode 100644 index 0000000..82fd3b2 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/checkpoint/detection_checkpoint.py @@ -0,0 +1,120 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import os +import pickle +import torch +from fvcore.common.checkpoint import Checkpointer +from torch.nn.parallel import DistributedDataParallel + +import detectron2.utils.comm as comm +from detectron2.utils.file_io import PathManager + +from .c2_model_loading import align_and_update_state_dicts + + +class DetectionCheckpointer(Checkpointer): + """ + Same as :class:`Checkpointer`, but is able to: + 1. handle models in detectron & detectron2 model zoo, and apply conversions for legacy models. + 2. correctly load checkpoints that are only available on the master worker + """ + + def __init__(self, model, save_dir="", *, save_to_disk=None, **checkpointables): + is_main_process = comm.is_main_process() + super().__init__( + model, + save_dir, + save_to_disk=is_main_process if save_to_disk is None else save_to_disk, + **checkpointables, + ) + self.path_manager = PathManager + + def load(self, path, *args, **kwargs): + need_sync = False + + if path and isinstance(self.model, DistributedDataParallel): + logger = logging.getLogger(__name__) + path = self.path_manager.get_local_path(path) + has_file = os.path.isfile(path) + all_has_file = comm.all_gather(has_file) + if not all_has_file[0]: + raise OSError(f"File {path} not found on main worker.") + if not all(all_has_file): + logger.warning( + f"Not all workers can read checkpoint {path}. " + "Training may fail to fully resume." + ) + # TODO: broadcast the checkpoint file contents from main + # worker, and load from it instead. + need_sync = True + if not has_file: + path = None # don't load if not readable + ret = super().load(path, *args, **kwargs) + + if need_sync: + logger.info("Broadcasting model states from main worker ...") + self.model._sync_params_and_buffers() + return ret + + def _load_file(self, filename): + if filename.endswith(".pkl"): + with PathManager.open(filename, "rb") as f: + data = pickle.load(f, encoding="latin1") + if "model" in data and "__author__" in data: + # file is in Detectron2 model zoo format + self.logger.info("Reading a file from '{}'".format(data["__author__"])) + return data + else: + # assume file is from Caffe2 / Detectron1 model zoo + if "blobs" in data: + # Detection models have "blobs", but ImageNet models don't + data = data["blobs"] + data = {k: v for k, v in data.items() if not k.endswith("_momentum")} + return {"model": data, "__author__": "Caffe2", "matching_heuristics": True} + elif filename.endswith(".pyth"): + # assume file is from pycls; no one else seems to use the ".pyth" extension + with PathManager.open(filename, "rb") as f: + data = torch.load(f) + assert ( + "model_state" in data + ), f"Cannot load .pyth file {filename}; pycls checkpoints must contain 'model_state'." + model_state = { + k: v + for k, v in data["model_state"].items() + if not k.endswith("num_batches_tracked") + } + return {"model": model_state, "__author__": "pycls", "matching_heuristics": True} + + loaded = super()._load_file(filename) # load native pth checkpoint + if "model" not in loaded: + loaded = {"model": loaded} + return loaded + + def _load_model(self, checkpoint): + if checkpoint.get("matching_heuristics", False): + self._convert_ndarray_to_tensor(checkpoint["model"]) + # convert weights by name-matching heuristics + checkpoint["model"] = align_and_update_state_dicts( + self.model.state_dict(), + checkpoint["model"], + c2_conversion=checkpoint.get("__author__", None) == "Caffe2", + ) + # for non-caffe2 models, use standard ways to load it + incompatible = super()._load_model(checkpoint) + + model_buffers = dict(self.model.named_buffers(recurse=False)) + for k in ["pixel_mean", "pixel_std"]: + # Ignore missing key message about pixel_mean/std. + # Though they may be missing in old checkpoints, they will be correctly + # initialized from config anyway. + if k in model_buffers: + try: + incompatible.missing_keys.remove(k) + except ValueError: + pass + for k in incompatible.unexpected_keys[:]: + # Ignore unexpected keys about cell anchors. They exist in old checkpoints + # but now they are non-persistent buffers and will not be in new checkpoints. + if "anchor_generator.cell_anchors" in k: + incompatible.unexpected_keys.remove(k) + return incompatible diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/config/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/config/__init__.py new file mode 100644 index 0000000..4e648e6 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/config/__init__.py @@ -0,0 +1,24 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .compat import downgrade_config, upgrade_config +from .config import CfgNode, get_cfg, global_cfg, set_global_cfg, configurable +from .instantiate import instantiate +from .lazy import LazyCall, LazyConfig + +__all__ = [ + "CfgNode", + "get_cfg", + "global_cfg", + "set_global_cfg", + "downgrade_config", + "upgrade_config", + "configurable", + "instantiate", + "LazyCall", + "LazyConfig", +] + + +from detectron2.utils.env import fixup_module_metadata + +fixup_module_metadata(__name__, globals(), __all__) +del fixup_module_metadata diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/config/compat.py b/motion-gan-pipeline/preprocessing/third/detectron2/config/compat.py new file mode 100644 index 0000000..11a08c4 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/config/compat.py @@ -0,0 +1,229 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +""" +Backward compatibility of configs. + +Instructions to bump version: ++ It's not needed to bump version if new keys are added. + It's only needed when backward-incompatible changes happen + (i.e., some existing keys disappear, or the meaning of a key changes) ++ To bump version, do the following: + 1. Increment _C.VERSION in defaults.py + 2. Add a converter in this file. + + Each ConverterVX has a function "upgrade" which in-place upgrades config from X-1 to X, + and a function "downgrade" which in-place downgrades config from X to X-1 + + In each function, VERSION is left unchanged. + + Each converter assumes that its input has the relevant keys + (i.e., the input is not a partial config). + 3. Run the tests (test_config.py) to make sure the upgrade & downgrade + functions are consistent. +""" + +import logging +from typing import List, Optional, Tuple + +from .config import CfgNode as CN +from .defaults import _C + +__all__ = ["upgrade_config", "downgrade_config"] + + +def upgrade_config(cfg: CN, to_version: Optional[int] = None) -> CN: + """ + Upgrade a config from its current version to a newer version. + + Args: + cfg (CfgNode): + to_version (int): defaults to the latest version. + """ + cfg = cfg.clone() + if to_version is None: + to_version = _C.VERSION + + assert cfg.VERSION <= to_version, "Cannot upgrade from v{} to v{}!".format( + cfg.VERSION, to_version + ) + for k in range(cfg.VERSION, to_version): + converter = globals()["ConverterV" + str(k + 1)] + converter.upgrade(cfg) + cfg.VERSION = k + 1 + return cfg + + +def downgrade_config(cfg: CN, to_version: int) -> CN: + """ + Downgrade a config from its current version to an older version. + + Args: + cfg (CfgNode): + to_version (int): + + Note: + A general downgrade of arbitrary configs is not always possible due to the + different functionalities in different versions. + The purpose of downgrade is only to recover the defaults in old versions, + allowing it to load an old partial yaml config. + Therefore, the implementation only needs to fill in the default values + in the old version when a general downgrade is not possible. + """ + cfg = cfg.clone() + assert cfg.VERSION >= to_version, "Cannot downgrade from v{} to v{}!".format( + cfg.VERSION, to_version + ) + for k in range(cfg.VERSION, to_version, -1): + converter = globals()["ConverterV" + str(k)] + converter.downgrade(cfg) + cfg.VERSION = k - 1 + return cfg + + +def guess_version(cfg: CN, filename: str) -> int: + """ + Guess the version of a partial config where the VERSION field is not specified. + Returns the version, or the latest if cannot make a guess. + + This makes it easier for users to migrate. + """ + logger = logging.getLogger(__name__) + + def _has(name: str) -> bool: + cur = cfg + for n in name.split("."): + if n not in cur: + return False + cur = cur[n] + return True + + # Most users' partial configs have "MODEL.WEIGHT", so guess on it + ret = None + if _has("MODEL.WEIGHT") or _has("TEST.AUG_ON"): + ret = 1 + + if ret is not None: + logger.warning("Config '{}' has no VERSION. Assuming it to be v{}.".format(filename, ret)) + else: + ret = _C.VERSION + logger.warning( + "Config '{}' has no VERSION. Assuming it to be compatible with latest v{}.".format( + filename, ret + ) + ) + return ret + + +def _rename(cfg: CN, old: str, new: str) -> None: + old_keys = old.split(".") + new_keys = new.split(".") + + def _set(key_seq: List[str], val: str) -> None: + cur = cfg + for k in key_seq[:-1]: + if k not in cur: + cur[k] = CN() + cur = cur[k] + cur[key_seq[-1]] = val + + def _get(key_seq: List[str]) -> CN: + cur = cfg + for k in key_seq: + cur = cur[k] + return cur + + def _del(key_seq: List[str]) -> None: + cur = cfg + for k in key_seq[:-1]: + cur = cur[k] + del cur[key_seq[-1]] + if len(cur) == 0 and len(key_seq) > 1: + _del(key_seq[:-1]) + + _set(new_keys, _get(old_keys)) + _del(old_keys) + + +class _RenameConverter: + """ + A converter that handles simple rename. + """ + + RENAME: List[Tuple[str, str]] = [] # list of tuples of (old name, new name) + + @classmethod + def upgrade(cls, cfg: CN) -> None: + for old, new in cls.RENAME: + _rename(cfg, old, new) + + @classmethod + def downgrade(cls, cfg: CN) -> None: + for old, new in cls.RENAME[::-1]: + _rename(cfg, new, old) + + +class ConverterV1(_RenameConverter): + RENAME = [("MODEL.RPN_HEAD.NAME", "MODEL.RPN.HEAD_NAME")] + + +class ConverterV2(_RenameConverter): + """ + A large bulk of rename, before public release. + """ + + RENAME = [ + ("MODEL.WEIGHT", "MODEL.WEIGHTS"), + ("MODEL.PANOPTIC_FPN.SEMANTIC_LOSS_SCALE", "MODEL.SEM_SEG_HEAD.LOSS_WEIGHT"), + ("MODEL.PANOPTIC_FPN.RPN_LOSS_SCALE", "MODEL.RPN.LOSS_WEIGHT"), + ("MODEL.PANOPTIC_FPN.INSTANCE_LOSS_SCALE", "MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT"), + ("MODEL.PANOPTIC_FPN.COMBINE_ON", "MODEL.PANOPTIC_FPN.COMBINE.ENABLED"), + ( + "MODEL.PANOPTIC_FPN.COMBINE_OVERLAP_THRESHOLD", + "MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH", + ), + ( + "MODEL.PANOPTIC_FPN.COMBINE_STUFF_AREA_LIMIT", + "MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT", + ), + ( + "MODEL.PANOPTIC_FPN.COMBINE_INSTANCES_CONFIDENCE_THRESHOLD", + "MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH", + ), + ("MODEL.ROI_HEADS.SCORE_THRESH", "MODEL.ROI_HEADS.SCORE_THRESH_TEST"), + ("MODEL.ROI_HEADS.NMS", "MODEL.ROI_HEADS.NMS_THRESH_TEST"), + ("MODEL.RETINANET.INFERENCE_SCORE_THRESHOLD", "MODEL.RETINANET.SCORE_THRESH_TEST"), + ("MODEL.RETINANET.INFERENCE_TOPK_CANDIDATES", "MODEL.RETINANET.TOPK_CANDIDATES_TEST"), + ("MODEL.RETINANET.INFERENCE_NMS_THRESHOLD", "MODEL.RETINANET.NMS_THRESH_TEST"), + ("TEST.DETECTIONS_PER_IMG", "TEST.DETECTIONS_PER_IMAGE"), + ("TEST.AUG_ON", "TEST.AUG.ENABLED"), + ("TEST.AUG_MIN_SIZES", "TEST.AUG.MIN_SIZES"), + ("TEST.AUG_MAX_SIZE", "TEST.AUG.MAX_SIZE"), + ("TEST.AUG_FLIP", "TEST.AUG.FLIP"), + ] + + @classmethod + def upgrade(cls, cfg: CN) -> None: + super().upgrade(cfg) + + if cfg.MODEL.META_ARCHITECTURE == "RetinaNet": + _rename( + cfg, "MODEL.RETINANET.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS" + ) + _rename(cfg, "MODEL.RETINANET.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES") + del cfg["MODEL"]["RPN"]["ANCHOR_SIZES"] + del cfg["MODEL"]["RPN"]["ANCHOR_ASPECT_RATIOS"] + else: + _rename(cfg, "MODEL.RPN.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS") + _rename(cfg, "MODEL.RPN.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES") + del cfg["MODEL"]["RETINANET"]["ANCHOR_SIZES"] + del cfg["MODEL"]["RETINANET"]["ANCHOR_ASPECT_RATIOS"] + del cfg["MODEL"]["RETINANET"]["ANCHOR_STRIDES"] + + @classmethod + def downgrade(cls, cfg: CN) -> None: + super().downgrade(cfg) + + _rename(cfg, "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS", "MODEL.RPN.ANCHOR_ASPECT_RATIOS") + _rename(cfg, "MODEL.ANCHOR_GENERATOR.SIZES", "MODEL.RPN.ANCHOR_SIZES") + cfg.MODEL.RETINANET.ANCHOR_ASPECT_RATIOS = cfg.MODEL.RPN.ANCHOR_ASPECT_RATIOS + cfg.MODEL.RETINANET.ANCHOR_SIZES = cfg.MODEL.RPN.ANCHOR_SIZES + cfg.MODEL.RETINANET.ANCHOR_STRIDES = [] # this is not used anywhere in any version diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/config/config.py b/motion-gan-pipeline/preprocessing/third/detectron2/config/config.py new file mode 100644 index 0000000..49a55b1 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/config/config.py @@ -0,0 +1,265 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import functools +import inspect +import logging +from fvcore.common.config import CfgNode as _CfgNode + +from detectron2.utils.file_io import PathManager + + +class CfgNode(_CfgNode): + """ + The same as `fvcore.common.config.CfgNode`, but different in: + + 1. Use unsafe yaml loading by default. + Note that this may lead to arbitrary code execution: you must not + load a config file from untrusted sources before manually inspecting + the content of the file. + 2. Support config versioning. + When attempting to merge an old config, it will convert the old config automatically. + + .. automethod:: clone + .. automethod:: freeze + .. automethod:: defrost + .. automethod:: is_frozen + .. automethod:: load_yaml_with_base + .. automethod:: merge_from_list + .. automethod:: merge_from_other_cfg + """ + + @classmethod + def _open_cfg(cls, filename): + return PathManager.open(filename, "r") + + # Note that the default value of allow_unsafe is changed to True + def merge_from_file(self, cfg_filename: str, allow_unsafe: bool = True) -> None: + """ + Load content from the given config file and merge it into self. + + Args: + cfg_filename: config filename + allow_unsafe: allow unsafe yaml syntax + """ + assert PathManager.isfile(cfg_filename), f"Config file '{cfg_filename}' does not exist!" + loaded_cfg = self.load_yaml_with_base(cfg_filename, allow_unsafe=allow_unsafe) + loaded_cfg = type(self)(loaded_cfg) + + # defaults.py needs to import CfgNode + from .defaults import _C + + latest_ver = _C.VERSION + assert ( + latest_ver == self.VERSION + ), "CfgNode.merge_from_file is only allowed on a config object of latest version!" + + logger = logging.getLogger(__name__) + + loaded_ver = loaded_cfg.get("VERSION", None) + if loaded_ver is None: + from .compat import guess_version + + loaded_ver = guess_version(loaded_cfg, cfg_filename) + assert loaded_ver <= self.VERSION, "Cannot merge a v{} config into a v{} config.".format( + loaded_ver, self.VERSION + ) + + if loaded_ver == self.VERSION: + self.merge_from_other_cfg(loaded_cfg) + else: + # compat.py needs to import CfgNode + from .compat import upgrade_config, downgrade_config + + logger.warning( + "Loading an old v{} config file '{}' by automatically upgrading to v{}. " + "See docs/CHANGELOG.md for instructions to update your files.".format( + loaded_ver, cfg_filename, self.VERSION + ) + ) + # To convert, first obtain a full config at an old version + old_self = downgrade_config(self, to_version=loaded_ver) + old_self.merge_from_other_cfg(loaded_cfg) + new_config = upgrade_config(old_self) + self.clear() + self.update(new_config) + + def dump(self, *args, **kwargs): + """ + Returns: + str: a yaml string representation of the config + """ + # to make it show up in docs + return super().dump(*args, **kwargs) + + +global_cfg = CfgNode() + + +def get_cfg() -> CfgNode: + """ + Get a copy of the default config. + + Returns: + a detectron2 CfgNode instance. + """ + from .defaults import _C + + return _C.clone() + + +def set_global_cfg(cfg: CfgNode) -> None: + """ + Let the global config point to the given cfg. + + Assume that the given "cfg" has the key "KEY", after calling + `set_global_cfg(cfg)`, the key can be accessed by: + :: + from detectron2.config import global_cfg + print(global_cfg.KEY) + + By using a hacky global config, you can access these configs anywhere, + without having to pass the config object or the values deep into the code. + This is a hacky feature introduced for quick prototyping / research exploration. + """ + global global_cfg + global_cfg.clear() + global_cfg.update(cfg) + + +def configurable(init_func=None, *, from_config=None): + """ + Decorate a function or a class's __init__ method so that it can be called + with a :class:`CfgNode` object using a :func:`from_config` function that translates + :class:`CfgNode` to arguments. + + Examples: + :: + # Usage 1: Decorator on __init__: + class A: + @configurable + def __init__(self, a, b=2, c=3): + pass + + @classmethod + def from_config(cls, cfg): # 'cfg' must be the first argument + # Returns kwargs to be passed to __init__ + return {"a": cfg.A, "b": cfg.B} + + a1 = A(a=1, b=2) # regular construction + a2 = A(cfg) # construct with a cfg + a3 = A(cfg, b=3, c=4) # construct with extra overwrite + + # Usage 2: Decorator on any function. Needs an extra from_config argument: + @configurable(from_config=lambda cfg: {"a: cfg.A, "b": cfg.B}) + def a_func(a, b=2, c=3): + pass + + a1 = a_func(a=1, b=2) # regular call + a2 = a_func(cfg) # call with a cfg + a3 = a_func(cfg, b=3, c=4) # call with extra overwrite + + Args: + init_func (callable): a class's ``__init__`` method in usage 1. The + class must have a ``from_config`` classmethod which takes `cfg` as + the first argument. + from_config (callable): the from_config function in usage 2. It must take `cfg` + as its first argument. + """ + + if init_func is not None: + assert ( + inspect.isfunction(init_func) + and from_config is None + and init_func.__name__ == "__init__" + ), "Incorrect use of @configurable. Check API documentation for examples." + + @functools.wraps(init_func) + def wrapped(self, *args, **kwargs): + try: + from_config_func = type(self).from_config + except AttributeError as e: + raise AttributeError( + "Class with @configurable must have a 'from_config' classmethod." + ) from e + if not inspect.ismethod(from_config_func): + raise TypeError("Class with @configurable must have a 'from_config' classmethod.") + + if _called_with_cfg(*args, **kwargs): + explicit_args = _get_args_from_config(from_config_func, *args, **kwargs) + init_func(self, **explicit_args) + else: + init_func(self, *args, **kwargs) + + return wrapped + + else: + if from_config is None: + return configurable # @configurable() is made equivalent to @configurable + assert inspect.isfunction( + from_config + ), "from_config argument of configurable must be a function!" + + def wrapper(orig_func): + @functools.wraps(orig_func) + def wrapped(*args, **kwargs): + if _called_with_cfg(*args, **kwargs): + explicit_args = _get_args_from_config(from_config, *args, **kwargs) + return orig_func(**explicit_args) + else: + return orig_func(*args, **kwargs) + + wrapped.from_config = from_config + return wrapped + + return wrapper + + +def _get_args_from_config(from_config_func, *args, **kwargs): + """ + Use `from_config` to obtain explicit arguments. + + Returns: + dict: arguments to be used for cls.__init__ + """ + signature = inspect.signature(from_config_func) + if list(signature.parameters.keys())[0] != "cfg": + if inspect.isfunction(from_config_func): + name = from_config_func.__name__ + else: + name = f"{from_config_func.__self__}.from_config" + raise TypeError(f"{name} must take 'cfg' as the first argument!") + support_var_arg = any( + param.kind in [param.VAR_POSITIONAL, param.VAR_KEYWORD] + for param in signature.parameters.values() + ) + if support_var_arg: # forward all arguments to from_config, if from_config accepts them + ret = from_config_func(*args, **kwargs) + else: + # forward supported arguments to from_config + supported_arg_names = set(signature.parameters.keys()) + extra_kwargs = {} + for name in list(kwargs.keys()): + if name not in supported_arg_names: + extra_kwargs[name] = kwargs.pop(name) + ret = from_config_func(*args, **kwargs) + # forward the other arguments to __init__ + ret.update(extra_kwargs) + return ret + + +def _called_with_cfg(*args, **kwargs): + """ + Returns: + bool: whether the arguments contain CfgNode and should be considered + forwarded to from_config. + """ + from omegaconf import DictConfig + + if len(args) and isinstance(args[0], (_CfgNode, DictConfig)): + return True + if isinstance(kwargs.pop("cfg", None), (_CfgNode, DictConfig)): + return True + # `from_config`'s first argument is forced to be "cfg". + # So the above check covers all cases. + return False diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/config/defaults.py b/motion-gan-pipeline/preprocessing/third/detectron2/config/defaults.py new file mode 100644 index 0000000..2a3a22a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/config/defaults.py @@ -0,0 +1,637 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .config import CfgNode as CN + +# NOTE: given the new config system +# (https://detectron2.readthedocs.io/en/latest/tutorials/lazyconfigs.html), +# we will stop adding new functionalities to default CfgNode. + +# ----------------------------------------------------------------------------- +# Convention about Training / Test specific parameters +# ----------------------------------------------------------------------------- +# Whenever an argument can be either used for training or for testing, the +# corresponding name will be post-fixed by a _TRAIN for a training parameter, +# or _TEST for a test-specific parameter. +# For example, the number of images during training will be +# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be +# IMAGES_PER_BATCH_TEST + +# ----------------------------------------------------------------------------- +# Config definition +# ----------------------------------------------------------------------------- + +_C = CN() + +# The version number, to upgrade from old configs to new ones if any +# changes happen. It's recommended to keep a VERSION in your config file. +_C.VERSION = 2 + +_C.MODEL = CN() +_C.MODEL.LOAD_PROPOSALS = False +_C.MODEL.MASK_ON = False +_C.MODEL.KEYPOINT_ON = False +_C.MODEL.DEVICE = "cuda" +_C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN" + +# Path (a file path, or URL like detectron2://.., https://..) to a checkpoint file +# to be loaded to the model. You can find available models in the model zoo. +_C.MODEL.WEIGHTS = "" + +# Values to be used for image normalization (BGR order, since INPUT.FORMAT defaults to BGR). +# To train on images of different number of channels, just set different mean & std. +# Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675] +_C.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675] +# When using pre-trained models in Detectron1 or any MSRA models, +# std has been absorbed into its conv1 weights, so the std needs to be set 1. +# Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std) +_C.MODEL.PIXEL_STD = [1.0, 1.0, 1.0] + + +# ----------------------------------------------------------------------------- +# INPUT +# ----------------------------------------------------------------------------- +_C.INPUT = CN() +# By default, {MIN,MAX}_SIZE options are used in transforms.ResizeShortestEdge. +# Please refer to ResizeShortestEdge for detailed definition. +# Size of the smallest side of the image during training +_C.INPUT.MIN_SIZE_TRAIN = (800,) +# Sample size of smallest side by choice or random selection from range give by +# INPUT.MIN_SIZE_TRAIN +_C.INPUT.MIN_SIZE_TRAIN_SAMPLING = "choice" +# Maximum size of the side of the image during training +_C.INPUT.MAX_SIZE_TRAIN = 1333 +# Size of the smallest side of the image during testing. Set to zero to disable resize in testing. +_C.INPUT.MIN_SIZE_TEST = 800 +# Maximum size of the side of the image during testing +_C.INPUT.MAX_SIZE_TEST = 1333 +# Mode for flipping images used in data augmentation during training +# choose one of ["horizontal, "vertical", "none"] +_C.INPUT.RANDOM_FLIP = "horizontal" + +# `True` if cropping is used for data augmentation during training +_C.INPUT.CROP = CN({"ENABLED": False}) +# Cropping type. See documentation of `detectron2.data.transforms.RandomCrop` for explanation. +_C.INPUT.CROP.TYPE = "relative_range" +# Size of crop in range (0, 1] if CROP.TYPE is "relative" or "relative_range" and in number of +# pixels if CROP.TYPE is "absolute" +_C.INPUT.CROP.SIZE = [0.9, 0.9] + + +# Whether the model needs RGB, YUV, HSV etc. +# Should be one of the modes defined here, as we use PIL to read the image: +# https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes +# with BGR being the one exception. One can set image format to BGR, we will +# internally use RGB for conversion and flip the channels over +_C.INPUT.FORMAT = "BGR" +# The ground truth mask format that the model will use. +# Mask R-CNN supports either "polygon" or "bitmask" as ground truth. +_C.INPUT.MASK_FORMAT = "polygon" # alternative: "bitmask" + + +# ----------------------------------------------------------------------------- +# Dataset +# ----------------------------------------------------------------------------- +_C.DATASETS = CN() +# List of the dataset names for training. Must be registered in DatasetCatalog +# Samples from these datasets will be merged and used as one dataset. +_C.DATASETS.TRAIN = () +# List of the pre-computed proposal files for training, which must be consistent +# with datasets listed in DATASETS.TRAIN. +_C.DATASETS.PROPOSAL_FILES_TRAIN = () +# Number of top scoring precomputed proposals to keep for training +_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN = 2000 +# List of the dataset names for testing. Must be registered in DatasetCatalog +_C.DATASETS.TEST = () +# List of the pre-computed proposal files for test, which must be consistent +# with datasets listed in DATASETS.TEST. +_C.DATASETS.PROPOSAL_FILES_TEST = () +# Number of top scoring precomputed proposals to keep for test +_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST = 1000 + +# ----------------------------------------------------------------------------- +# DataLoader +# ----------------------------------------------------------------------------- +_C.DATALOADER = CN() +# Number of data loading threads +_C.DATALOADER.NUM_WORKERS = 4 +# If True, each batch should contain only images for which the aspect ratio +# is compatible. This groups portrait images together, and landscape images +# are not batched with portrait images. +_C.DATALOADER.ASPECT_RATIO_GROUPING = True +# Options: TrainingSampler, RepeatFactorTrainingSampler +_C.DATALOADER.SAMPLER_TRAIN = "TrainingSampler" +# Repeat threshold for RepeatFactorTrainingSampler +_C.DATALOADER.REPEAT_THRESHOLD = 0.0 +# Tf True, when working on datasets that have instance annotations, the +# training dataloader will filter out images without associated annotations +_C.DATALOADER.FILTER_EMPTY_ANNOTATIONS = True + +# ---------------------------------------------------------------------------- # +# Backbone options +# ---------------------------------------------------------------------------- # +_C.MODEL.BACKBONE = CN() + +_C.MODEL.BACKBONE.NAME = "build_resnet_backbone" +# Freeze the first several stages so they are not trained. +# There are 5 stages in ResNet. The first is a convolution, and the following +# stages are each group of residual blocks. +_C.MODEL.BACKBONE.FREEZE_AT = 2 + + +# ---------------------------------------------------------------------------- # +# FPN options +# ---------------------------------------------------------------------------- # +_C.MODEL.FPN = CN() +# Names of the input feature maps to be used by FPN +# They must have contiguous power of 2 strides +# e.g., ["res2", "res3", "res4", "res5"] +_C.MODEL.FPN.IN_FEATURES = [] +_C.MODEL.FPN.OUT_CHANNELS = 256 + +# Options: "" (no norm), "GN" +_C.MODEL.FPN.NORM = "" + +# Types for fusing the FPN top-down and lateral features. Can be either "sum" or "avg" +_C.MODEL.FPN.FUSE_TYPE = "sum" + + +# ---------------------------------------------------------------------------- # +# Proposal generator options +# ---------------------------------------------------------------------------- # +_C.MODEL.PROPOSAL_GENERATOR = CN() +# Current proposal generators include "RPN", "RRPN" and "PrecomputedProposals" +_C.MODEL.PROPOSAL_GENERATOR.NAME = "RPN" +# Proposal height and width both need to be greater than MIN_SIZE +# (a the scale used during training or inference) +_C.MODEL.PROPOSAL_GENERATOR.MIN_SIZE = 0 + + +# ---------------------------------------------------------------------------- # +# Anchor generator options +# ---------------------------------------------------------------------------- # +_C.MODEL.ANCHOR_GENERATOR = CN() +# The generator can be any name in the ANCHOR_GENERATOR registry +_C.MODEL.ANCHOR_GENERATOR.NAME = "DefaultAnchorGenerator" +# Anchor sizes (i.e. sqrt of area) in absolute pixels w.r.t. the network input. +# Format: list[list[float]]. SIZES[i] specifies the list of sizes to use for +# IN_FEATURES[i]; len(SIZES) must be equal to len(IN_FEATURES) or 1. +# When len(SIZES) == 1, SIZES[0] is used for all IN_FEATURES. +_C.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64, 128, 256, 512]] +# Anchor aspect ratios. For each area given in `SIZES`, anchors with different aspect +# ratios are generated by an anchor generator. +# Format: list[list[float]]. ASPECT_RATIOS[i] specifies the list of aspect ratios (H/W) +# to use for IN_FEATURES[i]; len(ASPECT_RATIOS) == len(IN_FEATURES) must be true, +# or len(ASPECT_RATIOS) == 1 is true and aspect ratio list ASPECT_RATIOS[0] is used +# for all IN_FEATURES. +_C.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.5, 1.0, 2.0]] +# Anchor angles. +# list[list[float]], the angle in degrees, for each input feature map. +# ANGLES[i] specifies the list of angles for IN_FEATURES[i]. +_C.MODEL.ANCHOR_GENERATOR.ANGLES = [[-90, 0, 90]] +# Relative offset between the center of the first anchor and the top-left corner of the image +# Value has to be in [0, 1). Recommend to use 0.5, which means half stride. +# The value is not expected to affect model accuracy. +_C.MODEL.ANCHOR_GENERATOR.OFFSET = 0.0 + +# ---------------------------------------------------------------------------- # +# RPN options +# ---------------------------------------------------------------------------- # +_C.MODEL.RPN = CN() +_C.MODEL.RPN.HEAD_NAME = "StandardRPNHead" # used by RPN_HEAD_REGISTRY + +# Names of the input feature maps to be used by RPN +# e.g., ["p2", "p3", "p4", "p5", "p6"] for FPN +_C.MODEL.RPN.IN_FEATURES = ["res4"] +# Remove RPN anchors that go outside the image by BOUNDARY_THRESH pixels +# Set to -1 or a large value, e.g. 100000, to disable pruning anchors +_C.MODEL.RPN.BOUNDARY_THRESH = -1 +# IOU overlap ratios [BG_IOU_THRESHOLD, FG_IOU_THRESHOLD] +# Minimum overlap required between an anchor and ground-truth box for the +# (anchor, gt box) pair to be a positive example (IoU >= FG_IOU_THRESHOLD +# ==> positive RPN example: 1) +# Maximum overlap allowed between an anchor and ground-truth box for the +# (anchor, gt box) pair to be a negative examples (IoU < BG_IOU_THRESHOLD +# ==> negative RPN example: 0) +# Anchors with overlap in between (BG_IOU_THRESHOLD <= IoU < FG_IOU_THRESHOLD) +# are ignored (-1) +_C.MODEL.RPN.IOU_THRESHOLDS = [0.3, 0.7] +_C.MODEL.RPN.IOU_LABELS = [0, -1, 1] +# Number of regions per image used to train RPN +_C.MODEL.RPN.BATCH_SIZE_PER_IMAGE = 256 +# Target fraction of foreground (positive) examples per RPN minibatch +_C.MODEL.RPN.POSITIVE_FRACTION = 0.5 +# Options are: "smooth_l1", "giou", "diou", "ciou" +_C.MODEL.RPN.BBOX_REG_LOSS_TYPE = "smooth_l1" +_C.MODEL.RPN.BBOX_REG_LOSS_WEIGHT = 1.0 +# Weights on (dx, dy, dw, dh) for normalizing RPN anchor regression targets +_C.MODEL.RPN.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0) +# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1. +_C.MODEL.RPN.SMOOTH_L1_BETA = 0.0 +_C.MODEL.RPN.LOSS_WEIGHT = 1.0 +# Number of top scoring RPN proposals to keep before applying NMS +# When FPN is used, this is *per FPN level* (not total) +_C.MODEL.RPN.PRE_NMS_TOPK_TRAIN = 12000 +_C.MODEL.RPN.PRE_NMS_TOPK_TEST = 6000 +# Number of top scoring RPN proposals to keep after applying NMS +# When FPN is used, this limit is applied per level and then again to the union +# of proposals from all levels +# NOTE: When FPN is used, the meaning of this config is different from Detectron1. +# It means per-batch topk in Detectron1, but per-image topk here. +# See the "find_top_rpn_proposals" function for details. +_C.MODEL.RPN.POST_NMS_TOPK_TRAIN = 2000 +_C.MODEL.RPN.POST_NMS_TOPK_TEST = 1000 +# NMS threshold used on RPN proposals +_C.MODEL.RPN.NMS_THRESH = 0.7 +# Set this to -1 to use the same number of output channels as input channels. +_C.MODEL.RPN.CONV_DIMS = [-1] + +# ---------------------------------------------------------------------------- # +# ROI HEADS options +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_HEADS = CN() +_C.MODEL.ROI_HEADS.NAME = "Res5ROIHeads" +# Number of foreground classes +_C.MODEL.ROI_HEADS.NUM_CLASSES = 80 +# Names of the input feature maps to be used by ROI heads +# Currently all heads (box, mask, ...) use the same input feature map list +# e.g., ["p2", "p3", "p4", "p5"] is commonly used for FPN +_C.MODEL.ROI_HEADS.IN_FEATURES = ["res4"] +# IOU overlap ratios [IOU_THRESHOLD] +# Overlap threshold for an RoI to be considered background (if < IOU_THRESHOLD) +# Overlap threshold for an RoI to be considered foreground (if >= IOU_THRESHOLD) +_C.MODEL.ROI_HEADS.IOU_THRESHOLDS = [0.5] +_C.MODEL.ROI_HEADS.IOU_LABELS = [0, 1] +# RoI minibatch size *per image* (number of regions of interest [ROIs]) during training +# Total number of RoIs per training minibatch = +# ROI_HEADS.BATCH_SIZE_PER_IMAGE * SOLVER.IMS_PER_BATCH +# E.g., a common configuration is: 512 * 16 = 8192 +_C.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512 +# Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0) +_C.MODEL.ROI_HEADS.POSITIVE_FRACTION = 0.25 + +# Only used on test mode + +# Minimum score threshold (assuming scores in a [0, 1] range); a value chosen to +# balance obtaining high recall with not having too many low precision +# detections that will slow down inference post processing steps (like NMS) +# A default threshold of 0.0 increases AP by ~0.2-0.3 but significantly slows down +# inference. +_C.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.05 +# Overlap threshold used for non-maximum suppression (suppress boxes with +# IoU >= this threshold) +_C.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.5 +# If True, augment proposals with ground-truth boxes before sampling proposals to +# train ROI heads. +_C.MODEL.ROI_HEADS.PROPOSAL_APPEND_GT = True + +# ---------------------------------------------------------------------------- # +# Box Head +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_BOX_HEAD = CN() +# C4 don't use head name option +# Options for non-C4 models: FastRCNNConvFCHead, +_C.MODEL.ROI_BOX_HEAD.NAME = "" +# Options are: "smooth_l1", "giou", "diou", "ciou" +_C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_TYPE = "smooth_l1" +# The final scaling coefficient on the box regression loss, used to balance the magnitude of its +# gradients with other losses in the model. See also `MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT`. +_C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT = 1.0 +# Default weights on (dx, dy, dw, dh) for normalizing bbox regression targets +# These are empirically chosen to approximately lead to unit variance targets +_C.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10.0, 10.0, 5.0, 5.0) +# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1. +_C.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA = 0.0 +_C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14 +_C.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO = 0 +# Type of pooling operation applied to the incoming feature map for each RoI +_C.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2" + +_C.MODEL.ROI_BOX_HEAD.NUM_FC = 0 +# Hidden layer dimension for FC layers in the RoI box head +_C.MODEL.ROI_BOX_HEAD.FC_DIM = 1024 +_C.MODEL.ROI_BOX_HEAD.NUM_CONV = 0 +# Channel dimension for Conv layers in the RoI box head +_C.MODEL.ROI_BOX_HEAD.CONV_DIM = 256 +# Normalization method for the convolution layers. +# Options: "" (no norm), "GN", "SyncBN". +_C.MODEL.ROI_BOX_HEAD.NORM = "" +# Whether to use class agnostic for bbox regression +_C.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG = False +# If true, RoI heads use bounding boxes predicted by the box head rather than proposal boxes. +_C.MODEL.ROI_BOX_HEAD.TRAIN_ON_PRED_BOXES = False + +# ---------------------------------------------------------------------------- # +# Cascaded Box Head +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_BOX_CASCADE_HEAD = CN() +# The number of cascade stages is implicitly defined by the length of the following two configs. +_C.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS = ( + (10.0, 10.0, 5.0, 5.0), + (20.0, 20.0, 10.0, 10.0), + (30.0, 30.0, 15.0, 15.0), +) +_C.MODEL.ROI_BOX_CASCADE_HEAD.IOUS = (0.5, 0.6, 0.7) + + +# ---------------------------------------------------------------------------- # +# Mask Head +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_MASK_HEAD = CN() +_C.MODEL.ROI_MASK_HEAD.NAME = "MaskRCNNConvUpsampleHead" +_C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 14 +_C.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO = 0 +_C.MODEL.ROI_MASK_HEAD.NUM_CONV = 0 # The number of convs in the mask head +_C.MODEL.ROI_MASK_HEAD.CONV_DIM = 256 +# Normalization method for the convolution layers. +# Options: "" (no norm), "GN", "SyncBN". +_C.MODEL.ROI_MASK_HEAD.NORM = "" +# Whether to use class agnostic for mask prediction +_C.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK = False +# Type of pooling operation applied to the incoming feature map for each RoI +_C.MODEL.ROI_MASK_HEAD.POOLER_TYPE = "ROIAlignV2" + + +# ---------------------------------------------------------------------------- # +# Keypoint Head +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_KEYPOINT_HEAD = CN() +_C.MODEL.ROI_KEYPOINT_HEAD.NAME = "KRCNNConvDeconvUpsampleHead" +_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION = 14 +_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO = 0 +_C.MODEL.ROI_KEYPOINT_HEAD.CONV_DIMS = tuple(512 for _ in range(8)) +_C.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS = 17 # 17 is the number of keypoints in COCO. + +# Images with too few (or no) keypoints are excluded from training. +_C.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE = 1 +# Normalize by the total number of visible keypoints in the minibatch if True. +# Otherwise, normalize by the total number of keypoints that could ever exist +# in the minibatch. +# The keypoint softmax loss is only calculated on visible keypoints. +# Since the number of visible keypoints can vary significantly between +# minibatches, this has the effect of up-weighting the importance of +# minibatches with few visible keypoints. (Imagine the extreme case of +# only one visible keypoint versus N: in the case of N, each one +# contributes 1/N to the gradient compared to the single keypoint +# determining the gradient direction). Instead, we can normalize the +# loss by the total number of keypoints, if it were the case that all +# keypoints were visible in a full minibatch. (Returning to the example, +# this means that the one visible keypoint contributes as much as each +# of the N keypoints.) +_C.MODEL.ROI_KEYPOINT_HEAD.NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS = True +# Multi-task loss weight to use for keypoints +# Recommended values: +# - use 1.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is True +# - use 4.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is False +_C.MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT = 1.0 +# Type of pooling operation applied to the incoming feature map for each RoI +_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_TYPE = "ROIAlignV2" + +# ---------------------------------------------------------------------------- # +# Semantic Segmentation Head +# ---------------------------------------------------------------------------- # +_C.MODEL.SEM_SEG_HEAD = CN() +_C.MODEL.SEM_SEG_HEAD.NAME = "SemSegFPNHead" +_C.MODEL.SEM_SEG_HEAD.IN_FEATURES = ["p2", "p3", "p4", "p5"] +# Label in the semantic segmentation ground truth that is ignored, i.e., no loss is calculated for +# the correposnding pixel. +_C.MODEL.SEM_SEG_HEAD.IGNORE_VALUE = 255 +# Number of classes in the semantic segmentation head +_C.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 54 +# Number of channels in the 3x3 convs inside semantic-FPN heads. +_C.MODEL.SEM_SEG_HEAD.CONVS_DIM = 128 +# Outputs from semantic-FPN heads are up-scaled to the COMMON_STRIDE stride. +_C.MODEL.SEM_SEG_HEAD.COMMON_STRIDE = 4 +# Normalization method for the convolution layers. Options: "" (no norm), "GN". +_C.MODEL.SEM_SEG_HEAD.NORM = "GN" +_C.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT = 1.0 + +_C.MODEL.PANOPTIC_FPN = CN() +# Scaling of all losses from instance detection / segmentation head. +_C.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT = 1.0 + +# options when combining instance & semantic segmentation outputs +_C.MODEL.PANOPTIC_FPN.COMBINE = CN({"ENABLED": True}) # "COMBINE.ENABLED" is deprecated & not used +_C.MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH = 0.5 +_C.MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT = 4096 +_C.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = 0.5 + + +# ---------------------------------------------------------------------------- # +# RetinaNet Head +# ---------------------------------------------------------------------------- # +_C.MODEL.RETINANET = CN() + +# This is the number of foreground classes. +_C.MODEL.RETINANET.NUM_CLASSES = 80 + +_C.MODEL.RETINANET.IN_FEATURES = ["p3", "p4", "p5", "p6", "p7"] + +# Convolutions to use in the cls and bbox tower +# NOTE: this doesn't include the last conv for logits +_C.MODEL.RETINANET.NUM_CONVS = 4 + +# IoU overlap ratio [bg, fg] for labeling anchors. +# Anchors with < bg are labeled negative (0) +# Anchors with >= bg and < fg are ignored (-1) +# Anchors with >= fg are labeled positive (1) +_C.MODEL.RETINANET.IOU_THRESHOLDS = [0.4, 0.5] +_C.MODEL.RETINANET.IOU_LABELS = [0, -1, 1] + +# Prior prob for rare case (i.e. foreground) at the beginning of training. +# This is used to set the bias for the logits layer of the classifier subnet. +# This improves training stability in the case of heavy class imbalance. +_C.MODEL.RETINANET.PRIOR_PROB = 0.01 + +# Inference cls score threshold, only anchors with score > INFERENCE_TH are +# considered for inference (to improve speed) +_C.MODEL.RETINANET.SCORE_THRESH_TEST = 0.05 +# Select topk candidates before NMS +_C.MODEL.RETINANET.TOPK_CANDIDATES_TEST = 1000 +_C.MODEL.RETINANET.NMS_THRESH_TEST = 0.5 + +# Weights on (dx, dy, dw, dh) for normalizing Retinanet anchor regression targets +_C.MODEL.RETINANET.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0) + +# Loss parameters +_C.MODEL.RETINANET.FOCAL_LOSS_GAMMA = 2.0 +_C.MODEL.RETINANET.FOCAL_LOSS_ALPHA = 0.25 +_C.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA = 0.1 +# Options are: "smooth_l1", "giou", "diou", "ciou" +_C.MODEL.RETINANET.BBOX_REG_LOSS_TYPE = "smooth_l1" + +# One of BN, SyncBN, FrozenBN, GN +# Only supports GN until unshared norm is implemented +_C.MODEL.RETINANET.NORM = "" + + +# ---------------------------------------------------------------------------- # +# ResNe[X]t options (ResNets = {ResNet, ResNeXt} +# Note that parts of a resnet may be used for both the backbone and the head +# These options apply to both +# ---------------------------------------------------------------------------- # +_C.MODEL.RESNETS = CN() + +_C.MODEL.RESNETS.DEPTH = 50 +_C.MODEL.RESNETS.OUT_FEATURES = ["res4"] # res4 for C4 backbone, res2..5 for FPN backbone + +# Number of groups to use; 1 ==> ResNet; > 1 ==> ResNeXt +_C.MODEL.RESNETS.NUM_GROUPS = 1 + +# Options: FrozenBN, GN, "SyncBN", "BN" +_C.MODEL.RESNETS.NORM = "FrozenBN" + +# Baseline width of each group. +# Scaling this parameters will scale the width of all bottleneck layers. +_C.MODEL.RESNETS.WIDTH_PER_GROUP = 64 + +# Place the stride 2 conv on the 1x1 filter +# Use True only for the original MSRA ResNet; use False for C2 and Torch models +_C.MODEL.RESNETS.STRIDE_IN_1X1 = True + +# Apply dilation in stage "res5" +_C.MODEL.RESNETS.RES5_DILATION = 1 + +# Output width of res2. Scaling this parameters will scale the width of all 1x1 convs in ResNet +# For R18 and R34, this needs to be set to 64 +_C.MODEL.RESNETS.RES2_OUT_CHANNELS = 256 +_C.MODEL.RESNETS.STEM_OUT_CHANNELS = 64 + +# Apply Deformable Convolution in stages +# Specify if apply deform_conv on Res2, Res3, Res4, Res5 +_C.MODEL.RESNETS.DEFORM_ON_PER_STAGE = [False, False, False, False] +# Use True to use modulated deform_conv (DeformableV2, https://arxiv.org/abs/1811.11168); +# Use False for DeformableV1. +_C.MODEL.RESNETS.DEFORM_MODULATED = False +# Number of groups in deformable conv. +_C.MODEL.RESNETS.DEFORM_NUM_GROUPS = 1 + + +# ---------------------------------------------------------------------------- # +# Solver +# ---------------------------------------------------------------------------- # +_C.SOLVER = CN() + +# Options: WarmupMultiStepLR, WarmupCosineLR. +# See detectron2/solver/build.py for definition. +_C.SOLVER.LR_SCHEDULER_NAME = "WarmupMultiStepLR" + +_C.SOLVER.MAX_ITER = 40000 + +_C.SOLVER.BASE_LR = 0.001 +# The end lr, only used by WarmupCosineLR +_C.SOLVER.BASE_LR_END = 0.0 + +_C.SOLVER.MOMENTUM = 0.9 + +_C.SOLVER.NESTEROV = False + +_C.SOLVER.WEIGHT_DECAY = 0.0001 +# The weight decay that's applied to parameters of normalization layers +# (typically the affine transformation) +_C.SOLVER.WEIGHT_DECAY_NORM = 0.0 + +_C.SOLVER.GAMMA = 0.1 +# The iteration number to decrease learning rate by GAMMA. +_C.SOLVER.STEPS = (30000,) + +_C.SOLVER.WARMUP_FACTOR = 1.0 / 1000 +_C.SOLVER.WARMUP_ITERS = 1000 +_C.SOLVER.WARMUP_METHOD = "linear" + +# Save a checkpoint after every this number of iterations +_C.SOLVER.CHECKPOINT_PERIOD = 5000 + +# Number of images per batch across all machines. This is also the number +# of training images per step (i.e. per iteration). If we use 16 GPUs +# and IMS_PER_BATCH = 32, each GPU will see 2 images per batch. +# May be adjusted automatically if REFERENCE_WORLD_SIZE is set. +_C.SOLVER.IMS_PER_BATCH = 16 + +# The reference number of workers (GPUs) this config is meant to train with. +# It takes no effect when set to 0. +# With a non-zero value, it will be used by DefaultTrainer to compute a desired +# per-worker batch size, and then scale the other related configs (total batch size, +# learning rate, etc) to match the per-worker batch size. +# See documentation of `DefaultTrainer.auto_scale_workers` for details: +_C.SOLVER.REFERENCE_WORLD_SIZE = 0 + +# Detectron v1 (and previous detection code) used a 2x higher LR and 0 WD for +# biases. This is not useful (at least for recent models). You should avoid +# changing these and they exist only to reproduce Detectron v1 training if +# desired. +_C.SOLVER.BIAS_LR_FACTOR = 1.0 +_C.SOLVER.WEIGHT_DECAY_BIAS = None # None means following WEIGHT_DECAY + +# Gradient clipping +_C.SOLVER.CLIP_GRADIENTS = CN({"ENABLED": False}) +# Type of gradient clipping, currently 2 values are supported: +# - "value": the absolute values of elements of each gradients are clipped +# - "norm": the norm of the gradient for each parameter is clipped thus +# affecting all elements in the parameter +_C.SOLVER.CLIP_GRADIENTS.CLIP_TYPE = "value" +# Maximum absolute value used for clipping gradients +_C.SOLVER.CLIP_GRADIENTS.CLIP_VALUE = 1.0 +# Floating point number p for L-p norm to be used with the "norm" +# gradient clipping type; for L-inf, please specify .inf +_C.SOLVER.CLIP_GRADIENTS.NORM_TYPE = 2.0 + +# Enable automatic mixed precision for training +# Note that this does not change model's inference behavior. +# To use AMP in inference, run inference under autocast() +_C.SOLVER.AMP = CN({"ENABLED": False}) + +# ---------------------------------------------------------------------------- # +# Specific test options +# ---------------------------------------------------------------------------- # +_C.TEST = CN() +# For end-to-end tests to verify the expected accuracy. +# Each item is [task, metric, value, tolerance] +# e.g.: [['bbox', 'AP', 38.5, 0.2]] +_C.TEST.EXPECTED_RESULTS = [] +# The period (in terms of steps) to evaluate the model during training. +# Set to 0 to disable. +_C.TEST.EVAL_PERIOD = 0 +# The sigmas used to calculate keypoint OKS. See http://cocodataset.org/#keypoints-eval +# When empty, it will use the defaults in COCO. +# Otherwise it should be a list[float] with the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS. +_C.TEST.KEYPOINT_OKS_SIGMAS = [] +# Maximum number of detections to return per image during inference (100 is +# based on the limit established for the COCO dataset). +_C.TEST.DETECTIONS_PER_IMAGE = 100 + +_C.TEST.AUG = CN({"ENABLED": False}) +_C.TEST.AUG.MIN_SIZES = (400, 500, 600, 700, 800, 900, 1000, 1100, 1200) +_C.TEST.AUG.MAX_SIZE = 4000 +_C.TEST.AUG.FLIP = True + +_C.TEST.PRECISE_BN = CN({"ENABLED": False}) +_C.TEST.PRECISE_BN.NUM_ITER = 200 + +# ---------------------------------------------------------------------------- # +# Misc options +# ---------------------------------------------------------------------------- # +# Directory where output files are written +_C.OUTPUT_DIR = "./output" +# Set seed to negative to fully randomize everything. +# Set seed to positive to use a fixed seed. Note that a fixed seed increases +# reproducibility but does not guarantee fully deterministic behavior. +# Disabling all parallelism further increases reproducibility. +_C.SEED = -1 +# Benchmark different cudnn algorithms. +# If input images have very different sizes, this option will have large overhead +# for about 10k iterations. It usually hurts total time, but can benefit for certain models. +# If input images have the same or similar sizes, benchmark is often helpful. +_C.CUDNN_BENCHMARK = False +# The period (in terms of steps) for minibatch visualization at train time. +# Set to 0 to disable. +_C.VIS_PERIOD = 0 + +# global config is for quick hack purposes. +# You can set them in command line or config files, +# and access it with: +# +# from detectron2.config import global_cfg +# print(global_cfg.HACK) +# +# Do not commit any configs into it. +_C.GLOBAL = CN() +_C.GLOBAL.HACK = 1.0 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/config/instantiate.py b/motion-gan-pipeline/preprocessing/third/detectron2/config/instantiate.py new file mode 100644 index 0000000..93239a9 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/config/instantiate.py @@ -0,0 +1,82 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import dataclasses +import logging +from collections import abc +from typing import Any + +from detectron2.utils.registry import _convert_target_to_string, locate + +__all__ = ["dump_dataclass", "instantiate"] + + +def dump_dataclass(obj: Any): + """ + Dump a dataclass recursively into a dict that can be later instantiated. + + Args: + obj: a dataclass object + + Returns: + dict + """ + assert dataclasses.is_dataclass(obj) and not isinstance( + obj, type + ), "dump_dataclass() requires an instance of a dataclass." + ret = {"_target_": _convert_target_to_string(type(obj))} + for f in dataclasses.fields(obj): + v = getattr(obj, f.name) + if dataclasses.is_dataclass(v): + v = dump_dataclass(v) + if isinstance(v, (list, tuple)): + v = [dump_dataclass(x) if dataclasses.is_dataclass(x) else x for x in v] + ret[f.name] = v + return ret + + +def instantiate(cfg): + """ + Recursively instantiate objects defined in dictionaries by + "_target_" and arguments. + + Args: + cfg: a dict-like object with "_target_" that defines the caller, and + other keys that define the arguments + + Returns: + object instantiated by cfg + """ + from omegaconf import ListConfig + + if isinstance(cfg, ListConfig): + lst = [instantiate(x) for x in cfg] + return ListConfig(lst, flags={"allow_objects": True}) + if isinstance(cfg, list): + # Specialize for list, because many classes take + # list[objects] as arguments, such as ResNet, DatasetMapper + return [instantiate(x) for x in cfg] + + if isinstance(cfg, abc.Mapping) and "_target_" in cfg: + # conceptually equivalent to hydra.autils.instantiate(cfg) with _convert_=all, + # but faster: https://github.com/facebookresearch/hydra/issues/1200 + cfg = {k: instantiate(v) for k, v in cfg.items()} + cls = cfg.pop("_target_") + cls = instantiate(cls) + + if isinstance(cls, str): + cls_name = cls + cls = locate(cls_name) + assert cls is not None, cls_name + else: + try: + cls_name = cls.__module__ + "." + cls.__qualname__ + except Exception: + # target could be anything, so the above could fail + cls_name = str(cls) + assert callable(cls), f"_target_ {cls} does not define a callable object" + try: + return cls(**cfg) + except TypeError: + logger = logging.getLogger(__name__) + logger.error(f"Error when instantiating {cls_name}!") + raise + return cfg # return as-is if don't know what to do diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/config/lazy.py b/motion-gan-pipeline/preprocessing/third/detectron2/config/lazy.py new file mode 100644 index 0000000..fa5d86b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/config/lazy.py @@ -0,0 +1,399 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import ast +import builtins +import importlib +import inspect +import logging +import os +import uuid +from collections import abc +from contextlib import contextmanager +from copy import deepcopy +from dataclasses import is_dataclass +from typing import List, Tuple, Union +import cloudpickle +import yaml +from omegaconf import DictConfig, ListConfig, OmegaConf + +from detectron2.utils.file_io import PathManager +from detectron2.utils.registry import _convert_target_to_string + +__all__ = ["LazyCall", "LazyConfig"] + + +class LazyCall: + """ + Wrap a callable so that when it's called, the call will not be executed, + but returns a dict that describes the call. + + LazyCall object has to be called with only keyword arguments. Positional + arguments are not yet supported. + + Examples: + :: + from detectron2.config import instantiate, LazyCall + + layer_cfg = LazyCall(nn.Conv2d)(in_channels=32, out_channels=32) + layer_cfg.out_channels = 64 # can edit it afterwards + layer = instantiate(layer_cfg) + """ + + def __init__(self, target): + if not (callable(target) or isinstance(target, (str, abc.Mapping))): + raise TypeError( + f"target of LazyCall must be a callable or defines a callable! Got {target}" + ) + self._target = target + + def __call__(self, **kwargs): + if is_dataclass(self._target): + # omegaconf object cannot hold dataclass type + # https://github.com/omry/omegaconf/issues/784 + target = _convert_target_to_string(self._target) + else: + target = self._target + kwargs["_target_"] = target + + return DictConfig(content=kwargs, flags={"allow_objects": True}) + + +def _visit_dict_config(cfg, func): + """ + Apply func recursively to all DictConfig in cfg. + """ + if isinstance(cfg, DictConfig): + func(cfg) + for v in cfg.values(): + _visit_dict_config(v, func) + elif isinstance(cfg, ListConfig): + for v in cfg: + _visit_dict_config(v, func) + + +def _validate_py_syntax(filename): + # see also https://github.com/open-mmlab/mmcv/blob/master/mmcv/utils/config.py + with PathManager.open(filename, "r") as f: + content = f.read() + try: + ast.parse(content) + except SyntaxError as e: + raise SyntaxError(f"Config file {filename} has syntax error!") from e + + +def _cast_to_config(obj): + # if given a dict, return DictConfig instead + if isinstance(obj, dict): + return DictConfig(obj, flags={"allow_objects": True}) + return obj + + +_CFG_PACKAGE_NAME = "detectron2._cfg_loader" +""" +A namespace to put all imported config into. +""" + + +def _random_package_name(filename): + # generate a random package name when loading config files + return _CFG_PACKAGE_NAME + str(uuid.uuid4())[:4] + "." + os.path.basename(filename) + + +@contextmanager +def _patch_import(): + """ + Enhance relative import statements in config files, so that they: + 1. locate files purely based on relative location, regardless of packages. + e.g. you can import file without having __init__ + 2. do not cache modules globally; modifications of module states has no side effect + 3. support other storage system through PathManager + 4. imported dict are turned into omegaconf.DictConfig automatically + """ + old_import = builtins.__import__ + + def find_relative_file(original_file, relative_import_path, level): + cur_file = os.path.dirname(original_file) + for _ in range(level - 1): + cur_file = os.path.dirname(cur_file) + cur_name = relative_import_path.lstrip(".") + for part in cur_name.split("."): + cur_file = os.path.join(cur_file, part) + # NOTE: directory import is not handled. Because then it's unclear + # if such import should produce python module or DictConfig. This can + # be discussed further if needed. + if not cur_file.endswith(".py"): + cur_file += ".py" + if not PathManager.isfile(cur_file): + raise ImportError( + f"Cannot import name {relative_import_path} from " + f"{original_file}: {cur_file} has to exist." + ) + return cur_file + + def new_import(name, globals=None, locals=None, fromlist=(), level=0): + if ( + # Only deal with relative imports inside config files + level != 0 + and globals is not None + and (globals.get("__package__", "") or "").startswith(_CFG_PACKAGE_NAME) + ): + cur_file = find_relative_file(globals["__file__"], name, level) + _validate_py_syntax(cur_file) + spec = importlib.machinery.ModuleSpec( + _random_package_name(cur_file), None, origin=cur_file + ) + module = importlib.util.module_from_spec(spec) + module.__file__ = cur_file + with PathManager.open(cur_file) as f: + content = f.read() + exec(compile(content, cur_file, "exec"), module.__dict__) + for name in fromlist: # turn imported dict into DictConfig automatically + val = _cast_to_config(module.__dict__[name]) + module.__dict__[name] = val + return module + return old_import(name, globals, locals, fromlist=fromlist, level=level) + + builtins.__import__ = new_import + yield new_import + builtins.__import__ = old_import + + +class LazyConfig: + """ + Provide methods to save, load, and overrides an omegaconf config object + which may contain definition of lazily-constructed objects. + """ + + @staticmethod + def load_rel(filename: str, keys: Union[None, str, Tuple[str, ...]] = None): + """ + Similar to :meth:`load()`, but load path relative to the caller's + source file. + + This has the same functionality as a relative import, except that this method + accepts filename as a string, so more characters are allowed in the filename. + """ + caller_frame = inspect.stack()[1] + caller_fname = caller_frame[0].f_code.co_filename + assert caller_fname != "", "load_rel Unable to find caller" + caller_dir = os.path.dirname(caller_fname) + filename = os.path.join(caller_dir, filename) + return LazyConfig.load(filename, keys) + + @staticmethod + def load(filename: str, keys: Union[None, str, Tuple[str, ...]] = None): + """ + Load a config file. + + Args: + filename: absolute path or relative path w.r.t. the current working directory + keys: keys to load and return. If not given, return all keys + (whose values are config objects) in a dict. + """ + has_keys = keys is not None + filename = filename.replace("/./", "/") # redundant + if os.path.splitext(filename)[1] not in [".py", ".yaml", ".yml"]: + raise ValueError(f"Config file {filename} has to be a python or yaml file.") + if filename.endswith(".py"): + _validate_py_syntax(filename) + + with _patch_import(): + # Record the filename + module_namespace = { + "__file__": filename, + "__package__": _random_package_name(filename), + } + with PathManager.open(filename) as f: + content = f.read() + # Compile first with filename to: + # 1. make filename appears in stacktrace + # 2. make load_rel able to find its parent's (possibly remote) location + exec(compile(content, filename, "exec"), module_namespace) + + ret = module_namespace + else: + with PathManager.open(filename) as f: + obj = yaml.unsafe_load(f) + ret = OmegaConf.create(obj, flags={"allow_objects": True}) + + if has_keys: + if isinstance(keys, str): + return _cast_to_config(ret[keys]) + else: + return tuple(_cast_to_config(ret[a]) for a in keys) + else: + if filename.endswith(".py"): + # when not specified, only load those that are config objects + ret = DictConfig( + { + name: _cast_to_config(value) + for name, value in ret.items() + if isinstance(value, (DictConfig, ListConfig, dict)) + and not name.startswith("_") + }, + flags={"allow_objects": True}, + ) + return ret + + @staticmethod + def save(cfg, filename: str): + """ + Save a config object to a yaml file. + Note that when the config dictionary contains complex objects (e.g. lambda), + it can't be saved to yaml. In that case we will print an error and + attempt to save to a pkl file instead. + + Args: + cfg: an omegaconf config object + filename: yaml file name to save the config file + """ + logger = logging.getLogger(__name__) + try: + cfg = deepcopy(cfg) + except Exception: + pass + else: + # if it's deep-copyable, then... + def _replace_type_by_name(x): + if "_target_" in x and callable(x._target_): + try: + x._target_ = _convert_target_to_string(x._target_) + except AttributeError: + pass + + # not necessary, but makes yaml looks nicer + _visit_dict_config(cfg, _replace_type_by_name) + + save_pkl = False + try: + dict = OmegaConf.to_container(cfg, resolve=False) + dumped = yaml.dump(dict, default_flow_style=None, allow_unicode=True, width=9999) + with PathManager.open(filename, "w") as f: + f.write(dumped) + + try: + _ = yaml.unsafe_load(dumped) # test that it is loadable + except Exception: + logger.warning( + "The config contains objects that cannot serialize to a valid yaml. " + f"{filename} is human-readable but cannot be loaded." + ) + save_pkl = True + except Exception: + logger.exception("Unable to serialize the config to yaml. Error:") + save_pkl = True + + if save_pkl: + new_filename = filename + ".pkl" + try: + # retry by pickle + with PathManager.open(new_filename, "wb") as f: + cloudpickle.dump(cfg, f) + logger.warning(f"Config is saved using cloudpickle at {new_filename}.") + except Exception: + pass + + @staticmethod + def apply_overrides(cfg, overrides: List[str]): + """ + In-place override contents of cfg. + + Args: + cfg: an omegaconf config object + overrides: list of strings in the format of "a=b" to override configs. + See https://hydra.cc/docs/next/advanced/override_grammar/basic/ + for syntax. + + Returns: + the cfg object + """ + + def safe_update(cfg, key, value): + parts = key.split(".") + for idx in range(1, len(parts)): + prefix = ".".join(parts[:idx]) + v = OmegaConf.select(cfg, prefix, default=None) + if v is None: + break + if not OmegaConf.is_config(v): + raise KeyError( + f"Trying to update key {key}, but {prefix} " + f"is not a config, but has type {type(v)}." + ) + OmegaConf.update(cfg, key, value, merge=True) + + from hydra.core.override_parser.overrides_parser import OverridesParser + + parser = OverridesParser.create() + overrides = parser.parse_overrides(overrides) + for o in overrides: + key = o.key_or_group + value = o.value() + if o.is_delete(): + # TODO support this + raise NotImplementedError("deletion is not yet a supported override") + safe_update(cfg, key, value) + return cfg + + @staticmethod + def to_py(cfg, prefix: str = "cfg."): + """ + Try to convert a config object into Python-like psuedo code. + + Note that perfect conversion is not always possible. So the returned + results are mainly meant to be human-readable, and not meant to be executed. + + Args: + cfg: an omegaconf config object + prefix: root name for the resulting code (default: "cfg.") + + + Returns: + str of formatted Python code + """ + import black + + cfg = OmegaConf.to_container(cfg, resolve=True) + + def _to_str(obj, prefix=None, inside_call=False): + if prefix is None: + prefix = [] + if isinstance(obj, abc.Mapping) and "_target_" in obj: + # Dict representing a function call + target = _convert_target_to_string(obj.pop("_target_")) + args = [] + for k, v in sorted(obj.items()): + args.append(f"{k}={_to_str(v, inside_call=True)}") + args = ", ".join(args) + call = f"{target}({args})" + return "".join(prefix) + call + elif isinstance(obj, abc.Mapping) and not inside_call: + # Dict that is not inside a call is a list of top-level config objects that we + # render as one object per line with dot separated prefixes + key_list = [] + for k, v in sorted(obj.items()): + if isinstance(v, abc.Mapping) and "_target_" not in v: + key_list.append(_to_str(v, prefix=prefix + [k + "."])) + else: + key = "".join(prefix) + k + key_list.append(f"{key}={_to_str(v)}") + return "\n".join(key_list) + elif isinstance(obj, abc.Mapping): + # Dict that is inside a call is rendered as a regular dict + return ( + "{" + + ",".join( + f"{repr(k)}: {_to_str(v, inside_call=inside_call)}" + for k, v in sorted(obj.items()) + ) + + "}" + ) + elif isinstance(obj, list): + return "[" + ",".join(_to_str(x, inside_call=inside_call) for x in obj) + "]" + else: + return repr(obj) + + py_str = _to_str(cfg, prefix=[prefix]) + try: + return black.format_str(py_str, mode=black.Mode()) + except black.InvalidInput: + return py_str diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/__init__.py new file mode 100644 index 0000000..259f669 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/__init__.py @@ -0,0 +1,19 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from . import transforms # isort:skip + +from .build import ( + build_batch_data_loader, + build_detection_test_loader, + build_detection_train_loader, + get_detection_dataset_dicts, + load_proposals_into_dataset, + print_instances_class_histogram, +) +from .catalog import DatasetCatalog, MetadataCatalog, Metadata +from .common import DatasetFromList, MapDataset, ToIterableDataset +from .dataset_mapper import DatasetMapper + +# ensure the builtin datasets are registered +from . import datasets, samplers # isort:skip + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/benchmark.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/benchmark.py new file mode 100644 index 0000000..ac2f372 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/benchmark.py @@ -0,0 +1,225 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import numpy as np +from itertools import count +from typing import List, Tuple +import torch +import tqdm +from fvcore.common.timer import Timer + +from detectron2.utils import comm + +from .build import build_batch_data_loader +from .common import DatasetFromList, MapDataset +from .samplers import TrainingSampler + +logger = logging.getLogger(__name__) + + +class _EmptyMapDataset(torch.utils.data.Dataset): + """ + Map anything to emptiness. + """ + + def __init__(self, dataset): + self.ds = dataset + + def __len__(self): + return len(self.ds) + + def __getitem__(self, idx): + _ = self.ds[idx] + return [0] + + +def iter_benchmark( + iterator, num_iter: int, warmup: int = 5, max_time_seconds: float = 60 +) -> Tuple[float, List[float]]: + """ + Benchmark an iterator/iterable for `num_iter` iterations with an extra + `warmup` iterations of warmup. + End early if `max_time_seconds` time is spent on iterations. + + Returns: + float: average time (seconds) per iteration + list[float]: time spent on each iteration. Sometimes useful for further analysis. + """ + num_iter, warmup = int(num_iter), int(warmup) + + iterator = iter(iterator) + for _ in range(warmup): + next(iterator) + timer = Timer() + all_times = [] + for curr_iter in tqdm.trange(num_iter): + start = timer.seconds() + if start > max_time_seconds: + num_iter = curr_iter + break + next(iterator) + all_times.append(timer.seconds() - start) + avg = timer.seconds() / num_iter + return avg, all_times + + +class DataLoaderBenchmark: + """ + Some common benchmarks that help understand perf bottleneck of a standard dataloader + made of dataset, mapper and sampler. + """ + + def __init__( + self, + dataset, + *, + mapper, + sampler=None, + total_batch_size, + num_workers=0, + max_time_seconds: int = 90, + ): + """ + Args: + max_time_seconds (int): maximum time to spent for each benchmark + other args: same as in `build.py:build_detection_train_loader` + """ + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False, serialize=True) + if sampler is None: + sampler = TrainingSampler(len(dataset)) + + self.dataset = dataset + self.mapper = mapper + self.sampler = sampler + self.total_batch_size = total_batch_size + self.num_workers = num_workers + self.per_gpu_batch_size = self.total_batch_size // comm.get_world_size() + + self.max_time_seconds = max_time_seconds + + def _benchmark(self, iterator, num_iter, warmup, msg=None): + avg, all_times = iter_benchmark(iterator, num_iter, warmup, self.max_time_seconds) + if msg is not None: + self._log_time(msg, avg, all_times) + return avg, all_times + + def _log_time(self, msg, avg, all_times, distributed=False): + percentiles = [np.percentile(all_times, k, interpolation="nearest") for k in [1, 5, 95, 99]] + if not distributed: + logger.info( + f"{msg}: avg={1.0/avg:.1f} it/s, " + f"p1={percentiles[0]:.2g}s, p5={percentiles[1]:.2g}s, " + f"p95={percentiles[2]:.2g}s, p99={percentiles[3]:.2g}s." + ) + return + avg_per_gpu = comm.all_gather(avg) + percentiles_per_gpu = comm.all_gather(percentiles) + if comm.get_rank() > 0: + return + for idx, avg, percentiles in zip(count(), avg_per_gpu, percentiles_per_gpu): + logger.info( + f"GPU{idx} {msg}: avg={1.0/avg:.1f} it/s, " + f"p1={percentiles[0]:.2g}s, p5={percentiles[1]:.2g}s, " + f"p95={percentiles[2]:.2g}s, p99={percentiles[3]:.2g}s." + ) + + def benchmark_dataset(self, num_iter, warmup=5): + """ + Benchmark the speed of taking raw samples from the dataset. + """ + + def loader(): + while True: + for k in self.sampler: + yield self.dataset[k] + + self._benchmark(loader(), num_iter, warmup, "Dataset Alone") + + def benchmark_mapper(self, num_iter, warmup=5): + """ + Benchmark the speed of taking raw samples from the dataset and map + them in a single process. + """ + + def loader(): + while True: + for k in self.sampler: + yield self.mapper(self.dataset[k]) + + self._benchmark(loader(), num_iter, warmup, "Single Process Mapper (sec/sample)") + + def benchmark_workers(self, num_iter, warmup=10): + """ + Benchmark the dataloader by tuning num_workers to [0, 1, self.num_workers]. + """ + candidates = [0, 1] + if self.num_workers not in candidates: + candidates.append(self.num_workers) + + dataset = MapDataset(self.dataset, self.mapper) + for n in candidates: + loader = build_batch_data_loader( + dataset, + self.sampler, + self.total_batch_size, + num_workers=n, + ) + self._benchmark( + iter(loader), + num_iter * max(n, 1), + warmup * max(n, 1), + f"DataLoader ({n} workers, bs={self.per_gpu_batch_size})", + ) + del loader + + def benchmark_IPC(self, num_iter, warmup=10): + """ + Benchmark the dataloader where each worker outputs nothing. This + eliminates the IPC overhead compared to the regular dataloader. + + PyTorch multiprocessing's IPC only optimizes for torch tensors. + Large numpy arrays or other data structure may incur large IPC overhead. + """ + n = self.num_workers + dataset = _EmptyMapDataset(MapDataset(self.dataset, self.mapper)) + loader = build_batch_data_loader( + dataset, self.sampler, self.total_batch_size, num_workers=n + ) + self._benchmark( + iter(loader), + num_iter * max(n, 1), + warmup * max(n, 1), + f"DataLoader ({n} workers, bs={self.per_gpu_batch_size}) w/o comm", + ) + + def benchmark_distributed(self, num_iter, warmup=10): + """ + Benchmark the dataloader in each distributed worker, and log results of + all workers. This helps understand the final performance as well as + the variances among workers. + + It also prints startup time (first iter) of the dataloader. + """ + gpu = comm.get_world_size() + dataset = MapDataset(self.dataset, self.mapper) + n = self.num_workers + loader = build_batch_data_loader( + dataset, self.sampler, self.total_batch_size, num_workers=n + ) + + timer = Timer() + loader = iter(loader) + next(loader) + startup_time = timer.seconds() + logger.info("Dataloader startup time: {:.2f} seconds".format(startup_time)) + + comm.synchronize() + + avg, all_times = self._benchmark(loader, num_iter * max(n, 1), warmup * max(n, 1)) + del loader + self._log_time( + f"DataLoader ({gpu} GPUs x {n} workers, total bs={self.total_batch_size})", + avg, + all_times, + True, + ) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/build.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/build.py new file mode 100644 index 0000000..cf3a79b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/build.py @@ -0,0 +1,542 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +import logging +import numpy as np +import operator +import pickle +from typing import Any, Callable, Dict, List, Optional, Union +import torch +import torch.utils.data as torchdata +from tabulate import tabulate +from termcolor import colored + +from detectron2.config import configurable +from detectron2.structures import BoxMode +from detectron2.utils.comm import get_world_size +from detectron2.utils.env import seed_all_rng +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import _log_api_usage, log_first_n + +from .catalog import DatasetCatalog, MetadataCatalog +from .common import AspectRatioGroupedDataset, DatasetFromList, MapDataset, ToIterableDataset +from .dataset_mapper import DatasetMapper +from .detection_utils import check_metadata_consistency +from .samplers import ( + InferenceSampler, + RandomSubsetTrainingSampler, + RepeatFactorTrainingSampler, + TrainingSampler, +) + +""" +This file contains the default logic to build a dataloader for training or testing. +""" + +__all__ = [ + "build_batch_data_loader", + "build_detection_train_loader", + "build_detection_test_loader", + "get_detection_dataset_dicts", + "load_proposals_into_dataset", + "print_instances_class_histogram", +] + + +def filter_images_with_only_crowd_annotations(dataset_dicts): + """ + Filter out images with none annotations or only crowd annotations + (i.e., images without non-crowd annotations). + A common training-time preprocessing on COCO dataset. + + Args: + dataset_dicts (list[dict]): annotations in Detectron2 Dataset format. + + Returns: + list[dict]: the same format, but filtered. + """ + num_before = len(dataset_dicts) + + def valid(anns): + for ann in anns: + if ann.get("iscrowd", 0) == 0: + return True + return False + + dataset_dicts = [x for x in dataset_dicts if valid(x["annotations"])] + num_after = len(dataset_dicts) + logger = logging.getLogger(__name__) + logger.info( + "Removed {} images with no usable annotations. {} images left.".format( + num_before - num_after, num_after + ) + ) + return dataset_dicts + + +def filter_images_with_few_keypoints(dataset_dicts, min_keypoints_per_image): + """ + Filter out images with too few number of keypoints. + + Args: + dataset_dicts (list[dict]): annotations in Detectron2 Dataset format. + + Returns: + list[dict]: the same format as dataset_dicts, but filtered. + """ + num_before = len(dataset_dicts) + + def visible_keypoints_in_image(dic): + # Each keypoints field has the format [x1, y1, v1, ...], where v is visibility + annotations = dic["annotations"] + return sum( + (np.array(ann["keypoints"][2::3]) > 0).sum() + for ann in annotations + if "keypoints" in ann + ) + + dataset_dicts = [ + x for x in dataset_dicts if visible_keypoints_in_image(x) >= min_keypoints_per_image + ] + num_after = len(dataset_dicts) + logger = logging.getLogger(__name__) + logger.info( + "Removed {} images with fewer than {} keypoints.".format( + num_before - num_after, min_keypoints_per_image + ) + ) + return dataset_dicts + + +def load_proposals_into_dataset(dataset_dicts, proposal_file): + """ + Load precomputed object proposals into the dataset. + + The proposal file should be a pickled dict with the following keys: + + - "ids": list[int] or list[str], the image ids + - "boxes": list[np.ndarray], each is an Nx4 array of boxes corresponding to the image id + - "objectness_logits": list[np.ndarray], each is an N sized array of objectness scores + corresponding to the boxes. + - "bbox_mode": the BoxMode of the boxes array. Defaults to ``BoxMode.XYXY_ABS``. + + Args: + dataset_dicts (list[dict]): annotations in Detectron2 Dataset format. + proposal_file (str): file path of pre-computed proposals, in pkl format. + + Returns: + list[dict]: the same format as dataset_dicts, but added proposal field. + """ + logger = logging.getLogger(__name__) + logger.info("Loading proposals from: {}".format(proposal_file)) + + with PathManager.open(proposal_file, "rb") as f: + proposals = pickle.load(f, encoding="latin1") + + # Rename the key names in D1 proposal files + rename_keys = {"indexes": "ids", "scores": "objectness_logits"} + for key in rename_keys: + if key in proposals: + proposals[rename_keys[key]] = proposals.pop(key) + + # Fetch the indexes of all proposals that are in the dataset + # Convert image_id to str since they could be int. + img_ids = set({str(record["image_id"]) for record in dataset_dicts}) + id_to_index = {str(id): i for i, id in enumerate(proposals["ids"]) if str(id) in img_ids} + + # Assuming default bbox_mode of precomputed proposals are 'XYXY_ABS' + bbox_mode = BoxMode(proposals["bbox_mode"]) if "bbox_mode" in proposals else BoxMode.XYXY_ABS + + for record in dataset_dicts: + # Get the index of the proposal + i = id_to_index[str(record["image_id"])] + + boxes = proposals["boxes"][i] + objectness_logits = proposals["objectness_logits"][i] + # Sort the proposals in descending order of the scores + inds = objectness_logits.argsort()[::-1] + record["proposal_boxes"] = boxes[inds] + record["proposal_objectness_logits"] = objectness_logits[inds] + record["proposal_bbox_mode"] = bbox_mode + + return dataset_dicts + + +def print_instances_class_histogram(dataset_dicts, class_names): + """ + Args: + dataset_dicts (list[dict]): list of dataset dicts. + class_names (list[str]): list of class names (zero-indexed). + """ + num_classes = len(class_names) + hist_bins = np.arange(num_classes + 1) + histogram = np.zeros((num_classes,), dtype=np.int) + for entry in dataset_dicts: + annos = entry["annotations"] + classes = np.asarray( + [x["category_id"] for x in annos if not x.get("iscrowd", 0)], dtype=np.int + ) + if len(classes): + assert classes.min() >= 0, f"Got an invalid category_id={classes.min()}" + assert ( + classes.max() < num_classes + ), f"Got an invalid category_id={classes.max()} for a dataset of {num_classes} classes" + histogram += np.histogram(classes, bins=hist_bins)[0] + + N_COLS = min(6, len(class_names) * 2) + + def short_name(x): + # make long class names shorter. useful for lvis + if len(x) > 13: + return x[:11] + ".." + return x + + data = list( + itertools.chain(*[[short_name(class_names[i]), int(v)] for i, v in enumerate(histogram)]) + ) + total_num_instances = sum(data[1::2]) + data.extend([None] * (N_COLS - (len(data) % N_COLS))) + if num_classes > 1: + data.extend(["total", total_num_instances]) + data = itertools.zip_longest(*[data[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + data, + headers=["category", "#instances"] * (N_COLS // 2), + tablefmt="pipe", + numalign="left", + stralign="center", + ) + log_first_n( + logging.INFO, + "Distribution of instances among all {} categories:\n".format(num_classes) + + colored(table, "cyan"), + key="message", + ) + + +def get_detection_dataset_dicts( + names, + filter_empty=True, + min_keypoints=0, + proposal_files=None, + check_consistency=True, +): + """ + Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation. + + Args: + names (str or list[str]): a dataset name or a list of dataset names + filter_empty (bool): whether to filter out images without instance annotations + min_keypoints (int): filter out images with fewer keypoints than + `min_keypoints`. Set to 0 to do nothing. + proposal_files (list[str]): if given, a list of object proposal files + that match each dataset in `names`. + check_consistency (bool): whether to check if datasets have consistent metadata. + + Returns: + list[dict]: a list of dicts following the standard dataset dict format. + """ + if isinstance(names, str): + names = [names] + assert len(names), names + dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in names] + for dataset_name, dicts in zip(names, dataset_dicts): + assert len(dicts), "Dataset '{}' is empty!".format(dataset_name) + + if proposal_files is not None: + assert len(names) == len(proposal_files) + # load precomputed proposals from proposal files + dataset_dicts = [ + load_proposals_into_dataset(dataset_i_dicts, proposal_file) + for dataset_i_dicts, proposal_file in zip(dataset_dicts, proposal_files) + ] + + if isinstance(dataset_dicts[0], torchdata.Dataset): + return torchdata.ConcatDataset(dataset_dicts) + + dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts)) + + has_instances = "annotations" in dataset_dicts[0] + if filter_empty and has_instances: + dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts) + if min_keypoints > 0 and has_instances: + dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints) + + if check_consistency and has_instances: + try: + class_names = MetadataCatalog.get(names[0]).thing_classes + check_metadata_consistency("thing_classes", names) + print_instances_class_histogram(dataset_dicts, class_names) + except AttributeError: # class names are not available for this dataset + pass + + assert len(dataset_dicts), "No valid data found in {}.".format(",".join(names)) + return dataset_dicts + + +def build_batch_data_loader( + dataset, + sampler, + total_batch_size, + *, + aspect_ratio_grouping=False, + num_workers=0, + collate_fn=None, +): + """ + Build a batched dataloader. The main differences from `torch.autils.data.DataLoader` are: + 1. support aspect ratio grouping options + 2. use no "batch collation", because this is common for detection training + + Args: + dataset (torch.autils.data.Dataset): a pytorch map-style or iterable dataset. + sampler (torch.autils.data.sampler.Sampler or None): a sampler that produces indices. + Must be provided iff. ``dataset`` is a map-style dataset. + total_batch_size, aspect_ratio_grouping, num_workers, collate_fn: see + :func:`build_detection_train_loader`. + + Returns: + iterable[list]. Length of each list is the batch size of the current + GPU. Each element in the list comes from the dataset. + """ + world_size = get_world_size() + assert ( + total_batch_size > 0 and total_batch_size % world_size == 0 + ), "Total batch size ({}) must be divisible by the number of gpus ({}).".format( + total_batch_size, world_size + ) + batch_size = total_batch_size // world_size + + if isinstance(dataset, torchdata.IterableDataset): + assert sampler is None, "sampler must be None if dataset is IterableDataset" + else: + dataset = ToIterableDataset(dataset, sampler) + + if aspect_ratio_grouping: + data_loader = torchdata.DataLoader( + dataset, + num_workers=num_workers, + collate_fn=operator.itemgetter(0), # don't batch, but yield individual elements + worker_init_fn=worker_init_reset_seed, + ) # yield individual mapped dict + data_loader = AspectRatioGroupedDataset(data_loader, batch_size) + if collate_fn is None: + return data_loader + return MapDataset(data_loader, collate_fn) + else: + return torchdata.DataLoader( + dataset, + batch_size=batch_size, + drop_last=True, + num_workers=num_workers, + collate_fn=trivial_batch_collator if collate_fn is None else collate_fn, + worker_init_fn=worker_init_reset_seed, + ) + + +def _train_loader_from_config(cfg, mapper=None, *, dataset=None, sampler=None): + if dataset is None: + dataset = get_detection_dataset_dicts( + cfg.DATASETS.TRAIN, + filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, + min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE + if cfg.MODEL.KEYPOINT_ON + else 0, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + ) + _log_api_usage("dataset." + cfg.DATASETS.TRAIN[0]) + + if mapper is None: + mapper = DatasetMapper(cfg, True) + + if sampler is None: + sampler_name = cfg.DATALOADER.SAMPLER_TRAIN + logger = logging.getLogger(__name__) + logger.info("Using training sampler {}".format(sampler_name)) + if sampler_name == "TrainingSampler": + sampler = TrainingSampler(len(dataset)) + elif sampler_name == "RepeatFactorTrainingSampler": + repeat_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset, cfg.DATALOADER.REPEAT_THRESHOLD + ) + sampler = RepeatFactorTrainingSampler(repeat_factors) + elif sampler_name == "RandomSubsetTrainingSampler": + sampler = RandomSubsetTrainingSampler(len(dataset), cfg.DATALOADER.RANDOM_SUBSET_RATIO) + else: + raise ValueError("Unknown training sampler: {}".format(sampler_name)) + + return { + "dataset": dataset, + "sampler": sampler, + "mapper": mapper, + "total_batch_size": cfg.SOLVER.IMS_PER_BATCH, + "aspect_ratio_grouping": cfg.DATALOADER.ASPECT_RATIO_GROUPING, + "num_workers": cfg.DATALOADER.NUM_WORKERS, + } + + +@configurable(from_config=_train_loader_from_config) +def build_detection_train_loader( + dataset, + *, + mapper, + sampler=None, + total_batch_size, + aspect_ratio_grouping=True, + num_workers=0, + collate_fn=None, +): + """ + Build a dataloader for object detection with some default features. + + Args: + dataset (list or torch.autils.data.Dataset): a list of dataset dicts, + or a pytorch dataset (either map-style or iterable). It can be obtained + by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`. + mapper (callable): a callable which takes a sample (dict) from dataset and + returns the format to be consumed by the model. + When using cfg, the default choice is ``DatasetMapper(cfg, is_train=True)``. + sampler (torch.autils.data.sampler.Sampler or None): a sampler that produces + indices to be applied on ``dataset``. + If ``dataset`` is map-style, the default sampler is a :class:`TrainingSampler`, + which coordinates an infinite random shuffle sequence across all workers. + Sampler must be None if ``dataset`` is iterable. + total_batch_size (int): total batch size across all workers. + aspect_ratio_grouping (bool): whether to group images with similar + aspect ratio for efficiency. When enabled, it requires each + element in dataset be a dict with keys "width" and "height". + num_workers (int): number of parallel data loading workers + collate_fn: a function that determines how to do batching, same as the argument of + `torch.autils.data.DataLoader`. Defaults to do no collation and return a list of + data. No collation is OK for small batch size and simple data structures. + If your batch size is large and each sample contains too many small tensors, + it's more efficient to collate them in data loader. + + Returns: + torch.autils.data.DataLoader: + a dataloader. Each output from it is a ``list[mapped_element]`` of length + ``total_batch_size / num_workers``, where ``mapped_element`` is produced + by the ``mapper``. + """ + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False) + if mapper is not None: + dataset = MapDataset(dataset, mapper) + + if isinstance(dataset, torchdata.IterableDataset): + assert sampler is None, "sampler must be None if dataset is IterableDataset" + else: + if sampler is None: + sampler = TrainingSampler(len(dataset)) + assert isinstance(sampler, torchdata.Sampler), f"Expect a Sampler but got {type(sampler)}" + return build_batch_data_loader( + dataset, + sampler, + total_batch_size, + aspect_ratio_grouping=aspect_ratio_grouping, + num_workers=num_workers, + collate_fn=collate_fn, + ) + + +def _test_loader_from_config(cfg, dataset_name, mapper=None): + """ + Uses the given `dataset_name` argument (instead of the names in cfg), because the + standard practice is to evaluate each test set individually (not combining them). + """ + if isinstance(dataset_name, str): + dataset_name = [dataset_name] + + dataset = get_detection_dataset_dicts( + dataset_name, + filter_empty=False, + proposal_files=[ + cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(x)] for x in dataset_name + ] + if cfg.MODEL.LOAD_PROPOSALS + else None, + ) + if mapper is None: + mapper = DatasetMapper(cfg, False) + return { + "dataset": dataset, + "mapper": mapper, + "num_workers": cfg.DATALOADER.NUM_WORKERS, + "sampler": InferenceSampler(len(dataset)), + } + + +@configurable(from_config=_test_loader_from_config) +def build_detection_test_loader( + dataset: Union[List[Any], torchdata.Dataset], + *, + mapper: Callable[[Dict[str, Any]], Any], + sampler: Optional[torchdata.Sampler] = None, + batch_size: int = 1, + num_workers: int = 0, + collate_fn: Optional[Callable[[List[Any]], Any]] = None, +) -> torchdata.DataLoader: + """ + Similar to `build_detection_train_loader`, with default batch size = 1, + and sampler = :class:`InferenceSampler`. This sampler coordinates all workers + to produce the exact set of all samples. + + Args: + dataset: a list of dataset dicts, + or a pytorch dataset (either map-style or iterable). They can be obtained + by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`. + mapper: a callable which takes a sample (dict) from dataset + and returns the format to be consumed by the model. + When using cfg, the default choice is ``DatasetMapper(cfg, is_train=False)``. + sampler: a sampler that produces + indices to be applied on ``dataset``. Default to :class:`InferenceSampler`, + which splits the dataset across all workers. Sampler must be None + if `dataset` is iterable. + batch_size: the batch size of the data loader to be created. + Default to 1 image per worker since this is the standard when reporting + inference time in papers. + num_workers: number of parallel data loading workers + collate_fn: same as the argument of `torch.autils.data.DataLoader`. + Defaults to do no collation and return a list of data. + + Returns: + DataLoader: a torch DataLoader, that loads the given detection + dataset, with test-time transformation and batching. + + Examples: + :: + data_loader = build_detection_test_loader( + DatasetRegistry.get("my_test"), + mapper=DatasetMapper(...)) + + # or, instantiate with a CfgNode: + data_loader = build_detection_test_loader(cfg, "my_test") + """ + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False) + if mapper is not None: + dataset = MapDataset(dataset, mapper) + if isinstance(dataset, torchdata.IterableDataset): + assert sampler is None, "sampler must be None if dataset is IterableDataset" + else: + if sampler is None: + sampler = InferenceSampler(len(dataset)) + return torchdata.DataLoader( + dataset, + batch_size=batch_size, + sampler=sampler, + drop_last=False, + num_workers=num_workers, + collate_fn=trivial_batch_collator if collate_fn is None else collate_fn, + ) + + +def trivial_batch_collator(batch): + """ + A batch collator that does nothing. + """ + return batch + + +def worker_init_reset_seed(worker_id): + initial_seed = torch.initial_seed() % 2 ** 31 + seed_all_rng(initial_seed + worker_id) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/catalog.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/catalog.py new file mode 100644 index 0000000..45c110c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/catalog.py @@ -0,0 +1,236 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import logging +import types +from collections import UserDict +from typing import List + +from detectron2.utils.logger import log_first_n + +__all__ = ["DatasetCatalog", "MetadataCatalog", "Metadata"] + + +class _DatasetCatalog(UserDict): + """ + A global dictionary that stores information about the datasets and how to obtain them. + + It contains a mapping from strings + (which are names that identify a dataset, e.g. "coco_2014_train") + to a function which parses the dataset and returns the samples in the + format of `list[dict]`. + + The returned dicts should be in Detectron2 Dataset format (See DATASETS.md for details) + if used with the data loader functionalities in `data/build.py,data/detection_transform.py`. + + The purpose of having this catalog is to make it easy to choose + different datasets, by just using the strings in the config. + """ + + def register(self, name, func): + """ + Args: + name (str): the name that identifies a dataset, e.g. "coco_2014_train". + func (callable): a callable which takes no arguments and returns a list of dicts. + It must return the same results if called multiple times. + """ + assert callable(func), "You must register a function with `DatasetCatalog.register`!" + assert name not in self, "Dataset '{}' is already registered!".format(name) + self[name] = func + + def get(self, name): + """ + Call the registered function and return its results. + + Args: + name (str): the name that identifies a dataset, e.g. "coco_2014_train". + + Returns: + list[dict]: dataset annotations. + """ + try: + f = self[name] + except KeyError as e: + raise KeyError( + "Dataset '{}' is not registered! Available datasets are: {}".format( + name, ", ".join(list(self.keys())) + ) + ) from e + return f() + + def list(self) -> List[str]: + """ + List all registered datasets. + + Returns: + list[str] + """ + return list(self.keys()) + + def remove(self, name): + """ + Alias of ``pop``. + """ + self.pop(name) + + def __str__(self): + return "DatasetCatalog(registered datasets: {})".format(", ".join(self.keys())) + + __repr__ = __str__ + + +DatasetCatalog = _DatasetCatalog() +DatasetCatalog.__doc__ = ( + _DatasetCatalog.__doc__ + + """ + .. automethod:: detectron2.data.catalog.DatasetCatalog.register + .. automethod:: detectron2.data.catalog.DatasetCatalog.get +""" +) + + +class Metadata(types.SimpleNamespace): + """ + A class that supports simple attribute setter/getter. + It is intended for storing metadata of a dataset and make it accessible globally. + + Examples: + :: + # somewhere when you load the data: + MetadataCatalog.get("mydataset").thing_classes = ["person", "dog"] + + # somewhere when you print statistics or visualize: + classes = MetadataCatalog.get("mydataset").thing_classes + """ + + # the name of the dataset + # set default to N/A so that `self.name` in the errors will not trigger getattr again + name: str = "N/A" + + _RENAMED = { + "class_names": "thing_classes", + "dataset_id_to_contiguous_id": "thing_dataset_id_to_contiguous_id", + "stuff_class_names": "stuff_classes", + } + + def __getattr__(self, key): + if key in self._RENAMED: + log_first_n( + logging.WARNING, + "Metadata '{}' was renamed to '{}'!".format(key, self._RENAMED[key]), + n=10, + ) + return getattr(self, self._RENAMED[key]) + + # "name" exists in every metadata + if len(self.__dict__) > 1: + raise AttributeError( + "Attribute '{}' does not exist in the metadata of dataset '{}'. Available " + "keys are {}.".format(key, self.name, str(self.__dict__.keys())) + ) + else: + raise AttributeError( + f"Attribute '{key}' does not exist in the metadata of dataset '{self.name}': " + "metadata is empty." + ) + + def __setattr__(self, key, val): + if key in self._RENAMED: + log_first_n( + logging.WARNING, + "Metadata '{}' was renamed to '{}'!".format(key, self._RENAMED[key]), + n=10, + ) + setattr(self, self._RENAMED[key], val) + + # Ensure that metadata of the same name stays consistent + try: + oldval = getattr(self, key) + assert oldval == val, ( + "Attribute '{}' in the metadata of '{}' cannot be set " + "to a different value!\n{} != {}".format(key, self.name, oldval, val) + ) + except AttributeError: + super().__setattr__(key, val) + + def as_dict(self): + """ + Returns all the metadata as a dict. + Note that modifications to the returned dict will not reflect on the Metadata object. + """ + return copy.copy(self.__dict__) + + def set(self, **kwargs): + """ + Set multiple metadata with kwargs. + """ + for k, v in kwargs.items(): + setattr(self, k, v) + return self + + def get(self, key, default=None): + """ + Access an attribute and return its value if exists. + Otherwise return default. + """ + try: + return getattr(self, key) + except AttributeError: + return default + + +class _MetadataCatalog(UserDict): + """ + MetadataCatalog is a global dictionary that provides access to + :class:`Metadata` of a given dataset. + + The metadata associated with a certain name is a singleton: once created, the + metadata will stay alive and will be returned by future calls to ``get(name)``. + + It's like global variables, so don't abuse it. + It's meant for storing knowledge that's constant and shared across the execution + of the program, e.g.: the class names in COCO. + """ + + def get(self, name): + """ + Args: + name (str): name of a dataset (e.g. coco_2014_train). + + Returns: + Metadata: The :class:`Metadata` instance associated with this name, + or create an empty one if none is available. + """ + assert len(name) + r = super().get(name, None) + if r is None: + r = self[name] = Metadata(name=name) + return r + + def list(self): + """ + List all registered metadata. + + Returns: + list[str]: keys (names of datasets) of all registered metadata + """ + return list(self.keys()) + + def remove(self, name): + """ + Alias of ``pop``. + """ + self.pop(name) + + def __str__(self): + return "MetadataCatalog(registered metadata: {})".format(", ".join(self.keys())) + + __repr__ = __str__ + + +MetadataCatalog = _MetadataCatalog() +MetadataCatalog.__doc__ = ( + _MetadataCatalog.__doc__ + + """ + .. automethod:: detectron2.data.catalog.MetadataCatalog.get +""" +) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/common.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/common.py new file mode 100644 index 0000000..22080a9 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/common.py @@ -0,0 +1,244 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import itertools +import logging +import numpy as np +import pickle +import random +import torch.utils.data as data +from torch.utils.data.sampler import Sampler + +from detectron2.utils.serialize import PicklableWrapper + +__all__ = ["MapDataset", "DatasetFromList", "AspectRatioGroupedDataset", "ToIterableDataset"] + + +def _shard_iterator_dataloader_worker(iterable): + # Shard the iterable if we're currently inside pytorch dataloader worker. + worker_info = data.get_worker_info() + if worker_info is None or worker_info.num_workers == 1: + # do nothing + yield from iterable + else: + yield from itertools.islice(iterable, worker_info.id, None, worker_info.num_workers) + + +class _MapIterableDataset(data.IterableDataset): + """ + Map a function over elements in an IterableDataset. + + Similar to pytorch's MapIterDataPipe, but support filtering when map_func + returns None. + + This class is not public-facing. Will be called by `MapDataset`. + """ + + def __init__(self, dataset, map_func): + self._dataset = dataset + self._map_func = PicklableWrapper(map_func) # wrap so that a lambda will work + + def __len__(self): + return len(self._dataset) + + def __iter__(self): + for x in map(self._map_func, self._dataset): + if x is not None: + yield x + + +class MapDataset(data.Dataset): + """ + Map a function over the elements in a dataset. + """ + + def __init__(self, dataset, map_func): + """ + Args: + dataset: a dataset where map function is applied. Can be either + map-style or iterable dataset. When given an iterable dataset, + the returned object will also be an iterable dataset. + map_func: a callable which maps the element in dataset. map_func can + return None to skip the data (e.g. in case of errors). + How None is handled depends on the style of `dataset`. + If `dataset` is map-style, it randomly tries other elements. + If `dataset` is iterable, it skips the data and tries the next. + """ + self._dataset = dataset + self._map_func = PicklableWrapper(map_func) # wrap so that a lambda will work + + self._rng = random.Random(42) + self._fallback_candidates = set(range(len(dataset))) + + def __new__(cls, dataset, map_func): + is_iterable = isinstance(dataset, data.IterableDataset) + if is_iterable: + return _MapIterableDataset(dataset, map_func) + else: + return super().__new__(cls) + + def __getnewargs__(self): + return self._dataset, self._map_func + + def __len__(self): + return len(self._dataset) + + def __getitem__(self, idx): + retry_count = 0 + cur_idx = int(idx) + + while True: + data = self._map_func(self._dataset[cur_idx]) + if data is not None: + self._fallback_candidates.add(cur_idx) + return data + + # _map_func fails for this idx, use a random new index from the pool + retry_count += 1 + self._fallback_candidates.discard(cur_idx) + cur_idx = self._rng.sample(self._fallback_candidates, k=1)[0] + + if retry_count >= 3: + logger = logging.getLogger(__name__) + logger.warning( + "Failed to apply `_map_func` for idx: {}, retry count: {}".format( + idx, retry_count + ) + ) + + +class DatasetFromList(data.Dataset): + """ + Wrap a list to a torch Dataset. It produces elements of the list as data. + """ + + def __init__(self, lst: list, copy: bool = True, serialize: bool = True): + """ + Args: + lst (list): a list which contains elements to produce. + copy (bool): whether to deepcopy the element when producing it, + so that the result can be modified in place without affecting the + source in the list. + serialize (bool): whether to hold memory using serialized objects, when + enabled, data loader workers can use shared RAM from master + process instead of making a copy. + """ + self._lst = lst + self._copy = copy + self._serialize = serialize + + def _serialize(data): + buffer = pickle.dumps(data, protocol=-1) + return np.frombuffer(buffer, dtype=np.uint8) + + if self._serialize: + logger = logging.getLogger(__name__) + logger.info( + "Serializing {} elements to byte tensors and concatenating them all ...".format( + len(self._lst) + ) + ) + self._lst = [_serialize(x) for x in self._lst] + self._addr = np.asarray([len(x) for x in self._lst], dtype=np.int64) + self._addr = np.cumsum(self._addr) + self._lst = np.concatenate(self._lst) + logger.info("Serialized dataset takes {:.2f} MiB".format(len(self._lst) / 1024 ** 2)) + + def __len__(self): + if self._serialize: + return len(self._addr) + else: + return len(self._lst) + + def __getitem__(self, idx): + if self._serialize: + start_addr = 0 if idx == 0 else self._addr[idx - 1].item() + end_addr = self._addr[idx].item() + bytes = memoryview(self._lst[start_addr:end_addr]) + return pickle.loads(bytes) + elif self._copy: + return copy.deepcopy(self._lst[idx]) + else: + return self._lst[idx] + + +class ToIterableDataset(data.IterableDataset): + """ + Convert an old indices-based (also called map-style) dataset + to an iterable-style dataset. + """ + + def __init__(self, dataset: data.Dataset, sampler: Sampler, shard_sampler: bool = True): + """ + Args: + dataset: an old-style dataset with ``__getitem__`` + sampler: a cheap iterable that produces indices to be applied on ``dataset``. + shard_sampler: whether to shard the sampler based on the current pytorch data loader + worker id. When an IterableDataset is forked by pytorch's DataLoader into multiple + workers, it is responsible for sharding its data based on worker id so that workers + don't produce identical data. + + Most samplers (like our TrainingSampler) do not shard based on dataloader worker id + and this argument should be set to True. But certain samplers may be already + sharded, in that case this argument should be set to False. + """ + assert not isinstance(dataset, data.IterableDataset), dataset + assert isinstance(sampler, Sampler), sampler + self.dataset = dataset + self.sampler = sampler + self.shard_sampler = shard_sampler + + def __iter__(self): + if not self.shard_sampler: + sampler = self.sampler + else: + # With map-style dataset, `DataLoader(dataset, sampler)` runs the + # sampler in main process only. But `DataLoader(ToIterableDataset(dataset, sampler))` + # will run sampler in every of the N worker. So we should only keep 1/N of the ids on + # each worker. The assumption is that sampler is cheap to iterate so it's fine to + # discard ids in workers. + sampler = _shard_iterator_dataloader_worker(self.sampler) + for idx in sampler: + yield self.dataset[idx] + + def __len__(self): + return len(self.sampler) + + +class AspectRatioGroupedDataset(data.IterableDataset): + """ + Batch data that have similar aspect ratio together. + In this implementation, images whose aspect ratio < (or >) 1 will + be batched together. + This improves training speed because the images then need less padding + to form a batch. + + It assumes the underlying dataset produces dicts with "width" and "height" keys. + It will then produce a list of original dicts with length = batch_size, + all with similar aspect ratios. + """ + + def __init__(self, dataset, batch_size): + """ + Args: + dataset: an iterable. Each element must be a dict with keys + "width" and "height", which will be used to batch data. + batch_size (int): + """ + self.dataset = dataset + self.batch_size = batch_size + self._buckets = [[] for _ in range(2)] + # Hard-coded two aspect ratio groups: w > h and w < h. + # Can add support for more aspect ratio groups, but doesn't seem useful + + def __iter__(self): + for d in self.dataset: + w, h = d["width"], d["height"] + bucket_id = 0 if w > h else 1 + bucket = self._buckets[bucket_id] + bucket.append(d) + if len(bucket) == self.batch_size: + data = bucket[:] + # Clear bucket first, because code after yield is not + # guaranteed to execute + del bucket[:] + yield data diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/dataset_mapper.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/dataset_mapper.py new file mode 100644 index 0000000..a8714f7 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/dataset_mapper.py @@ -0,0 +1,191 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import logging +import numpy as np +from typing import List, Optional, Union +import torch + +from detectron2.config import configurable + +from . import detection_utils as utils +from . import transforms as T + +""" +This file contains the default mapping that's applied to "dataset dicts". +""" + +__all__ = ["DatasetMapper"] + + +class DatasetMapper: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by the model. + + This is the default callable to be used to map your dataset dict into training data. + You may need to follow it to implement your own one for customized logic, + such as a different way to read or transform images. + See :doc:`/tutorials/data_loading` for details. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies cropping/geometric transforms to the image and annotations + 3. Prepare data and annotations to Tensor and :class:`Instances` + """ + + @configurable + def __init__( + self, + is_train: bool, + *, + augmentations: List[Union[T.Augmentation, T.Transform]], + image_format: str, + use_instance_mask: bool = False, + use_keypoint: bool = False, + instance_mask_format: str = "polygon", + keypoint_hflip_indices: Optional[np.ndarray] = None, + precomputed_proposal_topk: Optional[int] = None, + recompute_boxes: bool = False, + ): + """ + NOTE: this interface is experimental. + + Args: + is_train: whether it's used in training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + use_instance_mask: whether to process instance segmentation annotations, if available + use_keypoint: whether to process keypoint annotations if available + instance_mask_format: one of "polygon" or "bitmask". Process instance segmentation + masks into this format. + keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices` + precomputed_proposal_topk: if given, will load pre-computed + proposals from dataset_dict and keep the top k proposals for each image. + recompute_boxes: whether to overwrite bounding box annotations + by computing tight bounding boxes from instance mask annotations. + """ + if recompute_boxes: + assert use_instance_mask, "recompute_boxes requires instance masks" + # fmt: off + self.is_train = is_train + self.augmentations = T.AugmentationList(augmentations) + self.image_format = image_format + self.use_instance_mask = use_instance_mask + self.instance_mask_format = instance_mask_format + self.use_keypoint = use_keypoint + self.keypoint_hflip_indices = keypoint_hflip_indices + self.proposal_topk = precomputed_proposal_topk + self.recompute_boxes = recompute_boxes + # fmt: on + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}") + + @classmethod + def from_config(cls, cfg, is_train: bool = True): + augs = utils.build_augmentation(cfg, is_train) + if cfg.INPUT.CROP.ENABLED and is_train: + augs.insert(0, T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE)) + recompute_boxes = cfg.MODEL.MASK_ON + else: + recompute_boxes = False + + ret = { + "is_train": is_train, + "augmentations": augs, + "image_format": cfg.INPUT.FORMAT, + "use_instance_mask": cfg.MODEL.MASK_ON, + "instance_mask_format": cfg.INPUT.MASK_FORMAT, + "use_keypoint": cfg.MODEL.KEYPOINT_ON, + "recompute_boxes": recompute_boxes, + } + + if cfg.MODEL.KEYPOINT_ON: + ret["keypoint_hflip_indices"] = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN) + + if cfg.MODEL.LOAD_PROPOSALS: + ret["precomputed_proposal_topk"] = ( + cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN + if is_train + else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST + ) + return ret + + def _transform_annotations(self, dataset_dict, transforms, image_shape): + # USER: Modify this if you want to keep them for some reason. + for anno in dataset_dict["annotations"]: + if not self.use_instance_mask: + anno.pop("segmentation", None) + if not self.use_keypoint: + anno.pop("keypoints", None) + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations( + obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices + ) + for obj in dataset_dict.pop("annotations") + if obj.get("iscrowd", 0) == 0 + ] + instances = utils.annotations_to_instances( + annos, image_shape, mask_format=self.instance_mask_format + ) + + # After transforms such as cropping are applied, the bounding box may no longer + # tightly bound the object. As an example, imagine a triangle object + # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight + # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to + # the intersection of original bounding box and the cropping box. + if self.recompute_boxes: + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + dataset_dict["instances"] = utils.filter_empty_instances(instances) + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + # USER: Write your own image loading if it's not from a file + image = utils.read_image(dataset_dict["file_name"], format=self.image_format) + utils.check_image_size(dataset_dict, image) + + # USER: Remove if you don't do semantic/panoptic segmentation. + if "sem_seg_file_name" in dataset_dict: + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2) + else: + sem_seg_gt = None + + aug_input = T.AugInput(image, sem_seg=sem_seg_gt) + transforms = self.augmentations(aug_input) + image, sem_seg_gt = aug_input.image, aug_input.sem_seg + + image_shape = image.shape[:2] # h, w + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + if sem_seg_gt is not None: + dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long")) + + # USER: Remove if you don't use pre-computed proposals. + # Most users would not need this feature. + if self.proposal_topk is not None: + utils.transform_proposals( + dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk + ) + + if not self.is_train: + # USER: Modify this if you want to keep them for some reason. + dataset_dict.pop("annotations", None) + dataset_dict.pop("sem_seg_file_name", None) + return dataset_dict + + if "annotations" in dataset_dict: + self._transform_annotations(dataset_dict, transforms, image_shape) + + return dataset_dict diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/README.md b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/README.md new file mode 100644 index 0000000..9fb3e4f --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/README.md @@ -0,0 +1,9 @@ + + +### Common Datasets + +The dataset implemented here do not need to load the data into the final format. +It should provide the minimal data structure needed to use the dataset, so it can be very efficient. + +For example, for an image dataset, just provide the file names and labels, but don't read the images. +Let the downstream decide how to read. diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/__init__.py new file mode 100644 index 0000000..a44bedc --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .coco import load_coco_json, load_sem_seg, register_coco_instances, convert_to_coco_json +from .coco_panoptic import register_coco_panoptic, register_coco_panoptic_separated +from .lvis import load_lvis_json, register_lvis_instances, get_lvis_instances_meta +from .pascal_voc import load_voc_instances, register_pascal_voc +from . import builtin as _builtin # ensure the builtin datasets are registered + + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/builtin.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/builtin.py new file mode 100644 index 0000000..c3a68aa --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/builtin.py @@ -0,0 +1,259 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + + +""" +This file registers pre-defined datasets at hard-coded paths, and their metadata. + +We hard-code metadata for common datasets. This will enable: +1. Consistency check when loading the datasets +2. Use models on these standard datasets directly and run demos, + without having to download the dataset annotations + +We hard-code some paths to the dataset that's assumed to +exist in "./datasets/". + +Users SHOULD NOT use this file to create new dataset / metadata for new dataset. +To add new dataset, refer to the tutorial "docs/DATASETS.md". +""" + +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog + +from .builtin_meta import ADE20K_SEM_SEG_CATEGORIES, _get_builtin_metadata +from .cityscapes import load_cityscapes_instances, load_cityscapes_semantic +from .cityscapes_panoptic import register_all_cityscapes_panoptic +from .coco import load_sem_seg, register_coco_instances +from .coco_panoptic import register_coco_panoptic, register_coco_panoptic_separated +from .lvis import get_lvis_instances_meta, register_lvis_instances +from .pascal_voc import register_pascal_voc + +# ==== Predefined datasets and splits for COCO ========== + +_PREDEFINED_SPLITS_COCO = {} +_PREDEFINED_SPLITS_COCO["coco"] = { + "coco_2014_train": ("coco/train2014", "coco/annotations/instances_train2014.json"), + "coco_2014_val": ("coco/val2014", "coco/annotations/instances_val2014.json"), + "coco_2014_minival": ("coco/val2014", "coco/annotations/instances_minival2014.json"), + "coco_2014_valminusminival": ( + "coco/val2014", + "coco/annotations/instances_valminusminival2014.json", + ), + "coco_2017_train": ("coco/train2017", "coco/annotations/instances_train2017.json"), + "coco_2017_val": ("coco/val2017", "coco/annotations/instances_val2017.json"), + "coco_2017_test": ("coco/test2017", "coco/annotations/image_info_test2017.json"), + "coco_2017_test-dev": ("coco/test2017", "coco/annotations/image_info_test-dev2017.json"), + "coco_2017_val_100": ("coco/val2017", "coco/annotations/instances_val2017_100.json"), +} + +_PREDEFINED_SPLITS_COCO["coco_person"] = { + "keypoints_coco_2014_train": ( + "coco/train2014", + "coco/annotations/person_keypoints_train2014.json", + ), + "keypoints_coco_2014_val": ("coco/val2014", "coco/annotations/person_keypoints_val2014.json"), + "keypoints_coco_2014_minival": ( + "coco/val2014", + "coco/annotations/person_keypoints_minival2014.json", + ), + "keypoints_coco_2014_valminusminival": ( + "coco/val2014", + "coco/annotations/person_keypoints_valminusminival2014.json", + ), + "keypoints_coco_2017_train": ( + "coco/train2017", + "coco/annotations/person_keypoints_train2017.json", + ), + "keypoints_coco_2017_val": ("coco/val2017", "coco/annotations/person_keypoints_val2017.json"), + "keypoints_coco_2017_val_100": ( + "coco/val2017", + "coco/annotations/person_keypoints_val2017_100.json", + ), +} + + +_PREDEFINED_SPLITS_COCO_PANOPTIC = { + "coco_2017_train_panoptic": ( + # This is the original panoptic annotation directory + "coco/panoptic_train2017", + "coco/annotations/panoptic_train2017.json", + # This directory contains semantic annotations that are + # converted from panoptic annotations. + # It is used by PanopticFPN. + # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py + # to create these directories. + "coco/panoptic_stuff_train2017", + ), + "coco_2017_val_panoptic": ( + "coco/panoptic_val2017", + "coco/annotations/panoptic_val2017.json", + "coco/panoptic_stuff_val2017", + ), + "coco_2017_val_100_panoptic": ( + "coco/panoptic_val2017_100", + "coco/annotations/panoptic_val2017_100.json", + "coco/panoptic_stuff_val2017_100", + ), +} + + +def register_all_coco(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_COCO.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + for ( + prefix, + (panoptic_root, panoptic_json, semantic_root), + ) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items(): + prefix_instances = prefix[: -len("_panoptic")] + instances_meta = MetadataCatalog.get(prefix_instances) + image_root, instances_json = instances_meta.image_root, instances_meta.json_file + # The "separated" version of COCO panoptic segmentation dataset, + # e.g. used by Panoptic FPN + register_coco_panoptic_separated( + prefix, + _get_builtin_metadata("coco_panoptic_separated"), + image_root, + os.path.join(root, panoptic_root), + os.path.join(root, panoptic_json), + os.path.join(root, semantic_root), + instances_json, + ) + # The "standard" version of COCO panoptic segmentation dataset, + # e.g. used by Panoptic-DeepLab + register_coco_panoptic( + prefix, + _get_builtin_metadata("coco_panoptic_standard"), + image_root, + os.path.join(root, panoptic_root), + os.path.join(root, panoptic_json), + instances_json, + ) + + +# ==== Predefined datasets and splits for LVIS ========== + + +_PREDEFINED_SPLITS_LVIS = { + "lvis_v1": { + "lvis_v1_train": ("coco/", "lvis/lvis_v1_train.json"), + "lvis_v1_val": ("coco/", "lvis/lvis_v1_val.json"), + "lvis_v1_test_dev": ("coco/", "lvis/lvis_v1_image_info_test_dev.json"), + "lvis_v1_test_challenge": ("coco/", "lvis/lvis_v1_image_info_test_challenge.json"), + }, + "lvis_v0.5": { + "lvis_v0.5_train": ("coco/", "lvis/lvis_v0.5_train.json"), + "lvis_v0.5_val": ("coco/", "lvis/lvis_v0.5_val.json"), + "lvis_v0.5_val_rand_100": ("coco/", "lvis/lvis_v0.5_val_rand_100.json"), + "lvis_v0.5_test": ("coco/", "lvis/lvis_v0.5_image_info_test.json"), + }, + "lvis_v0.5_cocofied": { + "lvis_v0.5_train_cocofied": ("coco/", "lvis/lvis_v0.5_train_cocofied.json"), + "lvis_v0.5_val_cocofied": ("coco/", "lvis/lvis_v0.5_val_cocofied.json"), + }, +} + + +def register_all_lvis(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_LVIS.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + register_lvis_instances( + key, + get_lvis_instances_meta(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +# ==== Predefined splits for raw cityscapes images =========== +_RAW_CITYSCAPES_SPLITS = { + "cityscapes_fine_{task}_train": ("cityscapes/leftImg8bit/train/", "cityscapes/gtFine/train/"), + "cityscapes_fine_{task}_val": ("cityscapes/leftImg8bit/val/", "cityscapes/gtFine/val/"), + "cityscapes_fine_{task}_test": ("cityscapes/leftImg8bit/test/", "cityscapes/gtFine/test/"), +} + + +def register_all_cityscapes(root): + for key, (image_dir, gt_dir) in _RAW_CITYSCAPES_SPLITS.items(): + meta = _get_builtin_metadata("cityscapes") + image_dir = os.path.join(root, image_dir) + gt_dir = os.path.join(root, gt_dir) + + inst_key = key.format(task="instance_seg") + DatasetCatalog.register( + inst_key, + lambda x=image_dir, y=gt_dir: load_cityscapes_instances( + x, y, from_json=True, to_polygons=True + ), + ) + MetadataCatalog.get(inst_key).set( + image_dir=image_dir, gt_dir=gt_dir, evaluator_type="cityscapes_instance", **meta + ) + + sem_key = key.format(task="sem_seg") + DatasetCatalog.register( + sem_key, lambda x=image_dir, y=gt_dir: load_cityscapes_semantic(x, y) + ) + MetadataCatalog.get(sem_key).set( + image_dir=image_dir, + gt_dir=gt_dir, + evaluator_type="cityscapes_sem_seg", + ignore_label=255, + **meta, + ) + + +# ==== Predefined splits for PASCAL VOC =========== +def register_all_pascal_voc(root): + SPLITS = [ + ("voc_2007_trainval", "VOC2007", "trainval"), + ("voc_2007_train", "VOC2007", "train"), + ("voc_2007_val", "VOC2007", "val"), + ("voc_2007_test", "VOC2007", "test"), + ("voc_2012_trainval", "VOC2012", "trainval"), + ("voc_2012_train", "VOC2012", "train"), + ("voc_2012_val", "VOC2012", "val"), + ] + for name, dirname, split in SPLITS: + year = 2007 if "2007" in name else 2012 + register_pascal_voc(name, os.path.join(root, dirname), split, year) + MetadataCatalog.get(name).evaluator_type = "pascal_voc" + + +def register_all_ade20k(root): + root = os.path.join(root, "ADEChallengeData2016") + for name, dirname in [("train", "training"), ("val", "validation")]: + image_dir = os.path.join(root, "images", dirname) + gt_dir = os.path.join(root, "annotations_detectron2", dirname) + name = f"ade20k_sem_seg_{name}" + DatasetCatalog.register( + name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg") + ) + MetadataCatalog.get(name).set( + stuff_classes=ADE20K_SEM_SEG_CATEGORIES[:], + image_root=image_dir, + sem_seg_root=gt_dir, + evaluator_type="sem_seg", + ignore_label=255, + ) + + +# True for open source; +# Internally at fb, we register them elsewhere +if __name__.endswith(".builtin"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.path.expanduser(os.getenv("DETECTRON2_DATASETS", "datasets")) + register_all_coco(_root) + register_all_lvis(_root) + register_all_cityscapes(_root) + register_all_cityscapes_panoptic(_root) + register_all_pascal_voc(_root) + register_all_ade20k(_root) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/builtin_meta.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/builtin_meta.py new file mode 100644 index 0000000..63c7a1a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/builtin_meta.py @@ -0,0 +1,350 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +""" +Note: +For your custom dataset, there is no need to hard-code metadata anywhere in the code. +For example, for COCO-format dataset, metadata will be obtained automatically +when calling `load_coco_json`. For other dataset, metadata may also be obtained in other ways +during loading. + +However, we hard-coded metadata for a few common dataset here. +The only goal is to allow users who don't have these dataset to use pre-trained models. +Users don't have to download a COCO json (which contains metadata), in order to visualize a +COCO model (with correct class names and colors). +""" + + +# All coco categories, together with their nice-looking visualization colors +# It's from https://github.com/cocodataset/panopticapi/blob/master/panoptic_coco_categories.json +COCO_CATEGORIES = [ + {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"}, + {"color": [119, 11, 32], "isthing": 1, "id": 2, "name": "bicycle"}, + {"color": [0, 0, 142], "isthing": 1, "id": 3, "name": "car"}, + {"color": [0, 0, 230], "isthing": 1, "id": 4, "name": "motorcycle"}, + {"color": [106, 0, 228], "isthing": 1, "id": 5, "name": "airplane"}, + {"color": [0, 60, 100], "isthing": 1, "id": 6, "name": "bus"}, + {"color": [0, 80, 100], "isthing": 1, "id": 7, "name": "train"}, + {"color": [0, 0, 70], "isthing": 1, "id": 8, "name": "truck"}, + {"color": [0, 0, 192], "isthing": 1, "id": 9, "name": "boat"}, + {"color": [250, 170, 30], "isthing": 1, "id": 10, "name": "traffic light"}, + {"color": [100, 170, 30], "isthing": 1, "id": 11, "name": "fire hydrant"}, + {"color": [220, 220, 0], "isthing": 1, "id": 13, "name": "stop sign"}, + {"color": [175, 116, 175], "isthing": 1, "id": 14, "name": "parking meter"}, + {"color": [250, 0, 30], "isthing": 1, "id": 15, "name": "bench"}, + {"color": [165, 42, 42], "isthing": 1, "id": 16, "name": "bird"}, + {"color": [255, 77, 255], "isthing": 1, "id": 17, "name": "cat"}, + {"color": [0, 226, 252], "isthing": 1, "id": 18, "name": "dog"}, + {"color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"}, + {"color": [0, 82, 0], "isthing": 1, "id": 20, "name": "sheep"}, + {"color": [120, 166, 157], "isthing": 1, "id": 21, "name": "cow"}, + {"color": [110, 76, 0], "isthing": 1, "id": 22, "name": "elephant"}, + {"color": [174, 57, 255], "isthing": 1, "id": 23, "name": "bear"}, + {"color": [199, 100, 0], "isthing": 1, "id": 24, "name": "zebra"}, + {"color": [72, 0, 118], "isthing": 1, "id": 25, "name": "giraffe"}, + {"color": [255, 179, 240], "isthing": 1, "id": 27, "name": "backpack"}, + {"color": [0, 125, 92], "isthing": 1, "id": 28, "name": "umbrella"}, + {"color": [209, 0, 151], "isthing": 1, "id": 31, "name": "handbag"}, + {"color": [188, 208, 182], "isthing": 1, "id": 32, "name": "tie"}, + {"color": [0, 220, 176], "isthing": 1, "id": 33, "name": "suitcase"}, + {"color": [255, 99, 164], "isthing": 1, "id": 34, "name": "frisbee"}, + {"color": [92, 0, 73], "isthing": 1, "id": 35, "name": "skis"}, + {"color": [133, 129, 255], "isthing": 1, "id": 36, "name": "snowboard"}, + {"color": [78, 180, 255], "isthing": 1, "id": 37, "name": "sports ball"}, + {"color": [0, 228, 0], "isthing": 1, "id": 38, "name": "kite"}, + {"color": [174, 255, 243], "isthing": 1, "id": 39, "name": "baseball bat"}, + {"color": [45, 89, 255], "isthing": 1, "id": 40, "name": "baseball glove"}, + {"color": [134, 134, 103], "isthing": 1, "id": 41, "name": "skateboard"}, + {"color": [145, 148, 174], "isthing": 1, "id": 42, "name": "surfboard"}, + {"color": [255, 208, 186], "isthing": 1, "id": 43, "name": "tennis racket"}, + {"color": [197, 226, 255], "isthing": 1, "id": 44, "name": "bottle"}, + {"color": [171, 134, 1], "isthing": 1, "id": 46, "name": "wine glass"}, + {"color": [109, 63, 54], "isthing": 1, "id": 47, "name": "cup"}, + {"color": [207, 138, 255], "isthing": 1, "id": 48, "name": "fork"}, + {"color": [151, 0, 95], "isthing": 1, "id": 49, "name": "knife"}, + {"color": [9, 80, 61], "isthing": 1, "id": 50, "name": "spoon"}, + {"color": [84, 105, 51], "isthing": 1, "id": 51, "name": "bowl"}, + {"color": [74, 65, 105], "isthing": 1, "id": 52, "name": "banana"}, + {"color": [166, 196, 102], "isthing": 1, "id": 53, "name": "apple"}, + {"color": [208, 195, 210], "isthing": 1, "id": 54, "name": "sandwich"}, + {"color": [255, 109, 65], "isthing": 1, "id": 55, "name": "orange"}, + {"color": [0, 143, 149], "isthing": 1, "id": 56, "name": "broccoli"}, + {"color": [179, 0, 194], "isthing": 1, "id": 57, "name": "carrot"}, + {"color": [209, 99, 106], "isthing": 1, "id": 58, "name": "hot dog"}, + {"color": [5, 121, 0], "isthing": 1, "id": 59, "name": "pizza"}, + {"color": [227, 255, 205], "isthing": 1, "id": 60, "name": "donut"}, + {"color": [147, 186, 208], "isthing": 1, "id": 61, "name": "cake"}, + {"color": [153, 69, 1], "isthing": 1, "id": 62, "name": "chair"}, + {"color": [3, 95, 161], "isthing": 1, "id": 63, "name": "couch"}, + {"color": [163, 255, 0], "isthing": 1, "id": 64, "name": "potted plant"}, + {"color": [119, 0, 170], "isthing": 1, "id": 65, "name": "bed"}, + {"color": [0, 182, 199], "isthing": 1, "id": 67, "name": "dining table"}, + {"color": [0, 165, 120], "isthing": 1, "id": 70, "name": "toilet"}, + {"color": [183, 130, 88], "isthing": 1, "id": 72, "name": "tv"}, + {"color": [95, 32, 0], "isthing": 1, "id": 73, "name": "laptop"}, + {"color": [130, 114, 135], "isthing": 1, "id": 74, "name": "mouse"}, + {"color": [110, 129, 133], "isthing": 1, "id": 75, "name": "remote"}, + {"color": [166, 74, 118], "isthing": 1, "id": 76, "name": "keyboard"}, + {"color": [219, 142, 185], "isthing": 1, "id": 77, "name": "cell phone"}, + {"color": [79, 210, 114], "isthing": 1, "id": 78, "name": "microwave"}, + {"color": [178, 90, 62], "isthing": 1, "id": 79, "name": "oven"}, + {"color": [65, 70, 15], "isthing": 1, "id": 80, "name": "toaster"}, + {"color": [127, 167, 115], "isthing": 1, "id": 81, "name": "sink"}, + {"color": [59, 105, 106], "isthing": 1, "id": 82, "name": "refrigerator"}, + {"color": [142, 108, 45], "isthing": 1, "id": 84, "name": "book"}, + {"color": [196, 172, 0], "isthing": 1, "id": 85, "name": "clock"}, + {"color": [95, 54, 80], "isthing": 1, "id": 86, "name": "vase"}, + {"color": [128, 76, 255], "isthing": 1, "id": 87, "name": "scissors"}, + {"color": [201, 57, 1], "isthing": 1, "id": 88, "name": "teddy bear"}, + {"color": [246, 0, 122], "isthing": 1, "id": 89, "name": "hair drier"}, + {"color": [191, 162, 208], "isthing": 1, "id": 90, "name": "toothbrush"}, + {"color": [255, 255, 128], "isthing": 0, "id": 92, "name": "banner"}, + {"color": [147, 211, 203], "isthing": 0, "id": 93, "name": "blanket"}, + {"color": [150, 100, 100], "isthing": 0, "id": 95, "name": "bridge"}, + {"color": [168, 171, 172], "isthing": 0, "id": 100, "name": "cardboard"}, + {"color": [146, 112, 198], "isthing": 0, "id": 107, "name": "counter"}, + {"color": [210, 170, 100], "isthing": 0, "id": 109, "name": "curtain"}, + {"color": [92, 136, 89], "isthing": 0, "id": 112, "name": "door-stuff"}, + {"color": [218, 88, 184], "isthing": 0, "id": 118, "name": "floor-wood"}, + {"color": [241, 129, 0], "isthing": 0, "id": 119, "name": "flower"}, + {"color": [217, 17, 255], "isthing": 0, "id": 122, "name": "fruit"}, + {"color": [124, 74, 181], "isthing": 0, "id": 125, "name": "gravel"}, + {"color": [70, 70, 70], "isthing": 0, "id": 128, "name": "house"}, + {"color": [255, 228, 255], "isthing": 0, "id": 130, "name": "light"}, + {"color": [154, 208, 0], "isthing": 0, "id": 133, "name": "mirror-stuff"}, + {"color": [193, 0, 92], "isthing": 0, "id": 138, "name": "net"}, + {"color": [76, 91, 113], "isthing": 0, "id": 141, "name": "pillow"}, + {"color": [255, 180, 195], "isthing": 0, "id": 144, "name": "platform"}, + {"color": [106, 154, 176], "isthing": 0, "id": 145, "name": "playingfield"}, + {"color": [230, 150, 140], "isthing": 0, "id": 147, "name": "railroad"}, + {"color": [60, 143, 255], "isthing": 0, "id": 148, "name": "river"}, + {"color": [128, 64, 128], "isthing": 0, "id": 149, "name": "road"}, + {"color": [92, 82, 55], "isthing": 0, "id": 151, "name": "roof"}, + {"color": [254, 212, 124], "isthing": 0, "id": 154, "name": "sand"}, + {"color": [73, 77, 174], "isthing": 0, "id": 155, "name": "sea"}, + {"color": [255, 160, 98], "isthing": 0, "id": 156, "name": "shelf"}, + {"color": [255, 255, 255], "isthing": 0, "id": 159, "name": "snow"}, + {"color": [104, 84, 109], "isthing": 0, "id": 161, "name": "stairs"}, + {"color": [169, 164, 131], "isthing": 0, "id": 166, "name": "tent"}, + {"color": [225, 199, 255], "isthing": 0, "id": 168, "name": "towel"}, + {"color": [137, 54, 74], "isthing": 0, "id": 171, "name": "wall-brick"}, + {"color": [135, 158, 223], "isthing": 0, "id": 175, "name": "wall-stone"}, + {"color": [7, 246, 231], "isthing": 0, "id": 176, "name": "wall-tile"}, + {"color": [107, 255, 200], "isthing": 0, "id": 177, "name": "wall-wood"}, + {"color": [58, 41, 149], "isthing": 0, "id": 178, "name": "water-other"}, + {"color": [183, 121, 142], "isthing": 0, "id": 180, "name": "window-blind"}, + {"color": [255, 73, 97], "isthing": 0, "id": 181, "name": "window-other"}, + {"color": [107, 142, 35], "isthing": 0, "id": 184, "name": "tree-merged"}, + {"color": [190, 153, 153], "isthing": 0, "id": 185, "name": "fence-merged"}, + {"color": [146, 139, 141], "isthing": 0, "id": 186, "name": "ceiling-merged"}, + {"color": [70, 130, 180], "isthing": 0, "id": 187, "name": "sky-other-merged"}, + {"color": [134, 199, 156], "isthing": 0, "id": 188, "name": "cabinet-merged"}, + {"color": [209, 226, 140], "isthing": 0, "id": 189, "name": "table-merged"}, + {"color": [96, 36, 108], "isthing": 0, "id": 190, "name": "floor-other-merged"}, + {"color": [96, 96, 96], "isthing": 0, "id": 191, "name": "pavement-merged"}, + {"color": [64, 170, 64], "isthing": 0, "id": 192, "name": "mountain-merged"}, + {"color": [152, 251, 152], "isthing": 0, "id": 193, "name": "grass-merged"}, + {"color": [208, 229, 228], "isthing": 0, "id": 194, "name": "dirt-merged"}, + {"color": [206, 186, 171], "isthing": 0, "id": 195, "name": "paper-merged"}, + {"color": [152, 161, 64], "isthing": 0, "id": 196, "name": "food-other-merged"}, + {"color": [116, 112, 0], "isthing": 0, "id": 197, "name": "building-other-merged"}, + {"color": [0, 114, 143], "isthing": 0, "id": 198, "name": "rock-merged"}, + {"color": [102, 102, 156], "isthing": 0, "id": 199, "name": "wall-other-merged"}, + {"color": [250, 141, 255], "isthing": 0, "id": 200, "name": "rug-merged"}, +] + +# fmt: off +COCO_PERSON_KEYPOINT_NAMES = ( + "nose", + "left_eye", "right_eye", + "left_ear", "right_ear", + "left_shoulder", "right_shoulder", + "left_elbow", "right_elbow", + "left_wrist", "right_wrist", + "left_hip", "right_hip", + "left_knee", "right_knee", + "left_ankle", "right_ankle", +) +# fmt: on + +# Pairs of keypoints that should be exchanged under horizontal flipping +COCO_PERSON_KEYPOINT_FLIP_MAP = ( + ("left_eye", "right_eye"), + ("left_ear", "right_ear"), + ("left_shoulder", "right_shoulder"), + ("left_elbow", "right_elbow"), + ("left_wrist", "right_wrist"), + ("left_hip", "right_hip"), + ("left_knee", "right_knee"), + ("left_ankle", "right_ankle"), +) + +# rules for pairs of keypoints to draw a line between, and the line color to use. +KEYPOINT_CONNECTION_RULES = [ + # face + ("left_ear", "left_eye", (102, 204, 255)), + ("right_ear", "right_eye", (51, 153, 255)), + ("left_eye", "nose", (102, 0, 204)), + ("nose", "right_eye", (51, 102, 255)), + # upper-body + ("left_shoulder", "right_shoulder", (255, 128, 0)), + ("left_shoulder", "left_elbow", (153, 255, 204)), + ("right_shoulder", "right_elbow", (128, 229, 255)), + ("left_elbow", "left_wrist", (153, 255, 153)), + ("right_elbow", "right_wrist", (102, 255, 224)), + # lower-body + ("left_hip", "right_hip", (255, 102, 0)), + ("left_hip", "left_knee", (255, 255, 77)), + ("right_hip", "right_knee", (153, 255, 204)), + ("left_knee", "left_ankle", (191, 255, 128)), + ("right_knee", "right_ankle", (255, 195, 77)), +] + +# All Cityscapes categories, together with their nice-looking visualization colors +# It's from https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py # noqa +CITYSCAPES_CATEGORIES = [ + {"color": (128, 64, 128), "isthing": 0, "id": 7, "trainId": 0, "name": "road"}, + {"color": (244, 35, 232), "isthing": 0, "id": 8, "trainId": 1, "name": "sidewalk"}, + {"color": (70, 70, 70), "isthing": 0, "id": 11, "trainId": 2, "name": "building"}, + {"color": (102, 102, 156), "isthing": 0, "id": 12, "trainId": 3, "name": "wall"}, + {"color": (190, 153, 153), "isthing": 0, "id": 13, "trainId": 4, "name": "fence"}, + {"color": (153, 153, 153), "isthing": 0, "id": 17, "trainId": 5, "name": "pole"}, + {"color": (250, 170, 30), "isthing": 0, "id": 19, "trainId": 6, "name": "traffic light"}, + {"color": (220, 220, 0), "isthing": 0, "id": 20, "trainId": 7, "name": "traffic sign"}, + {"color": (107, 142, 35), "isthing": 0, "id": 21, "trainId": 8, "name": "vegetation"}, + {"color": (152, 251, 152), "isthing": 0, "id": 22, "trainId": 9, "name": "terrain"}, + {"color": (70, 130, 180), "isthing": 0, "id": 23, "trainId": 10, "name": "sky"}, + {"color": (220, 20, 60), "isthing": 1, "id": 24, "trainId": 11, "name": "person"}, + {"color": (255, 0, 0), "isthing": 1, "id": 25, "trainId": 12, "name": "rider"}, + {"color": (0, 0, 142), "isthing": 1, "id": 26, "trainId": 13, "name": "car"}, + {"color": (0, 0, 70), "isthing": 1, "id": 27, "trainId": 14, "name": "truck"}, + {"color": (0, 60, 100), "isthing": 1, "id": 28, "trainId": 15, "name": "bus"}, + {"color": (0, 80, 100), "isthing": 1, "id": 31, "trainId": 16, "name": "train"}, + {"color": (0, 0, 230), "isthing": 1, "id": 32, "trainId": 17, "name": "motorcycle"}, + {"color": (119, 11, 32), "isthing": 1, "id": 33, "trainId": 18, "name": "bicycle"}, +] + +# fmt: off +ADE20K_SEM_SEG_CATEGORIES = [ + "wall", "building", "sky", "floor", "tree", "ceiling", "road, route", "bed", "window ", "grass", "cabinet", "sidewalk, pavement", "person", "earth, ground", "door", "table", "mountain, mount", "plant", "curtain", "chair", "car", "water", "painting, picture", "sofa", "shelf", "house", "sea", "mirror", "rug", "field", "armchair", "seat", "fence", "desk", "rock, stone", "wardrobe, closet, press", "lamp", "tub", "rail", "cushion", "base, pedestal, stand", "box", "column, pillar", "signboard, sign", "chest of drawers, chest, bureau, dresser", "counter", "sand", "sink", "skyscraper", "fireplace", "refrigerator, icebox", "grandstand, covered stand", "path", "stairs", "runway", "case, display case, showcase, vitrine", "pool table, billiard table, snooker table", "pillow", "screen door, screen", "stairway, staircase", "river", "bridge, span", "bookcase", "blind, screen", "coffee table", "toilet, can, commode, crapper, pot, potty, stool, throne", "flower", "book", "hill", "bench", "countertop", "stove", "palm, palm tree", "kitchen island", "computer", "swivel chair", "boat", "bar", "arcade machine", "hovel, hut, hutch, shack, shanty", "bus", "towel", "light", "truck", "tower", "chandelier", "awning, sunshade, sunblind", "street lamp", "booth", "tv", "plane", "dirt track", "clothes", "pole", "land, ground, soil", "bannister, banister, balustrade, balusters, handrail", "escalator, moving staircase, moving stairway", "ottoman, pouf, pouffe, puff, hassock", "bottle", "buffet, counter, sideboard", "poster, posting, placard, notice, bill, card", "stage", "van", "ship", "fountain", "conveyer belt, conveyor belt, conveyer, conveyor, transporter", "canopy", "washer, automatic washer, washing machine", "plaything, toy", "pool", "stool", "barrel, cask", "basket, handbasket", "falls", "tent", "bag", "minibike, motorbike", "cradle", "oven", "ball", "food, solid food", "step, stair", "tank, storage tank", "trade name", "microwave", "pot", "animal", "bicycle", "lake", "dishwasher", "screen", "blanket, cover", "sculpture", "hood, exhaust hood", "sconce", "vase", "traffic light", "tray", "trash can", "fan", "pier", "crt screen", "plate", "monitor", "bulletin board", "shower", "radiator", "glass, drinking glass", "clock", "flag", # noqa +] +# After processed by `prepare_ade20k_sem_seg.py`, id 255 means ignore +# fmt: on + + +def _get_coco_instances_meta(): + thing_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 1] + thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1] + assert len(thing_ids) == 80, len(thing_ids) + # Mapping from the incontiguous COCO category id to an id in [0, 79] + thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} + thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1] + ret = { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + "thing_colors": thing_colors, + } + return ret + + +def _get_coco_panoptic_separated_meta(): + """ + Returns metadata for "separated" version of the panoptic segmentation dataset. + """ + stuff_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 0] + assert len(stuff_ids) == 53, len(stuff_ids) + + # For semantic segmentation, this mapping maps from contiguous stuff id + # (in [0, 53], used in models) to ids in the dataset (used for processing results) + # The id 0 is mapped to an extra category "thing". + stuff_dataset_id_to_contiguous_id = {k: i + 1 for i, k in enumerate(stuff_ids)} + # When converting COCO panoptic annotations to semantic annotations + # We label the "thing" category to 0 + stuff_dataset_id_to_contiguous_id[0] = 0 + + # 54 names for COCO stuff categories (including "things") + stuff_classes = ["things"] + [ + k["name"].replace("-other", "").replace("-merged", "") + for k in COCO_CATEGORIES + if k["isthing"] == 0 + ] + + # NOTE: I randomly picked a color for things + stuff_colors = [[82, 18, 128]] + [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 0] + ret = { + "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id, + "stuff_classes": stuff_classes, + "stuff_colors": stuff_colors, + } + ret.update(_get_coco_instances_meta()) + return ret + + +def _get_builtin_metadata(dataset_name): + if dataset_name == "coco": + return _get_coco_instances_meta() + if dataset_name == "coco_panoptic_separated": + return _get_coco_panoptic_separated_meta() + elif dataset_name == "coco_panoptic_standard": + meta = {} + # The following metadata maps contiguous id from [0, #thing categories + + # #stuff categories) to their names and colors. We have to replica of the + # same name and color under "thing_*" and "stuff_*" because the current + # visualization function in D2 handles thing and class classes differently + # due to some heuristic used in Panoptic FPN. We keep the same naming to + # enable reusing existing visualization functions. + thing_classes = [k["name"] for k in COCO_CATEGORIES] + thing_colors = [k["color"] for k in COCO_CATEGORIES] + stuff_classes = [k["name"] for k in COCO_CATEGORIES] + stuff_colors = [k["color"] for k in COCO_CATEGORIES] + + meta["thing_classes"] = thing_classes + meta["thing_colors"] = thing_colors + meta["stuff_classes"] = stuff_classes + meta["stuff_colors"] = stuff_colors + + # Convert category id for training: + # category id: like semantic segmentation, it is the class id for each + # pixel. Since there are some classes not used in evaluation, the category + # id is not always contiguous and thus we have two set of category ids: + # - original category id: category id in the original dataset, mainly + # used for evaluation. + # - contiguous category id: [0, #classes), in order to train the linear + # softmax classifier. + thing_dataset_id_to_contiguous_id = {} + stuff_dataset_id_to_contiguous_id = {} + + for i, cat in enumerate(COCO_CATEGORIES): + if cat["isthing"]: + thing_dataset_id_to_contiguous_id[cat["id"]] = i + else: + stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id + meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id + + return meta + elif dataset_name == "coco_person": + return { + "thing_classes": ["person"], + "keypoint_names": COCO_PERSON_KEYPOINT_NAMES, + "keypoint_flip_map": COCO_PERSON_KEYPOINT_FLIP_MAP, + "keypoint_connection_rules": KEYPOINT_CONNECTION_RULES, + } + elif dataset_name == "cityscapes": + # fmt: off + CITYSCAPES_THING_CLASSES = [ + "person", "rider", "car", "truck", + "bus", "train", "motorcycle", "bicycle", + ] + CITYSCAPES_STUFF_CLASSES = [ + "road", "sidewalk", "building", "wall", "fence", "pole", "traffic light", + "traffic sign", "vegetation", "terrain", "sky", "person", "rider", "car", + "truck", "bus", "train", "motorcycle", "bicycle", + ] + # fmt: on + return { + "thing_classes": CITYSCAPES_THING_CLASSES, + "stuff_classes": CITYSCAPES_STUFF_CLASSES, + } + raise KeyError("No built-in metadata for dataset {}".format(dataset_name)) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/cityscapes.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/cityscapes.py new file mode 100644 index 0000000..1e84a5b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/cityscapes.py @@ -0,0 +1,329 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import functools +import json +import logging +import multiprocessing as mp +import numpy as np +import os +from itertools import chain +import pycocotools.mask as mask_util +from PIL import Image + +from detectron2.structures import BoxMode +from detectron2.utils.comm import get_world_size +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import setup_logger + +try: + import cv2 # noqa +except ImportError: + # OpenCV is an optional dependency at the moment + pass + + +logger = logging.getLogger(__name__) + + +def _get_cityscapes_files(image_dir, gt_dir): + files = [] + # scan through the directory + cities = PathManager.ls(image_dir) + logger.info(f"{len(cities)} cities found in '{image_dir}'.") + for city in cities: + city_img_dir = os.path.join(image_dir, city) + city_gt_dir = os.path.join(gt_dir, city) + for basename in PathManager.ls(city_img_dir): + image_file = os.path.join(city_img_dir, basename) + + suffix = "leftImg8bit.png" + assert basename.endswith(suffix), basename + basename = basename[: -len(suffix)] + + instance_file = os.path.join(city_gt_dir, basename + "gtFine_instanceIds.png") + label_file = os.path.join(city_gt_dir, basename + "gtFine_labelIds.png") + json_file = os.path.join(city_gt_dir, basename + "gtFine_polygons.json") + + files.append((image_file, instance_file, label_file, json_file)) + assert len(files), "No images found in {}".format(image_dir) + for f in files[0]: + assert PathManager.isfile(f), f + return files + + +def load_cityscapes_instances(image_dir, gt_dir, from_json=True, to_polygons=True): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train". + gt_dir (str): path to the raw annotations. e.g., "~/cityscapes/gtFine/train". + from_json (bool): whether to read annotations from the raw json file or the png files. + to_polygons (bool): whether to represent the segmentation as polygons + (COCO's format) instead of masks (cityscapes's format). + + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + """ + if from_json: + assert to_polygons, ( + "Cityscapes's json annotations are in polygon format. " + "Converting to mask format is not supported now." + ) + files = _get_cityscapes_files(image_dir, gt_dir) + + logger.info("Preprocessing cityscapes annotations ...") + # This is still not fast: all workers will execute duplicate works and will + # take up to 10m on a 8GPU server. + pool = mp.Pool(processes=max(mp.cpu_count() // get_world_size() // 2, 4)) + + ret = pool.map( + functools.partial(_cityscapes_files_to_dict, from_json=from_json, to_polygons=to_polygons), + files, + ) + logger.info("Loaded {} images from {}".format(len(ret), image_dir)) + + # Map cityscape ids to contiguous ids + from cityscapesscripts.helpers.labels import labels + + labels = [l for l in labels if l.hasInstances and not l.ignoreInEval] + dataset_id_to_contiguous_id = {l.id: idx for idx, l in enumerate(labels)} + for dict_per_image in ret: + for anno in dict_per_image["annotations"]: + anno["category_id"] = dataset_id_to_contiguous_id[anno["category_id"]] + return ret + + +def load_cityscapes_semantic(image_dir, gt_dir): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train". + gt_dir (str): path to the raw annotations. e.g., "~/cityscapes/gtFine/train". + + Returns: + list[dict]: a list of dict, each has "file_name" and + "sem_seg_file_name". + """ + ret = [] + # gt_dir is small and contain many small files. make sense to fetch to local first + gt_dir = PathManager.get_local_path(gt_dir) + for image_file, _, label_file, json_file in _get_cityscapes_files(image_dir, gt_dir): + label_file = label_file.replace("labelIds", "labelTrainIds") + + with PathManager.open(json_file, "r") as f: + jsonobj = json.load(f) + ret.append( + { + "file_name": image_file, + "sem_seg_file_name": label_file, + "height": jsonobj["imgHeight"], + "width": jsonobj["imgWidth"], + } + ) + assert len(ret), f"No images found in {image_dir}!" + assert PathManager.isfile( + ret[0]["sem_seg_file_name"] + ), "Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py" # noqa + return ret + + +def _cityscapes_files_to_dict(files, from_json, to_polygons): + """ + Parse cityscapes annotation files to a instance segmentation dataset dict. + + Args: + files (tuple): consists of (image_file, instance_id_file, label_id_file, json_file) + from_json (bool): whether to read annotations from the raw json file or the png files. + to_polygons (bool): whether to represent the segmentation as polygons + (COCO's format) instead of masks (cityscapes's format). + + Returns: + A dict in Detectron2 Dataset format. + """ + from cityscapesscripts.helpers.labels import id2label, name2label + + image_file, instance_id_file, _, json_file = files + + annos = [] + + if from_json: + from shapely.geometry import MultiPolygon, Polygon + + with PathManager.open(json_file, "r") as f: + jsonobj = json.load(f) + ret = { + "file_name": image_file, + "image_id": os.path.basename(image_file), + "height": jsonobj["imgHeight"], + "width": jsonobj["imgWidth"], + } + + # `polygons_union` contains the union of all valid polygons. + polygons_union = Polygon() + + # CityscapesScripts draw the polygons in sequential order + # and each polygon *overwrites* existing ones. See + # (https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/preparation/json2instanceImg.py) # noqa + # We use reverse order, and each polygon *avoids* early ones. + # This will resolve the ploygon overlaps in the same way as CityscapesScripts. + for obj in jsonobj["objects"][::-1]: + if "deleted" in obj: # cityscapes data format specific + continue + label_name = obj["label"] + + try: + label = name2label[label_name] + except KeyError: + if label_name.endswith("group"): # crowd area + label = name2label[label_name[: -len("group")]] + else: + raise + if label.id < 0: # cityscapes data format + continue + + # Cityscapes's raw annotations uses integer coordinates + # Therefore +0.5 here + poly_coord = np.asarray(obj["polygon"], dtype="f4") + 0.5 + # CityscapesScript uses PIL.ImageDraw.polygon to rasterize + # polygons for evaluation. This function operates in integer space + # and draws each pixel whose center falls into the polygon. + # Therefore it draws a polygon which is 0.5 "fatter" in expectation. + # We therefore dilate the input polygon by 0.5 as our input. + poly = Polygon(poly_coord).buffer(0.5, resolution=4) + + if not label.hasInstances or label.ignoreInEval: + # even if we won't store the polygon it still contributes to overlaps resolution + polygons_union = polygons_union.union(poly) + continue + + # Take non-overlapping part of the polygon + poly_wo_overlaps = poly.difference(polygons_union) + if poly_wo_overlaps.is_empty: + continue + polygons_union = polygons_union.union(poly) + + anno = {} + anno["iscrowd"] = label_name.endswith("group") + anno["category_id"] = label.id + + if isinstance(poly_wo_overlaps, Polygon): + poly_list = [poly_wo_overlaps] + elif isinstance(poly_wo_overlaps, MultiPolygon): + poly_list = poly_wo_overlaps.geoms + else: + raise NotImplementedError("Unknown geometric structure {}".format(poly_wo_overlaps)) + + poly_coord = [] + for poly_el in poly_list: + # COCO API can work only with exterior boundaries now, hence we store only them. + # TODO: store both exterior and interior boundaries once other parts of the + # codebase support holes in polygons. + poly_coord.append(list(chain(*poly_el.exterior.coords))) + anno["segmentation"] = poly_coord + (xmin, ymin, xmax, ymax) = poly_wo_overlaps.bounds + + anno["bbox"] = (xmin, ymin, xmax, ymax) + anno["bbox_mode"] = BoxMode.XYXY_ABS + + annos.append(anno) + else: + # See also the official annotation parsing scripts at + # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/instances2dict.py # noqa + with PathManager.open(instance_id_file, "rb") as f: + inst_image = np.asarray(Image.open(f), order="F") + # ids < 24 are stuff labels (filtering them first is about 5% faster) + flattened_ids = np.unique(inst_image[inst_image >= 24]) + + ret = { + "file_name": image_file, + "image_id": os.path.basename(image_file), + "height": inst_image.shape[0], + "width": inst_image.shape[1], + } + + for instance_id in flattened_ids: + # For non-crowd annotations, instance_id // 1000 is the label_id + # Crowd annotations have <1000 instance ids + label_id = instance_id // 1000 if instance_id >= 1000 else instance_id + label = id2label[label_id] + if not label.hasInstances or label.ignoreInEval: + continue + + anno = {} + anno["iscrowd"] = instance_id < 1000 + anno["category_id"] = label.id + + mask = np.asarray(inst_image == instance_id, dtype=np.uint8, order="F") + + inds = np.nonzero(mask) + ymin, ymax = inds[0].min(), inds[0].max() + xmin, xmax = inds[1].min(), inds[1].max() + anno["bbox"] = (xmin, ymin, xmax, ymax) + if xmax <= xmin or ymax <= ymin: + continue + anno["bbox_mode"] = BoxMode.XYXY_ABS + if to_polygons: + # This conversion comes from D4809743 and D5171122, + # when Mask-RCNN was first developed. + contours = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[ + -2 + ] + polygons = [c.reshape(-1).tolist() for c in contours if len(c) >= 3] + # opencv's can produce invalid polygons + if len(polygons) == 0: + continue + anno["segmentation"] = polygons + else: + anno["segmentation"] = mask_util.encode(mask[:, :, None])[0] + annos.append(anno) + ret["annotations"] = annos + return ret + + +if __name__ == "__main__": + """ + Test the cityscapes dataset loader. + + Usage: + python -m detectron2.data.datasets.cityscapes \ + cityscapes/leftImg8bit/train cityscapes/gtFine/train + """ + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("image_dir") + parser.add_argument("gt_dir") + parser.add_argument("--type", choices=["instance", "semantic"], default="instance") + args = parser.parse_args() + from detectron2.data.catalog import Metadata + from detectron2.utils.visualizer import Visualizer + from cityscapesscripts.helpers.labels import labels + + logger = setup_logger(name=__name__) + + dirname = "cityscapes-data-vis" + os.makedirs(dirname, exist_ok=True) + + if args.type == "instance": + dicts = load_cityscapes_instances( + args.image_dir, args.gt_dir, from_json=True, to_polygons=True + ) + logger.info("Done loading {} samples.".format(len(dicts))) + + thing_classes = [k.name for k in labels if k.hasInstances and not k.ignoreInEval] + meta = Metadata().set(thing_classes=thing_classes) + + else: + dicts = load_cityscapes_semantic(args.image_dir, args.gt_dir) + logger.info("Done loading {} samples.".format(len(dicts))) + + stuff_classes = [k.name for k in labels if k.trainId != 255] + stuff_colors = [k.color for k in labels if k.trainId != 255] + meta = Metadata().set(stuff_classes=stuff_classes, stuff_colors=stuff_colors) + + for d in dicts: + img = np.array(Image.open(PathManager.open(d["file_name"], "rb"))) + visualizer = Visualizer(img, metadata=meta) + vis = visualizer.draw_dataset_dict(d) + # cv2.imshow("a", vis.get_image()[:, :, ::-1]) + # cv2.waitKey() + fpath = os.path.join(dirname, os.path.basename(d["file_name"])) + vis.save(fpath) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/cityscapes_panoptic.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/cityscapes_panoptic.py new file mode 100644 index 0000000..48c136f --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/cityscapes_panoptic.py @@ -0,0 +1,187 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import json +import logging +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets.builtin_meta import CITYSCAPES_CATEGORIES +from detectron2.utils.file_io import PathManager + +""" +This file contains functions to register the Cityscapes panoptic dataset to the DatasetCatalog. +""" + + +logger = logging.getLogger(__name__) + + +def get_cityscapes_panoptic_files(image_dir, gt_dir, json_info): + files = [] + # scan through the directory + cities = PathManager.ls(image_dir) + logger.info(f"{len(cities)} cities found in '{image_dir}'.") + image_dict = {} + for city in cities: + city_img_dir = os.path.join(image_dir, city) + for basename in PathManager.ls(city_img_dir): + image_file = os.path.join(city_img_dir, basename) + + suffix = "_leftImg8bit.png" + assert basename.endswith(suffix), basename + basename = os.path.basename(basename)[: -len(suffix)] + + image_dict[basename] = image_file + + for ann in json_info["annotations"]: + image_file = image_dict.get(ann["image_id"], None) + assert image_file is not None, "No image {} found for annotation {}".format( + ann["image_id"], ann["file_name"] + ) + label_file = os.path.join(gt_dir, ann["file_name"]) + segments_info = ann["segments_info"] + + files.append((image_file, label_file, segments_info)) + + assert len(files), "No images found in {}".format(image_dir) + assert PathManager.isfile(files[0][0]), files[0][0] + assert PathManager.isfile(files[0][1]), files[0][1] + return files + + +def load_cityscapes_panoptic(image_dir, gt_dir, gt_json, meta): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train". + gt_dir (str): path to the raw annotations. e.g., + "~/cityscapes/gtFine/cityscapes_panoptic_train". + gt_json (str): path to the json file. e.g., + "~/cityscapes/gtFine/cityscapes_panoptic_train.json". + meta (dict): dictionary containing "thing_dataset_id_to_contiguous_id" + and "stuff_dataset_id_to_contiguous_id" to map category ids to + contiguous ids for training. + + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + """ + + def _convert_category_id(segment_info, meta): + if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]: + segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + else: + segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + return segment_info + + assert os.path.exists( + gt_json + ), "Please run `python cityscapesscripts/preparation/createPanopticImgs.py` to generate label files." # noqa + with open(gt_json) as f: + json_info = json.load(f) + files = get_cityscapes_panoptic_files(image_dir, gt_dir, json_info) + ret = [] + for image_file, label_file, segments_info in files: + sem_label_file = ( + image_file.replace("leftImg8bit", "gtFine").split(".")[0] + "_labelTrainIds.png" + ) + segments_info = [_convert_category_id(x, meta) for x in segments_info] + ret.append( + { + "file_name": image_file, + "image_id": "_".join( + os.path.splitext(os.path.basename(image_file))[0].split("_")[:3] + ), + "sem_seg_file_name": sem_label_file, + "pan_seg_file_name": label_file, + "segments_info": segments_info, + } + ) + assert len(ret), f"No images found in {image_dir}!" + assert PathManager.isfile( + ret[0]["sem_seg_file_name"] + ), "Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py" # noqa + assert PathManager.isfile( + ret[0]["pan_seg_file_name"] + ), "Please generate panoptic annotation with python cityscapesscripts/preparation/createPanopticImgs.py" # noqa + return ret + + +_RAW_CITYSCAPES_PANOPTIC_SPLITS = { + "cityscapes_fine_panoptic_train": ( + "cityscapes/leftImg8bit/train", + "cityscapes/gtFine/cityscapes_panoptic_train", + "cityscapes/gtFine/cityscapes_panoptic_train.json", + ), + "cityscapes_fine_panoptic_val": ( + "cityscapes/leftImg8bit/val", + "cityscapes/gtFine/cityscapes_panoptic_val", + "cityscapes/gtFine/cityscapes_panoptic_val.json", + ), + # "cityscapes_fine_panoptic_test": not supported yet +} + + +def register_all_cityscapes_panoptic(root): + meta = {} + # The following metadata maps contiguous id from [0, #thing categories + + # #stuff categories) to their names and colors. We have to replica of the + # same name and color under "thing_*" and "stuff_*" because the current + # visualization function in D2 handles thing and class classes differently + # due to some heuristic used in Panoptic FPN. We keep the same naming to + # enable reusing existing visualization functions. + thing_classes = [k["name"] for k in CITYSCAPES_CATEGORIES] + thing_colors = [k["color"] for k in CITYSCAPES_CATEGORIES] + stuff_classes = [k["name"] for k in CITYSCAPES_CATEGORIES] + stuff_colors = [k["color"] for k in CITYSCAPES_CATEGORIES] + + meta["thing_classes"] = thing_classes + meta["thing_colors"] = thing_colors + meta["stuff_classes"] = stuff_classes + meta["stuff_colors"] = stuff_colors + + # There are three types of ids in cityscapes panoptic segmentation: + # (1) category id: like semantic segmentation, it is the class id for each + # pixel. Since there are some classes not used in evaluation, the category + # id is not always contiguous and thus we have two set of category ids: + # - original category id: category id in the original dataset, mainly + # used for evaluation. + # - contiguous category id: [0, #classes), in order to train the classifier + # (2) instance id: this id is used to differentiate different instances from + # the same category. For "stuff" classes, the instance id is always 0; for + # "thing" classes, the instance id starts from 1 and 0 is reserved for + # ignored instances (e.g. crowd annotation). + # (3) panoptic id: this is the compact id that encode both category and + # instance id by: category_id * 1000 + instance_id. + thing_dataset_id_to_contiguous_id = {} + stuff_dataset_id_to_contiguous_id = {} + + for k in CITYSCAPES_CATEGORIES: + if k["isthing"] == 1: + thing_dataset_id_to_contiguous_id[k["id"]] = k["trainId"] + else: + stuff_dataset_id_to_contiguous_id[k["id"]] = k["trainId"] + + meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id + meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id + + for key, (image_dir, gt_dir, gt_json) in _RAW_CITYSCAPES_PANOPTIC_SPLITS.items(): + image_dir = os.path.join(root, image_dir) + gt_dir = os.path.join(root, gt_dir) + gt_json = os.path.join(root, gt_json) + + DatasetCatalog.register( + key, lambda x=image_dir, y=gt_dir, z=gt_json: load_cityscapes_panoptic(x, y, z, meta) + ) + MetadataCatalog.get(key).set( + panoptic_root=gt_dir, + image_root=image_dir, + panoptic_json=gt_json, + gt_dir=gt_dir.replace("cityscapes_panoptic_", ""), + evaluator_type="cityscapes_panoptic_seg", + ignore_label=255, + label_divisor=1000, + **meta, + ) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/coco.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/coco.py new file mode 100644 index 0000000..ed4f7cc --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/coco.py @@ -0,0 +1,539 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import contextlib +import datetime +import io +import json +import logging +import numpy as np +import os +import shutil +import pycocotools.mask as mask_util +from fvcore.common.timer import Timer +from iopath.common.file_io import file_lock +from PIL import Image + +from detectron2.structures import Boxes, BoxMode, PolygonMasks, RotatedBoxes +from detectron2.utils.file_io import PathManager + +from .. import DatasetCatalog, MetadataCatalog + +""" +This file contains functions to parse COCO-format annotations into dicts in "Detectron2 format". +""" + + +logger = logging.getLogger(__name__) + +__all__ = ["load_coco_json", "load_sem_seg", "convert_to_coco_json", "register_coco_instances"] + + +def load_coco_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None): + """ + Load a json file with COCO's instances annotation format. + Currently supports instance detection, instance segmentation, + and person keypoints annotations. + + Args: + json_file (str): full path to the json file in COCO instances annotation format. + image_root (str or path-like): the directory where the images in this json file exists. + dataset_name (str or None): the name of the dataset (e.g., coco_2017_train). + When provided, this function will also do the following: + + * Put "thing_classes" into the metadata associated with this dataset. + * Map the category ids into a contiguous range (needed by standard dataset format), + and add "thing_dataset_id_to_contiguous_id" to the metadata associated + with this dataset. + + This option should usually be provided, unless users need to load + the original json content and apply more processing manually. + extra_annotation_keys (list[str]): list of per-annotation keys that should also be + loaded into the dataset dict (besides "iscrowd", "bbox", "keypoints", + "category_id", "segmentation"). The values for these keys will be returned as-is. + For example, the densepose annotations are loaded in this way. + + Returns: + list[dict]: a list of dicts in Detectron2 standard dataset dicts format (See + `Using Custom Datasets `_ ) when `dataset_name` is not None. + If `dataset_name` is None, the returned `category_ids` may be + incontiguous and may not conform to the Detectron2 standard format. + + Notes: + 1. This function does not read the image files. + The results do not have the "image" field. + """ + from pycocotools.coco import COCO + + timer = Timer() + json_file = PathManager.get_local_path(json_file) + with contextlib.redirect_stdout(io.StringIO()): + coco_api = COCO(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) + + id_map = None + if dataset_name is not None: + meta = MetadataCatalog.get(dataset_name) + cat_ids = sorted(coco_api.getCatIds()) + cats = coco_api.loadCats(cat_ids) + # The categories in a custom json file may not be sorted. + thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])] + meta.thing_classes = thing_classes + + # In COCO, certain category ids are artificially removed, + # and by convention they are always ignored. + # We deal with COCO's id issue and translate + # the category ids to contiguous ids in [0, 80). + + # It works by looking at the "categories" field in the json, therefore + # if users' own json also have incontiguous ids, we'll + # apply this mapping as well but print a warning. + if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)): + if "coco" not in dataset_name: + logger.warning( + """ +Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you. +""" + ) + id_map = {v: i for i, v in enumerate(cat_ids)} + meta.thing_dataset_id_to_contiguous_id = id_map + + # sort indices for reproducible results + img_ids = sorted(coco_api.imgs.keys()) + # imgs is a list of dicts, each looks something like: + # {'license': 4, + # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', + # 'file_name': 'COCO_val2014_000000001268.jpg', + # 'height': 427, + # 'width': 640, + # 'date_captured': '2013-11-17 05:57:24', + # 'id': 1268} + imgs = coco_api.loadImgs(img_ids) + # anns is a list[list[dict]], where each dict is an annotation + # record for an object. The inner list enumerates the objects in an image + # and the outer list enumerates over images. Example of anns[0]: + # [{'segmentation': [[192.81, + # 247.09, + # ... + # 219.03, + # 249.06]], + # 'area': 1035.749, + # 'iscrowd': 0, + # 'image_id': 1268, + # 'bbox': [192.81, 224.8, 74.73, 33.43], + # 'category_id': 16, + # 'id': 42986}, + # ...] + anns = [coco_api.imgToAnns[img_id] for img_id in img_ids] + total_num_valid_anns = sum([len(x) for x in anns]) + total_num_anns = len(coco_api.anns) + if total_num_valid_anns < total_num_anns: + logger.warning( + f"{json_file} contains {total_num_anns} annotations, but only " + f"{total_num_valid_anns} of them match to images in the file." + ) + + if "minival" not in json_file: + # The popular valminusminival & minival annotations for COCO2014 contain this bug. + # However the ratio of buggy annotations there is tiny and does not affect accuracy. + # Therefore we explicitly white-list them. + ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] + assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format( + json_file + ) + + imgs_anns = list(zip(imgs, anns)) + logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file)) + + dataset_dicts = [] + + ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"] + (extra_annotation_keys or []) + + num_instances_without_valid_segmentation = 0 + + for (img_dict, anno_dict_list) in imgs_anns: + record = {} + record["file_name"] = os.path.join(image_root, img_dict["file_name"]) + record["height"] = img_dict["height"] + record["width"] = img_dict["width"] + image_id = record["image_id"] = img_dict["id"] + + objs = [] + for anno in anno_dict_list: + # Check that the image_id in this annotation is the same as + # the image_id we're looking at. + # This fails only when the data parsing logic or the annotation file is buggy. + + # The original COCO valminusminival2014 & minival2014 annotation files + # actually contains bugs that, together with certain ways of using COCO API, + # can trigger this assertion. + assert anno["image_id"] == image_id + + assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.' + + obj = {key: anno[key] for key in ann_keys if key in anno} + if "bbox" in obj and len(obj["bbox"]) == 0: + raise ValueError( + f"One annotation of image {image_id} contains empty 'bbox' value! " + "This json does not have valid COCO format." + ) + + segm = anno.get("segmentation", None) + if segm: # either list[list[float]] or dict(RLE) + if isinstance(segm, dict): + if isinstance(segm["counts"], list): + # convert to compressed RLE + segm = mask_util.frPyObjects(segm, *segm["size"]) + else: + # filter out invalid polygons (< 3 points) + segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + if len(segm) == 0: + num_instances_without_valid_segmentation += 1 + continue # ignore this instance + obj["segmentation"] = segm + + keypts = anno.get("keypoints", None) + if keypts: # list[int] + for idx, v in enumerate(keypts): + if idx % 3 != 2: + # COCO's segmentation coordinates are floating points in [0, H or W], + # but keypoint coordinates are integers in [0, H-1 or W-1] + # Therefore we assume the coordinates are "pixel indices" and + # add 0.5 to convert to floating point coordinates. + keypts[idx] = v + 0.5 + obj["keypoints"] = keypts + + obj["bbox_mode"] = BoxMode.XYWH_ABS + if id_map: + annotation_category_id = obj["category_id"] + try: + obj["category_id"] = id_map[annotation_category_id] + except KeyError as e: + raise KeyError( + f"Encountered category_id={annotation_category_id} " + "but this id does not exist in 'categories' of the json file." + ) from e + objs.append(obj) + record["annotations"] = objs + dataset_dicts.append(record) + + if num_instances_without_valid_segmentation > 0: + logger.warning( + "Filtered out {} instances without valid segmentation. ".format( + num_instances_without_valid_segmentation + ) + + "There might be issues in your dataset generation process. Please " + "check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully" + ) + return dataset_dicts + + +def load_sem_seg(gt_root, image_root, gt_ext="png", image_ext="jpg"): + """ + Load semantic segmentation datasets. All files under "gt_root" with "gt_ext" extension are + treated as ground truth annotations and all files under "image_root" with "image_ext" extension + as input images. Ground truth and input images are matched using file paths relative to + "gt_root" and "image_root" respectively without taking into account file extensions. + This works for COCO as well as some other datasets. + + Args: + gt_root (str): full path to ground truth semantic segmentation files. Semantic segmentation + annotations are stored as images with integer values in pixels that represent + corresponding semantic labels. + image_root (str): the directory where the input images are. + gt_ext (str): file extension for ground truth annotations. + image_ext (str): file extension for input images. + + Returns: + list[dict]: + a list of dicts in detectron2 standard format without instance-level + annotation. + + Notes: + 1. This function does not read the image and ground truth files. + The results do not have the "image" and "sem_seg" fields. + """ + + # We match input images with ground truth based on their relative filepaths (without file + # extensions) starting from 'image_root' and 'gt_root' respectively. + def file2id(folder_path, file_path): + # extract relative path starting from `folder_path` + image_id = os.path.normpath(os.path.relpath(file_path, start=folder_path)) + # remove file extension + image_id = os.path.splitext(image_id)[0] + return image_id + + input_files = sorted( + (os.path.join(image_root, f) for f in PathManager.ls(image_root) if f.endswith(image_ext)), + key=lambda file_path: file2id(image_root, file_path), + ) + gt_files = sorted( + (os.path.join(gt_root, f) for f in PathManager.ls(gt_root) if f.endswith(gt_ext)), + key=lambda file_path: file2id(gt_root, file_path), + ) + + assert len(gt_files) > 0, "No annotations found in {}.".format(gt_root) + + # Use the intersection, so that val2017_100 annotations can run smoothly with val2017 images + if len(input_files) != len(gt_files): + logger.warn( + "Directory {} and {} has {} and {} files, respectively.".format( + image_root, gt_root, len(input_files), len(gt_files) + ) + ) + input_basenames = [os.path.basename(f)[: -len(image_ext)] for f in input_files] + gt_basenames = [os.path.basename(f)[: -len(gt_ext)] for f in gt_files] + intersect = list(set(input_basenames) & set(gt_basenames)) + # sort, otherwise each worker may obtain a list[dict] in different order + intersect = sorted(intersect) + logger.warn("Will use their intersection of {} files.".format(len(intersect))) + input_files = [os.path.join(image_root, f + image_ext) for f in intersect] + gt_files = [os.path.join(gt_root, f + gt_ext) for f in intersect] + + logger.info( + "Loaded {} images with semantic segmentation from {}".format(len(input_files), image_root) + ) + + dataset_dicts = [] + for (img_path, gt_path) in zip(input_files, gt_files): + record = {} + record["file_name"] = img_path + record["sem_seg_file_name"] = gt_path + dataset_dicts.append(record) + + return dataset_dicts + + +def convert_to_coco_dict(dataset_name): + """ + Convert an instance detection/segmentation or keypoint detection dataset + in detectron2's standard format into COCO json format. + + Generic dataset description can be found here: + https://detectron2.readthedocs.io/tutorials/datasets.html#register-a-dataset + + COCO data format description can be found here: + http://cocodataset.org/#format-data + + Args: + dataset_name (str): + name of the source dataset + Must be registered in DatastCatalog and in detectron2's standard format. + Must have corresponding metadata "thing_classes" + Returns: + coco_dict: serializable dict in COCO json format + """ + + dataset_dicts = DatasetCatalog.get(dataset_name) + metadata = MetadataCatalog.get(dataset_name) + + # unmap the category mapping ids for COCO + if hasattr(metadata, "thing_dataset_id_to_contiguous_id"): + reverse_id_mapping = {v: k for k, v in metadata.thing_dataset_id_to_contiguous_id.items()} + reverse_id_mapper = lambda contiguous_id: reverse_id_mapping[contiguous_id] # noqa + else: + reverse_id_mapper = lambda contiguous_id: contiguous_id # noqa + + categories = [ + {"id": reverse_id_mapper(id), "name": name} + for id, name in enumerate(metadata.thing_classes) + ] + + logger.info("Converting dataset dicts into COCO format") + coco_images = [] + coco_annotations = [] + + for image_id, image_dict in enumerate(dataset_dicts): + coco_image = { + "id": image_dict.get("image_id", image_id), + "width": int(image_dict["width"]), + "height": int(image_dict["height"]), + "file_name": str(image_dict["file_name"]), + } + coco_images.append(coco_image) + + anns_per_image = image_dict.get("annotations", []) + for annotation in anns_per_image: + # create a new dict with only COCO fields + coco_annotation = {} + + # COCO requirement: XYWH box format for axis-align and XYWHA for rotated + bbox = annotation["bbox"] + if isinstance(bbox, np.ndarray): + if bbox.ndim != 1: + raise ValueError(f"bbox has to be 1-dimensional. Got shape={bbox.shape}.") + bbox = bbox.tolist() + if len(bbox) not in [4, 5]: + raise ValueError(f"bbox has to has length 4 or 5. Got {bbox}.") + from_bbox_mode = annotation["bbox_mode"] + to_bbox_mode = BoxMode.XYWH_ABS if len(bbox) == 4 else BoxMode.XYWHA_ABS + bbox = BoxMode.convert(bbox, from_bbox_mode, to_bbox_mode) + + # COCO requirement: instance area + if "segmentation" in annotation: + # Computing areas for instances by counting the pixels + segmentation = annotation["segmentation"] + # TODO: check segmentation type: RLE, BinaryMask or Polygon + if isinstance(segmentation, list): + polygons = PolygonMasks([segmentation]) + area = polygons.area()[0].item() + elif isinstance(segmentation, dict): # RLE + area = mask_util.area(segmentation).item() + else: + raise TypeError(f"Unknown segmentation type {type(segmentation)}!") + else: + # Computing areas using bounding boxes + if to_bbox_mode == BoxMode.XYWH_ABS: + bbox_xy = BoxMode.convert(bbox, to_bbox_mode, BoxMode.XYXY_ABS) + area = Boxes([bbox_xy]).area()[0].item() + else: + area = RotatedBoxes([bbox]).area()[0].item() + + if "keypoints" in annotation: + keypoints = annotation["keypoints"] # list[int] + for idx, v in enumerate(keypoints): + if idx % 3 != 2: + # COCO's segmentation coordinates are floating points in [0, H or W], + # but keypoint coordinates are integers in [0, H-1 or W-1] + # For COCO format consistency we substract 0.5 + # https://github.com/facebookresearch/detectron2/pull/175#issuecomment-551202163 + keypoints[idx] = v - 0.5 + if "num_keypoints" in annotation: + num_keypoints = annotation["num_keypoints"] + else: + num_keypoints = sum(kp > 0 for kp in keypoints[2::3]) + + # COCO requirement: + # linking annotations to images + # "id" field must start with 1 + coco_annotation["id"] = len(coco_annotations) + 1 + coco_annotation["image_id"] = coco_image["id"] + coco_annotation["bbox"] = [round(float(x), 3) for x in bbox] + coco_annotation["area"] = float(area) + coco_annotation["iscrowd"] = int(annotation.get("iscrowd", 0)) + coco_annotation["category_id"] = int(reverse_id_mapper(annotation["category_id"])) + + # Add optional fields + if "keypoints" in annotation: + coco_annotation["keypoints"] = keypoints + coco_annotation["num_keypoints"] = num_keypoints + + if "segmentation" in annotation: + seg = coco_annotation["segmentation"] = annotation["segmentation"] + if isinstance(seg, dict): # RLE + counts = seg["counts"] + if not isinstance(counts, str): + # make it json-serializable + seg["counts"] = counts.decode("ascii") + + coco_annotations.append(coco_annotation) + + logger.info( + "Conversion finished, " + f"#images: {len(coco_images)}, #annotations: {len(coco_annotations)}" + ) + + info = { + "date_created": str(datetime.datetime.now()), + "description": "Automatically generated COCO json file for Detectron2.", + } + coco_dict = {"info": info, "images": coco_images, "categories": categories, "licenses": None} + if len(coco_annotations) > 0: + coco_dict["annotations"] = coco_annotations + return coco_dict + + +def convert_to_coco_json(dataset_name, output_file, allow_cached=True): + """ + Converts dataset into COCO format and saves it to a json file. + dataset_name must be registered in DatasetCatalog and in detectron2's standard format. + + Args: + dataset_name: + reference from the config file to the catalogs + must be registered in DatasetCatalog and in detectron2's standard format + output_file: path of json file that will be saved to + allow_cached: if json file is already present then skip conversion + """ + + # TODO: The dataset or the conversion script *may* change, + # a checksum would be useful for validating the cached data + + PathManager.mkdirs(os.path.dirname(output_file)) + with file_lock(output_file): + if PathManager.exists(output_file) and allow_cached: + logger.warning( + f"Using previously cached COCO format annotations at '{output_file}'. " + "You need to clear the cache file if your dataset has been modified." + ) + else: + logger.info(f"Converting annotations of dataset '{dataset_name}' to COCO format ...)") + coco_dict = convert_to_coco_dict(dataset_name) + + logger.info(f"Caching COCO format annotations at '{output_file}' ...") + tmp_file = output_file + ".tmp" + with PathManager.open(tmp_file, "w") as f: + json.dump(coco_dict, f) + shutil.move(tmp_file, output_file) + + +def register_coco_instances(name, metadata, json_file, image_root): + """ + Register a dataset in COCO's json annotation format for + instance detection, instance segmentation and keypoint detection. + (i.e., Type 1 and 2 in http://cocodataset.org/#format-data. + `instances*.json` and `person_keypoints*.json` in the dataset). + + This is an example of how to register a new dataset. + You can do something similar to this function, to register new datasets. + + Args: + name (str): the name that identifies a dataset, e.g. "coco_2014_train". + metadata (dict): extra metadata associated with this dataset. You can + leave it as an empty dict. + json_file (str): path to the json instance annotation file. + image_root (str or path-like): directory which contains all the images. + """ + assert isinstance(name, str), name + assert isinstance(json_file, (str, os.PathLike)), json_file + assert isinstance(image_root, (str, os.PathLike)), image_root + # 1. register a function which returns dicts + DatasetCatalog.register(name, lambda: load_coco_json(json_file, image_root, name)) + + # 2. Optionally, add metadata about this dataset, + # since they might be useful in evaluation, visualization or logging + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, evaluator_type="coco", **metadata + ) + + +if __name__ == "__main__": + """ + Test the COCO json dataset loader. + + Usage: + python -m detectron2.data.datasets.coco \ + path/to/json path/to/image_root dataset_name + + "dataset_name" can be "coco_2014_minival_100", or other + pre-registered ones + """ + from detectron2.utils.logger import setup_logger + from detectron2.utils.visualizer import Visualizer + import detectron2.data.datasets # noqa # add pre-defined metadata + import sys + + logger = setup_logger(name=__name__) + assert sys.argv[3] in DatasetCatalog.list() + meta = MetadataCatalog.get(sys.argv[3]) + + dicts = load_coco_json(sys.argv[1], sys.argv[2], sys.argv[3]) + logger.info("Done loading {} samples.".format(len(dicts))) + + dirname = "coco-data-vis" + os.makedirs(dirname, exist_ok=True) + for d in dicts: + img = np.array(Image.open(d["file_name"])) + visualizer = Visualizer(img, metadata=meta) + vis = visualizer.draw_dataset_dict(d) + fpath = os.path.join(dirname, os.path.basename(d["file_name"])) + vis.save(fpath) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/coco_panoptic.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/coco_panoptic.py new file mode 100644 index 0000000..b8dae44 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/coco_panoptic.py @@ -0,0 +1,228 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import json +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.utils.file_io import PathManager + +from .coco import load_coco_json, load_sem_seg + +__all__ = ["register_coco_panoptic", "register_coco_panoptic_separated"] + + +def load_coco_panoptic_json(json_file, image_dir, gt_dir, meta): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". + gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017". + json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json". + + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + """ + + def _convert_category_id(segment_info, meta): + if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]: + segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = True + else: + segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = False + return segment_info + + with PathManager.open(json_file) as f: + json_info = json.load(f) + + ret = [] + for ann in json_info["annotations"]: + image_id = int(ann["image_id"]) + # TODO: currently we assume image and label has the same filename but + # different extension, and images have extension ".jpg" for COCO. Need + # to make image extension a user-provided argument if we extend this + # function to support other COCO-like datasets. + image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg") + label_file = os.path.join(gt_dir, ann["file_name"]) + segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]] + ret.append( + { + "file_name": image_file, + "image_id": image_id, + "pan_seg_file_name": label_file, + "segments_info": segments_info, + } + ) + assert len(ret), f"No images found in {image_dir}!" + assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"] + assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"] + return ret + + +def register_coco_panoptic( + name, metadata, image_root, panoptic_root, panoptic_json, instances_json=None +): + """ + Register a "standard" version of COCO panoptic segmentation dataset named `name`. + The dictionaries in this registered dataset follows detectron2's standard format. + Hence it's called "standard". + + Args: + name (str): the name that identifies a dataset, + e.g. "coco_2017_train_panoptic" + metadata (dict): extra metadata associated with this dataset. + image_root (str): directory which contains all the images + panoptic_root (str): directory which contains panoptic annotation images in COCO format + panoptic_json (str): path to the json panoptic annotation file in COCO format + sem_seg_root (none): not used, to be consistent with + `register_coco_panoptic_separated`. + instances_json (str): path to the json instance annotation file + """ + panoptic_name = name + DatasetCatalog.register( + panoptic_name, + lambda: load_coco_panoptic_json(panoptic_json, image_root, panoptic_root, metadata), + ) + MetadataCatalog.get(panoptic_name).set( + panoptic_root=panoptic_root, + image_root=image_root, + panoptic_json=panoptic_json, + json_file=instances_json, + evaluator_type="coco_panoptic_seg", + ignore_label=255, + label_divisor=1000, + **metadata, + ) + + +def register_coco_panoptic_separated( + name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json +): + """ + Register a "separated" version of COCO panoptic segmentation dataset named `name`. + The annotations in this registered dataset will contain both instance annotations and + semantic annotations, each with its own contiguous ids. Hence it's called "separated". + + It follows the setting used by the PanopticFPN paper: + + 1. The instance annotations directly come from polygons in the COCO + instances annotation task, rather than from the masks in the COCO panoptic annotations. + + The two format have small differences: + Polygons in the instance annotations may have overlaps. + The mask annotations are produced by labeling the overlapped polygons + with depth ordering. + + 2. The semantic annotations are converted from panoptic annotations, where + all "things" are assigned a semantic id of 0. + All semantic categories will therefore have ids in contiguous + range [1, #stuff_categories]. + + This function will also register a pure semantic segmentation dataset + named ``name + '_stuffonly'``. + + Args: + name (str): the name that identifies a dataset, + e.g. "coco_2017_train_panoptic" + metadata (dict): extra metadata associated with this dataset. + image_root (str): directory which contains all the images + panoptic_root (str): directory which contains panoptic annotation images + panoptic_json (str): path to the json panoptic annotation file + sem_seg_root (str): directory which contains all the ground truth segmentation annotations. + instances_json (str): path to the json instance annotation file + """ + panoptic_name = name + "_separated" + DatasetCatalog.register( + panoptic_name, + lambda: merge_to_panoptic( + load_coco_json(instances_json, image_root, panoptic_name), + load_sem_seg(sem_seg_root, image_root), + ), + ) + MetadataCatalog.get(panoptic_name).set( + panoptic_root=panoptic_root, + image_root=image_root, + panoptic_json=panoptic_json, + sem_seg_root=sem_seg_root, + json_file=instances_json, # TODO rename + evaluator_type="coco_panoptic_seg", + ignore_label=255, + **metadata, + ) + + semantic_name = name + "_stuffonly" + DatasetCatalog.register(semantic_name, lambda: load_sem_seg(sem_seg_root, image_root)) + MetadataCatalog.get(semantic_name).set( + sem_seg_root=sem_seg_root, + image_root=image_root, + evaluator_type="sem_seg", + ignore_label=255, + **metadata, + ) + + +def merge_to_panoptic(detection_dicts, sem_seg_dicts): + """ + Create dataset dicts for panoptic segmentation, by + merging two dicts using "file_name" field to match their entries. + + Args: + detection_dicts (list[dict]): lists of dicts for object detection or instance segmentation. + sem_seg_dicts (list[dict]): lists of dicts for semantic segmentation. + + Returns: + list[dict] (one per input image): Each dict contains all (key, value) pairs from dicts in + both detection_dicts and sem_seg_dicts that correspond to the same image. + The function assumes that the same key in different dicts has the same value. + """ + results = [] + sem_seg_file_to_entry = {x["file_name"]: x for x in sem_seg_dicts} + assert len(sem_seg_file_to_entry) > 0 + + for det_dict in detection_dicts: + dic = copy.copy(det_dict) + dic.update(sem_seg_file_to_entry[dic["file_name"]]) + results.append(dic) + return results + + +if __name__ == "__main__": + """ + Test the COCO panoptic dataset loader. + + Usage: + python -m detectron2.data.datasets.coco_panoptic \ + path/to/image_root path/to/panoptic_root path/to/panoptic_json dataset_name 10 + + "dataset_name" can be "coco_2017_train_panoptic", or other + pre-registered ones + """ + from detectron2.utils.logger import setup_logger + from detectron2.utils.visualizer import Visualizer + import detectron2.data.datasets # noqa # add pre-defined metadata + import sys + from PIL import Image + import numpy as np + + logger = setup_logger(name=__name__) + assert sys.argv[4] in DatasetCatalog.list() + meta = MetadataCatalog.get(sys.argv[4]) + + dicts = load_coco_panoptic_json(sys.argv[3], sys.argv[1], sys.argv[2], meta.as_dict()) + logger.info("Done loading {} samples.".format(len(dicts))) + + dirname = "coco-data-vis" + os.makedirs(dirname, exist_ok=True) + num_imgs_to_vis = int(sys.argv[5]) + for i, d in enumerate(dicts): + img = np.array(Image.open(d["file_name"])) + visualizer = Visualizer(img, metadata=meta) + vis = visualizer.draw_dataset_dict(d) + fpath = os.path.join(dirname, os.path.basename(d["file_name"])) + vis.save(fpath) + if i + 1 >= num_imgs_to_vis: + break diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/lvis.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/lvis.py new file mode 100644 index 0000000..78b3965 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/lvis.py @@ -0,0 +1,240 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import os +from fvcore.common.timer import Timer + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.structures import BoxMode +from detectron2.utils.file_io import PathManager + +from .builtin_meta import _get_coco_instances_meta +from .lvis_v0_5_categories import LVIS_CATEGORIES as LVIS_V0_5_CATEGORIES +from .lvis_v1_categories import LVIS_CATEGORIES as LVIS_V1_CATEGORIES + +""" +This file contains functions to parse LVIS-format annotations into dicts in the +"Detectron2 format". +""" + +logger = logging.getLogger(__name__) + +__all__ = ["load_lvis_json", "register_lvis_instances", "get_lvis_instances_meta"] + + +def register_lvis_instances(name, metadata, json_file, image_root): + """ + Register a dataset in LVIS's json annotation format for instance detection and segmentation. + + Args: + name (str): a name that identifies the dataset, e.g. "lvis_v0.5_train". + metadata (dict): extra metadata associated with this dataset. It can be an empty dict. + json_file (str): path to the json instance annotation file. + image_root (str or path-like): directory which contains all the images. + """ + DatasetCatalog.register(name, lambda: load_lvis_json(json_file, image_root, name)) + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, evaluator_type="lvis", **metadata + ) + + +def load_lvis_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None): + """ + Load a json file in LVIS's annotation format. + + Args: + json_file (str): full path to the LVIS json annotation file. + image_root (str): the directory where the images in this json file exists. + dataset_name (str): the name of the dataset (e.g., "lvis_v0.5_train"). + If provided, this function will put "thing_classes" into the metadata + associated with this dataset. + extra_annotation_keys (list[str]): list of per-annotation keys that should also be + loaded into the dataset dict (besides "bbox", "bbox_mode", "category_id", + "segmentation"). The values for these keys will be returned as-is. + + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + + Notes: + 1. This function does not read the image files. + The results do not have the "image" field. + """ + from lvis import LVIS + + json_file = PathManager.get_local_path(json_file) + + timer = Timer() + lvis_api = LVIS(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) + + if dataset_name is not None: + meta = get_lvis_instances_meta(dataset_name) + MetadataCatalog.get(dataset_name).set(**meta) + + # sort indices for reproducible results + img_ids = sorted(lvis_api.imgs.keys()) + # imgs is a list of dicts, each looks something like: + # {'license': 4, + # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', + # 'file_name': 'COCO_val2014_000000001268.jpg', + # 'height': 427, + # 'width': 640, + # 'date_captured': '2013-11-17 05:57:24', + # 'id': 1268} + imgs = lvis_api.load_imgs(img_ids) + # anns is a list[list[dict]], where each dict is an annotation + # record for an object. The inner list enumerates the objects in an image + # and the outer list enumerates over images. Example of anns[0]: + # [{'segmentation': [[192.81, + # 247.09, + # ... + # 219.03, + # 249.06]], + # 'area': 1035.749, + # 'image_id': 1268, + # 'bbox': [192.81, 224.8, 74.73, 33.43], + # 'category_id': 16, + # 'id': 42986}, + # ...] + anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids] + + # Sanity check that each annotation has a unique id + ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] + assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique".format( + json_file + ) + + imgs_anns = list(zip(imgs, anns)) + + logger.info("Loaded {} images in the LVIS format from {}".format(len(imgs_anns), json_file)) + + if extra_annotation_keys: + logger.info( + "The following extra annotation keys will be loaded: {} ".format(extra_annotation_keys) + ) + else: + extra_annotation_keys = [] + + def get_file_name(img_root, img_dict): + # Determine the path including the split folder ("train2017", "val2017", "test2017") from + # the coco_url field. Example: + # 'coco_url': 'http://images.cocodataset.org/train2017/000000155379.jpg' + split_folder, file_name = img_dict["coco_url"].split("/")[-2:] + return os.path.join(img_root + split_folder, file_name) + + dataset_dicts = [] + + for (img_dict, anno_dict_list) in imgs_anns: + record = {} + record["file_name"] = get_file_name(image_root, img_dict) + record["height"] = img_dict["height"] + record["width"] = img_dict["width"] + record["not_exhaustive_category_ids"] = img_dict.get("not_exhaustive_category_ids", []) + record["neg_category_ids"] = img_dict.get("neg_category_ids", []) + image_id = record["image_id"] = img_dict["id"] + + objs = [] + for anno in anno_dict_list: + # Check that the image_id in this annotation is the same as + # the image_id we're looking at. + # This fails only when the data parsing logic or the annotation file is buggy. + assert anno["image_id"] == image_id + obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS} + # LVIS data loader can be used to load COCO dataset categories. In this case `meta` + # variable will have a field with COCO-specific category mapping. + if dataset_name is not None and "thing_dataset_id_to_contiguous_id" in meta: + obj["category_id"] = meta["thing_dataset_id_to_contiguous_id"][anno["category_id"]] + else: + obj["category_id"] = anno["category_id"] - 1 # Convert 1-indexed to 0-indexed + segm = anno["segmentation"] # list[list[float]] + # filter out invalid polygons (< 3 points) + valid_segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + assert len(segm) == len( + valid_segm + ), "Annotation contains an invalid polygon with < 3 points" + assert len(segm) > 0 + obj["segmentation"] = segm + for extra_ann_key in extra_annotation_keys: + obj[extra_ann_key] = anno[extra_ann_key] + objs.append(obj) + record["annotations"] = objs + dataset_dicts.append(record) + + return dataset_dicts + + +def get_lvis_instances_meta(dataset_name): + """ + Load LVIS metadata. + + Args: + dataset_name (str): LVIS dataset name without the split name (e.g., "lvis_v0.5"). + + Returns: + dict: LVIS metadata with keys: thing_classes + """ + if "cocofied" in dataset_name: + return _get_coco_instances_meta() + if "v0.5" in dataset_name: + return _get_lvis_instances_meta_v0_5() + elif "v1" in dataset_name: + return _get_lvis_instances_meta_v1() + raise ValueError("No built-in metadata for dataset {}".format(dataset_name)) + + +def _get_lvis_instances_meta_v0_5(): + assert len(LVIS_V0_5_CATEGORIES) == 1230 + cat_ids = [k["id"] for k in LVIS_V0_5_CATEGORIES] + assert min(cat_ids) == 1 and max(cat_ids) == len( + cat_ids + ), "Category ids are not in [1, #categories], as expected" + # Ensure that the category list is sorted by id + lvis_categories = sorted(LVIS_V0_5_CATEGORIES, key=lambda x: x["id"]) + thing_classes = [k["synonyms"][0] for k in lvis_categories] + meta = {"thing_classes": thing_classes} + return meta + + +def _get_lvis_instances_meta_v1(): + assert len(LVIS_V1_CATEGORIES) == 1203 + cat_ids = [k["id"] for k in LVIS_V1_CATEGORIES] + assert min(cat_ids) == 1 and max(cat_ids) == len( + cat_ids + ), "Category ids are not in [1, #categories], as expected" + # Ensure that the category list is sorted by id + lvis_categories = sorted(LVIS_V1_CATEGORIES, key=lambda x: x["id"]) + thing_classes = [k["synonyms"][0] for k in lvis_categories] + meta = {"thing_classes": thing_classes} + return meta + + +if __name__ == "__main__": + """ + Test the LVIS json dataset loader. + + Usage: + python -m detectron2.data.datasets.lvis \ + path/to/json path/to/image_root dataset_name vis_limit + """ + import sys + import numpy as np + from detectron2.utils.logger import setup_logger + from PIL import Image + import detectron2.data.datasets # noqa # add pre-defined metadata + from detectron2.utils.visualizer import Visualizer + + logger = setup_logger(name=__name__) + meta = MetadataCatalog.get(sys.argv[3]) + + dicts = load_lvis_json(sys.argv[1], sys.argv[2], sys.argv[3]) + logger.info("Done loading {} samples.".format(len(dicts))) + + dirname = "lvis-data-vis" + os.makedirs(dirname, exist_ok=True) + for d in dicts[: int(sys.argv[4])]: + img = np.array(Image.open(d["file_name"])) + visualizer = Visualizer(img, metadata=meta) + vis = visualizer.draw_dataset_dict(d) + fpath = os.path.join(dirname, os.path.basename(d["file_name"])) + vis.save(fpath) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/lvis_v0_5_categories.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/lvis_v0_5_categories.py new file mode 100644 index 0000000..d3dab61 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/lvis_v0_5_categories.py @@ -0,0 +1,13 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Autogen with +# with open("lvis_v0.5_val.json", "r") as f: +# a = json.load(f) +# c = a["categories"] +# for x in c: +# del x["image_count"] +# del x["instance_count"] +# LVIS_CATEGORIES = repr(c) + " # noqa" + +# fmt: off +LVIS_CATEGORIES = [{'frequency': 'r', 'id': 1, 'synset': 'acorn.n.01', 'synonyms': ['acorn'], 'def': 'nut from an oak tree', 'name': 'acorn'}, {'frequency': 'c', 'id': 2, 'synset': 'aerosol.n.02', 'synonyms': ['aerosol_can', 'spray_can'], 'def': 'a dispenser that holds a substance under pressure', 'name': 'aerosol_can'}, {'frequency': 'f', 'id': 3, 'synset': 'air_conditioner.n.01', 'synonyms': ['air_conditioner'], 'def': 'a machine that keeps air cool and dry', 'name': 'air_conditioner'}, {'frequency': 'f', 'id': 4, 'synset': 'airplane.n.01', 'synonyms': ['airplane', 'aeroplane'], 'def': 'an aircraft that has a fixed wing and is powered by propellers or jets', 'name': 'airplane'}, {'frequency': 'c', 'id': 5, 'synset': 'alarm_clock.n.01', 'synonyms': ['alarm_clock'], 'def': 'a clock that wakes a sleeper at some preset time', 'name': 'alarm_clock'}, {'frequency': 'c', 'id': 6, 'synset': 'alcohol.n.01', 'synonyms': ['alcohol', 'alcoholic_beverage'], 'def': 'a liquor or brew containing alcohol as the active agent', 'name': 'alcohol'}, {'frequency': 'r', 'id': 7, 'synset': 'alligator.n.02', 'synonyms': ['alligator', 'gator'], 'def': 'amphibious reptiles related to crocodiles but with shorter broader snouts', 'name': 'alligator'}, {'frequency': 'c', 'id': 8, 'synset': 'almond.n.02', 'synonyms': ['almond'], 'def': 'oval-shaped edible seed of the almond tree', 'name': 'almond'}, {'frequency': 'c', 'id': 9, 'synset': 'ambulance.n.01', 'synonyms': ['ambulance'], 'def': 'a vehicle that takes people to and from hospitals', 'name': 'ambulance'}, {'frequency': 'r', 'id': 10, 'synset': 'amplifier.n.01', 'synonyms': ['amplifier'], 'def': 'electronic equipment that increases strength of signals', 'name': 'amplifier'}, {'frequency': 'c', 'id': 11, 'synset': 'anklet.n.03', 'synonyms': ['anklet', 'ankle_bracelet'], 'def': 'an ornament worn around the ankle', 'name': 'anklet'}, {'frequency': 'f', 'id': 12, 'synset': 'antenna.n.01', 'synonyms': ['antenna', 'aerial', 'transmitting_aerial'], 'def': 'an electrical device that sends or receives radio or television signals', 'name': 'antenna'}, {'frequency': 'f', 'id': 13, 'synset': 'apple.n.01', 'synonyms': ['apple'], 'def': 'fruit with red or yellow or green skin and sweet to tart crisp whitish flesh', 'name': 'apple'}, {'frequency': 'r', 'id': 14, 'synset': 'apple_juice.n.01', 'synonyms': ['apple_juice'], 'def': 'the juice of apples', 'name': 'apple_juice'}, {'frequency': 'r', 'id': 15, 'synset': 'applesauce.n.01', 'synonyms': ['applesauce'], 'def': 'puree of stewed apples usually sweetened and spiced', 'name': 'applesauce'}, {'frequency': 'r', 'id': 16, 'synset': 'apricot.n.02', 'synonyms': ['apricot'], 'def': 'downy yellow to rosy-colored fruit resembling a small peach', 'name': 'apricot'}, {'frequency': 'f', 'id': 17, 'synset': 'apron.n.01', 'synonyms': ['apron'], 'def': 'a garment of cloth that is tied about the waist and worn to protect clothing', 'name': 'apron'}, {'frequency': 'c', 'id': 18, 'synset': 'aquarium.n.01', 'synonyms': ['aquarium', 'fish_tank'], 'def': 'a tank/pool/bowl filled with water for keeping live fish and underwater animals', 'name': 'aquarium'}, {'frequency': 'c', 'id': 19, 'synset': 'armband.n.02', 'synonyms': ['armband'], 'def': 'a band worn around the upper arm', 'name': 'armband'}, {'frequency': 'f', 'id': 20, 'synset': 'armchair.n.01', 'synonyms': ['armchair'], 'def': 'chair with a support on each side for arms', 'name': 'armchair'}, {'frequency': 'r', 'id': 21, 'synset': 'armoire.n.01', 'synonyms': ['armoire'], 'def': 'a large wardrobe or cabinet', 'name': 'armoire'}, {'frequency': 'r', 'id': 22, 'synset': 'armor.n.01', 'synonyms': ['armor', 'armour'], 'def': 'protective covering made of metal and used in combat', 'name': 'armor'}, {'frequency': 'c', 'id': 23, 'synset': 'artichoke.n.02', 'synonyms': ['artichoke'], 'def': 'a thistlelike flower head with edible fleshy leaves and heart', 'name': 'artichoke'}, {'frequency': 'f', 'id': 24, 'synset': 'ashcan.n.01', 'synonyms': ['trash_can', 'garbage_can', 'wastebin', 'dustbin', 'trash_barrel', 'trash_bin'], 'def': 'a bin that holds rubbish until it is collected', 'name': 'trash_can'}, {'frequency': 'c', 'id': 25, 'synset': 'ashtray.n.01', 'synonyms': ['ashtray'], 'def': "a receptacle for the ash from smokers' cigars or cigarettes", 'name': 'ashtray'}, {'frequency': 'c', 'id': 26, 'synset': 'asparagus.n.02', 'synonyms': ['asparagus'], 'def': 'edible young shoots of the asparagus plant', 'name': 'asparagus'}, {'frequency': 'c', 'id': 27, 'synset': 'atomizer.n.01', 'synonyms': ['atomizer', 'atomiser', 'spray', 'sprayer', 'nebulizer', 'nebuliser'], 'def': 'a dispenser that turns a liquid (such as perfume) into a fine mist', 'name': 'atomizer'}, {'frequency': 'c', 'id': 28, 'synset': 'avocado.n.01', 'synonyms': ['avocado'], 'def': 'a pear-shaped fruit with green or blackish skin and rich yellowish pulp enclosing a single large seed', 'name': 'avocado'}, {'frequency': 'c', 'id': 29, 'synset': 'award.n.02', 'synonyms': ['award', 'accolade'], 'def': 'a tangible symbol signifying approval or distinction', 'name': 'award'}, {'frequency': 'f', 'id': 30, 'synset': 'awning.n.01', 'synonyms': ['awning'], 'def': 'a canopy made of canvas to shelter people or things from rain or sun', 'name': 'awning'}, {'frequency': 'r', 'id': 31, 'synset': 'ax.n.01', 'synonyms': ['ax', 'axe'], 'def': 'an edge tool with a heavy bladed head mounted across a handle', 'name': 'ax'}, {'frequency': 'f', 'id': 32, 'synset': 'baby_buggy.n.01', 'synonyms': ['baby_buggy', 'baby_carriage', 'perambulator', 'pram', 'stroller'], 'def': 'a small vehicle with four wheels in which a baby or child is pushed around', 'name': 'baby_buggy'}, {'frequency': 'c', 'id': 33, 'synset': 'backboard.n.01', 'synonyms': ['basketball_backboard'], 'def': 'a raised vertical board with basket attached; used to play basketball', 'name': 'basketball_backboard'}, {'frequency': 'f', 'id': 34, 'synset': 'backpack.n.01', 'synonyms': ['backpack', 'knapsack', 'packsack', 'rucksack', 'haversack'], 'def': 'a bag carried by a strap on your back or shoulder', 'name': 'backpack'}, {'frequency': 'f', 'id': 35, 'synset': 'bag.n.04', 'synonyms': ['handbag', 'purse', 'pocketbook'], 'def': 'a container used for carrying money and small personal items or accessories', 'name': 'handbag'}, {'frequency': 'f', 'id': 36, 'synset': 'bag.n.06', 'synonyms': ['suitcase', 'baggage', 'luggage'], 'def': 'cases used to carry belongings when traveling', 'name': 'suitcase'}, {'frequency': 'c', 'id': 37, 'synset': 'bagel.n.01', 'synonyms': ['bagel', 'beigel'], 'def': 'glazed yeast-raised doughnut-shaped roll with hard crust', 'name': 'bagel'}, {'frequency': 'r', 'id': 38, 'synset': 'bagpipe.n.01', 'synonyms': ['bagpipe'], 'def': 'a tubular wind instrument; the player blows air into a bag and squeezes it out', 'name': 'bagpipe'}, {'frequency': 'r', 'id': 39, 'synset': 'baguet.n.01', 'synonyms': ['baguet', 'baguette'], 'def': 'narrow French stick loaf', 'name': 'baguet'}, {'frequency': 'r', 'id': 40, 'synset': 'bait.n.02', 'synonyms': ['bait', 'lure'], 'def': 'something used to lure fish or other animals into danger so they can be trapped or killed', 'name': 'bait'}, {'frequency': 'f', 'id': 41, 'synset': 'ball.n.06', 'synonyms': ['ball'], 'def': 'a spherical object used as a plaything', 'name': 'ball'}, {'frequency': 'r', 'id': 42, 'synset': 'ballet_skirt.n.01', 'synonyms': ['ballet_skirt', 'tutu'], 'def': 'very short skirt worn by ballerinas', 'name': 'ballet_skirt'}, {'frequency': 'f', 'id': 43, 'synset': 'balloon.n.01', 'synonyms': ['balloon'], 'def': 'large tough nonrigid bag filled with gas or heated air', 'name': 'balloon'}, {'frequency': 'c', 'id': 44, 'synset': 'bamboo.n.02', 'synonyms': ['bamboo'], 'def': 'woody tropical grass having hollow woody stems', 'name': 'bamboo'}, {'frequency': 'f', 'id': 45, 'synset': 'banana.n.02', 'synonyms': ['banana'], 'def': 'elongated crescent-shaped yellow fruit with soft sweet flesh', 'name': 'banana'}, {'frequency': 'r', 'id': 46, 'synset': 'band_aid.n.01', 'synonyms': ['Band_Aid'], 'def': 'trade name for an adhesive bandage to cover small cuts or blisters', 'name': 'Band_Aid'}, {'frequency': 'c', 'id': 47, 'synset': 'bandage.n.01', 'synonyms': ['bandage'], 'def': 'a piece of soft material that covers and protects an injured part of the body', 'name': 'bandage'}, {'frequency': 'c', 'id': 48, 'synset': 'bandanna.n.01', 'synonyms': ['bandanna', 'bandana'], 'def': 'large and brightly colored handkerchief; often used as a neckerchief', 'name': 'bandanna'}, {'frequency': 'r', 'id': 49, 'synset': 'banjo.n.01', 'synonyms': ['banjo'], 'def': 'a stringed instrument of the guitar family with a long neck and circular body', 'name': 'banjo'}, {'frequency': 'f', 'id': 50, 'synset': 'banner.n.01', 'synonyms': ['banner', 'streamer'], 'def': 'long strip of cloth or paper used for decoration or advertising', 'name': 'banner'}, {'frequency': 'r', 'id': 51, 'synset': 'barbell.n.01', 'synonyms': ['barbell'], 'def': 'a bar to which heavy discs are attached at each end; used in weightlifting', 'name': 'barbell'}, {'frequency': 'r', 'id': 52, 'synset': 'barge.n.01', 'synonyms': ['barge'], 'def': 'a flatbottom boat for carrying heavy loads (especially on canals)', 'name': 'barge'}, {'frequency': 'f', 'id': 53, 'synset': 'barrel.n.02', 'synonyms': ['barrel', 'cask'], 'def': 'a cylindrical container that holds liquids', 'name': 'barrel'}, {'frequency': 'c', 'id': 54, 'synset': 'barrette.n.01', 'synonyms': ['barrette'], 'def': "a pin for holding women's hair in place", 'name': 'barrette'}, {'frequency': 'c', 'id': 55, 'synset': 'barrow.n.03', 'synonyms': ['barrow', 'garden_cart', 'lawn_cart', 'wheelbarrow'], 'def': 'a cart for carrying small loads; has handles and one or more wheels', 'name': 'barrow'}, {'frequency': 'f', 'id': 56, 'synset': 'base.n.03', 'synonyms': ['baseball_base'], 'def': 'a place that the runner must touch before scoring', 'name': 'baseball_base'}, {'frequency': 'f', 'id': 57, 'synset': 'baseball.n.02', 'synonyms': ['baseball'], 'def': 'a ball used in playing baseball', 'name': 'baseball'}, {'frequency': 'f', 'id': 58, 'synset': 'baseball_bat.n.01', 'synonyms': ['baseball_bat'], 'def': 'an implement used in baseball by the batter', 'name': 'baseball_bat'}, {'frequency': 'f', 'id': 59, 'synset': 'baseball_cap.n.01', 'synonyms': ['baseball_cap', 'jockey_cap', 'golf_cap'], 'def': 'a cap with a bill', 'name': 'baseball_cap'}, {'frequency': 'f', 'id': 60, 'synset': 'baseball_glove.n.01', 'synonyms': ['baseball_glove', 'baseball_mitt'], 'def': 'the handwear used by fielders in playing baseball', 'name': 'baseball_glove'}, {'frequency': 'f', 'id': 61, 'synset': 'basket.n.01', 'synonyms': ['basket', 'handbasket'], 'def': 'a container that is usually woven and has handles', 'name': 'basket'}, {'frequency': 'c', 'id': 62, 'synset': 'basket.n.03', 'synonyms': ['basketball_hoop'], 'def': 'metal hoop supporting a net through which players try to throw the basketball', 'name': 'basketball_hoop'}, {'frequency': 'c', 'id': 63, 'synset': 'basketball.n.02', 'synonyms': ['basketball'], 'def': 'an inflated ball used in playing basketball', 'name': 'basketball'}, {'frequency': 'r', 'id': 64, 'synset': 'bass_horn.n.01', 'synonyms': ['bass_horn', 'sousaphone', 'tuba'], 'def': 'the lowest brass wind instrument', 'name': 'bass_horn'}, {'frequency': 'r', 'id': 65, 'synset': 'bat.n.01', 'synonyms': ['bat_(animal)'], 'def': 'nocturnal mouselike mammal with forelimbs modified to form membranous wings', 'name': 'bat_(animal)'}, {'frequency': 'f', 'id': 66, 'synset': 'bath_mat.n.01', 'synonyms': ['bath_mat'], 'def': 'a heavy towel or mat to stand on while drying yourself after a bath', 'name': 'bath_mat'}, {'frequency': 'f', 'id': 67, 'synset': 'bath_towel.n.01', 'synonyms': ['bath_towel'], 'def': 'a large towel; to dry yourself after a bath', 'name': 'bath_towel'}, {'frequency': 'c', 'id': 68, 'synset': 'bathrobe.n.01', 'synonyms': ['bathrobe'], 'def': 'a loose-fitting robe of towelling; worn after a bath or swim', 'name': 'bathrobe'}, {'frequency': 'f', 'id': 69, 'synset': 'bathtub.n.01', 'synonyms': ['bathtub', 'bathing_tub'], 'def': 'a large open container that you fill with water and use to wash the body', 'name': 'bathtub'}, {'frequency': 'r', 'id': 70, 'synset': 'batter.n.02', 'synonyms': ['batter_(food)'], 'def': 'a liquid or semiliquid mixture, as of flour, eggs, and milk, used in cooking', 'name': 'batter_(food)'}, {'frequency': 'c', 'id': 71, 'synset': 'battery.n.02', 'synonyms': ['battery'], 'def': 'a portable device that produces electricity', 'name': 'battery'}, {'frequency': 'r', 'id': 72, 'synset': 'beach_ball.n.01', 'synonyms': ['beachball'], 'def': 'large and light ball; for play at the seaside', 'name': 'beachball'}, {'frequency': 'c', 'id': 73, 'synset': 'bead.n.01', 'synonyms': ['bead'], 'def': 'a small ball with a hole through the middle used for ornamentation, jewellery, etc.', 'name': 'bead'}, {'frequency': 'r', 'id': 74, 'synset': 'beaker.n.01', 'synonyms': ['beaker'], 'def': 'a flatbottomed jar made of glass or plastic; used for chemistry', 'name': 'beaker'}, {'frequency': 'c', 'id': 75, 'synset': 'bean_curd.n.01', 'synonyms': ['bean_curd', 'tofu'], 'def': 'cheeselike food made of curdled soybean milk', 'name': 'bean_curd'}, {'frequency': 'c', 'id': 76, 'synset': 'beanbag.n.01', 'synonyms': ['beanbag'], 'def': 'a bag filled with dried beans or similar items; used in games or to sit on', 'name': 'beanbag'}, {'frequency': 'f', 'id': 77, 'synset': 'beanie.n.01', 'synonyms': ['beanie', 'beany'], 'def': 'a small skullcap; formerly worn by schoolboys and college freshmen', 'name': 'beanie'}, {'frequency': 'f', 'id': 78, 'synset': 'bear.n.01', 'synonyms': ['bear'], 'def': 'large carnivorous or omnivorous mammals with shaggy coats and claws', 'name': 'bear'}, {'frequency': 'f', 'id': 79, 'synset': 'bed.n.01', 'synonyms': ['bed'], 'def': 'a piece of furniture that provides a place to sleep', 'name': 'bed'}, {'frequency': 'c', 'id': 80, 'synset': 'bedspread.n.01', 'synonyms': ['bedspread', 'bedcover', 'bed_covering', 'counterpane', 'spread'], 'def': 'decorative cover for a bed', 'name': 'bedspread'}, {'frequency': 'f', 'id': 81, 'synset': 'beef.n.01', 'synonyms': ['cow'], 'def': 'cattle that are reared for their meat', 'name': 'cow'}, {'frequency': 'c', 'id': 82, 'synset': 'beef.n.02', 'synonyms': ['beef_(food)', 'boeuf_(food)'], 'def': 'meat from an adult domestic bovine', 'name': 'beef_(food)'}, {'frequency': 'r', 'id': 83, 'synset': 'beeper.n.01', 'synonyms': ['beeper', 'pager'], 'def': 'an device that beeps when the person carrying it is being paged', 'name': 'beeper'}, {'frequency': 'f', 'id': 84, 'synset': 'beer_bottle.n.01', 'synonyms': ['beer_bottle'], 'def': 'a bottle that holds beer', 'name': 'beer_bottle'}, {'frequency': 'c', 'id': 85, 'synset': 'beer_can.n.01', 'synonyms': ['beer_can'], 'def': 'a can that holds beer', 'name': 'beer_can'}, {'frequency': 'r', 'id': 86, 'synset': 'beetle.n.01', 'synonyms': ['beetle'], 'def': 'insect with hard wing covers', 'name': 'beetle'}, {'frequency': 'f', 'id': 87, 'synset': 'bell.n.01', 'synonyms': ['bell'], 'def': 'a hollow device made of metal that makes a ringing sound when struck', 'name': 'bell'}, {'frequency': 'f', 'id': 88, 'synset': 'bell_pepper.n.02', 'synonyms': ['bell_pepper', 'capsicum'], 'def': 'large bell-shaped sweet pepper in green or red or yellow or orange or black varieties', 'name': 'bell_pepper'}, {'frequency': 'f', 'id': 89, 'synset': 'belt.n.02', 'synonyms': ['belt'], 'def': 'a band to tie or buckle around the body (usually at the waist)', 'name': 'belt'}, {'frequency': 'f', 'id': 90, 'synset': 'belt_buckle.n.01', 'synonyms': ['belt_buckle'], 'def': 'the buckle used to fasten a belt', 'name': 'belt_buckle'}, {'frequency': 'f', 'id': 91, 'synset': 'bench.n.01', 'synonyms': ['bench'], 'def': 'a long seat for more than one person', 'name': 'bench'}, {'frequency': 'c', 'id': 92, 'synset': 'beret.n.01', 'synonyms': ['beret'], 'def': 'a cap with no brim or bill; made of soft cloth', 'name': 'beret'}, {'frequency': 'c', 'id': 93, 'synset': 'bib.n.02', 'synonyms': ['bib'], 'def': 'a napkin tied under the chin of a child while eating', 'name': 'bib'}, {'frequency': 'r', 'id': 94, 'synset': 'bible.n.01', 'synonyms': ['Bible'], 'def': 'the sacred writings of the Christian religions', 'name': 'Bible'}, {'frequency': 'f', 'id': 95, 'synset': 'bicycle.n.01', 'synonyms': ['bicycle', 'bike_(bicycle)'], 'def': 'a wheeled vehicle that has two wheels and is moved by foot pedals', 'name': 'bicycle'}, {'frequency': 'f', 'id': 96, 'synset': 'bill.n.09', 'synonyms': ['visor', 'vizor'], 'def': 'a brim that projects to the front to shade the eyes', 'name': 'visor'}, {'frequency': 'c', 'id': 97, 'synset': 'binder.n.03', 'synonyms': ['binder', 'ring-binder'], 'def': 'holds loose papers or magazines', 'name': 'binder'}, {'frequency': 'c', 'id': 98, 'synset': 'binoculars.n.01', 'synonyms': ['binoculars', 'field_glasses', 'opera_glasses'], 'def': 'an optical instrument designed for simultaneous use by both eyes', 'name': 'binoculars'}, {'frequency': 'f', 'id': 99, 'synset': 'bird.n.01', 'synonyms': ['bird'], 'def': 'animal characterized by feathers and wings', 'name': 'bird'}, {'frequency': 'r', 'id': 100, 'synset': 'bird_feeder.n.01', 'synonyms': ['birdfeeder'], 'def': 'an outdoor device that supplies food for wild birds', 'name': 'birdfeeder'}, {'frequency': 'r', 'id': 101, 'synset': 'birdbath.n.01', 'synonyms': ['birdbath'], 'def': 'an ornamental basin (usually in a garden) for birds to bathe in', 'name': 'birdbath'}, {'frequency': 'c', 'id': 102, 'synset': 'birdcage.n.01', 'synonyms': ['birdcage'], 'def': 'a cage in which a bird can be kept', 'name': 'birdcage'}, {'frequency': 'c', 'id': 103, 'synset': 'birdhouse.n.01', 'synonyms': ['birdhouse'], 'def': 'a shelter for birds', 'name': 'birdhouse'}, {'frequency': 'f', 'id': 104, 'synset': 'birthday_cake.n.01', 'synonyms': ['birthday_cake'], 'def': 'decorated cake served at a birthday party', 'name': 'birthday_cake'}, {'frequency': 'r', 'id': 105, 'synset': 'birthday_card.n.01', 'synonyms': ['birthday_card'], 'def': 'a card expressing a birthday greeting', 'name': 'birthday_card'}, {'frequency': 'r', 'id': 106, 'synset': 'biscuit.n.01', 'synonyms': ['biscuit_(bread)'], 'def': 'small round bread leavened with baking-powder or soda', 'name': 'biscuit_(bread)'}, {'frequency': 'r', 'id': 107, 'synset': 'black_flag.n.01', 'synonyms': ['pirate_flag'], 'def': 'a flag usually bearing a white skull and crossbones on a black background', 'name': 'pirate_flag'}, {'frequency': 'c', 'id': 108, 'synset': 'black_sheep.n.02', 'synonyms': ['black_sheep'], 'def': 'sheep with a black coat', 'name': 'black_sheep'}, {'frequency': 'c', 'id': 109, 'synset': 'blackboard.n.01', 'synonyms': ['blackboard', 'chalkboard'], 'def': 'sheet of slate; for writing with chalk', 'name': 'blackboard'}, {'frequency': 'f', 'id': 110, 'synset': 'blanket.n.01', 'synonyms': ['blanket'], 'def': 'bedding that keeps a person warm in bed', 'name': 'blanket'}, {'frequency': 'c', 'id': 111, 'synset': 'blazer.n.01', 'synonyms': ['blazer', 'sport_jacket', 'sport_coat', 'sports_jacket', 'sports_coat'], 'def': 'lightweight jacket; often striped in the colors of a club or school', 'name': 'blazer'}, {'frequency': 'f', 'id': 112, 'synset': 'blender.n.01', 'synonyms': ['blender', 'liquidizer', 'liquidiser'], 'def': 'an electrically powered mixer that mix or chop or liquefy foods', 'name': 'blender'}, {'frequency': 'r', 'id': 113, 'synset': 'blimp.n.02', 'synonyms': ['blimp'], 'def': 'a small nonrigid airship used for observation or as a barrage balloon', 'name': 'blimp'}, {'frequency': 'c', 'id': 114, 'synset': 'blinker.n.01', 'synonyms': ['blinker', 'flasher'], 'def': 'a light that flashes on and off; used as a signal or to send messages', 'name': 'blinker'}, {'frequency': 'c', 'id': 115, 'synset': 'blueberry.n.02', 'synonyms': ['blueberry'], 'def': 'sweet edible dark-blue berries of blueberry plants', 'name': 'blueberry'}, {'frequency': 'r', 'id': 116, 'synset': 'boar.n.02', 'synonyms': ['boar'], 'def': 'an uncastrated male hog', 'name': 'boar'}, {'frequency': 'r', 'id': 117, 'synset': 'board.n.09', 'synonyms': ['gameboard'], 'def': 'a flat portable surface (usually rectangular) designed for board games', 'name': 'gameboard'}, {'frequency': 'f', 'id': 118, 'synset': 'boat.n.01', 'synonyms': ['boat', 'ship_(boat)'], 'def': 'a vessel for travel on water', 'name': 'boat'}, {'frequency': 'c', 'id': 119, 'synset': 'bobbin.n.01', 'synonyms': ['bobbin', 'spool', 'reel'], 'def': 'a thing around which thread/tape/film or other flexible materials can be wound', 'name': 'bobbin'}, {'frequency': 'r', 'id': 120, 'synset': 'bobby_pin.n.01', 'synonyms': ['bobby_pin', 'hairgrip'], 'def': 'a flat wire hairpin used to hold bobbed hair in place', 'name': 'bobby_pin'}, {'frequency': 'c', 'id': 121, 'synset': 'boiled_egg.n.01', 'synonyms': ['boiled_egg', 'coddled_egg'], 'def': 'egg cooked briefly in the shell in gently boiling water', 'name': 'boiled_egg'}, {'frequency': 'r', 'id': 122, 'synset': 'bolo_tie.n.01', 'synonyms': ['bolo_tie', 'bolo', 'bola_tie', 'bola'], 'def': 'a cord fastened around the neck with an ornamental clasp and worn as a necktie', 'name': 'bolo_tie'}, {'frequency': 'c', 'id': 123, 'synset': 'bolt.n.03', 'synonyms': ['deadbolt'], 'def': 'the part of a lock that is engaged or withdrawn with a key', 'name': 'deadbolt'}, {'frequency': 'f', 'id': 124, 'synset': 'bolt.n.06', 'synonyms': ['bolt'], 'def': 'a screw that screws into a nut to form a fastener', 'name': 'bolt'}, {'frequency': 'r', 'id': 125, 'synset': 'bonnet.n.01', 'synonyms': ['bonnet'], 'def': 'a hat tied under the chin', 'name': 'bonnet'}, {'frequency': 'f', 'id': 126, 'synset': 'book.n.01', 'synonyms': ['book'], 'def': 'a written work or composition that has been published', 'name': 'book'}, {'frequency': 'r', 'id': 127, 'synset': 'book_bag.n.01', 'synonyms': ['book_bag'], 'def': 'a bag in which students carry their books', 'name': 'book_bag'}, {'frequency': 'c', 'id': 128, 'synset': 'bookcase.n.01', 'synonyms': ['bookcase'], 'def': 'a piece of furniture with shelves for storing books', 'name': 'bookcase'}, {'frequency': 'c', 'id': 129, 'synset': 'booklet.n.01', 'synonyms': ['booklet', 'brochure', 'leaflet', 'pamphlet'], 'def': 'a small book usually having a paper cover', 'name': 'booklet'}, {'frequency': 'r', 'id': 130, 'synset': 'bookmark.n.01', 'synonyms': ['bookmark', 'bookmarker'], 'def': 'a marker (a piece of paper or ribbon) placed between the pages of a book', 'name': 'bookmark'}, {'frequency': 'r', 'id': 131, 'synset': 'boom.n.04', 'synonyms': ['boom_microphone', 'microphone_boom'], 'def': 'a pole carrying an overhead microphone projected over a film or tv set', 'name': 'boom_microphone'}, {'frequency': 'f', 'id': 132, 'synset': 'boot.n.01', 'synonyms': ['boot'], 'def': 'footwear that covers the whole foot and lower leg', 'name': 'boot'}, {'frequency': 'f', 'id': 133, 'synset': 'bottle.n.01', 'synonyms': ['bottle'], 'def': 'a glass or plastic vessel used for storing drinks or other liquids', 'name': 'bottle'}, {'frequency': 'c', 'id': 134, 'synset': 'bottle_opener.n.01', 'synonyms': ['bottle_opener'], 'def': 'an opener for removing caps or corks from bottles', 'name': 'bottle_opener'}, {'frequency': 'c', 'id': 135, 'synset': 'bouquet.n.01', 'synonyms': ['bouquet'], 'def': 'an arrangement of flowers that is usually given as a present', 'name': 'bouquet'}, {'frequency': 'r', 'id': 136, 'synset': 'bow.n.04', 'synonyms': ['bow_(weapon)'], 'def': 'a weapon for shooting arrows', 'name': 'bow_(weapon)'}, {'frequency': 'f', 'id': 137, 'synset': 'bow.n.08', 'synonyms': ['bow_(decorative_ribbons)'], 'def': 'a decorative interlacing of ribbons', 'name': 'bow_(decorative_ribbons)'}, {'frequency': 'f', 'id': 138, 'synset': 'bow_tie.n.01', 'synonyms': ['bow-tie', 'bowtie'], 'def': "a man's tie that ties in a bow", 'name': 'bow-tie'}, {'frequency': 'f', 'id': 139, 'synset': 'bowl.n.03', 'synonyms': ['bowl'], 'def': 'a dish that is round and open at the top for serving foods', 'name': 'bowl'}, {'frequency': 'r', 'id': 140, 'synset': 'bowl.n.08', 'synonyms': ['pipe_bowl'], 'def': 'a small round container that is open at the top for holding tobacco', 'name': 'pipe_bowl'}, {'frequency': 'c', 'id': 141, 'synset': 'bowler_hat.n.01', 'synonyms': ['bowler_hat', 'bowler', 'derby_hat', 'derby', 'plug_hat'], 'def': 'a felt hat that is round and hard with a narrow brim', 'name': 'bowler_hat'}, {'frequency': 'r', 'id': 142, 'synset': 'bowling_ball.n.01', 'synonyms': ['bowling_ball'], 'def': 'a large ball with finger holes used in the sport of bowling', 'name': 'bowling_ball'}, {'frequency': 'r', 'id': 143, 'synset': 'bowling_pin.n.01', 'synonyms': ['bowling_pin'], 'def': 'a club-shaped wooden object used in bowling', 'name': 'bowling_pin'}, {'frequency': 'r', 'id': 144, 'synset': 'boxing_glove.n.01', 'synonyms': ['boxing_glove'], 'def': 'large glove coverings the fists of a fighter worn for the sport of boxing', 'name': 'boxing_glove'}, {'frequency': 'c', 'id': 145, 'synset': 'brace.n.06', 'synonyms': ['suspenders'], 'def': 'elastic straps that hold trousers up (usually used in the plural)', 'name': 'suspenders'}, {'frequency': 'f', 'id': 146, 'synset': 'bracelet.n.02', 'synonyms': ['bracelet', 'bangle'], 'def': 'jewelry worn around the wrist for decoration', 'name': 'bracelet'}, {'frequency': 'r', 'id': 147, 'synset': 'brass.n.07', 'synonyms': ['brass_plaque'], 'def': 'a memorial made of brass', 'name': 'brass_plaque'}, {'frequency': 'c', 'id': 148, 'synset': 'brassiere.n.01', 'synonyms': ['brassiere', 'bra', 'bandeau'], 'def': 'an undergarment worn by women to support their breasts', 'name': 'brassiere'}, {'frequency': 'c', 'id': 149, 'synset': 'bread-bin.n.01', 'synonyms': ['bread-bin', 'breadbox'], 'def': 'a container used to keep bread or cake in', 'name': 'bread-bin'}, {'frequency': 'r', 'id': 150, 'synset': 'breechcloth.n.01', 'synonyms': ['breechcloth', 'breechclout', 'loincloth'], 'def': 'a garment that provides covering for the loins', 'name': 'breechcloth'}, {'frequency': 'c', 'id': 151, 'synset': 'bridal_gown.n.01', 'synonyms': ['bridal_gown', 'wedding_gown', 'wedding_dress'], 'def': 'a gown worn by the bride at a wedding', 'name': 'bridal_gown'}, {'frequency': 'c', 'id': 152, 'synset': 'briefcase.n.01', 'synonyms': ['briefcase'], 'def': 'a case with a handle; for carrying papers or files or books', 'name': 'briefcase'}, {'frequency': 'c', 'id': 153, 'synset': 'bristle_brush.n.01', 'synonyms': ['bristle_brush'], 'def': 'a brush that is made with the short stiff hairs of an animal or plant', 'name': 'bristle_brush'}, {'frequency': 'f', 'id': 154, 'synset': 'broccoli.n.01', 'synonyms': ['broccoli'], 'def': 'plant with dense clusters of tight green flower buds', 'name': 'broccoli'}, {'frequency': 'r', 'id': 155, 'synset': 'brooch.n.01', 'synonyms': ['broach'], 'def': 'a decorative pin worn by women', 'name': 'broach'}, {'frequency': 'c', 'id': 156, 'synset': 'broom.n.01', 'synonyms': ['broom'], 'def': 'bundle of straws or twigs attached to a long handle; used for cleaning', 'name': 'broom'}, {'frequency': 'c', 'id': 157, 'synset': 'brownie.n.03', 'synonyms': ['brownie'], 'def': 'square or bar of very rich chocolate cake usually with nuts', 'name': 'brownie'}, {'frequency': 'c', 'id': 158, 'synset': 'brussels_sprouts.n.01', 'synonyms': ['brussels_sprouts'], 'def': 'the small edible cabbage-like buds growing along a stalk', 'name': 'brussels_sprouts'}, {'frequency': 'r', 'id': 159, 'synset': 'bubble_gum.n.01', 'synonyms': ['bubble_gum'], 'def': 'a kind of chewing gum that can be blown into bubbles', 'name': 'bubble_gum'}, {'frequency': 'f', 'id': 160, 'synset': 'bucket.n.01', 'synonyms': ['bucket', 'pail'], 'def': 'a roughly cylindrical vessel that is open at the top', 'name': 'bucket'}, {'frequency': 'r', 'id': 161, 'synset': 'buggy.n.01', 'synonyms': ['horse_buggy'], 'def': 'a small lightweight carriage; drawn by a single horse', 'name': 'horse_buggy'}, {'frequency': 'c', 'id': 162, 'synset': 'bull.n.11', 'synonyms': ['bull'], 'def': 'mature male cow', 'name': 'bull'}, {'frequency': 'r', 'id': 163, 'synset': 'bulldog.n.01', 'synonyms': ['bulldog'], 'def': 'a thickset short-haired dog with a large head and strong undershot lower jaw', 'name': 'bulldog'}, {'frequency': 'r', 'id': 164, 'synset': 'bulldozer.n.01', 'synonyms': ['bulldozer', 'dozer'], 'def': 'large powerful tractor; a large blade in front flattens areas of ground', 'name': 'bulldozer'}, {'frequency': 'c', 'id': 165, 'synset': 'bullet_train.n.01', 'synonyms': ['bullet_train'], 'def': 'a high-speed passenger train', 'name': 'bullet_train'}, {'frequency': 'c', 'id': 166, 'synset': 'bulletin_board.n.02', 'synonyms': ['bulletin_board', 'notice_board'], 'def': 'a board that hangs on a wall; displays announcements', 'name': 'bulletin_board'}, {'frequency': 'r', 'id': 167, 'synset': 'bulletproof_vest.n.01', 'synonyms': ['bulletproof_vest'], 'def': 'a vest capable of resisting the impact of a bullet', 'name': 'bulletproof_vest'}, {'frequency': 'c', 'id': 168, 'synset': 'bullhorn.n.01', 'synonyms': ['bullhorn', 'megaphone'], 'def': 'a portable loudspeaker with built-in microphone and amplifier', 'name': 'bullhorn'}, {'frequency': 'r', 'id': 169, 'synset': 'bully_beef.n.01', 'synonyms': ['corned_beef', 'corn_beef'], 'def': 'beef cured or pickled in brine', 'name': 'corned_beef'}, {'frequency': 'f', 'id': 170, 'synset': 'bun.n.01', 'synonyms': ['bun', 'roll'], 'def': 'small rounded bread either plain or sweet', 'name': 'bun'}, {'frequency': 'c', 'id': 171, 'synset': 'bunk_bed.n.01', 'synonyms': ['bunk_bed'], 'def': 'beds built one above the other', 'name': 'bunk_bed'}, {'frequency': 'f', 'id': 172, 'synset': 'buoy.n.01', 'synonyms': ['buoy'], 'def': 'a float attached by rope to the seabed to mark channels in a harbor or underwater hazards', 'name': 'buoy'}, {'frequency': 'r', 'id': 173, 'synset': 'burrito.n.01', 'synonyms': ['burrito'], 'def': 'a flour tortilla folded around a filling', 'name': 'burrito'}, {'frequency': 'f', 'id': 174, 'synset': 'bus.n.01', 'synonyms': ['bus_(vehicle)', 'autobus', 'charabanc', 'double-decker', 'motorbus', 'motorcoach'], 'def': 'a vehicle carrying many passengers; used for public transport', 'name': 'bus_(vehicle)'}, {'frequency': 'c', 'id': 175, 'synset': 'business_card.n.01', 'synonyms': ['business_card'], 'def': "a card on which are printed the person's name and business affiliation", 'name': 'business_card'}, {'frequency': 'c', 'id': 176, 'synset': 'butcher_knife.n.01', 'synonyms': ['butcher_knife'], 'def': 'a large sharp knife for cutting or trimming meat', 'name': 'butcher_knife'}, {'frequency': 'c', 'id': 177, 'synset': 'butter.n.01', 'synonyms': ['butter'], 'def': 'an edible emulsion of fat globules made by churning milk or cream; for cooking and table use', 'name': 'butter'}, {'frequency': 'c', 'id': 178, 'synset': 'butterfly.n.01', 'synonyms': ['butterfly'], 'def': 'insect typically having a slender body with knobbed antennae and broad colorful wings', 'name': 'butterfly'}, {'frequency': 'f', 'id': 179, 'synset': 'button.n.01', 'synonyms': ['button'], 'def': 'a round fastener sewn to shirts and coats etc to fit through buttonholes', 'name': 'button'}, {'frequency': 'f', 'id': 180, 'synset': 'cab.n.03', 'synonyms': ['cab_(taxi)', 'taxi', 'taxicab'], 'def': 'a car that takes passengers where they want to go in exchange for money', 'name': 'cab_(taxi)'}, {'frequency': 'r', 'id': 181, 'synset': 'cabana.n.01', 'synonyms': ['cabana'], 'def': 'a small tent used as a dressing room beside the sea or a swimming pool', 'name': 'cabana'}, {'frequency': 'r', 'id': 182, 'synset': 'cabin_car.n.01', 'synonyms': ['cabin_car', 'caboose'], 'def': 'a car on a freight train for use of the train crew; usually the last car on the train', 'name': 'cabin_car'}, {'frequency': 'f', 'id': 183, 'synset': 'cabinet.n.01', 'synonyms': ['cabinet'], 'def': 'a piece of furniture resembling a cupboard with doors and shelves and drawers', 'name': 'cabinet'}, {'frequency': 'r', 'id': 184, 'synset': 'cabinet.n.03', 'synonyms': ['locker', 'storage_locker'], 'def': 'a storage compartment for clothes and valuables; usually it has a lock', 'name': 'locker'}, {'frequency': 'f', 'id': 185, 'synset': 'cake.n.03', 'synonyms': ['cake'], 'def': 'baked goods made from or based on a mixture of flour, sugar, eggs, and fat', 'name': 'cake'}, {'frequency': 'c', 'id': 186, 'synset': 'calculator.n.02', 'synonyms': ['calculator'], 'def': 'a small machine that is used for mathematical calculations', 'name': 'calculator'}, {'frequency': 'f', 'id': 187, 'synset': 'calendar.n.02', 'synonyms': ['calendar'], 'def': 'a list or register of events (appointments/social events/court cases, etc)', 'name': 'calendar'}, {'frequency': 'c', 'id': 188, 'synset': 'calf.n.01', 'synonyms': ['calf'], 'def': 'young of domestic cattle', 'name': 'calf'}, {'frequency': 'c', 'id': 189, 'synset': 'camcorder.n.01', 'synonyms': ['camcorder'], 'def': 'a portable television camera and videocassette recorder', 'name': 'camcorder'}, {'frequency': 'c', 'id': 190, 'synset': 'camel.n.01', 'synonyms': ['camel'], 'def': 'cud-chewing mammal used as a draft or saddle animal in desert regions', 'name': 'camel'}, {'frequency': 'f', 'id': 191, 'synset': 'camera.n.01', 'synonyms': ['camera'], 'def': 'equipment for taking photographs', 'name': 'camera'}, {'frequency': 'c', 'id': 192, 'synset': 'camera_lens.n.01', 'synonyms': ['camera_lens'], 'def': 'a lens that focuses the image in a camera', 'name': 'camera_lens'}, {'frequency': 'c', 'id': 193, 'synset': 'camper.n.02', 'synonyms': ['camper_(vehicle)', 'camping_bus', 'motor_home'], 'def': 'a recreational vehicle equipped for camping out while traveling', 'name': 'camper_(vehicle)'}, {'frequency': 'f', 'id': 194, 'synset': 'can.n.01', 'synonyms': ['can', 'tin_can'], 'def': 'airtight sealed metal container for food or drink or paint etc.', 'name': 'can'}, {'frequency': 'c', 'id': 195, 'synset': 'can_opener.n.01', 'synonyms': ['can_opener', 'tin_opener'], 'def': 'a device for cutting cans open', 'name': 'can_opener'}, {'frequency': 'r', 'id': 196, 'synset': 'candelabrum.n.01', 'synonyms': ['candelabrum', 'candelabra'], 'def': 'branched candlestick; ornamental; has several lights', 'name': 'candelabrum'}, {'frequency': 'f', 'id': 197, 'synset': 'candle.n.01', 'synonyms': ['candle', 'candlestick'], 'def': 'stick of wax with a wick in the middle', 'name': 'candle'}, {'frequency': 'f', 'id': 198, 'synset': 'candlestick.n.01', 'synonyms': ['candle_holder'], 'def': 'a holder with sockets for candles', 'name': 'candle_holder'}, {'frequency': 'r', 'id': 199, 'synset': 'candy_bar.n.01', 'synonyms': ['candy_bar'], 'def': 'a candy shaped as a bar', 'name': 'candy_bar'}, {'frequency': 'c', 'id': 200, 'synset': 'candy_cane.n.01', 'synonyms': ['candy_cane'], 'def': 'a hard candy in the shape of a rod (usually with stripes)', 'name': 'candy_cane'}, {'frequency': 'c', 'id': 201, 'synset': 'cane.n.01', 'synonyms': ['walking_cane'], 'def': 'a stick that people can lean on to help them walk', 'name': 'walking_cane'}, {'frequency': 'c', 'id': 202, 'synset': 'canister.n.02', 'synonyms': ['canister', 'cannister'], 'def': 'metal container for storing dry foods such as tea or flour', 'name': 'canister'}, {'frequency': 'r', 'id': 203, 'synset': 'cannon.n.02', 'synonyms': ['cannon'], 'def': 'heavy gun fired from a tank', 'name': 'cannon'}, {'frequency': 'c', 'id': 204, 'synset': 'canoe.n.01', 'synonyms': ['canoe'], 'def': 'small and light boat; pointed at both ends; propelled with a paddle', 'name': 'canoe'}, {'frequency': 'r', 'id': 205, 'synset': 'cantaloup.n.02', 'synonyms': ['cantaloup', 'cantaloupe'], 'def': 'the fruit of a cantaloup vine; small to medium-sized melon with yellowish flesh', 'name': 'cantaloup'}, {'frequency': 'r', 'id': 206, 'synset': 'canteen.n.01', 'synonyms': ['canteen'], 'def': 'a flask for carrying water; used by soldiers or travelers', 'name': 'canteen'}, {'frequency': 'c', 'id': 207, 'synset': 'cap.n.01', 'synonyms': ['cap_(headwear)'], 'def': 'a tight-fitting headwear', 'name': 'cap_(headwear)'}, {'frequency': 'f', 'id': 208, 'synset': 'cap.n.02', 'synonyms': ['bottle_cap', 'cap_(container_lid)'], 'def': 'a top (as for a bottle)', 'name': 'bottle_cap'}, {'frequency': 'r', 'id': 209, 'synset': 'cape.n.02', 'synonyms': ['cape'], 'def': 'a sleeveless garment like a cloak but shorter', 'name': 'cape'}, {'frequency': 'c', 'id': 210, 'synset': 'cappuccino.n.01', 'synonyms': ['cappuccino', 'coffee_cappuccino'], 'def': 'equal parts of espresso and steamed milk', 'name': 'cappuccino'}, {'frequency': 'f', 'id': 211, 'synset': 'car.n.01', 'synonyms': ['car_(automobile)', 'auto_(automobile)', 'automobile'], 'def': 'a motor vehicle with four wheels', 'name': 'car_(automobile)'}, {'frequency': 'f', 'id': 212, 'synset': 'car.n.02', 'synonyms': ['railcar_(part_of_a_train)', 'railway_car_(part_of_a_train)', 'railroad_car_(part_of_a_train)'], 'def': 'a wheeled vehicle adapted to the rails of railroad', 'name': 'railcar_(part_of_a_train)'}, {'frequency': 'r', 'id': 213, 'synset': 'car.n.04', 'synonyms': ['elevator_car'], 'def': 'where passengers ride up and down', 'name': 'elevator_car'}, {'frequency': 'r', 'id': 214, 'synset': 'car_battery.n.01', 'synonyms': ['car_battery', 'automobile_battery'], 'def': 'a battery in a motor vehicle', 'name': 'car_battery'}, {'frequency': 'c', 'id': 215, 'synset': 'card.n.02', 'synonyms': ['identity_card'], 'def': 'a card certifying the identity of the bearer', 'name': 'identity_card'}, {'frequency': 'c', 'id': 216, 'synset': 'card.n.03', 'synonyms': ['card'], 'def': 'a rectangular piece of paper used to send messages (e.g. greetings or pictures)', 'name': 'card'}, {'frequency': 'r', 'id': 217, 'synset': 'cardigan.n.01', 'synonyms': ['cardigan'], 'def': 'knitted jacket that is fastened up the front with buttons or a zipper', 'name': 'cardigan'}, {'frequency': 'r', 'id': 218, 'synset': 'cargo_ship.n.01', 'synonyms': ['cargo_ship', 'cargo_vessel'], 'def': 'a ship designed to carry cargo', 'name': 'cargo_ship'}, {'frequency': 'r', 'id': 219, 'synset': 'carnation.n.01', 'synonyms': ['carnation'], 'def': 'plant with pink to purple-red spice-scented usually double flowers', 'name': 'carnation'}, {'frequency': 'c', 'id': 220, 'synset': 'carriage.n.02', 'synonyms': ['horse_carriage'], 'def': 'a vehicle with wheels drawn by one or more horses', 'name': 'horse_carriage'}, {'frequency': 'f', 'id': 221, 'synset': 'carrot.n.01', 'synonyms': ['carrot'], 'def': 'deep orange edible root of the cultivated carrot plant', 'name': 'carrot'}, {'frequency': 'c', 'id': 222, 'synset': 'carryall.n.01', 'synonyms': ['tote_bag'], 'def': 'a capacious bag or basket', 'name': 'tote_bag'}, {'frequency': 'c', 'id': 223, 'synset': 'cart.n.01', 'synonyms': ['cart'], 'def': 'a heavy open wagon usually having two wheels and drawn by an animal', 'name': 'cart'}, {'frequency': 'c', 'id': 224, 'synset': 'carton.n.02', 'synonyms': ['carton'], 'def': 'a box made of cardboard; opens by flaps on top', 'name': 'carton'}, {'frequency': 'c', 'id': 225, 'synset': 'cash_register.n.01', 'synonyms': ['cash_register', 'register_(for_cash_transactions)'], 'def': 'a cashbox with an adding machine to register transactions', 'name': 'cash_register'}, {'frequency': 'r', 'id': 226, 'synset': 'casserole.n.01', 'synonyms': ['casserole'], 'def': 'food cooked and served in a casserole', 'name': 'casserole'}, {'frequency': 'r', 'id': 227, 'synset': 'cassette.n.01', 'synonyms': ['cassette'], 'def': 'a container that holds a magnetic tape used for recording or playing sound or video', 'name': 'cassette'}, {'frequency': 'c', 'id': 228, 'synset': 'cast.n.05', 'synonyms': ['cast', 'plaster_cast', 'plaster_bandage'], 'def': 'bandage consisting of a firm covering that immobilizes broken bones while they heal', 'name': 'cast'}, {'frequency': 'f', 'id': 229, 'synset': 'cat.n.01', 'synonyms': ['cat'], 'def': 'a domestic house cat', 'name': 'cat'}, {'frequency': 'c', 'id': 230, 'synset': 'cauliflower.n.02', 'synonyms': ['cauliflower'], 'def': 'edible compact head of white undeveloped flowers', 'name': 'cauliflower'}, {'frequency': 'r', 'id': 231, 'synset': 'caviar.n.01', 'synonyms': ['caviar', 'caviare'], 'def': "salted roe of sturgeon or other large fish; usually served as an hors d'oeuvre", 'name': 'caviar'}, {'frequency': 'c', 'id': 232, 'synset': 'cayenne.n.02', 'synonyms': ['cayenne_(spice)', 'cayenne_pepper_(spice)', 'red_pepper_(spice)'], 'def': 'ground pods and seeds of pungent red peppers of the genus Capsicum', 'name': 'cayenne_(spice)'}, {'frequency': 'c', 'id': 233, 'synset': 'cd_player.n.01', 'synonyms': ['CD_player'], 'def': 'electronic equipment for playing compact discs (CDs)', 'name': 'CD_player'}, {'frequency': 'c', 'id': 234, 'synset': 'celery.n.01', 'synonyms': ['celery'], 'def': 'widely cultivated herb with aromatic leaf stalks that are eaten raw or cooked', 'name': 'celery'}, {'frequency': 'f', 'id': 235, 'synset': 'cellular_telephone.n.01', 'synonyms': ['cellular_telephone', 'cellular_phone', 'cellphone', 'mobile_phone', 'smart_phone'], 'def': 'a hand-held mobile telephone', 'name': 'cellular_telephone'}, {'frequency': 'r', 'id': 236, 'synset': 'chain_mail.n.01', 'synonyms': ['chain_mail', 'ring_mail', 'chain_armor', 'chain_armour', 'ring_armor', 'ring_armour'], 'def': '(Middle Ages) flexible armor made of interlinked metal rings', 'name': 'chain_mail'}, {'frequency': 'f', 'id': 237, 'synset': 'chair.n.01', 'synonyms': ['chair'], 'def': 'a seat for one person, with a support for the back', 'name': 'chair'}, {'frequency': 'r', 'id': 238, 'synset': 'chaise_longue.n.01', 'synonyms': ['chaise_longue', 'chaise', 'daybed'], 'def': 'a long chair; for reclining', 'name': 'chaise_longue'}, {'frequency': 'r', 'id': 239, 'synset': 'champagne.n.01', 'synonyms': ['champagne'], 'def': 'a white sparkling wine produced in Champagne or resembling that produced there', 'name': 'champagne'}, {'frequency': 'f', 'id': 240, 'synset': 'chandelier.n.01', 'synonyms': ['chandelier'], 'def': 'branched lighting fixture; often ornate; hangs from the ceiling', 'name': 'chandelier'}, {'frequency': 'r', 'id': 241, 'synset': 'chap.n.04', 'synonyms': ['chap'], 'def': 'leather leggings without a seat; worn over trousers by cowboys to protect their legs', 'name': 'chap'}, {'frequency': 'r', 'id': 242, 'synset': 'checkbook.n.01', 'synonyms': ['checkbook', 'chequebook'], 'def': 'a book issued to holders of checking accounts', 'name': 'checkbook'}, {'frequency': 'r', 'id': 243, 'synset': 'checkerboard.n.01', 'synonyms': ['checkerboard'], 'def': 'a board having 64 squares of two alternating colors', 'name': 'checkerboard'}, {'frequency': 'c', 'id': 244, 'synset': 'cherry.n.03', 'synonyms': ['cherry'], 'def': 'a red fruit with a single hard stone', 'name': 'cherry'}, {'frequency': 'r', 'id': 245, 'synset': 'chessboard.n.01', 'synonyms': ['chessboard'], 'def': 'a checkerboard used to play chess', 'name': 'chessboard'}, {'frequency': 'r', 'id': 246, 'synset': 'chest_of_drawers.n.01', 'synonyms': ['chest_of_drawers_(furniture)', 'bureau_(furniture)', 'chest_(furniture)'], 'def': 'furniture with drawers for keeping clothes', 'name': 'chest_of_drawers_(furniture)'}, {'frequency': 'c', 'id': 247, 'synset': 'chicken.n.02', 'synonyms': ['chicken_(animal)'], 'def': 'a domestic fowl bred for flesh or eggs', 'name': 'chicken_(animal)'}, {'frequency': 'c', 'id': 248, 'synset': 'chicken_wire.n.01', 'synonyms': ['chicken_wire'], 'def': 'a galvanized wire network with a hexagonal mesh; used to build fences', 'name': 'chicken_wire'}, {'frequency': 'r', 'id': 249, 'synset': 'chickpea.n.01', 'synonyms': ['chickpea', 'garbanzo'], 'def': 'the seed of the chickpea plant; usually dried', 'name': 'chickpea'}, {'frequency': 'r', 'id': 250, 'synset': 'chihuahua.n.03', 'synonyms': ['Chihuahua'], 'def': 'an old breed of tiny short-haired dog with protruding eyes from Mexico', 'name': 'Chihuahua'}, {'frequency': 'r', 'id': 251, 'synset': 'chili.n.02', 'synonyms': ['chili_(vegetable)', 'chili_pepper_(vegetable)', 'chilli_(vegetable)', 'chilly_(vegetable)', 'chile_(vegetable)'], 'def': 'very hot and finely tapering pepper of special pungency', 'name': 'chili_(vegetable)'}, {'frequency': 'r', 'id': 252, 'synset': 'chime.n.01', 'synonyms': ['chime', 'gong'], 'def': 'an instrument consisting of a set of bells that are struck with a hammer', 'name': 'chime'}, {'frequency': 'r', 'id': 253, 'synset': 'chinaware.n.01', 'synonyms': ['chinaware'], 'def': 'dishware made of high quality porcelain', 'name': 'chinaware'}, {'frequency': 'c', 'id': 254, 'synset': 'chip.n.04', 'synonyms': ['crisp_(potato_chip)', 'potato_chip'], 'def': 'a thin crisp slice of potato fried in deep fat', 'name': 'crisp_(potato_chip)'}, {'frequency': 'r', 'id': 255, 'synset': 'chip.n.06', 'synonyms': ['poker_chip'], 'def': 'a small disk-shaped counter used to represent money when gambling', 'name': 'poker_chip'}, {'frequency': 'c', 'id': 256, 'synset': 'chocolate_bar.n.01', 'synonyms': ['chocolate_bar'], 'def': 'a bar of chocolate candy', 'name': 'chocolate_bar'}, {'frequency': 'c', 'id': 257, 'synset': 'chocolate_cake.n.01', 'synonyms': ['chocolate_cake'], 'def': 'cake containing chocolate', 'name': 'chocolate_cake'}, {'frequency': 'r', 'id': 258, 'synset': 'chocolate_milk.n.01', 'synonyms': ['chocolate_milk'], 'def': 'milk flavored with chocolate syrup', 'name': 'chocolate_milk'}, {'frequency': 'r', 'id': 259, 'synset': 'chocolate_mousse.n.01', 'synonyms': ['chocolate_mousse'], 'def': 'dessert mousse made with chocolate', 'name': 'chocolate_mousse'}, {'frequency': 'f', 'id': 260, 'synset': 'choker.n.03', 'synonyms': ['choker', 'collar', 'neckband'], 'def': 'necklace that fits tightly around the neck', 'name': 'choker'}, {'frequency': 'f', 'id': 261, 'synset': 'chopping_board.n.01', 'synonyms': ['chopping_board', 'cutting_board', 'chopping_block'], 'def': 'a wooden board where meats or vegetables can be cut', 'name': 'chopping_board'}, {'frequency': 'c', 'id': 262, 'synset': 'chopstick.n.01', 'synonyms': ['chopstick'], 'def': 'one of a pair of slender sticks used as oriental tableware to eat food with', 'name': 'chopstick'}, {'frequency': 'f', 'id': 263, 'synset': 'christmas_tree.n.05', 'synonyms': ['Christmas_tree'], 'def': 'an ornamented evergreen used as a Christmas decoration', 'name': 'Christmas_tree'}, {'frequency': 'c', 'id': 264, 'synset': 'chute.n.02', 'synonyms': ['slide'], 'def': 'sloping channel through which things can descend', 'name': 'slide'}, {'frequency': 'r', 'id': 265, 'synset': 'cider.n.01', 'synonyms': ['cider', 'cyder'], 'def': 'a beverage made from juice pressed from apples', 'name': 'cider'}, {'frequency': 'r', 'id': 266, 'synset': 'cigar_box.n.01', 'synonyms': ['cigar_box'], 'def': 'a box for holding cigars', 'name': 'cigar_box'}, {'frequency': 'c', 'id': 267, 'synset': 'cigarette.n.01', 'synonyms': ['cigarette'], 'def': 'finely ground tobacco wrapped in paper; for smoking', 'name': 'cigarette'}, {'frequency': 'c', 'id': 268, 'synset': 'cigarette_case.n.01', 'synonyms': ['cigarette_case', 'cigarette_pack'], 'def': 'a small flat case for holding cigarettes', 'name': 'cigarette_case'}, {'frequency': 'f', 'id': 269, 'synset': 'cistern.n.02', 'synonyms': ['cistern', 'water_tank'], 'def': 'a tank that holds the water used to flush a toilet', 'name': 'cistern'}, {'frequency': 'r', 'id': 270, 'synset': 'clarinet.n.01', 'synonyms': ['clarinet'], 'def': 'a single-reed instrument with a straight tube', 'name': 'clarinet'}, {'frequency': 'r', 'id': 271, 'synset': 'clasp.n.01', 'synonyms': ['clasp'], 'def': 'a fastener (as a buckle or hook) that is used to hold two things together', 'name': 'clasp'}, {'frequency': 'c', 'id': 272, 'synset': 'cleansing_agent.n.01', 'synonyms': ['cleansing_agent', 'cleanser', 'cleaner'], 'def': 'a preparation used in cleaning something', 'name': 'cleansing_agent'}, {'frequency': 'r', 'id': 273, 'synset': 'clementine.n.01', 'synonyms': ['clementine'], 'def': 'a variety of mandarin orange', 'name': 'clementine'}, {'frequency': 'c', 'id': 274, 'synset': 'clip.n.03', 'synonyms': ['clip'], 'def': 'any of various small fasteners used to hold loose articles together', 'name': 'clip'}, {'frequency': 'c', 'id': 275, 'synset': 'clipboard.n.01', 'synonyms': ['clipboard'], 'def': 'a small writing board with a clip at the top for holding papers', 'name': 'clipboard'}, {'frequency': 'f', 'id': 276, 'synset': 'clock.n.01', 'synonyms': ['clock', 'timepiece', 'timekeeper'], 'def': 'a timepiece that shows the time of day', 'name': 'clock'}, {'frequency': 'f', 'id': 277, 'synset': 'clock_tower.n.01', 'synonyms': ['clock_tower'], 'def': 'a tower with a large clock visible high up on an outside face', 'name': 'clock_tower'}, {'frequency': 'c', 'id': 278, 'synset': 'clothes_hamper.n.01', 'synonyms': ['clothes_hamper', 'laundry_basket', 'clothes_basket'], 'def': 'a hamper that holds dirty clothes to be washed or wet clothes to be dried', 'name': 'clothes_hamper'}, {'frequency': 'c', 'id': 279, 'synset': 'clothespin.n.01', 'synonyms': ['clothespin', 'clothes_peg'], 'def': 'wood or plastic fastener; for holding clothes on a clothesline', 'name': 'clothespin'}, {'frequency': 'r', 'id': 280, 'synset': 'clutch_bag.n.01', 'synonyms': ['clutch_bag'], 'def': "a woman's strapless purse that is carried in the hand", 'name': 'clutch_bag'}, {'frequency': 'f', 'id': 281, 'synset': 'coaster.n.03', 'synonyms': ['coaster'], 'def': 'a covering (plate or mat) that protects the surface of a table', 'name': 'coaster'}, {'frequency': 'f', 'id': 282, 'synset': 'coat.n.01', 'synonyms': ['coat'], 'def': 'an outer garment that has sleeves and covers the body from shoulder down', 'name': 'coat'}, {'frequency': 'c', 'id': 283, 'synset': 'coat_hanger.n.01', 'synonyms': ['coat_hanger', 'clothes_hanger', 'dress_hanger'], 'def': "a hanger that is shaped like a person's shoulders", 'name': 'coat_hanger'}, {'frequency': 'r', 'id': 284, 'synset': 'coatrack.n.01', 'synonyms': ['coatrack', 'hatrack'], 'def': 'a rack with hooks for temporarily holding coats and hats', 'name': 'coatrack'}, {'frequency': 'c', 'id': 285, 'synset': 'cock.n.04', 'synonyms': ['cock', 'rooster'], 'def': 'adult male chicken', 'name': 'cock'}, {'frequency': 'c', 'id': 286, 'synset': 'coconut.n.02', 'synonyms': ['coconut', 'cocoanut'], 'def': 'large hard-shelled brown oval nut with a fibrous husk', 'name': 'coconut'}, {'frequency': 'r', 'id': 287, 'synset': 'coffee_filter.n.01', 'synonyms': ['coffee_filter'], 'def': 'filter (usually of paper) that passes the coffee and retains the coffee grounds', 'name': 'coffee_filter'}, {'frequency': 'f', 'id': 288, 'synset': 'coffee_maker.n.01', 'synonyms': ['coffee_maker', 'coffee_machine'], 'def': 'a kitchen appliance for brewing coffee automatically', 'name': 'coffee_maker'}, {'frequency': 'f', 'id': 289, 'synset': 'coffee_table.n.01', 'synonyms': ['coffee_table', 'cocktail_table'], 'def': 'low table where magazines can be placed and coffee or cocktails are served', 'name': 'coffee_table'}, {'frequency': 'c', 'id': 290, 'synset': 'coffeepot.n.01', 'synonyms': ['coffeepot'], 'def': 'tall pot in which coffee is brewed', 'name': 'coffeepot'}, {'frequency': 'r', 'id': 291, 'synset': 'coil.n.05', 'synonyms': ['coil'], 'def': 'tubing that is wound in a spiral', 'name': 'coil'}, {'frequency': 'c', 'id': 292, 'synset': 'coin.n.01', 'synonyms': ['coin'], 'def': 'a flat metal piece (usually a disc) used as money', 'name': 'coin'}, {'frequency': 'r', 'id': 293, 'synset': 'colander.n.01', 'synonyms': ['colander', 'cullender'], 'def': 'bowl-shaped strainer; used to wash or drain foods', 'name': 'colander'}, {'frequency': 'c', 'id': 294, 'synset': 'coleslaw.n.01', 'synonyms': ['coleslaw', 'slaw'], 'def': 'basically shredded cabbage', 'name': 'coleslaw'}, {'frequency': 'r', 'id': 295, 'synset': 'coloring_material.n.01', 'synonyms': ['coloring_material', 'colouring_material'], 'def': 'any material used for its color', 'name': 'coloring_material'}, {'frequency': 'r', 'id': 296, 'synset': 'combination_lock.n.01', 'synonyms': ['combination_lock'], 'def': 'lock that can be opened only by turning dials in a special sequence', 'name': 'combination_lock'}, {'frequency': 'c', 'id': 297, 'synset': 'comforter.n.04', 'synonyms': ['pacifier', 'teething_ring'], 'def': 'device used for an infant to suck or bite on', 'name': 'pacifier'}, {'frequency': 'r', 'id': 298, 'synset': 'comic_book.n.01', 'synonyms': ['comic_book'], 'def': 'a magazine devoted to comic strips', 'name': 'comic_book'}, {'frequency': 'f', 'id': 299, 'synset': 'computer_keyboard.n.01', 'synonyms': ['computer_keyboard', 'keyboard_(computer)'], 'def': 'a keyboard that is a data input device for computers', 'name': 'computer_keyboard'}, {'frequency': 'r', 'id': 300, 'synset': 'concrete_mixer.n.01', 'synonyms': ['concrete_mixer', 'cement_mixer'], 'def': 'a machine with a large revolving drum in which cement/concrete is mixed', 'name': 'concrete_mixer'}, {'frequency': 'f', 'id': 301, 'synset': 'cone.n.01', 'synonyms': ['cone', 'traffic_cone'], 'def': 'a cone-shaped object used to direct traffic', 'name': 'cone'}, {'frequency': 'f', 'id': 302, 'synset': 'control.n.09', 'synonyms': ['control', 'controller'], 'def': 'a mechanism that controls the operation of a machine', 'name': 'control'}, {'frequency': 'r', 'id': 303, 'synset': 'convertible.n.01', 'synonyms': ['convertible_(automobile)'], 'def': 'a car that has top that can be folded or removed', 'name': 'convertible_(automobile)'}, {'frequency': 'r', 'id': 304, 'synset': 'convertible.n.03', 'synonyms': ['sofa_bed'], 'def': 'a sofa that can be converted into a bed', 'name': 'sofa_bed'}, {'frequency': 'c', 'id': 305, 'synset': 'cookie.n.01', 'synonyms': ['cookie', 'cooky', 'biscuit_(cookie)'], 'def': "any of various small flat sweet cakes (`biscuit' is the British term)", 'name': 'cookie'}, {'frequency': 'r', 'id': 306, 'synset': 'cookie_jar.n.01', 'synonyms': ['cookie_jar', 'cooky_jar'], 'def': 'a jar in which cookies are kept (and sometimes money is hidden)', 'name': 'cookie_jar'}, {'frequency': 'r', 'id': 307, 'synset': 'cooking_utensil.n.01', 'synonyms': ['cooking_utensil'], 'def': 'a kitchen utensil made of material that does not melt easily; used for cooking', 'name': 'cooking_utensil'}, {'frequency': 'f', 'id': 308, 'synset': 'cooler.n.01', 'synonyms': ['cooler_(for_food)', 'ice_chest'], 'def': 'an insulated box for storing food often with ice', 'name': 'cooler_(for_food)'}, {'frequency': 'c', 'id': 309, 'synset': 'cork.n.04', 'synonyms': ['cork_(bottle_plug)', 'bottle_cork'], 'def': 'the plug in the mouth of a bottle (especially a wine bottle)', 'name': 'cork_(bottle_plug)'}, {'frequency': 'r', 'id': 310, 'synset': 'corkboard.n.01', 'synonyms': ['corkboard'], 'def': 'a sheet consisting of cork granules', 'name': 'corkboard'}, {'frequency': 'r', 'id': 311, 'synset': 'corkscrew.n.01', 'synonyms': ['corkscrew', 'bottle_screw'], 'def': 'a bottle opener that pulls corks', 'name': 'corkscrew'}, {'frequency': 'c', 'id': 312, 'synset': 'corn.n.03', 'synonyms': ['edible_corn', 'corn', 'maize'], 'def': 'ears of corn that can be prepared and served for human food', 'name': 'edible_corn'}, {'frequency': 'r', 'id': 313, 'synset': 'cornbread.n.01', 'synonyms': ['cornbread'], 'def': 'bread made primarily of cornmeal', 'name': 'cornbread'}, {'frequency': 'c', 'id': 314, 'synset': 'cornet.n.01', 'synonyms': ['cornet', 'horn', 'trumpet'], 'def': 'a brass musical instrument with a narrow tube and a flared bell and many valves', 'name': 'cornet'}, {'frequency': 'c', 'id': 315, 'synset': 'cornice.n.01', 'synonyms': ['cornice', 'valance', 'valance_board', 'pelmet'], 'def': 'a decorative framework to conceal curtain fixtures at the top of a window casing', 'name': 'cornice'}, {'frequency': 'r', 'id': 316, 'synset': 'cornmeal.n.01', 'synonyms': ['cornmeal'], 'def': 'coarsely ground corn', 'name': 'cornmeal'}, {'frequency': 'r', 'id': 317, 'synset': 'corset.n.01', 'synonyms': ['corset', 'girdle'], 'def': "a woman's close-fitting foundation garment", 'name': 'corset'}, {'frequency': 'r', 'id': 318, 'synset': 'cos.n.02', 'synonyms': ['romaine_lettuce'], 'def': 'lettuce with long dark-green leaves in a loosely packed elongated head', 'name': 'romaine_lettuce'}, {'frequency': 'c', 'id': 319, 'synset': 'costume.n.04', 'synonyms': ['costume'], 'def': 'the attire characteristic of a country or a time or a social class', 'name': 'costume'}, {'frequency': 'r', 'id': 320, 'synset': 'cougar.n.01', 'synonyms': ['cougar', 'puma', 'catamount', 'mountain_lion', 'panther'], 'def': 'large American feline resembling a lion', 'name': 'cougar'}, {'frequency': 'r', 'id': 321, 'synset': 'coverall.n.01', 'synonyms': ['coverall'], 'def': 'a loose-fitting protective garment that is worn over other clothing', 'name': 'coverall'}, {'frequency': 'r', 'id': 322, 'synset': 'cowbell.n.01', 'synonyms': ['cowbell'], 'def': 'a bell hung around the neck of cow so that the cow can be easily located', 'name': 'cowbell'}, {'frequency': 'f', 'id': 323, 'synset': 'cowboy_hat.n.01', 'synonyms': ['cowboy_hat', 'ten-gallon_hat'], 'def': 'a hat with a wide brim and a soft crown; worn by American ranch hands', 'name': 'cowboy_hat'}, {'frequency': 'r', 'id': 324, 'synset': 'crab.n.01', 'synonyms': ['crab_(animal)'], 'def': 'decapod having eyes on short stalks and a broad flattened shell and pincers', 'name': 'crab_(animal)'}, {'frequency': 'c', 'id': 325, 'synset': 'cracker.n.01', 'synonyms': ['cracker'], 'def': 'a thin crisp wafer', 'name': 'cracker'}, {'frequency': 'r', 'id': 326, 'synset': 'crape.n.01', 'synonyms': ['crape', 'crepe', 'French_pancake'], 'def': 'small very thin pancake', 'name': 'crape'}, {'frequency': 'f', 'id': 327, 'synset': 'crate.n.01', 'synonyms': ['crate'], 'def': 'a rugged box (usually made of wood); used for shipping', 'name': 'crate'}, {'frequency': 'r', 'id': 328, 'synset': 'crayon.n.01', 'synonyms': ['crayon', 'wax_crayon'], 'def': 'writing or drawing implement made of a colored stick of composition wax', 'name': 'crayon'}, {'frequency': 'r', 'id': 329, 'synset': 'cream_pitcher.n.01', 'synonyms': ['cream_pitcher'], 'def': 'a small pitcher for serving cream', 'name': 'cream_pitcher'}, {'frequency': 'r', 'id': 330, 'synset': 'credit_card.n.01', 'synonyms': ['credit_card', 'charge_card', 'debit_card'], 'def': 'a card, usually plastic, used to pay for goods and services', 'name': 'credit_card'}, {'frequency': 'c', 'id': 331, 'synset': 'crescent_roll.n.01', 'synonyms': ['crescent_roll', 'croissant'], 'def': 'very rich flaky crescent-shaped roll', 'name': 'crescent_roll'}, {'frequency': 'c', 'id': 332, 'synset': 'crib.n.01', 'synonyms': ['crib', 'cot'], 'def': 'baby bed with high sides made of slats', 'name': 'crib'}, {'frequency': 'c', 'id': 333, 'synset': 'crock.n.03', 'synonyms': ['crock_pot', 'earthenware_jar'], 'def': 'an earthen jar (made of baked clay)', 'name': 'crock_pot'}, {'frequency': 'f', 'id': 334, 'synset': 'crossbar.n.01', 'synonyms': ['crossbar'], 'def': 'a horizontal bar that goes across something', 'name': 'crossbar'}, {'frequency': 'r', 'id': 335, 'synset': 'crouton.n.01', 'synonyms': ['crouton'], 'def': 'a small piece of toasted or fried bread; served in soup or salads', 'name': 'crouton'}, {'frequency': 'r', 'id': 336, 'synset': 'crow.n.01', 'synonyms': ['crow'], 'def': 'black birds having a raucous call', 'name': 'crow'}, {'frequency': 'c', 'id': 337, 'synset': 'crown.n.04', 'synonyms': ['crown'], 'def': 'an ornamental jeweled headdress signifying sovereignty', 'name': 'crown'}, {'frequency': 'c', 'id': 338, 'synset': 'crucifix.n.01', 'synonyms': ['crucifix'], 'def': 'representation of the cross on which Jesus died', 'name': 'crucifix'}, {'frequency': 'c', 'id': 339, 'synset': 'cruise_ship.n.01', 'synonyms': ['cruise_ship', 'cruise_liner'], 'def': 'a passenger ship used commercially for pleasure cruises', 'name': 'cruise_ship'}, {'frequency': 'c', 'id': 340, 'synset': 'cruiser.n.01', 'synonyms': ['police_cruiser', 'patrol_car', 'police_car', 'squad_car'], 'def': 'a car in which policemen cruise the streets', 'name': 'police_cruiser'}, {'frequency': 'c', 'id': 341, 'synset': 'crumb.n.03', 'synonyms': ['crumb'], 'def': 'small piece of e.g. bread or cake', 'name': 'crumb'}, {'frequency': 'r', 'id': 342, 'synset': 'crutch.n.01', 'synonyms': ['crutch'], 'def': 'a wooden or metal staff that fits under the armpit and reaches to the ground', 'name': 'crutch'}, {'frequency': 'c', 'id': 343, 'synset': 'cub.n.03', 'synonyms': ['cub_(animal)'], 'def': 'the young of certain carnivorous mammals such as the bear or wolf or lion', 'name': 'cub_(animal)'}, {'frequency': 'r', 'id': 344, 'synset': 'cube.n.05', 'synonyms': ['cube', 'square_block'], 'def': 'a block in the (approximate) shape of a cube', 'name': 'cube'}, {'frequency': 'f', 'id': 345, 'synset': 'cucumber.n.02', 'synonyms': ['cucumber', 'cuke'], 'def': 'cylindrical green fruit with thin green rind and white flesh eaten as a vegetable', 'name': 'cucumber'}, {'frequency': 'c', 'id': 346, 'synset': 'cufflink.n.01', 'synonyms': ['cufflink'], 'def': 'jewelry consisting of linked buttons used to fasten the cuffs of a shirt', 'name': 'cufflink'}, {'frequency': 'f', 'id': 347, 'synset': 'cup.n.01', 'synonyms': ['cup'], 'def': 'a small open container usually used for drinking; usually has a handle', 'name': 'cup'}, {'frequency': 'c', 'id': 348, 'synset': 'cup.n.08', 'synonyms': ['trophy_cup'], 'def': 'a metal vessel with handles that is awarded as a trophy to a competition winner', 'name': 'trophy_cup'}, {'frequency': 'c', 'id': 349, 'synset': 'cupcake.n.01', 'synonyms': ['cupcake'], 'def': 'small cake baked in a muffin tin', 'name': 'cupcake'}, {'frequency': 'r', 'id': 350, 'synset': 'curler.n.01', 'synonyms': ['hair_curler', 'hair_roller', 'hair_crimper'], 'def': 'a cylindrical tube around which the hair is wound to curl it', 'name': 'hair_curler'}, {'frequency': 'r', 'id': 351, 'synset': 'curling_iron.n.01', 'synonyms': ['curling_iron'], 'def': 'a cylindrical home appliance that heats hair that has been curled around it', 'name': 'curling_iron'}, {'frequency': 'f', 'id': 352, 'synset': 'curtain.n.01', 'synonyms': ['curtain', 'drapery'], 'def': 'hanging cloth used as a blind (especially for a window)', 'name': 'curtain'}, {'frequency': 'f', 'id': 353, 'synset': 'cushion.n.03', 'synonyms': ['cushion'], 'def': 'a soft bag filled with air or padding such as feathers or foam rubber', 'name': 'cushion'}, {'frequency': 'r', 'id': 354, 'synset': 'custard.n.01', 'synonyms': ['custard'], 'def': 'sweetened mixture of milk and eggs baked or boiled or frozen', 'name': 'custard'}, {'frequency': 'c', 'id': 355, 'synset': 'cutter.n.06', 'synonyms': ['cutting_tool'], 'def': 'a cutting implement; a tool for cutting', 'name': 'cutting_tool'}, {'frequency': 'r', 'id': 356, 'synset': 'cylinder.n.04', 'synonyms': ['cylinder'], 'def': 'a cylindrical container', 'name': 'cylinder'}, {'frequency': 'r', 'id': 357, 'synset': 'cymbal.n.01', 'synonyms': ['cymbal'], 'def': 'a percussion instrument consisting of a concave brass disk', 'name': 'cymbal'}, {'frequency': 'r', 'id': 358, 'synset': 'dachshund.n.01', 'synonyms': ['dachshund', 'dachsie', 'badger_dog'], 'def': 'small long-bodied short-legged breed of dog having a short sleek coat and long drooping ears', 'name': 'dachshund'}, {'frequency': 'r', 'id': 359, 'synset': 'dagger.n.01', 'synonyms': ['dagger'], 'def': 'a short knife with a pointed blade used for piercing or stabbing', 'name': 'dagger'}, {'frequency': 'r', 'id': 360, 'synset': 'dartboard.n.01', 'synonyms': ['dartboard'], 'def': 'a circular board of wood or cork used as the target in the game of darts', 'name': 'dartboard'}, {'frequency': 'r', 'id': 361, 'synset': 'date.n.08', 'synonyms': ['date_(fruit)'], 'def': 'sweet edible fruit of the date palm with a single long woody seed', 'name': 'date_(fruit)'}, {'frequency': 'f', 'id': 362, 'synset': 'deck_chair.n.01', 'synonyms': ['deck_chair', 'beach_chair'], 'def': 'a folding chair for use outdoors; a wooden frame supports a length of canvas', 'name': 'deck_chair'}, {'frequency': 'c', 'id': 363, 'synset': 'deer.n.01', 'synonyms': ['deer', 'cervid'], 'def': "distinguished from Bovidae by the male's having solid deciduous antlers", 'name': 'deer'}, {'frequency': 'c', 'id': 364, 'synset': 'dental_floss.n.01', 'synonyms': ['dental_floss', 'floss'], 'def': 'a soft thread for cleaning the spaces between the teeth', 'name': 'dental_floss'}, {'frequency': 'f', 'id': 365, 'synset': 'desk.n.01', 'synonyms': ['desk'], 'def': 'a piece of furniture with a writing surface and usually drawers or other compartments', 'name': 'desk'}, {'frequency': 'r', 'id': 366, 'synset': 'detergent.n.01', 'synonyms': ['detergent'], 'def': 'a surface-active chemical widely used in industry and laundering', 'name': 'detergent'}, {'frequency': 'c', 'id': 367, 'synset': 'diaper.n.01', 'synonyms': ['diaper'], 'def': 'garment consisting of a folded cloth drawn up between the legs and fastened at the waist', 'name': 'diaper'}, {'frequency': 'r', 'id': 368, 'synset': 'diary.n.01', 'synonyms': ['diary', 'journal'], 'def': 'a daily written record of (usually personal) experiences and observations', 'name': 'diary'}, {'frequency': 'r', 'id': 369, 'synset': 'die.n.01', 'synonyms': ['die', 'dice'], 'def': 'a small cube with 1 to 6 spots on the six faces; used in gambling', 'name': 'die'}, {'frequency': 'r', 'id': 370, 'synset': 'dinghy.n.01', 'synonyms': ['dinghy', 'dory', 'rowboat'], 'def': 'a small boat of shallow draft with seats and oars with which it is propelled', 'name': 'dinghy'}, {'frequency': 'f', 'id': 371, 'synset': 'dining_table.n.01', 'synonyms': ['dining_table'], 'def': 'a table at which meals are served', 'name': 'dining_table'}, {'frequency': 'r', 'id': 372, 'synset': 'dinner_jacket.n.01', 'synonyms': ['tux', 'tuxedo'], 'def': 'semiformal evening dress for men', 'name': 'tux'}, {'frequency': 'c', 'id': 373, 'synset': 'dish.n.01', 'synonyms': ['dish'], 'def': 'a piece of dishware normally used as a container for holding or serving food', 'name': 'dish'}, {'frequency': 'c', 'id': 374, 'synset': 'dish.n.05', 'synonyms': ['dish_antenna'], 'def': 'directional antenna consisting of a parabolic reflector', 'name': 'dish_antenna'}, {'frequency': 'c', 'id': 375, 'synset': 'dishrag.n.01', 'synonyms': ['dishrag', 'dishcloth'], 'def': 'a cloth for washing dishes', 'name': 'dishrag'}, {'frequency': 'c', 'id': 376, 'synset': 'dishtowel.n.01', 'synonyms': ['dishtowel', 'tea_towel'], 'def': 'a towel for drying dishes', 'name': 'dishtowel'}, {'frequency': 'f', 'id': 377, 'synset': 'dishwasher.n.01', 'synonyms': ['dishwasher', 'dishwashing_machine'], 'def': 'a machine for washing dishes', 'name': 'dishwasher'}, {'frequency': 'r', 'id': 378, 'synset': 'dishwasher_detergent.n.01', 'synonyms': ['dishwasher_detergent', 'dishwashing_detergent', 'dishwashing_liquid'], 'def': 'a low-sudsing detergent designed for use in dishwashers', 'name': 'dishwasher_detergent'}, {'frequency': 'r', 'id': 379, 'synset': 'diskette.n.01', 'synonyms': ['diskette', 'floppy', 'floppy_disk'], 'def': 'a small plastic magnetic disk enclosed in a stiff envelope used to store data', 'name': 'diskette'}, {'frequency': 'c', 'id': 380, 'synset': 'dispenser.n.01', 'synonyms': ['dispenser'], 'def': 'a container so designed that the contents can be used in prescribed amounts', 'name': 'dispenser'}, {'frequency': 'c', 'id': 381, 'synset': 'dixie_cup.n.01', 'synonyms': ['Dixie_cup', 'paper_cup'], 'def': 'a disposable cup made of paper; for holding drinks', 'name': 'Dixie_cup'}, {'frequency': 'f', 'id': 382, 'synset': 'dog.n.01', 'synonyms': ['dog'], 'def': 'a common domesticated dog', 'name': 'dog'}, {'frequency': 'f', 'id': 383, 'synset': 'dog_collar.n.01', 'synonyms': ['dog_collar'], 'def': 'a collar for a dog', 'name': 'dog_collar'}, {'frequency': 'c', 'id': 384, 'synset': 'doll.n.01', 'synonyms': ['doll'], 'def': 'a toy replica of a HUMAN (NOT AN ANIMAL)', 'name': 'doll'}, {'frequency': 'r', 'id': 385, 'synset': 'dollar.n.02', 'synonyms': ['dollar', 'dollar_bill', 'one_dollar_bill'], 'def': 'a piece of paper money worth one dollar', 'name': 'dollar'}, {'frequency': 'r', 'id': 386, 'synset': 'dolphin.n.02', 'synonyms': ['dolphin'], 'def': 'any of various small toothed whales with a beaklike snout; larger than porpoises', 'name': 'dolphin'}, {'frequency': 'c', 'id': 387, 'synset': 'domestic_ass.n.01', 'synonyms': ['domestic_ass', 'donkey'], 'def': 'domestic beast of burden descended from the African wild ass; patient but stubborn', 'name': 'domestic_ass'}, {'frequency': 'r', 'id': 388, 'synset': 'domino.n.03', 'synonyms': ['eye_mask'], 'def': 'a mask covering the upper part of the face but with holes for the eyes', 'name': 'eye_mask'}, {'frequency': 'r', 'id': 389, 'synset': 'doorbell.n.01', 'synonyms': ['doorbell', 'buzzer'], 'def': 'a button at an outer door that gives a ringing or buzzing signal when pushed', 'name': 'doorbell'}, {'frequency': 'f', 'id': 390, 'synset': 'doorknob.n.01', 'synonyms': ['doorknob', 'doorhandle'], 'def': "a knob used to open a door (often called `doorhandle' in Great Britain)", 'name': 'doorknob'}, {'frequency': 'c', 'id': 391, 'synset': 'doormat.n.02', 'synonyms': ['doormat', 'welcome_mat'], 'def': 'a mat placed outside an exterior door for wiping the shoes before entering', 'name': 'doormat'}, {'frequency': 'f', 'id': 392, 'synset': 'doughnut.n.02', 'synonyms': ['doughnut', 'donut'], 'def': 'a small ring-shaped friedcake', 'name': 'doughnut'}, {'frequency': 'r', 'id': 393, 'synset': 'dove.n.01', 'synonyms': ['dove'], 'def': 'any of numerous small pigeons', 'name': 'dove'}, {'frequency': 'r', 'id': 394, 'synset': 'dragonfly.n.01', 'synonyms': ['dragonfly'], 'def': 'slender-bodied non-stinging insect having iridescent wings that are outspread at rest', 'name': 'dragonfly'}, {'frequency': 'f', 'id': 395, 'synset': 'drawer.n.01', 'synonyms': ['drawer'], 'def': 'a boxlike container in a piece of furniture; made so as to slide in and out', 'name': 'drawer'}, {'frequency': 'c', 'id': 396, 'synset': 'drawers.n.01', 'synonyms': ['underdrawers', 'boxers', 'boxershorts'], 'def': 'underpants worn by men', 'name': 'underdrawers'}, {'frequency': 'f', 'id': 397, 'synset': 'dress.n.01', 'synonyms': ['dress', 'frock'], 'def': 'a one-piece garment for a woman; has skirt and bodice', 'name': 'dress'}, {'frequency': 'c', 'id': 398, 'synset': 'dress_hat.n.01', 'synonyms': ['dress_hat', 'high_hat', 'opera_hat', 'silk_hat', 'top_hat'], 'def': "a man's hat with a tall crown; usually covered with silk or with beaver fur", 'name': 'dress_hat'}, {'frequency': 'c', 'id': 399, 'synset': 'dress_suit.n.01', 'synonyms': ['dress_suit'], 'def': 'formalwear consisting of full evening dress for men', 'name': 'dress_suit'}, {'frequency': 'c', 'id': 400, 'synset': 'dresser.n.05', 'synonyms': ['dresser'], 'def': 'a cabinet with shelves', 'name': 'dresser'}, {'frequency': 'c', 'id': 401, 'synset': 'drill.n.01', 'synonyms': ['drill'], 'def': 'a tool with a sharp rotating point for making holes in hard materials', 'name': 'drill'}, {'frequency': 'r', 'id': 402, 'synset': 'drinking_fountain.n.01', 'synonyms': ['drinking_fountain'], 'def': 'a public fountain to provide a jet of drinking water', 'name': 'drinking_fountain'}, {'frequency': 'r', 'id': 403, 'synset': 'drone.n.04', 'synonyms': ['drone'], 'def': 'an aircraft without a pilot that is operated by remote control', 'name': 'drone'}, {'frequency': 'r', 'id': 404, 'synset': 'dropper.n.01', 'synonyms': ['dropper', 'eye_dropper'], 'def': 'pipet consisting of a small tube with a vacuum bulb at one end for drawing liquid in and releasing it a drop at a time', 'name': 'dropper'}, {'frequency': 'c', 'id': 405, 'synset': 'drum.n.01', 'synonyms': ['drum_(musical_instrument)'], 'def': 'a musical percussion instrument; usually consists of a hollow cylinder with a membrane stretched across each end', 'name': 'drum_(musical_instrument)'}, {'frequency': 'r', 'id': 406, 'synset': 'drumstick.n.02', 'synonyms': ['drumstick'], 'def': 'a stick used for playing a drum', 'name': 'drumstick'}, {'frequency': 'f', 'id': 407, 'synset': 'duck.n.01', 'synonyms': ['duck'], 'def': 'small web-footed broad-billed swimming bird', 'name': 'duck'}, {'frequency': 'r', 'id': 408, 'synset': 'duckling.n.02', 'synonyms': ['duckling'], 'def': 'young duck', 'name': 'duckling'}, {'frequency': 'c', 'id': 409, 'synset': 'duct_tape.n.01', 'synonyms': ['duct_tape'], 'def': 'a wide silvery adhesive tape', 'name': 'duct_tape'}, {'frequency': 'f', 'id': 410, 'synset': 'duffel_bag.n.01', 'synonyms': ['duffel_bag', 'duffle_bag', 'duffel', 'duffle'], 'def': 'a large cylindrical bag of heavy cloth', 'name': 'duffel_bag'}, {'frequency': 'r', 'id': 411, 'synset': 'dumbbell.n.01', 'synonyms': ['dumbbell'], 'def': 'an exercising weight with two ball-like ends connected by a short handle', 'name': 'dumbbell'}, {'frequency': 'c', 'id': 412, 'synset': 'dumpster.n.01', 'synonyms': ['dumpster'], 'def': 'a container designed to receive and transport and dump waste', 'name': 'dumpster'}, {'frequency': 'r', 'id': 413, 'synset': 'dustpan.n.02', 'synonyms': ['dustpan'], 'def': 'a short-handled receptacle into which dust can be swept', 'name': 'dustpan'}, {'frequency': 'r', 'id': 414, 'synset': 'dutch_oven.n.02', 'synonyms': ['Dutch_oven'], 'def': 'iron or earthenware cooking pot; used for stews', 'name': 'Dutch_oven'}, {'frequency': 'c', 'id': 415, 'synset': 'eagle.n.01', 'synonyms': ['eagle'], 'def': 'large birds of prey noted for their broad wings and strong soaring flight', 'name': 'eagle'}, {'frequency': 'f', 'id': 416, 'synset': 'earphone.n.01', 'synonyms': ['earphone', 'earpiece', 'headphone'], 'def': 'device for listening to audio that is held over or inserted into the ear', 'name': 'earphone'}, {'frequency': 'r', 'id': 417, 'synset': 'earplug.n.01', 'synonyms': ['earplug'], 'def': 'a soft plug that is inserted into the ear canal to block sound', 'name': 'earplug'}, {'frequency': 'f', 'id': 418, 'synset': 'earring.n.01', 'synonyms': ['earring'], 'def': 'jewelry to ornament the ear', 'name': 'earring'}, {'frequency': 'c', 'id': 419, 'synset': 'easel.n.01', 'synonyms': ['easel'], 'def': "an upright tripod for displaying something (usually an artist's canvas)", 'name': 'easel'}, {'frequency': 'r', 'id': 420, 'synset': 'eclair.n.01', 'synonyms': ['eclair'], 'def': 'oblong cream puff', 'name': 'eclair'}, {'frequency': 'r', 'id': 421, 'synset': 'eel.n.01', 'synonyms': ['eel'], 'def': 'an elongate fish with fatty flesh', 'name': 'eel'}, {'frequency': 'f', 'id': 422, 'synset': 'egg.n.02', 'synonyms': ['egg', 'eggs'], 'def': 'oval reproductive body of a fowl (especially a hen) used as food', 'name': 'egg'}, {'frequency': 'r', 'id': 423, 'synset': 'egg_roll.n.01', 'synonyms': ['egg_roll', 'spring_roll'], 'def': 'minced vegetables and meat wrapped in a pancake and fried', 'name': 'egg_roll'}, {'frequency': 'c', 'id': 424, 'synset': 'egg_yolk.n.01', 'synonyms': ['egg_yolk', 'yolk_(egg)'], 'def': 'the yellow spherical part of an egg', 'name': 'egg_yolk'}, {'frequency': 'c', 'id': 425, 'synset': 'eggbeater.n.02', 'synonyms': ['eggbeater', 'eggwhisk'], 'def': 'a mixer for beating eggs or whipping cream', 'name': 'eggbeater'}, {'frequency': 'c', 'id': 426, 'synset': 'eggplant.n.01', 'synonyms': ['eggplant', 'aubergine'], 'def': 'egg-shaped vegetable having a shiny skin typically dark purple', 'name': 'eggplant'}, {'frequency': 'r', 'id': 427, 'synset': 'electric_chair.n.01', 'synonyms': ['electric_chair'], 'def': 'a chair-shaped instrument of execution by electrocution', 'name': 'electric_chair'}, {'frequency': 'f', 'id': 428, 'synset': 'electric_refrigerator.n.01', 'synonyms': ['refrigerator'], 'def': 'a refrigerator in which the coolant is pumped around by an electric motor', 'name': 'refrigerator'}, {'frequency': 'f', 'id': 429, 'synset': 'elephant.n.01', 'synonyms': ['elephant'], 'def': 'a common elephant', 'name': 'elephant'}, {'frequency': 'r', 'id': 430, 'synset': 'elk.n.01', 'synonyms': ['elk', 'moose'], 'def': 'large northern deer with enormous flattened antlers in the male', 'name': 'elk'}, {'frequency': 'c', 'id': 431, 'synset': 'envelope.n.01', 'synonyms': ['envelope'], 'def': 'a flat (usually rectangular) container for a letter, thin package, etc.', 'name': 'envelope'}, {'frequency': 'c', 'id': 432, 'synset': 'eraser.n.01', 'synonyms': ['eraser'], 'def': 'an implement used to erase something', 'name': 'eraser'}, {'frequency': 'r', 'id': 433, 'synset': 'escargot.n.01', 'synonyms': ['escargot'], 'def': 'edible snail usually served in the shell with a sauce of melted butter and garlic', 'name': 'escargot'}, {'frequency': 'r', 'id': 434, 'synset': 'eyepatch.n.01', 'synonyms': ['eyepatch'], 'def': 'a protective cloth covering for an injured eye', 'name': 'eyepatch'}, {'frequency': 'r', 'id': 435, 'synset': 'falcon.n.01', 'synonyms': ['falcon'], 'def': 'birds of prey having long pointed powerful wings adapted for swift flight', 'name': 'falcon'}, {'frequency': 'f', 'id': 436, 'synset': 'fan.n.01', 'synonyms': ['fan'], 'def': 'a device for creating a current of air by movement of a surface or surfaces', 'name': 'fan'}, {'frequency': 'f', 'id': 437, 'synset': 'faucet.n.01', 'synonyms': ['faucet', 'spigot', 'tap'], 'def': 'a regulator for controlling the flow of a liquid from a reservoir', 'name': 'faucet'}, {'frequency': 'r', 'id': 438, 'synset': 'fedora.n.01', 'synonyms': ['fedora'], 'def': 'a hat made of felt with a creased crown', 'name': 'fedora'}, {'frequency': 'r', 'id': 439, 'synset': 'ferret.n.02', 'synonyms': ['ferret'], 'def': 'domesticated albino variety of the European polecat bred for hunting rats and rabbits', 'name': 'ferret'}, {'frequency': 'c', 'id': 440, 'synset': 'ferris_wheel.n.01', 'synonyms': ['Ferris_wheel'], 'def': 'a large wheel with suspended seats that remain upright as the wheel rotates', 'name': 'Ferris_wheel'}, {'frequency': 'r', 'id': 441, 'synset': 'ferry.n.01', 'synonyms': ['ferry', 'ferryboat'], 'def': 'a boat that transports people or vehicles across a body of water and operates on a regular schedule', 'name': 'ferry'}, {'frequency': 'r', 'id': 442, 'synset': 'fig.n.04', 'synonyms': ['fig_(fruit)'], 'def': 'fleshy sweet pear-shaped yellowish or purple fruit eaten fresh or preserved or dried', 'name': 'fig_(fruit)'}, {'frequency': 'c', 'id': 443, 'synset': 'fighter.n.02', 'synonyms': ['fighter_jet', 'fighter_aircraft', 'attack_aircraft'], 'def': 'a high-speed military or naval airplane designed to destroy enemy targets', 'name': 'fighter_jet'}, {'frequency': 'f', 'id': 444, 'synset': 'figurine.n.01', 'synonyms': ['figurine'], 'def': 'a small carved or molded figure', 'name': 'figurine'}, {'frequency': 'c', 'id': 445, 'synset': 'file.n.03', 'synonyms': ['file_cabinet', 'filing_cabinet'], 'def': 'office furniture consisting of a container for keeping papers in order', 'name': 'file_cabinet'}, {'frequency': 'r', 'id': 446, 'synset': 'file.n.04', 'synonyms': ['file_(tool)'], 'def': 'a steel hand tool with small sharp teeth on some or all of its surfaces; used for smoothing wood or metal', 'name': 'file_(tool)'}, {'frequency': 'f', 'id': 447, 'synset': 'fire_alarm.n.02', 'synonyms': ['fire_alarm', 'smoke_alarm'], 'def': 'an alarm that is tripped off by fire or smoke', 'name': 'fire_alarm'}, {'frequency': 'c', 'id': 448, 'synset': 'fire_engine.n.01', 'synonyms': ['fire_engine', 'fire_truck'], 'def': 'large trucks that carry firefighters and equipment to the site of a fire', 'name': 'fire_engine'}, {'frequency': 'c', 'id': 449, 'synset': 'fire_extinguisher.n.01', 'synonyms': ['fire_extinguisher', 'extinguisher'], 'def': 'a manually operated device for extinguishing small fires', 'name': 'fire_extinguisher'}, {'frequency': 'c', 'id': 450, 'synset': 'fire_hose.n.01', 'synonyms': ['fire_hose'], 'def': 'a large hose that carries water from a fire hydrant to the site of the fire', 'name': 'fire_hose'}, {'frequency': 'f', 'id': 451, 'synset': 'fireplace.n.01', 'synonyms': ['fireplace'], 'def': 'an open recess in a wall at the base of a chimney where a fire can be built', 'name': 'fireplace'}, {'frequency': 'f', 'id': 452, 'synset': 'fireplug.n.01', 'synonyms': ['fireplug', 'fire_hydrant', 'hydrant'], 'def': 'an upright hydrant for drawing water to use in fighting a fire', 'name': 'fireplug'}, {'frequency': 'c', 'id': 453, 'synset': 'fish.n.01', 'synonyms': ['fish'], 'def': 'any of various mostly cold-blooded aquatic vertebrates usually having scales and breathing through gills', 'name': 'fish'}, {'frequency': 'r', 'id': 454, 'synset': 'fish.n.02', 'synonyms': ['fish_(food)'], 'def': 'the flesh of fish used as food', 'name': 'fish_(food)'}, {'frequency': 'r', 'id': 455, 'synset': 'fishbowl.n.02', 'synonyms': ['fishbowl', 'goldfish_bowl'], 'def': 'a transparent bowl in which small fish are kept', 'name': 'fishbowl'}, {'frequency': 'r', 'id': 456, 'synset': 'fishing_boat.n.01', 'synonyms': ['fishing_boat', 'fishing_vessel'], 'def': 'a vessel for fishing', 'name': 'fishing_boat'}, {'frequency': 'c', 'id': 457, 'synset': 'fishing_rod.n.01', 'synonyms': ['fishing_rod', 'fishing_pole'], 'def': 'a rod that is used in fishing to extend the fishing line', 'name': 'fishing_rod'}, {'frequency': 'f', 'id': 458, 'synset': 'flag.n.01', 'synonyms': ['flag'], 'def': 'emblem usually consisting of a rectangular piece of cloth of distinctive design (do not include pole)', 'name': 'flag'}, {'frequency': 'f', 'id': 459, 'synset': 'flagpole.n.02', 'synonyms': ['flagpole', 'flagstaff'], 'def': 'a tall staff or pole on which a flag is raised', 'name': 'flagpole'}, {'frequency': 'c', 'id': 460, 'synset': 'flamingo.n.01', 'synonyms': ['flamingo'], 'def': 'large pink web-footed bird with down-bent bill', 'name': 'flamingo'}, {'frequency': 'c', 'id': 461, 'synset': 'flannel.n.01', 'synonyms': ['flannel'], 'def': 'a soft light woolen fabric; used for clothing', 'name': 'flannel'}, {'frequency': 'r', 'id': 462, 'synset': 'flash.n.10', 'synonyms': ['flash', 'flashbulb'], 'def': 'a lamp for providing momentary light to take a photograph', 'name': 'flash'}, {'frequency': 'c', 'id': 463, 'synset': 'flashlight.n.01', 'synonyms': ['flashlight', 'torch'], 'def': 'a small portable battery-powered electric lamp', 'name': 'flashlight'}, {'frequency': 'r', 'id': 464, 'synset': 'fleece.n.03', 'synonyms': ['fleece'], 'def': 'a soft bulky fabric with deep pile; used chiefly for clothing', 'name': 'fleece'}, {'frequency': 'f', 'id': 465, 'synset': 'flip-flop.n.02', 'synonyms': ['flip-flop_(sandal)'], 'def': 'a backless sandal held to the foot by a thong between two toes', 'name': 'flip-flop_(sandal)'}, {'frequency': 'c', 'id': 466, 'synset': 'flipper.n.01', 'synonyms': ['flipper_(footwear)', 'fin_(footwear)'], 'def': 'a shoe to aid a person in swimming', 'name': 'flipper_(footwear)'}, {'frequency': 'f', 'id': 467, 'synset': 'flower_arrangement.n.01', 'synonyms': ['flower_arrangement', 'floral_arrangement'], 'def': 'a decorative arrangement of flowers', 'name': 'flower_arrangement'}, {'frequency': 'c', 'id': 468, 'synset': 'flute.n.02', 'synonyms': ['flute_glass', 'champagne_flute'], 'def': 'a tall narrow wineglass', 'name': 'flute_glass'}, {'frequency': 'r', 'id': 469, 'synset': 'foal.n.01', 'synonyms': ['foal'], 'def': 'a young horse', 'name': 'foal'}, {'frequency': 'c', 'id': 470, 'synset': 'folding_chair.n.01', 'synonyms': ['folding_chair'], 'def': 'a chair that can be folded flat for storage', 'name': 'folding_chair'}, {'frequency': 'c', 'id': 471, 'synset': 'food_processor.n.01', 'synonyms': ['food_processor'], 'def': 'a kitchen appliance for shredding, blending, chopping, or slicing food', 'name': 'food_processor'}, {'frequency': 'c', 'id': 472, 'synset': 'football.n.02', 'synonyms': ['football_(American)'], 'def': 'the inflated oblong ball used in playing American football', 'name': 'football_(American)'}, {'frequency': 'r', 'id': 473, 'synset': 'football_helmet.n.01', 'synonyms': ['football_helmet'], 'def': 'a padded helmet with a face mask to protect the head of football players', 'name': 'football_helmet'}, {'frequency': 'c', 'id': 474, 'synset': 'footstool.n.01', 'synonyms': ['footstool', 'footrest'], 'def': 'a low seat or a stool to rest the feet of a seated person', 'name': 'footstool'}, {'frequency': 'f', 'id': 475, 'synset': 'fork.n.01', 'synonyms': ['fork'], 'def': 'cutlery used for serving and eating food', 'name': 'fork'}, {'frequency': 'r', 'id': 476, 'synset': 'forklift.n.01', 'synonyms': ['forklift'], 'def': 'an industrial vehicle with a power operated fork in front that can be inserted under loads to lift and move them', 'name': 'forklift'}, {'frequency': 'r', 'id': 477, 'synset': 'freight_car.n.01', 'synonyms': ['freight_car'], 'def': 'a railway car that carries freight', 'name': 'freight_car'}, {'frequency': 'r', 'id': 478, 'synset': 'french_toast.n.01', 'synonyms': ['French_toast'], 'def': 'bread slice dipped in egg and milk and fried', 'name': 'French_toast'}, {'frequency': 'c', 'id': 479, 'synset': 'freshener.n.01', 'synonyms': ['freshener', 'air_freshener'], 'def': 'anything that freshens', 'name': 'freshener'}, {'frequency': 'f', 'id': 480, 'synset': 'frisbee.n.01', 'synonyms': ['frisbee'], 'def': 'a light, plastic disk propelled with a flip of the wrist for recreation or competition', 'name': 'frisbee'}, {'frequency': 'c', 'id': 481, 'synset': 'frog.n.01', 'synonyms': ['frog', 'toad', 'toad_frog'], 'def': 'a tailless stout-bodied amphibians with long hind limbs for leaping', 'name': 'frog'}, {'frequency': 'c', 'id': 482, 'synset': 'fruit_juice.n.01', 'synonyms': ['fruit_juice'], 'def': 'drink produced by squeezing or crushing fruit', 'name': 'fruit_juice'}, {'frequency': 'r', 'id': 483, 'synset': 'fruit_salad.n.01', 'synonyms': ['fruit_salad'], 'def': 'salad composed of fruits', 'name': 'fruit_salad'}, {'frequency': 'c', 'id': 484, 'synset': 'frying_pan.n.01', 'synonyms': ['frying_pan', 'frypan', 'skillet'], 'def': 'a pan used for frying foods', 'name': 'frying_pan'}, {'frequency': 'r', 'id': 485, 'synset': 'fudge.n.01', 'synonyms': ['fudge'], 'def': 'soft creamy candy', 'name': 'fudge'}, {'frequency': 'r', 'id': 486, 'synset': 'funnel.n.02', 'synonyms': ['funnel'], 'def': 'a cone-shaped utensil used to channel a substance into a container with a small mouth', 'name': 'funnel'}, {'frequency': 'c', 'id': 487, 'synset': 'futon.n.01', 'synonyms': ['futon'], 'def': 'a pad that is used for sleeping on the floor or on a raised frame', 'name': 'futon'}, {'frequency': 'r', 'id': 488, 'synset': 'gag.n.02', 'synonyms': ['gag', 'muzzle'], 'def': "restraint put into a person's mouth to prevent speaking or shouting", 'name': 'gag'}, {'frequency': 'r', 'id': 489, 'synset': 'garbage.n.03', 'synonyms': ['garbage'], 'def': 'a receptacle where waste can be discarded', 'name': 'garbage'}, {'frequency': 'c', 'id': 490, 'synset': 'garbage_truck.n.01', 'synonyms': ['garbage_truck'], 'def': 'a truck for collecting domestic refuse', 'name': 'garbage_truck'}, {'frequency': 'c', 'id': 491, 'synset': 'garden_hose.n.01', 'synonyms': ['garden_hose'], 'def': 'a hose used for watering a lawn or garden', 'name': 'garden_hose'}, {'frequency': 'c', 'id': 492, 'synset': 'gargle.n.01', 'synonyms': ['gargle', 'mouthwash'], 'def': 'a medicated solution used for gargling and rinsing the mouth', 'name': 'gargle'}, {'frequency': 'r', 'id': 493, 'synset': 'gargoyle.n.02', 'synonyms': ['gargoyle'], 'def': 'an ornament consisting of a grotesquely carved figure of a person or animal', 'name': 'gargoyle'}, {'frequency': 'c', 'id': 494, 'synset': 'garlic.n.02', 'synonyms': ['garlic', 'ail'], 'def': 'aromatic bulb used as seasoning', 'name': 'garlic'}, {'frequency': 'r', 'id': 495, 'synset': 'gasmask.n.01', 'synonyms': ['gasmask', 'respirator', 'gas_helmet'], 'def': 'a protective face mask with a filter', 'name': 'gasmask'}, {'frequency': 'r', 'id': 496, 'synset': 'gazelle.n.01', 'synonyms': ['gazelle'], 'def': 'small swift graceful antelope of Africa and Asia having lustrous eyes', 'name': 'gazelle'}, {'frequency': 'c', 'id': 497, 'synset': 'gelatin.n.02', 'synonyms': ['gelatin', 'jelly'], 'def': 'an edible jelly made with gelatin and used as a dessert or salad base or a coating for foods', 'name': 'gelatin'}, {'frequency': 'r', 'id': 498, 'synset': 'gem.n.02', 'synonyms': ['gemstone'], 'def': 'a crystalline rock that can be cut and polished for jewelry', 'name': 'gemstone'}, {'frequency': 'c', 'id': 499, 'synset': 'giant_panda.n.01', 'synonyms': ['giant_panda', 'panda', 'panda_bear'], 'def': 'large black-and-white herbivorous mammal of bamboo forests of China and Tibet', 'name': 'giant_panda'}, {'frequency': 'c', 'id': 500, 'synset': 'gift_wrap.n.01', 'synonyms': ['gift_wrap'], 'def': 'attractive wrapping paper suitable for wrapping gifts', 'name': 'gift_wrap'}, {'frequency': 'c', 'id': 501, 'synset': 'ginger.n.03', 'synonyms': ['ginger', 'gingerroot'], 'def': 'the root of the common ginger plant; used fresh as a seasoning', 'name': 'ginger'}, {'frequency': 'f', 'id': 502, 'synset': 'giraffe.n.01', 'synonyms': ['giraffe'], 'def': 'tall animal having a spotted coat and small horns and very long neck and legs', 'name': 'giraffe'}, {'frequency': 'c', 'id': 503, 'synset': 'girdle.n.02', 'synonyms': ['cincture', 'sash', 'waistband', 'waistcloth'], 'def': 'a band of material around the waist that strengthens a skirt or trousers', 'name': 'cincture'}, {'frequency': 'f', 'id': 504, 'synset': 'glass.n.02', 'synonyms': ['glass_(drink_container)', 'drinking_glass'], 'def': 'a container for holding liquids while drinking', 'name': 'glass_(drink_container)'}, {'frequency': 'c', 'id': 505, 'synset': 'globe.n.03', 'synonyms': ['globe'], 'def': 'a sphere on which a map (especially of the earth) is represented', 'name': 'globe'}, {'frequency': 'f', 'id': 506, 'synset': 'glove.n.02', 'synonyms': ['glove'], 'def': 'handwear covering the hand', 'name': 'glove'}, {'frequency': 'c', 'id': 507, 'synset': 'goat.n.01', 'synonyms': ['goat'], 'def': 'a common goat', 'name': 'goat'}, {'frequency': 'f', 'id': 508, 'synset': 'goggles.n.01', 'synonyms': ['goggles'], 'def': 'tight-fitting spectacles worn to protect the eyes', 'name': 'goggles'}, {'frequency': 'r', 'id': 509, 'synset': 'goldfish.n.01', 'synonyms': ['goldfish'], 'def': 'small golden or orange-red freshwater fishes used as pond or aquarium pets', 'name': 'goldfish'}, {'frequency': 'r', 'id': 510, 'synset': 'golf_club.n.02', 'synonyms': ['golf_club', 'golf-club'], 'def': 'golf equipment used by a golfer to hit a golf ball', 'name': 'golf_club'}, {'frequency': 'c', 'id': 511, 'synset': 'golfcart.n.01', 'synonyms': ['golfcart'], 'def': 'a small motor vehicle in which golfers can ride between shots', 'name': 'golfcart'}, {'frequency': 'r', 'id': 512, 'synset': 'gondola.n.02', 'synonyms': ['gondola_(boat)'], 'def': 'long narrow flat-bottomed boat propelled by sculling; traditionally used on canals of Venice', 'name': 'gondola_(boat)'}, {'frequency': 'c', 'id': 513, 'synset': 'goose.n.01', 'synonyms': ['goose'], 'def': 'loud, web-footed long-necked aquatic birds usually larger than ducks', 'name': 'goose'}, {'frequency': 'r', 'id': 514, 'synset': 'gorilla.n.01', 'synonyms': ['gorilla'], 'def': 'largest ape', 'name': 'gorilla'}, {'frequency': 'r', 'id': 515, 'synset': 'gourd.n.02', 'synonyms': ['gourd'], 'def': 'any of numerous inedible fruits with hard rinds', 'name': 'gourd'}, {'frequency': 'r', 'id': 516, 'synset': 'gown.n.04', 'synonyms': ['surgical_gown', 'scrubs_(surgical_clothing)'], 'def': 'protective garment worn by surgeons during operations', 'name': 'surgical_gown'}, {'frequency': 'f', 'id': 517, 'synset': 'grape.n.01', 'synonyms': ['grape'], 'def': 'any of various juicy fruit with green or purple skins; grow in clusters', 'name': 'grape'}, {'frequency': 'r', 'id': 518, 'synset': 'grasshopper.n.01', 'synonyms': ['grasshopper'], 'def': 'plant-eating insect with hind legs adapted for leaping', 'name': 'grasshopper'}, {'frequency': 'c', 'id': 519, 'synset': 'grater.n.01', 'synonyms': ['grater'], 'def': 'utensil with sharp perforations for shredding foods (as vegetables or cheese)', 'name': 'grater'}, {'frequency': 'c', 'id': 520, 'synset': 'gravestone.n.01', 'synonyms': ['gravestone', 'headstone', 'tombstone'], 'def': 'a stone that is used to mark a grave', 'name': 'gravestone'}, {'frequency': 'r', 'id': 521, 'synset': 'gravy_boat.n.01', 'synonyms': ['gravy_boat', 'gravy_holder'], 'def': 'a dish (often boat-shaped) for serving gravy or sauce', 'name': 'gravy_boat'}, {'frequency': 'c', 'id': 522, 'synset': 'green_bean.n.02', 'synonyms': ['green_bean'], 'def': 'a common bean plant cultivated for its slender green edible pods', 'name': 'green_bean'}, {'frequency': 'c', 'id': 523, 'synset': 'green_onion.n.01', 'synonyms': ['green_onion', 'spring_onion', 'scallion'], 'def': 'a young onion before the bulb has enlarged', 'name': 'green_onion'}, {'frequency': 'r', 'id': 524, 'synset': 'griddle.n.01', 'synonyms': ['griddle'], 'def': 'cooking utensil consisting of a flat heated surface on which food is cooked', 'name': 'griddle'}, {'frequency': 'r', 'id': 525, 'synset': 'grillroom.n.01', 'synonyms': ['grillroom', 'grill_(restaurant)'], 'def': 'a restaurant where food is cooked on a grill', 'name': 'grillroom'}, {'frequency': 'r', 'id': 526, 'synset': 'grinder.n.04', 'synonyms': ['grinder_(tool)'], 'def': 'a machine tool that polishes metal', 'name': 'grinder_(tool)'}, {'frequency': 'r', 'id': 527, 'synset': 'grits.n.01', 'synonyms': ['grits', 'hominy_grits'], 'def': 'coarsely ground corn boiled as a breakfast dish', 'name': 'grits'}, {'frequency': 'c', 'id': 528, 'synset': 'grizzly.n.01', 'synonyms': ['grizzly', 'grizzly_bear'], 'def': 'powerful brownish-yellow bear of the uplands of western North America', 'name': 'grizzly'}, {'frequency': 'c', 'id': 529, 'synset': 'grocery_bag.n.01', 'synonyms': ['grocery_bag'], 'def': "a sack for holding customer's groceries", 'name': 'grocery_bag'}, {'frequency': 'r', 'id': 530, 'synset': 'guacamole.n.01', 'synonyms': ['guacamole'], 'def': 'a dip made of mashed avocado mixed with chopped onions and other seasonings', 'name': 'guacamole'}, {'frequency': 'f', 'id': 531, 'synset': 'guitar.n.01', 'synonyms': ['guitar'], 'def': 'a stringed instrument usually having six strings; played by strumming or plucking', 'name': 'guitar'}, {'frequency': 'c', 'id': 532, 'synset': 'gull.n.02', 'synonyms': ['gull', 'seagull'], 'def': 'mostly white aquatic bird having long pointed wings and short legs', 'name': 'gull'}, {'frequency': 'c', 'id': 533, 'synset': 'gun.n.01', 'synonyms': ['gun'], 'def': 'a weapon that discharges a bullet at high velocity from a metal tube', 'name': 'gun'}, {'frequency': 'r', 'id': 534, 'synset': 'hair_spray.n.01', 'synonyms': ['hair_spray'], 'def': 'substance sprayed on the hair to hold it in place', 'name': 'hair_spray'}, {'frequency': 'c', 'id': 535, 'synset': 'hairbrush.n.01', 'synonyms': ['hairbrush'], 'def': "a brush used to groom a person's hair", 'name': 'hairbrush'}, {'frequency': 'c', 'id': 536, 'synset': 'hairnet.n.01', 'synonyms': ['hairnet'], 'def': 'a small net that someone wears over their hair to keep it in place', 'name': 'hairnet'}, {'frequency': 'c', 'id': 537, 'synset': 'hairpin.n.01', 'synonyms': ['hairpin'], 'def': "a double pronged pin used to hold women's hair in place", 'name': 'hairpin'}, {'frequency': 'f', 'id': 538, 'synset': 'ham.n.01', 'synonyms': ['ham', 'jambon', 'gammon'], 'def': 'meat cut from the thigh of a hog (usually smoked)', 'name': 'ham'}, {'frequency': 'c', 'id': 539, 'synset': 'hamburger.n.01', 'synonyms': ['hamburger', 'beefburger', 'burger'], 'def': 'a sandwich consisting of a patty of minced beef served on a bun', 'name': 'hamburger'}, {'frequency': 'c', 'id': 540, 'synset': 'hammer.n.02', 'synonyms': ['hammer'], 'def': 'a hand tool with a heavy head and a handle; used to deliver an impulsive force by striking', 'name': 'hammer'}, {'frequency': 'r', 'id': 541, 'synset': 'hammock.n.02', 'synonyms': ['hammock'], 'def': 'a hanging bed of canvas or rope netting (usually suspended between two trees)', 'name': 'hammock'}, {'frequency': 'r', 'id': 542, 'synset': 'hamper.n.02', 'synonyms': ['hamper'], 'def': 'a basket usually with a cover', 'name': 'hamper'}, {'frequency': 'r', 'id': 543, 'synset': 'hamster.n.01', 'synonyms': ['hamster'], 'def': 'short-tailed burrowing rodent with large cheek pouches', 'name': 'hamster'}, {'frequency': 'c', 'id': 544, 'synset': 'hand_blower.n.01', 'synonyms': ['hair_dryer'], 'def': 'a hand-held electric blower that can blow warm air onto the hair', 'name': 'hair_dryer'}, {'frequency': 'r', 'id': 545, 'synset': 'hand_glass.n.01', 'synonyms': ['hand_glass', 'hand_mirror'], 'def': 'a mirror intended to be held in the hand', 'name': 'hand_glass'}, {'frequency': 'f', 'id': 546, 'synset': 'hand_towel.n.01', 'synonyms': ['hand_towel', 'face_towel'], 'def': 'a small towel used to dry the hands or face', 'name': 'hand_towel'}, {'frequency': 'c', 'id': 547, 'synset': 'handcart.n.01', 'synonyms': ['handcart', 'pushcart', 'hand_truck'], 'def': 'wheeled vehicle that can be pushed by a person', 'name': 'handcart'}, {'frequency': 'r', 'id': 548, 'synset': 'handcuff.n.01', 'synonyms': ['handcuff'], 'def': 'shackle that consists of a metal loop that can be locked around the wrist', 'name': 'handcuff'}, {'frequency': 'c', 'id': 549, 'synset': 'handkerchief.n.01', 'synonyms': ['handkerchief'], 'def': 'a square piece of cloth used for wiping the eyes or nose or as a costume accessory', 'name': 'handkerchief'}, {'frequency': 'f', 'id': 550, 'synset': 'handle.n.01', 'synonyms': ['handle', 'grip', 'handgrip'], 'def': 'the appendage to an object that is designed to be held in order to use or move it', 'name': 'handle'}, {'frequency': 'r', 'id': 551, 'synset': 'handsaw.n.01', 'synonyms': ['handsaw', "carpenter's_saw"], 'def': 'a saw used with one hand for cutting wood', 'name': 'handsaw'}, {'frequency': 'r', 'id': 552, 'synset': 'hardback.n.01', 'synonyms': ['hardback_book', 'hardcover_book'], 'def': 'a book with cardboard or cloth or leather covers', 'name': 'hardback_book'}, {'frequency': 'r', 'id': 553, 'synset': 'harmonium.n.01', 'synonyms': ['harmonium', 'organ_(musical_instrument)', 'reed_organ_(musical_instrument)'], 'def': 'a free-reed instrument in which air is forced through the reeds by bellows', 'name': 'harmonium'}, {'frequency': 'f', 'id': 554, 'synset': 'hat.n.01', 'synonyms': ['hat'], 'def': 'headwear that protects the head from bad weather, sun, or worn for fashion', 'name': 'hat'}, {'frequency': 'r', 'id': 555, 'synset': 'hatbox.n.01', 'synonyms': ['hatbox'], 'def': 'a round piece of luggage for carrying hats', 'name': 'hatbox'}, {'frequency': 'r', 'id': 556, 'synset': 'hatch.n.03', 'synonyms': ['hatch'], 'def': 'a movable barrier covering a hatchway', 'name': 'hatch'}, {'frequency': 'c', 'id': 557, 'synset': 'head_covering.n.01', 'synonyms': ['veil'], 'def': 'a garment that covers the head and face', 'name': 'veil'}, {'frequency': 'f', 'id': 558, 'synset': 'headband.n.01', 'synonyms': ['headband'], 'def': 'a band worn around or over the head', 'name': 'headband'}, {'frequency': 'f', 'id': 559, 'synset': 'headboard.n.01', 'synonyms': ['headboard'], 'def': 'a vertical board or panel forming the head of a bedstead', 'name': 'headboard'}, {'frequency': 'f', 'id': 560, 'synset': 'headlight.n.01', 'synonyms': ['headlight', 'headlamp'], 'def': 'a powerful light with reflector; attached to the front of an automobile or locomotive', 'name': 'headlight'}, {'frequency': 'c', 'id': 561, 'synset': 'headscarf.n.01', 'synonyms': ['headscarf'], 'def': 'a kerchief worn over the head and tied under the chin', 'name': 'headscarf'}, {'frequency': 'r', 'id': 562, 'synset': 'headset.n.01', 'synonyms': ['headset'], 'def': 'receiver consisting of a pair of headphones', 'name': 'headset'}, {'frequency': 'c', 'id': 563, 'synset': 'headstall.n.01', 'synonyms': ['headstall_(for_horses)', 'headpiece_(for_horses)'], 'def': "the band that is the part of a bridle that fits around a horse's head", 'name': 'headstall_(for_horses)'}, {'frequency': 'r', 'id': 564, 'synset': 'hearing_aid.n.02', 'synonyms': ['hearing_aid'], 'def': 'an acoustic device used to direct sound to the ear of a hearing-impaired person', 'name': 'hearing_aid'}, {'frequency': 'c', 'id': 565, 'synset': 'heart.n.02', 'synonyms': ['heart'], 'def': 'a muscular organ; its contractions move the blood through the body', 'name': 'heart'}, {'frequency': 'c', 'id': 566, 'synset': 'heater.n.01', 'synonyms': ['heater', 'warmer'], 'def': 'device that heats water or supplies warmth to a room', 'name': 'heater'}, {'frequency': 'c', 'id': 567, 'synset': 'helicopter.n.01', 'synonyms': ['helicopter'], 'def': 'an aircraft without wings that obtains its lift from the rotation of overhead blades', 'name': 'helicopter'}, {'frequency': 'f', 'id': 568, 'synset': 'helmet.n.02', 'synonyms': ['helmet'], 'def': 'a protective headgear made of hard material to resist blows', 'name': 'helmet'}, {'frequency': 'r', 'id': 569, 'synset': 'heron.n.02', 'synonyms': ['heron'], 'def': 'grey or white wading bird with long neck and long legs and (usually) long bill', 'name': 'heron'}, {'frequency': 'c', 'id': 570, 'synset': 'highchair.n.01', 'synonyms': ['highchair', 'feeding_chair'], 'def': 'a chair for feeding a very young child', 'name': 'highchair'}, {'frequency': 'f', 'id': 571, 'synset': 'hinge.n.01', 'synonyms': ['hinge'], 'def': 'a joint that holds two parts together so that one can swing relative to the other', 'name': 'hinge'}, {'frequency': 'r', 'id': 572, 'synset': 'hippopotamus.n.01', 'synonyms': ['hippopotamus'], 'def': 'massive thick-skinned animal living in or around rivers of tropical Africa', 'name': 'hippopotamus'}, {'frequency': 'r', 'id': 573, 'synset': 'hockey_stick.n.01', 'synonyms': ['hockey_stick'], 'def': 'sports implement consisting of a stick used by hockey players to move the puck', 'name': 'hockey_stick'}, {'frequency': 'c', 'id': 574, 'synset': 'hog.n.03', 'synonyms': ['hog', 'pig'], 'def': 'domestic swine', 'name': 'hog'}, {'frequency': 'f', 'id': 575, 'synset': 'home_plate.n.01', 'synonyms': ['home_plate_(baseball)', 'home_base_(baseball)'], 'def': '(baseball) a rubber slab where the batter stands; it must be touched by a base runner in order to score', 'name': 'home_plate_(baseball)'}, {'frequency': 'c', 'id': 576, 'synset': 'honey.n.01', 'synonyms': ['honey'], 'def': 'a sweet yellow liquid produced by bees', 'name': 'honey'}, {'frequency': 'f', 'id': 577, 'synset': 'hood.n.06', 'synonyms': ['fume_hood', 'exhaust_hood'], 'def': 'metal covering leading to a vent that exhausts smoke or fumes', 'name': 'fume_hood'}, {'frequency': 'f', 'id': 578, 'synset': 'hook.n.05', 'synonyms': ['hook'], 'def': 'a curved or bent implement for suspending or pulling something', 'name': 'hook'}, {'frequency': 'f', 'id': 579, 'synset': 'horse.n.01', 'synonyms': ['horse'], 'def': 'a common horse', 'name': 'horse'}, {'frequency': 'f', 'id': 580, 'synset': 'hose.n.03', 'synonyms': ['hose', 'hosepipe'], 'def': 'a flexible pipe for conveying a liquid or gas', 'name': 'hose'}, {'frequency': 'r', 'id': 581, 'synset': 'hot-air_balloon.n.01', 'synonyms': ['hot-air_balloon'], 'def': 'balloon for travel through the air in a basket suspended below a large bag of heated air', 'name': 'hot-air_balloon'}, {'frequency': 'r', 'id': 582, 'synset': 'hot_plate.n.01', 'synonyms': ['hotplate'], 'def': 'a portable electric appliance for heating or cooking or keeping food warm', 'name': 'hotplate'}, {'frequency': 'c', 'id': 583, 'synset': 'hot_sauce.n.01', 'synonyms': ['hot_sauce'], 'def': 'a pungent peppery sauce', 'name': 'hot_sauce'}, {'frequency': 'r', 'id': 584, 'synset': 'hourglass.n.01', 'synonyms': ['hourglass'], 'def': 'a sandglass timer that runs for sixty minutes', 'name': 'hourglass'}, {'frequency': 'r', 'id': 585, 'synset': 'houseboat.n.01', 'synonyms': ['houseboat'], 'def': 'a barge that is designed and equipped for use as a dwelling', 'name': 'houseboat'}, {'frequency': 'r', 'id': 586, 'synset': 'hummingbird.n.01', 'synonyms': ['hummingbird'], 'def': 'tiny American bird having brilliant iridescent plumage and long slender bills', 'name': 'hummingbird'}, {'frequency': 'r', 'id': 587, 'synset': 'hummus.n.01', 'synonyms': ['hummus', 'humus', 'hommos', 'hoummos', 'humous'], 'def': 'a thick spread made from mashed chickpeas', 'name': 'hummus'}, {'frequency': 'c', 'id': 588, 'synset': 'ice_bear.n.01', 'synonyms': ['polar_bear'], 'def': 'white bear of Arctic regions', 'name': 'polar_bear'}, {'frequency': 'c', 'id': 589, 'synset': 'ice_cream.n.01', 'synonyms': ['icecream'], 'def': 'frozen dessert containing cream and sugar and flavoring', 'name': 'icecream'}, {'frequency': 'r', 'id': 590, 'synset': 'ice_lolly.n.01', 'synonyms': ['popsicle'], 'def': 'ice cream or water ice on a small wooden stick', 'name': 'popsicle'}, {'frequency': 'c', 'id': 591, 'synset': 'ice_maker.n.01', 'synonyms': ['ice_maker'], 'def': 'an appliance included in some electric refrigerators for making ice cubes', 'name': 'ice_maker'}, {'frequency': 'r', 'id': 592, 'synset': 'ice_pack.n.01', 'synonyms': ['ice_pack', 'ice_bag'], 'def': 'a waterproof bag filled with ice: applied to the body (especially the head) to cool or reduce swelling', 'name': 'ice_pack'}, {'frequency': 'r', 'id': 593, 'synset': 'ice_skate.n.01', 'synonyms': ['ice_skate'], 'def': 'skate consisting of a boot with a steel blade fitted to the sole', 'name': 'ice_skate'}, {'frequency': 'r', 'id': 594, 'synset': 'ice_tea.n.01', 'synonyms': ['ice_tea', 'iced_tea'], 'def': 'strong tea served over ice', 'name': 'ice_tea'}, {'frequency': 'c', 'id': 595, 'synset': 'igniter.n.01', 'synonyms': ['igniter', 'ignitor', 'lighter'], 'def': 'a substance or device used to start a fire', 'name': 'igniter'}, {'frequency': 'r', 'id': 596, 'synset': 'incense.n.01', 'synonyms': ['incense'], 'def': 'a substance that produces a fragrant odor when burned', 'name': 'incense'}, {'frequency': 'r', 'id': 597, 'synset': 'inhaler.n.01', 'synonyms': ['inhaler', 'inhalator'], 'def': 'a dispenser that produces a chemical vapor to be inhaled through mouth or nose', 'name': 'inhaler'}, {'frequency': 'c', 'id': 598, 'synset': 'ipod.n.01', 'synonyms': ['iPod'], 'def': 'a pocket-sized device used to play music files', 'name': 'iPod'}, {'frequency': 'c', 'id': 599, 'synset': 'iron.n.04', 'synonyms': ['iron_(for_clothing)', 'smoothing_iron_(for_clothing)'], 'def': 'home appliance consisting of a flat metal base that is heated and used to smooth cloth', 'name': 'iron_(for_clothing)'}, {'frequency': 'r', 'id': 600, 'synset': 'ironing_board.n.01', 'synonyms': ['ironing_board'], 'def': 'narrow padded board on collapsible supports; used for ironing clothes', 'name': 'ironing_board'}, {'frequency': 'f', 'id': 601, 'synset': 'jacket.n.01', 'synonyms': ['jacket'], 'def': 'a waist-length coat', 'name': 'jacket'}, {'frequency': 'r', 'id': 602, 'synset': 'jam.n.01', 'synonyms': ['jam'], 'def': 'preserve of crushed fruit', 'name': 'jam'}, {'frequency': 'f', 'id': 603, 'synset': 'jean.n.01', 'synonyms': ['jean', 'blue_jean', 'denim'], 'def': '(usually plural) close-fitting trousers of heavy denim for manual work or casual wear', 'name': 'jean'}, {'frequency': 'c', 'id': 604, 'synset': 'jeep.n.01', 'synonyms': ['jeep', 'landrover'], 'def': 'a car suitable for traveling over rough terrain', 'name': 'jeep'}, {'frequency': 'r', 'id': 605, 'synset': 'jelly_bean.n.01', 'synonyms': ['jelly_bean', 'jelly_egg'], 'def': 'sugar-glazed jellied candy', 'name': 'jelly_bean'}, {'frequency': 'f', 'id': 606, 'synset': 'jersey.n.03', 'synonyms': ['jersey', 'T-shirt', 'tee_shirt'], 'def': 'a close-fitting pullover shirt', 'name': 'jersey'}, {'frequency': 'c', 'id': 607, 'synset': 'jet.n.01', 'synonyms': ['jet_plane', 'jet-propelled_plane'], 'def': 'an airplane powered by one or more jet engines', 'name': 'jet_plane'}, {'frequency': 'c', 'id': 608, 'synset': 'jewelry.n.01', 'synonyms': ['jewelry', 'jewellery'], 'def': 'an adornment (as a bracelet or ring or necklace) made of precious metals and set with gems (or imitation gems)', 'name': 'jewelry'}, {'frequency': 'r', 'id': 609, 'synset': 'joystick.n.02', 'synonyms': ['joystick'], 'def': 'a control device for computers consisting of a vertical handle that can move freely in two directions', 'name': 'joystick'}, {'frequency': 'r', 'id': 610, 'synset': 'jump_suit.n.01', 'synonyms': ['jumpsuit'], 'def': "one-piece garment fashioned after a parachutist's uniform", 'name': 'jumpsuit'}, {'frequency': 'c', 'id': 611, 'synset': 'kayak.n.01', 'synonyms': ['kayak'], 'def': 'a small canoe consisting of a light frame made watertight with animal skins', 'name': 'kayak'}, {'frequency': 'r', 'id': 612, 'synset': 'keg.n.02', 'synonyms': ['keg'], 'def': 'small cask or barrel', 'name': 'keg'}, {'frequency': 'r', 'id': 613, 'synset': 'kennel.n.01', 'synonyms': ['kennel', 'doghouse'], 'def': 'outbuilding that serves as a shelter for a dog', 'name': 'kennel'}, {'frequency': 'c', 'id': 614, 'synset': 'kettle.n.01', 'synonyms': ['kettle', 'boiler'], 'def': 'a metal pot for stewing or boiling; usually has a lid', 'name': 'kettle'}, {'frequency': 'f', 'id': 615, 'synset': 'key.n.01', 'synonyms': ['key'], 'def': 'metal instrument used to unlock a lock', 'name': 'key'}, {'frequency': 'r', 'id': 616, 'synset': 'keycard.n.01', 'synonyms': ['keycard'], 'def': 'a plastic card used to gain access typically to a door', 'name': 'keycard'}, {'frequency': 'r', 'id': 617, 'synset': 'kilt.n.01', 'synonyms': ['kilt'], 'def': 'a knee-length pleated tartan skirt worn by men as part of the traditional dress in the Highlands of northern Scotland', 'name': 'kilt'}, {'frequency': 'c', 'id': 618, 'synset': 'kimono.n.01', 'synonyms': ['kimono'], 'def': 'a loose robe; imitated from robes originally worn by Japanese', 'name': 'kimono'}, {'frequency': 'f', 'id': 619, 'synset': 'kitchen_sink.n.01', 'synonyms': ['kitchen_sink'], 'def': 'a sink in a kitchen', 'name': 'kitchen_sink'}, {'frequency': 'c', 'id': 620, 'synset': 'kitchen_table.n.01', 'synonyms': ['kitchen_table'], 'def': 'a table in the kitchen', 'name': 'kitchen_table'}, {'frequency': 'f', 'id': 621, 'synset': 'kite.n.03', 'synonyms': ['kite'], 'def': 'plaything consisting of a light frame covered with tissue paper; flown in wind at end of a string', 'name': 'kite'}, {'frequency': 'c', 'id': 622, 'synset': 'kitten.n.01', 'synonyms': ['kitten', 'kitty'], 'def': 'young domestic cat', 'name': 'kitten'}, {'frequency': 'c', 'id': 623, 'synset': 'kiwi.n.03', 'synonyms': ['kiwi_fruit'], 'def': 'fuzzy brown egg-shaped fruit with slightly tart green flesh', 'name': 'kiwi_fruit'}, {'frequency': 'f', 'id': 624, 'synset': 'knee_pad.n.01', 'synonyms': ['knee_pad'], 'def': 'protective garment consisting of a pad worn by football or baseball or hockey players', 'name': 'knee_pad'}, {'frequency': 'f', 'id': 625, 'synset': 'knife.n.01', 'synonyms': ['knife'], 'def': 'tool with a blade and point used as a cutting instrument', 'name': 'knife'}, {'frequency': 'r', 'id': 626, 'synset': 'knight.n.02', 'synonyms': ['knight_(chess_piece)', 'horse_(chess_piece)'], 'def': 'a chess game piece shaped to resemble the head of a horse', 'name': 'knight_(chess_piece)'}, {'frequency': 'r', 'id': 627, 'synset': 'knitting_needle.n.01', 'synonyms': ['knitting_needle'], 'def': 'needle consisting of a slender rod with pointed ends; usually used in pairs', 'name': 'knitting_needle'}, {'frequency': 'f', 'id': 628, 'synset': 'knob.n.02', 'synonyms': ['knob'], 'def': 'a round handle often found on a door', 'name': 'knob'}, {'frequency': 'r', 'id': 629, 'synset': 'knocker.n.05', 'synonyms': ['knocker_(on_a_door)', 'doorknocker'], 'def': 'a device (usually metal and ornamental) attached by a hinge to a door', 'name': 'knocker_(on_a_door)'}, {'frequency': 'r', 'id': 630, 'synset': 'koala.n.01', 'synonyms': ['koala', 'koala_bear'], 'def': 'sluggish tailless Australian marsupial with grey furry ears and coat', 'name': 'koala'}, {'frequency': 'r', 'id': 631, 'synset': 'lab_coat.n.01', 'synonyms': ['lab_coat', 'laboratory_coat'], 'def': 'a light coat worn to protect clothing from substances used while working in a laboratory', 'name': 'lab_coat'}, {'frequency': 'f', 'id': 632, 'synset': 'ladder.n.01', 'synonyms': ['ladder'], 'def': 'steps consisting of two parallel members connected by rungs', 'name': 'ladder'}, {'frequency': 'c', 'id': 633, 'synset': 'ladle.n.01', 'synonyms': ['ladle'], 'def': 'a spoon-shaped vessel with a long handle frequently used to transfer liquids', 'name': 'ladle'}, {'frequency': 'r', 'id': 634, 'synset': 'ladybug.n.01', 'synonyms': ['ladybug', 'ladybeetle', 'ladybird_beetle'], 'def': 'small round bright-colored and spotted beetle, typically red and black', 'name': 'ladybug'}, {'frequency': 'c', 'id': 635, 'synset': 'lamb.n.01', 'synonyms': ['lamb_(animal)'], 'def': 'young sheep', 'name': 'lamb_(animal)'}, {'frequency': 'r', 'id': 636, 'synset': 'lamb_chop.n.01', 'synonyms': ['lamb-chop', 'lambchop'], 'def': 'chop cut from a lamb', 'name': 'lamb-chop'}, {'frequency': 'f', 'id': 637, 'synset': 'lamp.n.02', 'synonyms': ['lamp'], 'def': 'a piece of furniture holding one or more electric light bulbs', 'name': 'lamp'}, {'frequency': 'f', 'id': 638, 'synset': 'lamppost.n.01', 'synonyms': ['lamppost'], 'def': 'a metal post supporting an outdoor lamp (such as a streetlight)', 'name': 'lamppost'}, {'frequency': 'f', 'id': 639, 'synset': 'lampshade.n.01', 'synonyms': ['lampshade'], 'def': 'a protective ornamental shade used to screen a light bulb from direct view', 'name': 'lampshade'}, {'frequency': 'c', 'id': 640, 'synset': 'lantern.n.01', 'synonyms': ['lantern'], 'def': 'light in a transparent protective case', 'name': 'lantern'}, {'frequency': 'f', 'id': 641, 'synset': 'lanyard.n.02', 'synonyms': ['lanyard', 'laniard'], 'def': 'a cord worn around the neck to hold a knife or whistle, etc.', 'name': 'lanyard'}, {'frequency': 'f', 'id': 642, 'synset': 'laptop.n.01', 'synonyms': ['laptop_computer', 'notebook_computer'], 'def': 'a portable computer small enough to use in your lap', 'name': 'laptop_computer'}, {'frequency': 'r', 'id': 643, 'synset': 'lasagna.n.01', 'synonyms': ['lasagna', 'lasagne'], 'def': 'baked dish of layers of lasagna pasta with sauce and cheese and meat or vegetables', 'name': 'lasagna'}, {'frequency': 'c', 'id': 644, 'synset': 'latch.n.02', 'synonyms': ['latch'], 'def': 'a bar that can be lowered or slid into a groove to fasten a door or gate', 'name': 'latch'}, {'frequency': 'r', 'id': 645, 'synset': 'lawn_mower.n.01', 'synonyms': ['lawn_mower'], 'def': 'garden tool for mowing grass on lawns', 'name': 'lawn_mower'}, {'frequency': 'r', 'id': 646, 'synset': 'leather.n.01', 'synonyms': ['leather'], 'def': 'an animal skin made smooth and flexible by removing the hair and then tanning', 'name': 'leather'}, {'frequency': 'c', 'id': 647, 'synset': 'legging.n.01', 'synonyms': ['legging_(clothing)', 'leging_(clothing)', 'leg_covering'], 'def': 'a garment covering the leg (usually extending from the knee to the ankle)', 'name': 'legging_(clothing)'}, {'frequency': 'c', 'id': 648, 'synset': 'lego.n.01', 'synonyms': ['Lego', 'Lego_set'], 'def': "a child's plastic construction set for making models from blocks", 'name': 'Lego'}, {'frequency': 'f', 'id': 649, 'synset': 'lemon.n.01', 'synonyms': ['lemon'], 'def': 'yellow oval fruit with juicy acidic flesh', 'name': 'lemon'}, {'frequency': 'r', 'id': 650, 'synset': 'lemonade.n.01', 'synonyms': ['lemonade'], 'def': 'sweetened beverage of diluted lemon juice', 'name': 'lemonade'}, {'frequency': 'f', 'id': 651, 'synset': 'lettuce.n.02', 'synonyms': ['lettuce'], 'def': 'leafy plant commonly eaten in salad or on sandwiches', 'name': 'lettuce'}, {'frequency': 'f', 'id': 652, 'synset': 'license_plate.n.01', 'synonyms': ['license_plate', 'numberplate'], 'def': "a plate mounted on the front and back of car and bearing the car's registration number", 'name': 'license_plate'}, {'frequency': 'f', 'id': 653, 'synset': 'life_buoy.n.01', 'synonyms': ['life_buoy', 'lifesaver', 'life_belt', 'life_ring'], 'def': 'a ring-shaped life preserver used to prevent drowning (NOT a life-jacket or vest)', 'name': 'life_buoy'}, {'frequency': 'f', 'id': 654, 'synset': 'life_jacket.n.01', 'synonyms': ['life_jacket', 'life_vest'], 'def': 'life preserver consisting of a sleeveless jacket of buoyant or inflatable design', 'name': 'life_jacket'}, {'frequency': 'f', 'id': 655, 'synset': 'light_bulb.n.01', 'synonyms': ['lightbulb'], 'def': 'glass bulb or tube shaped electric device that emits light (DO NOT MARK LAMPS AS A WHOLE)', 'name': 'lightbulb'}, {'frequency': 'r', 'id': 656, 'synset': 'lightning_rod.n.02', 'synonyms': ['lightning_rod', 'lightning_conductor'], 'def': 'a metallic conductor that is attached to a high point and leads to the ground', 'name': 'lightning_rod'}, {'frequency': 'c', 'id': 657, 'synset': 'lime.n.06', 'synonyms': ['lime'], 'def': 'the green acidic fruit of any of various lime trees', 'name': 'lime'}, {'frequency': 'r', 'id': 658, 'synset': 'limousine.n.01', 'synonyms': ['limousine'], 'def': 'long luxurious car; usually driven by a chauffeur', 'name': 'limousine'}, {'frequency': 'r', 'id': 659, 'synset': 'linen.n.02', 'synonyms': ['linen_paper'], 'def': 'a high-quality paper made of linen fibers or with a linen finish', 'name': 'linen_paper'}, {'frequency': 'c', 'id': 660, 'synset': 'lion.n.01', 'synonyms': ['lion'], 'def': 'large gregarious predatory cat of Africa and India', 'name': 'lion'}, {'frequency': 'c', 'id': 661, 'synset': 'lip_balm.n.01', 'synonyms': ['lip_balm'], 'def': 'a balm applied to the lips', 'name': 'lip_balm'}, {'frequency': 'c', 'id': 662, 'synset': 'lipstick.n.01', 'synonyms': ['lipstick', 'lip_rouge'], 'def': 'makeup that is used to color the lips', 'name': 'lipstick'}, {'frequency': 'r', 'id': 663, 'synset': 'liquor.n.01', 'synonyms': ['liquor', 'spirits', 'hard_liquor', 'liqueur', 'cordial'], 'def': 'an alcoholic beverage that is distilled rather than fermented', 'name': 'liquor'}, {'frequency': 'r', 'id': 664, 'synset': 'lizard.n.01', 'synonyms': ['lizard'], 'def': 'a reptile with usually two pairs of legs and a tapering tail', 'name': 'lizard'}, {'frequency': 'r', 'id': 665, 'synset': 'loafer.n.02', 'synonyms': ['Loafer_(type_of_shoe)'], 'def': 'a low leather step-in shoe', 'name': 'Loafer_(type_of_shoe)'}, {'frequency': 'f', 'id': 666, 'synset': 'log.n.01', 'synonyms': ['log'], 'def': 'a segment of the trunk of a tree when stripped of branches', 'name': 'log'}, {'frequency': 'c', 'id': 667, 'synset': 'lollipop.n.02', 'synonyms': ['lollipop'], 'def': 'hard candy on a stick', 'name': 'lollipop'}, {'frequency': 'c', 'id': 668, 'synset': 'lotion.n.01', 'synonyms': ['lotion'], 'def': 'any of various cosmetic preparations that are applied to the skin', 'name': 'lotion'}, {'frequency': 'f', 'id': 669, 'synset': 'loudspeaker.n.01', 'synonyms': ['speaker_(stero_equipment)'], 'def': 'electronic device that produces sound often as part of a stereo system', 'name': 'speaker_(stero_equipment)'}, {'frequency': 'c', 'id': 670, 'synset': 'love_seat.n.01', 'synonyms': ['loveseat'], 'def': 'small sofa that seats two people', 'name': 'loveseat'}, {'frequency': 'r', 'id': 671, 'synset': 'machine_gun.n.01', 'synonyms': ['machine_gun'], 'def': 'a rapidly firing automatic gun', 'name': 'machine_gun'}, {'frequency': 'f', 'id': 672, 'synset': 'magazine.n.02', 'synonyms': ['magazine'], 'def': 'a paperback periodic publication', 'name': 'magazine'}, {'frequency': 'f', 'id': 673, 'synset': 'magnet.n.01', 'synonyms': ['magnet'], 'def': 'a device that attracts iron and produces a magnetic field', 'name': 'magnet'}, {'frequency': 'r', 'id': 674, 'synset': 'mail_slot.n.01', 'synonyms': ['mail_slot'], 'def': 'a slot (usually in a door) through which mail can be delivered', 'name': 'mail_slot'}, {'frequency': 'c', 'id': 675, 'synset': 'mailbox.n.01', 'synonyms': ['mailbox_(at_home)', 'letter_box_(at_home)'], 'def': 'a private box for delivery of mail', 'name': 'mailbox_(at_home)'}, {'frequency': 'r', 'id': 676, 'synset': 'mallet.n.01', 'synonyms': ['mallet'], 'def': 'a sports implement with a long handle and a hammer-like head used to hit a ball', 'name': 'mallet'}, {'frequency': 'r', 'id': 677, 'synset': 'mammoth.n.01', 'synonyms': ['mammoth'], 'def': 'any of numerous extinct elephants widely distributed in the Pleistocene', 'name': 'mammoth'}, {'frequency': 'c', 'id': 678, 'synset': 'mandarin.n.05', 'synonyms': ['mandarin_orange'], 'def': 'a somewhat flat reddish-orange loose skinned citrus of China', 'name': 'mandarin_orange'}, {'frequency': 'c', 'id': 679, 'synset': 'manger.n.01', 'synonyms': ['manger', 'trough'], 'def': 'a container (usually in a barn or stable) from which cattle or horses feed', 'name': 'manger'}, {'frequency': 'f', 'id': 680, 'synset': 'manhole.n.01', 'synonyms': ['manhole'], 'def': 'a hole (usually with a flush cover) through which a person can gain access to an underground structure', 'name': 'manhole'}, {'frequency': 'c', 'id': 681, 'synset': 'map.n.01', 'synonyms': ['map'], 'def': "a diagrammatic representation of the earth's surface (or part of it)", 'name': 'map'}, {'frequency': 'c', 'id': 682, 'synset': 'marker.n.03', 'synonyms': ['marker'], 'def': 'a writing implement for making a mark', 'name': 'marker'}, {'frequency': 'r', 'id': 683, 'synset': 'martini.n.01', 'synonyms': ['martini'], 'def': 'a cocktail made of gin (or vodka) with dry vermouth', 'name': 'martini'}, {'frequency': 'r', 'id': 684, 'synset': 'mascot.n.01', 'synonyms': ['mascot'], 'def': 'a person or animal that is adopted by a team or other group as a symbolic figure', 'name': 'mascot'}, {'frequency': 'c', 'id': 685, 'synset': 'mashed_potato.n.01', 'synonyms': ['mashed_potato'], 'def': 'potato that has been peeled and boiled and then mashed', 'name': 'mashed_potato'}, {'frequency': 'r', 'id': 686, 'synset': 'masher.n.02', 'synonyms': ['masher'], 'def': 'a kitchen utensil used for mashing (e.g. potatoes)', 'name': 'masher'}, {'frequency': 'f', 'id': 687, 'synset': 'mask.n.04', 'synonyms': ['mask', 'facemask'], 'def': 'a protective covering worn over the face', 'name': 'mask'}, {'frequency': 'f', 'id': 688, 'synset': 'mast.n.01', 'synonyms': ['mast'], 'def': 'a vertical spar for supporting sails', 'name': 'mast'}, {'frequency': 'c', 'id': 689, 'synset': 'mat.n.03', 'synonyms': ['mat_(gym_equipment)', 'gym_mat'], 'def': 'sports equipment consisting of a piece of thick padding on the floor for gymnastics', 'name': 'mat_(gym_equipment)'}, {'frequency': 'r', 'id': 690, 'synset': 'matchbox.n.01', 'synonyms': ['matchbox'], 'def': 'a box for holding matches', 'name': 'matchbox'}, {'frequency': 'f', 'id': 691, 'synset': 'mattress.n.01', 'synonyms': ['mattress'], 'def': 'a thick pad filled with resilient material used as a bed or part of a bed', 'name': 'mattress'}, {'frequency': 'c', 'id': 692, 'synset': 'measuring_cup.n.01', 'synonyms': ['measuring_cup'], 'def': 'graduated cup used to measure liquid or granular ingredients', 'name': 'measuring_cup'}, {'frequency': 'c', 'id': 693, 'synset': 'measuring_stick.n.01', 'synonyms': ['measuring_stick', 'ruler_(measuring_stick)', 'measuring_rod'], 'def': 'measuring instrument having a sequence of marks at regular intervals', 'name': 'measuring_stick'}, {'frequency': 'c', 'id': 694, 'synset': 'meatball.n.01', 'synonyms': ['meatball'], 'def': 'ground meat formed into a ball and fried or simmered in broth', 'name': 'meatball'}, {'frequency': 'c', 'id': 695, 'synset': 'medicine.n.02', 'synonyms': ['medicine'], 'def': 'something that treats or prevents or alleviates the symptoms of disease', 'name': 'medicine'}, {'frequency': 'r', 'id': 696, 'synset': 'melon.n.01', 'synonyms': ['melon'], 'def': 'fruit of the gourd family having a hard rind and sweet juicy flesh', 'name': 'melon'}, {'frequency': 'f', 'id': 697, 'synset': 'microphone.n.01', 'synonyms': ['microphone'], 'def': 'device for converting sound waves into electrical energy', 'name': 'microphone'}, {'frequency': 'r', 'id': 698, 'synset': 'microscope.n.01', 'synonyms': ['microscope'], 'def': 'magnifier of the image of small objects', 'name': 'microscope'}, {'frequency': 'f', 'id': 699, 'synset': 'microwave.n.02', 'synonyms': ['microwave_oven'], 'def': 'kitchen appliance that cooks food by passing an electromagnetic wave through it', 'name': 'microwave_oven'}, {'frequency': 'r', 'id': 700, 'synset': 'milestone.n.01', 'synonyms': ['milestone', 'milepost'], 'def': 'stone post at side of a road to show distances', 'name': 'milestone'}, {'frequency': 'c', 'id': 701, 'synset': 'milk.n.01', 'synonyms': ['milk'], 'def': 'a white nutritious liquid secreted by mammals and used as food by human beings', 'name': 'milk'}, {'frequency': 'f', 'id': 702, 'synset': 'minivan.n.01', 'synonyms': ['minivan'], 'def': 'a small box-shaped passenger van', 'name': 'minivan'}, {'frequency': 'r', 'id': 703, 'synset': 'mint.n.05', 'synonyms': ['mint_candy'], 'def': 'a candy that is flavored with a mint oil', 'name': 'mint_candy'}, {'frequency': 'f', 'id': 704, 'synset': 'mirror.n.01', 'synonyms': ['mirror'], 'def': 'polished surface that forms images by reflecting light', 'name': 'mirror'}, {'frequency': 'c', 'id': 705, 'synset': 'mitten.n.01', 'synonyms': ['mitten'], 'def': 'glove that encases the thumb separately and the other four fingers together', 'name': 'mitten'}, {'frequency': 'c', 'id': 706, 'synset': 'mixer.n.04', 'synonyms': ['mixer_(kitchen_tool)', 'stand_mixer'], 'def': 'a kitchen utensil that is used for mixing foods', 'name': 'mixer_(kitchen_tool)'}, {'frequency': 'c', 'id': 707, 'synset': 'money.n.03', 'synonyms': ['money'], 'def': 'the official currency issued by a government or national bank', 'name': 'money'}, {'frequency': 'f', 'id': 708, 'synset': 'monitor.n.04', 'synonyms': ['monitor_(computer_equipment) computer_monitor'], 'def': 'a computer monitor', 'name': 'monitor_(computer_equipment) computer_monitor'}, {'frequency': 'c', 'id': 709, 'synset': 'monkey.n.01', 'synonyms': ['monkey'], 'def': 'any of various long-tailed primates', 'name': 'monkey'}, {'frequency': 'f', 'id': 710, 'synset': 'motor.n.01', 'synonyms': ['motor'], 'def': 'machine that converts other forms of energy into mechanical energy and so imparts motion', 'name': 'motor'}, {'frequency': 'f', 'id': 711, 'synset': 'motor_scooter.n.01', 'synonyms': ['motor_scooter', 'scooter'], 'def': 'a wheeled vehicle with small wheels and a low-powered engine', 'name': 'motor_scooter'}, {'frequency': 'r', 'id': 712, 'synset': 'motor_vehicle.n.01', 'synonyms': ['motor_vehicle', 'automotive_vehicle'], 'def': 'a self-propelled wheeled vehicle that does not run on rails', 'name': 'motor_vehicle'}, {'frequency': 'r', 'id': 713, 'synset': 'motorboat.n.01', 'synonyms': ['motorboat', 'powerboat'], 'def': 'a boat propelled by an internal-combustion engine', 'name': 'motorboat'}, {'frequency': 'f', 'id': 714, 'synset': 'motorcycle.n.01', 'synonyms': ['motorcycle'], 'def': 'a motor vehicle with two wheels and a strong frame', 'name': 'motorcycle'}, {'frequency': 'f', 'id': 715, 'synset': 'mound.n.01', 'synonyms': ['mound_(baseball)', "pitcher's_mound"], 'def': '(baseball) the slight elevation on which the pitcher stands', 'name': 'mound_(baseball)'}, {'frequency': 'r', 'id': 716, 'synset': 'mouse.n.01', 'synonyms': ['mouse_(animal_rodent)'], 'def': 'a small rodent with pointed snouts and small ears on elongated bodies with slender usually hairless tails', 'name': 'mouse_(animal_rodent)'}, {'frequency': 'f', 'id': 717, 'synset': 'mouse.n.04', 'synonyms': ['mouse_(computer_equipment)', 'computer_mouse'], 'def': 'a computer input device that controls an on-screen pointer', 'name': 'mouse_(computer_equipment)'}, {'frequency': 'f', 'id': 718, 'synset': 'mousepad.n.01', 'synonyms': ['mousepad'], 'def': 'a small portable pad that provides an operating surface for a computer mouse', 'name': 'mousepad'}, {'frequency': 'c', 'id': 719, 'synset': 'muffin.n.01', 'synonyms': ['muffin'], 'def': 'a sweet quick bread baked in a cup-shaped pan', 'name': 'muffin'}, {'frequency': 'f', 'id': 720, 'synset': 'mug.n.04', 'synonyms': ['mug'], 'def': 'with handle and usually cylindrical', 'name': 'mug'}, {'frequency': 'f', 'id': 721, 'synset': 'mushroom.n.02', 'synonyms': ['mushroom'], 'def': 'a common mushroom', 'name': 'mushroom'}, {'frequency': 'r', 'id': 722, 'synset': 'music_stool.n.01', 'synonyms': ['music_stool', 'piano_stool'], 'def': 'a stool for piano players; usually adjustable in height', 'name': 'music_stool'}, {'frequency': 'r', 'id': 723, 'synset': 'musical_instrument.n.01', 'synonyms': ['musical_instrument', 'instrument_(musical)'], 'def': 'any of various devices or contrivances that can be used to produce musical tones or sounds', 'name': 'musical_instrument'}, {'frequency': 'r', 'id': 724, 'synset': 'nailfile.n.01', 'synonyms': ['nailfile'], 'def': 'a small flat file for shaping the nails', 'name': 'nailfile'}, {'frequency': 'r', 'id': 725, 'synset': 'nameplate.n.01', 'synonyms': ['nameplate'], 'def': 'a plate bearing a name', 'name': 'nameplate'}, {'frequency': 'f', 'id': 726, 'synset': 'napkin.n.01', 'synonyms': ['napkin', 'table_napkin', 'serviette'], 'def': 'a small piece of table linen or paper that is used to wipe the mouth and to cover the lap in order to protect clothing', 'name': 'napkin'}, {'frequency': 'r', 'id': 727, 'synset': 'neckerchief.n.01', 'synonyms': ['neckerchief'], 'def': 'a kerchief worn around the neck', 'name': 'neckerchief'}, {'frequency': 'f', 'id': 728, 'synset': 'necklace.n.01', 'synonyms': ['necklace'], 'def': 'jewelry consisting of a cord or chain (often bearing gems) worn about the neck as an ornament', 'name': 'necklace'}, {'frequency': 'f', 'id': 729, 'synset': 'necktie.n.01', 'synonyms': ['necktie', 'tie_(necktie)'], 'def': 'neckwear consisting of a long narrow piece of material worn under a collar and tied in knot at the front', 'name': 'necktie'}, {'frequency': 'r', 'id': 730, 'synset': 'needle.n.03', 'synonyms': ['needle'], 'def': 'a sharp pointed implement (usually metal)', 'name': 'needle'}, {'frequency': 'c', 'id': 731, 'synset': 'nest.n.01', 'synonyms': ['nest'], 'def': 'a structure in which animals lay eggs or give birth to their young', 'name': 'nest'}, {'frequency': 'r', 'id': 732, 'synset': 'newsstand.n.01', 'synonyms': ['newsstand'], 'def': 'a stall where newspapers and other periodicals are sold', 'name': 'newsstand'}, {'frequency': 'c', 'id': 733, 'synset': 'nightwear.n.01', 'synonyms': ['nightshirt', 'nightwear', 'sleepwear', 'nightclothes'], 'def': 'garments designed to be worn in bed', 'name': 'nightshirt'}, {'frequency': 'r', 'id': 734, 'synset': 'nosebag.n.01', 'synonyms': ['nosebag_(for_animals)', 'feedbag'], 'def': 'a canvas bag that is used to feed an animal (such as a horse); covers the muzzle and fastens at the top of the head', 'name': 'nosebag_(for_animals)'}, {'frequency': 'r', 'id': 735, 'synset': 'noseband.n.01', 'synonyms': ['noseband_(for_animals)', 'nosepiece_(for_animals)'], 'def': "a strap that is the part of a bridle that goes over the animal's nose", 'name': 'noseband_(for_animals)'}, {'frequency': 'f', 'id': 736, 'synset': 'notebook.n.01', 'synonyms': ['notebook'], 'def': 'a book with blank pages for recording notes or memoranda', 'name': 'notebook'}, {'frequency': 'c', 'id': 737, 'synset': 'notepad.n.01', 'synonyms': ['notepad'], 'def': 'a pad of paper for keeping notes', 'name': 'notepad'}, {'frequency': 'c', 'id': 738, 'synset': 'nut.n.03', 'synonyms': ['nut'], 'def': 'a small metal block (usually square or hexagonal) with internal screw thread to be fitted onto a bolt', 'name': 'nut'}, {'frequency': 'r', 'id': 739, 'synset': 'nutcracker.n.01', 'synonyms': ['nutcracker'], 'def': 'a hand tool used to crack nuts open', 'name': 'nutcracker'}, {'frequency': 'c', 'id': 740, 'synset': 'oar.n.01', 'synonyms': ['oar'], 'def': 'an implement used to propel or steer a boat', 'name': 'oar'}, {'frequency': 'r', 'id': 741, 'synset': 'octopus.n.01', 'synonyms': ['octopus_(food)'], 'def': 'tentacles of octopus prepared as food', 'name': 'octopus_(food)'}, {'frequency': 'r', 'id': 742, 'synset': 'octopus.n.02', 'synonyms': ['octopus_(animal)'], 'def': 'bottom-living cephalopod having a soft oval body with eight long tentacles', 'name': 'octopus_(animal)'}, {'frequency': 'c', 'id': 743, 'synset': 'oil_lamp.n.01', 'synonyms': ['oil_lamp', 'kerosene_lamp', 'kerosine_lamp'], 'def': 'a lamp that burns oil (as kerosine) for light', 'name': 'oil_lamp'}, {'frequency': 'c', 'id': 744, 'synset': 'olive_oil.n.01', 'synonyms': ['olive_oil'], 'def': 'oil from olives', 'name': 'olive_oil'}, {'frequency': 'r', 'id': 745, 'synset': 'omelet.n.01', 'synonyms': ['omelet', 'omelette'], 'def': 'beaten eggs cooked until just set; may be folded around e.g. ham or cheese or jelly', 'name': 'omelet'}, {'frequency': 'f', 'id': 746, 'synset': 'onion.n.01', 'synonyms': ['onion'], 'def': 'the bulb of an onion plant', 'name': 'onion'}, {'frequency': 'f', 'id': 747, 'synset': 'orange.n.01', 'synonyms': ['orange_(fruit)'], 'def': 'orange (FRUIT of an orange tree)', 'name': 'orange_(fruit)'}, {'frequency': 'c', 'id': 748, 'synset': 'orange_juice.n.01', 'synonyms': ['orange_juice'], 'def': 'bottled or freshly squeezed juice of oranges', 'name': 'orange_juice'}, {'frequency': 'r', 'id': 749, 'synset': 'oregano.n.01', 'synonyms': ['oregano', 'marjoram'], 'def': 'aromatic Eurasian perennial herb used in cooking and baking', 'name': 'oregano'}, {'frequency': 'c', 'id': 750, 'synset': 'ostrich.n.02', 'synonyms': ['ostrich'], 'def': 'fast-running African flightless bird with two-toed feet; largest living bird', 'name': 'ostrich'}, {'frequency': 'c', 'id': 751, 'synset': 'ottoman.n.03', 'synonyms': ['ottoman', 'pouf', 'pouffe', 'hassock'], 'def': 'thick cushion used as a seat', 'name': 'ottoman'}, {'frequency': 'c', 'id': 752, 'synset': 'overall.n.01', 'synonyms': ['overalls_(clothing)'], 'def': 'work clothing consisting of denim trousers usually with a bib and shoulder straps', 'name': 'overalls_(clothing)'}, {'frequency': 'c', 'id': 753, 'synset': 'owl.n.01', 'synonyms': ['owl'], 'def': 'nocturnal bird of prey with hawk-like beak and claws and large head with front-facing eyes', 'name': 'owl'}, {'frequency': 'c', 'id': 754, 'synset': 'packet.n.03', 'synonyms': ['packet'], 'def': 'a small package or bundle', 'name': 'packet'}, {'frequency': 'r', 'id': 755, 'synset': 'pad.n.03', 'synonyms': ['inkpad', 'inking_pad', 'stamp_pad'], 'def': 'absorbent material saturated with ink used to transfer ink evenly to a rubber stamp', 'name': 'inkpad'}, {'frequency': 'c', 'id': 756, 'synset': 'pad.n.04', 'synonyms': ['pad'], 'def': 'a flat mass of soft material used for protection, stuffing, or comfort', 'name': 'pad'}, {'frequency': 'c', 'id': 757, 'synset': 'paddle.n.04', 'synonyms': ['paddle', 'boat_paddle'], 'def': 'a short light oar used without an oarlock to propel a canoe or small boat', 'name': 'paddle'}, {'frequency': 'c', 'id': 758, 'synset': 'padlock.n.01', 'synonyms': ['padlock'], 'def': 'a detachable, portable lock', 'name': 'padlock'}, {'frequency': 'r', 'id': 759, 'synset': 'paintbox.n.01', 'synonyms': ['paintbox'], 'def': "a box containing a collection of cubes or tubes of artists' paint", 'name': 'paintbox'}, {'frequency': 'c', 'id': 760, 'synset': 'paintbrush.n.01', 'synonyms': ['paintbrush'], 'def': 'a brush used as an applicator to apply paint', 'name': 'paintbrush'}, {'frequency': 'f', 'id': 761, 'synset': 'painting.n.01', 'synonyms': ['painting'], 'def': 'graphic art consisting of an artistic composition made by applying paints to a surface', 'name': 'painting'}, {'frequency': 'c', 'id': 762, 'synset': 'pajama.n.02', 'synonyms': ['pajamas', 'pyjamas'], 'def': 'loose-fitting nightclothes worn for sleeping or lounging', 'name': 'pajamas'}, {'frequency': 'c', 'id': 763, 'synset': 'palette.n.02', 'synonyms': ['palette', 'pallet'], 'def': 'board that provides a flat surface on which artists mix paints and the range of colors used', 'name': 'palette'}, {'frequency': 'f', 'id': 764, 'synset': 'pan.n.01', 'synonyms': ['pan_(for_cooking)', 'cooking_pan'], 'def': 'cooking utensil consisting of a wide metal vessel', 'name': 'pan_(for_cooking)'}, {'frequency': 'r', 'id': 765, 'synset': 'pan.n.03', 'synonyms': ['pan_(metal_container)'], 'def': 'shallow container made of metal', 'name': 'pan_(metal_container)'}, {'frequency': 'c', 'id': 766, 'synset': 'pancake.n.01', 'synonyms': ['pancake'], 'def': 'a flat cake of thin batter fried on both sides on a griddle', 'name': 'pancake'}, {'frequency': 'r', 'id': 767, 'synset': 'pantyhose.n.01', 'synonyms': ['pantyhose'], 'def': "a woman's tights consisting of underpants and stockings", 'name': 'pantyhose'}, {'frequency': 'r', 'id': 768, 'synset': 'papaya.n.02', 'synonyms': ['papaya'], 'def': 'large oval melon-like tropical fruit with yellowish flesh', 'name': 'papaya'}, {'frequency': 'r', 'id': 769, 'synset': 'paper_clip.n.01', 'synonyms': ['paperclip'], 'def': 'a wire or plastic clip for holding sheets of paper together', 'name': 'paperclip'}, {'frequency': 'f', 'id': 770, 'synset': 'paper_plate.n.01', 'synonyms': ['paper_plate'], 'def': 'a disposable plate made of cardboard', 'name': 'paper_plate'}, {'frequency': 'f', 'id': 771, 'synset': 'paper_towel.n.01', 'synonyms': ['paper_towel'], 'def': 'a disposable towel made of absorbent paper', 'name': 'paper_towel'}, {'frequency': 'r', 'id': 772, 'synset': 'paperback_book.n.01', 'synonyms': ['paperback_book', 'paper-back_book', 'softback_book', 'soft-cover_book'], 'def': 'a book with paper covers', 'name': 'paperback_book'}, {'frequency': 'r', 'id': 773, 'synset': 'paperweight.n.01', 'synonyms': ['paperweight'], 'def': 'a weight used to hold down a stack of papers', 'name': 'paperweight'}, {'frequency': 'c', 'id': 774, 'synset': 'parachute.n.01', 'synonyms': ['parachute'], 'def': 'rescue equipment consisting of a device that fills with air and retards your fall', 'name': 'parachute'}, {'frequency': 'r', 'id': 775, 'synset': 'parakeet.n.01', 'synonyms': ['parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet', 'parroquet'], 'def': 'any of numerous small slender long-tailed parrots', 'name': 'parakeet'}, {'frequency': 'c', 'id': 776, 'synset': 'parasail.n.01', 'synonyms': ['parasail_(sports)'], 'def': 'parachute that will lift a person up into the air when it is towed by a motorboat or a car', 'name': 'parasail_(sports)'}, {'frequency': 'r', 'id': 777, 'synset': 'parchment.n.01', 'synonyms': ['parchment'], 'def': 'a superior paper resembling sheepskin', 'name': 'parchment'}, {'frequency': 'r', 'id': 778, 'synset': 'parka.n.01', 'synonyms': ['parka', 'anorak'], 'def': "a kind of heavy jacket (`windcheater' is a British term)", 'name': 'parka'}, {'frequency': 'f', 'id': 779, 'synset': 'parking_meter.n.01', 'synonyms': ['parking_meter'], 'def': 'a coin-operated timer located next to a parking space', 'name': 'parking_meter'}, {'frequency': 'c', 'id': 780, 'synset': 'parrot.n.01', 'synonyms': ['parrot'], 'def': 'usually brightly colored tropical birds with short hooked beaks and the ability to mimic sounds', 'name': 'parrot'}, {'frequency': 'c', 'id': 781, 'synset': 'passenger_car.n.01', 'synonyms': ['passenger_car_(part_of_a_train)', 'coach_(part_of_a_train)'], 'def': 'a railcar where passengers ride', 'name': 'passenger_car_(part_of_a_train)'}, {'frequency': 'r', 'id': 782, 'synset': 'passenger_ship.n.01', 'synonyms': ['passenger_ship'], 'def': 'a ship built to carry passengers', 'name': 'passenger_ship'}, {'frequency': 'r', 'id': 783, 'synset': 'passport.n.02', 'synonyms': ['passport'], 'def': 'a document issued by a country to a citizen allowing that person to travel abroad and re-enter the home country', 'name': 'passport'}, {'frequency': 'f', 'id': 784, 'synset': 'pastry.n.02', 'synonyms': ['pastry'], 'def': 'any of various baked foods made of dough or batter', 'name': 'pastry'}, {'frequency': 'r', 'id': 785, 'synset': 'patty.n.01', 'synonyms': ['patty_(food)'], 'def': 'small flat mass of chopped food', 'name': 'patty_(food)'}, {'frequency': 'c', 'id': 786, 'synset': 'pea.n.01', 'synonyms': ['pea_(food)'], 'def': 'seed of a pea plant used for food', 'name': 'pea_(food)'}, {'frequency': 'c', 'id': 787, 'synset': 'peach.n.03', 'synonyms': ['peach'], 'def': 'downy juicy fruit with sweet yellowish or whitish flesh', 'name': 'peach'}, {'frequency': 'c', 'id': 788, 'synset': 'peanut_butter.n.01', 'synonyms': ['peanut_butter'], 'def': 'a spread made from ground peanuts', 'name': 'peanut_butter'}, {'frequency': 'c', 'id': 789, 'synset': 'pear.n.01', 'synonyms': ['pear'], 'def': 'sweet juicy gritty-textured fruit available in many varieties', 'name': 'pear'}, {'frequency': 'r', 'id': 790, 'synset': 'peeler.n.03', 'synonyms': ['peeler_(tool_for_fruit_and_vegetables)'], 'def': 'a device for peeling vegetables or fruits', 'name': 'peeler_(tool_for_fruit_and_vegetables)'}, {'frequency': 'r', 'id': 791, 'synset': 'pegboard.n.01', 'synonyms': ['pegboard'], 'def': 'a board perforated with regularly spaced holes into which pegs can be fitted', 'name': 'pegboard'}, {'frequency': 'c', 'id': 792, 'synset': 'pelican.n.01', 'synonyms': ['pelican'], 'def': 'large long-winged warm-water seabird having a large bill with a distensible pouch for fish', 'name': 'pelican'}, {'frequency': 'f', 'id': 793, 'synset': 'pen.n.01', 'synonyms': ['pen'], 'def': 'a writing implement with a point from which ink flows', 'name': 'pen'}, {'frequency': 'c', 'id': 794, 'synset': 'pencil.n.01', 'synonyms': ['pencil'], 'def': 'a thin cylindrical pointed writing implement made of wood and graphite', 'name': 'pencil'}, {'frequency': 'r', 'id': 795, 'synset': 'pencil_box.n.01', 'synonyms': ['pencil_box', 'pencil_case'], 'def': 'a box for holding pencils', 'name': 'pencil_box'}, {'frequency': 'r', 'id': 796, 'synset': 'pencil_sharpener.n.01', 'synonyms': ['pencil_sharpener'], 'def': 'a rotary implement for sharpening the point on pencils', 'name': 'pencil_sharpener'}, {'frequency': 'r', 'id': 797, 'synset': 'pendulum.n.01', 'synonyms': ['pendulum'], 'def': 'an apparatus consisting of an object mounted so that it swings freely under the influence of gravity', 'name': 'pendulum'}, {'frequency': 'c', 'id': 798, 'synset': 'penguin.n.01', 'synonyms': ['penguin'], 'def': 'short-legged flightless birds of cold southern regions having webbed feet and wings modified as flippers', 'name': 'penguin'}, {'frequency': 'r', 'id': 799, 'synset': 'pennant.n.02', 'synonyms': ['pennant'], 'def': 'a flag longer than it is wide (and often tapering)', 'name': 'pennant'}, {'frequency': 'r', 'id': 800, 'synset': 'penny.n.02', 'synonyms': ['penny_(coin)'], 'def': 'a coin worth one-hundredth of the value of the basic unit', 'name': 'penny_(coin)'}, {'frequency': 'c', 'id': 801, 'synset': 'pepper.n.03', 'synonyms': ['pepper', 'peppercorn'], 'def': 'pungent seasoning from the berry of the common pepper plant; whole or ground', 'name': 'pepper'}, {'frequency': 'c', 'id': 802, 'synset': 'pepper_mill.n.01', 'synonyms': ['pepper_mill', 'pepper_grinder'], 'def': 'a mill for grinding pepper', 'name': 'pepper_mill'}, {'frequency': 'c', 'id': 803, 'synset': 'perfume.n.02', 'synonyms': ['perfume'], 'def': 'a toiletry that emits and diffuses a fragrant odor', 'name': 'perfume'}, {'frequency': 'r', 'id': 804, 'synset': 'persimmon.n.02', 'synonyms': ['persimmon'], 'def': 'orange fruit resembling a plum; edible when fully ripe', 'name': 'persimmon'}, {'frequency': 'f', 'id': 805, 'synset': 'person.n.01', 'synonyms': ['baby', 'child', 'boy', 'girl', 'man', 'woman', 'person', 'human'], 'def': 'a human being', 'name': 'baby'}, {'frequency': 'r', 'id': 806, 'synset': 'pet.n.01', 'synonyms': ['pet'], 'def': 'a domesticated animal kept for companionship or amusement', 'name': 'pet'}, {'frequency': 'r', 'id': 807, 'synset': 'petfood.n.01', 'synonyms': ['petfood', 'pet-food'], 'def': 'food prepared for animal pets', 'name': 'petfood'}, {'frequency': 'r', 'id': 808, 'synset': 'pew.n.01', 'synonyms': ['pew_(church_bench)', 'church_bench'], 'def': 'long bench with backs; used in church by the congregation', 'name': 'pew_(church_bench)'}, {'frequency': 'r', 'id': 809, 'synset': 'phonebook.n.01', 'synonyms': ['phonebook', 'telephone_book', 'telephone_directory'], 'def': 'a directory containing an alphabetical list of telephone subscribers and their telephone numbers', 'name': 'phonebook'}, {'frequency': 'c', 'id': 810, 'synset': 'phonograph_record.n.01', 'synonyms': ['phonograph_record', 'phonograph_recording', 'record_(phonograph_recording)'], 'def': 'sound recording consisting of a typically black disk with a continuous groove', 'name': 'phonograph_record'}, {'frequency': 'c', 'id': 811, 'synset': 'piano.n.01', 'synonyms': ['piano'], 'def': 'a keyboard instrument that is played by depressing keys that cause hammers to strike tuned strings and produce sounds', 'name': 'piano'}, {'frequency': 'f', 'id': 812, 'synset': 'pickle.n.01', 'synonyms': ['pickle'], 'def': 'vegetables (especially cucumbers) preserved in brine or vinegar', 'name': 'pickle'}, {'frequency': 'f', 'id': 813, 'synset': 'pickup.n.01', 'synonyms': ['pickup_truck'], 'def': 'a light truck with an open body and low sides and a tailboard', 'name': 'pickup_truck'}, {'frequency': 'c', 'id': 814, 'synset': 'pie.n.01', 'synonyms': ['pie'], 'def': 'dish baked in pastry-lined pan often with a pastry top', 'name': 'pie'}, {'frequency': 'c', 'id': 815, 'synset': 'pigeon.n.01', 'synonyms': ['pigeon'], 'def': 'wild and domesticated birds having a heavy body and short legs', 'name': 'pigeon'}, {'frequency': 'r', 'id': 816, 'synset': 'piggy_bank.n.01', 'synonyms': ['piggy_bank', 'penny_bank'], 'def': "a child's coin bank (often shaped like a pig)", 'name': 'piggy_bank'}, {'frequency': 'f', 'id': 817, 'synset': 'pillow.n.01', 'synonyms': ['pillow'], 'def': 'a cushion to support the head of a sleeping person', 'name': 'pillow'}, {'frequency': 'r', 'id': 818, 'synset': 'pin.n.09', 'synonyms': ['pin_(non_jewelry)'], 'def': 'a small slender (often pointed) piece of wood or metal used to support or fasten or attach things', 'name': 'pin_(non_jewelry)'}, {'frequency': 'f', 'id': 819, 'synset': 'pineapple.n.02', 'synonyms': ['pineapple'], 'def': 'large sweet fleshy tropical fruit with a tuft of stiff leaves', 'name': 'pineapple'}, {'frequency': 'c', 'id': 820, 'synset': 'pinecone.n.01', 'synonyms': ['pinecone'], 'def': 'the seed-producing cone of a pine tree', 'name': 'pinecone'}, {'frequency': 'r', 'id': 821, 'synset': 'ping-pong_ball.n.01', 'synonyms': ['ping-pong_ball'], 'def': 'light hollow ball used in playing table tennis', 'name': 'ping-pong_ball'}, {'frequency': 'r', 'id': 822, 'synset': 'pinwheel.n.03', 'synonyms': ['pinwheel'], 'def': 'a toy consisting of vanes of colored paper or plastic that is pinned to a stick and spins when it is pointed into the wind', 'name': 'pinwheel'}, {'frequency': 'r', 'id': 823, 'synset': 'pipe.n.01', 'synonyms': ['tobacco_pipe'], 'def': 'a tube with a small bowl at one end; used for smoking tobacco', 'name': 'tobacco_pipe'}, {'frequency': 'f', 'id': 824, 'synset': 'pipe.n.02', 'synonyms': ['pipe', 'piping'], 'def': 'a long tube made of metal or plastic that is used to carry water or oil or gas etc.', 'name': 'pipe'}, {'frequency': 'r', 'id': 825, 'synset': 'pistol.n.01', 'synonyms': ['pistol', 'handgun'], 'def': 'a firearm that is held and fired with one hand', 'name': 'pistol'}, {'frequency': 'r', 'id': 826, 'synset': 'pita.n.01', 'synonyms': ['pita_(bread)', 'pocket_bread'], 'def': 'usually small round bread that can open into a pocket for filling', 'name': 'pita_(bread)'}, {'frequency': 'f', 'id': 827, 'synset': 'pitcher.n.02', 'synonyms': ['pitcher_(vessel_for_liquid)', 'ewer'], 'def': 'an open vessel with a handle and a spout for pouring', 'name': 'pitcher_(vessel_for_liquid)'}, {'frequency': 'r', 'id': 828, 'synset': 'pitchfork.n.01', 'synonyms': ['pitchfork'], 'def': 'a long-handled hand tool with sharp widely spaced prongs for lifting and pitching hay', 'name': 'pitchfork'}, {'frequency': 'f', 'id': 829, 'synset': 'pizza.n.01', 'synonyms': ['pizza'], 'def': 'Italian open pie made of thin bread dough spread with a spiced mixture of e.g. tomato sauce and cheese', 'name': 'pizza'}, {'frequency': 'f', 'id': 830, 'synset': 'place_mat.n.01', 'synonyms': ['place_mat'], 'def': 'a mat placed on a table for an individual place setting', 'name': 'place_mat'}, {'frequency': 'f', 'id': 831, 'synset': 'plate.n.04', 'synonyms': ['plate'], 'def': 'dish on which food is served or from which food is eaten', 'name': 'plate'}, {'frequency': 'c', 'id': 832, 'synset': 'platter.n.01', 'synonyms': ['platter'], 'def': 'a large shallow dish used for serving food', 'name': 'platter'}, {'frequency': 'r', 'id': 833, 'synset': 'playing_card.n.01', 'synonyms': ['playing_card'], 'def': 'one of a pack of cards that are used to play card games', 'name': 'playing_card'}, {'frequency': 'r', 'id': 834, 'synset': 'playpen.n.01', 'synonyms': ['playpen'], 'def': 'a portable enclosure in which babies may be left to play', 'name': 'playpen'}, {'frequency': 'c', 'id': 835, 'synset': 'pliers.n.01', 'synonyms': ['pliers', 'plyers'], 'def': 'a gripping hand tool with two hinged arms and (usually) serrated jaws', 'name': 'pliers'}, {'frequency': 'r', 'id': 836, 'synset': 'plow.n.01', 'synonyms': ['plow_(farm_equipment)', 'plough_(farm_equipment)'], 'def': 'a farm tool having one or more heavy blades to break the soil and cut a furrow prior to sowing', 'name': 'plow_(farm_equipment)'}, {'frequency': 'r', 'id': 837, 'synset': 'pocket_watch.n.01', 'synonyms': ['pocket_watch'], 'def': 'a watch that is carried in a small watch pocket', 'name': 'pocket_watch'}, {'frequency': 'c', 'id': 838, 'synset': 'pocketknife.n.01', 'synonyms': ['pocketknife'], 'def': 'a knife with a blade that folds into the handle; suitable for carrying in the pocket', 'name': 'pocketknife'}, {'frequency': 'c', 'id': 839, 'synset': 'poker.n.01', 'synonyms': ['poker_(fire_stirring_tool)', 'stove_poker', 'fire_hook'], 'def': 'fire iron consisting of a metal rod with a handle; used to stir a fire', 'name': 'poker_(fire_stirring_tool)'}, {'frequency': 'f', 'id': 840, 'synset': 'pole.n.01', 'synonyms': ['pole', 'post'], 'def': 'a long (usually round) rod of wood or metal or plastic', 'name': 'pole'}, {'frequency': 'r', 'id': 841, 'synset': 'police_van.n.01', 'synonyms': ['police_van', 'police_wagon', 'paddy_wagon', 'patrol_wagon'], 'def': 'van used by police to transport prisoners', 'name': 'police_van'}, {'frequency': 'f', 'id': 842, 'synset': 'polo_shirt.n.01', 'synonyms': ['polo_shirt', 'sport_shirt'], 'def': 'a shirt with short sleeves designed for comfort and casual wear', 'name': 'polo_shirt'}, {'frequency': 'r', 'id': 843, 'synset': 'poncho.n.01', 'synonyms': ['poncho'], 'def': 'a blanket-like cloak with a hole in the center for the head', 'name': 'poncho'}, {'frequency': 'c', 'id': 844, 'synset': 'pony.n.05', 'synonyms': ['pony'], 'def': 'any of various breeds of small gentle horses usually less than five feet high at the shoulder', 'name': 'pony'}, {'frequency': 'r', 'id': 845, 'synset': 'pool_table.n.01', 'synonyms': ['pool_table', 'billiard_table', 'snooker_table'], 'def': 'game equipment consisting of a heavy table on which pool is played', 'name': 'pool_table'}, {'frequency': 'f', 'id': 846, 'synset': 'pop.n.02', 'synonyms': ['pop_(soda)', 'soda_(pop)', 'tonic', 'soft_drink'], 'def': 'a sweet drink containing carbonated water and flavoring', 'name': 'pop_(soda)'}, {'frequency': 'r', 'id': 847, 'synset': 'portrait.n.02', 'synonyms': ['portrait', 'portrayal'], 'def': 'any likeness of a person, in any medium', 'name': 'portrait'}, {'frequency': 'c', 'id': 848, 'synset': 'postbox.n.01', 'synonyms': ['postbox_(public)', 'mailbox_(public)'], 'def': 'public box for deposit of mail', 'name': 'postbox_(public)'}, {'frequency': 'c', 'id': 849, 'synset': 'postcard.n.01', 'synonyms': ['postcard', 'postal_card', 'mailing-card'], 'def': 'a card for sending messages by post without an envelope', 'name': 'postcard'}, {'frequency': 'f', 'id': 850, 'synset': 'poster.n.01', 'synonyms': ['poster', 'placard'], 'def': 'a sign posted in a public place as an advertisement', 'name': 'poster'}, {'frequency': 'f', 'id': 851, 'synset': 'pot.n.01', 'synonyms': ['pot'], 'def': 'metal or earthenware cooking vessel that is usually round and deep; often has a handle and lid', 'name': 'pot'}, {'frequency': 'f', 'id': 852, 'synset': 'pot.n.04', 'synonyms': ['flowerpot'], 'def': 'a container in which plants are cultivated', 'name': 'flowerpot'}, {'frequency': 'f', 'id': 853, 'synset': 'potato.n.01', 'synonyms': ['potato'], 'def': 'an edible tuber native to South America', 'name': 'potato'}, {'frequency': 'c', 'id': 854, 'synset': 'potholder.n.01', 'synonyms': ['potholder'], 'def': 'an insulated pad for holding hot pots', 'name': 'potholder'}, {'frequency': 'c', 'id': 855, 'synset': 'pottery.n.01', 'synonyms': ['pottery', 'clayware'], 'def': 'ceramic ware made from clay and baked in a kiln', 'name': 'pottery'}, {'frequency': 'c', 'id': 856, 'synset': 'pouch.n.01', 'synonyms': ['pouch'], 'def': 'a small or medium size container for holding or carrying things', 'name': 'pouch'}, {'frequency': 'r', 'id': 857, 'synset': 'power_shovel.n.01', 'synonyms': ['power_shovel', 'excavator', 'digger'], 'def': 'a machine for excavating', 'name': 'power_shovel'}, {'frequency': 'c', 'id': 858, 'synset': 'prawn.n.01', 'synonyms': ['prawn', 'shrimp'], 'def': 'any of various edible decapod crustaceans', 'name': 'prawn'}, {'frequency': 'f', 'id': 859, 'synset': 'printer.n.03', 'synonyms': ['printer', 'printing_machine'], 'def': 'a machine that prints', 'name': 'printer'}, {'frequency': 'c', 'id': 860, 'synset': 'projectile.n.01', 'synonyms': ['projectile_(weapon)', 'missile'], 'def': 'a weapon that is forcibly thrown or projected at a targets', 'name': 'projectile_(weapon)'}, {'frequency': 'c', 'id': 861, 'synset': 'projector.n.02', 'synonyms': ['projector'], 'def': 'an optical instrument that projects an enlarged image onto a screen', 'name': 'projector'}, {'frequency': 'f', 'id': 862, 'synset': 'propeller.n.01', 'synonyms': ['propeller', 'propellor'], 'def': 'a mechanical device that rotates to push against air or water', 'name': 'propeller'}, {'frequency': 'r', 'id': 863, 'synset': 'prune.n.01', 'synonyms': ['prune'], 'def': 'dried plum', 'name': 'prune'}, {'frequency': 'r', 'id': 864, 'synset': 'pudding.n.01', 'synonyms': ['pudding'], 'def': 'any of various soft thick unsweetened baked dishes', 'name': 'pudding'}, {'frequency': 'r', 'id': 865, 'synset': 'puffer.n.02', 'synonyms': ['puffer_(fish)', 'pufferfish', 'blowfish', 'globefish'], 'def': 'fishes whose elongated spiny body can inflate itself with water or air to form a globe', 'name': 'puffer_(fish)'}, {'frequency': 'r', 'id': 866, 'synset': 'puffin.n.01', 'synonyms': ['puffin'], 'def': 'seabirds having short necks and brightly colored compressed bills', 'name': 'puffin'}, {'frequency': 'r', 'id': 867, 'synset': 'pug.n.01', 'synonyms': ['pug-dog'], 'def': 'small compact smooth-coated breed of Asiatic origin having a tightly curled tail and broad flat wrinkled muzzle', 'name': 'pug-dog'}, {'frequency': 'c', 'id': 868, 'synset': 'pumpkin.n.02', 'synonyms': ['pumpkin'], 'def': 'usually large pulpy deep-yellow round fruit of the squash family maturing in late summer or early autumn', 'name': 'pumpkin'}, {'frequency': 'r', 'id': 869, 'synset': 'punch.n.03', 'synonyms': ['puncher'], 'def': 'a tool for making holes or indentations', 'name': 'puncher'}, {'frequency': 'r', 'id': 870, 'synset': 'puppet.n.01', 'synonyms': ['puppet', 'marionette'], 'def': 'a small figure of a person operated from above with strings by a puppeteer', 'name': 'puppet'}, {'frequency': 'r', 'id': 871, 'synset': 'puppy.n.01', 'synonyms': ['puppy'], 'def': 'a young dog', 'name': 'puppy'}, {'frequency': 'r', 'id': 872, 'synset': 'quesadilla.n.01', 'synonyms': ['quesadilla'], 'def': 'a tortilla that is filled with cheese and heated', 'name': 'quesadilla'}, {'frequency': 'r', 'id': 873, 'synset': 'quiche.n.02', 'synonyms': ['quiche'], 'def': 'a tart filled with rich unsweetened custard; often contains other ingredients (as cheese or ham or seafood or vegetables)', 'name': 'quiche'}, {'frequency': 'f', 'id': 874, 'synset': 'quilt.n.01', 'synonyms': ['quilt', 'comforter'], 'def': 'bedding made of two layers of cloth filled with stuffing and stitched together', 'name': 'quilt'}, {'frequency': 'c', 'id': 875, 'synset': 'rabbit.n.01', 'synonyms': ['rabbit'], 'def': 'any of various burrowing animals of the family Leporidae having long ears and short tails', 'name': 'rabbit'}, {'frequency': 'r', 'id': 876, 'synset': 'racer.n.02', 'synonyms': ['race_car', 'racing_car'], 'def': 'a fast car that competes in races', 'name': 'race_car'}, {'frequency': 'c', 'id': 877, 'synset': 'racket.n.04', 'synonyms': ['racket', 'racquet'], 'def': 'a sports implement used to strike a ball in various games', 'name': 'racket'}, {'frequency': 'r', 'id': 878, 'synset': 'radar.n.01', 'synonyms': ['radar'], 'def': 'measuring instrument in which the echo of a pulse of microwave radiation is used to detect and locate distant objects', 'name': 'radar'}, {'frequency': 'c', 'id': 879, 'synset': 'radiator.n.03', 'synonyms': ['radiator'], 'def': 'a mechanism consisting of a metal honeycomb through which hot fluids circulate', 'name': 'radiator'}, {'frequency': 'c', 'id': 880, 'synset': 'radio_receiver.n.01', 'synonyms': ['radio_receiver', 'radio_set', 'radio', 'tuner_(radio)'], 'def': 'an electronic receiver that detects and demodulates and amplifies transmitted radio signals', 'name': 'radio_receiver'}, {'frequency': 'c', 'id': 881, 'synset': 'radish.n.03', 'synonyms': ['radish', 'daikon'], 'def': 'pungent edible root of any of various cultivated radish plants', 'name': 'radish'}, {'frequency': 'c', 'id': 882, 'synset': 'raft.n.01', 'synonyms': ['raft'], 'def': 'a flat float (usually made of logs or planks) that can be used for transport or as a platform for swimmers', 'name': 'raft'}, {'frequency': 'r', 'id': 883, 'synset': 'rag_doll.n.01', 'synonyms': ['rag_doll'], 'def': 'a cloth doll that is stuffed and (usually) painted', 'name': 'rag_doll'}, {'frequency': 'c', 'id': 884, 'synset': 'raincoat.n.01', 'synonyms': ['raincoat', 'waterproof_jacket'], 'def': 'a water-resistant coat', 'name': 'raincoat'}, {'frequency': 'c', 'id': 885, 'synset': 'ram.n.05', 'synonyms': ['ram_(animal)'], 'def': 'uncastrated adult male sheep', 'name': 'ram_(animal)'}, {'frequency': 'c', 'id': 886, 'synset': 'raspberry.n.02', 'synonyms': ['raspberry'], 'def': 'red or black edible aggregate berries usually smaller than the related blackberries', 'name': 'raspberry'}, {'frequency': 'r', 'id': 887, 'synset': 'rat.n.01', 'synonyms': ['rat'], 'def': 'any of various long-tailed rodents similar to but larger than a mouse', 'name': 'rat'}, {'frequency': 'c', 'id': 888, 'synset': 'razorblade.n.01', 'synonyms': ['razorblade'], 'def': 'a blade that has very sharp edge', 'name': 'razorblade'}, {'frequency': 'c', 'id': 889, 'synset': 'reamer.n.01', 'synonyms': ['reamer_(juicer)', 'juicer', 'juice_reamer'], 'def': 'a squeezer with a conical ridged center that is used for squeezing juice from citrus fruit', 'name': 'reamer_(juicer)'}, {'frequency': 'f', 'id': 890, 'synset': 'rearview_mirror.n.01', 'synonyms': ['rearview_mirror'], 'def': 'car mirror that reflects the view out of the rear window', 'name': 'rearview_mirror'}, {'frequency': 'c', 'id': 891, 'synset': 'receipt.n.02', 'synonyms': ['receipt'], 'def': 'an acknowledgment (usually tangible) that payment has been made', 'name': 'receipt'}, {'frequency': 'c', 'id': 892, 'synset': 'recliner.n.01', 'synonyms': ['recliner', 'reclining_chair', 'lounger_(chair)'], 'def': 'an armchair whose back can be lowered and foot can be raised to allow the sitter to recline in it', 'name': 'recliner'}, {'frequency': 'r', 'id': 893, 'synset': 'record_player.n.01', 'synonyms': ['record_player', 'phonograph_(record_player)', 'turntable'], 'def': 'machine in which rotating records cause a stylus to vibrate and the vibrations are amplified acoustically or electronically', 'name': 'record_player'}, {'frequency': 'r', 'id': 894, 'synset': 'red_cabbage.n.02', 'synonyms': ['red_cabbage'], 'def': 'compact head of purplish-red leaves', 'name': 'red_cabbage'}, {'frequency': 'f', 'id': 895, 'synset': 'reflector.n.01', 'synonyms': ['reflector'], 'def': 'device that reflects light, radiation, etc.', 'name': 'reflector'}, {'frequency': 'f', 'id': 896, 'synset': 'remote_control.n.01', 'synonyms': ['remote_control'], 'def': 'a device that can be used to control a machine or apparatus from a distance', 'name': 'remote_control'}, {'frequency': 'c', 'id': 897, 'synset': 'rhinoceros.n.01', 'synonyms': ['rhinoceros'], 'def': 'massive powerful herbivorous odd-toed ungulate of southeast Asia and Africa having very thick skin and one or two horns on the snout', 'name': 'rhinoceros'}, {'frequency': 'r', 'id': 898, 'synset': 'rib.n.03', 'synonyms': ['rib_(food)'], 'def': 'cut of meat including one or more ribs', 'name': 'rib_(food)'}, {'frequency': 'r', 'id': 899, 'synset': 'rifle.n.01', 'synonyms': ['rifle'], 'def': 'a shoulder firearm with a long barrel', 'name': 'rifle'}, {'frequency': 'f', 'id': 900, 'synset': 'ring.n.08', 'synonyms': ['ring'], 'def': 'jewelry consisting of a circlet of precious metal (often set with jewels) worn on the finger', 'name': 'ring'}, {'frequency': 'r', 'id': 901, 'synset': 'river_boat.n.01', 'synonyms': ['river_boat'], 'def': 'a boat used on rivers or to ply a river', 'name': 'river_boat'}, {'frequency': 'r', 'id': 902, 'synset': 'road_map.n.02', 'synonyms': ['road_map'], 'def': '(NOT A ROAD) a MAP showing roads (for automobile travel)', 'name': 'road_map'}, {'frequency': 'c', 'id': 903, 'synset': 'robe.n.01', 'synonyms': ['robe'], 'def': 'any loose flowing garment', 'name': 'robe'}, {'frequency': 'c', 'id': 904, 'synset': 'rocking_chair.n.01', 'synonyms': ['rocking_chair'], 'def': 'a chair mounted on rockers', 'name': 'rocking_chair'}, {'frequency': 'r', 'id': 905, 'synset': 'roller_skate.n.01', 'synonyms': ['roller_skate'], 'def': 'a shoe with pairs of rollers (small hard wheels) fixed to the sole', 'name': 'roller_skate'}, {'frequency': 'r', 'id': 906, 'synset': 'rollerblade.n.01', 'synonyms': ['Rollerblade'], 'def': 'an in-line variant of a roller skate', 'name': 'Rollerblade'}, {'frequency': 'c', 'id': 907, 'synset': 'rolling_pin.n.01', 'synonyms': ['rolling_pin'], 'def': 'utensil consisting of a cylinder (usually of wood) with a handle at each end; used to roll out dough', 'name': 'rolling_pin'}, {'frequency': 'r', 'id': 908, 'synset': 'root_beer.n.01', 'synonyms': ['root_beer'], 'def': 'carbonated drink containing extracts of roots and herbs', 'name': 'root_beer'}, {'frequency': 'c', 'id': 909, 'synset': 'router.n.02', 'synonyms': ['router_(computer_equipment)'], 'def': 'a device that forwards data packets between computer networks', 'name': 'router_(computer_equipment)'}, {'frequency': 'f', 'id': 910, 'synset': 'rubber_band.n.01', 'synonyms': ['rubber_band', 'elastic_band'], 'def': 'a narrow band of elastic rubber used to hold things (such as papers) together', 'name': 'rubber_band'}, {'frequency': 'c', 'id': 911, 'synset': 'runner.n.08', 'synonyms': ['runner_(carpet)'], 'def': 'a long narrow carpet', 'name': 'runner_(carpet)'}, {'frequency': 'f', 'id': 912, 'synset': 'sack.n.01', 'synonyms': ['plastic_bag', 'paper_bag'], 'def': "a bag made of paper or plastic for holding customer's purchases", 'name': 'plastic_bag'}, {'frequency': 'f', 'id': 913, 'synset': 'saddle.n.01', 'synonyms': ['saddle_(on_an_animal)'], 'def': 'a seat for the rider of a horse or camel', 'name': 'saddle_(on_an_animal)'}, {'frequency': 'f', 'id': 914, 'synset': 'saddle_blanket.n.01', 'synonyms': ['saddle_blanket', 'saddlecloth', 'horse_blanket'], 'def': 'stable gear consisting of a blanket placed under the saddle', 'name': 'saddle_blanket'}, {'frequency': 'c', 'id': 915, 'synset': 'saddlebag.n.01', 'synonyms': ['saddlebag'], 'def': 'a large bag (or pair of bags) hung over a saddle', 'name': 'saddlebag'}, {'frequency': 'r', 'id': 916, 'synset': 'safety_pin.n.01', 'synonyms': ['safety_pin'], 'def': 'a pin in the form of a clasp; has a guard so the point of the pin will not stick the user', 'name': 'safety_pin'}, {'frequency': 'c', 'id': 917, 'synset': 'sail.n.01', 'synonyms': ['sail'], 'def': 'a large piece of fabric by means of which wind is used to propel a sailing vessel', 'name': 'sail'}, {'frequency': 'c', 'id': 918, 'synset': 'salad.n.01', 'synonyms': ['salad'], 'def': 'food mixtures either arranged on a plate or tossed and served with a moist dressing; usually consisting of or including greens', 'name': 'salad'}, {'frequency': 'r', 'id': 919, 'synset': 'salad_plate.n.01', 'synonyms': ['salad_plate', 'salad_bowl'], 'def': 'a plate or bowl for individual servings of salad', 'name': 'salad_plate'}, {'frequency': 'r', 'id': 920, 'synset': 'salami.n.01', 'synonyms': ['salami'], 'def': 'highly seasoned fatty sausage of pork and beef usually dried', 'name': 'salami'}, {'frequency': 'r', 'id': 921, 'synset': 'salmon.n.01', 'synonyms': ['salmon_(fish)'], 'def': 'any of various large food and game fishes of northern waters', 'name': 'salmon_(fish)'}, {'frequency': 'r', 'id': 922, 'synset': 'salmon.n.03', 'synonyms': ['salmon_(food)'], 'def': 'flesh of any of various marine or freshwater fish of the family Salmonidae', 'name': 'salmon_(food)'}, {'frequency': 'r', 'id': 923, 'synset': 'salsa.n.01', 'synonyms': ['salsa'], 'def': 'spicy sauce of tomatoes and onions and chili peppers to accompany Mexican foods', 'name': 'salsa'}, {'frequency': 'f', 'id': 924, 'synset': 'saltshaker.n.01', 'synonyms': ['saltshaker'], 'def': 'a shaker with a perforated top for sprinkling salt', 'name': 'saltshaker'}, {'frequency': 'f', 'id': 925, 'synset': 'sandal.n.01', 'synonyms': ['sandal_(type_of_shoe)'], 'def': 'a shoe consisting of a sole fastened by straps to the foot', 'name': 'sandal_(type_of_shoe)'}, {'frequency': 'f', 'id': 926, 'synset': 'sandwich.n.01', 'synonyms': ['sandwich'], 'def': 'two (or more) slices of bread with a filling between them', 'name': 'sandwich'}, {'frequency': 'r', 'id': 927, 'synset': 'satchel.n.01', 'synonyms': ['satchel'], 'def': 'luggage consisting of a small case with a flat bottom and (usually) a shoulder strap', 'name': 'satchel'}, {'frequency': 'r', 'id': 928, 'synset': 'saucepan.n.01', 'synonyms': ['saucepan'], 'def': 'a deep pan with a handle; used for stewing or boiling', 'name': 'saucepan'}, {'frequency': 'f', 'id': 929, 'synset': 'saucer.n.02', 'synonyms': ['saucer'], 'def': 'a small shallow dish for holding a cup at the table', 'name': 'saucer'}, {'frequency': 'f', 'id': 930, 'synset': 'sausage.n.01', 'synonyms': ['sausage'], 'def': 'highly seasoned minced meat stuffed in casings', 'name': 'sausage'}, {'frequency': 'r', 'id': 931, 'synset': 'sawhorse.n.01', 'synonyms': ['sawhorse', 'sawbuck'], 'def': 'a framework for holding wood that is being sawed', 'name': 'sawhorse'}, {'frequency': 'r', 'id': 932, 'synset': 'sax.n.02', 'synonyms': ['saxophone'], 'def': "a wind instrument with a `J'-shaped form typically made of brass", 'name': 'saxophone'}, {'frequency': 'f', 'id': 933, 'synset': 'scale.n.07', 'synonyms': ['scale_(measuring_instrument)'], 'def': 'a measuring instrument for weighing; shows amount of mass', 'name': 'scale_(measuring_instrument)'}, {'frequency': 'r', 'id': 934, 'synset': 'scarecrow.n.01', 'synonyms': ['scarecrow', 'strawman'], 'def': 'an effigy in the shape of a man to frighten birds away from seeds', 'name': 'scarecrow'}, {'frequency': 'f', 'id': 935, 'synset': 'scarf.n.01', 'synonyms': ['scarf'], 'def': 'a garment worn around the head or neck or shoulders for warmth or decoration', 'name': 'scarf'}, {'frequency': 'c', 'id': 936, 'synset': 'school_bus.n.01', 'synonyms': ['school_bus'], 'def': 'a bus used to transport children to or from school', 'name': 'school_bus'}, {'frequency': 'f', 'id': 937, 'synset': 'scissors.n.01', 'synonyms': ['scissors'], 'def': 'a tool having two crossed pivoting blades with looped handles', 'name': 'scissors'}, {'frequency': 'c', 'id': 938, 'synset': 'scoreboard.n.01', 'synonyms': ['scoreboard'], 'def': 'a large board for displaying the score of a contest (and some other information)', 'name': 'scoreboard'}, {'frequency': 'c', 'id': 939, 'synset': 'scrambled_eggs.n.01', 'synonyms': ['scrambled_eggs'], 'def': 'eggs beaten and cooked to a soft firm consistency while stirring', 'name': 'scrambled_eggs'}, {'frequency': 'r', 'id': 940, 'synset': 'scraper.n.01', 'synonyms': ['scraper'], 'def': 'any of various hand tools for scraping', 'name': 'scraper'}, {'frequency': 'r', 'id': 941, 'synset': 'scratcher.n.03', 'synonyms': ['scratcher'], 'def': 'a device used for scratching', 'name': 'scratcher'}, {'frequency': 'c', 'id': 942, 'synset': 'screwdriver.n.01', 'synonyms': ['screwdriver'], 'def': 'a hand tool for driving screws; has a tip that fits into the head of a screw', 'name': 'screwdriver'}, {'frequency': 'c', 'id': 943, 'synset': 'scrub_brush.n.01', 'synonyms': ['scrubbing_brush'], 'def': 'a brush with short stiff bristles for heavy cleaning', 'name': 'scrubbing_brush'}, {'frequency': 'c', 'id': 944, 'synset': 'sculpture.n.01', 'synonyms': ['sculpture'], 'def': 'a three-dimensional work of art', 'name': 'sculpture'}, {'frequency': 'r', 'id': 945, 'synset': 'seabird.n.01', 'synonyms': ['seabird', 'seafowl'], 'def': 'a bird that frequents coastal waters and the open ocean: gulls; pelicans; gannets; cormorants; albatrosses; petrels; etc.', 'name': 'seabird'}, {'frequency': 'r', 'id': 946, 'synset': 'seahorse.n.02', 'synonyms': ['seahorse'], 'def': 'small fish with horse-like heads bent sharply downward and curled tails', 'name': 'seahorse'}, {'frequency': 'r', 'id': 947, 'synset': 'seaplane.n.01', 'synonyms': ['seaplane', 'hydroplane'], 'def': 'an airplane that can land on or take off from water', 'name': 'seaplane'}, {'frequency': 'c', 'id': 948, 'synset': 'seashell.n.01', 'synonyms': ['seashell'], 'def': 'the shell of a marine organism', 'name': 'seashell'}, {'frequency': 'r', 'id': 949, 'synset': 'seedling.n.01', 'synonyms': ['seedling'], 'def': 'young plant or tree grown from a seed', 'name': 'seedling'}, {'frequency': 'c', 'id': 950, 'synset': 'serving_dish.n.01', 'synonyms': ['serving_dish'], 'def': 'a dish used for serving food', 'name': 'serving_dish'}, {'frequency': 'r', 'id': 951, 'synset': 'sewing_machine.n.01', 'synonyms': ['sewing_machine'], 'def': 'a textile machine used as a home appliance for sewing', 'name': 'sewing_machine'}, {'frequency': 'r', 'id': 952, 'synset': 'shaker.n.03', 'synonyms': ['shaker'], 'def': 'a container in which something can be shaken', 'name': 'shaker'}, {'frequency': 'c', 'id': 953, 'synset': 'shampoo.n.01', 'synonyms': ['shampoo'], 'def': 'cleansing agent consisting of soaps or detergents used for washing the hair', 'name': 'shampoo'}, {'frequency': 'r', 'id': 954, 'synset': 'shark.n.01', 'synonyms': ['shark'], 'def': 'typically large carnivorous fishes with sharpe teeth', 'name': 'shark'}, {'frequency': 'r', 'id': 955, 'synset': 'sharpener.n.01', 'synonyms': ['sharpener'], 'def': 'any implement that is used to make something (an edge or a point) sharper', 'name': 'sharpener'}, {'frequency': 'r', 'id': 956, 'synset': 'sharpie.n.03', 'synonyms': ['Sharpie'], 'def': 'a pen with indelible ink that will write on any surface', 'name': 'Sharpie'}, {'frequency': 'r', 'id': 957, 'synset': 'shaver.n.03', 'synonyms': ['shaver_(electric)', 'electric_shaver', 'electric_razor'], 'def': 'a razor powered by an electric motor', 'name': 'shaver_(electric)'}, {'frequency': 'c', 'id': 958, 'synset': 'shaving_cream.n.01', 'synonyms': ['shaving_cream', 'shaving_soap'], 'def': 'toiletry consisting that forms a rich lather for softening the beard before shaving', 'name': 'shaving_cream'}, {'frequency': 'r', 'id': 959, 'synset': 'shawl.n.01', 'synonyms': ['shawl'], 'def': 'cloak consisting of an oblong piece of cloth used to cover the head and shoulders', 'name': 'shawl'}, {'frequency': 'r', 'id': 960, 'synset': 'shears.n.01', 'synonyms': ['shears'], 'def': 'large scissors with strong blades', 'name': 'shears'}, {'frequency': 'f', 'id': 961, 'synset': 'sheep.n.01', 'synonyms': ['sheep'], 'def': 'woolly usually horned ruminant mammal related to the goat', 'name': 'sheep'}, {'frequency': 'r', 'id': 962, 'synset': 'shepherd_dog.n.01', 'synonyms': ['shepherd_dog', 'sheepdog'], 'def': 'any of various usually long-haired breeds of dog reared to herd and guard sheep', 'name': 'shepherd_dog'}, {'frequency': 'r', 'id': 963, 'synset': 'sherbert.n.01', 'synonyms': ['sherbert', 'sherbet'], 'def': 'a frozen dessert made primarily of fruit juice and sugar', 'name': 'sherbert'}, {'frequency': 'r', 'id': 964, 'synset': 'shield.n.02', 'synonyms': ['shield'], 'def': 'armor carried on the arm to intercept blows', 'name': 'shield'}, {'frequency': 'f', 'id': 965, 'synset': 'shirt.n.01', 'synonyms': ['shirt'], 'def': 'a garment worn on the upper half of the body', 'name': 'shirt'}, {'frequency': 'f', 'id': 966, 'synset': 'shoe.n.01', 'synonyms': ['shoe', 'sneaker_(type_of_shoe)', 'tennis_shoe'], 'def': 'common footwear covering the foot', 'name': 'shoe'}, {'frequency': 'c', 'id': 967, 'synset': 'shopping_bag.n.01', 'synonyms': ['shopping_bag'], 'def': 'a bag made of plastic or strong paper (often with handles); used to transport goods after shopping', 'name': 'shopping_bag'}, {'frequency': 'c', 'id': 968, 'synset': 'shopping_cart.n.01', 'synonyms': ['shopping_cart'], 'def': 'a handcart that holds groceries or other goods while shopping', 'name': 'shopping_cart'}, {'frequency': 'f', 'id': 969, 'synset': 'short_pants.n.01', 'synonyms': ['short_pants', 'shorts_(clothing)', 'trunks_(clothing)'], 'def': 'trousers that end at or above the knee', 'name': 'short_pants'}, {'frequency': 'r', 'id': 970, 'synset': 'shot_glass.n.01', 'synonyms': ['shot_glass'], 'def': 'a small glass adequate to hold a single swallow of whiskey', 'name': 'shot_glass'}, {'frequency': 'c', 'id': 971, 'synset': 'shoulder_bag.n.01', 'synonyms': ['shoulder_bag'], 'def': 'a large handbag that can be carried by a strap looped over the shoulder', 'name': 'shoulder_bag'}, {'frequency': 'c', 'id': 972, 'synset': 'shovel.n.01', 'synonyms': ['shovel'], 'def': 'a hand tool for lifting loose material such as snow, dirt, etc.', 'name': 'shovel'}, {'frequency': 'f', 'id': 973, 'synset': 'shower.n.01', 'synonyms': ['shower_head'], 'def': 'a plumbing fixture that sprays water over you', 'name': 'shower_head'}, {'frequency': 'f', 'id': 974, 'synset': 'shower_curtain.n.01', 'synonyms': ['shower_curtain'], 'def': 'a curtain that keeps water from splashing out of the shower area', 'name': 'shower_curtain'}, {'frequency': 'r', 'id': 975, 'synset': 'shredder.n.01', 'synonyms': ['shredder_(for_paper)'], 'def': 'a device that shreds documents', 'name': 'shredder_(for_paper)'}, {'frequency': 'r', 'id': 976, 'synset': 'sieve.n.01', 'synonyms': ['sieve', 'screen_(sieve)'], 'def': 'a strainer for separating lumps from powdered material or grading particles', 'name': 'sieve'}, {'frequency': 'f', 'id': 977, 'synset': 'signboard.n.01', 'synonyms': ['signboard'], 'def': 'structure displaying a board on which advertisements can be posted', 'name': 'signboard'}, {'frequency': 'c', 'id': 978, 'synset': 'silo.n.01', 'synonyms': ['silo'], 'def': 'a cylindrical tower used for storing goods', 'name': 'silo'}, {'frequency': 'f', 'id': 979, 'synset': 'sink.n.01', 'synonyms': ['sink'], 'def': 'plumbing fixture consisting of a water basin fixed to a wall or floor and having a drainpipe', 'name': 'sink'}, {'frequency': 'f', 'id': 980, 'synset': 'skateboard.n.01', 'synonyms': ['skateboard'], 'def': 'a board with wheels that is ridden in a standing or crouching position and propelled by foot', 'name': 'skateboard'}, {'frequency': 'c', 'id': 981, 'synset': 'skewer.n.01', 'synonyms': ['skewer'], 'def': 'a long pin for holding meat in position while it is being roasted', 'name': 'skewer'}, {'frequency': 'f', 'id': 982, 'synset': 'ski.n.01', 'synonyms': ['ski'], 'def': 'sports equipment for skiing on snow', 'name': 'ski'}, {'frequency': 'f', 'id': 983, 'synset': 'ski_boot.n.01', 'synonyms': ['ski_boot'], 'def': 'a stiff boot that is fastened to a ski with a ski binding', 'name': 'ski_boot'}, {'frequency': 'f', 'id': 984, 'synset': 'ski_parka.n.01', 'synonyms': ['ski_parka', 'ski_jacket'], 'def': 'a parka to be worn while skiing', 'name': 'ski_parka'}, {'frequency': 'f', 'id': 985, 'synset': 'ski_pole.n.01', 'synonyms': ['ski_pole'], 'def': 'a pole with metal points used as an aid in skiing', 'name': 'ski_pole'}, {'frequency': 'f', 'id': 986, 'synset': 'skirt.n.02', 'synonyms': ['skirt'], 'def': 'a garment hanging from the waist; worn mainly by girls and women', 'name': 'skirt'}, {'frequency': 'c', 'id': 987, 'synset': 'sled.n.01', 'synonyms': ['sled', 'sledge', 'sleigh'], 'def': 'a vehicle or flat object for transportation over snow by sliding or pulled by dogs, etc.', 'name': 'sled'}, {'frequency': 'c', 'id': 988, 'synset': 'sleeping_bag.n.01', 'synonyms': ['sleeping_bag'], 'def': 'large padded bag designed to be slept in outdoors', 'name': 'sleeping_bag'}, {'frequency': 'r', 'id': 989, 'synset': 'sling.n.05', 'synonyms': ['sling_(bandage)', 'triangular_bandage'], 'def': 'bandage to support an injured forearm; slung over the shoulder or neck', 'name': 'sling_(bandage)'}, {'frequency': 'c', 'id': 990, 'synset': 'slipper.n.01', 'synonyms': ['slipper_(footwear)', 'carpet_slipper_(footwear)'], 'def': 'low footwear that can be slipped on and off easily; usually worn indoors', 'name': 'slipper_(footwear)'}, {'frequency': 'r', 'id': 991, 'synset': 'smoothie.n.02', 'synonyms': ['smoothie'], 'def': 'a thick smooth drink consisting of fresh fruit pureed with ice cream or yoghurt or milk', 'name': 'smoothie'}, {'frequency': 'r', 'id': 992, 'synset': 'snake.n.01', 'synonyms': ['snake', 'serpent'], 'def': 'limbless scaly elongate reptile; some are venomous', 'name': 'snake'}, {'frequency': 'f', 'id': 993, 'synset': 'snowboard.n.01', 'synonyms': ['snowboard'], 'def': 'a board that resembles a broad ski or a small surfboard; used in a standing position to slide down snow-covered slopes', 'name': 'snowboard'}, {'frequency': 'c', 'id': 994, 'synset': 'snowman.n.01', 'synonyms': ['snowman'], 'def': 'a figure of a person made of packed snow', 'name': 'snowman'}, {'frequency': 'c', 'id': 995, 'synset': 'snowmobile.n.01', 'synonyms': ['snowmobile'], 'def': 'tracked vehicle for travel on snow having skis in front', 'name': 'snowmobile'}, {'frequency': 'f', 'id': 996, 'synset': 'soap.n.01', 'synonyms': ['soap'], 'def': 'a cleansing agent made from the salts of vegetable or animal fats', 'name': 'soap'}, {'frequency': 'f', 'id': 997, 'synset': 'soccer_ball.n.01', 'synonyms': ['soccer_ball'], 'def': "an inflated ball used in playing soccer (called `football' outside of the United States)", 'name': 'soccer_ball'}, {'frequency': 'f', 'id': 998, 'synset': 'sock.n.01', 'synonyms': ['sock'], 'def': 'cloth covering for the foot; worn inside the shoe; reaches to between the ankle and the knee', 'name': 'sock'}, {'frequency': 'r', 'id': 999, 'synset': 'soda_fountain.n.02', 'synonyms': ['soda_fountain'], 'def': 'an apparatus for dispensing soda water', 'name': 'soda_fountain'}, {'frequency': 'r', 'id': 1000, 'synset': 'soda_water.n.01', 'synonyms': ['carbonated_water', 'club_soda', 'seltzer', 'sparkling_water'], 'def': 'effervescent beverage artificially charged with carbon dioxide', 'name': 'carbonated_water'}, {'frequency': 'f', 'id': 1001, 'synset': 'sofa.n.01', 'synonyms': ['sofa', 'couch', 'lounge'], 'def': 'an upholstered seat for more than one person', 'name': 'sofa'}, {'frequency': 'r', 'id': 1002, 'synset': 'softball.n.01', 'synonyms': ['softball'], 'def': 'ball used in playing softball', 'name': 'softball'}, {'frequency': 'c', 'id': 1003, 'synset': 'solar_array.n.01', 'synonyms': ['solar_array', 'solar_battery', 'solar_panel'], 'def': 'electrical device consisting of a large array of connected solar cells', 'name': 'solar_array'}, {'frequency': 'r', 'id': 1004, 'synset': 'sombrero.n.02', 'synonyms': ['sombrero'], 'def': 'a straw hat with a tall crown and broad brim; worn in American southwest and in Mexico', 'name': 'sombrero'}, {'frequency': 'c', 'id': 1005, 'synset': 'soup.n.01', 'synonyms': ['soup'], 'def': 'liquid food especially of meat or fish or vegetable stock often containing pieces of solid food', 'name': 'soup'}, {'frequency': 'r', 'id': 1006, 'synset': 'soup_bowl.n.01', 'synonyms': ['soup_bowl'], 'def': 'a bowl for serving soup', 'name': 'soup_bowl'}, {'frequency': 'c', 'id': 1007, 'synset': 'soupspoon.n.01', 'synonyms': ['soupspoon'], 'def': 'a spoon with a rounded bowl for eating soup', 'name': 'soupspoon'}, {'frequency': 'c', 'id': 1008, 'synset': 'sour_cream.n.01', 'synonyms': ['sour_cream', 'soured_cream'], 'def': 'soured light cream', 'name': 'sour_cream'}, {'frequency': 'r', 'id': 1009, 'synset': 'soya_milk.n.01', 'synonyms': ['soya_milk', 'soybean_milk', 'soymilk'], 'def': 'a milk substitute containing soybean flour and water; used in some infant formulas and in making tofu', 'name': 'soya_milk'}, {'frequency': 'r', 'id': 1010, 'synset': 'space_shuttle.n.01', 'synonyms': ['space_shuttle'], 'def': "a reusable spacecraft with wings for a controlled descent through the Earth's atmosphere", 'name': 'space_shuttle'}, {'frequency': 'r', 'id': 1011, 'synset': 'sparkler.n.02', 'synonyms': ['sparkler_(fireworks)'], 'def': 'a firework that burns slowly and throws out a shower of sparks', 'name': 'sparkler_(fireworks)'}, {'frequency': 'f', 'id': 1012, 'synset': 'spatula.n.02', 'synonyms': ['spatula'], 'def': 'a hand tool with a thin flexible blade used to mix or spread soft substances', 'name': 'spatula'}, {'frequency': 'r', 'id': 1013, 'synset': 'spear.n.01', 'synonyms': ['spear', 'lance'], 'def': 'a long pointed rod used as a tool or weapon', 'name': 'spear'}, {'frequency': 'f', 'id': 1014, 'synset': 'spectacles.n.01', 'synonyms': ['spectacles', 'specs', 'eyeglasses', 'glasses'], 'def': 'optical instrument consisting of a frame that holds a pair of lenses for correcting defective vision', 'name': 'spectacles'}, {'frequency': 'c', 'id': 1015, 'synset': 'spice_rack.n.01', 'synonyms': ['spice_rack'], 'def': 'a rack for displaying containers filled with spices', 'name': 'spice_rack'}, {'frequency': 'r', 'id': 1016, 'synset': 'spider.n.01', 'synonyms': ['spider'], 'def': 'predatory arachnid with eight legs, two poison fangs, two feelers, and usually two silk-spinning organs at the back end of the body', 'name': 'spider'}, {'frequency': 'c', 'id': 1017, 'synset': 'sponge.n.01', 'synonyms': ['sponge'], 'def': 'a porous mass usable to absorb water typically used for cleaning', 'name': 'sponge'}, {'frequency': 'f', 'id': 1018, 'synset': 'spoon.n.01', 'synonyms': ['spoon'], 'def': 'a piece of cutlery with a shallow bowl-shaped container and a handle', 'name': 'spoon'}, {'frequency': 'c', 'id': 1019, 'synset': 'sportswear.n.01', 'synonyms': ['sportswear', 'athletic_wear', 'activewear'], 'def': 'attire worn for sport or for casual wear', 'name': 'sportswear'}, {'frequency': 'c', 'id': 1020, 'synset': 'spotlight.n.02', 'synonyms': ['spotlight'], 'def': 'a lamp that produces a strong beam of light to illuminate a restricted area; used to focus attention of a stage performer', 'name': 'spotlight'}, {'frequency': 'r', 'id': 1021, 'synset': 'squirrel.n.01', 'synonyms': ['squirrel'], 'def': 'a kind of arboreal rodent having a long bushy tail', 'name': 'squirrel'}, {'frequency': 'c', 'id': 1022, 'synset': 'stapler.n.01', 'synonyms': ['stapler_(stapling_machine)'], 'def': 'a machine that inserts staples into sheets of paper in order to fasten them together', 'name': 'stapler_(stapling_machine)'}, {'frequency': 'r', 'id': 1023, 'synset': 'starfish.n.01', 'synonyms': ['starfish', 'sea_star'], 'def': 'echinoderms characterized by five arms extending from a central disk', 'name': 'starfish'}, {'frequency': 'f', 'id': 1024, 'synset': 'statue.n.01', 'synonyms': ['statue_(sculpture)'], 'def': 'a sculpture representing a human or animal', 'name': 'statue_(sculpture)'}, {'frequency': 'c', 'id': 1025, 'synset': 'steak.n.01', 'synonyms': ['steak_(food)'], 'def': 'a slice of meat cut from the fleshy part of an animal or large fish', 'name': 'steak_(food)'}, {'frequency': 'r', 'id': 1026, 'synset': 'steak_knife.n.01', 'synonyms': ['steak_knife'], 'def': 'a sharp table knife used in eating steak', 'name': 'steak_knife'}, {'frequency': 'r', 'id': 1027, 'synset': 'steamer.n.02', 'synonyms': ['steamer_(kitchen_appliance)'], 'def': 'a cooking utensil that can be used to cook food by steaming it', 'name': 'steamer_(kitchen_appliance)'}, {'frequency': 'f', 'id': 1028, 'synset': 'steering_wheel.n.01', 'synonyms': ['steering_wheel'], 'def': 'a handwheel that is used for steering', 'name': 'steering_wheel'}, {'frequency': 'r', 'id': 1029, 'synset': 'stencil.n.01', 'synonyms': ['stencil'], 'def': 'a sheet of material (metal, plastic, etc.) that has been perforated with a pattern; ink or paint can pass through the perforations to create the printed pattern on the surface below', 'name': 'stencil'}, {'frequency': 'r', 'id': 1030, 'synset': 'step_ladder.n.01', 'synonyms': ['stepladder'], 'def': 'a folding portable ladder hinged at the top', 'name': 'stepladder'}, {'frequency': 'c', 'id': 1031, 'synset': 'step_stool.n.01', 'synonyms': ['step_stool'], 'def': 'a stool that has one or two steps that fold under the seat', 'name': 'step_stool'}, {'frequency': 'c', 'id': 1032, 'synset': 'stereo.n.01', 'synonyms': ['stereo_(sound_system)'], 'def': 'electronic device for playing audio', 'name': 'stereo_(sound_system)'}, {'frequency': 'r', 'id': 1033, 'synset': 'stew.n.02', 'synonyms': ['stew'], 'def': 'food prepared by stewing especially meat or fish with vegetables', 'name': 'stew'}, {'frequency': 'r', 'id': 1034, 'synset': 'stirrer.n.02', 'synonyms': ['stirrer'], 'def': 'an implement used for stirring', 'name': 'stirrer'}, {'frequency': 'f', 'id': 1035, 'synset': 'stirrup.n.01', 'synonyms': ['stirrup'], 'def': "support consisting of metal loops into which rider's feet go", 'name': 'stirrup'}, {'frequency': 'c', 'id': 1036, 'synset': 'stocking.n.01', 'synonyms': ['stockings_(leg_wear)'], 'def': 'close-fitting hosiery to cover the foot and leg; come in matched pairs', 'name': 'stockings_(leg_wear)'}, {'frequency': 'f', 'id': 1037, 'synset': 'stool.n.01', 'synonyms': ['stool'], 'def': 'a simple seat without a back or arms', 'name': 'stool'}, {'frequency': 'f', 'id': 1038, 'synset': 'stop_sign.n.01', 'synonyms': ['stop_sign'], 'def': 'a traffic sign to notify drivers that they must come to a complete stop', 'name': 'stop_sign'}, {'frequency': 'f', 'id': 1039, 'synset': 'stoplight.n.01', 'synonyms': ['brake_light'], 'def': 'a red light on the rear of a motor vehicle that signals when the brakes are applied', 'name': 'brake_light'}, {'frequency': 'f', 'id': 1040, 'synset': 'stove.n.01', 'synonyms': ['stove', 'kitchen_stove', 'range_(kitchen_appliance)', 'kitchen_range', 'cooking_stove'], 'def': 'a kitchen appliance used for cooking food', 'name': 'stove'}, {'frequency': 'c', 'id': 1041, 'synset': 'strainer.n.01', 'synonyms': ['strainer'], 'def': 'a filter to retain larger pieces while smaller pieces and liquids pass through', 'name': 'strainer'}, {'frequency': 'f', 'id': 1042, 'synset': 'strap.n.01', 'synonyms': ['strap'], 'def': 'an elongated strip of material for binding things together or holding', 'name': 'strap'}, {'frequency': 'f', 'id': 1043, 'synset': 'straw.n.04', 'synonyms': ['straw_(for_drinking)', 'drinking_straw'], 'def': 'a thin paper or plastic tube used to suck liquids into the mouth', 'name': 'straw_(for_drinking)'}, {'frequency': 'f', 'id': 1044, 'synset': 'strawberry.n.01', 'synonyms': ['strawberry'], 'def': 'sweet fleshy red fruit', 'name': 'strawberry'}, {'frequency': 'f', 'id': 1045, 'synset': 'street_sign.n.01', 'synonyms': ['street_sign'], 'def': 'a sign visible from the street', 'name': 'street_sign'}, {'frequency': 'f', 'id': 1046, 'synset': 'streetlight.n.01', 'synonyms': ['streetlight', 'street_lamp'], 'def': 'a lamp supported on a lamppost; for illuminating a street', 'name': 'streetlight'}, {'frequency': 'r', 'id': 1047, 'synset': 'string_cheese.n.01', 'synonyms': ['string_cheese'], 'def': 'cheese formed in long strings twisted together', 'name': 'string_cheese'}, {'frequency': 'r', 'id': 1048, 'synset': 'stylus.n.02', 'synonyms': ['stylus'], 'def': 'a pointed tool for writing or drawing or engraving', 'name': 'stylus'}, {'frequency': 'r', 'id': 1049, 'synset': 'subwoofer.n.01', 'synonyms': ['subwoofer'], 'def': 'a loudspeaker that is designed to reproduce very low bass frequencies', 'name': 'subwoofer'}, {'frequency': 'r', 'id': 1050, 'synset': 'sugar_bowl.n.01', 'synonyms': ['sugar_bowl'], 'def': 'a dish in which sugar is served', 'name': 'sugar_bowl'}, {'frequency': 'r', 'id': 1051, 'synset': 'sugarcane.n.01', 'synonyms': ['sugarcane_(plant)'], 'def': 'juicy canes whose sap is a source of molasses and commercial sugar; fresh canes are sometimes chewed for the juice', 'name': 'sugarcane_(plant)'}, {'frequency': 'c', 'id': 1052, 'synset': 'suit.n.01', 'synonyms': ['suit_(clothing)'], 'def': 'a set of garments (usually including a jacket and trousers or skirt) for outerwear all of the same fabric and color', 'name': 'suit_(clothing)'}, {'frequency': 'c', 'id': 1053, 'synset': 'sunflower.n.01', 'synonyms': ['sunflower'], 'def': 'any plant of the genus Helianthus having large flower heads with dark disk florets and showy yellow rays', 'name': 'sunflower'}, {'frequency': 'f', 'id': 1054, 'synset': 'sunglasses.n.01', 'synonyms': ['sunglasses'], 'def': 'spectacles that are darkened or polarized to protect the eyes from the glare of the sun', 'name': 'sunglasses'}, {'frequency': 'c', 'id': 1055, 'synset': 'sunhat.n.01', 'synonyms': ['sunhat'], 'def': 'a hat with a broad brim that protects the face from direct exposure to the sun', 'name': 'sunhat'}, {'frequency': 'r', 'id': 1056, 'synset': 'sunscreen.n.01', 'synonyms': ['sunscreen', 'sunblock'], 'def': 'a cream spread on the skin; contains a chemical to filter out ultraviolet light and so protect from sunburn', 'name': 'sunscreen'}, {'frequency': 'f', 'id': 1057, 'synset': 'surfboard.n.01', 'synonyms': ['surfboard'], 'def': 'a narrow buoyant board for riding surf', 'name': 'surfboard'}, {'frequency': 'c', 'id': 1058, 'synset': 'sushi.n.01', 'synonyms': ['sushi'], 'def': 'rice (with raw fish) wrapped in seaweed', 'name': 'sushi'}, {'frequency': 'c', 'id': 1059, 'synset': 'swab.n.02', 'synonyms': ['mop'], 'def': 'cleaning implement consisting of absorbent material fastened to a handle; for cleaning floors', 'name': 'mop'}, {'frequency': 'c', 'id': 1060, 'synset': 'sweat_pants.n.01', 'synonyms': ['sweat_pants'], 'def': 'loose-fitting trousers with elastic cuffs; worn by athletes', 'name': 'sweat_pants'}, {'frequency': 'c', 'id': 1061, 'synset': 'sweatband.n.02', 'synonyms': ['sweatband'], 'def': 'a band of material tied around the forehead or wrist to absorb sweat', 'name': 'sweatband'}, {'frequency': 'f', 'id': 1062, 'synset': 'sweater.n.01', 'synonyms': ['sweater'], 'def': 'a crocheted or knitted garment covering the upper part of the body', 'name': 'sweater'}, {'frequency': 'f', 'id': 1063, 'synset': 'sweatshirt.n.01', 'synonyms': ['sweatshirt'], 'def': 'cotton knit pullover with long sleeves worn during athletic activity', 'name': 'sweatshirt'}, {'frequency': 'c', 'id': 1064, 'synset': 'sweet_potato.n.02', 'synonyms': ['sweet_potato'], 'def': 'the edible tuberous root of the sweet potato vine', 'name': 'sweet_potato'}, {'frequency': 'f', 'id': 1065, 'synset': 'swimsuit.n.01', 'synonyms': ['swimsuit', 'swimwear', 'bathing_suit', 'swimming_costume', 'bathing_costume', 'swimming_trunks', 'bathing_trunks'], 'def': 'garment worn for swimming', 'name': 'swimsuit'}, {'frequency': 'c', 'id': 1066, 'synset': 'sword.n.01', 'synonyms': ['sword'], 'def': 'a cutting or thrusting weapon that has a long metal blade', 'name': 'sword'}, {'frequency': 'r', 'id': 1067, 'synset': 'syringe.n.01', 'synonyms': ['syringe'], 'def': 'a medical instrument used to inject or withdraw fluids', 'name': 'syringe'}, {'frequency': 'r', 'id': 1068, 'synset': 'tabasco.n.02', 'synonyms': ['Tabasco_sauce'], 'def': 'very spicy sauce (trade name Tabasco) made from fully-aged red peppers', 'name': 'Tabasco_sauce'}, {'frequency': 'r', 'id': 1069, 'synset': 'table-tennis_table.n.01', 'synonyms': ['table-tennis_table', 'ping-pong_table'], 'def': 'a table used for playing table tennis', 'name': 'table-tennis_table'}, {'frequency': 'f', 'id': 1070, 'synset': 'table.n.02', 'synonyms': ['table'], 'def': 'a piece of furniture having a smooth flat top that is usually supported by one or more vertical legs', 'name': 'table'}, {'frequency': 'c', 'id': 1071, 'synset': 'table_lamp.n.01', 'synonyms': ['table_lamp'], 'def': 'a lamp that sits on a table', 'name': 'table_lamp'}, {'frequency': 'f', 'id': 1072, 'synset': 'tablecloth.n.01', 'synonyms': ['tablecloth'], 'def': 'a covering spread over a dining table', 'name': 'tablecloth'}, {'frequency': 'r', 'id': 1073, 'synset': 'tachometer.n.01', 'synonyms': ['tachometer'], 'def': 'measuring instrument for indicating speed of rotation', 'name': 'tachometer'}, {'frequency': 'r', 'id': 1074, 'synset': 'taco.n.02', 'synonyms': ['taco'], 'def': 'a small tortilla cupped around a filling', 'name': 'taco'}, {'frequency': 'f', 'id': 1075, 'synset': 'tag.n.02', 'synonyms': ['tag'], 'def': 'a label associated with something for the purpose of identification or information', 'name': 'tag'}, {'frequency': 'f', 'id': 1076, 'synset': 'taillight.n.01', 'synonyms': ['taillight', 'rear_light'], 'def': 'lamp (usually red) mounted at the rear of a motor vehicle', 'name': 'taillight'}, {'frequency': 'r', 'id': 1077, 'synset': 'tambourine.n.01', 'synonyms': ['tambourine'], 'def': 'a shallow drum with a single drumhead and with metallic disks in the sides', 'name': 'tambourine'}, {'frequency': 'r', 'id': 1078, 'synset': 'tank.n.01', 'synonyms': ['army_tank', 'armored_combat_vehicle', 'armoured_combat_vehicle'], 'def': 'an enclosed armored military vehicle; has a cannon and moves on caterpillar treads', 'name': 'army_tank'}, {'frequency': 'c', 'id': 1079, 'synset': 'tank.n.02', 'synonyms': ['tank_(storage_vessel)', 'storage_tank'], 'def': 'a large (usually metallic) vessel for holding gases or liquids', 'name': 'tank_(storage_vessel)'}, {'frequency': 'f', 'id': 1080, 'synset': 'tank_top.n.01', 'synonyms': ['tank_top_(clothing)'], 'def': 'a tight-fitting sleeveless shirt with wide shoulder straps and low neck and no front opening', 'name': 'tank_top_(clothing)'}, {'frequency': 'c', 'id': 1081, 'synset': 'tape.n.01', 'synonyms': ['tape_(sticky_cloth_or_paper)'], 'def': 'a long thin piece of cloth or paper as used for binding or fastening', 'name': 'tape_(sticky_cloth_or_paper)'}, {'frequency': 'c', 'id': 1082, 'synset': 'tape.n.04', 'synonyms': ['tape_measure', 'measuring_tape'], 'def': 'measuring instrument consisting of a narrow strip (cloth or metal) marked in inches or centimeters and used for measuring lengths', 'name': 'tape_measure'}, {'frequency': 'c', 'id': 1083, 'synset': 'tapestry.n.02', 'synonyms': ['tapestry'], 'def': 'a heavy textile with a woven design; used for curtains and upholstery', 'name': 'tapestry'}, {'frequency': 'f', 'id': 1084, 'synset': 'tarpaulin.n.01', 'synonyms': ['tarp'], 'def': 'waterproofed canvas', 'name': 'tarp'}, {'frequency': 'c', 'id': 1085, 'synset': 'tartan.n.01', 'synonyms': ['tartan', 'plaid'], 'def': 'a cloth having a crisscross design', 'name': 'tartan'}, {'frequency': 'c', 'id': 1086, 'synset': 'tassel.n.01', 'synonyms': ['tassel'], 'def': 'adornment consisting of a bunch of cords fastened at one end', 'name': 'tassel'}, {'frequency': 'r', 'id': 1087, 'synset': 'tea_bag.n.01', 'synonyms': ['tea_bag'], 'def': 'a measured amount of tea in a bag for an individual serving of tea', 'name': 'tea_bag'}, {'frequency': 'c', 'id': 1088, 'synset': 'teacup.n.02', 'synonyms': ['teacup'], 'def': 'a cup from which tea is drunk', 'name': 'teacup'}, {'frequency': 'c', 'id': 1089, 'synset': 'teakettle.n.01', 'synonyms': ['teakettle'], 'def': 'kettle for boiling water to make tea', 'name': 'teakettle'}, {'frequency': 'c', 'id': 1090, 'synset': 'teapot.n.01', 'synonyms': ['teapot'], 'def': 'pot for brewing tea; usually has a spout and handle', 'name': 'teapot'}, {'frequency': 'f', 'id': 1091, 'synset': 'teddy.n.01', 'synonyms': ['teddy_bear'], 'def': "plaything consisting of a child's toy bear (usually plush and stuffed with soft materials)", 'name': 'teddy_bear'}, {'frequency': 'f', 'id': 1092, 'synset': 'telephone.n.01', 'synonyms': ['telephone', 'phone', 'telephone_set'], 'def': 'electronic device for communicating by voice over long distances', 'name': 'telephone'}, {'frequency': 'c', 'id': 1093, 'synset': 'telephone_booth.n.01', 'synonyms': ['telephone_booth', 'phone_booth', 'call_box', 'telephone_box', 'telephone_kiosk'], 'def': 'booth for using a telephone', 'name': 'telephone_booth'}, {'frequency': 'f', 'id': 1094, 'synset': 'telephone_pole.n.01', 'synonyms': ['telephone_pole', 'telegraph_pole', 'telegraph_post'], 'def': 'tall pole supporting telephone wires', 'name': 'telephone_pole'}, {'frequency': 'r', 'id': 1095, 'synset': 'telephoto_lens.n.01', 'synonyms': ['telephoto_lens', 'zoom_lens'], 'def': 'a camera lens that magnifies the image', 'name': 'telephoto_lens'}, {'frequency': 'c', 'id': 1096, 'synset': 'television_camera.n.01', 'synonyms': ['television_camera', 'tv_camera'], 'def': 'television equipment for capturing and recording video', 'name': 'television_camera'}, {'frequency': 'f', 'id': 1097, 'synset': 'television_receiver.n.01', 'synonyms': ['television_set', 'tv', 'tv_set'], 'def': 'an electronic device that receives television signals and displays them on a screen', 'name': 'television_set'}, {'frequency': 'f', 'id': 1098, 'synset': 'tennis_ball.n.01', 'synonyms': ['tennis_ball'], 'def': 'ball about the size of a fist used in playing tennis', 'name': 'tennis_ball'}, {'frequency': 'f', 'id': 1099, 'synset': 'tennis_racket.n.01', 'synonyms': ['tennis_racket'], 'def': 'a racket used to play tennis', 'name': 'tennis_racket'}, {'frequency': 'r', 'id': 1100, 'synset': 'tequila.n.01', 'synonyms': ['tequila'], 'def': 'Mexican liquor made from fermented juices of an agave plant', 'name': 'tequila'}, {'frequency': 'c', 'id': 1101, 'synset': 'thermometer.n.01', 'synonyms': ['thermometer'], 'def': 'measuring instrument for measuring temperature', 'name': 'thermometer'}, {'frequency': 'c', 'id': 1102, 'synset': 'thermos.n.01', 'synonyms': ['thermos_bottle'], 'def': 'vacuum flask that preserves temperature of hot or cold drinks', 'name': 'thermos_bottle'}, {'frequency': 'c', 'id': 1103, 'synset': 'thermostat.n.01', 'synonyms': ['thermostat'], 'def': 'a regulator for automatically regulating temperature by starting or stopping the supply of heat', 'name': 'thermostat'}, {'frequency': 'r', 'id': 1104, 'synset': 'thimble.n.02', 'synonyms': ['thimble'], 'def': 'a small metal cap to protect the finger while sewing; can be used as a small container', 'name': 'thimble'}, {'frequency': 'c', 'id': 1105, 'synset': 'thread.n.01', 'synonyms': ['thread', 'yarn'], 'def': 'a fine cord of twisted fibers (of cotton or silk or wool or nylon etc.) used in sewing and weaving', 'name': 'thread'}, {'frequency': 'c', 'id': 1106, 'synset': 'thumbtack.n.01', 'synonyms': ['thumbtack', 'drawing_pin', 'pushpin'], 'def': 'a tack for attaching papers to a bulletin board or drawing board', 'name': 'thumbtack'}, {'frequency': 'c', 'id': 1107, 'synset': 'tiara.n.01', 'synonyms': ['tiara'], 'def': 'a jeweled headdress worn by women on formal occasions', 'name': 'tiara'}, {'frequency': 'c', 'id': 1108, 'synset': 'tiger.n.02', 'synonyms': ['tiger'], 'def': 'large feline of forests in most of Asia having a tawny coat with black stripes', 'name': 'tiger'}, {'frequency': 'c', 'id': 1109, 'synset': 'tights.n.01', 'synonyms': ['tights_(clothing)', 'leotards'], 'def': 'skintight knit hose covering the body from the waist to the feet worn by acrobats and dancers and as stockings by women and girls', 'name': 'tights_(clothing)'}, {'frequency': 'c', 'id': 1110, 'synset': 'timer.n.01', 'synonyms': ['timer', 'stopwatch'], 'def': 'a timepiece that measures a time interval and signals its end', 'name': 'timer'}, {'frequency': 'f', 'id': 1111, 'synset': 'tinfoil.n.01', 'synonyms': ['tinfoil'], 'def': 'foil made of tin or an alloy of tin and lead', 'name': 'tinfoil'}, {'frequency': 'r', 'id': 1112, 'synset': 'tinsel.n.01', 'synonyms': ['tinsel'], 'def': 'a showy decoration that is basically valueless', 'name': 'tinsel'}, {'frequency': 'f', 'id': 1113, 'synset': 'tissue.n.02', 'synonyms': ['tissue_paper'], 'def': 'a soft thin (usually translucent) paper', 'name': 'tissue_paper'}, {'frequency': 'c', 'id': 1114, 'synset': 'toast.n.01', 'synonyms': ['toast_(food)'], 'def': 'slice of bread that has been toasted', 'name': 'toast_(food)'}, {'frequency': 'f', 'id': 1115, 'synset': 'toaster.n.02', 'synonyms': ['toaster'], 'def': 'a kitchen appliance (usually electric) for toasting bread', 'name': 'toaster'}, {'frequency': 'c', 'id': 1116, 'synset': 'toaster_oven.n.01', 'synonyms': ['toaster_oven'], 'def': 'kitchen appliance consisting of a small electric oven for toasting or warming food', 'name': 'toaster_oven'}, {'frequency': 'f', 'id': 1117, 'synset': 'toilet.n.02', 'synonyms': ['toilet'], 'def': 'a plumbing fixture for defecation and urination', 'name': 'toilet'}, {'frequency': 'f', 'id': 1118, 'synset': 'toilet_tissue.n.01', 'synonyms': ['toilet_tissue', 'toilet_paper', 'bathroom_tissue'], 'def': 'a soft thin absorbent paper for use in toilets', 'name': 'toilet_tissue'}, {'frequency': 'f', 'id': 1119, 'synset': 'tomato.n.01', 'synonyms': ['tomato'], 'def': 'mildly acid red or yellow pulpy fruit eaten as a vegetable', 'name': 'tomato'}, {'frequency': 'c', 'id': 1120, 'synset': 'tongs.n.01', 'synonyms': ['tongs'], 'def': 'any of various devices for taking hold of objects; usually have two hinged legs with handles above and pointed hooks below', 'name': 'tongs'}, {'frequency': 'c', 'id': 1121, 'synset': 'toolbox.n.01', 'synonyms': ['toolbox'], 'def': 'a box or chest or cabinet for holding hand tools', 'name': 'toolbox'}, {'frequency': 'f', 'id': 1122, 'synset': 'toothbrush.n.01', 'synonyms': ['toothbrush'], 'def': 'small brush; has long handle; used to clean teeth', 'name': 'toothbrush'}, {'frequency': 'f', 'id': 1123, 'synset': 'toothpaste.n.01', 'synonyms': ['toothpaste'], 'def': 'a dentifrice in the form of a paste', 'name': 'toothpaste'}, {'frequency': 'c', 'id': 1124, 'synset': 'toothpick.n.01', 'synonyms': ['toothpick'], 'def': 'pick consisting of a small strip of wood or plastic; used to pick food from between the teeth', 'name': 'toothpick'}, {'frequency': 'c', 'id': 1125, 'synset': 'top.n.09', 'synonyms': ['cover'], 'def': 'covering for a hole (especially a hole in the top of a container)', 'name': 'cover'}, {'frequency': 'c', 'id': 1126, 'synset': 'tortilla.n.01', 'synonyms': ['tortilla'], 'def': 'thin unleavened pancake made from cornmeal or wheat flour', 'name': 'tortilla'}, {'frequency': 'c', 'id': 1127, 'synset': 'tow_truck.n.01', 'synonyms': ['tow_truck'], 'def': 'a truck equipped to hoist and pull wrecked cars (or to remove cars from no-parking zones)', 'name': 'tow_truck'}, {'frequency': 'f', 'id': 1128, 'synset': 'towel.n.01', 'synonyms': ['towel'], 'def': 'a rectangular piece of absorbent cloth (or paper) for drying or wiping', 'name': 'towel'}, {'frequency': 'f', 'id': 1129, 'synset': 'towel_rack.n.01', 'synonyms': ['towel_rack', 'towel_rail', 'towel_bar'], 'def': 'a rack consisting of one or more bars on which towels can be hung', 'name': 'towel_rack'}, {'frequency': 'f', 'id': 1130, 'synset': 'toy.n.03', 'synonyms': ['toy'], 'def': 'a device regarded as providing amusement', 'name': 'toy'}, {'frequency': 'c', 'id': 1131, 'synset': 'tractor.n.01', 'synonyms': ['tractor_(farm_equipment)'], 'def': 'a wheeled vehicle with large wheels; used in farming and other applications', 'name': 'tractor_(farm_equipment)'}, {'frequency': 'f', 'id': 1132, 'synset': 'traffic_light.n.01', 'synonyms': ['traffic_light'], 'def': 'a device to control vehicle traffic often consisting of three or more lights', 'name': 'traffic_light'}, {'frequency': 'r', 'id': 1133, 'synset': 'trail_bike.n.01', 'synonyms': ['dirt_bike'], 'def': 'a lightweight motorcycle equipped with rugged tires and suspension for off-road use', 'name': 'dirt_bike'}, {'frequency': 'c', 'id': 1134, 'synset': 'trailer_truck.n.01', 'synonyms': ['trailer_truck', 'tractor_trailer', 'trucking_rig', 'articulated_lorry', 'semi_truck'], 'def': 'a truck consisting of a tractor and trailer together', 'name': 'trailer_truck'}, {'frequency': 'f', 'id': 1135, 'synset': 'train.n.01', 'synonyms': ['train_(railroad_vehicle)', 'railroad_train'], 'def': 'public or private transport provided by a line of railway cars coupled together and drawn by a locomotive', 'name': 'train_(railroad_vehicle)'}, {'frequency': 'r', 'id': 1136, 'synset': 'trampoline.n.01', 'synonyms': ['trampoline'], 'def': 'gymnastic apparatus consisting of a strong canvas sheet attached with springs to a metal frame', 'name': 'trampoline'}, {'frequency': 'f', 'id': 1137, 'synset': 'tray.n.01', 'synonyms': ['tray'], 'def': 'an open receptacle for holding or displaying or serving articles or food', 'name': 'tray'}, {'frequency': 'r', 'id': 1138, 'synset': 'tree_house.n.01', 'synonyms': ['tree_house'], 'def': '(NOT A TREE) a PLAYHOUSE built in the branches of a tree', 'name': 'tree_house'}, {'frequency': 'r', 'id': 1139, 'synset': 'trench_coat.n.01', 'synonyms': ['trench_coat'], 'def': 'a military style raincoat; belted with deep pockets', 'name': 'trench_coat'}, {'frequency': 'r', 'id': 1140, 'synset': 'triangle.n.05', 'synonyms': ['triangle_(musical_instrument)'], 'def': 'a percussion instrument consisting of a metal bar bent in the shape of an open triangle', 'name': 'triangle_(musical_instrument)'}, {'frequency': 'r', 'id': 1141, 'synset': 'tricycle.n.01', 'synonyms': ['tricycle'], 'def': 'a vehicle with three wheels that is moved by foot pedals', 'name': 'tricycle'}, {'frequency': 'c', 'id': 1142, 'synset': 'tripod.n.01', 'synonyms': ['tripod'], 'def': 'a three-legged rack used for support', 'name': 'tripod'}, {'frequency': 'f', 'id': 1143, 'synset': 'trouser.n.01', 'synonyms': ['trousers', 'pants_(clothing)'], 'def': 'a garment extending from the waist to the knee or ankle, covering each leg separately', 'name': 'trousers'}, {'frequency': 'f', 'id': 1144, 'synset': 'truck.n.01', 'synonyms': ['truck'], 'def': 'an automotive vehicle suitable for hauling', 'name': 'truck'}, {'frequency': 'r', 'id': 1145, 'synset': 'truffle.n.03', 'synonyms': ['truffle_(chocolate)', 'chocolate_truffle'], 'def': 'creamy chocolate candy', 'name': 'truffle_(chocolate)'}, {'frequency': 'c', 'id': 1146, 'synset': 'trunk.n.02', 'synonyms': ['trunk'], 'def': 'luggage consisting of a large strong case used when traveling or for storage', 'name': 'trunk'}, {'frequency': 'r', 'id': 1147, 'synset': 'tub.n.02', 'synonyms': ['vat'], 'def': 'a large open vessel for holding or storing liquids', 'name': 'vat'}, {'frequency': 'c', 'id': 1148, 'synset': 'turban.n.01', 'synonyms': ['turban'], 'def': 'a traditional headdress consisting of a long scarf wrapped around the head', 'name': 'turban'}, {'frequency': 'r', 'id': 1149, 'synset': 'turkey.n.01', 'synonyms': ['turkey_(bird)'], 'def': 'large gallinaceous bird with fan-shaped tail; widely domesticated for food', 'name': 'turkey_(bird)'}, {'frequency': 'c', 'id': 1150, 'synset': 'turkey.n.04', 'synonyms': ['turkey_(food)'], 'def': 'flesh of large domesticated fowl usually roasted', 'name': 'turkey_(food)'}, {'frequency': 'r', 'id': 1151, 'synset': 'turnip.n.01', 'synonyms': ['turnip'], 'def': 'widely cultivated plant having a large fleshy edible white or yellow root', 'name': 'turnip'}, {'frequency': 'c', 'id': 1152, 'synset': 'turtle.n.02', 'synonyms': ['turtle'], 'def': 'any of various aquatic and land reptiles having a bony shell and flipper-like limbs for swimming', 'name': 'turtle'}, {'frequency': 'r', 'id': 1153, 'synset': 'turtleneck.n.01', 'synonyms': ['turtleneck_(clothing)', 'polo-neck'], 'def': 'a sweater or jersey with a high close-fitting collar', 'name': 'turtleneck_(clothing)'}, {'frequency': 'r', 'id': 1154, 'synset': 'typewriter.n.01', 'synonyms': ['typewriter'], 'def': 'hand-operated character printer for printing written messages one character at a time', 'name': 'typewriter'}, {'frequency': 'f', 'id': 1155, 'synset': 'umbrella.n.01', 'synonyms': ['umbrella'], 'def': 'a lightweight handheld collapsible canopy', 'name': 'umbrella'}, {'frequency': 'c', 'id': 1156, 'synset': 'underwear.n.01', 'synonyms': ['underwear', 'underclothes', 'underclothing', 'underpants'], 'def': 'undergarment worn next to the skin and under the outer garments', 'name': 'underwear'}, {'frequency': 'r', 'id': 1157, 'synset': 'unicycle.n.01', 'synonyms': ['unicycle'], 'def': 'a vehicle with a single wheel that is driven by pedals', 'name': 'unicycle'}, {'frequency': 'c', 'id': 1158, 'synset': 'urinal.n.01', 'synonyms': ['urinal'], 'def': 'a plumbing fixture (usually attached to the wall) used by men to urinate', 'name': 'urinal'}, {'frequency': 'r', 'id': 1159, 'synset': 'urn.n.01', 'synonyms': ['urn'], 'def': 'a large vase that usually has a pedestal or feet', 'name': 'urn'}, {'frequency': 'c', 'id': 1160, 'synset': 'vacuum.n.04', 'synonyms': ['vacuum_cleaner'], 'def': 'an electrical home appliance that cleans by suction', 'name': 'vacuum_cleaner'}, {'frequency': 'c', 'id': 1161, 'synset': 'valve.n.03', 'synonyms': ['valve'], 'def': 'control consisting of a mechanical device for controlling the flow of a fluid', 'name': 'valve'}, {'frequency': 'f', 'id': 1162, 'synset': 'vase.n.01', 'synonyms': ['vase'], 'def': 'an open jar of glass or porcelain used as an ornament or to hold flowers', 'name': 'vase'}, {'frequency': 'c', 'id': 1163, 'synset': 'vending_machine.n.01', 'synonyms': ['vending_machine'], 'def': 'a slot machine for selling goods', 'name': 'vending_machine'}, {'frequency': 'f', 'id': 1164, 'synset': 'vent.n.01', 'synonyms': ['vent', 'blowhole', 'air_vent'], 'def': 'a hole for the escape of gas or air', 'name': 'vent'}, {'frequency': 'c', 'id': 1165, 'synset': 'videotape.n.01', 'synonyms': ['videotape'], 'def': 'a video recording made on magnetic tape', 'name': 'videotape'}, {'frequency': 'r', 'id': 1166, 'synset': 'vinegar.n.01', 'synonyms': ['vinegar'], 'def': 'sour-tasting liquid produced usually by oxidation of the alcohol in wine or cider and used as a condiment or food preservative', 'name': 'vinegar'}, {'frequency': 'r', 'id': 1167, 'synset': 'violin.n.01', 'synonyms': ['violin', 'fiddle'], 'def': 'bowed stringed instrument that is the highest member of the violin family', 'name': 'violin'}, {'frequency': 'r', 'id': 1168, 'synset': 'vodka.n.01', 'synonyms': ['vodka'], 'def': 'unaged colorless liquor originating in Russia', 'name': 'vodka'}, {'frequency': 'r', 'id': 1169, 'synset': 'volleyball.n.02', 'synonyms': ['volleyball'], 'def': 'an inflated ball used in playing volleyball', 'name': 'volleyball'}, {'frequency': 'r', 'id': 1170, 'synset': 'vulture.n.01', 'synonyms': ['vulture'], 'def': 'any of various large birds of prey having naked heads and weak claws and feeding chiefly on carrion', 'name': 'vulture'}, {'frequency': 'c', 'id': 1171, 'synset': 'waffle.n.01', 'synonyms': ['waffle'], 'def': 'pancake batter baked in a waffle iron', 'name': 'waffle'}, {'frequency': 'r', 'id': 1172, 'synset': 'waffle_iron.n.01', 'synonyms': ['waffle_iron'], 'def': 'a kitchen appliance for baking waffles', 'name': 'waffle_iron'}, {'frequency': 'c', 'id': 1173, 'synset': 'wagon.n.01', 'synonyms': ['wagon'], 'def': 'any of various kinds of wheeled vehicles drawn by an animal or a tractor', 'name': 'wagon'}, {'frequency': 'c', 'id': 1174, 'synset': 'wagon_wheel.n.01', 'synonyms': ['wagon_wheel'], 'def': 'a wheel of a wagon', 'name': 'wagon_wheel'}, {'frequency': 'c', 'id': 1175, 'synset': 'walking_stick.n.01', 'synonyms': ['walking_stick'], 'def': 'a stick carried in the hand for support in walking', 'name': 'walking_stick'}, {'frequency': 'c', 'id': 1176, 'synset': 'wall_clock.n.01', 'synonyms': ['wall_clock'], 'def': 'a clock mounted on a wall', 'name': 'wall_clock'}, {'frequency': 'f', 'id': 1177, 'synset': 'wall_socket.n.01', 'synonyms': ['wall_socket', 'wall_plug', 'electric_outlet', 'electrical_outlet', 'outlet', 'electric_receptacle'], 'def': 'receptacle providing a place in a wiring system where current can be taken to run electrical devices', 'name': 'wall_socket'}, {'frequency': 'c', 'id': 1178, 'synset': 'wallet.n.01', 'synonyms': ['wallet', 'billfold'], 'def': 'a pocket-size case for holding papers and paper money', 'name': 'wallet'}, {'frequency': 'r', 'id': 1179, 'synset': 'walrus.n.01', 'synonyms': ['walrus'], 'def': 'either of two large northern marine mammals having ivory tusks and tough hide over thick blubber', 'name': 'walrus'}, {'frequency': 'r', 'id': 1180, 'synset': 'wardrobe.n.01', 'synonyms': ['wardrobe'], 'def': 'a tall piece of furniture that provides storage space for clothes; has a door and rails or hooks for hanging clothes', 'name': 'wardrobe'}, {'frequency': 'r', 'id': 1181, 'synset': 'wasabi.n.02', 'synonyms': ['wasabi'], 'def': 'the thick green root of the wasabi plant that the Japanese use in cooking and that tastes like strong horseradish', 'name': 'wasabi'}, {'frequency': 'c', 'id': 1182, 'synset': 'washer.n.03', 'synonyms': ['automatic_washer', 'washing_machine'], 'def': 'a home appliance for washing clothes and linens automatically', 'name': 'automatic_washer'}, {'frequency': 'f', 'id': 1183, 'synset': 'watch.n.01', 'synonyms': ['watch', 'wristwatch'], 'def': 'a small, portable timepiece', 'name': 'watch'}, {'frequency': 'f', 'id': 1184, 'synset': 'water_bottle.n.01', 'synonyms': ['water_bottle'], 'def': 'a bottle for holding water', 'name': 'water_bottle'}, {'frequency': 'c', 'id': 1185, 'synset': 'water_cooler.n.01', 'synonyms': ['water_cooler'], 'def': 'a device for cooling and dispensing drinking water', 'name': 'water_cooler'}, {'frequency': 'c', 'id': 1186, 'synset': 'water_faucet.n.01', 'synonyms': ['water_faucet', 'water_tap', 'tap_(water_faucet)'], 'def': 'a faucet for drawing water from a pipe or cask', 'name': 'water_faucet'}, {'frequency': 'r', 'id': 1187, 'synset': 'water_filter.n.01', 'synonyms': ['water_filter'], 'def': 'a filter to remove impurities from the water supply', 'name': 'water_filter'}, {'frequency': 'r', 'id': 1188, 'synset': 'water_heater.n.01', 'synonyms': ['water_heater', 'hot-water_heater'], 'def': 'a heater and storage tank to supply heated water', 'name': 'water_heater'}, {'frequency': 'r', 'id': 1189, 'synset': 'water_jug.n.01', 'synonyms': ['water_jug'], 'def': 'a jug that holds water', 'name': 'water_jug'}, {'frequency': 'r', 'id': 1190, 'synset': 'water_pistol.n.01', 'synonyms': ['water_gun', 'squirt_gun'], 'def': 'plaything consisting of a toy pistol that squirts water', 'name': 'water_gun'}, {'frequency': 'c', 'id': 1191, 'synset': 'water_scooter.n.01', 'synonyms': ['water_scooter', 'sea_scooter', 'jet_ski'], 'def': 'a motorboat resembling a motor scooter (NOT A SURFBOARD OR WATER SKI)', 'name': 'water_scooter'}, {'frequency': 'c', 'id': 1192, 'synset': 'water_ski.n.01', 'synonyms': ['water_ski'], 'def': 'broad ski for skimming over water towed by a speedboat (DO NOT MARK WATER)', 'name': 'water_ski'}, {'frequency': 'c', 'id': 1193, 'synset': 'water_tower.n.01', 'synonyms': ['water_tower'], 'def': 'a large reservoir for water', 'name': 'water_tower'}, {'frequency': 'c', 'id': 1194, 'synset': 'watering_can.n.01', 'synonyms': ['watering_can'], 'def': 'a container with a handle and a spout with a perforated nozzle; used to sprinkle water over plants', 'name': 'watering_can'}, {'frequency': 'c', 'id': 1195, 'synset': 'watermelon.n.02', 'synonyms': ['watermelon'], 'def': 'large oblong or roundish melon with a hard green rind and sweet watery red or occasionally yellowish pulp', 'name': 'watermelon'}, {'frequency': 'f', 'id': 1196, 'synset': 'weathervane.n.01', 'synonyms': ['weathervane', 'vane_(weathervane)', 'wind_vane'], 'def': 'mechanical device attached to an elevated structure; rotates freely to show the direction of the wind', 'name': 'weathervane'}, {'frequency': 'c', 'id': 1197, 'synset': 'webcam.n.01', 'synonyms': ['webcam'], 'def': 'a digital camera designed to take digital photographs and transmit them over the internet', 'name': 'webcam'}, {'frequency': 'c', 'id': 1198, 'synset': 'wedding_cake.n.01', 'synonyms': ['wedding_cake', 'bridecake'], 'def': 'a rich cake with two or more tiers and covered with frosting and decorations; served at a wedding reception', 'name': 'wedding_cake'}, {'frequency': 'c', 'id': 1199, 'synset': 'wedding_ring.n.01', 'synonyms': ['wedding_ring', 'wedding_band'], 'def': 'a ring given to the bride and/or groom at the wedding', 'name': 'wedding_ring'}, {'frequency': 'f', 'id': 1200, 'synset': 'wet_suit.n.01', 'synonyms': ['wet_suit'], 'def': 'a close-fitting garment made of a permeable material; worn in cold water to retain body heat', 'name': 'wet_suit'}, {'frequency': 'f', 'id': 1201, 'synset': 'wheel.n.01', 'synonyms': ['wheel'], 'def': 'a circular frame with spokes (or a solid disc) that can rotate on a shaft or axle', 'name': 'wheel'}, {'frequency': 'c', 'id': 1202, 'synset': 'wheelchair.n.01', 'synonyms': ['wheelchair'], 'def': 'a movable chair mounted on large wheels', 'name': 'wheelchair'}, {'frequency': 'c', 'id': 1203, 'synset': 'whipped_cream.n.01', 'synonyms': ['whipped_cream'], 'def': 'cream that has been beaten until light and fluffy', 'name': 'whipped_cream'}, {'frequency': 'r', 'id': 1204, 'synset': 'whiskey.n.01', 'synonyms': ['whiskey'], 'def': 'a liquor made from fermented mash of grain', 'name': 'whiskey'}, {'frequency': 'r', 'id': 1205, 'synset': 'whistle.n.03', 'synonyms': ['whistle'], 'def': 'a small wind instrument that produces a whistling sound by blowing into it', 'name': 'whistle'}, {'frequency': 'r', 'id': 1206, 'synset': 'wick.n.02', 'synonyms': ['wick'], 'def': 'a loosely woven cord in a candle or oil lamp that is lit on fire', 'name': 'wick'}, {'frequency': 'c', 'id': 1207, 'synset': 'wig.n.01', 'synonyms': ['wig'], 'def': 'hairpiece covering the head and made of real or synthetic hair', 'name': 'wig'}, {'frequency': 'c', 'id': 1208, 'synset': 'wind_chime.n.01', 'synonyms': ['wind_chime'], 'def': 'a decorative arrangement of pieces of metal or glass or pottery that hang together loosely so the wind can cause them to tinkle', 'name': 'wind_chime'}, {'frequency': 'c', 'id': 1209, 'synset': 'windmill.n.01', 'synonyms': ['windmill'], 'def': 'a mill that is powered by the wind', 'name': 'windmill'}, {'frequency': 'c', 'id': 1210, 'synset': 'window_box.n.01', 'synonyms': ['window_box_(for_plants)'], 'def': 'a container for growing plants on a windowsill', 'name': 'window_box_(for_plants)'}, {'frequency': 'f', 'id': 1211, 'synset': 'windshield_wiper.n.01', 'synonyms': ['windshield_wiper', 'windscreen_wiper', 'wiper_(for_windshield/screen)'], 'def': 'a mechanical device that cleans the windshield', 'name': 'windshield_wiper'}, {'frequency': 'c', 'id': 1212, 'synset': 'windsock.n.01', 'synonyms': ['windsock', 'air_sock', 'air-sleeve', 'wind_sleeve', 'wind_cone'], 'def': 'a truncated cloth cone mounted on a mast/pole; shows wind direction', 'name': 'windsock'}, {'frequency': 'f', 'id': 1213, 'synset': 'wine_bottle.n.01', 'synonyms': ['wine_bottle'], 'def': 'a bottle for holding wine', 'name': 'wine_bottle'}, {'frequency': 'r', 'id': 1214, 'synset': 'wine_bucket.n.01', 'synonyms': ['wine_bucket', 'wine_cooler'], 'def': 'a bucket of ice used to chill a bottle of wine', 'name': 'wine_bucket'}, {'frequency': 'f', 'id': 1215, 'synset': 'wineglass.n.01', 'synonyms': ['wineglass'], 'def': 'a glass that has a stem and in which wine is served', 'name': 'wineglass'}, {'frequency': 'r', 'id': 1216, 'synset': 'wing_chair.n.01', 'synonyms': ['wing_chair'], 'def': 'easy chair having wings on each side of a high back', 'name': 'wing_chair'}, {'frequency': 'c', 'id': 1217, 'synset': 'winker.n.02', 'synonyms': ['blinder_(for_horses)'], 'def': 'blinds that prevent a horse from seeing something on either side', 'name': 'blinder_(for_horses)'}, {'frequency': 'c', 'id': 1218, 'synset': 'wok.n.01', 'synonyms': ['wok'], 'def': 'pan with a convex bottom; used for frying in Chinese cooking', 'name': 'wok'}, {'frequency': 'r', 'id': 1219, 'synset': 'wolf.n.01', 'synonyms': ['wolf'], 'def': 'a wild carnivorous mammal of the dog family, living and hunting in packs', 'name': 'wolf'}, {'frequency': 'c', 'id': 1220, 'synset': 'wooden_spoon.n.02', 'synonyms': ['wooden_spoon'], 'def': 'a spoon made of wood', 'name': 'wooden_spoon'}, {'frequency': 'c', 'id': 1221, 'synset': 'wreath.n.01', 'synonyms': ['wreath'], 'def': 'an arrangement of flowers, leaves, or stems fastened in a ring', 'name': 'wreath'}, {'frequency': 'c', 'id': 1222, 'synset': 'wrench.n.03', 'synonyms': ['wrench', 'spanner'], 'def': 'a hand tool that is used to hold or twist a nut or bolt', 'name': 'wrench'}, {'frequency': 'c', 'id': 1223, 'synset': 'wristband.n.01', 'synonyms': ['wristband'], 'def': 'band consisting of a part of a sleeve that covers the wrist', 'name': 'wristband'}, {'frequency': 'f', 'id': 1224, 'synset': 'wristlet.n.01', 'synonyms': ['wristlet', 'wrist_band'], 'def': 'a band or bracelet worn around the wrist', 'name': 'wristlet'}, {'frequency': 'r', 'id': 1225, 'synset': 'yacht.n.01', 'synonyms': ['yacht'], 'def': 'an expensive vessel propelled by sail or power and used for cruising or racing', 'name': 'yacht'}, {'frequency': 'r', 'id': 1226, 'synset': 'yak.n.02', 'synonyms': ['yak'], 'def': 'large long-haired wild ox of Tibet often domesticated', 'name': 'yak'}, {'frequency': 'c', 'id': 1227, 'synset': 'yogurt.n.01', 'synonyms': ['yogurt', 'yoghurt', 'yoghourt'], 'def': 'a custard-like food made from curdled milk', 'name': 'yogurt'}, {'frequency': 'r', 'id': 1228, 'synset': 'yoke.n.07', 'synonyms': ['yoke_(animal_equipment)'], 'def': 'gear joining two animals at the neck; NOT egg yolk', 'name': 'yoke_(animal_equipment)'}, {'frequency': 'f', 'id': 1229, 'synset': 'zebra.n.01', 'synonyms': ['zebra'], 'def': 'any of several fleet black-and-white striped African equines', 'name': 'zebra'}, {'frequency': 'c', 'id': 1230, 'synset': 'zucchini.n.02', 'synonyms': ['zucchini', 'courgette'], 'def': 'small cucumber-shaped vegetable marrow; typically dark green', 'name': 'zucchini'}] # noqa +# fmt: on diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/lvis_v1_categories.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/lvis_v1_categories.py new file mode 100644 index 0000000..7374e69 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/lvis_v1_categories.py @@ -0,0 +1,16 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Autogen with +# with open("lvis_v1_val.json", "r") as f: +# a = json.load(f) +# c = a["categories"] +# for x in c: +# del x["image_count"] +# del x["instance_count"] +# LVIS_CATEGORIES = repr(c) + " # noqa" +# with open("/tmp/lvis_categories.py", "wt") as f: +# f.write(f"LVIS_CATEGORIES = {LVIS_CATEGORIES}") +# Then paste the contents of that file below + +# fmt: off +LVIS_CATEGORIES = [{'frequency': 'c', 'synset': 'aerosol.n.02', 'synonyms': ['aerosol_can', 'spray_can'], 'id': 1, 'def': 'a dispenser that holds a substance under pressure', 'name': 'aerosol_can'}, {'frequency': 'f', 'synset': 'air_conditioner.n.01', 'synonyms': ['air_conditioner'], 'id': 2, 'def': 'a machine that keeps air cool and dry', 'name': 'air_conditioner'}, {'frequency': 'f', 'synset': 'airplane.n.01', 'synonyms': ['airplane', 'aeroplane'], 'id': 3, 'def': 'an aircraft that has a fixed wing and is powered by propellers or jets', 'name': 'airplane'}, {'frequency': 'f', 'synset': 'alarm_clock.n.01', 'synonyms': ['alarm_clock'], 'id': 4, 'def': 'a clock that wakes a sleeper at some preset time', 'name': 'alarm_clock'}, {'frequency': 'c', 'synset': 'alcohol.n.01', 'synonyms': ['alcohol', 'alcoholic_beverage'], 'id': 5, 'def': 'a liquor or brew containing alcohol as the active agent', 'name': 'alcohol'}, {'frequency': 'c', 'synset': 'alligator.n.02', 'synonyms': ['alligator', 'gator'], 'id': 6, 'def': 'amphibious reptiles related to crocodiles but with shorter broader snouts', 'name': 'alligator'}, {'frequency': 'c', 'synset': 'almond.n.02', 'synonyms': ['almond'], 'id': 7, 'def': 'oval-shaped edible seed of the almond tree', 'name': 'almond'}, {'frequency': 'c', 'synset': 'ambulance.n.01', 'synonyms': ['ambulance'], 'id': 8, 'def': 'a vehicle that takes people to and from hospitals', 'name': 'ambulance'}, {'frequency': 'c', 'synset': 'amplifier.n.01', 'synonyms': ['amplifier'], 'id': 9, 'def': 'electronic equipment that increases strength of signals', 'name': 'amplifier'}, {'frequency': 'c', 'synset': 'anklet.n.03', 'synonyms': ['anklet', 'ankle_bracelet'], 'id': 10, 'def': 'an ornament worn around the ankle', 'name': 'anklet'}, {'frequency': 'f', 'synset': 'antenna.n.01', 'synonyms': ['antenna', 'aerial', 'transmitting_aerial'], 'id': 11, 'def': 'an electrical device that sends or receives radio or television signals', 'name': 'antenna'}, {'frequency': 'f', 'synset': 'apple.n.01', 'synonyms': ['apple'], 'id': 12, 'def': 'fruit with red or yellow or green skin and sweet to tart crisp whitish flesh', 'name': 'apple'}, {'frequency': 'r', 'synset': 'applesauce.n.01', 'synonyms': ['applesauce'], 'id': 13, 'def': 'puree of stewed apples usually sweetened and spiced', 'name': 'applesauce'}, {'frequency': 'r', 'synset': 'apricot.n.02', 'synonyms': ['apricot'], 'id': 14, 'def': 'downy yellow to rosy-colored fruit resembling a small peach', 'name': 'apricot'}, {'frequency': 'f', 'synset': 'apron.n.01', 'synonyms': ['apron'], 'id': 15, 'def': 'a garment of cloth that is tied about the waist and worn to protect clothing', 'name': 'apron'}, {'frequency': 'c', 'synset': 'aquarium.n.01', 'synonyms': ['aquarium', 'fish_tank'], 'id': 16, 'def': 'a tank/pool/bowl filled with water for keeping live fish and underwater animals', 'name': 'aquarium'}, {'frequency': 'r', 'synset': 'arctic.n.02', 'synonyms': ['arctic_(type_of_shoe)', 'galosh', 'golosh', 'rubber_(type_of_shoe)', 'gumshoe'], 'id': 17, 'def': 'a waterproof overshoe that protects shoes from water or snow', 'name': 'arctic_(type_of_shoe)'}, {'frequency': 'c', 'synset': 'armband.n.02', 'synonyms': ['armband'], 'id': 18, 'def': 'a band worn around the upper arm', 'name': 'armband'}, {'frequency': 'f', 'synset': 'armchair.n.01', 'synonyms': ['armchair'], 'id': 19, 'def': 'chair with a support on each side for arms', 'name': 'armchair'}, {'frequency': 'r', 'synset': 'armoire.n.01', 'synonyms': ['armoire'], 'id': 20, 'def': 'a large wardrobe or cabinet', 'name': 'armoire'}, {'frequency': 'r', 'synset': 'armor.n.01', 'synonyms': ['armor', 'armour'], 'id': 21, 'def': 'protective covering made of metal and used in combat', 'name': 'armor'}, {'frequency': 'c', 'synset': 'artichoke.n.02', 'synonyms': ['artichoke'], 'id': 22, 'def': 'a thistlelike flower head with edible fleshy leaves and heart', 'name': 'artichoke'}, {'frequency': 'f', 'synset': 'ashcan.n.01', 'synonyms': ['trash_can', 'garbage_can', 'wastebin', 'dustbin', 'trash_barrel', 'trash_bin'], 'id': 23, 'def': 'a bin that holds rubbish until it is collected', 'name': 'trash_can'}, {'frequency': 'c', 'synset': 'ashtray.n.01', 'synonyms': ['ashtray'], 'id': 24, 'def': "a receptacle for the ash from smokers' cigars or cigarettes", 'name': 'ashtray'}, {'frequency': 'c', 'synset': 'asparagus.n.02', 'synonyms': ['asparagus'], 'id': 25, 'def': 'edible young shoots of the asparagus plant', 'name': 'asparagus'}, {'frequency': 'c', 'synset': 'atomizer.n.01', 'synonyms': ['atomizer', 'atomiser', 'spray', 'sprayer', 'nebulizer', 'nebuliser'], 'id': 26, 'def': 'a dispenser that turns a liquid (such as perfume) into a fine mist', 'name': 'atomizer'}, {'frequency': 'f', 'synset': 'avocado.n.01', 'synonyms': ['avocado'], 'id': 27, 'def': 'a pear-shaped fruit with green or blackish skin and rich yellowish pulp enclosing a single large seed', 'name': 'avocado'}, {'frequency': 'c', 'synset': 'award.n.02', 'synonyms': ['award', 'accolade'], 'id': 28, 'def': 'a tangible symbol signifying approval or distinction', 'name': 'award'}, {'frequency': 'f', 'synset': 'awning.n.01', 'synonyms': ['awning'], 'id': 29, 'def': 'a canopy made of canvas to shelter people or things from rain or sun', 'name': 'awning'}, {'frequency': 'r', 'synset': 'ax.n.01', 'synonyms': ['ax', 'axe'], 'id': 30, 'def': 'an edge tool with a heavy bladed head mounted across a handle', 'name': 'ax'}, {'frequency': 'r', 'synset': 'baboon.n.01', 'synonyms': ['baboon'], 'id': 31, 'def': 'large terrestrial monkeys having doglike muzzles', 'name': 'baboon'}, {'frequency': 'f', 'synset': 'baby_buggy.n.01', 'synonyms': ['baby_buggy', 'baby_carriage', 'perambulator', 'pram', 'stroller'], 'id': 32, 'def': 'a small vehicle with four wheels in which a baby or child is pushed around', 'name': 'baby_buggy'}, {'frequency': 'c', 'synset': 'backboard.n.01', 'synonyms': ['basketball_backboard'], 'id': 33, 'def': 'a raised vertical board with basket attached; used to play basketball', 'name': 'basketball_backboard'}, {'frequency': 'f', 'synset': 'backpack.n.01', 'synonyms': ['backpack', 'knapsack', 'packsack', 'rucksack', 'haversack'], 'id': 34, 'def': 'a bag carried by a strap on your back or shoulder', 'name': 'backpack'}, {'frequency': 'f', 'synset': 'bag.n.04', 'synonyms': ['handbag', 'purse', 'pocketbook'], 'id': 35, 'def': 'a container used for carrying money and small personal items or accessories', 'name': 'handbag'}, {'frequency': 'f', 'synset': 'bag.n.06', 'synonyms': ['suitcase', 'baggage', 'luggage'], 'id': 36, 'def': 'cases used to carry belongings when traveling', 'name': 'suitcase'}, {'frequency': 'c', 'synset': 'bagel.n.01', 'synonyms': ['bagel', 'beigel'], 'id': 37, 'def': 'glazed yeast-raised doughnut-shaped roll with hard crust', 'name': 'bagel'}, {'frequency': 'r', 'synset': 'bagpipe.n.01', 'synonyms': ['bagpipe'], 'id': 38, 'def': 'a tubular wind instrument; the player blows air into a bag and squeezes it out', 'name': 'bagpipe'}, {'frequency': 'r', 'synset': 'baguet.n.01', 'synonyms': ['baguet', 'baguette'], 'id': 39, 'def': 'narrow French stick loaf', 'name': 'baguet'}, {'frequency': 'r', 'synset': 'bait.n.02', 'synonyms': ['bait', 'lure'], 'id': 40, 'def': 'something used to lure fish or other animals into danger so they can be trapped or killed', 'name': 'bait'}, {'frequency': 'f', 'synset': 'ball.n.06', 'synonyms': ['ball'], 'id': 41, 'def': 'a spherical object used as a plaything', 'name': 'ball'}, {'frequency': 'r', 'synset': 'ballet_skirt.n.01', 'synonyms': ['ballet_skirt', 'tutu'], 'id': 42, 'def': 'very short skirt worn by ballerinas', 'name': 'ballet_skirt'}, {'frequency': 'f', 'synset': 'balloon.n.01', 'synonyms': ['balloon'], 'id': 43, 'def': 'large tough nonrigid bag filled with gas or heated air', 'name': 'balloon'}, {'frequency': 'c', 'synset': 'bamboo.n.02', 'synonyms': ['bamboo'], 'id': 44, 'def': 'woody tropical grass having hollow woody stems', 'name': 'bamboo'}, {'frequency': 'f', 'synset': 'banana.n.02', 'synonyms': ['banana'], 'id': 45, 'def': 'elongated crescent-shaped yellow fruit with soft sweet flesh', 'name': 'banana'}, {'frequency': 'c', 'synset': 'band_aid.n.01', 'synonyms': ['Band_Aid'], 'id': 46, 'def': 'trade name for an adhesive bandage to cover small cuts or blisters', 'name': 'Band_Aid'}, {'frequency': 'c', 'synset': 'bandage.n.01', 'synonyms': ['bandage'], 'id': 47, 'def': 'a piece of soft material that covers and protects an injured part of the body', 'name': 'bandage'}, {'frequency': 'f', 'synset': 'bandanna.n.01', 'synonyms': ['bandanna', 'bandana'], 'id': 48, 'def': 'large and brightly colored handkerchief; often used as a neckerchief', 'name': 'bandanna'}, {'frequency': 'r', 'synset': 'banjo.n.01', 'synonyms': ['banjo'], 'id': 49, 'def': 'a stringed instrument of the guitar family with a long neck and circular body', 'name': 'banjo'}, {'frequency': 'f', 'synset': 'banner.n.01', 'synonyms': ['banner', 'streamer'], 'id': 50, 'def': 'long strip of cloth or paper used for decoration or advertising', 'name': 'banner'}, {'frequency': 'r', 'synset': 'barbell.n.01', 'synonyms': ['barbell'], 'id': 51, 'def': 'a bar to which heavy discs are attached at each end; used in weightlifting', 'name': 'barbell'}, {'frequency': 'r', 'synset': 'barge.n.01', 'synonyms': ['barge'], 'id': 52, 'def': 'a flatbottom boat for carrying heavy loads (especially on canals)', 'name': 'barge'}, {'frequency': 'f', 'synset': 'barrel.n.02', 'synonyms': ['barrel', 'cask'], 'id': 53, 'def': 'a cylindrical container that holds liquids', 'name': 'barrel'}, {'frequency': 'c', 'synset': 'barrette.n.01', 'synonyms': ['barrette'], 'id': 54, 'def': "a pin for holding women's hair in place", 'name': 'barrette'}, {'frequency': 'c', 'synset': 'barrow.n.03', 'synonyms': ['barrow', 'garden_cart', 'lawn_cart', 'wheelbarrow'], 'id': 55, 'def': 'a cart for carrying small loads; has handles and one or more wheels', 'name': 'barrow'}, {'frequency': 'f', 'synset': 'base.n.03', 'synonyms': ['baseball_base'], 'id': 56, 'def': 'a place that the runner must touch before scoring', 'name': 'baseball_base'}, {'frequency': 'f', 'synset': 'baseball.n.02', 'synonyms': ['baseball'], 'id': 57, 'def': 'a ball used in playing baseball', 'name': 'baseball'}, {'frequency': 'f', 'synset': 'baseball_bat.n.01', 'synonyms': ['baseball_bat'], 'id': 58, 'def': 'an implement used in baseball by the batter', 'name': 'baseball_bat'}, {'frequency': 'f', 'synset': 'baseball_cap.n.01', 'synonyms': ['baseball_cap', 'jockey_cap', 'golf_cap'], 'id': 59, 'def': 'a cap with a bill', 'name': 'baseball_cap'}, {'frequency': 'f', 'synset': 'baseball_glove.n.01', 'synonyms': ['baseball_glove', 'baseball_mitt'], 'id': 60, 'def': 'the handwear used by fielders in playing baseball', 'name': 'baseball_glove'}, {'frequency': 'f', 'synset': 'basket.n.01', 'synonyms': ['basket', 'handbasket'], 'id': 61, 'def': 'a container that is usually woven and has handles', 'name': 'basket'}, {'frequency': 'c', 'synset': 'basketball.n.02', 'synonyms': ['basketball'], 'id': 62, 'def': 'an inflated ball used in playing basketball', 'name': 'basketball'}, {'frequency': 'r', 'synset': 'bass_horn.n.01', 'synonyms': ['bass_horn', 'sousaphone', 'tuba'], 'id': 63, 'def': 'the lowest brass wind instrument', 'name': 'bass_horn'}, {'frequency': 'c', 'synset': 'bat.n.01', 'synonyms': ['bat_(animal)'], 'id': 64, 'def': 'nocturnal mouselike mammal with forelimbs modified to form membranous wings', 'name': 'bat_(animal)'}, {'frequency': 'f', 'synset': 'bath_mat.n.01', 'synonyms': ['bath_mat'], 'id': 65, 'def': 'a heavy towel or mat to stand on while drying yourself after a bath', 'name': 'bath_mat'}, {'frequency': 'f', 'synset': 'bath_towel.n.01', 'synonyms': ['bath_towel'], 'id': 66, 'def': 'a large towel; to dry yourself after a bath', 'name': 'bath_towel'}, {'frequency': 'c', 'synset': 'bathrobe.n.01', 'synonyms': ['bathrobe'], 'id': 67, 'def': 'a loose-fitting robe of towelling; worn after a bath or swim', 'name': 'bathrobe'}, {'frequency': 'f', 'synset': 'bathtub.n.01', 'synonyms': ['bathtub', 'bathing_tub'], 'id': 68, 'def': 'a large open container that you fill with water and use to wash the body', 'name': 'bathtub'}, {'frequency': 'r', 'synset': 'batter.n.02', 'synonyms': ['batter_(food)'], 'id': 69, 'def': 'a liquid or semiliquid mixture, as of flour, eggs, and milk, used in cooking', 'name': 'batter_(food)'}, {'frequency': 'c', 'synset': 'battery.n.02', 'synonyms': ['battery'], 'id': 70, 'def': 'a portable device that produces electricity', 'name': 'battery'}, {'frequency': 'r', 'synset': 'beach_ball.n.01', 'synonyms': ['beachball'], 'id': 71, 'def': 'large and light ball; for play at the seaside', 'name': 'beachball'}, {'frequency': 'c', 'synset': 'bead.n.01', 'synonyms': ['bead'], 'id': 72, 'def': 'a small ball with a hole through the middle used for ornamentation, jewellery, etc.', 'name': 'bead'}, {'frequency': 'c', 'synset': 'bean_curd.n.01', 'synonyms': ['bean_curd', 'tofu'], 'id': 73, 'def': 'cheeselike food made of curdled soybean milk', 'name': 'bean_curd'}, {'frequency': 'c', 'synset': 'beanbag.n.01', 'synonyms': ['beanbag'], 'id': 74, 'def': 'a bag filled with dried beans or similar items; used in games or to sit on', 'name': 'beanbag'}, {'frequency': 'f', 'synset': 'beanie.n.01', 'synonyms': ['beanie', 'beany'], 'id': 75, 'def': 'a small skullcap; formerly worn by schoolboys and college freshmen', 'name': 'beanie'}, {'frequency': 'f', 'synset': 'bear.n.01', 'synonyms': ['bear'], 'id': 76, 'def': 'large carnivorous or omnivorous mammals with shaggy coats and claws', 'name': 'bear'}, {'frequency': 'f', 'synset': 'bed.n.01', 'synonyms': ['bed'], 'id': 77, 'def': 'a piece of furniture that provides a place to sleep', 'name': 'bed'}, {'frequency': 'r', 'synset': 'bedpan.n.01', 'synonyms': ['bedpan'], 'id': 78, 'def': 'a shallow vessel used by a bedridden patient for defecation and urination', 'name': 'bedpan'}, {'frequency': 'f', 'synset': 'bedspread.n.01', 'synonyms': ['bedspread', 'bedcover', 'bed_covering', 'counterpane', 'spread'], 'id': 79, 'def': 'decorative cover for a bed', 'name': 'bedspread'}, {'frequency': 'f', 'synset': 'beef.n.01', 'synonyms': ['cow'], 'id': 80, 'def': 'cattle/cow', 'name': 'cow'}, {'frequency': 'f', 'synset': 'beef.n.02', 'synonyms': ['beef_(food)', 'boeuf_(food)'], 'id': 81, 'def': 'meat from an adult domestic bovine', 'name': 'beef_(food)'}, {'frequency': 'r', 'synset': 'beeper.n.01', 'synonyms': ['beeper', 'pager'], 'id': 82, 'def': 'an device that beeps when the person carrying it is being paged', 'name': 'beeper'}, {'frequency': 'f', 'synset': 'beer_bottle.n.01', 'synonyms': ['beer_bottle'], 'id': 83, 'def': 'a bottle that holds beer', 'name': 'beer_bottle'}, {'frequency': 'c', 'synset': 'beer_can.n.01', 'synonyms': ['beer_can'], 'id': 84, 'def': 'a can that holds beer', 'name': 'beer_can'}, {'frequency': 'r', 'synset': 'beetle.n.01', 'synonyms': ['beetle'], 'id': 85, 'def': 'insect with hard wing covers', 'name': 'beetle'}, {'frequency': 'f', 'synset': 'bell.n.01', 'synonyms': ['bell'], 'id': 86, 'def': 'a hollow device made of metal that makes a ringing sound when struck', 'name': 'bell'}, {'frequency': 'f', 'synset': 'bell_pepper.n.02', 'synonyms': ['bell_pepper', 'capsicum'], 'id': 87, 'def': 'large bell-shaped sweet pepper in green or red or yellow or orange or black varieties', 'name': 'bell_pepper'}, {'frequency': 'f', 'synset': 'belt.n.02', 'synonyms': ['belt'], 'id': 88, 'def': 'a band to tie or buckle around the body (usually at the waist)', 'name': 'belt'}, {'frequency': 'f', 'synset': 'belt_buckle.n.01', 'synonyms': ['belt_buckle'], 'id': 89, 'def': 'the buckle used to fasten a belt', 'name': 'belt_buckle'}, {'frequency': 'f', 'synset': 'bench.n.01', 'synonyms': ['bench'], 'id': 90, 'def': 'a long seat for more than one person', 'name': 'bench'}, {'frequency': 'c', 'synset': 'beret.n.01', 'synonyms': ['beret'], 'id': 91, 'def': 'a cap with no brim or bill; made of soft cloth', 'name': 'beret'}, {'frequency': 'c', 'synset': 'bib.n.02', 'synonyms': ['bib'], 'id': 92, 'def': 'a napkin tied under the chin of a child while eating', 'name': 'bib'}, {'frequency': 'r', 'synset': 'bible.n.01', 'synonyms': ['Bible'], 'id': 93, 'def': 'the sacred writings of the Christian religions', 'name': 'Bible'}, {'frequency': 'f', 'synset': 'bicycle.n.01', 'synonyms': ['bicycle', 'bike_(bicycle)'], 'id': 94, 'def': 'a wheeled vehicle that has two wheels and is moved by foot pedals', 'name': 'bicycle'}, {'frequency': 'f', 'synset': 'bill.n.09', 'synonyms': ['visor', 'vizor'], 'id': 95, 'def': 'a brim that projects to the front to shade the eyes', 'name': 'visor'}, {'frequency': 'f', 'synset': 'billboard.n.01', 'synonyms': ['billboard'], 'id': 96, 'def': 'large outdoor signboard', 'name': 'billboard'}, {'frequency': 'c', 'synset': 'binder.n.03', 'synonyms': ['binder', 'ring-binder'], 'id': 97, 'def': 'holds loose papers or magazines', 'name': 'binder'}, {'frequency': 'c', 'synset': 'binoculars.n.01', 'synonyms': ['binoculars', 'field_glasses', 'opera_glasses'], 'id': 98, 'def': 'an optical instrument designed for simultaneous use by both eyes', 'name': 'binoculars'}, {'frequency': 'f', 'synset': 'bird.n.01', 'synonyms': ['bird'], 'id': 99, 'def': 'animal characterized by feathers and wings', 'name': 'bird'}, {'frequency': 'c', 'synset': 'bird_feeder.n.01', 'synonyms': ['birdfeeder'], 'id': 100, 'def': 'an outdoor device that supplies food for wild birds', 'name': 'birdfeeder'}, {'frequency': 'c', 'synset': 'birdbath.n.01', 'synonyms': ['birdbath'], 'id': 101, 'def': 'an ornamental basin (usually in a garden) for birds to bathe in', 'name': 'birdbath'}, {'frequency': 'c', 'synset': 'birdcage.n.01', 'synonyms': ['birdcage'], 'id': 102, 'def': 'a cage in which a bird can be kept', 'name': 'birdcage'}, {'frequency': 'c', 'synset': 'birdhouse.n.01', 'synonyms': ['birdhouse'], 'id': 103, 'def': 'a shelter for birds', 'name': 'birdhouse'}, {'frequency': 'f', 'synset': 'birthday_cake.n.01', 'synonyms': ['birthday_cake'], 'id': 104, 'def': 'decorated cake served at a birthday party', 'name': 'birthday_cake'}, {'frequency': 'r', 'synset': 'birthday_card.n.01', 'synonyms': ['birthday_card'], 'id': 105, 'def': 'a card expressing a birthday greeting', 'name': 'birthday_card'}, {'frequency': 'r', 'synset': 'black_flag.n.01', 'synonyms': ['pirate_flag'], 'id': 106, 'def': 'a flag usually bearing a white skull and crossbones on a black background', 'name': 'pirate_flag'}, {'frequency': 'c', 'synset': 'black_sheep.n.02', 'synonyms': ['black_sheep'], 'id': 107, 'def': 'sheep with a black coat', 'name': 'black_sheep'}, {'frequency': 'c', 'synset': 'blackberry.n.01', 'synonyms': ['blackberry'], 'id': 108, 'def': 'large sweet black or very dark purple edible aggregate fruit', 'name': 'blackberry'}, {'frequency': 'f', 'synset': 'blackboard.n.01', 'synonyms': ['blackboard', 'chalkboard'], 'id': 109, 'def': 'sheet of slate; for writing with chalk', 'name': 'blackboard'}, {'frequency': 'f', 'synset': 'blanket.n.01', 'synonyms': ['blanket'], 'id': 110, 'def': 'bedding that keeps a person warm in bed', 'name': 'blanket'}, {'frequency': 'c', 'synset': 'blazer.n.01', 'synonyms': ['blazer', 'sport_jacket', 'sport_coat', 'sports_jacket', 'sports_coat'], 'id': 111, 'def': 'lightweight jacket; often striped in the colors of a club or school', 'name': 'blazer'}, {'frequency': 'f', 'synset': 'blender.n.01', 'synonyms': ['blender', 'liquidizer', 'liquidiser'], 'id': 112, 'def': 'an electrically powered mixer that mix or chop or liquefy foods', 'name': 'blender'}, {'frequency': 'r', 'synset': 'blimp.n.02', 'synonyms': ['blimp'], 'id': 113, 'def': 'a small nonrigid airship used for observation or as a barrage balloon', 'name': 'blimp'}, {'frequency': 'f', 'synset': 'blinker.n.01', 'synonyms': ['blinker', 'flasher'], 'id': 114, 'def': 'a light that flashes on and off; used as a signal or to send messages', 'name': 'blinker'}, {'frequency': 'f', 'synset': 'blouse.n.01', 'synonyms': ['blouse'], 'id': 115, 'def': 'a top worn by women', 'name': 'blouse'}, {'frequency': 'f', 'synset': 'blueberry.n.02', 'synonyms': ['blueberry'], 'id': 116, 'def': 'sweet edible dark-blue berries of blueberry plants', 'name': 'blueberry'}, {'frequency': 'r', 'synset': 'board.n.09', 'synonyms': ['gameboard'], 'id': 117, 'def': 'a flat portable surface (usually rectangular) designed for board games', 'name': 'gameboard'}, {'frequency': 'f', 'synset': 'boat.n.01', 'synonyms': ['boat', 'ship_(boat)'], 'id': 118, 'def': 'a vessel for travel on water', 'name': 'boat'}, {'frequency': 'r', 'synset': 'bob.n.05', 'synonyms': ['bob', 'bobber', 'bobfloat'], 'id': 119, 'def': 'a small float usually made of cork; attached to a fishing line', 'name': 'bob'}, {'frequency': 'c', 'synset': 'bobbin.n.01', 'synonyms': ['bobbin', 'spool', 'reel'], 'id': 120, 'def': 'a thing around which thread/tape/film or other flexible materials can be wound', 'name': 'bobbin'}, {'frequency': 'c', 'synset': 'bobby_pin.n.01', 'synonyms': ['bobby_pin', 'hairgrip'], 'id': 121, 'def': 'a flat wire hairpin used to hold bobbed hair in place', 'name': 'bobby_pin'}, {'frequency': 'c', 'synset': 'boiled_egg.n.01', 'synonyms': ['boiled_egg', 'coddled_egg'], 'id': 122, 'def': 'egg cooked briefly in the shell in gently boiling water', 'name': 'boiled_egg'}, {'frequency': 'r', 'synset': 'bolo_tie.n.01', 'synonyms': ['bolo_tie', 'bolo', 'bola_tie', 'bola'], 'id': 123, 'def': 'a cord fastened around the neck with an ornamental clasp and worn as a necktie', 'name': 'bolo_tie'}, {'frequency': 'c', 'synset': 'bolt.n.03', 'synonyms': ['deadbolt'], 'id': 124, 'def': 'the part of a lock that is engaged or withdrawn with a key', 'name': 'deadbolt'}, {'frequency': 'f', 'synset': 'bolt.n.06', 'synonyms': ['bolt'], 'id': 125, 'def': 'a screw that screws into a nut to form a fastener', 'name': 'bolt'}, {'frequency': 'r', 'synset': 'bonnet.n.01', 'synonyms': ['bonnet'], 'id': 126, 'def': 'a hat tied under the chin', 'name': 'bonnet'}, {'frequency': 'f', 'synset': 'book.n.01', 'synonyms': ['book'], 'id': 127, 'def': 'a written work or composition that has been published', 'name': 'book'}, {'frequency': 'c', 'synset': 'bookcase.n.01', 'synonyms': ['bookcase'], 'id': 128, 'def': 'a piece of furniture with shelves for storing books', 'name': 'bookcase'}, {'frequency': 'c', 'synset': 'booklet.n.01', 'synonyms': ['booklet', 'brochure', 'leaflet', 'pamphlet'], 'id': 129, 'def': 'a small book usually having a paper cover', 'name': 'booklet'}, {'frequency': 'r', 'synset': 'bookmark.n.01', 'synonyms': ['bookmark', 'bookmarker'], 'id': 130, 'def': 'a marker (a piece of paper or ribbon) placed between the pages of a book', 'name': 'bookmark'}, {'frequency': 'r', 'synset': 'boom.n.04', 'synonyms': ['boom_microphone', 'microphone_boom'], 'id': 131, 'def': 'a pole carrying an overhead microphone projected over a film or tv set', 'name': 'boom_microphone'}, {'frequency': 'f', 'synset': 'boot.n.01', 'synonyms': ['boot'], 'id': 132, 'def': 'footwear that covers the whole foot and lower leg', 'name': 'boot'}, {'frequency': 'f', 'synset': 'bottle.n.01', 'synonyms': ['bottle'], 'id': 133, 'def': 'a glass or plastic vessel used for storing drinks or other liquids', 'name': 'bottle'}, {'frequency': 'c', 'synset': 'bottle_opener.n.01', 'synonyms': ['bottle_opener'], 'id': 134, 'def': 'an opener for removing caps or corks from bottles', 'name': 'bottle_opener'}, {'frequency': 'c', 'synset': 'bouquet.n.01', 'synonyms': ['bouquet'], 'id': 135, 'def': 'an arrangement of flowers that is usually given as a present', 'name': 'bouquet'}, {'frequency': 'r', 'synset': 'bow.n.04', 'synonyms': ['bow_(weapon)'], 'id': 136, 'def': 'a weapon for shooting arrows', 'name': 'bow_(weapon)'}, {'frequency': 'f', 'synset': 'bow.n.08', 'synonyms': ['bow_(decorative_ribbons)'], 'id': 137, 'def': 'a decorative interlacing of ribbons', 'name': 'bow_(decorative_ribbons)'}, {'frequency': 'f', 'synset': 'bow_tie.n.01', 'synonyms': ['bow-tie', 'bowtie'], 'id': 138, 'def': "a man's tie that ties in a bow", 'name': 'bow-tie'}, {'frequency': 'f', 'synset': 'bowl.n.03', 'synonyms': ['bowl'], 'id': 139, 'def': 'a dish that is round and open at the top for serving foods', 'name': 'bowl'}, {'frequency': 'r', 'synset': 'bowl.n.08', 'synonyms': ['pipe_bowl'], 'id': 140, 'def': 'a small round container that is open at the top for holding tobacco', 'name': 'pipe_bowl'}, {'frequency': 'c', 'synset': 'bowler_hat.n.01', 'synonyms': ['bowler_hat', 'bowler', 'derby_hat', 'derby', 'plug_hat'], 'id': 141, 'def': 'a felt hat that is round and hard with a narrow brim', 'name': 'bowler_hat'}, {'frequency': 'r', 'synset': 'bowling_ball.n.01', 'synonyms': ['bowling_ball'], 'id': 142, 'def': 'a large ball with finger holes used in the sport of bowling', 'name': 'bowling_ball'}, {'frequency': 'f', 'synset': 'box.n.01', 'synonyms': ['box'], 'id': 143, 'def': 'a (usually rectangular) container; may have a lid', 'name': 'box'}, {'frequency': 'r', 'synset': 'boxing_glove.n.01', 'synonyms': ['boxing_glove'], 'id': 144, 'def': 'large glove coverings the fists of a fighter worn for the sport of boxing', 'name': 'boxing_glove'}, {'frequency': 'c', 'synset': 'brace.n.06', 'synonyms': ['suspenders'], 'id': 145, 'def': 'elastic straps that hold trousers up (usually used in the plural)', 'name': 'suspenders'}, {'frequency': 'f', 'synset': 'bracelet.n.02', 'synonyms': ['bracelet', 'bangle'], 'id': 146, 'def': 'jewelry worn around the wrist for decoration', 'name': 'bracelet'}, {'frequency': 'r', 'synset': 'brass.n.07', 'synonyms': ['brass_plaque'], 'id': 147, 'def': 'a memorial made of brass', 'name': 'brass_plaque'}, {'frequency': 'c', 'synset': 'brassiere.n.01', 'synonyms': ['brassiere', 'bra', 'bandeau'], 'id': 148, 'def': 'an undergarment worn by women to support their breasts', 'name': 'brassiere'}, {'frequency': 'c', 'synset': 'bread-bin.n.01', 'synonyms': ['bread-bin', 'breadbox'], 'id': 149, 'def': 'a container used to keep bread or cake in', 'name': 'bread-bin'}, {'frequency': 'f', 'synset': 'bread.n.01', 'synonyms': ['bread'], 'id': 150, 'def': 'food made from dough of flour or meal and usually raised with yeast or baking powder and then baked', 'name': 'bread'}, {'frequency': 'r', 'synset': 'breechcloth.n.01', 'synonyms': ['breechcloth', 'breechclout', 'loincloth'], 'id': 151, 'def': 'a garment that provides covering for the loins', 'name': 'breechcloth'}, {'frequency': 'f', 'synset': 'bridal_gown.n.01', 'synonyms': ['bridal_gown', 'wedding_gown', 'wedding_dress'], 'id': 152, 'def': 'a gown worn by the bride at a wedding', 'name': 'bridal_gown'}, {'frequency': 'c', 'synset': 'briefcase.n.01', 'synonyms': ['briefcase'], 'id': 153, 'def': 'a case with a handle; for carrying papers or files or books', 'name': 'briefcase'}, {'frequency': 'f', 'synset': 'broccoli.n.01', 'synonyms': ['broccoli'], 'id': 154, 'def': 'plant with dense clusters of tight green flower buds', 'name': 'broccoli'}, {'frequency': 'r', 'synset': 'brooch.n.01', 'synonyms': ['broach'], 'id': 155, 'def': 'a decorative pin worn by women', 'name': 'broach'}, {'frequency': 'c', 'synset': 'broom.n.01', 'synonyms': ['broom'], 'id': 156, 'def': 'bundle of straws or twigs attached to a long handle; used for cleaning', 'name': 'broom'}, {'frequency': 'c', 'synset': 'brownie.n.03', 'synonyms': ['brownie'], 'id': 157, 'def': 'square or bar of very rich chocolate cake usually with nuts', 'name': 'brownie'}, {'frequency': 'c', 'synset': 'brussels_sprouts.n.01', 'synonyms': ['brussels_sprouts'], 'id': 158, 'def': 'the small edible cabbage-like buds growing along a stalk', 'name': 'brussels_sprouts'}, {'frequency': 'r', 'synset': 'bubble_gum.n.01', 'synonyms': ['bubble_gum'], 'id': 159, 'def': 'a kind of chewing gum that can be blown into bubbles', 'name': 'bubble_gum'}, {'frequency': 'f', 'synset': 'bucket.n.01', 'synonyms': ['bucket', 'pail'], 'id': 160, 'def': 'a roughly cylindrical vessel that is open at the top', 'name': 'bucket'}, {'frequency': 'r', 'synset': 'buggy.n.01', 'synonyms': ['horse_buggy'], 'id': 161, 'def': 'a small lightweight carriage; drawn by a single horse', 'name': 'horse_buggy'}, {'frequency': 'c', 'synset': 'bull.n.11', 'synonyms': ['horned_cow'], 'id': 162, 'def': 'a cow with horns', 'name': 'bull'}, {'frequency': 'c', 'synset': 'bulldog.n.01', 'synonyms': ['bulldog'], 'id': 163, 'def': 'a thickset short-haired dog with a large head and strong undershot lower jaw', 'name': 'bulldog'}, {'frequency': 'r', 'synset': 'bulldozer.n.01', 'synonyms': ['bulldozer', 'dozer'], 'id': 164, 'def': 'large powerful tractor; a large blade in front flattens areas of ground', 'name': 'bulldozer'}, {'frequency': 'c', 'synset': 'bullet_train.n.01', 'synonyms': ['bullet_train'], 'id': 165, 'def': 'a high-speed passenger train', 'name': 'bullet_train'}, {'frequency': 'c', 'synset': 'bulletin_board.n.02', 'synonyms': ['bulletin_board', 'notice_board'], 'id': 166, 'def': 'a board that hangs on a wall; displays announcements', 'name': 'bulletin_board'}, {'frequency': 'r', 'synset': 'bulletproof_vest.n.01', 'synonyms': ['bulletproof_vest'], 'id': 167, 'def': 'a vest capable of resisting the impact of a bullet', 'name': 'bulletproof_vest'}, {'frequency': 'c', 'synset': 'bullhorn.n.01', 'synonyms': ['bullhorn', 'megaphone'], 'id': 168, 'def': 'a portable loudspeaker with built-in microphone and amplifier', 'name': 'bullhorn'}, {'frequency': 'f', 'synset': 'bun.n.01', 'synonyms': ['bun', 'roll'], 'id': 169, 'def': 'small rounded bread either plain or sweet', 'name': 'bun'}, {'frequency': 'c', 'synset': 'bunk_bed.n.01', 'synonyms': ['bunk_bed'], 'id': 170, 'def': 'beds built one above the other', 'name': 'bunk_bed'}, {'frequency': 'f', 'synset': 'buoy.n.01', 'synonyms': ['buoy'], 'id': 171, 'def': 'a float attached by rope to the seabed to mark channels in a harbor or underwater hazards', 'name': 'buoy'}, {'frequency': 'r', 'synset': 'burrito.n.01', 'synonyms': ['burrito'], 'id': 172, 'def': 'a flour tortilla folded around a filling', 'name': 'burrito'}, {'frequency': 'f', 'synset': 'bus.n.01', 'synonyms': ['bus_(vehicle)', 'autobus', 'charabanc', 'double-decker', 'motorbus', 'motorcoach'], 'id': 173, 'def': 'a vehicle carrying many passengers; used for public transport', 'name': 'bus_(vehicle)'}, {'frequency': 'c', 'synset': 'business_card.n.01', 'synonyms': ['business_card'], 'id': 174, 'def': "a card on which are printed the person's name and business affiliation", 'name': 'business_card'}, {'frequency': 'f', 'synset': 'butter.n.01', 'synonyms': ['butter'], 'id': 175, 'def': 'an edible emulsion of fat globules made by churning milk or cream; for cooking and table use', 'name': 'butter'}, {'frequency': 'c', 'synset': 'butterfly.n.01', 'synonyms': ['butterfly'], 'id': 176, 'def': 'insect typically having a slender body with knobbed antennae and broad colorful wings', 'name': 'butterfly'}, {'frequency': 'f', 'synset': 'button.n.01', 'synonyms': ['button'], 'id': 177, 'def': 'a round fastener sewn to shirts and coats etc to fit through buttonholes', 'name': 'button'}, {'frequency': 'f', 'synset': 'cab.n.03', 'synonyms': ['cab_(taxi)', 'taxi', 'taxicab'], 'id': 178, 'def': 'a car that takes passengers where they want to go in exchange for money', 'name': 'cab_(taxi)'}, {'frequency': 'r', 'synset': 'cabana.n.01', 'synonyms': ['cabana'], 'id': 179, 'def': 'a small tent used as a dressing room beside the sea or a swimming pool', 'name': 'cabana'}, {'frequency': 'c', 'synset': 'cabin_car.n.01', 'synonyms': ['cabin_car', 'caboose'], 'id': 180, 'def': 'a car on a freight train for use of the train crew; usually the last car on the train', 'name': 'cabin_car'}, {'frequency': 'f', 'synset': 'cabinet.n.01', 'synonyms': ['cabinet'], 'id': 181, 'def': 'a piece of furniture resembling a cupboard with doors and shelves and drawers', 'name': 'cabinet'}, {'frequency': 'r', 'synset': 'cabinet.n.03', 'synonyms': ['locker', 'storage_locker'], 'id': 182, 'def': 'a storage compartment for clothes and valuables; usually it has a lock', 'name': 'locker'}, {'frequency': 'f', 'synset': 'cake.n.03', 'synonyms': ['cake'], 'id': 183, 'def': 'baked goods made from or based on a mixture of flour, sugar, eggs, and fat', 'name': 'cake'}, {'frequency': 'c', 'synset': 'calculator.n.02', 'synonyms': ['calculator'], 'id': 184, 'def': 'a small machine that is used for mathematical calculations', 'name': 'calculator'}, {'frequency': 'f', 'synset': 'calendar.n.02', 'synonyms': ['calendar'], 'id': 185, 'def': 'a list or register of events (appointments/social events/court cases, etc)', 'name': 'calendar'}, {'frequency': 'c', 'synset': 'calf.n.01', 'synonyms': ['calf'], 'id': 186, 'def': 'young of domestic cattle', 'name': 'calf'}, {'frequency': 'c', 'synset': 'camcorder.n.01', 'synonyms': ['camcorder'], 'id': 187, 'def': 'a portable television camera and videocassette recorder', 'name': 'camcorder'}, {'frequency': 'c', 'synset': 'camel.n.01', 'synonyms': ['camel'], 'id': 188, 'def': 'cud-chewing mammal used as a draft or saddle animal in desert regions', 'name': 'camel'}, {'frequency': 'f', 'synset': 'camera.n.01', 'synonyms': ['camera'], 'id': 189, 'def': 'equipment for taking photographs', 'name': 'camera'}, {'frequency': 'c', 'synset': 'camera_lens.n.01', 'synonyms': ['camera_lens'], 'id': 190, 'def': 'a lens that focuses the image in a camera', 'name': 'camera_lens'}, {'frequency': 'c', 'synset': 'camper.n.02', 'synonyms': ['camper_(vehicle)', 'camping_bus', 'motor_home'], 'id': 191, 'def': 'a recreational vehicle equipped for camping out while traveling', 'name': 'camper_(vehicle)'}, {'frequency': 'f', 'synset': 'can.n.01', 'synonyms': ['can', 'tin_can'], 'id': 192, 'def': 'airtight sealed metal container for food or drink or paint etc.', 'name': 'can'}, {'frequency': 'c', 'synset': 'can_opener.n.01', 'synonyms': ['can_opener', 'tin_opener'], 'id': 193, 'def': 'a device for cutting cans open', 'name': 'can_opener'}, {'frequency': 'f', 'synset': 'candle.n.01', 'synonyms': ['candle', 'candlestick'], 'id': 194, 'def': 'stick of wax with a wick in the middle', 'name': 'candle'}, {'frequency': 'f', 'synset': 'candlestick.n.01', 'synonyms': ['candle_holder'], 'id': 195, 'def': 'a holder with sockets for candles', 'name': 'candle_holder'}, {'frequency': 'r', 'synset': 'candy_bar.n.01', 'synonyms': ['candy_bar'], 'id': 196, 'def': 'a candy shaped as a bar', 'name': 'candy_bar'}, {'frequency': 'c', 'synset': 'candy_cane.n.01', 'synonyms': ['candy_cane'], 'id': 197, 'def': 'a hard candy in the shape of a rod (usually with stripes)', 'name': 'candy_cane'}, {'frequency': 'c', 'synset': 'cane.n.01', 'synonyms': ['walking_cane'], 'id': 198, 'def': 'a stick that people can lean on to help them walk', 'name': 'walking_cane'}, {'frequency': 'c', 'synset': 'canister.n.02', 'synonyms': ['canister', 'cannister'], 'id': 199, 'def': 'metal container for storing dry foods such as tea or flour', 'name': 'canister'}, {'frequency': 'c', 'synset': 'canoe.n.01', 'synonyms': ['canoe'], 'id': 200, 'def': 'small and light boat; pointed at both ends; propelled with a paddle', 'name': 'canoe'}, {'frequency': 'c', 'synset': 'cantaloup.n.02', 'synonyms': ['cantaloup', 'cantaloupe'], 'id': 201, 'def': 'the fruit of a cantaloup vine; small to medium-sized melon with yellowish flesh', 'name': 'cantaloup'}, {'frequency': 'r', 'synset': 'canteen.n.01', 'synonyms': ['canteen'], 'id': 202, 'def': 'a flask for carrying water; used by soldiers or travelers', 'name': 'canteen'}, {'frequency': 'f', 'synset': 'cap.n.01', 'synonyms': ['cap_(headwear)'], 'id': 203, 'def': 'a tight-fitting headwear', 'name': 'cap_(headwear)'}, {'frequency': 'f', 'synset': 'cap.n.02', 'synonyms': ['bottle_cap', 'cap_(container_lid)'], 'id': 204, 'def': 'a top (as for a bottle)', 'name': 'bottle_cap'}, {'frequency': 'c', 'synset': 'cape.n.02', 'synonyms': ['cape'], 'id': 205, 'def': 'a sleeveless garment like a cloak but shorter', 'name': 'cape'}, {'frequency': 'c', 'synset': 'cappuccino.n.01', 'synonyms': ['cappuccino', 'coffee_cappuccino'], 'id': 206, 'def': 'equal parts of espresso and steamed milk', 'name': 'cappuccino'}, {'frequency': 'f', 'synset': 'car.n.01', 'synonyms': ['car_(automobile)', 'auto_(automobile)', 'automobile'], 'id': 207, 'def': 'a motor vehicle with four wheels', 'name': 'car_(automobile)'}, {'frequency': 'f', 'synset': 'car.n.02', 'synonyms': ['railcar_(part_of_a_train)', 'railway_car_(part_of_a_train)', 'railroad_car_(part_of_a_train)'], 'id': 208, 'def': 'a wheeled vehicle adapted to the rails of railroad (mark each individual railcar separately)', 'name': 'railcar_(part_of_a_train)'}, {'frequency': 'r', 'synset': 'car.n.04', 'synonyms': ['elevator_car'], 'id': 209, 'def': 'where passengers ride up and down', 'name': 'elevator_car'}, {'frequency': 'r', 'synset': 'car_battery.n.01', 'synonyms': ['car_battery', 'automobile_battery'], 'id': 210, 'def': 'a battery in a motor vehicle', 'name': 'car_battery'}, {'frequency': 'c', 'synset': 'card.n.02', 'synonyms': ['identity_card'], 'id': 211, 'def': 'a card certifying the identity of the bearer', 'name': 'identity_card'}, {'frequency': 'c', 'synset': 'card.n.03', 'synonyms': ['card'], 'id': 212, 'def': 'a rectangular piece of paper used to send messages (e.g. greetings or pictures)', 'name': 'card'}, {'frequency': 'c', 'synset': 'cardigan.n.01', 'synonyms': ['cardigan'], 'id': 213, 'def': 'knitted jacket that is fastened up the front with buttons or a zipper', 'name': 'cardigan'}, {'frequency': 'r', 'synset': 'cargo_ship.n.01', 'synonyms': ['cargo_ship', 'cargo_vessel'], 'id': 214, 'def': 'a ship designed to carry cargo', 'name': 'cargo_ship'}, {'frequency': 'r', 'synset': 'carnation.n.01', 'synonyms': ['carnation'], 'id': 215, 'def': 'plant with pink to purple-red spice-scented usually double flowers', 'name': 'carnation'}, {'frequency': 'c', 'synset': 'carriage.n.02', 'synonyms': ['horse_carriage'], 'id': 216, 'def': 'a vehicle with wheels drawn by one or more horses', 'name': 'horse_carriage'}, {'frequency': 'f', 'synset': 'carrot.n.01', 'synonyms': ['carrot'], 'id': 217, 'def': 'deep orange edible root of the cultivated carrot plant', 'name': 'carrot'}, {'frequency': 'f', 'synset': 'carryall.n.01', 'synonyms': ['tote_bag'], 'id': 218, 'def': 'a capacious bag or basket', 'name': 'tote_bag'}, {'frequency': 'c', 'synset': 'cart.n.01', 'synonyms': ['cart'], 'id': 219, 'def': 'a heavy open wagon usually having two wheels and drawn by an animal', 'name': 'cart'}, {'frequency': 'c', 'synset': 'carton.n.02', 'synonyms': ['carton'], 'id': 220, 'def': 'a container made of cardboard for holding food or drink', 'name': 'carton'}, {'frequency': 'c', 'synset': 'cash_register.n.01', 'synonyms': ['cash_register', 'register_(for_cash_transactions)'], 'id': 221, 'def': 'a cashbox with an adding machine to register transactions', 'name': 'cash_register'}, {'frequency': 'r', 'synset': 'casserole.n.01', 'synonyms': ['casserole'], 'id': 222, 'def': 'food cooked and served in a casserole', 'name': 'casserole'}, {'frequency': 'r', 'synset': 'cassette.n.01', 'synonyms': ['cassette'], 'id': 223, 'def': 'a container that holds a magnetic tape used for recording or playing sound or video', 'name': 'cassette'}, {'frequency': 'c', 'synset': 'cast.n.05', 'synonyms': ['cast', 'plaster_cast', 'plaster_bandage'], 'id': 224, 'def': 'bandage consisting of a firm covering that immobilizes broken bones while they heal', 'name': 'cast'}, {'frequency': 'f', 'synset': 'cat.n.01', 'synonyms': ['cat'], 'id': 225, 'def': 'a domestic house cat', 'name': 'cat'}, {'frequency': 'f', 'synset': 'cauliflower.n.02', 'synonyms': ['cauliflower'], 'id': 226, 'def': 'edible compact head of white undeveloped flowers', 'name': 'cauliflower'}, {'frequency': 'c', 'synset': 'cayenne.n.02', 'synonyms': ['cayenne_(spice)', 'cayenne_pepper_(spice)', 'red_pepper_(spice)'], 'id': 227, 'def': 'ground pods and seeds of pungent red peppers of the genus Capsicum', 'name': 'cayenne_(spice)'}, {'frequency': 'c', 'synset': 'cd_player.n.01', 'synonyms': ['CD_player'], 'id': 228, 'def': 'electronic equipment for playing compact discs (CDs)', 'name': 'CD_player'}, {'frequency': 'f', 'synset': 'celery.n.01', 'synonyms': ['celery'], 'id': 229, 'def': 'widely cultivated herb with aromatic leaf stalks that are eaten raw or cooked', 'name': 'celery'}, {'frequency': 'f', 'synset': 'cellular_telephone.n.01', 'synonyms': ['cellular_telephone', 'cellular_phone', 'cellphone', 'mobile_phone', 'smart_phone'], 'id': 230, 'def': 'a hand-held mobile telephone', 'name': 'cellular_telephone'}, {'frequency': 'r', 'synset': 'chain_mail.n.01', 'synonyms': ['chain_mail', 'ring_mail', 'chain_armor', 'chain_armour', 'ring_armor', 'ring_armour'], 'id': 231, 'def': '(Middle Ages) flexible armor made of interlinked metal rings', 'name': 'chain_mail'}, {'frequency': 'f', 'synset': 'chair.n.01', 'synonyms': ['chair'], 'id': 232, 'def': 'a seat for one person, with a support for the back', 'name': 'chair'}, {'frequency': 'r', 'synset': 'chaise_longue.n.01', 'synonyms': ['chaise_longue', 'chaise', 'daybed'], 'id': 233, 'def': 'a long chair; for reclining', 'name': 'chaise_longue'}, {'frequency': 'r', 'synset': 'chalice.n.01', 'synonyms': ['chalice'], 'id': 234, 'def': 'a bowl-shaped drinking vessel; especially the Eucharistic cup', 'name': 'chalice'}, {'frequency': 'f', 'synset': 'chandelier.n.01', 'synonyms': ['chandelier'], 'id': 235, 'def': 'branched lighting fixture; often ornate; hangs from the ceiling', 'name': 'chandelier'}, {'frequency': 'r', 'synset': 'chap.n.04', 'synonyms': ['chap'], 'id': 236, 'def': 'leather leggings without a seat; worn over trousers by cowboys to protect their legs', 'name': 'chap'}, {'frequency': 'r', 'synset': 'checkbook.n.01', 'synonyms': ['checkbook', 'chequebook'], 'id': 237, 'def': 'a book issued to holders of checking accounts', 'name': 'checkbook'}, {'frequency': 'r', 'synset': 'checkerboard.n.01', 'synonyms': ['checkerboard'], 'id': 238, 'def': 'a board having 64 squares of two alternating colors', 'name': 'checkerboard'}, {'frequency': 'c', 'synset': 'cherry.n.03', 'synonyms': ['cherry'], 'id': 239, 'def': 'a red fruit with a single hard stone', 'name': 'cherry'}, {'frequency': 'r', 'synset': 'chessboard.n.01', 'synonyms': ['chessboard'], 'id': 240, 'def': 'a checkerboard used to play chess', 'name': 'chessboard'}, {'frequency': 'c', 'synset': 'chicken.n.02', 'synonyms': ['chicken_(animal)'], 'id': 241, 'def': 'a domestic fowl bred for flesh or eggs', 'name': 'chicken_(animal)'}, {'frequency': 'c', 'synset': 'chickpea.n.01', 'synonyms': ['chickpea', 'garbanzo'], 'id': 242, 'def': 'the seed of the chickpea plant; usually dried', 'name': 'chickpea'}, {'frequency': 'c', 'synset': 'chili.n.02', 'synonyms': ['chili_(vegetable)', 'chili_pepper_(vegetable)', 'chilli_(vegetable)', 'chilly_(vegetable)', 'chile_(vegetable)'], 'id': 243, 'def': 'very hot and finely tapering pepper of special pungency', 'name': 'chili_(vegetable)'}, {'frequency': 'r', 'synset': 'chime.n.01', 'synonyms': ['chime', 'gong'], 'id': 244, 'def': 'an instrument consisting of a set of bells that are struck with a hammer', 'name': 'chime'}, {'frequency': 'r', 'synset': 'chinaware.n.01', 'synonyms': ['chinaware'], 'id': 245, 'def': 'dishware made of high quality porcelain', 'name': 'chinaware'}, {'frequency': 'c', 'synset': 'chip.n.04', 'synonyms': ['crisp_(potato_chip)', 'potato_chip'], 'id': 246, 'def': 'a thin crisp slice of potato fried in deep fat', 'name': 'crisp_(potato_chip)'}, {'frequency': 'r', 'synset': 'chip.n.06', 'synonyms': ['poker_chip'], 'id': 247, 'def': 'a small disk-shaped counter used to represent money when gambling', 'name': 'poker_chip'}, {'frequency': 'c', 'synset': 'chocolate_bar.n.01', 'synonyms': ['chocolate_bar'], 'id': 248, 'def': 'a bar of chocolate candy', 'name': 'chocolate_bar'}, {'frequency': 'c', 'synset': 'chocolate_cake.n.01', 'synonyms': ['chocolate_cake'], 'id': 249, 'def': 'cake containing chocolate', 'name': 'chocolate_cake'}, {'frequency': 'r', 'synset': 'chocolate_milk.n.01', 'synonyms': ['chocolate_milk'], 'id': 250, 'def': 'milk flavored with chocolate syrup', 'name': 'chocolate_milk'}, {'frequency': 'r', 'synset': 'chocolate_mousse.n.01', 'synonyms': ['chocolate_mousse'], 'id': 251, 'def': 'dessert mousse made with chocolate', 'name': 'chocolate_mousse'}, {'frequency': 'f', 'synset': 'choker.n.03', 'synonyms': ['choker', 'collar', 'neckband'], 'id': 252, 'def': 'shirt collar, animal collar, or tight-fitting necklace', 'name': 'choker'}, {'frequency': 'f', 'synset': 'chopping_board.n.01', 'synonyms': ['chopping_board', 'cutting_board', 'chopping_block'], 'id': 253, 'def': 'a wooden board where meats or vegetables can be cut', 'name': 'chopping_board'}, {'frequency': 'f', 'synset': 'chopstick.n.01', 'synonyms': ['chopstick'], 'id': 254, 'def': 'one of a pair of slender sticks used as oriental tableware to eat food with', 'name': 'chopstick'}, {'frequency': 'f', 'synset': 'christmas_tree.n.05', 'synonyms': ['Christmas_tree'], 'id': 255, 'def': 'an ornamented evergreen used as a Christmas decoration', 'name': 'Christmas_tree'}, {'frequency': 'c', 'synset': 'chute.n.02', 'synonyms': ['slide'], 'id': 256, 'def': 'sloping channel through which things can descend', 'name': 'slide'}, {'frequency': 'r', 'synset': 'cider.n.01', 'synonyms': ['cider', 'cyder'], 'id': 257, 'def': 'a beverage made from juice pressed from apples', 'name': 'cider'}, {'frequency': 'r', 'synset': 'cigar_box.n.01', 'synonyms': ['cigar_box'], 'id': 258, 'def': 'a box for holding cigars', 'name': 'cigar_box'}, {'frequency': 'f', 'synset': 'cigarette.n.01', 'synonyms': ['cigarette'], 'id': 259, 'def': 'finely ground tobacco wrapped in paper; for smoking', 'name': 'cigarette'}, {'frequency': 'c', 'synset': 'cigarette_case.n.01', 'synonyms': ['cigarette_case', 'cigarette_pack'], 'id': 260, 'def': 'a small flat case for holding cigarettes', 'name': 'cigarette_case'}, {'frequency': 'f', 'synset': 'cistern.n.02', 'synonyms': ['cistern', 'water_tank'], 'id': 261, 'def': 'a tank that holds the water used to flush a toilet', 'name': 'cistern'}, {'frequency': 'r', 'synset': 'clarinet.n.01', 'synonyms': ['clarinet'], 'id': 262, 'def': 'a single-reed instrument with a straight tube', 'name': 'clarinet'}, {'frequency': 'c', 'synset': 'clasp.n.01', 'synonyms': ['clasp'], 'id': 263, 'def': 'a fastener (as a buckle or hook) that is used to hold two things together', 'name': 'clasp'}, {'frequency': 'c', 'synset': 'cleansing_agent.n.01', 'synonyms': ['cleansing_agent', 'cleanser', 'cleaner'], 'id': 264, 'def': 'a preparation used in cleaning something', 'name': 'cleansing_agent'}, {'frequency': 'r', 'synset': 'cleat.n.02', 'synonyms': ['cleat_(for_securing_rope)'], 'id': 265, 'def': 'a fastener (usually with two projecting horns) around which a rope can be secured', 'name': 'cleat_(for_securing_rope)'}, {'frequency': 'r', 'synset': 'clementine.n.01', 'synonyms': ['clementine'], 'id': 266, 'def': 'a variety of mandarin orange', 'name': 'clementine'}, {'frequency': 'c', 'synset': 'clip.n.03', 'synonyms': ['clip'], 'id': 267, 'def': 'any of various small fasteners used to hold loose articles together', 'name': 'clip'}, {'frequency': 'c', 'synset': 'clipboard.n.01', 'synonyms': ['clipboard'], 'id': 268, 'def': 'a small writing board with a clip at the top for holding papers', 'name': 'clipboard'}, {'frequency': 'r', 'synset': 'clipper.n.03', 'synonyms': ['clippers_(for_plants)'], 'id': 269, 'def': 'shears for cutting grass or shrubbery (often used in the plural)', 'name': 'clippers_(for_plants)'}, {'frequency': 'r', 'synset': 'cloak.n.02', 'synonyms': ['cloak'], 'id': 270, 'def': 'a loose outer garment', 'name': 'cloak'}, {'frequency': 'f', 'synset': 'clock.n.01', 'synonyms': ['clock', 'timepiece', 'timekeeper'], 'id': 271, 'def': 'a timepiece that shows the time of day', 'name': 'clock'}, {'frequency': 'f', 'synset': 'clock_tower.n.01', 'synonyms': ['clock_tower'], 'id': 272, 'def': 'a tower with a large clock visible high up on an outside face', 'name': 'clock_tower'}, {'frequency': 'c', 'synset': 'clothes_hamper.n.01', 'synonyms': ['clothes_hamper', 'laundry_basket', 'clothes_basket'], 'id': 273, 'def': 'a hamper that holds dirty clothes to be washed or wet clothes to be dried', 'name': 'clothes_hamper'}, {'frequency': 'c', 'synset': 'clothespin.n.01', 'synonyms': ['clothespin', 'clothes_peg'], 'id': 274, 'def': 'wood or plastic fastener; for holding clothes on a clothesline', 'name': 'clothespin'}, {'frequency': 'r', 'synset': 'clutch_bag.n.01', 'synonyms': ['clutch_bag'], 'id': 275, 'def': "a woman's strapless purse that is carried in the hand", 'name': 'clutch_bag'}, {'frequency': 'f', 'synset': 'coaster.n.03', 'synonyms': ['coaster'], 'id': 276, 'def': 'a covering (plate or mat) that protects the surface of a table', 'name': 'coaster'}, {'frequency': 'f', 'synset': 'coat.n.01', 'synonyms': ['coat'], 'id': 277, 'def': 'an outer garment that has sleeves and covers the body from shoulder down', 'name': 'coat'}, {'frequency': 'c', 'synset': 'coat_hanger.n.01', 'synonyms': ['coat_hanger', 'clothes_hanger', 'dress_hanger'], 'id': 278, 'def': "a hanger that is shaped like a person's shoulders", 'name': 'coat_hanger'}, {'frequency': 'c', 'synset': 'coatrack.n.01', 'synonyms': ['coatrack', 'hatrack'], 'id': 279, 'def': 'a rack with hooks for temporarily holding coats and hats', 'name': 'coatrack'}, {'frequency': 'c', 'synset': 'cock.n.04', 'synonyms': ['cock', 'rooster'], 'id': 280, 'def': 'adult male chicken', 'name': 'cock'}, {'frequency': 'r', 'synset': 'cockroach.n.01', 'synonyms': ['cockroach'], 'id': 281, 'def': 'any of numerous chiefly nocturnal insects; some are domestic pests', 'name': 'cockroach'}, {'frequency': 'r', 'synset': 'cocoa.n.01', 'synonyms': ['cocoa_(beverage)', 'hot_chocolate_(beverage)', 'drinking_chocolate'], 'id': 282, 'def': 'a beverage made from cocoa powder and milk and sugar; usually drunk hot', 'name': 'cocoa_(beverage)'}, {'frequency': 'c', 'synset': 'coconut.n.02', 'synonyms': ['coconut', 'cocoanut'], 'id': 283, 'def': 'large hard-shelled brown oval nut with a fibrous husk', 'name': 'coconut'}, {'frequency': 'f', 'synset': 'coffee_maker.n.01', 'synonyms': ['coffee_maker', 'coffee_machine'], 'id': 284, 'def': 'a kitchen appliance for brewing coffee automatically', 'name': 'coffee_maker'}, {'frequency': 'f', 'synset': 'coffee_table.n.01', 'synonyms': ['coffee_table', 'cocktail_table'], 'id': 285, 'def': 'low table where magazines can be placed and coffee or cocktails are served', 'name': 'coffee_table'}, {'frequency': 'c', 'synset': 'coffeepot.n.01', 'synonyms': ['coffeepot'], 'id': 286, 'def': 'tall pot in which coffee is brewed', 'name': 'coffeepot'}, {'frequency': 'r', 'synset': 'coil.n.05', 'synonyms': ['coil'], 'id': 287, 'def': 'tubing that is wound in a spiral', 'name': 'coil'}, {'frequency': 'c', 'synset': 'coin.n.01', 'synonyms': ['coin'], 'id': 288, 'def': 'a flat metal piece (usually a disc) used as money', 'name': 'coin'}, {'frequency': 'c', 'synset': 'colander.n.01', 'synonyms': ['colander', 'cullender'], 'id': 289, 'def': 'bowl-shaped strainer; used to wash or drain foods', 'name': 'colander'}, {'frequency': 'c', 'synset': 'coleslaw.n.01', 'synonyms': ['coleslaw', 'slaw'], 'id': 290, 'def': 'basically shredded cabbage', 'name': 'coleslaw'}, {'frequency': 'r', 'synset': 'coloring_material.n.01', 'synonyms': ['coloring_material', 'colouring_material'], 'id': 291, 'def': 'any material used for its color', 'name': 'coloring_material'}, {'frequency': 'r', 'synset': 'combination_lock.n.01', 'synonyms': ['combination_lock'], 'id': 292, 'def': 'lock that can be opened only by turning dials in a special sequence', 'name': 'combination_lock'}, {'frequency': 'c', 'synset': 'comforter.n.04', 'synonyms': ['pacifier', 'teething_ring'], 'id': 293, 'def': 'device used for an infant to suck or bite on', 'name': 'pacifier'}, {'frequency': 'r', 'synset': 'comic_book.n.01', 'synonyms': ['comic_book'], 'id': 294, 'def': 'a magazine devoted to comic strips', 'name': 'comic_book'}, {'frequency': 'r', 'synset': 'compass.n.01', 'synonyms': ['compass'], 'id': 295, 'def': 'navigational instrument for finding directions', 'name': 'compass'}, {'frequency': 'f', 'synset': 'computer_keyboard.n.01', 'synonyms': ['computer_keyboard', 'keyboard_(computer)'], 'id': 296, 'def': 'a keyboard that is a data input device for computers', 'name': 'computer_keyboard'}, {'frequency': 'f', 'synset': 'condiment.n.01', 'synonyms': ['condiment'], 'id': 297, 'def': 'a preparation (a sauce or relish or spice) to enhance flavor or enjoyment', 'name': 'condiment'}, {'frequency': 'f', 'synset': 'cone.n.01', 'synonyms': ['cone', 'traffic_cone'], 'id': 298, 'def': 'a cone-shaped object used to direct traffic', 'name': 'cone'}, {'frequency': 'f', 'synset': 'control.n.09', 'synonyms': ['control', 'controller'], 'id': 299, 'def': 'a mechanism that controls the operation of a machine', 'name': 'control'}, {'frequency': 'r', 'synset': 'convertible.n.01', 'synonyms': ['convertible_(automobile)'], 'id': 300, 'def': 'a car that has top that can be folded or removed', 'name': 'convertible_(automobile)'}, {'frequency': 'r', 'synset': 'convertible.n.03', 'synonyms': ['sofa_bed'], 'id': 301, 'def': 'a sofa that can be converted into a bed', 'name': 'sofa_bed'}, {'frequency': 'r', 'synset': 'cooker.n.01', 'synonyms': ['cooker'], 'id': 302, 'def': 'a utensil for cooking', 'name': 'cooker'}, {'frequency': 'f', 'synset': 'cookie.n.01', 'synonyms': ['cookie', 'cooky', 'biscuit_(cookie)'], 'id': 303, 'def': "any of various small flat sweet cakes (`biscuit' is the British term)", 'name': 'cookie'}, {'frequency': 'r', 'synset': 'cooking_utensil.n.01', 'synonyms': ['cooking_utensil'], 'id': 304, 'def': 'a kitchen utensil made of material that does not melt easily; used for cooking', 'name': 'cooking_utensil'}, {'frequency': 'f', 'synset': 'cooler.n.01', 'synonyms': ['cooler_(for_food)', 'ice_chest'], 'id': 305, 'def': 'an insulated box for storing food often with ice', 'name': 'cooler_(for_food)'}, {'frequency': 'f', 'synset': 'cork.n.04', 'synonyms': ['cork_(bottle_plug)', 'bottle_cork'], 'id': 306, 'def': 'the plug in the mouth of a bottle (especially a wine bottle)', 'name': 'cork_(bottle_plug)'}, {'frequency': 'r', 'synset': 'corkboard.n.01', 'synonyms': ['corkboard'], 'id': 307, 'def': 'a sheet consisting of cork granules', 'name': 'corkboard'}, {'frequency': 'c', 'synset': 'corkscrew.n.01', 'synonyms': ['corkscrew', 'bottle_screw'], 'id': 308, 'def': 'a bottle opener that pulls corks', 'name': 'corkscrew'}, {'frequency': 'f', 'synset': 'corn.n.03', 'synonyms': ['edible_corn', 'corn', 'maize'], 'id': 309, 'def': 'ears or kernels of corn that can be prepared and served for human food (only mark individual ears or kernels)', 'name': 'edible_corn'}, {'frequency': 'r', 'synset': 'cornbread.n.01', 'synonyms': ['cornbread'], 'id': 310, 'def': 'bread made primarily of cornmeal', 'name': 'cornbread'}, {'frequency': 'c', 'synset': 'cornet.n.01', 'synonyms': ['cornet', 'horn', 'trumpet'], 'id': 311, 'def': 'a brass musical instrument with a narrow tube and a flared bell and many valves', 'name': 'cornet'}, {'frequency': 'c', 'synset': 'cornice.n.01', 'synonyms': ['cornice', 'valance', 'valance_board', 'pelmet'], 'id': 312, 'def': 'a decorative framework to conceal curtain fixtures at the top of a window casing', 'name': 'cornice'}, {'frequency': 'r', 'synset': 'cornmeal.n.01', 'synonyms': ['cornmeal'], 'id': 313, 'def': 'coarsely ground corn', 'name': 'cornmeal'}, {'frequency': 'c', 'synset': 'corset.n.01', 'synonyms': ['corset', 'girdle'], 'id': 314, 'def': "a woman's close-fitting foundation garment", 'name': 'corset'}, {'frequency': 'c', 'synset': 'costume.n.04', 'synonyms': ['costume'], 'id': 315, 'def': 'the attire characteristic of a country or a time or a social class', 'name': 'costume'}, {'frequency': 'r', 'synset': 'cougar.n.01', 'synonyms': ['cougar', 'puma', 'catamount', 'mountain_lion', 'panther'], 'id': 316, 'def': 'large American feline resembling a lion', 'name': 'cougar'}, {'frequency': 'r', 'synset': 'coverall.n.01', 'synonyms': ['coverall'], 'id': 317, 'def': 'a loose-fitting protective garment that is worn over other clothing', 'name': 'coverall'}, {'frequency': 'c', 'synset': 'cowbell.n.01', 'synonyms': ['cowbell'], 'id': 318, 'def': 'a bell hung around the neck of cow so that the cow can be easily located', 'name': 'cowbell'}, {'frequency': 'f', 'synset': 'cowboy_hat.n.01', 'synonyms': ['cowboy_hat', 'ten-gallon_hat'], 'id': 319, 'def': 'a hat with a wide brim and a soft crown; worn by American ranch hands', 'name': 'cowboy_hat'}, {'frequency': 'c', 'synset': 'crab.n.01', 'synonyms': ['crab_(animal)'], 'id': 320, 'def': 'decapod having eyes on short stalks and a broad flattened shell and pincers', 'name': 'crab_(animal)'}, {'frequency': 'r', 'synset': 'crab.n.05', 'synonyms': ['crabmeat'], 'id': 321, 'def': 'the edible flesh of any of various crabs', 'name': 'crabmeat'}, {'frequency': 'c', 'synset': 'cracker.n.01', 'synonyms': ['cracker'], 'id': 322, 'def': 'a thin crisp wafer', 'name': 'cracker'}, {'frequency': 'r', 'synset': 'crape.n.01', 'synonyms': ['crape', 'crepe', 'French_pancake'], 'id': 323, 'def': 'small very thin pancake', 'name': 'crape'}, {'frequency': 'f', 'synset': 'crate.n.01', 'synonyms': ['crate'], 'id': 324, 'def': 'a rugged box (usually made of wood); used for shipping', 'name': 'crate'}, {'frequency': 'c', 'synset': 'crayon.n.01', 'synonyms': ['crayon', 'wax_crayon'], 'id': 325, 'def': 'writing or drawing implement made of a colored stick of composition wax', 'name': 'crayon'}, {'frequency': 'r', 'synset': 'cream_pitcher.n.01', 'synonyms': ['cream_pitcher'], 'id': 326, 'def': 'a small pitcher for serving cream', 'name': 'cream_pitcher'}, {'frequency': 'c', 'synset': 'crescent_roll.n.01', 'synonyms': ['crescent_roll', 'croissant'], 'id': 327, 'def': 'very rich flaky crescent-shaped roll', 'name': 'crescent_roll'}, {'frequency': 'c', 'synset': 'crib.n.01', 'synonyms': ['crib', 'cot'], 'id': 328, 'def': 'baby bed with high sides made of slats', 'name': 'crib'}, {'frequency': 'c', 'synset': 'crock.n.03', 'synonyms': ['crock_pot', 'earthenware_jar'], 'id': 329, 'def': 'an earthen jar (made of baked clay) or a modern electric crockpot', 'name': 'crock_pot'}, {'frequency': 'f', 'synset': 'crossbar.n.01', 'synonyms': ['crossbar'], 'id': 330, 'def': 'a horizontal bar that goes across something', 'name': 'crossbar'}, {'frequency': 'r', 'synset': 'crouton.n.01', 'synonyms': ['crouton'], 'id': 331, 'def': 'a small piece of toasted or fried bread; served in soup or salads', 'name': 'crouton'}, {'frequency': 'c', 'synset': 'crow.n.01', 'synonyms': ['crow'], 'id': 332, 'def': 'black birds having a raucous call', 'name': 'crow'}, {'frequency': 'r', 'synset': 'crowbar.n.01', 'synonyms': ['crowbar', 'wrecking_bar', 'pry_bar'], 'id': 333, 'def': 'a heavy iron lever with one end forged into a wedge', 'name': 'crowbar'}, {'frequency': 'c', 'synset': 'crown.n.04', 'synonyms': ['crown'], 'id': 334, 'def': 'an ornamental jeweled headdress signifying sovereignty', 'name': 'crown'}, {'frequency': 'c', 'synset': 'crucifix.n.01', 'synonyms': ['crucifix'], 'id': 335, 'def': 'representation of the cross on which Jesus died', 'name': 'crucifix'}, {'frequency': 'c', 'synset': 'cruise_ship.n.01', 'synonyms': ['cruise_ship', 'cruise_liner'], 'id': 336, 'def': 'a passenger ship used commercially for pleasure cruises', 'name': 'cruise_ship'}, {'frequency': 'c', 'synset': 'cruiser.n.01', 'synonyms': ['police_cruiser', 'patrol_car', 'police_car', 'squad_car'], 'id': 337, 'def': 'a car in which policemen cruise the streets', 'name': 'police_cruiser'}, {'frequency': 'f', 'synset': 'crumb.n.03', 'synonyms': ['crumb'], 'id': 338, 'def': 'small piece of e.g. bread or cake', 'name': 'crumb'}, {'frequency': 'c', 'synset': 'crutch.n.01', 'synonyms': ['crutch'], 'id': 339, 'def': 'a wooden or metal staff that fits under the armpit and reaches to the ground', 'name': 'crutch'}, {'frequency': 'c', 'synset': 'cub.n.03', 'synonyms': ['cub_(animal)'], 'id': 340, 'def': 'the young of certain carnivorous mammals such as the bear or wolf or lion', 'name': 'cub_(animal)'}, {'frequency': 'c', 'synset': 'cube.n.05', 'synonyms': ['cube', 'square_block'], 'id': 341, 'def': 'a block in the (approximate) shape of a cube', 'name': 'cube'}, {'frequency': 'f', 'synset': 'cucumber.n.02', 'synonyms': ['cucumber', 'cuke'], 'id': 342, 'def': 'cylindrical green fruit with thin green rind and white flesh eaten as a vegetable', 'name': 'cucumber'}, {'frequency': 'c', 'synset': 'cufflink.n.01', 'synonyms': ['cufflink'], 'id': 343, 'def': 'jewelry consisting of linked buttons used to fasten the cuffs of a shirt', 'name': 'cufflink'}, {'frequency': 'f', 'synset': 'cup.n.01', 'synonyms': ['cup'], 'id': 344, 'def': 'a small open container usually used for drinking; usually has a handle', 'name': 'cup'}, {'frequency': 'c', 'synset': 'cup.n.08', 'synonyms': ['trophy_cup'], 'id': 345, 'def': 'a metal award or cup-shaped vessel with handles that is awarded as a trophy to a competition winner', 'name': 'trophy_cup'}, {'frequency': 'f', 'synset': 'cupboard.n.01', 'synonyms': ['cupboard', 'closet'], 'id': 346, 'def': 'a small room (or recess) or cabinet used for storage space', 'name': 'cupboard'}, {'frequency': 'f', 'synset': 'cupcake.n.01', 'synonyms': ['cupcake'], 'id': 347, 'def': 'small cake baked in a muffin tin', 'name': 'cupcake'}, {'frequency': 'r', 'synset': 'curler.n.01', 'synonyms': ['hair_curler', 'hair_roller', 'hair_crimper'], 'id': 348, 'def': 'a cylindrical tube around which the hair is wound to curl it', 'name': 'hair_curler'}, {'frequency': 'r', 'synset': 'curling_iron.n.01', 'synonyms': ['curling_iron'], 'id': 349, 'def': 'a cylindrical home appliance that heats hair that has been curled around it', 'name': 'curling_iron'}, {'frequency': 'f', 'synset': 'curtain.n.01', 'synonyms': ['curtain', 'drapery'], 'id': 350, 'def': 'hanging cloth used as a blind (especially for a window)', 'name': 'curtain'}, {'frequency': 'f', 'synset': 'cushion.n.03', 'synonyms': ['cushion'], 'id': 351, 'def': 'a soft bag filled with air or padding such as feathers or foam rubber', 'name': 'cushion'}, {'frequency': 'r', 'synset': 'cylinder.n.04', 'synonyms': ['cylinder'], 'id': 352, 'def': 'a cylindrical container', 'name': 'cylinder'}, {'frequency': 'r', 'synset': 'cymbal.n.01', 'synonyms': ['cymbal'], 'id': 353, 'def': 'a percussion instrument consisting of a concave brass disk', 'name': 'cymbal'}, {'frequency': 'r', 'synset': 'dagger.n.01', 'synonyms': ['dagger'], 'id': 354, 'def': 'a short knife with a pointed blade used for piercing or stabbing', 'name': 'dagger'}, {'frequency': 'r', 'synset': 'dalmatian.n.02', 'synonyms': ['dalmatian'], 'id': 355, 'def': 'a large breed having a smooth white coat with black or brown spots', 'name': 'dalmatian'}, {'frequency': 'c', 'synset': 'dartboard.n.01', 'synonyms': ['dartboard'], 'id': 356, 'def': 'a circular board of wood or cork used as the target in the game of darts', 'name': 'dartboard'}, {'frequency': 'r', 'synset': 'date.n.08', 'synonyms': ['date_(fruit)'], 'id': 357, 'def': 'sweet edible fruit of the date palm with a single long woody seed', 'name': 'date_(fruit)'}, {'frequency': 'f', 'synset': 'deck_chair.n.01', 'synonyms': ['deck_chair', 'beach_chair'], 'id': 358, 'def': 'a folding chair for use outdoors; a wooden frame supports a length of canvas', 'name': 'deck_chair'}, {'frequency': 'c', 'synset': 'deer.n.01', 'synonyms': ['deer', 'cervid'], 'id': 359, 'def': "distinguished from Bovidae by the male's having solid deciduous antlers", 'name': 'deer'}, {'frequency': 'c', 'synset': 'dental_floss.n.01', 'synonyms': ['dental_floss', 'floss'], 'id': 360, 'def': 'a soft thread for cleaning the spaces between the teeth', 'name': 'dental_floss'}, {'frequency': 'f', 'synset': 'desk.n.01', 'synonyms': ['desk'], 'id': 361, 'def': 'a piece of furniture with a writing surface and usually drawers or other compartments', 'name': 'desk'}, {'frequency': 'r', 'synset': 'detergent.n.01', 'synonyms': ['detergent'], 'id': 362, 'def': 'a surface-active chemical widely used in industry and laundering', 'name': 'detergent'}, {'frequency': 'c', 'synset': 'diaper.n.01', 'synonyms': ['diaper'], 'id': 363, 'def': 'garment consisting of a folded cloth drawn up between the legs and fastened at the waist', 'name': 'diaper'}, {'frequency': 'r', 'synset': 'diary.n.01', 'synonyms': ['diary', 'journal'], 'id': 364, 'def': 'yearly planner book', 'name': 'diary'}, {'frequency': 'r', 'synset': 'die.n.01', 'synonyms': ['die', 'dice'], 'id': 365, 'def': 'a small cube with 1 to 6 spots on the six faces; used in gambling', 'name': 'die'}, {'frequency': 'r', 'synset': 'dinghy.n.01', 'synonyms': ['dinghy', 'dory', 'rowboat'], 'id': 366, 'def': 'a small boat of shallow draft with seats and oars with which it is propelled', 'name': 'dinghy'}, {'frequency': 'f', 'synset': 'dining_table.n.01', 'synonyms': ['dining_table'], 'id': 367, 'def': 'a table at which meals are served', 'name': 'dining_table'}, {'frequency': 'r', 'synset': 'dinner_jacket.n.01', 'synonyms': ['tux', 'tuxedo'], 'id': 368, 'def': 'semiformal evening dress for men', 'name': 'tux'}, {'frequency': 'f', 'synset': 'dish.n.01', 'synonyms': ['dish'], 'id': 369, 'def': 'a piece of dishware normally used as a container for holding or serving food', 'name': 'dish'}, {'frequency': 'c', 'synset': 'dish.n.05', 'synonyms': ['dish_antenna'], 'id': 370, 'def': 'directional antenna consisting of a parabolic reflector', 'name': 'dish_antenna'}, {'frequency': 'c', 'synset': 'dishrag.n.01', 'synonyms': ['dishrag', 'dishcloth'], 'id': 371, 'def': 'a cloth for washing dishes or cleaning in general', 'name': 'dishrag'}, {'frequency': 'f', 'synset': 'dishtowel.n.01', 'synonyms': ['dishtowel', 'tea_towel'], 'id': 372, 'def': 'a towel for drying dishes', 'name': 'dishtowel'}, {'frequency': 'f', 'synset': 'dishwasher.n.01', 'synonyms': ['dishwasher', 'dishwashing_machine'], 'id': 373, 'def': 'a machine for washing dishes', 'name': 'dishwasher'}, {'frequency': 'r', 'synset': 'dishwasher_detergent.n.01', 'synonyms': ['dishwasher_detergent', 'dishwashing_detergent', 'dishwashing_liquid', 'dishsoap'], 'id': 374, 'def': 'dishsoap or dish detergent designed for use in dishwashers', 'name': 'dishwasher_detergent'}, {'frequency': 'f', 'synset': 'dispenser.n.01', 'synonyms': ['dispenser'], 'id': 375, 'def': 'a container so designed that the contents can be used in prescribed amounts', 'name': 'dispenser'}, {'frequency': 'r', 'synset': 'diving_board.n.01', 'synonyms': ['diving_board'], 'id': 376, 'def': 'a springboard from which swimmers can dive', 'name': 'diving_board'}, {'frequency': 'f', 'synset': 'dixie_cup.n.01', 'synonyms': ['Dixie_cup', 'paper_cup'], 'id': 377, 'def': 'a disposable cup made of paper; for holding drinks', 'name': 'Dixie_cup'}, {'frequency': 'f', 'synset': 'dog.n.01', 'synonyms': ['dog'], 'id': 378, 'def': 'a common domesticated dog', 'name': 'dog'}, {'frequency': 'f', 'synset': 'dog_collar.n.01', 'synonyms': ['dog_collar'], 'id': 379, 'def': 'a collar for a dog', 'name': 'dog_collar'}, {'frequency': 'f', 'synset': 'doll.n.01', 'synonyms': ['doll'], 'id': 380, 'def': 'a toy replica of a HUMAN (NOT AN ANIMAL)', 'name': 'doll'}, {'frequency': 'r', 'synset': 'dollar.n.02', 'synonyms': ['dollar', 'dollar_bill', 'one_dollar_bill'], 'id': 381, 'def': 'a piece of paper money worth one dollar', 'name': 'dollar'}, {'frequency': 'r', 'synset': 'dollhouse.n.01', 'synonyms': ['dollhouse', "doll's_house"], 'id': 382, 'def': "a house so small that it is likened to a child's plaything", 'name': 'dollhouse'}, {'frequency': 'c', 'synset': 'dolphin.n.02', 'synonyms': ['dolphin'], 'id': 383, 'def': 'any of various small toothed whales with a beaklike snout; larger than porpoises', 'name': 'dolphin'}, {'frequency': 'c', 'synset': 'domestic_ass.n.01', 'synonyms': ['domestic_ass', 'donkey'], 'id': 384, 'def': 'domestic beast of burden descended from the African wild ass; patient but stubborn', 'name': 'domestic_ass'}, {'frequency': 'f', 'synset': 'doorknob.n.01', 'synonyms': ['doorknob', 'doorhandle'], 'id': 385, 'def': "a knob used to open a door (often called `doorhandle' in Great Britain)", 'name': 'doorknob'}, {'frequency': 'c', 'synset': 'doormat.n.02', 'synonyms': ['doormat', 'welcome_mat'], 'id': 386, 'def': 'a mat placed outside an exterior door for wiping the shoes before entering', 'name': 'doormat'}, {'frequency': 'f', 'synset': 'doughnut.n.02', 'synonyms': ['doughnut', 'donut'], 'id': 387, 'def': 'a small ring-shaped friedcake', 'name': 'doughnut'}, {'frequency': 'r', 'synset': 'dove.n.01', 'synonyms': ['dove'], 'id': 388, 'def': 'any of numerous small pigeons', 'name': 'dove'}, {'frequency': 'r', 'synset': 'dragonfly.n.01', 'synonyms': ['dragonfly'], 'id': 389, 'def': 'slender-bodied non-stinging insect having iridescent wings that are outspread at rest', 'name': 'dragonfly'}, {'frequency': 'f', 'synset': 'drawer.n.01', 'synonyms': ['drawer'], 'id': 390, 'def': 'a boxlike container in a piece of furniture; made so as to slide in and out', 'name': 'drawer'}, {'frequency': 'c', 'synset': 'drawers.n.01', 'synonyms': ['underdrawers', 'boxers', 'boxershorts'], 'id': 391, 'def': 'underpants worn by men', 'name': 'underdrawers'}, {'frequency': 'f', 'synset': 'dress.n.01', 'synonyms': ['dress', 'frock'], 'id': 392, 'def': 'a one-piece garment for a woman; has skirt and bodice', 'name': 'dress'}, {'frequency': 'c', 'synset': 'dress_hat.n.01', 'synonyms': ['dress_hat', 'high_hat', 'opera_hat', 'silk_hat', 'top_hat'], 'id': 393, 'def': "a man's hat with a tall crown; usually covered with silk or with beaver fur", 'name': 'dress_hat'}, {'frequency': 'f', 'synset': 'dress_suit.n.01', 'synonyms': ['dress_suit'], 'id': 394, 'def': 'formalwear consisting of full evening dress for men', 'name': 'dress_suit'}, {'frequency': 'f', 'synset': 'dresser.n.05', 'synonyms': ['dresser'], 'id': 395, 'def': 'a cabinet with shelves', 'name': 'dresser'}, {'frequency': 'c', 'synset': 'drill.n.01', 'synonyms': ['drill'], 'id': 396, 'def': 'a tool with a sharp rotating point for making holes in hard materials', 'name': 'drill'}, {'frequency': 'r', 'synset': 'drone.n.04', 'synonyms': ['drone'], 'id': 397, 'def': 'an aircraft without a pilot that is operated by remote control', 'name': 'drone'}, {'frequency': 'r', 'synset': 'dropper.n.01', 'synonyms': ['dropper', 'eye_dropper'], 'id': 398, 'def': 'pipet consisting of a small tube with a vacuum bulb at one end for drawing liquid in and releasing it a drop at a time', 'name': 'dropper'}, {'frequency': 'c', 'synset': 'drum.n.01', 'synonyms': ['drum_(musical_instrument)'], 'id': 399, 'def': 'a musical percussion instrument; usually consists of a hollow cylinder with a membrane stretched across each end', 'name': 'drum_(musical_instrument)'}, {'frequency': 'r', 'synset': 'drumstick.n.02', 'synonyms': ['drumstick'], 'id': 400, 'def': 'a stick used for playing a drum', 'name': 'drumstick'}, {'frequency': 'f', 'synset': 'duck.n.01', 'synonyms': ['duck'], 'id': 401, 'def': 'small web-footed broad-billed swimming bird', 'name': 'duck'}, {'frequency': 'c', 'synset': 'duckling.n.02', 'synonyms': ['duckling'], 'id': 402, 'def': 'young duck', 'name': 'duckling'}, {'frequency': 'c', 'synset': 'duct_tape.n.01', 'synonyms': ['duct_tape'], 'id': 403, 'def': 'a wide silvery adhesive tape', 'name': 'duct_tape'}, {'frequency': 'f', 'synset': 'duffel_bag.n.01', 'synonyms': ['duffel_bag', 'duffle_bag', 'duffel', 'duffle'], 'id': 404, 'def': 'a large cylindrical bag of heavy cloth (does not include suitcases)', 'name': 'duffel_bag'}, {'frequency': 'r', 'synset': 'dumbbell.n.01', 'synonyms': ['dumbbell'], 'id': 405, 'def': 'an exercising weight with two ball-like ends connected by a short handle', 'name': 'dumbbell'}, {'frequency': 'c', 'synset': 'dumpster.n.01', 'synonyms': ['dumpster'], 'id': 406, 'def': 'a container designed to receive and transport and dump waste', 'name': 'dumpster'}, {'frequency': 'r', 'synset': 'dustpan.n.02', 'synonyms': ['dustpan'], 'id': 407, 'def': 'a short-handled receptacle into which dust can be swept', 'name': 'dustpan'}, {'frequency': 'c', 'synset': 'eagle.n.01', 'synonyms': ['eagle'], 'id': 408, 'def': 'large birds of prey noted for their broad wings and strong soaring flight', 'name': 'eagle'}, {'frequency': 'f', 'synset': 'earphone.n.01', 'synonyms': ['earphone', 'earpiece', 'headphone'], 'id': 409, 'def': 'device for listening to audio that is held over or inserted into the ear', 'name': 'earphone'}, {'frequency': 'r', 'synset': 'earplug.n.01', 'synonyms': ['earplug'], 'id': 410, 'def': 'a soft plug that is inserted into the ear canal to block sound', 'name': 'earplug'}, {'frequency': 'f', 'synset': 'earring.n.01', 'synonyms': ['earring'], 'id': 411, 'def': 'jewelry to ornament the ear', 'name': 'earring'}, {'frequency': 'c', 'synset': 'easel.n.01', 'synonyms': ['easel'], 'id': 412, 'def': "an upright tripod for displaying something (usually an artist's canvas)", 'name': 'easel'}, {'frequency': 'r', 'synset': 'eclair.n.01', 'synonyms': ['eclair'], 'id': 413, 'def': 'oblong cream puff', 'name': 'eclair'}, {'frequency': 'r', 'synset': 'eel.n.01', 'synonyms': ['eel'], 'id': 414, 'def': 'an elongate fish with fatty flesh', 'name': 'eel'}, {'frequency': 'f', 'synset': 'egg.n.02', 'synonyms': ['egg', 'eggs'], 'id': 415, 'def': 'oval reproductive body of a fowl (especially a hen) used as food', 'name': 'egg'}, {'frequency': 'r', 'synset': 'egg_roll.n.01', 'synonyms': ['egg_roll', 'spring_roll'], 'id': 416, 'def': 'minced vegetables and meat wrapped in a pancake and fried', 'name': 'egg_roll'}, {'frequency': 'c', 'synset': 'egg_yolk.n.01', 'synonyms': ['egg_yolk', 'yolk_(egg)'], 'id': 417, 'def': 'the yellow spherical part of an egg', 'name': 'egg_yolk'}, {'frequency': 'c', 'synset': 'eggbeater.n.02', 'synonyms': ['eggbeater', 'eggwhisk'], 'id': 418, 'def': 'a mixer for beating eggs or whipping cream', 'name': 'eggbeater'}, {'frequency': 'c', 'synset': 'eggplant.n.01', 'synonyms': ['eggplant', 'aubergine'], 'id': 419, 'def': 'egg-shaped vegetable having a shiny skin typically dark purple', 'name': 'eggplant'}, {'frequency': 'r', 'synset': 'electric_chair.n.01', 'synonyms': ['electric_chair'], 'id': 420, 'def': 'a chair-shaped instrument of execution by electrocution', 'name': 'electric_chair'}, {'frequency': 'f', 'synset': 'electric_refrigerator.n.01', 'synonyms': ['refrigerator'], 'id': 421, 'def': 'a refrigerator in which the coolant is pumped around by an electric motor', 'name': 'refrigerator'}, {'frequency': 'f', 'synset': 'elephant.n.01', 'synonyms': ['elephant'], 'id': 422, 'def': 'a common elephant', 'name': 'elephant'}, {'frequency': 'c', 'synset': 'elk.n.01', 'synonyms': ['elk', 'moose'], 'id': 423, 'def': 'large northern deer with enormous flattened antlers in the male', 'name': 'elk'}, {'frequency': 'c', 'synset': 'envelope.n.01', 'synonyms': ['envelope'], 'id': 424, 'def': 'a flat (usually rectangular) container for a letter, thin package, etc.', 'name': 'envelope'}, {'frequency': 'c', 'synset': 'eraser.n.01', 'synonyms': ['eraser'], 'id': 425, 'def': 'an implement used to erase something', 'name': 'eraser'}, {'frequency': 'r', 'synset': 'escargot.n.01', 'synonyms': ['escargot'], 'id': 426, 'def': 'edible snail usually served in the shell with a sauce of melted butter and garlic', 'name': 'escargot'}, {'frequency': 'r', 'synset': 'eyepatch.n.01', 'synonyms': ['eyepatch'], 'id': 427, 'def': 'a protective cloth covering for an injured eye', 'name': 'eyepatch'}, {'frequency': 'r', 'synset': 'falcon.n.01', 'synonyms': ['falcon'], 'id': 428, 'def': 'birds of prey having long pointed powerful wings adapted for swift flight', 'name': 'falcon'}, {'frequency': 'f', 'synset': 'fan.n.01', 'synonyms': ['fan'], 'id': 429, 'def': 'a device for creating a current of air by movement of a surface or surfaces', 'name': 'fan'}, {'frequency': 'f', 'synset': 'faucet.n.01', 'synonyms': ['faucet', 'spigot', 'tap'], 'id': 430, 'def': 'a regulator for controlling the flow of a liquid from a reservoir', 'name': 'faucet'}, {'frequency': 'r', 'synset': 'fedora.n.01', 'synonyms': ['fedora'], 'id': 431, 'def': 'a hat made of felt with a creased crown', 'name': 'fedora'}, {'frequency': 'r', 'synset': 'ferret.n.02', 'synonyms': ['ferret'], 'id': 432, 'def': 'domesticated albino variety of the European polecat bred for hunting rats and rabbits', 'name': 'ferret'}, {'frequency': 'c', 'synset': 'ferris_wheel.n.01', 'synonyms': ['Ferris_wheel'], 'id': 433, 'def': 'a large wheel with suspended seats that remain upright as the wheel rotates', 'name': 'Ferris_wheel'}, {'frequency': 'c', 'synset': 'ferry.n.01', 'synonyms': ['ferry', 'ferryboat'], 'id': 434, 'def': 'a boat that transports people or vehicles across a body of water and operates on a regular schedule', 'name': 'ferry'}, {'frequency': 'r', 'synset': 'fig.n.04', 'synonyms': ['fig_(fruit)'], 'id': 435, 'def': 'fleshy sweet pear-shaped yellowish or purple fruit eaten fresh or preserved or dried', 'name': 'fig_(fruit)'}, {'frequency': 'c', 'synset': 'fighter.n.02', 'synonyms': ['fighter_jet', 'fighter_aircraft', 'attack_aircraft'], 'id': 436, 'def': 'a high-speed military or naval airplane designed to destroy enemy targets', 'name': 'fighter_jet'}, {'frequency': 'f', 'synset': 'figurine.n.01', 'synonyms': ['figurine'], 'id': 437, 'def': 'a small carved or molded figure', 'name': 'figurine'}, {'frequency': 'c', 'synset': 'file.n.03', 'synonyms': ['file_cabinet', 'filing_cabinet'], 'id': 438, 'def': 'office furniture consisting of a container for keeping papers in order', 'name': 'file_cabinet'}, {'frequency': 'r', 'synset': 'file.n.04', 'synonyms': ['file_(tool)'], 'id': 439, 'def': 'a steel hand tool with small sharp teeth on some or all of its surfaces; used for smoothing wood or metal', 'name': 'file_(tool)'}, {'frequency': 'f', 'synset': 'fire_alarm.n.02', 'synonyms': ['fire_alarm', 'smoke_alarm'], 'id': 440, 'def': 'an alarm that is tripped off by fire or smoke', 'name': 'fire_alarm'}, {'frequency': 'f', 'synset': 'fire_engine.n.01', 'synonyms': ['fire_engine', 'fire_truck'], 'id': 441, 'def': 'large trucks that carry firefighters and equipment to the site of a fire', 'name': 'fire_engine'}, {'frequency': 'f', 'synset': 'fire_extinguisher.n.01', 'synonyms': ['fire_extinguisher', 'extinguisher'], 'id': 442, 'def': 'a manually operated device for extinguishing small fires', 'name': 'fire_extinguisher'}, {'frequency': 'c', 'synset': 'fire_hose.n.01', 'synonyms': ['fire_hose'], 'id': 443, 'def': 'a large hose that carries water from a fire hydrant to the site of the fire', 'name': 'fire_hose'}, {'frequency': 'f', 'synset': 'fireplace.n.01', 'synonyms': ['fireplace'], 'id': 444, 'def': 'an open recess in a wall at the base of a chimney where a fire can be built', 'name': 'fireplace'}, {'frequency': 'f', 'synset': 'fireplug.n.01', 'synonyms': ['fireplug', 'fire_hydrant', 'hydrant'], 'id': 445, 'def': 'an upright hydrant for drawing water to use in fighting a fire', 'name': 'fireplug'}, {'frequency': 'r', 'synset': 'first-aid_kit.n.01', 'synonyms': ['first-aid_kit'], 'id': 446, 'def': 'kit consisting of a set of bandages and medicines for giving first aid', 'name': 'first-aid_kit'}, {'frequency': 'f', 'synset': 'fish.n.01', 'synonyms': ['fish'], 'id': 447, 'def': 'any of various mostly cold-blooded aquatic vertebrates usually having scales and breathing through gills', 'name': 'fish'}, {'frequency': 'c', 'synset': 'fish.n.02', 'synonyms': ['fish_(food)'], 'id': 448, 'def': 'the flesh of fish used as food', 'name': 'fish_(food)'}, {'frequency': 'r', 'synset': 'fishbowl.n.02', 'synonyms': ['fishbowl', 'goldfish_bowl'], 'id': 449, 'def': 'a transparent bowl in which small fish are kept', 'name': 'fishbowl'}, {'frequency': 'c', 'synset': 'fishing_rod.n.01', 'synonyms': ['fishing_rod', 'fishing_pole'], 'id': 450, 'def': 'a rod that is used in fishing to extend the fishing line', 'name': 'fishing_rod'}, {'frequency': 'f', 'synset': 'flag.n.01', 'synonyms': ['flag'], 'id': 451, 'def': 'emblem usually consisting of a rectangular piece of cloth of distinctive design (do not include pole)', 'name': 'flag'}, {'frequency': 'f', 'synset': 'flagpole.n.02', 'synonyms': ['flagpole', 'flagstaff'], 'id': 452, 'def': 'a tall staff or pole on which a flag is raised', 'name': 'flagpole'}, {'frequency': 'c', 'synset': 'flamingo.n.01', 'synonyms': ['flamingo'], 'id': 453, 'def': 'large pink web-footed bird with down-bent bill', 'name': 'flamingo'}, {'frequency': 'c', 'synset': 'flannel.n.01', 'synonyms': ['flannel'], 'id': 454, 'def': 'a soft light woolen fabric; used for clothing', 'name': 'flannel'}, {'frequency': 'c', 'synset': 'flap.n.01', 'synonyms': ['flap'], 'id': 455, 'def': 'any broad thin covering attached at one edge, such as a mud flap next to a wheel or a flap on an airplane wing', 'name': 'flap'}, {'frequency': 'r', 'synset': 'flash.n.10', 'synonyms': ['flash', 'flashbulb'], 'id': 456, 'def': 'a lamp for providing momentary light to take a photograph', 'name': 'flash'}, {'frequency': 'c', 'synset': 'flashlight.n.01', 'synonyms': ['flashlight', 'torch'], 'id': 457, 'def': 'a small portable battery-powered electric lamp', 'name': 'flashlight'}, {'frequency': 'r', 'synset': 'fleece.n.03', 'synonyms': ['fleece'], 'id': 458, 'def': 'a soft bulky fabric with deep pile; used chiefly for clothing', 'name': 'fleece'}, {'frequency': 'f', 'synset': 'flip-flop.n.02', 'synonyms': ['flip-flop_(sandal)'], 'id': 459, 'def': 'a backless sandal held to the foot by a thong between two toes', 'name': 'flip-flop_(sandal)'}, {'frequency': 'c', 'synset': 'flipper.n.01', 'synonyms': ['flipper_(footwear)', 'fin_(footwear)'], 'id': 460, 'def': 'a shoe to aid a person in swimming', 'name': 'flipper_(footwear)'}, {'frequency': 'f', 'synset': 'flower_arrangement.n.01', 'synonyms': ['flower_arrangement', 'floral_arrangement'], 'id': 461, 'def': 'a decorative arrangement of flowers', 'name': 'flower_arrangement'}, {'frequency': 'c', 'synset': 'flute.n.02', 'synonyms': ['flute_glass', 'champagne_flute'], 'id': 462, 'def': 'a tall narrow wineglass', 'name': 'flute_glass'}, {'frequency': 'c', 'synset': 'foal.n.01', 'synonyms': ['foal'], 'id': 463, 'def': 'a young horse', 'name': 'foal'}, {'frequency': 'c', 'synset': 'folding_chair.n.01', 'synonyms': ['folding_chair'], 'id': 464, 'def': 'a chair that can be folded flat for storage', 'name': 'folding_chair'}, {'frequency': 'c', 'synset': 'food_processor.n.01', 'synonyms': ['food_processor'], 'id': 465, 'def': 'a kitchen appliance for shredding, blending, chopping, or slicing food', 'name': 'food_processor'}, {'frequency': 'c', 'synset': 'football.n.02', 'synonyms': ['football_(American)'], 'id': 466, 'def': 'the inflated oblong ball used in playing American football', 'name': 'football_(American)'}, {'frequency': 'r', 'synset': 'football_helmet.n.01', 'synonyms': ['football_helmet'], 'id': 467, 'def': 'a padded helmet with a face mask to protect the head of football players', 'name': 'football_helmet'}, {'frequency': 'c', 'synset': 'footstool.n.01', 'synonyms': ['footstool', 'footrest'], 'id': 468, 'def': 'a low seat or a stool to rest the feet of a seated person', 'name': 'footstool'}, {'frequency': 'f', 'synset': 'fork.n.01', 'synonyms': ['fork'], 'id': 469, 'def': 'cutlery used for serving and eating food', 'name': 'fork'}, {'frequency': 'c', 'synset': 'forklift.n.01', 'synonyms': ['forklift'], 'id': 470, 'def': 'an industrial vehicle with a power operated fork in front that can be inserted under loads to lift and move them', 'name': 'forklift'}, {'frequency': 'c', 'synset': 'freight_car.n.01', 'synonyms': ['freight_car'], 'id': 471, 'def': 'a railway car that carries freight', 'name': 'freight_car'}, {'frequency': 'c', 'synset': 'french_toast.n.01', 'synonyms': ['French_toast'], 'id': 472, 'def': 'bread slice dipped in egg and milk and fried', 'name': 'French_toast'}, {'frequency': 'c', 'synset': 'freshener.n.01', 'synonyms': ['freshener', 'air_freshener'], 'id': 473, 'def': 'anything that freshens air by removing or covering odor', 'name': 'freshener'}, {'frequency': 'f', 'synset': 'frisbee.n.01', 'synonyms': ['frisbee'], 'id': 474, 'def': 'a light, plastic disk propelled with a flip of the wrist for recreation or competition', 'name': 'frisbee'}, {'frequency': 'c', 'synset': 'frog.n.01', 'synonyms': ['frog', 'toad', 'toad_frog'], 'id': 475, 'def': 'a tailless stout-bodied amphibians with long hind limbs for leaping', 'name': 'frog'}, {'frequency': 'c', 'synset': 'fruit_juice.n.01', 'synonyms': ['fruit_juice'], 'id': 476, 'def': 'drink produced by squeezing or crushing fruit', 'name': 'fruit_juice'}, {'frequency': 'f', 'synset': 'frying_pan.n.01', 'synonyms': ['frying_pan', 'frypan', 'skillet'], 'id': 477, 'def': 'a pan used for frying foods', 'name': 'frying_pan'}, {'frequency': 'r', 'synset': 'fudge.n.01', 'synonyms': ['fudge'], 'id': 478, 'def': 'soft creamy candy', 'name': 'fudge'}, {'frequency': 'r', 'synset': 'funnel.n.02', 'synonyms': ['funnel'], 'id': 479, 'def': 'a cone-shaped utensil used to channel a substance into a container with a small mouth', 'name': 'funnel'}, {'frequency': 'r', 'synset': 'futon.n.01', 'synonyms': ['futon'], 'id': 480, 'def': 'a pad that is used for sleeping on the floor or on a raised frame', 'name': 'futon'}, {'frequency': 'r', 'synset': 'gag.n.02', 'synonyms': ['gag', 'muzzle'], 'id': 481, 'def': "restraint put into a person's mouth to prevent speaking or shouting", 'name': 'gag'}, {'frequency': 'r', 'synset': 'garbage.n.03', 'synonyms': ['garbage'], 'id': 482, 'def': 'a receptacle where waste can be discarded', 'name': 'garbage'}, {'frequency': 'c', 'synset': 'garbage_truck.n.01', 'synonyms': ['garbage_truck'], 'id': 483, 'def': 'a truck for collecting domestic refuse', 'name': 'garbage_truck'}, {'frequency': 'c', 'synset': 'garden_hose.n.01', 'synonyms': ['garden_hose'], 'id': 484, 'def': 'a hose used for watering a lawn or garden', 'name': 'garden_hose'}, {'frequency': 'c', 'synset': 'gargle.n.01', 'synonyms': ['gargle', 'mouthwash'], 'id': 485, 'def': 'a medicated solution used for gargling and rinsing the mouth', 'name': 'gargle'}, {'frequency': 'r', 'synset': 'gargoyle.n.02', 'synonyms': ['gargoyle'], 'id': 486, 'def': 'an ornament consisting of a grotesquely carved figure of a person or animal', 'name': 'gargoyle'}, {'frequency': 'c', 'synset': 'garlic.n.02', 'synonyms': ['garlic', 'ail'], 'id': 487, 'def': 'aromatic bulb used as seasoning', 'name': 'garlic'}, {'frequency': 'r', 'synset': 'gasmask.n.01', 'synonyms': ['gasmask', 'respirator', 'gas_helmet'], 'id': 488, 'def': 'a protective face mask with a filter', 'name': 'gasmask'}, {'frequency': 'c', 'synset': 'gazelle.n.01', 'synonyms': ['gazelle'], 'id': 489, 'def': 'small swift graceful antelope of Africa and Asia having lustrous eyes', 'name': 'gazelle'}, {'frequency': 'c', 'synset': 'gelatin.n.02', 'synonyms': ['gelatin', 'jelly'], 'id': 490, 'def': 'an edible jelly made with gelatin and used as a dessert or salad base or a coating for foods', 'name': 'gelatin'}, {'frequency': 'r', 'synset': 'gem.n.02', 'synonyms': ['gemstone'], 'id': 491, 'def': 'a crystalline rock that can be cut and polished for jewelry', 'name': 'gemstone'}, {'frequency': 'r', 'synset': 'generator.n.02', 'synonyms': ['generator'], 'id': 492, 'def': 'engine that converts mechanical energy into electrical energy by electromagnetic induction', 'name': 'generator'}, {'frequency': 'c', 'synset': 'giant_panda.n.01', 'synonyms': ['giant_panda', 'panda', 'panda_bear'], 'id': 493, 'def': 'large black-and-white herbivorous mammal of bamboo forests of China and Tibet', 'name': 'giant_panda'}, {'frequency': 'c', 'synset': 'gift_wrap.n.01', 'synonyms': ['gift_wrap'], 'id': 494, 'def': 'attractive wrapping paper suitable for wrapping gifts', 'name': 'gift_wrap'}, {'frequency': 'c', 'synset': 'ginger.n.03', 'synonyms': ['ginger', 'gingerroot'], 'id': 495, 'def': 'the root of the common ginger plant; used fresh as a seasoning', 'name': 'ginger'}, {'frequency': 'f', 'synset': 'giraffe.n.01', 'synonyms': ['giraffe'], 'id': 496, 'def': 'tall animal having a spotted coat and small horns and very long neck and legs', 'name': 'giraffe'}, {'frequency': 'c', 'synset': 'girdle.n.02', 'synonyms': ['cincture', 'sash', 'waistband', 'waistcloth'], 'id': 497, 'def': 'a band of material around the waist that strengthens a skirt or trousers', 'name': 'cincture'}, {'frequency': 'f', 'synset': 'glass.n.02', 'synonyms': ['glass_(drink_container)', 'drinking_glass'], 'id': 498, 'def': 'a container for holding liquids while drinking', 'name': 'glass_(drink_container)'}, {'frequency': 'c', 'synset': 'globe.n.03', 'synonyms': ['globe'], 'id': 499, 'def': 'a sphere on which a map (especially of the earth) is represented', 'name': 'globe'}, {'frequency': 'f', 'synset': 'glove.n.02', 'synonyms': ['glove'], 'id': 500, 'def': 'handwear covering the hand', 'name': 'glove'}, {'frequency': 'c', 'synset': 'goat.n.01', 'synonyms': ['goat'], 'id': 501, 'def': 'a common goat', 'name': 'goat'}, {'frequency': 'f', 'synset': 'goggles.n.01', 'synonyms': ['goggles'], 'id': 502, 'def': 'tight-fitting spectacles worn to protect the eyes', 'name': 'goggles'}, {'frequency': 'r', 'synset': 'goldfish.n.01', 'synonyms': ['goldfish'], 'id': 503, 'def': 'small golden or orange-red freshwater fishes used as pond or aquarium pets', 'name': 'goldfish'}, {'frequency': 'c', 'synset': 'golf_club.n.02', 'synonyms': ['golf_club', 'golf-club'], 'id': 504, 'def': 'golf equipment used by a golfer to hit a golf ball', 'name': 'golf_club'}, {'frequency': 'c', 'synset': 'golfcart.n.01', 'synonyms': ['golfcart'], 'id': 505, 'def': 'a small motor vehicle in which golfers can ride between shots', 'name': 'golfcart'}, {'frequency': 'r', 'synset': 'gondola.n.02', 'synonyms': ['gondola_(boat)'], 'id': 506, 'def': 'long narrow flat-bottomed boat propelled by sculling; traditionally used on canals of Venice', 'name': 'gondola_(boat)'}, {'frequency': 'c', 'synset': 'goose.n.01', 'synonyms': ['goose'], 'id': 507, 'def': 'loud, web-footed long-necked aquatic birds usually larger than ducks', 'name': 'goose'}, {'frequency': 'r', 'synset': 'gorilla.n.01', 'synonyms': ['gorilla'], 'id': 508, 'def': 'largest ape', 'name': 'gorilla'}, {'frequency': 'r', 'synset': 'gourd.n.02', 'synonyms': ['gourd'], 'id': 509, 'def': 'any of numerous inedible fruits with hard rinds', 'name': 'gourd'}, {'frequency': 'f', 'synset': 'grape.n.01', 'synonyms': ['grape'], 'id': 510, 'def': 'any of various juicy fruit with green or purple skins; grow in clusters', 'name': 'grape'}, {'frequency': 'c', 'synset': 'grater.n.01', 'synonyms': ['grater'], 'id': 511, 'def': 'utensil with sharp perforations for shredding foods (as vegetables or cheese)', 'name': 'grater'}, {'frequency': 'c', 'synset': 'gravestone.n.01', 'synonyms': ['gravestone', 'headstone', 'tombstone'], 'id': 512, 'def': 'a stone that is used to mark a grave', 'name': 'gravestone'}, {'frequency': 'r', 'synset': 'gravy_boat.n.01', 'synonyms': ['gravy_boat', 'gravy_holder'], 'id': 513, 'def': 'a dish (often boat-shaped) for serving gravy or sauce', 'name': 'gravy_boat'}, {'frequency': 'f', 'synset': 'green_bean.n.02', 'synonyms': ['green_bean'], 'id': 514, 'def': 'a common bean plant cultivated for its slender green edible pods', 'name': 'green_bean'}, {'frequency': 'f', 'synset': 'green_onion.n.01', 'synonyms': ['green_onion', 'spring_onion', 'scallion'], 'id': 515, 'def': 'a young onion before the bulb has enlarged', 'name': 'green_onion'}, {'frequency': 'r', 'synset': 'griddle.n.01', 'synonyms': ['griddle'], 'id': 516, 'def': 'cooking utensil consisting of a flat heated surface on which food is cooked', 'name': 'griddle'}, {'frequency': 'f', 'synset': 'grill.n.02', 'synonyms': ['grill', 'grille', 'grillwork', 'radiator_grille'], 'id': 517, 'def': 'a framework of metal bars used as a partition or a grate', 'name': 'grill'}, {'frequency': 'r', 'synset': 'grits.n.01', 'synonyms': ['grits', 'hominy_grits'], 'id': 518, 'def': 'coarsely ground corn boiled as a breakfast dish', 'name': 'grits'}, {'frequency': 'c', 'synset': 'grizzly.n.01', 'synonyms': ['grizzly', 'grizzly_bear'], 'id': 519, 'def': 'powerful brownish-yellow bear of the uplands of western North America', 'name': 'grizzly'}, {'frequency': 'c', 'synset': 'grocery_bag.n.01', 'synonyms': ['grocery_bag'], 'id': 520, 'def': "a sack for holding customer's groceries", 'name': 'grocery_bag'}, {'frequency': 'f', 'synset': 'guitar.n.01', 'synonyms': ['guitar'], 'id': 521, 'def': 'a stringed instrument usually having six strings; played by strumming or plucking', 'name': 'guitar'}, {'frequency': 'c', 'synset': 'gull.n.02', 'synonyms': ['gull', 'seagull'], 'id': 522, 'def': 'mostly white aquatic bird having long pointed wings and short legs', 'name': 'gull'}, {'frequency': 'c', 'synset': 'gun.n.01', 'synonyms': ['gun'], 'id': 523, 'def': 'a weapon that discharges a bullet at high velocity from a metal tube', 'name': 'gun'}, {'frequency': 'f', 'synset': 'hairbrush.n.01', 'synonyms': ['hairbrush'], 'id': 524, 'def': "a brush used to groom a person's hair", 'name': 'hairbrush'}, {'frequency': 'c', 'synset': 'hairnet.n.01', 'synonyms': ['hairnet'], 'id': 525, 'def': 'a small net that someone wears over their hair to keep it in place', 'name': 'hairnet'}, {'frequency': 'c', 'synset': 'hairpin.n.01', 'synonyms': ['hairpin'], 'id': 526, 'def': "a double pronged pin used to hold women's hair in place", 'name': 'hairpin'}, {'frequency': 'r', 'synset': 'halter.n.03', 'synonyms': ['halter_top'], 'id': 527, 'def': "a woman's top that fastens behind the back and neck leaving the back and arms uncovered", 'name': 'halter_top'}, {'frequency': 'f', 'synset': 'ham.n.01', 'synonyms': ['ham', 'jambon', 'gammon'], 'id': 528, 'def': 'meat cut from the thigh of a hog (usually smoked)', 'name': 'ham'}, {'frequency': 'c', 'synset': 'hamburger.n.01', 'synonyms': ['hamburger', 'beefburger', 'burger'], 'id': 529, 'def': 'a sandwich consisting of a patty of minced beef served on a bun', 'name': 'hamburger'}, {'frequency': 'c', 'synset': 'hammer.n.02', 'synonyms': ['hammer'], 'id': 530, 'def': 'a hand tool with a heavy head and a handle; used to deliver an impulsive force by striking', 'name': 'hammer'}, {'frequency': 'c', 'synset': 'hammock.n.02', 'synonyms': ['hammock'], 'id': 531, 'def': 'a hanging bed of canvas or rope netting (usually suspended between two trees)', 'name': 'hammock'}, {'frequency': 'r', 'synset': 'hamper.n.02', 'synonyms': ['hamper'], 'id': 532, 'def': 'a basket usually with a cover', 'name': 'hamper'}, {'frequency': 'c', 'synset': 'hamster.n.01', 'synonyms': ['hamster'], 'id': 533, 'def': 'short-tailed burrowing rodent with large cheek pouches', 'name': 'hamster'}, {'frequency': 'f', 'synset': 'hand_blower.n.01', 'synonyms': ['hair_dryer'], 'id': 534, 'def': 'a hand-held electric blower that can blow warm air onto the hair', 'name': 'hair_dryer'}, {'frequency': 'r', 'synset': 'hand_glass.n.01', 'synonyms': ['hand_glass', 'hand_mirror'], 'id': 535, 'def': 'a mirror intended to be held in the hand', 'name': 'hand_glass'}, {'frequency': 'f', 'synset': 'hand_towel.n.01', 'synonyms': ['hand_towel', 'face_towel'], 'id': 536, 'def': 'a small towel used to dry the hands or face', 'name': 'hand_towel'}, {'frequency': 'c', 'synset': 'handcart.n.01', 'synonyms': ['handcart', 'pushcart', 'hand_truck'], 'id': 537, 'def': 'wheeled vehicle that can be pushed by a person', 'name': 'handcart'}, {'frequency': 'r', 'synset': 'handcuff.n.01', 'synonyms': ['handcuff'], 'id': 538, 'def': 'shackle that consists of a metal loop that can be locked around the wrist', 'name': 'handcuff'}, {'frequency': 'c', 'synset': 'handkerchief.n.01', 'synonyms': ['handkerchief'], 'id': 539, 'def': 'a square piece of cloth used for wiping the eyes or nose or as a costume accessory', 'name': 'handkerchief'}, {'frequency': 'f', 'synset': 'handle.n.01', 'synonyms': ['handle', 'grip', 'handgrip'], 'id': 540, 'def': 'the appendage to an object that is designed to be held in order to use or move it', 'name': 'handle'}, {'frequency': 'r', 'synset': 'handsaw.n.01', 'synonyms': ['handsaw', "carpenter's_saw"], 'id': 541, 'def': 'a saw used with one hand for cutting wood', 'name': 'handsaw'}, {'frequency': 'r', 'synset': 'hardback.n.01', 'synonyms': ['hardback_book', 'hardcover_book'], 'id': 542, 'def': 'a book with cardboard or cloth or leather covers', 'name': 'hardback_book'}, {'frequency': 'r', 'synset': 'harmonium.n.01', 'synonyms': ['harmonium', 'organ_(musical_instrument)', 'reed_organ_(musical_instrument)'], 'id': 543, 'def': 'a free-reed instrument in which air is forced through the reeds by bellows', 'name': 'harmonium'}, {'frequency': 'f', 'synset': 'hat.n.01', 'synonyms': ['hat'], 'id': 544, 'def': 'headwear that protects the head from bad weather, sun, or worn for fashion', 'name': 'hat'}, {'frequency': 'r', 'synset': 'hatbox.n.01', 'synonyms': ['hatbox'], 'id': 545, 'def': 'a round piece of luggage for carrying hats', 'name': 'hatbox'}, {'frequency': 'c', 'synset': 'head_covering.n.01', 'synonyms': ['veil'], 'id': 546, 'def': 'a garment that covers the head OR face', 'name': 'veil'}, {'frequency': 'f', 'synset': 'headband.n.01', 'synonyms': ['headband'], 'id': 547, 'def': 'a band worn around or over the head', 'name': 'headband'}, {'frequency': 'f', 'synset': 'headboard.n.01', 'synonyms': ['headboard'], 'id': 548, 'def': 'a vertical board or panel forming the head of a bedstead', 'name': 'headboard'}, {'frequency': 'f', 'synset': 'headlight.n.01', 'synonyms': ['headlight', 'headlamp'], 'id': 549, 'def': 'a powerful light with reflector; attached to the front of an automobile or locomotive', 'name': 'headlight'}, {'frequency': 'c', 'synset': 'headscarf.n.01', 'synonyms': ['headscarf'], 'id': 550, 'def': 'a kerchief worn over the head and tied under the chin', 'name': 'headscarf'}, {'frequency': 'r', 'synset': 'headset.n.01', 'synonyms': ['headset'], 'id': 551, 'def': 'receiver consisting of a pair of headphones', 'name': 'headset'}, {'frequency': 'c', 'synset': 'headstall.n.01', 'synonyms': ['headstall_(for_horses)', 'headpiece_(for_horses)'], 'id': 552, 'def': "the band that is the part of a bridle that fits around a horse's head", 'name': 'headstall_(for_horses)'}, {'frequency': 'c', 'synset': 'heart.n.02', 'synonyms': ['heart'], 'id': 553, 'def': 'a muscular organ; its contractions move the blood through the body', 'name': 'heart'}, {'frequency': 'c', 'synset': 'heater.n.01', 'synonyms': ['heater', 'warmer'], 'id': 554, 'def': 'device that heats water or supplies warmth to a room', 'name': 'heater'}, {'frequency': 'c', 'synset': 'helicopter.n.01', 'synonyms': ['helicopter'], 'id': 555, 'def': 'an aircraft without wings that obtains its lift from the rotation of overhead blades', 'name': 'helicopter'}, {'frequency': 'f', 'synset': 'helmet.n.02', 'synonyms': ['helmet'], 'id': 556, 'def': 'a protective headgear made of hard material to resist blows', 'name': 'helmet'}, {'frequency': 'r', 'synset': 'heron.n.02', 'synonyms': ['heron'], 'id': 557, 'def': 'grey or white wading bird with long neck and long legs and (usually) long bill', 'name': 'heron'}, {'frequency': 'c', 'synset': 'highchair.n.01', 'synonyms': ['highchair', 'feeding_chair'], 'id': 558, 'def': 'a chair for feeding a very young child', 'name': 'highchair'}, {'frequency': 'f', 'synset': 'hinge.n.01', 'synonyms': ['hinge'], 'id': 559, 'def': 'a joint that holds two parts together so that one can swing relative to the other', 'name': 'hinge'}, {'frequency': 'r', 'synset': 'hippopotamus.n.01', 'synonyms': ['hippopotamus'], 'id': 560, 'def': 'massive thick-skinned animal living in or around rivers of tropical Africa', 'name': 'hippopotamus'}, {'frequency': 'r', 'synset': 'hockey_stick.n.01', 'synonyms': ['hockey_stick'], 'id': 561, 'def': 'sports implement consisting of a stick used by hockey players to move the puck', 'name': 'hockey_stick'}, {'frequency': 'c', 'synset': 'hog.n.03', 'synonyms': ['hog', 'pig'], 'id': 562, 'def': 'domestic swine', 'name': 'hog'}, {'frequency': 'f', 'synset': 'home_plate.n.01', 'synonyms': ['home_plate_(baseball)', 'home_base_(baseball)'], 'id': 563, 'def': '(baseball) a rubber slab where the batter stands; it must be touched by a base runner in order to score', 'name': 'home_plate_(baseball)'}, {'frequency': 'c', 'synset': 'honey.n.01', 'synonyms': ['honey'], 'id': 564, 'def': 'a sweet yellow liquid produced by bees', 'name': 'honey'}, {'frequency': 'f', 'synset': 'hood.n.06', 'synonyms': ['fume_hood', 'exhaust_hood'], 'id': 565, 'def': 'metal covering leading to a vent that exhausts smoke or fumes', 'name': 'fume_hood'}, {'frequency': 'f', 'synset': 'hook.n.05', 'synonyms': ['hook'], 'id': 566, 'def': 'a curved or bent implement for suspending or pulling something', 'name': 'hook'}, {'frequency': 'r', 'synset': 'hookah.n.01', 'synonyms': ['hookah', 'narghile', 'nargileh', 'sheesha', 'shisha', 'water_pipe'], 'id': 567, 'def': 'a tobacco pipe with a long flexible tube connected to a container where the smoke is cooled by passing through water', 'name': 'hookah'}, {'frequency': 'r', 'synset': 'hornet.n.01', 'synonyms': ['hornet'], 'id': 568, 'def': 'large stinging wasp', 'name': 'hornet'}, {'frequency': 'f', 'synset': 'horse.n.01', 'synonyms': ['horse'], 'id': 569, 'def': 'a common horse', 'name': 'horse'}, {'frequency': 'f', 'synset': 'hose.n.03', 'synonyms': ['hose', 'hosepipe'], 'id': 570, 'def': 'a flexible pipe for conveying a liquid or gas', 'name': 'hose'}, {'frequency': 'r', 'synset': 'hot-air_balloon.n.01', 'synonyms': ['hot-air_balloon'], 'id': 571, 'def': 'balloon for travel through the air in a basket suspended below a large bag of heated air', 'name': 'hot-air_balloon'}, {'frequency': 'r', 'synset': 'hot_plate.n.01', 'synonyms': ['hotplate'], 'id': 572, 'def': 'a portable electric appliance for heating or cooking or keeping food warm', 'name': 'hotplate'}, {'frequency': 'c', 'synset': 'hot_sauce.n.01', 'synonyms': ['hot_sauce'], 'id': 573, 'def': 'a pungent peppery sauce', 'name': 'hot_sauce'}, {'frequency': 'r', 'synset': 'hourglass.n.01', 'synonyms': ['hourglass'], 'id': 574, 'def': 'a sandglass timer that runs for sixty minutes', 'name': 'hourglass'}, {'frequency': 'r', 'synset': 'houseboat.n.01', 'synonyms': ['houseboat'], 'id': 575, 'def': 'a barge that is designed and equipped for use as a dwelling', 'name': 'houseboat'}, {'frequency': 'c', 'synset': 'hummingbird.n.01', 'synonyms': ['hummingbird'], 'id': 576, 'def': 'tiny American bird having brilliant iridescent plumage and long slender bills', 'name': 'hummingbird'}, {'frequency': 'r', 'synset': 'hummus.n.01', 'synonyms': ['hummus', 'humus', 'hommos', 'hoummos', 'humous'], 'id': 577, 'def': 'a thick spread made from mashed chickpeas', 'name': 'hummus'}, {'frequency': 'f', 'synset': 'ice_bear.n.01', 'synonyms': ['polar_bear'], 'id': 578, 'def': 'white bear of Arctic regions', 'name': 'polar_bear'}, {'frequency': 'c', 'synset': 'ice_cream.n.01', 'synonyms': ['icecream'], 'id': 579, 'def': 'frozen dessert containing cream and sugar and flavoring', 'name': 'icecream'}, {'frequency': 'r', 'synset': 'ice_lolly.n.01', 'synonyms': ['popsicle'], 'id': 580, 'def': 'ice cream or water ice on a small wooden stick', 'name': 'popsicle'}, {'frequency': 'c', 'synset': 'ice_maker.n.01', 'synonyms': ['ice_maker'], 'id': 581, 'def': 'an appliance included in some electric refrigerators for making ice cubes', 'name': 'ice_maker'}, {'frequency': 'r', 'synset': 'ice_pack.n.01', 'synonyms': ['ice_pack', 'ice_bag'], 'id': 582, 'def': 'a waterproof bag filled with ice: applied to the body (especially the head) to cool or reduce swelling', 'name': 'ice_pack'}, {'frequency': 'r', 'synset': 'ice_skate.n.01', 'synonyms': ['ice_skate'], 'id': 583, 'def': 'skate consisting of a boot with a steel blade fitted to the sole', 'name': 'ice_skate'}, {'frequency': 'c', 'synset': 'igniter.n.01', 'synonyms': ['igniter', 'ignitor', 'lighter'], 'id': 584, 'def': 'a substance or device used to start a fire', 'name': 'igniter'}, {'frequency': 'r', 'synset': 'inhaler.n.01', 'synonyms': ['inhaler', 'inhalator'], 'id': 585, 'def': 'a dispenser that produces a chemical vapor to be inhaled through mouth or nose', 'name': 'inhaler'}, {'frequency': 'f', 'synset': 'ipod.n.01', 'synonyms': ['iPod'], 'id': 586, 'def': 'a pocket-sized device used to play music files', 'name': 'iPod'}, {'frequency': 'c', 'synset': 'iron.n.04', 'synonyms': ['iron_(for_clothing)', 'smoothing_iron_(for_clothing)'], 'id': 587, 'def': 'home appliance consisting of a flat metal base that is heated and used to smooth cloth', 'name': 'iron_(for_clothing)'}, {'frequency': 'c', 'synset': 'ironing_board.n.01', 'synonyms': ['ironing_board'], 'id': 588, 'def': 'narrow padded board on collapsible supports; used for ironing clothes', 'name': 'ironing_board'}, {'frequency': 'f', 'synset': 'jacket.n.01', 'synonyms': ['jacket'], 'id': 589, 'def': 'a waist-length coat', 'name': 'jacket'}, {'frequency': 'c', 'synset': 'jam.n.01', 'synonyms': ['jam'], 'id': 590, 'def': 'preserve of crushed fruit', 'name': 'jam'}, {'frequency': 'f', 'synset': 'jar.n.01', 'synonyms': ['jar'], 'id': 591, 'def': 'a vessel (usually cylindrical) with a wide mouth and without handles', 'name': 'jar'}, {'frequency': 'f', 'synset': 'jean.n.01', 'synonyms': ['jean', 'blue_jean', 'denim'], 'id': 592, 'def': '(usually plural) close-fitting trousers of heavy denim for manual work or casual wear', 'name': 'jean'}, {'frequency': 'c', 'synset': 'jeep.n.01', 'synonyms': ['jeep', 'landrover'], 'id': 593, 'def': 'a car suitable for traveling over rough terrain', 'name': 'jeep'}, {'frequency': 'r', 'synset': 'jelly_bean.n.01', 'synonyms': ['jelly_bean', 'jelly_egg'], 'id': 594, 'def': 'sugar-glazed jellied candy', 'name': 'jelly_bean'}, {'frequency': 'f', 'synset': 'jersey.n.03', 'synonyms': ['jersey', 'T-shirt', 'tee_shirt'], 'id': 595, 'def': 'a close-fitting pullover shirt', 'name': 'jersey'}, {'frequency': 'c', 'synset': 'jet.n.01', 'synonyms': ['jet_plane', 'jet-propelled_plane'], 'id': 596, 'def': 'an airplane powered by one or more jet engines', 'name': 'jet_plane'}, {'frequency': 'r', 'synset': 'jewel.n.01', 'synonyms': ['jewel', 'gem', 'precious_stone'], 'id': 597, 'def': 'a precious or semiprecious stone incorporated into a piece of jewelry', 'name': 'jewel'}, {'frequency': 'c', 'synset': 'jewelry.n.01', 'synonyms': ['jewelry', 'jewellery'], 'id': 598, 'def': 'an adornment (as a bracelet or ring or necklace) made of precious metals and set with gems (or imitation gems)', 'name': 'jewelry'}, {'frequency': 'r', 'synset': 'joystick.n.02', 'synonyms': ['joystick'], 'id': 599, 'def': 'a control device for computers consisting of a vertical handle that can move freely in two directions', 'name': 'joystick'}, {'frequency': 'c', 'synset': 'jump_suit.n.01', 'synonyms': ['jumpsuit'], 'id': 600, 'def': "one-piece garment fashioned after a parachutist's uniform", 'name': 'jumpsuit'}, {'frequency': 'c', 'synset': 'kayak.n.01', 'synonyms': ['kayak'], 'id': 601, 'def': 'a small canoe consisting of a light frame made watertight with animal skins', 'name': 'kayak'}, {'frequency': 'r', 'synset': 'keg.n.02', 'synonyms': ['keg'], 'id': 602, 'def': 'small cask or barrel', 'name': 'keg'}, {'frequency': 'r', 'synset': 'kennel.n.01', 'synonyms': ['kennel', 'doghouse'], 'id': 603, 'def': 'outbuilding that serves as a shelter for a dog', 'name': 'kennel'}, {'frequency': 'c', 'synset': 'kettle.n.01', 'synonyms': ['kettle', 'boiler'], 'id': 604, 'def': 'a metal pot for stewing or boiling; usually has a lid', 'name': 'kettle'}, {'frequency': 'f', 'synset': 'key.n.01', 'synonyms': ['key'], 'id': 605, 'def': 'metal instrument used to unlock a lock', 'name': 'key'}, {'frequency': 'r', 'synset': 'keycard.n.01', 'synonyms': ['keycard'], 'id': 606, 'def': 'a plastic card used to gain access typically to a door', 'name': 'keycard'}, {'frequency': 'c', 'synset': 'kilt.n.01', 'synonyms': ['kilt'], 'id': 607, 'def': 'a knee-length pleated tartan skirt worn by men as part of the traditional dress in the Highlands of northern Scotland', 'name': 'kilt'}, {'frequency': 'c', 'synset': 'kimono.n.01', 'synonyms': ['kimono'], 'id': 608, 'def': 'a loose robe; imitated from robes originally worn by Japanese', 'name': 'kimono'}, {'frequency': 'f', 'synset': 'kitchen_sink.n.01', 'synonyms': ['kitchen_sink'], 'id': 609, 'def': 'a sink in a kitchen', 'name': 'kitchen_sink'}, {'frequency': 'r', 'synset': 'kitchen_table.n.01', 'synonyms': ['kitchen_table'], 'id': 610, 'def': 'a table in the kitchen', 'name': 'kitchen_table'}, {'frequency': 'f', 'synset': 'kite.n.03', 'synonyms': ['kite'], 'id': 611, 'def': 'plaything consisting of a light frame covered with tissue paper; flown in wind at end of a string', 'name': 'kite'}, {'frequency': 'c', 'synset': 'kitten.n.01', 'synonyms': ['kitten', 'kitty'], 'id': 612, 'def': 'young domestic cat', 'name': 'kitten'}, {'frequency': 'c', 'synset': 'kiwi.n.03', 'synonyms': ['kiwi_fruit'], 'id': 613, 'def': 'fuzzy brown egg-shaped fruit with slightly tart green flesh', 'name': 'kiwi_fruit'}, {'frequency': 'f', 'synset': 'knee_pad.n.01', 'synonyms': ['knee_pad'], 'id': 614, 'def': 'protective garment consisting of a pad worn by football or baseball or hockey players', 'name': 'knee_pad'}, {'frequency': 'f', 'synset': 'knife.n.01', 'synonyms': ['knife'], 'id': 615, 'def': 'tool with a blade and point used as a cutting instrument', 'name': 'knife'}, {'frequency': 'r', 'synset': 'knitting_needle.n.01', 'synonyms': ['knitting_needle'], 'id': 616, 'def': 'needle consisting of a slender rod with pointed ends; usually used in pairs', 'name': 'knitting_needle'}, {'frequency': 'f', 'synset': 'knob.n.02', 'synonyms': ['knob'], 'id': 617, 'def': 'a round handle often found on a door', 'name': 'knob'}, {'frequency': 'r', 'synset': 'knocker.n.05', 'synonyms': ['knocker_(on_a_door)', 'doorknocker'], 'id': 618, 'def': 'a device (usually metal and ornamental) attached by a hinge to a door', 'name': 'knocker_(on_a_door)'}, {'frequency': 'r', 'synset': 'koala.n.01', 'synonyms': ['koala', 'koala_bear'], 'id': 619, 'def': 'sluggish tailless Australian marsupial with grey furry ears and coat', 'name': 'koala'}, {'frequency': 'r', 'synset': 'lab_coat.n.01', 'synonyms': ['lab_coat', 'laboratory_coat'], 'id': 620, 'def': 'a light coat worn to protect clothing from substances used while working in a laboratory', 'name': 'lab_coat'}, {'frequency': 'f', 'synset': 'ladder.n.01', 'synonyms': ['ladder'], 'id': 621, 'def': 'steps consisting of two parallel members connected by rungs', 'name': 'ladder'}, {'frequency': 'c', 'synset': 'ladle.n.01', 'synonyms': ['ladle'], 'id': 622, 'def': 'a spoon-shaped vessel with a long handle frequently used to transfer liquids', 'name': 'ladle'}, {'frequency': 'c', 'synset': 'ladybug.n.01', 'synonyms': ['ladybug', 'ladybeetle', 'ladybird_beetle'], 'id': 623, 'def': 'small round bright-colored and spotted beetle, typically red and black', 'name': 'ladybug'}, {'frequency': 'f', 'synset': 'lamb.n.01', 'synonyms': ['lamb_(animal)'], 'id': 624, 'def': 'young sheep', 'name': 'lamb_(animal)'}, {'frequency': 'r', 'synset': 'lamb_chop.n.01', 'synonyms': ['lamb-chop', 'lambchop'], 'id': 625, 'def': 'chop cut from a lamb', 'name': 'lamb-chop'}, {'frequency': 'f', 'synset': 'lamp.n.02', 'synonyms': ['lamp'], 'id': 626, 'def': 'a piece of furniture holding one or more electric light bulbs', 'name': 'lamp'}, {'frequency': 'f', 'synset': 'lamppost.n.01', 'synonyms': ['lamppost'], 'id': 627, 'def': 'a metal post supporting an outdoor lamp (such as a streetlight)', 'name': 'lamppost'}, {'frequency': 'f', 'synset': 'lampshade.n.01', 'synonyms': ['lampshade'], 'id': 628, 'def': 'a protective ornamental shade used to screen a light bulb from direct view', 'name': 'lampshade'}, {'frequency': 'c', 'synset': 'lantern.n.01', 'synonyms': ['lantern'], 'id': 629, 'def': 'light in a transparent protective case', 'name': 'lantern'}, {'frequency': 'f', 'synset': 'lanyard.n.02', 'synonyms': ['lanyard', 'laniard'], 'id': 630, 'def': 'a cord worn around the neck to hold a knife or whistle, etc.', 'name': 'lanyard'}, {'frequency': 'f', 'synset': 'laptop.n.01', 'synonyms': ['laptop_computer', 'notebook_computer'], 'id': 631, 'def': 'a portable computer small enough to use in your lap', 'name': 'laptop_computer'}, {'frequency': 'r', 'synset': 'lasagna.n.01', 'synonyms': ['lasagna', 'lasagne'], 'id': 632, 'def': 'baked dish of layers of lasagna pasta with sauce and cheese and meat or vegetables', 'name': 'lasagna'}, {'frequency': 'f', 'synset': 'latch.n.02', 'synonyms': ['latch'], 'id': 633, 'def': 'a bar that can be lowered or slid into a groove to fasten a door or gate', 'name': 'latch'}, {'frequency': 'r', 'synset': 'lawn_mower.n.01', 'synonyms': ['lawn_mower'], 'id': 634, 'def': 'garden tool for mowing grass on lawns', 'name': 'lawn_mower'}, {'frequency': 'r', 'synset': 'leather.n.01', 'synonyms': ['leather'], 'id': 635, 'def': 'an animal skin made smooth and flexible by removing the hair and then tanning', 'name': 'leather'}, {'frequency': 'c', 'synset': 'legging.n.01', 'synonyms': ['legging_(clothing)', 'leging_(clothing)', 'leg_covering'], 'id': 636, 'def': 'a garment covering the leg (usually extending from the knee to the ankle)', 'name': 'legging_(clothing)'}, {'frequency': 'c', 'synset': 'lego.n.01', 'synonyms': ['Lego', 'Lego_set'], 'id': 637, 'def': "a child's plastic construction set for making models from blocks", 'name': 'Lego'}, {'frequency': 'r', 'synset': 'legume.n.02', 'synonyms': ['legume'], 'id': 638, 'def': 'the fruit or seed of bean or pea plants', 'name': 'legume'}, {'frequency': 'f', 'synset': 'lemon.n.01', 'synonyms': ['lemon'], 'id': 639, 'def': 'yellow oval fruit with juicy acidic flesh', 'name': 'lemon'}, {'frequency': 'r', 'synset': 'lemonade.n.01', 'synonyms': ['lemonade'], 'id': 640, 'def': 'sweetened beverage of diluted lemon juice', 'name': 'lemonade'}, {'frequency': 'f', 'synset': 'lettuce.n.02', 'synonyms': ['lettuce'], 'id': 641, 'def': 'leafy plant commonly eaten in salad or on sandwiches', 'name': 'lettuce'}, {'frequency': 'f', 'synset': 'license_plate.n.01', 'synonyms': ['license_plate', 'numberplate'], 'id': 642, 'def': "a plate mounted on the front and back of car and bearing the car's registration number", 'name': 'license_plate'}, {'frequency': 'f', 'synset': 'life_buoy.n.01', 'synonyms': ['life_buoy', 'lifesaver', 'life_belt', 'life_ring'], 'id': 643, 'def': 'a ring-shaped life preserver used to prevent drowning (NOT a life-jacket or vest)', 'name': 'life_buoy'}, {'frequency': 'f', 'synset': 'life_jacket.n.01', 'synonyms': ['life_jacket', 'life_vest'], 'id': 644, 'def': 'life preserver consisting of a sleeveless jacket of buoyant or inflatable design', 'name': 'life_jacket'}, {'frequency': 'f', 'synset': 'light_bulb.n.01', 'synonyms': ['lightbulb'], 'id': 645, 'def': 'lightblub/source of light', 'name': 'lightbulb'}, {'frequency': 'r', 'synset': 'lightning_rod.n.02', 'synonyms': ['lightning_rod', 'lightning_conductor'], 'id': 646, 'def': 'a metallic conductor that is attached to a high point and leads to the ground', 'name': 'lightning_rod'}, {'frequency': 'f', 'synset': 'lime.n.06', 'synonyms': ['lime'], 'id': 647, 'def': 'the green acidic fruit of any of various lime trees', 'name': 'lime'}, {'frequency': 'r', 'synset': 'limousine.n.01', 'synonyms': ['limousine'], 'id': 648, 'def': 'long luxurious car; usually driven by a chauffeur', 'name': 'limousine'}, {'frequency': 'c', 'synset': 'lion.n.01', 'synonyms': ['lion'], 'id': 649, 'def': 'large gregarious predatory cat of Africa and India', 'name': 'lion'}, {'frequency': 'c', 'synset': 'lip_balm.n.01', 'synonyms': ['lip_balm'], 'id': 650, 'def': 'a balm applied to the lips', 'name': 'lip_balm'}, {'frequency': 'r', 'synset': 'liquor.n.01', 'synonyms': ['liquor', 'spirits', 'hard_liquor', 'liqueur', 'cordial'], 'id': 651, 'def': 'liquor or beer', 'name': 'liquor'}, {'frequency': 'c', 'synset': 'lizard.n.01', 'synonyms': ['lizard'], 'id': 652, 'def': 'a reptile with usually two pairs of legs and a tapering tail', 'name': 'lizard'}, {'frequency': 'f', 'synset': 'log.n.01', 'synonyms': ['log'], 'id': 653, 'def': 'a segment of the trunk of a tree when stripped of branches', 'name': 'log'}, {'frequency': 'c', 'synset': 'lollipop.n.02', 'synonyms': ['lollipop'], 'id': 654, 'def': 'hard candy on a stick', 'name': 'lollipop'}, {'frequency': 'f', 'synset': 'loudspeaker.n.01', 'synonyms': ['speaker_(stero_equipment)'], 'id': 655, 'def': 'electronic device that produces sound often as part of a stereo system', 'name': 'speaker_(stero_equipment)'}, {'frequency': 'c', 'synset': 'love_seat.n.01', 'synonyms': ['loveseat'], 'id': 656, 'def': 'small sofa that seats two people', 'name': 'loveseat'}, {'frequency': 'r', 'synset': 'machine_gun.n.01', 'synonyms': ['machine_gun'], 'id': 657, 'def': 'a rapidly firing automatic gun', 'name': 'machine_gun'}, {'frequency': 'f', 'synset': 'magazine.n.02', 'synonyms': ['magazine'], 'id': 658, 'def': 'a paperback periodic publication', 'name': 'magazine'}, {'frequency': 'f', 'synset': 'magnet.n.01', 'synonyms': ['magnet'], 'id': 659, 'def': 'a device that attracts iron and produces a magnetic field', 'name': 'magnet'}, {'frequency': 'c', 'synset': 'mail_slot.n.01', 'synonyms': ['mail_slot'], 'id': 660, 'def': 'a slot (usually in a door) through which mail can be delivered', 'name': 'mail_slot'}, {'frequency': 'f', 'synset': 'mailbox.n.01', 'synonyms': ['mailbox_(at_home)', 'letter_box_(at_home)'], 'id': 661, 'def': 'a private box for delivery of mail', 'name': 'mailbox_(at_home)'}, {'frequency': 'r', 'synset': 'mallard.n.01', 'synonyms': ['mallard'], 'id': 662, 'def': 'wild dabbling duck from which domestic ducks are descended', 'name': 'mallard'}, {'frequency': 'r', 'synset': 'mallet.n.01', 'synonyms': ['mallet'], 'id': 663, 'def': 'a sports implement with a long handle and a hammer-like head used to hit a ball', 'name': 'mallet'}, {'frequency': 'r', 'synset': 'mammoth.n.01', 'synonyms': ['mammoth'], 'id': 664, 'def': 'any of numerous extinct elephants widely distributed in the Pleistocene', 'name': 'mammoth'}, {'frequency': 'r', 'synset': 'manatee.n.01', 'synonyms': ['manatee'], 'id': 665, 'def': 'sirenian mammal of tropical coastal waters of America', 'name': 'manatee'}, {'frequency': 'c', 'synset': 'mandarin.n.05', 'synonyms': ['mandarin_orange'], 'id': 666, 'def': 'a somewhat flat reddish-orange loose skinned citrus of China', 'name': 'mandarin_orange'}, {'frequency': 'c', 'synset': 'manger.n.01', 'synonyms': ['manger', 'trough'], 'id': 667, 'def': 'a container (usually in a barn or stable) from which cattle or horses feed', 'name': 'manger'}, {'frequency': 'f', 'synset': 'manhole.n.01', 'synonyms': ['manhole'], 'id': 668, 'def': 'a hole (usually with a flush cover) through which a person can gain access to an underground structure', 'name': 'manhole'}, {'frequency': 'f', 'synset': 'map.n.01', 'synonyms': ['map'], 'id': 669, 'def': "a diagrammatic representation of the earth's surface (or part of it)", 'name': 'map'}, {'frequency': 'f', 'synset': 'marker.n.03', 'synonyms': ['marker'], 'id': 670, 'def': 'a writing implement for making a mark', 'name': 'marker'}, {'frequency': 'r', 'synset': 'martini.n.01', 'synonyms': ['martini'], 'id': 671, 'def': 'a cocktail made of gin (or vodka) with dry vermouth', 'name': 'martini'}, {'frequency': 'r', 'synset': 'mascot.n.01', 'synonyms': ['mascot'], 'id': 672, 'def': 'a person or animal that is adopted by a team or other group as a symbolic figure', 'name': 'mascot'}, {'frequency': 'c', 'synset': 'mashed_potato.n.01', 'synonyms': ['mashed_potato'], 'id': 673, 'def': 'potato that has been peeled and boiled and then mashed', 'name': 'mashed_potato'}, {'frequency': 'r', 'synset': 'masher.n.02', 'synonyms': ['masher'], 'id': 674, 'def': 'a kitchen utensil used for mashing (e.g. potatoes)', 'name': 'masher'}, {'frequency': 'f', 'synset': 'mask.n.04', 'synonyms': ['mask', 'facemask'], 'id': 675, 'def': 'a protective covering worn over the face', 'name': 'mask'}, {'frequency': 'f', 'synset': 'mast.n.01', 'synonyms': ['mast'], 'id': 676, 'def': 'a vertical spar for supporting sails', 'name': 'mast'}, {'frequency': 'c', 'synset': 'mat.n.03', 'synonyms': ['mat_(gym_equipment)', 'gym_mat'], 'id': 677, 'def': 'sports equipment consisting of a piece of thick padding on the floor for gymnastics', 'name': 'mat_(gym_equipment)'}, {'frequency': 'r', 'synset': 'matchbox.n.01', 'synonyms': ['matchbox'], 'id': 678, 'def': 'a box for holding matches', 'name': 'matchbox'}, {'frequency': 'f', 'synset': 'mattress.n.01', 'synonyms': ['mattress'], 'id': 679, 'def': 'a thick pad filled with resilient material used as a bed or part of a bed', 'name': 'mattress'}, {'frequency': 'c', 'synset': 'measuring_cup.n.01', 'synonyms': ['measuring_cup'], 'id': 680, 'def': 'graduated cup used to measure liquid or granular ingredients', 'name': 'measuring_cup'}, {'frequency': 'c', 'synset': 'measuring_stick.n.01', 'synonyms': ['measuring_stick', 'ruler_(measuring_stick)', 'measuring_rod'], 'id': 681, 'def': 'measuring instrument having a sequence of marks at regular intervals', 'name': 'measuring_stick'}, {'frequency': 'c', 'synset': 'meatball.n.01', 'synonyms': ['meatball'], 'id': 682, 'def': 'ground meat formed into a ball and fried or simmered in broth', 'name': 'meatball'}, {'frequency': 'c', 'synset': 'medicine.n.02', 'synonyms': ['medicine'], 'id': 683, 'def': 'something that treats or prevents or alleviates the symptoms of disease', 'name': 'medicine'}, {'frequency': 'c', 'synset': 'melon.n.01', 'synonyms': ['melon'], 'id': 684, 'def': 'fruit of the gourd family having a hard rind and sweet juicy flesh', 'name': 'melon'}, {'frequency': 'f', 'synset': 'microphone.n.01', 'synonyms': ['microphone'], 'id': 685, 'def': 'device for converting sound waves into electrical energy', 'name': 'microphone'}, {'frequency': 'r', 'synset': 'microscope.n.01', 'synonyms': ['microscope'], 'id': 686, 'def': 'magnifier of the image of small objects', 'name': 'microscope'}, {'frequency': 'f', 'synset': 'microwave.n.02', 'synonyms': ['microwave_oven'], 'id': 687, 'def': 'kitchen appliance that cooks food by passing an electromagnetic wave through it', 'name': 'microwave_oven'}, {'frequency': 'r', 'synset': 'milestone.n.01', 'synonyms': ['milestone', 'milepost'], 'id': 688, 'def': 'stone post at side of a road to show distances', 'name': 'milestone'}, {'frequency': 'f', 'synset': 'milk.n.01', 'synonyms': ['milk'], 'id': 689, 'def': 'a white nutritious liquid secreted by mammals and used as food by human beings', 'name': 'milk'}, {'frequency': 'r', 'synset': 'milk_can.n.01', 'synonyms': ['milk_can'], 'id': 690, 'def': 'can for transporting milk', 'name': 'milk_can'}, {'frequency': 'r', 'synset': 'milkshake.n.01', 'synonyms': ['milkshake'], 'id': 691, 'def': 'frothy drink of milk and flavoring and sometimes fruit or ice cream', 'name': 'milkshake'}, {'frequency': 'f', 'synset': 'minivan.n.01', 'synonyms': ['minivan'], 'id': 692, 'def': 'a small box-shaped passenger van', 'name': 'minivan'}, {'frequency': 'r', 'synset': 'mint.n.05', 'synonyms': ['mint_candy'], 'id': 693, 'def': 'a candy that is flavored with a mint oil', 'name': 'mint_candy'}, {'frequency': 'f', 'synset': 'mirror.n.01', 'synonyms': ['mirror'], 'id': 694, 'def': 'polished surface that forms images by reflecting light', 'name': 'mirror'}, {'frequency': 'c', 'synset': 'mitten.n.01', 'synonyms': ['mitten'], 'id': 695, 'def': 'glove that encases the thumb separately and the other four fingers together', 'name': 'mitten'}, {'frequency': 'c', 'synset': 'mixer.n.04', 'synonyms': ['mixer_(kitchen_tool)', 'stand_mixer'], 'id': 696, 'def': 'a kitchen utensil that is used for mixing foods', 'name': 'mixer_(kitchen_tool)'}, {'frequency': 'c', 'synset': 'money.n.03', 'synonyms': ['money'], 'id': 697, 'def': 'the official currency issued by a government or national bank', 'name': 'money'}, {'frequency': 'f', 'synset': 'monitor.n.04', 'synonyms': ['monitor_(computer_equipment) computer_monitor'], 'id': 698, 'def': 'a computer monitor', 'name': 'monitor_(computer_equipment) computer_monitor'}, {'frequency': 'c', 'synset': 'monkey.n.01', 'synonyms': ['monkey'], 'id': 699, 'def': 'any of various long-tailed primates', 'name': 'monkey'}, {'frequency': 'f', 'synset': 'motor.n.01', 'synonyms': ['motor'], 'id': 700, 'def': 'machine that converts other forms of energy into mechanical energy and so imparts motion', 'name': 'motor'}, {'frequency': 'f', 'synset': 'motor_scooter.n.01', 'synonyms': ['motor_scooter', 'scooter'], 'id': 701, 'def': 'a wheeled vehicle with small wheels and a low-powered engine', 'name': 'motor_scooter'}, {'frequency': 'r', 'synset': 'motor_vehicle.n.01', 'synonyms': ['motor_vehicle', 'automotive_vehicle'], 'id': 702, 'def': 'a self-propelled wheeled vehicle that does not run on rails', 'name': 'motor_vehicle'}, {'frequency': 'f', 'synset': 'motorcycle.n.01', 'synonyms': ['motorcycle'], 'id': 703, 'def': 'a motor vehicle with two wheels and a strong frame', 'name': 'motorcycle'}, {'frequency': 'f', 'synset': 'mound.n.01', 'synonyms': ['mound_(baseball)', "pitcher's_mound"], 'id': 704, 'def': '(baseball) the slight elevation on which the pitcher stands', 'name': 'mound_(baseball)'}, {'frequency': 'f', 'synset': 'mouse.n.04', 'synonyms': ['mouse_(computer_equipment)', 'computer_mouse'], 'id': 705, 'def': 'a computer input device that controls an on-screen pointer (does not include trackpads / touchpads)', 'name': 'mouse_(computer_equipment)'}, {'frequency': 'f', 'synset': 'mousepad.n.01', 'synonyms': ['mousepad'], 'id': 706, 'def': 'a small portable pad that provides an operating surface for a computer mouse', 'name': 'mousepad'}, {'frequency': 'c', 'synset': 'muffin.n.01', 'synonyms': ['muffin'], 'id': 707, 'def': 'a sweet quick bread baked in a cup-shaped pan', 'name': 'muffin'}, {'frequency': 'f', 'synset': 'mug.n.04', 'synonyms': ['mug'], 'id': 708, 'def': 'with handle and usually cylindrical', 'name': 'mug'}, {'frequency': 'f', 'synset': 'mushroom.n.02', 'synonyms': ['mushroom'], 'id': 709, 'def': 'a common mushroom', 'name': 'mushroom'}, {'frequency': 'r', 'synset': 'music_stool.n.01', 'synonyms': ['music_stool', 'piano_stool'], 'id': 710, 'def': 'a stool for piano players; usually adjustable in height', 'name': 'music_stool'}, {'frequency': 'c', 'synset': 'musical_instrument.n.01', 'synonyms': ['musical_instrument', 'instrument_(musical)'], 'id': 711, 'def': 'any of various devices or contrivances that can be used to produce musical tones or sounds', 'name': 'musical_instrument'}, {'frequency': 'r', 'synset': 'nailfile.n.01', 'synonyms': ['nailfile'], 'id': 712, 'def': 'a small flat file for shaping the nails', 'name': 'nailfile'}, {'frequency': 'f', 'synset': 'napkin.n.01', 'synonyms': ['napkin', 'table_napkin', 'serviette'], 'id': 713, 'def': 'a small piece of table linen or paper that is used to wipe the mouth and to cover the lap in order to protect clothing', 'name': 'napkin'}, {'frequency': 'r', 'synset': 'neckerchief.n.01', 'synonyms': ['neckerchief'], 'id': 714, 'def': 'a kerchief worn around the neck', 'name': 'neckerchief'}, {'frequency': 'f', 'synset': 'necklace.n.01', 'synonyms': ['necklace'], 'id': 715, 'def': 'jewelry consisting of a cord or chain (often bearing gems) worn about the neck as an ornament', 'name': 'necklace'}, {'frequency': 'f', 'synset': 'necktie.n.01', 'synonyms': ['necktie', 'tie_(necktie)'], 'id': 716, 'def': 'neckwear consisting of a long narrow piece of material worn under a collar and tied in knot at the front', 'name': 'necktie'}, {'frequency': 'c', 'synset': 'needle.n.03', 'synonyms': ['needle'], 'id': 717, 'def': 'a sharp pointed implement (usually metal)', 'name': 'needle'}, {'frequency': 'c', 'synset': 'nest.n.01', 'synonyms': ['nest'], 'id': 718, 'def': 'a structure in which animals lay eggs or give birth to their young', 'name': 'nest'}, {'frequency': 'f', 'synset': 'newspaper.n.01', 'synonyms': ['newspaper', 'paper_(newspaper)'], 'id': 719, 'def': 'a daily or weekly publication on folded sheets containing news, articles, and advertisements', 'name': 'newspaper'}, {'frequency': 'c', 'synset': 'newsstand.n.01', 'synonyms': ['newsstand'], 'id': 720, 'def': 'a stall where newspapers and other periodicals are sold', 'name': 'newsstand'}, {'frequency': 'c', 'synset': 'nightwear.n.01', 'synonyms': ['nightshirt', 'nightwear', 'sleepwear', 'nightclothes'], 'id': 721, 'def': 'garments designed to be worn in bed', 'name': 'nightshirt'}, {'frequency': 'r', 'synset': 'nosebag.n.01', 'synonyms': ['nosebag_(for_animals)', 'feedbag'], 'id': 722, 'def': 'a canvas bag that is used to feed an animal (such as a horse); covers the muzzle and fastens at the top of the head', 'name': 'nosebag_(for_animals)'}, {'frequency': 'c', 'synset': 'noseband.n.01', 'synonyms': ['noseband_(for_animals)', 'nosepiece_(for_animals)'], 'id': 723, 'def': "a strap that is the part of a bridle that goes over the animal's nose", 'name': 'noseband_(for_animals)'}, {'frequency': 'f', 'synset': 'notebook.n.01', 'synonyms': ['notebook'], 'id': 724, 'def': 'a book with blank pages for recording notes or memoranda', 'name': 'notebook'}, {'frequency': 'c', 'synset': 'notepad.n.01', 'synonyms': ['notepad'], 'id': 725, 'def': 'a pad of paper for keeping notes', 'name': 'notepad'}, {'frequency': 'f', 'synset': 'nut.n.03', 'synonyms': ['nut'], 'id': 726, 'def': 'a small metal block (usually square or hexagonal) with internal screw thread to be fitted onto a bolt', 'name': 'nut'}, {'frequency': 'r', 'synset': 'nutcracker.n.01', 'synonyms': ['nutcracker'], 'id': 727, 'def': 'a hand tool used to crack nuts open', 'name': 'nutcracker'}, {'frequency': 'f', 'synset': 'oar.n.01', 'synonyms': ['oar'], 'id': 728, 'def': 'an implement used to propel or steer a boat', 'name': 'oar'}, {'frequency': 'r', 'synset': 'octopus.n.01', 'synonyms': ['octopus_(food)'], 'id': 729, 'def': 'tentacles of octopus prepared as food', 'name': 'octopus_(food)'}, {'frequency': 'r', 'synset': 'octopus.n.02', 'synonyms': ['octopus_(animal)'], 'id': 730, 'def': 'bottom-living cephalopod having a soft oval body with eight long tentacles', 'name': 'octopus_(animal)'}, {'frequency': 'c', 'synset': 'oil_lamp.n.01', 'synonyms': ['oil_lamp', 'kerosene_lamp', 'kerosine_lamp'], 'id': 731, 'def': 'a lamp that burns oil (as kerosine) for light', 'name': 'oil_lamp'}, {'frequency': 'c', 'synset': 'olive_oil.n.01', 'synonyms': ['olive_oil'], 'id': 732, 'def': 'oil from olives', 'name': 'olive_oil'}, {'frequency': 'r', 'synset': 'omelet.n.01', 'synonyms': ['omelet', 'omelette'], 'id': 733, 'def': 'beaten eggs cooked until just set; may be folded around e.g. ham or cheese or jelly', 'name': 'omelet'}, {'frequency': 'f', 'synset': 'onion.n.01', 'synonyms': ['onion'], 'id': 734, 'def': 'the bulb of an onion plant', 'name': 'onion'}, {'frequency': 'f', 'synset': 'orange.n.01', 'synonyms': ['orange_(fruit)'], 'id': 735, 'def': 'orange (FRUIT of an orange tree)', 'name': 'orange_(fruit)'}, {'frequency': 'c', 'synset': 'orange_juice.n.01', 'synonyms': ['orange_juice'], 'id': 736, 'def': 'bottled or freshly squeezed juice of oranges', 'name': 'orange_juice'}, {'frequency': 'c', 'synset': 'ostrich.n.02', 'synonyms': ['ostrich'], 'id': 737, 'def': 'fast-running African flightless bird with two-toed feet; largest living bird', 'name': 'ostrich'}, {'frequency': 'f', 'synset': 'ottoman.n.03', 'synonyms': ['ottoman', 'pouf', 'pouffe', 'hassock'], 'id': 738, 'def': 'a thick standalone cushion used as a seat or footrest, often next to a chair', 'name': 'ottoman'}, {'frequency': 'f', 'synset': 'oven.n.01', 'synonyms': ['oven'], 'id': 739, 'def': 'kitchen appliance used for baking or roasting', 'name': 'oven'}, {'frequency': 'c', 'synset': 'overall.n.01', 'synonyms': ['overalls_(clothing)'], 'id': 740, 'def': 'work clothing consisting of denim trousers usually with a bib and shoulder straps', 'name': 'overalls_(clothing)'}, {'frequency': 'c', 'synset': 'owl.n.01', 'synonyms': ['owl'], 'id': 741, 'def': 'nocturnal bird of prey with hawk-like beak and claws and large head with front-facing eyes', 'name': 'owl'}, {'frequency': 'c', 'synset': 'packet.n.03', 'synonyms': ['packet'], 'id': 742, 'def': 'a small package or bundle', 'name': 'packet'}, {'frequency': 'r', 'synset': 'pad.n.03', 'synonyms': ['inkpad', 'inking_pad', 'stamp_pad'], 'id': 743, 'def': 'absorbent material saturated with ink used to transfer ink evenly to a rubber stamp', 'name': 'inkpad'}, {'frequency': 'c', 'synset': 'pad.n.04', 'synonyms': ['pad'], 'id': 744, 'def': 'mostly arm/knee pads labeled', 'name': 'pad'}, {'frequency': 'f', 'synset': 'paddle.n.04', 'synonyms': ['paddle', 'boat_paddle'], 'id': 745, 'def': 'a short light oar used without an oarlock to propel a canoe or small boat', 'name': 'paddle'}, {'frequency': 'c', 'synset': 'padlock.n.01', 'synonyms': ['padlock'], 'id': 746, 'def': 'a detachable, portable lock', 'name': 'padlock'}, {'frequency': 'c', 'synset': 'paintbrush.n.01', 'synonyms': ['paintbrush'], 'id': 747, 'def': 'a brush used as an applicator to apply paint', 'name': 'paintbrush'}, {'frequency': 'f', 'synset': 'painting.n.01', 'synonyms': ['painting'], 'id': 748, 'def': 'graphic art consisting of an artistic composition made by applying paints to a surface', 'name': 'painting'}, {'frequency': 'f', 'synset': 'pajama.n.02', 'synonyms': ['pajamas', 'pyjamas'], 'id': 749, 'def': 'loose-fitting nightclothes worn for sleeping or lounging', 'name': 'pajamas'}, {'frequency': 'c', 'synset': 'palette.n.02', 'synonyms': ['palette', 'pallet'], 'id': 750, 'def': 'board that provides a flat surface on which artists mix paints and the range of colors used', 'name': 'palette'}, {'frequency': 'f', 'synset': 'pan.n.01', 'synonyms': ['pan_(for_cooking)', 'cooking_pan'], 'id': 751, 'def': 'cooking utensil consisting of a wide metal vessel', 'name': 'pan_(for_cooking)'}, {'frequency': 'r', 'synset': 'pan.n.03', 'synonyms': ['pan_(metal_container)'], 'id': 752, 'def': 'shallow container made of metal', 'name': 'pan_(metal_container)'}, {'frequency': 'c', 'synset': 'pancake.n.01', 'synonyms': ['pancake'], 'id': 753, 'def': 'a flat cake of thin batter fried on both sides on a griddle', 'name': 'pancake'}, {'frequency': 'r', 'synset': 'pantyhose.n.01', 'synonyms': ['pantyhose'], 'id': 754, 'def': "a woman's tights consisting of underpants and stockings", 'name': 'pantyhose'}, {'frequency': 'r', 'synset': 'papaya.n.02', 'synonyms': ['papaya'], 'id': 755, 'def': 'large oval melon-like tropical fruit with yellowish flesh', 'name': 'papaya'}, {'frequency': 'f', 'synset': 'paper_plate.n.01', 'synonyms': ['paper_plate'], 'id': 756, 'def': 'a disposable plate made of cardboard', 'name': 'paper_plate'}, {'frequency': 'f', 'synset': 'paper_towel.n.01', 'synonyms': ['paper_towel'], 'id': 757, 'def': 'a disposable towel made of absorbent paper', 'name': 'paper_towel'}, {'frequency': 'r', 'synset': 'paperback_book.n.01', 'synonyms': ['paperback_book', 'paper-back_book', 'softback_book', 'soft-cover_book'], 'id': 758, 'def': 'a book with paper covers', 'name': 'paperback_book'}, {'frequency': 'r', 'synset': 'paperweight.n.01', 'synonyms': ['paperweight'], 'id': 759, 'def': 'a weight used to hold down a stack of papers', 'name': 'paperweight'}, {'frequency': 'c', 'synset': 'parachute.n.01', 'synonyms': ['parachute'], 'id': 760, 'def': 'rescue equipment consisting of a device that fills with air and retards your fall', 'name': 'parachute'}, {'frequency': 'c', 'synset': 'parakeet.n.01', 'synonyms': ['parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet', 'parroquet'], 'id': 761, 'def': 'any of numerous small slender long-tailed parrots', 'name': 'parakeet'}, {'frequency': 'c', 'synset': 'parasail.n.01', 'synonyms': ['parasail_(sports)'], 'id': 762, 'def': 'parachute that will lift a person up into the air when it is towed by a motorboat or a car', 'name': 'parasail_(sports)'}, {'frequency': 'c', 'synset': 'parasol.n.01', 'synonyms': ['parasol', 'sunshade'], 'id': 763, 'def': 'a handheld collapsible source of shade', 'name': 'parasol'}, {'frequency': 'r', 'synset': 'parchment.n.01', 'synonyms': ['parchment'], 'id': 764, 'def': 'a superior paper resembling sheepskin', 'name': 'parchment'}, {'frequency': 'c', 'synset': 'parka.n.01', 'synonyms': ['parka', 'anorak'], 'id': 765, 'def': "a kind of heavy jacket (`windcheater' is a British term)", 'name': 'parka'}, {'frequency': 'f', 'synset': 'parking_meter.n.01', 'synonyms': ['parking_meter'], 'id': 766, 'def': 'a coin-operated timer located next to a parking space', 'name': 'parking_meter'}, {'frequency': 'c', 'synset': 'parrot.n.01', 'synonyms': ['parrot'], 'id': 767, 'def': 'usually brightly colored tropical birds with short hooked beaks and the ability to mimic sounds', 'name': 'parrot'}, {'frequency': 'c', 'synset': 'passenger_car.n.01', 'synonyms': ['passenger_car_(part_of_a_train)', 'coach_(part_of_a_train)'], 'id': 768, 'def': 'a railcar where passengers ride', 'name': 'passenger_car_(part_of_a_train)'}, {'frequency': 'r', 'synset': 'passenger_ship.n.01', 'synonyms': ['passenger_ship'], 'id': 769, 'def': 'a ship built to carry passengers', 'name': 'passenger_ship'}, {'frequency': 'c', 'synset': 'passport.n.02', 'synonyms': ['passport'], 'id': 770, 'def': 'a document issued by a country to a citizen allowing that person to travel abroad and re-enter the home country', 'name': 'passport'}, {'frequency': 'f', 'synset': 'pastry.n.02', 'synonyms': ['pastry'], 'id': 771, 'def': 'any of various baked foods made of dough or batter', 'name': 'pastry'}, {'frequency': 'r', 'synset': 'patty.n.01', 'synonyms': ['patty_(food)'], 'id': 772, 'def': 'small flat mass of chopped food', 'name': 'patty_(food)'}, {'frequency': 'c', 'synset': 'pea.n.01', 'synonyms': ['pea_(food)'], 'id': 773, 'def': 'seed of a pea plant used for food', 'name': 'pea_(food)'}, {'frequency': 'c', 'synset': 'peach.n.03', 'synonyms': ['peach'], 'id': 774, 'def': 'downy juicy fruit with sweet yellowish or whitish flesh', 'name': 'peach'}, {'frequency': 'c', 'synset': 'peanut_butter.n.01', 'synonyms': ['peanut_butter'], 'id': 775, 'def': 'a spread made from ground peanuts', 'name': 'peanut_butter'}, {'frequency': 'f', 'synset': 'pear.n.01', 'synonyms': ['pear'], 'id': 776, 'def': 'sweet juicy gritty-textured fruit available in many varieties', 'name': 'pear'}, {'frequency': 'c', 'synset': 'peeler.n.03', 'synonyms': ['peeler_(tool_for_fruit_and_vegetables)'], 'id': 777, 'def': 'a device for peeling vegetables or fruits', 'name': 'peeler_(tool_for_fruit_and_vegetables)'}, {'frequency': 'r', 'synset': 'peg.n.04', 'synonyms': ['wooden_leg', 'pegleg'], 'id': 778, 'def': 'a prosthesis that replaces a missing leg', 'name': 'wooden_leg'}, {'frequency': 'r', 'synset': 'pegboard.n.01', 'synonyms': ['pegboard'], 'id': 779, 'def': 'a board perforated with regularly spaced holes into which pegs can be fitted', 'name': 'pegboard'}, {'frequency': 'c', 'synset': 'pelican.n.01', 'synonyms': ['pelican'], 'id': 780, 'def': 'large long-winged warm-water seabird having a large bill with a distensible pouch for fish', 'name': 'pelican'}, {'frequency': 'f', 'synset': 'pen.n.01', 'synonyms': ['pen'], 'id': 781, 'def': 'a writing implement with a point from which ink flows', 'name': 'pen'}, {'frequency': 'f', 'synset': 'pencil.n.01', 'synonyms': ['pencil'], 'id': 782, 'def': 'a thin cylindrical pointed writing implement made of wood and graphite', 'name': 'pencil'}, {'frequency': 'r', 'synset': 'pencil_box.n.01', 'synonyms': ['pencil_box', 'pencil_case'], 'id': 783, 'def': 'a box for holding pencils', 'name': 'pencil_box'}, {'frequency': 'r', 'synset': 'pencil_sharpener.n.01', 'synonyms': ['pencil_sharpener'], 'id': 784, 'def': 'a rotary implement for sharpening the point on pencils', 'name': 'pencil_sharpener'}, {'frequency': 'r', 'synset': 'pendulum.n.01', 'synonyms': ['pendulum'], 'id': 785, 'def': 'an apparatus consisting of an object mounted so that it swings freely under the influence of gravity', 'name': 'pendulum'}, {'frequency': 'c', 'synset': 'penguin.n.01', 'synonyms': ['penguin'], 'id': 786, 'def': 'short-legged flightless birds of cold southern regions having webbed feet and wings modified as flippers', 'name': 'penguin'}, {'frequency': 'r', 'synset': 'pennant.n.02', 'synonyms': ['pennant'], 'id': 787, 'def': 'a flag longer than it is wide (and often tapering)', 'name': 'pennant'}, {'frequency': 'r', 'synset': 'penny.n.02', 'synonyms': ['penny_(coin)'], 'id': 788, 'def': 'a coin worth one-hundredth of the value of the basic unit', 'name': 'penny_(coin)'}, {'frequency': 'f', 'synset': 'pepper.n.03', 'synonyms': ['pepper', 'peppercorn'], 'id': 789, 'def': 'pungent seasoning from the berry of the common pepper plant; whole or ground', 'name': 'pepper'}, {'frequency': 'c', 'synset': 'pepper_mill.n.01', 'synonyms': ['pepper_mill', 'pepper_grinder'], 'id': 790, 'def': 'a mill for grinding pepper', 'name': 'pepper_mill'}, {'frequency': 'c', 'synset': 'perfume.n.02', 'synonyms': ['perfume'], 'id': 791, 'def': 'a toiletry that emits and diffuses a fragrant odor', 'name': 'perfume'}, {'frequency': 'r', 'synset': 'persimmon.n.02', 'synonyms': ['persimmon'], 'id': 792, 'def': 'orange fruit resembling a plum; edible when fully ripe', 'name': 'persimmon'}, {'frequency': 'f', 'synset': 'person.n.01', 'synonyms': ['person', 'baby', 'child', 'boy', 'girl', 'man', 'woman', 'human'], 'id': 793, 'def': 'a human being', 'name': 'person'}, {'frequency': 'c', 'synset': 'pet.n.01', 'synonyms': ['pet'], 'id': 794, 'def': 'a domesticated animal kept for companionship or amusement', 'name': 'pet'}, {'frequency': 'c', 'synset': 'pew.n.01', 'synonyms': ['pew_(church_bench)', 'church_bench'], 'id': 795, 'def': 'long bench with backs; used in church by the congregation', 'name': 'pew_(church_bench)'}, {'frequency': 'r', 'synset': 'phonebook.n.01', 'synonyms': ['phonebook', 'telephone_book', 'telephone_directory'], 'id': 796, 'def': 'a directory containing an alphabetical list of telephone subscribers and their telephone numbers', 'name': 'phonebook'}, {'frequency': 'c', 'synset': 'phonograph_record.n.01', 'synonyms': ['phonograph_record', 'phonograph_recording', 'record_(phonograph_recording)'], 'id': 797, 'def': 'sound recording consisting of a typically black disk with a continuous groove', 'name': 'phonograph_record'}, {'frequency': 'f', 'synset': 'piano.n.01', 'synonyms': ['piano'], 'id': 798, 'def': 'a keyboard instrument that is played by depressing keys that cause hammers to strike tuned strings and produce sounds', 'name': 'piano'}, {'frequency': 'f', 'synset': 'pickle.n.01', 'synonyms': ['pickle'], 'id': 799, 'def': 'vegetables (especially cucumbers) preserved in brine or vinegar', 'name': 'pickle'}, {'frequency': 'f', 'synset': 'pickup.n.01', 'synonyms': ['pickup_truck'], 'id': 800, 'def': 'a light truck with an open body and low sides and a tailboard', 'name': 'pickup_truck'}, {'frequency': 'c', 'synset': 'pie.n.01', 'synonyms': ['pie'], 'id': 801, 'def': 'dish baked in pastry-lined pan often with a pastry top', 'name': 'pie'}, {'frequency': 'c', 'synset': 'pigeon.n.01', 'synonyms': ['pigeon'], 'id': 802, 'def': 'wild and domesticated birds having a heavy body and short legs', 'name': 'pigeon'}, {'frequency': 'r', 'synset': 'piggy_bank.n.01', 'synonyms': ['piggy_bank', 'penny_bank'], 'id': 803, 'def': "a child's coin bank (often shaped like a pig)", 'name': 'piggy_bank'}, {'frequency': 'f', 'synset': 'pillow.n.01', 'synonyms': ['pillow'], 'id': 804, 'def': 'a cushion to support the head of a sleeping person', 'name': 'pillow'}, {'frequency': 'r', 'synset': 'pin.n.09', 'synonyms': ['pin_(non_jewelry)'], 'id': 805, 'def': 'a small slender (often pointed) piece of wood or metal used to support or fasten or attach things', 'name': 'pin_(non_jewelry)'}, {'frequency': 'f', 'synset': 'pineapple.n.02', 'synonyms': ['pineapple'], 'id': 806, 'def': 'large sweet fleshy tropical fruit with a tuft of stiff leaves', 'name': 'pineapple'}, {'frequency': 'c', 'synset': 'pinecone.n.01', 'synonyms': ['pinecone'], 'id': 807, 'def': 'the seed-producing cone of a pine tree', 'name': 'pinecone'}, {'frequency': 'r', 'synset': 'ping-pong_ball.n.01', 'synonyms': ['ping-pong_ball'], 'id': 808, 'def': 'light hollow ball used in playing table tennis', 'name': 'ping-pong_ball'}, {'frequency': 'r', 'synset': 'pinwheel.n.03', 'synonyms': ['pinwheel'], 'id': 809, 'def': 'a toy consisting of vanes of colored paper or plastic that is pinned to a stick and spins when it is pointed into the wind', 'name': 'pinwheel'}, {'frequency': 'r', 'synset': 'pipe.n.01', 'synonyms': ['tobacco_pipe'], 'id': 810, 'def': 'a tube with a small bowl at one end; used for smoking tobacco', 'name': 'tobacco_pipe'}, {'frequency': 'f', 'synset': 'pipe.n.02', 'synonyms': ['pipe', 'piping'], 'id': 811, 'def': 'a long tube made of metal or plastic that is used to carry water or oil or gas etc.', 'name': 'pipe'}, {'frequency': 'r', 'synset': 'pistol.n.01', 'synonyms': ['pistol', 'handgun'], 'id': 812, 'def': 'a firearm that is held and fired with one hand', 'name': 'pistol'}, {'frequency': 'c', 'synset': 'pita.n.01', 'synonyms': ['pita_(bread)', 'pocket_bread'], 'id': 813, 'def': 'usually small round bread that can open into a pocket for filling', 'name': 'pita_(bread)'}, {'frequency': 'f', 'synset': 'pitcher.n.02', 'synonyms': ['pitcher_(vessel_for_liquid)', 'ewer'], 'id': 814, 'def': 'an open vessel with a handle and a spout for pouring', 'name': 'pitcher_(vessel_for_liquid)'}, {'frequency': 'r', 'synset': 'pitchfork.n.01', 'synonyms': ['pitchfork'], 'id': 815, 'def': 'a long-handled hand tool with sharp widely spaced prongs for lifting and pitching hay', 'name': 'pitchfork'}, {'frequency': 'f', 'synset': 'pizza.n.01', 'synonyms': ['pizza'], 'id': 816, 'def': 'Italian open pie made of thin bread dough spread with a spiced mixture of e.g. tomato sauce and cheese', 'name': 'pizza'}, {'frequency': 'f', 'synset': 'place_mat.n.01', 'synonyms': ['place_mat'], 'id': 817, 'def': 'a mat placed on a table for an individual place setting', 'name': 'place_mat'}, {'frequency': 'f', 'synset': 'plate.n.04', 'synonyms': ['plate'], 'id': 818, 'def': 'dish on which food is served or from which food is eaten', 'name': 'plate'}, {'frequency': 'c', 'synset': 'platter.n.01', 'synonyms': ['platter'], 'id': 819, 'def': 'a large shallow dish used for serving food', 'name': 'platter'}, {'frequency': 'r', 'synset': 'playpen.n.01', 'synonyms': ['playpen'], 'id': 820, 'def': 'a portable enclosure in which babies may be left to play', 'name': 'playpen'}, {'frequency': 'c', 'synset': 'pliers.n.01', 'synonyms': ['pliers', 'plyers'], 'id': 821, 'def': 'a gripping hand tool with two hinged arms and (usually) serrated jaws', 'name': 'pliers'}, {'frequency': 'r', 'synset': 'plow.n.01', 'synonyms': ['plow_(farm_equipment)', 'plough_(farm_equipment)'], 'id': 822, 'def': 'a farm tool having one or more heavy blades to break the soil and cut a furrow prior to sowing', 'name': 'plow_(farm_equipment)'}, {'frequency': 'r', 'synset': 'plume.n.02', 'synonyms': ['plume'], 'id': 823, 'def': 'a feather or cluster of feathers worn as an ornament', 'name': 'plume'}, {'frequency': 'r', 'synset': 'pocket_watch.n.01', 'synonyms': ['pocket_watch'], 'id': 824, 'def': 'a watch that is carried in a small watch pocket', 'name': 'pocket_watch'}, {'frequency': 'c', 'synset': 'pocketknife.n.01', 'synonyms': ['pocketknife'], 'id': 825, 'def': 'a knife with a blade that folds into the handle; suitable for carrying in the pocket', 'name': 'pocketknife'}, {'frequency': 'c', 'synset': 'poker.n.01', 'synonyms': ['poker_(fire_stirring_tool)', 'stove_poker', 'fire_hook'], 'id': 826, 'def': 'fire iron consisting of a metal rod with a handle; used to stir a fire', 'name': 'poker_(fire_stirring_tool)'}, {'frequency': 'f', 'synset': 'pole.n.01', 'synonyms': ['pole', 'post'], 'id': 827, 'def': 'a long (usually round) rod of wood or metal or plastic', 'name': 'pole'}, {'frequency': 'f', 'synset': 'polo_shirt.n.01', 'synonyms': ['polo_shirt', 'sport_shirt'], 'id': 828, 'def': 'a shirt with short sleeves designed for comfort and casual wear', 'name': 'polo_shirt'}, {'frequency': 'r', 'synset': 'poncho.n.01', 'synonyms': ['poncho'], 'id': 829, 'def': 'a blanket-like cloak with a hole in the center for the head', 'name': 'poncho'}, {'frequency': 'c', 'synset': 'pony.n.05', 'synonyms': ['pony'], 'id': 830, 'def': 'any of various breeds of small gentle horses usually less than five feet high at the shoulder', 'name': 'pony'}, {'frequency': 'r', 'synset': 'pool_table.n.01', 'synonyms': ['pool_table', 'billiard_table', 'snooker_table'], 'id': 831, 'def': 'game equipment consisting of a heavy table on which pool is played', 'name': 'pool_table'}, {'frequency': 'f', 'synset': 'pop.n.02', 'synonyms': ['pop_(soda)', 'soda_(pop)', 'tonic', 'soft_drink'], 'id': 832, 'def': 'a sweet drink containing carbonated water and flavoring', 'name': 'pop_(soda)'}, {'frequency': 'c', 'synset': 'postbox.n.01', 'synonyms': ['postbox_(public)', 'mailbox_(public)'], 'id': 833, 'def': 'public box for deposit of mail', 'name': 'postbox_(public)'}, {'frequency': 'c', 'synset': 'postcard.n.01', 'synonyms': ['postcard', 'postal_card', 'mailing-card'], 'id': 834, 'def': 'a card for sending messages by post without an envelope', 'name': 'postcard'}, {'frequency': 'f', 'synset': 'poster.n.01', 'synonyms': ['poster', 'placard'], 'id': 835, 'def': 'a sign posted in a public place as an advertisement', 'name': 'poster'}, {'frequency': 'f', 'synset': 'pot.n.01', 'synonyms': ['pot'], 'id': 836, 'def': 'metal or earthenware cooking vessel that is usually round and deep; often has a handle and lid', 'name': 'pot'}, {'frequency': 'f', 'synset': 'pot.n.04', 'synonyms': ['flowerpot'], 'id': 837, 'def': 'a container in which plants are cultivated', 'name': 'flowerpot'}, {'frequency': 'f', 'synset': 'potato.n.01', 'synonyms': ['potato'], 'id': 838, 'def': 'an edible tuber native to South America', 'name': 'potato'}, {'frequency': 'c', 'synset': 'potholder.n.01', 'synonyms': ['potholder'], 'id': 839, 'def': 'an insulated pad for holding hot pots', 'name': 'potholder'}, {'frequency': 'c', 'synset': 'pottery.n.01', 'synonyms': ['pottery', 'clayware'], 'id': 840, 'def': 'ceramic ware made from clay and baked in a kiln', 'name': 'pottery'}, {'frequency': 'c', 'synset': 'pouch.n.01', 'synonyms': ['pouch'], 'id': 841, 'def': 'a small or medium size container for holding or carrying things', 'name': 'pouch'}, {'frequency': 'c', 'synset': 'power_shovel.n.01', 'synonyms': ['power_shovel', 'excavator', 'digger'], 'id': 842, 'def': 'a machine for excavating', 'name': 'power_shovel'}, {'frequency': 'c', 'synset': 'prawn.n.01', 'synonyms': ['prawn', 'shrimp'], 'id': 843, 'def': 'any of various edible decapod crustaceans', 'name': 'prawn'}, {'frequency': 'c', 'synset': 'pretzel.n.01', 'synonyms': ['pretzel'], 'id': 844, 'def': 'glazed and salted cracker typically in the shape of a loose knot', 'name': 'pretzel'}, {'frequency': 'f', 'synset': 'printer.n.03', 'synonyms': ['printer', 'printing_machine'], 'id': 845, 'def': 'a machine that prints', 'name': 'printer'}, {'frequency': 'c', 'synset': 'projectile.n.01', 'synonyms': ['projectile_(weapon)', 'missile'], 'id': 846, 'def': 'a weapon that is forcibly thrown or projected at a targets', 'name': 'projectile_(weapon)'}, {'frequency': 'c', 'synset': 'projector.n.02', 'synonyms': ['projector'], 'id': 847, 'def': 'an optical instrument that projects an enlarged image onto a screen', 'name': 'projector'}, {'frequency': 'f', 'synset': 'propeller.n.01', 'synonyms': ['propeller', 'propellor'], 'id': 848, 'def': 'a mechanical device that rotates to push against air or water', 'name': 'propeller'}, {'frequency': 'r', 'synset': 'prune.n.01', 'synonyms': ['prune'], 'id': 849, 'def': 'dried plum', 'name': 'prune'}, {'frequency': 'r', 'synset': 'pudding.n.01', 'synonyms': ['pudding'], 'id': 850, 'def': 'any of various soft thick unsweetened baked dishes', 'name': 'pudding'}, {'frequency': 'r', 'synset': 'puffer.n.02', 'synonyms': ['puffer_(fish)', 'pufferfish', 'blowfish', 'globefish'], 'id': 851, 'def': 'fishes whose elongated spiny body can inflate itself with water or air to form a globe', 'name': 'puffer_(fish)'}, {'frequency': 'r', 'synset': 'puffin.n.01', 'synonyms': ['puffin'], 'id': 852, 'def': 'seabirds having short necks and brightly colored compressed bills', 'name': 'puffin'}, {'frequency': 'r', 'synset': 'pug.n.01', 'synonyms': ['pug-dog'], 'id': 853, 'def': 'small compact smooth-coated breed of Asiatic origin having a tightly curled tail and broad flat wrinkled muzzle', 'name': 'pug-dog'}, {'frequency': 'c', 'synset': 'pumpkin.n.02', 'synonyms': ['pumpkin'], 'id': 854, 'def': 'usually large pulpy deep-yellow round fruit of the squash family maturing in late summer or early autumn', 'name': 'pumpkin'}, {'frequency': 'r', 'synset': 'punch.n.03', 'synonyms': ['puncher'], 'id': 855, 'def': 'a tool for making holes or indentations', 'name': 'puncher'}, {'frequency': 'r', 'synset': 'puppet.n.01', 'synonyms': ['puppet', 'marionette'], 'id': 856, 'def': 'a small figure of a person operated from above with strings by a puppeteer', 'name': 'puppet'}, {'frequency': 'c', 'synset': 'puppy.n.01', 'synonyms': ['puppy'], 'id': 857, 'def': 'a young dog', 'name': 'puppy'}, {'frequency': 'r', 'synset': 'quesadilla.n.01', 'synonyms': ['quesadilla'], 'id': 858, 'def': 'a tortilla that is filled with cheese and heated', 'name': 'quesadilla'}, {'frequency': 'r', 'synset': 'quiche.n.02', 'synonyms': ['quiche'], 'id': 859, 'def': 'a tart filled with rich unsweetened custard; often contains other ingredients (as cheese or ham or seafood or vegetables)', 'name': 'quiche'}, {'frequency': 'f', 'synset': 'quilt.n.01', 'synonyms': ['quilt', 'comforter'], 'id': 860, 'def': 'bedding made of two layers of cloth filled with stuffing and stitched together', 'name': 'quilt'}, {'frequency': 'c', 'synset': 'rabbit.n.01', 'synonyms': ['rabbit'], 'id': 861, 'def': 'any of various burrowing animals of the family Leporidae having long ears and short tails', 'name': 'rabbit'}, {'frequency': 'r', 'synset': 'racer.n.02', 'synonyms': ['race_car', 'racing_car'], 'id': 862, 'def': 'a fast car that competes in races', 'name': 'race_car'}, {'frequency': 'c', 'synset': 'racket.n.04', 'synonyms': ['racket', 'racquet'], 'id': 863, 'def': 'a sports implement used to strike a ball in various games', 'name': 'racket'}, {'frequency': 'r', 'synset': 'radar.n.01', 'synonyms': ['radar'], 'id': 864, 'def': 'measuring instrument in which the echo of a pulse of microwave radiation is used to detect and locate distant objects', 'name': 'radar'}, {'frequency': 'f', 'synset': 'radiator.n.03', 'synonyms': ['radiator'], 'id': 865, 'def': 'a mechanism consisting of a metal honeycomb through which hot fluids circulate', 'name': 'radiator'}, {'frequency': 'c', 'synset': 'radio_receiver.n.01', 'synonyms': ['radio_receiver', 'radio_set', 'radio', 'tuner_(radio)'], 'id': 866, 'def': 'an electronic receiver that detects and demodulates and amplifies transmitted radio signals', 'name': 'radio_receiver'}, {'frequency': 'c', 'synset': 'radish.n.03', 'synonyms': ['radish', 'daikon'], 'id': 867, 'def': 'pungent edible root of any of various cultivated radish plants', 'name': 'radish'}, {'frequency': 'c', 'synset': 'raft.n.01', 'synonyms': ['raft'], 'id': 868, 'def': 'a flat float (usually made of logs or planks) that can be used for transport or as a platform for swimmers', 'name': 'raft'}, {'frequency': 'r', 'synset': 'rag_doll.n.01', 'synonyms': ['rag_doll'], 'id': 869, 'def': 'a cloth doll that is stuffed and (usually) painted', 'name': 'rag_doll'}, {'frequency': 'c', 'synset': 'raincoat.n.01', 'synonyms': ['raincoat', 'waterproof_jacket'], 'id': 870, 'def': 'a water-resistant coat', 'name': 'raincoat'}, {'frequency': 'c', 'synset': 'ram.n.05', 'synonyms': ['ram_(animal)'], 'id': 871, 'def': 'uncastrated adult male sheep', 'name': 'ram_(animal)'}, {'frequency': 'c', 'synset': 'raspberry.n.02', 'synonyms': ['raspberry'], 'id': 872, 'def': 'red or black edible aggregate berries usually smaller than the related blackberries', 'name': 'raspberry'}, {'frequency': 'r', 'synset': 'rat.n.01', 'synonyms': ['rat'], 'id': 873, 'def': 'any of various long-tailed rodents similar to but larger than a mouse', 'name': 'rat'}, {'frequency': 'c', 'synset': 'razorblade.n.01', 'synonyms': ['razorblade'], 'id': 874, 'def': 'a blade that has very sharp edge', 'name': 'razorblade'}, {'frequency': 'c', 'synset': 'reamer.n.01', 'synonyms': ['reamer_(juicer)', 'juicer', 'juice_reamer'], 'id': 875, 'def': 'a squeezer with a conical ridged center that is used for squeezing juice from citrus fruit', 'name': 'reamer_(juicer)'}, {'frequency': 'f', 'synset': 'rearview_mirror.n.01', 'synonyms': ['rearview_mirror'], 'id': 876, 'def': 'vehicle mirror (side or rearview)', 'name': 'rearview_mirror'}, {'frequency': 'c', 'synset': 'receipt.n.02', 'synonyms': ['receipt'], 'id': 877, 'def': 'an acknowledgment (usually tangible) that payment has been made', 'name': 'receipt'}, {'frequency': 'c', 'synset': 'recliner.n.01', 'synonyms': ['recliner', 'reclining_chair', 'lounger_(chair)'], 'id': 878, 'def': 'an armchair whose back can be lowered and foot can be raised to allow the sitter to recline in it', 'name': 'recliner'}, {'frequency': 'c', 'synset': 'record_player.n.01', 'synonyms': ['record_player', 'phonograph_(record_player)', 'turntable'], 'id': 879, 'def': 'machine in which rotating records cause a stylus to vibrate and the vibrations are amplified acoustically or electronically', 'name': 'record_player'}, {'frequency': 'f', 'synset': 'reflector.n.01', 'synonyms': ['reflector'], 'id': 880, 'def': 'device that reflects light, radiation, etc.', 'name': 'reflector'}, {'frequency': 'f', 'synset': 'remote_control.n.01', 'synonyms': ['remote_control'], 'id': 881, 'def': 'a device that can be used to control a machine or apparatus from a distance', 'name': 'remote_control'}, {'frequency': 'c', 'synset': 'rhinoceros.n.01', 'synonyms': ['rhinoceros'], 'id': 882, 'def': 'massive powerful herbivorous odd-toed ungulate of southeast Asia and Africa having very thick skin and one or two horns on the snout', 'name': 'rhinoceros'}, {'frequency': 'r', 'synset': 'rib.n.03', 'synonyms': ['rib_(food)'], 'id': 883, 'def': 'cut of meat including one or more ribs', 'name': 'rib_(food)'}, {'frequency': 'c', 'synset': 'rifle.n.01', 'synonyms': ['rifle'], 'id': 884, 'def': 'a shoulder firearm with a long barrel', 'name': 'rifle'}, {'frequency': 'f', 'synset': 'ring.n.08', 'synonyms': ['ring'], 'id': 885, 'def': 'jewelry consisting of a circlet of precious metal (often set with jewels) worn on the finger', 'name': 'ring'}, {'frequency': 'r', 'synset': 'river_boat.n.01', 'synonyms': ['river_boat'], 'id': 886, 'def': 'a boat used on rivers or to ply a river', 'name': 'river_boat'}, {'frequency': 'r', 'synset': 'road_map.n.02', 'synonyms': ['road_map'], 'id': 887, 'def': '(NOT A ROAD) a MAP showing roads (for automobile travel)', 'name': 'road_map'}, {'frequency': 'c', 'synset': 'robe.n.01', 'synonyms': ['robe'], 'id': 888, 'def': 'any loose flowing garment', 'name': 'robe'}, {'frequency': 'c', 'synset': 'rocking_chair.n.01', 'synonyms': ['rocking_chair'], 'id': 889, 'def': 'a chair mounted on rockers', 'name': 'rocking_chair'}, {'frequency': 'r', 'synset': 'rodent.n.01', 'synonyms': ['rodent'], 'id': 890, 'def': 'relatively small placental mammals having a single pair of constantly growing incisor teeth specialized for gnawing', 'name': 'rodent'}, {'frequency': 'r', 'synset': 'roller_skate.n.01', 'synonyms': ['roller_skate'], 'id': 891, 'def': 'a shoe with pairs of rollers (small hard wheels) fixed to the sole', 'name': 'roller_skate'}, {'frequency': 'r', 'synset': 'rollerblade.n.01', 'synonyms': ['Rollerblade'], 'id': 892, 'def': 'an in-line variant of a roller skate', 'name': 'Rollerblade'}, {'frequency': 'c', 'synset': 'rolling_pin.n.01', 'synonyms': ['rolling_pin'], 'id': 893, 'def': 'utensil consisting of a cylinder (usually of wood) with a handle at each end; used to roll out dough', 'name': 'rolling_pin'}, {'frequency': 'r', 'synset': 'root_beer.n.01', 'synonyms': ['root_beer'], 'id': 894, 'def': 'carbonated drink containing extracts of roots and herbs', 'name': 'root_beer'}, {'frequency': 'c', 'synset': 'router.n.02', 'synonyms': ['router_(computer_equipment)'], 'id': 895, 'def': 'a device that forwards data packets between computer networks', 'name': 'router_(computer_equipment)'}, {'frequency': 'f', 'synset': 'rubber_band.n.01', 'synonyms': ['rubber_band', 'elastic_band'], 'id': 896, 'def': 'a narrow band of elastic rubber used to hold things (such as papers) together', 'name': 'rubber_band'}, {'frequency': 'c', 'synset': 'runner.n.08', 'synonyms': ['runner_(carpet)'], 'id': 897, 'def': 'a long narrow carpet', 'name': 'runner_(carpet)'}, {'frequency': 'f', 'synset': 'sack.n.01', 'synonyms': ['plastic_bag', 'paper_bag'], 'id': 898, 'def': "a bag made of paper or plastic for holding customer's purchases", 'name': 'plastic_bag'}, {'frequency': 'f', 'synset': 'saddle.n.01', 'synonyms': ['saddle_(on_an_animal)'], 'id': 899, 'def': 'a seat for the rider of a horse or camel', 'name': 'saddle_(on_an_animal)'}, {'frequency': 'f', 'synset': 'saddle_blanket.n.01', 'synonyms': ['saddle_blanket', 'saddlecloth', 'horse_blanket'], 'id': 900, 'def': 'stable gear consisting of a blanket placed under the saddle', 'name': 'saddle_blanket'}, {'frequency': 'c', 'synset': 'saddlebag.n.01', 'synonyms': ['saddlebag'], 'id': 901, 'def': 'a large bag (or pair of bags) hung over a saddle', 'name': 'saddlebag'}, {'frequency': 'r', 'synset': 'safety_pin.n.01', 'synonyms': ['safety_pin'], 'id': 902, 'def': 'a pin in the form of a clasp; has a guard so the point of the pin will not stick the user', 'name': 'safety_pin'}, {'frequency': 'f', 'synset': 'sail.n.01', 'synonyms': ['sail'], 'id': 903, 'def': 'a large piece of fabric by means of which wind is used to propel a sailing vessel', 'name': 'sail'}, {'frequency': 'f', 'synset': 'salad.n.01', 'synonyms': ['salad'], 'id': 904, 'def': 'food mixtures either arranged on a plate or tossed and served with a moist dressing; usually consisting of or including greens', 'name': 'salad'}, {'frequency': 'r', 'synset': 'salad_plate.n.01', 'synonyms': ['salad_plate', 'salad_bowl'], 'id': 905, 'def': 'a plate or bowl for individual servings of salad', 'name': 'salad_plate'}, {'frequency': 'c', 'synset': 'salami.n.01', 'synonyms': ['salami'], 'id': 906, 'def': 'highly seasoned fatty sausage of pork and beef usually dried', 'name': 'salami'}, {'frequency': 'c', 'synset': 'salmon.n.01', 'synonyms': ['salmon_(fish)'], 'id': 907, 'def': 'any of various large food and game fishes of northern waters', 'name': 'salmon_(fish)'}, {'frequency': 'r', 'synset': 'salmon.n.03', 'synonyms': ['salmon_(food)'], 'id': 908, 'def': 'flesh of any of various marine or freshwater fish of the family Salmonidae', 'name': 'salmon_(food)'}, {'frequency': 'c', 'synset': 'salsa.n.01', 'synonyms': ['salsa'], 'id': 909, 'def': 'spicy sauce of tomatoes and onions and chili peppers to accompany Mexican foods', 'name': 'salsa'}, {'frequency': 'f', 'synset': 'saltshaker.n.01', 'synonyms': ['saltshaker'], 'id': 910, 'def': 'a shaker with a perforated top for sprinkling salt', 'name': 'saltshaker'}, {'frequency': 'f', 'synset': 'sandal.n.01', 'synonyms': ['sandal_(type_of_shoe)'], 'id': 911, 'def': 'a shoe consisting of a sole fastened by straps to the foot', 'name': 'sandal_(type_of_shoe)'}, {'frequency': 'f', 'synset': 'sandwich.n.01', 'synonyms': ['sandwich'], 'id': 912, 'def': 'two (or more) slices of bread with a filling between them', 'name': 'sandwich'}, {'frequency': 'r', 'synset': 'satchel.n.01', 'synonyms': ['satchel'], 'id': 913, 'def': 'luggage consisting of a small case with a flat bottom and (usually) a shoulder strap', 'name': 'satchel'}, {'frequency': 'r', 'synset': 'saucepan.n.01', 'synonyms': ['saucepan'], 'id': 914, 'def': 'a deep pan with a handle; used for stewing or boiling', 'name': 'saucepan'}, {'frequency': 'f', 'synset': 'saucer.n.02', 'synonyms': ['saucer'], 'id': 915, 'def': 'a small shallow dish for holding a cup at the table', 'name': 'saucer'}, {'frequency': 'f', 'synset': 'sausage.n.01', 'synonyms': ['sausage'], 'id': 916, 'def': 'highly seasoned minced meat stuffed in casings', 'name': 'sausage'}, {'frequency': 'r', 'synset': 'sawhorse.n.01', 'synonyms': ['sawhorse', 'sawbuck'], 'id': 917, 'def': 'a framework for holding wood that is being sawed', 'name': 'sawhorse'}, {'frequency': 'r', 'synset': 'sax.n.02', 'synonyms': ['saxophone'], 'id': 918, 'def': "a wind instrument with a `J'-shaped form typically made of brass", 'name': 'saxophone'}, {'frequency': 'f', 'synset': 'scale.n.07', 'synonyms': ['scale_(measuring_instrument)'], 'id': 919, 'def': 'a measuring instrument for weighing; shows amount of mass', 'name': 'scale_(measuring_instrument)'}, {'frequency': 'r', 'synset': 'scarecrow.n.01', 'synonyms': ['scarecrow', 'strawman'], 'id': 920, 'def': 'an effigy in the shape of a man to frighten birds away from seeds', 'name': 'scarecrow'}, {'frequency': 'f', 'synset': 'scarf.n.01', 'synonyms': ['scarf'], 'id': 921, 'def': 'a garment worn around the head or neck or shoulders for warmth or decoration', 'name': 'scarf'}, {'frequency': 'c', 'synset': 'school_bus.n.01', 'synonyms': ['school_bus'], 'id': 922, 'def': 'a bus used to transport children to or from school', 'name': 'school_bus'}, {'frequency': 'f', 'synset': 'scissors.n.01', 'synonyms': ['scissors'], 'id': 923, 'def': 'a tool having two crossed pivoting blades with looped handles', 'name': 'scissors'}, {'frequency': 'f', 'synset': 'scoreboard.n.01', 'synonyms': ['scoreboard'], 'id': 924, 'def': 'a large board for displaying the score of a contest (and some other information)', 'name': 'scoreboard'}, {'frequency': 'r', 'synset': 'scraper.n.01', 'synonyms': ['scraper'], 'id': 925, 'def': 'any of various hand tools for scraping', 'name': 'scraper'}, {'frequency': 'c', 'synset': 'screwdriver.n.01', 'synonyms': ['screwdriver'], 'id': 926, 'def': 'a hand tool for driving screws; has a tip that fits into the head of a screw', 'name': 'screwdriver'}, {'frequency': 'f', 'synset': 'scrub_brush.n.01', 'synonyms': ['scrubbing_brush'], 'id': 927, 'def': 'a brush with short stiff bristles for heavy cleaning', 'name': 'scrubbing_brush'}, {'frequency': 'c', 'synset': 'sculpture.n.01', 'synonyms': ['sculpture'], 'id': 928, 'def': 'a three-dimensional work of art', 'name': 'sculpture'}, {'frequency': 'c', 'synset': 'seabird.n.01', 'synonyms': ['seabird', 'seafowl'], 'id': 929, 'def': 'a bird that frequents coastal waters and the open ocean: gulls; pelicans; gannets; cormorants; albatrosses; petrels; etc.', 'name': 'seabird'}, {'frequency': 'c', 'synset': 'seahorse.n.02', 'synonyms': ['seahorse'], 'id': 930, 'def': 'small fish with horse-like heads bent sharply downward and curled tails', 'name': 'seahorse'}, {'frequency': 'r', 'synset': 'seaplane.n.01', 'synonyms': ['seaplane', 'hydroplane'], 'id': 931, 'def': 'an airplane that can land on or take off from water', 'name': 'seaplane'}, {'frequency': 'c', 'synset': 'seashell.n.01', 'synonyms': ['seashell'], 'id': 932, 'def': 'the shell of a marine organism', 'name': 'seashell'}, {'frequency': 'c', 'synset': 'sewing_machine.n.01', 'synonyms': ['sewing_machine'], 'id': 933, 'def': 'a textile machine used as a home appliance for sewing', 'name': 'sewing_machine'}, {'frequency': 'c', 'synset': 'shaker.n.03', 'synonyms': ['shaker'], 'id': 934, 'def': 'a container in which something can be shaken', 'name': 'shaker'}, {'frequency': 'c', 'synset': 'shampoo.n.01', 'synonyms': ['shampoo'], 'id': 935, 'def': 'cleansing agent consisting of soaps or detergents used for washing the hair', 'name': 'shampoo'}, {'frequency': 'c', 'synset': 'shark.n.01', 'synonyms': ['shark'], 'id': 936, 'def': 'typically large carnivorous fishes with sharpe teeth', 'name': 'shark'}, {'frequency': 'r', 'synset': 'sharpener.n.01', 'synonyms': ['sharpener'], 'id': 937, 'def': 'any implement that is used to make something (an edge or a point) sharper', 'name': 'sharpener'}, {'frequency': 'r', 'synset': 'sharpie.n.03', 'synonyms': ['Sharpie'], 'id': 938, 'def': 'a pen with indelible ink that will write on any surface', 'name': 'Sharpie'}, {'frequency': 'r', 'synset': 'shaver.n.03', 'synonyms': ['shaver_(electric)', 'electric_shaver', 'electric_razor'], 'id': 939, 'def': 'a razor powered by an electric motor', 'name': 'shaver_(electric)'}, {'frequency': 'c', 'synset': 'shaving_cream.n.01', 'synonyms': ['shaving_cream', 'shaving_soap'], 'id': 940, 'def': 'toiletry consisting that forms a rich lather for softening the beard before shaving', 'name': 'shaving_cream'}, {'frequency': 'r', 'synset': 'shawl.n.01', 'synonyms': ['shawl'], 'id': 941, 'def': 'cloak consisting of an oblong piece of cloth used to cover the head and shoulders', 'name': 'shawl'}, {'frequency': 'r', 'synset': 'shears.n.01', 'synonyms': ['shears'], 'id': 942, 'def': 'large scissors with strong blades', 'name': 'shears'}, {'frequency': 'f', 'synset': 'sheep.n.01', 'synonyms': ['sheep'], 'id': 943, 'def': 'woolly usually horned ruminant mammal related to the goat', 'name': 'sheep'}, {'frequency': 'r', 'synset': 'shepherd_dog.n.01', 'synonyms': ['shepherd_dog', 'sheepdog'], 'id': 944, 'def': 'any of various usually long-haired breeds of dog reared to herd and guard sheep', 'name': 'shepherd_dog'}, {'frequency': 'r', 'synset': 'sherbert.n.01', 'synonyms': ['sherbert', 'sherbet'], 'id': 945, 'def': 'a frozen dessert made primarily of fruit juice and sugar', 'name': 'sherbert'}, {'frequency': 'c', 'synset': 'shield.n.02', 'synonyms': ['shield'], 'id': 946, 'def': 'armor carried on the arm to intercept blows', 'name': 'shield'}, {'frequency': 'f', 'synset': 'shirt.n.01', 'synonyms': ['shirt'], 'id': 947, 'def': 'a garment worn on the upper half of the body', 'name': 'shirt'}, {'frequency': 'f', 'synset': 'shoe.n.01', 'synonyms': ['shoe', 'sneaker_(type_of_shoe)', 'tennis_shoe'], 'id': 948, 'def': 'common footwear covering the foot', 'name': 'shoe'}, {'frequency': 'f', 'synset': 'shopping_bag.n.01', 'synonyms': ['shopping_bag'], 'id': 949, 'def': 'a bag made of plastic or strong paper (often with handles); used to transport goods after shopping', 'name': 'shopping_bag'}, {'frequency': 'c', 'synset': 'shopping_cart.n.01', 'synonyms': ['shopping_cart'], 'id': 950, 'def': 'a handcart that holds groceries or other goods while shopping', 'name': 'shopping_cart'}, {'frequency': 'f', 'synset': 'short_pants.n.01', 'synonyms': ['short_pants', 'shorts_(clothing)', 'trunks_(clothing)'], 'id': 951, 'def': 'trousers that end at or above the knee', 'name': 'short_pants'}, {'frequency': 'r', 'synset': 'shot_glass.n.01', 'synonyms': ['shot_glass'], 'id': 952, 'def': 'a small glass adequate to hold a single swallow of whiskey', 'name': 'shot_glass'}, {'frequency': 'f', 'synset': 'shoulder_bag.n.01', 'synonyms': ['shoulder_bag'], 'id': 953, 'def': 'a large handbag that can be carried by a strap looped over the shoulder', 'name': 'shoulder_bag'}, {'frequency': 'c', 'synset': 'shovel.n.01', 'synonyms': ['shovel'], 'id': 954, 'def': 'a hand tool for lifting loose material such as snow, dirt, etc.', 'name': 'shovel'}, {'frequency': 'f', 'synset': 'shower.n.01', 'synonyms': ['shower_head'], 'id': 955, 'def': 'a plumbing fixture that sprays water over you', 'name': 'shower_head'}, {'frequency': 'r', 'synset': 'shower_cap.n.01', 'synonyms': ['shower_cap'], 'id': 956, 'def': 'a tight cap worn to keep hair dry while showering', 'name': 'shower_cap'}, {'frequency': 'f', 'synset': 'shower_curtain.n.01', 'synonyms': ['shower_curtain'], 'id': 957, 'def': 'a curtain that keeps water from splashing out of the shower area', 'name': 'shower_curtain'}, {'frequency': 'r', 'synset': 'shredder.n.01', 'synonyms': ['shredder_(for_paper)'], 'id': 958, 'def': 'a device that shreds documents', 'name': 'shredder_(for_paper)'}, {'frequency': 'f', 'synset': 'signboard.n.01', 'synonyms': ['signboard'], 'id': 959, 'def': 'structure displaying a board on which advertisements can be posted', 'name': 'signboard'}, {'frequency': 'c', 'synset': 'silo.n.01', 'synonyms': ['silo'], 'id': 960, 'def': 'a cylindrical tower used for storing goods', 'name': 'silo'}, {'frequency': 'f', 'synset': 'sink.n.01', 'synonyms': ['sink'], 'id': 961, 'def': 'plumbing fixture consisting of a water basin fixed to a wall or floor and having a drainpipe', 'name': 'sink'}, {'frequency': 'f', 'synset': 'skateboard.n.01', 'synonyms': ['skateboard'], 'id': 962, 'def': 'a board with wheels that is ridden in a standing or crouching position and propelled by foot', 'name': 'skateboard'}, {'frequency': 'c', 'synset': 'skewer.n.01', 'synonyms': ['skewer'], 'id': 963, 'def': 'a long pin for holding meat in position while it is being roasted', 'name': 'skewer'}, {'frequency': 'f', 'synset': 'ski.n.01', 'synonyms': ['ski'], 'id': 964, 'def': 'sports equipment for skiing on snow', 'name': 'ski'}, {'frequency': 'f', 'synset': 'ski_boot.n.01', 'synonyms': ['ski_boot'], 'id': 965, 'def': 'a stiff boot that is fastened to a ski with a ski binding', 'name': 'ski_boot'}, {'frequency': 'f', 'synset': 'ski_parka.n.01', 'synonyms': ['ski_parka', 'ski_jacket'], 'id': 966, 'def': 'a parka to be worn while skiing', 'name': 'ski_parka'}, {'frequency': 'f', 'synset': 'ski_pole.n.01', 'synonyms': ['ski_pole'], 'id': 967, 'def': 'a pole with metal points used as an aid in skiing', 'name': 'ski_pole'}, {'frequency': 'f', 'synset': 'skirt.n.02', 'synonyms': ['skirt'], 'id': 968, 'def': 'a garment hanging from the waist; worn mainly by girls and women', 'name': 'skirt'}, {'frequency': 'r', 'synset': 'skullcap.n.01', 'synonyms': ['skullcap'], 'id': 969, 'def': 'rounded brimless cap fitting the crown of the head', 'name': 'skullcap'}, {'frequency': 'c', 'synset': 'sled.n.01', 'synonyms': ['sled', 'sledge', 'sleigh'], 'id': 970, 'def': 'a vehicle or flat object for transportation over snow by sliding or pulled by dogs, etc.', 'name': 'sled'}, {'frequency': 'c', 'synset': 'sleeping_bag.n.01', 'synonyms': ['sleeping_bag'], 'id': 971, 'def': 'large padded bag designed to be slept in outdoors', 'name': 'sleeping_bag'}, {'frequency': 'r', 'synset': 'sling.n.05', 'synonyms': ['sling_(bandage)', 'triangular_bandage'], 'id': 972, 'def': 'bandage to support an injured forearm; slung over the shoulder or neck', 'name': 'sling_(bandage)'}, {'frequency': 'c', 'synset': 'slipper.n.01', 'synonyms': ['slipper_(footwear)', 'carpet_slipper_(footwear)'], 'id': 973, 'def': 'low footwear that can be slipped on and off easily; usually worn indoors', 'name': 'slipper_(footwear)'}, {'frequency': 'r', 'synset': 'smoothie.n.02', 'synonyms': ['smoothie'], 'id': 974, 'def': 'a thick smooth drink consisting of fresh fruit pureed with ice cream or yoghurt or milk', 'name': 'smoothie'}, {'frequency': 'r', 'synset': 'snake.n.01', 'synonyms': ['snake', 'serpent'], 'id': 975, 'def': 'limbless scaly elongate reptile; some are venomous', 'name': 'snake'}, {'frequency': 'f', 'synset': 'snowboard.n.01', 'synonyms': ['snowboard'], 'id': 976, 'def': 'a board that resembles a broad ski or a small surfboard; used in a standing position to slide down snow-covered slopes', 'name': 'snowboard'}, {'frequency': 'c', 'synset': 'snowman.n.01', 'synonyms': ['snowman'], 'id': 977, 'def': 'a figure of a person made of packed snow', 'name': 'snowman'}, {'frequency': 'c', 'synset': 'snowmobile.n.01', 'synonyms': ['snowmobile'], 'id': 978, 'def': 'tracked vehicle for travel on snow having skis in front', 'name': 'snowmobile'}, {'frequency': 'f', 'synset': 'soap.n.01', 'synonyms': ['soap'], 'id': 979, 'def': 'a cleansing agent made from the salts of vegetable or animal fats', 'name': 'soap'}, {'frequency': 'f', 'synset': 'soccer_ball.n.01', 'synonyms': ['soccer_ball'], 'id': 980, 'def': "an inflated ball used in playing soccer (called `football' outside of the United States)", 'name': 'soccer_ball'}, {'frequency': 'f', 'synset': 'sock.n.01', 'synonyms': ['sock'], 'id': 981, 'def': 'cloth covering for the foot; worn inside the shoe; reaches to between the ankle and the knee', 'name': 'sock'}, {'frequency': 'f', 'synset': 'sofa.n.01', 'synonyms': ['sofa', 'couch', 'lounge'], 'id': 982, 'def': 'an upholstered seat for more than one person', 'name': 'sofa'}, {'frequency': 'r', 'synset': 'softball.n.01', 'synonyms': ['softball'], 'id': 983, 'def': 'ball used in playing softball', 'name': 'softball'}, {'frequency': 'c', 'synset': 'solar_array.n.01', 'synonyms': ['solar_array', 'solar_battery', 'solar_panel'], 'id': 984, 'def': 'electrical device consisting of a large array of connected solar cells', 'name': 'solar_array'}, {'frequency': 'r', 'synset': 'sombrero.n.02', 'synonyms': ['sombrero'], 'id': 985, 'def': 'a straw hat with a tall crown and broad brim; worn in American southwest and in Mexico', 'name': 'sombrero'}, {'frequency': 'f', 'synset': 'soup.n.01', 'synonyms': ['soup'], 'id': 986, 'def': 'liquid food especially of meat or fish or vegetable stock often containing pieces of solid food', 'name': 'soup'}, {'frequency': 'r', 'synset': 'soup_bowl.n.01', 'synonyms': ['soup_bowl'], 'id': 987, 'def': 'a bowl for serving soup', 'name': 'soup_bowl'}, {'frequency': 'c', 'synset': 'soupspoon.n.01', 'synonyms': ['soupspoon'], 'id': 988, 'def': 'a spoon with a rounded bowl for eating soup', 'name': 'soupspoon'}, {'frequency': 'c', 'synset': 'sour_cream.n.01', 'synonyms': ['sour_cream', 'soured_cream'], 'id': 989, 'def': 'soured light cream', 'name': 'sour_cream'}, {'frequency': 'r', 'synset': 'soya_milk.n.01', 'synonyms': ['soya_milk', 'soybean_milk', 'soymilk'], 'id': 990, 'def': 'a milk substitute containing soybean flour and water; used in some infant formulas and in making tofu', 'name': 'soya_milk'}, {'frequency': 'r', 'synset': 'space_shuttle.n.01', 'synonyms': ['space_shuttle'], 'id': 991, 'def': "a reusable spacecraft with wings for a controlled descent through the Earth's atmosphere", 'name': 'space_shuttle'}, {'frequency': 'r', 'synset': 'sparkler.n.02', 'synonyms': ['sparkler_(fireworks)'], 'id': 992, 'def': 'a firework that burns slowly and throws out a shower of sparks', 'name': 'sparkler_(fireworks)'}, {'frequency': 'f', 'synset': 'spatula.n.02', 'synonyms': ['spatula'], 'id': 993, 'def': 'a hand tool with a thin flexible blade used to mix or spread soft substances', 'name': 'spatula'}, {'frequency': 'r', 'synset': 'spear.n.01', 'synonyms': ['spear', 'lance'], 'id': 994, 'def': 'a long pointed rod used as a tool or weapon', 'name': 'spear'}, {'frequency': 'f', 'synset': 'spectacles.n.01', 'synonyms': ['spectacles', 'specs', 'eyeglasses', 'glasses'], 'id': 995, 'def': 'optical instrument consisting of a frame that holds a pair of lenses for correcting defective vision', 'name': 'spectacles'}, {'frequency': 'c', 'synset': 'spice_rack.n.01', 'synonyms': ['spice_rack'], 'id': 996, 'def': 'a rack for displaying containers filled with spices', 'name': 'spice_rack'}, {'frequency': 'c', 'synset': 'spider.n.01', 'synonyms': ['spider'], 'id': 997, 'def': 'predatory arachnid with eight legs, two poison fangs, two feelers, and usually two silk-spinning organs at the back end of the body', 'name': 'spider'}, {'frequency': 'r', 'synset': 'spiny_lobster.n.02', 'synonyms': ['crawfish', 'crayfish'], 'id': 998, 'def': 'large edible marine crustacean having a spiny carapace but lacking the large pincers of true lobsters', 'name': 'crawfish'}, {'frequency': 'c', 'synset': 'sponge.n.01', 'synonyms': ['sponge'], 'id': 999, 'def': 'a porous mass usable to absorb water typically used for cleaning', 'name': 'sponge'}, {'frequency': 'f', 'synset': 'spoon.n.01', 'synonyms': ['spoon'], 'id': 1000, 'def': 'a piece of cutlery with a shallow bowl-shaped container and a handle', 'name': 'spoon'}, {'frequency': 'c', 'synset': 'sportswear.n.01', 'synonyms': ['sportswear', 'athletic_wear', 'activewear'], 'id': 1001, 'def': 'attire worn for sport or for casual wear', 'name': 'sportswear'}, {'frequency': 'c', 'synset': 'spotlight.n.02', 'synonyms': ['spotlight'], 'id': 1002, 'def': 'a lamp that produces a strong beam of light to illuminate a restricted area; used to focus attention of a stage performer', 'name': 'spotlight'}, {'frequency': 'r', 'synset': 'squid.n.01', 'synonyms': ['squid_(food)', 'calamari', 'calamary'], 'id': 1003, 'def': '(Italian cuisine) squid prepared as food', 'name': 'squid_(food)'}, {'frequency': 'c', 'synset': 'squirrel.n.01', 'synonyms': ['squirrel'], 'id': 1004, 'def': 'a kind of arboreal rodent having a long bushy tail', 'name': 'squirrel'}, {'frequency': 'r', 'synset': 'stagecoach.n.01', 'synonyms': ['stagecoach'], 'id': 1005, 'def': 'a large coach-and-four formerly used to carry passengers and mail on regular routes between towns', 'name': 'stagecoach'}, {'frequency': 'c', 'synset': 'stapler.n.01', 'synonyms': ['stapler_(stapling_machine)'], 'id': 1006, 'def': 'a machine that inserts staples into sheets of paper in order to fasten them together', 'name': 'stapler_(stapling_machine)'}, {'frequency': 'c', 'synset': 'starfish.n.01', 'synonyms': ['starfish', 'sea_star'], 'id': 1007, 'def': 'echinoderms characterized by five arms extending from a central disk', 'name': 'starfish'}, {'frequency': 'f', 'synset': 'statue.n.01', 'synonyms': ['statue_(sculpture)'], 'id': 1008, 'def': 'a sculpture representing a human or animal', 'name': 'statue_(sculpture)'}, {'frequency': 'c', 'synset': 'steak.n.01', 'synonyms': ['steak_(food)'], 'id': 1009, 'def': 'a slice of meat cut from the fleshy part of an animal or large fish', 'name': 'steak_(food)'}, {'frequency': 'r', 'synset': 'steak_knife.n.01', 'synonyms': ['steak_knife'], 'id': 1010, 'def': 'a sharp table knife used in eating steak', 'name': 'steak_knife'}, {'frequency': 'f', 'synset': 'steering_wheel.n.01', 'synonyms': ['steering_wheel'], 'id': 1011, 'def': 'a handwheel that is used for steering', 'name': 'steering_wheel'}, {'frequency': 'r', 'synset': 'step_ladder.n.01', 'synonyms': ['stepladder'], 'id': 1012, 'def': 'a folding portable ladder hinged at the top', 'name': 'stepladder'}, {'frequency': 'c', 'synset': 'step_stool.n.01', 'synonyms': ['step_stool'], 'id': 1013, 'def': 'a stool that has one or two steps that fold under the seat', 'name': 'step_stool'}, {'frequency': 'c', 'synset': 'stereo.n.01', 'synonyms': ['stereo_(sound_system)'], 'id': 1014, 'def': 'electronic device for playing audio', 'name': 'stereo_(sound_system)'}, {'frequency': 'r', 'synset': 'stew.n.02', 'synonyms': ['stew'], 'id': 1015, 'def': 'food prepared by stewing especially meat or fish with vegetables', 'name': 'stew'}, {'frequency': 'r', 'synset': 'stirrer.n.02', 'synonyms': ['stirrer'], 'id': 1016, 'def': 'an implement used for stirring', 'name': 'stirrer'}, {'frequency': 'f', 'synset': 'stirrup.n.01', 'synonyms': ['stirrup'], 'id': 1017, 'def': "support consisting of metal loops into which rider's feet go", 'name': 'stirrup'}, {'frequency': 'f', 'synset': 'stool.n.01', 'synonyms': ['stool'], 'id': 1018, 'def': 'a simple seat without a back or arms', 'name': 'stool'}, {'frequency': 'f', 'synset': 'stop_sign.n.01', 'synonyms': ['stop_sign'], 'id': 1019, 'def': 'a traffic sign to notify drivers that they must come to a complete stop', 'name': 'stop_sign'}, {'frequency': 'f', 'synset': 'stoplight.n.01', 'synonyms': ['brake_light'], 'id': 1020, 'def': 'a red light on the rear of a motor vehicle that signals when the brakes are applied', 'name': 'brake_light'}, {'frequency': 'f', 'synset': 'stove.n.01', 'synonyms': ['stove', 'kitchen_stove', 'range_(kitchen_appliance)', 'kitchen_range', 'cooking_stove'], 'id': 1021, 'def': 'a kitchen appliance used for cooking food', 'name': 'stove'}, {'frequency': 'c', 'synset': 'strainer.n.01', 'synonyms': ['strainer'], 'id': 1022, 'def': 'a filter to retain larger pieces while smaller pieces and liquids pass through', 'name': 'strainer'}, {'frequency': 'f', 'synset': 'strap.n.01', 'synonyms': ['strap'], 'id': 1023, 'def': 'an elongated strip of material for binding things together or holding', 'name': 'strap'}, {'frequency': 'f', 'synset': 'straw.n.04', 'synonyms': ['straw_(for_drinking)', 'drinking_straw'], 'id': 1024, 'def': 'a thin paper or plastic tube used to suck liquids into the mouth', 'name': 'straw_(for_drinking)'}, {'frequency': 'f', 'synset': 'strawberry.n.01', 'synonyms': ['strawberry'], 'id': 1025, 'def': 'sweet fleshy red fruit', 'name': 'strawberry'}, {'frequency': 'f', 'synset': 'street_sign.n.01', 'synonyms': ['street_sign'], 'id': 1026, 'def': 'a sign visible from the street', 'name': 'street_sign'}, {'frequency': 'f', 'synset': 'streetlight.n.01', 'synonyms': ['streetlight', 'street_lamp'], 'id': 1027, 'def': 'a lamp supported on a lamppost; for illuminating a street', 'name': 'streetlight'}, {'frequency': 'r', 'synset': 'string_cheese.n.01', 'synonyms': ['string_cheese'], 'id': 1028, 'def': 'cheese formed in long strings twisted together', 'name': 'string_cheese'}, {'frequency': 'r', 'synset': 'stylus.n.02', 'synonyms': ['stylus'], 'id': 1029, 'def': 'a pointed tool for writing or drawing or engraving, including pens', 'name': 'stylus'}, {'frequency': 'r', 'synset': 'subwoofer.n.01', 'synonyms': ['subwoofer'], 'id': 1030, 'def': 'a loudspeaker that is designed to reproduce very low bass frequencies', 'name': 'subwoofer'}, {'frequency': 'r', 'synset': 'sugar_bowl.n.01', 'synonyms': ['sugar_bowl'], 'id': 1031, 'def': 'a dish in which sugar is served', 'name': 'sugar_bowl'}, {'frequency': 'r', 'synset': 'sugarcane.n.01', 'synonyms': ['sugarcane_(plant)'], 'id': 1032, 'def': 'juicy canes whose sap is a source of molasses and commercial sugar; fresh canes are sometimes chewed for the juice', 'name': 'sugarcane_(plant)'}, {'frequency': 'f', 'synset': 'suit.n.01', 'synonyms': ['suit_(clothing)'], 'id': 1033, 'def': 'a set of garments (usually including a jacket and trousers or skirt) for outerwear all of the same fabric and color', 'name': 'suit_(clothing)'}, {'frequency': 'c', 'synset': 'sunflower.n.01', 'synonyms': ['sunflower'], 'id': 1034, 'def': 'any plant of the genus Helianthus having large flower heads with dark disk florets and showy yellow rays', 'name': 'sunflower'}, {'frequency': 'f', 'synset': 'sunglasses.n.01', 'synonyms': ['sunglasses'], 'id': 1035, 'def': 'spectacles that are darkened or polarized to protect the eyes from the glare of the sun', 'name': 'sunglasses'}, {'frequency': 'c', 'synset': 'sunhat.n.01', 'synonyms': ['sunhat'], 'id': 1036, 'def': 'a hat with a broad brim that protects the face from direct exposure to the sun', 'name': 'sunhat'}, {'frequency': 'f', 'synset': 'surfboard.n.01', 'synonyms': ['surfboard'], 'id': 1037, 'def': 'a narrow buoyant board for riding surf', 'name': 'surfboard'}, {'frequency': 'c', 'synset': 'sushi.n.01', 'synonyms': ['sushi'], 'id': 1038, 'def': 'rice (with raw fish) wrapped in seaweed', 'name': 'sushi'}, {'frequency': 'c', 'synset': 'swab.n.02', 'synonyms': ['mop'], 'id': 1039, 'def': 'cleaning implement consisting of absorbent material fastened to a handle; for cleaning floors', 'name': 'mop'}, {'frequency': 'c', 'synset': 'sweat_pants.n.01', 'synonyms': ['sweat_pants'], 'id': 1040, 'def': 'loose-fitting trousers with elastic cuffs; worn by athletes', 'name': 'sweat_pants'}, {'frequency': 'c', 'synset': 'sweatband.n.02', 'synonyms': ['sweatband'], 'id': 1041, 'def': 'a band of material tied around the forehead or wrist to absorb sweat', 'name': 'sweatband'}, {'frequency': 'f', 'synset': 'sweater.n.01', 'synonyms': ['sweater'], 'id': 1042, 'def': 'a crocheted or knitted garment covering the upper part of the body', 'name': 'sweater'}, {'frequency': 'f', 'synset': 'sweatshirt.n.01', 'synonyms': ['sweatshirt'], 'id': 1043, 'def': 'cotton knit pullover with long sleeves worn during athletic activity', 'name': 'sweatshirt'}, {'frequency': 'c', 'synset': 'sweet_potato.n.02', 'synonyms': ['sweet_potato'], 'id': 1044, 'def': 'the edible tuberous root of the sweet potato vine', 'name': 'sweet_potato'}, {'frequency': 'f', 'synset': 'swimsuit.n.01', 'synonyms': ['swimsuit', 'swimwear', 'bathing_suit', 'swimming_costume', 'bathing_costume', 'swimming_trunks', 'bathing_trunks'], 'id': 1045, 'def': 'garment worn for swimming', 'name': 'swimsuit'}, {'frequency': 'c', 'synset': 'sword.n.01', 'synonyms': ['sword'], 'id': 1046, 'def': 'a cutting or thrusting weapon that has a long metal blade', 'name': 'sword'}, {'frequency': 'r', 'synset': 'syringe.n.01', 'synonyms': ['syringe'], 'id': 1047, 'def': 'a medical instrument used to inject or withdraw fluids', 'name': 'syringe'}, {'frequency': 'r', 'synset': 'tabasco.n.02', 'synonyms': ['Tabasco_sauce'], 'id': 1048, 'def': 'very spicy sauce (trade name Tabasco) made from fully-aged red peppers', 'name': 'Tabasco_sauce'}, {'frequency': 'r', 'synset': 'table-tennis_table.n.01', 'synonyms': ['table-tennis_table', 'ping-pong_table'], 'id': 1049, 'def': 'a table used for playing table tennis', 'name': 'table-tennis_table'}, {'frequency': 'f', 'synset': 'table.n.02', 'synonyms': ['table'], 'id': 1050, 'def': 'a piece of furniture having a smooth flat top that is usually supported by one or more vertical legs', 'name': 'table'}, {'frequency': 'c', 'synset': 'table_lamp.n.01', 'synonyms': ['table_lamp'], 'id': 1051, 'def': 'a lamp that sits on a table', 'name': 'table_lamp'}, {'frequency': 'f', 'synset': 'tablecloth.n.01', 'synonyms': ['tablecloth'], 'id': 1052, 'def': 'a covering spread over a dining table', 'name': 'tablecloth'}, {'frequency': 'r', 'synset': 'tachometer.n.01', 'synonyms': ['tachometer'], 'id': 1053, 'def': 'measuring instrument for indicating speed of rotation', 'name': 'tachometer'}, {'frequency': 'r', 'synset': 'taco.n.02', 'synonyms': ['taco'], 'id': 1054, 'def': 'a small tortilla cupped around a filling', 'name': 'taco'}, {'frequency': 'f', 'synset': 'tag.n.02', 'synonyms': ['tag'], 'id': 1055, 'def': 'a label associated with something for the purpose of identification or information', 'name': 'tag'}, {'frequency': 'f', 'synset': 'taillight.n.01', 'synonyms': ['taillight', 'rear_light'], 'id': 1056, 'def': 'lamp (usually red) mounted at the rear of a motor vehicle', 'name': 'taillight'}, {'frequency': 'r', 'synset': 'tambourine.n.01', 'synonyms': ['tambourine'], 'id': 1057, 'def': 'a shallow drum with a single drumhead and with metallic disks in the sides', 'name': 'tambourine'}, {'frequency': 'r', 'synset': 'tank.n.01', 'synonyms': ['army_tank', 'armored_combat_vehicle', 'armoured_combat_vehicle'], 'id': 1058, 'def': 'an enclosed armored military vehicle; has a cannon and moves on caterpillar treads', 'name': 'army_tank'}, {'frequency': 'f', 'synset': 'tank.n.02', 'synonyms': ['tank_(storage_vessel)', 'storage_tank'], 'id': 1059, 'def': 'a large (usually metallic) vessel for holding gases or liquids', 'name': 'tank_(storage_vessel)'}, {'frequency': 'f', 'synset': 'tank_top.n.01', 'synonyms': ['tank_top_(clothing)'], 'id': 1060, 'def': 'a tight-fitting sleeveless shirt with wide shoulder straps and low neck and no front opening', 'name': 'tank_top_(clothing)'}, {'frequency': 'f', 'synset': 'tape.n.01', 'synonyms': ['tape_(sticky_cloth_or_paper)'], 'id': 1061, 'def': 'a long thin piece of cloth or paper as used for binding or fastening', 'name': 'tape_(sticky_cloth_or_paper)'}, {'frequency': 'c', 'synset': 'tape.n.04', 'synonyms': ['tape_measure', 'measuring_tape'], 'id': 1062, 'def': 'measuring instrument consisting of a narrow strip (cloth or metal) marked in inches or centimeters and used for measuring lengths', 'name': 'tape_measure'}, {'frequency': 'c', 'synset': 'tapestry.n.02', 'synonyms': ['tapestry'], 'id': 1063, 'def': 'a heavy textile with a woven design; used for curtains and upholstery', 'name': 'tapestry'}, {'frequency': 'f', 'synset': 'tarpaulin.n.01', 'synonyms': ['tarp'], 'id': 1064, 'def': 'waterproofed canvas', 'name': 'tarp'}, {'frequency': 'c', 'synset': 'tartan.n.01', 'synonyms': ['tartan', 'plaid'], 'id': 1065, 'def': 'a cloth having a crisscross design', 'name': 'tartan'}, {'frequency': 'c', 'synset': 'tassel.n.01', 'synonyms': ['tassel'], 'id': 1066, 'def': 'adornment consisting of a bunch of cords fastened at one end', 'name': 'tassel'}, {'frequency': 'c', 'synset': 'tea_bag.n.01', 'synonyms': ['tea_bag'], 'id': 1067, 'def': 'a measured amount of tea in a bag for an individual serving of tea', 'name': 'tea_bag'}, {'frequency': 'c', 'synset': 'teacup.n.02', 'synonyms': ['teacup'], 'id': 1068, 'def': 'a cup from which tea is drunk', 'name': 'teacup'}, {'frequency': 'c', 'synset': 'teakettle.n.01', 'synonyms': ['teakettle'], 'id': 1069, 'def': 'kettle for boiling water to make tea', 'name': 'teakettle'}, {'frequency': 'f', 'synset': 'teapot.n.01', 'synonyms': ['teapot'], 'id': 1070, 'def': 'pot for brewing tea; usually has a spout and handle', 'name': 'teapot'}, {'frequency': 'f', 'synset': 'teddy.n.01', 'synonyms': ['teddy_bear'], 'id': 1071, 'def': "plaything consisting of a child's toy bear (usually plush and stuffed with soft materials)", 'name': 'teddy_bear'}, {'frequency': 'f', 'synset': 'telephone.n.01', 'synonyms': ['telephone', 'phone', 'telephone_set'], 'id': 1072, 'def': 'electronic device for communicating by voice over long distances (includes wired and wireless/cell phones)', 'name': 'telephone'}, {'frequency': 'c', 'synset': 'telephone_booth.n.01', 'synonyms': ['telephone_booth', 'phone_booth', 'call_box', 'telephone_box', 'telephone_kiosk'], 'id': 1073, 'def': 'booth for using a telephone', 'name': 'telephone_booth'}, {'frequency': 'f', 'synset': 'telephone_pole.n.01', 'synonyms': ['telephone_pole', 'telegraph_pole', 'telegraph_post'], 'id': 1074, 'def': 'tall pole supporting telephone wires', 'name': 'telephone_pole'}, {'frequency': 'r', 'synset': 'telephoto_lens.n.01', 'synonyms': ['telephoto_lens', 'zoom_lens'], 'id': 1075, 'def': 'a camera lens that magnifies the image', 'name': 'telephoto_lens'}, {'frequency': 'c', 'synset': 'television_camera.n.01', 'synonyms': ['television_camera', 'tv_camera'], 'id': 1076, 'def': 'television equipment for capturing and recording video', 'name': 'television_camera'}, {'frequency': 'f', 'synset': 'television_receiver.n.01', 'synonyms': ['television_set', 'tv', 'tv_set'], 'id': 1077, 'def': 'an electronic device that receives television signals and displays them on a screen', 'name': 'television_set'}, {'frequency': 'f', 'synset': 'tennis_ball.n.01', 'synonyms': ['tennis_ball'], 'id': 1078, 'def': 'ball about the size of a fist used in playing tennis', 'name': 'tennis_ball'}, {'frequency': 'f', 'synset': 'tennis_racket.n.01', 'synonyms': ['tennis_racket'], 'id': 1079, 'def': 'a racket used to play tennis', 'name': 'tennis_racket'}, {'frequency': 'r', 'synset': 'tequila.n.01', 'synonyms': ['tequila'], 'id': 1080, 'def': 'Mexican liquor made from fermented juices of an agave plant', 'name': 'tequila'}, {'frequency': 'c', 'synset': 'thermometer.n.01', 'synonyms': ['thermometer'], 'id': 1081, 'def': 'measuring instrument for measuring temperature', 'name': 'thermometer'}, {'frequency': 'c', 'synset': 'thermos.n.01', 'synonyms': ['thermos_bottle'], 'id': 1082, 'def': 'vacuum flask that preserves temperature of hot or cold drinks', 'name': 'thermos_bottle'}, {'frequency': 'f', 'synset': 'thermostat.n.01', 'synonyms': ['thermostat'], 'id': 1083, 'def': 'a regulator for automatically regulating temperature by starting or stopping the supply of heat', 'name': 'thermostat'}, {'frequency': 'r', 'synset': 'thimble.n.02', 'synonyms': ['thimble'], 'id': 1084, 'def': 'a small metal cap to protect the finger while sewing; can be used as a small container', 'name': 'thimble'}, {'frequency': 'c', 'synset': 'thread.n.01', 'synonyms': ['thread', 'yarn'], 'id': 1085, 'def': 'a fine cord of twisted fibers (of cotton or silk or wool or nylon etc.) used in sewing and weaving', 'name': 'thread'}, {'frequency': 'c', 'synset': 'thumbtack.n.01', 'synonyms': ['thumbtack', 'drawing_pin', 'pushpin'], 'id': 1086, 'def': 'a tack for attaching papers to a bulletin board or drawing board', 'name': 'thumbtack'}, {'frequency': 'c', 'synset': 'tiara.n.01', 'synonyms': ['tiara'], 'id': 1087, 'def': 'a jeweled headdress worn by women on formal occasions', 'name': 'tiara'}, {'frequency': 'c', 'synset': 'tiger.n.02', 'synonyms': ['tiger'], 'id': 1088, 'def': 'large feline of forests in most of Asia having a tawny coat with black stripes', 'name': 'tiger'}, {'frequency': 'c', 'synset': 'tights.n.01', 'synonyms': ['tights_(clothing)', 'leotards'], 'id': 1089, 'def': 'skintight knit hose covering the body from the waist to the feet worn by acrobats and dancers and as stockings by women and girls', 'name': 'tights_(clothing)'}, {'frequency': 'c', 'synset': 'timer.n.01', 'synonyms': ['timer', 'stopwatch'], 'id': 1090, 'def': 'a timepiece that measures a time interval and signals its end', 'name': 'timer'}, {'frequency': 'f', 'synset': 'tinfoil.n.01', 'synonyms': ['tinfoil'], 'id': 1091, 'def': 'foil made of tin or an alloy of tin and lead', 'name': 'tinfoil'}, {'frequency': 'c', 'synset': 'tinsel.n.01', 'synonyms': ['tinsel'], 'id': 1092, 'def': 'a showy decoration that is basically valueless', 'name': 'tinsel'}, {'frequency': 'f', 'synset': 'tissue.n.02', 'synonyms': ['tissue_paper'], 'id': 1093, 'def': 'a soft thin (usually translucent) paper', 'name': 'tissue_paper'}, {'frequency': 'c', 'synset': 'toast.n.01', 'synonyms': ['toast_(food)'], 'id': 1094, 'def': 'slice of bread that has been toasted', 'name': 'toast_(food)'}, {'frequency': 'f', 'synset': 'toaster.n.02', 'synonyms': ['toaster'], 'id': 1095, 'def': 'a kitchen appliance (usually electric) for toasting bread', 'name': 'toaster'}, {'frequency': 'f', 'synset': 'toaster_oven.n.01', 'synonyms': ['toaster_oven'], 'id': 1096, 'def': 'kitchen appliance consisting of a small electric oven for toasting or warming food', 'name': 'toaster_oven'}, {'frequency': 'f', 'synset': 'toilet.n.02', 'synonyms': ['toilet'], 'id': 1097, 'def': 'a plumbing fixture for defecation and urination', 'name': 'toilet'}, {'frequency': 'f', 'synset': 'toilet_tissue.n.01', 'synonyms': ['toilet_tissue', 'toilet_paper', 'bathroom_tissue'], 'id': 1098, 'def': 'a soft thin absorbent paper for use in toilets', 'name': 'toilet_tissue'}, {'frequency': 'f', 'synset': 'tomato.n.01', 'synonyms': ['tomato'], 'id': 1099, 'def': 'mildly acid red or yellow pulpy fruit eaten as a vegetable', 'name': 'tomato'}, {'frequency': 'f', 'synset': 'tongs.n.01', 'synonyms': ['tongs'], 'id': 1100, 'def': 'any of various devices for taking hold of objects; usually have two hinged legs with handles above and pointed hooks below', 'name': 'tongs'}, {'frequency': 'c', 'synset': 'toolbox.n.01', 'synonyms': ['toolbox'], 'id': 1101, 'def': 'a box or chest or cabinet for holding hand tools', 'name': 'toolbox'}, {'frequency': 'f', 'synset': 'toothbrush.n.01', 'synonyms': ['toothbrush'], 'id': 1102, 'def': 'small brush; has long handle; used to clean teeth', 'name': 'toothbrush'}, {'frequency': 'f', 'synset': 'toothpaste.n.01', 'synonyms': ['toothpaste'], 'id': 1103, 'def': 'a dentifrice in the form of a paste', 'name': 'toothpaste'}, {'frequency': 'f', 'synset': 'toothpick.n.01', 'synonyms': ['toothpick'], 'id': 1104, 'def': 'pick consisting of a small strip of wood or plastic; used to pick food from between the teeth', 'name': 'toothpick'}, {'frequency': 'f', 'synset': 'top.n.09', 'synonyms': ['cover'], 'id': 1105, 'def': 'covering for a hole (especially a hole in the top of a container)', 'name': 'cover'}, {'frequency': 'c', 'synset': 'tortilla.n.01', 'synonyms': ['tortilla'], 'id': 1106, 'def': 'thin unleavened pancake made from cornmeal or wheat flour', 'name': 'tortilla'}, {'frequency': 'c', 'synset': 'tow_truck.n.01', 'synonyms': ['tow_truck'], 'id': 1107, 'def': 'a truck equipped to hoist and pull wrecked cars (or to remove cars from no-parking zones)', 'name': 'tow_truck'}, {'frequency': 'f', 'synset': 'towel.n.01', 'synonyms': ['towel'], 'id': 1108, 'def': 'a rectangular piece of absorbent cloth (or paper) for drying or wiping', 'name': 'towel'}, {'frequency': 'f', 'synset': 'towel_rack.n.01', 'synonyms': ['towel_rack', 'towel_rail', 'towel_bar'], 'id': 1109, 'def': 'a rack consisting of one or more bars on which towels can be hung', 'name': 'towel_rack'}, {'frequency': 'f', 'synset': 'toy.n.03', 'synonyms': ['toy'], 'id': 1110, 'def': 'a device regarded as providing amusement', 'name': 'toy'}, {'frequency': 'c', 'synset': 'tractor.n.01', 'synonyms': ['tractor_(farm_equipment)'], 'id': 1111, 'def': 'a wheeled vehicle with large wheels; used in farming and other applications', 'name': 'tractor_(farm_equipment)'}, {'frequency': 'f', 'synset': 'traffic_light.n.01', 'synonyms': ['traffic_light'], 'id': 1112, 'def': 'a device to control vehicle traffic often consisting of three or more lights', 'name': 'traffic_light'}, {'frequency': 'c', 'synset': 'trail_bike.n.01', 'synonyms': ['dirt_bike'], 'id': 1113, 'def': 'a lightweight motorcycle equipped with rugged tires and suspension for off-road use', 'name': 'dirt_bike'}, {'frequency': 'f', 'synset': 'trailer_truck.n.01', 'synonyms': ['trailer_truck', 'tractor_trailer', 'trucking_rig', 'articulated_lorry', 'semi_truck'], 'id': 1114, 'def': 'a truck consisting of a tractor and trailer together', 'name': 'trailer_truck'}, {'frequency': 'f', 'synset': 'train.n.01', 'synonyms': ['train_(railroad_vehicle)', 'railroad_train'], 'id': 1115, 'def': 'public or private transport provided by a line of railway cars coupled together and drawn by a locomotive', 'name': 'train_(railroad_vehicle)'}, {'frequency': 'r', 'synset': 'trampoline.n.01', 'synonyms': ['trampoline'], 'id': 1116, 'def': 'gymnastic apparatus consisting of a strong canvas sheet attached with springs to a metal frame', 'name': 'trampoline'}, {'frequency': 'f', 'synset': 'tray.n.01', 'synonyms': ['tray'], 'id': 1117, 'def': 'an open receptacle for holding or displaying or serving articles or food', 'name': 'tray'}, {'frequency': 'r', 'synset': 'trench_coat.n.01', 'synonyms': ['trench_coat'], 'id': 1118, 'def': 'a military style raincoat; belted with deep pockets', 'name': 'trench_coat'}, {'frequency': 'r', 'synset': 'triangle.n.05', 'synonyms': ['triangle_(musical_instrument)'], 'id': 1119, 'def': 'a percussion instrument consisting of a metal bar bent in the shape of an open triangle', 'name': 'triangle_(musical_instrument)'}, {'frequency': 'c', 'synset': 'tricycle.n.01', 'synonyms': ['tricycle'], 'id': 1120, 'def': 'a vehicle with three wheels that is moved by foot pedals', 'name': 'tricycle'}, {'frequency': 'f', 'synset': 'tripod.n.01', 'synonyms': ['tripod'], 'id': 1121, 'def': 'a three-legged rack used for support', 'name': 'tripod'}, {'frequency': 'f', 'synset': 'trouser.n.01', 'synonyms': ['trousers', 'pants_(clothing)'], 'id': 1122, 'def': 'a garment extending from the waist to the knee or ankle, covering each leg separately', 'name': 'trousers'}, {'frequency': 'f', 'synset': 'truck.n.01', 'synonyms': ['truck'], 'id': 1123, 'def': 'an automotive vehicle suitable for hauling', 'name': 'truck'}, {'frequency': 'r', 'synset': 'truffle.n.03', 'synonyms': ['truffle_(chocolate)', 'chocolate_truffle'], 'id': 1124, 'def': 'creamy chocolate candy', 'name': 'truffle_(chocolate)'}, {'frequency': 'c', 'synset': 'trunk.n.02', 'synonyms': ['trunk'], 'id': 1125, 'def': 'luggage consisting of a large strong case used when traveling or for storage', 'name': 'trunk'}, {'frequency': 'r', 'synset': 'tub.n.02', 'synonyms': ['vat'], 'id': 1126, 'def': 'a large vessel for holding or storing liquids', 'name': 'vat'}, {'frequency': 'c', 'synset': 'turban.n.01', 'synonyms': ['turban'], 'id': 1127, 'def': 'a traditional headdress consisting of a long scarf wrapped around the head', 'name': 'turban'}, {'frequency': 'c', 'synset': 'turkey.n.04', 'synonyms': ['turkey_(food)'], 'id': 1128, 'def': 'flesh of large domesticated fowl usually roasted', 'name': 'turkey_(food)'}, {'frequency': 'r', 'synset': 'turnip.n.01', 'synonyms': ['turnip'], 'id': 1129, 'def': 'widely cultivated plant having a large fleshy edible white or yellow root', 'name': 'turnip'}, {'frequency': 'c', 'synset': 'turtle.n.02', 'synonyms': ['turtle'], 'id': 1130, 'def': 'any of various aquatic and land reptiles having a bony shell and flipper-like limbs for swimming', 'name': 'turtle'}, {'frequency': 'c', 'synset': 'turtleneck.n.01', 'synonyms': ['turtleneck_(clothing)', 'polo-neck'], 'id': 1131, 'def': 'a sweater or jersey with a high close-fitting collar', 'name': 'turtleneck_(clothing)'}, {'frequency': 'c', 'synset': 'typewriter.n.01', 'synonyms': ['typewriter'], 'id': 1132, 'def': 'hand-operated character printer for printing written messages one character at a time', 'name': 'typewriter'}, {'frequency': 'f', 'synset': 'umbrella.n.01', 'synonyms': ['umbrella'], 'id': 1133, 'def': 'a lightweight handheld collapsible canopy', 'name': 'umbrella'}, {'frequency': 'f', 'synset': 'underwear.n.01', 'synonyms': ['underwear', 'underclothes', 'underclothing', 'underpants'], 'id': 1134, 'def': 'undergarment worn next to the skin and under the outer garments', 'name': 'underwear'}, {'frequency': 'r', 'synset': 'unicycle.n.01', 'synonyms': ['unicycle'], 'id': 1135, 'def': 'a vehicle with a single wheel that is driven by pedals', 'name': 'unicycle'}, {'frequency': 'f', 'synset': 'urinal.n.01', 'synonyms': ['urinal'], 'id': 1136, 'def': 'a plumbing fixture (usually attached to the wall) used by men to urinate', 'name': 'urinal'}, {'frequency': 'c', 'synset': 'urn.n.01', 'synonyms': ['urn'], 'id': 1137, 'def': 'a large vase that usually has a pedestal or feet', 'name': 'urn'}, {'frequency': 'c', 'synset': 'vacuum.n.04', 'synonyms': ['vacuum_cleaner'], 'id': 1138, 'def': 'an electrical home appliance that cleans by suction', 'name': 'vacuum_cleaner'}, {'frequency': 'f', 'synset': 'vase.n.01', 'synonyms': ['vase'], 'id': 1139, 'def': 'an open jar of glass or porcelain used as an ornament or to hold flowers', 'name': 'vase'}, {'frequency': 'c', 'synset': 'vending_machine.n.01', 'synonyms': ['vending_machine'], 'id': 1140, 'def': 'a slot machine for selling goods', 'name': 'vending_machine'}, {'frequency': 'f', 'synset': 'vent.n.01', 'synonyms': ['vent', 'blowhole', 'air_vent'], 'id': 1141, 'def': 'a hole for the escape of gas or air', 'name': 'vent'}, {'frequency': 'f', 'synset': 'vest.n.01', 'synonyms': ['vest', 'waistcoat'], 'id': 1142, 'def': "a man's sleeveless garment worn underneath a coat", 'name': 'vest'}, {'frequency': 'c', 'synset': 'videotape.n.01', 'synonyms': ['videotape'], 'id': 1143, 'def': 'a video recording made on magnetic tape', 'name': 'videotape'}, {'frequency': 'r', 'synset': 'vinegar.n.01', 'synonyms': ['vinegar'], 'id': 1144, 'def': 'sour-tasting liquid produced usually by oxidation of the alcohol in wine or cider and used as a condiment or food preservative', 'name': 'vinegar'}, {'frequency': 'r', 'synset': 'violin.n.01', 'synonyms': ['violin', 'fiddle'], 'id': 1145, 'def': 'bowed stringed instrument that is the highest member of the violin family', 'name': 'violin'}, {'frequency': 'r', 'synset': 'vodka.n.01', 'synonyms': ['vodka'], 'id': 1146, 'def': 'unaged colorless liquor originating in Russia', 'name': 'vodka'}, {'frequency': 'c', 'synset': 'volleyball.n.02', 'synonyms': ['volleyball'], 'id': 1147, 'def': 'an inflated ball used in playing volleyball', 'name': 'volleyball'}, {'frequency': 'r', 'synset': 'vulture.n.01', 'synonyms': ['vulture'], 'id': 1148, 'def': 'any of various large birds of prey having naked heads and weak claws and feeding chiefly on carrion', 'name': 'vulture'}, {'frequency': 'c', 'synset': 'waffle.n.01', 'synonyms': ['waffle'], 'id': 1149, 'def': 'pancake batter baked in a waffle iron', 'name': 'waffle'}, {'frequency': 'r', 'synset': 'waffle_iron.n.01', 'synonyms': ['waffle_iron'], 'id': 1150, 'def': 'a kitchen appliance for baking waffles', 'name': 'waffle_iron'}, {'frequency': 'c', 'synset': 'wagon.n.01', 'synonyms': ['wagon'], 'id': 1151, 'def': 'any of various kinds of wheeled vehicles drawn by an animal or a tractor', 'name': 'wagon'}, {'frequency': 'c', 'synset': 'wagon_wheel.n.01', 'synonyms': ['wagon_wheel'], 'id': 1152, 'def': 'a wheel of a wagon', 'name': 'wagon_wheel'}, {'frequency': 'c', 'synset': 'walking_stick.n.01', 'synonyms': ['walking_stick'], 'id': 1153, 'def': 'a stick carried in the hand for support in walking', 'name': 'walking_stick'}, {'frequency': 'c', 'synset': 'wall_clock.n.01', 'synonyms': ['wall_clock'], 'id': 1154, 'def': 'a clock mounted on a wall', 'name': 'wall_clock'}, {'frequency': 'f', 'synset': 'wall_socket.n.01', 'synonyms': ['wall_socket', 'wall_plug', 'electric_outlet', 'electrical_outlet', 'outlet', 'electric_receptacle'], 'id': 1155, 'def': 'receptacle providing a place in a wiring system where current can be taken to run electrical devices', 'name': 'wall_socket'}, {'frequency': 'f', 'synset': 'wallet.n.01', 'synonyms': ['wallet', 'billfold'], 'id': 1156, 'def': 'a pocket-size case for holding papers and paper money', 'name': 'wallet'}, {'frequency': 'r', 'synset': 'walrus.n.01', 'synonyms': ['walrus'], 'id': 1157, 'def': 'either of two large northern marine mammals having ivory tusks and tough hide over thick blubber', 'name': 'walrus'}, {'frequency': 'r', 'synset': 'wardrobe.n.01', 'synonyms': ['wardrobe'], 'id': 1158, 'def': 'a tall piece of furniture that provides storage space for clothes; has a door and rails or hooks for hanging clothes', 'name': 'wardrobe'}, {'frequency': 'r', 'synset': 'washbasin.n.01', 'synonyms': ['washbasin', 'basin_(for_washing)', 'washbowl', 'washstand', 'handbasin'], 'id': 1159, 'def': 'a bathroom sink that is permanently installed and connected to a water supply and drainpipe; where you can wash your hands and face', 'name': 'washbasin'}, {'frequency': 'c', 'synset': 'washer.n.03', 'synonyms': ['automatic_washer', 'washing_machine'], 'id': 1160, 'def': 'a home appliance for washing clothes and linens automatically', 'name': 'automatic_washer'}, {'frequency': 'f', 'synset': 'watch.n.01', 'synonyms': ['watch', 'wristwatch'], 'id': 1161, 'def': 'a small, portable timepiece', 'name': 'watch'}, {'frequency': 'f', 'synset': 'water_bottle.n.01', 'synonyms': ['water_bottle'], 'id': 1162, 'def': 'a bottle for holding water', 'name': 'water_bottle'}, {'frequency': 'c', 'synset': 'water_cooler.n.01', 'synonyms': ['water_cooler'], 'id': 1163, 'def': 'a device for cooling and dispensing drinking water', 'name': 'water_cooler'}, {'frequency': 'c', 'synset': 'water_faucet.n.01', 'synonyms': ['water_faucet', 'water_tap', 'tap_(water_faucet)'], 'id': 1164, 'def': 'a faucet for drawing water from a pipe or cask', 'name': 'water_faucet'}, {'frequency': 'r', 'synset': 'water_heater.n.01', 'synonyms': ['water_heater', 'hot-water_heater'], 'id': 1165, 'def': 'a heater and storage tank to supply heated water', 'name': 'water_heater'}, {'frequency': 'c', 'synset': 'water_jug.n.01', 'synonyms': ['water_jug'], 'id': 1166, 'def': 'a jug that holds water', 'name': 'water_jug'}, {'frequency': 'r', 'synset': 'water_pistol.n.01', 'synonyms': ['water_gun', 'squirt_gun'], 'id': 1167, 'def': 'plaything consisting of a toy pistol that squirts water', 'name': 'water_gun'}, {'frequency': 'c', 'synset': 'water_scooter.n.01', 'synonyms': ['water_scooter', 'sea_scooter', 'jet_ski'], 'id': 1168, 'def': 'a motorboat resembling a motor scooter (NOT A SURFBOARD OR WATER SKI)', 'name': 'water_scooter'}, {'frequency': 'c', 'synset': 'water_ski.n.01', 'synonyms': ['water_ski'], 'id': 1169, 'def': 'broad ski for skimming over water towed by a speedboat (DO NOT MARK WATER)', 'name': 'water_ski'}, {'frequency': 'c', 'synset': 'water_tower.n.01', 'synonyms': ['water_tower'], 'id': 1170, 'def': 'a large reservoir for water', 'name': 'water_tower'}, {'frequency': 'c', 'synset': 'watering_can.n.01', 'synonyms': ['watering_can'], 'id': 1171, 'def': 'a container with a handle and a spout with a perforated nozzle; used to sprinkle water over plants', 'name': 'watering_can'}, {'frequency': 'f', 'synset': 'watermelon.n.02', 'synonyms': ['watermelon'], 'id': 1172, 'def': 'large oblong or roundish melon with a hard green rind and sweet watery red or occasionally yellowish pulp', 'name': 'watermelon'}, {'frequency': 'f', 'synset': 'weathervane.n.01', 'synonyms': ['weathervane', 'vane_(weathervane)', 'wind_vane'], 'id': 1173, 'def': 'mechanical device attached to an elevated structure; rotates freely to show the direction of the wind', 'name': 'weathervane'}, {'frequency': 'c', 'synset': 'webcam.n.01', 'synonyms': ['webcam'], 'id': 1174, 'def': 'a digital camera designed to take digital photographs and transmit them over the internet', 'name': 'webcam'}, {'frequency': 'c', 'synset': 'wedding_cake.n.01', 'synonyms': ['wedding_cake', 'bridecake'], 'id': 1175, 'def': 'a rich cake with two or more tiers and covered with frosting and decorations; served at a wedding reception', 'name': 'wedding_cake'}, {'frequency': 'c', 'synset': 'wedding_ring.n.01', 'synonyms': ['wedding_ring', 'wedding_band'], 'id': 1176, 'def': 'a ring given to the bride and/or groom at the wedding', 'name': 'wedding_ring'}, {'frequency': 'f', 'synset': 'wet_suit.n.01', 'synonyms': ['wet_suit'], 'id': 1177, 'def': 'a close-fitting garment made of a permeable material; worn in cold water to retain body heat', 'name': 'wet_suit'}, {'frequency': 'f', 'synset': 'wheel.n.01', 'synonyms': ['wheel'], 'id': 1178, 'def': 'a circular frame with spokes (or a solid disc) that can rotate on a shaft or axle', 'name': 'wheel'}, {'frequency': 'c', 'synset': 'wheelchair.n.01', 'synonyms': ['wheelchair'], 'id': 1179, 'def': 'a movable chair mounted on large wheels', 'name': 'wheelchair'}, {'frequency': 'c', 'synset': 'whipped_cream.n.01', 'synonyms': ['whipped_cream'], 'id': 1180, 'def': 'cream that has been beaten until light and fluffy', 'name': 'whipped_cream'}, {'frequency': 'c', 'synset': 'whistle.n.03', 'synonyms': ['whistle'], 'id': 1181, 'def': 'a small wind instrument that produces a whistling sound by blowing into it', 'name': 'whistle'}, {'frequency': 'c', 'synset': 'wig.n.01', 'synonyms': ['wig'], 'id': 1182, 'def': 'hairpiece covering the head and made of real or synthetic hair', 'name': 'wig'}, {'frequency': 'c', 'synset': 'wind_chime.n.01', 'synonyms': ['wind_chime'], 'id': 1183, 'def': 'a decorative arrangement of pieces of metal or glass or pottery that hang together loosely so the wind can cause them to tinkle', 'name': 'wind_chime'}, {'frequency': 'c', 'synset': 'windmill.n.01', 'synonyms': ['windmill'], 'id': 1184, 'def': 'A mill or turbine that is powered by wind', 'name': 'windmill'}, {'frequency': 'c', 'synset': 'window_box.n.01', 'synonyms': ['window_box_(for_plants)'], 'id': 1185, 'def': 'a container for growing plants on a windowsill', 'name': 'window_box_(for_plants)'}, {'frequency': 'f', 'synset': 'windshield_wiper.n.01', 'synonyms': ['windshield_wiper', 'windscreen_wiper', 'wiper_(for_windshield/screen)'], 'id': 1186, 'def': 'a mechanical device that cleans the windshield', 'name': 'windshield_wiper'}, {'frequency': 'c', 'synset': 'windsock.n.01', 'synonyms': ['windsock', 'air_sock', 'air-sleeve', 'wind_sleeve', 'wind_cone'], 'id': 1187, 'def': 'a truncated cloth cone mounted on a mast/pole; shows wind direction', 'name': 'windsock'}, {'frequency': 'f', 'synset': 'wine_bottle.n.01', 'synonyms': ['wine_bottle'], 'id': 1188, 'def': 'a bottle for holding wine', 'name': 'wine_bottle'}, {'frequency': 'c', 'synset': 'wine_bucket.n.01', 'synonyms': ['wine_bucket', 'wine_cooler'], 'id': 1189, 'def': 'a bucket of ice used to chill a bottle of wine', 'name': 'wine_bucket'}, {'frequency': 'f', 'synset': 'wineglass.n.01', 'synonyms': ['wineglass'], 'id': 1190, 'def': 'a glass that has a stem and in which wine is served', 'name': 'wineglass'}, {'frequency': 'f', 'synset': 'winker.n.02', 'synonyms': ['blinder_(for_horses)'], 'id': 1191, 'def': 'blinds that prevent a horse from seeing something on either side', 'name': 'blinder_(for_horses)'}, {'frequency': 'c', 'synset': 'wok.n.01', 'synonyms': ['wok'], 'id': 1192, 'def': 'pan with a convex bottom; used for frying in Chinese cooking', 'name': 'wok'}, {'frequency': 'r', 'synset': 'wolf.n.01', 'synonyms': ['wolf'], 'id': 1193, 'def': 'a wild carnivorous mammal of the dog family, living and hunting in packs', 'name': 'wolf'}, {'frequency': 'c', 'synset': 'wooden_spoon.n.02', 'synonyms': ['wooden_spoon'], 'id': 1194, 'def': 'a spoon made of wood', 'name': 'wooden_spoon'}, {'frequency': 'c', 'synset': 'wreath.n.01', 'synonyms': ['wreath'], 'id': 1195, 'def': 'an arrangement of flowers, leaves, or stems fastened in a ring', 'name': 'wreath'}, {'frequency': 'c', 'synset': 'wrench.n.03', 'synonyms': ['wrench', 'spanner'], 'id': 1196, 'def': 'a hand tool that is used to hold or twist a nut or bolt', 'name': 'wrench'}, {'frequency': 'f', 'synset': 'wristband.n.01', 'synonyms': ['wristband'], 'id': 1197, 'def': 'band consisting of a part of a sleeve that covers the wrist', 'name': 'wristband'}, {'frequency': 'f', 'synset': 'wristlet.n.01', 'synonyms': ['wristlet', 'wrist_band'], 'id': 1198, 'def': 'a band or bracelet worn around the wrist', 'name': 'wristlet'}, {'frequency': 'c', 'synset': 'yacht.n.01', 'synonyms': ['yacht'], 'id': 1199, 'def': 'an expensive vessel propelled by sail or power and used for cruising or racing', 'name': 'yacht'}, {'frequency': 'c', 'synset': 'yogurt.n.01', 'synonyms': ['yogurt', 'yoghurt', 'yoghourt'], 'id': 1200, 'def': 'a custard-like food made from curdled milk', 'name': 'yogurt'}, {'frequency': 'c', 'synset': 'yoke.n.07', 'synonyms': ['yoke_(animal_equipment)'], 'id': 1201, 'def': 'gear joining two animals at the neck; NOT egg yolk', 'name': 'yoke_(animal_equipment)'}, {'frequency': 'f', 'synset': 'zebra.n.01', 'synonyms': ['zebra'], 'id': 1202, 'def': 'any of several fleet black-and-white striped African equines', 'name': 'zebra'}, {'frequency': 'c', 'synset': 'zucchini.n.02', 'synonyms': ['zucchini', 'courgette'], 'id': 1203, 'def': 'small cucumber-shaped vegetable marrow; typically dark green', 'name': 'zucchini'}] # noqa +# fmt: on diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/pascal_voc.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/pascal_voc.py new file mode 100644 index 0000000..dbbf82c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/pascal_voc.py @@ -0,0 +1,82 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import numpy as np +import os +import xml.etree.ElementTree as ET +from typing import List, Tuple, Union + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.structures import BoxMode +from detectron2.utils.file_io import PathManager + +__all__ = ["load_voc_instances", "register_pascal_voc"] + + +# fmt: off +CLASS_NAMES = ( + "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", + "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", + "pottedplant", "sheep", "sofa", "train", "tvmonitor" +) +# fmt: on + + +def load_voc_instances(dirname: str, split: str, class_names: Union[List[str], Tuple[str, ...]]): + """ + Load Pascal VOC detection annotations to Detectron2 format. + + Args: + dirname: Contain "Annotations", "ImageSets", "JPEGImages" + split (str): one of "train", "test", "val", "trainval" + class_names: list or tuple of class names + """ + with PathManager.open(os.path.join(dirname, "ImageSets", "Main", split + ".txt")) as f: + fileids = np.loadtxt(f, dtype=np.str) + + # Needs to read many small annotation files. Makes sense at local + annotation_dirname = PathManager.get_local_path(os.path.join(dirname, "Annotations/")) + dicts = [] + for fileid in fileids: + anno_file = os.path.join(annotation_dirname, fileid + ".xml") + jpeg_file = os.path.join(dirname, "JPEGImages", fileid + ".jpg") + + with PathManager.open(anno_file) as f: + tree = ET.parse(f) + + r = { + "file_name": jpeg_file, + "image_id": fileid, + "height": int(tree.findall("./size/height")[0].text), + "width": int(tree.findall("./size/width")[0].text), + } + instances = [] + + for obj in tree.findall("object"): + cls = obj.find("name").text + # We include "difficult" samples in training. + # Based on limited experiments, they don't hurt accuracy. + # difficult = int(obj.find("difficult").text) + # if difficult == 1: + # continue + bbox = obj.find("bndbox") + bbox = [float(bbox.find(x).text) for x in ["xmin", "ymin", "xmax", "ymax"]] + # Original annotations are integers in the range [1, W or H] + # Assuming they mean 1-based pixel indices (inclusive), + # a box with annotation (xmin=1, xmax=W) covers the whole image. + # In coordinate space this is represented by (xmin=0, xmax=W) + bbox[0] -= 1.0 + bbox[1] -= 1.0 + instances.append( + {"category_id": class_names.index(cls), "bbox": bbox, "bbox_mode": BoxMode.XYXY_ABS} + ) + r["annotations"] = instances + dicts.append(r) + return dicts + + +def register_pascal_voc(name, dirname, split, year, class_names=CLASS_NAMES): + DatasetCatalog.register(name, lambda: load_voc_instances(dirname, split, class_names)) + MetadataCatalog.get(name).set( + thing_classes=list(class_names), dirname=dirname, year=year, split=split + ) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/register_coco.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/register_coco.py new file mode 100644 index 0000000..e564438 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/datasets/register_coco.py @@ -0,0 +1,3 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .coco import register_coco_instances # noqa +from .coco_panoptic import register_coco_panoptic_separated # noqa diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/detection_utils.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/detection_utils.py new file mode 100644 index 0000000..2707eb4 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/detection_utils.py @@ -0,0 +1,623 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +""" +Common data processing utilities that are used in a +typical object detection data pipeline. +""" +import logging +import numpy as np +from typing import List, Union +import pycocotools.mask as mask_util +import torch +from PIL import Image + +from detectron2.structures import ( + BitMasks, + Boxes, + BoxMode, + Instances, + Keypoints, + PolygonMasks, + RotatedBoxes, + polygons_to_bitmask, +) +from detectron2.utils.file_io import PathManager + +from . import transforms as T +from .catalog import MetadataCatalog + +__all__ = [ + "SizeMismatchError", + "convert_image_to_rgb", + "check_image_size", + "transform_proposals", + "transform_instance_annotations", + "annotations_to_instances", + "annotations_to_instances_rotated", + "build_augmentation", + "build_transform_gen", + "create_keypoint_hflip_indices", + "filter_empty_instances", + "read_image", +] + + +class SizeMismatchError(ValueError): + """ + When loaded image has difference width/height compared with annotation. + """ + + +# https://en.wikipedia.org/wiki/YUV#SDTV_with_BT.601 +_M_RGB2YUV = [[0.299, 0.587, 0.114], [-0.14713, -0.28886, 0.436], [0.615, -0.51499, -0.10001]] +_M_YUV2RGB = [[1.0, 0.0, 1.13983], [1.0, -0.39465, -0.58060], [1.0, 2.03211, 0.0]] + +# https://www.exiv2.org/tags.html +_EXIF_ORIENT = 274 # exif 'Orientation' tag + + +def convert_PIL_to_numpy(image, format): + """ + Convert PIL image to numpy array of target format. + + Args: + image (PIL.Image): a PIL image + format (str): the format of output image + + Returns: + (np.ndarray): also see `read_image` + """ + if format is not None: + # PIL only supports RGB, so convert to RGB and flip channels over below + conversion_format = format + if format in ["BGR", "YUV-BT.601"]: + conversion_format = "RGB" + image = image.convert(conversion_format) + image = np.asarray(image) + # PIL squeezes out the channel dimension for "L", so make it HWC + if format == "L": + image = np.expand_dims(image, -1) + + # handle formats not supported by PIL + elif format == "BGR": + # flip channels if needed + image = image[:, :, ::-1] + elif format == "YUV-BT.601": + image = image / 255.0 + image = np.dot(image, np.array(_M_RGB2YUV).T) + + return image + + +def convert_image_to_rgb(image, format): + """ + Convert an image from given format to RGB. + + Args: + image (np.ndarray or Tensor): an HWC image + format (str): the format of input image, also see `read_image` + + Returns: + (np.ndarray): (H,W,3) RGB image in 0-255 range, can be either float or uint8 + """ + if isinstance(image, torch.Tensor): + image = image.cpu().numpy() + if format == "BGR": + image = image[:, :, [2, 1, 0]] + elif format == "YUV-BT.601": + image = np.dot(image, np.array(_M_YUV2RGB).T) + image = image * 255.0 + else: + if format == "L": + image = image[:, :, 0] + image = image.astype(np.uint8) + image = np.asarray(Image.fromarray(image, mode=format).convert("RGB")) + return image + + +def _apply_exif_orientation(image): + """ + Applies the exif orientation correctly. + + This code exists per the bug: + https://github.com/python-pillow/Pillow/issues/3973 + with the function `ImageOps.exif_transpose`. The Pillow source raises errors with + various methods, especially `tobytes` + + Function based on: + https://github.com/wkentaro/labelme/blob/v4.5.4/labelme/utils/image.py#L59 + https://github.com/python-pillow/Pillow/blob/7.1.2/src/PIL/ImageOps.py#L527 + + Args: + image (PIL.Image): a PIL image + + Returns: + (PIL.Image): the PIL image with exif orientation applied, if applicable + """ + if not hasattr(image, "getexif"): + return image + + try: + exif = image.getexif() + except Exception: # https://github.com/facebookresearch/detectron2/issues/1885 + exif = None + + if exif is None: + return image + + orientation = exif.get(_EXIF_ORIENT) + + method = { + 2: Image.FLIP_LEFT_RIGHT, + 3: Image.ROTATE_180, + 4: Image.FLIP_TOP_BOTTOM, + 5: Image.TRANSPOSE, + 6: Image.ROTATE_270, + 7: Image.TRANSVERSE, + 8: Image.ROTATE_90, + }.get(orientation) + + if method is not None: + return image.transpose(method) + return image + + +def read_image(file_name, format=None): + """ + Read an image into the given format. + Will apply rotation and flipping if the image has such exif information. + + Args: + file_name (str): image file path + format (str): one of the supported image modes in PIL, or "BGR" or "YUV-BT.601". + + Returns: + image (np.ndarray): + an HWC image in the given format, which is 0-255, uint8 for + supported image modes in PIL or "BGR"; float (0-1 for Y) for YUV-BT.601. + """ + with PathManager.open(file_name, "rb") as f: + image = Image.open(f) + + # work around this bug: https://github.com/python-pillow/Pillow/issues/3973 + image = _apply_exif_orientation(image) + return convert_PIL_to_numpy(image, format) + + +def check_image_size(dataset_dict, image): + """ + Raise an error if the image does not match the size specified in the dict. + """ + if "width" in dataset_dict or "height" in dataset_dict: + image_wh = (image.shape[1], image.shape[0]) + expected_wh = (dataset_dict["width"], dataset_dict["height"]) + if not image_wh == expected_wh: + raise SizeMismatchError( + "Mismatched image shape{}, got {}, expect {}.".format( + " for image " + dataset_dict["file_name"] + if "file_name" in dataset_dict + else "", + image_wh, + expected_wh, + ) + + " Please check the width/height in your annotation." + ) + + # To ensure bbox always remap to original image size + if "width" not in dataset_dict: + dataset_dict["width"] = image.shape[1] + if "height" not in dataset_dict: + dataset_dict["height"] = image.shape[0] + + +def transform_proposals(dataset_dict, image_shape, transforms, *, proposal_topk, min_box_size=0): + """ + Apply transformations to the proposals in dataset_dict, if any. + + Args: + dataset_dict (dict): a dict read from the dataset, possibly + contains fields "proposal_boxes", "proposal_objectness_logits", "proposal_bbox_mode" + image_shape (tuple): height, width + transforms (TransformList): + proposal_topk (int): only keep top-K scoring proposals + min_box_size (int): proposals with either side smaller than this + threshold are removed + + The input dict is modified in-place, with abovementioned keys removed. A new + key "proposals" will be added. Its value is an `Instances` + object which contains the transformed proposals in its field + "proposal_boxes" and "objectness_logits". + """ + if "proposal_boxes" in dataset_dict: + # Transform proposal boxes + boxes = transforms.apply_box( + BoxMode.convert( + dataset_dict.pop("proposal_boxes"), + dataset_dict.pop("proposal_bbox_mode"), + BoxMode.XYXY_ABS, + ) + ) + boxes = Boxes(boxes) + objectness_logits = torch.as_tensor( + dataset_dict.pop("proposal_objectness_logits").astype("float32") + ) + + boxes.clip(image_shape) + keep = boxes.nonempty(threshold=min_box_size) + boxes = boxes[keep] + objectness_logits = objectness_logits[keep] + + proposals = Instances(image_shape) + proposals.proposal_boxes = boxes[:proposal_topk] + proposals.objectness_logits = objectness_logits[:proposal_topk] + dataset_dict["proposals"] = proposals + + +def transform_instance_annotations( + annotation, transforms, image_size, *, keypoint_hflip_indices=None +): + """ + Apply transforms to box, segmentation and keypoints annotations of a single instance. + + It will use `transforms.apply_box` for the box, and + `transforms.apply_coords` for segmentation polygons & keypoints. + If you need anything more specially designed for each data structure, + you'll need to implement your own version of this function or the transforms. + + Args: + annotation (dict): dict of instance annotations for a single instance. + It will be modified in-place. + transforms (TransformList or list[Transform]): + image_size (tuple): the height, width of the transformed image + keypoint_hflip_indices (ndarray[int]): see `create_keypoint_hflip_indices`. + + Returns: + dict: + the same input dict with fields "bbox", "segmentation", "keypoints" + transformed according to `transforms`. + The "bbox_mode" field will be set to XYXY_ABS. + """ + if isinstance(transforms, (tuple, list)): + transforms = T.TransformList(transforms) + # bbox is 1d (per-instance bounding box) + bbox = BoxMode.convert(annotation["bbox"], annotation["bbox_mode"], BoxMode.XYXY_ABS) + # clip transformed bbox to image size + bbox = transforms.apply_box(np.array([bbox]))[0].clip(min=0) + annotation["bbox"] = np.minimum(bbox, list(image_size + image_size)[::-1]) + annotation["bbox_mode"] = BoxMode.XYXY_ABS + + if "segmentation" in annotation: + # each instance contains 1 or more polygons + segm = annotation["segmentation"] + if isinstance(segm, list): + # polygons + polygons = [np.asarray(p).reshape(-1, 2) for p in segm] + annotation["segmentation"] = [ + p.reshape(-1) for p in transforms.apply_polygons(polygons) + ] + elif isinstance(segm, dict): + # RLE + mask = mask_util.decode(segm) + mask = transforms.apply_segmentation(mask) + assert tuple(mask.shape[:2]) == image_size + annotation["segmentation"] = mask + else: + raise ValueError( + "Cannot transform segmentation of type '{}'!" + "Supported types are: polygons as list[list[float] or ndarray]," + " COCO-style RLE as a dict.".format(type(segm)) + ) + + if "keypoints" in annotation: + keypoints = transform_keypoint_annotations( + annotation["keypoints"], transforms, image_size, keypoint_hflip_indices + ) + annotation["keypoints"] = keypoints + + return annotation + + +def transform_keypoint_annotations(keypoints, transforms, image_size, keypoint_hflip_indices=None): + """ + Transform keypoint annotations of an image. + If a keypoint is transformed out of image boundary, it will be marked "unlabeled" (visibility=0) + + Args: + keypoints (list[float]): Nx3 float in Detectron2's Dataset format. + Each point is represented by (x, y, visibility). + transforms (TransformList): + image_size (tuple): the height, width of the transformed image + keypoint_hflip_indices (ndarray[int]): see `create_keypoint_hflip_indices`. + When `transforms` includes horizontal flip, will use the index + mapping to flip keypoints. + """ + # (N*3,) -> (N, 3) + keypoints = np.asarray(keypoints, dtype="float64").reshape(-1, 3) + keypoints_xy = transforms.apply_coords(keypoints[:, :2]) + + # Set all out-of-boundary points to "unlabeled" + inside = (keypoints_xy >= np.array([0, 0])) & (keypoints_xy <= np.array(image_size[::-1])) + inside = inside.all(axis=1) + keypoints[:, :2] = keypoints_xy + keypoints[:, 2][~inside] = 0 + + # This assumes that HorizFlipTransform is the only one that does flip + do_hflip = sum(isinstance(t, T.HFlipTransform) for t in transforms.transforms) % 2 == 1 + + # Alternative way: check if probe points was horizontally flipped. + # probe = np.asarray([[0.0, 0.0], [image_width, 0.0]]) + # probe_aug = transforms.apply_coords(probe.copy()) + # do_hflip = np.sign(probe[1][0] - probe[0][0]) != np.sign(probe_aug[1][0] - probe_aug[0][0]) # noqa + + # If flipped, swap each keypoint with its opposite-handed equivalent + if do_hflip: + if keypoint_hflip_indices is None: + raise ValueError("Cannot flip keypoints without providing flip indices!") + if len(keypoints) != len(keypoint_hflip_indices): + raise ValueError( + "Keypoint data has {} points, but metadata " + "contains {} points!".format(len(keypoints), len(keypoint_hflip_indices)) + ) + keypoints = keypoints[np.asarray(keypoint_hflip_indices, dtype=np.int32), :] + + # Maintain COCO convention that if visibility == 0 (unlabeled), then x, y = 0 + keypoints[keypoints[:, 2] == 0] = 0 + return keypoints + + +def annotations_to_instances(annos, image_size, mask_format="polygon"): + """ + Create an :class:`Instances` object used by the models, + from instance annotations in the dataset dict. + + Args: + annos (list[dict]): a list of instance annotations in one image, each + element for one instance. + image_size (tuple): height, width + + Returns: + Instances: + It will contain fields "gt_boxes", "gt_classes", + "gt_masks", "gt_keypoints", if they can be obtained from `annos`. + This is the format that builtin models expect. + """ + boxes = ( + np.stack( + [BoxMode.convert(obj["bbox"], obj["bbox_mode"], BoxMode.XYXY_ABS) for obj in annos] + ) + if len(annos) + else np.zeros((0, 4)) + ) + target = Instances(image_size) + target.gt_boxes = Boxes(boxes) + + classes = [int(obj["category_id"]) for obj in annos] + classes = torch.tensor(classes, dtype=torch.int64) + target.gt_classes = classes + + if len(annos) and "segmentation" in annos[0]: + segms = [obj["segmentation"] for obj in annos] + if mask_format == "polygon": + try: + masks = PolygonMasks(segms) + except ValueError as e: + raise ValueError( + "Failed to use mask_format=='polygon' from the given annotations!" + ) from e + else: + assert mask_format == "bitmask", mask_format + masks = [] + for segm in segms: + if isinstance(segm, list): + # polygon + masks.append(polygons_to_bitmask(segm, *image_size)) + elif isinstance(segm, dict): + # COCO RLE + masks.append(mask_util.decode(segm)) + elif isinstance(segm, np.ndarray): + assert segm.ndim == 2, "Expect segmentation of 2 dimensions, got {}.".format( + segm.ndim + ) + # mask array + masks.append(segm) + else: + raise ValueError( + "Cannot convert segmentation of type '{}' to BitMasks!" + "Supported types are: polygons as list[list[float] or ndarray]," + " COCO-style RLE as a dict, or a binary segmentation mask " + " in a 2D numpy array of shape HxW.".format(type(segm)) + ) + # torch.from_numpy does not support array with negative stride. + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x)) for x in masks]) + ) + target.gt_masks = masks + + if len(annos) and "keypoints" in annos[0]: + kpts = [obj.get("keypoints", []) for obj in annos] + target.gt_keypoints = Keypoints(kpts) + + return target + + +def annotations_to_instances_rotated(annos, image_size): + """ + Create an :class:`Instances` object used by the models, + from instance annotations in the dataset dict. + Compared to `annotations_to_instances`, this function is for rotated boxes only + + Args: + annos (list[dict]): a list of instance annotations in one image, each + element for one instance. + image_size (tuple): height, width + + Returns: + Instances: + Containing fields "gt_boxes", "gt_classes", + if they can be obtained from `annos`. + This is the format that builtin models expect. + """ + boxes = [obj["bbox"] for obj in annos] + target = Instances(image_size) + boxes = target.gt_boxes = RotatedBoxes(boxes) + boxes.clip(image_size) + + classes = [obj["category_id"] for obj in annos] + classes = torch.tensor(classes, dtype=torch.int64) + target.gt_classes = classes + + return target + + +def filter_empty_instances( + instances, by_box=True, by_mask=True, box_threshold=1e-5, return_mask=False +): + """ + Filter out empty instances in an `Instances` object. + + Args: + instances (Instances): + by_box (bool): whether to filter out instances with empty boxes + by_mask (bool): whether to filter out instances with empty masks + box_threshold (float): minimum width and height to be considered non-empty + return_mask (bool): whether to return boolean mask of filtered instances + + Returns: + Instances: the filtered instances. + tensor[bool], optional: boolean mask of filtered instances + """ + assert by_box or by_mask + r = [] + if by_box: + r.append(instances.gt_boxes.nonempty(threshold=box_threshold)) + if instances.has("gt_masks") and by_mask: + r.append(instances.gt_masks.nonempty()) + + # TODO: can also filter visible keypoints + + if not r: + return instances + m = r[0] + for x in r[1:]: + m = m & x + if return_mask: + return instances[m], m + return instances[m] + + +def create_keypoint_hflip_indices(dataset_names: Union[str, List[str]]) -> List[int]: + """ + Args: + dataset_names: list of dataset names + + Returns: + list[int]: a list of size=#keypoints, storing the + horizontally-flipped keypoint indices. + """ + if isinstance(dataset_names, str): + dataset_names = [dataset_names] + + check_metadata_consistency("keypoint_names", dataset_names) + check_metadata_consistency("keypoint_flip_map", dataset_names) + + meta = MetadataCatalog.get(dataset_names[0]) + names = meta.keypoint_names + # TODO flip -> hflip + flip_map = dict(meta.keypoint_flip_map) + flip_map.update({v: k for k, v in flip_map.items()}) + flipped_names = [i if i not in flip_map else flip_map[i] for i in names] + flip_indices = [names.index(i) for i in flipped_names] + return flip_indices + + +def gen_crop_transform_with_instance(crop_size, image_size, instance): + """ + Generate a CropTransform so that the cropping region contains + the center of the given instance. + + Args: + crop_size (tuple): h, w in pixels + image_size (tuple): h, w + instance (dict): an annotation dict of one instance, in Detectron2's + dataset format. + """ + crop_size = np.asarray(crop_size, dtype=np.int32) + bbox = BoxMode.convert(instance["bbox"], instance["bbox_mode"], BoxMode.XYXY_ABS) + center_yx = (bbox[1] + bbox[3]) * 0.5, (bbox[0] + bbox[2]) * 0.5 + assert ( + image_size[0] >= center_yx[0] and image_size[1] >= center_yx[1] + ), "The annotation bounding box is outside of the image!" + assert ( + image_size[0] >= crop_size[0] and image_size[1] >= crop_size[1] + ), "Crop size is larger than image size!" + + min_yx = np.maximum(np.floor(center_yx).astype(np.int32) - crop_size, 0) + max_yx = np.maximum(np.asarray(image_size, dtype=np.int32) - crop_size, 0) + max_yx = np.minimum(max_yx, np.ceil(center_yx).astype(np.int32)) + + y0 = np.random.randint(min_yx[0], max_yx[0] + 1) + x0 = np.random.randint(min_yx[1], max_yx[1] + 1) + return T.CropTransform(x0, y0, crop_size[1], crop_size[0]) + + +def check_metadata_consistency(key, dataset_names): + """ + Check that the datasets have consistent metadata. + + Args: + key (str): a metadata key + dataset_names (list[str]): a list of dataset names + + Raises: + AttributeError: if the key does not exist in the metadata + ValueError: if the given datasets do not have the same metadata values defined by key + """ + if len(dataset_names) == 0: + return + logger = logging.getLogger(__name__) + entries_per_dataset = [getattr(MetadataCatalog.get(d), key) for d in dataset_names] + for idx, entry in enumerate(entries_per_dataset): + if entry != entries_per_dataset[0]: + logger.error( + "Metadata '{}' for dataset '{}' is '{}'".format(key, dataset_names[idx], str(entry)) + ) + logger.error( + "Metadata '{}' for dataset '{}' is '{}'".format( + key, dataset_names[0], str(entries_per_dataset[0]) + ) + ) + raise ValueError("Datasets have different metadata '{}'!".format(key)) + + +def build_augmentation(cfg, is_train): + """ + Create a list of default :class:`Augmentation` from config. + Now it includes resizing and flipping. + + Returns: + list[Augmentation] + """ + if is_train: + min_size = cfg.INPUT.MIN_SIZE_TRAIN + max_size = cfg.INPUT.MAX_SIZE_TRAIN + sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING + else: + min_size = cfg.INPUT.MIN_SIZE_TEST + max_size = cfg.INPUT.MAX_SIZE_TEST + sample_style = "choice" + augmentation = [T.ResizeShortestEdge(min_size, max_size, sample_style)] + if is_train and cfg.INPUT.RANDOM_FLIP != "none": + augmentation.append( + T.RandomFlip( + horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal", + vertical=cfg.INPUT.RANDOM_FLIP == "vertical", + ) + ) + return augmentation + + +build_transform_gen = build_augmentation +""" +Alias for backward-compatibility. +""" diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/samplers/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/samplers/__init__.py new file mode 100644 index 0000000..85c9f1a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/samplers/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .distributed_sampler import ( + InferenceSampler, + RandomSubsetTrainingSampler, + RepeatFactorTrainingSampler, + TrainingSampler, +) + +from .grouped_batch_sampler import GroupedBatchSampler + +__all__ = [ + "GroupedBatchSampler", + "TrainingSampler", + "RandomSubsetTrainingSampler", + "InferenceSampler", + "RepeatFactorTrainingSampler", +] diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/samplers/distributed_sampler.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/samplers/distributed_sampler.py new file mode 100644 index 0000000..a098e6a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/samplers/distributed_sampler.py @@ -0,0 +1,278 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +import logging +import math +from collections import defaultdict +from typing import Optional +import torch +from torch.utils.data.sampler import Sampler + +from detectron2.utils import comm + +logger = logging.getLogger(__name__) + + +class TrainingSampler(Sampler): + """ + In training, we only care about the "infinite stream" of training data. + So this sampler produces an infinite stream of indices and + all workers cooperate to correctly shuffle the indices and sample different indices. + + The samplers in each worker effectively produces `indices[worker_id::num_workers]` + where `indices` is an infinite stream of indices consisting of + `shuffle(range(size)) + shuffle(range(size)) + ...` (if shuffle is True) + or `range(size) + range(size) + ...` (if shuffle is False) + + Note that this sampler does not shard based on pytorch DataLoader worker id. + A sampler passed to pytorch DataLoader is used only with map-style dataset + and will not be executed inside workers. + But if this sampler is used in a way that it gets execute inside a dataloader + worker, then extra work needs to be done to shard its outputs based on worker id. + This is required so that workers don't produce identical data. + :class:`ToIterableDataset` implements this logic. + This note is true for all samplers in detectron2. + """ + + def __init__(self, size: int, shuffle: bool = True, seed: Optional[int] = None): + """ + Args: + size (int): the total number of data of the underlying dataset to sample from + shuffle (bool): whether to shuffle the indices or not + seed (int): the initial seed of the shuffle. Must be the same + across all workers. If None, will use a random seed shared + among workers (require synchronization among all workers). + """ + if not isinstance(size, int): + raise TypeError(f"TrainingSampler(size=) expects an int. Got type {type(size)}.") + if size <= 0: + raise ValueError(f"TrainingSampler(size=) expects a positive int. Got {size}.") + self._size = size + self._shuffle = shuffle + if seed is None: + seed = comm.shared_random_seed() + self._seed = int(seed) + + self._rank = comm.get_rank() + self._world_size = comm.get_world_size() + + def __iter__(self): + start = self._rank + yield from itertools.islice(self._infinite_indices(), start, None, self._world_size) + + def _infinite_indices(self): + g = torch.Generator() + g.manual_seed(self._seed) + while True: + if self._shuffle: + yield from torch.randperm(self._size, generator=g).tolist() + else: + yield from torch.arange(self._size).tolist() + + +class RandomSubsetTrainingSampler(TrainingSampler): + """ + Similar to TrainingSampler, but only sample a random subset of indices. + This is useful when you want to estimate the accuracy vs data-number curves by + training the model with different subset_ratio. + """ + + def __init__( + self, + size: int, + subset_ratio: float, + shuffle: bool = True, + seed_shuffle: Optional[int] = None, + seed_subset: Optional[int] = None, + ): + """ + Args: + size (int): the total number of data of the underlying dataset to sample from + subset_ratio (float): the ratio of subset data to sample from the underlying dataset + shuffle (bool): whether to shuffle the indices or not + seed_shuffle (int): the initial seed of the shuffle. Must be the same + across all workers. If None, will use a random seed shared + among workers (require synchronization among all workers). + seed_subset (int): the seed to randomize the subset to be sampled. + Must be the same across all workers. If None, will use a random seed shared + among workers (require synchronization among all workers). + """ + super().__init__(size=size, shuffle=shuffle, seed=seed_shuffle) + + assert 0.0 < subset_ratio <= 1.0 + self._size_subset = int(size * subset_ratio) + assert self._size_subset > 0 + if seed_subset is None: + seed_subset = comm.shared_random_seed() + self._seed_subset = int(seed_subset) + + # randomly generate the subset indexes to be sampled from + g = torch.Generator() + g.manual_seed(self._seed_subset) + indexes_randperm = torch.randperm(self._size, generator=g) + self._indexes_subset = indexes_randperm[: self._size_subset] + + logger.info("Using RandomSubsetTrainingSampler......") + logger.info(f"Randomly sample {self._size_subset} data from the original {self._size} data") + + def _infinite_indices(self): + g = torch.Generator() + g.manual_seed(self._seed) # self._seed equals seed_shuffle from __init__() + while True: + if self._shuffle: + # generate a random permutation to shuffle self._indexes_subset + randperm = torch.randperm(self._size_subset, generator=g) + yield from self._indexes_subset[randperm].tolist() + else: + yield from self._indexes_subset.tolist() + + +class RepeatFactorTrainingSampler(Sampler): + """ + Similar to TrainingSampler, but a sample may appear more times than others based + on its "repeat factor". This is suitable for training on class imbalanced datasets like LVIS. + """ + + def __init__(self, repeat_factors, *, shuffle=True, seed=None): + """ + Args: + repeat_factors (Tensor): a float vector, the repeat factor for each indice. When it's + full of ones, it is equivalent to ``TrainingSampler(len(repeat_factors), ...)``. + shuffle (bool): whether to shuffle the indices or not + seed (int): the initial seed of the shuffle. Must be the same + across all workers. If None, will use a random seed shared + among workers (require synchronization among all workers). + """ + self._shuffle = shuffle + if seed is None: + seed = comm.shared_random_seed() + self._seed = int(seed) + + self._rank = comm.get_rank() + self._world_size = comm.get_world_size() + + # Split into whole number (_int_part) and fractional (_frac_part) parts. + self._int_part = torch.trunc(repeat_factors) + self._frac_part = repeat_factors - self._int_part + + @staticmethod + def repeat_factors_from_category_frequency(dataset_dicts, repeat_thresh): + """ + Compute (fractional) per-image repeat factors based on category frequency. + The repeat factor for an image is a function of the frequency of the rarest + category labeled in that image. The "frequency of category c" in [0, 1] is defined + as the fraction of images in the training set (without repeats) in which category c + appears. + See :paper:`lvis` (>= v2) Appendix B.2. + + Args: + dataset_dicts (list[dict]): annotations in Detectron2 dataset format. + repeat_thresh (float): frequency threshold below which data is repeated. + If the frequency is half of `repeat_thresh`, the image will be + repeated twice. + + Returns: + torch.Tensor: + the i-th element is the repeat factor for the dataset image at index i. + """ + # 1. For each category c, compute the fraction of images that contain it: f(c) + category_freq = defaultdict(int) + for dataset_dict in dataset_dicts: # For each image (without repeats) + cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} + for cat_id in cat_ids: + category_freq[cat_id] += 1 + num_images = len(dataset_dicts) + for k, v in category_freq.items(): + category_freq[k] = v / num_images + + # 2. For each category c, compute the category-level repeat factor: + # r(c) = max(1, sqrt(t / f(c))) + category_rep = { + cat_id: max(1.0, math.sqrt(repeat_thresh / cat_freq)) + for cat_id, cat_freq in category_freq.items() + } + + # 3. For each image I, compute the image-level repeat factor: + # r(I) = max_{c in I} r(c) + rep_factors = [] + for dataset_dict in dataset_dicts: + cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]} + rep_factor = max({category_rep[cat_id] for cat_id in cat_ids}, default=1.0) + rep_factors.append(rep_factor) + + return torch.tensor(rep_factors, dtype=torch.float32) + + def _get_epoch_indices(self, generator): + """ + Create a list of dataset indices (with repeats) to use for one epoch. + + Args: + generator (torch.Generator): pseudo random number generator used for + stochastic rounding. + + Returns: + torch.Tensor: list of dataset indices to use in one epoch. Each index + is repeated based on its calculated repeat factor. + """ + # Since repeat factors are fractional, we use stochastic rounding so + # that the target repeat factor is achieved in expectation over the + # course of training + rands = torch.rand(len(self._frac_part), generator=generator) + rep_factors = self._int_part + (rands < self._frac_part).float() + # Construct a list of indices in which we repeat images as specified + indices = [] + for dataset_index, rep_factor in enumerate(rep_factors): + indices.extend([dataset_index] * int(rep_factor.item())) + return torch.tensor(indices, dtype=torch.int64) + + def __iter__(self): + start = self._rank + yield from itertools.islice(self._infinite_indices(), start, None, self._world_size) + + def _infinite_indices(self): + g = torch.Generator() + g.manual_seed(self._seed) + while True: + # Sample indices with repeats determined by stochastic rounding; each + # "epoch" may have a slightly different size due to the rounding. + indices = self._get_epoch_indices(g) + if self._shuffle: + randperm = torch.randperm(len(indices), generator=g) + yield from indices[randperm].tolist() + else: + yield from indices.tolist() + + +class InferenceSampler(Sampler): + """ + Produce indices for inference across all workers. + Inference needs to run on the __exact__ set of samples, + therefore when the total number of samples is not divisible by the number of workers, + this sampler produces different number of samples on different workers. + """ + + def __init__(self, size: int): + """ + Args: + size (int): the total number of data of the underlying dataset to sample from + """ + self._size = size + assert size > 0 + self._rank = comm.get_rank() + self._world_size = comm.get_world_size() + self._local_indices = self._get_local_indices(size, self._world_size, self._rank) + + @staticmethod + def _get_local_indices(total_size, world_size, rank): + shard_size = total_size // world_size + left = total_size % world_size + shard_sizes = [shard_size + int(r < left) for r in range(world_size)] + + begin = sum(shard_sizes[:rank]) + end = min(sum(shard_sizes[: rank + 1]), total_size) + return range(begin, end) + + def __iter__(self): + yield from self._local_indices + + def __len__(self): + return len(self._local_indices) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/samplers/grouped_batch_sampler.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/samplers/grouped_batch_sampler.py new file mode 100644 index 0000000..6ba3c01 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/samplers/grouped_batch_sampler.py @@ -0,0 +1,47 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +from torch.utils.data.sampler import BatchSampler, Sampler + + +class GroupedBatchSampler(BatchSampler): + """ + Wraps another sampler to yield a mini-batch of indices. + It enforces that the batch only contain elements from the same group. + It also tries to provide mini-batches which follows an ordering which is + as close as possible to the ordering from the original sampler. + """ + + def __init__(self, sampler, group_ids, batch_size): + """ + Args: + sampler (Sampler): Base sampler. + group_ids (list[int]): If the sampler produces indices in range [0, N), + `group_ids` must be a list of `N` ints which contains the group id of each sample. + The group ids must be a set of integers in the range [0, num_groups). + batch_size (int): Size of mini-batch. + """ + if not isinstance(sampler, Sampler): + raise ValueError( + "sampler should be an instance of " + "torch.autils.data.Sampler, but got sampler={}".format(sampler) + ) + self.sampler = sampler + self.group_ids = np.asarray(group_ids) + assert self.group_ids.ndim == 1 + self.batch_size = batch_size + groups = np.unique(self.group_ids).tolist() + + # buffer the indices of each group until batch size is reached + self.buffer_per_group = {k: [] for k in groups} + + def __iter__(self): + for idx in self.sampler: + group_id = self.group_ids[idx] + group_buffer = self.buffer_per_group[group_id] + group_buffer.append(idx) + if len(group_buffer) == self.batch_size: + yield group_buffer[:] # yield a copy of the list + del group_buffer[:] + + def __len__(self): + raise NotImplementedError("len() of GroupedBatchSampler is not well-defined.") diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/transforms/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/transforms/__init__.py new file mode 100644 index 0000000..ab3c63b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/transforms/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from fvcore.transforms.transform import Transform, TransformList # order them first +from fvcore.transforms.transform import * +from .transform import * +from .augmentation import * +from .augmentation_impl import * + +__all__ = [k for k in globals().keys() if not k.startswith("_")] + + +from detectron2.utils.env import fixup_module_metadata + +fixup_module_metadata(__name__, globals(), __all__) +del fixup_module_metadata diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/transforms/augmentation.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/transforms/augmentation.py new file mode 100644 index 0000000..48be5b1 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/transforms/augmentation.py @@ -0,0 +1,377 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import inspect +import numpy as np +import pprint +from typing import Any, List, Optional, Tuple, Union +from fvcore.transforms.transform import Transform, TransformList + +""" +See "Data Augmentation" tutorial for an overview of the system: +https://detectron2.readthedocs.io/tutorials/augmentation.html +""" + + +__all__ = [ + "Augmentation", + "AugmentationList", + "AugInput", + "TransformGen", + "apply_transform_gens", + "StandardAugInput", + "apply_augmentations", +] + + +def _check_img_dtype(img): + assert isinstance(img, np.ndarray), "[Augmentation] Needs an numpy array, but got a {}!".format( + type(img) + ) + assert not isinstance(img.dtype, np.integer) or ( + img.dtype == np.uint8 + ), "[Augmentation] Got image of type {}, use uint8 or floating points instead!".format( + img.dtype + ) + assert img.ndim in [2, 3], img.ndim + + +def _get_aug_input_args(aug, aug_input) -> List[Any]: + """ + Get the arguments to be passed to ``aug.get_transform`` from the input ``aug_input``. + """ + if aug.input_args is None: + # Decide what attributes are needed automatically + prms = list(inspect.signature(aug.get_transform).parameters.items()) + # The default behavior is: if there is one parameter, then its "image" + # (work automatically for majority of use cases, and also avoid BC breaking), + # Otherwise, use the argument names. + if len(prms) == 1: + names = ("image",) + else: + names = [] + for name, prm in prms: + if prm.kind in (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD): + raise TypeError( + f""" \ +The default implementation of `{type(aug)}.__call__` does not allow \ +`{type(aug)}.get_transform` to use variable-length arguments (*args, **kwargs)! \ +If arguments are unknown, reimplement `__call__` instead. \ +""" + ) + names.append(name) + aug.input_args = tuple(names) + + args = [] + for f in aug.input_args: + try: + args.append(getattr(aug_input, f)) + except AttributeError as e: + raise AttributeError( + f"{type(aug)}.get_transform needs input attribute '{f}', " + f"but it is not an attribute of {type(aug_input)}!" + ) from e + return args + + +class Augmentation: + """ + Augmentation defines (often random) policies/strategies to generate :class:`Transform` + from data. It is often used for pre-processing of input data. + + A "policy" that generates a :class:`Transform` may, in the most general case, + need arbitrary information from input data in order to determine what transforms + to apply. Therefore, each :class:`Augmentation` instance defines the arguments + needed by its :meth:`get_transform` method. When called with the positional arguments, + the :meth:`get_transform` method executes the policy. + + Note that :class:`Augmentation` defines the policies to create a :class:`Transform`, + but not how to execute the actual transform operations to those data. + Its :meth:`__call__` method will use :meth:`AugInput.transform` to execute the transform. + + The returned `Transform` object is meant to describe deterministic transformation, which means + it can be re-applied on associated data, e.g. the geometry of an image and its segmentation + masks need to be transformed together. + (If such re-application is not needed, then determinism is not a crucial requirement.) + """ + + input_args: Optional[Tuple[str]] = None + """ + Stores the attribute names needed by :meth:`get_transform`, e.g. ``("image", "sem_seg")``. + By default, it is just a tuple of argument names in :meth:`self.get_transform`, which often only + contain "image". As long as the argument name convention is followed, there is no need for + users to touch this attribute. + """ + + def _init(self, params=None): + if params: + for k, v in params.items(): + if k != "self" and not k.startswith("_"): + setattr(self, k, v) + + def get_transform(self, *args) -> Transform: + """ + Execute the policy based on input data, and decide what transform to apply to inputs. + + Args: + args: Any fixed-length positional arguments. By default, the name of the arguments + should exist in the :class:`AugInput` to be used. + + Returns: + Transform: Returns the deterministic transform to apply to the input. + + Examples: + :: + class MyAug: + # if a policy needs to know both image and semantic segmentation + def get_transform(image, sem_seg) -> T.Transform: + pass + tfm: Transform = MyAug().get_transform(image, sem_seg) + new_image = tfm.apply_image(image) + + Notes: + Users can freely use arbitrary new argument names in custom + :meth:`get_transform` method, as long as they are available in the + input data. In detectron2 we use the following convention: + + * image: (H,W) or (H,W,C) ndarray of type uint8 in range [0, 255], or + floating point in range [0, 1] or [0, 255]. + * boxes: (N,4) ndarray of float32. It represents the instance bounding boxes + of N instances. Each is in XYXY format in unit of absolute coordinates. + * sem_seg: (H,W) ndarray of type uint8. Each element is an integer label of pixel. + + We do not specify convention for other types and do not include builtin + :class:`Augmentation` that uses other types in detectron2. + """ + raise NotImplementedError + + def __call__(self, aug_input) -> Transform: + """ + Augment the given `aug_input` **in-place**, and return the transform that's used. + + This method will be called to apply the augmentation. In most augmentation, it + is enough to use the default implementation, which calls :meth:`get_transform` + using the inputs. But a subclass can overwrite it to have more complicated logic. + + Args: + aug_input (AugInput): an object that has attributes needed by this augmentation + (defined by ``self.get_transform``). Its ``transform`` method will be called + to in-place transform it. + + Returns: + Transform: the transform that is applied on the input. + """ + args = _get_aug_input_args(self, aug_input) + tfm = self.get_transform(*args) + assert isinstance(tfm, (Transform, TransformList)), ( + f"{type(self)}.get_transform must return an instance of Transform! " + f"Got {type(tfm)} instead." + ) + aug_input.transform(tfm) + return tfm + + def _rand_range(self, low=1.0, high=None, size=None): + """ + Uniform float random number between low and high. + """ + if high is None: + low, high = 0, low + if size is None: + size = [] + return np.random.uniform(low, high, size) + + def __repr__(self): + """ + Produce something like: + "MyAugmentation(field1={self.field1}, field2={self.field2})" + """ + try: + sig = inspect.signature(self.__init__) + classname = type(self).__name__ + argstr = [] + for name, param in sig.parameters.items(): + assert ( + param.kind != param.VAR_POSITIONAL and param.kind != param.VAR_KEYWORD + ), "The default __repr__ doesn't support *args or **kwargs" + assert hasattr(self, name), ( + "Attribute {} not found! " + "Default __repr__ only works if attributes match the constructor.".format(name) + ) + attr = getattr(self, name) + default = param.default + if default is attr: + continue + attr_str = pprint.pformat(attr) + if "\n" in attr_str: + # don't show it if pformat decides to use >1 lines + attr_str = "..." + argstr.append("{}={}".format(name, attr_str)) + return "{}({})".format(classname, ", ".join(argstr)) + except AssertionError: + return super().__repr__() + + __str__ = __repr__ + + +def _transform_to_aug(tfm_or_aug): + """ + Wrap Transform into Augmentation. + Private, used internally to implement augmentations. + """ + assert isinstance(tfm_or_aug, (Transform, Augmentation)), tfm_or_aug + if isinstance(tfm_or_aug, Augmentation): + return tfm_or_aug + else: + + class _TransformToAug(Augmentation): + def __init__(self, tfm: Transform): + self.tfm = tfm + + def get_transform(self, *args): + return self.tfm + + def __repr__(self): + return repr(self.tfm) + + __str__ = __repr__ + + return _TransformToAug(tfm_or_aug) + + +class AugmentationList(Augmentation): + """ + Apply a sequence of augmentations. + + It has ``__call__`` method to apply the augmentations. + + Note that :meth:`get_transform` method is impossible (will throw error if called) + for :class:`AugmentationList`, because in order to apply a sequence of augmentations, + the kth augmentation must be applied first, to provide inputs needed by the (k+1)th + augmentation. + """ + + def __init__(self, augs): + """ + Args: + augs (list[Augmentation or Transform]): + """ + super().__init__() + self.augs = [_transform_to_aug(x) for x in augs] + + def __call__(self, aug_input) -> Transform: + tfms = [] + for x in self.augs: + tfm = x(aug_input) + tfms.append(tfm) + return TransformList(tfms) + + def __repr__(self): + msgs = [str(x) for x in self.augs] + return "AugmentationList[{}]".format(", ".join(msgs)) + + __str__ = __repr__ + + +class AugInput: + """ + Input that can be used with :meth:`Augmentation.__call__`. + This is a standard implementation for the majority of use cases. + This class provides the standard attributes **"image", "boxes", "sem_seg"** + defined in :meth:`__init__` and they may be needed by different augmentations. + Most augmentation policies do not need attributes beyond these three. + + After applying augmentations to these attributes (using :meth:`AugInput.transform`), + the returned transforms can then be used to transform other data structures that users have. + + Examples: + :: + input = AugInput(image, boxes=boxes) + tfms = augmentation(input) + transformed_image = input.image + transformed_boxes = input.boxes + transformed_other_data = tfms.apply_other(other_data) + + An extended project that works with new data types may implement augmentation policies + that need other inputs. An algorithm may need to transform inputs in a way different + from the standard approach defined in this class. In those rare situations, users can + implement a class similar to this class, that satify the following condition: + + * The input must provide access to these data in the form of attribute access + (``getattr``). For example, if an :class:`Augmentation` to be applied needs "image" + and "sem_seg" arguments, its input must have the attribute "image" and "sem_seg". + * The input must have a ``transform(tfm: Transform) -> None`` method which + in-place transforms all its attributes. + """ + + # TODO maybe should support more builtin data types here + def __init__( + self, + image: np.ndarray, + *, + boxes: Optional[np.ndarray] = None, + sem_seg: Optional[np.ndarray] = None, + ): + """ + Args: + image (ndarray): (H,W) or (H,W,C) ndarray of type uint8 in range [0, 255], or + floating point in range [0, 1] or [0, 255]. The meaning of C is up + to users. + boxes (ndarray or None): Nx4 float32 boxes in XYXY_ABS mode + sem_seg (ndarray or None): HxW uint8 semantic segmentation mask. Each element + is an integer label of pixel. + """ + _check_img_dtype(image) + self.image = image + self.boxes = boxes + self.sem_seg = sem_seg + + def transform(self, tfm: Transform) -> None: + """ + In-place transform all attributes of this class. + + By "in-place", it means after calling this method, accessing an attribute such + as ``self.image`` will return transformed data. + """ + self.image = tfm.apply_image(self.image) + if self.boxes is not None: + self.boxes = tfm.apply_box(self.boxes) + if self.sem_seg is not None: + self.sem_seg = tfm.apply_segmentation(self.sem_seg) + + def apply_augmentations( + self, augmentations: List[Union[Augmentation, Transform]] + ) -> TransformList: + """ + Equivalent of ``AugmentationList(augmentations)(self)`` + """ + return AugmentationList(augmentations)(self) + + +def apply_augmentations(augmentations: List[Union[Transform, Augmentation]], inputs): + """ + Use ``T.AugmentationList(augmentations)(inputs)`` instead. + """ + if isinstance(inputs, np.ndarray): + # handle the common case of image-only Augmentation, also for backward compatibility + image_only = True + inputs = AugInput(inputs) + else: + image_only = False + tfms = inputs.apply_augmentations(augmentations) + return inputs.image if image_only else inputs, tfms + + +apply_transform_gens = apply_augmentations +""" +Alias for backward-compatibility. +""" + +TransformGen = Augmentation +""" +Alias for Augmentation, since it is something that generates :class:`Transform`s +""" + +StandardAugInput = AugInput +""" +Alias for compatibility. It's not worth the complexity to have two classes. +""" diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/transforms/augmentation_impl.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/transforms/augmentation_impl.py new file mode 100644 index 0000000..652a34a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/transforms/augmentation_impl.py @@ -0,0 +1,614 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +""" +Implement many useful :class:`Augmentation`. +""" +import numpy as np +import sys +from typing import Tuple +import torch +from fvcore.transforms.transform import ( + BlendTransform, + CropTransform, + HFlipTransform, + NoOpTransform, + PadTransform, + Transform, + TransformList, + VFlipTransform, +) +from PIL import Image + +from .augmentation import Augmentation, _transform_to_aug +from .transform import ExtentTransform, ResizeTransform, RotationTransform + +__all__ = [ + "FixedSizeCrop", + "RandomApply", + "RandomBrightness", + "RandomContrast", + "RandomCrop", + "RandomExtent", + "RandomFlip", + "RandomSaturation", + "RandomLighting", + "RandomRotation", + "Resize", + "ResizeScale", + "ResizeShortestEdge", + "RandomCrop_CategoryAreaConstraint", +] + + +class RandomApply(Augmentation): + """ + Randomly apply an augmentation with a given probability. + """ + + def __init__(self, tfm_or_aug, prob=0.5): + """ + Args: + tfm_or_aug (Transform, Augmentation): the transform or augmentation + to be applied. It can either be a `Transform` or `Augmentation` + instance. + prob (float): probability between 0.0 and 1.0 that + the wrapper transformation is applied + """ + super().__init__() + self.aug = _transform_to_aug(tfm_or_aug) + assert 0.0 <= prob <= 1.0, f"Probablity must be between 0.0 and 1.0 (given: {prob})" + self.prob = prob + + def get_transform(self, *args): + do = self._rand_range() < self.prob + if do: + return self.aug.get_transform(*args) + else: + return NoOpTransform() + + def __call__(self, aug_input): + do = self._rand_range() < self.prob + if do: + return self.aug(aug_input) + else: + return NoOpTransform() + + +class RandomFlip(Augmentation): + """ + Flip the image horizontally or vertically with the given probability. + """ + + def __init__(self, prob=0.5, *, horizontal=True, vertical=False): + """ + Args: + prob (float): probability of flip. + horizontal (boolean): whether to apply horizontal flipping + vertical (boolean): whether to apply vertical flipping + """ + super().__init__() + + if horizontal and vertical: + raise ValueError("Cannot do both horiz and vert. Please use two Flip instead.") + if not horizontal and not vertical: + raise ValueError("At least one of horiz or vert has to be True!") + self._init(locals()) + + def get_transform(self, image): + h, w = image.shape[:2] + do = self._rand_range() < self.prob + if do: + if self.horizontal: + return HFlipTransform(w) + elif self.vertical: + return VFlipTransform(h) + else: + return NoOpTransform() + + +class Resize(Augmentation): + """Resize image to a fixed target size""" + + def __init__(self, shape, interp=Image.BILINEAR): + """ + Args: + shape: (h, w) tuple or a int + interp: PIL interpolation method + """ + if isinstance(shape, int): + shape = (shape, shape) + shape = tuple(shape) + self._init(locals()) + + def get_transform(self, image): + return ResizeTransform( + image.shape[0], image.shape[1], self.shape[0], self.shape[1], self.interp + ) + + +class ResizeShortestEdge(Augmentation): + """ + Resize the image while keeping the aspect ratio unchanged. + It attempts to scale the shorter edge to the given `short_edge_length`, + as long as the longer edge does not exceed `max_size`. + If `max_size` is reached, then downscale so that the longer edge does not exceed max_size. + """ + + @torch.jit.unused + def __init__( + self, short_edge_length, max_size=sys.maxsize, sample_style="range", interp=Image.BILINEAR + ): + """ + Args: + short_edge_length (list[int]): If ``sample_style=="range"``, + a [min, max] interval from which to sample the shortest edge length. + If ``sample_style=="choice"``, a list of shortest edge lengths to sample from. + max_size (int): maximum allowed longest edge length. + sample_style (str): either "range" or "choice". + """ + super().__init__() + assert sample_style in ["range", "choice"], sample_style + + self.is_range = sample_style == "range" + if isinstance(short_edge_length, int): + short_edge_length = (short_edge_length, short_edge_length) + if self.is_range: + assert len(short_edge_length) == 2, ( + "short_edge_length must be two values using 'range' sample style." + f" Got {short_edge_length}!" + ) + self._init(locals()) + + @torch.jit.unused + def get_transform(self, image): + h, w = image.shape[:2] + if self.is_range: + size = np.random.randint(self.short_edge_length[0], self.short_edge_length[1] + 1) + else: + size = np.random.choice(self.short_edge_length) + if size == 0: + return NoOpTransform() + + newh, neww = ResizeShortestEdge.get_output_shape(h, w, size, self.max_size) + return ResizeTransform(h, w, newh, neww, self.interp) + + @staticmethod + def get_output_shape( + oldh: int, oldw: int, short_edge_length: int, max_size: int + ) -> Tuple[int, int]: + """ + Compute the output size given input size and target short edge length. + """ + h, w = oldh, oldw + size = short_edge_length * 1.0 + scale = size / min(h, w) + if h < w: + newh, neww = size, scale * w + else: + newh, neww = scale * h, size + if max(newh, neww) > max_size: + scale = max_size * 1.0 / max(newh, neww) + newh = newh * scale + neww = neww * scale + neww = int(neww + 0.5) + newh = int(newh + 0.5) + return (newh, neww) + + +class ResizeScale(Augmentation): + """ + Takes target size as input and randomly scales the given target size between `min_scale` + and `max_scale`. It then scales the input image such that it fits inside the scaled target + box, keeping the aspect ratio constant. + This implements the resize part of the Google's 'resize_and_crop' data augmentation: + https://github.com/tensorflow/tpu/blob/master/models/official/detection/utils/input_utils.py#L127 + """ + + def __init__( + self, + min_scale: float, + max_scale: float, + target_height: int, + target_width: int, + interp: int = Image.BILINEAR, + ): + """ + Args: + min_scale: minimum image scale range. + max_scale: maximum image scale range. + target_height: target image height. + target_width: target image width. + interp: image interpolation method. + """ + super().__init__() + self._init(locals()) + + def _get_resize(self, image: np.ndarray, scale: float) -> Transform: + input_size = image.shape[:2] + + # Compute new target size given a scale. + target_size = (self.target_height, self.target_width) + target_scale_size = np.multiply(target_size, scale) + + # Compute actual rescaling applied to input image and output size. + output_scale = np.minimum( + target_scale_size[0] / input_size[0], target_scale_size[1] / input_size[1] + ) + output_size = np.round(np.multiply(input_size, output_scale)).astype(int) + + return ResizeTransform( + input_size[0], input_size[1], output_size[0], output_size[1], self.interp + ) + + def get_transform(self, image: np.ndarray) -> Transform: + random_scale = np.random.uniform(self.min_scale, self.max_scale) + return self._get_resize(image, random_scale) + + +class RandomRotation(Augmentation): + """ + This method returns a copy of this image, rotated the given + number of degrees counter clockwise around the given center. + """ + + def __init__(self, angle, expand=True, center=None, sample_style="range", interp=None): + """ + Args: + angle (list[float]): If ``sample_style=="range"``, + a [min, max] interval from which to sample the angle (in degrees). + If ``sample_style=="choice"``, a list of angles to sample from + expand (bool): choose if the image should be resized to fit the whole + rotated image (default), or simply cropped + center (list[[float, float]]): If ``sample_style=="range"``, + a [[minx, miny], [maxx, maxy]] relative interval from which to sample the center, + [0, 0] being the top left of the image and [1, 1] the bottom right. + If ``sample_style=="choice"``, a list of centers to sample from + Default: None, which means that the center of rotation is the center of the image + center has no effect if expand=True because it only affects shifting + """ + super().__init__() + assert sample_style in ["range", "choice"], sample_style + self.is_range = sample_style == "range" + if isinstance(angle, (float, int)): + angle = (angle, angle) + if center is not None and isinstance(center[0], (float, int)): + center = (center, center) + self._init(locals()) + + def get_transform(self, image): + h, w = image.shape[:2] + center = None + if self.is_range: + angle = np.random.uniform(self.angle[0], self.angle[1]) + if self.center is not None: + center = ( + np.random.uniform(self.center[0][0], self.center[1][0]), + np.random.uniform(self.center[0][1], self.center[1][1]), + ) + else: + angle = np.random.choice(self.angle) + if self.center is not None: + center = np.random.choice(self.center) + + if center is not None: + center = (w * center[0], h * center[1]) # Convert to absolute coordinates + + if angle % 360 == 0: + return NoOpTransform() + + return RotationTransform(h, w, angle, expand=self.expand, center=center, interp=self.interp) + + +class FixedSizeCrop(Augmentation): + """ + If `crop_size` is smaller than the input image size, then it uses a random crop of + the crop size. If `crop_size` is larger than the input image size, then it pads + the right and the bottom of the image to the crop size if `pad` is True, otherwise + it returns the smaller image. + """ + + def __init__(self, crop_size: Tuple[int], pad: bool = True, pad_value: float = 128.0): + """ + Args: + crop_size: target image (height, width). + pad: if True, will pad images smaller than `crop_size` up to `crop_size` + pad_value: the padding value. + """ + super().__init__() + self._init(locals()) + + def _get_crop(self, image: np.ndarray) -> Transform: + # Compute the image scale and scaled size. + input_size = image.shape[:2] + output_size = self.crop_size + + # Add random crop if the image is scaled up. + max_offset = np.subtract(input_size, output_size) + max_offset = np.maximum(max_offset, 0) + offset = np.multiply(max_offset, np.random.uniform(0.0, 1.0)) + offset = np.round(offset).astype(int) + return CropTransform( + offset[1], offset[0], output_size[1], output_size[0], input_size[1], input_size[0] + ) + + def _get_pad(self, image: np.ndarray) -> Transform: + # Compute the image scale and scaled size. + input_size = image.shape[:2] + output_size = self.crop_size + + # Add padding if the image is scaled down. + pad_size = np.subtract(output_size, input_size) + pad_size = np.maximum(pad_size, 0) + original_size = np.minimum(input_size, output_size) + return PadTransform( + 0, 0, pad_size[1], pad_size[0], original_size[1], original_size[0], self.pad_value + ) + + def get_transform(self, image: np.ndarray) -> TransformList: + transforms = [self._get_crop(image)] + if self.pad: + transforms.append(self._get_pad(image)) + return TransformList(transforms) + + +class RandomCrop(Augmentation): + """ + Randomly crop a rectangle region out of an image. + """ + + def __init__(self, crop_type: str, crop_size): + """ + Args: + crop_type (str): one of "relative_range", "relative", "absolute", "absolute_range". + crop_size (tuple[float, float]): two floats, explained below. + + - "relative": crop a (H * crop_size[0], W * crop_size[1]) region from an input image of + size (H, W). crop size should be in (0, 1] + - "relative_range": uniformly sample two values from [crop_size[0], 1] + and [crop_size[1]], 1], and use them as in "relative" crop type. + - "absolute" crop a (crop_size[0], crop_size[1]) region from input image. + crop_size must be smaller than the input image size. + - "absolute_range", for an input of size (H, W), uniformly sample H_crop in + [crop_size[0], min(H, crop_size[1])] and W_crop in [crop_size[0], min(W, crop_size[1])]. + Then crop a region (H_crop, W_crop). + """ + # TODO style of relative_range and absolute_range are not consistent: + # one takes (h, w) but another takes (min, max) + super().__init__() + assert crop_type in ["relative_range", "relative", "absolute", "absolute_range"] + self._init(locals()) + + def get_transform(self, image): + h, w = image.shape[:2] + croph, cropw = self.get_crop_size((h, w)) + assert h >= croph and w >= cropw, "Shape computation in {} has bugs.".format(self) + h0 = np.random.randint(h - croph + 1) + w0 = np.random.randint(w - cropw + 1) + return CropTransform(w0, h0, cropw, croph) + + def get_crop_size(self, image_size): + """ + Args: + image_size (tuple): height, width + + Returns: + crop_size (tuple): height, width in absolute pixels + """ + h, w = image_size + if self.crop_type == "relative": + ch, cw = self.crop_size + return int(h * ch + 0.5), int(w * cw + 0.5) + elif self.crop_type == "relative_range": + crop_size = np.asarray(self.crop_size, dtype=np.float32) + ch, cw = crop_size + np.random.rand(2) * (1 - crop_size) + return int(h * ch + 0.5), int(w * cw + 0.5) + elif self.crop_type == "absolute": + return (min(self.crop_size[0], h), min(self.crop_size[1], w)) + elif self.crop_type == "absolute_range": + assert self.crop_size[0] <= self.crop_size[1] + ch = np.random.randint(min(h, self.crop_size[0]), min(h, self.crop_size[1]) + 1) + cw = np.random.randint(min(w, self.crop_size[0]), min(w, self.crop_size[1]) + 1) + return ch, cw + else: + raise NotImplementedError("Unknown crop type {}".format(self.crop_type)) + + +class RandomCrop_CategoryAreaConstraint(Augmentation): + """ + Similar to :class:`RandomCrop`, but find a cropping window such that no single category + occupies a ratio of more than `single_category_max_area` in semantic segmentation ground + truth, which can cause unstability in training. The function attempts to find such a valid + cropping window for at most 10 times. + """ + + def __init__( + self, + crop_type: str, + crop_size, + single_category_max_area: float = 1.0, + ignored_category: int = None, + ): + """ + Args: + crop_type, crop_size: same as in :class:`RandomCrop` + single_category_max_area: the maximum allowed area ratio of a + category. Set to 1.0 to disable + ignored_category: allow this category in the semantic segmentation + ground truth to exceed the area ratio. Usually set to the category + that's ignored in training. + """ + self.crop_aug = RandomCrop(crop_type, crop_size) + self._init(locals()) + + def get_transform(self, image, sem_seg): + if self.single_category_max_area >= 1.0: + return self.crop_aug.get_transform(image) + else: + h, w = sem_seg.shape + for _ in range(10): + crop_size = self.crop_aug.get_crop_size((h, w)) + y0 = np.random.randint(h - crop_size[0] + 1) + x0 = np.random.randint(w - crop_size[1] + 1) + sem_seg_temp = sem_seg[y0 : y0 + crop_size[0], x0 : x0 + crop_size[1]] + labels, cnt = np.unique(sem_seg_temp, return_counts=True) + if self.ignored_category is not None: + cnt = cnt[labels != self.ignored_category] + if len(cnt) > 1 and np.max(cnt) < np.sum(cnt) * self.single_category_max_area: + break + crop_tfm = CropTransform(x0, y0, crop_size[1], crop_size[0]) + return crop_tfm + + +class RandomExtent(Augmentation): + """ + Outputs an image by cropping a random "subrect" of the source image. + + The subrect can be parameterized to include pixels outside the source image, + in which case they will be set to zeros (i.e. black). The size of the output + image will vary with the size of the random subrect. + """ + + def __init__(self, scale_range, shift_range): + """ + Args: + output_size (h, w): Dimensions of output image + scale_range (l, h): Range of input-to-output size scaling factor + shift_range (x, y): Range of shifts of the cropped subrect. The rect + is shifted by [w / 2 * Uniform(-x, x), h / 2 * Uniform(-y, y)], + where (w, h) is the (width, height) of the input image. Set each + component to zero to crop at the image's center. + """ + super().__init__() + self._init(locals()) + + def get_transform(self, image): + img_h, img_w = image.shape[:2] + + # Initialize src_rect to fit the input image. + src_rect = np.array([-0.5 * img_w, -0.5 * img_h, 0.5 * img_w, 0.5 * img_h]) + + # Apply a random scaling to the src_rect. + src_rect *= np.random.uniform(self.scale_range[0], self.scale_range[1]) + + # Apply a random shift to the coordinates origin. + src_rect[0::2] += self.shift_range[0] * img_w * (np.random.rand() - 0.5) + src_rect[1::2] += self.shift_range[1] * img_h * (np.random.rand() - 0.5) + + # Map src_rect coordinates into image coordinates (center at corner). + src_rect[0::2] += 0.5 * img_w + src_rect[1::2] += 0.5 * img_h + + return ExtentTransform( + src_rect=(src_rect[0], src_rect[1], src_rect[2], src_rect[3]), + output_size=(int(src_rect[3] - src_rect[1]), int(src_rect[2] - src_rect[0])), + ) + + +class RandomContrast(Augmentation): + """ + Randomly transforms image contrast. + + Contrast intensity is uniformly sampled in (intensity_min, intensity_max). + - intensity < 1 will reduce contrast + - intensity = 1 will preserve the input image + - intensity > 1 will increase contrast + + See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html + """ + + def __init__(self, intensity_min, intensity_max): + """ + Args: + intensity_min (float): Minimum augmentation + intensity_max (float): Maximum augmentation + """ + super().__init__() + self._init(locals()) + + def get_transform(self, image): + w = np.random.uniform(self.intensity_min, self.intensity_max) + return BlendTransform(src_image=image.mean(), src_weight=1 - w, dst_weight=w) + + +class RandomBrightness(Augmentation): + """ + Randomly transforms image brightness. + + Brightness intensity is uniformly sampled in (intensity_min, intensity_max). + - intensity < 1 will reduce brightness + - intensity = 1 will preserve the input image + - intensity > 1 will increase brightness + + See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html + """ + + def __init__(self, intensity_min, intensity_max): + """ + Args: + intensity_min (float): Minimum augmentation + intensity_max (float): Maximum augmentation + """ + super().__init__() + self._init(locals()) + + def get_transform(self, image): + w = np.random.uniform(self.intensity_min, self.intensity_max) + return BlendTransform(src_image=0, src_weight=1 - w, dst_weight=w) + + +class RandomSaturation(Augmentation): + """ + Randomly transforms saturation of an RGB image. + Input images are assumed to have 'RGB' channel order. + + Saturation intensity is uniformly sampled in (intensity_min, intensity_max). + - intensity < 1 will reduce saturation (make the image more grayscale) + - intensity = 1 will preserve the input image + - intensity > 1 will increase saturation + + See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html + """ + + def __init__(self, intensity_min, intensity_max): + """ + Args: + intensity_min (float): Minimum augmentation (1 preserves input). + intensity_max (float): Maximum augmentation (1 preserves input). + """ + super().__init__() + self._init(locals()) + + def get_transform(self, image): + assert image.shape[-1] == 3, "RandomSaturation only works on RGB images" + w = np.random.uniform(self.intensity_min, self.intensity_max) + grayscale = image.dot([0.299, 0.587, 0.114])[:, :, np.newaxis] + return BlendTransform(src_image=grayscale, src_weight=1 - w, dst_weight=w) + + +class RandomLighting(Augmentation): + """ + The "lighting" augmentation described in AlexNet, using fixed PCA over ImageNet. + Input images are assumed to have 'RGB' channel order. + + The degree of color jittering is randomly sampled via a normal distribution, + with standard deviation given by the scale parameter. + """ + + def __init__(self, scale): + """ + Args: + scale (float): Standard deviation of principal component weighting. + """ + super().__init__() + self._init(locals()) + self.eigen_vecs = np.array( + [[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140], [-0.5836, -0.6948, 0.4203]] + ) + self.eigen_vals = np.array([0.2175, 0.0188, 0.0045]) + + def get_transform(self, image): + assert image.shape[-1] == 3, "RandomLighting only works on RGB images" + weights = np.random.normal(scale=self.scale, size=3) + return BlendTransform( + src_image=self.eigen_vecs.dot(weights * self.eigen_vals), src_weight=1.0, dst_weight=1.0 + ) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/data/transforms/transform.py b/motion-gan-pipeline/preprocessing/third/detectron2/data/transforms/transform.py new file mode 100644 index 0000000..de44b99 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/data/transforms/transform.py @@ -0,0 +1,351 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +""" +See "Data Augmentation" tutorial for an overview of the system: +https://detectron2.readthedocs.io/tutorials/augmentation.html +""" + +import numpy as np +import torch +import torch.nn.functional as F +from fvcore.transforms.transform import ( + CropTransform, + HFlipTransform, + NoOpTransform, + Transform, + TransformList, +) +from PIL import Image + +try: + import cv2 # noqa +except ImportError: + # OpenCV is an optional dependency at the moment + pass + +__all__ = [ + "ExtentTransform", + "ResizeTransform", + "RotationTransform", + "ColorTransform", + "PILColorTransform", +] + + +class ExtentTransform(Transform): + """ + Extracts a subregion from the source image and scales it to the output size. + + The fill color is used to map pixels from the source rect that fall outside + the source image. + + See: https://pillow.readthedocs.io/en/latest/PIL.html#PIL.ImageTransform.ExtentTransform + """ + + def __init__(self, src_rect, output_size, interp=Image.LINEAR, fill=0): + """ + Args: + src_rect (x0, y0, x1, y1): src coordinates + output_size (h, w): dst image size + interp: PIL interpolation methods + fill: Fill color used when src_rect extends outside image + """ + super().__init__() + self._set_attributes(locals()) + + def apply_image(self, img, interp=None): + h, w = self.output_size + if len(img.shape) > 2 and img.shape[2] == 1: + pil_image = Image.fromarray(img[:, :, 0], mode="L") + else: + pil_image = Image.fromarray(img) + pil_image = pil_image.transform( + size=(w, h), + method=Image.EXTENT, + data=self.src_rect, + resample=interp if interp else self.interp, + fill=self.fill, + ) + ret = np.asarray(pil_image) + if len(img.shape) > 2 and img.shape[2] == 1: + ret = np.expand_dims(ret, -1) + return ret + + def apply_coords(self, coords): + # Transform image center from source coordinates into output coordinates + # and then map the new origin to the corner of the output image. + h, w = self.output_size + x0, y0, x1, y1 = self.src_rect + new_coords = coords.astype(np.float32) + new_coords[:, 0] -= 0.5 * (x0 + x1) + new_coords[:, 1] -= 0.5 * (y0 + y1) + new_coords[:, 0] *= w / (x1 - x0) + new_coords[:, 1] *= h / (y1 - y0) + new_coords[:, 0] += 0.5 * w + new_coords[:, 1] += 0.5 * h + return new_coords + + def apply_segmentation(self, segmentation): + segmentation = self.apply_image(segmentation, interp=Image.NEAREST) + return segmentation + + +class ResizeTransform(Transform): + """ + Resize the image to a target size. + """ + + def __init__(self, h, w, new_h, new_w, interp=None): + """ + Args: + h, w (int): original image size + new_h, new_w (int): new image size + interp: PIL interpolation methods, defaults to bilinear. + """ + # TODO decide on PIL vs opencv + super().__init__() + if interp is None: + interp = Image.BILINEAR + self._set_attributes(locals()) + + def apply_image(self, img, interp=None): + assert img.shape[:2] == (self.h, self.w) + assert len(img.shape) <= 4 + interp_method = interp if interp is not None else self.interp + + if img.dtype == np.uint8: + if len(img.shape) > 2 and img.shape[2] == 1: + pil_image = Image.fromarray(img[:, :, 0], mode="L") + else: + pil_image = Image.fromarray(img) + pil_image = pil_image.resize((self.new_w, self.new_h), interp_method) + ret = np.asarray(pil_image) + if len(img.shape) > 2 and img.shape[2] == 1: + ret = np.expand_dims(ret, -1) + else: + # PIL only supports uint8 + if any(x < 0 for x in img.strides): + img = np.ascontiguousarray(img) + img = torch.from_numpy(img) + shape = list(img.shape) + shape_4d = shape[:2] + [1] * (4 - len(shape)) + shape[2:] + img = img.view(shape_4d).permute(2, 3, 0, 1) # hw(c) -> nchw + _PIL_RESIZE_TO_INTERPOLATE_MODE = { + Image.NEAREST: "nearest", + Image.BILINEAR: "bilinear", + Image.BICUBIC: "bicubic", + } + mode = _PIL_RESIZE_TO_INTERPOLATE_MODE[interp_method] + align_corners = None if mode == "nearest" else False + img = F.interpolate( + img, (self.new_h, self.new_w), mode=mode, align_corners=align_corners + ) + shape[:2] = (self.new_h, self.new_w) + ret = img.permute(2, 3, 0, 1).view(shape).numpy() # nchw -> hw(c) + + return ret + + def apply_coords(self, coords): + coords[:, 0] = coords[:, 0] * (self.new_w * 1.0 / self.w) + coords[:, 1] = coords[:, 1] * (self.new_h * 1.0 / self.h) + return coords + + def apply_segmentation(self, segmentation): + segmentation = self.apply_image(segmentation, interp=Image.NEAREST) + return segmentation + + def inverse(self): + return ResizeTransform(self.new_h, self.new_w, self.h, self.w, self.interp) + + +class RotationTransform(Transform): + """ + This method returns a copy of this image, rotated the given + number of degrees counter clockwise around its center. + """ + + def __init__(self, h, w, angle, expand=True, center=None, interp=None): + """ + Args: + h, w (int): original image size + angle (float): degrees for rotation + expand (bool): choose if the image should be resized to fit the whole + rotated image (default), or simply cropped + center (tuple (width, height)): coordinates of the rotation center + if left to None, the center will be fit to the center of each image + center has no effect if expand=True because it only affects shifting + interp: cv2 interpolation method, default cv2.INTER_LINEAR + """ + super().__init__() + image_center = np.array((w / 2, h / 2)) + if center is None: + center = image_center + if interp is None: + interp = cv2.INTER_LINEAR + abs_cos, abs_sin = (abs(np.cos(np.deg2rad(angle))), abs(np.sin(np.deg2rad(angle)))) + if expand: + # find the new width and height bounds + bound_w, bound_h = np.rint( + [h * abs_sin + w * abs_cos, h * abs_cos + w * abs_sin] + ).astype(int) + else: + bound_w, bound_h = w, h + + self._set_attributes(locals()) + self.rm_coords = self.create_rotation_matrix() + # Needed because of this problem https://github.com/opencv/opencv/issues/11784 + self.rm_image = self.create_rotation_matrix(offset=-0.5) + + def apply_image(self, img, interp=None): + """ + img should be a numpy array, formatted as Height * Width * Nchannels + """ + if len(img) == 0 or self.angle % 360 == 0: + return img + assert img.shape[:2] == (self.h, self.w) + interp = interp if interp is not None else self.interp + return cv2.warpAffine(img, self.rm_image, (self.bound_w, self.bound_h), flags=interp) + + def apply_coords(self, coords): + """ + coords should be a N * 2 array-like, containing N couples of (x, y) points + """ + coords = np.asarray(coords, dtype=float) + if len(coords) == 0 or self.angle % 360 == 0: + return coords + return cv2.transform(coords[:, np.newaxis, :], self.rm_coords)[:, 0, :] + + def apply_segmentation(self, segmentation): + segmentation = self.apply_image(segmentation, interp=cv2.INTER_NEAREST) + return segmentation + + def create_rotation_matrix(self, offset=0): + center = (self.center[0] + offset, self.center[1] + offset) + rm = cv2.getRotationMatrix2D(tuple(center), self.angle, 1) + if self.expand: + # Find the coordinates of the center of rotation in the new image + # The only point for which we know the future coordinates is the center of the image + rot_im_center = cv2.transform(self.image_center[None, None, :] + offset, rm)[0, 0, :] + new_center = np.array([self.bound_w / 2, self.bound_h / 2]) + offset - rot_im_center + # shift the rotation center to the new coordinates + rm[:, 2] += new_center + return rm + + def inverse(self): + """ + The inverse is to rotate it back with expand, and crop to get the original shape. + """ + if not self.expand: # Not possible to inverse if a part of the image is lost + raise NotImplementedError() + rotation = RotationTransform( + self.bound_h, self.bound_w, -self.angle, True, None, self.interp + ) + crop = CropTransform( + (rotation.bound_w - self.w) // 2, (rotation.bound_h - self.h) // 2, self.w, self.h + ) + return TransformList([rotation, crop]) + + +class ColorTransform(Transform): + """ + Generic wrapper for any photometric transforms. + These transformations should only affect the color space and + not the coordinate space of the image (e.g. annotation + coordinates such as bounding boxes should not be changed) + """ + + def __init__(self, op): + """ + Args: + op (Callable): operation to be applied to the image, + which takes in an ndarray and returns an ndarray. + """ + if not callable(op): + raise ValueError("op parameter should be callable") + super().__init__() + self._set_attributes(locals()) + + def apply_image(self, img): + return self.op(img) + + def apply_coords(self, coords): + return coords + + def inverse(self): + return NoOpTransform() + + def apply_segmentation(self, segmentation): + return segmentation + + +class PILColorTransform(ColorTransform): + """ + Generic wrapper for PIL Photometric image transforms, + which affect the color space and not the coordinate + space of the image + """ + + def __init__(self, op): + """ + Args: + op (Callable): operation to be applied to the image, + which takes in a PIL Image and returns a transformed + PIL Image. + For reference on possible operations see: + - https://pillow.readthedocs.io/en/stable/ + """ + if not callable(op): + raise ValueError("op parameter should be callable") + super().__init__(op) + + def apply_image(self, img): + img = Image.fromarray(img) + return np.asarray(super().apply_image(img)) + + +def HFlip_rotated_box(transform, rotated_boxes): + """ + Apply the horizontal flip transform on rotated boxes. + + Args: + rotated_boxes (ndarray): Nx5 floating point array of + (x_center, y_center, width, height, angle_degrees) format + in absolute coordinates. + """ + # Transform x_center + rotated_boxes[:, 0] = transform.width - rotated_boxes[:, 0] + # Transform angle + rotated_boxes[:, 4] = -rotated_boxes[:, 4] + return rotated_boxes + + +def Resize_rotated_box(transform, rotated_boxes): + """ + Apply the resizing transform on rotated boxes. For details of how these (approximation) + formulas are derived, please refer to :meth:`RotatedBoxes.scale`. + + Args: + rotated_boxes (ndarray): Nx5 floating point array of + (x_center, y_center, width, height, angle_degrees) format + in absolute coordinates. + """ + scale_factor_x = transform.new_w * 1.0 / transform.w + scale_factor_y = transform.new_h * 1.0 / transform.h + rotated_boxes[:, 0] *= scale_factor_x + rotated_boxes[:, 1] *= scale_factor_y + theta = rotated_boxes[:, 4] * np.pi / 180.0 + c = np.cos(theta) + s = np.sin(theta) + rotated_boxes[:, 2] *= np.sqrt(np.square(scale_factor_x * c) + np.square(scale_factor_y * s)) + rotated_boxes[:, 3] *= np.sqrt(np.square(scale_factor_x * s) + np.square(scale_factor_y * c)) + rotated_boxes[:, 4] = np.arctan2(scale_factor_x * s, scale_factor_y * c) * 180 / np.pi + + return rotated_boxes + + +HFlipTransform.register_type("rotated_box", HFlip_rotated_box) +ResizeTransform.register_type("rotated_box", Resize_rotated_box) + +# not necessary any more with latest fvcore +NoOpTransform.register_type("rotated_box", lambda t, x: x) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/engine/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/engine/__init__.py new file mode 100644 index 0000000..08a6157 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/engine/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from .launch import * +from .train_loop import * + +__all__ = [k for k in globals().keys() if not k.startswith("_")] + + +# prefer to let hooks and defaults live in separate namespaces (therefore not in __all__) +# but still make them available here +from .hooks import * +from .defaults import * diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/engine/defaults.py b/motion-gan-pipeline/preprocessing/third/detectron2/engine/defaults.py new file mode 100644 index 0000000..cc3faa1 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/engine/defaults.py @@ -0,0 +1,715 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +""" +This file contains components with some default boilerplate logic user may need +in training / testing. They will not work for everyone, but many users may find them useful. + +The behavior of functions/classes in this file is subject to change, +since they are meant to represent the "common default behavior" people need in their projects. +""" + +import argparse +import logging +import os +import sys +import weakref +from collections import OrderedDict +from typing import Optional +import torch +from fvcore.nn.precise_bn import get_bn_modules +from omegaconf import OmegaConf +from torch.nn.parallel import DistributedDataParallel + +import detectron2.data.transforms as T +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import CfgNode, LazyConfig +from detectron2.data import ( + MetadataCatalog, + build_detection_test_loader, + build_detection_train_loader, +) +from detectron2.evaluation import ( + DatasetEvaluator, + inference_on_dataset, + print_csv_format, + verify_results, +) +from detectron2.modeling import build_model +from detectron2.solver import build_lr_scheduler, build_optimizer +from detectron2.utils import comm +from detectron2.utils.collect_env import collect_env_info +from detectron2.utils.env import seed_all_rng +from detectron2.utils.events import CommonMetricPrinter, JSONWriter, TensorboardXWriter +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import setup_logger + +from . import hooks +from .train_loop import AMPTrainer, SimpleTrainer, TrainerBase + +__all__ = [ + "create_ddp_model", + "default_argument_parser", + "default_setup", + "default_writers", + "DefaultPredictor", + "DefaultTrainer", +] + + +def create_ddp_model(model, *, fp16_compression=False, **kwargs): + """ + Create a DistributedDataParallel model if there are >1 processes. + + Args: + model: a torch.nn.Module + fp16_compression: add fp16 compression hooks to the ddp object. + See more at https://pytorch.org/docs/stable/ddp_comm_hooks.html#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_hook + kwargs: other arguments of :module:`torch.nn.parallel.DistributedDataParallel`. + """ # noqa + if comm.get_world_size() == 1: + return model + if "device_ids" not in kwargs: + kwargs["device_ids"] = [comm.get_local_rank()] + ddp = DistributedDataParallel(model, **kwargs) + if fp16_compression: + from torch.distributed.algorithms.ddp_comm_hooks import default as comm_hooks + + ddp.register_comm_hook(state=None, hook=comm_hooks.fp16_compress_hook) + return ddp + + +def default_argument_parser(epilog=None): + """ + Create a parser with some common arguments used by detectron2 users. + + Args: + epilog (str): epilog passed to ArgumentParser describing the usage. + + Returns: + argparse.ArgumentParser: + """ + parser = argparse.ArgumentParser( + epilog=epilog + or f""" +Examples: + +Run on single machine: + $ {sys.argv[0]} --num-gpus 8 --config-file cfg.yaml + +Change some config options: + $ {sys.argv[0]} --config-file cfg.yaml MODEL.WEIGHTS /path/to/weight.pth SOLVER.BASE_LR 0.001 + +Run on multiple machines: + (machine0)$ {sys.argv[0]} --machine-rank 0 --num-machines 2 --dist-url [--other-flags] + (machine1)$ {sys.argv[0]} --machine-rank 1 --num-machines 2 --dist-url [--other-flags] +""", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file") + parser.add_argument( + "--resume", + action="store_true", + help="Whether to attempt to resume from the checkpoint directory. " + "See documentation of `DefaultTrainer.resume_or_load()` for what it means.", + ) + parser.add_argument("--eval-only", action="store_true", help="perform evaluation only") + parser.add_argument("--num-gpus", type=int, default=1, help="number of gpus *per machine*") + parser.add_argument("--num-machines", type=int, default=1, help="total number of machines") + parser.add_argument( + "--machine-rank", type=int, default=0, help="the rank of this machine (unique per machine)" + ) + + # PyTorch still may leave orphan processes in multi-gpu training. + # Therefore we use a deterministic way to obtain port, + # so that users are aware of orphan processes by seeing the port occupied. + port = 2 ** 15 + 2 ** 14 + hash(os.getuid() if sys.platform != "win32" else 1) % 2 ** 14 + parser.add_argument( + "--dist-url", + default="tcp://127.0.0.1:{}".format(port), + help="initialization URL for pytorch distributed backend. See " + "https://pytorch.org/docs/stable/distributed.html for details.", + ) + parser.add_argument( + "opts", + help=""" +Modify config options at the end of the command. For Yacs configs, use +space-separated "PATH.KEY VALUE" pairs. +For python-based LazyConfig, use "path.key=value". + """.strip(), + default=None, + nargs=argparse.REMAINDER, + ) + return parser + + +def _try_get_key(cfg, *keys, default=None): + """ + Try select keys from cfg until the first key that exists. Otherwise return default. + """ + if isinstance(cfg, CfgNode): + cfg = OmegaConf.create(cfg.dump()) + for k in keys: + none = object() + p = OmegaConf.select(cfg, k, default=none) + if p is not none: + return p + return default + + +def _highlight(code, filename): + try: + import pygments + except ImportError: + return code + + from pygments.lexers import Python3Lexer, YamlLexer + from pygments.formatters import Terminal256Formatter + + lexer = Python3Lexer() if filename.endswith(".py") else YamlLexer() + code = pygments.highlight(code, lexer, Terminal256Formatter(style="monokai")) + return code + + +def default_setup(cfg, args): + """ + Perform some basic common setups at the beginning of a job, including: + + 1. Set up the detectron2 logger + 2. Log basic information about environment, cmdline arguments, and config + 3. Backup the config to the output directory + + Args: + cfg (CfgNode or omegaconf.DictConfig): the full config to be used + args (argparse.NameSpace): the command line arguments to be logged + """ + output_dir = _try_get_key(cfg, "OUTPUT_DIR", "output_dir", "train.output_dir") + if comm.is_main_process() and output_dir: + PathManager.mkdirs(output_dir) + + rank = comm.get_rank() + setup_logger(output_dir, distributed_rank=rank, name="fvcore") + logger = setup_logger(output_dir, distributed_rank=rank) + + logger.info("Rank of current process: {}. World size: {}".format(rank, comm.get_world_size())) + logger.info("Environment info:\n" + collect_env_info()) + + logger.info("Command line arguments: " + str(args)) + if hasattr(args, "config_file") and args.config_file != "": + logger.info( + "Contents of args.config_file={}:\n{}".format( + args.config_file, + _highlight(PathManager.open(args.config_file, "r").read(), args.config_file), + ) + ) + + if comm.is_main_process() and output_dir: + # Note: some of our scripts may expect the existence of + # config.yaml in output directory + path = os.path.join(output_dir, "config.yaml") + if isinstance(cfg, CfgNode): + logger.info("Running with full config:\n{}".format(_highlight(cfg.dump(), ".yaml"))) + with PathManager.open(path, "w") as f: + f.write(cfg.dump()) + else: + LazyConfig.save(cfg, path) + logger.info("Full config saved to {}".format(path)) + + # make sure each worker has a different, yet deterministic seed if specified + seed = _try_get_key(cfg, "SEED", "train.seed", default=-1) + seed_all_rng(None if seed < 0 else seed + rank) + + # cudnn benchmark has large overhead. It shouldn't be used considering the small size of + # typical validation set. + if not (hasattr(args, "eval_only") and args.eval_only): + torch.backends.cudnn.benchmark = _try_get_key( + cfg, "CUDNN_BENCHMARK", "train.cudnn_benchmark", default=False + ) + + +def default_writers(output_dir: str, max_iter: Optional[int] = None): + """ + Build a list of :class:`EventWriter` to be used. + It now consists of a :class:`CommonMetricPrinter`, + :class:`TensorboardXWriter` and :class:`JSONWriter`. + + Args: + output_dir: directory to store JSON metrics and tensorboard events + max_iter: the total number of iterations + + Returns: + list[EventWriter]: a list of :class:`EventWriter` objects. + """ + PathManager.mkdirs(output_dir) + return [ + # It may not always print what you want to see, since it prints "common" metrics only. + CommonMetricPrinter(max_iter), + JSONWriter(os.path.join(output_dir, "metrics.json")), + TensorboardXWriter(output_dir), + ] + + +class DefaultPredictor: + """ + Create a simple end-to-end predictor with the given config that runs on + single device for a single input image. + + Compared to using the model directly, this class does the following additions: + + 1. Load checkpoint from `cfg.MODEL.WEIGHTS`. + 2. Always take BGR image as the input and apply conversion defined by `cfg.INPUT.FORMAT`. + 3. Apply resizing defined by `cfg.INPUT.{MIN,MAX}_SIZE_TEST`. + 4. Take one input image and produce a single output, instead of a batch. + + This is meant for simple demo purposes, so it does the above steps automatically. + This is not meant for benchmarks or running complicated inference logic. + If you'd like to do anything more complicated, please refer to its source code as + examples to build and use the model manually. + + Attributes: + metadata (Metadata): the metadata of the underlying dataset, obtained from + cfg.DATASETS.TEST. + + Examples: + :: + pred = DefaultPredictor(cfg) + inputs = cv2.imread("input.jpg") + outputs = pred(inputs) + """ + + def __init__(self, cfg): + self.cfg = cfg.clone() # cfg can be modified by model + self.model = build_model(self.cfg) + self.model.eval() + if len(cfg.DATASETS.TEST): + self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0]) + + checkpointer = DetectionCheckpointer(self.model) + checkpointer.load(cfg.MODEL.WEIGHTS) + + self.aug = T.ResizeShortestEdge( + [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST + ) + + self.input_format = cfg.INPUT.FORMAT + assert self.input_format in ["RGB", "BGR"], self.input_format + + def __call__(self, original_image): + """ + Args: + original_image (np.ndarray): an image of shape (H, W, C) (in BGR order). + + Returns: + predictions (dict): + the output of the model for one image only. + See :doc:`/tutorials/models` for details about the format. + """ + with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258 + # Apply pre-processing to image. + if self.input_format == "RGB": + # whether the model expects BGR inputs or RGB + original_image = original_image[:, :, ::-1] + height, width = original_image.shape[:2] + image = self.aug.get_transform(original_image).apply_image(original_image) + image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1)) + + inputs = {"image": image, "height": height, "width": width} + predictions = self.model([inputs])[0] + return predictions + + +class DefaultTrainer(TrainerBase): + """ + A trainer with default training logic. It does the following: + + 1. Create a :class:`SimpleTrainer` using model, optimizer, dataloader + defined by the given config. Create a LR scheduler defined by the config. + 2. Load the last checkpoint or `cfg.MODEL.WEIGHTS`, if exists, when + `resume_or_load` is called. + 3. Register a few common hooks defined by the config. + + It is created to simplify the **standard model training workflow** and reduce code boilerplate + for users who only need the standard training workflow, with standard features. + It means this class makes *many assumptions* about your training logic that + may easily become invalid in a new research. In fact, any assumptions beyond those made in the + :class:`SimpleTrainer` are too much for research. + + The code of this class has been annotated about restrictive assumptions it makes. + When they do not work for you, you're encouraged to: + + 1. Overwrite methods of this class, OR: + 2. Use :class:`SimpleTrainer`, which only does minimal SGD training and + nothing else. You can then add your own hooks if needed. OR: + 3. Write your own training loop similar to `tools/plain_train_net.py`. + + See the :doc:`/tutorials/training` tutorials for more details. + + Note that the behavior of this class, like other functions/classes in + this file, is not stable, since it is meant to represent the "common default behavior". + It is only guaranteed to work well with the standard models and training workflow in detectron2. + To obtain more stable behavior, write your own training logic with other public APIs. + + Examples: + :: + trainer = DefaultTrainer(cfg) + trainer.resume_or_load() # load last checkpoint or MODEL.WEIGHTS + trainer.train() + + Attributes: + scheduler: + checkpointer (DetectionCheckpointer): + cfg (CfgNode): + """ + + def __init__(self, cfg): + """ + Args: + cfg (CfgNode): + """ + super().__init__() + logger = logging.getLogger("detectron2") + if not logger.isEnabledFor(logging.INFO): # setup_logger is not called for d2 + setup_logger() + cfg = DefaultTrainer.auto_scale_workers(cfg, comm.get_world_size()) + + # Assume these objects must be constructed in this order. + model = self.build_model(cfg) + optimizer = self.build_optimizer(cfg, model) + data_loader = self.build_train_loader(cfg) + + model = create_ddp_model(model, broadcast_buffers=False) + self._trainer = (AMPTrainer if cfg.SOLVER.AMP.ENABLED else SimpleTrainer)( + model, data_loader, optimizer + ) + + self.scheduler = self.build_lr_scheduler(cfg, optimizer) + self.checkpointer = DetectionCheckpointer( + # Assume you want to save checkpoints together with logs/statistics + model, + cfg.OUTPUT_DIR, + trainer=weakref.proxy(self), + ) + self.start_iter = 0 + self.max_iter = cfg.SOLVER.MAX_ITER + self.cfg = cfg + + self.register_hooks(self.build_hooks()) + + def resume_or_load(self, resume=True): + """ + If `resume==True` and `cfg.OUTPUT_DIR` contains the last checkpoint (defined by + a `last_checkpoint` file), resume from the file. Resuming means loading all + available states (eg. optimizer and scheduler) and update iteration counter + from the checkpoint. ``cfg.MODEL.WEIGHTS`` will not be used. + + Otherwise, this is considered as an independent training. The method will load model + weights from the file `cfg.MODEL.WEIGHTS` (but will not load other states) and start + from iteration 0. + + Args: + resume (bool): whether to do resume or not + """ + self.checkpointer.resume_or_load(self.cfg.MODEL.WEIGHTS, resume=resume) + if resume and self.checkpointer.has_checkpoint(): + # The checkpoint stores the training iteration that just finished, thus we start + # at the next iteration + self.start_iter = self.iter + 1 + + def build_hooks(self): + """ + Build a list of default hooks, including timing, evaluation, + checkpointing, lr scheduling, precise BN, writing events. + + Returns: + list[HookBase]: + """ + cfg = self.cfg.clone() + cfg.defrost() + cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN + + ret = [ + hooks.IterationTimer(), + hooks.LRScheduler(), + hooks.PreciseBN( + # Run at the same freq as (but before) evaluation. + cfg.TEST.EVAL_PERIOD, + self.model, + # Build a new data loader to not affect training + self.build_train_loader(cfg), + cfg.TEST.PRECISE_BN.NUM_ITER, + ) + if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model) + else None, + ] + + # Do PreciseBN before checkpointer, because it updates the model and need to + # be saved by checkpointer. + # This is not always the best: if checkpointing has a different frequency, + # some checkpoints may have more precise statistics than others. + if comm.is_main_process(): + ret.append(hooks.PeriodicCheckpointer(self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD)) + + def test_and_save_results(): + self._last_eval_results = self.test(self.cfg, self.model) + return self._last_eval_results + + # Do evaluation after checkpointer, because then if it fails, + # we can use the saved checkpoint to debug. + ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results)) + + if comm.is_main_process(): + # Here the default print/log frequency of each writer is used. + # run writers in the end, so that evaluation metrics are written + ret.append(hooks.PeriodicWriter(self.build_writers(), period=20)) + return ret + + def build_writers(self): + """ + Build a list of writers to be used using :func:`default_writers()`. + If you'd like a different list of writers, you can overwrite it in + your trainer. + + Returns: + list[EventWriter]: a list of :class:`EventWriter` objects. + """ + return default_writers(self.cfg.OUTPUT_DIR, self.max_iter) + + def train(self): + """ + Run training. + + Returns: + OrderedDict of results, if evaluation is enabled. Otherwise None. + """ + super().train(self.start_iter, self.max_iter) + if len(self.cfg.TEST.EXPECTED_RESULTS) and comm.is_main_process(): + assert hasattr( + self, "_last_eval_results" + ), "No evaluation results obtained during training!" + verify_results(self.cfg, self._last_eval_results) + return self._last_eval_results + + def run_step(self): + self._trainer.iter = self.iter + self._trainer.run_step() + + def state_dict(self): + ret = super().state_dict() + ret["_trainer"] = self._trainer.state_dict() + return ret + + def load_state_dict(self, state_dict): + super().load_state_dict(state_dict) + self._trainer.load_state_dict(state_dict["_trainer"]) + + @classmethod + def build_model(cls, cfg): + """ + Returns: + torch.nn.Module: + + It now calls :func:`detectron2.modeling.build_model`. + Overwrite it if you'd like a different model. + """ + model = build_model(cfg) + logger = logging.getLogger(__name__) + logger.info("Model:\n{}".format(model)) + return model + + @classmethod + def build_optimizer(cls, cfg, model): + """ + Returns: + torch.optim.Optimizer: + + It now calls :func:`detectron2.solver.build_optimizer`. + Overwrite it if you'd like a different optimizer. + """ + return build_optimizer(cfg, model) + + @classmethod + def build_lr_scheduler(cls, cfg, optimizer): + """ + It now calls :func:`detectron2.solver.build_lr_scheduler`. + Overwrite it if you'd like a different scheduler. + """ + return build_lr_scheduler(cfg, optimizer) + + @classmethod + def build_train_loader(cls, cfg): + """ + Returns: + iterable + + It now calls :func:`detectron2.data.build_detection_train_loader`. + Overwrite it if you'd like a different data loader. + """ + return build_detection_train_loader(cfg) + + @classmethod + def build_test_loader(cls, cfg, dataset_name): + """ + Returns: + iterable + + It now calls :func:`detectron2.data.build_detection_test_loader`. + Overwrite it if you'd like a different data loader. + """ + return build_detection_test_loader(cfg, dataset_name) + + @classmethod + def build_evaluator(cls, cfg, dataset_name): + """ + Returns: + DatasetEvaluator or None + + It is not implemented by default. + """ + raise NotImplementedError( + """ +If you want DefaultTrainer to automatically run evaluation, +please implement `build_evaluator()` in subclasses (see train_net.py for example). +Alternatively, you can call evaluation functions yourself (see Colab balloon tutorial for example). +""" + ) + + @classmethod + def test(cls, cfg, model, evaluators=None): + """ + Evaluate the given model. The given model is expected to already contain + weights to evaluate. + + Args: + cfg (CfgNode): + model (nn.Module): + evaluators (list[DatasetEvaluator] or None): if None, will call + :meth:`build_evaluator`. Otherwise, must have the same length as + ``cfg.DATASETS.TEST``. + + Returns: + dict: a dict of result metrics + """ + logger = logging.getLogger(__name__) + if isinstance(evaluators, DatasetEvaluator): + evaluators = [evaluators] + if evaluators is not None: + assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format( + len(cfg.DATASETS.TEST), len(evaluators) + ) + + results = OrderedDict() + for idx, dataset_name in enumerate(cfg.DATASETS.TEST): + data_loader = cls.build_test_loader(cfg, dataset_name) + # When evaluators are passed in as arguments, + # implicitly assume that evaluators can be created before data_loader. + if evaluators is not None: + evaluator = evaluators[idx] + else: + try: + evaluator = cls.build_evaluator(cfg, dataset_name) + except NotImplementedError: + logger.warn( + "No evaluator found. Use `DefaultTrainer.test(evaluators=)`, " + "or implement its `build_evaluator` method." + ) + results[dataset_name] = {} + continue + results_i = inference_on_dataset(model, data_loader, evaluator) + results[dataset_name] = results_i + if comm.is_main_process(): + assert isinstance( + results_i, dict + ), "Evaluator must return a dict on the main process. Got {} instead.".format( + results_i + ) + logger.info("Evaluation results for {} in csv format:".format(dataset_name)) + print_csv_format(results_i) + + if len(results) == 1: + results = list(results.values())[0] + return results + + @staticmethod + def auto_scale_workers(cfg, num_workers: int): + """ + When the config is defined for certain number of workers (according to + ``cfg.SOLVER.REFERENCE_WORLD_SIZE``) that's different from the number of + workers currently in use, returns a new cfg where the total batch size + is scaled so that the per-GPU batch size stays the same as the + original ``IMS_PER_BATCH // REFERENCE_WORLD_SIZE``. + + Other config options are also scaled accordingly: + * training steps and warmup steps are scaled inverse proportionally. + * learning rate are scaled proportionally, following :paper:`ImageNet in 1h`. + + For example, with the original config like the following: + + .. code-block:: yaml + + IMS_PER_BATCH: 16 + BASE_LR: 0.1 + REFERENCE_WORLD_SIZE: 8 + MAX_ITER: 5000 + STEPS: (4000,) + CHECKPOINT_PERIOD: 1000 + + When this config is used on 16 GPUs instead of the reference number 8, + calling this method will return a new config with: + + .. code-block:: yaml + + IMS_PER_BATCH: 32 + BASE_LR: 0.2 + REFERENCE_WORLD_SIZE: 16 + MAX_ITER: 2500 + STEPS: (2000,) + CHECKPOINT_PERIOD: 500 + + Note that both the original config and this new config can be trained on 16 GPUs. + It's up to user whether to enable this feature (by setting ``REFERENCE_WORLD_SIZE``). + + Returns: + CfgNode: a new config. Same as original if ``cfg.SOLVER.REFERENCE_WORLD_SIZE==0``. + """ + old_world_size = cfg.SOLVER.REFERENCE_WORLD_SIZE + if old_world_size == 0 or old_world_size == num_workers: + return cfg + cfg = cfg.clone() + frozen = cfg.is_frozen() + cfg.defrost() + + assert ( + cfg.SOLVER.IMS_PER_BATCH % old_world_size == 0 + ), "Invalid REFERENCE_WORLD_SIZE in config!" + scale = num_workers / old_world_size + bs = cfg.SOLVER.IMS_PER_BATCH = int(round(cfg.SOLVER.IMS_PER_BATCH * scale)) + lr = cfg.SOLVER.BASE_LR = cfg.SOLVER.BASE_LR * scale + max_iter = cfg.SOLVER.MAX_ITER = int(round(cfg.SOLVER.MAX_ITER / scale)) + warmup_iter = cfg.SOLVER.WARMUP_ITERS = int(round(cfg.SOLVER.WARMUP_ITERS / scale)) + cfg.SOLVER.STEPS = tuple(int(round(s / scale)) for s in cfg.SOLVER.STEPS) + cfg.TEST.EVAL_PERIOD = int(round(cfg.TEST.EVAL_PERIOD / scale)) + cfg.SOLVER.CHECKPOINT_PERIOD = int(round(cfg.SOLVER.CHECKPOINT_PERIOD / scale)) + cfg.SOLVER.REFERENCE_WORLD_SIZE = num_workers # maintain invariant + logger = logging.getLogger(__name__) + logger.info( + f"Auto-scaling the config to batch_size={bs}, learning_rate={lr}, " + f"max_iter={max_iter}, warmup={warmup_iter}." + ) + + if frozen: + cfg.freeze() + return cfg + + +# Access basic attributes from the underlying trainer +for _attr in ["model", "data_loader", "optimizer"]: + setattr( + DefaultTrainer, + _attr, + property( + # getter + lambda self, x=_attr: getattr(self._trainer, x), + # setter + lambda self, value, x=_attr: setattr(self._trainer, x, value), + ), + ) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/engine/hooks.py b/motion-gan-pipeline/preprocessing/third/detectron2/engine/hooks.py new file mode 100644 index 0000000..52c321f --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/engine/hooks.py @@ -0,0 +1,686 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import datetime +import itertools +import logging +import math +import operator +import os +import tempfile +import time +import warnings +from collections import Counter +import torch +from fvcore.common.checkpoint import Checkpointer +from fvcore.common.checkpoint import PeriodicCheckpointer as _PeriodicCheckpointer +from fvcore.common.param_scheduler import ParamScheduler +from fvcore.common.timer import Timer +from fvcore.nn.precise_bn import get_bn_modules, update_bn_stats + +import detectron2.utils.comm as comm +from detectron2.evaluation.testing import flatten_results_dict +from detectron2.solver import LRMultiplier +from detectron2.utils.events import EventStorage, EventWriter +from detectron2.utils.file_io import PathManager + +from .train_loop import HookBase + +__all__ = [ + "CallbackHook", + "IterationTimer", + "PeriodicWriter", + "PeriodicCheckpointer", + "BestCheckpointer", + "LRScheduler", + "AutogradProfiler", + "EvalHook", + "PreciseBN", + "TorchProfiler", + "TorchMemoryStats", +] + + +""" +Implement some common hooks. +""" + + +class CallbackHook(HookBase): + """ + Create a hook using callback functions provided by the user. + """ + + def __init__(self, *, before_train=None, after_train=None, before_step=None, after_step=None): + """ + Each argument is a function that takes one argument: the trainer. + """ + self._before_train = before_train + self._before_step = before_step + self._after_step = after_step + self._after_train = after_train + + def before_train(self): + if self._before_train: + self._before_train(self.trainer) + + def after_train(self): + if self._after_train: + self._after_train(self.trainer) + # The functions may be closures that hold reference to the trainer + # Therefore, delete them to avoid circular reference. + del self._before_train, self._after_train + del self._before_step, self._after_step + + def before_step(self): + if self._before_step: + self._before_step(self.trainer) + + def after_step(self): + if self._after_step: + self._after_step(self.trainer) + + +class IterationTimer(HookBase): + """ + Track the time spent for each iteration (each run_step call in the trainer). + Print a summary in the end of training. + + This hook uses the time between the call to its :meth:`before_step` + and :meth:`after_step` methods. + Under the convention that :meth:`before_step` of all hooks should only + take negligible amount of time, the :class:`IterationTimer` hook should be + placed at the beginning of the list of hooks to obtain accurate timing. + """ + + def __init__(self, warmup_iter=3): + """ + Args: + warmup_iter (int): the number of iterations at the beginning to exclude + from timing. + """ + self._warmup_iter = warmup_iter + self._step_timer = Timer() + self._start_time = time.perf_counter() + self._total_timer = Timer() + + def before_train(self): + self._start_time = time.perf_counter() + self._total_timer.reset() + self._total_timer.pause() + + def after_train(self): + logger = logging.getLogger(__name__) + total_time = time.perf_counter() - self._start_time + total_time_minus_hooks = self._total_timer.seconds() + hook_time = total_time - total_time_minus_hooks + + num_iter = self.trainer.storage.iter + 1 - self.trainer.start_iter - self._warmup_iter + + if num_iter > 0 and total_time_minus_hooks > 0: + # Speed is meaningful only after warmup + # NOTE this format is parsed by grep in some scripts + logger.info( + "Overall training speed: {} iterations in {} ({:.4f} s / it)".format( + num_iter, + str(datetime.timedelta(seconds=int(total_time_minus_hooks))), + total_time_minus_hooks / num_iter, + ) + ) + + logger.info( + "Total training time: {} ({} on hooks)".format( + str(datetime.timedelta(seconds=int(total_time))), + str(datetime.timedelta(seconds=int(hook_time))), + ) + ) + + def before_step(self): + self._step_timer.reset() + self._total_timer.resume() + + def after_step(self): + # +1 because we're in after_step, the current step is done + # but not yet counted + iter_done = self.trainer.storage.iter - self.trainer.start_iter + 1 + if iter_done >= self._warmup_iter: + sec = self._step_timer.seconds() + self.trainer.storage.put_scalars(time=sec) + else: + self._start_time = time.perf_counter() + self._total_timer.reset() + + self._total_timer.pause() + + +class PeriodicWriter(HookBase): + """ + Write events to EventStorage (by calling ``writer.write()``) periodically. + + It is executed every ``period`` iterations and after the last iteration. + Note that ``period`` does not affect how data is smoothed by each writer. + """ + + def __init__(self, writers, period=20): + """ + Args: + writers (list[EventWriter]): a list of EventWriter objects + period (int): + """ + self._writers = writers + for w in writers: + assert isinstance(w, EventWriter), w + self._period = period + + def after_step(self): + if (self.trainer.iter + 1) % self._period == 0 or ( + self.trainer.iter == self.trainer.max_iter - 1 + ): + for writer in self._writers: + writer.write() + + def after_train(self): + for writer in self._writers: + # If any new data is found (e.g. produced by other after_train), + # write them before closing + writer.write() + writer.close() + + +class PeriodicCheckpointer(_PeriodicCheckpointer, HookBase): + """ + Same as :class:`detectron2.checkpoint.PeriodicCheckpointer`, but as a hook. + + Note that when used as a hook, + it is unable to save additional data other than what's defined + by the given `checkpointer`. + + It is executed every ``period`` iterations and after the last iteration. + """ + + def before_train(self): + self.max_iter = self.trainer.max_iter + + def after_step(self): + # No way to use **kwargs + self.step(self.trainer.iter) + + +class BestCheckpointer(HookBase): + """ + Checkpoints best weights based off given metric. + + This hook should be used in conjunction to and executed after the hook + that produces the metric, e.g. `EvalHook`. + """ + + def __init__( + self, + eval_period: int, + checkpointer: Checkpointer, + val_metric: str, + mode: str = "max", + file_prefix: str = "model_best", + ) -> None: + """ + Args: + eval_period (int): the period `EvalHook` is set to run. + checkpointer: the checkpointer object used to save checkpoints. + val_metric (str): validation metric to track for best checkpoint, e.g. "bbox/AP50" + mode (str): one of {'max', 'min'}. controls whether the chosen val metric should be + maximized or minimized, e.g. for "bbox/AP50" it should be "max" + file_prefix (str): the prefix of checkpoint's filename, defaults to "model_best" + """ + self._logger = logging.getLogger(__name__) + self._period = eval_period + self._val_metric = val_metric + assert mode in [ + "max", + "min", + ], f'Mode "{mode}" to `BestCheckpointer` is unknown. It should be one of {"max", "min"}.' + if mode == "max": + self._compare = operator.gt + else: + self._compare = operator.lt + self._checkpointer = checkpointer + self._file_prefix = file_prefix + self.best_metric = None + self.best_iter = None + + def _update_best(self, val, iteration): + if math.isnan(val) or math.isinf(val): + return False + self.best_metric = val + self.best_iter = iteration + return True + + def _best_checking(self): + metric_tuple = self.trainer.storage.latest().get(self._val_metric) + if metric_tuple is None: + self._logger.warning( + f"Given val metric {self._val_metric} does not seem to be computed/stored." + "Will not be checkpointing based on it." + ) + return + else: + latest_metric, metric_iter = metric_tuple + + if self.best_metric is None: + if self._update_best(latest_metric, metric_iter): + additional_state = {"iteration": metric_iter} + self._checkpointer.save(f"{self._file_prefix}", **additional_state) + self._logger.info( + f"Saved first model at {self.best_metric:0.5f} @ {self.best_iter} steps" + ) + elif self._compare(latest_metric, self.best_metric): + additional_state = {"iteration": metric_iter} + self._checkpointer.save(f"{self._file_prefix}", **additional_state) + self._logger.info( + f"Saved best model as latest eval score for {self._val_metric} is " + f"{latest_metric:0.5f}, better than last best score " + f"{self.best_metric:0.5f} @ iteration {self.best_iter}." + ) + self._update_best(latest_metric, metric_iter) + else: + self._logger.info( + f"Not saving as latest eval score for {self._val_metric} is {latest_metric:0.5f}, " + f"not better than best score {self.best_metric:0.5f} @ iteration {self.best_iter}." + ) + + def after_step(self): + # same conditions as `EvalHook` + next_iter = self.trainer.iter + 1 + if ( + self._period > 0 + and next_iter % self._period == 0 + and next_iter != self.trainer.max_iter + ): + self._best_checking() + + def after_train(self): + # same conditions as `EvalHook` + if self.trainer.iter + 1 >= self.trainer.max_iter: + self._best_checking() + + +class LRScheduler(HookBase): + """ + A hook which executes a torch builtin LR scheduler and summarizes the LR. + It is executed after every iteration. + """ + + def __init__(self, optimizer=None, scheduler=None): + """ + Args: + optimizer (torch.optim.Optimizer): + scheduler (torch.optim.LRScheduler or fvcore.common.param_scheduler.ParamScheduler): + if a :class:`ParamScheduler` object, it defines the multiplier over the base LR + in the optimizer. + + If any argument is not given, will try to obtain it from the trainer. + """ + self._optimizer = optimizer + self._scheduler = scheduler + + def before_train(self): + self._optimizer = self._optimizer or self.trainer.optimizer + if isinstance(self.scheduler, ParamScheduler): + self._scheduler = LRMultiplier( + self._optimizer, + self.scheduler, + self.trainer.max_iter, + last_iter=self.trainer.iter - 1, + ) + self._best_param_group_id = LRScheduler.get_best_param_group_id(self._optimizer) + + @staticmethod + def get_best_param_group_id(optimizer): + # NOTE: some heuristics on what LR to summarize + # summarize the param group with most parameters + largest_group = max(len(g["params"]) for g in optimizer.param_groups) + + if largest_group == 1: + # If all groups have one parameter, + # then find the most common initial LR, and use it for summary + lr_count = Counter([g["lr"] for g in optimizer.param_groups]) + lr = lr_count.most_common()[0][0] + for i, g in enumerate(optimizer.param_groups): + if g["lr"] == lr: + return i + else: + for i, g in enumerate(optimizer.param_groups): + if len(g["params"]) == largest_group: + return i + + def after_step(self): + lr = self._optimizer.param_groups[self._best_param_group_id]["lr"] + self.trainer.storage.put_scalar("lr", lr, smoothing_hint=False) + self.scheduler.step() + + @property + def scheduler(self): + return self._scheduler or self.trainer.scheduler + + def state_dict(self): + if isinstance(self.scheduler, torch.optim.lr_scheduler._LRScheduler): + return self.scheduler.state_dict() + return {} + + def load_state_dict(self, state_dict): + if isinstance(self.scheduler, torch.optim.lr_scheduler._LRScheduler): + logger = logging.getLogger(__name__) + logger.info("Loading scheduler from state_dict ...") + self.scheduler.load_state_dict(state_dict) + + +class TorchProfiler(HookBase): + """ + A hook which runs `torch.profiler.profile`. + + Examples: + :: + hooks.TorchProfiler( + lambda trainer: 10 < trainer.iter < 20, self.cfg.OUTPUT_DIR + ) + + The above example will run the profiler for iteration 10~20 and dump + results to ``OUTPUT_DIR``. We did not profile the first few iterations + because they are typically slower than the rest. + The result files can be loaded in the ``chrome://tracing`` page in chrome browser, + and the tensorboard visualizations can be visualized using + ``tensorboard --logdir OUTPUT_DIR/log`` + """ + + def __init__(self, enable_predicate, output_dir, *, activities=None, save_tensorboard=True): + """ + Args: + enable_predicate (callable[trainer -> bool]): a function which takes a trainer, + and returns whether to enable the profiler. + It will be called once every step, and can be used to select which steps to profile. + output_dir (str): the output directory to dump tracing files. + activities (iterable): same as in `torch.profiler.profile`. + save_tensorboard (bool): whether to save tensorboard visualizations at (output_dir)/log/ + """ + self._enable_predicate = enable_predicate + self._activities = activities + self._output_dir = output_dir + self._save_tensorboard = save_tensorboard + + def before_step(self): + if self._enable_predicate(self.trainer): + if self._save_tensorboard: + on_trace_ready = torch.profiler.tensorboard_trace_handler( + os.path.join( + self._output_dir, + "log", + "profiler-tensorboard-iter{}".format(self.trainer.iter), + ), + f"worker{comm.get_rank()}", + ) + else: + on_trace_ready = None + self._profiler = torch.profiler.profile( + activities=self._activities, + on_trace_ready=on_trace_ready, + record_shapes=True, + profile_memory=True, + with_stack=True, + with_flops=True, + ) + self._profiler.__enter__() + else: + self._profiler = None + + def after_step(self): + if self._profiler is None: + return + self._profiler.__exit__(None, None, None) + if not self._save_tensorboard: + PathManager.mkdirs(self._output_dir) + out_file = os.path.join( + self._output_dir, "profiler-trace-iter{}.json".format(self.trainer.iter) + ) + if "://" not in out_file: + self._profiler.export_chrome_trace(out_file) + else: + # Support non-posix filesystems + with tempfile.TemporaryDirectory(prefix="detectron2_profiler") as d: + tmp_file = os.path.join(d, "tmp.json") + self._profiler.export_chrome_trace(tmp_file) + with open(tmp_file) as f: + content = f.read() + with PathManager.open(out_file, "w") as f: + f.write(content) + + +class AutogradProfiler(TorchProfiler): + """ + A hook which runs `torch.autograd.profiler.profile`. + + Examples: + :: + hooks.AutogradProfiler( + lambda trainer: 10 < trainer.iter < 20, self.cfg.OUTPUT_DIR + ) + + The above example will run the profiler for iteration 10~20 and dump + results to ``OUTPUT_DIR``. We did not profile the first few iterations + because they are typically slower than the rest. + The result files can be loaded in the ``chrome://tracing`` page in chrome browser. + + Note: + When used together with NCCL on older version of GPUs, + autograd profiler may cause deadlock because it unnecessarily allocates + memory on every device it sees. The memory management calls, if + interleaved with NCCL calls, lead to deadlock on GPUs that do not + support ``cudaLaunchCooperativeKernelMultiDevice``. + """ + + def __init__(self, enable_predicate, output_dir, *, use_cuda=True): + """ + Args: + enable_predicate (callable[trainer -> bool]): a function which takes a trainer, + and returns whether to enable the profiler. + It will be called once every step, and can be used to select which steps to profile. + output_dir (str): the output directory to dump tracing files. + use_cuda (bool): same as in `torch.autograd.profiler.profile`. + """ + warnings.warn("AutogradProfiler has been deprecated in favor of TorchProfiler.") + self._enable_predicate = enable_predicate + self._use_cuda = use_cuda + self._output_dir = output_dir + + def before_step(self): + if self._enable_predicate(self.trainer): + self._profiler = torch.autograd.profiler.profile(use_cuda=self._use_cuda) + self._profiler.__enter__() + else: + self._profiler = None + + +class EvalHook(HookBase): + """ + Run an evaluation function periodically, and at the end of training. + + It is executed every ``eval_period`` iterations and after the last iteration. + """ + + def __init__(self, eval_period, eval_function): + """ + Args: + eval_period (int): the period to run `eval_function`. Set to 0 to + not evaluate periodically (but still after the last iteration). + eval_function (callable): a function which takes no arguments, and + returns a nested dict of evaluation metrics. + + Note: + This hook must be enabled in all or none workers. + If you would like only certain workers to perform evaluation, + give other workers a no-op function (`eval_function=lambda: None`). + """ + self._period = eval_period + self._func = eval_function + + def _do_eval(self): + results = self._func() + + if results: + assert isinstance( + results, dict + ), "Eval function must return a dict. Got {} instead.".format(results) + + flattened_results = flatten_results_dict(results) + for k, v in flattened_results.items(): + try: + v = float(v) + except Exception as e: + raise ValueError( + "[EvalHook] eval_function should return a nested dict of float. " + "Got '{}: {}' instead.".format(k, v) + ) from e + self.trainer.storage.put_scalars(**flattened_results, smoothing_hint=False) + + # Evaluation may take different time among workers. + # A barrier make them start the next iteration together. + comm.synchronize() + + def after_step(self): + next_iter = self.trainer.iter + 1 + if self._period > 0 and next_iter % self._period == 0: + # do the last eval in after_train + if next_iter != self.trainer.max_iter: + self._do_eval() + + def after_train(self): + # This condition is to prevent the eval from running after a failed training + if self.trainer.iter + 1 >= self.trainer.max_iter: + self._do_eval() + # func is likely a closure that holds reference to the trainer + # therefore we clean it to avoid circular reference in the end + del self._func + + +class PreciseBN(HookBase): + """ + The standard implementation of BatchNorm uses EMA in inference, which is + sometimes suboptimal. + This class computes the true average of statistics rather than the moving average, + and put true averages to every BN layer in the given model. + + It is executed every ``period`` iterations and after the last iteration. + """ + + def __init__(self, period, model, data_loader, num_iter): + """ + Args: + period (int): the period this hook is run, or 0 to not run during training. + The hook will always run in the end of training. + model (nn.Module): a module whose all BN layers in training mode will be + updated by precise BN. + Note that user is responsible for ensuring the BN layers to be + updated are in training mode when this hook is triggered. + data_loader (iterable): it will produce data to be run by `model(data)`. + num_iter (int): number of iterations used to compute the precise + statistics. + """ + self._logger = logging.getLogger(__name__) + if len(get_bn_modules(model)) == 0: + self._logger.info( + "PreciseBN is disabled because model does not contain BN layers in training mode." + ) + self._disabled = True + return + + self._model = model + self._data_loader = data_loader + self._num_iter = num_iter + self._period = period + self._disabled = False + + self._data_iter = None + + def after_step(self): + next_iter = self.trainer.iter + 1 + is_final = next_iter == self.trainer.max_iter + if is_final or (self._period > 0 and next_iter % self._period == 0): + self.update_stats() + + def update_stats(self): + """ + Update the model with precise statistics. Users can manually call this method. + """ + if self._disabled: + return + + if self._data_iter is None: + self._data_iter = iter(self._data_loader) + + def data_loader(): + for num_iter in itertools.count(1): + if num_iter % 100 == 0: + self._logger.info( + "Running precise-BN ... {}/{} iterations.".format(num_iter, self._num_iter) + ) + # This way we can reuse the same iterator + yield next(self._data_iter) + + with EventStorage(): # capture events in a new storage to discard them + self._logger.info( + "Running precise-BN for {} iterations... ".format(self._num_iter) + + "Note that this could produce different statistics every time." + ) + update_bn_stats(self._model, data_loader(), self._num_iter) + + +class TorchMemoryStats(HookBase): + """ + Writes pytorch's cuda memory statistics periodically. + """ + + def __init__(self, period=20, max_runs=10): + """ + Args: + period (int): Output stats each 'period' iterations + max_runs (int): Stop the logging after 'max_runs' + """ + + self._logger = logging.getLogger(__name__) + self._period = period + self._max_runs = max_runs + self._runs = 0 + + def after_step(self): + if self._runs > self._max_runs: + return + + if (self.trainer.iter + 1) % self._period == 0 or ( + self.trainer.iter == self.trainer.max_iter - 1 + ): + if torch.cuda.is_available(): + max_reserved_mb = torch.cuda.max_memory_reserved() / 1024.0 / 1024.0 + reserved_mb = torch.cuda.memory_reserved() / 1024.0 / 1024.0 + max_allocated_mb = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0 + allocated_mb = torch.cuda.memory_allocated() / 1024.0 / 1024.0 + + self._logger.info( + ( + " iter: {} " + " max_reserved_mem: {:.0f}MB " + " reserved_mem: {:.0f}MB " + " max_allocated_mem: {:.0f}MB " + " allocated_mem: {:.0f}MB " + ).format( + self.trainer.iter, + max_reserved_mb, + reserved_mb, + max_allocated_mb, + allocated_mb, + ) + ) + + self._runs += 1 + if self._runs == self._max_runs: + mem_summary = torch.cuda.memory_summary() + self._logger.info("\n" + mem_summary) + + torch.cuda.reset_peak_memory_stats() diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/engine/launch.py b/motion-gan-pipeline/preprocessing/third/detectron2/engine/launch.py new file mode 100644 index 0000000..46f9869 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/engine/launch.py @@ -0,0 +1,126 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +from datetime import timedelta +import torch +import torch.distributed as dist +import torch.multiprocessing as mp + +from detectron2.utils import comm + +__all__ = ["DEFAULT_TIMEOUT", "launch"] + +DEFAULT_TIMEOUT = timedelta(minutes=30) + + +def _find_free_port(): + import socket + + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + # Binding to port 0 will cause the OS to find an available port for us + sock.bind(("", 0)) + port = sock.getsockname()[1] + sock.close() + # NOTE: there is still a chance the port could be taken by other processes. + return port + + +def launch( + main_func, + num_gpus_per_machine, + num_machines=1, + machine_rank=0, + dist_url=None, + args=(), + timeout=DEFAULT_TIMEOUT, +): + """ + Launch multi-gpu or distributed training. + This function must be called on all machines involved in the training. + It will spawn child processes (defined by ``num_gpus_per_machine``) on each machine. + + Args: + main_func: a function that will be called by `main_func(*args)` + num_gpus_per_machine (int): number of GPUs per machine + num_machines (int): the total number of machines + machine_rank (int): the rank of this machine + dist_url (str): url to connect to for distributed jobs, including protocol + e.g. "tcp://127.0.0.1:8686". + Can be set to "auto" to automatically select a free port on localhost + timeout (timedelta): timeout of the distributed workers + args (tuple): arguments passed to main_func + """ + world_size = num_machines * num_gpus_per_machine + if world_size > 1: + # https://github.com/pytorch/pytorch/pull/14391 + # TODO prctl in spawned processes + + if dist_url == "auto": + assert num_machines == 1, "dist_url=auto not supported in multi-machine jobs." + port = _find_free_port() + dist_url = f"tcp://127.0.0.1:{port}" + if num_machines > 1 and dist_url.startswith("file://"): + logger = logging.getLogger(__name__) + logger.warning( + "file:// is not a reliable init_method in multi-machine jobs. Prefer tcp://" + ) + + mp.spawn( + _distributed_worker, + nprocs=num_gpus_per_machine, + args=( + main_func, + world_size, + num_gpus_per_machine, + machine_rank, + dist_url, + args, + timeout, + ), + daemon=False, + ) + else: + main_func(*args) + + +def _distributed_worker( + local_rank, + main_func, + world_size, + num_gpus_per_machine, + machine_rank, + dist_url, + args, + timeout=DEFAULT_TIMEOUT, +): + assert torch.cuda.is_available(), "cuda is not available. Please check your installation." + global_rank = machine_rank * num_gpus_per_machine + local_rank + try: + dist.init_process_group( + backend="NCCL", + init_method=dist_url, + world_size=world_size, + rank=global_rank, + timeout=timeout, + ) + except Exception as e: + logger = logging.getLogger(__name__) + logger.error("Process group URL: {}".format(dist_url)) + raise e + + # Setup the local process group (which contains ranks within the same machine) + assert comm._LOCAL_PROCESS_GROUP is None + num_machines = world_size // num_gpus_per_machine + for i in range(num_machines): + ranks_on_i = list(range(i * num_gpus_per_machine, (i + 1) * num_gpus_per_machine)) + pg = dist.new_group(ranks_on_i) + if i == machine_rank: + comm._LOCAL_PROCESS_GROUP = pg + + assert num_gpus_per_machine <= torch.cuda.device_count() + torch.cuda.set_device(local_rank) + + # synchronize is needed here to prevent a possible timeout after calling init_process_group + # See: https://github.com/facebookresearch/maskrcnn-benchmark/issues/172 + comm.synchronize() + + main_func(*args) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/engine/train_loop.py b/motion-gan-pipeline/preprocessing/third/detectron2/engine/train_loop.py new file mode 100644 index 0000000..c4a86b5 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/engine/train_loop.py @@ -0,0 +1,417 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import logging +import numpy as np +import time +import weakref +from typing import List, Mapping, Optional +import torch +from torch.nn.parallel import DataParallel, DistributedDataParallel + +import detectron2.utils.comm as comm +from detectron2.utils.events import EventStorage, get_event_storage +from detectron2.utils.logger import _log_api_usage + +__all__ = ["HookBase", "TrainerBase", "SimpleTrainer", "AMPTrainer"] + + +class HookBase: + """ + Base class for hooks that can be registered with :class:`TrainerBase`. + + Each hook can implement 4 methods. The way they are called is demonstrated + in the following snippet: + :: + hook.before_train() + for iter in range(start_iter, max_iter): + hook.before_step() + trainer.run_step() + hook.after_step() + iter += 1 + hook.after_train() + + Notes: + 1. In the hook method, users can access ``self.trainer`` to access more + properties about the context (e.g., model, current iteration, or config + if using :class:`DefaultTrainer`). + + 2. A hook that does something in :meth:`before_step` can often be + implemented equivalently in :meth:`after_step`. + If the hook takes non-trivial time, it is strongly recommended to + implement the hook in :meth:`after_step` instead of :meth:`before_step`. + The convention is that :meth:`before_step` should only take negligible time. + + Following this convention will allow hooks that do care about the difference + between :meth:`before_step` and :meth:`after_step` (e.g., timer) to + function properly. + + """ + + trainer: "TrainerBase" = None + """ + A weak reference to the trainer object. Set by the trainer when the hook is registered. + """ + + def before_train(self): + """ + Called before the first iteration. + """ + pass + + def after_train(self): + """ + Called after the last iteration. + """ + pass + + def before_step(self): + """ + Called before each iteration. + """ + pass + + def after_step(self): + """ + Called after each iteration. + """ + pass + + def state_dict(self): + """ + Hooks are stateless by default, but can be made checkpointable by + implementing `state_dict` and `load_state_dict`. + """ + return {} + + +class TrainerBase: + """ + Base class for iterative trainer with hooks. + + The only assumption we made here is: the training runs in a loop. + A subclass can implement what the loop is. + We made no assumptions about the existence of dataloader, optimizer, model, etc. + + Attributes: + iter(int): the current iteration. + + start_iter(int): The iteration to start with. + By convention the minimum possible value is 0. + + max_iter(int): The iteration to end training. + + storage(EventStorage): An EventStorage that's opened during the course of training. + """ + + def __init__(self) -> None: + self._hooks: List[HookBase] = [] + self.iter: int = 0 + self.start_iter: int = 0 + self.max_iter: int + self.storage: EventStorage + _log_api_usage("trainer." + self.__class__.__name__) + + def register_hooks(self, hooks: List[Optional[HookBase]]) -> None: + """ + Register hooks to the trainer. The hooks are executed in the order + they are registered. + + Args: + hooks (list[Optional[HookBase]]): list of hooks + """ + hooks = [h for h in hooks if h is not None] + for h in hooks: + assert isinstance(h, HookBase) + # To avoid circular reference, hooks and trainer cannot own each other. + # This normally does not matter, but will cause memory leak if the + # involved objects contain __del__: + # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/ + h.trainer = weakref.proxy(self) + self._hooks.extend(hooks) + + def train(self, start_iter: int, max_iter: int): + """ + Args: + start_iter, max_iter (int): See docs above + """ + logger = logging.getLogger(__name__) + logger.info("Starting training from iteration {}".format(start_iter)) + + self.iter = self.start_iter = start_iter + self.max_iter = max_iter + + with EventStorage(start_iter) as self.storage: + try: + self.before_train() + for self.iter in range(start_iter, max_iter): + self.before_step() + self.run_step() + self.after_step() + # self.iter == max_iter can be used by `after_train` to + # tell whether the training successfully finished or failed + # due to exceptions. + self.iter += 1 + except Exception: + logger.exception("Exception during training:") + raise + finally: + self.after_train() + + def before_train(self): + for h in self._hooks: + h.before_train() + + def after_train(self): + self.storage.iter = self.iter + for h in self._hooks: + h.after_train() + + def before_step(self): + # Maintain the invariant that storage.iter == trainer.iter + # for the entire execution of each step + self.storage.iter = self.iter + + for h in self._hooks: + h.before_step() + + def after_step(self): + for h in self._hooks: + h.after_step() + + def run_step(self): + raise NotImplementedError + + def state_dict(self): + ret = {"iteration": self.iter} + hooks_state = {} + for h in self._hooks: + sd = h.state_dict() + if sd: + name = type(h).__qualname__ + if name in hooks_state: + # TODO handle repetitive stateful hooks + continue + hooks_state[name] = sd + if hooks_state: + ret["hooks"] = hooks_state + return ret + + def load_state_dict(self, state_dict): + logger = logging.getLogger(__name__) + self.iter = state_dict["iteration"] + for key, value in state_dict.get("hooks", {}).items(): + for h in self._hooks: + try: + name = type(h).__qualname__ + except AttributeError: + continue + if name == key: + h.load_state_dict(value) + break + else: + logger.warning(f"Cannot find the hook '{key}', its state_dict is ignored.") + + +class SimpleTrainer(TrainerBase): + """ + A simple trainer for the most common type of task: + single-cost single-optimizer single-data-source iterative optimization, + optionally using data-parallelism. + It assumes that every step, you: + + 1. Compute the loss with a data from the data_loader. + 2. Compute the gradients with the above loss. + 3. Update the model with the optimizer. + + All other tasks during training (checkpointing, logging, evaluation, LR schedule) + are maintained by hooks, which can be registered by :meth:`TrainerBase.register_hooks`. + + If you want to do anything fancier than this, + either subclass TrainerBase and implement your own `run_step`, + or write your own training loop. + """ + + def __init__(self, model, data_loader, optimizer): + """ + Args: + model: a torch Module. Takes a data from data_loader and returns a + dict of losses. + data_loader: an iterable. Contains data to be used to call model. + optimizer: a torch optimizer. + """ + super().__init__() + + """ + We set the model to training mode in the trainer. + However it's valid to train a model that's in eval mode. + If you want your model (or a submodule of it) to behave + like evaluation during training, you can overwrite its train() method. + """ + model.train() + + self.model = model + self.data_loader = data_loader + self._data_loader_iter = iter(data_loader) + self.optimizer = optimizer + + def run_step(self): + """ + Implement the standard training logic described above. + """ + assert self.model.training, "[SimpleTrainer] model was changed to eval mode!" + start = time.perf_counter() + """ + If you want to do something with the data, you can wrap the dataloader. + """ + data = next(self._data_loader_iter) + data_time = time.perf_counter() - start + + """ + If you want to do something with the losses, you can wrap the model. + """ + loss_dict = self.model(data) + if isinstance(loss_dict, torch.Tensor): + losses = loss_dict + loss_dict = {"total_loss": loss_dict} + else: + losses = sum(loss_dict.values()) + + """ + If you need to accumulate gradients or do something similar, you can + wrap the optimizer with your custom `zero_grad()` method. + """ + self.optimizer.zero_grad() + losses.backward() + + self._write_metrics(loss_dict, data_time) + + """ + If you need gradient clipping/scaling or other processing, you can + wrap the optimizer with your custom `step()` method. But it is + suboptimal as explained in https://arxiv.org/abs/2006.15704 Sec 3.2.4 + """ + self.optimizer.step() + + def _write_metrics( + self, + loss_dict: Mapping[str, torch.Tensor], + data_time: float, + prefix: str = "", + ) -> None: + SimpleTrainer.write_metrics(loss_dict, data_time, prefix) + + @staticmethod + def write_metrics( + loss_dict: Mapping[str, torch.Tensor], + data_time: float, + prefix: str = "", + ) -> None: + """ + Args: + loss_dict (dict): dict of scalar losses + data_time (float): time taken by the dataloader iteration + prefix (str): prefix for logging keys + """ + metrics_dict = {k: v.detach().cpu().item() for k, v in loss_dict.items()} + metrics_dict["data_time"] = data_time + + # Gather metrics among all workers for logging + # This assumes we do DDP-style training, which is currently the only + # supported method in detectron2. + all_metrics_dict = comm.gather(metrics_dict) + + if comm.is_main_process(): + storage = get_event_storage() + + # data_time among workers can have high variance. The actual latency + # caused by data_time is the maximum among workers. + data_time = np.max([x.pop("data_time") for x in all_metrics_dict]) + storage.put_scalar("data_time", data_time) + + # average the rest metrics + metrics_dict = { + k: np.mean([x[k] for x in all_metrics_dict]) for k in all_metrics_dict[0].keys() + } + total_losses_reduced = sum(metrics_dict.values()) + if not np.isfinite(total_losses_reduced): + raise FloatingPointError( + f"Loss became infinite or NaN at iteration={storage.iter}!\n" + f"loss_dict = {metrics_dict}" + ) + + storage.put_scalar("{}total_loss".format(prefix), total_losses_reduced) + if len(metrics_dict) > 1: + storage.put_scalars(**metrics_dict) + + def state_dict(self): + ret = super().state_dict() + ret["optimizer"] = self.optimizer.state_dict() + return ret + + def load_state_dict(self, state_dict): + super().load_state_dict(state_dict) + self.optimizer.load_state_dict(state_dict["optimizer"]) + + +class AMPTrainer(SimpleTrainer): + """ + Like :class:`SimpleTrainer`, but uses PyTorch's native automatic mixed precision + in the training loop. + """ + + def __init__(self, model, data_loader, optimizer, grad_scaler=None): + """ + Args: + model, data_loader, optimizer: same as in :class:`SimpleTrainer`. + grad_scaler: torch GradScaler to automatically scale gradients. + """ + unsupported = "AMPTrainer does not support single-process multi-device training!" + if isinstance(model, DistributedDataParallel): + assert not (model.device_ids and len(model.device_ids) > 1), unsupported + assert not isinstance(model, DataParallel), unsupported + + super().__init__(model, data_loader, optimizer) + + if grad_scaler is None: + from torch.cuda.amp import GradScaler + + grad_scaler = GradScaler() + self.grad_scaler = grad_scaler + + def run_step(self): + """ + Implement the AMP training logic. + """ + assert self.model.training, "[AMPTrainer] model was changed to eval mode!" + assert torch.cuda.is_available(), "[AMPTrainer] CUDA is required for AMP training!" + from torch.cuda.amp import autocast + + start = time.perf_counter() + data = next(self._data_loader_iter) + data_time = time.perf_counter() - start + + with autocast(): + loss_dict = self.model(data) + if isinstance(loss_dict, torch.Tensor): + losses = loss_dict + loss_dict = {"total_loss": loss_dict} + else: + losses = sum(loss_dict.values()) + + self.optimizer.zero_grad() + self.grad_scaler.scale(losses).backward() + + self._write_metrics(loss_dict, data_time) + + self.grad_scaler.step(self.optimizer) + self.grad_scaler.update() + + def state_dict(self): + ret = super().state_dict() + ret["grad_scaler"] = self.grad_scaler.state_dict() + return ret + + def load_state_dict(self, state_dict): + super().load_state_dict(state_dict) + self.grad_scaler.load_state_dict(state_dict["grad_scaler"]) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/__init__.py new file mode 100644 index 0000000..d96609e --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .cityscapes_evaluation import CityscapesInstanceEvaluator, CityscapesSemSegEvaluator +from .coco_evaluation import COCOEvaluator +from .rotated_coco_evaluation import RotatedCOCOEvaluator +from .evaluator import DatasetEvaluator, DatasetEvaluators, inference_context, inference_on_dataset +from .lvis_evaluation import LVISEvaluator +from .panoptic_evaluation import COCOPanopticEvaluator +from .pascal_voc_evaluation import PascalVOCDetectionEvaluator +from .sem_seg_evaluation import SemSegEvaluator +from .testing import print_csv_format, verify_results + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/cityscapes_evaluation.py b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/cityscapes_evaluation.py new file mode 100644 index 0000000..9cc7888 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/cityscapes_evaluation.py @@ -0,0 +1,197 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import glob +import logging +import numpy as np +import os +import tempfile +from collections import OrderedDict +import torch +from PIL import Image + +from detectron2.data import MetadataCatalog +from detectron2.utils import comm +from detectron2.utils.file_io import PathManager + +from .evaluator import DatasetEvaluator + + +class CityscapesEvaluator(DatasetEvaluator): + """ + Base class for evaluation using cityscapes API. + """ + + def __init__(self, dataset_name): + """ + Args: + dataset_name (str): the name of the dataset. + It must have the following metadata associated with it: + "thing_classes", "gt_dir". + """ + self._metadata = MetadataCatalog.get(dataset_name) + self._cpu_device = torch.device("cpu") + self._logger = logging.getLogger(__name__) + + def reset(self): + self._working_dir = tempfile.TemporaryDirectory(prefix="cityscapes_eval_") + self._temp_dir = self._working_dir.name + # All workers will write to the same results directory + # TODO this does not work in distributed training + assert ( + comm.get_local_size() == comm.get_world_size() + ), "CityscapesEvaluator currently do not work with multiple machines." + self._temp_dir = comm.all_gather(self._temp_dir)[0] + if self._temp_dir != self._working_dir.name: + self._working_dir.cleanup() + self._logger.info( + "Writing cityscapes results to temporary directory {} ...".format(self._temp_dir) + ) + + +class CityscapesInstanceEvaluator(CityscapesEvaluator): + """ + Evaluate instance segmentation results on cityscapes dataset using cityscapes API. + + Note: + * It does not work in multi-machine distributed training. + * It contains a synchronization, therefore has to be used on all ranks. + * Only the main process runs evaluation. + """ + + def process(self, inputs, outputs): + from cityscapesscripts.helpers.labels import name2label + + for input, output in zip(inputs, outputs): + file_name = input["file_name"] + basename = os.path.splitext(os.path.basename(file_name))[0] + pred_txt = os.path.join(self._temp_dir, basename + "_pred.txt") + + if "instances" in output: + output = output["instances"].to(self._cpu_device) + num_instances = len(output) + with open(pred_txt, "w") as fout: + for i in range(num_instances): + pred_class = output.pred_classes[i] + classes = self._metadata.thing_classes[pred_class] + class_id = name2label[classes].id + score = output.scores[i] + mask = output.pred_masks[i].numpy().astype("uint8") + png_filename = os.path.join( + self._temp_dir, basename + "_{}_{}.png".format(i, classes) + ) + + Image.fromarray(mask * 255).save(png_filename) + fout.write( + "{} {} {}\n".format(os.path.basename(png_filename), class_id, score) + ) + else: + # Cityscapes requires a prediction file for every ground truth image. + with open(pred_txt, "w") as fout: + pass + + def evaluate(self): + """ + Returns: + dict: has a key "segm", whose value is a dict of "AP" and "AP50". + """ + comm.synchronize() + if comm.get_rank() > 0: + return + import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval + + self._logger.info("Evaluating results under {} ...".format(self._temp_dir)) + + # set some global states in cityscapes evaluation API, before evaluating + cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir) + cityscapes_eval.args.predictionWalk = None + cityscapes_eval.args.JSONOutput = False + cityscapes_eval.args.colorized = False + cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json") + + # These lines are adopted from + # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa + gt_dir = PathManager.get_local_path(self._metadata.gt_dir) + groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_instanceIds.png")) + assert len( + groundTruthImgList + ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format( + cityscapes_eval.args.groundTruthSearch + ) + predictionImgList = [] + for gt in groundTruthImgList: + predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args)) + results = cityscapes_eval.evaluateImgLists( + predictionImgList, groundTruthImgList, cityscapes_eval.args + )["averages"] + + ret = OrderedDict() + ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100} + self._working_dir.cleanup() + return ret + + +class CityscapesSemSegEvaluator(CityscapesEvaluator): + """ + Evaluate semantic segmentation results on cityscapes dataset using cityscapes API. + + Note: + * It does not work in multi-machine distributed training. + * It contains a synchronization, therefore has to be used on all ranks. + * Only the main process runs evaluation. + """ + + def process(self, inputs, outputs): + from cityscapesscripts.helpers.labels import trainId2label + + for input, output in zip(inputs, outputs): + file_name = input["file_name"] + basename = os.path.splitext(os.path.basename(file_name))[0] + pred_filename = os.path.join(self._temp_dir, basename + "_pred.png") + + output = output["sem_seg"].argmax(dim=0).to(self._cpu_device).numpy() + pred = 255 * np.ones(output.shape, dtype=np.uint8) + for train_id, label in trainId2label.items(): + if label.ignoreInEval: + continue + pred[output == train_id] = label.id + Image.fromarray(pred).save(pred_filename) + + def evaluate(self): + comm.synchronize() + if comm.get_rank() > 0: + return + # Load the Cityscapes eval script *after* setting the required env var, + # since the script reads CITYSCAPES_DATASET into global variables at load time. + import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval + + self._logger.info("Evaluating results under {} ...".format(self._temp_dir)) + + # set some global states in cityscapes evaluation API, before evaluating + cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir) + cityscapes_eval.args.predictionWalk = None + cityscapes_eval.args.JSONOutput = False + cityscapes_eval.args.colorized = False + + # These lines are adopted from + # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa + gt_dir = PathManager.get_local_path(self._metadata.gt_dir) + groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_labelIds.png")) + assert len( + groundTruthImgList + ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format( + cityscapes_eval.args.groundTruthSearch + ) + predictionImgList = [] + for gt in groundTruthImgList: + predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt)) + results = cityscapes_eval.evaluateImgLists( + predictionImgList, groundTruthImgList, cityscapes_eval.args + ) + ret = OrderedDict() + ret["sem_seg"] = { + "IoU": 100.0 * results["averageScoreClasses"], + "iIoU": 100.0 * results["averageScoreInstClasses"], + "IoU_sup": 100.0 * results["averageScoreCategories"], + "iIoU_sup": 100.0 * results["averageScoreInstCategories"], + } + self._working_dir.cleanup() + return ret diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/coco_evaluation.py b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/coco_evaluation.py new file mode 100644 index 0000000..e3884d2 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/coco_evaluation.py @@ -0,0 +1,722 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import contextlib +import copy +import io +import itertools +import json +import logging +import numpy as np +import os +import pickle +from collections import OrderedDict +import pycocotools.mask as mask_util +import torch +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval +from tabulate import tabulate + +import detectron2.utils.comm as comm +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.data.datasets.coco import convert_to_coco_json +from detectron2.structures import Boxes, BoxMode, pairwise_iou +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import create_small_table + +from .evaluator import DatasetEvaluator + +try: + from detectron2.evaluation.fast_eval_api import COCOeval_opt +except ImportError: + COCOeval_opt = COCOeval + + +class COCOEvaluator(DatasetEvaluator): + """ + Evaluate AR for object proposals, AP for instance detection/segmentation, AP + for keypoint detection outputs using COCO's metrics. + See http://cocodataset.org/#detection-eval and + http://cocodataset.org/#keypoints-eval to understand its metrics. + The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means + the metric cannot be computed (e.g. due to no predictions made). + + In addition to COCO, this evaluator is able to support any bounding box detection, + instance segmentation, or keypoint detection dataset. + """ + + def __init__( + self, + dataset_name, + tasks=None, + distributed=True, + output_dir=None, + *, + max_dets_per_image=None, + use_fast_impl=True, + kpt_oks_sigmas=(), + allow_cached_coco=True, + ): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + It must have either the following corresponding metadata: + + "json_file": the path to the COCO format annotation + + Or it must be in detectron2's standard dataset format + so it can be converted to COCO format automatically. + tasks (tuple[str]): tasks that can be evaluated under the given + configuration. A task is one of "bbox", "segm", "keypoints". + By default, will infer this automatically from predictions. + distributed (True): if True, will collect results from all ranks and run evaluation + in the main process. + Otherwise, will only evaluate the results in the current process. + output_dir (str): optional, an output directory to dump all + results predicted on the dataset. The dump contains two files: + + 1. "instances_predictions.pth" a file that can be loaded with `torch.load` and + contains all the results in the format they are produced by the model. + 2. "coco_instances_results.json" a json file in COCO's result format. + max_dets_per_image (int): limit on the maximum number of detections per image. + By default in COCO, this limit is to 100, but this can be customized + to be greater, as is needed in evaluation metrics AP fixed and AP pool + (see https://arxiv.org/pdf/2102.01066.pdf) + This doesn't affect keypoint evaluation. + use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP. + Although the results should be very close to the official implementation in COCO + API, it is still recommended to compute results with the official API for use in + papers. The faster implementation also uses more RAM. + kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS. + See http://cocodataset.org/#keypoints-eval + When empty, it will use the defaults in COCO. + Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS. + allow_cached_coco (bool): Whether to use cached coco json from previous validation + runs. You should set this to False if you need to use different validation data. + Defaults to True. + """ + self._logger = logging.getLogger(__name__) + self._distributed = distributed + self._output_dir = output_dir + + if use_fast_impl and (COCOeval_opt is COCOeval): + self._logger.info("Fast COCO eval is not built. Falling back to official COCO eval.") + use_fast_impl = False + self._use_fast_impl = use_fast_impl + + # COCOeval requires the limit on the number of detections per image (maxDets) to be a list + # with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the + # 3rd element (100) is used as the limit on the number of detections per image when + # evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval, + # we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults. + if max_dets_per_image is None: + max_dets_per_image = [1, 10, 100] + else: + max_dets_per_image = [1, 10, max_dets_per_image] + self._max_dets_per_image = max_dets_per_image + + if tasks is not None and isinstance(tasks, CfgNode): + kpt_oks_sigmas = ( + tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas + ) + self._logger.warn( + "COCO Evaluator instantiated using config, this is deprecated behavior." + " Please pass in explicit arguments instead." + ) + self._tasks = None # Infering it from predictions should be better + else: + self._tasks = tasks + + self._cpu_device = torch.device("cpu") + + self._metadata = MetadataCatalog.get(dataset_name) + if not hasattr(self._metadata, "json_file"): + if output_dir is None: + raise ValueError( + "output_dir must be provided to COCOEvaluator " + "for datasets not in COCO format." + ) + self._logger.info(f"Trying to convert '{dataset_name}' to COCO format ...") + + cache_path = os.path.join(output_dir, f"{dataset_name}_coco_format.json") + self._metadata.json_file = cache_path + convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco) + + json_file = PathManager.get_local_path(self._metadata.json_file) + with contextlib.redirect_stdout(io.StringIO()): + self._coco_api = COCO(json_file) + + # Test set json files do not contain annotations (evaluation must be + # performed using the COCO evaluation server). + self._do_evaluation = "annotations" in self._coco_api.dataset + if self._do_evaluation: + self._kpt_oks_sigmas = kpt_oks_sigmas + + def reset(self): + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a COCO model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + for input, output in zip(inputs, outputs): + prediction = {"image_id": input["image_id"]} + + if "instances" in output: + instances = output["instances"].to(self._cpu_device) + prediction["instances"] = instances_to_coco_json(instances, input["image_id"]) + if "proposals" in output: + prediction["proposals"] = output["proposals"].to(self._cpu_device) + if len(prediction) > 1: + self._predictions.append(prediction) + + def evaluate(self, img_ids=None): + """ + Args: + img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset + """ + if self._distributed: + comm.synchronize() + predictions = comm.gather(self._predictions, dst=0) + predictions = list(itertools.chain(*predictions)) + + if not comm.is_main_process(): + return {} + else: + predictions = self._predictions + + if len(predictions) == 0: + self._logger.warning("[COCOEvaluator] Did not receive valid predictions.") + return {} + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "instances_predictions.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(predictions, f) + + self._results = OrderedDict() + if "proposals" in predictions[0]: + self._eval_box_proposals(predictions) + if "instances" in predictions[0]: + self._eval_predictions(predictions, img_ids=img_ids) + # Copy so the caller can do whatever with results + return copy.deepcopy(self._results) + + def _tasks_from_predictions(self, predictions): + """ + Get COCO API "tasks" (i.e. iou_type) from COCO-format predictions. + """ + tasks = {"bbox"} + for pred in predictions: + if "segmentation" in pred: + tasks.add("segm") + if "keypoints" in pred: + tasks.add("keypoints") + return sorted(tasks) + + def _eval_predictions(self, predictions, img_ids=None): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for COCO format ...") + coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) + tasks = self._tasks or self._tasks_from_predictions(coco_results) + + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id + all_contiguous_ids = list(dataset_id_to_contiguous_id.values()) + num_classes = len(all_contiguous_ids) + assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1 + + reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()} + for result in coco_results: + category_id = result["category_id"] + assert category_id < num_classes, ( + f"A prediction has class={category_id}, " + f"but the dataset only has {num_classes} classes and " + f"predicted class id should be in [0, {num_classes - 1}]." + ) + result["category_id"] = reverse_id_mapping[category_id] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "coco_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(coco_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info( + "Evaluating predictions with {} COCO API...".format( + "unofficial" if self._use_fast_impl else "official" + ) + ) + for task in sorted(tasks): + assert task in {"bbox", "segm", "keypoints"}, f"Got unknown task: {task}!" + coco_eval = ( + _evaluate_predictions_on_coco( + self._coco_api, + coco_results, + task, + kpt_oks_sigmas=self._kpt_oks_sigmas, + use_fast_impl=self._use_fast_impl, + img_ids=img_ids, + max_dets_per_image=self._max_dets_per_image, + ) + if len(coco_results) > 0 + else None # cocoapi does not handle empty results very well + ) + + res = self._derive_coco_results( + coco_eval, task, class_names=self._metadata.get("thing_classes") + ) + self._results[task] = res + + def _eval_box_proposals(self, predictions): + """ + Evaluate the box proposals in predictions. + Fill self._results with the metrics for "box_proposals" task. + """ + if self._output_dir: + # Saving generated box proposals to file. + # Predicted box_proposals are in XYXY_ABS mode. + bbox_mode = BoxMode.XYXY_ABS.value + ids, boxes, objectness_logits = [], [], [] + for prediction in predictions: + ids.append(prediction["image_id"]) + boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy()) + objectness_logits.append(prediction["proposals"].objectness_logits.numpy()) + + proposal_data = { + "boxes": boxes, + "objectness_logits": objectness_logits, + "ids": ids, + "bbox_mode": bbox_mode, + } + with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f: + pickle.dump(proposal_data, f) + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating bbox proposals ...") + res = {} + areas = {"all": "", "small": "s", "medium": "m", "large": "l"} + for limit in [100, 1000]: + for area, suffix in areas.items(): + stats = _evaluate_box_proposals(predictions, self._coco_api, area=area, limit=limit) + key = "AR{}@{:d}".format(suffix, limit) + res[key] = float(stats["ar"].item() * 100) + self._logger.info("Proposal metrics: \n" + create_small_table(res)) + self._results["box_proposals"] = res + + def _derive_coco_results(self, coco_eval, iou_type, class_names=None): + """ + Derive the desired score numbers from summarized COCOeval. + + Args: + coco_eval (None or COCOEval): None represents no predictions from model. + iou_type (str): + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + + Returns: + a dict of {metric name: score} + """ + + metrics = { + "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"], + "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl"], + "keypoints": ["AP", "AP50", "AP75", "APm", "APl"], + }[iou_type] + + if coco_eval is None: + self._logger.warn("No predictions from the model!") + return {metric: float("nan") for metric in metrics} + + # the standard metrics + results = { + metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan") + for idx, metric in enumerate(metrics) + } + self._logger.info( + "Evaluation results for {}: \n".format(iou_type) + create_small_table(results) + ) + if not np.isfinite(sum(results.values())): + self._logger.info("Some metrics cannot be computed and is shown as NaN.") + + if class_names is None or len(class_names) <= 1: + return results + # Compute per-category AP + # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa + precisions = coco_eval.eval["precision"] + # precision has dims (iou, recall, cls, area range, max dets) + assert len(class_names) == precisions.shape[2] + + results_per_category = [] + for idx, name in enumerate(class_names): + # area range index 0: all area ranges + # max dets index -1: typically 100 per image + precision = precisions[:, :, idx, 0, -1] + precision = precision[precision > -1] + ap = np.mean(precision) if precision.size else float("nan") + results_per_category.append(("{}".format(name), float(ap * 100))) + + # tabulate it + N_COLS = min(6, len(results_per_category) * 2) + results_flatten = list(itertools.chain(*results_per_category)) + results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + results_2d, + tablefmt="pipe", + floatfmt=".3f", + headers=["category", "AP"] * (N_COLS // 2), + numalign="left", + ) + self._logger.info("Per-category {} AP: \n".format(iou_type) + table) + + results.update({"AP-" + name: ap for name, ap in results_per_category}) + return results + + +def instances_to_coco_json(instances, img_id): + """ + Dump an "Instances" object to a COCO-format json that's used for evaluation. + + Args: + instances (Instances): + img_id (int): the image id + + Returns: + list[dict]: list of json annotations in COCO format. + """ + num_instance = len(instances) + if num_instance == 0: + return [] + + boxes = instances.pred_boxes.tensor.numpy() + boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + boxes = boxes.tolist() + scores = instances.scores.tolist() + classes = instances.pred_classes.tolist() + + has_mask = instances.has("pred_masks") + if has_mask: + # use RLE to encode the masks, because they are too large and takes memory + # since this evaluator stores outputs of the entire dataset + rles = [ + mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0] + for mask in instances.pred_masks + ] + for rle in rles: + # "counts" is an array encoded by mask_util as a byte-stream. Python3's + # json writer which always produces strings cannot serialize a bytestream + # unless you decode it. Thankfully, utf-8 works out (which is also what + # the pycocotools/_mask.pyx does). + rle["counts"] = rle["counts"].decode("utf-8") + + has_keypoints = instances.has("pred_keypoints") + if has_keypoints: + keypoints = instances.pred_keypoints + + results = [] + for k in range(num_instance): + result = { + "image_id": img_id, + "category_id": classes[k], + "bbox": boxes[k], + "score": scores[k], + } + if has_mask: + result["segmentation"] = rles[k] + if has_keypoints: + # In COCO annotations, + # keypoints coordinates are pixel indices. + # However our predictions are floating point coordinates. + # Therefore we subtract 0.5 to be consistent with the annotation format. + # This is the inverse of data loading logic in `datasets/coco.py`. + keypoints[k][:, :2] -= 0.5 + result["keypoints"] = keypoints[k].flatten().tolist() + results.append(result) + return results + + +# inspired from Detectron: +# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa +def _evaluate_box_proposals(dataset_predictions, coco_api, thresholds=None, area="all", limit=None): + """ + Evaluate detection proposal recall metrics. This function is a much + faster alternative to the official COCO API recall evaluation code. However, + it produces slightly different results. + """ + # Record max overlap value for each gt box + # Return vector of overlap values + areas = { + "all": 0, + "small": 1, + "medium": 2, + "large": 3, + "96-128": 4, + "128-256": 5, + "256-512": 6, + "512-inf": 7, + } + area_ranges = [ + [0 ** 2, 1e5 ** 2], # all + [0 ** 2, 32 ** 2], # small + [32 ** 2, 96 ** 2], # medium + [96 ** 2, 1e5 ** 2], # large + [96 ** 2, 128 ** 2], # 96-128 + [128 ** 2, 256 ** 2], # 128-256 + [256 ** 2, 512 ** 2], # 256-512 + [512 ** 2, 1e5 ** 2], + ] # 512-inf + assert area in areas, "Unknown area range: {}".format(area) + area_range = area_ranges[areas[area]] + gt_overlaps = [] + num_pos = 0 + + for prediction_dict in dataset_predictions: + predictions = prediction_dict["proposals"] + + # sort predictions in descending order + # TODO maybe remove this and make it explicit in the documentation + inds = predictions.objectness_logits.sort(descending=True)[1] + predictions = predictions[inds] + + ann_ids = coco_api.getAnnIds(imgIds=prediction_dict["image_id"]) + anno = coco_api.loadAnns(ann_ids) + gt_boxes = [ + BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) + for obj in anno + if obj["iscrowd"] == 0 + ] + gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes + gt_boxes = Boxes(gt_boxes) + gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0]) + + if len(gt_boxes) == 0 or len(predictions) == 0: + continue + + valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1]) + gt_boxes = gt_boxes[valid_gt_inds] + + num_pos += len(gt_boxes) + + if len(gt_boxes) == 0: + continue + + if limit is not None and len(predictions) > limit: + predictions = predictions[:limit] + + overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes) + + _gt_overlaps = torch.zeros(len(gt_boxes)) + for j in range(min(len(predictions), len(gt_boxes))): + # find which proposal box maximally covers each gt box + # and get the iou amount of coverage for each gt box + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + + # find which gt box is 'best' covered (i.e. 'best' = most iou) + gt_ovr, gt_ind = max_overlaps.max(dim=0) + assert gt_ovr >= 0 + # find the proposal box that covers the best covered gt box + box_ind = argmax_overlaps[gt_ind] + # record the iou coverage of this gt box + _gt_overlaps[j] = overlaps[box_ind, gt_ind] + assert _gt_overlaps[j] == gt_ovr + # mark the proposal box and the gt box as used + overlaps[box_ind, :] = -1 + overlaps[:, gt_ind] = -1 + + # append recorded iou coverage level + gt_overlaps.append(_gt_overlaps) + gt_overlaps = ( + torch.cat(gt_overlaps, dim=0) if len(gt_overlaps) else torch.zeros(0, dtype=torch.float32) + ) + gt_overlaps, _ = torch.sort(gt_overlaps) + + if thresholds is None: + step = 0.05 + thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32) + recalls = torch.zeros_like(thresholds) + # compute recall for each iou threshold + for i, t in enumerate(thresholds): + recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos) + # ar = 2 * np.trapz(recalls, thresholds) + ar = recalls.mean() + return { + "ar": ar, + "recalls": recalls, + "thresholds": thresholds, + "gt_overlaps": gt_overlaps, + "num_pos": num_pos, + } + + +def _evaluate_predictions_on_coco( + coco_gt, + coco_results, + iou_type, + kpt_oks_sigmas=None, + use_fast_impl=True, + img_ids=None, + max_dets_per_image=None, +): + """ + Evaluate the coco results using COCOEval API. + """ + assert len(coco_results) > 0 + + if iou_type == "segm": + coco_results = copy.deepcopy(coco_results) + # When evaluating mask AP, if the results contain bbox, cocoapi will + # use the box area as the area of the instance, instead of the mask area. + # This leads to a different definition of small/medium/large. + # We remove the bbox field to let mask AP use mask area. + for c in coco_results: + c.pop("bbox", None) + + coco_dt = coco_gt.loadRes(coco_results) + coco_eval = (COCOeval_opt if use_fast_impl else COCOeval)(coco_gt, coco_dt, iou_type) + # For COCO, the default max_dets_per_image is [1, 10, 100]. + if max_dets_per_image is None: + max_dets_per_image = [1, 10, 100] # Default from COCOEval + else: + assert ( + len(max_dets_per_image) >= 3 + ), "COCOeval requires maxDets (and max_dets_per_image) to have length at least 3" + # In the case that user supplies a custom input for max_dets_per_image, + # apply COCOevalMaxDets to evaluate AP with the custom input. + if max_dets_per_image[2] != 100: + coco_eval = COCOevalMaxDets(coco_gt, coco_dt, iou_type) + if iou_type != "keypoints": + coco_eval.params.maxDets = max_dets_per_image + + if img_ids is not None: + coco_eval.params.imgIds = img_ids + + if iou_type == "keypoints": + # Use the COCO default keypoint OKS sigmas unless overrides are specified + if kpt_oks_sigmas: + assert hasattr(coco_eval.params, "kpt_oks_sigmas"), "pycocotools is too old!" + coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas) + # COCOAPI requires every detection and every gt to have keypoints, so + # we just take the first entry from both + num_keypoints_dt = len(coco_results[0]["keypoints"]) // 3 + num_keypoints_gt = len(next(iter(coco_gt.anns.values()))["keypoints"]) // 3 + num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas) + assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, ( + f"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. " + f"Ground truth contains {num_keypoints_gt} keypoints. " + f"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. " + "They have to agree with each other. For meaning of OKS, please refer to " + "http://cocodataset.org/#keypoints-eval." + ) + + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + + return coco_eval + + +class COCOevalMaxDets(COCOeval): + """ + Modified version of COCOeval for evaluating AP with a custom + maxDets (by default for COCO, maxDets is 100) + """ + + def summarize(self): + """ + Compute and display summary metrics for evaluation results given + a custom value for max_dets_per_image + """ + + def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100): + p = self.params + iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}" + titleStr = "Average Precision" if ap == 1 else "Average Recall" + typeStr = "(AP)" if ap == 1 else "(AR)" + iouStr = ( + "{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1]) + if iouThr is None + else "{:0.2f}".format(iouThr) + ) + + aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] + mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] + if ap == 1: + # dimension of precision: [TxRxKxAxM] + s = self.eval["precision"] + # IoU + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, :, aind, mind] + else: + # dimension of recall: [TxKxAxM] + s = self.eval["recall"] + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, aind, mind] + if len(s[s > -1]) == 0: + mean_s = -1 + else: + mean_s = np.mean(s[s > -1]) + print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s)) + return mean_s + + def _summarizeDets(): + stats = np.zeros((12,)) + # Evaluate AP using the custom limit on maximum detections per image + stats[0] = _summarize(1, maxDets=self.params.maxDets[2]) + stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2]) + stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2]) + stats[3] = _summarize(1, areaRng="small", maxDets=self.params.maxDets[2]) + stats[4] = _summarize(1, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[5] = _summarize(1, areaRng="large", maxDets=self.params.maxDets[2]) + stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) + stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) + stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) + stats[9] = _summarize(0, areaRng="small", maxDets=self.params.maxDets[2]) + stats[10] = _summarize(0, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[11] = _summarize(0, areaRng="large", maxDets=self.params.maxDets[2]) + return stats + + def _summarizeKps(): + stats = np.zeros((10,)) + stats[0] = _summarize(1, maxDets=20) + stats[1] = _summarize(1, maxDets=20, iouThr=0.5) + stats[2] = _summarize(1, maxDets=20, iouThr=0.75) + stats[3] = _summarize(1, maxDets=20, areaRng="medium") + stats[4] = _summarize(1, maxDets=20, areaRng="large") + stats[5] = _summarize(0, maxDets=20) + stats[6] = _summarize(0, maxDets=20, iouThr=0.5) + stats[7] = _summarize(0, maxDets=20, iouThr=0.75) + stats[8] = _summarize(0, maxDets=20, areaRng="medium") + stats[9] = _summarize(0, maxDets=20, areaRng="large") + return stats + + if not self.eval: + raise Exception("Please run accumulate() first") + iouType = self.params.iouType + if iouType == "segm" or iouType == "bbox": + summarize = _summarizeDets + elif iouType == "keypoints": + summarize = _summarizeKps + self.stats = summarize() + + def __str__(self): + self.summarize() diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/evaluator.py b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/evaluator.py new file mode 100644 index 0000000..baf9960 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/evaluator.py @@ -0,0 +1,224 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import datetime +import logging +import time +from collections import OrderedDict, abc +from contextlib import ExitStack, contextmanager +from typing import List, Union +import torch +from torch import nn + +from detectron2.utils.comm import get_world_size, is_main_process +from detectron2.utils.logger import log_every_n_seconds + + +class DatasetEvaluator: + """ + Base class for a dataset evaluator. + + The function :func:`inference_on_dataset` runs the model over + all samples in the dataset, and have a DatasetEvaluator to process the inputs/outputs. + + This class will accumulate information of the inputs/outputs (by :meth:`process`), + and produce evaluation results in the end (by :meth:`evaluate`). + """ + + def reset(self): + """ + Preparation for a new round of evaluation. + Should be called before starting a round of evaluation. + """ + pass + + def process(self, inputs, outputs): + """ + Process the pair of inputs and outputs. + If they contain batches, the pairs can be consumed one-by-one using `zip`: + + .. code-block:: python + + for input_, output in zip(inputs, outputs): + # do evaluation on single input/output pair + ... + + Args: + inputs (list): the inputs that's used to call the model. + outputs (list): the return value of `model(inputs)` + """ + pass + + def evaluate(self): + """ + Evaluate/summarize the performance, after processing all input/output pairs. + + Returns: + dict: + A new evaluator class can return a dict of arbitrary format + as long as the user can process the results. + In our train_net.py, we expect the following format: + + * key: the name of the task (e.g., bbox) + * value: a dict of {metric name: score}, e.g.: {"AP50": 80} + """ + pass + + +class DatasetEvaluators(DatasetEvaluator): + """ + Wrapper class to combine multiple :class:`DatasetEvaluator` instances. + + This class dispatches every evaluation call to + all of its :class:`DatasetEvaluator`. + """ + + def __init__(self, evaluators): + """ + Args: + evaluators (list): the evaluators to combine. + """ + super().__init__() + self._evaluators = evaluators + + def reset(self): + for evaluator in self._evaluators: + evaluator.reset() + + def process(self, inputs, outputs): + for evaluator in self._evaluators: + evaluator.process(inputs, outputs) + + def evaluate(self): + results = OrderedDict() + for evaluator in self._evaluators: + result = evaluator.evaluate() + if is_main_process() and result is not None: + for k, v in result.items(): + assert ( + k not in results + ), "Different evaluators produce results with the same key {}".format(k) + results[k] = v + return results + + +def inference_on_dataset( + model, data_loader, evaluator: Union[DatasetEvaluator, List[DatasetEvaluator], None] +): + """ + Run model on the data_loader and evaluate the metrics with evaluator. + Also benchmark the inference speed of `model.__call__` accurately. + The model will be used in eval mode. + + Args: + model (callable): a callable which takes an object from + `data_loader` and returns some outputs. + + If it's an nn.Module, it will be temporarily set to `eval` mode. + If you wish to evaluate a model in `training` mode instead, you can + wrap the given model and override its behavior of `.eval()` and `.train()`. + data_loader: an iterable object with a length. + The elements it generates will be the inputs to the model. + evaluator: the evaluator(s) to run. Use `None` if you only want to benchmark, + but don't want to do any evaluation. + + Returns: + The return value of `evaluator.evaluate()` + """ + num_devices = get_world_size() + logger = logging.getLogger(__name__) + logger.info("Start inference on {} batches".format(len(data_loader))) + + total = len(data_loader) # inference data loader must have a fixed length + if evaluator is None: + # create a no-op evaluator + evaluator = DatasetEvaluators([]) + if isinstance(evaluator, abc.MutableSequence): + evaluator = DatasetEvaluators(evaluator) + evaluator.reset() + + num_warmup = min(5, total - 1) + start_time = time.perf_counter() + total_data_time = 0 + total_compute_time = 0 + total_eval_time = 0 + with ExitStack() as stack: + if isinstance(model, nn.Module): + stack.enter_context(inference_context(model)) + stack.enter_context(torch.no_grad()) + + start_data_time = time.perf_counter() + for idx, inputs in enumerate(data_loader): + total_data_time += time.perf_counter() - start_data_time + if idx == num_warmup: + start_time = time.perf_counter() + total_data_time = 0 + total_compute_time = 0 + total_eval_time = 0 + + start_compute_time = time.perf_counter() + outputs = model(inputs) + if torch.cuda.is_available(): + torch.cuda.synchronize() + total_compute_time += time.perf_counter() - start_compute_time + + start_eval_time = time.perf_counter() + evaluator.process(inputs, outputs) + total_eval_time += time.perf_counter() - start_eval_time + + iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup) + data_seconds_per_iter = total_data_time / iters_after_start + compute_seconds_per_iter = total_compute_time / iters_after_start + eval_seconds_per_iter = total_eval_time / iters_after_start + total_seconds_per_iter = (time.perf_counter() - start_time) / iters_after_start + if idx >= num_warmup * 2 or compute_seconds_per_iter > 5: + eta = datetime.timedelta(seconds=int(total_seconds_per_iter * (total - idx - 1))) + log_every_n_seconds( + logging.INFO, + ( + f"Inference done {idx + 1}/{total}. " + f"Dataloading: {data_seconds_per_iter:.4f} s/iter. " + f"Inference: {compute_seconds_per_iter:.4f} s/iter. " + f"Eval: {eval_seconds_per_iter:.4f} s/iter. " + f"Total: {total_seconds_per_iter:.4f} s/iter. " + f"ETA={eta}" + ), + n=5, + ) + start_data_time = time.perf_counter() + + # Measure the time only for this worker (before the synchronization barrier) + total_time = time.perf_counter() - start_time + total_time_str = str(datetime.timedelta(seconds=total_time)) + # NOTE this format is parsed by grep + logger.info( + "Total inference time: {} ({:.6f} s / iter per device, on {} devices)".format( + total_time_str, total_time / (total - num_warmup), num_devices + ) + ) + total_compute_time_str = str(datetime.timedelta(seconds=int(total_compute_time))) + logger.info( + "Total inference pure compute time: {} ({:.6f} s / iter per device, on {} devices)".format( + total_compute_time_str, total_compute_time / (total - num_warmup), num_devices + ) + ) + + results = evaluator.evaluate() + # An evaluator may return None when not in main process. + # Replace it by an empty dict instead to make it easier for downstream code to handle + if results is None: + results = {} + return results + + +@contextmanager +def inference_context(model): + """ + A context where the model is temporarily changed to eval mode, + and restored to previous mode afterwards. + + Args: + model: a torch Module + """ + training_mode = model.training + model.eval() + yield + model.train(training_mode) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/fast_eval_api.py b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/fast_eval_api.py new file mode 100644 index 0000000..2eb202b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/fast_eval_api.py @@ -0,0 +1,121 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import logging +import numpy as np +import time +from pycocotools.cocoeval import COCOeval + +from detectron2 import _C + +logger = logging.getLogger(__name__) + + +class COCOeval_opt(COCOeval): + """ + This is a slightly modified version of the original COCO API, where the functions evaluateImg() + and accumulate() are implemented in C++ to speedup evaluation + """ + + def evaluate(self): + """ + Run per image evaluation on given images and store results in self.evalImgs_cpp, a + datastructure that isn't readable from Python but is used by a c++ implementation of + accumulate(). Unlike the original COCO PythonAPI, we don't populate the datastructure + self.evalImgs because this datastructure is a computational bottleneck. + :return: None + """ + tic = time.time() + + p = self.params + # add backward compatibility if useSegm is specified in params + if p.useSegm is not None: + p.iouType = "segm" if p.useSegm == 1 else "bbox" + logger.info("Evaluate annotation type *{}*".format(p.iouType)) + p.imgIds = list(np.unique(p.imgIds)) + if p.useCats: + p.catIds = list(np.unique(p.catIds)) + p.maxDets = sorted(p.maxDets) + self.params = p + + self._prepare() # bottleneck + + # loop through images, area range, max detection number + catIds = p.catIds if p.useCats else [-1] + + if p.iouType == "segm" or p.iouType == "bbox": + computeIoU = self.computeIoU + elif p.iouType == "keypoints": + computeIoU = self.computeOks + self.ious = { + (imgId, catId): computeIoU(imgId, catId) for imgId in p.imgIds for catId in catIds + } # bottleneck + + maxDet = p.maxDets[-1] + + # <<<< Beginning of code differences with original COCO API + def convert_instances_to_cpp(instances, is_det=False): + # Convert annotations for a list of instances in an image to a format that's fast + # to access in C++ + instances_cpp = [] + for instance in instances: + instance_cpp = _C.InstanceAnnotation( + int(instance["id"]), + instance["score"] if is_det else instance.get("score", 0.0), + instance["area"], + bool(instance.get("iscrowd", 0)), + bool(instance.get("ignore", 0)), + ) + instances_cpp.append(instance_cpp) + return instances_cpp + + # Convert GT annotations, detections, and IOUs to a format that's fast to access in C++ + ground_truth_instances = [ + [convert_instances_to_cpp(self._gts[imgId, catId]) for catId in p.catIds] + for imgId in p.imgIds + ] + detected_instances = [ + [convert_instances_to_cpp(self._dts[imgId, catId], is_det=True) for catId in p.catIds] + for imgId in p.imgIds + ] + ious = [[self.ious[imgId, catId] for catId in catIds] for imgId in p.imgIds] + + if not p.useCats: + # For each image, flatten per-category lists into a single list + ground_truth_instances = [[[o for c in i for o in c]] for i in ground_truth_instances] + detected_instances = [[[o for c in i for o in c]] for i in detected_instances] + + # Call C++ implementation of self.evaluateImgs() + self._evalImgs_cpp = _C.COCOevalEvaluateImages( + p.areaRng, maxDet, p.iouThrs, ious, ground_truth_instances, detected_instances + ) + self._evalImgs = None + + self._paramsEval = copy.deepcopy(self.params) + toc = time.time() + logger.info("COCOeval_opt.evaluate() finished in {:0.2f} seconds.".format(toc - tic)) + # >>>> End of code differences with original COCO API + + def accumulate(self): + """ + Accumulate per image evaluation results and store the result in self.eval. Does not + support changing parameter settings from those used by self.evaluate() + """ + logger.info("Accumulating evaluation results...") + tic = time.time() + assert hasattr( + self, "_evalImgs_cpp" + ), "evaluate() must be called before accmulate() is called." + + self.eval = _C.COCOevalAccumulate(self._paramsEval, self._evalImgs_cpp) + + # recall is num_iou_thresholds X num_categories X num_area_ranges X num_max_detections + self.eval["recall"] = np.array(self.eval["recall"]).reshape( + self.eval["counts"][:1] + self.eval["counts"][2:] + ) + + # precision and scores are num_iou_thresholds X num_recall_thresholds X num_categories X + # num_area_ranges X num_max_detections + self.eval["precision"] = np.array(self.eval["precision"]).reshape(self.eval["counts"]) + self.eval["scores"] = np.array(self.eval["scores"]).reshape(self.eval["counts"]) + toc = time.time() + logger.info("COCOeval_opt.accumulate() finished in {:0.2f} seconds.".format(toc - tic)) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/lvis_evaluation.py b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/lvis_evaluation.py new file mode 100644 index 0000000..0604fea --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/lvis_evaluation.py @@ -0,0 +1,380 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import itertools +import json +import logging +import os +import pickle +from collections import OrderedDict +import torch + +import detectron2.utils.comm as comm +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.structures import Boxes, BoxMode, pairwise_iou +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import create_small_table + +from .coco_evaluation import instances_to_coco_json +from .evaluator import DatasetEvaluator + + +class LVISEvaluator(DatasetEvaluator): + """ + Evaluate object proposal and instance detection/segmentation outputs using + LVIS's metrics and evaluation API. + """ + + def __init__( + self, + dataset_name, + tasks=None, + distributed=True, + output_dir=None, + *, + max_dets_per_image=None, + ): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + It must have the following corresponding metadata: + "json_file": the path to the LVIS format annotation + tasks (tuple[str]): tasks that can be evaluated under the given + configuration. A task is one of "bbox", "segm". + By default, will infer this automatically from predictions. + distributed (True): if True, will collect results from all ranks for evaluation. + Otherwise, will evaluate the results in the current process. + output_dir (str): optional, an output directory to dump results. + max_dets_per_image (None or int): limit on maximum detections per image in evaluating AP + This limit, by default of the LVIS dataset, is 300. + """ + from lvis import LVIS + + self._logger = logging.getLogger(__name__) + + if tasks is not None and isinstance(tasks, CfgNode): + self._logger.warn( + "COCO Evaluator instantiated using config, this is deprecated behavior." + " Please pass in explicit arguments instead." + ) + self._tasks = None # Infering it from predictions should be better + else: + self._tasks = tasks + + self._distributed = distributed + self._output_dir = output_dir + self._max_dets_per_image = max_dets_per_image + + self._cpu_device = torch.device("cpu") + + self._metadata = MetadataCatalog.get(dataset_name) + json_file = PathManager.get_local_path(self._metadata.json_file) + self._lvis_api = LVIS(json_file) + # Test set json files do not contain annotations (evaluation must be + # performed using the LVIS evaluation server). + self._do_evaluation = len(self._lvis_api.get_ann_ids()) > 0 + + def reset(self): + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a LVIS model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a LVIS model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + for input, output in zip(inputs, outputs): + prediction = {"image_id": input["image_id"]} + + if "instances" in output: + instances = output["instances"].to(self._cpu_device) + prediction["instances"] = instances_to_coco_json(instances, input["image_id"]) + if "proposals" in output: + prediction["proposals"] = output["proposals"].to(self._cpu_device) + self._predictions.append(prediction) + + def evaluate(self): + if self._distributed: + comm.synchronize() + predictions = comm.gather(self._predictions, dst=0) + predictions = list(itertools.chain(*predictions)) + + if not comm.is_main_process(): + return + else: + predictions = self._predictions + + if len(predictions) == 0: + self._logger.warning("[LVISEvaluator] Did not receive valid predictions.") + return {} + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "instances_predictions.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(predictions, f) + + self._results = OrderedDict() + if "proposals" in predictions[0]: + self._eval_box_proposals(predictions) + if "instances" in predictions[0]: + self._eval_predictions(predictions) + # Copy so the caller can do whatever with results + return copy.deepcopy(self._results) + + def _tasks_from_predictions(self, predictions): + for pred in predictions: + if "segmentation" in pred: + return ("bbox", "segm") + return ("bbox",) + + def _eval_predictions(self, predictions): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + + Args: + predictions (list[dict]): list of outputs from the model + """ + self._logger.info("Preparing results in the LVIS format ...") + lvis_results = list(itertools.chain(*[x["instances"] for x in predictions])) + tasks = self._tasks or self._tasks_from_predictions(lvis_results) + + # LVIS evaluator can be used to evaluate results for COCO dataset categories. + # In this case `_metadata` variable will have a field with COCO-specific category mapping. + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + reverse_id_mapping = { + v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items() + } + for result in lvis_results: + result["category_id"] = reverse_id_mapping[result["category_id"]] + else: + # unmap the category ids for LVIS (from 0-indexed to 1-indexed) + for result in lvis_results: + result["category_id"] += 1 + + if self._output_dir: + file_path = os.path.join(self._output_dir, "lvis_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(lvis_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating predictions ...") + for task in sorted(tasks): + res = _evaluate_predictions_on_lvis( + self._lvis_api, + lvis_results, + task, + max_dets_per_image=self._max_dets_per_image, + class_names=self._metadata.get("thing_classes"), + ) + self._results[task] = res + + def _eval_box_proposals(self, predictions): + """ + Evaluate the box proposals in predictions. + Fill self._results with the metrics for "box_proposals" task. + """ + if self._output_dir: + # Saving generated box proposals to file. + # Predicted box_proposals are in XYXY_ABS mode. + bbox_mode = BoxMode.XYXY_ABS.value + ids, boxes, objectness_logits = [], [], [] + for prediction in predictions: + ids.append(prediction["image_id"]) + boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy()) + objectness_logits.append(prediction["proposals"].objectness_logits.numpy()) + + proposal_data = { + "boxes": boxes, + "objectness_logits": objectness_logits, + "ids": ids, + "bbox_mode": bbox_mode, + } + with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f: + pickle.dump(proposal_data, f) + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating bbox proposals ...") + res = {} + areas = {"all": "", "small": "s", "medium": "m", "large": "l"} + for limit in [100, 1000]: + for area, suffix in areas.items(): + stats = _evaluate_box_proposals(predictions, self._lvis_api, area=area, limit=limit) + key = "AR{}@{:d}".format(suffix, limit) + res[key] = float(stats["ar"].item() * 100) + self._logger.info("Proposal metrics: \n" + create_small_table(res)) + self._results["box_proposals"] = res + + +# inspired from Detectron: +# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa +def _evaluate_box_proposals(dataset_predictions, lvis_api, thresholds=None, area="all", limit=None): + """ + Evaluate detection proposal recall metrics. This function is a much + faster alternative to the official LVIS API recall evaluation code. However, + it produces slightly different results. + """ + # Record max overlap value for each gt box + # Return vector of overlap values + areas = { + "all": 0, + "small": 1, + "medium": 2, + "large": 3, + "96-128": 4, + "128-256": 5, + "256-512": 6, + "512-inf": 7, + } + area_ranges = [ + [0 ** 2, 1e5 ** 2], # all + [0 ** 2, 32 ** 2], # small + [32 ** 2, 96 ** 2], # medium + [96 ** 2, 1e5 ** 2], # large + [96 ** 2, 128 ** 2], # 96-128 + [128 ** 2, 256 ** 2], # 128-256 + [256 ** 2, 512 ** 2], # 256-512 + [512 ** 2, 1e5 ** 2], + ] # 512-inf + assert area in areas, "Unknown area range: {}".format(area) + area_range = area_ranges[areas[area]] + gt_overlaps = [] + num_pos = 0 + + for prediction_dict in dataset_predictions: + predictions = prediction_dict["proposals"] + + # sort predictions in descending order + # TODO maybe remove this and make it explicit in the documentation + inds = predictions.objectness_logits.sort(descending=True)[1] + predictions = predictions[inds] + + ann_ids = lvis_api.get_ann_ids(img_ids=[prediction_dict["image_id"]]) + anno = lvis_api.load_anns(ann_ids) + gt_boxes = [ + BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) for obj in anno + ] + gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes + gt_boxes = Boxes(gt_boxes) + gt_areas = torch.as_tensor([obj["area"] for obj in anno]) + + if len(gt_boxes) == 0 or len(predictions) == 0: + continue + + valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1]) + gt_boxes = gt_boxes[valid_gt_inds] + + num_pos += len(gt_boxes) + + if len(gt_boxes) == 0: + continue + + if limit is not None and len(predictions) > limit: + predictions = predictions[:limit] + + overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes) + + _gt_overlaps = torch.zeros(len(gt_boxes)) + for j in range(min(len(predictions), len(gt_boxes))): + # find which proposal box maximally covers each gt box + # and get the iou amount of coverage for each gt box + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + + # find which gt box is 'best' covered (i.e. 'best' = most iou) + gt_ovr, gt_ind = max_overlaps.max(dim=0) + assert gt_ovr >= 0 + # find the proposal box that covers the best covered gt box + box_ind = argmax_overlaps[gt_ind] + # record the iou coverage of this gt box + _gt_overlaps[j] = overlaps[box_ind, gt_ind] + assert _gt_overlaps[j] == gt_ovr + # mark the proposal box and the gt box as used + overlaps[box_ind, :] = -1 + overlaps[:, gt_ind] = -1 + + # append recorded iou coverage level + gt_overlaps.append(_gt_overlaps) + gt_overlaps = ( + torch.cat(gt_overlaps, dim=0) if len(gt_overlaps) else torch.zeros(0, dtype=torch.float32) + ) + gt_overlaps, _ = torch.sort(gt_overlaps) + + if thresholds is None: + step = 0.05 + thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32) + recalls = torch.zeros_like(thresholds) + # compute recall for each iou threshold + for i, t in enumerate(thresholds): + recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos) + # ar = 2 * np.trapz(recalls, thresholds) + ar = recalls.mean() + return { + "ar": ar, + "recalls": recalls, + "thresholds": thresholds, + "gt_overlaps": gt_overlaps, + "num_pos": num_pos, + } + + +def _evaluate_predictions_on_lvis( + lvis_gt, lvis_results, iou_type, max_dets_per_image=None, class_names=None +): + """ + Args: + iou_type (str): + max_dets_per_image (None or int): limit on maximum detections per image in evaluating AP + This limit, by default of the LVIS dataset, is 300. + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + + Returns: + a dict of {metric name: score} + """ + metrics = { + "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"], + "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"], + }[iou_type] + + logger = logging.getLogger(__name__) + + if len(lvis_results) == 0: # TODO: check if needed + logger.warn("No predictions from the model!") + return {metric: float("nan") for metric in metrics} + + if iou_type == "segm": + lvis_results = copy.deepcopy(lvis_results) + # When evaluating mask AP, if the results contain bbox, LVIS API will + # use the box area as the area of the instance, instead of the mask area. + # This leads to a different definition of small/medium/large. + # We remove the bbox field to let mask AP use mask area. + for c in lvis_results: + c.pop("bbox", None) + + if max_dets_per_image is None: + max_dets_per_image = 300 # Default for LVIS dataset + + from lvis import LVISEval, LVISResults + + logger.info(f"Evaluating with max detections per image = {max_dets_per_image}") + lvis_results = LVISResults(lvis_gt, lvis_results, max_dets=max_dets_per_image) + lvis_eval = LVISEval(lvis_gt, lvis_results, iou_type) + lvis_eval.run() + lvis_eval.print_results() + + # Pull the standard metrics from the LVIS results + results = lvis_eval.get_results() + results = {metric: float(results[metric] * 100) for metric in metrics} + logger.info("Evaluation results for {}: \n".format(iou_type) + create_small_table(results)) + return results diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/panoptic_evaluation.py b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/panoptic_evaluation.py new file mode 100644 index 0000000..9fb3462 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/panoptic_evaluation.py @@ -0,0 +1,199 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import contextlib +import io +import itertools +import json +import logging +import numpy as np +import os +import tempfile +from collections import OrderedDict +from typing import Optional +from PIL import Image +from tabulate import tabulate + +from detectron2.data import MetadataCatalog +from detectron2.utils import comm +from detectron2.utils.file_io import PathManager + +from .evaluator import DatasetEvaluator + +logger = logging.getLogger(__name__) + + +class COCOPanopticEvaluator(DatasetEvaluator): + """ + Evaluate Panoptic Quality metrics on COCO using PanopticAPI. + It saves panoptic segmentation prediction in `output_dir` + + It contains a synchronize call and has to be called from all workers. + """ + + def __init__(self, dataset_name: str, output_dir: Optional[str] = None): + """ + Args: + dataset_name: name of the dataset + output_dir: output directory to save results for evaluation. + """ + self._metadata = MetadataCatalog.get(dataset_name) + self._thing_contiguous_id_to_dataset_id = { + v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items() + } + self._stuff_contiguous_id_to_dataset_id = { + v: k for k, v in self._metadata.stuff_dataset_id_to_contiguous_id.items() + } + + self._output_dir = output_dir + if self._output_dir is not None: + PathManager.mkdirs(self._output_dir) + + def reset(self): + self._predictions = [] + + def _convert_category_id(self, segment_info): + isthing = segment_info.pop("isthing", None) + if isthing is None: + # the model produces panoptic category id directly. No more conversion needed + return segment_info + if isthing is True: + segment_info["category_id"] = self._thing_contiguous_id_to_dataset_id[ + segment_info["category_id"] + ] + else: + segment_info["category_id"] = self._stuff_contiguous_id_to_dataset_id[ + segment_info["category_id"] + ] + return segment_info + + def process(self, inputs, outputs): + from panopticapi.utils import id2rgb + + for input, output in zip(inputs, outputs): + panoptic_img, segments_info = output["panoptic_seg"] + panoptic_img = panoptic_img.cpu().numpy() + if segments_info is None: + # If "segments_info" is None, we assume "panoptic_img" is a + # H*W int32 image storing the panoptic_id in the format of + # category_id * label_divisor + instance_id. We reserve -1 for + # VOID label, and add 1 to panoptic_img since the official + # evaluation script uses 0 for VOID label. + label_divisor = self._metadata.label_divisor + segments_info = [] + for panoptic_label in np.unique(panoptic_img): + if panoptic_label == -1: + # VOID region. + continue + pred_class = panoptic_label // label_divisor + isthing = ( + pred_class in self._metadata.thing_dataset_id_to_contiguous_id.values() + ) + segments_info.append( + { + "id": int(panoptic_label) + 1, + "category_id": int(pred_class), + "isthing": bool(isthing), + } + ) + # Official evaluation script uses 0 for VOID label. + panoptic_img += 1 + + file_name = os.path.basename(input["file_name"]) + file_name_png = os.path.splitext(file_name)[0] + ".png" + with io.BytesIO() as out: + Image.fromarray(id2rgb(panoptic_img)).save(out, format="PNG") + segments_info = [self._convert_category_id(x) for x in segments_info] + self._predictions.append( + { + "image_id": input["image_id"], + "file_name": file_name_png, + "png_string": out.getvalue(), + "segments_info": segments_info, + } + ) + + def evaluate(self): + comm.synchronize() + + self._predictions = comm.gather(self._predictions) + self._predictions = list(itertools.chain(*self._predictions)) + if not comm.is_main_process(): + return + + # PanopticApi requires local files + gt_json = PathManager.get_local_path(self._metadata.panoptic_json) + gt_folder = PathManager.get_local_path(self._metadata.panoptic_root) + + with tempfile.TemporaryDirectory(prefix="panoptic_eval") as pred_dir: + logger.info("Writing all panoptic predictions to {} ...".format(pred_dir)) + for p in self._predictions: + with open(os.path.join(pred_dir, p["file_name"]), "wb") as f: + f.write(p.pop("png_string")) + + with open(gt_json, "r") as f: + json_data = json.load(f) + json_data["annotations"] = self._predictions + + output_dir = self._output_dir or pred_dir + predictions_json = os.path.join(output_dir, "predictions.json") + with PathManager.open(predictions_json, "w") as f: + f.write(json.dumps(json_data)) + + from panopticapi.evaluation import pq_compute + + with contextlib.redirect_stdout(io.StringIO()): + pq_res = pq_compute( + gt_json, + PathManager.get_local_path(predictions_json), + gt_folder=gt_folder, + pred_folder=pred_dir, + ) + + res = {} + res["PQ"] = 100 * pq_res["All"]["pq"] + res["SQ"] = 100 * pq_res["All"]["sq"] + res["RQ"] = 100 * pq_res["All"]["rq"] + res["PQ_th"] = 100 * pq_res["Things"]["pq"] + res["SQ_th"] = 100 * pq_res["Things"]["sq"] + res["RQ_th"] = 100 * pq_res["Things"]["rq"] + res["PQ_st"] = 100 * pq_res["Stuff"]["pq"] + res["SQ_st"] = 100 * pq_res["Stuff"]["sq"] + res["RQ_st"] = 100 * pq_res["Stuff"]["rq"] + + results = OrderedDict({"panoptic_seg": res}) + _print_panoptic_results(pq_res) + + return results + + +def _print_panoptic_results(pq_res): + headers = ["", "PQ", "SQ", "RQ", "#categories"] + data = [] + for name in ["All", "Things", "Stuff"]: + row = [name] + [pq_res[name][k] * 100 for k in ["pq", "sq", "rq"]] + [pq_res[name]["n"]] + data.append(row) + table = tabulate( + data, headers=headers, tablefmt="pipe", floatfmt=".3f", stralign="center", numalign="center" + ) + logger.info("Panoptic Evaluation Results:\n" + table) + + +if __name__ == "__main__": + from detectron2.utils.logger import setup_logger + + logger = setup_logger() + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--gt-json") + parser.add_argument("--gt-dir") + parser.add_argument("--pred-json") + parser.add_argument("--pred-dir") + args = parser.parse_args() + + from panopticapi.evaluation import pq_compute + + with contextlib.redirect_stdout(io.StringIO()): + pq_res = pq_compute( + args.gt_json, args.pred_json, gt_folder=args.gt_dir, pred_folder=args.pred_dir + ) + _print_panoptic_results(pq_res) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/pascal_voc_evaluation.py b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/pascal_voc_evaluation.py new file mode 100644 index 0000000..1d1abcd --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/pascal_voc_evaluation.py @@ -0,0 +1,300 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import logging +import numpy as np +import os +import tempfile +import xml.etree.ElementTree as ET +from collections import OrderedDict, defaultdict +from functools import lru_cache +import torch + +from detectron2.data import MetadataCatalog +from detectron2.utils import comm +from detectron2.utils.file_io import PathManager + +from .evaluator import DatasetEvaluator + + +class PascalVOCDetectionEvaluator(DatasetEvaluator): + """ + Evaluate Pascal VOC style AP for Pascal VOC dataset. + It contains a synchronization, therefore has to be called from all ranks. + + Note that the concept of AP can be implemented in different ways and may not + produce identical results. This class mimics the implementation of the official + Pascal VOC Matlab API, and should produce similar but not identical results to the + official API. + """ + + def __init__(self, dataset_name): + """ + Args: + dataset_name (str): name of the dataset, e.g., "voc_2007_test" + """ + self._dataset_name = dataset_name + meta = MetadataCatalog.get(dataset_name) + + # Too many tiny files, download all to local for speed. + annotation_dir_local = PathManager.get_local_path( + os.path.join(meta.dirname, "Annotations/") + ) + self._anno_file_template = os.path.join(annotation_dir_local, "{}.xml") + self._image_set_path = os.path.join(meta.dirname, "ImageSets", "Main", meta.split + ".txt") + self._class_names = meta.thing_classes + assert meta.year in [2007, 2012], meta.year + self._is_2007 = meta.year == 2007 + self._cpu_device = torch.device("cpu") + self._logger = logging.getLogger(__name__) + + def reset(self): + self._predictions = defaultdict(list) # class name -> list of prediction strings + + def process(self, inputs, outputs): + for input, output in zip(inputs, outputs): + image_id = input["image_id"] + instances = output["instances"].to(self._cpu_device) + boxes = instances.pred_boxes.tensor.numpy() + scores = instances.scores.tolist() + classes = instances.pred_classes.tolist() + for box, score, cls in zip(boxes, scores, classes): + xmin, ymin, xmax, ymax = box + # The inverse of data loading logic in `datasets/pascal_voc.py` + xmin += 1 + ymin += 1 + self._predictions[cls].append( + f"{image_id} {score:.3f} {xmin:.1f} {ymin:.1f} {xmax:.1f} {ymax:.1f}" + ) + + def evaluate(self): + """ + Returns: + dict: has a key "segm", whose value is a dict of "AP", "AP50", and "AP75". + """ + all_predictions = comm.gather(self._predictions, dst=0) + if not comm.is_main_process(): + return + predictions = defaultdict(list) + for predictions_per_rank in all_predictions: + for clsid, lines in predictions_per_rank.items(): + predictions[clsid].extend(lines) + del all_predictions + + self._logger.info( + "Evaluating {} using {} metric. " + "Note that results do not use the official Matlab API.".format( + self._dataset_name, 2007 if self._is_2007 else 2012 + ) + ) + + with tempfile.TemporaryDirectory(prefix="pascal_voc_eval_") as dirname: + res_file_template = os.path.join(dirname, "{}.txt") + + aps = defaultdict(list) # iou -> ap per class + for cls_id, cls_name in enumerate(self._class_names): + lines = predictions.get(cls_id, [""]) + + with open(res_file_template.format(cls_name), "w") as f: + f.write("\n".join(lines)) + + for thresh in range(50, 100, 5): + rec, prec, ap = voc_eval( + res_file_template, + self._anno_file_template, + self._image_set_path, + cls_name, + ovthresh=thresh / 100.0, + use_07_metric=self._is_2007, + ) + aps[thresh].append(ap * 100) + + ret = OrderedDict() + mAP = {iou: np.mean(x) for iou, x in aps.items()} + ret["bbox"] = {"AP": np.mean(list(mAP.values())), "AP50": mAP[50], "AP75": mAP[75]} + return ret + + +############################################################################## +# +# Below code is modified from +# https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py +# -------------------------------------------------------- +# Fast/er R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Bharath Hariharan +# -------------------------------------------------------- + +"""Python implementation of the PASCAL VOC devkit's AP evaluation code.""" + + +@lru_cache(maxsize=None) +def parse_rec(filename): + """Parse a PASCAL VOC xml file.""" + with PathManager.open(filename) as f: + tree = ET.parse(f) + objects = [] + for obj in tree.findall("object"): + obj_struct = {} + obj_struct["name"] = obj.find("name").text + obj_struct["pose"] = obj.find("pose").text + obj_struct["truncated"] = int(obj.find("truncated").text) + obj_struct["difficult"] = int(obj.find("difficult").text) + bbox = obj.find("bndbox") + obj_struct["bbox"] = [ + int(bbox.find("xmin").text), + int(bbox.find("ymin").text), + int(bbox.find("xmax").text), + int(bbox.find("ymax").text), + ] + objects.append(obj_struct) + + return objects + + +def voc_ap(rec, prec, use_07_metric=False): + """Compute VOC AP given precision and recall. If use_07_metric is true, uses + the VOC 07 11-point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0.0 + for t in np.arange(0.0, 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11.0 + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.0], rec, [1.0])) + mpre = np.concatenate(([0.0], prec, [0.0])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + +def voc_eval(detpath, annopath, imagesetfile, classname, ovthresh=0.5, use_07_metric=False): + """rec, prec, ap = voc_eval(detpath, + annopath, + imagesetfile, + classname, + [ovthresh], + [use_07_metric]) + + Top level function that does the PASCAL VOC evaluation. + + detpath: Path to detections + detpath.format(classname) should produce the detection results file. + annopath: Path to annotations + annopath.format(imagename) should be the xml annotations file. + imagesetfile: Text file containing the list of images, one image per line. + classname: Category name (duh) + [ovthresh]: Overlap threshold (default = 0.5) + [use_07_metric]: Whether to use VOC07's 11 point AP computation + (default False) + """ + # assumes detections are in detpath.format(classname) + # assumes annotations are in annopath.format(imagename) + # assumes imagesetfile is a text file with each line an image name + + # first load gt + # read list of images + with PathManager.open(imagesetfile, "r") as f: + lines = f.readlines() + imagenames = [x.strip() for x in lines] + + # load annots + recs = {} + for imagename in imagenames: + recs[imagename] = parse_rec(annopath.format(imagename)) + + # extract gt objects for this class + class_recs = {} + npos = 0 + for imagename in imagenames: + R = [obj for obj in recs[imagename] if obj["name"] == classname] + bbox = np.array([x["bbox"] for x in R]) + difficult = np.array([x["difficult"] for x in R]).astype(np.bool) + # difficult = np.array([False for x in R]).astype(np.bool) # treat all "difficult" as GT + det = [False] * len(R) + npos = npos + sum(~difficult) + class_recs[imagename] = {"bbox": bbox, "difficult": difficult, "det": det} + + # read dets + detfile = detpath.format(classname) + with open(detfile, "r") as f: + lines = f.readlines() + + splitlines = [x.strip().split(" ") for x in lines] + image_ids = [x[0] for x in splitlines] + confidence = np.array([float(x[1]) for x in splitlines]) + BB = np.array([[float(z) for z in x[2:]] for x in splitlines]).reshape(-1, 4) + + # sort by confidence + sorted_ind = np.argsort(-confidence) + BB = BB[sorted_ind, :] + image_ids = [image_ids[x] for x in sorted_ind] + + # go down dets and mark TPs and FPs + nd = len(image_ids) + tp = np.zeros(nd) + fp = np.zeros(nd) + for d in range(nd): + R = class_recs[image_ids[d]] + bb = BB[d, :].astype(float) + ovmax = -np.inf + BBGT = R["bbox"].astype(float) + + if BBGT.size > 0: + # compute overlaps + # intersection + ixmin = np.maximum(BBGT[:, 0], bb[0]) + iymin = np.maximum(BBGT[:, 1], bb[1]) + ixmax = np.minimum(BBGT[:, 2], bb[2]) + iymax = np.minimum(BBGT[:, 3], bb[3]) + iw = np.maximum(ixmax - ixmin + 1.0, 0.0) + ih = np.maximum(iymax - iymin + 1.0, 0.0) + inters = iw * ih + + # union + uni = ( + (bb[2] - bb[0] + 1.0) * (bb[3] - bb[1] + 1.0) + + (BBGT[:, 2] - BBGT[:, 0] + 1.0) * (BBGT[:, 3] - BBGT[:, 1] + 1.0) + - inters + ) + + overlaps = inters / uni + ovmax = np.max(overlaps) + jmax = np.argmax(overlaps) + + if ovmax > ovthresh: + if not R["difficult"][jmax]: + if not R["det"][jmax]: + tp[d] = 1.0 + R["det"][jmax] = 1 + else: + fp[d] = 1.0 + else: + fp[d] = 1.0 + + # compute precision recall + fp = np.cumsum(fp) + tp = np.cumsum(tp) + rec = tp / float(npos) + # avoid divide by zero in case the first detection matches a difficult + # ground truth + prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) + ap = voc_ap(rec, prec, use_07_metric) + + return rec, prec, ap diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/rotated_coco_evaluation.py b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/rotated_coco_evaluation.py new file mode 100644 index 0000000..ea6d1b3 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/rotated_coco_evaluation.py @@ -0,0 +1,207 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +import json +import numpy as np +import os +import torch +from pycocotools.cocoeval import COCOeval, maskUtils + +from detectron2.structures import BoxMode, RotatedBoxes, pairwise_iou_rotated +from detectron2.utils.file_io import PathManager + +from .coco_evaluation import COCOEvaluator + + +class RotatedCOCOeval(COCOeval): + @staticmethod + def is_rotated(box_list): + if type(box_list) == np.ndarray: + return box_list.shape[1] == 5 + elif type(box_list) == list: + if box_list == []: # cannot decide the box_dim + return False + return np.all( + np.array( + [ + (len(obj) == 5) and ((type(obj) == list) or (type(obj) == np.ndarray)) + for obj in box_list + ] + ) + ) + return False + + @staticmethod + def boxlist_to_tensor(boxlist, output_box_dim): + if type(boxlist) == np.ndarray: + box_tensor = torch.from_numpy(boxlist) + elif type(boxlist) == list: + if boxlist == []: + return torch.zeros((0, output_box_dim), dtype=torch.float32) + else: + box_tensor = torch.FloatTensor(boxlist) + else: + raise Exception("Unrecognized boxlist type") + + input_box_dim = box_tensor.shape[1] + if input_box_dim != output_box_dim: + if input_box_dim == 4 and output_box_dim == 5: + box_tensor = BoxMode.convert(box_tensor, BoxMode.XYWH_ABS, BoxMode.XYWHA_ABS) + else: + raise Exception( + "Unable to convert from {}-dim box to {}-dim box".format( + input_box_dim, output_box_dim + ) + ) + return box_tensor + + def compute_iou_dt_gt(self, dt, gt, is_crowd): + if self.is_rotated(dt) or self.is_rotated(gt): + # TODO: take is_crowd into consideration + assert all(c == 0 for c in is_crowd) + dt = RotatedBoxes(self.boxlist_to_tensor(dt, output_box_dim=5)) + gt = RotatedBoxes(self.boxlist_to_tensor(gt, output_box_dim=5)) + return pairwise_iou_rotated(dt, gt) + else: + # This is the same as the classical COCO evaluation + return maskUtils.iou(dt, gt, is_crowd) + + def computeIoU(self, imgId, catId): + p = self.params + if p.useCats: + gt = self._gts[imgId, catId] + dt = self._dts[imgId, catId] + else: + gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]] + dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]] + if len(gt) == 0 and len(dt) == 0: + return [] + inds = np.argsort([-d["score"] for d in dt], kind="mergesort") + dt = [dt[i] for i in inds] + if len(dt) > p.maxDets[-1]: + dt = dt[0 : p.maxDets[-1]] + + assert p.iouType == "bbox", "unsupported iouType for iou computation" + + g = [g["bbox"] for g in gt] + d = [d["bbox"] for d in dt] + + # compute iou between each dt and gt region + iscrowd = [int(o["iscrowd"]) for o in gt] + + # Note: this function is copied from cocoeval.py in cocoapi + # and the major difference is here. + ious = self.compute_iou_dt_gt(d, g, iscrowd) + return ious + + +class RotatedCOCOEvaluator(COCOEvaluator): + """ + Evaluate object proposal/instance detection outputs using COCO-like metrics and APIs, + with rotated boxes support. + Note: this uses IOU only and does not consider angle differences. + """ + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a COCO model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + for input, output in zip(inputs, outputs): + prediction = {"image_id": input["image_id"]} + + if "instances" in output: + instances = output["instances"].to(self._cpu_device) + + prediction["instances"] = self.instances_to_json(instances, input["image_id"]) + if "proposals" in output: + prediction["proposals"] = output["proposals"].to(self._cpu_device) + self._predictions.append(prediction) + + def instances_to_json(self, instances, img_id): + num_instance = len(instances) + if num_instance == 0: + return [] + + boxes = instances.pred_boxes.tensor.numpy() + if boxes.shape[1] == 4: + boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + boxes = boxes.tolist() + scores = instances.scores.tolist() + classes = instances.pred_classes.tolist() + + results = [] + for k in range(num_instance): + result = { + "image_id": img_id, + "category_id": classes[k], + "bbox": boxes[k], + "score": scores[k], + } + + results.append(result) + return results + + def _eval_predictions(self, predictions, img_ids=None): # img_ids: unused + """ + Evaluate predictions on the given tasks. + Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for COCO format ...") + coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) + + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + reverse_id_mapping = { + v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items() + } + for result in coco_results: + result["category_id"] = reverse_id_mapping[result["category_id"]] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "coco_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(coco_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating predictions ...") + + assert self._tasks is None or set(self._tasks) == { + "bbox" + }, "[RotatedCOCOEvaluator] Only bbox evaluation is supported" + coco_eval = ( + self._evaluate_predictions_on_coco(self._coco_api, coco_results) + if len(coco_results) > 0 + else None # cocoapi does not handle empty results very well + ) + + task = "bbox" + res = self._derive_coco_results( + coco_eval, task, class_names=self._metadata.get("thing_classes") + ) + self._results[task] = res + + def _evaluate_predictions_on_coco(self, coco_gt, coco_results): + """ + Evaluate the coco results using COCOEval API. + """ + assert len(coco_results) > 0 + + coco_dt = coco_gt.loadRes(coco_results) + + # Only bbox is supported for now + coco_eval = RotatedCOCOeval(coco_gt, coco_dt, iouType="bbox") + + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + + return coco_eval diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/sem_seg_evaluation.py b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/sem_seg_evaluation.py new file mode 100644 index 0000000..7a19db7 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/sem_seg_evaluation.py @@ -0,0 +1,184 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +import json +import logging +import numpy as np +import os +from collections import OrderedDict +import PIL.Image as Image +import pycocotools.mask as mask_util +import torch + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.utils.comm import all_gather, is_main_process, synchronize +from detectron2.utils.file_io import PathManager + +from .evaluator import DatasetEvaluator + + +class SemSegEvaluator(DatasetEvaluator): + """ + Evaluate semantic segmentation metrics. + """ + + def __init__( + self, + dataset_name, + distributed=True, + output_dir=None, + *, + num_classes=None, + ignore_label=None, + ): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + distributed (bool): if True, will collect results from all ranks for evaluation. + Otherwise, will evaluate the results in the current process. + output_dir (str): an output directory to dump results. + num_classes, ignore_label: deprecated argument + """ + self._logger = logging.getLogger(__name__) + if num_classes is not None: + self._logger.warn( + "SemSegEvaluator(num_classes) is deprecated! It should be obtained from metadata." + ) + if ignore_label is not None: + self._logger.warn( + "SemSegEvaluator(ignore_label) is deprecated! It should be obtained from metadata." + ) + self._dataset_name = dataset_name + self._distributed = distributed + self._output_dir = output_dir + + self._cpu_device = torch.device("cpu") + + self.input_file_to_gt_file = { + dataset_record["file_name"]: dataset_record["sem_seg_file_name"] + for dataset_record in DatasetCatalog.get(dataset_name) + } + + meta = MetadataCatalog.get(dataset_name) + # Dict that maps contiguous training ids to COCO category ids + try: + c2d = meta.stuff_dataset_id_to_contiguous_id + self._contiguous_id_to_dataset_id = {v: k for k, v in c2d.items()} + except AttributeError: + self._contiguous_id_to_dataset_id = None + self._class_names = meta.stuff_classes + self._num_classes = len(meta.stuff_classes) + if num_classes is not None: + assert self._num_classes == num_classes, f"{self._num_classes} != {num_classes}" + self._ignore_label = ignore_label if ignore_label is not None else meta.ignore_label + + def reset(self): + self._conf_matrix = np.zeros((self._num_classes + 1, self._num_classes + 1), dtype=np.int64) + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a model. + It is a list of dicts. Each dict corresponds to an image and + contains keys like "height", "width", "file_name". + outputs: the outputs of a model. It is either list of semantic segmentation predictions + (Tensor [H, W]) or list of dicts with key "sem_seg" that contains semantic + segmentation prediction in the same format. + """ + for input, output in zip(inputs, outputs): + output = output["sem_seg"].argmax(dim=0).to(self._cpu_device) + pred = np.array(output, dtype=np.int) + with PathManager.open(self.input_file_to_gt_file[input["file_name"]], "rb") as f: + gt = np.array(Image.open(f), dtype=np.int) + + gt[gt == self._ignore_label] = self._num_classes + + self._conf_matrix += np.bincount( + (self._num_classes + 1) * pred.reshape(-1) + gt.reshape(-1), + minlength=self._conf_matrix.size, + ).reshape(self._conf_matrix.shape) + + self._predictions.extend(self.encode_json_sem_seg(pred, input["file_name"])) + + def evaluate(self): + """ + Evaluates standard semantic segmentation metrics (http://cocodataset.org/#stuff-eval): + + * Mean intersection-over-union averaged across classes (mIoU) + * Frequency Weighted IoU (fwIoU) + * Mean pixel accuracy averaged across classes (mACC) + * Pixel Accuracy (pACC) + """ + if self._distributed: + synchronize() + conf_matrix_list = all_gather(self._conf_matrix) + self._predictions = all_gather(self._predictions) + self._predictions = list(itertools.chain(*self._predictions)) + if not is_main_process(): + return + + self._conf_matrix = np.zeros_like(self._conf_matrix) + for conf_matrix in conf_matrix_list: + self._conf_matrix += conf_matrix + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "sem_seg_predictions.json") + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(self._predictions)) + + acc = np.full(self._num_classes, np.nan, dtype=np.float) + iou = np.full(self._num_classes, np.nan, dtype=np.float) + tp = self._conf_matrix.diagonal()[:-1].astype(np.float) + pos_gt = np.sum(self._conf_matrix[:-1, :-1], axis=0).astype(np.float) + class_weights = pos_gt / np.sum(pos_gt) + pos_pred = np.sum(self._conf_matrix[:-1, :-1], axis=1).astype(np.float) + acc_valid = pos_gt > 0 + acc[acc_valid] = tp[acc_valid] / pos_gt[acc_valid] + iou_valid = (pos_gt + pos_pred) > 0 + union = pos_gt + pos_pred - tp + iou[acc_valid] = tp[acc_valid] / union[acc_valid] + macc = np.sum(acc[acc_valid]) / np.sum(acc_valid) + miou = np.sum(iou[acc_valid]) / np.sum(iou_valid) + fiou = np.sum(iou[acc_valid] * class_weights[acc_valid]) + pacc = np.sum(tp) / np.sum(pos_gt) + + res = {} + res["mIoU"] = 100 * miou + res["fwIoU"] = 100 * fiou + for i, name in enumerate(self._class_names): + res["IoU-{}".format(name)] = 100 * iou[i] + res["mACC"] = 100 * macc + res["pACC"] = 100 * pacc + for i, name in enumerate(self._class_names): + res["ACC-{}".format(name)] = 100 * acc[i] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "sem_seg_evaluation.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(res, f) + results = OrderedDict({"sem_seg": res}) + self._logger.info(results) + return results + + def encode_json_sem_seg(self, sem_seg, input_file_name): + """ + Convert semantic segmentation to COCO stuff format with segments encoded as RLEs. + See http://cocodataset.org/#format-results + """ + json_list = [] + for label in np.unique(sem_seg): + if self._contiguous_id_to_dataset_id is not None: + assert ( + label in self._contiguous_id_to_dataset_id + ), "Label {} is not in the metadata info for {}".format(label, self._dataset_name) + dataset_id = self._contiguous_id_to_dataset_id[label] + else: + dataset_id = int(label) + mask = (sem_seg == label).astype(np.uint8) + mask_rle = mask_util.encode(np.array(mask[:, :, None], order="F"))[0] + mask_rle["counts"] = mask_rle["counts"].decode("utf-8") + json_list.append( + {"file_name": input_file_name, "category_id": dataset_id, "segmentation": mask_rle} + ) + return json_list diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/testing.py b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/testing.py new file mode 100644 index 0000000..9e5ae62 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/evaluation/testing.py @@ -0,0 +1,85 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import numpy as np +import pprint +import sys +from collections.abc import Mapping + + +def print_csv_format(results): + """ + Print main metrics in a format similar to Detectron, + so that they are easy to copypaste into a spreadsheet. + + Args: + results (OrderedDict[dict]): task_name -> {metric -> score} + unordered dict can also be printed, but in arbitrary order + """ + assert isinstance(results, Mapping) or not len(results), results + logger = logging.getLogger(__name__) + for task, res in results.items(): + if isinstance(res, Mapping): + # Don't print "AP-category" metrics since they are usually not tracked. + important_res = [(k, v) for k, v in res.items() if "-" not in k] + logger.info("copypaste: Task: {}".format(task)) + logger.info("copypaste: " + ",".join([k[0] for k in important_res])) + logger.info("copypaste: " + ",".join(["{0:.4f}".format(k[1]) for k in important_res])) + else: + logger.info(f"copypaste: {task}={res}") + + +def verify_results(cfg, results): + """ + Args: + results (OrderedDict[dict]): task_name -> {metric -> score} + + Returns: + bool: whether the verification succeeds or not + """ + expected_results = cfg.TEST.EXPECTED_RESULTS + if not len(expected_results): + return True + + ok = True + for task, metric, expected, tolerance in expected_results: + actual = results[task].get(metric, None) + if actual is None: + ok = False + continue + if not np.isfinite(actual): + ok = False + continue + diff = abs(actual - expected) + if diff > tolerance: + ok = False + + logger = logging.getLogger(__name__) + if not ok: + logger.error("Result verification failed!") + logger.error("Expected Results: " + str(expected_results)) + logger.error("Actual Results: " + pprint.pformat(results)) + + sys.exit(1) + else: + logger.info("Results verification passed.") + return ok + + +def flatten_results_dict(results): + """ + Expand a hierarchical dict of scalars into a flat dict of scalars. + If results[k1][k2][k3] = v, the returned dict will have the entry + {"k1/k2/k3": v}. + + Args: + results (dict): + """ + r = {} + for k, v in results.items(): + if isinstance(v, Mapping): + v = flatten_results_dict(v) + for kk, vv in v.items(): + r[k + "/" + kk] = vv + else: + r[k] = v + return r diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/export/README.md b/motion-gan-pipeline/preprocessing/third/detectron2/export/README.md new file mode 100644 index 0000000..9fcd335 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/export/README.md @@ -0,0 +1,13 @@ + +This directory contains code to prepare a detectron2 model for deployment. +Currently it supports exporting a detectron2 model to Caffe2 format through ONNX. + +Please see [documentation](https://detectron2.readthedocs.io/tutorials/deployment.html) for its usage. + + +### Acknowledgements + +Thanks to Mobile Vision team at Facebook for developing the Caffe2 conversion tools. + +Thanks to Computing Platform Department - PAI team at Alibaba Group (@bddpqq, @chenbohua3) who +help export Detectron2 models to TorchScript. diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/export/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/export/__init__.py new file mode 100644 index 0000000..25e5c94 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/export/__init__.py @@ -0,0 +1,15 @@ +# -*- coding: utf-8 -*- + +try: + from caffe2.proto import caffe2_pb2 as _tmp + + # caffe2 is optional +except ImportError: + pass +else: + from .api import * + +from .flatten import TracingAdapter +from .torchscript import scripting_with_instances, dump_torchscript_IR + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/export/api.py b/motion-gan-pipeline/preprocessing/third/detectron2/export/api.py new file mode 100644 index 0000000..ad42721 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/export/api.py @@ -0,0 +1,235 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import logging +import os +import torch +from caffe2.proto import caffe2_pb2 +from torch import nn + +from detectron2.config import CfgNode +from detectron2.utils.file_io import PathManager + +from .caffe2_inference import ProtobufDetectionModel +from .caffe2_modeling import META_ARCH_CAFFE2_EXPORT_TYPE_MAP, convert_batched_inputs_to_c2_format +from .shared import get_pb_arg_vali, get_pb_arg_vals, save_graph + +__all__ = [ + "add_export_config", + "Caffe2Model", + "Caffe2Tracer", +] + + +def add_export_config(cfg): + return cfg + + +class Caffe2Tracer: + """ + Make a detectron2 model traceable with Caffe2 operators. + This class creates a traceable version of a detectron2 model which: + + 1. Rewrite parts of the model using ops in Caffe2. Note that some ops do + not have GPU implementation in Caffe2. + 2. Remove post-processing and only produce raw layer outputs + + After making a traceable model, the class provide methods to export such a + model to different deployment formats. + Exported graph produced by this class take two input tensors: + + 1. (1, C, H, W) float "data" which is an image (usually in [0, 255]). + (H, W) often has to be padded to multiple of 32 (depend on the model + architecture). + 2. 1x3 float "im_info", each row of which is (height, width, 1.0). + Height and width are true image shapes before padding. + + The class currently only supports models using builtin meta architectures. + Batch inference is not supported, and contributions are welcome. + """ + + def __init__(self, cfg: CfgNode, model: nn.Module, inputs): + """ + Args: + cfg (CfgNode): a detectron2 config used to construct caffe2-compatible model. + model (nn.Module): An original pytorch model. Must be among a few official models + in detectron2 that can be converted to become caffe2-compatible automatically. + Weights have to be already loaded to this model. + inputs: sample inputs that the given model takes for inference. + Will be used to trace the model. For most models, random inputs with + no detected objects will not work as they lead to wrong traces. + """ + assert isinstance(cfg, CfgNode), cfg + assert isinstance(model, torch.nn.Module), type(model) + + # TODO make it support custom models, by passing in c2 model directly + C2MetaArch = META_ARCH_CAFFE2_EXPORT_TYPE_MAP[cfg.MODEL.META_ARCHITECTURE] + self.traceable_model = C2MetaArch(cfg, copy.deepcopy(model)) + self.inputs = inputs + self.traceable_inputs = self.traceable_model.get_caffe2_inputs(inputs) + + def export_caffe2(self): + """ + Export the model to Caffe2's protobuf format. + The returned object can be saved with its :meth:`.save_protobuf()` method. + The result can be loaded and executed using Caffe2 runtime. + + Returns: + :class:`Caffe2Model` + """ + from .caffe2_export import export_caffe2_detection_model + + predict_net, init_net = export_caffe2_detection_model( + self.traceable_model, self.traceable_inputs + ) + return Caffe2Model(predict_net, init_net) + + def export_onnx(self): + """ + Export the model to ONNX format. + Note that the exported model contains custom ops only available in caffe2, therefore it + cannot be directly executed by other runtime (such as onnxruntime or TensorRT). + Post-processing or transformation passes may be applied on the model to accommodate + different runtimes, but we currently do not provide support for them. + + Returns: + onnx.ModelProto: an onnx model. + """ + from .caffe2_export import export_onnx_model as export_onnx_model_impl + + return export_onnx_model_impl(self.traceable_model, (self.traceable_inputs,)) + + def export_torchscript(self): + """ + Export the model to a ``torch.jit.TracedModule`` by tracing. + The returned object can be saved to a file by ``.save()``. + + Returns: + torch.jit.TracedModule: a torch TracedModule + """ + logger = logging.getLogger(__name__) + logger.info("Tracing the model with torch.jit.trace ...") + with torch.no_grad(): + return torch.jit.trace(self.traceable_model, (self.traceable_inputs,)) + + +class Caffe2Model(nn.Module): + """ + A wrapper around the traced model in Caffe2's protobuf format. + The exported graph has different inputs/outputs from the original Pytorch + model, as explained in :class:`Caffe2Tracer`. This class wraps around the + exported graph to simulate the same interface as the original Pytorch model. + It also provides functions to save/load models in Caffe2's format.' + + Examples: + :: + c2_model = Caffe2Tracer(cfg, torch_model, inputs).export_caffe2() + inputs = [{"image": img_tensor_CHW}] + outputs = c2_model(inputs) + orig_outputs = torch_model(inputs) + """ + + def __init__(self, predict_net, init_net): + super().__init__() + self.eval() # always in eval mode + self._predict_net = predict_net + self._init_net = init_net + self._predictor = None + + __init__.__HIDE_SPHINX_DOC__ = True + + @property + def predict_net(self): + """ + caffe2.core.Net: the underlying caffe2 predict net + """ + return self._predict_net + + @property + def init_net(self): + """ + caffe2.core.Net: the underlying caffe2 init net + """ + return self._init_net + + def save_protobuf(self, output_dir): + """ + Save the model as caffe2's protobuf format. + It saves the following files: + + * "model.pb": definition of the graph. Can be visualized with + tools like `netron `_. + * "model_init.pb": model parameters + * "model.pbtxt": human-readable definition of the graph. Not + needed for deployment. + + Args: + output_dir (str): the output directory to save protobuf files. + """ + logger = logging.getLogger(__name__) + logger.info("Saving model to {} ...".format(output_dir)) + if not PathManager.exists(output_dir): + PathManager.mkdirs(output_dir) + + with PathManager.open(os.path.join(output_dir, "model.pb"), "wb") as f: + f.write(self._predict_net.SerializeToString()) + with PathManager.open(os.path.join(output_dir, "model.pbtxt"), "w") as f: + f.write(str(self._predict_net)) + with PathManager.open(os.path.join(output_dir, "model_init.pb"), "wb") as f: + f.write(self._init_net.SerializeToString()) + + def save_graph(self, output_file, inputs=None): + """ + Save the graph as SVG format. + + Args: + output_file (str): a SVG file + inputs: optional inputs given to the model. + If given, the inputs will be used to run the graph to record + shape of every tensor. The shape information will be + saved together with the graph. + """ + from .caffe2_export import run_and_save_graph + + if inputs is None: + save_graph(self._predict_net, output_file, op_only=False) + else: + size_divisibility = get_pb_arg_vali(self._predict_net, "size_divisibility", 0) + device = get_pb_arg_vals(self._predict_net, "device", b"cpu").decode("ascii") + inputs = convert_batched_inputs_to_c2_format(inputs, size_divisibility, device) + inputs = [x.cpu().numpy() for x in inputs] + run_and_save_graph(self._predict_net, self._init_net, inputs, output_file) + + @staticmethod + def load_protobuf(dir): + """ + Args: + dir (str): a directory used to save Caffe2Model with + :meth:`save_protobuf`. + The files "model.pb" and "model_init.pb" are needed. + + Returns: + Caffe2Model: the caffe2 model loaded from this directory. + """ + predict_net = caffe2_pb2.NetDef() + with PathManager.open(os.path.join(dir, "model.pb"), "rb") as f: + predict_net.ParseFromString(f.read()) + + init_net = caffe2_pb2.NetDef() + with PathManager.open(os.path.join(dir, "model_init.pb"), "rb") as f: + init_net.ParseFromString(f.read()) + + return Caffe2Model(predict_net, init_net) + + def __call__(self, inputs): + """ + An interface that wraps around a Caffe2 model and mimics detectron2's models' + input/output format. See details about the format at :doc:`/tutorials/models`. + This is used to compare the outputs of caffe2 model with its original torch model. + + Due to the extra conversion between Pytorch/Caffe2, this method is not meant for + benchmark. Because of the conversion, this method also has dependency + on detectron2 in order to convert to detectron2's output format. + """ + if self._predictor is None: + self._predictor = ProtobufDetectionModel(self._predict_net, self._init_net) + return self._predictor(inputs) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/export/c10.py b/motion-gan-pipeline/preprocessing/third/detectron2/export/c10.py new file mode 100644 index 0000000..25ee230 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/export/c10.py @@ -0,0 +1,534 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import math +import torch +import torch.nn.functional as F + +from detectron2.layers import cat +from detectron2.layers.roi_align_rotated import ROIAlignRotated +from detectron2.modeling import poolers +from detectron2.modeling.proposal_generator import rpn +from detectron2.modeling.roi_heads.mask_head import mask_rcnn_inference +from detectron2.structures import Boxes, ImageList, Instances, Keypoints + +from .shared import alias, to_device + + +""" +This file contains caffe2-compatible implementation of several detectron2 components. +""" + + +class Caffe2Boxes(Boxes): + """ + Representing a list of detectron2.structures.Boxes from minibatch, each box + is represented by a 5d vector (batch index + 4 coordinates), or a 6d vector + (batch index + 5 coordinates) for RotatedBoxes. + """ + + def __init__(self, tensor): + assert isinstance(tensor, torch.Tensor) + assert tensor.dim() == 2 and tensor.size(-1) in [4, 5, 6], tensor.size() + # TODO: make tensor immutable when dim is Nx5 for Boxes, + # and Nx6 for RotatedBoxes? + self.tensor = tensor + + +# TODO clean up this class, maybe just extend Instances +class InstancesList(object): + """ + Tensor representation of a list of Instances object for a batch of images. + + When dealing with a batch of images with Caffe2 ops, a list of bboxes + (instances) are usually represented by single Tensor with size + (sigma(Ni), 5) or (sigma(Ni), 4) plus a batch split Tensor. This class is + for providing common functions to convert between these two representations. + """ + + def __init__(self, im_info, indices, extra_fields=None): + # [N, 3] -> (H, W, Scale) + self.im_info = im_info + # [N,] -> indice of batch to which the instance belongs + self.indices = indices + # [N, ...] + self.batch_extra_fields = extra_fields or {} + + self.image_size = self.im_info + + def get_fields(self): + """like `get_fields` in the Instances object, + but return each field in tensor representations""" + ret = {} + for k, v in self.batch_extra_fields.items(): + # if isinstance(v, torch.Tensor): + # tensor_rep = v + # elif isinstance(v, (Boxes, Keypoints)): + # tensor_rep = v.tensor + # else: + # raise ValueError("Can't find tensor representation for: {}".format()) + ret[k] = v + return ret + + def has(self, name): + return name in self.batch_extra_fields + + def set(self, name, value): + data_len = len(value) + if len(self.batch_extra_fields): + assert ( + len(self) == data_len + ), "Adding a field of length {} to a Instances of length {}".format(data_len, len(self)) + self.batch_extra_fields[name] = value + + def __setattr__(self, name, val): + if name in ["im_info", "indices", "batch_extra_fields", "image_size"]: + super().__setattr__(name, val) + else: + self.set(name, val) + + def __getattr__(self, name): + if name not in self.batch_extra_fields: + raise AttributeError("Cannot find field '{}' in the given Instances!".format(name)) + return self.batch_extra_fields[name] + + def __len__(self): + return len(self.indices) + + def flatten(self): + ret = [] + for _, v in self.batch_extra_fields.items(): + if isinstance(v, (Boxes, Keypoints)): + ret.append(v.tensor) + else: + ret.append(v) + return ret + + @staticmethod + def to_d2_instances_list(instances_list): + """ + Convert InstancesList to List[Instances]. The input `instances_list` can + also be a List[Instances], in this case this method is a non-op. + """ + if not isinstance(instances_list, InstancesList): + assert all(isinstance(x, Instances) for x in instances_list) + return instances_list + + ret = [] + for i, info in enumerate(instances_list.im_info): + instances = Instances(torch.Size([int(info[0].item()), int(info[1].item())])) + + ids = instances_list.indices == i + for k, v in instances_list.batch_extra_fields.items(): + if isinstance(v, torch.Tensor): + instances.set(k, v[ids]) + continue + elif isinstance(v, Boxes): + instances.set(k, v[ids, -4:]) + continue + + target_type, tensor_source = v + assert isinstance(tensor_source, torch.Tensor) + assert tensor_source.shape[0] == instances_list.indices.shape[0] + tensor_source = tensor_source[ids] + + if issubclass(target_type, Boxes): + instances.set(k, Boxes(tensor_source[:, -4:])) + elif issubclass(target_type, Keypoints): + instances.set(k, Keypoints(tensor_source)) + elif issubclass(target_type, torch.Tensor): + instances.set(k, tensor_source) + else: + raise ValueError("Can't handle targe type: {}".format(target_type)) + + ret.append(instances) + return ret + + +class Caffe2Compatible(object): + """ + A model can inherit this class to indicate that it can be traced and deployed with caffe2. + """ + + def _get_tensor_mode(self): + return self._tensor_mode + + def _set_tensor_mode(self, v): + self._tensor_mode = v + + tensor_mode = property(_get_tensor_mode, _set_tensor_mode) + """ + If true, the model expects C2-style tensor only inputs/outputs format. + """ + + +class Caffe2RPN(Caffe2Compatible, rpn.RPN): + def _generate_proposals( + self, images, objectness_logits_pred, anchor_deltas_pred, gt_instances=None + ): + assert isinstance(images, ImageList) + if self.tensor_mode: + im_info = images.image_sizes + else: + im_info = torch.tensor([[im_sz[0], im_sz[1], 1.0] for im_sz in images.image_sizes]).to( + images.tensor.device + ) + assert isinstance(im_info, torch.Tensor) + + rpn_rois_list = [] + rpn_roi_probs_list = [] + for scores, bbox_deltas, cell_anchors_tensor, feat_stride in zip( + objectness_logits_pred, + anchor_deltas_pred, + iter(self.anchor_generator.cell_anchors), + self.anchor_generator.strides, + ): + scores = scores.detach() + bbox_deltas = bbox_deltas.detach() + + rpn_rois, rpn_roi_probs = torch.ops._caffe2.GenerateProposals( + scores, + bbox_deltas, + im_info, + cell_anchors_tensor, + spatial_scale=1.0 / feat_stride, + pre_nms_topN=self.pre_nms_topk[self.training], + post_nms_topN=self.post_nms_topk[self.training], + nms_thresh=self.nms_thresh, + min_size=self.min_box_size, + # correct_transform_coords=True, # deprecated argument + angle_bound_on=True, # Default + angle_bound_lo=-180, + angle_bound_hi=180, + clip_angle_thresh=1.0, # Default + legacy_plus_one=False, + ) + rpn_rois_list.append(rpn_rois) + rpn_roi_probs_list.append(rpn_roi_probs) + + # For FPN in D2, in RPN all proposals from different levels are concated + # together, ranked and picked by top post_nms_topk. Then in ROIPooler + # it calculates level_assignments and calls the RoIAlign from + # the corresponding level. + + if len(objectness_logits_pred) == 1: + rpn_rois = rpn_rois_list[0] + rpn_roi_probs = rpn_roi_probs_list[0] + else: + assert len(rpn_rois_list) == len(rpn_roi_probs_list) + rpn_post_nms_topN = self.post_nms_topk[self.training] + + device = rpn_rois_list[0].device + input_list = [to_device(x, "cpu") for x in (rpn_rois_list + rpn_roi_probs_list)] + + # TODO remove this after confirming rpn_max_level/rpn_min_level + # is not needed in CollectRpnProposals. + feature_strides = list(self.anchor_generator.strides) + rpn_min_level = int(math.log2(feature_strides[0])) + rpn_max_level = int(math.log2(feature_strides[-1])) + assert (rpn_max_level - rpn_min_level + 1) == len( + rpn_rois_list + ), "CollectRpnProposals requires continuous levels" + + rpn_rois = torch.ops._caffe2.CollectRpnProposals( + input_list, + # NOTE: in current implementation, rpn_max_level and rpn_min_level + # are not needed, only the subtraction of two matters and it + # can be infer from the number of inputs. Keep them now for + # consistency. + rpn_max_level=2 + len(rpn_rois_list) - 1, + rpn_min_level=2, + rpn_post_nms_topN=rpn_post_nms_topN, + ) + rpn_rois = to_device(rpn_rois, device) + rpn_roi_probs = [] + + proposals = self.c2_postprocess(im_info, rpn_rois, rpn_roi_probs, self.tensor_mode) + return proposals, {} + + def forward(self, images, features, gt_instances=None): + assert not self.training + features = [features[f] for f in self.in_features] + objectness_logits_pred, anchor_deltas_pred = self.rpn_head(features) + return self._generate_proposals( + images, + objectness_logits_pred, + anchor_deltas_pred, + gt_instances, + ) + + @staticmethod + def c2_postprocess(im_info, rpn_rois, rpn_roi_probs, tensor_mode): + proposals = InstancesList( + im_info=im_info, + indices=rpn_rois[:, 0], + extra_fields={ + "proposal_boxes": Caffe2Boxes(rpn_rois), + "objectness_logits": (torch.Tensor, rpn_roi_probs), + }, + ) + if not tensor_mode: + proposals = InstancesList.to_d2_instances_list(proposals) + else: + proposals = [proposals] + return proposals + + +class Caffe2ROIPooler(Caffe2Compatible, poolers.ROIPooler): + @staticmethod + def c2_preprocess(box_lists): + assert all(isinstance(x, Boxes) for x in box_lists) + if all(isinstance(x, Caffe2Boxes) for x in box_lists): + # input is pure-tensor based + assert len(box_lists) == 1 + pooler_fmt_boxes = box_lists[0].tensor + else: + pooler_fmt_boxes = poolers.convert_boxes_to_pooler_format(box_lists) + return pooler_fmt_boxes + + def forward(self, x, box_lists): + assert not self.training + + pooler_fmt_boxes = self.c2_preprocess(box_lists) + num_level_assignments = len(self.level_poolers) + + if num_level_assignments == 1: + if isinstance(self.level_poolers[0], ROIAlignRotated): + c2_roi_align = torch.ops._caffe2.RoIAlignRotated + aligned = True + else: + c2_roi_align = torch.ops._caffe2.RoIAlign + aligned = self.level_poolers[0].aligned + + x0 = x[0] + if x0.is_quantized: + x0 = x0.dequantize() + + out = c2_roi_align( + x0, + pooler_fmt_boxes, + order="NCHW", + spatial_scale=float(self.level_poolers[0].spatial_scale), + pooled_h=int(self.output_size[0]), + pooled_w=int(self.output_size[1]), + sampling_ratio=int(self.level_poolers[0].sampling_ratio), + aligned=aligned, + ) + return out + + device = pooler_fmt_boxes.device + assert ( + self.max_level - self.min_level + 1 == 4 + ), "Currently DistributeFpnProposals only support 4 levels" + fpn_outputs = torch.ops._caffe2.DistributeFpnProposals( + to_device(pooler_fmt_boxes, "cpu"), + roi_canonical_scale=self.canonical_box_size, + roi_canonical_level=self.canonical_level, + roi_max_level=self.max_level, + roi_min_level=self.min_level, + legacy_plus_one=False, + ) + fpn_outputs = [to_device(x, device) for x in fpn_outputs] + + rois_fpn_list = fpn_outputs[:-1] + rois_idx_restore_int32 = fpn_outputs[-1] + + roi_feat_fpn_list = [] + for roi_fpn, x_level, pooler in zip(rois_fpn_list, x, self.level_poolers): + if isinstance(pooler, ROIAlignRotated): + c2_roi_align = torch.ops._caffe2.RoIAlignRotated + aligned = True + else: + c2_roi_align = torch.ops._caffe2.RoIAlign + aligned = bool(pooler.aligned) + + if x_level.is_quantized: + x_level = x_level.dequantize() + + roi_feat_fpn = c2_roi_align( + x_level, + roi_fpn, + order="NCHW", + spatial_scale=float(pooler.spatial_scale), + pooled_h=int(self.output_size[0]), + pooled_w=int(self.output_size[1]), + sampling_ratio=int(pooler.sampling_ratio), + aligned=aligned, + ) + roi_feat_fpn_list.append(roi_feat_fpn) + + roi_feat_shuffled = cat(roi_feat_fpn_list, dim=0) + assert roi_feat_shuffled.numel() > 0 and rois_idx_restore_int32.numel() > 0, ( + "Caffe2 export requires tracing with a model checkpoint + input that can produce valid" + " detections. But no detections were obtained with the given checkpoint and input!" + ) + roi_feat = torch.ops._caffe2.BatchPermutation(roi_feat_shuffled, rois_idx_restore_int32) + return roi_feat + + +class Caffe2FastRCNNOutputsInference: + def __init__(self, tensor_mode): + self.tensor_mode = tensor_mode # whether the output is caffe2 tensor mode + + def __call__(self, box_predictor, predictions, proposals): + """equivalent to FastRCNNOutputLayers.inference""" + num_classes = box_predictor.num_classes + score_thresh = box_predictor.test_score_thresh + nms_thresh = box_predictor.test_nms_thresh + topk_per_image = box_predictor.test_topk_per_image + is_rotated = len(box_predictor.box2box_transform.weights) == 5 + + if is_rotated: + box_dim = 5 + assert box_predictor.box2box_transform.weights[4] == 1, ( + "The weights for Rotated BBoxTransform in C2 have only 4 dimensions," + + " thus enforcing the angle weight to be 1 for now" + ) + box2box_transform_weights = box_predictor.box2box_transform.weights[:4] + else: + box_dim = 4 + box2box_transform_weights = box_predictor.box2box_transform.weights + + class_logits, box_regression = predictions + if num_classes + 1 == class_logits.shape[1]: + class_prob = F.softmax(class_logits, -1) + else: + assert num_classes == class_logits.shape[1] + class_prob = F.sigmoid(class_logits) + # BoxWithNMSLimit will infer num_classes from the shape of the class_prob + # So append a zero column as placeholder for the background class + class_prob = torch.cat((class_prob, torch.zeros(class_prob.shape[0], 1)), dim=1) + + assert box_regression.shape[1] % box_dim == 0 + cls_agnostic_bbox_reg = box_regression.shape[1] // box_dim == 1 + + input_tensor_mode = proposals[0].proposal_boxes.tensor.shape[1] == box_dim + 1 + + rois = type(proposals[0].proposal_boxes).cat([p.proposal_boxes for p in proposals]) + device, dtype = rois.tensor.device, rois.tensor.dtype + if input_tensor_mode: + im_info = proposals[0].image_size + rois = rois.tensor + else: + im_info = torch.tensor( + [[sz[0], sz[1], 1.0] for sz in [x.image_size for x in proposals]] + ) + batch_ids = cat( + [ + torch.full((b, 1), i, dtype=dtype, device=device) + for i, b in enumerate(len(p) for p in proposals) + ], + dim=0, + ) + rois = torch.cat([batch_ids, rois.tensor], dim=1) + + roi_pred_bbox, roi_batch_splits = torch.ops._caffe2.BBoxTransform( + to_device(rois, "cpu"), + to_device(box_regression, "cpu"), + to_device(im_info, "cpu"), + weights=box2box_transform_weights, + apply_scale=True, + rotated=is_rotated, + angle_bound_on=True, + angle_bound_lo=-180, + angle_bound_hi=180, + clip_angle_thresh=1.0, + legacy_plus_one=False, + ) + roi_pred_bbox = to_device(roi_pred_bbox, device) + roi_batch_splits = to_device(roi_batch_splits, device) + + nms_outputs = torch.ops._caffe2.BoxWithNMSLimit( + to_device(class_prob, "cpu"), + to_device(roi_pred_bbox, "cpu"), + to_device(roi_batch_splits, "cpu"), + score_thresh=float(score_thresh), + nms=float(nms_thresh), + detections_per_im=int(topk_per_image), + soft_nms_enabled=False, + soft_nms_method="linear", + soft_nms_sigma=0.5, + soft_nms_min_score_thres=0.001, + rotated=is_rotated, + cls_agnostic_bbox_reg=cls_agnostic_bbox_reg, + input_boxes_include_bg_cls=False, + output_classes_include_bg_cls=False, + legacy_plus_one=False, + ) + roi_score_nms = to_device(nms_outputs[0], device) + roi_bbox_nms = to_device(nms_outputs[1], device) + roi_class_nms = to_device(nms_outputs[2], device) + roi_batch_splits_nms = to_device(nms_outputs[3], device) + roi_keeps_nms = to_device(nms_outputs[4], device) + roi_keeps_size_nms = to_device(nms_outputs[5], device) + if not self.tensor_mode: + roi_class_nms = roi_class_nms.to(torch.int64) + + roi_batch_ids = cat( + [ + torch.full((b, 1), i, dtype=dtype, device=device) + for i, b in enumerate(int(x.item()) for x in roi_batch_splits_nms) + ], + dim=0, + ) + + roi_class_nms = alias(roi_class_nms, "class_nms") + roi_score_nms = alias(roi_score_nms, "score_nms") + roi_bbox_nms = alias(roi_bbox_nms, "bbox_nms") + roi_batch_splits_nms = alias(roi_batch_splits_nms, "batch_splits_nms") + roi_keeps_nms = alias(roi_keeps_nms, "keeps_nms") + roi_keeps_size_nms = alias(roi_keeps_size_nms, "keeps_size_nms") + + results = InstancesList( + im_info=im_info, + indices=roi_batch_ids[:, 0], + extra_fields={ + "pred_boxes": Caffe2Boxes(roi_bbox_nms), + "scores": roi_score_nms, + "pred_classes": roi_class_nms, + }, + ) + + if not self.tensor_mode: + results = InstancesList.to_d2_instances_list(results) + batch_splits = roi_batch_splits_nms.int().tolist() + kept_indices = list(roi_keeps_nms.to(torch.int64).split(batch_splits)) + else: + results = [results] + kept_indices = [roi_keeps_nms] + + return results, kept_indices + + +class Caffe2MaskRCNNInference: + def __call__(self, pred_mask_logits, pred_instances): + """equivalent to mask_head.mask_rcnn_inference""" + if all(isinstance(x, InstancesList) for x in pred_instances): + assert len(pred_instances) == 1 + mask_probs_pred = pred_mask_logits.sigmoid() + mask_probs_pred = alias(mask_probs_pred, "mask_fcn_probs") + pred_instances[0].pred_masks = mask_probs_pred + else: + mask_rcnn_inference(pred_mask_logits, pred_instances) + + +class Caffe2KeypointRCNNInference: + def __init__(self, use_heatmap_max_keypoint): + self.use_heatmap_max_keypoint = use_heatmap_max_keypoint + + def __call__(self, pred_keypoint_logits, pred_instances): + # just return the keypoint heatmap for now, + # there will be option to call HeatmapMaxKeypointOp + output = alias(pred_keypoint_logits, "kps_score") + if all(isinstance(x, InstancesList) for x in pred_instances): + assert len(pred_instances) == 1 + if self.use_heatmap_max_keypoint: + device = output.device + output = torch.ops._caffe2.HeatmapMaxKeypoint( + to_device(output, "cpu"), + pred_instances[0].pred_boxes.tensor, + should_output_softmax=True, # worth make it configerable? + ) + output = to_device(output, device) + output = alias(output, "keypoints_out") + pred_instances[0].pred_keypoints = output + return pred_keypoint_logits diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/export/caffe2_export.py b/motion-gan-pipeline/preprocessing/third/detectron2/export/caffe2_export.py new file mode 100644 index 0000000..74ac123 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/export/caffe2_export.py @@ -0,0 +1,207 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import copy +import io +import logging +import numpy as np +from typing import List +import onnx +import torch +from caffe2.proto import caffe2_pb2 +from caffe2.python import core +from caffe2.python.onnx.backend import Caffe2Backend +from tabulate import tabulate +from termcolor import colored +from torch.onnx import OperatorExportTypes + +from .shared import ( + ScopedWS, + construct_init_net_from_params, + fuse_alias_placeholder, + fuse_copy_between_cpu_and_gpu, + get_params_from_init_net, + group_norm_replace_aten_with_caffe2, + infer_device_type, + remove_dead_end_ops, + remove_reshape_for_fc, + save_graph, +) + +logger = logging.getLogger(__name__) + + +def export_onnx_model(model, inputs): + """ + Trace and export a model to onnx format. + + Args: + model (nn.Module): + inputs (tuple[args]): the model will be called by `model(*inputs)` + + Returns: + an onnx model + """ + assert isinstance(model, torch.nn.Module) + + # make sure all modules are in eval mode, onnx may change the training state + # of the module if the states are not consistent + def _check_eval(module): + assert not module.training + + model.apply(_check_eval) + + # Export the model to ONNX + with torch.no_grad(): + with io.BytesIO() as f: + torch.onnx.export( + model, + inputs, + f, + operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK, + # verbose=True, # NOTE: uncomment this for debugging + # export_params=True, + ) + onnx_model = onnx.load_from_string(f.getvalue()) + + # Apply ONNX's Optimization + all_passes = onnx.optimizer.get_available_passes() + passes = ["fuse_bn_into_conv"] + assert all(p in all_passes for p in passes) + onnx_model = onnx.optimizer.optimize(onnx_model, passes) + return onnx_model + + +def _op_stats(net_def): + type_count = {} + for t in [op.type for op in net_def.op]: + type_count[t] = type_count.get(t, 0) + 1 + type_count_list = sorted(type_count.items(), key=lambda kv: kv[0]) # alphabet + type_count_list = sorted(type_count_list, key=lambda kv: -kv[1]) # count + return "\n".join("{:>4}x {}".format(count, name) for name, count in type_count_list) + + +def _assign_device_option( + predict_net: caffe2_pb2.NetDef, init_net: caffe2_pb2.NetDef, tensor_inputs: List[torch.Tensor] +): + """ + ONNX exported network doesn't have concept of device, assign necessary + device option for each op in order to make it runable on GPU runtime. + """ + + def _get_device_type(torch_tensor): + assert torch_tensor.device.type in ["cpu", "cuda"] + assert torch_tensor.device.index == 0 + return torch_tensor.device.type + + def _assign_op_device_option(net_proto, net_ssa, blob_device_types): + for op, ssa_i in zip(net_proto.op, net_ssa): + if op.type in ["CopyCPUToGPU", "CopyGPUToCPU"]: + op.device_option.CopyFrom(core.DeviceOption(caffe2_pb2.CUDA, 0)) + else: + devices = [blob_device_types[b] for b in ssa_i[0] + ssa_i[1]] + assert all(d == devices[0] for d in devices) + if devices[0] == "cuda": + op.device_option.CopyFrom(core.DeviceOption(caffe2_pb2.CUDA, 0)) + + # update ops in predict_net + predict_net_input_device_types = { + (name, 0): _get_device_type(tensor) + for name, tensor in zip(predict_net.external_input, tensor_inputs) + } + predict_net_device_types = infer_device_type( + predict_net, known_status=predict_net_input_device_types, device_name_style="pytorch" + ) + predict_net_ssa, _ = core.get_ssa(predict_net) + _assign_op_device_option(predict_net, predict_net_ssa, predict_net_device_types) + + # update ops in init_net + init_net_ssa, versions = core.get_ssa(init_net) + init_net_output_device_types = { + (name, versions[name]): predict_net_device_types[(name, 0)] + for name in init_net.external_output + } + init_net_device_types = infer_device_type( + init_net, known_status=init_net_output_device_types, device_name_style="pytorch" + ) + _assign_op_device_option(init_net, init_net_ssa, init_net_device_types) + + +def export_caffe2_detection_model(model: torch.nn.Module, tensor_inputs: List[torch.Tensor]): + """ + Export a caffe2-compatible Detectron2 model to caffe2 format via ONNX. + + Arg: + model: a caffe2-compatible version of detectron2 model, defined in caffe2_modeling.py + tensor_inputs: a list of tensors that caffe2 model takes as input. + """ + model = copy.deepcopy(model) + assert isinstance(model, torch.nn.Module) + assert hasattr(model, "encode_additional_info") + + # Export via ONNX + logger.info( + "Exporting a {} model via ONNX ...".format(type(model).__name__) + + " Some warnings from ONNX are expected and are usually not to worry about." + ) + onnx_model = export_onnx_model(model, (tensor_inputs,)) + # Convert ONNX model to Caffe2 protobuf + init_net, predict_net = Caffe2Backend.onnx_graph_to_caffe2_net(onnx_model) + ops_table = [[op.type, op.input, op.output] for op in predict_net.op] + table = tabulate(ops_table, headers=["type", "input", "output"], tablefmt="pipe") + logger.info( + "ONNX export Done. Exported predict_net (before optimizations):\n" + colored(table, "cyan") + ) + + # Apply protobuf optimization + fuse_alias_placeholder(predict_net, init_net) + if any(t.device.type != "cpu" for t in tensor_inputs): + fuse_copy_between_cpu_and_gpu(predict_net) + remove_dead_end_ops(init_net) + _assign_device_option(predict_net, init_net, tensor_inputs) + params, device_options = get_params_from_init_net(init_net) + predict_net, params = remove_reshape_for_fc(predict_net, params) + init_net = construct_init_net_from_params(params, device_options) + group_norm_replace_aten_with_caffe2(predict_net) + + # Record necessary information for running the pb model in Detectron2 system. + model.encode_additional_info(predict_net, init_net) + + logger.info("Operators used in predict_net: \n{}".format(_op_stats(predict_net))) + logger.info("Operators used in init_net: \n{}".format(_op_stats(init_net))) + + return predict_net, init_net + + +def run_and_save_graph(predict_net, init_net, tensor_inputs, graph_save_path): + """ + Run the caffe2 model on given inputs, recording the shape and draw the graph. + + predict_net/init_net: caffe2 model. + tensor_inputs: a list of tensors that caffe2 model takes as input. + graph_save_path: path for saving graph of exported model. + """ + + logger.info("Saving graph of ONNX exported model to {} ...".format(graph_save_path)) + save_graph(predict_net, graph_save_path, op_only=False) + + # Run the exported Caffe2 net + logger.info("Running ONNX exported model ...") + with ScopedWS("__ws_tmp__", True) as ws: + ws.RunNetOnce(init_net) + initialized_blobs = set(ws.Blobs()) + uninitialized = [inp for inp in predict_net.external_input if inp not in initialized_blobs] + for name, blob in zip(uninitialized, tensor_inputs): + ws.FeedBlob(name, blob) + + try: + ws.RunNetOnce(predict_net) + except RuntimeError as e: + logger.warning("Encountered RuntimeError: \n{}".format(str(e))) + + ws_blobs = {b: ws.FetchBlob(b) for b in ws.Blobs()} + blob_sizes = {b: ws_blobs[b].shape for b in ws_blobs if isinstance(ws_blobs[b], np.ndarray)} + + logger.info("Saving graph with blob shapes to {} ...".format(graph_save_path)) + save_graph(predict_net, graph_save_path, op_only=False, blob_sizes=blob_sizes) + + return ws_blobs diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/export/caffe2_inference.py b/motion-gan-pipeline/preprocessing/third/detectron2/export/caffe2_inference.py new file mode 100644 index 0000000..deb886c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/export/caffe2_inference.py @@ -0,0 +1,161 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import logging +import numpy as np +from itertools import count +import torch +from caffe2.proto import caffe2_pb2 +from caffe2.python import core + +from .caffe2_modeling import META_ARCH_CAFFE2_EXPORT_TYPE_MAP, convert_batched_inputs_to_c2_format +from .shared import ScopedWS, get_pb_arg_vali, get_pb_arg_vals, infer_device_type + +logger = logging.getLogger(__name__) + + +# ===== ref: mobile-vision predictor's 'Caffe2Wrapper' class ====== +class ProtobufModel(torch.nn.Module): + """ + Wrapper of a caffe2's protobuf model. + It works just like nn.Module, but running caffe2 under the hood. + Input/Output are tuple[tensor] that match the caffe2 net's external_input/output. + """ + + _ids = count(0) + + def __init__(self, predict_net, init_net): + logger.info(f"Initializing ProtobufModel for: {predict_net.name} ...") + super().__init__() + assert isinstance(predict_net, caffe2_pb2.NetDef) + assert isinstance(init_net, caffe2_pb2.NetDef) + # create unique temporary workspace for each instance + self.ws_name = "__tmp_ProtobufModel_{}__".format(next(self._ids)) + self.net = core.Net(predict_net) + + logger.info("Running init_net once to fill the parameters ...") + with ScopedWS(self.ws_name, is_reset=True, is_cleanup=False) as ws: + ws.RunNetOnce(init_net) + uninitialized_external_input = [] + for blob in self.net.Proto().external_input: + if blob not in ws.Blobs(): + uninitialized_external_input.append(blob) + ws.CreateBlob(blob) + ws.CreateNet(self.net) + + self._error_msgs = set() + self._input_blobs = uninitialized_external_input + + def _infer_output_devices(self, inputs): + """ + Returns: + list[str]: list of device for each external output + """ + + def _get_device_type(torch_tensor): + assert torch_tensor.device.type in ["cpu", "cuda"] + assert torch_tensor.device.index == 0 + return torch_tensor.device.type + + predict_net = self.net.Proto() + input_device_types = { + (name, 0): _get_device_type(tensor) for name, tensor in zip(self._input_blobs, inputs) + } + device_type_map = infer_device_type( + predict_net, known_status=input_device_types, device_name_style="pytorch" + ) + ssa, versions = core.get_ssa(predict_net) + versioned_outputs = [(name, versions[name]) for name in predict_net.external_output] + output_devices = [device_type_map[outp] for outp in versioned_outputs] + return output_devices + + def forward(self, inputs): + """ + Args: + inputs (tuple[torch.Tensor]) + + Returns: + tuple[torch.Tensor] + """ + assert len(inputs) == len(self._input_blobs), ( + f"Length of inputs ({len(inputs)}) " + f"doesn't match the required input blobs: {self._input_blobs}" + ) + + with ScopedWS(self.ws_name, is_reset=False, is_cleanup=False) as ws: + for b, tensor in zip(self._input_blobs, inputs): + ws.FeedBlob(b, tensor) + + try: + ws.RunNet(self.net.Proto().name) + except RuntimeError as e: + if not str(e) in self._error_msgs: + self._error_msgs.add(str(e)) + logger.warning("Encountered new RuntimeError: \n{}".format(str(e))) + logger.warning("Catch the error and use partial results.") + + c2_outputs = [ws.FetchBlob(b) for b in self.net.Proto().external_output] + # Remove outputs of current run, this is necessary in order to + # prevent fetching the result from previous run if the model fails + # in the middle. + for b in self.net.Proto().external_output: + # Needs to create uninitialized blob to make the net runable. + # This is "equivalent" to: ws.RemoveBlob(b) then ws.CreateBlob(b), + # but there'no such API. + ws.FeedBlob(b, f"{b}, a C++ native class of type nullptr (uninitialized).") + + # Cast output to torch.Tensor on the desired device + output_devices = ( + self._infer_output_devices(inputs) + if any(t.device.type != "cpu" for t in inputs) + else ["cpu" for _ in self.net.Proto().external_output] + ) + + outputs = [] + for name, c2_output, device in zip( + self.net.Proto().external_output, c2_outputs, output_devices + ): + if not isinstance(c2_output, np.ndarray): + raise RuntimeError( + "Invalid output for blob {}, received: {}".format(name, c2_output) + ) + outputs.append(torch.tensor(c2_output).to(device=device)) + return tuple(outputs) + + +class ProtobufDetectionModel(torch.nn.Module): + """ + A class works just like a pytorch meta arch in terms of inference, but running + caffe2 model under the hood. + """ + + def __init__(self, predict_net, init_net, *, convert_outputs=None): + """ + Args: + predict_net, init_net (core.Net): caffe2 nets + convert_outptus (callable): a function that converts caffe2 + outputs to the same format of the original pytorch model. + By default, use the one defined in the caffe2 meta_arch. + """ + super().__init__() + self.protobuf_model = ProtobufModel(predict_net, init_net) + self.size_divisibility = get_pb_arg_vali(predict_net, "size_divisibility", 0) + self.device = get_pb_arg_vals(predict_net, "device", b"cpu").decode("ascii") + + if convert_outputs is None: + meta_arch = get_pb_arg_vals(predict_net, "meta_architecture", b"GeneralizedRCNN") + meta_arch = META_ARCH_CAFFE2_EXPORT_TYPE_MAP[meta_arch.decode("ascii")] + self._convert_outputs = meta_arch.get_outputs_converter(predict_net, init_net) + else: + self._convert_outputs = convert_outputs + + def _convert_inputs(self, batched_inputs): + # currently all models convert inputs in the same way + return convert_batched_inputs_to_c2_format( + batched_inputs, self.size_divisibility, self.device + ) + + def forward(self, batched_inputs): + c2_inputs = self._convert_inputs(batched_inputs) + c2_results = self.protobuf_model(c2_inputs) + c2_results = dict(zip(self.protobuf_model.net.Proto().external_output, c2_results)) + return self._convert_outputs(batched_inputs, c2_inputs, c2_results) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/export/caffe2_modeling.py b/motion-gan-pipeline/preprocessing/third/detectron2/export/caffe2_modeling.py new file mode 100644 index 0000000..e00de4a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/export/caffe2_modeling.py @@ -0,0 +1,419 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import functools +import io +import struct +import types +import torch + +from detectron2.modeling import meta_arch +from detectron2.modeling.box_regression import Box2BoxTransform +from detectron2.modeling.roi_heads import keypoint_head +from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes + +from .c10 import Caffe2Compatible +from .caffe2_patch import ROIHeadsPatcher, patch_generalized_rcnn +from .shared import ( + alias, + check_set_pb_arg, + get_pb_arg_floats, + get_pb_arg_valf, + get_pb_arg_vali, + get_pb_arg_vals, + mock_torch_nn_functional_interpolate, +) + + +def assemble_rcnn_outputs_by_name(image_sizes, tensor_outputs, force_mask_on=False): + """ + A function to assemble caffe2 model's outputs (i.e. Dict[str, Tensor]) + to detectron2's format (i.e. list of Instances instance). + This only works when the model follows the Caffe2 detectron's naming convention. + + Args: + image_sizes (List[List[int, int]]): [H, W] of every image. + tensor_outputs (Dict[str, Tensor]): external_output to its tensor. + + force_mask_on (Bool): if true, the it make sure there'll be pred_masks even + if the mask is not found from tensor_outputs (usually due to model crash) + """ + + results = [Instances(image_size) for image_size in image_sizes] + + batch_splits = tensor_outputs.get("batch_splits", None) + if batch_splits: + raise NotImplementedError() + assert len(image_sizes) == 1 + result = results[0] + + bbox_nms = tensor_outputs["bbox_nms"] + score_nms = tensor_outputs["score_nms"] + class_nms = tensor_outputs["class_nms"] + # Detection will always success because Conv support 0-batch + assert bbox_nms is not None + assert score_nms is not None + assert class_nms is not None + if bbox_nms.shape[1] == 5: + result.pred_boxes = RotatedBoxes(bbox_nms) + else: + result.pred_boxes = Boxes(bbox_nms) + result.scores = score_nms + result.pred_classes = class_nms.to(torch.int64) + + mask_fcn_probs = tensor_outputs.get("mask_fcn_probs", None) + if mask_fcn_probs is not None: + # finish the mask pred + mask_probs_pred = mask_fcn_probs + num_masks = mask_probs_pred.shape[0] + class_pred = result.pred_classes + indices = torch.arange(num_masks, device=class_pred.device) + mask_probs_pred = mask_probs_pred[indices, class_pred][:, None] + result.pred_masks = mask_probs_pred + elif force_mask_on: + # NOTE: there's no way to know the height/width of mask here, it won't be + # used anyway when batch size is 0, so just set them to 0. + result.pred_masks = torch.zeros([0, 1, 0, 0], dtype=torch.uint8) + + keypoints_out = tensor_outputs.get("keypoints_out", None) + kps_score = tensor_outputs.get("kps_score", None) + if keypoints_out is not None: + # keypoints_out: [N, 4, #kypoints], where 4 is in order of (x, y, score, prob) + keypoints_tensor = keypoints_out + # NOTE: it's possible that prob is not calculated if "should_output_softmax" + # is set to False in HeatmapMaxKeypoint, so just using raw score, seems + # it doesn't affect mAP. TODO: check more carefully. + keypoint_xyp = keypoints_tensor.transpose(1, 2)[:, :, [0, 1, 2]] + result.pred_keypoints = keypoint_xyp + elif kps_score is not None: + # keypoint heatmap to sparse data structure + pred_keypoint_logits = kps_score + keypoint_head.keypoint_rcnn_inference(pred_keypoint_logits, [result]) + + return results + + +def _cast_to_f32(f64): + return struct.unpack("f", struct.pack("f", f64))[0] + + +def set_caffe2_compatible_tensor_mode(model, enable=True): + def _fn(m): + if isinstance(m, Caffe2Compatible): + m.tensor_mode = enable + + model.apply(_fn) + + +def convert_batched_inputs_to_c2_format(batched_inputs, size_divisibility, device): + """ + See get_caffe2_inputs() below. + """ + assert all(isinstance(x, dict) for x in batched_inputs) + assert all(x["image"].dim() == 3 for x in batched_inputs) + + images = [x["image"] for x in batched_inputs] + images = ImageList.from_tensors(images, size_divisibility) + + im_info = [] + for input_per_image, image_size in zip(batched_inputs, images.image_sizes): + target_height = input_per_image.get("height", image_size[0]) + target_width = input_per_image.get("width", image_size[1]) # noqa + # NOTE: The scale inside im_info is kept as convention and for providing + # post-processing information if further processing is needed. For + # current Caffe2 model definitions that don't include post-processing inside + # the model, this number is not used. + # NOTE: There can be a slight difference between width and height + # scales, using a single number can results in numerical difference + # compared with D2's post-processing. + scale = target_height / image_size[0] + im_info.append([image_size[0], image_size[1], scale]) + im_info = torch.Tensor(im_info) + + return images.tensor.to(device), im_info.to(device) + + +class Caffe2MetaArch(Caffe2Compatible, torch.nn.Module): + """ + Base class for caffe2-compatible implementation of a meta architecture. + The forward is traceable and its traced graph can be converted to caffe2 + graph through ONNX. + """ + + def __init__(self, cfg, torch_model): + """ + Args: + cfg (CfgNode): + torch_model (nn.Module): the detectron2 model (meta_arch) to be + converted. + """ + super().__init__() + self._wrapped_model = torch_model + self.eval() + set_caffe2_compatible_tensor_mode(self, True) + + def get_caffe2_inputs(self, batched_inputs): + """ + Convert pytorch-style structured inputs to caffe2-style inputs that + are tuples of tensors. + + Args: + batched_inputs (list[dict]): inputs to a detectron2 model + in its standard format. Each dict has "image" (CHW tensor), and optionally + "height" and "width". + + Returns: + tuple[Tensor]: + tuple of tensors that will be the inputs to the + :meth:`forward` method. For existing models, the first + is an NCHW tensor (padded and batched); the second is + a im_info Nx3 tensor, where the rows are + (height, width, unused legacy parameter) + """ + return convert_batched_inputs_to_c2_format( + batched_inputs, + self._wrapped_model.backbone.size_divisibility, + self._wrapped_model.device, + ) + + def encode_additional_info(self, predict_net, init_net): + """ + Save extra metadata that will be used by inference in the output protobuf. + """ + pass + + def forward(self, inputs): + """ + Run the forward in caffe2-style. It has to use caffe2-compatible ops + and the method will be used for tracing. + + Args: + inputs (tuple[Tensor]): inputs defined by :meth:`get_caffe2_input`. + They will be the inputs of the converted caffe2 graph. + + Returns: + tuple[Tensor]: output tensors. They will be the outputs of the + converted caffe2 graph. + """ + raise NotImplementedError + + def _caffe2_preprocess_image(self, inputs): + """ + Caffe2 implementation of preprocess_image, which is called inside each MetaArch's forward. + It normalizes the input images, and the final caffe2 graph assumes the + inputs have been batched already. + """ + data, im_info = inputs + data = alias(data, "data") + im_info = alias(im_info, "im_info") + mean, std = self._wrapped_model.pixel_mean, self._wrapped_model.pixel_std + normalized_data = (data - mean) / std + normalized_data = alias(normalized_data, "normalized_data") + + # Pack (data, im_info) into ImageList which is recognized by self.inference. + images = ImageList(tensor=normalized_data, image_sizes=im_info) + return images + + @staticmethod + def get_outputs_converter(predict_net, init_net): + """ + Creates a function that converts outputs of the caffe2 model to + detectron2's standard format. + The function uses information in `predict_net` and `init_net` that are + available at inferene time. Therefore the function logic can be used in inference. + + The returned function has the following signature: + + def convert(batched_inputs, c2_inputs, c2_results) -> detectron2_outputs + + Where + + * batched_inputs (list[dict]): the original input format of the meta arch + * c2_inputs (tuple[Tensor]): the caffe2 inputs. + * c2_results (dict[str, Tensor]): the caffe2 output format, + corresponding to the outputs of the :meth:`forward` function. + * detectron2_outputs: the original output format of the meta arch. + + This function can be used to compare the outputs of the original meta arch and + the converted caffe2 graph. + + Returns: + callable: a callable of the above signature. + """ + raise NotImplementedError + + +class Caffe2GeneralizedRCNN(Caffe2MetaArch): + def __init__(self, cfg, torch_model): + assert isinstance(torch_model, meta_arch.GeneralizedRCNN) + torch_model = patch_generalized_rcnn(torch_model) + super().__init__(cfg, torch_model) + + try: + use_heatmap_max_keypoint = cfg.EXPORT_CAFFE2.USE_HEATMAP_MAX_KEYPOINT + except AttributeError: + use_heatmap_max_keypoint = False + self.roi_heads_patcher = ROIHeadsPatcher( + self._wrapped_model.roi_heads, use_heatmap_max_keypoint + ) + + def encode_additional_info(self, predict_net, init_net): + size_divisibility = self._wrapped_model.backbone.size_divisibility + check_set_pb_arg(predict_net, "size_divisibility", "i", size_divisibility) + check_set_pb_arg( + predict_net, "device", "s", str.encode(str(self._wrapped_model.device), "ascii") + ) + check_set_pb_arg(predict_net, "meta_architecture", "s", b"GeneralizedRCNN") + + @mock_torch_nn_functional_interpolate() + def forward(self, inputs): + if not self.tensor_mode: + return self._wrapped_model.inference(inputs) + images = self._caffe2_preprocess_image(inputs) + features = self._wrapped_model.backbone(images.tensor) + proposals, _ = self._wrapped_model.proposal_generator(images, features) + with self.roi_heads_patcher.mock_roi_heads(): + detector_results, _ = self._wrapped_model.roi_heads(images, features, proposals) + return tuple(detector_results[0].flatten()) + + @staticmethod + def get_outputs_converter(predict_net, init_net): + def f(batched_inputs, c2_inputs, c2_results): + _, im_info = c2_inputs + image_sizes = [[int(im[0]), int(im[1])] for im in im_info] + results = assemble_rcnn_outputs_by_name(image_sizes, c2_results) + return meta_arch.GeneralizedRCNN._postprocess(results, batched_inputs, image_sizes) + + return f + + +class Caffe2RetinaNet(Caffe2MetaArch): + def __init__(self, cfg, torch_model): + assert isinstance(torch_model, meta_arch.RetinaNet) + super().__init__(cfg, torch_model) + + @mock_torch_nn_functional_interpolate() + def forward(self, inputs): + assert self.tensor_mode + images = self._caffe2_preprocess_image(inputs) + + # explicitly return the images sizes to avoid removing "im_info" by ONNX + # since it's not used in the forward path + return_tensors = [images.image_sizes] + + features = self._wrapped_model.backbone(images.tensor) + features = [features[f] for f in self._wrapped_model.head_in_features] + for i, feature_i in enumerate(features): + features[i] = alias(feature_i, "feature_{}".format(i), is_backward=True) + return_tensors.append(features[i]) + + pred_logits, pred_anchor_deltas = self._wrapped_model.head(features) + for i, (box_cls_i, box_delta_i) in enumerate(zip(pred_logits, pred_anchor_deltas)): + return_tensors.append(alias(box_cls_i, "box_cls_{}".format(i))) + return_tensors.append(alias(box_delta_i, "box_delta_{}".format(i))) + + return tuple(return_tensors) + + def encode_additional_info(self, predict_net, init_net): + size_divisibility = self._wrapped_model.backbone.size_divisibility + check_set_pb_arg(predict_net, "size_divisibility", "i", size_divisibility) + check_set_pb_arg( + predict_net, "device", "s", str.encode(str(self._wrapped_model.device), "ascii") + ) + check_set_pb_arg(predict_net, "meta_architecture", "s", b"RetinaNet") + + # Inference parameters: + check_set_pb_arg( + predict_net, "score_threshold", "f", _cast_to_f32(self._wrapped_model.test_score_thresh) + ) + check_set_pb_arg( + predict_net, "topk_candidates", "i", self._wrapped_model.test_topk_candidates + ) + check_set_pb_arg( + predict_net, "nms_threshold", "f", _cast_to_f32(self._wrapped_model.test_nms_thresh) + ) + check_set_pb_arg( + predict_net, + "max_detections_per_image", + "i", + self._wrapped_model.max_detections_per_image, + ) + + check_set_pb_arg( + predict_net, + "bbox_reg_weights", + "floats", + [_cast_to_f32(w) for w in self._wrapped_model.box2box_transform.weights], + ) + self._encode_anchor_generator_cfg(predict_net) + + def _encode_anchor_generator_cfg(self, predict_net): + # serialize anchor_generator for future use + serialized_anchor_generator = io.BytesIO() + torch.save(self._wrapped_model.anchor_generator, serialized_anchor_generator) + # Ideally we can put anchor generating inside the model, then we don't + # need to store this information. + bytes = serialized_anchor_generator.getvalue() + check_set_pb_arg(predict_net, "serialized_anchor_generator", "s", bytes) + + @staticmethod + def get_outputs_converter(predict_net, init_net): + self = types.SimpleNamespace() + serialized_anchor_generator = io.BytesIO( + get_pb_arg_vals(predict_net, "serialized_anchor_generator", None) + ) + self.anchor_generator = torch.load(serialized_anchor_generator) + bbox_reg_weights = get_pb_arg_floats(predict_net, "bbox_reg_weights", None) + self.box2box_transform = Box2BoxTransform(weights=tuple(bbox_reg_weights)) + self.test_score_thresh = get_pb_arg_valf(predict_net, "score_threshold", None) + self.test_topk_candidates = get_pb_arg_vali(predict_net, "topk_candidates", None) + self.test_nms_thresh = get_pb_arg_valf(predict_net, "nms_threshold", None) + self.max_detections_per_image = get_pb_arg_vali( + predict_net, "max_detections_per_image", None + ) + + # hack to reuse inference code from RetinaNet + for meth in [ + "forward_inference", + "inference_single_image", + "_transpose_dense_predictions", + "_decode_multi_level_predictions", + "_decode_per_level_predictions", + ]: + setattr(self, meth, functools.partial(getattr(meta_arch.RetinaNet, meth), self)) + + def f(batched_inputs, c2_inputs, c2_results): + _, im_info = c2_inputs + image_sizes = [[int(im[0]), int(im[1])] for im in im_info] + dummy_images = ImageList( + torch.randn( + ( + len(im_info), + 3, + ) + + tuple(image_sizes[0]) + ), + image_sizes, + ) + + num_features = len([x for x in c2_results.keys() if x.startswith("box_cls_")]) + pred_logits = [c2_results["box_cls_{}".format(i)] for i in range(num_features)] + pred_anchor_deltas = [c2_results["box_delta_{}".format(i)] for i in range(num_features)] + + # For each feature level, feature should have the same batch size and + # spatial dimension as the box_cls and box_delta. + dummy_features = [x.clone()[:, 0:0, :, :] for x in pred_logits] + # self.num_classess can be inferred + self.num_classes = pred_logits[0].shape[1] // (pred_anchor_deltas[0].shape[1] // 4) + + results = self.forward_inference( + dummy_images, dummy_features, [pred_logits, pred_anchor_deltas] + ) + return meta_arch.GeneralizedRCNN._postprocess(results, batched_inputs, image_sizes) + + return f + + +META_ARCH_CAFFE2_EXPORT_TYPE_MAP = { + "GeneralizedRCNN": Caffe2GeneralizedRCNN, + "RetinaNet": Caffe2RetinaNet, +} diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/export/caffe2_patch.py b/motion-gan-pipeline/preprocessing/third/detectron2/export/caffe2_patch.py new file mode 100644 index 0000000..c9eee59 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/export/caffe2_patch.py @@ -0,0 +1,152 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import contextlib +from unittest import mock +import torch + +from detectron2.modeling import poolers +from detectron2.modeling.proposal_generator import rpn +from detectron2.modeling.roi_heads import keypoint_head, mask_head +from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers + +from .c10 import ( + Caffe2Compatible, + Caffe2FastRCNNOutputsInference, + Caffe2KeypointRCNNInference, + Caffe2MaskRCNNInference, + Caffe2ROIPooler, + Caffe2RPN, +) + + +class GenericMixin(object): + pass + + +class Caffe2CompatibleConverter(object): + """ + A GenericUpdater which implements the `create_from` interface, by modifying + module object and assign it with another class replaceCls. + """ + + def __init__(self, replaceCls): + self.replaceCls = replaceCls + + def create_from(self, module): + # update module's class to the new class + assert isinstance(module, torch.nn.Module) + if issubclass(self.replaceCls, GenericMixin): + # replaceCls should act as mixin, create a new class on-the-fly + new_class = type( + "{}MixedWith{}".format(self.replaceCls.__name__, module.__class__.__name__), + (self.replaceCls, module.__class__), + {}, # {"new_method": lambda self: ...}, + ) + module.__class__ = new_class + else: + # replaceCls is complete class, this allow arbitrary class swap + module.__class__ = self.replaceCls + + # initialize Caffe2Compatible + if isinstance(module, Caffe2Compatible): + module.tensor_mode = False + + return module + + +def patch(model, target, updater, *args, **kwargs): + """ + recursively (post-order) update all modules with the target type and its + subclasses, make a initialization/composition/inheritance/... via the + updater.create_from. + """ + for name, module in model.named_children(): + model._modules[name] = patch(module, target, updater, *args, **kwargs) + if isinstance(model, target): + return updater.create_from(model, *args, **kwargs) + return model + + +def patch_generalized_rcnn(model): + ccc = Caffe2CompatibleConverter + model = patch(model, rpn.RPN, ccc(Caffe2RPN)) + model = patch(model, poolers.ROIPooler, ccc(Caffe2ROIPooler)) + + return model + + +@contextlib.contextmanager +def mock_fastrcnn_outputs_inference( + tensor_mode, check=True, box_predictor_type=FastRCNNOutputLayers +): + with mock.patch.object( + box_predictor_type, + "inference", + autospec=True, + side_effect=Caffe2FastRCNNOutputsInference(tensor_mode), + ) as mocked_func: + yield + if check: + assert mocked_func.call_count > 0 + + +@contextlib.contextmanager +def mock_mask_rcnn_inference(tensor_mode, patched_module, check=True): + with mock.patch( + "{}.mask_rcnn_inference".format(patched_module), side_effect=Caffe2MaskRCNNInference() + ) as mocked_func: + yield + if check: + assert mocked_func.call_count > 0 + + +@contextlib.contextmanager +def mock_keypoint_rcnn_inference(tensor_mode, patched_module, use_heatmap_max_keypoint, check=True): + with mock.patch( + "{}.keypoint_rcnn_inference".format(patched_module), + side_effect=Caffe2KeypointRCNNInference(use_heatmap_max_keypoint), + ) as mocked_func: + yield + if check: + assert mocked_func.call_count > 0 + + +class ROIHeadsPatcher: + def __init__(self, heads, use_heatmap_max_keypoint): + self.heads = heads + self.use_heatmap_max_keypoint = use_heatmap_max_keypoint + + @contextlib.contextmanager + def mock_roi_heads(self, tensor_mode=True): + """ + Patching several inference functions inside ROIHeads and its subclasses + + Args: + tensor_mode (bool): whether the inputs/outputs are caffe2's tensor + format or not. Default to True. + """ + # NOTE: this requries the `keypoint_rcnn_inference` and `mask_rcnn_inference` + # are called inside the same file as BaseXxxHead due to using mock.patch. + kpt_heads_mod = keypoint_head.BaseKeypointRCNNHead.__module__ + mask_head_mod = mask_head.BaseMaskRCNNHead.__module__ + + mock_ctx_managers = [ + mock_fastrcnn_outputs_inference( + tensor_mode=tensor_mode, + check=True, + box_predictor_type=type(self.heads.box_predictor), + ) + ] + if getattr(self.heads, "keypoint_on", False): + mock_ctx_managers += [ + mock_keypoint_rcnn_inference( + tensor_mode, kpt_heads_mod, self.use_heatmap_max_keypoint + ) + ] + if getattr(self.heads, "mask_on", False): + mock_ctx_managers += [mock_mask_rcnn_inference(tensor_mode, mask_head_mod)] + + with contextlib.ExitStack() as stack: # python 3.3+ + for mgr in mock_ctx_managers: + stack.enter_context(mgr) + yield diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/export/flatten.py b/motion-gan-pipeline/preprocessing/third/detectron2/export/flatten.py new file mode 100644 index 0000000..f5ba429 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/export/flatten.py @@ -0,0 +1,330 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import collections +from dataclasses import dataclass +from typing import Callable, List, Optional, Tuple +import torch +from torch import nn + +from detectron2.structures import Boxes, Instances, ROIMasks +from detectron2.utils.registry import _convert_target_to_string, locate + +from .torchscript_patch import patch_builtin_len + + +@dataclass +class Schema: + """ + A Schema defines how to flatten a possibly hierarchical object into tuple of + primitive objects, so it can be used as inputs/outputs of PyTorch's tracing. + + PyTorch does not support tracing a function that produces rich output + structures (e.g. dict, Instances, Boxes). To trace such a function, we + flatten the rich object into tuple of tensors, and return this tuple of tensors + instead. Meanwhile, we also need to know how to "rebuild" the original object + from the flattened results, so we can evaluate the flattened results. + A Schema defines how to flatten an object, and while flattening it, it records + necessary schemas so that the object can be rebuilt using the flattened outputs. + + The flattened object and the schema object is returned by ``.flatten`` classmethod. + Then the original object can be rebuilt with the ``__call__`` method of schema. + + A Schema is a dataclass that can be serialized easily. + """ + + # inspired by FetchMapper in tensorflow/python/client/session.py + + @classmethod + def flatten(cls, obj): + raise NotImplementedError + + def __call__(self, values): + raise NotImplementedError + + @staticmethod + def _concat(values): + ret = () + sizes = [] + for v in values: + assert isinstance(v, tuple), "Flattened results must be a tuple" + ret = ret + v + sizes.append(len(v)) + return ret, sizes + + @staticmethod + def _split(values, sizes): + if len(sizes): + expected_len = sum(sizes) + assert ( + len(values) == expected_len + ), f"Values has length {len(values)} but expect length {expected_len}." + ret = [] + for k in range(len(sizes)): + begin, end = sum(sizes[:k]), sum(sizes[: k + 1]) + ret.append(values[begin:end]) + return ret + + +@dataclass +class ListSchema(Schema): + schemas: List[Schema] # the schemas that define how to flatten each element in the list + sizes: List[int] # the flattened length of each element + + def __call__(self, values): + values = self._split(values, self.sizes) + if len(values) != len(self.schemas): + raise ValueError( + f"Values has length {len(values)} but schemas " f"has length {len(self.schemas)}!" + ) + values = [m(v) for m, v in zip(self.schemas, values)] + return list(values) + + @classmethod + def flatten(cls, obj): + res = [flatten_to_tuple(k) for k in obj] + values, sizes = cls._concat([k[0] for k in res]) + return values, cls([k[1] for k in res], sizes) + + +@dataclass +class TupleSchema(ListSchema): + def __call__(self, values): + return tuple(super().__call__(values)) + + +@dataclass +class IdentitySchema(Schema): + def __call__(self, values): + return values[0] + + @classmethod + def flatten(cls, obj): + return (obj,), cls() + + +@dataclass +class DictSchema(ListSchema): + keys: List[str] + + def __call__(self, values): + values = super().__call__(values) + return dict(zip(self.keys, values)) + + @classmethod + def flatten(cls, obj): + for k in obj.keys(): + if not isinstance(k, str): + raise KeyError("Only support flattening dictionaries if keys are str.") + keys = sorted(obj.keys()) + values = [obj[k] for k in keys] + ret, schema = ListSchema.flatten(values) + return ret, cls(schema.schemas, schema.sizes, keys) + + +@dataclass +class InstancesSchema(DictSchema): + def __call__(self, values): + image_size, fields = values[-1], values[:-1] + fields = super().__call__(fields) + return Instances(image_size, **fields) + + @classmethod + def flatten(cls, obj): + ret, schema = super().flatten(obj.get_fields()) + size = obj.image_size + if not isinstance(size, torch.Tensor): + size = torch.tensor(size) + return ret + (size,), schema + + +@dataclass +class TensorWrapSchema(Schema): + """ + For classes that are simple wrapper of tensors, e.g. + Boxes, RotatedBoxes, BitMasks + """ + + class_name: str + + def __call__(self, values): + return locate(self.class_name)(values[0]) + + @classmethod + def flatten(cls, obj): + return (obj.tensor,), cls(_convert_target_to_string(type(obj))) + + +# if more custom structures needed in the future, can allow +# passing in extra schemas for custom types +def flatten_to_tuple(obj): + """ + Flatten an object so it can be used for PyTorch tracing. + Also returns how to rebuild the original object from the flattened outputs. + + Returns: + res (tuple): the flattened results that can be used as tracing outputs + schema: an object with a ``__call__`` method such that ``schema(res) == obj``. + It is a pure dataclass that can be serialized. + """ + schemas = [ + ((str, bytes), IdentitySchema), + (list, ListSchema), + (tuple, TupleSchema), + (collections.abc.Mapping, DictSchema), + (Instances, InstancesSchema), + ((Boxes, ROIMasks), TensorWrapSchema), + ] + for klass, schema in schemas: + if isinstance(obj, klass): + F = schema + break + else: + F = IdentitySchema + + return F.flatten(obj) + + +class TracingAdapter(nn.Module): + """ + A model may take rich input/output format (e.g. dict or custom classes), + but `torch.jit.trace` requires tuple of tensors as input/output. + This adapter flattens input/output format of a model so it becomes traceable. + + It also records the necessary schema to rebuild model's inputs/outputs from flattened + inputs/outputs. + + Example: + :: + outputs = model(inputs) # inputs/outputs may be rich structure + adapter = TracingAdapter(model, inputs) + + # can now trace the model, with adapter.flattened_inputs, or another + # tuple of tensors with the same length and meaning + traced = torch.jit.trace(adapter, adapter.flattened_inputs) + + # traced model can only produce flattened outputs (tuple of tensors) + flattened_outputs = traced(*adapter.flattened_inputs) + # adapter knows the schema to convert it back (new_outputs == outputs) + new_outputs = adapter.outputs_schema(flattened_outputs) + """ + + flattened_inputs: Tuple[torch.Tensor] = None + """ + Flattened version of inputs given to this class's constructor. + """ + + inputs_schema: Schema = None + """ + Schema of the inputs given to this class's constructor. + """ + + outputs_schema: Schema = None + """ + Schema of the output produced by calling the given model with inputs. + """ + + def __init__( + self, + model: nn.Module, + inputs, + inference_func: Optional[Callable] = None, + allow_non_tensor: bool = False, + ): + """ + Args: + model: an nn.Module + inputs: An input argument or a tuple of input arguments used to call model. + After flattening, it has to only consist of tensors. + inference_func: a callable that takes (model, *inputs), calls the + model with inputs, and return outputs. By default it + is ``lambda model, *inputs: model(*inputs)``. Can be override + if you need to call the model differently. + allow_non_tensor: allow inputs/outputs to contain non-tensor objects. + This option will filter out non-tensor objects to make the + model traceable, but ``inputs_schema``/``outputs_schema`` cannot be + used anymore because inputs/outputs cannot be rebuilt from pure tensors. + This is useful when you're only interested in the single trace of + execution (e.g. for flop count), but not interested in + generalizing the traced graph to new inputs. + """ + super().__init__() + if isinstance(model, (nn.parallel.distributed.DistributedDataParallel, nn.DataParallel)): + model = model.module + self.model = model + if not isinstance(inputs, tuple): + inputs = (inputs,) + self.inputs = inputs + self.allow_non_tensor = allow_non_tensor + + if inference_func is None: + inference_func = lambda model, *inputs: model(*inputs) # noqa + self.inference_func = inference_func + + self.flattened_inputs, self.inputs_schema = flatten_to_tuple(inputs) + + if all(isinstance(x, torch.Tensor) for x in self.flattened_inputs): + return + if self.allow_non_tensor: + self.flattened_inputs = tuple( + [x for x in self.flattened_inputs if isinstance(x, torch.Tensor)] + ) + self.inputs_schema = None + else: + for input in self.flattened_inputs: + if not isinstance(input, torch.Tensor): + raise ValueError( + "Inputs for tracing must only contain tensors. " + f"Got a {type(input)} instead." + ) + + def forward(self, *args: torch.Tensor): + with torch.no_grad(), patch_builtin_len(): + if self.inputs_schema is not None: + inputs_orig_format = self.inputs_schema(args) + else: + if len(args) != len(self.flattened_inputs) or any( + x is not y for x, y in zip(args, self.flattened_inputs) + ): + raise ValueError( + "TracingAdapter does not contain valid inputs_schema." + " So it cannot generalize to other inputs and must be" + " traced with `.flattened_inputs`." + ) + inputs_orig_format = self.inputs + + outputs = self.inference_func(self.model, *inputs_orig_format) + flattened_outputs, schema = flatten_to_tuple(outputs) + + flattened_output_tensors = tuple( + [x for x in flattened_outputs if isinstance(x, torch.Tensor)] + ) + if len(flattened_output_tensors) < len(flattened_outputs): + if self.allow_non_tensor: + flattened_outputs = flattened_output_tensors + self.outputs_schema = None + else: + raise ValueError( + "Model cannot be traced because some model outputs " + "cannot flatten to tensors." + ) + else: # schema is valid + if self.outputs_schema is None: + self.outputs_schema = schema + else: + assert self.outputs_schema == schema, ( + "Model should always return outputs with the same " + "structure so it can be traced!" + ) + return flattened_outputs + + def _create_wrapper(self, traced_model): + """ + Return a function that has an input/output interface the same as the + original model, but it calls the given traced model under the hood. + """ + + def forward(*args): + flattened_inputs, _ = flatten_to_tuple(args) + flattened_outputs = traced_model(*flattened_inputs) + return self.outputs_schema(flattened_outputs) + + return forward diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/export/shared.py b/motion-gan-pipeline/preprocessing/third/detectron2/export/shared.py new file mode 100644 index 0000000..2d0f7bf --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/export/shared.py @@ -0,0 +1,1034 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import collections +import contextlib +import copy +import functools +import logging +import numpy as np +import os +from typing import Any, Callable, Dict, List, Optional, Tuple, Union +from unittest import mock +import caffe2.python.utils as putils +import torch +import torch.nn.functional as F +from caffe2.proto import caffe2_pb2 +from caffe2.python import core, net_drawer, workspace +from torch.nn.functional import interpolate as interp + +logger = logging.getLogger(__name__) + + +# ==== torch/utils_toffee/cast.py ======================================= + + +def to_device(t, device_str): + """ + This function is a replacement of .to(another_device) such that it allows the + casting to be traced properly by explicitly calling the underlying copy ops. + It also avoids introducing unncessary op when casting to the same device. + """ + src = t.device + dst = torch.device(device_str) + + if src == dst: + return t + elif src.type == "cuda" and dst.type == "cpu": + return torch.ops._caffe2.CopyGPUToCPU(t) + elif src.type == "cpu" and dst.type == "cuda": + return torch.ops._caffe2.CopyCPUToGPU(t) + else: + raise RuntimeError("Can't cast tensor from device {} to device {}".format(src, dst)) + + +# ==== torch/utils_toffee/interpolate.py ======================================= + + +# Note: borrowed from vision/detection/fair/detectron/detectron/modeling/detector.py +def BilinearInterpolation(tensor_in, up_scale): + assert up_scale % 2 == 0, "Scale should be even" + + def upsample_filt(size): + factor = (size + 1) // 2 + if size % 2 == 1: + center = factor - 1 + else: + center = factor - 0.5 + + og = np.ogrid[:size, :size] + return (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) / factor) + + kernel_size = int(up_scale) * 2 + bil_filt = upsample_filt(kernel_size) + + dim = int(tensor_in.shape[1]) + kernel = np.zeros((dim, dim, kernel_size, kernel_size), dtype=np.float32) + kernel[range(dim), range(dim), :, :] = bil_filt + + tensor_out = F.conv_transpose2d( + tensor_in, + weight=to_device(torch.Tensor(kernel), tensor_in.device), + bias=None, + stride=int(up_scale), + padding=int(up_scale / 2), + ) + + return tensor_out + + +# NOTE: ONNX is incompatible with traced torch.nn.functional.interpolate if +# using dynamic `scale_factor` rather than static `size`. (T43166860) +# NOTE: Caffe2 Int8 conversion might not be able to quantize `size` properly. +def onnx_compatibale_interpolate( + input, size=None, scale_factor=None, mode="nearest", align_corners=None +): + # NOTE: The input dimensions are interpreted in the form: + # `mini-batch x channels x [optional depth] x [optional height] x width`. + if size is None and scale_factor is not None: + if input.dim() == 4: + if isinstance(scale_factor, (int, float)): + height_scale, width_scale = (scale_factor, scale_factor) + else: + assert isinstance(scale_factor, (tuple, list)) + assert len(scale_factor) == 2 + height_scale, width_scale = scale_factor + + assert not align_corners, "No matching C2 op for align_corners == True" + if mode == "nearest": + return torch.ops._caffe2.ResizeNearest( + input, order="NCHW", width_scale=width_scale, height_scale=height_scale + ) + elif mode == "bilinear": + logger.warning( + "Use F.conv_transpose2d for bilinear interpolate" + " because there's no such C2 op, this may cause significant" + " slowdown and the boundary pixels won't be as same as" + " using F.interpolate due to padding." + ) + assert height_scale == width_scale + return BilinearInterpolation(input, up_scale=height_scale) + logger.warning("Output size is not static, it might cause ONNX conversion issue") + + return interp(input, size, scale_factor, mode, align_corners) + + +@contextlib.contextmanager +def mock_torch_nn_functional_interpolate(): + if torch.onnx.is_in_onnx_export(): + with mock.patch( + "torch.nn.functional.interpolate", side_effect=onnx_compatibale_interpolate + ): + yield + else: + yield + + +# ==== torch/utils_caffe2/ws_utils.py ========================================== + + +class ScopedWS(object): + def __init__(self, ws_name, is_reset, is_cleanup=False): + self.ws_name = ws_name + self.is_reset = is_reset + self.is_cleanup = is_cleanup + self.org_ws = "" + + def __enter__(self): + self.org_ws = workspace.CurrentWorkspace() + if self.ws_name is not None: + workspace.SwitchWorkspace(self.ws_name, True) + if self.is_reset: + workspace.ResetWorkspace() + + return workspace + + def __exit__(self, *args): + if self.is_cleanup: + workspace.ResetWorkspace() + if self.ws_name is not None: + workspace.SwitchWorkspace(self.org_ws) + + +def fetch_any_blob(name): + bb = None + try: + bb = workspace.FetchBlob(name) + except TypeError: + bb = workspace.FetchInt8Blob(name) + except Exception as e: + logger.error("Get blob {} error: {}".format(name, e)) + + return bb + + +# ==== torch/utils_caffe2/protobuf.py ========================================== + + +def get_pb_arg(pb, arg_name): + for x in pb.arg: + if x.name == arg_name: + return x + return None + + +def get_pb_arg_valf(pb, arg_name, default_val): + arg = get_pb_arg(pb, arg_name) + return arg.f if arg is not None else default_val + + +def get_pb_arg_floats(pb, arg_name, default_val): + arg = get_pb_arg(pb, arg_name) + return list(map(float, arg.floats)) if arg is not None else default_val + + +def get_pb_arg_ints(pb, arg_name, default_val): + arg = get_pb_arg(pb, arg_name) + return list(map(int, arg.ints)) if arg is not None else default_val + + +def get_pb_arg_vali(pb, arg_name, default_val): + arg = get_pb_arg(pb, arg_name) + return arg.i if arg is not None else default_val + + +def get_pb_arg_vals(pb, arg_name, default_val): + arg = get_pb_arg(pb, arg_name) + return arg.s if arg is not None else default_val + + +def get_pb_arg_valstrings(pb, arg_name, default_val): + arg = get_pb_arg(pb, arg_name) + return list(arg.strings) if arg is not None else default_val + + +def check_set_pb_arg(pb, arg_name, arg_attr, arg_value, allow_override=False): + arg = get_pb_arg(pb, arg_name) + if arg is None: + arg = putils.MakeArgument(arg_name, arg_value) + assert hasattr(arg, arg_attr) + pb.arg.extend([arg]) + if allow_override and getattr(arg, arg_attr) != arg_value: + logger.warning( + "Override argument {}: {} -> {}".format(arg_name, getattr(arg, arg_attr), arg_value) + ) + setattr(arg, arg_attr, arg_value) + else: + assert arg is not None + assert getattr(arg, arg_attr) == arg_value, "Existing value {}, new value {}".format( + getattr(arg, arg_attr), arg_value + ) + + +def _create_const_fill_op_from_numpy(name, tensor, device_option=None): + assert type(tensor) == np.ndarray + kTypeNameMapper = { + np.dtype("float32"): "GivenTensorFill", + np.dtype("int32"): "GivenTensorIntFill", + np.dtype("int64"): "GivenTensorInt64Fill", + np.dtype("uint8"): "GivenTensorStringFill", + } + + args_dict = {} + if tensor.dtype == np.dtype("uint8"): + args_dict.update({"values": [str(tensor.data)], "shape": [1]}) + else: + args_dict.update({"values": tensor, "shape": tensor.shape}) + + if device_option is not None: + args_dict["device_option"] = device_option + + return core.CreateOperator(kTypeNameMapper[tensor.dtype], [], [name], **args_dict) + + +def _create_const_fill_op_from_c2_int8_tensor(name, int8_tensor): + assert type(int8_tensor) == workspace.Int8Tensor + kTypeNameMapper = { + np.dtype("int32"): "Int8GivenIntTensorFill", + np.dtype("uint8"): "Int8GivenTensorFill", + } + + tensor = int8_tensor.data + assert tensor.dtype in [np.dtype("uint8"), np.dtype("int32")] + values = tensor.tobytes() if tensor.dtype == np.dtype("uint8") else tensor + + return core.CreateOperator( + kTypeNameMapper[tensor.dtype], + [], + [name], + values=values, + shape=tensor.shape, + Y_scale=int8_tensor.scale, + Y_zero_point=int8_tensor.zero_point, + ) + + +def create_const_fill_op( + name: str, + blob: Union[np.ndarray, workspace.Int8Tensor], + device_option: Optional[caffe2_pb2.DeviceOption] = None, +) -> caffe2_pb2.OperatorDef: + """ + Given a blob object, return the Caffe2 operator that creates this blob + as constant. Currently support NumPy tensor and Caffe2 Int8Tensor. + """ + + tensor_type = type(blob) + assert tensor_type in [ + np.ndarray, + workspace.Int8Tensor, + ], 'Error when creating const fill op for "{}", unsupported blob type: {}'.format( + name, type(blob) + ) + + if tensor_type == np.ndarray: + return _create_const_fill_op_from_numpy(name, blob, device_option) + elif tensor_type == workspace.Int8Tensor: + assert device_option is None + return _create_const_fill_op_from_c2_int8_tensor(name, blob) + + +def construct_init_net_from_params( + params: Dict[str, Any], device_options: Optional[Dict[str, caffe2_pb2.DeviceOption]] = None +) -> caffe2_pb2.NetDef: + """ + Construct the init_net from params dictionary + """ + init_net = caffe2_pb2.NetDef() + device_options = device_options or {} + for name, blob in params.items(): + if isinstance(blob, str): + logger.warning( + ( + "Blob {} with type {} is not supported in generating init net," + " skipped.".format(name, type(blob)) + ) + ) + continue + init_net.op.extend( + [create_const_fill_op(name, blob, device_option=device_options.get(name, None))] + ) + init_net.external_output.append(name) + return init_net + + +def get_producer_map(ssa): + """ + Return dict from versioned blob to (i, j), + where i is index of producer op, j is the index of output of that op. + """ + producer_map = {} + for i in range(len(ssa)): + outputs = ssa[i][1] + for j, outp in enumerate(outputs): + producer_map[outp] = (i, j) + return producer_map + + +def get_consumer_map(ssa): + """ + Return dict from versioned blob to list of (i, j), + where i is index of consumer op, j is the index of input of that op. + """ + consumer_map = collections.defaultdict(list) + for i in range(len(ssa)): + inputs = ssa[i][0] + for j, inp in enumerate(inputs): + consumer_map[inp].append((i, j)) + return consumer_map + + +def get_params_from_init_net( + init_net: caffe2_pb2.NetDef, +) -> [Dict[str, Any], Dict[str, caffe2_pb2.DeviceOption]]: + """ + Take the output blobs from init_net by running it. + Outputs: + params: dict from blob name to numpy array + device_options: dict from blob name to the device option of its creating op + """ + # NOTE: this assumes that the params is determined by producer op with the + # only exception be CopyGPUToCPU which is CUDA op but returns CPU tensor. + def _get_device_option(producer_op): + if producer_op.type == "CopyGPUToCPU": + return caffe2_pb2.DeviceOption() + else: + return producer_op.device_option + + with ScopedWS("__get_params_from_init_net__", is_reset=True, is_cleanup=True) as ws: + ws.RunNetOnce(init_net) + params = {b: fetch_any_blob(b) for b in init_net.external_output} + ssa, versions = core.get_ssa(init_net) + producer_map = get_producer_map(ssa) + device_options = { + b: _get_device_option(init_net.op[producer_map[(b, versions[b])][0]]) + for b in init_net.external_output + } + return params, device_options + + +def _updater_raise(op, input_types, output_types): + raise RuntimeError( + "Failed to apply updater for op {} given input_types {} and" + " output_types {}".format(op, input_types, output_types) + ) + + +def _generic_status_identifier( + predict_net: caffe2_pb2.NetDef, + status_updater: Callable, + known_status: Dict[Tuple[str, int], Any], +) -> Dict[Tuple[str, int], Any]: + """ + Statically infer the status of each blob, the status can be such as device type + (CPU/GPU), layout (NCHW/NHWC), data type (float32/int8), etc. "Blob" here + is versioned blob (Tuple[str, int]) in the format compatible with ssa. + Inputs: + predict_net: the caffe2 network + status_updater: a callable, given an op and the status of its input/output, + it returns the updated status of input/output. `None` is used for + representing unknown status. + known_status: a dict containing known status, used as initialization. + Outputs: + A dict mapping from versioned blob to its status + """ + ssa, versions = core.get_ssa(predict_net) + versioned_ext_input = [(b, 0) for b in predict_net.external_input] + versioned_ext_output = [(b, versions[b]) for b in predict_net.external_output] + all_versioned_blobs = set().union(*[set(x[0] + x[1]) for x in ssa]) + + allowed_vbs = all_versioned_blobs.union(versioned_ext_input).union(versioned_ext_output) + assert all(k in allowed_vbs for k in known_status) + assert all(v is not None for v in known_status.values()) + _known_status = copy.deepcopy(known_status) + + def _check_and_update(key, value): + assert value is not None + if key in _known_status: + if not _known_status[key] == value: + raise RuntimeError( + "Confilict status for {}, existing status {}, new status {}".format( + key, _known_status[key], value + ) + ) + _known_status[key] = value + + def _update_i(op, ssa_i): + versioned_inputs = ssa_i[0] + versioned_outputs = ssa_i[1] + + inputs_status = [_known_status.get(b, None) for b in versioned_inputs] + outputs_status = [_known_status.get(b, None) for b in versioned_outputs] + + new_inputs_status, new_outputs_status = status_updater(op, inputs_status, outputs_status) + + for versioned_blob, status in zip( + versioned_inputs + versioned_outputs, new_inputs_status + new_outputs_status + ): + if status is not None: + _check_and_update(versioned_blob, status) + + for op, ssa_i in zip(predict_net.op, ssa): + _update_i(op, ssa_i) + for op, ssa_i in zip(reversed(predict_net.op), reversed(ssa)): + _update_i(op, ssa_i) + + # NOTE: This strictly checks all the blob from predict_net must be assgined + # a known status. However sometimes it's impossible (eg. having deadend op), + # we may relax this constraint if + for k in all_versioned_blobs: + if k not in _known_status: + raise NotImplementedError( + "Can not infer the status for {}. Currently only support the case where" + " a single forward and backward pass can identify status for all blobs.".format(k) + ) + + return _known_status + + +def infer_device_type( + predict_net: caffe2_pb2.NetDef, + known_status: Dict[Tuple[str, int], Any], + device_name_style: str = "caffe2", +) -> Dict[Tuple[str, int], str]: + """Return the device type ("cpu" or "gpu"/"cuda") of each (versioned) blob""" + + assert device_name_style in ["caffe2", "pytorch"] + _CPU_STR = "cpu" + _GPU_STR = "gpu" if device_name_style == "caffe2" else "cuda" + + def _copy_cpu_to_gpu_updater(op, input_types, output_types): + if input_types[0] == _GPU_STR or output_types[0] == _CPU_STR: + _updater_raise(op, input_types, output_types) + return ([_CPU_STR], [_GPU_STR]) + + def _copy_gpu_to_cpu_updater(op, input_types, output_types): + if input_types[0] == _CPU_STR or output_types[0] == _GPU_STR: + _updater_raise(op, input_types, output_types) + return ([_GPU_STR], [_CPU_STR]) + + def _other_ops_updater(op, input_types, output_types): + non_none_types = [x for x in input_types + output_types if x is not None] + if len(non_none_types) > 0: + the_type = non_none_types[0] + if not all(x == the_type for x in non_none_types): + _updater_raise(op, input_types, output_types) + else: + the_type = None + return ([the_type for _ in op.input], [the_type for _ in op.output]) + + def _device_updater(op, *args, **kwargs): + return { + "CopyCPUToGPU": _copy_cpu_to_gpu_updater, + "CopyGPUToCPU": _copy_gpu_to_cpu_updater, + }.get(op.type, _other_ops_updater)(op, *args, **kwargs) + + return _generic_status_identifier(predict_net, _device_updater, known_status) + + +# ==== torch/utils_caffe2/vis.py =============================================== + + +def _modify_blob_names(ops, blob_rename_f): + ret = [] + + def _replace_list(blob_list, replaced_list): + del blob_list[:] + blob_list.extend(replaced_list) + + for x in ops: + cur = copy.deepcopy(x) + _replace_list(cur.input, list(map(blob_rename_f, cur.input))) + _replace_list(cur.output, list(map(blob_rename_f, cur.output))) + ret.append(cur) + + return ret + + +def _rename_blob(name, blob_sizes, blob_ranges): + def _list_to_str(bsize): + ret = ", ".join([str(x) for x in bsize]) + ret = "[" + ret + "]" + return ret + + ret = name + if blob_sizes is not None and name in blob_sizes: + ret += "\n" + _list_to_str(blob_sizes[name]) + if blob_ranges is not None and name in blob_ranges: + ret += "\n" + _list_to_str(blob_ranges[name]) + + return ret + + +# graph_name could not contain word 'graph' +def save_graph(net, file_name, graph_name="net", op_only=True, blob_sizes=None, blob_ranges=None): + blob_rename_f = functools.partial(_rename_blob, blob_sizes=blob_sizes, blob_ranges=blob_ranges) + return save_graph_base(net, file_name, graph_name, op_only, blob_rename_f) + + +def save_graph_base(net, file_name, graph_name="net", op_only=True, blob_rename_func=None): + graph = None + ops = net.op + if blob_rename_func is not None: + ops = _modify_blob_names(ops, blob_rename_func) + if not op_only: + graph = net_drawer.GetPydotGraph(ops, graph_name, rankdir="TB") + else: + graph = net_drawer.GetPydotGraphMinimal( + ops, graph_name, rankdir="TB", minimal_dependency=True + ) + + try: + par_dir = os.path.dirname(file_name) + if not os.path.exists(par_dir): + os.makedirs(par_dir) + + format = os.path.splitext(os.path.basename(file_name))[-1] + if format == ".png": + graph.write_png(file_name) + elif format == ".pdf": + graph.write_pdf(file_name) + elif format == ".svg": + graph.write_svg(file_name) + else: + print("Incorrect format {}".format(format)) + except Exception as e: + print("Error when writing graph to image {}".format(e)) + + return graph + + +# ==== torch/utils_toffee/aten_to_caffe2.py ==================================== + + +def group_norm_replace_aten_with_caffe2(predict_net: caffe2_pb2.NetDef): + """ + For ONNX exported model, GroupNorm will be represented as ATen op, + this can be a drop in replacement from ATen to GroupNorm + """ + count = 0 + for op in predict_net.op: + if op.type == "ATen": + op_name = get_pb_arg_vals(op, "operator", None) # return byte in py3 + if op_name and op_name.decode() == "group_norm": + op.arg.remove(get_pb_arg(op, "operator")) + + if get_pb_arg_vali(op, "cudnn_enabled", None): + op.arg.remove(get_pb_arg(op, "cudnn_enabled")) + + num_groups = get_pb_arg_vali(op, "num_groups", None) + if num_groups is not None: + op.arg.remove(get_pb_arg(op, "num_groups")) + check_set_pb_arg(op, "group", "i", num_groups) + + op.type = "GroupNorm" + count += 1 + if count > 1: + logger.info("Replaced {} ATen operator to GroupNormOp".format(count)) + + +# ==== torch/utils_toffee/alias.py ============================================= + + +def alias(x, name, is_backward=False): + if not torch.onnx.is_in_onnx_export(): + return x + assert isinstance(x, torch.Tensor) + return torch.ops._caffe2.AliasWithName(x, name, is_backward=is_backward) + + +def fuse_alias_placeholder(predict_net, init_net): + """Remove AliasWithName placeholder and rename the input/output of it""" + # First we finish all the re-naming + for i, op in enumerate(predict_net.op): + if op.type == "AliasWithName": + assert len(op.input) == 1 + assert len(op.output) == 1 + name = get_pb_arg_vals(op, "name", None).decode() + is_backward = bool(get_pb_arg_vali(op, "is_backward", 0)) + rename_op_input(predict_net, init_net, i, 0, name, from_producer=is_backward) + rename_op_output(predict_net, i, 0, name) + + # Remove AliasWithName, should be very safe since it's a non-op + new_ops = [] + for op in predict_net.op: + if op.type != "AliasWithName": + new_ops.append(op) + else: + # safety check + assert op.input == op.output + assert op.input[0] == op.arg[0].s.decode() + del predict_net.op[:] + predict_net.op.extend(new_ops) + + +# ==== torch/utils_caffe2/graph_transform.py =================================== + + +class IllegalGraphTransformError(ValueError): + """When a graph transform function call can't be executed.""" + + +def _rename_versioned_blob_in_proto( + proto: caffe2_pb2.NetDef, + old_name: str, + new_name: str, + version: int, + ssa: List[Tuple[List[Tuple[str, int]], List[Tuple[str, int]]]], + start_versions: Dict[str, int], + end_versions: Dict[str, int], +): + """In given proto, rename all blobs with matched version""" + # Operater list + for op, i_th_ssa in zip(proto.op, ssa): + versioned_inputs, versioned_outputs = i_th_ssa + for i in range(len(op.input)): + if versioned_inputs[i] == (old_name, version): + op.input[i] = new_name + for i in range(len(op.output)): + if versioned_outputs[i] == (old_name, version): + op.output[i] = new_name + # external_input + if start_versions.get(old_name, 0) == version: + for i in range(len(proto.external_input)): + if proto.external_input[i] == old_name: + proto.external_input[i] = new_name + # external_output + if end_versions.get(old_name, 0) == version: + for i in range(len(proto.external_output)): + if proto.external_output[i] == old_name: + proto.external_output[i] = new_name + + +def rename_op_input( + predict_net: caffe2_pb2.NetDef, + init_net: caffe2_pb2.NetDef, + op_id: int, + input_id: int, + new_name: str, + from_producer: bool = False, +): + """ + Rename the op_id-th operator in predict_net, change it's input_id-th input's + name to the new_name. It also does automatic re-route and change + external_input and init_net if necessary. + - It requires the input is only consumed by this op. + - This function modifies predict_net and init_net in-place. + - When from_producer is enable, this also updates other operators that consumes + the same input. Be cautious because may trigger unintended behavior. + """ + assert isinstance(predict_net, caffe2_pb2.NetDef) + assert isinstance(init_net, caffe2_pb2.NetDef) + + init_net_ssa, init_net_versions = core.get_ssa(init_net) + predict_net_ssa, predict_net_versions = core.get_ssa( + predict_net, copy.deepcopy(init_net_versions) + ) + + versioned_inputs, versioned_outputs = predict_net_ssa[op_id] + old_name, version = versioned_inputs[input_id] + + if from_producer: + producer_map = get_producer_map(predict_net_ssa) + if not (old_name, version) in producer_map: + raise NotImplementedError( + "Can't find producer, the input {} is probably from" + " init_net, this is not supported yet.".format(old_name) + ) + producer = producer_map[(old_name, version)] + rename_op_output(predict_net, producer[0], producer[1], new_name) + return + + def contain_targets(op_ssa): + return (old_name, version) in op_ssa[0] + + is_consumer = [contain_targets(op_ssa) for op_ssa in predict_net_ssa] + if sum(is_consumer) > 1: + raise IllegalGraphTransformError( + ( + "Input '{}' of operator(#{}) are consumed by other ops, please use" + + " rename_op_output on the producer instead. Offending op: \n{}" + ).format(old_name, op_id, predict_net.op[op_id]) + ) + + # update init_net + _rename_versioned_blob_in_proto( + init_net, old_name, new_name, version, init_net_ssa, {}, init_net_versions + ) + # update predict_net + _rename_versioned_blob_in_proto( + predict_net, + old_name, + new_name, + version, + predict_net_ssa, + init_net_versions, + predict_net_versions, + ) + + +def rename_op_output(predict_net: caffe2_pb2.NetDef, op_id: int, output_id: int, new_name: str): + """ + Rename the op_id-th operator in predict_net, change it's output_id-th input's + name to the new_name. It also does automatic re-route and change + external_output and if necessary. + - It allows multiple consumers of its output. + - This function modifies predict_net in-place, doesn't need init_net. + """ + assert isinstance(predict_net, caffe2_pb2.NetDef) + + ssa, blob_versions = core.get_ssa(predict_net) + + versioned_inputs, versioned_outputs = ssa[op_id] + old_name, version = versioned_outputs[output_id] + + # update predict_net + _rename_versioned_blob_in_proto( + predict_net, old_name, new_name, version, ssa, {}, blob_versions + ) + + +def get_sub_graph_external_input_output( + predict_net: caffe2_pb2.NetDef, sub_graph_op_indices: List[int] +) -> Tuple[List[Tuple[str, int]], List[Tuple[str, int]]]: + """ + Return the list of external input/output of sub-graph, + each element is tuple of the name and corresponding version in predict_net. + + external input/output is defined the same way as caffe2 NetDef. + """ + ssa, versions = core.get_ssa(predict_net) + + all_inputs = [] + all_outputs = [] + for op_id in sub_graph_op_indices: + all_inputs += [inp for inp in ssa[op_id][0] if inp not in all_inputs] + all_outputs += list(ssa[op_id][1]) # ssa output won't repeat + + # for versioned blobs, external inputs are just those blob in all_inputs + # but not in all_outputs + ext_inputs = [inp for inp in all_inputs if inp not in all_outputs] + + # external outputs are essentially outputs of this subgraph that are used + # outside of this sub-graph (including predict_net.external_output) + all_other_inputs = sum( + (ssa[i][0] for i in range(len(ssa)) if i not in sub_graph_op_indices), + [(outp, versions[outp]) for outp in predict_net.external_output], + ) + ext_outputs = [outp for outp in all_outputs if outp in set(all_other_inputs)] + + return ext_inputs, ext_outputs + + +class DiGraph: + """A DAG representation of caffe2 graph, each vertice is a versioned blob.""" + + def __init__(self): + self.vertices = set() + self.graph = collections.defaultdict(list) + + def add_edge(self, u, v): + self.graph[u].append(v) + self.vertices.add(u) + self.vertices.add(v) + + # grab from https://www.geeksforgeeks.org/find-paths-given-source-destination/ + def get_all_paths(self, s, d): + visited = {k: False for k in self.vertices} + path = [] + all_paths = [] + + def _get_all_paths_util(graph, u, d, visited, path): + visited[u] = True + path.append(u) + if u == d: + all_paths.append(copy.deepcopy(path)) + else: + for i in graph[u]: + if not visited[i]: + _get_all_paths_util(graph, i, d, visited, path) + path.pop() + visited[u] = False + + _get_all_paths_util(self.graph, s, d, visited, path) + return all_paths + + @staticmethod + def from_ssa(ssa): + graph = DiGraph() + for op_id in range(len(ssa)): + for inp in ssa[op_id][0]: + for outp in ssa[op_id][1]: + graph.add_edge(inp, outp) + return graph + + +def _get_dependency_chain(ssa, versioned_target, versioned_source): + """ + Return the index list of relevant operator to produce target blob from source blob, + if there's no dependency, return empty list. + """ + + # finding all paths between nodes can be O(N!), thus we can only search + # in the subgraph using the op starting from the first consumer of source blob + # to the producer of the target blob. + consumer_map = get_consumer_map(ssa) + producer_map = get_producer_map(ssa) + start_op = min(x[0] for x in consumer_map[versioned_source]) - 15 + end_op = ( + producer_map[versioned_target][0] + 15 if versioned_target in producer_map else start_op + ) + sub_graph_ssa = ssa[start_op : end_op + 1] + if len(sub_graph_ssa) > 30: + logger.warning( + "Subgraph bebetween {} and {} is large (from op#{} to op#{}), it" + " might take non-trival time to find all paths between them.".format( + versioned_source, versioned_target, start_op, end_op + ) + ) + + dag = DiGraph.from_ssa(sub_graph_ssa) + paths = dag.get_all_paths(versioned_source, versioned_target) # include two ends + ops_in_paths = [[producer_map[blob][0] for blob in path[1:]] for path in paths] + return sorted(set().union(*[set(ops) for ops in ops_in_paths])) + + +def identify_reshape_sub_graph(predict_net: caffe2_pb2.NetDef) -> List[List[int]]: + """ + Idenfity the reshape sub-graph in a protobuf. + The reshape sub-graph is defined as matching the following pattern: + + (input_blob) -> Op_1 -> ... -> Op_N -> (new_shape) -─┐ + └-------------------------------------------> Reshape -> (output_blob) + + Return: + List of sub-graphs, each sub-graph is represented as a list of indices + of the relavent ops, [Op_1, Op_2, ..., Op_N, Reshape] + """ + + ssa, _ = core.get_ssa(predict_net) + + ret = [] + for i, op in enumerate(predict_net.op): + if op.type == "Reshape": + assert len(op.input) == 2 + input_ssa = ssa[i][0] + data_source = input_ssa[0] + shape_source = input_ssa[1] + op_indices = _get_dependency_chain(ssa, shape_source, data_source) + ret.append(op_indices + [i]) + return ret + + +def remove_reshape_for_fc(predict_net, params): + """ + In PyTorch nn.Linear has to take 2D tensor, this often leads to reshape + a 4D tensor to 2D by calling .view(). However this (dynamic) reshaping + doesn't work well with ONNX and Int8 tools, and cause using extra + ops (eg. ExpandDims) that might not be available on mobile. + Luckily Caffe2 supports 4D tensor for FC, so we can remove those reshape + after exporting ONNX model. + """ + from caffe2.python import core + + # find all reshape sub-graph that can be removed, which is now all Reshape + # sub-graph whose output is only consumed by FC. + # TODO: to make it safer, we may need the actually value to better determine + # if a Reshape before FC is removable. + reshape_sub_graphs = identify_reshape_sub_graph(predict_net) + sub_graphs_to_remove = [] + for reshape_sub_graph in reshape_sub_graphs: + reshape_op_id = reshape_sub_graph[-1] + assert predict_net.op[reshape_op_id].type == "Reshape" + ssa, _ = core.get_ssa(predict_net) + reshape_output = ssa[reshape_op_id][1][0] + consumers = [i for i in range(len(ssa)) if reshape_output in ssa[i][0]] + if all(predict_net.op[consumer].type == "FC" for consumer in consumers): + # safety check if the sub-graph is isolated, for this reshape sub-graph, + # it means it has one non-param external input and one external output. + ext_inputs, ext_outputs = get_sub_graph_external_input_output( + predict_net, reshape_sub_graph + ) + non_params_ext_inputs = [inp for inp in ext_inputs if inp[1] != 0] + if len(non_params_ext_inputs) == 1 and len(ext_outputs) == 1: + sub_graphs_to_remove.append(reshape_sub_graph) + + # perform removing subgraph by: + # 1: rename the Reshape's output to its input, then the graph can be + # seen as in-place itentify, meaning whose external input/output are the same. + # 2: simply remove those ops. + remove_op_ids = [] + params_to_remove = [] + for sub_graph in sub_graphs_to_remove: + logger.info( + "Remove Reshape sub-graph:\n{}".format( + "".join(["(#{:>4})\n{}".format(i, predict_net.op[i]) for i in sub_graph]) + ) + ) + reshape_op_id = sub_graph[-1] + new_reshap_output = predict_net.op[reshape_op_id].input[0] + rename_op_output(predict_net, reshape_op_id, 0, new_reshap_output) + ext_inputs, ext_outputs = get_sub_graph_external_input_output(predict_net, sub_graph) + non_params_ext_inputs = [inp for inp in ext_inputs if inp[1] != 0] + params_ext_inputs = [inp for inp in ext_inputs if inp[1] == 0] + assert len(non_params_ext_inputs) == 1 and len(ext_outputs) == 1 + assert ext_outputs[0][0] == non_params_ext_inputs[0][0] + assert ext_outputs[0][1] == non_params_ext_inputs[0][1] + 1 + remove_op_ids.extend(sub_graph) + params_to_remove.extend(params_ext_inputs) + + predict_net = copy.deepcopy(predict_net) + new_ops = [op for i, op in enumerate(predict_net.op) if i not in remove_op_ids] + del predict_net.op[:] + predict_net.op.extend(new_ops) + for versioned_params in params_to_remove: + name = versioned_params[0] + logger.info("Remove params: {} from init_net and predict_net.external_input".format(name)) + del params[name] + predict_net.external_input.remove(name) + + return predict_net, params + + +def fuse_copy_between_cpu_and_gpu(predict_net: caffe2_pb2.NetDef): + """ + In-place fuse extra copy ops between cpu/gpu for the following case: + a -CopyAToB-> b -CopyBToA> c1 -NextOp1-> d1 + -CopyBToA> c2 -NextOp2-> d2 + The fused network will look like: + a -NextOp1-> d1 + -NextOp2-> d2 + """ + + _COPY_OPS = ["CopyCPUToGPU", "CopyGPUToCPU"] + + def _fuse_once(predict_net): + ssa, blob_versions = core.get_ssa(predict_net) + consumer_map = get_consumer_map(ssa) + versioned_external_output = [ + (name, blob_versions[name]) for name in predict_net.external_output + ] + + for op_id, op in enumerate(predict_net.op): + if op.type in _COPY_OPS: + fw_copy_versioned_output = ssa[op_id][1][0] + consumer_ids = [x[0] for x in consumer_map[fw_copy_versioned_output]] + reverse_op_type = _COPY_OPS[1 - _COPY_OPS.index(op.type)] + + is_fusable = ( + len(consumer_ids) > 0 + and fw_copy_versioned_output not in versioned_external_output + and all( + predict_net.op[_op_id].type == reverse_op_type + and ssa[_op_id][1][0] not in versioned_external_output + for _op_id in consumer_ids + ) + ) + + if is_fusable: + for rv_copy_op_id in consumer_ids: + # making each NextOp uses "a" directly and removing Copy ops + rs_copy_versioned_output = ssa[rv_copy_op_id][1][0] + next_op_id, inp_id = consumer_map[rs_copy_versioned_output][0] + predict_net.op[next_op_id].input[inp_id] = op.input[0] + # remove CopyOps + new_ops = [ + op + for i, op in enumerate(predict_net.op) + if i != op_id and i not in consumer_ids + ] + del predict_net.op[:] + predict_net.op.extend(new_ops) + return True + + return False + + # _fuse_once returns False is nothing can be fused + while _fuse_once(predict_net): + pass + + +def remove_dead_end_ops(net_def: caffe2_pb2.NetDef): + """remove ops if its output is not used or not in external_output""" + ssa, versions = core.get_ssa(net_def) + versioned_external_output = [(name, versions[name]) for name in net_def.external_output] + consumer_map = get_consumer_map(ssa) + removed_op_ids = set() + + def _is_dead_end(versioned_blob): + return not ( + versioned_blob in versioned_external_output + or ( + len(consumer_map[versioned_blob]) > 0 + and all(x[0] not in removed_op_ids for x in consumer_map[versioned_blob]) + ) + ) + + for i, ssa_i in reversed(list(enumerate(ssa))): + versioned_outputs = ssa_i[1] + if all(_is_dead_end(outp) for outp in versioned_outputs): + removed_op_ids.add(i) + + # simply removing those deadend ops should have no effect to external_output + new_ops = [op for i, op in enumerate(net_def.op) if i not in removed_op_ids] + del net_def.op[:] + net_def.op.extend(new_ops) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/export/torchscript.py b/motion-gan-pipeline/preprocessing/third/detectron2/export/torchscript.py new file mode 100644 index 0000000..24fe59b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/export/torchscript.py @@ -0,0 +1,132 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import os +import torch + +from detectron2.utils.file_io import PathManager + +from .torchscript_patch import freeze_training_mode, patch_instances + +__all__ = ["scripting_with_instances", "dump_torchscript_IR"] + + +def scripting_with_instances(model, fields): + """ + Run :func:`torch.jit.script` on a model that uses the :class:`Instances` class. Since + attributes of :class:`Instances` are "dynamically" added in eager mode，it is difficult + for scripting to support it out of the box. This function is made to support scripting + a model that uses :class:`Instances`. It does the following: + + 1. Create a scriptable ``new_Instances`` class which behaves similarly to ``Instances``, + but with all attributes been "static". + The attributes need to be statically declared in the ``fields`` argument. + 2. Register ``new_Instances``, and force scripting compiler to + use it when trying to compile ``Instances``. + + After this function, the process will be reverted. User should be able to script another model + using different fields. + + Example: + Assume that ``Instances`` in the model consist of two attributes named + ``proposal_boxes`` and ``objectness_logits`` with type :class:`Boxes` and + :class:`Tensor` respectively during inference. You can call this function like: + :: + fields = {"proposal_boxes": Boxes, "objectness_logits": torch.Tensor} + torchscipt_model = scripting_with_instances(model, fields) + + Note: + It only support models in evaluation mode. + + Args: + model (nn.Module): The input model to be exported by scripting. + fields (Dict[str, type]): Attribute names and corresponding type that + ``Instances`` will use in the model. Note that all attributes used in ``Instances`` + need to be added, regardless of whether they are inputs/outputs of the model. + Data type not defined in detectron2 is not supported for now. + + Returns: + torch.jit.ScriptModule: the model in torchscript format + """ + assert ( + not model.training + ), "Currently we only support exporting models in evaluation mode to torchscript" + + with freeze_training_mode(model), patch_instances(fields): + scripted_model = torch.jit.script(model) + return scripted_model + + +# alias for old name +export_torchscript_with_instances = scripting_with_instances + + +def dump_torchscript_IR(model, dir): + """ + Dump IR of a TracedModule/ScriptModule/Function in various format (code, graph, + inlined graph). Useful for debugging. + + Args: + model (TracedModule/ScriptModule/ScriptFUnction): traced or scripted module + dir (str): output directory to dump files. + """ + dir = os.path.expanduser(dir) + PathManager.mkdirs(dir) + + def _get_script_mod(mod): + if isinstance(mod, torch.jit.TracedModule): + return mod._actual_script_module + return mod + + # Dump pretty-printed code: https://pytorch.org/docs/stable/jit.html#inspecting-code + with PathManager.open(os.path.join(dir, "model_ts_code.txt"), "w") as f: + + def get_code(mod): + # Try a few ways to get code using private attributes. + try: + # This contains more information than just `mod.code` + return _get_script_mod(mod)._c.code + except AttributeError: + pass + try: + return mod.code + except AttributeError: + return None + + def dump_code(prefix, mod): + code = get_code(mod) + name = prefix or "root model" + if code is None: + f.write(f"Could not found code for {name} (type={mod.original_name})\n") + f.write("\n") + else: + f.write(f"\nCode for {name}, type={mod.original_name}:\n") + f.write(code) + f.write("\n") + f.write("-" * 80) + + for name, m in mod.named_children(): + dump_code(prefix + "." + name, m) + + if isinstance(model, torch.jit.ScriptFunction): + f.write(get_code(model)) + else: + dump_code("", model) + + def _get_graph(model): + try: + # Recursively dump IR of all modules + return _get_script_mod(model)._c.dump_to_str(True, False, False) + except AttributeError: + return model.graph.str() + + with PathManager.open(os.path.join(dir, "model_ts_IR.txt"), "w") as f: + f.write(_get_graph(model)) + + # Dump IR of the entire graph (all submodules inlined) + with PathManager.open(os.path.join(dir, "model_ts_IR_inlined.txt"), "w") as f: + f.write(str(model.inlined_graph)) + + if not isinstance(model, torch.jit.ScriptFunction): + # Dump the model structure in pytorch style + with PathManager.open(os.path.join(dir, "model.txt"), "w") as f: + f.write(str(model)) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/export/torchscript_patch.py b/motion-gan-pipeline/preprocessing/third/detectron2/export/torchscript_patch.py new file mode 100644 index 0000000..da9b324 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/export/torchscript_patch.py @@ -0,0 +1,406 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import os +import sys +import tempfile +from contextlib import ExitStack, contextmanager +from copy import deepcopy +from unittest import mock +import torch +from torch import nn + +# need some explicit imports due to https://github.com/pytorch/pytorch/issues/38964 +import detectron2 # noqa F401 +from detectron2.structures import Boxes, Instances +from detectron2.utils.env import _import_file + +_counter = 0 + + +def _clear_jit_cache(): + from torch.jit._recursive import concrete_type_store + from torch.jit._state import _jit_caching_layer + + concrete_type_store.type_store.clear() # for modules + _jit_caching_layer.clear() # for free functions + + +def _add_instances_conversion_methods(newInstances): + """ + Add from_instances methods to the scripted Instances class. + """ + cls_name = newInstances.__name__ + + @torch.jit.unused + def from_instances(instances: Instances): + """ + Create scripted Instances from original Instances + """ + fields = instances.get_fields() + image_size = instances.image_size + ret = newInstances(image_size) + for name, val in fields.items(): + assert hasattr(ret, f"_{name}"), f"No attribute named {name} in {cls_name}" + setattr(ret, name, deepcopy(val)) + return ret + + newInstances.from_instances = from_instances + + +@contextmanager +def patch_instances(fields): + """ + A contextmanager, under which the Instances class in detectron2 is replaced + by a statically-typed scriptable class, defined by `fields`. + See more in `scripting_with_instances`. + """ + + with tempfile.TemporaryDirectory(prefix="detectron2") as dir, tempfile.NamedTemporaryFile( + mode="w", encoding="utf-8", suffix=".py", dir=dir, delete=False + ) as f: + try: + # Objects that use Instances should not reuse previously-compiled + # results in cache, because `Instances` could be a new class each time. + _clear_jit_cache() + + cls_name, s = _gen_instance_module(fields) + f.write(s) + f.flush() + f.close() + + module = _import(f.name) + new_instances = getattr(module, cls_name) + _ = torch.jit.script(new_instances) + # let torchscript think Instances was scripted already + Instances.__torch_script_class__ = True + # let torchscript find new_instances when looking for the jit type of Instances + Instances._jit_override_qualname = torch._jit_internal._qualified_name(new_instances) + + _add_instances_conversion_methods(new_instances) + yield new_instances + finally: + try: + del Instances.__torch_script_class__ + del Instances._jit_override_qualname + except AttributeError: + pass + sys.modules.pop(module.__name__) + + +def _gen_instance_class(fields): + """ + Args: + fields (dict[name: type]) + """ + + class _FieldType: + def __init__(self, name, type_): + assert isinstance(name, str), f"Field name must be str, got {name}" + self.name = name + self.type_ = type_ + self.annotation = f"{type_.__module__}.{type_.__name__}" + + fields = [_FieldType(k, v) for k, v in fields.items()] + + def indent(level, s): + return " " * 4 * level + s + + lines = [] + + global _counter + _counter += 1 + + cls_name = "ScriptedInstances{}".format(_counter) + + field_names = tuple(x.name for x in fields) + extra_args = ", ".join([f"{f.name}: Optional[{f.annotation}] = None" for f in fields]) + lines.append( + f""" +class {cls_name}: + def __init__(self, image_size: Tuple[int, int], {extra_args}): + self.image_size = image_size + self._field_names = {field_names} +""" + ) + + for f in fields: + lines.append( + indent(2, f"self._{f.name} = torch.jit.annotate(Optional[{f.annotation}], {f.name})") + ) + + for f in fields: + lines.append( + f""" + @property + def {f.name}(self) -> {f.annotation}: + # has to use a local for type refinement + # https://pytorch.org/docs/stable/jit_language_reference.html#optional-type-refinement + t = self._{f.name} + assert t is not None, "{f.name} is None and cannot be accessed!" + return t + + @{f.name}.setter + def {f.name}(self, value: {f.annotation}) -> None: + self._{f.name} = value +""" + ) + + # support method `__len__` + lines.append( + """ + def __len__(self) -> int: +""" + ) + for f in fields: + lines.append( + f""" + t = self._{f.name} + if t is not None: + return len(t) +""" + ) + lines.append( + """ + raise NotImplementedError("Empty Instances does not support __len__!") +""" + ) + + # support method `has` + lines.append( + """ + def has(self, name: str) -> bool: +""" + ) + for f in fields: + lines.append( + f""" + if name == "{f.name}": + return self._{f.name} is not None +""" + ) + lines.append( + """ + return False +""" + ) + + # support method `to` + none_args = ", None" * len(fields) + lines.append( + f""" + def to(self, device: torch.device) -> "{cls_name}": + ret = {cls_name}(self.image_size{none_args}) +""" + ) + for f in fields: + if hasattr(f.type_, "to"): + lines.append( + f""" + t = self._{f.name} + if t is not None: + ret._{f.name} = t.to(device) +""" + ) + else: + # For now, ignore fields that cannot be moved to devices. + # Maybe can support other tensor-like classes (e.g. __torch_function__) + pass + lines.append( + """ + return ret +""" + ) + + # support method `getitem` + none_args = ", None" * len(fields) + lines.append( + f""" + def __getitem__(self, item) -> "{cls_name}": + ret = {cls_name}(self.image_size{none_args}) +""" + ) + for f in fields: + lines.append( + f""" + t = self._{f.name} + if t is not None: + ret._{f.name} = t[item] +""" + ) + lines.append( + """ + return ret +""" + ) + + # support method `cat` + # this version does not contain checks that all instances have same size and fields + none_args = ", None" * len(fields) + lines.append( + f""" + def cat(self, instances: List["{cls_name}"]) -> "{cls_name}": + ret = {cls_name}(self.image_size{none_args}) +""" + ) + for f in fields: + lines.append( + f""" + t = self._{f.name} + if t is not None: + values: List[{f.annotation}] = [x.{f.name} for x in instances] + if torch.jit.isinstance(t, torch.Tensor): + ret._{f.name} = torch.cat(values, dim=0) + else: + ret._{f.name} = t.cat(values) +""" + ) + lines.append( + """ + return ret""" + ) + + # support method `get_fields()` + lines.append( + """ + def get_fields(self) -> Dict[str, Tensor]: + ret = {} + """ + ) + for f in fields: + if f.type_ == Boxes: + stmt = "t.tensor" + elif f.type_ == torch.Tensor: + stmt = "t" + else: + stmt = f'assert False, "unsupported type {str(f.type_)}"' + lines.append( + f""" + t = self._{f.name} + if t is not None: + ret["{f.name}"] = {stmt} + """ + ) + lines.append( + """ + return ret""" + ) + return cls_name, os.linesep.join(lines) + + +def _gen_instance_module(fields): + # TODO: find a more automatic way to enable import of other classes + s = """ +from copy import deepcopy +import torch +from torch import Tensor +import typing +from typing import * + +import detectron2 +from detectron2.structures import Boxes, Instances + +""" + + cls_name, cls_def = _gen_instance_class(fields) + s += cls_def + return cls_name, s + + +def _import(path): + return _import_file( + "{}{}".format(sys.modules[__name__].__name__, _counter), path, make_importable=True + ) + + +@contextmanager +def patch_builtin_len(modules=()): + """ + Patch the builtin len() function of a few detectron2 modules + to use __len__ instead, because __len__ does not convert values to + integers and therefore is friendly to tracing. + + Args: + modules (list[stsr]): names of extra modules to patch len(), in + addition to those in detectron2. + """ + + def _new_len(obj): + return obj.__len__() + + with ExitStack() as stack: + MODULES = [ + "detectron2.modeling.roi_heads.fast_rcnn", + "detectron2.modeling.roi_heads.mask_head", + "detectron2.modeling.roi_heads.keypoint_head", + ] + list(modules) + ctxs = [stack.enter_context(mock.patch(mod + ".len")) for mod in MODULES] + for m in ctxs: + m.side_effect = _new_len + yield + + +def patch_nonscriptable_classes(): + """ + Apply patches on a few nonscriptable detectron2 classes. + Should not have side-effects on eager usage. + """ + # __prepare_scriptable__ can also be added to models for easier maintenance. + # But it complicates the clean model code. + + from detectron2.modeling.backbone import ResNet, FPN + + # Due to https://github.com/pytorch/pytorch/issues/36061, + # we change backbone to use ModuleList for scripting. + # (note: this changes param names in state_dict) + + def prepare_resnet(self): + ret = deepcopy(self) + ret.stages = nn.ModuleList(ret.stages) + for k in self.stage_names: + delattr(ret, k) + return ret + + ResNet.__prepare_scriptable__ = prepare_resnet + + def prepare_fpn(self): + ret = deepcopy(self) + ret.lateral_convs = nn.ModuleList(ret.lateral_convs) + ret.output_convs = nn.ModuleList(ret.output_convs) + for name, _ in self.named_children(): + if name.startswith("fpn_"): + delattr(ret, name) + return ret + + FPN.__prepare_scriptable__ = prepare_fpn + + # Annotate some attributes to be constants for the purpose of scripting, + # even though they are not constants in eager mode. + from detectron2.modeling.roi_heads import StandardROIHeads + + if hasattr(StandardROIHeads, "__annotations__"): + # copy first to avoid editing annotations of base class + StandardROIHeads.__annotations__ = deepcopy(StandardROIHeads.__annotations__) + StandardROIHeads.__annotations__["mask_on"] = torch.jit.Final[bool] + StandardROIHeads.__annotations__["keypoint_on"] = torch.jit.Final[bool] + + +# These patches are not supposed to have side-effects. +patch_nonscriptable_classes() + + +@contextmanager +def freeze_training_mode(model): + """ + A context manager that annotates the "training" attribute of every submodule + to constant, so that the training codepath in these modules can be + meta-compiled away. Upon exiting, the annotations are reverted. + """ + classes = {type(x) for x in model.modules()} + # __constants__ is the old way to annotate constants and not compatible + # with __annotations__ . + classes = {x for x in classes if not hasattr(x, "__constants__")} + for cls in classes: + cls.__annotations__["training"] = torch.jit.Final[bool] + yield + for cls in classes: + cls.__annotations__["training"] = bool diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/layers/__init__.py new file mode 100644 index 0000000..3d015c5 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/__init__.py @@ -0,0 +1,24 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .batch_norm import FrozenBatchNorm2d, get_norm, NaiveSyncBatchNorm, CycleBatchNormList +from .deform_conv import DeformConv, ModulatedDeformConv +from .mask_ops import paste_masks_in_image +from .nms import batched_nms, batched_nms_rotated, nms, nms_rotated +from .roi_align import ROIAlign, roi_align +from .roi_align_rotated import ROIAlignRotated, roi_align_rotated +from .shape_spec import ShapeSpec +from .wrappers import ( + BatchNorm2d, + Conv2d, + ConvTranspose2d, + cat, + interpolate, + Linear, + nonzero_tuple, + cross_entropy, + shapes_to_tensor, +) +from .blocks import CNNBlockBase, DepthwiseSeparableConv2d +from .aspp import ASPP +from .losses import ciou_loss, diou_loss + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/aspp.py b/motion-gan-pipeline/preprocessing/third/detectron2/layers/aspp.py new file mode 100644 index 0000000..14861aa --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/aspp.py @@ -0,0 +1,144 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from copy import deepcopy +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F + +from .batch_norm import get_norm +from .blocks import DepthwiseSeparableConv2d +from .wrappers import Conv2d + + +class ASPP(nn.Module): + """ + Atrous Spatial Pyramid Pooling (ASPP). + """ + + def __init__( + self, + in_channels, + out_channels, + dilations, + *, + norm, + activation, + pool_kernel_size=None, + dropout: float = 0.0, + use_depthwise_separable_conv=False, + ): + """ + Args: + in_channels (int): number of input channels for ASPP. + out_channels (int): number of output channels. + dilations (list): a list of 3 dilations in ASPP. + norm (str or callable): normalization for all conv layers. + See :func:`layers.get_norm` for supported format. norm is + applied to all conv layers except the conv following + global average pooling. + activation (callable): activation function. + pool_kernel_size (tuple, list): the average pooling size (kh, kw) + for image pooling layer in ASPP. If set to None, it always + performs global average pooling. If not None, it must be + divisible by the shape of inputs in forward(). It is recommended + to use a fixed input feature size in training, and set this + option to match this size, so that it performs global average + pooling in training, and the size of the pooling window stays + consistent in inference. + dropout (float): apply dropout on the output of ASPP. It is used in + the official DeepLab implementation with a rate of 0.1: + https://github.com/tensorflow/models/blob/21b73d22f3ed05b650e85ac50849408dd36de32e/research/deeplab/model.py#L532 # noqa + use_depthwise_separable_conv (bool): use DepthwiseSeparableConv2d + for 3x3 convs in ASPP, proposed in :paper:`DeepLabV3+`. + """ + super(ASPP, self).__init__() + assert len(dilations) == 3, "ASPP expects 3 dilations, got {}".format(len(dilations)) + self.pool_kernel_size = pool_kernel_size + self.dropout = dropout + use_bias = norm == "" + self.convs = nn.ModuleList() + # conv 1x1 + self.convs.append( + Conv2d( + in_channels, + out_channels, + kernel_size=1, + bias=use_bias, + norm=get_norm(norm, out_channels), + activation=deepcopy(activation), + ) + ) + weight_init.c2_xavier_fill(self.convs[-1]) + # atrous convs + for dilation in dilations: + if use_depthwise_separable_conv: + self.convs.append( + DepthwiseSeparableConv2d( + in_channels, + out_channels, + kernel_size=3, + padding=dilation, + dilation=dilation, + norm1=norm, + activation1=deepcopy(activation), + norm2=norm, + activation2=deepcopy(activation), + ) + ) + else: + self.convs.append( + Conv2d( + in_channels, + out_channels, + kernel_size=3, + padding=dilation, + dilation=dilation, + bias=use_bias, + norm=get_norm(norm, out_channels), + activation=deepcopy(activation), + ) + ) + weight_init.c2_xavier_fill(self.convs[-1]) + # image pooling + # We do not add BatchNorm because the spatial resolution is 1x1, + # the original TF implementation has BatchNorm. + if pool_kernel_size is None: + image_pooling = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + Conv2d(in_channels, out_channels, 1, bias=True, activation=deepcopy(activation)), + ) + else: + image_pooling = nn.Sequential( + nn.AvgPool2d(kernel_size=pool_kernel_size, stride=1), + Conv2d(in_channels, out_channels, 1, bias=True, activation=deepcopy(activation)), + ) + weight_init.c2_xavier_fill(image_pooling[1]) + self.convs.append(image_pooling) + + self.project = Conv2d( + 5 * out_channels, + out_channels, + kernel_size=1, + bias=use_bias, + norm=get_norm(norm, out_channels), + activation=deepcopy(activation), + ) + weight_init.c2_xavier_fill(self.project) + + def forward(self, x): + size = x.shape[-2:] + if self.pool_kernel_size is not None: + if size[0] % self.pool_kernel_size[0] or size[1] % self.pool_kernel_size[1]: + raise ValueError( + "`pool_kernel_size` must be divisible by the shape of inputs. " + "Input size: {} `pool_kernel_size`: {}".format(size, self.pool_kernel_size) + ) + res = [] + for conv in self.convs: + res.append(conv(x)) + res[-1] = F.interpolate(res[-1], size=size, mode="bilinear", align_corners=False) + res = torch.cat(res, dim=1) + res = self.project(res) + res = F.dropout(res, self.dropout, training=self.training) if self.dropout > 0 else res + return res diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/batch_norm.py b/motion-gan-pipeline/preprocessing/third/detectron2/layers/batch_norm.py new file mode 100644 index 0000000..09a6c66 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/batch_norm.py @@ -0,0 +1,276 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import torch +import torch.distributed as dist +from fvcore.nn.distributed import differentiable_all_reduce +from torch import nn +from torch.nn import functional as F + +from detectron2.utils import comm, env + +from .wrappers import BatchNorm2d + + +class FrozenBatchNorm2d(nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters are fixed. + + It contains non-trainable buffers called + "weight" and "bias", "running_mean", "running_var", + initialized to perform identity transformation. + + The pre-trained backbone models from Caffe2 only contain "weight" and "bias", + which are computed from the original four parameters of BN. + The affine transform `x * weight + bias` will perform the equivalent + computation of `(x - running_mean) / sqrt(running_var) * weight + bias`. + When loading a backbone model from Caffe2, "running_mean" and "running_var" + will be left unchanged as identity transformation. + + Other pre-trained backbone models may contain all 4 parameters. + + The forward is implemented by `F.batch_norm(..., training=False)`. + """ + + _version = 3 + + def __init__(self, num_features, eps=1e-5): + super().__init__() + self.num_features = num_features + self.eps = eps + self.register_buffer("weight", torch.ones(num_features)) + self.register_buffer("bias", torch.zeros(num_features)) + self.register_buffer("running_mean", torch.zeros(num_features)) + self.register_buffer("running_var", torch.ones(num_features) - eps) + + def forward(self, x): + if x.requires_grad: + # When gradients are needed, F.batch_norm will use extra memory + # because its backward op computes gradients for weight/bias as well. + scale = self.weight * (self.running_var + self.eps).rsqrt() + bias = self.bias - self.running_mean * scale + scale = scale.reshape(1, -1, 1, 1) + bias = bias.reshape(1, -1, 1, 1) + out_dtype = x.dtype # may be half + return x * scale.to(out_dtype) + bias.to(out_dtype) + else: + # When gradients are not needed, F.batch_norm is a single fused op + # and provide more optimization opportunities. + return F.batch_norm( + x, + self.running_mean, + self.running_var, + self.weight, + self.bias, + training=False, + eps=self.eps, + ) + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + version = local_metadata.get("version", None) + + if version is None or version < 2: + # No running_mean/var in early versions + # This will silent the warnings + if prefix + "running_mean" not in state_dict: + state_dict[prefix + "running_mean"] = torch.zeros_like(self.running_mean) + if prefix + "running_var" not in state_dict: + state_dict[prefix + "running_var"] = torch.ones_like(self.running_var) + + super()._load_from_state_dict( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ) + + def __repr__(self): + return "FrozenBatchNorm2d(num_features={}, eps={})".format(self.num_features, self.eps) + + @classmethod + def convert_frozen_batchnorm(cls, module): + """ + Convert all BatchNorm/SyncBatchNorm in module into FrozenBatchNorm. + + Args: + module (torch.nn.Module): + + Returns: + If module is BatchNorm/SyncBatchNorm, returns a new module. + Otherwise, in-place convert module and return it. + + Similar to convert_sync_batchnorm in + https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/batchnorm.py + """ + bn_module = nn.modules.batchnorm + bn_module = (bn_module.BatchNorm2d, bn_module.SyncBatchNorm) + res = module + if isinstance(module, bn_module): + res = cls(module.num_features) + if module.affine: + res.weight.data = module.weight.data.clone().detach() + res.bias.data = module.bias.data.clone().detach() + res.running_mean.data = module.running_mean.data + res.running_var.data = module.running_var.data + res.eps = module.eps + else: + for name, child in module.named_children(): + new_child = cls.convert_frozen_batchnorm(child) + if new_child is not child: + res.add_module(name, new_child) + return res + + +def get_norm(norm, out_channels): + """ + Args: + norm (str or callable): either one of BN, SyncBN, FrozenBN, GN; + or a callable that takes a channel number and returns + the normalization layer as a nn.Module. + + Returns: + nn.Module or None: the normalization layer + """ + if norm is None: + return None + if isinstance(norm, str): + if len(norm) == 0: + return None + norm = { + "BN": BatchNorm2d, + # Fixed in https://github.com/pytorch/pytorch/pull/36382 + "SyncBN": NaiveSyncBatchNorm if env.TORCH_VERSION <= (1, 5) else nn.SyncBatchNorm, + "FrozenBN": FrozenBatchNorm2d, + "GN": lambda channels: nn.GroupNorm(32, channels), + # for debugging: + "nnSyncBN": nn.SyncBatchNorm, + "naiveSyncBN": NaiveSyncBatchNorm, + # expose stats_mode N as an option to caller, required for zero-len inputs + "naiveSyncBN_N": lambda channels: NaiveSyncBatchNorm(channels, stats_mode="N"), + }[norm] + return norm(out_channels) + + +class NaiveSyncBatchNorm(BatchNorm2d): + """ + In PyTorch<=1.5, ``nn.SyncBatchNorm`` has incorrect gradient + when the batch size on each worker is different. + (e.g., when scale augmentation is used, or when it is applied to mask head). + + This is a slower but correct alternative to `nn.SyncBatchNorm`. + + Note: + There isn't a single definition of Sync BatchNorm. + + When ``stats_mode==""``, this module computes overall statistics by using + statistics of each worker with equal weight. The result is true statistics + of all samples (as if they are all on one worker) only when all workers + have the same (N, H, W). This mode does not support inputs with zero batch size. + + When ``stats_mode=="N"``, this module computes overall statistics by weighting + the statistics of each worker by their ``N``. The result is true statistics + of all samples (as if they are all on one worker) only when all workers + have the same (H, W). It is slower than ``stats_mode==""``. + + Even though the result of this module may not be the true statistics of all samples, + it may still be reasonable because it might be preferrable to assign equal weights + to all workers, regardless of their (H, W) dimension, instead of putting larger weight + on larger images. From preliminary experiments, little difference is found between such + a simplified implementation and an accurate computation of overall mean & variance. + """ + + def __init__(self, *args, stats_mode="", **kwargs): + super().__init__(*args, **kwargs) + assert stats_mode in ["", "N"] + self._stats_mode = stats_mode + + def forward(self, input): + if comm.get_world_size() == 1 or not self.training: + return super().forward(input) + + B, C = input.shape[0], input.shape[1] + + half_input = input.dtype == torch.float16 + if half_input: + # fp16 does not have good enough numerics for the reduction here + input = input.float() + mean = torch.mean(input, dim=[0, 2, 3]) + meansqr = torch.mean(input * input, dim=[0, 2, 3]) + + if self._stats_mode == "": + assert B > 0, 'SyncBatchNorm(stats_mode="") does not support zero batch size.' + vec = torch.cat([mean, meansqr], dim=0) + vec = differentiable_all_reduce(vec) * (1.0 / dist.get_world_size()) + mean, meansqr = torch.split(vec, C) + momentum = self.momentum + else: + if B == 0: + vec = torch.zeros([2 * C + 1], device=mean.device, dtype=mean.dtype) + vec = vec + input.sum() # make sure there is gradient w.r.t input + else: + vec = torch.cat( + [mean, meansqr, torch.ones([1], device=mean.device, dtype=mean.dtype)], dim=0 + ) + vec = differentiable_all_reduce(vec * B) + + total_batch = vec[-1].detach() + momentum = total_batch.clamp(max=1) * self.momentum # no update if total_batch is 0 + mean, meansqr, _ = torch.split(vec / total_batch.clamp(min=1), C) # avoid div-by-zero + + var = meansqr - mean * mean + invstd = torch.rsqrt(var + self.eps) + scale = self.weight * invstd + bias = self.bias - mean * scale + scale = scale.reshape(1, -1, 1, 1) + bias = bias.reshape(1, -1, 1, 1) + + self.running_mean += momentum * (mean.detach() - self.running_mean) + self.running_var += momentum * (var.detach() - self.running_var) + ret = input * scale + bias + if half_input: + ret = ret.half() + return ret + + +class CycleBatchNormList(nn.ModuleList): + """ + Implement domain-specific BatchNorm by cycling. + + When a BatchNorm layer is used for multiple input domains or input + features, it might need to maintain a separate test-time statistics + for each domain. See Sec 5.2 in :paper:`rethinking-batchnorm`. + + This module implements it by using N separate BN layers + and it cycles through them every time a forward() is called. + + NOTE: The caller of this module MUST guarantee to always call + this module by multiple of N times. Otherwise its test-time statistics + will be incorrect. + """ + + def __init__(self, length: int, bn_class=nn.BatchNorm2d, **kwargs): + """ + Args: + length: number of BatchNorm layers to cycle. + bn_class: the BatchNorm class to use + kwargs: arguments of the BatchNorm class, such as num_features. + """ + self._affine = kwargs.pop("affine", True) + super().__init__([bn_class(**kwargs, affine=False) for k in range(length)]) + if self._affine: + # shared affine, domain-specific BN + channels = self[0].num_features + self.weight = nn.Parameter(torch.ones(channels)) + self.bias = nn.Parameter(torch.zeros(channels)) + self._pos = 0 + + def forward(self, x): + ret = self[self._pos](x) + self._pos = (self._pos + 1) % len(self) + + if self._affine: + w = self.weight.reshape(1, -1, 1, 1) + b = self.bias.reshape(1, -1, 1, 1) + return ret * w + b + else: + return ret + + def extra_repr(self): + return f"affine={self._affine}" diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/blocks.py b/motion-gan-pipeline/preprocessing/third/detectron2/layers/blocks.py new file mode 100644 index 0000000..1995a4b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/blocks.py @@ -0,0 +1,111 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import fvcore.nn.weight_init as weight_init +from torch import nn + +from .batch_norm import FrozenBatchNorm2d, get_norm +from .wrappers import Conv2d + + +""" +CNN building blocks. +""" + + +class CNNBlockBase(nn.Module): + """ + A CNN block is assumed to have input channels, output channels and a stride. + The input and output of `forward()` method must be NCHW tensors. + The method can perform arbitrary computation but must match the given + channels and stride specification. + + Attribute: + in_channels (int): + out_channels (int): + stride (int): + """ + + def __init__(self, in_channels, out_channels, stride): + """ + The `__init__` method of any subclass should also contain these arguments. + + Args: + in_channels (int): + out_channels (int): + stride (int): + """ + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.stride = stride + + def freeze(self): + """ + Make this block not trainable. + This method sets all parameters to `requires_grad=False`, + and convert all BatchNorm layers to FrozenBatchNorm + + Returns: + the block itself + """ + for p in self.parameters(): + p.requires_grad = False + FrozenBatchNorm2d.convert_frozen_batchnorm(self) + return self + + +class DepthwiseSeparableConv2d(nn.Module): + """ + A kxk depthwise convolution + a 1x1 convolution. + + In :paper:`xception`, norm & activation are applied on the second conv. + :paper:`mobilenet` uses norm & activation on both convs. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size=3, + padding=1, + dilation=1, + *, + norm1=None, + activation1=None, + norm2=None, + activation2=None, + ): + """ + Args: + norm1, norm2 (str or callable): normalization for the two conv layers. + activation1, activation2 (callable(Tensor) -> Tensor): activation + function for the two conv layers. + """ + super().__init__() + self.depthwise = Conv2d( + in_channels, + in_channels, + kernel_size=kernel_size, + padding=padding, + dilation=dilation, + groups=in_channels, + bias=not norm1, + norm=get_norm(norm1, in_channels), + activation=activation1, + ) + self.pointwise = Conv2d( + in_channels, + out_channels, + kernel_size=1, + bias=not norm2, + norm=get_norm(norm2, out_channels), + activation=activation2, + ) + + # default initialization + weight_init.c2_msra_fill(self.depthwise) + weight_init.c2_msra_fill(self.pointwise) + + def forward(self, x): + return self.pointwise(self.depthwise(x)) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/README.md b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/README.md new file mode 100644 index 0000000..778ed3d --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/README.md @@ -0,0 +1,7 @@ + + +To add a new Op: + +1. Create a new directory +2. Implement new ops there +3. Delcare its Python interface in `vision.cpp`. diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h new file mode 100644 index 0000000..03f4211 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h @@ -0,0 +1,115 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#pragma once +#include + +namespace detectron2 { + +at::Tensor ROIAlignRotated_forward_cpu( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio); + +at::Tensor ROIAlignRotated_backward_cpu( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio); + +#if defined(WITH_CUDA) || defined(WITH_HIP) +at::Tensor ROIAlignRotated_forward_cuda( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio); + +at::Tensor ROIAlignRotated_backward_cuda( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio); +#endif + +// Interface for Python +inline at::Tensor ROIAlignRotated_forward( + const at::Tensor& input, + const at::Tensor& rois, + const double spatial_scale, + const int64_t pooled_height, + const int64_t pooled_width, + const int64_t sampling_ratio) { + if (input.is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + return ROIAlignRotated_forward_cuda( + input, + rois, + spatial_scale, + pooled_height, + pooled_width, + sampling_ratio); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + return ROIAlignRotated_forward_cpu( + input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio); +} + +inline at::Tensor ROIAlignRotated_backward( + const at::Tensor& grad, + const at::Tensor& rois, + const double spatial_scale, + const int64_t pooled_height, + const int64_t pooled_width, + const int64_t batch_size, + const int64_t channels, + const int64_t height, + const int64_t width, + const int64_t sampling_ratio) { + if (grad.is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + return ROIAlignRotated_backward_cuda( + grad, + rois, + spatial_scale, + pooled_height, + pooled_width, + batch_size, + channels, + height, + width, + sampling_ratio); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + return ROIAlignRotated_backward_cpu( + grad, + rois, + spatial_scale, + pooled_height, + pooled_width, + batch_size, + channels, + height, + width, + sampling_ratio); +} + +} // namespace detectron2 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cpu.cpp b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cpu.cpp new file mode 100644 index 0000000..2a3d305 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cpu.cpp @@ -0,0 +1,522 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#include +#include "ROIAlignRotated.h" + +// Note: this implementation originates from the Caffe2 ROIAlignRotated Op +// and PyTorch ROIAlign (non-rotated) Op implementations. +// The key difference between this implementation and those ones is +// we don't do "legacy offset" in this version, as there aren't many previous +// works, if any, using the "legacy" ROIAlignRotated Op. +// This would make the interface a bit cleaner. + +namespace detectron2 { + +namespace { +template +struct PreCalc { + int pos1; + int pos2; + int pos3; + int pos4; + T w1; + T w2; + T w3; + T w4; +}; + +template +void pre_calc_for_bilinear_interpolate( + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int iy_upper, + const int ix_upper, + T roi_start_h, + T roi_start_w, + T bin_size_h, + T bin_size_w, + int roi_bin_grid_h, + int roi_bin_grid_w, + T roi_center_h, + T roi_center_w, + T cos_theta, + T sin_theta, + std::vector>& pre_calc) { + int pre_calc_index = 0; + for (int ph = 0; ph < pooled_height; ph++) { + for (int pw = 0; pw < pooled_width; pw++) { + for (int iy = 0; iy < iy_upper; iy++) { + const T yy = roi_start_h + ph * bin_size_h + + static_cast(iy + .5f) * bin_size_h / + static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < ix_upper; ix++) { + const T xx = roi_start_w + pw * bin_size_w + + static_cast(ix + .5f) * bin_size_w / + static_cast(roi_bin_grid_w); + + // Rotate by theta around the center and translate + // In image space, (y, x) is the order for Right Handed System, + // and this is essentially multiplying the point by a rotation matrix + // to rotate it counterclockwise through angle theta. + T y = yy * cos_theta - xx * sin_theta + roi_center_h; + T x = yy * sin_theta + xx * cos_theta + roi_center_w; + // deal with: inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + PreCalc pc; + pc.pos1 = 0; + pc.pos2 = 0; + pc.pos3 = 0; + pc.pos4 = 0; + pc.w1 = 0; + pc.w2 = 0; + pc.w3 = 0; + pc.w4 = 0; + pre_calc[pre_calc_index] = pc; + pre_calc_index += 1; + continue; + } + + if (y < 0) { + y = 0; + } + if (x < 0) { + x = 0; + } + + int y_low = (int)y; + int x_low = (int)x; + int y_high; + int x_high; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T)y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T)x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + // save weights and indices + PreCalc pc; + pc.pos1 = y_low * width + x_low; + pc.pos2 = y_low * width + x_high; + pc.pos3 = y_high * width + x_low; + pc.pos4 = y_high * width + x_high; + pc.w1 = w1; + pc.w2 = w2; + pc.w3 = w3; + pc.w4 = w4; + pre_calc[pre_calc_index] = pc; + + pre_calc_index += 1; + } + } + } + } +} + +template +void bilinear_interpolate_gradient( + const int height, + const int width, + T y, + T x, + T& w1, + T& w2, + T& w3, + T& w4, + int& x_low, + int& x_high, + int& y_low, + int& y_high) { + // deal with cases that inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + w1 = w2 = w3 = w4 = 0.; + x_low = x_high = y_low = y_high = -1; + return; + } + + if (y < 0) { + y = 0; + } + + if (x < 0) { + x = 0; + } + + y_low = (int)y; + x_low = (int)x; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T)y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T)x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + + // reference in forward + // T v1 = input[y_low * width + x_low]; + // T v2 = input[y_low * width + x_high]; + // T v3 = input[y_high * width + x_low]; + // T v4 = input[y_high * width + x_high]; + // T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + + w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + return; +} + +template +inline void add(T* address, const T& val) { + *address += val; +} + +} // namespace + +template +void ROIAlignRotatedForward( + const int nthreads, + const T* input, + const T& spatial_scale, + const int channels, + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + const T* rois, + T* output) { + int n_rois = nthreads / channels / pooled_width / pooled_height; + // (n, c, ph, pw) is an element in the pooled output + // can be parallelized using omp + // #pragma omp parallel for num_threads(32) + for (int n = 0; n < n_rois; n++) { + int index_n = n * channels * pooled_width * pooled_height; + + const T* current_roi = rois + n * 6; + int roi_batch_ind = current_roi[0]; + + // Do not use rounding; this implementation detail is critical + // ROIAlignRotated supports align == true, i.e., continuous coordinate + // by default, thus the 0.5 offset + T offset = (T)0.5; + T roi_center_w = current_roi[1] * spatial_scale - offset; + T roi_center_h = current_roi[2] * spatial_scale - offset; + T roi_width = current_roi[3] * spatial_scale; + T roi_height = current_roi[4] * spatial_scale; + T theta = current_roi[5] * M_PI / 180.0; + T cos_theta = cos(theta); + T sin_theta = sin(theta); + + AT_ASSERTM( + roi_width >= 0 && roi_height >= 0, + "ROIs in ROIAlignRotated do not have non-negative size!"); + + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) + ? sampling_ratio + : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = + (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // We do average (integral) pooling inside a bin + const T count = std::max(roi_bin_grid_h * roi_bin_grid_w, 1); // e.g. = 4 + + // we want to precalculate indices and weights shared by all channels, + // this is the key point of optimization + std::vector> pre_calc( + roi_bin_grid_h * roi_bin_grid_w * pooled_width * pooled_height); + + // roi_start_h and roi_start_w are computed wrt the center of RoI (x, y). + // Appropriate translation needs to be applied after. + T roi_start_h = -roi_height / 2.0; + T roi_start_w = -roi_width / 2.0; + + pre_calc_for_bilinear_interpolate( + height, + width, + pooled_height, + pooled_width, + roi_bin_grid_h, + roi_bin_grid_w, + roi_start_h, + roi_start_w, + bin_size_h, + bin_size_w, + roi_bin_grid_h, + roi_bin_grid_w, + roi_center_h, + roi_center_w, + cos_theta, + sin_theta, + pre_calc); + + for (int c = 0; c < channels; c++) { + int index_n_c = index_n + c * pooled_width * pooled_height; + const T* offset_input = + input + (roi_batch_ind * channels + c) * height * width; + int pre_calc_index = 0; + + for (int ph = 0; ph < pooled_height; ph++) { + for (int pw = 0; pw < pooled_width; pw++) { + int index = index_n_c + ph * pooled_width + pw; + + T output_val = 0.; + for (int iy = 0; iy < roi_bin_grid_h; iy++) { + for (int ix = 0; ix < roi_bin_grid_w; ix++) { + PreCalc pc = pre_calc[pre_calc_index]; + output_val += pc.w1 * offset_input[pc.pos1] + + pc.w2 * offset_input[pc.pos2] + + pc.w3 * offset_input[pc.pos3] + pc.w4 * offset_input[pc.pos4]; + + pre_calc_index += 1; + } + } + output_val /= count; + + output[index] = output_val; + } // for pw + } // for ph + } // for c + } // for n +} + +template +void ROIAlignRotatedBackward( + const int nthreads, + // may not be contiguous. should index using n_stride, etc + const T* grad_output, + const T& spatial_scale, + const int channels, + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + T* grad_input, + const T* rois, + const int n_stride, + const int c_stride, + const int h_stride, + const int w_stride) { + for (int index = 0; index < nthreads; index++) { + // (n, c, ph, pw) is an element in the pooled output + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int c = (index / pooled_width / pooled_height) % channels; + int n = index / pooled_width / pooled_height / channels; + + const T* current_roi = rois + n * 6; + int roi_batch_ind = current_roi[0]; + + // Do not use rounding; this implementation detail is critical + // ROIAlignRotated supports align == true, i.e., continuous coordinate + // by default, thus the 0.5 offset + T offset = (T)0.5; + T roi_center_w = current_roi[1] * spatial_scale - offset; + T roi_center_h = current_roi[2] * spatial_scale - offset; + T roi_width = current_roi[3] * spatial_scale; + T roi_height = current_roi[4] * spatial_scale; + T theta = current_roi[5] * M_PI / 180.0; + T cos_theta = cos(theta); + T sin_theta = sin(theta); + + AT_ASSERTM( + roi_width >= 0 && roi_height >= 0, + "ROIs in ROIAlignRotated do not have non-negative size!"); + + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + T* offset_grad_input = + grad_input + ((roi_batch_ind * channels + c) * height * width); + + int output_offset = n * n_stride + c * c_stride; + const T* offset_grad_output = grad_output + output_offset; + const T grad_output_this_bin = + offset_grad_output[ph * h_stride + pw * w_stride]; + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) + ? sampling_ratio + : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = + (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // roi_start_h and roi_start_w are computed wrt the center of RoI (x, y). + // Appropriate translation needs to be applied after. + T roi_start_h = -roi_height / 2.0; + T roi_start_w = -roi_width / 2.0; + + // We do average (integral) pooling inside a bin + const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4 + + for (int iy = 0; iy < roi_bin_grid_h; iy++) { + const T yy = roi_start_h + ph * bin_size_h + + static_cast(iy + .5f) * bin_size_h / + static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < roi_bin_grid_w; ix++) { + const T xx = roi_start_w + pw * bin_size_w + + static_cast(ix + .5f) * bin_size_w / + static_cast(roi_bin_grid_w); + + // Rotate by theta around the center and translate + T y = yy * cos_theta - xx * sin_theta + roi_center_h; + T x = yy * sin_theta + xx * cos_theta + roi_center_w; + + T w1, w2, w3, w4; + int x_low, x_high, y_low, y_high; + + bilinear_interpolate_gradient( + height, width, y, x, w1, w2, w3, w4, x_low, x_high, y_low, y_high); + + T g1 = grad_output_this_bin * w1 / count; + T g2 = grad_output_this_bin * w2 / count; + T g3 = grad_output_this_bin * w3 / count; + T g4 = grad_output_this_bin * w4 / count; + + if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) { + // atomic add is not needed for now since it is single threaded + add(offset_grad_input + y_low * width + x_low, static_cast(g1)); + add(offset_grad_input + y_low * width + x_high, static_cast(g2)); + add(offset_grad_input + y_high * width + x_low, static_cast(g3)); + add(offset_grad_input + y_high * width + x_high, static_cast(g4)); + } // if + } // ix + } // iy + } // for +} // ROIAlignRotatedBackward + +at::Tensor ROIAlignRotated_forward_cpu( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio) { + AT_ASSERTM(input.device().is_cpu(), "input must be a CPU tensor"); + AT_ASSERTM(rois.device().is_cpu(), "rois must be a CPU tensor"); + + at::TensorArg input_t{input, "input", 1}, rois_t{rois, "rois", 2}; + + at::CheckedFrom c = "ROIAlign_forward_cpu"; + at::checkAllSameType(c, {input_t, rois_t}); + + auto num_rois = rois.size(0); + auto channels = input.size(1); + auto height = input.size(2); + auto width = input.size(3); + + at::Tensor output = at::zeros( + {num_rois, channels, pooled_height, pooled_width}, input.options()); + + auto output_size = num_rois * pooled_height * pooled_width * channels; + + if (output.numel() == 0) { + return output; + } + + auto input_ = input.contiguous(), rois_ = rois.contiguous(); + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + input.scalar_type(), "ROIAlignRotated_forward", [&] { + ROIAlignRotatedForward( + output_size, + input_.data_ptr(), + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + rois_.data_ptr(), + output.data_ptr()); + }); + return output; +} + +at::Tensor ROIAlignRotated_backward_cpu( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio) { + AT_ASSERTM(grad.device().is_cpu(), "grad must be a CPU tensor"); + AT_ASSERTM(rois.device().is_cpu(), "rois must be a CPU tensor"); + + at::TensorArg grad_t{grad, "grad", 1}, rois_t{rois, "rois", 2}; + + at::CheckedFrom c = "ROIAlignRotated_backward_cpu"; + at::checkAllSameType(c, {grad_t, rois_t}); + + at::Tensor grad_input = + at::zeros({batch_size, channels, height, width}, grad.options()); + + // handle possibly empty gradients + if (grad.numel() == 0) { + return grad_input; + } + + // get stride values to ensure indexing into gradients is correct. + int n_stride = grad.stride(0); + int c_stride = grad.stride(1); + int h_stride = grad.stride(2); + int w_stride = grad.stride(3); + + auto rois_ = rois.contiguous(); + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + grad.scalar_type(), "ROIAlignRotated_forward", [&] { + ROIAlignRotatedBackward( + grad.numel(), + grad.data_ptr(), + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + grad_input.data_ptr(), + rois_.data_ptr(), + n_stride, + c_stride, + h_stride, + w_stride); + }); + return grad_input; +} + +} // namespace detectron2 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cuda.cu b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cuda.cu new file mode 100644 index 0000000..fca1865 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cuda.cu @@ -0,0 +1,443 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#include +#include +#include +#include + +// TODO make it in a common file +#define CUDA_1D_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \ + i += blockDim.x * gridDim.x) + +// Note: this implementation originates from the Caffe2 ROIAlignRotated Op +// and PyTorch ROIAlign (non-rotated) Op implementations. +// The key difference between this implementation and those ones is +// we don't do "legacy offset" in this version, as there aren't many previous +// works, if any, using the "legacy" ROIAlignRotated Op. +// This would make the interface a bit cleaner. + +namespace detectron2 { + +namespace { + +template +__device__ T bilinear_interpolate( + const T* input, + const int height, + const int width, + T y, + T x) { + // deal with cases that inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + return 0; + } + + if (y < 0) { + y = 0; + } + + if (x < 0) { + x = 0; + } + + int y_low = (int)y; + int x_low = (int)x; + int y_high; + int x_high; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T)y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T)x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + // do bilinear interpolation + T v1 = input[y_low * width + x_low]; + T v2 = input[y_low * width + x_high]; + T v3 = input[y_high * width + x_low]; + T v4 = input[y_high * width + x_high]; + T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + + return val; +} + +template +__device__ void bilinear_interpolate_gradient( + const int height, + const int width, + T y, + T x, + T& w1, + T& w2, + T& w3, + T& w4, + int& x_low, + int& x_high, + int& y_low, + int& y_high) { + // deal with cases that inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + w1 = w2 = w3 = w4 = 0.; + x_low = x_high = y_low = y_high = -1; + return; + } + + if (y < 0) { + y = 0; + } + + if (x < 0) { + x = 0; + } + + y_low = (int)y; + x_low = (int)x; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T)y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T)x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + + // reference in forward + // T v1 = input[y_low * width + x_low]; + // T v2 = input[y_low * width + x_high]; + // T v3 = input[y_high * width + x_low]; + // T v4 = input[y_high * width + x_high]; + // T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + + w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + return; +} + +} // namespace + +template +__global__ void RoIAlignRotatedForward( + const int nthreads, + const T* input, + const T spatial_scale, + const int channels, + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + const T* rois, + T* top_data) { + CUDA_1D_KERNEL_LOOP(index, nthreads) { + // (n, c, ph, pw) is an element in the pooled output + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int c = (index / pooled_width / pooled_height) % channels; + int n = index / pooled_width / pooled_height / channels; + + const T* current_roi = rois + n * 6; + int roi_batch_ind = current_roi[0]; + + // Do not use rounding; this implementation detail is critical + // ROIAlignRotated supports align == true, i.e., continuous coordinate + // by default, thus the 0.5 offset + T offset = (T)0.5; + T roi_center_w = current_roi[1] * spatial_scale - offset; + T roi_center_h = current_roi[2] * spatial_scale - offset; + T roi_width = current_roi[3] * spatial_scale; + T roi_height = current_roi[4] * spatial_scale; + T theta = current_roi[5] * M_PI / 180.0; + T cos_theta = cos(theta); + T sin_theta = sin(theta); + + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + const T* offset_input = + input + (roi_batch_ind * channels + c) * height * width; + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) + ? sampling_ratio + : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = + (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // roi_start_h and roi_start_w are computed wrt the center of RoI (x, y). + // Appropriate translation needs to be applied after. + T roi_start_h = -roi_height / 2.0; + T roi_start_w = -roi_width / 2.0; + + // We do average (inte gral) pooling inside a bin + const T count = max(roi_bin_grid_h * roi_bin_grid_w, 1); // e.g. = 4 + + T output_val = 0.; + for (int iy = 0; iy < roi_bin_grid_h; iy++) // e.g., iy = 0, 1 + { + const T yy = roi_start_h + ph * bin_size_h + + static_cast(iy + .5f) * bin_size_h / + static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < roi_bin_grid_w; ix++) { + const T xx = roi_start_w + pw * bin_size_w + + static_cast(ix + .5f) * bin_size_w / + static_cast(roi_bin_grid_w); + + // Rotate by theta around the center and translate + T y = yy * cos_theta - xx * sin_theta + roi_center_h; + T x = yy * sin_theta + xx * cos_theta + roi_center_w; + + T val = bilinear_interpolate(offset_input, height, width, y, x); + output_val += val; + } + } + output_val /= count; + + top_data[index] = output_val; + } +} + +template +__global__ void RoIAlignRotatedBackwardFeature( + const int nthreads, + const T* top_diff, + const int num_rois, + const T spatial_scale, + const int channels, + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + T* bottom_diff, + const T* rois) { + CUDA_1D_KERNEL_LOOP(index, nthreads) { + // (n, c, ph, pw) is an element in the pooled output + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int c = (index / pooled_width / pooled_height) % channels; + int n = index / pooled_width / pooled_height / channels; + + const T* current_roi = rois + n * 6; + int roi_batch_ind = current_roi[0]; + + // Do not use rounding; this implementation detail is critical + // ROIAlignRotated supports align == true, i.e., continuous coordinate + // by default, thus the 0.5 offset + T offset = (T)0.5; + T roi_center_w = current_roi[1] * spatial_scale - offset; + T roi_center_h = current_roi[2] * spatial_scale - offset; + T roi_width = current_roi[3] * spatial_scale; + T roi_height = current_roi[4] * spatial_scale; + T theta = current_roi[5] * M_PI / 180.0; + T cos_theta = cos(theta); + T sin_theta = sin(theta); + + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + T* offset_bottom_diff = + bottom_diff + (roi_batch_ind * channels + c) * height * width; + + int top_offset = (n * channels + c) * pooled_height * pooled_width; + const T* offset_top_diff = top_diff + top_offset; + const T top_diff_this_bin = offset_top_diff[ph * pooled_width + pw]; + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) + ? sampling_ratio + : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = + (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // roi_start_h and roi_start_w are computed wrt the center of RoI (x, y). + // Appropriate translation needs to be applied after. + T roi_start_h = -roi_height / 2.0; + T roi_start_w = -roi_width / 2.0; + + // We do average (integral) pooling inside a bin + const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4 + + for (int iy = 0; iy < roi_bin_grid_h; iy++) // e.g., iy = 0, 1 + { + const T yy = roi_start_h + ph * bin_size_h + + static_cast(iy + .5f) * bin_size_h / + static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < roi_bin_grid_w; ix++) { + const T xx = roi_start_w + pw * bin_size_w + + static_cast(ix + .5f) * bin_size_w / + static_cast(roi_bin_grid_w); + + // Rotate by theta around the center and translate + T y = yy * cos_theta - xx * sin_theta + roi_center_h; + T x = yy * sin_theta + xx * cos_theta + roi_center_w; + + T w1, w2, w3, w4; + int x_low, x_high, y_low, y_high; + + bilinear_interpolate_gradient( + height, width, y, x, w1, w2, w3, w4, x_low, x_high, y_low, y_high); + + T g1 = top_diff_this_bin * w1 / count; + T g2 = top_diff_this_bin * w2 / count; + T g3 = top_diff_this_bin * w3 / count; + T g4 = top_diff_this_bin * w4 / count; + + if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) { + atomicAdd( + offset_bottom_diff + y_low * width + x_low, static_cast(g1)); + atomicAdd( + offset_bottom_diff + y_low * width + x_high, static_cast(g2)); + atomicAdd( + offset_bottom_diff + y_high * width + x_low, static_cast(g3)); + atomicAdd( + offset_bottom_diff + y_high * width + x_high, static_cast(g4)); + } // if + } // ix + } // iy + } // CUDA_1D_KERNEL_LOOP +} // RoIAlignRotatedBackward + +at::Tensor ROIAlignRotated_forward_cuda( + const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio) { + AT_ASSERTM(input.device().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(rois.device().is_cuda(), "rois must be a CUDA tensor"); + at::TensorArg input_t{input, "input", 1}, rois_t{rois, "rois", 2}; + + at::CheckedFrom c = "ROIAlignRotated_forward_cuda"; + at::checkAllSameGPU(c, {input_t, rois_t}); + at::checkAllSameType(c, {input_t, rois_t}); + at::cuda::CUDAGuard device_guard(input.device()); + + auto num_rois = rois.size(0); + auto channels = input.size(1); + auto height = input.size(2); + auto width = input.size(3); + + auto output = at::empty( + {num_rois, channels, pooled_height, pooled_width}, input.options()); + auto output_size = num_rois * pooled_height * pooled_width * channels; + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + dim3 grid(std::min( + at::cuda::ATenCeilDiv( + static_cast(output_size), static_cast(512)), + static_cast(4096))); + dim3 block(512); + + if (output.numel() == 0) { + AT_CUDA_CHECK(cudaGetLastError()); + return output; + } + + auto input_ = input.contiguous(), rois_ = rois.contiguous(); + AT_DISPATCH_FLOATING_TYPES( + input.scalar_type(), "ROIAlignRotated_forward", [&] { + RoIAlignRotatedForward<<>>( + output_size, + input_.data_ptr(), + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + rois_.data_ptr(), + output.data_ptr()); + }); + cudaDeviceSynchronize(); + AT_CUDA_CHECK(cudaGetLastError()); + return output; +} + +// TODO remove the dependency on input and use instead its sizes -> save memory +at::Tensor ROIAlignRotated_backward_cuda( + const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio) { + AT_ASSERTM(grad.device().is_cuda(), "grad must be a CUDA tensor"); + AT_ASSERTM(rois.device().is_cuda(), "rois must be a CUDA tensor"); + + at::TensorArg grad_t{grad, "grad", 1}, rois_t{rois, "rois", 2}; + at::CheckedFrom c = "ROIAlign_backward_cuda"; + at::checkAllSameGPU(c, {grad_t, rois_t}); + at::checkAllSameType(c, {grad_t, rois_t}); + at::cuda::CUDAGuard device_guard(grad.device()); + + auto num_rois = rois.size(0); + auto grad_input = + at::zeros({batch_size, channels, height, width}, grad.options()); + + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + dim3 grid(std::min( + at::cuda::ATenCeilDiv( + static_cast(grad.numel()), static_cast(512)), + static_cast(4096))); + dim3 block(512); + + // handle possibly empty gradients + if (grad.numel() == 0) { + AT_CUDA_CHECK(cudaGetLastError()); + return grad_input; + } + + auto grad_ = grad.contiguous(), rois_ = rois.contiguous(); + AT_DISPATCH_FLOATING_TYPES( + grad.scalar_type(), "ROIAlignRotated_backward", [&] { + RoIAlignRotatedBackwardFeature<<>>( + grad.numel(), + grad_.data_ptr(), + num_rois, + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + grad_input.data_ptr(), + rois_.data_ptr()); + }); + AT_CUDA_CHECK(cudaGetLastError()); + return grad_input; +} + +} // namespace detectron2 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated.h b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated.h new file mode 100644 index 0000000..3bf383b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated.h @@ -0,0 +1,35 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#pragma once +#include + +namespace detectron2 { + +at::Tensor box_iou_rotated_cpu( + const at::Tensor& boxes1, + const at::Tensor& boxes2); + +#if defined(WITH_CUDA) || defined(WITH_HIP) +at::Tensor box_iou_rotated_cuda( + const at::Tensor& boxes1, + const at::Tensor& boxes2); +#endif + +// Interface for Python +// inline is needed to prevent multiple function definitions when this header is +// included by different cpps +inline at::Tensor box_iou_rotated( + const at::Tensor& boxes1, + const at::Tensor& boxes2) { + assert(boxes1.device().is_cuda() == boxes2.device().is_cuda()); + if (boxes1.device().is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + return box_iou_rotated_cuda(boxes1.contiguous(), boxes2.contiguous()); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + + return box_iou_rotated_cpu(boxes1.contiguous(), boxes2.contiguous()); +} + +} // namespace detectron2 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.cpp b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.cpp new file mode 100644 index 0000000..c843487 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.cpp @@ -0,0 +1,39 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#include "box_iou_rotated.h" +#include "box_iou_rotated_utils.h" + +namespace detectron2 { + +template +void box_iou_rotated_cpu_kernel( + const at::Tensor& boxes1, + const at::Tensor& boxes2, + at::Tensor& ious) { + auto num_boxes1 = boxes1.size(0); + auto num_boxes2 = boxes2.size(0); + + for (int i = 0; i < num_boxes1; i++) { + for (int j = 0; j < num_boxes2; j++) { + ious[i * num_boxes2 + j] = single_box_iou_rotated( + boxes1[i].data_ptr(), boxes2[j].data_ptr()); + } + } +} + +at::Tensor box_iou_rotated_cpu( + // input must be contiguous: + const at::Tensor& boxes1, + const at::Tensor& boxes2) { + auto num_boxes1 = boxes1.size(0); + auto num_boxes2 = boxes2.size(0); + at::Tensor ious = + at::empty({num_boxes1 * num_boxes2}, boxes1.options().dtype(at::kFloat)); + + box_iou_rotated_cpu_kernel(boxes1, boxes2, ious); + + // reshape from 1d array to 2d array + auto shape = std::vector{num_boxes1, num_boxes2}; + return ious.reshape(shape); +} + +} // namespace detectron2 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cuda.cu b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cuda.cu new file mode 100644 index 0000000..952710e --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cuda.cu @@ -0,0 +1,130 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#include +#include +#include +#include +#include "box_iou_rotated_utils.h" + +namespace detectron2 { + +// 2D block with 32 * 16 = 512 threads per block +const int BLOCK_DIM_X = 32; +const int BLOCK_DIM_Y = 16; + +template +__global__ void box_iou_rotated_cuda_kernel( + const int n_boxes1, + const int n_boxes2, + const T* dev_boxes1, + const T* dev_boxes2, + T* dev_ious) { + const int row_start = blockIdx.x * blockDim.x; + const int col_start = blockIdx.y * blockDim.y; + + const int row_size = min(n_boxes1 - row_start, blockDim.x); + const int col_size = min(n_boxes2 - col_start, blockDim.y); + + __shared__ float block_boxes1[BLOCK_DIM_X * 5]; + __shared__ float block_boxes2[BLOCK_DIM_Y * 5]; + + // It's safe to copy using threadIdx.x since BLOCK_DIM_X >= BLOCK_DIM_Y + if (threadIdx.x < row_size && threadIdx.y == 0) { + block_boxes1[threadIdx.x * 5 + 0] = + dev_boxes1[(row_start + threadIdx.x) * 5 + 0]; + block_boxes1[threadIdx.x * 5 + 1] = + dev_boxes1[(row_start + threadIdx.x) * 5 + 1]; + block_boxes1[threadIdx.x * 5 + 2] = + dev_boxes1[(row_start + threadIdx.x) * 5 + 2]; + block_boxes1[threadIdx.x * 5 + 3] = + dev_boxes1[(row_start + threadIdx.x) * 5 + 3]; + block_boxes1[threadIdx.x * 5 + 4] = + dev_boxes1[(row_start + threadIdx.x) * 5 + 4]; + } + + if (threadIdx.x < col_size && threadIdx.y == 0) { + block_boxes2[threadIdx.x * 5 + 0] = + dev_boxes2[(col_start + threadIdx.x) * 5 + 0]; + block_boxes2[threadIdx.x * 5 + 1] = + dev_boxes2[(col_start + threadIdx.x) * 5 + 1]; + block_boxes2[threadIdx.x * 5 + 2] = + dev_boxes2[(col_start + threadIdx.x) * 5 + 2]; + block_boxes2[threadIdx.x * 5 + 3] = + dev_boxes2[(col_start + threadIdx.x) * 5 + 3]; + block_boxes2[threadIdx.x * 5 + 4] = + dev_boxes2[(col_start + threadIdx.x) * 5 + 4]; + } + __syncthreads(); + + if (threadIdx.x < row_size && threadIdx.y < col_size) { + int offset = (row_start + threadIdx.x) * n_boxes2 + col_start + threadIdx.y; + dev_ious[offset] = single_box_iou_rotated( + block_boxes1 + threadIdx.x * 5, block_boxes2 + threadIdx.y * 5); + } +} + +at::Tensor box_iou_rotated_cuda( + // input must be contiguous + const at::Tensor& boxes1, + const at::Tensor& boxes2) { + using scalar_t = float; + AT_ASSERTM( + boxes1.scalar_type() == at::kFloat, "boxes1 must be a float tensor"); + AT_ASSERTM( + boxes2.scalar_type() == at::kFloat, "boxes2 must be a float tensor"); + AT_ASSERTM(boxes1.is_cuda(), "boxes1 must be a CUDA tensor"); + AT_ASSERTM(boxes2.is_cuda(), "boxes2 must be a CUDA tensor"); + at::cuda::CUDAGuard device_guard(boxes1.device()); + + auto num_boxes1 = boxes1.size(0); + auto num_boxes2 = boxes2.size(0); + + at::Tensor ious = + at::empty({num_boxes1 * num_boxes2}, boxes1.options().dtype(at::kFloat)); + + bool transpose = false; + if (num_boxes1 > 0 && num_boxes2 > 0) { + scalar_t *data1 = boxes1.data_ptr(), + *data2 = boxes2.data_ptr(); + + if (num_boxes2 > 65535 * BLOCK_DIM_Y) { + AT_ASSERTM( + num_boxes1 <= 65535 * BLOCK_DIM_Y, + "Too many boxes for box_iou_rotated_cuda!"); + // x dim is allowed to be large, but y dim cannot, + // so we transpose the two to avoid "invalid configuration argument" + // error. We assume one of them is small. Otherwise the result is hard to + // fit in memory anyway. + std::swap(num_boxes1, num_boxes2); + std::swap(data1, data2); + transpose = true; + } + + const int blocks_x = + at::cuda::ATenCeilDiv(static_cast(num_boxes1), BLOCK_DIM_X); + const int blocks_y = + at::cuda::ATenCeilDiv(static_cast(num_boxes2), BLOCK_DIM_Y); + + dim3 blocks(blocks_x, blocks_y); + dim3 threads(BLOCK_DIM_X, BLOCK_DIM_Y); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + box_iou_rotated_cuda_kernel<<>>( + num_boxes1, + num_boxes2, + data1, + data2, + (scalar_t*)ious.data_ptr()); + + AT_CUDA_CHECK(cudaGetLastError()); + } + + // reshape from 1d array to 2d array + auto shape = std::vector{num_boxes1, num_boxes2}; + if (transpose) { + return ious.view(shape).t(); + } else { + return ious.view(shape); + } +} + +} // namespace detectron2 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h new file mode 100644 index 0000000..b54a5dd --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h @@ -0,0 +1,370 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#pragma once + +#include +#include + +#if defined(__CUDACC__) || __HCC__ == 1 || __HIP__ == 1 +// Designates functions callable from the host (CPU) and the device (GPU) +#define HOST_DEVICE __host__ __device__ +#define HOST_DEVICE_INLINE HOST_DEVICE __forceinline__ +#else +#include +#define HOST_DEVICE +#define HOST_DEVICE_INLINE HOST_DEVICE inline +#endif + +namespace detectron2 { + +namespace { + +template +struct RotatedBox { + T x_ctr, y_ctr, w, h, a; +}; + +template +struct Point { + T x, y; + HOST_DEVICE_INLINE Point(const T& px = 0, const T& py = 0) : x(px), y(py) {} + HOST_DEVICE_INLINE Point operator+(const Point& p) const { + return Point(x + p.x, y + p.y); + } + HOST_DEVICE_INLINE Point& operator+=(const Point& p) { + x += p.x; + y += p.y; + return *this; + } + HOST_DEVICE_INLINE Point operator-(const Point& p) const { + return Point(x - p.x, y - p.y); + } + HOST_DEVICE_INLINE Point operator*(const T coeff) const { + return Point(x * coeff, y * coeff); + } +}; + +template +HOST_DEVICE_INLINE T dot_2d(const Point& A, const Point& B) { + return A.x * B.x + A.y * B.y; +} + +// R: result type. can be different from input type +template +HOST_DEVICE_INLINE R cross_2d(const Point& A, const Point& B) { + return static_cast(A.x) * static_cast(B.y) - + static_cast(B.x) * static_cast(A.y); +} + +template +HOST_DEVICE_INLINE void get_rotated_vertices( + const RotatedBox& box, + Point (&pts)[4]) { + // M_PI / 180. == 0.01745329251 + double theta = box.a * 0.01745329251; + T cosTheta2 = (T)cos(theta) * 0.5f; + T sinTheta2 = (T)sin(theta) * 0.5f; + + // y: top --> down; x: left --> right + pts[0].x = box.x_ctr + sinTheta2 * box.h + cosTheta2 * box.w; + pts[0].y = box.y_ctr + cosTheta2 * box.h - sinTheta2 * box.w; + pts[1].x = box.x_ctr - sinTheta2 * box.h + cosTheta2 * box.w; + pts[1].y = box.y_ctr - cosTheta2 * box.h - sinTheta2 * box.w; + pts[2].x = 2 * box.x_ctr - pts[0].x; + pts[2].y = 2 * box.y_ctr - pts[0].y; + pts[3].x = 2 * box.x_ctr - pts[1].x; + pts[3].y = 2 * box.y_ctr - pts[1].y; +} + +template +HOST_DEVICE_INLINE int get_intersection_points( + const Point (&pts1)[4], + const Point (&pts2)[4], + Point (&intersections)[24]) { + // Line vector + // A line from p1 to p2 is: p1 + (p2-p1)*t, t=[0,1] + Point vec1[4], vec2[4]; + for (int i = 0; i < 4; i++) { + vec1[i] = pts1[(i + 1) % 4] - pts1[i]; + vec2[i] = pts2[(i + 1) % 4] - pts2[i]; + } + + // When computing the intersection area, it doesn't hurt if we have + // more (duplicated/approximate) intersections/vertices than needed, + // while it can cause drastic difference if we miss an intersection/vertex. + // Therefore, we add an epsilon to relax the comparisons between + // the float point numbers that decide the intersection points. + double EPS = 1e-5; + + // Line test - test all line combos for intersection + int num = 0; // number of intersections + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + // Solve for 2x2 Ax=b + T det = cross_2d(vec2[j], vec1[i]); + + // This takes care of parallel lines + if (fabs(det) <= 1e-14) { + continue; + } + + auto vec12 = pts2[j] - pts1[i]; + + T t1 = cross_2d(vec2[j], vec12) / det; + T t2 = cross_2d(vec1[i], vec12) / det; + + if (t1 > -EPS && t1 < 1.0f + EPS && t2 > -EPS && t2 < 1.0f + EPS) { + intersections[num++] = pts1[i] + vec1[i] * t1; + } + } + } + + // Check for vertices of rect1 inside rect2 + { + const auto& AB = vec2[0]; + const auto& DA = vec2[3]; + auto ABdotAB = dot_2d(AB, AB); + auto ADdotAD = dot_2d(DA, DA); + for (int i = 0; i < 4; i++) { + // assume ABCD is the rectangle, and P is the point to be judged + // P is inside ABCD iff. P's projection on AB lies within AB + // and P's projection on AD lies within AD + + auto AP = pts1[i] - pts2[0]; + + auto APdotAB = dot_2d(AP, AB); + auto APdotAD = -dot_2d(AP, DA); + + if ((APdotAB > -EPS) && (APdotAD > -EPS) && (APdotAB < ABdotAB + EPS) && + (APdotAD < ADdotAD + EPS)) { + intersections[num++] = pts1[i]; + } + } + } + + // Reverse the check - check for vertices of rect2 inside rect1 + { + const auto& AB = vec1[0]; + const auto& DA = vec1[3]; + auto ABdotAB = dot_2d(AB, AB); + auto ADdotAD = dot_2d(DA, DA); + for (int i = 0; i < 4; i++) { + auto AP = pts2[i] - pts1[0]; + + auto APdotAB = dot_2d(AP, AB); + auto APdotAD = -dot_2d(AP, DA); + + if ((APdotAB > -EPS) && (APdotAD > -EPS) && (APdotAB < ABdotAB + EPS) && + (APdotAD < ADdotAD + EPS)) { + intersections[num++] = pts2[i]; + } + } + } + + return num; +} + +template +HOST_DEVICE_INLINE int convex_hull_graham( + const Point (&p)[24], + const int& num_in, + Point (&q)[24], + bool shift_to_zero = false) { + assert(num_in >= 2); + + // Step 1: + // Find point with minimum y + // if more than 1 points have the same minimum y, + // pick the one with the minimum x. + int t = 0; + for (int i = 1; i < num_in; i++) { + if (p[i].y < p[t].y || (p[i].y == p[t].y && p[i].x < p[t].x)) { + t = i; + } + } + auto& start = p[t]; // starting point + + // Step 2: + // Subtract starting point from every points (for sorting in the next step) + for (int i = 0; i < num_in; i++) { + q[i] = p[i] - start; + } + + // Swap the starting point to position 0 + auto tmp = q[0]; + q[0] = q[t]; + q[t] = tmp; + + // Step 3: + // Sort point 1 ~ num_in according to their relative cross-product values + // (essentially sorting according to angles) + // If the angles are the same, sort according to their distance to origin + T dist[24]; +#if defined(__CUDACC__) || __HCC__ == 1 || __HIP__ == 1 + // compute distance to origin before sort, and sort them together with the + // points + for (int i = 0; i < num_in; i++) { + dist[i] = dot_2d(q[i], q[i]); + } + + // CUDA version + // In the future, we can potentially use thrust + // for sorting here to improve speed (though not guaranteed) + for (int i = 1; i < num_in - 1; i++) { + for (int j = i + 1; j < num_in; j++) { + T crossProduct = cross_2d(q[i], q[j]); + if ((crossProduct < -1e-6) || + (fabs(crossProduct) < 1e-6 && dist[i] > dist[j])) { + auto q_tmp = q[i]; + q[i] = q[j]; + q[j] = q_tmp; + auto dist_tmp = dist[i]; + dist[i] = dist[j]; + dist[j] = dist_tmp; + } + } + } +#else + // CPU version + std::sort( + q + 1, q + num_in, [](const Point& A, const Point& B) -> bool { + T temp = cross_2d(A, B); + if (fabs(temp) < 1e-6) { + return dot_2d(A, A) < dot_2d(B, B); + } else { + return temp > 0; + } + }); + // compute distance to origin after sort, since the points are now different. + for (int i = 0; i < num_in; i++) { + dist[i] = dot_2d(q[i], q[i]); + } +#endif + + // Step 4: + // Make sure there are at least 2 points (that don't overlap with each other) + // in the stack + int k; // index of the non-overlapped second point + for (k = 1; k < num_in; k++) { + if (dist[k] > 1e-8) { + break; + } + } + if (k == num_in) { + // We reach the end, which means the convex hull is just one point + q[0] = p[t]; + return 1; + } + q[1] = q[k]; + int m = 2; // 2 points in the stack + // Step 5: + // Finally we can start the scanning process. + // When a non-convex relationship between the 3 points is found + // (either concave shape or duplicated points), + // we pop the previous point from the stack + // until the 3-point relationship is convex again, or + // until the stack only contains two points + for (int i = k + 1; i < num_in; i++) { + while (m > 1) { + auto q1 = q[i] - q[m - 2], q2 = q[m - 1] - q[m - 2]; + // cross_2d() uses FMA and therefore computes round(round(q1.x*q2.y) - + // q2.x*q1.y) So it may not return 0 even when q1==q2. Therefore we + // compare round(q1.x*q2.y) and round(q2.x*q1.y) directly. (round means + // round to nearest floating point). + if (q1.x * q2.y >= q2.x * q1.y) + m--; + else + break; + } + // Using double also helps, but float can solve the issue for now. + // while (m > 1 && cross_2d(q[i] - q[m - 2], q[m - 1] - q[m - 2]) + // >= 0) { + // m--; + // } + q[m++] = q[i]; + } + + // Step 6 (Optional): + // In general sense we need the original coordinates, so we + // need to shift the points back (reverting Step 2) + // But if we're only interested in getting the area/perimeter of the shape + // We can simply return. + if (!shift_to_zero) { + for (int i = 0; i < m; i++) { + q[i] += start; + } + } + + return m; +} + +template +HOST_DEVICE_INLINE T polygon_area(const Point (&q)[24], const int& m) { + if (m <= 2) { + return 0; + } + + T area = 0; + for (int i = 1; i < m - 1; i++) { + area += fabs(cross_2d(q[i] - q[0], q[i + 1] - q[0])); + } + + return area / 2.0; +} + +template +HOST_DEVICE_INLINE T rotated_boxes_intersection( + const RotatedBox& box1, + const RotatedBox& box2) { + // There are up to 4 x 4 + 4 + 4 = 24 intersections (including dups) returned + // from rotated_rect_intersection_pts + Point intersectPts[24], orderedPts[24]; + + Point pts1[4]; + Point pts2[4]; + get_rotated_vertices(box1, pts1); + get_rotated_vertices(box2, pts2); + + int num = get_intersection_points(pts1, pts2, intersectPts); + + if (num <= 2) { + return 0.0; + } + + // Convex Hull to order the intersection points in clockwise order and find + // the contour area. + int num_convex = convex_hull_graham(intersectPts, num, orderedPts, true); + return polygon_area(orderedPts, num_convex); +} + +} // namespace + +template +HOST_DEVICE_INLINE T +single_box_iou_rotated(T const* const box1_raw, T const* const box2_raw) { + // shift center to the middle point to achieve higher precision in result + RotatedBox box1, box2; + auto center_shift_x = (box1_raw[0] + box2_raw[0]) / 2.0; + auto center_shift_y = (box1_raw[1] + box2_raw[1]) / 2.0; + box1.x_ctr = box1_raw[0] - center_shift_x; + box1.y_ctr = box1_raw[1] - center_shift_y; + box1.w = box1_raw[2]; + box1.h = box1_raw[3]; + box1.a = box1_raw[4]; + box2.x_ctr = box2_raw[0] - center_shift_x; + box2.y_ctr = box2_raw[1] - center_shift_y; + box2.w = box2_raw[2]; + box2.h = box2_raw[3]; + box2.a = box2_raw[4]; + + T area1 = box1.w * box1.h; + T area2 = box2.w * box2.h; + if (area1 < 1e-14 || area2 < 1e-14) { + return 0.f; + } + + T intersection = rotated_boxes_intersection(box1, box2); + T iou = intersection / (area1 + area2 - intersection); + return iou; +} + +} // namespace detectron2 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/cocoeval/cocoeval.cpp b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/cocoeval/cocoeval.cpp new file mode 100644 index 0000000..0a5b7b9 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/cocoeval/cocoeval.cpp @@ -0,0 +1,507 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#include "cocoeval.h" +#include +#include +#include +#include + +using namespace pybind11::literals; + +namespace detectron2 { + +namespace COCOeval { + +// Sort detections from highest score to lowest, such that +// detection_instances[detection_sorted_indices[t]] >= +// detection_instances[detection_sorted_indices[t+1]]. Use stable_sort to match +// original COCO API +void SortInstancesByDetectionScore( + const std::vector& detection_instances, + std::vector* detection_sorted_indices) { + detection_sorted_indices->resize(detection_instances.size()); + std::iota( + detection_sorted_indices->begin(), detection_sorted_indices->end(), 0); + std::stable_sort( + detection_sorted_indices->begin(), + detection_sorted_indices->end(), + [&detection_instances](size_t j1, size_t j2) { + return detection_instances[j1].score > detection_instances[j2].score; + }); +} + +// Partition the ground truth objects based on whether or not to ignore them +// based on area +void SortInstancesByIgnore( + const std::array& area_range, + const std::vector& ground_truth_instances, + std::vector* ground_truth_sorted_indices, + std::vector* ignores) { + ignores->clear(); + ignores->reserve(ground_truth_instances.size()); + for (auto o : ground_truth_instances) { + ignores->push_back( + o.ignore || o.area < area_range[0] || o.area > area_range[1]); + } + + ground_truth_sorted_indices->resize(ground_truth_instances.size()); + std::iota( + ground_truth_sorted_indices->begin(), + ground_truth_sorted_indices->end(), + 0); + std::stable_sort( + ground_truth_sorted_indices->begin(), + ground_truth_sorted_indices->end(), + [&ignores](size_t j1, size_t j2) { + return (int)(*ignores)[j1] < (int)(*ignores)[j2]; + }); +} + +// For each IOU threshold, greedily match each detected instance to a ground +// truth instance (if possible) and store the results +void MatchDetectionsToGroundTruth( + const std::vector& detection_instances, + const std::vector& detection_sorted_indices, + const std::vector& ground_truth_instances, + const std::vector& ground_truth_sorted_indices, + const std::vector& ignores, + const std::vector>& ious, + const std::vector& iou_thresholds, + const std::array& area_range, + ImageEvaluation* results) { + // Initialize memory to store return data matches and ignore + const int num_iou_thresholds = iou_thresholds.size(); + const int num_ground_truth = ground_truth_sorted_indices.size(); + const int num_detections = detection_sorted_indices.size(); + std::vector ground_truth_matches( + num_iou_thresholds * num_ground_truth, 0); + std::vector& detection_matches = results->detection_matches; + std::vector& detection_ignores = results->detection_ignores; + std::vector& ground_truth_ignores = results->ground_truth_ignores; + detection_matches.resize(num_iou_thresholds * num_detections, 0); + detection_ignores.resize(num_iou_thresholds * num_detections, false); + ground_truth_ignores.resize(num_ground_truth); + for (auto g = 0; g < num_ground_truth; ++g) { + ground_truth_ignores[g] = ignores[ground_truth_sorted_indices[g]]; + } + + for (auto t = 0; t < num_iou_thresholds; ++t) { + for (auto d = 0; d < num_detections; ++d) { + // information about best match so far (match=-1 -> unmatched) + double best_iou = std::min(iou_thresholds[t], 1 - 1e-10); + int match = -1; + for (auto g = 0; g < num_ground_truth; ++g) { + // if this ground truth instance is already matched and not a + // crowd, it cannot be matched to another detection + if (ground_truth_matches[t * num_ground_truth + g] > 0 && + !ground_truth_instances[ground_truth_sorted_indices[g]].is_crowd) { + continue; + } + + // if detected instance matched to a regular ground truth + // instance, we can break on the first ground truth instance + // tagged as ignore (because they are sorted by the ignore tag) + if (match >= 0 && !ground_truth_ignores[match] && + ground_truth_ignores[g]) { + break; + } + + // if IOU overlap is the best so far, store the match appropriately + if (ious[d][ground_truth_sorted_indices[g]] >= best_iou) { + best_iou = ious[d][ground_truth_sorted_indices[g]]; + match = g; + } + } + // if match was made, store id of match for both detection and + // ground truth + if (match >= 0) { + detection_ignores[t * num_detections + d] = ground_truth_ignores[match]; + detection_matches[t * num_detections + d] = + ground_truth_instances[ground_truth_sorted_indices[match]].id; + ground_truth_matches[t * num_ground_truth + match] = + detection_instances[detection_sorted_indices[d]].id; + } + + // set unmatched detections outside of area range to ignore + const InstanceAnnotation& detection = + detection_instances[detection_sorted_indices[d]]; + detection_ignores[t * num_detections + d] = + detection_ignores[t * num_detections + d] || + (detection_matches[t * num_detections + d] == 0 && + (detection.area < area_range[0] || detection.area > area_range[1])); + } + } + + // store detection score results + results->detection_scores.resize(detection_sorted_indices.size()); + for (size_t d = 0; d < detection_sorted_indices.size(); ++d) { + results->detection_scores[d] = + detection_instances[detection_sorted_indices[d]].score; + } +} + +std::vector EvaluateImages( + const std::vector>& area_ranges, + int max_detections, + const std::vector& iou_thresholds, + const ImageCategoryInstances>& image_category_ious, + const ImageCategoryInstances& + image_category_ground_truth_instances, + const ImageCategoryInstances& + image_category_detection_instances) { + const int num_area_ranges = area_ranges.size(); + const int num_images = image_category_ground_truth_instances.size(); + const int num_categories = + image_category_ious.size() > 0 ? image_category_ious[0].size() : 0; + std::vector detection_sorted_indices; + std::vector ground_truth_sorted_indices; + std::vector ignores; + std::vector results_all( + num_images * num_area_ranges * num_categories); + + // Store results for each image, category, and area range combination. Results + // for each IOU threshold are packed into the same ImageEvaluation object + for (auto i = 0; i < num_images; ++i) { + for (auto c = 0; c < num_categories; ++c) { + const std::vector& ground_truth_instances = + image_category_ground_truth_instances[i][c]; + const std::vector& detection_instances = + image_category_detection_instances[i][c]; + + SortInstancesByDetectionScore( + detection_instances, &detection_sorted_indices); + if ((int)detection_sorted_indices.size() > max_detections) { + detection_sorted_indices.resize(max_detections); + } + + for (size_t a = 0; a < area_ranges.size(); ++a) { + SortInstancesByIgnore( + area_ranges[a], + ground_truth_instances, + &ground_truth_sorted_indices, + &ignores); + + MatchDetectionsToGroundTruth( + detection_instances, + detection_sorted_indices, + ground_truth_instances, + ground_truth_sorted_indices, + ignores, + image_category_ious[i][c], + iou_thresholds, + area_ranges[a], + &results_all + [c * num_area_ranges * num_images + a * num_images + i]); + } + } + } + + return results_all; +} + +// Convert a python list to a vector +template +std::vector list_to_vec(const py::list& l) { + std::vector v(py::len(l)); + for (int i = 0; i < (int)py::len(l); ++i) { + v[i] = l[i].cast(); + } + return v; +} + +// Helper function to Accumulate() +// Considers the evaluation results applicable to a particular category, area +// range, and max_detections parameter setting, which begin at +// evaluations[evaluation_index]. Extracts a sorted list of length n of all +// applicable detection instances concatenated across all images in the dataset, +// which are represented by the outputs evaluation_indices, detection_scores, +// image_detection_indices, and detection_sorted_indices--all of which are +// length n. evaluation_indices[i] stores the applicable index into +// evaluations[] for instance i, which has detection score detection_score[i], +// and is the image_detection_indices[i]'th of the list of detections +// for the image containing i. detection_sorted_indices[] defines a sorted +// permutation of the 3 other outputs +int BuildSortedDetectionList( + const std::vector& evaluations, + const int64_t evaluation_index, + const int64_t num_images, + const int max_detections, + std::vector* evaluation_indices, + std::vector* detection_scores, + std::vector* detection_sorted_indices, + std::vector* image_detection_indices) { + assert(evaluations.size() >= evaluation_index + num_images); + + // Extract a list of object instances of the applicable category, area + // range, and max detections requirements such that they can be sorted + image_detection_indices->clear(); + evaluation_indices->clear(); + detection_scores->clear(); + image_detection_indices->reserve(num_images * max_detections); + evaluation_indices->reserve(num_images * max_detections); + detection_scores->reserve(num_images * max_detections); + int num_valid_ground_truth = 0; + for (auto i = 0; i < num_images; ++i) { + const ImageEvaluation& evaluation = evaluations[evaluation_index + i]; + + for (int d = 0; + d < (int)evaluation.detection_scores.size() && d < max_detections; + ++d) { // detected instances + evaluation_indices->push_back(evaluation_index + i); + image_detection_indices->push_back(d); + detection_scores->push_back(evaluation.detection_scores[d]); + } + for (auto ground_truth_ignore : evaluation.ground_truth_ignores) { + if (!ground_truth_ignore) { + ++num_valid_ground_truth; + } + } + } + + // Sort detections by decreasing score, using stable sort to match + // python implementation + detection_sorted_indices->resize(detection_scores->size()); + std::iota( + detection_sorted_indices->begin(), detection_sorted_indices->end(), 0); + std::stable_sort( + detection_sorted_indices->begin(), + detection_sorted_indices->end(), + [&detection_scores](size_t j1, size_t j2) { + return (*detection_scores)[j1] > (*detection_scores)[j2]; + }); + + return num_valid_ground_truth; +} + +// Helper function to Accumulate() +// Compute a precision recall curve given a sorted list of detected instances +// encoded in evaluations, evaluation_indices, detection_scores, +// detection_sorted_indices, image_detection_indices (see +// BuildSortedDetectionList()). Using vectors precisions and recalls +// and temporary storage, output the results into precisions_out, recalls_out, +// and scores_out, which are large buffers containing many precion/recall curves +// for all possible parameter settings, with precisions_out_index and +// recalls_out_index defining the applicable indices to store results. +void ComputePrecisionRecallCurve( + const int64_t precisions_out_index, + const int64_t precisions_out_stride, + const int64_t recalls_out_index, + const std::vector& recall_thresholds, + const int iou_threshold_index, + const int num_iou_thresholds, + const int num_valid_ground_truth, + const std::vector& evaluations, + const std::vector& evaluation_indices, + const std::vector& detection_scores, + const std::vector& detection_sorted_indices, + const std::vector& image_detection_indices, + std::vector* precisions, + std::vector* recalls, + std::vector* precisions_out, + std::vector* scores_out, + std::vector* recalls_out) { + assert(recalls_out->size() > recalls_out_index); + + // Compute precision/recall for each instance in the sorted list of detections + int64_t true_positives_sum = 0, false_positives_sum = 0; + precisions->clear(); + recalls->clear(); + precisions->reserve(detection_sorted_indices.size()); + recalls->reserve(detection_sorted_indices.size()); + assert(!evaluations.empty() || detection_sorted_indices.empty()); + for (auto detection_sorted_index : detection_sorted_indices) { + const ImageEvaluation& evaluation = + evaluations[evaluation_indices[detection_sorted_index]]; + const auto num_detections = + evaluation.detection_matches.size() / num_iou_thresholds; + const auto detection_index = iou_threshold_index * num_detections + + image_detection_indices[detection_sorted_index]; + assert(evaluation.detection_matches.size() > detection_index); + assert(evaluation.detection_ignores.size() > detection_index); + const int64_t detection_match = + evaluation.detection_matches[detection_index]; + const bool detection_ignores = + evaluation.detection_ignores[detection_index]; + const auto true_positive = detection_match > 0 && !detection_ignores; + const auto false_positive = detection_match == 0 && !detection_ignores; + if (true_positive) { + ++true_positives_sum; + } + if (false_positive) { + ++false_positives_sum; + } + + const double recall = + static_cast(true_positives_sum) / num_valid_ground_truth; + recalls->push_back(recall); + const int64_t num_valid_detections = + true_positives_sum + false_positives_sum; + const double precision = num_valid_detections > 0 + ? static_cast(true_positives_sum) / num_valid_detections + : 0.0; + precisions->push_back(precision); + } + + (*recalls_out)[recalls_out_index] = !recalls->empty() ? recalls->back() : 0; + + for (int64_t i = static_cast(precisions->size()) - 1; i > 0; --i) { + if ((*precisions)[i] > (*precisions)[i - 1]) { + (*precisions)[i - 1] = (*precisions)[i]; + } + } + + // Sample the per instance precision/recall list at each recall threshold + for (size_t r = 0; r < recall_thresholds.size(); ++r) { + // first index in recalls >= recall_thresholds[r] + std::vector::iterator low = std::lower_bound( + recalls->begin(), recalls->end(), recall_thresholds[r]); + size_t precisions_index = low - recalls->begin(); + + const auto results_ind = precisions_out_index + r * precisions_out_stride; + assert(results_ind < precisions_out->size()); + assert(results_ind < scores_out->size()); + if (precisions_index < precisions->size()) { + (*precisions_out)[results_ind] = (*precisions)[precisions_index]; + (*scores_out)[results_ind] = + detection_scores[detection_sorted_indices[precisions_index]]; + } else { + (*precisions_out)[results_ind] = 0; + (*scores_out)[results_ind] = 0; + } + } +} +py::dict Accumulate( + const py::object& params, + const std::vector& evaluations) { + const std::vector recall_thresholds = + list_to_vec(params.attr("recThrs")); + const std::vector max_detections = + list_to_vec(params.attr("maxDets")); + const int num_iou_thresholds = py::len(params.attr("iouThrs")); + const int num_recall_thresholds = py::len(params.attr("recThrs")); + const int num_categories = params.attr("useCats").cast() == 1 + ? py::len(params.attr("catIds")) + : 1; + const int num_area_ranges = py::len(params.attr("areaRng")); + const int num_max_detections = py::len(params.attr("maxDets")); + const int num_images = py::len(params.attr("imgIds")); + + std::vector precisions_out( + num_iou_thresholds * num_recall_thresholds * num_categories * + num_area_ranges * num_max_detections, + -1); + std::vector recalls_out( + num_iou_thresholds * num_categories * num_area_ranges * + num_max_detections, + -1); + std::vector scores_out( + num_iou_thresholds * num_recall_thresholds * num_categories * + num_area_ranges * num_max_detections, + -1); + + // Consider the list of all detected instances in the entire dataset in one + // large list. evaluation_indices, detection_scores, + // image_detection_indices, and detection_sorted_indices all have the same + // length as this list, such that each entry corresponds to one detected + // instance + std::vector evaluation_indices; // indices into evaluations[] + std::vector detection_scores; // detection scores of each instance + std::vector detection_sorted_indices; // sorted indices of all + // instances in the dataset + std::vector + image_detection_indices; // indices into the list of detected instances in + // the same image as each instance + std::vector precisions, recalls; + + for (auto c = 0; c < num_categories; ++c) { + for (auto a = 0; a < num_area_ranges; ++a) { + for (auto m = 0; m < num_max_detections; ++m) { + // The COCO PythonAPI assumes evaluations[] (the return value of + // COCOeval::EvaluateImages() is one long list storing results for each + // combination of category, area range, and image id, with categories in + // the outermost loop and images in the innermost loop. + const int64_t evaluations_index = + c * num_area_ranges * num_images + a * num_images; + int num_valid_ground_truth = BuildSortedDetectionList( + evaluations, + evaluations_index, + num_images, + max_detections[m], + &evaluation_indices, + &detection_scores, + &detection_sorted_indices, + &image_detection_indices); + + if (num_valid_ground_truth == 0) { + continue; + } + + for (auto t = 0; t < num_iou_thresholds; ++t) { + // recalls_out is a flattened vectors representing a + // num_iou_thresholds X num_categories X num_area_ranges X + // num_max_detections matrix + const int64_t recalls_out_index = + t * num_categories * num_area_ranges * num_max_detections + + c * num_area_ranges * num_max_detections + + a * num_max_detections + m; + + // precisions_out and scores_out are flattened vectors + // representing a num_iou_thresholds X num_recall_thresholds X + // num_categories X num_area_ranges X num_max_detections matrix + const int64_t precisions_out_stride = + num_categories * num_area_ranges * num_max_detections; + const int64_t precisions_out_index = t * num_recall_thresholds * + num_categories * num_area_ranges * num_max_detections + + c * num_area_ranges * num_max_detections + + a * num_max_detections + m; + + ComputePrecisionRecallCurve( + precisions_out_index, + precisions_out_stride, + recalls_out_index, + recall_thresholds, + t, + num_iou_thresholds, + num_valid_ground_truth, + evaluations, + evaluation_indices, + detection_scores, + detection_sorted_indices, + image_detection_indices, + &precisions, + &recalls, + &precisions_out, + &scores_out, + &recalls_out); + } + } + } + } + + time_t rawtime; + struct tm local_time; + std::array buffer; + time(&rawtime); +#ifdef _WIN32 + localtime_s(&local_time, &rawtime); +#else + localtime_r(&rawtime, &local_time); +#endif + strftime( + buffer.data(), 200, "%Y-%m-%d %H:%num_max_detections:%S", &local_time); + return py::dict( + "params"_a = params, + "counts"_a = std::vector( + {num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections}), + "date"_a = buffer, + "precision"_a = precisions_out, + "recall"_a = recalls_out, + "scores"_a = scores_out); +} + +} // namespace COCOeval + +} // namespace detectron2 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/cocoeval/cocoeval.h b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/cocoeval/cocoeval.h new file mode 100644 index 0000000..db246e4 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/cocoeval/cocoeval.h @@ -0,0 +1,88 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#pragma once + +#include +#include +#include +#include +#include + +namespace py = pybind11; + +namespace detectron2 { + +namespace COCOeval { + +// Annotation data for a single object instance in an image +struct InstanceAnnotation { + InstanceAnnotation( + uint64_t id, + double score, + double area, + bool is_crowd, + bool ignore) + : id{id}, score{score}, area{area}, is_crowd{is_crowd}, ignore{ignore} {} + uint64_t id; + double score = 0.; + double area = 0.; + bool is_crowd = false; + bool ignore = false; +}; + +// Stores intermediate results for evaluating detection results for a single +// image that has D detected instances and G ground truth instances. This stores +// matches between detected and ground truth instances +struct ImageEvaluation { + // For each of the D detected instances, the id of the matched ground truth + // instance, or 0 if unmatched + std::vector detection_matches; + + // The detection score of each of the D detected instances + std::vector detection_scores; + + // Marks whether or not each of G instances was ignored from evaluation (e.g., + // because it's outside area_range) + std::vector ground_truth_ignores; + + // Marks whether or not each of D instances was ignored from evaluation (e.g., + // because it's outside aRng) + std::vector detection_ignores; +}; + +template +using ImageCategoryInstances = std::vector>>; + +// C++ implementation of COCO API cocoeval.py::COCOeval.evaluateImg(). For each +// combination of image, category, area range settings, and IOU thresholds to +// evaluate, it matches detected instances to ground truth instances and stores +// the results into a vector of ImageEvaluation results, which will be +// interpreted by the COCOeval::Accumulate() function to produce precion-recall +// curves. The parameters of nested vectors have the following semantics: +// image_category_ious[i][c][d][g] is the intersection over union of the d'th +// detected instance and g'th ground truth instance of +// category category_ids[c] in image image_ids[i] +// image_category_ground_truth_instances[i][c] is a vector of ground truth +// instances in image image_ids[i] of category category_ids[c] +// image_category_detection_instances[i][c] is a vector of detected +// instances in image image_ids[i] of category category_ids[c] +std::vector EvaluateImages( + const std::vector>& area_ranges, // vector of 2-tuples + int max_detections, + const std::vector& iou_thresholds, + const ImageCategoryInstances>& image_category_ious, + const ImageCategoryInstances& + image_category_ground_truth_instances, + const ImageCategoryInstances& + image_category_detection_instances); + +// C++ implementation of COCOeval.accumulate(), which generates precision +// recall curves for each set of category, IOU threshold, detection area range, +// and max number of detections parameters. It is assumed that the parameter +// evaluations is the return value of the functon COCOeval::EvaluateImages(), +// which was called with the same parameter settings params +py::dict Accumulate( + const py::object& params, + const std::vector& evalutations); + +} // namespace COCOeval +} // namespace detectron2 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/cuda_version.cu b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/cuda_version.cu new file mode 100644 index 0000000..6dfe1b9 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/cuda_version.cu @@ -0,0 +1,26 @@ +// Copyright (c) Facebook, Inc. and its affiliates. + +#include + +namespace detectron2 { +int get_cudart_version() { +// Not a ROCM platform: Either HIP is not used, or +// it is used, but platform is not ROCM (i.e. it is CUDA) +#if !defined(__HIP_PLATFORM_HCC__) + return CUDART_VERSION; +#else + int version = 0; + +#if HIP_VERSION_MAJOR != 0 + // Create a convention similar to that of CUDA, as assumed by other + // parts of the code. + + version = HIP_VERSION_MINOR; + version += (HIP_VERSION_MAJOR * 100); +#else + hipRuntimeGetVersion(&version); +#endif + return version; +#endif +} +} // namespace detectron2 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/deformable/deform_conv.h b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/deformable/deform_conv.h new file mode 100644 index 0000000..965c1bf --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/deformable/deform_conv.h @@ -0,0 +1,377 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#pragma once +#include + +namespace detectron2 { + +#if defined(WITH_CUDA) || defined(WITH_HIP) +int deform_conv_forward_cuda( + at::Tensor input, + at::Tensor weight, + at::Tensor offset, + at::Tensor output, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step); + +int deform_conv_backward_input_cuda( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradInput, + at::Tensor gradOffset, + at::Tensor weight, + at::Tensor columns, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step); + +int deform_conv_backward_parameters_cuda( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradWeight, // at::Tensor gradBias, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + float scale, + int im2col_step); + +void modulated_deform_conv_cuda_forward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor output, + at::Tensor columns, + int kernel_h, + int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int group, + const int deformable_group, + const bool with_bias); + +void modulated_deform_conv_cuda_backward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor columns, + at::Tensor grad_input, + at::Tensor grad_weight, + at::Tensor grad_bias, + at::Tensor grad_offset, + at::Tensor grad_mask, + at::Tensor grad_output, + int kernel_h, + int kernel_w, + int stride_h, + int stride_w, + int pad_h, + int pad_w, + int dilation_h, + int dilation_w, + int group, + int deformable_group, + const bool with_bias); + +#endif + +inline int deform_conv_forward( + at::Tensor input, + at::Tensor weight, + at::Tensor offset, + at::Tensor output, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step) { + if (input.is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + TORCH_CHECK(weight.is_cuda(), "weight tensor is not on GPU!"); + TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!"); + return deform_conv_forward_cuda( + input, + weight, + offset, + output, + columns, + ones, + kW, + kH, + dW, + dH, + padW, + padH, + dilationW, + dilationH, + group, + deformable_group, + im2col_step); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + AT_ERROR("This operator is not implemented on CPU"); +} + +inline int deform_conv_backward_input( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradInput, + at::Tensor gradOffset, + at::Tensor weight, + at::Tensor columns, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step) { + if (gradOutput.is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + TORCH_CHECK(input.is_cuda(), "input tensor is not on GPU!"); + TORCH_CHECK(weight.is_cuda(), "weight tensor is not on GPU!"); + TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!"); + return deform_conv_backward_input_cuda( + input, + offset, + gradOutput, + gradInput, + gradOffset, + weight, + columns, + kW, + kH, + dW, + dH, + padW, + padH, + dilationW, + dilationH, + group, + deformable_group, + im2col_step); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + AT_ERROR("This operator is not implemented on CPU"); +} + +inline int deform_conv_backward_filter( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradWeight, // at::Tensor gradBias, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + float scale, + int im2col_step) { + if (gradOutput.is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + TORCH_CHECK(input.is_cuda(), "input tensor is not on GPU!"); + TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!"); + return deform_conv_backward_parameters_cuda( + input, + offset, + gradOutput, + gradWeight, + columns, + ones, + kW, + kH, + dW, + dH, + padW, + padH, + dilationW, + dilationH, + group, + deformable_group, + scale, + im2col_step); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + AT_ERROR("This operator is not implemented on CPU"); +} + +inline void modulated_deform_conv_forward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor output, + at::Tensor columns, + int kernel_h, + int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int group, + const int deformable_group, + const bool with_bias) { + if (input.is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + TORCH_CHECK(weight.is_cuda(), "weight tensor is not on GPU!"); + TORCH_CHECK(bias.is_cuda(), "bias tensor is not on GPU!"); + TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!"); + return modulated_deform_conv_cuda_forward( + input, + weight, + bias, + ones, + offset, + mask, + output, + columns, + kernel_h, + kernel_w, + stride_h, + stride_w, + pad_h, + pad_w, + dilation_h, + dilation_w, + group, + deformable_group, + with_bias); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + AT_ERROR("This operator is not implemented on CPU"); +} + +inline void modulated_deform_conv_backward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor columns, + at::Tensor grad_input, + at::Tensor grad_weight, + at::Tensor grad_bias, + at::Tensor grad_offset, + at::Tensor grad_mask, + at::Tensor grad_output, + int kernel_h, + int kernel_w, + int stride_h, + int stride_w, + int pad_h, + int pad_w, + int dilation_h, + int dilation_w, + int group, + int deformable_group, + const bool with_bias) { + if (grad_output.is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + TORCH_CHECK(input.is_cuda(), "input tensor is not on GPU!"); + TORCH_CHECK(weight.is_cuda(), "weight tensor is not on GPU!"); + TORCH_CHECK(bias.is_cuda(), "bias tensor is not on GPU!"); + TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!"); + return modulated_deform_conv_cuda_backward( + input, + weight, + bias, + ones, + offset, + mask, + columns, + grad_input, + grad_weight, + grad_bias, + grad_offset, + grad_mask, + grad_output, + kernel_h, + kernel_w, + stride_h, + stride_w, + pad_h, + pad_w, + dilation_h, + dilation_w, + group, + deformable_group, + with_bias); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + AT_ERROR("This operator is not implemented on CPU"); +} + +} // namespace detectron2 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/deformable/deform_conv_cuda.cu b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/deformable/deform_conv_cuda.cu new file mode 100644 index 0000000..2072bb8 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/deformable/deform_conv_cuda.cu @@ -0,0 +1,1223 @@ +// Copyright (c) Facebook, Inc. and its affiliates. + +// modified from +// https://github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/dcn/src/deform_conv_cuda.cpp +// Original license: Apache 2.0 + +// modify from +// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c +// Original license: Apache 2.0 + +#include + +#include "deform_conv.h" + +#include +#include + +namespace detectron2 { + +void deformable_im2col( + const at::Tensor data_im, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor data_col); + +void deformable_col2im( + const at::Tensor data_col, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor grad_im); + +void deformable_col2im_coord( + const at::Tensor data_col, + const at::Tensor data_im, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor grad_offset); + +void modulated_deformable_im2col_cuda( + const at::Tensor data_im, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kenerl_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor data_col); + +void modulated_deformable_col2im_cuda( + const at::Tensor data_col, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kenerl_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor grad_im); + +void modulated_deformable_col2im_coord_cuda( + const at::Tensor data_col, + const at::Tensor data_im, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kenerl_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor grad_offset, + at::Tensor grad_mask); + +void shape_check( + at::Tensor input, + at::Tensor offset, + at::Tensor* gradOutput, + at::Tensor weight, + int kH, + int kW, + int dH, + int dW, + int padH, + int padW, + int dilationH, + int dilationW, + int group, + int deformable_group) { + TORCH_CHECK( + weight.ndimension() == 4, + "4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, " + "but got: %s", + weight.ndimension()); + + TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); + + TORCH_CHECK( + kW > 0 && kH > 0, + "kernel size should be greater than zero, but got kH: %d kW: %d", + kH, + kW); + + TORCH_CHECK( + (weight.size(2) == kH && weight.size(3) == kW), + "kernel size should be consistent with weight, ", + "but got kH: %d kW: %d weight.size(2): %d, weight.size(3): %d", + kH, + kW, + weight.size(2), + weight.size(3)); + + TORCH_CHECK( + dW > 0 && dH > 0, + "stride should be greater than zero, but got dH: %d dW: %d", + dH, + dW); + + TORCH_CHECK( + dilationW > 0 && dilationH > 0, + "dilation should be greater than 0, but got dilationH: %d dilationW: %d", + dilationH, + dilationW); + + int ndim = input.ndimension(); + int dimf = 0; + int dimh = 1; + int dimw = 2; + + if (ndim == 4) { + dimf++; + dimh++; + dimw++; + } + + TORCH_CHECK( + ndim == 3 || ndim == 4, + "3D or 4D input tensor expected but got: %s", + ndim); + + long nInputPlane = weight.size(1) * group; + long inputHeight = input.size(dimh); + long inputWidth = input.size(dimw); + long nOutputPlane = weight.size(0); + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + + TORCH_CHECK( + nInputPlane % deformable_group == 0, + "input channels must divide deformable group size"); + + if (outputWidth < 1 || outputHeight < 1) + AT_ERROR( + "Given input size: (%ld x %ld x %ld). " + "Calculated output size: (%ld x %ld x %ld). Output size is too small", + nInputPlane, + inputHeight, + inputWidth, + nOutputPlane, + outputHeight, + outputWidth); + + TORCH_CHECK( + input.size(1) == nInputPlane, + "invalid number of input planes, expected: %d, but got: %d", + nInputPlane, + input.size(1)); + + TORCH_CHECK( + (inputHeight + 2 * padH >= kH && inputWidth + 2 * padW >= kW), + "input image is smaller than kernel"); + + TORCH_CHECK( + (offset.size(2) == outputHeight && offset.size(3) == outputWidth), + "invalid spatial size of offset, expected height: %d width: %d, but " + "got height: %d width: %d", + outputHeight, + outputWidth, + offset.size(2), + offset.size(3)); + + TORCH_CHECK( + (offset.size(1) == deformable_group * 2 * kH * kW), + "invalid number of channels of offset"); + + if (gradOutput != NULL) { + TORCH_CHECK( + gradOutput->size(dimf) == nOutputPlane, + "invalid number of gradOutput planes, expected: %d, but got: %d", + nOutputPlane, + gradOutput->size(dimf)); + + TORCH_CHECK( + (gradOutput->size(dimh) == outputHeight && + gradOutput->size(dimw) == outputWidth), + "invalid size of gradOutput, expected height: %d width: %d , but " + "got height: %d width: %d", + outputHeight, + outputWidth, + gradOutput->size(dimh), + gradOutput->size(dimw)); + } +} + +int deform_conv_forward_cuda( + at::Tensor input, + at::Tensor weight, + at::Tensor offset, + at::Tensor output, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step) { + // todo: resize columns to include im2col: done + // todo: add im2col_step as input + // todo: add new output buffer and transpose it to output (or directly + // transpose output) todo: possibly change data indexing because of + // parallel_imgs + + shape_check( + input, + offset, + NULL, + weight, + kH, + kW, + dH, + dW, + padH, + padW, + dilationH, + dilationW, + group, + deformable_group); + + input = input.contiguous(); + offset = offset.contiguous(); + weight = weight.contiguous(); + + int batch = 1; + if (input.ndimension() == 3) { + // Force batch + batch = 0; + input.unsqueeze_(0); + offset.unsqueeze_(0); + } + + // todo: assert batchsize dividable by im2col_step + + long batchSize = input.size(0); + long nInputPlane = input.size(1); + long inputHeight = input.size(2); + long inputWidth = input.size(3); + + long nOutputPlane = weight.size(0); + + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); + + output = output.view( + {batchSize / im2col_step, + im2col_step, + nOutputPlane, + outputHeight, + outputWidth}); + columns = at::zeros( + {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, + input.options()); + + if (ones.ndimension() != 2 || + ones.size(0) * ones.size(1) < outputHeight * outputWidth) { + ones = at::ones({outputHeight, outputWidth}, input.options()); + } + + input = input.view( + {batchSize / im2col_step, + im2col_step, + nInputPlane, + inputHeight, + inputWidth}); + offset = offset.view( + {batchSize / im2col_step, + im2col_step, + deformable_group * 2 * kH * kW, + outputHeight, + outputWidth}); + + at::Tensor output_buffer = at::zeros( + {batchSize / im2col_step, + nOutputPlane, + im2col_step * outputHeight, + outputWidth}, + output.options()); + + output_buffer = output_buffer.view( + {output_buffer.size(0), + group, + output_buffer.size(1) / group, + output_buffer.size(2), + output_buffer.size(3)}); + + for (int elt = 0; elt < batchSize / im2col_step; elt++) { + deformable_im2col( + input[elt], + offset[elt], + nInputPlane, + inputHeight, + inputWidth, + kH, + kW, + padH, + padW, + dH, + dW, + dilationH, + dilationW, + im2col_step, + deformable_group, + columns); + + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + weight = weight.view( + {group, + weight.size(0) / group, + weight.size(1), + weight.size(2), + weight.size(3)}); + + for (int g = 0; g < group; g++) { + output_buffer[elt][g] = output_buffer[elt][g] + .flatten(1) + .addmm_(weight[g].flatten(1), columns[g]) + .view_as(output_buffer[elt][g]); + } + } + + output_buffer = output_buffer.view( + {output_buffer.size(0), + output_buffer.size(1) * output_buffer.size(2), + output_buffer.size(3), + output_buffer.size(4)}); + + output_buffer = output_buffer.view( + {batchSize / im2col_step, + nOutputPlane, + im2col_step, + outputHeight, + outputWidth}); + output_buffer.transpose_(1, 2); + output.copy_(output_buffer); + output = output.view({batchSize, nOutputPlane, outputHeight, outputWidth}); + + input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); + offset = offset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + if (batch == 0) { + output = output.view({nOutputPlane, outputHeight, outputWidth}); + input = input.view({nInputPlane, inputHeight, inputWidth}); + offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); + } + + return 1; +} + +int deform_conv_backward_input_cuda( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradInput, + at::Tensor gradOffset, + at::Tensor weight, + at::Tensor columns, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step) { + shape_check( + input, + offset, + &gradOutput, + weight, + kH, + kW, + dH, + dW, + padH, + padW, + dilationH, + dilationW, + group, + deformable_group); + + input = input.contiguous(); + offset = offset.contiguous(); + gradOutput = gradOutput.contiguous(); + weight = weight.contiguous(); + + int batch = 1; + + if (input.ndimension() == 3) { + // Force batch + batch = 0; + input = input.view({1, input.size(0), input.size(1), input.size(2)}); + offset = offset.view({1, offset.size(0), offset.size(1), offset.size(2)}); + gradOutput = gradOutput.view( + {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); + } + + long batchSize = input.size(0); + long nInputPlane = input.size(1); + long inputHeight = input.size(2); + long inputWidth = input.size(3); + + long nOutputPlane = weight.size(0); + + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + TORCH_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset"); + gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); + columns = at::zeros( + {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, + input.options()); + + // change order of grad output + gradOutput = gradOutput.view( + {batchSize / im2col_step, + im2col_step, + nOutputPlane, + outputHeight, + outputWidth}); + gradOutput.transpose_(1, 2); + + gradInput = gradInput.view( + {batchSize / im2col_step, + im2col_step, + nInputPlane, + inputHeight, + inputWidth}); + input = input.view( + {batchSize / im2col_step, + im2col_step, + nInputPlane, + inputHeight, + inputWidth}); + gradOffset = gradOffset.view( + {batchSize / im2col_step, + im2col_step, + deformable_group * 2 * kH * kW, + outputHeight, + outputWidth}); + offset = offset.view( + {batchSize / im2col_step, + im2col_step, + deformable_group * 2 * kH * kW, + outputHeight, + outputWidth}); + + for (int elt = 0; elt < batchSize / im2col_step; elt++) { + // divide into groups + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + weight = weight.view( + {group, + weight.size(0) / group, + weight.size(1), + weight.size(2), + weight.size(3)}); + gradOutput = gradOutput.view( + {gradOutput.size(0), + group, + gradOutput.size(1) / group, + gradOutput.size(2), + gradOutput.size(3), + gradOutput.size(4)}); + + for (int g = 0; g < group; g++) { + columns[g] = columns[g].addmm_( + weight[g].flatten(1).transpose(0, 1), + gradOutput[elt][g].flatten(1), + 0.0f, + 1.0f); + } + + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + gradOutput = gradOutput.view( + {gradOutput.size(0), + gradOutput.size(1) * gradOutput.size(2), + gradOutput.size(3), + gradOutput.size(4), + gradOutput.size(5)}); + + deformable_col2im_coord( + columns, + input[elt], + offset[elt], + nInputPlane, + inputHeight, + inputWidth, + kH, + kW, + padH, + padW, + dH, + dW, + dilationH, + dilationW, + im2col_step, + deformable_group, + gradOffset[elt]); + + deformable_col2im( + columns, + offset[elt], + nInputPlane, + inputHeight, + inputWidth, + kH, + kW, + padH, + padW, + dH, + dW, + dilationH, + dilationW, + im2col_step, + deformable_group, + gradInput[elt]); + } + + gradOutput.transpose_(1, 2); + gradOutput = + gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); + + gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); + input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); + gradOffset = gradOffset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + offset = offset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + if (batch == 0) { + gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); + input = input.view({nInputPlane, inputHeight, inputWidth}); + gradInput = gradInput.view({nInputPlane, inputHeight, inputWidth}); + offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); + gradOffset = + gradOffset.view({offset.size(1), offset.size(2), offset.size(3)}); + } + + return 1; +} + +int deform_conv_backward_parameters_cuda( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradWeight, // at::Tensor gradBias, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + float scale, + int im2col_step) { + // todo: transpose and reshape outGrad + // todo: reshape columns + // todo: add im2col_step as input + + shape_check( + input, + offset, + &gradOutput, + gradWeight, + kH, + kW, + dH, + dW, + padH, + padW, + dilationH, + dilationW, + group, + deformable_group); + + input = input.contiguous(); + offset = offset.contiguous(); + gradOutput = gradOutput.contiguous(); + + int batch = 1; + + if (input.ndimension() == 3) { + // Force batch + batch = 0; + input = input.view( + at::IntList({1, input.size(0), input.size(1), input.size(2)})); + gradOutput = gradOutput.view( + {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); + } + + long batchSize = input.size(0); + long nInputPlane = input.size(1); + long inputHeight = input.size(2); + long inputWidth = input.size(3); + + long nOutputPlane = gradWeight.size(0); + + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); + + columns = at::zeros( + {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, + input.options()); + + gradOutput = gradOutput.view( + {batchSize / im2col_step, + im2col_step, + nOutputPlane, + outputHeight, + outputWidth}); + gradOutput.transpose_(1, 2); + + at::Tensor gradOutputBuffer = at::zeros_like(gradOutput); + gradOutputBuffer = gradOutputBuffer.view( + {batchSize / im2col_step, + nOutputPlane, + im2col_step, + outputHeight, + outputWidth}); + gradOutputBuffer.copy_(gradOutput); + // gradOutput is not contiguous, so we do reshape (instead of view) next + gradOutputBuffer = gradOutputBuffer.reshape( + {batchSize / im2col_step, + nOutputPlane, + im2col_step * outputHeight, + outputWidth}); + + gradOutput.transpose_(1, 2); + gradOutput = + gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); + + input = input.view( + {batchSize / im2col_step, + im2col_step, + nInputPlane, + inputHeight, + inputWidth}); + offset = offset.view( + {batchSize / im2col_step, + im2col_step, + deformable_group * 2 * kH * kW, + outputHeight, + outputWidth}); + + for (int elt = 0; elt < batchSize / im2col_step; elt++) { + deformable_im2col( + input[elt], + offset[elt], + nInputPlane, + inputHeight, + inputWidth, + kH, + kW, + padH, + padW, + dH, + dW, + dilationH, + dilationW, + im2col_step, + deformable_group, + columns); + + // divide into group + gradOutputBuffer = gradOutputBuffer.view( + {gradOutputBuffer.size(0), + group, + gradOutputBuffer.size(1) / group, + gradOutputBuffer.size(2), + gradOutputBuffer.size(3)}); + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + gradWeight = gradWeight.view( + {group, + gradWeight.size(0) / group, + gradWeight.size(1), + gradWeight.size(2), + gradWeight.size(3)}); + + for (int g = 0; g < group; g++) { + gradWeight[g] = gradWeight[g] + .flatten(1) + .addmm_( + gradOutputBuffer[elt][g].flatten(1), + columns[g].transpose(1, 0), + 1.0, + scale) + .view_as(gradWeight[g]); + } + gradOutputBuffer = gradOutputBuffer.view( + {gradOutputBuffer.size(0), + gradOutputBuffer.size(1) * gradOutputBuffer.size(2), + gradOutputBuffer.size(3), + gradOutputBuffer.size(4)}); + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + gradWeight = gradWeight.view( + {gradWeight.size(0) * gradWeight.size(1), + gradWeight.size(2), + gradWeight.size(3), + gradWeight.size(4)}); + } + + input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); + offset = offset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + if (batch == 0) { + gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); + input = input.view({nInputPlane, inputHeight, inputWidth}); + } + + return 1; +} + +void modulated_deform_conv_cuda_forward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor output, + at::Tensor columns, + int kernel_h, + int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int group, + const int deformable_group, + const bool with_bias) { + shape_check( + input, + offset, + NULL, + weight, + kernel_h, + kernel_w, + stride_h, + stride_w, + pad_h, + pad_w, + dilation_h, + dilation_w, + group, + deformable_group); + + TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); + TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_out = weight.size(0); + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + + if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) + AT_ERROR( + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", + kernel_h_, + kernel_w, + kernel_h_, + kernel_w_); + if (channels != channels_kernel * group) + AT_ERROR( + "Input shape and kernel channels wont match: (%d vs %d).", + channels, + channels_kernel * group); + + const int height_out = + (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = + (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + // mask shape check + TORCH_CHECK( + (mask.size(2) == height_out && mask.size(3) == width_out), + "invalid spatial size of mask, expected height: %d width: %d, but " + "got height: %d width: %d", + height_out, + width_out, + mask.size(2), + mask.size(3)); + + TORCH_CHECK( + (mask.size(1) == deformable_group * kernel_h * kernel_w), + "invalid number of channels of mask"); + + if (ones.ndimension() != 2 || + ones.size(0) * ones.size(1) < height_out * width_out) { + // Resize plane and fill with ones... + ones = at::ones({height_out, width_out}, input.options()); + } + + // resize output + output = output.view({batch, channels_out, height_out, width_out}).zero_(); + // resize temporary columns + columns = at::zeros( + {channels * kernel_h * kernel_w, 1 * height_out * width_out}, + input.options()); + + output = output.view( + {output.size(0), + group, + output.size(1) / group, + output.size(2), + output.size(3)}); + + for (int b = 0; b < batch; b++) { + modulated_deformable_im2col_cuda( + input[b], + offset[b], + mask[b], + 1, + channels, + height, + width, + height_out, + width_out, + kernel_h, + kernel_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + deformable_group, + columns); + + // divide into group + weight = weight.view( + {group, + weight.size(0) / group, + weight.size(1), + weight.size(2), + weight.size(3)}); + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + + for (int g = 0; g < group; g++) { + output[b][g] = output[b][g] + .flatten(1) + .addmm_(weight[g].flatten(1), columns[g]) + .view_as(output[b][g]); + } + + weight = weight.view( + {weight.size(0) * weight.size(1), + weight.size(2), + weight.size(3), + weight.size(4)}); + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + } + + output = output.view( + {output.size(0), + output.size(1) * output.size(2), + output.size(3), + output.size(4)}); + + if (with_bias) { + output += bias.view({1, bias.size(0), 1, 1}); + } +} + +void modulated_deform_conv_cuda_backward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor columns, + at::Tensor grad_input, + at::Tensor grad_weight, + at::Tensor grad_bias, + at::Tensor grad_offset, + at::Tensor grad_mask, + at::Tensor grad_output, + int kernel_h, + int kernel_w, + int stride_h, + int stride_w, + int pad_h, + int pad_w, + int dilation_h, + int dilation_w, + int group, + int deformable_group, + const bool with_bias) { + shape_check( + input, + offset, + &grad_output, + weight, + kernel_h, + kernel_w, + stride_h, + stride_w, + pad_h, + pad_w, + dilation_h, + dilation_w, + group, + deformable_group); + + TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); + TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) + AT_ERROR( + "Input shape and kernel shape wont match: (%d x %d vs %d x %d).", + kernel_h_, + kernel_w, + kernel_h_, + kernel_w_); + if (channels != channels_kernel * group) + AT_ERROR( + "Input shape and kernel channels wont match: (%d vs %d).", + channels, + channels_kernel * group); + + const int height_out = + (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = + (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + // mask shape check + TORCH_CHECK( + (mask.size(2) == height_out && mask.size(3) == width_out), + "invalid spatial size of mask, expected height: %d width: %d, but " + "got height: %d width: %d", + height_out, + width_out, + mask.size(2), + mask.size(3)); + + TORCH_CHECK( + (mask.size(1) == deformable_group * kernel_h * kernel_w), + "invalid number of channels of mask"); + + if (ones.ndimension() != 2 || + ones.size(0) * ones.size(1) < height_out * width_out) { + // Resize plane and fill with ones... + ones = at::ones({height_out, width_out}, input.options()); + } + + grad_input = grad_input.view({batch, channels, height, width}); + columns = at::zeros( + {channels * kernel_h * kernel_w, height_out * width_out}, + input.options()); + + grad_output = grad_output.view( + {grad_output.size(0), + group, + grad_output.size(1) / group, + grad_output.size(2), + grad_output.size(3)}); + + for (int b = 0; b < batch; b++) { + // divide int group + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + weight = weight.view( + {group, + weight.size(0) / group, + weight.size(1), + weight.size(2), + weight.size(3)}); + + for (int g = 0; g < group; g++) { + columns[g].addmm_( + weight[g].flatten(1).transpose(0, 1), + grad_output[b][g].flatten(1), + 0.0f, + 1.0f); + } + + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + weight = weight.view( + {weight.size(0) * weight.size(1), + weight.size(2), + weight.size(3), + weight.size(4)}); + + // gradient w.r.t. input coordinate data + modulated_deformable_col2im_coord_cuda( + columns, + input[b], + offset[b], + mask[b], + 1, + channels, + height, + width, + height_out, + width_out, + kernel_h, + kernel_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + deformable_group, + grad_offset[b], + grad_mask[b]); + // gradient w.r.t. input data + modulated_deformable_col2im_cuda( + columns, + offset[b], + mask[b], + 1, + channels, + height, + width, + height_out, + width_out, + kernel_h, + kernel_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + deformable_group, + grad_input[b]); + + // gradient w.r.t. weight, dWeight should accumulate across the batch and + // group + modulated_deformable_im2col_cuda( + input[b], + offset[b], + mask[b], + 1, + channels, + height, + width, + height_out, + width_out, + kernel_h, + kernel_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + deformable_group, + columns); + + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + grad_weight = grad_weight.view( + {group, + grad_weight.size(0) / group, + grad_weight.size(1), + grad_weight.size(2), + grad_weight.size(3)}); + if (with_bias) + grad_bias = grad_bias.view({group, grad_bias.size(0) / group}); + + for (int g = 0; g < group; g++) { + grad_weight[g] = + grad_weight[g] + .flatten(1) + .addmm_(grad_output[b][g].flatten(1), columns[g].transpose(0, 1)) + .view_as(grad_weight[g]); + if (with_bias) { + grad_bias[g] = + grad_bias[g] + .view({-1, 1}) + .addmm_(grad_output[b][g].flatten(1), ones.view({-1, 1})) + .view(-1); + } + } + + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + grad_weight = grad_weight.view( + {grad_weight.size(0) * grad_weight.size(1), + grad_weight.size(2), + grad_weight.size(3), + grad_weight.size(4)}); + if (with_bias) + grad_bias = grad_bias.view({grad_bias.size(0) * grad_bias.size(1)}); + } + grad_output = grad_output.view( + {grad_output.size(0) * grad_output.size(1), + grad_output.size(2), + grad_output.size(3), + grad_output.size(4)}); +} + +} // namespace detectron2 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/deformable/deform_conv_cuda_kernel.cu b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/deformable/deform_conv_cuda_kernel.cu new file mode 100644 index 0000000..f299c7a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/deformable/deform_conv_cuda_kernel.cu @@ -0,0 +1,1288 @@ +// Copyright (c) Facebook, Inc. and its affiliates. + +// modified from +// https://github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu +// Original license: Apache 2.0 +// clang-format off + +// modify from +// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu + +/*! + ******************* BEGIN Caffe Copyright Notice and Disclaimer ***************** + * + * COPYRIGHT + * + * All contributions by the University of California: + * Copyright (c) 2014-2017 The Regents of the University of California (Regents) + * All rights reserved. + * + * All other contributions: + * Copyright (c) 2014-2017, the respective contributors + * All rights reserved. + * + * Caffe uses a shared copyright model: each contributor holds copyright over + * their contributions to Caffe. The project versioning records all such + * contribution and copyright details. If a contributor wants to further mark + * their specific copyright on a particular contribution, they should indicate + * their copyright solely in the commit message of the change when it is + * committed. + * + * LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + *AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + *IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE + *FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + *DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + *SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + *CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, + *OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + *OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * CONTRIBUTION AGREEMENT + * + * By contributing to the BVLC/caffe repository through pull-request, comment, + * or otherwise, the contributor releases their content to the + * license and copyright terms herein. + * + ***************** END Caffe Copyright Notice and Disclaimer ********************* + * + * Copyright (c) 2018 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file modulated_deformable_im2col.cuh + * \brief Function definitions of converting an image to + * column matrix based on kernel, padding, dilation, and offset. + * These functions are mainly used in deformable convolution operators. + * \ref: https://arxiv.org/abs/1703.06211 + * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu, Dazhi Cheng + */ + +#include +#include +#include +#include +#include +#include + +using namespace at; + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ + i += blockDim.x * gridDim.x) + + +namespace { + +const int CUDA_NUM_THREADS = 1024; +const int kMaxGridNum = 65535; + +inline int GET_BLOCKS(const int N) { + return std::min(kMaxGridNum, (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS); +} + +} + +template +__device__ scalar_t deformable_im2col_bilinear( + const scalar_t* bottom_data, + const int data_width, + const int height, + const int width, + scalar_t h, + scalar_t w) { + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + scalar_t lh = h - h_low; + scalar_t lw = w - w_low; + scalar_t hh = 1 - lh, hw = 1 - lw; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + v1 = bottom_data[h_low * data_width + w_low]; + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + v2 = bottom_data[h_low * data_width + w_high]; + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + v3 = bottom_data[h_high * data_width + w_low]; + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + v4 = bottom_data[h_high * data_width + w_high]; + + scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +template +__device__ scalar_t get_gradient_weight( + scalar_t argmax_h, + scalar_t argmax_w, + const int h, + const int w, + const int height, + const int width) { + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || + argmax_w >= width) { + // empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + if (h == argmax_h_low && w == argmax_w_low) + weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) + weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) + weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) + weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +template +__device__ scalar_t get_coordinate_weight( + scalar_t argmax_h, + scalar_t argmax_w, + const int height, + const int width, + const scalar_t* im_data, + const int data_width, + const int bp_dir) { + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || + argmax_w >= width) { + // empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + + if (bp_dir == 0) { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_w_low + 1 - argmax_w) * + im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * + im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * + im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * + im_data[argmax_h_high * data_width + argmax_w_high]; + } else if (bp_dir == 1) { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_h_low + 1 - argmax_h) * + im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * + im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * + im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * + im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +template +__global__ void deformable_im2col_gpu_kernel( + const int n, + const scalar_t* data_im, + const scalar_t* data_offset, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int num_channels, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* data_col) { + CUDA_KERNEL_LOOP(index, n) { + // index index of output matrix + const int w_col = index % width_col; + const int h_col = (index / width_col) % height_col; + const int b_col = (index / width_col / height_col) % batch_size; + const int c_im = (index / width_col / height_col) / batch_size; + const int c_col = c_im * kernel_h * kernel_w; + + // compute deformable group index + const int deformable_group_index = c_im / channel_per_deformable_group; + + const int h_in = h_col * stride_h - pad_h; + const int w_in = w_col * stride_w - pad_w; + scalar_t* data_col_ptr = data_col + + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; + // const scalar_t* data_im_ptr = data_im + ((b_col * num_channels + c_im) * + // height + h_in) * width + w_in; + const scalar_t* data_im_ptr = + data_im + (b_col * num_channels + c_im) * height * width; + const scalar_t* data_offset_ptr = data_offset + + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + + for (int i = 0; i < kernel_h; ++i) { + for (int j = 0; j < kernel_w; ++j) { + const int data_offset_h_ptr = + ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; + const int data_offset_w_ptr = + ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + + w_col; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + scalar_t val = static_cast(0); + const scalar_t h_im = h_in + i * dilation_h + offset_h; + const scalar_t w_im = w_in + j * dilation_w + offset_w; + if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) { + // const scalar_t map_h = i * dilation_h + offset_h; + // const scalar_t map_w = j * dilation_w + offset_w; + // const int cur_height = height - h_in; + // const int cur_width = width - w_in; + // val = deformable_im2col_bilinear(data_im_ptr, width, cur_height, + // cur_width, map_h, map_w); + val = deformable_im2col_bilinear( + data_im_ptr, width, height, width, h_im, w_im); + } + *data_col_ptr = val; + data_col_ptr += batch_size * height_col * width_col; + } + } + } +} + + +template +__global__ void deformable_col2im_gpu_kernel( + const int n, + const scalar_t* data_col, + const scalar_t* data_offset, + const int channels, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* grad_im) { + CUDA_KERNEL_LOOP(index, n) { + const int j = (index / width_col / height_col / batch_size) % kernel_w; + const int i = + (index / width_col / height_col / batch_size / kernel_w) % kernel_h; + const int c = + index / width_col / height_col / batch_size / kernel_w / kernel_h; + // compute the start and end of the output + + const int deformable_group_index = c / channel_per_deformable_group; + + int w_out = index % width_col; + int h_out = (index / width_col) % height_col; + int b = (index / width_col / height_col) % batch_size; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + + const scalar_t* data_offset_ptr = data_offset + + (b * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + const int data_offset_h_ptr = + ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; + const int data_offset_w_ptr = + ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; + const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; + + const scalar_t cur_top_grad = data_col[index]; + const int cur_h = (int)cur_inv_h_data; + const int cur_w = (int)cur_inv_w_data; + for (int dy = -2; dy <= 2; dy++) { + for (int dx = -2; dx <= 2; dx++) { + if (cur_h + dy >= 0 && cur_h + dy < height && cur_w + dx >= 0 && + cur_w + dx < width && abs(cur_inv_h_data - (cur_h + dy)) < 1 && + abs(cur_inv_w_data - (cur_w + dx)) < 1) { + int cur_bottom_grad_pos = + ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; + scalar_t weight = get_gradient_weight( + cur_inv_h_data, + cur_inv_w_data, + cur_h + dy, + cur_w + dx, + height, + width); + atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); + } + } + } + } +} + + +template +__global__ void deformable_col2im_coord_gpu_kernel( + const int n, + const scalar_t* data_col, + const scalar_t* data_im, + const scalar_t* data_offset, + const int channels, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int offset_channels, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* grad_offset) { + CUDA_KERNEL_LOOP(index, n) { + scalar_t val = 0; + int w = index % width_col; + int h = (index / width_col) % height_col; + int c = (index / width_col / height_col) % offset_channels; + int b = (index / width_col / height_col) / offset_channels; + // compute the start and end of the output + + const int deformable_group_index = c / (2 * kernel_h * kernel_w); + const int col_step = kernel_h * kernel_w; + int cnt = 0; + const scalar_t* data_col_ptr = data_col + + deformable_group_index * channel_per_deformable_group * batch_size * + width_col * height_col; + const scalar_t* data_im_ptr = data_im + + (b * deformable_group + deformable_group_index) * + channel_per_deformable_group / kernel_h / kernel_w * height * width; + const scalar_t* data_offset_ptr = data_offset + + (b * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + + const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; + + for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; + col_c += col_step) { + const int col_pos = + (((col_c * batch_size + b) * height_col) + h) * width_col + w; + const int bp_dir = offset_c % 2; + + int j = (col_pos / width_col / height_col / batch_size) % kernel_w; + int i = + (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; + int w_out = col_pos % width_col; + int h_out = (col_pos / width_col) % height_col; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + const int data_offset_h_ptr = + (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); + const int data_offset_w_ptr = + (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + + w_out); + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + scalar_t inv_h = h_in + i * dilation_h + offset_h; + scalar_t inv_w = w_in + j * dilation_w + offset_w; + if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) { + inv_h = inv_w = -2; + } + const scalar_t weight = get_coordinate_weight( + inv_h, + inv_w, + height, + width, + data_im_ptr + cnt * height * width, + width, + bp_dir); + val += weight * data_col_ptr[col_pos]; + cnt += 1; + } + + grad_offset[index] = val; + } +} + + +namespace detectron2 { + +void deformable_im2col( + const at::Tensor data_im, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor data_col) { + // num_axes should be smaller than block size + // todo: check parallel_imgs is correctly passed in + int height_col = + (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; + int width_col = + (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; + int num_kernels = channels * height_col * width_col * parallel_imgs; + int channel_per_deformable_group = channels / deformable_group; + + at::cuda::CUDAGuard device_guard(data_im.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_im.scalar_type(), "deformable_im2col_gpu", ([&] { + const scalar_t* data_im_ = data_im.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + scalar_t* data_col_ = data_col.data_ptr(); + + deformable_im2col_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_im_, + data_offset_, + height, + width, + ksize_h, + ksize_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + parallel_imgs, + channels, + deformable_group, + height_col, + width_col, + data_col_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf("error in deformable_im2col: %s\n", cudaGetErrorString(err)); + } +} + + +void deformable_col2im( + const at::Tensor data_col, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor grad_im) { + // todo: make sure parallel_imgs is passed in correctly + int height_col = + (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; + int width_col = + (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; + int num_kernels = + channels * ksize_h * ksize_w * height_col * width_col * parallel_imgs; + int channel_per_deformable_group = channels / deformable_group; + + at::cuda::CUDAGuard device_guard(data_col.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.scalar_type(), "deformable_col2im_gpu", ([&] { + const scalar_t* data_col_ = data_col.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + scalar_t* grad_im_ = grad_im.data_ptr(); + + deformable_col2im_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_col_, + data_offset_, + channels, + height, + width, + ksize_h, + ksize_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + parallel_imgs, + deformable_group, + height_col, + width_col, + grad_im_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf("error in deformable_col2im: %s\n", cudaGetErrorString(err)); + } +} + + +void deformable_col2im_coord( + const at::Tensor data_col, + const at::Tensor data_im, + const at::Tensor data_offset, + const int channels, + const int height, + const int width, + const int ksize_h, + const int ksize_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int parallel_imgs, + const int deformable_group, + at::Tensor grad_offset) { + int height_col = + (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; + int width_col = + (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; + int num_kernels = height_col * width_col * 2 * ksize_h * ksize_w * + deformable_group * parallel_imgs; + int channel_per_deformable_group = + channels * ksize_h * ksize_w / deformable_group; + + at::cuda::CUDAGuard device_guard(data_col.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.scalar_type(), "deformable_col2im_coord_gpu", ([&] { + const scalar_t* data_col_ = data_col.data_ptr(); + const scalar_t* data_im_ = data_im.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + scalar_t* grad_offset_ = grad_offset.data_ptr(); + + deformable_col2im_coord_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_col_, + data_im_, + data_offset_, + channels, + height, + width, + ksize_h, + ksize_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + parallel_imgs, + 2 * ksize_h * ksize_w * deformable_group, + deformable_group, + height_col, + width_col, + grad_offset_); + })); +} + +} // namespace detectron2 + + +template +__device__ scalar_t dmcn_im2col_bilinear( + const scalar_t* bottom_data, + const int data_width, + const int height, + const int width, + scalar_t h, + scalar_t w) { + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + scalar_t lh = h - h_low; + scalar_t lw = w - w_low; + scalar_t hh = 1 - lh, hw = 1 - lw; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + v1 = bottom_data[h_low * data_width + w_low]; + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + v2 = bottom_data[h_low * data_width + w_high]; + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + v3 = bottom_data[h_high * data_width + w_low]; + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + v4 = bottom_data[h_high * data_width + w_high]; + + scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +template +__device__ scalar_t dmcn_get_gradient_weight( + scalar_t argmax_h, + scalar_t argmax_w, + const int h, + const int w, + const int height, + const int width) { + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || + argmax_w >= width) { + // empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + if (h == argmax_h_low && w == argmax_w_low) + weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) + weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) + weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) + weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +template +__device__ scalar_t dmcn_get_coordinate_weight( + scalar_t argmax_h, + scalar_t argmax_w, + const int height, + const int width, + const scalar_t* im_data, + const int data_width, + const int bp_dir) { + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || + argmax_w >= width) { + // empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + + if (bp_dir == 0) { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_w_low + 1 - argmax_w) * + im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * + im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * + im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * + im_data[argmax_h_high * data_width + argmax_w_high]; + } else if (bp_dir == 1) { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_h_low + 1 - argmax_h) * + im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * + im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * + im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * + im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +template +__global__ void modulated_deformable_im2col_gpu_kernel( + const int n, + const scalar_t* data_im, + const scalar_t* data_offset, + const scalar_t* data_mask, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int num_channels, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* data_col) { + CUDA_KERNEL_LOOP(index, n) { + // index index of output matrix + const int w_col = index % width_col; + const int h_col = (index / width_col) % height_col; + const int b_col = (index / width_col / height_col) % batch_size; + const int c_im = (index / width_col / height_col) / batch_size; + const int c_col = c_im * kernel_h * kernel_w; + + // compute deformable group index + const int deformable_group_index = c_im / channel_per_deformable_group; + + const int h_in = h_col * stride_h - pad_h; + const int w_in = w_col * stride_w - pad_w; + + scalar_t* data_col_ptr = data_col + + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; + // const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * + // height + h_in) * width + w_in; + const scalar_t* data_im_ptr = + data_im + (b_col * num_channels + c_im) * height * width; + const scalar_t* data_offset_ptr = data_offset + + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + + const scalar_t* data_mask_ptr = data_mask + + (b_col * deformable_group + deformable_group_index) * kernel_h * + kernel_w * height_col * width_col; + + for (int i = 0; i < kernel_h; ++i) { + for (int j = 0; j < kernel_w; ++j) { + const int data_offset_h_ptr = + ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; + const int data_offset_w_ptr = + ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + + w_col; + const int data_mask_hw_ptr = + ((i * kernel_w + j) * height_col + h_col) * width_col + w_col; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; + scalar_t val = static_cast(0); + const scalar_t h_im = h_in + i * dilation_h + offset_h; + const scalar_t w_im = w_in + j * dilation_w + offset_w; + // if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { + if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) { + // const float map_h = i * dilation_h + offset_h; + // const float map_w = j * dilation_w + offset_w; + // const int cur_height = height - h_in; + // const int cur_width = width - w_in; + // val = dmcn_im2col_bilinear(data_im_ptr, width, cur_height, + // cur_width, map_h, map_w); + val = dmcn_im2col_bilinear( + data_im_ptr, width, height, width, h_im, w_im); + } + *data_col_ptr = val * mask; + data_col_ptr += batch_size * height_col * width_col; + // data_col_ptr += height_col * width_col; + } + } + } +} + +template +__global__ void modulated_deformable_col2im_gpu_kernel( + const int n, + const scalar_t* data_col, + const scalar_t* data_offset, + const scalar_t* data_mask, + const int channels, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* grad_im) { + CUDA_KERNEL_LOOP(index, n) { + const int j = (index / width_col / height_col / batch_size) % kernel_w; + const int i = + (index / width_col / height_col / batch_size / kernel_w) % kernel_h; + const int c = + index / width_col / height_col / batch_size / kernel_w / kernel_h; + // compute the start and end of the output + + const int deformable_group_index = c / channel_per_deformable_group; + + int w_out = index % width_col; + int h_out = (index / width_col) % height_col; + int b = (index / width_col / height_col) % batch_size; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + + const scalar_t* data_offset_ptr = data_offset + + (b * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + const scalar_t* data_mask_ptr = data_mask + + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * + height_col * width_col; + const int data_offset_h_ptr = + ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; + const int data_offset_w_ptr = + ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; + const int data_mask_hw_ptr = + ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; + const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; + const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; + + const scalar_t cur_top_grad = data_col[index] * mask; + const int cur_h = (int)cur_inv_h_data; + const int cur_w = (int)cur_inv_w_data; + for (int dy = -2; dy <= 2; dy++) { + for (int dx = -2; dx <= 2; dx++) { + if (cur_h + dy >= 0 && cur_h + dy < height && cur_w + dx >= 0 && + cur_w + dx < width && abs(cur_inv_h_data - (cur_h + dy)) < 1 && + abs(cur_inv_w_data - (cur_w + dx)) < 1) { + int cur_bottom_grad_pos = + ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; + scalar_t weight = dmcn_get_gradient_weight( + cur_inv_h_data, + cur_inv_w_data, + cur_h + dy, + cur_w + dx, + height, + width); + atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); + } + } + } + } +} + +template +__global__ void modulated_deformable_col2im_coord_gpu_kernel( + const int n, + const scalar_t* data_col, + const scalar_t* data_im, + const scalar_t* data_offset, + const scalar_t* data_mask, + const int channels, + const int height, + const int width, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, + const int offset_channels, + const int deformable_group, + const int height_col, + const int width_col, + scalar_t* grad_offset, + scalar_t* grad_mask) { + CUDA_KERNEL_LOOP(index, n) { + scalar_t val = 0, mval = 0; + int w = index % width_col; + int h = (index / width_col) % height_col; + int c = (index / width_col / height_col) % offset_channels; + int b = (index / width_col / height_col) / offset_channels; + // compute the start and end of the output + + const int deformable_group_index = c / (2 * kernel_h * kernel_w); + const int col_step = kernel_h * kernel_w; + int cnt = 0; + const scalar_t* data_col_ptr = data_col + + deformable_group_index * channel_per_deformable_group * batch_size * + width_col * height_col; + const scalar_t* data_im_ptr = data_im + + (b * deformable_group + deformable_group_index) * + channel_per_deformable_group / kernel_h / kernel_w * height * width; + const scalar_t* data_offset_ptr = data_offset + + (b * deformable_group + deformable_group_index) * 2 * kernel_h * + kernel_w * height_col * width_col; + const scalar_t* data_mask_ptr = data_mask + + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * + height_col * width_col; + + const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; + + for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; + col_c += col_step) { + const int col_pos = + (((col_c * batch_size + b) * height_col) + h) * width_col + w; + const int bp_dir = offset_c % 2; + + int j = (col_pos / width_col / height_col / batch_size) % kernel_w; + int i = + (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; + int w_out = col_pos % width_col; + int h_out = (col_pos / width_col) % height_col; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + const int data_offset_h_ptr = + (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); + const int data_offset_w_ptr = + (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + + w_out); + const int data_mask_hw_ptr = + (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; + scalar_t inv_h = h_in + i * dilation_h + offset_h; + scalar_t inv_w = w_in + j * dilation_w + offset_w; + if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) { + inv_h = inv_w = -2; + } else { + mval += data_col_ptr[col_pos] * + dmcn_im2col_bilinear( + data_im_ptr + cnt * height * width, + width, + height, + width, + inv_h, + inv_w); + } + const scalar_t weight = dmcn_get_coordinate_weight( + inv_h, + inv_w, + height, + width, + data_im_ptr + cnt * height * width, + width, + bp_dir); + val += weight * data_col_ptr[col_pos] * mask; + cnt += 1; + } + // KERNEL_ASSIGN(grad_offset[index], offset_req, val); + grad_offset[index] = val; + if (offset_c % 2 == 0) + // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + + // deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * + // height_col + h) * width_col + w], mask_req, mval); + grad_mask + [(((b * deformable_group + deformable_group_index) * kernel_h * + kernel_w + + offset_c / 2) * + height_col + + h) * + width_col + + w] = mval; + } +} + + +namespace detectron2 { + +void modulated_deformable_im2col_cuda( + const at::Tensor data_im, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kenerl_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor data_col) { + // num_axes should be smaller than block size + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * batch_size * height_col * width_col; + + at::cuda::CUDAGuard device_guard(data_im.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_im.scalar_type(), "modulated_deformable_im2col_gpu", ([&] { + const scalar_t* data_im_ = data_im.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + const scalar_t* data_mask_ = data_mask.data_ptr(); + scalar_t* data_col_ = data_col.data_ptr(); + + modulated_deformable_im2col_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_im_, + data_offset_, + data_mask_, + height_im, + width_im, + kernel_h, + kenerl_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + batch_size, + channels, + deformable_group, + height_col, + width_col, + data_col_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf( + "error in modulated_deformable_im2col_cuda: %s\n", + cudaGetErrorString(err)); + } +} + +void modulated_deformable_col2im_cuda( + const at::Tensor data_col, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor grad_im) { + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = + channels * kernel_h * kernel_w * batch_size * height_col * width_col; + + at::cuda::CUDAGuard device_guard(data_col.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.scalar_type(), "modulated_deformable_col2im_gpu", ([&] { + const scalar_t* data_col_ = data_col.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + const scalar_t* data_mask_ = data_mask.data_ptr(); + scalar_t* grad_im_ = grad_im.data_ptr(); + + modulated_deformable_col2im_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_col_, + data_offset_, + data_mask_, + channels, + height_im, + width_im, + kernel_h, + kernel_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + batch_size, + deformable_group, + height_col, + width_col, + grad_im_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf( + "error in modulated_deformable_col2im_cuda: %s\n", + cudaGetErrorString(err)); + } +} + +void modulated_deformable_col2im_coord_cuda( + const at::Tensor data_col, + const at::Tensor data_im, + const at::Tensor data_offset, + const at::Tensor data_mask, + const int batch_size, + const int channels, + const int height_im, + const int width_im, + const int height_col, + const int width_col, + const int kernel_h, + const int kernel_w, + const int pad_h, + const int pad_w, + const int stride_h, + const int stride_w, + const int dilation_h, + const int dilation_w, + const int deformable_group, + at::Tensor grad_offset, + at::Tensor grad_mask) { + const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * + kernel_w * deformable_group; + const int channel_per_deformable_group = + channels * kernel_h * kernel_w / deformable_group; + + at::cuda::CUDAGuard device_guard(data_col.device()); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.scalar_type(), "modulated_deformable_col2im_coord_gpu", ([&] { + const scalar_t* data_col_ = data_col.data_ptr(); + const scalar_t* data_im_ = data_im.data_ptr(); + const scalar_t* data_offset_ = data_offset.data_ptr(); + const scalar_t* data_mask_ = data_mask.data_ptr(); + scalar_t* grad_offset_ = grad_offset.data_ptr(); + scalar_t* grad_mask_ = grad_mask.data_ptr(); + + modulated_deformable_col2im_coord_gpu_kernel<<< + GET_BLOCKS(num_kernels), + CUDA_NUM_THREADS, + 0, + stream>>>( + num_kernels, + data_col_, + data_im_, + data_offset_, + data_mask_, + channels, + height_im, + width_im, + kernel_h, + kernel_w, + pad_h, + pad_w, + stride_h, + stride_w, + dilation_h, + dilation_w, + channel_per_deformable_group, + batch_size, + 2 * kernel_h * kernel_w * deformable_group, + deformable_group, + height_col, + width_col, + grad_offset_, + grad_mask_); + })); + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf( + "error in modulated_deformable_col2im_coord_cuda: %s\n", + cudaGetErrorString(err)); + } +} + +} // namespace detectron2 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/nms_rotated/nms_rotated.h b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/nms_rotated/nms_rotated.h new file mode 100644 index 0000000..12aca38 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/nms_rotated/nms_rotated.h @@ -0,0 +1,39 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#pragma once +#include + +namespace detectron2 { + +at::Tensor nms_rotated_cpu( + const at::Tensor& dets, + const at::Tensor& scores, + const double iou_threshold); + +#if defined(WITH_CUDA) || defined(WITH_HIP) +at::Tensor nms_rotated_cuda( + const at::Tensor& dets, + const at::Tensor& scores, + const double iou_threshold); +#endif + +// Interface for Python +// inline is needed to prevent multiple function definitions when this header is +// included by different cpps +inline at::Tensor nms_rotated( + const at::Tensor& dets, + const at::Tensor& scores, + const double iou_threshold) { + assert(dets.device().is_cuda() == scores.device().is_cuda()); + if (dets.device().is_cuda()) { +#if defined(WITH_CUDA) || defined(WITH_HIP) + return nms_rotated_cuda( + dets.contiguous(), scores.contiguous(), iou_threshold); +#else + AT_ERROR("Detectron2 is not compiled with GPU support!"); +#endif + } + + return nms_rotated_cpu(dets.contiguous(), scores.contiguous(), iou_threshold); +} + +} // namespace detectron2 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/nms_rotated/nms_rotated_cpu.cpp b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/nms_rotated/nms_rotated_cpu.cpp new file mode 100644 index 0000000..d7556e6 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/nms_rotated/nms_rotated_cpu.cpp @@ -0,0 +1,75 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#include "../box_iou_rotated/box_iou_rotated_utils.h" +#include "nms_rotated.h" + +namespace detectron2 { + +template +at::Tensor nms_rotated_cpu_kernel( + const at::Tensor& dets, + const at::Tensor& scores, + const double iou_threshold) { + // nms_rotated_cpu_kernel is modified from torchvision's nms_cpu_kernel, + // however, the code in this function is much shorter because + // we delegate the IoU computation for rotated boxes to + // the single_box_iou_rotated function in box_iou_rotated_utils.h + AT_ASSERTM(dets.device().is_cpu(), "dets must be a CPU tensor"); + AT_ASSERTM(scores.device().is_cpu(), "scores must be a CPU tensor"); + AT_ASSERTM( + dets.scalar_type() == scores.scalar_type(), + "dets should have the same type as scores"); + + if (dets.numel() == 0) { + return at::empty({0}, dets.options().dtype(at::kLong)); + } + + auto order_t = std::get<1>(scores.sort(0, /* descending=*/true)); + + auto ndets = dets.size(0); + at::Tensor suppressed_t = at::zeros({ndets}, dets.options().dtype(at::kByte)); + at::Tensor keep_t = at::zeros({ndets}, dets.options().dtype(at::kLong)); + + auto suppressed = suppressed_t.data_ptr(); + auto keep = keep_t.data_ptr(); + auto order = order_t.data_ptr(); + + int64_t num_to_keep = 0; + + for (int64_t _i = 0; _i < ndets; _i++) { + auto i = order[_i]; + if (suppressed[i] == 1) { + continue; + } + + keep[num_to_keep++] = i; + + for (int64_t _j = _i + 1; _j < ndets; _j++) { + auto j = order[_j]; + if (suppressed[j] == 1) { + continue; + } + + auto ovr = single_box_iou_rotated( + dets[i].data_ptr(), dets[j].data_ptr()); + if (ovr >= iou_threshold) { + suppressed[j] = 1; + } + } + } + return keep_t.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep); +} + +at::Tensor nms_rotated_cpu( + // input must be contiguous + const at::Tensor& dets, + const at::Tensor& scores, + const double iou_threshold) { + auto result = at::empty({0}, dets.options()); + + AT_DISPATCH_FLOATING_TYPES(dets.scalar_type(), "nms_rotated", [&] { + result = nms_rotated_cpu_kernel(dets, scores, iou_threshold); + }); + return result; +} + +} // namespace detectron2 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/nms_rotated/nms_rotated_cuda.cu b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/nms_rotated/nms_rotated_cuda.cu new file mode 100644 index 0000000..2a3db5c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/nms_rotated/nms_rotated_cuda.cu @@ -0,0 +1,145 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +#include +#include +#include +#include +#ifdef WITH_CUDA +#include "../box_iou_rotated/box_iou_rotated_utils.h" +#endif +// TODO avoid this when pytorch supports "same directory" hipification +#ifdef WITH_HIP +#include "box_iou_rotated/box_iou_rotated_utils.h" +#endif + +using namespace detectron2; + +namespace { +int const threadsPerBlock = sizeof(unsigned long long) * 8; +} + +template +__global__ void nms_rotated_cuda_kernel( + const int n_boxes, + const double iou_threshold, + const T* dev_boxes, + unsigned long long* dev_mask) { + // nms_rotated_cuda_kernel is modified from torchvision's nms_cuda_kernel + + const int row_start = blockIdx.y; + const int col_start = blockIdx.x; + + // if (row_start > col_start) return; + + const int row_size = + min(n_boxes - row_start * threadsPerBlock, threadsPerBlock); + const int col_size = + min(n_boxes - col_start * threadsPerBlock, threadsPerBlock); + + // Compared to nms_cuda_kernel, where each box is represented with 4 values + // (x1, y1, x2, y2), each rotated box is represented with 5 values + // (x_center, y_center, width, height, angle_degrees) here. + __shared__ T block_boxes[threadsPerBlock * 5]; + if (threadIdx.x < col_size) { + block_boxes[threadIdx.x * 5 + 0] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0]; + block_boxes[threadIdx.x * 5 + 1] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1]; + block_boxes[threadIdx.x * 5 + 2] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2]; + block_boxes[threadIdx.x * 5 + 3] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3]; + block_boxes[threadIdx.x * 5 + 4] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4]; + } + __syncthreads(); + + if (threadIdx.x < row_size) { + const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x; + const T* cur_box = dev_boxes + cur_box_idx * 5; + int i = 0; + unsigned long long t = 0; + int start = 0; + if (row_start == col_start) { + start = threadIdx.x + 1; + } + for (i = start; i < col_size; i++) { + // Instead of devIoU used by original horizontal nms, here + // we use the single_box_iou_rotated function from box_iou_rotated_utils.h + if (single_box_iou_rotated(cur_box, block_boxes + i * 5) > + iou_threshold) { + t |= 1ULL << i; + } + } + const int col_blocks = at::cuda::ATenCeilDiv(n_boxes, threadsPerBlock); + dev_mask[cur_box_idx * col_blocks + col_start] = t; + } +} + +namespace detectron2 { + +at::Tensor nms_rotated_cuda( + // input must be contiguous + const at::Tensor& dets, + const at::Tensor& scores, + double iou_threshold) { + // using scalar_t = float; + AT_ASSERTM(dets.is_cuda(), "dets must be a CUDA tensor"); + AT_ASSERTM(scores.is_cuda(), "scores must be a CUDA tensor"); + at::cuda::CUDAGuard device_guard(dets.device()); + + auto order_t = std::get<1>(scores.sort(0, /* descending=*/true)); + auto dets_sorted = dets.index_select(0, order_t); + + auto dets_num = dets.size(0); + + const int col_blocks = + at::cuda::ATenCeilDiv(static_cast(dets_num), threadsPerBlock); + + at::Tensor mask = + at::empty({dets_num * col_blocks}, dets.options().dtype(at::kLong)); + + dim3 blocks(col_blocks, col_blocks); + dim3 threads(threadsPerBlock); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES( + dets_sorted.scalar_type(), "nms_rotated_kernel_cuda", [&] { + nms_rotated_cuda_kernel<<>>( + dets_num, + iou_threshold, + dets_sorted.data_ptr(), + (unsigned long long*)mask.data_ptr()); + }); + + at::Tensor mask_cpu = mask.to(at::kCPU); + unsigned long long* mask_host = + (unsigned long long*)mask_cpu.data_ptr(); + + std::vector remv(col_blocks); + memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); + + at::Tensor keep = + at::empty({dets_num}, dets.options().dtype(at::kLong).device(at::kCPU)); + int64_t* keep_out = keep.data_ptr(); + + int num_to_keep = 0; + for (int i = 0; i < dets_num; i++) { + int nblock = i / threadsPerBlock; + int inblock = i % threadsPerBlock; + + if (!(remv[nblock] & (1ULL << inblock))) { + keep_out[num_to_keep++] = i; + unsigned long long* p = mask_host + i * col_blocks; + for (int j = nblock; j < col_blocks; j++) { + remv[j] |= p[j]; + } + } + } + + AT_CUDA_CHECK(cudaGetLastError()); + return order_t.index( + {keep.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep) + .to(order_t.device(), keep.scalar_type())}); +} + +} // namespace detectron2 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/vision.cpp b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/vision.cpp new file mode 100644 index 0000000..c9a2cd4 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/csrc/vision.cpp @@ -0,0 +1,117 @@ +// Copyright (c) Facebook, Inc. and its affiliates. + +#include +#include "ROIAlignRotated/ROIAlignRotated.h" +#include "box_iou_rotated/box_iou_rotated.h" +#include "cocoeval/cocoeval.h" +#include "deformable/deform_conv.h" +#include "nms_rotated/nms_rotated.h" + +namespace detectron2 { + +#if defined(WITH_CUDA) || defined(WITH_HIP) +extern int get_cudart_version(); +#endif + +std::string get_cuda_version() { +#if defined(WITH_CUDA) || defined(WITH_HIP) + std::ostringstream oss; + +#if defined(WITH_CUDA) + oss << "CUDA "; +#else + oss << "HIP "; +#endif + + // copied from + // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231 + auto printCudaStyleVersion = [&](int v) { + oss << (v / 1000) << "." << (v / 10 % 100); + if (v % 10 != 0) { + oss << "." << (v % 10); + } + }; + printCudaStyleVersion(get_cudart_version()); + return oss.str(); +#else // neither CUDA nor HIP + return std::string("not available"); +#endif +} + +bool has_cuda() { +#if defined(WITH_CUDA) + return true; +#else + return false; +#endif +} + +// similar to +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp +std::string get_compiler_version() { + std::ostringstream ss; +#if defined(__GNUC__) +#ifndef __clang__ + +#if ((__GNUC__ <= 4) && (__GNUC_MINOR__ <= 8)) +#error "GCC >= 4.9 is required!" +#endif + + { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; } +#endif +#endif + +#if defined(__clang_major__) + { + ss << "clang " << __clang_major__ << "." << __clang_minor__ << "." + << __clang_patchlevel__; + } +#endif + +#if defined(_MSC_VER) + { ss << "MSVC " << _MSC_FULL_VER; } +#endif + return ss.str(); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("get_compiler_version", &get_compiler_version, "get_compiler_version"); + m.def("get_cuda_version", &get_cuda_version, "get_cuda_version"); + m.def("has_cuda", &has_cuda, "has_cuda"); + + m.def("deform_conv_forward", &deform_conv_forward, "deform_conv_forward"); + m.def( + "deform_conv_backward_input", + &deform_conv_backward_input, + "deform_conv_backward_input"); + m.def( + "deform_conv_backward_filter", + &deform_conv_backward_filter, + "deform_conv_backward_filter"); + m.def( + "modulated_deform_conv_forward", + &modulated_deform_conv_forward, + "modulated_deform_conv_forward"); + m.def( + "modulated_deform_conv_backward", + &modulated_deform_conv_backward, + "modulated_deform_conv_backward"); + + m.def("COCOevalAccumulate", &COCOeval::Accumulate, "COCOeval::Accumulate"); + m.def( + "COCOevalEvaluateImages", + &COCOeval::EvaluateImages, + "COCOeval::EvaluateImages"); + pybind11::class_(m, "InstanceAnnotation") + .def(pybind11::init()); + pybind11::class_(m, "ImageEvaluation") + .def(pybind11::init<>()); +} + +TORCH_LIBRARY(detectron2, m) { + m.def("nms_rotated", &nms_rotated); + m.def("box_iou_rotated", &box_iou_rotated); + m.def("roi_align_rotated_forward", &ROIAlignRotated_forward); + m.def("roi_align_rotated_backward", &ROIAlignRotated_backward); +} +} // namespace detectron2 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/deform_conv.py b/motion-gan-pipeline/preprocessing/third/detectron2/layers/deform_conv.py new file mode 100644 index 0000000..dffb720 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/deform_conv.py @@ -0,0 +1,514 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import math +from functools import lru_cache +import torch +from torch import nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.utils import _pair +from torchvision.ops import deform_conv2d + +from detectron2.utils.develop import create_dummy_class, create_dummy_func + +from .wrappers import _NewEmptyTensorOp + + +class _DeformConv(Function): + @staticmethod + def forward( + ctx, + input, + offset, + weight, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + im2col_step=64, + ): + if input is not None and input.dim() != 4: + raise ValueError( + "Expected 4D tensor as input, got {}D tensor instead.".format(input.dim()) + ) + ctx.stride = _pair(stride) + ctx.padding = _pair(padding) + ctx.dilation = _pair(dilation) + ctx.groups = groups + ctx.deformable_groups = deformable_groups + ctx.im2col_step = im2col_step + + ctx.save_for_backward(input, offset, weight) + + output = input.new_empty( + _DeformConv._output_size(input, weight, ctx.padding, ctx.dilation, ctx.stride) + ) + + ctx.bufs_ = [input.new_empty(0), input.new_empty(0)] # columns, ones + + if not input.is_cuda: + # TODO: let torchvision support full features of our deformconv. + if deformable_groups != 1: + raise NotImplementedError( + "Deformable Conv with deformable_groups != 1 is not supported on CPUs!" + ) + return deform_conv2d( + input, offset, weight, stride=stride, padding=padding, dilation=dilation + ) + else: + cur_im2col_step = _DeformConv._cal_im2col_step(input.shape[0], ctx.im2col_step) + assert (input.shape[0] % cur_im2col_step) == 0, "im2col step must divide batchsize" + + _C.deform_conv_forward( + input, + weight, + offset, + output, + ctx.bufs_[0], + ctx.bufs_[1], + weight.size(3), + weight.size(2), + ctx.stride[1], + ctx.stride[0], + ctx.padding[1], + ctx.padding[0], + ctx.dilation[1], + ctx.dilation[0], + ctx.groups, + ctx.deformable_groups, + cur_im2col_step, + ) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + input, offset, weight = ctx.saved_tensors + + grad_input = grad_offset = grad_weight = None + + if not grad_output.is_cuda: + raise NotImplementedError("Deformable Conv is not supported on CPUs!") + else: + cur_im2col_step = _DeformConv._cal_im2col_step(input.shape[0], ctx.im2col_step) + assert (input.shape[0] % cur_im2col_step) == 0, "im2col step must divide batchsize" + + if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: + grad_input = torch.zeros_like(input) + grad_offset = torch.zeros_like(offset) + _C.deform_conv_backward_input( + input, + offset, + grad_output, + grad_input, + grad_offset, + weight, + ctx.bufs_[0], + weight.size(3), + weight.size(2), + ctx.stride[1], + ctx.stride[0], + ctx.padding[1], + ctx.padding[0], + ctx.dilation[1], + ctx.dilation[0], + ctx.groups, + ctx.deformable_groups, + cur_im2col_step, + ) + + if ctx.needs_input_grad[2]: + grad_weight = torch.zeros_like(weight) + _C.deform_conv_backward_filter( + input, + offset, + grad_output, + grad_weight, + ctx.bufs_[0], + ctx.bufs_[1], + weight.size(3), + weight.size(2), + ctx.stride[1], + ctx.stride[0], + ctx.padding[1], + ctx.padding[0], + ctx.dilation[1], + ctx.dilation[0], + ctx.groups, + ctx.deformable_groups, + 1, + cur_im2col_step, + ) + + return grad_input, grad_offset, grad_weight, None, None, None, None, None, None + + @staticmethod + def _output_size(input, weight, padding, dilation, stride): + channels = weight.size(0) + output_size = (input.size(0), channels) + for d in range(input.dim() - 2): + in_size = input.size(d + 2) + pad = padding[d] + kernel = dilation[d] * (weight.size(d + 2) - 1) + 1 + stride_ = stride[d] + output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1,) + if not all(map(lambda s: s > 0, output_size)): + raise ValueError( + "convolution input is too small (output would be {})".format( + "x".join(map(str, output_size)) + ) + ) + return output_size + + @staticmethod + @lru_cache(maxsize=128) + def _cal_im2col_step(input_size, default_size): + """ + Calculate proper im2col step size, which should be divisible by input_size and not larger + than prefer_size. Meanwhile the step size should be as large as possible to be more + efficient. So we choose the largest one among all divisors of input_size which are smaller + than prefer_size. + :param input_size: input batch size . + :param default_size: default preferred im2col step size. + :return: the largest proper step size. + """ + if input_size <= default_size: + return input_size + best_step = 1 + for step in range(2, min(int(math.sqrt(input_size)) + 1, default_size)): + if input_size % step == 0: + if input_size // step <= default_size: + return input_size // step + best_step = step + + return best_step + + +class _ModulatedDeformConv(Function): + @staticmethod + def forward( + ctx, + input, + offset, + mask, + weight, + bias=None, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + ): + ctx.stride = stride + ctx.padding = padding + ctx.dilation = dilation + ctx.groups = groups + ctx.deformable_groups = deformable_groups + ctx.with_bias = bias is not None + if not ctx.with_bias: + bias = input.new_empty(1) # fake tensor + if not input.is_cuda: + raise NotImplementedError("Deformable Conv is not supported on CPUs!") + if ( + weight.requires_grad + or mask.requires_grad + or offset.requires_grad + or input.requires_grad + ): + ctx.save_for_backward(input, offset, mask, weight, bias) + output = input.new_empty(_ModulatedDeformConv._infer_shape(ctx, input, weight)) + ctx._bufs = [input.new_empty(0), input.new_empty(0)] + _C.modulated_deform_conv_forward( + input, + weight, + bias, + ctx._bufs[0], + offset, + mask, + output, + ctx._bufs[1], + weight.shape[2], + weight.shape[3], + ctx.stride, + ctx.stride, + ctx.padding, + ctx.padding, + ctx.dilation, + ctx.dilation, + ctx.groups, + ctx.deformable_groups, + ctx.with_bias, + ) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + if not grad_output.is_cuda: + raise NotImplementedError("Deformable Conv is not supported on CPUs!") + input, offset, mask, weight, bias = ctx.saved_tensors + grad_input = torch.zeros_like(input) + grad_offset = torch.zeros_like(offset) + grad_mask = torch.zeros_like(mask) + grad_weight = torch.zeros_like(weight) + grad_bias = torch.zeros_like(bias) + _C.modulated_deform_conv_backward( + input, + weight, + bias, + ctx._bufs[0], + offset, + mask, + ctx._bufs[1], + grad_input, + grad_weight, + grad_bias, + grad_offset, + grad_mask, + grad_output, + weight.shape[2], + weight.shape[3], + ctx.stride, + ctx.stride, + ctx.padding, + ctx.padding, + ctx.dilation, + ctx.dilation, + ctx.groups, + ctx.deformable_groups, + ctx.with_bias, + ) + if not ctx.with_bias: + grad_bias = None + + return ( + grad_input, + grad_offset, + grad_mask, + grad_weight, + grad_bias, + None, + None, + None, + None, + None, + ) + + @staticmethod + def _infer_shape(ctx, input, weight): + n = input.size(0) + channels_out = weight.size(0) + height, width = input.shape[2:4] + kernel_h, kernel_w = weight.shape[2:4] + height_out = ( + height + 2 * ctx.padding - (ctx.dilation * (kernel_h - 1) + 1) + ) // ctx.stride + 1 + width_out = ( + width + 2 * ctx.padding - (ctx.dilation * (kernel_w - 1) + 1) + ) // ctx.stride + 1 + return n, channels_out, height_out, width_out + + +deform_conv = _DeformConv.apply +modulated_deform_conv = _ModulatedDeformConv.apply + + +class DeformConv(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + bias=False, + norm=None, + activation=None, + ): + """ + Deformable convolution from :paper:`deformconv`. + + Arguments are similar to :class:`Conv2D`. Extra arguments: + + Args: + deformable_groups (int): number of groups used in deformable convolution. + norm (nn.Module, optional): a normalization layer + activation (callable(Tensor) -> Tensor): a callable activation function + """ + super(DeformConv, self).__init__() + + assert not bias + assert in_channels % groups == 0, "in_channels {} cannot be divisible by groups {}".format( + in_channels, groups + ) + assert ( + out_channels % groups == 0 + ), "out_channels {} cannot be divisible by groups {}".format(out_channels, groups) + + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.stride = _pair(stride) + self.padding = _pair(padding) + self.dilation = _pair(dilation) + self.groups = groups + self.deformable_groups = deformable_groups + self.norm = norm + self.activation = activation + + self.weight = nn.Parameter( + torch.Tensor(out_channels, in_channels // self.groups, *self.kernel_size) + ) + self.bias = None + + nn.init.kaiming_uniform_(self.weight, nonlinearity="relu") + + def forward(self, x, offset): + if x.numel() == 0: + # When input is empty, we want to return a empty tensor with "correct" shape, + # So that the following operations will not panic + # if they check for the shape of the tensor. + # This computes the height and width of the output tensor + output_shape = [ + (i + 2 * p - (di * (k - 1) + 1)) // s + 1 + for i, p, di, k, s in zip( + x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride + ) + ] + output_shape = [x.shape[0], self.weight.shape[0]] + output_shape + return _NewEmptyTensorOp.apply(x, output_shape) + + x = deform_conv( + x, + offset, + self.weight, + self.stride, + self.padding, + self.dilation, + self.groups, + self.deformable_groups, + ) + if self.norm is not None: + x = self.norm(x) + if self.activation is not None: + x = self.activation(x) + return x + + def extra_repr(self): + tmpstr = "in_channels=" + str(self.in_channels) + tmpstr += ", out_channels=" + str(self.out_channels) + tmpstr += ", kernel_size=" + str(self.kernel_size) + tmpstr += ", stride=" + str(self.stride) + tmpstr += ", padding=" + str(self.padding) + tmpstr += ", dilation=" + str(self.dilation) + tmpstr += ", groups=" + str(self.groups) + tmpstr += ", deformable_groups=" + str(self.deformable_groups) + tmpstr += ", bias=False" + return tmpstr + + +class ModulatedDeformConv(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + bias=True, + norm=None, + activation=None, + ): + """ + Modulated deformable convolution from :paper:`deformconv2`. + + Arguments are similar to :class:`Conv2D`. Extra arguments: + + Args: + deformable_groups (int): number of groups used in deformable convolution. + norm (nn.Module, optional): a normalization layer + activation (callable(Tensor) -> Tensor): a callable activation function + """ + super(ModulatedDeformConv, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.stride = stride + self.padding = padding + self.dilation = dilation + self.groups = groups + self.deformable_groups = deformable_groups + self.with_bias = bias + self.norm = norm + self.activation = activation + + self.weight = nn.Parameter( + torch.Tensor(out_channels, in_channels // groups, *self.kernel_size) + ) + if bias: + self.bias = nn.Parameter(torch.Tensor(out_channels)) + else: + self.bias = None + + nn.init.kaiming_uniform_(self.weight, nonlinearity="relu") + if self.bias is not None: + nn.init.constant_(self.bias, 0) + + def forward(self, x, offset, mask): + if x.numel() == 0: + output_shape = [ + (i + 2 * p - (di * (k - 1) + 1)) // s + 1 + for i, p, di, k, s in zip( + x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride + ) + ] + output_shape = [x.shape[0], self.weight.shape[0]] + output_shape + return _NewEmptyTensorOp.apply(x, output_shape) + + x = modulated_deform_conv( + x, + offset, + mask, + self.weight, + self.bias, + self.stride, + self.padding, + self.dilation, + self.groups, + self.deformable_groups, + ) + if self.norm is not None: + x = self.norm(x) + if self.activation is not None: + x = self.activation(x) + return x + + def extra_repr(self): + tmpstr = "in_channels=" + str(self.in_channels) + tmpstr += ", out_channels=" + str(self.out_channels) + tmpstr += ", kernel_size=" + str(self.kernel_size) + tmpstr += ", stride=" + str(self.stride) + tmpstr += ", padding=" + str(self.padding) + tmpstr += ", dilation=" + str(self.dilation) + tmpstr += ", groups=" + str(self.groups) + tmpstr += ", deformable_groups=" + str(self.deformable_groups) + tmpstr += ", bias=" + str(self.with_bias) + return tmpstr + + +try: + from detectron2 import _C +except ImportError: + # TODO: register ops natively so there is no need to import _C. + _msg = "detectron2 is not compiled successfully, please build following the instructions!" + _args = ("detectron2._C", _msg) + DeformConv = create_dummy_class("DeformConv", *_args) + ModulatedDeformConv = create_dummy_class("ModulatedDeformConv", *_args) + deform_conv = create_dummy_func("deform_conv", *_args) + modulated_deform_conv = create_dummy_func("modulated_deform_conv", *_args) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/losses.py b/motion-gan-pipeline/preprocessing/third/detectron2/layers/losses.py new file mode 100644 index 0000000..cf4d5e9 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/losses.py @@ -0,0 +1,133 @@ +import math +import torch + + +def diou_loss( + boxes1: torch.Tensor, + boxes2: torch.Tensor, + reduction: str = "none", + eps: float = 1e-7, +) -> torch.Tensor: + """ + Distance Intersection over Union Loss (Zhaohui Zheng et. al) + https://arxiv.org/abs/1911.08287 + Args: + boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,). + reduction: 'none' | 'mean' | 'sum' + 'none': No reduction will be applied to the output. + 'mean': The output will be averaged. + 'sum': The output will be summed. + eps (float): small number to prevent division by zero + """ + + x1, y1, x2, y2 = boxes1.unbind(dim=-1) + x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1) + + # TODO: use torch._assert_async() when pytorch 1.8 support is dropped + assert (x2 >= x1).all(), "bad box: x1 larger than x2" + assert (y2 >= y1).all(), "bad box: y1 larger than y2" + + # Intersection keypoints + xkis1 = torch.max(x1, x1g) + ykis1 = torch.max(y1, y1g) + xkis2 = torch.min(x2, x2g) + ykis2 = torch.min(y2, y2g) + + intsct = torch.zeros_like(x1) + mask = (ykis2 > ykis1) & (xkis2 > xkis1) + intsct[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask]) + union = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsct + eps + iou = intsct / union + + # smallest enclosing box + xc1 = torch.min(x1, x1g) + yc1 = torch.min(y1, y1g) + xc2 = torch.max(x2, x2g) + yc2 = torch.max(y2, y2g) + diag_len = ((xc2 - xc1) ** 2) + ((yc2 - yc1) ** 2) + eps + + # centers of boxes + x_p = (x2 + x1) / 2 + y_p = (y2 + y1) / 2 + x_g = (x1g + x2g) / 2 + y_g = (y1g + y2g) / 2 + distance = ((x_p - x_g) ** 2) + ((y_p - y_g) ** 2) + + # Eqn. (7) + loss = 1 - iou + (distance / diag_len) + if reduction == "mean": + loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum() + elif reduction == "sum": + loss = loss.sum() + + return loss + + +def ciou_loss( + boxes1: torch.Tensor, + boxes2: torch.Tensor, + reduction: str = "none", + eps: float = 1e-7, +) -> torch.Tensor: + """ + Complete Intersection over Union Loss (Zhaohui Zheng et. al) + https://arxiv.org/abs/1911.08287 + Args: + boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,). + reduction: 'none' | 'mean' | 'sum' + 'none': No reduction will be applied to the output. + 'mean': The output will be averaged. + 'sum': The output will be summed. + eps (float): small number to prevent division by zero + """ + + x1, y1, x2, y2 = boxes1.unbind(dim=-1) + x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1) + + # TODO: use torch._assert_async() when pytorch 1.8 support is dropped + assert (x2 >= x1).all(), "bad box: x1 larger than x2" + assert (y2 >= y1).all(), "bad box: y1 larger than y2" + + # Intersection keypoints + xkis1 = torch.max(x1, x1g) + ykis1 = torch.max(y1, y1g) + xkis2 = torch.min(x2, x2g) + ykis2 = torch.min(y2, y2g) + + intsct = torch.zeros_like(x1) + mask = (ykis2 > ykis1) & (xkis2 > xkis1) + intsct[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask]) + union = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsct + eps + iou = intsct / union + + # smallest enclosing box + xc1 = torch.min(x1, x1g) + yc1 = torch.min(y1, y1g) + xc2 = torch.max(x2, x2g) + yc2 = torch.max(y2, y2g) + diag_len = ((xc2 - xc1) ** 2) + ((yc2 - yc1) ** 2) + eps + + # centers of boxes + x_p = (x2 + x1) / 2 + y_p = (y2 + y1) / 2 + x_g = (x1g + x2g) / 2 + y_g = (y1g + y2g) / 2 + distance = ((x_p - x_g) ** 2) + ((y_p - y_g) ** 2) + + # width and height of boxes + w_pred = x2 - x1 + h_pred = y2 - y1 + w_gt = x2g - x1g + h_gt = y2g - y1g + v = (4 / (math.pi ** 2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2) + with torch.no_grad(): + alpha = v / (1 - iou + v + eps) + + # Eqn. (10) + loss = 1 - iou + (distance / diag_len) + alpha * v + if reduction == "mean": + loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum() + elif reduction == "sum": + loss = loss.sum() + + return loss diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/mask_ops.py b/motion-gan-pipeline/preprocessing/third/detectron2/layers/mask_ops.py new file mode 100644 index 0000000..e7a9f3a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/mask_ops.py @@ -0,0 +1,275 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +from typing import Tuple +import torch +from PIL import Image +from torch.nn import functional as F + +__all__ = ["paste_masks_in_image"] + + +BYTES_PER_FLOAT = 4 +# TODO: This memory limit may be too much or too little. It would be better to +# determine it based on available resources. +GPU_MEM_LIMIT = 1024 ** 3 # 1 GB memory limit + + +def _do_paste_mask(masks, boxes, img_h: int, img_w: int, skip_empty: bool = True): + """ + Args: + masks: N, 1, H, W + boxes: N, 4 + img_h, img_w (int): + skip_empty (bool): only paste masks within the region that + tightly bound all boxes, and returns the results this region only. + An important optimization for CPU. + + Returns: + if skip_empty == False, a mask of shape (N, img_h, img_w) + if skip_empty == True, a mask of shape (N, h', w'), and the slice + object for the corresponding region. + """ + # On GPU, paste all masks together (up to chunk size) + # by using the entire image to sample the masks + # Compared to pasting them one by one, + # this has more operations but is faster on COCO-scale dataset. + device = masks.device + + if skip_empty and not torch.jit.is_scripting(): + x0_int, y0_int = torch.clamp(boxes.min(dim=0).values.floor()[:2] - 1, min=0).to( + dtype=torch.int32 + ) + x1_int = torch.clamp(boxes[:, 2].max().ceil() + 1, max=img_w).to(dtype=torch.int32) + y1_int = torch.clamp(boxes[:, 3].max().ceil() + 1, max=img_h).to(dtype=torch.int32) + else: + x0_int, y0_int = 0, 0 + x1_int, y1_int = img_w, img_h + x0, y0, x1, y1 = torch.split(boxes, 1, dim=1) # each is Nx1 + + N = masks.shape[0] + + img_y = torch.arange(y0_int, y1_int, device=device, dtype=torch.float32) + 0.5 + img_x = torch.arange(x0_int, x1_int, device=device, dtype=torch.float32) + 0.5 + img_y = (img_y - y0) / (y1 - y0) * 2 - 1 + img_x = (img_x - x0) / (x1 - x0) * 2 - 1 + # img_x, img_y have shapes (N, w), (N, h) + + gx = img_x[:, None, :].expand(N, img_y.size(1), img_x.size(1)) + gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1)) + grid = torch.stack([gx, gy], dim=3) + + if not torch.jit.is_scripting(): + if not masks.dtype.is_floating_point: + masks = masks.float() + img_masks = F.grid_sample(masks, grid.to(masks.dtype), align_corners=False) + + if skip_empty and not torch.jit.is_scripting(): + return img_masks[:, 0], (slice(y0_int, y1_int), slice(x0_int, x1_int)) + else: + return img_masks[:, 0], () + + +# Annotate boxes as Tensor (but not Boxes) in order to use scripting +@torch.jit.script_if_tracing +def paste_masks_in_image( + masks: torch.Tensor, boxes: torch.Tensor, image_shape: Tuple[int, int], threshold: float = 0.5 +): + """ + Paste a set of masks that are of a fixed resolution (e.g., 28 x 28) into an image. + The location, height, and width for pasting each mask is determined by their + corresponding bounding boxes in boxes. + + Note: + This is a complicated but more accurate implementation. In actual deployment, it is + often enough to use a faster but less accurate implementation. + See :func:`paste_mask_in_image_old` in this file for an alternative implementation. + + Args: + masks (tensor): Tensor of shape (Bimg, Hmask, Wmask), where Bimg is the number of + detected object instances in the image and Hmask, Wmask are the mask width and mask + height of the predicted mask (e.g., Hmask = Wmask = 28). Values are in [0, 1]. + boxes (Boxes or Tensor): A Boxes of length Bimg or Tensor of shape (Bimg, 4). + boxes[i] and masks[i] correspond to the same object instance. + image_shape (tuple): height, width + threshold (float): A threshold in [0, 1] for converting the (soft) masks to + binary masks. + + Returns: + img_masks (Tensor): A tensor of shape (Bimg, Himage, Wimage), where Bimg is the + number of detected object instances and Himage, Wimage are the image width + and height. img_masks[i] is a binary mask for object instance i. + """ + + assert masks.shape[-1] == masks.shape[-2], "Only square mask predictions are supported" + N = len(masks) + if N == 0: + return masks.new_empty((0,) + image_shape, dtype=torch.uint8) + if not isinstance(boxes, torch.Tensor): + boxes = boxes.tensor + device = boxes.device + assert len(boxes) == N, boxes.shape + + img_h, img_w = image_shape + + # The actual implementation split the input into chunks, + # and paste them chunk by chunk. + if device.type == "cpu" or torch.jit.is_scripting(): + # CPU is most efficient when they are pasted one by one with skip_empty=True + # so that it performs minimal number of operations. + num_chunks = N + else: + # GPU benefits from parallelism for larger chunks, but may have memory issue + # int(img_h) because shape may be tensors in tracing + num_chunks = int(np.ceil(N * int(img_h) * int(img_w) * BYTES_PER_FLOAT / GPU_MEM_LIMIT)) + assert ( + num_chunks <= N + ), "Default GPU_MEM_LIMIT in mask_ops.py is too small; try increasing it" + chunks = torch.chunk(torch.arange(N, device=device), num_chunks) + + img_masks = torch.zeros( + N, img_h, img_w, device=device, dtype=torch.bool if threshold >= 0 else torch.uint8 + ) + for inds in chunks: + masks_chunk, spatial_inds = _do_paste_mask( + masks[inds, None, :, :], boxes[inds], img_h, img_w, skip_empty=device.type == "cpu" + ) + + if threshold >= 0: + masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool) + else: + # for visualization and debugging + masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8) + + if torch.jit.is_scripting(): # Scripting does not use the optimized codepath + img_masks[inds] = masks_chunk + else: + img_masks[(inds,) + spatial_inds] = masks_chunk + return img_masks + + +# The below are the original paste function (from Detectron1) which has +# larger quantization error. +# It is faster on CPU, while the aligned one is faster on GPU thanks to grid_sample. + + +def paste_mask_in_image_old(mask, box, img_h, img_w, threshold): + """ + Paste a single mask in an image. + This is a per-box implementation of :func:`paste_masks_in_image`. + This function has larger quantization error due to incorrect pixel + modeling and is not used any more. + + Args: + mask (Tensor): A tensor of shape (Hmask, Wmask) storing the mask of a single + object instance. Values are in [0, 1]. + box (Tensor): A tensor of shape (4, ) storing the x0, y0, x1, y1 box corners + of the object instance. + img_h, img_w (int): Image height and width. + threshold (float): Mask binarization threshold in [0, 1]. + + Returns: + im_mask (Tensor): + The resized and binarized object mask pasted into the original + image plane (a tensor of shape (img_h, img_w)). + """ + # Conversion from continuous box coordinates to discrete pixel coordinates + # via truncation (cast to int32). This determines which pixels to paste the + # mask onto. + box = box.to(dtype=torch.int32) # Continuous to discrete coordinate conversion + # An example (1D) box with continuous coordinates (x0=0.7, x1=4.3) will map to + # a discrete coordinates (x0=0, x1=4). Note that box is mapped to 5 = x1 - x0 + 1 + # pixels (not x1 - x0 pixels). + samples_w = box[2] - box[0] + 1 # Number of pixel samples, *not* geometric width + samples_h = box[3] - box[1] + 1 # Number of pixel samples, *not* geometric height + + # Resample the mask from it's original grid to the new samples_w x samples_h grid + mask = Image.fromarray(mask.cpu().numpy()) + mask = mask.resize((samples_w, samples_h), resample=Image.BILINEAR) + mask = np.array(mask, copy=False) + + if threshold >= 0: + mask = np.array(mask > threshold, dtype=np.uint8) + mask = torch.from_numpy(mask) + else: + # for visualization and debugging, we also + # allow it to return an unmodified mask + mask = torch.from_numpy(mask * 255).to(torch.uint8) + + im_mask = torch.zeros((img_h, img_w), dtype=torch.uint8) + x_0 = max(box[0], 0) + x_1 = min(box[2] + 1, img_w) + y_0 = max(box[1], 0) + y_1 = min(box[3] + 1, img_h) + + im_mask[y_0:y_1, x_0:x_1] = mask[ + (y_0 - box[1]) : (y_1 - box[1]), (x_0 - box[0]) : (x_1 - box[0]) + ] + return im_mask + + +# Our pixel modeling requires extrapolation for any continuous +# coordinate < 0.5 or > length - 0.5. When sampling pixels on the masks, +# we would like this extrapolation to be an interpolation between boundary values and zero, +# instead of using absolute zero or boundary values. +# Therefore `paste_mask_in_image_old` is often used with zero padding around the masks like this: +# masks, scale = pad_masks(masks[:, 0, :, :], 1) +# boxes = scale_boxes(boxes.tensor, scale) + + +def pad_masks(masks, padding): + """ + Args: + masks (tensor): A tensor of shape (B, M, M) representing B masks. + padding (int): Number of cells to pad on all sides. + + Returns: + The padded masks and the scale factor of the padding size / original size. + """ + B = masks.shape[0] + M = masks.shape[-1] + pad2 = 2 * padding + scale = float(M + pad2) / M + padded_masks = masks.new_zeros((B, M + pad2, M + pad2)) + padded_masks[:, padding:-padding, padding:-padding] = masks + return padded_masks, scale + + +def scale_boxes(boxes, scale): + """ + Args: + boxes (tensor): A tensor of shape (B, 4) representing B boxes with 4 + coords representing the corners x0, y0, x1, y1, + scale (float): The box scaling factor. + + Returns: + Scaled boxes. + """ + w_half = (boxes[:, 2] - boxes[:, 0]) * 0.5 + h_half = (boxes[:, 3] - boxes[:, 1]) * 0.5 + x_c = (boxes[:, 2] + boxes[:, 0]) * 0.5 + y_c = (boxes[:, 3] + boxes[:, 1]) * 0.5 + + w_half *= scale + h_half *= scale + + scaled_boxes = torch.zeros_like(boxes) + scaled_boxes[:, 0] = x_c - w_half + scaled_boxes[:, 2] = x_c + w_half + scaled_boxes[:, 1] = y_c - h_half + scaled_boxes[:, 3] = y_c + h_half + return scaled_boxes + + +@torch.jit.script_if_tracing +def _paste_masks_tensor_shape( + masks: torch.Tensor, + boxes: torch.Tensor, + image_shape: Tuple[torch.Tensor, torch.Tensor], + threshold: float = 0.5, +): + """ + A wrapper of paste_masks_in_image where image_shape is Tensor. + During tracing, shapes might be tensors instead of ints. The Tensor->int + conversion should be scripted rather than traced. + """ + return paste_masks_in_image(masks, boxes, (int(image_shape[0]), int(image_shape[1])), threshold) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/nms.py b/motion-gan-pipeline/preprocessing/third/detectron2/layers/nms.py new file mode 100644 index 0000000..6b6be71 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/nms.py @@ -0,0 +1,139 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import torch +from torchvision.ops import boxes as box_ops +from torchvision.ops import nms # noqa . for compatibility + + +def batched_nms( + boxes: torch.Tensor, scores: torch.Tensor, idxs: torch.Tensor, iou_threshold: float +): + """ + Same as torchvision.ops.boxes.batched_nms, but with float(). + """ + assert boxes.shape[-1] == 4 + # Note: Torchvision already has a strategy (https://github.com/pytorch/vision/issues/1311) + # to decide whether to use coordinate trick or for loop to implement batched_nms. So we + # just call it directly. + # Fp16 does not have enough range for batched NMS, so adding float(). + return box_ops.batched_nms(boxes.float(), scores, idxs, iou_threshold) + + +# Note: this function (nms_rotated) might be moved into +# torchvision/ops/boxes.py in the future +def nms_rotated(boxes, scores, iou_threshold): + """ + Performs non-maximum suppression (NMS) on the rotated boxes according + to their intersection-over-union (IoU). + + Rotated NMS iteratively removes lower scoring rotated boxes which have an + IoU greater than iou_threshold with another (higher scoring) rotated box. + + Note that RotatedBox (5, 3, 4, 2, -90) covers exactly the same region as + RotatedBox (5, 3, 4, 2, 90) does, and their IoU will be 1. However, they + can be representing completely different objects in certain tasks, e.g., OCR. + + As for the question of whether rotated-NMS should treat them as faraway boxes + even though their IOU is 1, it depends on the application and/or ground truth annotation. + + As an extreme example, consider a single character v and the square box around it. + + If the angle is 0 degree, the object (text) would be read as 'v'; + + If the angle is 90 degrees, the object (text) would become '>'; + + If the angle is 180 degrees, the object (text) would become '^'; + + If the angle is 270/-90 degrees, the object (text) would become '<' + + All of these cases have IoU of 1 to each other, and rotated NMS that only + uses IoU as criterion would only keep one of them with the highest score - + which, practically, still makes sense in most cases because typically + only one of theses orientations is the correct one. Also, it does not matter + as much if the box is only used to classify the object (instead of transcribing + them with a sequential OCR recognition model) later. + + On the other hand, when we use IoU to filter proposals that are close to the + ground truth during training, we should definitely take the angle into account if + we know the ground truth is labeled with the strictly correct orientation (as in, + upside-down words are annotated with -180 degrees even though they can be covered + with a 0/90/-90 degree box, etc.) + + The way the original dataset is annotated also matters. For example, if the dataset + is a 4-point polygon dataset that does not enforce ordering of vertices/orientation, + we can estimate a minimum rotated bounding box to this polygon, but there's no way + we can tell the correct angle with 100% confidence (as shown above, there could be 4 different + rotated boxes, with angles differed by 90 degrees to each other, covering the exactly + same region). In that case we have to just use IoU to determine the box + proximity (as many detection benchmarks (even for text) do) unless there're other + assumptions we can make (like width is always larger than height, or the object is not + rotated by more than 90 degrees CCW/CW, etc.) + + In summary, not considering angles in rotated NMS seems to be a good option for now, + but we should be aware of its implications. + + Args: + boxes (Tensor[N, 5]): Rotated boxes to perform NMS on. They are expected to be in + (x_center, y_center, width, height, angle_degrees) format. + scores (Tensor[N]): Scores for each one of the rotated boxes + iou_threshold (float): Discards all overlapping rotated boxes with IoU < iou_threshold + + Returns: + keep (Tensor): int64 tensor with the indices of the elements that have been kept + by Rotated NMS, sorted in decreasing order of scores + """ + return torch.ops.detectron2.nms_rotated(boxes, scores, iou_threshold) + + +# Note: this function (batched_nms_rotated) might be moved into +# torchvision/ops/boxes.py in the future +def batched_nms_rotated(boxes, scores, idxs, iou_threshold): + """ + Performs non-maximum suppression in a batched fashion. + + Each index value correspond to a category, and NMS + will not be applied between elements of different categories. + + Args: + boxes (Tensor[N, 5]): + boxes where NMS will be performed. They + are expected to be in (x_ctr, y_ctr, width, height, angle_degrees) format + scores (Tensor[N]): + scores for each one of the boxes + idxs (Tensor[N]): + indices of the categories for each one of the boxes. + iou_threshold (float): + discards all overlapping boxes + with IoU < iou_threshold + + Returns: + Tensor: + int64 tensor with the indices of the elements that have been kept + by NMS, sorted in decreasing order of scores + """ + assert boxes.shape[-1] == 5 + + if boxes.numel() == 0: + return torch.empty((0,), dtype=torch.int64, device=boxes.device) + boxes = boxes.float() # fp16 does not have enough range for batched NMS + # Strategy: in order to perform NMS independently per class, + # we add an offset to all the boxes. The offset is dependent + # only on the class idx, and is large enough so that boxes + # from different classes do not overlap + + # Note that batched_nms in torchvision/ops/boxes.py only uses max_coordinate, + # which won't handle negative coordinates correctly. + # Here by using min_coordinate we can make sure the negative coordinates are + # correctly handled. + max_coordinate = ( + torch.max(boxes[:, 0], boxes[:, 1]) + torch.max(boxes[:, 2], boxes[:, 3]) / 2 + ).max() + min_coordinate = ( + torch.min(boxes[:, 0], boxes[:, 1]) - torch.max(boxes[:, 2], boxes[:, 3]) / 2 + ).min() + offsets = idxs.to(boxes) * (max_coordinate - min_coordinate + 1) + boxes_for_nms = boxes.clone() # avoid modifying the original values in boxes + boxes_for_nms[:, :2] += offsets[:, None] + keep = nms_rotated(boxes_for_nms, scores, iou_threshold) + return keep diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/roi_align.py b/motion-gan-pipeline/preprocessing/third/detectron2/layers/roi_align.py new file mode 100644 index 0000000..163462e --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/roi_align.py @@ -0,0 +1,74 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from torch import nn +from torchvision.ops import roi_align + + +# NOTE: torchvision's RoIAlign has a different default aligned=False +class ROIAlign(nn.Module): + def __init__(self, output_size, spatial_scale, sampling_ratio, aligned=True): + """ + Args: + output_size (tuple): h, w + spatial_scale (float): scale the input boxes by this number + sampling_ratio (int): number of inputs samples to take for each output + sample. 0 to take samples densely. + aligned (bool): if False, use the legacy implementation in + Detectron. If True, align the results more perfectly. + + Note: + The meaning of aligned=True: + + Given a continuous coordinate c, its two neighboring pixel indices (in our + pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example, + c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled + from the underlying signal at continuous coordinates 0.5 and 1.5). But the original + roi_align (aligned=False) does not subtract the 0.5 when computing neighboring + pixel indices and therefore it uses pixels with a slightly incorrect alignment + (relative to our pixel model) when performing bilinear interpolation. + + With `aligned=True`, + we first appropriately scale the ROI and then shift it by -0.5 + prior to calling roi_align. This produces the correct neighbors; see + detectron2/tests/test_roi_align.py for verification. + + The difference does not make a difference to the model's performance if + ROIAlign is used together with conv layers. + """ + super().__init__() + self.output_size = output_size + self.spatial_scale = spatial_scale + self.sampling_ratio = sampling_ratio + self.aligned = aligned + + from torchvision import __version__ + + version = tuple(int(x) for x in __version__.split(".")[:2]) + # https://github.com/pytorch/vision/pull/2438 + assert version >= (0, 7), "Require torchvision >= 0.7" + + def forward(self, input, rois): + """ + Args: + input: NCHW images + rois: Bx5 boxes. First column is the index into N. The other 4 columns are xyxy. + """ + assert rois.dim() == 2 and rois.size(1) == 5 + if input.is_quantized: + input = input.dequantize() + return roi_align( + input, + rois.to(dtype=input.dtype), + self.output_size, + self.spatial_scale, + self.sampling_ratio, + self.aligned, + ) + + def __repr__(self): + tmpstr = self.__class__.__name__ + "(" + tmpstr += "output_size=" + str(self.output_size) + tmpstr += ", spatial_scale=" + str(self.spatial_scale) + tmpstr += ", sampling_ratio=" + str(self.sampling_ratio) + tmpstr += ", aligned=" + str(self.aligned) + tmpstr += ")" + return tmpstr diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/roi_align_rotated.py b/motion-gan-pipeline/preprocessing/third/detectron2/layers/roi_align_rotated.py new file mode 100644 index 0000000..d097326 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/roi_align_rotated.py @@ -0,0 +1,91 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import torch +from torch import nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.utils import _pair + + +class _ROIAlignRotated(Function): + @staticmethod + def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio): + ctx.save_for_backward(roi) + ctx.output_size = _pair(output_size) + ctx.spatial_scale = spatial_scale + ctx.sampling_ratio = sampling_ratio + ctx.input_shape = input.size() + output = torch.ops.detectron2.roi_align_rotated_forward( + input, roi, spatial_scale, output_size[0], output_size[1], sampling_ratio + ) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + (rois,) = ctx.saved_tensors + output_size = ctx.output_size + spatial_scale = ctx.spatial_scale + sampling_ratio = ctx.sampling_ratio + bs, ch, h, w = ctx.input_shape + grad_input = torch.ops.detectron2.roi_align_rotated_backward( + grad_output, + rois, + spatial_scale, + output_size[0], + output_size[1], + bs, + ch, + h, + w, + sampling_ratio, + ) + return grad_input, None, None, None, None, None + + +roi_align_rotated = _ROIAlignRotated.apply + + +class ROIAlignRotated(nn.Module): + def __init__(self, output_size, spatial_scale, sampling_ratio): + """ + Args: + output_size (tuple): h, w + spatial_scale (float): scale the input boxes by this number + sampling_ratio (int): number of inputs samples to take for each output + sample. 0 to take samples densely. + + Note: + ROIAlignRotated supports continuous coordinate by default: + Given a continuous coordinate c, its two neighboring pixel indices (in our + pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example, + c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled + from the underlying signal at continuous coordinates 0.5 and 1.5). + """ + super(ROIAlignRotated, self).__init__() + self.output_size = output_size + self.spatial_scale = spatial_scale + self.sampling_ratio = sampling_ratio + + def forward(self, input, rois): + """ + Args: + input: NCHW images + rois: Bx6 boxes. First column is the index into N. + The other 5 columns are (x_ctr, y_ctr, width, height, angle_degrees). + """ + assert rois.dim() == 2 and rois.size(1) == 6 + orig_dtype = input.dtype + if orig_dtype == torch.float16: + input = input.float() + rois = rois.float() + return roi_align_rotated( + input, rois, self.output_size, self.spatial_scale, self.sampling_ratio + ).to(dtype=orig_dtype) + + def __repr__(self): + tmpstr = self.__class__.__name__ + "(" + tmpstr += "output_size=" + str(self.output_size) + tmpstr += ", spatial_scale=" + str(self.spatial_scale) + tmpstr += ", sampling_ratio=" + str(self.sampling_ratio) + tmpstr += ")" + return tmpstr diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/rotated_boxes.py b/motion-gan-pipeline/preprocessing/third/detectron2/layers/rotated_boxes.py new file mode 100644 index 0000000..03f73b3 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/rotated_boxes.py @@ -0,0 +1,21 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from __future__ import absolute_import, division, print_function, unicode_literals +import torch + + +def pairwise_iou_rotated(boxes1, boxes2): + """ + Return intersection-over-union (Jaccard index) of boxes. + + Both sets of boxes are expected to be in + (x_center, y_center, width, height, angle) format. + + Arguments: + boxes1 (Tensor[N, 5]) + boxes2 (Tensor[M, 5]) + + Returns: + iou (Tensor[N, M]): the NxM matrix containing the pairwise + IoU values for every element in boxes1 and boxes2 + """ + return torch.ops.detectron2.box_iou_rotated(boxes1, boxes2) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/shape_spec.py b/motion-gan-pipeline/preprocessing/third/detectron2/layers/shape_spec.py new file mode 100644 index 0000000..fe7e8e2 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/shape_spec.py @@ -0,0 +1,20 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +from collections import namedtuple + + +class ShapeSpec(namedtuple("_ShapeSpec", ["channels", "height", "width", "stride"])): + """ + A simple structure that contains basic shape specification about a tensor. + It is often used as the auxiliary inputs/outputs of models, + to complement the lack of shape inference ability among pytorch modules. + + Attributes: + channels: + height: + width: + stride: + """ + + def __new__(cls, channels=None, height=None, width=None, stride=None): + return super().__new__(cls, channels, height, width, stride) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/layers/wrappers.py b/motion-gan-pipeline/preprocessing/third/detectron2/layers/wrappers.py new file mode 100644 index 0000000..29d0ef9 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/layers/wrappers.py @@ -0,0 +1,132 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +""" +Wrappers around on some nn functions, mainly to support empty tensors. + +Ideally, add support directly in PyTorch to empty tensors in those functions. + +These can be removed once https://github.com/pytorch/pytorch/issues/12013 +is implemented +""" + +from typing import List, Optional +import torch +from torch.nn import functional as F + + +def shapes_to_tensor(x: List[int], device: Optional[torch.device] = None) -> torch.Tensor: + """ + Turn a list of integer scalars or integer Tensor scalars into a vector, + in a way that's both traceable and scriptable. + + In tracing, `x` should be a list of scalar Tensor, so the output can trace to the inputs. + In scripting or eager, `x` should be a list of int. + """ + if torch.jit.is_scripting(): + return torch.as_tensor(x, device=device) + if torch.jit.is_tracing(): + assert all( + [isinstance(t, torch.Tensor) for t in x] + ), "Shape should be tensor during tracing!" + # as_tensor should not be used in tracing because it records a constant + ret = torch.stack(x) + if ret.device != device: # avoid recording a hard-coded device if not necessary + ret = ret.to(device=device) + return ret + return torch.as_tensor(x, device=device) + + +def cat(tensors: List[torch.Tensor], dim: int = 0): + """ + Efficient version of torch.cat that avoids a copy if there is only a single element in a list + """ + assert isinstance(tensors, (list, tuple)) + if len(tensors) == 1: + return tensors[0] + return torch.cat(tensors, dim) + + +def cross_entropy(input, target, *, reduction="mean", **kwargs): + """ + Same as `torch.nn.functional.cross_entropy`, but returns 0 (instead of nan) + for empty inputs. + """ + if target.numel() == 0 and reduction == "mean": + return input.sum() * 0.0 # connect the gradient + return F.cross_entropy(input, target, reduction=reduction, **kwargs) + + +class _NewEmptyTensorOp(torch.autograd.Function): + @staticmethod + def forward(ctx, x, new_shape): + ctx.shape = x.shape + return x.new_empty(new_shape) + + @staticmethod + def backward(ctx, grad): + shape = ctx.shape + return _NewEmptyTensorOp.apply(grad, shape), None + + +class Conv2d(torch.nn.Conv2d): + """ + A wrapper around :class:`torch.nn.Conv2d` to support empty inputs and more features. + """ + + def __init__(self, *args, **kwargs): + """ + Extra keyword arguments supported in addition to those in `torch.nn.Conv2d`: + + Args: + norm (nn.Module, optional): a normalization layer + activation (callable(Tensor) -> Tensor): a callable activation function + + It assumes that norm layer is used before activation. + """ + norm = kwargs.pop("norm", None) + activation = kwargs.pop("activation", None) + super().__init__(*args, **kwargs) + + self.norm = norm + self.activation = activation + + def forward(self, x): + # torchscript does not support SyncBatchNorm yet + # https://github.com/pytorch/pytorch/issues/40507 + # and we skip these codes in torchscript since: + # 1. currently we only support torchscript in evaluation mode + # 2. features needed by exporting module to torchscript are added in PyTorch 1.6 or + # later version, `Conv2d` in these PyTorch versions has already supported empty inputs. + if not torch.jit.is_scripting(): + if x.numel() == 0 and self.training: + # https://github.com/pytorch/pytorch/issues/12013 + assert not isinstance( + self.norm, torch.nn.SyncBatchNorm + ), "SyncBatchNorm does not support empty inputs!" + + x = F.conv2d( + x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups + ) + if self.norm is not None: + x = self.norm(x) + if self.activation is not None: + x = self.activation(x) + return x + + +ConvTranspose2d = torch.nn.ConvTranspose2d +BatchNorm2d = torch.nn.BatchNorm2d +interpolate = F.interpolate +Linear = torch.nn.Linear + + +def nonzero_tuple(x): + """ + A 'as_tuple=True' version of torch.nonzero to support torchscript. + because of https://github.com/pytorch/pytorch/issues/38718 + """ + if torch.jit.is_scripting(): + if x.dim() == 0: + return x.unsqueeze(0).nonzero().unbind(1) + return x.nonzero().unbind(1) + else: + return x.nonzero(as_tuple=True) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/model_zoo/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/model_zoo/__init__.py new file mode 100644 index 0000000..6204208 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/model_zoo/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +""" +Model Zoo API for Detectron2: a collection of functions to create common model architectures +listed in `MODEL_ZOO.md `_, +and optionally load their pre-trained weights. +""" + +from .model_zoo import get, get_config_file, get_checkpoint_url, get_config + +__all__ = ["get_checkpoint_url", "get", "get_config_file", "get_config"] diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/model_zoo/model_zoo.py b/motion-gan-pipeline/preprocessing/third/detectron2/model_zoo/model_zoo.py new file mode 100644 index 0000000..5b90bc9 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/model_zoo/model_zoo.py @@ -0,0 +1,213 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import os +from typing import Optional +import pkg_resources +import torch + +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate +from detectron2.modeling import build_model + + +class _ModelZooUrls(object): + """ + Mapping from names to officially released Detectron2 pre-trained models. + """ + + S3_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/" + + # format: {config_path.yaml} -> model_id/model_final_{commit}.pkl + CONFIG_PATH_TO_URL_SUFFIX = { + # COCO Detection with Faster R-CNN + "COCO-Detection/faster_rcnn_R_50_C4_1x": "137257644/model_final_721ade.pkl", + "COCO-Detection/faster_rcnn_R_50_DC5_1x": "137847829/model_final_51d356.pkl", + "COCO-Detection/faster_rcnn_R_50_FPN_1x": "137257794/model_final_b275ba.pkl", + "COCO-Detection/faster_rcnn_R_50_C4_3x": "137849393/model_final_f97cb7.pkl", + "COCO-Detection/faster_rcnn_R_50_DC5_3x": "137849425/model_final_68d202.pkl", + "COCO-Detection/faster_rcnn_R_50_FPN_3x": "137849458/model_final_280758.pkl", + "COCO-Detection/faster_rcnn_R_101_C4_3x": "138204752/model_final_298dad.pkl", + "COCO-Detection/faster_rcnn_R_101_DC5_3x": "138204841/model_final_3e0943.pkl", + "COCO-Detection/faster_rcnn_R_101_FPN_3x": "137851257/model_final_f6e8b1.pkl", + "COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x": "139173657/model_final_68b088.pkl", + # COCO Detection with RetinaNet + "COCO-Detection/retinanet_R_50_FPN_1x": "190397773/model_final_bfca0b.pkl", + "COCO-Detection/retinanet_R_50_FPN_3x": "190397829/model_final_5bd44e.pkl", + "COCO-Detection/retinanet_R_101_FPN_3x": "190397697/model_final_971ab9.pkl", + # COCO Detection with RPN and Fast R-CNN + "COCO-Detection/rpn_R_50_C4_1x": "137258005/model_final_450694.pkl", + "COCO-Detection/rpn_R_50_FPN_1x": "137258492/model_final_02ce48.pkl", + "COCO-Detection/fast_rcnn_R_50_FPN_1x": "137635226/model_final_e5f7ce.pkl", + # COCO Instance Segmentation Baselines with Mask R-CNN + "COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x": "137259246/model_final_9243eb.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x": "137260150/model_final_4f86c3.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x": "137260431/model_final_a54504.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x": "137849525/model_final_4ce675.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x": "137849551/model_final_84107b.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x": "137849600/model_final_f10217.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x": "138363239/model_final_a2914c.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x": "138363294/model_final_0464b7.pkl", + "COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x": "138205316/model_final_a3ec72.pkl", + "COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x": "139653917/model_final_2d9806.pkl", # noqa + # New baselines using Large-Scale Jitter and Longer Training Schedule + "new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ": "42047764/model_final_bb69de.pkl", + "new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ": "42047638/model_final_89a8d3.pkl", + "new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ": "42019571/model_final_14d201.pkl", + "new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ": "42025812/model_final_4f7b58.pkl", + "new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ": "42131867/model_final_0bb7ae.pkl", + "new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ": "42073830/model_final_f96b26.pkl", + "new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ": "42047771/model_final_b7fbab.pkl", # noqa + "new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ": "42132721/model_final_5d87c1.pkl", # noqa + "new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ": "42025447/model_final_f1362d.pkl", # noqa + "new_baselines/mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ": "42047784/model_final_6ba57e.pkl", # noqa + "new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ": "42047642/model_final_27b9c1.pkl", # noqa + "new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ": "42045954/model_final_ef3a80.pkl", # noqa + # COCO Person Keypoint Detection Baselines with Keypoint R-CNN + "COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x": "137261548/model_final_04e291.pkl", + "COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x": "137849621/model_final_a6e10b.pkl", + "COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x": "138363331/model_final_997cc7.pkl", + "COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x": "139686956/model_final_5ad38f.pkl", + # COCO Panoptic Segmentation Baselines with Panoptic FPN + "COCO-PanopticSegmentation/panoptic_fpn_R_50_1x": "139514544/model_final_dbfeb4.pkl", + "COCO-PanopticSegmentation/panoptic_fpn_R_50_3x": "139514569/model_final_c10459.pkl", + "COCO-PanopticSegmentation/panoptic_fpn_R_101_3x": "139514519/model_final_cafdb1.pkl", + # LVIS Instance Segmentation Baselines with Mask R-CNN + "LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x": "144219072/model_final_571f7c.pkl", # noqa + "LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x": "144219035/model_final_824ab5.pkl", # noqa + "LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x": "144219108/model_final_5e3439.pkl", # noqa + # Cityscapes & Pascal VOC Baselines + "Cityscapes/mask_rcnn_R_50_FPN": "142423278/model_final_af9cf5.pkl", + "PascalVOC-Detection/faster_rcnn_R_50_C4": "142202221/model_final_b1acc2.pkl", + # Other Settings + "Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5": "138602867/model_final_65c703.pkl", + "Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5": "144998336/model_final_821d0b.pkl", + "Misc/cascade_mask_rcnn_R_50_FPN_1x": "138602847/model_final_e9d89b.pkl", + "Misc/cascade_mask_rcnn_R_50_FPN_3x": "144998488/model_final_480dd8.pkl", + "Misc/mask_rcnn_R_50_FPN_3x_syncbn": "169527823/model_final_3b3c51.pkl", + "Misc/mask_rcnn_R_50_FPN_3x_gn": "138602888/model_final_dc5d9e.pkl", + "Misc/scratch_mask_rcnn_R_50_FPN_3x_gn": "138602908/model_final_01ca85.pkl", + "Misc/scratch_mask_rcnn_R_50_FPN_9x_gn": "183808979/model_final_da7b4c.pkl", + "Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn": "184226666/model_final_5ce33e.pkl", + "Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x": "139797668/model_final_be35db.pkl", + "Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv": "18131413/model_0039999_e76410.pkl", # noqa + # D1 Comparisons + "Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x": "137781054/model_final_7ab50c.pkl", # noqa + "Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x": "137781281/model_final_62ca52.pkl", # noqa + "Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x": "137781195/model_final_cce136.pkl", + } + + @staticmethod + def query(config_path: str) -> Optional[str]: + """ + Args: + config_path: relative config filename + """ + name = config_path.replace(".yaml", "").replace(".py", "") + if name in _ModelZooUrls.CONFIG_PATH_TO_URL_SUFFIX: + suffix = _ModelZooUrls.CONFIG_PATH_TO_URL_SUFFIX[name] + return _ModelZooUrls.S3_PREFIX + name + "/" + suffix + return None + + +def get_checkpoint_url(config_path): + """ + Returns the URL to the model trained using the given config + + Args: + config_path (str): config file name relative to detectron2's "configs/" + directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" + + Returns: + str: a URL to the model + """ + url = _ModelZooUrls.query(config_path) + if url is None: + raise RuntimeError("Pretrained model for {} is not available!".format(config_path)) + return url + + +def get_config_file(config_path): + """ + Returns path to a builtin config file. + + Args: + config_path (str): config file name relative to detectron2's "configs/" + directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" + + Returns: + str: the real path to the config file. + """ + cfg_file = pkg_resources.resource_filename( + "detectron2.model_zoo", os.path.join("configs", config_path) + ) + if not os.path.exists(cfg_file): + raise RuntimeError("{} not available in Model Zoo!".format(config_path)) + return cfg_file + + +def get_config(config_path, trained: bool = False): + """ + Returns a config object for a model in model zoo. + + Args: + config_path (str): config file name relative to detectron2's "configs/" + directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" + trained (bool): If True, will set ``MODEL.WEIGHTS`` to trained model zoo weights. + If False, the checkpoint specified in the config file's ``MODEL.WEIGHTS`` is used + instead; this will typically (though not always) initialize a subset of weights using + an ImageNet pre-trained model, while randomly initializing the other weights. + + Returns: + CfgNode or omegaconf.DictConfig: a config object + """ + cfg_file = get_config_file(config_path) + if cfg_file.endswith(".yaml"): + cfg = get_cfg() + cfg.merge_from_file(cfg_file) + if trained: + cfg.MODEL.WEIGHTS = get_checkpoint_url(config_path) + return cfg + elif cfg_file.endswith(".py"): + cfg = LazyConfig.load(cfg_file) + if trained: + url = get_checkpoint_url(config_path) + if "train" in cfg and "init_checkpoint" in cfg.train: + cfg.train.init_checkpoint = url + else: + raise NotImplementedError + return cfg + + +def get(config_path, trained: bool = False, device: Optional[str] = None): + """ + Get a model specified by relative path under Detectron2's official ``configs/`` directory. + + Args: + config_path (str): config file name relative to detectron2's "configs/" + directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" + trained (bool): see :func:`get_config`. + device (str or None): overwrite the device in config, if given. + + Returns: + nn.Module: a detectron2 model. Will be in training mode. + + Example: + :: + from detectron2 import model_zoo + model = model_zoo.get("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml", trained=True) + """ + cfg = get_config(config_path, trained) + if device is None and not torch.cuda.is_available(): + device = "cpu" + if device is not None and isinstance(cfg, CfgNode): + cfg.MODEL.DEVICE = device + + if isinstance(cfg, CfgNode): + model = build_model(cfg) + DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS) + else: + model = instantiate(cfg.model) + if device is not None: + model = model.to(device) + if "train" in cfg and "init_checkpoint" in cfg.train: + DetectionCheckpointer(model).load(cfg.train.init_checkpoint) + return model diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/__init__.py new file mode 100644 index 0000000..576493d --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/__init__.py @@ -0,0 +1,59 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from detectron2.layers import ShapeSpec + +from .anchor_generator import build_anchor_generator, ANCHOR_GENERATOR_REGISTRY +from .backbone import ( + BACKBONE_REGISTRY, + FPN, + Backbone, + ResNet, + ResNetBlockBase, + build_backbone, + build_resnet_backbone, + make_stage, +) +from .meta_arch import ( + META_ARCH_REGISTRY, + SEM_SEG_HEADS_REGISTRY, + GeneralizedRCNN, + PanopticFPN, + ProposalNetwork, + RetinaNet, + SemanticSegmentor, + build_model, + build_sem_seg_head, + FCOS, +) +from .postprocessing import detector_postprocess +from .proposal_generator import ( + PROPOSAL_GENERATOR_REGISTRY, + build_proposal_generator, + RPN_HEAD_REGISTRY, + build_rpn_head, +) +from .roi_heads import ( + ROI_BOX_HEAD_REGISTRY, + ROI_HEADS_REGISTRY, + ROI_KEYPOINT_HEAD_REGISTRY, + ROI_MASK_HEAD_REGISTRY, + ROIHeads, + StandardROIHeads, + BaseMaskRCNNHead, + BaseKeypointRCNNHead, + FastRCNNOutputLayers, + build_box_head, + build_keypoint_head, + build_mask_head, + build_roi_heads, +) +from .test_time_augmentation import DatasetMapperTTA, GeneralizedRCNNWithTTA +from .mmdet_wrapper import MMDetBackbone, MMDetDetector + +_EXCLUDE = {"ShapeSpec"} +__all__ = [k for k in globals().keys() if k not in _EXCLUDE and not k.startswith("_")] + + +from detectron2.utils.env import fixup_module_metadata + +fixup_module_metadata(__name__, globals(), __all__) +del fixup_module_metadata diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/anchor_generator.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/anchor_generator.py new file mode 100644 index 0000000..ee4b988 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/anchor_generator.py @@ -0,0 +1,382 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import collections +import math +from typing import List +import torch +from torch import nn + +from detectron2.config import configurable +from detectron2.layers import ShapeSpec +from detectron2.structures import Boxes, RotatedBoxes +from detectron2.utils.registry import Registry + +ANCHOR_GENERATOR_REGISTRY = Registry("ANCHOR_GENERATOR") +ANCHOR_GENERATOR_REGISTRY.__doc__ = """ +Registry for modules that creates object detection anchors for feature maps. + +The registered object will be called with `obj(cfg, input_shape)`. +""" + + +class BufferList(nn.Module): + """ + Similar to nn.ParameterList, but for buffers + """ + + def __init__(self, buffers): + super().__init__() + for i, buffer in enumerate(buffers): + # Use non-persistent buffer so the values are not saved in checkpoint + self.register_buffer(str(i), buffer, persistent=False) + + def __len__(self): + return len(self._buffers) + + def __iter__(self): + return iter(self._buffers.values()) + + +def _create_grid_offsets(size: List[int], stride: int, offset: float, device: torch.device): + grid_height, grid_width = size + shifts_x = torch.arange( + offset * stride, grid_width * stride, step=stride, dtype=torch.float32, device=device + ) + shifts_y = torch.arange( + offset * stride, grid_height * stride, step=stride, dtype=torch.float32, device=device + ) + + shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) + shift_x = shift_x.reshape(-1) + shift_y = shift_y.reshape(-1) + return shift_x, shift_y + + +def _broadcast_params(params, num_features, name): + """ + If one size (or aspect ratio) is specified and there are multiple feature + maps, we "broadcast" anchors of that single size (or aspect ratio) + over all feature maps. + + If params is list[float], or list[list[float]] with len(params) == 1, repeat + it num_features time. + + Returns: + list[list[float]]: param for each feature + """ + assert isinstance( + params, collections.abc.Sequence + ), f"{name} in anchor generator has to be a list! Got {params}." + assert len(params), f"{name} in anchor generator cannot be empty!" + if not isinstance(params[0], collections.abc.Sequence): # params is list[float] + return [params] * num_features + if len(params) == 1: + return list(params) * num_features + assert len(params) == num_features, ( + f"Got {name} of length {len(params)} in anchor generator, " + f"but the number of input features is {num_features}!" + ) + return params + + +@ANCHOR_GENERATOR_REGISTRY.register() +class DefaultAnchorGenerator(nn.Module): + """ + Compute anchors in the standard ways described in + "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks". + """ + + box_dim: torch.jit.Final[int] = 4 + """ + the dimension of each anchor box. + """ + + @configurable + def __init__(self, *, sizes, aspect_ratios, strides, offset=0.5): + """ + This interface is experimental. + + Args: + sizes (list[list[float]] or list[float]): + If ``sizes`` is list[list[float]], ``sizes[i]`` is the list of anchor sizes + (i.e. sqrt of anchor area) to use for the i-th feature map. + If ``sizes`` is list[float], ``sizes`` is used for all feature maps. + Anchor sizes are given in absolute lengths in units of + the input image; they do not dynamically scale if the input image size changes. + aspect_ratios (list[list[float]] or list[float]): list of aspect ratios + (i.e. height / width) to use for anchors. Same "broadcast" rule for `sizes` applies. + strides (list[int]): stride of each input feature. + offset (float): Relative offset between the center of the first anchor and the top-left + corner of the image. Value has to be in [0, 1). + Recommend to use 0.5, which means half stride. + """ + super().__init__() + + self.strides = strides + self.num_features = len(self.strides) + sizes = _broadcast_params(sizes, self.num_features, "sizes") + aspect_ratios = _broadcast_params(aspect_ratios, self.num_features, "aspect_ratios") + self.cell_anchors = self._calculate_anchors(sizes, aspect_ratios) + + self.offset = offset + assert 0.0 <= self.offset < 1.0, self.offset + + @classmethod + def from_config(cls, cfg, input_shape: List[ShapeSpec]): + return { + "sizes": cfg.MODEL.ANCHOR_GENERATOR.SIZES, + "aspect_ratios": cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS, + "strides": [x.stride for x in input_shape], + "offset": cfg.MODEL.ANCHOR_GENERATOR.OFFSET, + } + + def _calculate_anchors(self, sizes, aspect_ratios): + cell_anchors = [ + self.generate_cell_anchors(s, a).float() for s, a in zip(sizes, aspect_ratios) + ] + return BufferList(cell_anchors) + + @property + @torch.jit.unused + def num_cell_anchors(self): + """ + Alias of `num_anchors`. + """ + return self.num_anchors + + @property + @torch.jit.unused + def num_anchors(self): + """ + Returns: + list[int]: Each int is the number of anchors at every pixel + location, on that feature map. + For example, if at every pixel we use anchors of 3 aspect + ratios and 5 sizes, the number of anchors is 15. + (See also ANCHOR_GENERATOR.SIZES and ANCHOR_GENERATOR.ASPECT_RATIOS in config) + + In standard RPN models, `num_anchors` on every feature map is the same. + """ + return [len(cell_anchors) for cell_anchors in self.cell_anchors] + + def _grid_anchors(self, grid_sizes: List[List[int]]): + """ + Returns: + list[Tensor]: #featuremap tensors, each is (#locations x #cell_anchors) x 4 + """ + anchors = [] + # buffers() not supported by torchscript. use named_buffers() instead + buffers: List[torch.Tensor] = [x[1] for x in self.cell_anchors.named_buffers()] + for size, stride, base_anchors in zip(grid_sizes, self.strides, buffers): + shift_x, shift_y = _create_grid_offsets(size, stride, self.offset, base_anchors.device) + shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) + + anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) + + return anchors + + def generate_cell_anchors(self, sizes=(32, 64, 128, 256, 512), aspect_ratios=(0.5, 1, 2)): + """ + Generate a tensor storing canonical anchor boxes, which are all anchor + boxes of different sizes and aspect_ratios centered at (0, 0). + We can later build the set of anchors for a full feature map by + shifting and tiling these tensors (see `meth:_grid_anchors`). + + Args: + sizes (tuple[float]): + aspect_ratios (tuple[float]]): + + Returns: + Tensor of shape (len(sizes) * len(aspect_ratios), 4) storing anchor boxes + in XYXY format. + """ + + # This is different from the anchor generator defined in the original Faster R-CNN + # code or Detectron. They yield the same AP, however the old version defines cell + # anchors in a less natural way with a shift relative to the feature grid and + # quantization that results in slightly different sizes for different aspect ratios. + # See also https://github.com/facebookresearch/Detectron/issues/227 + + anchors = [] + for size in sizes: + area = size ** 2.0 + for aspect_ratio in aspect_ratios: + # s * s = w * h + # a = h / w + # ... some algebra ... + # w = sqrt(s * s / a) + # h = a * w + w = math.sqrt(area / aspect_ratio) + h = aspect_ratio * w + x0, y0, x1, y1 = -w / 2.0, -h / 2.0, w / 2.0, h / 2.0 + anchors.append([x0, y0, x1, y1]) + return torch.tensor(anchors) + + def forward(self, features: List[torch.Tensor]): + """ + Args: + features (list[Tensor]): list of backbone feature maps on which to generate anchors. + + Returns: + list[Boxes]: a list of Boxes containing all the anchors for each feature map + (i.e. the cell anchors repeated over all locations in the feature map). + The number of anchors of each feature map is Hi x Wi x num_cell_anchors, + where Hi, Wi are resolution of the feature map divided by anchor stride. + """ + grid_sizes = [feature_map.shape[-2:] for feature_map in features] + anchors_over_all_feature_maps = self._grid_anchors(grid_sizes) + return [Boxes(x) for x in anchors_over_all_feature_maps] + + +@ANCHOR_GENERATOR_REGISTRY.register() +class RotatedAnchorGenerator(nn.Module): + """ + Compute rotated anchors used by Rotated RPN (RRPN), described in + "Arbitrary-Oriented Scene Text Detection via Rotation Proposals". + """ + + box_dim: int = 5 + """ + the dimension of each anchor box. + """ + + @configurable + def __init__(self, *, sizes, aspect_ratios, strides, angles, offset=0.5): + """ + This interface is experimental. + + Args: + sizes (list[list[float]] or list[float]): + If sizes is list[list[float]], sizes[i] is the list of anchor sizes + (i.e. sqrt of anchor area) to use for the i-th feature map. + If sizes is list[float], the sizes are used for all feature maps. + Anchor sizes are given in absolute lengths in units of + the input image; they do not dynamically scale if the input image size changes. + aspect_ratios (list[list[float]] or list[float]): list of aspect ratios + (i.e. height / width) to use for anchors. Same "broadcast" rule for `sizes` applies. + strides (list[int]): stride of each input feature. + angles (list[list[float]] or list[float]): list of angles (in degrees CCW) + to use for anchors. Same "broadcast" rule for `sizes` applies. + offset (float): Relative offset between the center of the first anchor and the top-left + corner of the image. Value has to be in [0, 1). + Recommend to use 0.5, which means half stride. + """ + super().__init__() + + self.strides = strides + self.num_features = len(self.strides) + sizes = _broadcast_params(sizes, self.num_features, "sizes") + aspect_ratios = _broadcast_params(aspect_ratios, self.num_features, "aspect_ratios") + angles = _broadcast_params(angles, self.num_features, "angles") + self.cell_anchors = self._calculate_anchors(sizes, aspect_ratios, angles) + + self.offset = offset + assert 0.0 <= self.offset < 1.0, self.offset + + @classmethod + def from_config(cls, cfg, input_shape: List[ShapeSpec]): + return { + "sizes": cfg.MODEL.ANCHOR_GENERATOR.SIZES, + "aspect_ratios": cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS, + "strides": [x.stride for x in input_shape], + "offset": cfg.MODEL.ANCHOR_GENERATOR.OFFSET, + "angles": cfg.MODEL.ANCHOR_GENERATOR.ANGLES, + } + + def _calculate_anchors(self, sizes, aspect_ratios, angles): + cell_anchors = [ + self.generate_cell_anchors(size, aspect_ratio, angle).float() + for size, aspect_ratio, angle in zip(sizes, aspect_ratios, angles) + ] + return BufferList(cell_anchors) + + @property + def num_cell_anchors(self): + """ + Alias of `num_anchors`. + """ + return self.num_anchors + + @property + def num_anchors(self): + """ + Returns: + list[int]: Each int is the number of anchors at every pixel + location, on that feature map. + For example, if at every pixel we use anchors of 3 aspect + ratios, 2 sizes and 5 angles, the number of anchors is 30. + (See also ANCHOR_GENERATOR.SIZES, ANCHOR_GENERATOR.ASPECT_RATIOS + and ANCHOR_GENERATOR.ANGLES in config) + + In standard RRPN models, `num_anchors` on every feature map is the same. + """ + return [len(cell_anchors) for cell_anchors in self.cell_anchors] + + def _grid_anchors(self, grid_sizes): + anchors = [] + for size, stride, base_anchors in zip(grid_sizes, self.strides, self.cell_anchors): + shift_x, shift_y = _create_grid_offsets(size, stride, self.offset, base_anchors.device) + zeros = torch.zeros_like(shift_x) + shifts = torch.stack((shift_x, shift_y, zeros, zeros, zeros), dim=1) + + anchors.append((shifts.view(-1, 1, 5) + base_anchors.view(1, -1, 5)).reshape(-1, 5)) + + return anchors + + def generate_cell_anchors( + self, + sizes=(32, 64, 128, 256, 512), + aspect_ratios=(0.5, 1, 2), + angles=(-90, -60, -30, 0, 30, 60, 90), + ): + """ + Generate a tensor storing canonical anchor boxes, which are all anchor + boxes of different sizes, aspect_ratios, angles centered at (0, 0). + We can later build the set of anchors for a full feature map by + shifting and tiling these tensors (see `meth:_grid_anchors`). + + Args: + sizes (tuple[float]): + aspect_ratios (tuple[float]]): + angles (tuple[float]]): + + Returns: + Tensor of shape (len(sizes) * len(aspect_ratios) * len(angles), 5) + storing anchor boxes in (x_ctr, y_ctr, w, h, angle) format. + """ + anchors = [] + for size in sizes: + area = size ** 2.0 + for aspect_ratio in aspect_ratios: + # s * s = w * h + # a = h / w + # ... some algebra ... + # w = sqrt(s * s / a) + # h = a * w + w = math.sqrt(area / aspect_ratio) + h = aspect_ratio * w + anchors.extend([0, 0, w, h, a] for a in angles) + + return torch.tensor(anchors) + + def forward(self, features): + """ + Args: + features (list[Tensor]): list of backbone feature maps on which to generate anchors. + + Returns: + list[RotatedBoxes]: a list of Boxes containing all the anchors for each feature map + (i.e. the cell anchors repeated over all locations in the feature map). + The number of anchors of each feature map is Hi x Wi x num_cell_anchors, + where Hi, Wi are resolution of the feature map divided by anchor stride. + """ + grid_sizes = [feature_map.shape[-2:] for feature_map in features] + anchors_over_all_feature_maps = self._grid_anchors(grid_sizes) + return [RotatedBoxes(x) for x in anchors_over_all_feature_maps] + + +def build_anchor_generator(cfg, input_shape): + """ + Built an anchor generator from `cfg.MODEL.ANCHOR_GENERATOR.NAME`. + """ + anchor_generator = cfg.MODEL.ANCHOR_GENERATOR.NAME + return ANCHOR_GENERATOR_REGISTRY.get(anchor_generator)(cfg, input_shape) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/__init__.py new file mode 100644 index 0000000..55b265d --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .build import build_backbone, BACKBONE_REGISTRY # noqa F401 isort:skip + +from .backbone import Backbone +from .fpn import FPN +from .regnet import RegNet +from .resnet import ( + BasicStem, + ResNet, + ResNetBlockBase, + build_resnet_backbone, + make_stage, + BottleneckBlock, +) + +__all__ = [k for k in globals().keys() if not k.startswith("_")] +# TODO can expose more resnet blocks after careful consideration diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/backbone.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/backbone.py new file mode 100644 index 0000000..369fb88 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/backbone.py @@ -0,0 +1,53 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from abc import ABCMeta, abstractmethod +import torch.nn as nn + +from detectron2.layers import ShapeSpec + +__all__ = ["Backbone"] + + +class Backbone(nn.Module, metaclass=ABCMeta): + """ + Abstract base class for network backbones. + """ + + def __init__(self): + """ + The `__init__` method of any subclass can specify its own set of arguments. + """ + super().__init__() + + @abstractmethod + def forward(self): + """ + Subclasses must override this method, but adhere to the same return type. + + Returns: + dict[str->Tensor]: mapping from feature name (e.g., "res2") to tensor + """ + pass + + @property + def size_divisibility(self) -> int: + """ + Some backbones require the input height and width to be divisible by a + specific integer. This is typically true for encoder / decoder type networks + with lateral connection (e.g., FPN) for which feature maps need to match + dimension in the "bottom up" and "top down" paths. Set to 0 if no specific + input size divisibility is required. + """ + return 0 + + def output_shape(self): + """ + Returns: + dict[str->ShapeSpec] + """ + # this is a backward-compatible default + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) + for name in self._out_features + } diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/build.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/build.py new file mode 100644 index 0000000..af02141 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/build.py @@ -0,0 +1,33 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from detectron2.layers import ShapeSpec +from detectron2.utils.registry import Registry + +from .backbone import Backbone + +BACKBONE_REGISTRY = Registry("BACKBONE") +BACKBONE_REGISTRY.__doc__ = """ +Registry for backbones, which extract feature maps from images + +The registered object must be a callable that accepts two arguments: + +1. A :class:`detectron2.config.CfgNode` +2. A :class:`detectron2.layers.ShapeSpec`, which contains the input shape specification. + +Registered object must return instance of :class:`Backbone`. +""" + + +def build_backbone(cfg, input_shape=None): + """ + Build a backbone from `cfg.MODEL.BACKBONE.NAME`. + + Returns: + an instance of :class:`Backbone` + """ + if input_shape is None: + input_shape = ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN)) + + backbone_name = cfg.MODEL.BACKBONE.NAME + backbone = BACKBONE_REGISTRY.get(backbone_name)(cfg, input_shape) + assert isinstance(backbone, Backbone) + return backbone diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/fpn.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/fpn.py new file mode 100644 index 0000000..d0bdfc9 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/fpn.py @@ -0,0 +1,255 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import math +import fvcore.nn.weight_init as weight_init +import torch +import torch.nn.functional as F +from torch import nn + +from detectron2.layers import Conv2d, ShapeSpec, get_norm + +from .backbone import Backbone +from .build import BACKBONE_REGISTRY +from .resnet import build_resnet_backbone + +__all__ = ["build_resnet_fpn_backbone", "build_retinanet_resnet_fpn_backbone", "FPN"] + + +class FPN(Backbone): + """ + This module implements :paper:`FPN`. + It creates pyramid features built on top of some input feature maps. + """ + + _fuse_type: torch.jit.Final[str] + + def __init__( + self, bottom_up, in_features, out_channels, norm="", top_block=None, fuse_type="sum" + ): + """ + Args: + bottom_up (Backbone): module representing the bottom up subnetwork. + Must be a subclass of :class:`Backbone`. The multi-scale feature + maps generated by the bottom up network, and listed in `in_features`, + are used to generate FPN levels. + in_features (list[str]): names of the input feature maps coming + from the backbone to which FPN is attached. For example, if the + backbone produces ["res2", "res3", "res4"], any *contiguous* sublist + of these may be used; order must be from high to low resolution. + out_channels (int): number of channels in the output feature maps. + norm (str): the normalization to use. + top_block (nn.Module or None): if provided, an extra operation will + be performed on the output of the last (smallest resolution) + FPN output, and the result will extend the result list. The top_block + further downsamples the feature map. It must have an attribute + "num_levels", meaning the number of extra FPN levels added by + this block, and "in_feature", which is a string representing + its input feature (e.g., p5). + fuse_type (str): types for fusing the top down features and the lateral + ones. It can be "sum" (default), which sums up element-wise; or "avg", + which takes the element-wise mean of the two. + """ + super(FPN, self).__init__() + assert isinstance(bottom_up, Backbone) + assert in_features, in_features + + # Feature map strides and channels from the bottom up network (e.g. ResNet) + input_shapes = bottom_up.output_shape() + strides = [input_shapes[f].stride for f in in_features] + in_channels_per_feature = [input_shapes[f].channels for f in in_features] + + _assert_strides_are_log2_contiguous(strides) + lateral_convs = [] + output_convs = [] + + use_bias = norm == "" + for idx, in_channels in enumerate(in_channels_per_feature): + lateral_norm = get_norm(norm, out_channels) + output_norm = get_norm(norm, out_channels) + + lateral_conv = Conv2d( + in_channels, out_channels, kernel_size=1, bias=use_bias, norm=lateral_norm + ) + output_conv = Conv2d( + out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + norm=output_norm, + ) + weight_init.c2_xavier_fill(lateral_conv) + weight_init.c2_xavier_fill(output_conv) + stage = int(math.log2(strides[idx])) + self.add_module("fpn_lateral{}".format(stage), lateral_conv) + self.add_module("fpn_output{}".format(stage), output_conv) + + lateral_convs.append(lateral_conv) + output_convs.append(output_conv) + # Place convs into top-down order (from low to high resolution) + # to make the top-down computation in forward clearer. + self.lateral_convs = lateral_convs[::-1] + self.output_convs = output_convs[::-1] + self.top_block = top_block + self.in_features = tuple(in_features) + self.bottom_up = bottom_up + # Return feature names are "p", like ["p2", "p3", ..., "p6"] + self._out_feature_strides = {"p{}".format(int(math.log2(s))): s for s in strides} + # top block output feature maps. + if self.top_block is not None: + for s in range(stage, stage + self.top_block.num_levels): + self._out_feature_strides["p{}".format(s + 1)] = 2 ** (s + 1) + + self._out_features = list(self._out_feature_strides.keys()) + self._out_feature_channels = {k: out_channels for k in self._out_features} + self._size_divisibility = strides[-1] + assert fuse_type in {"avg", "sum"} + self._fuse_type = fuse_type + + @property + def size_divisibility(self): + return self._size_divisibility + + def forward(self, x): + """ + Args: + input (dict[str->Tensor]): mapping feature map name (e.g., "res5") to + feature map tensor for each feature level in high to low resolution order. + + Returns: + dict[str->Tensor]: + mapping from feature map name to FPN feature map tensor + in high to low resolution order. Returned feature names follow the FPN + paper convention: "p", where stage has stride = 2 ** stage e.g., + ["p2", "p3", ..., "p6"]. + """ + bottom_up_features = self.bottom_up(x) + results = [] + prev_features = self.lateral_convs[0](bottom_up_features[self.in_features[-1]]) + results.append(self.output_convs[0](prev_features)) + + # Reverse feature maps into top-down order (from low to high resolution) + for idx, (lateral_conv, output_conv) in enumerate( + zip(self.lateral_convs, self.output_convs) + ): + # Slicing of ModuleList is not supported https://github.com/pytorch/pytorch/issues/47336 + # Therefore we loop over all modules but skip the first one + if idx > 0: + features = self.in_features[-idx - 1] + features = bottom_up_features[features] + top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest") + lateral_features = lateral_conv(features) + prev_features = lateral_features + top_down_features + if self._fuse_type == "avg": + prev_features /= 2 + results.insert(0, output_conv(prev_features)) + + if self.top_block is not None: + if self.top_block.in_feature in bottom_up_features: + top_block_in_feature = bottom_up_features[self.top_block.in_feature] + else: + top_block_in_feature = results[self._out_features.index(self.top_block.in_feature)] + results.extend(self.top_block(top_block_in_feature)) + assert len(self._out_features) == len(results) + return {f: res for f, res in zip(self._out_features, results)} + + def output_shape(self): + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) + for name in self._out_features + } + + +def _assert_strides_are_log2_contiguous(strides): + """ + Assert that each stride is 2x times its preceding stride, i.e. "contiguous in log2". + """ + for i, stride in enumerate(strides[1:], 1): + assert stride == 2 * strides[i - 1], "Strides {} {} are not log2 contiguous".format( + stride, strides[i - 1] + ) + + +class LastLevelMaxPool(nn.Module): + """ + This module is used in the original FPN to generate a downsampled + P6 feature from P5. + """ + + def __init__(self): + super().__init__() + self.num_levels = 1 + self.in_feature = "p5" + + def forward(self, x): + return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)] + + +class LastLevelP6P7(nn.Module): + """ + This module is used in RetinaNet to generate extra layers, P6 and P7 from + C5 feature. + """ + + def __init__(self, in_channels, out_channels, in_feature="res5"): + super().__init__() + self.num_levels = 2 + self.in_feature = in_feature + self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1) + self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1) + for module in [self.p6, self.p7]: + weight_init.c2_xavier_fill(module) + + def forward(self, c5): + p6 = self.p6(c5) + p7 = self.p7(F.relu(p6)) + return [p6, p7] + + +@BACKBONE_REGISTRY.register() +def build_resnet_fpn_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + bottom_up = build_resnet_backbone(cfg, input_shape) + in_features = cfg.MODEL.FPN.IN_FEATURES + out_channels = cfg.MODEL.FPN.OUT_CHANNELS + backbone = FPN( + bottom_up=bottom_up, + in_features=in_features, + out_channels=out_channels, + norm=cfg.MODEL.FPN.NORM, + top_block=LastLevelMaxPool(), + fuse_type=cfg.MODEL.FPN.FUSE_TYPE, + ) + return backbone + + +@BACKBONE_REGISTRY.register() +def build_retinanet_resnet_fpn_backbone(cfg, input_shape: ShapeSpec): + """ + Args: + cfg: a detectron2 CfgNode + + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + bottom_up = build_resnet_backbone(cfg, input_shape) + in_features = cfg.MODEL.FPN.IN_FEATURES + out_channels = cfg.MODEL.FPN.OUT_CHANNELS + in_channels_p6p7 = bottom_up.output_shape()["res5"].channels + backbone = FPN( + bottom_up=bottom_up, + in_features=in_features, + out_channels=out_channels, + norm=cfg.MODEL.FPN.NORM, + top_block=LastLevelP6P7(in_channels_p6p7, out_channels), + fuse_type=cfg.MODEL.FPN.FUSE_TYPE, + ) + return backbone diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/regnet.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/regnet.py new file mode 100644 index 0000000..3533d63 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/regnet.py @@ -0,0 +1,452 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Implementation of RegNet models from :paper:`dds` and :paper:`scaling`. + +This code is adapted from https://github.com/facebookresearch/pycls with minimal modifications. +Some code duplication exists between RegNet and ResNets (e.g., ResStem) in order to simplify +model loading. +""" + +import numpy as np +from torch import nn + +from detectron2.layers import CNNBlockBase, ShapeSpec, get_norm + +from .backbone import Backbone + +__all__ = [ + "AnyNet", + "RegNet", + "ResStem", + "SimpleStem", + "VanillaBlock", + "ResBasicBlock", + "ResBottleneckBlock", +] + + +def conv2d(w_in, w_out, k, *, stride=1, groups=1, bias=False): + """Helper for building a conv2d layer.""" + assert k % 2 == 1, "Only odd size kernels supported to avoid padding issues." + s, p, g, b = stride, (k - 1) // 2, groups, bias + return nn.Conv2d(w_in, w_out, k, stride=s, padding=p, groups=g, bias=b) + + +def gap2d(): + """Helper for building a global average pooling layer.""" + return nn.AdaptiveAvgPool2d((1, 1)) + + +def pool2d(k, *, stride=1): + """Helper for building a pool2d layer.""" + assert k % 2 == 1, "Only odd size kernels supported to avoid padding issues." + return nn.MaxPool2d(k, stride=stride, padding=(k - 1) // 2) + + +def init_weights(m): + """Performs ResNet-style weight initialization.""" + if isinstance(m, nn.Conv2d): + # Note that there is no bias due to BN + fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(mean=0.0, std=np.sqrt(2.0 / fan_out)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1.0) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + m.weight.data.normal_(mean=0.0, std=0.01) + m.bias.data.zero_() + + +class ResStem(CNNBlockBase): + """ResNet stem for ImageNet: 7x7, BN, AF, MaxPool.""" + + def __init__(self, w_in, w_out, norm, activation_class): + super().__init__(w_in, w_out, 4) + self.conv = conv2d(w_in, w_out, 7, stride=2) + self.bn = get_norm(norm, w_out) + self.af = activation_class() + self.pool = pool2d(3, stride=2) + + def forward(self, x): + for layer in self.children(): + x = layer(x) + return x + + +class SimpleStem(CNNBlockBase): + """Simple stem for ImageNet: 3x3, BN, AF.""" + + def __init__(self, w_in, w_out, norm, activation_class): + super().__init__(w_in, w_out, 2) + self.conv = conv2d(w_in, w_out, 3, stride=2) + self.bn = get_norm(norm, w_out) + self.af = activation_class() + + def forward(self, x): + for layer in self.children(): + x = layer(x) + return x + + +class SE(nn.Module): + """Squeeze-and-Excitation (SE) block: AvgPool, FC, Act, FC, Sigmoid.""" + + def __init__(self, w_in, w_se, activation_class): + super().__init__() + self.avg_pool = gap2d() + self.f_ex = nn.Sequential( + conv2d(w_in, w_se, 1, bias=True), + activation_class(), + conv2d(w_se, w_in, 1, bias=True), + nn.Sigmoid(), + ) + + def forward(self, x): + return x * self.f_ex(self.avg_pool(x)) + + +class VanillaBlock(CNNBlockBase): + """Vanilla block: [3x3 conv, BN, Relu] x2.""" + + def __init__(self, w_in, w_out, stride, norm, activation_class, _params): + super().__init__(w_in, w_out, stride) + self.a = conv2d(w_in, w_out, 3, stride=stride) + self.a_bn = get_norm(norm, w_out) + self.a_af = activation_class() + self.b = conv2d(w_out, w_out, 3) + self.b_bn = get_norm(norm, w_out) + self.b_af = activation_class() + + def forward(self, x): + for layer in self.children(): + x = layer(x) + return x + + +class BasicTransform(nn.Module): + """Basic transformation: [3x3 conv, BN, Relu] x2.""" + + def __init__(self, w_in, w_out, stride, norm, activation_class, _params): + super().__init__() + self.a = conv2d(w_in, w_out, 3, stride=stride) + self.a_bn = get_norm(norm, w_out) + self.a_af = activation_class() + self.b = conv2d(w_out, w_out, 3) + self.b_bn = get_norm(norm, w_out) + self.b_bn.final_bn = True + + def forward(self, x): + for layer in self.children(): + x = layer(x) + return x + + +class ResBasicBlock(CNNBlockBase): + """Residual basic block: x + f(x), f = basic transform.""" + + def __init__(self, w_in, w_out, stride, norm, activation_class, params): + super().__init__(w_in, w_out, stride) + self.proj, self.bn = None, None + if (w_in != w_out) or (stride != 1): + self.proj = conv2d(w_in, w_out, 1, stride=stride) + self.bn = get_norm(norm, w_out) + self.f = BasicTransform(w_in, w_out, stride, norm, activation_class, params) + self.af = activation_class() + + def forward(self, x): + x_p = self.bn(self.proj(x)) if self.proj else x + return self.af(x_p + self.f(x)) + + +class BottleneckTransform(nn.Module): + """Bottleneck transformation: 1x1, 3x3 [+SE], 1x1.""" + + def __init__(self, w_in, w_out, stride, norm, activation_class, params): + super().__init__() + w_b = int(round(w_out * params["bot_mul"])) + w_se = int(round(w_in * params["se_r"])) + groups = w_b // params["group_w"] + self.a = conv2d(w_in, w_b, 1) + self.a_bn = get_norm(norm, w_b) + self.a_af = activation_class() + self.b = conv2d(w_b, w_b, 3, stride=stride, groups=groups) + self.b_bn = get_norm(norm, w_b) + self.b_af = activation_class() + self.se = SE(w_b, w_se, activation_class) if w_se else None + self.c = conv2d(w_b, w_out, 1) + self.c_bn = get_norm(norm, w_out) + self.c_bn.final_bn = True + + def forward(self, x): + for layer in self.children(): + x = layer(x) + return x + + +class ResBottleneckBlock(CNNBlockBase): + """Residual bottleneck block: x + f(x), f = bottleneck transform.""" + + def __init__(self, w_in, w_out, stride, norm, activation_class, params): + super().__init__(w_in, w_out, stride) + self.proj, self.bn = None, None + if (w_in != w_out) or (stride != 1): + self.proj = conv2d(w_in, w_out, 1, stride=stride) + self.bn = get_norm(norm, w_out) + self.f = BottleneckTransform(w_in, w_out, stride, norm, activation_class, params) + self.af = activation_class() + + def forward(self, x): + x_p = self.bn(self.proj(x)) if self.proj else x + return self.af(x_p + self.f(x)) + + +class AnyStage(nn.Module): + """AnyNet stage (sequence of blocks w/ the same output shape).""" + + def __init__(self, w_in, w_out, stride, d, block_class, norm, activation_class, params): + super().__init__() + for i in range(d): + block = block_class(w_in, w_out, stride, norm, activation_class, params) + self.add_module("b{}".format(i + 1), block) + stride, w_in = 1, w_out + + def forward(self, x): + for block in self.children(): + x = block(x) + return x + + +class AnyNet(Backbone): + """AnyNet model. See :paper:`dds`.""" + + def __init__( + self, + *, + stem_class, + stem_width, + block_class, + depths, + widths, + group_widths, + strides, + bottleneck_ratios, + se_ratio, + activation_class, + freeze_at=0, + norm="BN", + out_features=None, + ): + """ + Args: + stem_class (callable): A callable taking 4 arguments (channels in, channels out, + normalization, callable returning an activation function) that returns another + callable implementing the stem module. + stem_width (int): The number of output channels that the stem produces. + block_class (callable): A callable taking 6 arguments (channels in, channels out, + stride, normalization, callable returning an activation function, a dict of + block-specific parameters) that returns another callable implementing the repeated + block module. + depths (list[int]): Number of blocks in each stage. + widths (list[int]): For each stage, the number of output channels of each block. + group_widths (list[int]): For each stage, the number of channels per group in group + convolution, if the block uses group convolution. + strides (list[int]): The stride that each network stage applies to its input. + bottleneck_ratios (list[float]): For each stage, the ratio of the number of bottleneck + channels to the number of block input channels (or, equivalently, output channels), + if the block uses a bottleneck. + se_ratio (float): The ratio of the number of channels used inside the squeeze-excitation + (SE) module to it number of input channels, if SE the block uses SE. + activation_class (callable): A callable taking no arguments that returns another + callable implementing an activation function. + freeze_at (int): The number of stages at the beginning to freeze. + see :meth:`freeze` for detailed explanation. + norm (str or callable): normalization for all conv layers. + See :func:`layers.get_norm` for supported format. + out_features (list[str]): name of the layers whose outputs should + be returned in forward. RegNet's use "stem" and "s1", "s2", etc for the stages after + the stem. If None, will return the output of the last layer. + """ + super().__init__() + self.stem = stem_class(3, stem_width, norm, activation_class) + + current_stride = self.stem.stride + self._out_feature_strides = {"stem": current_stride} + self._out_feature_channels = {"stem": self.stem.out_channels} + self.stages_and_names = [] + prev_w = stem_width + + for i, (d, w, s, b, g) in enumerate( + zip(depths, widths, strides, bottleneck_ratios, group_widths) + ): + params = {"bot_mul": b, "group_w": g, "se_r": se_ratio} + stage = AnyStage(prev_w, w, s, d, block_class, norm, activation_class, params) + name = "s{}".format(i + 1) + self.add_module(name, stage) + self.stages_and_names.append((stage, name)) + self._out_feature_strides[name] = current_stride = int( + current_stride * np.prod([k.stride for k in stage.children()]) + ) + self._out_feature_channels[name] = list(stage.children())[-1].out_channels + prev_w = w + + self.apply(init_weights) + + if out_features is None: + out_features = [name] + self._out_features = out_features + assert len(self._out_features) + children = [x[0] for x in self.named_children()] + for out_feature in self._out_features: + assert out_feature in children, "Available children: {} does not include {}".format( + ", ".join(children), out_feature + ) + self.freeze(freeze_at) + + def forward(self, x): + """ + Args: + x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. + + Returns: + dict[str->Tensor]: names and the corresponding features + """ + assert x.dim() == 4, f"Model takes an input of shape (N, C, H, W). Got {x.shape} instead!" + outputs = {} + x = self.stem(x) + if "stem" in self._out_features: + outputs["stem"] = x + for stage, name in self.stages_and_names: + x = stage(x) + if name in self._out_features: + outputs[name] = x + return outputs + + def output_shape(self): + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) + for name in self._out_features + } + + def freeze(self, freeze_at=0): + """ + Freeze the first several stages of the model. Commonly used in fine-tuning. + + Layers that produce the same feature map spatial size are defined as one + "stage" by :paper:`FPN`. + + Args: + freeze_at (int): number of stages to freeze. + `1` means freezing the stem. `2` means freezing the stem and + one residual stage, etc. + + Returns: + nn.Module: this model itself + """ + if freeze_at >= 1: + self.stem.freeze() + for idx, (stage, _) in enumerate(self.stages_and_names, start=2): + if freeze_at >= idx: + for block in stage.children(): + block.freeze() + return self + + +def adjust_block_compatibility(ws, bs, gs): + """Adjusts the compatibility of widths, bottlenecks, and groups.""" + assert len(ws) == len(bs) == len(gs) + assert all(w > 0 and b > 0 and g > 0 for w, b, g in zip(ws, bs, gs)) + vs = [int(max(1, w * b)) for w, b in zip(ws, bs)] + gs = [int(min(g, v)) for g, v in zip(gs, vs)] + ms = [np.lcm(g, b) if b > 1 else g for g, b in zip(gs, bs)] + vs = [max(m, int(round(v / m) * m)) for v, m in zip(vs, ms)] + ws = [int(v / b) for v, b in zip(vs, bs)] + assert all(w * b % g == 0 for w, b, g in zip(ws, bs, gs)) + return ws, bs, gs + + +def generate_regnet_parameters(w_a, w_0, w_m, d, q=8): + """Generates per stage widths and depths from RegNet parameters.""" + assert w_a >= 0 and w_0 > 0 and w_m > 1 and w_0 % q == 0 + # Generate continuous per-block ws + ws_cont = np.arange(d) * w_a + w_0 + # Generate quantized per-block ws + ks = np.round(np.log(ws_cont / w_0) / np.log(w_m)) + ws_all = w_0 * np.power(w_m, ks) + ws_all = np.round(np.divide(ws_all, q)).astype(int) * q + # Generate per stage ws and ds (assumes ws_all are sorted) + ws, ds = np.unique(ws_all, return_counts=True) + # Compute number of actual stages and total possible stages + num_stages, total_stages = len(ws), ks.max() + 1 + # Convert numpy arrays to lists and return + ws, ds, ws_all, ws_cont = (x.tolist() for x in (ws, ds, ws_all, ws_cont)) + return ws, ds, num_stages, total_stages, ws_all, ws_cont + + +class RegNet(AnyNet): + """RegNet model. See :paper:`dds`.""" + + def __init__( + self, + *, + stem_class, + stem_width, + block_class, + depth, + w_a, + w_0, + w_m, + group_width, + stride=2, + bottleneck_ratio=1.0, + se_ratio=0.0, + activation_class=None, + freeze_at=0, + norm="BN", + out_features=None, + ): + """ + Build a RegNet from the parameterization described in :paper:`dds` Section 3.3. + + Args: + See :class:`AnyNet` for arguments that are not listed here. + depth (int): Total number of blocks in the RegNet. + w_a (float): Factor by which block width would increase prior to quantizing block widths + by stage. See :paper:`dds` Section 3.3. + w_0 (int): Initial block width. See :paper:`dds` Section 3.3. + w_m (float): Parameter controlling block width quantization. + See :paper:`dds` Section 3.3. + group_width (int): Number of channels per group in group convolution, if the block uses + group convolution. + bottleneck_ratio (float): The ratio of the number of bottleneck channels to the number + of block input channels (or, equivalently, output channels), if the block uses a + bottleneck. + stride (int): The stride that each network stage applies to its input. + """ + ws, ds = generate_regnet_parameters(w_a, w_0, w_m, depth)[0:2] + ss = [stride for _ in ws] + bs = [bottleneck_ratio for _ in ws] + gs = [group_width for _ in ws] + ws, bs, gs = adjust_block_compatibility(ws, bs, gs) + + def default_activation_class(): + return nn.ReLU(inplace=True) + + super().__init__( + stem_class=stem_class, + stem_width=stem_width, + block_class=block_class, + depths=ds, + widths=ws, + strides=ss, + group_widths=gs, + bottleneck_ratios=bs, + se_ratio=se_ratio, + activation_class=default_activation_class + if activation_class is None + else activation_class, + freeze_at=freeze_at, + norm=norm, + out_features=out_features, + ) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/resnet.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/resnet.py new file mode 100644 index 0000000..5b8e842 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/backbone/resnet.py @@ -0,0 +1,694 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +import fvcore.nn.weight_init as weight_init +import torch +import torch.nn.functional as F +from torch import nn + +from detectron2.layers import ( + CNNBlockBase, + Conv2d, + DeformConv, + ModulatedDeformConv, + ShapeSpec, + get_norm, +) + +from .backbone import Backbone +from .build import BACKBONE_REGISTRY + +__all__ = [ + "ResNetBlockBase", + "BasicBlock", + "BottleneckBlock", + "DeformBottleneckBlock", + "BasicStem", + "ResNet", + "make_stage", + "build_resnet_backbone", +] + + +class BasicBlock(CNNBlockBase): + """ + The basic residual block for ResNet-18 and ResNet-34 defined in :paper:`ResNet`, + with two 3x3 conv layers and a projection shortcut if needed. + """ + + def __init__(self, in_channels, out_channels, *, stride=1, norm="BN"): + """ + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + stride (int): Stride for the first conv. + norm (str or callable): normalization for all conv layers. + See :func:`layers.get_norm` for supported format. + """ + super().__init__(in_channels, out_channels, stride) + + if in_channels != out_channels: + self.shortcut = Conv2d( + in_channels, + out_channels, + kernel_size=1, + stride=stride, + bias=False, + norm=get_norm(norm, out_channels), + ) + else: + self.shortcut = None + + self.conv1 = Conv2d( + in_channels, + out_channels, + kernel_size=3, + stride=stride, + padding=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + + self.conv2 = Conv2d( + out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + + for layer in [self.conv1, self.conv2, self.shortcut]: + if layer is not None: # shortcut can be None + weight_init.c2_msra_fill(layer) + + def forward(self, x): + out = self.conv1(x) + out = F.relu_(out) + out = self.conv2(out) + + if self.shortcut is not None: + shortcut = self.shortcut(x) + else: + shortcut = x + + out += shortcut + out = F.relu_(out) + return out + + +class BottleneckBlock(CNNBlockBase): + """ + The standard bottleneck residual block used by ResNet-50, 101 and 152 + defined in :paper:`ResNet`. It contains 3 conv layers with kernels + 1x1, 3x3, 1x1, and a projection shortcut if needed. + """ + + def __init__( + self, + in_channels, + out_channels, + *, + bottleneck_channels, + stride=1, + num_groups=1, + norm="BN", + stride_in_1x1=False, + dilation=1, + ): + """ + Args: + bottleneck_channels (int): number of output channels for the 3x3 + "bottleneck" conv layers. + num_groups (int): number of groups for the 3x3 conv layer. + norm (str or callable): normalization for all conv layers. + See :func:`layers.get_norm` for supported format. + stride_in_1x1 (bool): when stride>1, whether to put stride in the + first 1x1 convolution or the bottleneck 3x3 convolution. + dilation (int): the dilation rate of the 3x3 conv layer. + """ + super().__init__(in_channels, out_channels, stride) + + if in_channels != out_channels: + self.shortcut = Conv2d( + in_channels, + out_channels, + kernel_size=1, + stride=stride, + bias=False, + norm=get_norm(norm, out_channels), + ) + else: + self.shortcut = None + + # The original MSRA ResNet models have stride in the first 1x1 conv + # The subsequent fb.torch.resnet and Caffe2 ResNe[X]t implementations have + # stride in the 3x3 conv + stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride) + + self.conv1 = Conv2d( + in_channels, + bottleneck_channels, + kernel_size=1, + stride=stride_1x1, + bias=False, + norm=get_norm(norm, bottleneck_channels), + ) + + self.conv2 = Conv2d( + bottleneck_channels, + bottleneck_channels, + kernel_size=3, + stride=stride_3x3, + padding=1 * dilation, + bias=False, + groups=num_groups, + dilation=dilation, + norm=get_norm(norm, bottleneck_channels), + ) + + self.conv3 = Conv2d( + bottleneck_channels, + out_channels, + kernel_size=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + + for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]: + if layer is not None: # shortcut can be None + weight_init.c2_msra_fill(layer) + + # Zero-initialize the last normalization in each residual branch, + # so that at the beginning, the residual branch starts with zeros, + # and each residual block behaves like an identity. + # See Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour": + # "For BN layers, the learnable scaling coefficient γ is initialized + # to be 1, except for each residual block's last BN + # where γ is initialized to be 0." + + # nn.init.constant_(self.conv3.norm.weight, 0) + # TODO this somehow hurts performance when training GN models from scratch. + # Add it as an option when we need to use this code to train a backbone. + + def forward(self, x): + out = self.conv1(x) + out = F.relu_(out) + + out = self.conv2(out) + out = F.relu_(out) + + out = self.conv3(out) + + if self.shortcut is not None: + shortcut = self.shortcut(x) + else: + shortcut = x + + out += shortcut + out = F.relu_(out) + return out + + +class DeformBottleneckBlock(CNNBlockBase): + """ + Similar to :class:`BottleneckBlock`, but with :paper:`deformable conv ` + in the 3x3 convolution. + """ + + def __init__( + self, + in_channels, + out_channels, + *, + bottleneck_channels, + stride=1, + num_groups=1, + norm="BN", + stride_in_1x1=False, + dilation=1, + deform_modulated=False, + deform_num_groups=1, + ): + super().__init__(in_channels, out_channels, stride) + self.deform_modulated = deform_modulated + + if in_channels != out_channels: + self.shortcut = Conv2d( + in_channels, + out_channels, + kernel_size=1, + stride=stride, + bias=False, + norm=get_norm(norm, out_channels), + ) + else: + self.shortcut = None + + stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride) + + self.conv1 = Conv2d( + in_channels, + bottleneck_channels, + kernel_size=1, + stride=stride_1x1, + bias=False, + norm=get_norm(norm, bottleneck_channels), + ) + + if deform_modulated: + deform_conv_op = ModulatedDeformConv + # offset channels are 2 or 3 (if with modulated) * kernel_size * kernel_size + offset_channels = 27 + else: + deform_conv_op = DeformConv + offset_channels = 18 + + self.conv2_offset = Conv2d( + bottleneck_channels, + offset_channels * deform_num_groups, + kernel_size=3, + stride=stride_3x3, + padding=1 * dilation, + dilation=dilation, + ) + self.conv2 = deform_conv_op( + bottleneck_channels, + bottleneck_channels, + kernel_size=3, + stride=stride_3x3, + padding=1 * dilation, + bias=False, + groups=num_groups, + dilation=dilation, + deformable_groups=deform_num_groups, + norm=get_norm(norm, bottleneck_channels), + ) + + self.conv3 = Conv2d( + bottleneck_channels, + out_channels, + kernel_size=1, + bias=False, + norm=get_norm(norm, out_channels), + ) + + for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]: + if layer is not None: # shortcut can be None + weight_init.c2_msra_fill(layer) + + nn.init.constant_(self.conv2_offset.weight, 0) + nn.init.constant_(self.conv2_offset.bias, 0) + + def forward(self, x): + out = self.conv1(x) + out = F.relu_(out) + + if self.deform_modulated: + offset_mask = self.conv2_offset(out) + offset_x, offset_y, mask = torch.chunk(offset_mask, 3, dim=1) + offset = torch.cat((offset_x, offset_y), dim=1) + mask = mask.sigmoid() + out = self.conv2(out, offset, mask) + else: + offset = self.conv2_offset(out) + out = self.conv2(out, offset) + out = F.relu_(out) + + out = self.conv3(out) + + if self.shortcut is not None: + shortcut = self.shortcut(x) + else: + shortcut = x + + out += shortcut + out = F.relu_(out) + return out + + +class BasicStem(CNNBlockBase): + """ + The standard ResNet stem (layers before the first residual block), + with a conv, relu and max_pool. + """ + + def __init__(self, in_channels=3, out_channels=64, norm="BN"): + """ + Args: + norm (str or callable): norm after the first conv layer. + See :func:`layers.get_norm` for supported format. + """ + super().__init__(in_channels, out_channels, 4) + self.in_channels = in_channels + self.conv1 = Conv2d( + in_channels, + out_channels, + kernel_size=7, + stride=2, + padding=3, + bias=False, + norm=get_norm(norm, out_channels), + ) + weight_init.c2_msra_fill(self.conv1) + + def forward(self, x): + x = self.conv1(x) + x = F.relu_(x) + x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) + return x + + +class ResNet(Backbone): + """ + Implement :paper:`ResNet`. + """ + + def __init__(self, stem, stages, num_classes=None, out_features=None, freeze_at=0): + """ + Args: + stem (nn.Module): a stem module + stages (list[list[CNNBlockBase]]): several (typically 4) stages, + each contains multiple :class:`CNNBlockBase`. + num_classes (None or int): if None, will not perform classification. + Otherwise, will create a linear layer. + out_features (list[str]): name of the layers whose outputs should + be returned in forward. Can be anything in "stem", "linear", or "res2" ... + If None, will return the output of the last layer. + freeze_at (int): The number of stages at the beginning to freeze. + see :meth:`freeze` for detailed explanation. + """ + super().__init__() + self.stem = stem + self.num_classes = num_classes + + current_stride = self.stem.stride + self._out_feature_strides = {"stem": current_stride} + self._out_feature_channels = {"stem": self.stem.out_channels} + + self.stage_names, self.stages = [], [] + + if out_features is not None: + # Avoid keeping unused layers in this module. They consume extra memory + # and may cause allreduce to fail + num_stages = max( + [{"res2": 1, "res3": 2, "res4": 3, "res5": 4}.get(f, 0) for f in out_features] + ) + stages = stages[:num_stages] + for i, blocks in enumerate(stages): + assert len(blocks) > 0, len(blocks) + for block in blocks: + assert isinstance(block, CNNBlockBase), block + + name = "res" + str(i + 2) + stage = nn.Sequential(*blocks) + + self.add_module(name, stage) + self.stage_names.append(name) + self.stages.append(stage) + + self._out_feature_strides[name] = current_stride = int( + current_stride * np.prod([k.stride for k in blocks]) + ) + self._out_feature_channels[name] = curr_channels = blocks[-1].out_channels + self.stage_names = tuple(self.stage_names) # Make it static for scripting + + if num_classes is not None: + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.linear = nn.Linear(curr_channels, num_classes) + + # Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour": + # "The 1000-way fully-connected layer is initialized by + # drawing weights from a zero-mean Gaussian with standard deviation of 0.01." + nn.init.normal_(self.linear.weight, std=0.01) + name = "linear" + + if out_features is None: + out_features = [name] + self._out_features = out_features + assert len(self._out_features) + children = [x[0] for x in self.named_children()] + for out_feature in self._out_features: + assert out_feature in children, "Available children: {}".format(", ".join(children)) + self.freeze(freeze_at) + + def forward(self, x): + """ + Args: + x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. + + Returns: + dict[str->Tensor]: names and the corresponding features + """ + assert x.dim() == 4, f"ResNet takes an input of shape (N, C, H, W). Got {x.shape} instead!" + outputs = {} + x = self.stem(x) + if "stem" in self._out_features: + outputs["stem"] = x + for name, stage in zip(self.stage_names, self.stages): + x = stage(x) + if name in self._out_features: + outputs[name] = x + if self.num_classes is not None: + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.linear(x) + if "linear" in self._out_features: + outputs["linear"] = x + return outputs + + def output_shape(self): + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) + for name in self._out_features + } + + def freeze(self, freeze_at=0): + """ + Freeze the first several stages of the ResNet. Commonly used in + fine-tuning. + + Layers that produce the same feature map spatial size are defined as one + "stage" by :paper:`FPN`. + + Args: + freeze_at (int): number of stages to freeze. + `1` means freezing the stem. `2` means freezing the stem and + one residual stage, etc. + + Returns: + nn.Module: this ResNet itself + """ + if freeze_at >= 1: + self.stem.freeze() + for idx, stage in enumerate(self.stages, start=2): + if freeze_at >= idx: + for block in stage.children(): + block.freeze() + return self + + @staticmethod + def make_stage(block_class, num_blocks, *, in_channels, out_channels, **kwargs): + """ + Create a list of blocks of the same type that forms one ResNet stage. + + Args: + block_class (type): a subclass of CNNBlockBase that's used to create all blocks in this + stage. A module of this type must not change spatial resolution of inputs unless its + stride != 1. + num_blocks (int): number of blocks in this stage + in_channels (int): input channels of the entire stage. + out_channels (int): output channels of **every block** in the stage. + kwargs: other arguments passed to the constructor of + `block_class`. If the argument name is "xx_per_block", the + argument is a list of values to be passed to each block in the + stage. Otherwise, the same argument is passed to every block + in the stage. + + Returns: + list[CNNBlockBase]: a list of block module. + + Examples: + :: + stage = ResNet.make_stage( + BottleneckBlock, 3, in_channels=16, out_channels=64, + bottleneck_channels=16, num_groups=1, + stride_per_block=[2, 1, 1], + dilations_per_block=[1, 1, 2] + ) + + Usually, layers that produce the same feature map spatial size are defined as one + "stage" (in :paper:`FPN`). Under such definition, ``stride_per_block[1:]`` should + all be 1. + """ + blocks = [] + for i in range(num_blocks): + curr_kwargs = {} + for k, v in kwargs.items(): + if k.endswith("_per_block"): + assert len(v) == num_blocks, ( + f"Argument '{k}' of make_stage should have the " + f"same length as num_blocks={num_blocks}." + ) + newk = k[: -len("_per_block")] + assert newk not in kwargs, f"Cannot call make_stage with both {k} and {newk}!" + curr_kwargs[newk] = v[i] + else: + curr_kwargs[k] = v + + blocks.append( + block_class(in_channels=in_channels, out_channels=out_channels, **curr_kwargs) + ) + in_channels = out_channels + return blocks + + @staticmethod + def make_default_stages(depth, block_class=None, **kwargs): + """ + Created list of ResNet stages from pre-defined depth (one of 18, 34, 50, 101, 152). + If it doesn't create the ResNet variant you need, please use :meth:`make_stage` + instead for fine-grained customization. + + Args: + depth (int): depth of ResNet + block_class (type): the CNN block class. Has to accept + `bottleneck_channels` argument for depth > 50. + By default it is BasicBlock or BottleneckBlock, based on the + depth. + kwargs: + other arguments to pass to `make_stage`. Should not contain + stride and channels, as they are predefined for each depth. + + Returns: + list[list[CNNBlockBase]]: modules in all stages; see arguments of + :class:`ResNet.__init__`. + """ + num_blocks_per_stage = { + 18: [2, 2, 2, 2], + 34: [3, 4, 6, 3], + 50: [3, 4, 6, 3], + 101: [3, 4, 23, 3], + 152: [3, 8, 36, 3], + }[depth] + if block_class is None: + block_class = BasicBlock if depth < 50 else BottleneckBlock + if depth < 50: + in_channels = [64, 64, 128, 256] + out_channels = [64, 128, 256, 512] + else: + in_channels = [64, 256, 512, 1024] + out_channels = [256, 512, 1024, 2048] + ret = [] + for (n, s, i, o) in zip(num_blocks_per_stage, [1, 2, 2, 2], in_channels, out_channels): + if depth >= 50: + kwargs["bottleneck_channels"] = o // 4 + ret.append( + ResNet.make_stage( + block_class=block_class, + num_blocks=n, + stride_per_block=[s] + [1] * (n - 1), + in_channels=i, + out_channels=o, + **kwargs, + ) + ) + return ret + + +ResNetBlockBase = CNNBlockBase +""" +Alias for backward compatibiltiy. +""" + + +def make_stage(*args, **kwargs): + """ + Deprecated alias for backward compatibiltiy. + """ + return ResNet.make_stage(*args, **kwargs) + + +@BACKBONE_REGISTRY.register() +def build_resnet_backbone(cfg, input_shape): + """ + Create a ResNet instance from config. + + Returns: + ResNet: a :class:`ResNet` instance. + """ + # need registration of new blocks/stems? + norm = cfg.MODEL.RESNETS.NORM + stem = BasicStem( + in_channels=input_shape.channels, + out_channels=cfg.MODEL.RESNETS.STEM_OUT_CHANNELS, + norm=norm, + ) + + # fmt: off + freeze_at = cfg.MODEL.BACKBONE.FREEZE_AT + out_features = cfg.MODEL.RESNETS.OUT_FEATURES + depth = cfg.MODEL.RESNETS.DEPTH + num_groups = cfg.MODEL.RESNETS.NUM_GROUPS + width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP + bottleneck_channels = num_groups * width_per_group + in_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS + out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS + stride_in_1x1 = cfg.MODEL.RESNETS.STRIDE_IN_1X1 + res5_dilation = cfg.MODEL.RESNETS.RES5_DILATION + deform_on_per_stage = cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE + deform_modulated = cfg.MODEL.RESNETS.DEFORM_MODULATED + deform_num_groups = cfg.MODEL.RESNETS.DEFORM_NUM_GROUPS + # fmt: on + assert res5_dilation in {1, 2}, "res5_dilation cannot be {}.".format(res5_dilation) + + num_blocks_per_stage = { + 18: [2, 2, 2, 2], + 34: [3, 4, 6, 3], + 50: [3, 4, 6, 3], + 101: [3, 4, 23, 3], + 152: [3, 8, 36, 3], + }[depth] + + if depth in [18, 34]: + assert out_channels == 64, "Must set MODEL.RESNETS.RES2_OUT_CHANNELS = 64 for R18/R34" + assert not any( + deform_on_per_stage + ), "MODEL.RESNETS.DEFORM_ON_PER_STAGE unsupported for R18/R34" + assert res5_dilation == 1, "Must set MODEL.RESNETS.RES5_DILATION = 1 for R18/R34" + assert num_groups == 1, "Must set MODEL.RESNETS.NUM_GROUPS = 1 for R18/R34" + + stages = [] + + for idx, stage_idx in enumerate(range(2, 6)): + # res5_dilation is used this way as a convention in R-FCN & Deformable Conv paper + dilation = res5_dilation if stage_idx == 5 else 1 + first_stride = 1 if idx == 0 or (stage_idx == 5 and dilation == 2) else 2 + stage_kargs = { + "num_blocks": num_blocks_per_stage[idx], + "stride_per_block": [first_stride] + [1] * (num_blocks_per_stage[idx] - 1), + "in_channels": in_channels, + "out_channels": out_channels, + "norm": norm, + } + # Use BasicBlock for R18 and R34. + if depth in [18, 34]: + stage_kargs["block_class"] = BasicBlock + else: + stage_kargs["bottleneck_channels"] = bottleneck_channels + stage_kargs["stride_in_1x1"] = stride_in_1x1 + stage_kargs["dilation"] = dilation + stage_kargs["num_groups"] = num_groups + if deform_on_per_stage[idx]: + stage_kargs["block_class"] = DeformBottleneckBlock + stage_kargs["deform_modulated"] = deform_modulated + stage_kargs["deform_num_groups"] = deform_num_groups + else: + stage_kargs["block_class"] = BottleneckBlock + blocks = ResNet.make_stage(**stage_kargs) + in_channels = out_channels + out_channels *= 2 + bottleneck_channels *= 2 + stages.append(blocks) + return ResNet(stem, stages, out_features=out_features, freeze_at=freeze_at) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/box_regression.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/box_regression.py new file mode 100644 index 0000000..b24c123 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/box_regression.py @@ -0,0 +1,369 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import math +from typing import List, Tuple, Union +import torch +from fvcore.nn import giou_loss, smooth_l1_loss +from torch.nn import functional as F + +from detectron2.layers import cat, ciou_loss, diou_loss +from detectron2.structures import Boxes + +# Value for clamping large dw and dh predictions. The heuristic is that we clamp +# such that dw and dh are no larger than what would transform a 16px box into a +# 1000px box (based on a small anchor, 16px, and a typical image size, 1000px). +_DEFAULT_SCALE_CLAMP = math.log(1000.0 / 16) + + +__all__ = ["Box2BoxTransform", "Box2BoxTransformRotated", "Box2BoxTransformLinear"] + + +@torch.jit.script +class Box2BoxTransform(object): + """ + The box-to-box transform defined in R-CNN. The transformation is parameterized + by 4 deltas: (dx, dy, dw, dh). The transformation scales the box's width and height + by exp(dw), exp(dh) and shifts a box's center by the offset (dx * width, dy * height). + """ + + def __init__( + self, weights: Tuple[float, float, float, float], scale_clamp: float = _DEFAULT_SCALE_CLAMP + ): + """ + Args: + weights (4-element tuple): Scaling factors that are applied to the + (dx, dy, dw, dh) deltas. In Fast R-CNN, these were originally set + such that the deltas have unit variance; now they are treated as + hyperparameters of the system. + scale_clamp (float): When predicting deltas, the predicted box scaling + factors (dw and dh) are clamped such that they are <= scale_clamp. + """ + self.weights = weights + self.scale_clamp = scale_clamp + + def get_deltas(self, src_boxes, target_boxes): + """ + Get box regression transformation deltas (dx, dy, dw, dh) that can be used + to transform the `src_boxes` into the `target_boxes`. That is, the relation + ``target_boxes == self.apply_deltas(deltas, src_boxes)`` is true (unless + any delta is too large and is clamped). + + Args: + src_boxes (Tensor): source boxes, e.g., object proposals + target_boxes (Tensor): target of the transformation, e.g., ground-truth + boxes. + """ + assert isinstance(src_boxes, torch.Tensor), type(src_boxes) + assert isinstance(target_boxes, torch.Tensor), type(target_boxes) + + src_widths = src_boxes[:, 2] - src_boxes[:, 0] + src_heights = src_boxes[:, 3] - src_boxes[:, 1] + src_ctr_x = src_boxes[:, 0] + 0.5 * src_widths + src_ctr_y = src_boxes[:, 1] + 0.5 * src_heights + + target_widths = target_boxes[:, 2] - target_boxes[:, 0] + target_heights = target_boxes[:, 3] - target_boxes[:, 1] + target_ctr_x = target_boxes[:, 0] + 0.5 * target_widths + target_ctr_y = target_boxes[:, 1] + 0.5 * target_heights + + wx, wy, ww, wh = self.weights + dx = wx * (target_ctr_x - src_ctr_x) / src_widths + dy = wy * (target_ctr_y - src_ctr_y) / src_heights + dw = ww * torch.log(target_widths / src_widths) + dh = wh * torch.log(target_heights / src_heights) + + deltas = torch.stack((dx, dy, dw, dh), dim=1) + assert (src_widths > 0).all().item(), "Input boxes to Box2BoxTransform are not valid!" + return deltas + + def apply_deltas(self, deltas, boxes): + """ + Apply transformation `deltas` (dx, dy, dw, dh) to `boxes`. + + Args: + deltas (Tensor): transformation deltas of shape (N, k*4), where k >= 1. + deltas[i] represents k potentially different class-specific + box transformations for the single box boxes[i]. + boxes (Tensor): boxes to transform, of shape (N, 4) + """ + deltas = deltas.float() # ensure fp32 for decoding precision + boxes = boxes.to(deltas.dtype) + + widths = boxes[:, 2] - boxes[:, 0] + heights = boxes[:, 3] - boxes[:, 1] + ctr_x = boxes[:, 0] + 0.5 * widths + ctr_y = boxes[:, 1] + 0.5 * heights + + wx, wy, ww, wh = self.weights + dx = deltas[:, 0::4] / wx + dy = deltas[:, 1::4] / wy + dw = deltas[:, 2::4] / ww + dh = deltas[:, 3::4] / wh + + # Prevent sending too large values into torch.exp() + dw = torch.clamp(dw, max=self.scale_clamp) + dh = torch.clamp(dh, max=self.scale_clamp) + + pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] + pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] + pred_w = torch.exp(dw) * widths[:, None] + pred_h = torch.exp(dh) * heights[:, None] + + x1 = pred_ctr_x - 0.5 * pred_w + y1 = pred_ctr_y - 0.5 * pred_h + x2 = pred_ctr_x + 0.5 * pred_w + y2 = pred_ctr_y + 0.5 * pred_h + pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1) + return pred_boxes.reshape(deltas.shape) + + +@torch.jit.script +class Box2BoxTransformRotated(object): + """ + The box-to-box transform defined in Rotated R-CNN. The transformation is parameterized + by 5 deltas: (dx, dy, dw, dh, da). The transformation scales the box's width and height + by exp(dw), exp(dh), shifts a box's center by the offset (dx * width, dy * height), + and rotate a box's angle by da (radians). + Note: angles of deltas are in radians while angles of boxes are in degrees. + """ + + def __init__( + self, + weights: Tuple[float, float, float, float, float], + scale_clamp: float = _DEFAULT_SCALE_CLAMP, + ): + """ + Args: + weights (5-element tuple): Scaling factors that are applied to the + (dx, dy, dw, dh, da) deltas. These are treated as + hyperparameters of the system. + scale_clamp (float): When predicting deltas, the predicted box scaling + factors (dw and dh) are clamped such that they are <= scale_clamp. + """ + self.weights = weights + self.scale_clamp = scale_clamp + + def get_deltas(self, src_boxes, target_boxes): + """ + Get box regression transformation deltas (dx, dy, dw, dh, da) that can be used + to transform the `src_boxes` into the `target_boxes`. That is, the relation + ``target_boxes == self.apply_deltas(deltas, src_boxes)`` is true (unless + any delta is too large and is clamped). + + Args: + src_boxes (Tensor): Nx5 source boxes, e.g., object proposals + target_boxes (Tensor): Nx5 target of the transformation, e.g., ground-truth + boxes. + """ + assert isinstance(src_boxes, torch.Tensor), type(src_boxes) + assert isinstance(target_boxes, torch.Tensor), type(target_boxes) + + src_ctr_x, src_ctr_y, src_widths, src_heights, src_angles = torch.unbind(src_boxes, dim=1) + + target_ctr_x, target_ctr_y, target_widths, target_heights, target_angles = torch.unbind( + target_boxes, dim=1 + ) + + wx, wy, ww, wh, wa = self.weights + dx = wx * (target_ctr_x - src_ctr_x) / src_widths + dy = wy * (target_ctr_y - src_ctr_y) / src_heights + dw = ww * torch.log(target_widths / src_widths) + dh = wh * torch.log(target_heights / src_heights) + # Angles of deltas are in radians while angles of boxes are in degrees. + # the conversion to radians serve as a way to normalize the values + da = target_angles - src_angles + da = (da + 180.0) % 360.0 - 180.0 # make it in [-180, 180) + da *= wa * math.pi / 180.0 + + deltas = torch.stack((dx, dy, dw, dh, da), dim=1) + assert ( + (src_widths > 0).all().item() + ), "Input boxes to Box2BoxTransformRotated are not valid!" + return deltas + + def apply_deltas(self, deltas, boxes): + """ + Apply transformation `deltas` (dx, dy, dw, dh, da) to `boxes`. + + Args: + deltas (Tensor): transformation deltas of shape (N, k*5). + deltas[i] represents box transformation for the single box boxes[i]. + boxes (Tensor): boxes to transform, of shape (N, 5) + """ + assert deltas.shape[1] % 5 == 0 and boxes.shape[1] == 5 + + boxes = boxes.to(deltas.dtype).unsqueeze(2) + + ctr_x = boxes[:, 0] + ctr_y = boxes[:, 1] + widths = boxes[:, 2] + heights = boxes[:, 3] + angles = boxes[:, 4] + + wx, wy, ww, wh, wa = self.weights + + dx = deltas[:, 0::5] / wx + dy = deltas[:, 1::5] / wy + dw = deltas[:, 2::5] / ww + dh = deltas[:, 3::5] / wh + da = deltas[:, 4::5] / wa + + # Prevent sending too large values into torch.exp() + dw = torch.clamp(dw, max=self.scale_clamp) + dh = torch.clamp(dh, max=self.scale_clamp) + + pred_boxes = torch.zeros_like(deltas) + pred_boxes[:, 0::5] = dx * widths + ctr_x # x_ctr + pred_boxes[:, 1::5] = dy * heights + ctr_y # y_ctr + pred_boxes[:, 2::5] = torch.exp(dw) * widths # width + pred_boxes[:, 3::5] = torch.exp(dh) * heights # height + + # Following original RRPN implementation, + # angles of deltas are in radians while angles of boxes are in degrees. + pred_angle = da * 180.0 / math.pi + angles + pred_angle = (pred_angle + 180.0) % 360.0 - 180.0 # make it in [-180, 180) + + pred_boxes[:, 4::5] = pred_angle + + return pred_boxes + + +class Box2BoxTransformLinear(object): + """ + The linear box-to-box transform defined in FCOS. The transformation is parameterized + by the distance from the center of (square) src box to 4 edges of the target box. + """ + + def __init__(self, normalize_by_size=True): + """ + Args: + normalize_by_size: normalize deltas by the size of src (anchor) boxes. + """ + self.normalize_by_size = normalize_by_size + + def get_deltas(self, src_boxes, target_boxes): + """ + Get box regression transformation deltas (dx1, dy1, dx2, dy2) that can be used + to transform the `src_boxes` into the `target_boxes`. That is, the relation + ``target_boxes == self.apply_deltas(deltas, src_boxes)`` is true. + The center of src must be inside target boxes. + + Args: + src_boxes (Tensor): square source boxes, e.g., anchors + target_boxes (Tensor): target of the transformation, e.g., ground-truth + boxes. + """ + assert isinstance(src_boxes, torch.Tensor), type(src_boxes) + assert isinstance(target_boxes, torch.Tensor), type(target_boxes) + + src_ctr_x = 0.5 * (src_boxes[:, 0] + src_boxes[:, 2]) + src_ctr_y = 0.5 * (src_boxes[:, 1] + src_boxes[:, 3]) + + target_l = src_ctr_x - target_boxes[:, 0] + target_t = src_ctr_y - target_boxes[:, 1] + target_r = target_boxes[:, 2] - src_ctr_x + target_b = target_boxes[:, 3] - src_ctr_y + + deltas = torch.stack((target_l, target_t, target_r, target_b), dim=1) + if self.normalize_by_size: + stride_w = src_boxes[:, 2] - src_boxes[:, 0] + stride_h = src_boxes[:, 3] - src_boxes[:, 1] + strides = torch.stack([stride_w, stride_h, stride_w, stride_h], axis=1) + deltas = deltas / strides + + return deltas + + def apply_deltas(self, deltas, boxes): + """ + Apply transformation `deltas` (dx1, dy1, dx2, dy2) to `boxes`. + + Args: + deltas (Tensor): transformation deltas of shape (N, k*4), where k >= 1. + deltas[i] represents k potentially different class-specific + box transformations for the single box boxes[i]. + boxes (Tensor): boxes to transform, of shape (N, 4) + """ + # Ensure the output is a valid box. See Sec 2.1 of https://arxiv.org/abs/2006.09214 + deltas = F.relu(deltas) + boxes = boxes.to(deltas.dtype) + + ctr_x = 0.5 * (boxes[:, 0] + boxes[:, 2]) + ctr_y = 0.5 * (boxes[:, 1] + boxes[:, 3]) + if self.normalize_by_size: + stride_w = boxes[:, 2] - boxes[:, 0] + stride_h = boxes[:, 3] - boxes[:, 1] + strides = torch.stack([stride_w, stride_h, stride_w, stride_h], axis=1) + deltas = deltas * strides + + l = deltas[:, 0::4] + t = deltas[:, 1::4] + r = deltas[:, 2::4] + b = deltas[:, 3::4] + + pred_boxes = torch.zeros_like(deltas) + pred_boxes[:, 0::4] = ctr_x[:, None] - l # x1 + pred_boxes[:, 1::4] = ctr_y[:, None] - t # y1 + pred_boxes[:, 2::4] = ctr_x[:, None] + r # x2 + pred_boxes[:, 3::4] = ctr_y[:, None] + b # y2 + return pred_boxes + + +def _dense_box_regression_loss( + anchors: List[Union[Boxes, torch.Tensor]], + box2box_transform: Box2BoxTransform, + pred_anchor_deltas: List[torch.Tensor], + gt_boxes: List[torch.Tensor], + fg_mask: torch.Tensor, + box_reg_loss_type="smooth_l1", + smooth_l1_beta=0.0, +): + """ + Compute loss for dense multi-level box regression. + Loss is accumulated over ``fg_mask``. + + Args: + anchors: #lvl anchor boxes, each is (HixWixA, 4) + pred_anchor_deltas: #lvl predictions, each is (N, HixWixA, 4) + gt_boxes: N ground truth boxes, each has shape (R, 4) (R = sum(Hi * Wi * A)) + fg_mask: the foreground boolean mask of shape (N, R) to compute loss on + box_reg_loss_type (str): Loss type to use. Supported losses: "smooth_l1", "giou", + "diou", "ciou". + smooth_l1_beta (float): beta parameter for the smooth L1 regression loss. Default to + use L1 loss. Only used when `box_reg_loss_type` is "smooth_l1" + """ + if isinstance(anchors[0], Boxes): + anchors = type(anchors[0]).cat(anchors).tensor # (R, 4) + else: + anchors = cat(anchors) + if box_reg_loss_type == "smooth_l1": + gt_anchor_deltas = [box2box_transform.get_deltas(anchors, k) for k in gt_boxes] + gt_anchor_deltas = torch.stack(gt_anchor_deltas) # (N, R, 4) + loss_box_reg = smooth_l1_loss( + cat(pred_anchor_deltas, dim=1)[fg_mask], + gt_anchor_deltas[fg_mask], + beta=smooth_l1_beta, + reduction="sum", + ) + elif box_reg_loss_type == "giou": + pred_boxes = [ + box2box_transform.apply_deltas(k, anchors) for k in cat(pred_anchor_deltas, dim=1) + ] + loss_box_reg = giou_loss( + torch.stack(pred_boxes)[fg_mask], torch.stack(gt_boxes)[fg_mask], reduction="sum" + ) + elif box_reg_loss_type == "diou": + pred_boxes = [ + box2box_transform.apply_deltas(k, anchors) for k in cat(pred_anchor_deltas, dim=1) + ] + loss_box_reg = diou_loss( + torch.stack(pred_boxes)[fg_mask], torch.stack(gt_boxes)[fg_mask], reduction="sum" + ) + elif box_reg_loss_type == "ciou": + pred_boxes = [ + box2box_transform.apply_deltas(k, anchors) for k in cat(pred_anchor_deltas, dim=1) + ] + loss_box_reg = ciou_loss( + torch.stack(pred_boxes)[fg_mask], torch.stack(gt_boxes)[fg_mask], reduction="sum" + ) + else: + raise ValueError(f"Invalid dense box regression loss type '{box_reg_loss_type}'") + return loss_box_reg diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/matcher.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/matcher.py new file mode 100644 index 0000000..c7597ca --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/matcher.py @@ -0,0 +1,127 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from typing import List +import torch + +from detectron2.layers import nonzero_tuple + + +# TODO: the name is too general +class Matcher(object): + """ + This class assigns to each predicted "element" (e.g., a box) a ground-truth + element. Each predicted element will have exactly zero or one matches; each + ground-truth element may be matched to zero or more predicted elements. + + The matching is determined by the MxN match_quality_matrix, that characterizes + how well each (ground-truth, prediction)-pair match each other. For example, + if the elements are boxes, this matrix may contain box intersection-over-union + overlap values. + + The matcher returns (a) a vector of length N containing the index of the + ground-truth element m in [0, M) that matches to prediction n in [0, N). + (b) a vector of length N containing the labels for each prediction. + """ + + def __init__( + self, thresholds: List[float], labels: List[int], allow_low_quality_matches: bool = False + ): + """ + Args: + thresholds (list): a list of thresholds used to stratify predictions + into levels. + labels (list): a list of values to label predictions belonging at + each level. A label can be one of {-1, 0, 1} signifying + {ignore, negative class, positive class}, respectively. + allow_low_quality_matches (bool): if True, produce additional matches + for predictions with maximum match quality lower than high_threshold. + See set_low_quality_matches_ for more details. + + For example, + thresholds = [0.3, 0.5] + labels = [0, -1, 1] + All predictions with iou < 0.3 will be marked with 0 and + thus will be considered as false positives while training. + All predictions with 0.3 <= iou < 0.5 will be marked with -1 and + thus will be ignored. + All predictions with 0.5 <= iou will be marked with 1 and + thus will be considered as true positives. + """ + # Add -inf and +inf to first and last position in thresholds + thresholds = thresholds[:] + assert thresholds[0] > 0 + thresholds.insert(0, -float("inf")) + thresholds.append(float("inf")) + # Currently torchscript does not support all + generator + assert all([low <= high for (low, high) in zip(thresholds[:-1], thresholds[1:])]) + assert all([l in [-1, 0, 1] for l in labels]) + assert len(labels) == len(thresholds) - 1 + self.thresholds = thresholds + self.labels = labels + self.allow_low_quality_matches = allow_low_quality_matches + + def __call__(self, match_quality_matrix): + """ + Args: + match_quality_matrix (Tensor[float]): an MxN tensor, containing the + pairwise quality between M ground-truth elements and N predicted + elements. All elements must be >= 0 (due to the us of `torch.nonzero` + for selecting indices in :meth:`set_low_quality_matches_`). + + Returns: + matches (Tensor[int64]): a vector of length N, where matches[i] is a matched + ground-truth index in [0, M) + match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates + whether a prediction is a true or false positive or ignored + """ + assert match_quality_matrix.dim() == 2 + if match_quality_matrix.numel() == 0: + default_matches = match_quality_matrix.new_full( + (match_quality_matrix.size(1),), 0, dtype=torch.int64 + ) + # When no gt boxes exist, we define IOU = 0 and therefore set labels + # to `self.labels[0]`, which usually defaults to background class 0 + # To choose to ignore instead, can make labels=[-1,0,-1,1] + set appropriate thresholds + default_match_labels = match_quality_matrix.new_full( + (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8 + ) + return default_matches, default_match_labels + + assert torch.all(match_quality_matrix >= 0) + + # match_quality_matrix is M (gt) x N (predicted) + # Max over gt elements (dim 0) to find best gt candidate for each prediction + matched_vals, matches = match_quality_matrix.max(dim=0) + + match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8) + + for (l, low, high) in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]): + low_high = (matched_vals >= low) & (matched_vals < high) + match_labels[low_high] = l + + if self.allow_low_quality_matches: + self.set_low_quality_matches_(match_labels, match_quality_matrix) + + return matches, match_labels + + def set_low_quality_matches_(self, match_labels, match_quality_matrix): + """ + Produce additional matches for predictions that have only low-quality matches. + Specifically, for each ground-truth G find the set of predictions that have + maximum overlap with it (including ties); for each prediction in that set, if + it is unmatched, then match it to the ground-truth G. + + This function implements the RPN assignment case (i) in Sec. 3.1.2 of + :paper:`Faster R-CNN`. + """ + # For each gt, find the prediction with which it has highest quality + highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1) + # Find the highest quality match available, even if it is low, including ties. + # Note that the matches qualities must be positive due to the use of + # `torch.nonzero`. + _, pred_inds_with_highest_quality = nonzero_tuple( + match_quality_matrix == highest_quality_foreach_gt[:, None] + ) + # If an anchor was labeled positive only due to a low-quality match + # with gt_A, but it has larger overlap with gt_B, it's matched index will still be gt_B. + # This follows the implementation in Detectron, and is found to have no significant impact. + match_labels[pred_inds_with_highest_quality] = 1 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/__init__.py new file mode 100644 index 0000000..6b06681 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/__init__.py @@ -0,0 +1,16 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +from .build import META_ARCH_REGISTRY, build_model # isort:skip + +from .panoptic_fpn import PanopticFPN + +# import all the meta_arch, so they will be registered +from .rcnn import GeneralizedRCNN, ProposalNetwork +from .dense_detector import DenseDetector +from .retinanet import RetinaNet +from .fcos import FCOS +from .semantic_seg import SEM_SEG_HEADS_REGISTRY, SemanticSegmentor, build_sem_seg_head + + +__all__ = list(globals().keys()) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/build.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/build.py new file mode 100644 index 0000000..3427215 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/build.py @@ -0,0 +1,25 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import torch + +from detectron2.utils.logger import _log_api_usage +from detectron2.utils.registry import Registry + +META_ARCH_REGISTRY = Registry("META_ARCH") # noqa F401 isort:skip +META_ARCH_REGISTRY.__doc__ = """ +Registry for meta-architectures, i.e. the whole model. + +The registered object will be called with `obj(cfg)` +and expected to return a `nn.Module` object. +""" + + +def build_model(cfg): + """ + Build the whole model architecture, defined by ``cfg.MODEL.META_ARCHITECTURE``. + Note that it does not load any weights from ``cfg``. + """ + meta_arch = cfg.MODEL.META_ARCHITECTURE + model = META_ARCH_REGISTRY.get(meta_arch)(cfg) + model.to(torch.device(cfg.MODEL.DEVICE)) + _log_api_usage("modeling.meta_arch." + meta_arch) + return model diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/dense_detector.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/dense_detector.py new file mode 100644 index 0000000..382eab9 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/dense_detector.py @@ -0,0 +1,282 @@ +import numpy as np +from typing import Dict, List, Optional, Tuple +import torch +from torch import Tensor, nn + +from detectron2.data.detection_utils import convert_image_to_rgb +from detectron2.modeling import Backbone +from detectron2.structures import Boxes, ImageList, Instances +from detectron2.utils.events import get_event_storage + +from ..postprocessing import detector_postprocess + + +def permute_to_N_HWA_K(tensor, K: int): + """ + Transpose/reshape a tensor from (N, (Ai x K), H, W) to (N, (HxWxAi), K) + """ + assert tensor.dim() == 4, tensor.shape + N, _, H, W = tensor.shape + tensor = tensor.view(N, -1, K, H, W) + tensor = tensor.permute(0, 3, 4, 1, 2) + tensor = tensor.reshape(N, -1, K) # Size=(N,HWA,K) + return tensor + + +class DenseDetector(nn.Module): + """ + Base class for dense detector. We define a dense detector as a fully-convolutional model that + makes per-pixel (i.e. dense) predictions. + """ + + def __init__( + self, + backbone: Backbone, + head: nn.Module, + head_in_features: Optional[List[str]] = None, + *, + pixel_mean, + pixel_std, + ): + """ + Args: + backbone: backbone module + head: head module + head_in_features: backbone features to use in head. Default to all backbone features. + pixel_mean (Tuple[float]): + Values to be used for image normalization (BGR order). + To train on images of different number of channels, set different mean & std. + Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675] + pixel_std (Tuple[float]): + When using pre-trained models in Detectron1 or any MSRA models, + std has been absorbed into its conv1 weights, so the std needs to be set 1. + Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std) + """ + super().__init__() + + self.backbone = backbone + self.head = head + if head_in_features is None: + shapes = self.backbone.output_shape() + self.head_in_features = sorted(shapes.keys(), key=lambda x: shapes[x].stride) + else: + self.head_in_features = head_in_features + + self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False) + + @property + def device(self): + return self.pixel_mean.device + + def forward(self, batched_inputs: List[Dict[str, Tensor]]): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper` . + Each item in the list contains the inputs for one image. + For now, each item in the list is a dict that contains: + + * image: Tensor, image in (C, H, W) format. + * instances: Instances + + Other information that's included in the original dicts, such as: + + * "height", "width" (int): the output resolution of the model, used in inference. + See :meth:`postprocess` for details. + + Returns: + In training, dict[str, Tensor]: mapping from a named loss to a tensor storing the + loss. Used during training only. In inference, the standard output format, described + in :doc:`/tutorials/models`. + """ + images = self.preprocess_image(batched_inputs) + features = self.backbone(images.tensor) + features = [features[f] for f in self.head_in_features] + predictions = self.head(features) + + if self.training: + assert not torch.jit.is_scripting(), "Not supported" + assert "instances" in batched_inputs[0], "Instance annotations are missing in training!" + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + return self.forward_training(images, features, predictions, gt_instances) + else: + results = self.forward_inference(images, features, predictions) + if torch.jit.is_scripting(): + return results + + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + results, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"instances": r}) + return processed_results + + def forward_training(self, images, features, predictions, gt_instances): + raise NotImplementedError() + + def preprocess_image(self, batched_inputs: List[Dict[str, Tensor]]): + """ + Normalize, pad and batch the input images. + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors(images, self.backbone.size_divisibility) + return images + + def _transpose_dense_predictions( + self, predictions: List[List[Tensor]], dims_per_anchor: List[int] + ) -> List[List[Tensor]]: + """ + Transpose the dense per-level predictions. + + Args: + predictions: a list of outputs, each is a list of per-level + predictions with shape (N, Ai x K, Hi, Wi), where N is the + number of images, Ai is the number of anchors per location on + level i, K is the dimension of predictions per anchor. + dims_per_anchor: the value of K for each predictions. e.g. 4 for + box prediction, #classes for classification prediction. + + Returns: + List[List[Tensor]]: each prediction is transposed to (N, Hi x Wi x Ai, K). + """ + assert len(predictions) == len(dims_per_anchor) + res: List[List[Tensor]] = [] + for pred, dim_per_anchor in zip(predictions, dims_per_anchor): + pred = [permute_to_N_HWA_K(x, dim_per_anchor) for x in pred] + res.append(pred) + return res + + def _ema_update(self, name: str, value: float, initial_value: float, momentum: float = 0.9): + """ + Apply EMA update to `self.name` using `value`. + + This is mainly used for loss normalizer. In Detectron1, loss is normalized by number + of foreground samples in the batch. When batch size is 1 per GPU, #foreground has a + large variance and using it lead to lower performance. Therefore we maintain an EMA of + #foreground to stabilize the normalizer. + + Args: + name: name of the normalizer + value: the new value to update + initial_value: the initial value to start with + momentum: momentum of EMA + + Returns: + float: the updated EMA value + """ + if hasattr(self, name): + old = getattr(self, name) + else: + old = initial_value + new = old * momentum + value * (1 - momentum) + setattr(self, name, new) + return new + + def _decode_per_level_predictions( + self, + anchors: Boxes, + pred_scores: Tensor, + pred_deltas: Tensor, + score_thresh: float, + topk_candidates: int, + image_size: Tuple[int, int], + ) -> Instances: + """ + Decode boxes and classification predictions of one featuer level, by + the following steps: + 1. filter the predictions based on score threshold and top K scores. + 2. transform the box regression outputs + 3. return the predicted scores, classes and boxes + + Args: + anchors: Boxes, anchor for this feature level + pred_scores: HxWxA,K + pred_deltas: HxWxA,4 + + Returns: + Instances: with field "scores", "pred_boxes", "pred_classes". + """ + # Apply two filtering to make NMS faster. + # 1. Keep boxes with confidence score higher than threshold + keep_idxs = pred_scores > score_thresh + pred_scores = pred_scores[keep_idxs] + topk_idxs = torch.nonzero(keep_idxs) # Kx2 + + # 2. Keep top k top scoring boxes only + num_topk = min(topk_candidates, topk_idxs.size(0)) + pred_scores, idxs = pred_scores.topk(num_topk) + topk_idxs = topk_idxs[idxs] + + anchor_idxs, classes_idxs = topk_idxs.unbind(dim=1) + + pred_boxes = self.box2box_transform.apply_deltas( + pred_deltas[anchor_idxs], anchors.tensor[anchor_idxs] + ) + return Instances( + image_size, pred_boxes=Boxes(pred_boxes), scores=pred_scores, pred_classes=classes_idxs + ) + + def _decode_multi_level_predictions( + self, + anchors: List[Boxes], + pred_scores: List[Tensor], + pred_deltas: List[Tensor], + score_thresh: float, + topk_candidates: int, + image_size: Tuple[int, int], + ) -> Instances: + """ + Run `_decode_per_level_predictions` for all feature levels and concat the results. + """ + predictions = [ + self._decode_per_level_predictions( + anchors_i, + box_cls_i, + box_reg_i, + self.test_score_thresh, + self.test_topk_candidates, + image_size, + ) + # Iterate over every feature level + for box_cls_i, box_reg_i, anchors_i in zip(pred_scores, pred_deltas, anchors) + ] + return predictions[0].cat(predictions) # 'Instances.cat' is not scriptale but this is + + def visualize_training(self, batched_inputs, results): + """ + A function used to visualize ground truth images and final network predictions. + It shows ground truth bounding boxes on the original image and up to 20 + predicted object bounding boxes on the original image. + + Args: + batched_inputs (list): a list that contains input to the model. + results (List[Instances]): a list of #images elements returned by forward_inference(). + """ + from detectron2.utils.visualizer import Visualizer + + assert len(batched_inputs) == len( + results + ), "Cannot visualize inputs and results of different sizes" + storage = get_event_storage() + max_boxes = 20 + + image_index = 0 # only visualize a single image + img = batched_inputs[image_index]["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format) + v_gt = Visualizer(img, None) + v_gt = v_gt.overlay_instances(boxes=batched_inputs[image_index]["instances"].gt_boxes) + anno_img = v_gt.get_image() + processed_results = detector_postprocess(results[image_index], img.shape[0], img.shape[1]) + predicted_boxes = processed_results.pred_boxes.tensor.detach().cpu().numpy() + + v_pred = Visualizer(img, None) + v_pred = v_pred.overlay_instances(boxes=predicted_boxes[0:max_boxes]) + prop_img = v_pred.get_image() + vis_img = np.vstack((anno_img, prop_img)) + vis_img = vis_img.transpose(2, 0, 1) + vis_name = f"Top: GT bounding boxes; Bottom: {max_boxes} Highest Scoring Results" + storage.put_image(vis_name, vis_img) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/fcos.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/fcos.py new file mode 100644 index 0000000..55cdb76 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/fcos.py @@ -0,0 +1,303 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import logging +from typing import List, Optional, Tuple +import torch +from fvcore.nn import sigmoid_focal_loss_jit +from torch import Tensor, nn +from torch.nn import functional as F + +from detectron2.layers import ShapeSpec, batched_nms +from detectron2.structures import Boxes, ImageList, Instances, pairwise_point_box_distance +from detectron2.utils.events import get_event_storage + +from ..anchor_generator import DefaultAnchorGenerator +from ..backbone import Backbone +from ..box_regression import Box2BoxTransformLinear, _dense_box_regression_loss +from .dense_detector import DenseDetector +from .retinanet import RetinaNetHead + +__all__ = ["FCOS"] + + +logger = logging.getLogger(__name__) + + +class FCOS(DenseDetector): + """ + Implement FCOS in :paper:`fcos`. + """ + + def __init__( + self, + *, + backbone: Backbone, + head: nn.Module, + head_in_features: Optional[List[str]] = None, + box2box_transform=None, + num_classes, + center_sampling_radius: float = 1.5, + focal_loss_alpha=0.25, + focal_loss_gamma=2.0, + test_score_thresh=0.2, + test_topk_candidates=1000, + test_nms_thresh=0.6, + max_detections_per_image=100, + pixel_mean, + pixel_std, + ): + """ + Args: + center_sampling_radius: radius of the "center" of a groundtruth box, + within which all anchor points are labeled positive. + Other arguments mean the same as in :class:`RetinaNet`. + """ + super().__init__( + backbone, head, head_in_features, pixel_mean=pixel_mean, pixel_std=pixel_std + ) + + self.num_classes = num_classes + + # FCOS uses one anchor point per location. + # We represent the anchor point by a box whose size equals the anchor stride. + feature_shapes = backbone.output_shape() + fpn_strides = [feature_shapes[k].stride for k in self.head_in_features] + self.anchor_generator = DefaultAnchorGenerator( + sizes=[[k] for k in fpn_strides], aspect_ratios=[1.0], strides=fpn_strides + ) + + # FCOS parameterizes box regression by a linear transform, + # where predictions are normalized by anchor stride (equal to anchor size). + if box2box_transform is None: + box2box_transform = Box2BoxTransformLinear(normalize_by_size=True) + self.box2box_transform = box2box_transform + + self.center_sampling_radius = float(center_sampling_radius) + + # Loss parameters: + self.focal_loss_alpha = focal_loss_alpha + self.focal_loss_gamma = focal_loss_gamma + + # Inference parameters: + self.test_score_thresh = test_score_thresh + self.test_topk_candidates = test_topk_candidates + self.test_nms_thresh = test_nms_thresh + self.max_detections_per_image = max_detections_per_image + + def forward_training(self, images, features, predictions, gt_instances): + # Transpose the Hi*Wi*A dimension to the middle: + pred_logits, pred_anchor_deltas, pred_centerness = self._transpose_dense_predictions( + predictions, [self.num_classes, 4, 1] + ) + anchors = self.anchor_generator(features) + gt_labels, gt_boxes = self.label_anchors(anchors, gt_instances) + return self.losses( + anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes, pred_centerness + ) + + @torch.no_grad() + def match_anchors(self, anchors: List[Boxes], gt_instances: List[Instances]): + """ + Match anchors with ground truth boxes. + + Args: + anchors: #level boxes, from the highest resolution to lower resolution + gt_instances: ground truth instances per image + + Returns: + List[Tensor]: + #image tensors, each is a vector of matched gt + indices (or -1 for unmatched anchors) for all anchors. + """ + num_anchors_per_level = [len(x) for x in anchors] + anchors = Boxes.cat(anchors) # Rx4 + anchor_centers = anchors.get_centers() # Rx2 + anchor_sizes = anchors.tensor[:, 2] - anchors.tensor[:, 0] # R + + lower_bound = anchor_sizes * 4 + lower_bound[: num_anchors_per_level[0]] = 0 + upper_bound = anchor_sizes * 8 + upper_bound[-num_anchors_per_level[-1] :] = float("inf") + + matched_indices = [] + for gt_per_image in gt_instances: + gt_centers = gt_per_image.gt_boxes.get_centers() # Nx2 + # FCOS with center sampling: anchor point must be close enough to gt center. + pairwise_match = (anchor_centers[:, None, :] - gt_centers[None, :, :]).abs_().max( + dim=2 + ).values < self.center_sampling_radius * anchor_sizes[:, None] + pairwise_dist = pairwise_point_box_distance(anchor_centers, gt_per_image.gt_boxes) + + # The original FCOS anchor matching rule: anchor point must be inside gt + pairwise_match &= pairwise_dist.min(dim=2).values > 0 + + # Multilevel anchor matching in FCOS: each anchor is only responsible + # for certain scale range. + pairwise_dist = pairwise_dist.max(dim=2).values + pairwise_match &= (pairwise_dist > lower_bound[:, None]) & ( + pairwise_dist < upper_bound[:, None] + ) + + # Match the GT box with minimum area, if there are multiple GT matches + gt_areas = gt_per_image.gt_boxes.area() # N + pairwise_match = pairwise_match.to(torch.float32) * (1e8 - gt_areas[None, :]) + min_values, matched_idx = pairwise_match.max(dim=1) # R, per-anchor match + matched_idx[min_values < 1e-5] = -1 # Unmatched anchors are assigned -1 + + matched_indices.append(matched_idx) + return matched_indices + + @torch.no_grad() + def label_anchors(self, anchors, gt_instances): + """ + Same interface as :meth:`RetinaNet.label_anchors`, but implemented with FCOS + anchor matching rule. + + Unlike RetinaNet, there are no ignored anchors. + """ + matched_indices = self.match_anchors(anchors, gt_instances) + + matched_labels, matched_boxes = [], [] + for gt_index, gt_per_image in zip(matched_indices, gt_instances): + label = gt_per_image.gt_classes[gt_index.clip(min=0)] + label[gt_index < 0] = self.num_classes # background + + matched_gt_boxes = gt_per_image.gt_boxes[gt_index.clip(min=0)] + + matched_labels.append(label) + matched_boxes.append(matched_gt_boxes) + return matched_labels, matched_boxes + + def losses( + self, anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes, pred_centerness + ): + """ + This method is almost identical to :meth:`RetinaNet.losses`, with an extra + "loss_centerness" in the returned dict. + """ + num_images = len(gt_labels) + gt_labels = torch.stack(gt_labels) # (N, R) + + pos_mask = (gt_labels >= 0) & (gt_labels != self.num_classes) + num_pos_anchors = pos_mask.sum().item() + get_event_storage().put_scalar("num_pos_anchors", num_pos_anchors / num_images) + normalizer = self._ema_update("loss_normalizer", max(num_pos_anchors, 1), 300) + + # classification and regression loss + gt_labels_target = F.one_hot(gt_labels, num_classes=self.num_classes + 1)[ + :, :, :-1 + ] # no loss for the last (background) class + loss_cls = sigmoid_focal_loss_jit( + torch.cat(pred_logits, dim=1), + gt_labels_target.to(pred_logits[0].dtype), + alpha=self.focal_loss_alpha, + gamma=self.focal_loss_gamma, + reduction="sum", + ) + + loss_box_reg = _dense_box_regression_loss( + anchors, + self.box2box_transform, + pred_anchor_deltas, + [x.tensor for x in gt_boxes], + pos_mask, + box_reg_loss_type="giou", + ) + + ctrness_targets = self.compute_ctrness_targets(anchors, gt_boxes) # NxR + pred_centerness = torch.cat(pred_centerness, dim=1).squeeze(dim=2) # NxR + ctrness_loss = F.binary_cross_entropy_with_logits( + pred_centerness[pos_mask], ctrness_targets[pos_mask], reduction="sum" + ) + return { + "loss_fcos_cls": loss_cls / normalizer, + "loss_fcos_loc": loss_box_reg / normalizer, + "loss_fcos_ctr": ctrness_loss / normalizer, + } + + def compute_ctrness_targets(self, anchors, gt_boxes): # NxR + anchors = Boxes.cat(anchors).tensor # Rx4 + reg_targets = [self.box2box_transform.get_deltas(anchors, m.tensor) for m in gt_boxes] + reg_targets = torch.stack(reg_targets, dim=0) # NxRx4 + if len(reg_targets) == 0: + return reg_targets.new_zeros(len(reg_targets)) + left_right = reg_targets[:, :, [0, 2]] + top_bottom = reg_targets[:, :, [1, 3]] + ctrness = (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) * ( + top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0] + ) + return torch.sqrt(ctrness) + + def forward_inference( + self, images: ImageList, features: List[Tensor], predictions: List[List[Tensor]] + ): + pred_logits, pred_anchor_deltas, pred_centerness = self._transpose_dense_predictions( + predictions, [self.num_classes, 4, 1] + ) + anchors = self.anchor_generator(features) + + results: List[Instances] = [] + for img_idx, image_size in enumerate(images.image_sizes): + scores_per_image = [ + # Multiply and sqrt centerness & classification scores + # (See eqn. 4 in https://arxiv.org/abs/2006.09214) + torch.sqrt(x[img_idx].sigmoid_() * y[img_idx].sigmoid_()) + for x, y in zip(pred_logits, pred_centerness) + ] + deltas_per_image = [x[img_idx] for x in pred_anchor_deltas] + results_per_image = self.inference_single_image( + anchors, scores_per_image, deltas_per_image, image_size + ) + results.append(results_per_image) + return results + + def inference_single_image( + self, + anchors: List[Boxes], + box_cls: List[Tensor], + box_delta: List[Tensor], + image_size: Tuple[int, int], + ): + """ + Identical to :meth:`RetinaNet.inference_single_image. + """ + pred = self._decode_multi_level_predictions( + anchors, + box_cls, + box_delta, + self.test_score_thresh, + self.test_topk_candidates, + image_size, + ) + keep = batched_nms( + pred.pred_boxes.tensor, pred.scores, pred.pred_classes, self.test_nms_thresh + ) + return pred[keep[: self.max_detections_per_image]] + + +class FCOSHead(RetinaNetHead): + """ + The head used in :paper:`fcos`. It adds an additional centerness + prediction branch on top of :class:`RetinaNetHead`. + """ + + def __init__(self, *, input_shape: List[ShapeSpec], conv_dims: List[int], **kwargs): + super().__init__(input_shape=input_shape, conv_dims=conv_dims, num_anchors=1, **kwargs) + # Unlike original FCOS, we do not add an additional learnable scale layer + # because it's found to have no benefits after normalizing regression targets by stride. + self._num_features = len(input_shape) + self.ctrness = nn.Conv2d(conv_dims[-1], 1, kernel_size=3, stride=1, padding=1) + torch.nn.init.normal_(self.ctrness.weight, std=0.01) + torch.nn.init.constant_(self.ctrness.bias, 0) + + def forward(self, features): + assert len(features) == self._num_features + logits = [] + bbox_reg = [] + ctrness = [] + for feature in features: + logits.append(self.cls_score(self.cls_subnet(feature))) + bbox_feature = self.bbox_subnet(feature) + bbox_reg.append(self.bbox_pred(bbox_feature)) + ctrness.append(self.ctrness(bbox_feature)) + return logits, bbox_reg, ctrness diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/panoptic_fpn.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/panoptic_fpn.py new file mode 100644 index 0000000..13aeabc --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/panoptic_fpn.py @@ -0,0 +1,266 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import logging +from typing import Dict, List +import torch +from torch import nn + +from detectron2.config import configurable +from detectron2.structures import ImageList + +from ..postprocessing import detector_postprocess, sem_seg_postprocess +from .build import META_ARCH_REGISTRY +from .rcnn import GeneralizedRCNN +from .semantic_seg import build_sem_seg_head + +__all__ = ["PanopticFPN"] + + +@META_ARCH_REGISTRY.register() +class PanopticFPN(GeneralizedRCNN): + """ + Implement the paper :paper:`PanopticFPN`. + """ + + @configurable + def __init__( + self, + *, + sem_seg_head: nn.Module, + combine_overlap_thresh: float = 0.5, + combine_stuff_area_thresh: float = 4096, + combine_instances_score_thresh: float = 0.5, + **kwargs, + ): + """ + NOTE: this interface is experimental. + + Args: + sem_seg_head: a module for the semantic segmentation head. + combine_overlap_thresh: combine masks into one instances if + they have enough overlap + combine_stuff_area_thresh: ignore stuff areas smaller than this threshold + combine_instances_score_thresh: ignore instances whose score is + smaller than this threshold + + Other arguments are the same as :class:`GeneralizedRCNN`. + """ + super().__init__(**kwargs) + self.sem_seg_head = sem_seg_head + # options when combining instance & semantic outputs + self.combine_overlap_thresh = combine_overlap_thresh + self.combine_stuff_area_thresh = combine_stuff_area_thresh + self.combine_instances_score_thresh = combine_instances_score_thresh + + @classmethod + def from_config(cls, cfg): + ret = super().from_config(cfg) + ret.update( + { + "combine_overlap_thresh": cfg.MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH, + "combine_stuff_area_thresh": cfg.MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT, + "combine_instances_score_thresh": cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH, # noqa + } + ) + ret["sem_seg_head"] = build_sem_seg_head(cfg, ret["backbone"].output_shape()) + logger = logging.getLogger(__name__) + if not cfg.MODEL.PANOPTIC_FPN.COMBINE.ENABLED: + logger.warning( + "PANOPTIC_FPN.COMBINED.ENABLED is no longer used. " + " model.inference(do_postprocess=) should be used to toggle postprocessing." + ) + if cfg.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT != 1.0: + w = cfg.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT + logger.warning( + "PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT should be replaced by weights on each ROI head." + ) + + def update_weight(x): + if isinstance(x, dict): + return {k: v * w for k, v in x.items()} + else: + return x * w + + roi_heads = ret["roi_heads"] + roi_heads.box_predictor.loss_weight = update_weight(roi_heads.box_predictor.loss_weight) + roi_heads.mask_head.loss_weight = update_weight(roi_heads.mask_head.loss_weight) + return ret + + def forward(self, batched_inputs): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper`. + Each item in the list contains the inputs for one image. + + For now, each item in the list is a dict that contains: + + * "image": Tensor, image in (C, H, W) format. + * "instances": Instances + * "sem_seg": semantic segmentation ground truth. + * Other information that's included in the original dicts, such as: + "height", "width" (int): the output resolution of the model, used in inference. + See :meth:`postprocess` for details. + + Returns: + list[dict]: + each dict has the results for one image. The dict contains the following keys: + + * "instances": see :meth:`GeneralizedRCNN.forward` for its format. + * "sem_seg": see :meth:`SemanticSegmentor.forward` for its format. + * "panoptic_seg": See the return value of + :func:`combine_semantic_and_instance_outputs` for its format. + """ + if not self.training: + return self.inference(batched_inputs) + images = self.preprocess_image(batched_inputs) + features = self.backbone(images.tensor) + + assert "sem_seg" in batched_inputs[0] + gt_sem_seg = [x["sem_seg"].to(self.device) for x in batched_inputs] + gt_sem_seg = ImageList.from_tensors( + gt_sem_seg, self.backbone.size_divisibility, self.sem_seg_head.ignore_value + ).tensor + sem_seg_results, sem_seg_losses = self.sem_seg_head(features, gt_sem_seg) + + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + proposals, proposal_losses = self.proposal_generator(images, features, gt_instances) + detector_results, detector_losses = self.roi_heads( + images, features, proposals, gt_instances + ) + + losses = sem_seg_losses + losses.update(proposal_losses) + losses.update(detector_losses) + return losses + + def inference(self, batched_inputs: List[Dict[str, torch.Tensor]], do_postprocess: bool = True): + """ + Run inference on the given inputs. + + Args: + batched_inputs (list[dict]): same as in :meth:`forward` + do_postprocess (bool): whether to apply post-processing on the outputs. + + Returns: + When do_postprocess=True, see docs in :meth:`forward`. + Otherwise, returns a (list[Instances], list[Tensor]) that contains + the raw detector outputs, and raw semantic segmentation outputs. + """ + images = self.preprocess_image(batched_inputs) + features = self.backbone(images.tensor) + sem_seg_results, sem_seg_losses = self.sem_seg_head(features, None) + proposals, _ = self.proposal_generator(images, features, None) + detector_results, _ = self.roi_heads(images, features, proposals, None) + + if do_postprocess: + processed_results = [] + for sem_seg_result, detector_result, input_per_image, image_size in zip( + sem_seg_results, detector_results, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + sem_seg_r = sem_seg_postprocess(sem_seg_result, image_size, height, width) + detector_r = detector_postprocess(detector_result, height, width) + + processed_results.append({"sem_seg": sem_seg_r, "instances": detector_r}) + + panoptic_r = combine_semantic_and_instance_outputs( + detector_r, + sem_seg_r.argmax(dim=0), + self.combine_overlap_thresh, + self.combine_stuff_area_thresh, + self.combine_instances_score_thresh, + ) + processed_results[-1]["panoptic_seg"] = panoptic_r + return processed_results + else: + return detector_results, sem_seg_results + + +def combine_semantic_and_instance_outputs( + instance_results, + semantic_results, + overlap_threshold, + stuff_area_thresh, + instances_score_thresh, +): + """ + Implement a simple combining logic following + "combine_semantic_and_instance_predictions.py" in panopticapi + to produce panoptic segmentation outputs. + + Args: + instance_results: output of :func:`detector_postprocess`. + semantic_results: an (H, W) tensor, each element is the contiguous semantic + category id + + Returns: + panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment. + segments_info (list[dict]): Describe each segment in `panoptic_seg`. + Each dict contains keys "id", "category_id", "isthing". + """ + panoptic_seg = torch.zeros_like(semantic_results, dtype=torch.int32) + + # sort instance outputs by scores + sorted_inds = torch.argsort(-instance_results.scores) + + current_segment_id = 0 + segments_info = [] + + instance_masks = instance_results.pred_masks.to(dtype=torch.bool, device=panoptic_seg.device) + + # Add instances one-by-one, check for overlaps with existing ones + for inst_id in sorted_inds: + score = instance_results.scores[inst_id].item() + if score < instances_score_thresh: + break + mask = instance_masks[inst_id] # H,W + mask_area = mask.sum().item() + + if mask_area == 0: + continue + + intersect = (mask > 0) & (panoptic_seg > 0) + intersect_area = intersect.sum().item() + + if intersect_area * 1.0 / mask_area > overlap_threshold: + continue + + if intersect_area > 0: + mask = mask & (panoptic_seg == 0) + + current_segment_id += 1 + panoptic_seg[mask] = current_segment_id + segments_info.append( + { + "id": current_segment_id, + "isthing": True, + "score": score, + "category_id": instance_results.pred_classes[inst_id].item(), + "instance_id": inst_id.item(), + } + ) + + # Add semantic results to remaining empty areas + semantic_labels = torch.unique(semantic_results).cpu().tolist() + for semantic_label in semantic_labels: + if semantic_label == 0: # 0 is a special "thing" class + continue + mask = (semantic_results == semantic_label) & (panoptic_seg == 0) + mask_area = mask.sum().item() + if mask_area < stuff_area_thresh: + continue + + current_segment_id += 1 + panoptic_seg[mask] = current_segment_id + segments_info.append( + { + "id": current_segment_id, + "isthing": False, + "category_id": semantic_label, + "area": mask_area, + } + ) + + return panoptic_seg, segments_info diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/rcnn.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/rcnn.py new file mode 100644 index 0000000..7b45363 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/rcnn.py @@ -0,0 +1,327 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import numpy as np +from typing import Dict, List, Optional, Tuple +import torch +from torch import nn + +from detectron2.config import configurable +from detectron2.data.detection_utils import convert_image_to_rgb +from detectron2.structures import ImageList, Instances +from detectron2.utils.events import get_event_storage +from detectron2.utils.logger import log_first_n + +from ..backbone import Backbone, build_backbone +from ..postprocessing import detector_postprocess +from ..proposal_generator import build_proposal_generator +from ..roi_heads import build_roi_heads +from .build import META_ARCH_REGISTRY + +__all__ = ["GeneralizedRCNN", "ProposalNetwork"] + + +@META_ARCH_REGISTRY.register() +class GeneralizedRCNN(nn.Module): + """ + Generalized R-CNN. Any models that contains the following three components: + 1. Per-image feature extraction (aka backbone) + 2. Region proposal generation + 3. Per-region feature extraction and prediction + """ + + @configurable + def __init__( + self, + *, + backbone: Backbone, + proposal_generator: nn.Module, + roi_heads: nn.Module, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + input_format: Optional[str] = None, + vis_period: int = 0, + ): + """ + Args: + backbone: a backbone module, must follow detectron2's backbone interface + proposal_generator: a module that generates proposals using backbone features + roi_heads: a ROI head that performs per-region computation + pixel_mean, pixel_std: list or tuple with #channels element, representing + the per-channel mean and std to be used to normalize the input image + input_format: describe the meaning of channels of input. Needed by visualization + vis_period: the period to run visualization. Set to 0 to disable. + """ + super().__init__() + self.backbone = backbone + self.proposal_generator = proposal_generator + self.roi_heads = roi_heads + + self.input_format = input_format + self.vis_period = vis_period + if vis_period > 0: + assert input_format is not None, "input_format is required for visualization!" + + self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False) + assert ( + self.pixel_mean.shape == self.pixel_std.shape + ), f"{self.pixel_mean} and {self.pixel_std} have different shapes!" + + @classmethod + def from_config(cls, cfg): + backbone = build_backbone(cfg) + return { + "backbone": backbone, + "proposal_generator": build_proposal_generator(cfg, backbone.output_shape()), + "roi_heads": build_roi_heads(cfg, backbone.output_shape()), + "input_format": cfg.INPUT.FORMAT, + "vis_period": cfg.VIS_PERIOD, + "pixel_mean": cfg.MODEL.PIXEL_MEAN, + "pixel_std": cfg.MODEL.PIXEL_STD, + } + + @property + def device(self): + return self.pixel_mean.device + + def visualize_training(self, batched_inputs, proposals): + """ + A function used to visualize images and proposals. It shows ground truth + bounding boxes on the original image and up to 20 top-scoring predicted + object proposals on the original image. Users can implement different + visualization functions for different models. + + Args: + batched_inputs (list): a list that contains input to the model. + proposals (list): a list that contains predicted proposals. Both + batched_inputs and proposals should have the same length. + """ + from detectron2.utils.visualizer import Visualizer + + storage = get_event_storage() + max_vis_prop = 20 + + for input, prop in zip(batched_inputs, proposals): + img = input["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format) + v_gt = Visualizer(img, None) + v_gt = v_gt.overlay_instances(boxes=input["instances"].gt_boxes) + anno_img = v_gt.get_image() + box_size = min(len(prop.proposal_boxes), max_vis_prop) + v_pred = Visualizer(img, None) + v_pred = v_pred.overlay_instances( + boxes=prop.proposal_boxes[0:box_size].tensor.cpu().numpy() + ) + prop_img = v_pred.get_image() + vis_img = np.concatenate((anno_img, prop_img), axis=1) + vis_img = vis_img.transpose(2, 0, 1) + vis_name = "Left: GT bounding boxes; Right: Predicted proposals" + storage.put_image(vis_name, vis_img) + break # only visualize one image in a batch + + def forward(self, batched_inputs: List[Dict[str, torch.Tensor]]): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper` . + Each item in the list contains the inputs for one image. + For now, each item in the list is a dict that contains: + + * image: Tensor, image in (C, H, W) format. + * instances (optional): groundtruth :class:`Instances` + * proposals (optional): :class:`Instances`, precomputed proposals. + + Other information that's included in the original dicts, such as: + + * "height", "width" (int): the output resolution of the model, used in inference. + See :meth:`postprocess` for details. + + Returns: + list[dict]: + Each dict is the output for one input image. + The dict contains one key "instances" whose value is a :class:`Instances`. + The :class:`Instances` object has the following keys: + "pred_boxes", "pred_classes", "scores", "pred_masks", "pred_keypoints" + """ + if not self.training: + return self.inference(batched_inputs) + + images = self.preprocess_image(batched_inputs) + if "instances" in batched_inputs[0]: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + else: + gt_instances = None + + features = self.backbone(images.tensor) + + if self.proposal_generator is not None: + proposals, proposal_losses = self.proposal_generator(images, features, gt_instances) + else: + assert "proposals" in batched_inputs[0] + proposals = [x["proposals"].to(self.device) for x in batched_inputs] + proposal_losses = {} + + _, detector_losses = self.roi_heads(images, features, proposals, gt_instances) + if self.vis_period > 0: + storage = get_event_storage() + if storage.iter % self.vis_period == 0: + self.visualize_training(batched_inputs, proposals) + + losses = {} + losses.update(detector_losses) + losses.update(proposal_losses) + return losses + + def inference( + self, + batched_inputs: List[Dict[str, torch.Tensor]], + detected_instances: Optional[List[Instances]] = None, + do_postprocess: bool = True, + ): + """ + Run inference on the given inputs. + + Args: + batched_inputs (list[dict]): same as in :meth:`forward` + detected_instances (None or list[Instances]): if not None, it + contains an `Instances` object per image. The `Instances` + object contains "pred_boxes" and "pred_classes" which are + known boxes in the image. + The inference will then skip the detection of bounding boxes, + and only predict other per-ROI outputs. + do_postprocess (bool): whether to apply post-processing on the outputs. + + Returns: + When do_postprocess=True, same as in :meth:`forward`. + Otherwise, a list[Instances] containing raw network outputs. + """ + assert not self.training + + images = self.preprocess_image(batched_inputs) + features = self.backbone(images.tensor) + + if detected_instances is None: + if self.proposal_generator is not None: + proposals, _ = self.proposal_generator(images, features, None) + else: + assert "proposals" in batched_inputs[0] + proposals = [x["proposals"].to(self.device) for x in batched_inputs] + + results, _ = self.roi_heads(images, features, proposals, None) + else: + detected_instances = [x.to(self.device) for x in detected_instances] + results = self.roi_heads.forward_with_given_boxes(features, detected_instances) + + if do_postprocess: + assert not torch.jit.is_scripting(), "Scripting is not supported for postprocess." + return GeneralizedRCNN._postprocess(results, batched_inputs, images.image_sizes) + else: + return results + + def preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]): + """ + Normalize, pad and batch the input images. + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors(images, self.backbone.size_divisibility) + return images + + @staticmethod + def _postprocess(instances, batched_inputs: List[Dict[str, torch.Tensor]], image_sizes): + """ + Rescale the output instances to the target size. + """ + # note: private function; subject to changes + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + instances, batched_inputs, image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"instances": r}) + return processed_results + + +@META_ARCH_REGISTRY.register() +class ProposalNetwork(nn.Module): + """ + A meta architecture that only predicts object proposals. + """ + + @configurable + def __init__( + self, + *, + backbone: Backbone, + proposal_generator: nn.Module, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + ): + """ + Args: + backbone: a backbone module, must follow detectron2's backbone interface + proposal_generator: a module that generates proposals using backbone features + pixel_mean, pixel_std: list or tuple with #channels element, representing + the per-channel mean and std to be used to normalize the input image + """ + super().__init__() + self.backbone = backbone + self.proposal_generator = proposal_generator + self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False) + + @classmethod + def from_config(cls, cfg): + backbone = build_backbone(cfg) + return { + "backbone": backbone, + "proposal_generator": build_proposal_generator(cfg, backbone.output_shape()), + "pixel_mean": cfg.MODEL.PIXEL_MEAN, + "pixel_std": cfg.MODEL.PIXEL_STD, + } + + @property + def device(self): + return self.pixel_mean.device + + def forward(self, batched_inputs): + """ + Args: + Same as in :class:`GeneralizedRCNN.forward` + + Returns: + list[dict]: + Each dict is the output for one input image. + The dict contains one key "proposals" whose value is a + :class:`Instances` with keys "proposal_boxes" and "objectness_logits". + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors(images, self.backbone.size_divisibility) + features = self.backbone(images.tensor) + + if "instances" in batched_inputs[0]: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + elif "targets" in batched_inputs[0]: + log_first_n( + logging.WARN, "'targets' in the model inputs is now renamed to 'instances'!", n=10 + ) + gt_instances = [x["targets"].to(self.device) for x in batched_inputs] + else: + gt_instances = None + proposals, proposal_losses = self.proposal_generator(images, features, gt_instances) + # In training, the proposals are not useful at all but we generate them anyway. + # This makes RPN-only models about 5% slower. + if self.training: + return proposal_losses + + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + proposals, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"proposals": r}) + return processed_results diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/retinanet.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/retinanet.py new file mode 100644 index 0000000..3ea88f6 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/retinanet.py @@ -0,0 +1,439 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import math +from typing import List, Tuple +import torch +from fvcore.nn import sigmoid_focal_loss_jit +from torch import Tensor, nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import CycleBatchNormList, ShapeSpec, batched_nms, cat, get_norm +from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou +from detectron2.utils.events import get_event_storage + +from ..anchor_generator import build_anchor_generator +from ..backbone import Backbone, build_backbone +from ..box_regression import Box2BoxTransform, _dense_box_regression_loss +from ..matcher import Matcher +from .build import META_ARCH_REGISTRY +from .dense_detector import DenseDetector, permute_to_N_HWA_K # noqa + +__all__ = ["RetinaNet"] + + +logger = logging.getLogger(__name__) + + +@META_ARCH_REGISTRY.register() +class RetinaNet(DenseDetector): + """ + Implement RetinaNet in :paper:`RetinaNet`. + """ + + @configurable + def __init__( + self, + *, + backbone: Backbone, + head: nn.Module, + head_in_features, + anchor_generator, + box2box_transform, + anchor_matcher, + num_classes, + focal_loss_alpha=0.25, + focal_loss_gamma=2.0, + smooth_l1_beta=0.0, + box_reg_loss_type="smooth_l1", + test_score_thresh=0.05, + test_topk_candidates=1000, + test_nms_thresh=0.5, + max_detections_per_image=100, + pixel_mean, + pixel_std, + vis_period=0, + input_format="BGR", + ): + """ + NOTE: this interface is experimental. + + Args: + backbone: a backbone module, must follow detectron2's backbone interface + head (nn.Module): a module that predicts logits and regression deltas + for each level from a list of per-level features + head_in_features (Tuple[str]): Names of the input feature maps to be used in head + anchor_generator (nn.Module): a module that creates anchors from a + list of features. Usually an instance of :class:`AnchorGenerator` + box2box_transform (Box2BoxTransform): defines the transform from anchors boxes to + instance boxes + anchor_matcher (Matcher): label the anchors by matching them with ground truth. + num_classes (int): number of classes. Used to label background proposals. + + # Loss parameters: + focal_loss_alpha (float): focal_loss_alpha + focal_loss_gamma (float): focal_loss_gamma + smooth_l1_beta (float): smooth_l1_beta + box_reg_loss_type (str): Options are "smooth_l1", "giou", "diou", "ciou" + + # Inference parameters: + test_score_thresh (float): Inference cls score threshold, only anchors with + score > INFERENCE_TH are considered for inference (to improve speed) + test_topk_candidates (int): Select topk candidates before NMS + test_nms_thresh (float): Overlap threshold used for non-maximum suppression + (suppress boxes with IoU >= this threshold) + max_detections_per_image (int): + Maximum number of detections to return per image during inference + (100 is based on the limit established for the COCO dataset). + + pixel_mean, pixel_std: see :class:`DenseDetector`. + """ + super().__init__( + backbone, head, head_in_features, pixel_mean=pixel_mean, pixel_std=pixel_std + ) + self.num_classes = num_classes + + # Anchors + self.anchor_generator = anchor_generator + self.box2box_transform = box2box_transform + self.anchor_matcher = anchor_matcher + + # Loss parameters: + self.focal_loss_alpha = focal_loss_alpha + self.focal_loss_gamma = focal_loss_gamma + self.smooth_l1_beta = smooth_l1_beta + self.box_reg_loss_type = box_reg_loss_type + # Inference parameters: + self.test_score_thresh = test_score_thresh + self.test_topk_candidates = test_topk_candidates + self.test_nms_thresh = test_nms_thresh + self.max_detections_per_image = max_detections_per_image + # Vis parameters + self.vis_period = vis_period + self.input_format = input_format + + @classmethod + def from_config(cls, cfg): + backbone = build_backbone(cfg) + backbone_shape = backbone.output_shape() + feature_shapes = [backbone_shape[f] for f in cfg.MODEL.RETINANET.IN_FEATURES] + head = RetinaNetHead(cfg, feature_shapes) + anchor_generator = build_anchor_generator(cfg, feature_shapes) + return { + "backbone": backbone, + "head": head, + "anchor_generator": anchor_generator, + "box2box_transform": Box2BoxTransform(weights=cfg.MODEL.RETINANET.BBOX_REG_WEIGHTS), + "anchor_matcher": Matcher( + cfg.MODEL.RETINANET.IOU_THRESHOLDS, + cfg.MODEL.RETINANET.IOU_LABELS, + allow_low_quality_matches=True, + ), + "pixel_mean": cfg.MODEL.PIXEL_MEAN, + "pixel_std": cfg.MODEL.PIXEL_STD, + "num_classes": cfg.MODEL.RETINANET.NUM_CLASSES, + "head_in_features": cfg.MODEL.RETINANET.IN_FEATURES, + # Loss parameters: + "focal_loss_alpha": cfg.MODEL.RETINANET.FOCAL_LOSS_ALPHA, + "focal_loss_gamma": cfg.MODEL.RETINANET.FOCAL_LOSS_GAMMA, + "smooth_l1_beta": cfg.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA, + "box_reg_loss_type": cfg.MODEL.RETINANET.BBOX_REG_LOSS_TYPE, + # Inference parameters: + "test_score_thresh": cfg.MODEL.RETINANET.SCORE_THRESH_TEST, + "test_topk_candidates": cfg.MODEL.RETINANET.TOPK_CANDIDATES_TEST, + "test_nms_thresh": cfg.MODEL.RETINANET.NMS_THRESH_TEST, + "max_detections_per_image": cfg.TEST.DETECTIONS_PER_IMAGE, + # Vis parameters + "vis_period": cfg.VIS_PERIOD, + "input_format": cfg.INPUT.FORMAT, + } + + def forward_training(self, images, features, predictions, gt_instances): + # Transpose the Hi*Wi*A dimension to the middle: + pred_logits, pred_anchor_deltas = self._transpose_dense_predictions( + predictions, [self.num_classes, 4] + ) + anchors = self.anchor_generator(features) + gt_labels, gt_boxes = self.label_anchors(anchors, gt_instances) + return self.losses(anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes) + + def losses(self, anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes): + """ + Args: + anchors (list[Boxes]): a list of #feature level Boxes + gt_labels, gt_boxes: see output of :meth:`RetinaNet.label_anchors`. + Their shapes are (N, R) and (N, R, 4), respectively, where R is + the total number of anchors across levels, i.e. sum(Hi x Wi x Ai) + pred_logits, pred_anchor_deltas: both are list[Tensor]. Each element in the + list corresponds to one level and has shape (N, Hi * Wi * Ai, K or 4). + Where K is the number of classes used in `pred_logits`. + + Returns: + dict[str, Tensor]: + mapping from a named loss to a scalar tensor storing the loss. + Used during training only. The dict keys are: "loss_cls" and "loss_box_reg" + """ + num_images = len(gt_labels) + gt_labels = torch.stack(gt_labels) # (N, R) + + valid_mask = gt_labels >= 0 + pos_mask = (gt_labels >= 0) & (gt_labels != self.num_classes) + num_pos_anchors = pos_mask.sum().item() + get_event_storage().put_scalar("num_pos_anchors", num_pos_anchors / num_images) + normalizer = self._ema_update("loss_normalizer", max(num_pos_anchors, 1), 100) + + # classification and regression loss + gt_labels_target = F.one_hot(gt_labels[valid_mask], num_classes=self.num_classes + 1)[ + :, :-1 + ] # no loss for the last (background) class + loss_cls = sigmoid_focal_loss_jit( + cat(pred_logits, dim=1)[valid_mask], + gt_labels_target.to(pred_logits[0].dtype), + alpha=self.focal_loss_alpha, + gamma=self.focal_loss_gamma, + reduction="sum", + ) + + loss_box_reg = _dense_box_regression_loss( + anchors, + self.box2box_transform, + pred_anchor_deltas, + gt_boxes, + pos_mask, + box_reg_loss_type=self.box_reg_loss_type, + smooth_l1_beta=self.smooth_l1_beta, + ) + + return { + "loss_cls": loss_cls / normalizer, + "loss_box_reg": loss_box_reg / normalizer, + } + + @torch.no_grad() + def label_anchors(self, anchors, gt_instances): + """ + Args: + anchors (list[Boxes]): A list of #feature level Boxes. + The Boxes contains anchors of this image on the specific feature level. + gt_instances (list[Instances]): a list of N `Instances`s. The i-th + `Instances` contains the ground-truth per-instance annotations + for the i-th input image. + + Returns: + list[Tensor]: List of #img tensors. i-th element is a vector of labels whose length is + the total number of anchors across all feature maps (sum(Hi * Wi * A)). + Label values are in {-1, 0, ..., K}, with -1 means ignore, and K means background. + + list[Tensor]: i-th element is a Rx4 tensor, where R is the total number of anchors + across feature maps. The values are the matched gt boxes for each anchor. + Values are undefined for those anchors not labeled as foreground. + """ + anchors = Boxes.cat(anchors) # Rx4 + + gt_labels = [] + matched_gt_boxes = [] + for gt_per_image in gt_instances: + match_quality_matrix = pairwise_iou(gt_per_image.gt_boxes, anchors) + matched_idxs, anchor_labels = self.anchor_matcher(match_quality_matrix) + del match_quality_matrix + + if len(gt_per_image) > 0: + matched_gt_boxes_i = gt_per_image.gt_boxes.tensor[matched_idxs] + + gt_labels_i = gt_per_image.gt_classes[matched_idxs] + # Anchors with label 0 are treated as background. + gt_labels_i[anchor_labels == 0] = self.num_classes + # Anchors with label -1 are ignored. + gt_labels_i[anchor_labels == -1] = -1 + else: + matched_gt_boxes_i = torch.zeros_like(anchors.tensor) + gt_labels_i = torch.zeros_like(matched_idxs) + self.num_classes + + gt_labels.append(gt_labels_i) + matched_gt_boxes.append(matched_gt_boxes_i) + + return gt_labels, matched_gt_boxes + + def forward_inference( + self, images: ImageList, features: List[Tensor], predictions: List[List[Tensor]] + ): + pred_logits, pred_anchor_deltas = self._transpose_dense_predictions( + predictions, [self.num_classes, 4] + ) + anchors = self.anchor_generator(features) + + results: List[Instances] = [] + for img_idx, image_size in enumerate(images.image_sizes): + scores_per_image = [x[img_idx].sigmoid_() for x in pred_logits] + deltas_per_image = [x[img_idx] for x in pred_anchor_deltas] + results_per_image = self.inference_single_image( + anchors, scores_per_image, deltas_per_image, image_size + ) + results.append(results_per_image) + return results + + def inference_single_image( + self, + anchors: List[Boxes], + box_cls: List[Tensor], + box_delta: List[Tensor], + image_size: Tuple[int, int], + ): + """ + Single-image inference. Return bounding-box detection results by thresholding + on scores and applying non-maximum suppression (NMS). + + Arguments: + anchors (list[Boxes]): list of #feature levels. Each entry contains + a Boxes object, which contains all the anchors in that feature level. + box_cls (list[Tensor]): list of #feature levels. Each entry contains + tensor of size (H x W x A, K) + box_delta (list[Tensor]): Same shape as 'box_cls' except that K becomes 4. + image_size (tuple(H, W)): a tuple of the image height and width. + + Returns: + Same as `inference`, but for only one image. + """ + pred = self._decode_multi_level_predictions( + anchors, + box_cls, + box_delta, + self.test_score_thresh, + self.test_topk_candidates, + image_size, + ) + keep = batched_nms( # per-class NMS + pred.pred_boxes.tensor, pred.scores, pred.pred_classes, self.test_nms_thresh + ) + return pred[keep[: self.max_detections_per_image]] + + +class RetinaNetHead(nn.Module): + """ + The head used in RetinaNet for object classification and box regression. + It has two subnets for the two tasks, with a common structure but separate parameters. + """ + + @configurable + def __init__( + self, + *, + input_shape: List[ShapeSpec], + num_classes, + num_anchors, + conv_dims: List[int], + norm="", + prior_prob=0.01, + ): + """ + NOTE: this interface is experimental. + + Args: + input_shape (List[ShapeSpec]): input shape + num_classes (int): number of classes. Used to label background proposals. + num_anchors (int): number of generated anchors + conv_dims (List[int]): dimensions for each convolution layer + norm (str or callable): + Normalization for conv layers except for the two output layers. + See :func:`detectron2.layers.get_norm` for supported types. + prior_prob (float): Prior weight for computing bias + """ + super().__init__() + + self._num_features = len(input_shape) + if norm == "BN" or norm == "SyncBN": + logger.info( + f"Using domain-specific {norm} in RetinaNetHead with len={self._num_features}." + ) + bn_class = nn.BatchNorm2d if norm == "BN" else nn.SyncBatchNorm + + def norm(c): + return CycleBatchNormList( + length=self._num_features, bn_class=bn_class, num_features=c + ) + + else: + norm_name = str(type(get_norm(norm, 1))) + if "BN" in norm_name: + logger.warning( + f"Shared BatchNorm (type={norm_name}) may not work well in RetinaNetHead." + ) + + cls_subnet = [] + bbox_subnet = [] + for in_channels, out_channels in zip( + [input_shape[0].channels] + list(conv_dims), conv_dims + ): + cls_subnet.append( + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + ) + if norm: + cls_subnet.append(get_norm(norm, out_channels)) + cls_subnet.append(nn.ReLU()) + bbox_subnet.append( + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + ) + if norm: + bbox_subnet.append(get_norm(norm, out_channels)) + bbox_subnet.append(nn.ReLU()) + + self.cls_subnet = nn.Sequential(*cls_subnet) + self.bbox_subnet = nn.Sequential(*bbox_subnet) + self.cls_score = nn.Conv2d( + conv_dims[-1], num_anchors * num_classes, kernel_size=3, stride=1, padding=1 + ) + self.bbox_pred = nn.Conv2d( + conv_dims[-1], num_anchors * 4, kernel_size=3, stride=1, padding=1 + ) + + # Initialization + for modules in [self.cls_subnet, self.bbox_subnet, self.cls_score, self.bbox_pred]: + for layer in modules.modules(): + if isinstance(layer, nn.Conv2d): + torch.nn.init.normal_(layer.weight, mean=0, std=0.01) + torch.nn.init.constant_(layer.bias, 0) + + # Use prior in model initialization to improve stability + bias_value = -(math.log((1 - prior_prob) / prior_prob)) + torch.nn.init.constant_(self.cls_score.bias, bias_value) + + @classmethod + def from_config(cls, cfg, input_shape: List[ShapeSpec]): + num_anchors = build_anchor_generator(cfg, input_shape).num_cell_anchors + assert ( + len(set(num_anchors)) == 1 + ), "Using different number of anchors between levels is not currently supported!" + num_anchors = num_anchors[0] + + return { + "input_shape": input_shape, + "num_classes": cfg.MODEL.RETINANET.NUM_CLASSES, + "conv_dims": [input_shape[0].channels] * cfg.MODEL.RETINANET.NUM_CONVS, + "prior_prob": cfg.MODEL.RETINANET.PRIOR_PROB, + "norm": cfg.MODEL.RETINANET.NORM, + "num_anchors": num_anchors, + } + + def forward(self, features: List[Tensor]): + """ + Arguments: + features (list[Tensor]): FPN feature map tensors in high to low resolution. + Each tensor in the list correspond to different feature levels. + + Returns: + logits (list[Tensor]): #lvl tensors, each has shape (N, AxK, Hi, Wi). + The tensor predicts the classification probability + at each spatial position for each of the A anchors and K object + classes. + bbox_reg (list[Tensor]): #lvl tensors, each has shape (N, Ax4, Hi, Wi). + The tensor predicts 4-vector (dx,dy,dw,dh) box + regression values for every anchor. These values are the + relative offset between the anchor and the ground truth box. + """ + assert len(features) == self._num_features + logits = [] + bbox_reg = [] + for feature in features: + logits.append(self.cls_score(self.cls_subnet(feature))) + bbox_reg.append(self.bbox_pred(self.bbox_subnet(feature))) + return logits, bbox_reg diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/semantic_seg.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/semantic_seg.py new file mode 100644 index 0000000..6dd3dc2 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/meta_arch/semantic_seg.py @@ -0,0 +1,260 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +from typing import Callable, Dict, Optional, Tuple, Union +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ShapeSpec, get_norm +from detectron2.structures import ImageList +from detectron2.utils.registry import Registry + +from ..backbone import Backbone, build_backbone +from ..postprocessing import sem_seg_postprocess +from .build import META_ARCH_REGISTRY + +__all__ = [ + "SemanticSegmentor", + "SEM_SEG_HEADS_REGISTRY", + "SemSegFPNHead", + "build_sem_seg_head", +] + + +SEM_SEG_HEADS_REGISTRY = Registry("SEM_SEG_HEADS") +SEM_SEG_HEADS_REGISTRY.__doc__ = """ +Registry for semantic segmentation heads, which make semantic segmentation predictions +from feature maps. +""" + + +@META_ARCH_REGISTRY.register() +class SemanticSegmentor(nn.Module): + """ + Main class for semantic segmentation architectures. + """ + + @configurable + def __init__( + self, + *, + backbone: Backbone, + sem_seg_head: nn.Module, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + ): + """ + Args: + backbone: a backbone module, must follow detectron2's backbone interface + sem_seg_head: a module that predicts semantic segmentation from backbone features + pixel_mean, pixel_std: list or tuple with #channels element, representing + the per-channel mean and std to be used to normalize the input image + """ + super().__init__() + self.backbone = backbone + self.sem_seg_head = sem_seg_head + self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False) + + @classmethod + def from_config(cls, cfg): + backbone = build_backbone(cfg) + sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape()) + return { + "backbone": backbone, + "sem_seg_head": sem_seg_head, + "pixel_mean": cfg.MODEL.PIXEL_MEAN, + "pixel_std": cfg.MODEL.PIXEL_STD, + } + + @property + def device(self): + return self.pixel_mean.device + + def forward(self, batched_inputs): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper`. + Each item in the list contains the inputs for one image. + + For now, each item in the list is a dict that contains: + + * "image": Tensor, image in (C, H, W) format. + * "sem_seg": semantic segmentation ground truth + * Other information that's included in the original dicts, such as: + "height", "width" (int): the output resolution of the model (may be different + from input resolution), used in inference. + + + Returns: + list[dict]: + Each dict is the output for one input image. + The dict contains one key "sem_seg" whose value is a + Tensor that represents the + per-pixel segmentation prediced by the head. + The prediction has shape KxHxW that represents the logits of + each class for each pixel. + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors(images, self.backbone.size_divisibility) + + features = self.backbone(images.tensor) + + if "sem_seg" in batched_inputs[0]: + targets = [x["sem_seg"].to(self.device) for x in batched_inputs] + targets = ImageList.from_tensors( + targets, self.backbone.size_divisibility, self.sem_seg_head.ignore_value + ).tensor + else: + targets = None + results, losses = self.sem_seg_head(features, targets) + + if self.training: + return losses + + processed_results = [] + for result, input_per_image, image_size in zip(results, batched_inputs, images.image_sizes): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = sem_seg_postprocess(result, image_size, height, width) + processed_results.append({"sem_seg": r}) + return processed_results + + +def build_sem_seg_head(cfg, input_shape): + """ + Build a semantic segmentation head from `cfg.MODEL.SEM_SEG_HEAD.NAME`. + """ + name = cfg.MODEL.SEM_SEG_HEAD.NAME + return SEM_SEG_HEADS_REGISTRY.get(name)(cfg, input_shape) + + +@SEM_SEG_HEADS_REGISTRY.register() +class SemSegFPNHead(nn.Module): + """ + A semantic segmentation head described in :paper:`PanopticFPN`. + It takes a list of FPN features as input, and applies a sequence of + 3x3 convs and upsampling to scale all of them to the stride defined by + ``common_stride``. Then these features are added and used to make final + predictions by another 1x1 conv layer. + """ + + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + num_classes: int, + conv_dims: int, + common_stride: int, + loss_weight: float = 1.0, + norm: Optional[Union[str, Callable]] = None, + ignore_value: int = -1, + ): + """ + NOTE: this interface is experimental. + + Args: + input_shape: shapes (channels and stride) of the input features + num_classes: number of classes to predict + conv_dims: number of output channels for the intermediate conv layers. + common_stride: the common stride that all features will be upscaled to + loss_weight: loss weight + norm (str or callable): normalization for all conv layers + ignore_value: category id to be ignored during training. + """ + super().__init__() + input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) + if not len(input_shape): + raise ValueError("SemSegFPNHead(input_shape=) cannot be empty!") + self.in_features = [k for k, v in input_shape] + feature_strides = [v.stride for k, v in input_shape] + feature_channels = [v.channels for k, v in input_shape] + + self.ignore_value = ignore_value + self.common_stride = common_stride + self.loss_weight = loss_weight + + self.scale_heads = [] + for in_feature, stride, channels in zip( + self.in_features, feature_strides, feature_channels + ): + head_ops = [] + head_length = max(1, int(np.log2(stride) - np.log2(self.common_stride))) + for k in range(head_length): + norm_module = get_norm(norm, conv_dims) + conv = Conv2d( + channels if k == 0 else conv_dims, + conv_dims, + kernel_size=3, + stride=1, + padding=1, + bias=not norm, + norm=norm_module, + activation=F.relu, + ) + weight_init.c2_msra_fill(conv) + head_ops.append(conv) + if stride != self.common_stride: + head_ops.append( + nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False) + ) + self.scale_heads.append(nn.Sequential(*head_ops)) + self.add_module(in_feature, self.scale_heads[-1]) + self.predictor = Conv2d(conv_dims, num_classes, kernel_size=1, stride=1, padding=0) + weight_init.c2_msra_fill(self.predictor) + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + return { + "input_shape": { + k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES + }, + "ignore_value": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE, + "num_classes": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES, + "conv_dims": cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM, + "common_stride": cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE, + "norm": cfg.MODEL.SEM_SEG_HEAD.NORM, + "loss_weight": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT, + } + + def forward(self, features, targets=None): + """ + Returns: + In training, returns (None, dict of losses) + In inference, returns (CxHxW logits, {}) + """ + x = self.layers(features) + if self.training: + return None, self.losses(x, targets) + else: + x = F.interpolate( + x, scale_factor=self.common_stride, mode="bilinear", align_corners=False + ) + return x, {} + + def layers(self, features): + for i, f in enumerate(self.in_features): + if i == 0: + x = self.scale_heads[i](features[f]) + else: + x = x + self.scale_heads[i](features[f]) + x = self.predictor(x) + return x + + def losses(self, predictions, targets): + predictions = predictions.float() # https://github.com/pytorch/pytorch/issues/48163 + predictions = F.interpolate( + predictions, + scale_factor=self.common_stride, + mode="bilinear", + align_corners=False, + ) + loss = F.cross_entropy( + predictions, targets, reduction="mean", ignore_index=self.ignore_value + ) + losses = {"loss_sem_seg": loss * self.loss_weight} + return losses diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/mmdet_wrapper.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/mmdet_wrapper.py new file mode 100644 index 0000000..386e929 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/mmdet_wrapper.py @@ -0,0 +1,274 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +import logging +import numpy as np +from collections import OrderedDict +from collections.abc import Mapping +from typing import Dict, List, Optional, Tuple, Union +import torch +from omegaconf import DictConfig, OmegaConf +from torch import Tensor, nn + +from detectron2.layers import ShapeSpec +from detectron2.structures import BitMasks, Boxes, ImageList, Instances +from detectron2.utils.events import get_event_storage + +from .backbone import Backbone + +logger = logging.getLogger(__name__) + + +def _to_container(cfg): + """ + mmdet will assert the type of dict/list. + So convert omegaconf objects to dict/list. + """ + if isinstance(cfg, DictConfig): + cfg = OmegaConf.to_container(cfg, resolve=True) + from mmcv.utils import ConfigDict + + return ConfigDict(cfg) + + +class MMDetBackbone(Backbone): + """ + Wrapper of mmdetection backbones to use in detectron2. + + mmdet backbones produce list/tuple of tensors, while detectron2 backbones + produce a dict of tensors. This class wraps the given backbone to produce + output in detectron2's convention, so it can be used in place of detectron2 + backbones. + """ + + def __init__( + self, + backbone: Union[nn.Module, Mapping], + neck: Union[nn.Module, Mapping, None] = None, + *, + output_shapes: List[ShapeSpec], + output_names: Optional[List[str]] = None, + ): + """ + Args: + backbone: either a backbone module or a mmdet config dict that defines a + backbone. The backbone takes a 4D image tensor and returns a + sequence of tensors. + neck: either a backbone module or a mmdet config dict that defines a + neck. The neck takes outputs of backbone and returns a + sequence of tensors. If None, no neck is used. + pretrained_backbone: defines the backbone weights that can be loaded by + mmdet, such as "torchvision://resnet50". + output_shapes: shape for every output of the backbone (or neck, if given). + stride and channels are often needed. + output_names: names for every output of the backbone (or neck, if given). + By default, will use "out0", "out1", ... + """ + super().__init__() + if isinstance(backbone, Mapping): + from mmdet.models import build_backbone + + backbone = build_backbone(_to_container(backbone)) + self.backbone = backbone + + if isinstance(neck, Mapping): + from mmdet.models import build_neck + + neck = build_neck(_to_container(neck)) + self.neck = neck + + # "Neck" weights, if any, are part of neck itself. This is the interface + # of mmdet so we follow it. Reference: + # https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/detectors/two_stage.py + logger.info("Initializing mmdet backbone weights...") + self.backbone.init_weights() + # train() in mmdet modules is non-trivial, and has to be explicitly + # called. Reference: + # https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/backbones/resnet.py + self.backbone.train() + if self.neck is not None: + logger.info("Initializing mmdet neck weights ...") + if isinstance(self.neck, nn.Sequential): + for m in self.neck: + m.init_weights() + else: + self.neck.init_weights() + self.neck.train() + + self._output_shapes = output_shapes + if not output_names: + output_names = [f"out{i}" for i in range(len(output_shapes))] + self._output_names = output_names + + def forward(self, x) -> Dict[str, Tensor]: + outs = self.backbone(x) + if self.neck is not None: + outs = self.neck(outs) + assert isinstance( + outs, (list, tuple) + ), "mmdet backbone should return a list/tuple of tensors!" + if len(outs) != len(self._output_shapes): + raise ValueError( + "Length of output_shapes does not match outputs from the mmdet backbone: " + f"{len(outs)} != {len(self._output_shapes)}" + ) + return {k: v for k, v in zip(self._output_names, outs)} + + def output_shape(self) -> Dict[str, ShapeSpec]: + return {k: v for k, v in zip(self._output_names, self._output_shapes)} + + +class MMDetDetector(nn.Module): + """ + Wrapper of a mmdetection detector model, for detection and instance segmentation. + Input/output formats of this class follow detectron2's convention, so a + mmdetection model can be trained and evaluated in detectron2. + """ + + def __init__( + self, + detector: Union[nn.Module, Mapping], + *, + # Default is 32 regardless of model: + # https://github.com/open-mmlab/mmdetection/tree/master/configs/_base_/datasets + size_divisibility=32, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + ): + """ + Args: + detector: a mmdet detector, or a mmdet config dict that defines a detector. + size_divisibility: pad input images to multiple of this number + pixel_mean: per-channel mean to normalize input image + pixel_std: per-channel stddev to normalize input image + """ + super().__init__() + if isinstance(detector, Mapping): + from mmdet.models import build_detector + + detector = build_detector(_to_container(detector)) + self.detector = detector + self.size_divisibility = size_divisibility + + self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False) + assert ( + self.pixel_mean.shape == self.pixel_std.shape + ), f"{self.pixel_mean} and {self.pixel_std} have different shapes!" + + def forward(self, batched_inputs: List[Dict[str, torch.Tensor]]): + images = [x["image"].to(self.device) for x in batched_inputs] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors(images, size_divisibility=self.size_divisibility).tensor + metas = [] + rescale = {"height" in x for x in batched_inputs} + if len(rescale) != 1: + raise ValueError("Some inputs have original height/width, but some don't!") + rescale = list(rescale)[0] + output_shapes = [] + for input in batched_inputs: + meta = {} + c, h, w = input["image"].shape + meta["img_shape"] = meta["ori_shape"] = (h, w, c) + if rescale: + scale_factor = np.array( + [w / input["width"], h / input["height"]] * 2, dtype="float32" + ) + ori_shape = (input["height"], input["width"]) + output_shapes.append(ori_shape) + meta["ori_shape"] = ori_shape + (c,) + else: + scale_factor = 1.0 + output_shapes.append((h, w)) + meta["scale_factor"] = scale_factor + meta["flip"] = False + padh, padw = images.shape[-2:] + meta["pad_shape"] = (padh, padw, c) + metas.append(meta) + + if self.training: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + if gt_instances[0].has("gt_masks"): + from mmdet.core import PolygonMasks as mm_PolygonMasks, BitmapMasks as mm_BitMasks + + def convert_mask(m, shape): + # mmdet mask format + if isinstance(m, BitMasks): + return mm_BitMasks(m.tensor.cpu().numpy(), shape[0], shape[1]) + else: + return mm_PolygonMasks(m.polygons, shape[0], shape[1]) + + gt_masks = [convert_mask(x.gt_masks, x.image_size) for x in gt_instances] + losses_and_metrics = self.detector.forward_train( + images, + metas, + [x.gt_boxes.tensor for x in gt_instances], + [x.gt_classes for x in gt_instances], + gt_masks=gt_masks, + ) + else: + losses_and_metrics = self.detector.forward_train( + images, + metas, + [x.gt_boxes.tensor for x in gt_instances], + [x.gt_classes for x in gt_instances], + ) + return _parse_losses(losses_and_metrics) + else: + results = self.detector.simple_test(images, metas, rescale=rescale) + results = [ + {"instances": _convert_mmdet_result(r, shape)} + for r, shape in zip(results, output_shapes) + ] + return results + + @property + def device(self): + return self.pixel_mean.device + + +# Reference: show_result() in +# https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/detectors/base.py +def _convert_mmdet_result(result, shape: Tuple[int, int]) -> Instances: + if isinstance(result, tuple): + bbox_result, segm_result = result + if isinstance(segm_result, tuple): + segm_result = segm_result[0] + else: + bbox_result, segm_result = result, None + + bboxes = torch.from_numpy(np.vstack(bbox_result)) # Nx5 + bboxes, scores = bboxes[:, :4], bboxes[:, -1] + labels = [ + torch.full((bbox.shape[0],), i, dtype=torch.int32) for i, bbox in enumerate(bbox_result) + ] + labels = torch.cat(labels) + inst = Instances(shape) + inst.pred_boxes = Boxes(bboxes) + inst.scores = scores + inst.pred_classes = labels + + if segm_result is not None and len(labels) > 0: + segm_result = list(itertools.chain(*segm_result)) + segm_result = [torch.from_numpy(x) if isinstance(x, np.ndarray) else x for x in segm_result] + segm_result = torch.stack(segm_result, dim=0) + inst.pred_masks = segm_result + return inst + + +# reference: https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/detectors/base.py +def _parse_losses(losses: Dict[str, Tensor]) -> Dict[str, Tensor]: + log_vars = OrderedDict() + for loss_name, loss_value in losses.items(): + if isinstance(loss_value, torch.Tensor): + log_vars[loss_name] = loss_value.mean() + elif isinstance(loss_value, list): + log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value) + else: + raise TypeError(f"{loss_name} is not a tensor or list of tensors") + + if "loss" not in loss_name: + # put metrics to storage; don't return them + storage = get_event_storage() + value = log_vars.pop(loss_name).cpu().item() + storage.put_scalar(loss_name, value) + return log_vars diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/poolers.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/poolers.py new file mode 100644 index 0000000..6bea77a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/poolers.py @@ -0,0 +1,245 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import math +from typing import List +import torch +from torch import nn +from torchvision.ops import RoIPool + +from detectron2.layers import ROIAlign, ROIAlignRotated, cat, nonzero_tuple, shapes_to_tensor +from detectron2.structures import Boxes + +""" +To export ROIPooler to torchscript, in this file, variables that should be annotated with +`Union[List[Boxes], List[RotatedBoxes]]` are only annotated with `List[Boxes]`. + +TODO: Correct these annotations when torchscript support `Union`. +https://github.com/pytorch/pytorch/issues/41412 +""" + +__all__ = ["ROIPooler"] + + +def assign_boxes_to_levels( + box_lists: List[Boxes], + min_level: int, + max_level: int, + canonical_box_size: int, + canonical_level: int, +): + """ + Map each box in `box_lists` to a feature map level index and return the assignment + vector. + + Args: + box_lists (list[Boxes] | list[RotatedBoxes]): A list of N Boxes or N RotatedBoxes, + where N is the number of images in the batch. + min_level (int): Smallest feature map level index. The input is considered index 0, + the output of stage 1 is index 1, and so. + max_level (int): Largest feature map level index. + canonical_box_size (int): A canonical box size in pixels (sqrt(box area)). + canonical_level (int): The feature map level index on which a canonically-sized box + should be placed. + + Returns: + A tensor of length M, where M is the total number of boxes aggregated over all + N batch images. The memory layout corresponds to the concatenation of boxes + from all images. Each element is the feature map index, as an offset from + `self.min_level`, for the corresponding box (so value i means the box is at + `self.min_level + i`). + """ + box_sizes = torch.sqrt(cat([boxes.area() for boxes in box_lists])) + # Eqn.(1) in FPN paper + level_assignments = torch.floor( + canonical_level + torch.log2(box_sizes / canonical_box_size + 1e-8) + ) + # clamp level to (min, max), in case the box size is too large or too small + # for the available feature maps + level_assignments = torch.clamp(level_assignments, min=min_level, max=max_level) + return level_assignments.to(torch.int64) - min_level + + +def convert_boxes_to_pooler_format(box_lists: List[Boxes]): + """ + Convert all boxes in `box_lists` to the low-level format used by ROI pooling ops + (see description under Returns). + + Args: + box_lists (list[Boxes] | list[RotatedBoxes]): + A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch. + + Returns: + When input is list[Boxes]: + A tensor of shape (M, 5), where M is the total number of boxes aggregated over all + N batch images. + The 5 columns are (batch index, x0, y0, x1, y1), where batch index + is the index in [0, N) identifying which batch image the box with corners at + (x0, y0, x1, y1) comes from. + When input is list[RotatedBoxes]: + A tensor of shape (M, 6), where M is the total number of boxes aggregated over all + N batch images. + The 6 columns are (batch index, x_ctr, y_ctr, width, height, angle_degrees), + where batch index is the index in [0, N) identifying which batch image the + rotated box (x_ctr, y_ctr, width, height, angle_degrees) comes from. + """ + boxes = torch.cat([x.tensor for x in box_lists], dim=0) + # __len__ returns Tensor in tracing. + sizes = shapes_to_tensor([x.__len__() for x in box_lists], device=boxes.device) + indices = torch.repeat_interleave( + torch.arange(len(box_lists), dtype=boxes.dtype, device=boxes.device), sizes + ) + return cat([indices[:, None], boxes], dim=1) + + +class ROIPooler(nn.Module): + """ + Region of interest feature map pooler that supports pooling from one or more + feature maps. + """ + + def __init__( + self, + output_size, + scales, + sampling_ratio, + pooler_type, + canonical_box_size=224, + canonical_level=4, + ): + """ + Args: + output_size (int, tuple[int] or list[int]): output size of the pooled region, + e.g., 14 x 14. If tuple or list is given, the length must be 2. + scales (list[float]): The scale for each low-level pooling op relative to + the input image. For a feature map with stride s relative to the input + image, scale is defined as 1/s. The stride must be power of 2. + When there are multiple scales, they must form a pyramid, i.e. they must be + a monotically decreasing geometric sequence with a factor of 1/2. + sampling_ratio (int): The `sampling_ratio` parameter for the ROIAlign op. + pooler_type (string): Name of the type of pooling operation that should be applied. + For instance, "ROIPool" or "ROIAlignV2". + canonical_box_size (int): A canonical box size in pixels (sqrt(box area)). The default + is heuristically defined as 224 pixels in the FPN paper (based on ImageNet + pre-training). + canonical_level (int): The feature map level index from which a canonically-sized box + should be placed. The default is defined as level 4 (stride=16) in the FPN paper, + i.e., a box of size 224x224 will be placed on the feature with stride=16. + The box placement for all boxes will be determined from their sizes w.r.t + canonical_box_size. For example, a box whose area is 4x that of a canonical box + should be used to pool features from feature level ``canonical_level+1``. + + Note that the actual input feature maps given to this module may not have + sufficiently many levels for the input boxes. If the boxes are too large or too + small for the input feature maps, the closest level will be used. + """ + super().__init__() + + if isinstance(output_size, int): + output_size = (output_size, output_size) + assert len(output_size) == 2 + assert isinstance(output_size[0], int) and isinstance(output_size[1], int) + self.output_size = output_size + + if pooler_type == "ROIAlign": + self.level_poolers = nn.ModuleList( + ROIAlign( + output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=False + ) + for scale in scales + ) + elif pooler_type == "ROIAlignV2": + self.level_poolers = nn.ModuleList( + ROIAlign( + output_size, spatial_scale=scale, sampling_ratio=sampling_ratio, aligned=True + ) + for scale in scales + ) + elif pooler_type == "ROIPool": + self.level_poolers = nn.ModuleList( + RoIPool(output_size, spatial_scale=scale) for scale in scales + ) + elif pooler_type == "ROIAlignRotated": + self.level_poolers = nn.ModuleList( + ROIAlignRotated(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio) + for scale in scales + ) + else: + raise ValueError("Unknown pooler type: {}".format(pooler_type)) + + # Map scale (defined as 1 / stride) to its feature map level under the + # assumption that stride is a power of 2. + min_level = -(math.log2(scales[0])) + max_level = -(math.log2(scales[-1])) + assert math.isclose(min_level, int(min_level)) and math.isclose( + max_level, int(max_level) + ), "Featuremap stride is not power of 2!" + self.min_level = int(min_level) + self.max_level = int(max_level) + assert ( + len(scales) == self.max_level - self.min_level + 1 + ), "[ROIPooler] Sizes of input featuremaps do not form a pyramid!" + assert 0 <= self.min_level and self.min_level <= self.max_level + self.canonical_level = canonical_level + assert canonical_box_size > 0 + self.canonical_box_size = canonical_box_size + + def forward(self, x: List[torch.Tensor], box_lists: List[Boxes]): + """ + Args: + x (list[Tensor]): A list of feature maps of NCHW shape, with scales matching those + used to construct this module. + box_lists (list[Boxes] | list[RotatedBoxes]): + A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch. + The box coordinates are defined on the original image and + will be scaled by the `scales` argument of :class:`ROIPooler`. + + Returns: + Tensor: + A tensor of shape (M, C, output_size, output_size) where M is the total number of + boxes aggregated over all N batch images and C is the number of channels in `x`. + """ + num_level_assignments = len(self.level_poolers) + + assert isinstance(x, list) and isinstance( + box_lists, list + ), "Arguments to pooler must be lists" + assert ( + len(x) == num_level_assignments + ), "unequal value, num_level_assignments={}, but x is list of {} Tensors".format( + num_level_assignments, len(x) + ) + + assert len(box_lists) == x[0].size( + 0 + ), "unequal value, x[0] batch dim 0 is {}, but box_list has length {}".format( + x[0].size(0), len(box_lists) + ) + if len(box_lists) == 0: + return torch.zeros( + (0, x[0].shape[1]) + self.output_size, device=x[0].device, dtype=x[0].dtype + ) + + pooler_fmt_boxes = convert_boxes_to_pooler_format(box_lists) + + if num_level_assignments == 1: + return self.level_poolers[0](x[0], pooler_fmt_boxes) + + level_assignments = assign_boxes_to_levels( + box_lists, self.min_level, self.max_level, self.canonical_box_size, self.canonical_level + ) + + num_boxes = pooler_fmt_boxes.size(0) + num_channels = x[0].shape[1] + output_size = self.output_size[0] + + dtype, device = x[0].dtype, x[0].device + output = torch.zeros( + (num_boxes, num_channels, output_size, output_size), dtype=dtype, device=device + ) + + for level, pooler in enumerate(self.level_poolers): + inds = nonzero_tuple(level_assignments == level)[0] + pooler_fmt_boxes_level = pooler_fmt_boxes[inds] + # Use index_put_ instead of advance indexing, to avoid pytorch/issues/49852 + output.index_put_((inds,), pooler(x[level], pooler_fmt_boxes_level)) + + return output diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/postprocessing.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/postprocessing.py new file mode 100644 index 0000000..52f273b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/postprocessing.py @@ -0,0 +1,101 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import torch +from torch.nn import functional as F + +from detectron2.structures import Instances, ROIMasks + + +# perhaps should rename to "resize_instance" +def detector_postprocess( + results: Instances, output_height: int, output_width: int, mask_threshold: float = 0.5 +): + """ + Resize the output instances. + The input images are often resized when entering an object detector. + As a result, we often need the outputs of the detector in a different + resolution from its inputs. + + This function will resize the raw outputs of an R-CNN detector + to produce outputs according to the desired output resolution. + + Args: + results (Instances): the raw outputs from the detector. + `results.image_size` contains the input image resolution the detector sees. + This object might be modified in-place. + output_height, output_width: the desired output resolution. + + Returns: + Instances: the resized output from the model, based on the output resolution + """ + if isinstance(output_width, torch.Tensor): + # This shape might (but not necessarily) be tensors during tracing. + # Converts integer tensors to float temporaries to ensure true + # division is performed when computing scale_x and scale_y. + output_width_tmp = output_width.float() + output_height_tmp = output_height.float() + new_size = torch.stack([output_height, output_width]) + else: + new_size = (output_height, output_width) + output_width_tmp = output_width + output_height_tmp = output_height + + scale_x, scale_y = ( + output_width_tmp / results.image_size[1], + output_height_tmp / results.image_size[0], + ) + results = Instances(new_size, **results.get_fields()) + + if results.has("pred_boxes"): + output_boxes = results.pred_boxes + elif results.has("proposal_boxes"): + output_boxes = results.proposal_boxes + else: + output_boxes = None + assert output_boxes is not None, "Predictions must contain boxes!" + + output_boxes.scale(scale_x, scale_y) + output_boxes.clip(results.image_size) + + results = results[output_boxes.nonempty()] + + if results.has("pred_masks"): + if isinstance(results.pred_masks, ROIMasks): + roi_masks = results.pred_masks + else: + # pred_masks is a tensor of shape (N, 1, M, M) + roi_masks = ROIMasks(results.pred_masks[:, 0, :, :]) + results.pred_masks = roi_masks.to_bitmasks( + results.pred_boxes, output_height, output_width, mask_threshold + ).tensor # TODO return ROIMasks/BitMask object in the future + + if results.has("pred_keypoints"): + results.pred_keypoints[:, :, 0] *= scale_x + results.pred_keypoints[:, :, 1] *= scale_y + + return results + + +def sem_seg_postprocess(result, img_size, output_height, output_width): + """ + Return semantic segmentation predictions in the original resolution. + + The input images are often resized when entering semantic segmentor. Moreover, in same + cases, they also padded inside segmentor to be divisible by maximum network stride. + As a result, we often need the predictions of the segmentor in a different + resolution from its inputs. + + Args: + result (Tensor): semantic segmentation prediction logits. A tensor of shape (C, H, W), + where C is the number of classes, and H, W are the height and width of the prediction. + img_size (tuple): image size that segmentor is taking as input. + output_height, output_width: the desired output resolution. + + Returns: + semantic segmentation prediction (Tensor): A tensor of the shape + (C, output_height, output_width) that contains per-pixel soft predictions. + """ + result = result[:, : img_size[0], : img_size[1]].expand(1, -1, -1, -1) + result = F.interpolate( + result, size=(output_height, output_width), mode="bilinear", align_corners=False + )[0] + return result diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/proposal_generator/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/proposal_generator/__init__.py new file mode 100644 index 0000000..3f4e4df --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/proposal_generator/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .build import PROPOSAL_GENERATOR_REGISTRY, build_proposal_generator +from .rpn import RPN_HEAD_REGISTRY, build_rpn_head, RPN, StandardRPNHead + +__all__ = list(globals().keys()) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/proposal_generator/build.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/proposal_generator/build.py new file mode 100644 index 0000000..34eb12d --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/proposal_generator/build.py @@ -0,0 +1,24 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from detectron2.utils.registry import Registry + +PROPOSAL_GENERATOR_REGISTRY = Registry("PROPOSAL_GENERATOR") +PROPOSAL_GENERATOR_REGISTRY.__doc__ = """ +Registry for proposal generator, which produces object proposals from feature maps. + +The registered object will be called with `obj(cfg, input_shape)`. +The call should return a `nn.Module` object. +""" + +from . import rpn, rrpn # noqa F401 isort:skip + + +def build_proposal_generator(cfg, input_shape): + """ + Build a proposal generator from `cfg.MODEL.PROPOSAL_GENERATOR.NAME`. + The name can be "PrecomputedProposals" to use no proposal generator. + """ + name = cfg.MODEL.PROPOSAL_GENERATOR.NAME + if name == "PrecomputedProposals": + return None + + return PROPOSAL_GENERATOR_REGISTRY.get(name)(cfg, input_shape) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/proposal_generator/proposal_utils.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/proposal_generator/proposal_utils.py new file mode 100644 index 0000000..4703219 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/proposal_generator/proposal_utils.py @@ -0,0 +1,196 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import math +from typing import List, Tuple, Union +import torch + +from detectron2.layers import batched_nms, cat +from detectron2.structures import Boxes, Instances + +logger = logging.getLogger(__name__) + + +def _is_tracing(): + # (fixed in TORCH_VERSION >= 1.9) + if torch.jit.is_scripting(): + # https://github.com/pytorch/pytorch/issues/47379 + return False + else: + return torch.jit.is_tracing() + + +def find_top_rpn_proposals( + proposals: List[torch.Tensor], + pred_objectness_logits: List[torch.Tensor], + image_sizes: List[Tuple[int, int]], + nms_thresh: float, + pre_nms_topk: int, + post_nms_topk: int, + min_box_size: float, + training: bool, +): + """ + For each feature map, select the `pre_nms_topk` highest scoring proposals, + apply NMS, clip proposals, and remove small boxes. Return the `post_nms_topk` + highest scoring proposals among all the feature maps for each image. + + Args: + proposals (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A, 4). + All proposal predictions on the feature maps. + pred_objectness_logits (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A). + image_sizes (list[tuple]): sizes (h, w) for each image + nms_thresh (float): IoU threshold to use for NMS + pre_nms_topk (int): number of top k scoring proposals to keep before applying NMS. + When RPN is run on multiple feature maps (as in FPN) this number is per + feature map. + post_nms_topk (int): number of top k scoring proposals to keep after applying NMS. + When RPN is run on multiple feature maps (as in FPN) this number is total, + over all feature maps. + min_box_size (float): minimum proposal box side length in pixels (absolute units + wrt input images). + training (bool): True if proposals are to be used in training, otherwise False. + This arg exists only to support a legacy bug; look for the "NB: Legacy bug ..." + comment. + + Returns: + list[Instances]: list of N Instances. The i-th Instances + stores post_nms_topk object proposals for image i, sorted by their + objectness score in descending order. + """ + num_images = len(image_sizes) + device = proposals[0].device + + # 1. Select top-k anchor for every level and every image + topk_scores = [] # #lvl Tensor, each of shape N x topk + topk_proposals = [] + level_ids = [] # #lvl Tensor, each of shape (topk,) + batch_idx = torch.arange(num_images, device=device) + for level_id, (proposals_i, logits_i) in enumerate(zip(proposals, pred_objectness_logits)): + Hi_Wi_A = logits_i.shape[1] + if isinstance(Hi_Wi_A, torch.Tensor): # it's a tensor in tracing + num_proposals_i = torch.clamp(Hi_Wi_A, max=pre_nms_topk) + else: + num_proposals_i = min(Hi_Wi_A, pre_nms_topk) + + topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) + + # each is N x topk + topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4 + + topk_proposals.append(topk_proposals_i) + topk_scores.append(topk_scores_i) + level_ids.append(torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device)) + + # 2. Concat all levels together + topk_scores = cat(topk_scores, dim=1) + topk_proposals = cat(topk_proposals, dim=1) + level_ids = cat(level_ids, dim=0) + + # 3. For each image, run a per-level NMS, and choose topk results. + results: List[Instances] = [] + for n, image_size in enumerate(image_sizes): + boxes = Boxes(topk_proposals[n]) + scores_per_img = topk_scores[n] + lvl = level_ids + + valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) + if not valid_mask.all(): + if training: + raise FloatingPointError( + "Predicted boxes or scores contain Inf/NaN. Training has diverged." + ) + boxes = boxes[valid_mask] + scores_per_img = scores_per_img[valid_mask] + lvl = lvl[valid_mask] + boxes.clip(image_size) + + # filter empty boxes + keep = boxes.nonempty(threshold=min_box_size) + if _is_tracing() or keep.sum().item() != len(boxes): + boxes, scores_per_img, lvl = boxes[keep], scores_per_img[keep], lvl[keep] + + keep = batched_nms(boxes.tensor, scores_per_img, lvl, nms_thresh) + # In Detectron1, there was different behavior during training vs. testing. + # (https://github.com/facebookresearch/Detectron/issues/459) + # During training, topk is over the proposals from *all* images in the training batch. + # During testing, it is over the proposals for each image separately. + # As a result, the training behavior becomes batch-dependent, + # and the configuration "POST_NMS_TOPK_TRAIN" end up relying on the batch size. + # This bug is addressed in Detectron2 to make the behavior independent of batch size. + keep = keep[:post_nms_topk] # keep is already sorted + + res = Instances(image_size) + res.proposal_boxes = boxes[keep] + res.objectness_logits = scores_per_img[keep] + results.append(res) + return results + + +def add_ground_truth_to_proposals( + gt: Union[List[Instances], List[Boxes]], proposals: List[Instances] +) -> List[Instances]: + """ + Call `add_ground_truth_to_proposals_single_image` for all images. + + Args: + gt(Union[List[Instances], List[Boxes]): list of N elements. Element i is a Instances + representing the ground-truth for image i. + proposals (list[Instances]): list of N elements. Element i is a Instances + representing the proposals for image i. + + Returns: + list[Instances]: list of N Instances. Each is the proposals for the image, + with field "proposal_boxes" and "objectness_logits". + """ + assert gt is not None + + if len(proposals) != len(gt): + raise ValueError("proposals and gt should have the same length as the number of images!") + if len(proposals) == 0: + return proposals + + return [ + add_ground_truth_to_proposals_single_image(gt_i, proposals_i) + for gt_i, proposals_i in zip(gt, proposals) + ] + + +def add_ground_truth_to_proposals_single_image( + gt: Union[Instances, Boxes], proposals: Instances +) -> Instances: + """ + Augment `proposals` with `gt`. + + Args: + Same as `add_ground_truth_to_proposals`, but with gt and proposals + per image. + + Returns: + Same as `add_ground_truth_to_proposals`, but for only one image. + """ + if isinstance(gt, Boxes): + # convert Boxes to Instances + gt = Instances(proposals.image_size, gt_boxes=gt) + + gt_boxes = gt.gt_boxes + device = proposals.objectness_logits.device + # Assign all ground-truth boxes an objectness logit corresponding to + # P(object) = sigmoid(logit) =~ 1. + gt_logit_value = math.log((1.0 - 1e-10) / (1 - (1.0 - 1e-10))) + gt_logits = gt_logit_value * torch.ones(len(gt_boxes), device=device) + + # Concatenating gt_boxes with proposals requires them to have the same fields + gt_proposal = Instances(proposals.image_size, **gt.get_fields()) + gt_proposal.proposal_boxes = gt_boxes + gt_proposal.objectness_logits = gt_logits + + for key in proposals.get_fields().keys(): + assert gt_proposal.has( + key + ), "The attribute '{}' in `proposals` does not exist in `gt`".format(key) + + # NOTE: Instances.cat only use fields from the first item. Extra fields in latter items + # will be thrown away. + new_proposals = Instances.cat([proposals, gt_proposal]) + + return new_proposals diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/proposal_generator/rpn.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/proposal_generator/rpn.py new file mode 100644 index 0000000..99cd536 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/proposal_generator/rpn.py @@ -0,0 +1,533 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from typing import Dict, List, Optional, Tuple, Union +import torch +import torch.nn.functional as F +from torch import nn + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ShapeSpec, cat +from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou +from detectron2.utils.events import get_event_storage +from detectron2.utils.memory import retry_if_cuda_oom +from detectron2.utils.registry import Registry + +from ..anchor_generator import build_anchor_generator +from ..box_regression import Box2BoxTransform, _dense_box_regression_loss +from ..matcher import Matcher +from ..sampling import subsample_labels +from .build import PROPOSAL_GENERATOR_REGISTRY +from .proposal_utils import find_top_rpn_proposals + +RPN_HEAD_REGISTRY = Registry("RPN_HEAD") +RPN_HEAD_REGISTRY.__doc__ = """ +Registry for RPN heads, which take feature maps and perform +objectness classification and bounding box regression for anchors. + +The registered object will be called with `obj(cfg, input_shape)`. +The call should return a `nn.Module` object. +""" + + +""" +Shape shorthand in this module: + + N: number of images in the minibatch + L: number of feature maps per image on which RPN is run + A: number of cell anchors (must be the same for all feature maps) + Hi, Wi: height and width of the i-th feature map + B: size of the box parameterization + +Naming convention: + + objectness: refers to the binary classification of an anchor as object vs. not object. + + deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box2box + transform (see :class:`box_regression.Box2BoxTransform`), or 5d for rotated boxes. + + pred_objectness_logits: predicted objectness scores in [-inf, +inf]; use + sigmoid(pred_objectness_logits) to estimate P(object). + + gt_labels: ground-truth binary classification labels for objectness + + pred_anchor_deltas: predicted box2box transform deltas + + gt_anchor_deltas: ground-truth box2box transform deltas +""" + + +def build_rpn_head(cfg, input_shape): + """ + Build an RPN head defined by `cfg.MODEL.RPN.HEAD_NAME`. + """ + name = cfg.MODEL.RPN.HEAD_NAME + return RPN_HEAD_REGISTRY.get(name)(cfg, input_shape) + + +@RPN_HEAD_REGISTRY.register() +class StandardRPNHead(nn.Module): + """ + Standard RPN classification and regression heads described in :paper:`Faster R-CNN`. + Uses a 3x3 conv to produce a shared hidden state from which one 1x1 conv predicts + objectness logits for each anchor and a second 1x1 conv predicts bounding-box deltas + specifying how to deform each anchor into an object proposal. + """ + + @configurable + def __init__( + self, *, in_channels: int, num_anchors: int, box_dim: int = 4, conv_dims: List[int] = (-1,) + ): + """ + NOTE: this interface is experimental. + + Args: + in_channels (int): number of input feature channels. When using multiple + input features, they must have the same number of channels. + num_anchors (int): number of anchors to predict for *each spatial position* + on the feature map. The total number of anchors for each + feature map will be `num_anchors * H * W`. + box_dim (int): dimension of a box, which is also the number of box regression + predictions to make for each anchor. An axis aligned box has + box_dim=4, while a rotated box has box_dim=5. + conv_dims (list[int]): a list of integers representing the output channels + of N conv layers. Set it to -1 to use the same number of output channels + as input channels. + """ + super().__init__() + cur_channels = in_channels + # Keeping the old variable names and structure for backwards compatiblity. + # Otherwise the old checkpoints will fail to load. + if len(conv_dims) == 1: + out_channels = cur_channels if conv_dims[0] == -1 else conv_dims[0] + # 3x3 conv for the hidden representation + self.conv = self._get_rpn_conv(cur_channels, out_channels) + cur_channels = out_channels + else: + self.conv = nn.Sequential() + for k, conv_dim in enumerate(conv_dims): + out_channels = cur_channels if conv_dim == -1 else conv_dim + if out_channels <= 0: + raise ValueError( + f"Conv output channels should be greater than 0. Got {out_channels}" + ) + conv = self._get_rpn_conv(cur_channels, out_channels) + self.conv.add_module(f"conv{k}", conv) + cur_channels = out_channels + # 1x1 conv for predicting objectness logits + self.objectness_logits = nn.Conv2d(cur_channels, num_anchors, kernel_size=1, stride=1) + # 1x1 conv for predicting box2box transform deltas + self.anchor_deltas = nn.Conv2d(cur_channels, num_anchors * box_dim, kernel_size=1, stride=1) + + # Keeping the order of weights initialization same for backwards compatiblility. + for layer in self.modules(): + if isinstance(layer, nn.Conv2d): + nn.init.normal_(layer.weight, std=0.01) + nn.init.constant_(layer.bias, 0) + + def _get_rpn_conv(self, in_channels, out_channels): + return Conv2d( + in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + activation=nn.ReLU(), + ) + + @classmethod + def from_config(cls, cfg, input_shape): + # Standard RPN is shared across levels: + in_channels = [s.channels for s in input_shape] + assert len(set(in_channels)) == 1, "Each level must have the same channel!" + in_channels = in_channels[0] + + # RPNHead should take the same input as anchor generator + # NOTE: it assumes that creating an anchor generator does not have unwanted side effect. + anchor_generator = build_anchor_generator(cfg, input_shape) + num_anchors = anchor_generator.num_anchors + box_dim = anchor_generator.box_dim + assert ( + len(set(num_anchors)) == 1 + ), "Each level must have the same number of anchors per spatial position" + return { + "in_channels": in_channels, + "num_anchors": num_anchors[0], + "box_dim": box_dim, + "conv_dims": cfg.MODEL.RPN.CONV_DIMS, + } + + def forward(self, features: List[torch.Tensor]): + """ + Args: + features (list[Tensor]): list of feature maps + + Returns: + list[Tensor]: A list of L elements. + Element i is a tensor of shape (N, A, Hi, Wi) representing + the predicted objectness logits for all anchors. A is the number of cell anchors. + list[Tensor]: A list of L elements. Element i is a tensor of shape + (N, A*box_dim, Hi, Wi) representing the predicted "deltas" used to transform anchors + to proposals. + """ + pred_objectness_logits = [] + pred_anchor_deltas = [] + for x in features: + t = self.conv(x) + pred_objectness_logits.append(self.objectness_logits(t)) + pred_anchor_deltas.append(self.anchor_deltas(t)) + return pred_objectness_logits, pred_anchor_deltas + + +@PROPOSAL_GENERATOR_REGISTRY.register() +class RPN(nn.Module): + """ + Region Proposal Network, introduced by :paper:`Faster R-CNN`. + """ + + @configurable + def __init__( + self, + *, + in_features: List[str], + head: nn.Module, + anchor_generator: nn.Module, + anchor_matcher: Matcher, + box2box_transform: Box2BoxTransform, + batch_size_per_image: int, + positive_fraction: float, + pre_nms_topk: Tuple[float, float], + post_nms_topk: Tuple[float, float], + nms_thresh: float = 0.7, + min_box_size: float = 0.0, + anchor_boundary_thresh: float = -1.0, + loss_weight: Union[float, Dict[str, float]] = 1.0, + box_reg_loss_type: str = "smooth_l1", + smooth_l1_beta: float = 0.0, + ): + """ + NOTE: this interface is experimental. + + Args: + in_features (list[str]): list of names of input features to use + head (nn.Module): a module that predicts logits and regression deltas + for each level from a list of per-level features + anchor_generator (nn.Module): a module that creates anchors from a + list of features. Usually an instance of :class:`AnchorGenerator` + anchor_matcher (Matcher): label the anchors by matching them with ground truth. + box2box_transform (Box2BoxTransform): defines the transform from anchors boxes to + instance boxes + batch_size_per_image (int): number of anchors per image to sample for training + positive_fraction (float): fraction of foreground anchors to sample for training + pre_nms_topk (tuple[float]): (train, test) that represents the + number of top k proposals to select before NMS, in + training and testing. + post_nms_topk (tuple[float]): (train, test) that represents the + number of top k proposals to select after NMS, in + training and testing. + nms_thresh (float): NMS threshold used to de-duplicate the predicted proposals + min_box_size (float): remove proposal boxes with any side smaller than this threshold, + in the unit of input image pixels + anchor_boundary_thresh (float): legacy option + loss_weight (float|dict): weights to use for losses. Can be single float for weighting + all rpn losses together, or a dict of individual weightings. Valid dict keys are: + "loss_rpn_cls" - applied to classification loss + "loss_rpn_loc" - applied to box regression loss + box_reg_loss_type (str): Loss type to use. Supported losses: "smooth_l1", "giou". + smooth_l1_beta (float): beta parameter for the smooth L1 regression loss. Default to + use L1 loss. Only used when `box_reg_loss_type` is "smooth_l1" + """ + super().__init__() + self.in_features = in_features + self.rpn_head = head + self.anchor_generator = anchor_generator + self.anchor_matcher = anchor_matcher + self.box2box_transform = box2box_transform + self.batch_size_per_image = batch_size_per_image + self.positive_fraction = positive_fraction + # Map from self.training state to train/test settings + self.pre_nms_topk = {True: pre_nms_topk[0], False: pre_nms_topk[1]} + self.post_nms_topk = {True: post_nms_topk[0], False: post_nms_topk[1]} + self.nms_thresh = nms_thresh + self.min_box_size = float(min_box_size) + self.anchor_boundary_thresh = anchor_boundary_thresh + if isinstance(loss_weight, float): + loss_weight = {"loss_rpn_cls": loss_weight, "loss_rpn_loc": loss_weight} + self.loss_weight = loss_weight + self.box_reg_loss_type = box_reg_loss_type + self.smooth_l1_beta = smooth_l1_beta + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + in_features = cfg.MODEL.RPN.IN_FEATURES + ret = { + "in_features": in_features, + "min_box_size": cfg.MODEL.PROPOSAL_GENERATOR.MIN_SIZE, + "nms_thresh": cfg.MODEL.RPN.NMS_THRESH, + "batch_size_per_image": cfg.MODEL.RPN.BATCH_SIZE_PER_IMAGE, + "positive_fraction": cfg.MODEL.RPN.POSITIVE_FRACTION, + "loss_weight": { + "loss_rpn_cls": cfg.MODEL.RPN.LOSS_WEIGHT, + "loss_rpn_loc": cfg.MODEL.RPN.BBOX_REG_LOSS_WEIGHT * cfg.MODEL.RPN.LOSS_WEIGHT, + }, + "anchor_boundary_thresh": cfg.MODEL.RPN.BOUNDARY_THRESH, + "box2box_transform": Box2BoxTransform(weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS), + "box_reg_loss_type": cfg.MODEL.RPN.BBOX_REG_LOSS_TYPE, + "smooth_l1_beta": cfg.MODEL.RPN.SMOOTH_L1_BETA, + } + + ret["pre_nms_topk"] = (cfg.MODEL.RPN.PRE_NMS_TOPK_TRAIN, cfg.MODEL.RPN.PRE_NMS_TOPK_TEST) + ret["post_nms_topk"] = (cfg.MODEL.RPN.POST_NMS_TOPK_TRAIN, cfg.MODEL.RPN.POST_NMS_TOPK_TEST) + + ret["anchor_generator"] = build_anchor_generator(cfg, [input_shape[f] for f in in_features]) + ret["anchor_matcher"] = Matcher( + cfg.MODEL.RPN.IOU_THRESHOLDS, cfg.MODEL.RPN.IOU_LABELS, allow_low_quality_matches=True + ) + ret["head"] = build_rpn_head(cfg, [input_shape[f] for f in in_features]) + return ret + + def _subsample_labels(self, label): + """ + Randomly sample a subset of positive and negative examples, and overwrite + the label vector to the ignore value (-1) for all elements that are not + included in the sample. + + Args: + labels (Tensor): a vector of -1, 0, 1. Will be modified in-place and returned. + """ + pos_idx, neg_idx = subsample_labels( + label, self.batch_size_per_image, self.positive_fraction, 0 + ) + # Fill with the ignore label (-1), then set positive and negative labels + label.fill_(-1) + label.scatter_(0, pos_idx, 1) + label.scatter_(0, neg_idx, 0) + return label + + @torch.jit.unused + @torch.no_grad() + def label_and_sample_anchors( + self, anchors: List[Boxes], gt_instances: List[Instances] + ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: + """ + Args: + anchors (list[Boxes]): anchors for each feature map. + gt_instances: the ground-truth instances for each image. + + Returns: + list[Tensor]: + List of #img tensors. i-th element is a vector of labels whose length is + the total number of anchors across all feature maps R = sum(Hi * Wi * A). + Label values are in {-1, 0, 1}, with meanings: -1 = ignore; 0 = negative + class; 1 = positive class. + list[Tensor]: + i-th element is a Rx4 tensor. The values are the matched gt boxes for each + anchor. Values are undefined for those anchors not labeled as 1. + """ + anchors = Boxes.cat(anchors) + + gt_boxes = [x.gt_boxes for x in gt_instances] + image_sizes = [x.image_size for x in gt_instances] + del gt_instances + + gt_labels = [] + matched_gt_boxes = [] + for image_size_i, gt_boxes_i in zip(image_sizes, gt_boxes): + """ + image_size_i: (h, w) for the i-th image + gt_boxes_i: ground-truth boxes for i-th image + """ + + match_quality_matrix = retry_if_cuda_oom(pairwise_iou)(gt_boxes_i, anchors) + matched_idxs, gt_labels_i = retry_if_cuda_oom(self.anchor_matcher)(match_quality_matrix) + # Matching is memory-expensive and may result in CPU tensors. But the result is small + gt_labels_i = gt_labels_i.to(device=gt_boxes_i.device) + del match_quality_matrix + + if self.anchor_boundary_thresh >= 0: + # Discard anchors that go out of the boundaries of the image + # NOTE: This is legacy functionality that is turned off by default in Detectron2 + anchors_inside_image = anchors.inside_box(image_size_i, self.anchor_boundary_thresh) + gt_labels_i[~anchors_inside_image] = -1 + + # A vector of labels (-1, 0, 1) for each anchor + gt_labels_i = self._subsample_labels(gt_labels_i) + + if len(gt_boxes_i) == 0: + # These values won't be used anyway since the anchor is labeled as background + matched_gt_boxes_i = torch.zeros_like(anchors.tensor) + else: + # TODO wasted indexing computation for ignored boxes + matched_gt_boxes_i = gt_boxes_i[matched_idxs].tensor + + gt_labels.append(gt_labels_i) # N,AHW + matched_gt_boxes.append(matched_gt_boxes_i) + return gt_labels, matched_gt_boxes + + @torch.jit.unused + def losses( + self, + anchors: List[Boxes], + pred_objectness_logits: List[torch.Tensor], + gt_labels: List[torch.Tensor], + pred_anchor_deltas: List[torch.Tensor], + gt_boxes: List[torch.Tensor], + ) -> Dict[str, torch.Tensor]: + """ + Return the losses from a set of RPN predictions and their associated ground-truth. + + Args: + anchors (list[Boxes or RotatedBoxes]): anchors for each feature map, each + has shape (Hi*Wi*A, B), where B is box dimension (4 or 5). + pred_objectness_logits (list[Tensor]): A list of L elements. + Element i is a tensor of shape (N, Hi*Wi*A) representing + the predicted objectness logits for all anchors. + gt_labels (list[Tensor]): Output of :meth:`label_and_sample_anchors`. + pred_anchor_deltas (list[Tensor]): A list of L elements. Element i is a tensor of shape + (N, Hi*Wi*A, 4 or 5) representing the predicted "deltas" used to transform anchors + to proposals. + gt_boxes (list[Tensor]): Output of :meth:`label_and_sample_anchors`. + + Returns: + dict[loss name -> loss value]: A dict mapping from loss name to loss value. + Loss names are: `loss_rpn_cls` for objectness classification and + `loss_rpn_loc` for proposal localization. + """ + num_images = len(gt_labels) + gt_labels = torch.stack(gt_labels) # (N, sum(Hi*Wi*Ai)) + + # Log the number of positive/negative anchors per-image that's used in training + pos_mask = gt_labels == 1 + num_pos_anchors = pos_mask.sum().item() + num_neg_anchors = (gt_labels == 0).sum().item() + storage = get_event_storage() + storage.put_scalar("rpn/num_pos_anchors", num_pos_anchors / num_images) + storage.put_scalar("rpn/num_neg_anchors", num_neg_anchors / num_images) + + localization_loss = _dense_box_regression_loss( + anchors, + self.box2box_transform, + pred_anchor_deltas, + gt_boxes, + pos_mask, + box_reg_loss_type=self.box_reg_loss_type, + smooth_l1_beta=self.smooth_l1_beta, + ) + + valid_mask = gt_labels >= 0 + objectness_loss = F.binary_cross_entropy_with_logits( + cat(pred_objectness_logits, dim=1)[valid_mask], + gt_labels[valid_mask].to(torch.float32), + reduction="sum", + ) + normalizer = self.batch_size_per_image * num_images + losses = { + "loss_rpn_cls": objectness_loss / normalizer, + # The original Faster R-CNN paper uses a slightly different normalizer + # for loc loss. But it doesn't matter in practice + "loss_rpn_loc": localization_loss / normalizer, + } + losses = {k: v * self.loss_weight.get(k, 1.0) for k, v in losses.items()} + return losses + + def forward( + self, + images: ImageList, + features: Dict[str, torch.Tensor], + gt_instances: Optional[List[Instances]] = None, + ): + """ + Args: + images (ImageList): input images of length `N` + features (dict[str, Tensor]): input data as a mapping from feature + map name to tensor. Axis 0 represents the number of images `N` in + the input data; axes 1-3 are channels, height, and width, which may + vary between feature maps (e.g., if a feature pyramid is used). + gt_instances (list[Instances], optional): a length `N` list of `Instances`s. + Each `Instances` stores ground-truth instances for the corresponding image. + + Returns: + proposals: list[Instances]: contains fields "proposal_boxes", "objectness_logits" + loss: dict[Tensor] or None + """ + features = [features[f] for f in self.in_features] + anchors = self.anchor_generator(features) + + pred_objectness_logits, pred_anchor_deltas = self.rpn_head(features) + # Transpose the Hi*Wi*A dimension to the middle: + pred_objectness_logits = [ + # (N, A, Hi, Wi) -> (N, Hi, Wi, A) -> (N, Hi*Wi*A) + score.permute(0, 2, 3, 1).flatten(1) + for score in pred_objectness_logits + ] + pred_anchor_deltas = [ + # (N, A*B, Hi, Wi) -> (N, A, B, Hi, Wi) -> (N, Hi, Wi, A, B) -> (N, Hi*Wi*A, B) + x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1]) + .permute(0, 3, 4, 1, 2) + .flatten(1, -2) + for x in pred_anchor_deltas + ] + + if self.training: + assert gt_instances is not None, "RPN requires gt_instances in training!" + gt_labels, gt_boxes = self.label_and_sample_anchors(anchors, gt_instances) + losses = self.losses( + anchors, pred_objectness_logits, gt_labels, pred_anchor_deltas, gt_boxes + ) + else: + losses = {} + proposals = self.predict_proposals( + anchors, pred_objectness_logits, pred_anchor_deltas, images.image_sizes + ) + return proposals, losses + + def predict_proposals( + self, + anchors: List[Boxes], + pred_objectness_logits: List[torch.Tensor], + pred_anchor_deltas: List[torch.Tensor], + image_sizes: List[Tuple[int, int]], + ): + """ + Decode all the predicted box regression deltas to proposals. Find the top proposals + by applying NMS and removing boxes that are too small. + + Returns: + proposals (list[Instances]): list of N Instances. The i-th Instances + stores post_nms_topk object proposals for image i, sorted by their + objectness score in descending order. + """ + # The proposals are treated as fixed for joint training with roi heads. + # This approach ignores the derivative w.r.t. the proposal boxes’ coordinates that + # are also network responses. + with torch.no_grad(): + pred_proposals = self._decode_proposals(anchors, pred_anchor_deltas) + return find_top_rpn_proposals( + pred_proposals, + pred_objectness_logits, + image_sizes, + self.nms_thresh, + self.pre_nms_topk[self.training], + self.post_nms_topk[self.training], + self.min_box_size, + self.training, + ) + + def _decode_proposals(self, anchors: List[Boxes], pred_anchor_deltas: List[torch.Tensor]): + """ + Transform anchors into proposals by applying the predicted anchor deltas. + + Returns: + proposals (list[Tensor]): A list of L tensors. Tensor i has shape + (N, Hi*Wi*A, B) + """ + N = pred_anchor_deltas[0].shape[0] + proposals = [] + # For each feature map + for anchors_i, pred_anchor_deltas_i in zip(anchors, pred_anchor_deltas): + B = anchors_i.tensor.size(1) + pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B) + # Expand anchors to shape (N*Hi*Wi*A, B) + anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B) + proposals_i = self.box2box_transform.apply_deltas(pred_anchor_deltas_i, anchors_i) + # Append feature map proposals with shape (N, Hi*Wi*A, B) + proposals.append(proposals_i.view(N, -1, B)) + return proposals diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/proposal_generator/rrpn.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/proposal_generator/rrpn.py new file mode 100644 index 0000000..1a3cd28 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/proposal_generator/rrpn.py @@ -0,0 +1,209 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +import logging +from typing import Dict, List +import torch + +from detectron2.config import configurable +from detectron2.layers import ShapeSpec, batched_nms_rotated, cat +from detectron2.structures import Instances, RotatedBoxes, pairwise_iou_rotated +from detectron2.utils.memory import retry_if_cuda_oom + +from ..box_regression import Box2BoxTransformRotated +from .build import PROPOSAL_GENERATOR_REGISTRY +from .proposal_utils import _is_tracing +from .rpn import RPN + +logger = logging.getLogger(__name__) + + +def find_top_rrpn_proposals( + proposals, + pred_objectness_logits, + image_sizes, + nms_thresh, + pre_nms_topk, + post_nms_topk, + min_box_size, + training, +): + """ + For each feature map, select the `pre_nms_topk` highest scoring proposals, + apply NMS, clip proposals, and remove small boxes. Return the `post_nms_topk` + highest scoring proposals among all the feature maps if `training` is True, + otherwise, returns the highest `post_nms_topk` scoring proposals for each + feature map. + + Args: + proposals (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A, 5). + All proposal predictions on the feature maps. + pred_objectness_logits (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A). + image_sizes (list[tuple]): sizes (h, w) for each image + nms_thresh (float): IoU threshold to use for NMS + pre_nms_topk (int): number of top k scoring proposals to keep before applying NMS. + When RRPN is run on multiple feature maps (as in FPN) this number is per + feature map. + post_nms_topk (int): number of top k scoring proposals to keep after applying NMS. + When RRPN is run on multiple feature maps (as in FPN) this number is total, + over all feature maps. + min_box_size(float): minimum proposal box side length in pixels (absolute units wrt + input images). + training (bool): True if proposals are to be used in training, otherwise False. + This arg exists only to support a legacy bug; look for the "NB: Legacy bug ..." + comment. + + Returns: + proposals (list[Instances]): list of N Instances. The i-th Instances + stores post_nms_topk object proposals for image i. + """ + num_images = len(image_sizes) + device = proposals[0].device + + # 1. Select top-k anchor for every level and every image + topk_scores = [] # #lvl Tensor, each of shape N x topk + topk_proposals = [] + level_ids = [] # #lvl Tensor, each of shape (topk,) + batch_idx = torch.arange(num_images, device=device) + for level_id, proposals_i, logits_i in zip( + itertools.count(), proposals, pred_objectness_logits + ): + Hi_Wi_A = logits_i.shape[1] + if isinstance(Hi_Wi_A, torch.Tensor): # it's a tensor in tracing + num_proposals_i = torch.clamp(Hi_Wi_A, max=pre_nms_topk) + else: + num_proposals_i = min(Hi_Wi_A, pre_nms_topk) + + topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) + + # each is N x topk + topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 5 + + topk_proposals.append(topk_proposals_i) + topk_scores.append(topk_scores_i) + level_ids.append(torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device)) + + # 2. Concat all levels together + topk_scores = cat(topk_scores, dim=1) + topk_proposals = cat(topk_proposals, dim=1) + level_ids = cat(level_ids, dim=0) + + # 3. For each image, run a per-level NMS, and choose topk results. + results = [] + for n, image_size in enumerate(image_sizes): + boxes = RotatedBoxes(topk_proposals[n]) + scores_per_img = topk_scores[n] + lvl = level_ids + + valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) + if not valid_mask.all(): + if training: + raise FloatingPointError( + "Predicted boxes or scores contain Inf/NaN. Training has diverged." + ) + boxes = boxes[valid_mask] + scores_per_img = scores_per_img[valid_mask] + lvl = lvl[valid_mask] + boxes.clip(image_size) + + # filter empty boxes + keep = boxes.nonempty(threshold=min_box_size) + if _is_tracing() or keep.sum().item() != len(boxes): + boxes, scores_per_img, lvl = (boxes[keep], scores_per_img[keep], lvl[keep]) + + keep = batched_nms_rotated(boxes.tensor, scores_per_img, lvl, nms_thresh) + # In Detectron1, there was different behavior during training vs. testing. + # (https://github.com/facebookresearch/Detectron/issues/459) + # During training, topk is over the proposals from *all* images in the training batch. + # During testing, it is over the proposals for each image separately. + # As a result, the training behavior becomes batch-dependent, + # and the configuration "POST_NMS_TOPK_TRAIN" end up relying on the batch size. + # This bug is addressed in Detectron2 to make the behavior independent of batch size. + keep = keep[:post_nms_topk] + + res = Instances(image_size) + res.proposal_boxes = boxes[keep] + res.objectness_logits = scores_per_img[keep] + results.append(res) + return results + + +@PROPOSAL_GENERATOR_REGISTRY.register() +class RRPN(RPN): + """ + Rotated Region Proposal Network described in :paper:`RRPN`. + """ + + @configurable + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + if self.anchor_boundary_thresh >= 0: + raise NotImplementedError( + "anchor_boundary_thresh is a legacy option not implemented for RRPN." + ) + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + ret = super().from_config(cfg, input_shape) + ret["box2box_transform"] = Box2BoxTransformRotated(weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS) + return ret + + @torch.no_grad() + def label_and_sample_anchors(self, anchors: List[RotatedBoxes], gt_instances: List[Instances]): + """ + Args: + anchors (list[RotatedBoxes]): anchors for each feature map. + gt_instances: the ground-truth instances for each image. + + Returns: + list[Tensor]: + List of #img tensors. i-th element is a vector of labels whose length is + the total number of anchors across feature maps. Label values are in {-1, 0, 1}, + with meanings: -1 = ignore; 0 = negative class; 1 = positive class. + list[Tensor]: + i-th element is a Nx5 tensor, where N is the total number of anchors across + feature maps. The values are the matched gt boxes for each anchor. + Values are undefined for those anchors not labeled as 1. + """ + anchors = RotatedBoxes.cat(anchors) + + gt_boxes = [x.gt_boxes for x in gt_instances] + del gt_instances + + gt_labels = [] + matched_gt_boxes = [] + for gt_boxes_i in gt_boxes: + """ + gt_boxes_i: ground-truth boxes for i-th image + """ + match_quality_matrix = retry_if_cuda_oom(pairwise_iou_rotated)(gt_boxes_i, anchors) + matched_idxs, gt_labels_i = retry_if_cuda_oom(self.anchor_matcher)(match_quality_matrix) + # Matching is memory-expensive and may result in CPU tensors. But the result is small + gt_labels_i = gt_labels_i.to(device=gt_boxes_i.device) + + # A vector of labels (-1, 0, 1) for each anchor + gt_labels_i = self._subsample_labels(gt_labels_i) + + if len(gt_boxes_i) == 0: + # These values won't be used anyway since the anchor is labeled as background + matched_gt_boxes_i = torch.zeros_like(anchors.tensor) + else: + # TODO wasted indexing computation for ignored boxes + matched_gt_boxes_i = gt_boxes_i[matched_idxs].tensor + + gt_labels.append(gt_labels_i) # N,AHW + matched_gt_boxes.append(matched_gt_boxes_i) + return gt_labels, matched_gt_boxes + + @torch.no_grad() + def predict_proposals(self, anchors, pred_objectness_logits, pred_anchor_deltas, image_sizes): + pred_proposals = self._decode_proposals(anchors, pred_anchor_deltas) + return find_top_rrpn_proposals( + pred_proposals, + pred_objectness_logits, + image_sizes, + self.nms_thresh, + self.pre_nms_topk[self.training], + self.post_nms_topk[self.training], + self.min_box_size, + self.training, + ) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/__init__.py new file mode 100644 index 0000000..d13e9c5 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/__init__.py @@ -0,0 +1,29 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .box_head import ROI_BOX_HEAD_REGISTRY, build_box_head, FastRCNNConvFCHead +from .keypoint_head import ( + ROI_KEYPOINT_HEAD_REGISTRY, + build_keypoint_head, + BaseKeypointRCNNHead, + KRCNNConvDeconvUpsampleHead, +) +from .mask_head import ( + ROI_MASK_HEAD_REGISTRY, + build_mask_head, + BaseMaskRCNNHead, + MaskRCNNConvUpsampleHead, +) +from .roi_heads import ( + ROI_HEADS_REGISTRY, + ROIHeads, + Res5ROIHeads, + StandardROIHeads, + build_roi_heads, + select_foreground_proposals, +) +from .cascade_rcnn import CascadeROIHeads +from .rotated_fast_rcnn import RROIHeads +from .fast_rcnn import FastRCNNOutputLayers + +from . import cascade_rcnn # isort:skip + +__all__ = list(globals().keys()) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/box_head.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/box_head.py new file mode 100644 index 0000000..5d0370b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/box_head.py @@ -0,0 +1,118 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +from typing import List +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ShapeSpec, get_norm +from detectron2.utils.registry import Registry + +__all__ = ["FastRCNNConvFCHead", "build_box_head", "ROI_BOX_HEAD_REGISTRY"] + +ROI_BOX_HEAD_REGISTRY = Registry("ROI_BOX_HEAD") +ROI_BOX_HEAD_REGISTRY.__doc__ = """ +Registry for box heads, which make box predictions from per-region features. + +The registered object will be called with `obj(cfg, input_shape)`. +""" + + +# To get torchscript support, we make the head a subclass of `nn.Sequential`. +# Therefore, to add new layers in this head class, please make sure they are +# added in the order they will be used in forward(). +@ROI_BOX_HEAD_REGISTRY.register() +class FastRCNNConvFCHead(nn.Sequential): + """ + A head with several 3x3 conv layers (each followed by norm & relu) and then + several fc layers (each followed by relu). + """ + + @configurable + def __init__( + self, input_shape: ShapeSpec, *, conv_dims: List[int], fc_dims: List[int], conv_norm="" + ): + """ + NOTE: this interface is experimental. + + Args: + input_shape (ShapeSpec): shape of the input feature. + conv_dims (list[int]): the output dimensions of the conv layers + fc_dims (list[int]): the output dimensions of the fc layers + conv_norm (str or callable): normalization for the conv layers. + See :func:`detectron2.layers.get_norm` for supported types. + """ + super().__init__() + assert len(conv_dims) + len(fc_dims) > 0 + + self._output_size = (input_shape.channels, input_shape.height, input_shape.width) + + self.conv_norm_relus = [] + for k, conv_dim in enumerate(conv_dims): + conv = Conv2d( + self._output_size[0], + conv_dim, + kernel_size=3, + padding=1, + bias=not conv_norm, + norm=get_norm(conv_norm, conv_dim), + activation=nn.ReLU(), + ) + self.add_module("conv{}".format(k + 1), conv) + self.conv_norm_relus.append(conv) + self._output_size = (conv_dim, self._output_size[1], self._output_size[2]) + + self.fcs = [] + for k, fc_dim in enumerate(fc_dims): + if k == 0: + self.add_module("flatten", nn.Flatten()) + fc = nn.Linear(int(np.prod(self._output_size)), fc_dim) + self.add_module("fc{}".format(k + 1), fc) + self.add_module("fc_relu{}".format(k + 1), nn.ReLU()) + self.fcs.append(fc) + self._output_size = fc_dim + + for layer in self.conv_norm_relus: + weight_init.c2_msra_fill(layer) + for layer in self.fcs: + weight_init.c2_xavier_fill(layer) + + @classmethod + def from_config(cls, cfg, input_shape): + num_conv = cfg.MODEL.ROI_BOX_HEAD.NUM_CONV + conv_dim = cfg.MODEL.ROI_BOX_HEAD.CONV_DIM + num_fc = cfg.MODEL.ROI_BOX_HEAD.NUM_FC + fc_dim = cfg.MODEL.ROI_BOX_HEAD.FC_DIM + return { + "input_shape": input_shape, + "conv_dims": [conv_dim] * num_conv, + "fc_dims": [fc_dim] * num_fc, + "conv_norm": cfg.MODEL.ROI_BOX_HEAD.NORM, + } + + def forward(self, x): + for layer in self: + x = layer(x) + return x + + @property + @torch.jit.unused + def output_shape(self): + """ + Returns: + ShapeSpec: the output feature shape + """ + o = self._output_size + if isinstance(o, int): + return ShapeSpec(channels=o) + else: + return ShapeSpec(channels=o[0], height=o[1], width=o[2]) + + +def build_box_head(cfg, input_shape): + """ + Build a box head defined by `cfg.MODEL.ROI_BOX_HEAD.NAME`. + """ + name = cfg.MODEL.ROI_BOX_HEAD.NAME + return ROI_BOX_HEAD_REGISTRY.get(name)(cfg, input_shape) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/cascade_rcnn.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/cascade_rcnn.py new file mode 100644 index 0000000..a0ca70f --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/cascade_rcnn.py @@ -0,0 +1,299 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from typing import List +import torch +from torch import nn +from torch.autograd.function import Function + +from detectron2.config import configurable +from detectron2.layers import ShapeSpec +from detectron2.structures import Boxes, Instances, pairwise_iou +from detectron2.utils.events import get_event_storage + +from ..box_regression import Box2BoxTransform +from ..matcher import Matcher +from ..poolers import ROIPooler +from .box_head import build_box_head +from .fast_rcnn import FastRCNNOutputLayers, fast_rcnn_inference +from .roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads + + +class _ScaleGradient(Function): + @staticmethod + def forward(ctx, input, scale): + ctx.scale = scale + return input + + @staticmethod + def backward(ctx, grad_output): + return grad_output * ctx.scale, None + + +@ROI_HEADS_REGISTRY.register() +class CascadeROIHeads(StandardROIHeads): + """ + The ROI heads that implement :paper:`Cascade R-CNN`. + """ + + @configurable + def __init__( + self, + *, + box_in_features: List[str], + box_pooler: ROIPooler, + box_heads: List[nn.Module], + box_predictors: List[nn.Module], + proposal_matchers: List[Matcher], + **kwargs, + ): + """ + NOTE: this interface is experimental. + + Args: + box_pooler (ROIPooler): pooler that extracts region features from given boxes + box_heads (list[nn.Module]): box head for each cascade stage + box_predictors (list[nn.Module]): box predictor for each cascade stage + proposal_matchers (list[Matcher]): matcher with different IoU thresholds to + match boxes with ground truth for each stage. The first matcher matches + RPN proposals with ground truth, the other matchers use boxes predicted + by the previous stage as proposals and match them with ground truth. + """ + assert "proposal_matcher" not in kwargs, ( + "CascadeROIHeads takes 'proposal_matchers=' for each stage instead " + "of one 'proposal_matcher='." + ) + # The first matcher matches RPN proposals with ground truth, done in the base class + kwargs["proposal_matcher"] = proposal_matchers[0] + num_stages = self.num_cascade_stages = len(box_heads) + box_heads = nn.ModuleList(box_heads) + box_predictors = nn.ModuleList(box_predictors) + assert len(box_predictors) == num_stages, f"{len(box_predictors)} != {num_stages}!" + assert len(proposal_matchers) == num_stages, f"{len(proposal_matchers)} != {num_stages}!" + super().__init__( + box_in_features=box_in_features, + box_pooler=box_pooler, + box_head=box_heads, + box_predictor=box_predictors, + **kwargs, + ) + self.proposal_matchers = proposal_matchers + + @classmethod + def from_config(cls, cfg, input_shape): + ret = super().from_config(cfg, input_shape) + ret.pop("proposal_matcher") + return ret + + @classmethod + def _init_box_head(cls, cfg, input_shape): + # fmt: off + in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / input_shape[k].stride for k in in_features) + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE + cascade_bbox_reg_weights = cfg.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS + cascade_ious = cfg.MODEL.ROI_BOX_CASCADE_HEAD.IOUS + assert len(cascade_bbox_reg_weights) == len(cascade_ious) + assert cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG, \ + "CascadeROIHeads only support class-agnostic regression now!" + assert cascade_ious[0] == cfg.MODEL.ROI_HEADS.IOU_THRESHOLDS[0] + # fmt: on + + in_channels = [input_shape[f].channels for f in in_features] + # Check all channel counts are equal + assert len(set(in_channels)) == 1, in_channels + in_channels = in_channels[0] + + box_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + pooled_shape = ShapeSpec( + channels=in_channels, width=pooler_resolution, height=pooler_resolution + ) + + box_heads, box_predictors, proposal_matchers = [], [], [] + for match_iou, bbox_reg_weights in zip(cascade_ious, cascade_bbox_reg_weights): + box_head = build_box_head(cfg, pooled_shape) + box_heads.append(box_head) + box_predictors.append( + FastRCNNOutputLayers( + cfg, + box_head.output_shape, + box2box_transform=Box2BoxTransform(weights=bbox_reg_weights), + ) + ) + proposal_matchers.append(Matcher([match_iou], [0, 1], allow_low_quality_matches=False)) + return { + "box_in_features": in_features, + "box_pooler": box_pooler, + "box_heads": box_heads, + "box_predictors": box_predictors, + "proposal_matchers": proposal_matchers, + } + + def forward(self, images, features, proposals, targets=None): + del images + if self.training: + proposals = self.label_and_sample_proposals(proposals, targets) + + if self.training: + # Need targets to box head + losses = self._forward_box(features, proposals, targets) + losses.update(self._forward_mask(features, proposals)) + losses.update(self._forward_keypoint(features, proposals)) + return proposals, losses + else: + pred_instances = self._forward_box(features, proposals) + pred_instances = self.forward_with_given_boxes(features, pred_instances) + return pred_instances, {} + + def _forward_box(self, features, proposals, targets=None): + """ + Args: + features, targets: the same as in + Same as in :meth:`ROIHeads.forward`. + proposals (list[Instances]): the per-image object proposals with + their matching ground truth. + Each has fields "proposal_boxes", and "objectness_logits", + "gt_classes", "gt_boxes". + """ + features = [features[f] for f in self.box_in_features] + head_outputs = [] # (predictor, predictions, proposals) + prev_pred_boxes = None + image_sizes = [x.image_size for x in proposals] + for k in range(self.num_cascade_stages): + if k > 0: + # The output boxes of the previous stage are used to create the input + # proposals of the next stage. + proposals = self._create_proposals_from_boxes(prev_pred_boxes, image_sizes) + if self.training: + proposals = self._match_and_label_boxes(proposals, k, targets) + predictions = self._run_stage(features, proposals, k) + prev_pred_boxes = self.box_predictor[k].predict_boxes(predictions, proposals) + head_outputs.append((self.box_predictor[k], predictions, proposals)) + + if self.training: + losses = {} + storage = get_event_storage() + for stage, (predictor, predictions, proposals) in enumerate(head_outputs): + with storage.name_scope("stage{}".format(stage)): + stage_losses = predictor.losses(predictions, proposals) + losses.update({k + "_stage{}".format(stage): v for k, v in stage_losses.items()}) + return losses + else: + # Each is a list[Tensor] of length #image. Each tensor is Ri x (K+1) + scores_per_stage = [h[0].predict_probs(h[1], h[2]) for h in head_outputs] + + # Average the scores across heads + scores = [ + sum(list(scores_per_image)) * (1.0 / self.num_cascade_stages) + for scores_per_image in zip(*scores_per_stage) + ] + # Use the boxes of the last head + predictor, predictions, proposals = head_outputs[-1] + boxes = predictor.predict_boxes(predictions, proposals) + pred_instances, _ = fast_rcnn_inference( + boxes, + scores, + image_sizes, + predictor.test_score_thresh, + predictor.test_nms_thresh, + predictor.test_topk_per_image, + ) + return pred_instances + + @torch.no_grad() + def _match_and_label_boxes(self, proposals, stage, targets): + """ + Match proposals with groundtruth using the matcher at the given stage. + Label the proposals as foreground or background based on the match. + + Args: + proposals (list[Instances]): One Instances for each image, with + the field "proposal_boxes". + stage (int): the current stage + targets (list[Instances]): the ground truth instances + + Returns: + list[Instances]: the same proposals, but with fields "gt_classes" and "gt_boxes" + """ + num_fg_samples, num_bg_samples = [], [] + for proposals_per_image, targets_per_image in zip(proposals, targets): + match_quality_matrix = pairwise_iou( + targets_per_image.gt_boxes, proposals_per_image.proposal_boxes + ) + # proposal_labels are 0 or 1 + matched_idxs, proposal_labels = self.proposal_matchers[stage](match_quality_matrix) + if len(targets_per_image) > 0: + gt_classes = targets_per_image.gt_classes[matched_idxs] + # Label unmatched proposals (0 label from matcher) as background (label=num_classes) + gt_classes[proposal_labels == 0] = self.num_classes + gt_boxes = targets_per_image.gt_boxes[matched_idxs] + else: + gt_classes = torch.zeros_like(matched_idxs) + self.num_classes + gt_boxes = Boxes( + targets_per_image.gt_boxes.tensor.new_zeros((len(proposals_per_image), 4)) + ) + proposals_per_image.gt_classes = gt_classes + proposals_per_image.gt_boxes = gt_boxes + + num_fg_samples.append((proposal_labels == 1).sum().item()) + num_bg_samples.append(proposal_labels.numel() - num_fg_samples[-1]) + + # Log the number of fg/bg samples in each stage + storage = get_event_storage() + storage.put_scalar( + "stage{}/roi_head/num_fg_samples".format(stage), + sum(num_fg_samples) / len(num_fg_samples), + ) + storage.put_scalar( + "stage{}/roi_head/num_bg_samples".format(stage), + sum(num_bg_samples) / len(num_bg_samples), + ) + return proposals + + def _run_stage(self, features, proposals, stage): + """ + Args: + features (list[Tensor]): #lvl input features to ROIHeads + proposals (list[Instances]): #image Instances, with the field "proposal_boxes" + stage (int): the current stage + + Returns: + Same output as `FastRCNNOutputLayers.forward()`. + """ + box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals]) + # The original implementation averages the losses among heads, + # but scale up the parameter gradients of the heads. + # This is equivalent to adding the losses among heads, + # but scale down the gradients on features. + if self.training: + box_features = _ScaleGradient.apply(box_features, 1.0 / self.num_cascade_stages) + box_features = self.box_head[stage](box_features) + return self.box_predictor[stage](box_features) + + def _create_proposals_from_boxes(self, boxes, image_sizes): + """ + Args: + boxes (list[Tensor]): per-image predicted boxes, each of shape Ri x 4 + image_sizes (list[tuple]): list of image shapes in (h, w) + + Returns: + list[Instances]: per-image proposals with the given boxes. + """ + # Just like RPN, the proposals should not have gradients + boxes = [Boxes(b.detach()) for b in boxes] + proposals = [] + for boxes_per_image, image_size in zip(boxes, image_sizes): + boxes_per_image.clip(image_size) + if self.training: + # do not filter empty boxes at inference time, + # because the scores from each stage need to be aligned and added later + boxes_per_image = boxes_per_image[boxes_per_image.nonempty()] + prop = Instances(image_size) + prop.proposal_boxes = boxes_per_image + proposals.append(prop) + return proposals diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/fast_rcnn.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/fast_rcnn.py new file mode 100644 index 0000000..42eba21 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/fast_rcnn.py @@ -0,0 +1,462 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +from typing import Dict, List, Tuple, Union +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import ShapeSpec, batched_nms, cat, cross_entropy, nonzero_tuple +from detectron2.modeling.box_regression import Box2BoxTransform, _dense_box_regression_loss +from detectron2.structures import Boxes, Instances +from detectron2.utils.events import get_event_storage + +__all__ = ["fast_rcnn_inference", "FastRCNNOutputLayers"] + + +logger = logging.getLogger(__name__) + +""" +Shape shorthand in this module: + + N: number of images in the minibatch + R: number of ROIs, combined over all images, in the minibatch + Ri: number of ROIs in image i + K: number of foreground classes. E.g.,there are 80 foreground classes in COCO. + +Naming convention: + + deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box2box + transform (see :class:`box_regression.Box2BoxTransform`). + + pred_class_logits: predicted class scores in [-inf, +inf]; use + softmax(pred_class_logits) to estimate P(class). + + gt_classes: ground-truth classification labels in [0, K], where [0, K) represent + foreground object classes and K represents the background class. + + pred_proposal_deltas: predicted box2box transform deltas for transforming proposals + to detection box predictions. + + gt_proposal_deltas: ground-truth box2box transform deltas +""" + + +def fast_rcnn_inference( + boxes: List[torch.Tensor], + scores: List[torch.Tensor], + image_shapes: List[Tuple[int, int]], + score_thresh: float, + nms_thresh: float, + topk_per_image: int, +): + """ + Call `fast_rcnn_inference_single_image` for all images. + + Args: + boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic + boxes for each image. Element i has shape (Ri, K * 4) if doing + class-specific regression, or (Ri, 4) if doing class-agnostic + regression, where Ri is the number of predicted objects for image i. + This is compatible with the output of :meth:`FastRCNNOutputLayers.predict_boxes`. + scores (list[Tensor]): A list of Tensors of predicted class scores for each image. + Element i has shape (Ri, K + 1), where Ri is the number of predicted objects + for image i. Compatible with the output of :meth:`FastRCNNOutputLayers.predict_probs`. + image_shapes (list[tuple]): A list of (width, height) tuples for each image in the batch. + score_thresh (float): Only return detections with a confidence score exceeding this + threshold. + nms_thresh (float): The threshold to use for box non-maximum suppression. Value in [0, 1]. + topk_per_image (int): The number of top scoring detections to return. Set < 0 to return + all detections. + + Returns: + instances: (list[Instances]): A list of N instances, one for each image in the batch, + that stores the topk most confidence detections. + kept_indices: (list[Tensor]): A list of 1D tensor of length of N, each element indicates + the corresponding boxes/scores index in [0, Ri) from the input, for image i. + """ + result_per_image = [ + fast_rcnn_inference_single_image( + boxes_per_image, scores_per_image, image_shape, score_thresh, nms_thresh, topk_per_image + ) + for scores_per_image, boxes_per_image, image_shape in zip(scores, boxes, image_shapes) + ] + return [x[0] for x in result_per_image], [x[1] for x in result_per_image] + + +def _log_classification_stats(pred_logits, gt_classes, prefix="fast_rcnn"): + """ + Log the classification metrics to EventStorage. + + Args: + pred_logits: Rx(K+1) logits. The last column is for background class. + gt_classes: R labels + """ + num_instances = gt_classes.numel() + if num_instances == 0: + return + pred_classes = pred_logits.argmax(dim=1) + bg_class_ind = pred_logits.shape[1] - 1 + + fg_inds = (gt_classes >= 0) & (gt_classes < bg_class_ind) + num_fg = fg_inds.nonzero().numel() + fg_gt_classes = gt_classes[fg_inds] + fg_pred_classes = pred_classes[fg_inds] + + num_false_negative = (fg_pred_classes == bg_class_ind).nonzero().numel() + num_accurate = (pred_classes == gt_classes).nonzero().numel() + fg_num_accurate = (fg_pred_classes == fg_gt_classes).nonzero().numel() + + storage = get_event_storage() + storage.put_scalar(f"{prefix}/cls_accuracy", num_accurate / num_instances) + if num_fg > 0: + storage.put_scalar(f"{prefix}/fg_cls_accuracy", fg_num_accurate / num_fg) + storage.put_scalar(f"{prefix}/false_negative", num_false_negative / num_fg) + + +def fast_rcnn_inference_single_image( + boxes, + scores, + image_shape: Tuple[int, int], + score_thresh: float, + nms_thresh: float, + topk_per_image: int, +): + """ + Single-image inference. Return bounding-box detection results by thresholding + on scores and applying non-maximum suppression (NMS). + + Args: + Same as `fast_rcnn_inference`, but with boxes, scores, and image shapes + per image. + + Returns: + Same as `fast_rcnn_inference`, but for only one image. + """ + valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) + if not valid_mask.all(): + boxes = boxes[valid_mask] + scores = scores[valid_mask] + + scores = scores[:, :-1] + num_bbox_reg_classes = boxes.shape[1] // 4 + # Convert to Boxes to use the `clip` function ... + boxes = Boxes(boxes.reshape(-1, 4)) + boxes.clip(image_shape) + boxes = boxes.tensor.view(-1, num_bbox_reg_classes, 4) # R x C x 4 + + # 1. Filter results based on detection scores. It can make NMS more efficient + # by filtering out low-confidence detections. + filter_mask = scores > score_thresh # R x K + # R' x 2. First column contains indices of the R predictions; + # Second column contains indices of classes. + filter_inds = filter_mask.nonzero() + if num_bbox_reg_classes == 1: + boxes = boxes[filter_inds[:, 0], 0] + else: + boxes = boxes[filter_mask] + scores = scores[filter_mask] + + # 2. Apply NMS for each class independently. + keep = batched_nms(boxes, scores, filter_inds[:, 1], nms_thresh) + if topk_per_image >= 0: + keep = keep[:topk_per_image] + boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep] + + result = Instances(image_shape) + result.pred_boxes = Boxes(boxes) + result.scores = scores + result.pred_classes = filter_inds[:, 1] + return result, filter_inds[:, 0] + + +class FastRCNNOutputLayers(nn.Module): + """ + Two linear layers for predicting Fast R-CNN outputs: + + 1. proposal-to-detection box regression deltas + 2. classification scores + """ + + @configurable + def __init__( + self, + input_shape: ShapeSpec, + *, + box2box_transform, + num_classes: int, + test_score_thresh: float = 0.0, + test_nms_thresh: float = 0.5, + test_topk_per_image: int = 100, + cls_agnostic_bbox_reg: bool = False, + smooth_l1_beta: float = 0.0, + box_reg_loss_type: str = "smooth_l1", + loss_weight: Union[float, Dict[str, float]] = 1.0, + ): + """ + NOTE: this interface is experimental. + + Args: + input_shape (ShapeSpec): shape of the input feature to this module + box2box_transform (Box2BoxTransform or Box2BoxTransformRotated): + num_classes (int): number of foreground classes + test_score_thresh (float): threshold to filter predictions results. + test_nms_thresh (float): NMS threshold for prediction results. + test_topk_per_image (int): number of top predictions to produce per image. + cls_agnostic_bbox_reg (bool): whether to use class agnostic for bbox regression + smooth_l1_beta (float): transition point from L1 to L2 loss. Only used if + `box_reg_loss_type` is "smooth_l1" + box_reg_loss_type (str): Box regression loss type. One of: "smooth_l1", "giou", + "diou", "ciou" + loss_weight (float|dict): weights to use for losses. Can be single float for weighting + all losses, or a dict of individual weightings. Valid dict keys are: + * "loss_cls": applied to classification loss + * "loss_box_reg": applied to box regression loss + """ + super().__init__() + if isinstance(input_shape, int): # some backward compatibility + input_shape = ShapeSpec(channels=input_shape) + self.num_classes = num_classes + input_size = input_shape.channels * (input_shape.width or 1) * (input_shape.height or 1) + # prediction layer for num_classes foreground classes and one background class (hence + 1) + self.cls_score = nn.Linear(input_size, num_classes + 1) + num_bbox_reg_classes = 1 if cls_agnostic_bbox_reg else num_classes + box_dim = len(box2box_transform.weights) + self.bbox_pred = nn.Linear(input_size, num_bbox_reg_classes * box_dim) + + nn.init.normal_(self.cls_score.weight, std=0.01) + nn.init.normal_(self.bbox_pred.weight, std=0.001) + for l in [self.cls_score, self.bbox_pred]: + nn.init.constant_(l.bias, 0) + + self.box2box_transform = box2box_transform + self.smooth_l1_beta = smooth_l1_beta + self.test_score_thresh = test_score_thresh + self.test_nms_thresh = test_nms_thresh + self.test_topk_per_image = test_topk_per_image + self.box_reg_loss_type = box_reg_loss_type + if isinstance(loss_weight, float): + loss_weight = {"loss_cls": loss_weight, "loss_box_reg": loss_weight} + self.loss_weight = loss_weight + + @classmethod + def from_config(cls, cfg, input_shape): + return { + "input_shape": input_shape, + "box2box_transform": Box2BoxTransform(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS), + # fmt: off + "num_classes" : cfg.MODEL.ROI_HEADS.NUM_CLASSES, + "cls_agnostic_bbox_reg" : cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG, + "smooth_l1_beta" : cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA, + "test_score_thresh" : cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST, + "test_nms_thresh" : cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST, + "test_topk_per_image" : cfg.TEST.DETECTIONS_PER_IMAGE, + "box_reg_loss_type" : cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_TYPE, + "loss_weight" : {"loss_box_reg": cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT}, + # fmt: on + } + + def forward(self, x): + """ + Args: + x: per-region features of shape (N, ...) for N bounding boxes to predict. + + Returns: + (Tensor, Tensor): + First tensor: shape (N,K+1), scores for each of the N box. Each row contains the + scores for K object categories and 1 background class. + + Second tensor: bounding box regression deltas for each box. Shape is shape (N,Kx4), + or (N,4) for class-agnostic regression. + """ + if x.dim() > 2: + x = torch.flatten(x, start_dim=1) + scores = self.cls_score(x) + proposal_deltas = self.bbox_pred(x) + return scores, proposal_deltas + + def losses(self, predictions, proposals): + """ + Args: + predictions: return values of :meth:`forward()`. + proposals (list[Instances]): proposals that match the features that were used + to compute predictions. The fields ``proposal_boxes``, ``gt_boxes``, + ``gt_classes`` are expected. + + Returns: + Dict[str, Tensor]: dict of losses + """ + scores, proposal_deltas = predictions + + # parse classification outputs + gt_classes = ( + cat([p.gt_classes for p in proposals], dim=0) if len(proposals) else torch.empty(0) + ) + _log_classification_stats(scores, gt_classes) + + # parse box regression outputs + if len(proposals): + proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0) # Nx4 + assert not proposal_boxes.requires_grad, "Proposals should not require gradients!" + # If "gt_boxes" does not exist, the proposals must be all negative and + # should not be included in regression loss computation. + # Here we just use proposal_boxes as an arbitrary placeholder because its + # value won't be used in self.box_reg_loss(). + gt_boxes = cat( + [(p.gt_boxes if p.has("gt_boxes") else p.proposal_boxes).tensor for p in proposals], + dim=0, + ) + else: + proposal_boxes = gt_boxes = torch.empty((0, 4), device=proposal_deltas.device) + + losses = { + "loss_cls": cross_entropy(scores, gt_classes, reduction="mean"), + "loss_box_reg": self.box_reg_loss( + proposal_boxes, gt_boxes, proposal_deltas, gt_classes + ), + } + return {k: v * self.loss_weight.get(k, 1.0) for k, v in losses.items()} + + def box_reg_loss(self, proposal_boxes, gt_boxes, pred_deltas, gt_classes): + """ + Args: + proposal_boxes/gt_boxes are tensors with the same shape (R, 4 or 5). + pred_deltas has shape (R, 4 or 5), or (R, num_classes * (4 or 5)). + gt_classes is a long tensor of shape R, the gt class label of each proposal. + R shall be the number of proposals. + """ + box_dim = proposal_boxes.shape[1] # 4 or 5 + # Regression loss is only computed for foreground proposals (those matched to a GT) + fg_inds = nonzero_tuple((gt_classes >= 0) & (gt_classes < self.num_classes))[0] + if pred_deltas.shape[1] == box_dim: # cls-agnostic regression + fg_pred_deltas = pred_deltas[fg_inds] + else: + fg_pred_deltas = pred_deltas.view(-1, self.num_classes, box_dim)[ + fg_inds, gt_classes[fg_inds] + ] + + loss_box_reg = _dense_box_regression_loss( + [proposal_boxes[fg_inds]], + self.box2box_transform, + [fg_pred_deltas.unsqueeze(0)], + [gt_boxes[fg_inds]], + ..., + self.box_reg_loss_type, + self.smooth_l1_beta, + ) + + # The reg loss is normalized using the total number of regions (R), not the number + # of foreground regions even though the box regression loss is only defined on + # foreground regions. Why? Because doing so gives equal training influence to + # each foreground example. To see how, consider two different minibatches: + # (1) Contains a single foreground region + # (2) Contains 100 foreground regions + # If we normalize by the number of foreground regions, the single example in + # minibatch (1) will be given 100 times as much influence as each foreground + # example in minibatch (2). Normalizing by the total number of regions, R, + # means that the single example in minibatch (1) and each of the 100 examples + # in minibatch (2) are given equal influence. + return loss_box_reg / max(gt_classes.numel(), 1.0) # return 0 if empty + + def inference(self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances]): + """ + Args: + predictions: return values of :meth:`forward()`. + proposals (list[Instances]): proposals that match the features that were + used to compute predictions. The ``proposal_boxes`` field is expected. + + Returns: + list[Instances]: same as `fast_rcnn_inference`. + list[Tensor]: same as `fast_rcnn_inference`. + """ + boxes = self.predict_boxes(predictions, proposals) + scores = self.predict_probs(predictions, proposals) + image_shapes = [x.image_size for x in proposals] + return fast_rcnn_inference( + boxes, + scores, + image_shapes, + self.test_score_thresh, + self.test_nms_thresh, + self.test_topk_per_image, + ) + + def predict_boxes_for_gt_classes(self, predictions, proposals): + """ + Args: + predictions: return values of :meth:`forward()`. + proposals (list[Instances]): proposals that match the features that were used + to compute predictions. The fields ``proposal_boxes``, ``gt_classes`` are expected. + + Returns: + list[Tensor]: + A list of Tensors of predicted boxes for GT classes in case of + class-specific box head. Element i of the list has shape (Ri, B), where Ri is + the number of proposals for image i and B is the box dimension (4 or 5) + """ + if not len(proposals): + return [] + scores, proposal_deltas = predictions + proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0) + N, B = proposal_boxes.shape + predict_boxes = self.box2box_transform.apply_deltas( + proposal_deltas, proposal_boxes + ) # Nx(KxB) + + K = predict_boxes.shape[1] // B + if K > 1: + gt_classes = torch.cat([p.gt_classes for p in proposals], dim=0) + # Some proposals are ignored or have a background class. Their gt_classes + # cannot be used as index. + gt_classes = gt_classes.clamp_(0, K - 1) + + predict_boxes = predict_boxes.view(N, K, B)[ + torch.arange(N, dtype=torch.long, device=predict_boxes.device), gt_classes + ] + num_prop_per_image = [len(p) for p in proposals] + return predict_boxes.split(num_prop_per_image) + + def predict_boxes( + self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances] + ): + """ + Args: + predictions: return values of :meth:`forward()`. + proposals (list[Instances]): proposals that match the features that were + used to compute predictions. The ``proposal_boxes`` field is expected. + + Returns: + list[Tensor]: + A list of Tensors of predicted class-specific or class-agnostic boxes + for each image. Element i has shape (Ri, K * B) or (Ri, B), where Ri is + the number of proposals for image i and B is the box dimension (4 or 5) + """ + if not len(proposals): + return [] + _, proposal_deltas = predictions + num_prop_per_image = [len(p) for p in proposals] + proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0) + predict_boxes = self.box2box_transform.apply_deltas( + proposal_deltas, + proposal_boxes, + ) # Nx(KxB) + return predict_boxes.split(num_prop_per_image) + + def predict_probs( + self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances] + ): + """ + Args: + predictions: return values of :meth:`forward()`. + proposals (list[Instances]): proposals that match the features that were + used to compute predictions. + + Returns: + list[Tensor]: + A list of Tensors of predicted class probabilities for each image. + Element i has shape (Ri, K + 1), where Ri is the number of proposals for image i. + """ + scores, _ = predictions + num_inst_per_image = [len(p) for p in proposals] + probs = F.softmax(scores, dim=-1) + return probs.split(num_inst_per_image, dim=0) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/keypoint_head.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/keypoint_head.py new file mode 100644 index 0000000..e0acc13 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/keypoint_head.py @@ -0,0 +1,272 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from typing import List +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ConvTranspose2d, cat, interpolate +from detectron2.structures import Instances, heatmaps_to_keypoints +from detectron2.utils.events import get_event_storage +from detectron2.utils.registry import Registry + +_TOTAL_SKIPPED = 0 + + +__all__ = [ + "ROI_KEYPOINT_HEAD_REGISTRY", + "build_keypoint_head", + "BaseKeypointRCNNHead", + "KRCNNConvDeconvUpsampleHead", +] + + +ROI_KEYPOINT_HEAD_REGISTRY = Registry("ROI_KEYPOINT_HEAD") +ROI_KEYPOINT_HEAD_REGISTRY.__doc__ = """ +Registry for keypoint heads, which make keypoint predictions from per-region features. + +The registered object will be called with `obj(cfg, input_shape)`. +""" + + +def build_keypoint_head(cfg, input_shape): + """ + Build a keypoint head from `cfg.MODEL.ROI_KEYPOINT_HEAD.NAME`. + """ + name = cfg.MODEL.ROI_KEYPOINT_HEAD.NAME + return ROI_KEYPOINT_HEAD_REGISTRY.get(name)(cfg, input_shape) + + +def keypoint_rcnn_loss(pred_keypoint_logits, instances, normalizer): + """ + Arguments: + pred_keypoint_logits (Tensor): A tensor of shape (N, K, S, S) where N is the total number + of instances in the batch, K is the number of keypoints, and S is the side length + of the keypoint heatmap. The values are spatial logits. + instances (list[Instances]): A list of M Instances, where M is the batch size. + These instances are predictions from the model + that are in 1:1 correspondence with pred_keypoint_logits. + Each Instances should contain a `gt_keypoints` field containing a `structures.Keypoint` + instance. + normalizer (float): Normalize the loss by this amount. + If not specified, we normalize by the number of visible keypoints in the minibatch. + + Returns a scalar tensor containing the loss. + """ + heatmaps = [] + valid = [] + + keypoint_side_len = pred_keypoint_logits.shape[2] + for instances_per_image in instances: + if len(instances_per_image) == 0: + continue + keypoints = instances_per_image.gt_keypoints + heatmaps_per_image, valid_per_image = keypoints.to_heatmap( + instances_per_image.proposal_boxes.tensor, keypoint_side_len + ) + heatmaps.append(heatmaps_per_image.view(-1)) + valid.append(valid_per_image.view(-1)) + + if len(heatmaps): + keypoint_targets = cat(heatmaps, dim=0) + valid = cat(valid, dim=0).to(dtype=torch.uint8) + valid = torch.nonzero(valid).squeeze(1) + + # torch.mean (in binary_cross_entropy_with_logits) doesn't + # accept empty tensors, so handle it separately + if len(heatmaps) == 0 or valid.numel() == 0: + global _TOTAL_SKIPPED + _TOTAL_SKIPPED += 1 + storage = get_event_storage() + storage.put_scalar("kpts_num_skipped_batches", _TOTAL_SKIPPED, smoothing_hint=False) + return pred_keypoint_logits.sum() * 0 + + N, K, H, W = pred_keypoint_logits.shape + pred_keypoint_logits = pred_keypoint_logits.view(N * K, H * W) + + keypoint_loss = F.cross_entropy( + pred_keypoint_logits[valid], keypoint_targets[valid], reduction="sum" + ) + + # If a normalizer isn't specified, normalize by the number of visible keypoints in the minibatch + if normalizer is None: + normalizer = valid.numel() + keypoint_loss /= normalizer + + return keypoint_loss + + +def keypoint_rcnn_inference(pred_keypoint_logits: torch.Tensor, pred_instances: List[Instances]): + """ + Post process each predicted keypoint heatmap in `pred_keypoint_logits` into (x, y, score) + and add it to the `pred_instances` as a `pred_keypoints` field. + + Args: + pred_keypoint_logits (Tensor): A tensor of shape (R, K, S, S) where R is the total number + of instances in the batch, K is the number of keypoints, and S is the side length of + the keypoint heatmap. The values are spatial logits. + pred_instances (list[Instances]): A list of N Instances, where N is the number of images. + + Returns: + None. Each element in pred_instances will contain extra "pred_keypoints" and + "pred_keypoint_heatmaps" fields. "pred_keypoints" is a tensor of shape + (#instance, K, 3) where the last dimension corresponds to (x, y, score). + The scores are larger than 0. "pred_keypoint_heatmaps" contains the raw + keypoint logits as passed to this function. + """ + # flatten all bboxes from all images together (list[Boxes] -> Rx4 tensor) + bboxes_flat = cat([b.pred_boxes.tensor for b in pred_instances], dim=0) + + pred_keypoint_logits = pred_keypoint_logits.detach() + keypoint_results = heatmaps_to_keypoints(pred_keypoint_logits, bboxes_flat.detach()) + num_instances_per_image = [len(i) for i in pred_instances] + keypoint_results = keypoint_results[:, :, [0, 1, 3]].split(num_instances_per_image, dim=0) + heatmap_results = pred_keypoint_logits.split(num_instances_per_image, dim=0) + + for keypoint_results_per_image, heatmap_results_per_image, instances_per_image in zip( + keypoint_results, heatmap_results, pred_instances + ): + # keypoint_results_per_image is (num instances)x(num keypoints)x(x, y, score) + # heatmap_results_per_image is (num instances)x(num keypoints)x(side)x(side) + instances_per_image.pred_keypoints = keypoint_results_per_image + instances_per_image.pred_keypoint_heatmaps = heatmap_results_per_image + + +class BaseKeypointRCNNHead(nn.Module): + """ + Implement the basic Keypoint R-CNN losses and inference logic described in + Sec. 5 of :paper:`Mask R-CNN`. + """ + + @configurable + def __init__(self, *, num_keypoints, loss_weight=1.0, loss_normalizer=1.0): + """ + NOTE: this interface is experimental. + + Args: + num_keypoints (int): number of keypoints to predict + loss_weight (float): weight to multiple on the keypoint loss + loss_normalizer (float or str): + If float, divide the loss by `loss_normalizer * #images`. + If 'visible', the loss is normalized by the total number of + visible keypoints across images. + """ + super().__init__() + self.num_keypoints = num_keypoints + self.loss_weight = loss_weight + assert loss_normalizer == "visible" or isinstance(loss_normalizer, float), loss_normalizer + self.loss_normalizer = loss_normalizer + + @classmethod + def from_config(cls, cfg, input_shape): + ret = { + "loss_weight": cfg.MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT, + "num_keypoints": cfg.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS, + } + normalize_by_visible = ( + cfg.MODEL.ROI_KEYPOINT_HEAD.NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS + ) # noqa + if not normalize_by_visible: + batch_size_per_image = cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE + positive_sample_fraction = cfg.MODEL.ROI_HEADS.POSITIVE_FRACTION + ret["loss_normalizer"] = ( + ret["num_keypoints"] * batch_size_per_image * positive_sample_fraction + ) + else: + ret["loss_normalizer"] = "visible" + return ret + + def forward(self, x, instances: List[Instances]): + """ + Args: + x: input 4D region feature(s) provided by :class:`ROIHeads`. + instances (list[Instances]): contains the boxes & labels corresponding + to the input features. + Exact format is up to its caller to decide. + Typically, this is the foreground instances in training, with + "proposal_boxes" field and other gt annotations. + In inference, it contains boxes that are already predicted. + + Returns: + A dict of losses if in training. The predicted "instances" if in inference. + """ + x = self.layers(x) + if self.training: + num_images = len(instances) + normalizer = ( + None if self.loss_normalizer == "visible" else num_images * self.loss_normalizer + ) + return { + "loss_keypoint": keypoint_rcnn_loss(x, instances, normalizer=normalizer) + * self.loss_weight + } + else: + keypoint_rcnn_inference(x, instances) + return instances + + def layers(self, x): + """ + Neural network layers that makes predictions from regional input features. + """ + raise NotImplementedError + + +# To get torchscript support, we make the head a subclass of `nn.Sequential`. +# Therefore, to add new layers in this head class, please make sure they are +# added in the order they will be used in forward(). +@ROI_KEYPOINT_HEAD_REGISTRY.register() +class KRCNNConvDeconvUpsampleHead(BaseKeypointRCNNHead, nn.Sequential): + """ + A standard keypoint head containing a series of 3x3 convs, followed by + a transpose convolution and bilinear interpolation for upsampling. + It is described in Sec. 5 of :paper:`Mask R-CNN`. + """ + + @configurable + def __init__(self, input_shape, *, num_keypoints, conv_dims, **kwargs): + """ + NOTE: this interface is experimental. + + Args: + input_shape (ShapeSpec): shape of the input feature + conv_dims: an iterable of output channel counts for each conv in the head + e.g. (512, 512, 512) for three convs outputting 512 channels. + """ + super().__init__(num_keypoints=num_keypoints, **kwargs) + + # default up_scale to 2.0 (this can be made an option) + up_scale = 2.0 + in_channels = input_shape.channels + + for idx, layer_channels in enumerate(conv_dims, 1): + module = Conv2d(in_channels, layer_channels, 3, stride=1, padding=1) + self.add_module("conv_fcn{}".format(idx), module) + self.add_module("conv_fcn_relu{}".format(idx), nn.ReLU()) + in_channels = layer_channels + + deconv_kernel = 4 + self.score_lowres = ConvTranspose2d( + in_channels, num_keypoints, deconv_kernel, stride=2, padding=deconv_kernel // 2 - 1 + ) + self.up_scale = up_scale + + for name, param in self.named_parameters(): + if "bias" in name: + nn.init.constant_(param, 0) + elif "weight" in name: + # Caffe2 implementation uses MSRAFill, which in fact + # corresponds to kaiming_normal_ in PyTorch + nn.init.kaiming_normal_(param, mode="fan_out", nonlinearity="relu") + + @classmethod + def from_config(cls, cfg, input_shape): + ret = super().from_config(cfg, input_shape) + ret["input_shape"] = input_shape + ret["conv_dims"] = cfg.MODEL.ROI_KEYPOINT_HEAD.CONV_DIMS + return ret + + def layers(self, x): + for layer in self: + x = layer(x) + x = interpolate(x, scale_factor=self.up_scale, mode="bilinear", align_corners=False) + return x diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/mask_head.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/mask_head.py new file mode 100644 index 0000000..5ac5c4b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/mask_head.py @@ -0,0 +1,292 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from typing import List +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ConvTranspose2d, ShapeSpec, cat, get_norm +from detectron2.structures import Instances +from detectron2.utils.events import get_event_storage +from detectron2.utils.registry import Registry + +__all__ = [ + "BaseMaskRCNNHead", + "MaskRCNNConvUpsampleHead", + "build_mask_head", + "ROI_MASK_HEAD_REGISTRY", +] + + +ROI_MASK_HEAD_REGISTRY = Registry("ROI_MASK_HEAD") +ROI_MASK_HEAD_REGISTRY.__doc__ = """ +Registry for mask heads, which predicts instance masks given +per-region features. + +The registered object will be called with `obj(cfg, input_shape)`. +""" + + +@torch.jit.unused +def mask_rcnn_loss(pred_mask_logits: torch.Tensor, instances: List[Instances], vis_period: int = 0): + """ + Compute the mask prediction loss defined in the Mask R-CNN paper. + + Args: + pred_mask_logits (Tensor): A tensor of shape (B, C, Hmask, Wmask) or (B, 1, Hmask, Wmask) + for class-specific or class-agnostic, where B is the total number of predicted masks + in all images, C is the number of foreground classes, and Hmask, Wmask are the height + and width of the mask predictions. The values are logits. + instances (list[Instances]): A list of N Instances, where N is the number of images + in the batch. These instances are in 1:1 + correspondence with the pred_mask_logits. The ground-truth labels (class, box, mask, + ...) associated with each instance are stored in fields. + vis_period (int): the period (in steps) to dump visualization. + + Returns: + mask_loss (Tensor): A scalar tensor containing the loss. + """ + cls_agnostic_mask = pred_mask_logits.size(1) == 1 + total_num_masks = pred_mask_logits.size(0) + mask_side_len = pred_mask_logits.size(2) + assert pred_mask_logits.size(2) == pred_mask_logits.size(3), "Mask prediction must be square!" + + gt_classes = [] + gt_masks = [] + for instances_per_image in instances: + if len(instances_per_image) == 0: + continue + if not cls_agnostic_mask: + gt_classes_per_image = instances_per_image.gt_classes.to(dtype=torch.int64) + gt_classes.append(gt_classes_per_image) + + gt_masks_per_image = instances_per_image.gt_masks.crop_and_resize( + instances_per_image.proposal_boxes.tensor, mask_side_len + ).to(device=pred_mask_logits.device) + # A tensor of shape (N, M, M), N=#instances in the image; M=mask_side_len + gt_masks.append(gt_masks_per_image) + + if len(gt_masks) == 0: + return pred_mask_logits.sum() * 0 + + gt_masks = cat(gt_masks, dim=0) + + if cls_agnostic_mask: + pred_mask_logits = pred_mask_logits[:, 0] + else: + indices = torch.arange(total_num_masks) + gt_classes = cat(gt_classes, dim=0) + pred_mask_logits = pred_mask_logits[indices, gt_classes] + + if gt_masks.dtype == torch.bool: + gt_masks_bool = gt_masks + else: + # Here we allow gt_masks to be float as well (depend on the implementation of rasterize()) + gt_masks_bool = gt_masks > 0.5 + gt_masks = gt_masks.to(dtype=torch.float32) + + # Log the training accuracy (using gt classes and 0.5 threshold) + mask_incorrect = (pred_mask_logits > 0.0) != gt_masks_bool + mask_accuracy = 1 - (mask_incorrect.sum().item() / max(mask_incorrect.numel(), 1.0)) + num_positive = gt_masks_bool.sum().item() + false_positive = (mask_incorrect & ~gt_masks_bool).sum().item() / max( + gt_masks_bool.numel() - num_positive, 1.0 + ) + false_negative = (mask_incorrect & gt_masks_bool).sum().item() / max(num_positive, 1.0) + + storage = get_event_storage() + storage.put_scalar("mask_rcnn/accuracy", mask_accuracy) + storage.put_scalar("mask_rcnn/false_positive", false_positive) + storage.put_scalar("mask_rcnn/false_negative", false_negative) + if vis_period > 0 and storage.iter % vis_period == 0: + pred_masks = pred_mask_logits.sigmoid() + vis_masks = torch.cat([pred_masks, gt_masks], axis=2) + name = "Left: mask prediction; Right: mask GT" + for idx, vis_mask in enumerate(vis_masks): + vis_mask = torch.stack([vis_mask] * 3, axis=0) + storage.put_image(name + f" ({idx})", vis_mask) + + mask_loss = F.binary_cross_entropy_with_logits(pred_mask_logits, gt_masks, reduction="mean") + return mask_loss + + +def mask_rcnn_inference(pred_mask_logits: torch.Tensor, pred_instances: List[Instances]): + """ + Convert pred_mask_logits to estimated foreground probability masks while also + extracting only the masks for the predicted classes in pred_instances. For each + predicted box, the mask of the same class is attached to the instance by adding a + new "pred_masks" field to pred_instances. + + Args: + pred_mask_logits (Tensor): A tensor of shape (B, C, Hmask, Wmask) or (B, 1, Hmask, Wmask) + for class-specific or class-agnostic, where B is the total number of predicted masks + in all images, C is the number of foreground classes, and Hmask, Wmask are the height + and width of the mask predictions. The values are logits. + pred_instances (list[Instances]): A list of N Instances, where N is the number of images + in the batch. Each Instances must have field "pred_classes". + + Returns: + None. pred_instances will contain an extra "pred_masks" field storing a mask of size (Hmask, + Wmask) for predicted class. Note that the masks are returned as a soft (non-quantized) + masks the resolution predicted by the network; post-processing steps, such as resizing + the predicted masks to the original image resolution and/or binarizing them, is left + to the caller. + """ + cls_agnostic_mask = pred_mask_logits.size(1) == 1 + + if cls_agnostic_mask: + mask_probs_pred = pred_mask_logits.sigmoid() + else: + # Select masks corresponding to the predicted classes + num_masks = pred_mask_logits.shape[0] + class_pred = cat([i.pred_classes for i in pred_instances]) + indices = torch.arange(num_masks, device=class_pred.device) + mask_probs_pred = pred_mask_logits[indices, class_pred][:, None].sigmoid() + # mask_probs_pred.shape: (B, 1, Hmask, Wmask) + + num_boxes_per_image = [len(i) for i in pred_instances] + mask_probs_pred = mask_probs_pred.split(num_boxes_per_image, dim=0) + + for prob, instances in zip(mask_probs_pred, pred_instances): + instances.pred_masks = prob # (1, Hmask, Wmask) + + +class BaseMaskRCNNHead(nn.Module): + """ + Implement the basic Mask R-CNN losses and inference logic described in :paper:`Mask R-CNN` + """ + + @configurable + def __init__(self, *, loss_weight: float = 1.0, vis_period: int = 0): + """ + NOTE: this interface is experimental. + + Args: + loss_weight (float): multiplier of the loss + vis_period (int): visualization period + """ + super().__init__() + self.vis_period = vis_period + self.loss_weight = loss_weight + + @classmethod + def from_config(cls, cfg, input_shape): + return {"vis_period": cfg.VIS_PERIOD} + + def forward(self, x, instances: List[Instances]): + """ + Args: + x: input region feature(s) provided by :class:`ROIHeads`. + instances (list[Instances]): contains the boxes & labels corresponding + to the input features. + Exact format is up to its caller to decide. + Typically, this is the foreground instances in training, with + "proposal_boxes" field and other gt annotations. + In inference, it contains boxes that are already predicted. + + Returns: + A dict of losses in training. The predicted "instances" in inference. + """ + x = self.layers(x) + if self.training: + return {"loss_mask": mask_rcnn_loss(x, instances, self.vis_period) * self.loss_weight} + else: + mask_rcnn_inference(x, instances) + return instances + + def layers(self, x): + """ + Neural network layers that makes predictions from input features. + """ + raise NotImplementedError + + +# To get torchscript support, we make the head a subclass of `nn.Sequential`. +# Therefore, to add new layers in this head class, please make sure they are +# added in the order they will be used in forward(). +@ROI_MASK_HEAD_REGISTRY.register() +class MaskRCNNConvUpsampleHead(BaseMaskRCNNHead, nn.Sequential): + """ + A mask head with several conv layers, plus an upsample layer (with `ConvTranspose2d`). + Predictions are made with a final 1x1 conv layer. + """ + + @configurable + def __init__(self, input_shape: ShapeSpec, *, num_classes, conv_dims, conv_norm="", **kwargs): + """ + NOTE: this interface is experimental. + + Args: + input_shape (ShapeSpec): shape of the input feature + num_classes (int): the number of foreground classes (i.e. background is not + included). 1 if using class agnostic prediction. + conv_dims (list[int]): a list of N>0 integers representing the output dimensions + of N-1 conv layers and the last upsample layer. + conv_norm (str or callable): normalization for the conv layers. + See :func:`detectron2.layers.get_norm` for supported types. + """ + super().__init__(**kwargs) + assert len(conv_dims) >= 1, "conv_dims have to be non-empty!" + + self.conv_norm_relus = [] + + cur_channels = input_shape.channels + for k, conv_dim in enumerate(conv_dims[:-1]): + conv = Conv2d( + cur_channels, + conv_dim, + kernel_size=3, + stride=1, + padding=1, + bias=not conv_norm, + norm=get_norm(conv_norm, conv_dim), + activation=nn.ReLU(), + ) + self.add_module("mask_fcn{}".format(k + 1), conv) + self.conv_norm_relus.append(conv) + cur_channels = conv_dim + + self.deconv = ConvTranspose2d( + cur_channels, conv_dims[-1], kernel_size=2, stride=2, padding=0 + ) + self.add_module("deconv_relu", nn.ReLU()) + cur_channels = conv_dims[-1] + + self.predictor = Conv2d(cur_channels, num_classes, kernel_size=1, stride=1, padding=0) + + for layer in self.conv_norm_relus + [self.deconv]: + weight_init.c2_msra_fill(layer) + # use normal distribution initialization for mask prediction layer + nn.init.normal_(self.predictor.weight, std=0.001) + if self.predictor.bias is not None: + nn.init.constant_(self.predictor.bias, 0) + + @classmethod + def from_config(cls, cfg, input_shape): + ret = super().from_config(cfg, input_shape) + conv_dim = cfg.MODEL.ROI_MASK_HEAD.CONV_DIM + num_conv = cfg.MODEL.ROI_MASK_HEAD.NUM_CONV + ret.update( + conv_dims=[conv_dim] * (num_conv + 1), # +1 for ConvTranspose + conv_norm=cfg.MODEL.ROI_MASK_HEAD.NORM, + input_shape=input_shape, + ) + if cfg.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK: + ret["num_classes"] = 1 + else: + ret["num_classes"] = cfg.MODEL.ROI_HEADS.NUM_CLASSES + return ret + + def layers(self, x): + for layer in self: + x = layer(x) + return x + + +def build_mask_head(cfg, input_shape): + """ + Build a mask head defined by `cfg.MODEL.ROI_MASK_HEAD.NAME`. + """ + name = cfg.MODEL.ROI_MASK_HEAD.NAME + return ROI_MASK_HEAD_REGISTRY.get(name)(cfg, input_shape) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/roi_heads.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/roi_heads.py new file mode 100644 index 0000000..13dd57a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/roi_heads.py @@ -0,0 +1,877 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import inspect +import logging +import numpy as np +from typing import Dict, List, Optional, Tuple +import torch +from torch import nn + +from detectron2.config import configurable +from detectron2.layers import ShapeSpec, nonzero_tuple +from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou +from detectron2.utils.events import get_event_storage +from detectron2.utils.registry import Registry + +from ..backbone.resnet import BottleneckBlock, ResNet +from ..matcher import Matcher +from ..poolers import ROIPooler +from ..proposal_generator.proposal_utils import add_ground_truth_to_proposals +from ..sampling import subsample_labels +from .box_head import build_box_head +from .fast_rcnn import FastRCNNOutputLayers +from .keypoint_head import build_keypoint_head +from .mask_head import build_mask_head + +ROI_HEADS_REGISTRY = Registry("ROI_HEADS") +ROI_HEADS_REGISTRY.__doc__ = """ +Registry for ROI heads in a generalized R-CNN model. +ROIHeads take feature maps and region proposals, and +perform per-region computation. + +The registered object will be called with `obj(cfg, input_shape)`. +The call is expected to return an :class:`ROIHeads`. +""" + +logger = logging.getLogger(__name__) + + +def build_roi_heads(cfg, input_shape): + """ + Build ROIHeads defined by `cfg.MODEL.ROI_HEADS.NAME`. + """ + name = cfg.MODEL.ROI_HEADS.NAME + return ROI_HEADS_REGISTRY.get(name)(cfg, input_shape) + + +def select_foreground_proposals( + proposals: List[Instances], bg_label: int +) -> Tuple[List[Instances], List[torch.Tensor]]: + """ + Given a list of N Instances (for N images), each containing a `gt_classes` field, + return a list of Instances that contain only instances with `gt_classes != -1 && + gt_classes != bg_label`. + + Args: + proposals (list[Instances]): A list of N Instances, where N is the number of + images in the batch. + bg_label: label index of background class. + + Returns: + list[Instances]: N Instances, each contains only the selected foreground instances. + list[Tensor]: N boolean vector, correspond to the selection mask of + each Instances object. True for selected instances. + """ + assert isinstance(proposals, (list, tuple)) + assert isinstance(proposals[0], Instances) + assert proposals[0].has("gt_classes") + fg_proposals = [] + fg_selection_masks = [] + for proposals_per_image in proposals: + gt_classes = proposals_per_image.gt_classes + fg_selection_mask = (gt_classes != -1) & (gt_classes != bg_label) + fg_idxs = fg_selection_mask.nonzero().squeeze(1) + fg_proposals.append(proposals_per_image[fg_idxs]) + fg_selection_masks.append(fg_selection_mask) + return fg_proposals, fg_selection_masks + + +def select_proposals_with_visible_keypoints(proposals: List[Instances]) -> List[Instances]: + """ + Args: + proposals (list[Instances]): a list of N Instances, where N is the + number of images. + + Returns: + proposals: only contains proposals with at least one visible keypoint. + + Note that this is still slightly different from Detectron. + In Detectron, proposals for training keypoint head are re-sampled from + all the proposals with IOU>threshold & >=1 visible keypoint. + + Here, the proposals are first sampled from all proposals with + IOU>threshold, then proposals with no visible keypoint are filtered out. + This strategy seems to make no difference on Detectron and is easier to implement. + """ + ret = [] + all_num_fg = [] + for proposals_per_image in proposals: + # If empty/unannotated image (hard negatives), skip filtering for train + if len(proposals_per_image) == 0: + ret.append(proposals_per_image) + continue + gt_keypoints = proposals_per_image.gt_keypoints.tensor + # #fg x K x 3 + vis_mask = gt_keypoints[:, :, 2] >= 1 + xs, ys = gt_keypoints[:, :, 0], gt_keypoints[:, :, 1] + proposal_boxes = proposals_per_image.proposal_boxes.tensor.unsqueeze(dim=1) # #fg x 1 x 4 + kp_in_box = ( + (xs >= proposal_boxes[:, :, 0]) + & (xs <= proposal_boxes[:, :, 2]) + & (ys >= proposal_boxes[:, :, 1]) + & (ys <= proposal_boxes[:, :, 3]) + ) + selection = (kp_in_box & vis_mask).any(dim=1) + selection_idxs = nonzero_tuple(selection)[0] + all_num_fg.append(selection_idxs.numel()) + ret.append(proposals_per_image[selection_idxs]) + + storage = get_event_storage() + storage.put_scalar("keypoint_head/num_fg_samples", np.mean(all_num_fg)) + return ret + + +class ROIHeads(torch.nn.Module): + """ + ROIHeads perform all per-region computation in an R-CNN. + + It typically contains logic to + + 1. (in training only) match proposals with ground truth and sample them + 2. crop the regions and extract per-region features using proposals + 3. make per-region predictions with different heads + + It can have many variants, implemented as subclasses of this class. + This base class contains the logic to match/sample proposals. + But it is not necessary to inherit this class if the sampling logic is not needed. + """ + + @configurable + def __init__( + self, + *, + num_classes, + batch_size_per_image, + positive_fraction, + proposal_matcher, + proposal_append_gt=True, + ): + """ + NOTE: this interface is experimental. + + Args: + num_classes (int): number of foreground classes (i.e. background is not included) + batch_size_per_image (int): number of proposals to sample for training + positive_fraction (float): fraction of positive (foreground) proposals + to sample for training. + proposal_matcher (Matcher): matcher that matches proposals and ground truth + proposal_append_gt (bool): whether to include ground truth as proposals as well + """ + super().__init__() + self.batch_size_per_image = batch_size_per_image + self.positive_fraction = positive_fraction + self.num_classes = num_classes + self.proposal_matcher = proposal_matcher + self.proposal_append_gt = proposal_append_gt + + @classmethod + def from_config(cls, cfg): + return { + "batch_size_per_image": cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE, + "positive_fraction": cfg.MODEL.ROI_HEADS.POSITIVE_FRACTION, + "num_classes": cfg.MODEL.ROI_HEADS.NUM_CLASSES, + "proposal_append_gt": cfg.MODEL.ROI_HEADS.PROPOSAL_APPEND_GT, + # Matcher to assign box proposals to gt boxes + "proposal_matcher": Matcher( + cfg.MODEL.ROI_HEADS.IOU_THRESHOLDS, + cfg.MODEL.ROI_HEADS.IOU_LABELS, + allow_low_quality_matches=False, + ), + } + + def _sample_proposals( + self, matched_idxs: torch.Tensor, matched_labels: torch.Tensor, gt_classes: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Based on the matching between N proposals and M groundtruth, + sample the proposals and set their classification labels. + + Args: + matched_idxs (Tensor): a vector of length N, each is the best-matched + gt index in [0, M) for each proposal. + matched_labels (Tensor): a vector of length N, the matcher's label + (one of cfg.MODEL.ROI_HEADS.IOU_LABELS) for each proposal. + gt_classes (Tensor): a vector of length M. + + Returns: + Tensor: a vector of indices of sampled proposals. Each is in [0, N). + Tensor: a vector of the same length, the classification label for + each sampled proposal. Each sample is labeled as either a category in + [0, num_classes) or the background (num_classes). + """ + has_gt = gt_classes.numel() > 0 + # Get the corresponding GT for each proposal + if has_gt: + gt_classes = gt_classes[matched_idxs] + # Label unmatched proposals (0 label from matcher) as background (label=num_classes) + gt_classes[matched_labels == 0] = self.num_classes + # Label ignore proposals (-1 label) + gt_classes[matched_labels == -1] = -1 + else: + gt_classes = torch.zeros_like(matched_idxs) + self.num_classes + + sampled_fg_idxs, sampled_bg_idxs = subsample_labels( + gt_classes, self.batch_size_per_image, self.positive_fraction, self.num_classes + ) + + sampled_idxs = torch.cat([sampled_fg_idxs, sampled_bg_idxs], dim=0) + return sampled_idxs, gt_classes[sampled_idxs] + + @torch.no_grad() + def label_and_sample_proposals( + self, proposals: List[Instances], targets: List[Instances] + ) -> List[Instances]: + """ + Prepare some proposals to be used to train the ROI heads. + It performs box matching between `proposals` and `targets`, and assigns + training labels to the proposals. + It returns ``self.batch_size_per_image`` random samples from proposals and groundtruth + boxes, with a fraction of positives that is no larger than + ``self.positive_fraction``. + + Args: + See :meth:`ROIHeads.forward` + + Returns: + list[Instances]: + length `N` list of `Instances`s containing the proposals + sampled for training. Each `Instances` has the following fields: + + - proposal_boxes: the proposal boxes + - gt_boxes: the ground-truth box that the proposal is assigned to + (this is only meaningful if the proposal has a label > 0; if label = 0 + then the ground-truth box is random) + + Other fields such as "gt_classes", "gt_masks", that's included in `targets`. + """ + # Augment proposals with ground-truth boxes. + # In the case of learned proposals (e.g., RPN), when training starts + # the proposals will be low quality due to random initialization. + # It's possible that none of these initial + # proposals have high enough overlap with the gt objects to be used + # as positive examples for the second stage components (box head, + # cls head, mask head). Adding the gt boxes to the set of proposals + # ensures that the second stage components will have some positive + # examples from the start of training. For RPN, this augmentation improves + # convergence and empirically improves box AP on COCO by about 0.5 + # points (under one tested configuration). + if self.proposal_append_gt: + proposals = add_ground_truth_to_proposals(targets, proposals) + + proposals_with_gt = [] + + num_fg_samples = [] + num_bg_samples = [] + for proposals_per_image, targets_per_image in zip(proposals, targets): + has_gt = len(targets_per_image) > 0 + match_quality_matrix = pairwise_iou( + targets_per_image.gt_boxes, proposals_per_image.proposal_boxes + ) + matched_idxs, matched_labels = self.proposal_matcher(match_quality_matrix) + sampled_idxs, gt_classes = self._sample_proposals( + matched_idxs, matched_labels, targets_per_image.gt_classes + ) + + # Set target attributes of the sampled proposals: + proposals_per_image = proposals_per_image[sampled_idxs] + proposals_per_image.gt_classes = gt_classes + + if has_gt: + sampled_targets = matched_idxs[sampled_idxs] + # We index all the attributes of targets that start with "gt_" + # and have not been added to proposals yet (="gt_classes"). + # NOTE: here the indexing waste some compute, because heads + # like masks, keypoints, etc, will filter the proposals again, + # (by foreground/background, or number of keypoints in the image, etc) + # so we essentially index the data twice. + for (trg_name, trg_value) in targets_per_image.get_fields().items(): + if trg_name.startswith("gt_") and not proposals_per_image.has(trg_name): + proposals_per_image.set(trg_name, trg_value[sampled_targets]) + # If no GT is given in the image, we don't know what a dummy gt value can be. + # Therefore the returned proposals won't have any gt_* fields, except for a + # gt_classes full of background label. + + num_bg_samples.append((gt_classes == self.num_classes).sum().item()) + num_fg_samples.append(gt_classes.numel() - num_bg_samples[-1]) + proposals_with_gt.append(proposals_per_image) + + # Log the number of fg/bg samples that are selected for training ROI heads + storage = get_event_storage() + storage.put_scalar("roi_head/num_fg_samples", np.mean(num_fg_samples)) + storage.put_scalar("roi_head/num_bg_samples", np.mean(num_bg_samples)) + + return proposals_with_gt + + def forward( + self, + images: ImageList, + features: Dict[str, torch.Tensor], + proposals: List[Instances], + targets: Optional[List[Instances]] = None, + ) -> Tuple[List[Instances], Dict[str, torch.Tensor]]: + """ + Args: + images (ImageList): + features (dict[str,Tensor]): input data as a mapping from feature + map name to tensor. Axis 0 represents the number of images `N` in + the input data; axes 1-3 are channels, height, and width, which may + vary between feature maps (e.g., if a feature pyramid is used). + proposals (list[Instances]): length `N` list of `Instances`. The i-th + `Instances` contains object proposals for the i-th input image, + with fields "proposal_boxes" and "objectness_logits". + targets (list[Instances], optional): length `N` list of `Instances`. The i-th + `Instances` contains the ground-truth per-instance annotations + for the i-th input image. Specify `targets` during training only. + It may have the following fields: + + - gt_boxes: the bounding box of each instance. + - gt_classes: the label for each instance with a category ranging in [0, #class]. + - gt_masks: PolygonMasks or BitMasks, the ground-truth masks of each instance. + - gt_keypoints: NxKx3, the groud-truth keypoints for each instance. + + Returns: + list[Instances]: length `N` list of `Instances` containing the + detected instances. Returned during inference only; may be [] during training. + + dict[str->Tensor]: + mapping from a named loss to a tensor storing the loss. Used during training only. + """ + raise NotImplementedError() + + +@ROI_HEADS_REGISTRY.register() +class Res5ROIHeads(ROIHeads): + """ + The ROIHeads in a typical "C4" R-CNN model, where + the box and mask head share the cropping and + the per-region feature computation by a Res5 block. + See :paper:`ResNet` Appendix A. + """ + + @configurable + def __init__( + self, + *, + in_features: List[str], + pooler: ROIPooler, + res5: nn.Module, + box_predictor: nn.Module, + mask_head: Optional[nn.Module] = None, + **kwargs, + ): + """ + NOTE: this interface is experimental. + + Args: + in_features (list[str]): list of backbone feature map names to use for + feature extraction + pooler (ROIPooler): pooler to extra region features from backbone + res5 (nn.Sequential): a CNN to compute per-region features, to be used by + ``box_predictor`` and ``mask_head``. Typically this is a "res5" + block from a ResNet. + box_predictor (nn.Module): make box predictions from the feature. + Should have the same interface as :class:`FastRCNNOutputLayers`. + mask_head (nn.Module): transform features to make mask predictions + """ + super().__init__(**kwargs) + self.in_features = in_features + self.pooler = pooler + if isinstance(res5, (list, tuple)): + res5 = nn.Sequential(*res5) + self.res5 = res5 + self.box_predictor = box_predictor + self.mask_on = mask_head is not None + if self.mask_on: + self.mask_head = mask_head + + @classmethod + def from_config(cls, cfg, input_shape): + # fmt: off + ret = super().from_config(cfg) + in_features = ret["in_features"] = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE + pooler_scales = (1.0 / input_shape[in_features[0]].stride, ) + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + mask_on = cfg.MODEL.MASK_ON + # fmt: on + assert not cfg.MODEL.KEYPOINT_ON + assert len(in_features) == 1 + + ret["pooler"] = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + + # Compatbility with old moco code. Might be useful. + # See notes in StandardROIHeads.from_config + if not inspect.ismethod(cls._build_res5_block): + logger.warning( + "The behavior of _build_res5_block may change. " + "Please do not depend on private methods." + ) + cls._build_res5_block = classmethod(cls._build_res5_block) + + ret["res5"], out_channels = cls._build_res5_block(cfg) + ret["box_predictor"] = FastRCNNOutputLayers( + cfg, ShapeSpec(channels=out_channels, height=1, width=1) + ) + + if mask_on: + ret["mask_head"] = build_mask_head( + cfg, + ShapeSpec(channels=out_channels, width=pooler_resolution, height=pooler_resolution), + ) + return ret + + @classmethod + def _build_res5_block(cls, cfg): + # fmt: off + stage_channel_factor = 2 ** 3 # res5 is 8x res2 + num_groups = cfg.MODEL.RESNETS.NUM_GROUPS + width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP + bottleneck_channels = num_groups * width_per_group * stage_channel_factor + out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS * stage_channel_factor + stride_in_1x1 = cfg.MODEL.RESNETS.STRIDE_IN_1X1 + norm = cfg.MODEL.RESNETS.NORM + assert not cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE[-1], \ + "Deformable conv is not yet supported in res5 head." + # fmt: on + + blocks = ResNet.make_stage( + BottleneckBlock, + 3, + stride_per_block=[2, 1, 1], + in_channels=out_channels // 2, + bottleneck_channels=bottleneck_channels, + out_channels=out_channels, + num_groups=num_groups, + norm=norm, + stride_in_1x1=stride_in_1x1, + ) + return nn.Sequential(*blocks), out_channels + + def _shared_roi_transform(self, features: List[torch.Tensor], boxes: List[Boxes]): + x = self.pooler(features, boxes) + return self.res5(x) + + def forward( + self, + images: ImageList, + features: Dict[str, torch.Tensor], + proposals: List[Instances], + targets: Optional[List[Instances]] = None, + ): + """ + See :meth:`ROIHeads.forward`. + """ + del images + + if self.training: + assert targets + proposals = self.label_and_sample_proposals(proposals, targets) + del targets + + proposal_boxes = [x.proposal_boxes for x in proposals] + box_features = self._shared_roi_transform( + [features[f] for f in self.in_features], proposal_boxes + ) + predictions = self.box_predictor(box_features.mean(dim=[2, 3])) + + if self.training: + del features + losses = self.box_predictor.losses(predictions, proposals) + if self.mask_on: + proposals, fg_selection_masks = select_foreground_proposals( + proposals, self.num_classes + ) + # Since the ROI feature transform is shared between boxes and masks, + # we don't need to recompute features. The mask loss is only defined + # on foreground proposals, so we need to select out the foreground + # features. + mask_features = box_features[torch.cat(fg_selection_masks, dim=0)] + del box_features + losses.update(self.mask_head(mask_features, proposals)) + return [], losses + else: + pred_instances, _ = self.box_predictor.inference(predictions, proposals) + pred_instances = self.forward_with_given_boxes(features, pred_instances) + return pred_instances, {} + + def forward_with_given_boxes( + self, features: Dict[str, torch.Tensor], instances: List[Instances] + ) -> List[Instances]: + """ + Use the given boxes in `instances` to produce other (non-box) per-ROI outputs. + + Args: + features: same as in `forward()` + instances (list[Instances]): instances to predict other outputs. Expect the keys + "pred_boxes" and "pred_classes" to exist. + + Returns: + instances (Instances): + the same `Instances` object, with extra + fields such as `pred_masks` or `pred_keypoints`. + """ + assert not self.training + assert instances[0].has("pred_boxes") and instances[0].has("pred_classes") + + if self.mask_on: + feature_list = [features[f] for f in self.in_features] + x = self._shared_roi_transform(feature_list, [x.pred_boxes for x in instances]) + return self.mask_head(x, instances) + else: + return instances + + +@ROI_HEADS_REGISTRY.register() +class StandardROIHeads(ROIHeads): + """ + It's "standard" in a sense that there is no ROI transform sharing + or feature sharing between tasks. + Each head independently processes the input features by each head's + own pooler and head. + + This class is used by most models, such as FPN and C5. + To implement more models, you can subclass it and implement a different + :meth:`forward()` or a head. + """ + + @configurable + def __init__( + self, + *, + box_in_features: List[str], + box_pooler: ROIPooler, + box_head: nn.Module, + box_predictor: nn.Module, + mask_in_features: Optional[List[str]] = None, + mask_pooler: Optional[ROIPooler] = None, + mask_head: Optional[nn.Module] = None, + keypoint_in_features: Optional[List[str]] = None, + keypoint_pooler: Optional[ROIPooler] = None, + keypoint_head: Optional[nn.Module] = None, + train_on_pred_boxes: bool = False, + **kwargs, + ): + """ + NOTE: this interface is experimental. + + Args: + box_in_features (list[str]): list of feature names to use for the box head. + box_pooler (ROIPooler): pooler to extra region features for box head + box_head (nn.Module): transform features to make box predictions + box_predictor (nn.Module): make box predictions from the feature. + Should have the same interface as :class:`FastRCNNOutputLayers`. + mask_in_features (list[str]): list of feature names to use for the mask + pooler or mask head. None if not using mask head. + mask_pooler (ROIPooler): pooler to extract region features from image features. + The mask head will then take region features to make predictions. + If None, the mask head will directly take the dict of image features + defined by `mask_in_features` + mask_head (nn.Module): transform features to make mask predictions + keypoint_in_features, keypoint_pooler, keypoint_head: similar to ``mask_*``. + train_on_pred_boxes (bool): whether to use proposal boxes or + predicted boxes from the box head to train other heads. + """ + super().__init__(**kwargs) + # keep self.in_features for backward compatibility + self.in_features = self.box_in_features = box_in_features + self.box_pooler = box_pooler + self.box_head = box_head + self.box_predictor = box_predictor + + self.mask_on = mask_in_features is not None + if self.mask_on: + self.mask_in_features = mask_in_features + self.mask_pooler = mask_pooler + self.mask_head = mask_head + + self.keypoint_on = keypoint_in_features is not None + if self.keypoint_on: + self.keypoint_in_features = keypoint_in_features + self.keypoint_pooler = keypoint_pooler + self.keypoint_head = keypoint_head + + self.train_on_pred_boxes = train_on_pred_boxes + + @classmethod + def from_config(cls, cfg, input_shape): + ret = super().from_config(cfg) + ret["train_on_pred_boxes"] = cfg.MODEL.ROI_BOX_HEAD.TRAIN_ON_PRED_BOXES + # Subclasses that have not been updated to use from_config style construction + # may have overridden _init_*_head methods. In this case, those overridden methods + # will not be classmethods and we need to avoid trying to call them here. + # We test for this with ismethod which only returns True for bound methods of cls. + # Such subclasses will need to handle calling their overridden _init_*_head methods. + if inspect.ismethod(cls._init_box_head): + ret.update(cls._init_box_head(cfg, input_shape)) + if inspect.ismethod(cls._init_mask_head): + ret.update(cls._init_mask_head(cfg, input_shape)) + if inspect.ismethod(cls._init_keypoint_head): + ret.update(cls._init_keypoint_head(cfg, input_shape)) + return ret + + @classmethod + def _init_box_head(cls, cfg, input_shape): + # fmt: off + in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / input_shape[k].stride for k in in_features) + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE + # fmt: on + + # If StandardROIHeads is applied on multiple feature maps (as in FPN), + # then we share the same predictors and therefore the channel counts must be the same + in_channels = [input_shape[f].channels for f in in_features] + # Check all channel counts are equal + assert len(set(in_channels)) == 1, in_channels + in_channels = in_channels[0] + + box_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + # Here we split "box head" and "box predictor", which is mainly due to historical reasons. + # They are used together so the "box predictor" layers should be part of the "box head". + # New subclasses of ROIHeads do not need "box predictor"s. + box_head = build_box_head( + cfg, ShapeSpec(channels=in_channels, height=pooler_resolution, width=pooler_resolution) + ) + box_predictor = FastRCNNOutputLayers(cfg, box_head.output_shape) + return { + "box_in_features": in_features, + "box_pooler": box_pooler, + "box_head": box_head, + "box_predictor": box_predictor, + } + + @classmethod + def _init_mask_head(cls, cfg, input_shape): + if not cfg.MODEL.MASK_ON: + return {} + # fmt: off + in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_resolution = cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / input_shape[k].stride for k in in_features) + sampling_ratio = cfg.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_MASK_HEAD.POOLER_TYPE + # fmt: on + + in_channels = [input_shape[f].channels for f in in_features][0] + + ret = {"mask_in_features": in_features} + ret["mask_pooler"] = ( + ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + if pooler_type + else None + ) + if pooler_type: + shape = ShapeSpec( + channels=in_channels, width=pooler_resolution, height=pooler_resolution + ) + else: + shape = {f: input_shape[f] for f in in_features} + ret["mask_head"] = build_mask_head(cfg, shape) + return ret + + @classmethod + def _init_keypoint_head(cls, cfg, input_shape): + if not cfg.MODEL.KEYPOINT_ON: + return {} + # fmt: off + in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_resolution = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / input_shape[k].stride for k in in_features) # noqa + sampling_ratio = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_TYPE + # fmt: on + + in_channels = [input_shape[f].channels for f in in_features][0] + + ret = {"keypoint_in_features": in_features} + ret["keypoint_pooler"] = ( + ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + if pooler_type + else None + ) + if pooler_type: + shape = ShapeSpec( + channels=in_channels, width=pooler_resolution, height=pooler_resolution + ) + else: + shape = {f: input_shape[f] for f in in_features} + ret["keypoint_head"] = build_keypoint_head(cfg, shape) + return ret + + def forward( + self, + images: ImageList, + features: Dict[str, torch.Tensor], + proposals: List[Instances], + targets: Optional[List[Instances]] = None, + ) -> Tuple[List[Instances], Dict[str, torch.Tensor]]: + """ + See :class:`ROIHeads.forward`. + """ + del images + if self.training: + assert targets, "'targets' argument is required during training" + proposals = self.label_and_sample_proposals(proposals, targets) + del targets + + if self.training: + losses = self._forward_box(features, proposals) + # Usually the original proposals used by the box head are used by the mask, keypoint + # heads. But when `self.train_on_pred_boxes is True`, proposals will contain boxes + # predicted by the box head. + losses.update(self._forward_mask(features, proposals)) + losses.update(self._forward_keypoint(features, proposals)) + return proposals, losses + else: + pred_instances = self._forward_box(features, proposals) + # During inference cascaded prediction is used: the mask and keypoints heads are only + # applied to the top scoring box detections. + pred_instances = self.forward_with_given_boxes(features, pred_instances) + return pred_instances, {} + + def forward_with_given_boxes( + self, features: Dict[str, torch.Tensor], instances: List[Instances] + ) -> List[Instances]: + """ + Use the given boxes in `instances` to produce other (non-box) per-ROI outputs. + + This is useful for downstream tasks where a box is known, but need to obtain + other attributes (outputs of other heads). + Test-time augmentation also uses this. + + Args: + features: same as in `forward()` + instances (list[Instances]): instances to predict other outputs. Expect the keys + "pred_boxes" and "pred_classes" to exist. + + Returns: + list[Instances]: + the same `Instances` objects, with extra + fields such as `pred_masks` or `pred_keypoints`. + """ + assert not self.training + assert instances[0].has("pred_boxes") and instances[0].has("pred_classes") + + instances = self._forward_mask(features, instances) + instances = self._forward_keypoint(features, instances) + return instances + + def _forward_box(self, features: Dict[str, torch.Tensor], proposals: List[Instances]): + """ + Forward logic of the box prediction branch. If `self.train_on_pred_boxes is True`, + the function puts predicted boxes in the `proposal_boxes` field of `proposals` argument. + + Args: + features (dict[str, Tensor]): mapping from feature map names to tensor. + Same as in :meth:`ROIHeads.forward`. + proposals (list[Instances]): the per-image object proposals with + their matching ground truth. + Each has fields "proposal_boxes", and "objectness_logits", + "gt_classes", "gt_boxes". + + Returns: + In training, a dict of losses. + In inference, a list of `Instances`, the predicted instances. + """ + features = [features[f] for f in self.box_in_features] + box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals]) + box_features = self.box_head(box_features) + predictions = self.box_predictor(box_features) + del box_features + + if self.training: + losses = self.box_predictor.losses(predictions, proposals) + # proposals is modified in-place below, so losses must be computed first. + if self.train_on_pred_boxes: + with torch.no_grad(): + pred_boxes = self.box_predictor.predict_boxes_for_gt_classes( + predictions, proposals + ) + for proposals_per_image, pred_boxes_per_image in zip(proposals, pred_boxes): + proposals_per_image.proposal_boxes = Boxes(pred_boxes_per_image) + return losses + else: + pred_instances, _ = self.box_predictor.inference(predictions, proposals) + return pred_instances + + def _forward_mask(self, features: Dict[str, torch.Tensor], instances: List[Instances]): + """ + Forward logic of the mask prediction branch. + + Args: + features (dict[str, Tensor]): mapping from feature map names to tensor. + Same as in :meth:`ROIHeads.forward`. + instances (list[Instances]): the per-image instances to train/predict masks. + In training, they can be the proposals. + In inference, they can be the boxes predicted by R-CNN box head. + + Returns: + In training, a dict of losses. + In inference, update `instances` with new fields "pred_masks" and return it. + """ + if not self.mask_on: + return {} if self.training else instances + + if self.training: + # head is only trained on positive proposals. + instances, _ = select_foreground_proposals(instances, self.num_classes) + + if self.mask_pooler is not None: + features = [features[f] for f in self.mask_in_features] + boxes = [x.proposal_boxes if self.training else x.pred_boxes for x in instances] + features = self.mask_pooler(features, boxes) + else: + features = {f: features[f] for f in self.mask_in_features} + return self.mask_head(features, instances) + + def _forward_keypoint(self, features: Dict[str, torch.Tensor], instances: List[Instances]): + """ + Forward logic of the keypoint prediction branch. + + Args: + features (dict[str, Tensor]): mapping from feature map names to tensor. + Same as in :meth:`ROIHeads.forward`. + instances (list[Instances]): the per-image instances to train/predict keypoints. + In training, they can be the proposals. + In inference, they can be the boxes predicted by R-CNN box head. + + Returns: + In training, a dict of losses. + In inference, update `instances` with new fields "pred_keypoints" and return it. + """ + if not self.keypoint_on: + return {} if self.training else instances + + if self.training: + # head is only trained on positive proposals with >=1 visible keypoints. + instances, _ = select_foreground_proposals(instances, self.num_classes) + instances = select_proposals_with_visible_keypoints(instances) + + if self.keypoint_pooler is not None: + features = [features[f] for f in self.keypoint_in_features] + boxes = [x.proposal_boxes if self.training else x.pred_boxes for x in instances] + features = self.keypoint_pooler(features, boxes) + else: + features = {f: features[f] for f in self.keypoint_in_features} + return self.keypoint_head(features, instances) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/rotated_fast_rcnn.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/rotated_fast_rcnn.py new file mode 100644 index 0000000..b1eedee --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/roi_heads/rotated_fast_rcnn.py @@ -0,0 +1,270 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import numpy as np +import torch + +from detectron2.config import configurable +from detectron2.layers import ShapeSpec, batched_nms_rotated +from detectron2.structures import Instances, RotatedBoxes, pairwise_iou_rotated +from detectron2.utils.events import get_event_storage + +from ..box_regression import Box2BoxTransformRotated +from ..poolers import ROIPooler +from ..proposal_generator.proposal_utils import add_ground_truth_to_proposals +from .box_head import build_box_head +from .fast_rcnn import FastRCNNOutputLayers +from .roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads + +logger = logging.getLogger(__name__) + +""" +Shape shorthand in this module: + + N: number of images in the minibatch + R: number of ROIs, combined over all images, in the minibatch + Ri: number of ROIs in image i + K: number of foreground classes. E.g.,there are 80 foreground classes in COCO. + +Naming convention: + + deltas: refers to the 5-d (dx, dy, dw, dh, da) deltas that parameterize the box2box + transform (see :class:`box_regression.Box2BoxTransformRotated`). + + pred_class_logits: predicted class scores in [-inf, +inf]; use + softmax(pred_class_logits) to estimate P(class). + + gt_classes: ground-truth classification labels in [0, K], where [0, K) represent + foreground object classes and K represents the background class. + + pred_proposal_deltas: predicted rotated box2box transform deltas for transforming proposals + to detection box predictions. + + gt_proposal_deltas: ground-truth rotated box2box transform deltas +""" + + +def fast_rcnn_inference_rotated( + boxes, scores, image_shapes, score_thresh, nms_thresh, topk_per_image +): + """ + Call `fast_rcnn_inference_single_image_rotated` for all images. + + Args: + boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic + boxes for each image. Element i has shape (Ri, K * 5) if doing + class-specific regression, or (Ri, 5) if doing class-agnostic + regression, where Ri is the number of predicted objects for image i. + This is compatible with the output of :meth:`FastRCNNOutputLayers.predict_boxes`. + scores (list[Tensor]): A list of Tensors of predicted class scores for each image. + Element i has shape (Ri, K + 1), where Ri is the number of predicted objects + for image i. Compatible with the output of :meth:`FastRCNNOutputLayers.predict_probs`. + image_shapes (list[tuple]): A list of (width, height) tuples for each image in the batch. + score_thresh (float): Only return detections with a confidence score exceeding this + threshold. + nms_thresh (float): The threshold to use for box non-maximum suppression. Value in [0, 1]. + topk_per_image (int): The number of top scoring detections to return. Set < 0 to return + all detections. + + Returns: + instances: (list[Instances]): A list of N instances, one for each image in the batch, + that stores the topk most confidence detections. + kept_indices: (list[Tensor]): A list of 1D tensor of length of N, each element indicates + the corresponding boxes/scores index in [0, Ri) from the input, for image i. + """ + result_per_image = [ + fast_rcnn_inference_single_image_rotated( + boxes_per_image, scores_per_image, image_shape, score_thresh, nms_thresh, topk_per_image + ) + for scores_per_image, boxes_per_image, image_shape in zip(scores, boxes, image_shapes) + ] + return [x[0] for x in result_per_image], [x[1] for x in result_per_image] + + +def fast_rcnn_inference_single_image_rotated( + boxes, scores, image_shape, score_thresh, nms_thresh, topk_per_image +): + """ + Single-image inference. Return rotated bounding-box detection results by thresholding + on scores and applying rotated non-maximum suppression (Rotated NMS). + + Args: + Same as `fast_rcnn_inference_rotated`, but with rotated boxes, scores, and image shapes + per image. + + Returns: + Same as `fast_rcnn_inference_rotated`, but for only one image. + """ + valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) + if not valid_mask.all(): + boxes = boxes[valid_mask] + scores = scores[valid_mask] + + B = 5 # box dimension + scores = scores[:, :-1] + num_bbox_reg_classes = boxes.shape[1] // B + # Convert to Boxes to use the `clip` function ... + boxes = RotatedBoxes(boxes.reshape(-1, B)) + boxes.clip(image_shape) + boxes = boxes.tensor.view(-1, num_bbox_reg_classes, B) # R x C x B + # Filter results based on detection scores + filter_mask = scores > score_thresh # R x K + # R' x 2. First column contains indices of the R predictions; + # Second column contains indices of classes. + filter_inds = filter_mask.nonzero() + if num_bbox_reg_classes == 1: + boxes = boxes[filter_inds[:, 0], 0] + else: + boxes = boxes[filter_mask] + scores = scores[filter_mask] + + # Apply per-class Rotated NMS + keep = batched_nms_rotated(boxes, scores, filter_inds[:, 1], nms_thresh) + if topk_per_image >= 0: + keep = keep[:topk_per_image] + boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep] + + result = Instances(image_shape) + result.pred_boxes = RotatedBoxes(boxes) + result.scores = scores + result.pred_classes = filter_inds[:, 1] + + return result, filter_inds[:, 0] + + +class RotatedFastRCNNOutputLayers(FastRCNNOutputLayers): + """ + Two linear layers for predicting Rotated Fast R-CNN outputs. + """ + + @classmethod + def from_config(cls, cfg, input_shape): + args = super().from_config(cfg, input_shape) + args["box2box_transform"] = Box2BoxTransformRotated( + weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS + ) + return args + + def inference(self, predictions, proposals): + """ + Returns: + list[Instances]: same as `fast_rcnn_inference_rotated`. + list[Tensor]: same as `fast_rcnn_inference_rotated`. + """ + boxes = self.predict_boxes(predictions, proposals) + scores = self.predict_probs(predictions, proposals) + image_shapes = [x.image_size for x in proposals] + + return fast_rcnn_inference_rotated( + boxes, + scores, + image_shapes, + self.test_score_thresh, + self.test_nms_thresh, + self.test_topk_per_image, + ) + + +@ROI_HEADS_REGISTRY.register() +class RROIHeads(StandardROIHeads): + """ + This class is used by Rotated Fast R-CNN to detect rotated boxes. + For now, it only supports box predictions but not mask or keypoints. + """ + + @configurable + def __init__(self, **kwargs): + """ + NOTE: this interface is experimental. + """ + super().__init__(**kwargs) + assert ( + not self.mask_on and not self.keypoint_on + ), "Mask/Keypoints not supported in Rotated ROIHeads." + assert not self.train_on_pred_boxes, "train_on_pred_boxes not implemented for RROIHeads!" + + @classmethod + def _init_box_head(cls, cfg, input_shape): + # fmt: off + in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / input_shape[k].stride for k in in_features) + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE + # fmt: on + assert pooler_type in ["ROIAlignRotated"], pooler_type + # assume all channel counts are equal + in_channels = [input_shape[f].channels for f in in_features][0] + + box_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + box_head = build_box_head( + cfg, ShapeSpec(channels=in_channels, height=pooler_resolution, width=pooler_resolution) + ) + # This line is the only difference v.s. StandardROIHeads + box_predictor = RotatedFastRCNNOutputLayers(cfg, box_head.output_shape) + return { + "box_in_features": in_features, + "box_pooler": box_pooler, + "box_head": box_head, + "box_predictor": box_predictor, + } + + @torch.no_grad() + def label_and_sample_proposals(self, proposals, targets): + """ + Prepare some proposals to be used to train the RROI heads. + It performs box matching between `proposals` and `targets`, and assigns + training labels to the proposals. + It returns `self.batch_size_per_image` random samples from proposals and groundtruth boxes, + with a fraction of positives that is no larger than `self.positive_sample_fraction. + + Args: + See :meth:`StandardROIHeads.forward` + + Returns: + list[Instances]: length `N` list of `Instances`s containing the proposals + sampled for training. Each `Instances` has the following fields: + - proposal_boxes: the rotated proposal boxes + - gt_boxes: the ground-truth rotated boxes that the proposal is assigned to + (this is only meaningful if the proposal has a label > 0; if label = 0 + then the ground-truth box is random) + - gt_classes: the ground-truth classification lable for each proposal + """ + if self.proposal_append_gt: + proposals = add_ground_truth_to_proposals(targets, proposals) + + proposals_with_gt = [] + + num_fg_samples = [] + num_bg_samples = [] + for proposals_per_image, targets_per_image in zip(proposals, targets): + has_gt = len(targets_per_image) > 0 + match_quality_matrix = pairwise_iou_rotated( + targets_per_image.gt_boxes, proposals_per_image.proposal_boxes + ) + matched_idxs, matched_labels = self.proposal_matcher(match_quality_matrix) + sampled_idxs, gt_classes = self._sample_proposals( + matched_idxs, matched_labels, targets_per_image.gt_classes + ) + + proposals_per_image = proposals_per_image[sampled_idxs] + proposals_per_image.gt_classes = gt_classes + + if has_gt: + sampled_targets = matched_idxs[sampled_idxs] + proposals_per_image.gt_boxes = targets_per_image.gt_boxes[sampled_targets] + + num_bg_samples.append((gt_classes == self.num_classes).sum().item()) + num_fg_samples.append(gt_classes.numel() - num_bg_samples[-1]) + proposals_with_gt.append(proposals_per_image) + + # Log the number of fg/bg samples that are selected for training ROI heads + storage = get_event_storage() + storage.put_scalar("roi_head/num_fg_samples", np.mean(num_fg_samples)) + storage.put_scalar("roi_head/num_bg_samples", np.mean(num_bg_samples)) + + return proposals_with_gt diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/sampling.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/sampling.py new file mode 100644 index 0000000..a2d0f66 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/sampling.py @@ -0,0 +1,54 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import torch + +from detectron2.layers import nonzero_tuple + +__all__ = ["subsample_labels"] + + +def subsample_labels( + labels: torch.Tensor, num_samples: int, positive_fraction: float, bg_label: int +): + """ + Return `num_samples` (or fewer, if not enough found) + random samples from `labels` which is a mixture of positives & negatives. + It will try to return as many positives as possible without + exceeding `positive_fraction * num_samples`, and then try to + fill the remaining slots with negatives. + + Args: + labels (Tensor): (N, ) label vector with values: + * -1: ignore + * bg_label: background ("negative") class + * otherwise: one or more foreground ("positive") classes + num_samples (int): The total number of labels with value >= 0 to return. + Values that are not sampled will be filled with -1 (ignore). + positive_fraction (float): The number of subsampled labels with values > 0 + is `min(num_positives, int(positive_fraction * num_samples))`. The number + of negatives sampled is `min(num_negatives, num_samples - num_positives_sampled)`. + In order words, if there are not enough positives, the sample is filled with + negatives. If there are also not enough negatives, then as many elements are + sampled as is possible. + bg_label (int): label index of background ("negative") class. + + Returns: + pos_idx, neg_idx (Tensor): + 1D vector of indices. The total length of both is `num_samples` or fewer. + """ + positive = nonzero_tuple((labels != -1) & (labels != bg_label))[0] + negative = nonzero_tuple(labels == bg_label)[0] + + num_pos = int(num_samples * positive_fraction) + # protect against not enough positive examples + num_pos = min(positive.numel(), num_pos) + num_neg = num_samples - num_pos + # protect against not enough negative examples + num_neg = min(negative.numel(), num_neg) + + # randomly select positive and negative examples + perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos] + perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg] + + pos_idx = positive[perm1] + neg_idx = negative[perm2] + return pos_idx, neg_idx diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/modeling/test_time_augmentation.py b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/test_time_augmentation.py new file mode 100644 index 0000000..373e6bf --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/modeling/test_time_augmentation.py @@ -0,0 +1,307 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import numpy as np +from contextlib import contextmanager +from itertools import count +from typing import List +import torch +from fvcore.transforms import HFlipTransform, NoOpTransform +from torch import nn +from torch.nn.parallel import DistributedDataParallel + +from detectron2.config import configurable +from detectron2.data.detection_utils import read_image +from detectron2.data.transforms import ( + RandomFlip, + ResizeShortestEdge, + ResizeTransform, + apply_augmentations, +) +from detectron2.structures import Boxes, Instances + +from .meta_arch import GeneralizedRCNN +from .postprocessing import detector_postprocess +from .roi_heads.fast_rcnn import fast_rcnn_inference_single_image + +__all__ = ["DatasetMapperTTA", "GeneralizedRCNNWithTTA"] + + +class DatasetMapperTTA: + """ + Implement test-time augmentation for detection data. + It is a callable which takes a dataset dict from a detection dataset, + and returns a list of dataset dicts where the images + are augmented from the input image by the transformations defined in the config. + This is used for test-time augmentation. + """ + + @configurable + def __init__(self, min_sizes: List[int], max_size: int, flip: bool): + """ + Args: + min_sizes: list of short-edge size to resize the image to + max_size: maximum height or width of resized images + flip: whether to apply flipping augmentation + """ + self.min_sizes = min_sizes + self.max_size = max_size + self.flip = flip + + @classmethod + def from_config(cls, cfg): + return { + "min_sizes": cfg.TEST.AUG.MIN_SIZES, + "max_size": cfg.TEST.AUG.MAX_SIZE, + "flip": cfg.TEST.AUG.FLIP, + } + + def __call__(self, dataset_dict): + """ + Args: + dict: a dict in standard model input format. See tutorials for details. + + Returns: + list[dict]: + a list of dicts, which contain augmented version of the input image. + The total number of dicts is ``len(min_sizes) * (2 if flip else 1)``. + Each dict has field "transforms" which is a TransformList, + containing the transforms that are used to generate this image. + """ + numpy_image = dataset_dict["image"].permute(1, 2, 0).numpy() + shape = numpy_image.shape + orig_shape = (dataset_dict["height"], dataset_dict["width"]) + if shape[:2] != orig_shape: + # It transforms the "original" image in the dataset to the input image + pre_tfm = ResizeTransform(orig_shape[0], orig_shape[1], shape[0], shape[1]) + else: + pre_tfm = NoOpTransform() + + # Create all combinations of augmentations to use + aug_candidates = [] # each element is a list[Augmentation] + for min_size in self.min_sizes: + resize = ResizeShortestEdge(min_size, self.max_size) + aug_candidates.append([resize]) # resize only + if self.flip: + flip = RandomFlip(prob=1.0) + aug_candidates.append([resize, flip]) # resize + flip + + # Apply all the augmentations + ret = [] + for aug in aug_candidates: + new_image, tfms = apply_augmentations(aug, np.copy(numpy_image)) + torch_image = torch.from_numpy(np.ascontiguousarray(new_image.transpose(2, 0, 1))) + + dic = copy.deepcopy(dataset_dict) + dic["transforms"] = pre_tfm + tfms + dic["image"] = torch_image + ret.append(dic) + return ret + + +class GeneralizedRCNNWithTTA(nn.Module): + """ + A GeneralizedRCNN with test-time augmentation enabled. + Its :meth:`__call__` method has the same interface as :meth:`GeneralizedRCNN.forward`. + """ + + def __init__(self, cfg, model, tta_mapper=None, batch_size=3): + """ + Args: + cfg (CfgNode): + model (GeneralizedRCNN): a GeneralizedRCNN to apply TTA on. + tta_mapper (callable): takes a dataset dict and returns a list of + augmented versions of the dataset dict. Defaults to + `DatasetMapperTTA(cfg)`. + batch_size (int): batch the augmented images into this batch size for inference. + """ + super().__init__() + if isinstance(model, DistributedDataParallel): + model = model.module + assert isinstance( + model, GeneralizedRCNN + ), "TTA is only supported on GeneralizedRCNN. Got a model of type {}".format(type(model)) + self.cfg = cfg.clone() + assert not self.cfg.MODEL.KEYPOINT_ON, "TTA for keypoint is not supported yet" + assert ( + not self.cfg.MODEL.LOAD_PROPOSALS + ), "TTA for pre-computed proposals is not supported yet" + + self.model = model + + if tta_mapper is None: + tta_mapper = DatasetMapperTTA(cfg) + self.tta_mapper = tta_mapper + self.batch_size = batch_size + + @contextmanager + def _turn_off_roi_heads(self, attrs): + """ + Open a context where some heads in `model.roi_heads` are temporarily turned off. + Args: + attr (list[str]): the attribute in `model.roi_heads` which can be used + to turn off a specific head, e.g., "mask_on", "keypoint_on". + """ + roi_heads = self.model.roi_heads + old = {} + for attr in attrs: + try: + old[attr] = getattr(roi_heads, attr) + except AttributeError: + # The head may not be implemented in certain ROIHeads + pass + + if len(old.keys()) == 0: + yield + else: + for attr in old.keys(): + setattr(roi_heads, attr, False) + yield + for attr in old.keys(): + setattr(roi_heads, attr, old[attr]) + + def _batch_inference(self, batched_inputs, detected_instances=None): + """ + Execute inference on a list of inputs, + using batch size = self.batch_size, instead of the length of the list. + + Inputs & outputs have the same format as :meth:`GeneralizedRCNN.inference` + """ + if detected_instances is None: + detected_instances = [None] * len(batched_inputs) + + outputs = [] + inputs, instances = [], [] + for idx, input, instance in zip(count(), batched_inputs, detected_instances): + inputs.append(input) + instances.append(instance) + if len(inputs) == self.batch_size or idx == len(batched_inputs) - 1: + outputs.extend( + self.model.inference( + inputs, + instances if instances[0] is not None else None, + do_postprocess=False, + ) + ) + inputs, instances = [], [] + return outputs + + def __call__(self, batched_inputs): + """ + Same input/output format as :meth:`GeneralizedRCNN.forward` + """ + + def _maybe_read_image(dataset_dict): + ret = copy.copy(dataset_dict) + if "image" not in ret: + image = read_image(ret.pop("file_name"), self.model.input_format) + image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW + ret["image"] = image + if "height" not in ret and "width" not in ret: + ret["height"] = image.shape[1] + ret["width"] = image.shape[2] + return ret + + return [self._inference_one_image(_maybe_read_image(x)) for x in batched_inputs] + + def _inference_one_image(self, input): + """ + Args: + input (dict): one dataset dict with "image" field being a CHW tensor + + Returns: + dict: one output dict + """ + orig_shape = (input["height"], input["width"]) + augmented_inputs, tfms = self._get_augmented_inputs(input) + # Detect boxes from all augmented versions + with self._turn_off_roi_heads(["mask_on", "keypoint_on"]): + # temporarily disable roi heads + all_boxes, all_scores, all_classes = self._get_augmented_boxes(augmented_inputs, tfms) + # merge all detected boxes to obtain final predictions for boxes + merged_instances = self._merge_detections(all_boxes, all_scores, all_classes, orig_shape) + + if self.cfg.MODEL.MASK_ON: + # Use the detected boxes to obtain masks + augmented_instances = self._rescale_detected_boxes( + augmented_inputs, merged_instances, tfms + ) + # run forward on the detected boxes + outputs = self._batch_inference(augmented_inputs, augmented_instances) + # Delete now useless variables to avoid being out of memory + del augmented_inputs, augmented_instances + # average the predictions + merged_instances.pred_masks = self._reduce_pred_masks(outputs, tfms) + merged_instances = detector_postprocess(merged_instances, *orig_shape) + return {"instances": merged_instances} + else: + return {"instances": merged_instances} + + def _get_augmented_inputs(self, input): + augmented_inputs = self.tta_mapper(input) + tfms = [x.pop("transforms") for x in augmented_inputs] + return augmented_inputs, tfms + + def _get_augmented_boxes(self, augmented_inputs, tfms): + # 1: forward with all augmented images + outputs = self._batch_inference(augmented_inputs) + # 2: union the results + all_boxes = [] + all_scores = [] + all_classes = [] + for output, tfm in zip(outputs, tfms): + # Need to inverse the transforms on boxes, to obtain results on original image + pred_boxes = output.pred_boxes.tensor + original_pred_boxes = tfm.inverse().apply_box(pred_boxes.cpu().numpy()) + all_boxes.append(torch.from_numpy(original_pred_boxes).to(pred_boxes.device)) + + all_scores.extend(output.scores) + all_classes.extend(output.pred_classes) + all_boxes = torch.cat(all_boxes, dim=0) + return all_boxes, all_scores, all_classes + + def _merge_detections(self, all_boxes, all_scores, all_classes, shape_hw): + # select from the union of all results + num_boxes = len(all_boxes) + num_classes = self.cfg.MODEL.ROI_HEADS.NUM_CLASSES + # +1 because fast_rcnn_inference expects background scores as well + all_scores_2d = torch.zeros(num_boxes, num_classes + 1, device=all_boxes.device) + for idx, cls, score in zip(count(), all_classes, all_scores): + all_scores_2d[idx, cls] = score + + merged_instances, _ = fast_rcnn_inference_single_image( + all_boxes, + all_scores_2d, + shape_hw, + 1e-8, + self.cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST, + self.cfg.TEST.DETECTIONS_PER_IMAGE, + ) + + return merged_instances + + def _rescale_detected_boxes(self, augmented_inputs, merged_instances, tfms): + augmented_instances = [] + for input, tfm in zip(augmented_inputs, tfms): + # Transform the target box to the augmented image's coordinate space + pred_boxes = merged_instances.pred_boxes.tensor.cpu().numpy() + pred_boxes = torch.from_numpy(tfm.apply_box(pred_boxes)) + + aug_instances = Instances( + image_size=input["image"].shape[1:3], + pred_boxes=Boxes(pred_boxes), + pred_classes=merged_instances.pred_classes, + scores=merged_instances.scores, + ) + augmented_instances.append(aug_instances) + return augmented_instances + + def _reduce_pred_masks(self, outputs, tfms): + # Should apply inverse transforms on masks. + # We assume only resize & flip are used. pred_masks is a scale-invariant + # representation, so we handle flip specially + for output, tfm in zip(outputs, tfms): + if any(isinstance(t, HFlipTransform) for t in tfm.transforms): + output.pred_masks = output.pred_masks.flip(dims=[3]) + all_pred_masks = torch.stack([o.pred_masks for o in outputs], dim=0) + avg_pred_masks = torch.mean(all_pred_masks, dim=0) + return avg_pred_masks diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/projects/README.md b/motion-gan-pipeline/preprocessing/third/detectron2/projects/README.md new file mode 100644 index 0000000..95afe7f --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/projects/README.md @@ -0,0 +1,2 @@ + +Projects live in the [`projects` directory](../../projects) under the root of this repository, but not here. diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/projects/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/projects/__init__.py new file mode 100644 index 0000000..a68207d --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/projects/__init__.py @@ -0,0 +1,31 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import importlib +from pathlib import Path + +_PROJECTS = { + "point_rend": "PointRend", + "deeplab": "DeepLab", + "panoptic_deeplab": "Panoptic-DeepLab", +} +_PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent / "projects" + +if _PROJECT_ROOT.is_dir(): + # This is true only for in-place installation (pip install -e, setup.py develop), + # where setup(package_dir=) does not work: https://github.com/pypa/setuptools/issues/230 + + class _D2ProjectsFinder(importlib.abc.MetaPathFinder): + def find_spec(self, name, path, target=None): + if not name.startswith("detectron2.projects."): + return + project_name = name.split(".")[-1] + project_dir = _PROJECTS.get(project_name) + if not project_dir: + return + target_file = _PROJECT_ROOT / f"{project_dir}/{project_name}/__init__.py" + if not target_file.is_file(): + return + return importlib.util.spec_from_file_location(name, target_file) + + import sys + + sys.meta_path.append(_D2ProjectsFinder()) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/solver/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/solver/__init__.py new file mode 100644 index 0000000..9a2dbd3 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/solver/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .build import build_lr_scheduler, build_optimizer, get_default_optimizer_params +from .lr_scheduler import WarmupCosineLR, WarmupMultiStepLR, LRMultiplier, WarmupParamScheduler + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/solver/build.py b/motion-gan-pipeline/preprocessing/third/detectron2/solver/build.py new file mode 100644 index 0000000..42c6e88 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/solver/build.py @@ -0,0 +1,287 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import itertools +import logging +from collections import defaultdict +from enum import Enum +from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Type, Union +import torch +from fvcore.common.param_scheduler import CosineParamScheduler, MultiStepParamScheduler + +from detectron2.config import CfgNode + +from .lr_scheduler import LRMultiplier, WarmupParamScheduler + +_GradientClipperInput = Union[torch.Tensor, Iterable[torch.Tensor]] +_GradientClipper = Callable[[_GradientClipperInput], None] + + +class GradientClipType(Enum): + VALUE = "value" + NORM = "norm" + + +def _create_gradient_clipper(cfg: CfgNode) -> _GradientClipper: + """ + Creates gradient clipping closure to clip by value or by norm, + according to the provided config. + """ + cfg = copy.deepcopy(cfg) + + def clip_grad_norm(p: _GradientClipperInput): + torch.nn.utils.clip_grad_norm_(p, cfg.CLIP_VALUE, cfg.NORM_TYPE) + + def clip_grad_value(p: _GradientClipperInput): + torch.nn.utils.clip_grad_value_(p, cfg.CLIP_VALUE) + + _GRADIENT_CLIP_TYPE_TO_CLIPPER = { + GradientClipType.VALUE: clip_grad_value, + GradientClipType.NORM: clip_grad_norm, + } + return _GRADIENT_CLIP_TYPE_TO_CLIPPER[GradientClipType(cfg.CLIP_TYPE)] + + +def _generate_optimizer_class_with_gradient_clipping( + optimizer: Type[torch.optim.Optimizer], + *, + per_param_clipper: Optional[_GradientClipper] = None, + global_clipper: Optional[_GradientClipper] = None, +) -> Type[torch.optim.Optimizer]: + """ + Dynamically creates a new type that inherits the type of a given instance + and overrides the `step` method to add gradient clipping + """ + assert ( + per_param_clipper is None or global_clipper is None + ), "Not allowed to use both per-parameter clipping and global clipping" + + def optimizer_wgc_step(self, closure=None): + if per_param_clipper is not None: + for group in self.param_groups: + for p in group["params"]: + per_param_clipper(p) + else: + # global clipper for future use with detr + # (https://github.com/facebookresearch/detr/pull/287) + all_params = itertools.chain(*[g["params"] for g in self.param_groups]) + global_clipper(all_params) + super(type(self), self).step(closure) + + OptimizerWithGradientClip = type( + optimizer.__name__ + "WithGradientClip", + (optimizer,), + {"step": optimizer_wgc_step}, + ) + return OptimizerWithGradientClip + + +def maybe_add_gradient_clipping( + cfg: CfgNode, optimizer: Type[torch.optim.Optimizer] +) -> Type[torch.optim.Optimizer]: + """ + If gradient clipping is enabled through config options, wraps the existing + optimizer type to become a new dynamically created class OptimizerWithGradientClip + that inherits the given optimizer and overrides the `step` method to + include gradient clipping. + + Args: + cfg: CfgNode, configuration options + optimizer: type. A subclass of torch.optim.Optimizer + + Return: + type: either the input `optimizer` (if gradient clipping is disabled), or + a subclass of it with gradient clipping included in the `step` method. + """ + if not cfg.SOLVER.CLIP_GRADIENTS.ENABLED: + return optimizer + if isinstance(optimizer, torch.optim.Optimizer): + optimizer_type = type(optimizer) + else: + assert issubclass(optimizer, torch.optim.Optimizer), optimizer + optimizer_type = optimizer + + grad_clipper = _create_gradient_clipper(cfg.SOLVER.CLIP_GRADIENTS) + OptimizerWithGradientClip = _generate_optimizer_class_with_gradient_clipping( + optimizer_type, per_param_clipper=grad_clipper + ) + if isinstance(optimizer, torch.optim.Optimizer): + optimizer.__class__ = OptimizerWithGradientClip # a bit hacky, not recommended + return optimizer + else: + return OptimizerWithGradientClip + + +def build_optimizer(cfg: CfgNode, model: torch.nn.Module) -> torch.optim.Optimizer: + """ + Build an optimizer from config. + """ + params = get_default_optimizer_params( + model, + base_lr=cfg.SOLVER.BASE_LR, + weight_decay_norm=cfg.SOLVER.WEIGHT_DECAY_NORM, + bias_lr_factor=cfg.SOLVER.BIAS_LR_FACTOR, + weight_decay_bias=cfg.SOLVER.WEIGHT_DECAY_BIAS, + ) + return maybe_add_gradient_clipping(cfg, torch.optim.SGD)( + params, + lr=cfg.SOLVER.BASE_LR, + momentum=cfg.SOLVER.MOMENTUM, + nesterov=cfg.SOLVER.NESTEROV, + weight_decay=cfg.SOLVER.WEIGHT_DECAY, + ) + + +def get_default_optimizer_params( + model: torch.nn.Module, + base_lr: Optional[float] = None, + weight_decay: Optional[float] = None, + weight_decay_norm: Optional[float] = None, + bias_lr_factor: Optional[float] = 1.0, + weight_decay_bias: Optional[float] = None, + overrides: Optional[Dict[str, Dict[str, float]]] = None, +) -> List[Dict[str, Any]]: + """ + Get default param list for optimizer, with support for a few types of + overrides. If no overrides needed, this is equivalent to `model.parameters()`. + + Args: + base_lr: lr for every group by default. Can be omitted to use the one in optimizer. + weight_decay: weight decay for every group by default. Can be omitted to use the one + in optimizer. + weight_decay_norm: override weight decay for params in normalization layers + bias_lr_factor: multiplier of lr for bias parameters. + weight_decay_bias: override weight decay for bias parameters + overrides: if not `None`, provides values for optimizer hyperparameters + (LR, weight decay) for module parameters with a given name; e.g. + ``{"embedding": {"lr": 0.01, "weight_decay": 0.1}}`` will set the LR and + weight decay values for all module parameters named `embedding`. + + For common detection models, ``weight_decay_norm`` is the only option + needed to be set. ``bias_lr_factor,weight_decay_bias`` are legacy settings + from Detectron1 that are not found useful. + + Example: + :: + torch.optim.SGD(get_default_optimizer_params(model, weight_decay_norm=0), + lr=0.01, weight_decay=1e-4, momentum=0.9) + """ + if overrides is None: + overrides = {} + defaults = {} + if base_lr is not None: + defaults["lr"] = base_lr + if weight_decay is not None: + defaults["weight_decay"] = weight_decay + bias_overrides = {} + if bias_lr_factor is not None and bias_lr_factor != 1.0: + # NOTE: unlike Detectron v1, we now by default make bias hyperparameters + # exactly the same as regular weights. + if base_lr is None: + raise ValueError("bias_lr_factor requires base_lr") + bias_overrides["lr"] = base_lr * bias_lr_factor + if weight_decay_bias is not None: + bias_overrides["weight_decay"] = weight_decay_bias + if len(bias_overrides): + if "bias" in overrides: + raise ValueError("Conflicting overrides for 'bias'") + overrides["bias"] = bias_overrides + + norm_module_types = ( + torch.nn.BatchNorm1d, + torch.nn.BatchNorm2d, + torch.nn.BatchNorm3d, + torch.nn.SyncBatchNorm, + # NaiveSyncBatchNorm inherits from BatchNorm2d + torch.nn.GroupNorm, + torch.nn.InstanceNorm1d, + torch.nn.InstanceNorm2d, + torch.nn.InstanceNorm3d, + torch.nn.LayerNorm, + torch.nn.LocalResponseNorm, + ) + params: List[Dict[str, Any]] = [] + memo: Set[torch.nn.parameter.Parameter] = set() + for module in model.modules(): + for module_param_name, value in module.named_parameters(recurse=False): + if not value.requires_grad: + continue + # Avoid duplicating parameters + if value in memo: + continue + memo.add(value) + + hyperparams = copy.copy(defaults) + if isinstance(module, norm_module_types) and weight_decay_norm is not None: + hyperparams["weight_decay"] = weight_decay_norm + hyperparams.update(overrides.get(module_param_name, {})) + params.append({"params": [value], **hyperparams}) + return reduce_param_groups(params) + + +def _expand_param_groups(params: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + # Transform parameter groups into per-parameter structure. + # Later items in `params` can overwrite parameters set in previous items. + ret = defaultdict(dict) + for item in params: + assert "params" in item + cur_params = {x: y for x, y in item.items() if x != "params"} + for param in item["params"]: + ret[param].update({"params": [param], **cur_params}) + return list(ret.values()) + + +def reduce_param_groups(params: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + # Reorganize the parameter groups and merge duplicated groups. + # The number of parameter groups needs to be as small as possible in order + # to efficiently use the PyTorch multi-tensor optimizer. Therefore instead + # of using a parameter_group per single parameter, we reorganize the + # parameter groups and merge duplicated groups. This approach speeds + # up multi-tensor optimizer significantly. + params = _expand_param_groups(params) + groups = defaultdict(list) # re-group all parameter groups by their hyperparams + for item in params: + cur_params = tuple((x, y) for x, y in item.items() if x != "params") + groups[cur_params].extend(item["params"]) + ret = [] + for param_keys, param_values in groups.items(): + cur = {kv[0]: kv[1] for kv in param_keys} + cur["params"] = param_values + ret.append(cur) + return ret + + +def build_lr_scheduler( + cfg: CfgNode, optimizer: torch.optim.Optimizer +) -> torch.optim.lr_scheduler._LRScheduler: + """ + Build a LR scheduler from config. + """ + name = cfg.SOLVER.LR_SCHEDULER_NAME + + if name == "WarmupMultiStepLR": + steps = [x for x in cfg.SOLVER.STEPS if x <= cfg.SOLVER.MAX_ITER] + if len(steps) != len(cfg.SOLVER.STEPS): + logger = logging.getLogger(__name__) + logger.warning( + "SOLVER.STEPS contains values larger than SOLVER.MAX_ITER. " + "These values will be ignored." + ) + sched = MultiStepParamScheduler( + values=[cfg.SOLVER.GAMMA ** k for k in range(len(steps) + 1)], + milestones=steps, + num_updates=cfg.SOLVER.MAX_ITER, + ) + elif name == "WarmupCosineLR": + end_value = cfg.SOLVER.BASE_LR_END / cfg.SOLVER.BASE_LR + assert end_value >= 0.0 and end_value <= 1.0, end_value + sched = CosineParamScheduler(1, end_value) + else: + raise ValueError("Unknown LR scheduler: {}".format(name)) + + sched = WarmupParamScheduler( + sched, + cfg.SOLVER.WARMUP_FACTOR, + min(cfg.SOLVER.WARMUP_ITERS / cfg.SOLVER.MAX_ITER, 1.0), + cfg.SOLVER.WARMUP_METHOD, + ) + return LRMultiplier(optimizer, multiplier=sched, max_iter=cfg.SOLVER.MAX_ITER) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/solver/lr_scheduler.py b/motion-gan-pipeline/preprocessing/third/detectron2/solver/lr_scheduler.py new file mode 100644 index 0000000..8803e87 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/solver/lr_scheduler.py @@ -0,0 +1,238 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import math +from bisect import bisect_right +from typing import List +import torch +from fvcore.common.param_scheduler import ( + CompositeParamScheduler, + ConstantParamScheduler, + LinearParamScheduler, + ParamScheduler, +) + +logger = logging.getLogger(__name__) + + +class WarmupParamScheduler(CompositeParamScheduler): + """ + Add an initial warmup stage to another scheduler. + """ + + def __init__( + self, + scheduler: ParamScheduler, + warmup_factor: float, + warmup_length: float, + warmup_method: str = "linear", + ): + """ + Args: + scheduler: warmup will be added at the beginning of this scheduler + warmup_factor: the factor w.r.t the initial value of ``scheduler``, e.g. 0.001 + warmup_length: the relative length (in [0, 1]) of warmup steps w.r.t the entire + training, e.g. 0.01 + warmup_method: one of "linear" or "constant" + """ + end_value = scheduler(warmup_length) # the value to reach when warmup ends + start_value = warmup_factor * scheduler(0.0) + if warmup_method == "constant": + warmup = ConstantParamScheduler(start_value) + elif warmup_method == "linear": + warmup = LinearParamScheduler(start_value, end_value) + else: + raise ValueError("Unknown warmup method: {}".format(warmup_method)) + super().__init__( + [warmup, scheduler], + interval_scaling=["rescaled", "fixed"], + lengths=[warmup_length, 1 - warmup_length], + ) + + +class LRMultiplier(torch.optim.lr_scheduler._LRScheduler): + """ + A LRScheduler which uses fvcore :class:`ParamScheduler` to multiply the + learning rate of each param in the optimizer. + Every step, the learning rate of each parameter becomes its initial value + multiplied by the output of the given :class:`ParamScheduler`. + + The absolute learning rate value of each parameter can be different. + This scheduler can be used as long as the relative scale among them do + not change during training. + + Examples: + :: + LRMultiplier( + opt, + WarmupParamScheduler( + MultiStepParamScheduler( + [1, 0.1, 0.01], + milestones=[60000, 80000], + num_updates=90000, + ), 0.001, 100 / 90000 + ), + max_iter=90000 + ) + """ + + # NOTES: in the most general case, every LR can use its own scheduler. + # Supporting this requires interaction with the optimizer when its parameter + # group is initialized. For example, classyvision implements its own optimizer + # that allows different schedulers for every parameter group. + # To avoid this complexity, we use this class to support the most common cases + # where the relative scale among all LRs stay unchanged during training. In this + # case we only need a total of one scheduler that defines the relative LR multiplier. + + def __init__( + self, + optimizer: torch.optim.Optimizer, + multiplier: ParamScheduler, + max_iter: int, + last_iter: int = -1, + ): + """ + Args: + optimizer, last_iter: See ``torch.optim.lr_scheduler._LRScheduler``. + ``last_iter`` is the same as ``last_epoch``. + multiplier: a fvcore ParamScheduler that defines the multiplier on + every LR of the optimizer + max_iter: the total number of training iterations + """ + if not isinstance(multiplier, ParamScheduler): + raise ValueError( + "_LRMultiplier(multiplier=) must be an instance of fvcore " + f"ParamScheduler. Got {multiplier} instead." + ) + self._multiplier = multiplier + self._max_iter = max_iter + super().__init__(optimizer, last_epoch=last_iter) + + def state_dict(self): + # fvcore schedulers are stateless. Only keep pytorch scheduler states + return {"base_lrs": self.base_lrs, "last_epoch": self.last_epoch} + + def get_lr(self) -> List[float]: + multiplier = self._multiplier(self.last_epoch / self._max_iter) + return [base_lr * multiplier for base_lr in self.base_lrs] + + +""" +Content below is no longer needed! +""" + +# NOTE: PyTorch's LR scheduler interface uses names that assume the LR changes +# only on epoch boundaries. We typically use iteration based schedules instead. +# As a result, "epoch" (e.g., as in self.last_epoch) should be understood to mean +# "iteration" instead. + +# FIXME: ideally this would be achieved with a CombinedLRScheduler, separating +# MultiStepLR with WarmupLR but the current LRScheduler design doesn't allow it. + + +class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler): + def __init__( + self, + optimizer: torch.optim.Optimizer, + milestones: List[int], + gamma: float = 0.1, + warmup_factor: float = 0.001, + warmup_iters: int = 1000, + warmup_method: str = "linear", + last_epoch: int = -1, + ): + logger.warning( + "WarmupMultiStepLR is deprecated! Use LRMultipilier with fvcore ParamScheduler instead!" + ) + if not list(milestones) == sorted(milestones): + raise ValueError( + "Milestones should be a list of" " increasing integers. Got {}", milestones + ) + self.milestones = milestones + self.gamma = gamma + self.warmup_factor = warmup_factor + self.warmup_iters = warmup_iters + self.warmup_method = warmup_method + super().__init__(optimizer, last_epoch) + + def get_lr(self) -> List[float]: + warmup_factor = _get_warmup_factor_at_iter( + self.warmup_method, self.last_epoch, self.warmup_iters, self.warmup_factor + ) + return [ + base_lr * warmup_factor * self.gamma ** bisect_right(self.milestones, self.last_epoch) + for base_lr in self.base_lrs + ] + + def _compute_values(self) -> List[float]: + # The new interface + return self.get_lr() + + +class WarmupCosineLR(torch.optim.lr_scheduler._LRScheduler): + def __init__( + self, + optimizer: torch.optim.Optimizer, + max_iters: int, + warmup_factor: float = 0.001, + warmup_iters: int = 1000, + warmup_method: str = "linear", + last_epoch: int = -1, + ): + logger.warning( + "WarmupCosineLR is deprecated! Use LRMultipilier with fvcore ParamScheduler instead!" + ) + self.max_iters = max_iters + self.warmup_factor = warmup_factor + self.warmup_iters = warmup_iters + self.warmup_method = warmup_method + super().__init__(optimizer, last_epoch) + + def get_lr(self) -> List[float]: + warmup_factor = _get_warmup_factor_at_iter( + self.warmup_method, self.last_epoch, self.warmup_iters, self.warmup_factor + ) + # Different definitions of half-cosine with warmup are possible. For + # simplicity we multiply the standard half-cosine schedule by the warmup + # factor. An alternative is to start the period of the cosine at warmup_iters + # instead of at 0. In the case that warmup_iters << max_iters the two are + # very close to each other. + return [ + base_lr + * warmup_factor + * 0.5 + * (1.0 + math.cos(math.pi * self.last_epoch / self.max_iters)) + for base_lr in self.base_lrs + ] + + def _compute_values(self) -> List[float]: + # The new interface + return self.get_lr() + + +def _get_warmup_factor_at_iter( + method: str, iter: int, warmup_iters: int, warmup_factor: float +) -> float: + """ + Return the learning rate warmup factor at a specific iteration. + See :paper:`ImageNet in 1h` for more details. + + Args: + method (str): warmup method; either "constant" or "linear". + iter (int): iteration at which to calculate the warmup factor. + warmup_iters (int): the number of warmup iterations. + warmup_factor (float): the base warmup factor (the meaning changes according + to the method used). + + Returns: + float: the effective warmup factor at the given iteration. + """ + if iter >= warmup_iters: + return 1.0 + + if method == "constant": + return warmup_factor + elif method == "linear": + alpha = iter / warmup_iters + return warmup_factor * (1 - alpha) + alpha + else: + raise ValueError("Unknown warmup method: {}".format(method)) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/structures/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/structures/__init__.py new file mode 100644 index 0000000..f3ee605 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/structures/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .boxes import Boxes, BoxMode, pairwise_iou, pairwise_ioa, pairwise_point_box_distance +from .image_list import ImageList + +from .instances import Instances +from .keypoints import Keypoints, heatmaps_to_keypoints +from .masks import BitMasks, PolygonMasks, polygons_to_bitmask, ROIMasks +from .rotated_boxes import RotatedBoxes +from .rotated_boxes import pairwise_iou as pairwise_iou_rotated + +__all__ = [k for k in globals().keys() if not k.startswith("_")] + + +from detectron2.utils.env import fixup_module_metadata + +fixup_module_metadata(__name__, globals(), __all__) +del fixup_module_metadata diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/structures/boxes.py b/motion-gan-pipeline/preprocessing/third/detectron2/structures/boxes.py new file mode 100644 index 0000000..ae543c6 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/structures/boxes.py @@ -0,0 +1,423 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import math +import numpy as np +from enum import IntEnum, unique +from typing import List, Tuple, Union +import torch +from torch import device + +_RawBoxType = Union[List[float], Tuple[float, ...], torch.Tensor, np.ndarray] + + +@unique +class BoxMode(IntEnum): + """ + Enum of different ways to represent a box. + """ + + XYXY_ABS = 0 + """ + (x0, y0, x1, y1) in absolute floating points coordinates. + The coordinates in range [0, width or height]. + """ + XYWH_ABS = 1 + """ + (x0, y0, w, h) in absolute floating points coordinates. + """ + XYXY_REL = 2 + """ + Not yet supported! + (x0, y0, x1, y1) in range [0, 1]. They are relative to the size of the image. + """ + XYWH_REL = 3 + """ + Not yet supported! + (x0, y0, w, h) in range [0, 1]. They are relative to the size of the image. + """ + XYWHA_ABS = 4 + """ + (xc, yc, w, h, a) in absolute floating points coordinates. + (xc, yc) is the center of the rotated box, and the angle a is in degrees ccw. + """ + + @staticmethod + def convert(box: _RawBoxType, from_mode: "BoxMode", to_mode: "BoxMode") -> _RawBoxType: + """ + Args: + box: can be a k-tuple, k-list or an Nxk array/tensor, where k = 4 or 5 + from_mode, to_mode (BoxMode) + + Returns: + The converted box of the same type. + """ + if from_mode == to_mode: + return box + + original_type = type(box) + is_numpy = isinstance(box, np.ndarray) + single_box = isinstance(box, (list, tuple)) + if single_box: + assert len(box) == 4 or len(box) == 5, ( + "BoxMode.convert takes either a k-tuple/list or an Nxk array/tensor," + " where k == 4 or 5" + ) + arr = torch.tensor(box)[None, :] + else: + # avoid modifying the input box + if is_numpy: + arr = torch.from_numpy(np.asarray(box)).clone() + else: + arr = box.clone() + + assert to_mode not in [BoxMode.XYXY_REL, BoxMode.XYWH_REL] and from_mode not in [ + BoxMode.XYXY_REL, + BoxMode.XYWH_REL, + ], "Relative mode not yet supported!" + + if from_mode == BoxMode.XYWHA_ABS and to_mode == BoxMode.XYXY_ABS: + assert ( + arr.shape[-1] == 5 + ), "The last dimension of input shape must be 5 for XYWHA format" + original_dtype = arr.dtype + arr = arr.double() + + w = arr[:, 2] + h = arr[:, 3] + a = arr[:, 4] + c = torch.abs(torch.cos(a * math.pi / 180.0)) + s = torch.abs(torch.sin(a * math.pi / 180.0)) + # This basically computes the horizontal bounding rectangle of the rotated box + new_w = c * w + s * h + new_h = c * h + s * w + + # convert center to top-left corner + arr[:, 0] -= new_w / 2.0 + arr[:, 1] -= new_h / 2.0 + # bottom-right corner + arr[:, 2] = arr[:, 0] + new_w + arr[:, 3] = arr[:, 1] + new_h + + arr = arr[:, :4].to(dtype=original_dtype) + elif from_mode == BoxMode.XYWH_ABS and to_mode == BoxMode.XYWHA_ABS: + original_dtype = arr.dtype + arr = arr.double() + arr[:, 0] += arr[:, 2] / 2.0 + arr[:, 1] += arr[:, 3] / 2.0 + angles = torch.zeros((arr.shape[0], 1), dtype=arr.dtype) + arr = torch.cat((arr, angles), axis=1).to(dtype=original_dtype) + else: + if to_mode == BoxMode.XYXY_ABS and from_mode == BoxMode.XYWH_ABS: + arr[:, 2] += arr[:, 0] + arr[:, 3] += arr[:, 1] + elif from_mode == BoxMode.XYXY_ABS and to_mode == BoxMode.XYWH_ABS: + arr[:, 2] -= arr[:, 0] + arr[:, 3] -= arr[:, 1] + else: + raise NotImplementedError( + "Conversion from BoxMode {} to {} is not supported yet".format( + from_mode, to_mode + ) + ) + + if single_box: + return original_type(arr.flatten().tolist()) + if is_numpy: + return arr.numpy() + else: + return arr + + +class Boxes: + """ + This structure stores a list of boxes as a Nx4 torch.Tensor. + It supports some common methods about boxes + (`area`, `clip`, `nonempty`, etc), + and also behaves like a Tensor + (support indexing, `to(device)`, `.device`, and iteration over all boxes) + + Attributes: + tensor (torch.Tensor): float matrix of Nx4. Each row is (x1, y1, x2, y2). + """ + + def __init__(self, tensor: torch.Tensor): + """ + Args: + tensor (Tensor[float]): a Nx4 matrix. Each row is (x1, y1, x2, y2). + """ + device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu") + tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device) + if tensor.numel() == 0: + # Use reshape, so we don't end up creating a new tensor that does not depend on + # the inputs (and consequently confuses jit) + tensor = tensor.reshape((-1, 4)).to(dtype=torch.float32, device=device) + assert tensor.dim() == 2 and tensor.size(-1) == 4, tensor.size() + + self.tensor = tensor + + def clone(self) -> "Boxes": + """ + Clone the Boxes. + + Returns: + Boxes + """ + return Boxes(self.tensor.clone()) + + def to(self, device: torch.device): + # Boxes are assumed float32 and does not support to(dtype) + return Boxes(self.tensor.to(device=device)) + + def area(self) -> torch.Tensor: + """ + Computes the area of all the boxes. + + Returns: + torch.Tensor: a vector with areas of each box. + """ + box = self.tensor + area = (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1]) + return area + + def clip(self, box_size: Tuple[int, int]) -> None: + """ + Clip (in place) the boxes by limiting x coordinates to the range [0, width] + and y coordinates to the range [0, height]. + + Args: + box_size (height, width): The clipping box's size. + """ + assert torch.isfinite(self.tensor).all(), "Box tensor contains infinite or NaN!" + h, w = box_size + x1 = self.tensor[:, 0].clamp(min=0, max=w) + y1 = self.tensor[:, 1].clamp(min=0, max=h) + x2 = self.tensor[:, 2].clamp(min=0, max=w) + y2 = self.tensor[:, 3].clamp(min=0, max=h) + self.tensor = torch.stack((x1, y1, x2, y2), dim=-1) + + def nonempty(self, threshold: float = 0.0) -> torch.Tensor: + """ + Find boxes that are non-empty. + A box is considered empty, if either of its side is no larger than threshold. + + Returns: + Tensor: + a binary vector which represents whether each box is empty + (False) or non-empty (True). + """ + box = self.tensor + widths = box[:, 2] - box[:, 0] + heights = box[:, 3] - box[:, 1] + keep = (widths > threshold) & (heights > threshold) + return keep + + def __getitem__(self, item) -> "Boxes": + """ + Args: + item: int, slice, or a BoolTensor + + Returns: + Boxes: Create a new :class:`Boxes` by indexing. + + The following usage are allowed: + + 1. `new_boxes = boxes[3]`: return a `Boxes` which contains only one box. + 2. `new_boxes = boxes[2:10]`: return a slice of boxes. + 3. `new_boxes = boxes[vector]`, where vector is a torch.BoolTensor + with `length = len(boxes)`. Nonzero elements in the vector will be selected. + + Note that the returned Boxes might share storage with this Boxes, + subject to Pytorch's indexing semantics. + """ + if isinstance(item, int): + return Boxes(self.tensor[item].view(1, -1)) + b = self.tensor[item] + assert b.dim() == 2, "Indexing on Boxes with {} failed to return a matrix!".format(item) + return Boxes(b) + + def __len__(self) -> int: + return self.tensor.shape[0] + + def __repr__(self) -> str: + return "Boxes(" + str(self.tensor) + ")" + + def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor: + """ + Args: + box_size (height, width): Size of the reference box. + boundary_threshold (int): Boxes that extend beyond the reference box + boundary by more than boundary_threshold are considered "outside". + + Returns: + a binary vector, indicating whether each box is inside the reference box. + """ + height, width = box_size + inds_inside = ( + (self.tensor[..., 0] >= -boundary_threshold) + & (self.tensor[..., 1] >= -boundary_threshold) + & (self.tensor[..., 2] < width + boundary_threshold) + & (self.tensor[..., 3] < height + boundary_threshold) + ) + return inds_inside + + def get_centers(self) -> torch.Tensor: + """ + Returns: + The box centers in a Nx2 array of (x, y). + """ + return (self.tensor[:, :2] + self.tensor[:, 2:]) / 2 + + def scale(self, scale_x: float, scale_y: float) -> None: + """ + Scale the box with horizontal and vertical scaling factors + """ + self.tensor[:, 0::2] *= scale_x + self.tensor[:, 1::2] *= scale_y + + @classmethod + def cat(cls, boxes_list: List["Boxes"]) -> "Boxes": + """ + Concatenates a list of Boxes into a single Boxes + + Arguments: + boxes_list (list[Boxes]) + + Returns: + Boxes: the concatenated Boxes + """ + assert isinstance(boxes_list, (list, tuple)) + if len(boxes_list) == 0: + return cls(torch.empty(0)) + assert all([isinstance(box, Boxes) for box in boxes_list]) + + # use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input + cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0)) + return cat_boxes + + @property + def device(self) -> device: + return self.tensor.device + + # type "Iterator[torch.Tensor]", yield, and iter() not supported by torchscript + # https://github.com/pytorch/pytorch/issues/18627 + @torch.jit.unused + def __iter__(self): + """ + Yield a box as a Tensor of shape (4,) at a time. + """ + yield from self.tensor + + +def pairwise_intersection(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor: + """ + Given two lists of boxes of size N and M, + compute the intersection area between __all__ N x M pairs of boxes. + The box order must be (xmin, ymin, xmax, ymax) + + Args: + boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. + + Returns: + Tensor: intersection, sized [N,M]. + """ + boxes1, boxes2 = boxes1.tensor, boxes2.tensor + width_height = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) - torch.max( + boxes1[:, None, :2], boxes2[:, :2] + ) # [N,M,2] + + width_height.clamp_(min=0) # [N,M,2] + intersection = width_height.prod(dim=2) # [N,M] + return intersection + + +# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py +# with slight modifications +def pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor: + """ + Given two lists of boxes of size N and M, compute the IoU + (intersection over union) between **all** N x M pairs of boxes. + The box order must be (xmin, ymin, xmax, ymax). + + Args: + boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. + + Returns: + Tensor: IoU, sized [N,M]. + """ + area1 = boxes1.area() # [N] + area2 = boxes2.area() # [M] + inter = pairwise_intersection(boxes1, boxes2) + + # handle empty boxes + iou = torch.where( + inter > 0, + inter / (area1[:, None] + area2 - inter), + torch.zeros(1, dtype=inter.dtype, device=inter.device), + ) + return iou + + +def pairwise_ioa(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor: + """ + Similar to :func:`pariwise_iou` but compute the IoA (intersection over boxes2 area). + + Args: + boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. + + Returns: + Tensor: IoA, sized [N,M]. + """ + area2 = boxes2.area() # [M] + inter = pairwise_intersection(boxes1, boxes2) + + # handle empty boxes + ioa = torch.where( + inter > 0, inter / area2, torch.zeros(1, dtype=inter.dtype, device=inter.device) + ) + return ioa + + +def pairwise_point_box_distance(points: torch.Tensor, boxes: Boxes): + """ + Pairwise distance between N points and M boxes. The distance between a + point and a box is represented by the distance from the point to 4 edges + of the box. Distances are all positive when the point is inside the box. + + Args: + points: Nx2 coordinates. Each row is (x, y) + boxes: M boxes + + Returns: + Tensor: distances of size (N, M, 4). The 4 values are distances from + the point to the left, top, right, bottom of the box. + """ + x, y = points.unsqueeze(dim=2).unbind(dim=1) # (N, 1) + x0, y0, x1, y1 = boxes.tensor.unsqueeze(dim=0).unbind(dim=2) # (1, M) + return torch.stack([x - x0, y - y0, x1 - x, y1 - y], dim=2) + + +def matched_pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor: + """ + Compute pairwise intersection over union (IOU) of two sets of matched + boxes that have the same number of boxes. + Similar to :func:`pairwise_iou`, but computes only diagonal elements of the matrix. + + Args: + boxes1 (Boxes): bounding boxes, sized [N,4]. + boxes2 (Boxes): same length as boxes1 + Returns: + Tensor: iou, sized [N]. + """ + assert len(boxes1) == len( + boxes2 + ), "boxlists should have the same" "number of entries, got {}, {}".format( + len(boxes1), len(boxes2) + ) + area1 = boxes1.area() # [N] + area2 = boxes2.area() # [N] + box1, box2 = boxes1.tensor, boxes2.tensor + lt = torch.max(box1[:, :2], box2[:, :2]) # [N,2] + rb = torch.min(box1[:, 2:], box2[:, 2:]) # [N,2] + wh = (rb - lt).clamp(min=0) # [N,2] + inter = wh[:, 0] * wh[:, 1] # [N] + iou = inter / (area1 + area2 - inter) # [N] + return iou diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/structures/image_list.py b/motion-gan-pipeline/preprocessing/third/detectron2/structures/image_list.py new file mode 100644 index 0000000..b31b2d3 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/structures/image_list.py @@ -0,0 +1,110 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from __future__ import division +from typing import Any, List, Tuple +import torch +from torch import device +from torch.nn import functional as F + +from detectron2.layers.wrappers import shapes_to_tensor + + +class ImageList(object): + """ + Structure that holds a list of images (of possibly + varying sizes) as a single tensor. + This works by padding the images to the same size. + The original sizes of each image is stored in `image_sizes`. + + Attributes: + image_sizes (list[tuple[int, int]]): each tuple is (h, w). + During tracing, it becomes list[Tensor] instead. + """ + + def __init__(self, tensor: torch.Tensor, image_sizes: List[Tuple[int, int]]): + """ + Arguments: + tensor (Tensor): of shape (N, H, W) or (N, C_1, ..., C_K, H, W) where K >= 1 + image_sizes (list[tuple[int, int]]): Each tuple is (h, w). It can + be smaller than (H, W) due to padding. + """ + self.tensor = tensor + self.image_sizes = image_sizes + + def __len__(self) -> int: + return len(self.image_sizes) + + def __getitem__(self, idx) -> torch.Tensor: + """ + Access the individual image in its original size. + + Args: + idx: int or slice + + Returns: + Tensor: an image of shape (H, W) or (C_1, ..., C_K, H, W) where K >= 1 + """ + size = self.image_sizes[idx] + return self.tensor[idx, ..., : size[0], : size[1]] + + @torch.jit.unused + def to(self, *args: Any, **kwargs: Any) -> "ImageList": + cast_tensor = self.tensor.to(*args, **kwargs) + return ImageList(cast_tensor, self.image_sizes) + + @property + def device(self) -> device: + return self.tensor.device + + @staticmethod + def from_tensors( + tensors: List[torch.Tensor], size_divisibility: int = 0, pad_value: float = 0.0 + ) -> "ImageList": + """ + Args: + tensors: a tuple or list of `torch.Tensor`, each of shape (Hi, Wi) or + (C_1, ..., C_K, Hi, Wi) where K >= 1. The Tensors will be padded + to the same shape with `pad_value`. + size_divisibility (int): If `size_divisibility > 0`, add padding to ensure + the common height and width is divisible by `size_divisibility`. + This depends on the model and many models need a divisibility of 32. + pad_value (float): value to pad + + Returns: + an `ImageList`. + """ + assert len(tensors) > 0 + assert isinstance(tensors, (tuple, list)) + for t in tensors: + assert isinstance(t, torch.Tensor), type(t) + assert t.shape[:-2] == tensors[0].shape[:-2], t.shape + + image_sizes = [(im.shape[-2], im.shape[-1]) for im in tensors] + image_sizes_tensor = [shapes_to_tensor(x) for x in image_sizes] + max_size = torch.stack(image_sizes_tensor).max(0).values + + if size_divisibility > 1: + stride = size_divisibility + # the last two dims are H,W, both subject to divisibility requirement + max_size = (max_size + (stride - 1)).div(stride, rounding_mode="floor") * stride + + # handle weirdness of scripting and tracing ... + if torch.jit.is_scripting(): + max_size: List[int] = max_size.to(dtype=torch.long).tolist() + else: + if torch.jit.is_tracing(): + image_sizes = image_sizes_tensor + + if len(tensors) == 1: + # This seems slightly (2%) faster. + # TODO: check whether it's faster for multiple images as well + image_size = image_sizes[0] + padding_size = [0, max_size[-1] - image_size[1], 0, max_size[-2] - image_size[0]] + batched_imgs = F.pad(tensors[0], padding_size, value=pad_value).unsqueeze_(0) + else: + # max_size can be a tensor in tracing mode, therefore convert to list + batch_shape = [len(tensors)] + list(tensors[0].shape[:-2]) + list(max_size) + batched_imgs = tensors[0].new_full(batch_shape, pad_value) + for img, pad_img in zip(tensors, batched_imgs): + pad_img[..., : img.shape[-2], : img.shape[-1]].copy_(img) + + return ImageList(batched_imgs.contiguous(), image_sizes) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/structures/instances.py b/motion-gan-pipeline/preprocessing/third/detectron2/structures/instances.py new file mode 100644 index 0000000..612e66f --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/structures/instances.py @@ -0,0 +1,192 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import itertools +from typing import Any, Dict, List, Tuple, Union +import torch + + +class Instances: + """ + This class represents a list of instances in an image. + It stores the attributes of instances (e.g., boxes, masks, labels, scores) as "fields". + All fields must have the same ``__len__`` which is the number of instances. + + All other (non-field) attributes of this class are considered private: + they must start with '_' and are not modifiable by a user. + + Some basic usage: + + 1. Set/get/check a field: + + .. code-block:: python + + instances.gt_boxes = Boxes(...) + print(instances.pred_masks) # a tensor of shape (N, H, W) + print('gt_masks' in instances) + + 2. ``len(instances)`` returns the number of instances + 3. Indexing: ``instances[indices]`` will apply the indexing on all the fields + and returns a new :class:`Instances`. + Typically, ``indices`` is a integer vector of indices, + or a binary mask of length ``num_instances`` + + .. code-block:: python + + category_3_detections = instances[instances.pred_classes == 3] + confident_detections = instances[instances.scores > 0.9] + """ + + def __init__(self, image_size: Tuple[int, int], **kwargs: Any): + """ + Args: + image_size (height, width): the spatial size of the image. + kwargs: fields to add to this `Instances`. + """ + self._image_size = image_size + self._fields: Dict[str, Any] = {} + for k, v in kwargs.items(): + self.set(k, v) + + @property + def image_size(self) -> Tuple[int, int]: + """ + Returns: + tuple: height, width + """ + return self._image_size + + def __setattr__(self, name: str, val: Any) -> None: + if name.startswith("_"): + super().__setattr__(name, val) + else: + self.set(name, val) + + def __getattr__(self, name: str) -> Any: + if name == "_fields" or name not in self._fields: + raise AttributeError("Cannot find field '{}' in the given Instances!".format(name)) + return self._fields[name] + + def set(self, name: str, value: Any) -> None: + """ + Set the field named `name` to `value`. + The length of `value` must be the number of instances, + and must agree with other existing fields in this object. + """ + data_len = len(value) + if len(self._fields): + assert ( + len(self) == data_len + ), "Adding a field of length {} to a Instances of length {}".format(data_len, len(self)) + self._fields[name] = value + + def has(self, name: str) -> bool: + """ + Returns: + bool: whether the field called `name` exists. + """ + return name in self._fields + + def remove(self, name: str) -> None: + """ + Remove the field called `name`. + """ + del self._fields[name] + + def get(self, name: str) -> Any: + """ + Returns the field called `name`. + """ + return self._fields[name] + + def get_fields(self) -> Dict[str, Any]: + """ + Returns: + dict: a dict which maps names (str) to data of the fields + + Modifying the returned dict will modify this instance. + """ + return self._fields + + # Tensor-like methods + def to(self, *args: Any, **kwargs: Any) -> "Instances": + """ + Returns: + Instances: all fields are called with a `to(device)`, if the field has this method. + """ + ret = Instances(self._image_size) + for k, v in self._fields.items(): + if hasattr(v, "to"): + v = v.to(*args, **kwargs) + ret.set(k, v) + return ret + + def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "Instances": + """ + Args: + item: an index-like object and will be used to index all the fields. + + Returns: + If `item` is a string, return the data in the corresponding field. + Otherwise, returns an `Instances` where all fields are indexed by `item`. + """ + if type(item) == int: + if item >= len(self) or item < -len(self): + raise IndexError("Instances index out of range!") + else: + item = slice(item, None, len(self)) + + ret = Instances(self._image_size) + for k, v in self._fields.items(): + ret.set(k, v[item]) + return ret + + def __len__(self) -> int: + for v in self._fields.values(): + # use __len__ because len() has to be int and is not friendly to tracing + return v.__len__() + raise NotImplementedError("Empty Instances does not support __len__!") + + def __iter__(self): + raise NotImplementedError("`Instances` object is not iterable!") + + @staticmethod + def cat(instance_lists: List["Instances"]) -> "Instances": + """ + Args: + instance_lists (list[Instances]) + + Returns: + Instances + """ + assert all(isinstance(i, Instances) for i in instance_lists) + assert len(instance_lists) > 0 + if len(instance_lists) == 1: + return instance_lists[0] + + image_size = instance_lists[0].image_size + if not isinstance(image_size, torch.Tensor): # could be a tensor in tracing + for i in instance_lists[1:]: + assert i.image_size == image_size + ret = Instances(image_size) + for k in instance_lists[0]._fields.keys(): + values = [i.get(k) for i in instance_lists] + v0 = values[0] + if isinstance(v0, torch.Tensor): + values = torch.cat(values, dim=0) + elif isinstance(v0, list): + values = list(itertools.chain(*values)) + elif hasattr(type(v0), "cat"): + values = type(v0).cat(values) + else: + raise ValueError("Unsupported type {} for concatenation".format(type(v0))) + ret.set(k, values) + return ret + + def __str__(self) -> str: + s = self.__class__.__name__ + "(" + s += "num_instances={}, ".format(len(self)) + s += "image_height={}, ".format(self._image_size[0]) + s += "image_width={}, ".format(self._image_size[1]) + s += "fields=[{}])".format(", ".join((f"{k}: {v}" for k, v in self._fields.items()))) + return s + + __repr__ = __str__ diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/structures/keypoints.py b/motion-gan-pipeline/preprocessing/third/detectron2/structures/keypoints.py new file mode 100644 index 0000000..d0ee872 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/structures/keypoints.py @@ -0,0 +1,239 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +from typing import Any, List, Tuple, Union +import torch +from torch.nn import functional as F + + +class Keypoints: + """ + Stores keypoint **annotation** data. GT Instances have a `gt_keypoints` property + containing the x,y location and visibility flag of each keypoint. This tensor has shape + (N, K, 3) where N is the number of instances and K is the number of keypoints per instance. + + The visibility flag follows the COCO format and must be one of three integers: + + * v=0: not labeled (in which case x=y=0) + * v=1: labeled but not visible + * v=2: labeled and visible + """ + + def __init__(self, keypoints: Union[torch.Tensor, np.ndarray, List[List[float]]]): + """ + Arguments: + keypoints: A Tensor, numpy array, or list of the x, y, and visibility of each keypoint. + The shape should be (N, K, 3) where N is the number of + instances, and K is the number of keypoints per instance. + """ + device = keypoints.device if isinstance(keypoints, torch.Tensor) else torch.device("cpu") + keypoints = torch.as_tensor(keypoints, dtype=torch.float32, device=device) + assert keypoints.dim() == 3 and keypoints.shape[2] == 3, keypoints.shape + self.tensor = keypoints + + def __len__(self) -> int: + return self.tensor.size(0) + + def to(self, *args: Any, **kwargs: Any) -> "Keypoints": + return type(self)(self.tensor.to(*args, **kwargs)) + + @property + def device(self) -> torch.device: + return self.tensor.device + + def to_heatmap(self, boxes: torch.Tensor, heatmap_size: int) -> torch.Tensor: + """ + Convert keypoint annotations to a heatmap of one-hot labels for training, + as described in :paper:`Mask R-CNN`. + + Arguments: + boxes: Nx4 tensor, the boxes to draw the keypoints to + + Returns: + heatmaps: + A tensor of shape (N, K), each element is integer spatial label + in the range [0, heatmap_size**2 - 1] for each keypoint in the input. + valid: + A tensor of shape (N, K) containing whether each keypoint is in the roi or not. + """ + return _keypoints_to_heatmap(self.tensor, boxes, heatmap_size) + + def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "Keypoints": + """ + Create a new `Keypoints` by indexing on this `Keypoints`. + + The following usage are allowed: + + 1. `new_kpts = kpts[3]`: return a `Keypoints` which contains only one instance. + 2. `new_kpts = kpts[2:10]`: return a slice of key points. + 3. `new_kpts = kpts[vector]`, where vector is a torch.ByteTensor + with `length = len(kpts)`. Nonzero elements in the vector will be selected. + + Note that the returned Keypoints might share storage with this Keypoints, + subject to Pytorch's indexing semantics. + """ + if isinstance(item, int): + return Keypoints([self.tensor[item]]) + return Keypoints(self.tensor[item]) + + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "num_instances={})".format(len(self.tensor)) + return s + + @staticmethod + def cat(keypoints_list: List["Keypoints"]) -> "Keypoints": + """ + Concatenates a list of Keypoints into a single Keypoints + + Arguments: + keypoints_list (list[Keypoints]) + + Returns: + Keypoints: the concatenated Keypoints + """ + assert isinstance(keypoints_list, (list, tuple)) + assert len(keypoints_list) > 0 + assert all(isinstance(keypoints, Keypoints) for keypoints in keypoints_list) + + cat_kpts = type(keypoints_list[0])( + torch.cat([kpts.tensor for kpts in keypoints_list], dim=0) + ) + return cat_kpts + + +# TODO make this nicer, this is a direct translation from C2 (but removing the inner loop) +def _keypoints_to_heatmap( + keypoints: torch.Tensor, rois: torch.Tensor, heatmap_size: int +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Encode keypoint locations into a target heatmap for use in SoftmaxWithLoss across space. + + Maps keypoints from the half-open interval [x1, x2) on continuous image coordinates to the + closed interval [0, heatmap_size - 1] on discrete image coordinates. We use the + continuous-discrete conversion from Heckbert 1990 ("What is the coordinate of a pixel?"): + d = floor(c) and c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate. + + Arguments: + keypoints: tensor of keypoint locations in of shape (N, K, 3). + rois: Nx4 tensor of rois in xyxy format + heatmap_size: integer side length of square heatmap. + + Returns: + heatmaps: A tensor of shape (N, K) containing an integer spatial label + in the range [0, heatmap_size**2 - 1] for each keypoint in the input. + valid: A tensor of shape (N, K) containing whether each keypoint is in + the roi or not. + """ + + if rois.numel() == 0: + return rois.new().long(), rois.new().long() + offset_x = rois[:, 0] + offset_y = rois[:, 1] + scale_x = heatmap_size / (rois[:, 2] - rois[:, 0]) + scale_y = heatmap_size / (rois[:, 3] - rois[:, 1]) + + offset_x = offset_x[:, None] + offset_y = offset_y[:, None] + scale_x = scale_x[:, None] + scale_y = scale_y[:, None] + + x = keypoints[..., 0] + y = keypoints[..., 1] + + x_boundary_inds = x == rois[:, 2][:, None] + y_boundary_inds = y == rois[:, 3][:, None] + + x = (x - offset_x) * scale_x + x = x.floor().long() + y = (y - offset_y) * scale_y + y = y.floor().long() + + x[x_boundary_inds] = heatmap_size - 1 + y[y_boundary_inds] = heatmap_size - 1 + + valid_loc = (x >= 0) & (y >= 0) & (x < heatmap_size) & (y < heatmap_size) + vis = keypoints[..., 2] > 0 + valid = (valid_loc & vis).long() + + lin_ind = y * heatmap_size + x + heatmaps = lin_ind * valid + + return heatmaps, valid + + +@torch.jit.script_if_tracing +def heatmaps_to_keypoints(maps: torch.Tensor, rois: torch.Tensor) -> torch.Tensor: + """ + Extract predicted keypoint locations from heatmaps. + + Args: + maps (Tensor): (#ROIs, #keypoints, POOL_H, POOL_W). The predicted heatmap of logits for + each ROI and each keypoint. + rois (Tensor): (#ROIs, 4). The box of each ROI. + + Returns: + Tensor of shape (#ROIs, #keypoints, 4) with the last dimension corresponding to + (x, y, logit, score) for each keypoint. + + When converting discrete pixel indices in an NxN image to a continuous keypoint coordinate, + we maintain consistency with :meth:`Keypoints.to_heatmap` by using the conversion from + Heckbert 1990: c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate. + """ + # The decorator use of torch.no_grad() was not supported by torchscript. + # https://github.com/pytorch/pytorch/issues/44768 + maps = maps.detach() + rois = rois.detach() + + offset_x = rois[:, 0] + offset_y = rois[:, 1] + + widths = (rois[:, 2] - rois[:, 0]).clamp(min=1) + heights = (rois[:, 3] - rois[:, 1]).clamp(min=1) + widths_ceil = widths.ceil() + heights_ceil = heights.ceil() + + num_rois, num_keypoints = maps.shape[:2] + xy_preds = maps.new_zeros(rois.shape[0], num_keypoints, 4) + + width_corrections = widths / widths_ceil + height_corrections = heights / heights_ceil + + keypoints_idx = torch.arange(num_keypoints, device=maps.device) + + for i in range(num_rois): + outsize = (int(heights_ceil[i]), int(widths_ceil[i])) + roi_map = F.interpolate( + maps[[i]], size=outsize, mode="bicubic", align_corners=False + ).squeeze( + 0 + ) # #keypoints x H x W + + # softmax over the spatial region + max_score, _ = roi_map.view(num_keypoints, -1).max(1) + max_score = max_score.view(num_keypoints, 1, 1) + tmp_full_resolution = (roi_map - max_score).exp_() + tmp_pool_resolution = (maps[i] - max_score).exp_() + # Produce scores over the region H x W, but normalize with POOL_H x POOL_W, + # so that the scores of objects of different absolute sizes will be more comparable + roi_map_scores = tmp_full_resolution / tmp_pool_resolution.sum((1, 2), keepdim=True) + + w = roi_map.shape[2] + pos = roi_map.view(num_keypoints, -1).argmax(1) + + x_int = pos % w + y_int = (pos - x_int) // w + + assert ( + roi_map_scores[keypoints_idx, y_int, x_int] + == roi_map_scores.view(num_keypoints, -1).max(1)[0] + ).all() + + x = (x_int.float() + 0.5) * width_corrections[i] + y = (y_int.float() + 0.5) * height_corrections[i] + + xy_preds[i, :, 0] = x + offset_x[i] + xy_preds[i, :, 1] = y + offset_y[i] + xy_preds[i, :, 2] = roi_map[keypoints_idx, y_int, x_int] + xy_preds[i, :, 3] = roi_map_scores[keypoints_idx, y_int, x_int] + + return xy_preds diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/structures/masks.py b/motion-gan-pipeline/preprocessing/third/detectron2/structures/masks.py new file mode 100644 index 0000000..8f8e72d --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/structures/masks.py @@ -0,0 +1,532 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import itertools +import numpy as np +from typing import Any, Iterator, List, Union +import pycocotools.mask as mask_util +import torch +from torch import device + +from detectron2.layers.roi_align import ROIAlign +from detectron2.utils.memory import retry_if_cuda_oom + +from .boxes import Boxes + + +def polygon_area(x, y): + # Using the shoelace formula + # https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates + return 0.5 * np.abs(np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1))) + + +def polygons_to_bitmask(polygons: List[np.ndarray], height: int, width: int) -> np.ndarray: + """ + Args: + polygons (list[ndarray]): each array has shape (Nx2,) + height, width (int) + + Returns: + ndarray: a bool mask of shape (height, width) + """ + if len(polygons) == 0: + # COCOAPI does not support empty polygons + return np.zeros((height, width)).astype(np.bool) + rles = mask_util.frPyObjects(polygons, height, width) + rle = mask_util.merge(rles) + return mask_util.decode(rle).astype(np.bool) + + +def rasterize_polygons_within_box( + polygons: List[np.ndarray], box: np.ndarray, mask_size: int +) -> torch.Tensor: + """ + Rasterize the polygons into a mask image and + crop the mask content in the given box. + The cropped mask is resized to (mask_size, mask_size). + + This function is used when generating training targets for mask head in Mask R-CNN. + Given original ground-truth masks for an image, new ground-truth mask + training targets in the size of `mask_size x mask_size` + must be provided for each predicted box. This function will be called to + produce such targets. + + Args: + polygons (list[ndarray[float]]): a list of polygons, which represents an instance. + box: 4-element numpy array + mask_size (int): + + Returns: + Tensor: BoolTensor of shape (mask_size, mask_size) + """ + # 1. Shift the polygons w.r.t the boxes + w, h = box[2] - box[0], box[3] - box[1] + + polygons = copy.deepcopy(polygons) + for p in polygons: + p[0::2] = p[0::2] - box[0] + p[1::2] = p[1::2] - box[1] + + # 2. Rescale the polygons to the new box size + # max() to avoid division by small number + ratio_h = mask_size / max(h, 0.1) + ratio_w = mask_size / max(w, 0.1) + + if ratio_h == ratio_w: + for p in polygons: + p *= ratio_h + else: + for p in polygons: + p[0::2] *= ratio_w + p[1::2] *= ratio_h + + # 3. Rasterize the polygons with coco api + mask = polygons_to_bitmask(polygons, mask_size, mask_size) + mask = torch.from_numpy(mask) + return mask + + +class BitMasks: + """ + This class stores the segmentation masks for all objects in one image, in + the form of bitmaps. + + Attributes: + tensor: bool Tensor of N,H,W, representing N instances in the image. + """ + + def __init__(self, tensor: Union[torch.Tensor, np.ndarray]): + """ + Args: + tensor: bool Tensor of N,H,W, representing N instances in the image. + """ + device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu") + tensor = torch.as_tensor(tensor, dtype=torch.bool, device=device) + assert tensor.dim() == 3, tensor.size() + self.image_size = tensor.shape[1:] + self.tensor = tensor + + @torch.jit.unused + def to(self, *args: Any, **kwargs: Any) -> "BitMasks": + return BitMasks(self.tensor.to(*args, **kwargs)) + + @property + def device(self) -> torch.device: + return self.tensor.device + + @torch.jit.unused + def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "BitMasks": + """ + Returns: + BitMasks: Create a new :class:`BitMasks` by indexing. + + The following usage are allowed: + + 1. `new_masks = masks[3]`: return a `BitMasks` which contains only one mask. + 2. `new_masks = masks[2:10]`: return a slice of masks. + 3. `new_masks = masks[vector]`, where vector is a torch.BoolTensor + with `length = len(masks)`. Nonzero elements in the vector will be selected. + + Note that the returned object might share storage with this object, + subject to Pytorch's indexing semantics. + """ + if isinstance(item, int): + return BitMasks(self.tensor[item].unsqueeze(0)) + m = self.tensor[item] + assert m.dim() == 3, "Indexing on BitMasks with {} returns a tensor with shape {}!".format( + item, m.shape + ) + return BitMasks(m) + + @torch.jit.unused + def __iter__(self) -> torch.Tensor: + yield from self.tensor + + @torch.jit.unused + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "num_instances={})".format(len(self.tensor)) + return s + + def __len__(self) -> int: + return self.tensor.shape[0] + + def nonempty(self) -> torch.Tensor: + """ + Find masks that are non-empty. + + Returns: + Tensor: a BoolTensor which represents + whether each mask is empty (False) or non-empty (True). + """ + return self.tensor.flatten(1).any(dim=1) + + @staticmethod + def from_polygon_masks( + polygon_masks: Union["PolygonMasks", List[List[np.ndarray]]], height: int, width: int + ) -> "BitMasks": + """ + Args: + polygon_masks (list[list[ndarray]] or PolygonMasks) + height, width (int) + """ + if isinstance(polygon_masks, PolygonMasks): + polygon_masks = polygon_masks.polygons + masks = [polygons_to_bitmask(p, height, width) for p in polygon_masks] + if len(masks): + return BitMasks(torch.stack([torch.from_numpy(x) for x in masks])) + else: + return BitMasks(torch.empty(0, height, width, dtype=torch.bool)) + + @staticmethod + def from_roi_masks(roi_masks: "ROIMasks", height: int, width: int) -> "BitMasks": + """ + Args: + roi_masks: + height, width (int): + """ + return roi_masks.to_bitmasks(height, width) + + def crop_and_resize(self, boxes: torch.Tensor, mask_size: int) -> torch.Tensor: + """ + Crop each bitmask by the given box, and resize results to (mask_size, mask_size). + This can be used to prepare training targets for Mask R-CNN. + It has less reconstruction error compared to rasterization with polygons. + However we observe no difference in accuracy, + but BitMasks requires more memory to store all the masks. + + Args: + boxes (Tensor): Nx4 tensor storing the boxes for each mask + mask_size (int): the size of the rasterized mask. + + Returns: + Tensor: + A bool tensor of shape (N, mask_size, mask_size), where + N is the number of predicted boxes for this image. + """ + assert len(boxes) == len(self), "{} != {}".format(len(boxes), len(self)) + device = self.tensor.device + + batch_inds = torch.arange(len(boxes), device=device).to(dtype=boxes.dtype)[:, None] + rois = torch.cat([batch_inds, boxes], dim=1) # Nx5 + + bit_masks = self.tensor.to(dtype=torch.float32) + rois = rois.to(device=device) + output = ( + ROIAlign((mask_size, mask_size), 1.0, 0, aligned=True) + .forward(bit_masks[:, None, :, :], rois) + .squeeze(1) + ) + output = output >= 0.5 + return output + + def get_bounding_boxes(self) -> Boxes: + """ + Returns: + Boxes: tight bounding boxes around bitmasks. + If a mask is empty, it's bounding box will be all zero. + """ + boxes = torch.zeros(self.tensor.shape[0], 4, dtype=torch.float32) + x_any = torch.any(self.tensor, dim=1) + y_any = torch.any(self.tensor, dim=2) + for idx in range(self.tensor.shape[0]): + x = torch.where(x_any[idx, :])[0] + y = torch.where(y_any[idx, :])[0] + if len(x) > 0 and len(y) > 0: + boxes[idx, :] = torch.as_tensor( + [x[0], y[0], x[-1] + 1, y[-1] + 1], dtype=torch.float32 + ) + return Boxes(boxes) + + @staticmethod + def cat(bitmasks_list: List["BitMasks"]) -> "BitMasks": + """ + Concatenates a list of BitMasks into a single BitMasks + + Arguments: + bitmasks_list (list[BitMasks]) + + Returns: + BitMasks: the concatenated BitMasks + """ + assert isinstance(bitmasks_list, (list, tuple)) + assert len(bitmasks_list) > 0 + assert all(isinstance(bitmask, BitMasks) for bitmask in bitmasks_list) + + cat_bitmasks = type(bitmasks_list[0])(torch.cat([bm.tensor for bm in bitmasks_list], dim=0)) + return cat_bitmasks + + +class PolygonMasks: + """ + This class stores the segmentation masks for all objects in one image, in the form of polygons. + + Attributes: + polygons: list[list[ndarray]]. Each ndarray is a float64 vector representing a polygon. + """ + + def __init__(self, polygons: List[List[Union[torch.Tensor, np.ndarray]]]): + """ + Arguments: + polygons (list[list[np.ndarray]]): The first + level of the list correspond to individual instances, + the second level to all the polygons that compose the + instance, and the third level to the polygon coordinates. + The third level array should have the format of + [x0, y0, x1, y1, ..., xn, yn] (n >= 3). + """ + if not isinstance(polygons, list): + raise ValueError( + "Cannot create PolygonMasks: Expect a list of list of polygons per image. " + "Got '{}' instead.".format(type(polygons)) + ) + + def _make_array(t: Union[torch.Tensor, np.ndarray]) -> np.ndarray: + # Use float64 for higher precision, because why not? + # Always put polygons on CPU (self.to is a no-op) since they + # are supposed to be small tensors. + # May need to change this assumption if GPU placement becomes useful + if isinstance(t, torch.Tensor): + t = t.cpu().numpy() + return np.asarray(t).astype("float64") + + def process_polygons( + polygons_per_instance: List[Union[torch.Tensor, np.ndarray]] + ) -> List[np.ndarray]: + if not isinstance(polygons_per_instance, list): + raise ValueError( + "Cannot create polygons: Expect a list of polygons per instance. " + "Got '{}' instead.".format(type(polygons_per_instance)) + ) + # transform each polygon to a numpy array + polygons_per_instance = [_make_array(p) for p in polygons_per_instance] + for polygon in polygons_per_instance: + if len(polygon) % 2 != 0 or len(polygon) < 6: + raise ValueError(f"Cannot create a polygon from {len(polygon)} coordinates.") + return polygons_per_instance + + self.polygons: List[List[np.ndarray]] = [ + process_polygons(polygons_per_instance) for polygons_per_instance in polygons + ] + + def to(self, *args: Any, **kwargs: Any) -> "PolygonMasks": + return self + + @property + def device(self) -> torch.device: + return torch.device("cpu") + + def get_bounding_boxes(self) -> Boxes: + """ + Returns: + Boxes: tight bounding boxes around polygon masks. + """ + boxes = torch.zeros(len(self.polygons), 4, dtype=torch.float32) + for idx, polygons_per_instance in enumerate(self.polygons): + minxy = torch.as_tensor([float("inf"), float("inf")], dtype=torch.float32) + maxxy = torch.zeros(2, dtype=torch.float32) + for polygon in polygons_per_instance: + coords = torch.from_numpy(polygon).view(-1, 2).to(dtype=torch.float32) + minxy = torch.min(minxy, torch.min(coords, dim=0).values) + maxxy = torch.max(maxxy, torch.max(coords, dim=0).values) + boxes[idx, :2] = minxy + boxes[idx, 2:] = maxxy + return Boxes(boxes) + + def nonempty(self) -> torch.Tensor: + """ + Find masks that are non-empty. + + Returns: + Tensor: + a BoolTensor which represents whether each mask is empty (False) or not (True). + """ + keep = [1 if len(polygon) > 0 else 0 for polygon in self.polygons] + return torch.from_numpy(np.asarray(keep, dtype=np.bool)) + + def __getitem__(self, item: Union[int, slice, List[int], torch.BoolTensor]) -> "PolygonMasks": + """ + Support indexing over the instances and return a `PolygonMasks` object. + `item` can be: + + 1. An integer. It will return an object with only one instance. + 2. A slice. It will return an object with the selected instances. + 3. A list[int]. It will return an object with the selected instances, + correpsonding to the indices in the list. + 4. A vector mask of type BoolTensor, whose length is num_instances. + It will return an object with the instances whose mask is nonzero. + """ + if isinstance(item, int): + selected_polygons = [self.polygons[item]] + elif isinstance(item, slice): + selected_polygons = self.polygons[item] + elif isinstance(item, list): + selected_polygons = [self.polygons[i] for i in item] + elif isinstance(item, torch.Tensor): + # Polygons is a list, so we have to move the indices back to CPU. + if item.dtype == torch.bool: + assert item.dim() == 1, item.shape + item = item.nonzero().squeeze(1).cpu().numpy().tolist() + elif item.dtype in [torch.int32, torch.int64]: + item = item.cpu().numpy().tolist() + else: + raise ValueError("Unsupported tensor dtype={} for indexing!".format(item.dtype)) + selected_polygons = [self.polygons[i] for i in item] + return PolygonMasks(selected_polygons) + + def __iter__(self) -> Iterator[List[np.ndarray]]: + """ + Yields: + list[ndarray]: the polygons for one instance. + Each Tensor is a float64 vector representing a polygon. + """ + return iter(self.polygons) + + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "num_instances={})".format(len(self.polygons)) + return s + + def __len__(self) -> int: + return len(self.polygons) + + def crop_and_resize(self, boxes: torch.Tensor, mask_size: int) -> torch.Tensor: + """ + Crop each mask by the given box, and resize results to (mask_size, mask_size). + This can be used to prepare training targets for Mask R-CNN. + + Args: + boxes (Tensor): Nx4 tensor storing the boxes for each mask + mask_size (int): the size of the rasterized mask. + + Returns: + Tensor: A bool tensor of shape (N, mask_size, mask_size), where + N is the number of predicted boxes for this image. + """ + assert len(boxes) == len(self), "{} != {}".format(len(boxes), len(self)) + + device = boxes.device + # Put boxes on the CPU, as the polygon representation is not efficient GPU-wise + # (several small tensors for representing a single instance mask) + boxes = boxes.to(torch.device("cpu")) + + results = [ + rasterize_polygons_within_box(poly, box.numpy(), mask_size) + for poly, box in zip(self.polygons, boxes) + ] + """ + poly: list[list[float]], the polygons for one instance + box: a tensor of shape (4,) + """ + if len(results) == 0: + return torch.empty(0, mask_size, mask_size, dtype=torch.bool, device=device) + return torch.stack(results, dim=0).to(device=device) + + def area(self): + """ + Computes area of the mask. + Only works with Polygons, using the shoelace formula: + https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates + + Returns: + Tensor: a vector, area for each instance + """ + + area = [] + for polygons_per_instance in self.polygons: + area_per_instance = 0 + for p in polygons_per_instance: + area_per_instance += polygon_area(p[0::2], p[1::2]) + area.append(area_per_instance) + + return torch.tensor(area) + + @staticmethod + def cat(polymasks_list: List["PolygonMasks"]) -> "PolygonMasks": + """ + Concatenates a list of PolygonMasks into a single PolygonMasks + + Arguments: + polymasks_list (list[PolygonMasks]) + + Returns: + PolygonMasks: the concatenated PolygonMasks + """ + assert isinstance(polymasks_list, (list, tuple)) + assert len(polymasks_list) > 0 + assert all(isinstance(polymask, PolygonMasks) for polymask in polymasks_list) + + cat_polymasks = type(polymasks_list[0])( + list(itertools.chain.from_iterable(pm.polygons for pm in polymasks_list)) + ) + return cat_polymasks + + +class ROIMasks: + """ + Represent masks by N smaller masks defined in some ROIs. Once ROI boxes are given, + full-image bitmask can be obtained by "pasting" the mask on the region defined + by the corresponding ROI box. + """ + + def __init__(self, tensor: torch.Tensor): + """ + Args: + tensor: (N, M, M) mask tensor that defines the mask within each ROI. + """ + if tensor.dim() != 3: + raise ValueError("ROIMasks must take a masks of 3 dimension.") + self.tensor = tensor + + def to(self, device: torch.device) -> "ROIMasks": + return ROIMasks(self.tensor.to(device)) + + @property + def device(self) -> device: + return self.tensor.device + + def __len__(self): + return self.tensor.shape[0] + + def __getitem__(self, item) -> "ROIMasks": + """ + Returns: + ROIMasks: Create a new :class:`ROIMasks` by indexing. + + The following usage are allowed: + + 1. `new_masks = masks[2:10]`: return a slice of masks. + 2. `new_masks = masks[vector]`, where vector is a torch.BoolTensor + with `length = len(masks)`. Nonzero elements in the vector will be selected. + + Note that the returned object might share storage with this object, + subject to Pytorch's indexing semantics. + """ + t = self.tensor[item] + if t.dim() != 3: + raise ValueError( + f"Indexing on ROIMasks with {item} returns a tensor with shape {t.shape}!" + ) + return ROIMasks(t) + + @torch.jit.unused + def __repr__(self) -> str: + s = self.__class__.__name__ + "(" + s += "num_instances={})".format(len(self.tensor)) + return s + + @torch.jit.unused + def to_bitmasks(self, boxes: torch.Tensor, height, width, threshold=0.5): + """ + Args: see documentation of :func:`paste_masks_in_image`. + """ + from detectron2.layers.mask_ops import paste_masks_in_image, _paste_masks_tensor_shape + + if torch.jit.is_tracing(): + if isinstance(height, torch.Tensor): + paste_func = _paste_masks_tensor_shape + else: + paste_func = paste_masks_in_image + else: + paste_func = retry_if_cuda_oom(paste_masks_in_image) + bitmasks = paste_func(self.tensor, boxes.tensor, (height, width), threshold=threshold) + return BitMasks(bitmasks) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/structures/rotated_boxes.py b/motion-gan-pipeline/preprocessing/third/detectron2/structures/rotated_boxes.py new file mode 100644 index 0000000..4ec8e4c --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/structures/rotated_boxes.py @@ -0,0 +1,503 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import math +from typing import List, Tuple +import torch + +from detectron2.layers.rotated_boxes import pairwise_iou_rotated + +from .boxes import Boxes + + +class RotatedBoxes(Boxes): + """ + This structure stores a list of rotated boxes as a Nx5 torch.Tensor. + It supports some common methods about boxes + (`area`, `clip`, `nonempty`, etc), + and also behaves like a Tensor + (support indexing, `to(device)`, `.device`, and iteration over all boxes) + """ + + def __init__(self, tensor: torch.Tensor): + """ + Args: + tensor (Tensor[float]): a Nx5 matrix. Each row is + (x_center, y_center, width, height, angle), + in which angle is represented in degrees. + While there's no strict range restriction for it, + the recommended principal range is between [-180, 180) degrees. + + Assume we have a horizontal box B = (x_center, y_center, width, height), + where width is along the x-axis and height is along the y-axis. + The rotated box B_rot (x_center, y_center, width, height, angle) + can be seen as: + + 1. When angle == 0: + B_rot == B + 2. When angle > 0: + B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CCW; + 3. When angle < 0: + B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CW. + + Mathematically, since the right-handed coordinate system for image space + is (y, x), where y is top->down and x is left->right, the 4 vertices of the + rotated rectangle :math:`(yr_i, xr_i)` (i = 1, 2, 3, 4) can be obtained from + the vertices of the horizontal rectangle :math:`(y_i, x_i)` (i = 1, 2, 3, 4) + in the following way (:math:`\\theta = angle*\\pi/180` is the angle in radians, + :math:`(y_c, x_c)` is the center of the rectangle): + + .. math:: + + yr_i = \\cos(\\theta) (y_i - y_c) - \\sin(\\theta) (x_i - x_c) + y_c, + + xr_i = \\sin(\\theta) (y_i - y_c) + \\cos(\\theta) (x_i - x_c) + x_c, + + which is the standard rigid-body rotation transformation. + + Intuitively, the angle is + (1) the rotation angle from y-axis in image space + to the height vector (top->down in the box's local coordinate system) + of the box in CCW, and + (2) the rotation angle from x-axis in image space + to the width vector (left->right in the box's local coordinate system) + of the box in CCW. + + More intuitively, consider the following horizontal box ABCD represented + in (x1, y1, x2, y2): (3, 2, 7, 4), + covering the [3, 7] x [2, 4] region of the continuous coordinate system + which looks like this: + + .. code:: none + + O--------> x + | + | A---B + | | | + | D---C + | + v y + + Note that each capital letter represents one 0-dimensional geometric point + instead of a 'square pixel' here. + + In the example above, using (x, y) to represent a point we have: + + .. math:: + + O = (0, 0), A = (3, 2), B = (7, 2), C = (7, 4), D = (3, 4) + + We name vector AB = vector DC as the width vector in box's local coordinate system, and + vector AD = vector BC as the height vector in box's local coordinate system. Initially, + when angle = 0 degree, they're aligned with the positive directions of x-axis and y-axis + in the image space, respectively. + + For better illustration, we denote the center of the box as E, + + .. code:: none + + O--------> x + | + | A---B + | | E | + | D---C + | + v y + + where the center E = ((3+7)/2, (2+4)/2) = (5, 3). + + Also, + + .. math:: + + width = |AB| = |CD| = 7 - 3 = 4, + height = |AD| = |BC| = 4 - 2 = 2. + + Therefore, the corresponding representation for the same shape in rotated box in + (x_center, y_center, width, height, angle) format is: + + (5, 3, 4, 2, 0), + + Now, let's consider (5, 3, 4, 2, 90), which is rotated by 90 degrees + CCW (counter-clockwise) by definition. It looks like this: + + .. code:: none + + O--------> x + | B-C + | | | + | |E| + | | | + | A-D + v y + + The center E is still located at the same point (5, 3), while the vertices + ABCD are rotated by 90 degrees CCW with regard to E: + A = (4, 5), B = (4, 1), C = (6, 1), D = (6, 5) + + Here, 90 degrees can be seen as the CCW angle to rotate from y-axis to + vector AD or vector BC (the top->down height vector in box's local coordinate system), + or the CCW angle to rotate from x-axis to vector AB or vector DC (the left->right + width vector in box's local coordinate system). + + .. math:: + + width = |AB| = |CD| = 5 - 1 = 4, + height = |AD| = |BC| = 6 - 4 = 2. + + Next, how about (5, 3, 4, 2, -90), which is rotated by 90 degrees CW (clockwise) + by definition? It looks like this: + + .. code:: none + + O--------> x + | D-A + | | | + | |E| + | | | + | C-B + v y + + The center E is still located at the same point (5, 3), while the vertices + ABCD are rotated by 90 degrees CW with regard to E: + A = (6, 1), B = (6, 5), C = (4, 5), D = (4, 1) + + .. math:: + + width = |AB| = |CD| = 5 - 1 = 4, + height = |AD| = |BC| = 6 - 4 = 2. + + This covers exactly the same region as (5, 3, 4, 2, 90) does, and their IoU + will be 1. However, these two will generate different RoI Pooling results and + should not be treated as an identical box. + + On the other hand, it's easy to see that (X, Y, W, H, A) is identical to + (X, Y, W, H, A+360N), for any integer N. For example (5, 3, 4, 2, 270) would be + identical to (5, 3, 4, 2, -90), because rotating the shape 270 degrees CCW is + equivalent to rotating the same shape 90 degrees CW. + + We could rotate further to get (5, 3, 4, 2, 180), or (5, 3, 4, 2, -180): + + .. code:: none + + O--------> x + | + | C---D + | | E | + | B---A + | + v y + + .. math:: + + A = (7, 4), B = (3, 4), C = (3, 2), D = (7, 2), + + width = |AB| = |CD| = 7 - 3 = 4, + height = |AD| = |BC| = 4 - 2 = 2. + + Finally, this is a very inaccurate (heavily quantized) illustration of + how (5, 3, 4, 2, 60) looks like in case anyone wonders: + + .. code:: none + + O--------> x + | B\ + | / C + | /E / + | A / + | `D + v y + + It's still a rectangle with center of (5, 3), width of 4 and height of 2, + but its angle (and thus orientation) is somewhere between + (5, 3, 4, 2, 0) and (5, 3, 4, 2, 90). + """ + device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu") + tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device) + if tensor.numel() == 0: + # Use reshape, so we don't end up creating a new tensor that does not depend on + # the inputs (and consequently confuses jit) + tensor = tensor.reshape((0, 5)).to(dtype=torch.float32, device=device) + assert tensor.dim() == 2 and tensor.size(-1) == 5, tensor.size() + + self.tensor = tensor + + def clone(self) -> "RotatedBoxes": + """ + Clone the RotatedBoxes. + + Returns: + RotatedBoxes + """ + return RotatedBoxes(self.tensor.clone()) + + def to(self, device: torch.device): + # Boxes are assumed float32 and does not support to(dtype) + return RotatedBoxes(self.tensor.to(device=device)) + + def area(self) -> torch.Tensor: + """ + Computes the area of all the boxes. + + Returns: + torch.Tensor: a vector with areas of each box. + """ + box = self.tensor + area = box[:, 2] * box[:, 3] + return area + + def normalize_angles(self) -> None: + """ + Restrict angles to the range of [-180, 180) degrees + """ + self.tensor[:, 4] = (self.tensor[:, 4] + 180.0) % 360.0 - 180.0 + + def clip(self, box_size: Tuple[int, int], clip_angle_threshold: float = 1.0) -> None: + """ + Clip (in place) the boxes by limiting x coordinates to the range [0, width] + and y coordinates to the range [0, height]. + + For RRPN: + Only clip boxes that are almost horizontal with a tolerance of + clip_angle_threshold to maintain backward compatibility. + + Rotated boxes beyond this threshold are not clipped for two reasons: + + 1. There are potentially multiple ways to clip a rotated box to make it + fit within the image. + 2. It's tricky to make the entire rectangular box fit within the image + and still be able to not leave out pixels of interest. + + Therefore we rely on ops like RoIAlignRotated to safely handle this. + + Args: + box_size (height, width): The clipping box's size. + clip_angle_threshold: + Iff. abs(normalized(angle)) <= clip_angle_threshold (in degrees), + we do the clipping as horizontal boxes. + """ + h, w = box_size + + # normalize angles to be within (-180, 180] degrees + self.normalize_angles() + + idx = torch.where(torch.abs(self.tensor[:, 4]) <= clip_angle_threshold)[0] + + # convert to (x1, y1, x2, y2) + x1 = self.tensor[idx, 0] - self.tensor[idx, 2] / 2.0 + y1 = self.tensor[idx, 1] - self.tensor[idx, 3] / 2.0 + x2 = self.tensor[idx, 0] + self.tensor[idx, 2] / 2.0 + y2 = self.tensor[idx, 1] + self.tensor[idx, 3] / 2.0 + + # clip + x1.clamp_(min=0, max=w) + y1.clamp_(min=0, max=h) + x2.clamp_(min=0, max=w) + y2.clamp_(min=0, max=h) + + # convert back to (xc, yc, w, h) + self.tensor[idx, 0] = (x1 + x2) / 2.0 + self.tensor[idx, 1] = (y1 + y2) / 2.0 + # make sure widths and heights do not increase due to numerical errors + self.tensor[idx, 2] = torch.min(self.tensor[idx, 2], x2 - x1) + self.tensor[idx, 3] = torch.min(self.tensor[idx, 3], y2 - y1) + + def nonempty(self, threshold: float = 0.0) -> torch.Tensor: + """ + Find boxes that are non-empty. + A box is considered empty, if either of its side is no larger than threshold. + + Returns: + Tensor: a binary vector which represents + whether each box is empty (False) or non-empty (True). + """ + box = self.tensor + widths = box[:, 2] + heights = box[:, 3] + keep = (widths > threshold) & (heights > threshold) + return keep + + def __getitem__(self, item) -> "RotatedBoxes": + """ + Returns: + RotatedBoxes: Create a new :class:`RotatedBoxes` by indexing. + + The following usage are allowed: + + 1. `new_boxes = boxes[3]`: return a `RotatedBoxes` which contains only one box. + 2. `new_boxes = boxes[2:10]`: return a slice of boxes. + 3. `new_boxes = boxes[vector]`, where vector is a torch.ByteTensor + with `length = len(boxes)`. Nonzero elements in the vector will be selected. + + Note that the returned RotatedBoxes might share storage with this RotatedBoxes, + subject to Pytorch's indexing semantics. + """ + if isinstance(item, int): + return RotatedBoxes(self.tensor[item].view(1, -1)) + b = self.tensor[item] + assert b.dim() == 2, "Indexing on RotatedBoxes with {} failed to return a matrix!".format( + item + ) + return RotatedBoxes(b) + + def __len__(self) -> int: + return self.tensor.shape[0] + + def __repr__(self) -> str: + return "RotatedBoxes(" + str(self.tensor) + ")" + + def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor: + """ + Args: + box_size (height, width): Size of the reference box covering + [0, width] x [0, height] + boundary_threshold (int): Boxes that extend beyond the reference box + boundary by more than boundary_threshold are considered "outside". + + For RRPN, it might not be necessary to call this function since it's common + for rotated box to extend to outside of the image boundaries + (the clip function only clips the near-horizontal boxes) + + Returns: + a binary vector, indicating whether each box is inside the reference box. + """ + height, width = box_size + + cnt_x = self.tensor[..., 0] + cnt_y = self.tensor[..., 1] + half_w = self.tensor[..., 2] / 2.0 + half_h = self.tensor[..., 3] / 2.0 + a = self.tensor[..., 4] + c = torch.abs(torch.cos(a * math.pi / 180.0)) + s = torch.abs(torch.sin(a * math.pi / 180.0)) + # This basically computes the horizontal bounding rectangle of the rotated box + max_rect_dx = c * half_w + s * half_h + max_rect_dy = c * half_h + s * half_w + + inds_inside = ( + (cnt_x - max_rect_dx >= -boundary_threshold) + & (cnt_y - max_rect_dy >= -boundary_threshold) + & (cnt_x + max_rect_dx < width + boundary_threshold) + & (cnt_y + max_rect_dy < height + boundary_threshold) + ) + + return inds_inside + + def get_centers(self) -> torch.Tensor: + """ + Returns: + The box centers in a Nx2 array of (x, y). + """ + return self.tensor[:, :2] + + def scale(self, scale_x: float, scale_y: float) -> None: + """ + Scale the rotated box with horizontal and vertical scaling factors + Note: when scale_factor_x != scale_factor_y, + the rotated box does not preserve the rectangular shape when the angle + is not a multiple of 90 degrees under resize transformation. + Instead, the shape is a parallelogram (that has skew) + Here we make an approximation by fitting a rotated rectangle to the parallelogram. + """ + self.tensor[:, 0] *= scale_x + self.tensor[:, 1] *= scale_y + theta = self.tensor[:, 4] * math.pi / 180.0 + c = torch.cos(theta) + s = torch.sin(theta) + + # In image space, y is top->down and x is left->right + # Consider the local coordintate system for the rotated box, + # where the box center is located at (0, 0), and the four vertices ABCD are + # A(-w / 2, -h / 2), B(w / 2, -h / 2), C(w / 2, h / 2), D(-w / 2, h / 2) + # the midpoint of the left edge AD of the rotated box E is: + # E = (A+D)/2 = (-w / 2, 0) + # the midpoint of the top edge AB of the rotated box F is: + # F(0, -h / 2) + # To get the old coordinates in the global system, apply the rotation transformation + # (Note: the right-handed coordinate system for image space is yOx): + # (old_x, old_y) = (s * y + c * x, c * y - s * x) + # E(old) = (s * 0 + c * (-w/2), c * 0 - s * (-w/2)) = (-c * w / 2, s * w / 2) + # F(old) = (s * (-h / 2) + c * 0, c * (-h / 2) - s * 0) = (-s * h / 2, -c * h / 2) + # After applying the scaling factor (sfx, sfy): + # E(new) = (-sfx * c * w / 2, sfy * s * w / 2) + # F(new) = (-sfx * s * h / 2, -sfy * c * h / 2) + # The new width after scaling tranformation becomes: + + # w(new) = |E(new) - O| * 2 + # = sqrt[(sfx * c * w / 2)^2 + (sfy * s * w / 2)^2] * 2 + # = sqrt[(sfx * c)^2 + (sfy * s)^2] * w + # i.e., scale_factor_w = sqrt[(sfx * c)^2 + (sfy * s)^2] + # + # For example, + # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_w == scale_factor_x; + # when |angle| = 90, c = 0, |s| = 1, scale_factor_w == scale_factor_y + self.tensor[:, 2] *= torch.sqrt((scale_x * c) ** 2 + (scale_y * s) ** 2) + + # h(new) = |F(new) - O| * 2 + # = sqrt[(sfx * s * h / 2)^2 + (sfy * c * h / 2)^2] * 2 + # = sqrt[(sfx * s)^2 + (sfy * c)^2] * h + # i.e., scale_factor_h = sqrt[(sfx * s)^2 + (sfy * c)^2] + # + # For example, + # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_h == scale_factor_y; + # when |angle| = 90, c = 0, |s| = 1, scale_factor_h == scale_factor_x + self.tensor[:, 3] *= torch.sqrt((scale_x * s) ** 2 + (scale_y * c) ** 2) + + # The angle is the rotation angle from y-axis in image space to the height + # vector (top->down in the box's local coordinate system) of the box in CCW. + # + # angle(new) = angle_yOx(O - F(new)) + # = angle_yOx( (sfx * s * h / 2, sfy * c * h / 2) ) + # = atan2(sfx * s * h / 2, sfy * c * h / 2) + # = atan2(sfx * s, sfy * c) + # + # For example, + # when sfx == sfy, angle(new) == atan2(s, c) == angle(old) + self.tensor[:, 4] = torch.atan2(scale_x * s, scale_y * c) * 180 / math.pi + + @classmethod + def cat(cls, boxes_list: List["RotatedBoxes"]) -> "RotatedBoxes": + """ + Concatenates a list of RotatedBoxes into a single RotatedBoxes + + Arguments: + boxes_list (list[RotatedBoxes]) + + Returns: + RotatedBoxes: the concatenated RotatedBoxes + """ + assert isinstance(boxes_list, (list, tuple)) + if len(boxes_list) == 0: + return cls(torch.empty(0)) + assert all([isinstance(box, RotatedBoxes) for box in boxes_list]) + + # use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input + cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0)) + return cat_boxes + + @property + def device(self) -> torch.device: + return self.tensor.device + + @torch.jit.unused + def __iter__(self): + """ + Yield a box as a Tensor of shape (5,) at a time. + """ + yield from self.tensor + + +def pairwise_iou(boxes1: RotatedBoxes, boxes2: RotatedBoxes) -> None: + """ + Given two lists of rotated boxes of size N and M, + compute the IoU (intersection over union) + between **all** N x M pairs of boxes. + The box order must be (x_center, y_center, width, height, angle). + + Args: + boxes1, boxes2 (RotatedBoxes): + two `RotatedBoxes`. Contains N & M rotated boxes, respectively. + + Returns: + Tensor: IoU, sized [N,M]. + """ + + return pairwise_iou_rotated(boxes1.tensor, boxes2.tensor) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/tracking/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/tracking/__init__.py new file mode 100644 index 0000000..aaf477d --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/tracking/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .base_tracker import ( # noqa + BaseTracker, + build_tracker_head, + TRACKER_HEADS_REGISTRY, +) +from .bbox_iou_tracker import BBoxIOUTracker # noqa +from .hungarian_tracker import BaseHungarianTracker # noqa +from .iou_weighted_hungarian_bbox_iou_tracker import ( # noqa + IOUWeightedHungarianBBoxIOUTracker, +) +from .utils import create_prediction_pairs # noqa +from .vanilla_hungarian_bbox_iou_tracker import VanillaHungarianBBoxIOUTracker # noqa + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/tracking/base_tracker.py b/motion-gan-pipeline/preprocessing/third/detectron2/tracking/base_tracker.py new file mode 100644 index 0000000..e2dea77 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/tracking/base_tracker.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. +from ..structures import Instances +from detectron2.utils.registry import Registry +from ..config.config import CfgNode as CfgNode_ +from detectron2.config import configurable + + +TRACKER_HEADS_REGISTRY = Registry("TRACKER_HEADS") +TRACKER_HEADS_REGISTRY.__doc__ = """ +Registry for tracking classes. +""" + + +class BaseTracker(object): + """ + A parent class for all trackers + """ + + @configurable + def __init__(self, **kwargs): + self._prev_instances = None # (D2)instances for previous frame + self._matched_idx = set() # indices in prev_instances found matching + self._matched_ID = set() # idendities in prev_instances found matching + self._untracked_prev_idx = set() # indices in prev_instances not found matching + self._id_count = 0 # used to assign new id + + @classmethod + def from_config(cls, cfg: CfgNode_): + raise NotImplementedError("Calling BaseTracker::from_config") + + def update(self, predictions: Instances) -> Instances: + """ + Args: + predictions: D2 Instances for predictions of the current frame + Return: + D2 Instances for predictions of the current frame with ID assigned + + _prev_instances and instances will have the following fields: + .pred_boxes (shape=[N, 4]) + .scores (shape=[N,]) + .pred_classes (shape=[N,]) + .pred_keypoints (shape=[N, M, 3], Optional) + .pred_masks (shape=List[2D_MASK], Optional) 2D_MASK: shape=[H, W] + .ID (shape=[N,]) + + N: # of detected bboxes + H and W: height and width of 2D mask + """ + raise NotImplementedError("Calling BaseTracker::update") + + +def build_tracker_head(cfg: CfgNode_) -> BaseTracker: + """ + Build a tracker head from `cfg.TRACKER_HEADS.TRACKER_NAME`. + + Args: + cfg: D2 CfgNode, config file with tracker information + Return: + tracker object + """ + name = cfg.TRACKER_HEADS.TRACKER_NAME + tracker_class = TRACKER_HEADS_REGISTRY.get(name) + return tracker_class(cfg) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/tracking/bbox_iou_tracker.py b/motion-gan-pipeline/preprocessing/third/detectron2/tracking/bbox_iou_tracker.py new file mode 100644 index 0000000..d7eb3aa --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/tracking/bbox_iou_tracker.py @@ -0,0 +1,260 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. +import copy +from typing import List + +import numpy as np +import torch +from detectron2.config import configurable +from detectron2.structures import Boxes, Instances +from detectron2.structures.boxes import pairwise_iou + +from ..config.config import CfgNode as CfgNode_ +from .base_tracker import BaseTracker, TRACKER_HEADS_REGISTRY + + +@TRACKER_HEADS_REGISTRY.register() +class BBoxIOUTracker(BaseTracker): + """ + A bounding box tracker to assign ID based on IoU between current and previous instances + """ + @configurable + def __init__( + self, + *, + video_height: int, + video_width: int, + max_num_instances: int = 200, + max_lost_frame_count: int = 0, + min_box_rel_dim: float = 0.02, + min_instance_period: int = 1, + track_iou_threshold: float = 0.5, + **kwargs + ): + """ + Args: + video_height: height the video frame + video_width: width of the video frame + max_num_instances: maximum number of id allowed to be tracked + max_lost_frame_count: maximum number of frame an id can lost tracking + exceed this number, an id is considered as lost + forever + min_box_rel_dim: a percentage, smaller than this dimension, a bbox is + removed from tracking + min_instance_period: an instance will be shown after this number of period + since its first showing up in the video + track_iou_threshold: iou threshold, below this number a bbox pair is removed + from tracking + """ + super().__init__(**kwargs) + self._video_height = video_height + self._video_width = video_width + self._max_num_instances = max_num_instances + self._max_lost_frame_count = max_lost_frame_count + self._min_box_rel_dim = min_box_rel_dim + self._min_instance_period = min_instance_period + self._track_iou_threshold = track_iou_threshold + + @classmethod + def from_config(cls, cfg: CfgNode_): + """ + Old style initialization using CfgNode + + Args: + cfg: D2 CfgNode, config file + Return: + dictionary storing arguments for __init__ method + """ + assert "VIDEO_HEIGHT" in cfg.TRACKER_HEADS + assert "VIDEO_WIDTH" in cfg.TRACKER_HEADS + video_height = cfg.TRACKER_HEADS.get("VIDEO_HEIGHT") + video_width = cfg.TRACKER_HEADS.get("VIDEO_WIDTH") + max_num_instances = cfg.TRACKER_HEADS.get("MAX_NUM_INSTANCES", 200) + max_lost_frame_count = cfg.TRACKER_HEADS.get("MAX_LOST_FRAME_COUNT", 0) + min_box_rel_dim = cfg.TRACKER_HEADS.get("MIN_BOX_REL_DIM", 0.02) + min_instance_period = cfg.TRACKER_HEADS.get("MIN_INSTANCE_PERIOD", 1) + track_iou_threshold = cfg.TRACKER_HEADS.get("TRACK_IOU_THRESHOLD", 0.5) + return { + "_target_": "detectron2.tracking.bbox_iou_tracker.BBoxIOUTracker", + "video_height": video_height, + "video_width": video_width, + "max_num_instances": max_num_instances, + "max_lost_frame_count": max_lost_frame_count, + "min_box_rel_dim": min_box_rel_dim, + "min_instance_period": min_instance_period, + "track_iou_threshold": track_iou_threshold + } + + def update(self, instances: Instances) -> Instances: + """ + See BaseTracker description + """ + if instances.has("pred_keypoints"): + raise NotImplementedError("Need to add support for keypoints") + instances = self._initialize_extra_fields(instances) + if self._prev_instances is not None: + # calculate IoU of all bbox pairs + iou_all = pairwise_iou( + boxes1=instances.pred_boxes, + boxes2=self._prev_instances.pred_boxes, + ) + # sort IoU in descending order + bbox_pairs = self._create_prediction_pairs(instances, iou_all) + # assign previous ID to current bbox if IoU > track_iou_threshold + self._reset_fields() + for bbox_pair in bbox_pairs: + idx = bbox_pair["idx"] + prev_id = bbox_pair["prev_id"] + if idx in self._matched_idx \ + or prev_id in self._matched_ID \ + or bbox_pair["IoU"] < self._track_iou_threshold: + continue + instances.ID[idx] = prev_id + instances.ID_period[idx] = bbox_pair["prev_period"] + 1 + instances.lost_frame_count[idx] = 0 + self._matched_idx.add(idx) + self._matched_ID.add(prev_id) + self._untracked_prev_idx.remove(bbox_pair["prev_idx"]) + instances = self._assign_new_id(instances) + instances = self._merge_untracked_instances(instances) + self._prev_instances = copy.deepcopy(instances) + return instances + + def _create_prediction_pairs( + self, instances: Instances, iou_all: np.ndarray + ) -> List: + """ + For all instances in previous and current frames, create pairs. For each + pair, store index of the instance in current frame predcitions, index in + previous predictions, ID in previous predictions, IoU of the bboxes in this + pair, period in previous predictions. + + Args: + instances: D2 Instances, for predictions of the current frame + iou_all: IoU for all bboxes pairs + Return: + A list of IoU for all pairs + """ + bbox_pairs = [] + for i in range(len(instances)): + for j in range(len(self._prev_instances)): + bbox_pairs.append( + { + "idx": i, + "prev_idx": j, + "prev_id": self._prev_instances.ID[j], + "IoU": iou_all[i, j], + "prev_period": self._prev_instances.ID_period[j], + } + ) + return bbox_pairs + + def _initialize_extra_fields(self, instances: Instances) -> Instances: + """ + If input instances don't have ID, ID_period, lost_frame_count fields, + this method is used to initialize these fields. + + Args: + instances: D2 Instances, for predictions of the current frame + Return: + D2 Instances with extra fields added + """ + if not instances.has("ID"): + instances.set("ID", [None] * len(instances)) + if not instances.has("ID_period"): + instances.set("ID_period", [None] * len(instances)) + if not instances.has("lost_frame_count"): + instances.set("lost_frame_count", [None] * len(instances)) + if self._prev_instances is None: + instances.ID = list(range(len(instances))) + self._id_count += len(instances) + instances.ID_period = [1] * len(instances) + instances.lost_frame_count = [0] * len(instances) + return instances + + def _reset_fields(self): + """ + Before each uodate call, reset fields first + """ + self._matched_idx = set() + self._matched_ID = set() + self._untracked_prev_idx = set(range(len(self._prev_instances))) + + def _assign_new_id(self, instances: Instances) -> Instances: + """ + For each untracked instance, assign a new id + + Args: + instances: D2 Instances, for predictions of the current frame + Return: + D2 Instances with new ID assigned + """ + untracked_idx = set(range(len(instances))).difference(self._matched_idx) + for idx in untracked_idx: + instances.ID[idx] = self._id_count + self._id_count += 1 + instances.ID_period[idx] = 1 + instances.lost_frame_count[idx] = 0 + return instances + + def _merge_untracked_instances(self, instances: Instances) -> Instances: + """ + For untracked previous instances, under certain condition, still keep them + in tracking and merge with the current instances. + + Args: + instances: D2 Instances, for predictions of the current frame + Return: + D2 Instances merging current instances and instances from previous + frame decided to keep tracking + """ + untracked_instances = Instances( + image_size=instances.image_size, + pred_boxes=[], + pred_masks=[], + pred_classes=[], + scores=[], + ID=[], + ID_period=[], + lost_frame_count=[], + ) + prev_bboxes = list(self._prev_instances.pred_boxes) + prev_classes = list(self._prev_instances.pred_classes) + prev_scores = list(self._prev_instances.scores) + prev_ID_period = self._prev_instances.ID_period + if instances.has("pred_masks"): + prev_masks = list(self._prev_instances.pred_masks) + for idx in self._untracked_prev_idx: + x_left, y_top, x_right, y_bot = prev_bboxes[idx] + if ( + (1.0 * (x_right - x_left) / self._video_width < self._min_box_rel_dim) + or (1.0 * (y_bot - y_top) / self._video_height < self._min_box_rel_dim) + or self._prev_instances.lost_frame_count[idx] >= self._max_lost_frame_count + or prev_ID_period[idx] <= self._min_instance_period + ): + continue + untracked_instances.pred_boxes.append(list(prev_bboxes[idx].numpy())) + untracked_instances.pred_classes.append(int(prev_classes[idx])) + untracked_instances.scores.append(float(prev_scores[idx])) + untracked_instances.ID.append(self._prev_instances.ID[idx]) + untracked_instances.ID_period.append(self._prev_instances.ID_period[idx]) + untracked_instances.lost_frame_count.append( + self._prev_instances.lost_frame_count[idx] + 1 + ) + if instances.has("pred_masks"): + untracked_instances.pred_masks.append(prev_masks[idx].numpy().astype(np.uint8)) + + untracked_instances.pred_boxes = Boxes(torch.FloatTensor(untracked_instances.pred_boxes)) + untracked_instances.pred_classes = torch.IntTensor(untracked_instances.pred_classes) + untracked_instances.scores = torch.FloatTensor(untracked_instances.scores) + if instances.has("pred_masks"): + untracked_instances.pred_masks = torch.IntTensor(untracked_instances.pred_masks) + else: + untracked_instances.remove("pred_masks") + + return Instances.cat( + [ + instances, + untracked_instances, + ] + ) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/tracking/hungarian_tracker.py b/motion-gan-pipeline/preprocessing/third/detectron2/tracking/hungarian_tracker.py new file mode 100644 index 0000000..a080157 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/tracking/hungarian_tracker.py @@ -0,0 +1,176 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. +import copy + +import numpy as np +import torch +from detectron2.structures import Boxes, Instances + +from .base_tracker import BaseTracker +from scipy.optimize import linear_sum_assignment +from ..config.config import CfgNode as CfgNode_ +from typing import Dict +from detectron2.config import configurable + + +class BaseHungarianTracker(BaseTracker): + """ + A base class for all Hungarian trackers + """ + + @configurable + def __init__( + self, + video_height: int, + video_width: int, + max_num_instances: int = 200, + max_lost_frame_count: int = 0, + min_box_rel_dim: float = 0.02, + min_instance_period: int = 1, + **kwargs + ): + """ + Args: + video_height: height the video frame + video_width: width of the video frame + max_num_instances: maximum number of id allowed to be tracked + max_lost_frame_count: maximum number of frame an id can lost tracking + exceed this number, an id is considered as lost + forever + min_box_rel_dim: a percentage, smaller than this dimension, a bbox is + removed from tracking + min_instance_period: an instance will be shown after this number of period + since its first showing up in the video + """ + super().__init__(**kwargs) + self._video_height = video_height + self._video_width = video_width + self._max_num_instances = max_num_instances + self._max_lost_frame_count = max_lost_frame_count + self._min_box_rel_dim = min_box_rel_dim + self._min_instance_period = min_instance_period + + @classmethod + def from_config(cls, cfg: CfgNode_) -> Dict: + raise NotImplementedError("Calling HungarianTracker::from_config") + + def build_cost_matrix(self, instances: Instances, prev_instances: Instances) -> np.ndarray: + raise NotImplementedError("Calling HungarianTracker::build_matrix") + + def update(self, instances: Instances) -> Instances: + if instances.has("pred_keypoints"): + raise NotImplementedError("Need to add support for keypoints") + instances = self._initialize_extra_fields(instances) + if self._prev_instances is not None: + self._untracked_prev_idx = set(range(len(self._prev_instances))) + cost_matrix = self.build_cost_matrix(instances, self._prev_instances) + matched_idx, matched_prev_idx = linear_sum_assignment(cost_matrix) + instances = self._process_matched_idx(instances, matched_idx, matched_prev_idx) + instances = self._process_unmatched_idx(instances, matched_idx) + instances = self._process_unmatched_prev_idx(instances, matched_prev_idx) + self._prev_instances = copy.deepcopy(instances) + return instances + + def _initialize_extra_fields(self, instances: Instances) -> Instances: + """ + If input instances don't have ID, ID_period, lost_frame_count fields, + this method is used to initialize these fields. + + Args: + instances: D2 Instances, for predictions of the current frame + Return: + D2 Instances with extra fields added + """ + if not instances.has("ID"): + instances.set("ID", [None] * len(instances)) + if not instances.has("ID_period"): + instances.set("ID_period", [None] * len(instances)) + if not instances.has("lost_frame_count"): + instances.set("lost_frame_count", [None] * len(instances)) + if self._prev_instances is None: + instances.ID = list(range(len(instances))) + self._id_count += len(instances) + instances.ID_period = [1] * len(instances) + instances.lost_frame_count = [0] * len(instances) + return instances + + def _process_matched_idx( + self, + instances: Instances, + matched_idx: np.ndarray, + matched_prev_idx: np.ndarray + ) -> Instances: + assert matched_idx.size == matched_prev_idx.size + for i in range(matched_idx.size): + instances.ID[matched_idx[i]] = self._prev_instances.ID[matched_prev_idx[i]] + instances.ID_period[matched_idx[i]] = \ + self._prev_instances.ID_period[matched_prev_idx[i]] + 1 + instances.lost_frame_count[matched_idx[i]] = 0 + return instances + + def _process_unmatched_idx(self, instances: Instances, matched_idx: np.ndarray) -> Instances: + untracked_idx = set(range(len(instances))).difference(set(matched_idx)) + for idx in untracked_idx: + instances.ID[idx] = self._id_count + self._id_count += 1 + instances.ID_period[idx] = 1 + instances.lost_frame_count[idx] = 0 + return instances + + def _process_unmatched_prev_idx( + self, + instances: Instances, + matched_prev_idx: + np.ndarray + ) -> Instances: + untracked_instances = Instances( + image_size=instances.image_size, + pred_boxes=[], + pred_masks=[], + pred_classes=[], + scores=[], + ID=[], + ID_period=[], + lost_frame_count=[], + ) + prev_bboxes = list(self._prev_instances.pred_boxes) + prev_classes = list(self._prev_instances.pred_classes) + prev_scores = list(self._prev_instances.scores) + prev_ID_period = self._prev_instances.ID_period + if instances.has("pred_masks"): + prev_masks = list(self._prev_instances.pred_masks) + untracked_prev_idx = set(range(len(self._prev_instances))).difference(set(matched_prev_idx)) + for idx in untracked_prev_idx: + x_left, y_top, x_right, y_bot = prev_bboxes[idx] + if ( + (1.0 * (x_right - x_left) / self._video_width < self._min_box_rel_dim) + or (1.0 * (y_bot - y_top) / self._video_height < self._min_box_rel_dim) + or self._prev_instances.lost_frame_count[idx] >= self._max_lost_frame_count + or prev_ID_period[idx] <= self._min_instance_period + ): + continue + untracked_instances.pred_boxes.append(list(prev_bboxes[idx].numpy())) + untracked_instances.pred_classes.append(int(prev_classes[idx])) + untracked_instances.scores.append(float(prev_scores[idx])) + untracked_instances.ID.append(self._prev_instances.ID[idx]) + untracked_instances.ID_period.append(self._prev_instances.ID_period[idx]) + untracked_instances.lost_frame_count.append( + self._prev_instances.lost_frame_count[idx] + 1 + ) + if instances.has("pred_masks"): + untracked_instances.pred_masks.append(prev_masks[idx].numpy().astype(np.uint8)) + + untracked_instances.pred_boxes = Boxes(torch.FloatTensor(untracked_instances.pred_boxes)) + untracked_instances.pred_classes = torch.IntTensor(untracked_instances.pred_classes) + untracked_instances.scores = torch.FloatTensor(untracked_instances.scores) + if instances.has("pred_masks"): + untracked_instances.pred_masks = torch.IntTensor(untracked_instances.pred_masks) + else: + untracked_instances.remove("pred_masks") + + return Instances.cat( + [ + instances, + untracked_instances, + ] + ) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/tracking/iou_weighted_hungarian_bbox_iou_tracker.py b/motion-gan-pipeline/preprocessing/third/detectron2/tracking/iou_weighted_hungarian_bbox_iou_tracker.py new file mode 100644 index 0000000..94bf342 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/tracking/iou_weighted_hungarian_bbox_iou_tracker.py @@ -0,0 +1,101 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +from typing import List + +import numpy as np + +from .base_tracker import TRACKER_HEADS_REGISTRY +from .vanilla_hungarian_bbox_iou_tracker import VanillaHungarianBBoxIOUTracker +from detectron2.config import configurable, CfgNode as CfgNode_ + + +@TRACKER_HEADS_REGISTRY.register() +class IOUWeightedHungarianBBoxIOUTracker(VanillaHungarianBBoxIOUTracker): + """ + A tracker using IoU as weight in Hungarian algorithm, also known + as Munkres or Kuhn-Munkres algorithm + """ + + @configurable + def __init__( + self, + *, + video_height: int, + video_width: int, + max_num_instances: int = 200, + max_lost_frame_count: int = 0, + min_box_rel_dim: float = 0.02, + min_instance_period: int = 1, + track_iou_threshold: float = 0.5, + **kwargs + ): + """ + Args: + video_height: height the video frame + video_width: width of the video frame + max_num_instances: maximum number of id allowed to be tracked + max_lost_frame_count: maximum number of frame an id can lost tracking + exceed this number, an id is considered as lost + forever + min_box_rel_dim: a percentage, smaller than this dimension, a bbox is + removed from tracking + min_instance_period: an instance will be shown after this number of period + since its first showing up in the video + track_iou_threshold: iou threshold, below this number a bbox pair is removed + from tracking + """ + super().__init__( + video_height=video_height, + video_width=video_width, + max_num_instances=max_num_instances, + max_lost_frame_count=max_lost_frame_count, + min_box_rel_dim=min_box_rel_dim, + min_instance_period=min_instance_period, + track_iou_threshold=track_iou_threshold + ) + + @classmethod + def from_config(cls, cfg: CfgNode_): + """ + Old style initialization using CfgNode + + Args: + cfg: D2 CfgNode, config file + Return: + dictionary storing arguments for __init__ method + """ + assert "VIDEO_HEIGHT" in cfg.TRACKER_HEADS + assert "VIDEO_WIDTH" in cfg.TRACKER_HEADS + video_height = cfg.TRACKER_HEADS.get("VIDEO_HEIGHT") + video_width = cfg.TRACKER_HEADS.get("VIDEO_WIDTH") + max_num_instances = cfg.TRACKER_HEADS.get("MAX_NUM_INSTANCES", 200) + max_lost_frame_count = cfg.TRACKER_HEADS.get("MAX_LOST_FRAME_COUNT", 0) + min_box_rel_dim = cfg.TRACKER_HEADS.get("MIN_BOX_REL_DIM", 0.02) + min_instance_period = cfg.TRACKER_HEADS.get("MIN_INSTANCE_PERIOD", 1) + track_iou_threshold = cfg.TRACKER_HEADS.get("TRACK_IOU_THRESHOLD", 0.5) + return { + "_target_": "detectron2.tracking.iou_weighted_hungarian_bbox_iou_tracker.IOUWeightedHungarianBBoxIOUTracker", # noqa + "video_height": video_height, + "video_width": video_width, + "max_num_instances": max_num_instances, + "max_lost_frame_count": max_lost_frame_count, + "min_box_rel_dim": min_box_rel_dim, + "min_instance_period": min_instance_period, + "track_iou_threshold": track_iou_threshold + } + + def assign_cost_matrix_values(self, cost_matrix: np.ndarray, bbox_pairs: List) -> np.ndarray: + """ + Based on IoU for each pair of bbox, assign the associated value in cost matrix + + Args: + cost_matrix: np.ndarray, initialized 2D array with target dimensions + bbox_pairs: list of bbox pair, in each pair, iou value is stored + Return: + np.ndarray, cost_matrix with assigned values + """ + for pair in bbox_pairs: + # assign (-1 * IoU) for above threshold pairs, algorithms will minimize cost + cost_matrix[pair["idx"]][pair["prev_idx"]] = -1 * pair["IoU"] + return cost_matrix diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/tracking/utils.py b/motion-gan-pipeline/preprocessing/third/detectron2/tracking/utils.py new file mode 100644 index 0000000..7dbcf2a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/tracking/utils.py @@ -0,0 +1,39 @@ +#!/usr/bin/env python3 +from detectron2.structures import Instances +import numpy as np +from typing import List + + +def create_prediction_pairs( + instances: Instances, + prev_instances: Instances, + iou_all: np.ndarray, + threshold: float = 0.5, +) -> List: + """ + Args: + instances: predictions from current frame + prev_instances: predictions from previous frame + iou_all: 2D numpy array containing iou for each bbox pair + threshold: below the threshold, doesn't consider the pair of bbox is valid + Return: + List of bbox pairs + """ + bbox_pairs = [] + for i in range(len(instances)): + for j in range(len(prev_instances)): + if iou_all[i, j] < threshold: + continue + bbox_pairs.append( + { + "idx": i, + "prev_idx": j, + "prev_id": prev_instances.ID[j], + "IoU": iou_all[i, j], + "prev_period": prev_instances.ID_period[j], + } + ) + return bbox_pairs + + +LARGE_COST_VALUE = 100000 diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/tracking/vanilla_hungarian_bbox_iou_tracker.py b/motion-gan-pipeline/preprocessing/third/detectron2/tracking/vanilla_hungarian_bbox_iou_tracker.py new file mode 100644 index 0000000..9c1bf5a --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/tracking/vanilla_hungarian_bbox_iou_tracker.py @@ -0,0 +1,131 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +from typing import List + +import numpy as np +from detectron2.structures import Instances +from detectron2.structures.boxes import pairwise_iou +from detectron2.tracking.utils import create_prediction_pairs, LARGE_COST_VALUE + +from .base_tracker import TRACKER_HEADS_REGISTRY +from .hungarian_tracker import BaseHungarianTracker +from detectron2.config import configurable, CfgNode as CfgNode_ + + +@TRACKER_HEADS_REGISTRY.register() +class VanillaHungarianBBoxIOUTracker(BaseHungarianTracker): + """ + Hungarian algo based tracker using bbox iou as metric + """ + + @configurable + def __init__( + self, + *, + video_height: int, + video_width: int, + max_num_instances: int = 200, + max_lost_frame_count: int = 0, + min_box_rel_dim: float = 0.02, + min_instance_period: int = 1, + track_iou_threshold: float = 0.5, + **kwargs + ): + """ + Args: + video_height: height the video frame + video_width: width of the video frame + max_num_instances: maximum number of id allowed to be tracked + max_lost_frame_count: maximum number of frame an id can lost tracking + exceed this number, an id is considered as lost + forever + min_box_rel_dim: a percentage, smaller than this dimension, a bbox is + removed from tracking + min_instance_period: an instance will be shown after this number of period + since its first showing up in the video + track_iou_threshold: iou threshold, below this number a bbox pair is removed + from tracking + """ + super().__init__( + video_height=video_height, + video_width=video_width, + max_num_instances=max_num_instances, + max_lost_frame_count=max_lost_frame_count, + min_box_rel_dim=min_box_rel_dim, + min_instance_period=min_instance_period + ) + self._track_iou_threshold = track_iou_threshold + + @classmethod + def from_config(cls, cfg: CfgNode_): + """ + Old style initialization using CfgNode + + Args: + cfg: D2 CfgNode, config file + Return: + dictionary storing arguments for __init__ method + """ + assert "VIDEO_HEIGHT" in cfg.TRACKER_HEADS + assert "VIDEO_WIDTH" in cfg.TRACKER_HEADS + video_height = cfg.TRACKER_HEADS.get("VIDEO_HEIGHT") + video_width = cfg.TRACKER_HEADS.get("VIDEO_WIDTH") + max_num_instances = cfg.TRACKER_HEADS.get("MAX_NUM_INSTANCES", 200) + max_lost_frame_count = cfg.TRACKER_HEADS.get("MAX_LOST_FRAME_COUNT", 0) + min_box_rel_dim = cfg.TRACKER_HEADS.get("MIN_BOX_REL_DIM", 0.02) + min_instance_period = cfg.TRACKER_HEADS.get("MIN_INSTANCE_PERIOD", 1) + track_iou_threshold = cfg.TRACKER_HEADS.get("TRACK_IOU_THRESHOLD", 0.5) + return { + "_target_": "detectron2.tracking.vanilla_hungarian_bbox_iou_tracker.VanillaHungarianBBoxIOUTracker", # noqa + "video_height": video_height, + "video_width": video_width, + "max_num_instances": max_num_instances, + "max_lost_frame_count": max_lost_frame_count, + "min_box_rel_dim": min_box_rel_dim, + "min_instance_period": min_instance_period, + "track_iou_threshold": track_iou_threshold + } + + def build_cost_matrix(self, instances: Instances, prev_instances: Instances) -> np.ndarray: + """ + Build the cost matrix for assignment problem + (https://en.wikipedia.org/wiki/Assignment_problem) + + Args: + instances: D2 Instances, for current frame predictions + prev_instances: D2 Instances, for previous frame predictions + + Return: + the cost matrix in numpy array + """ + assert instances is not None and prev_instances is not None + # calculate IoU of all bbox pairs + iou_all = pairwise_iou( + boxes1=instances.pred_boxes, + boxes2=self._prev_instances.pred_boxes, + ) + bbox_pairs = create_prediction_pairs( + instances, + self._prev_instances, + iou_all, + threshold=self._track_iou_threshold + ) + # assign large cost value to make sure pair below IoU threshold won't be matched + cost_matrix = np.full((len(instances), len(prev_instances)), LARGE_COST_VALUE) + return self.assign_cost_matrix_values(cost_matrix, bbox_pairs) + + def assign_cost_matrix_values(self, cost_matrix: np.ndarray, bbox_pairs: List) -> np.ndarray: + """ + Based on IoU for each pair of bbox, assign the associated value in cost matrix + + Args: + cost_matrix: np.ndarray, initialized 2D array with target dimensions + bbox_pairs: list of bbox pair, in each pair, iou value is stored + Return: + np.ndarray, cost_matrix with assigned values + """ + for pair in bbox_pairs: + # assign -1 for IoU above threshold pairs, algorithms will minimize cost + cost_matrix[pair["idx"]][pair["prev_idx"]] = -1 + return cost_matrix diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/utils/README.md b/motion-gan-pipeline/preprocessing/third/detectron2/utils/README.md new file mode 100644 index 0000000..9765b24 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/utils/README.md @@ -0,0 +1,5 @@ +# Utility functions + +This folder contain utility functions that are not used in the +core library, but are useful for building models or training +code using the config system. diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/utils/__init__.py b/motion-gan-pipeline/preprocessing/third/detectron2/utils/__init__.py new file mode 100644 index 0000000..9020c2d --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/utils/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/utils/analysis.py b/motion-gan-pipeline/preprocessing/third/detectron2/utils/analysis.py new file mode 100644 index 0000000..178da79 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/utils/analysis.py @@ -0,0 +1,188 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# -*- coding: utf-8 -*- + +import typing +from typing import Any, List +import fvcore +from fvcore.nn import activation_count, flop_count, parameter_count, parameter_count_table +from torch import nn + +from detectron2.export import TracingAdapter + +__all__ = [ + "activation_count_operators", + "flop_count_operators", + "parameter_count_table", + "parameter_count", + "FlopCountAnalysis", +] + +FLOPS_MODE = "flops" +ACTIVATIONS_MODE = "activations" + + +# Some extra ops to ignore from counting, including elementwise and reduction ops +_IGNORED_OPS = { + "aten::add", + "aten::add_", + "aten::argmax", + "aten::argsort", + "aten::batch_norm", + "aten::constant_pad_nd", + "aten::div", + "aten::div_", + "aten::exp", + "aten::log2", + "aten::max_pool2d", + "aten::meshgrid", + "aten::mul", + "aten::mul_", + "aten::neg", + "aten::nonzero_numpy", + "aten::reciprocal", + "aten::repeat_interleave", + "aten::rsub", + "aten::sigmoid", + "aten::sigmoid_", + "aten::softmax", + "aten::sort", + "aten::sqrt", + "aten::sub", + "torchvision::nms", # TODO estimate flop for nms +} + + +class FlopCountAnalysis(fvcore.nn.FlopCountAnalysis): + """ + Same as :class:`fvcore.nn.FlopCountAnalysis`, but supports detectron2 models. + """ + + def __init__(self, model, inputs): + """ + Args: + model (nn.Module): + inputs (Any): inputs of the given model. Does not have to be tuple of tensors. + """ + wrapper = TracingAdapter(model, inputs, allow_non_tensor=True) + super().__init__(wrapper, wrapper.flattened_inputs) + self.set_op_handle(**{k: None for k in _IGNORED_OPS}) + + +def flop_count_operators(model: nn.Module, inputs: list) -> typing.DefaultDict[str, float]: + """ + Implement operator-level flops counting using jit. + This is a wrapper of :func:`fvcore.nn.flop_count` and adds supports for standard + detection models in detectron2. + Please use :class:`FlopCountAnalysis` for more advanced functionalities. + + Note: + The function runs the input through the model to compute flops. + The flops of a detection model is often input-dependent, for example, + the flops of box & mask head depends on the number of proposals & + the number of detected objects. + Therefore, the flops counting using a single input may not accurately + reflect the computation cost of a model. It's recommended to average + across a number of inputs. + + Args: + model: a detectron2 model that takes `list[dict]` as input. + inputs (list[dict]): inputs to model, in detectron2's standard format. + Only "image" key will be used. + supported_ops (dict[str, Handle]): see documentation of :func:`fvcore.nn.flop_count` + + Returns: + Counter: Gflop count per operator + """ + old_train = model.training + model.eval() + ret = FlopCountAnalysis(model, inputs).by_operator() + model.train(old_train) + return {k: v / 1e9 for k, v in ret.items()} + + +def activation_count_operators( + model: nn.Module, inputs: list, **kwargs +) -> typing.DefaultDict[str, float]: + """ + Implement operator-level activations counting using jit. + This is a wrapper of fvcore.nn.activation_count, that supports standard detection models + in detectron2. + + Note: + The function runs the input through the model to compute activations. + The activations of a detection model is often input-dependent, for example, + the activations of box & mask head depends on the number of proposals & + the number of detected objects. + + Args: + model: a detectron2 model that takes `list[dict]` as input. + inputs (list[dict]): inputs to model, in detectron2's standard format. + Only "image" key will be used. + + Returns: + Counter: activation count per operator + """ + return _wrapper_count_operators(model=model, inputs=inputs, mode=ACTIVATIONS_MODE, **kwargs) + + +def _wrapper_count_operators( + model: nn.Module, inputs: list, mode: str, **kwargs +) -> typing.DefaultDict[str, float]: + # ignore some ops + supported_ops = {k: lambda *args, **kwargs: {} for k in _IGNORED_OPS} + supported_ops.update(kwargs.pop("supported_ops", {})) + kwargs["supported_ops"] = supported_ops + + assert len(inputs) == 1, "Please use batch size=1" + tensor_input = inputs[0]["image"] + inputs = [{"image": tensor_input}] # remove other keys, in case there are any + + old_train = model.training + if isinstance(model, (nn.parallel.distributed.DistributedDataParallel, nn.DataParallel)): + model = model.module + wrapper = TracingAdapter(model, inputs) + wrapper.eval() + if mode == FLOPS_MODE: + ret = flop_count(wrapper, (tensor_input,), **kwargs) + elif mode == ACTIVATIONS_MODE: + ret = activation_count(wrapper, (tensor_input,), **kwargs) + else: + raise NotImplementedError("Count for mode {} is not supported yet.".format(mode)) + # compatible with change in fvcore + if isinstance(ret, tuple): + ret = ret[0] + model.train(old_train) + return ret + + +def find_unused_parameters(model: nn.Module, inputs: Any) -> List[str]: + """ + Given a model, find parameters that do not contribute + to the loss. + + Args: + model: a model in training mode that returns losses + inputs: argument or a tuple of arguments. Inputs of the model + + Returns: + list[str]: the name of unused parameters + """ + assert model.training + for _, prm in model.named_parameters(): + prm.grad = None + + if isinstance(inputs, tuple): + losses = model(*inputs) + else: + losses = model(inputs) + + if isinstance(losses, dict): + losses = sum(losses.values()) + losses.backward() + + unused: List[str] = [] + for name, prm in model.named_parameters(): + if prm.grad is None: + unused.append(name) + prm.grad = None + return unused diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/utils/collect_env.py b/motion-gan-pipeline/preprocessing/third/detectron2/utils/collect_env.py new file mode 100644 index 0000000..1b23548 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/utils/collect_env.py @@ -0,0 +1,242 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import importlib +import numpy as np +import os +import re +import subprocess +import sys +from collections import defaultdict +import PIL +import torch +import torchvision +from tabulate import tabulate + +__all__ = ["collect_env_info"] + + +def collect_torch_env(): + try: + import torch.__config__ + + return torch.__config__.show() + except ImportError: + # compatible with older versions of pytorch + from torch.utils.collect_env import get_pretty_env_info + + return get_pretty_env_info() + + +def get_env_module(): + var_name = "DETECTRON2_ENV_MODULE" + return var_name, os.environ.get(var_name, "") + + +def detect_compute_compatibility(CUDA_HOME, so_file): + try: + cuobjdump = os.path.join(CUDA_HOME, "bin", "cuobjdump") + if os.path.isfile(cuobjdump): + output = subprocess.check_output( + "'{}' --list-elf '{}'".format(cuobjdump, so_file), shell=True + ) + output = output.decode("utf-8").strip().split("\n") + arch = [] + for line in output: + line = re.findall(r"\.sm_([0-9]*)\.", line)[0] + arch.append(".".join(line)) + arch = sorted(set(arch)) + return ", ".join(arch) + else: + return so_file + "; cannot find cuobjdump" + except Exception: + # unhandled failure + return so_file + + +def collect_env_info(): + has_gpu = torch.cuda.is_available() # true for both CUDA & ROCM + torch_version = torch.__version__ + + # NOTE that CUDA_HOME/ROCM_HOME could be None even when CUDA runtime libs are functional + from torch.utils.cpp_extension import CUDA_HOME, ROCM_HOME + + has_rocm = False + if (getattr(torch.version, "hip", None) is not None) and (ROCM_HOME is not None): + has_rocm = True + has_cuda = has_gpu and (not has_rocm) + + data = [] + data.append(("sys.platform", sys.platform)) # check-template.yml depends on it + data.append(("Python", sys.version.replace("\n", ""))) + data.append(("numpy", np.__version__)) + + try: + import detectron2 # noqa + + data.append( + ("detectron2", detectron2.__version__ + " @" + os.path.dirname(detectron2.__file__)) + ) + except ImportError: + data.append(("detectron2", "failed to import")) + except AttributeError: + data.append(("detectron2", "imported a wrong installation")) + + try: + import detectron2._C as _C + except ImportError as e: + data.append(("detectron2._C", f"not built correctly: {e}")) + + # print system compilers when extension fails to build + if sys.platform != "win32": # don't know what to do for windows + try: + # this is how torch/autils/cpp_extensions.py choose compiler + cxx = os.environ.get("CXX", "c++") + cxx = subprocess.check_output("'{}' --version".format(cxx), shell=True) + cxx = cxx.decode("utf-8").strip().split("\n")[0] + except subprocess.SubprocessError: + cxx = "Not found" + data.append(("Compiler ($CXX)", cxx)) + + if has_cuda and CUDA_HOME is not None: + try: + nvcc = os.path.join(CUDA_HOME, "bin", "nvcc") + nvcc = subprocess.check_output("'{}' -V".format(nvcc), shell=True) + nvcc = nvcc.decode("utf-8").strip().split("\n")[-1] + except subprocess.SubprocessError: + nvcc = "Not found" + data.append(("CUDA compiler", nvcc)) + if has_cuda and sys.platform != "win32": + try: + so_file = importlib.util.find_spec("detectron2._C").origin + except (ImportError, AttributeError): + pass + else: + data.append( + ("detectron2 arch flags", detect_compute_compatibility(CUDA_HOME, so_file)) + ) + else: + # print compilers that are used to build extension + data.append(("Compiler", _C.get_compiler_version())) + data.append(("CUDA compiler", _C.get_cuda_version())) # cuda or hip + if has_cuda and getattr(_C, "has_cuda", lambda: True)(): + data.append( + ("detectron2 arch flags", detect_compute_compatibility(CUDA_HOME, _C.__file__)) + ) + + data.append(get_env_module()) + data.append(("PyTorch", torch_version + " @" + os.path.dirname(torch.__file__))) + data.append(("PyTorch debug build", torch.version.debug)) + + if not has_gpu: + has_gpu_text = "No: torch.cuda.is_available() == False" + else: + has_gpu_text = "Yes" + data.append(("GPU available", has_gpu_text)) + if has_gpu: + devices = defaultdict(list) + for k in range(torch.cuda.device_count()): + cap = ".".join((str(x) for x in torch.cuda.get_device_capability(k))) + name = torch.cuda.get_device_name(k) + f" (arch={cap})" + devices[name].append(str(k)) + for name, devids in devices.items(): + data.append(("GPU " + ",".join(devids), name)) + + if has_rocm: + msg = " - invalid!" if not (ROCM_HOME and os.path.isdir(ROCM_HOME)) else "" + data.append(("ROCM_HOME", str(ROCM_HOME) + msg)) + else: + try: + from torch.utils.collect_env import get_nvidia_driver_version, run as _run + + data.append(("Driver version", get_nvidia_driver_version(_run))) + except Exception: + pass + msg = " - invalid!" if not (CUDA_HOME and os.path.isdir(CUDA_HOME)) else "" + data.append(("CUDA_HOME", str(CUDA_HOME) + msg)) + + cuda_arch_list = os.environ.get("TORCH_CUDA_ARCH_LIST", None) + if cuda_arch_list: + data.append(("TORCH_CUDA_ARCH_LIST", cuda_arch_list)) + data.append(("Pillow", PIL.__version__)) + + try: + data.append( + ( + "torchvision", + str(torchvision.__version__) + " @" + os.path.dirname(torchvision.__file__), + ) + ) + if has_cuda: + try: + torchvision_C = importlib.util.find_spec("torchvision._C").origin + msg = detect_compute_compatibility(CUDA_HOME, torchvision_C) + data.append(("torchvision arch flags", msg)) + except (ImportError, AttributeError): + data.append(("torchvision._C", "Not found")) + except AttributeError: + data.append(("torchvision", "unknown")) + + try: + import fvcore + + data.append(("fvcore", fvcore.__version__)) + except (ImportError, AttributeError): + pass + + try: + import iopath + + data.append(("iopath", iopath.__version__)) + except (ImportError, AttributeError): + pass + + try: + import cv2 + + data.append(("cv2", cv2.__version__)) + except (ImportError, AttributeError): + data.append(("cv2", "Not found")) + env_str = tabulate(data) + "\n" + env_str += collect_torch_env() + return env_str + + +def test_nccl_ops(): + num_gpu = torch.cuda.device_count() + if os.access("/tmp", os.W_OK): + import torch.multiprocessing as mp + + dist_url = "file:///tmp/nccl_tmp_file" + print("Testing NCCL connectivity ... this should not hang.") + mp.spawn(_test_nccl_worker, nprocs=num_gpu, args=(num_gpu, dist_url), daemon=False) + print("NCCL succeeded.") + + +def _test_nccl_worker(rank, num_gpu, dist_url): + import torch.distributed as dist + + dist.init_process_group(backend="NCCL", init_method=dist_url, rank=rank, world_size=num_gpu) + dist.barrier(device_ids=[rank]) + + +if __name__ == "__main__": + try: + from detectron2.utils.collect_env import collect_env_info as f + + print(f()) + except ImportError: + print(collect_env_info()) + + if torch.cuda.is_available(): + num_gpu = torch.cuda.device_count() + for k in range(num_gpu): + device = f"cuda:{k}" + try: + x = torch.tensor([1, 2.0], dtype=torch.float32) + x = x.to(device) + except Exception as e: + print( + f"Unable to copy tensor to device={device}: {e}. " + "Your CUDA environment is broken." + ) + if num_gpu > 1: + test_nccl_ops() diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/utils/colormap.py b/motion-gan-pipeline/preprocessing/third/detectron2/utils/colormap.py new file mode 100644 index 0000000..14ded16 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/utils/colormap.py @@ -0,0 +1,158 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +""" +An awesome colormap for really neat visualizations. +Copied from Detectron, and removed gray colors. +""" + +import numpy as np +import random + +__all__ = ["colormap", "random_color", "random_colors"] + +# fmt: off +# RGB: +_COLORS = np.array( + [ + 0.000, 0.447, 0.741, + 0.850, 0.325, 0.098, + 0.929, 0.694, 0.125, + 0.494, 0.184, 0.556, + 0.466, 0.674, 0.188, + 0.301, 0.745, 0.933, + 0.635, 0.078, 0.184, + 0.300, 0.300, 0.300, + 0.600, 0.600, 0.600, + 1.000, 0.000, 0.000, + 1.000, 0.500, 0.000, + 0.749, 0.749, 0.000, + 0.000, 1.000, 0.000, + 0.000, 0.000, 1.000, + 0.667, 0.000, 1.000, + 0.333, 0.333, 0.000, + 0.333, 0.667, 0.000, + 0.333, 1.000, 0.000, + 0.667, 0.333, 0.000, + 0.667, 0.667, 0.000, + 0.667, 1.000, 0.000, + 1.000, 0.333, 0.000, + 1.000, 0.667, 0.000, + 1.000, 1.000, 0.000, + 0.000, 0.333, 0.500, + 0.000, 0.667, 0.500, + 0.000, 1.000, 0.500, + 0.333, 0.000, 0.500, + 0.333, 0.333, 0.500, + 0.333, 0.667, 0.500, + 0.333, 1.000, 0.500, + 0.667, 0.000, 0.500, + 0.667, 0.333, 0.500, + 0.667, 0.667, 0.500, + 0.667, 1.000, 0.500, + 1.000, 0.000, 0.500, + 1.000, 0.333, 0.500, + 1.000, 0.667, 0.500, + 1.000, 1.000, 0.500, + 0.000, 0.333, 1.000, + 0.000, 0.667, 1.000, + 0.000, 1.000, 1.000, + 0.333, 0.000, 1.000, + 0.333, 0.333, 1.000, + 0.333, 0.667, 1.000, + 0.333, 1.000, 1.000, + 0.667, 0.000, 1.000, + 0.667, 0.333, 1.000, + 0.667, 0.667, 1.000, + 0.667, 1.000, 1.000, + 1.000, 0.000, 1.000, + 1.000, 0.333, 1.000, + 1.000, 0.667, 1.000, + 0.333, 0.000, 0.000, + 0.500, 0.000, 0.000, + 0.667, 0.000, 0.000, + 0.833, 0.000, 0.000, + 1.000, 0.000, 0.000, + 0.000, 0.167, 0.000, + 0.000, 0.333, 0.000, + 0.000, 0.500, 0.000, + 0.000, 0.667, 0.000, + 0.000, 0.833, 0.000, + 0.000, 1.000, 0.000, + 0.000, 0.000, 0.167, + 0.000, 0.000, 0.333, + 0.000, 0.000, 0.500, + 0.000, 0.000, 0.667, + 0.000, 0.000, 0.833, + 0.000, 0.000, 1.000, + 0.000, 0.000, 0.000, + 0.143, 0.143, 0.143, + 0.857, 0.857, 0.857, + 1.000, 1.000, 1.000 + ] +).astype(np.float32).reshape(-1, 3) +# fmt: on + + +def colormap(rgb=False, maximum=255): + """ + Args: + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + + Returns: + ndarray: a float32 array of Nx3 colors, in range [0, 255] or [0, 1] + """ + assert maximum in [255, 1], maximum + c = _COLORS * maximum + if not rgb: + c = c[:, ::-1] + return c + + +def random_color(rgb=False, maximum=255): + """ + Args: + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + + Returns: + ndarray: a vector of 3 numbers + """ + idx = np.random.randint(0, len(_COLORS)) + ret = _COLORS[idx] * maximum + if not rgb: + ret = ret[::-1] + return ret + + +def random_colors(N, rgb=False, maximum=255): + """ + Args: + N (int): number of unique colors needed + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + + Returns: + ndarray: a list of random_color + """ + indices = random.sample(range(len(_COLORS)), N) + ret = [_COLORS[i] * maximum for i in indices] + if not rgb: + ret = [x[::-1] for x in ret] + return ret + + +if __name__ == "__main__": + import cv2 + + size = 100 + H, W = 10, 10 + canvas = np.random.rand(H * size, W * size, 3).astype("float32") + for h in range(H): + for w in range(W): + idx = h * W + w + if idx >= len(_COLORS): + break + canvas[h * size : (h + 1) * size, w * size : (w + 1) * size] = _COLORS[idx] + cv2.imshow("a", canvas) + cv2.waitKey(0) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/utils/comm.py b/motion-gan-pipeline/preprocessing/third/detectron2/utils/comm.py new file mode 100644 index 0000000..7e2a0c4 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/utils/comm.py @@ -0,0 +1,199 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +""" +This file contains primitives for multi-gpu communication. +This is useful when doing distributed training. +""" + +import functools +import numpy as np +import torch +import torch.distributed as dist + +_LOCAL_PROCESS_GROUP = None +""" +A torch process group which only includes processes that on the same machine as the current process. +This variable is set when processes are spawned by `launch()` in "engine/launch.py". +""" + + +def get_world_size() -> int: + if not dist.is_available(): + return 1 + if not dist.is_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank() -> int: + if not dist.is_available(): + return 0 + if not dist.is_initialized(): + return 0 + return dist.get_rank() + + +def get_local_rank() -> int: + """ + Returns: + The rank of the current process within the local (per-machine) process group. + """ + if not dist.is_available(): + return 0 + if not dist.is_initialized(): + return 0 + assert ( + _LOCAL_PROCESS_GROUP is not None + ), "Local process group is not created! Please use launch() to spawn processes!" + return dist.get_rank(group=_LOCAL_PROCESS_GROUP) + + +def get_local_size() -> int: + """ + Returns: + The size of the per-machine process group, + i.e. the number of processes per machine. + """ + if not dist.is_available(): + return 1 + if not dist.is_initialized(): + return 1 + return dist.get_world_size(group=_LOCAL_PROCESS_GROUP) + + +def is_main_process() -> bool: + return get_rank() == 0 + + +def synchronize(): + """ + Helper function to synchronize (barrier) among all processes when + using distributed training + """ + if not dist.is_available(): + return + if not dist.is_initialized(): + return + world_size = dist.get_world_size() + if world_size == 1: + return + if dist.get_backend() == dist.Backend.NCCL: + # This argument is needed to avoid warnings. + # It's valid only for NCCL backend. + dist.barrier(device_ids=[torch.cuda.current_device()]) + else: + dist.barrier() + + +@functools.lru_cache() +def _get_global_gloo_group(): + """ + Return a process group based on gloo backend, containing all the ranks + The result is cached. + """ + if dist.get_backend() == "nccl": + return dist.new_group(backend="gloo") + else: + return dist.group.WORLD + + +def all_gather(data, group=None): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors). + + Args: + data: any picklable object + group: a torch process group. By default, will use a group which + contains all ranks on gloo backend. + + Returns: + list[data]: list of data gathered from each rank + """ + if get_world_size() == 1: + return [data] + if group is None: + group = _get_global_gloo_group() # use CPU group by default, to reduce GPU RAM usage. + world_size = dist.get_world_size(group) + if world_size == 1: + return [data] + + output = [None for _ in range(world_size)] + dist.all_gather_object(output, data, group=group) + return output + + +def gather(data, dst=0, group=None): + """ + Run gather on arbitrary picklable data (not necessarily tensors). + + Args: + data: any picklable object + dst (int): destination rank + group: a torch process group. By default, will use a group which + contains all ranks on gloo backend. + + Returns: + list[data]: on dst, a list of data gathered from each rank. Otherwise, + an empty list. + """ + if get_world_size() == 1: + return [data] + if group is None: + group = _get_global_gloo_group() + world_size = dist.get_world_size(group=group) + if world_size == 1: + return [data] + rank = dist.get_rank(group=group) + + if rank == dst: + output = [None for _ in range(world_size)] + dist.gather_object(data, output, dst=dst, group=group) + return output + else: + dist.gather_object(data, None, dst=dst, group=group) + return [] + + +def shared_random_seed(): + """ + Returns: + int: a random number that is the same across all workers. + If workers need a shared RNG, they can use this shared seed to + create one. + + All workers must call this function, otherwise it will deadlock. + """ + ints = np.random.randint(2 ** 31) + all_ints = all_gather(ints) + return all_ints[0] + + +def reduce_dict(input_dict, average=True): + """ + Reduce the values in the dictionary from all processes so that process with rank + 0 has the reduced results. + + Args: + input_dict (dict): inputs to be reduced. All the values must be scalar CUDA Tensor. + average (bool): whether to do average or sum + + Returns: + a dict with the same keys as input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.reduce(values, dst=0) + if dist.get_rank() == 0 and average: + # only main process gets accumulated, so only divide by + # world_size in this case + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/utils/develop.py b/motion-gan-pipeline/preprocessing/third/detectron2/utils/develop.py new file mode 100644 index 0000000..7ff7bf1 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/utils/develop.py @@ -0,0 +1,59 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +""" Utilities for developers only. +These are not visible to users (not automatically imported). And should not +appeared in docs.""" +# adapted from https://github.com/tensorpack/tensorpack/blob/master/tensorpack/utils/develop.py + + +def create_dummy_class(klass, dependency, message=""): + """ + When a dependency of a class is not available, create a dummy class which throws ImportError + when used. + + Args: + klass (str): name of the class. + dependency (str): name of the dependency. + message: extra message to print + Returns: + class: a class object + """ + err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, klass) + if message: + err = err + " " + message + + class _DummyMetaClass(type): + # throw error on class attribute access + def __getattr__(_, __): + raise ImportError(err) + + class _Dummy(object, metaclass=_DummyMetaClass): + # throw error on constructor + def __init__(self, *args, **kwargs): + raise ImportError(err) + + return _Dummy + + +def create_dummy_func(func, dependency, message=""): + """ + When a dependency of a function is not available, create a dummy function which throws + ImportError when used. + + Args: + func (str): name of the function. + dependency (str or list[str]): name(s) of the dependency. + message: extra message to print + Returns: + function: a function object + """ + err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, func) + if message: + err = err + " " + message + + if isinstance(dependency, (list, tuple)): + dependency = ",".join(dependency) + + def _dummy(*args, **kwargs): + raise ImportError(err) + + return _dummy diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/utils/env.py b/motion-gan-pipeline/preprocessing/third/detectron2/utils/env.py new file mode 100644 index 0000000..2f2c0fb --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/utils/env.py @@ -0,0 +1,170 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import importlib +import importlib.util +import logging +import numpy as np +import os +import random +import sys +from datetime import datetime +import torch + +__all__ = ["seed_all_rng"] + + +TORCH_VERSION = tuple(int(x) for x in torch.__version__.split(".")[:2]) +""" +PyTorch version as a tuple of 2 ints. Useful for comparison. +""" + + +DOC_BUILDING = os.getenv("_DOC_BUILDING", False) # set in docs/conf.py +""" +Whether we're building documentation. +""" + + +def seed_all_rng(seed=None): + """ + Set the random seed for the RNG in torch, numpy and python. + + Args: + seed (int): if None, will use a strong random seed. + """ + if seed is None: + seed = ( + os.getpid() + + int(datetime.now().strftime("%S%f")) + + int.from_bytes(os.urandom(2), "big") + ) + logger = logging.getLogger(__name__) + logger.info("Using a generated random seed {}".format(seed)) + np.random.seed(seed) + torch.manual_seed(seed) + random.seed(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + + +# from https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path +def _import_file(module_name, file_path, make_importable=False): + spec = importlib.util.spec_from_file_location(module_name, file_path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + if make_importable: + sys.modules[module_name] = module + return module + + +def _configure_libraries(): + """ + Configurations for some libraries. + """ + # An environment option to disable `import cv2` globally, + # in case it leads to negative performance impact + disable_cv2 = int(os.environ.get("DETECTRON2_DISABLE_CV2", False)) + if disable_cv2: + sys.modules["cv2"] = None + else: + # Disable opencl in opencv since its interaction with cuda often has negative effects + # This envvar is supported after OpenCV 3.4.0 + os.environ["OPENCV_OPENCL_RUNTIME"] = "disabled" + try: + import cv2 + + if int(cv2.__version__.split(".")[0]) >= 3: + cv2.ocl.setUseOpenCL(False) + except ModuleNotFoundError: + # Other types of ImportError, if happened, should not be ignored. + # Because a failed opencv import could mess up address space + # https://github.com/skvark/opencv-python/issues/381 + pass + + def get_version(module, digit=2): + return tuple(map(int, module.__version__.split(".")[:digit])) + + # fmt: off + assert get_version(torch) >= (1, 4), "Requires torch>=1.4" + import fvcore + assert get_version(fvcore, 3) >= (0, 1, 2), "Requires fvcore>=0.1.2" + import yaml + assert get_version(yaml) >= (5, 1), "Requires pyyaml>=5.1" + # fmt: on + + +_ENV_SETUP_DONE = False + + +def setup_environment(): + """Perform environment setup work. The default setup is a no-op, but this + function allows the user to specify a Python source file or a module in + the $DETECTRON2_ENV_MODULE environment variable, that performs + custom setup work that may be necessary to their computing environment. + """ + global _ENV_SETUP_DONE + if _ENV_SETUP_DONE: + return + _ENV_SETUP_DONE = True + + _configure_libraries() + + custom_module_path = os.environ.get("DETECTRON2_ENV_MODULE") + + if custom_module_path: + setup_custom_environment(custom_module_path) + else: + # The default setup is a no-op + pass + + +def setup_custom_environment(custom_module): + """ + Load custom environment setup by importing a Python source file or a + module, and run the setup function. + """ + if custom_module.endswith(".py"): + module = _import_file("detectron2.autils.env.custom_module", custom_module) + else: + module = importlib.import_module(custom_module) + assert hasattr(module, "setup_environment") and callable(module.setup_environment), ( + "Custom environment module defined in {} does not have the " + "required callable attribute 'setup_environment'." + ).format(custom_module) + module.setup_environment() + + +def fixup_module_metadata(module_name, namespace, keys=None): + """ + Fix the __qualname__ of module members to be their exported api name, so + when they are referenced in docs, sphinx can find them. Reference: + https://github.com/python-trio/trio/blob/6754c74eacfad9cc5c92d5c24727a2f3b620624e/trio/_util.py#L216-L241 + """ + if not DOC_BUILDING: + return + seen_ids = set() + + def fix_one(qualname, name, obj): + # avoid infinite recursion (relevant when using + # typing.Generic, for example) + if id(obj) in seen_ids: + return + seen_ids.add(id(obj)) + + mod = getattr(obj, "__module__", None) + if mod is not None and (mod.startswith(module_name) or mod.startswith("fvcore.")): + obj.__module__ = module_name + # Modules, unlike everything else in Python, put fully-qualitied + # names into their __name__ attribute. We check for "." to avoid + # rewriting these. + if hasattr(obj, "__name__") and "." not in obj.__name__: + obj.__name__ = name + obj.__qualname__ = qualname + if isinstance(obj, type): + for attr_name, attr_value in obj.__dict__.items(): + fix_one(objname + "." + attr_name, attr_name, attr_value) + + if keys is None: + keys = namespace.keys() + for objname in keys: + if not objname.startswith("_"): + obj = namespace[objname] + fix_one(objname, objname, obj) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/utils/events.py b/motion-gan-pipeline/preprocessing/third/detectron2/utils/events.py new file mode 100644 index 0000000..b696698 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/utils/events.py @@ -0,0 +1,486 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import datetime +import json +import logging +import os +import time +from collections import defaultdict +from contextlib import contextmanager +from typing import Optional +import torch +from fvcore.common.history_buffer import HistoryBuffer + +from detectron2.utils.file_io import PathManager + +__all__ = [ + "get_event_storage", + "JSONWriter", + "TensorboardXWriter", + "CommonMetricPrinter", + "EventStorage", +] + +_CURRENT_STORAGE_STACK = [] + + +def get_event_storage(): + """ + Returns: + The :class:`EventStorage` object that's currently being used. + Throws an error if no :class:`EventStorage` is currently enabled. + """ + assert len( + _CURRENT_STORAGE_STACK + ), "get_event_storage() has to be called inside a 'with EventStorage(...)' context!" + return _CURRENT_STORAGE_STACK[-1] + + +class EventWriter: + """ + Base class for writers that obtain events from :class:`EventStorage` and process them. + """ + + def write(self): + raise NotImplementedError + + def close(self): + pass + + +class JSONWriter(EventWriter): + """ + Write scalars to a json file. + + It saves scalars as one json per line (instead of a big json) for easy parsing. + + Examples parsing such a json file: + :: + $ cat metrics.json | jq -s '.[0:2]' + [ + { + "data_time": 0.008433341979980469, + "iteration": 19, + "loss": 1.9228371381759644, + "loss_box_reg": 0.050025828182697296, + "loss_classifier": 0.5316952466964722, + "loss_mask": 0.7236229181289673, + "loss_rpn_box": 0.0856662318110466, + "loss_rpn_cls": 0.48198649287223816, + "lr": 0.007173333333333333, + "time": 0.25401854515075684 + }, + { + "data_time": 0.007216215133666992, + "iteration": 39, + "loss": 1.282649278640747, + "loss_box_reg": 0.06222952902317047, + "loss_classifier": 0.30682939291000366, + "loss_mask": 0.6970193982124329, + "loss_rpn_box": 0.038663312792778015, + "loss_rpn_cls": 0.1471673548221588, + "lr": 0.007706666666666667, + "time": 0.2490077018737793 + } + ] + + $ cat metrics.json | jq '.loss_mask' + 0.7126231789588928 + 0.689423680305481 + 0.6776131987571716 + ... + + """ + + def __init__(self, json_file, window_size=20): + """ + Args: + json_file (str): path to the json file. New data will be appended if the file exists. + window_size (int): the window size of median smoothing for the scalars whose + `smoothing_hint` are True. + """ + self._file_handle = PathManager.open(json_file, "a") + self._window_size = window_size + self._last_write = -1 + + def write(self): + storage = get_event_storage() + to_save = defaultdict(dict) + + for k, (v, iter) in storage.latest_with_smoothing_hint(self._window_size).items(): + # keep scalars that have not been written + if iter <= self._last_write: + continue + to_save[iter][k] = v + if len(to_save): + all_iters = sorted(to_save.keys()) + self._last_write = max(all_iters) + + for itr, scalars_per_iter in to_save.items(): + scalars_per_iter["iteration"] = itr + self._file_handle.write(json.dumps(scalars_per_iter, sort_keys=True) + "\n") + self._file_handle.flush() + try: + os.fsync(self._file_handle.fileno()) + except AttributeError: + pass + + def close(self): + self._file_handle.close() + + +class TensorboardXWriter(EventWriter): + """ + Write all scalars to a tensorboard file. + """ + + def __init__(self, log_dir: str, window_size: int = 20, **kwargs): + """ + Args: + log_dir (str): the directory to save the output events + window_size (int): the scalars will be median-smoothed by this window size + + kwargs: other arguments passed to `torch.autils.tensorboard.SummaryWriter(...)` + """ + self._window_size = window_size + from torch.utils.tensorboard import SummaryWriter + + self._writer = SummaryWriter(log_dir, **kwargs) + self._last_write = -1 + + def write(self): + storage = get_event_storage() + new_last_write = self._last_write + for k, (v, iter) in storage.latest_with_smoothing_hint(self._window_size).items(): + if iter > self._last_write: + self._writer.add_scalar(k, v, iter) + new_last_write = max(new_last_write, iter) + self._last_write = new_last_write + + # storage.put_{image,histogram} is only meant to be used by + # tensorboard writer. So we access its internal fields directly from here. + if len(storage._vis_data) >= 1: + for img_name, img, step_num in storage._vis_data: + self._writer.add_image(img_name, img, step_num) + # Storage stores all image data and rely on this writer to clear them. + # As a result it assumes only one writer will use its image data. + # An alternative design is to let storage store limited recent + # data (e.g. only the most recent image) that all writers can access. + # In that case a writer may not see all image data if its period is long. + storage.clear_images() + + if len(storage._histograms) >= 1: + for params in storage._histograms: + self._writer.add_histogram_raw(**params) + storage.clear_histograms() + + def close(self): + if hasattr(self, "_writer"): # doesn't exist when the code fails at import + self._writer.close() + + +class CommonMetricPrinter(EventWriter): + """ + Print **common** metrics to the terminal, including + iteration time, ETA, memory, all losses, and the learning rate. + It also applies smoothing using a window of 20 elements. + + It's meant to print common metrics in common ways. + To print something in more customized ways, please implement a similar printer by yourself. + """ + + def __init__(self, max_iter: Optional[int] = None, window_size: int = 20): + """ + Args: + max_iter: the maximum number of iterations to train. + Used to compute ETA. If not given, ETA will not be printed. + window_size (int): the losses will be median-smoothed by this window size + """ + self.logger = logging.getLogger(__name__) + self._max_iter = max_iter + self._window_size = window_size + self._last_write = None # (step, time) of last call to write(). Used to compute ETA + + def _get_eta(self, storage) -> Optional[str]: + if self._max_iter is None: + return "" + iteration = storage.iter + try: + eta_seconds = storage.history("time").median(1000) * (self._max_iter - iteration - 1) + storage.put_scalar("eta_seconds", eta_seconds, smoothing_hint=False) + return str(datetime.timedelta(seconds=int(eta_seconds))) + except KeyError: + # estimate eta on our own - more noisy + eta_string = None + if self._last_write is not None: + estimate_iter_time = (time.perf_counter() - self._last_write[1]) / ( + iteration - self._last_write[0] + ) + eta_seconds = estimate_iter_time * (self._max_iter - iteration - 1) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + self._last_write = (iteration, time.perf_counter()) + return eta_string + + def write(self): + storage = get_event_storage() + iteration = storage.iter + if iteration == self._max_iter: + # This hook only reports training progress (loss, ETA, etc) but not other data, + # therefore do not write anything after training succeeds, even if this method + # is called. + return + + try: + data_time = storage.history("data_time").avg(20) + except KeyError: + # they may not exist in the first few iterations (due to warmup) + # or when SimpleTrainer is not used + data_time = None + try: + iter_time = storage.history("time").global_avg() + except KeyError: + iter_time = None + try: + lr = "{:.5g}".format(storage.history("lr").latest()) + except KeyError: + lr = "N/A" + + eta_string = self._get_eta(storage) + + if torch.cuda.is_available(): + max_mem_mb = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0 + else: + max_mem_mb = None + + # NOTE: max_mem is parsed by grep in "dev/parse_results.sh" + self.logger.info( + " {eta}iter: {iter} {losses} {time}{data_time}lr: {lr} {memory}".format( + eta=f"eta: {eta_string} " if eta_string else "", + iter=iteration, + losses=" ".join( + [ + "{}: {:.4g}".format(k, v.median(self._window_size)) + for k, v in storage.histories().items() + if "loss" in k + ] + ), + time="time: {:.4f} ".format(iter_time) if iter_time is not None else "", + data_time="data_time: {:.4f} ".format(data_time) if data_time is not None else "", + lr=lr, + memory="max_mem: {:.0f}M".format(max_mem_mb) if max_mem_mb is not None else "", + ) + ) + + +class EventStorage: + """ + The user-facing class that provides metric storage functionalities. + + In the future we may add support for storing / logging other types of data if needed. + """ + + def __init__(self, start_iter=0): + """ + Args: + start_iter (int): the iteration number to start with + """ + self._history = defaultdict(HistoryBuffer) + self._smoothing_hints = {} + self._latest_scalars = {} + self._iter = start_iter + self._current_prefix = "" + self._vis_data = [] + self._histograms = [] + + def put_image(self, img_name, img_tensor): + """ + Add an `img_tensor` associated with `img_name`, to be shown on + tensorboard. + + Args: + img_name (str): The name of the image to put into tensorboard. + img_tensor (torch.Tensor or numpy.array): An `uint8` or `float` + Tensor of shape `[channel, height, width]` where `channel` is + 3. The image format should be RGB. The elements in img_tensor + can either have values in [0, 1] (float32) or [0, 255] (uint8). + The `img_tensor` will be visualized in tensorboard. + """ + self._vis_data.append((img_name, img_tensor, self._iter)) + + def put_scalar(self, name, value, smoothing_hint=True): + """ + Add a scalar `value` to the `HistoryBuffer` associated with `name`. + + Args: + smoothing_hint (bool): a 'hint' on whether this scalar is noisy and should be + smoothed when logged. The hint will be accessible through + :meth:`EventStorage.smoothing_hints`. A writer may ignore the hint + and apply custom smoothing rule. + + It defaults to True because most scalars we save need to be smoothed to + provide any useful signal. + """ + name = self._current_prefix + name + history = self._history[name] + value = float(value) + history.update(value, self._iter) + self._latest_scalars[name] = (value, self._iter) + + existing_hint = self._smoothing_hints.get(name) + if existing_hint is not None: + assert ( + existing_hint == smoothing_hint + ), "Scalar {} was put with a different smoothing_hint!".format(name) + else: + self._smoothing_hints[name] = smoothing_hint + + def put_scalars(self, *, smoothing_hint=True, **kwargs): + """ + Put multiple scalars from keyword arguments. + + Examples: + + storage.put_scalars(loss=my_loss, accuracy=my_accuracy, smoothing_hint=True) + """ + for k, v in kwargs.items(): + self.put_scalar(k, v, smoothing_hint=smoothing_hint) + + def put_histogram(self, hist_name, hist_tensor, bins=1000): + """ + Create a histogram from a tensor. + + Args: + hist_name (str): The name of the histogram to put into tensorboard. + hist_tensor (torch.Tensor): A Tensor of arbitrary shape to be converted + into a histogram. + bins (int): Number of histogram bins. + """ + ht_min, ht_max = hist_tensor.min().item(), hist_tensor.max().item() + + # Create a histogram with PyTorch + hist_counts = torch.histc(hist_tensor, bins=bins) + hist_edges = torch.linspace(start=ht_min, end=ht_max, steps=bins + 1, dtype=torch.float32) + + # Parameter for the add_histogram_raw function of SummaryWriter + hist_params = dict( + tag=hist_name, + min=ht_min, + max=ht_max, + num=len(hist_tensor), + sum=float(hist_tensor.sum()), + sum_squares=float(torch.sum(hist_tensor ** 2)), + bucket_limits=hist_edges[1:].tolist(), + bucket_counts=hist_counts.tolist(), + global_step=self._iter, + ) + self._histograms.append(hist_params) + + def history(self, name): + """ + Returns: + HistoryBuffer: the scalar history for name + """ + ret = self._history.get(name, None) + if ret is None: + raise KeyError("No history metric available for {}!".format(name)) + return ret + + def histories(self): + """ + Returns: + dict[name -> HistoryBuffer]: the HistoryBuffer for all scalars + """ + return self._history + + def latest(self): + """ + Returns: + dict[str -> (float, int)]: mapping from the name of each scalar to the most + recent value and the iteration number its added. + """ + return self._latest_scalars + + def latest_with_smoothing_hint(self, window_size=20): + """ + Similar to :meth:`latest`, but the returned values + are either the un-smoothed original latest value, + or a median of the given window_size, + depend on whether the smoothing_hint is True. + + This provides a default behavior that other writers can use. + """ + result = {} + for k, (v, itr) in self._latest_scalars.items(): + result[k] = ( + self._history[k].median(window_size) if self._smoothing_hints[k] else v, + itr, + ) + return result + + def smoothing_hints(self): + """ + Returns: + dict[name -> bool]: the user-provided hint on whether the scalar + is noisy and needs smoothing. + """ + return self._smoothing_hints + + def step(self): + """ + User should either: (1) Call this function to increment storage.iter when needed. Or + (2) Set `storage.iter` to the correct iteration number before each iteration. + + The storage will then be able to associate the new data with an iteration number. + """ + self._iter += 1 + + @property + def iter(self): + """ + Returns: + int: The current iteration number. When used together with a trainer, + this is ensured to be the same as trainer.iter. + """ + return self._iter + + @iter.setter + def iter(self, val): + self._iter = int(val) + + @property + def iteration(self): + # for backward compatibility + return self._iter + + def __enter__(self): + _CURRENT_STORAGE_STACK.append(self) + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + assert _CURRENT_STORAGE_STACK[-1] == self + _CURRENT_STORAGE_STACK.pop() + + @contextmanager + def name_scope(self, name): + """ + Yields: + A context within which all the events added to this storage + will be prefixed by the name scope. + """ + old_prefix = self._current_prefix + self._current_prefix = name.rstrip("/") + "/" + yield + self._current_prefix = old_prefix + + def clear_images(self): + """ + Delete all the stored images for visualization. This should be called + after images are written to tensorboard. + """ + self._vis_data = [] + + def clear_histograms(self): + """ + Delete all the stored histograms for visualization. + This should be called after histograms are written to tensorboard. + """ + self._histograms = [] diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/utils/file_io.py b/motion-gan-pipeline/preprocessing/third/detectron2/utils/file_io.py new file mode 100644 index 0000000..46ee4ec --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/utils/file_io.py @@ -0,0 +1,37 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from iopath.common.file_io import HTTPURLHandler, OneDrivePathHandler, PathHandler +from iopath.common.file_io import PathManager as PathManagerBase + +__all__ = ["PathManager", "PathHandler"] + + +PathManager = PathManagerBase() +""" +This is a detectron2 project-specific PathManager. +We try to stay away from global PathManager in fvcore as it +introduces potential conflicts among other libraries. +""" + + +class Detectron2Handler(PathHandler): + """ + Resolve anything that's hosted under detectron2's namespace. + """ + + PREFIX = "detectron2://" + S3_DETECTRON2_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/" + + def _get_supported_prefixes(self): + return [self.PREFIX] + + def _get_local_path(self, path, **kwargs): + name = path[len(self.PREFIX) :] + return PathManager.get_local_path(self.S3_DETECTRON2_PREFIX + name, **kwargs) + + def _open(self, path, mode="r", **kwargs): + return PathManager.open(self._get_local_path(path), mode, **kwargs) + + +PathManager.register_handler(HTTPURLHandler()) +PathManager.register_handler(OneDrivePathHandler()) +PathManager.register_handler(Detectron2Handler()) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/utils/logger.py b/motion-gan-pipeline/preprocessing/third/detectron2/utils/logger.py new file mode 100644 index 0000000..c0c19c4 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/utils/logger.py @@ -0,0 +1,237 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import atexit +import functools +import logging +import os +import sys +import time +from collections import Counter +import torch +from tabulate import tabulate +from termcolor import colored + +from detectron2.utils.file_io import PathManager + +__all__ = ["setup_logger", "log_first_n", "log_every_n", "log_every_n_seconds"] + + +class _ColorfulFormatter(logging.Formatter): + def __init__(self, *args, **kwargs): + self._root_name = kwargs.pop("root_name") + "." + self._abbrev_name = kwargs.pop("abbrev_name", "") + if len(self._abbrev_name): + self._abbrev_name = self._abbrev_name + "." + super(_ColorfulFormatter, self).__init__(*args, **kwargs) + + def formatMessage(self, record): + record.name = record.name.replace(self._root_name, self._abbrev_name) + log = super(_ColorfulFormatter, self).formatMessage(record) + if record.levelno == logging.WARNING: + prefix = colored("WARNING", "red", attrs=["blink"]) + elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL: + prefix = colored("ERROR", "red", attrs=["blink", "underline"]) + else: + return log + return prefix + " " + log + + +@functools.lru_cache() # so that calling setup_logger multiple times won't add many handlers +def setup_logger( + output=None, distributed_rank=0, *, color=True, name="detectron2", abbrev_name=None +): + """ + Initialize the detectron2 logger and set its verbosity level to "DEBUG". + + Args: + output (str): a file name or a directory to save log. If None, will not save log file. + If ends with ".txt" or ".log", assumed to be a file name. + Otherwise, logs will be saved to `output/log.txt`. + name (str): the root module name of this logger + abbrev_name (str): an abbreviation of the module, to avoid long names in logs. + Set to "" to not log the root module in logs. + By default, will abbreviate "detectron2" to "d2" and leave other + modules unchanged. + + Returns: + logging.Logger: a logger + """ + logger = logging.getLogger(name) + logger.setLevel(logging.DEBUG) + logger.propagate = False + + if abbrev_name is None: + abbrev_name = "d2" if name == "detectron2" else name + + plain_formatter = logging.Formatter( + "[%(asctime)s] %(name)s %(levelname)s: %(message)s", datefmt="%m/%d %H:%M:%S" + ) + # stdout logging: master only + if distributed_rank == 0: + ch = logging.StreamHandler(stream=sys.stdout) + ch.setLevel(logging.DEBUG) + if color: + formatter = _ColorfulFormatter( + colored("[%(asctime)s %(name)s]: ", "green") + "%(message)s", + datefmt="%m/%d %H:%M:%S", + root_name=name, + abbrev_name=str(abbrev_name), + ) + else: + formatter = plain_formatter + ch.setFormatter(formatter) + logger.addHandler(ch) + + # file logging: all workers + if output is not None: + if output.endswith(".txt") or output.endswith(".log"): + filename = output + else: + filename = os.path.join(output, "log.txt") + if distributed_rank > 0: + filename = filename + ".rank{}".format(distributed_rank) + PathManager.mkdirs(os.path.dirname(filename)) + + fh = logging.StreamHandler(_cached_log_stream(filename)) + fh.setLevel(logging.DEBUG) + fh.setFormatter(plain_formatter) + logger.addHandler(fh) + + return logger + + +# cache the opened file object, so that different calls to `setup_logger` +# with the same file name can safely write to the same file. +@functools.lru_cache(maxsize=None) +def _cached_log_stream(filename): + # use 1K buffer if writing to cloud storage + io = PathManager.open(filename, "a", buffering=1024 if "://" in filename else -1) + atexit.register(io.close) + return io + + +""" +Below are some other convenient logging methods. +They are mainly adopted from +https://github.com/abseil/abseil-py/blob/master/absl/logging/__init__.py +""" + + +def _find_caller(): + """ + Returns: + str: module name of the caller + tuple: a hashable key to be used to identify different callers + """ + frame = sys._getframe(2) + while frame: + code = frame.f_code + if os.path.join("autils", "logger.") not in code.co_filename: + mod_name = frame.f_globals["__name__"] + if mod_name == "__main__": + mod_name = "detectron2" + return mod_name, (code.co_filename, frame.f_lineno, code.co_name) + frame = frame.f_back + + +_LOG_COUNTER = Counter() +_LOG_TIMER = {} + + +def log_first_n(lvl, msg, n=1, *, name=None, key="caller"): + """ + Log only for the first n times. + + Args: + lvl (int): the logging level + msg (str): + n (int): + name (str): name of the logger to use. Will use the caller's module by default. + key (str or tuple[str]): the string(s) can be one of "caller" or + "message", which defines how to identify duplicated logs. + For example, if called with `n=1, key="caller"`, this function + will only log the first call from the same caller, regardless of + the message content. + If called with `n=1, key="message"`, this function will log the + same content only once, even if they are called from different places. + If called with `n=1, key=("caller", "message")`, this function + will not log only if the same caller has logged the same message before. + """ + if isinstance(key, str): + key = (key,) + assert len(key) > 0 + + caller_module, caller_key = _find_caller() + hash_key = () + if "caller" in key: + hash_key = hash_key + caller_key + if "message" in key: + hash_key = hash_key + (msg,) + + _LOG_COUNTER[hash_key] += 1 + if _LOG_COUNTER[hash_key] <= n: + logging.getLogger(name or caller_module).log(lvl, msg) + + +def log_every_n(lvl, msg, n=1, *, name=None): + """ + Log once per n times. + + Args: + lvl (int): the logging level + msg (str): + n (int): + name (str): name of the logger to use. Will use the caller's module by default. + """ + caller_module, key = _find_caller() + _LOG_COUNTER[key] += 1 + if n == 1 or _LOG_COUNTER[key] % n == 1: + logging.getLogger(name or caller_module).log(lvl, msg) + + +def log_every_n_seconds(lvl, msg, n=1, *, name=None): + """ + Log no more than once per n seconds. + + Args: + lvl (int): the logging level + msg (str): + n (int): + name (str): name of the logger to use. Will use the caller's module by default. + """ + caller_module, key = _find_caller() + last_logged = _LOG_TIMER.get(key, None) + current_time = time.time() + if last_logged is None or current_time - last_logged >= n: + logging.getLogger(name or caller_module).log(lvl, msg) + _LOG_TIMER[key] = current_time + + +def create_small_table(small_dict): + """ + Create a small table using the keys of small_dict as headers. This is only + suitable for small dictionaries. + + Args: + small_dict (dict): a result dictionary of only a few items. + + Returns: + str: the table as a string. + """ + keys, values = tuple(zip(*small_dict.items())) + table = tabulate( + [values], + headers=keys, + tablefmt="pipe", + floatfmt=".3f", + stralign="center", + numalign="center", + ) + return table + + +def _log_api_usage(identifier: str): + """ + Internal function used to log the usage of different detectron2 components + inside facebook's infra. + """ + torch._C._log_api_usage_once("detectron2." + identifier) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/utils/memory.py b/motion-gan-pipeline/preprocessing/third/detectron2/utils/memory.py new file mode 100644 index 0000000..bd49478 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/utils/memory.py @@ -0,0 +1,84 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import logging +from contextlib import contextmanager +from functools import wraps +import torch + +__all__ = ["retry_if_cuda_oom"] + + +@contextmanager +def _ignore_torch_cuda_oom(): + """ + A context which ignores CUDA OOM exception from pytorch. + """ + try: + yield + except RuntimeError as e: + # NOTE: the string may change? + if "CUDA out of memory. " in str(e): + pass + else: + raise + + +def retry_if_cuda_oom(func): + """ + Makes a function retry itself after encountering + pytorch's CUDA OOM error. + It will first retry after calling `torch.cuda.empty_cache()`. + + If that still fails, it will then retry by trying to convert inputs to CPUs. + In this case, it expects the function to dispatch to CPU implementation. + The return values may become CPU tensors as well and it's user's + responsibility to convert it back to CUDA tensor if needed. + + Args: + func: a stateless callable that takes tensor-like objects as arguments + + Returns: + a callable which retries `func` if OOM is encountered. + + Examples: + :: + output = retry_if_cuda_oom(some_torch_function)(input1, input2) + # output may be on CPU even if inputs are on GPU + + Note: + 1. When converting inputs to CPU, it will only look at each argument and check + if it has `.device` and `.to` for conversion. Nested structures of tensors + are not supported. + + 2. Since the function might be called more than once, it has to be + stateless. + """ + + def maybe_to_cpu(x): + try: + like_gpu_tensor = x.device.type == "cuda" and hasattr(x, "to") + except AttributeError: + like_gpu_tensor = False + if like_gpu_tensor: + return x.to(device="cpu") + else: + return x + + @wraps(func) + def wrapped(*args, **kwargs): + with _ignore_torch_cuda_oom(): + return func(*args, **kwargs) + + # Clear cache and retry + torch.cuda.empty_cache() + with _ignore_torch_cuda_oom(): + return func(*args, **kwargs) + + # Try on CPU. This slows down the code significantly, therefore print a notice. + logger = logging.getLogger(__name__) + logger.info("Attempting to copy inputs of {} to CPU due to CUDA OOM".format(str(func))) + new_args = (maybe_to_cpu(x) for x in args) + new_kwargs = {k: maybe_to_cpu(v) for k, v in kwargs.items()} + return func(*new_args, **new_kwargs) + + return wrapped diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/utils/registry.py b/motion-gan-pipeline/preprocessing/third/detectron2/utils/registry.py new file mode 100644 index 0000000..f67d809 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/utils/registry.py @@ -0,0 +1,60 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +from typing import Any +import pydoc +from fvcore.common.registry import Registry # for backward compatibility. + +""" +``Registry`` and `locate` provide ways to map a string (typically found +in config files) to callable objects. +""" + +__all__ = ["Registry", "locate"] + + +def _convert_target_to_string(t: Any) -> str: + """ + Inverse of ``locate()``. + + Args: + t: any object with ``__module__`` and ``__qualname__`` + """ + module, qualname = t.__module__, t.__qualname__ + + # Compress the path to this object, e.g. ``module.submodule._impl.class`` + # may become ``module.submodule.class``, if the later also resolves to the same + # object. This simplifies the string, and also is less affected by moving the + # class implementation. + module_parts = module.split(".") + for k in range(1, len(module_parts)): + prefix = ".".join(module_parts[:k]) + candidate = f"{prefix}.{qualname}" + try: + if locate(candidate) is t: + return candidate + except ImportError: + pass + return f"{module}.{qualname}" + + +def locate(name: str) -> Any: + """ + Locate and return an object ``x`` using an input string ``{x.__module__}.{x.__qualname__}``, + such as "module.submodule.class_name". + + Raise Exception if it cannot be found. + """ + obj = pydoc.locate(name) + + # Some cases (e.g. torch.optim.sgd.SGD) not handled correctly + # by pydoc.locate. Try a private function from hydra. + if obj is None: + try: + # from hydra.autils import get_method - will print many errors + from hydra.utils import _locate + except ImportError as e: + raise ImportError(f"Cannot dynamically locate object {name}!") from e + else: + obj = _locate(name) # it raises if fails + + return obj diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/utils/serialize.py b/motion-gan-pipeline/preprocessing/third/detectron2/utils/serialize.py new file mode 100644 index 0000000..0b38862 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/utils/serialize.py @@ -0,0 +1,32 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import cloudpickle + + +class PicklableWrapper(object): + """ + Wrap an object to make it more picklable, note that it uses + heavy weight serialization libraries that are slower than pickle. + It's best to use it only on closures (which are usually not picklable). + + This is a simplified version of + https://github.com/joblib/joblib/blob/master/joblib/externals/loky/cloudpickle_wrapper.py + """ + + def __init__(self, obj): + while isinstance(obj, PicklableWrapper): + # Wrapping an object twice is no-op + obj = obj._obj + self._obj = obj + + def __reduce__(self): + s = cloudpickle.dumps(self._obj) + return cloudpickle.loads, (s,) + + def __call__(self, *args, **kwargs): + return self._obj(*args, **kwargs) + + def __getattr__(self, attr): + # Ensure that the wrapped object can be used seamlessly as the previous object. + if attr not in ["_obj"]: + return getattr(self._obj, attr) + return getattr(self, attr) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/utils/testing.py b/motion-gan-pipeline/preprocessing/third/detectron2/utils/testing.py new file mode 100644 index 0000000..161fa6b --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/utils/testing.py @@ -0,0 +1,137 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import io +import numpy as np +import torch + +from detectron2 import model_zoo +from detectron2.data import DatasetCatalog +from detectron2.data.detection_utils import read_image +from detectron2.modeling import build_model +from detectron2.structures import Boxes, Instances, ROIMasks +from detectron2.utils.file_io import PathManager + + +""" +Internal utilities for tests. Don't use except for writing tests. +""" + + +def get_model_no_weights(config_path): + """ + Like model_zoo.get, but do not load any weights (even pretrained) + """ + cfg = model_zoo.get_config(config_path) + if not torch.cuda.is_available(): + cfg.MODEL.DEVICE = "cpu" + return build_model(cfg) + + +def random_boxes(num_boxes, max_coord=100, device="cpu"): + """ + Create a random Nx4 boxes tensor, with coordinates < max_coord. + """ + boxes = torch.rand(num_boxes, 4, device=device) * (max_coord * 0.5) + boxes.clamp_(min=1.0) # tiny boxes cause numerical instability in box regression + # Note: the implementation of this function in torchvision is: + # boxes[:, 2:] += torch.rand(N, 2) * 100 + # but it does not guarantee non-negative widths/heights constraints: + # boxes[:, 2] >= boxes[:, 0] and boxes[:, 3] >= boxes[:, 1]: + boxes[:, 2:] += boxes[:, :2] + return boxes + + +def get_sample_coco_image(tensor=True): + """ + Args: + tensor (bool): if True, returns 3xHxW tensor. + else, returns a HxWx3 numpy array. + + Returns: + an image, in BGR color. + """ + try: + file_name = DatasetCatalog.get("coco_2017_val_100")[0]["file_name"] + if not PathManager.exists(file_name): + raise FileNotFoundError() + except IOError: + # for public CI to run + file_name = PathManager.get_local_path( + "http://images.cocodataset.org/train2017/000000000009.jpg" + ) + ret = read_image(file_name, format="BGR") + if tensor: + ret = torch.from_numpy(np.ascontiguousarray(ret.transpose(2, 0, 1))) + return ret + + +def convert_scripted_instances(instances): + """ + Convert a scripted Instances object to a regular :class:`Instances` object + """ + assert hasattr( + instances, "image_size" + ), f"Expect an Instances object, but got {type(instances)}!" + ret = Instances(instances.image_size) + for name in instances._field_names: + val = getattr(instances, "_" + name, None) + if val is not None: + ret.set(name, val) + return ret + + +def assert_instances_allclose(input, other, *, rtol=1e-5, msg="", size_as_tensor=False): + """ + Args: + input, other (Instances): + size_as_tensor: compare image_size of the Instances as tensors (instead of tuples). + Useful for comparing outputs of tracing. + """ + if not isinstance(input, Instances): + input = convert_scripted_instances(input) + if not isinstance(other, Instances): + other = convert_scripted_instances(other) + + if not msg: + msg = "Two Instances are different! " + else: + msg = msg.rstrip() + " " + + size_error_msg = msg + f"image_size is {input.image_size} vs. {other.image_size}!" + if size_as_tensor: + assert torch.equal( + torch.tensor(input.image_size), torch.tensor(other.image_size) + ), size_error_msg + else: + assert input.image_size == other.image_size, size_error_msg + fields = sorted(input.get_fields().keys()) + fields_other = sorted(other.get_fields().keys()) + assert fields == fields_other, msg + f"Fields are {fields} vs {fields_other}!" + + for f in fields: + val1, val2 = input.get(f), other.get(f) + if isinstance(val1, (Boxes, ROIMasks)): + # boxes in the range of O(100) and can have a larger tolerance + assert torch.allclose(val1.tensor, val2.tensor, atol=100 * rtol), ( + msg + f"Field {f} differs too much!" + ) + elif isinstance(val1, torch.Tensor): + if val1.dtype.is_floating_point: + mag = torch.abs(val1).max().cpu().item() + assert torch.allclose(val1, val2, atol=mag * rtol), ( + msg + f"Field {f} differs too much!" + ) + else: + assert torch.equal(val1, val2), msg + f"Field {f} is different!" + else: + raise ValueError(f"Don't know how to compare type {type(val1)}") + + +def reload_script_model(module): + """ + Save a jit module and load it back. + Similar to the `getExportImportCopy` function in torch/testing/ + """ + buffer = io.BytesIO() + torch.jit.save(module, buffer) + buffer.seek(0) + return torch.jit.load(buffer) diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/utils/video_visualizer.py b/motion-gan-pipeline/preprocessing/third/detectron2/utils/video_visualizer.py new file mode 100644 index 0000000..5ec4de1 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/utils/video_visualizer.py @@ -0,0 +1,282 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import numpy as np +import pycocotools.mask as mask_util + +from detectron2.utils.visualizer import ( + ColorMode, + Visualizer, + _create_text_labels, + _PanopticPrediction, +) + +from .colormap import random_color, random_colors +from detectron2.structures import Instances +from typing import List + + +class _DetectedInstance: + """ + Used to store data about detected objects in video frame, + in order to transfer color to objects in the future frames. + + Attributes: + label (int): + bbox (tuple[float]): + mask_rle (dict): + color (tuple[float]): RGB colors in range (0, 1) + ttl (int): time-to-live for the instance. For example, if ttl=2, + the instance color can be transferred to objects in the next two frames. + """ + + __slots__ = ["label", "bbox", "mask_rle", "color", "ttl"] + + def __init__(self, label, bbox, mask_rle, color, ttl): + self.label = label + self.bbox = bbox + self.mask_rle = mask_rle + self.color = color + self.ttl = ttl + + +class VideoVisualizer: + def __init__(self, metadata, instance_mode=ColorMode.IMAGE): + """ + Args: + metadata (MetadataCatalog): image metadata. + """ + self.metadata = metadata + self._old_instances = [] + assert instance_mode in [ + ColorMode.IMAGE, + ColorMode.IMAGE_BW, + ], "Other mode not supported yet." + self._instance_mode = instance_mode + self._max_num_instances = self.metadata.get("max_num_instances", 74) + self._assigned_colors = {} + self._color_pool = random_colors(self._max_num_instances, rgb=True, maximum=1) + self._color_idx_set = set(range(len(self._color_pool))) + + def draw_instance_predictions(self, frame, predictions): + """ + Draw instance-level prediction results on an image. + + Args: + frame (ndarray): an RGB image of shape (H, W, C), in the range [0, 255]. + predictions (Instances): the output of an instance detection/segmentation + model. Following fields will be used to draw: + "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle"). + + Returns: + output (VisImage): image object with visualizations. + """ + frame_visualizer = Visualizer(frame, self.metadata) + num_instances = len(predictions) + if num_instances == 0: + return frame_visualizer.output + + boxes = predictions.pred_boxes.tensor.numpy() if predictions.has("pred_boxes") else None + scores = predictions.scores if predictions.has("scores") else None + classes = predictions.pred_classes.numpy() if predictions.has("pred_classes") else None + keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None + colors = predictions.COLOR if predictions.has("COLOR") else [None] * len(predictions) + periods = predictions.ID_period if predictions.has("ID_period") else None + period_threshold = self.metadata.get("period_threshold", 0) + visibilities = [True] * len(predictions) if periods is None else [ + x > period_threshold for x in periods] + + if predictions.has("pred_masks"): + masks = predictions.pred_masks + # mask IOU is not yet enabled + # masks_rles = mask_util.encode(np.asarray(masks.permute(1, 2, 0), order="F")) + # assert len(masks_rles) == num_instances + else: + masks = None + + if not predictions.has("COLOR"): + if predictions.has("ID"): + colors = self._assign_colors_by_id(predictions) + else: + # ToDo: clean old assign color method and use a default tracker to assign id + detected = [ + _DetectedInstance(classes[i], boxes[i], mask_rle=None, color=colors[i], ttl=8) + for i in range(num_instances) + ] + colors = self._assign_colors(detected) + + labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None)) + + if self._instance_mode == ColorMode.IMAGE_BW: + # any() returns uint8 tensor + frame_visualizer.output.reset_image( + frame_visualizer._create_grayscale_image( + (masks.any(dim=0) > 0).numpy() if masks is not None else None + ) + ) + alpha = 0.3 + else: + alpha = 0.5 + + labels = ( + None + if labels is None + else [y[0] for y in filter(lambda x: x[1], zip(labels, visibilities))] + ) # noqa + assigned_colors = ( + None + if colors is None + else [y[0] for y in filter(lambda x: x[1], zip(colors, visibilities))] + ) # noqa + frame_visualizer.overlay_instances( + boxes=None if masks is not None else boxes[visibilities], # boxes are a bit distracting + masks=None if masks is None else masks[visibilities], + labels=labels, + keypoints=None if keypoints is None else keypoints[visibilities], + assigned_colors=assigned_colors, + alpha=alpha, + ) + + return frame_visualizer.output + + def draw_sem_seg(self, frame, sem_seg, area_threshold=None): + """ + Args: + sem_seg (ndarray or Tensor): semantic segmentation of shape (H, W), + each value is the integer label. + area_threshold (Optional[int]): only draw segmentations larger than the threshold + """ + # don't need to do anything special + frame_visualizer = Visualizer(frame, self.metadata) + frame_visualizer.draw_sem_seg(sem_seg, area_threshold=None) + return frame_visualizer.output + + def draw_panoptic_seg_predictions( + self, frame, panoptic_seg, segments_info, area_threshold=None, alpha=0.5 + ): + frame_visualizer = Visualizer(frame, self.metadata) + pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata) + + if self._instance_mode == ColorMode.IMAGE_BW: + frame_visualizer.output.reset_image( + frame_visualizer._create_grayscale_image(pred.non_empty_mask()) + ) + + # draw mask for all semantic segments first i.e. "stuff" + for mask, sinfo in pred.semantic_masks(): + category_idx = sinfo["category_id"] + try: + mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]] + except AttributeError: + mask_color = None + + frame_visualizer.draw_binary_mask( + mask, + color=mask_color, + text=self.metadata.stuff_classes[category_idx], + alpha=alpha, + area_threshold=area_threshold, + ) + + all_instances = list(pred.instance_masks()) + if len(all_instances) == 0: + return frame_visualizer.output + # draw mask for all instances second + masks, sinfo = list(zip(*all_instances)) + num_instances = len(masks) + masks_rles = mask_util.encode( + np.asarray(np.asarray(masks).transpose(1, 2, 0), dtype=np.uint8, order="F") + ) + assert len(masks_rles) == num_instances + + category_ids = [x["category_id"] for x in sinfo] + detected = [ + _DetectedInstance(category_ids[i], bbox=None, mask_rle=masks_rles[i], color=None, ttl=8) + for i in range(num_instances) + ] + colors = self._assign_colors(detected) + labels = [self.metadata.thing_classes[k] for k in category_ids] + + frame_visualizer.overlay_instances( + boxes=None, + masks=masks, + labels=labels, + keypoints=None, + assigned_colors=colors, + alpha=alpha, + ) + return frame_visualizer.output + + def _assign_colors(self, instances): + """ + Naive tracking heuristics to assign same color to the same instance, + will update the internal state of tracked instances. + + Returns: + list[tuple[float]]: list of colors. + """ + + # Compute iou with either boxes or masks: + is_crowd = np.zeros((len(instances),), dtype=np.bool) + if instances[0].bbox is None: + assert instances[0].mask_rle is not None + # use mask iou only when box iou is None + # because box seems good enough + rles_old = [x.mask_rle for x in self._old_instances] + rles_new = [x.mask_rle for x in instances] + ious = mask_util.iou(rles_old, rles_new, is_crowd) + threshold = 0.5 + else: + boxes_old = [x.bbox for x in self._old_instances] + boxes_new = [x.bbox for x in instances] + ious = mask_util.iou(boxes_old, boxes_new, is_crowd) + threshold = 0.6 + if len(ious) == 0: + ious = np.zeros((len(self._old_instances), len(instances)), dtype="float32") + + # Only allow matching instances of the same label: + for old_idx, old in enumerate(self._old_instances): + for new_idx, new in enumerate(instances): + if old.label != new.label: + ious[old_idx, new_idx] = 0 + + matched_new_per_old = np.asarray(ious).argmax(axis=1) + max_iou_per_old = np.asarray(ious).max(axis=1) + + # Try to find match for each old instance: + extra_instances = [] + for idx, inst in enumerate(self._old_instances): + if max_iou_per_old[idx] > threshold: + newidx = matched_new_per_old[idx] + if instances[newidx].color is None: + instances[newidx].color = inst.color + continue + # If an old instance does not match any new instances, + # keep it for the next frame in case it is just missed by the detector + inst.ttl -= 1 + if inst.ttl > 0: + extra_instances.append(inst) + + # Assign random color to newly-detected instances: + for inst in instances: + if inst.color is None: + inst.color = random_color(rgb=True, maximum=1) + self._old_instances = instances[:] + extra_instances + return [d.color for d in instances] + + def _assign_colors_by_id(self, instances: Instances) -> List: + colors = [] + untracked_ids = set(self._assigned_colors.keys()) + for id in instances.ID: + if id in self._assigned_colors: + colors.append(self._color_pool[self._assigned_colors[id]]) + untracked_ids.remove(id) + else: + assert len(self._color_idx_set) >= 1, f"Number of id exceeded maximum, \ + max = {self._max_num_instances}" + idx = self._color_idx_set.pop() + color = self._color_pool[idx] + self._assigned_colors[id] = idx + colors.append(color) + for id in untracked_ids: + self._color_idx_set.add(self._assigned_colors[id]) + del self._assigned_colors[id] + return colors diff --git a/motion-gan-pipeline/preprocessing/third/detectron2/utils/visualizer.py b/motion-gan-pipeline/preprocessing/third/detectron2/utils/visualizer.py new file mode 100644 index 0000000..8e14518 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/detectron2/utils/visualizer.py @@ -0,0 +1,1267 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import colorsys +import logging +import math +import numpy as np +from enum import Enum, unique +import cv2 +import matplotlib as mpl +import matplotlib.colors as mplc +import matplotlib.figure as mplfigure +import pycocotools.mask as mask_util +import torch +from matplotlib.backends.backend_agg import FigureCanvasAgg +from PIL import Image + +from detectron2.data import MetadataCatalog +from detectron2.structures import BitMasks, Boxes, BoxMode, Keypoints, PolygonMasks, RotatedBoxes +from detectron2.utils.file_io import PathManager + +from .colormap import random_color + +logger = logging.getLogger(__name__) + +__all__ = ["ColorMode", "VisImage", "Visualizer"] + + +_SMALL_OBJECT_AREA_THRESH = 1000 +_LARGE_MASK_AREA_THRESH = 120000 +_OFF_WHITE = (1.0, 1.0, 240.0 / 255) +_BLACK = (0, 0, 0) +_RED = (1.0, 0, 0) + +_KEYPOINT_THRESHOLD = 0.05 + + +@unique +class ColorMode(Enum): + """ + Enum of different color modes to use for instance visualizations. + """ + + IMAGE = 0 + """ + Picks a random color for every instance and overlay segmentations with low opacity. + """ + SEGMENTATION = 1 + """ + Let instances of the same category have similar colors + (from metadata.thing_colors), and overlay them with + high opacity. This provides more attention on the quality of segmentation. + """ + IMAGE_BW = 2 + """ + Same as IMAGE, but convert all areas without masks to gray-scale. + Only available for drawing per-instance mask predictions. + """ + + +class GenericMask: + """ + Attribute: + polygons (list[ndarray]): list[ndarray]: polygons for this mask. + Each ndarray has format [x, y, x, y, ...] + mask (ndarray): a binary mask + """ + + def __init__(self, mask_or_polygons, height, width): + self._mask = self._polygons = self._has_holes = None + self.height = height + self.width = width + + m = mask_or_polygons + if isinstance(m, dict): + # RLEs + assert "counts" in m and "size" in m + if isinstance(m["counts"], list): # uncompressed RLEs + h, w = m["size"] + assert h == height and w == width + m = mask_util.frPyObjects(m, h, w) + self._mask = mask_util.decode(m)[:, :] + return + + if isinstance(m, list): # list[ndarray] + self._polygons = [np.asarray(x).reshape(-1) for x in m] + return + + if isinstance(m, np.ndarray): # assumed to be a binary mask + assert m.shape[1] != 2, m.shape + assert m.shape == ( + height, + width, + ), f"mask shape: {m.shape}, target dims: {height}, {width}" + self._mask = m.astype("uint8") + return + + raise ValueError("GenericMask cannot handle object {} of type '{}'".format(m, type(m))) + + @property + def mask(self): + if self._mask is None: + self._mask = self.polygons_to_mask(self._polygons) + return self._mask + + @property + def polygons(self): + if self._polygons is None: + self._polygons, self._has_holes = self.mask_to_polygons(self._mask) + return self._polygons + + @property + def has_holes(self): + if self._has_holes is None: + if self._mask is not None: + self._polygons, self._has_holes = self.mask_to_polygons(self._mask) + else: + self._has_holes = False # if original format is polygon, does not have holes + return self._has_holes + + def mask_to_polygons(self, mask): + # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level + # hierarchy. External contours (boundary) of the object are placed in hierarchy-1. + # Internal contours (holes) are placed in hierarchy-2. + # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours. + mask = np.ascontiguousarray(mask) # some versions of cv2 does not support incontiguous arr + res = cv2.findContours(mask.astype("uint8"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE) + hierarchy = res[-1] + if hierarchy is None: # empty mask + return [], False + has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0 + res = res[-2] + res = [x.flatten() for x in res] + # These coordinates from OpenCV are integers in range [0, W-1 or H-1]. + # We add 0.5 to turn them into real-value coordinate space. A better solution + # would be to first +0.5 and then dilate the returned polygon by 0.5. + res = [x + 0.5 for x in res if len(x) >= 6] + return res, has_holes + + def polygons_to_mask(self, polygons): + rle = mask_util.frPyObjects(polygons, self.height, self.width) + rle = mask_util.merge(rle) + return mask_util.decode(rle)[:, :] + + def area(self): + return self.mask.sum() + + def bbox(self): + p = mask_util.frPyObjects(self.polygons, self.height, self.width) + p = mask_util.merge(p) + bbox = mask_util.toBbox(p) + bbox[2] += bbox[0] + bbox[3] += bbox[1] + return bbox + + +class _PanopticPrediction: + """ + Unify different panoptic annotation/prediction formats + """ + + def __init__(self, panoptic_seg, segments_info, metadata=None): + if segments_info is None: + assert metadata is not None + # If "segments_info" is None, we assume "panoptic_img" is a + # H*W int32 image storing the panoptic_id in the format of + # category_id * label_divisor + instance_id. We reserve -1 for + # VOID label. + label_divisor = metadata.label_divisor + segments_info = [] + for panoptic_label in np.unique(panoptic_seg.numpy()): + if panoptic_label == -1: + # VOID region. + continue + pred_class = panoptic_label // label_divisor + isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values() + segments_info.append( + { + "id": int(panoptic_label), + "category_id": int(pred_class), + "isthing": bool(isthing), + } + ) + del metadata + + self._seg = panoptic_seg + + self._sinfo = {s["id"]: s for s in segments_info} # seg id -> seg info + segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True) + areas = areas.numpy() + sorted_idxs = np.argsort(-areas) + self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs] + self._seg_ids = self._seg_ids.tolist() + for sid, area in zip(self._seg_ids, self._seg_areas): + if sid in self._sinfo: + self._sinfo[sid]["area"] = float(area) + + def non_empty_mask(self): + """ + Returns: + (H, W) array, a mask for all pixels that have a prediction + """ + empty_ids = [] + for id in self._seg_ids: + if id not in self._sinfo: + empty_ids.append(id) + if len(empty_ids) == 0: + return np.zeros(self._seg.shape, dtype=np.uint8) + assert ( + len(empty_ids) == 1 + ), ">1 ids corresponds to no labels. This is currently not supported" + return (self._seg != empty_ids[0]).numpy().astype(np.bool) + + def semantic_masks(self): + for sid in self._seg_ids: + sinfo = self._sinfo.get(sid) + if sinfo is None or sinfo["isthing"]: + # Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions. + continue + yield (self._seg == sid).numpy().astype(np.bool), sinfo + + def instance_masks(self): + for sid in self._seg_ids: + sinfo = self._sinfo.get(sid) + if sinfo is None or not sinfo["isthing"]: + continue + mask = (self._seg == sid).numpy().astype(np.bool) + if mask.sum() > 0: + yield mask, sinfo + + +def _create_text_labels(classes, scores, class_names, is_crowd=None): + """ + Args: + classes (list[int] or None): + scores (list[float] or None): + class_names (list[str] or None): + is_crowd (list[bool] or None): + + Returns: + list[str] or None + """ + labels = None + if classes is not None: + if class_names is not None and len(class_names) > 0: + labels = [class_names[i] for i in classes] + else: + labels = [str(i) for i in classes] + if scores is not None: + if labels is None: + labels = ["{:.0f}%".format(s * 100) for s in scores] + else: + labels = ["{} {:.0f}%".format(l, s * 100) for l, s in zip(labels, scores)] + if labels is not None and is_crowd is not None: + labels = [l + ("|crowd" if crowd else "") for l, crowd in zip(labels, is_crowd)] + return labels + + +class VisImage: + def __init__(self, img, scale=1.0): + """ + Args: + img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255]. + scale (float): scale the input image + """ + self.img = img + self.scale = scale + self.width, self.height = img.shape[1], img.shape[0] + self._setup_figure(img) + + def _setup_figure(self, img): + """ + Args: + Same as in :meth:`__init__()`. + + Returns: + fig (matplotlib.pyplot.figure): top level container for all the image plot elements. + ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system. + """ + fig = mplfigure.Figure(frameon=False) + self.dpi = fig.get_dpi() + # add a small 1e-2 to avoid precision lost due to matplotlib's truncation + # (https://github.com/matplotlib/matplotlib/issues/15363) + fig.set_size_inches( + (self.width * self.scale + 1e-2) / self.dpi, + (self.height * self.scale + 1e-2) / self.dpi, + ) + self.canvas = FigureCanvasAgg(fig) + # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig) + ax = fig.add_axes([0.0, 0.0, 1.0, 1.0]) + ax.axis("off") + self.fig = fig + self.ax = ax + self.reset_image(img) + + def reset_image(self, img): + """ + Args: + img: same as in __init__ + """ + img = img.astype("uint8") + self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation="nearest") + + def save(self, filepath): + """ + Args: + filepath (str): a string that contains the absolute path, including the file name, where + the visualized image will be saved. + """ + self.fig.savefig(filepath) + + def get_image(self): + """ + Returns: + ndarray: + the visualized image of shape (H, W, 3) (RGB) in uint8 type. + The shape is scaled w.r.t the input image using the given `scale` argument. + """ + canvas = self.canvas + s, (width, height) = canvas.print_to_buffer() + # buf = io.BytesIO() # works for cairo backend + # canvas.print_rgba(buf) + # width, height = self.width, self.height + # s = buf.getvalue() + + buffer = np.frombuffer(s, dtype="uint8") + + img_rgba = buffer.reshape(height, width, 4) + rgb, alpha = np.split(img_rgba, [3], axis=2) + return rgb.astype("uint8") + + +class Visualizer: + """ + Visualizer that draws data about detection/segmentation on images. + + It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}` + that draw primitive objects to images, as well as high-level wrappers like + `draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}` + that draw composite data in some pre-defined style. + + Note that the exact visualization style for the high-level wrappers are subject to change. + Style such as color, opacity, label contents, visibility of labels, or even the visibility + of objects themselves (e.g. when the object is too small) may change according + to different heuristics, as long as the results still look visually reasonable. + + To obtain a consistent style, you can implement custom drawing functions with the + abovementioned primitive methods instead. If you need more customized visualization + styles, you can process the data yourself following their format documented in + tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not + intend to satisfy everyone's preference on drawing styles. + + This visualizer focuses on high rendering quality rather than performance. It is not + designed to be used for real-time applications. + """ + + # TODO implement a fast, rasterized version using OpenCV + + def __init__(self, img_rgb, metadata=None, scale=1.0, instance_mode=ColorMode.IMAGE): + """ + Args: + img_rgb: a numpy array of shape (H, W, C), where H and W correspond to + the height and width of the image respectively. C is the number of + color channels. The image is required to be in RGB format since that + is a requirement of the Matplotlib library. The image is also expected + to be in the range [0, 255]. + metadata (Metadata): dataset metadata (e.g. class names and colors) + instance_mode (ColorMode): defines one of the pre-defined style for drawing + instances on an image. + """ + self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8) + if metadata is None: + metadata = MetadataCatalog.get("__nonexist__") + self.metadata = metadata + self.output = VisImage(self.img, scale=scale) + self.cpu_device = torch.device("cpu") + + # too small texts are useless, therefore clamp to 9 + self._default_font_size = max( + np.sqrt(self.output.height * self.output.width) // 90, 10 // scale + ) + self._instance_mode = instance_mode + self.keypoint_threshold = _KEYPOINT_THRESHOLD + + def draw_instance_predictions(self, predictions): + """ + Draw instance-level prediction results on an image. + + Args: + predictions (Instances): the output of an instance detection/segmentation + model. Following fields will be used to draw: + "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle"). + + Returns: + output (VisImage): image object with visualizations. + """ + boxes = predictions.pred_boxes if predictions.has("pred_boxes") else None + scores = predictions.scores if predictions.has("scores") else None + classes = predictions.pred_classes.tolist() if predictions.has("pred_classes") else None + labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None)) + keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None + + if predictions.has("pred_masks"): + masks = np.asarray(predictions.pred_masks) + masks = [GenericMask(x, self.output.height, self.output.width) for x in masks] + else: + masks = None + + if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"): + colors = [ + self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes + ] + alpha = 0.8 + else: + colors = None + alpha = 0.5 + + if self._instance_mode == ColorMode.IMAGE_BW: + self.output.reset_image( + self._create_grayscale_image( + (predictions.pred_masks.any(dim=0) > 0).numpy() + if predictions.has("pred_masks") + else None + ) + ) + alpha = 0.3 + + self.overlay_instances( + masks=masks, + boxes=boxes, + labels=labels, + keypoints=keypoints, + assigned_colors=colors, + alpha=alpha, + ) + return self.output + + def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8): + """ + Draw semantic segmentation predictions/labels. + + Args: + sem_seg (Tensor or ndarray): the segmentation of shape (H, W). + Each value is the integer label of the pixel. + area_threshold (int): segments with less than `area_threshold` are not drawn. + alpha (float): the larger it is, the more opaque the segmentations are. + + Returns: + output (VisImage): image object with visualizations. + """ + if isinstance(sem_seg, torch.Tensor): + sem_seg = sem_seg.numpy() + labels, areas = np.unique(sem_seg, return_counts=True) + sorted_idxs = np.argsort(-areas).tolist() + labels = labels[sorted_idxs] + for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels): + try: + mask_color = [x / 255 for x in self.metadata.stuff_colors[label]] + except (AttributeError, IndexError): + mask_color = None + + binary_mask = (sem_seg == label).astype(np.uint8) + text = self.metadata.stuff_classes[label] + self.draw_binary_mask( + binary_mask, + color=mask_color, + edge_color=_OFF_WHITE, + text=text, + alpha=alpha, + area_threshold=area_threshold, + ) + return self.output + + def draw_panoptic_seg(self, panoptic_seg, segments_info, area_threshold=None, alpha=0.7): + """ + Draw panoptic prediction annotations or results. + + Args: + panoptic_seg (Tensor): of shape (height, width) where the values are ids for each + segment. + segments_info (list[dict] or None): Describe each segment in `panoptic_seg`. + If it is a ``list[dict]``, each dict contains keys "id", "category_id". + If None, category id of each pixel is computed by + ``pixel // metadata.label_divisor``. + area_threshold (int): stuff segments with less than `area_threshold` are not drawn. + + Returns: + output (VisImage): image object with visualizations. + """ + pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata) + + if self._instance_mode == ColorMode.IMAGE_BW: + self.output.reset_image(self._create_grayscale_image(pred.non_empty_mask())) + + # draw mask for all semantic segments first i.e. "stuff" + for mask, sinfo in pred.semantic_masks(): + category_idx = sinfo["category_id"] + try: + mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]] + except AttributeError: + mask_color = None + + text = self.metadata.stuff_classes[category_idx] + self.draw_binary_mask( + mask, + color=mask_color, + edge_color=_OFF_WHITE, + text=text, + alpha=alpha, + area_threshold=area_threshold, + ) + + # draw mask for all instances second + all_instances = list(pred.instance_masks()) + if len(all_instances) == 0: + return self.output + masks, sinfo = list(zip(*all_instances)) + category_ids = [x["category_id"] for x in sinfo] + + try: + scores = [x["score"] for x in sinfo] + except KeyError: + scores = None + labels = _create_text_labels( + category_ids, scores, self.metadata.thing_classes, [x.get("iscrowd", 0) for x in sinfo] + ) + + try: + colors = [ + self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in category_ids + ] + except AttributeError: + colors = None + self.overlay_instances(masks=masks, labels=labels, assigned_colors=colors, alpha=alpha) + + return self.output + + draw_panoptic_seg_predictions = draw_panoptic_seg # backward compatibility + + def draw_dataset_dict(self, dic): + """ + Draw annotations/segmentaions in Detectron2 Dataset format. + + Args: + dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format. + + Returns: + output (VisImage): image object with visualizations. + """ + annos = dic.get("annotations", None) + if annos: + if "segmentation" in annos[0]: + masks = [x["segmentation"] for x in annos] + else: + masks = None + if "keypoints" in annos[0]: + keypts = [x["keypoints"] for x in annos] + keypts = np.array(keypts).reshape(len(annos), -1, 3) + else: + keypts = None + + boxes = [ + BoxMode.convert(x["bbox"], x["bbox_mode"], BoxMode.XYXY_ABS) + if len(x["bbox"]) == 4 + else x["bbox"] + for x in annos + ] + + colors = None + category_ids = [x["category_id"] for x in annos] + if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"): + colors = [ + self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) + for c in category_ids + ] + names = self.metadata.get("thing_classes", None) + labels = _create_text_labels( + category_ids, + scores=None, + class_names=names, + is_crowd=[x.get("iscrowd", 0) for x in annos], + ) + self.overlay_instances( + labels=labels, boxes=boxes, masks=masks, keypoints=keypts, assigned_colors=colors + ) + + sem_seg = dic.get("sem_seg", None) + if sem_seg is None and "sem_seg_file_name" in dic: + with PathManager.open(dic["sem_seg_file_name"], "rb") as f: + sem_seg = Image.open(f) + sem_seg = np.asarray(sem_seg, dtype="uint8") + if sem_seg is not None: + self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5) + + pan_seg = dic.get("pan_seg", None) + if pan_seg is None and "pan_seg_file_name" in dic: + with PathManager.open(dic["pan_seg_file_name"], "rb") as f: + pan_seg = Image.open(f) + pan_seg = np.asarray(pan_seg) + from panopticapi.utils import rgb2id + + pan_seg = rgb2id(pan_seg) + if pan_seg is not None: + segments_info = dic["segments_info"] + pan_seg = torch.tensor(pan_seg) + self.draw_panoptic_seg(pan_seg, segments_info, area_threshold=0, alpha=0.5) + return self.output + + def overlay_instances( + self, + *, + boxes=None, + labels=None, + masks=None, + keypoints=None, + assigned_colors=None, + alpha=0.5, + ): + """ + Args: + boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`, + or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image, + or a :class:`RotatedBoxes`, + or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format + for the N objects in a single image, + labels (list[str]): the text to be displayed for each instance. + masks (masks-like object): Supported types are: + + * :class:`detectron2.structures.PolygonMasks`, + :class:`detectron2.structures.BitMasks`. + * list[list[ndarray]]: contains the segmentation masks for all objects in one image. + The first level of the list corresponds to individual instances. The second + level to all the polygon that compose the instance, and the third level + to the polygon coordinates. The third level should have the format of + [x0, y0, x1, y1, ..., xn, yn] (n >= 3). + * list[ndarray]: each ndarray is a binary mask of shape (H, W). + * list[dict]: each dict is a COCO-style RLE. + keypoints (Keypoint or array like): an array-like object of shape (N, K, 3), + where the N is the number of instances and K is the number of keypoints. + The last dimension corresponds to (x, y, visibility or score). + assigned_colors (list[matplotlib.colors]): a list of colors, where each color + corresponds to each mask or box in the image. Refer to 'matplotlib.colors' + for full list of formats that the colors are accepted in. + Returns: + output (VisImage): image object with visualizations. + """ + num_instances = 0 + if boxes is not None: + boxes = self._convert_boxes(boxes) + num_instances = len(boxes) + if masks is not None: + masks = self._convert_masks(masks) + if num_instances: + assert len(masks) == num_instances + else: + num_instances = len(masks) + if keypoints is not None: + if num_instances: + assert len(keypoints) == num_instances + else: + num_instances = len(keypoints) + keypoints = self._convert_keypoints(keypoints) + if labels is not None: + assert len(labels) == num_instances + if assigned_colors is None: + assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)] + if num_instances == 0: + return self.output + if boxes is not None and boxes.shape[1] == 5: + return self.overlay_rotated_instances( + boxes=boxes, labels=labels, assigned_colors=assigned_colors + ) + + # Display in largest to smallest order to reduce occlusion. + areas = None + if boxes is not None: + areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1) + elif masks is not None: + areas = np.asarray([x.area() for x in masks]) + + if areas is not None: + sorted_idxs = np.argsort(-areas).tolist() + # Re-order overlapped instances in descending order. + boxes = boxes[sorted_idxs] if boxes is not None else None + labels = [labels[k] for k in sorted_idxs] if labels is not None else None + masks = [masks[idx] for idx in sorted_idxs] if masks is not None else None + assigned_colors = [assigned_colors[idx] for idx in sorted_idxs] + keypoints = keypoints[sorted_idxs] if keypoints is not None else None + + for i in range(num_instances): + color = assigned_colors[i] + if boxes is not None: + self.draw_box(boxes[i], edge_color=color) + + if masks is not None: + for segment in masks[i].polygons: + self.draw_polygon(segment.reshape(-1, 2), color, alpha=alpha) + + if labels is not None: + # first get a box + if boxes is not None: + x0, y0, x1, y1 = boxes[i] + text_pos = (x0, y0) # if drawing boxes, put text on the box corner. + horiz_align = "left" + elif masks is not None: + # skip small mask without polygon + if len(masks[i].polygons) == 0: + continue + + x0, y0, x1, y1 = masks[i].bbox() + + # draw text in the center (defined by median) when box is not drawn + # median is less sensitive to outliers. + text_pos = np.median(masks[i].mask.nonzero(), axis=1)[::-1] + horiz_align = "center" + else: + continue # drawing the box confidence for keypoints isn't very useful. + # for small objects, draw text at the side to avoid occlusion + instance_area = (y1 - y0) * (x1 - x0) + if ( + instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale + or y1 - y0 < 40 * self.output.scale + ): + if y1 >= self.output.height - 5: + text_pos = (x1, y0) + else: + text_pos = (x0, y1) + + height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width) + lighter_color = self._change_color_brightness(color, brightness_factor=0.7) + font_size = ( + np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) + * 0.5 + * self._default_font_size + ) + self.draw_text( + labels[i], + text_pos, + color=lighter_color, + horizontal_alignment=horiz_align, + font_size=font_size, + ) + + # draw keypoints + if keypoints is not None: + for keypoints_per_instance in keypoints: + self.draw_and_connect_keypoints(keypoints_per_instance) + + return self.output + + def overlay_rotated_instances(self, boxes=None, labels=None, assigned_colors=None): + """ + Args: + boxes (ndarray): an Nx5 numpy array of + (x_center, y_center, width, height, angle_degrees) format + for the N objects in a single image. + labels (list[str]): the text to be displayed for each instance. + assigned_colors (list[matplotlib.colors]): a list of colors, where each color + corresponds to each mask or box in the image. Refer to 'matplotlib.colors' + for full list of formats that the colors are accepted in. + + Returns: + output (VisImage): image object with visualizations. + """ + num_instances = len(boxes) + + if assigned_colors is None: + assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)] + if num_instances == 0: + return self.output + + # Display in largest to smallest order to reduce occlusion. + if boxes is not None: + areas = boxes[:, 2] * boxes[:, 3] + + sorted_idxs = np.argsort(-areas).tolist() + # Re-order overlapped instances in descending order. + boxes = boxes[sorted_idxs] + labels = [labels[k] for k in sorted_idxs] if labels is not None else None + colors = [assigned_colors[idx] for idx in sorted_idxs] + + for i in range(num_instances): + self.draw_rotated_box_with_label( + boxes[i], edge_color=colors[i], label=labels[i] if labels is not None else None + ) + + return self.output + + def draw_and_connect_keypoints(self, keypoints): + """ + Draws keypoints of an instance and follows the rules for keypoint connections + to draw lines between appropriate keypoints. This follows color heuristics for + line color. + + Args: + keypoints (Tensor): a tensor of shape (K, 3), where K is the number of keypoints + and the last dimension corresponds to (x, y, probability). + + Returns: + output (VisImage): image object with visualizations. + """ + visible = {} + keypoint_names = self.metadata.get("keypoint_names") + for idx, keypoint in enumerate(keypoints): + + # draw keypoint + x, y, prob = keypoint + if prob > self.keypoint_threshold: + self.draw_circle((x, y), color=_RED) + if keypoint_names: + keypoint_name = keypoint_names[idx] + visible[keypoint_name] = (x, y) + + if self.metadata.get("keypoint_connection_rules"): + for kp0, kp1, color in self.metadata.keypoint_connection_rules: + if kp0 in visible and kp1 in visible: + x0, y0 = visible[kp0] + x1, y1 = visible[kp1] + color = tuple(x / 255.0 for x in color) + self.draw_line([x0, x1], [y0, y1], color=color) + + # draw lines from nose to mid-shoulder and mid-shoulder to mid-hip + # Note that this strategy is specific to person keypoints. + # For other keypoints, it should just do nothing + try: + ls_x, ls_y = visible["left_shoulder"] + rs_x, rs_y = visible["right_shoulder"] + mid_shoulder_x, mid_shoulder_y = (ls_x + rs_x) / 2, (ls_y + rs_y) / 2 + except KeyError: + pass + else: + # draw line from nose to mid-shoulder + nose_x, nose_y = visible.get("nose", (None, None)) + if nose_x is not None: + self.draw_line([nose_x, mid_shoulder_x], [nose_y, mid_shoulder_y], color=_RED) + + try: + # draw line from mid-shoulder to mid-hip + lh_x, lh_y = visible["left_hip"] + rh_x, rh_y = visible["right_hip"] + except KeyError: + pass + else: + mid_hip_x, mid_hip_y = (lh_x + rh_x) / 2, (lh_y + rh_y) / 2 + self.draw_line([mid_hip_x, mid_shoulder_x], [mid_hip_y, mid_shoulder_y], color=_RED) + return self.output + + """ + Primitive drawing functions: + """ + + def draw_text( + self, + text, + position, + *, + font_size=None, + color="g", + horizontal_alignment="center", + rotation=0, + ): + """ + Args: + text (str): class label + position (tuple): a tuple of the x and y coordinates to place text on image. + font_size (int, optional): font of the text. If not provided, a font size + proportional to the image width is calculated and used. + color: color of the text. Refer to `matplotlib.colors` for full list + of formats that are accepted. + horizontal_alignment (str): see `matplotlib.text.Text` + rotation: rotation angle in degrees CCW + + Returns: + output (VisImage): image object with text drawn. + """ + if not font_size: + font_size = self._default_font_size + + # since the text background is dark, we don't want the text to be dark + color = np.maximum(list(mplc.to_rgb(color)), 0.2) + color[np.argmax(color)] = max(0.8, np.max(color)) + + x, y = position + self.output.ax.text( + x, + y, + text, + size=font_size * self.output.scale, + family="sans-serif", + bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"}, + verticalalignment="top", + horizontalalignment=horizontal_alignment, + color=color, + zorder=10, + rotation=rotation, + ) + return self.output + + def draw_box(self, box_coord, alpha=0.5, edge_color="g", line_style="-"): + """ + Args: + box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0 + are the coordinates of the image's top left corner. x1 and y1 are the + coordinates of the image's bottom right corner. + alpha (float): blending efficient. Smaller values lead to more transparent masks. + edge_color: color of the outline of the box. Refer to `matplotlib.colors` + for full list of formats that are accepted. + line_style (string): the string to use to create the outline of the boxes. + + Returns: + output (VisImage): image object with box drawn. + """ + x0, y0, x1, y1 = box_coord + width = x1 - x0 + height = y1 - y0 + + linewidth = max(self._default_font_size / 4, 1) + + self.output.ax.add_patch( + mpl.patches.Rectangle( + (x0, y0), + width, + height, + fill=False, + edgecolor=edge_color, + linewidth=linewidth * self.output.scale, + alpha=alpha, + linestyle=line_style, + ) + ) + return self.output + + def draw_rotated_box_with_label( + self, rotated_box, alpha=0.5, edge_color="g", line_style="-", label=None + ): + """ + Draw a rotated box with label on its top-left corner. + + Args: + rotated_box (tuple): a tuple containing (cnt_x, cnt_y, w, h, angle), + where cnt_x and cnt_y are the center coordinates of the box. + w and h are the width and height of the box. angle represents how + many degrees the box is rotated CCW with regard to the 0-degree box. + alpha (float): blending efficient. Smaller values lead to more transparent masks. + edge_color: color of the outline of the box. Refer to `matplotlib.colors` + for full list of formats that are accepted. + line_style (string): the string to use to create the outline of the boxes. + label (string): label for rotated box. It will not be rendered when set to None. + + Returns: + output (VisImage): image object with box drawn. + """ + cnt_x, cnt_y, w, h, angle = rotated_box + area = w * h + # use thinner lines when the box is small + linewidth = self._default_font_size / ( + 6 if area < _SMALL_OBJECT_AREA_THRESH * self.output.scale else 3 + ) + + theta = angle * math.pi / 180.0 + c = math.cos(theta) + s = math.sin(theta) + rect = [(-w / 2, h / 2), (-w / 2, -h / 2), (w / 2, -h / 2), (w / 2, h / 2)] + # x: left->right ; y: top->down + rotated_rect = [(s * yy + c * xx + cnt_x, c * yy - s * xx + cnt_y) for (xx, yy) in rect] + for k in range(4): + j = (k + 1) % 4 + self.draw_line( + [rotated_rect[k][0], rotated_rect[j][0]], + [rotated_rect[k][1], rotated_rect[j][1]], + color=edge_color, + linestyle="--" if k == 1 else line_style, + linewidth=linewidth, + ) + + if label is not None: + text_pos = rotated_rect[1] # topleft corner + + height_ratio = h / np.sqrt(self.output.height * self.output.width) + label_color = self._change_color_brightness(edge_color, brightness_factor=0.7) + font_size = ( + np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) * 0.5 * self._default_font_size + ) + self.draw_text(label, text_pos, color=label_color, font_size=font_size, rotation=angle) + + return self.output + + def draw_circle(self, circle_coord, color, radius=3): + """ + Args: + circle_coord (list(int) or tuple(int)): contains the x and y coordinates + of the center of the circle. + color: color of the polygon. Refer to `matplotlib.colors` for a full list of + formats that are accepted. + radius (int): radius of the circle. + + Returns: + output (VisImage): image object with box drawn. + """ + x, y = circle_coord + self.output.ax.add_patch( + mpl.patches.Circle(circle_coord, radius=radius, fill=True, color=color) + ) + return self.output + + def draw_line(self, x_data, y_data, color, linestyle="-", linewidth=None): + """ + Args: + x_data (list[int]): a list containing x values of all the points being drawn. + Length of list should match the length of y_data. + y_data (list[int]): a list containing y values of all the points being drawn. + Length of list should match the length of x_data. + color: color of the line. Refer to `matplotlib.colors` for a full list of + formats that are accepted. + linestyle: style of the line. Refer to `matplotlib.lines.Line2D` + for a full list of formats that are accepted. + linewidth (float or None): width of the line. When it's None, + a default value will be computed and used. + + Returns: + output (VisImage): image object with line drawn. + """ + if linewidth is None: + linewidth = self._default_font_size / 3 + linewidth = max(linewidth, 1) + self.output.ax.add_line( + mpl.lines.Line2D( + x_data, + y_data, + linewidth=linewidth * self.output.scale, + color=color, + linestyle=linestyle, + ) + ) + return self.output + + def draw_binary_mask( + self, binary_mask, color=None, *, edge_color=None, text=None, alpha=0.5, area_threshold=10 + ): + """ + Args: + binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and + W is the image width. Each value in the array is either a 0 or 1 value of uint8 + type. + color: color of the mask. Refer to `matplotlib.colors` for a full list of + formats that are accepted. If None, will pick a random color. + edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a + full list of formats that are accepted. + text (str): if None, will be drawn on the object + alpha (float): blending efficient. Smaller values lead to more transparent masks. + area_threshold (float): a connected component smaller than this area will not be shown. + + Returns: + output (VisImage): image object with mask drawn. + """ + if color is None: + color = random_color(rgb=True, maximum=1) + color = mplc.to_rgb(color) + + has_valid_segment = False + binary_mask = binary_mask.astype("uint8") # opencv needs uint8 + mask = GenericMask(binary_mask, self.output.height, self.output.width) + shape2d = (binary_mask.shape[0], binary_mask.shape[1]) + + if not mask.has_holes: + # draw polygons for regular masks + for segment in mask.polygons: + area = mask_util.area(mask_util.frPyObjects([segment], shape2d[0], shape2d[1])) + if area < (area_threshold or 0): + continue + has_valid_segment = True + segment = segment.reshape(-1, 2) + self.draw_polygon(segment, color=color, edge_color=edge_color, alpha=alpha) + else: + # TODO: Use Path/PathPatch to draw vector graphics: + # https://stackoverflow.com/questions/8919719/how-to-plot-a-complex-polygon + rgba = np.zeros(shape2d + (4,), dtype="float32") + rgba[:, :, :3] = color + rgba[:, :, 3] = (mask.mask == 1).astype("float32") * alpha + has_valid_segment = True + self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0)) + + if text is not None and has_valid_segment: + lighter_color = self._change_color_brightness(color, brightness_factor=0.7) + self._draw_text_in_mask(binary_mask, text, lighter_color) + return self.output + + def draw_soft_mask(self, soft_mask, color=None, *, text=None, alpha=0.5): + """ + Args: + soft_mask (ndarray): float array of shape (H, W), each value in [0, 1]. + color: color of the mask. Refer to `matplotlib.colors` for a full list of + formats that are accepted. If None, will pick a random color. + text (str): if None, will be drawn on the object + alpha (float): blending efficient. Smaller values lead to more transparent masks. + + Returns: + output (VisImage): image object with mask drawn. + """ + if color is None: + color = random_color(rgb=True, maximum=1) + color = mplc.to_rgb(color) + + shape2d = (soft_mask.shape[0], soft_mask.shape[1]) + rgba = np.zeros(shape2d + (4,), dtype="float32") + rgba[:, :, :3] = color + rgba[:, :, 3] = soft_mask * alpha + self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0)) + + if text is not None: + lighter_color = self._change_color_brightness(color, brightness_factor=0.7) + binary_mask = (soft_mask > 0.5).astype("uint8") + self._draw_text_in_mask(binary_mask, text, lighter_color) + return self.output + + def draw_polygon(self, segment, color, edge_color=None, alpha=0.5): + """ + Args: + segment: numpy array of shape Nx2, containing all the points in the polygon. + color: color of the polygon. Refer to `matplotlib.colors` for a full list of + formats that are accepted. + edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a + full list of formats that are accepted. If not provided, a darker shade + of the polygon color will be used instead. + alpha (float): blending efficient. Smaller values lead to more transparent masks. + + Returns: + output (VisImage): image object with polygon drawn. + """ + if edge_color is None: + # make edge color darker than the polygon color + if alpha > 0.8: + edge_color = self._change_color_brightness(color, brightness_factor=-0.7) + else: + edge_color = color + edge_color = mplc.to_rgb(edge_color) + (1,) + + polygon = mpl.patches.Polygon( + segment, + fill=True, + facecolor=mplc.to_rgb(color) + (alpha,), + edgecolor=edge_color, + linewidth=max(self._default_font_size // 15 * self.output.scale, 1), + ) + self.output.ax.add_patch(polygon) + return self.output + + """ + Internal methods: + """ + + def _jitter(self, color): + """ + Randomly modifies given color to produce a slightly different color than the color given. + + Args: + color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color + picked. The values in the list are in the [0.0, 1.0] range. + + Returns: + jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the + color after being jittered. The values in the list are in the [0.0, 1.0] range. + """ + color = mplc.to_rgb(color) + vec = np.random.rand(3) + # better to do it in another color space + vec = vec / np.linalg.norm(vec) * 0.5 + res = np.clip(vec + color, 0, 1) + return tuple(res) + + def _create_grayscale_image(self, mask=None): + """ + Create a grayscale version of the original image. + The colors in masked area, if given, will be kept. + """ + img_bw = self.img.astype("f4").mean(axis=2) + img_bw = np.stack([img_bw] * 3, axis=2) + if mask is not None: + img_bw[mask] = self.img[mask] + return img_bw + + def _change_color_brightness(self, color, brightness_factor): + """ + Depending on the brightness_factor, gives a lighter or darker color i.e. a color with + less or more saturation than the original color. + + Args: + color: color of the polygon. Refer to `matplotlib.colors` for a full list of + formats that are accepted. + brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of + 0 will correspond to no change, a factor in [-1.0, 0) range will result in + a darker color and a factor in (0, 1.0] range will result in a lighter color. + + Returns: + modified_color (tuple[double]): a tuple containing the RGB values of the + modified color. Each value in the tuple is in the [0.0, 1.0] range. + """ + assert brightness_factor >= -1.0 and brightness_factor <= 1.0 + color = mplc.to_rgb(color) + polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color)) + modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1]) + modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness + modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness + modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2]) + return modified_color + + def _convert_boxes(self, boxes): + """ + Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension. + """ + if isinstance(boxes, Boxes) or isinstance(boxes, RotatedBoxes): + return boxes.tensor.detach().numpy() + else: + return np.asarray(boxes) + + def _convert_masks(self, masks_or_polygons): + """ + Convert different format of masks or polygons to a tuple of masks and polygons. + + Returns: + list[GenericMask]: + """ + + m = masks_or_polygons + if isinstance(m, PolygonMasks): + m = m.polygons + if isinstance(m, BitMasks): + m = m.tensor.numpy() + if isinstance(m, torch.Tensor): + m = m.numpy() + ret = [] + for x in m: + if isinstance(x, GenericMask): + ret.append(x) + else: + ret.append(GenericMask(x, self.output.height, self.output.width)) + return ret + + def _draw_text_in_mask(self, binary_mask, text, color): + """ + Find proper places to draw text given a binary mask. + """ + # TODO sometimes drawn on wrong objects. the heuristics here can improve. + _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8) + if stats[1:, -1].size == 0: + return + largest_component_id = np.argmax(stats[1:, -1]) + 1 + + # draw text on the largest component, as well as other very large components. + for cid in range(1, _num_cc): + if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH: + # median is more stable than centroid + # center = centroids[largest_component_id] + center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1] + self.draw_text(text, center, color=color) + + def _convert_keypoints(self, keypoints): + if isinstance(keypoints, Keypoints): + keypoints = keypoints.tensor + keypoints = np.asarray(keypoints) + return keypoints + + def get_output(self): + """ + Returns: + output (VisImage): the image output containing the visualizations added + to the image. + """ + return self.output diff --git a/motion-gan-pipeline/preprocessing/third/mmflow/flownet2CS/flownet2cs_8x1_sfine_flyingthings3d_subset_384x768.pth b/motion-gan-pipeline/preprocessing/third/mmflow/flownet2CS/flownet2cs_8x1_sfine_flyingthings3d_subset_384x768.pth new file mode 100644 index 0000000..cf64f7f --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/mmflow/flownet2CS/flownet2cs_8x1_sfine_flyingthings3d_subset_384x768.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f97c353079f97b7801b8b4c6984703f0f0788be5543b974863e52248b771118f +size 311523031 diff --git a/motion-gan-pipeline/preprocessing/third/mmflow/flownet2CS/flownet2cs_8x1_sfine_flyingthings3d_subset_384x768.py b/motion-gan-pipeline/preprocessing/third/mmflow/flownet2CS/flownet2cs_8x1_sfine_flyingthings3d_subset_384x768.py new file mode 100644 index 0000000..4433c09 --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/mmflow/flownet2CS/flownet2cs_8x1_sfine_flyingthings3d_subset_384x768.py @@ -0,0 +1,466 @@ +FlowNetC_checkpoint = 'work_dir/upload_ready/flownet/flownetc_8x1_sfine_flyingthings3d_subset_384x768.pth' +model = dict( + type='FlowNetCSS', + flownetC=dict( + freeze_net=True, + type='FlowNetC', + encoder=dict( + type='FlowNetEncoder', + in_channels=3, + pyramid_levels=['level1', 'level2', 'level3'], + out_channels=(64, 128, 256), + kernel_size=(7, 5, 5), + strides=(2, 2, 2), + num_convs=(1, 1, 1), + dilations=(1, 1, 1), + act_cfg=dict(type='LeakyReLU', negative_slope=0.1)), + corr_level='level3', + corr_encoder=dict( + type='CorrEncoder', + in_channels=473, + pyramid_levels=['level3', 'level4', 'level5', 'level6'], + kernel_size=(3, 3, 3, 3), + num_convs=(1, 2, 2, 2), + out_channels=(256, 512, 512, 1024), + redir_in_channels=256, + redir_channels=32, + strides=(1, 2, 2, 2), + dilations=(1, 1, 1, 1), + corr_cfg=dict( + type='Correlation', + kernel_size=1, + max_displacement=10, + stride=1, + padding=0, + dilation_patch=2), + scaled=False, + conv_cfg=None, + norm_cfg=None, + act_cfg=dict(type='LeakyReLU', negative_slope=0.1)), + decoder=dict( + type='FlowNetCDecoder', + in_channels=dict( + level6=1024, level5=1026, level4=770, level3=386, level2=194), + out_channels=dict(level6=512, level5=256, level4=128, level3=64), + deconv_bias=True, + pred_bias=True, + upsample_bias=True, + norm_cfg=None, + act_cfg=dict(type='LeakyReLU', negative_slope=0.1)), + init_cfg=dict( + type='Pretrained', + checkpoint= + 'work_dir/upload_ready/flownet/flownetc_8x1_sfine_flyingthings3d_subset_384x768.pth' + ), + train_cfg=dict(), + test_cfg=dict()), + flownetS1=dict( + type='FlowNetS', + encoder=dict( + type='FlowNetEncoder', + in_channels=12, + pyramid_levels=[ + 'level1', 'level2', 'level3', 'level4', 'level5', 'level6' + ], + num_convs=(1, 1, 2, 2, 2, 2), + out_channels=(64, 128, 256, 512, 512, 1024), + kernel_size=(7, 5, (5, 3), 3, 3, 3), + strides=(2, 2, 2, 2, 2, 2), + dilations=(1, 1, 1, 1, 1, 1), + act_cfg=dict(type='LeakyReLU', negative_slope=0.1)), + decoder=dict( + type='FlowNetSDecoder', + in_channels=dict( + level6=1024, level5=1026, level4=770, level3=386, level2=194), + out_channels=dict(level6=512, level5=256, level4=128, level3=64), + deconv_bias=True, + pred_bias=True, + upsample_bias=False, + act_cfg=dict(type='LeakyReLU', negative_slope=0.1), + flow_loss=dict( + type='MultiLevelEPE', + p=2, + reduction='sum', + weights=dict( + level2=0.005, + level3=0.01, + level4=0.02, + level5=0.08, + level6=0.32))), + init_cfg=[ + dict( + type='Kaiming', + layer=['Conv2d', 'ConvTranspose2d'], + a=0.1, + mode='fan_in', + nonlinearity='leaky_relu', + bias=0), + dict(type='Constant', layer='BatchNorm2d', val=1, bias=0) + ], + train_cfg=dict(), + test_cfg=dict()), + link_cfg=dict(scale_factor=4, mode='bilinear'), + out_level='level2') +test_dataset_type = 'Sintel' +test_data_root = 'data/Sintel' +img_norm_cfg = dict( + mean=[0.0, 0.0, 0.0], std=[255.0, 255.0, 255.0], to_rgb=False) +global_transform = dict( + translates=(0.05, 0.05), + zoom=(1.0, 1.5), + shear=(0.86, 1.16), + rotate=(-10.0, 10.0)) +relative_transform = dict( + translates=(0.00375, 0.00375), + zoom=(0.985, 1.015), + shear=(1.0, 1.0), + rotate=(-1.0, 1.0)) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + type='ColorJitter', + brightness=0.5, + contrast=0.5, + saturation=0.5, + hue=0.5), + dict(type='RandomGamma', gamma_range=(0.7, 1.5)), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='GaussianNoise', sigma_range=(0, 0.04), clamp_range=(0.0, 1.0)), + dict(type='RandomFlip', prob=0.5, direction='horizontal'), + dict(type='RandomFlip', prob=0.5, direction='vertical'), + dict( + type='RandomAffine', + global_transform=dict( + translates=(0.05, 0.05), + zoom=(1.0, 1.5), + shear=(0.86, 1.16), + rotate=(-10.0, 10.0)), + relative_transform=dict( + translates=(0.00375, 0.00375), + zoom=(0.985, 1.015), + shear=(1.0, 1.0), + rotate=(-1.0, 1.0))), + dict(type='RandomCrop', crop_size=(384, 768)), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['imgs', 'flow_gt'], + meta_keys=[ + 'img_fields', 'ann_fields', 'filename1', 'filename2', + 'ori_filename1', 'ori_filename2', 'filename_flow', + 'ori_filename_flow', 'ori_shape', 'img_shape', 'img_norm_cfg' + ]) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='InputResize', exponent=6), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='TestFormatBundle'), + dict( + type='Collect', + keys=['imgs'], + meta_keys=[ + 'flow_gt', 'filename1', 'filename2', 'ori_filename1', + 'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg', + 'scale_factor', 'pad_shape' + ]) +] +flyingthings3d_subset_train = dict( + type='FlyingThings3DSubset', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + type='ColorJitter', + brightness=0.5, + contrast=0.5, + saturation=0.5, + hue=0.5), + dict(type='RandomGamma', gamma_range=(0.7, 1.5)), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict( + type='GaussianNoise', + sigma_range=(0, 0.04), + clamp_range=(0.0, 1.0)), + dict(type='RandomFlip', prob=0.5, direction='horizontal'), + dict(type='RandomFlip', prob=0.5, direction='vertical'), + dict( + type='RandomAffine', + global_transform=dict( + translates=(0.05, 0.05), + zoom=(1.0, 1.5), + shear=(0.86, 1.16), + rotate=(-10.0, 10.0)), + relative_transform=dict( + translates=(0.00375, 0.00375), + zoom=(0.985, 1.015), + shear=(1.0, 1.0), + rotate=(-1.0, 1.0))), + dict(type='RandomCrop', crop_size=(384, 768)), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['imgs', 'flow_gt'], + meta_keys=[ + 'img_fields', 'ann_fields', 'filename1', 'filename2', + 'ori_filename1', 'ori_filename2', 'filename_flow', + 'ori_filename_flow', 'ori_shape', 'img_shape', 'img_norm_cfg' + ]) + ], + data_root='data/FlyingThings3D_subset', + test_mode=False, + direction='forward', + scene='left') +test_data_cleanpass = dict( + type='Sintel', + data_root='data/Sintel', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='InputResize', exponent=6), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='TestFormatBundle'), + dict( + type='Collect', + keys=['imgs'], + meta_keys=[ + 'flow_gt', 'filename1', 'filename2', 'ori_filename1', + 'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg', + 'scale_factor', 'pad_shape' + ]) + ], + test_mode=True, + pass_style='clean') +test_data_finalpass = dict( + type='Sintel', + data_root='data/Sintel', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='InputResize', exponent=6), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='TestFormatBundle'), + dict( + type='Collect', + keys=['imgs'], + meta_keys=[ + 'flow_gt', 'filename1', 'filename2', 'ori_filename1', + 'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg', + 'scale_factor', 'pad_shape' + ]) + ], + test_mode=True, + pass_style='final') +data = dict( + train_dataloader=dict( + samples_per_gpu=1, + workers_per_gpu=5, + drop_last=True, + persistent_workers=True), + val_dataloader=dict(samples_per_gpu=1, workers_per_gpu=5, shuffle=False), + test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=5, shuffle=False), + train=dict( + type='FlyingThings3DSubset', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + type='ColorJitter', + brightness=0.5, + contrast=0.5, + saturation=0.5, + hue=0.5), + dict(type='RandomGamma', gamma_range=(0.7, 1.5)), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict( + type='GaussianNoise', + sigma_range=(0, 0.04), + clamp_range=(0.0, 1.0)), + dict(type='RandomFlip', prob=0.5, direction='horizontal'), + dict(type='RandomFlip', prob=0.5, direction='vertical'), + dict( + type='RandomAffine', + global_transform=dict( + translates=(0.05, 0.05), + zoom=(1.0, 1.5), + shear=(0.86, 1.16), + rotate=(-10.0, 10.0)), + relative_transform=dict( + translates=(0.00375, 0.00375), + zoom=(0.985, 1.015), + shear=(1.0, 1.0), + rotate=(-1.0, 1.0))), + dict(type='RandomCrop', crop_size=(384, 768)), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['imgs', 'flow_gt'], + meta_keys=[ + 'img_fields', 'ann_fields', 'filename1', 'filename2', + 'ori_filename1', 'ori_filename2', 'filename_flow', + 'ori_filename_flow', 'ori_shape', 'img_shape', + 'img_norm_cfg' + ]) + ], + data_root='data/FlyingThings3D_subset', + test_mode=False, + direction='forward', + scene='left'), + val=dict( + type='ConcatDataset', + datasets=[ + dict( + type='Sintel', + data_root='data/Sintel', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='InputResize', exponent=6), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='TestFormatBundle'), + dict( + type='Collect', + keys=['imgs'], + meta_keys=[ + 'flow_gt', 'filename1', 'filename2', + 'ori_filename1', 'ori_filename2', 'ori_shape', + 'img_shape', 'img_norm_cfg', 'scale_factor', + 'pad_shape' + ]) + ], + test_mode=True, + pass_style='clean'), + dict( + type='Sintel', + data_root='data/Sintel', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='InputResize', exponent=6), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='TestFormatBundle'), + dict( + type='Collect', + keys=['imgs'], + meta_keys=[ + 'flow_gt', 'filename1', 'filename2', + 'ori_filename1', 'ori_filename2', 'ori_shape', + 'img_shape', 'img_norm_cfg', 'scale_factor', + 'pad_shape' + ]) + ], + test_mode=True, + pass_style='final') + ], + separate_eval=True), + test=dict( + type='ConcatDataset', + datasets=[ + dict( + type='Sintel', + data_root='data/Sintel', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='InputResize', exponent=6), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='TestFormatBundle'), + dict( + type='Collect', + keys=['imgs'], + meta_keys=[ + 'flow_gt', 'filename1', 'filename2', + 'ori_filename1', 'ori_filename2', 'ori_shape', + 'img_shape', 'img_norm_cfg', 'scale_factor', + 'pad_shape' + ]) + ], + test_mode=True, + pass_style='clean'), + dict( + type='Sintel', + data_root='data/Sintel', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='InputResize', exponent=6), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='TestFormatBundle'), + dict( + type='Collect', + keys=['imgs'], + meta_keys=[ + 'flow_gt', 'filename1', 'filename2', + 'ori_filename1', 'ori_filename2', 'ori_shape', + 'img_shape', 'img_norm_cfg', 'scale_factor', + 'pad_shape' + ]) + ], + test_mode=True, + pass_style='final') + ], + separate_eval=True)) +optimizer = dict( + type='Adam', lr=1e-05, weight_decay=0.0004, betas=(0.9, 0.999)) +optimizer_config = dict(grad_clip=None) +lr_config = dict( + policy='step', + by_epoch=False, + gamma=0.5, + step=[200000, 300000, 400000, 500000]) +runner = dict(type='IterBasedRunner', max_iters=600000) +checkpoint_config = dict(by_epoch=False, interval=50000) +evaluation = dict(interval=50000, metric='EPE') +log_config = dict( + interval=50, + hooks=[dict(type='TextLoggerHook'), + dict(type='TensorboardLoggerHook')]) +dist_params = dict(backend='nccl') +log_level = 'INFO' +load_from = 'work_dir/cs_c2/latest.pth' +resume_from = None +workflow = [('train', 1)] +work_dir = 'work_dir/cs_t2' +gpu_ids = range(0, 1) diff --git a/motion-gan-pipeline/preprocessing/third/mmflow/pwcnet/pwcnet_ft_4x1_300k_sintel_final_384x768.pth b/motion-gan-pipeline/preprocessing/third/mmflow/pwcnet/pwcnet_ft_4x1_300k_sintel_final_384x768.pth new file mode 100644 index 0000000..1385a0e --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/mmflow/pwcnet/pwcnet_ft_4x1_300k_sintel_final_384x768.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:48b53f3900969f13bd522e98c7ac8990850daa8a59312f01ed41a4cc31c0422a +size 112646841 diff --git a/motion-gan-pipeline/preprocessing/third/mmflow/pwcnet/pwcnet_ft_4x1_300k_sintel_final_384x768.py b/motion-gan-pipeline/preprocessing/third/mmflow/pwcnet/pwcnet_ft_4x1_300k_sintel_final_384x768.py new file mode 100644 index 0000000..f0360fd --- /dev/null +++ b/motion-gan-pipeline/preprocessing/third/mmflow/pwcnet/pwcnet_ft_4x1_300k_sintel_final_384x768.py @@ -0,0 +1,409 @@ +dataset_type = 'Sintel' +data_root = 'data/Sintel' +img_norm_cfg = dict( + mean=[0.0, 0.0, 0.0], std=[255.0, 255.0, 255.0], to_rgb=False) +crop_size = (384, 768) +global_transform = dict( + translates=(0.05, 0.05), + zoom=(1.0, 1.5), + shear=(0.86, 1.16), + rotate=(-10.0, 10.0)) +relative_transform = dict( + translates=(0.00375, 0.00375), + zoom=(0.985, 1.015), + shear=(1.0, 1.0), + rotate=(-1.0, 1.0)) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_occ=True), + dict( + type='ColorJitter', + brightness=0.5, + contrast=0.5, + saturation=0.5, + hue=0.5), + dict(type='RandomGamma', gamma_range=(0.7, 1.5)), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='RandomFlip', prob=0.5, direction='horizontal'), + dict(type='RandomFlip', prob=0.5, direction='vertical'), + dict( + type='RandomAffine', + global_transform=dict( + translates=(0.05, 0.05), + zoom=(1.0, 1.5), + shear=(0.86, 1.16), + rotate=(-10.0, 10.0)), + relative_transform=dict( + translates=(0.00375, 0.00375), + zoom=(0.985, 1.015), + shear=(1.0, 1.0), + rotate=(-1.0, 1.0))), + dict(type='RandomCrop', crop_size=(384, 768)), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['imgs', 'flow_gt'], + meta_keys=[ + 'img_fields', 'ann_fields', 'filename1', 'filename2', + 'ori_filename1', 'ori_filename2', 'filename_flow', + 'ori_filename_flow', 'ori_shape', 'img_shape', 'img_norm_cfg' + ]) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='InputResize', exponent=6), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='TestFormatBundle'), + dict( + type='Collect', + keys=['imgs'], + meta_keys=[ + 'flow_gt', 'filename1', 'filename2', 'ori_filename1', + 'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg', + 'scale_factor', 'pad_shape' + ]) +] +sintel_final_train = dict( + type='Sintel', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_occ=True), + dict( + type='ColorJitter', + brightness=0.5, + contrast=0.5, + saturation=0.5, + hue=0.5), + dict(type='RandomGamma', gamma_range=(0.7, 1.5)), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='RandomFlip', prob=0.5, direction='horizontal'), + dict(type='RandomFlip', prob=0.5, direction='vertical'), + dict( + type='RandomAffine', + global_transform=dict( + translates=(0.05, 0.05), + zoom=(1.0, 1.5), + shear=(0.86, 1.16), + rotate=(-10.0, 10.0)), + relative_transform=dict( + translates=(0.00375, 0.00375), + zoom=(0.985, 1.015), + shear=(1.0, 1.0), + rotate=(-1.0, 1.0))), + dict(type='RandomCrop', crop_size=(384, 768)), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['imgs', 'flow_gt'], + meta_keys=[ + 'img_fields', 'ann_fields', 'filename1', 'filename2', + 'ori_filename1', 'ori_filename2', 'filename_flow', + 'ori_filename_flow', 'ori_shape', 'img_shape', 'img_norm_cfg' + ]) + ], + data_root='data/Sintel', + test_mode=False, + pass_style='final') +sintel_clean_test = dict( + type='Sintel', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='InputResize', exponent=6), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='TestFormatBundle'), + dict( + type='Collect', + keys=['imgs'], + meta_keys=[ + 'flow_gt', 'filename1', 'filename2', 'ori_filename1', + 'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg', + 'scale_factor', 'pad_shape' + ]) + ], + data_root='data/Sintel', + test_mode=True, + pass_style='clean') +sintel_final_test = dict( + type='Sintel', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='InputResize', exponent=6), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='TestFormatBundle'), + dict( + type='Collect', + keys=['imgs'], + meta_keys=[ + 'flow_gt', 'filename1', 'filename2', 'ori_filename1', + 'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg', + 'scale_factor', 'pad_shape' + ]) + ], + data_root='data/Sintel', + test_mode=True, + pass_style='final') +data = dict( + train_dataloader=dict( + samples_per_gpu=1, + workers_per_gpu=5, + drop_last=True, + persistent_workers=True), + val_dataloader=dict( + samples_per_gpu=1, + workers_per_gpu=5, + shuffle=False, + persistent_workers=True), + test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=5, shuffle=False), + train=[ + dict( + type='Sintel', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_occ=True), + dict( + type='ColorJitter', + brightness=0.5, + contrast=0.5, + saturation=0.5, + hue=0.5), + dict(type='RandomGamma', gamma_range=(0.7, 1.5)), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='RandomFlip', prob=0.5, direction='horizontal'), + dict(type='RandomFlip', prob=0.5, direction='vertical'), + dict( + type='RandomAffine', + global_transform=dict( + translates=(0.05, 0.05), + zoom=(1.0, 1.5), + shear=(0.86, 1.16), + rotate=(-10.0, 10.0)), + relative_transform=dict( + translates=(0.00375, 0.00375), + zoom=(0.985, 1.015), + shear=(1.0, 1.0), + rotate=(-1.0, 1.0))), + dict(type='RandomCrop', crop_size=(384, 768)), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['imgs', 'flow_gt'], + meta_keys=[ + 'img_fields', 'ann_fields', 'filename1', 'filename2', + 'ori_filename1', 'ori_filename2', 'filename_flow', + 'ori_filename_flow', 'ori_shape', 'img_shape', + 'img_norm_cfg' + ]) + ], + data_root='data/Sintel', + test_mode=False, + pass_style='final') + ], + val=dict( + type='ConcatDataset', + datasets=[ + dict( + type='Sintel', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='InputResize', exponent=6), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='TestFormatBundle'), + dict( + type='Collect', + keys=['imgs'], + meta_keys=[ + 'flow_gt', 'filename1', 'filename2', + 'ori_filename1', 'ori_filename2', 'ori_shape', + 'img_shape', 'img_norm_cfg', 'scale_factor', + 'pad_shape' + ]) + ], + data_root='data/Sintel', + test_mode=True, + pass_style='clean'), + dict( + type='Sintel', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='InputResize', exponent=6), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='TestFormatBundle'), + dict( + type='Collect', + keys=['imgs'], + meta_keys=[ + 'flow_gt', 'filename1', 'filename2', + 'ori_filename1', 'ori_filename2', 'ori_shape', + 'img_shape', 'img_norm_cfg', 'scale_factor', + 'pad_shape' + ]) + ], + data_root='data/Sintel', + test_mode=True, + pass_style='final') + ], + separate_eval=True), + test=dict( + type='ConcatDataset', + datasets=[ + dict( + type='Sintel', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='InputResize', exponent=6), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='TestFormatBundle'), + dict( + type='Collect', + keys=['imgs'], + meta_keys=[ + 'flow_gt', 'filename1', 'filename2', + 'ori_filename1', 'ori_filename2', 'ori_shape', + 'img_shape', 'img_norm_cfg', 'scale_factor', + 'pad_shape' + ]) + ], + data_root='data/Sintel', + test_mode=True, + pass_style='clean'), + dict( + type='Sintel', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict(type='InputResize', exponent=6), + dict( + type='Normalize', + mean=[0.0, 0.0, 0.0], + std=[255.0, 255.0, 255.0], + to_rgb=False), + dict(type='TestFormatBundle'), + dict( + type='Collect', + keys=['imgs'], + meta_keys=[ + 'flow_gt', 'filename1', 'filename2', + 'ori_filename1', 'ori_filename2', 'ori_shape', + 'img_shape', 'img_norm_cfg', 'scale_factor', + 'pad_shape' + ]) + ], + data_root='data/Sintel', + test_mode=True, + pass_style='final') + ], + separate_eval=True)) +optimizer = dict( + type='Adam', lr=3e-05, weight_decay=0.0004, betas=(0.9, 0.999)) +optimizer_config = dict(grad_clip=None) +lr_config = dict( + policy='MultiStage', + by_epoch=False, + gammas=[0.5, 0.5], + milestone_lrs=[3e-05, 2e-05], + milestone_iters=[0, 150000], + steps=[[ + 45000, 65000, 85000, 95000, 97500, 100000, 110000, 120000, 130000, + 140000 + ], + [ + 195000, 215000, 235000, 245000, 247500, 250000, 260000, 270000, + 280000, 290000 + ]]) +runner = dict(type='IterBasedRunner', max_iters=300000) +checkpoint_config = dict(by_epoch=False, interval=50000) +evaluation = dict(interval=50000, metric='EPE') +log_config = dict( + interval=50, + hooks=[dict(type='TextLoggerHook'), + dict(type='TensorboardLoggerHook')]) +dist_params = dict(backend='nccl') +log_level = 'INFO' +load_from = 'https://download.openmmlab.com/mmflow/pwcnet/pwcnet_ft_4x1_300k_sintel_384x768.pth' +resume_from = None +workflow = [('train', 1)] +model = dict( + type='PWCNet', + encoder=dict( + type='PWCNetEncoder', + in_channels=3, + net_type='Basic', + pyramid_levels=[ + 'level1', 'level2', 'level3', 'level4', 'level5', 'level6' + ], + out_channels=(16, 32, 64, 96, 128, 196), + strides=(2, 2, 2, 2, 2, 2), + dilations=(1, 1, 1, 1, 1, 1), + act_cfg=dict(type='LeakyReLU', negative_slope=0.1)), + decoder=dict( + type='PWCNetDecoder', + in_channels=dict( + level6=81, level5=213, level4=181, level3=149, level2=117), + flow_div=20.0, + corr_cfg=dict(type='Correlation', max_displacement=4, padding=0), + warp_cfg=dict(type='Warp', align_corners=True, use_mask=True), + act_cfg=dict(type='LeakyReLU', negative_slope=0.1), + scaled=False, + post_processor=dict(type='ContextNet', in_channels=565), + flow_loss=dict( + type='MultiLevelEPE', + p=1, + q=0.4, + eps=0.01, + reduction='sum', + weights=dict( + level2=0.005, + level3=0.01, + level4=0.02, + level5=0.08, + level6=0.32))), + train_cfg=dict(), + test_cfg=dict(), + init_cfg=dict( + type='Kaiming', + nonlinearity='leaky_relu', + layer=['Conv2d', 'ConvTranspose2d'], + mode='fan_in', + bias=0)) diff --git a/requirements.api.txt b/requirements.api.txt new file mode 100644 index 0000000..25810f0 --- /dev/null +++ b/requirements.api.txt @@ -0,0 +1,7 @@ +tinydb~=4.7.1 +python-multipart~=0.0.6 +httpx~=0.23.1 +chardet~=5.1.0 +fastapi~=0.95.0 + +git+https://github.com/mediatechnologycenter/api-utils.git@0.1.0 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..6125a92 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,36 @@ +wavio +soundfile +opencv-python +scikit-image +tqdm +noisereduce +resampy +mediapipe +matplotlib +h5py +face_alignment +python_speech_features +moviepy +pydub +chumpy +av +pims +dominate +setuptools==59.5.0 +configargparse +wandb +pytorch-ignite +basicsr +facexlib +basicsr>=1.4.2 +facexlib>=0.2.5 +lmdb +pyyaml +scipy +tb-nightly +yapf +realesrgan +pytorch-fid +# tensorflow-gpu==1.15 +ninja +# git+https://github.com/facebookresearch/pytorch3d.git@stable \ No newline at end of file diff --git a/requirements_deepspeech.txt b/requirements_deepspeech.txt new file mode 100644 index 0000000..c7a94de --- /dev/null +++ b/requirements_deepspeech.txt @@ -0,0 +1,37 @@ +soundfile +librosa +wavio +opencv-python +scikit-image +tqdm +noisereduce +resampy +mediapipe +matplotlib +h5py +face_alignment +python_speech_features +moviepy +pydub +chumpy +av +pims +dominate +setuptools==59.5.0 +configargparse +wandb +pytorch-ignite +basicsr +facexlib +basicsr>=1.4.2 +facexlib>=0.2.5 +lmdb +pyyaml +scipy +tb-nightly +yapf +realesrgan +# pytorch-fid +tensorflow-gpu==1.15 +ninja +torchvision \ No newline at end of file