CV/Using VFP Pretrained Models.ipynb

{
 "nbformat": 4,
 "nbformat_minor": 0,
 "metadata": {
  "colab": {
   "name": "““Copy of Using VFP pretrained models”的副本”的副本",
   "provenance": [],
   "collapsed_sections": []
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "language_info": {
   "name": "python"
  },
  "accelerator": "GPU"
 },
 "cells": [
  {
   "cell_type": "code",
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive', force_remount=True)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XrMb4_HUly-E",
    "outputId": "188f6c76-a2ba-4bf9-f084-2ab8bdfb46ac",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "STW7GcJs6DNk",
    "outputId": "fbdc64d0-9707-4860-b942-59849375e030",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
      "Collecting torch==1.8.0+cu111\n",
      "  Downloading https://download.pytorch.org/whl/cu111/torch-1.8.0%2Bcu111-cp37-cp37m-linux_x86_64.whl (1982.2 MB)\n",
      "\u001B[K     |█████████████▌                  | 834.1 MB 1.2 MB/s eta 0:15:26tcmalloc: large alloc 1147494400 bytes == 0x3abb0000 @  0x7f4401b35615 0x592b76 0x4df71e 0x59afff 0x515655 0x549576 0x593fce 0x548ae9 0x51566f 0x549576 0x593fce 0x548ae9 0x5127f1 0x598e3b 0x511f68 0x598e3b 0x511f68 0x598e3b 0x511f68 0x4bc98a 0x532e76 0x594b72 0x515600 0x549576 0x593fce 0x548ae9 0x5127f1 0x549576 0x593fce 0x5118f8 0x593dd7\n",
      "\u001B[K     |█████████████████               | 1055.7 MB 1.3 MB/s eta 0:11:37tcmalloc: large alloc 1434370048 bytes == 0x7f206000 @  0x7f4401b35615 0x592b76 0x4df71e 0x59afff 0x515655 0x549576 0x593fce 0x548ae9 0x51566f 0x549576 0x593fce 0x548ae9 0x5127f1 0x598e3b 0x511f68 0x598e3b 0x511f68 0x598e3b 0x511f68 0x4bc98a 0x532e76 0x594b72 0x515600 0x549576 0x593fce 0x548ae9 0x5127f1 0x549576 0x593fce 0x5118f8 0x593dd7\n",
      "\u001B[K     |█████████████████████▋          | 1336.2 MB 1.3 MB/s eta 0:08:20tcmalloc: large alloc 1792966656 bytes == 0x4038000 @  0x7f4401b35615 0x592b76 0x4df71e 0x59afff 0x515655 0x549576 0x593fce 0x548ae9 0x51566f 0x549576 0x593fce 0x548ae9 0x5127f1 0x598e3b 0x511f68 0x598e3b 0x511f68 0x598e3b 0x511f68 0x4bc98a 0x532e76 0x594b72 0x515600 0x549576 0x593fce 0x548ae9 0x5127f1 0x549576 0x593fce 0x5118f8 0x593dd7\n",
      "\u001B[K     |███████████████████████████▎    | 1691.1 MB 1.2 MB/s eta 0:04:00tcmalloc: large alloc 2241208320 bytes == 0x6ee20000 @  0x7f4401b35615 0x592b76 0x4df71e 0x59afff 0x515655 0x549576 0x593fce 0x548ae9 0x51566f 0x549576 0x593fce 0x548ae9 0x5127f1 0x598e3b 0x511f68 0x598e3b 0x511f68 0x598e3b 0x511f68 0x4bc98a 0x532e76 0x594b72 0x515600 0x549576 0x593fce 0x548ae9 0x5127f1 0x549576 0x593fce 0x5118f8 0x593dd7\n",
      "\u001B[K     |████████████████████████████████| 1982.2 MB 1.2 MB/s eta 0:00:01tcmalloc: large alloc 1982251008 bytes == 0xf4782000 @  0x7f4401b341e7 0x4a3940 0x4a39cc 0x592b76 0x4df71e 0x59afff 0x515655 0x549576 0x593fce 0x511e2c 0x549576 0x593fce 0x511e2c 0x549576 0x593fce 0x511e2c 0x549576 0x593fce 0x511e2c 0x549576 0x593fce 0x511e2c 0x593dd7 0x511e2c 0x549576 0x593fce 0x548ae9 0x5127f1 0x549576 0x593fce 0x548ae9\n",
      "tcmalloc: large alloc 2477817856 bytes == 0x16a9ee000 @  0x7f4401b35615 0x592b76 0x4df71e 0x59afff 0x515655 0x549576 0x593fce 0x511e2c 0x549576 0x593fce 0x511e2c 0x549576 0x593fce 0x511e2c 0x549576 0x593fce 0x511e2c 0x549576 0x593fce 0x511e2c 0x593dd7 0x511e2c 0x549576 0x593fce 0x548ae9 0x5127f1 0x549576 0x593fce 0x548ae9 0x5127f1 0x549576\n",
      "\u001B[K     |████████████████████████████████| 1982.2 MB 5.3 kB/s \n",
      "\u001B[?25hCollecting torchvision==0.9.0+cu111\n",
      "  Downloading https://download.pytorch.org/whl/cu111/torchvision-0.9.0%2Bcu111-cp37-cp37m-linux_x86_64.whl (17.6 MB)\n",
      "\u001B[K     |████████████████████████████████| 17.6 MB 1.4 MB/s \n",
      "\u001B[?25hCollecting torchaudio==0.8.0\n",
      "  Downloading torchaudio-0.8.0-cp37-cp37m-manylinux1_x86_64.whl (1.9 MB)\n",
      "\u001B[K     |████████████████████████████████| 1.9 MB 4.8 MB/s \n",
      "\u001B[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch==1.8.0+cu111) (1.21.6)\n",
      "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.8.0+cu111) (4.2.0)\n",
      "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision==0.9.0+cu111) (7.1.2)\n",
      "Installing collected packages: torch, torchvision, torchaudio\n",
      "  Attempting uninstall: torch\n",
      "    Found existing installation: torch 1.11.0+cu113\n",
      "    Uninstalling torch-1.11.0+cu113:\n",
      "      Successfully uninstalled torch-1.11.0+cu113\n",
      "  Attempting uninstall: torchvision\n",
      "    Found existing installation: torchvision 0.12.0+cu113\n",
      "    Uninstalling torchvision-0.12.0+cu113:\n",
      "      Successfully uninstalled torchvision-0.12.0+cu113\n",
      "  Attempting uninstall: torchaudio\n",
      "    Found existing installation: torchaudio 0.11.0+cu113\n",
      "    Uninstalling torchaudio-0.11.0+cu113:\n",
      "      Successfully uninstalled torchaudio-0.11.0+cu113\n",
      "\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "torchtext 0.12.0 requires torch==1.11.0, but you have torch 1.8.0+cu111 which is incompatible.\u001B[0m\n",
      "Successfully installed torch-1.8.0+cu111 torchaudio-0.8.0 torchvision-0.9.0+cu111\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!pip install openmim\n",
    "!mim install mmdet\n",
    "!pip install future tensorboard\n",
    "!pip install munch tqdm xmltodict seaborn"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7ac9NtuB6bRz",
    "outputId": "4fd0fc9e-b962-4600-dae7-a1e509a35def",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting openmim\n",
      "  Downloading openmim-0.1.5.tar.gz (35 kB)\n",
      "Requirement already satisfied: Click==7.1.2 in /usr/local/lib/python3.7/dist-packages (from openmim) (7.1.2)\n",
      "Collecting colorama\n",
      "  Downloading colorama-0.4.4-py2.py3-none-any.whl (16 kB)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from openmim) (2.23.0)\n",
      "Collecting model-index\n",
      "  Downloading model_index-0.1.11-py3-none-any.whl (34 kB)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from openmim) (1.3.5)\n",
      "Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from openmim) (0.8.9)\n",
      "Collecting ordered-set\n",
      "  Downloading ordered_set-4.1.0-py3-none-any.whl (7.6 kB)\n",
      "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from model-index->openmim) (3.13)\n",
      "Requirement already satisfied: markdown in /usr/local/lib/python3.7/dist-packages (from model-index->openmim) (3.3.7)\n",
      "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown->model-index->openmim) (4.11.4)\n",
      "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown->model-index->openmim) (4.2.0)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown->model-index->openmim) (3.8.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->openmim) (2022.1)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas->openmim) (1.21.6)\n",
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      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->openmim) (1.15.0)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->openmim) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->openmim) (2022.5.18.1)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->openmim) (1.24.3)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->openmim) (2.10)\n",
      "Building wheels for collected packages: openmim\n",
      "  Building wheel for openmim (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "  Created wheel for openmim: filename=openmim-0.1.5-py2.py3-none-any.whl size=42503 sha256=1bfbf811608489104ba12e52f9c7102320ade6056287beca34d5d1bfcb322464\n",
      "  Stored in directory: /root/.cache/pip/wheels/16/8b/e1/bdebbbc687aa50224a5ce46fe97a040a0c59f92b34bfc750b6\n",
      "Successfully built openmim\n",
      "Installing collected packages: ordered-set, model-index, colorama, openmim\n",
      "Successfully installed colorama-0.4.4 model-index-0.1.11 openmim-0.1.5 ordered-set-4.1.0\n",
      "installing mmdet from https://github.com/open-mmlab/mmdetection.git.\n",
      "Cloning into '/tmp/tmpebrtuub7/mmdetection'...\n",
      "remote: Enumerating objects: 24921, done.\u001B[K\n",
      "remote: Counting objects: 100% (5/5), done.\u001B[K\n",
      "remote: Compressing objects: 100% (5/5), done.\u001B[K\n",
      "remote: Total 24921 (delta 0), reused 4 (delta 0), pack-reused 24916\u001B[K\n",
      "Receiving objects: 100% (24921/24921), 37.75 MiB | 33.04 MiB/s, done.\n",
      "Resolving deltas: 100% (17467/17467), done.\n",
      "Note: checking out 'ca11860f4f3c3ca2ce8340e2686eeaec05b29111'.\n",
      "\n",
      "You are in 'detached HEAD' state. You can look around, make experimental\n",
      "changes and commit them, and you can discard any commits you make in this\n",
      "state without impacting any branches by performing another checkout.\n",
      "\n",
      "If you want to create a new branch to retain commits you create, you may\n",
      "do so (now or later) by using -b with the checkout command again. Example:\n",
      "\n",
      "  git checkout -b <new-branch-name>\n",
      "\n",
      "\u001B[32minstalling dependency: mmcv-full\u001B[0m\n",
      "installing mmcv-full from wheel.\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Looking in links: https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html\n",
      "Collecting mmcv-full==1.5.2\n",
      "  Downloading https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/mmcv_full-1.5.2-cp37-cp37m-manylinux1_x86_64.whl (44.9 MB)\n",
      "\u001B[K     |████████████████████████████████| 44.9 MB 9.0 MB/s \n",
      "\u001B[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcv-full==1.5.2) (1.21.6)\n",
      "Collecting addict\n",
      "  Downloading addict-2.4.0-py3-none-any.whl (3.8 kB)\n",
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      "  Downloading yapf-0.32.0-py2.py3-none-any.whl (190 kB)\n",
      "\u001B[K     |████████████████████████████████| 190 kB 5.0 MB/s \n",
      "\u001B[?25hRequirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv-full==1.5.2) (7.1.2)\n",
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      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->mmcv-full==1.5.2) (3.0.9)\n",
      "Installing collected packages: yapf, addict, mmcv-full\n",
      "Successfully installed addict-2.4.0 mmcv-full-1.5.2 yapf-0.32.0\n",
      "\u001B[32mSuccessfully installed mmcv-full.\u001B[0m\n",
      "\u001B[32mSuccessfully installed dependencies.\u001B[0m\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Requirement already satisfied: cython in /usr/local/lib/python3.7/dist-packages (from -r /tmp/tmpebrtuub7/mmdetection/requirements/build.txt (line 2)) (0.29.30)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from -r /tmp/tmpebrtuub7/mmdetection/requirements/build.txt (line 3)) (1.21.6)\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Processing /tmp/tmpebrtuub7/mmdetection\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mmdet==2.25.0) (3.2.2)\n",
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      "Collecting terminaltables\n",
      "  Downloading terminaltables-3.1.10-py2.py3-none-any.whl (15 kB)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmdet==2.25.0) (1.4.2)\n",
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      "Building wheels for collected packages: mmdet\n",
      "  Building wheel for mmdet (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "  Created wheel for mmdet: filename=mmdet-2.25.0-py3-none-any.whl size=1440375 sha256=94d1853d9a2f760cd225c03ad606e30d6fae53774a730d1fb7c96fe8f8801fb2\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-pmb62ry0/wheels/4c/e3/a6/c13cbf899fac388b3594bdfcef6f4a14d3346cf2230745e7e6\n",
      "Successfully built mmdet\n",
      "Installing collected packages: terminaltables, mmdet\n",
      "Successfully installed mmdet-2.25.0 terminaltables-3.1.10\n",
      "\u001B[32mSuccessfully installed mmdet.\u001B[0m\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (0.16.0)\n",
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      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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      "Installing collected packages: xmltodict, munch\n",
      "Successfully installed munch-2.5.0 xmltodict-0.13.0\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!git clone https://github.com/DASH-Lab/VFP290K"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3mD1MhYy7Z0r",
    "outputId": "a7276d17-6785-416e-c473-2009f8b89e00",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Cloning into 'VFP290K'...\n",
      "remote: Enumerating objects: 1996, done.\u001B[K\n",
      "remote: Counting objects: 100% (879/879), done.\u001B[K\n",
      "remote: Compressing objects: 100% (390/390), done.\u001B[K\n",
      "remote: Total 1996 (delta 482), reused 860 (delta 468), pack-reused 1117\u001B[K\n",
      "Receiving objects: 100% (1996/1996), 30.45 MiB | 34.80 MiB/s, done.\n",
      "Resolving deltas: 100% (755/755), done.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!pip3 install openmim\n",
    "!mim install mmcv-full\n",
    "!git clone https://github.com/open-mmlab/mmdetection.git\n",
    "%cd mmdetection\n",
    "!pip install -e .\n",
    "%cd .."
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aMvxSWos7v5q",
    "outputId": "9c313121-da80-44f7-cdfc-ca17212f4196",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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      "\u001B[33mmmcv-full==1.5.2 existed.\u001B[0m\n",
      "Cloning into 'mmdetection'...\n",
      "remote: Enumerating objects: 24921, done.\u001B[K\n",
      "remote: Counting objects: 100% (5/5), done.\u001B[K\n",
      "remote: Compressing objects: 100% (5/5), done.\u001B[K\n",
      "remote: Total 24921 (delta 0), reused 4 (delta 0), pack-reused 24916\u001B[K\n",
      "Receiving objects: 100% (24921/24921), 37.75 MiB | 21.41 MiB/s, done.\n",
      "Resolving deltas: 100% (17467/17467), done.\n",
      "/content/mmdetection\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Obtaining file:///content/mmdetection\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mmdet==2.25.0) (3.2.2)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmdet==2.25.0) (1.21.6)\n",
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      "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->mmdet==2.25.0) (4.2.0)\n",
      "Installing collected packages: mmdet\n",
      "  Attempting uninstall: mmdet\n",
      "    Found existing installation: mmdet 2.25.0\n",
      "    Uninstalling mmdet-2.25.0:\n",
      "      Successfully uninstalled mmdet-2.25.0\n",
      "  Running setup.py develop for mmdet\n",
      "Successfully installed mmdet-2.25.0\n",
      "/content\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!unzip \"/content/drive/MyDrive/TIL 2022/CV/CV_data_with_egg2.zip\" -d /content"
   ],
   "metadata": {
    "id": "p1wO5ePu9giR",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "8315dcc0-9b44-4c4e-eec6-4db418f2c678",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
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     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import json\n",
    "from copy import deepcopy\n",
    "from pathlib import Path\n",
    "\n",
    "train_data = {\n",
    "  \"licenses\": [\n",
    "    {\n",
    "      \"name\": \"\",\n",
    "      \"id\": 0,\n",
    "      \"url\": \"\"\n",
    "    }\n",
    "  ],\n",
    "  \"info\": {\n",
    "    \"contributor\": \"\",\n",
    "    \"date_created\": \"\",\n",
    "    \"description\": \"\",\n",
    "    \"url\": \"\",\n",
    "    \"version\": \"\",\n",
    "    \"year\": \"\"\n",
    "  },\n",
    "  \"categories\": [\n",
    "    {\n",
    "      \"id\": 1,\n",
    "      \"name\": \"fallen\",\n",
    "      \"supercategory\": \"\"\n",
    "    },\n",
    "    {\n",
    "      \"id\": 2,\n",
    "      \"name\": \"standing\",\n",
    "      \"supercategory\": \"\"\n",
    "    }\n",
    "  ],\n",
    "  \"images\": [],\n",
    "  \"annotations\": []\n",
    "}\n",
    "val_data = deepcopy(train_data)\n",
    "\n",
    "with open(\"/content/CV_data/train_egg2_merged.json\") as all_data_json:\n",
    "  all_data = json.load(all_data_json)\n",
    "\n",
    "filename_to_img = {img[\"file_name\"]: img for img in all_data[\"images\"]}\n",
    "img_id_to_annotations = {}\n",
    "for annotation in all_data[\"annotations\"]:\n",
    "  if annotation[\"image_id\"] not in img_id_to_annotations:\n",
    "    img_id_to_annotations[annotation[\"image_id\"]] = []\n",
    "  img_id_to_annotations[annotation[\"image_id\"]].append(annotation)\n",
    "\n",
    "for train_img_path in Path(\"/content/CV_data/train/images\").glob(\"*\"):\n",
    "  train_data[\"images\"].append(filename_to_img[train_img_path.name])\n",
    "  train_data[\"annotations\"].extend(img_id_to_annotations[filename_to_img[train_img_path.name][\"id\"]])\n",
    "\n",
    "for val_img_path in Path(\"/content/CV_data/val/images\").glob(\"*\"):\n",
    "  val_data[\"images\"].append(filename_to_img[val_img_path.name])\n",
    "  val_data[\"annotations\"].extend(img_id_to_annotations[filename_to_img[val_img_path.name][\"id\"]])\n",
    "\n",
    "with open(\"/content/CV_data/train/vfp_train.json\", \"w\") as train_json:\n",
    "  json.dump(train_data, train_json)\n",
    "with open(\"/content/CV_data/val/vfp_val.json\", \"w\") as val_json:\n",
    "  json.dump(val_data, val_json)"
   ],
   "metadata": {
    "id": "rr7zAFNwDq5y",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 9,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "%%writefile /content/VFP290K/configs/VFP290K/retinanet_r50_1x_benchmark.py\n",
    "# model settings\n",
    "model = dict(\n",
    "    type='RetinaNet',\n",
    "    pretrained='torchvision://resnet50',\n",
    "    backbone=dict(\n",
    "        type='ResNet',\n",
    "        depth=50,\n",
    "        num_stages=4,\n",
    "        out_indices=(0, 1, 2, 3),\n",
    "        frozen_stages=1,\n",
    "        norm_cfg=dict(type='BN', requires_grad=True),\n",
    "        norm_eval=True,\n",
    "        style='pytorch'),\n",
    "    neck=dict(\n",
    "        type='FPN',\n",
    "        in_channels=[256, 512, 1024, 2048],\n",
    "        out_channels=256,\n",
    "        start_level=1,\n",
    "        add_extra_convs='on_input',\n",
    "        num_outs=5),\n",
    "    bbox_head=dict(\n",
    "        type='RetinaHead',\n",
    "        num_classes=2,\n",
    "        in_channels=256,\n",
    "        stacked_convs=4,\n",
    "        feat_channels=256,\n",
    "        anchor_generator=dict(\n",
    "            type='AnchorGenerator',\n",
    "            octave_base_scale=4,\n",
    "            scales_per_octave=3,\n",
    "            ratios=[0.5, 1.0, 2.0],\n",
    "            strides=[8, 16, 32, 64, 128]),\n",
    "        bbox_coder=dict(\n",
    "            type='DeltaXYWHBBoxCoder',\n",
    "            target_means=[.0, .0, .0, .0],\n",
    "            target_stds=[1.0, 1.0, 1.0, 1.0]),\n",
    "        loss_cls=dict(\n",
    "            type='FocalLoss',\n",
    "            use_sigmoid=True,\n",
    "            gamma=2.0,\n",
    "            alpha=0.25,\n",
    "            loss_weight=1.0),\n",
    "        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n",
    "    # model training and testing settings\n",
    "    train_cfg=dict(\n",
    "        assigner=dict(\n",
    "            type='MaxIoUAssigner',\n",
    "            pos_iou_thr=0.5,\n",
    "            neg_iou_thr=0.4,\n",
    "            min_pos_iou=0,\n",
    "            ignore_iof_thr=-1),\n",
    "        allowed_border=-1,\n",
    "        pos_weight=-1,\n",
    "        debug=False),\n",
    "    test_cfg=dict(\n",
    "        nms_pre=1000,\n",
    "        min_bbox_size=0,\n",
    "        score_thr=0.05,\n",
    "        nms=dict(type='nms', iou_threshold=0.5),\n",
    "        max_per_img=100))\n",
    "\n",
    "\n",
    "\n",
    "dataset_type = 'CocoDataset'\n",
    "# classes = \"/<YOUR labesl.txt DIRECTORY>\"\n",
    "classes = (\"fallen\", \"standing\")\n",
    "data_root = '/content/CV_data' # root path of the dataset folder\n",
    "\n",
    "img_norm_cfg = dict(\n",
    "    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\n",
    "train_pipeline = [\n",
    "    dict(type='LoadImageFromFile'),\n",
    "    dict(type='LoadAnnotations', with_bbox=True),\n",
    "    dict(type='Resize', img_scale=(1920, 1080), keep_ratio=True),\n",
    "    dict(type='RandomFlip', flip_ratio=0.5),\n",
    "    dict(type='Normalize', **img_norm_cfg),\n",
    "    dict(type='Pad', size_divisor=32),\n",
    "    dict(type='DefaultFormatBundle'),\n",
    "    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),\n",
    "]\n",
    "\n",
    "test_pipeline = [\n",
    "    dict(type='LoadImageFromFile'),\n",
    "    dict(\n",
    "        type='MultiScaleFlipAug',\n",
    "        img_scale=(1920, 1080),\n",
    "        flip=False,\n",
    "        transforms=[\n",
    "            dict(type='Resize', keep_ratio=True),\n",
    "            dict(type='RandomFlip'),\n",
    "            dict(type='Normalize', **img_norm_cfg),\n",
    "            dict(type='Pad', size_divisor=32),\n",
    "            dict(type='ImageToTensor', keys=['img']),\n",
    "            dict(type='Collect', keys=['img']),\n",
    "        ])\n",
    "]\n",
    "\n",
    "data = dict(\n",
    "    samples_per_gpu=20, # equivalent to batch size because we only have 1 GPU\n",
    "    workers_per_gpu=2,\n",
    "    train=dict(\n",
    "        type=dataset_type,\n",
    "        classes=classes,\n",
    "        ann_file=data_root + '/train/vfp_train.json',\n",
    "        img_prefix=data_root + '/train/images',\n",
    "        pipeline=train_pipeline),\n",
    "    val=dict(\n",
    "        type=dataset_type,\n",
    "        classes=classes,\n",
    "        ann_file=data_root + '/val/vfp_val.json',\n",
    "        img_prefix=data_root + '/val/images',\n",
    "        pipeline=test_pipeline),\n",
    "    test=dict(\n",
    "        type=dataset_type,\n",
    "        classes=classes,\n",
    "        ann_file=data_root + '/test/interim_no_annotations.json',\n",
    "        img_prefix=data_root + '/test/images',\n",
    "        pipeline=test_pipeline))\n",
    "evaluation = dict(interval=1, metric='bbox')\n",
    "\n",
    "\n",
    "# optimizer\n",
    "optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)\n",
    "optimizer_config = dict(grad_clip=None)\n",
    "# learning policy\n",
    "lr_config = dict(\n",
    "    policy='step',\n",
    "    warmup='linear',\n",
    "    warmup_iters=500,\n",
    "    warmup_ratio=0.001,\n",
    "    step=[8, 11])\n",
    "runner = dict(type='EpochBasedRunner', max_epochs=300)\n",
    "\n",
    "checkpoint_config = dict(interval=1)\n",
    "# yapf:disable\n",
    "log_config = dict(\n",
    "    interval=50,\n",
    "    hooks=[\n",
    "        dict(type='TextLoggerHook'),\n",
    "        # dict(type='TensorboardLoggerHook')\n",
    "    ])\n",
    "# yapf:enable\n",
    "custom_hooks = [dict(type='NumClassCheckHook')]\n",
    "\n",
    "dist_params = dict(backend='nccl')\n",
    "log_level = 'INFO'\n",
    "load_from = None\n",
    "resume_from = None\n",
    "workflow = [('train', 1), ('val', 1)]\n",
    "\n",
    "fp16 = dict(loss_scale=512.)\n",
    "\n",
    "# load pretrained model\n",
    "load_from = '/content/VFP290K-Pretrained_Models/retinanet_benchmark/latest.pth'\n",
    "# load_from = 'checkpoints/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth'"
   ],
   "metadata": {
    "id": "AYxjOO9n8R-t",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "321c0c54-2337-45ab-a759-1647423b60d7",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Overwriting /content/VFP290K/configs/VFP290K/retinanet_r50_1x_benchmark.py\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%cd /content/VFP290K\n",
    "!python tools/train.py \\\n",
    "  /content/VFP290K/configs/VFP290K/retinanet_r50_1x_benchmark.py \\\n",
    "  --work-dir \"/content/drive/MyDrive/TIL 2022/CV/retinanet_training\" \\\n",
    "  --resume-from \"/content/VFP290K-Pretrained_Models/retinanet_benchmark/latest.pth\" \\\n",
    "  --gpu-ids 0"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C7G4xrix7e-a",
    "outputId": "a544913c-4169-4f54-93e8-49508b450c35",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "/content/VFP290K\n",
      "2022-06-05 14:35:18,242 - mmdet - INFO - Environment info:\n",
      "------------------------------------------------------------\n",
      "sys.platform: linux\n",
      "Python: 3.7.13 (default, Apr 24 2022, 01:04:09) [GCC 7.5.0]\n",
      "CUDA available: True\n",
      "GPU 0: Tesla T4\n",
      "CUDA_HOME: /usr/local/cuda\n",
      "NVCC: Cuda compilation tools, release 11.1, V11.1.105\n",
      "GCC: x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
      "PyTorch: 1.8.0+cu111\n",
      "PyTorch compiling details: PyTorch built with:\n",
      "  - GCC 7.3\n",
      "  - C++ Version: 201402\n",
      "  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n",
      "  - Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)\n",
      "  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n",
      "  - NNPACK is enabled\n",
      "  - CPU capability usage: AVX2\n",
      "  - CUDA Runtime 11.1\n",
      "  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n",
      "  - CuDNN 8.0.5\n",
      "  - Magma 2.5.2\n",
      "  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.8.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n",
      "\n",
      "TorchVision: 0.9.0+cu111\n",
      "OpenCV: 4.1.2\n",
      "MMCV: 1.5.2\n",
      "MMCV Compiler: GCC 7.3\n",
      "MMCV CUDA Compiler: 11.1\n",
      "MMDetection: 2.25.0+4ff57ac\n",
      "------------------------------------------------------------\n",
      "\n",
      "2022-06-05 14:35:18,507 - mmdet - INFO - Distributed training: False\n",
      "2022-06-05 14:35:18,682 - mmdet - INFO - Config:\n",
      "model = dict(\n",
      "    type='RetinaNet',\n",
      "    pretrained='torchvision://resnet50',\n",
      "    backbone=dict(\n",
      "        type='ResNet',\n",
      "        depth=50,\n",
      "        num_stages=4,\n",
      "        out_indices=(0, 1, 2, 3),\n",
      "        frozen_stages=1,\n",
      "        norm_cfg=dict(type='BN', requires_grad=True),\n",
      "        norm_eval=True,\n",
      "        style='pytorch'),\n",
      "    neck=dict(\n",
      "        type='FPN',\n",
      "        in_channels=[256, 512, 1024, 2048],\n",
      "        out_channels=256,\n",
      "        start_level=1,\n",
      "        add_extra_convs='on_input',\n",
      "        num_outs=5),\n",
      "    bbox_head=dict(\n",
      "        type='RetinaHead',\n",
      "        num_classes=2,\n",
      "        in_channels=256,\n",
      "        stacked_convs=4,\n",
      "        feat_channels=256,\n",
      "        anchor_generator=dict(\n",
      "            type='AnchorGenerator',\n",
      "            octave_base_scale=4,\n",
      "            scales_per_octave=3,\n",
      "            ratios=[0.5, 1.0, 2.0],\n",
      "            strides=[8, 16, 32, 64, 128]),\n",
      "        bbox_coder=dict(\n",
      "            type='DeltaXYWHBBoxCoder',\n",
      "            target_means=[0.0, 0.0, 0.0, 0.0],\n",
      "            target_stds=[1.0, 1.0, 1.0, 1.0]),\n",
      "        loss_cls=dict(\n",
      "            type='FocalLoss',\n",
      "            use_sigmoid=True,\n",
      "            gamma=2.0,\n",
      "            alpha=0.25,\n",
      "            loss_weight=1.0),\n",
      "        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n",
      "    train_cfg=dict(\n",
      "        assigner=dict(\n",
      "            type='MaxIoUAssigner',\n",
      "            pos_iou_thr=0.5,\n",
      "            neg_iou_thr=0.4,\n",
      "            min_pos_iou=0,\n",
      "            ignore_iof_thr=-1),\n",
      "        allowed_border=-1,\n",
      "        pos_weight=-1,\n",
      "        debug=False),\n",
      "    test_cfg=dict(\n",
      "        nms_pre=1000,\n",
      "        min_bbox_size=0,\n",
      "        score_thr=0.05,\n",
      "        nms=dict(type='nms', iou_threshold=0.5),\n",
      "        max_per_img=100))\n",
      "dataset_type = 'CocoDataset'\n",
      "classes = ('fallen', 'standing')\n",
      "data_root = '/content/CV_data'\n",
      "img_norm_cfg = dict(\n",
      "    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\n",
      "train_pipeline = [\n",
      "    dict(type='LoadImageFromFile'),\n",
      "    dict(type='LoadAnnotations', with_bbox=True),\n",
      "    dict(type='Resize', img_scale=(1280, 720), keep_ratio=True),\n",
      "    dict(type='RandomFlip', flip_ratio=0.5),\n",
      "    dict(\n",
      "        type='Normalize',\n",
      "        mean=[123.675, 116.28, 103.53],\n",
      "        std=[58.395, 57.12, 57.375],\n",
      "        to_rgb=True),\n",
      "    dict(type='Pad', size_divisor=32),\n",
      "    dict(type='DefaultFormatBundle'),\n",
      "    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n",
      "]\n",
      "test_pipeline = [\n",
      "    dict(type='LoadImageFromFile'),\n",
      "    dict(\n",
      "        type='MultiScaleFlipAug',\n",
      "        img_scale=(1280, 720),\n",
      "        flip=False,\n",
      "        transforms=[\n",
      "            dict(type='Resize', keep_ratio=True),\n",
      "            dict(type='RandomFlip'),\n",
      "            dict(\n",
      "                type='Normalize',\n",
      "                mean=[123.675, 116.28, 103.53],\n",
      "                std=[58.395, 57.12, 57.375],\n",
      "                to_rgb=True),\n",
      "            dict(type='Pad', size_divisor=32),\n",
      "            dict(type='ImageToTensor', keys=['img']),\n",
      "            dict(type='Collect', keys=['img'])\n",
      "        ])\n",
      "]\n",
      "data = dict(\n",
      "    samples_per_gpu=20,\n",
      "    workers_per_gpu=2,\n",
      "    train=dict(\n",
      "        type='CocoDataset',\n",
      "        classes=('fallen', 'standing'),\n",
      "        ann_file='/content/CV_data/train/vfp_train.json',\n",
      "        img_prefix='/content/CV_data/train/images',\n",
      "        pipeline=[\n",
      "            dict(type='LoadImageFromFile'),\n",
      "            dict(type='LoadAnnotations', with_bbox=True),\n",
      "            dict(type='Resize', img_scale=(1280, 720), keep_ratio=True),\n",
      "            dict(type='RandomFlip', flip_ratio=0.5),\n",
      "            dict(\n",
      "                type='Normalize',\n",
      "                mean=[123.675, 116.28, 103.53],\n",
      "                std=[58.395, 57.12, 57.375],\n",
      "                to_rgb=True),\n",
      "            dict(type='Pad', size_divisor=32),\n",
      "            dict(type='DefaultFormatBundle'),\n",
      "            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n",
      "        ]),\n",
      "    val=dict(\n",
      "        type='CocoDataset',\n",
      "        classes=('fallen', 'standing'),\n",
      "        ann_file='/content/CV_data/val/vfp_val.json',\n",
      "        img_prefix='/content/CV_data/val/images',\n",
      "        pipeline=[\n",
      "            dict(type='LoadImageFromFile'),\n",
      "            dict(\n",
      "                type='MultiScaleFlipAug',\n",
      "                img_scale=(1280, 720),\n",
      "                flip=False,\n",
      "                transforms=[\n",
      "                    dict(type='Resize', keep_ratio=True),\n",
      "                    dict(type='RandomFlip'),\n",
      "                    dict(\n",
      "                        type='Normalize',\n",
      "                        mean=[123.675, 116.28, 103.53],\n",
      "                        std=[58.395, 57.12, 57.375],\n",
      "                        to_rgb=True),\n",
      "                    dict(type='Pad', size_divisor=32),\n",
      "                    dict(type='ImageToTensor', keys=['img']),\n",
      "                    dict(type='Collect', keys=['img'])\n",
      "                ])\n",
      "        ]),\n",
      "    test=dict(\n",
      "        type='CocoDataset',\n",
      "        classes=('fallen', 'standing'),\n",
      "        ann_file='/content/CV_data/test/interim_no_annotations.json',\n",
      "        img_prefix='/content/CV_data/test/images',\n",
      "        pipeline=[\n",
      "            dict(type='LoadImageFromFile'),\n",
      "            dict(\n",
      "                type='MultiScaleFlipAug',\n",
      "                img_scale=(1280, 720),\n",
      "                flip=False,\n",
      "                transforms=[\n",
      "                    dict(type='Resize', keep_ratio=True),\n",
      "                    dict(type='RandomFlip'),\n",
      "                    dict(\n",
      "                        type='Normalize',\n",
      "                        mean=[123.675, 116.28, 103.53],\n",
      "                        std=[58.395, 57.12, 57.375],\n",
      "                        to_rgb=True),\n",
      "                    dict(type='Pad', size_divisor=32),\n",
      "                    dict(type='ImageToTensor', keys=['img']),\n",
      "                    dict(type='Collect', keys=['img'])\n",
      "                ])\n",
      "        ]))\n",
      "evaluation = dict(interval=1, metric='bbox')\n",
      "optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)\n",
      "optimizer_config = dict(grad_clip=None)\n",
      "lr_config = dict(\n",
      "    policy='step',\n",
      "    warmup='linear',\n",
      "    warmup_iters=500,\n",
      "    warmup_ratio=0.001,\n",
      "    step=[8, 11])\n",
      "runner = dict(type='EpochBasedRunner', max_epochs=300)\n",
      "checkpoint_config = dict(interval=1)\n",
      "log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\n",
      "custom_hooks = [dict(type='NumClassCheckHook')]\n",
      "dist_params = dict(backend='nccl')\n",
      "log_level = 'INFO'\n",
      "load_from = '/content/VFP290K-Pretrained_Models/retinanet_benchmark/latest.pth'\n",
      "resume_from = '/content/VFP290K-Pretrained_Models/retinanet_benchmark/latest.pth'\n",
      "workflow = [('train', 1), ('val', 1)]\n",
      "fp16 = dict(loss_scale=512.0)\n",
      "work_dir = '/content/drive/MyDrive/TIL 2022/CV/retinanet_training'\n",
      "gpu_ids = [0]\n",
      "\n",
      "/content/mmdetection/mmdet/models/detectors/single_stage.py:29: UserWarning: DeprecationWarning: pretrained is deprecated, please use \"init_cfg\" instead\n",
      "  warnings.warn('DeprecationWarning: pretrained is deprecated, '\n",
      "/content/mmdetection/mmdet/models/backbones/resnet.py:401: UserWarning: DeprecationWarning: pretrained is deprecated, please use \"init_cfg\" instead\n",
      "  warnings.warn('DeprecationWarning: pretrained is deprecated, '\n",
      "2022-06-05 14:35:19,119 - mmdet - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'}\n",
      "2022-06-05 14:35:19,120 - mmcv - INFO - load model from: torchvision://resnet50\n",
      "2022-06-05 14:35:19,120 - mmcv - INFO - load checkpoint from torchvision path: torchvision://resnet50\n",
      "2022-06-05 14:35:19,360 - mmcv - WARNING - The model and loaded state dict do not match exactly\n",
      "\n",
      "unexpected key in source state_dict: fc.weight, fc.bias\n",
      "\n",
      "2022-06-05 14:35:19,389 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}\n",
      "2022-06-05 14:35:19,442 - mmdet - INFO - initialize RetinaHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01, 'override': {'type': 'Normal', 'name': 'retina_cls', 'std': 0.01, 'bias_prob': 0.01}}\n",
      "loading annotations into memory...\n",
      "Done (t=0.14s)\n",
      "creating index...\n",
      "index created!\n",
      "loading annotations into memory...\n",
      "Done (t=0.02s)\n",
      "creating index...\n",
      "index created!\n",
      "2022-06-05 14:35:23,518 - mmdet - INFO - Automatic scaling of learning rate (LR) has been disabled.\n",
      "loading annotations into memory...\n",
      "Done (t=0.01s)\n",
      "creating index...\n",
      "index created!\n",
      "2022-06-05 14:35:23,546 - mmdet - INFO - load checkpoint from local path: /content/VFP290K-Pretrained_Models/retinanet_benchmark/latest.pth\n",
      "2022-06-05 14:35:23,791 - mmdet - INFO - the iteration number is changed due to change of GPU number\n",
      "2022-06-05 14:35:23,802 - mmdet - INFO - resumed epoch 12, iter 104928\n",
      "2022-06-05 14:35:23,803 - mmdet - INFO - Start running, host: root@a8aedb2b4358, work_dir: /content/drive/MyDrive/TIL 2022/CV/retinanet_training\n",
      "2022-06-05 14:35:23,803 - mmdet - INFO - Hooks will be executed in the following order:\n",
      "before_run:\n",
      "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
      "(ABOVE_NORMAL) Fp16OptimizerHook                  \n",
      "(NORMAL      ) CheckpointHook                     \n",
      "(LOW         ) EvalHook                           \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "before_train_epoch:\n",
      "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
      "(NORMAL      ) NumClassCheckHook                  \n",
      "(LOW         ) IterTimerHook                      \n",
      "(LOW         ) EvalHook                           \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "before_train_iter:\n",
      "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
      "(LOW         ) IterTimerHook                      \n",
      "(LOW         ) EvalHook                           \n",
      " -------------------- \n",
      "after_train_iter:\n",
      "(ABOVE_NORMAL) Fp16OptimizerHook                  \n",
      "(NORMAL      ) CheckpointHook                     \n",
      "(LOW         ) IterTimerHook                      \n",
      "(LOW         ) EvalHook                           \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "after_train_epoch:\n",
      "(NORMAL      ) CheckpointHook                     \n",
      "(LOW         ) EvalHook                           \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "before_val_epoch:\n",
      "(NORMAL      ) NumClassCheckHook                  \n",
      "(LOW         ) IterTimerHook                      \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "before_val_iter:\n",
      "(LOW         ) IterTimerHook                      \n",
      " -------------------- \n",
      "after_val_iter:\n",
      "(LOW         ) IterTimerHook                      \n",
      " -------------------- \n",
      "after_val_epoch:\n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "after_run:\n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "2022-06-05 14:35:23,804 - mmdet - INFO - workflow: [('train', 1), ('val', 1)], max: 300 epochs\n",
      "2022-06-05 14:35:23,805 - mmdet - INFO - Checkpoints will be saved to /content/drive/MyDrive/TIL 2022/CV/retinanet_training by HardDiskBackend.\n",
      "2022-06-05 14:37:28,798 - mmdet - INFO - Epoch [13][50/246]\tlr: 1.000e-04, eta: -1 day, 2:21:06, time: 2.500, data_time: 0.162, memory: 11945, loss_cls: 0.7344, loss_bbox: 0.0861, loss: 0.8206\n",
      "2022-06-05 14:39:27,522 - mmdet - INFO - Epoch [13][100/246]\tlr: 1.000e-04, eta: -1 day, 2:51:36, time: 2.374, data_time: 0.060, memory: 11945, loss_cls: 0.1722, loss_bbox: 0.0848, loss: 0.2570\n",
      "2022-06-05 14:41:26,712 - mmdet - INFO - Epoch [13][150/246]\tlr: 1.000e-04, eta: -1 day, 2:58:49, time: 2.384, data_time: 0.063, memory: 11945, loss_cls: 0.1486, loss_bbox: 0.0788, loss: 0.2274\n",
      "2022-06-05 14:43:25,279 - mmdet - INFO - Epoch [13][200/246]\tlr: 1.000e-04, eta: -1 day, 3:03:04, time: 2.371, data_time: 0.061, memory: 11945, loss_cls: 0.1365, loss_bbox: 0.0792, loss: 0.2156\n",
      "2022-06-05 14:45:14,090 - mmdet - INFO - Saving checkpoint at 13 epochs\n",
      "[>>] 1218/1218, 13.1 task/s, elapsed: 93s, ETA:     0s2022-06-05 14:46:49,531 - mmdet - INFO - Evaluating bbox...\n",
      "Loading and preparing results...\n",
      "DONE (t=0.17s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=3.19s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=1.11s).\n",
      "2022-06-05 14:46:54,161 - mmdet - INFO - \n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.774\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.874\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.826\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.208\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.751\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.798\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.903\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.903\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.903\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.412\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.857\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.940\n",
      "\n",
      "2022-06-05 14:46:54,203 - mmdet - INFO - Exp name: retinanet_r50_1x_benchmark.py\n",
      "2022-06-05 14:46:54,204 - mmdet - INFO - Epoch(val) [13][1218]\tbbox_mAP: 0.7740, bbox_mAP_50: 0.8740, bbox_mAP_75: 0.8260, bbox_mAP_s: 0.2080, bbox_mAP_m: 0.7510, bbox_mAP_l: 0.7980, bbox_mAP_copypaste: 0.774 0.874 0.826 0.208 0.751 0.798\n",
      "2022-06-05 14:48:28,191 - mmdet - INFO - Exp name: retinanet_r50_1x_benchmark.py\n",
      "2022-06-05 14:48:28,191 - mmdet - INFO - Epoch(val) [13][61]\tloss_cls: 0.1217, loss_bbox: 0.0744, loss: 0.1961\n",
      "2022-06-05 14:50:33,190 - mmdet - INFO - Epoch [14][50/246]\tlr: 1.000e-04, eta: -1 day, 6:07:01, time: 2.499, data_time: 0.167, memory: 11945, loss_cls: 0.1117, loss_bbox: 0.0742, loss: 0.1859\n",
      "2022-06-05 14:52:31,369 - mmdet - INFO - Epoch [14][100/246]\tlr: 1.000e-04, eta: -1 day, 5:41:27, time: 2.364, data_time: 0.062, memory: 11945, loss_cls: 0.1137, loss_bbox: 0.0764, loss: 0.1902\n",
      "2022-06-05 14:54:30,261 - mmdet - INFO - Epoch [14][150/246]\tlr: 1.000e-04, eta: -1 day, 5:20:53, time: 2.378, data_time: 0.064, memory: 11945, loss_cls: 0.1067, loss_bbox: 0.0761, loss: 0.1828\n",
      "2022-06-05 14:56:28,415 - mmdet - INFO - Epoch [14][200/246]\tlr: 1.000e-04, eta: -1 day, 5:05:22, time: 2.363, data_time: 0.061, memory: 11945, loss_cls: 0.0935, loss_bbox: 0.0712, loss: 0.1647\n",
      "2022-06-05 14:58:17,042 - mmdet - INFO - Saving checkpoint at 14 epochs\n",
      "[>>] 1218/1218, 12.9 task/s, elapsed: 94s, ETA:     0s2022-06-05 14:59:54,179 - mmdet - INFO - Evaluating bbox...\n",
      "Loading and preparing results...\n",
      "DONE (t=0.17s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=2.61s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.92s).\n",
      "2022-06-05 14:59:57,995 - mmdet - INFO - \n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.820\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.921\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.870\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.225\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.779\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.859\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.907\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.907\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.907\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.400\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.862\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.943\n",
      "\n",
      "2022-06-05 14:59:58,033 - mmdet - INFO - Exp name: retinanet_r50_1x_benchmark.py\n",
      "2022-06-05 14:59:58,033 - mmdet - INFO - Epoch(val) [14][1218]\tbbox_mAP: 0.8200, bbox_mAP_50: 0.9210, bbox_mAP_75: 0.8700, bbox_mAP_s: 0.2250, bbox_mAP_m: 0.7790, bbox_mAP_l: 0.8590, bbox_mAP_copypaste: 0.820 0.921 0.870 0.225 0.779 0.859\n",
      "2022-06-05 15:01:34,467 - mmdet - INFO - Exp name: retinanet_r50_1x_benchmark.py\n",
      "2022-06-05 15:01:34,468 - mmdet - INFO - Epoch(val) [14][61]\tloss_cls: 0.0918, loss_bbox: 0.0710, loss: 0.1629\n",
      "2022-06-05 15:03:39,452 - mmdet - INFO - Epoch [15][50/246]\tlr: 1.000e-04, eta: -1 day, 6:21:49, time: 2.499, data_time: 0.172, memory: 11945, loss_cls: 0.0843, loss_bbox: 0.0710, loss: 0.1553\n",
      "2022-06-05 15:05:37,476 - mmdet - INFO - Epoch [15][100/246]\tlr: 1.000e-04, eta: -1 day, 6:04:16, time: 2.360, data_time: 0.062, memory: 11945, loss_cls: 0.0838, loss_bbox: 0.0728, loss: 0.1566\n",
      "2022-06-05 15:07:35,443 - mmdet - INFO - Epoch [15][150/246]\tlr: 1.000e-04, eta: -1 day, 5:49:11, time: 2.359, data_time: 0.062, memory: 11945, loss_cls: 0.0849, loss_bbox: 0.0716, loss: 0.1564\n",
      "2022-06-05 15:09:33,539 - mmdet - INFO - Epoch [15][200/246]\tlr: 1.000e-04, eta: -1 day, 5:35:54, time: 2.362, data_time: 0.062, memory: 11945, loss_cls: 0.0797, loss_bbox: 0.0725, loss: 0.1523\n",
      "2022-06-05 15:11:22,641 - mmdet - INFO - Saving checkpoint at 15 epochs\n",
      "[>>] 1218/1218, 13.0 task/s, elapsed: 94s, ETA:     0s2022-06-05 15:12:58,661 - mmdet - INFO - Evaluating bbox...\n",
      "Loading and preparing results...\n",
      "DONE (t=0.16s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=2.33s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.69s).\n",
      "2022-06-05 15:13:01,938 - mmdet - INFO - \n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.842\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.941\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.892\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.222\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.793\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.885\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.910\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.910\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.910\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.412\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.866\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.946\n",
      "\n",
      "2022-06-05 15:13:01,974 - mmdet - INFO - Exp name: retinanet_r50_1x_benchmark.py\n",
      "2022-06-05 15:13:01,974 - mmdet - INFO - Epoch(val) [15][1218]\tbbox_mAP: 0.8420, bbox_mAP_50: 0.9410, bbox_mAP_75: 0.8920, bbox_mAP_s: 0.2220, bbox_mAP_m: 0.7930, bbox_mAP_l: 0.8850, bbox_mAP_copypaste: 0.842 0.941 0.892 0.222 0.793 0.885\n",
      "2022-06-05 15:14:38,824 - mmdet - INFO - Exp name: retinanet_r50_1x_benchmark.py\n",
      "2022-06-05 15:14:38,824 - mmdet - INFO - Epoch(val) [15][61]\tloss_cls: 0.0753, loss_bbox: 0.0691, loss: 0.1443\n",
      "2022-06-05 15:16:42,781 - mmdet - INFO - Epoch [16][50/246]\tlr: 1.000e-04, eta: -1 day, 6:23:50, time: 2.478, data_time: 0.168, memory: 11945, loss_cls: 0.0715, loss_bbox: 0.0690, loss: 0.1405\n",
      "2022-06-05 15:18:40,713 - mmdet - INFO - Epoch [16][100/246]\tlr: 1.000e-04, eta: -1 day, 6:10:19, time: 2.359, data_time: 0.063, memory: 11945, loss_cls: 0.0696, loss_bbox: 0.0668, loss: 0.1364\n",
      "2022-06-05 15:20:39,350 - mmdet - INFO - Epoch [16][150/246]\tlr: 1.000e-04, eta: -1 day, 5:57:41, time: 2.373, data_time: 0.065, memory: 11945, loss_cls: 0.0701, loss_bbox: 0.0698, loss: 0.1399\n",
      "2022-06-05 15:22:37,240 - mmdet - INFO - Epoch [16][200/246]\tlr: 1.000e-04, eta: -1 day, 5:46:37, time: 2.358, data_time: 0.061, memory: 11945, loss_cls: 0.0644, loss_bbox: 0.0716, loss: 0.1360\n",
      "2022-06-05 15:24:25,574 - mmdet - INFO - Saving checkpoint at 16 epochs\n",
      "[>>] 1218/1218, 13.1 task/s, elapsed: 93s, ETA:     0s2022-06-05 15:26:01,535 - mmdet - INFO - Evaluating bbox...\n",
      "Loading and preparing results...\n",
      "DONE (t=0.21s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=2.82s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.62s).\n",
      "2022-06-05 15:26:05,362 - mmdet - INFO - \n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.855\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.953\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.906\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.215\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.802\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.898\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.913\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.913\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.913\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.412\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.869\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.948\n",
      "\n",
      "2022-06-05 15:26:05,394 - mmdet - INFO - Exp name: retinanet_r50_1x_benchmark.py\n",
      "2022-06-05 15:26:05,394 - mmdet - INFO - Epoch(val) [16][1218]\tbbox_mAP: 0.8550, bbox_mAP_50: 0.9530, bbox_mAP_75: 0.9060, bbox_mAP_s: 0.2150, bbox_mAP_m: 0.8020, bbox_mAP_l: 0.8980, bbox_mAP_copypaste: 0.855 0.953 0.906 0.215 0.802 0.898\n",
      "2022-06-05 15:27:39,358 - mmdet - INFO - Exp name: retinanet_r50_1x_benchmark.py\n",
      "2022-06-05 15:27:39,358 - mmdet - INFO - Epoch(val) [16][61]\tloss_cls: 0.0642, loss_bbox: 0.0675, loss: 0.1317\n",
      "2022-06-05 15:29:43,388 - mmdet - INFO - Epoch [17][50/246]\tlr: 1.000e-04, eta: -1 day, 6:20:53, time: 2.480, data_time: 0.166, memory: 11945, loss_cls: 0.0616, loss_bbox: 0.0680, loss: 0.1296\n",
      "2022-06-05 15:31:41,093 - mmdet - INFO - Epoch [17][100/246]\tlr: 1.000e-04, eta: -1 day, 6:09:52, time: 2.354, data_time: 0.062, memory: 11945, loss_cls: 0.0619, loss_bbox: 0.0681, loss: 0.1300\n",
      "2022-06-05 15:33:39,420 - mmdet - INFO - Epoch [17][150/246]\tlr: 1.000e-04, eta: -1 day, 5:59:21, time: 2.367, data_time: 0.064, memory: 11945, loss_cls: 0.0608, loss_bbox: 0.0688, loss: 0.1296\n",
      "2022-06-05 15:35:37,342 - mmdet - INFO - Epoch [17][200/246]\tlr: 1.000e-04, eta: -1 day, 5:49:45, time: 2.358, data_time: 0.062, memory: 11945, loss_cls: 0.0602, loss_bbox: 0.0659, loss: 0.1261\n",
      "2022-06-05 15:37:25,615 - mmdet - INFO - Saving checkpoint at 17 epochs\n",
      "[>>] 1218/1218, 13.1 task/s, elapsed: 93s, ETA:     0s2022-06-05 15:39:01,007 - mmdet - INFO - Evaluating bbox...\n",
      "Loading and preparing results...\n",
      "DONE (t=0.14s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=2.21s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.58s).\n",
      "2022-06-05 15:39:04,030 - mmdet - INFO - \n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.864\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.959\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.914\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.216\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.810\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.907\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.914\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.914\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.914\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.396\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.871\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.949\n",
      "\n",
      "2022-06-05 15:39:04,069 - mmdet - INFO - Exp name: retinanet_r50_1x_benchmark.py\n",
      "2022-06-05 15:39:04,070 - mmdet - INFO - Epoch(val) [17][1218]\tbbox_mAP: 0.8640, bbox_mAP_50: 0.9590, bbox_mAP_75: 0.9140, bbox_mAP_s: 0.2160, bbox_mAP_m: 0.8100, bbox_mAP_l: 0.9070, bbox_mAP_copypaste: 0.864 0.959 0.914 0.216 0.810 0.907\n",
      "2022-06-05 15:40:40,285 - mmdet - INFO - Exp name: retinanet_r50_1x_benchmark.py\n",
      "2022-06-05 15:40:40,285 - mmdet - INFO - Epoch(val) [17][61]\tloss_cls: 0.0560, loss_bbox: 0.0663, loss: 0.1223\n",
      "2022-06-05 15:42:44,110 - mmdet - INFO - Epoch [18][50/246]\tlr: 1.000e-04, eta: -1 day, 6:16:18, time: 2.476, data_time: 0.163, memory: 11945, loss_cls: 0.0532, loss_bbox: 0.0646, loss: 0.1177\n",
      "2022-06-05 15:44:42,481 - mmdet - INFO - Epoch [18][100/246]\tlr: 1.000e-04, eta: -1 day, 6:06:33, time: 2.367, data_time: 0.065, memory: 11945, loss_cls: 0.0540, loss_bbox: 0.0643, loss: 0.1183\n",
      "2022-06-05 15:46:40,394 - mmdet - INFO - Epoch [18][150/246]\tlr: 1.000e-04, eta: -1 day, 5:57:34, time: 2.358, data_time: 0.062, memory: 11945, loss_cls: 0.0551, loss_bbox: 0.0653, loss: 0.1204\n",
      "2022-06-05 15:48:38,063 - mmdet - INFO - Epoch [18][200/246]\tlr: 1.000e-04, eta: -1 day, 5:49:09, time: 2.353, data_time: 0.061, memory: 11945, loss_cls: 0.0544, loss_bbox: 0.0683, loss: 0.1227\n",
      "2022-06-05 15:50:26,901 - mmdet - INFO - Saving checkpoint at 18 epochs\n",
      "[  ] 35/1218, 11.7 task/s, elapsed: 3s, ETA:   101s"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%writefile /content/VFP290K/configs/VFP290K/cascade_rcnn_benchmark.py\n",
    "_base_ = [\n",
    "    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'\n",
    "]\n",
    "\n",
    "\n",
    "# model settings\n",
    "model = dict(\n",
    "    type='CascadeRCNN',\n",
    "    backbone=dict(\n",
    "        type='ResNet',\n",
    "        depth=50,\n",
    "        num_stages=4,\n",
    "        out_indices=(0, 1, 2, 3),\n",
    "        frozen_stages=1,\n",
    "        norm_cfg=dict(type='BN', requires_grad=True),\n",
    "        norm_eval=True,\n",
    "        style='pytorch',\n",
    "        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),\n",
    "    neck=dict(\n",
    "        type='FPN',\n",
    "        in_channels=[256, 512, 1024, 2048],\n",
    "        out_channels=256,\n",
    "        num_outs=5),\n",
    "    rpn_head=dict(\n",
    "        type='RPNHead',\n",
    "        in_channels=256,\n",
    "        feat_channels=256,\n",
    "        anchor_generator=dict(\n",
    "            type='AnchorGenerator',\n",
    "            scales=[8],\n",
    "            ratios=[0.5, 1.0, 2.0],\n",
    "            strides=[4, 8, 16, 32, 64]),\n",
    "        bbox_coder=dict(\n",
    "            type='DeltaXYWHBBoxCoder',\n",
    "            target_means=[.0, .0, .0, .0],\n",
    "            target_stds=[1.0, 1.0, 1.0, 1.0]),\n",
    "        loss_cls=dict(\n",
    "            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n",
    "        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),\n",
    "    roi_head=dict(\n",
    "        type='CascadeRoIHead',\n",
    "        num_stages=3,\n",
    "        stage_loss_weights=[1, 0.5, 0.25],\n",
    "        bbox_roi_extractor=dict(\n",
    "            type='SingleRoIExtractor',\n",
    "            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n",
    "            out_channels=256,\n",
    "            featmap_strides=[4, 8, 16, 32]),\n",
    "        bbox_head=[\n",
    "            dict(\n",
    "                type='Shared2FCBBoxHead',\n",
    "                in_channels=256,\n",
    "                fc_out_channels=1024,\n",
    "                roi_feat_size=7,\n",
    "                num_classes=2,\n",
    "                bbox_coder=dict(\n",
    "                    type='DeltaXYWHBBoxCoder',\n",
    "                    target_means=[0., 0., 0., 0.],\n",
    "                    target_stds=[0.1, 0.1, 0.2, 0.2]),\n",
    "                reg_class_agnostic=True,\n",
    "                loss_cls=dict(\n",
    "                    type='CrossEntropyLoss',\n",
    "                    use_sigmoid=False,\n",
    "                    loss_weight=1.0),\n",
    "                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,\n",
    "                               loss_weight=1.0)),\n",
    "            dict(\n",
    "                type='Shared2FCBBoxHead',\n",
    "                in_channels=256,\n",
    "                fc_out_channels=1024,\n",
    "                roi_feat_size=7,\n",
    "                num_classes=2,\n",
    "                bbox_coder=dict(\n",
    "                    type='DeltaXYWHBBoxCoder',\n",
    "                    target_means=[0., 0., 0., 0.],\n",
    "                    target_stds=[0.05, 0.05, 0.1, 0.1]),\n",
    "                reg_class_agnostic=True,\n",
    "                loss_cls=dict(\n",
    "                    type='CrossEntropyLoss',\n",
    "                    use_sigmoid=False,\n",
    "                    loss_weight=1.0),\n",
    "                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,\n",
    "                               loss_weight=1.0)),\n",
    "            dict(\n",
    "                type='Shared2FCBBoxHead',\n",
    "                in_channels=256,\n",
    "                fc_out_channels=1024,\n",
    "                roi_feat_size=7,\n",
    "                num_classes=2,\n",
    "                bbox_coder=dict(\n",
    "                    type='DeltaXYWHBBoxCoder',\n",
    "                    target_means=[0., 0., 0., 0.],\n",
    "                    target_stds=[0.033, 0.033, 0.067, 0.067]),\n",
    "                reg_class_agnostic=True,\n",
    "                loss_cls=dict(\n",
    "                    type='CrossEntropyLoss',\n",
    "                    use_sigmoid=False,\n",
    "                    loss_weight=1.0),\n",
    "                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))\n",
    "        ]),\n",
    "    # model training and testing settings\n",
    "    train_cfg=dict(\n",
    "        rpn=dict(\n",
    "            assigner=dict(\n",
    "                type='MaxIoUAssigner',\n",
    "                pos_iou_thr=0.7,\n",
    "                neg_iou_thr=0.3,\n",
    "                min_pos_iou=0.3,\n",
    "                match_low_quality=True,\n",
    "                ignore_iof_thr=-1),\n",
    "            sampler=dict(\n",
    "                type='RandomSampler',\n",
    "                num=256,\n",
    "                pos_fraction=0.5,\n",
    "                neg_pos_ub=-1,\n",
    "                add_gt_as_proposals=False),\n",
    "            allowed_border=0,\n",
    "            pos_weight=-1,\n",
    "            debug=False),\n",
    "        rpn_proposal=dict(\n",
    "            nms_pre=2000,\n",
    "            max_per_img=2000,\n",
    "            nms=dict(type='nms', iou_threshold=0.7),\n",
    "            min_bbox_size=0),\n",
    "        rcnn=[\n",
    "            dict(\n",
    "                assigner=dict(\n",
    "                    type='MaxIoUAssigner',\n",
    "                    pos_iou_thr=0.5,\n",
    "                    neg_iou_thr=0.5,\n",
    "                    min_pos_iou=0.5,\n",
    "                    match_low_quality=False,\n",
    "                    ignore_iof_thr=-1),\n",
    "                sampler=dict(\n",
    "                    type='RandomSampler',\n",
    "                    num=512,\n",
    "                    pos_fraction=0.25,\n",
    "                    neg_pos_ub=-1,\n",
    "                    add_gt_as_proposals=True),\n",
    "                pos_weight=-1,\n",
    "                debug=False),\n",
    "            dict(\n",
    "                assigner=dict(\n",
    "                    type='MaxIoUAssigner',\n",
    "                    pos_iou_thr=0.6,\n",
    "                    neg_iou_thr=0.6,\n",
    "                    min_pos_iou=0.6,\n",
    "                    match_low_quality=False,\n",
    "                    ignore_iof_thr=-1),\n",
    "                sampler=dict(\n",
    "                    type='RandomSampler',\n",
    "                    num=512,\n",
    "                    pos_fraction=0.25,\n",
    "                    neg_pos_ub=-1,\n",
    "                    add_gt_as_proposals=True),\n",
    "                pos_weight=-1,\n",
    "                debug=False),\n",
    "            dict(\n",
    "                assigner=dict(\n",
    "                    type='MaxIoUAssigner',\n",
    "                    pos_iou_thr=0.7,\n",
    "                    neg_iou_thr=0.7,\n",
    "                    min_pos_iou=0.7,\n",
    "                    match_low_quality=False,\n",
    "                    ignore_iof_thr=-1),\n",
    "                sampler=dict(\n",
    "                    type='RandomSampler',\n",
    "                    num=512,\n",
    "                    pos_fraction=0.25,\n",
    "                    neg_pos_ub=-1,\n",
    "                    add_gt_as_proposals=True),\n",
    "                pos_weight=-1,\n",
    "                debug=False)\n",
    "        ]),\n",
    "    test_cfg=dict(\n",
    "        rpn=dict(\n",
    "            nms_pre=1000,\n",
    "            max_per_img=1000,\n",
    "            nms=dict(type='nms', iou_threshold=0.7),\n",
    "            min_bbox_size=0),\n",
    "        rcnn=dict(\n",
    "            score_thr=0.05,\n",
    "            nms=dict(type='nms', iou_threshold=0.5),\n",
    "            max_per_img=100)))\n",
    "\n",
    "\n",
    "# dataset settings\n",
    "dataset_type = 'CocoDataset'\n",
    "# classes = \"/<YOUR labesl.txt DIRECTORY>\"\n",
    "classes = (\"fallen\", \"standing\")\n",
    "data_root = '/content/CV_data' # root path of the dataset folder\n",
    "img_norm_cfg = dict(\n",
    "    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\n",
    "train_pipeline = [\n",
    "    dict(type='LoadImageFromFile'),\n",
    "    dict(type='LoadAnnotations', with_bbox=True),\n",
    "    dict(type='Resize', img_scale=(1920, 1080), keep_ratio=True),\n",
    "    dict(type='RandomFlip', flip_ratio=0.5),\n",
    "    dict(type='Normalize', **img_norm_cfg),\n",
    "    dict(type='Pad', size_divisor=32),\n",
    "    dict(type='DefaultFormatBundle'),\n",
    "    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),\n",
    "]\n",
    "test_pipeline = [\n",
    "    dict(type='LoadImageFromFile'),\n",
    "    dict(\n",
    "        type='MultiScaleFlipAug',\n",
    "        img_scale=(1920, 1080),\n",
    "        flip=True,\n",
    "        transforms=[\n",
    "            dict(type='Resize', keep_ratio=True),\n",
    "            dict(type='RandomFlip'),\n",
    "            dict(type='Normalize', **img_norm_cfg),\n",
    "            dict(type='Pad', size_divisor=32),\n",
    "            dict(type='ImageToTensor', keys=['img']),\n",
    "            dict(type='Collect', keys=['img']),\n",
    "        ])\n",
    "]\n",
    "data = dict(\n",
    "    samples_per_gpu=6, # equivalent to batch size because we only have 1 GPU\n",
    "    workers_per_gpu=2,\n",
    "    train=dict(\n",
    "        type=dataset_type,\n",
    "        classes=classes,\n",
    "        ann_file=data_root + '/train/vfp_train.json',\n",
    "        img_prefix=data_root + '/train/images',\n",
    "        pipeline=train_pipeline),\n",
    "    val=dict(\n",
    "        type=dataset_type,\n",
    "        classes=classes,\n",
    "        ann_file=data_root + '/val/vfp_val.json',\n",
    "        img_prefix=data_root + '/val/images',\n",
    "        pipeline=test_pipeline),\n",
    "    test=dict(\n",
    "        type=dataset_type,\n",
    "        classes=classes,\n",
    "        ann_file=data_root + '/test/interim_no_annotations.json',\n",
    "        img_prefix=data_root + '/test/images',\n",
    "        pipeline=test_pipeline))\n",
    "evaluation = dict(interval=1, metric='bbox')\n",
    "\n",
    "# optimizer\n",
    "optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)\n",
    "optimizer_config = dict(grad_clip=None)\n",
    "# learning policy\n",
    "lr_config = dict(\n",
    "    policy='step',\n",
    "    warmup='linear',\n",
    "    warmup_iters=500,\n",
    "    warmup_ratio=0.001,\n",
    "    step=[8, 11])\n",
    "runner = dict(type='EpochBasedRunner', max_epochs=300)\n",
    "\n",
    "checkpoint_config = dict(interval=1)\n",
    "# yapf:disable\n",
    "log_config = dict(\n",
    "    interval=50,\n",
    "    hooks=[\n",
    "        dict(type='TextLoggerHook'),\n",
    "        # dict(type='TensorboardLoggerHook')\n",
    "    ])\n",
    "# yapf:enable\n",
    "custom_hooks = [dict(type='NumClassCheckHook')]\n",
    "\n",
    "dist_params = dict(backend='nccl')\n",
    "log_level = 'INFO'\n",
    "load_from = None\n",
    "resume_from = None\n",
    "workflow = [('train', 1), ('val', 1)]\n",
    "\n",
    "fp16 = dict(loss_scale=512.)\n",
    "# load pretrained model\n",
    "load_from = '/content/drive/MyDrive/TIL 2022/CV/pretrainedArchive/cascade.pth'"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Xn3GWoBtLdVY",
    "outputId": "41d17931-407c-46da-953c-abc02c521985",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Overwriting /content/VFP290K/configs/VFP290K/cascade_rcnn_benchmark.py\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%cd /content/VFP290K\n",
    "!python tools/train.py \\\n",
    "  /content/VFP290K/configs/VFP290K/cascade_rcnn_benchmark.py \\\n",
    "  --work-dir \"/content/drive/MyDrive/TIL 2022/CV/cascade_training\" \\\n",
    "  --resume-from '/content/drive/MyDrive/TIL 2022/CV/cascade_training/epoch_17.pth' \\\n",
    "  --gpu-ids 0 \\\n",
    "  --cfg-options device='cuda'"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "seHFz3U5MpPo",
    "outputId": "769d0ed8-f2a1-4bb4-a5e9-f7f7a0973617",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "/content/VFP290K\n",
      "2022-06-06 14:42:23,105 - mmdet - INFO - Environment info:\n",
      "------------------------------------------------------------\n",
      "sys.platform: linux\n",
      "Python: 3.7.13 (default, Apr 24 2022, 01:04:09) [GCC 7.5.0]\n",
      "CUDA available: True\n",
      "GPU 0: Tesla T4\n",
      "CUDA_HOME: /usr/local/cuda\n",
      "NVCC: Cuda compilation tools, release 11.1, V11.1.105\n",
      "GCC: x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
      "PyTorch: 1.8.0+cu111\n",
      "PyTorch compiling details: PyTorch built with:\n",
      "  - GCC 7.3\n",
      "  - C++ Version: 201402\n",
      "  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n",
      "  - Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)\n",
      "  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n",
      "  - NNPACK is enabled\n",
      "  - CPU capability usage: AVX2\n",
      "  - CUDA Runtime 11.1\n",
      "  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n",
      "  - CuDNN 8.0.5\n",
      "  - Magma 2.5.2\n",
      "  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.8.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n",
      "\n",
      "TorchVision: 0.9.0+cu111\n",
      "OpenCV: 4.1.2\n",
      "MMCV: 1.5.2\n",
      "MMCV Compiler: GCC 7.3\n",
      "MMCV CUDA Compiler: 11.1\n",
      "MMDetection: 2.25.0+4ff57ac\n",
      "------------------------------------------------------------\n",
      "\n",
      "2022-06-06 14:42:23,507 - mmdet - INFO - Distributed training: False\n",
      "2022-06-06 14:42:23,899 - mmdet - INFO - Config:\n",
      "optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)\n",
      "optimizer_config = dict(grad_clip=None)\n",
      "lr_config = dict(\n",
      "    policy='step',\n",
      "    warmup='linear',\n",
      "    warmup_iters=500,\n",
      "    warmup_ratio=0.001,\n",
      "    step=[8, 11])\n",
      "runner = dict(type='EpochBasedRunner', max_epochs=300)\n",
      "checkpoint_config = dict(interval=1)\n",
      "log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\n",
      "custom_hooks = [dict(type='NumClassCheckHook')]\n",
      "dist_params = dict(backend='nccl')\n",
      "log_level = 'INFO'\n",
      "load_from = '/content/drive/MyDrive/TIL 2022/CV/pretrainedArchive/cascade.pth'\n",
      "resume_from = '/content/drive/MyDrive/TIL 2022/CV/cascade_training/epoch_17.pth'\n",
      "workflow = [('train', 1), ('val', 1)]\n",
      "model = dict(\n",
      "    type='CascadeRCNN',\n",
      "    backbone=dict(\n",
      "        type='ResNet',\n",
      "        depth=50,\n",
      "        num_stages=4,\n",
      "        out_indices=(0, 1, 2, 3),\n",
      "        frozen_stages=1,\n",
      "        norm_cfg=dict(type='BN', requires_grad=True),\n",
      "        norm_eval=True,\n",
      "        style='pytorch',\n",
      "        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),\n",
      "    neck=dict(\n",
      "        type='FPN',\n",
      "        in_channels=[256, 512, 1024, 2048],\n",
      "        out_channels=256,\n",
      "        num_outs=5),\n",
      "    rpn_head=dict(\n",
      "        type='RPNHead',\n",
      "        in_channels=256,\n",
      "        feat_channels=256,\n",
      "        anchor_generator=dict(\n",
      "            type='AnchorGenerator',\n",
      "            scales=[8],\n",
      "            ratios=[0.5, 1.0, 2.0],\n",
      "            strides=[4, 8, 16, 32, 64]),\n",
      "        bbox_coder=dict(\n",
      "            type='DeltaXYWHBBoxCoder',\n",
      "            target_means=[0.0, 0.0, 0.0, 0.0],\n",
      "            target_stds=[1.0, 1.0, 1.0, 1.0]),\n",
      "        loss_cls=dict(\n",
      "            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n",
      "        loss_bbox=dict(\n",
      "            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),\n",
      "    roi_head=dict(\n",
      "        type='CascadeRoIHead',\n",
      "        num_stages=3,\n",
      "        stage_loss_weights=[1, 0.5, 0.25],\n",
      "        bbox_roi_extractor=dict(\n",
      "            type='SingleRoIExtractor',\n",
      "            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n",
      "            out_channels=256,\n",
      "            featmap_strides=[4, 8, 16, 32]),\n",
      "        bbox_head=[\n",
      "            dict(\n",
      "                type='Shared2FCBBoxHead',\n",
      "                in_channels=256,\n",
      "                fc_out_channels=1024,\n",
      "                roi_feat_size=7,\n",
      "                num_classes=2,\n",
      "                bbox_coder=dict(\n",
      "                    type='DeltaXYWHBBoxCoder',\n",
      "                    target_means=[0.0, 0.0, 0.0, 0.0],\n",
      "                    target_stds=[0.1, 0.1, 0.2, 0.2]),\n",
      "                reg_class_agnostic=True,\n",
      "                loss_cls=dict(\n",
      "                    type='CrossEntropyLoss',\n",
      "                    use_sigmoid=False,\n",
      "                    loss_weight=1.0),\n",
      "                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,\n",
      "                               loss_weight=1.0)),\n",
      "            dict(\n",
      "                type='Shared2FCBBoxHead',\n",
      "                in_channels=256,\n",
      "                fc_out_channels=1024,\n",
      "                roi_feat_size=7,\n",
      "                num_classes=2,\n",
      "                bbox_coder=dict(\n",
      "                    type='DeltaXYWHBBoxCoder',\n",
      "                    target_means=[0.0, 0.0, 0.0, 0.0],\n",
      "                    target_stds=[0.05, 0.05, 0.1, 0.1]),\n",
      "                reg_class_agnostic=True,\n",
      "                loss_cls=dict(\n",
      "                    type='CrossEntropyLoss',\n",
      "                    use_sigmoid=False,\n",
      "                    loss_weight=1.0),\n",
      "                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,\n",
      "                               loss_weight=1.0)),\n",
      "            dict(\n",
      "                type='Shared2FCBBoxHead',\n",
      "                in_channels=256,\n",
      "                fc_out_channels=1024,\n",
      "                roi_feat_size=7,\n",
      "                num_classes=2,\n",
      "                bbox_coder=dict(\n",
      "                    type='DeltaXYWHBBoxCoder',\n",
      "                    target_means=[0.0, 0.0, 0.0, 0.0],\n",
      "                    target_stds=[0.033, 0.033, 0.067, 0.067]),\n",
      "                reg_class_agnostic=True,\n",
      "                loss_cls=dict(\n",
      "                    type='CrossEntropyLoss',\n",
      "                    use_sigmoid=False,\n",
      "                    loss_weight=1.0),\n",
      "                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))\n",
      "        ]),\n",
      "    train_cfg=dict(\n",
      "        rpn=dict(\n",
      "            assigner=dict(\n",
      "                type='MaxIoUAssigner',\n",
      "                pos_iou_thr=0.7,\n",
      "                neg_iou_thr=0.3,\n",
      "                min_pos_iou=0.3,\n",
      "                match_low_quality=True,\n",
      "                ignore_iof_thr=-1),\n",
      "            sampler=dict(\n",
      "                type='RandomSampler',\n",
      "                num=256,\n",
      "                pos_fraction=0.5,\n",
      "                neg_pos_ub=-1,\n",
      "                add_gt_as_proposals=False),\n",
      "            allowed_border=0,\n",
      "            pos_weight=-1,\n",
      "            debug=False),\n",
      "        rpn_proposal=dict(\n",
      "            nms_pre=2000,\n",
      "            max_per_img=2000,\n",
      "            nms=dict(type='nms', iou_threshold=0.7),\n",
      "            min_bbox_size=0),\n",
      "        rcnn=[\n",
      "            dict(\n",
      "                assigner=dict(\n",
      "                    type='MaxIoUAssigner',\n",
      "                    pos_iou_thr=0.5,\n",
      "                    neg_iou_thr=0.5,\n",
      "                    min_pos_iou=0.5,\n",
      "                    match_low_quality=False,\n",
      "                    ignore_iof_thr=-1),\n",
      "                sampler=dict(\n",
      "                    type='RandomSampler',\n",
      "                    num=512,\n",
      "                    pos_fraction=0.25,\n",
      "                    neg_pos_ub=-1,\n",
      "                    add_gt_as_proposals=True),\n",
      "                pos_weight=-1,\n",
      "                debug=False),\n",
      "            dict(\n",
      "                assigner=dict(\n",
      "                    type='MaxIoUAssigner',\n",
      "                    pos_iou_thr=0.6,\n",
      "                    neg_iou_thr=0.6,\n",
      "                    min_pos_iou=0.6,\n",
      "                    match_low_quality=False,\n",
      "                    ignore_iof_thr=-1),\n",
      "                sampler=dict(\n",
      "                    type='RandomSampler',\n",
      "                    num=512,\n",
      "                    pos_fraction=0.25,\n",
      "                    neg_pos_ub=-1,\n",
      "                    add_gt_as_proposals=True),\n",
      "                pos_weight=-1,\n",
      "                debug=False),\n",
      "            dict(\n",
      "                assigner=dict(\n",
      "                    type='MaxIoUAssigner',\n",
      "                    pos_iou_thr=0.7,\n",
      "                    neg_iou_thr=0.7,\n",
      "                    min_pos_iou=0.7,\n",
      "                    match_low_quality=False,\n",
      "                    ignore_iof_thr=-1),\n",
      "                sampler=dict(\n",
      "                    type='RandomSampler',\n",
      "                    num=512,\n",
      "                    pos_fraction=0.25,\n",
      "                    neg_pos_ub=-1,\n",
      "                    add_gt_as_proposals=True),\n",
      "                pos_weight=-1,\n",
      "                debug=False)\n",
      "        ]),\n",
      "    test_cfg=dict(\n",
      "        rpn=dict(\n",
      "            nms_pre=1000,\n",
      "            max_per_img=1000,\n",
      "            nms=dict(type='nms', iou_threshold=0.7),\n",
      "            min_bbox_size=0),\n",
      "        rcnn=dict(\n",
      "            score_thr=0.05,\n",
      "            nms=dict(type='nms', iou_threshold=0.5),\n",
      "            max_per_img=100)))\n",
      "dataset_type = 'CocoDataset'\n",
      "classes = ('fallen', 'standing')\n",
      "data_root = '/content/CV_data'\n",
      "img_norm_cfg = dict(\n",
      "    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\n",
      "train_pipeline = [\n",
      "    dict(type='LoadImageFromFile'),\n",
      "    dict(type='LoadAnnotations', with_bbox=True),\n",
      "    dict(type='Resize', img_scale=(1920, 1080), keep_ratio=True),\n",
      "    dict(type='RandomFlip', flip_ratio=0.5),\n",
      "    dict(\n",
      "        type='Normalize',\n",
      "        mean=[123.675, 116.28, 103.53],\n",
      "        std=[58.395, 57.12, 57.375],\n",
      "        to_rgb=True),\n",
      "    dict(type='Pad', size_divisor=32),\n",
      "    dict(type='DefaultFormatBundle'),\n",
      "    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n",
      "]\n",
      "test_pipeline = [\n",
      "    dict(type='LoadImageFromFile'),\n",
      "    dict(\n",
      "        type='MultiScaleFlipAug',\n",
      "        img_scale=(1920, 1080),\n",
      "        flip=True,\n",
      "        transforms=[\n",
      "            dict(type='Resize', keep_ratio=True),\n",
      "            dict(type='RandomFlip'),\n",
      "            dict(\n",
      "                type='Normalize',\n",
      "                mean=[123.675, 116.28, 103.53],\n",
      "                std=[58.395, 57.12, 57.375],\n",
      "                to_rgb=True),\n",
      "            dict(type='Pad', size_divisor=32),\n",
      "            dict(type='ImageToTensor', keys=['img']),\n",
      "            dict(type='Collect', keys=['img'])\n",
      "        ])\n",
      "]\n",
      "data = dict(\n",
      "    samples_per_gpu=6,\n",
      "    workers_per_gpu=2,\n",
      "    train=dict(\n",
      "        type='CocoDataset',\n",
      "        classes=('fallen', 'standing'),\n",
      "        ann_file='/content/CV_data/train/vfp_train.json',\n",
      "        img_prefix='/content/CV_data/train/images',\n",
      "        pipeline=[\n",
      "            dict(type='LoadImageFromFile'),\n",
      "            dict(type='LoadAnnotations', with_bbox=True),\n",
      "            dict(type='Resize', img_scale=(1920, 1080), keep_ratio=True),\n",
      "            dict(type='RandomFlip', flip_ratio=0.5),\n",
      "            dict(\n",
      "                type='Normalize',\n",
      "                mean=[123.675, 116.28, 103.53],\n",
      "                std=[58.395, 57.12, 57.375],\n",
      "                to_rgb=True),\n",
      "            dict(type='Pad', size_divisor=32),\n",
      "            dict(type='DefaultFormatBundle'),\n",
      "            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n",
      "        ]),\n",
      "    val=dict(\n",
      "        type='CocoDataset',\n",
      "        classes=('fallen', 'standing'),\n",
      "        ann_file='/content/CV_data/val/vfp_val.json',\n",
      "        img_prefix='/content/CV_data/val/images',\n",
      "        pipeline=[\n",
      "            dict(type='LoadImageFromFile'),\n",
      "            dict(\n",
      "                type='MultiScaleFlipAug',\n",
      "                img_scale=(1920, 1080),\n",
      "                flip=True,\n",
      "                transforms=[\n",
      "                    dict(type='Resize', keep_ratio=True),\n",
      "                    dict(type='RandomFlip'),\n",
      "                    dict(\n",
      "                        type='Normalize',\n",
      "                        mean=[123.675, 116.28, 103.53],\n",
      "                        std=[58.395, 57.12, 57.375],\n",
      "                        to_rgb=True),\n",
      "                    dict(type='Pad', size_divisor=32),\n",
      "                    dict(type='ImageToTensor', keys=['img']),\n",
      "                    dict(type='Collect', keys=['img'])\n",
      "                ])\n",
      "        ]),\n",
      "    test=dict(\n",
      "        type='CocoDataset',\n",
      "        classes=('fallen', 'standing'),\n",
      "        ann_file='/content/CV_data/test/interim_no_annotations.json',\n",
      "        img_prefix='/content/CV_data/test/images',\n",
      "        pipeline=[\n",
      "            dict(type='LoadImageFromFile'),\n",
      "            dict(\n",
      "                type='MultiScaleFlipAug',\n",
      "                img_scale=(1920, 1080),\n",
      "                flip=True,\n",
      "                transforms=[\n",
      "                    dict(type='Resize', keep_ratio=True),\n",
      "                    dict(type='RandomFlip'),\n",
      "                    dict(\n",
      "                        type='Normalize',\n",
      "                        mean=[123.675, 116.28, 103.53],\n",
      "                        std=[58.395, 57.12, 57.375],\n",
      "                        to_rgb=True),\n",
      "                    dict(type='Pad', size_divisor=32),\n",
      "                    dict(type='ImageToTensor', keys=['img']),\n",
      "                    dict(type='Collect', keys=['img'])\n",
      "                ])\n",
      "        ]))\n",
      "evaluation = dict(interval=1, metric='bbox')\n",
      "fp16 = dict(loss_scale=512.0)\n",
      "device = 'cuda'\n",
      "work_dir = '/content/drive/MyDrive/TIL 2022/CV/cascade_training'\n",
      "gpu_ids = [0]\n",
      "\n",
      "2022-06-06 14:42:24,652 - mmdet - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'}\n",
      "2022-06-06 14:42:24,653 - mmcv - INFO - load model from: torchvision://resnet50\n",
      "2022-06-06 14:42:24,653 - mmcv - INFO - load checkpoint from torchvision path: torchvision://resnet50\n",
      "2022-06-06 14:42:24,887 - mmcv - WARNING - The model and loaded state dict do not match exactly\n",
      "\n",
      "unexpected key in source state_dict: fc.weight, fc.bias\n",
      "\n",
      "2022-06-06 14:42:24,918 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}\n",
      "2022-06-06 14:42:24,941 - mmdet - INFO - initialize RPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01}\n",
      "2022-06-06 14:42:24,949 - mmdet - INFO - initialize Shared2FCBBoxHead with init_cfg [{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'distribution': 'uniform', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}]\n",
      "2022-06-06 14:42:25,057 - mmdet - INFO - initialize Shared2FCBBoxHead with init_cfg [{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'distribution': 'uniform', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}]\n",
      "2022-06-06 14:42:25,165 - mmdet - INFO - initialize Shared2FCBBoxHead with init_cfg [{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'distribution': 'uniform', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}]\n",
      "loading annotations into memory...\n",
      "Done (t=0.06s)\n",
      "creating index...\n",
      "index created!\n",
      "loading annotations into memory...\n",
      "Done (t=0.02s)\n",
      "creating index...\n",
      "index created!\n",
      "2022-06-06 14:42:37,938 - mmdet - INFO - Automatic scaling of learning rate (LR) has been disabled.\n",
      "loading annotations into memory...\n",
      "Done (t=0.12s)\n",
      "creating index...\n",
      "index created!\n",
      "2022-06-06 14:42:38,077 - mmdet - INFO - load checkpoint from local path: /content/drive/MyDrive/TIL 2022/CV/cascade_training/epoch_17.pth\n",
      "2022-06-06 14:42:45,887 - mmdet - INFO - the iteration number is changed due to change of GPU number\n",
      "2022-06-06 14:42:45,899 - mmdet - INFO - resumed epoch 17, iter 855592\n",
      "2022-06-06 14:42:45,900 - mmdet - INFO - Start running, host: root@fd0161930f2b, work_dir: /content/drive/MyDrive/TIL 2022/CV/cascade_training\n",
      "2022-06-06 14:42:45,901 - mmdet - INFO - Hooks will be executed in the following order:\n",
      "before_run:\n",
      "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
      "(ABOVE_NORMAL) Fp16OptimizerHook                  \n",
      "(NORMAL      ) CheckpointHook                     \n",
      "(LOW         ) EvalHook                           \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "before_train_epoch:\n",
      "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
      "(NORMAL      ) NumClassCheckHook                  \n",
      "(LOW         ) IterTimerHook                      \n",
      "(LOW         ) EvalHook                           \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "before_train_iter:\n",
      "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
      "(LOW         ) IterTimerHook                      \n",
      "(LOW         ) EvalHook                           \n",
      " -------------------- \n",
      "after_train_iter:\n",
      "(ABOVE_NORMAL) Fp16OptimizerHook                  \n",
      "(NORMAL      ) CheckpointHook                     \n",
      "(LOW         ) IterTimerHook                      \n",
      "(LOW         ) EvalHook                           \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "after_train_epoch:\n",
      "(NORMAL      ) CheckpointHook                     \n",
      "(LOW         ) EvalHook                           \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "before_val_epoch:\n",
      "(NORMAL      ) NumClassCheckHook                  \n",
      "(LOW         ) IterTimerHook                      \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "before_val_iter:\n",
      "(LOW         ) IterTimerHook                      \n",
      " -------------------- \n",
      "after_val_iter:\n",
      "(LOW         ) IterTimerHook                      \n",
      " -------------------- \n",
      "after_val_epoch:\n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "after_run:\n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      " -------------------- \n",
      "2022-06-06 14:42:45,901 - mmdet - INFO - workflow: [('train', 1), ('val', 1)], max: 300 epochs\n",
      "2022-06-06 14:42:45,902 - mmdet - INFO - Checkpoints will be saved to /content/drive/MyDrive/TIL 2022/CV/cascade_training by HardDiskBackend.\n",
      "2022-06-06 14:44:29,121 - mmdet - INFO - Epoch [18][50/818]\tlr: 2.000e-04, eta: -15 days, 10:05:52, time: 2.064, data_time: 0.112, memory: 10078, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0020, s0.loss_cls: 0.0302, s0.acc: 98.8613, s0.loss_bbox: 0.0150, s1.loss_cls: 0.0094, s1.acc: 99.3580, s1.loss_bbox: 0.0221, s2.loss_cls: 0.0053, s2.acc: 99.3151, s2.loss_bbox: 0.0201, loss: 0.1048\n",
      "2022-06-06 14:46:09,451 - mmdet - INFO - Epoch [18][100/818]\tlr: 2.000e-04, eta: -15 days, 14:56:58, time: 2.007, data_time: 0.036, memory: 10079, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0023, s0.loss_cls: 0.0317, s0.acc: 98.7826, s0.loss_bbox: 0.0173, s1.loss_cls: 0.0103, s1.acc: 99.2310, s1.loss_bbox: 0.0254, s2.loss_cls: 0.0055, s2.acc: 99.2125, s2.loss_bbox: 0.0222, loss: 0.1155\n",
      "2022-06-06 14:47:49,360 - mmdet - INFO - Epoch [18][150/818]\tlr: 2.000e-04, eta: -15 days, 17:01:28, time: 1.998, data_time: 0.035, memory: 10080, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0023, s0.loss_cls: 0.0341, s0.acc: 98.8092, s0.loss_bbox: 0.0168, s1.loss_cls: 0.0111, s1.acc: 99.2804, s1.loss_bbox: 0.0262, s2.loss_cls: 0.0058, s2.acc: 99.2388, s2.loss_bbox: 0.0224, loss: 0.1198\n",
      "2022-06-06 14:49:29,815 - mmdet - INFO - Epoch [18][200/818]\tlr: 2.000e-04, eta: -15 days, 17:35:08, time: 2.009, data_time: 0.035, memory: 10080, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0022, s0.loss_cls: 0.0333, s0.acc: 98.7598, s0.loss_bbox: 0.0177, s1.loss_cls: 0.0112, s1.acc: 99.2048, s1.loss_bbox: 0.0267, s2.loss_cls: 0.0070, s2.acc: 99.0808, s2.loss_bbox: 0.0223, loss: 0.1212\n",
      "2022-06-06 14:51:09,894 - mmdet - INFO - Epoch [18][250/818]\tlr: 2.000e-04, eta: -15 days, 18:09:57, time: 2.002, data_time: 0.035, memory: 10080, loss_rpn_cls: 0.0017, loss_rpn_bbox: 0.0021, s0.loss_cls: 0.0326, s0.acc: 98.7760, s0.loss_bbox: 0.0169, s1.loss_cls: 0.0118, s1.acc: 99.2341, s1.loss_bbox: 0.0261, s2.loss_cls: 0.0070, s2.acc: 99.1082, s2.loss_bbox: 0.0227, loss: 0.1208\n",
      "2022-06-06 14:52:50,068 - mmdet - INFO - Epoch [18][300/818]\tlr: 2.000e-04, eta: -15 days, 18:29:23, time: 2.003, data_time: 0.035, memory: 10080, loss_rpn_cls: 0.0021, loss_rpn_bbox: 0.0021, s0.loss_cls: 0.0333, s0.acc: 98.7038, s0.loss_bbox: 0.0189, s1.loss_cls: 0.0127, s1.acc: 99.0877, s1.loss_bbox: 0.0274, s2.loss_cls: 0.0070, s2.acc: 98.9926, s2.loss_bbox: 0.0231, loss: 0.1264\n",
      "2022-06-06 14:54:30,711 - mmdet - INFO - Epoch [18][350/818]\tlr: 2.000e-04, eta: -15 days, 18:29:09, time: 2.013, data_time: 0.035, memory: 10080, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0020, s0.loss_cls: 0.0310, s0.acc: 98.8216, s0.loss_bbox: 0.0158, s1.loss_cls: 0.0098, s1.acc: 99.2819, s1.loss_bbox: 0.0241, s2.loss_cls: 0.0055, s2.acc: 99.2259, s2.loss_bbox: 0.0219, loss: 0.1112\n",
      "2022-06-06 14:56:10,876 - mmdet - INFO - Epoch [18][400/818]\tlr: 2.000e-04, eta: -15 days, 18:40:43, time: 2.003, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0015, loss_rpn_bbox: 0.0018, s0.loss_cls: 0.0312, s0.acc: 98.8932, s0.loss_bbox: 0.0150, s1.loss_cls: 0.0103, s1.acc: 99.3791, s1.loss_bbox: 0.0217, s2.loss_cls: 0.0056, s2.acc: 99.2856, s2.loss_bbox: 0.0198, loss: 0.1070\n",
      "2022-06-06 14:57:50,844 - mmdet - INFO - Epoch [18][450/818]\tlr: 2.000e-04, eta: -15 days, 18:53:48, time: 1.999, data_time: 0.035, memory: 10080, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0336, s0.acc: 98.7428, s0.loss_bbox: 0.0158, s1.loss_cls: 0.0114, s1.acc: 99.1746, s1.loss_bbox: 0.0240, s2.loss_cls: 0.0064, s2.acc: 99.1362, s2.loss_bbox: 0.0211, loss: 0.1146\n",
      "2022-06-06 14:59:31,746 - mmdet - INFO - Epoch [18][500/818]\tlr: 2.000e-04, eta: -15 days, 18:44:55, time: 2.018, data_time: 0.035, memory: 10080, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0022, s0.loss_cls: 0.0318, s0.acc: 98.8490, s0.loss_bbox: 0.0161, s1.loss_cls: 0.0100, s1.acc: 99.3209, s1.loss_bbox: 0.0249, s2.loss_cls: 0.0061, s2.acc: 99.2221, s2.loss_bbox: 0.0210, loss: 0.1129\n",
      "2022-06-06 15:01:11,458 - mmdet - INFO - Epoch [18][550/818]\tlr: 2.000e-04, eta: -15 days, 18:59:22, time: 1.994, data_time: 0.034, memory: 10080, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0018, s0.loss_cls: 0.0282, s0.acc: 98.9414, s0.loss_bbox: 0.0148, s1.loss_cls: 0.0080, s1.acc: 99.3986, s1.loss_bbox: 0.0222, s2.loss_cls: 0.0045, s2.acc: 99.3395, s2.loss_bbox: 0.0203, loss: 0.1007\n",
      "2022-06-06 15:02:51,380 - mmdet - INFO - Epoch [18][600/818]\tlr: 2.000e-04, eta: -15 days, 19:07:35, time: 1.998, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0016, s0.loss_cls: 0.0280, s0.acc: 98.9466, s0.loss_bbox: 0.0130, s1.loss_cls: 0.0084, s1.acc: 99.3960, s1.loss_bbox: 0.0193, s2.loss_cls: 0.0042, s2.acc: 99.4196, s2.loss_bbox: 0.0178, loss: 0.0935\n",
      "2022-06-06 15:04:31,830 - mmdet - INFO - Epoch [18][650/818]\tlr: 2.000e-04, eta: -15 days, 19:06:00, time: 2.009, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0019, s0.loss_cls: 0.0332, s0.acc: 98.7572, s0.loss_bbox: 0.0179, s1.loss_cls: 0.0114, s1.acc: 99.1961, s1.loss_bbox: 0.0274, s2.loss_cls: 0.0064, s2.acc: 99.1215, s2.loss_bbox: 0.0239, loss: 0.1227\n",
      "2022-06-06 15:06:11,513 - mmdet - INFO - Epoch [18][700/818]\tlr: 2.000e-04, eta: -15 days, 19:15:34, time: 1.994, data_time: 0.035, memory: 10080, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0019, s0.loss_cls: 0.0317, s0.acc: 98.8346, s0.loss_bbox: 0.0157, s1.loss_cls: 0.0114, s1.acc: 99.2618, s1.loss_bbox: 0.0241, s2.loss_cls: 0.0061, s2.acc: 99.2097, s2.loss_bbox: 0.0223, loss: 0.1141\n",
      "2022-06-06 15:07:51,272 - mmdet - INFO - Epoch [18][750/818]\tlr: 2.000e-04, eta: -15 days, 19:22:35, time: 1.995, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0012, loss_rpn_bbox: 0.0019, s0.loss_cls: 0.0291, s0.acc: 98.8939, s0.loss_bbox: 0.0145, s1.loss_cls: 0.0096, s1.acc: 99.3431, s1.loss_bbox: 0.0219, s2.loss_cls: 0.0052, s2.acc: 99.3323, s2.loss_bbox: 0.0208, loss: 0.1042\n",
      "2022-06-06 15:09:30,985 - mmdet - INFO - Epoch [18][800/818]\tlr: 2.000e-04, eta: -15 days, 19:29:07, time: 1.994, data_time: 0.035, memory: 10080, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0289, s0.acc: 98.8841, s0.loss_bbox: 0.0139, s1.loss_cls: 0.0089, s1.acc: 99.3609, s1.loss_bbox: 0.0212, s2.loss_cls: 0.0053, s2.acc: 99.2829, s2.loss_bbox: 0.0184, loss: 0.0991\n",
      "2022-06-06 15:10:06,735 - mmdet - INFO - Saving checkpoint at 18 epochs\n",
      "[>>] 1218/1218, 3.7 task/s, elapsed: 333s, ETA:     0s2022-06-06 15:15:44,721 - mmdet - INFO - Evaluating bbox...\n",
      "Loading and preparing results...\n",
      "DONE (t=0.01s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=1.34s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.21s).\n",
      "2022-06-06 15:15:46,305 - mmdet - INFO - \n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.912\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.988\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.966\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.396\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.868\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.947\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.934\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.934\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.934\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.492\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.896\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.965\n",
      "\n",
      "2022-06-06 15:15:46,320 - mmdet - INFO - Exp name: cascade_rcnn_benchmark.py\n",
      "2022-06-06 15:15:46,320 - mmdet - INFO - Epoch(val) [18][1218]\tbbox_mAP: 0.9120, bbox_mAP_50: 0.9880, bbox_mAP_75: 0.9660, bbox_mAP_s: 0.3960, bbox_mAP_m: 0.8680, bbox_mAP_l: 0.9470, bbox_mAP_copypaste: 0.912 0.988 0.966 0.396 0.868 0.947\n",
      "2022-06-06 15:18:59,916 - mmdet - INFO - Exp name: cascade_rcnn_benchmark.py\n",
      "2022-06-06 15:18:59,916 - mmdet - INFO - Epoch(val) [18][203]\tloss_rpn_cls: 0.0015, loss_rpn_bbox: 0.0021, s0.loss_cls: 0.0345, s0.acc: 98.6981, s0.loss_bbox: 0.0194, s1.loss_cls: 0.0115, s1.acc: 99.1746, s1.loss_bbox: 0.0290, s2.loss_cls: 0.0065, s2.acc: 99.0738, s2.loss_bbox: 0.0242, loss: 0.1285\n",
      "2022-06-06 15:20:44,090 - mmdet - INFO - Epoch [19][50/818]\tlr: 2.000e-04, eta: -14 days, 1:45:40, time: 2.083, data_time: 0.104, memory: 10080, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0019, s0.loss_cls: 0.0302, s0.acc: 98.8633, s0.loss_bbox: 0.0169, s1.loss_cls: 0.0105, s1.acc: 99.2757, s1.loss_bbox: 0.0249, s2.loss_cls: 0.0064, s2.acc: 99.0935, s2.loss_bbox: 0.0212, loss: 0.1128\n",
      "2022-06-06 15:22:24,161 - mmdet - INFO - Epoch [19][100/818]\tlr: 2.000e-04, eta: -14 days, 1:26:08, time: 2.001, data_time: 0.037, memory: 10080, loss_rpn_cls: 0.0016, loss_rpn_bbox: 0.0023, s0.loss_cls: 0.0307, s0.acc: 98.8848, s0.loss_bbox: 0.0152, s1.loss_cls: 0.0096, s1.acc: 99.3109, s1.loss_bbox: 0.0223, s2.loss_cls: 0.0053, s2.acc: 99.2260, s2.loss_bbox: 0.0196, loss: 0.1065\n",
      "2022-06-06 15:24:04,417 - mmdet - INFO - Epoch [19][150/818]\tlr: 2.000e-04, eta: -14 days, 1:06:29, time: 2.005, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0019, s0.loss_cls: 0.0296, s0.acc: 98.8633, s0.loss_bbox: 0.0154, s1.loss_cls: 0.0091, s1.acc: 99.3233, s1.loss_bbox: 0.0237, s2.loss_cls: 0.0050, s2.acc: 99.2929, s2.loss_bbox: 0.0215, loss: 0.1069\n",
      "2022-06-06 15:25:44,846 - mmdet - INFO - Epoch [19][200/818]\tlr: 2.000e-04, eta: -14 days, 0:46:52, time: 2.009, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0026, loss_rpn_bbox: 0.0019, s0.loss_cls: 0.0300, s0.acc: 98.8743, s0.loss_bbox: 0.0160, s1.loss_cls: 0.0096, s1.acc: 99.3588, s1.loss_bbox: 0.0243, s2.loss_cls: 0.0054, s2.acc: 99.3016, s2.loss_bbox: 0.0219, loss: 0.1118\n",
      "2022-06-06 15:27:25,289 - mmdet - INFO - Epoch [19][250/818]\tlr: 2.000e-04, eta: -14 days, 0:28:49, time: 2.009, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0018, s0.loss_cls: 0.0297, s0.acc: 98.8900, s0.loss_bbox: 0.0152, s1.loss_cls: 0.0102, s1.acc: 99.2901, s1.loss_bbox: 0.0229, s2.loss_cls: 0.0058, s2.acc: 99.2304, s2.loss_bbox: 0.0196, loss: 0.1063\n",
      "2022-06-06 15:29:05,305 - mmdet - INFO - Epoch [19][300/818]\tlr: 2.000e-04, eta: -14 days, 0:16:06, time: 2.000, data_time: 0.035, memory: 10080, loss_rpn_cls: 0.0012, loss_rpn_bbox: 0.0018, s0.loss_cls: 0.0283, s0.acc: 98.9167, s0.loss_bbox: 0.0145, s1.loss_cls: 0.0079, s1.acc: 99.4686, s1.loss_bbox: 0.0228, s2.loss_cls: 0.0043, s2.acc: 99.4209, s2.loss_bbox: 0.0201, loss: 0.1010\n",
      "2022-06-06 15:30:45,365 - mmdet - INFO - Epoch [19][350/818]\tlr: 2.000e-04, eta: -14 days, 0:03:57, time: 2.001, data_time: 0.035, memory: 10080, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0018, s0.loss_cls: 0.0284, s0.acc: 98.9408, s0.loss_bbox: 0.0139, s1.loss_cls: 0.0088, s1.acc: 99.3866, s1.loss_bbox: 0.0222, s2.loss_cls: 0.0050, s2.acc: 99.3686, s2.loss_bbox: 0.0209, loss: 0.1020\n",
      "2022-06-06 15:32:25,440 - mmdet - INFO - Epoch [19][400/818]\tlr: 2.000e-04, eta: -15 days, 23:52:33, time: 2.001, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0020, s0.loss_cls: 0.0313, s0.acc: 98.8770, s0.loss_bbox: 0.0150, s1.loss_cls: 0.0097, s1.acc: 99.3506, s1.loss_bbox: 0.0226, s2.loss_cls: 0.0054, s2.acc: 99.2255, s2.loss_bbox: 0.0206, loss: 0.1075\n",
      "2022-06-06 15:34:05,802 - mmdet - INFO - Epoch [19][450/818]\tlr: 2.000e-04, eta: -15 days, 23:39:36, time: 2.007, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0018, s0.loss_cls: 0.0287, s0.acc: 98.9075, s0.loss_bbox: 0.0160, s1.loss_cls: 0.0090, s1.acc: 99.3536, s1.loss_bbox: 0.0234, s2.loss_cls: 0.0047, s2.acc: 99.3322, s2.loss_bbox: 0.0210, loss: 0.1055\n",
      "2022-06-06 15:35:45,541 - mmdet - INFO - Epoch [19][500/818]\tlr: 2.000e-04, eta: -15 days, 23:32:19, time: 1.995, data_time: 0.035, memory: 10080, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0015, s0.loss_cls: 0.0281, s0.acc: 98.9030, s0.loss_bbox: 0.0132, s1.loss_cls: 0.0082, s1.acc: 99.4173, s1.loss_bbox: 0.0210, s2.loss_cls: 0.0045, s2.acc: 99.3712, s2.loss_bbox: 0.0190, loss: 0.0962\n",
      "2022-06-06 15:37:25,547 - mmdet - INFO - Epoch [19][550/818]\tlr: 2.000e-04, eta: -15 days, 23:23:28, time: 2.000, data_time: 0.037, memory: 10080, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0020, s0.loss_cls: 0.0343, s0.acc: 98.7415, s0.loss_bbox: 0.0169, s1.loss_cls: 0.0125, s1.acc: 99.1710, s1.loss_bbox: 0.0250, s2.loss_cls: 0.0067, s2.acc: 99.0574, s2.loss_bbox: 0.0218, loss: 0.1201\n",
      "2022-06-06 15:39:06,466 - mmdet - INFO - Epoch [19][600/818]\tlr: 2.000e-04, eta: -15 days, 23:08:33, time: 2.018, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0018, s0.loss_cls: 0.0280, s0.acc: 98.9460, s0.loss_bbox: 0.0150, s1.loss_cls: 0.0080, s1.acc: 99.4310, s1.loss_bbox: 0.0224, s2.loss_cls: 0.0043, s2.acc: 99.4108, s2.loss_bbox: 0.0203, loss: 0.1008\n",
      "2022-06-06 15:40:47,748 - mmdet - INFO - Epoch [19][650/818]\tlr: 2.000e-04, eta: -15 days, 22:52:01, time: 2.026, data_time: 0.037, memory: 10080, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0016, s0.loss_cls: 0.0286, s0.acc: 98.9264, s0.loss_bbox: 0.0139, s1.loss_cls: 0.0083, s1.acc: 99.4023, s1.loss_bbox: 0.0216, s2.loss_cls: 0.0043, s2.acc: 99.4303, s2.loss_bbox: 0.0197, loss: 0.0989\n",
      "2022-06-06 15:42:27,968 - mmdet - INFO - Epoch [19][700/818]\tlr: 2.000e-04, eta: -15 days, 22:43:36, time: 2.004, data_time: 0.037, memory: 10080, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0022, s0.loss_cls: 0.0276, s0.acc: 98.9349, s0.loss_bbox: 0.0138, s1.loss_cls: 0.0085, s1.acc: 99.3480, s1.loss_bbox: 0.0213, s2.loss_cls: 0.0043, s2.acc: 99.4057, s2.loss_bbox: 0.0198, loss: 0.0985\n",
      "2022-06-06 15:44:08,525 - mmdet - INFO - Epoch [19][750/818]\tlr: 2.000e-04, eta: -15 days, 22:33:25, time: 2.011, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0015, s0.loss_cls: 0.0290, s0.acc: 98.9134, s0.loss_bbox: 0.0142, s1.loss_cls: 0.0097, s1.acc: 99.3351, s1.loss_bbox: 0.0203, s2.loss_cls: 0.0054, s2.acc: 99.2828, s2.loss_bbox: 0.0176, loss: 0.0986\n",
      "2022-06-06 15:45:48,532 - mmdet - INFO - Epoch [19][800/818]\tlr: 2.000e-04, eta: -15 days, 22:27:14, time: 2.000, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0022, s0.loss_cls: 0.0269, s0.acc: 98.9857, s0.loss_bbox: 0.0152, s1.loss_cls: 0.0083, s1.acc: 99.4529, s1.loss_bbox: 0.0239, s2.loss_cls: 0.0044, s2.acc: 99.4270, s2.loss_bbox: 0.0210, loss: 0.1027\n",
      "2022-06-06 15:46:24,051 - mmdet - INFO - Saving checkpoint at 19 epochs\n",
      "[>>] 1218/1218, 3.7 task/s, elapsed: 333s, ETA:     0s2022-06-06 15:52:00,947 - mmdet - INFO - Evaluating bbox...\n",
      "Loading and preparing results...\n",
      "DONE (t=0.01s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=1.23s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.36s).\n",
      "2022-06-06 15:52:02,565 - mmdet - INFO - \n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.912\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.988\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.966\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.410\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.868\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.948\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.935\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.935\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.935\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.512\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.898\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.966\n",
      "\n",
      "2022-06-06 15:52:02,582 - mmdet - INFO - Exp name: cascade_rcnn_benchmark.py\n",
      "2022-06-06 15:52:02,582 - mmdet - INFO - Epoch(val) [19][1218]\tbbox_mAP: 0.9120, bbox_mAP_50: 0.9880, bbox_mAP_75: 0.9660, bbox_mAP_s: 0.4100, bbox_mAP_m: 0.8680, bbox_mAP_l: 0.9480, bbox_mAP_copypaste: 0.912 0.988 0.966 0.410 0.868 0.948\n",
      "2022-06-06 15:55:17,212 - mmdet - INFO - Exp name: cascade_rcnn_benchmark.py\n",
      "2022-06-06 15:55:17,213 - mmdet - INFO - Epoch(val) [19][203]\tloss_rpn_cls: 0.0015, loss_rpn_bbox: 0.0020, s0.loss_cls: 0.0332, s0.acc: 98.7747, s0.loss_bbox: 0.0183, s1.loss_cls: 0.0109, s1.acc: 99.2380, s1.loss_bbox: 0.0278, s2.loss_cls: 0.0062, s2.acc: 99.1550, s2.loss_bbox: 0.0234, loss: 0.1233\n",
      "2022-06-06 15:57:00,437 - mmdet - INFO - Epoch [20][50/818]\tlr: 2.000e-04, eta: -14 days, 1:37:44, time: 2.064, data_time: 0.102, memory: 10080, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0303, s0.acc: 98.8353, s0.loss_bbox: 0.0153, s1.loss_cls: 0.0099, s1.acc: 99.3261, s1.loss_bbox: 0.0230, s2.loss_cls: 0.0053, s2.acc: 99.2726, s2.loss_bbox: 0.0202, loss: 0.1066\n",
      "2022-06-06 15:58:40,477 - mmdet - INFO - Epoch [20][100/818]\tlr: 2.000e-04, eta: -14 days, 1:26:15, time: 2.001, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0018, s0.loss_cls: 0.0263, s0.acc: 98.9974, s0.loss_bbox: 0.0123, s1.loss_cls: 0.0071, s1.acc: 99.4990, s1.loss_bbox: 0.0184, s2.loss_cls: 0.0038, s2.acc: 99.4781, s2.loss_bbox: 0.0179, loss: 0.0887\n",
      "2022-06-06 16:00:20,969 - mmdet - INFO - Epoch [20][150/818]\tlr: 2.000e-04, eta: -14 days, 1:12:44, time: 2.010, data_time: 0.037, memory: 10080, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0020, s0.loss_cls: 0.0274, s0.acc: 98.9531, s0.loss_bbox: 0.0151, s1.loss_cls: 0.0073, s1.acc: 99.5108, s1.loss_bbox: 0.0223, s2.loss_cls: 0.0043, s2.acc: 99.4499, s2.loss_bbox: 0.0190, loss: 0.0981\n",
      "2022-06-06 16:02:02,452 - mmdet - INFO - Epoch [20][200/818]\tlr: 2.000e-04, eta: -14 days, 0:54:21, time: 2.030, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0260, s0.acc: 99.0462, s0.loss_bbox: 0.0134, s1.loss_cls: 0.0083, s1.acc: 99.4408, s1.loss_bbox: 0.0215, s2.loss_cls: 0.0044, s2.acc: 99.4131, s2.loss_bbox: 0.0197, loss: 0.0960\n",
      "2022-06-06 16:03:43,503 - mmdet - INFO - Epoch [20][250/818]\tlr: 2.000e-04, eta: -14 days, 0:39:12, time: 2.021, data_time: 0.039, memory: 10080, loss_rpn_cls: 0.0023, loss_rpn_bbox: 0.0019, s0.loss_cls: 0.0276, s0.acc: 98.9577, s0.loss_bbox: 0.0134, s1.loss_cls: 0.0088, s1.acc: 99.4097, s1.loss_bbox: 0.0201, s2.loss_cls: 0.0047, s2.acc: 99.3633, s2.loss_bbox: 0.0177, loss: 0.0963\n",
      "2022-06-06 16:05:24,113 - mmdet - INFO - Epoch [20][300/818]\tlr: 2.000e-04, eta: -14 days, 0:27:04, time: 2.012, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0013, loss_rpn_bbox: 0.0021, s0.loss_cls: 0.0275, s0.acc: 98.9557, s0.loss_bbox: 0.0162, s1.loss_cls: 0.0085, s1.acc: 99.3557, s1.loss_bbox: 0.0242, s2.loss_cls: 0.0046, s2.acc: 99.3655, s2.loss_bbox: 0.0215, loss: 0.1060\n",
      "2022-06-06 16:07:04,619 - mmdet - INFO - Epoch [20][350/818]\tlr: 2.000e-04, eta: -14 days, 0:16:00, time: 2.010, data_time: 0.037, memory: 10080, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0016, s0.loss_cls: 0.0261, s0.acc: 99.0371, s0.loss_bbox: 0.0126, s1.loss_cls: 0.0084, s1.acc: 99.4298, s1.loss_bbox: 0.0190, s2.loss_cls: 0.0048, s2.acc: 99.3178, s2.loss_bbox: 0.0176, loss: 0.0910\n",
      "2022-06-06 16:08:44,854 - mmdet - INFO - Epoch [20][400/818]\tlr: 2.000e-04, eta: -14 days, 0:06:44, time: 2.005, data_time: 0.037, memory: 10080, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0021, s0.loss_cls: 0.0275, s0.acc: 98.9831, s0.loss_bbox: 0.0140, s1.loss_cls: 0.0087, s1.acc: 99.3731, s1.loss_bbox: 0.0222, s2.loss_cls: 0.0047, s2.acc: 99.3251, s2.loss_bbox: 0.0212, loss: 0.1012\n",
      "2022-06-06 16:10:26,402 - mmdet - INFO - Epoch [20][450/818]\tlr: 2.000e-04, eta: -15 days, 23:51:26, time: 2.031, data_time: 0.039, memory: 10080, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0022, s0.loss_cls: 0.0304, s0.acc: 98.9115, s0.loss_bbox: 0.0143, s1.loss_cls: 0.0105, s1.acc: 99.3154, s1.loss_bbox: 0.0216, s2.loss_cls: 0.0058, s2.acc: 99.2712, s2.loss_bbox: 0.0193, loss: 0.1050\n",
      "2022-06-06 16:12:06,287 - mmdet - INFO - Epoch [20][500/818]\tlr: 2.000e-04, eta: -15 days, 23:44:42, time: 1.998, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0268, s0.acc: 98.9857, s0.loss_bbox: 0.0146, s1.loss_cls: 0.0079, s1.acc: 99.4047, s1.loss_bbox: 0.0217, s2.loss_cls: 0.0047, s2.acc: 99.3524, s2.loss_bbox: 0.0184, loss: 0.0963\n",
      "2022-06-06 16:13:46,022 - mmdet - INFO - Epoch [20][550/818]\tlr: 2.000e-04, eta: -15 days, 23:38:54, time: 1.995, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0018, s0.loss_cls: 0.0301, s0.acc: 98.8926, s0.loss_bbox: 0.0157, s1.loss_cls: 0.0105, s1.acc: 99.3098, s1.loss_bbox: 0.0226, s2.loss_cls: 0.0060, s2.acc: 99.2448, s2.loss_bbox: 0.0196, loss: 0.1075\n",
      "2022-06-06 16:15:26,142 - mmdet - INFO - Epoch [20][600/818]\tlr: 2.000e-04, eta: -15 days, 23:31:32, time: 2.002, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0018, s0.loss_cls: 0.0300, s0.acc: 98.8926, s0.loss_bbox: 0.0166, s1.loss_cls: 0.0089, s1.acc: 99.4287, s1.loss_bbox: 0.0237, s2.loss_cls: 0.0051, s2.acc: 99.4341, s2.loss_bbox: 0.0212, loss: 0.1084\n",
      "2022-06-06 16:17:06,044 - mmdet - INFO - Epoch [20][650/818]\tlr: 2.000e-04, eta: -15 days, 23:25:23, time: 1.998, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0295, s0.acc: 98.9089, s0.loss_bbox: 0.0137, s1.loss_cls: 0.0091, s1.acc: 99.3502, s1.loss_bbox: 0.0211, s2.loss_cls: 0.0047, s2.acc: 99.3523, s2.loss_bbox: 0.0198, loss: 0.1006\n",
      "2022-06-06 16:18:45,584 - mmdet - INFO - Epoch [20][700/818]\tlr: 2.000e-04, eta: -15 days, 23:21:01, time: 1.991, data_time: 0.037, memory: 10080, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0256, s0.acc: 99.0234, s0.loss_bbox: 0.0136, s1.loss_cls: 0.0071, s1.acc: 99.4855, s1.loss_bbox: 0.0199, s2.loss_cls: 0.0036, s2.acc: 99.5114, s2.loss_bbox: 0.0189, loss: 0.0909\n",
      "2022-06-06 16:20:25,138 - mmdet - INFO - Epoch [20][750/818]\tlr: 2.000e-04, eta: -15 days, 23:16:41, time: 1.991, data_time: 0.037, memory: 10080, loss_rpn_cls: 0.0012, loss_rpn_bbox: 0.0020, s0.loss_cls: 0.0281, s0.acc: 98.9635, s0.loss_bbox: 0.0151, s1.loss_cls: 0.0081, s1.acc: 99.4470, s1.loss_bbox: 0.0236, s2.loss_cls: 0.0050, s2.acc: 99.3264, s2.loss_bbox: 0.0196, loss: 0.1026\n",
      "2022-06-06 16:22:04,709 - mmdet - INFO - Epoch [20][800/818]\tlr: 2.000e-04, eta: -15 days, 23:12:24, time: 1.991, data_time: 0.037, memory: 10080, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0015, s0.loss_cls: 0.0284, s0.acc: 98.9277, s0.loss_bbox: 0.0144, s1.loss_cls: 0.0079, s1.acc: 99.4268, s1.loss_bbox: 0.0225, s2.loss_cls: 0.0046, s2.acc: 99.3592, s2.loss_bbox: 0.0204, loss: 0.1005\n",
      "2022-06-06 16:22:40,247 - mmdet - INFO - Saving checkpoint at 20 epochs\n",
      "[>>] 1218/1218, 3.6 task/s, elapsed: 334s, ETA:     0s2022-06-06 16:28:18,748 - mmdet - INFO - Evaluating bbox...\n",
      "Loading and preparing results...\n",
      "DONE (t=0.01s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=1.22s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.22s).\n",
      "2022-06-06 16:28:20,223 - mmdet - INFO - \n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.916\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.989\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.967\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.416\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.873\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.949\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.937\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.937\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.937\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.512\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.901\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.967\n",
      "\n",
      "2022-06-06 16:28:20,238 - mmdet - INFO - Exp name: cascade_rcnn_benchmark.py\n",
      "2022-06-06 16:28:20,239 - mmdet - INFO - Epoch(val) [20][1218]\tbbox_mAP: 0.9160, bbox_mAP_50: 0.9890, bbox_mAP_75: 0.9670, bbox_mAP_s: 0.4160, bbox_mAP_m: 0.8730, bbox_mAP_l: 0.9490, bbox_mAP_copypaste: 0.916 0.989 0.967 0.416 0.873 0.949\n",
      "2022-06-06 16:31:35,427 - mmdet - INFO - Exp name: cascade_rcnn_benchmark.py\n",
      "2022-06-06 16:31:35,427 - mmdet - INFO - Epoch(val) [20][203]\tloss_rpn_cls: 0.0012, loss_rpn_bbox: 0.0020, s0.loss_cls: 0.0322, s0.acc: 98.8118, s0.loss_bbox: 0.0183, s1.loss_cls: 0.0106, s1.acc: 99.2498, s1.loss_bbox: 0.0278, s2.loss_cls: 0.0061, s2.acc: 99.1426, s2.loss_bbox: 0.0237, loss: 0.1218\n",
      "2022-06-06 16:33:19,193 - mmdet - INFO - Epoch [21][50/818]\tlr: 2.000e-04, eta: -14 days, 1:15:57, time: 2.075, data_time: 0.104, memory: 10080, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0255, s0.acc: 99.0286, s0.loss_bbox: 0.0140, s1.loss_cls: 0.0073, s1.acc: 99.4485, s1.loss_bbox: 0.0216, s2.loss_cls: 0.0045, s2.acc: 99.3704, s2.loss_bbox: 0.0191, loss: 0.0944\n",
      "2022-06-06 16:34:59,512 - mmdet - INFO - Epoch [21][100/818]\tlr: 2.000e-04, eta: -14 days, 1:06:24, time: 2.006, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0016, s0.loss_cls: 0.0264, s0.acc: 99.0156, s0.loss_bbox: 0.0127, s1.loss_cls: 0.0078, s1.acc: 99.4251, s1.loss_bbox: 0.0200, s2.loss_cls: 0.0042, s2.acc: 99.4407, s2.loss_bbox: 0.0185, loss: 0.0921\n",
      "2022-06-06 16:36:40,269 - mmdet - INFO - Epoch [21][150/818]\tlr: 2.000e-04, eta: -14 days, 0:55:26, time: 2.015, data_time: 0.040, memory: 10080, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0016, s0.loss_cls: 0.0236, s0.acc: 99.1211, s0.loss_bbox: 0.0127, s1.loss_cls: 0.0057, s1.acc: 99.5982, s1.loss_bbox: 0.0204, s2.loss_cls: 0.0032, s2.acc: 99.5394, s2.loss_bbox: 0.0191, loss: 0.0868\n",
      "2022-06-06 16:38:22,340 - mmdet - INFO - Epoch [21][200/818]\tlr: 2.000e-04, eta: -14 days, 0:39:45, time: 2.041, data_time: 0.040, memory: 10080, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0272, s0.acc: 98.9909, s0.loss_bbox: 0.0154, s1.loss_cls: 0.0073, s1.acc: 99.5088, s1.loss_bbox: 0.0214, s2.loss_cls: 0.0038, s2.acc: 99.5130, s2.loss_bbox: 0.0186, loss: 0.0963\n",
      "2022-06-06 16:40:04,931 - mmdet - INFO - Epoch [21][250/818]\tlr: 2.000e-04, eta: -14 days, 0:22:38, time: 2.052, data_time: 0.041, memory: 10080, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0249, s0.acc: 99.0202, s0.loss_bbox: 0.0134, s1.loss_cls: 0.0066, s1.acc: 99.4911, s1.loss_bbox: 0.0200, s2.loss_cls: 0.0039, s2.acc: 99.4367, s2.loss_bbox: 0.0176, loss: 0.0886\n",
      "2022-06-06 16:41:47,636 - mmdet - INFO - Epoch [21][300/818]\tlr: 2.000e-04, eta: -14 days, 0:05:39, time: 2.054, data_time: 0.041, memory: 10080, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0016, s0.loss_cls: 0.0247, s0.acc: 99.0592, s0.loss_bbox: 0.0127, s1.loss_cls: 0.0073, s1.acc: 99.4608, s1.loss_bbox: 0.0193, s2.loss_cls: 0.0038, s2.acc: 99.4661, s2.loss_bbox: 0.0182, loss: 0.0886\n",
      "2022-06-06 16:43:30,231 - mmdet - INFO - Epoch [21][350/818]\tlr: 2.000e-04, eta: -15 days, 23:49:36, time: 2.052, data_time: 0.040, memory: 10080, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0021, s0.loss_cls: 0.0258, s0.acc: 99.0195, s0.loss_bbox: 0.0136, s1.loss_cls: 0.0074, s1.acc: 99.5028, s1.loss_bbox: 0.0208, s2.loss_cls: 0.0045, s2.acc: 99.4573, s2.loss_bbox: 0.0185, loss: 0.0937\n",
      "2022-06-06 16:45:11,549 - mmdet - INFO - Epoch [21][400/818]\tlr: 2.000e-04, eta: -15 days, 23:38:39, time: 2.026, data_time: 0.039, memory: 10080, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0020, s0.loss_cls: 0.0285, s0.acc: 98.9089, s0.loss_bbox: 0.0140, s1.loss_cls: 0.0084, s1.acc: 99.4178, s1.loss_bbox: 0.0219, s2.loss_cls: 0.0045, s2.acc: 99.4119, s2.loss_bbox: 0.0200, loss: 0.1000\n",
      "2022-06-06 16:46:51,923 - mmdet - INFO - Epoch [21][450/818]\tlr: 2.000e-04, eta: -15 days, 23:31:19, time: 2.007, data_time: 0.039, memory: 10080, loss_rpn_cls: 0.0013, loss_rpn_bbox: 0.0018, s0.loss_cls: 0.0294, s0.acc: 98.8991, s0.loss_bbox: 0.0141, s1.loss_cls: 0.0093, s1.acc: 99.3284, s1.loss_bbox: 0.0214, s2.loss_cls: 0.0049, s2.acc: 99.3591, s2.loss_bbox: 0.0190, loss: 0.1012\n",
      "2022-06-06 16:48:31,937 - mmdet - INFO - Epoch [21][500/818]\tlr: 2.000e-04, eta: -15 days, 23:25:26, time: 2.000, data_time: 0.037, memory: 10080, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0015, s0.loss_cls: 0.0246, s0.acc: 99.0736, s0.loss_bbox: 0.0137, s1.loss_cls: 0.0065, s1.acc: 99.5786, s1.loss_bbox: 0.0219, s2.loss_cls: 0.0040, s2.acc: 99.4706, s2.loss_bbox: 0.0190, loss: 0.0916\n",
      "2022-06-06 16:50:12,394 - mmdet - INFO - Epoch [21][550/818]\tlr: 2.000e-04, eta: -15 days, 23:18:10, time: 2.009, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0024, s0.loss_cls: 0.0272, s0.acc: 98.9486, s0.loss_bbox: 0.0154, s1.loss_cls: 0.0085, s1.acc: 99.3853, s1.loss_bbox: 0.0224, s2.loss_cls: 0.0047, s2.acc: 99.3554, s2.loss_bbox: 0.0198, loss: 0.1013\n",
      "2022-06-06 16:51:52,418 - mmdet - INFO - Epoch [21][600/818]\tlr: 2.000e-04, eta: -15 days, 23:12:33, time: 2.000, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0028, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0263, s0.acc: 98.9974, s0.loss_bbox: 0.0139, s1.loss_cls: 0.0078, s1.acc: 99.4591, s1.loss_bbox: 0.0202, s2.loss_cls: 0.0040, s2.acc: 99.4353, s2.loss_bbox: 0.0187, loss: 0.0954\n",
      "2022-06-06 16:53:32,520 - mmdet - INFO - Epoch [21][650/818]\tlr: 2.000e-04, eta: -15 days, 23:06:48, time: 2.002, data_time: 0.037, memory: 10080, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0018, s0.loss_cls: 0.0280, s0.acc: 98.9408, s0.loss_bbox: 0.0135, s1.loss_cls: 0.0083, s1.acc: 99.4096, s1.loss_bbox: 0.0194, s2.loss_cls: 0.0040, s2.acc: 99.4824, s2.loss_bbox: 0.0183, loss: 0.0938\n",
      "2022-06-06 16:55:12,723 - mmdet - INFO - Epoch [21][700/818]\tlr: 2.000e-04, eta: -15 days, 23:00:51, time: 2.004, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0020, s0.loss_cls: 0.0280, s0.acc: 98.9316, s0.loss_bbox: 0.0149, s1.loss_cls: 0.0100, s1.acc: 99.2715, s1.loss_bbox: 0.0212, s2.loss_cls: 0.0056, s2.acc: 99.1915, s2.loss_bbox: 0.0185, loss: 0.1009\n",
      "2022-06-06 16:56:52,985 - mmdet - INFO - Epoch [21][750/818]\tlr: 2.000e-04, eta: -15 days, 22:54:50, time: 2.005, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0273, s0.acc: 98.9596, s0.loss_bbox: 0.0147, s1.loss_cls: 0.0078, s1.acc: 99.4700, s1.loss_bbox: 0.0232, s2.loss_cls: 0.0045, s2.acc: 99.3917, s2.loss_bbox: 0.0207, loss: 0.1008\n",
      "2022-06-06 16:58:32,749 - mmdet - INFO - Epoch [21][800/818]\tlr: 2.000e-04, eta: -15 days, 22:50:32, time: 1.995, data_time: 0.037, memory: 10080, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0018, s0.loss_cls: 0.0260, s0.acc: 99.0111, s0.loss_bbox: 0.0134, s1.loss_cls: 0.0079, s1.acc: 99.4500, s1.loss_bbox: 0.0198, s2.loss_cls: 0.0043, s2.acc: 99.3697, s2.loss_bbox: 0.0187, loss: 0.0928\n",
      "2022-06-06 16:59:08,337 - mmdet - INFO - Saving checkpoint at 21 epochs\n",
      "[>>] 1218/1218, 3.6 task/s, elapsed: 343s, ETA:     0s2022-06-06 17:04:55,604 - mmdet - INFO - Evaluating bbox...\n",
      "Loading and preparing results...\n",
      "DONE (t=0.01s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=1.39s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.27s).\n",
      "2022-06-06 17:04:57,770 - mmdet - INFO - \n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.918\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.989\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.967\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.428\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.876\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.951\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.938\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.938\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.938\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.517\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.903\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.967\n",
      "\n",
      "2022-06-06 17:04:57,794 - mmdet - INFO - Exp name: cascade_rcnn_benchmark.py\n",
      "2022-06-06 17:04:57,794 - mmdet - INFO - Epoch(val) [21][1218]\tbbox_mAP: 0.9180, bbox_mAP_50: 0.9890, bbox_mAP_75: 0.9670, bbox_mAP_s: 0.4280, bbox_mAP_m: 0.8760, bbox_mAP_l: 0.9510, bbox_mAP_copypaste: 0.918 0.989 0.967 0.428 0.876 0.951\n",
      "2022-06-06 17:08:21,166 - mmdet - INFO - Exp name: cascade_rcnn_benchmark.py\n",
      "2022-06-06 17:08:21,166 - mmdet - INFO - Epoch(val) [21][203]\tloss_rpn_cls: 0.0012, loss_rpn_bbox: 0.0020, s0.loss_cls: 0.0317, s0.acc: 98.8232, s0.loss_bbox: 0.0178, s1.loss_cls: 0.0106, s1.acc: 99.2106, s1.loss_bbox: 0.0273, s2.loss_cls: 0.0060, s2.acc: 99.1367, s2.loss_bbox: 0.0233, loss: 0.1200\n",
      "2022-06-06 17:10:05,827 - mmdet - INFO - Epoch [22][50/818]\tlr: 2.000e-04, eta: -14 days, 0:20:22, time: 2.093, data_time: 0.107, memory: 10080, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0019, s0.loss_cls: 0.0252, s0.acc: 99.0221, s0.loss_bbox: 0.0146, s1.loss_cls: 0.0081, s1.acc: 99.3952, s1.loss_bbox: 0.0214, s2.loss_cls: 0.0047, s2.acc: 99.3674, s2.loss_bbox: 0.0179, loss: 0.0949\n",
      "2022-06-06 17:11:46,317 - mmdet - INFO - Epoch [22][100/818]\tlr: 2.000e-04, eta: -14 days, 0:12:38, time: 2.010, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0014, loss_rpn_bbox: 0.0020, s0.loss_cls: 0.0294, s0.acc: 98.8965, s0.loss_bbox: 0.0161, s1.loss_cls: 0.0087, s1.acc: 99.3855, s1.loss_bbox: 0.0249, s2.loss_cls: 0.0048, s2.acc: 99.3411, s2.loss_bbox: 0.0214, loss: 0.1086\n",
      "2022-06-06 17:13:26,891 - mmdet - INFO - Epoch [22][150/818]\tlr: 2.000e-04, eta: -14 days, 0:04:50, time: 2.011, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0246, s0.acc: 99.0417, s0.loss_bbox: 0.0129, s1.loss_cls: 0.0063, s1.acc: 99.5671, s1.loss_bbox: 0.0193, s2.loss_cls: 0.0036, s2.acc: 99.5241, s2.loss_bbox: 0.0173, loss: 0.0868\n",
      "2022-06-06 17:15:07,474 - mmdet - INFO - Epoch [22][200/818]\tlr: 2.000e-04, eta: -15 days, 23:57:10, time: 2.012, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0264, s0.acc: 99.0280, s0.loss_bbox: 0.0139, s1.loss_cls: 0.0081, s1.acc: 99.4400, s1.loss_bbox: 0.0207, s2.loss_cls: 0.0042, s2.acc: 99.4463, s2.loss_bbox: 0.0184, loss: 0.0942\n",
      "2022-06-06 17:16:47,881 - mmdet - INFO - Epoch [22][250/818]\tlr: 2.000e-04, eta: -15 days, 23:50:11, time: 2.008, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0249, s0.acc: 99.0540, s0.loss_bbox: 0.0124, s1.loss_cls: 0.0079, s1.acc: 99.4583, s1.loss_bbox: 0.0198, s2.loss_cls: 0.0041, s2.acc: 99.5164, s2.loss_bbox: 0.0177, loss: 0.0897\n",
      "2022-06-06 17:18:27,730 - mmdet - INFO - Epoch [22][300/818]\tlr: 2.000e-04, eta: -15 days, 23:44:57, time: 1.997, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0281, s0.acc: 98.9421, s0.loss_bbox: 0.0139, s1.loss_cls: 0.0082, s1.acc: 99.4431, s1.loss_bbox: 0.0206, s2.loss_cls: 0.0049, s2.acc: 99.3697, s2.loss_bbox: 0.0186, loss: 0.0968\n",
      "2022-06-06 17:20:08,719 - mmdet - INFO - Epoch [22][350/818]\tlr: 2.000e-04, eta: -15 days, 23:36:36, time: 2.020, data_time: 0.039, memory: 10080, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0240, s0.acc: 99.0566, s0.loss_bbox: 0.0139, s1.loss_cls: 0.0061, s1.acc: 99.5610, s1.loss_bbox: 0.0221, s2.loss_cls: 0.0032, s2.acc: 99.5411, s2.loss_bbox: 0.0202, loss: 0.0919\n",
      "2022-06-06 17:21:51,017 - mmdet - INFO - Epoch [22][400/818]\tlr: 2.000e-04, eta: -15 days, 23:24:47, time: 2.046, data_time: 0.040, memory: 10080, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0019, s0.loss_cls: 0.0240, s0.acc: 99.0560, s0.loss_bbox: 0.0126, s1.loss_cls: 0.0069, s1.acc: 99.4791, s1.loss_bbox: 0.0181, s2.loss_cls: 0.0035, s2.acc: 99.4822, s2.loss_bbox: 0.0170, loss: 0.0847\n",
      "2022-06-06 17:23:31,042 - mmdet - INFO - Epoch [22][450/818]\tlr: 2.000e-04, eta: -15 days, 23:19:29, time: 2.000, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0019, loss_rpn_bbox: 0.0015, s0.loss_cls: 0.0254, s0.acc: 99.0840, s0.loss_bbox: 0.0124, s1.loss_cls: 0.0070, s1.acc: 99.5641, s1.loss_bbox: 0.0184, s2.loss_cls: 0.0040, s2.acc: 99.5142, s2.loss_bbox: 0.0166, loss: 0.0872\n",
      "2022-06-06 17:25:11,498 - mmdet - INFO - Epoch [22][500/818]\tlr: 2.000e-04, eta: -15 days, 23:13:07, time: 2.009, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0017, s0.loss_cls: 0.0240, s0.acc: 99.1009, s0.loss_bbox: 0.0135, s1.loss_cls: 0.0059, s1.acc: 99.5602, s1.loss_bbox: 0.0211, s2.loss_cls: 0.0032, s2.acc: 99.5660, s2.loss_bbox: 0.0193, loss: 0.0891\n",
      "2022-06-06 17:26:51,551 - mmdet - INFO - Epoch [22][550/818]\tlr: 2.000e-04, eta: -15 days, 23:07:57, time: 2.001, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0013, s0.loss_cls: 0.0262, s0.acc: 99.0352, s0.loss_bbox: 0.0124, s1.loss_cls: 0.0072, s1.acc: 99.5181, s1.loss_bbox: 0.0189, s2.loss_cls: 0.0037, s2.acc: 99.5163, s2.loss_bbox: 0.0185, loss: 0.0888\n",
      "2022-06-06 17:28:32,375 - mmdet - INFO - Epoch [22][600/818]\tlr: 2.000e-04, eta: -15 days, 23:00:50, time: 2.016, data_time: 0.040, memory: 10080, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0019, s0.loss_cls: 0.0293, s0.acc: 98.8854, s0.loss_bbox: 0.0141, s1.loss_cls: 0.0098, s1.acc: 99.2825, s1.loss_bbox: 0.0218, s2.loss_cls: 0.0056, s2.acc: 99.1980, s2.loss_bbox: 0.0187, loss: 0.1020\n",
      "2022-06-06 17:30:13,749 - mmdet - INFO - Epoch [22][650/818]\tlr: 2.000e-04, eta: -15 days, 22:52:25, time: 2.027, data_time: 0.040, memory: 10080, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0022, s0.loss_cls: 0.0249, s0.acc: 99.0540, s0.loss_bbox: 0.0131, s1.loss_cls: 0.0070, s1.acc: 99.5000, s1.loss_bbox: 0.0196, s2.loss_cls: 0.0039, s2.acc: 99.5000, s2.loss_bbox: 0.0186, loss: 0.0900\n",
      "2022-06-06 17:31:54,520 - mmdet - INFO - Epoch [22][700/818]\tlr: 2.000e-04, eta: -15 days, 22:45:44, time: 2.015, data_time: 0.040, memory: 10080, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0014, s0.loss_cls: 0.0223, s0.acc: 99.1510, s0.loss_bbox: 0.0113, s1.loss_cls: 0.0066, s1.acc: 99.5376, s1.loss_bbox: 0.0171, s2.loss_cls: 0.0035, s2.acc: 99.5111, s2.loss_bbox: 0.0154, loss: 0.0781\n",
      "2022-06-06 17:33:35,373 - mmdet - INFO - Epoch [22][750/818]\tlr: 2.000e-04, eta: -15 days, 22:38:57, time: 2.017, data_time: 0.040, memory: 10080, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0019, s0.loss_cls: 0.0255, s0.acc: 99.0221, s0.loss_bbox: 0.0150, s1.loss_cls: 0.0075, s1.acc: 99.4415, s1.loss_bbox: 0.0226, s2.loss_cls: 0.0041, s2.acc: 99.4375, s2.loss_bbox: 0.0201, loss: 0.0974\n",
      "2022-06-06 17:35:15,971 - mmdet - INFO - Epoch [22][800/818]\tlr: 2.000e-04, eta: -15 days, 22:32:57, time: 2.012, data_time: 0.039, memory: 10080, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0016, s0.loss_cls: 0.0229, s0.acc: 99.1198, s0.loss_bbox: 0.0128, s1.loss_cls: 0.0057, s1.acc: 99.5910, s1.loss_bbox: 0.0199, s2.loss_cls: 0.0031, s2.acc: 99.5987, s2.loss_bbox: 0.0186, loss: 0.0853\n",
      "2022-06-06 17:35:51,586 - mmdet - INFO - Saving checkpoint at 22 epochs\n",
      "[>>] 1218/1218, 3.6 task/s, elapsed: 337s, ETA:     0s2022-06-06 17:41:32,731 - mmdet - INFO - Evaluating bbox...\n",
      "Loading and preparing results...\n",
      "DONE (t=0.12s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=1.20s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.22s).\n",
      "2022-06-06 17:41:34,303 - mmdet - INFO - \n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.920\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.989\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.967\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.440\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.878\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.953\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.939\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.939\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.939\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.529\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.904\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.968\n",
      "\n",
      "2022-06-06 17:41:34,318 - mmdet - INFO - Exp name: cascade_rcnn_benchmark.py\n",
      "2022-06-06 17:41:34,318 - mmdet - INFO - Epoch(val) [22][1218]\tbbox_mAP: 0.9200, bbox_mAP_50: 0.9890, bbox_mAP_75: 0.9670, bbox_mAP_s: 0.4400, bbox_mAP_m: 0.8780, bbox_mAP_l: 0.9530, bbox_mAP_copypaste: 0.920 0.989 0.967 0.440 0.878 0.953\n",
      "2022-06-06 17:44:52,667 - mmdet - INFO - Exp name: cascade_rcnn_benchmark.py\n",
      "2022-06-06 17:44:52,667 - mmdet - INFO - Epoch(val) [22][203]\tloss_rpn_cls: 0.0012, loss_rpn_bbox: 0.0019, s0.loss_cls: 0.0311, s0.acc: 98.8475, s0.loss_bbox: 0.0177, s1.loss_cls: 0.0099, s1.acc: 99.2921, s1.loss_bbox: 0.0270, s2.loss_cls: 0.0057, s2.acc: 99.1974, s2.loss_bbox: 0.0229, loss: 0.1173\n",
      "2022-06-06 17:46:36,523 - mmdet - INFO - Epoch [23][50/818]\tlr: 2.000e-04, eta: -15 days, 23:46:31, time: 2.077, data_time: 0.107, memory: 10080, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0016, s0.loss_cls: 0.0238, s0.acc: 99.0723, s0.loss_bbox: 0.0128, s1.loss_cls: 0.0069, s1.acc: 99.4863, s1.loss_bbox: 0.0194, s2.loss_cls: 0.0037, s2.acc: 99.5264, s2.loss_bbox: 0.0178, loss: 0.0864\n",
      "2022-06-06 17:48:16,909 - mmdet - INFO - Epoch [23][100/818]\tlr: 2.000e-04, eta: -15 days, 23:40:18, time: 2.008, data_time: 0.039, memory: 10080, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0014, s0.loss_cls: 0.0225, s0.acc: 99.1517, s0.loss_bbox: 0.0115, s1.loss_cls: 0.0063, s1.acc: 99.5959, s1.loss_bbox: 0.0176, s2.loss_cls: 0.0034, s2.acc: 99.5862, s2.loss_bbox: 0.0164, loss: 0.0797\n",
      "2022-06-06 17:49:57,697 - mmdet - INFO - Epoch [23][150/818]\tlr: 2.000e-04, eta: -15 days, 23:33:13, time: 2.016, data_time: 0.039, memory: 10080, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0019, s0.loss_cls: 0.0256, s0.acc: 99.0195, s0.loss_bbox: 0.0132, s1.loss_cls: 0.0075, s1.acc: 99.4708, s1.loss_bbox: 0.0207, s2.loss_cls: 0.0043, s2.acc: 99.4700, s2.loss_bbox: 0.0185, loss: 0.0927\n",
      "2022-06-06 17:51:37,825 - mmdet - INFO - Epoch [23][200/818]\tlr: 2.000e-04, eta: -15 days, 23:27:51, time: 2.003, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0015, s0.loss_cls: 0.0237, s0.acc: 99.0872, s0.loss_bbox: 0.0118, s1.loss_cls: 0.0060, s1.acc: 99.5586, s1.loss_bbox: 0.0178, s2.loss_cls: 0.0031, s2.acc: 99.5451, s2.loss_bbox: 0.0161, loss: 0.0807\n",
      "2022-06-06 17:53:17,428 - mmdet - INFO - Epoch [23][250/818]\tlr: 2.000e-04, eta: -15 days, 23:23:48, time: 1.992, data_time: 0.037, memory: 10080, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0019, s0.loss_cls: 0.0230, s0.acc: 99.1335, s0.loss_bbox: 0.0127, s1.loss_cls: 0.0061, s1.acc: 99.5772, s1.loss_bbox: 0.0192, s2.loss_cls: 0.0032, s2.acc: 99.5738, s2.loss_bbox: 0.0161, loss: 0.0826\n",
      "2022-06-06 17:54:57,985 - mmdet - INFO - Epoch [23][300/818]\tlr: 2.000e-04, eta: -15 days, 23:17:34, time: 2.011, data_time: 0.039, memory: 10080, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0013, s0.loss_cls: 0.0222, s0.acc: 99.1536, s0.loss_bbox: 0.0119, s1.loss_cls: 0.0064, s1.acc: 99.4976, s1.loss_bbox: 0.0179, s2.loss_cls: 0.0035, s2.acc: 99.4627, s2.loss_bbox: 0.0161, loss: 0.0797\n",
      "2022-06-06 17:56:37,812 - mmdet - INFO - Epoch [23][350/818]\tlr: 2.000e-04, eta: -15 days, 23:13:08, time: 1.997, data_time: 0.037, memory: 10080, loss_rpn_cls: 0.0017, loss_rpn_bbox: 0.0018, s0.loss_cls: 0.0232, s0.acc: 99.1595, s0.loss_bbox: 0.0129, s1.loss_cls: 0.0065, s1.acc: 99.5663, s1.loss_bbox: 0.0202, s2.loss_cls: 0.0036, s2.acc: 99.5051, s2.loss_bbox: 0.0185, loss: 0.0884\n",
      "2022-06-06 17:58:17,989 - mmdet - INFO - Epoch [23][400/818]\tlr: 2.000e-04, eta: -15 days, 23:07:57, time: 2.004, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0016, s0.loss_cls: 0.0247, s0.acc: 99.0430, s0.loss_bbox: 0.0129, s1.loss_cls: 0.0069, s1.acc: 99.5072, s1.loss_bbox: 0.0196, s2.loss_cls: 0.0038, s2.acc: 99.5014, s2.loss_bbox: 0.0179, loss: 0.0881\n",
      "2022-06-06 17:59:57,457 - mmdet - INFO - Epoch [23][450/818]\tlr: 2.000e-04, eta: -15 days, 23:04:28, time: 1.989, data_time: 0.037, memory: 10080, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0016, s0.loss_cls: 0.0241, s0.acc: 99.1165, s0.loss_bbox: 0.0132, s1.loss_cls: 0.0066, s1.acc: 99.5323, s1.loss_bbox: 0.0202, s2.loss_cls: 0.0039, s2.acc: 99.5415, s2.loss_bbox: 0.0184, loss: 0.0887\n",
      "2022-06-06 18:01:37,443 - mmdet - INFO - Epoch [23][500/818]\tlr: 2.000e-04, eta: -15 days, 22:59:51, time: 2.000, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0021, s0.loss_cls: 0.0255, s0.acc: 99.0339, s0.loss_bbox: 0.0131, s1.loss_cls: 0.0079, s1.acc: 99.4268, s1.loss_bbox: 0.0212, s2.loss_cls: 0.0043, s2.acc: 99.4005, s2.loss_bbox: 0.0199, loss: 0.0949\n",
      "2022-06-06 18:03:18,042 - mmdet - INFO - Epoch [23][550/818]\tlr: 2.000e-04, eta: -15 days, 22:53:57, time: 2.012, data_time: 0.037, memory: 10080, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0018, s0.loss_cls: 0.0225, s0.acc: 99.1458, s0.loss_bbox: 0.0114, s1.loss_cls: 0.0060, s1.acc: 99.5385, s1.loss_bbox: 0.0171, s2.loss_cls: 0.0029, s2.acc: 99.5778, s2.loss_bbox: 0.0166, loss: 0.0794\n",
      "2022-06-06 18:04:57,837 - mmdet - INFO - Epoch [23][600/818]\tlr: 2.000e-04, eta: -15 days, 22:49:53, time: 1.996, data_time: 0.038, memory: 10080, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0013, s0.loss_cls: 0.0245, s0.acc: 99.0462, s0.loss_bbox: 0.0123, s1.loss_cls: 0.0067, s1.acc: 99.5106, s1.loss_bbox: 0.0190, s2.loss_cls: 0.0035, s2.acc: 99.5194, s2.loss_bbox: 0.0182, loss: 0.0859\n",
      "2022-06-06 18:06:37,331 - mmdet - INFO - Epoch [23][650/818]\tlr: 2.000e-04, eta: -15 days, 22:46:32, time: 1.990, data_time: 0.036, memory: 10080, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0018, s0.loss_cls: 0.0237, s0.acc: 99.0742, s0.loss_bbox: 0.0127, s1.loss_cls: 0.0063, s1.acc: 99.5531, s1.loss_bbox: 0.0183, s2.loss_cls: 0.0031, s2.acc: 99.6200, s2.loss_bbox: 0.0165, loss: 0.0832\n",
      "2022-06-06 18:08:17,520 - mmdet - INFO - Epoch [23][700/818]\tlr: 2.000e-04, eta: -15 days, 22:41:43, time: 2.004, data_time: 0.039, memory: 10080, loss_rpn_cls: 0.0015, loss_rpn_bbox: 0.0019, s0.loss_cls: 0.0263, s0.acc: 99.0540, s0.loss_bbox: 0.0158, s1.loss_cls: 0.0083, s1.acc: 99.4628, s1.loss_bbox: 0.0221, s2.loss_cls: 0.0050, s2.acc: 99.3462, s2.loss_bbox: 0.0189, loss: 0.0998\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%cd /content/VFP290K/\n",
    "\n",
    "# cannot supply path with spaces for jsonfile_prefix, otherwise it'll save to the path with spaces removed\n",
    "!python tools/test.py \\\n",
    "  \"/content/VFP290K/configs/VFP290K/retinanet_r50_1x_benchmark.py\" \\\n",
    "  \"/content/drive/MyDrive/TIL 2022/CV/retinanet_training/epoch_50.pth\" \\\n",
    "  --fuse-conv-bn \\\n",
    "  --format-only \\\n",
    "  --gpu-collect \\\n",
    "  --gpu-ids 0 \\\n",
    "  --eval-options \"jsonfile_prefix=/content/retinanet_epoch50\"\n",
    "\n",
    "!mv \"/content/retinanet_epoch50.bbox.json\" \"/content/drive/MyDrive/TIL 2022/CV/preds/retinanet\""
   ],
   "metadata": {
    "id": "tJHqb8mlb8bS",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "%cd /content/VFP290K/\n",
    "\n",
    "# cannot supply path with spaces for jsonfile_prefix, otherwise it'll save to the path with spaces removed\n",
    "!python tools/test.py \\\n",
    "  \"/content/VFP290K/configs/VFP290K/cascade_rcnn_benchmark.py\" \\\n",
    "  \"/content/drive/MyDrive/TIL 2022/CV/cascade_training/epoch_22.pth\" \\\n",
    "  --fuse-conv-bn \\\n",
    "  --format-only \\\n",
    "  --gpu-collect \\\n",
    "  --gpu-ids 0 \\\n",
    "  --eval-options \"jsonfile_prefix=/content/cascade_epoch22_flip\"\n",
    "\n",
    "!mv \"/content/cascade_epoch22_flip.bbox.json\" \"/content/drive/MyDrive/TIL 2022/CV/preds/cascade/\""
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Pej3VtSPuNLv",
    "outputId": "0c1b9335-2523-4e67-f641-6f989c91bca8",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "/content/VFP290K\n",
      "loading annotations into memory...\n",
      "Done (t=0.01s)\n",
      "creating index...\n",
      "index created!\n",
      "load checkpoint from local path: /content/drive/MyDrive/TIL 2022/CV/cascade_training/epoch_22.pth\n",
      "[>>] 1020/1020, 3.7 task/s, elapsed: 272s, ETA:     0s"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import json\n",
    "\n",
    "pred_path = \"/content/drive/MyDrive/TIL 2022/CV/preds/cascade/cascade_pretrained_flip.bbox.json\"\n",
    "image_info = json.loads(open(\"/content/CV_data/test/interim_no_annotations.json\").read())[\"images\"]\n",
    "preds = json.loads(open(pred_path).read())\n",
    "filename_to_img = {}\n",
    "for img in image_info:\n",
    "  filename_to_img[img[\"file_name\"]] = img\n",
    "  filename_to_img[img[\"file_name\"]][\"has_pred\"] = False\n",
    "\n",
    "for filename in filename_to_img:\n",
    "  if not filename_to_img[filename][\"has_pred\"]:\n",
    "    # add dummy pred because DSTA grader gives error if one or more files has no preds\n",
    "    preds.append({\n",
    "      \"image_id\": filename_to_img[filename][\"id\"],\n",
    "      \"bbox\": [0., 0., 0., 0.],\n",
    "      \"category_id\": 2,\n",
    "      \"score\": 0.0000001,\n",
    "    })\n",
    "\n",
    "preds = sorted(preds, key=lambda x: x['image_id'])\n",
    "\n",
    "with open(pred_path, \"w+\") as all_preds_file:\n",
    "  json.dump(preds, all_preds_file, separators=(\",\", \":\"))"
   ],
   "metadata": {
    "id": "Pode2IT5kbDq",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "%cd /content/VFP290K"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ohmAmLSWAZ3m",
    "outputId": "f605481f-c454-4668-c2cb-394540a20bed",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "/content/VFP290K\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# NOT NEEDED TO RUN, RUN CELL ABOVE ONLY\n",
    "\n",
    "!mkdir \"/content/drive/MyDrive/TIL 2022/CV/preds/retinanet/\"\n",
    "\n",
    "import json\n",
    "import os\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "from mmdet.apis import init_detector, inference_detector\n",
    "import mmcv\n",
    "from tqdm import tqdm\n",
    "\n",
    "config_file = '/content/VFP290K/configs/VFP290K/retinanet_r50_1x_benchmark.py'\n",
    "checkpoint_file = '/content/drive/MyDrive/TIL 2022/CV/retinanet_training/epoch_52.pth'\n",
    "\n",
    "model = init_detector(config_file, checkpoint_file, device='cuda:0')\n",
    "\n",
    "with open(\"/content/CV_data/test/interim_no_annotations.json\") as img_info_file:\n",
    "  img_info = json.load(img_info_file)[\"images\"]\n",
    "filename_to_id = {img[\"file_name\"]: img[\"id\"] for img in img_info}\n",
    "\n",
    "preds = []\n",
    "confidence_threshold = 0.3\n",
    "for i, img_path in enumerate(tqdm(list(Path(\"/content/CV_data/test/images\").glob(\"*\")))):\n",
    "  person_detections = inference_detector(model, img_path)[0]\n",
    "  detections_filtered = person_detections[np.where(person_detections[:, 4] > confidence_threshold)]\n",
    "\n",
    "  img_id = filename_to_id[os.path.basename(img_path)]\n",
    "  for detection in detections_filtered:\n",
    "    x1, y1, x2, y2, confidence = [float(x) for x in detection] # convert from np.float32 to float\n",
    "    preds.append({\n",
    "      \"image_id\": img_id,\n",
    "      \"bbox\": [x1, y1, x2-x1, y2-y1],\n",
    "      \"category_id\": 1, # person\n",
    "      \"score\": confidence,\n",
    "    })\n",
    "\n",
    "with open(\"/content/drive/MyDrive/TIL 2022/CV/preds/retinanet/retinanet_epoch52.json\", \"w\") as preds_file:\n",
    "  json.dump(preds, preds_file, separators=(\",\", \":\"))"
   ],
   "metadata": {
    "id": "g21UEk2JnNMy",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "img_path = \"/content/CV_data/test/images/0zDqAqkTN9BUo3Xh499oOrMbpY_jpg.rf.195fd0df7ec946aa1bcfd46e339d0c01.jpg\"\n",
    "result = inference_detector(model, img_path)"
   ],
   "metadata": {
    "id": "Kj36ASWUoSKe",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "model.show_result(img_path, result, out_file=\"/content/result.jpg\", score_thr=0.4)"
   ],
   "metadata": {
    "id": "6hTWOx-Xocqc",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  }
 ]
}