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Migrate to UV: setup uv package manager, remove environment.yml, add pyproject.toml #743

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0 annotator/midas/midas/__init__.py → control_net/__init__.py
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0 annotator/canny/__init__.py → control_net/annotator/canny/__init__.py
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0 annotator/ckpts/ckpts.txt → control_net/annotator/ckpts/ckpts.txt
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2 changes: 1 addition & 1 deletion annotator/hed/__init__.py → control_net/annotator/hed/__init__.py
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Expand Up @@ -11,7 +11,7 @@
import numpy as np

from einops import rearrange
from annotator.util import annotator_ckpts_path
from control_net.annotator.util import annotator_ckpts_path


class DoubleConvBlock(torch.nn.Module):
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0 annotator/midas/LICENSE → control_net/annotator/midas/LICENSE
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0 annotator/midas/__init__.py → control_net/annotator/midas/__init__.py
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2 changes: 1 addition & 1 deletion annotator/midas/api.py → control_net/annotator/midas/api.py
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Expand Up @@ -10,7 +10,7 @@
from .midas.midas_net import MidasNet
from .midas.midas_net_custom import MidasNet_small
from .midas.transforms import Resize, NormalizeImage, PrepareForNet
from annotator.util import annotator_ckpts_path
from control_net.annotator.util import annotator_ckpts_path


ISL_PATHS = {
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0 ldm/data/__init__.py → ...rol_net/annotator/midas/midas/__init__.py
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0 annotator/midas/midas/base_model.py → ...l_net/annotator/midas/midas/base_model.py
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0 annotator/midas/midas/blocks.py → control_net/annotator/midas/midas/blocks.py
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0 annotator/midas/midas/vit.py → control_net/annotator/midas/midas/vit.py
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0 annotator/mlsd/LICENSE → control_net/annotator/mlsd/LICENSE
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from .models.mbv2_mlsd_large import MobileV2_MLSD_Large
from .utils import pred_lines

from annotator.util import annotator_ckpts_path
from control_net.annotator.util import annotator_ckpts_path


remote_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/mlsd_large_512_fp32.pth"
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0 annotator/mlsd/utils.py → control_net/annotator/mlsd/utils.py
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0 annotator/openpose/LICENSE → control_net/annotator/openpose/LICENSE
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from . import util
from .body import Body
from .hand import Hand
from annotator.util import annotator_ckpts_path
from control_net.annotator.util import annotator_ckpts_path


body_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/body_pose_model.pth"
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6 changes: 3 additions & 3 deletions annotator/uniformer/__init__.py → control_net/annotator/uniformer/__init__.py
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import os

from annotator.uniformer.mmseg.apis import init_segmentor, inference_segmentor, show_result_pyplot
from annotator.uniformer.mmseg.core.evaluation import get_palette
from annotator.util import annotator_ckpts_path
from control_net.annotator.uniformer.mmseg.apis import init_segmentor, inference_segmentor, show_result_pyplot
from control_net.annotator.uniformer.mmseg.core.evaluation import get_palette
from control_net.annotator.util import annotator_ckpts_path


checkpoint_file = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/upernet_global_small.pth"
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import torch.nn as nn
import torch.nn.functional as F

from annotator.uniformer.mmcv.utils import TORCH_VERSION, build_from_cfg, digit_version
from control_net.annotator.uniformer.mmcv.utils import TORCH_VERSION, build_from_cfg, digit_version
from .registry import ACTIVATION_LAYERS

for module in [
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import torch.nn as nn

from annotator.uniformer.mmcv.utils import _BatchNorm, _InstanceNorm
from control_net.annotator.uniformer.mmcv.utils import _BatchNorm, _InstanceNorm
from ..utils import constant_init, kaiming_init
from .activation import build_activation_layer
from .conv import build_conv_layer
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import torch
import torch.nn as nn

from annotator.uniformer.mmcv import build_from_cfg
from control_net.annotator.uniformer.mmcv import build_from_cfg
from .registry import DROPOUT_LAYERS


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import torch.nn as nn

from annotator.uniformer.mmcv.utils import is_tuple_of
from annotator.uniformer.mmcv.utils.parrots_wrapper import SyncBatchNorm, _BatchNorm, _InstanceNorm
from control_net.annotator.uniformer.mmcv.utils import is_tuple_of
from control_net.annotator.uniformer.mmcv.utils.parrots_wrapper import SyncBatchNorm, _BatchNorm, _InstanceNorm
from .registry import NORM_LAYERS

NORM_LAYERS.register_module('BN', module=nn.BatchNorm2d)
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@@ -1,5 +1,5 @@
# Copyright (c) OpenMMLab. All rights reserved.
from annotator.uniformer.mmcv.utils import Registry
from control_net.annotator.uniformer.mmcv.utils import Registry

CONV_LAYERS = Registry('conv layer')
NORM_LAYERS = Registry('norm layer')
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import torch
import torch.nn as nn

from annotator.uniformer.mmcv import ConfigDict, deprecated_api_warning
from annotator.uniformer.mmcv.cnn import Linear, build_activation_layer, build_norm_layer
from annotator.uniformer.mmcv.runner.base_module import BaseModule, ModuleList, Sequential
from annotator.uniformer.mmcv.utils import build_from_cfg
from control_net.annotator.uniformer.mmcv import ConfigDict, deprecated_api_warning
from control_net.annotator.uniformer.mmcv.cnn import Linear, build_activation_layer, build_norm_layer
from control_net.annotator.uniformer.mmcv.runner.base_module import BaseModule, ModuleList, Sequential
from control_net.annotator.uniformer.mmcv.utils import build_from_cfg
from .drop import build_dropout
from .registry import (ATTENTION, FEEDFORWARD_NETWORK, POSITIONAL_ENCODING,
TRANSFORMER_LAYER, TRANSFORMER_LAYER_SEQUENCE)
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import torch.nn as nn
from torch import Tensor

from annotator.uniformer.mmcv.utils import Registry, build_from_cfg, get_logger, print_log
from control_net.annotator.uniformer.mmcv.utils import Registry, build_from_cfg, get_logger, print_log

INITIALIZERS = Registry('initializer')

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import torch.distributed as dist

import annotator.uniformer.mmcv as mmcv
from annotator.uniformer.mmcv.runner import get_dist_info
from control_net.annotator.uniformer.mmcv.runner import get_dist_info


def single_gpu_test(model, data_loader):
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from urllib.request import urlopen

import annotator.uniformer.mmcv as mmcv
from annotator.uniformer.mmcv.utils.misc import has_method
from annotator.uniformer.mmcv.utils.path import is_filepath
from control_net.annotator.uniformer.mmcv.utils.misc import has_method
from control_net.annotator.uniformer.mmcv.utils.path import is_filepath


class BaseStorageBackend(metaclass=ABCMeta):
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from cv2 import (IMREAD_COLOR, IMREAD_GRAYSCALE, IMREAD_IGNORE_ORIENTATION,
IMREAD_UNCHANGED)

from annotator.uniformer.mmcv.utils import check_file_exist, is_str, mkdir_or_exist
from control_net.annotator.uniformer.mmcv.utils import check_file_exist, is_str, mkdir_or_exist

try:
from turbojpeg import TJCS_RGB, TJPF_BGR, TJPF_GRAY, TurboJPEG
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import torch.nn as nn
import torch.nn.functional as F

from annotator.uniformer.mmcv.cnn import PLUGIN_LAYERS, Scale
from control_net.annotator.uniformer.mmcv.cnn import PLUGIN_LAYERS, Scale


def NEG_INF_DIAG(n, device):
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from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair, _single

from annotator.uniformer.mmcv.utils import deprecated_api_warning
from control_net.annotator.uniformer.mmcv.utils import deprecated_api_warning
from ..cnn import CONV_LAYERS
from ..utils import ext_loader, print_log

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from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair, _single

from annotator.uniformer.mmcv.utils import deprecated_api_warning
from control_net.annotator.uniformer.mmcv.utils import deprecated_api_warning
from ..cnn import CONV_LAYERS
from ..utils import ext_loader, print_log

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import torch.nn.functional as F
from torch.autograd.function import Function, once_differentiable

from annotator.uniformer.mmcv import deprecated_api_warning
from annotator.uniformer.mmcv.cnn import constant_init, xavier_init
from annotator.uniformer.mmcv.cnn.bricks.registry import ATTENTION
from annotator.uniformer.mmcv.runner import BaseModule
from control_net.annotator.uniformer.mmcv import deprecated_api_warning
from control_net.annotator.uniformer.mmcv.cnn import constant_init, xavier_init
from control_net.annotator.uniformer.mmcv.cnn.bricks.registry import ATTENTION
from control_net.annotator.uniformer.mmcv.runner import BaseModule
from ..utils import ext_loader

ext_module = ext_loader.load_ext(
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import numpy as np
import torch

from annotator.uniformer.mmcv.utils import deprecated_api_warning
from control_net.annotator.uniformer.mmcv.utils import deprecated_api_warning
from ..utils import ext_loader

ext_module = ext_loader.load_ext(
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import torch
from torch import nn as nn

from annotator.uniformer.mmcv.runner import force_fp32
from control_net.annotator.uniformer.mmcv.runner import force_fp32
from .furthest_point_sample import (furthest_point_sample,
furthest_point_sample_with_dist)

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import torch.nn as nn
import torch.nn.functional as F

from annotator.uniformer.mmcv.cnn import CONV_LAYERS, ConvAWS2d, constant_init
from annotator.uniformer.mmcv.ops.deform_conv import deform_conv2d
from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from control_net.annotator.uniformer.mmcv.cnn import CONV_LAYERS, ConvAWS2d, constant_init
from control_net.annotator.uniformer.mmcv.ops.deform_conv import deform_conv2d
from control_net.annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version


@CONV_LAYERS.register_module(name='SAC')
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from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter

from annotator.uniformer.mmcv.cnn import NORM_LAYERS
from control_net.annotator.uniformer.mmcv.cnn import NORM_LAYERS
from ..utils import ext_loader

ext_module = ext_loader.load_ext('_ext', [
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from torch.autograd import Function
from torch.nn import functional as F

from annotator.uniformer.mmcv.utils import to_2tuple
from control_net.annotator.uniformer.mmcv.utils import to_2tuple
from ..utils import ext_loader

upfirdn2d_ext = ext_loader.load_ext('_ext', ['upfirdn2d'])
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from torch.nn.parallel.distributed import (DistributedDataParallel,
_find_tensors)

from annotator.uniformer.mmcv import print_log
from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from control_net.annotator.uniformer.mmcv import print_log
from control_net.annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from .scatter_gather import scatter_kwargs


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from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_dense_tensors)

from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from control_net.annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from .registry import MODULE_WRAPPERS
from .scatter_gather import scatter_kwargs

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@@ -1,7 +1,7 @@
# Copyright (c) OpenMMLab. All rights reserved.
from torch.nn.parallel import DataParallel, DistributedDataParallel

from annotator.uniformer.mmcv.utils import Registry
from control_net.annotator.uniformer.mmcv.utils import Registry

MODULE_WRAPPERS = Registry('module wrapper')
MODULE_WRAPPERS.register_module(module=DataParallel)
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import torch.nn as nn

from annotator.uniformer.mmcv.runner.dist_utils import master_only
from annotator.uniformer.mmcv.utils.logging import get_logger, logger_initialized, print_log
from control_net.annotator.uniformer.mmcv.runner.dist_utils import master_only
from control_net.annotator.uniformer.mmcv.utils.logging import get_logger, logger_initialized, print_log


class BaseModule(nn.Module, metaclass=ABCMeta):
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0 ...former/mmcv/runner/default_constructor.py → ...former/mmcv/runner/default_constructor.py
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import torch
import torch.nn as nn

from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from control_net.annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from .dist_utils import allreduce_grads as _allreduce_grads

try:
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import os.path as osp
import warnings

from annotator.uniformer.mmcv.fileio import FileClient
from control_net.annotator.uniformer.mmcv.fileio import FileClient
from ..dist_utils import allreduce_params, master_only
from .hook import HOOKS, Hook

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from torch.nn.modules.batchnorm import _BatchNorm
from torch.utils.data import DataLoader

from annotator.uniformer.mmcv.fileio import FileClient
from annotator.uniformer.mmcv.utils import is_seq_of
from control_net.annotator.uniformer.mmcv.fileio import FileClient
from control_net.annotator.uniformer.mmcv.utils import is_seq_of
from .hook import Hook
from .logger import LoggerHook

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@@ -1,5 +1,5 @@
# Copyright (c) OpenMMLab. All rights reserved.
from annotator.uniformer.mmcv.utils import Registry, is_method_overridden
from control_net.annotator.uniformer.mmcv.utils import Registry, is_method_overridden

HOOKS = Registry('hook')

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# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp

from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from control_net.annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from ...dist_utils import master_only
from ..hook import HOOKS
from .base import LoggerHook
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import torch.distributed as dist

import annotator.uniformer.mmcv as mmcv
from annotator.uniformer.mmcv.fileio.file_client import FileClient
from annotator.uniformer.mmcv.utils import is_tuple_of, scandir
from control_net.annotator.uniformer.mmcv.fileio.file_client import FileClient
from control_net.annotator.uniformer.mmcv.utils import is_tuple_of, scandir
from ..hook import HOOKS
from .base import LoggerHook

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from torch.nn.utils import clip_grad

from annotator.uniformer.mmcv.utils import TORCH_VERSION, _BatchNorm, digit_version
from control_net.annotator.uniformer.mmcv.utils import TORCH_VERSION, _BatchNorm, digit_version
from ..dist_utils import allreduce_grads
from ..fp16_utils import LossScaler, wrap_fp16_model
from .hook import HOOKS, Hook
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import torch
from torch.nn import GroupNorm, LayerNorm

from annotator.uniformer.mmcv.utils import _BatchNorm, _InstanceNorm, build_from_cfg, is_list_of
from annotator.uniformer.mmcv.utils.ext_loader import check_ops_exist
from control_net.annotator.uniformer.mmcv.utils import _BatchNorm, _InstanceNorm, build_from_cfg, is_list_of
from control_net.annotator.uniformer.mmcv.utils.ext_loader import check_ops_exist
from .builder import OPTIMIZER_BUILDERS, OPTIMIZERS


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import torch

from annotator.uniformer.mmcv.utils import digit_version
from control_net.annotator.uniformer.mmcv.utils import digit_version


def is_jit_tracing() -> bool:
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CAP_PROP_FRAME_HEIGHT, CAP_PROP_FRAME_WIDTH,
CAP_PROP_POS_FRAMES, VideoWriter_fourcc)

from annotator.uniformer.mmcv.utils import (check_file_exist, mkdir_or_exist, scandir,
from control_net.annotator.uniformer.mmcv.utils import (check_file_exist, mkdir_or_exist, scandir,
track_progress)


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import cv2
import numpy as np

from annotator.uniformer.mmcv.arraymisc import dequantize, quantize
from annotator.uniformer.mmcv.image import imread, imwrite
from annotator.uniformer.mmcv.utils import is_str
from control_net.annotator.uniformer.mmcv.arraymisc import dequantize, quantize
from control_net.annotator.uniformer.mmcv.image import imread, imwrite
from control_net.annotator.uniformer.mmcv.utils import is_str


def flowread(flow_or_path, quantize=False, concat_axis=0, *args, **kwargs):
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import subprocess
import tempfile

from annotator.uniformer.mmcv.utils import requires_executable
from control_net.annotator.uniformer.mmcv.utils import requires_executable


@requires_executable('ffmpeg')
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