repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
ControlNet | annotator/uniformer/mmseg/models/decode_heads/cc_head.py | .py | import torch
from ..builder import HEADS
from .fcn_head import FCNHead
try:
from annotator.uniformer.mmcv.ops import CrissCrossAttention
except ModuleNotFoundError:
CrissCrossAttention = None
@HEADS.register_module()
class CCHead(FCNHead):
"""CCNet: Criss-Cross Attention for Semantic Segmentation.
... | 43 | 1,303 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/ema_head.py | .py | import math
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule
from ..builder import HEADS
from .decode_head import BaseDecodeHead
def reduce_mean(tensor):
"""Reduce mean when distributed training."""
if not... | 169 | 5,796 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/sep_fcn_head.py | .py | from annotator.uniformer.mmcv.cnn import DepthwiseSeparableConvModule
from ..builder import HEADS
from .fcn_head import FCNHead
@HEADS.register_module()
class DepthwiseSeparableFCNHead(FCNHead):
"""Depthwise-Separable Fully Convolutional Network for Semantic
Segmentation.
This head is implemented accord... | 52 | 2,024 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/ann_head.py | .py | import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule
from ..builder import HEADS
from ..utils import SelfAttentionBlock as _SelfAttentionBlock
from .decode_head import BaseDecodeHead
class PPMConcat(nn.ModuleList):
"""Pyramid Pooling Module that only concat the features of each ... | 246 | 9,194 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/decode_head.py | .py | from abc import ABCMeta, abstractmethod
import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import normal_init
from annotator.uniformer.mmcv.runner import auto_fp16, force_fp32
from annotator.uniformer.mmseg.core import build_pixel_sampler
from annotator.uniformer.mmseg.ops import resize
from ..build... | 235 | 9,240 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/uper_head.py | .py | import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
from .psp_head import PPM
@HEADS.register_module()
class UPerHead(BaseDecodeHead):
"""Unified Perceptual Pars... | 127 | 4,012 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/point_head.py | .py | # Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend/point_head/point_head.py # noqa
import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule, normal_init
from annotator.uniformer.mmcv.ops import point_sample
from annotator.uniformer.mmseg.models... | 350 | 14,754 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/nl_head.py | .py | import torch
from annotator.uniformer.mmcv.cnn import NonLocal2d
from ..builder import HEADS
from .fcn_head import FCNHead
@HEADS.register_module()
class NLHead(FCNHead):
"""Non-local Neural Networks.
This head is the implementation of `NLNet
<https://arxiv.org/abs/1711.07971>`_.
Args:
redu... | 50 | 1,577 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/dm_head.py | .py | import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule, build_activation_layer, build_norm_layer
from ..builder import HEADS
from .decode_head import BaseDecodeHead
class DCM(nn.Module):
"""Dynamic Convolutional Module used in DMNet.
Args:
... | 141 | 5,004 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/da_head.py | .py | import torch
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule, Scale
from torch import nn
from annotator.uniformer.mmseg.core import add_prefix
from ..builder import HEADS
from ..utils import SelfAttentionBlock as _SelfAttentionBlock
from .decode_head import BaseDecodeHead
class PA... | 179 | 5,585 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/lraspp_head.py | .py | import torch
import torch.nn as nn
from annotator.uniformer.mmcv import is_tuple_of
from annotator.uniformer.mmcv.cnn import ConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
@HEADS.register_module()
class LRASPPHead(BaseDecodeHead):
"... | 91 | 3,098 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/sep_aspp_head.py | .py | import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from .aspp_head import ASPPHead, ASPPModule
class DepthwiseSeparableASPPModule(ASPPModule):
"""Atrous Spatial Pyramid P... | 102 | 3,527 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/cascade_decode_head.py | .py | from abc import ABCMeta, abstractmethod
from .decode_head import BaseDecodeHead
class BaseCascadeDecodeHead(BaseDecodeHead, metaclass=ABCMeta):
"""Base class for cascade decode head used in
:class:`CascadeEncoderDecoder."""
def __init__(self, *args, **kwargs):
super(BaseCascadeDecodeHead, self).... | 58 | 2,351 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/fpn_head.py | .py | import numpy as np
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
@HEADS.register_module()
class FPNHead(BaseDecodeHead):
"""Panoptic Feature Pyramid Networks.
This... | 69 | 2,422 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/ocr_head.py | .py | import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from ..utils import SelfAttentionBlock as _SelfAttentionBlock
from .cascade_decode_head import BaseCascadeDecodeHead
clas... | 128 | 4,319 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/psa_head.py | .py | import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
try:
from annotator.uniformer.mmcv.ops import PSAMask
except ModuleNotFoundErr... | 197 | 7,544 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/gc_head.py | .py | import torch
from annotator.uniformer.mmcv.cnn import ContextBlock
from ..builder import HEADS
from .fcn_head import FCNHead
@HEADS.register_module()
class GCHead(FCNHead):
"""GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond.
This head is the implementation of `GCNet
<https://arxiv.... | 48 | 1,611 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/dnl_head.py | .py | import torch
from annotator.uniformer.mmcv.cnn import NonLocal2d
from torch import nn
from ..builder import HEADS
from .fcn_head import FCNHead
class DisentangledNonLocal2d(NonLocal2d):
"""Disentangled Non-Local Blocks.
Args:
temperature (float): Temperature to adjust attention. Default: 0.05
""... | 132 | 4,591 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/aspp_head.py | .py | import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
class ASPPModule(nn.ModuleList):
"""Atrous Spatial Pyramid Pooling (ASPP) Module.
Args:
dilation... | 108 | 3,459 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/psp_head.py | .py | import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
class PPM(nn.ModuleList):
"""Pooling Pyramid Module used in PSPNet.
Args:
pool_scales (tuple[int... | 102 | 3,352 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/fcn_head.py | .py | import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule
from ..builder import HEADS
from .decode_head import BaseDecodeHead
@HEADS.register_module()
class FCNHead(BaseDecodeHead):
"""Fully Convolution Networks for Semantic Segmentation.
This head is implemented of `FCNNet <htt... | 82 | 2,817 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/apc_head.py | .py | import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
class ACM(nn.Module):
"""Adaptive Context Module used in APCNet.
Args:
... | 159 | 5,572 |
ControlNet | annotator/uniformer/mmseg/models/backbones/unet.py | .py | import torch.nn as nn
import torch.utils.checkpoint as cp
from annotator.uniformer.mmcv.cnn import (UPSAMPLE_LAYERS, ConvModule, build_activation_layer,
build_norm_layer, constant_init, kaiming_init)
from annotator.uniformer.mmcv.runner import load_checkpoint
from annotator.uniformer.mmcv.utils.pa... | 430 | 18,269 |
ControlNet | annotator/uniformer/mmseg/models/backbones/resnext.py | .py | import math
from annotator.uniformer.mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from ..utils import ResLayer
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
class Bottleneck(_Bottleneck):
"""Bottleneck block for ResNeXt.
If style is "pytorch"... | 146 | 5,161 |
ControlNet | annotator/uniformer/mmseg/models/backbones/fast_scnn.py | .py | import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import (ConvModule, DepthwiseSeparableConvModule, constant_init,
kaiming_init)
from torch.nn.modules.batchnorm import _BatchNorm
from annotator.uniformer.mmseg.models.decode_heads.psp_head import PPM
from annotator.uniformer.mms... | 376 | 14,436 |
ControlNet | annotator/uniformer/mmseg/models/backbones/mobilenet_v3.py | .py | import logging
import annotator.uniformer.mmcv as mmcv
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule, constant_init, kaiming_init
from annotator.uniformer.mmcv.cnn.bricks import Conv2dAdaptivePadding
from annotator.uniformer.mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm... | 256 | 10,390 |
ControlNet | annotator/uniformer/mmseg/models/backbones/resnet.py | .py | import torch.nn as nn
import torch.utils.checkpoint as cp
from annotator.uniformer.mmcv.cnn import (build_conv_layer, build_norm_layer, build_plugin_layer,
constant_init, kaiming_init)
from annotator.uniformer.mmcv.runner import load_checkpoint
from annotator.uniformer.mmcv.utils.parrots_wrapper i... | 689 | 24,310 |
ControlNet | annotator/uniformer/mmseg/models/backbones/__init__.py | .py | from .cgnet import CGNet
# from .fast_scnn import FastSCNN
from .hrnet import HRNet
from .mobilenet_v2 import MobileNetV2
from .mobilenet_v3 import MobileNetV3
from .resnest import ResNeSt
from .resnet import ResNet, ResNetV1c, ResNetV1d
from .resnext import ResNeXt
from .unet import UNet
from .vit import VisionTransfo... | 18 | 532 |
ControlNet | annotator/uniformer/mmseg/models/backbones/cgnet.py | .py | import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from annotator.uniformer.mmcv.cnn import (ConvModule, build_conv_layer, build_norm_layer,
constant_init, kaiming_init)
from annotator.uniformer.mmcv.runner import load_checkpoint
from annotator.uniformer.mmcv.utils.parrots_wrap... | 368 | 13,183 |
ControlNet | annotator/uniformer/mmseg/models/backbones/mobilenet_v2.py | .py | import logging
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule, constant_init, kaiming_init
from annotator.uniformer.mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
from ..utils import InvertedResidual, make_divisible
@BA... | 181 | 6,981 |
ControlNet | annotator/uniformer/mmseg/models/backbones/hrnet.py | .py | import torch.nn as nn
from annotator.uniformer.mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init,
kaiming_init)
from annotator.uniformer.mmcv.runner import load_checkpoint
from annotator.uniformer.mmcv.utils.parrots_wrapper import _BatchNorm
from annotator.uniformer.mmseg.ops imp... | 556 | 21,226 |
ControlNet | annotator/uniformer/mmseg/models/backbones/resnest.py | .py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from annotator.uniformer.mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from ..utils import ResLayer
from .resnet import Bottleneck as _Bottleneck
from .resnet import ... | 315 | 10,110 |
ControlNet | annotator/uniformer/mmseg/models/backbones/vit.py | .py | """Modified from https://github.com/rwightman/pytorch-image-
models/blob/master/timm/models/vision_transformer.py."""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from annotator.uniformer.mmcv.cnn import (Conv2d, Linear, build_activation_layer, bui... | 460 | 18,085 |
ControlNet | annotator/uniformer/mmseg/models/backbones/uniformer.py | .py | # --------------------------------------------------------
# UniFormer
# Copyright (c) 2022 SenseTime X-Lab
# Licensed under The MIT License [see LICENSE for details]
# Written by Kunchang Li
# --------------------------------------------------------
from collections import OrderedDict
import math
from functools impo... | 423 | 18,476 |
ControlNet | annotator/uniformer/mmseg/models/losses/lovasz_loss.py | .py | """Modified from https://github.com/bermanmaxim/LovaszSoftmax/blob/master/pytor
ch/lovasz_losses.py Lovasz-Softmax and Jaccard hinge loss in PyTorch Maxim
Berman 2018 ESAT-PSI KU Leuven (MIT License)"""
import annotator.uniformer.mmcv as mmcv
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..b... | 304 | 11,419 |
ControlNet | annotator/uniformer/mmseg/models/losses/utils.py | .py | import functools
import annotator.uniformer.mmcv as mmcv
import numpy as np
import torch.nn.functional as F
def get_class_weight(class_weight):
"""Get class weight for loss function.
Args:
class_weight (list[float] | str | None): If class_weight is a str,
take it as a file name and read ... | 122 | 3,718 |
ControlNet | annotator/uniformer/mmseg/models/losses/cross_entropy_loss.py | .py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import get_class_weight, weight_reduce_loss
def cross_entropy(pred,
label,
weight=None,
class_weight=None,
reduction='mean',
... | 199 | 7,437 |
ControlNet | annotator/uniformer/mmseg/models/losses/__init__.py | .py | from .accuracy import Accuracy, accuracy
from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
cross_entropy, mask_cross_entropy)
from .dice_loss import DiceLoss
from .lovasz_loss import LovaszLoss
from .utils import reduce_loss, weight_reduce_loss, weighted_loss
__a... | 13 | 529 |
ControlNet | annotator/uniformer/mmseg/models/losses/accuracy.py | .py | import torch.nn as nn
def accuracy(pred, target, topk=1, thresh=None):
"""Calculate accuracy according to the prediction and target.
Args:
pred (torch.Tensor): The model prediction, shape (N, num_class, ...)
target (torch.Tensor): The target of each prediction, shape (N, , ...)
topk (... | 79 | 2,970 |
ControlNet | annotator/uniformer/mmseg/models/losses/dice_loss.py | .py | """Modified from https://github.com/LikeLy-Journey/SegmenTron/blob/master/
segmentron/solver/loss.py (Apache-2.0 License)"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import get_class_weight, weighted_loss
@weighted_loss
def dice_loss(pred,
... | 120 | 4,239 |
ControlNet | annotator/uniformer/mmseg/models/utils/se_layer.py | .py | import annotator.uniformer.mmcv as mmcv
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule
from .make_divisible import make_divisible
class SELayer(nn.Module):
"""Squeeze-and-Excitation Module.
Args:
channels (int): The input (and output) channels of the SE layer.
rati... | 58 | 2,151 |
ControlNet | annotator/uniformer/mmseg/models/utils/res_layer.py | .py | from annotator.uniformer.mmcv.cnn import build_conv_layer, build_norm_layer
from torch import nn as nn
class ResLayer(nn.Sequential):
"""ResLayer to build ResNet style backbone.
Args:
block (nn.Module): block used to build ResLayer.
inplanes (int): inplanes of block.
planes (int): pla... | 95 | 3,335 |
ControlNet | annotator/uniformer/mmseg/models/utils/__init__.py | .py | from .drop import DropPath
from .inverted_residual import InvertedResidual, InvertedResidualV3
from .make_divisible import make_divisible
from .res_layer import ResLayer
from .se_layer import SELayer
from .self_attention_block import SelfAttentionBlock
from .up_conv_block import UpConvBlock
from .weight_init import tru... | 14 | 502 |
ControlNet | annotator/uniformer/mmseg/models/utils/inverted_residual.py | .py | from annotator.uniformer.mmcv.cnn import ConvModule
from torch import nn
from torch.utils import checkpoint as cp
from .se_layer import SELayer
class InvertedResidual(nn.Module):
"""InvertedResidual block for MobileNetV2.
Args:
in_channels (int): The input channels of the InvertedResidual block.
... | 209 | 7,025 |
ControlNet | annotator/uniformer/mmseg/models/utils/self_attention_block.py | .py | import torch
from annotator.uniformer.mmcv.cnn import ConvModule, constant_init
from torch import nn as nn
from torch.nn import functional as F
class SelfAttentionBlock(nn.Module):
"""General self-attention block/non-local block.
Please refer to https://arxiv.org/abs/1706.03762 for details about key,
que... | 160 | 6,145 |
ControlNet | annotator/uniformer/mmseg/models/utils/drop.py | .py | """Modified from https://github.com/rwightman/pytorch-image-
models/blob/master/timm/models/layers/drop.py."""
import torch
from torch import nn
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of
residual blocks).
Args:
drop_prob (float): Drop r... | 32 | 1,015 |
ControlNet | annotator/uniformer/mmseg/models/utils/make_divisible.py | .py | def make_divisible(value, divisor, min_value=None, min_ratio=0.9):
"""Make divisible function.
This function rounds the channel number to the nearest value that can be
divisible by the divisor. It is taken from the original tf repo. It ensures
that all layers have a channel number that is divisible by ... | 28 | 1,231 |
ControlNet | annotator/uniformer/mmseg/models/utils/weight_init.py | .py | """Modified from https://github.com/rwightman/pytorch-image-
models/blob/master/timm/models/layers/drop.py."""
import math
import warnings
import torch
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
"""Reference: https://people.sc.fsu.edu/~jburkardt/presentations
/truncated_normal.pdf"""
def norm... | 63 | 2,327 |
ControlNet | annotator/uniformer/mmseg/models/utils/up_conv_block.py | .py | import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule, build_upsample_layer
class UpConvBlock(nn.Module):
"""Upsample convolution block in decoder for UNet.
This upsample convolution block consists of one upsample module
followed by one convolution block. The upsample mod... | 102 | 3,988 |
ControlNet | annotator/uniformer/mmseg/models/segmentors/encoder_decoder.py | .py | import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmseg.core import add_prefix
from annotator.uniformer.mmseg.ops import resize
from .. import builder
from ..builder import SEGMENTORS
from .base import BaseSegmentor
@SEGMENTORS.register_module()
class EncoderDecoder(BaseSegm... | 299 | 11,344 |
ControlNet | annotator/uniformer/mmseg/models/segmentors/cascade_encoder_decoder.py | .py | from torch import nn
from annotator.uniformer.mmseg.core import add_prefix
from annotator.uniformer.mmseg.ops import resize
from .. import builder
from ..builder import SEGMENTORS
from .encoder_decoder import EncoderDecoder
@SEGMENTORS.register_module()
class CascadeEncoderDecoder(EncoderDecoder):
"""Cascade Enc... | 99 | 3,708 |
ControlNet | annotator/uniformer/mmseg/models/segmentors/base.py | .py | import logging
import warnings
from abc import ABCMeta, abstractmethod
from collections import OrderedDict
import annotator.uniformer.mmcv as mmcv
import numpy as np
import torch
import torch.distributed as dist
import torch.nn as nn
from annotator.uniformer.mmcv.runner import auto_fp16
class BaseSegmentor(nn.Module... | 274 | 10,398 |
ControlNet | annotator/uniformer/mmseg/models/necks/__init__.py | .py | from .fpn import FPN
from .multilevel_neck import MultiLevelNeck
__all__ = ['FPN', 'MultiLevelNeck']
| 5 | 102 |
ControlNet | annotator/uniformer/mmseg/models/necks/multilevel_neck.py | .py | import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule
from ..builder import NECKS
@NECKS.register_module()
class MultiLevelNeck(nn.Module):
"""MultiLevelNeck.
A neck structure connect vit backbone and decoder_heads.
Args:
in_channels (List[int]... | 71 | 2,454 |
ControlNet | annotator/uniformer/mmseg/models/necks/fpn.py | .py | import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule, xavier_init
from ..builder import NECKS
@NECKS.register_module()
class FPN(nn.Module):
"""Feature Pyramid Network.
This is an implementation of - Feature Pyramid Networks for Object
Detection (http... | 213 | 9,159 |
ControlNet | annotator/uniformer/mmcv/version.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '1.3.17'
def parse_version_info(version_str: str, length: int = 4) -> tuple:
"""Parse a version string into a tuple.
Args:
version_str (str): The version string.
length (int): The maximum number of version levels. Default: 4.
... | 36 | 1,177 |
ControlNet | annotator/uniformer/mmcv/fileio/parse.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from io import StringIO
from .file_client import FileClient
def list_from_file(filename,
prefix='',
offset=0,
max_num=0,
encoding='utf-8',
file_client_args=None):
"""Loa... | 98 | 3,458 |
ControlNet | annotator/uniformer/mmcv/fileio/io.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from io import BytesIO, StringIO
from pathlib import Path
from ..utils import is_list_of, is_str
from .file_client import FileClient
from .handlers import BaseFileHandler, JsonHandler, PickleHandler, YamlHandler
file_handlers = {
'json': JsonHandler(),
'yaml': Y... | 152 | 5,520 |
ControlNet | annotator/uniformer/mmcv/fileio/__init__.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from .file_client import BaseStorageBackend, FileClient
from .handlers import BaseFileHandler, JsonHandler, PickleHandler, YamlHandler
from .io import dump, load, register_handler
from .parse import dict_from_file, list_from_file
__all__ = [
'BaseStorageBackend', 'Fi... | 12 | 478 |
ControlNet | annotator/uniformer/mmcv/fileio/file_client.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import inspect
import os
import os.path as osp
import re
import tempfile
import warnings
from abc import ABCMeta, abstractmethod
from contextlib import contextmanager
from pathlib import Path
from typing import Iterable, Iterator, Optional, Tuple, Union
from urllib.reques... | 1,149 | 41,933 |
ControlNet | annotator/uniformer/mmcv/fileio/handlers/json_handler.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import json
import numpy as np
from .base import BaseFileHandler
def set_default(obj):
"""Set default json values for non-serializable values.
It helps convert ``set``, ``range`` and ``np.ndarray`` data types to list.
It also converts ``np.generic`` (incl... | 37 | 1,068 |
ControlNet | annotator/uniformer/mmcv/fileio/handlers/pickle_handler.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import pickle
from .base import BaseFileHandler
class PickleHandler(BaseFileHandler):
str_like = False
def load_from_fileobj(self, file, **kwargs):
return pickle.load(file, **kwargs)
def load_from_path(self, filepath, **kwargs):
return su... | 29 | 817 |
ControlNet | annotator/uniformer/mmcv/fileio/handlers/base.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
class BaseFileHandler(metaclass=ABCMeta):
# `str_like` is a flag to indicate whether the type of file object is
# str-like object or bytes-like object. Pickle only processes bytes-like
# objects but json only processes... | 31 | 993 |
ControlNet | annotator/uniformer/mmcv/fileio/handlers/yaml_handler.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import yaml
try:
from yaml import CLoader as Loader, CDumper as Dumper
except ImportError:
from yaml import Loader, Dumper
from .base import BaseFileHandler # isort:skip
class YamlHandler(BaseFileHandler):
def load_from_fileobj(self, file, **kwargs):
... | 25 | 665 |
ControlNet | annotator/uniformer/mmcv/image/misc.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import annotator.uniformer.mmcv as mmcv
try:
import torch
except ImportError:
torch = None
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
"""Convert tensor to 3-channel images.
Args:
tensor (torch.Tenso... | 45 | 1,410 |
ControlNet | annotator/uniformer/mmcv/image/io.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import io
import os.path as osp
from pathlib import Path
import cv2
import numpy as np
from cv2 import (IMREAD_COLOR, IMREAD_GRAYSCALE, IMREAD_IGNORE_ORIENTATION,
IMREAD_UNCHANGED)
from annotator.uniformer.mmcv.utils import check_file_exist, is_str, mkd... | 259 | 9,572 |
ControlNet | annotator/uniformer/mmcv/image/colorspace.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import cv2
import numpy as np
def imconvert(img, src, dst):
"""Convert an image from the src colorspace to dst colorspace.
Args:
img (ndarray): The input image.
src (str): The source colorspace, e.g., 'rgb', 'hsv'.
dst (str): The destina... | 307 | 9,907 |
ControlNet | annotator/uniformer/mmcv/image/__init__.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from .colorspace import (bgr2gray, bgr2hls, bgr2hsv, bgr2rgb, bgr2ycbcr,
gray2bgr, gray2rgb, hls2bgr, hsv2bgr, imconvert,
rgb2bgr, rgb2gray, rgb2ycbcr, ycbcr2bgr, ycbcr2rgb)
from .geometric import (cutout, imcrop, imflip, ... | 29 | 1,725 |
ControlNet | annotator/uniformer/mmcv/image/geometric.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import numbers
import cv2
import numpy as np
from ..utils import to_2tuple
from .io import imread_backend
try:
from PIL import Image
except ImportError:
Image = None
def _scale_size(size, scale):
"""Rescale a size by a ratio.
Args:
size (tupl... | 729 | 25,196 |
ControlNet | annotator/uniformer/mmcv/image/photometric.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import cv2
import numpy as np
from ..utils import is_tuple_of
from .colorspace import bgr2gray, gray2bgr
def imnormalize(img, mean, std, to_rgb=True):
"""Normalize an image with mean and std.
Args:
img (ndarray): Image to be normalized.
mean (n... | 429 | 14,999 |
ControlNet | annotator/uniformer/mmcv/parallel/distributed_deprecated.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.distributed as dist
import torch.nn as nn
from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_dense_tensors)
from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from .registry... | 71 | 2,837 |
ControlNet | annotator/uniformer/mmcv/parallel/utils.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from .registry import MODULE_WRAPPERS
def is_module_wrapper(module):
"""Check if a module is a module wrapper.
The following 3 modules in MMCV (and their subclasses) are regarded as
module wrappers: DataParallel, DistributedDataParallel,
MMDistributedDa... | 21 | 708 |
ControlNet | annotator/uniformer/mmcv/parallel/distributed.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
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 .scatter... | 113 | 4,857 |
ControlNet | annotator/uniformer/mmcv/parallel/__init__.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from .collate import collate
from .data_container import DataContainer
from .data_parallel import MMDataParallel
from .distributed import MMDistributedDataParallel
from .registry import MODULE_WRAPPERS
from .scatter_gather import scatter, scatter_kwargs
from .utils import... | 14 | 505 |
ControlNet | annotator/uniformer/mmcv/parallel/registry.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from torch.nn.parallel import DataParallel, DistributedDataParallel
from annotator.uniformer.mmcv.utils import Registry
MODULE_WRAPPERS = Registry('module wrapper')
MODULE_WRAPPERS.register_module(module=DataParallel)
MODULE_WRAPPERS.register_module(module=DistributedDa... | 9 | 332 |
ControlNet | annotator/uniformer/mmcv/parallel/collate.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from collections.abc import Mapping, Sequence
import torch
import torch.nn.functional as F
from torch.utils.data.dataloader import default_collate
from .data_container import DataContainer
def collate(batch, samples_per_gpu=1):
"""Puts each data field into a tenso... | 85 | 3,665 |
ControlNet | annotator/uniformer/mmcv/parallel/_functions.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch.nn.parallel._functions import _get_stream
def scatter(input, devices, streams=None):
"""Scatters tensor across multiple GPUs."""
if streams is None:
streams = [None] * len(devices)
if isinstance(input, list):
chunk_si... | 80 | 2,830 |
ControlNet | annotator/uniformer/mmcv/parallel/data_container.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import functools
import torch
def assert_tensor_type(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
if not isinstance(args[0].data, torch.Tensor):
raise AttributeError(
f'{args[0].__class__.__name__} has no attr... | 90 | 2,365 |
ControlNet | annotator/uniformer/mmcv/parallel/data_parallel.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from itertools import chain
from torch.nn.parallel import DataParallel
from .scatter_gather import scatter_kwargs
class MMDataParallel(DataParallel):
"""The DataParallel module that supports DataContainer.
MMDataParallel has two main differences with PyTorch ... | 90 | 3,912 |
ControlNet | annotator/uniformer/mmcv/parallel/scatter_gather.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch.nn.parallel._functions import Scatter as OrigScatter
from ._functions import Scatter
from .data_container import DataContainer
def scatter(inputs, target_gpus, dim=0):
"""Scatter inputs to target gpus.
The only difference from original ... | 60 | 2,307 |
ControlNet | annotator/uniformer/mmcv/engine/__init__.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from .test import (collect_results_cpu, collect_results_gpu, multi_gpu_test,
single_gpu_test)
__all__ = [
'collect_results_cpu', 'collect_results_gpu', 'multi_gpu_test',
'single_gpu_test'
]
| 9 | 266 |
ControlNet | annotator/uniformer/mmcv/engine/test.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import pickle
import shutil
import tempfile
import time
import torch
import torch.distributed as dist
import annotator.uniformer.mmcv as mmcv
from annotator.uniformer.mmcv.runner import get_dist_info
def single_gpu_test(model, data_loader):
"... | 203 | 7,196 |
ControlNet | annotator/uniformer/mmcv/visualization/color.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from enum import Enum
import numpy as np
from annotator.uniformer.mmcv.utils import is_str
class Color(Enum):
"""An enum that defines common colors.
Contains red, green, blue, cyan, yellow, magenta, white and black.
"""
red = (0, 0, 255)
green = (... | 52 | 1,381 |
ControlNet | annotator/uniformer/mmcv/visualization/optflow.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from __future__ import division
import numpy as np
from annotator.uniformer.mmcv.image import rgb2bgr
from annotator.uniformer.mmcv.video import flowread
from .image import imshow
def flowshow(flow, win_name='', wait_time=0):
"""Show optical flow.
Args:
... | 113 | 3,389 |
ControlNet | annotator/uniformer/mmcv/visualization/__init__.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from .color import Color, color_val
from .image import imshow, imshow_bboxes, imshow_det_bboxes
from .optflow import flow2rgb, flowshow, make_color_wheel
__all__ = [
'Color', 'color_val', 'imshow', 'imshow_bboxes', 'imshow_det_bboxes',
'flowshow', 'flow2rgb', 'ma... | 10 | 338 |
ControlNet | annotator/uniformer/mmcv/visualization/image.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import cv2
import numpy as np
from annotator.uniformer.mmcv.image import imread, imwrite
from .color import color_val
def imshow(img, win_name='', wait_time=0):
"""Show an image.
Args:
img (str or ndarray): The image to be displayed.
win_name (... | 153 | 5,145 |
ControlNet | annotator/uniformer/mmcv/runner/checkpoint.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimize... | 708 | 25,136 |
ControlNet | annotator/uniformer/mmcv/runner/default_constructor.py | .py | from .builder import RUNNER_BUILDERS, RUNNERS
@RUNNER_BUILDERS.register_module()
class DefaultRunnerConstructor:
"""Default constructor for runners.
Custom existing `Runner` like `EpocBasedRunner` though `RunnerConstructor`.
For example, We can inject some new properties and functions for `Runner`.
... | 45 | 1,928 |
ControlNet | annotator/uniformer/mmcv/runner/utils.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import random
import sys
import time
import warnings
from getpass import getuser
from socket import gethostname
import numpy as np
import torch
import annotator.uniformer.mmcv as mmcv
def get_host_info():
"""Get hostname and username.
Return empty s... | 94 | 2,936 |
ControlNet | annotator/uniformer/mmcv/runner/builder.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
from ..utils import Registry
RUNNERS = Registry('runner')
RUNNER_BUILDERS = Registry('runner builder')
def build_runner_constructor(cfg):
return RUNNER_BUILDERS.build(cfg)
def build_runner(cfg, default_args=None):
runner_cfg = copy.deepcopy(cfg)
... | 25 | 666 |
ControlNet | annotator/uniformer/mmcv/runner/epoch_based_runner.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import platform
import shutil
import time
import warnings
import torch
import annotator.uniformer.mmcv as mmcv
from .base_runner import BaseRunner
from .builder import RUNNERS
from .checkpoint import save_checkpoint
from .utils import get_host_info... | 188 | 7,565 |
ControlNet | annotator/uniformer/mmcv/runner/base_module.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import warnings
from abc import ABCMeta
from collections import defaultdict
from logging import FileHandler
import torch.nn as nn
from annotator.uniformer.mmcv.runner.dist_utils import master_only
from annotator.uniformer.mmcv.utils.logging import get_logger... | 196 | 7,502 |
ControlNet | annotator/uniformer/mmcv/runner/__init__.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from .base_module import BaseModule, ModuleList, Sequential
from .base_runner import BaseRunner
from .builder import RUNNERS, build_runner
from .checkpoint import (CheckpointLoader, _load_checkpoint,
_load_checkpoint_with_prefix, load_checkpoint,
... | 48 | 2,859 |
ControlNet | annotator/uniformer/mmcv/runner/priority.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from enum import Enum
class Priority(Enum):
"""Hook priority levels.
+--------------+------------+
| Level | Value |
+==============+============+
| HIGHEST | 0 |
+--------------+------------+
| VERY_HIGH | 10 ... | 61 | 1,598 |
ControlNet | annotator/uniformer/mmcv/runner/base_runner.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import logging
import os.path as osp
import warnings
from abc import ABCMeta, abstractmethod
import torch
from torch.optim import Optimizer
import annotator.uniformer.mmcv as mmcv
from ..parallel import is_module_wrapper
from .checkpoint import load_checkpoi... | 543 | 20,846 |
ControlNet | annotator/uniformer/mmcv/runner/fp16_utils.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import functools
import warnings
from collections import abc
from inspect import getfullargspec
import numpy as np
import torch
import torch.nn as nn
from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from .dist_utils import allreduce_grads as _allr... | 411 | 15,784 |
ControlNet | annotator/uniformer/mmcv/runner/iter_based_runner.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import platform
import shutil
import time
import warnings
import torch
from torch.optim import Optimizer
import annotator.uniformer.mmcv as mmcv
from .base_runner import BaseRunner
from .builder import RUNNERS
from .checkpoint import save_checkpoin... | 274 | 11,062 |
ControlNet | annotator/uniformer/mmcv/runner/log_buffer.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from collections import OrderedDict
import numpy as np
class LogBuffer:
def __init__(self):
self.val_history = OrderedDict()
self.n_history = OrderedDict()
self.output = OrderedDict()
self.ready = False
def clear(self):
... | 42 | 1,192 |
ControlNet | annotator/uniformer/mmcv/runner/dist_utils.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import functools
import os
import subprocess
from collections import OrderedDict
import torch
import torch.multiprocessing as mp
from torch import distributed as dist
from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_de... | 165 | 5,395 |
ControlNet | annotator/uniformer/mmcv/runner/hooks/iter_timer.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import time
from .hook import HOOKS, Hook
@HOOKS.register_module()
class IterTimerHook(Hook):
def before_epoch(self, runner):
self.t = time.time()
def before_iter(self, runner):
runner.log_buffer.update({'data_time': time.time() - self.t})
... | 19 | 446 |
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