repo stringlengths 7 90 | file_url stringlengths 81 315 | file_path stringlengths 4 228 | content stringlengths 0 32.8k | language stringclasses 1
value | license stringclasses 7
values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 14:38:15 2026-01-05 02:33:18 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/decode_head.py | annotator/uniformer/mmseg/models/decode_heads/decode_head.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/segmentors/cascade_encoder_decoder.py | annotator/uniformer/mmseg/models/segmentors/cascade_encoder_decoder.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/segmentors/__init__.py | annotator/uniformer/mmseg/models/segmentors/__init__.py | from .base import BaseSegmentor
from .cascade_encoder_decoder import CascadeEncoderDecoder
from .encoder_decoder import EncoderDecoder
__all__ = ['BaseSegmentor', 'EncoderDecoder', 'CascadeEncoderDecoder']
| python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/segmentors/base.py | annotator/uniformer/mmseg/models/segmentors/base.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/segmentors/encoder_decoder.py | annotator/uniformer/mmseg/models/segmentors/encoder_decoder.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/utils/weight_init.py | annotator/uniformer/mmseg/models/utils/weight_init.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/utils/drop.py | annotator/uniformer/mmseg/models/utils/drop.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/utils/se_layer.py | annotator/uniformer/mmseg/models/utils/se_layer.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/utils/inverted_residual.py | annotator/uniformer/mmseg/models/utils/inverted_residual.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.
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/utils/make_divisible.py | annotator/uniformer/mmseg/models/utils/make_divisible.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 ... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/utils/__init__.py | annotator/uniformer/mmseg/models/utils/__init__.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/utils/self_attention_block.py | annotator/uniformer/mmseg/models/utils/self_attention_block.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/utils/res_layer.py | annotator/uniformer/mmseg/models/utils/res_layer.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/utils/up_conv_block.py | annotator/uniformer/mmseg/models/utils/up_conv_block.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/backbones/fast_scnn.py | annotator/uniformer/mmseg/models/backbones/fast_scnn.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/backbones/unet.py | annotator/uniformer/mmseg/models/backbones/unet.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/backbones/mobilenet_v3.py | annotator/uniformer/mmseg/models/backbones/mobilenet_v3.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/backbones/mobilenet_v2.py | annotator/uniformer/mmseg/models/backbones/mobilenet_v2.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/backbones/resnest.py | annotator/uniformer/mmseg/models/backbones/resnest.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 ... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/backbones/hrnet.py | annotator/uniformer/mmseg/models/backbones/hrnet.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/backbones/vit.py | annotator/uniformer/mmseg/models/backbones/vit.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/backbones/resnet.py | annotator/uniformer/mmseg/models/backbones/resnet.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/backbones/uniformer.py | annotator/uniformer/mmseg/models/backbones/uniformer.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/backbones/resnext.py | annotator/uniformer/mmseg/models/backbones/resnext.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"... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/backbones/__init__.py | annotator/uniformer/mmseg/models/backbones/__init__.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/backbones/cgnet.py | annotator/uniformer/mmseg/models/backbones/cgnet.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/necks/multilevel_neck.py | annotator/uniformer/mmseg/models/necks/multilevel_neck.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]... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/necks/__init__.py | annotator/uniformer/mmseg/models/necks/__init__.py | from .fpn import FPN
from .multilevel_neck import MultiLevelNeck
__all__ = ['FPN', 'MultiLevelNeck']
| python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/necks/fpn.py | annotator/uniformer/mmseg/models/necks/fpn.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/utils/logger.py | annotator/uniformer/mmseg/utils/logger.py | import logging
from annotator.uniformer.mmcv.utils import get_logger
def get_root_logger(log_file=None, log_level=logging.INFO):
"""Get the root logger.
The logger will be initialized if it has not been initialized. By default a
StreamHandler will be added. If `log_file` is specified, a FileHandler will... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/utils/collect_env.py | annotator/uniformer/mmseg/utils/collect_env.py | from annotator.uniformer.mmcv.utils import collect_env as collect_base_env
from annotator.uniformer.mmcv.utils import get_git_hash
import annotator.uniformer.mmseg as mmseg
def collect_env():
"""Collect the information of the running environments."""
env_info = collect_base_env()
env_info['MMSegmentation... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/utils/__init__.py | annotator/uniformer/mmseg/utils/__init__.py | from .collect_env import collect_env
from .logger import get_root_logger
__all__ = ['get_root_logger', 'collect_env']
| python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/core/__init__.py | annotator/uniformer/mmseg/core/__init__.py | from .evaluation import * # noqa: F401, F403
from .seg import * # noqa: F401, F403
from .utils import * # noqa: F401, F403
| python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/core/utils/misc.py | annotator/uniformer/mmseg/core/utils/misc.py | def add_prefix(inputs, prefix):
"""Add prefix for dict.
Args:
inputs (dict): The input dict with str keys.
prefix (str): The prefix to add.
Returns:
dict: The dict with keys updated with ``prefix``.
"""
outputs = dict()
for name, value in inputs.items():
outpu... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/core/utils/__init__.py | annotator/uniformer/mmseg/core/utils/__init__.py | from .misc import add_prefix
__all__ = ['add_prefix']
| python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/core/seg/__init__.py | annotator/uniformer/mmseg/core/seg/__init__.py | from .builder import build_pixel_sampler
from .sampler import BasePixelSampler, OHEMPixelSampler
__all__ = ['build_pixel_sampler', 'BasePixelSampler', 'OHEMPixelSampler']
| python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/core/seg/builder.py | annotator/uniformer/mmseg/core/seg/builder.py | from annotator.uniformer.mmcv.utils import Registry, build_from_cfg
PIXEL_SAMPLERS = Registry('pixel sampler')
def build_pixel_sampler(cfg, **default_args):
"""Build pixel sampler for segmentation map."""
return build_from_cfg(cfg, PIXEL_SAMPLERS, default_args)
| python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/core/seg/sampler/base_pixel_sampler.py | annotator/uniformer/mmseg/core/seg/sampler/base_pixel_sampler.py | from abc import ABCMeta, abstractmethod
class BasePixelSampler(metaclass=ABCMeta):
"""Base class of pixel sampler."""
def __init__(self, **kwargs):
pass
@abstractmethod
def sample(self, seg_logit, seg_label):
"""Placeholder for sample function."""
| python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/core/seg/sampler/ohem_pixel_sampler.py | annotator/uniformer/mmseg/core/seg/sampler/ohem_pixel_sampler.py | import torch
import torch.nn.functional as F
from ..builder import PIXEL_SAMPLERS
from .base_pixel_sampler import BasePixelSampler
@PIXEL_SAMPLERS.register_module()
class OHEMPixelSampler(BasePixelSampler):
"""Online Hard Example Mining Sampler for segmentation.
Args:
context (nn.Module): The contex... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/core/seg/sampler/__init__.py | annotator/uniformer/mmseg/core/seg/sampler/__init__.py | from .base_pixel_sampler import BasePixelSampler
from .ohem_pixel_sampler import OHEMPixelSampler
__all__ = ['BasePixelSampler', 'OHEMPixelSampler']
| python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/core/evaluation/metrics.py | annotator/uniformer/mmseg/core/evaluation/metrics.py | from collections import OrderedDict
import annotator.uniformer.mmcv as mmcv
import numpy as np
import torch
def f_score(precision, recall, beta=1):
"""calcuate the f-score value.
Args:
precision (float | torch.Tensor): The precision value.
recall (float | torch.Tensor): The recall value.
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/core/evaluation/class_names.py | annotator/uniformer/mmseg/core/evaluation/class_names.py | import annotator.uniformer.mmcv as mmcv
def cityscapes_classes():
"""Cityscapes class names for external use."""
return [
'road', 'sidewalk', 'building', 'wall', 'fence', 'pole',
'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky',
'person', 'rider', 'car', 'truck', 'bus', ... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/core/evaluation/__init__.py | annotator/uniformer/mmseg/core/evaluation/__init__.py | from .class_names import get_classes, get_palette
from .eval_hooks import DistEvalHook, EvalHook
from .metrics import eval_metrics, mean_dice, mean_fscore, mean_iou
__all__ = [
'EvalHook', 'DistEvalHook', 'mean_dice', 'mean_iou', 'mean_fscore',
'eval_metrics', 'get_classes', 'get_palette'
]
| python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/core/evaluation/eval_hooks.py | annotator/uniformer/mmseg/core/evaluation/eval_hooks.py | import os.path as osp
from annotator.uniformer.mmcv.runner import DistEvalHook as _DistEvalHook
from annotator.uniformer.mmcv.runner import EvalHook as _EvalHook
class EvalHook(_EvalHook):
"""Single GPU EvalHook, with efficient test support.
Args:
by_epoch (bool): Determine perform evaluation by epo... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/apis/train.py | annotator/uniformer/mmseg/apis/train.py | import random
import warnings
import numpy as np
import torch
from annotator.uniformer.mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from annotator.uniformer.mmcv.runner import build_optimizer, build_runner
from annotator.uniformer.mmseg.core import DistEvalHook, EvalHook
from annotator.uniformer.mms... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/apis/inference.py | annotator/uniformer/mmseg/apis/inference.py | import matplotlib.pyplot as plt
import annotator.uniformer.mmcv as mmcv
import torch
from annotator.uniformer.mmcv.parallel import collate, scatter
from annotator.uniformer.mmcv.runner import load_checkpoint
from annotator.uniformer.mmseg.datasets.pipelines import Compose
from annotator.uniformer.mmseg.models import b... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/apis/__init__.py | annotator/uniformer/mmseg/apis/__init__.py | from .inference import inference_segmentor, init_segmentor, show_result_pyplot
from .test import multi_gpu_test, single_gpu_test
from .train import get_root_logger, set_random_seed, train_segmentor
__all__ = [
'get_root_logger', 'set_random_seed', 'train_segmentor', 'init_segmentor',
'inference_segmentor', 'mu... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/apis/test.py | annotator/uniformer/mmseg/apis/test.py | import os.path as osp
import pickle
import shutil
import tempfile
import annotator.uniformer.mmcv as mmcv
import numpy as np
import torch
import torch.distributed as dist
from annotator.uniformer.mmcv.image import tensor2imgs
from annotator.uniformer.mmcv.runner import get_dist_info
def np2tmp(array, temp_file_name=... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/ops/wrappers.py | annotator/uniformer/mmseg/ops/wrappers.py | import warnings
import torch.nn as nn
import torch.nn.functional as F
def resize(input,
size=None,
scale_factor=None,
mode='nearest',
align_corners=None,
warning=True):
if warning:
if size is not None and align_corners:
input_h, input_w =... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/ops/encoding.py | annotator/uniformer/mmseg/ops/encoding.py | import torch
from torch import nn
from torch.nn import functional as F
class Encoding(nn.Module):
"""Encoding Layer: a learnable residual encoder.
Input is of shape (batch_size, channels, height, width).
Output is of shape (batch_size, num_codes, channels).
Args:
channels: dimension of the ... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/ops/__init__.py | annotator/uniformer/mmseg/ops/__init__.py | from .encoding import Encoding
from .wrappers import Upsample, resize
__all__ = ['Upsample', 'resize', 'Encoding']
| python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/version.py | annotator/uniformer/mmcv/version.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.
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/__init__.py | annotator/uniformer/mmcv/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
# flake8: noqa
from .arraymisc import *
from .fileio import *
from .image import *
from .utils import *
from .version import *
from .video import *
from .visualization import *
# The following modules are not imported to this level, so mmcv may be used
# without PyTorch.... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/alexnet.py | annotator/uniformer/mmcv/cnn/alexnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import torch.nn as nn
class AlexNet(nn.Module):
"""AlexNet backbone.
Args:
num_classes (int): number of classes for classification.
"""
def __init__(self, num_classes=-1):
super(AlexNet, self).__init__()
self.num... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/vgg.py | annotator/uniformer/mmcv/cnn/vgg.py | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import torch.nn as nn
from .utils import constant_init, kaiming_init, normal_init
def conv3x3(in_planes, out_planes, dilation=1):
"""3x3 convolution with padding."""
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/resnet.py | annotator/uniformer/mmcv/cnn/resnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import torch.nn as nn
import torch.utils.checkpoint as cp
from .utils import constant_init, kaiming_init
def conv3x3(in_planes, out_planes, stride=1, dilation=1):
"""3x3 convolution with padding."""
return nn.Conv2d(
in_planes,
o... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/__init__.py | annotator/uniformer/mmcv/cnn/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .alexnet import AlexNet
# yapf: disable
from .bricks import (ACTIVATION_LAYERS, CONV_LAYERS, NORM_LAYERS,
PADDING_LAYERS, PLUGIN_LAYERS, UPSAMPLE_LAYERS,
ContextBlock, Conv2d, Conv3d, ConvAWS2d, ConvModule,
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/builder.py | annotator/uniformer/mmcv/cnn/builder.py | # Copyright (c) OpenMMLab. All rights reserved.
from ..runner import Sequential
from ..utils import Registry, build_from_cfg
def build_model_from_cfg(cfg, registry, default_args=None):
"""Build a PyTorch model from config dict(s). Different from
``build_from_cfg``, if cfg is a list, a ``nn.Sequential`` will b... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/activation.py | annotator/uniformer/mmcv/cnn/bricks/activation.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
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 .registry import ACTIVATION_LAYERS
for module in [
nn.ReLU, nn.LeakyReLU, nn.PReLU, nn.RReLU, nn.ReLU6... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/conv.py | annotator/uniformer/mmcv/cnn/bricks/conv.py | # Copyright (c) OpenMMLab. All rights reserved.
from torch import nn
from .registry import CONV_LAYERS
CONV_LAYERS.register_module('Conv1d', module=nn.Conv1d)
CONV_LAYERS.register_module('Conv2d', module=nn.Conv2d)
CONV_LAYERS.register_module('Conv3d', module=nn.Conv3d)
CONV_LAYERS.register_module('Conv', module=nn.C... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/drop.py | annotator/uniformer/mmcv/cnn/bricks/drop.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from annotator.uniformer.mmcv import build_from_cfg
from .registry import DROPOUT_LAYERS
def drop_path(x, drop_prob=0., training=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of
residual blocks... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/registry.py | annotator/uniformer/mmcv/cnn/bricks/registry.py | # Copyright (c) OpenMMLab. All rights reserved.
from annotator.uniformer.mmcv.utils import Registry
CONV_LAYERS = Registry('conv layer')
NORM_LAYERS = Registry('norm layer')
ACTIVATION_LAYERS = Registry('activation layer')
PADDING_LAYERS = Registry('padding layer')
UPSAMPLE_LAYERS = Registry('upsample layer')
PLUGIN_L... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/wrappers.py | annotator/uniformer/mmcv/cnn/bricks/wrappers.py | # Copyright (c) OpenMMLab. All rights reserved.
r"""Modified from https://github.com/facebookresearch/detectron2/blob/master/detectron2/layers/wrappers.py # noqa: E501
Wrap some nn modules to support empty tensor input. Currently, these wrappers
are mainly used in mask heads like fcn_mask_head and maskiou_heads since... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/conv2d_adaptive_padding.py | annotator/uniformer/mmcv/cnn/bricks/conv2d_adaptive_padding.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from torch import nn
from torch.nn import functional as F
from .registry import CONV_LAYERS
@CONV_LAYERS.register_module()
class Conv2dAdaptivePadding(nn.Conv2d):
"""Implementation of 2D convolution in tensorflow with `padding` as "same",
which app... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/plugin.py | annotator/uniformer/mmcv/cnn/bricks/plugin.py | import inspect
import platform
from .registry import PLUGIN_LAYERS
if platform.system() == 'Windows':
import regex as re
else:
import re
def infer_abbr(class_type):
"""Infer abbreviation from the class name.
This method will infer the abbreviation to map class types to
abbreviations.
Rule ... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/conv_ws.py | annotator/uniformer/mmcv/cnn/bricks/conv_ws.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from .registry import CONV_LAYERS
def conv_ws_2d(input,
weight,
bias=None,
stride=1,
padding=0,
dilation=1,
grou... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/non_local.py | annotator/uniformer/mmcv/cnn/bricks/non_local.py | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta
import torch
import torch.nn as nn
from ..utils import constant_init, normal_init
from .conv_module import ConvModule
from .registry import PLUGIN_LAYERS
class _NonLocalNd(nn.Module, metaclass=ABCMeta):
"""Basic Non-local module.
This ... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/upsample.py | annotator/uniformer/mmcv/cnn/bricks/upsample.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from ..utils import xavier_init
from .registry import UPSAMPLE_LAYERS
UPSAMPLE_LAYERS.register_module('nearest', module=nn.Upsample)
UPSAMPLE_LAYERS.register_module('bilinear', module=nn.Upsample)
@UPSAMPLE_LAYERS.... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/generalized_attention.py | annotator/uniformer/mmcv/cnn/bricks/generalized_attention.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..utils import kaiming_init
from .registry import PLUGIN_LAYERS
@PLUGIN_LAYERS.register_module()
class GeneralizedAttention(nn.Module):
"""GeneralizedAttention m... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/hswish.py | annotator/uniformer/mmcv/cnn/bricks/hswish.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from .registry import ACTIVATION_LAYERS
@ACTIVATION_LAYERS.register_module()
class HSwish(nn.Module):
"""Hard Swish Module.
This module applies the hard swish function:
.. math::
Hswish(x) = x * ReLU6(x + 3) / 6
Args:
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/conv_module.py | annotator/uniformer/mmcv/cnn/bricks/conv_module.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
from 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
from .norm import build_norm_laye... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/norm.py | annotator/uniformer/mmcv/cnn/bricks/norm.py | # Copyright (c) OpenMMLab. All rights reserved.
import inspect
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 .registry import NORM_LAYERS
NORM_LAYERS.register_module('BN', module=nn... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/scale.py | annotator/uniformer/mmcv/cnn/bricks/scale.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
class Scale(nn.Module):
"""A learnable scale parameter.
This layer scales the input by a learnable factor. It multiplies a
learnable scale parameter of shape (1,) with input of any shape.
Args:
scale (float): ... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/__init__.py | annotator/uniformer/mmcv/cnn/bricks/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .activation import build_activation_layer
from .context_block import ContextBlock
from .conv import build_conv_layer
from .conv2d_adaptive_padding import Conv2dAdaptivePadding
from .conv_module import ConvModule
from .conv_ws import ConvAWS2d, ConvWS2d, conv_ws_2d
fr... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/swish.py | annotator/uniformer/mmcv/cnn/bricks/swish.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from .registry import ACTIVATION_LAYERS
@ACTIVATION_LAYERS.register_module()
class Swish(nn.Module):
"""Swish Module.
This module applies the swish function:
.. math::
Swish(x) = x * Sigmoid(x)
Returns:
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py | annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from .conv_module import ConvModule
class DepthwiseSeparableConvModule(nn.Module):
"""Depthwise separable convolution module.
See https://arxiv.org/pdf/1704.04861.pdf for details.
This module can replace a ConvModule with the conv bl... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/transformer.py | annotator/uniformer/mmcv/cnn/bricks/transformer.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import warnings
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_mod... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/context_block.py | annotator/uniformer/mmcv/cnn/bricks/context_block.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch import nn
from ..utils import constant_init, kaiming_init
from .registry import PLUGIN_LAYERS
def last_zero_init(m):
if isinstance(m, nn.Sequential):
constant_init(m[-1], val=0)
else:
constant_init(m, val=0)
@PLUGIN_LAY... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/padding.py | annotator/uniformer/mmcv/cnn/bricks/padding.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from .registry import PADDING_LAYERS
PADDING_LAYERS.register_module('zero', module=nn.ZeroPad2d)
PADDING_LAYERS.register_module('reflect', module=nn.ReflectionPad2d)
PADDING_LAYERS.register_module('replicate', module=nn.ReplicationPad2d)
def buil... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/bricks/hsigmoid.py | annotator/uniformer/mmcv/cnn/bricks/hsigmoid.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from .registry import ACTIVATION_LAYERS
@ACTIVATION_LAYERS.register_module()
class HSigmoid(nn.Module):
"""Hard Sigmoid Module. Apply the hard sigmoid function:
Hsigmoid(x) = min(max((x + bias) / divisor, min_value), max_value)
Default... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/utils/weight_init.py | annotator/uniformer/mmcv/cnn/utils/weight_init.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import math
import warnings
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor
from annotator.uniformer.mmcv.utils import Registry, build_from_cfg, get_logger, print_log
INITIALIZERS = Registry('initializer')
def update_init_in... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/utils/fuse_conv_bn.py | annotator/uniformer/mmcv/cnn/utils/fuse_conv_bn.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
def _fuse_conv_bn(conv, bn):
"""Fuse conv and bn into one module.
Args:
conv (nn.Module): Conv to be fused.
bn (nn.Module): BN to be fused.
Returns:
nn.Module: Fused module.
"""
conv_w = co... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/utils/flops_counter.py | annotator/uniformer/mmcv/cnn/utils/flops_counter.py | # Modified from flops-counter.pytorch by Vladislav Sovrasov
# original repo: https://github.com/sovrasov/flops-counter.pytorch
# MIT License
# Copyright (c) 2018 Vladislav Sovrasov
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (th... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/utils/sync_bn.py | annotator/uniformer/mmcv/cnn/utils/sync_bn.py | import torch
import annotator.uniformer.mmcv as mmcv
class _BatchNormXd(torch.nn.modules.batchnorm._BatchNorm):
"""A general BatchNorm layer without input dimension check.
Reproduced from @kapily's work:
(https://github.com/pytorch/pytorch/issues/41081#issuecomment-783961547)
The only difference bet... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/cnn/utils/__init__.py | annotator/uniformer/mmcv/cnn/utils/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .flops_counter import get_model_complexity_info
from .fuse_conv_bn import fuse_conv_bn
from .sync_bn import revert_sync_batchnorm
from .weight_init import (INITIALIZERS, Caffe2XavierInit, ConstantInit,
KaimingInit, NormalInit, PretrainedInit... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/image/colorspace.py | annotator/uniformer/mmcv/image/colorspace.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/image/geometric.py | annotator/uniformer/mmcv/image/geometric.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/image/misc.py | annotator/uniformer/mmcv/image/misc.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/image/photometric.py | annotator/uniformer/mmcv/image/photometric.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/image/__init__.py | annotator/uniformer/mmcv/image/__init__.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, ... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/image/io.py | annotator/uniformer/mmcv/image/io.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/fileio/parse.py | annotator/uniformer/mmcv/fileio/parse.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/fileio/file_client.py | annotator/uniformer/mmcv/fileio/file_client.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | true |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/fileio/__init__.py | annotator/uniformer/mmcv/fileio/__init__.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/fileio/io.py | annotator/uniformer/mmcv/fileio/io.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/fileio/handlers/pickle_handler.py | annotator/uniformer/mmcv/fileio/handlers/pickle_handler.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/fileio/handlers/yaml_handler.py | annotator/uniformer/mmcv/fileio/handlers/yaml_handler.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):
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/fileio/handlers/json_handler.py | annotator/uniformer/mmcv/fileio/handlers/json_handler.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/fileio/handlers/__init__.py | annotator/uniformer/mmcv/fileio/handlers/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .base import BaseFileHandler
from .json_handler import JsonHandler
from .pickle_handler import PickleHandler
from .yaml_handler import YamlHandler
__all__ = ['BaseFileHandler', 'JsonHandler', 'PickleHandler', 'YamlHandler']
| python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmcv/fileio/handlers/base.py | annotator/uniformer/mmcv/fileio/handlers/base.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
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