repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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mmpretrain | mmpretrain-master/mmcls/models/backbones/seresnext.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from .resnet import ResLayer
from .seresnet import SEBottleneck as _SEBottleneck
from .seresnet import SEResNet
class SEBottleneck(_SEBottleneck):
"""SEBottleneck block for SERe... | 6,673 | 41.782051 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/hornet.py | # Copyright (c) OpenMMLab. All rights reserved.
# Adapted from official impl at https://github.com/raoyongming/HorNet.
try:
import torch.fft
fft = True
except ImportError:
fft = None
import copy
from functools import partial
from typing import Sequence
import torch
import torch.nn as nn
import torch.nn.fu... | 18,877 | 36.756 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/conformer.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import build_activation_layer, build_norm_layer
from mmcv.cnn.bricks.drop import DropPath
from mmcv.cnn.bricks.transformer import AdaptivePadding
from mmcv.cnn.ut... | 22,874 | 35.483254 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/resnext.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResLayer, ResNet
class Bottleneck(_Bottleneck):
"""Bottleneck block for ResNeXt.
Args:
in_channels... | 6,256 | 40.993289 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/t2t_vit.py | # Copyright (c) OpenMMLab. All rights reserved.
from copy import deepcopy
from typing import Sequence
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import build_norm_layer
from mmcv.cnn.bricks.transformer import FFN
from mmcv.cnn.utils.weight_init import trunc_normal_
from mmcv.runner.base_module... | 16,420 | 36.235828 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/vision_transformer.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import build_norm_layer
from mmcv.cnn.bricks.transformer import FFN, PatchEmbed
from mmcv.cnn.utils.weight_init import trunc_normal_
from mmcv.runner.base_module import BaseMo... | 14,734 | 37.372396 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/mobilenet_v3.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import ConvModule
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
from ..utils import InvertedResidual
from .base_backbone import BaseBackbone
@BACKBONES.register_module()
class MobileNetV3(BaseBackbone):
"""MobileNet... | 7,608 | 37.821429 | 78 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/efficientformer.py | # Copyright (c) OpenMMLab. All rights reserved.
import itertools
from typing import Optional, Sequence
import torch
import torch.nn as nn
from mmcv.cnn.bricks import (ConvModule, DropPath, build_activation_layer,
build_norm_layer)
from mmcv.runner import BaseModule, ModuleList, Sequential
... | 22,131 | 35.461285 | 97 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/repmlp.py | # Copyright (c) OpenMMLab. All rights reserved.
# Adapted from official impl at https://github.com/DingXiaoH/RepMLP.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import (ConvModule, build_activation_layer, build_conv_layer,
build_norm_layer)
from mmcv.cnn.bricks... | 22,843 | 38.454231 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/twins.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import Conv2d, build_norm_layer
from mmcv.cnn.bricks.drop import build_dropout
from mmcv.cnn.bricks.transformer import FFN, PatchEmbed
from mmcv.cnn.utils.weight_init import (con... | 30,204 | 40.719613 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/resnest.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResLayer, ResN... | 12,231 | 34.976471 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/resnet_cifar.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from .resnet import ResNet
@BACKBONES.register_module()
class ResNet_CIFAR(ResNet):
"""ResNet backbone for CIFAR.
Compared to standard ResNet, it uses... | 3,707 | 44.219512 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/lenet.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from ..builder import BACKBONES
from .base_backbone import BaseBackbone
@BACKBONES.register_module()
class LeNet5(BaseBackbone):
"""`LeNet5 <https://en.wikipedia.org/wiki/LeNet>`_ backbone.
The input for LeNet-5 is a 32×32 grayscale image... | 1,325 | 29.837209 | 67 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/shufflenet_v2.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule, constant_init, normal_init
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.utils import channel_shuffle
from ..b... | 10,733 | 34.193443 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/convnext.py | # Copyright (c) OpenMMLab. All rights reserved.
from functools import partial
from itertools import chain
from typing import Sequence
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn.bricks import (NORM_LAYERS, DropPath, build_activation_layer,
... | 12,948 | 35.997143 | 94 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/res2net.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner import ModuleList, Sequential
from ..builder import BACKBONES
from .resnet import Bottleneck as _Bottleneck
from .... | 10,770 | 34.084691 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/convmixer.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence
import torch
import torch.nn as nn
from mmcv.cnn.bricks import (Conv2dAdaptivePadding, build_activation_layer,
build_norm_layer)
from mmcv.utils import digit_version
from ..builder import BACKBONES
from .base_back... | 6,184 | 33.943503 | 95 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/van.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import Conv2d, build_activation_layer, build_norm_layer
from mmcv.cnn.bricks import DropPath
from mmcv.cnn.bricks.transformer import PatchEmbed
from mmcv.runner import BaseModule, ModuleList
from mmcv.utils.parrots_wrapper ... | 16,200 | 35.325112 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/poolformer.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence
import torch
import torch.nn as nn
from mmcv.cnn.bricks import DropPath, build_activation_layer, build_norm_layer
from mmcv.runner import BaseModule
from ..builder import BACKBONES
from .base_backbone import BaseBackbone
class PatchEmbed(nn... | 14,699 | 34.251799 | 89 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/swin_transformer_v2.py | # Copyright (c) OpenMMLab. All rights reserved.
from copy import deepcopy
from typing import Sequence
import numpy as np
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import build_norm_layer
from mmcv.cnn.bricks.transformer import FFN, PatchEmbed
from mmcv.cnn.utils.weight_init i... | 22,671 | 39.413547 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/cspnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Sequence
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmcv.cnn.bricks import DropPath
from mmcv.runner import BaseModule, Sequential
from torch.nn.modules.batchnorm import _BatchNorm
... | 25,481 | 36.473529 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/backbones/alexnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from ..builder import BACKBONES
from .base_backbone import BaseBackbone
@BACKBONES.register_module()
class AlexNet(BaseBackbone):
"""`AlexNet <https://en.wikipedia.org/wiki/AlexNet>`_ backbone.
The input for AlexNet is a 224x224 RGB image... | 1,884 | 32.070175 | 67 | py |
mmpretrain | mmpretrain-master/mmcls/models/heads/multi_label_csra_head.py | # Copyright (c) OpenMMLab. All rights reserved.
# Modified from https://github.com/Kevinz-code/CSRA
import torch
import torch.nn as nn
from mmcv.runner import BaseModule, ModuleList
from ..builder import HEADS
from .multi_label_head import MultiLabelClsHead
@HEADS.register_module()
class CSRAClsHead(MultiLabelClsHea... | 4,250 | 33.844262 | 74 | py |
mmpretrain | mmpretrain-master/mmcls/models/heads/conformer_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn.utils.weight_init import trunc_normal_
from ..builder import HEADS
from .cls_head import ClsHead
@HEADS.register_module()
class ConformerHead(ClsHead):
"""Linear classifier head.
Args:
... | 4,776 | 34.917293 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/heads/multi_label_linear_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from ..builder import HEADS
from .multi_label_head import MultiLabelClsHead
@HEADS.register_module()
class MultiLabelLinearClsHead(MultiLabelClsHead):
"""Linear classification head for multilabel task.
Args:
num_class... | 2,948 | 33.290698 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/heads/deit_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from mmcls.utils import get_root_logger
from ..builder import HEADS
from .vision_transformer_head import VisionTransformerClsHead
@HEADS.register_module()
class DeiTClsHead(VisionTransformerClsHead):
"""Distille... | 3,951 | 39.742268 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/heads/cls_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch
import torch.nn.functional as F
from mmcls.models.losses import Accuracy
from ..builder import HEADS, build_loss
from ..utils import is_tracing
from .base_head import BaseHead
@HEADS.register_module()
class ClsHead(BaseHead):
"""classi... | 3,939 | 32.675214 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/heads/stacked_head.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, Sequence
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import build_activation_layer, build_norm_layer
from mmcv.runner import BaseModule, ModuleList
from ..builder import HEADS
from .cls_head import ClsHead
class LinearB... | 5,456 | 32.27439 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/heads/multi_label_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import HEADS, build_loss
from ..utils import is_tracing
from .base_head import BaseHead
@HEADS.register_module()
class MultiLabelClsHead(BaseHead):
"""Classification head for multilabel task.
Args:
loss (dict): Config of cla... | 3,252 | 31.53 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/heads/efficientformer_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from ..builder import HEADS
from .cls_head import ClsHead
@HEADS.register_module()
class EfficientFormerClsHead(ClsHead):
"""EfficientFormer classifier head.
Args:
num_classes (int): Number of categ... | 3,476 | 34.845361 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/heads/linear_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from ..builder import HEADS
from .cls_head import ClsHead
@HEADS.register_module()
class LinearClsHead(ClsHead):
"""Linear classifier head.
Args:
num_classes (int): Number of categories excluding th... | 2,730 | 32.304878 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/models/heads/vision_transformer_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from collections import OrderedDict
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import build_activation_layer
from mmcv.cnn.utils.weight_init import trunc_normal_
from mmcv.runner import Sequential
from ..builder import HEADS
from .cl... | 4,527 | 35.516129 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/datasets/base_dataset.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
from abc import ABCMeta, abstractmethod
from os import PathLike
from typing import List
import mmcv
import numpy as np
from torch.utils.data import Dataset
from mmcls.core.evaluation import precision_recall_f1, support
from mmcls.models... | 7,686 | 33.626126 | 78 | py |
mmpretrain | mmpretrain-master/mmcls/datasets/dataset_wrappers.py | # Copyright (c) OpenMMLab. All rights reserved.
import bisect
import math
from collections import defaultdict
import numpy as np
from mmcv.utils import print_log
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
from .builder import DATASETS
@DATASETS.register_module()
class ConcatDataset(_Concat... | 12,464 | 36.432432 | 167 | py |
mmpretrain | mmpretrain-master/mmcls/datasets/builder.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import platform
import random
from functools import partial
import numpy as np
import torch
from mmcv.parallel import collate
from mmcv.runner import get_dist_info
from mmcv.utils import Registry, build_from_cfg, digit_version
from torch.utils.data import Dat... | 6,438 | 33.994565 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/datasets/cifar.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path
import pickle
import numpy as np
import torch.distributed as dist
from mmcv.runner import get_dist_info
from .base_dataset import BaseDataset
from .builder import DATASETS
from .utils import check_integrity, download_and_extract_archive
@DATAS... | 5,805 | 36.217949 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/datasets/imagenet.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Union
from .builder import DATASETS
from .custom import CustomDataset
@DATASETS.register_module()
class ImageNet(CustomDataset):
"""`ImageNet <http://www.image-net.org>`_ Dataset.
The dataset supports two kinds of annotat... | 35,468 | 32.461321 | 146 | py |
mmpretrain | mmpretrain-master/mmcls/datasets/mnist.py | # Copyright (c) OpenMMLab. All rights reserved.
import codecs
import os
import os.path as osp
import numpy as np
import torch
import torch.distributed as dist
from mmcv.runner import get_dist_info, master_only
from .base_dataset import BaseDataset
from .builder import DATASETS
from .utils import download_and_extract_... | 6,416 | 33.5 | 97 | py |
mmpretrain | mmpretrain-master/mmcls/datasets/samplers/distributed_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
from mmcls.core.utils import sync_random_seed
from mmcls.datasets import SAMPLERS
@SAMPLERS.register_module()
class DistributedSampler(_DistributedSampler):
def __init__(self,
... | 2,242 | 35.770492 | 77 | py |
mmpretrain | mmpretrain-master/mmcls/datasets/samplers/repeat_aug.py | import math
import torch
from mmcv.runner import get_dist_info
from torch.utils.data import Sampler
from mmcls.core.utils import sync_random_seed
from mmcls.datasets import SAMPLERS
@SAMPLERS.register_module()
class RepeatAugSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset for... | 4,228 | 38.523364 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/datasets/pipelines/formatting.py | # Copyright (c) OpenMMLab. All rights reserved.
from collections.abc import Sequence
import mmcv
import numpy as np
import torch
from mmcv.parallel import DataContainer as DC
from PIL import Image
from ..builder import PIPELINES
def to_tensor(data):
"""Convert objects of various python types to :obj:`torch.Tens... | 5,508 | 27.107143 | 78 | py |
mmpretrain | mmpretrain-master/mmcls/datasets/pipelines/auto_augment.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import inspect
import random
from math import ceil
from numbers import Number
from typing import Sequence
import mmcv
import numpy as np
from ..builder import PIPELINES
from .compose import Compose
# Default hyperparameters for all Ops
_HPARAMS_DEFAULT = di... | 37,200 | 39.348156 | 79 | py |
mmpretrain | mmpretrain-master/mmcls/utils/setup_env.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import platform
import warnings
import cv2
import torch.multiprocessing as mp
def setup_multi_processes(cfg):
"""Setup multi-processing environment variables."""
# set multi-process start method as `fork` to speed up the training
if platform.syste... | 2,219 | 45.25 | 112 | py |
mmpretrain | mmpretrain-master/mmcls/utils/distribution.py | # Copyright (c) OpenMMLab. All rights reserved.
def wrap_non_distributed_model(model, device='cuda', dim=0, *args, **kwargs):
"""Wrap module in non-distributed environment by device type.
- For CUDA, wrap as :obj:`mmcv.parallel.MMDataParallel`.
- For MPS, wrap as :obj:`mmcv.device.mps.MPSDataParallel`.
... | 2,889 | 34.679012 | 78 | py |
mmpretrain | mmpretrain-master/mmcls/utils/device.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmcv.utils import digit_version
def auto_select_device() -> str:
mmcv_version = digit_version(mmcv.__version__)
if mmcv_version >= digit_version('1.6.0'):
from mmcv.device import get_device
return get_device()
el... | 403 | 24.25 | 50 | py |
mmpretrain | mmpretrain-master/.dev_scripts/benchmark_regression/1-benchmark_valid.py | import logging
import re
from argparse import ArgumentParser
from pathlib import Path
from time import time
from typing import OrderedDict
import numpy as np
import torch
from mmcv import Config
from mmcv.parallel import collate, scatter
from modelindex.load_model_index import load
from rich.console import Console
fro... | 9,547 | 36.296875 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_runtime/test_optimizer.py | # Copyright (c) OpenMMLab. All rights reserved.
import functools
from collections import OrderedDict
from copy import deepcopy
from typing import Iterable
import torch
import torch.nn as nn
from mmcv.runner import build_optimizer
from mmcv.runner.optimizer.builder import OPTIMIZERS
from mmcv.utils.registry import buil... | 11,126 | 34.893548 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_runtime/test_eval_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import tempfile
from unittest.mock import MagicMock, patch
import mmcv.runner
import pytest
import torch
import torch.nn as nn
from mmcv.runner import obj_from_dict
from mmcv.runner.hooks import DistEvalHook, EvalHook
from torch.utils.data import DataLoade... | 7,069 | 33.487805 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_runtime/test_hooks.py | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import shutil
import tempfile
import numpy as np
import pytest
import torch
import torch.nn as nn
from mmcv.runner import build_runner
from mmcv.runner.hooks import Hook, IterTimerHook
from torch.utils.data import DataLoader
import mmcls.core # noqa: F40... | 4,838 | 29.433962 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_runtime/test_num_class_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import tempfile
from unittest.mock import MagicMock
import mmcv.runner as mmcv_runner
import pytest
import torch
from mmcv.runner import obj_from_dict
from torch.utils.data import DataLoader, Dataset
from mmcls.core.hook import ClassNumCheckHook
from mmcl... | 2,556 | 29.082353 | 76 | py |
mmpretrain | mmpretrain-master/tests/test_runtime/test_preciseBN_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
import torch.nn as nn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import EpochBasedRunner, IterBasedRunner, build_optimizer
from mmcv.utils import get_logger
from mmcv.utils.logging imp... | 8,349 | 29.363636 | 77 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_classifiers.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from copy import deepcopy
import numpy as np
import torch
from mmcv import ConfigDict
from mmcls.models import CLASSIFIERS
from mmcls.models.classifiers import ImageClassifier
def test_image_classifier():
model_cfg = dict(
... | 10,685 | 31.678899 | 77 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_heads.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import patch
import pytest
import torch
from mmcls.models.heads import (ClsHead, ConformerHead, CSRAClsHead,
DeiTClsHead, EfficientFormerClsHead,
LinearClsHead, MultiLabelClsHead,
... | 14,769 | 35.832918 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcls.models.necks import (GeneralizedMeanPooling, GlobalAveragePooling,
HRFuseScales)
def test_gap_neck():
# test 1d gap_neck
neck = GlobalAveragePooling(dim=1)
# batch_size, num_features, fe... | 2,348 | 25.693182 | 77 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_twins.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import pytest
import torch
import torch.nn as nn
from mmcls.models.backbones.twins import (PCPVT, SVT,
GlobalSubsampledAttention,
LocallyGroupedSelfAttention)
def test_LSA_... | 8,490 | 33.79918 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_vgg.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmcls.models.backbones import VGG
def check_norm_state(modules, train_state):
"""Check if norm layer is in correct train state."""
for mod in modules:
if isinstance(mod, _... | 4,069 | 28.071429 | 74 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_seresnext.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcls.models.backbones import SEResNeXt
from mmcls.models.backbones.seresnext import SEBottleneck as SEBottleneckX
def test_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
SEB... | 2,587 | 33.506667 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_mobilenet_v3.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.backbones import MobileNetV3
from mmcls.models.utils import InvertedResidual
def is_norm(modules):
"""Check if is one of the norms.... | 6,437 | 35.579545 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_res2net.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmcls.models.backbones import Res2Net
def check_norm_state(modules, train_state):
"""Check if norm layer is in correct train state."""
for mod in modules:
if isinstance(mo... | 2,320 | 31.236111 | 77 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_t2t_vit.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import os
import tempfile
from copy import deepcopy
from unittest import TestCase
import numpy as np
import torch
from mmcv.runner import load_checkpoint, save_checkpoint
from mmcls.models.backbones import T2T_ViT
from mmcls.models.backbones.t2t_vit import g... | 6,968 | 35.873016 | 109 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_timm_backbone.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch import nn
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.backbones import TIMMBackbone
def check_norm_state(modules, train_state):
"""Check if norm layer is in correct train state."""
for mod in modules... | 7,317 | 34.697561 | 76 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_convnext.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcls.models.backbones import ConvNeXt
def test_assertion():
with pytest.raises(AssertionError):
ConvNeXt(arch='unknown')
with pytest.raises(AssertionError):
# ConvNeXt arch dict should include 'embed_dims',
... | 2,895 | 28.85567 | 77 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_hornet.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from copy import deepcopy
from itertools import chain
from unittest import TestCase
import pytest
import torch
from mmcv.utils import digit_version
from mmcv.utils.parrots_wrapper import _BatchNorm
from torch import nn
from mmcls.models.backbones import HorN... | 5,944 | 32.971429 | 78 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_swin_transformer.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import os
import tempfile
from copy import deepcopy
from itertools import chain
from unittest import TestCase
import torch
from mmcv.runner import load_checkpoint, save_checkpoint
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmcls.models.backbones ... | 9,274 | 35.230469 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_efficientnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.backbones import EfficientNet
def is_norm(modules):
"""Check if is one of the norms."""
if isinstance(modules, (GroupNorm, _Bat... | 5,431 | 36.462069 | 78 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_resnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmcls.models.backbones import ResNet, ResNetV1c, ResNetV1d
from mmcls.models.backbones.resnet import (BasicBlock, Bottleneck, ResLayer,... | 21,285 | 33.387722 | 78 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_shufflenet_v1.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.backbones import ShuffleNetV1
from mmcls.models.backbones.shufflenet_v1 import ShuffleUnit
def is_block(modules):
"""Check if is Re... | 7,645 | 29.955466 | 78 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_deit.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import os
import tempfile
from copy import deepcopy
from unittest import TestCase
import torch
from mmcv.runner import load_checkpoint, save_checkpoint
from mmcls.models.backbones import DistilledVisionTransformer
from .utils import timm_resize_pos_embed
c... | 5,003 | 36.909091 | 77 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_vision_transformer.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import os
import tempfile
from copy import deepcopy
from unittest import TestCase
import torch
from mmcv.runner import load_checkpoint, save_checkpoint
from mmcls.models.backbones import VisionTransformer
from .utils import timm_resize_pos_embed
class Test... | 6,804 | 35.983696 | 78 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn.functional as F
def timm_resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()):
"""Timm version pos embed resize function.
copied from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vi... | 1,258 | 38.34375 | 111 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_conformer.py | # Copyright (c) OpenMMLab. All rights reserved.
from copy import deepcopy
import pytest
import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.backbones import Conformer
def is_norm(modules):
"""Check if is one of the norms."""
if isinstance(m... | 3,413 | 29.482143 | 76 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_mobilenet_v2.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.backbones import MobileNetV2
from mmcls.models.backbones.mobilenet_v2 import InvertedResidual
def is_block(modules):
"""Check if is... | 8,992 | 33.588462 | 77 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_hrnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.backbones import HRNet
def is_norm(modules):
"""Check if is one of the norms."""
if isinstance(modules, (GroupNorm, _BatchNorm)... | 2,625 | 26.93617 | 71 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_cspnet.py | # Copyright (c) OpenMMLab. All rights reserved.
from copy import deepcopy
from functools import partial
from unittest import TestCase
import torch
from mmcv.cnn import ConvModule
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmcls.models.backbones import CSPDarkNet, CSPResNet, CSPResNeXt
from mmcls.models.ba... | 5,069 | 33.256757 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_mlp_mixer.py | # Copyright (c) OpenMMLab. All rights reserved.
from copy import deepcopy
from unittest import TestCase
import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.backbones import MlpMixer
def is_norm(modules):
"""Check if is one of the norms."""
... | 3,683 | 29.7 | 76 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_mvit.py | # Copyright (c) OpenMMLab. All rights reserved.
from copy import deepcopy
from unittest import TestCase
import torch
from mmcls.models.backbones import MViT
class TestMViT(TestCase):
def setUp(self):
self.cfg = dict(arch='tiny', img_size=224, drop_path_rate=0.1)
def test_arch(self):
# Test... | 6,654 | 34.77957 | 78 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_resnet_cifar.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmcls.models.backbones import ResNet_CIFAR
def check_norm_state(modules, train_state):
"""Check if norm layer is in correct train state."""
for mod in modules:
if isinstan... | 2,157 | 30.735294 | 63 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_resnest.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcls.models.backbones import ResNeSt
from mmcls.models.backbones.resnest import Bottleneck as BottleneckS
def test_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
BottleneckS... | 1,450 | 31.244444 | 76 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_regnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcls.models.backbones import RegNet
regnet_test_data = [
('regnetx_400mf',
dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22,
bot_mul=1.0), [32, 64, 160, 384]),
('regnetx_800mf',
dict(w0=56, wa=35.73, wm=2.2... | 3,119 | 31.842105 | 73 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_tnt.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.backbones import TNT
def check_norm_state(modules, train_state):
"""Check if norm layer is in correct train state."""
for mod in modules:
if isinstance(mod, _... | 1,285 | 24.215686 | 56 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_poolformer.py | # Copyright (c) OpenMMLab. All rights reserved.
from copy import deepcopy
from unittest import TestCase
import torch
from mmcls.models.backbones import PoolFormer
from mmcls.models.backbones.poolformer import PoolFormerBlock
class TestPoolFormer(TestCase):
def setUp(self):
arch = 's12'
self.cfg... | 5,208 | 35.173611 | 77 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_van.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from copy import deepcopy
from itertools import chain
from unittest import TestCase
import torch
from mmcv.utils.parrots_wrapper import _BatchNorm
from torch import nn
from mmcls.models.backbones import VAN
def check_norm_state(modules, train_state):
"... | 6,637 | 34.121693 | 78 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_repvgg.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import tempfile
import pytest
import torch
from mmcv.runner import load_checkpoint, save_checkpoint
from torch import nn
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.backbones import RepVGG
from mmc... | 12,162 | 33.652422 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_shufflenet_v2.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.backbones import ShuffleNetV2
from mmcls.models.backbones.shufflenet_v2 import InvertedResidual
def is_block(modules):
"""Check if ... | 6,370 | 29.927184 | 78 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_efficientformer.py | # Copyright (c) OpenMMLab. All rights reserved.
from copy import deepcopy
from unittest import TestCase
import torch
from mmcv.cnn import ConvModule
from torch import nn
from mmcls.models.backbones import EfficientFormer
from mmcls.models.backbones.efficientformer import (AttentionWithBias, Flat,
... | 7,785 | 37.93 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_seresnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules import AvgPool2d
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.backbones import SEResNet
from mmcls.models.backbones.resnet import ResLayer
from mmcls.models.backbones.seresnet import SEBottleneck, SE... | 8,411 | 32.919355 | 78 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_resnext.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcls.models.backbones import ResNeXt
from mmcls.models.backbones.resnext import Bottleneck as BottleneckX
def test_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
BottleneckX... | 2,072 | 32.435484 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_densenet.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcls.models.backbones import DenseNet
def test_assertion():
with pytest.raises(AssertionError):
DenseNet(arch='unknown')
with pytest.raises(AssertionError):
# DenseNet arch dict should include essential_keys,
... | 2,721 | 27.354167 | 75 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_convmixer.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcls.models.backbones import ConvMixer
def test_assertion():
with pytest.raises(AssertionError):
ConvMixer(arch='unknown')
with pytest.raises(AssertionError):
# ConvMixer arch dict should include essential_keys,... | 2,087 | 23.564706 | 60 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_swin_transformer_v2.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import os
import tempfile
from copy import deepcopy
from itertools import chain
from unittest import TestCase
import torch
from mmcv.runner import load_checkpoint, save_checkpoint
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmcls.models.backbones ... | 8,897 | 35.467213 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_backbones/test_repmlp.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import tempfile
from copy import deepcopy
from unittest import TestCase
import torch
from mmcv.runner import load_checkpoint, save_checkpoint
from mmcls.models.backbones import RepMLPNet
class TestRepMLP(TestCase):
def setUp(self):
# default mod... | 5,915 | 33.196532 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_utils/test_layer_scale.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmcls.models.utils import LayerScale
class TestLayerScale(TestCase):
def test_init(self):
with self.assertRaisesRegex(AssertionError, "'data_format' could"):
cfg = dict(
dim=10,
... | 1,577 | 31.204082 | 75 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_utils/test_position_encoding.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcls.models.utils import ConditionalPositionEncoding
def test_conditional_position_encoding_module():
CPE = ConditionalPositionEncoding(in_channels=32, embed_dims=32, stride=2)
outs = CPE(torch.randn(1, 3136, 32), (56, 56))
assert outs.sh... | 352 | 31.090909 | 78 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_utils/test_augment.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcls.models.utils import Augments
augment_cfgs = [
dict(type='BatchCutMix', alpha=1., prob=1.),
dict(type='BatchMixup', alpha=1., prob=1.),
dict(type='Identity', prob=1.),
dict(type='BatchResizeMix', alpha=1., prob=1.)
]
... | 3,262 | 32.639175 | 78 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_utils/test_embed.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcls.models.backbones import VGG
from mmcls.models.utils import HybridEmbed, PatchEmbed, PatchMerging
def cal_unfold_dim(dim, kernel_size, stride, padding=0, dilation=1):
return (dim + 2 * padding - dilation * (kernel_size - 1) - 1)... | 3,256 | 35.595506 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_utils/test_misc.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv.utils import digit_version
from mmcls.models.utils import channel_shuffle, is_tracing, make_divisible
def test_make_divisible():
# test min_value is None
result = make_divisible(34, 8, None)
assert result == 32
# te... | 1,705 | 27.433333 | 77 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_utils/test_inverted_residual.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.utils import InvertedResidual, SELayer
def is_norm(modules):
"""Check if is one of the norms."""
if isinstance(modules, (GroupN... | 2,787 | 32.590361 | 71 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_utils/test_se.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.utils import SELayer
def is_norm(modules):
"""Check if is one of the norms."""
if isinstance(modules, (GroupNorm, _BatchNorm)):... | 2,963 | 29.875 | 62 | py |
mmpretrain | mmpretrain-master/tests/test_models/test_utils/test_attention.py | # Copyright (c) OpenMMLab. All rights reserved.
from functools import partial
from unittest import TestCase
from unittest.mock import ANY, MagicMock
import pytest
import torch
from mmcv.utils import TORCH_VERSION, digit_version
from mmcls.models.utils.attention import ShiftWindowMSA, WindowMSA
if digit_version(TORCH... | 8,102 | 37.770335 | 79 | py |
mmpretrain | mmpretrain-master/tests/test_data/test_builder.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from copy import deepcopy
from unittest.mock import patch
import torch
from mmcv.utils import digit_version
from mmcls.datasets import ImageNet, build_dataloader, build_dataset
from mmcls.datasets.dataset_wrappers import (ClassBalancedDataset,
... | 9,975 | 35.542125 | 79 | py |
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