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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mmselfsup-0.x | mmselfsup-0.x/tests/test_runtime/test_extractor.py | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import tempfile
from unittest.mock import MagicMock
import torch
import torch.nn as nn
from mmcv.parallel import MMDataParallel
from mmcv.runner import build_runner
from torch.utils.data import Dataset
from mmselfsup.core.optimizer import build_optimizer
... | 1,805 | 27.21875 | 76 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_runtime/test_extract_process.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import MagicMock
import pytest
import torch
import torch.nn as nn
from mmcv.parallel import MMDataParallel
from torch.utils.data import DataLoader, Dataset
from mmselfsup.models.utils import ExtractProcess, MultiExtractProcess
class ExampleDataset(D... | 2,293 | 29.184211 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_runtime/test_hooks/test_byol_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import tempfile
from unittest.mock import MagicMock
import torch
import torch.nn as nn
from mmcv.parallel import MMDataParallel
from mmcv.runner import build_runner, obj_from_dict
from torch.utils.data import DataLoader, Dataset
from mmselfsup.core.hooks ... | 2,565 | 31.075 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_runtime/test_hooks/test_deepcluster_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import tempfile
from unittest.mock import MagicMock
import torch
from mmcv.parallel import MMDataParallel
from mmcv.runner import build_runner
from torch.utils.data import Dataset
from mmselfsup.core.hooks import DeepClusterHook
from mmselfsup.core.optimi... | 2,405 | 29.075 | 76 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_runtime/test_hooks/test_optimizer_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import tempfile
from unittest.mock import MagicMock
import pytest
import torch
import torch.nn as nn
from mmcv.parallel import MMDataParallel
from mmcv.runner import build_runner, obj_from_dict
from torch.utils.data import DataLoader, Dataset
from mmselfs... | 4,247 | 32.448819 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_runtime/test_hooks/test_densecl_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import tempfile
from unittest.mock import MagicMock
import torch
import torch.nn as nn
from mmcv.parallel import MMDataParallel
from mmcv.runner import build_runner, obj_from_dict
from torch.utils.data import DataLoader, Dataset
from mmselfsup.core.hooks ... | 2,135 | 29.084507 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_runtime/test_hooks/test_swav_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import tempfile
from unittest.mock import MagicMock
import torch
import torch.nn as nn
from mmcv.parallel import MMDataParallel
from mmcv.runner import build_runner, obj_from_dict
from torch.utils.data import DataLoader, Dataset
from mmselfsup.core.hooks ... | 2,383 | 29.961039 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_runtime/test_hooks/test_simsiam_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import tempfile
from unittest.mock import MagicMock
import torch
import torch.nn as nn
from mmcv.parallel import MMDataParallel
from mmcv.runner import build_runner
from torch.utils.data import DataLoader, Dataset
from mmselfsup.core.hooks import SimSiamH... | 2,364 | 29.320513 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_heads.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn.functional as F
from mmselfsup.models.heads import (ClsHead, ContrastiveHead, LatentClsHead,
LatentCrossCorrelationHead,
LatentPredictHead, MAEFinetuneHead,
... | 4,331 | 27.88 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_backbones/test_resnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmselfsup.models.backbones import ResNet
from mmselfsup.models.backbones.resnet import BasicBlock, Bottleneck
def is_block(modules):
"""Check if is ResNet building block."""
if isinstance(modul... | 4,443 | 30.742857 | 68 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_backbones/test_mae_pretrain_vit.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.backbones import MAEViT
backbone = dict(arch='b', patch_size=16, mask_ratio=0.75)
@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
def test_mae_pretrain_vit():
mae_pre... | 555 | 26.8 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_backbones/test_mim_cls_vit.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.backbones import MIMVisionTransformer
finetune_backbone = dict(
arch='b', patch_size=16, drop_path_rate=0.1, final_norm=False)
finetune_backbone_norm = dict(
arch='b', patch_size=16, drop_path_ra... | 1,600 | 38.04878 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_backbones/test_maskfeat_pretrain_vit.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.backbones import MaskFeatViT
backbone = dict(arch='b', patch_size=16)
@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
def test_maskfeat_pretrain_vit():
maskfeat_pretr... | 618 | 28.47619 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_backbones/test_resnext.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmselfsup.models.backbones import ResNeXt
from mmselfsup.models.backbones.resnext import Bottleneck as BottleneckX
def test_resnext():
with pytest.raises(KeyError):
# ResNeXt depth should be in [50, 101, 152]
ResNeXt(... | 1,462 | 32.25 | 78 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_byol.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.algorithms import BYOL
backbone = dict(
type='ResNet',
depth=18,
in_channels=3,
out_indices=[4], # 0: conv-1, x: stage-x
norm_cfg=dict(type='BN'))
neck = dict(
type='NonLinearNeck... | 1,546 | 27.648148 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_mocov3.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models import MoCoV3
backbone = dict(
type='VisionTransformer',
arch='mocov3-small', # embed_dim = 384
img_size=224,
patch_size=16,
stop_grad_conv1=True)
neck = dict(
type='NonLinearNeck... | 1,544 | 25.637931 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_odc.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.algorithms import ODC
num_classes = 5
backbone = dict(
type='ResNet',
depth=18,
in_channels=3,
out_indices=[4], # 0: conv-1, x: stage-x
norm_cfg=dict(type='BN'))
neck = dict(
type... | 1,424 | 24.909091 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_swav.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.algorithms import SwAV
nmb_crops = [2, 6]
backbone = dict(
type='ResNet',
depth=18,
in_channels=3,
out_indices=[4], # 0: conv-1, x: stage-x
norm_cfg=dict(type='BN'),
zero_init_res... | 1,565 | 26.964286 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_maskfeat.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.algorithms import MaskFeat
backbone = dict(
type='MaskFeatViT',
arch='b',
patch_size=16,
drop_path_rate=0,
)
head = dict(type='MaskFeatPretrainHead', hog_dim=108)
hog_para = dict(nbins=9, ... | 1,074 | 30.617647 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_simmim.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.algorithms import SimMIM
@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
def test_simmim():
# model config
model_config = dict(
backbone=dict(
... | 868 | 28.965517 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_relative_loc.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.algorithms import RelativeLoc
backbone = dict(
type='ResNet',
depth=18,
in_channels=3,
out_indices=[4], # 0: conv-1, x: stage-x
norm_cfg=dict(type='BN'))
neck = dict(
type='Relati... | 1,831 | 32.309091 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_rotation_pred.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.algorithms import RotationPred
backbone = dict(
type='ResNet',
depth=18,
in_channels=3,
out_indices=[4], # 0: conv-1, x: stage-x
norm_cfg=dict(type='BN'))
head = dict(type='ClsHead', ... | 1,485 | 31.304348 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_classification.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.algorithms import Classification
@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
def test_classification():
# test ResNet
with_sobel = True,
backbone = dict(
... | 1,659 | 27.62069 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_barlowtwins.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.algorithms import BarlowTwins
backbone = dict(
type='ResNet',
depth=50,
in_channels=3,
out_indices=[4], # 0: conv-1, x: stage-x
norm_cfg=dict(type='BN'))
neck = dict(
type='NonLin... | 1,393 | 29.977778 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_simsiam.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.algorithms import SimSiam
backbone = dict(
type='ResNet',
depth=18,
in_channels=3,
out_indices=[4], # 0: conv-1, x: stage-x
norm_cfg=dict(type='BN'),
zero_init_residual=True)
neck... | 1,704 | 26.95082 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_deepcluster.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.algorithms import DeepCluster
num_classes = 5
with_sobel = True,
backbone = dict(
type='ResNet',
depth=18,
in_channels=2,
out_indices=[4], # 0: conv-1, x: stage-x
norm_cfg=dict(type='... | 1,373 | 28.869565 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_densecl.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
from unittest.mock import MagicMock
import pytest
import torch
import mmselfsup
from mmselfsup.models.algorithms import DenseCL
queue_len = 32
feat_dim = 2
momentum = 0.999
loss_lambda = 0.5
backbone = dict(
type='ResNet',
depth=18,
in_chann... | 2,689 | 28.56044 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_npid.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.algorithms import NPID
backbone = dict(
type='ResNet',
depth=18,
in_channels=3,
out_indices=[4], # 0: conv-1, x: stage-x
norm_cfg=dict(type='BN'))
neck = dict(
type='LinearNeck', ... | 1,185 | 31.944444 | 77 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_simclr.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.algorithms import SimCLR
backbone = dict(
type='ResNet',
depth=18,
in_channels=3,
out_indices=[4], # 0: conv-1, x: stage-x
norm_cfg=dict(type='BN'))
neck = dict(
type='NonLinearNe... | 1,171 | 28.3 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_cae.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.algorithms import CAE
# model settings
backbone = dict(
type='CAEViT', arch='b', patch_size=16, init_values=0.1, qkv_bias=False)
neck = dict(
type='CAENeck',
patch_size=16,
embed_dims=768,... | 1,421 | 28.020408 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_moco.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
from unittest.mock import MagicMock
import pytest
import torch
import mmselfsup
from mmselfsup.models.algorithms import MoCo
queue_len = 32
feat_dim = 2
momentum = 0.999
backbone = dict(
type='ResNet',
depth=18,
in_channels=3,
out_indice... | 1,991 | 26.666667 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_mmcls_classifier_wrapper.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import mmcls.models # noqa: F401
import pytest
import torch
from mmselfsup.models import ALGORITHMS
@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
def test_mmcls_classifier_wrapper():
model_config = dict(
t... | 1,948 | 32.033898 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_algorithms/test_mae.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.algorithms import MAE
backbone = dict(type='MAEViT', arch='b', patch_size=16, mask_ratio=0.75)
neck = dict(
type='MAEPretrainDecoder',
patch_size=16,
in_chans=3,
embed_dim=768,
decoder... | 1,310 | 30.97561 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_utils/test_knn_classifier.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmselfsup.models.utils import knn_classifier
def test_knn_classifier():
train_feats = torch.ones(200, 3)
train_labels = torch.ones(200).long()
test_feats = torch.ones(200, 3)
test_labels = torch.ones(200).long()
num_knn = [10, 20, ... | 541 | 29.111111 | 74 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_utils/test_dalle.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmselfsup.models.utils import Encoder
@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
def test_dalle():
model = Encoder()
fake_inputs = torch.rand((2, 3, 112, 112))
fake_output... | 401 | 22.647059 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_utils/test_multi_pooling.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmselfsup.models.utils import MultiPooling
def test_multi_pooling():
# adaptive
layer = MultiPooling(pool_type='adaptive', in_indices=(0, 1, 2))
fake_in = [
torch.rand((1, 32, 112, 112)),
torch.rand((1, 64, 56... | 1,094 | 27.815789 | 69 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_utils/test_multi_prototypes.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
import torch.nn as nn
from mmselfsup.models.utils import MultiPrototypes
def test_multi_prototypes():
with pytest.raises(AssertionError):
layer = MultiPrototypes(output_dim=16, num_prototypes=2)
layer = MultiPrototypes(output... | 746 | 30.125 | 68 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_utils/test_sobel.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmselfsup.models.utils import Sobel
def test_sobel():
sobel_layer = Sobel()
fake_input = torch.rand((1, 3, 224, 224))
fake_res = sobel_layer(fake_input)
assert fake_res.shape == (1, 2, 224, 224)
for p in sobel_layer.sobel.paramete... | 366 | 23.466667 | 47 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_necks/test_linear_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmselfsup.models.necks import LinearNeck
def test_linear_neck():
neck = LinearNeck(16, 32, with_avg_pool=True)
assert isinstance(neck.avgpool, nn.Module)
assert neck.fc.in_features == 16
assert neck.fc.out_feature... | 702 | 28.291667 | 52 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_necks/test_relative_loc_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmselfsup.models.necks import RelativeLocNeck
def test_relative_loc_neck():
neck = RelativeLocNeck(16, 32)
assert neck.fc.in_features == 32
assert neck.fc.out_features == 32
assert neck.bn.num_features == 32
# test neck with avgpo... | 672 | 28.26087 | 55 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_necks/test_odc_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmselfsup.models.necks import ODCNeck
def test_odc_neck():
neck = ODCNeck(16, 32, 16, norm_cfg=dict(type='BN1d'))
assert neck.fc0.in_features == 16
assert neck.fc0.out_features == 32
assert neck.bn0.num_features == 32
assert neck.f... | 783 | 30.36 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_necks/test_densecl_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmselfsup.models.necks import DenseCLNeck
def test_densecl_neck():
neck = DenseCLNeck(16, 32, 16)
assert isinstance(neck.mlp, nn.Sequential)
assert isinstance(neck.mlp2, nn.Sequential)
assert neck.mlp[0].in_featur... | 1,153 | 33.969697 | 56 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_necks/test_swav_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmselfsup.models.necks import SwAVNeck
def test_swav_neck():
neck = SwAVNeck(16, 32, 16, norm_cfg=dict(type='BN1d'))
assert isinstance(neck.projection_neck, (nn.Module, nn.Sequential))
# test neck with avgpool
fa... | 539 | 30.764706 | 74 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_necks/test_mae_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmselfsup.models.necks import MAEPretrainDecoder
def test_linear_neck():
decoder = MAEPretrainDecoder()
decoder.init_weights()
decoder.eval()
inputs = torch.rand(1, 50, 1024)
ids_restore = torch.arange(0, 196).unsqueeze(0)
leve... | 424 | 27.333333 | 56 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_necks/test_nonlinear_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmselfsup.models.necks import NonLinearNeck
def test_nonlinear_neck():
# test neck arch
neck = NonLinearNeck(16, 32, 16, norm_cfg=dict(type='BN1d'))
assert neck.fc0.in_features == 16
assert neck.fc0.out_features == 32
assert neck.b... | 1,297 | 30.658537 | 68 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_necks/test_avgpool_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmselfsup.models.necks import AvgPool2dNeck
def test_avgpool2d_neck():
fake_in = [torch.randn((2, 3, 8, 8))]
# test default
neck = AvgPool2dNeck()
fake_out = neck(fake_in)
assert fake_out[0].shape == (2, 3, 1, 1)
# test custo... | 550 | 21.958333 | 48 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_models/test_necks/test_mocov2_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmselfsup.models.necks import MoCoV2Neck
def test_mocov2_neck():
neck = MoCoV2Neck(16, 32, 16)
assert isinstance(neck.mlp, nn.Sequential)
assert neck.mlp[0].in_features == 16
assert neck.mlp[2].in_features == 32
... | 739 | 28.6 | 54 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_apis/test_train.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import platform
import tempfile
import time
import mmcv
import pytest
import torch
import torch.nn as nn
from mmcv import Config
from torch.utils.data import Dataset
from mmselfsup.apis import init_random_seed, set_random_seed, train_model
class ... | 2,928 | 25.387387 | 103 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_apis/test_inference.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import platform
import pytest
import torch
import torch.nn as nn
from mmcv import Config
from PIL import Image
from mmselfsup.apis import inference_model
from mmselfsup.models import BaseModel
class ExampleModel(BaseModel):
def __init__(self... | 1,548 | 24.816667 | 99 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_data/test_pipeline.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmcv.utils import build_from_cfg
from PIL import Image
from mmselfsup.datasets.builder import PIPELINES
def test_random_applied_trans():
img = Image.fromarray(np.ones((224, 224, 3), dtype=np.uint8))
# p=0.5
... | 5,715 | 27.58 | 77 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_utils/test_misc.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
import pytest
import torch
from mmselfsup.utils.misc import find_latest_checkpoint, tensor2imgs
def test_tensor2imgs():
with pytest.raises(AssertionError):
tensor2imgs(torch.rand((3, 16, 16)))
fake_tensor = torch.r... | 1,842 | 31.910714 | 68 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_utils/test_alias_multinomial.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmselfsup.utils import AliasMethod
def test_alias_multinomial():
example_in = torch.Tensor([1, 2, 3, 4])
example_alias_method = AliasMethod(example_in)
assert (example_alias_method.prob.numpy() <= 1).all()
assert len(exam... | 644 | 28.318182 | 61 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_utils/test_test_helper.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import MagicMock
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from mmselfsup.utils.test_helper import single_gpu_test
class ExampleDataset(Dataset):
def __getitem__(self, idx):
r... | 1,174 | 24.543478 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_metrics/test_accuracy.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmselfsup.models.utils import Accuracy
def test_accuracy():
pred = torch.Tensor([[0.2, 0.3, 0.5], [0.25, 0.15, 0.6], [0.9, 0.05, 0.05],
[0.8, 0.1, 0.1], [0.55, 0.15, 0.3]])
target = torch.zeros(5)
acc = Accuracy((... | 427 | 25.75 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/apis/inference.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Tuple, Union
import mmcv
import torch
from mmcv.parallel import collate, scatter
from mmcv.runner import load_checkpoint
from mmcv.utils import build_from_cfg
from PIL import Image
from torch import nn
from torchvision.transforms import Compo... | 3,036 | 34.313953 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/apis/train.py | # Copyright (c) OpenMMLab. All rights reserved.
import random
import numpy as np
import torch
import torch.distributed as dist
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (HOOKS, DistEvalHook, DistSamplerSeedHook, EvalHook,
build_runner, get_dist... | 8,228 | 36.747706 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/core/hooks/optimizer_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmcv.runner import (HOOKS, Fp16OptimizerHook, OptimizerHook,
allreduce_grads)
from mmcv.utils import TORCH_VERSION, _BatchNorm, digit_version
@HOOKS.register_module()
class DistOptimizerHook(OptimizerHook):
"""Optimizer hook for distrib... | 10,923 | 40.694656 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/core/hooks/interclr_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
import torch.distributed as dist
from mmcv.runner import HOOKS, Hook
from mmcv.utils import print_log
from mmselfsup.utils import Extractor
from mmselfsup.utils import clustering as _clustering
@HOOKS.register_module()
class InterCLRHook... | 7,998 | 42.005376 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/core/hooks/swav_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import torch
import torch.distributed as dist
from mmcv.runner import HOOKS, Hook
@HOOKS.register_module()
class SwAVHook(Hook):
"""Hook for SwAV.
This hook builds the queue in SwAV according to ``epoch_queue_starts``.
The queue will ... | 3,060 | 36.329268 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/core/hooks/deepcluster_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
import torch.distributed as dist
from mmcv.runner import HOOKS, Hook
from mmcv.utils import print_log
from mmselfsup.utils import Extractor
from mmselfsup.utils import clustering as _clustering
from mmselfsup.utils import get_root_logger
... | 5,575 | 37.993007 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/core/optimizer/optimizers.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.runner.optimizer.builder import OPTIMIZERS
from torch.optim import * # noqa: F401,F403
from torch.optim.optimizer import Optimizer, required
@OPTIMIZERS.register_module()
class LARS(Optimizer):
"""Implements layer-wise adaptive rate scaling f... | 4,827 | 35.575758 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/core/optimizer/constructor.py | # Copyright (c) OpenMMLab. All rights reserved.
import re
import torch.distributed as dist
from mmcv.runner.optimizer.builder import OPTIMIZER_BUILDERS, OPTIMIZERS
from mmcv.utils import build_from_cfg, print_log
@OPTIMIZER_BUILDERS.register_module(force=True)
class DefaultOptimizerConstructor:
"""Rewrote defaul... | 3,606 | 43.530864 | 78 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/core/optimizer/transformer_finetune_constructor.py | # Copyright (c) OpenMMLab. All rights reserved.
import re
import torch.distributed as dist
from mmcv.runner.optimizer.builder import OPTIMIZER_BUILDERS, OPTIMIZERS
from mmcv.utils import build_from_cfg, print_log
@OPTIMIZER_BUILDERS.register_module()
class TransformerFinetuneConstructor:
"""Rewrote default const... | 6,889 | 42.607595 | 77 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/core/optimizer/builder.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
from mmcv.runner.optimizer.builder import build_optimizer_constructor
def build_optimizer(model, optimizer_cfg):
"""Build optimizer from configs.
Args:
model (:obj:`nn.Module`): The model with parameters to be optimized.
optimizer_c... | 2,002 | 40.729167 | 76 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/base.py | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from collections import OrderedDict
import torch
import torch.distributed as dist
from mmcv.runner import BaseModule, auto_fp16
class BaseModel(BaseModule, metaclass=ABCMeta):
"""Base model class for self-supervised learning.... | 5,902 | 35.89375 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/densecl.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.utils.logging import logger_initialized, print_log
from mmselfsup.utils import (batch_shuffle_ddp, batch_unshuffle_ddp,
concat_all_gather)
from ..builder import ALGORITHMS, build_backbone, build_he... | 9,241 | 36.266129 | 78 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/simclr.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import ALGORITHMS, build_backbone, build_head, build_neck
from ..utils import GatherLayer
from .base import BaseModel
@ALGORITHMS.register_module()
class SimCLR(BaseModel):
"""SimCLR.
Implementation of `A Simple Framework for Contra... | 3,191 | 34.466667 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/mae.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, Optional, Tuple
import torch
from ..builder import ALGORITHMS, build_backbone, build_head, build_neck
from .base import BaseModel
@ALGORITHMS.register_module()
class MAE(BaseModel):
"""MAE.
Implementation of `Masked Autoencoders Are S... | 3,194 | 33.354839 | 73 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/rotation_pred.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.runner import auto_fp16
from ..builder import ALGORITHMS, build_backbone, build_head
from .base import BaseModel
@ALGORITHMS.register_module()
class RotationPred(BaseModel):
"""Rotation prediction.
Implementation of `Unsupervised Represe... | 3,354 | 33.587629 | 76 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/mocov3.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from ..builder import ALGORITHMS, build_backbone, build_head, build_neck
from .base import BaseModel
@ALGORITHMS.register_module()
class MoCoV3(BaseModel):
"""MoCo v3.
Implementation of `An Empirical Study of Training Self-Su... | 3,769 | 34.233645 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/barlowtwins.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional
import torch
from ..builder import ALGORITHMS, build_backbone, build_head, build_neck
from .base import BaseModel
@ALGORITHMS.register_module()
class BarlowTwins(BaseModel):
"""BarlowTwins.
Implementation of `Barlow Twins: Se... | 2,478 | 32.053333 | 78 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/deepcluster.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
import torch.nn as nn
from ..builder import ALGORITHMS, build_backbone, build_head, build_neck
from ..utils import Sobel
from .base import BaseModel
@ALGORITHMS.register_module()
class DeepCluster(BaseModel):
"""DeepCluster.
Imp... | 4,071 | 33.218487 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/relative_loc.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import ALGORITHMS, build_backbone, build_head, build_neck
from .base import BaseModel
@ALGORITHMS.register_module()
class RelativeLoc(BaseModel):
"""Relative patch location.
Implementation of `Unsupervised Visual Representation Lear... | 3,876 | 35.233645 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/moco.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmselfsup.utils import (batch_shuffle_ddp, batch_unshuffle_ddp,
concat_all_gather)
from ..builder import ALGORITHMS, build_backbone, build_head, build_neck
from .base import BaseModel
@ALGORITHMS.regi... | 5,124 | 33.166667 | 78 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/simsiam.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from ..builder import ALGORITHMS, build_backbone, build_head, build_neck
from .base import BaseModel
@ALGORITHMS.register_module()
class SimSiam(BaseModel):
"""SimSiam.
Implementation of `Exploring Simple Siamese Representation Learning
... | 2,240 | 30.125 | 78 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/simmim.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional
import torch
from ..builder import ALGORITHMS, build_backbone, build_head, build_neck
from .base import BaseModel
@ALGORITHMS.register_module()
class SimMIM(BaseModel):
"""SimMIM.
Implementation of `SimMIM: A Simple Framework... | 1,952 | 29.046154 | 75 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/maskfeat.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional
import torch
from ..builder import ALGORITHMS, build_backbone, build_head
from ..utils.hog_layer import HOGLayerC
from .base import BaseModel
@ALGORITHMS.register_module()
class MaskFeat(BaseModel):
"""MaskFeat.
Implementatio... | 2,324 | 31.746479 | 77 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/swav.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import ALGORITHMS, build_backbone, build_head, build_neck
from .base import BaseModel
@ALGORITHMS.register_module()
class SwAV(BaseModel):
"""SwAV.
Implementation of `Unsupervised Learning of Visual Features by Contrasting
Clust... | 2,393 | 30.090909 | 78 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/cae.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence
import torch
from torchvision.transforms import Normalize
from ..builder import ALGORITHMS, build_backbone, build_head, build_neck
from .base import BaseModel
@ALGORITHMS.register_module()
class CAE(BaseModel):
"""CAE.
Implementati... | 4,123 | 36.490909 | 77 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/mmcls_classifier_wrapper.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcls.models import ImageClassifier
from mmcv.runner import auto_fp16
from ..builder import ALGORITHMS
@ALGORITHMS.register_module()
class MMClsImageClassifierWrapper(ImageClassifier):
"""Workaround to use models from mmclassificaiton.
Since ... | 4,003 | 35.4 | 78 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/interclr_moco.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import numpy as np
import torch
import torch.nn as nn
from mmselfsup.utils import (batch_shuffle_ddp, batch_unshuffle_ddp,
concat_all_gather)
from ..builder import (ALGORITHMS, build_backbone, build_head, build_memory,
... | 19,120 | 41.303097 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/npid.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from ..builder import (ALGORITHMS, build_backbone, build_head, build_memory,
build_neck)
from .base import BaseModel
@ALGORITHMS.register_module()
class NPID(BaseModel):
"""NPID.
Implementation of `Unsu... | 3,946 | 34.558559 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/byol.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from ..builder import ALGORITHMS, build_backbone, build_head, build_neck
from .base import BaseModel
@ALGORITHMS.register_module()
class BYOL(BaseModel):
"""BYOL.
Implementation of `Bootstrap Your Own Latent: A New Approach t... | 3,490 | 33.564356 | 75 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/algorithms/odc.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
import torch.nn as nn
from ..builder import (ALGORITHMS, build_backbone, build_head, build_memory,
build_neck)
from ..utils import Sobel
from .base import BaseModel
@ALGORITHMS.register_module()
class ODC(BaseModel... | 5,013 | 34.814286 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/necks/relative_loc_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import build_norm_layer
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class RelativeLocNeck(BaseModule):
"""The neck of relative patch location: fc-bn-relu-dropout.
Args:
in_... | 1,885 | 32.678571 | 76 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/necks/swav_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import build_norm_layer
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class SwAVNeck(BaseModule):
"""The non-linear neck of SwAV: fc-bn-relu-fc-normalization.
Args:
... | 2,733 | 35.453333 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/necks/odc_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import build_norm_layer
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class ODCNeck(BaseModule):
"""The non-linear neck of ODC: fc-bn-relu-dropout-fc-relu.
Args:
in_channels ... | 2,010 | 32.516667 | 76 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/necks/mocov2_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class MoCoV2Neck(BaseModule):
"""The non-linear neck of MoCo v2: fc-relu-fc.
Args:
in_channels (int): Number of input channels.
hid_ch... | 1,354 | 31.261905 | 76 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/necks/simmim_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class SimMIMNeck(BaseModule):
"""Pre-train Neck For SimMIM.
This neck reconstructs the original image from the shrunk feature map.
A... | 921 | 25.342857 | 74 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/necks/linear_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class LinearNeck(BaseModule):
"""The linear neck: fc only.
Args:
in_channels (int): Number of input channels.
out_channels (int): Numb... | 1,146 | 29.184211 | 76 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/necks/nonlinear_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import build_norm_layer
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class NonLinearNeck(BaseModule):
"""The non-linear neck.
Structure: fc-bn-[relu-fc-bn] where the substructure in... | 4,223 | 37.4 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/necks/avgpool2d_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class AvgPool2dNeck(BaseModule):
"""The average pooling 2d neck."""
def __init__(self, output_size=1):
super(AvgPool2dNeck, self).__init__()
... | 498 | 23.95 | 56 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/necks/cae_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
import torch
import torch.nn as nn
from mmcv.cnn import build_norm_layer
from mmcv.cnn.utils.weight_init import trunc_normal_
from mmcv.runner import BaseModule
from ..builder import NECKS
from ..utils import CAETransformerRegressorLayer, Transf... | 6,546 | 37.739645 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/necks/densecl_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class DenseCLNeck(BaseModule):
"""The non-linear neck of DenseCL.
Single and dense neck in parallel: fc-relu-fc, conv-relu-conv.
Borrowed from the... | 2,202 | 33.968254 | 77 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/necks/mae_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcls.models.backbones.vision_transformer import TransformerEncoderLayer
from mmcv.cnn import build_norm_layer
from mmcv.runner import BaseModule
from ..builder import NECKS
from ..utils import build_2d_sincos_position_embedding
... | 4,735 | 33.569343 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/memories/simple_memory.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.distributed as dist
import torch.nn as nn
from mmcv.runner import BaseModule, get_dist_info
from mmselfsup.utils import AliasMethod
from ..builder import MEMORIES
@MEMORIES.register_module()
class SimpleMemory(BaseModule):
"""Simple memory... | 2,494 | 34.642857 | 77 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/memories/odc_memory.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
import torch.distributed as dist
from mmcv.runner import BaseModule, get_dist_info
from sklearn.cluster import KMeans
from ..builder import MEMORIES
@MEMORIES.register_module()
class ODCMemory(BaseModule):
"""Memory module for ODC.
... | 10,403 | 44.038961 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/memories/interclr_memory.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
import torch.distributed as dist
import torch.nn as nn
from mmcv.runner import BaseModule, get_dist_info
from sklearn.cluster import KMeans
from mmselfsup.utils import AliasMethod
from ..builder import MEMORIES
@MEMORIES.register_module(... | 11,583 | 44.427451 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/utils/dall_e.py | # Copyright (c)
# https://github.com/microsoft/unilm/blob/master/beit/dall_e/encoder.py
# Copied from BEiT
import math
from collections import OrderedDict
from functools import partial
import attr
import torch
import torch.nn as nn
import torch.nn.functional as F
@attr.s(eq=False)
class Conv2d(nn.Module):
n_in: ... | 6,647 | 36.988571 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/utils/position_embedding.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
def build_2d_sincos_position_embedding(patches_resolution,
embed_dims,
temperature=10000.,
cls_token=False):
"""The function is to build... | 1,430 | 32.27907 | 75 | py |
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