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* [Feature]: MAE pre-training with fp16 (#271) * [Feature]: MAE pre-training with fp16 * [Fix]: Fix lint * [Fix]: Fix SimMIM config link, and add SimMIM to model_zoo (#272) * [Fix]: Fix link error * [Fix]: Add SimMIM to model zoo * [Fix]: Fix lint * [Fix] fix 'no init_cfg' error for pre-trained model backbones (#256) * [UT] add unit test for apis (#276) * [UT] add unit test for apis * ignore pytest log * [Feature] Add extra dataloader settings in configs. (#264) * [Feature] support to set validation samples per gpu independently * set default to be cfg.data.samples_per_gpu * modify the tools/test.py * using 'train_dataloader', 'val_dataloader', 'test_dataloader' for specific settings * test 'evaluation' branch * [Fix]: Change imgs_per_gpu to samples_per_gpu MAE (#278) * [Feature]: Add SimMIM 192 pt 224 ft (#280) * [Feature]: Add SimMIM 192 pt 224 ft * [Feature]: Add simmim 192 pt 224 ft to readme * [Fix] fix key error bug when registering custom hooks (#273) * [UT] remove pytorch1.5 test (#288) * [Benchmark] rename linear probing config file names (#281) * [Benchmark] rename linear probing config file names * update config links * Avoid GPU memory leak with prefetch dataloader (#277) * [Feature] barlowtwins (#207) * [Fix]: Fix mmcls upgrade bug (#235) * [Feature]: Add multi machine dist_train (#232) * [Feature]: Add multi machine dist_train * [Fix]: Change bash to sh * [Fix]: Fix missing sh suffix * [Refactor]: Change bash to sh * [Refactor] Add unit test (#234) * [Refactor] add unit test * update workflow * update * [Fix] fix lint * update test * refactor moco and densecl unit test * fix lint * add unit test * update unit test * remove modification * [Feature]: Add MAE metafile (#238) * [Feature]: Add MAE metafile * [Fix]: Fix lint * [Fix]: Change LARS to AdamW in the metafile of MAE * Add barlowtwins * Add unit test for barlowtwins * Adjust training params * add decorator to pass CI * adjust params * Add barlowtwins * Add unit test for barlowtwins * Adjust training params * add decorator to pass CI * adjust params * add barlowtwins configs * revise LatentCrossCorrelationHead * modify ut to save memory * add metafile * add barlowtwins results to model zoo * add barlow twins to homepage * fix batch size bug * add algorithm readme * add type hints * reorganize the model zoo * remove one config * recover the config * add missing docstring * revise barlowtwins * reorganize coco and voc benchmark * add barlowtwins to index.rst * revise docstring Co-authored-by: Yuan Liu <[email protected]> Co-authored-by: Yixiao Fang <[email protected]> Co-authored-by: fangyixiao18 <[email protected]> * [Fix] fix --local-rank (#290) * [UT] reduce memory usage while runing unit test (#291) * [Feature]: CAE Supported (#284) * [Feature]: Add mc * [Feature]: Add dataset of CAE * [Feature]: Init version of CAE * [Feature]: Add mc * [Fix]: Change beta to (0.9, 0.999) * [Fix]: New feature * [Fix]: Decouple the qkv bias * [Feature]: Decouple qkv bias in MultiheadAttention * [Feature]: New mask generator * [Fix]: Fix TransformEncoderLayer bug * [Feature]: Add MAE CAE linear prob * [Fix]: Fix config * [Fix]: Delete redundant mc * [Fix]: Add init value in mim cls vit * [Fix]: Fix cae ft config * [Fix]: Delete repeated init_values * [Fix]: Change bs from 64 to 128 in CAE ft * [Fix]: Add mc in cae pt * [Fix]: Fix momemtum update bug * [Fix]: Add no weight_decay for gamma * [Feature]: Add mc for cae pt * [Fix]: Delete mc * [Fix]: Delete redundant files * [Fix]: Fix lint * [Feature]: Add docstring to algo, backbone, neck and head * [Fix]: Fix lint * [Fix]: network * [Feature]: Add docstrings for network blocks * [Feature]: Add docstring to ToTensor * [Feature]: Add docstring to transoform * [Fix]: Add type hint to BEiTMaskGenerator * [Fix]: Fix lint * [Fix]: Add copyright to dalle_e * [Fix]: Fix BlockwiseMaskGenerator * [Feature]: Add UT for CAE * [Fix]: Fix dalle state_dict path not existed bug * [Fix]: Delete file_client_args related code * [Fix]: Remove redundant code * [Refactor]: Add fp16 to the name of cae pre-train config * [Refactor]: Use FFN from mmcv * [Refactor]: Change network_blocks to trasformer_blocks * [Fix]: Fix mask generator name bug * [Fix]: cae pre-train config bug * [Fix]: Fix docstring grammar * [Fix]: Fix mc related code * [Fix]: Add object parent to transform * [Fix]: Delete unnecessary modification * [Fix]: Change blockwisemask generator to simmim mask generator * [Refactor]: Change cae mae pretrain vit to cae mae vit * [Refactor]: Change lamb to lambd * [Fix]: Remove blank line * [Fix]: Fix lint * [Fix]: Fix UT * [Fix]: Delete modification to swin * [Fix]: Fix lint * [Feature]: Add README and metafile * [Feature]: Update index.rst * [Fix]: Update model_zoo * [Fix]: Change MAE to CAE in algorithm * [Fix]: Change SimMIMMaskGenerator to CAEMaskGenerator * [Fix]: Fix model zoo * [Fix]: Change to dalle_encoder * [Feature]: Add download link for dalle * [Fix]: Fix lint * [Fix]: Fix UT * [Fix]: Update metafile * [Fix]: Change b to base * [Feature]: Add dalle download link in warning * [Fix] add arxiv link in readme Co-authored-by: Jiahao Xie <[email protected]> * [Enhance] update SimCLR models and results (#295) * [Enhance] update simclr models and results * [Fix] revise comments to indicate settings * Update version (#296) * [Feature]: Update to 0.9.0 * [Feature]: Add version constrain for mmcls * [Fix]: Fix bug * [Fix]: Fix version bug * [Feature]: Update version in install.md * update changelog * update readme * [Fix] fix uppercase * [Fix] fix uppercase * [Fix] fix uppercase * update version dependency * add cae to readme Co-authored-by: fangyixiao18 <[email protected]> Co-authored-by: Jiahao Xie <[email protected]> Co-authored-by: Yixiao Fang <[email protected]> Co-authored-by: Ming Li <[email protected]> Co-authored-by: xcnick <[email protected]> Co-authored-by: fangyixiao18 <[email protected]> Co-authored-by: Jiahao Xie <[email protected]>
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2 changes: 1 addition & 1 deletion
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.../resnet50-sobel_8xb32-steplr-100e_in1k.py → ...50-sobel_linear-8xb32-steplr-100e_in1k.py
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|---|---|---|
| @@ -1,4 +1,4 @@ | ||
| _base_ = 'resnet50_8xb32-steplr-100e_in1k.py' | ||
| _base_ = 'resnet50_linear-8xb32-steplr-100e_in1k.py' | ||
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| # model settings | ||
| model = dict(with_sobel=True, backbone=dict(in_channels=2, frozen_stages=4)) |
2 changes: 1 addition & 1 deletion
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...et50-sobel_mhead_8xb32-steplr-90e_in1k.py → ...bel_mhead_linear-8xb32-steplr-90e_in1k.py
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,4 +1,4 @@ | ||
| _base_ = 'resnet50_mhead_8xb32-steplr-90e_in1k.py' | ||
| _base_ = 'resnet50_mhead_linear-8xb32-steplr-90e_in1k.py' | ||
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| # model settings | ||
| model = dict(with_sobel=True, backbone=dict(in_channels=2, frozen_stages=4)) |
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34 changes: 34 additions & 0 deletions
34
configs/benchmarks/classification/imagenet/swin-base_ft-8xb256-coslr-100e_in1k-224.py
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| _base_ = 'swin-base_ft-8xb256-coslr-100e_in1k.py' | ||
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| # model | ||
| model = dict( | ||
| backbone=dict( | ||
| img_size=224, stage_cfgs=dict(block_cfgs=dict(window_size=7)))) | ||
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| # dataset | ||
| img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | ||
| train_pipeline = [ | ||
| dict( | ||
| type='RandomAug', | ||
| input_size=224, | ||
| color_jitter=0.4, | ||
| auto_augment='rand-m9-mstd0.5-inc1', | ||
| interpolation='bicubic', | ||
| re_prob=0.25, | ||
| re_mode='pixel', | ||
| re_count=1, | ||
| mean=(0.485, 0.456, 0.406), | ||
| std=(0.229, 0.224, 0.225)) | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='Resize', size=256, interpolation=3), | ||
| dict(type='CenterCrop', size=224), | ||
| dict(type='ToTensor'), | ||
| dict(type='Normalize', **img_norm_cfg) | ||
| ] | ||
| data = dict( | ||
| samples_per_gpu=256, | ||
| drop_last=False, | ||
| workers_per_gpu=32, | ||
| train=dict(pipeline=train_pipeline), | ||
| val=dict(pipeline=test_pipeline)) |
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configs/benchmarks/classification/imagenet/vit-base-p16_ft-8xb128-coslr-100e-rpe_in1k.py
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| Original file line number | Diff line number | Diff line change |
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| _base_ = 'vit-base-p16_ft-8xb128-coslr-100e_in1k.py' | ||
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| # model | ||
| model = dict(backbone=dict(use_window=True, init_values=0.1)) | ||
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| # optimizer | ||
| optimizer = dict(lr=8e-3) | ||
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| # learning policy | ||
| lr_config = dict(warmup_iters=5) | ||
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| # dataset | ||
| img_norm_cfg = dict(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) | ||
| train_pipeline = [ | ||
| dict( | ||
| type='RandomAug', | ||
| input_size=224, | ||
| color_jitter=0.4, | ||
| auto_augment='rand-m9-mstd0.5-inc1', | ||
| interpolation='bicubic', | ||
| re_prob=0.25, | ||
| re_mode='pixel', | ||
| re_count=1, | ||
| mean=(0.5, 0.5, 0.5), | ||
| std=(0.5, 0.5, 0.5)) | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='Resize', size=256, interpolation=3), | ||
| dict(type='CenterCrop', size=224), | ||
| dict(type='ToTensor'), | ||
| dict(type='Normalize', **img_norm_cfg) | ||
| ] | ||
| data = dict( | ||
| train=dict(pipeline=train_pipeline), | ||
| val=dict(pipeline=test_pipeline), | ||
| samples_per_gpu=128) | ||
|
|
||
| find_unused_parameters = True |
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|---|---|---|
| @@ -0,0 +1,40 @@ | ||
| # dataset settings | ||
| data_source = 'ImageNet' | ||
| dataset_type = 'SingleViewDataset' | ||
| img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | ||
| train_pipeline = [ | ||
| dict(type='RandomHorizontalFlip', p=0.5), | ||
| dict( | ||
| type='RandomResizedCropAndInterpolationWithTwoPic', | ||
| size=224, | ||
| second_size=112, | ||
| interpolation='bicubic', | ||
| second_interpolation='lanczos', | ||
| scale=(0.08, 1.0)), | ||
| ] | ||
|
|
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| # prefetch | ||
| prefetch = False | ||
| if not prefetch: | ||
| train_pipeline.extend([dict(type='ToTensor')]) | ||
|
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||
| train_pipeline.append( | ||
| dict( | ||
| type='BEiTMaskGenerator', | ||
| input_size=(14, 14), | ||
| num_masking_patches=75, | ||
| max_num_patches=None, | ||
| min_num_patches=16)) | ||
|
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| # dataset summary | ||
| data = dict( | ||
| samples_per_gpu=256, | ||
| workers_per_gpu=8, | ||
| train=dict( | ||
| type=dataset_type, | ||
| data_source=dict( | ||
| type=data_source, | ||
| data_prefix='data/imagenet/train', | ||
| ann_file='data/imagenet/meta/train.txt'), | ||
| pipeline=train_pipeline, | ||
| prefetch=prefetch)) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,22 @@ | ||
| # model settings | ||
| model = dict( | ||
| type='BarlowTwins', | ||
| backbone=dict( | ||
| type='ResNet', | ||
| depth=50, | ||
| in_channels=3, | ||
| out_indices=[4], # 0: conv-1, x: stage-x | ||
| norm_cfg=dict(type='SyncBN'), | ||
| zero_init_residual=True), | ||
| neck=dict( | ||
| type='NonLinearNeck', | ||
| in_channels=2048, | ||
| hid_channels=8192, | ||
| out_channels=8192, | ||
| num_layers=3, | ||
| with_last_bn=False, | ||
| with_last_bn_affine=False, | ||
| with_avg_pool=True, | ||
| init_cfg=dict( | ||
| type='Kaiming', distribution='uniform', layer=['Linear'])), | ||
| head=dict(type='LatentCrossCorrelationHead', in_channels=8192)) |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,17 @@ | ||
| # model settings | ||
| model = dict( | ||
| type='CAE', | ||
| backbone=dict(type='CAEViT', arch='b', patch_size=16, init_values=0.1), | ||
| neck=dict( | ||
| type='CAENeck', | ||
| patch_size=16, | ||
| embed_dims=768, | ||
| num_heads=12, | ||
| regressor_depth=4, | ||
| decoder_depth=4, | ||
| mlp_ratio=4, | ||
| init_values=0.1, | ||
| ), | ||
| head=dict( | ||
| type='CAEHead', tokenizer_path='cae_ckpt/dalle_encoder.pth', lambd=2), | ||
| base_momentum=0.0) |
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