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* [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 * [Fix] fix codecov bug (#241) * [Fix] fix codecov bug * update comment * [Refactor] Using MMCls backbones (#233) * [Refactor] using backbones from MMCls * [Refactor] modify the unit test * [Fix] modify default setting of out_indices * [Docs] fix lint * [Refactor] modify super init * [Refactore] remove res_layer.py * using mmcv PatchEmbed * [Fix]: Fix outdated problem (#249) * [Fix]: Fix outdated problem * [Fix]: Update MoCov3 bibtex * [Fix]: Use abs path in README * [Fix]: Reformat MAE bibtex * [Fix]: Reformat MoCov3 bibtex * [Feature] Resume from the latest checkpoint automatically. (#245) * [Feature] Resume from the latest checkpoint automatically. * fix windows path problem * fix lint * add code reference * [Docs] add docstring for ResNet and ResNeXt (#252) * [Feature] support KNN benchmark (#243) * [Feature] support KNN benchmark * [Fix] add docstring and multi-machine testing * [Fix] fix lint * [Fix] change args format and check init_cfg * [Docs] add benchmark tutorial * [Docs] add benchmark results * [Feature]: SimMIM supported (#239) * [Feature]: SimMIM Pretrain * [Feature]: Add mix precision and 16x128 config * [Fix]: Fix config import bug * [Fix]: Fix config bug * [Feature]: Simim Finetune * [Fix]: Log every 100 * [Fix]: Fix eval problem * [Feature]: Add docstring for simmim * [Refactor]: Merge layer wise lr decay to Default constructor * [Fix]:Fix simmim evaluation bug * [Fix]: Change model to be compatible to latest version of mmcls * [Fix]: Fix lint * [Fix]: Rewrite forward_train for classification cls * [Feature]: Add UT * [Fix]: Fix lint * [Feature]: Add 32 gpus training for simmim ft * [Fix]: Rename mmcls classifier wrapper * [Fix]: Add docstring to SimMIMNeck * [Feature]: Generate docstring for the forward function of simmim encoder * [Fix]: Rewrite the class docstring for constructor * [Fix]: Fix lint * [Fix]: Fix UT * [Fix]: Reformat config * [Fix]: Add img resolution * [Feature]: Add readme and metafile * [Fix]: Fix typo in README.md * [Fix]: Change BlackMaskGen to BlockwiseMaskGenerator * [Fix]: Change the name of SwinForSimMIM * [Fix]: Delete irrelevant files * [Feature]: Create extra transformerfinetuneconstructor * [Fix]: Fix lint * [Fix]: Update SimMIM README * [Fix]: Change SimMIMPretrainHead to SimMIMHead * [Fix]: Fix the docstring of ft constructor * [Fix]: Fix UT * [Fix]: Recover deletion Co-authored-by: Your <you@example.com> * [Fix] add seed to distributed sampler (#250) * [Fix] add seed to distributed sampler * fix lint * [Feature] Add ImageNet21k (#225) * solve memory leak by limited implementation * fix lint problem Co-authored-by: liming <liming.ai@bytedance.com> * [Refactor] change args format to '--a-b' (#253) * [Refactor] change args format to `--a-b` * modify tsne script * modify 'sh' files * modify getting_started.md * modify getting_started.md * [Fix] fix 'mkdir' error in prepare_voc07_cls.sh (#261) * [Fix] fix positional parameter error (#260) * [Fix] fix command errors in benchmarks tutorial (#263) * [Docs] add brief installation steps in README.md (#265) * [Docs] add colab tutorial (#247) * [Docs] add colab tutorial * fix lint * modify the colab tutorial, using API to train the model * modify the description * remove # * modify the command * [Docs] translate 6_benchmarks.md into Chinese (#262) * [Docs] translate 6_benchmarks.md into Chinese * Update 6_benchmarks.md change 基准 to 基准评测 * Update 6_benchmarks.md (1) Add Chinese translation of ‘1 folder for ImageNet nearest-neighbor classification task’ (2) 数据预准备 -> 数据准备 * [Docs] remove install scripts in README (#267) * [Docs] Update version information in dev branch (#268) * update version to v0.8.0 * fix lint * [Fix]: Install the latest mmcls * [Fix]: Add SimMIM in RAEDME Co-authored-by: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com> Co-authored-by: Jiahao Xie <52497952+Jiahao000@users.noreply.github.com> Co-authored-by: Your <you@example.com> Co-authored-by: Ming Li <73068772+mitming@users.noreply.github.com> Co-authored-by: liming <liming.ai@bytedance.com> Co-authored-by: RenQin <45731309+soonera@users.noreply.github.com> Co-authored-by: YuanLiuuuuuu <3463423099@qq.com>
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31 changes: 31 additions & 0 deletions
31
configs/benchmarks/classification/_base_/models/swin-base.py
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| # model settings | ||
|
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| custom_imports = dict(imports='mmcls.models', allow_failed_imports=False) | ||
|
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| model = dict( | ||
| type='MMClsImageClassifierWrapper', | ||
| backbone=dict( | ||
| type='mmcls.SwinTransformer', | ||
| arch='base', | ||
| img_size=192, | ||
| drop_path_rate=0.1, | ||
| stage_cfgs=dict(block_cfgs=dict(window_size=6))), | ||
| neck=dict(type='mmcls.GlobalAveragePooling'), | ||
| head=dict( | ||
| type='mmcls.LinearClsHead', | ||
| num_classes=1000, | ||
| in_channels=1024, | ||
| init_cfg=None, # suppress the default init_cfg of LinearClsHead. | ||
| loss=dict( | ||
| type='mmcls.LabelSmoothLoss', | ||
| label_smooth_val=0.1, | ||
| mode='original'), | ||
| cal_acc=False), | ||
| init_cfg=[ | ||
| dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), | ||
| dict(type='Constant', layer='LayerNorm', val=1., bias=0.) | ||
| ], | ||
| train_cfg=dict(augments=[ | ||
| dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), | ||
| dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) | ||
| ])) |
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configs/benchmarks/classification/imagenet/swin-base_ft-32xb64-coslr-100e_in1k.py
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| _base_ = 'swin-base_ft-8xb256-coslr-100e_in1k.py' | ||
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| data = dict(samples_per_gpu=64, workers_per_gpu=8) |
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configs/benchmarks/classification/imagenet/swin-base_ft-8xb256-coslr-100e_in1k.py
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| _base_ = [ | ||
| '../_base_/models/swin-base.py', | ||
| '../_base_/datasets/imagenet.py', | ||
| '../_base_/schedules/adamw_coslr-100e_in1k.py', | ||
| '../_base_/default_runtime.py', | ||
| ] | ||
|
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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=192, | ||
| 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=219, interpolation=3), | ||
| dict(type='CenterCrop', size=192), | ||
| 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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| # model | ||
| model = dict(backbone=dict(init_cfg=dict())) | ||
|
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| # optimizer | ||
| optimizer = dict( | ||
| lr=1.25e-3 * 2048 / 512, | ||
| paramwise_options={ | ||
| 'norm': dict(weight_decay=0.), | ||
| 'bias': dict(weight_decay=0.), | ||
| 'absolute_pos_embed': dict(weight_decay=0.), | ||
| 'relative_position_bias_table': dict(weight_decay=0.) | ||
| }, | ||
| constructor='TransformerFinetuneConstructor', | ||
| model_type='swin', | ||
| layer_decay=0.9) | ||
|
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| # clip gradient | ||
| optimizer_config = dict(grad_clip=dict(max_norm=5.0)) | ||
|
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| # learning policy | ||
| lr_config = dict( | ||
| policy='CosineAnnealing', | ||
| min_lr=2.5e-7 * 2048 / 512, | ||
| warmup='linear', | ||
| warmup_iters=20, | ||
| warmup_ratio=2.5e-7 / 1.25e-3, | ||
| warmup_by_epoch=True, | ||
| by_epoch=False) | ||
|
|
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| # mixed precision | ||
| fp16 = dict(loss_scale='dynamic') | ||
|
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| # runtime | ||
| checkpoint_config = dict(interval=1, max_keep_ckpts=3, out_dir='') | ||
| persistent_workers = True | ||
| log_config = dict( | ||
| interval=100, hooks=[ | ||
| dict(type='TextLoggerHook'), | ||
| ]) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| data_source = 'ImageNet' | ||
| dataset_type = 'SingleViewDataset' | ||
| img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | ||
| pipeline = [ | ||
| dict(type='Resize', size=256), | ||
| dict(type='CenterCrop', size=224), | ||
| dict(type='ToTensor'), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| ] | ||
|
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| 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=pipeline), | ||
| val=dict( | ||
| type=dataset_type, | ||
| data_source=dict( | ||
| type=data_source, | ||
| data_prefix='data/imagenet/val', | ||
| ann_file='data/imagenet/meta/val.txt', | ||
| ), | ||
| pipeline=pipeline)) |
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| # 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='RandomResizedCrop', | ||
| size=192, | ||
| scale=(0.67, 1.0), | ||
| ratio=(3. / 4., 4. / 3.)), | ||
| dict(type='RandomHorizontalFlip') | ||
| ] | ||
|
|
||
| # prefetch | ||
| prefetch = False | ||
| if not prefetch: | ||
| train_pipeline.extend( | ||
| [dict(type='ToTensor'), | ||
| dict(type='Normalize', **img_norm_cfg)]) | ||
|
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| train_pipeline.append( | ||
| dict( | ||
| type='BlockwiseMaskGenerator', | ||
| input_size=192, | ||
| mask_patch_size=32, | ||
| model_patch_size=4, | ||
| mask_ratio=0.6)) | ||
|
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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 |
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| @@ -0,0 +1,10 @@ | ||
| # model settings | ||
| model = dict( | ||
| type='SimMIM', | ||
| backbone=dict( | ||
| type='SimMIMSwinTransformer', | ||
| arch='B', | ||
| img_size=192, | ||
| stage_cfgs=dict(block_cfgs=dict(window_size=6))), | ||
| neck=dict(type='SimMIMNeck', in_channels=128 * 2**3, encoder_stride=32), | ||
| head=dict(type='SimMIMHead', patch_size=4, encoder_in_channels=3)) |
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