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[Enhance] Support the Training of ActionClip (#2620)
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162 changes: 162 additions & 0 deletions
162
projects/actionclip/configs/actionclip_vit-base-p16-res224-clip-pre_g8xb16_1x1x8_k400-rgb.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,162 @@ | ||
| custom_imports = dict(imports='models') | ||
|
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| num_segs = 8 | ||
|
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| model = dict( | ||
| type='ActionClip', | ||
| clip_arch='ViT-B/16', | ||
| num_adapter_segs=num_segs, | ||
| num_adapter_layers=6, | ||
| to_float32=True, | ||
| labels_or_label_file='configs/label_map_k400.txt', | ||
| data_preprocessor=dict( | ||
| type='ActionDataPreprocessor', | ||
| mean=[122.771, 116.746, 104.093], | ||
| std=[68.500, 66.632, 70.323], | ||
| format_shape='NCHW')) | ||
|
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| dataset_type = 'VideoDataset' | ||
| data_root = 'data/kinetics400/videos_train' | ||
| data_root_val = 'data/kinetics400/videos_val' | ||
| ann_file_train = 'data/kinetics400/kinetics400_train_list_videos.txt' | ||
| ann_file_val = 'data/kinetics400/kinetics400_val_list_videos.txt' | ||
| ann_file_test = 'data/kinetics400/kinetics400_val_list_videos.txt' | ||
|
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| file_client_args = dict(io_backend='disk') | ||
| file_client_args = dict( | ||
| io_backend='petrel', | ||
| path_mapping=dict( | ||
| {'data/kinetics400/': 's3://openmmlab/datasets/action/Kinetics400/'})) | ||
|
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| train_pipeline = [ | ||
| dict(type='DecordInit', **file_client_args), | ||
| dict( | ||
| type='SampleFrames', clip_len=1, frame_interval=1, num_clips=num_segs), | ||
| dict(type='DecordDecode'), | ||
| dict(type='Resize', scale=(-1, 256)), | ||
| dict(type='RandomResizedCrop'), | ||
| dict( | ||
| type='MultiScaleCrop', | ||
| input_size=224, | ||
| scales=(1, .875, .75, .66), | ||
| random_crop=False, | ||
| num_fixed_crops=13, | ||
| max_wh_scale_gap=1), | ||
| dict(type='Resize', scale=(224, 224), keep_ratio=False), | ||
| dict(type='Flip', flip_ratio=0.5), | ||
| dict(type='FormatShape', input_format='NCHW'), | ||
| dict(type='PackActionInputs') | ||
| ] | ||
|
|
||
| val_pipeline = [ | ||
| dict(type='DecordInit', **file_client_args), | ||
| dict( | ||
| type='SampleFrames', | ||
| clip_len=1, | ||
| frame_interval=1, | ||
| num_clips=num_segs, | ||
| test_mode=True), | ||
| dict(type='DecordDecode'), | ||
| dict(type='Resize', scale=(-1, 256)), | ||
| dict(type='CenterCrop', crop_size=224), | ||
| dict(type='FormatShape', input_format='NCHW'), | ||
| dict(type='PackActionInputs') | ||
| ] | ||
|
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||
| test_pipeline = val_pipeline | ||
|
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| train_dataloader = dict( | ||
| batch_size=16, | ||
| num_workers=16, | ||
| persistent_workers=True, | ||
| sampler=dict(type='DefaultSampler', shuffle=True), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| ann_file=ann_file_train, | ||
| data_prefix=dict(video=data_root), | ||
| pipeline=train_pipeline)) | ||
| val_dataloader = dict( | ||
| batch_size=16, | ||
| num_workers=16, | ||
| persistent_workers=True, | ||
| sampler=dict(type='DefaultSampler', shuffle=False), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| ann_file=ann_file_val, | ||
| data_prefix=dict(video=data_root_val), | ||
| pipeline=val_pipeline, | ||
| test_mode=True)) | ||
| test_dataloader = dict( | ||
| batch_size=1, | ||
| num_workers=16, | ||
| persistent_workers=True, | ||
| sampler=dict(type='DefaultSampler', shuffle=False), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| ann_file=ann_file_test, | ||
| data_prefix=dict(video=data_root_val), | ||
| pipeline=test_pipeline, | ||
| test_mode=True)) | ||
|
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| val_evaluator = dict(type='AccMetric') | ||
| test_evaluator = val_evaluator | ||
|
|
||
| train_cfg = dict( | ||
| type='EpochBasedTrainLoop', max_epochs=50, val_begin=1, val_interval=1) | ||
| val_cfg = dict(type='ValLoop') | ||
| test_cfg = dict(type='TestLoop') | ||
|
|
||
| optim_wrapper = dict( | ||
| optimizer=dict( | ||
| type='AdamW', lr=5e-6, betas=(0.9, 0.98), eps=1e-08, weight_decay=0.2), | ||
| paramwise_cfg=dict(custom_keys=dict(adapter=dict(lr_mult=10)))) | ||
|
|
||
| param_scheduler = [ | ||
| dict( | ||
| type='LinearLR', | ||
| start_factor=0.01, | ||
| by_epoch=True, | ||
| begin=0, | ||
| end=5, | ||
| convert_to_iter_based=True), | ||
| dict( | ||
| type='CosineAnnealingLR', | ||
| T_max=45, | ||
| eta_min=0, | ||
| by_epoch=True, | ||
| begin=5, | ||
| end=50, | ||
| convert_to_iter_based=True) | ||
| ] | ||
|
|
||
| # Default setting for scaling LR automatically | ||
| # - `enable` means enable scaling LR automatically | ||
| # or not by default. | ||
| # - `base_batch_size` = (8 GPUs) x (16 samples per GPU). | ||
| auto_scale_lr = dict(enable=False, base_batch_size=128) | ||
|
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| default_scope = 'mmaction' | ||
|
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| default_hooks = dict( | ||
| runtime_info=dict(type='RuntimeInfoHook'), | ||
| timer=dict(type='IterTimerHook'), | ||
| logger=dict(type='LoggerHook', interval=100, ignore_last=False), | ||
| param_scheduler=dict(type='ParamSchedulerHook'), | ||
| checkpoint=dict( | ||
| type='CheckpointHook', interval=1, save_best='auto', max_keep_ckpts=5), | ||
| sampler_seed=dict(type='DistSamplerSeedHook'), | ||
| sync_buffers=dict(type='SyncBuffersHook')) | ||
|
|
||
| env_cfg = dict( | ||
| cudnn_benchmark=False, | ||
| mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), | ||
| dist_cfg=dict(backend='nccl')) | ||
|
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| log_processor = dict(type='LogProcessor', window_size=20, by_epoch=True) | ||
|
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| vis_backends = [dict(type='LocalVisBackend')] | ||
| visualizer = dict(type='ActionVisualizer', vis_backends=vis_backends) | ||
|
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| log_level = 'INFO' | ||
| load_from = None | ||
| resume = False |
162 changes: 162 additions & 0 deletions
162
projects/actionclip/configs/actionclip_vit-base-p32-res224-clip-pre_g8xb16_1x1x8_k400-rgb.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,162 @@ | ||
| custom_imports = dict(imports='models') | ||
|
|
||
| num_segs = 8 | ||
|
|
||
| model = dict( | ||
| type='ActionClip', | ||
| clip_arch='ViT-B/32', | ||
| num_adapter_segs=num_segs, | ||
| num_adapter_layers=6, | ||
| to_float32=True, | ||
| labels_or_label_file='configs/label_map_k400.txt', | ||
| data_preprocessor=dict( | ||
| type='ActionDataPreprocessor', | ||
| mean=[122.771, 116.746, 104.093], | ||
| std=[68.500, 66.632, 70.323], | ||
| format_shape='NCHW')) | ||
|
|
||
| dataset_type = 'VideoDataset' | ||
| data_root = 'data/kinetics400/videos_train' | ||
| data_root_val = 'data/kinetics400/videos_val' | ||
| ann_file_train = 'data/kinetics400/kinetics400_train_list_videos.txt' | ||
| ann_file_val = 'data/kinetics400/kinetics400_val_list_videos.txt' | ||
| ann_file_test = 'data/kinetics400/kinetics400_val_list_videos.txt' | ||
|
|
||
| file_client_args = dict(io_backend='disk') | ||
| file_client_args = dict( | ||
| io_backend='petrel', | ||
| path_mapping=dict( | ||
| {'data/kinetics400/': 's3://openmmlab/datasets/action/Kinetics400/'})) | ||
|
|
||
| train_pipeline = [ | ||
| dict(type='DecordInit', **file_client_args), | ||
| dict( | ||
| type='SampleFrames', clip_len=1, frame_interval=1, num_clips=num_segs), | ||
| dict(type='DecordDecode'), | ||
| dict(type='Resize', scale=(-1, 256)), | ||
| dict(type='RandomResizedCrop'), | ||
| dict( | ||
| type='MultiScaleCrop', | ||
| input_size=224, | ||
| scales=(1, .875, .75, .66), | ||
| random_crop=False, | ||
| num_fixed_crops=13, | ||
| max_wh_scale_gap=1), | ||
| dict(type='Resize', scale=(224, 224), keep_ratio=False), | ||
| dict(type='Flip', flip_ratio=0.5), | ||
| dict(type='FormatShape', input_format='NCHW'), | ||
| dict(type='PackActionInputs') | ||
| ] | ||
|
|
||
| val_pipeline = [ | ||
| dict(type='DecordInit', **file_client_args), | ||
| dict( | ||
| type='SampleFrames', | ||
| clip_len=1, | ||
| frame_interval=1, | ||
| num_clips=num_segs, | ||
| test_mode=True), | ||
| dict(type='DecordDecode'), | ||
| dict(type='Resize', scale=(-1, 256)), | ||
| dict(type='CenterCrop', crop_size=224), | ||
| dict(type='FormatShape', input_format='NCHW'), | ||
| dict(type='PackActionInputs') | ||
| ] | ||
|
|
||
| test_pipeline = val_pipeline | ||
|
|
||
| train_dataloader = dict( | ||
| batch_size=16, | ||
| num_workers=16, | ||
| persistent_workers=True, | ||
| sampler=dict(type='DefaultSampler', shuffle=True), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| ann_file=ann_file_train, | ||
| data_prefix=dict(video=data_root), | ||
| pipeline=train_pipeline)) | ||
| val_dataloader = dict( | ||
| batch_size=16, | ||
| num_workers=16, | ||
| persistent_workers=True, | ||
| sampler=dict(type='DefaultSampler', shuffle=False), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| ann_file=ann_file_val, | ||
| data_prefix=dict(video=data_root_val), | ||
| pipeline=val_pipeline, | ||
| test_mode=True)) | ||
| test_dataloader = dict( | ||
| batch_size=1, | ||
| num_workers=16, | ||
| persistent_workers=True, | ||
| sampler=dict(type='DefaultSampler', shuffle=False), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| ann_file=ann_file_test, | ||
| data_prefix=dict(video=data_root_val), | ||
| pipeline=test_pipeline, | ||
| test_mode=True)) | ||
|
|
||
| val_evaluator = dict(type='AccMetric') | ||
| test_evaluator = val_evaluator | ||
|
|
||
| train_cfg = dict( | ||
| type='EpochBasedTrainLoop', max_epochs=50, val_begin=1, val_interval=1) | ||
| val_cfg = dict(type='ValLoop') | ||
| test_cfg = dict(type='TestLoop') | ||
|
|
||
| optim_wrapper = dict( | ||
| optimizer=dict( | ||
| type='AdamW', lr=5e-6, betas=(0.9, 0.98), eps=1e-08, weight_decay=0.2), | ||
| paramwise_cfg=dict(custom_keys=dict(adapter=dict(lr_mult=10)))) | ||
|
|
||
| param_scheduler = [ | ||
| dict( | ||
| type='LinearLR', | ||
| start_factor=0.01, | ||
| by_epoch=True, | ||
| begin=0, | ||
| end=5, | ||
| convert_to_iter_based=True), | ||
| dict( | ||
| type='CosineAnnealingLR', | ||
| T_max=45, | ||
| eta_min=0, | ||
| by_epoch=True, | ||
| begin=5, | ||
| end=50, | ||
| convert_to_iter_based=True) | ||
| ] | ||
|
|
||
| # Default setting for scaling LR automatically | ||
| # - `enable` means enable scaling LR automatically | ||
| # or not by default. | ||
| # - `base_batch_size` = (8 GPUs) x (16 samples per GPU). | ||
| auto_scale_lr = dict(enable=False, base_batch_size=128) | ||
|
|
||
| default_scope = 'mmaction' | ||
|
|
||
| default_hooks = dict( | ||
| runtime_info=dict(type='RuntimeInfoHook'), | ||
| timer=dict(type='IterTimerHook'), | ||
| logger=dict(type='LoggerHook', interval=100, ignore_last=False), | ||
| param_scheduler=dict(type='ParamSchedulerHook'), | ||
| checkpoint=dict( | ||
| type='CheckpointHook', interval=1, save_best='auto', max_keep_ckpts=5), | ||
| sampler_seed=dict(type='DistSamplerSeedHook'), | ||
| sync_buffers=dict(type='SyncBuffersHook')) | ||
|
|
||
| env_cfg = dict( | ||
| cudnn_benchmark=False, | ||
| mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), | ||
| dist_cfg=dict(backend='nccl')) | ||
|
|
||
| log_processor = dict(type='LogProcessor', window_size=20, by_epoch=True) | ||
|
|
||
| vis_backends = [dict(type='LocalVisBackend')] | ||
| visualizer = dict(type='ActionVisualizer', vis_backends=vis_backends) | ||
|
|
||
| log_level = 'INFO' | ||
| load_from = None | ||
| resume = False |
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