-
Notifications
You must be signed in to change notification settings - Fork 1.2k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[Enhance] Support the Training of ActionClip (#2620)
- Loading branch information
1 parent
baf385e
commit 17b88a3
Showing
4 changed files
with
437 additions
and
26 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
162 changes: 162 additions & 0 deletions
162
projects/actionclip/configs/actionclip_vit-base-p16-res224-clip-pre_g8xb16_1x1x8_k400-rgb.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| 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/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')) | ||
|
|
||
| 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 |
162 changes: 162 additions & 0 deletions
162
projects/actionclip/configs/actionclip_vit-base-p32-res224-clip-pre_g8xb16_1x1x8_k400-rgb.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| 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 |
Oops, something went wrong.