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* resolve comments * update changelog * add hvu training configs * update README * add missing ckpt links * update changelog * make mean_average_percision robust (some category may missing in testing) * fix * fix bad configs * check if results is empty
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configs/recognition/tsn/hvu/tsn_r18_1x1x8_100e_hvu_action_rgb.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,126 @@ | ||
| # model settings | ||
| category_nums = dict( | ||
| action=729, attribute=117, concept=291, event=69, object=1679, scene=248) | ||
| target_cate = 'action' | ||
|
|
||
| model = dict( | ||
| type='Recognizer2D', | ||
| backbone=dict( | ||
| type='ResNet', | ||
| pretrained='torchvision://resnet18', | ||
| depth=18, | ||
| norm_eval=False), | ||
| cls_head=dict( | ||
| type='TSNHead', | ||
| num_classes=category_nums[target_cate], | ||
| in_channels=512, | ||
| spatial_type='avg', | ||
| multi_class=True, | ||
| consensus=dict(type='AvgConsensus', dim=1), | ||
| loss_cls=dict(type='BCELossWithLogits', loss_weight=333.), | ||
| dropout_ratio=0.4, | ||
| init_std=0.01)) | ||
| # model training and testing settings | ||
| train_cfg = None | ||
| test_cfg = dict(average_clips=None) | ||
| # dataset settings | ||
| dataset_type = 'RawframeDataset' | ||
| data_root = 'data/hvu/rawframes_train' | ||
| data_root_val = 'data/hvu/rawframes_val' | ||
| ann_file_train = f'data/hvu/hvu_{target_cate}_train.json' | ||
| ann_file_val = f'data/hvu/hvu_{target_cate}_val.json' | ||
| ann_file_test = f'data/hvu/hvu_{target_cate}_val.json' | ||
| img_norm_cfg = dict( | ||
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False) | ||
| train_pipeline = [ | ||
| dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8), | ||
| dict(type='RawFrameDecode'), | ||
| dict(type='Resize', scale=(-1, 256)), | ||
| dict(type='RandomResizedCrop'), | ||
| dict(type='Resize', scale=(224, 224), keep_ratio=False), | ||
| dict(type='Flip', flip_ratio=0.5), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='FormatShape', input_format='NCHW'), | ||
| dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), | ||
| dict(type='ToTensor', keys=['imgs', 'label']) | ||
| ] | ||
| val_pipeline = [ | ||
| dict( | ||
| type='SampleFrames', | ||
| clip_len=1, | ||
| frame_interval=1, | ||
| num_clips=8, | ||
| test_mode=True), | ||
| dict(type='RawFrameDecode'), | ||
| dict(type='Resize', scale=(-1, 256)), | ||
| dict(type='CenterCrop', crop_size=256), | ||
| dict(type='Flip', flip_ratio=0), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='FormatShape', input_format='NCHW'), | ||
| dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), | ||
| dict(type='ToTensor', keys=['imgs']) | ||
| ] | ||
| test_pipeline = [ | ||
| dict( | ||
| type='SampleFrames', | ||
| clip_len=1, | ||
| frame_interval=1, | ||
| num_clips=25, | ||
| test_mode=True), | ||
| dict(type='RawFrameDecode'), | ||
| dict(type='Resize', scale=(-1, 256)), | ||
| dict(type='ThreeCrop', crop_size=256), | ||
| dict(type='Flip', flip_ratio=0), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='FormatShape', input_format='NCHW'), | ||
| dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), | ||
| dict(type='ToTensor', keys=['imgs']) | ||
| ] | ||
| data = dict( | ||
| videos_per_gpu=32, | ||
| workers_per_gpu=2, | ||
| train=dict( | ||
| type=dataset_type, | ||
| ann_file=ann_file_train, | ||
| data_prefix=data_root, | ||
| pipeline=train_pipeline, | ||
| multi_class=True, | ||
| num_classes=category_nums[target_cate], | ||
| filename_tmpl='img_{:05d}.jpg'), | ||
| val=dict( | ||
| type=dataset_type, | ||
| ann_file=ann_file_val, | ||
| data_prefix=data_root_val, | ||
| pipeline=val_pipeline, | ||
| multi_class=True, | ||
| num_classes=category_nums[target_cate], | ||
| filename_tmpl='img_{:05d}.jpg'), | ||
| test=dict( | ||
| type=dataset_type, | ||
| ann_file=ann_file_test, | ||
| data_prefix=data_root_val, | ||
| pipeline=test_pipeline, | ||
| multi_class=True, | ||
| num_classes=category_nums[target_cate], | ||
| filename_tmpl='img_{:05d}.jpg')) | ||
| # optimizer | ||
| optimizer = dict( | ||
| type='SGD', lr=0.01, momentum=0.9, | ||
| weight_decay=0.0001) # this lr is used for 8 gpus | ||
| optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) | ||
| # learning policy | ||
| lr_config = dict(policy='step', step=[40, 80]) | ||
| total_epochs = 100 | ||
| checkpoint_config = dict(interval=1) | ||
| evaluation = dict(interval=2, metrics=['mean_average_precision']) | ||
| log_config = dict( | ||
| interval=20, hooks=[ | ||
| dict(type='TextLoggerHook'), | ||
| ]) | ||
| # runtime settings | ||
| dist_params = dict(backend='nccl') | ||
| log_level = 'INFO' | ||
| work_dir = f'./work_dirs/tsn_r18_1x1x8_100e_hvu_{target_cate}_rgb/' | ||
| load_from = None | ||
| resume_from = None | ||
| workflow = [('train', 1)] |
126 changes: 126 additions & 0 deletions
126
configs/recognition/tsn/hvu/tsn_r18_1x1x8_100e_hvu_attribute_rgb.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,126 @@ | ||
| # model settings | ||
| category_nums = dict( | ||
| action=729, attribute=117, concept=291, event=69, object=1679, scene=248) | ||
| target_cate = 'attribute' | ||
|
|
||
| model = dict( | ||
| type='Recognizer2D', | ||
| backbone=dict( | ||
| type='ResNet', | ||
| pretrained='torchvision://resnet18', | ||
| depth=18, | ||
| norm_eval=False), | ||
| cls_head=dict( | ||
| type='TSNHead', | ||
| num_classes=category_nums[target_cate], | ||
| in_channels=512, | ||
| spatial_type='avg', | ||
| multi_class=True, | ||
| consensus=dict(type='AvgConsensus', dim=1), | ||
| loss_cls=dict(type='BCELossWithLogits', loss_weight=333.), | ||
| dropout_ratio=0.4, | ||
| init_std=0.01)) | ||
| # model training and testing settings | ||
| train_cfg = None | ||
| test_cfg = dict(average_clips=None) | ||
| # dataset settings | ||
| dataset_type = 'RawframeDataset' | ||
| data_root = 'data/hvu/rawframes_train' | ||
| data_root_val = 'data/hvu/rawframes_val' | ||
| ann_file_train = f'data/hvu/hvu_{target_cate}_train.json' | ||
| ann_file_val = f'data/hvu/hvu_{target_cate}_val.json' | ||
| ann_file_test = f'data/hvu/hvu_{target_cate}_val.json' | ||
| img_norm_cfg = dict( | ||
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False) | ||
| train_pipeline = [ | ||
| dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8), | ||
| dict(type='RawFrameDecode'), | ||
| dict(type='Resize', scale=(-1, 256)), | ||
| dict(type='RandomResizedCrop'), | ||
| dict(type='Resize', scale=(224, 224), keep_ratio=False), | ||
| dict(type='Flip', flip_ratio=0.5), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='FormatShape', input_format='NCHW'), | ||
| dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), | ||
| dict(type='ToTensor', keys=['imgs', 'label']) | ||
| ] | ||
| val_pipeline = [ | ||
| dict( | ||
| type='SampleFrames', | ||
| clip_len=1, | ||
| frame_interval=1, | ||
| num_clips=8, | ||
| test_mode=True), | ||
| dict(type='RawFrameDecode'), | ||
| dict(type='Resize', scale=(-1, 256)), | ||
| dict(type='CenterCrop', crop_size=256), | ||
| dict(type='Flip', flip_ratio=0), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='FormatShape', input_format='NCHW'), | ||
| dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), | ||
| dict(type='ToTensor', keys=['imgs']) | ||
| ] | ||
| test_pipeline = [ | ||
| dict( | ||
| type='SampleFrames', | ||
| clip_len=1, | ||
| frame_interval=1, | ||
| num_clips=25, | ||
| test_mode=True), | ||
| dict(type='RawFrameDecode'), | ||
| dict(type='Resize', scale=(-1, 256)), | ||
| dict(type='ThreeCrop', crop_size=256), | ||
| dict(type='Flip', flip_ratio=0), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='FormatShape', input_format='NCHW'), | ||
| dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), | ||
| dict(type='ToTensor', keys=['imgs']) | ||
| ] | ||
| data = dict( | ||
| videos_per_gpu=32, | ||
| workers_per_gpu=2, | ||
| train=dict( | ||
| type=dataset_type, | ||
| ann_file=ann_file_train, | ||
| data_prefix=data_root, | ||
| pipeline=train_pipeline, | ||
| multi_class=True, | ||
| num_classes=category_nums[target_cate], | ||
| filename_tmpl='img_{:05d}.jpg'), | ||
| val=dict( | ||
| type=dataset_type, | ||
| ann_file=ann_file_val, | ||
| data_prefix=data_root_val, | ||
| pipeline=val_pipeline, | ||
| multi_class=True, | ||
| num_classes=category_nums[target_cate], | ||
| filename_tmpl='img_{:05d}.jpg'), | ||
| test=dict( | ||
| type=dataset_type, | ||
| ann_file=ann_file_test, | ||
| data_prefix=data_root_val, | ||
| pipeline=test_pipeline, | ||
| multi_class=True, | ||
| num_classes=category_nums[target_cate], | ||
| filename_tmpl='img_{:05d}.jpg')) | ||
| # optimizer | ||
| optimizer = dict( | ||
| type='SGD', lr=0.01, momentum=0.9, | ||
| weight_decay=0.0001) # this lr is used for 8 gpus | ||
| optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) | ||
| # learning policy | ||
| lr_config = dict(policy='step', step=[40, 80]) | ||
| total_epochs = 100 | ||
| checkpoint_config = dict(interval=1) | ||
| evaluation = dict(interval=2, metrics=['mean_average_precision']) | ||
| log_config = dict( | ||
| interval=20, hooks=[ | ||
| dict(type='TextLoggerHook'), | ||
| ]) | ||
| # runtime settings | ||
| dist_params = dict(backend='nccl') | ||
| log_level = 'INFO' | ||
| work_dir = f'./work_dirs/tsn_r18_1x1x8_100e_hvu_{target_cate}_rgb/' | ||
| load_from = None | ||
| resume_from = None | ||
| workflow = [('train', 1)] |
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