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[Feature] Add Omnisource training configs (#296)
* resolve comments * update changelog * update README * add training config * reorganize path * update README * add omnisource model links * resolve comments * update README.md * update changelog * remove css classes
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...wonly_r50_8x8x1_256e_minikinetics/slowonly_r50_8x8x1_256e_minikinetics_googleimage_rgb.py
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| @@ -0,0 +1,147 @@ | ||
| # model settings | ||
| model = dict( | ||
| type='Recognizer3D', | ||
| backbone=dict( | ||
| type='ResNet3dSlowOnly', | ||
| depth=50, | ||
| pretrained=None, | ||
| lateral=False, | ||
| conv1_kernel=(1, 7, 7), | ||
| conv1_stride_t=1, | ||
| pool1_stride_t=1, | ||
| inflate=(0, 0, 1, 1), | ||
| norm_eval=False), | ||
| cls_head=dict( | ||
| type='I3DHead', | ||
| in_channels=2048, | ||
| num_classes=200, | ||
| spatial_type='avg', | ||
| dropout_ratio=0.5)) | ||
| # model training and testing settings | ||
| train_cfg = None | ||
| test_cfg = dict(average_clips='prob') | ||
| # dataset settings | ||
| dataset_type = 'VideoDataset' | ||
|
|
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| data_root = 'data/OmniSource/kinetics_200_train' | ||
| data_root_val = 'data/OmniSource/kinetics_200_val' | ||
| gg_root = 'data/OmniSource/googleimage_200' | ||
|
|
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| ann_file_train = 'data/OmniSource/annotations/kinetics_200/k200_train.txt' | ||
| ann_file_gg = ('data/OmniSource/annotations/googleimage_200/' | ||
| 'tsn_8seg_googleimage_200_wodup.txt') | ||
|
|
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| ann_file_val = 'data/OmniSource/annotations/kinetics_200/k200_val.txt' | ||
| ann_file_test = 'data/OmniSource/annotations/kinetics_200/k200_val.txt' | ||
|
|
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| img_norm_cfg = dict( | ||
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False) | ||
|
|
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| train_pipeline = [ | ||
| dict(type='DecordInit'), | ||
| dict(type='SampleFrames', clip_len=8, frame_interval=8, num_clips=1), | ||
| dict(type='DecordDecode'), | ||
| 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='NCTHW'), | ||
| dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), | ||
| dict(type='ToTensor', keys=['imgs', 'label']) | ||
| ] | ||
| train_gg_pipeline = [ | ||
| dict(type='ImageDecode'), | ||
| 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='PseudoClipBuilder', clip_len=8), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='FormatShape', input_format='NCTHW'), | ||
| dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), | ||
| dict(type='ToTensor', keys=['imgs', 'label']) | ||
| ] | ||
|
|
||
| val_pipeline = [ | ||
| dict(type='DecordInit'), | ||
| dict( | ||
| type='SampleFrames', | ||
| clip_len=8, | ||
| frame_interval=8, | ||
| num_clips=1, | ||
| test_mode=True), | ||
| dict(type='DecordDecode'), | ||
| dict(type='Resize', scale=(-1, 256)), | ||
| dict(type='CenterCrop', crop_size=256), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='FormatShape', input_format='NCTHW'), | ||
| dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), | ||
| dict(type='ToTensor', keys=['imgs']) | ||
| ] | ||
|
|
||
| test_pipeline = [ | ||
| dict(type='DecordInit'), | ||
| dict( | ||
| type='SampleFrames', | ||
| clip_len=8, | ||
| frame_interval=8, | ||
| num_clips=10, | ||
| test_mode=True), | ||
| dict(type='DecordDecode'), | ||
| dict(type='Resize', scale=(-1, 256)), | ||
| dict(type='ThreeCrop', crop_size=256), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='FormatShape', input_format='NCTHW'), | ||
| dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), | ||
| dict(type='ToTensor', keys=['imgs']) | ||
| ] | ||
|
|
||
| data = dict( | ||
| videos_per_gpu=12, | ||
| workers_per_gpu=2, | ||
| train=[ | ||
| dict( | ||
| type=dataset_type, | ||
| ann_file=ann_file_train, | ||
| data_prefix=data_root, | ||
| pipeline=train_pipeline), | ||
| dict( | ||
| type='ImageDataset', | ||
| ann_file=ann_file_gg, | ||
| data_prefix=gg_root, | ||
| pipeline=train_gg_pipeline) | ||
| ], | ||
| val=dict( | ||
| type=dataset_type, | ||
| ann_file=ann_file_val, | ||
| data_prefix=data_root_val, | ||
| pipeline=val_pipeline), | ||
| test=dict( | ||
| type=dataset_type, | ||
| ann_file=ann_file_test, | ||
| data_prefix=data_root_val, | ||
| pipeline=test_pipeline)) | ||
| # optimizer | ||
| optimizer = dict( | ||
| type='SGD', lr=0.15, 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='CosineAnnealing', min_lr=0) | ||
| total_epochs = 256 | ||
| checkpoint_config = dict(interval=8) | ||
| workflow = [('train', 1)] | ||
| evaluation = dict( | ||
| interval=8, metrics=['top_k_accuracy', 'mean_class_accuracy']) | ||
| log_config = dict( | ||
| interval=20, hooks=[ | ||
| dict(type='TextLoggerHook'), | ||
| ]) | ||
| dist_params = dict(backend='nccl') | ||
| log_level = 'INFO' | ||
| work_dir = ('./work_dirs/omnisource/' | ||
| 'slowonly_r50_8x8x1_256e_minikinetics_googleimage_rgb') | ||
| load_from = None | ||
| resume_from = None | ||
| find_unused_parameters = False |
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