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Add Sub-jhmdb dataset #292
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139742f
add jhmdb dataset
jin-s13 21a6ae1
add cfg
jin-s13 4f6a901
add cfg
jin-s13 a5b8b62
add cfg
jin-s13 e339813
add cfg
jin-s13 127c2e0
add cfg
jin-s13 9442dd4
add cfg
jin-s13 259e3ca
add torso normalization
jin-s13 bee18b5
shift 1 pixel
jin-s13 2652aeb
add tPCK for evaluation
jin-s13 ccc024a
add tPCK for evaluation
jin-s13 4452bb9
add doc
jin-s13 91e4107
upload models
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resolve comments
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,145 @@ | ||
| log_level = 'INFO' | ||
| load_from = 'https://download.openmmlab.com/mmpose/top_down/cpm/cpm_mpii_368x368-116e62b8_20200822.pth' # noqa: E501 | ||
| resume_from = None | ||
| dist_params = dict(backend='nccl') | ||
| workflow = [('train', 1)] | ||
| checkpoint_config = dict(interval=1) | ||
| evaluation = dict(interval=1, metric=['PCK', 'tPCK'], key_indicator='Mean PCK') | ||
|
|
||
| optimizer = dict( | ||
| type='Adam', | ||
| lr=5e-4, | ||
| ) | ||
| optimizer_config = dict(grad_clip=None) | ||
| # learning policy | ||
| lr_config = dict( | ||
| policy='step', | ||
| warmup='linear', | ||
| warmup_iters=500, | ||
| warmup_ratio=0.001, | ||
| step=[20, 30]) | ||
| total_epochs = 40 | ||
| log_config = dict( | ||
| interval=50, | ||
| hooks=[ | ||
| dict(type='TextLoggerHook'), | ||
| # dict(type='TensorboardLoggerHook') | ||
| ]) | ||
|
|
||
| channel_cfg = dict( | ||
| num_output_channels=15, | ||
| dataset_joints=15, | ||
| dataset_channel=[ | ||
| [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], | ||
| ], | ||
| inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]) | ||
|
|
||
| # model settings | ||
| model = dict( | ||
| type='TopDown', | ||
| pretrained=None, | ||
| backbone=dict( | ||
| type='CPM', | ||
| in_channels=3, | ||
| out_channels=channel_cfg['num_output_channels'], | ||
| feat_channels=128, | ||
| num_stages=6), | ||
| keypoint_head=dict( | ||
| type='TopDownMultiStageHead', | ||
| in_channels=channel_cfg['num_output_channels'], | ||
| out_channels=channel_cfg['num_output_channels'], | ||
| num_stages=6, | ||
| num_deconv_layers=0, | ||
| extra=dict(final_conv_kernel=0, ), | ||
| ), | ||
| train_cfg=dict(), | ||
| test_cfg=dict( | ||
| flip_test=True, | ||
| post_process=True, | ||
| shift_heatmap=True, | ||
| unbiased_decoding=False, | ||
| modulate_kernel=11), | ||
| loss_pose=dict(type='JointsMSELoss', use_target_weight=True)) | ||
|
|
||
| data_cfg = dict( | ||
| image_size=[368, 368], | ||
| heatmap_size=[46, 46], | ||
| num_output_channels=channel_cfg['num_output_channels'], | ||
| num_joints=channel_cfg['dataset_joints'], | ||
| dataset_channel=channel_cfg['dataset_channel'], | ||
| inference_channel=channel_cfg['inference_channel'], | ||
| soft_nms=False, | ||
| nms_thr=1.0, | ||
| oks_thr=0.9, | ||
| vis_thr=0.2, | ||
| bbox_thr=1.0, | ||
| use_gt_bbox=True, | ||
| image_thr=0.0, | ||
| bbox_file='', | ||
| ) | ||
|
|
||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='TopDownRandomFlip', flip_prob=0.5), | ||
| dict( | ||
| type='TopDownGetRandomScaleRotation', rot_factor=30, | ||
| scale_factor=0.25), | ||
| dict(type='TopDownAffine'), | ||
| dict(type='ToTensor'), | ||
| dict( | ||
| type='NormalizeTensor', | ||
| mean=[0.485, 0.456, 0.406], | ||
| std=[0.229, 0.224, 0.225]), | ||
| dict(type='TopDownGenerateTarget', sigma=2), | ||
| dict( | ||
| type='Collect', | ||
| keys=['img', 'target', 'target_weight'], | ||
| meta_keys=[ | ||
| 'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', | ||
| 'rotation', 'bbox', 'flip_pairs' | ||
| ]), | ||
| ] | ||
|
|
||
| val_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='TopDownAffine'), | ||
| dict(type='ToTensor'), | ||
| dict( | ||
| type='NormalizeTensor', | ||
| mean=[0.485, 0.456, 0.406], | ||
| std=[0.229, 0.224, 0.225]), | ||
| dict( | ||
| type='Collect', | ||
| keys=[ | ||
| 'img', | ||
| ], | ||
| meta_keys=[ | ||
| 'image_file', 'center', 'scale', 'rotation', 'bbox', 'flip_pairs' | ||
| ]), | ||
| ] | ||
|
|
||
| test_pipeline = val_pipeline | ||
|
|
||
| data_root = 'data/jhmdb' | ||
| data = dict( | ||
| samples_per_gpu=32, | ||
| workers_per_gpu=2, | ||
| train=dict( | ||
| type='TopDownJhmdbDataset', | ||
| ann_file=f'{data_root}/annotations/Sub1_train.json', | ||
| img_prefix=f'{data_root}/', | ||
| data_cfg=data_cfg, | ||
| pipeline=train_pipeline), | ||
| val=dict( | ||
| type='TopDownJhmdbDataset', | ||
| ann_file=f'{data_root}/annotations/Sub1_test.json', | ||
| img_prefix=f'{data_root}/', | ||
| data_cfg=data_cfg, | ||
| pipeline=val_pipeline), | ||
| test=dict( | ||
| type='TopDownJhmdbDataset', | ||
| ann_file=f'{data_root}/annotations/Sub1_test.json', | ||
| img_prefix=f'{data_root}/', | ||
| data_cfg=data_cfg, | ||
| pipeline=val_pipeline), | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,145 @@ | ||
| log_level = 'INFO' | ||
| load_from = 'https://download.openmmlab.com/mmpose/top_down/cpm/cpm_mpii_368x368-116e62b8_20200822.pth' # noqa: E501 | ||
| resume_from = None | ||
| dist_params = dict(backend='nccl') | ||
| workflow = [('train', 1)] | ||
| checkpoint_config = dict(interval=1) | ||
| evaluation = dict(interval=1, metric=['PCK', 'tPCK'], key_indicator='Mean PCK') | ||
|
|
||
| optimizer = dict( | ||
| type='Adam', | ||
| lr=5e-4, | ||
| ) | ||
| optimizer_config = dict(grad_clip=None) | ||
| # learning policy | ||
| lr_config = dict( | ||
| policy='step', | ||
| warmup='linear', | ||
| warmup_iters=500, | ||
| warmup_ratio=0.001, | ||
| step=[20, 30]) | ||
| total_epochs = 40 | ||
| log_config = dict( | ||
| interval=50, | ||
| hooks=[ | ||
| dict(type='TextLoggerHook'), | ||
| # dict(type='TensorboardLoggerHook') | ||
| ]) | ||
|
|
||
| channel_cfg = dict( | ||
| num_output_channels=15, | ||
| dataset_joints=15, | ||
| dataset_channel=[ | ||
| [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], | ||
| ], | ||
| inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]) | ||
|
|
||
| # model settings | ||
| model = dict( | ||
| type='TopDown', | ||
| pretrained=None, | ||
| backbone=dict( | ||
| type='CPM', | ||
| in_channels=3, | ||
| out_channels=channel_cfg['num_output_channels'], | ||
| feat_channels=128, | ||
| num_stages=6), | ||
| keypoint_head=dict( | ||
| type='TopDownMultiStageHead', | ||
| in_channels=channel_cfg['num_output_channels'], | ||
| out_channels=channel_cfg['num_output_channels'], | ||
| num_stages=6, | ||
| num_deconv_layers=0, | ||
| extra=dict(final_conv_kernel=0, ), | ||
| ), | ||
| train_cfg=dict(), | ||
| test_cfg=dict( | ||
| flip_test=True, | ||
| post_process=True, | ||
| shift_heatmap=True, | ||
| unbiased_decoding=False, | ||
| modulate_kernel=11), | ||
| loss_pose=dict(type='JointsMSELoss', use_target_weight=True)) | ||
|
|
||
| data_cfg = dict( | ||
| image_size=[368, 368], | ||
| heatmap_size=[46, 46], | ||
| num_output_channels=channel_cfg['num_output_channels'], | ||
| num_joints=channel_cfg['dataset_joints'], | ||
| dataset_channel=channel_cfg['dataset_channel'], | ||
| inference_channel=channel_cfg['inference_channel'], | ||
| soft_nms=False, | ||
| nms_thr=1.0, | ||
| oks_thr=0.9, | ||
| vis_thr=0.2, | ||
| bbox_thr=1.0, | ||
| use_gt_bbox=True, | ||
| image_thr=0.0, | ||
| bbox_file='', | ||
| ) | ||
|
|
||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='TopDownRandomFlip', flip_prob=0.5), | ||
| dict( | ||
| type='TopDownGetRandomScaleRotation', rot_factor=30, | ||
| scale_factor=0.25), | ||
| dict(type='TopDownAffine'), | ||
| dict(type='ToTensor'), | ||
| dict( | ||
| type='NormalizeTensor', | ||
| mean=[0.485, 0.456, 0.406], | ||
| std=[0.229, 0.224, 0.225]), | ||
| dict(type='TopDownGenerateTarget', sigma=2), | ||
| dict( | ||
| type='Collect', | ||
| keys=['img', 'target', 'target_weight'], | ||
| meta_keys=[ | ||
| 'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', | ||
| 'rotation', 'bbox', 'flip_pairs' | ||
| ]), | ||
| ] | ||
|
|
||
| val_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='TopDownAffine'), | ||
| dict(type='ToTensor'), | ||
| dict( | ||
| type='NormalizeTensor', | ||
| mean=[0.485, 0.456, 0.406], | ||
| std=[0.229, 0.224, 0.225]), | ||
| dict( | ||
| type='Collect', | ||
| keys=[ | ||
| 'img', | ||
| ], | ||
| meta_keys=[ | ||
| 'image_file', 'center', 'scale', 'rotation', 'bbox', 'flip_pairs' | ||
| ]), | ||
| ] | ||
|
|
||
| test_pipeline = val_pipeline | ||
|
|
||
| data_root = 'data/jhmdb' | ||
| data = dict( | ||
| samples_per_gpu=32, | ||
| workers_per_gpu=2, | ||
| train=dict( | ||
| type='TopDownJhmdbDataset', | ||
| ann_file=f'{data_root}/annotations/Sub2_train.json', | ||
| img_prefix=f'{data_root}/', | ||
| data_cfg=data_cfg, | ||
| pipeline=train_pipeline), | ||
| val=dict( | ||
| type='TopDownJhmdbDataset', | ||
| ann_file=f'{data_root}/annotations/Sub2_test.json', | ||
| img_prefix=f'{data_root}/', | ||
| data_cfg=data_cfg, | ||
| pipeline=val_pipeline), | ||
| test=dict( | ||
| type='TopDownJhmdbDataset', | ||
| ann_file=f'{data_root}/annotations/Sub2_test.json', | ||
| img_prefix=f'{data_root}/', | ||
| data_cfg=data_cfg, | ||
| pipeline=val_pipeline), | ||
| ) |
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What does
sub-mean?There was a problem hiding this comment.
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Only a subset of JHMDB
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JHMDB is initially proposed for action recognition. Some works also use a subset of JHMDB (sub-JHMDB) for pose estimation.
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Good to know. Put it in the intro of preparation will be helpful