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[Feature] Support ubody dataset (2d keypoints) (#2588)
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configs/wholebody_2d_keypoint/topdown_heatmap/ubody2d/hrnet_coco-wholebody.yml
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| Models: | ||
| - Config: configs/wholebody_2d_keypoint/topdown_heatmap/ubody2d/td-hm_hrnet-w32_8xb64-210e_ubody-256x192.py | ||
| In Collection: HRNet | ||
| Metadata: | ||
| Architecture: &id001 | ||
| - HRNet | ||
| Training Data: UBody-COCO-WholeBody | ||
| Name: td-hm_hrnet-w32_8xb64-210e_ubody-256x192 | ||
| Results: | ||
| - Dataset: COCO-WholeBody | ||
| Metrics: | ||
| Body AP: 0.678 | ||
| Body AR: 0.755 | ||
| Face AP: 0.630 | ||
| Face AR: 0.708 | ||
| Foot AP: 0.543 | ||
| Foot AR: 0.661 | ||
| Hand AP: 0.467 | ||
| Hand AR: 0.566 | ||
| Whole AP: 0.536 | ||
| Whole AR: 0.636 | ||
| Task: Wholebody 2D Keypoint | ||
| Weights: https://download.openmmlab.com/mmpose/v1/wholebody_2d_keypoint/ubody/td-hm_hrnet-w32_8xb64-210e_ubody-coco-256x192-7c227391_20230807.pth |
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...igs/wholebody_2d_keypoint/topdown_heatmap/ubody2d/hrnet_ubody-coco-wholebody.md
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| <!-- [ALGORITHM] --> | ||
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| <details> | ||
| <summary align="right"><a href="http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html">HRNet (CVPR'2019)</a></summary> | ||
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| ```bibtex | ||
| @inproceedings{sun2019deep, | ||
| title={Deep high-resolution representation learning for human pose estimation}, | ||
| author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong}, | ||
| booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, | ||
| pages={5693--5703}, | ||
| year={2019} | ||
| } | ||
| ``` | ||
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| </details> | ||
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| <!-- [DATASET] --> | ||
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| <details> | ||
| <summary align="right"><a href="https://arxiv.org/abs/2303.16160">UBody (CVPR'2023)</a></summary> | ||
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| ```bibtex | ||
| @article{lin2023one, | ||
| title={One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer}, | ||
| author={Lin, Jing and Zeng, Ailing and Wang, Haoqian and Zhang, Lei and Li, Yu}, | ||
| booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, | ||
| year={2023}, | ||
| } | ||
| ``` | ||
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| </details> | ||
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| Results on COCO-WholeBody v1.0 val with detector having human AP of 56.4 on COCO val2017 dataset | ||
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| | Arch | Input Size | Body AP | Body AR | Foot AP | Foot AR | Face AP | Face AR | Hand AP | Hand AR | Whole AP | Whole AR | ckpt | log | | ||
| | :-------------------------------------- | :--------: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :------: | :------: | :--------------------------------------: | :-------------------------------------: | | ||
| | [pose_hrnet_w32](/configs/wholebody_2d_keypoint/topdown_heatmap/ubody/td-hm_hrnet-w32_8xb64-210e_coco-wholebody-256x192.py) | 256x192 | 0.685 | 0.759 | 0.564 | 0.675 | 0.625 | 0.705 | 0.516 | 0.609 | 0.549 | 0.646 | [ckpt](https://download.openmmlab.com/mmpose/v1/wholebody_2d_keypoint/ubody/td-hm_hrnet-w32_8xb64-210e_ubody-coco-256x192-7c227391_20230807.pth) | [log](https://download.openmmlab.com/mmpose/v1/wholebody_2d_keypoint/ubody/td-hm_hrnet-w32_8xb64-210e_ubody-coco-256x192-7c227391_20230807.json) | |
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...wholebody_2d_keypoint/topdown_heatmap/ubody2d/td-hm_hrnet-w32_8xb64-210e_ubody-256x192.py
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| _base_ = ['../../../_base_/default_runtime.py'] | ||
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| # runtime | ||
| train_cfg = dict(max_epochs=210, val_interval=10) | ||
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| # optimizer | ||
| optim_wrapper = dict(optimizer=dict( | ||
| type='Adam', | ||
| lr=5e-4, | ||
| )) | ||
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| # learning policy | ||
| param_scheduler = [ | ||
| dict( | ||
| type='LinearLR', begin=0, end=500, start_factor=0.001, | ||
| by_epoch=False), # warm-up | ||
| dict( | ||
| type='MultiStepLR', | ||
| begin=0, | ||
| end=210, | ||
| milestones=[170, 200], | ||
| gamma=0.1, | ||
| by_epoch=True) | ||
| ] | ||
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| # automatically scaling LR based on the actual training batch size | ||
| auto_scale_lr = dict(base_batch_size=512) | ||
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| # hooks | ||
| default_hooks = dict( | ||
| checkpoint=dict(save_best='coco-wholebody/AP', rule='greater')) | ||
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| # codec settings | ||
| codec = dict( | ||
| type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) | ||
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| # model settings | ||
| model = dict( | ||
| type='TopdownPoseEstimator', | ||
| data_preprocessor=dict( | ||
| type='PoseDataPreprocessor', | ||
| mean=[123.675, 116.28, 103.53], | ||
| std=[58.395, 57.12, 57.375], | ||
| bgr_to_rgb=True), | ||
| backbone=dict( | ||
| type='HRNet', | ||
| in_channels=3, | ||
| extra=dict( | ||
| stage1=dict( | ||
| num_modules=1, | ||
| num_branches=1, | ||
| block='BOTTLENECK', | ||
| num_blocks=(4, ), | ||
| num_channels=(64, )), | ||
| stage2=dict( | ||
| num_modules=1, | ||
| num_branches=2, | ||
| block='BASIC', | ||
| num_blocks=(4, 4), | ||
| num_channels=(32, 64)), | ||
| stage3=dict( | ||
| num_modules=4, | ||
| num_branches=3, | ||
| block='BASIC', | ||
| num_blocks=(4, 4, 4), | ||
| num_channels=(32, 64, 128)), | ||
| stage4=dict( | ||
| num_modules=3, | ||
| num_branches=4, | ||
| block='BASIC', | ||
| num_blocks=(4, 4, 4, 4), | ||
| num_channels=(32, 64, 128, 256))), | ||
| init_cfg=dict( | ||
| type='Pretrained', | ||
| checkpoint='https://download.openmmlab.com/mmpose/' | ||
| 'pretrain_models/hrnet_w32-36af842e.pth'), | ||
| ), | ||
| head=dict( | ||
| type='HeatmapHead', | ||
| in_channels=32, | ||
| out_channels=133, | ||
| deconv_out_channels=None, | ||
| loss=dict(type='KeypointMSELoss', use_target_weight=True), | ||
| decoder=codec), | ||
| test_cfg=dict( | ||
| flip_test=True, | ||
| flip_mode='heatmap', | ||
| shift_heatmap=True, | ||
| )) | ||
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| # base dataset settings | ||
| dataset_type = 'UBody2dDataset' | ||
| data_mode = 'topdown' | ||
| data_root = 'data/UBody/' | ||
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| scenes = [ | ||
| 'Magic_show', 'Entertainment', 'ConductMusic', 'Online_class', 'TalkShow', | ||
| 'Speech', 'Fitness', 'Interview', 'Olympic', 'TVShow', 'Singing', | ||
| 'SignLanguage', 'Movie', 'LiveVlog', 'VideoConference' | ||
| ] | ||
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| train_datasets = [ | ||
| dict( | ||
| type='CocoWholeBodyDataset', | ||
| data_root='data/coco/', | ||
| data_mode=data_mode, | ||
| ann_file='annotations/coco_wholebody_train_v1.0.json', | ||
| data_prefix=dict(img='train2017/'), | ||
| pipeline=[]) | ||
| ] | ||
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| for scene in scenes: | ||
| train_dataset = dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| data_mode=data_mode, | ||
| ann_file=f'annotations/{scene}/train_annotations.json', | ||
| data_prefix=dict(img='images/'), | ||
| pipeline=[], | ||
| sample_interval=10) | ||
| train_datasets.append(train_dataset) | ||
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| # pipelines | ||
| train_pipeline = [ | ||
| dict(type='LoadImage'), | ||
| dict(type='GetBBoxCenterScale'), | ||
| dict(type='RandomFlip', direction='horizontal'), | ||
| dict(type='RandomHalfBody'), | ||
| dict(type='RandomBBoxTransform'), | ||
| dict(type='TopdownAffine', input_size=codec['input_size']), | ||
| dict(type='GenerateTarget', encoder=codec), | ||
| dict(type='PackPoseInputs') | ||
| ] | ||
| val_pipeline = [ | ||
| dict(type='LoadImage'), | ||
| dict(type='GetBBoxCenterScale'), | ||
| dict(type='TopdownAffine', input_size=codec['input_size']), | ||
| dict(type='PackPoseInputs') | ||
| ] | ||
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| # data loaders | ||
| train_dataloader = dict( | ||
| batch_size=64, | ||
| num_workers=2, | ||
| persistent_workers=True, | ||
| sampler=dict(type='DefaultSampler', shuffle=True), | ||
| dataset=dict( | ||
| type='CombinedDataset', | ||
| metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'), | ||
| datasets=train_datasets, | ||
| pipeline=train_pipeline, | ||
| test_mode=False, | ||
| )) | ||
| val_dataloader = dict( | ||
| batch_size=32, | ||
| num_workers=2, | ||
| persistent_workers=True, | ||
| drop_last=False, | ||
| sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), | ||
| dataset=dict( | ||
| type='CocoWholeBodyDataset', | ||
| ann_file='data/coco/annotations/coco_wholebody_val_v1.0.json', | ||
| data_prefix=dict(img='data/coco/val2017/'), | ||
| pipeline=val_pipeline, | ||
| bbox_file='data/coco/person_detection_results/' | ||
| 'COCO_val2017_detections_AP_H_56_person.json', | ||
| test_mode=True)) | ||
| test_dataloader = val_dataloader | ||
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| val_evaluator = dict( | ||
| type='CocoWholeBodyMetric', | ||
| ann_file='data/coco/annotations/coco_wholebody_val_v1.0.json') | ||
| test_evaluator = val_evaluator |
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