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Merge branch 'dev-1.x' of https://github.com/open-mmlab/mmpose into dev-1.x
2 parents 9e5aa6b + 7bfa713 commit 0e27c3b

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‎configs/_base_/datasets/exlpose.py‎

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dataset_info = dict(
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dataset_name='exlpose',
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paper_info=dict(
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author='Sohyun Lee, Jaesung Rim, Boseung Jeong, Geonu Kim,'
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'ByungJu Woo, Haechan Lee, Sunghyun Cho, Suha Kwak',
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title='Human Pose Estimation in Extremely Low-Light Conditions',
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container='arXiv',
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year='2023',
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homepage='https://arxiv.org/abs/2303.15410',
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),
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keypoint_info={
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0:
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dict(
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name='left_shoulder',
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id=0,
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color=[0, 255, 0],
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type='upper',
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swap='right_shoulder'),
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1:
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dict(
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name='right_shoulder',
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id=1,
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color=[255, 128, 0],
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type='upper',
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swap='left_shoulder'),
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2:
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dict(
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name='left_elbow',
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id=2,
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color=[0, 255, 0],
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type='upper',
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swap='right_elbow'),
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3:
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dict(
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name='right_elbow',
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id=3,
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color=[255, 128, 0],
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type='upper',
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swap='left_elbow'),
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4:
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dict(
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name='left_wrist',
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id=4,
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color=[0, 255, 0],
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type='upper',
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swap='right_wrist'),
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5:
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dict(
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name='right_wrist',
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id=5,
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color=[255, 128, 0],
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type='upper',
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swap='left_wrist'),
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6:
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dict(
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name='left_hip',
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id=6,
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color=[0, 255, 0],
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type='lower',
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swap='right_hip'),
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7:
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dict(
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name='right_hip',
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id=7,
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color=[255, 128, 0],
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type='lower',
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swap='left_hip'),
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8:
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dict(
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name='left_knee',
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id=8,
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color=[0, 255, 0],
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type='lower',
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swap='right_knee'),
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9:
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dict(
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name='right_knee',
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id=9,
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color=[255, 128, 0],
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type='lower',
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swap='left_knee'),
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10:
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dict(
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name='left_ankle',
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id=10,
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color=[0, 255, 0],
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type='lower',
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swap='right_ankle'),
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11:
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dict(
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name='right_ankle',
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id=11,
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color=[255, 128, 0],
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type='lower',
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swap='left_ankle'),
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12:
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dict(name='head', id=12, color=[51, 153, 255], type='upper', swap=''),
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13:
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dict(name='neck', id=13, color=[51, 153, 255], type='upper', swap='')
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},
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skeleton_info={
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0: dict(link=('head', 'neck'), id=0, color=[51, 153, 255]),
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1: dict(link=('neck', 'left_shoulder'), id=1, color=[51, 153, 255]),
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2: dict(link=('neck', 'right_shoulder'), id=2, color=[51, 153, 255]),
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3: dict(link=('left_shoulder', 'left_elbow'), id=3, color=[0, 255, 0]),
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4: dict(link=('left_elbow', 'left_wrist'), id=4, color=[0, 255, 0]),
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5: dict(
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link=('right_shoulder', 'right_elbow'), id=5, color=[255, 128, 0]),
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6:
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dict(link=('right_elbow', 'right_wrist'), id=6, color=[255, 128, 0]),
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7: dict(link=('neck', 'right_hip'), id=7, color=[51, 153, 255]),
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8: dict(link=('neck', 'left_hip'), id=8, color=[51, 153, 255]),
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9: dict(link=('right_hip', 'right_knee'), id=9, color=[255, 128, 0]),
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10:
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dict(link=('right_knee', 'right_ankle'), id=10, color=[255, 128, 0]),
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11: dict(link=('left_hip', 'left_knee'), id=11, color=[0, 255, 0]),
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12: dict(link=('left_knee', 'left_ankle'), id=12, color=[0, 255, 0]),
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},
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joint_weights=[
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0.2, 0.2, 0.2, 1.3, 1.5, 0.2, 1.3, 1.5, 0.2, 0.2, 0.5, 0.2, 0.2, 0.5
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],
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sigmas=[
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0.079, 0.079, 0.072, 0.072, 0.062, 0.062, 0.107, 0.107, 0.087, 0.087,
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0.089, 0.089, 0.079, 0.079
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])

‎configs/body_2d_keypoint/topdown_heatmap/exlpose/hrnet_exlpose.md‎

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<!-- [ALGORITHM] -->
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<details>
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<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
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@inproceedings{sun2019deep,
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title={Deep high-resolution representation learning for human pose estimation},
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author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
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booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
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pages={5693--5703},
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year={2019}
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}
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```
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</details>
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<!-- [DATASET] -->
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<details>
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<summary align="right"><a href="http://cg.postech.ac.kr/research/ExLPose/">ExLPose (2023)</a></summary>
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```bibtex
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@inproceedings{ExLPose_2023_CVPR,
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title={Human Pose Estimation in Extremely Low-Light Conditions},
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author={Sohyun Lee, Jaesung Rim, Boseung Jeong, Geonu Kim, ByungJu Woo, Haechan Lee, Sunghyun Cho, Suha Kwak},
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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year={2023}
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}
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```
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</details>
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Results on ExLPose-LLA val set with ground-truth bounding boxes
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| Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AR | AR<sup>50</sup> | ckpt | log |
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| :-------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------: | :-------------------------------------------: |
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| [pose_hrnet_w32](/configs/body_2d_keypoint/topdown_heatmap/exlpose/td-hm_hrnet-w32_8xb64-210e_exlpose-256x192.py) | 256x192 | 0.401 | 0.64 | 0.40 | 0.452 | 0.693 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/exlpose/td-hm_hrnet-w32_8xb64-210e_exlpose-ll-256x192.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/exlpose/td-hm_hrnet-w32_8xb64-210e_exlpose-ll-256x192.json) |

‎configs/body_2d_keypoint/topdown_heatmap/exlpose/hrnet_exlpose.yml‎

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Models:
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- Config: configs/body_2d_keypoint/topdown_heatmap/exlpose/td-hm_hrnet-w32_8xb64-210e_exlpose-256x192.py
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In Collection: HRNet
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Metadata:
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Architecture:
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- HRNet
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Training Data: ExLPose-LL
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Name: td-hm_hrnet-w32_8xb64-210e_exlpose-256x192
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Results:
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- Dataset: ExLPose
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Metrics:
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AP: 0.401
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[email protected]: 0.64
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[email protected]: 0.40
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AR: 0.452
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[email protected]: 0.693
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Task: Body 2D Keypoint
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Weights: https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/exlpose/td-hm_hrnet-w32_8xb64-210e_exlpose-ll-256x192.pth

‎configs/body_2d_keypoint/topdown_heatmap/exlpose/td-hm_hrnet-w32_8xb64-210e_exlpose-256x192.py‎

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_base_ = ['../../../_base_/default_runtime.py']
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# runtime
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train_cfg = dict(max_epochs=210, val_interval=10)
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# optimizer
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optim_wrapper = dict(optimizer=dict(
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type='Adam',
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lr=5e-4,
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))
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# learning policy
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param_scheduler = [
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dict(
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type='LinearLR', begin=0, end=500, start_factor=0.001,
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by_epoch=False), # warm-up
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dict(
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type='MultiStepLR',
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begin=0,
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end=210,
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milestones=[170, 200],
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gamma=0.1,
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by_epoch=True)
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]
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# automatically scaling LR based on the actual training batch size
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auto_scale_lr = dict(base_batch_size=512)
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# hooks
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default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater'))
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# codec settings
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codec = dict(
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type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)
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# model settings
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model = dict(
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type='TopdownPoseEstimator',
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data_preprocessor=dict(
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type='PoseDataPreprocessor',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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bgr_to_rgb=True),
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backbone=dict(
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type='HRNet',
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in_channels=3,
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extra=dict(
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stage1=dict(
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num_modules=1,
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num_branches=1,
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block='BOTTLENECK',
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num_blocks=(4, ),
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num_channels=(64, )),
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stage2=dict(
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num_modules=1,
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num_branches=2,
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block='BASIC',
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num_blocks=(4, 4),
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num_channels=(32, 64)),
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stage3=dict(
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num_modules=4,
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num_branches=3,
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block='BASIC',
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num_blocks=(4, 4, 4),
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num_channels=(32, 64, 128)),
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stage4=dict(
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num_modules=3,
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num_branches=4,
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block='BASIC',
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num_blocks=(4, 4, 4, 4),
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num_channels=(32, 64, 128, 256))),
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init_cfg=dict(
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type='Pretrained',
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checkpoint='https://download.openmmlab.com/mmpose/'
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'pretrain_models/hrnet_w32-36af842e.pth'),
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),
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head=dict(
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type='HeatmapHead',
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in_channels=32,
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out_channels=14,
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deconv_out_channels=None,
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loss=dict(type='KeypointMSELoss', use_target_weight=True),
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decoder=codec),
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test_cfg=dict(
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flip_test=True,
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flip_mode='heatmap',
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shift_heatmap=True,
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))
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# base dataset settings
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dataset_type = 'ExlposeDataset'
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data_mode = 'topdown'
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data_root = 'data/ExLPose/'
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# pipelines
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train_pipeline = [
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dict(type='LoadImage'),
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dict(type='GetBBoxCenterScale'),
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dict(type='RandomFlip', direction='horizontal'),
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dict(type='RandomHalfBody'),
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dict(type='RandomBBoxTransform'),
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dict(type='TopdownAffine', input_size=codec['input_size']),
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dict(type='GenerateTarget', encoder=codec),
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dict(type='PackPoseInputs')
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]
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val_pipeline = [
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dict(type='LoadImage'),
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dict(type='GetBBoxCenterScale'),
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dict(type='TopdownAffine', input_size=codec['input_size']),
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dict(type='PackPoseInputs')
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]
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# data loaders
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train_dataloader = dict(
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batch_size=64,
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num_workers=2,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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data_mode=data_mode,
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ann_file='annotations/ExLPose/ExLPose_train_LL.json',
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data_prefix=dict(img=''),
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pipeline=train_pipeline,
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))
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val_dataloader = dict(
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batch_size=32,
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num_workers=2,
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persistent_workers=True,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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data_mode=data_mode,
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ann_file='annotations/ExLPose/ExLPose_test_LL-A.json',
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data_prefix=dict(img=''),
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test_mode=True,
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pipeline=val_pipeline,
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))
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test_dataloader = val_dataloader
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# evaluators
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val_evaluator = dict(
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type='CocoMetric',
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ann_file=data_root + 'annotations/ExLPose/ExLPose_test_LL-A.json',
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use_area=False)
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test_evaluator = val_evaluator

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