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[Docs] Refine docs #1656
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[Docs] Refine docs #1656
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10343e0
refine docstring and docs
Tau-J f94ca0b
fix typo
Tau-J 201d109
update
Tau-J 9e516e4
refine docs
Tau-J 43ca33d
fix doc
Tau-J 4e8003b
fix dsnt doc
Tau-J b604923
update debias doc
Tau-J 1c7318c
fix typo
Tau-J 978f856
update simcc config
Tau-J 6cb9bd2
add simcc 256x192
Tau-J b9452e7
update simcc config
Tau-J 55ef206
update img
Tau-J d92814a
update img
Tau-J 9f2409d
add mobilenetv2 rle
Tau-J 36ebac6
reorganize config
Tau-J 85fe00d
fix typo
Tau-J 045b43c
add normalize in simcc label
Tau-J f13f7db
refine simcc normalize
Tau-J e1b1ff6
support vipnas head in simcc
Tau-J e2b23af
update vipnas simcc config
Tau-J aedddc6
update algorithm doc
Tau-J 9ce7e3a
add docs
Tau-J 46d0b72
fix normalize
Tau-J 315c75b
update config
Tau-J 0bbac9c
update ipr doc
Tau-J 80e4731
update simcc args
Tau-J 7cac002
update simcc dcos
Tau-J e6a354a
update ckpt and log links
Tau-J 570d97f
fix ut
Tau-J ce40cb4
fix ut
Tau-J 0e67d65
Update docs/zh_cn/overview.md
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,55 @@ | ||
| <!-- [ALGORITHM] --> | ||
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| <details> | ||
| <summary align="right"><a href="https://arxiv.org/abs/2107.03332">SimCC (ECCV'2022)</a></summary> | ||
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| ```bibtex | ||
| @misc{https://doi.org/10.48550/arxiv.2107.03332, | ||
| title={SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation}, | ||
| author={Li, Yanjie and Yang, Sen and Liu, Peidong and Zhang, Shoukui and Wang, Yunxiao and Wang, Zhicheng and Yang, Wankou and Xia, Shu-Tao}, | ||
| year={2021} | ||
| } | ||
| ``` | ||
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| </details> | ||
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| <!-- [BACKBONE] --> | ||
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| <details> | ||
| <summary align="right"><a href="http://openaccess.thecvf.com/content_cvpr_2018/html/Sandler_MobileNetV2_Inverted_Residuals_CVPR_2018_paper.html">MobilenetV2 (CVPR'2018)</a></summary> | ||
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| ```bibtex | ||
| @inproceedings{sandler2018mobilenetv2, | ||
| title={Mobilenetv2: Inverted residuals and linear bottlenecks}, | ||
| author={Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh}, | ||
| booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, | ||
| pages={4510--4520}, | ||
| year={2018} | ||
| } | ||
| ``` | ||
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| </details> | ||
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| <!-- [DATASET] --> | ||
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| <details> | ||
| <summary align="right"><a href="https://link.springer.com/chapter/10.1007/978-3-319-10602-1_48">COCO (ECCV'2014)</a></summary> | ||
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| ```bibtex | ||
| @inproceedings{lin2014microsoft, | ||
| title={Microsoft coco: Common objects in context}, | ||
| author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence}, | ||
| booktitle={European conference on computer vision}, | ||
| pages={740--755}, | ||
| year={2014}, | ||
| organization={Springer} | ||
| } | ||
| ``` | ||
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| </details> | ||
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| Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset | ||
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| | Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AR | AR<sup>50</sup> | ckpt | log | | ||
| | :-------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------: | :-------------------------------------------: | | ||
| | [simcc_mobilenetv2_wo_deconv](/configs/body_2d_keypoint/simcc/coco/simcc_mobilenetv2_wo-deconv-8xb64-210e_coco-256x192.py) | 256x192 | 0.620 | 0.855 | 0.697 | 0.678 | 0.902 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/simcc/coco/simcc_mobilenetv2_wo-deconv-8xb64-210e_coco-256x192-e0cc028d_20220922.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/simcc/coco/simcc_mobilenetv2_wo-deconv-8xb64-210e_coco-256x192-e0cc028d_20220922.log.json) | |
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127 changes: 127 additions & 0 deletions
127
configs/body_2d_keypoint/simcc/coco/simcc_mobilenetv2_wo-deconv-8xb64-210e_coco-256x192.py
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| Original file line number | Diff line number | Diff line change |
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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=train_cfg['max_epochs'], | ||
| 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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| # codec settings | ||
| codec = dict( | ||
| type='SimCCLabel', input_size=(192, 256), sigma=6.0, simcc_split_ratio=2.0) | ||
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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='MobileNetV2', | ||
| widen_factor=1., | ||
| out_indices=(7, ), | ||
| init_cfg=dict( | ||
| type='Pretrained', | ||
| checkpoint='mmcls://mobilenet_v2', | ||
| )), | ||
| head=dict( | ||
| type='SimCCHead', | ||
| in_channels=1280, | ||
| out_channels=17, | ||
| input_size=codec['input_size'], | ||
| in_featuremap_size=(6, 8), | ||
| simcc_split_ratio=codec['simcc_split_ratio'], | ||
| deconv_out_channels=None, | ||
| loss=dict(type='KLDiscretLoss', use_target_weight=True), | ||
| decoder=codec), | ||
| test_cfg=dict(flip_test=True, )) | ||
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| # base dataset settings | ||
| dataset_type = 'CocoDataset' | ||
| data_mode = 'topdown' | ||
| data_root = 'data/coco/' | ||
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| file_client_args = dict(backend='disk') | ||
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| # pipelines | ||
| train_pipeline = [ | ||
| dict(type='LoadImage', file_client_args=file_client_args), | ||
| 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', target_type='keypoint_xy_label', encoder=codec), | ||
| dict(type='PackPoseInputs') | ||
| ] | ||
| val_pipeline = [ | ||
| dict(type='LoadImage', file_client_args=file_client_args), | ||
| 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=dataset_type, | ||
| data_root=data_root, | ||
| data_mode=data_mode, | ||
| ann_file='annotations/person_keypoints_train2017.json', | ||
| data_prefix=dict(img='train2017/'), | ||
| pipeline=train_pipeline, | ||
| )) | ||
| 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=dataset_type, | ||
| data_root=data_root, | ||
| data_mode=data_mode, | ||
| ann_file='annotations/person_keypoints_val2017.json', | ||
| bbox_file=f'{data_root}person_detection_results/' | ||
| 'COCO_val2017_detections_AP_H_56_person.json', | ||
| data_prefix=dict(img='val2017/'), | ||
| test_mode=True, | ||
| pipeline=val_pipeline, | ||
| )) | ||
| test_dataloader = val_dataloader | ||
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| # hooks | ||
| default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) | ||
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| # evaluators | ||
| val_evaluator = dict( | ||
| type='CocoMetric', | ||
| ann_file=data_root + 'annotations/person_keypoints_val2017.json') | ||
| test_evaluator = val_evaluator |
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What about using the default value
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I think
end=train_cfg['max_epochs']is more intuitive for users to understand