[Feature] support DEKR

configs/body/2d_kpt_sview_rgb_img/dekr/README.md

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+# Bottom-up Human Pose Estimation via Disentangled Keypoint Regression (DEKR)
+
+<!-- [ALGORITHM] -->
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/2104.02300">DEKR (CVPR'2021)</a></summary>
+
+```bibtex
+@inproceedings{geng2021bottom,
+  title={Bottom-up human pose estimation via disentangled keypoint regression},
+  author={Geng, Zigang and Sun, Ke and Xiao, Bin and Zhang, Zhaoxiang and Wang, Jingdong},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={14676--14686},
+  year={2021}
+}
+```
+
+</details>
+
+DEKR is a popular 2D bottom-up pose estimation approach that simultaneously detects all the instances and regresses the offsets from the instance centers to joints.
+
+In order to predict the offsets more accurately, the offsets of different joints are regressed using separated branches with deformable convolutional layers. Thus convolution kernels with different shapes are adopted to extract features for the corresponding joint.

configs/body/2d_kpt_sview_rgb_img/dekr/coco/hrnet_coco.md

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+<!-- [ALGORITHM] -->
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/2104.02300">DEKR (CVPR'2021)</a></summary>
+
+```bibtex
+@inproceedings{geng2021bottom,
+  title={Bottom-up human pose estimation via disentangled keypoint regression},
+  author={Geng, Zigang and Sun, Ke and Xiao, Bin and Zhang, Zhaoxiang and Wang, Jingdong},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={14676--14686},
+  year={2021}
+}
+```
+
+</details>
+
+<!-- [ALGORITHM] -->
+
+<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>
+
+```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}
+}
+```
+
+</details>
+
+<!-- [DATASET] -->
+
+<details>
+<summary align="right"><a href="https://link.springer.com/chapter/10.1007/978-3-319-10602-1_48">COCO (ECCV'2014)</a></summary>
+
+```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}
+}
+```
+
+</details>
+
+Results on COCO val2017 without multi-scale test
+
+| Arch                                          | Input Size |  AP   | AP<sup>50</sup> | AP<sup>75</sup> |  AR   | AR<sup>50</sup> |                     ckpt                      |                      log                      |
+| :-------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------: | :-------------------------------------------: |
+| [HRNet-w32](/configs/body/2d_kpt_sview_rgb_img/disentangled_keypoint_regression/coco/hrnet_w32_coco_512x512.py) |  512x512   | 0.680 |      0.868      |      0.745      | 0.728 |      0.897      | [ckpt](https://download.openmmlab.com/mmpose/bottom_up/dekr/hrnet_w32_coco_512x512-2a3056de_20220928.pth) | [log](https://download.openmmlab.com/mmpose/bottom_up/dekr/hrnet_w32_coco_512x512-20220928.log.json) |
+| [HRNet-w48](/configs/body/2d_kpt_sview_rgb_img/disentangled_keypoint_regression/coco/hrnet_w48_coco_640x640.py) |  640x640   | 0.709 |      0.876      |      0.773      | 0.758 |      0.909      | [ckpt](https://download.openmmlab.com/mmpose/bottom_up/dekr/hrnet_w48_coco_640x640-8854b2f1_20220930.pth) | [log](https://download.openmmlab.com/mmpose/bottom_up/dekr/hrnet_w48_coco_640x640-20220930.log.json) |
+
+Results on COCO val2017 with multi-scale test. 3 default scales (\[2, 1, 0.5\]) are used
+
+| Arch                                                                | Input Size |  AP   | AP<sup>50</sup> | AP<sup>75</sup> |  AR   | AR<sup>50</sup> |                                 ckpt                                 |
+| :------------------------------------------------------------------ | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :------------------------------------------------------------------: |
+| [HRNet-w32](/configs/body/2d_kpt_sview_rgb_img/disentangled_keypoint_regression/coco/hrnet_w32_coco_512x512_multiscale.py)\* |  512x512   | 0.705 |      0.878      |      0.767      | 0.759 |      0.921      | [ckpt](https://download.openmmlab.com/mmpose/bottom_up/dekr/hrnet_w32_coco_512x512-2a3056de_20220928.pth) |
+| [HRNet-w48](/configs/body/2d_kpt_sview_rgb_img/disentangled_keypoint_regression/coco/hrnet_w48_coco_640x640_multiscale.py)\* |  640x640   | 0.722 |      0.882      |      0.785      | 0.778 |      0.928      | [ckpt](https://download.openmmlab.com/mmpose/bottom_up/dekr/hrnet_w48_coco_640x640-8854b2f1_20220930.pth) |
+
+\* these configs are generally used for evaluation. The training settings are identical to their single-scale counterparts.
+
+The results of models provided by the authors on COCO val2017 using the same evaluation protocol
+
+| Arch      | Input Size |   Setting    |  AP   | AP<sup>50</sup> | AP<sup>75</sup> |  AR   | AR<sup>50</sup> |                             ckpt                             |
+| :-------- | :--------: | :----------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :----------------------------------------------------------: |
+| HRNet-w32 |  512x512   | single-scale | 0.678 |      0.868      |      0.744      | 0.728 |      0.897      | see [official implementation](https://github.com/HRNet/DEKR) |
+| HRNet-w48 |  640x640   | single-scale | 0.707 |      0.876      |      0.773      | 0.757 |      0.909      | see [official implementation](https://github.com/HRNet/DEKR) |
+| HRNet-w32 |  512x512   | multi-scale  | 0.708 |      0.880      |      0.773      | 0.763 |      0.921      | see [official implementation](https://github.com/HRNet/DEKR) |
+| HRNet-w48 |  640x640   | multi-scale  | 0.721 |      0.881      |      0.786      | 0.779 |      0.927      | see [official implementation](https://github.com/HRNet/DEKR) |
+
+The discrepancy between these results and that shown in paper is attributed to the differences in implementation details in evaluation process.

configs/body/2d_kpt_sview_rgb_img/dekr/coco/hrnet_coco.yml

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+Collections:
+- Name: DEKR
+  Paper:
+    Title: Bottom-up human pose estimation via disentangled keypoint regression
+    URL: https://arxiv.org/abs/2104.02300
+  README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/algorithms/dekr.md
+Models:
+- Config: configs/body/2d_kpt_sview_rgb_img/disentangled_keypoint_regression/coco/hrnet_w32_coco_512x512.py
+  In Collection: DEKR
+  Metadata:
+    Architecture: &id001
+    - DEKR
+    - HRNet
+    Training Data: COCO
+  Name: disentangled_keypoint_regression_hrnet_w32_coco_512x512
+  Results:
+  - Dataset: COCO
+    Metrics:
+      AP: 0.68
+      AP@0.5: 0.868
+      AP@0.75: 0.745
+      AR: 0.728
+      AR@0.5: 0.897
+    Task: Body 2D Keypoint
+  Weights: https://download.openmmlab.com/mmpose/bottom_up/dekr/hrnet_w32_coco_512x512-2a3056de_20220928.pth
+- Config: configs/body/2d_kpt_sview_rgb_img/disentangled_keypoint_regression/coco/hrnet_w48_coco_640x640.py
+  In Collection: DEKR
+  Metadata:
+    Architecture: *id001
+    Training Data: COCO
+  Name: disentangled_keypoint_regression_hrnet_w48_coco_640x640
+  Results:
+  - Dataset: COCO
+    Metrics:
+      AP: 0.709
+      AP@0.5: 0.876
+      AP@0.75: 0.773
+      AR: 0.758
+      AR@0.5: 0.909
+    Task: Body 2D Keypoint
+  Weights: https://download.openmmlab.com/mmpose/bottom_up/dekr/hrnet_w48_coco_640x640-8854b2f1_20220930.pth
+- Config: configs/body/2d_kpt_sview_rgb_img/disentangled_keypoint_regression/coco/hrnet_w32_coco_512x512_multiscale.py
+  In Collection: DEKR
+  Metadata:
+    Architecture: *id001
+    Training Data: COCO
+  Name: disentangled_keypoint_regression_hrnet_w32_coco_512x512_multiscale
+  Results:
+  - Dataset: COCO
+    Metrics:
+      AP: 0.705
+      AP@0.5: 0.878
+      AP@0.75: 0.767
+      AR: 0.759
+      AR@0.5: 0.921
+    Task: Body 2D Keypoint
+  Weights: https://download.openmmlab.com/mmpose/bottom_up/dekr/hrnet_w32_coco_512x512-2a3056de_20220928.pth
+- Config: configs/body/2d_kpt_sview_rgb_img/disentangled_keypoint_regression/coco/hrnet_w48_coco_640x640_multiscale.py
+  In Collection: DEKR
+  Metadata:
+    Architecture: *id001
+    Training Data: COCO
+  Name: disentangled_keypoint_regression_hrnet_w48_coco_640x640_multiscale
+  Results:
+  - Dataset: COCO
+    Metrics:
+      AP: 0.722
+      AP@0.5: 0.882
+      AP@0.75: 0.785
+      AR: 0.778
+      AR@0.5: 0.928
+    Task: Body 2D Keypoint
+  Weights: https://download.openmmlab.com/mmpose/bottom_up/dekr/hrnet_w48_coco_640x640-8854b2f1_20220930.pth

configs/body/2d_kpt_sview_rgb_img/dekr/coco/hrnet_w32_coco_512x512.py

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+_base_ = [
+    '../../../../_base_/default_runtime.py',
+    '../../../../_base_/datasets/coco.py'
+]
+checkpoint_config = dict(interval=20)
+evaluation = dict(interval=20, metric='mAP', save_best='AP')
+
+optimizer = dict(
+    type='Adam',
+    lr=0.001,
+)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[90, 120])
+total_epochs = 140
+channel_cfg = dict(
+    dataset_joints=17,
+    dataset_channel=[
+        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
+    ],
+    inference_channel=[
+        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
+    ])
+
+data_cfg = dict(
+    image_size=512,
+    base_size=256,
+    base_sigma=2,
+    heatmap_size=[128],
+    num_joints=channel_cfg['dataset_joints'],
+    dataset_channel=channel_cfg['dataset_channel'],
+    inference_channel=channel_cfg['inference_channel'],
+    num_scales=1,
+    scale_aware_sigma=False,
+)
+
+# model settings
+model = dict(
+    type='DisentangledKeypointRegressor',
+    pretrained='https://download.openmmlab.com/mmpose/'
+    'pretrain_models/hrnet_w32-36af842e.pth',
+    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),
+                multiscale_output=True)),
+    ),
+    keypoint_head=dict(
+        type='DEKRHead',
+        in_channels=(32, 64, 128, 256),
+        in_index=(0, 1, 2, 3),
+        num_heatmap_filters=32,
+        num_joints=channel_cfg['dataset_joints'],
+        input_transform='resize_concat',
+        heatmap_loss=dict(
+            type='JointsMSELoss',
+            use_target_weight=True,
+            loss_weight=1.0,
+        ),
+        offset_loss=dict(
+            type='SoftWeightSmoothL1Loss',
+            use_target_weight=True,
+            supervise_empty=False,
+            loss_weight=0.002,
+            beta=1 / 9.0,
+        )),
+    train_cfg=dict(),
+    test_cfg=dict(
+        num_joints=channel_cfg['dataset_joints'],
+        max_num_people=30,
+        project2image=False,
+        align_corners=False,
+        max_pool_kernel=5,
+        use_nms=True,
+        nms_dist_thr=0.05,
+        nms_joints_thr=8,
+        keypoint_threshold=0.01,
+        rescore_cfg=dict(
+            in_channels=74,
+            norm_indexes=(5, 6),
+            pretrained='https://download.openmmlab.com/mmpose/'
+            'pretrain_models/kpt_rescore_coco-33d58c5c.pth'),
+        flip_test=True))
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='BottomUpRandomAffine',
+        rot_factor=30,
+        scale_factor=[0.75, 1.5],
+        scale_type='short',
+        trans_factor=40),
+    dict(type='BottomUpRandomFlip', flip_prob=0.5),
+    dict(type='ToTensor'),
+    dict(
+        type='NormalizeTensor',
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]),
+    dict(type='GetKeypointCenterArea'),
+    dict(
+        type='BottomUpGenerateHeatmapTarget',
+        sigma=(2, 4),
+        gen_center_heatmap=True,
+        bg_weight=0.1,
+    ),
+    dict(
+        type='BottomUpGenerateOffsetTarget',
+        radius=4,
+    ),
+    dict(
+        type='Collect',
+        keys=['img', 'heatmaps', 'masks', 'offsets', 'offset_weights'],
+        meta_keys=[]),
+]
+
+val_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='BottomUpGetImgSize', test_scale_factor=[1]),
+    dict(
+        type='BottomUpResizeAlign',
+        transforms=[
+            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', 'aug_data', 'test_scale_factor', 'base_size',
+            'center', 'scale', 'flip_index', 'num_joints', 'skeleton',
+            'image_size', 'heatmap_size'
+        ]),
+]
+
+test_pipeline = val_pipeline
+
+data_root = 'data/coco'
+data = dict(
+    workers_per_gpu=4,
+    train_dataloader=dict(samples_per_gpu=10),
+    val_dataloader=dict(samples_per_gpu=1),
+    test_dataloader=dict(samples_per_gpu=1),
+    train=dict(
+        type='BottomUpCocoDataset',
+        ann_file=f'{data_root}/annotations/person_keypoints_train2017.json',
+        img_prefix=f'{data_root}/train2017/',
+        data_cfg=data_cfg,
+        pipeline=train_pipeline,
+        dataset_info={{_base_.dataset_info}}),
+    val=dict(
+        type='BottomUpCocoDataset',
+        ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
+        img_prefix=f'{data_root}/val2017/',
+        data_cfg=data_cfg,
+        pipeline=val_pipeline,
+        dataset_info={{_base_.dataset_info}}),
+    test=dict(
+        type='BottomUpCocoDataset',
+        ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
+        img_prefix=f'{data_root}/val2017/',
+        data_cfg=data_cfg,
+        pipeline=test_pipeline,
+        dataset_info={{_base_.dataset_info}}),
+)