[Feature] Add RTMPose-Wholebody

configs/wholebody_2d_keypoint/rtmpose/cocktail13/rtmw_cocktail13.md

@@ -0,0 +1,76 @@
+<!-- [ALGORITHM] -->
+
+<details>
+<summary align="right"><a href="https://link.springer.com/chapter/10.1007/978-3-030-58580-8_27">RTMPose (arXiv'2023)</a></summary>
+
+```bibtex
+@misc{https://doi.org/10.48550/arxiv.2303.07399,
+  doi = {10.48550/ARXIV.2303.07399},
+  url = {https://arxiv.org/abs/2303.07399},
+  author = {Jiang, Tao and Lu, Peng and Zhang, Li and Ma, Ningsheng and Han, Rui and Lyu, Chengqi and Li, Yining and Chen, Kai},
+  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  title = {RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose},
+  publisher = {arXiv},
+  year = {2023},
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+
+```
+
+</details>
+
+<!-- [BACKBONE] -->
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/2212.07784">RTMDet (arXiv'2022)</a></summary>
+
+```bibtex
+@misc{lyu2022rtmdet,
+      title={RTMDet: An Empirical Study of Designing Real-Time Object Detectors},
+      author={Chengqi Lyu and Wenwei Zhang and Haian Huang and Yue Zhou and Yudong Wang and Yanyi Liu and Shilong Zhang and Kai Chen},
+      year={2022},
+      eprint={2212.07784},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+</details>
+
+<!-- [DATASET] -->
+
+<details>
+<summary align="right"><a href="https://link.springer.com/chapter/10.1007/978-3-030-58545-7_12">COCO-WholeBody (ECCV'2020)</a></summary>
+
+```bibtex
+@inproceedings{jin2020whole,
+  title={Whole-Body Human Pose Estimation in the Wild},
+  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
+  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
+  year={2020}
+}
+```
+
+</details>
+
+- `Cocktail13` denotes model trained on 13 public datasets:
+  - [AI Challenger](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#aic)
+  - [CrowdPose](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#crowdpose)
+  - [MPII](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#mpii)
+  - [sub-JHMDB](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#sub-jhmdb-dataset)
+  - [Halpe](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_wholebody_keypoint.html#halpe)
+  - [PoseTrack18](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#posetrack18)
+  - [COCO-Wholebody](https://github.com/jin-s13/COCO-WholeBody/)
+  - [UBody](https://github.com/IDEA-Research/OSX)
+  - [Human-Art](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#human-art-dataset)
+  - [WFLW](https://wywu.github.io/projects/LAB/WFLW.html)
+  - [300W](https://ibug.doc.ic.ac.uk/resources/300-W/)
+  - [COFW](http://www.vision.caltech.edu/xpburgos/ICCV13/)
+  - [LaPa](https://github.com/JDAI-CV/lapa-dataset)
+
+Results on COCO-WholeBody v1.0 val with detector having human AP of 56.4 on COCO val2017 dataset
+
+| 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                   |
+| :-------------------------------------- | :--------: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :------: | :------: | :--------------------------------------: | :-------------------------------------: |
+| [rtmw-x](/configs/wholebody_2d_keypoint/rtmpose/cocktail13/rtmw-x_8xb320-270e_cocktail13-256x192.py) |  256x192   |  0.753  |  0.815  |  0.773  |  0.869  |  0.843  |  0.894  |  0.602  |  0.703  |  0.672   |  0.754   | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-x_simcc-cocktail13_pt-ucoco_270e-256x192-fbef0d61_20230925.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-x_simcc-cocktail13_pt-ucoco_270e-256x192-fbef0d61_20230925.json) |
+| [rtmw-x](/configs/wholebody_2d_keypoint/rtmpose/cocktail13/rtmw-x_8xb320-270e_cocktail13-384x288.py) |  384x288   |  0.764  |  0.825  |  0.791  |  0.883  |  0.882  |  0.922  |  0.654  |  0.744  |  0.702   |  0.779   | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-x_simcc-cocktail13_pt-ucoco_270e-384x288-0949e3a9_20230925.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-x_simcc-cocktail13_pt-ucoco_270e-384x288-0949e3a9_20230925.json) |

mmpose/datasets/transforms/__init__.py

@@ -6,7 +6,7 @@
                                 GenerateTarget, GetBBoxCenterScale,
                                 PhotometricDistortion, RandomBBoxTransform,
                                 RandomFlip, RandomHalfBody, YOLOXHSVRandomAug)
-from .converting import KeypointConverter
+from .converting import KeypointConverter, SingleHandConverter
 from .formatting import PackPoseInputs
 from .hand_transforms import HandRandomFlip
 from .loading import LoadImage
@@ -21,5 +21,6 @@
     'BottomupGetHeatmapMask', 'BottomupRandomAffine', 'BottomupResize',
     'GenerateTarget', 'KeypointConverter', 'RandomFlipAroundRoot',
     'FilterAnnotations', 'YOLOXHSVRandomAug', 'YOLOXMixUp', 'Mosaic',
-    'BottomupRandomCrop', 'BottomupRandomChoiceResize', 'HandRandomFlip'
+    'BottomupRandomCrop', 'BottomupRandomChoiceResize', 'HandRandomFlip',
+    'SingleHandConverter'
 ]

mmpose/datasets/transforms/common_transforms.py

@@ -503,21 +503,26 @@ def _get_transform_params(self, num_bboxes: int) -> Tuple:
             - scale (np.ndarray): Scaling factor of each bbox in shape (n, 1)
             - rotate (np.ndarray): Rotation degree of each bbox in shape (n,)
         """
+        random_v = self._truncnorm(size=(num_bboxes, 4))
+        offset_v = random_v[:, :2]
+        scale_v = random_v[:, 2:3]
+        rotate_v = random_v[:, 3]
+
         # Get shift parameters
-        offset = self._truncnorm(size=(num_bboxes, 2)) * self.shift_factor
+        offset = offset_v * self.shift_factor
         offset = np.where(
             np.random.rand(num_bboxes, 1) < self.shift_prob, offset, 0.)
 
         # Get scaling parameters
         scale_min, scale_max = self.scale_factor
         mu = (scale_max + scale_min) * 0.5
         sigma = (scale_max - scale_min) * 0.5
-        scale = self._truncnorm(size=(num_bboxes, 1)) * sigma + mu
+        scale = scale_v * sigma + mu
         scale = np.where(
             np.random.rand(num_bboxes, 1) < self.scale_prob, scale, 1.)
 
         # Get rotation parameters
-        rotate = self._truncnorm(size=(num_bboxes, )) * self.rotate_factor
+        rotate = rotate_v * self.rotate_factor
         rotate = np.where(
             np.random.rand(num_bboxes) < self.rotate_prob, rotate, 0.)
 

mmpose/datasets/transforms/converting.py

@@ -62,7 +62,10 @@ def __init__(self, num_keypoints: int,
                                                                   int]]]):
         self.num_keypoints = num_keypoints
         self.mapping = mapping
-        source_index, target_index = zip(*mapping)
+        if len(mapping):
+            source_index, target_index = zip(*mapping)
+        else:
+            source_index, target_index = [], []
 
         src1, src2 = [], []
         interpolation = False
@@ -89,6 +92,9 @@ def __init__(self, num_keypoints: int,
     def transform(self, results: dict) -> dict:
         """Transforms the keypoint results to match the target keypoints."""
         num_instances = results['keypoints'].shape[0]
+        if len(results['keypoints_visible'].shape) > 2:
+            results['keypoints_visible'] = results['keypoints_visible'][:, :,
+                                                                        0]
 
         # Initialize output arrays
         keypoints = np.zeros((num_instances, self.num_keypoints, 3))
@@ -186,3 +192,83 @@ def __repr__(self) -> str:
         repr_str += f'(num_keypoints={self.num_keypoints}, '\
                     f'mapping={self.mapping})'
         return repr_str
+
+
+@TRANSFORMS.register_module()
+class SingleHandConverter(BaseTransform):
+    """Mapping a single hand keypoints into double hands according to the given
+    mapping and hand type.
+
+    Required Keys:
+
+        - keypoints
+        - keypoints_visible
+        - hand_type
+
+    Modified Keys:
+
+        - keypoints
+        - keypoints_visible
+
+    Args:
+        num_keypoints (int): The number of keypoints in target dataset.
+        left_hand_mapping (list): A list containing mapping indexes. Each
+            element has format (source_index, target_index)
+        right_hand_mapping (list): A list containing mapping indexes. Each
+            element has format (source_index, target_index)
+
+    Example:
+        >>> import numpy as np
+        >>> self = SingleHandConverter(
+        >>>     num_keypoints=42,
+        >>>     left_hand_mapping=[
+        >>>         (0, 0), (1, 1), (2, 2), (3, 3)
+        >>>     ],
+        >>>     right_hand_mapping=[
+        >>>         (0, 21), (1, 22), (2, 23), (3, 24)
+        >>>     ])
+        >>> results = dict(
+        >>>     keypoints=np.arange(84).reshape(2, 21, 2),
+        >>>     keypoints_visible=np.arange(84).reshape(2, 21, 2) % 2,
+        >>>     hand_type=np.array([[0, 1], [1, 0]]))
+        >>> results = self(results)
+    """
+
+    def __init__(self, num_keypoints: int,
+                 left_hand_mapping: Union[List[Tuple[int, int]],
+                                          List[Tuple[Tuple, int]]],
+                 right_hand_mapping: Union[List[Tuple[int, int]],
+                                           List[Tuple[Tuple, int]]]):
+        self.num_keypoints = num_keypoints
+        self.left_hand_converter = KeypointConverter(num_keypoints,
+                                                     left_hand_mapping)
+        self.right_hand_converter = KeypointConverter(num_keypoints,
+                                                      right_hand_mapping)
+
+    def transform(self, results: dict) -> dict:
+        """Transforms the keypoint results to match the target keypoints."""
+        assert 'hand_type' in results, (
+            'hand_type should be provided in results')
+        hand_type = results['hand_type']
+
+        if np.sum(hand_type - [[0, 1]]) <= 1e-6:
+            # left hand
+            results = self.left_hand_converter(results)
+        elif np.sum(hand_type - [[1, 0]]) <= 1e-6:
+            results = self.right_hand_converter(results)
+        else:
+            raise ValueError('hand_type should be left or right')
+
+        return results
+
+    def __repr__(self) -> str:
+        """print the basic information of the transform.
+
+        Returns:
+            str: Formatted string.
+        """
+        repr_str = self.__class__.__name__
+        repr_str += f'(num_keypoints={self.num_keypoints}, '\
+                    f'left_hand_converter={self.left_hand_converter}, '\
+                    f'right_hand_converter={self.right_hand_converter})'
+        return repr_str

mmpose/models/heads/__init__.py

@@ -1,6 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .base_head import BaseHead
-from .coord_cls_heads import RTMCCHead, SimCCHead
+from .coord_cls_heads import RTMCCHead, RTMWHead, SimCCHead
 from .heatmap_heads import (AssociativeEmbeddingHead, CIDHead, CPMHead,
                             HeatmapHead, InternetHead, MSPNHead, ViPNASHead)
 from .hybrid_heads import DEKRHead, VisPredictHead
@@ -16,5 +16,5 @@
     'DSNTHead', 'AssociativeEmbeddingHead', 'DEKRHead', 'VisPredictHead',
     'CIDHead', 'RTMCCHead', 'TemporalRegressionHead',
     'TrajectoryRegressionHead', 'MotionRegressionHead', 'EDPoseHead',
-    'InternetHead'
+    'InternetHead', 'RTMWHead'
 ]

mmpose/models/heads/coord_cls_heads/__init__.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .rtmcc_head import RTMCCHead
+from .rtmw_head import RTMWHead
 from .simcc_head import SimCCHead
 
-__all__ = ['SimCCHead', 'RTMCCHead']
+__all__ = ['SimCCHead', 'RTMCCHead', 'RTMWHead']