[Refactor] Update CrowdPose evaluation results

configs/body_2d_keypoint/topdown_heatmap/crowdpose/hrnet_crowdpose.md

@@ -33,6 +33,6 @@
 
 Results on CrowdPose test with [YOLOv3](https://github.com/eriklindernoren/PyTorch-YOLOv3) human detector
 
-| Arch                                          | Input Size |  AP   | AP<sup>50</sup> | AP<sup>75</sup> |  AR   | AR<sup>50</sup> |                     ckpt                      |                      log                      |
-| :-------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------: | :-------------------------------------------: |
-| [pose_hrnet_w32](/configs/body_2d_keypoint/topdown_heatmap/crowdpose/td-hm_hrnet-w32_8xb64-210e_crowdpose-256x192.py) |  256x192   | 0.675 |      0.825      |      0.729      | 0.816 |      0.769      | [ckpt](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_crowdpose_256x192-960be101_20201227.pth) | [log](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_crowdpose_256x192_20201227.log.json) |
+| Arch                                           | Input Size |  AP   | AP<sup>50</sup> | AP<sup>75</sup> | AP (E) | AP (M) | AP (H) |                      ckpt                      |                      log                      |
+| :--------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :----: | :----: | :----: | :--------------------------------------------: | :-------------------------------------------: |
+| [pose_hrnet_w32](/configs/body_2d_keypoint/topdown_heatmap/crowdpose/td-hm_hrnet-w32_8xb64-210e_crowdpose-256x192.py) |  256x192   | 0.675 |      0.825      |      0.729      | 0.770  | 0.687  | 0.553  | [ckpt](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_crowdpose_256x192-960be101_20201227.pth) | [log](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_crowdpose_256x192_20201227.log.json) |

configs/body_2d_keypoint/topdown_heatmap/crowdpose/resnet_crowdpose.md

@@ -50,9 +50,9 @@
 
 Results on CrowdPose test with [YOLOv3](https://github.com/eriklindernoren/PyTorch-YOLOv3) human detector
 
-| Arch                                          | Input Size |  AP   | AP<sup>50</sup> | AP<sup>75</sup> |  AR   | AR<sup>50</sup> |                     ckpt                      |                      log                      |
-| :-------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------: | :-------------------------------------------: |
-| [pose_resnet_50](/configs/body_2d_keypoint/topdown_heatmap/crowdpose/td-hm_res50_8xb64-210e_crowdpose-256x192.py) |  256x192   | 0.637 |      0.808      |      0.692      | 0.785 |      0.738      | [ckpt](https://download.openmmlab.com/mmpose/top_down/resnet/res50_crowdpose_256x192-c6a526b6_20201227.pth) | [log](https://download.openmmlab.com/mmpose/top_down/resnet/res50_crowdpose_256x192_20201227.log.json) |
-| [pose_resnet_101](/configs/body_2d_keypoint/topdown_heatmap/crowdpose/td-hm_res101_8xb64-210e_crowdpose-256x192.py) |  256x192   | 0.647 |      0.810      |      0.703      | 0.796 |      0.746      | [ckpt](https://download.openmmlab.com/mmpose/top_down/resnet/res101_crowdpose_256x192-8f5870f4_20201227.pth) | [log](https://download.openmmlab.com/mmpose/top_down/resnet/res101_crowdpose_256x192_20201227.log.json) |
-| [pose_resnet_101](/configs/body_2d_keypoint/topdown_heatmap/crowdpose/td-hm_res101_8xb64-210e_crowdpose-320x256.py) |  320x256   | 0.661 |      0.821      |      0.714      | 0.800 |      0.759      | [ckpt](https://download.openmmlab.com/mmpose/top_down/resnet/res101_crowdpose_320x256-c88c512a_20201227.pth) | [log](https://download.openmmlab.com/mmpose/top_down/resnet/res101_crowdpose_320x256_20201227.log.json) |
-| [pose_resnet_152](/configs/body_2d_keypoint/topdown_heatmap/crowdpose/td-hm_res152_8xb64-210e_crowdpose-256x192.py) |  256x192   | 0.656 |      0.818      |      0.712      | 0.803 |      0.754      | [ckpt](https://download.openmmlab.com/mmpose/top_down/resnet/res152_crowdpose_256x192-dbd49aba_20201227.pth) | [log](https://download.openmmlab.com/mmpose/top_down/resnet/res152_crowdpose_256x192_20201227.log.json) |
+| Arch                                           | Input Size |  AP   | AP<sup>50</sup> | AP<sup>75</sup> | AP (E) | AP (M) | AP (H) |                      ckpt                      |                      log                      |
+| :--------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :----: | :----: | :----: | :--------------------------------------------: | :-------------------------------------------: |
+| [pose_resnet_50](/configs/body_2d_keypoint/topdown_heatmap/crowdpose/td-hm_res50_8xb64-210e_crowdpose-256x192.py) |  256x192   | 0.637 |      0.808      |      0.692      | 0.738  | 0.650  | 0.506  | [ckpt](https://download.openmmlab.com/mmpose/top_down/resnet/res50_crowdpose_256x192-c6a526b6_20201227.pth) | [log](https://download.openmmlab.com/mmpose/top_down/resnet/res50_crowdpose_256x192_20201227.log.json) |
+| [pose_resnet_101](/configs/body_2d_keypoint/topdown_heatmap/crowdpose/td-hm_res101_8xb64-210e_crowdpose-256x192.py) |  256x192   | 0.647 |      0.810      |      0.703      | 0.745  | 0.658  | 0.521  | [ckpt](https://download.openmmlab.com/mmpose/top_down/resnet/res101_crowdpose_256x192-8f5870f4_20201227.pth) | [log](https://download.openmmlab.com/mmpose/top_down/resnet/res101_crowdpose_256x192_20201227.log.json) |
+| [pose_resnet_101](/configs/body_2d_keypoint/topdown_heatmap/crowdpose/td-hm_res101_8xb64-210e_crowdpose-320x256.py) |  320x256   | 0.661 |      0.821      |      0.714      | 0.759  | 0.672  | 0.534  | [ckpt](https://download.openmmlab.com/mmpose/top_down/resnet/res101_crowdpose_320x256-c88c512a_20201227.pth) | [log](https://download.openmmlab.com/mmpose/top_down/resnet/res101_crowdpose_320x256_20201227.log.json) |
+| [pose_resnet_152](/configs/body_2d_keypoint/topdown_heatmap/crowdpose/td-hm_res152_8xb64-210e_crowdpose-256x192.py) |  256x192   | 0.656 |      0.818      |      0.712      | 0.754  | 0.666  | 0.533  | [ckpt](https://download.openmmlab.com/mmpose/top_down/resnet/res152_crowdpose_256x192-dbd49aba_20201227.pth) | [log](https://download.openmmlab.com/mmpose/top_down/resnet/res152_crowdpose_256x192_20201227.log.json) |

mmpose/datasets/datasets/wholebody/coco_wholebody_dataset.py

@@ -1,4 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import copy
 import os.path as osp
 from typing import Optional
 
@@ -117,6 +118,10 @@ def parse_data_info(self, raw_data_info: dict) -> Optional[dict]:
             'iscrowd': ann['iscrowd'],
             'segmentation': ann['segmentation'],
             'id': ann['id'],
+            'category_id': ann['category_id'],
+            # store the raw annotation of the instance
+            # it is useful for evaluation without providing ann_file
+            'raw_ann_info': copy.deepcopy(ann),
         }
 
         return data_info

mmpose/evaluation/metrics/coco_metric.py

@@ -18,7 +18,7 @@
 
 @METRICS.register_module()
 class CocoMetric(BaseMetric):
-    """COCO evaluation metric.
+    """COCO pose estimation task evaluation metric.
 
     Evaluate AR, AP, and mAP for keypoint detection tasks. Support COCO
     dataset and other datasets in COCO format. Please refer to
@@ -32,7 +32,7 @@ class CocoMetric(BaseMetric):
         use_area (bool): Whether to use ``'area'`` message in the annotations.
             If the ground truth annotations (e.g. CrowdPose, AIC) do not have
             the field ``'area'``, please set ``use_area=False``.
-            Default: ``True``
+            Defaults to ``True``
         iou_type (str): The same parameter as `iouType` in
             :class:`xtcocotools.COCOeval`, which can be ``'keypoints'``, or
             ``'keypoints_crowd'`` (used in CrowdPose dataset).
@@ -72,10 +72,9 @@ class CocoMetric(BaseMetric):
             test submission when the ground truth annotations are absent. If
             set to ``True``, ``outfile_prefix`` should specify the path to
             store the output results. Defaults to ``False``
-        outfile_prefix (str, optional): The prefix of json files. It includes
+        outfile_prefix (str | None): The prefix of json files. It includes
             the file path and the prefix of filename, e.g., ``'a/b/prefix'``.
-            If not specified, a temp file will be created.
-            Defaults to ``None``
+            If not specified, a temp file will be created. Defaults to ``None``
         collect_device (str): Device name used for collecting results from
             different ranks during distributed training. Must be ``'cpu'`` or
             ``'gpu'``. Defaults to ``'cpu'``

mmpose/evaluation/metrics/coco_wholebody_metric.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from typing import Dict, Optional
+import datetime
+from typing import Dict, Optional, Sequence
 
 import numpy as np
 from mmengine.fileio import dump
@@ -19,15 +20,17 @@ class CocoWholeBodyMetric(CocoMetric):
     for more details.
 
     Args:
-        ann_file (str): Path to the coco format annotation file.
+        ann_file (str, optional): Path to the coco format annotation file.
+            If not specified, ground truth annotations from the dataset will
+            be converted to coco format. Defaults to None
         use_area (bool): Whether to use ``'area'`` message in the annotations.
             If the ground truth annotations (e.g. CrowdPose, AIC) do not have
             the field ``'area'``, please set ``use_area=False``.
-            Default: ``True``.
+            Defaults to ``True``
         iou_type (str): The same parameter as `iouType` in
             :class:`xtcocotools.COCOeval`, which can be ``'keypoints'``, or
             ``'keypoints_crowd'`` (used in CrowdPose dataset).
-            Defaults to ``'keypoints'``.
+            Defaults to ``'keypoints'``
         score_mode (str): The mode to score the prediction results which
             should be one of the following options:
 
@@ -62,17 +65,11 @@ class CocoWholeBodyMetric(CocoMetric):
             doing quantitative evaluation. This is designed for the need of
             test submission when the ground truth annotations are absent. If
             set to ``True``, ``outfile_prefix`` should specify the path to
-            store the output results. Default: ``False``.
+            store the output results. Defaults to ``False``
         outfile_prefix (str | None): The prefix of json files. It includes
             the file path and the prefix of filename, e.g., ``'a/b/prefix'``.
-            If not specified, a temp file will be created. Default: ``None``.
-        collect_device (str): Device name used for collecting results from
-            different ranks during distributed training. Must be ``'cpu'`` or
-            ``'gpu'``. Default: ``'cpu'``.
-        prefix (str, optional): The prefix that will be added in the metric
-            names to disambiguate homonymous metrics of different evaluators.
-            If prefix is not provided in the argument, ``self.default_prefix``
-            will be used instead. Default: ``None``.
+            If not specified, a temp file will be created. Defaults to ``None``
+        **kwargs: Keyword parameters passed to :class:`mmeval.BaseMetric`
     """
     default_prefix: Optional[str] = 'coco-wholebody'
     body_num = 17
@@ -81,6 +78,101 @@ class CocoWholeBodyMetric(CocoMetric):
     left_hand_num = 21
     right_hand_num = 21
 
+    def gt_to_coco_json(self, gt_dicts: Sequence[dict],
+                        outfile_prefix: str) -> str:
+        """Convert ground truth to coco format json file.
+
+        Args:
+            gt_dicts (Sequence[dict]): Ground truth of the dataset. Each dict
+                contains the ground truth information about the data sample.
+                Required keys of the each `gt_dict` in `gt_dicts`:
+                    - `img_id`: image id of the data sample
+                    - `width`: original image width
+                    - `height`: original image height
+                    - `raw_ann_info`: the raw annotation information
+                Optional keys:
+                    - `crowd_index`: measure the crowding level of an image,
+                        defined in CrowdPose dataset
+                It is worth mentioning that, in order to compute `CocoMetric`,
+                there are some required keys in the `raw_ann_info`:
+                    - `id`: the id to distinguish different annotations
+                    - `image_id`: the image id of this annotation
+                    - `category_id`: the category of the instance.
+                    - `bbox`: the object bounding box
+                    - `keypoints`: the keypoints cooridinates along with their
+                        visibilities. Note that it need to be aligned
+                        with the official COCO format, e.g., a list with length
+                        N * 3, in which N is the number of keypoints. And each
+                        triplet represent the [x, y, visible] of the keypoint.
+                    - 'keypoints'
+                    - `iscrowd`: indicating whether the annotation is a crowd.
+                        It is useful when matching the detection results to
+                        the ground truth.
+                There are some optional keys as well:
+                    - `area`: it is necessary when `self.use_area` is `True`
+                    - `num_keypoints`: it is necessary when `self.iou_type`
+                        is set as `keypoints_crowd`.
+            outfile_prefix (str): The filename prefix of the json files. If the
+                prefix is "somepath/xxx", the json file will be named
+                "somepath/xxx.gt.json".
+        Returns:
+            str: The filename of the json file.
+        """
+        image_infos = []
+        annotations = []
+        img_ids = []
+        ann_ids = []
+
+        for gt_dict in gt_dicts:
+            # filter duplicate image_info
+            if gt_dict['img_id'] not in img_ids:
+                image_info = dict(
+                    id=gt_dict['img_id'],
+                    width=gt_dict['width'],
+                    height=gt_dict['height'],
+                )
+                if self.iou_type == 'keypoints_crowd':
+                    image_info['crowdIndex'] = gt_dict['crowd_index']
+
+                image_infos.append(image_info)
+                img_ids.append(gt_dict['img_id'])
+
+            # filter duplicate annotations
+            for ann in gt_dict['raw_ann_info']:
+                annotation = dict(
+                    id=ann['id'],
+                    image_id=ann['image_id'],
+                    category_id=ann['category_id'],
+                    bbox=ann['bbox'],
+                    keypoints=ann['keypoints'],
+                    foot_kpts=ann['foot_kpts'],
+                    face_kpts=ann['face_kpts'],
+                    lefthand_kpts=ann['lefthand_kpts'],
+                    righthand_kpts=ann['righthand_kpts'],
+                    iscrowd=ann['iscrowd'],
+                )
+                if self.use_area:
+                    assert 'area' in ann, \
+                        '`area` is required when `self.use_area` is `True`'
+                    annotation['area'] = ann['area']
+
+                annotations.append(annotation)
+                ann_ids.append(ann['id'])
+
+        info = dict(
+            date_created=str(datetime.datetime.now()),
+            description='Coco json file converted by mmpose CocoMetric.')
+        coco_json: dict = dict(
+            info=info,
+            images=image_infos,
+            categories=self.dataset_meta['CLASSES'],
+            licenses=None,
+            annotations=annotations,
+        )
+        converted_json_path = f'{outfile_prefix}.gt.json'
+        dump(coco_json, converted_json_path, sort_keys=True, indent=4)
+        return converted_json_path
+
     def results2json(self, keypoints: Dict[int, list],
                      outfile_prefix: str) -> str:
         """Dump the keypoint detection results to a COCO style json file.

mmpose/evaluation/metrics/posetrack18_metric.py

@@ -28,7 +28,9 @@ class PoseTrack18Metric(CocoMetric):
     for more details.
 
     Args:
-        ann_file (str): Path to the annotation file.
+        ann_file (str, optional): Path to the coco format annotation file.
+            If not specified, ground truth annotations from the dataset will
+            be converted to coco format. Defaults to None
         score_mode (str): The mode to score the prediction results which
             should be one of the following options:
 
@@ -37,7 +39,7 @@ class PoseTrack18Metric(CocoMetric):
                 - ``'bbox_keypoint'``: Use keypoint score to rescore the
                     prediction results.
 
-            Defaults to ``'bbox'`
+            Defaults to ``'bbox_keypoint'`
         keypoint_score_thr (float): The threshold of keypoint score. The
             keypoints with score lower than it will not be included to
             rescore the prediction results. Valid only when ``score_mode`` is
@@ -61,22 +63,16 @@ class PoseTrack18Metric(CocoMetric):
             doing quantitative evaluation. This is designed for the need of
             test submission when the ground truth annotations are absent. If
             set to ``True``, ``outfile_prefix`` should specify the path to
-            store the output results. Default: ``False``.
+            store the output results. Defaults to ``False``
         outfile_prefix (str | None): The prefix of json files. It includes
             the file path and the prefix of filename, e.g., ``'a/b/prefix'``.
-            If not specified, a temp file will be created. Default: ``None``.
-        collect_device (str): Device name used for collecting results from
-            different ranks during distributed training. Must be ``'cpu'`` or
-            ``'gpu'``. Default: ``'cpu'``.
-        prefix (str, optional): The prefix that will be added in the metric
-            names to disambiguate homonymous metrics of different evaluators.
-            If prefix is not provided in the argument, ``self.default_prefix``
-            will be used instead. Default: ``None``.
+            If not specified, a temp file will be created. Defaults to ``None``
+        **kwargs: Keyword parameters passed to :class:`mmeval.BaseMetric`
     """
     default_prefix: Optional[str] = 'posetrack18'
 
     def __init__(self,
-                 ann_file: str,
+                 ann_file: Optional[str] = None,
                  score_mode: str = 'bbox_keypoint',
                  keypoint_score_thr: float = 0.2,
                  nms_mode: str = 'oks_nms',
@@ -216,7 +212,7 @@ def _do_python_keypoint_eval(self, outfile_prefix: str) -> List[tuple]:
 
         stats_names = [
             'Head AP', 'Shou AP', 'Elb AP', 'Wri AP', 'Hip AP', 'Knee AP',
-            'Ankl AP', 'Total AP'
+            'Ankl AP', 'AP'
         ]
 
         info_str = list(zip(stats_names, stats))