Method evaluate

PATH/core/solvers/utils/pos_tester_dev.py:414–533

Evaluate PCKh for MPII dataset. Adapted from https://github.com/leoxiaobin/deep-high-resolution-net.pytorch Copyright (c) Microsoft, under the MIT License. Note: - batch_size: N - num_keypoints: K - heatmap height: H - heatmap w

(self, res_folder=None, metric='PCKh', **kwargs)

        #  note: sync if multi-gpu

    def evaluate(self, res_folder=None, metric='PCKh', **kwargs):
        """Evaluate PCKh for MPII dataset. Adapted from
        https://github.com/leoxiaobin/deep-high-resolution-net.pytorch
        Copyright (c) Microsoft, under the MIT License.
        Note:
            - batch_size: N
            - num_keypoints: K
            - heatmap height: H
            - heatmap width: W
        Args:
            results (list[dict]): Testing results containing the following
                items:
                - preds (np.ndarray[N,K,3]): The first two dimensions are \
                    coordinates, score is the third dimension of the array.
                - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
                    scale[1],area, score]
                - image_paths (list[str]): For example, ['/val2017/000000\
                    397133.jpg']
                - heatmap (np.ndarray[N, K, H, W]): model output heatmap.
            res_folder (str, optional): The folder to save the testing
                results. Default: None.
            metric (str | list[str]): Metrics to be performed.
                Defaults: 'PCKh'.
        Returns:
            dict: PCKh for each joint
        """

        metrics = metric if isinstance(metric, list) else [metric]
        allowed_metrics = ['PCKh']
        for metric in metrics:
            if metric not in allowed_metrics:
                raise KeyError(f'metric {metric} is not supported')

        kpts = []
        for result in self.results:
            preds = result['preds']
            bbox_ids = result['bbox_ids']
            batch_size = len(bbox_ids)
            for i in range(batch_size):
                kpts.append({'keypoints': preds[i], 'bbox_id': bbox_ids[i]})
        kpts = self._sort_and_unique_bboxes(kpts)

        preds = np.stack([kpt['keypoints'] for kpt in kpts])

        # convert 0-based index to 1-based index,
        # and get the first two dimensions.
        preds = preds[..., :2] + 1.0

        if res_folder:
            pred_file = os.path.join(res_folder, 'pred.mat')
            savemat(pred_file, mdict={'preds': preds})

        SC_BIAS = 0.6
        threshold = 0.5

        gt_file = os.path.join(os.path.dirname(self.annot_root), 'mpii_gt_val.mat')
        gt_dict = loadmat(gt_file)
        dataset_joints = gt_dict['dataset_joints']