def simple_test(self, inputs, img_metas, rescale=False):
        """Test function without test time augmentation.

        Args:
            inputs (list[paddle.Tensor]): List of multiple images.
            img_metas (list[dict]): List of image information.
            rescale (bool, optional): Whether to rescale the results.
                Defaults to False.

        Returns:
            list[list[np.ndarray]]: BBox and keypoint results of each image
                and classes. The outer list corresponds to each image.
                The inner list corresponds to each class.
        """
        batch_size = len(img_metas)
        assert batch_size == 1, 'Currently only batch_size 1 for inference ' \
            f'mode is supported. Found batch_size {batch_size}.'
        feat = self.extract_feat(inputs)
        results_list = self.bbox_head.simple_test(
            feat, img_metas, rescale=rescale)

        bbox_kpt_results = [
            self.bbox_kpt2result(det_bboxes, det_labels, det_kpts,
                                 self.bbox_head.num_classes)
            for det_bboxes, det_labels, det_kpts in results_list
        ]
        return bbox_kpt_results
Test function without test time augmentation.

        Args:
            inputs (list[paddle.Tensor]): List of multiple images.
            img_metas (list[dict]): List of image information.
            rescale (bool, optional): Whether to rescale the results.
                Defaults to False.

        Returns:
            list[list[np.ndarray]]: BBox and keypoint results of each image
                and classes. The outer list corresponds to each image.
                The inner list corresponds to each class.
        
ppdet/modeling/architectures/keypoint_petr.py