def simple_test(self, feats, img_metas, rescale=False):
        """Test det bboxes without test-time augmentation.

        Args:
            feats (tuple[paddle.Tensor]): Multi-level features from the
                upstream network, each is a 4D-tensor.
            img_metas (list[dict]): List of image information.
            rescale (bool, optional): Whether to rescale the results.
                Defaults to False.

        Returns:
            list[tuple[Tensor, Tensor, Tensor]]: Each item in result_list is
                3-tuple. The first item is ``bboxes`` with shape (n, 5),
                where 5 represent (tl_x, tl_y, br_x, br_y, score).
                The shape of the second tensor in the tuple is ``labels``
                with shape (n,). The third item is ``kpts`` with shape
                (n, K, 3), in [p^{1}_x, p^{1}_y, p^{1}_v, p^{K}_x, p^{K}_y,
                p^{K}_v] format.
        """
        # forward of this head requires img_metas
        outs = self.forward(feats, img_metas)
        results_list = self.get_bboxes(*outs, img_metas, rescale=rescale)
        return results_list
Test det bboxes without test-time augmentation.

        Args:
            feats (tuple[paddle.Tensor]): Multi-level features from the
                upstream network, each is a 4D-tensor.
            img_metas (list[dict]): List of image information.
            rescale (bool, optional): Whether to rescale the results.
                Defaults to False.

        Returns:
            list[tuple[Tensor, Tensor, Tensor]]: Each item in result_list is
                3-tuple. The first item is ``bboxes`` with shape (n, 5),
                where 5 represent (tl_x, tl_y, br_x, br_y, score).
                The shape of the second tensor in the tuple is ``labels``
                with shape (n,). The third item is ``kpts`` with shape
                (n, K, 3), in [p^{1}_x, p^{1}_y, p^{1}_v, p^{K}_x, p^{K}_y,
                p^{K}_v] format.
        
ppdet/modeling/heads/petr_head.py