def _check_inside_boxes_limited(self, gt_bbox, xs, ys,
                                    num_points_each_level):
        """
        check if points is within the clipped boxes
        :param gt_bbox: bounding boxes
        :param xs: horizontal coordinate of points
        :param ys: vertical coordinate of points
        :return: the mask of points is within gt_box or not
        """
        bboxes = np.reshape(
            gt_bbox, newshape=[1, gt_bbox.shape[0], gt_bbox.shape[1]])
        bboxes = np.tile(bboxes, reps=[xs.shape[0], 1, 1])
        ct_x = (bboxes[:, :, 0] + bboxes[:, :, 2]) / 2
        ct_y = (bboxes[:, :, 1] + bboxes[:, :, 3]) / 2
        beg = 0
        clipped_box = bboxes.copy()
        for lvl, stride in enumerate(self.downsample_ratios):
            end = beg + num_points_each_level[lvl]
            stride_exp = self.center_sampling_radius * stride
            clipped_box[beg:end, :, 0] = np.maximum(
                bboxes[beg:end, :, 0], ct_x[beg:end, :] - stride_exp)
            clipped_box[beg:end, :, 1] = np.maximum(
                bboxes[beg:end, :, 1], ct_y[beg:end, :] - stride_exp)
            clipped_box[beg:end, :, 2] = np.minimum(
                bboxes[beg:end, :, 2], ct_x[beg:end, :] + stride_exp)
            clipped_box[beg:end, :, 3] = np.minimum(
                bboxes[beg:end, :, 3], ct_y[beg:end, :] + stride_exp)
            beg = end
        l_res = xs - clipped_box[:, :, 0]
        r_res = clipped_box[:, :, 2] - xs
        t_res = ys - clipped_box[:, :, 1]
        b_res = clipped_box[:, :, 3] - ys
        clipped_box_reg_targets = np.stack([l_res, t_res, r_res, b_res], axis=2)
        inside_gt_box = np.min(clipped_box_reg_targets, axis=2) > 0
        return inside_gt_box

        check if points is within the clipped boxes
        :param gt_bbox: bounding boxes
        :param xs: horizontal coordinate of points
        :param ys: vertical coordinate of points
        :return: the mask of points is within gt_box or not
        
ppdet/data/transform/batch_operators.py