def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
        """
        Rescale, clip and filter the bbox from the output of NMS to 
        get final prediction. 

        Notes:
        Currently only support bs = 1.

        Args:
            bboxes (Tensor): The output bboxes with shape [N, 6] after decode
                and NMS, including labels, scores and bboxes.
            bbox_num (Tensor): The number of prediction boxes of each batch with
                shape [1], and is N.
            im_shape (Tensor): The shape of the input image.
            scale_factor (Tensor): The scale factor of the input image.
        Returns:
            pred_result (Tensor): The final prediction results with shape [N, 6]
                including labels, scores and bboxes.
        """
        if self.export_eb:
            # enable rcnn models for edgeboard hw to skip the following postprocess.
            return bboxes, bboxes, bbox_num

        if not self.export_onnx:
            bboxes_list = []
            bbox_num_list = []
            id_start = 0
            fake_bboxes = paddle.to_tensor(
                np.array(
                    [[0., 0.0, 0.0, 0.0, 1.0, 1.0]], dtype='float32'))
            fake_bbox_num = paddle.to_tensor(np.array([1], dtype='int32'))

            # add fake bbox when output is empty for each batch
            for i in range(bbox_num.shape[0]):
                if bbox_num[i] == 0:
                    bboxes_i = fake_bboxes
                    bbox_num_i = fake_bbox_num
                else:
                    bboxes_i = bboxes[id_start:id_start + bbox_num[i], :]
                    bbox_num_i = bbox_num[i:i + 1]
                    # id_start: 0-dim, bbox_num: 1-dim. Use bbox_num[i] instead of bbox_num[i:i+1] in pir.
                    id_start += bbox_num[i]
                bboxes_list.append(bboxes_i)
                bbox_num_list.append(bbox_num_i)
            bboxes = paddle.concat(bboxes_list)
            bbox_num = paddle.concat(bbox_num_list)

        origin_shape = paddle.floor(im_shape / scale_factor + 0.5)

        if not self.export_onnx:
            origin_shape_list = []
            scale_factor_list = []
            # scale_factor: scale_y, scale_x
            for i in range(bbox_num.shape[0]):
                expand_shape = paddle.expand(origin_shape[i:i + 1, :],
                                             [bbox_num[i:i + 1], 2])                          
                scale_y, scale_x = scale_factor[i, 0], scale_factor[i, 1]
                # TODO(PIR): something wrong with slice op, remove unsqueeze in the future.
                scale_y = paddle.unsqueeze(scale_y, 0)
                scale_x = paddle.unsqueeze(scale_x, 0)
                scale = paddle.concat([scale_x, scale_y, scale_x, scale_y])
                expand_scale = paddle.expand(scale, [bbox_num[i:i + 1], 4])
                origin_shape_list.append(expand_shape)
                scale_factor_list.append(expand_scale)

            self.origin_shape_list = paddle.concat(origin_shape_list)
            scale_factor_list = paddle.concat(scale_factor_list)

        else:
            # simplify the computation for bs=1 when exporting onnx
            scale_y, scale_x = scale_factor[0][0], scale_factor[0][1]
            scale = paddle.concat(
                [scale_x, scale_y, scale_x, scale_y]).unsqueeze(0)
            self.origin_shape_list = paddle.expand(origin_shape,
                                                   [bbox_num[0:1], 2])
            scale_factor_list = paddle.expand(scale, [bbox_num[0:1], 4])

        # bboxes: [N, 6], label, score, bbox
        pred_label = bboxes[:, 0:1]
        pred_score = bboxes[:, 1:2]
        pred_bbox = bboxes[:, 2:]
        # rescale bbox to original image
        scaled_bbox = pred_bbox / scale_factor_list
        origin_h = self.origin_shape_list[:, 0]
        origin_w = self.origin_shape_list[:, 1]
        zeros = paddle.zeros_like(origin_h)
        # clip bbox to [0, original_size]
        x1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 0], origin_w), zeros)
        y1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 1], origin_h), zeros)
        x2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 2], origin_w), zeros)
        y2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 3], origin_h), zeros)
        pred_bbox = paddle.stack([x1, y1, x2, y2], axis=-1)
        # filter empty bbox
        keep_mask = nonempty_bbox(pred_bbox, return_mask=True)
        keep_mask = paddle.unsqueeze(keep_mask, [1])
        pred_label = paddle.where(keep_mask, pred_label,
                                  paddle.ones_like(pred_label) * -1)
        pred_result = paddle.concat([pred_label, pred_score, pred_bbox], axis=1)
        return bboxes, pred_result, bbox_num

        Rescale, clip and filter the bbox from the output of NMS to 
        get final prediction. 

        Notes:
        Currently only support bs = 1.

        Args:
            bboxes (Tensor): The output bboxes with shape [N, 6] after decode
                and NMS, including labels, scores and bboxes.
            bbox_num (Tensor): The number of prediction boxes of each batch with
                shape [1], and is N.
            im_shape (Tensor): The shape of the input image.
            scale_factor (Tensor): The scale factor of the input image.
        Returns:
            pred_result (Tensor): The final prediction results with shape [N, 6]
                including labels, scores and bboxes.
        
ppdet/modeling/post_process.py