def forward(self, pred_scores, pred_bboxes):
    '\n        pred_scores : [N, M]  score\n        pred_bboxes : [N, 5]  xc, yc, w, h, a\n        im_shape : [N, 2]  im_shape\n        scale_factor : [N, 2]  scale_factor\n        '
    pred_ploys0 = rbox2poly(pred_bboxes)
    pred_ploys = paddle.unsqueeze(pred_ploys0, axis=0)
    pred_scores0 = paddle.transpose(pred_scores, [1, 0])
    pred_scores = paddle.unsqueeze(pred_scores0, axis=0)
    (pred_cls_score_bbox, bbox_num, _) = self.nms(pred_ploys, pred_scores, self.num_classes)
    if ((pred_cls_score_bbox.shape[0] <= 0) or (pred_cls_score_bbox.shape[1] <= 1)):
        pred_cls_score_bbox = self.fake_pred_cls_score_bbox
        bbox_num = self.fake_bbox_num
    pred_cls_score_bbox = paddle.reshape(pred_cls_score_bbox, [(- 1), 10])
    return (pred_cls_score_bbox, bbox_num)
pred_scores : [N, M]  score
pred_bboxes : [N, 5]  xc, yc, w, h, a
im_shape : [N, 2]  im_shape
scale_factor : [N, 2]  scale_factor
def forward(self, pred_scores, pred_bboxes):
    '\n        pred_scores : [N, M]  score\n        pred_bboxes : [N, 5]  xc, yc, w, h, a\n        im_shape : [N, 2]  im_shape\n        scale_factor : [N, 2]  scale_factor\n        '
    pred_ploys0 = rbox2poly(pred_bboxes)
    pred_ploys = paddle.unsqueeze(pred_ploys0, axis=0)
    pred_scores0 = paddle.transpose(pred_scores, [1, 0])
    pred_scores = paddle.unsqueeze(pred_scores0, axis=0)
    (pred_cls_score_bbox, bbox_num, _) = self.nms(pred_ploys, pred_scores, self.num_classes)
    if ((pred_cls_score_bbox.shape[0] <= 0) or (pred_cls_score_bbox.shape[1] <= 1)):
        pred_cls_score_bbox = self.fake_pred_cls_score_bbox
        bbox_num = self.fake_bbox_num
    pred_cls_score_bbox = paddle.reshape(pred_cls_score_bbox, [(- 1), 10])
    return (pred_cls_score_bbox, bbox_num)