code: def test_loss(self):
      x, gtbox, gtlabel, gtscore, target = (
          self.x,
          self.gtbox,
          self.gtlabel,
          self.gtscore,
          self.target,
      )
      yolo_loss = YOLOv3Loss(
          ignore_thresh=self.ignore_thresh,
          label_smooth=self.use_label_smooth,
          num_classes=self.class_num,
          downsample=self.downsample_ratio,
          scale_x_y=self.scale_x_y,
      )
      x = paddle.to_tensor(x.astype(np.float32))
      gtbox = paddle.to_tensor(gtbox.astype(np.float32))
      gtlabel = paddle.to_tensor(gtlabel.astype(np.float32))
      gtscore = paddle.to_tensor(gtscore.astype(np.float32))
      t = paddle.to_tensor(target.astype(np.float32))
      anchor = [self.anchors[i] for i in self.anchor_mask]
      (yolo_loss1, px, py, tx, ty) = fine_grained_loss(
          output=x,
          target=t,
          gt_box=gtbox,
          batch_size=self.b,
          num_classes=self.class_num,
          anchors=self.mask_anchors,
          ignore_thresh=self.ignore_thresh,
          downsample=self.downsample_ratio,
          scale_x_y=self.scale_x_y,
      )
      yolo_loss2 = yolo_loss.yolov3_loss(
          x, t, gtbox, anchor, self.downsample_ratio, self.scale_x_y
      )
      for k in yolo_loss2:
          self.assertAlmostEqual(
              float(yolo_loss1[k]),
              float(yolo_loss2[k]),
              delta=1e-2,
              msg=k,
          )
  
imports: from __future__ import division
  import unittest
  import paddle
  import paddle.nn.functional
  import os
  import sys
  from ppdet.modeling.losses import YOLOv3Loss
  from ppdet.data.transform.op_helper import jaccard_overlap
  from ppdet.modeling.bbox_utils import iou_similarity
  import numpy
label_desc: Use image node_id 3 for calling the TestYolov3LossOp obj's underlying member method code with example usage: obj.test_loss() without return types