code: def fine_grained_loss(
      output,
      target,
      gt_box,
      batch_size,
      num_classes,
      anchors,
      ignore_thresh,
      downsample,
      scale_x_y=1.0,
      eps=1e-10,
  ):
      an_num = len(anchors) // 2
      x, y, w, h, obj, cls = _split_output(output, an_num, num_classes)
      tx, ty, tw, th, tscale, tobj, tcls = _split_target(target)
  
      tscale_tobj = tscale * tobj
  
      scale_x_y = scale_x_y
  
      if abs(scale_x_y - 1.0) < eps:
          x = F.sigmoid(x)
          y = F.sigmoid(y)
          loss_x = (
              F.binary_cross_entropy(x, tx, reduction="none")
              * tscale_tobj
          )
          loss_x = paddle.sum(loss_x, axis=[1, 2, 3])
          loss_y = (
              F.binary_cross_entropy(y, ty, reduction="none")
              * tscale_tobj
          )
          loss_y = paddle.sum(loss_y, axis=[1, 2, 3])
      else:
          dx = scale_x_y * F.sigmoid(x) - 0.5 * (scale_x_y - 1.0)
          dy = scale_x_y * F.sigmoid(y) - 0.5 * (scale_x_y - 1.0)
          loss_x = paddle.abs(dx - tx) * tscale_tobj
          loss_x = paddle.sum(loss_x, axis=[1, 2, 3])
          loss_y = paddle.abs(dy - ty) * tscale_tobj
          loss_y = paddle.sum(loss_y, axis=[1, 2, 3])
  
      # NOTE: we refined loss function of (w, h) as L1Loss
      loss_w = paddle.abs(w - tw) * tscale_tobj
      loss_w = paddle.sum(loss_w, axis=[1, 2, 3])
      loss_h = paddle.abs(h - th) * tscale_tobj
      loss_h = paddle.sum(loss_h, axis=[1, 2, 3])
  
      loss_obj_pos, loss_obj_neg = _calc_obj_loss(
          output,
          obj,
          tobj,
          gt_box,
          batch_size,
          anchors,
          num_classes,
          downsample,
          ignore_thresh,
          scale_x_y,
      )
  
      cls = F.sigmoid(cls)
      loss_cls = F.binary_cross_entropy(cls, tcls, reduction="none")
      tobj = paddle.unsqueeze(tobj, axis=-1)
  
      loss_cls = paddle.multiply(loss_cls, tobj)
      loss_cls = paddle.sum(loss_cls, axis=[1, 2, 3, 4])
  
      loss_xys = paddle.mean(loss_x + loss_y)
      loss_whs = paddle.mean(loss_w + loss_h)
      loss_objs = paddle.mean(loss_obj_pos + loss_obj_neg)
      loss_clss = paddle.mean(loss_cls)
  
      losses_all = {
          "loss_xy": paddle.sum(loss_xys),
          "loss_wh": paddle.sum(loss_whs),
          "loss_loc": paddle.sum(loss_xys) + paddle.sum(loss_whs),
          "loss_obj": paddle.sum(loss_objs),
          "loss_cls": paddle.sum(loss_clss),
      }
      return losses_all, x, y, tx, ty
  
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 4 for calling a global function with example usage: fine_grained_loss(output, target, gt_box, batch_size, num_classes, anchors, ignore_thresh, downsample, scale_x_y, eps) and returns: losses_all,  x,  y,  tx,  ty