code: def yolov3_loss(
      self, p, t, gt_box, anchor, downsample, scale=1.0, eps=1e-10
  ):
      na = len(anchor)
      b, c, h, w = p.shape
      if self.iou_aware_loss:
          ioup, p = p[:, 0:na, :, :], p[:, na:, :, :]
          ioup = ioup.unsqueeze(-1)
      p = p.reshape((b, na, -1, h, w)).transpose((0, 1, 3, 4, 2))
      x, y = p[:, :, :, :, 0:1], p[:, :, :, :, 1:2]
      w, h = p[:, :, :, :, 2:3], p[:, :, :, :, 3:4]
      obj, pcls = p[:, :, :, :, 4:5], p[:, :, :, :, 5:]
      self.distill_pairs.append([x, y, w, h, obj, pcls])
  
      t = t.transpose((0, 1, 3, 4, 2))
      tx, ty = t[:, :, :, :, 0:1], t[:, :, :, :, 1:2]
      tw, th = t[:, :, :, :, 2:3], t[:, :, :, :, 3:4]
      tscale = t[:, :, :, :, 4:5]
      tobj, tcls = t[:, :, :, :, 5:6], t[:, :, :, :, 6:]
  
      tscale_obj = tscale * tobj
      loss = dict()
  
      x = scale * F.sigmoid(x) - 0.5 * (scale - 1.0)
      y = scale * F.sigmoid(y) - 0.5 * (scale - 1.0)
  
      if abs(scale - 1.0) < eps:
          loss_x = F.binary_cross_entropy(x, tx, reduction="none")
          loss_y = F.binary_cross_entropy(y, ty, reduction="none")
          loss_xy = tscale_obj * (loss_x + loss_y)
      else:
          loss_x = paddle.abs(x - tx)
          loss_y = paddle.abs(y - ty)
          loss_xy = tscale_obj * (loss_x + loss_y)
  
      loss_xy = loss_xy.sum([1, 2, 3, 4]).mean()
  
      loss_w = paddle.abs(w - tw)
      loss_h = paddle.abs(h - th)
      loss_wh = tscale_obj * (loss_w + loss_h)
      loss_wh = loss_wh.sum([1, 2, 3, 4]).mean()
  
      loss["loss_xy"] = loss_xy
      loss["loss_wh"] = loss_wh
  
      if self.iou_loss is not None:
          # warn: do not modify x, y, w, h in place
          box, tbox = [x, y, w, h], [tx, ty, tw, th]
          pbox = bbox_transform(box, anchor, downsample)
          gbox = bbox_transform(tbox, anchor, downsample)
          loss_iou = self.iou_loss(pbox, gbox)
          loss_iou = loss_iou * tscale_obj
          loss_iou = loss_iou.sum([1, 2, 3, 4]).mean()
          loss["loss_iou"] = loss_iou
  
      if self.iou_aware_loss is not None:
          box, tbox = [x, y, w, h], [tx, ty, tw, th]
          pbox = bbox_transform(box, anchor, downsample)
          gbox = bbox_transform(tbox, anchor, downsample)
          loss_iou_aware = self.iou_aware_loss(ioup, pbox, gbox)
          loss_iou_aware = loss_iou_aware * tobj
          loss_iou_aware = loss_iou_aware.sum([1, 2, 3, 4]).mean()
          loss["loss_iou_aware"] = loss_iou_aware
  
      box = [x, y, w, h]
      loss_obj = self.obj_loss(
          box, gt_box, obj, tobj, anchor, downsample
      )
      loss_obj = loss_obj.sum(-1).mean()
      loss["loss_obj"] = loss_obj
      loss_cls = self.cls_loss(pcls, tcls) * tobj
      loss_cls = loss_cls.sum([1, 2, 3, 4]).mean()
      loss["loss_cls"] = loss_cls
      return loss
  
file_path: PaddleDetection/ppdet/modeling/losses/yolo_loss.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from ..bbox_utils import decode_yolo, xywh2xyxy, batch_iou_similarity
label_desc: Use image node_id 4 for calling the YOLOv3Loss obj's underlying member method code with example usage: obj.yolov3_loss(p, t, gt_box, anchor, downsample, scale, eps) and returns: loss