class_docstr: iou aware loss, see https://arxiv.org/abs/1912.05992
  Args:
      loss_weight (float): iou aware loss weight, default is 1.0
      max_height (int): max height of input to support random shape input
      max_width (int): max width of input to support random shape input
class_docstr_tok: ["iou","aware","loss",",","see","https",":","\/\/arxiv.org\/abs\/1912.05992","Args",":","loss_weight","(","float",")",":","iou","aware","loss","weight",",","default","is","1.0","max_height","(","int",")",":","max","height","of","input","to","support","random","shape","input","max_width","(","int",")",":","max","width","of","input","to","support","random","shape","input"]
code: def __call__(self, ioup, pbox, gbox):
      iou = bbox_iou(
          pbox, gbox, giou=self.giou, diou=self.diou, ciou=self.ciou
      )
      iou.stop_gradient = True
      loss_iou_aware = F.binary_cross_entropy_with_logits(
          ioup, iou, reduction="none"
      )
      loss_iou_aware = loss_iou_aware * self.loss_weight
      return loss_iou_aware
  
file_path: PaddleDetection/ppdet/modeling/losses/iou_aware_loss.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle.nn.functional
  from ppdet.core.workspace import register, serializable
  from .iou_loss import IouLoss
  from ..bbox_utils import bbox_iou
label_desc: Use image node_id 2 for calling the IouAwareLoss obj's underlying member method code with example usage: obj.__call__(ioup, pbox, gbox) and returns: loss_iou_aware