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 __init__(
      self, loss_weight=1.0, giou=False, diou=False, ciou=False
  ):
      super(IouAwareLoss, self).__init__(
          loss_weight=loss_weight, giou=giou, diou=diou, ciou=ciou
      )
  
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 1 to create a new IouAwareLoss object from inherited base classes: IouLoss with example: obj = IouAwareLoss(loss_weight, giou, diou, ciou)