class_docstr: Distribution Focal Loss (DFL) is a variant of `Generalized Focal Loss:
  Learning Qualified and Distributed Bounding Boxes for Dense Object
  Detection <https://arxiv.org/abs/2006.04388>`_.
  Args:
      reduction (str): Options are `'none'`, `'mean'` and `'sum'`.
      loss_weight (float): Loss weight of current loss.
class_docstr_tok: ["Distribution","Focal","Loss","(","DFL",")","is","a","variant","of","`","Generalized","Focal","Loss",":","Learning","Qualified","and","Distributed","Bounding","Boxes","for","Dense","Object","Detection","<","https",":","\/\/arxiv.org\/abs\/2006.04388",">","`","_",".","Args",":","reduction","(","str",")",":","Options","are","`","'none","'","`",",","`","'mean","'","`","and","`","'sum","'","`",".","loss_weight","(","float",")",":","Loss","weight","of","current","loss","."]
docstr: Forward function.
  Args:
      pred (Tensor): Predicted general distribution of bounding
          boxes (before softmax) with shape (N, n+1), n is the max value
          of the integral set `{0, ..., n}` in paper.
      target (Tensor): Target distance label for bounding boxes
          with shape (N,).
      weight (Tensor, optional): The weight of loss for each
          prediction. Defaults to None.
      avg_factor (int, optional): Average factor that is used to average
          the loss. Defaults to None.
docstr_tok: ["Forward","function",".","Args",":","pred","(","Tensor",")",":","Predicted","general","distribution","of","bounding","boxes","(","before","softmax",")","with","shape","(","N",",","n+1",")",",","n","is","the","max","value","of","the","integral","set","`","{","0",",","...",",","n","}","`","in","paper",".","target","(","Tensor",")",":","Target","distance","label","for","bounding","boxes","with","shape","(","N",",",")",".","weight","(","Tensor",",","optional",")",":","The","weight","of","loss","for","each","prediction",".","Defaults","to","None",".","avg_factor","(","int",",","optional",")",":","Average","factor","that","is","used","to","average","the","loss",".","Defaults","to","None","."]
code: def forward(self, pred, target, weight=None, avg_factor=None):
      """Forward function.
      Args:
          pred (Tensor): Predicted general distribution of bounding
              boxes (before softmax) with shape (N, n+1), n is the max value
              of the integral set `{0, ..., n}` in paper.
          target (Tensor): Target distance label for bounding boxes
              with shape (N,).
          weight (Tensor, optional): The weight of loss for each
              prediction. Defaults to None.
          avg_factor (int, optional): Average factor that is used to average
              the loss. Defaults to None.
      """
      loss = self.loss_weight * distribution_focal_loss(pred, target)
      if weight is not None:
          loss = loss * weight
      if avg_factor is None:
          if self.reduction == "none":
              return loss
          elif self.reduction == "mean":
              return loss.mean()
          elif self.reduction == "sum":
              return loss.sum()
      else:
          # if reduction is mean, then average the loss by avg_factor
          if self.reduction == "mean":
              loss = loss.sum() / avg_factor
          # if reduction is 'none', then do nothing, otherwise raise an error
          elif self.reduction != "none":
              raise ValueError(
                  'avg_factor can not be used with reduction="sum"'
              )
      return loss
  
file_path: PaddleDetection/ppdet/modeling/losses/gfocal_loss.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register, serializable
  from ppdet.modeling import ops
label_desc: Use image node_id 2 for calling the DistributionFocalLoss obj's underlying member method code with example usage: obj.forward(pred, target, weight, avg_factor) and returns: loss, loss, loss, loss