code: def varifocal_loss(
      pred,
      target,
      alpha=0.75,
      gamma=2.0,
      iou_weighted=True,
      use_sigmoid=True,
  ):
      """`Varifocal Loss <https://arxiv.org/abs/2008.13367>`_
  
      Args:
          pred (Tensor): The prediction with shape (N, C), C is the
              number of classes
          target (Tensor): The learning target of the iou-aware
              classification score with shape (N, C), C is the number of classes.
          alpha (float, optional): A balance factor for the negative part of
              Varifocal Loss, which is different from the alpha of Focal Loss.
              Defaults to 0.75.
          gamma (float, optional): The gamma for calculating the modulating
              factor. Defaults to 2.0.
          iou_weighted (bool, optional): Whether to weight the loss of the
              positive example with the iou target. Defaults to True.
      """
      # pred and target should be of the same size
      assert pred.shape == target.shape
      if use_sigmoid:
          pred_new = F.sigmoid(pred)
      else:
          pred_new = pred
      target = target.cast(pred.dtype)
      if iou_weighted:
          focal_weight = target * (target > 0.0).cast(
              "float32"
          ) + alpha * (pred_new - target).abs().pow(gamma) * (
              target <= 0.0
          ).cast(
              "float32"
          )
      else:
          focal_weight = (target > 0.0).cast("float32") + alpha * (
              pred_new - target
          ).abs().pow(gamma) * (target <= 0.0).cast("float32")
  
      if use_sigmoid:
          loss = (
              F.binary_cross_entropy_with_logits(
                  pred, target, reduction="none"
              )
              * focal_weight
          )
      else:
          loss = (
              F.binary_cross_entropy(pred, target, reduction="none")
              * focal_weight
          )
          loss = loss.sum(axis=1)
      return loss
  
file_path: PaddleDetection/ppdet/modeling/losses/varifocal_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 1 for calling a global function with example usage: varifocal_loss(pred, target, alpha, gamma, iou_weighted, use_sigmoid) and returns: loss