(self,
               prediction_tensor,
               target_tensor,
               ignore_nan_targets=False,
               scope=None,
               **params)
return self._compute_loss(prediction_tensor, target_tensor, **params)
Call the loss function.

    Args:
      prediction_tensor: an N-d tensor of shape [batch, anchors, ...]
        representing predicted quantities.
      target_tensor: an N-d tensor of shape [batch, anchors, ...] representing
        regression or classification targets.
      ignore_nan_targets: whether to ignore nan targets in the loss computation.
        E.g. can be used if the target tensor is missing groundtruth data that
        shouldn't be factored into the loss.
      scope: Op scope name. Defaults to 'Loss' if None.
      **params: Additional keyword arguments for specific implementations of
              the Loss.

    Returns:
      loss: a tensor representing the value of the loss function.
Call the loss function.
Call the loss function .
def __call__(self,
               prediction_tensor,
               target_tensor,
               ignore_nan_targets=False,
               scope=None,
               **params):
    """Call the loss function.

    Args:
      prediction_tensor: an N-d tensor of shape [batch, anchors, ...]
        representing predicted quantities.
      target_tensor: an N-d tensor of shape [batch, anchors, ...] representing
        regression or classification targets.
      ignore_nan_targets: whether to ignore nan targets in the loss computation.
        E.g. can be used if the target tensor is missing groundtruth data that
        shouldn't be factored into the loss.
      scope: Op scope name. Defaults to 'Loss' if None.
      **params: Additional keyword arguments for specific implementations of
              the Loss.

    Returns:
      loss: a tensor representing the value of the loss function.
    """
    if ignore_nan_targets:
      target_tensor = torch.where(torch.isnan(target_tensor),
                                prediction_tensor,
                                target_tensor)
    return self._compute_loss(prediction_tensor, target_tensor, **params)
def __call__ ( self , prediction_tensor , target_tensor , ignore_nan_targets = False , scope = None , * * params ) : if ignore_nan_targets : target_tensor = torch . where ( torch . isnan ( target_tensor ) , prediction_tensor , target_tensor ) return self . _compute_loss ( prediction_tensor , target_tensor , * * params )