Method update

metrics/rce.py:246–273

Update the current nrce. Args: predictions: Predicted values. target: Ground truth. Should have same shape as predictions. weight: The weight to use for the predicted values. Shape should be broadcastable to that of predictions.

(
    self,
    predictions: torch.Tensor,
    target: torch.Tensor,
    weight: Union[float, torch.Tensor] = 1.0,
  )

    self.batch_metric = copy.deepcopy(self)

  def update(
    self,
    predictions: torch.Tensor,
    target: torch.Tensor,
    weight: Union[float, torch.Tensor] = 1.0,
  ):
    """
    Update the current nrce.
    Args:
      predictions: Predicted values.
      target: Ground truth. Should have same shape as predictions.
      weight: The weight to use for the predicted values. Shape should be broadcastable to that of
       predictions.
    """
    predictions = torch.sigmoid(predictions) if self.nrce_from_logits else predictions

    target = _smooth(target, self.nrce_label_smoothing)
    self.mean_label.update(target, weight)

    self.mean_prediction.update(predictions, weight)

    normalizer = self.mean_label.compute() / self.mean_prediction.compute()

    predictions = predictions * normalizer

    self.binary_cross_entropy.update(
      self.bce_loss_fn(predictions, target, reduction="none"), weight
    )

  def reset(self):
    """