c7395e3c5b71f97983129c7c4fddced5ce341147,allennlp/training/metrics/f1_measure.py,F1Measure,__call__,#F1Measure#Any#Any#Any#,26

Before Change

        // If you actually passed in Variables here instead of Tensors, this will be a huge memory
        // leak, because it will prevent garbage collection for the computation graph.  We"ll ensure
        // that we"re using tensors here first.
        if isinstance(predictions, Variable):
            predictions = predictions.data
        if isinstance(gold_labels, Variable):
            gold_labels = gold_labels.data
        if isinstance(mask, Variable):
            mask = mask.data

        num_classes = predictions.size(-1)
        if (gold_labels >= num_classes).any():
            raise ConfigurationError("A gold label passed to F1Measure contains an id >= {}, "
                                     "the number of classes.".format(num_classes))
        if mask is None:
            mask = ones_like(gold_labels)
        mask = mask.float()
        gold_labels = gold_labels.float()
        positive_label_mask = gold_labels.eq(self._positive_label).float()
        negative_label_mask = 1.0 - positive_label_mask

        argmax_predictions = predictions.topk(1, -1)[1].float().squeeze(-1)

        // True Negatives: correct non-positive predictions.
        correct_null_predictions = (argmax_predictions !=
                                    self._positive_label).float() * negative_label_mask
        self._true_negatives += (correct_null_predictions.float() * mask).sum()

        // True Positives: correct positively labeled predictions.
        correct_non_null_predictions = (argmax_predictions ==
                                        self._positive_label).float() * positive_label_mask
        self._true_positives += (correct_non_null_predictions * mask).sum()

        // False Negatives: incorrect negatively labeled predictions.
        incorrect_null_predictions = (argmax_predictions !=

After Change

            A masking tensor the same size as ``gold_labels``.
        
        // Get the data from the Variables.
        predictions, gold_labels, mask = self.unwrap_to_tensors(predictions, gold_labels, mask)

        num_classes = predictions.size(-1)
        if (gold_labels >= num_classes).any():
            raise ConfigurationError("A gold label passed to F1Measure contains an id >= {}, "

Instances