0a456a7582da2ab4271756d7775bba84a75c8c0d,allennlp/training/metrics/categorical_accuracy.py,CategoricalAccuracy,__call__,#CategoricalAccuracy#Any#Any#Any#,35

Before Change

            _correct_count = torch.tensor(self.correct_count).to(device)
            _total_count = torch.tensor(self.total_count).to(device)
            dist.all_reduce(_correct_count, op=dist.ReduceOp.SUM)
            dist.all_reduce(_total_count, op=dist.ReduceOp.SUM)
            self.correct_count = _correct_count.item()
            self.total_count = _total_count.item()

    def get_metric(self, reset: bool = False):

After Change

            correct *= mask.view(-1, 1)
            total_count_diff = mask.sum()
        else:
            total_count_diff = torch.tensor(gold_labels.numel())
        correct_count_diff = correct.sum()

        if is_distributed():
            dist.all_reduce(correct_count_diff, op=dist.ReduceOp.SUM)
            dist.all_reduce(total_count_diff, op=dist.ReduceOp.SUM)
            self.correct_count += correct_count_diff.item()
            self.total_count += total_count_diff.item()
        else:
            self.correct_count += correct_count_diff
            self.total_count += total_count_diff

Instances