e839626c28d8dc6030ce71f3619e934b36fb6627,catalyst/metrics/functional.py,,process_multiclass_components,#,33

Before Change

    if targets.max() != len(torch.unique(targets)) - 1:
        if raise_class_labels_mismatch:
            raise Exception(
                "`targets` maximum does not represent number of classes"
            )
        // mapping classes
        outputs, targets = map_labels_to_classes(outputs, targets)
    return outputs, targets

After Change

        targets = torch.from_numpy(targets)

    // @TODO: move to process_multiclass_components ?
    if outputs.dim() == targets.dim() + 1:
        // looks like we have scores/probabilities in our outputs
        // let"s convert them to final model predictions
        num_classes = max(
            outputs.shape[argmax_dim], int(targets.max().detach().item() + 1)
        )
        outputs = torch.argmax(outputs, dim=argmax_dim)
    if num_classes is None:
        // as far as we expect the outputs/targets tensors to be int64
        // we could find number of classes as max available number
        num_classes = max(

Instances