e839626c28d8dc6030ce71f3619e934b36fb6627,catalyst/metrics/functional.py,,get_multiclass_statistics,#Any#Any#Any#Any#,167

Before Change

        tensor([1., 1., 0., 1., 1.])
    
    // @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(

After Change

        tensor([0., 0., 0., 1., 1.]), tensor([1., 1., 0., 0., 0.]),
        tensor([1., 1., 0., 1., 1.])
    
    outputs, targets, num_classes = process_multiclass_components(
        outputs=outputs,
        targets=targets,
        argmax_dim=argmax_dim,

Instances