Make optional argmax for y_pred in Confusion Matrix, Precision, Recall, Accuracy

🚀 Feature

Today, the conditions on the input of the Confusion Matrix, (and Precision, Recall, Accuracy in multiclass case) are the following:

    - `y_pred` must contain logits and has the following shape (batch_size, num_categories, ...)
    - `y` should have the following shape (batch_size, ...) and contains ground-truth class indices
        with or without the background class. During the computation, argmax of `y_pred` is taken to determine predicted classes.

Taking argmax on y_pred can be an option if we would like to determine winning class by some other rule. Let’s keep argmax as default behaviour if y_pred is (N, C, ...) and do not apply it if y_pred.shape == y.shape and (N, ...).

[sdesrozis]

Ok I do it soon

[vfdev-5]

argmax should be optional and user should be able to give their own rule.

If y_pred has C dimension like (N, C, ...) there is no way to compute a metric without taking argmax with y_true as (N, ...). In this case we should take argmax without an option, IMO.

if y_pred.shape == y.shape and (N, …), do not apply

yes. In this case, user can perform winning class selection in output_transform or anywhere before metrics update.