Required input to confusion matrix

I understand that the input to the confusion matrix metric has to be in the shape of

batch_size x num_classes, e.g. for a binary classification problem

Sample 1:     0.75 0.25
Sample 2:     0.35 0.65

Also the target is to expected to be of int type rather than float due to the use of torch.bincount.

I am wondering, if it would make sense to change the API so that input of the shape batch_size suffices, i.e.

Sample 1:     1
Sample 2:     0

Also a more consistent metric handling would be desirable because currently for some metrics like Accuracy the user has to manually round output before passing it to the metric whereas for others this is not necessary. I dont think its good to clutter ones entire code with output_transform closures for this purpose

[CDitzel]

mh so my issue is literally based on my ignorance of the Ignite lib and the Deep Learning Matter as a whole, Embarrassing. Thank you Sir

[vfdev-5]

No problems 😃 Feel free to close the issue if it answers your question. Thanks