Potential bug/confusion when is_multilabel=True for some metrics

Hello,

in the documentation for Precision, Recall and ClassificationReport, if the task is multilabel, following examples are given:

        Multilabel case, the shapes must be (batch_size, num_categories, ...)

        .. testcode:: 2

            metric = ClassificationReport(output_dict=True, is_multilabel=True)
            metric.attach(default_evaluator, "cr")
            y_true = torch.Tensor([
                [0, 0, 1],
                [0, 0, 0],
                [0, 0, 0],
                [1, 0, 0],
                [0, 1, 1],
            ]).unsqueeze(0)
            y_pred = torch.Tensor([
                [1, 1, 0],
                [1, 0, 1],
                [1, 0, 0],
                [1, 0, 1],
                [1, 1, 0],
            ]).unsqueeze(0)

In all metric docs, a first-dim is added by unsqueeze(0) to both y_true and y_pred, but it is also told that the shapes must be (batch_size, num_categories, ...). From my understanding of the latter, both y_true and y_pred should have n_samples X num_categories shape. In this example, this amounts to 5 x 3 e.g. 5 examples/samples and three output labels, each binary.

• Why do we add the dummy singleton dim in the first place? If we don’t add it, per-label computation is not correct as it takes the sample dimension as the label dimension i.e. if 256 examples are given, 256 metrics are computed for each of them.

Thanks!

[sadra-barikbin]

@vfdev-5 I’ll do it, if you will

[louis-she]

Sorry i’m a little busy these days, i have not started this one yet. @nishantb06 you can take this one. I will focus on #2423