i want to ask question about dice.py

[1583364217]

average:

            Defines the reduction that is applied. Should be one of the following:
            - ``'micro'`` [default]: Calculate the metric globally, across all samples and classes.
            - ``'macro'``: Calculate the metric for each class separately, and average the
              metrics across classes (with equal weights for each class).
            - ``'weighted'``: Calculate the metric for each class separately, and average the
              metrics across classes, weighting each class by its support (``tp + fn``).
            - ``'none'`` or ``None``: Calculate the metric for each class separately, and return
              the metric for every class.
            - ``'samples'``: Calculate the metric for each sample, and average the metrics
              across samples (with equal weights for each sample).
            .. note::
               What is considered a sample in the multi-dimensional multi-class case
               depends on the value of ``mdmc_average``.

   if average not in ["micro", "macro", "samples"]:
            raise ValueError(f"The `reduce` {average} is not valid.")

i used None to get a list including all classes dice, but raise a error. In the exegesis, You can pass in the parameter None. how i to do to get each classes dice? rewrite Dice?

[Borda]

Setting average=None in TorchMetrics’ Dice metric should return per-class scores as per the docs. If this causes errors, it’s likely due to an outdated version or input format issues.
Make sure to update to the latest TorchMetrics version via:

pip install --upgrade torchmetrics

Check input shapes and types match expected formats. Rewriting Dice is usually unnecessary since average=None is supported to get class-wise metrics. If problems persist, consider opening an issue with a reproducible example for help.