Clarify input shape expectation in classification for samplewise reduction

src/torchmetrics/classification/accuracy.py

@@ -49,7 +49,8 @@ class BinaryAccuracy(BinaryStatScores):
           If ``multidim_average`` is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a scalar
           value per sample.
 
-    Additional dimension ``...`` will be flattened into the batch dimension.
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
 
     Args:
         threshold: Threshold for transforming probability to binary {0,1} predictions
@@ -176,6 +177,9 @@ class MulticlassAccuracy(MulticlassStatScores):
               - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
               - If ``average=None/'none'``, the shape will be ``(N, C)``
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         num_classes: Integer specifying the number of classes
         average:
@@ -325,6 +329,9 @@ class MultilabelAccuracy(MultilabelStatScores):
           - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
           - If ``average=None/'none'``, the shape will be ``(N, C)``
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         num_labels: Integer specifying the number of labels
         threshold: Threshold for transforming probability to binary (0,1) predictions

src/torchmetrics/classification/exact_match.py

@@ -54,14 +54,16 @@ class MulticlassExactMatch(Metric):
       probabilities/logits into an int tensor.
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.
 
-
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
     - ``mcem`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``multidim_average`` argument:
 
         - If ``multidim_average`` is set to ``global`` the output will be a scalar tensor
         - If ``multidim_average`` is set to ``samplewise`` the output will be a tensor of shape ``(N,)``
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         num_classes: Integer specifying the number of labels
         multidim_average:
@@ -206,14 +208,16 @@ class MultilabelExactMatch(Metric):
       sigmoid per element. Additionally, we convert to int tensor with thresholding using the value in ``threshold``.
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``.
 
-
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
     - ``mlem`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``multidim_average`` argument:
 
         - If ``multidim_average`` is set to ``global`` the output will be a scalar tensor
         - If ``multidim_average`` is set to ``samplewise`` the output will be a tensor of shape ``(N,)``
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         num_labels: Integer specifying the number of labels
         threshold: Threshold for transforming probability to binary (0,1) predictions

src/torchmetrics/classification/f_beta.py

@@ -66,6 +66,9 @@ class BinaryFBetaScore(BinaryStatScores):
         - If ``multidim_average`` is set to ``samplewise`` the output will be a tensor of shape ``(N,)`` consisting of
           a scalar value per sample.
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         beta: Weighting between precision and recall in calculation. Setting to 1 corresponds to equal weight
         threshold: Threshold for transforming probability to binary {0,1} predictions
@@ -202,7 +205,6 @@ class MulticlassFBetaScore(MulticlassStatScores):
       probabilities/logits into an int tensor.
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.
 
-
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
     - ``mcfbs`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``average`` and
@@ -218,6 +220,9 @@ class MulticlassFBetaScore(MulticlassStatScores):
           - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
           - If ``average=None/'none'``, the shape will be ``(N, C)``
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         beta: Weighting between precision and recall in calculation. Setting to 1 corresponds to equal weight
         num_classes: Integer specifying the number of classes
@@ -382,7 +387,6 @@ class MultilabelFBetaScore(MultilabelStatScores):
       per element. Additionally, we convert to int tensor with thresholding using the value in ``threshold``.
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``.
 
-
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
     - ``mlfbs`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``average`` and
@@ -398,6 +402,9 @@ class MultilabelFBetaScore(MultilabelStatScores):
           - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
           - If ``average=None/'none'``, the shape will be ``(N, C)``
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         beta: Weighting between precision and recall in calculation. Setting to 1 corresponds to equal weight
         num_labels: Integer specifying the number of labels
@@ -566,6 +573,9 @@ class BinaryF1Score(BinaryFBetaScore):
         - If ``multidim_average`` is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a scalar
           value per sample.
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         threshold: Threshold for transforming probability to binary {0,1} predictions
         multidim_average:
@@ -706,6 +716,9 @@ class MulticlassF1Score(MulticlassFBetaScore):
           - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
           - If ``average=None/'none'``, the shape will be ``(N, C)``
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         preds: Tensor with predictions
         target: Tensor with true labels
@@ -876,6 +889,9 @@ class MultilabelF1Score(MultilabelFBetaScore):
           - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
           - If ``average=None/'none'``, the shape will be ``(N, C)```
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         num_labels: Integer specifying the number of labels
         threshold: Threshold for transforming probability to binary (0,1) predictions

src/torchmetrics/classification/hamming.py

@@ -58,6 +58,9 @@ class BinaryHammingDistance(BinaryStatScores):
         - If ``multidim_average`` is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a
           scalar value per sample.
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         threshold: Threshold for transforming probability to binary {0,1} predictions
         multidim_average:
@@ -171,7 +174,6 @@ class MulticlassHammingDistance(MulticlassStatScores):
       probabilities/logits into an int tensor.
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.
 
-
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
     - ``mchd`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``average`` and
@@ -187,6 +189,9 @@ class MulticlassHammingDistance(MulticlassStatScores):
           - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
           - If ``average=None/'none'``, the shape will be ``(N, C)``
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         num_classes: Integer specifying the number of classes
         average:
@@ -324,7 +329,6 @@ class MultilabelHammingDistance(MultilabelStatScores):
       ``threshold``.
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``.
 
-
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
     - ``mlhd`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``average`` and
@@ -340,6 +344,9 @@ class MultilabelHammingDistance(MultilabelStatScores):
           - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
           - If ``average=None/'none'``, the shape will be ``(N, C)``
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         num_labels: Integer specifying the number of labels
         threshold: Threshold for transforming probability to binary (0,1) predictions

src/torchmetrics/classification/precision_recall.py

@@ -57,6 +57,9 @@ class BinaryPrecision(BinaryStatScores):
       value. If ``multidim_average`` is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a
       scalar value per sample.
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         threshold: Threshold for transforming probability to binary {0,1} predictions
         multidim_average:
@@ -187,6 +190,9 @@ class MulticlassPrecision(MulticlassStatScores):
           - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
           - If ``average=None/'none'``, the shape will be ``(N, C)``
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         num_classes: Integer specifying the number of classes
         average:
@@ -340,6 +346,9 @@ class MultilabelPrecision(MultilabelStatScores):
           - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
           - If ``average=None/'none'``, the shape will be ``(N, C)``
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         num_labels: Integer specifying the number of labels
         threshold: Threshold for transforming probability to binary (0,1) predictions
@@ -479,6 +488,9 @@ class BinaryRecall(BinaryStatScores):
       value. If ``multidim_average`` is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of
       a scalar value per sample.
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         threshold: Threshold for transforming probability to binary {0,1} predictions
         multidim_average:
@@ -608,6 +620,9 @@ class MulticlassRecall(MulticlassStatScores):
           - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
           - If ``average=None/'none'``, the shape will be ``(N, C)``
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         num_classes: Integer specifying the number of classes
         average:
@@ -760,6 +775,9 @@ class MultilabelRecall(MultilabelStatScores):
           - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
           - If ``average=None/'none'``, the shape will be ``(N, C)``
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         num_labels: Integer specifying the number of labels
         threshold: Threshold for transforming probability to binary (0,1) predictions

src/torchmetrics/classification/specificity.py

@@ -50,6 +50,9 @@ class BinarySpecificity(BinaryStatScores):
       If ``multidim_average`` is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a scalar value
       per sample.
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         threshold: Threshold for transforming probability to binary {0,1} predictions
         multidim_average:
@@ -174,6 +177,9 @@ class MulticlassSpecificity(MulticlassStatScores):
           - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
           - If ``average=None/'none'``, the shape will be ``(N, C)``
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         num_classes: Integer specifying the number of classes
         average:
@@ -307,7 +313,6 @@ class MultilabelSpecificity(MultilabelStatScores):
       per element. Additionally, we convert to int tensor with thresholding using the value in ``threshold``.
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``
 
-
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
     - ``mls`` (:class:`~torch.Tensor`): The returned shape depends on the ``average`` and ``multidim_average``
@@ -323,6 +328,9 @@ class MultilabelSpecificity(MultilabelStatScores):
           - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
           - If ``average=None/'none'``, the shape will be ``(N, C)``
 
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
+
     Args:
         num_labels: Integer specifying the number of labels
         threshold: Threshold for transforming probability to binary (0,1) predictions

src/torchmetrics/classification/stat_scores.py

@@ -107,8 +107,11 @@ class BinaryStatScores(_AbstractStatScores):
       to ``[tp, fp, tn, fn, sup]`` (``sup`` stands for support and equals ``tp + fn``). The shape
       depends on the ``multidim_average`` parameter:
 
-    - If ``multidim_average`` is set to ``global``, the shape will be ``(5,)``
-    - If ``multidim_average`` is set to ``samplewise``, the shape will be ``(N, 5)``
+      - If ``multidim_average`` is set to ``global``, the shape will be ``(5,)``
+      - If ``multidim_average`` is set to ``samplewise``, the shape will be ``(N, 5)``
+
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
 
     Args:
         threshold: Threshold for transforming probability to binary {0,1} predictions
@@ -208,12 +211,18 @@ class MulticlassStatScores(_AbstractStatScores):
       to ``[tp, fp, tn, fn, sup]`` (``sup`` stands for support and equals ``tp + fn``). The shape
       depends on ``average`` and ``multidim_average`` parameters:
 
-    - If ``multidim_average`` is set to ``global``
-    - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(5,)``
-    - If ``average=None/'none'``, the shape will be ``(C, 5)``
-    - If ``multidim_average`` is set to ``samplewise``
-    - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N, 5)``
-    - If ``average=None/'none'``, the shape will be ``(N, C, 5)``
+      - If ``multidim_average`` is set to ``global``:
+
+        - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(5,)``
+        - If ``average=None/'none'``, the shape will be ``(C, 5)``
+
+      - If ``multidim_average`` is set to ``samplewise``:
+
+        - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N, 5)``
+        - If ``average=None/'none'``, the shape will be ``(N, C, 5)``
+
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
 
     Args:
         num_classes: Integer specifying the number of classes
@@ -352,12 +361,18 @@ class MultilabelStatScores(_AbstractStatScores):
       to ``[tp, fp, tn, fn, sup]`` (``sup`` stands for support and equals ``tp + fn``). The shape
       depends on ``average`` and ``multidim_average`` parameters:
 
-    - If ``multidim_average`` is set to ``global``
-    - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(5,)``
-    - If ``average=None/'none'``, the shape will be ``(C, 5)``
-    - If ``multidim_average`` is set to ``samplewise``
-    - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N, 5)``
-    - If ``average=None/'none'``, the shape will be ``(N, C, 5)``
+      - If ``multidim_average`` is set to ``global``:
+
+        - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(5,)``
+        - If ``average=None/'none'``, the shape will be ``(C, 5)``
+
+      - If ``multidim_average`` is set to ``samplewise``:
+
+        - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N, 5)``
+        - If ``average=None/'none'``, the shape will be ``(N, C, 5)``
+
+    If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
+    which the reduction will then be applied over instead of the sample dimension ``N``.
 
     Args:
         num_labels: Integer specifying the number of labels