Merge branch 'master' into lightning_logging

docs/source/conf.py

@@ -79,7 +79,7 @@ def _transform_changelog(path_in: str, path_out: str) -> None:
 
 # If your documentation needs a minimal Sphinx version, state it here.
 
-needs_sphinx = "4.0"
+needs_sphinx = "6.2"
 
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
@@ -138,7 +138,7 @@ def _transform_changelog(path_in: str, path_out: str) -> None:
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = "en"
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.

requirements/docs.txt

@@ -1,17 +1,17 @@
-sphinx>=4.0, <5.0
+sphinx >6.0, <7.0
 myst-parser
-nbsphinx>=0.8
-pandoc>=1.0
-docutils>=0.16
-sphinxcontrib-fulltoc>=1.0
+nbsphinx >=0.8
+pandoc >=1.0
+docutils >=0.16
+sphinxcontrib-fulltoc >=1.0
 sphinxcontrib-mockautodoc
 https://github.com/Lightning-AI/lightning_sphinx_theme/archive/master.zip#egg=pt-lightning-sphinx-theme
-sphinx-autodoc-typehints>=1.0
-sphinx-paramlinks>=0.5.1
-sphinx-togglebutton>=0.2
-sphinx-copybutton>=0.3
+sphinx-autodoc-typehints >=1.0
+sphinx-paramlinks >=0.5.1
+sphinx-togglebutton >=0.2
+sphinx-copybutton >=0.3
 
-lightning>=1.8.0, <3.0.0
+lightning >=1.8.0, <3.0.0
 
 # integrations
 -r integrate.txt

requirements/multimodal.txt

@@ -1,4 +1,4 @@
 # NOTE: the upper bound for the package version is only set for CI stability, and it is dropped while installing this package
 #  in case you want to preserve/enforce restrictions on the latest compatible version, add "strict" as an in-line comment
 
-transformers >=4.10.0, <4.29.3
+transformers >=4.10.0, <4.30.2

requirements/text_test.txt

@@ -4,6 +4,6 @@
 jiwer >=2.3.0, <=3.0.1
 rouge-score >0.1.0, <=0.1.2
 bert_score ==0.3.13
-transformers >4.4.0, <4.29.3
+transformers >4.4.0, <4.30.2
 huggingface-hub <0.16  # hotfix, failing SDR for latest PT 1.11
 sacrebleu >=2.0.0, <=2.3.1

src/torchmetrics/classification/accuracy.py

@@ -48,6 +48,8 @@ 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.
+
     Args:
         threshold: Threshold for transforming probability to binary {0,1} predictions
         multidim_average:

src/torchmetrics/classification/confusion_matrix.py

@@ -57,12 +57,11 @@ class BinaryConfusionMatrix(Metric):
       element. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``.
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.
 
-    .. note::
-       Additional dimension ``...`` will be flattened into the batch dimension.
-
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
-    - ``bcm`` (:class:`~torch.Tensor`): A tensor containing a ``(2, 2)`` matrix
+    - ``confusion_matrix`` (:class:`~torch.Tensor`): A tensor containing a ``(2, 2)`` matrix
+
+    Additional dimension ``...`` will be flattened into the batch dimension.
 
     Args:
         threshold: Threshold for transforming probability to binary (0,1) predictions
@@ -179,27 +178,9 @@ class MulticlassConfusionMatrix(Metric):
       element. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``.
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.
 
-    .. note::
-       Additional dimension ``...`` will be flattened into the batch dimension.
-
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
-    - ``bcm`` (:class:`~torch.Tensor`): A tensor containing a ``(2, 2)`` matrix
-
-    ---
-
-    As input to 'update' the metric accepts the following input:
-
-    - ``preds``: ``(N, ...)`` (int tensor) or ``(N, C, ..)`` (float tensor). If preds is a floating point
-      we apply ``torch.argmax`` along the ``C`` dimension to automatically convert probabilities/logits into
-      an int tensor.
-    - ``target`` (int tensor): ``(N, ...)``
-
-    Additional dimension ``...`` will be flattened into the batch dimension.
-
-    As output of 'compute' the metric returns the following output:
-
-    - ``confusion matrix``: [num_classes, num_classes] matrix
+    - ``confusion_matrix``: [num_classes, num_classes] matrix
 
     Args:
         num_classes: Integer specifing the number of classes
@@ -322,8 +303,6 @@ class MultilabelConfusionMatrix(Metric):
       we convert to int tensor with thresholding using the value in ``threshold``.
     - ``target`` (int tensor): ``(N, C, ...)``
 
-    Additional dimension ``...`` will be flattened into the batch dimension.
-
     As output of 'compute' the metric returns the following output:
 
     - ``confusion matrix``: [num_labels,2,2] matrix