Merge branch 'newmetric/move_dice' of https://github.com/Lightning-AI…

…/torchmetrics into newmetric/move_dice

.github/workflows/docs-build.yml

@@ -48,18 +48,11 @@ jobs:
           pytorch-version: ${{ matrix.pytorch-version }}
           pypi-dir: ${{ env.PYPI_CACHE }}
 
-      - name: Install Latex
-        if: ${{ matrix.target == 'html' }}
-        # install Texlive, see https://linuxconfig.org/how-to-install-latex-on-ubuntu-20-04-focal-fossa-linux
-        run: |
-          sudo apt-get update --fix-missing
-          sudo apt-get install -y \
-            texlive-latex-extra texlive-pictures texlive-fonts-recommended dvipng cm-super
-
       - name: Install package & dependencies
         run: |
           make get-sphinx-template
-          pip install . -U -r requirements/_docs.txt \
+          # install with -e so the path to source link comes from this project not from the installed package
+          pip install -e . -U -r requirements/_docs.txt \
             --find-links="${PYPI_CACHE}" --find-links="${TORCH_URL}"
       - run: pip list
       - name: Full build for deployment
@@ -70,7 +63,10 @@ jobs:
         run: echo "SPHINX_ENABLE_GALLERY=0" >> $GITHUB_ENV
       - name: make ${{ matrix.target }}
         working-directory: ./docs
-        run: make ${{ matrix.target }} --debug --jobs $(nproc) SPHINXOPTS="-W --keep-going"
+        run: |
+          pwd
+          ls -la
+          make ${{ matrix.target }} --debug --jobs $(nproc) SPHINXOPTS="-W --keep-going"
 
       - name: Upload built docs
         if: ${{ matrix.target == 'html' && github.event_name != 'pull_request' }}

CHANGELOG.md

@@ -38,16 +38,33 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Deprecated Dice from classification metrics ([#2725](https://github.com/Lightning-AI/torchmetrics/pull/2725))
 
 
+### Deprecated
+
+- Deprecated `num_outputs` in `R2Score` ([#2705](https://github.com/Lightning-AI/torchmetrics/pull/2705))
+
+
 ### Removed
 
 -
 
 
 ### Fixed
 
+- Fixed wrong aggregation in `segmentation.MeanIoU` ([#2698](https://github.com/Lightning-AI/torchmetrics/pull/2698))
+
+
+- Fixed handling zero division error in binary IoU (Jaccard index) calculation ([#2726](https://github.com/Lightning-AI/torchmetrics/pull/2726))
+
+
 - Correct the padding related calculation errors in SSIM ([#2721](https://github.com/Lightning-AI/torchmetrics/pull/2721))
 
 
+- Fixed compatibility of audio domain with new `scipy` ([#2733](https://github.com/Lightning-AI/torchmetrics/pull/2733))
+
+
+- Fixed how `prefix`/`postfix` works in `MultitaskWrapper` ([#2722](https://github.com/Lightning-AI/torchmetrics/pull/2722))
+
+
 ## [1.4.1] - 2024-08-02
 
 ### Changed

docs/source/conf.py

@@ -269,6 +269,11 @@ def _set_root_image_path(page_path: str) -> None:
     ),
 ]
 
+# MathJax configuration
+mathjax3_config = {
+    "tex": {"packages": {"[+]": ["ams", "newcommand", "configMacros"]}},
+}
+
 # -- Options for Epub output -------------------------------------------------
 
 # Bibliographic Dublin Core info.
@@ -358,8 +363,7 @@ def package_list_from_file(file: str) -> list[str]:
 autodoc_mock_imports = MOCK_PACKAGES
 
 
-# Resolve function
-# This function is used to populate the (source) links in the API
+# Resolve function - this function is used to populate the (source) links in the API
 def linkcode_resolve(domain, info) -> Optional[str]:  # noqa: ANN001
     return _linkcode_resolve(domain, info=info, github_user="Lightning-AI", github_repo="torchmetrics")
 

docs/source/links.rst

@@ -91,7 +91,7 @@
 .. _CER: https://rechtsprechung-im-ostseeraum.archiv.uni-greifswald.de/word-error-rate-character-error-rate-how-to-evaluate-a-model
 .. _MER: https://www.isca-speech.org/archive/interspeech_2004/morris04_interspeech.html
 .. _WIL: https://www.isca-speech.org/archive/interspeech_2004/morris04_interspeech.html
-.. _WIP: https://infoscience.epfl.ch/entities/publication/9983d013-8239-422e-a3f7-a1500d309474
+.. _WIP: https://www.isca-archive.org/interspeech_2004/morris04_interspeech.pdf
 .. _TV: https://en.wikipedia.org/wiki/Total_variation_denoising
 .. _InfoLM: https://arxiv.org/abs/2112.01589
 .. _alpha divergence: https://static.renyi.hu/renyi_cikkek/1961_on_measures_of_entropy_and_information.pdf

requirements/classification_test.txt

@@ -1,7 +1,7 @@
 # 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
 
-pandas >=1.4.0, <=2.2.2
+pandas >1.4.0, <=2.2.2
 netcal >1.0.0, <1.4.0 # calibration_error
 numpy <2.2.0
 fairlearn # group_fairness

requirements/detection_test.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
 
-faster-coco-eval >=1.3.3
+faster-coco-eval ==1.5.*

requirements/nominal_test.txt

@@ -1,7 +1,7 @@
 # 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
 
-pandas >1.0.0, <=2.2.2 # cannot pin version due to numpy version incompatibility
-dython <=0.7.7
+pandas >1.4.0, <=2.2.2 # cannot pin version due to numpy version incompatibility
+dython ~=0.7.6
 scipy >1.0.0, <1.15.0 # cannot pin version due to some version conflicts with `oldest` CI configuration
 statsmodels >0.13.5, <0.15.0

requirements/typing.txt

@@ -1,5 +1,5 @@
 mypy ==1.11.2
-torch ==2.4.0
+torch ==2.4.1
 
 types-PyYAML
 types-emoji

requirements/visual.txt

@@ -1,5 +1,5 @@
 # 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
 
-matplotlib >=3.3.0, <3.10.0
+matplotlib >=3.6.0, <3.10.0
 SciencePlots >= 2.0.0, <2.2.0

src/torchmetrics/__init__.py

@@ -20,6 +20,13 @@
     if not hasattr(PIL, "PILLOW_VERSION"):
         PIL.PILLOW_VERSION = PIL.__version__
 
+if package_available("scipy"):
+    import scipy.signal
+
+    # back compatibility patch due to SMRMpy using scipy.signal.hamming
+    if not hasattr(scipy.signal, "hamming"):
+        scipy.signal.hamming = scipy.signal.windows.hamming
+
 from torchmetrics import functional  # noqa: E402
 from torchmetrics.aggregation import (  # noqa: E402
     CatMetric,

src/torchmetrics/classification/dice.py

@@ -116,9 +116,9 @@ class Dice(Metric):
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     .. warning::
-        The `dice` metrics is being deprecated from the classification subpackage in v1.6.0 of torchmetrics and will be
-        removed in v1.7.0. Please instead consider using `f1score` metric from the classification subpackage as it
-        provides the same functionality. Additionally, we are going to re-add the `dice` metric in the segmentation
+        The ``dice`` metrics is being deprecated from the classification subpackage in v1.6.0 of torchmetrics and will be
+        removed in v1.7.0. Please instead consider using ``f1score`` metric from the classification subpackage as it
+        provides the same functionality. Additionally, we are going to re-add the ``dice`` metric in the segmentation
         domain in v1.6.0 with slight modifications to functionality.
 
     Raises:

src/torchmetrics/functional/classification/dice.py

@@ -152,9 +152,9 @@ def dice(
             than what they appear to be.
 
     .. warning::
-        The `dice` metrics is being deprecated from the classification subpackage in v1.6.0 of torchmetrics and will be
-        removed in v1.7.0. Please instead consider using `f1score` metric from the classification subpackage as it
-        provides the same functionality. Additionally, we are going to re-add the `dice` metric in the segmentation
+        The ``dice`` metrics is being deprecated from the classification subpackage in v1.6.0 of torchmetrics and will be
+        removed in v1.7.0. Please instead consider using ``f1score`` metric from the classification subpackage as it
+        provides the same functionality. Additionally, we are going to re-add the ``dice`` metric in the segmentation
         domain in v1.6.0 with slight modifications to functionality.
 
     Return:

src/torchmetrics/functional/classification/jaccard.py

@@ -67,7 +67,7 @@ def _jaccard_index_reduce(
         raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
     confmat = confmat.float()
     if average == "binary":
-        return confmat[1, 1] / (confmat[0, 1] + confmat[1, 0] + confmat[1, 1])
+        return _safe_divide(confmat[1, 1], (confmat[0, 1] + confmat[1, 0] + confmat[1, 1]), zero_division=zero_division)
 
     ignore_index_cond = ignore_index is not None and 0 <= ignore_index < confmat.shape[0]
     multilabel = confmat.ndim == 3

src/torchmetrics/functional/nominal/__init__.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
 from torchmetrics.functional.nominal.cramers import cramers_v, cramers_v_matrix
 from torchmetrics.functional.nominal.fleiss_kappa import fleiss_kappa
 from torchmetrics.functional.nominal.pearson import (

src/torchmetrics/metric.py

@@ -284,7 +284,7 @@ def forward(self, *args: Any, **kwargs: Any) -> Any:
         """Aggregate and evaluate batch input directly.
 
         Serves the dual purpose of both computing the metric on the current batch of inputs but also add the batch
-        statistics to the overall accumululating metric state. Input arguments are the exact same as corresponding
+        statistics to the overall accumulating metric state. Input arguments are the exact same as corresponding
         ``update`` method. The returned output is the exact same as the output of ``compute``.
 
         Args:
@@ -361,7 +361,7 @@ def _forward_full_state_update(self, *args: Any, **kwargs: Any) -> Any:
     def _forward_reduce_state_update(self, *args: Any, **kwargs: Any) -> Any:
         """Forward computation using single call to `update`.
 
-        This can be done when the global metric state is a sinple reduction of batch states. This can be unsafe for
+        This can be done when the global metric state is a simple reduction of batch states. This can be unsafe for
         certain metric cases but is also the fastest way to both accumulate globally and compute locally.
 
         """
@@ -802,7 +802,7 @@ def _apply(self, fn: Callable, exclude_state: Sequence[str] = "") -> Module:
         """Overwrite `_apply` function such that we can also move metric states to the correct device.
 
         This method is called by the base ``nn.Module`` class whenever `.to`, `.cuda`, `.float`, `.half` etc. methods
-        are called. Dtype conversion is garded and will only happen through the special `set_dtype` method.
+        are called. Dtype conversion is guarded and will only happen through the special `set_dtype` method.
 
         Args:
             fn: the function to apply
@@ -1166,15 +1166,15 @@ def _sync_dist(self, dist_sync_fn: Optional[Callable] = None, process_group: Opt
         """
 
     def update(self, *args: Any, **kwargs: Any) -> None:
-        """Redirect the call to the input which the conposition was formed from."""
+        """Redirect the call to the input which the composition was formed from."""
         if isinstance(self.metric_a, Metric):
             self.metric_a.update(*args, **self.metric_a._filter_kwargs(**kwargs))
 
         if isinstance(self.metric_b, Metric):
             self.metric_b.update(*args, **self.metric_b._filter_kwargs(**kwargs))
 
     def compute(self) -> Any:
-        """Redirect the call to the input which the conposition was formed from."""
+        """Redirect the call to the input which the composition was formed from."""
         # also some parsing for kwargs?
         val_a = self.metric_a.compute() if isinstance(self.metric_a, Metric) else self.metric_a
         val_b = self.metric_b.compute() if isinstance(self.metric_b, Metric) else self.metric_b
@@ -1216,7 +1216,7 @@ def forward(self, *args: Any, **kwargs: Any) -> Any:
         return self._forward_cache
 
     def reset(self) -> None:
-        """Redirect the call to the input which the conposition was formed from."""
+        """Redirect the call to the input which the composition was formed from."""
         if isinstance(self.metric_a, Metric):
             self.metric_a.reset()
 

src/torchmetrics/nominal/__init__.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
 from torchmetrics.nominal.cramers import CramersV
 from torchmetrics.nominal.fleiss_kappa import FleissKappa
 from torchmetrics.nominal.pearson import PearsonsContingencyCoefficient

src/torchmetrics/regression/r2.py

@@ -13,11 +13,11 @@
 # limitations under the License.
 from typing import Any, Optional, Sequence, Union
 
-import torch
 from torch import Tensor, tensor
 
 from torchmetrics.functional.regression.r2 import _r2_score_compute, _r2_score_update
 from torchmetrics.metric import Metric
+from torchmetrics.utilities import rank_zero_warn
 from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
 from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
 
@@ -65,23 +65,32 @@ class R2Score(Metric):
             * ``'variance_weighted'`` scores are weighted by their individual variances
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
+    .. warning::
+        Argument ``num_outputs`` in ``R2Score`` has been deprecated because it is no longer necessary and will be
+        removed in v1.6.0 of TorchMetrics. The number of outputs is now automatically inferred from the shape
+        of the input tensors.
+
     Raises:
         ValueError:
             If ``adjusted`` parameter is not an integer larger or equal to 0.
         ValueError:
             If ``multioutput`` is not one of ``"raw_values"``, ``"uniform_average"`` or ``"variance_weighted"``.
 
-    Example:
+    Example (single output):
+        >>> from torch import tensor
         >>> from torchmetrics.regression import R2Score
-        >>> target = torch.tensor([3, -0.5, 2, 7])
-        >>> preds = torch.tensor([2.5, 0.0, 2, 8])
+        >>> target = tensor([3, -0.5, 2, 7])
+        >>> preds = tensor([2.5, 0.0, 2, 8])
         >>> r2score = R2Score()
         >>> r2score(preds, target)
         tensor(0.9486)
 
-        >>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]])
-        >>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]])
-        >>> r2score = R2Score(num_outputs=2, multioutput='raw_values')
+    Example (multioutput):
+        >>> from torch import tensor
+        >>> from torchmetrics.regression import R2Score
+        >>> target = tensor([[0.5, 1], [-1, 1], [7, -6]])
+        >>> preds = tensor([[0, 2], [-1, 2], [8, -5]])
+        >>> r2score = R2Score(multioutput='raw_values')
         >>> r2score(preds, target)
         tensor([0.9654, 0.9082])
 
@@ -100,14 +109,20 @@ class R2Score(Metric):
 
     def __init__(
         self,
-        num_outputs: int = 1,
+        num_outputs: Optional[int] = None,
         adjusted: int = 0,
         multioutput: str = "uniform_average",
         **kwargs: Any,
     ) -> None:
         super().__init__(**kwargs)
 
-        self.num_outputs = num_outputs
+        if num_outputs is not None:
+            rank_zero_warn(
+                "Argument `num_outputs` in `R2Score` has been deprecated because it is no longer necessary and will be"
+                "removed in v1.6.0 of TorchMetrics. The number of outputs is now automatically inferred from the shape"
+                "of the input tensors.",
+                DeprecationWarning,
+            )
 
         if adjusted < 0 or not isinstance(adjusted, int):
             raise ValueError("`adjusted` parameter should be an integer larger or equal to 0.")
@@ -120,19 +135,19 @@ def __init__(
             )
         self.multioutput = multioutput
 
-        self.add_state("sum_squared_error", default=torch.zeros(self.num_outputs), dist_reduce_fx="sum")
-        self.add_state("sum_error", default=torch.zeros(self.num_outputs), dist_reduce_fx="sum")
-        self.add_state("residual", default=torch.zeros(self.num_outputs), dist_reduce_fx="sum")
+        self.add_state("sum_squared_error", default=tensor(0.0), dist_reduce_fx="sum")
+        self.add_state("sum_error", default=tensor(0.0), dist_reduce_fx="sum")
+        self.add_state("residual", default=tensor(0.0), dist_reduce_fx="sum")
         self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
 
     def update(self, preds: Tensor, target: Tensor) -> None:
         """Update state with predictions and targets."""
         sum_squared_error, sum_error, residual, total = _r2_score_update(preds, target)
 
-        self.sum_squared_error += sum_squared_error
-        self.sum_error += sum_error
-        self.residual += residual
-        self.total += total
+        self.sum_squared_error = self.sum_squared_error + sum_squared_error
+        self.sum_error = self.sum_error + sum_error
+        self.residual = self.residual + residual
+        self.total = self.total + total
 
     def compute(self) -> Tensor:
         """Compute r2 score over the metric states."""

src/torchmetrics/segmentation/mean_iou.py

@@ -110,7 +110,8 @@ def __init__(
         self.input_format = input_format
 
         num_classes = num_classes - 1 if not include_background else num_classes
-        self.add_state("score", default=torch.zeros(num_classes if per_class else 1), dist_reduce_fx="mean")
+        self.add_state("score", default=torch.zeros(num_classes if per_class else 1), dist_reduce_fx="sum")
+        self.add_state("num_batches", default=torch.tensor(0), dist_reduce_fx="sum")
 
     def update(self, preds: Tensor, target: Tensor) -> None:
         """Update the state with the new data."""
@@ -119,10 +120,11 @@ def update(self, preds: Tensor, target: Tensor) -> None:
         )
         score = _mean_iou_compute(intersection, union, per_class=self.per_class)
         self.score += score.mean(0) if self.per_class else score.mean()
+        self.num_batches += 1
 
     def compute(self) -> Tensor:
-        """Update the state with the new data."""
-        return self.score  # / self.num_batches
+        """Compute the final Mean Intersection over Union (mIoU)."""
+        return self.score / self.num_batches
 
     def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_AX_TYPE] = None) -> _PLOT_OUT_TYPE:
         """Plot a single or multiple values from the metric.

src/torchmetrics/utilities/imports.py

@@ -64,6 +64,7 @@
 _MECAB_KO_DIC_AVAILABLE = RequirementCache("mecab_ko_dic")
 _IPADIC_AVAILABLE = RequirementCache("ipadic")
 _SENTENCEPIECE_AVAILABLE = RequirementCache("sentencepiece")
+_SCIPI_AVAILABLE = RequirementCache("scipy")
 _SKLEARN_GREATER_EQUAL_1_3 = RequirementCache("scikit-learn>=1.3.0")
 
 _LATEX_AVAILABLE: bool = shutil.which("latex") is not None