releasing minor 1.5.2 [rebase & merge]

.github/CONTRIBUTING.md

@@ -103,7 +103,7 @@ def my_func(param_a: int, param_b: Optional[float] = None) -> str:
         >>> my_func(1, 2)
         3
 
-    .. note:: If you want to add something.
+    .. hint:: If you want to add something.
     """
     p = param_b if param_b else 0
     return str(param_a + p)

.github/workflows/ci-checks.yml

@@ -13,29 +13,29 @@ concurrency:
 
 jobs:
   check-code:
-    uses: Lightning-AI/utilities/.github/workflows/[email protected].7
+    uses: Lightning-AI/utilities/.github/workflows/[email protected].8
     with:
-      actions-ref: v0.11.7
+      actions-ref: v0.11.8
       extra-typing: "typing"
 
   check-schema:
-    uses: Lightning-AI/utilities/.github/workflows/[email protected].7
+    uses: Lightning-AI/utilities/.github/workflows/[email protected].8
 
   check-package:
     if: github.event.pull_request.draft == false
-    uses: Lightning-AI/utilities/.github/workflows/[email protected].7
+    uses: Lightning-AI/utilities/.github/workflows/[email protected].8
     with:
-      actions-ref: v0.11.7
+      actions-ref: v0.11.8
       artifact-name: dist-packages-${{ github.sha }}
       import-name: "torchmetrics"
       testing-matrix: |
         {
-          "os": ["ubuntu-22.04", "macos-12", "windows-2022"],
+          "os": ["ubuntu-22.04", "macos-13", "windows-2022"],
           "python-version": ["3.8", "3.11"]
         }
 
   check-md-links:
-    uses: Lightning-AI/utilities/.github/workflows/[email protected].7
+    uses: Lightning-AI/utilities/.github/workflows/[email protected].8
     with:
       base-branch: master
       config-file: ".github/markdown-links-config.json"

.github/workflows/ci-integrate.yml

@@ -26,12 +26,12 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: ["ubuntu-22.04", "macOS-12", "windows-2022"]
-        python-version: ["3.8", "3.10"]
+        os: ["ubuntu-22.04", "macOS-13", "windows-2022"]
+        python-version: ["3.9", "3.11"]
         requires: ["oldest", "latest"]
         exclude:
-          - { python-version: "3.10", requires: "oldest" }
-          - { python-version: "3.10", os: "windows" } # todo: https://discuss.pytorch.org/t/numpy-is-not-available-error/146192
+          - { python-version: "3.11", requires: "oldest" }
+          - { python-version: "3.11", os: "windows" } # todo: https://discuss.pytorch.org/t/numpy-is-not-available-error/146192
         include:
           - { python-version: "3.10", requires: "latest", os: "ubuntu-22.04" }
           # - { python-version: "3.10", requires: "latest", os: "macOS-14" } # M1 machine # todo: crashing for MPS out of memory
@@ -53,6 +53,8 @@ jobs:
 
       - name: source cashing
         uses: ./.github/actions/pull-caches
+        with:
+          requires: ${{ matrix.requires }}
       - name: set oldest if/only for integrations
         if: matrix.requires == 'oldest'
         run: python .github/assistant.py set-oldest-versions --req_files='["requirements/_integrate.txt"]'

.github/workflows/ci-tests.yml

@@ -46,7 +46,7 @@ jobs:
           - "2.5.0"
         include:
           # cover additional python and PT combinations
-          - { os: "ubuntu-22.04", python-version: "3.8", pytorch-version: "1.13.1" }
+          - { os: "ubuntu-20.04", python-version: "3.8", pytorch-version: "1.13.1", requires: "oldest" }
           - { os: "ubuntu-22.04", python-version: "3.10", pytorch-version: "2.0.1" }
           - { os: "ubuntu-22.04", python-version: "3.10", pytorch-version: "2.2.2" }
           - { os: "ubuntu-22.04", python-version: "3.11", pytorch-version: "2.3.1" }

.github/workflows/clear-cache.yml

@@ -23,7 +23,7 @@ on:
 jobs:
   cron-clear:
     if: github.event_name == 'schedule' || github.event_name == 'pull_request'
-    uses: Lightning-AI/utilities/.github/workflows/[email protected].7
+    uses: Lightning-AI/utilities/.github/workflows/[email protected].8
     with:
       scripts-ref: v0.11.7
       dry-run: ${{ github.event_name == 'pull_request' }}
@@ -32,9 +32,9 @@ jobs:
 
   direct-clear:
     if: github.event_name == 'workflow_dispatch' || github.event_name == 'pull_request'
-    uses: Lightning-AI/utilities/.github/workflows/[email protected].7
+    uses: Lightning-AI/utilities/.github/workflows/[email protected].8
     with:
-      scripts-ref: v0.11.7
+      scripts-ref: v0.11.8
       dry-run: ${{ github.event_name == 'pull_request' }}
       pattern: ${{ inputs.pattern || 'pypi_wheels' }} # setting str in case of PR / debugging
       age-days: ${{ fromJSON(inputs.age-days) || 0 }} # setting 0 in case of PR / debugging

.github/workflows/docs-build.yml

@@ -44,7 +44,6 @@ jobs:
       - name: source cashing
         uses: ./.github/actions/pull-caches
         with:
-          requires: ${{ matrix.requires }}
           pytorch-version: ${{ matrix.pytorch-version }}
           pypi-dir: ${{ env.PYPI_CACHE }}
 

.github/workflows/publish-pkg.yml

@@ -67,7 +67,7 @@ jobs:
       - run: ls -lh dist/
       # We do this, since failures on test.pypi aren't that bad
       - name: Publish to Test PyPI
-        uses: pypa/gh-action-pypi-publish@v1.10.2
+        uses: pypa/gh-action-pypi-publish@v1.11.0
         with:
           user: __token__
           password: ${{ secrets.test_pypi_password }}
@@ -94,7 +94,7 @@ jobs:
           path: dist
       - run: ls -lh dist/
       - name: Publish distribution 📦 to PyPI
-        uses: pypa/gh-action-pypi-publish@v1.10.2
+        uses: pypa/gh-action-pypi-publish@v1.11.0
         with:
           user: __token__
           password: ${{ secrets.pypi_password }}

CHANGELOG.md

@@ -6,6 +6,22 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 **Note: we move fast, but still we preserve 0.1 version (one feature release) back compatibility.**
 
+---
+
+## [1.5.2] - 2024-11-07
+
+### Changed
+
+- Re-adding `numpy` 2+ support ([#2804](https://github.com/Lightning-AI/torchmetrics/pull/2804))
+
+### Fixed
+
+- Fixed iou scores in detection for either empty predictions/targets leading to wrong scores ([#2805](https://github.com/Lightning-AI/torchmetrics/pull/2805))
+- Fixed `MetricCollection` compatibility with `torch.jit.script` ([#2813](https://github.com/Lightning-AI/torchmetrics/pull/2813))
+- Fixed assert in PIT ([#2811](https://github.com/Lightning-AI/torchmetrics/pull/2811))
+- Pathed `np.Inf` for `numpy` 2.0+ ([#2826](https://github.com/Lightning-AI/torchmetrics/pull/2826))
+
+
 ---
 
 ## [1.5.1] - 2024-10-22

docs/source/pages/implement.rst

@@ -257,7 +257,7 @@ and tests gets formatted in the following way:
   3. ``new_metric(...)``: essentially wraps the ``_update`` and ``_compute`` private functions into one public function that
      makes up the functional interface for the metric.
 
-  .. note::
+  .. hint::
      The `functional mean squared error <https://github.com/Lightning-AI/torchmetrics/blob/master/src/torchmetrics/functional/regression/mse.py>`_
      metric is a is a great example of how to divide the logic.
 
@@ -270,9 +270,9 @@ and tests gets formatted in the following way:
      ``_new_metric_compute(...)`` function in its ``compute``. No logic should really be implemented in the module interface.
      We do this to not have duplicate code to maintain.
 
-   .. note::
-     The module `MeanSquaredError <https://github.com/Lightning-AI/torchmetrics/blob/master/src/torchmetrics/regression/mse.py>`_
-     metric that corresponds to the above functional example showcases these steps.
+  .. note::
+    The module `MeanSquaredError <https://github.com/Lightning-AI/torchmetrics/blob/master/src/torchmetrics/regression/mse.py>`_
+    metric that corresponds to the above functional example showcases these steps.
 
 4. Remember to add binding to the different relevant ``__init__`` files.
 
@@ -291,7 +291,7 @@ and tests gets formatted in the following way:
      so that different combinations of inputs and parameters get tested.
   5. (optional) If your metric raises any exception, please add tests that showcase this.
 
-  .. note::
+  .. hint::
     The `test file for MSE <https://github.com/Lightning-AI/torchmetrics/blob/master/tests/unittests/regression/test_mean_error.py>`_
     metric shows how to implement such tests.
 

docs/source/pages/lightning.rst

@@ -13,7 +13,7 @@ TorchMetrics in PyTorch Lightning
 TorchMetrics was originally created as part of `PyTorch Lightning <https://github.com/Lightning-AI/pytorch-lightning>`_, a powerful deep learning research
 framework designed for scaling models without boilerplate.
 
-.. note::
+.. caution::
 
     TorchMetrics always offers compatibility with the last 2 major PyTorch Lightning versions, but we recommend always
     keeping both frameworks up-to-date for the best experience.
@@ -69,9 +69,9 @@ LightningModule `self.log <https://lightning.ai/docs/pytorch/stable/extensions/l
 method, Lightning will log the metric based on ``on_step`` and ``on_epoch`` flags present in ``self.log(...)``. If
 ``on_epoch`` is True, the logger automatically logs the end of epoch metric value by calling ``.compute()``.
 
-.. note::
+.. caution::
 
-    ``sync_dist``, ``sync_dist_group`` and ``reduce_fx`` flags from ``self.log(...)`` don't affect the metric logging
+    The ``sync_dist``, ``sync_dist_group`` and ``reduce_fx`` flags from ``self.log(...)`` don't affect the metric logging
     in any manner. The metric class contains its own distributed synchronization logic.
 
     This, however is only true for metrics that inherit the base class ``Metric``,
@@ -136,7 +136,7 @@ Note that logging metrics this way will require you to manually reset the metric
 In general, we recommend logging the metric object to make sure that metrics are correctly computed and reset.
 Additionally, we highly recommend that the two ways of logging are not mixed as it can lead to wrong results.
 
-.. note::
+.. hint::
 
     When using any Modular metric, calling ``self.metric(...)`` or ``self.metric.forward(...)`` serves the dual purpose
     of calling ``self.metric.update()`` on its input and simultaneously returning the metric value over the provided

docs/source/pages/overview.rst

@@ -61,13 +61,13 @@ This metrics API is independent of PyTorch Lightning. Metrics can directly be us
     It is highly recommended to re-initialize the metric per mode as
     shown in the examples above.
 
-.. note::
+.. caution::
 
     Metric states are **not** added to the models ``state_dict`` by default.
     To change this, after initializing the metric, the method ``.persistent(mode)`` can
     be used to enable (``mode=True``) or disable (``mode=False``) this behaviour.
 
-.. note::
+.. important::
 
     Due to specialized logic around metric states, we in general do **not** recommend
     that metrics are initialized inside other metrics (nested metrics), as this can lead
@@ -306,7 +306,7 @@ This pattern is implemented for the following operators (with ``a`` being metric
 * Positive Value (``pos(a)``)
 * Indexing (``a[0]``)
 
-.. note::
+.. caution::
 
     Some of these operations are only fully supported from Pytorch v1.4 and onwards, explicitly we found:
     ``add``, ``mul``, ``rmatmul``, ``rsub``, ``rmod``
@@ -381,7 +381,7 @@ inside your LightningModule. In most cases we just have to replace ``self.log``
             # remember to reset metrics at the end of the epoch
             self.valid_metrics.reset()
 
-.. note::
+.. important::
 
     `MetricCollection` as default assumes that all the metrics in the collection
     have the same call signature. If this is not the case, input that should be

requirements/_tests.txt

@@ -18,4 +18,4 @@ fire ==0.7.*
 cloudpickle >1.3, <=3.1.0
 scikit-learn ==1.2.*; python_version < "3.9"
 scikit-learn ==1.5.*; python_version > "3.8"  # we do not use `> =` because of oldest replcement
-cachier ==3.0.1
+cachier ==3.1.2

requirements/audio.txt

@@ -3,9 +3,10 @@
 
 # this need to be the same as used inside speechmetrics
 pesq >=0.0.4, <0.0.5
+numpy <2.0  # strict, for compatibility reasons
 pystoi >=0.4.0, <0.5.0
 torchaudio >=0.10.0, <2.6.0
 gammatone >=1.0.0, <1.1.0
 librosa >=0.9.0, <0.11.0
-onnxruntime >=1.12.0, <1.20 # installing onnxruntime_gpu-gpu failed on macos
+onnxruntime >=1.12.0, <1.21 # installing onnxruntime_gpu-gpu failed on macos
 requests >=2.19.0, <2.33.0

requirements/base.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
 
-numpy >1.20.0, <2.0  # strict, for compatibility reasons
+numpy >1.20.0
 packaging >17.1
 torch >=1.10.0, <2.6.0
 typing-extensions; python_version < '3.9'

requirements/multimodal.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
 
-transformers >=4.42.3, <4.46.0
+transformers >=4.42.3, <4.47.0
 piq <=0.8.0

requirements/text.txt

@@ -4,7 +4,7 @@
 nltk >3.8.1, <=3.9.1
 tqdm <4.67.0
 regex >=2021.9.24, <=2024.9.11
-transformers >4.4.0, <4.46.0
+transformers >4.4.0, <4.47.0
 mecab-python3 >=1.0.6, <1.1.0
 ipadic >=1.0.0, <1.1.0
 sentencepiece >=0.2.0, <0.3.0

requirements/typing.txt

@@ -1,5 +1,5 @@
-mypy ==1.11.2
-torch ==2.5.0
+mypy ==1.13.0
+torch ==2.5.1
 
 types-PyYAML
 types-emoji

src/conftest.py

@@ -36,5 +36,5 @@ def collect(self) -> GeneratorExit:
     def pytest_collect_file(parent: Path, path: Path) -> Optional[DoctestModule]:
         """Collect doctests and add the reset_random_seed fixture."""
         if path.ext == ".py":
-            return DoctestModule.from_parent(parent, fspath=path)
+            return DoctestModule.from_parent(parent, path=Path(path))
         return None

src/torchmetrics/__about__.py

@@ -1,4 +1,4 @@
-__version__ = "1.5.1"
+__version__ = "1.5.2"
 __author__ = "Lightning-AI et al."
 __author_email__ = "[email protected]"
 __license__ = "Apache-2.0"

src/torchmetrics/__init__.py

@@ -14,6 +14,13 @@
 _PACKAGE_ROOT = os.path.dirname(__file__)
 _PROJECT_ROOT = os.path.dirname(_PACKAGE_ROOT)
 
+if package_available("numpy"):
+    # compatibility for AttributeError: `np.Inf` was removed in the NumPy 2.0 release. Use `np.inf` instead
+    import numpy
+
+    numpy.Inf = numpy.inf
+
+
 if package_available("PIL"):
     import PIL
 

src/torchmetrics/audio/dnsmos.py

@@ -54,11 +54,13 @@ class DeepNoiseSuppressionMeanOpinionScore(Metric):
     - ``dnsmos`` (:class:`~torch.Tensor`): float tensor of DNSMOS values reduced across the batch
         with shape ``(...,4)`` indicating [p808_mos, mos_sig, mos_bak, mos_ovr] in the last dim.
 
-    .. note:: using this metric requires you to have ``librosa``, ``onnxruntime`` and ``requests`` installed.
+    .. hint::
+        Using this metric requires you to have ``librosa``, ``onnxruntime`` and ``requests`` installed.
         Install as ``pip install torchmetrics['audio']`` or alternatively `pip install librosa onnxruntime-gpu requests`
         (if you do not have GPU enabled machine install `onnxruntime` instead of `onnxruntime-gpu`)
 
-    .. note:: the ``forward`` and ``compute`` methods in this class return a reduced DNSMOS value
+    .. caution::
+        The ``forward`` and ``compute`` methods in this class return a reduced DNSMOS value
         for a batch. To obtain the DNSMOS value for each sample, you may use the functional counterpart in
         :func:`~torchmetrics.functional.audio.dnsmos.deep_noise_suppression_mean_opinion_score`.
 

src/torchmetrics/audio/pesq.py

@@ -45,12 +45,14 @@ class PerceptualEvaluationSpeechQuality(Metric):
 
     - ``pesq`` (:class:`~torch.Tensor`): float tensor of PESQ value reduced across the batch
 
-    .. note:: using this metrics requires you to have ``pesq`` install. Either install as ``pip install
+    .. hint::
+        Using this metrics requires you to have ``pesq`` install. Either install as ``pip install
         torchmetrics[audio]`` or ``pip install pesq``. ``pesq`` will compile with your currently
         installed version of numpy, meaning that if you upgrade numpy at some point in the future you will
         most likely have to reinstall ``pesq``.
 
-    .. note:: the ``forward`` and ``compute`` methods in this class return a single (reduced) PESQ value
+    .. caution::
+        The ``forward`` and ``compute`` methods in this class return a single (reduced) PESQ value
         for a batch. To obtain a PESQ value for each sample, you may use the functional counterpart in
         :func:`~torchmetrics.functional.audio.pesq.perceptual_evaluation_speech_quality`.
 

src/torchmetrics/audio/srmr.py

@@ -49,11 +49,12 @@ class SpeechReverberationModulationEnergyRatio(Metric):
 
     - ``srmr`` (:class:`~torch.Tensor`): float scaler tensor
 
-    .. note:: using this metrics requires you to have ``gammatone`` and ``torchaudio`` installed.
+    .. hint::
+        Using this metrics requires you to have ``gammatone`` and ``torchaudio`` installed.
         Either install as ``pip install torchmetrics[audio]`` or ``pip install torchaudio``
         and ``pip install git+https://github.com/detly/gammatone``.
 
-    .. note::
+    .. attention::
         This implementation is experimental, and might not be consistent with the matlab
         implementation `SRMRToolbox`_, especially the fast implementation.
         The slow versions, a) fast=False, norm=False, max_cf=128, b) fast=False, norm=True, max_cf=30, have

src/torchmetrics/audio/stoi.py

@@ -50,7 +50,8 @@ class ShortTimeObjectiveIntelligibility(Metric):
 
     - ``stoi`` (:class:`~torch.Tensor`): float scalar tensor
 
-    .. note:: using this metrics requires you to have ``pystoi`` install. Either install as ``pip install
+    .. hint::
+        Using this metrics requires you to have ``pystoi`` install. Either install as ``pip install
         torchmetrics[audio]`` or ``pip install pystoi``.
 
     Args:

src/torchmetrics/classification/calibration_error.py

@@ -214,7 +214,7 @@ class MulticlassCalibrationError(Metric):
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)`` containing ground truth labels, and
       therefore only contain values in the [0, n_classes-1] range (except if `ignore_index` is specified).
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output: