Merge branch 'master' into ci/PT-2.6

.github/workflows/ci-checks.yml

@@ -13,19 +13,19 @@ concurrency:
 
 jobs:
   check-code:
-    uses: Lightning-AI/utilities/.github/workflows/[email protected].8
+    uses: Lightning-AI/utilities/.github/workflows/[email protected].9
     with:
-      actions-ref: v0.11.8
+      actions-ref: v0.11.9
       extra-typing: "typing"
 
   check-schema:
-    uses: Lightning-AI/utilities/.github/workflows/[email protected].8
+    uses: Lightning-AI/utilities/.github/workflows/[email protected].9
 
   check-package:
     if: github.event.pull_request.draft == false
-    uses: Lightning-AI/utilities/.github/workflows/[email protected].8
+    uses: Lightning-AI/utilities/.github/workflows/[email protected].9
     with:
-      actions-ref: v0.11.8
+      actions-ref: v0.11.9
       artifact-name: dist-packages-${{ github.sha }}
       import-name: "torchmetrics"
       testing-matrix: |
@@ -35,7 +35,7 @@ jobs:
         }
 
   check-md-links:
-    uses: Lightning-AI/utilities/.github/workflows/[email protected].8
+    uses: Lightning-AI/utilities/.github/workflows/[email protected].9
     with:
       base-branch: master
       config-file: ".github/markdown-links-config.json"

.github/workflows/ci-tests.yml

@@ -198,7 +198,7 @@ jobs:
       - name: Upload coverage to Codecov
         # skip for PR if there is nothing to test, note that outside PR there is default 'unittests'
         if: ${{ env.TEST_DIRS != '' }}
-        uses: codecov/codecov-action@v4
+        uses: codecov/codecov-action@v5
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
           file: tests/coverage.xml

.github/workflows/clear-cache.yml

@@ -23,18 +23,18 @@ on:
 jobs:
   cron-clear:
     if: github.event_name == 'schedule' || github.event_name == 'pull_request'
-    uses: Lightning-AI/utilities/.github/workflows/[email protected].8
+    uses: Lightning-AI/utilities/.github/workflows/[email protected].9
     with:
-      scripts-ref: v0.11.7
+      scripts-ref: v0.11.9
       dry-run: ${{ github.event_name == 'pull_request' }}
       pattern: "pip-latest"
       age-days: 7
 
   direct-clear:
     if: github.event_name == 'workflow_dispatch' || github.event_name == 'pull_request'
-    uses: Lightning-AI/utilities/.github/workflows/[email protected].8
+    uses: Lightning-AI/utilities/.github/workflows/[email protected].9
     with:
-      scripts-ref: v0.11.8
+      scripts-ref: v0.11.9
       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/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@v1.11.0
+        uses: pypa/gh-action-pypi-publish@v1.12.2
         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@v1.11.0
+        uses: pypa/gh-action-pypi-publish@v1.12.2
         with:
           user: __token__
           password: ${{ secrets.pypi_password }}

CHANGELOG.md

@@ -27,7 +27,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
--
+- Fixed issue with shared state in metric collection when using dice score ([#2848](https://github.com/PyTorchLightning/metrics/pull/2848))
 
 
 ---

requirements/_docs.txt

@@ -13,7 +13,7 @@ sphinx-autobuild ==2024.10.3
 sphinx-gallery ==0.18.0
 
 lightning >=1.8.0, <2.5.0
-lightning-utilities ==0.11.8
+lightning-utilities ==0.11.9
 pydantic > 1.0.0, < 3.0.0
 
 # integrations

requirements/_doctest.txt

@@ -2,5 +2,5 @@
 #  in case you want to preserve/enforce restrictions on the latest compatible version, add "strict" as an in-line comment
 
 pytest >=8.0, <9.0
-pytest-doctestplus >=1.0, <1.3
-pytest-rerunfailures >=13.0, <15.0
+pytest-doctestplus >=1.0, <1.4
+pytest-rerunfailures >=13.0, <16.0

requirements/_tests.txt

@@ -6,8 +6,8 @@ coverage ==7.6.*
 codecov ==2.1.13
 pytest ==8.3.*
 pytest-cov ==6.0.0
-pytest-doctestplus ==1.2.1
-pytest-rerunfailures ==14.0
+pytest-doctestplus ==1.3.0
+pytest-rerunfailures ==15.0
 pytest-timeout ==2.3.1
 pytest-xdist ==3.6.1
 phmdoctest ==1.4.0
@@ -18,5 +18,5 @@ 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
+scikit-learn ==1.6.*; python_version > "3.8"  # we do not use `> =` because of oldest replcement
 cachier ==3.1.2

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, <4.47.0
+transformers >=4.42.3, <4.48.0
 piq <=0.8.0

requirements/text.txt

@@ -4,7 +4,7 @@
 nltk >3.8.1, <=3.9.1
 tqdm <4.68.0
 regex >=2021.9.24, <=2024.11.6
-transformers >4.4.0, <4.47.0
+transformers >4.4.0, <4.48.0
 mecab-python3 >=1.0.6, <1.1.0
 ipadic >=1.0.0, <1.1.0
 sentencepiece >=0.2.0, <0.3.0

src/torchmetrics/functional/classification/stat_scores.py

@@ -291,7 +291,7 @@ def _multiclass_stat_scores_tensor_validation(
             )
         if multidim_average != "global" and preds.ndim < 3:
             raise ValueError(
-                "If `preds` have one dimension more than `target`, the shape of `preds` should "
+                "If `preds` have one dimension more than `target`, the shape of `preds` should be"
                 " at least 3D when multidim_average is set to `samplewise`"
             )
 
@@ -303,7 +303,7 @@ def _multiclass_stat_scores_tensor_validation(
             )
         if multidim_average != "global" and preds.ndim < 2:
             raise ValueError(
-                "When `preds` and `target` have the same shape, the shape of `preds` should "
+                "When `preds` and `target` have the same shape, the shape of `preds` should be"
                 " at least 2D when multidim_average is set to `samplewise`"
             )
     else:

src/torchmetrics/segmentation/dice.py

@@ -131,8 +131,7 @@ def update(self, preds: Tensor, target: Tensor) -> None:
         )
         self.numerator.append(numerator)
         self.denominator.append(denominator)
-        if self.average == "weighted":
-            self.support.append(support)
+        self.support.append(support)
 
     def compute(self) -> Tensor:
         """Computes the Dice Score."""

tests/unittests/segmentation/test_dice.py

@@ -16,6 +16,7 @@
 import pytest
 import torch
 from sklearn.metrics import f1_score
+from torchmetrics import MetricCollection
 from torchmetrics.functional.segmentation.dice import dice_score
 from torchmetrics.segmentation.dice import DiceScore
 
@@ -106,3 +107,32 @@ def test_dice_score_functional(self, preds, target, input_format, include_backgr
                 "input_format": input_format,
             },
         )
+
+
+@pytest.mark.parametrize("compute_groups", [True, False])
+def test_dice_score_metric_collection(compute_groups: bool, num_batches: int = 4):
+    """Test that the metric works within a metric collection with and without compute groups."""
+    metric_collection = MetricCollection(
+        metrics={
+            "DiceScore (micro)": DiceScore(
+                num_classes=NUM_CLASSES,
+                average="micro",
+            ),
+            "DiceScore (macro)": DiceScore(
+                num_classes=NUM_CLASSES,
+                average="macro",
+            ),
+            "DiceScore (weighted)": DiceScore(
+                num_classes=NUM_CLASSES,
+                average="weighted",
+            ),
+        },
+        compute_groups=compute_groups,
+    )
+
+    for _ in range(num_batches):
+        metric_collection.update(_inputs1.preds, _inputs1.target)
+    result = metric_collection.compute()
+
+    assert isinstance(result, dict)
+    assert len(set(metric_collection.keys()) - set(result.keys())) == 0