Fix issue with shared state of MetricCollection compute group when using DiceScore(average="weighted")

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))
 
 
 ---

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