Fix missing reset in classwise wrapper

CHANGELOG.md

@@ -40,7 +40,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
--
+- Fixed missing reset in `ClasswiseWrapper` ([#1129](https://github.com/Lightning-AI/metrics/pull/1129))
 
 
 -

src/torchmetrics/wrappers/classwise.py

@@ -51,6 +51,8 @@ class ClasswiseWrapper(Metric):
         'recall_horse': tensor(0.), 'recall_fish': tensor(0.3333), 'recall_dog': tensor(0.4000)}
     """
 
+    full_state_update: Optional[bool] = True
+
     def __init__(self, metric: Metric, labels: Optional[List[str]] = None) -> None:
         super().__init__()
         if not isinstance(metric, Metric):
@@ -71,3 +73,6 @@ def update(self, *args: Any, **kwargs: Any) -> None:
 
     def compute(self) -> Dict[str, Tensor]:
         return self._convert(self.metric.compute())
+
+    def reset(self) -> None:
+        self.metric.reset()

tests/unittests/wrappers/test_classwise.py

@@ -15,27 +15,35 @@ def test_raises_error_on_wrong_input():
 
 def test_output_no_labels():
     """Test that wrapper works with no label input."""
+    base = Accuracy(num_classes=3, average=None)
     metric = ClasswiseWrapper(Accuracy(num_classes=3, average=None))
-    preds = torch.randn(10, 3).softmax(dim=-1)
-    target = torch.randint(3, (10,))
-    val = metric(preds, target)
-    assert isinstance(val, dict)
-    assert len(val) == 3
-    for i in range(3):
-        assert f"accuracy_{i}" in val
+    for _ in range(2):
+        preds = torch.randn(10, 3).softmax(dim=-1)
+        target = torch.randint(3, (10,))
+        val = metric(preds, target)
+        val_base = base(preds, target)
+        assert isinstance(val, dict)
+        assert len(val) == 3
+        for i in range(3):
+            assert f"accuracy_{i}" in val
+            assert val[f"accuracy_{i}"] == val_base[i]
 
 
 def test_output_with_labels():
     """Test that wrapper works with label input."""
     labels = ["horse", "fish", "cat"]
+    base = Accuracy(num_classes=3, average=None)
     metric = ClasswiseWrapper(Accuracy(num_classes=3, average=None), labels=labels)
-    preds = torch.randn(10, 3).softmax(dim=-1)
-    target = torch.randint(3, (10,))
-    val = metric(preds, target)
-    assert isinstance(val, dict)
-    assert len(val) == 3
-    for lab in labels:
-        assert f"accuracy_{lab}" in val
+    for _ in range(2):
+        preds = torch.randn(10, 3).softmax(dim=-1)
+        target = torch.randint(3, (10,))
+        val = metric(preds, target)
+        val_base = base(preds, target)
+        assert isinstance(val, dict)
+        assert len(val) == 3
+        for i, lab in enumerate(labels):
+            assert f"accuracy_{lab}" in val
+            assert val[f"accuracy_{lab}"] == val_base[i]
 
 
 @pytest.mark.parametrize("prefix", [None, "pre_"])