Fix broken reset method in MultioutputWrapper (#1460)

* code fix
* tests
* changelog

(cherry picked from commit f3c82e2)

CHANGELOG.md

@@ -14,6 +14,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 -
 
 
+
 ### Changed
 
 -
@@ -34,8 +35,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed type checking on the `maximize` parameter at the initialization of `MetricTracker` ([#1428](https://github.com/Lightning-AI/metrics/issues/1428))
 
 
+- Fixed wrongly reset method in `MultioutputWrapper` ([#1460](https://github.com/Lightning-AI/metrics/issues/1460))
+
+
 - Fix dtype checking in `PrecisionRecallCurve` for `target` tensor ([#1457](https://github.com/Lightning-AI/metrics/pull/1457))
 
+
 ## [0.11.0] - 2022-11-30
 
 ### Added

src/torchmetrics/wrappers/multioutput.py

@@ -1,5 +1,5 @@
 from copy import deepcopy
-from typing import Any, List, Tuple
+from typing import Any, Callable, List, Tuple
 
 import torch
 from torch import Tensor
@@ -132,3 +132,12 @@ def reset(self) -> None:
         """Reset all underlying metrics."""
         for metric in self.metrics:
             metric.reset()
+        super().reset()
+
+    def _wrap_update(self, update: Callable) -> Callable:
+        """Overwrite to do nothing."""
+        return update
+
+    def _wrap_compute(self, compute: Callable) -> Callable:
+        """Overwrite to do nothing."""
+        return compute

tests/unittests/wrappers/test_multioutput.py

@@ -5,10 +5,10 @@
 import torch
 from sklearn.metrics import accuracy_score
 from sklearn.metrics import r2_score as sk_r2score
-from torch import Tensor
+from torch import Tensor, tensor
 
 from torchmetrics import Metric
-from torchmetrics.classification import MulticlassAccuracy
+from torchmetrics.classification import ConfusionMatrix, MulticlassAccuracy
 from torchmetrics.regression import R2Score
 from torchmetrics.wrappers.multioutput import MultioutputWrapper
 from unittests.helpers import seed_all
@@ -122,3 +122,17 @@ def test_multioutput_wrapper(
             dist_sync_on_step,
             metric_args=dict(num_outputs=num_outputs, base_metric_class=base_metric_class, **metric_kwargs),
         )
+
+
+def test_reset_called_correctly():
+    """Check that underlying metric is being correctly reset when calling forward."""
+    base_metric = ConfusionMatrix(task="multiclass", num_classes=2)
+    cf = MultioutputWrapper(base_metric, num_outputs=2)
+
+    res = cf(tensor([[0, 0]]), tensor([[0, 0]]))
+    assert torch.allclose(res[0], tensor([[1, 0], [0, 0]]))
+    assert torch.allclose(res[1], tensor([[1, 0], [0, 0]]))
+    cf.reset()
+    res = cf(tensor([[1, 1]]), tensor([[0, 0]]))
+    assert torch.allclose(res[0], tensor([[0, 1], [0, 0]]))
+    assert torch.allclose(res[1], tensor([[0, 1], [0, 0]]))