Add test that functional metrics return the correct compute value

tests/unit_tests/metrics.py

@@ -1,5 +1,6 @@
 import torch
 from torch.nn import MSELoss
+from torchmetrics import MeanSquaredError
 
 from qusi.internal.train_session import update_logging_metrics
 
@@ -12,3 +13,15 @@ def test_update_logging_metrics_for_functional_metrics():
     metric = MSELoss()
     update_logging_metrics(predicted_targets, targets, [metric], metric_totals)
     assert metric_totals == expected_metric_totals
+
+
+def test_update_logging_metrics_for_state_based_torchmetrics():
+    metric = MeanSquaredError()
+    value = metric(torch.tensor([1.]), torch.tensor([2.]))  # Add some already stored metric value.
+    predicted_targets = torch.tensor([1.])
+    targets = torch.tensor([3.])
+    metric_totals = torch.tensor([0.])
+    expected_computed_metric_value = torch.tensor(2.5)  # Average of 1 and 4.
+    update_logging_metrics(predicted_targets, targets, [metric], metric_totals)
+    computed_metric_value = metric.compute()
+    assert computed_metric_value == expected_computed_metric_value