add metrics module tests

src/eva/metrics/core/module.py

@@ -22,9 +22,9 @@ def __init__(
         """Initializes the metrics for the Trainer.
 
         Args:
-            train: The training metric collection to be tracked.
-            val: The validation metric collection to be tracked.
-            test: The test metric collection to be tracked.
+            train: The training metric collection.
+            val: The validation metric collection.
+            test: The test metric collection.
         """
         super().__init__()
 
@@ -49,9 +49,9 @@ def from_metrics(
         """Initializes a metric module from a list of metrics.
 
         Args:
-            train: A list of metrics for the training process.
-            val: A list of metrics for the val process.
-            test: A list of metrics for the test process.
+            train: Metrics for the training stage.
+            val: Metrics for the validation stage.
+            test: Metrics for the test stage.
             separator: The separator between the group name of the metric
                 and the metric itself. Defaults to `"/"`.
         """
@@ -89,7 +89,7 @@ def training_metrics(self) -> collection.MetricCollection:
 
     @property
     def validation_metrics(self) -> collection.MetricCollection:
-        """Returns the metrics of the val dataset."""
+        """Returns the metrics of the validation dataset."""
         return self._val
 
     @property
@@ -104,7 +104,7 @@ def _create_collection_from_metrics(
     """Create a unique collection from metrics.
 
     Args:
-        metrics: A list of metrics.
+        metrics: The desired metrics.
         prefix: A prefix to added to the collection. Defaults to `None`.
 
     Returns:

tests/eva/metrics/core/test_metric_module.py

@@ -0,0 +1,33 @@
+"""MetricModule tests."""
+from typing import List
+
+import pytest
+import torchmetrics
+
+from eva.metrics import core
+
+
+@pytest.mark.parametrize(
+    "schema, expected",
+    [
+        (core.MetricsSchema(train=torchmetrics.Dice()), [1, 0, 0]),
+        (core.MetricsSchema(evaluation=torchmetrics.Dice()), [0, 1, 1]),
+        (core.MetricsSchema(common=torchmetrics.Dice()), [1, 1, 1]),
+        (core.MetricsSchema(train=torchmetrics.Dice(), evaluation=torchmetrics.Dice()), [1, 1, 1]),
+    ],
+)
+def test_metric_module(metric_module: core.MetricModule, expected: List[int]) -> None:
+    """Tests the MetricModule."""
+    assert len(metric_module.training_metrics) == expected[0]
+    assert len(metric_module.validation_metrics) == expected[1]
+    assert len(metric_module.test_metrics) == expected[2]
+    # test that the metrics are copied and are not the same object
+    assert metric_module.training_metrics != metric_module.validation_metrics
+    assert metric_module.training_metrics != metric_module.test_metrics
+    assert metric_module.validation_metrics != metric_module.test_metrics
+
+
+@pytest.fixture(scope="function")
+def metric_module(schema: core.MetricsSchema) -> core.MetricModule:
+    """MetricModule fixture."""
+    return core.MetricModule.from_schema(schema=schema)

tests/eva/metrics/core/test_schemas.py

@@ -9,9 +9,27 @@
 @pytest.mark.parametrize(
     "common, train, evaluation, expected_train, expected_evaluation",
     [
-        (torchmetrics.Accuracy("binary"), None, None, "BinaryAccuracy()", "BinaryAccuracy()"),
-        (None, torchmetrics.Accuracy("binary"), None, "BinaryAccuracy()", "None"),
-        (None, None, torchmetrics.Accuracy("binary"), "None", "BinaryAccuracy()"),
+        (
+            torchmetrics.Accuracy("binary"),
+            None,
+            None,
+            "BinaryAccuracy()",
+            "BinaryAccuracy()",
+        ),
+        (
+            None,
+            torchmetrics.Accuracy("binary"),
+            None,
+            "BinaryAccuracy()",
+            "None",
+        ),
+        (
+            None,
+            None,
+            torchmetrics.Accuracy("binary"),
+            "None",
+            "BinaryAccuracy()",
+        ),
         (
             torchmetrics.Accuracy("binary"),
             torchmetrics.Dice(),