Allow passing metrics objects directly to create_metrics_collection

CHANGELOG.md

@@ -10,6 +10,8 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- 🔨 Allow passing metrics objects directly to `create_metrics_collection` by @ashwinvaidya17 in https://github.com/openvinotoolkit/anomalib/pull/2212
+
 ### Deprecated
 
 ### Fixed

src/anomalib/metrics/__init__.py

@@ -10,6 +10,7 @@
 
 import torchmetrics
 from omegaconf import DictConfig, ListConfig
+from torchmetrics import Metric
 
 from .anomaly_score_distribution import AnomalyScoreDistribution
 from .aupr import AUPR
@@ -162,7 +163,7 @@
 
 
 def create_metric_collection(
-    metrics: list[str] | dict[str, dict[str, Any]],
+    metrics: list[str] | dict[str, dict[str, Any]] | Metric | list[Metric],
     prefix: str | None = None,
 ) -> AnomalibMetricCollection:
     """Create a metric collection from a list of metric names or dictionaries.
@@ -171,25 +172,31 @@
 
         - if list[str] (names of metrics): see `metric_collection_from_names`
         - if dict[str, dict[str, Any]] (path and init args of a class): see `metric_collection_from_dicts`
+        - if list[Metric] (metric objects): A collection is returned with those metrics.
 
     The function will first try to retrieve the metric from the metrics defined in Anomalib metrics module,
     then in TorchMetrics package.
 
     Args:
-        metrics (list[str] | dict[str, dict[str, Any]]): List of metrics or dictionaries to create metric collection.
+        metrics (list[str] | dict[str, dict[str, Any]] | Metric | list[Metric]): List of metrics or dictionaries to
+            create metric collection.
         prefix (str | None): Prefix to assign to the metrics in the collection.
 
     Returns:
         AnomalibMetricCollection: Collection of metrics.
     """
-    # fallback is using the names
-
     if isinstance(metrics, ListConfig | list):
-        if not all(isinstance(metric, str) for metric in metrics):
-            msg = f"All metrics must be strings, found {metrics}"
+        if not (
+            all(isinstance(metric, str) for metric in metrics) or all(isinstance(metric, Metric) for metric in metrics)
+        ):
+            msg = f"All metrics must be either string or Metric objects, found {metrics}"
             raise TypeError(msg)
+        if all(isinstance(metric, str) for metric in metrics):
+            return metric_collection_from_names(metrics, prefix)
+        return AnomalibMetricCollection(metrics, prefix)
 
-        return metric_collection_from_names(metrics, prefix)
+    if isinstance(metrics, Metric):
+        return AnomalibMetricCollection([metrics], prefix)
 
     if isinstance(metrics, DictConfig | dict):
         _validate_metrics_dict(metrics)

tests/unit/metrics/test_create_metrics.py

@@ -0,0 +1,45 @@
+"""Test metrics collection creation."""
+
+from torchmetrics.classification import Accuracy
+
+from anomalib.metrics import AUPRO, create_metric_collection
+
+
+def test_string_initialization() -> None:
+    """Pass metrics as a list of string."""
+    metrics_list = ["AUROC", "AUPR"]
+    collection = create_metric_collection(metrics_list, prefix=None)
+    assert len(collection) == 2
+    assert "AUROC" in collection
+    assert "AUPR" in collection
+
+
+def test_dict_initialization() -> None:
+    """Pass metrics as a dictionary."""
+    metrics_dict = {
+        "PixelWiseAUROC": {
+            "class_path": "anomalib.metrics.AUROC",
+            "init_args": {},
+        },
+        "Precision": {
+            "class_path": "torchmetrics.Precision",
+            "init_args": {"task": "binary"},
+        },
+    }
+    collection = create_metric_collection(metrics_dict, prefix=None)
+    assert len(collection) == 2
+    assert "PixelWiseAUROC" in collection
+    assert "Precision" in collection
+
+
+def test_metric_object_initialization() -> None:
+    """Pass metrics as a list of metric objects."""
+    metrics_list = [AUPRO(), Accuracy(task="binary")]
+    collection = create_metric_collection(metrics_list, prefix=None)
+    assert len(collection) == 2
+    assert "AUPRO" in collection
+    assert "BinaryAccuracy" in collection
+
+    collection = create_metric_collection(AUPRO(), prefix=None)
+    assert len(collection) == 1
+    assert "AUPRO" in collection