Fix binary classification metric task (#3928)

* Fix binary classification

* Add unit-tests

Author: harimkang

src/otx/core/metrics/accuracy.py

@@ -346,8 +346,10 @@ def compute(self) -> torch.Tensor:
 
 
 def _multi_class_cls_metric_callable(label_info: LabelInfo) -> MetricCollection:
+    num_classes = label_info.num_classes
+    task = "binary" if num_classes == 1 else "multiclass"
     return MetricCollection(
-        {"accuracy": TorchmetricAcc(task="multiclass", num_classes=label_info.num_classes)},
+        {"accuracy": TorchmetricAcc(task=task, num_classes=num_classes)},
     )
 
 

tests/unit/core/metrics/test_accuracy.py

@@ -9,9 +9,11 @@
     HlabelAccuracy,
     MixedHLabelAccuracy,
     MulticlassAccuracywithLabelGroup,
+    MultiClassClsMetricCallable,
     MultilabelAccuracywithLabelGroup,
 )
 from otx.core.types.label import HLabelInfo, LabelInfo
+from torchmetrics.classification.accuracy import BinaryAccuracy, MulticlassAccuracy
 
 
 class TestAccuracy:
@@ -45,6 +47,16 @@ def test_multiclass_accuracy(self, fxt_multiclass_labelinfo: LabelInfo) -> None:
         acc = result["accuracy"]
         assert round(acc.item(), 3) == 0.792
 
+    def test_default_multi_class_cls_metric_callable(self, fxt_multiclass_labelinfo: LabelInfo) -> None:
+        assert fxt_multiclass_labelinfo.num_classes > 1
+        metric = MultiClassClsMetricCallable(fxt_multiclass_labelinfo)
+        assert isinstance(metric.accuracy, MulticlassAccuracy)
+
+        one_class_label_info = LabelInfo(label_names=["class1"], label_groups=[["class1"]])
+        assert one_class_label_info.num_classes == 1
+        binary_metric = MultiClassClsMetricCallable(one_class_label_info)
+        assert isinstance(binary_metric.accuracy, BinaryAccuracy)
+
     def test_multilabel_accuracy(self, fxt_multilabel_labelinfo: LabelInfo) -> None:
         """Check whether accuracy is same with OTX1.x version."""
         preds = [