Add better errors when wrong input is received in detection intersection metrics

CHANGELOG.md

@@ -18,6 +18,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added `input_format` argument to segmentation metrics ([#2572](https://github.com/Lightning-AI/torchmetrics/pull/2572))
 
 
+- Added better error messages for intersection detection metrics for wrong user input ([#2577](https://github.com/Lightning-AI/torchmetrics/pull/2577))
+
+
 ### Changed
 
 -

src/torchmetrics/functional/detection/ciou.py

@@ -24,6 +24,11 @@
 def _ciou_update(
     preds: torch.Tensor, target: torch.Tensor, iou_threshold: Optional[float], replacement_val: float = 0
 ) -> torch.Tensor:
+    if preds.ndim != 2 or preds.shape[-1] != 4:
+        raise ValueError(f"Expected preds to be of shape (N, 4) but got {preds.shape}")
+    if target.ndim != 2 or target.shape[-1] != 4:
+        raise ValueError(f"Expected target to be of shape (N, 4) but got {target.shape}")
+
     from torchvision.ops import complete_box_iou
 
     iou = complete_box_iou(preds, target)

src/torchmetrics/functional/detection/diou.py

@@ -24,6 +24,11 @@
 def _diou_update(
     preds: torch.Tensor, target: torch.Tensor, iou_threshold: Optional[float], replacement_val: float = 0
 ) -> torch.Tensor:
+    if preds.ndim != 2 or preds.shape[-1] != 4:
+        raise ValueError(f"Expected preds to be of shape (N, 4) but got {preds.shape}")
+    if target.ndim != 2 or target.shape[-1] != 4:
+        raise ValueError(f"Expected target to be of shape (N, 4) but got {target.shape}")
+
     from torchvision.ops import distance_box_iou
 
     iou = distance_box_iou(preds, target)

src/torchmetrics/functional/detection/giou.py

@@ -24,6 +24,11 @@
 def _giou_update(
     preds: torch.Tensor, target: torch.Tensor, iou_threshold: Optional[float], replacement_val: float = 0
 ) -> torch.Tensor:
+    if preds.ndim != 2 or preds.shape[-1] != 4:
+        raise ValueError(f"Expected preds to be of shape (N, 4) but got {preds.shape}")
+    if target.ndim != 2 or target.shape[-1] != 4:
+        raise ValueError(f"Expected target to be of shape (N, 4) but got {target.shape}")
+
     from torchvision.ops import generalized_box_iou
 
     iou = generalized_box_iou(preds, target)

src/torchmetrics/functional/detection/iou.py

@@ -24,6 +24,12 @@
 def _iou_update(
     preds: torch.Tensor, target: torch.Tensor, iou_threshold: Optional[float], replacement_val: float = 0
 ) -> torch.Tensor:
+    """Compute the IoU matrix between two sets of boxes."""
+    if preds.ndim != 2 or preds.shape[-1] != 4:
+        raise ValueError(f"Expected preds to be of shape (N, 4) but got {preds.shape}")
+    if target.ndim != 2 or target.shape[-1] != 4:
+        raise ValueError(f"Expected target to be of shape (N, 4) but got {target.shape}")
+
     from torchvision.ops import box_iou
 
     iou = box_iou(preds, target)

tests/unittests/detection/test_intersection.py

@@ -314,6 +314,20 @@ def test_error_on_wrong_input(self, class_metric, functional_metric, reference_m
                 [{"boxes": Tensor(), "labels": []}],
             )
 
+    def test_functional_error_on_wrong_input_shape(self, class_metric, functional_metric, reference_metric):
+        """Test functional input validation."""
+        with pytest.raises(ValueError, match="Expected preds to be of shape.*"):
+            functional_metric(torch.randn(25), torch.randn(25, 4))
+
+        with pytest.raises(ValueError, match="Expected target to be of shape.*"):
+            functional_metric(torch.randn(25, 4), torch.randn(25))
+
+        with pytest.raises(ValueError, match="Expected preds to be of shape.*"):
+            functional_metric(torch.randn(25, 25), torch.randn(25, 4))
+
+        with pytest.raises(ValueError, match="Expected target to be of shape.*"):
+            functional_metric(torch.randn(25, 4), torch.randn(25, 25))
+
 
 def test_corner_case():
     """See issue: https://github.com/Lightning-AI/torchmetrics/issues/1921."""