Fix corner case when single empty pred is provided to detection IOU…

… metric (#2780)

CHANGELOG.md

@@ -53,7 +53,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
--
+- Fixed corner case in `Iou` metric for single empty prediction tensors ([#2780](https://github.com/Lightning-AI/torchmetrics/pull/2780))
 
 
 ## [1.4.3] - 2024-10-10

src/torchmetrics/detection/iou.py

@@ -211,7 +211,8 @@ def compute(self) -> dict:
         """Computes IoU based on inputs passed in to ``update`` previously."""
         score = torch.cat([mat[mat != self._invalid_val] for mat in self.iou_matrix], 0).mean()
         results: Dict[str, Tensor] = {f"{self._iou_type}": score}
-
+        if torch.isnan(score):  # if no valid boxes are found
+            results[f"{self._iou_type}"] = torch.tensor(0.0, device=score.device)
         if self.class_metrics:
             gt_labels = dim_zero_cat(self.groundtruth_labels)
             classes = gt_labels.unique().tolist() if len(gt_labels) > 0 else []

tests/unittests/detection/test_intersection.py

@@ -63,6 +63,8 @@ def _tv_wrapper_class(preds, target, base_fn, respect_labels, iou_threshold, cla
     base_name = {tv_ciou: "ciou", tv_diou: "diou", tv_giou: "giou", tv_iou: "iou"}[base_fn]
 
     result = {f"{base_name}": score.cpu()}
+    if torch.isnan(score):
+        result.update({f"{base_name}": torch.tensor(0.0)})
     if class_metrics:
         for cl in torch.cat(classes).unique().tolist():
             class_score, numel = 0, 0
@@ -71,7 +73,6 @@ def _tv_wrapper_class(preds, target, base_fn, respect_labels, iou_threshold, cla
                 class_score += masked_s[masked_s != -1].sum()
                 numel += masked_s[masked_s != -1].numel()
             result.update({f"{base_name}/cl_{cl}": class_score.cpu() / numel})
-
     return result
 
 
@@ -328,6 +329,32 @@ def test_functional_error_on_wrong_input_shape(self, class_metric, functional_me
         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_only_one_empty_prediction(self, class_metric, functional_metric, reference_metric):
+        """Test that the metric does not crash when there is only one empty prediction."""
+        target = [
+            {
+                "boxes": torch.tensor([
+                    [8.0000, 70.0000, 76.0000, 110.0000],
+                    [247.0000, 131.0000, 315.0000, 175.0000],
+                    [361.0000, 177.0000, 395.0000, 203.0000],
+                ]),
+                "labels": torch.tensor([0, 0, 0]),
+            }
+        ]
+        preds = [
+            {
+                "boxes": torch.empty(size=(0, 4)),
+                "labels": torch.tensor([], dtype=torch.int64),
+                "scores": torch.tensor([]),
+            }
+        ]
+
+        metric = class_metric()
+        metric.update(preds, target)
+        res = metric.compute()
+        for val in res.values():
+            assert val == torch.tensor(0.0)
+
 
 def test_corner_case():
     """See issue: https://github.com/Lightning-AI/torchmetrics/issues/1921."""