MAP metric - fix empty predictions

tests/detection/test_map.py

@@ -206,6 +206,30 @@ def test_error_on_wrong_init():
         MAP(class_metrics=0)
 
 
+@pytest.mark.skipif(_pytest_condition, reason="test requires that pycocotools and torchvision=>0.8.0 is installed")
+def test_empty_preds():
+    """Test empty predictions."""
+
+    metric = MAP()
+
+    metric.update(
+        [
+            dict(
+                boxes=torch.Tensor([[]]),
+                scores=torch.Tensor([]),
+                labels=torch.IntTensor([]),
+            ),
+        ],
+        [
+            dict(
+                boxes=torch.Tensor([[214.1500, 41.2900, 562.4100, 285.0700]]),
+                labels=torch.IntTensor([4]),
+            ),
+        ],
+    )
+    metric.compute()
+
+
 @pytest.mark.skipif(_pytest_condition, reason="test requires that pycocotools and torchvision=>0.8.0 is installed")
 def test_error_on_wrong_input():
     """Test class input validation."""

torchmetrics/detection/map.py

@@ -365,7 +365,7 @@ def _get_coco_format(
         annotations = []
         annotation_id = 1  # has to start with 1, otherwise COCOEval results are wrong
 
-        boxes = [box_convert(box, in_fmt="xyxy", out_fmt="xywh") for box in boxes]
+        boxes = [box_convert(box, in_fmt="xyxy", out_fmt="xywh") if boxes[0].size(1) == 4 else box for box in boxes]
         for image_id, (image_boxes, image_labels) in enumerate(zip(boxes, labels)):
             image_boxes = image_boxes.cpu().tolist()
             image_labels = image_labels.cpu().tolist()