MeanAveragePrecision: Skip box conversion if no boxes are present

CHANGELOG.md

@@ -42,9 +42,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Fixed mAP calculation for areas with 0 predictions ([#1080](https://github.com/PyTorchLightning/metrics/pull/1080))
 
+
 - Fixed bug where avg precision state and auroc state was not merge when using MetricCollections ([#1086](https://github.com/PyTorchLightning/metrics/pull/1086))
 
 
+- Skip box conversion if no boxes are present in `MeanAveragePrecision` ([#1097](https://github.com/PyTorchLightning/metrics/pull/1097))
+
+
 ## [0.9.1] - 2022-06-08
 
 ### Added

src/torchmetrics/detection/mean_ap.py

@@ -416,7 +416,8 @@ def _get_safe_item_values(self, item: Dict[str, Any]) -> Union[Tensor, Tuple]:
 
  if self.iou_type == "bbox":
  boxes = _fix_empty_tensors(item["boxes"])
- boxes = box_convert(boxes, in_fmt=self.box_format, out_fmt="xyxy")
+ if boxes.numel() > 0:
+ boxes = box_convert(boxes, in_fmt=self.box_format, out_fmt="xyxy")
  return boxes
  elif self.iou_type == "segm":
  masks = []

tests/unittests/detection/test_map.py

@@ -56,7 +56,6 @@
  ],
 )
 
-
 _inputs = Input(
  preds=[
  [
@@ -299,7 +298,6 @@ class TestMAP(MetricTester):
 
  @pytest.mark.parametrize("ddp", [False, True])
  def test_map_bbox(self, compute_on_cpu, ddp):
-
  """Test modular implementation for correctness."""
  self.run_class_metric_test(
  ddp=ddp,
@@ -344,12 +342,8 @@ def test_empty_preds():
  metric = MeanAveragePrecision()
 
  metric.update(
- [
- dict(boxes=Tensor([]), scores=Tensor([]), labels=IntTensor([])),
- ],
- [
- dict(boxes=Tensor([[214.1500, 41.2900, 562.4100, 285.0700]]), labels=IntTensor([4])),
- ],
+ [dict(boxes=Tensor([]), scores=Tensor([]), labels=IntTensor([]))],
+ [dict(boxes=Tensor([[214.1500, 41.2900, 562.4100, 285.0700]]), labels=IntTensor([4]))],
  )
  metric.compute()
 
@@ -360,16 +354,56 @@ def test_empty_ground_truths():
  metric = MeanAveragePrecision()
 
  metric.update(
- [
- dict(
- boxes=Tensor([[214.1500, 41.2900, 562.4100, 285.0700]]),
- scores=Tensor([0.5]),
- labels=IntTensor([4]),
- ),
- ],
- [
- dict(boxes=Tensor([]), labels=IntTensor([])),
- ],
+ [dict(boxes=Tensor([[214.1500, 41.2900, 562.4100, 285.0700]]), scores=Tensor([0.5]), labels=IntTensor([4]))],
+ [dict(boxes=Tensor([]), labels=IntTensor([]))],
+ )
+ metric.compute()
+
+
+@pytest.mark.skipif(_pytest_condition, reason="test requires that torchvision=>0.8.0 is installed")
+def test_empty_ground_truths_xywh():
+ """Test empty ground truths in xywh format."""
+ metric = MeanAveragePrecision(box_format="xywh")
+
+ metric.update(
+ [dict(boxes=Tensor([[214.1500, 41.2900, 348.2600, 243.7800]]), scores=Tensor([0.5]), labels=IntTensor([4]))],
+ [dict(boxes=Tensor([]), labels=IntTensor([]))],
+ )
+ metric.compute()
+
+
+@pytest.mark.skipif(_pytest_condition, reason="test requires that torchvision=>0.8.0 is installed")
+def test_empty_preds_xywh():
+ """Test empty predictions in xywh format."""
+ metric = MeanAveragePrecision(box_format="xywh")
+
+ metric.update(
+ [dict(boxes=Tensor([]), scores=Tensor([]), labels=IntTensor([]))],
+ [dict(boxes=Tensor([[214.1500, 41.2900, 348.2600, 243.7800]]), labels=IntTensor([4]))],
+ )
+ metric.compute()
+
+
+@pytest.mark.skipif(_pytest_condition, reason="test requires that torchvision=>0.8.0 is installed")
+def test_empty_ground_truths_cxcywh():
+ """Test empty ground truths in cxcywh format."""
+ metric = MeanAveragePrecision(box_format="cxcywh")
+
+ metric.update(
+ [dict(boxes=Tensor([[388.2800, 163.1800, 348.2600, 243.7800]]), scores=Tensor([0.5]), labels=IntTensor([4]))],
+ [dict(boxes=Tensor([]), labels=IntTensor([]))],
+ )
+ metric.compute()
+
+
+@pytest.mark.skipif(_pytest_condition, reason="test requires that torchvision=>0.8.0 is installed")
+def test_empty_preds_cxcywh():
+ """Test empty predictions in cxcywh format."""
+ metric = MeanAveragePrecision(box_format="cxcywh")
+
+ metric.update(
+ [dict(boxes=Tensor([]), scores=Tensor([]), labels=IntTensor([]))],
+ [dict(boxes=Tensor([[388.2800, 163.1800, 348.2600, 243.7800]]), labels=IntTensor([4]))],
  )
  metric.compute()
 
@@ -467,16 +501,8 @@ def test_segm_iou_empty_gt_mask():
  metric = MeanAveragePrecision(iou_type="segm")
 
  metric.update(
- [
- dict(
- masks=torch.randint(0, 1, (1, 10, 10)).bool(),
- scores=Tensor([0.5]),
- labels=IntTensor([4]),
- ),
- ],
- [
- dict(masks=Tensor([]), labels=IntTensor([])),
- ],
+ [dict(masks=torch.randint(0, 1, (1, 10, 10)).bool(), scores=Tensor([0.5]), labels=IntTensor([4]))],
+ [dict(masks=Tensor([]), labels=IntTensor([]))],
  )
 
  metric.compute()
@@ -488,16 +514,8 @@ def test_segm_iou_empty_pred_mask():
  metric = MeanAveragePrecision(iou_type="segm")
 
  metric.update(
- [
- dict(
- masks=torch.BoolTensor([]),
- scores=Tensor([]),
- labels=IntTensor([]),
- ),
- ],
- [
- dict(masks=torch.randint(0, 1, (1, 10, 10)).bool(), labels=IntTensor([4])),
- ],
+ [dict(masks=torch.BoolTensor([]), scores=Tensor([]), labels=IntTensor([]))],
+ [dict(masks=torch.randint(0, 1, (1, 10, 10)).bool(), labels=IntTensor([4]))],
  )
 
  metric.compute()