Fix mAP calculation for areas with 0 predictions (#1080)

* Fix mAP calculation for areas with 0 predictions The issue was first dicussed here: #1061 (comment) * chlog Co-authored-by: Jirka <[email protected]> Co-authored-by: Daniel Stancl <[email protected]> (cherry picked from commit 84274ab)
Lightning-AI · Jun 14, 2022 · 659216b · 659216b
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -7,17 +7,56 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 **Note: we move fast, but still we preserve 0.1 version (one feature release) back compatibility.**
 
 
+## [UnReleased] - 2022-MM-DD
+
+### Added
+
+-
+
+
+-
+
+
+### Changed
+
+-
+
+-
+
+
+### Deprecated
+
+-
+
+-
+
+
+### Removed
+
+-
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+-
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+
+### Fixed
+
+- Fixed mAP calculation for areas with 0 predictions ([#1080](https://github.com/PyTorchLightning/metrics/pull/1080))
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+-
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+
 ## [0.9.1] - 2022-06-08
 
 ### Added
 
-- Added specific `RuntimeError` when metric object is on wrong device ([#1056](https://github.com/PyTorchLightning/metrics/pull/1056))
+- Added specific `RuntimeError` when metric object is on the wrong device ([#1056](https://github.com/PyTorchLightning/metrics/pull/1056))
 - Added an option to specify own n-gram weights for `BLEUScore` and `SacreBLEUScore` instead of using uniform weights only. ([#1075](https://github.com/PyTorchLightning/metrics/pull/1075))
 
 ### Fixed
 
 - Fixed aggregation metrics when input only contains zero ([#1070](https://github.com/PyTorchLightning/metrics/pull/1070))
-- Fixed `TypeError` when providing superclass arguments as kwargs ([#1069](https://github.com/PyTorchLightning/metrics/pull/1069))
+- Fixed `TypeError` when providing superclass arguments as `kwargs` ([#1069](https://github.com/PyTorchLightning/metrics/pull/1069))
 - Fixed bug related to state reference in metric collection when using compute groups ([#1076](https://github.com/PyTorchLightning/metrics/pull/1076))
 
 

diff --git a/tests/detection/test_map.py b/tests/detection/test_map.py
@@ -462,7 +462,7 @@ def test_missing_gt():
 
 
 @pytest.mark.skipif(_pytest_condition, reason="test requires that torchvision=>0.8.0 is installed")
-def test_segm_iou_empty_mask():
+def test_segm_iou_empty_gt_mask():
     """Test empty ground truths."""
     metric = MeanAveragePrecision(iou_type="segm")
 
@@ -482,6 +482,27 @@ def test_segm_iou_empty_mask():
     metric.compute()
 
 
+@pytest.mark.skipif(_pytest_condition, reason="test requires that torchvision=>0.8.0 is installed")
+def test_segm_iou_empty_pred_mask():
+    """Test empty predictions."""
+    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])),
+        ],
+    )
+
+    metric.compute()
+
+
 @pytest.mark.skipif(_pytest_condition, reason="test requires that torchvision=>0.8.0 is installed")
 def test_error_on_wrong_input():
     """Test class input validation."""

diff --git a/torchmetrics/detection/mean_ap.py b/torchmetrics/detection/mean_ap.py
@@ -482,9 +482,9 @@ def __evaluate_image_gt_no_preds(
     ) -> Dict[str, Any]:
         """Some GT but no predictions."""
         # GTs
-        gt = gt[gt_label_mask]
+        gt = [gt[i] for i in gt_label_mask]
         nb_gt = len(gt)
-        areas = box_area(gt)
+        areas = compute_area(gt, iou_type=self.iou_type).to(self.device)
         ignore_area = (areas < area_range[0]) | (areas > area_range[1])
         gt_ignore, _ = torch.sort(ignore_area.to(torch.uint8))
         gt_ignore = gt_ignore.to(torch.bool)