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Supress warnings in MeanAveragePrecision when intended #2501

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merged 3 commits into from
Apr 15, 2024
Merged

Supress warnings in MeanAveragePrecision when intended #2501

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0cd5fb9
suppress warnings when needed
SkafteNicki Apr 14, 2024
83d61f2
changelog
SkafteNicki Apr 14, 2024
c4ca271
Merge branch 'master' into bugfix/supress_map_warning
SkafteNicki Apr 15, 2024
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3 changes: 3 additions & 0 deletions CHANGELOG.md
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Expand Up @@ -36,6 +36,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
- Fixed axis names with Precision-Recall curve ([#2462](https://github.com/Lightning-AI/torchmetrics/pull/2462))


- Fixed warnings being suppressed in `MeanAveragePrecision` when requested ([#2501](https://github.com/Lightning-AI/torchmetrics/pull/2501))


## [1.3.2] - 2024-03-18

### Fixed
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5 changes: 3 additions & 2 deletions src/torchmetrics/detection/mean_ap.py
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Expand Up @@ -827,8 +827,9 @@ def _get_safe_item_values(
rle = self.mask_utils.encode(np.asfortranarray(i))
masks.append((tuple(rle["size"]), rle["counts"]))
output[1] = tuple(masks) # type: ignore[call-overload]
if (output[0] is not None and len(output[0]) > self.max_detection_thresholds[-1]) or (
output[1] is not None and len(output[1]) > self.max_detection_thresholds[-1]
if warn and (
(output[0] is not None and len(output[0]) > self.max_detection_thresholds[-1])
or (output[1] is not None and len(output[1]) > self.max_detection_thresholds[-1])
):
_warning_on_too_many_detections(self.max_detection_thresholds[-1])
return output # type: ignore[return-value]
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12 changes: 9 additions & 3 deletions tests/unittests/detection/test_map.py
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Expand Up @@ -712,7 +712,8 @@ def test_for_box_format(self, box_format, iou_val_expected, map_val_expected, ba
assert round(float(result["ious"][(0, 0)]), 3) == iou_val_expected

@pytest.mark.parametrize("iou_type", ["bbox", "segm"])
def test_warning_on_many_detections(self, iou_type, backend):
@pytest.mark.parametrize("warn_on_many_detections", [False, True])
def test_warning_on_many_detections(self, iou_type, warn_on_many_detections, backend, recwarn):
"""Test that a warning is raised when there are many detections."""
if iou_type == "bbox":
preds = [
Expand All @@ -727,8 +728,13 @@ def test_warning_on_many_detections(self, iou_type, backend):
preds, targets = _generate_random_segm_input("cpu", 1, 101, 10, False)

metric = MeanAveragePrecision(iou_type=iou_type, backend=backend)
with pytest.warns(UserWarning, match="Encountered more than 100 detections in a single image.*"):
metric.update(preds, targets)
metric.warn_on_many_detections = warn_on_many_detections

if warn_on_many_detections:
with pytest.warns(UserWarning, match="Encountered more than 100 detections in a single image.*"):
metric.update(preds, targets)
else:
assert len(recwarn) == 0

@pytest.mark.parametrize(
("preds", "target", "expected_iou_len", "iou_keys", "precision_shape", "recall_shape", "scores_shape"),
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