Fixed MAP metric when using custom list of thresholds

CHANGELOG.md

@@ -76,6 +76,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed `CalibrationError` to work on logit input ([#985](https://github.com/PyTorchLightning/metrics/pull/985))
 
 
+- Fixed MAP metric when using custom list of thresholds ([#995](https://github.com/PyTorchLightning/metrics/issues/995))
+
+
 ## [0.8.0] - 2022-04-14
 
 ### Added

tests/detection/test_map.py

@@ -313,6 +313,19 @@ def test_map_gpu(inputs):
  metric.compute()
 
 
+@pytest.mark.skipif(_pytest_condition, reason="test requires that torchvision=>0.8.0 is installed")
+@pytest.mark.skipif(_gpu_test_condition, reason="test requires CUDA availability")
+def test_map_with_custom_thresholds():
+ """Test that map works with custom iou thresholds."""
+ metric = MeanAveragePrecision(iou_thresholds=[0.1, 0.2])
+ metric = metric.to("cuda")
+ for preds, targets in zip(_inputs.preds, _inputs.target):
+ metric.update(_move_to_gpu(preds), _move_to_gpu(targets))
+ res = metric.compute()
+ assert res["map_50"].item() == -1
+ assert res["map_75"].item() == -1
+
+
 @pytest.mark.skipif(_pytest_condition, reason="test requires that pycocotools and torchvision=>0.8.0 is installed")
 def test_empty_metric():
  """Test empty metric."""

torchmetrics/detection/mean_ap.py

@@ -653,8 +653,14 @@ def _summarize_results(self, precisions: Tensor, recalls: Tensor) -> Tuple[MAPMe
  map_metrics = MAPMetricResults()
  map_metrics.map = self._summarize(results, True)
  last_max_det_thr = self.max_detection_thresholds[-1]
- map_metrics.map_50 = self._summarize(results, True, iou_threshold=0.5, max_dets=last_max_det_thr)
- map_metrics.map_75 = self._summarize(results, True, iou_threshold=0.75, max_dets=last_max_det_thr)
+ if 0.5 in self.iou_thresholds:
+ map_metrics.map_50 = self._summarize(results, True, iou_threshold=0.5, max_dets=last_max_det_thr)
+ else:
+ map_metrics.map_50 = torch.tensor([-1])
+ if 0.75 in self.iou_thresholds:
+ map_metrics.map_75 = self._summarize(results, True, iou_threshold=0.75, max_dets=last_max_det_thr)
+ else:
+ map_metrics.map_75 = torch.tensor([-1])
  map_metrics.map_small = self._summarize(results, True, area_range="small", max_dets=last_max_det_thr)
  map_metrics.map_medium = self._summarize(results, True, area_range="medium", max_dets=last_max_det_thr)
  map_metrics.map_large = self._summarize(results, True, area_range="large", max_dets=last_max_det_thr)
@@ -750,8 +756,6 @@ def compute(self) -> dict:
  dict containing
 
  - map: ``torch.Tensor``
- - map_50: ``torch.Tensor``
- - map_75: ``torch.Tensor``
  - map_small: ``torch.Tensor``
  - map_medium: ``torch.Tensor``
  - map_large: ``torch.Tensor``
@@ -761,6 +765,8 @@ def compute(self) -> dict:
  - mar_small: ``torch.Tensor``
  - mar_medium: ``torch.Tensor``
  - mar_large: ``torch.Tensor``
+ - map_50: ``torch.Tensor`` (-1 if 0.5 not in the list of iou thresholds)
+ - map_75: ``torch.Tensor`` (-1 if 0.75 not in the list of iou thresholds)
  - map_per_class: ``torch.Tensor`` (-1 if class metrics are disabled)
  - mar_100_per_class: ``torch.Tensor`` (-1 if class metrics are disabled)
  """