MAP: change bool to float32 (#1150)

* Change bool to float32. * add test case * changelog Co-authored-by: André Aquilina <[email protected]> Co-authored-by: Nicki Skafte Detlefsen <[email protected]> Co-authored-by: thomas chaton <[email protected]> (cherry picked from commit d3c4c82)
Lightning-AI · Oct 21, 2022 · e491f47 · e491f47
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -21,6 +21,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed unintentional downloading of `nltk.punkt` when `lsum` not in `rouge_keys` ([#1258](https://github.com/Lightning-AI/metrics/pull/1258))
 
 
+- Fixed type casting in `MAP` metric between `bool` and `float32` ([#1150](https://github.com/Lightning-AI/metrics/pull/1150))
+
+
 ## [0.10.0] - 2022-10-04
 
 ### Added

diff --git a/src/torchmetrics/detection/mean_ap.py b/src/torchmetrics/detection/mean_ap.py
@@ -500,7 +500,7 @@ def __evaluate_image_gt_no_preds(
         return {
             "dtMatches": torch.zeros((nb_iou_thrs, nb_det), dtype=torch.bool, device=self.device),
             "gtMatches": torch.zeros((nb_iou_thrs, nb_gt), dtype=torch.bool, device=self.device),
-            "dtScores": torch.zeros(nb_det, dtype=torch.bool, device=self.device),
+            "dtScores": torch.zeros(nb_det, dtype=torch.float32, device=self.device),
             "gtIgnore": gt_ignore,
             "dtIgnore": det_ignore,
         }

diff --git a/tests/unittests/detection/test_map.py b/tests/unittests/detection/test_map.py
@@ -168,17 +168,31 @@
 
 # Test empty preds case, to ensure bool inputs are properly casted to uint8
 # From https://github.com/Lightning-AI/metrics/issues/981
+# and https://github.com/Lightning-AI/metrics/issues/1147
 _inputs3 = Input(
     preds=[
+        [
+            dict(
+                boxes=Tensor([[258.0, 41.0, 606.0, 285.0]]),
+                scores=Tensor([0.536]),
+                labels=IntTensor([0]),
+            ),
+        ],
         [
             dict(boxes=Tensor([]), scores=Tensor([]), labels=Tensor([])),
         ],
     ],
     target=[
+        [
+            dict(
+                boxes=Tensor([[214.0, 41.0, 562.0, 285.0]]),
+                labels=IntTensor([0]),
+            )
+        ],
         [
             dict(
                 boxes=Tensor([[1.0, 2.0, 3.0, 4.0]]),
-                scores=Tensor([0.8]),
+                scores=Tensor([0.8]),  # target does not have scores
                 labels=Tensor([1]),
             ),
         ],