simple

torchmetrics/detection/mean_ap.py

@@ -695,11 +695,9 @@ def __calculate_recall_precision_scores(
         # different sorting method generates slightly different results.
         # mergesort is used to be consistent as Matlab implementation.
         # Sort in PyTorch does not support bool types on CUDA (yet, 1.11.0)
-        if det_scores.is_cuda and det_scores.dtype is torch.bool:
-            # Explicitly cast to uint8 to avoid error for bool inputs on CUDA to argsort
-            inds = torch.argsort(det_scores.to(torch.uint8), descending=True)
-        else:
-            inds = torch.argsort(det_scores, descending=True)
+        dtype = torch.uint8 if det_scores.is_cuda and det_scores.dtype is torch.bool else det_scores.dtype
+        # Explicitly cast to uint8 to avoid error for bool inputs on CUDA to argsort
+        inds = torch.argsort(det_scores.to(dtype), descending=True)
         det_scores_sorted = det_scores[inds]
 
         det_matches = torch.cat([e["dtMatches"][:, :max_det] for e in img_eval_cls_bbox], axis=1)[:, inds]