Fix a bug related to MPS

Author: sadra-barikbin

ignite/metrics/vision/object_detection_average_precision_recall.py

@@ -124,7 +124,7 @@ def box_iou(pred_boxes: torch.Tensor, gt_boxes: torch.Tensor, iscrowd: torch.Boo
             rec_thresholds=rec_thresholds,
             class_mean=None,
         )
-        precision = torch.double if torch.device(device) != torch.device("mps") else torch.float32
+        precision = torch.double if torch.device(device).type != "mps" else torch.float32
         self.rec_thresholds = self.rec_thresholds.to(device=device, dtype=precision)
 
     @reinit__is_reduced
@@ -207,12 +207,12 @@ def _compute_recall_and_precision(
         indices = torch.argsort(scores, dim=-1, stable=True, descending=True)
         tp = TP[..., indices]
         tp_summation = tp.cumsum(dim=-1)
-        if tp_summation.device != torch.device("mps"):
+        if tp_summation.device.type != "mps":
             tp_summation = tp_summation.double()
 
         fp = FP[..., indices]
         fp_summation = fp.cumsum(dim=-1)
-        if fp_summation.device != torch.device("mps"):
+        if fp_summation.device.type != "mps":
             fp_summation = fp_summation.double()
 
         recall = tp_summation / y_true_count
@@ -342,7 +342,7 @@ def update(self, output: Tuple[List[Dict[str, torch.Tensor]], List[Dict[str, tor
                     )
 
                 scores = pred["scores"][max_best_detections_index]
-                if self._device == torch.device("mps") and scores.dtype == torch.double:
+                if self._device.type == "mps" and scores.dtype == torch.double:
                     scores = scores.to(dtype=torch.float32)
                 self._scores.append(scores.to(self._device))
                 self._y_pred_labels.append(pred_labels.to(dtype=torch.int, device=self._device))
@@ -352,7 +352,7 @@ def _compute(self) -> torch.Tensor:
         pred_labels = _cat_and_agg_tensors(self._y_pred_labels, cast(Tuple[int], ()), torch.int, self._device)
         TP = _cat_and_agg_tensors(self._tps, (len(self._iou_thresholds),), torch.uint8, self._device)
         FP = _cat_and_agg_tensors(self._fps, (len(self._iou_thresholds),), torch.uint8, self._device)
-        fp_precision = torch.double if self._device != torch.device("mps") else torch.float32
+        fp_precision = torch.double if self._device.type != "mps" else torch.float32
         scores = _cat_and_agg_tensors(self._scores, cast(Tuple[int], ()), fp_precision, self._device)
 
         average_precisions_recalls = -torch.ones(