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docs/source/metrics.rst

@@ -341,7 +341,7 @@ Complete list of metrics
     MultiLabelConfusionMatrix
     MutualInformation
     ObjectDetectionAvgPrecisionRecall
-    CommonObjDetectionMetrics
+    CommonObjectDetectionMetrics
     vision.object_detection_average_precision_recall.coco_tensor_list_to_dict_list
     precision.Precision
     PSNR

ignite/metrics/__init__.py

@@ -40,7 +40,7 @@
 from ignite.metrics.top_k_categorical_accuracy import TopKCategoricalAccuracy
 from ignite.metrics.vision.object_detection_average_precision_recall import (
     coco_tensor_list_to_dict_list,
-    CommonObjDetectionMetrics,
+    CommonObjectDetectionMetrics,
     ObjectDetectionAvgPrecisionRecall,
 )
 
@@ -94,6 +94,6 @@
     "ROC_AUC",
     "MeanAveragePrecision",
     "ObjectDetectionAvgPrecisionRecall",
-    "CommonObjDetectionMetrics",
+    "CommonObjectDetectionMetrics",
     "coco_tensor_list_to_dict_list",
 ]

ignite/metrics/vision/object_detection_average_precision_recall.py

@@ -234,7 +234,15 @@ def _compute_average_precision(self, recall: torch.Tensor, precision: torch.Tens
         Returns:
             average_precision: (n-1)-dimensional tensor containing the average precision for mean dimensions.
         """
-        precision_integrand = precision.flip(-1).cummax(dim=-1).values.flip(-1)
+        if precision.device.type == "mps":
+            # Manual fallback to CPU if precision is on MPS due to the error:
+            # NotImplementedError: The operator 'aten::_cummax_helper' is not currently implemented for the MPS device
+            device = precision.device
+            precision_integrand = precision.flip(-1).cpu()
+            precision_integrand = precision_integrand.cummax(dim=-1).values
+            precision_integrand = precision_integrand.to(device=device).flip(-1)
+        else:
+            precision_integrand = precision.flip(-1).cummax(dim=-1).values.flip(-1)
         rec_thresholds = cast(torch.Tensor, self.rec_thresholds).repeat((*recall.shape[:-1], 1))
         rec_thresh_indices = (
             torch.searchsorted(recall, rec_thresholds)
@@ -386,9 +394,9 @@ def compute(self) -> Tuple[float, float]:
         return ap, ar
 
 
-class CommonObjDetectionMetrics(MetricGroup):
+class CommonObjectDetectionMetrics(MetricGroup):
     """
-    Common Object detection metrics. Included metrics are as follows:
+    Common Object Detection metrics. Included metrics are as follows:
 
     =============== ==========================================
     **Metric name**    **Description**

tests/ignite/metrics/vision/test_object_detection_map.py

@@ -17,7 +17,7 @@
 
 import ignite.distributed as idist
 from ignite.engine import Engine
-from ignite.metrics import CommonObjDetectionMetrics, ObjectDetectionAvgPrecisionRecall
+from ignite.metrics import CommonObjectDetectionMetrics, ObjectDetectionAvgPrecisionRecall
 from ignite.metrics.vision.object_detection_average_precision_recall import coco_tensor_list_to_dict_list
 from ignite.utils import manual_seed
 
@@ -895,7 +895,7 @@ def test_compute(sample):
     print(all_res)
     assert np.allclose(all_res, sample.mAP)
 
-    common_metrics = CommonObjDetectionMetrics(device=device)
+    common_metrics = CommonObjectDetectionMetrics(device=device)
     common_metrics.update(sample.data)
     res = common_metrics.compute()
     common_metrics_res = [
@@ -1021,7 +1021,7 @@ def test_distrib_update_compute(distributed, sample):
     all_res = [AP_50_95, AP_50, AP_75, AP_S, AP_M, AP_L, AR_1, AR_10, AR_100, AR_S, AR_M, AR_L]
     assert np.allclose(all_res, sample.mAP)
 
-    common_metrics = CommonObjDetectionMetrics(device=device)
+    common_metrics = CommonObjectDetectionMetrics(device=device)
     common_metrics.update((y_pred_rank, y_rank))
     res = common_metrics.compute()
     common_metrics_res = [