Return also classes for MAP metric

.azure/gpu-pipeline.yml

@@ -109,7 +109,7 @@ jobs:
         ls -lh $(HF_CACHE_DIR)  # show what was restored...
       displayName: 'Show HF cache'
 
-    - bash: python -m pytest torchmetrics --cov=torchmetrics --timeout=120 --durations=50
+    - bash: python -m pytest torchmetrics --cov=torchmetrics --timeout=150 --durations=50
       workingDirectory: src
       displayName: 'DocTesting'
 

CHANGELOG.md

@@ -13,6 +13,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Added support for plotting of metrics through `.plot()` method ([#1328](https://github.com/Lightning-AI/metrics/pull/1328))
 
+- Added `classes` to output from `MAP` metric ([#1419](https://github.com/Lightning-AI/metrics/pull/1419))
 
 ### Changed
 

src/torchmetrics/detection/mean_ap.py

@@ -94,7 +94,7 @@ def __delattr__(self, key: str) -> None:
 class MAPMetricResults(BaseMetricResults):
     """Class to wrap the final mAP results."""
 
-    __slots__ = ("map", "map_50", "map_75", "map_small", "map_medium", "map_large")
+    __slots__ = ("map", "map_50", "map_75", "map_small", "map_medium", "map_large", "classes")
 
 
 class MARMetricResults(BaseMetricResults):
@@ -248,6 +248,7 @@ class MeanAveragePrecision(Metric):
         - map_75: (:class:`~torch.Tensor`) (-1 if 0.75 not in the list of iou thresholds)
         - map_per_class: (:class:`~torch.Tensor`) (-1 if class metrics are disabled)
         - mar_100_per_class: (:class:`~torch.Tensor`) (-1 if class metrics are disabled)
+        - classes (:class:`~torch.Tensor`)
 
     For an example on how to use this metric check the `torchmetrics examples
     <https://github.com/Lightning-AI/metrics/blob/master/examples/detection_map.py>`_
@@ -332,7 +333,8 @@ class MeanAveragePrecision(Metric):
         >>> metric.update(preds, target)
         >>> from pprint import pprint
         >>> pprint(metric.compute())
-        {'map': tensor(0.6000),
+        {'classes': tensor(0, dtype=torch.int32),
+         'map': tensor(0.6000),
          'map_50': tensor(1.),
          'map_75': tensor(1.),
          'map_large': tensor(0.6000),
@@ -923,5 +925,5 @@ def compute(self) -> dict:
         metrics.update(mar_val)
         metrics.map_per_class = map_per_class_values
         metrics[f"mar_{self.max_detection_thresholds[-1]}_per_class"] = mar_max_dets_per_class_values
-
+        metrics.classes = torch.tensor(classes, dtype=torch.int)
         return metrics

tests/unittests/detection/test_map.py

@@ -282,6 +282,7 @@ def _compare_fn(preds, target) -> dict:
         "mar_large": Tensor([0.633]),
         "map_per_class": Tensor([0.725, 0.800, 0.454, -1.000, 0.650, 0.556]),
         "mar_100_per_class": Tensor([0.780, 0.800, 0.450, -1.000, 0.650, 0.580]),
+        "classes": Tensor([0, 1, 2, 3, 4, 49]),
     }
 
 
@@ -317,6 +318,7 @@ def _compare_fn_segm(preds, target) -> dict:
         "mar_large": Tensor([0.35]),
         "map_per_class": Tensor([0.4039604, -1.0, 0.3]),
         "mar_100_per_class": Tensor([0.4, -1.0, 0.3]),
+        "classes": Tensor([2, 3, 4]),
     }