Feature/tracker higher is better integration

src/torchmetrics/utilities/imports.py

@@ -26,6 +26,7 @@
 _TORCH_GREATER_EQUAL_2_0 = RequirementCache("torch>=2.0.0")
 _TORCH_GREATER_EQUAL_2_1 = RequirementCache("torch>=2.1.0")
 _TORCH_GREATER_EQUAL_2_2 = RequirementCache("torch>=2.2.0")
+_TORCHMETRICS_GREATER_EQUAL_1_6 = RequirementCache("torchmetrics>=1.7.0")
 
 _NLTK_AVAILABLE = RequirementCache("nltk")
 _ROUGE_SCORE_AVAILABLE = RequirementCache("rouge_score")

src/torchmetrics/wrappers/tracker.py

@@ -105,21 +105,50 @@ class MetricTracker(ModuleList):
 
     """
 
-    def __init__(self, metric: Union[Metric, MetricCollection], maximize: Union[bool, List[bool]] = True) -> None:
+    def __init__(
+        self, metric: Union[Metric, MetricCollection], maximize: Optional[Union[bool, List[bool]]] = True
+    ) -> None:
         super().__init__()
         if not isinstance(metric, (Metric, MetricCollection)):
             raise TypeError(
                 "Metric arg need to be an instance of a torchmetrics"
                 f" `Metric` or `MetricCollection` but got {metric}"
             )
         self._base_metric = metric
-        if not isinstance(maximize, (bool, list)):
-            raise ValueError("Argument `maximize` should either be a single bool or list of bool")
-        if isinstance(maximize, list) and isinstance(metric, MetricCollection) and len(maximize) != len(metric):
-            raise ValueError("The len of argument `maximize` should match the length of the metric collection")
-        if isinstance(metric, Metric) and not isinstance(maximize, bool):
-            raise ValueError("Argument `maximize` should be a single bool when `metric` is a single Metric")
-        self.maximize = maximize
+
+        if maximize is None:
+            if isinstance(metric, Metric):
+                if not hasattr(metric, "higher_is_better"):
+                    raise AttributeError(
+                        f"The metric '{metric.__class__.__name__}' does not have a 'higher_is_better' attribute."
+                        " Please provide the `maximize` argument explicitly."
+                    )
+                self.maximize = metric.higher_is_better
+            elif isinstance(metric, MetricCollection):
+                self.maximize = []
+                for name, m in metric.items():
+                    if not hasattr(m, "higher_is_better"):
+                        raise AttributeError(
+                            f"The metric '{name}' in the MetricCollection does not have a 'higher_is_better' attribute."
+                            " Please provide the `maximize` argument explicitly."
+                        )
+                    self.maximize.append(m.higher_is_better)
+        else:
+            rank_zero_warn(
+                "The default value for `maximize` will be changed from `True` to `None` in v1.7.0 of TorchMetrics,"
+                "will automatically infer the value based on the `higher_is_better` attribute of the metric"
+                " (if such attribute exists) or raise an error if it does not. If you are explicitly setting the"
+                " `maximize` argument to either `True` or `False` already, you can ignore this warning.",
+                FutureWarning,
+            )
+
+            if not isinstance(maximize, (bool, list)):
+                raise ValueError("Argument `maximize` should either be a single bool or list of bool")
+            if isinstance(maximize, list) and isinstance(metric, MetricCollection) and len(maximize) != len(metric):
+                raise ValueError("The len of argument `maximize` should match the length of the metric collection")
+            if isinstance(metric, Metric) and not isinstance(maximize, bool):
+                raise ValueError("Argument `maximize` should be a single bool when `metric` is a single Metric")
+            self.maximize = maximize
 
         self._increment_called = False
 

tests/unittests/wrappers/test_tracker.py

@@ -12,16 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import warnings
+
 import pytest
 import torch
-from torchmetrics import MetricCollection
+from torchmetrics import Metric, MetricCollection
 from torchmetrics.classification import (
     MulticlassAccuracy,
     MulticlassConfusionMatrix,
     MulticlassPrecision,
     MulticlassRecall,
 )
 from torchmetrics.regression import MeanAbsoluteError, MeanSquaredError
+from torchmetrics.utilities.imports import _TORCHMETRICS_GREATER_EQUAL_1_6
 from torchmetrics.wrappers import MetricTracker, MultioutputWrapper
 
 from unittests._helpers import seed_all
@@ -216,3 +219,37 @@ def test_metric_tracker_and_collection_multioutput(input_to_tracker, assert_type
     else:
         assert best_metric is None
         assert which_epoch is None
+
+
+def test_tracker_futurewarning():
+    """Check that future warning is raised for the maximize argument.
+
+    Also to make sure that we remove it in future versions of TM.
+
+    """
+    if _TORCHMETRICS_GREATER_EQUAL_1_6:
+        # Check that for future versions that we remove the warning
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
+            MetricTracker(MeanSquaredError(), maximize=True)
+    else:
+        with pytest.warns(FutureWarning, match="The default value for `maximize` will be changed from `True` to.*"):
+            MetricTracker(MeanSquaredError(), maximize=True)
+
+
+@pytest.mark.parametrize(
+    "base_metric",
+    [
+        MeanSquaredError(),
+        MeanAbsoluteError(),
+        MulticlassAccuracy(num_classes=10),
+        MetricCollection([MeanSquaredError(), MeanAbsoluteError()]),
+    ],
+)
+def test_tracker_higher_is_better_integration(base_metric):
+    """Check that the maximize argument is correctly set based on the metric higher_is_better attribute."""
+    tracker = MetricTracker(base_metric, maximize=None)
+    if isinstance(base_metric, Metric):
+        assert tracker.maximize == base_metric.higher_is_better
+    else:
+        assert tracker.maximize == [m.higher_is_better for m in base_metric.values()]