Add updates from comments

monai/metrics/generalized_dice.py

@@ -14,7 +14,7 @@
 import torch
 
 from monai.metrics.utils import do_metric_reduction, ignore_background
-from monai.utils import MetricReduction, Weight, look_up_option
+from monai.utils import MetricReduction, Weight, deprecated_arg, deprecated_arg_default, look_up_option
 
 from .metric import CumulativeIterationMetric
 
@@ -44,16 +44,27 @@ class GeneralizedDiceScore(CumulativeIterationMetric):
         ValueError: When the `reduction` is not one of MetricReduction enum.
     """
 
+    @deprecated_arg_default(
+        "reduction",
+        MetricReduction.MEAN_BATCH,
+        MetricReduction.MEAN,
+        since="1.4.0",
+        replaced="1.4.0",
+        msg_suffix="Old versions computed `mean` when `mean_batch` was provided due to bug in reduction",
+    )
     def __init__(
-        self, include_background: bool = True, reduction: str = "mean", weight_type: Weight | str = Weight.SQUARE
+        self,
+        include_background: bool = True,
+        reduction: str = MetricReduction.MEAN,
+        weight_type: Weight | str = Weight.SQUARE,
     ) -> None:
         super().__init__()
         self.include_background = include_background
         self.reduction = look_up_option(reduction, MetricReduction)
         self.weight_type = look_up_option(weight_type, Weight)
-        self.sum_over_labels = self.reduction == MetricReduction.SUM or self.reduction == MetricReduction.MEAN
+        self.sum_over_classes = self.reduction == MetricReduction.SUM or self.reduction == MetricReduction.MEAN
 
-    def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+    def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:  # type: ignore[override]
         """
         Computes the Generalized Dice Score and returns a tensor with its per image values.
 
@@ -63,7 +74,7 @@ def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor
             y: Binarized ground-truth. It must be in one-hot format and have the same shape as `y_pred`.
 
         Returns:
-            torch.Tensor: Per batch and per class Generalized Dice Score.
+            torch.Tensor: Generalized Dice Score averaged across batch and class
 
         Raises:
             ValueError: If `y_pred` and `y` have less than 3 dimensions, or `y_pred` and `y` don't have the same shape.
@@ -73,10 +84,16 @@ def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor
             y=y,
             include_background=self.include_background,
             weight_type=self.weight_type,
-            sum_over_labels=self.sum_over_labels,
+            sum_over_classes=self.sum_over_classes,
         )
 
-    def aggregate(self) -> torch.Tensor:
+    @deprecated_arg(
+        "reduction",
+        since="1.4.0",
+        removed="1.7.0",
+        msg_suffix="Reduction will be ignored. Set reduction during init. as gen.dice needs it during compute",
+    )
+    def aggregate(self, reduction: MetricReduction | str | None = None) -> torch.Tensor:
         """
         Execute reduction logic for the output of `compute_generalized_dice`.
 
@@ -101,7 +118,7 @@ def compute_generalized_dice(
     y: torch.Tensor,
     include_background: bool = True,
     weight_type: Weight | str = Weight.SQUARE,
-    sum_over_labels: bool = False,
+    sum_over_classes: bool = False,
 ) -> torch.Tensor:
     """
     Computes the Generalized Dice Score and returns a tensor with its per image values.
@@ -158,7 +175,7 @@ def compute_generalized_dice(
         b[infs] = torch.max(b)
 
     # Compute the weighted numerator and denominator, summing along the class axis when sum_over_labels is True
-    if sum_over_labels:
+    if sum_over_classes:
         numer = 2.0 * (intersection * w).sum(dim=1, keepdim=True)
         denom = (denominator * w).sum(dim=1, keepdim=True)
         y_pred_o = y_pred_o.sum(dim=-1, keepdim=True)