3919 Use enums for reduction in DiceCELoss

monai/losses/dice.py

@@ -21,7 +21,7 @@
 from monai.losses.focal_loss import FocalLoss
 from monai.losses.spatial_mask import MaskedLoss
 from monai.networks import one_hot
-from monai.utils import LossReduction, Weight, look_up_option
+from monai.utils import DiceCEReduction, LossReduction, Weight, look_up_option
 
 
 class DiceLoss(_Loss):
@@ -671,6 +671,7 @@ def __init__(
 
         """
         super().__init__()
+        reduction = look_up_option(reduction, DiceCEReduction).value
         self.dice = DiceLoss(
             include_background=include_background,
             to_onehot_y=to_onehot_y,

monai/utils/__init__.py

@@ -19,6 +19,7 @@
     BlendMode,
     ChannelMatching,
     CommonKeys,
+    DiceCEReduction,
     ForwardMode,
     GridSampleMode,
     GridSamplePadMode,

monai/utils/enums.py

@@ -25,6 +25,7 @@
     "Average",
     "MetricReduction",
     "LossReduction",
+    "DiceCEReduction",
     "Weight",
     "ChannelMatching",
     "SkipMode",
@@ -166,6 +167,16 @@ class LossReduction(Enum):
     SUM = "sum"
 
 
+class DiceCEReduction(Enum):
+    """
+    See also:
+        - :py:class:`monai.losses.dice.DiceCELoss`
+    """
+
+    MEAN = "mean"
+    SUM = "sum"
+
+
 class Weight(Enum):
     """
     See also: :py:class:`monai.losses.dice.GeneralizedDiceLoss`

tests/test_dice_ce_loss.py

@@ -80,6 +80,11 @@ def test_ill_shape(self):
         with self.assertRaisesRegex(ValueError, ""):
             loss(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3)))
 
+    def test_ill_reduction(self):
+        with self.assertRaisesRegex(ValueError, ""):
+            loss = DiceCELoss(reduction="none")
+            loss(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3)))
+
     @SkipIfBeforePyTorchVersion((1, 7, 0))
     def test_script(self):
         loss = DiceCELoss()