3460 - deprecate reduction in contrastive loss

monai/losses/contrastive.py

@@ -9,13 +9,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Union
-
 import torch
 from torch.nn import functional as F
 from torch.nn.modules.loss import _Loss
 
-from monai.utils import LossReduction
+from monai.utils import deprecated_arg
 
 
 class ContrastiveLoss(_Loss):
@@ -31,19 +29,23 @@ class ContrastiveLoss(_Loss):
 
     """
 
-    def __init__(
-        self, temperature: float = 0.5, batch_size: int = 1, reduction: Union[LossReduction, str] = LossReduction.SUM
-    ) -> None:
+    @deprecated_arg(name="reduction", since="0.8", msg_suffix="`reduction` is no longer supported.")
+    def __init__(self, temperature: float = 0.5, batch_size: int = 1, reduction="sum") -> None:
         """
         Args:
             temperature: Can be scaled between 0 and 1 for learning from negative samples, ideally set to 0.5.
+            batch_size: The number of samples.
 
         Raises:
-            AssertionError: When an input of dimension length > 2 is passed
-            AssertionError: When input and target are of different shapes
+            ValueError: When an input of dimension length > 2 is passed
+            ValueError: When input and target are of different shapes
+
+        .. deprecated:: 0.8.0
+
+            `reduction` is no longer supported.
 
         """
-        super().__init__(reduction=LossReduction(reduction).value)
+        super().__init__()
 
         self.batch_size = batch_size
         self.temperature = temperature
@@ -53,18 +55,15 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         Args:
             input: the shape should be B[F].
             target: the shape should be B[F].
-
-        Raises:
-            ValueError: When ``self.reduction`` is not one of ["sum", "none"].
         """
         if len(target.shape) > 2 or len(input.shape) > 2:
-            raise AssertionError(
+            raise ValueError(
                 f"Either target or input has dimensions greater than 2 where target "
                 f"shape is ({target.shape}) and input shape is ({input.shape})"
             )
 
         if target.shape != input.shape:
-            raise AssertionError(f"ground truth has differing shape ({target.shape}) from input ({input.shape})")
+            raise ValueError(f"ground truth has differing shape ({target.shape}) from input ({input.shape})")
 
         temperature_tensor = torch.tensor(self.temperature).to(input.device)
 
@@ -86,6 +85,4 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
 
         loss_partial = -torch.log(nominator / torch.sum(denominator, dim=1))
 
-        if self.reduction == LossReduction.SUM.value:
-            return torch.sum(loss_partial) / (2 * self.batch_size)
-        raise ValueError(f"Unsupported reduction: {self.reduction}, " f'available options are ["mean", "sum", "none"].')
+        return torch.sum(loss_partial) / (2 * self.batch_size)

tests/test_contrastive_loss.py

@@ -61,7 +61,7 @@ def test_result(self, input_param, input_data, expected_val):
 
     def test_ill_shape(self):
         loss = ContrastiveLoss(temperature=0.5, batch_size=1)
-        with self.assertRaisesRegex(AssertionError, ""):
+        with self.assertRaisesRegex(ValueError, ""):
             loss(torch.ones((1, 2, 3)), torch.ones((1, 1, 2, 3)))
 
     def test_with_cuda(self):