Merge pull request #86 from constantinpape/update-loss-masking

Use ApplyMask in other masking loss wrappers

torch_em/loss/wrapper.py

@@ -41,27 +41,24 @@ def forward(self, prediction, target, **kwargs):
 # Loss transformations
 #
 
+
+class ApplyMask:
+    def __call__(self, prediction, target, mask):
+        mask.requires_grad = False
+        prediction = prediction * mask
+        target = target * mask
+        return prediction, target
+
+
 class ApplyAndRemoveMask:
     def __call__(self, prediction, target):
         assert target.dim() == prediction.dim(), f"{target.dim()}, {prediction.dim()}"
         assert target.size(1) == 2 * prediction.size(1), f"{target.size(1)}, {prediction.size(1)}"
         assert target.shape[2:] == prediction.shape[2:], f"{str(target.shape)}, {str(prediction.shape)}"
-
         seperating_channel = target.size(1) // 2
         mask = target[:, seperating_channel:]
         target = target[:, :seperating_channel]
-        mask.requires_grad = False
-
-        # mask the prediction
-        prediction = prediction * mask
-        return prediction, target
-
-
-class ApplyMask:
-    def __call__(self, prediction, target, mask):
-        mask.requires_grad = False
-        prediction = prediction * mask
-        target = target * mask
+        prediction, target = ApplyMask()(prediction, target, mask)
         return prediction, target
 
 
@@ -71,7 +68,5 @@ def __init__(self, ignore_label=-1):
 
     def __call__(self, prediction, target):
         mask = (target != self.ignore_label)
-        mask.requires_grad = False
-        prediction = prediction * mask
-        target = target * mask
+        prediction, target = ApplyMask()(prediction, target, mask)
         return prediction, target