[normalizer] Do not call to super().set_normalizer() as it was moved

src/mednet/libs/classification/models/alexnet.py

@@ -115,7 +115,13 @@ class Alexnet(Model):
             )
             self.normalizer = make_imagenet_normalizer()
         else:
-            super().set_normalizer(dataloader)
+            from .normalizer import make_z_normalizer
+
+            logger.info(
+                f"Uninitialised {self.name} model - "
+                f"computing z-norm factors from train dataloader.",
+            )
+            self.normalizer = make_z_normalizer(dataloader)
 
     def training_step(self, batch, _):
         images = batch[0]

src/mednet/libs/classification/models/densenet.py

@@ -118,7 +118,13 @@ class Densenet(Model):
             )
             self.normalizer = make_imagenet_normalizer()
         else:
-            super().set_normalizer(dataloader)
+            from .normalizer import make_z_normalizer
+
+            logger.info(
+                f"Uninitialised {self.name} model - "
+                f"computing z-norm factors from train dataloader.",
+            )
+            self.normalizer = make_z_normalizer(dataloader)
 
     def training_step(self, batch, _):
         images = batch[0]

src/mednet/libs/segmentation/models/driu.py

@@ -158,7 +158,13 @@ class DRIU(Model):
             )
             self.normalizer = make_imagenet_normalizer()
         else:
-            super().set_normalizer(dataloader)
+            from .normalizer import make_z_normalizer
+
+            logger.info(
+                f"Uninitialised {self.name} model - "
+                f"computing z-norm factors from train dataloader.",
+            )
+            self.normalizer = make_z_normalizer(dataloader)
 
     def training_step(self, batch, batch_idx):
         images = batch[0]["image"]

src/mednet/libs/segmentation/models/driu_bn.py

@@ -161,7 +161,13 @@ class DRIUBN(Model):
             )
             self.normalizer = make_imagenet_normalizer()
         else:
-            super().set_normalizer(dataloader)
+            from .normalizer import make_z_normalizer
+
+            logger.info(
+                f"Uninitialised {self.name} model - "
+                f"computing z-norm factors from train dataloader.",
+            )
+            self.normalizer = make_z_normalizer(dataloader)
 
     def training_step(self, batch, batch_idx):
         images = batch[0]["image"]

src/mednet/libs/segmentation/models/driu_od.py

@@ -143,7 +143,13 @@ class DRIUOD(Model):
             )
             self.normalizer = make_imagenet_normalizer()
         else:
-            super().set_normalizer(dataloader)
+            from .normalizer import make_z_normalizer
+
+            logger.info(
+                f"Uninitialised {self.name} model - "
+                f"computing z-norm factors from train dataloader.",
+            )
+            self.normalizer = make_z_normalizer(dataloader)
 
     def training_step(self, batch, batch_idx):
         images = batch[0]["image"]

src/mednet/libs/segmentation/models/driu_pix.py

@@ -147,7 +147,13 @@ class DRIUPix(Model):
             )
             self.normalizer = make_imagenet_normalizer()
         else:
-            super().set_normalizer(dataloader)
+            from .normalizer import make_z_normalizer
+
+            logger.info(
+                f"Uninitialised {self.name} model - "
+                f"computing z-norm factors from train dataloader.",
+            )
+            self.normalizer = make_z_normalizer(dataloader)
 
     def training_step(self, batch, batch_idx):
         images = batch[0]["image"]

src/mednet/libs/segmentation/models/hed.py

@@ -162,7 +162,13 @@ class HED(Model):
             )
             self.normalizer = make_imagenet_normalizer()
         else:
-            super().set_normalizer(dataloader)
+            from .normalizer import make_z_normalizer
+
+            logger.info(
+                f"Uninitialised {self.name} model - "
+                f"computing z-norm factors from train dataloader.",
+            )
+            self.normalizer = make_z_normalizer(dataloader)
 
     def training_step(self, batch, batch_idx):
         images = batch[0]["image"]

src/mednet/libs/segmentation/models/m2unet.py

@@ -210,7 +210,13 @@ class M2UNET(Model):
             )
             self.normalizer = make_imagenet_normalizer()
         else:
-            super().set_normalizer(dataloader)
+            from .normalizer import make_z_normalizer
+
+            logger.info(
+                f"Uninitialised {self.name} model - "
+                f"computing z-norm factors from train dataloader.",
+            )
+            self.normalizer = make_z_normalizer(dataloader)
 
     def training_step(self, batch, batch_idx):
         images = batch[0]["image"]

src/mednet/libs/segmentation/models/unet.py

@@ -151,7 +151,13 @@ class Unet(Model):
             )
             self.normalizer = make_imagenet_normalizer()
         else:
-            super().set_normalizer(dataloader)
+            from .normalizer import make_z_normalizer
+
+            logger.info(
+                f"Uninitialised {self.name} model - "
+                f"computing z-norm factors from train dataloader.",
+            )
+            self.normalizer = make_z_normalizer(dataloader)
 
     def training_step(self, batch, batch_idx):
         images = batch[0]["image"]