Convert labels to float instead of double

src/ptbench/models/alexnet.py

@@ -74,7 +74,7 @@ class Alexnet(pl.LightningModule):
         # Forward pass on the network
         outputs = self(images)
 
-        training_loss = self.hparams.criterion(outputs, labels.double())
+        training_loss = self.hparams.criterion(outputs, labels.float())
 
         return {"loss": training_loss}
 
@@ -89,7 +89,7 @@ class Alexnet(pl.LightningModule):
 
         # data forwarding on the existing network
         outputs = self(images)
-        validation_loss = self.hparams.criterion_valid(outputs, labels.double())
+        validation_loss = self.hparams.criterion_valid(outputs, labels.float())
 
         return {"validation_loss": validation_loss}
 

src/ptbench/models/densenet.py

@@ -75,7 +75,7 @@ class Densenet(pl.LightningModule):
         # Forward pass on the network
         outputs = self(images)
 
-        training_loss = self.hparams.criterion(outputs, labels.double())
+        training_loss = self.hparams.criterion(outputs, labels.float())
 
         return {"loss": training_loss}
 
@@ -90,7 +90,7 @@ class Densenet(pl.LightningModule):
 
         # data forwarding on the existing network
         outputs = self(images)
-        validation_loss = self.hparams.criterion_valid(outputs, labels.double())
+        validation_loss = self.hparams.criterion_valid(outputs, labels.float())
 
         return {"validation_loss": validation_loss}
 

src/ptbench/models/densenet_rs.py

@@ -69,7 +69,7 @@ class DensenetRS(pl.LightningModule):
         # Forward pass on the network
         outputs = self(images)
 
-        training_loss = self.hparams.criterion(outputs, labels.double())
+        training_loss = self.hparams.criterion(outputs, labels.float())
 
         return {"loss": training_loss}
 
@@ -84,7 +84,7 @@ class DensenetRS(pl.LightningModule):
 
         # data forwarding on the existing network
         outputs = self(images)
-        validation_loss = self.hparams.criterion_valid(outputs, labels.double())
+        validation_loss = self.hparams.criterion_valid(outputs, labels.float())
 
         return {"validation_loss": validation_loss}
 

src/ptbench/models/logistic_regression.py

@@ -57,7 +57,7 @@ class LogisticRegression(pl.LightningModule):
         # Forward pass on the network
         outputs = self(images)
 
-        training_loss = self.hparams.criterion(outputs, labels.double())
+        training_loss = self.hparams.criterion(outputs, labels.float())
 
         return {"loss": training_loss}
 
@@ -72,7 +72,7 @@ class LogisticRegression(pl.LightningModule):
 
         # data forwarding on the existing network
         outputs = self(images)
-        validation_loss = self.hparams.criterion_valid(outputs, labels.double())
+        validation_loss = self.hparams.criterion_valid(outputs, labels.float())
 
         return {"validation_loss": validation_loss}
 

src/ptbench/models/pasa.py

@@ -166,7 +166,7 @@ class PASA(pl.LightningModule):
         # Forward pass on the network
         outputs = self(images)
 
-        training_loss = self.hparams.criterion(outputs, labels.double())
+        training_loss = self.hparams.criterion(outputs, labels.float())
 
         return {"loss": training_loss}
 
@@ -181,7 +181,7 @@ class PASA(pl.LightningModule):
 
         # data forwarding on the existing network
         outputs = self(images)
-        validation_loss = self.hparams.criterion_valid(outputs, labels.double())
+        validation_loss = self.hparams.criterion_valid(outputs, labels.float())
 
         return {"validation_loss": validation_loss}
 

src/ptbench/models/signs_to_tb.py

@@ -64,7 +64,7 @@ class SignsToTB(pl.LightningModule):
         # Forward pass on the network
         outputs = self(images)
 
-        training_loss = self.hparams.criterion(outputs, labels.double())
+        training_loss = self.hparams.criterion(outputs, labels.float())
 
         return {"loss": training_loss}
 
@@ -79,7 +79,7 @@ class SignsToTB(pl.LightningModule):
 
         # data forwarding on the existing network
         outputs = self(images)
-        validation_loss = self.hparams.criterion_valid(outputs, labels.double())
+        validation_loss = self.hparams.criterion_valid(outputs, labels.float())
 
         return {"validation_loss": validation_loss}