prepared execution of first VA code + fixed bugs

solver.py

@@ -62,9 +62,9 @@ class Solver():
                  experiment_name,
                  optimizer,
                  optimizer_arguments={},
-                 #  loss_function=MSELoss(),
-                 #  loss_function=torch.nn.L1Loss(),
-                 loss_function=torch.nn.CosineEmbeddingLoss(),
+                 loss_function=MSELoss(),
+                #  loss_function=torch.nn.L1Loss(),
+                #  loss_function=torch.nn.CosineEmbeddingLoss(),
                  model_name='BrainMapper',
                  labels=None,
                  number_epochs=10,
@@ -208,9 +208,8 @@ class Solver():
 
                     y_hat = torch.mul(y_hat, MNI152_T1_2mm_brain_mask)
 
-                    # loss = self.loss_function(y_hat, y)  # Loss computation
-                    loss = self.loss_function(
-                        y_hat+1e-4, y+1e-4, torch.tensor(1.0).cuda(self.device, non_blocking=True))
+                    loss = self.loss_function(y_hat, y)  # Loss computation
+                    # loss = self.loss_function(y_hat+1e-4, y+1e-4, torch.tensor(1.0).cuda(self.device, non_blocking=True))
 
                     # We also calculate a separate MSE for cost function comparison!
                     MSE = self.MSE(y_hat, y)

utils/modules.py

@@ -309,8 +309,8 @@ class ResNetClassifierBlock3D(nn.Module):
         self.normalization = nn.InstanceNorm3d(
             num_features=parameters['number_of_classes'])
 
-        # self.activation = nn.Sigmoid()
-        self.activation = nn.Tanh()
+        self.activation = nn.Sigmoid()
+        # self.activation = nn.Tanh()
 
         # TODO: Might be wworth looking at GANS for image generation, and adding padding