updated .gitlab-ci.yml with latest one

Drop Python 2 support

bob/learn/pytorch/architectures/ConditionalGAN.py

@@ -80,10 +80,13 @@ class ConditionalGAN_generator(nn.Module):
     
     """
     generator_input = torch.cat((z, y), 1)
-    if isinstance(generator_input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
-      output = nn.parallel.data_parallel(self.main, generator_input, range(self.ngpu))
-    else:
-      output = self.main(generator_input)
+    #if isinstance(generator_input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
+    #  output = nn.parallel.data_parallel(self.main, generator_input, range(self.ngpu))
+    #else:
+    #  output = self.main(generator_input)
+    
+    # let's assume that we will never face the case where more than a GPU is used ...
+    output = self.main(generator_input)
     return output
 
 
@@ -159,8 +162,11 @@ class ConditionalGAN_discriminator(nn.Module):
       the output of the discriminator
     """
     input_discriminator = torch.cat((images, y), 1)
-    if isinstance(input_discriminator.data, torch.cuda.FloatTensor) and self.ngpu > 1:
-      output = nn.parallel.data_parallel(self.main, input_discriminator, range(self.ngpu))
-    else:
-      output = self.main(input_discriminator)
+    #if isinstance(input_discriminator.data, torch.cuda.FloatTensor) and self.ngpu > 1:
+    #  output = nn.parallel.data_parallel(self.main, input_discriminator, range(self.ngpu))
+    #else:
+    #  output = self.main(input_discriminator)
+    
+    # let's assume that we will never face the case where more than a GPU is used ...
+    output = self.main(input_discriminator)
     return output.view(-1, 1).squeeze(1)