[script] modified the script to train on Multi-PIE to take into account renaming

bob/learn/pytorch/scripts/train_conditionalgan_multipie.py

@@ -8,7 +8,6 @@ Usage:
   %(prog)s [--noise-dim=<int>] [--conditional-dim=<int>] 
            [--batch-size=<int>] [--epochs=<int>] [--sample=<int>]
            [--output-dir=<path>] [--use-gpu] [--seed=<int>] [--verbose ...]
-           [--second]
 
 Options:
   -h, --help                    Show this screen.
@@ -22,7 +21,6 @@ Options:
   -g, --use-gpu                 Use the GPU 
   -S, --seed=<int>              The random seed [default: 3] 
   -v, --verbose                 Increase the verbosity (may appear multiple times).
-      --second                  Use the "direct" architecture.
 
 Example:
 
@@ -63,7 +61,9 @@ from bob.learn.pytorch.datasets import Normalize
 
 from bob.learn.pytorch.architectures import weights_init
 
-
+from bob.learn.pytorch.architectures import ConditionalGAN_generator as cgenerator
+from bob.learn.pytorch.architectures import ConditionalGAN_discriminator as cdiscriminator
+from bob.learn.pytorch.trainers import ConditionalGANTrainer as ctrainer
 
 def main(user_input=None):
   
@@ -75,7 +75,7 @@ def main(user_input=None):
 
   prog = os.path.basename(sys.argv[0])
   completions = dict(prog=prog, version=version,)
-  args = docopt(__doc__ % completions,argv=arguments,version='Train DR-GAN (%s)' % version,)
+  args = docopt(__doc__ % completions,argv=arguments,version='Train conditional GAN (%s)' % version,)
 
   # verbosity
   verbosity_level = args['--verbose']
@@ -105,15 +105,6 @@ def main(user_input=None):
   bob.io.base.create_directories_safe(log_dir)
   bob.io.base.create_directories_safe(images_dir)
 
-  if bool(args['--second']):
-    from bob.learn.pytorch.architectures import ConditionalGAN_generator2 as cgenerator
-    from bob.learn.pytorch.architectures import ConditionalGAN_discriminator2 as cdiscriminator
-    from bob.learn.pytorch.trainers import ConditionalGANTrainer2 as ctrainer
-  else:
-    from bob.learn.pytorch.architectures import ConditionalGAN_generator as cgenerator
-    from bob.learn.pytorch.architectures import ConditionalGAN_discriminator as cdiscriminator
-    from bob.learn.pytorch.trainers import ConditionalGANTrainer as ctrainer
-
   # ============
   # === DATA ===
   # ============
@@ -146,7 +137,6 @@ def main(user_input=None):
   discriminator.apply(weights_init)
   logger.info("Discriminator architecture: {}".format(discriminator))
 
-
   # ===============
   # === TRAINER ===
   # ===============