[script, trainer] various fixes to DCGAN

bob/learn/pytorch/scripts/train_dcgan.py

@@ -69,6 +69,7 @@ def main(user_input=None):
   configuration = load([os.path.join(args['<configuration>'])])
   
   # get various parameters, either from config file or command-line 
+  noise_dim = get_parameter(args, configuration, 'noise_dim', 100)
   batch_size = get_parameter(args, configuration, 'batch_size', 64)
   epochs = get_parameter(args, configuration, 'epochs', 20)
   sample = get_parameter(args, configuration, 'sample', 1e10)
@@ -83,7 +84,7 @@ def main(user_input=None):
   # print parameters
   logger.debug("Batch size = {}".format(batch_size))
   logger.debug("Epochs = {}".format(epochs))
-  logger.debug("Sample = {}".format(learning_rate))
+  logger.debug("Sample = {}".format(sample))
   logger.debug("Seed = {}".format(seed))
   logger.debug("Output directory = {}".format(output_dir))
   logger.debug("Use GPU = {}".format(use_gpu))
@@ -98,14 +99,14 @@ def main(user_input=None):
    # get data
   if hasattr(configuration, 'dataset'):
     dataloader = torch.utils.data.DataLoader(configuration.dataset, batch_size=batch_size, shuffle=True)
-    logger.info("There are {} training images from {} categories".format(len(configuration.dataset)))
+    logger.info("There are {} training images".format(len(configuration.dataset)))
   else:
     logger.error("Please provide a dataset in your configuration file !")
     sys.exit()
   
   # train the model
   if hasattr(configuration, 'generator') and hasattr(configuration, 'discriminator'):
-    trainer = DCGANTrainer(generator, discriminator, batch_size=batch_size, noise_dim=noise_dim, use_gpu=use_gpu, verbosity_level=verbosity_level)
+    trainer = DCGANTrainer(configuration.generator, configuration.discriminator, batch_size=batch_size, noise_dim=noise_dim, use_gpu=use_gpu, verbosity_level=verbosity_level)
     trainer.train(dataloader, n_epochs=epochs, output_dir=output_dir)
   else:
     logger.error("Please provide both a generator and a discriminator in your configuration file !")

bob/learn/pytorch/trainers/DCGANTrainer.py

@@ -158,7 +158,8 @@ class DCGANTrainer(object):
         optimizerG.step()
 
         end = time.time()
-        logger.info("[{}/{}][{}/{}] => Loss D = {} -- Loss G = {} (time spent: {})".format(epoch, n_epochs, i, len(dataloader), errD.data[0], errG.data[0], (end-start)))
+        #logger.info("[{}/{}][{}/{}] => Loss D = {} -- Loss G = {} (time spent: {})".format(epoch, n_epochs, i, len(dataloader), errD.data[0], errG.data[0], (end-start)))
+        logger.info("[{}/{}][{}/{}] => Loss D = {} -- Loss G = {} (time spent: {})".format(epoch, n_epochs, i, len(dataloader), errD.item(), errG.item(), (end-start)))
       
       # save generated images at every epoch
       fake = self.netG(self.fixed_noise)