📚 notes

labml_nn/gan/cycle_gan.py

@@ -611,22 +611,29 @@ def setup_dataloader(self: Configs):
 
 
 def train():
+    """
+    ## Train Cycle GAN
+    """
+    # Create configurations
     conf = Configs()
+    # Create an experiment
     experiment.create(name='cycle_gan')
-    experiment.configs(conf, {
-        'dataset_name': 'summer2winter_yosemite'
-    }, 'run')
+    # Calculate configurations.
+    # It will calculate `conf.run` and all other configs required by it.
+    experiment.configs(conf, {'dataset_name': 'summer2winter_yosemite'}, 'run')
     # Register models for saving and loading.
     # `get_modules` gives a dictionary of `nn.Modules` in `conf`.
     # You can also specify a custom dictionary of models.
     experiment.add_pytorch_models(get_modules(conf))
+    # Start and watch the experiment
     with experiment.start():
+        # Run the training
         conf.run()
 
 
 def plot_image(img: torch.Tensor):
     """
-    Plots an image with matplotlib
+    ### Plots an image with matplotlib
     """
     from matplotlib import pyplot as plt
 
@@ -641,7 +648,10 @@ def plot_image(img: torch.Tensor):
     plt.show()
 
 
-def sample():
+def evaluate():
+    """
+    ## Evaluate trained Cycle GAN
+    """
     # Set the run uuid from the training run
     trained_run_uuid = 'f73c1164184711eb9190b74249275441'
     # Create configs object
@@ -704,4 +714,4 @@ def sample():
 
 if __name__ == '__main__':
     train()
-    # sample()
+    # evaluate()