[trainers] fixed docstrings in ConditionalGANTrainer

bob/learn/pytorch/trainers/ConditionalGANTrainer.py

 #!/usr/bin/env python
 # encoding: utf-8
 
-import numpy
-import time
 
 import torch
 import torch.nn as nn
@@ -13,38 +11,58 @@ import torchvision.utils as vutils
 import bob.core
 logger = bob.core.log.setup("bob.learn.pytorch")
 
-class ConditionalGANTrainer(object):
-  """
-  Class to train a Conditional GAN
+import time
 
-  **Parameters**
+class ConditionalGANTrainer(object):
+  """Class to train a Conditional GAN
 
-  generator: pytorch nn.Module
+  Attributes
+  ----------
+  generator : :py:class:`torch.nn.Module`
     The generator network
-
-  discriminator: pytorch nn.Module
+  discriminator : :py:class:`torch.nn.Module`
     The discriminator network
-
-  image_size: list
+  image_size: list of int
     The size of the images in this format: [channels,height, width]
-
   batch_size: int
     The size of your minibatch
-
   noise_dim: int
     The dimension of the noise (input to the generator)
-
   conditional_dim: int
     The dimension of the conditioning variable
-
-  use_gpu: boolean
+  use_gpu: bool
     If you would like to use the gpu
-
-  verbosity_level: int
-    The level of verbosity output to stdout
+  fixed_noise : :py:class:`torch.Tensor`
+    The fixed input noise to the generator.
+  fixed_one_hot : :py:class:`torch.Tensor`
+    The set of fixed one-hot encoded conditioning variable 
+  criterion : :py:class:`torch.nn.BCELoss`
+    The binary cross-entropy loss
+  
   """
   def __init__(self, netG, netD, image_size, batch_size=64, noise_dim=100, conditional_dim=13, use_gpu=False, verbosity_level=2):
-   
+    """Init function
+
+    Parameters
+    ----------
+    netG : :py:class:`torch.nn.Module`
+      The generator network
+    netD : :py:class:`torch.nn.Module`
+      The discriminator network
+    image_size: list of int
+      The size of the images in this format: [channels,height, width]
+    batch_size: int
+      The size of your minibatch
+    noise_dim: int
+      The dimension of the noise (input to the generator)
+    conditional_dim: int
+      The dimension of the conditioning variable
+    use_gpu: bool
+      If you would like to use the gpu
+    verbosity_level: int
+      The level of verbosity output to stdout
+  
+    """
     bob.core.log.set_verbosity_level(logger, verbosity_level)
     
     self.netG = netG
@@ -76,25 +94,21 @@ class ConditionalGANTrainer(object):
 
 
   def train(self, dataloader, n_epochs=10, learning_rate=0.0002, beta1=0.5, output_dir='out'):
-    """
-    Function that performs the training.
-
-    **Parameters**
+    """trains the Conditional GAN.
 
-    dataloader: pytorch DataLoader
+    Parameters
+    ----------
+    dataloader: :py:class:`torch.utils.data.DataLoader`
       The dataloader for your data
-
     n_epochs: int
       The number of epochs you would like to train for
-
     learning_rate: float
       The learning rate for Adam optimizer
-
     beta1: float
       The beta1 for Adam optimizer
-
-    output_dir: path
+    output_dir: str
       The directory where you would like to output images and models
+
     """
     real_label = 1
     fake_label = 0