[architectures] fixed docstrings in ConditionalGAN

bob/learn/pytorch/architectures/ConditionalGAN.py

@@ -5,43 +5,37 @@
 import torch
 import torch.nn as nn
 
-def weights_init(m):
-  """
-  Weights initialization
-  
-  **Parameters**
-  
-  m: 
-    The model
-  """
-  classname = m.__class__.__name__
-  if classname.find('Conv') != -1:
-    m.weight.data.normal_(0.0, 0.02)
-  elif classname.find('BatchNorm') != -1:
-    m.weight.data.normal_(1.0, 0.02)
-    m.bias.data.fill_(0)
 
 
 class ConditionalGAN_generator(nn.Module):
-  """
-  Class defining the Conditional GAN generator.
-
-  **Parameters**
+  """ Class implementating the conditional GAN generator
 
-  noise_dim: int
-    The dimension of the noise.
+  This network is introduced in the following publication:
+  Mehdi Mirza, Simon Osindero: "Conditional Generative Adversarial Nets"
 
-  conditional_dim: int
-    The dimension of the conditioning variable.
+  Attributes
+  ----------
+  ngpu : int
+    The number of available GPU devices
+  main : :py:class:`torch.nn.Sequential`
+    The sequential container
 
-  channels: int
-    The number of channels in the input image (default: 3).
-
-  ngpu: int
-    The number of GPU (default: 1)
-  """ 
+  """
   def __init__(self, noise_dim, conditional_dim, channels=3, ngpu=1):
-    
+    """Init function
+
+    Parameters
+    ----------
+    noise_dim : int
+      The dimension of the noise 
+    conditional_dim : int
+      The dimension of the conditioning variable 
+    channels : int
+      The number of channels in the image 
+    ngpu : int
+      The number of available GPU devices
+
+    """
     super(ConditionalGAN_generator, self).__init__()
     self.ngpu = ngpu
     self.conditional_dim = conditional_dim
@@ -73,16 +67,20 @@ class ConditionalGAN_generator(nn.Module):
     )
 
   def forward(self, z, y):
-    """
-      Forward function for the generator.
+    """Forward function
 
-    **Parameters**
-
-    z: pyTorch Variable
+    Parameters
+    ----------
+    z : :py:class: `torch.autograd.Variable`
       The minibatch of noise.
-
-    y: pyTorch Variable
+    y : :py:class: `torch.autograd.Variable`
       The conditional one hot encoded vector for the minibatch.
+    
+    Returns
+    -------
+    :py:class:`torch.Tensor`
+      the output of the generator (i.e. an image)
+    
     """
     generator_input = torch.cat((z, y), 1)
     if isinstance(generator_input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
@@ -93,22 +91,33 @@ class ConditionalGAN_generator(nn.Module):
 
 
 class ConditionalGAN_discriminator(nn.Module):
-  """
-  Class defining the Conditional GAN discriminator.
-
-  **Parameters**
+  """ Class implementating the conditional GAN discriminator
 
+  Attributes
+  ----------
   conditional_dim: int
     The dimension of the conditioning variable.
-
   channels: int
     The number of channels in the input image (default: 3).
+  ngpu : int
+    The number of available GPU devices
+  main : :py:class:`torch.nn.Sequential`
+    The sequential container
 
-  ngpu: int
-    The number of GPU (default: 1)
   """ 
   def __init__(self, conditional_dim, channels=3, ngpu=1):
-    
+    """Init function
+
+    Parameters
+    ----------
+    conditional_dim: int
+      The dimension of the conditioning variable.
+    channels: int
+      The number of channels in the input image (default: 3).
+    ngpu : int
+      The number of available GPU devices
+   
+    """
     super(ConditionalGAN_discriminator, self).__init__()
     self.conditional_dim = conditional_dim
     self.ngpu = ngpu
@@ -139,16 +148,19 @@ class ConditionalGAN_discriminator(nn.Module):
   
 
   def forward(self, images, y):
-    """
-      Forward function for the discriminator.
+    """Forward function
 
-    **Parameters**
-
-    images: pyTorch Variable
+    Parameters
+    ----------
+    images : :py:class: `torch.autograd.Variable`
       The minibatch of input images.
-
-    y: pyTorch Variable
+    y : :py:class: `torch.autograd.Variable`
       The corresponding conditional feature maps.
+    
+    Returns
+    -------
+    :py:class:`torch.Tensor`
+      the output of the discriminator
     """
     input_discriminator = torch.cat((images, y), 1)
     if isinstance(input_discriminator.data, torch.cuda.FloatTensor) and self.ngpu > 1: