Resolve "Add GANs"

Closes #4 (closed)

bob/learn/pytorch/architectures/ConditionalGAN.py

+#!/usr/bin/env python
+# encoding: utf-8
+
+
+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**
+
+  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 input image (default: 3).
+
+  ngpu: int
+    The number of GPU (default: 1)
+  """ 
+  def __init__(self, noise_dim, conditional_dim, channels=3, ngpu=1):
+    
+    super(ConditionalGAN_generator, self).__init__()
+    self.ngpu = ngpu
+    self.conditional_dim = conditional_dim
+    
+    # output dimension
+    ngf = 64
+
+    self.main = nn.Sequential(
+      # input is Z, going into a convolution
+      nn.ConvTranspose2d((noise_dim + conditional_dim), ngf * 8, 4, 1, 0, bias=False),
+      nn.BatchNorm2d(ngf * 8),
+      nn.ReLU(True),
+      # state size. (ngf*8) x 4 x 4
+      nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
+      nn.BatchNorm2d(ngf * 4),
+      nn.ReLU(True),
+      # state size. (ngf*4) x 8 x 8
+      nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
+      nn.BatchNorm2d(ngf * 2),
+      nn.ReLU(True),
+      # state size. (ngf*2) x 16 x 16
+      nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
+      nn.BatchNorm2d(ngf),
+      nn.ReLU(True),
+      # state size. (ngf) x 32 x 32
+      nn.ConvTranspose2d(ngf, channels, 4, 2, 1, bias=False),
+      nn.Tanh()
+      # state size. (nc) x 64 x 64
+    )
+
+  def forward(self, z, y):
+    """
+      Forward function for the generator.
+
+    **Parameters**
+
+    z: pyTorch Variable
+      The minibatch of noise.
+
+    y: pyTorch Variable
+      The conditional one hot encoded vector for the minibatch.
+    """
+    generator_input = torch.cat((z, y), 1)
+    if isinstance(generator_input.data, torch.cuda.FloatTensor) and self.ngpu > 1:
+      output = nn.parallel.data_parallel(self.main, generator_input, range(self.ngpu))
+    else:
+      output = self.main(generator_input)
+    return output
+
+
+class ConditionalGAN_discriminator(nn.Module):
+  """
+  Class defining the Conditional GAN discriminator.
+
+  **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 GPU (default: 1)
+  """ 
+  def __init__(self, conditional_dim, channels=3, ngpu=1):
+    
+    super(ConditionalGAN_discriminator, self).__init__()
+    self.conditional_dim = conditional_dim
+    self.ngpu = ngpu
+    
+    # input dimension
+    ndf = 64
+       
+    self.main = nn.Sequential(
+      # input is (nc) x 64 x 64
+      nn.Conv2d((channels + conditional_dim), ndf, 4, 2, 1, bias=False),
+      nn.LeakyReLU(0.2, inplace=True),
+      # state size. (ndf) x 32 x 32
+      nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
+      nn.BatchNorm2d(ndf * 2),
+      nn.LeakyReLU(0.2, inplace=True),
+      # state size. (ndf*2) x 16 x 16
+      nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
+      nn.BatchNorm2d(ndf * 4),
+      nn.LeakyReLU(0.2, inplace=True),
+      # state size. (ndf*4) x 8 x 8
+      nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
+      nn.BatchNorm2d(ndf * 8),
+      nn.LeakyReLU(0.2, inplace=True),
+      # state size. (ndf*8) x 4 x 4
+      nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
+      nn.Sigmoid()
+    )
+  
+
+  def forward(self, images, y):
+    """
+      Forward function for the discriminator.
+
+    **Parameters**
+
+    images: pyTorch Variable
+      The minibatch of input images.
+
+    y: pyTorch Variable
+      The corresponding conditional feature maps.
+    """
+    input_discriminator = torch.cat((images, y), 1)
+    if isinstance(input_discriminator.data, torch.cuda.FloatTensor) and self.ngpu > 1:
+      output = nn.parallel.data_parallel(self.main, input_discriminator, range(self.ngpu))
+    else:
+      output = self.main(input_discriminator)
+    return output.view(-1, 1).squeeze(1)