text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  """
  Architecture based on InfoGAN paper.
  """
  
  class Generator(nn.Module):
  	def __init__(self):
  		super().__init__()
  
  		self.tconv1 = nn.ConvTranspose2d(228, 448, 2, 1, bias=False)
  		self.bn1 = nn.BatchNorm2d(448)
  
  		self.tconv2 = nn.ConvTranspose2d(448, 256, 4, 2, padding=1, bias=False)
  		self.bn2 = nn.BatchNorm2d(256)
  
  		self.tconv3 = nn.ConvTranspose2d(256, 128, 4, 2, padding=1, bias=False)
  
  		self.tconv4 = nn.ConvTranspose2d(128, 64, 4, 2, padding=1, bias=False)
  
  		self.tconv5 = nn.ConvTranspose2d(64, 3, 4, 2, padding=1, bias=False)
  
  	def forward(self, x):
  		x = F.relu(self.bn1(self.tconv1(x)))
  		x = F.relu(self.bn2(self.tconv2(x)))
  		x = F.relu(self.tconv3(x))
  		x = F.relu(self.tconv4(x))
  
  		img = torch.tanh(self.tconv5(x))
  
  		return img
  
  class Discriminator(nn.Module):
  	def __init__(self):
  		super().__init__()
  
  		self.conv1 = nn.Conv2d(3, 64, 4, 2, 1)
  
  		self.conv2 = nn.Conv2d(64, 128, 4, 2, 1, bias=False)
  		self.bn2 = nn.BatchNorm2d(128)
  
  		self.conv3 = nn.Conv2d(128, 256, 4, 2, 1, bias=False)
  		self.bn3 = nn.BatchNorm2d(256)
  
  	def forward(self, x):
  		x = F.leaky_relu(self.conv1(x), 0.1, inplace=True)
  		x = F.leaky_relu(self.bn2(self.conv2(x)), 0.1, inplace=True)
  		x = F.leaky_relu(self.bn3(self.conv3(x)), 0.1, inplace=True)
  
  		return x
  
  class DHead(nn.Module):
  	def __init__(self):
  		super().__init__()
  
  		self.conv = nn.Conv2d(256, 1, 4)
  
  	def forward(self, x):
  		output = torch.sigmoid(self.conv(x))
  
  		return output
  
  class QHead(nn.Module):
  	def __init__(self):
  		super().__init__()
  
  		self.conv1 = nn.Conv2d(256, 128, 4, bias=False)
  		self.bn1 = nn.BatchNorm2d(128)
  
  		self.conv_disc = nn.Conv2d(128, 100, 1)
  
  		self.conv_mu = nn.Conv2d(128, 1, 1)
  		self.conv_var = nn.Conv2d(128, 1, 1)
  
  	def forward(self, x):
  		x = F.leaky_relu(self.bn1(self.conv1(x)), 0.1, inplace=True)
  
  		disc_logits = self.conv_disc(x).squeeze()
  
  		# Not used during training for celeba dataset.
  		mu = self.conv_mu(x).squeeze()
  		var = torch.exp(self.conv_var(x).squeeze())
  
  		return disc_logits, mu, var
  