#!/usr/bin/env python
# encoding: utf-8
import torch
import torch.nn as nn
[docs]class ConditionalGAN_generator(nn.Module):
""" Class implementating the conditional GAN generator
This network is introduced in the following publication:
Mehdi Mirza, Simon Osindero: "Conditional Generative Adversarial Nets"
Attributes
----------
ngpu : int
The number of available GPU devices
main : :py:class:`torch.nn.Sequential`
The sequential container
"""
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
# 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
)
[docs] def forward(self, z, y):
"""Forward function
Parameters
----------
z : :py:class: `torch.autograd.Variable`
The minibatch of noise.
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:
# output = nn.parallel.data_parallel(self.main, generator_input, range(self.ngpu))
# else:
# output = self.main(generator_input)
# let's assume that we will never face the case where more than a GPU is used ...
output = self.main(generator_input)
return output
[docs]class ConditionalGAN_discriminator(nn.Module):
""" 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
"""
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
# 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(),
)
[docs] def forward(self, images, y):
"""Forward function
Parameters
----------
images : :py:class: `torch.autograd.Variable`
The minibatch of input images.
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:
# output = nn.parallel.data_parallel(self.main, input_discriminator, range(self.ngpu))
# else:
# output = self.main(input_discriminator)
# let's assume that we will never face the case where more than a GPU is used ...
output = self.main(input_discriminator)
return output.view(-1, 1).squeeze(1)