wgan_gp.py

import argparse
import os
import numpy as np
import math
import sys

import torchvision.transforms as transforms
from torchvision.utils import save_image

from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable

import torch.nn as nn
import torch.nn.functional as F
import torch.autograd as autograd
import torch

os.makedirs('images', exist_ok=True)

parser = argparse.ArgumentParser()
parser.add_argument('--n_epochs', type=int, default=200, help='number of epochs of training')
parser.add_argument('--batch_size', type=int, default=64, help='size of the batches')
parser.add_argument('--lr', type=float, default=0.0002, help='adam: learning rate')
parser.add_argument('--b1', type=float, default=0.5, help='adam: decay of first order momentum of gradient')
parser.add_argument('--b2', type=float, default=0.999, help='adam: decay of first order momentum of gradient')
parser.add_argument('--n_cpu', type=int, default=8, help='number of cpu threads to use during batch generation')
parser.add_argument('--latent_dim', type=int, default=100, help='dimensionality of the latent space')
parser.add_argument('--img_size', type=int, default=28, help='size of each image dimension')
parser.add_argument('--channels', type=int, default=1, help='number of image channels')
parser.add_argument('--n_critic', type=int, default=5, help='number of training steps for discriminator per iter')
parser.add_argument('--clip_value', type=float, default=0.01, help='lower and upper clip value for disc. weights')
parser.add_argument('--sample_interval', type=int, default=400, help='interval betwen image samples')
opt = parser.parse_args()
print(opt)

img_shape = (opt.channels, opt.img_size, opt.img_size)

cuda = True if torch.cuda.is_available() else False

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()

        def block(in_feat, out_feat, normalize=True):
            layers = [  nn.Linear(in_feat, out_feat)]
            if normalize:
                layers.append(nn.BatchNorm1d(out_feat, 0.8))
            layers.append(nn.LeakyReLU(0.2, inplace=True))
            return layers

        self.model = nn.Sequential(
            *block(opt.latent_dim, 128, normalize=False),
            *block(128, 256),
            *block(256, 512),
            *block(512, 1024),
            nn.Linear(1024, int(np.prod(img_shape))),
            nn.Tanh()
        )

    def forward(self, z):
        img = self.model(z)
        img = img.view(img.shape[0], *img_shape)
        return img

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()

        self.model = nn.Sequential(
            nn.Linear(int(np.prod(img_shape)), 512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(256, 1)
        )

    def forward(self, img):
        img_flat = img.view(img.shape[0], -1)
        validity = self.model(img_flat)
        return validity

# Loss weight for gradient penalty
lambda_gp = 10

# Initialize generator and discriminator
generator = Generator()
discriminator = Discriminator()

if cuda:
    generator.cuda()
    discriminator.cuda()

# Configure data loader
os.makedirs('../../data/mnist', exist_ok=True)
dataloader = torch.utils.data.DataLoader(
    datasets.MNIST('../../data/mnist', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
                   ])),
    batch_size=opt.batch_size, shuffle=True)

# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))

Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor

def compute_gradient_penalty(D, real_samples, fake_samples):
    """Calculates the gradient penalty loss for WGAN GP"""
    # Random weight term for interpolation between real and fake samples
    alpha = Tensor(np.random.random((real_samples.size(0), 1, 1, 1)))
    # Get random interpolation between real and fake samples
    interpolates = (alpha * real_samples + ((1 - alpha) * fake_samples)).requires_grad_(True)
    d_interpolates = D(interpolates)
    fake = Variable(Tensor(real_samples.shape[0], 1).fill_(1.0), requires_grad=False)
    # Get gradient w.r.t. interpolates
    gradients = autograd.grad(outputs=d_interpolates, inputs=interpolates,
                              grad_outputs=fake, create_graph=True, retain_graph=True,
                              only_inputs=True)[0]
    gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()
    return gradient_penalty

# ----------
#  Training
# ----------

batches_done = 0
for epoch in range(opt.n_epochs):
    for i, (imgs, _) in enumerate(dataloader):

        # Configure input
        real_imgs = Variable(imgs.type(Tensor))

        # ---------------------
        #  Train Discriminator
        # ---------------------

        optimizer_D.zero_grad()

        # Sample noise as generator input
        z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0], opt.latent_dim))))

        # Generate a batch of images
        fake_imgs = generator(z)

        # Real images
        real_validity = discriminator(real_imgs)
        # Fake images
        fake_validity = discriminator(fake_imgs)
        # Gradient penalty
        gradient_penalty = compute_gradient_penalty(discriminator, real_imgs.data, fake_imgs.data)
        # Adversarial loss
        d_loss = -torch.mean(real_validity) + torch.mean(fake_validity) + lambda_gp * gradient_penalty

        d_loss.backward()
        optimizer_D.step()

        optimizer_G.zero_grad()

        # Train the generator every n_critic steps
        if i % opt.n_critic == 0:

            # -----------------
            #  Train Generator
            # -----------------

            # Generate a batch of images
            fake_imgs = generator(z)
            # Loss measures generator's ability to fool the discriminator
            # Train on fake images
            fake_validity = discriminator(fake_imgs)
            g_loss = -torch.mean(fake_validity)

            g_loss.backward()
            optimizer_G.step()

            print ("[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]" % (epoch, opt.n_epochs,
                                                            i, len(dataloader),
                                                            d_loss.item(), g_loss.item()))

            if batches_done % opt.sample_interval == 0:
                save_image(fake_imgs.data[:25], 'images/%d.png' % batches_done, nrow=5, normalize=True)

            batches_done += opt.n_critic