wgan_gp.py

import argparse
import os
import numpy as np
import math

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



def arg_parse():
    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")
    return parser.parse_args()



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

        self.img_shape = img_shape

        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(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], *(self.img_shape))
        return img


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

        self.img_shape = img_shape
        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


def compute_gradient_penalty(D, real_samples, fake_samples):
    """
    Calculates the gradient penalty loss for WGAN GP

    :param            D: The discriminator (the critic)
    :param real_samples: The real samples
    :param fake_samples: The fake samples
    """
    # Random weight term for interpolation between real and fake samples
    t = Tensor(np.random.random((real_samples.size(0), 1, 1, 1)))

    # Get random interpolation between real and fake samples
    interpolates = (t * real_samples + ((1 - t) * 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]

    gradients = gradients.view(gradients.size(0), -1)
    gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()

    return gradient_penalty



if __name__ == '__main__':
    os.makedirs("images", exist_ok=True)
    args = arg_parse()
    channels, img_size = args.channels, args.img_size
    img_shape = (channels, img_size, img_size)
    latent_dim = args.latent_dim

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

    # Loss weight for gradient penalty
    lambda_gp = 10

    # Initialize generator and discriminator
    generator = Generator(latent_dim=latent_dim, img_shape=img_shape)
    discriminator = Discriminator(img_shape=img_shape)

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


    batch_size = args.batch_size
    lr = args.lr
    b1, b2 = args.b1, args.b2

    # 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.Resize(img_size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]
            ),
        ),
        batch_size=batch_size,
        shuffle=True,
    )

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

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


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

    n_epochs, n_critic, sample_interval = args.n_epochs, args.n_critic, args.sample_interval
    batches_done = 0
    
    for epoch in range(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], 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 % 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, n_epochs, i, len(dataloader), d_loss.item(), g_loss.item())
                )

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

                batches_done += n_critic