wgan_gp.py

import argparse
import os
import numpy as np
import math
import sys
from datetime import date

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
from config import *
from utils import *
from data import Fashion_attr_prediction

os.makedirs("images", exist_ok=True)
os.makedirs("images/wgan_gp", exist_ok=True)

parser = argparse.ArgumentParser()
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("--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")
# included in config
parser.add_argument("--n_epochs", type=int, default=10, help="number of epochs of training")
parser.add_argument("--batch_size", type=int, default=64, help="size of the batches")
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")
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")
opt = parser.parse_args()

opt.n_epochs = N_EPOCHS
opt.batch_size = TRAIN_BATCH_SIZE
opt.channels = IMG_CHANNELS
opt.sample_interval = SAMPLE_INTERVAL
opt.latent_dim = LATENT_DIM
opt.img_size = IMG_SIZE
print(opt)

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

cuda = True if torch.cuda.is_available() else False
today = date.today().strftime("%Y%m%d")

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
dataloader = torch.utils.data.DataLoader(
    Fashion_attr_prediction(
        categories=CATEGORIES,
        type="train",
        transform=TRANSFORM_FN,
        crop=True
    ),
    batch_size=TRAIN_BATCH_SIZE,
    num_workers=NUM_WORKERS,
    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]
    gradients = gradients.view(gradients.size(0), -1)
    gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()
    return gradient_penalty


# ----------
#  Training
# ----------
if __name__=="__main__":
    torch.multiprocessing.freeze_support()
    batches_done = 0
    G_losses = []
    D_losses = []
    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()
            D_losses.append(d_loss.item())
            # 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()
                G_losses.append(g_loss.item())
                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/wgan_gp/%s_%s_%d.png".format(CONFIG_AS_STR, today, batches_done), nrow=5, normalize=True)

                batches_done += opt.n_critic

    plot_losses("wgan_gp", G_losses, D_losses, CONFIG_AS_STR, today)
    print("Saved Encoder to {}".format(save_model(generator, "wgan_gp_generator", CONFIG_AS_STR, today)))
    print("Saved Decoder to {}".format(save_model(discriminator, "wgan_gp_discriminator", CONFIG_AS_STR, today)))