cogan.py

import argparse
import os
import numpy as np
import math
import scipy
import itertools

import mnistm

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

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=32, 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=32, help='size of each image dimension')
parser.add_argument('--channels', type=int, default=3, help='number of image channels')
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

def weights_init_normal(m):
    classname = m.__class__.__name__
    if classname.find('Linear') != -1:
        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
        torch.nn.init.constant_(m.bias.data, 0.0)

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

        self.init_size = opt.img_size // 4
        self.fc = nn.Sequential(nn.Linear(opt.latent_dim, 128*self.init_size**2))

        self.shared_conv = nn.Sequential(
            nn.BatchNorm2d(128),
            nn.Upsample(scale_factor=2),
            nn.Conv2d(128, 128, 3, stride=1, padding=1),
            nn.BatchNorm2d(128, 0.8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Upsample(scale_factor=2),
        )
        self.G1 = nn.Sequential(
            nn.Conv2d(128, 64, 3, stride=1, padding=1),
            nn.BatchNorm2d(64, 0.8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(64, opt.channels, 3, stride=1, padding=1),
            nn.Tanh()
        )
        self.G2 = nn.Sequential(
            nn.Conv2d(128, 64, 3, stride=1, padding=1),
            nn.BatchNorm2d(64, 0.8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(64, opt.channels, 3, stride=1, padding=1),
            nn.Tanh()
        )

    def forward(self, noise):
        out = self.fc(noise)
        out = out.view(out.shape[0], 128, self.init_size, self.init_size)
        img_emb = self.shared_conv(out)
        img1 = self.G1(img_emb)
        img2 = self.G2(img_emb)
        return img1, img2

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

        def discriminator_block(in_filters, out_filters, bn=True):
            block = [nn.Conv2d(in_filters, out_filters, 3, 2, 1)]
            if bn:
                block.append(nn.BatchNorm2d(out_filters, 0.8))
            block.extend([nn.LeakyReLU(0.2, inplace=True), nn.Dropout2d(0.25)])
            return block

        self.shared_conv = nn.Sequential(
            *discriminator_block(opt.channels, 16, bn=False),
            *discriminator_block(16, 32),
            *discriminator_block(32, 64),
            *discriminator_block(64, 128),
        )
        # The height and width of downsampled image
        ds_size = opt.img_size // 2**4
        self.D1 = nn.Linear(128*ds_size**2, 1)
        self.D2 = nn.Linear(128*ds_size**2, 1)

    def forward(self, img1, img2):
        # Determine validity of first image
        out = self.shared_conv(img1)
        out = out.view(out.shape[0], -1)
        validity1 = self.D1(out)
        # Determine validity of second image
        out = self.shared_conv(img2)
        out = out.view(out.shape[0], -1)
        validity2 = self.D2(out)

        return validity1, validity2

# Loss function
adversarial_loss = torch.nn.MSELoss()

# Initialize models
coupled_generators = CoupledGenerators()
coupled_discriminators = CoupledDiscriminators()

if cuda:
    coupled_generators.cuda()
    coupled_discriminators.cuda()

# Initialize weights
coupled_generators.apply(weights_init_normal)
coupled_discriminators.apply(weights_init_normal)

# Configure data loader
os.makedirs('../../data/mnist', exist_ok=True)
dataloader1 = torch.utils.data.DataLoader(
    datasets.MNIST('../../data/mnist', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.Resize(opt.img_size),
                       transforms.ToTensor(),
                       transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
                   ])),
    batch_size=opt.batch_size, shuffle=True)

os.makedirs('../../data/mnistm', exist_ok=True)
dataloader2 = torch.utils.data.DataLoader(
    mnistm.MNISTM('../../data/mnistm', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.Resize(opt.img_size),
                       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(coupled_generators.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(coupled_discriminators.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))

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

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

for epoch in range(opt.n_epochs):
    for i, ((imgs1, _), (imgs2, _)) in enumerate(zip(dataloader1, dataloader2)):

        batch_size = imgs1.shape[0]

        # Adversarial ground truths
        valid = Variable(Tensor(batch_size, 1).fill_(1.0), requires_grad=False)
        fake = Variable(Tensor(batch_size, 1).fill_(0.0), requires_grad=False)

        # Configure input
        imgs1 = Variable(imgs1.type(Tensor).expand(imgs1.size(0), 3, opt.img_size, opt.img_size))
        imgs2 = Variable(imgs2.type(Tensor))

        # ------------------
        #  Train Generators
        # ------------------

        optimizer_G.zero_grad()

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

        # Generate a batch of images
        gen_imgs1, gen_imgs2 = coupled_generators(z)
        # Determine validity of generated images
        validity1, validity2 = coupled_discriminators(gen_imgs1, gen_imgs2)

        g_loss =    (adversarial_loss(validity1, valid) + \
                    adversarial_loss(validity2, valid)) / 2

        g_loss.backward()
        optimizer_G.step()

        # ----------------------
        #  Train Discriminators
        # ----------------------

        optimizer_D.zero_grad()

        # Determine validity of real and generated images
        validity1_real, validity2_real = coupled_discriminators(imgs1, imgs2)
        validity1_fake, validity2_fake = coupled_discriminators(gen_imgs1.detach(), gen_imgs2.detach())

        d_loss =    (adversarial_loss(validity1_real, valid) + \
                    adversarial_loss(validity1_fake, fake) + \
                    adversarial_loss(validity2_real, valid) + \
                    adversarial_loss(validity2_fake, fake)) / 4

        d_loss.backward()
        optimizer_D.step()

        print ("[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]" % (epoch, opt.n_epochs, i, len(dataloader1),
                                                            d_loss.item(), g_loss.item()))

        batches_done = epoch * len(dataloader1) + i
        if batches_done % opt.sample_interval == 0:
            gen_imgs = torch.cat((gen_imgs1.data, gen_imgs2.data), 0)
            save_image(gen_imgs, 'images/%d.png' % batches_done, nrow=8, normalize=True)