dcgan_train.py

import torch
import torch.nn as nn
import torchvision.datasets as dataset
import torch.optim as optim
import torchvision.transforms as transforms
import numpy as np
import matplotlib.pyplot as plt
from dcgan import Discriminator, Generator, weights_init
from preprocessing import Dataset


lr = 2e-4
beta1 = 0.5
epoch_num = 32
batch_size = 8
nz = 100  # length of noise
ngpu = 0
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")


def main():
    # load training data
    trainset = Dataset('./data/brilliant_blue')

    trainloader = torch.utils.data.DataLoader(
        trainset, batch_size=batch_size, shuffle=True
    )

    # init netD and netG
    netD = Discriminator().to(device)
    netD.apply(weights_init)

    netG = Generator(nz).to(device)
    netG.apply(weights_init)


    criterion = nn.BCELoss()

    # used for visualzing training process
    fixed_noise = torch.randn(16, nz, 1, device=device)

    real_label = 1.
    fake_label = 0.

    optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
    optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))

    for epoch in range(epoch_num):
        for step, (data, _) in enumerate(trainloader):

            real_cpu = data.to(device)
            b_size = real_cpu.size(0)

            # train netD
            label = torch.full((b_size,), real_label,
                               dtype=torch.float, device=device)
            netD.zero_grad()
            output = netD(real_cpu).view(-1)
            errD_real = criterion(output, label)
            errD_real.backward()
            D_x = output.mean().item()

            # train netG
            noise = torch.randn(b_size, nz, 1, device=device)
            fake = netG(noise)
            label.fill_(fake_label)
            output = netD(fake.detach()).view(-1)
            errD_fake = criterion(output, label)
            errD_fake.backward()
            D_G_z1 = output.mean().item()
            errD = errD_real + errD_fake
            optimizerD.step()
            netG.zero_grad()

            label.fill_(real_label)
            output = netD(fake).view(-1)
            errG = criterion(output, label)
            errG.backward()
            D_G_z2 = output.mean().item()
            optimizerG.step()

            print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'
                  % (epoch, epoch_num, step, len(trainloader),
                     errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))

        # save training process
        with torch.no_grad():
            fake = netG(fixed_noise).detach().cpu()
            f, a = plt.subplots(4, 4, figsize=(8, 8))
            for i in range(4):
                for j in range(4):
                    a[i][j].plot(fake[i * 4 + j].view(-1))
                    a[i][j].set_xticks(())
                    a[i][j].set_yticks(())
            plt.savefig('./img/dcgan_epoch_%d.png' % epoch)
            plt.close()
    
    # save models
    torch.save(netG, './nets/dcgan_netG.pkl')
    torch.save(netD, './nets/dcgan_netD.pkl')


if __name__ == '__main__':
    main()