gans/train.py at master · satvick16/gans · GitHub

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from __future__ import print_function
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim as optim
import torch.utils.data
import torchvision.utils as vutils

from config import *
from data_prep import get_data
from models import *
from display_results import *

# Ensure reproducibility by fixing random seed
manualSeed = 999

print("Random Seed: ", manualSeed)
random.seed(manualSeed)
torch.manual_seed(manualSeed)

# Prepare data
dataset, dataloader = get_data()

device = torch.device("cuda:0" if (
    torch.cuda.is_available() and ngpu > 0) else "cpu")

# Instantiate models
netG = Generator(ngpu).to(device)
netG.apply(weights_init)

netD = Discriminator(ngpu).to(device)
netD.apply(weights_init)

# Optimizers
criterion = nn.BCELoss()

fixed_noise = torch.randn(64, nz, 1, 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))

# Training Loop

img_list = []
G_losses = []
D_losses = []
iters = 0

print("Starting Training Loop...")

for epoch in range(num_epochs):
    for i, data in enumerate(dataloader, 0):
        # give disc real data
        netD.zero_grad()
        real_cpu = data[0].to(device)
        b_size = real_cpu.size(0)
        label = torch.full((b_size,), real_label,
                           dtype=torch.float, device=device)

        output = netD(real_cpu).view(-1)
        errD_real = criterion(output, label)
        errD_real.backward()
        D_x = output.mean().item()

        # give disc fake data
        noise = torch.randn(b_size, nz, 1, 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()

        # train gen
        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()

        # Output training stats
        if i % 50 == 0:
            print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'
                  % (epoch, num_epochs, i, len(dataloader),
                     errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))

        G_losses.append(errG.item())
        D_losses.append(errD.item())

        if (iters % 500 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)):
            with torch.no_grad():
                fake = netG(fixed_noise).detach().cpu()
            img_list.append(vutils.make_grid(fake, padding=2, normalize=True))

        iters += 1

        # save checkpoints
        if epoch % 100 == 0:
            torch.save(netG.state_dict(), "generator" +
                       "-" + str(epoch) + "-" + "epochs.pt")

# Save final model
torch.save(netG.state_dict(), "generator.pt")

# Visualize results
loss_curves(G_losses, D_losses)
visualize_gen(img_list)
visualize_comparison(dataloader, device, img_list)