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import os
import torch
import torch.nn as nn
import torch.optim as optim
import pickle
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import TensorDataset, DataLoader
from torchvision.datasets import CIFAR10
from torchvision.transforms import Compose, ToTensor, Resize, Normalize
from torchvision.utils import save_image
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from models import *
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torch.manual_seed(0)
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device = 'cuda'
num_channels, latent_dim = 3, 100
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dataset = CIFAR10(root='.', download=True, transform=Compose([Resize(32), ToTensor(), Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))]))
dataloader = DataLoader(dataset, batch_size=256, shuffle=True)
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G = Generator(nc=num_channels, ld=latent_dim).to(device)
D = Discriminator(nc=num_channels, ld=latent_dim).to(device)
E = Encoder(nc=num_channels, ld=latent_dim).to(device)
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adver_criterion = nn.BCELoss().to(device)
recon_criterion = nn.L1Loss(reduction='sum').to(device)
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D_optimizer = optim.Adam(D.parameters(), lr=0.0002)
G_optimizer = optim.Adam(G.parameters(), lr=0.0002)
E_optimizer = optim.Adam(E.parameters(), lr=0.0002)
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fixed_latent = (2*torch.rand(64,100,1,1)-1).to(device)
outdir = 'cifar10_bigan'
os.makedirs(outdir, exist_ok=True)
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losses = {
'D':[],
'G':[],
'E':[],
'I':[]
}
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for epoch in range(25):
for idx, (x, _) in enumerate(dataloader):
batch_size = x.shape[0]
x_real = x.detach().to(device)
# Train D
D.zero_grad()
z_real = E(x_real).detach()
z_fake = 2*torch.rand(batch_size, latent_dim, 1, 1).to(device)-1
x_fake = G(z_fake).detach()
real_pred = D(x_real, z_real)
fake_pred = D(x_fake, z_fake)
d_real_target = torch.ones(batch_size, 1).to(device)
d_fake_target = torch.zeros(batch_size, 1).to(device)
D_loss = adver_criterion(fake_pred, d_fake_target) + adver_criterion(real_pred, d_real_target)
D_loss.backward()
D_optimizer.step()
# Train G
G.zero_grad()
z_fake = 2*torch.rand(batch_size, latent_dim, 1, 1).to(device)-1
x_fake = G(z_fake)
fake_pred = D(x_fake, z_fake)
g_target = d_real_target.clone()
G_loss = adver_criterion(fake_pred, g_target)
G_loss.backward()
G_optimizer.step()
# Train E
E.zero_grad()
z_real = E(x_real)
real_pred = D(x_real, z_real)
e_target = torch.ones(batch_size, 1).to(device)
E_loss = adver_criterion(real_pred, e_target)
E_loss.backward()
E_optimizer.step()
# latent identity Loss
E.zero_grad()
G.zero_grad()
z_recon0 = E(x_real)
x_recon1 = G(z_recon0)
z_recon1 = E(x_recon1)
I_loss = recon_criterion(x_recon1, x_real)
# I_loss.backward(retain_graph=True)
# E_optimizer.step()
# G_optimizer.step()
losses['D'].append(D_loss.item())
losses['G'].append(G_loss.item())
losses['E'].append(E_loss.item())
losses['I'].append(I_loss.item())
save_image(G(fixed_latent), f'{outdir}/fixed_{epoch+1}.png')
torch.save(G, f'{outdir}/G_{epoch+1}.pth')
torch.save(D, f'{outdir}/D_{epoch+1}.pth')
torch.save(E, f'{outdir}/E_{epoch+1}.pth')
with open(f'{outdir}/losses.dat', 'wb') as fp:
pickle.dump(losses, fp)
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plt.plot(losses['G'], label='Generator')
plt.plot(losses['D'], label='Discriminator')
plt.legend()
plt.show()
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plt.plot(losses['E'], label='Encoder')
plt.legend()
plt.show()
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plt.plot(losses['I'], label='Identity')
plt.legend()
plt.show()
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numTests = 5
_ = G.eval(), E.eval()
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test_dataset = CIFAR10(root='.', download=True, train=False, transform=Compose([Resize(32), ToTensor(), Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))]))
test_dataloader = DataLoader(test_dataset, batch_size=256, shuffle=True)
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ad_losses, post_losses = [], []
for test in range(numTests):
torch.manual_seed(100+test)
E_post = Encoder().to(device)
ft_optimizer = optim.Adam(E_post.parameters(), lr=0.0002)
for it in range(5000):
E_post.zero_grad()
z_recon0 = 2*torch.rand(256, latent_dim, 1, 1).to(device)-1
x_recon0 = G(z_recon0)
z_recon1 = E_post(x_recon0)
x_recon1 = G(z_recon1)
ft_loss = recon_criterion(x_recon1, x_recon0) + recon_criterion(z_recon1, z_recon0)
ft_loss.backward(retain_graph=True)
ft_optimizer.step()
E_post.eval()
torch.save(E_post, f'{outdir}/Epost_{test+1}.pth')
ad_loss, post_loss = 0, 0
for idx, (x, _) in enumerate(test_dataloader):
batch_size = x.shape[0]
x_real = x.detach().to(device)
ad_loss += recon_criterion(G(E(x_real)), x_real).item()
post_loss += recon_criterion(G(E_post(x_real)), x_real).item()
ad_loss, post_loss = ad_loss/len(test_dataset), post_loss/len(test_dataset)
ad_losses.append(ad_loss)
post_losses.append(post_loss)
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print((np.mean(ad_losses), np.std(ad_losses)), (np.mean(post_losses), np.std(post_losses)))