optimizer.zero_grad()
# output by doing a forward pass of the fake data through discriminator
output = discriminator(data_fake).squeeze()
loss = criterion(output, real_label)
# compute gradients of loss
loss.backward()
# update generator parameters
optimizer.step()
return loss
# create the noise vector
noise = create_noise(sample_size, nz)
generator.train()
discriminator.train()
for epoch in range(epochs):
loss_g = 0.0
loss_d = 0.0
for bi, data in tqdm(enumerate(train_loader),
total=int(len(train_data) / train_loader.batch_size)):
image, _ = data
image = image.to(device)
b_size = len(image)
# forward pass through generator to create fake data
data_fake = generator(create_noise(b_size, nz)).detach()
data_real = image
loss_d += train_discriminator(optim_d, data_real, data_fake)
data_fake = generator(create_noise(b_size, nz))
loss_g += train_generator(optim_g, data_fake)
def train(z_channels,
c_channels,
epoch_num,
batch_size,
lr=0.0002,
beta1=0.5,
model_path='models/dcgan_checkpoint.pth'):
use_cuda = torch.cuda.is_available()
device = torch.device('cuda' if use_cuda else 'cpu')
if use_cuda:
cudnn.benchmark = True
else:
print("***** Warning: Cuda isn't available! *****")
loader = load_mnist(batch_size)
generator = Generator(z_channels, c_channels).to(device)
discriminator = Discriminator(c_channels).to(device)
g_optimizer = optim.Adam(generator.parameters(),
lr=lr,
betas=(beta1, 0.999))
d_optimizer = optim.Adam(discriminator.parameters(),
lr=lr,
betas=(beta1, 0.999))
start_epoch = 0
if os.path.exists(model_path):
checkpoint = torch.load(model_path)
generator.load_state_dict(checkpoint['g'])
discriminator.load_state_dict(checkpoint['d'])
g_optimizer.load_state_dict(checkpoint['g_optim'])
d_optimizer.load_state_dict(checkpoint['d_optim'])
start_epoch = checkpoint['epoch'] + 1
criterion = nn.BCELoss().to(device)
generator.train()
discriminator.train()
std = 0.1
for epoch in range(start_epoch, start_epoch + epoch_num):
d_loss_sum, g_loss_sum = 0, 0
print('---- epoch: %d ----' % (epoch, ))
for i, (real_image, number) in enumerate(loader):
real_image = real_image.to(device)
image_noise = torch.randn(real_image.size(),
device=device).normal_(0, std)
d_optimizer.zero_grad()
real_label = torch.randn(number.size(),
device=device).normal_(0.9, 0.1)
real_image.add_(image_noise)
out = discriminator(real_image)
d_real_loss = criterion(out, real_label)
d_real_loss.backward()
noise_z = torch.randn((number.size(0), z_channels, 1, 1),
device=device)
fake_image = generator(noise_z)
fake_label = torch.zeros(number.size(), device=device)
fake_image = fake_image.add(image_noise)
out = discriminator(fake_image.detach())
d_fake_loss = criterion(out, fake_label)
d_fake_loss.backward()
d_optimizer.step()
g_optimizer.zero_grad()
out = discriminator(fake_image)
g_loss = criterion(out, real_label)
g_loss.backward()
g_optimizer.step()
d_loss_sum += d_real_loss.item() + d_fake_loss.item()
g_loss_sum += g_loss.item()
# if i % 10 == 0:
# print(d_loss, g_loss)
print('d_loss: %f \t\t g_loss: %f' % (d_loss_sum /
(i + 1), g_loss_sum / (i + 1)))
std *= 0.9
if epoch % 1 == 0:
checkpoint = {
'g': generator.state_dict(),
'd': discriminator.state_dict(),
'g_optim': g_optimizer.state_dict(),
'd_optim': d_optimizer.state_dict(),
'epoch': epoch,
}
save_image(fake_image,
'out/fake_samples_epoch_%03d.png' % (epoch, ),
normalize=False)
torch.save(checkpoint, model_path)
os.system('cp ' + model_path + ' models/model%d' % (epoch, ))
print('saved!')
gen = Generator(z_dim, channels_img, features_gen).to(device)
disc = Discriminator(channels_img, features_disc).to(device)
load_model(disc, disc_file, device)
load_model(gen, gen_file, device)
opt_gen = optim.Adam(gen.parameters(), lr=learning_rate, betas=(0.5, 0.999))
opt_disc = optim.Adam(disc.parameters(), lr=learning_rate, betas=(0.5, 0.999))
criterion = nn.BCELoss()
fixed_noise = torch.randn(32, z_dim, 1, 1).to(device)
writer_real = SummaryWriter('runs/dcgan_mnist/real')
writer_fake = SummaryWriter('runs/dcgan_mnist/fake')
step = 0
gen.train()
disc.train()
for epoch in range(num_epochs):
for batch_idx, (real, _) in enumerate(loader):
real = real.to(device)
noise = torch.randn((batch_size, z_dim, 1, 1)).to(device)
fake = gen(noise)
# Training Discriminator max log(D(x)) + log(1 - D(G(z)))
disc_real = disc(real).view(-1)
loss_disc_real = criterion(disc_real, torch.ones_like(disc_real))
disc_fake = disc(fake).view(-1)
loss_disc_fake = criterion(disc_fake, torch.zeros_like(disc_fake))
loss_disc = (loss_disc_real + loss_disc_fake) / 2
