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')
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)
# used for visualizing training process
fixed_noise = torch.randn(16, nz, 1, device=device)
# optimizers
optimizerD = optim.RMSprop(netD.parameters(), lr=lr)
optimizerG = optim.RMSprop(netG.parameters(), lr=lr)
for epoch in range(epoch_num):
for step, (data, _) in enumerate(trainloader):
# training netD
real_cpu = data.to(device)
b_size = real_cpu.size(0)
netD.zero_grad()
noise = torch.randn(b_size, nz, 1, device=device)
fake = netG(noise)
loss_D = -torch.mean(netD(real_cpu)) + torch.mean(netD(fake))
loss_D.backward()
optimizerD.step()
for p in netD.parameters():
p.data.clamp_(-clip_value, clip_value)
if step % n_critic == 0:
# training netG
noise = torch.randn(b_size, nz, 1, device=device)
netG.zero_grad()
fake = netG(noise)
loss_G = -torch.mean(netD(fake))
netD.zero_grad()
netG.zero_grad()
loss_G.backward()
optimizerG.step()
if step % 5 == 0:
print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f' %
(epoch, epoch_num, step, len(trainloader), loss_D.item(),
loss_G.item()))
# 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/wgan_epoch_%d.png' % epoch)
plt.close()
# save model
torch.save(netG, './nets/wgan_netG.pkl')
torch.save(netD, './nets/wgan_netD.pkl')
#
#plt.show()
# Create the generator.
netG = Generator(params).to(device)
# Apply the weights_init() function to randomly initialize all
# weights to mean=0.0, stddev=0.2
netG.apply(weights_init)
# Print the model.
print(netG)
# Create the discriminator.
netD = Discriminator(params).to(device)
# Apply the weights_init() function to randomly initialize all
# weights to mean=0.0, stddev=0.2
netD.apply(weights_init)
# Print the model.
print(netD)
# Binary Cross Entropy loss function.
criterion = nn.BCELoss()
fixed_noise = torch.randn(64, params['nz'], 1, 1, device=device)
real_label = 1
fake_label = 0
# Optimizer for the discriminator.
optimizerD = optim.Adam(netD.parameters(),
lr=params['lr'],
betas=(params['beta1'], 0.999))
train=True,
download=True,
transform=transform)
mini_train_data, mnist_restset = torch.utils.data.random_split(
train_data, [int(0.9 * len(train_data)),
int(0.1 * len(train_data))])
train_loader = DataLoader(mini_train_data, batch_size=batch_size, shuffle=True)
# initialize models
generator = Generator(nz).to(device)
discriminator = Discriminator().to(device)
# initialize generator weights
generator.apply(weights_init)
# initialize discriminator weights
discriminator.apply(weights_init)
print('##### GENERATOR #####')
print(generator)
params = list(generator.parameters())
for i in range(13):
print(params[i].size()) # conv1's .weight
print('######################')
print('\n##### DISCRIMINATOR #####')
print(discriminator)
print('######################')
# optimizers
optim_g = optim.Adam(generator.parameters(), lr=lr, betas=(beta1, 0.999))
optim_d = optim.Adam(discriminator.parameters(), lr=lr, betas=(beta1, 0.999))
# loss function
def main():
# Loss function
adversarial_loss = torch.nn.BCELoss()
# Initialize generator and discriminator
generator = Generator()
discriminator = Discriminator()
# Initialize weights
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)
# DataParallel
generator = nn.DataParallel(generator).to(device)
discriminator = nn.DataParallel(discriminator).to(device)
# Dataloader
# data preparation, loaders
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# cudnn.benchmark = True
# preparing the training laoder
train_loader = torch.utils.data.DataLoader(
ImageLoader(
opt.img_path,
transforms.Compose([
transforms.Scale(
128
), # rescale the image keeping the original aspect ratio
transforms.CenterCrop(
128), # we get only the center of that rescaled
transforms.RandomCrop(
128), # random crop within the center crop
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]),
data_path=opt.data_path,
partition='train'),
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.workers,
pin_memory=True)
print('Training loader prepared.')
# preparing validation loader
val_loader = torch.utils.data.DataLoader(
ImageLoader(
opt.img_path,
transforms.Compose([
transforms.Scale(
128
), # rescale the image keeping the original aspect ratio
transforms.CenterCrop(
128), # we get only the center of that rescaled
transforms.ToTensor(),
normalize,
]),
data_path=opt.data_path,
partition='val'),
batch_size=opt.batch_size,
shuffle=False,
num_workers=opt.workers,
pin_memory=True)
print('Validation loader prepared.')
# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(),
lr=opt.lr,
betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(),
lr=opt.lr,
betas=(opt.b1, opt.b2))
# ----------
# Training
# ----------
for epoch in range(opt.n_epochs):
pbar = tqdm(total=len(train_loader))
start_time = time.time()
for i, data in enumerate(train_loader):
input_var = list()
for j in range(len(data)):
# if j>1:
input_var.append(data[j].to(device))
imgs = input_var[0]
# Adversarial ground truths
valid = np.ones((imgs.shape[0], 1))
valid = torch.FloatTensor(valid).to(device)
fake = np.zeros((imgs.shape[0], 1))
fake = torch.FloatTensor(fake).to(device)
# -----------------
# Train Generator
# -----------------
optimizer_G.zero_grad()
# Sample noise as generator input
z = np.random.normal(0, 1, (imgs.shape[0], opt.latent_dim))
z = torch.FloatTensor(z).to(device)
# Generate a batch of images
gen_imgs = generator(z, input_var[1], input_var[2], input_var[3],
input_var[4])
# Loss measures generator's ability to fool the discriminator
g_loss = adversarial_loss(discriminator(gen_imgs), valid)
g_loss.backward()
optimizer_G.step()
# ---------------------
# Train Discriminator
# ---------------------
optimizer_D.zero_grad()
# Measure discriminator's ability to classify real from generated samples
real_loss = adversarial_loss(discriminator(imgs), valid)
fake_loss = adversarial_loss(discriminator(gen_imgs.detach()),
fake)
d_loss = (real_loss + fake_loss) / 2
d_loss.backward()
optimizer_D.step()
pbar.update(1)
pbar.close()
print(
"[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f] [Time Elapsed: %f]"
% (epoch, opt.n_epochs, i, len(train_loader), d_loss.item(),
g_loss.item(), time.time() - start_time))
if epoch % opt.sample_interval == 0:
save_samples(epoch, gen_imgs.data[:25])
save_model(epoch, generator.state_dict(),
discriminator.state_dict())
import torch.nn.init as init
# from config import *
import config
import torch.optim as optim
import data
if __name__ == "__main__":
# Create the nets
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dataset, dataloader = data.create_dataset()
gen_net = Generator().to(device)
gen_net.apply(weights_init)
dis_net = Discriminator().to(device)
dis_net.apply(weights_init)
# Imporant. We need to add noise to images to learn properly
fixed_noise = torch.randn(config.batchSize, config.nz, 1, 1, device=device)
real_label = 1
fake_label = 0
criterion = nn.BCELoss()
# We need 2 seperate optimizers, the Generator and the Discriminator
gen_opt = optim.Adam(gen_net.parameters(),
lr=config.lr,
betas=(config.beta1, 0.999))
dis_opt = optim.Adam(dis_net.parameters(),
lr=config.lr,
drop_last=True)
else:
raise Exception("Not a valid dataset")
G = Generator(args.num_noises, NUM_COLORS, args.depths, IMAGE_SIZE).to(device)
D = Discriminator(NUM_COLORS, args.depths, IMAGE_SIZE).to(device)
def init_weight(model):
classname = model.__class__.__name__
if classname.find('conv') != -1:
torch.nn.init.normal_(model.weight.data, 0, 0.02)
G.apply(init_weight)
D.apply(init_weight)
criterion_d = torch.nn.BCELoss()
criterion_g = torch.nn.MSELoss(
) if args.feature_matching else torch.nn.BCELoss()
optimizer_g = torch.optim.Adam(G.parameters(),
lr=args.learning_rate,
betas=[args.beta_1, args.beta_2])
optimizer_d = torch.optim.Adam(D.parameters(),
lr=args.learning_rate,
betas=[args.beta_1, args.beta_2])
if __name__ == "__main__":
# One-sided label smoothing
pos_labels = torch.full((args.batch_size, 1), args.alpha, device=device)
neg_labels = torch.zeros((args.batch_size, 1), device=device)
# Loss function
adversarial_loss = torch.nn.BCELoss()
# Initialize generator and discriminator
generator = Generator()
discriminator = Discriminator()
cuda = True if torch.cuda.is_available() else False
if cuda:
generator.cuda()
discriminator.cuda()
adversarial_loss.cuda()
# Initialize weights
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)
# Configure data loader
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])
print('Using {} dataloader workers every process'.format(nw))
dataloader = torch.utils.data.DataLoader(
LoadData(
dped_dir=dped_dir,
dataset_size=train_size,
image_size=PATCH_SIZE,
test=False,
),
batch_size=batch_size,
shuffle=True,
num_workers=nw,
pin_memory=True,
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)
# used for visualizing training process
fixed_noise = torch.randn(16, nz, 1, device=device)
# optimizers
# optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, beta2))
# optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, beta2))
optimizerD = optim.RMSprop(netD.parameters(), lr=lr)
optimizerG = optim.RMSprop(netG.parameters(), lr=lr)
for epoch in range(epoch_num):
for step, (data, _) in enumerate(trainloader):
# training netD
real_cpu = data.to(device)
b_size = real_cpu.size(0)
netD.zero_grad()
noise = torch.randn(b_size, nz, 1, device=device)
fake = netG(noise)
# gradient penalty
eps = torch.Tensor(b_size, 1, 1).uniform_(0, 1)
x_p = eps * data + (1 - eps) * fake
grad = autograd.grad(netD(x_p).mean(),
x_p,
create_graph=True,
retain_graph=True)[0].view(b_size, -1)
grad_norm = torch.norm(grad, 2, 1)
grad_penalty = p_coeff * torch.pow(grad_norm - 1, 2)
loss_D = torch.mean(netD(fake) - netD(real_cpu))
loss_D.backward()
optimizerD.step()
for p in netD.parameters():
p.data.clamp_(-0.01, 0.01)
if step % n_critic == 0:
# training netG
noise = torch.randn(b_size, nz, 1, device=device)
netG.zero_grad()
fake = netG(noise)
loss_G = -torch.mean(netD(fake))
netD.zero_grad()
netG.zero_grad()
loss_G.backward()
optimizerG.step()
if step % 5 == 0:
print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f' %
(epoch, epoch_num, step, len(trainloader), loss_D.item(),
loss_G.item()))
# 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/wgan_gp_epoch_%d.png' % epoch)
plt.close()
# save model
torch.save(netG, './nets/wgan_gp_netG.pkl')
torch.save(netD, './nets/wgan_gp_netD.pkl')
def train():
os.makedirs('log', exist_ok=True)
ds = datasets.ImageFolder(root=data_root,
transform=transforms.Compose([
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
dataloader = DataLoader(ds, batch_size=batch_size, shuffle=True)
net_g = Generator(n_latent_vector, n_g_filters).to(device)
net_g.apply(weight_init)
net_d = Discriminator(n_d_filters).to(device)
net_d.apply(weight_init)
if os.path.exists(model_save_path):
all_state_dict = torch.load(model_save_path)
net_d.load_state_dict(all_state_dict['d_state_dict'])
net_g.load_state_dict(all_state_dict['g_state_dict'])
print('model restored from {}'.format(model_save_path))
criterion = nn.BCELoss()
fixed_noise = torch.randn(1, n_latent_vector, 1, 1, device=device)
real_label = 1
fake_label = 0
optimizer_d = optim.Adam(net_d.parameters(), lr=lr, betas=(0.5, 0.999))
optimizer_g = optim.Adam(net_g.parameters(), lr=lr, betas=(0.5, 0.999))
print('start training...')
try:
for epoch in range(epochs):
for i, data in enumerate(dataloader, 0):
# update Discrinimator, maximize d loss
net_d.zero_grad()
real_cpu = data[0].to(device)
b_size = real_cpu.size(0)
label = torch.full((b_size,), real_label, device=device)
output = net_d(real_cpu).view(-1)
err_d_real = criterion(output, label)
err_d_real.backward()
d_x = output.mean().item()
# train with fake batch
noise = torch.randn(b_size, n_latent_vector, 1, 1, device=device)
fake = net_g(noise)
label.fill_(fake_label)
output = net_d(fake.detach()).view(-1)
err_d_fake = criterion(output, label)
err_d_fake.backward()
d_g_z1 = output.mean().item()
err_d = err_d_real + err_d_fake
optimizer_d.step()
# update Generator
net_g.zero_grad()
label.fill_(real_label)
output = net_d(fake).view(-1)
err_g = criterion(output, label)
err_g.backward()
d_g_z2 = output.mean().item()
optimizer_d.step()
if i % 50 == 0:
print(f'Epoch: {epoch}, loss_d: {err_d.item()}, loss_g: {err_g.item()}')
if epoch % 2 == 0 and epoch != 0:
with torch.no_grad():
fake = net_g(fixed_noise).detach().cpu().numpy()
print(fake.shape)
fake = np.transpose(np.squeeze(fake, axis=0), (1, 2, 0))
print(fake.shape)
cv2.imwrite('log/{}_fake.png'.format(epoch), fake)
print('record a fake image to local.')
except KeyboardInterrupt:
print('interrupted, try saving the model')
all_state_dict = {
'd_state_dict': net_d.state_dict(),
'g_state_dict': net_g.state_dict(),
}
torch.save(all_state_dict, model_save_path)
print('model saved...')