def train(model_config, train_config):
mode = 'train'
dataset = ShakespeareModern(train_shakespeare_path, test_shakespeare_path, train_modern_path, test_modern_path, mode=mode)
dataloader = DataLoader(dataset, batch_size=train_config['batch_size'], shuffle=False)
print(dataset.domain_A_max_len)
shakespeare_disc = Discriminator(model_config['embedding_size'], model_config['hidden_dim'], len(dataset.vocab), batch_size=train_config['batch_size']).cuda()
shakespeare_disc.train()
if train_config['continue_train']:
shakespeare_disc.load_state_dict(torch.load(train_config['model_path']))
criterion = nn.BCELoss().cuda()
optimizer = torch.optim.Adam(shakespeare_disc.parameters(), lr=train_config['base_lr'],
weight_decay=1e-5)
real_label = torch.ones((train_config['batch_size'], 1)).cuda()
fake_label = torch.zeros((train_config['batch_size'], 1)).cuda()
for epoch in range(train_config['num_epochs']):
for idx, (s, s_addn_feats, m, m_addn_feats) in tqdm(enumerate(dataloader)):
s = s.transpose(0, 1)
m = m.transpose(0, 1)
s = Variable(s).cuda()
s_output = shakespeare_disc(s, s_addn_feats)
s_loss = criterion(s_output, real_label)
s_loss = 100 * s_loss
optimizer.zero_grad()
s_loss.backward()
optimizer.step()
shakespeare_disc.hidden = shakespeare_disc.init_hidden()
m = Variable(m).cuda()
m_output = shakespeare_disc(m, m_addn_feats)
m_loss = criterion(m_output, fake_label)
m_loss = 100 * m_loss
optimizer.zero_grad()
m_loss.backward()
optimizer.step()
shakespeare_disc.hidden = shakespeare_disc.init_hidden()
if idx % 100 == 0:
print('\tepoch [{}/{}], iter: {}, s_loss: {:.4f}, m_loss: {:.4f}, preds: s: {}, {}, m: {}, {}'
.format(epoch+1, train_config['num_epochs'], idx, s_loss.item(), m_loss.item(), s_output.item(), round(s_output.item()), m_output.item(), round(m_output.item())))
print('\tepoch [{}/{}]'.format(epoch+1, train_config['num_epochs']))
torch.save(shakespeare_disc.state_dict(), './shakespeare_disc.pth')
def main():
parser = argparse.ArgumentParser(
description='Train Cartoon avatar Gan models')
parser.add_argument('--crop_size',
default=64,
type=int,
help='Training images crop size')
parser.add_argument('--num_epochs',
default=50,
type=int,
help='Train epoch number')
parser.add_argument('--data_root',
default='data/cartoon',
help='Root directory for dataset')
parser.add_argument('--worker',
default=2,
type=int,
help='Number of workers for dataloader')
parser.add_argument('--batch_size',
default=16,
type=int,
help='Batch size during training')
parser.add_argument('--channels',
default=3,
type=int,
help='Number of channels in the training images')
parser.add_argument('--nz',
default=100,
type=int,
help='Size of generator input')
parser.add_argument('--ngf',
default=64,
type=int,
help='Size of feature maps in generator')
parser.add_argument('--ndf',
default=64,
type=int,
help='Size of feature maps in descriminator')
parser.add_argument('--lr',
default=0.0002,
type=float,
help='Learning rate for optimizer')
parser.add_argument('--beta1',
default=0.5,
type=float,
help='Beta1 hyperparam for Adam optimizers')
parser.add_argument('--beta2',
default=0.999,
type=float,
help='Beta2 hyperparam for Adam optimizers')
parser.add_argument('--ngpu',
default=1,
type=int,
help='Number of GPUs , use 0 for CPU mode')
parser.add_argument(
'--latent_vector_num',
default=8,
type=int,
help=
'latent vectors that we will use to visualize , 8 means that it will visualize 8 images during training'
)
opt = parser.parse_args()
dataroot = opt.data_root
workers = opt.worker
batch_size = opt.batch_size
image_size = opt.crop_size
nc = opt.channels
nz = opt.nz
ngf = opt.ngf
ndf = opt.ndf
num_epochs = opt.num_epochs
lr = opt.lr
beta1 = opt.beta1
beta2 = opt.beta2
ngpu = opt.ngpu
latent_vector_num = opt.latent_vector_num
# Create the dataset
dataset = dset.ImageFolder(root=dataroot,
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)),
]))
# Create the dataloader
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
num_workers=workers)
# Decide which device we want to run on
device = torch.device("cuda:0" if (
torch.cuda.is_available() and ngpu > 0) else "cpu")
# Create the generator
netG = Generator(ngpu, nz, ngf, nc).to(device)
# Create the Discriminator
netD = Discriminator(ngpu, nc, ndf).to(device)
# Handle multi-gpu if desired
if (device.type == 'cuda') and (ngpu > 1):
netG = nn.DataParallel(netG, list(range(ngpu)))
netD = nn.DataParallel(netD, list(range(ngpu)))
# Apply the weights_init function to randomly initialize all weights
# to mean=0, stdev=0.2.
netG.apply(weights_init)
netD.apply(weights_init)
# Setup Adam optimizers for both G and D
optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))
# Print models
print(netG)
print(netD)
# Initialize BCELoss function
criterion = nn.BCELoss()
# Create batch of latent vectors that we will use to visualize
fixed_noise = torch.randn(latent_vector_num, nz, 1, 1, device=device)
#real and fake labels during training
real_label = 1
fake_label = 0
# Lists to keep track of progress
img_list = []
G_losses = []
D_losses = []
iters = 0
print("Starting Training ...")
for epoch in range(num_epochs):
for i, data in enumerate(dataloader, 0):
############################
# (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
###########################
## Train with all-real batch
netD.zero_grad()
# Format batch
real_cpu = data[0].to(device)
b_size = real_cpu.size(0)
label = torch.full((b_size, ), real_label, device=device)
# Forward pass real batch through D
output = netD(real_cpu).view(-1)
# Calculate loss on all-real batch
errD_real = criterion(output, label)
# Calculate gradients for D in backward pass
errD_real.backward()
D_x = output.mean().item()
## Train with all-fake batch
# Generate batch of latent vectors
noise = torch.randn(b_size, nz, 1, 1, device=device)
# Generate fake image batch with G
fake = netG(noise)
label.fill_(fake_label)
# Classify all fake batch with D
output = netD(fake.detach()).view(-1)
# Calculate D's loss on the all-fake batch
errD_fake = criterion(output, label)
# Calculate the gradients for this batch
errD_fake.backward()
D_G_z1 = output.mean().item()
# Add the gradients from the all-real and all-fake batches
errD = errD_real + errD_fake
# Update D
optimizerD.step()
############################
# (2) Update G network: maximize log(D(G(z)))
###########################
netG.zero_grad()
label.fill_(real_label)
# fake labels are real for generator cost
# Since we just updated D, perform another forward pass of all-fake batch through D
output = netD(fake).view(-1)
# Calculate G's loss based on this output
errG = criterion(output, label)
# Calculate gradients for G
errG.backward()
D_G_z2 = output.mean().item()
# Update G
optimizerG.step()
# Output training stats
if i % 50 == 0:
# Save model data
torch.save(netG.state_dict(),
'pretrained_model/netG_epoch_%d.pth' % (iters))
torch.save(netD.state_dict(),
'pretrained_model/netD_epoch_%d.pth' % (iters))
# Print training stats
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))
# Save Losses for plotting later
G_losses.append(errG.item())
D_losses.append(errD.item())
# Check how the generator is doing by saving G's output on fixed_noise
if (iters % 650 == 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
# Display and Save samples GIF
fig = plt.figure(figsize=(8, 8))
plt.axis("off")
ims = [[plt.imshow(np.transpose(i, (1, 2, 0)), animated=True)]
for i in img_list]
ani = animation.ArtistAnimation(fig,
ims,
interval=1000,
repeat_delay=1000,
blit=True)
ani.save('output/samples.gif', writer='imagemagick', fps=100)
if opt['parallel']:
torch.save(
model.module.state_dict(),
opt['weights_path'] + "/" + MODE + "_" + str(epoch) + ".ckpt")
else:
torch.save(
model.state_dict(),
opt['weights_path'] + "/" + MODE + "_" + str(epoch) + ".ckpt")
if MODE == 'GAN':
if opt['parallel']:
torch.save(
model_d.module.state_dict(), opt['weights_path'] + "/" +
MODE + "_D_" + str(epoch) + ".ckpt")
else:
torch.save(
model_d.state_dict(), opt['weights_path'] + "/" + MODE +
"_D_" + str(epoch) + ".ckpt")
if opt['log'] == 1:
with open(
opt['weights_path'] + "/" + MODE + "_" + str(epoch) +
".json", "w") as js:
json.dump(dict(
msg.split(':')
for msg in latest_msg.replace(" ", "").split(',')),
js,
indent=4,
separators=(',', ': '))
# Stop early if loss is not improving
if early_stop == opt['early_stop']:
print("Loss has not improved in {} epochs. Stopping early.".format(
torch.cuda.manual_seed(opt.seed)
# Networks
if opt.upsample == 'ori':
netG_A2B = Generator_ori(opt.input_nc, opt.output_nc)
netG_B2A = Generator_ori(opt.output_nc, opt.input_nc)
else:
netG_A2B = Generator(opt.input_nc, opt.output_nc)
netG_B2A = Generator(opt.output_nc, opt.input_nc)
netD_A = Discriminator(opt.input_nc)
netD_B = Discriminator(opt.output_nc)
netG_A2B.cuda()
netG_B2A.cuda()
netD_A.cuda()
netD_B.cuda()
netG_A2B.apply(weights_init_normal)
netG_B2A.apply(weights_init_normal)
netD_A.apply(weights_init_normal)
netD_B.apply(weights_init_normal)
torch.save(netG_A2B.state_dict(),
"initial_weights/netG_A2B_seed_{}.pth.tar".format(opt.seed))
torch.save(netG_B2A.state_dict(),
"initial_weights/netG_B2A_seed_{}.pth.tar".format(opt.seed))
torch.save(netD_A.state_dict(),
"initial_weights/netD_A_seed_{}.pth.tar".format(opt.seed))
torch.save(netD_B.state_dict(),
"initial_weights/netD_B_seed_{}.pth.tar".format(opt.seed))
