def check_cycle_generator():
"""Checks the output and number of parameters of the CycleGenerator class.
"""
state = torch.load('checker_files/cycle_generator.pt')
G_XtoY = CycleGenerator(conv_dim=32, init_zero_weights=False)
G_XtoY.load_state_dict(state['state_dict'])
images = state['input']
cycle_generator_expected = state['output']
output = G_XtoY(images)
output_np = output.data.cpu().numpy()
if np.allclose(output_np, cycle_generator_expected):
print('CycleGenerator output: EQUAL')
else:
print('CycleGenerator output: NOT EQUAL')
num_params = count_parameters(G_XtoY)
expected_params = 105856
print('CycleGenerator #params = {}, expected #params = {}, {}'.format(
num_params, expected_params,
'EQUAL' if num_params == expected_params else 'NOT EQUAL'))
print('-' * 80)
def create_model(opts):
"""Builds the generators and discriminators.
"""
G_XtoY = CycleGenerator(conv_dim=opts.g_conv_dim, init_zero_weights=opts.init_zero_weights)
G_YtoX = CycleGenerator(conv_dim=opts.g_conv_dim, init_zero_weights=opts.init_zero_weights)
D_X = DCDiscriminator(conv_dim=opts.d_conv_dim)
D_Y = DCDiscriminator(conv_dim=opts.d_conv_dim)
return G_XtoY, G_YtoX, D_X, D_Y
def load_checkpoint(opts):
"""Loads the generator and discriminator models from checkpoints.
"""
G_XtoY_path = os.path.join(opts.load, 'G_XtoY.pkl')
G_YtoX_path = os.path.join(opts.load, 'G_YtoX.pkl')
D_X_path = os.path.join(opts.load, 'D_X.pkl')
D_Y_path = os.path.join(opts.load, 'D_Y.pkl')
G_XtoY = CycleGenerator(conv_dim=opts.g_conv_dim,
init_zero_weights=opts.init_zero_weights)
G_YtoX = CycleGenerator(conv_dim=opts.g_conv_dim,
init_zero_weights=opts.init_zero_weights)
D_X = DCDiscriminator(conv_dim=opts.d_conv_dim)
D_Y = DCDiscriminator(conv_dim=opts.d_conv_dim)
G_XtoY.load_state_dict(
torch.load(G_XtoY_path, map_location=lambda storage, loc: storage))
G_YtoX.load_state_dict(
torch.load(G_YtoX_path, map_location=lambda storage, loc: storage))
D_X.load_state_dict(
torch.load(D_X_path, map_location=lambda storage, loc: storage))
D_Y.load_state_dict(
torch.load(D_Y_path, map_location=lambda storage, loc: storage))
# if torch.cuda.is_available():
# G_XtoY.cuda()
# G_YtoX.cuda()
# D_X.cuda()
# D_Y.cuda()
# print('Models moved to GPU.')
return G_XtoY, G_YtoX, D_X, D_Y
def check_cycle_generator():
"""Checks the output and number of parameters of the CycleGenerator class.
"""
set_random_seeds(RANDOM_SEED)
G_XtoY = CycleGenerator(conv_dim=32, init_zero_weights=False)
dataloader_X, test_dataloader_X = get_emoji_loader(emoji_type='Apple')
images, labels = iter(dataloader_X).next()
images = Variable(images)
output = G_XtoY(images)
output_np = output.data.cpu().numpy()
# np.save('checker_files/cycle_generator.npy', output_np)
cycle_generator_expected = np.load('checker_files/cycle_generator.npy')
if np.allclose(output_np, cycle_generator_expected):
print('CycleGenerator output: EQUAL')
else:
print('CycleGenerator output: NOT EQUAL')
num_params = count_parameters(G_XtoY)
expected_params = 105856
print('CycleGenerator #params = {}, expected #params = {}, {}'.format(
num_params, expected_params, 'EQUAL' if num_params == expected_params else 'NOT EQUAL'))
print('-' * 80)
def create_model(opts):
"""Builds the generators and discriminators.
"""
G_XtoY = CycleGenerator(conv_dim=opts.g_conv_dim,
init_zero_weights=opts.init_zero_weights)
G_YtoX = CycleGenerator(conv_dim=opts.g_conv_dim,
init_zero_weights=opts.init_zero_weights)
D_X = DCDiscriminator(conv_dim=opts.d_conv_dim)
D_Y = DCDiscriminator(conv_dim=opts.d_conv_dim)
print_models(G_XtoY, G_YtoX, D_X, D_Y)
# if torch.cuda.is_available():
# G_XtoY.cuda()
# G_YtoX.cuda()
# D_X.cuda()
# D_Y.cuda()
# print('Models moved to GPU.')
return G_XtoY, G_YtoX, D_X, D_Y
def create_model(opts):
G_XtoY = CycleGenerator(init_zero_weights=opts.init_zero_weights)
G_YtoX = CycleGenerator(init_zero_weights=opts.init_zero_weights)
D_X = PatchGANDiscriminator()
D_Y = PatchGANDiscriminator()
if torch.cuda.is_available():
G_XtoY.cuda()
G_YtoX.cuda()
D_X.cuda()
D_Y.cuda()
print('Models moved to GPU.')
return G_XtoY, G_YtoX, D_X, D_Y
def create_model(opts):
"""Builds the generators and discriminators.
"""
G_XtoY = CycleGenerator(conv_dim=opts.g_conv_dim,
init_zero_weights=opts.init_zero_weights)
G_YtoX = CycleGenerator(conv_dim=opts.g_conv_dim,
init_zero_weights=opts.init_zero_weights)
D_X = DCDiscriminator(conv_dim=opts.d_conv_dim)
D_Y = DCDiscriminator(conv_dim=opts.d_conv_dim)
if torch.cuda.is_available():
G_XtoY.cuda()
G_YtoX.cuda()
D_X.cuda()
D_Y.cuda()
return G_XtoY, G_YtoX, D_X, D_Y
def train_loop(opts):
if opts.image_height == 128:
res_blocks = 6
elif opts.image_height >= 256:
res_blocks = 9
# Create networks
G_AB = CycleGenerator(opts.a_channels, opts.b_channels,
res_blocks).to(device)
G_BA = CycleGenerator(opts.b_channels, opts.a_channels,
res_blocks).to(device)
D_A = Discriminator(opts.a_channels, opts.d_conv_dim).to(device)
D_B = Discriminator(opts.b_channels, opts.d_conv_dim).to(device)
# Print network architecture
print(" G_AtoB ")
print("---------------------------------------")
print(G_AB)
print("---------------------------------------")
print(" G_BtoA ")
print("---------------------------------------")
print(G_BA)
print("---------------------------------------")
print(" D_A ")
print("---------------------------------------")
print(D_A)
print("---------------------------------------")
print(" D_B ")
print("---------------------------------------")
print(D_B)
print("---------------------------------------")
# Create losses
criterion_gan = torch.nn.MSELoss()
criterion_cycle = torch.nn.L1Loss()
criterion_identity = torch.nn.L1Loss()
if opts.load:
#TODO
pass
# Weights cycle loss and identity loss
lambda_cycle = 10
lambda_id = 0.5 * lambda_cycle
# Create optimizers
g_optimizer = torch.optim.Adam(itertools.chain(G_AB.parameters(),
G_BA.parameters()),
lr=opts.lr,
betas=(opts.beta1, opts.beta2))
d_a_optimizer = torch.optim.Adam(D_A.parameters(),
lr=opts.lr,
betas=(opts.beta1, opts.beta2))
d_b_optimizer = torch.optim.Adam(D_B.parameters(),
lr=opts.lr,
betas=(opts.beta1, opts.beta2))
# Create learning rate update schedulers
LambdaLR = get_lambda_rule(opts)
g_lr_scheduler = torch.optim.lr_scheduler.LambdaLR(g_optimizer,
lr_lambda=LambdaLR)
d_a_lr_scheduler = torch.optim.lr_scheduler.LambdaLR(d_a_optimizer,
lr_lambda=LambdaLR)
d_b_lr_scheduler = torch.optim.lr_scheduler.LambdaLR(d_b_optimizer,
lr_lambda=LambdaLR)
# Image transformations
transform = transforms.Compose([
transforms.Resize(int(opts.image_height * 1.12), Image.BICUBIC),
transforms.RandomCrop((opts.image_height, opts.image_width)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataloader = DataLoader(CycleGANDataset(opts.dataroot_dir,
opts.dataset_name,
transform),
batch_size=opts.batch_size,
shuffle=True,
num_workers=opts.n_cpu)
test_dataloader = DataLoader(CycleGANDataset(opts.dataroot_dir,
opts.dataset_name,
transform,
mode='test'),
batch_size=5,
shuffle=False,
num_workers=1)
end_epoch = opts.epochs + opts.start_epoch
total_batch = len(train_dataloader) * opts.epochs
for epoch in range(opts.start_epoch, end_epoch):
for index, batch in enumerate(train_dataloader):
# Create adversarial target
real_A = Variable(batch['A'].to(device))
real_B = Variable(batch['B'].to(device))
fake_A, fake_B = G_BA(real_B), G_AB(real_A)
# Train discriminator A
d_a_optimizer.zero_grad()
patch_real = D_A(real_A)
loss_a_real = criterion_gan(
patch_real,
torch.tensor(1.0).expand_as(patch_real).to(device))
patch_fake = D_A(fake_A)
loss_a_fake = criterion_gan(
patch_fake,
torch.tensor(0.0).expand_as(patch_fake).to(device))
loss_d_a = (loss_a_real + loss_a_fake) / 2
loss_d_a.backward()
d_a_optimizer.step()
# Train discriminator B
d_b_optimizer.zero_grad()
patch_real = D_B(real_B)
loss_b_real = criterion_gan(
patch_real,
torch.tensor(1.0).expand_as(patch_real).to(device))
patch_fake = D_B(fake_B)
loss_b_fake = criterion_gan(
patch_fake,
torch.tensor(0.0).expand_as(patch_fake).to(device))
loss_d_b = (loss_b_real + loss_b_fake) / 2
loss_d_b.backward()
d_b_optimizer.step()
# Train generator
g_optimizer.zero_grad()
fake_A, fake_B = G_BA(real_B), G_AB(real_A)
reconstructed_A, reconstructed_B = G_BA(fake_B), G_AB(fake_A)
# GAN loss
patch_a = D_A(fake_A)
loss_gan_ba = criterion_gan(
patch_a,
torch.tensor(1.0).expand_as(patch_a).to(device))
patch_b = D_B(fake_B)
loss_gan_ab = criterion_gan(
patch_b,
torch.tensor(1.0).expand_as(patch_b).to(device))
loss_gan = (loss_gan_ab + loss_gan_ba) / 2
# Cycle loss
loss_cycle_a = criterion_cycle(reconstructed_A, real_A)
loss_cycle_b = criterion_cycle(reconstructed_B, real_B)
loss_cycle = (loss_cycle_a + loss_cycle_b) / 2
# Identity loss
loss_id_a = criterion_identity(G_BA(real_A), real_A)
loss_id_b = criterion_identity(G_AB(real_B), real_B)
loss_identity = (loss_id_a + loss_id_b) / 2
# Total loss
loss_g = loss_gan + lambda_cycle * loss_cycle + lambda_id * loss_identity
loss_g.backward()
g_optimizer.step()
current_batch = epoch * len(train_dataloader) + index
sys.stdout.write(
f"\r[Epoch {epoch+1}/{opts.epochs-opts.start_epoch}] [Index {index}/{len(train_dataloader)}] [D_A loss: {loss_d_a.item():.4f}] [D_B loss: {loss_d_b.item():.4f}] [G loss: adv: {loss_gan.item():.4f}, cycle: {loss_cycle.item():.4f}, identity: {loss_identity.item():.4f}]"
)
if current_batch % opts.sample_every == 0:
save_sample(G_AB, G_BA, current_batch, opts, test_dataloader)
# Update learning reate
g_lr_scheduler.step()
d_a_lr_scheduler.step()
d_b_lr_scheduler.step()
if epoch % opts.checkpoint_every == 0:
torch.save(
G_AB.state_dict(),
f'{opts.checkpoint_dir}/{opts.dataset_name}/G_AB_{epoch}.pth')
torch.save(
G_BA.state_dict(),
f'{opts.checkpoint_dir}/{opts.dataset_name}/G_BA_{epoch}.pth')
torch.save(
D_A.state_dict(),
f'{opts.checkpoint_dir}/{opts.dataset_name}/D_A_{epoch}.pth')
torch.save(
D_B.state_dict(),
f'{opts.checkpoint_dir}/{opts.dataset_name}/D_B_{epoch}.pth')
opt.B_nc = 3
opt.cuda = 1
opt.lr = 0.0002
opt.epoch_count = 0
opt.n_epochs = 100
opt.n_epochs_decay = 100
opt.port = 35850
opt.test = 1
print(opt)
if torch.cuda.is_available() and not opt.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
###### Definition of variables ######
# Networks
netG_A2B = CycleGenerator(opt.A_nc, opt.B_nc)
netG_B2A = CycleGenerator(opt.B_nc, opt.A_nc)
if opt.cuda:
netG_A2B.cuda()
netG_B2A.cuda()
# Load state dicts
netG_A2B.load_state_dict(torch.load(opt.generator_A2B))
netG_B2A.load_state_dict(torch.load(opt.generator_B2A))
# Set model's test mode
netG_A2B.eval()
netG_B2A.eval()
# Inputs & targets memory allocation
