class UNIT_Trainer(nn.Module):
def __init__(self, hyperparameters):
super(UNIT_Trainer, self).__init__()
lr = hyperparameters['lr']
# Initiate the networks
self.gen_a = VAEGen(hyperparameters['input_dim_a'], hyperparameters['gen']) # auto-encoder for domain a
self.gen_b = VAEGen(hyperparameters['input_dim_b'], hyperparameters['gen']) # auto-encoder for domain b
self.dis_a = MsImageDis(hyperparameters['input_dim_a'], hyperparameters['dis']) # discriminator for domain a
self.dis_b = MsImageDis(hyperparameters['input_dim_b'], hyperparameters['dis']) # discriminator for domain b
self.instancenorm = nn.InstanceNorm2d(512, affine=False)
# Setup the optimizers
beta1 = hyperparameters['beta1']
beta2 = hyperparameters['beta2']
dis_params = list(self.dis_a.parameters()) + list(self.dis_b.parameters())
gen_params = list(self.gen_a.parameters()) + list(self.gen_b.parameters())
self.dis_opt = torch.optim.Adam([p for p in dis_params if p.requires_grad],
lr=lr, betas=(beta1, beta2), weight_decay=hyperparameters['weight_decay'])
self.gen_opt = torch.optim.Adam([p for p in gen_params if p.requires_grad],
lr=lr, betas=(beta1, beta2), weight_decay=hyperparameters['weight_decay'])
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters)
# Network weight initialization
self.apply(weights_init(hyperparameters['init']))
self.dis_a.apply(weights_init('gaussian'))
self.dis_b.apply(weights_init('gaussian'))
# Load VGG model if needed
if 'vgg_w' in hyperparameters.keys() and hyperparameters['vgg_w'] > 0:
self.vgg = load_vgg16(hyperparameters['vgg_model_path'] + '/models')
self.vgg.eval()
for param in self.vgg.parameters():
param.requires_grad = False
def recon_criterion(self, input, target):
return torch.mean(torch.abs(input - target))
def forward(self, x_a, x_b):
self.eval()
h_a, _ = self.gen_a.encode(x_a)
h_b, _ = self.gen_b.encode(x_b)
x_ba = self.gen_a.decode(h_b)
x_ab = self.gen_b.decode(h_a)
self.train()
return x_ab, x_ba
def __compute_kl(self, mu):
# def _compute_kl(self, mu, sd):
# mu_2 = torch.pow(mu, 2)
# sd_2 = torch.pow(sd, 2)
# encoding_loss = (mu_2 + sd_2 - torch.log(sd_2)).sum() / mu_2.size(0)
# return encoding_loss
mu_2 = torch.pow(mu, 2)
encoding_loss = torch.mean(mu_2)
return encoding_loss
def gen_update(self, x_a, x_b, hyperparameters):
self.gen_opt.zero_grad()
# encode
h_a, n_a = self.gen_a.encode(x_a)
h_b, n_b = self.gen_b.encode(x_b)
# decode (within domain)
x_a_recon = self.gen_a.decode(h_a + n_a)
x_b_recon = self.gen_b.decode(h_b + n_b)
# decode (cross domain)
x_ba = self.gen_a.decode(h_b + n_b)
x_ab = self.gen_b.decode(h_a + n_a)
# encode again
h_b_recon, n_b_recon = self.gen_a.encode(x_ba)
h_a_recon, n_a_recon = self.gen_b.encode(x_ab)
# decode again (if needed)
x_aba = self.gen_a.decode(h_a_recon + n_a_recon) if hyperparameters['recon_x_cyc_w'] > 0 else None
x_bab = self.gen_b.decode(h_b_recon + n_b_recon) if hyperparameters['recon_x_cyc_w'] > 0 else None
# reconstruction loss
self.loss_gen_recon_x_a = self.recon_criterion(x_a_recon, x_a)
self.loss_gen_recon_x_b = self.recon_criterion(x_b_recon, x_b)
self.loss_gen_recon_kl_a = self.__compute_kl(h_a)
self.loss_gen_recon_kl_b = self.__compute_kl(h_b)
self.loss_gen_cyc_x_a = self.recon_criterion(x_aba, x_a)
self.loss_gen_cyc_x_b = self.recon_criterion(x_bab, x_b)
self.loss_gen_recon_kl_cyc_aba = self.__compute_kl(h_a_recon)
self.loss_gen_recon_kl_cyc_bab = self.__compute_kl(h_b_recon)
# GAN loss
self.loss_gen_adv_a = self.dis_a.calc_gen_loss(x_ba)
self.loss_gen_adv_b = self.dis_b.calc_gen_loss(x_ab)
# domain-invariant perceptual loss
self.loss_gen_vgg_a = self.compute_vgg_loss(self.vgg, x_ba, x_b) if hyperparameters['vgg_w'] > 0 else 0
self.loss_gen_vgg_b = self.compute_vgg_loss(self.vgg, x_ab, x_a) if hyperparameters['vgg_w'] > 0 else 0
# total loss
self.loss_gen_total = hyperparameters['gan_w'] * self.loss_gen_adv_a + \
hyperparameters['gan_w'] * self.loss_gen_adv_b + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_a + \
hyperparameters['recon_kl_w'] * self.loss_gen_recon_kl_a + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_b + \
hyperparameters['recon_kl_w'] * self.loss_gen_recon_kl_b + \
hyperparameters['recon_x_cyc_w'] * self.loss_gen_cyc_x_a + \
hyperparameters['recon_kl_cyc_w'] * self.loss_gen_recon_kl_cyc_aba + \
hyperparameters['recon_x_cyc_w'] * self.loss_gen_cyc_x_b + \
hyperparameters['recon_kl_cyc_w'] * self.loss_gen_recon_kl_cyc_bab + \
hyperparameters['vgg_w'] * self.loss_gen_vgg_a + \
hyperparameters['vgg_w'] * self.loss_gen_vgg_b
self.loss_gen_total.backward()
self.gen_opt.step()
def compute_vgg_loss(self, vgg, img, target):
img_vgg = vgg_preprocess(img)
target_vgg = vgg_preprocess(target)
img_fea = vgg(img_vgg)
target_fea = vgg(target_vgg)
return torch.mean((self.instancenorm(img_fea) - self.instancenorm(target_fea)) ** 2)
def sample(self, x_a, x_b):
self.eval()
x_a_recon, x_b_recon, x_ba, x_ab = [], [], [], []
for i in range(x_a.size(0)):
h_a, _ = self.gen_a.encode(x_a[i].unsqueeze(0))
h_b, _ = self.gen_b.encode(x_b[i].unsqueeze(0))
x_a_recon.append(self.gen_a.decode(h_a))
x_b_recon.append(self.gen_b.decode(h_b))
x_ba.append(self.gen_a.decode(h_b))
x_ab.append(self.gen_b.decode(h_a))
x_a_recon, x_b_recon = torch.cat(x_a_recon), torch.cat(x_b_recon)
x_ba = torch.cat(x_ba)
x_ab = torch.cat(x_ab)
self.train()
return x_a, x_a_recon, x_ab, x_b, x_b_recon, x_ba
def dis_update(self, x_a, x_b, hyperparameters):
self.dis_opt.zero_grad()
# encode
h_a, n_a = self.gen_a.encode(x_a)
h_b, n_b = self.gen_b.encode(x_b)
# decode (cross domain)
x_ba = self.gen_a.decode(h_b + n_b)
x_ab = self.gen_b.decode(h_a + n_a)
# D loss
self.loss_dis_a = self.dis_a.calc_dis_loss(x_ba.detach(), x_a)
self.loss_dis_b = self.dis_b.calc_dis_loss(x_ab.detach(), x_b)
self.loss_dis_total = hyperparameters['gan_w'] * self.loss_dis_a + hyperparameters['gan_w'] * self.loss_dis_b
self.loss_dis_total.backward()
self.dis_opt.step()
def update_learning_rate(self):
if self.dis_scheduler is not None:
self.dis_scheduler.step()
if self.gen_scheduler is not None:
self.gen_scheduler.step()
def resume(self, checkpoint_dir, hyperparameters):
# Load generators
last_model_name = get_model_list(checkpoint_dir, "gen")
state_dict = torch.load(last_model_name)
self.gen_a.load_state_dict(state_dict['a'])
self.gen_b.load_state_dict(state_dict['b'])
iterations = int(last_model_name[-11:-3])
# Load discriminators
last_model_name = get_model_list(checkpoint_dir, "dis")
state_dict = torch.load(last_model_name)
self.dis_a.load_state_dict(state_dict['a'])
self.dis_b.load_state_dict(state_dict['b'])
# Load optimizers
state_dict = torch.load(os.path.join(checkpoint_dir, 'optimizer.pt'))
self.dis_opt.load_state_dict(state_dict['dis'])
self.gen_opt.load_state_dict(state_dict['gen'])
# Reinitilize schedulers
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters, iterations)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters, iterations)
print('Resume from iteration %d' % iterations)
return iterations
def save(self, snapshot_dir, iterations):
# Save generators, discriminators, and optimizers
gen_name = os.path.join(snapshot_dir, 'gen_%08d.pt' % (iterations + 1))
dis_name = os.path.join(snapshot_dir, 'dis_%08d.pt' % (iterations + 1))
opt_name = os.path.join(snapshot_dir, 'optimizer.pt')
torch.save({'a': self.gen_a.state_dict(), 'b': self.gen_b.state_dict()}, gen_name)
torch.save({'a': self.dis_a.state_dict(), 'b': self.dis_b.state_dict()}, dis_name)
torch.save({'gen': self.gen_opt.state_dict(), 'dis': self.dis_opt.state_dict()}, opt_name)
class Trainer(nn.Module):
def __init__(self, hyperparameters):
super(Trainer, self).__init__()
lr = hyperparameters['lr']
# Initiate the networks
# auto-encoder for domain a
self.trait_dim = hyperparameters['gen']['trait_dim']
self.gen_a = VAEGen(hyperparameters['input_dim'],
hyperparameters['basis_encoder_dims'],
hyperparameters['trait_encoder_dims'],
hyperparameters['decoder_dims'], self.trait_dim)
# auto-encoder for domain b
self.gen_b = VAEGen(hyperparameters['input_dim'],
hyperparameters['basis_encoder_dims'],
hyperparameters['trait_encoder_dims'],
hyperparameters['decoder_dims'], self.trait_dim)
# discriminator for domain a
self.dis_a = Discriminator(hyperparameters['input_dim'],
hyperparameters['dis_dims'], 1)
# discriminator for domain b
self.dis_b = Discriminator(hyperparameters['input_dim'],
hyperparameters['dis_dims'], 1)
# fix the noise used in sampling
self.trait_a = torch.randn(8, self.trait_dim, 1, 1)
self.trait_b = torch.randn(8, self.trait_dim, 1, 1)
# Setup the optimizers
dis_params = list(self.dis_a.parameters()) + \
list(self.dis_b.parameters())
gen_params = list(self.gen_a.parameters()) + \
list(self.gen_b.parameters())
for _p in gen_params:
print(_p.data.shape)
self.dis_opt = torch.optim.Adam(
[p for p in dis_params if p.requires_grad],
lr=lr,
weight_decay=hyperparameters['weight_decay'])
self.gen_opt = torch.optim.Adam(
[p for p in gen_params if p.requires_grad],
lr=lr,
weight_decay=hyperparameters['weight_decay'])
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters)
# Network weight initialization
self.apply(weights_init(hyperparameters['init']))
self.gen_a.apply(weights_init('gaussian'))
self.gen_b.apply(weights_init('gaussian'))
self.dis_a.apply(weights_init('gaussian'))
self.dis_b.apply(weights_init('gaussian'))
def recon_criterion(self, input, target):
return torch.mean(torch.abs(input - target))
def forward(self, x_a, x_b):
self.eval()
trait_a = Variable(self.trait_a)
trait_b = Variable(self.trait_b)
basis_a, trait_a_fake = self.gen_a.encode(x_a)
basis_b, trait_b_fake = self.gen_b.encode(x_b)
x_ba = self.gen_a.decode(basis_b, trait_a)
x_ab = self.gen_b.decode(basis_a, trait_b)
self.train()
return x_ab, x_ba
def gen_update(self, x_a, x_b, hyperparameters):
self.gen_opt.zero_grad()
trait_a = Variable(torch.randn(x_a.size(0), self.trait_dim))
trait_b = Variable(torch.randn(x_b.size(0), self.trait_dim))
# encode
basis_a, trait_a_prime = self.gen_a.encode(x_a)
basis_b, trait_b_prime = self.gen_b.encode(x_b)
# decode (within domain)
x_a_recon = self.gen_a.decode(basis_a, trait_a_prime)
x_b_recon = self.gen_b.decode(basis_b, trait_b_prime)
# decode (cross domain)
x_ba = self.gen_a.decode(basis_b, trait_a)
x_ab = self.gen_b.decode(basis_a, trait_b)
# encode again
basis_b_recon, trait_a_recon = self.gen_a.encode(x_ba)
basis_a_recon, trait_b_recon = self.gen_b.encode(x_ab)
# decode again (if needed)
x_aba = self.gen_a.decode(
basis_a_recon,
trait_a_prime) if hyperparameters['recon_x_cyc_w'] > 0 else None
x_bab = self.gen_b.decode(
basis_b_recon,
trait_b_prime) if hyperparameters['recon_x_cyc_w'] > 0 else None
# reconstruction loss
self.loss_gen_recon_x_a = self.recon_criterion(x_a_recon, x_a)
self.loss_gen_recon_x_b = self.recon_criterion(x_b_recon, x_b)
self.loss_gen_recon_trait_a = self.recon_criterion(
trait_a_recon, trait_a)
self.loss_gen_recon_trait_b = self.recon_criterion(
trait_b_recon, trait_b)
self.loss_gen_recon_basis_a = self.recon_criterion(
basis_a_recon, basis_a)
self.loss_gen_recon_basis_b = self.recon_criterion(
basis_b_recon, basis_b)
self.loss_gen_cycrecon_x_a = self.recon_criterion(
x_aba, x_a) if hyperparameters['recon_x_cyc_w'] > 0 else 0
self.loss_gen_cycrecon_x_b = self.recon_criterion(
x_bab, x_b) if hyperparameters['recon_x_cyc_w'] > 0 else 0
# GAN loss
self.loss_gen_adv_a = self.dis_a.calc_gen_loss(x_ba)
self.loss_gen_adv_b = self.dis_b.calc_gen_loss(x_ab)
# total loss
self.loss_gen_total = hyperparameters[
'gan_w'] * self.loss_gen_adv_a + \
hyperparameters['gan_w'] * self.loss_gen_adv_b + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_a + \
hyperparameters['recon_trait_w'] * self.loss_gen_recon_trait_a + \
hyperparameters['recon_basis_w'] * self.loss_gen_recon_basis_a + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_b + \
hyperparameters['recon_trait_w'] * self.loss_gen_recon_trait_b + \
hyperparameters['recon_basis_w'] * self.loss_gen_recon_basis_b + \
hyperparameters['recon_x_cyc_w'] * self.loss_gen_cycrecon_x_a + \
hyperparameters['recon_x_cyc_w'] * self.loss_gen_cycrecon_x_b
self.loss_gen_total.backward()
self.gen_opt.step()
# def sample(self, x_a, x_b):
# self.eval()
# s_a1 = Variable(self.s_a)
# s_b1 = Variable(self.s_b)
# s_a2 = Variable(torch.randn(x_a.size(0), self.trait_dim, 1, 1))
# s_b2 = Variable(torch.randn(x_b.size(0), self.trait_dim, 1, 1))
# x_a_recon, x_b_recon, x_ba1, x_ba2, x_ab1, x_ab2 = [], [], [], [], [], []
# for i in range(x_a.size(0)):
# c_a, s_a_fake = self.gen_a.encode(x_a[i].unsqueeze(0))
# c_b, s_b_fake = self.gen_b.encode(x_b[i].unsqueeze(0))
# x_a_recon.append(self.gen_a.decode(c_a, s_a_fake))
# x_b_recon.append(self.gen_b.decode(c_b, s_b_fake))
# x_ba1.append(self.gen_a.decode(c_b, s_a1[i].unsqueeze(0)))
# x_ba2.append(self.gen_a.decode(c_b, s_a2[i].unsqueeze(0)))
# x_ab1.append(self.gen_b.decode(c_a, s_b1[i].unsqueeze(0)))
# x_ab2.append(self.gen_b.decode(c_a, s_b2[i].unsqueeze(0)))
# x_a_recon, x_b_recon = torch.cat(x_a_recon), torch.cat(x_b_recon)
# x_ba1, x_ba2 = torch.cat(x_ba1), torch.cat(x_ba2)
# x_ab1, x_ab2 = torch.cat(x_ab1), torch.cat(x_ab2)
# self.train()
# return x_a, x_a_recon, x_ab1, x_ab2, x_b, x_b_recon, x_ba1, x_ba2
def dis_update(self, x_a, x_b, hyperparameters):
self.dis_opt.zero_grad()
trait_a = Variable(torch.randn(x_a.size(0), self.trait_dim))
trait_b = Variable(torch.randn(x_b.size(0), self.trait_dim))
# encode
basis_a, _ = self.gen_a.encode(x_a)
basis_b, _ = self.gen_b.encode(x_b)
# decode (cross domain)
x_ba = self.gen_a.decode(basis_b, trait_a)
x_ab = self.gen_b.decode(basis_a, trait_b)
# D loss
self.loss_dis_a = self.dis_a.calc_dis_loss(x_ba, x_a)
self.loss_dis_b = self.dis_b.calc_dis_loss(x_ab, x_b)
self.loss_dis_total = hyperparameters['gan_w'] * \
self.loss_dis_a + hyperparameters['gan_w'] * self.loss_dis_b
self.loss_dis_total.backward()
self.dis_opt.step()
def update_learning_rate(self):
if self.dis_scheduler is not None:
self.dis_scheduler.step()
if self.gen_scheduler is not None:
self.gen_scheduler.step()
def resume(self, checkpoint_dir, hyperparameters):
# Load generators
last_model_name = get_model_list(checkpoint_dir, "gen")
state_dict = torch.load(last_model_name)
self.gen_a.load_state_dict(state_dict['a'])
self.gen_b.load_state_dict(state_dict['b'])
iterations = int(last_model_name[-11:-3])
# Load discriminators
last_model_name = get_model_list(checkpoint_dir, "dis")
state_dict = torch.load(last_model_name)
self.dis_a.load_state_dict(state_dict['a'])
self.dis_b.load_state_dict(state_dict['b'])
# Load optimizers
state_dict = torch.load(os.path.join(checkpoint_dir, 'optimizer.pt'))
self.dis_opt.load_state_dict(state_dict['dis'])
self.gen_opt.load_state_dict(state_dict['gen'])
# Reinitilize schedulers
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters,
iterations)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters,
iterations)
print('Resume from iteration %d' % iterations)
return iterations
def save(self, snapshot_dir, iterations):
# Save generators, discriminators, and optimizers
gen_name = os.path.join(snapshot_dir, 'gen_%08d.pt' % (iterations + 1))
dis_name = os.path.join(snapshot_dir, 'dis_%08d.pt' % (iterations + 1))
opt_name = os.path.join(snapshot_dir, 'optimizer.pt')
torch.save({
'a': self.gen_a.state_dict(),
'b': self.gen_b.state_dict()
}, gen_name)
torch.save({
'a': self.dis_a.state_dict(),
'b': self.dis_b.state_dict()
}, dis_name)
torch.save(
{
'gen': self.gen_opt.state_dict(),
'dis': self.dis_opt.state_dict()
}, opt_name)
class UNIT_Trainer(nn.Module):
def __init__(self, hyperparameters):
super(UNIT_Trainer, self).__init__()
lr = hyperparameters['lr']
# Initiate the networks
self.gen_a = VAEGen(hyperparameters['input_dim_a'], hyperparameters['gen']) # auto-encoder for domain a
self.gen_b = VAEGen(hyperparameters['input_dim_b'], hyperparameters['gen']) # auto-encoder for domain b
self.dis_a = MsImageDis(hyperparameters['input_dim_a'], hyperparameters['dis']) # discriminator for domain a
self.dis_b = MsImageDis(hyperparameters['input_dim_b'], hyperparameters['dis']) # discriminator for domain b
self.instancenorm = nn.InstanceNorm2d(512, affine=False)
# Setup the optimizers
beta1 = hyperparameters['beta1']
beta2 = hyperparameters['beta2']
dis_params = list(self.dis_a.parameters()) + list(self.dis_b.parameters())
gen_params = list(self.gen_a.parameters()) + list(self.gen_b.parameters())
self.dis_opt = torch.optim.Adam([p for p in dis_params if p.requires_grad],
lr=lr, betas=(beta1, beta2), weight_decay=hyperparameters['weight_decay'])
self.gen_opt = torch.optim.Adam([p for p in gen_params if p.requires_grad],
lr=lr, betas=(beta1, beta2), weight_decay=hyperparameters['weight_decay'])
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters)
# Network weight initialization
self.apply(weights_init(hyperparameters['init']))
self.dis_a.apply(weights_init('gaussian'))
self.dis_b.apply(weights_init('gaussian'))
# Load VGG model if needed
if 'vgg_w' in hyperparameters.keys() and hyperparameters['vgg_w'] > 0:
self.vgg = load_vgg16(hyperparameters['vgg_model_path'] + '/models')
self.vgg.eval()
for param in self.vgg.parameters():
param.requires_grad = False
def recon_criterion(self, input, target):
return torch.mean(torch.abs(input - target))
def forward(self, x_a, x_b):
self.eval()
x_a.volatile = True
x_b.volatile = True
h_a, _ = self.gen_a.encode(x_a)
h_b, _ = self.gen_b.encode(x_b)
x_ba = self.gen_a.decode(h_b)
x_ab = self.gen_b.decode(h_a)
self.train()
return x_ab, x_ba
def __compute_kl(self, mu):
# def _compute_kl(self, mu, sd):
# mu_2 = torch.pow(mu, 2)
# sd_2 = torch.pow(sd, 2)
# encoding_loss = (mu_2 + sd_2 - torch.log(sd_2)).sum() / mu_2.size(0)
# return encoding_loss
mu_2 = torch.pow(mu, 2)
encoding_loss = torch.mean(mu_2)
return encoding_loss
def gen_update(self, x_a, x_b, hyperparameters):
self.gen_opt.zero_grad()
# encode
h_a, n_a = self.gen_a.encode(x_a)
h_b, n_b = self.gen_b.encode(x_b)
# decode (within domain)
x_a_recon = self.gen_a.decode(h_a + n_a)
x_b_recon = self.gen_b.decode(h_b + n_b)
# decode (cross domain)
x_ba = self.gen_a.decode(h_b + n_b)
x_ab = self.gen_b.decode(h_a + n_a)
# encode again
h_b_recon, n_b_recon = self.gen_a.encode(x_ba)
h_a_recon, n_a_recon = self.gen_b.encode(x_ab)
# decode again (if needed)
x_aba = self.gen_a.decode(h_a_recon + n_a_recon) if hyperparameters['recon_x_cyc_w'] > 0 else None
x_bab = self.gen_b.decode(h_b_recon + n_b_recon) if hyperparameters['recon_x_cyc_w'] > 0 else None
# reconstruction loss
self.loss_gen_recon_x_a = self.recon_criterion(x_a_recon, x_a)
self.loss_gen_recon_x_b = self.recon_criterion(x_b_recon, x_b)
self.loss_gen_recon_kl_a = self.__compute_kl(h_a)
self.loss_gen_recon_kl_b = self.__compute_kl(h_b)
self.loss_gen_cyc_x_a = self.recon_criterion(x_aba, x_a)
self.loss_gen_cyc_x_b = self.recon_criterion(x_bab, x_b)
self.loss_gen_recon_kl_cyc_aba = self.__compute_kl(h_a_recon)
self.loss_gen_recon_kl_cyc_bab = self.__compute_kl(h_b_recon)
# GAN loss
self.loss_gen_adv_a = self.dis_a.calc_gen_loss(x_ba)
self.loss_gen_adv_b = self.dis_b.calc_gen_loss(x_ab)
# domain-invariant perceptual loss
self.loss_gen_vgg_a = self.compute_vgg_loss(self.vgg, x_ba, x_b) if hyperparameters['vgg_w'] > 0 else 0
self.loss_gen_vgg_b = self.compute_vgg_loss(self.vgg, x_ab, x_a) if hyperparameters['vgg_w'] > 0 else 0
# total loss
self.loss_gen_total = hyperparameters['gan_w'] * self.loss_gen_adv_a + \
hyperparameters['gan_w'] * self.loss_gen_adv_b + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_a + \
hyperparameters['recon_kl_w'] * self.loss_gen_recon_kl_a + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_b + \
hyperparameters['recon_kl_w'] * self.loss_gen_recon_kl_b + \
hyperparameters['recon_x_cyc_w'] * self.loss_gen_cyc_x_a + \
hyperparameters['recon_kl_cyc_w'] * self.loss_gen_recon_kl_cyc_aba + \
hyperparameters['recon_x_cyc_w'] * self.loss_gen_cyc_x_b + \
hyperparameters['recon_kl_cyc_w'] * self.loss_gen_recon_kl_cyc_bab + \
hyperparameters['vgg_w'] * self.loss_gen_vgg_a + \
hyperparameters['vgg_w'] * self.loss_gen_vgg_b
self.loss_gen_total.backward()
self.gen_opt.step()
def compute_vgg_loss(self, vgg, img, target):
img_vgg = vgg_preprocess(img)
target_vgg = vgg_preprocess(target)
img_fea = vgg(img_vgg)
target_fea = vgg(target_vgg)
return torch.mean((self.instancenorm(img_fea) - self.instancenorm(target_fea)) ** 2)
def sample(self, x_a, x_b):
self.eval()
x_a.volatile = True
x_b.volatile = True
x_a_recon, x_b_recon, x_ba, x_ab = [], [], [], []
for i in range(x_a.size(0)):
h_a, _ = self.gen_a.encode(x_a[i].unsqueeze(0))
h_b, _ = self.gen_b.encode(x_b[i].unsqueeze(0))
x_a_recon.append(self.gen_a.decode(h_a))
x_b_recon.append(self.gen_b.decode(h_b))
x_ba.append(self.gen_a.decode(h_b))
x_ab.append(self.gen_b.decode(h_a))
x_a_recon, x_b_recon = torch.cat(x_a_recon), torch.cat(x_b_recon)
x_ba = torch.cat(x_ba)
x_ab = torch.cat(x_ab)
self.train()
return x_a, x_a_recon, x_ab, x_b, x_b_recon, x_ba
def dis_update(self, x_a, x_b, hyperparameters):
self.dis_opt.zero_grad()
# encode
h_a, n_a = self.gen_a.encode(x_a)
h_b, n_b = self.gen_b.encode(x_b)
# decode (cross domain)
x_ba = self.gen_a.decode(h_b + n_b)
x_ab = self.gen_b.decode(h_a + n_a)
# D loss
self.loss_dis_a = self.dis_a.calc_dis_loss(x_ba.detach(), x_a)
self.loss_dis_b = self.dis_b.calc_dis_loss(x_ab.detach(), x_b)
self.loss_dis_total = hyperparameters['gan_w'] * self.loss_dis_a + hyperparameters['gan_w'] * self.loss_dis_b
self.loss_dis_total.backward()
self.dis_opt.step()
def update_learning_rate(self):
if self.dis_scheduler is not None:
self.dis_scheduler.step()
if self.gen_scheduler is not None:
self.gen_scheduler.step()
def resume(self, checkpoint_dir, hyperparameters):
# Load generators
last_model_name = get_model_list(checkpoint_dir, "gen")
state_dict = torch.load(last_model_name)
self.gen_a.load_state_dict(state_dict['a'])
self.gen_b.load_state_dict(state_dict['b'])
iterations = int(last_model_name[-11:-3])
# Load discriminators
last_model_name = get_model_list(checkpoint_dir, "dis")
state_dict = torch.load(last_model_name)
self.dis_a.load_state_dict(state_dict['a'])
self.dis_b.load_state_dict(state_dict['b'])
# Load optimizers
state_dict = torch.load(os.path.join(checkpoint_dir, 'optimizer.pt'))
self.dis_opt.load_state_dict(state_dict['dis'])
self.gen_opt.load_state_dict(state_dict['gen'])
# Reinitilize schedulers
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters, iterations)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters, iterations)
print('Resume from iteration %d' % iterations)
return iterations
def save(self, snapshot_dir, iterations):
# Save generators, discriminators, and optimizers
gen_name = os.path.join(snapshot_dir, 'gen_%08d.pt' % (iterations + 1))
dis_name = os.path.join(snapshot_dir, 'dis_%08d.pt' % (iterations + 1))
opt_name = os.path.join(snapshot_dir, 'optimizer.pt')
torch.save({'a': self.gen_a.state_dict(), 'b': self.gen_b.state_dict()}, gen_name)
torch.save({'a': self.dis_a.state_dict(), 'b': self.dis_b.state_dict()}, dis_name)
torch.save({'gen': self.gen_opt.state_dict(), 'dis': self.dis_opt.state_dict()}, opt_name)
class UNIT_Trainer(nn.Module):
def __init__(self, hyperparameters):
super(UNIT_Trainer, self).__init__()
lr = hyperparameters['lr']
# Initiate the networks
self.gen_a = VAEGen(hyperparameters['input_dim_a'], hyperparameters['gen']) # auto-encoder for domain a
self.gen_b = VAEGen(hyperparameters['input_dim_b'], hyperparameters['gen']) # auto-encoder for domain b
if not hyperparameters['origin']:
self.dis_a = MultiscaleDiscriminator(hyperparameters['input_dim_a'], # discriminator for a
ndf=64, n_layers=3, norm_layer=nn.InstanceNorm2d, use_sigmoid=False,
num_D=2, getIntermFeat=True
)
self.dis_b = MultiscaleDiscriminator(hyperparameters['input_dim_b'], # discriminator for b
ndf=64, n_layers=3, norm_layer=nn.InstanceNorm2d, use_sigmoid=False,
num_D=2, getIntermFeat=True
)
self.criterionGAN = GANLoss(use_lsgan=True, tensor=torch.cuda.FloatTensor)
else:
self.dis_a = MsImageDis(hyperparameters['input_dim_a'], hyperparameters['dis'])
self.dis_b = MsImageDis(hyperparameters['input_dim_b'], hyperparameters['dis'])
self.instancenorm = nn.InstanceNorm2d(512, affine=False)
# Setup the optimizers
beta1 = hyperparameters['beta1']
beta2 = hyperparameters['beta2']
dis_params = list(self.dis_a.parameters()) + list(self.dis_b.parameters())
gen_params = list(self.gen_a.parameters()) + list(self.gen_b.parameters())
self.dis_opt = torch.optim.Adam([p for p in dis_params if p.requires_grad],
lr=lr, betas=(beta1, beta2), weight_decay=hyperparameters['weight_decay'])
self.gen_opt = torch.optim.Adam([p for p in gen_params if p.requires_grad],
lr=lr, betas=(beta1, beta2), weight_decay=hyperparameters['weight_decay'])
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters)
# Network weight initialization
self.apply(weights_init(hyperparameters['init']))
self.dis_a.apply(weights_init('gaussian'))
self.dis_b.apply(weights_init('gaussian'))
# Load VGG model if needed
if 'vgg_w' in hyperparameters.keys() and hyperparameters['vgg_w'] > 0:
self.vgg = load_vgg16(hyperparameters['vgg_model_path'] + '/models')
self.vgg.eval()
for param in self.vgg.parameters():
param.requires_grad = False
def compute_digits_differnce(self, digits1, digits2, weight=1.0):
feat_diff = 0
feat_weights = 4.0 / (3 + 1) # 3 layers's discrminator
D_weights = 1.0 / 2.0 # number of discrminator
for i in range(2):
for j in range(len(digits2[i])-1):
feat_diff += D_weights * feat_weights * \
F.l1_loss(digits2[i][j],
digits1[i][j].detach()) * weight
return feat_diff
def compute_gan_loss(self, real_digits, fake_digits, gan_cri,
loss_at='None'):
errD = None
errG = None
errG_feat = None
if gan_cri is not None:
if loss_at == 'D':
errD = (gan_cri(real_digits, True) \
+ gan_cri(fake_digits, False)) * 0.5
elif loss_at == 'G':
errG = gan_cri(fake_digits, True)
errG_feat = self.compute_digits_differnce(real_digits, fake_digits,
weight=10.0)
return errD, errG, errG_feat
def recon_criterion(self, input, target):
return torch.mean(torch.abs(input - target))
def forward(self, x_a, x_b):
self.eval()
h_a, _ = self.gen_a.encode(x_a)
h_b, _ = self.gen_b.encode(x_b)
x_ba, _ = self.gen_a.decode(h_b)
x_ab, _ = self.gen_b.decode(h_a)
self.train()
return x_ab, x_ba
def __compute_kl(self, mu):
# def _compute_kl(self, mu, sd):
# mu_2 = torch.pow(mu, 2)
# sd_2 = torch.pow(sd, 2)
# encoding_loss = (mu_2 + sd_2 - torch.log(sd_2)).sum() / mu_2.size(0)
# return encoding_loss
mu_2 = torch.pow(mu, 2)
encoding_loss = torch.mean(mu_2)
return encoding_loss
def gen_update(self, x_a, x_b, hyperparameters):
self.gen_opt.zero_grad()
############ Encode #########################################$
h_a, n_a = self.gen_a.encode(x_a)
h_b, n_b = self.gen_b.encode(x_b)
# decode (within domain)
if hyperparameters['zero_z']:
pre_Z = Variable(torch.zeros(hyperparameters['batch_size'],
hyperparameters['gen']['z_num']).cuda())
else:
pre_Z = None
########### Reconstruction ###################################
x_a_recon, _ = self.gen_a.decode(h_a + n_a, z_var=pre_Z)
x_b_recon, _ = self.gen_b.decode(h_b + n_b, z_var=pre_Z)
##############################################################
########### Decode (Cross Domain) ############################
##############################################################
########## with random vector ################
x_ba, z_var_ba_1 = self.gen_a.decode(h_b + n_b)
x_ab, z_var_ab_1 = self.gen_b.decode(h_a + n_a)
########## with zero latent vector ###########
x_ba_zero, _ = self.gen_a.decode(h_b + n_b, z_var=pre_Z)
x_ab_zero, _ = self.gen_b.decode(h_a + n_a, z_var=pre_Z)
######## decode (cross domain the second time) ################
if hyperparameters['loss_eg_weight'] != 0:
x_ba_eg, z_var_ba_2 = self.gen_a.decode(h_b + n_b)
x_ab_eg, z_var_ab_2 = self.gen_b.decode(h_a + n_a)
x_ba_eg = x_ba_eg.detach()
x_ab_eg = x_ab_eg.detach()
if not hyperparameters['origin']:
x_ba_eg_digits = self.dis_a(x_ba_eg)
x_ab_eg_digits = self.dis_b(x_ab_eg)
# encode again
h_b_recon, n_b_recon = self.gen_a.encode(x_ba_zero)
h_a_recon, n_a_recon = self.gen_b.encode(x_ab_zero)
# decode again (if needed)
x_aba, _ = self.gen_a.decode(h_a_recon + n_a_recon,
z_var=pre_Z
) if hyperparameters['recon_x_cyc_w'] > 0 else (None, 0)
x_bab, _ = self.gen_b.decode(h_b_recon + n_b_recon,
z_var=pre_Z
) if hyperparameters['recon_x_cyc_w'] > 0 else (None, 0)
# reconstruction loss
self.loss_gen_recon_x_a = self.recon_criterion(x_a_recon, x_a)
self.loss_gen_recon_x_b = self.recon_criterion(x_b_recon, x_b)
self.loss_gen_recon_kl_a = self.__compute_kl(h_a)
self.loss_gen_recon_kl_b = self.__compute_kl(h_b)
self.loss_gen_cyc_x_a = self.recon_criterion(x_aba, x_a) if x_aba is not None else 0
self.loss_gen_cyc_x_b = self.recon_criterion(x_bab, x_b) if x_bab is not None else 0
self.loss_gen_recon_kl_cyc_aba = self.__compute_kl(h_a_recon)
self.loss_gen_recon_kl_cyc_bab = self.__compute_kl(h_b_recon)
if hyperparameters['loss_eg_weight'] == 0:
self.loss_gen_adv_a, self.loss_gen_adv_b, self.loss_gan_feat_a, \
self.loss_gan_feat_b = 0, 0, 0, 0
elif not hyperparameters['origin']:
x_ba_digits = self.dis_a(x_ba)
x_a_digits = self.dis_a(x_a)
_, self.loss_gen_adv_a, self.loss_gan_feat_a = \
self.compute_gan_loss(x_a_digits, x_ba_digits,
self.criterionGAN, loss_at='G')
x_ab_digits = self.dis_a(x_ab)
x_b_digits = self.dis_a(x_b)
_, self.loss_gen_adv_b, self.loss_gan_feat_b = \
self.compute_gan_loss(x_b_digits, x_ab_digits,
self.criterionGAN, loss_at='G')
else:
self.loss_gen_adv_a = self.dis_a.calc_gen_loss(x_ba)
self.loss_gen_adv_b = self.dis_b.calc_gen_loss(x_ab)
self.loss_gan_feat_a, self.loss_gan_feat_b = 0, 0
# GAN loss
# self.loss_gen_adv_a = self.dis_a.calc_gen_loss(x_ba)
# self.loss_gen_adv_b = self.dis_b.calc_gen_loss(x_ab)
# domain-invariant perceptual loss
self.loss_gen_vgg_a = self.compute_vgg_loss(self.vgg, x_ba, x_b) if hyperparameters['vgg_w'] > 0 else 0
self.loss_gen_vgg_b = self.compute_vgg_loss(self.vgg, x_ab, x_a) if hyperparameters['vgg_w'] > 0 else 0
if hyperparameters['loss_eg_weight'] == 0:
self.loss_eg = 0.0
elif not hyperparameters['origin']:
self.loss_eg = compute_eg_loss(x_ba_digits, x_ba_eg_digits,
x_ab_digits, x_ab_eg_digits, z_var_ba_1, z_var_ba_2,
z_var_ab_1, z_var_ab_2, hyperparameters)
else:
self.loss_eg = compute_eg_loss(x_ba, x_ba_eg,
x_ab, x_ab_eg, z_var_ba_1, z_var_ba_2, z_var_ab_1, z_var_ab_2,
hyperparameters)
# total loss
self.loss_gen_total = hyperparameters['gan_w'] * self.loss_gen_adv_a + \
hyperparameters['gan_w'] * self.loss_gen_adv_b + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_a + \
hyperparameters['recon_kl_w'] * self.loss_gen_recon_kl_a + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_b + \
hyperparameters['recon_kl_w'] * self.loss_gen_recon_kl_b + \
hyperparameters['recon_x_cyc_w'] * self.loss_gen_cyc_x_a + \
hyperparameters['recon_kl_cyc_w'] * self.loss_gen_recon_kl_cyc_aba + \
hyperparameters['recon_x_cyc_w'] * self.loss_gen_cyc_x_b + \
hyperparameters['recon_kl_cyc_w'] * self.loss_gen_recon_kl_cyc_bab + \
hyperparameters['vgg_w'] * self.loss_gen_vgg_a + \
self.loss_gan_feat_b + self.loss_gan_feat_a + \
hyperparameters['vgg_w'] * self.loss_gen_vgg_b + \
hyperparameters['loss_eg_weight'] * self.loss_eg
self.loss_gen_total.backward()
self.gen_opt.step()
def compute_vgg_loss(self, vgg, img, target):
img_vgg = vgg_preprocess(img)
target_vgg = vgg_preprocess(target)
img_fea = vgg(img_vgg)
target_fea = vgg(target_vgg)
return torch.mean((self.instancenorm(img_fea) - self.instancenorm(target_fea)) ** 2)
def sample(self, x_a, x_b):
self.eval()
x_a_recon, x_b_recon, x_ba, x_ab = [], [], [], []
for i in range(x_a.size(0)):
h_a, _ = self.gen_a.encode(x_a[i].unsqueeze(0))
h_b, _ = self.gen_b.encode(x_b[i].unsqueeze(0))
x_a_recon.append(self.gen_a.decode(h_a)[0])
x_b_recon.append(self.gen_b.decode(h_b)[0])
x_ba.append(self.gen_a.decode(h_b)[0])
x_ab.append(self.gen_b.decode(h_a)[0])
x_a_recon, x_b_recon = torch.cat(x_a_recon), torch.cat(x_b_recon)
x_ba = torch.cat(x_ba)
x_ab = torch.cat(x_ab)
self.train()
return x_a, x_a_recon, x_ab, x_b, x_b_recon, x_ba
def dis_update(self, x_a, x_b, hyperparameters):
self.dis_opt.zero_grad()
# encode
h_a, n_a = self.gen_a.encode(x_a)
h_b, n_b = self.gen_b.encode(x_b)
# decode (cross domain)
x_ba, _ = self.gen_a.decode(h_b + n_b)
x_ab, _ = self.gen_b.decode(h_a + n_a)
# D loss
if not hyperparameters['origin']:
real_digits_a = self.dis_a(x_a)
fake_digits_a = self.dis_a(x_ba.detach())
real_digits_b = self.dis_b(x_b)
fake_digits_b = self.dis_b(x_ab.detach())
self.loss_dis_a, _, _ = self.compute_gan_loss(real_digits_a, fake_digits_a,
self.criterionGAN, loss_at='D')
self.loss_dis_b, _, _ = self.compute_gan_loss(real_digits_b, fake_digits_b,
self.criterionGAN, loss_at='D')
else:
self.loss_dis_a = self.dis_a.calc_dis_loss(x_ba.detach(), x_a)
self.loss_dis_b = self.dis_b.calc_dis_loss(x_ab.detach(), x_b)
self.loss_dis_total = hyperparameters['gan_w'] * self.loss_dis_a + hyperparameters['gan_w'] * self.loss_dis_b
self.loss_dis_total.backward()
self.dis_opt.step()
def update_learning_rate(self):
if self.dis_scheduler is not None:
self.dis_scheduler.step()
if self.gen_scheduler is not None:
self.gen_scheduler.step()
def resume(self, checkpoint_dir, hyperparameters):
# Load generators
last_model_name = get_model_list(checkpoint_dir, "gen")
state_dict = torch.load(last_model_name)
self.gen_a.load_state_dict(state_dict['a'])
self.gen_b.load_state_dict(state_dict['b'])
iterations = int(last_model_name[-11:-3])
# Load discriminators
last_model_name = get_model_list(checkpoint_dir, "dis")
state_dict = torch.load(last_model_name)
self.dis_a.load_state_dict(state_dict['a'])
self.dis_b.load_state_dict(state_dict['b'])
# Load optimizers
state_dict = torch.load(os.path.join(checkpoint_dir, 'optimizer.pt'))
self.dis_opt.load_state_dict(state_dict['dis'])
self.gen_opt.load_state_dict(state_dict['gen'])
# Reinitilize schedulers
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters, iterations)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters, iterations)
print('Resume from iteration %d' % iterations)
return iterations
def save(self, snapshot_dir, iterations):
# Save generators, discriminators, and optimizers
gen_name = os.path.join(snapshot_dir, 'gen_%08d.pt' % (iterations + 1))
dis_name = os.path.join(snapshot_dir, 'dis_%08d.pt' % (iterations + 1))
opt_name = os.path.join(snapshot_dir, 'optimizer.pt')
torch.save({'a': self.gen_a.state_dict(), 'b': self.gen_b.state_dict()}, gen_name)
torch.save({'a': self.dis_a.state_dict(), 'b': self.dis_b.state_dict()}, dis_name)
torch.save({'gen': self.gen_opt.state_dict(), 'dis': self.dis_opt.state_dict()}, opt_name)
class UNIT_Trainer(nn.Module):
def __init__(self, hyperparameters, resume_epoch=-1, snapshot_dir=None):
super(UNIT_Trainer, self).__init__()
lr = hyperparameters['lr']
# Initiate the networks.
self.gen = VAEGen(
hyperparameters['input_dim'] + hyperparameters['n_datasets'],
hyperparameters['gen'],
hyperparameters['n_datasets']) # Auto-encoder for domain a.
self.dis = MsImageDis(
hyperparameters['input_dim'] + hyperparameters['n_datasets'],
hyperparameters['dis']) # Discriminator for domain a.
self.instancenorm = nn.InstanceNorm2d(512, affine=False)
self.sup = UNet(input_channels=hyperparameters['input_dim'],
num_classes=2).cuda()
# Setup the optimizers.
beta1 = hyperparameters['beta1']
beta2 = hyperparameters['beta2']
dis_params = list(self.dis.parameters())
gen_params = list(self.gen.parameters()) + list(self.sup.parameters())
self.dis_opt = torch.optim.Adam(
[p for p in dis_params if p.requires_grad],
lr=lr,
betas=(beta1, beta2),
weight_decay=hyperparameters['weight_decay'])
self.gen_opt = torch.optim.Adam(
[p for p in gen_params if p.requires_grad],
lr=lr,
betas=(beta1, beta2),
weight_decay=hyperparameters['weight_decay'])
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters)
# Network weight initialization.
self.apply(weights_init(hyperparameters['init']))
self.dis.apply(weights_init('gaussian'))
# Presetting one hot encoding vectors.
self.one_hot_img = torch.zeros(hyperparameters['n_datasets'],
hyperparameters['batch_size'],
hyperparameters['n_datasets'], 256,
256).cuda()
self.one_hot_h = torch.zeros(hyperparameters['n_datasets'],
hyperparameters['batch_size'],
hyperparameters['n_datasets'], 64,
64).cuda()
for i in range(hyperparameters['n_datasets']):
self.one_hot_img[i, :, i, :, :].fill_(1)
self.one_hot_h[i, :, i, :, :].fill_(1)
if resume_epoch != -1:
self.resume(snapshot_dir, hyperparameters)
def recon_criterion(self, input, target):
return torch.mean(torch.abs(input - target))
def semi_criterion(self, input, target):
loss = CrossEntropyLoss2d(size_average=False).cuda()
return loss(input, target)
def forward(self, x_a, x_b):
self.eval()
x_a.volatile = True
x_b.volatile = True
h_a, _ = self.gen_a.encode(x_a)
h_b, _ = self.gen_b.encode(x_b)
x_ba = self.gen_a.decode(h_b)
x_ab = self.gen_b.decode(h_a)
self.train()
return x_ab, x_ba
def __compute_kl(self, mu):
# def _compute_kl(self, mu, sd):
# mu_2 = torch.pow(mu, 2)
# sd_2 = torch.pow(sd, 2)
# encoding_loss = (mu_2 + sd_2 - torch.log(sd_2)).sum() / mu_2.size(0)
# return encoding_loss
mu_2 = torch.pow(mu, 2)
encoding_loss = torch.mean(mu_2)
return encoding_loss
def set_gen_trainable(self, train_bool):
if train_bool:
self.gen.train()
for param in self.gen.parameters():
param.requires_grad = True
else:
self.gen.eval()
for param in self.gen.parameters():
param.requires_grad = True
def set_sup_trainable(self, train_bool):
if train_bool:
self.sup.train()
for param in self.sup.parameters():
param.requires_grad = True
else:
self.sup.eval()
for param in self.sup.parameters():
param.requires_grad = True
def sup_update(self, x_a, x_b, y_a, y_b, d_index_a, d_index_b, use_a,
use_b, hyperparameters):
self.gen_opt.zero_grad()
# Encode.
one_hot_x_a = torch.cat([x_a, self.one_hot_img[d_index_a]], 1)
one_hot_x_b = torch.cat([x_b, self.one_hot_img[d_index_b]], 1)
h_a, n_a = self.gen.encode(one_hot_x_a)
h_b, n_b = self.gen.encode(one_hot_x_b)
# Decode (within domain).
one_hot_h_a = torch.cat([h_a + n_a, self.one_hot_h[d_index_a]], 1)
one_hot_h_b = torch.cat([h_b + n_b, self.one_hot_h[d_index_b]], 1)
x_a_recon = self.gen.decode(one_hot_h_a)
x_b_recon = self.gen.decode(one_hot_h_b)
# Decode (cross domain).
one_hot_h_ab = torch.cat([h_a + n_a, self.one_hot_h[d_index_b]], 1)
one_hot_h_ba = torch.cat([h_b + n_b, self.one_hot_h[d_index_a]], 1)
x_ba = self.gen.decode(one_hot_h_ba)
x_ab = self.gen.decode(one_hot_h_ab)
# Encode again.
one_hot_x_ba = torch.cat([x_ba, self.one_hot_img[d_index_a]], 1)
one_hot_x_ab = torch.cat([x_ab, self.one_hot_img[d_index_b]], 1)
h_b_recon, n_b_recon = self.gen.encode(one_hot_x_ba)
h_a_recon, n_a_recon = self.gen.encode(one_hot_x_ab)
# Decode again (if needed).
one_hot_h_a_recon = torch.cat(
[h_a_recon + n_a_recon, self.one_hot_h[d_index_a]], 1)
one_hot_h_b_recon = torch.cat(
[h_b_recon + n_b_recon, self.one_hot_h[d_index_b]], 1)
x_aba = self.gen.decode(
one_hot_h_a_recon
) if hyperparameters['recon_x_cyc_w'] > 0 else None
x_bab = self.gen.decode(
one_hot_h_b_recon
) if hyperparameters['recon_x_cyc_w'] > 0 else None
# Forwarding through supervised model.
p_a = None
p_b = None
loss_semi_a = None
loss_semi_b = None
has_a_label = (h_a[use_a, :, :, :].size(0) != 0)
if has_a_label:
p_a = self.sup(h_a, use_a, True)
p_a_recon = self.sup(h_a_recon, use_a, True)
loss_semi_a = self.semi_criterion(p_a, y_a[use_a, :, :]) + \
self.semi_criterion(p_a_recon, y_a[use_a, :, :])
has_b_label = (h_b[use_b, :, :, :].size(0) != 0)
if has_b_label:
p_b = self.sup(h_b, use_b, True)
p_b_recon = self.sup(h_b, use_b, True)
loss_semi_b = self.semi_criterion(p_b, y_b[use_b, :, :]) + \
self.semi_criterion(p_b_recon, y_b[use_b, :, :])
self.loss_gen_total = None
if loss_semi_a is not None and loss_semi_b is not None:
self.loss_gen_total = loss_semi_a + loss_semi_b
elif loss_semi_a is not None:
self.loss_gen_total = loss_semi_a
elif loss_semi_b is not None:
self.loss_gen_total = loss_semi_b
if self.loss_gen_total is not None:
self.loss_gen_total.backward()
self.gen_opt.step()
def sup_forward(self, x, y, d_index, hyperparameters):
self.sup.eval()
# Encoding content image.
one_hot_x = torch.cat([x, self.one_hot_img[d_index, 0].unsqueeze(0)],
1)
hidden, _ = self.gen.encode(one_hot_x)
# Forwarding on supervised model.
y_pred = self.sup(hidden, only_prediction=True)
# Computing metrics.
pred = y_pred.data.max(1)[1].squeeze_(1).squeeze_(0).cpu().numpy()
jacc = jaccard(pred, y.cpu().squeeze(0).numpy())
return jacc, pred, hidden
def gen_update(self, x_a, x_b, d_index_a, d_index_b, hyperparameters):
self.gen_opt.zero_grad()
# Encode.
one_hot_x_a = torch.cat([x_a, self.one_hot_img[d_index_a]], 1)
one_hot_x_b = torch.cat([x_b, self.one_hot_img[d_index_b]], 1)
h_a, n_a = self.gen.encode(one_hot_x_a)
h_b, n_b = self.gen.encode(one_hot_x_b)
# Decode (within domain).
one_hot_h_a = torch.cat([h_a + n_a, self.one_hot_h[d_index_a]], 1)
one_hot_h_b = torch.cat([h_b + n_b, self.one_hot_h[d_index_b]], 1)
x_a_recon = self.gen.decode(one_hot_h_a)
x_b_recon = self.gen.decode(one_hot_h_b)
# Decode (cross domain).
one_hot_h_ab = torch.cat([h_a + n_a, self.one_hot_h[d_index_b]], 1)
one_hot_h_ba = torch.cat([h_b + n_b, self.one_hot_h[d_index_a]], 1)
x_ba = self.gen.decode(one_hot_h_ba)
x_ab = self.gen.decode(one_hot_h_ab)
# Encode again.
one_hot_x_ba = torch.cat([x_ba, self.one_hot_img[d_index_a]], 1)
one_hot_x_ab = torch.cat([x_ab, self.one_hot_img[d_index_b]], 1)
h_b_recon, n_b_recon = self.gen.encode(one_hot_x_ba)
h_a_recon, n_a_recon = self.gen.encode(one_hot_x_ab)
# Decode again (if needed).
one_hot_h_a_recon = torch.cat(
[h_a_recon + n_a_recon, self.one_hot_h[d_index_a]], 1)
one_hot_h_b_recon = torch.cat(
[h_b_recon + n_b_recon, self.one_hot_h[d_index_b]], 1)
x_aba = self.gen.decode(
one_hot_h_a_recon
) if hyperparameters['recon_x_cyc_w'] > 0 else None
x_bab = self.gen.decode(
one_hot_h_b_recon
) if hyperparameters['recon_x_cyc_w'] > 0 else None
# Reconstruction loss.
self.loss_gen_recon_x_a = self.recon_criterion(x_a_recon, x_a)
self.loss_gen_recon_x_b = self.recon_criterion(x_b_recon, x_b)
self.loss_gen_recon_kl_a = self.__compute_kl(h_a)
self.loss_gen_recon_kl_b = self.__compute_kl(h_b)
self.loss_gen_cyc_x_a = self.recon_criterion(x_aba, x_a)
self.loss_gen_cyc_x_b = self.recon_criterion(x_bab, x_b)
self.loss_gen_recon_kl_cyc_aba = self.__compute_kl(h_a_recon)
self.loss_gen_recon_kl_cyc_bab = self.__compute_kl(h_b_recon)
# GAN loss.
self.loss_gen_adv_a = self.dis.calc_gen_loss(one_hot_x_ba)
self.loss_gen_adv_b = self.dis.calc_gen_loss(one_hot_x_ab)
# Total loss.
self.loss_gen_total = hyperparameters['gan_w'] * self.loss_gen_adv_a + \
hyperparameters['gan_w'] * self.loss_gen_adv_b + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_a + \
hyperparameters['recon_kl_w'] * self.loss_gen_recon_kl_a + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_b + \
hyperparameters['recon_kl_w'] * self.loss_gen_recon_kl_b + \
hyperparameters['recon_x_cyc_w'] * self.loss_gen_cyc_x_a + \
hyperparameters['recon_kl_cyc_w'] * self.loss_gen_recon_kl_cyc_aba + \
hyperparameters['recon_x_cyc_w'] * self.loss_gen_cyc_x_b + \
hyperparameters['recon_kl_cyc_w'] * self.loss_gen_recon_kl_cyc_bab
self.loss_gen_total.backward()
self.gen_opt.step()
def sample(self, x_a, x_b):
self.eval()
x_a.volatile = True
x_b.volatile = True
x_a_recon, x_b_recon, x_ba, x_ab = [], [], [], []
for i in range(x_a.size(0)):
h_a, _ = self.gen_a.encode(x_a[i].unsqueeze(0))
h_b, _ = self.gen_b.encode(x_b[i].unsqueeze(0))
x_a_recon.append(self.gen_a.decode(h_a))
x_b_recon.append(self.gen_b.decode(h_b))
x_ba.append(self.gen_a.decode(h_b))
x_ab.append(self.gen_b.decode(h_a))
x_a_recon, x_b_recon = torch.cat(x_a_recon), torch.cat(x_b_recon)
x_ba = torch.cat(x_ba)
x_ab = torch.cat(x_ab)
self.train()
return x_a, x_a_recon, x_ab, x_b, x_b_recon, x_ba
def dis_update(self, x_a, x_b, d_index_a, d_index_b, hyperparameters):
self.dis_opt.zero_grad()
# Encode.
one_hot_x_a = torch.cat([x_a, self.one_hot_img[d_index_a]], 1)
one_hot_x_b = torch.cat([x_b, self.one_hot_img[d_index_b]], 1)
h_a, n_a = self.gen.encode(one_hot_x_a)
h_b, n_b = self.gen.encode(one_hot_x_b)
# Decode (cross domain).
one_hot_h_ab = torch.cat([h_a + n_a, self.one_hot_h[d_index_b]], 1)
one_hot_h_ba = torch.cat([h_b + n_b, self.one_hot_h[d_index_a]], 1)
x_ba = self.gen.decode(one_hot_h_ba)
x_ab = self.gen.decode(one_hot_h_ab)
# D loss.
one_hot_x_ba = torch.cat([x_ba, self.one_hot_img[d_index_a]], 1)
one_hot_x_ab = torch.cat([x_ab, self.one_hot_img[d_index_b]], 1)
self.loss_dis_a = self.dis.calc_dis_loss(one_hot_x_ba.detach(),
one_hot_x_a)
self.loss_dis_b = self.dis.calc_dis_loss(one_hot_x_ab.detach(),
one_hot_x_b)
self.loss_dis_total = hyperparameters['gan_w'] * self.loss_dis_a + \
hyperparameters['gan_w'] * self.loss_dis_b
self.loss_dis_total.backward()
self.dis_opt.step()
def update_learning_rate(self):
if self.dis_scheduler is not None:
self.dis_scheduler.step()
if self.gen_scheduler is not None:
self.gen_scheduler.step()
def resume(self, checkpoint_dir, hyperparameters):
# Load generators.
last_model_name = get_model_list(checkpoint_dir, "gen")
state_dict = torch.load(last_model_name)
self.gen.load_state_dict(state_dict)
epochs = int(last_model_name[-11:-3])
# Load discriminators.
last_model_name = get_model_list(checkpoint_dir, "dis")
state_dict = torch.load(last_model_name)
self.dis.load_state_dict(state_dict)
# Load supervised model.
last_model_name = get_model_list(checkpoint_dir, "sup")
state_dict = torch.load(last_model_name)
self.sup.load_state_dict(state_dict)
# Load optimizers.
last_model_name = get_model_list(checkpoint_dir, "opt")
state_dict = torch.load(last_model_name)
self.dis_opt.load_state_dict(state_dict['dis'])
self.gen_opt.load_state_dict(state_dict['gen'])
for state in self.dis_opt.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
for state in self.gen_opt.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
# Reinitilize schedulers.
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters,
epochs)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters,
epochs)
print('Resume from iteration %d' % epochs)
return epochs
def save(self, snapshot_dir, epoch):
# Save generators, discriminators, and optimizers.
gen_name = os.path.join(snapshot_dir, 'gen_%08d.pt' % epoch)
dis_name = os.path.join(snapshot_dir, 'dis_%08d.pt' % epoch)
sup_name = os.path.join(snapshot_dir, 'sup_%08d.pt' % epoch)
opt_name = os.path.join(snapshot_dir, 'opt_%08d.pt' % epoch)
torch.save(self.gen.state_dict(), gen_name)
torch.save(self.dis.state_dict(), dis_name)
torch.save(self.sup.state_dict(), sup_name)
torch.save(
{
'dis': self.dis_opt.state_dict(),
'gen': self.gen_opt.state_dict()
}, opt_name)