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.dis_content = Dis_content()
self.gpuid = hyperparameters['gpuID']
# @ add backgound discriminator for each domain
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.content_opt = torch.optim.Adam(
self.dis_content.parameters(),
lr=lr / 2.,
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)
self.content_scheduler = get_scheduler(self.content_opt,
hyperparameters)
# Network weight initialization
self.gen_a.apply(weights_init(hyperparameters['init']))
self.gen_b.apply(weights_init(hyperparameters['init']))
self.dis_a.apply(weights_init('gaussian'))
self.dis_b.apply(weights_init('gaussian'))
self.dis_content.apply(weights_init('gaussian'))
# initialize the blur network
self.BGBlur_kernel = [5, 9, 15]
self.BlurNet = [
GaussionSmoothLayer(3, k_size, 25).cuda(self.gpuid)
for k_size in self.BGBlur_kernel
]
self.BlurWeight = [0.25, 0.5, 1.]
self.Gradient = GradientLoss(3, 3)
# # Load VGG model if needed for test
if 'vgg_w' in hyperparameters.keys() and hyperparameters['vgg_w'] > 0:
self.vgg = load_vgg19()
if torch.cuda.is_available():
self.vgg.cuda(self.gpuid)
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_cont(x_a)
# h_a_sty = self.gen_a.encode_sty(x_a)
# h_b = self.gen_b.encode_cont(x_b)
x_ab = self.gen_b.decode_cont(h_a)
# h_c = torch.cat((h_b, h_a_sty), 1)
# x_ba = self.gen_a.decode_recs(h_c)
# self.train()
return x_ab #, x_ba
def __compute_kl(self, mu):
# def _compute_kl(self, mu, sd):
mu_2 = torch.pow(mu, 2)
encoding_loss = torch.mean(mu_2)
return encoding_loss
def content_update(self, x_a, x_b, hyperparameters): #
# encode
self.content_opt.zero_grad()
enc_a = self.gen_a.encode_cont(x_a)
enc_b = self.gen_b.encode_cont(x_b)
pred_fake = self.dis_content.forward(enc_a)
pred_real = self.dis_content.forward(enc_b)
loss_D = 0
if hyperparameters['gan_type'] == 'lsgan':
loss_D += torch.mean((pred_fake - 0)**2) + torch.mean(
(pred_real - 1)**2)
elif hyperparameters['gan_type'] == 'nsgan':
all0 = Variable(torch.zeros_like(pred_fake.data).cuda(self.gpuid),
requires_grad=False)
all1 = Variable(torch.ones_like(pred_real.data).cuda(self.gpuid),
requires_grad=False)
loss_D += torch.mean(
F.binary_cross_entropy(F.sigmoid(pred_fake), all0) +
F.binary_cross_entropy(F.sigmoid(pred_real), all1))
else:
assert 0, "Unsupported GAN type: {}".format(
hyperparameters['gan_type'])
loss_D.backward()
nn.utils.clip_grad_norm_(self.dis_content.parameters(), 5)
self.content_opt.step()
def gen_update(self, x_a, x_b, hyperparameters):
self.gen_opt.zero_grad()
self.content_opt.zero_grad()
# encode
h_a = self.gen_a.encode_cont(x_a)
h_b = self.gen_b.encode_cont(x_b)
h_a_sty = self.gen_a.encode_sty(x_a)
# add domain adverisal loss for generator
out_a = self.dis_content(h_a)
out_b = self.dis_content(h_b)
self.loss_ContentD = 0
if hyperparameters['gan_type'] == 'lsgan':
self.loss_ContentD += torch.mean((out_a - 0.5)**2) + torch.mean(
(out_b - 0.5)**2)
elif hyperparameters['gan_type'] == 'nsgan':
all1 = Variable(0.5 * torch.ones_like(out_b.data).cuda(self.gpuid),
requires_grad=False)
self.loss_ContentD += torch.mean(
F.binary_cross_entropy(F.sigmoid(out_a), all1) +
F.binary_cross_entropy(F.sigmoid(out_b), all1))
else:
assert 0, "Unsupported GAN type: {}".format(
hyperparameters['gan_type'])
# decode (within domain)
h_a_cont = torch.cat((h_a, h_a_sty), 1)
noise_a = torch.randn(h_a_cont.size()).cuda(h_a_cont.data.get_device())
x_a_recon = self.gen_a.decode_recs(h_a_cont + noise_a)
noise_b = torch.randn(h_b.size()).cuda(h_b.data.get_device())
x_b_recon = self.gen_b.decode_cont(h_b + noise_b)
# decode (cross domain)
h_ba_cont = torch.cat((h_b, h_a_sty), 1)
x_ba = self.gen_a.decode_recs(h_ba_cont + noise_a)
x_ab = self.gen_b.decode_cont(h_a + noise_b)
# encode again
h_b_recon = self.gen_a.encode_cont(x_ba)
h_b_sty_recon = self.gen_a.encode_sty(x_ba)
h_a_recon = self.gen_b.encode_cont(x_ab)
# decode again (if needed)
h_a_cat_recs = torch.cat((h_a_recon, h_b_sty_recon), 1)
x_aba = self.gen_a.decode_recs(
h_a_cat_recs +
noise_a) if hyperparameters['recon_x_cyc_w'] > 0 else None
x_bab = self.gen_b.decode_cont(
h_b_recon +
noise_b) 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_recon_kl_sty = self.__compute_kl(h_a_sty)
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_aba 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)
self.loss_gen_recon_kl_cyc_sty = self.__compute_kl(h_b_sty_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
# add background guide loss
self.loss_bgm = 0
if hyperparameters['BGM'] != 0:
for index, weight in enumerate(self.BlurWeight):
out_b = self.BlurNet[index](x_ba)
out_real_b = self.BlurNet[index](x_b)
out_a = self.BlurNet[index](x_ab)
out_real_a = self.BlurNet[index](x_a)
grad_loss_b = self.recon_criterion(out_b, out_real_b)
grad_loss_a = self.recon_criterion(out_a, out_real_a)
self.loss_bgm += weight * (grad_loss_a + grad_loss_b)
# 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_kl_w'] * self.loss_gen_recon_kl_sty + \
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['recon_kl_cyc_w'] * self.loss_gen_recon_kl_cyc_sty + \
hyperparameters['vgg_w'] * self.loss_gen_vgg_a + \
hyperparameters['vgg_w'] * self.loss_gen_vgg_b + \
hyperparameters['BGM'] * self.loss_bgm + \
hyperparameters['gan_w'] * self.loss_ContentD
self.loss_gen_total.backward()
self.gen_opt.step()
self.content_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):
if x_a is None or x_b is None:
return None
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_cont(x_a[i].unsqueeze(0))
h_a_sty = self.gen_a.encode_sty(x_a[i].unsqueeze(0))
h_b = self.gen_b.encode_cont(x_b[i].unsqueeze(0))
h_ba_cont = torch.cat((h_b, h_a_sty), 1)
h_aa_cont = torch.cat((h_a, h_a_sty), 1)
x_a_recon.append(self.gen_a.decode_recs(h_aa_cont))
x_b_recon.append(self.gen_b.decode_cont(h_b))
x_ba.append(self.gen_a.decode_recs(h_ba_cont))
x_ab.append(self.gen_b.decode_cont(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()
self.content_opt.zero_grad()
# encode
h_a = self.gen_a.encode_cont(x_a)
h_a_sty = self.gen_a.encode_sty(x_a)
h_b = self.gen_b.encode_cont(x_b)
# # @ add content adversial
out_a = self.dis_content(h_a)
out_b = self.dis_content(h_b)
self.loss_ContentD = 0
if hyperparameters['gan_type'] == 'lsgan':
self.loss_ContentD += torch.mean((out_a - 0)**2) + torch.mean(
(out_b - 1)**2)
elif hyperparameters['gan_type'] == 'nsgan':
all0 = Variable(torch.zeros_like(out_a.data).cuda(self.gpuid),
requires_grad=False)
all1 = Variable(torch.ones_like(out_b.data).cuda(self.gpuid),
requires_grad=False)
self.loss_ContentD += torch.mean(
F.binary_cross_entropy(F.sigmoid(out_a), all0) +
F.binary_cross_entropy(F.sigmoid(out_b), all1))
else:
assert 0, "Unsupported GAN type: {}".format(
hyperparameters['gan_type'])
# decode (cross domain)
h_cat = torch.cat((h_b, h_a_sty), 1)
noise_b = torch.randn(h_cat.size()).cuda(h_cat.data.get_device())
x_ba = self.gen_a.decode_recs(h_cat + noise_b)
noise_a = torch.randn(h_a.size()).cuda(h_a.data.get_device())
x_ab = self.gen_b.decode_cont(h_a + noise_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 + self.loss_dis_b + self.loss_ContentD)
self.loss_dis_total.backward()
nn.utils.clip_grad_norm_(self.dis_content.parameters(),
5) # dis_content update
self.dis_opt.step()
self.content_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()
if self.content_scheduler is not None:
self.content_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_00188000")
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 discontent discriminator
last_model_name = get_model_list(checkpoint_dir, "dis_Content")
state_dict = torch.load(last_model_name)
self.dis_content.load_state_dict(state_dict['dis_c'])
# 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'])
self.content_opt.load_state_dict(state_dict['dis_content'])
# Reinitilize schedulers
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters,
iterations)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters,
iterations)
self.content_scheduler = get_scheduler(self.content_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))
dis_Con_name = os.path.join(snapshot_dir,
'dis_Content_%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({'dis_c': self.dis_content.state_dict()}, dis_Con_name)
# opt state
torch.save({'gen': self.gen_opt.state_dict(), 'dis': self.dis_opt.state_dict(), \
'dis_content':self.content_opt.state_dict()}, opt_name)