def main():
net = UNet(dtype, image_size=opt.image_size).type(dtype)
print(net)
if opt.test:
runTest(net)
return
physical_loss = PhysicalLoss()
optimizer = optim.Adam(net.parameters(), lr=opt.learning_rate)
fixed_sample_0, fixed_solution_0, fixed_sample_1, fixed_solution_1 = makeSamples(
opt.image_size)
data = torch.zeros(opt.batch_size, 1, opt.image_size, opt.image_size)
print("Training Started")
for epoch in range(opt.epochs):
mean_loss = 0
for sample in range(opt.epoch_size):
data[:, :, :, 0] = np.random.uniform(100)
data[:, :, 0, :] = np.random.uniform(100)
data[:, :, :, -1] = np.random.uniform(100)
data[:, :, -1, :] = np.random.uniform(100)
img = Variable(data).type(dtype)
output = net(img)
loss = physical_loss(output)
optimizer.zero_grad()
loss.backward()
optimizer.step()
mean_loss += loss.data[0]
mean_loss /= opt.epoch_size
print('epoch [{}/{}], loss:{:.4f}'.format(epoch + 1, opt.epochs,
mean_loss))
plotSamples(fixed_solution_0, net(fixed_sample_0), fixed_solution_1,
net(fixed_sample_1), opt.experiment, epoch)
# checkpoint networks
if epoch + 1 % 50 == 0:
torch.save(net.state_dict(),
'%s/net_epoch_%d.pth' % (opt.experiment, epoch + 1))
print("Training Complete")
torch.save(net.state_dict(),
'%s/net_epoch_%d.pth' % (opt.experiment, epoch + 1))
print("Network Weights Saved in %s" % opt.experiment)
train_loader, test_loader = load_train_test(data, valid_size=0.01)
print("The size of training examples is : {}".format(len(
train_loader.dataset)))
print("The size of testing examples is : {}".format(len(test_loader.dataset)))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
model = UNet(num_class).to(device)
summary(model, (1, 512, 512))
d = open('sample.json', 'w+')
# Observe that all parameters are being optimized
optimizer = optim.Adam(model.parameters(), lr=4e-4)
#model.load_state_dict(torch.load("../outputs5/checkpoints/ckpt_0_31.pth"))
transforms1 = transforms.Compose(
[transforms.Resize((512, 512)),
transforms.ToTensor()])
test_data = SegDataset1(test_dirs,
mask_dir=mask_dirs1,
transform=transforms1,
train=False)
t_dataloader = DataLoader(test_data,
batch_size=1,
shuffle=False,
pin_memory=False)
print(len(test_data))
data = SegDataset("train_data", transform=train_transforms)
#print("THe size of the complete dataset is {}".format(data.__len__()))
train_loader, test_loader = load_train_test(data, valid_size=0.01)
#print("The size of training examples is : {}".format(len(train_loader.dataset)))
#print("The size of testing examples is : {}".format(len(test_loader.dataset)))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
model = UNet(num_class).to(device)
#summary(model,(1,512,512))
# d = open('sample.json', 'w+')
optimizer = optim.Adam(model.parameters(), lr=0.0001)
#def weights_init(m):
# if isinstance(m, nn.Conv2d):
# xavier_uniform(m.weight.data)
# xavier_uniform(m.bias.data)
#model.apply(weights_init)
model.load_state_dict(torch.load("../outputs12/checkpoints/ckpt_0_0.pth"))
transforms1 = transforms.Compose([transforms.ToTensor()])
#print(torch.load("../outputs4/checkpoints/ckpt_0_160.pth").keys())
#pretrained_dict = torch.load("../outputs4/checkpoints/ckpt_0_160.pth")
#model_dict = model.state_dict()
#print("M", model_dict.keys())
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)
class MUNIT_Trainer(nn.Module):
def __init__(self, hyperparameters, resume_epoch=-1, snapshot_dir=None):
super(MUNIT_Trainer, self).__init__()
lr = hyperparameters['lr']
# Initiate the networks.
self.gen = AdaINGen(
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.style_dim = hyperparameters['gen']['style_dim']
self.beta1 = hyperparameters['beta1']
self.beta2 = hyperparameters['beta2']
self.weight_decay = hyperparameters['weight_decay']
# Initiating and loader pretrained UNet.
self.sup = UNet(input_channels=hyperparameters['input_dim'],
num_classes=2).cuda()
# Fix the noise used in sampling.
self.s_a = torch.randn(8, self.style_dim, 1, 1).cuda()
self.s_b = torch.randn(8, self.style_dim, 1, 1).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=(self.beta1, self.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=(self.beta1, self.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_c = 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_c[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
s_a = Variable(self.s_a, volatile=True)
s_b = Variable(self.s_b, volatile=True)
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)
c_a, s_a_fake = self.gen.encode(one_hot_x_a)
c_b, s_b_fake = self.gen.encode(one_hot_x_b)
one_hot_c_b = torch.cat([c_b, self.one_hot_c[d_index_a]], 1)
one_hot_c_a = torch.cat([c_a, self.one_hot_c[d_index_b]], 1)
x_ba = self.gen.decode(one_hot_c_b, s_a)
x_ab = self.gen.decode(one_hot_c_a, s_b)
self.train()
return x_ab, x_ba
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()
s_a = Variable(torch.randn(x_a.size(0), self.style_dim, 1, 1).cuda())
s_b = Variable(torch.randn(x_b.size(0), self.style_dim, 1, 1).cuda())
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)
# Encode.
c_a, s_a_prime = self.gen.encode(one_hot_x_a)
c_b, s_b_prime = self.gen.encode(one_hot_x_b)
# Decode (within domain).
one_hot_c_a = torch.cat([c_a, self.one_hot_c[d_index_a]], 1)
one_hot_c_b = torch.cat([c_b, self.one_hot_c[d_index_b]], 1)
x_a_recon = self.gen.decode(one_hot_c_a, s_a_prime)
x_b_recon = self.gen.decode(one_hot_c_b, s_b_prime)
# Decode (cross domain).
one_hot_c_ab = torch.cat([c_a, self.one_hot_c[d_index_b]], 1)
one_hot_c_ba = torch.cat([c_b, self.one_hot_c[d_index_a]], 1)
x_ba = self.gen.decode(one_hot_c_ba, s_a)
x_ab = self.gen.decode(one_hot_c_ab, s_b)
# 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)
c_b_recon, s_a_recon = self.gen.encode(one_hot_x_ba)
c_a_recon, s_b_recon = self.gen.encode(one_hot_x_ab)
# Forwarding through supervised model.
p_a = None
p_b = None
loss_semi_a = None
loss_semi_b = None
has_a_label = (c_a[use_a, :, :, :].size(0) != 0)
if has_a_label:
p_a = self.sup(c_a, use_a, True)
p_a_recon = self.sup(c_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 = (c_b[use_b, :, :, :].size(0) != 0)
if has_b_label:
p_b = self.sup(c_b, use_b, True)
p_b_recon = self.sup(c_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)
content, _ = self.gen.encode(one_hot_x)
# Forwarding on supervised model.
y_pred = self.sup(content, 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, content
def gen_update(self, x_a, x_b, d_index_a, d_index_b, hyperparameters):
self.gen_opt.zero_grad()
s_a = Variable(torch.randn(x_a.size(0), self.style_dim, 1, 1).cuda())
s_b = Variable(torch.randn(x_b.size(0), self.style_dim, 1, 1).cuda())
# 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)
c_a, s_a_prime = self.gen.encode(one_hot_x_a)
c_b, s_b_prime = self.gen.encode(one_hot_x_b)
# Decode (within domain).
one_hot_c_a = torch.cat([c_a, self.one_hot_c[d_index_a]], 1)
one_hot_c_b = torch.cat([c_b, self.one_hot_c[d_index_b]], 1)
x_a_recon = self.gen.decode(one_hot_c_a, s_a_prime)
x_b_recon = self.gen.decode(one_hot_c_b, s_b_prime)
# Decode (cross domain).
one_hot_c_ab = torch.cat([c_a, self.one_hot_c[d_index_b]], 1)
one_hot_c_ba = torch.cat([c_b, self.one_hot_c[d_index_a]], 1)
x_ba = self.gen.decode(one_hot_c_ba, s_a)
x_ab = self.gen.decode(one_hot_c_ab, s_b)
# 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)
c_b_recon, s_a_recon = self.gen.encode(one_hot_x_ba)
c_a_recon, s_b_recon = self.gen.encode(one_hot_x_ab)
# Decode again (if needed).
one_hot_c_aba_recon = torch.cat([c_a_recon, self.one_hot_c[d_index_a]],
1)
one_hot_c_bab_recon = torch.cat([c_b_recon, self.one_hot_c[d_index_b]],
1)
x_aba = self.gen.decode(one_hot_c_aba_recon, s_a_prime)
x_bab = self.gen.decode(one_hot_c_bab_recon, s_b_prime)
# 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_s_a = self.recon_criterion(s_a_recon, s_a)
self.loss_gen_recon_s_b = self.recon_criterion(s_b_recon, s_b)
self.loss_gen_recon_c_a = self.recon_criterion(c_a_recon, c_a)
self.loss_gen_recon_c_b = self.recon_criterion(c_b_recon, c_b)
self.loss_gen_cycrecon_x_a = self.recon_criterion(x_aba, x_a)
self.loss_gen_cycrecon_x_b = self.recon_criterion(x_bab, x_b)
# 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_s_w'] * self.loss_gen_recon_s_a + \
hyperparameters['recon_c_w'] * self.loss_gen_recon_c_a + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_b + \
hyperparameters['recon_s_w'] * self.loss_gen_recon_s_b + \
hyperparameters['recon_c_w'] * self.loss_gen_recon_c_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 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
s_a1 = Variable(self.s_a, volatile=True)
s_b1 = Variable(self.s_b, volatile=True)
s_a2 = Variable(torch.randn(x_a.size(0), self.style_dim, 1, 1).cuda(),
volatile=True)
s_b2 = Variable(torch.randn(x_b.size(0), self.style_dim, 1, 1).cuda(),
volatile=True)
x_a_recon, x_b_recon, x_ba1, x_ba2, x_ab1, x_ab2 = [], [], [], [], [], []
for i in range(x_a.size(0)):
one_hot_x_a = torch.cat(
[x_a[i].unsqueeze(0), self.one_hot_img_a[i].unsqueeze(0)], 1)
one_hot_x_b = torch.cat(
[x_b[i].unsqueeze(0), self.one_hot_img_b[i].unsqueeze(0)], 1)
c_a, s_a_fake = self.gen.encode(one_hot_x_a)
c_b, s_b_fake = self.gen.encode(one_hot_x_b)
x_a_recon.append(self.gen.decode(c_a, s_a_fake))
x_b_recon.append(self.gen.decode(c_b, s_b_fake))
x_ba1.append(self.gen.decode(c_b, s_a1[i].unsqueeze(0)))
x_ba2.append(self.gen.decode(c_b, s_a2[i].unsqueeze(0)))
x_ab1.append(self.gen.decode(c_a, s_b1[i].unsqueeze(0)))
x_ab2.append(self.gen.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, d_index_a, d_index_b, hyperparameters):
self.dis_opt.zero_grad()
s_a = Variable(torch.randn(x_a.size(0), self.style_dim, 1, 1).cuda())
s_b = Variable(torch.randn(x_b.size(0), self.style_dim, 1, 1).cuda())
# 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)
c_a, _ = self.gen.encode(one_hot_x_a)
c_b, _ = self.gen.encode(one_hot_x_b)
one_hot_c_ba = torch.cat([c_b, self.one_hot_c[d_index_a]], 1)
one_hot_c_ab = torch.cat([c_a, self.one_hot_c[d_index_b]], 1)
# Decode (cross domain).
x_ba = self.gen.decode(one_hot_c_ba, s_a)
x_ab = self.gen.decode(one_hot_c_ab, s_b)
# 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, one_hot_x_a)
self.loss_dis_b = self.dis.calc_dis_loss(one_hot_x_ab, 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):
print("--> " + checkpoint_dir)
# Load generator.
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 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 discriminator.
last_model_name = get_model_list(checkpoint_dir, "dis")
state_dict = torch.load(last_model_name)
self.dis.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 epoch %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(
{
'gen': self.gen_opt.state_dict(),
'dis': self.dis_opt.state_dict()
}, opt_name)
class MUNIT_Trainer(nn.Module):
def __init__(self, hyperparameters, resume_epoch=-1, snapshot_dir=None):
super(MUNIT_Trainer, self).__init__()
lr = hyperparameters['lr']
# Initiate the networks.
self.gen = AdaINGen2(
hyperparameters['input_dim'],
hyperparameters['gen']) # Auto-encoder for domain a.
self.dis = NLayerDiscriminator(
hyperparameters['input_dim']) # Discriminator for domain a.
self.dis2 = NLayerDiscriminator(3 * hyperparameters['input_dim'],
n_layers=4)
self.instancenorm = nn.InstanceNorm2d(512, affine=False)
self.style_dim = hyperparameters['gen']['style_dim']
self.beta1 = hyperparameters['beta1']
self.beta2 = hyperparameters['beta2']
self.weight_decay = hyperparameters['weight_decay']
# Initiating and loader pretrained UNet.
self.sup = UNet(input_channels=hyperparameters['input_dim'],
num_classes=3).cuda()
# Fix the noise used in sampling.
self.s_a = torch.randn(8, self.style_dim, 1, 1).cuda()
self.s_b = torch.randn(8, self.style_dim, 1, 1).cuda()
# Setup the optimizers.
beta1 = hyperparameters['beta1']
beta2 = hyperparameters['beta2']
dis_params = list(self.dis.parameters())
dis2_params = list(self.dis2.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=(self.beta1, self.beta2),
weight_decay=hyperparameters['weight_decay'])
self.dis2_opt = torch.optim.Adam(
[p for p in dis2_params if p.requires_grad],
lr=lr,
betas=(self.beta1, self.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=(self.beta1, self.beta2),
weight_decay=hyperparameters['weight_decay'])
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters)
self.dis2_scheduler = get_scheduler(self.dis2_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'))
self.dis2.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_c = 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_c[i, :, i, :, :].fill_(1)
if resume_epoch != -1:
self.resume(snapshot_dir, hyperparameters, resume_epoch)
def recon_criterion(self, input, target):
return torch.mean(torch.abs(input - target))
def semi_criterion(self, input, target):
loss = CrossEntropyLoss2d(size_average=True).cuda()
return loss(input, target)
def forward(self, x_a, x_b):
self.eval()
x_a.volatile = True
x_b.volatile = True
s_a = Variable(self.s_a, volatile=True)
s_b = Variable(self.s_b, volatile=True)
c_a, s_a_fake = self.gen.encode(x_a)
c_b, s_b_fake = self.gen.encode(x_b)
x_ba = self.gen.decode(c_b, s_a)
x_ab = self.gen.decode(c_a, s_b)
self.train()
return x_ab, x_ba
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
##################################################################################
# Mainly adapted from https://github.com/hugo-oliveira/CoDAGANs ##################
##################################################################################
def sup_update(self, x_a, x_b, y_a, y_b, d_index_a, d_index_b, use_a,
use_b, ep, hyperparameters):
self.gen_opt.zero_grad()
# temp_open=hyperparameters['temp_open']
s_a = Variable(torch.randn(x_a.size(0), self.style_dim, 1, 1).cuda())
s_b = Variable(torch.randn(x_b.size(0), self.style_dim, 1, 1).cuda())
c_a, s_a_prime = self.gen.encode(x_a)
c_b, s_b_prime = self.gen.encode(x_b)
x_ba = self.gen.decode(c_b, s_a)
x_ab = self.gen.decode(c_a, s_b)
c_b_recon, s_a_recon = self.gen.encode(x_ba)
c_a_recon, s_b_recon = self.gen.encode(x_ab)
# Forwarding through supervised model.
p_a = None
p_b = None
loss_semi_a = None
loss_semi_b = None
# if temp_open==1:
c_a = c_a.detach()
c_b = c_b.detach()
c_b_recon = c_b_recon.detach()
c_a_recon = c_a_recon.detach()
p_a = self.sup(c_a, use_a, True)
p_a_recon = self.sup(c_a_recon, use_a, True)
p_b = self.sup(c_b, use_a, True)
p_b_recon = self.sup(c_b_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, :, :])
if (ep + 1) > 10:
loss_gen_b = self.dis2.calc_gen_loss(
p_b) + self.dis2.calc_gen_loss(p_b_recon)
else:
loss_gen_b = Variable(torch.tensor(0).cuda(), requires_grad=False)
self.loss_gen_total = None
weight_temp = hyperparameters['weight_temp']
if loss_semi_a is not None:
self.loss_gen_total = hyperparameters[
'recon_x_w'] * loss_semi_a + weight_temp * loss_gen_b
seg_loss = hyperparameters['recon_x_w'] * loss_semi_a
seg_gen_loss = weight_temp * loss_gen_b
if self.loss_gen_total is not None:
self.loss_gen_total.backward()
self.gen_opt.step()
return seg_loss.item(), seg_gen_loss.item()
def sup_forward(self, x, y, d_index, hyperparameters):
self.sup.eval()
# Encoding content image.
content, _ = self.gen.encode(x)
# Forwarding on supervised model.
y_pred = self.sup(content, only_prediction=True)
# Computing metrics.
pred = y_pred.data.max(1)[1].squeeze_(1).squeeze_(0).cpu().numpy()
jacc, jacc_cup = jaccard(pred, y.cpu().squeeze(0).numpy())
return jacc, jacc_cup, pred, content
def gen_update(self, x_a, x_b, d_index_a, d_index_b, hyperparameters):
self.gen_opt.zero_grad()
s_a = Variable(torch.randn(x_a.size(0), self.style_dim, 1, 1).cuda())
s_b = Variable(torch.randn(x_b.size(0), self.style_dim, 1, 1).cuda())
# Encode.
c_a, s_a_prime = self.gen.encode(x_a)
c_b, s_b_prime = self.gen.encode(x_b)
# Decode (within domain).
x_a_recon = self.gen.decode(c_a, s_a_prime)
x_b_recon = self.gen.decode(c_b, s_b_prime)
# Decode (cross domain).
x_ba = self.gen.decode(c_b, s_a)
x_ab = self.gen.decode(c_a, s_b)
# Encode again.
c_b_recon, s_a_recon = self.gen.encode(x_ba)
c_a_recon, s_b_recon = self.gen.encode(x_ab)
# Decode again (if needed).
x_aba = self.gen.decode(c_a_recon, s_a_prime)
x_bab = self.gen.decode(c_b_recon, s_b_prime)
# 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_s_a = self.recon_criterion(s_a_recon, s_a)
self.loss_gen_recon_s_b = self.recon_criterion(s_b_recon, s_b)
self.loss_gen_recon_c_a = self.recon_criterion(c_a_recon, c_a)
self.loss_gen_recon_c_b = self.recon_criterion(c_b_recon, c_b)
self.loss_gen_cycrecon_x_a = self.recon_criterion(x_aba, x_a)
self.loss_gen_cycrecon_x_b = self.recon_criterion(x_bab, x_b)
# GAN loss.
self.loss_gen_adv_a = self.dis.calc_gen_loss(x_ba)
self.loss_gen_adv_b = self.dis.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_s_w'] * self.loss_gen_recon_s_a + \
hyperparameters['recon_c_w'] * self.loss_gen_recon_c_a + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_b + \
hyperparameters['recon_s_w'] * self.loss_gen_recon_s_b + \
hyperparameters['recon_c_w'] * self.loss_gen_recon_c_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()
return self.loss_gen_total.item()
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 dis_update(self, x_a, x_b, d_index_a, d_index_b, hyperparameters):
self.dis_opt.zero_grad()
s_a = Variable(torch.randn(x_a.size(0), self.style_dim, 1, 1).cuda())
s_b = Variable(torch.randn(x_b.size(0), self.style_dim, 1, 1).cuda())
# Encode.
c_a, _ = self.gen.encode(x_a)
c_b, _ = self.gen.encode(x_b)
# Decode (cross domain).
x_ba = self.gen.decode(c_b, s_a)
x_ab = self.gen.decode(c_a, s_b)
# D loss.
self.loss_dis_a = self.dis.calc_dis_loss(x_ba.detach(), x_a)
self.loss_dis_b = self.dis.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()
return self.loss_dis_total.item()
def dis2_update(self, x_a, x_b, d_index_a, d_index_b, use_a, use_b,
hyperparameters):
self.dis2_opt.zero_grad()
s_a = Variable(torch.randn(x_a.size(0), self.style_dim, 1, 1).cuda())
s_b = Variable(torch.randn(x_b.size(0), self.style_dim, 1, 1).cuda())
# Encode.
c_a, s_a_prime = self.gen.encode(x_a)
c_b, s_b_prime = self.gen.encode(x_b)
# Decode (within domain).
x_a_recon = self.gen.decode(c_a, s_a_prime)
x_b_recon = self.gen.decode(c_b, s_b_prime)
# Decode (cross domain).
x_ba = self.gen.decode(c_b, s_a)
x_ab = self.gen.decode(c_a, s_b)
# Encode again.
c_b_recon, s_a_recon = self.gen.encode(x_ba)
c_a_recon, s_b_recon = self.gen.encode(x_ab)
p_b = self.sup(c_b, use_a, True)
p_b_recon = self.sup(c_b_recon, use_a, True)
p_a = self.sup(c_a, use_a, True)
p_a_recon = self.sup(c_a_recon, use_a, True)
self.loss_dis2_b = self.dis2.calc_dis_loss(
p_b.detach(), p_a.detach()) + self.dis2.calc_dis_loss(
p_b_recon.detach(), p_a_recon.detach())
self.loss_dis2_total = hyperparameters['gan_w'] * self.loss_dis2_b
self.loss_dis2_total.backward()
self.dis2_opt.step()
return self.loss_dis2_total.item()
def update_learning_rate(self):
if self.dis_scheduler is not None:
self.dis_scheduler.step()
if self.dis2_scheduler is not None:
self.dis2_scheduler.step()
if self.gen_scheduler is not None:
self.gen_scheduler.step()
def resume(self, checkpoint_dir, hyperparameters, resume_epoch):
print("--> " + checkpoint_dir)
# Load generator.
last_model_name = get_model_list(checkpoint_dir, "gen", resume_epoch)
# print('\n',last_model_name)
state_dict = torch.load(last_model_name)
self.gen.load_state_dict(state_dict)
epochs = int(last_model_name[-11:-3])
# Load supervised model.
last_model_name = get_model_list(checkpoint_dir, "sup", resume_epoch)
state_dict = torch.load(last_model_name)
self.sup.load_state_dict(state_dict)
# Load discriminator.
# last_model_name = get_model_list(checkpoint_dir, "dis", resume_epoch)
# state_dict = torch.load(last_model_name)
# self.dis.load_state_dict(state_dict)
# # Load discriminator2.
# last_model_name = get_model_list(checkpoint_dir, "dis2", resume_epoch)
# state_dict = torch.load(last_model_name)
# self.dis2.load_state_dict(state_dict)
# # Load optimizers.
# last_model_name = get_model_list(checkpoint_dir, "opt", resume_epoch)
# state_dict = torch.load(last_model_name)
# self.dis_opt.load_state_dict(state_dict['dis'])
# self.dis2_opt.load_state_dict(state_dict['dis2'])
# 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.dis2_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.dis2_scheduler = get_scheduler(self.dis2_opt, hyperparameters, epochs)
# self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters, epochs)
# print('Resume from epoch %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)
dis2_name = os.path.join(snapshot_dir, 'dis2_%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.dis2.state_dict(), dis2_name)
torch.save(self.sup.state_dict(), sup_name)
torch.save(
{
'gen': self.gen_opt.state_dict(),
'dis': self.dis_opt.state_dict(),
'dis2': self.dis2_opt.state_dict()
}, opt_name)