def build_model(self):
# self.G = net.Generator()
# 构建两个判别器（二分类器） D_X, D_Y
self.D_A = net.Discriminator(self.img_size, self.d_conv_dim, self.d_repeat_num, self.norm)
self.D_B = net.Discriminator(self.img_size, self.d_conv_dim, self.d_repeat_num, self.norm)
# 初始化网络参数，apply为从nn.module继承
self.G.apply(self.weights_init_xavier)
self.D_A.apply(self.weights_init_xavier)
self.D_B.apply(self.weights_init_xavier)
# 从checkpoint文件中加载网络参数
self.load_checkpoint()
# 循环一致性损失Cycle consistency loss
self.criterionL1 = torch.nn.L1Loss()
# 感知损失Perceptual loss
self.criterionL2 = torch.nn.MSELoss()
if self.device=='cuda':
self.criterionGAN = GANLoss(use_lsgan=True, tensor=torch.cuda.FloatTensor)
else:
self.criterionGAN = GANLoss(use_lsgan=True, tensor=torch.FloatTensor)
self.vgg = net.vgg16(pretrained=True)
# 妆容损失makeup loss
self.criterionHis = HistogramLoss()
# Optimizers 优化器，迭代优化生成器和判别器的参数
self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr, [self.beta1, self.beta2])
self.d_A_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, self.D_A.parameters()), self.d_lr, [self.beta1, self.beta2])
self.d_B_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, self.D_B.parameters()), self.d_lr, [self.beta1, self.beta2])
# Print networks
self.print_network(self.G, 'G')
self.print_network(self.D_A, 'D_A')
self.print_network(self.D_B, 'D_B')
if torch.cuda.is_available():
self.device = "cuda"
if torch.cuda.device_count() > 1:
self.G = nn.DataParallel(self.G)
self.D_A = nn.DataParallel(self.D_A)
self.D_B = nn.DataParallel(self.D_B)
self.vgg = nn.DataParallel(self.vgg)
self.criterionHis = nn.DataParallel(self.criterionHis)
self.criterionGAN = nn.DataParallel(self.criterionGAN)
self.criterionL1 = nn.DataParallel(self.criterionL1)
self.criterionL2 = nn.DataParallel(self.criterionL2)
self.criterionGAN = nn.DataParallel(self.criterionGAN)
self.G.cuda()
self.vgg.cuda()
self.criterionHis.cuda()
self.criterionGAN.cuda()
self.criterionL1.cuda()
self.criterionL2.cuda()
self.D_A.cuda()
self.D_B.cuda()
def build_model(self):
# self.G = net.Generator()
self.D_A = net.Discriminator(self.img_size, self.d_conv_dim,
self.d_repeat_num, self.norm)
self.D_B = net.Discriminator(self.img_size, self.d_conv_dim,
self.d_repeat_num, self.norm)
self.load_checkpoint()
self.criterionL1 = torch.nn.L1Loss()
self.criterionL2 = torch.nn.MSELoss()
self.criterionGAN = GANLoss(use_lsgan=True,
tensor=torch.cuda.FloatTensor)
self.vgg = net.vgg16(pretrained=True)
self.criterionHis = HistogramLoss()
# Optimizers
self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr,
[self.beta1, self.beta2])
self.d_A_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, self.D_A.parameters()),
self.d_lr, [self.beta1, self.beta2])
self.d_B_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, self.D_B.parameters()),
self.d_lr, [self.beta1, self.beta2])
# Print networks
self.print_network(self.G, 'G')
self.print_network(self.D_A, 'D_A')
self.print_network(self.D_B, 'D_B')
if torch.cuda.is_available():
self.device = "cuda"
if torch.cuda.device_count() > 1:
self.G = nn.DataParallel(self.G)
self.D_A = nn.DataParallel(self.D_A)
self.D_B = nn.DataParallel(self.D_B)
self.vgg = nn.DataParallel(self.vgg)
self.criterionHis = nn.DataParallel(self.criterionHis)
self.criterionGAN = nn.DataParallel(self.criterionGAN)
self.criterionL1 = nn.DataParallel(self.criterionL1)
self.criterionL2 = nn.DataParallel(self.criterionL2)
self.criterionGAN = nn.DataParallel(self.criterionGAN)
self.G.cuda()
self.vgg.cuda()
self.criterionHis.cuda()
self.criterionGAN.cuda()
self.criterionL1.cuda()
self.criterionL2.cuda()
self.D_A.cuda()
self.D_B.cuda()
def build_model(self):
# Define generators and discriminators
if self.whichG=='normal':
self.G = net.Generator_makeup(self.g_conv_dim, self.g_repeat_num)
if self.whichG=='branch':
self.G = net.Generator_branch(self.g_conv_dim, self.g_repeat_num)
for i in self.cls:
setattr(self, "D_" + i, net.Discriminator(self.img_size, self.d_conv_dim, self.d_repeat_num, self.norm))
self.criterionL1 = torch.nn.L1Loss()
self.criterionL2 = torch.nn.MSELoss()
self.criterionGAN = GANLoss(use_lsgan=True, tensor =torch.cuda.FloatTensor)
self.vgg=models.vgg16(pretrained=True)
# Optimizers
self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr, [self.beta1, self.beta2])
for i in self.cls:
setattr(self, "d_" + i + "_optimizer", \
torch.optim.Adam(filter(lambda p: p.requires_grad, getattr(self, "D_" + i).parameters()), \
self.d_lr, [self.beta1, self.beta2]))
# Weights initialization
self.G.apply(self.weights_init_xavier)
for i in self.cls:
getattr(self, "D_" + i).apply(self.weights_init_xavier)
if torch.cuda.is_available():
self.G.cuda()
self.vgg.cuda()
for i in self.cls:
getattr(self, "D_" + i).cuda()
def build_model(self):
# Define generators and discriminators
if self.whichG == 'normal':
self.G = net.Generator_makeup(self.g_conv_dim, self.g_repeat_num)
if self.whichG == 'branch':
self.G = net.Generator_branch(self.g_conv_dim, self.g_repeat_num)
for i in self.cls:
setattr(
self, "D_" + i,
net.Discriminator(self.img_size, self.d_conv_dim,
self.d_repeat_num, self.norm))
self.criterionL1 = torch.nn.L1Loss()
self.criterionL2 = torch.nn.MSELoss()
self.criterionGAN = GANLoss(use_lsgan=True,
tensor=torch.cuda.FloatTensor)
self.vgg = net.VGG()
self.vgg.load_state_dict(torch.load('addings/vgg_conv.pth'))
# self.vgg = models.vgg19_bn(pretrained=True)
# Optimizers
self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr,
[self.beta1, self.beta2])
for i in self.cls:
setattr(self, "d_" + i + "_optimizer", \
torch.optim.Adam(filter(lambda p: p.requires_grad, getattr(self, "D_" + i).parameters()), \
self.d_lr, [self.beta1, self.beta2]))
# Weights initialization
self.G.apply(self.weights_init_xavier)
for i in self.cls:
getattr(self, "D_" + i).apply(self.weights_init_xavier)
# Print networks
self.print_network(self.G, 'G')
for i in self.cls:
self.print_network(getattr(self, "D_" + i), "D_" + i)
"""
if torch.cuda.device_count() > 1:
self.G = torch.nn.DataParallel(self.G)
self.vgg = torch.nn.DataParallel(self.vgg)
for i in self.cls:
setattr(self, "D_" + i, torch.nn.DataParallel(getattr(self, "D_" + i)))
self.G.to(self.device)
self.vgg.to(self.device)
for i in self.cls:
getattr(self, "D_" + i).to(self.device)
"""
if torch.cuda.is_available():
self.G.cuda()
self.vgg.cuda()
for i in self.cls:
getattr(self, "D_" + i).cuda()
def build_model(self):
# Define generators and discriminators
self.E = network.Encoder(self.e_conv_dim)
self.G = network.Generator(self.g_conv_dim)
for i in self.cls:
setattr(
self, "D_" + i,
net.Discriminator(self.img_size, self.d_conv_dim,
self.d_repeat_num, self.norm))
# Define vgg for perceptual loss
self.vgg = net.VGG()
self.vgg.load_state_dict(torch.load('addings/vgg_conv.pth'))
# Define loss
self.criterionL1 = torch.nn.L1Loss()
self.criterionL2 = torch.nn.MSELoss()
self.criterionGAN = GANLoss(use_lsgan=True,
tensor=torch.cuda.FloatTensor)
# Optimizers
self.e_optimizer = torch.optim.Adam(self.E.parameters(), self.e_lr,
[self.beta1, self.beta2])
self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr,
[self.beta1, self.beta2])
for i in self.cls:
setattr(self, "d_" + i + "_optimizer", \
torch.optim.Adam(filter(lambda p: p.requires_grad, getattr(self, "D_" + i).parameters()), \
self.d_lr, [self.beta1, self.beta2]))
# Weights initialization
self.E.apply(self.weights_init_xavier)
self.G.apply(self.weights_init_xavier)
for i in self.cls:
getattr(self, "D_" + i).apply(self.weights_init_xavier)
# Print networks
self.print_network(self.E, 'E')
self.print_network(self.G, 'G')
for i in self.cls:
self.print_network(getattr(self, "D_" + i), "D_" + i)
if torch.cuda.is_available():
self.E.cuda()
self.G.cuda()
self.vgg.cuda()
for i in self.cls:
getattr(self, "D_" + i).cuda()
def build_model(self):
# Define generators and discriminators
self.G_A = net.Generator(self.g_conv_dim, self.g_repeat_num)
self.G_B = net.Generator(self.g_conv_dim, self.g_repeat_num)
self.D_A = net.Discriminator(self.img_size, self.d_conv_dim,
self.d_repeat_num)
self.D_B = net.Discriminator(self.img_size, self.d_conv_dim,
self.d_repeat_num)
self.criterionL1 = torch.nn.L1Loss()
self.criterionGAN = GANLoss(use_lsgan=True,
tensor=torch.cuda.FloatTensor)
# Optimizers
self.g_optimizer = torch.optim.Adam(
itertools.chain(self.G_A.parameters(), self.G_B.parameters()),
self.g_lr, [self.beta1, self.beta2])
self.d_A_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, self.D_A.parameters()),
self.d_lr, [self.beta1, self.beta2])
self.d_B_optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, self.D_B.parameters()),
self.d_lr, [self.beta1, self.beta2])
self.G_A.apply(self.weights_init_xavier)
self.D_A.apply(self.weights_init_xavier)
self.G_B.apply(self.weights_init_xavier)
self.D_B.apply(self.weights_init_xavier)
# Print networks
# self.print_network(self.E, 'E')
self.print_network(self.G_A, 'G_A')
self.print_network(self.D_A, 'D_A')
self.print_network(self.G_B, 'G_B')
self.print_network(self.D_B, 'D_B')
if torch.cuda.is_available():
self.G_A.cuda()
self.G_B.cuda()
self.D_A.cuda()
self.D_B.cuda()
class Solver(Track):
def __init__(self, config, device="cpu", data_loader=None, inference=False):
self.G = net.Generator()
if inference:
self.G.load_state_dict(torch.load(inference, map_location=torch.device(device)))
self.G = self.G.to(device).eval()
return
self.start_time = time.time()
self.checkpoint = config.MODEL.WEIGHTS
self.log_path = config.LOG.LOG_PATH
self.result_path = os.path.join(self.log_path, config.LOG.VIS_PATH)
self.snapshot_path = os.path.join(self.log_path, config.LOG.SNAPSHOT_PATH)
self.log_step = config.LOG.LOG_STEP
self.vis_step = config.LOG.VIS_STEP
if device=='cuda':
self.snapshot_step = config.LOG.SNAPSHOT_STEP // torch.cuda.device_count()
else:
self.snapshot_step = config.LOG.SNAPSHOT_STEP // 1
# Data loader
self.data_loader_train = data_loader
self.img_size = config.DATA.IMG_SIZE
self.num_epochs = config.TRAINING.NUM_EPOCHS
self.num_epochs_decay = config.TRAINING.NUM_EPOCHS_DECAY
self.g_lr = config.TRAINING.G_LR
self.d_lr = config.TRAINING.D_LR
self.g_step = config.TRAINING.G_STEP
self.beta1 = config.TRAINING.BETA1
self.beta2 = config.TRAINING.BETA2
self.lambda_idt = config.LOSS.LAMBDA_IDT
self.lambda_A = config.LOSS.LAMBDA_A
self.lambda_B = config.LOSS.LAMBDA_B
self.lambda_his_lip = config.LOSS.LAMBDA_HIS_LIP
self.lambda_his_skin = config.LOSS.LAMBDA_HIS_SKIN
self.lambda_his_eye = config.LOSS.LAMBDA_HIS_EYE
self.lambda_vgg = config.LOSS.LAMBDA_VGG
# Hyper-parameteres
self.d_conv_dim = config.MODEL.D_CONV_DIM
self.d_repeat_num = config.MODEL.D_REPEAT_NUM
self.norm = config.MODEL.NORM
self.device = device
self.build_model()
super(Solver, self).__init__()
# For generator
def weights_init_xavier(self, m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
init.xavier_normal(m.weight.data, gain=1.0)
elif classname.find('Linear') != -1:
init.xavier_normal(m.weight.data, gain=1.0)
def print_network(self, model, name):
num_params = 0
for p in model.parameters():
num_params += p.numel()
print(name)
print(model)
print("The number of parameters: {}".format(num_params))
def de_norm(self, x):
out = (x + 1) / 2
return out.clamp(0, 1)
def build_model(self):
# self.G = net.Generator()
# 构建两个判别器（二分类器） D_X, D_Y
self.D_A = net.Discriminator(self.img_size, self.d_conv_dim, self.d_repeat_num, self.norm)
self.D_B = net.Discriminator(self.img_size, self.d_conv_dim, self.d_repeat_num, self.norm)
# 初始化网络参数，apply为从nn.module继承
self.G.apply(self.weights_init_xavier)
self.D_A.apply(self.weights_init_xavier)
self.D_B.apply(self.weights_init_xavier)
# 从checkpoint文件中加载网络参数
self.load_checkpoint()
# 循环一致性损失Cycle consistency loss
self.criterionL1 = torch.nn.L1Loss()
# 感知损失Perceptual loss
self.criterionL2 = torch.nn.MSELoss()
if self.device=='cuda':
self.criterionGAN = GANLoss(use_lsgan=True, tensor=torch.cuda.FloatTensor)
else:
self.criterionGAN = GANLoss(use_lsgan=True, tensor=torch.FloatTensor)
self.vgg = net.vgg16(pretrained=True)
# 妆容损失makeup loss
self.criterionHis = HistogramLoss()
# Optimizers 优化器，迭代优化生成器和判别器的参数
self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr, [self.beta1, self.beta2])
self.d_A_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, self.D_A.parameters()), self.d_lr, [self.beta1, self.beta2])
self.d_B_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, self.D_B.parameters()), self.d_lr, [self.beta1, self.beta2])
# Print networks
self.print_network(self.G, 'G')
self.print_network(self.D_A, 'D_A')
self.print_network(self.D_B, 'D_B')
if torch.cuda.is_available():
self.device = "cuda"
if torch.cuda.device_count() > 1:
self.G = nn.DataParallel(self.G)
self.D_A = nn.DataParallel(self.D_A)
self.D_B = nn.DataParallel(self.D_B)
self.vgg = nn.DataParallel(self.vgg)
self.criterionHis = nn.DataParallel(self.criterionHis)
self.criterionGAN = nn.DataParallel(self.criterionGAN)
self.criterionL1 = nn.DataParallel(self.criterionL1)
self.criterionL2 = nn.DataParallel(self.criterionL2)
self.criterionGAN = nn.DataParallel(self.criterionGAN)
self.G.cuda()
self.vgg.cuda()
self.criterionHis.cuda()
self.criterionGAN.cuda()
self.criterionL1.cuda()
self.criterionL2.cuda()
self.D_A.cuda()
self.D_B.cuda()
def load_checkpoint(self):
G_path = os.path.join(self.checkpoint, 'G.pth')
if os.path.exists(G_path):
self.G.load_state_dict(torch.load(G_path, map_location=torch.device(self.device)))
print('loaded trained generator {}..!'.format(G_path))
D_A_path = os.path.join(self.checkpoint, 'D_A.pth')
if os.path.exists(D_A_path):
self.D_A.load_state_dict(torch.load(D_A_path, map_location=torch.device(self.device)))
print('loaded trained discriminator A {}..!'.format(D_A_path))
D_B_path = os.path.join(self.checkpoint, 'D_B.pth')
if os.path.exists(D_B_path):
self.D_B.load_state_dict(torch.load(D_B_path, map_location=torch.device(self.device)))
print('loaded trained discriminator B {}..!'.format(D_B_path))
def generate(self, org_A, ref_B, lms_A=None, lms_B=None, mask_A=None, mask_B=None,
diff_A=None, diff_B=None, gamma=None, beta=None, ret=False):
"""org_A is content, ref_B is style"""
res = self.G(org_A, ref_B, mask_A, mask_B, diff_A, diff_B, gamma, beta, ret)
return res
# mask attribute: 0:background 1:face 2:left-eyebrown 3:right-eyebrown 4:left-eye 5: right-eye 6: nose
# 7: upper-lip 8: teeth 9: under-lip 10:hair 11: left-ear 12: right-ear 13: neck
def test(self, real_A, mask_A, diff_A, real_B, mask_B, diff_B):
cur_prama = None
with torch.no_grad():
cur_prama = self.generate(real_A, real_B, None, None, mask_A, mask_B,
diff_A, diff_B, ret=True)
fake_A = self.generate(real_A, real_B, None, None, mask_A, mask_B,
diff_A, diff_B, gamma=cur_prama[0], beta=cur_prama[1])
fake_A = fake_A.squeeze(0)
# normalize
min_, max_ = fake_A.min(), fake_A.max()
fake_A.add_(-min_).div_(max_ - min_ + 1e-5)
return ToPILImage()(fake_A.cpu())
def train(self):
# The number of iterations per epoch
self.iters_per_epoch = len(self.data_loader_train)
# Start with trained model if exists
g_lr = self.g_lr
d_lr = self.d_lr
start = 0
for self.e in range(start, self.num_epochs): # epoch
for self.i, (source_input, reference_input) in enumerate(self.data_loader_train): # batch
# image, mask, dist
image_s, image_r = source_input[0].to(self.device), reference_input[0].to(self.device)
mask_s, mask_r = source_input[1].to(self.device), reference_input[1].to(self.device)
dist_s, dist_r = source_input[2].to(self.device), reference_input[2].to(self.device)
self.track("data")
# ================== Train D ================== #
# training D_A, D_A aims to distinguish class B 判断是否是“真reference” y
# Real
out = self.D_A(image_r)
self.track("D_A")
d_loss_real = self.criterionGAN(out, True)
self.track("D_A_loss")
# Fake
# 利用生成网络生成fake_y
fake_A = self.G(image_s, image_r, mask_s, mask_r, dist_s, dist_r)
self.track("G")
# 判别网络的输入，判别网络的损失 requires_grad=False
fake_A = Variable(fake_A.data).detach()
out = self.D_A(fake_A)
self.track("D_A_2")
d_loss_fake = self.criterionGAN(out, False)
self.track("D_A_loss_2")
# Backward + Optimize
# 判别器网络反向传播，更新网络参数
d_loss = (d_loss_real.mean() + d_loss_fake.mean()) * 0.5
self.d_A_optimizer.zero_grad()
d_loss.backward(retain_graph=False) ##retain_graph=False 释放计算图
self.d_A_optimizer.step()
# Logging
self.loss = {}
self.loss['D-A-loss_real'] = d_loss_real.mean().item()
# training D_B, D_B aims to distinguish class A 判断是否是“真source” x
# Real
out = self.D_B(image_s)
d_loss_real = self.criterionGAN(out, True)
# Fake 利用生成网络生成fake_x
self.track("G-before")
fake_B = self.G(image_r, image_s, mask_r, mask_s, dist_r, dist_s)
self.track("G-2")
fake_B = Variable(fake_B.data).detach()
out = self.D_B(fake_B)
d_loss_fake = self.criterionGAN(out, False)
# Backward + Optimize
d_loss = (d_loss_real.mean() + d_loss_fake.mean()) * 0.5
self.d_B_optimizer.zero_grad()
d_loss.backward(retain_graph=False)
self.d_B_optimizer.step()
# Logging
self.loss['D-B-loss_real'] = d_loss_real.mean().item()
# self.track("Discriminator backward")
# ================== Train G ================== #
if (self.i + 1) % self.g_step == 0:
# identity loss
assert self.lambda_idt > 0
# G should be identity if ref_B or org_A is fed
idt_A = self.G(image_s, image_s, mask_s, mask_s, dist_s, dist_s)
idt_B = self.G(image_r, image_r, mask_r, mask_r, dist_r, dist_r)
loss_idt_A = self.criterionL1(idt_A, image_s) * self.lambda_A * self.lambda_idt
loss_idt_B = self.criterionL1(idt_B, image_r) * self.lambda_B * self.lambda_idt
# loss_idt
loss_idt = (loss_idt_A + loss_idt_B) * 0.5
# loss_idt = loss_idt_A * 0.5
# self.track("Identical")
# GAN loss D_A(G_A(A))
# fake_A in class B, # 生成器对抗损失 L_G^adv
fake_A = self.G(image_s, image_r, mask_s, mask_r, dist_s, dist_r)
pred_fake = self.D_A(fake_A)
g_A_loss_adv = self.criterionGAN(pred_fake, True)
# GAN loss D_B(G_B(B))
fake_B = self.G(image_r, image_s, mask_r, mask_s, dist_r, dist_s)
pred_fake = self.D_B(fake_B)
g_B_loss_adv = self.criterionGAN(pred_fake, True)
# self.track("Generator forward")
# color_histogram loss
# 各局部颜色直方图损失 Makeup loss
g_A_loss_his = 0
g_B_loss_his = 0
g_A_lip_loss_his = self.criterionHis(
fake_A, image_r, mask_s[:, 0], mask_r[:, 0]
) * self.lambda_his_lip
g_B_lip_loss_his = self.criterionHis(
fake_B, image_s, mask_r[:, 0], mask_s[:, 0]
) * self.lambda_his_lip
g_A_loss_his += g_A_lip_loss_his
g_B_loss_his += g_B_lip_loss_his
g_A_skin_loss_his = self.criterionHis(
fake_A, image_r, mask_s[:, 1], mask_r[:, 1]
) * self.lambda_his_skin
g_B_skin_loss_his = self.criterionHis(
fake_B, image_s, mask_r[:, 1], mask_s[:, 1]
) * self.lambda_his_skin
g_A_loss_his += g_A_skin_loss_his
g_B_loss_his += g_B_skin_loss_his
g_A_eye_loss_his = self.criterionHis(
fake_A, image_r, mask_s[:, 2], mask_r[:, 2]
) * self.lambda_his_eye
g_B_eye_loss_his = self.criterionHis(
fake_B, image_s, mask_r[:, 2], mask_s[:, 2]
) * self.lambda_his_eye
g_A_loss_his += g_A_eye_loss_his
g_B_loss_his += g_B_eye_loss_his
# self.track("Generator histogram")
# cycle loss
# fake_A: fake_x/source
rec_A = self.G(fake_A, image_s, mask_s, mask_s, dist_s, dist_s)
rec_B = self.G(fake_B, image_r, mask_r, mask_r, dist_r, dist_r)
g_loss_rec_A = self.criterionL1(rec_A, image_s) * self.lambda_A
g_loss_rec_B = self.criterionL1(rec_B, image_r) * self.lambda_B
# self.track("Generator recover")
# vgg loss
# Perceptual loss
vgg_s = self.vgg(image_s)
vgg_s = Variable(vgg_s.data).detach()
vgg_fake_A = self.vgg(fake_A)
g_loss_A_vgg = self.criterionL2(vgg_fake_A, vgg_s) * self.lambda_A * self.lambda_vgg
# self.track("Generator vgg")
vgg_r = self.vgg(image_r)
vgg_r = Variable(vgg_r.data).detach()
vgg_fake_B = self.vgg(fake_B)
g_loss_B_vgg = self.criterionL2(vgg_fake_B, vgg_r) * self.lambda_B * self.lambda_vgg
loss_rec = (g_loss_rec_A + g_loss_rec_B + g_loss_A_vgg + g_loss_B_vgg) * 0.5
# loss_rec = (g_loss_rec_A + g_loss_A_vgg) * 0.5
# Combined loss
g_loss = (g_A_loss_adv + g_B_loss_adv + loss_rec + loss_idt + g_A_loss_his + g_B_loss_his).mean()
# g_loss = (g_A_loss_adv + loss_rec + loss_idt + g_A_loss_his).mean()
self.g_optimizer.zero_grad()
g_loss.backward(retain_graph=False)
self.g_optimizer.step()
# self.track("Generator backward")
# Logging
self.loss['G-A-loss-adv'] = g_A_loss_adv.mean().item()
self.loss['G-B-loss-adv'] = g_A_loss_adv.mean().item()
self.loss['G-loss-org'] = g_loss_rec_A.mean().item()
self.loss['G-loss-ref'] = g_loss_rec_B.mean().item()
self.loss['G-loss-idt'] = loss_idt.mean().item()
self.loss['G-loss-img-rec'] = (g_loss_rec_A + g_loss_rec_B).mean().item()
self.loss['G-loss-vgg-rec'] = (g_loss_A_vgg + g_loss_B_vgg).mean().item()
self.loss['G-loss-img-rec'] = g_loss_rec_A.mean().item()
self.loss['G-loss-vgg-rec'] = g_loss_A_vgg.mean().item()
self.loss['G-A-loss-his'] = g_A_loss_his.mean().item()
# Print out log info
if (self.i + 1) % self.log_step == 0:
self.log_terminal()
#plot the figures
for key_now in self.loss.keys():
plot_fig.plot(key_now, self.loss[key_now])
#save the images
if (self.i) % self.vis_step == 0:
print("Saving middle output...")
self.vis_train([image_s, image_r, fake_A, rec_A, mask_s[:, :, 0], mask_r[:, :, 0]])
# Save model checkpoints
if (self.i) % self.snapshot_step == 0:
self.save_models()
if (self.i % 100 == 99):
plot_fig.flush(self.log_path)
plot_fig.tick()
# Decay learning rate
if (self.e+1) > (self.num_epochs - self.num_epochs_decay):
g_lr -= (self.g_lr / float(self.num_epochs_decay))
d_lr -= (self.d_lr / float(self.num_epochs_decay))
self.update_lr(g_lr, d_lr)
print('Decay learning rate to g_lr: {}, d_lr:{}.'.format(g_lr, d_lr))
def update_lr(self, g_lr, d_lr):
for param_group in self.g_optimizer.param_groups:
param_group['lr'] = g_lr
for param_group in self.d_A_optimizer.param_groups:
param_group['lr'] = d_lr
for param_group in self.d_B_optimizer.param_groups:
param_group['lr'] = d_lr
def save_models(self):
if not osp.exists(self.snapshot_path):
os.makedirs(self.snapshot_path)
torch.save(
self.G.state_dict(),
os.path.join(
self.snapshot_path, '{}_{}_G.pth'.format(self.e + 1, self.i + 1)))
torch.save(
self.D_A.state_dict(),
os.path.join(
self.snapshot_path, '{}_{}_D_A.pth'.format(self.e + 1, self.i + 1)))
torch.save(
self.D_B.state_dict(),
os.path.join(
self.snapshot_path, '{}_{}_D_B.pth'.format(self.e + 1, self.i + 1)))
def vis_train(self, img_train_list):
# saving training results
mode = "train_vis"
img_train_list = torch.cat(img_train_list, dim=3)
result_path_train = osp.join(self.result_path, mode)
if not osp.exists(result_path_train):
os.makedirs(result_path_train)
save_path = os.path.join(result_path_train, '{}_{}_fake.jpg'.format(self.e, self.i))
save_image(self.de_norm(img_train_list.data), save_path, normalize=True)
def log_terminal(self):
elapsed = time.time() - self.start_time
elapsed = str(datetime.timedelta(seconds=elapsed))
log = "Elapsed [{}], Epoch [{}/{}], Iter [{}/{}]".format(
elapsed, self.e+1, self.num_epochs, self.i+1, self.iters_per_epoch)
for tag, value in self.loss.items():
log += ", {}: {:.4f}".format(tag, value)
print(log)
def to_var(self, x, requires_grad=True):
if torch.cuda.is_available():
x = x.cuda()
if not requires_grad:
return Variable(x, requires_grad=requires_grad)
else:
return Variable(x)