def train(self):
"""Train StarGAN within a single dataset."""
# Set data loader.
data_loader = self.celeba_loader
data_iter = iter(data_loader)
# Learning rate cache for decaying.
g_lr = self.g_lr
d_lr = self.d_lr
# Start training from scratch or resume training.
start_iters = 0
#加入加载模型
self.resume_iters = start_iters
if self.resume_iters: #参数resume_iters 设置为none
start_iters = self.resume_iters #可以不连续训练，从之前训练好后的结果处开始
self.restore_model(self.resume_iters, 'both')
# Start training.
print('Start training...')
start_time = time.time()
for i in range(start_iters, self.num_iters):
# =================================================================================== #
# 1. Preprocess input data #
# =================================================================================== #
# Fetch real images and labels.
try:
x_fixed, x_illumination,label_org = next(data_iter)
except:
data_iter = iter(data_loader)
x_fixed, x_illumination,label_org = next(data_iter)
x_fixed = x_fixed.to(self.device)
x_illumination = x_illumination.to(self.device)
label_org = label_org.to(self.device)
# =================================================================================== #
# #加上gluon中的网络，normalizaion #
# =================================================================================== #
fake_out = self.netG1(x_illumination)
# update D
self.set_requires_grad(self.netD1, True)
self.optimizer_D.zero_grad()
self.backward_D(x_illumination,fake_out,x_fixed)
self.optimizer_D.step()
# update G
self.set_requires_grad(self.netD1, False)
self.optimizer_G.zero_grad()
self.backward_G(x_illumination,fake_out,x_fixed,label_org)
self.optimizer_G.step()
# =================================================================================== #
# 2. Train the discriminator #
# =================================================================================== #
# Compute loss with real images.
out_src, out_cls = self.D(x_illumination) #D接受的就只是一幅图像,真实的具有光照的图像
#判别器以一个batch(16张)的真实图片为输入，输出out_src[16, 1, 2, 2]，用来判断图片真假。
#out_cls[16, 5]，得到图片的标签估计。
d_loss_real = - torch.mean(out_src) # d_loss_real最小，那么 out_src 最大==1 （针对图像）
d_loss_cls = self.classification_loss(out_cls, label_org, self.dataset) #针对标签
#d_loss_cls = -self.classification_loss(out_cls, label_org, dataset = 'RaFD')
##衡量真实标签与标签估计的差距
x_fake = self.G(x_fixed, label_org) #x_fake 生成一个图像数据
out_src, out_cls = self.D(x_fake.detach())#在这里表示不用求上面一行中的G的梯度
d_loss_fake = torch.mean(out_src) #假图像为0
#判定越接近为假，损失越小
#加到这个地方，归类生成图像的光照
#d_loss_cls = self.classification_loss(out_cls, label_org, self.dataset)
# Compute loss for gradient penalty.
#计算梯度惩罚因子alpha,根据alpha结合x_real,x_fake,输入判别网络,计算梯度,得到梯度损失函数,
alpha = torch.rand(x_fixed.size(0), 1, 1, 1).to(self.device)
# alpha是一个随机数 tensor([[[[ 0.7610]]]])
x_hat = (alpha * x_fixed.data + (1 - alpha) * x_fake.data).requires_grad_(True)
# x_hat是一个图像大小的张量数据，随着alpha的改变而变化
out_src, _ = self.D(x_hat) #x_hat 表示梯度惩罚因子
d_loss_gp = self.gradient_penalty(out_src, x_hat)
d_loss = d_loss_real + d_loss_fake + self.lambda_cls * d_loss_cls + self.lambda_gp * d_loss_gp
#print(d_loss_real,d_loss_fake,d_loss_cls,d_loss_gp)
#(1.00000e-04 *1.1113) (1.00000e-05 * -3.0589) (13.8667) (0.9953)
self.reset_grad()
d_loss.backward()
self.d_optimizer.step()
# Logging.
loss = {}
loss['D/loss_real'] = d_loss_real.item()
loss['D/loss_fake'] = d_loss_fake.item()
loss['D/loss_cls'] = (self.lambda_cls *d_loss_cls).item()
loss['D/loss_gp'] = (self.lambda_gp * d_loss_gp).item()
# =================================================================================== #
# 3. Train the generator #
# =================================================================================== #
#生成网络的作用是,输入original域的图可以生成目标域的图像,输入为目标域的图像,生成original域的图像（重建）
if (i+1) % self.n_critic == 0: #每更新5次判别器再更新一次生成器
# Original-to-target domain.
#将真实图像输入x_real和假的标签c_trg输入生成网络,得到生成图像x_fake
x_fake = self.G(x_fixed, label_org)
out_src, out_cls = self.D(x_fake)
g_loss_fake = - torch.mean(out_src) #这里是对抗损失，希望生成的假图像为1
g_loss_cls = self.classification_loss(out_cls, label_org, self.dataset)#向目标标签进行转化
#g_loss_cls = -self.classification_loss(out_cls, label_org, dataset = 'RaFD')
# Target-to-original domain.
# 这里结合另一个GAN 进行重建
#x_reconst = self.G(x_fake, c_org)
#g_loss_rec = torch.mean(torch.abs(x_fixed - x_reconst))
g_ground_truth = torch.mean(torch.abs(x_illumination - x_fake))
#和normlization结合进行重建
g_loss_rec = torch.mean(torch.abs(self.G(self.netG1(x_illumination),label_org) - x_illumination))
# Backward and optimize.
g_loss = g_loss_fake + 100 * g_ground_truth + self.lambda_cls * g_loss_cls +\
self.lambda_rec * g_loss_rec
#print(g_loss_fake,g_ground_truth,g_loss_cls,g_loss_rec)
#tensor(-0.4776) tensor(0.4306) tensor(5.2388) tensor(0.4283)
self.reset_grad()
g_loss.backward()
self.g_optimizer.step()
# Logging.
loss['G/loss_fake'] = g_loss_fake.item()
loss['G/loss_gt'] = (self.lambda_rec *g_ground_truth).item()
loss['G/loss_rec'] = (self.lambda_rec *g_loss_rec).item()
loss['G/loss_cls'] = g_loss_cls.item()
# =================================================================================== #
# 4. Miscellaneous #
# =================================================================================== #
# Print out training information.
if (i+1) % self.log_step == 0:
et = time.time() - start_time
et = str(datetime.timedelta(seconds=et))[:-7]
log = "Elapsed [{}], Iteration [{}/{}]".format(et, i+1, self.num_iters)
for tag, value in loss.items():
log += ", {}: {:.4f}".format(tag, value)
print(log)
if self.use_tensorboard:
for tag, value in loss.items():
self.logger.scalar_summary(tag, value, i+1)
# Translate fixed images for debugging. 每100轮保存一次图像
if (i+1) % self.sample_step == 0:
with torch.no_grad():
x_fake_list = [x_fixed]
x_fake_list.append(self.G(x_fixed, label_org))
x_concat = torch.cat(x_fake_list, dim=3)
sample_path = os.path.join(self.sample_dir, '{}-images.jpg'.format(i+1))
save_image(self.denorm(x_concat.data.cpu()), sample_path, nrow=1, padding=0)
print('Saved real and fake images into {}...'.format(sample_path))
# Save model checkpoints. 每100轮保存一下模型
if (i+1) % self.model_save_step == 0:
G_path = os.path.join(self.model_save_dir, '{}-G.ckpt'.format(i+1))
D_path = os.path.join(self.model_save_dir, '{}-D.ckpt'.format(i+1))
torch.save(self.G.state_dict(), G_path)
torch.save(self.D.state_dict(), D_path)
G1_path = os.path.join(self.model_save_dir, '{}-G1.ckpt'.format(i+1))
D1_path = os.path.join(self.model_save_dir, '{}-D1.ckpt'.format(i+1))
torch.save(self.netG1.state_dict(), G1_path)
torch.save(self.netD1.state_dict(), D1_path)
print('Saved model checkpoints into {}...'.format(self.model_save_dir))
# Decay learning rates. 降低学习率
if (i+1) % self.lr_update_step == 0 and (i+1) > (self.num_iters - self.num_iters_decay):
g_lr -= (self.g_lr / float(self.num_iters_decay))
d_lr -= (self.d_lr / float(self.num_iters_decay))
self.update_lr(g_lr, d_lr)
print ('Decayed learning rates, g_lr: {}, d_lr: {}.'.format(g_lr, d_lr))
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