【源碼解讀】cycleGAN（二) ：訓練

源碼地址：https://github.com/aitorzip/PyTorch-CycleGAN

訓練的代碼見於train.py，首先定義好網絡，兩個生成器A2B, B2A和兩個判別器A, B，以及對應的優化器（優化器的設置保證了只更新生成器或判別器，不會互相影響）

###### Definition of variables ######
# Networks
netG_A2B = Generator(opt.input_nc, opt.output_nc)
netG_B2A = Generator(opt.output_nc, opt.input_nc)
netD_A = Discriminator(opt.input_nc)
netD_B = Discriminator(opt.output_nc)

# Optimizers & LR schedulers
optimizer_G = torch.optim.Adam(itertools.chain(netG_A2B.parameters(), netG_B2A.parameters()),
                                lr=opt.lr, betas=(0.5, 0.999))
optimizer_D_A = torch.optim.Adam(netD_A.parameters(), lr=opt.lr, betas=(0.5, 0.999))
optimizer_D_B = torch.optim.Adam(netD_B.parameters(), lr=opt.lr, betas=(0.5, 0.999))

然后是數據

# Dataset loader
transforms_ = [ transforms.Resize(int(opt.size*1.12), Image.BICUBIC), 
                transforms.RandomCrop(opt.size), 
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5)) ]
dataloader = DataLoader(ImageDataset(opt.dataroot, transforms_=transforms_, unaligned=True), 
                        batch_size=opt.batchSize, shuffle=True, num_workers=opt.n_cpu)

接着就可以求取損失，反傳梯度，更新網絡，更新網絡的時候首先更新生成器，然后分別更新兩個判別器

生成器：損失函數=身份損失+對抗損失+循環一致損失

###### Generators A2B and B2A ######
        optimizer_G.zero_grad()

        # Identity loss
        # G_A2B(B) should equal B if real B is fed
        same_B = netG_A2B(real_B)
        loss_identity_B = criterion_identity(same_B, real_B)*5.0
        # G_B2A(A) should equal A if real A is fed
        same_A = netG_B2A(real_A)
        loss_identity_A = criterion_identity(same_A, real_A)*5.0

        # GAN loss
        fake_B = netG_A2B(real_A)
        pred_fake = netD_B(fake_B)
        loss_GAN_A2B = criterion_GAN(pred_fake, target_real)

        fake_A = netG_B2A(real_B)
        pred_fake = netD_A(fake_A)
        loss_GAN_B2A = criterion_GAN(pred_fake, target_real)

        # Cycle loss
        recovered_A = netG_B2A(fake_B)
        loss_cycle_ABA = criterion_cycle(recovered_A, real_A)*10.0

        recovered_B = netG_A2B(fake_A)
        loss_cycle_BAB = criterion_cycle(recovered_B, real_B)*10.0

        # Total loss
        loss_G = loss_identity_A + loss_identity_B + loss_GAN_A2B + loss_GAN_B2A + loss_cycle_ABA + loss_cycle_BAB
        loss_G.backward()
        
        optimizer_G.step()

判別器A  損失函數= 真實樣本分類損失 + 虛假樣本分類損失

###### Discriminator A ######
        optimizer_D_A.zero_grad()

        # Real loss
        pred_real = netD_A(real_A)
        loss_D_real = criterion_GAN(pred_real, target_real)

        # Fake loss
        fake_A = fake_A_buffer.push_and_pop(fake_A)
        pred_fake = netD_A(fake_A.detach())
        loss_D_fake = criterion_GAN(pred_fake, target_fake)

        # Total loss
        loss_D_A = (loss_D_real + loss_D_fake)*0.5
        loss_D_A.backward()

        optimizer_D_A.step()
        ###################################

判別器B 損失函數= 真實樣本分類損失 + 虛假樣本分類損失

###### Discriminator B ######
        optimizer_D_B.zero_grad()

        # Real loss
        pred_real = netD_B(real_B)
        loss_D_real = criterion_GAN(pred_real, target_real)
        
        # Fake loss
        fake_B = fake_B_buffer.push_and_pop(fake_B)
        pred_fake = netD_B(fake_B.detach())
        loss_D_fake = criterion_GAN(pred_fake, target_fake)

        # Total loss
        loss_D_B = (loss_D_real + loss_D_fake)*0.5
        loss_D_B.backward()

        optimizer_D_B.step()
        ###################################

可以注意到，判別器損失中，虛假樣本fake_A，fake_B都采用detach()操作，脫離計算圖，這樣判別器的損失進行反向傳播不會對整個網絡計算梯度，避免了不必要的計算