使用Pytorch實(shí)現(xiàn)頻譜歸一化生成對(duì)抗網(wǎng)絡(luò)(SN-GAN)

   
   

    
    

     
     

      
      

       
       

        
        
 來源：DeepHub IMBA 
        
       
       
      
      
     
     
    
    
   
   
   
   

    
    

     
     

      
      

       
       

        
        
本文約3800字，建議閱讀5分鐘
        
        
 自從擴(kuò)散模型發(fā)布以來，GAN的關(guān)注度和論文是越來越少了，但是它們里面的一些思路還是值得我們了解和學(xué)習(xí)。所以本文我們來使用Pytorch 來實(shí)現(xiàn)SN-GAN。 
        
       
       
      
      
     
     
    
    
   
   

• 更穩(wěn)定，更容易訓(xùn)練
• 可以生成更高質(zhì)量的圖像
• 更通用，可以用來生成更廣泛的內(nèi)容。

模式崩潰

Wassersteian損失

   
   

    
    

     
     

    
    
    
    
 JSD(P∥Q)=1/2(KL(P∥M)+KL(Q∥M))
   
   
   
   

    
   
   

1-Lipschitz Contiunity

   
   

     |f(x) — f(y)| <= |x — y|
   
   

   
   

     ∥∣D(x)?D(y)∣≤K?∥x?y∥
   
   

譜范數(shù)

代碼實(shí)現(xiàn)

   
   

    
    

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

    
    
    
    
 import torch from torch import nn from tqdm.auto import tqdm from torchvision import transforms from torchvision.datasets import MNIST from torchvision.utils import make_grid from torch.utils.data import DataLoader import matplotlib.pyplot as plt torch.manual_seed(0)  def show_tensor_images(image_tensor, num_images=25, size=(1, 28, 28)):    image_tensor = (image_tensor + 1) / 2    image_unflat = image_tensor.detach().cpu()    image_grid = make_grid(image_unflat[:num_images], nrow=5)    plt.imshow(image_grid.permute(1, 2, 0).squeeze())    plt.show()
   
   
   
   

    
   
   

 class Generator(nn.Module):      def __init__(self,z_dim=10,im_chan = 1,hidden_dim = 64):          super(Generatoe,self).__init__()          self.gen = nn.Sequential(          self.make_gen_block(z_dim,hidden_dim * 4),          self.make_gen_block(hidden_dim*4,hidden_dim * 2,kernel_size = 4,stride =1),          self.make_gen_block(hidden_dim * 2,hidden_dim),          self.make_gen_block(hidden_dim,im_chan,kernel_size=4,final_layer = True),          )    def make_gen_block(self,input_channels,output_channels,kernel_size=3,stride=2,final_layer = False):          if not final_layer :            return nn.Sequential(nn.ConvTranspose2D(input_layer,output_layer,kernel_size,stride),                    nn.BatchNorm2d(output_channels),                    nn.ReLU(inplace = True),                      )          else:            return nn.Sequential(nn.ConvTranspose2D(input_layer,output_layer,kernel_size,stride),                    nn.Tanh(),)  def unsqueeze_noise():        return noise.view(len(noise), self.z_dim, 1, 1)  def forward(self,noise):      x = self.unsqueeze_noise(noise)      return self.gen(x) def get_noise(n_samples, z_dim, device='cpu'):    return torch.randn(n_samples, z_dim, device=device)

   
   

    
    

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

    
    
    
    
 class Discriminator(nn.Module):      def __init__(self, im_chan=1, hidden_dim=16):        super(Discriminator, self).__init__()        self.disc = nn.Sequential(            self.make_disc_block(im_chan, hidden_dim),            self.make_disc_block(hidden_dim, hidden_dim * 2),            self.make_disc_block(hidden_dim * 2, 1, final_layer=True),        )      def make_disc_block(self, input_channels, output_channels, kernel_size=4, stride=2, final_layer=False):        if not final_layer:            return nn.Sequential(                nn.utils.spectral_norm(nn.Conv2d(input_channels, output_channels, kernel_size, stride)),                nn.BatchNorm2d(output_channels),                nn.LeakyReLU(0.2, inplace=True),            )        else:            return nn.Sequential(                nn.utils.spectral_norm(nn.Conv2d(input_channels, output_channels, kernel_size, stride)),            )    def forward(self, image):        disc_pred = self.disc(image)        return disc_pred.view(len(disc_pred), -1)
   
   
   
   

    
   
   

訓(xùn)練

   
   

    
    

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

    
    
    
    
 criterion = nn.BCEWithLogitsLoss() n_epochs = 50 z_dim = 64 display_step = 500 batch_size = 128 # A learning rate of 0.0002 works well on DCGAN lr = 0.0002 beta_1 = 0.5 beta_2 = 0.999 device = 'cuda' transform = transforms.Compose([    transforms.ToTensor(),    transforms.Normalize((0.5,), (0.5,)), ])  dataloader = DataLoader(    MNIST(".", download=True, transform=transform),    batch_size=batch_size,    shuffle=True)
   
   
   
   

    
   
   

   
   

    
    

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

    
    
    
    
 gen = Generator(z_dim).to(device) gen_opt = torch.optim.Adam(gen.parameters(), lr=lr, betas=(beta_1, beta_2)) disc = Discriminator().to(device) disc_opt = torch.optim.Adam(disc.parameters(), lr=lr, betas=(beta_1, beta_2))  # initialize the weights to the normal distribution # with mean 0 and standard deviation 0.02 def weights_init(m):    if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):        torch.nn.init.normal_(m.weight, 0.0, 0.02)    if isinstance(m, nn.BatchNorm2d):        torch.nn.init.normal_(m.weight, 0.0, 0.02)        torch.nn.init.constant_(m.bias, 0) gen = gen.apply(weights_init) disc = disc.apply(weights_init)
   
   
   
   

    
   
   

   
   

    
    

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

     
     

    
    
    
    
 cur_step = 0 mean_generator_loss = 0 mean_discriminator_loss = 0 for epoch in range(n_epochs):    # Dataloader returns the batches    for real, _ in tqdm(dataloader):        cur_batch_size = len(real)        real = real.to(device)         ## Update Discriminator ##        disc_opt.zero_grad()        fake_noise = get_noise(cur_batch_size, z_dim, device=device)        fake = gen(fake_noise)        disc_fake_pred = disc(fake.detach())        disc_fake_loss = criterion(disc_fake_pred, torch.zeros_like(disc_fake_pred))        disc_real_pred = disc(real)        disc_real_loss = criterion(disc_real_pred, torch.ones_like(disc_real_pred))        disc_loss = (disc_fake_loss + disc_real_loss) / 2         # Keep track of the average discriminator loss        mean_discriminator_loss += disc_loss.item() / display_step        # Update gradients        disc_loss.backward(retain_graph=True)        # Update optimizer        disc_opt.step()         ## Update Generator ##        gen_opt.zero_grad()        fake_noise_2 = get_noise(cur_batch_size, z_dim, device=device)        fake_2 = gen(fake_noise_2)        disc_fake_pred = disc(fake_2)        gen_loss = criterion(disc_fake_pred, torch.ones_like(disc_fake_pred))        gen_loss.backward()        gen_opt.step()         # Keep track of the average generator loss        mean_generator_loss += gen_loss.item() / display_step         ## Visualization code ##        if cur_step % display_step == 0 and cur_step > 0:            print(f"Step {cur_step}: Generator loss: {mean_generator_loss}, discriminator loss: {mean_discriminator_loss}")            show_tensor_images(fake)            show_tensor_images(real)            mean_generator_loss = 0            mean_discriminator_loss = 0        cur_step += 1
   
   
   
   

    
   
   

總結(jié)

編輯：文婧