我正在调查在PyTorch中使用带梯度惩罚的Wasserstein GAN，但始终会得到较大的正发电机损耗，并且随着时间的推移而增加。
我大量借用了Caogang's implementation，但使用了this implementation中使用的鉴别器和发电机损耗，因为如果我尝试用曹刚实现中使用的Invalid gradient at index 0 - expected shape[] but got [1]和.backward()参数调用one，就会得到mone。
我正在一个增强的WikiArt数据集（大于400k64x64图像）和CIFAR-10上进行训练，得到了一个正常的WGAN（可以进行重量削减）[即，它在25个时期后产生可通过的图像]，尽管所有时期的D和G损失都徘徊在3左右[我使用torch.mean(D_real)等计算它们]。然而，在WGAN-GP版本中，WikiArt和CIFAR-10数据集上的生成器丢失都急剧增加，并且完全无法在WikiArt上生成除噪声之外的任何其他东西。
以下是CIFAR-10上25个时期之后的损失示例：
我不使用任何技巧，如单面标签平滑，我训练的默认学习率为0.001，亚当优化器和我训练鉴别器5次，每次发电机更新。为什么会发生这种疯狂的减肥行为，为什么正常的减肥方法WGAN仍然在WikiArt上有效，但是WGANGP完全失败了？
这与结构无关，无论G和D都是dcgan还是使用this modified DCGAN, the Creative Adversarial Network时，这要求D能够对图像进行分类，并且G生成不明确的图像。
下面是我当前train方法的相关部分：
self.generator = Can64Generator(self.z_noise, self.channels, self.num_gen_filters).to(self.device)
self.discriminator =WCan64Discriminator(self.channels,self.y_dim, self.num_disc_filters).to(self.device)
style_criterion = nn.CrossEntropyLoss()
self.disc_optimizer = optim.Adam(self.discriminator.parameters(), lr=self.lr, betas=(self.beta1, 0.9))
self.gen_optimizer = optim.Adam(self.generator.parameters(), lr=self.lr, betas=(self.beta1, 0.9))
while i < len(dataloader):
j = 0
disc_loss_epoch = []
gen_loss_epoch = []
if self.type == "can":
disc_class_loss_epoch = []
gen_class_loss_epoch = []
if self.gradient_penalty == False:
# critic training methodology in official WGAN implementation
if gen_iterations < 25 or (gen_iterations % 500 == 0):
disc_iters = 100
else:
disc_iters = self.disc_iterations
while j < disc_iters and (i < len(dataloader)):
# if using wgan with weight clipping
if self.gradient_penalty == False:
# Train Discriminator
for param in self.discriminator.parameters():
param.data.clamp_(self.lower_clamp,self.upper_clamp)
for param in self.discriminator.parameters():
param.requires_grad_(True)
j+=1
i+=1
data = data_iterator.next()
self.discriminator.zero_grad()
real_images, image_labels = data
# image labels are the the image's classes (e.g. Impressionism)
real_images = real_images.to(self.device)
batch_size = real_images.size(0)
real_image_labels = torch.LongTensor(batch_size).to(self.device)
real_image_labels.copy_(image_labels)
labels = torch.full((batch_size,),real_label,device=self.device)
if self.type == 'can':
predicted_output_real, predicted_styles_real = self.discriminator(real_images.detach())
predicted_styles_real = predicted_styles_real.to(self.device)
disc_class_loss = style_criterion(predicted_styles_real,real_image_labels)
disc_class_loss.backward(retain_graph=True)
else:
predicted_output_real = self.discriminator(real_images.detach())
disc_loss_real = -torch.mean(predicted_output_real)
# fake
noise = torch.randn(batch_size,self.z_noise,1,1,device=self.device)
with torch.no_grad():
noise_g = noise.detach()
fake_images = self.generator(noise_g)
labels.fill_(fake_label)
if self.type == 'can':
predicted_output_fake, predicted_styles_fake = self.discriminator(fake_images)
else:
predicted_output_fake = self.discriminator(fake_images)
disc_gen_z_1 = predicted_output_fake.mean().item()
disc_loss_fake = torch.mean(predicted_output_fake)
#via https://github.com/znxlwm/pytorch-generative-model-collections/blob/master/WGAN_GP.py
if self.gradient_penalty:
# gradient penalty
alpha = torch.rand((real_images.size()[0], 1, 1, 1)).to(self.device)
x_hat = alpha * real_images.data + (1 - alpha) * fake_images.data
x_hat.requires_grad_(True)
if self.type == 'can':
pred_hat, _ = self.discriminator(x_hat)
else:
pred_hat = self.discriminator(x_hat)
gradients = grad(outputs=pred_hat, inputs=x_hat, grad_outputs=torch.ones(pred_hat.size()).to(self.device),
create_graph=True, retain_graph=True, only_inputs=True)[0]
gradient_penalty = lambda_ * ((gradients.view(gradients.size()[0], -1).norm(2, 1) - 1) ** 2).mean()
disc_loss = disc_loss_fake + disc_loss_real + gradient_penalty
else:
disc_loss = disc_loss_fake + disc_loss_real
if self.type == 'can':
disc_loss += disc_class_loss.mean()
disc_x = disc_loss.mean().item()
disc_loss.backward(retain_graph=True)
self.disc_optimizer.step()
# train generator
for param in self.discriminator.parameters():
param.requires_grad_(False)
self.generator.zero_grad()
labels.fill_(real_label)
if self.type == 'can':
predicted_output_fake, predicted_styles_fake = self.discriminator(fake_images)
predicted_styles_fake = predicted_styles_fake.to(self.device)
else:
predicted_output_fake = self.discriminator(fake_images)
gen_loss = -torch.mean(predicted_output_fake)
disc_gen_z_2 = gen_loss.mean().item()
if self.type == 'can':
fake_batch_labels = 1.0/self.y_dim * torch.ones_like(predicted_styles_fake)
fake_batch_labels = torch.mean(fake_batch_labels,1).long().to(self.device)
gen_class_loss = style_criterion(predicted_styles_fake,fake_batch_labels)
gen_class_loss.backward(retain_graph=True)
gen_loss += gen_class_loss.mean()
gen_loss.backward()
gen_iterations += 1
这是（DCGAN）生成器的代码：
class Can64Generator(nn.Module):
def __init__(self, z_noise, channels, num_gen_filters):
super(Can64Generator,self).__init__()
self.ngpu = 1
self.main = nn.Sequential(
nn.ConvTranspose2d(z_noise, num_gen_filters * 16, 4, 1, 0, bias=False),
nn.BatchNorm2d(num_gen_filters * 16),
nn.ReLU(True),
nn.ConvTranspose2d(num_gen_filters * 16, num_gen_filters * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(num_gen_filters * 4),
nn.ReLU(True),
nn.ConvTranspose2d(num_gen_filters * 4, num_gen_filters * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(num_gen_filters * 2),
nn.ReLU(True),
nn.ConvTranspose2d(num_gen_filters * 2, num_gen_filters, 4, 2, 1, bias=False),
nn.BatchNorm2d(num_gen_filters),
nn.ReLU(True),
nn.ConvTranspose2d(num_gen_filters, 3, 4, 2, 1, bias=False),
nn.Tanh()
)
def forward(self, inp):
output = self.main(inp)
return output
这是（当前的）CAN鉴别器，它有额外的层用于
样式（图像类）分类：
class Can64Discriminator(nn.Module):
def __init__(self, channels,y_dim, num_disc_filters):
super(Can64Discriminator, self).__init__()
self.ngpu = 1
self.conv = nn.Sequential(
nn.Conv2d(channels, num_disc_filters // 2, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(num_disc_filters // 2, num_disc_filters, 4, 2, 1, bias=False),
nn.BatchNorm2d(num_disc_filters),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(num_disc_filters, num_disc_filters * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(num_disc_filters * 2),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(num_disc_filters * 2, num_disc_filters * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(num_disc_filters * 4),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(num_disc_filters * 4, num_disc_filters * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(num_disc_filters * 8),
nn.LeakyReLU(0.2, inplace=True),
)
# was this
#self.final_conv = nn.Conv2d(num_disc_filters * 8, num_disc_filters * 8, 4, 2, 1, bias=False)
self.real_fake_head = nn.Linear(num_disc_filters * 8, 1)
# no bn and lrelu needed
self.sig = nn.Sigmoid()
self.fc = nn.Sequential()
self.fc.add_module("linear_layer{0}".format(num_disc_filters*16),nn.Linear(num_disc_filters*8,num_disc_filters*16))
self.fc.add_module("linear_layer{0}".format(num_disc_filters*8),nn.Linear(num_disc_filters*16,num_disc_filters*8))
self.fc.add_module("linear_layer{0}".format(num_disc_filters),nn.Linear(num_disc_filters*8,y_dim))
self.fc.add_module('softmax',nn.Softmax(dim=1))
def forward(self, inp):
x = self.conv(inp)
x = x.view(x.size(0),-1)
real_out = self.sig(self.real_fake_head(x))
real_out = real_out.view(-1,1).squeeze(1)
style = self.fc(x)
#style = torch.mean(style,1) # CrossEntropyLoss requires input be (N,C)
return real_out,style
我的GAN的WGANGP版本和WGAN版本之间的唯一区别是WGAN版本使用
RMSprop和lr=0.00005并根据WGAN论文剪辑鉴别器的权重。这是什么原因？我想做尽可能小的改变，因为我想比较损失函数单独。即使在CIFAR-10上使用未经修改的DCGAN鉴别器，也会遇到同样的问题。我遇到这种情况可能是因为我目前只接受了25个阶段的培训，还是另有原因？有趣的是，我的GAN在使用LSGAN（
nn.MSELoss()）时也完全不能产生除噪声以外的任何东西。提前谢谢！
最佳答案
基于梯度惩罚的分批标准化方法。作者自己也提倡使用层规范化，但是这在他们的论文中用粗体写得很清楚（https://papers.nips.cc/paper/7159-improved-training-of-wasserstein-gans.pdf）。很难说你的代码中是否还有其他的bug，但是我强烈建议你仔细阅读DCGAN和Wasserstein GAN的论文，并真正记录下超参数。弄错了它们会破坏GAN的性能，而进行超参数搜索会很快变得非常昂贵。
顺便说一下，转置卷积在输出图像中产生阶梯伪影。改为使用图像大小调整。对于这种现象的深入解释，我可以推荐以下资源（https://distill.pub/2016/deconv-checkerboard/）。