Trick1：Label平滑
如果有两个目标label：Real=1 和 Fake=0，那么对于每个新样本，如果是real，那么把label替换为0.7~1.2之间的随机值；如果样本是fake，那么把label替换为0.0~0.3之间的随机值。
在models/networks.py中的GANLoss类中的__init__函数中进行修改：
原代码：
def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0):
""" Initialize the GANLoss class.
Parameters:
gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp.
target_real_label (bool) - - label for a real image
target_fake_label (bool) - - label of a fake image
Note: Do not use sigmoid as the last layer of Discriminator.
LSGAN needs no sigmoid. vanilla GANs will handle it with BCEWithLogitsLoss.
"""
super(GANLoss, self).__init__()
self.register_buffer('real_label', torch.tensor(target_real_label))
self.register_buffer('fake_label', torch.tensor(target_fake_label))
self.gan_mode = gan_mode
if gan_mode == 'lsgan':
self.loss = nn.MSELoss()
elif gan_mode == 'vanilla':
self.loss = nn.BCEWithLogitsLoss()
elif gan_mode in ['wgangp']:
self.loss = None
else:
raise NotImplementedError('gan mode %s not implemented' % gan_mode)修改后的代码：
def __init__(self, gan_mode):
""" Initialize the GANLoss class.
Parameters:
gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp.
target_real_label (bool) - - label for a real image
target_fake_label (bool) - - label of a fake image
Note: Do not use sigmoid as the last layer of Discriminator.
LSGAN needs no sigmoid. vanilla GANs will handle it with BCEWithLogitsLoss.
"""
super(GANLoss, self).__init__()
target_real_label = random.randint(7, 12) * 0.1
target_fake_label = random.randint(0, 3) * 0.1
self.register_buffer('real_label', torch.tensor(target_real_label))
self.register_buffer('fake_label', torch.tensor(target_fake_label))
self.gan_mode = gan_mode
if gan_mode == 'lsgan':
self.loss = nn.MSELoss()
elif gan_mode == 'vanilla':
self.loss = nn.BCEWithLogitsLoss()
elif gan_mode in ['wgangp']:
self.loss = None
else:
raise NotImplementedError('gan mode %s not implemented' % gan_mode)Trick2：将图像输入鉴别器之前，将噪声添加到实际图像和生成的图像中
在models/cycle_gan_model.py中的CycleGANModle类中的backward_D_A函数和backward_D_B函数修改
原代码：
def backward_D_A(self):
"""Calculate GAN loss for discriminator D_A"""
fake_B = self.fake_B_pool.query(self.fake_B)
self.loss_D_A = self.backward_D_basic(self.netD_A, self.real_B, fake_B)
def backward_D_B(self):
"""Calculate GAN loss for discriminator D_B"""
fake_A = self.fake_A_pool.query(self.fake_A)
self.loss_D_B = self.backward_D_basic(self.netD_B, self.real_A, fake_A)修改后代码：
def backward_D_A(self):
"""Calculate GAN loss for discriminator D_A"""
real_B=self.real_B #(B C H W)
fake_B = self.fake_B_pool.query(self.fake_B) #(B C H W)
###给fake_B添加噪点
BatchSize_fake,C_fake,H_fake,W_fake=fake_B.size()
img_fake=fake_B.view(H_fake,W_fake,C_fake) #(H W C)
img_fake_np=img_fake.numpy() #将（H W C）的Tensor转为（H W C）的numpy
h_fake,w_fake,c_fake=img_fake_np.shape
Nd = 0.1
Sd = 1 - Nd
mask_fake = np.random.choice((0, 1, 2), size=(h_fake, w_fake, 1), p=[Nd / 2.0, Nd / 2.0, Sd]) # 生成一个通道的mask
mask_fake = np.repeat(mask_fake, c_fake, axis=2) # 在通道的维度复制，生成彩色的mask
img_fake_np[mask_fake==0]=0
img_fake_np[mask_fake==1]=255
img_fake_Tensor=torch.from_numpy(img_fake_np) #（H W C）numpy转为（H W C）的Tensor
H1_fake,W1_fake,C1_fake=img_fake_Tensor.size()
fake_B=img_fake_Tensor.view(BatchSize_fake,C1_fake,H1_fake,W1_fake) #将（H W C）的Tensor转为（B C H W）的Tensor
###给real_B添加噪点
BatchSize_real, C_real, H_real, W_real = real_B.size()
img_real = real_B.view(H_real, W_real, C_real)
img_real_np = img_real.numpy()
h_real, w_real, c_real = img_real_np.shape
mask_real = np.random.choice((0, 1, 2), size=(h_real, w_real, 1), p=[Nd / 2.0, Nd / 2.0, Sd]) # 生成一个通道的mask
mask_real = np.repeat(mask_real, c_real, axis=2) # 在通道的维度复制，生成彩色的mask
img_real_np[mask_real == 0] = 0
img_real_np[mask_real == 1] = 255
img_real_Tensor=torch.from_numpy(img_real_np)
H1_real,W1_real,C1_real=img_real_Tensor.size()
real_B=img_real_Tensor.view(BatchSize_real,C1_real,H1_real,W1_real)
self.loss_D_A = self.backward_D_basic(self.netD_A, real_B, fake_B)
def backward_D_B(self):
"""Calculate GAN loss for discriminator D_B"""
real_A=self.real_A
fake_A = self.fake_A_pool.query(self.fake_A)
###给fake_A添加噪点
BatchSize_fake, C_fake, H_fake, W_fake = fake_A.size()
img_fake = fake_A.view(H_fake, W_fake, C_fake)
img_fake_np = img_fake.numpy()
h_fake, w_fake, c_fake = img_fake_np.shape
Nd = 0.1
Sd = 1 - Nd
mask_fake = np.random.choice((0, 1, 2), size=(h_fake, w_fake, 1), p=[Nd / 2.0, Nd / 2.0, Sd]) # 生成一个通道的mask
mask_fake = np.repeat(mask_fake, c_fake, axis=2) # 在通道的维度复制，生成彩色的mask
img_fake_np[mask_fake == 0] = 0
img_fake_np[mask_fake == 1] = 255
img_fake_Tensor = torch.from_numpy(img_fake_np)
H1_fake, W1_fake, C1_fake = img_fake_Tensor.size()
fake_B = img_fake_Tensor.view(BatchSize_fake, C1_fake, H1_fake, W1_fake)
###给real_A添加噪点
BatchSize_real, C_real, H_real, W_real = real_A.size()
img_real = real_A.view(H_real, W_real, C_real)
img_real_np = img_real.numpy()
h_real, w_real, c_real = img_real_np.shape
mask_real = np.random.choice((0, 1, 2), size=(h_real, w_real, 1), p=[Nd / 2.0, Nd / 2.0, Sd]) # 生成一个通道的mask
mask_real = np.repeat(mask_real, c_real, axis=2) # 在通道的维度复制，生成彩色的mask
img_real_np[mask_real == 0] = 0
img_real_np[mask_real == 1] = 255
img_real_Tensor = torch.from_numpy(img_real_np)
H1_real, W1_real, C1_real = img_real_Tensor.size()
real_B = img_real_Tensor.view(BatchSize_real, C1_real, H1_real, W1_real)
self.loss_D_B = self.backward_D_basic(self.netD_B, real_A, fake_A)加噪点思路：参考了该文章的椒盐噪点
并且由于fake_A等变量是（B,C,H,W）的Tensor，而文章中的算法是基于numpy，因此只需要将fake_A等变量先从Tensor->numpy->算法->Tensor
Trick3： 判别器的优化频率高于生成器。
原代码中判别器的训练次数和生成器的训练次数的比例是1：1，即每一个epoch的时候只训练一次Generator和Discriminator。现在我尝试一个epoch的时候，训练一次Generator，然后用其生成的fake图片训练3次Discriminator。
原理：1）Trick2的噪点会使Discriminator难以训练。
2）多训练Discriminator，能够刺激Generator模型的训练从而生成好的效果图。
在models/cycle_gan_model.py中的CycleGANModel类中的optimize_parameters函数中进行修改
原代码：
def optimize_parameters(self):
"""Calculate losses, gradients, and update network weights; called in every training iteration"""
# forward
self.forward() # compute fake images and reconstruction images.
# G_A and G_B
self.set_requires_grad([self.netD_A, self.netD_B], False) # Ds require no gradients when optimizing Gs
self.optimizer_G.zero_grad() # set G_A and G_B's gradients to zero
self.backward_G() # calculate gradients for G_A and G_B
self.optimizer_G.step() # update G_A and G_B's weights
# D_A and D_B
self.set_requires_grad([self.netD_A, self.netD_B], True)
self.optimizer_D.zero_grad() # set D_A and D_B's gradients to zero
self.backward_D_A() # calculate gradients for D_A
self.backward_D_B() # calculate graidents for D_B
self.optimizer_D.step() # update D_A and D_B's weights修改后的代码：
def optimize_parameters(self):
"""Calculate losses, gradients, and update network weights; called in every training iteration"""
# forward
self.forward() # compute fake images and reconstruction images.
# G_A and G_B
self.set_requires_grad([self.netD_A, self.netD_B], False) # Ds require no gradients when optimizing Gs
self.optimizer_G.zero_grad() # set G_A and G_B's gradients to zero
self.backward_G() # calculate gradients for G_A and G_B
self.optimizer_G.step() # update G_A and G_B's weights
# D_A and D_B
self.set_requires_grad([self.netD_A, self.netD_B], True)
for i in range(3):
self.optimizer_D.zero_grad() # set D_A and D_B's gradients to zero
self.backward_D_A() # calculate gradients for D_A
self.backward_D_B() # calculate graidents for D_B
self.optimizer_D.step() # update D_A and D_B's weights