本文整理匯總了Python中net.Discriminator方法的典型用法代碼示例。如果您正苦於以下問題：Python net.Discriminator方法的具體用法？Python net.Discriminator怎麽用？Python net.Discriminator使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類net的用法示例。
在下文中一共展示了net.Discriminator方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: build_model
# 需要導入模塊: import net [as 別名]
# 或者: from net import Discriminator [as 別名]
def build_model(self):
# Define generators and discriminators
if self.whichG=='normal':
self.G = net.Generator_makeup(self.g_conv_dim, self.g_repeat_num)
if self.whichG=='branch':
self.G = net.Generator_branch(self.g_conv_dim, self.g_repeat_num)
for i in self.cls:
setattr(self, "D_" + i, net.Discriminator(self.img_size, self.d_conv_dim, self.d_repeat_num, self.norm))
self.criterionL1 = torch.nn.L1Loss()
self.criterionL2 = torch.nn.MSELoss()
self.criterionGAN = GANLoss(use_lsgan=True, tensor =torch.cuda.FloatTensor)
self.vgg = net.VGG()
self.vgg.load_state_dict(torch.load('addings/vgg_conv.pth'))
# Optimizers
self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr, [self.beta1, self.beta2])
for i in self.cls:
setattr(self, "d_" + i + "_optimizer", \
torch.optim.Adam(filter(lambda p: p.requires_grad, getattr(self, "D_" + i).parameters()), \
self.d_lr, [self.beta1, self.beta2]))
# Weights initialization
self.G.apply(self.weights_init_xavier)
for i in self.cls:
getattr(self, "D_" + i).apply(self.weights_init_xavier)
# Print networks
self.print_network(self.G, 'G')
for i in self.cls:
self.print_network(getattr(self, "D_" + i), "D_" + i)
if torch.cuda.is_available():
self.G.cuda()
self.vgg.cuda()
for i in self.cls:
getattr(self, "D_" + i).cuda() 示例2: build_model
# 需要導入模塊: import net [as 別名]
# 或者: from net import Discriminator [as 別名]
def build_model(self):
# Define generators and discriminators
self.G_A = net.Generator(self.g_conv_dim, self.g_repeat_num)
self.G_B = net.Generator(self.g_conv_dim, self.g_repeat_num)
self.D_A = net.Discriminator(self.img_size, self.d_conv_dim, self.d_repeat_num)
self.D_B = net.Discriminator(self.img_size, self.d_conv_dim, self.d_repeat_num)
self.criterionL1 = torch.nn.L1Loss()
self.criterionGAN = GANLoss(use_lsgan=True, tensor =torch.cuda.FloatTensor)
# Optimizers
self.g_optimizer = torch.optim.Adam(itertools.chain(self.G_A.parameters(), self.G_B.parameters()),
self.g_lr, [self.beta1, self.beta2])
self.d_A_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, self.D_A.parameters()), self.d_lr, [self.beta1, self.beta2])
self.d_B_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, self.D_B.parameters()), self.d_lr, [self.beta1, self.beta2])
self.G_A.apply(self.weights_init_xavier)
self.D_A.apply(self.weights_init_xavier)
self.G_B.apply(self.weights_init_xavier)
self.D_B.apply(self.weights_init_xavier)
# Print networks
# self.print_network(self.E, 'E')
self.print_network(self.G_A, 'G_A')
self.print_network(self.D_A, 'D_A')
self.print_network(self.G_B, 'G_B')
self.print_network(self.D_B, 'D_B')
if torch.cuda.is_available():
self.G_A.cuda()
self.G_B.cuda()
self.D_A.cuda()
self.D_B.cuda()