def initialize(self, opt):
BaseModel.initialize(self, opt)
self.is_Train = opt.is_Train
self.G = Generator(N_p=opt.N_p, N_z=opt.N_z)
self.D = Discriminator(N_p=opt.N_p, N_d=opt.N_d)
if self.is_Train:
self.optimizer_G = optim.Adam(self.G.parameters(),
lr=opt.lr_G,
betas=(opt.beta1, opt.beta2))
self.optimizer_D = optim.Adam(self.D.parameters(),
lr=opt.lr_D,
betas=(opt.beta1, opt.beta2))
self.criterion = nn.CrossEntropyLoss()
self.L1_criterion = nn.L1Loss()
self.w_L1 = opt.w_L1
self.N_z = opt.N_z
self.N_p = opt.N_p
self.N_d = opt.N_d
import torchvision.models as models
self.resnet18 = models.resnet18(pretrained=True)
if self.gpu_ids:
self.resnet18 = self.resnet18.cuda()
self.resnet18.fc = torch.nn.LeakyReLU(0.1)
for param in self.resnet18.parameters():
param.requires_grad = False
self.resnet18.eval()
torch.set_num_threads(1)
def initialize(self, opt):
BaseModel.initialize(self, opt)
self.is_Train = opt.is_Train
self.G = Generator(N_p=opt.N_p, N_z=opt.N_z,single = True)
self.D = Discriminator(N_p=opt.N_p, N_d=opt.N_d)
if self.is_Train:
self.optimizer_G = optim.Adam(self.G.parameters(), lr=opt.lr_G, betas=(opt.beta1, opt.beta2))
self.optimizer_D = optim.Adam(self.D.parameters(), lr=opt.lr_D, betas=(opt.beta1, opt.beta2))
self.criterion = nn.CrossEntropyLoss()
self.L1_criterion = nn.L1Loss()
self.w_L1 = opt.w_L1
self.N_z = opt.N_z
self.N_p = opt.N_p
self.N_d = opt.N_d