def load_models(args):
if args.task == 'AE':
if args.dataset == 'mnist':
netG = generators.MNISTgenerator(args).cuda()
netD = discriminators.MNISTdiscriminator(args).cuda()
netE = encoders.MNISTencoder(args).cuda()
elif args.dataset == 'cifar10':
netG = generators.CIFARgenerator(args).cuda()
netD = discriminators.CIFARdiscriminator(args).cuda()
netE = encoders.CIFARencoder(args).cuda()
if args.task == 'sr':
if args.dataset == 'cifar10':
netG = generators.genResNet(args).cuda()
netD = discriminators.SRdiscriminatorCIFAR(args).cuda()
vgg = vgg19_bn(pretrained=True).cuda()
netE = VGGextraction(vgg)
elif args.dataset == 'imagenet':
netG = generators.genResNet(args, (3, 224, 224)).cuda(0)
netD = discriminators.SRdiscriminatorCIFAR(args).cuda(0)
netD = None
vgg = vgg19_bn(pretrained=True).cuda(1)
netE = VGGextraction(vgg).cuda(1)
print(netG, netD, netE)
return (netG, netD, netE)
def load_models(args):
if args.dataset == 'mnist':
netG = generators.MNISTgenerator(args).cuda()
netD = discriminators.MNISTdiscriminator(args).cuda()
netE = encoders.MNISTencoder(args).cuda()
if args.dataset == 'cifar10':
netG = generators.CIFARgenerator(args).cuda()
netD = discriminators.CIFARdiscriminator(args).cuda()
netE = encoders.CIFARencoder(args).cuda()
print(netG, netD, netE)
return (netG, netD, netE)
def load_models(args):
if args.dataset in ['mnist', 'fmnist']:
netG = generators.MNISTgenerator(args).cuda()
netD = discriminators.MNISTdiscriminator(args).cuda()
netE = encoders.MNISTencoder(args).cuda()
if args.dataset in ['cifar', 'cifar_hidden']:
netG = generators.CIFARgenerator(args).cuda()
netD = discriminators.CIFARdiscriminator(args).cuda()
netE = encoders.CIFARencoder(args).cuda()
if args.dataset == 'celeba':
netG = generators.CELEBAgenerator(args).cuda()
netD = discriminators.CELEBAdiscriminator(args).cuda()
netE = encoders.CELEBAencoder(args).cuda()
print (netG, netD, netE)
return (netG, netD, netE)