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)