load_options.dataroot, "train", torchvision.transforms.Compose([ torchvision.transforms.Resize(opt.sizex, Image.NEAREST), torchvision.transforms.CenterCrop(opt.sizex), torchvision.transforms.ToTensor(), ])) if opt.dataset == 'flowers': dataset = datasets.FlowersDataset( load_options.dataroot, "train", torchvision.transforms.Compose([ torchvision.transforms.Resize(opt.sizex, Image.NEAREST), torchvision.transforms.CenterCrop(opt.sizex), torchvision.transforms.ToTensor(), ])) if opt.dataset == 'cmnist': dataset = datasets.CMNISTDataset(dataPath=load_options.dataroot, sets='train') loader = torch.utils.data.DataLoader(dataset, batch_size=load_options.batch_size, shuffle=True) xData, mData = next(iter(loader)) xData = xData.to(device) mData = mData.to(device) ## Use the same z for all images in batch: ## # z = torch.randn(1, opt.nMasks, opt.nz, 1, 1).repeat(batch_size, 1, 1, 1, 1).to(device) ## or use different z: ## z = torch.randn(load_options.batch_size, opt.nMasks, opt.nz, 1, 1).to(device) with torch.no_grad():
transforms.ToTensor(), ])) testset = datasets.FlowersDataset(opt.dataroot, "test", transforms.Compose([transforms.Resize(opt.sizex, Image.NEAREST), transforms.CenterCrop(opt.sizex), transforms.ToTensor(), ])) valset = datasets.FlowersDataset(opt.dataroot, "val", transforms.Compose([transforms.Resize(opt.sizex, Image.NEAREST), transforms.CenterCrop(opt.sizex), transforms.ToTensor(), ])) if opt.dataset == 'cmnist': trainset = datasets.CMNISTDataset(dataPath=opt.dataroot, sets='train') testset = datasets.CMNISTDataset(dataPath=opt.dataroot, sets='test') valset = datasets.CMNISTDataset(dataPath=opt.dataroot, sets='val') trainloader = torch.utils.data.DataLoader(trainset, batch_size=opt.batchSize, shuffle=True, num_workers=4, drop_last=True) def weights_init_ortho(m): if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Linear): nn.init.orthogonal_(m.weight, opt.initOrthoGain) netEncM = models._netEncM(sizex=opt.sizex, nIn=opt.nx, nMasks=opt.nMasks, nRes=opt.nResM, nf=opt.nfM, temperature=opt.temperature).to(device) netGenX = models._netGenX(sizex=opt.sizex, nOut=opt.nx, nc=opt.nz, nf=opt.nfX, nMasks=opt.nMasks, selfAtt=opt.useSelfAttG).to(device) netDX = models._resDiscriminator128(nIn=opt.nx, nf=opt.nfD, selfAtt=opt.useSelfAttD).to(device)
transforms.Resize(opt.sizex, Image.NEAREST), transforms.CenterCrop(opt.sizex), transforms.ToTensor(), ]), ) valset = datasets.FlowersDataset( opt.dataroot, "val", transforms.Compose([ transforms.Resize(opt.sizex, Image.NEAREST), transforms.CenterCrop(opt.sizex), transforms.ToTensor(), ]), ) if opt.dataset == "cmnist": trainset = datasets.CMNISTDataset(dataPath=opt.dataroot, sets="train") testset = datasets.CMNISTDataset(dataPath=opt.dataroot, sets="test") valset = datasets.CMNISTDataset(dataPath=opt.dataroot, sets="val") trainloader = torch.utils.data.DataLoader(trainset, batch_size=opt.batchSize, shuffle=True, num_workers=4, drop_last=True) def weights_init_ortho(m): if (isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Linear)): nn.init.orthogonal_(m.weight, opt.initOrthoGain)