testset = datasets.CUBDataset(opt.dataroot, "test", transforms.Compose([transforms.Resize(opt.sizex, Image.NEAREST), transforms.CenterCrop(opt.sizex), transforms.ToTensor(), ])) valset = datasets.CUBDataset(opt.dataroot, "val", transforms.Compose([transforms.Resize(opt.sizex, Image.NEAREST), transforms.CenterCrop(opt.sizex), transforms.ToTensor(), ])) if opt.dataset == 'flowers': trainset = datasets.FlowersDataset(opt.dataroot, "train", transforms.Compose([transforms.Resize(opt.sizex, Image.NEAREST), transforms.CenterCrop(opt.sizex), 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':
torchvision.transforms.ToTensor(), ]), ) if opt.dataset == 'cub': dataset = datasets.CUBDataset( 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: ##