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: ##