我们从Python开源项目中，提取了以下4个代码示例，用于说明如何使用torchvision.datasets.SVHN。
def get_loader(config): """Builds and returns Dataloader for MNIST and SVHN dataset.""" transform = transforms.Compose([ transforms.Scale(config.image_size), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) svhn = datasets.SVHN(root=config.svhn_path, download=True, transform=transform) mnist = datasets.MNIST(root=config.mnist_path, download=True, transform=transform) svhn_loader = torch.utils.data.DataLoader(dataset=svhn, batch_size=config.batch_size, shuffle=True, num_workers=config.num_workers) mnist_loader = torch.utils.data.DataLoader(dataset=mnist, batch_size=config.batch_size, shuffle=True, num_workers=config.num_workers) return svhn_loader, mnist_loader
def get_loader(config): """Builds and returns Dataloader for MNIST and SVHN dataset.""" transform = transforms.Compose([ transforms.Scale(config.image_size), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) svhn = datasets.SVHN(root=config.svhn_path, download=True, transform=transform, split='train') mnist = datasets.MNIST(root=config.mnist_path, download=True, transform=transform, train=True) svhn_test = datasets.SVHN(root=config.svhn_path, download=True, transform=transform, split='test') mnist_test = datasets.MNIST(root=config.mnist_path, download=True, transform=transform, train=False) svhn_loader = torch.utils.data.DataLoader(dataset=svhn, batch_size=config.batch_size, shuffle=True, num_workers=config.num_workers) mnist_loader = torch.utils.data.DataLoader(dataset=mnist, batch_size=config.batch_size, shuffle=True, num_workers=config.num_workers) svhn_test_loader = torch.utils.data.DataLoader(dataset=svhn_test, batch_size=config.batch_size, shuffle=False, num_workers=config.num_workers) mnist_test_loader = torch.utils.data.DataLoader(dataset=mnist_test, batch_size=config.batch_size, shuffle=False, num_workers=config.num_workers) return svhn_loader, mnist_loader, svhn_test_loader, mnist_test_loader
def get(batch_size, data_root='/tmp/public_dataset/pytorch', train=True, val=True, **kwargs): data_root = os.path.expanduser(os.path.join(data_root, 'svhn-data')) num_workers = kwargs.setdefault('num_workers', 1) kwargs.pop('input_size', None) print("Building SVHN data loader with {} workers".format(num_workers)) def target_transform(target): return int(target[0]) - 1 ds = [] if train: train_loader = torch.utils.data.DataLoader( datasets.SVHN( root=data_root, split='train', download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]), target_transform=target_transform, ), batch_size=batch_size, shuffle=True, **kwargs) ds.append(train_loader) if val: test_loader = torch.utils.data.DataLoader( datasets.SVHN( root=data_root, split='test', download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]), target_transform=target_transform ), batch_size=batch_size, shuffle=False, **kwargs) ds.append(test_loader) ds = ds[0] if len(ds) == 1 else ds return ds
def __init__(self, batchsize, train=True): Dataset.__init__(self) data_root = join(dirname(realpath(__file__)), 'SVHN_data') self.name = "svhn" self.range = [0.0, 1.0] self.data_dims = [3, 32, 32] self.batchsize = batchsize if train: split = "train" self.data = dsets.SVHN(root=data_root, download=True, split="train", transform=transforms.Compose([ transforms.ToTensor()])) self.dataloder = tdata.DataLoader(self.data, self.batchsize, shuffle=True) self.iter = iter(self.dataloder) self._index = 0
