Python torchvision.datasets 模块，STL10 实例源码

我们从Python开源项目中，提取了以下3个代码示例，用于说明如何使用torchvision.datasets.STL10。

项目：convNet.pytorch 作者：eladhoffer | 项目源码 | 文件源码

def get_dataset(name, split='train', transform=None,
                target_transform=None, download=True, datasets_path=__DATASETS_DEFAULT_PATH):
    train = (split == 'train')
    root = os.path.join(datasets_path, name)
    if name == 'cifar10':
        return datasets.CIFAR10(root=root,
                                train=train,
                                transform=transform,
                                target_transform=target_transform,
                                download=download)
    elif name == 'cifar100':
        return datasets.CIFAR100(root=root,
                                 train=train,
                                 transform=transform,
                                 target_transform=target_transform,
                                 download=download)
    elif name == 'mnist':
        return datasets.MNIST(root=root,
                              train=train,
                              transform=transform,
                              target_transform=target_transform,
                              download=download)
    elif name == 'stl10':
        return datasets.STL10(root=root,
                              split=split,
                              transform=transform,
                              target_transform=target_transform,
                              download=download)
    elif name == 'imagenet':
        if train:
            root = os.path.join(root, 'train')
        else:
            root = os.path.join(root, 'val')
        return datasets.ImageFolder(root=root,
                                    transform=transform,
                                    target_transform=target_transform)

项目：bigBatch 作者：eladhoffer | 项目源码 | 文件源码

def get_dataset(name, split='train', transform=None,
                target_transform=None, download=True, datasets_path=__DATASETS_DEFAULT_PATH):
    train = (split == 'train')
    root = os.path.join(datasets_path, name)
    if name == 'cifar10':
        return datasets.CIFAR10(root=root,
                                train=train,
                                transform=transform,
                                target_transform=target_transform,
                                download=download)
    elif name == 'cifar100':
        return datasets.CIFAR100(root=root,
                                 train=train,
                                 transform=transform,
                                 target_transform=target_transform,
                                 download=download)
    elif name == 'mnist':
        return datasets.MNIST(root=root,
                              train=train,
                              transform=transform,
                              target_transform=target_transform,
                              download=download)
    elif name == 'stl10':
        return datasets.STL10(root=root,
                              split=split,
                              transform=transform,
                              target_transform=target_transform,
                              download=download)
    elif name == 'imagenet':
        if train:
            root = os.path.join(root, 'train')
        else:
            root = os.path.join(root, 'val')
        return datasets.ImageFolder(root=root,
                                    transform=transform,
                                    target_transform=target_transform)

项目：pytorch-playground 作者：aaron-xichen | 项目源码 | 文件源码

def get(batch_size, data_root='/mnt/local0/public_dataset/pytorch/', train=True, val=True, **kwargs):
    data_root = os.path.expanduser(os.path.join(data_root, 'stl10-data'))
    num_workers = kwargs.setdefault('num_workers', 1)
    kwargs.pop('input_size', None)
    print("Building STL10 data loader with {} workers".format(num_workers))
    ds = []
    if train:
        train_loader = torch.utils.data.DataLoader(
            datasets.STL10(
                root=data_root, split='train', download=True,
                transform=transforms.Compose([
                    transforms.Pad(4),
                    transforms.RandomCrop(96),
                    transforms.RandomHorizontalFlip(),
                    transforms.ToTensor(),
                    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
                ])),
            batch_size=batch_size, shuffle=True, **kwargs)
        ds.append(train_loader)

    if val:
        test_loader = torch.utils.data.DataLoader(
            datasets.STL10(
                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)),
                ])),
            batch_size=batch_size, shuffle=False, **kwargs)
        ds.append(test_loader)

    ds = ds[0] if len(ds) == 1 else ds
    return ds