Class fashion

sweep/cnn/fashion_data.py:11–136

`MNIST `_ Dataset. Args: root (string): Root directory of dataset where ``processed/training.pt`` and ``processed/test.pt`` exist. train (bool, optional): If True, creates dataset from ``training.pt``, otherwise from ``t

class fashion(data.Dataset):
    """`MNIST <http://yann.lecun.com/exdb/mnist/>`_ Dataset.
    Args:
        root (string): Root directory of dataset where ``processed/training.pt``
            and  ``processed/test.pt`` exist.
        train (bool, optional): If True, creates dataset from ``training.pt``,
            otherwise from ``test.pt``.
        download (bool, optional): If true, downloads the dataset from the internet and
            puts it in root directory. If dataset is already downloaded, it is not
            downloaded again.
        transform (callable, optional): A function/transform that  takes in an PIL image
            and returns a transformed version. E.g, ``transforms.RandomCrop``
        target_transform (callable, optional): A function/transform that takes in the
            target and transforms it.
    """
    urls = [
        'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz',
        'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz',
        'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz',
        'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz',
    ]
    raw_folder = 'raw'
    processed_folder = 'processed'
    training_file = 'training.pt'
    test_file = 'test.pt'

    def __init__(self, root, train=True, transform=None, target_transform=None, download=False):
        self.root = os.path.expanduser(root)
        self.transform = transform
        self.target_transform = target_transform
        self.train = train  # training set or test set

        if download:
            self.download()

        if not self._check_exists():
            raise RuntimeError('Dataset not found.' +
                               ' You can use download=True to download it')

        if self.train:
            self.train_data, self.train_labels = torch.load(
                os.path.join(root, self.processed_folder, self.training_file))
        else:
            self.test_data, self.test_labels = torch.load(os.path.join(root, self.processed_folder, self.test_file))

    def __getitem__(self, index):
        """
        Args:
            index (int): Index
        Returns:
            tuple: (image, target) where target is index of the target class.
        """
        if self.train:
            img, target = self.train_data[index], self.train_labels[index]
        else:
            img, target = self.test_data[index], self.test_labels[index]

        # doing this so that it is consistent with all other datasets