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svhn

load_data ΒΆ

Load and return the Street View House Numbers (SVHN) dataset.

Parameters:

Name Type Description Default
root_dir Optional[str]

The path to store the downloaded data. When path is not provided, the data will be saved into fastestimator_data under the user's home directory.

 None

Returns:

Type Description
Tuple[PickleDataset, PickleDataset]

(train_data, test_data)
Source code in fastestimator/fastestimator/dataset/data/svhn.py 
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def load_data(root_dir: Optional[str] = None) -> Tuple[PickleDataset, PickleDataset]:
    """Load and return the Street View House Numbers (SVHN) dataset.

    Args:
        root_dir: The path to store the downloaded data. When `path` is not provided, the data will be saved into
            `fastestimator_data` under the user's home directory.

    Returns:
        (train_data, test_data)
    """
    home = str(Path.home())

    if root_dir is None:
        root_dir = os.path.join(home, 'fastestimator_data', 'SVHN')
    else:
        root_dir = os.path.join(os.path.abspath(root_dir), 'SVHN')
    os.makedirs(root_dir, exist_ok=True)

    train_file_path = os.path.join(root_dir, 'train.pickle')
    test_file_path = os.path.join(root_dir, 'test.pickle')
    train_compressed_path = os.path.join(root_dir, "train.tar.gz")
    test_compressed_path = os.path.join(root_dir, "test.tar.gz")
    train_folder_path = os.path.join(root_dir, "train")
    test_folder_path = os.path.join(root_dir, "test")

    if not os.path.exists(train_folder_path):
        # download
        if not os.path.exists(train_compressed_path):
            print("Downloading train data to {}".format(root_dir))
            wget.download('http://ufldl.stanford.edu/housenumbers/train.tar.gz', root_dir, bar=bar_custom)
        # extract
        print("\nExtracting files ...")
        with tarfile.open(train_compressed_path) as tar:
            tar.extractall(root_dir)

    if not os.path.exists(test_folder_path):
        # download
        if not os.path.exists(test_compressed_path):
            print("Downloading eval data to {}".format(root_dir))
            wget.download('http://ufldl.stanford.edu/housenumbers/test.tar.gz', root_dir, bar=bar_custom)
        # extract
        print("\nExtracting files ...")
        with tarfile.open(test_compressed_path) as tar:
            tar.extractall(root_dir)

    # glob and generate bbox files
    if not os.path.exists(train_file_path):
        print("\nConstructing bounding box data ...")
        _extract_metadata(train_folder_path, "train", train_file_path)
    if not os.path.exists(test_file_path):
        print("\nConstructing bounding box data ...")
        _extract_metadata(test_folder_path, "test", test_file_path)

    return PickleDataset(train_file_path), PickleDataset(test_file_path)