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cifar10

load_data

Load and return the CIFAR10 dataset.

Please consider using the ciFAIR10 dataset instead. CIFAR10 contains duplicates between its train and test sets.

Parameters:

Name Type Description Default
root_dir 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
image_key str

The key for image.

 'x'
label_key str

The key for label.

 'y'
channels_last bool

Whether channel is last

 True

Returns:

Type Description
Tuple[NumpyDataset, NumpyDataset]

(train_data, eval_data)
Source code in fastestimator/fastestimator/dataset/data/cifar10.py 
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def load_data(root_dir: str = None, image_key: str = "x", label_key: str = "y",
              channels_last: bool = True) -> Tuple[NumpyDataset, NumpyDataset]:
    """Load and return the CIFAR10 dataset.

    Please consider using the ciFAIR10 dataset instead. CIFAR10 contains duplicates between its train and test sets.

    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.
        image_key: The key for image.
        label_key: The key for label.
        channels_last: Whether channel is last

    Returns:
        (train_data, eval_data)
    """
    warn("Consider using the ciFAIR10 dataset instead.")
    home = str(Path.home())

    if root_dir is None:
        root_dir = os.path.join(home, 'fastestimator_data', 'cifar10')
    else:
        root_dir = os.path.join(os.path.abspath(root_dir), 'cifar10')
    os.makedirs(root_dir, exist_ok=True)

    image_compressed_path = os.path.join(root_dir, 'cifar10.tar.gz')
    image_extracted_path = os.path.join(root_dir, 'cifar-10-batches-py')

    if not os.path.exists(image_extracted_path):
        print("Downloading data to {}".format(root_dir))
        download_file_from_google_drive('1mYURe_Oq90Sg47uSK26v-fs3zzxqupCM', image_compressed_path)

        print("Extracting data to {}".format(root_dir))
        with tarfile.open(image_compressed_path) as img_tar:
            img_tar.extractall(root_dir)

    num_train_samples = 50000

    x_train = np.empty((num_train_samples, 3, 32, 32), dtype="uint8")
    y_train = np.empty((num_train_samples, ), dtype="uint8")

    for i in range(1, 6):
        fpath = os.path.join(image_extracted_path, "data_batch_" + str(i))
        (
            x_train[(i - 1) * 10000:i * 10000, :, :, :],
            y_train[(i - 1) * 10000:i * 10000],
        ) = load_batch(fpath)

    fpath = os.path.join(image_extracted_path, "test_batch")
    x_eval, y_eval = load_batch(fpath)

    y_eval = np.array(y_eval)

    if channels_last:
        x_train = x_train.transpose(0, 2, 3, 1)
        x_eval = x_eval.transpose(0, 2, 3, 1)

    x_eval = x_eval.astype(x_train.dtype)
    y_eval = y_eval.astype(y_train.dtype)

    train_data = NumpyDataset({image_key: x_train, label_key: y_train})
    eval_data = NumpyDataset({image_key: x_eval, label_key: y_eval})

    return train_data, eval_data