A-MNIST.py

"""Augmented MNIST Data Set"""


import struct

import numpy as np

import datasets
from datasets.tasks import ImageClassification

_DESCRIPTION = """\
The dataset is built on top of MNIST.
It consists from 130K of images in 10 classes - 120K training and 10K test samples.
The training set was augmented with additional 60K images.
"""

_URLS = {
    "train_images": "data/train-images-idx3-ubyte.gz",
    "train_labels": "data/train-labels-idx1-ubyte.gz",
    "test_images": "data/t10k-images-idx3-ubyte.gz",
    "test_labels": "data/t10k-labels-idx1-ubyte.gz",
}


class AMNIST(datasets.GeneratorBasedBuilder):
    """A-MNIST Data Set"""

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="amnist",
            version=datasets.Version("1.1.0"),
            description=_DESCRIPTION,
        )
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "label": datasets.features.ClassLabel(names=["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]),
                }
            ),
            supervised_keys=("image", "label"),
            task_templates=[
                ImageClassification(
                    image_column="image",
                    label_column="label",
                )
            ],
        )

    def _split_generators(self, dl_manager):
        urls_to_download = _URLS
        downloaded_files = dl_manager.download_and_extract(urls_to_download)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": [downloaded_files["train_images"],
                                 downloaded_files["train_labels"]],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": [downloaded_files["test_images"],
                                 downloaded_files["test_labels"]],
                    "split": "test",
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        """This function returns the examples in the raw form."""
        # Images
        with open(filepath[0], "rb") as f:
            # First 16 bytes contain some metadata
            _ = f.read(4)
            size = struct.unpack(">I", f.read(4))[0]
            _ = f.read(8)
            images = np.frombuffer(f.read(), dtype=np.uint8).reshape(size, 28, 28)

        # Labels
        with open(filepath[1], "rb") as f:
            # First 8 bytes contain some metadata
            _ = f.read(8)
            labels = np.frombuffer(f.read(), dtype=np.uint8)

        for idx in range(size):
            yield idx, {"image": images[idx], "label": str(labels[idx])}