vision/torchvision/datasets/semeion.py

import os.path
from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union

import numpy as np
from PIL import Image

from .utils import check_integrity, download_url
from .vision import VisionDataset


class SEMEION(VisionDataset):
    r"""`SEMEION <http://archive.ics.uci.edu/ml/datasets/semeion+handwritten+digit>`_ Dataset.

    Args:
        root (str or ``pathlib.Path``): Root directory of dataset where directory
            ``semeion.py`` exists.
        transform (callable, optional): A function/transform that takes in a 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.
        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.

    """
    url = "http://archive.ics.uci.edu/ml/machine-learning-databases/semeion/semeion.data"
    filename = "semeion.data"
    md5_checksum = "cb545d371d2ce14ec121470795a77432"

    def __init__(
        self,
        root: Union[str, Path],
        transform: Optional[Callable] = None,
        target_transform: Optional[Callable] = None,
        download: bool = True,
    ) -> None:
        super().__init__(root, transform=transform, target_transform=target_transform)

        if download:
            self.download()

        if not self._check_integrity():
            raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")

        fp = os.path.join(self.root, self.filename)
        data = np.loadtxt(fp)
        # convert value to 8 bit unsigned integer
        # color (white #255) the pixels
        self.data = (data[:, :256] * 255).astype("uint8")
        self.data = np.reshape(self.data, (-1, 16, 16))
        self.labels = np.nonzero(data[:, 256:])[1]

    def __getitem__(self, index: int) -> Tuple[Any, Any]:
        """
        Args:
            index (int): Index

        Returns:
            tuple: (image, target) where target is index of the target class.
        """
        img, target = self.data[index], int(self.labels[index])

        # doing this so that it is consistent with all other datasets
        # to return a PIL Image
        img = Image.fromarray(img, mode="L")

        if self.transform is not None:
            img = self.transform(img)

        if self.target_transform is not None:
            target = self.target_transform(target)

        return img, target

    def __len__(self) -> int:
        return len(self.data)

    def _check_integrity(self) -> bool:
        root = self.root
        fpath = os.path.join(root, self.filename)
        if not check_integrity(fpath, self.md5_checksum):
            return False
        return True

    def download(self) -> None:
        if self._check_integrity():
            return

        root = self.root
        download_url(self.url, root, self.filename, self.md5_checksum)