Source code for torchvision.datasets.semeion

from PIL import Image
import os
import os.path
import numpy as np
from typing import Any, Callable, Optional, Tuple
from .vision import VisionDataset
from .utils import download_url, check_integrity


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

    Args:
        root (string): Root directory of dataset where directory
            ``semeion.py`` exists.
        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.
        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: str,
            transform: Optional[Callable] = None,
            target_transform: Optional[Callable] = None,
            download: bool = True,
    ) -> None:
        super(SEMEION, self).__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():
            print('Files already downloaded and verified')
            return

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