venv/lib/python3.11/site-packages/torchvision/datasets/stl10.py

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

import numpy as np
from PIL import Image

from .utils import check_integrity, download_and_extract_archive, verify_str_arg
from .vision import VisionDataset


class STL10(VisionDataset):
    """`STL10 <https://cs.stanford.edu/~acoates/stl10/>`_ Dataset.

    Args:
        root (str or ``pathlib.Path``): Root directory of dataset where directory
            ``stl10_binary`` exists.
        split (string): One of {'train', 'test', 'unlabeled', 'train+unlabeled'}.
            Accordingly, dataset is selected.
        folds (int, optional): One of {0-9} or None.
            For training, loads one of the 10 pre-defined folds of 1k samples for the
            standard evaluation procedure. If no value is passed, loads the 5k samples.
        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.
    """

    base_folder = "stl10_binary"
    url = "http://ai.stanford.edu/~acoates/stl10/stl10_binary.tar.gz"
    filename = "stl10_binary.tar.gz"
    tgz_md5 = "91f7769df0f17e558f3565bffb0c7dfb"
    class_names_file = "class_names.txt"
    folds_list_file = "fold_indices.txt"
    train_list = [
        ["train_X.bin", "918c2871b30a85fa023e0c44e0bee87f"],
        ["train_y.bin", "5a34089d4802c674881badbb80307741"],
        ["unlabeled_X.bin", "5242ba1fed5e4be9e1e742405eb56ca4"],
    ]

    test_list = [["test_X.bin", "7f263ba9f9e0b06b93213547f721ac82"], ["test_y.bin", "36f9794fa4beb8a2c72628de14fa638e"]]
    splits = ("train", "train+unlabeled", "unlabeled", "test")

    def __init__(
        self,
        root: Union[str, Path],
        split: str = "train",
        folds: Optional[int] = None,
        transform: Optional[Callable] = None,
        target_transform: Optional[Callable] = None,
        download: bool = False,
    ) -> None:
        super().__init__(root, transform=transform, target_transform=target_transform)
        self.split = verify_str_arg(split, "split", self.splits)
        self.folds = self._verify_folds(folds)

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

        # now load the picked numpy arrays
        self.labels: Optional[np.ndarray]
        if self.split == "train":
            self.data, self.labels = self.__loadfile(self.train_list[0][0], self.train_list[1][0])
            self.labels = cast(np.ndarray, self.labels)
            self.__load_folds(folds)

        elif self.split == "train+unlabeled":
            self.data, self.labels = self.__loadfile(self.train_list[0][0], self.train_list[1][0])
            self.labels = cast(np.ndarray, self.labels)
            self.__load_folds(folds)
            unlabeled_data, _ = self.__loadfile(self.train_list[2][0])
            self.data = np.concatenate((self.data, unlabeled_data))
            self.labels = np.concatenate((self.labels, np.asarray([-1] * unlabeled_data.shape[0])))

        elif self.split == "unlabeled":
            self.data, _ = self.__loadfile(self.train_list[2][0])
            self.labels = np.asarray([-1] * self.data.shape[0])
        else:  # self.split == 'test':
            self.data, self.labels = self.__loadfile(self.test_list[0][0], self.test_list[1][0])

        class_file = os.path.join(self.root, self.base_folder, self.class_names_file)
        if os.path.isfile(class_file):
            with open(class_file) as f:
                self.classes = f.read().splitlines()

    def _verify_folds(self, folds: Optional[int]) -> Optional[int]:
        if folds is None:
            return folds
        elif isinstance(folds, int):
            if folds in range(10):
                return folds
            msg = "Value for argument folds should be in the range [0, 10), but got {}."
            raise ValueError(msg.format(folds))
        else:
            msg = "Expected type None or int for argument folds, but got type {}."
            raise ValueError(msg.format(type(folds)))

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

        Returns:
            tuple: (image, target) where target is index of the target class.
        """
        target: Optional[int]
        if self.labels is not None:
            img, target = self.data[index], int(self.labels[index])
        else:
            img, target = self.data[index], None

        # doing this so that it is consistent with all other datasets
        # to return a PIL Image
        img = Image.fromarray(np.transpose(img, (1, 2, 0)))

        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 self.data.shape[0]

    def __loadfile(self, data_file: str, labels_file: Optional[str] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
        labels = None
        if labels_file:
            path_to_labels = os.path.join(self.root, self.base_folder, labels_file)
            with open(path_to_labels, "rb") as f:
                labels = np.fromfile(f, dtype=np.uint8) - 1  # 0-based

        path_to_data = os.path.join(self.root, self.base_folder, data_file)
        with open(path_to_data, "rb") as f:
            # read whole file in uint8 chunks
            everything = np.fromfile(f, dtype=np.uint8)
            images = np.reshape(everything, (-1, 3, 96, 96))
            images = np.transpose(images, (0, 1, 3, 2))

        return images, labels

    def _check_integrity(self) -> bool:
        for filename, md5 in self.train_list + self.test_list:
            fpath = os.path.join(self.root, self.base_folder, filename)
            if not check_integrity(fpath, md5):
                return False
        return True

    def download(self) -> None:
        if self._check_integrity():
            return
        download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.tgz_md5)
        self._check_integrity()

    def extra_repr(self) -> str:
        return "Split: {split}".format(**self.__dict__)

    def __load_folds(self, folds: Optional[int]) -> None:
        # loads one of the folds if specified
        if folds is None:
            return
        path_to_folds = os.path.join(self.root, self.base_folder, self.folds_list_file)
        with open(path_to_folds) as f:
            str_idx = f.read().splitlines()[folds]
            list_idx = np.fromstring(str_idx, dtype=np.int64, sep=" ")
            self.data = self.data[list_idx, :, :, :]
            if self.labels is not None:
                self.labels = self.labels[list_idx]