Source code for torchvision.datasets.stl10

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

[docs]class STL10(VisionDataset): """`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 = "" 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: 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.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.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]) = np.concatenate((, unlabeled_data)) self.labels = np.concatenate((self.labels, np.asarray([-1] * unlabeled_data.shape[0]))) elif self.split == "unlabeled":, _ = self.__loadfile(self.train_list[2][0]) self.labels = np.asarray([-1] *[0]) else: # self.split == 'test':, 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 = 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)))
[docs] 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 =[index], int(self.labels[index]) else: img, target =[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[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(): print("Files already downloaded and verified") 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 =[folds] list_idx = np.fromstring(str_idx, dtype=np.int64, sep=" ") =[list_idx, :, :, :] if self.labels is not None: self.labels = self.labels[list_idx]


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