Source code for torchvision.datasets.stl10

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

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 (string): 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 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. """ 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: str, 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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