Source code for torchvision.datasets.stl10

from PIL import Image
import os
import os.path
import numpy as np

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

[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, split='train', folds=None, transform=None, target_transform=None, download=False): super(STL10, self).__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 if self.split == 'train':, self.labels = self.__loadfile( self.train_list[0][0], self.train_list[1][0]) self.__load_folds(folds) elif self.split == 'train+unlabeled':, self.labels = self.__loadfile( self.train_list[0][0], self.train_list[1][0]) 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): 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): """ Args: index (int): Index Returns: tuple: (image, target) where target is index of the target class. """ 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): return[0] def __loadfile(self, data_file, labels_file=None): 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): root = self.root for fentry in (self.train_list + self.test_list): filename, md5 = fentry[0], fentry[1] fpath = os.path.join(root, self.base_folder, filename) if not check_integrity(fpath, md5): return False return True def download(self): 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): return "Split: {split}".format(**self.__dict__) def __load_folds(self, folds): # 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, 'r') as f: str_idx =[folds] list_idx = np.fromstring(str_idx, dtype=np.uint8, sep=' '), self.labels =[list_idx, :, :, :], self.labels[list_idx]


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