from __future__ import print_function
import torch.utils.data as data
from PIL import Image
import os
import os.path
import errno
import numpy as np
import sys
from .cifar import CIFAR10
[docs]class STL10(CIFAR10):
"""`STL10 <https://cs.stanford.edu/~acoates/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.
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 = "http://ai.stanford.edu/~acoates/stl10/stl10_binary.tar.gz"
filename = "stl10_binary.tar.gz"
tgz_md5 = '91f7769df0f17e558f3565bffb0c7dfb'
class_names_file = 'class_names.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']
]
def __init__(self, root, split='train',
transform=None, target_transform=None, download=False):
self.root = os.path.expanduser(root)
self.transform = transform
self.target_transform = target_transform
self.split = split # train/test/unlabeled set
if download:
self.download()
if 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.data, self.labels = self.__loadfile(
self.train_list[0][0], self.train_list[1][0])
elif self.split == 'train+unlabeled':
self.data, self.labels = self.__loadfile(
self.train_list[0][0], self.train_list[1][0])
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()
[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 = 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):
return self.data.shape[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