add stl10 dataset (#83)

README.rst

@@ -49,6 +49,7 @@ The following dataset loaders are available:
 -  `ImageFolder <#imagefolder>`__
 -  `Imagenet-12 <#imagenet-12>`__
 -  `CIFAR10 and CIFAR100 <#cifar>`__
+-  `STL10 <#stl10>`__
 
 Datasets have the API: - ``__getitem__`` - ``__len__`` They all subclass
 from ``torch.utils.data.Dataset`` Hence, they can all be multi-threaded
@@ -156,6 +157,18 @@ CIFAR
    puts it in root directory. If dataset already downloaded, does not do
    anything.
 
+STL10
+~~~~~
+
+``dset.STL10(root, split='train', transform=None, target_transform=None, download=False)``
+
+-  ``root`` : root directory of dataset where there is folder ``stl10_binary``
+-  ``split`` : ``'train'`` = Training set, ``'test'`` = Test set, ``'unlabeled'`` = Unlabeled set,
+    ``'train+unlabeled'`` = Training + Unlabeled set (missing label marked as ``-1``)
+-  ``download`` : ``True`` = downloads the dataset from the internet and
+    puts it in root directory. If dataset already downloaded, does not do
+    anything.
+
 ImageFolder
 ~~~~~~~~~~~
 

torchvision/datasets/__init__.py

@@ -2,10 +2,11 @@ from .lsun import LSUN, LSUNClass
 from .folder import ImageFolder
 from .coco import CocoCaptions, CocoDetection
 from .cifar import CIFAR10, CIFAR100
+from .stl10 import STL10
 from .mnist import MNIST
 
 __all__ = ('LSUN', 'LSUNClass',
            'ImageFolder',
            'CocoCaptions', 'CocoDetection',
            'CIFAR10', 'CIFAR100',
-           'MNIST')
+           'MNIST', 'STL10')

torchvision/datasets/stl10.py

+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
+
+
+class STL10(CIFAR10):
+    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 = 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 = None
+        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(
+            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 __getitem__(self, index):
+        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