cifar 10 and 100

.gitignore

@@ -0,0 +1,7 @@
+build/
+dist/
+torchvision.egg-info/
+*/**/__pycache__
+*/**/*.pyc
+*/**/*~
+*~

README.md

@@ -29,6 +29,7 @@ The following dataset loaders are available:
 - [LSUN Classification](#lsun)
 - [ImageFolder](#imagefolder)
 - [Imagenet-12](#imagenet-12)
+- [CIFAR10 and CIFAR100](#cifar)
 
 Datasets have the API:
 - `__getitem__`
@@ -97,6 +98,15 @@ u'A mountain view with a plume of smoke in the background']
   - ['bedroom_train', 'church_train', ...] : a list of categories to load
 
 
+### CIFAR
+
+`dset.CIFAR10(root, train=True, transform=None, target_transform=None, download=False)`
+`dset.CIFAR100(root, train=True, transform=None, target_transform=None, download=False)`
+
+- `root` : root directory of dataset where there is folder `cifar-10-batches-py`
+- `train` : `True` = Training set, `False` = Test set
+- `download` : `True` = downloads the dataset from the internet and puts it in root directory. If dataset already downloaded, does not do anything.
+
 ### ImageFolder
 
 A generic data loader where the images are arranged in this way:

test/cifar.py

@@ -0,0 +1,12 @@
+import torch
+import torchvision.datasets as dset
+
+print('\n\nCifar 10')
+a = dset.CIFAR10(root="abc/def/ghi", download=True)
+
+print(a[3])
+
+print('\n\nCifar 100')
+a = dset.CIFAR100(root="abc/def/ghi", download=True)
+
+print(a[3])

torchvision/datasets/__init__.py

@@ -1,7 +1,9 @@
 from .lsun import LSUN, LSUNClass
 from .folder import ImageFolder
 from .coco import CocoCaptions, CocoDetection
+from .cifar import CIFAR10, CIFAR100
 
 __all__ = ('LSUN', 'LSUNClass',
            'ImageFolder',
-           'CocoCaptions', 'CocoDetection')
+           'CocoCaptions', 'CocoDetection',
+           'CIFAR10', 'CIFAR100')

torchvision/datasets/cifar.py

@@ -0,0 +1,159 @@
+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
+if sys.version_info[0] == 2:
+    import cPickle as pickle
+else:
+    import pickle
+
+class CIFAR10(data.Dataset):
+    base_folder = 'cifar-10-batches-py'
+    url = "http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
+    filename = "cifar-10-python.tar.gz"
+    tgz_mdf = 'c58f30108f718f92721af3b95e74349a'
+    train_list = [
+            ['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
+            ['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
+            ['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],
+            ['data_batch_4', '634d18415352ddfa80567beed471001a'],
+            ['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
+    ]
+
+    test_list = [
+            ['test_batch', '40351d587109b95175f43aff81a1287e'],
+    ]
+
+    def __init__(self, root, train=True, transform=None, target_transform=None, download=False):
+        self.root = root
+        self.transform = transform
+        self.target_transform = target_transform
+        self.train = train # training set or test 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
+        self.train_data = []
+        self.train_labels = []
+        for fentry in self.train_list:
+            f = fentry[0]
+            file = os.path.join(root, self.base_folder, f)
+            fo = open(file, 'rb')
+            entry = pickle.load(fo)
+            self.train_data.append(entry['data'])
+            if 'labels' in entry:
+                self.train_labels += entry['labels']
+            else:
+                self.train_labels += entry['fine_labels']
+            fo.close()
+
+        self.train_data = np.concatenate(self.train_data)
+
+        f = self.test_list[0][0]
+        file = os.path.join(root, self.base_folder, f)
+        fo = open(file, 'rb')
+        entry = pickle.load(fo)
+        self.test_data = entry['data']
+        if 'labels' in entry:
+            self.test_labels = entry['labels']
+        else:
+            self.test_labels = entry['fine_labels']
+        fo.close()
+
+        self.train_data = self.train_data.reshape((50000, 3, 32, 32))
+        self.test_data = self.test_data.reshape((10000, 3, 32, 32))
+
+    def __getitem__(self, index):
+        if self.train:
+            img, target = self.train_data[index], self.train_labels[index]
+        else:
+            img, target = self.test_data[index], self.test_labels[index]
+
+        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):
+        if self.train:
+            return 50000
+        else:
+            return 10000
+
+    def _check_integrity(self):
+        import hashlib
+        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 os.path.isfile(fpath):
+                return False
+            md5c = hashlib.md5(open(fpath, 'rb').read()).hexdigest()
+            if md5c != md5:
+                return False
+        return True
+
+    def download(self):
+        from six.moves import urllib
+        import tarfile
+        import hashlib
+
+        root = self.root
+        fpath = os.path.join(root, self.filename)
+
+        try:
+            os.makedirs(root)
+        except OSError as e:
+            if e.errno == errno.EEXIST:
+                pass
+            else:
+                raise
+
+        if self._check_integrity():
+            print('Files already downloaded and verified')
+            return
+
+        # downloads file
+        if os.path.isfile(fpath) and \
+           hashlib.md5(open(fpath, 'rb').read()).hexdigest() == self.tgz_md5:
+            print('Using downloaded file: ' + fpath)
+        else:
+            print('Downloading ' + self.url + ' to ' + fpath)
+            urllib.request.urlretrieve(self.url, fpath)
+
+        # extract file
+        cwd = os.getcwd()
+        print('Extracting tar file')
+        tar = tarfile.open(fpath, "r:gz")
+        os.chdir(root)        
+        tar.extractall()
+        tar.close()
+        os.chdir(cwd)
+        print('Done!')
+
+
+class CIFAR100(CIFAR10):
+    base_folder = 'cifar-100-python'
+    url = "http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz"
+    filename = "cifar-100-python.tar.gz"
+    tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85'
+    train_list = [
+            ['train', '16019d7e3df5f24257cddd939b257f8d'],
+    ]
+
+    test_list = [
+            ['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'],
+    ]
+