SVHN dataset for torchvision

README.rst

@@ -168,6 +168,17 @@ STL10
 -  ``download`` : ``True`` = downloads the dataset from the internet and
     puts it in root directory. If dataset already downloaded, does not do
     anything.
+
+SVHN
+~~~~~
+
+``dset.SVHN(root, split='train', transform=None, target_transform=None, download=False)``
+
+-  ``root`` : root directory of dataset where there is folder ``SVHN``
+-  ``split`` : ``'train'`` = Training set, ``'test'`` = Test set, ``'extra'`` = Extra training set
+-  ``download`` : ``True`` = downloads the dataset from the internet and
+    puts it in root directory. If dataset is already downloaded, does not do
+    anything.
 
 ImageFolder
 ~~~~~~~~~~~

torchvision/datasets/__init__.py

@@ -4,9 +4,10 @@
 from .cifar import CIFAR10, CIFAR100
 from .stl10 import STL10
 from .mnist import MNIST
+from .svhn import SVHN
 
 __all__ = ('LSUN', 'LSUNClass',
            'ImageFolder',
            'CocoCaptions', 'CocoDetection',
            'CIFAR10', 'CIFAR100',
-           'MNIST', 'STL10')
+           'MNIST', 'STL10', 'SVHN')

torchvision/datasets/svhn.py

@@ -0,0 +1,111 @@
+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
+
+
+class SVHN(data.Dataset):
+    url = ""
+    filename = ""
+    file_md5 = ""
+
+    split_list = {
+        'train': ["http://ufldl.stanford.edu/housenumbers/train_32x32.mat",
+                  "train_32x32.mat", "e26dedcc434d2e4c54c9b2d4a06d8373"],
+        'test': ["http://ufldl.stanford.edu/housenumbers/test_32x32.mat",
+                 "test_32x32.mat", "eb5a983be6a315427106f1b164d9cef3"],
+        'extra': ["http://ufldl.stanford.edu/housenumbers/extra_32x32.mat",
+                  "extra_32x32.mat", "a93ce644f1a588dc4d68dda5feec44a7"]}
+
+    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  # training set or test set or extra set
+
+        if self.split not in self.split_list:
+            raise ValueError('Wrong split entered! Please use split="train" or split="extra" or split="test"')
+
+        self.url = self.split_list[split][0]
+        self.filename = self.split_list[split][1]
+        self.file_md5 = self.split_list[split][2]
+
+        if download:
+            self.download()
+
+        if not self._check_integrity():
+            raise RuntimeError('Dataset not found or corrupted.' +
+                               ' You can use download=True to download it')
+
+        # import here rather than at top of file because this is
+        # an optional dependency for torchvision
+        import scipy.io as sio
+
+        # reading(loading) mat file as array
+        loaded_mat = sio.loadmat(os.path.join(root, self.filename))
+
+        self.data = loaded_mat['X']
+        self.labels = loaded_mat['y']
+        self.data = np.transpose(self.data, (3, 2, 0, 1))
+
+    def __getitem__(self, index):
+        img, target = self.data[index], self.labels[index]
+
+        # 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 len(self.data)
+
+    def _check_integrity(self):
+        import hashlib
+        root = self.root
+        md5 = self.split_list[self.split][2]
+        fpath = os.path.join(root, self.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):
+            print('Using downloaded file: ' + fpath)
+        else:
+            print('Downloading ' + self.url + ' to ' + fpath)
+            urllib.request.urlretrieve(self.url, fpath)
+            print ('Downloaded!')