Add SEMEION Dataset

torchvision/datasets/__init__.py

@@ -7,9 +7,10 @@
 from .svhn import SVHN
 from .phototour import PhotoTour
 from .fakedata import FakeData
+from .semeion import SEMEION
 
 __all__ = ('LSUN', 'LSUNClass',
            'ImageFolder', 'FakeData',
            'CocoCaptions', 'CocoDetection',
            'CIFAR10', 'CIFAR100', 'FashionMNIST',
-           'MNIST', 'STL10', 'SVHN', 'PhotoTour')
+           'MNIST', 'STL10', 'SVHN', 'PhotoTour', 'SEMEION')

torchvision/datasets/semeion.py

@@ -0,0 +1,123 @@
+from __future__ import print_function
+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
+
+import torch.utils.data as data
+from .utils import download_url, check_integrity
+
+
+class SEMEION(data.Dataset):
+    """`SEMEION <http://archive.ics.uci.edu/ml/datasets/semeion+handwritten+digit>`_ Dataset.
+    Args:
+        root (string): Root directory of dataset where directory
+            ``semeion.py`` exists.
+        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.
+    """
+    url = "http://archive.ics.uci.edu/ml/machine-learning-databases/semeion/semeion.data"
+    filename = "semeion.data"
+    md5_checksum = 'cb545d371d2ce14ec121470795a77432'
+
+    def __init__(self, root, transform=None, target_transform=None, download=True):
+        self.root = os.path.expanduser(root)
+        self.transform = transform
+        self.target_transform = target_transform
+
+        if download:
+            self.download()
+
+        if not self._check_integrity():
+            raise RuntimeError('Dataset not found or corrupted.' +
+                               ' You can use download=True to download it')
+
+        self.data = []
+        self.labels = []
+        fp = os.path.join(root, self.filename)
+        file = open(fp, 'r')
+        data = file.read()
+        file.close()
+        dataSplitted = data.split("\n")[:-1]
+        datasetLength = len(dataSplitted)
+        i = 0
+        while i < datasetLength:
+            # Get the 'i-th' row
+            strings = dataSplitted[i]
+
+            # Split row into numbers(string), and avoid blank at the end
+            stringsSplitted = (strings[:-1]).split(" ")
+
+            # Get data (which ends at column 256th), then in a numpy array.
+            rawData = stringsSplitted[:256]
+            dataFloat = [float(j) for j in rawData]
+            img = np.array(dataFloat[:16])
+            j = 16
+            k = 0
+            while j < len(dataFloat):
+                temp = np.array(dataFloat[k:j])
+                img = np.vstack((img, temp))
+
+                k = j
+                j += 16
+
+            self.data.append(img)
+
+            # Get label and convert it into numbers, then in a numpy array.
+            labelString = stringsSplitted[256:]
+            labelInt = [int(index) for index in labelString]
+            self.labels.append(np.array(labelInt))
+            i += 1
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+        Returns:
+            tuple: (image, target) where target is index of the target class.
+        """
+        img, target = self.data[index], self.labels[index]
+
+        # doing this so that it is consistent with all other datasets
+        # to return a PIL Image
+        # convert value to 8 bit unsigned integer
+        # color (white #255) the pixels
+        img = img.astype('uint8') * 255
+        img = Image.fromarray(img, mode='L')
+
+        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):
+        root = self.root
+        fpath = os.path.join(root, self.filename)
+        if not check_integrity(fpath, self.md5_checksum):
+            return False
+        return True
+
+    def download(self):
+        if self._check_integrity():
+            print('Files already downloaded and verified')
+            return
+
+        root = self.root
+        download_url(self.url, root, self.filename, self.md5_checksum)