Rewrite mnist.py and add mnist_test.py

python/paddle/v2/dataset/mnist.py

-import sklearn.datasets.mldata
-import sklearn.model_selection
+import paddle.v2.dataset.common
+import subprocess
 import numpy
-from common import DATA_HOME
 
-__all__ = ['train_creator', 'test_creator']
+URL_PREFIX = 'http://yann.lecun.com/exdb/mnist/'
 
+TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz'
+TEST_IMAGE_MD5 = '25e3cc63507ef6e98d5dc541e8672bb6'
 
-def __mnist_reader_creator__(data, target):
-    def reader():
-        n_samples = data.shape[0]
-        for i in xrange(n_samples):
-            yield (data[i] / 255.0).astype(numpy.float32), int(target[i])
+TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz'
+TEST_LABEL_MD5 = '4e9511fe019b2189026bd0421ba7b688'
+
+TRAIN_IMAGE_URL = URL_PREFIX + 'train-images-idx3-ubyte.gz'
+TRAIN_IMAGE_MD5 = 'f68b3c2dcbeaaa9fbdd348bbdeb94873'
 
-    return reader
+TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz'
+TRAIN_LABEL_MD5 = 'd53e105ee54ea40749a09fcbcd1e9432'
 
 
-TEST_SIZE = 10000
+def reader_creator(image_filename, label_filename, buffer_size):
+    def reader():
+        # According to http://stackoverflow.com/a/38061619/724872, we
+        # cannot use standard package gzip here.
+        m = subprocess.Popen(["zcat", image_filename], stdout=subprocess.PIPE)
+        m.stdout.read(16) # skip some magic bytes
+
+        l = subprocess.Popen(["zcat", label_filename], stdout=subprocess.PIPE)
+        l.stdout.read(8) # skip some magic bytes
 
-data = sklearn.datasets.mldata.fetch_mldata(
-    "MNIST original", data_home=DATA_HOME)
-X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
-    data.data, data.target, test_size=TEST_SIZE, random_state=0)
+        while True:
+            labels = numpy.fromfile(
+                l.stdout, 'ubyte', count=buffer_size
+            ).astype("int")
 
+            if labels.size != buffer_size:
+                break # numpy.fromfile returns empty slice after EOF.
 
-def train_creator():
-    return __mnist_reader_creator__(X_train, y_train)
+            images = numpy.fromfile(
+                m.stdout, 'ubyte', count=buffer_size * 28 * 28
+            ).reshape((buffer_size, 28 * 28)
+            ).astype('float32')
 
+            images = images / 255.0 * 2.0 - 1.0
 
-def test_creator():
-    return __mnist_reader_creator__(X_test, y_test)
+            for i in xrange(buffer_size):
+                yield images[i, :], labels[i]
 
+        m.terminate()
+        l.terminate()
 
-def unittest():
-    assert len(list(test_creator()())) == TEST_SIZE
+    return reader()
 
+def train():
+    return reader_creator(
+        paddle.v2.dataset.common.download(
+            TRAIN_IMAGE_URL, 'mnist', TRAIN_IMAGE_MD5),
+        paddle.v2.dataset.common.download(
+            TRAIN_LABEL_URL, 'mnist', TRAIN_LABEL_MD5),
+        100)
 
-if __name__ == '__main__':
-    unittest()
+def test():
+    return reader_creator(
+        paddle.v2.dataset.common.download(
+            TEST_IMAGE_URL, 'mnist', TEST_IMAGE_MD5),
+        paddle.v2.dataset.common.download(
+            TEST_LABEL_URL, 'mnist', TEST_LABEL_MD5),
+        100)

python/paddle/v2/dataset/tests/mnist_test.py

+import paddle.v2.dataset.mnist
+import unittest
+
+class TestMNIST(unittest.TestCase):
+    def check_reader(self, reader):
+        sum = 0
+        for l in reader:
+            self.assertEqual(l[0].size, 784)
+            self.assertEqual(l[1].size, 1)
+            self.assertLess(l[1], 10)
+            self.assertGreaterEqual(l[1], 0)
+            sum += 1
+        return sum
+
+    def test_train(self):
+        self.assertEqual(
+            self.check_reader(paddle.v2.dataset.mnist.train()),
+            60000)
+
+    def test_test(self):
+        self.assertEqual(
+            self.check_reader(paddle.v2.dataset.mnist.test()),
+            10000)
+
+
+if __name__ == '__main__':
+    unittest.main()