Merge pull request #1476 from wangkuiyi/dataset

Simplify CIFAR/MNIST Data Package, Remove Scipy/sklearn package dependencies.

python/paddle/v2/dataset/cifar.py

 """
-CIFAR Dataset.
-
-URL: https://www.cs.toronto.edu/~kriz/cifar.html
-
-the default train_creator, test_creator used for CIFAR-10 dataset.
+CIFAR dataset: https://www.cs.toronto.edu/~kriz/cifar.html
 """
 import cPickle
 import itertools
-import tarfile
-
 import numpy
+import paddle.v2.dataset.common
+import tarfile
 
-from config import download
-
-__all__ = [
-    'cifar_100_train_creator', 'cifar_100_test_creator', 'train_creator',
-    'test_creator'
-]
+__all__ = ['train100', 'test100', 'train10', 'test10']
 
-CIFAR10_URL = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
+URL_PREFIX = 'https://www.cs.toronto.edu/~kriz/'
+CIFAR10_URL = URL_PREFIX + 'cifar-10-python.tar.gz'
 CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a'
-CIFAR100_URL = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'
+CIFAR100_URL = URL_PREFIX + 'cifar-100-python.tar.gz'
 CIFAR100_MD5 = 'eb9058c3a382ffc7106e4002c42a8d85'
 
 
-def __read_batch__(filename, sub_name):
-    def reader():
-        def __read_one_batch_impl__(batch):
-            data = batch['data']
-            labels = batch.get('labels', batch.get('fine_labels', None))
-            assert labels is not None
-            for sample, label in itertools.izip(data, labels):
-                yield (sample / 255.0).astype(numpy.float32), int(label)
+def reader_creator(filename, sub_name):
+    def read_batch(batch):
+        data = batch['data']
+        labels = batch.get('labels', batch.get('fine_labels', None))
+        assert labels is not None
+        for sample, label in itertools.izip(data, labels):
+            yield (sample / 255.0).astype(numpy.float32), int(label)
 
+    def reader():
         with tarfile.open(filename, mode='r') as f:
             names = (each_item.name for each_item in f
                      if sub_name in each_item.name)
 
             for name in names:
                 batch = cPickle.load(f.extractfile(name))
-                for item in __read_one_batch_impl__(batch):
+                for item in read_batch(batch):
                     yield item
 
     return reader
 
 
-def cifar_100_train_creator():
-    fn = download(url=CIFAR100_URL, md5=CIFAR100_MD5)
-    return __read_batch__(fn, 'train')
-
-
-def cifar_100_test_creator():
-    fn = download(url=CIFAR100_URL, md5=CIFAR100_MD5)
-    return __read_batch__(fn, 'test')
-
-
-def train_creator():
-    """
-    Default train reader creator. Use CIFAR-10 dataset.
-    """
-    fn = download(url=CIFAR10_URL, md5=CIFAR10_MD5)
-    return __read_batch__(fn, 'data_batch')
+def train100():
+    return reader_creator(
+        paddle.v2.dataset.common.download(CIFAR100_URL, 'cifar', CIFAR100_MD5),
+        'train')
 
 
-def test_creator():
-    """
-    Default test reader creator. Use CIFAR-10 dataset.
-    """
-    fn = download(url=CIFAR10_URL, md5=CIFAR10_MD5)
-    return __read_batch__(fn, 'test_batch')
+def test100():
+    return reader_creator(
+        paddle.v2.dataset.common.download(CIFAR100_URL, 'cifar', CIFAR100_MD5),
+        'test')
 
 
-def unittest():
-    for _ in train_creator()():
-        pass
-    for _ in test_creator()():
-        pass
+def train10():
+    return reader_creator(
+        paddle.v2.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5),
+        'data_batch')
 
 
-if __name__ == '__main__':
-    unittest()
+def test10():
+    return reader_creator(
+        paddle.v2.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5),
+        'test_batch')

python/paddle/v2/dataset/common.py

+import requests
+import hashlib
+import os
+import shutil
+
+__all__ = ['DATA_HOME', 'download', 'md5file']
+
+DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset')
+
+if not os.path.exists(DATA_HOME):
+    os.makedirs(DATA_HOME)
+
+
+def md5file(fname):
+    hash_md5 = hashlib.md5()
+    f = open(fname, "rb")
+    for chunk in iter(lambda: f.read(4096), b""):
+        hash_md5.update(chunk)
+    f.close()
+    return hash_md5.hexdigest()
+
+
+def download(url, module_name, md5sum):
+    dirname = os.path.join(DATA_HOME, module_name)
+    if not os.path.exists(dirname):
+        os.makedirs(dirname)
+
+    filename = os.path.join(dirname, url.split('/')[-1])
+    if not (os.path.exists(filename) and md5file(filename) == md5sum):
+        r = requests.get(url, stream=True)
+        with open(filename, 'w') as f:
+            shutil.copyfileobj(r.raw, f)
+
+    return filename

python/paddle/v2/dataset/config.py

-import hashlib
-import os
-import shutil
-import urllib2
-
-__all__ = ['DATA_HOME', 'download']
-
-DATA_HOME = os.path.expanduser('~/.cache/paddle_data_set')
-
-if not os.path.exists(DATA_HOME):
-    os.makedirs(DATA_HOME)
-
-
-def download(url, md5):
-    filename = os.path.split(url)[-1]
-    assert DATA_HOME is not None
-    filepath = os.path.join(DATA_HOME, md5)
-    if not os.path.exists(filepath):
-        os.makedirs(filepath)
-    __full_file__ = os.path.join(filepath, filename)
-
-    def __file_ok__():
-        if not os.path.exists(__full_file__):
-            return False
-        md5_hash = hashlib.md5()
-        with open(__full_file__, 'rb') as f:
-            for chunk in iter(lambda: f.read(4096), b""):
-                md5_hash.update(chunk)
-
-        return md5_hash.hexdigest() == md5
-
-    while not __file_ok__():
-        response = urllib2.urlopen(url)
-        with open(__full_file__, mode='wb') as of:
-            shutil.copyfileobj(fsrc=response, fdst=of)
-    return __full_file__

python/paddle/v2/dataset/mnist.py

-import sklearn.datasets.mldata
-import sklearn.model_selection
+"""
+MNIST dataset.
+"""
 import numpy
-from config import DATA_HOME
+import paddle.v2.dataset.common
+import subprocess
 
-__all__ = ['train_creator', 'test_creator']
+__all__ = ['train', 'test']
 
+URL_PREFIX = 'http://yann.lecun.com/exdb/mnist/'
+TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz'
+TEST_IMAGE_MD5 = '25e3cc63507ef6e98d5dc541e8672bb6'
+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'
+TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz'
+TRAIN_LABEL_MD5 = 'd53e105ee54ea40749a09fcbcd1e9432'
 
-def __mnist_reader_creator__(data, target):
+
+def reader_creator(image_filename, label_filename, buffer_size):
     def reader():
-        n_samples = data.shape[0]
-        for i in xrange(n_samples):
-            yield (data[i] / 255.0).astype(numpy.float32), int(target[i])
+        # 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
 
-    return reader
+        l = subprocess.Popen(["zcat", label_filename], stdout=subprocess.PIPE)
+        l.stdout.read(8)  # skip some magic bytes
 
+        while True:
+            labels = numpy.fromfile(
+                l.stdout, 'ubyte', count=buffer_size).astype("int")
 
-TEST_SIZE = 10000
+            if labels.size != buffer_size:
+                break  # numpy.fromfile returns empty slice after EOF.
 
-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)
+            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 train_creator():
-    return __mnist_reader_creator__(X_train, y_train)
+            for i in xrange(buffer_size):
+                yield images[i, :], int(labels[i])
 
+        m.terminate()
+        l.terminate()
 
-def test_creator():
-    return __mnist_reader_creator__(X_test, y_test)
+    return reader
 
 
-def unittest():
-    assert len(list(test_creator()())) == TEST_SIZE
+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/movielens.py

 import zipfile
-from config import download
+from common import download
 import re
 import random
 import functools

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

+import paddle.v2.dataset.cifar
+import unittest
+
+
+class TestCIFAR(unittest.TestCase):
+    def check_reader(self, reader):
+        sum = 0
+        label = 0
+        for l in reader():
+            self.assertEqual(l[0].size, 3072)
+            if l[1] > label:
+                label = l[1]
+            sum += 1
+        return sum, label
+
+    def test_test10(self):
+        instances, max_label_value = self.check_reader(
+            paddle.v2.dataset.cifar.test10())
+        self.assertEqual(instances, 10000)
+        self.assertEqual(max_label_value, 9)
+
+    def test_train10(self):
+        instances, max_label_value = self.check_reader(
+            paddle.v2.dataset.cifar.train10())
+        self.assertEqual(instances, 50000)
+        self.assertEqual(max_label_value, 9)
+
+    def test_test100(self):
+        instances, max_label_value = self.check_reader(
+            paddle.v2.dataset.cifar.test100())
+        self.assertEqual(instances, 10000)
+        self.assertEqual(max_label_value, 99)
+
+    def test_train100(self):
+        instances, max_label_value = self.check_reader(
+            paddle.v2.dataset.cifar.train100())
+        self.assertEqual(instances, 50000)
+        self.assertEqual(max_label_value, 99)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

+import paddle.v2.dataset.common
+import unittest
+import tempfile
+
+
+class TestCommon(unittest.TestCase):
+    def test_md5file(self):
+        _, temp_path = tempfile.mkstemp()
+        with open(temp_path, 'w') as f:
+            f.write("Hello\n")
+        self.assertEqual('09f7e02f1290be211da707a266f153b3',
+                         paddle.v2.dataset.common.md5file(temp_path))
+
+    def test_download(self):
+        yi_avatar = 'https://avatars0.githubusercontent.com/u/1548775?v=3&s=460'
+        self.assertEqual(
+            paddle.v2.dataset.common.DATA_HOME + '/test/1548775?v=3&s=460',
+            paddle.v2.dataset.common.download(
+                yi_avatar, 'test', 'f75287202d6622414c706c36c16f8e0d'))
+
+
+if __name__ == '__main__':
+    unittest.main()

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
+        label = 0
+        for l in reader():
+            self.assertEqual(l[0].size, 784)
+            if l[1] > label:
+                label = l[1]
+            sum += 1
+        return sum, label
+
+    def test_train(self):
+        instances, max_label_value = self.check_reader(
+            paddle.v2.dataset.mnist.train())
+        self.assertEqual(instances, 60000)
+        self.assertEqual(max_label_value, 9)
+
+    def test_test(self):
+        instances, max_label_value = self.check_reader(
+            paddle.v2.dataset.mnist.test())
+        self.assertEqual(instances, 10000)
+        self.assertEqual(max_label_value, 9)
+
+
+if __name__ == '__main__':
+    unittest.main()