提交 e44f0538 编写于 作者: Y Yu Yang

Merge remote-tracking branch 'wangyi/dataset' into feature/clean_mnist_v2

"""
CIFAR Dataset.
URL: https://www.cs.toronto.edu/~kriz/cifar.html
the default train_creator, test_creator used for CIFAR-10 dataset.
"""
import cPickle
import itertools
import tarfile
import numpy
from common import download
__all__ = [
'cifar_100_train_creator', 'cifar_100_test_creator', 'train_creator',
'test_creator'
]
CIFAR10_URL = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a'
CIFAR100_URL = 'https://www.cs.toronto.edu/~kriz/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)
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):
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 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 unittest():
for _ in train_creator()():
pass
for _ in test_creator()():
pass
if __name__ == '__main__':
unittest()
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):
# If file doesn't exist or MD5 doesn't match, then download.
r = requests.get(url, stream=True)
with open(filename, 'w') as f:
shutil.copyfileobj(r.raw, f)
return filename
import os
__all__ = ['DATA_HOME']
DATA_HOME = os.path.expanduser('~/.cache/paddle_data_set')
if not os.path.exists(DATA_HOME):
os.makedirs(DATA_HOME)
import sklearn.datasets.mldata
import sklearn.model_selection
import paddle.v2.dataset.common
import subprocess
import numpy
from config import DATA_HOME
__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():
n_samples = data.shape[0]
for i in xrange(n_samples):
yield (data[i] / 255.0).astype(numpy.float32), int(target[i])
return reader
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
TEST_SIZE = 10000
X_train = None
X_test = None
y_train = None
y_test = None
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 __initialize_dataset__():
global X_train, X_test, y_train, y_test
if X_train is not None:
return
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():
__initialize_dataset__()
return __mnist_reader_creator__(X_train, y_train)
for i in xrange(buffer_size):
yield images[i, :], labels[i]
m.terminate()
l.terminate()
def test_creator():
__initialize_dataset__()
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)
import zipfile
from common import download
import re
import random
import functools
__all__ = ['train_creator', 'test_creator']
class MovieInfo(object):
def __init__(self, index, categories, title):
self.index = int(index)
self.categories = categories
self.title = title
def value(self):
return [
self.index, [CATEGORIES_DICT[c] for c in self.categories],
[MOVIE_TITLE_DICT[w.lower()] for w in self.title.split()]
]
class UserInfo(object):
def __init__(self, index, gender, age, job_id):
self.index = int(index)
self.is_male = gender == 'M'
self.age = [1, 18, 25, 35, 45, 50, 56].index(int(age))
self.job_id = int(job_id)
def value(self):
return [self.index, 0 if self.is_male else 1, self.age, self.job_id]
MOVIE_INFO = None
MOVIE_TITLE_DICT = None
CATEGORIES_DICT = None
USER_INFO = None
def __initialize_meta_info__():
fn = download(
url='http://files.grouplens.org/datasets/movielens/ml-1m.zip',
md5='c4d9eecfca2ab87c1945afe126590906')
global MOVIE_INFO
if MOVIE_INFO is None:
pattern = re.compile(r'^(.*)\((\d+)\)$')
with zipfile.ZipFile(file=fn) as package:
for info in package.infolist():
assert isinstance(info, zipfile.ZipInfo)
MOVIE_INFO = dict()
title_word_set = set()
categories_set = set()
with package.open('ml-1m/movies.dat') as movie_file:
for i, line in enumerate(movie_file):
movie_id, title, categories = line.strip().split('::')
categories = categories.split('|')
for c in categories:
categories_set.add(c)
title = pattern.match(title).group(1)
MOVIE_INFO[int(movie_id)] = MovieInfo(
index=movie_id, categories=categories, title=title)
for w in title.split():
title_word_set.add(w.lower())
global MOVIE_TITLE_DICT
MOVIE_TITLE_DICT = dict()
for i, w in enumerate(title_word_set):
MOVIE_TITLE_DICT[w] = i
global CATEGORIES_DICT
CATEGORIES_DICT = dict()
for i, c in enumerate(categories_set):
CATEGORIES_DICT[c] = i
global USER_INFO
USER_INFO = dict()
with package.open('ml-1m/users.dat') as user_file:
for line in user_file:
uid, gender, age, job, _ = line.strip().split("::")
USER_INFO[int(uid)] = UserInfo(
index=uid, gender=gender, age=age, job_id=job)
return fn
def __reader__(rand_seed=0, test_ratio=0.1, is_test=False):
fn = __initialize_meta_info__()
rand = random.Random(x=rand_seed)
with zipfile.ZipFile(file=fn) as package:
with package.open('ml-1m/ratings.dat') as rating:
for line in rating:
if (rand.random() < test_ratio) == is_test:
uid, mov_id, rating, _ = line.strip().split("::")
uid = int(uid)
mov_id = int(mov_id)
rating = float(rating) * 2 - 5.0
mov = MOVIE_INFO[mov_id]
usr = USER_INFO[uid]
yield usr.value() + mov.value() + [[rating]]
def __reader_creator__(**kwargs):
return lambda: __reader__(**kwargs)
train_creator = functools.partial(__reader_creator__, is_test=False)
test_creator = functools.partial(__reader_creator__, is_test=True)
def unittest():
for train_count, _ in enumerate(train_creator()()):
pass
for test_count, _ in enumerate(test_creator()()):
pass
print train_count, test_count
if __name__ == '__main__':
unittest()
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()
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()
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