Yu Yang
8 years ago
commit
cb9d156b84
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"""
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CIFAR Dataset.
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URL: https://www.cs.toronto.edu/~kriz/cifar.html
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the default train_creator, test_creator used for CIFAR-10 dataset.
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"""
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import cPickle
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import itertools
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import tarfile
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import numpy
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from common import download
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__all__ = [
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'cifar_100_train_creator', 'cifar_100_test_creator', 'train_creator',
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'test_creator'
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]
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CIFAR10_URL = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
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CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a'
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CIFAR100_URL = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'
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CIFAR100_MD5 = 'eb9058c3a382ffc7106e4002c42a8d85'
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def __read_batch__(filename, sub_name):
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def reader():
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def __read_one_batch_impl__(batch):
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data = batch['data']
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labels = batch.get('labels', batch.get('fine_labels', None))
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assert labels is not None
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for sample, label in itertools.izip(data, labels):
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yield (sample / 255.0).astype(numpy.float32), int(label)
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with tarfile.open(filename, mode='r') as f:
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names = (each_item.name for each_item in f
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if sub_name in each_item.name)
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for name in names:
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batch = cPickle.load(f.extractfile(name))
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for item in __read_one_batch_impl__(batch):
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yield item
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return reader
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def cifar_100_train_creator():
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fn = download(url=CIFAR100_URL, md5=CIFAR100_MD5)
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return __read_batch__(fn, 'train')
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def cifar_100_test_creator():
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fn = download(url=CIFAR100_URL, md5=CIFAR100_MD5)
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return __read_batch__(fn, 'test')
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def train_creator():
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"""
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Default train reader creator. Use CIFAR-10 dataset.
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"""
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fn = download(url=CIFAR10_URL, md5=CIFAR10_MD5)
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return __read_batch__(fn, 'data_batch')
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def test_creator():
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"""
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Default test reader creator. Use CIFAR-10 dataset.
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"""
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fn = download(url=CIFAR10_URL, md5=CIFAR10_MD5)
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return __read_batch__(fn, 'test_batch')
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def unittest():
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for _ in train_creator()():
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pass
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for _ in test_creator()():
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pass
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if __name__ == '__main__':
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unittest()
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@ -0,0 +1,35 @@
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import requests
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import hashlib
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import os
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import shutil
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__all__ = ['DATA_HOME', 'download', 'md5file']
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DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset')
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if not os.path.exists(DATA_HOME):
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os.makedirs(DATA_HOME)
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def md5file(fname):
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hash_md5 = hashlib.md5()
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f = open(fname, "rb")
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for chunk in iter(lambda: f.read(4096), b""):
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hash_md5.update(chunk)
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f.close()
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return hash_md5.hexdigest()
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def download(url, module_name, md5sum):
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dirname = os.path.join(DATA_HOME, module_name)
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if not os.path.exists(dirname):
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os.makedirs(dirname)
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filename = os.path.join(dirname, url.split('/')[-1])
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if not (os.path.exists(filename) and md5file(filename) == md5sum):
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# If file doesn't exist or MD5 doesn't match, then download.
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r = requests.get(url, stream=True)
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with open(filename, 'w') as f:
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shutil.copyfileobj(r.raw, f)
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return filename
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@ -1,8 +0,0 @@
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import os
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__all__ = ['DATA_HOME']
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DATA_HOME = os.path.expanduser('~/.cache/paddle_data_set')
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if not os.path.exists(DATA_HOME):
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os.makedirs(DATA_HOME)
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@ -1,39 +1,64 @@
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import sklearn.datasets.mldata
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import sklearn.model_selection
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import paddle.v2.dataset.common
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import subprocess
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import numpy
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from config import DATA_HOME
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__all__ = ['train_creator', 'test_creator']
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__all__ = ['train', 'test']
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URL_PREFIX = 'http://yann.lecun.com/exdb/mnist/'
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def __mnist_reader_creator__(data, target):
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TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz'
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TEST_IMAGE_MD5 = '25e3cc63507ef6e98d5dc541e8672bb6'
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TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz'
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TEST_LABEL_MD5 = '4e9511fe019b2189026bd0421ba7b688'
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TRAIN_IMAGE_URL = URL_PREFIX + 'train-images-idx3-ubyte.gz'
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TRAIN_IMAGE_MD5 = 'f68b3c2dcbeaaa9fbdd348bbdeb94873'
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TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz'
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TRAIN_LABEL_MD5 = 'd53e105ee54ea40749a09fcbcd1e9432'
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def reader_creator(image_filename, label_filename, buffer_size):
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def reader():
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n_samples = data.shape[0]
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for i in xrange(n_samples):
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yield (data[i] / 255.0).astype(numpy.float32), int(target[i])
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# According to http://stackoverflow.com/a/38061619/724872, we
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# cannot use standard package gzip here.
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m = subprocess.Popen(["zcat", image_filename], stdout=subprocess.PIPE)
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m.stdout.read(16) # skip some magic bytes
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return reader
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l = subprocess.Popen(["zcat", label_filename], stdout=subprocess.PIPE)
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l.stdout.read(8) # skip some magic bytes
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while True:
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labels = numpy.fromfile(
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l.stdout, 'ubyte', count=buffer_size).astype("int")
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TEST_SIZE = 10000
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if labels.size != buffer_size:
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break # numpy.fromfile returns empty slice after EOF.
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data = sklearn.datasets.mldata.fetch_mldata(
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"MNIST original", data_home=DATA_HOME)
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X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
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data.data, data.target, test_size=TEST_SIZE, random_state=0)
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images = numpy.fromfile(
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m.stdout, 'ubyte', count=buffer_size * 28 * 28).reshape(
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(buffer_size, 28 * 28)).astype('float32')
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images = images / 255.0 * 2.0 - 1.0
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def train_creator():
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return __mnist_reader_creator__(X_train, y_train)
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for i in xrange(buffer_size):
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yield images[i, :], labels[i]
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m.terminate()
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l.terminate()
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def test_creator():
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return __mnist_reader_creator__(X_test, y_test)
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return reader()
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def unittest():
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assert len(list(test_creator()())) == TEST_SIZE
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def train():
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return reader_creator(
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paddle.v2.dataset.common.download(TRAIN_IMAGE_URL, 'mnist',
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TRAIN_IMAGE_MD5),
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paddle.v2.dataset.common.download(TRAIN_LABEL_URL, 'mnist',
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TRAIN_LABEL_MD5), 100)
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if __name__ == '__main__':
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unittest()
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def test():
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return reader_creator(
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paddle.v2.dataset.common.download(TEST_IMAGE_URL, 'mnist',
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TEST_IMAGE_MD5),
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paddle.v2.dataset.common.download(TEST_LABEL_URL, 'mnist',
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TEST_LABEL_MD5), 100)
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@ -0,0 +1,120 @@
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import zipfile
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from common import download
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import re
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import random
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import functools
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__all__ = ['train_creator', 'test_creator']
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class MovieInfo(object):
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def __init__(self, index, categories, title):
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self.index = int(index)
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self.categories = categories
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self.title = title
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def value(self):
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return [
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self.index, [CATEGORIES_DICT[c] for c in self.categories],
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[MOVIE_TITLE_DICT[w.lower()] for w in self.title.split()]
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]
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class UserInfo(object):
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def __init__(self, index, gender, age, job_id):
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self.index = int(index)
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self.is_male = gender == 'M'
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self.age = [1, 18, 25, 35, 45, 50, 56].index(int(age))
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self.job_id = int(job_id)
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def value(self):
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return [self.index, 0 if self.is_male else 1, self.age, self.job_id]
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MOVIE_INFO = None
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MOVIE_TITLE_DICT = None
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CATEGORIES_DICT = None
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USER_INFO = None
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def __initialize_meta_info__():
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fn = download(
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url='http://files.grouplens.org/datasets/movielens/ml-1m.zip',
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md5='c4d9eecfca2ab87c1945afe126590906')
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global MOVIE_INFO
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if MOVIE_INFO is None:
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pattern = re.compile(r'^(.*)\((\d+)\)$')
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with zipfile.ZipFile(file=fn) as package:
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for info in package.infolist():
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assert isinstance(info, zipfile.ZipInfo)
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MOVIE_INFO = dict()
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title_word_set = set()
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categories_set = set()
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with package.open('ml-1m/movies.dat') as movie_file:
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for i, line in enumerate(movie_file):
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movie_id, title, categories = line.strip().split('::')
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categories = categories.split('|')
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for c in categories:
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categories_set.add(c)
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title = pattern.match(title).group(1)
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MOVIE_INFO[int(movie_id)] = MovieInfo(
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index=movie_id, categories=categories, title=title)
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for w in title.split():
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title_word_set.add(w.lower())
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global MOVIE_TITLE_DICT
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MOVIE_TITLE_DICT = dict()
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for i, w in enumerate(title_word_set):
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MOVIE_TITLE_DICT[w] = i
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global CATEGORIES_DICT
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CATEGORIES_DICT = dict()
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for i, c in enumerate(categories_set):
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CATEGORIES_DICT[c] = i
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global USER_INFO
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USER_INFO = dict()
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with package.open('ml-1m/users.dat') as user_file:
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for line in user_file:
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uid, gender, age, job, _ = line.strip().split("::")
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USER_INFO[int(uid)] = UserInfo(
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index=uid, gender=gender, age=age, job_id=job)
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return fn
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def __reader__(rand_seed=0, test_ratio=0.1, is_test=False):
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fn = __initialize_meta_info__()
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rand = random.Random(x=rand_seed)
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with zipfile.ZipFile(file=fn) as package:
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with package.open('ml-1m/ratings.dat') as rating:
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for line in rating:
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if (rand.random() < test_ratio) == is_test:
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uid, mov_id, rating, _ = line.strip().split("::")
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uid = int(uid)
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mov_id = int(mov_id)
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rating = float(rating) * 2 - 5.0
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mov = MOVIE_INFO[mov_id]
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usr = USER_INFO[uid]
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yield usr.value() + mov.value() + [[rating]]
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def __reader_creator__(**kwargs):
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return lambda: __reader__(**kwargs)
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train_creator = functools.partial(__reader_creator__, is_test=False)
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test_creator = functools.partial(__reader_creator__, is_test=True)
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def unittest():
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for train_count, _ in enumerate(train_creator()()):
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pass
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for test_count, _ in enumerate(test_creator()()):
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pass
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print train_count, test_count
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if __name__ == '__main__':
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unittest()
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@ -0,0 +1,23 @@
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import paddle.v2.dataset.common
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import unittest
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import tempfile
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class TestCommon(unittest.TestCase):
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def test_md5file(self):
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_, temp_path = tempfile.mkstemp()
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with open(temp_path, 'w') as f:
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f.write("Hello\n")
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self.assertEqual('09f7e02f1290be211da707a266f153b3',
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paddle.v2.dataset.common.md5file(temp_path))
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def test_download(self):
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yi_avatar = 'https://avatars0.githubusercontent.com/u/1548775?v=3&s=460'
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self.assertEqual(
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paddle.v2.dataset.common.DATA_HOME + '/test/1548775?v=3&s=460',
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paddle.v2.dataset.common.download(
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yi_avatar, 'test', 'f75287202d6622414c706c36c16f8e0d'))
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if __name__ == '__main__':
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unittest.main()
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@ -0,0 +1,26 @@
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import paddle.v2.dataset.mnist
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import unittest
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class TestMNIST(unittest.TestCase):
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def check_reader(self, reader):
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sum = 0
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for l in reader:
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self.assertEqual(l[0].size, 784)
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self.assertEqual(l[1].size, 1)
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self.assertLess(l[1], 10)
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self.assertGreaterEqual(l[1], 0)
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sum += 1
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return sum
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def test_train(self):
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self.assertEqual(
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self.check_reader(paddle.v2.dataset.mnist.train()), 60000)
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def test_test(self):
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self.assertEqual(
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self.check_reader(paddle.v2.dataset.mnist.test()), 10000)
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if __name__ == '__main__':
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unittest.main()
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Loading…
Reference in new issue