cifar.py

# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
CIFAR dataset.

This module will download dataset from https://www.cs.toronto.edu/~kriz/cifar.html and
parse train set and test set into paddle reader creators.

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 
images per class. There are 50000 training images and 10000 test images.

The CIFAR-100 dataset is just like the CIFAR-10, except it has 100 classes containing 
600 images each. There are 500 training images and 100 testing images per class. 

"""

import cPickle
import itertools
import numpy
from common import download
import tarfile

__all__ = ['train100', 'test100', 'train10', 'test10']

URL_PREFIX = 'https://www.cs.toronto.edu/~kriz/'
CIFAR10_URL = URL_PREFIX + 'cifar-10-python.tar.gz'
CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a'
CIFAR100_URL = URL_PREFIX + 'cifar-100-python.tar.gz'
CIFAR100_MD5 = 'eb9058c3a382ffc7106e4002c42a8d85'


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_batch(batch):
                    yield item

    return reader


def train100():
    """
    CIFAR-100 train set creator.

    It returns a reader creator, each sample in the reader is image pixels in
    [0, 1] and label in [0, 99].

    :return: Train reader creator
    :rtype: callable
    """
    return reader_creator(
        download(CIFAR100_URL, 'cifar', CIFAR100_MD5), 'train')


def test100():
    """
    CIFAR-100 test set cretor.

    It returns a reader creator, each sample in the reader is image pixels in
    [0, 1] and label in [0, 9].

    :return: Test reader creator.
    :rtype: callable
    """
    return reader_creator(download(CIFAR100_URL, 'cifar', CIFAR100_MD5), 'test')


def train10():
    """
    CIFAR-10 train set creator.

    It returns a reader creator, each sample in the reader is image pixels in
    [0, 1] and label in [0, 9].

    :return: Train reader creator
    :rtype: callable
    """
    return reader_creator(
        download(CIFAR10_URL, 'cifar', CIFAR10_MD5), 'data_batch')


def test10():
    """
    CIFAR-10 test set cretor.

    It returns a reader creator, each sample in the reader is image pixels in
    [0, 1] and label in [0, 9].

    :return: Test reader creator.
    :rtype: callable
    """
    return reader_creator(
        download(CIFAR10_URL, 'cifar', CIFAR10_MD5), 'test_batch')


def fetch():
    download(CIFAR10_URL, 'cifar', CIFAR10_MD5)
    download(CIFAR100_URL, 'cifar', CIFAR100_MD5)