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124 lines
4.3 KiB
124 lines
4.3 KiB
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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MNIST dataset.
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This module will download dataset from http://yann.lecun.com/exdb/mnist/ and
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parse training set and test set into paddle reader creators.
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"""
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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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import platform
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__all__ = ['train', 'test', 'convert']
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URL_PREFIX = 'http://yann.lecun.com/exdb/mnist/'
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TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz'
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TEST_IMAGE_MD5 = '9fb629c4189551a2d022fa330f9573f3'
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TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz'
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TEST_LABEL_MD5 = 'ec29112dd5afa0611ce80d1b7f02629c'
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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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if platform.system() == 'Darwin':
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zcat_cmd = 'gzcat'
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elif platform.system() == 'Linux':
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zcat_cmd = 'zcat'
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else:
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raise NotImplementedError()
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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_cmd, image_filename], stdout=subprocess.PIPE)
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m.stdout.read(16) # skip some magic bytes
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l = subprocess.Popen([zcat_cmd, label_filename], stdout=subprocess.PIPE)
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l.stdout.read(8) # skip some magic bytes
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try: # reader could be break.
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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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if labels.size != buffer_size:
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break # numpy.fromfile returns empty slice after EOF.
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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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for i in xrange(buffer_size):
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yield images[i, :], int(labels[i])
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finally:
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m.terminate()
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l.terminate()
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return reader
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def train():
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"""
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MNIST training set creator.
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It returns a reader creator, each sample in the reader is image pixels in
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[0, 1] and label in [0, 9].
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:return: Training reader creator
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:rtype: callable
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"""
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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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def test():
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"""
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MNIST test set creator.
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It returns a reader creator, each sample in the reader is image pixels in
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[0, 1] and label in [0, 9].
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:return: Test reader creator.
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:rtype: callable
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"""
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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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def fetch():
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paddle.v2.dataset.common.download(TRAIN_IMAGE_URL, 'mnist', TRAIN_IMAGE_MD5)
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paddle.v2.dataset.common.download(TRAIN_LABEL_URL, 'mnist', TRAIN_LABEL_MD5)
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paddle.v2.dataset.common.download(TEST_IMAGE_URL, 'mnist', TEST_IMAGE_MD5)
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paddle.v2.dataset.common.download(TEST_LABEL_URL, 'mnist', TRAIN_LABEL_MD5)
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def convert(path):
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"""
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Converts dataset to recordio format
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"""
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paddle.v2.dataset.common.convert(path, train(), 1000, "minist_train")
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paddle.v2.dataset.common.convert(path, test(), 1000, "minist_test")
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