You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Paddle/python/paddle/dataset/mnist.py

129 lines
4.6 KiB

# 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.
"""
MNIST dataset.
This module will download dataset from http://yann.lecun.com/exdb/mnist/ and
parse training set and test set into paddle reader creators.
"""
from __future__ import print_function
import paddle.dataset.common
import gzip
import numpy
import struct
from six.moves import range
__all__ = ['train', 'test']
URL_PREFIX = 'https://dataset.bj.bcebos.com/mnist/'
TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz'
TEST_IMAGE_MD5 = '9fb629c4189551a2d022fa330f9573f3'
TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz'
TEST_LABEL_MD5 = 'ec29112dd5afa0611ce80d1b7f02629c'
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 reader_creator(image_filename, label_filename, buffer_size):
def reader():
with gzip.GzipFile(image_filename, 'rb') as image_file:
img_buf = image_file.read()
with gzip.GzipFile(label_filename, 'rb') as label_file:
lab_buf = label_file.read()
step_label = 0
offset_img = 0
# read from Big-endian
# get file info from magic byte
# image file : 16B
magic_byte_img = '>IIII'
magic_img, image_num, rows, cols = struct.unpack_from(
magic_byte_img, img_buf, offset_img)
offset_img += struct.calcsize(magic_byte_img)
offset_lab = 0
# label file : 8B
magic_byte_lab = '>II'
magic_lab, label_num = struct.unpack_from(magic_byte_lab,
lab_buf, offset_lab)
offset_lab += struct.calcsize(magic_byte_lab)
while True:
if step_label >= label_num:
break
fmt_label = '>' + str(buffer_size) + 'B'
labels = struct.unpack_from(fmt_label, lab_buf, offset_lab)
offset_lab += struct.calcsize(fmt_label)
step_label += buffer_size
fmt_images = '>' + str(buffer_size * rows * cols) + 'B'
images_temp = struct.unpack_from(fmt_images, img_buf,
offset_img)
images = numpy.reshape(images_temp, (
buffer_size, rows * cols)).astype('float32')
offset_img += struct.calcsize(fmt_images)
images = images / 255.0
images = images * 2.0
images = images - 1.0
for i in range(buffer_size):
yield images[i, :], int(labels[i])
return reader
def train():
"""
MNIST training set creator.
It returns a reader creator, each sample in the reader is image pixels in
[-1, 1] and label in [0, 9].
:return: Training reader creator
:rtype: callable
"""
return reader_creator(
paddle.dataset.common.download(TRAIN_IMAGE_URL, 'mnist',
TRAIN_IMAGE_MD5),
paddle.dataset.common.download(TRAIN_LABEL_URL, 'mnist',
TRAIN_LABEL_MD5), 100)
def test():
"""
MNIST test set creator.
It returns a reader creator, each sample in the reader is image pixels in
[-1, 1] and label in [0, 9].
:return: Test reader creator.
:rtype: callable
"""
return reader_creator(
paddle.dataset.common.download(TEST_IMAGE_URL, 'mnist', TEST_IMAGE_MD5),
paddle.dataset.common.download(TEST_LABEL_URL, 'mnist', TEST_LABEL_MD5),
100)
def fetch():
paddle.dataset.common.download(TRAIN_IMAGE_URL, 'mnist', TRAIN_IMAGE_MD5)
paddle.dataset.common.download(TRAIN_LABEL_URL, 'mnist', TRAIN_LABEL_MD5)
paddle.dataset.common.download(TEST_IMAGE_URL, 'mnist', TEST_IMAGE_MD5)
paddle.dataset.common.download(TEST_LABEL_URL, 'mnist', TEST_LABEL_MD5)