# 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', 'convert'] 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 * 2.0 - 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) def convert(path): """ Converts dataset to recordio format """ paddle.dataset.common.convert(path, train(), 1000, "minist_train") paddle.dataset.common.convert(path, test(), 1000, "minist_test")