tiny_dnn  1.0.0
A header only, dependency-free deep learning framework in C++11
cifar10_parser.h
1 /*
2  Copyright (c) 2013, Taiga Nomi
3  All rights reserved.
4 
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6  modification, are permitted provided that the following conditions are met:
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9  * Redistributions in binary form must reproduce the above copyright
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14  derived from this software without specific prior written permission.
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16  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY
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26 */
27 #pragma once
28 #include "tiny_dnn/util/util.h"
29 #include <fstream>
30 #include <cstdint>
31 #include <algorithm>
32 
33 #define CIFAR10_IMAGE_DEPTH (3)
34 #define CIFAR10_IMAGE_WIDTH (32)
35 #define CIFAR10_IMAGE_HEIGHT (32)
36 #define CIFAR10_IMAGE_AREA (CIFAR10_IMAGE_WIDTH*CIFAR10_IMAGE_HEIGHT)
37 #define CIFAR10_IMAGE_SIZE (CIFAR10_IMAGE_AREA*CIFAR10_IMAGE_DEPTH)
38 
39 
40 namespace tiny_dnn {
41 
53 inline void parse_cifar10(const std::string& filename,
54  std::vector<vec_t> *train_images,
55  std::vector<label_t> *train_labels,
56  float_t scale_min,
57  float_t scale_max,
58  int x_padding,
59  int y_padding)
60 {
61  if (x_padding < 0 || y_padding < 0)
62  throw nn_error("padding size must not be negative");
63  if (scale_min >= scale_max)
64  throw nn_error("scale_max must be greater than scale_min");
65 
66  std::ifstream ifs(filename.c_str(), std::ios::in | std::ios::binary);
67  if (ifs.fail() || ifs.bad())
68  throw nn_error("failed to open file:" + filename);
69 
70  uint8_t label;
71  std::vector<unsigned char> buf(CIFAR10_IMAGE_SIZE);
72 
73  while (ifs.read((char*) &label, 1)) {
74  vec_t img;
75 
76  if (!ifs.read((char*) &buf[0], CIFAR10_IMAGE_SIZE)) break;
77 
78  if (x_padding || y_padding)
79  {
80  int w = CIFAR10_IMAGE_WIDTH + 2 * x_padding;
81  int h = CIFAR10_IMAGE_HEIGHT + 2 * y_padding;
82 
83  img.resize(w * h * CIFAR10_IMAGE_DEPTH, scale_min);
84 
85  for (int c = 0; c < CIFAR10_IMAGE_DEPTH; c++) {
86  for (int y = 0; y < CIFAR10_IMAGE_HEIGHT; y++) {
87  for (int x = 0; x < CIFAR10_IMAGE_WIDTH; x++) {
88  img[c * w * h + (y + y_padding) * w + x + x_padding]
89  = scale_min + (scale_max - scale_min) * buf[c * CIFAR10_IMAGE_AREA + y * CIFAR10_IMAGE_WIDTH + x] / 255;
90  }
91  }
92  }
93  }
94  else
95  {
96  std::transform(buf.begin(), buf.end(), std::back_inserter(img),
97  [=](unsigned char c) { return scale_min + (scale_max - scale_min) * c / 255; });
98  }
99 
100  train_images->push_back(img);
101  train_labels->push_back(label);
102  }
103 }
104 
105 } // namespace tiny_dnn