Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/lstm/networkio.h @ 17:dd9cdb856310
Remove PKG-INFO from the because it is regenerated automatically for the sdist
| author | Franz Glasner <fzglas.hg@dom66.de> |
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| date | Thu, 18 Sep 2025 17:40:40 +0200 |
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| rev | line source |
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1 /////////////////////////////////////////////////////////////////////// |
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2 // File: networkio.h |
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3 // Description: Network input/output data, allowing float/int implementations. |
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4 // Author: Ray Smith |
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5 // |
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6 // (C) Copyright 2014, Google Inc. |
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7 // Licensed under the Apache License, Version 2.0 (the "License"); |
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8 // you may not use this file except in compliance with the License. |
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9 // You may obtain a copy of the License at |
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10 // http://www.apache.org/licenses/LICENSE-2.0 |
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11 // Unless required by applicable law or agreed to in writing, software |
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12 // distributed under the License is distributed on an "AS IS" BASIS, |
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13 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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14 // See the License for the specific language governing permissions and |
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15 // limitations under the License. |
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16 /////////////////////////////////////////////////////////////////////// |
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17 |
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18 #ifndef TESSERACT_LSTM_NETWORKIO_H_ |
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19 #define TESSERACT_LSTM_NETWORKIO_H_ |
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20 |
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21 #include "helpers.h" |
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22 #include "image.h" |
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23 #include "static_shape.h" |
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24 #include "stridemap.h" |
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25 #include "weightmatrix.h" |
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26 |
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27 #include <cmath> |
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28 #include <cstdio> |
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29 #include <vector> |
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30 |
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31 struct Pix; |
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32 |
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33 namespace tesseract { |
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34 |
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35 // Class to contain all the input/output of a network, allowing for fixed or |
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36 // variable-strided 2d to 1d mapping, and float or int8_t values. Provides |
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37 // enough calculating functions to hide the detail of the implementation. |
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38 class TESS_API NetworkIO { |
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39 public: |
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40 NetworkIO() : int_mode_(false) {} |
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41 // Resizes the array (and stride), avoiding realloc if possible, to the given |
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42 // size from various size specs: |
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43 // Same stride size, but given number of features. |
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44 void Resize(const NetworkIO &src, int num_features) { |
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45 ResizeToMap(src.int_mode(), src.stride_map(), num_features); |
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46 } |
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47 // Resizes to a specific size as a 2-d temp buffer. No batches, no y-dim. |
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48 void Resize2d(bool int_mode, int width, int num_features); |
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49 // Resizes forcing a float representation with the stridemap of src and the |
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50 // given number of features. |
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51 void ResizeFloat(const NetworkIO &src, int num_features) { |
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52 ResizeToMap(false, src.stride_map(), num_features); |
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53 } |
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54 // Resizes to a specific stride_map. |
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55 void ResizeToMap(bool int_mode, const StrideMap &stride_map, int num_features); |
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56 // Shrinks image size by x_scale,y_scale, and use given number of features. |
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57 void ResizeScaled(const NetworkIO &src, int x_scale, int y_scale, int num_features); |
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58 // Resizes to just 1 x-coord, whatever the input. |
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59 void ResizeXTo1(const NetworkIO &src, int num_features); |
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60 // Initialize all the array to zero. |
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61 void Zero(); |
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62 // Initializes to zero all elements of the array that do not correspond to |
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63 // valid image positions. (If a batch of different-sized images are packed |
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64 // together, then there will be padding pixels.) |
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65 void ZeroInvalidElements(); |
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66 // Sets up the array from the given image, using the currently set int_mode_. |
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67 // If the image width doesn't match the shape, the image is truncated or |
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68 // padded with noise to match. |
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69 void FromPix(const StaticShape &shape, const Image pix, TRand *randomizer); |
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70 // Sets up the array from the given set of images, using the currently set |
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71 // int_mode_. If the image width doesn't match the shape, the images are |
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72 // truncated or padded with noise to match. |
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73 void FromPixes(const StaticShape &shape, const std::vector<Image> &pixes, |
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74 TRand *randomizer); |
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75 // Copies the given pix to *this at the given batch index, stretching and |
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76 // clipping the pixel values so that [black, black + 2*contrast] maps to the |
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77 // dynamic range of *this, ie [-1,1] for a float and (-127,127) for int. |
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78 // This is a 2-d operation in the sense that the output depth is the number |
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79 // of input channels, the height is the height of the image, and the width |
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80 // is the width of the image, or truncated/padded with noise if the width |
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81 // is a fixed size. |
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82 void Copy2DImage(int batch, Image pix, float black, float contrast, TRand *randomizer); |
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83 // Copies the given pix to *this at the given batch index, as Copy2DImage |
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84 // above, except that the output depth is the height of the input image, the |
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85 // output height is 1, and the output width as for Copy2DImage. |
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86 // The image is thus treated as a 1-d set of vertical pixel strips. |
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87 void Copy1DGreyImage(int batch, Image pix, float black, float contrast, TRand *randomizer); |
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88 // Helper stores the pixel value in i_ or f_ according to int_mode_. |
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89 // t: is the index from the StrideMap corresponding to the current |
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90 // [batch,y,x] position |
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91 // f: is the index into the depth/channel |
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92 // pixel: the value of the pixel from the image (in one channel) |
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93 // black: the pixel value to map to the lowest of the range of *this |
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94 // contrast: the range of pixel values to stretch to half the range of *this. |
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95 void SetPixel(int t, int f, int pixel, float black, float contrast); |
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96 // Converts the array to a Pix. Must be pixDestroyed after use. |
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97 Image ToPix() const; |
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98 // Prints the first and last num timesteps of the array for each feature. |
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99 void Print(int num) const; |
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100 |
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101 // Returns the timestep width. |
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102 int Width() const { |
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103 return int_mode_ ? i_.dim1() : f_.dim1(); |
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104 } |
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105 // Returns the number of features. |
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106 int NumFeatures() const { |
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107 return int_mode_ ? i_.dim2() : f_.dim2(); |
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108 } |
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109 // Accessor to a timestep of the float matrix. |
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110 float *f(int t) { |
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111 ASSERT_HOST(!int_mode_); |
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112 return f_[t]; |
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113 } |
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114 const float *f(int t) const { |
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115 ASSERT_HOST(!int_mode_); |
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116 return f_[t]; |
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117 } |
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118 const int8_t *i(int t) const { |
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119 ASSERT_HOST(int_mode_); |
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120 return i_[t]; |
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121 } |
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122 bool int_mode() const { |
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123 return int_mode_; |
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124 } |
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125 void set_int_mode(bool is_quantized) { |
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126 int_mode_ = is_quantized; |
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127 } |
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128 const StrideMap &stride_map() const { |
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129 return stride_map_; |
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130 } |
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131 void set_stride_map(const StrideMap &map) { |
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132 stride_map_ = map; |
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133 } |
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134 const GENERIC_2D_ARRAY<float> &float_array() const { |
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135 return f_; |
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136 } |
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137 GENERIC_2D_ARRAY<float> *mutable_float_array() { |
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138 return &f_; |
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139 } |
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140 |
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141 // Copies a single time step from src. |
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142 void CopyTimeStepFrom(int dest_t, const NetworkIO &src, int src_t); |
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143 // Copies a part of single time step from src. |
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144 void CopyTimeStepGeneral(int dest_t, int dest_offset, int num_features, const NetworkIO &src, |
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145 int src_t, int src_offset); |
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146 // Zeroes a single time step. |
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147 void ZeroTimeStep(int t) { |
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148 if (int_mode_) { |
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149 memset(i_[t], 0, sizeof(*i_[t]) * NumFeatures()); |
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150 } else { |
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151 memset(f_[t], 0, sizeof(*f_[t]) * NumFeatures()); |
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152 } |
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153 } |
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154 // Sets the given range to random values. |
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155 void Randomize(int t, int offset, int num_features, TRand *randomizer); |
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156 |
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157 // Helper returns the label and score of the best choice over a range. |
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158 int BestChoiceOverRange(int t_start, int t_end, int not_this, int null_ch, float *rating, |
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159 float *certainty) const; |
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160 // Helper returns the rating and certainty of the choice over a range in t. |
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161 void ScoresOverRange(int t_start, int t_end, int choice, int null_ch, float *rating, |
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162 float *certainty) const; |
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163 // Returns the index (label) of the best value at the given timestep, |
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164 // and if not null, sets the score to the log of the corresponding value. |
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165 int BestLabel(int t, float *score) const { |
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166 return BestLabel(t, -1, -1, score); |
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167 } |
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168 // Returns the index (label) of the best value at the given timestep, |
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169 // excluding not_this and not_that, and if not null, sets the score to the |
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170 // log of the corresponding value. |
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171 int BestLabel(int t, int not_this, int not_that, float *score) const; |
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172 // Returns the best start position out of range (into which both start and end |
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173 // must fit) to obtain the highest cumulative score for the given labels. |
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174 int PositionOfBestMatch(const std::vector<int> &labels, int start, int end) const; |
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175 // Returns the cumulative score of the given labels starting at start, and |
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176 // using one label per time-step. |
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177 TFloat ScoreOfLabels(const std::vector<int> &labels, int start) const; |
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178 // Helper function sets all the outputs for a single timestep, such that |
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179 // label has value ok_score, and the other labels share 1 - ok_score. |
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180 // Assumes float mode. |
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181 void SetActivations(int t, int label, float ok_score); |
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182 // Modifies the values, only if needed, so that the given label is |
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183 // the winner at the given time step t. |
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184 // Assumes float mode. |
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185 void EnsureBestLabel(int t, int label); |
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186 // Helper function converts prob to certainty taking the minimum into account. |
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187 static float ProbToCertainty(float prob); |
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188 // Returns true if there is any bad value that is suspiciously like a GT |
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189 // error. Assuming that *this is the difference(gradient) between target |
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190 // and forward output, returns true if there is a large negative value |
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191 // (correcting a very confident output) for which there is no corresponding |
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192 // positive value in an adjacent timestep for the same feature index. This |
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193 // allows the box-truthed samples to make fine adjustments to position while |
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194 // stopping other disagreements of confident output with ground truth. |
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195 bool AnySuspiciousTruth(float confidence_thr) const; |
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196 |
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197 // Reads a single timestep to floats in the range [-1, 1]. |
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198 void ReadTimeStep(int t, TFloat *output) const; |
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199 // Adds a single timestep to floats. |
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200 void AddTimeStep(int t, TFloat *inout) const; |
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201 // Adds part of a single timestep to floats. |
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202 void AddTimeStepPart(int t, int offset, int num_features, float *inout) const; |
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203 // Writes a single timestep from floats in the range [-1, 1]. |
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204 void WriteTimeStep(int t, const TFloat *input); |
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205 // Writes a single timestep from floats in the range [-1, 1] writing only |
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206 // num_features elements of input to (*this)[t], starting at offset. |
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207 void WriteTimeStepPart(int t, int offset, int num_features, const TFloat *input); |
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208 // Maxpools a single time step from src. |
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209 void MaxpoolTimeStep(int dest_t, const NetworkIO &src, int src_t, int *max_line); |
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210 // Runs maxpool backward, using maxes to index timesteps in *this. |
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211 void MaxpoolBackward(const NetworkIO &fwd, const GENERIC_2D_ARRAY<int> &maxes); |
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212 // Returns the min over time of the maxes over features of the outputs. |
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213 float MinOfMaxes() const; |
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214 // Returns the min over time. |
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215 float Max() const { |
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216 return int_mode_ ? i_.Max() : f_.Max(); |
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217 } |
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218 // Computes combined results for a combiner that chooses between an existing |
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219 // input and itself, with an additional output to indicate the choice. |
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220 void CombineOutputs(const NetworkIO &base_output, const NetworkIO &combiner_output); |
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221 // Computes deltas for a combiner that chooses between 2 sets of inputs. |
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222 void ComputeCombinerDeltas(const NetworkIO &fwd_deltas, const NetworkIO &base_output); |
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223 |
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224 // Copies the array checking that the types match. |
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225 void CopyAll(const NetworkIO &src); |
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226 // Adds the array to a float array, with scaling to [-1, 1] if the src is int. |
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227 void AddAllToFloat(const NetworkIO &src); |
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228 // Subtracts the array from a float array. src must also be float. |
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229 void SubtractAllFromFloat(const NetworkIO &src); |
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230 |
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231 // Copies src to *this, with maxabs normalization to match scale. |
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232 void CopyWithNormalization(const NetworkIO &src, const NetworkIO &scale); |
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233 // Multiplies the float data by the given factor. |
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234 void ScaleFloatBy(float factor) { |
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235 f_ *= factor; |
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236 } |
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237 // Copies src to *this with independent reversal of the y dimension. |
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238 void CopyWithYReversal(const NetworkIO &src); |
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239 // Copies src to *this with independent reversal of the x dimension. |
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240 void CopyWithXReversal(const NetworkIO &src); |
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241 // Copies src to *this with independent transpose of the x and y dimensions. |
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242 void CopyWithXYTranspose(const NetworkIO &src); |
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243 // Copies src to *this, at the given feature_offset, returning the total |
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244 // feature offset after the copy. Multiple calls will stack outputs from |
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245 // multiple sources in feature space. |
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246 int CopyPacking(const NetworkIO &src, int feature_offset); |
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247 // Opposite of CopyPacking, fills *this with a part of src, starting at |
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248 // feature_offset, and picking num_features. Resizes *this to match. |
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249 void CopyUnpacking(const NetworkIO &src, int feature_offset, int num_features); |
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250 // Transposes the float part of *this into dest. |
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251 void Transpose(TransposedArray *dest) const; |
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252 |
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253 // Clips the content of a single time-step to +/-range. |
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254 void ClipVector(int t, float range); |
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255 |
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256 // Applies Func to timestep t of *this (u) and multiplies the result by v |
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257 // component-wise, putting the product in *product. |
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258 // *this and v may be int or float, but must match. The outputs are TFloat. |
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259 template <class Func> |
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260 void FuncMultiply(const NetworkIO &v_io, int t, TFloat *product) { |
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261 Func f; |
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262 ASSERT_HOST(!int_mode_); |
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263 ASSERT_HOST(!v_io.int_mode_); |
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264 int dim = f_.dim2(); |
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265 if (int_mode_) { |
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266 const int8_t *u = i_[t]; |
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267 const int8_t *v = v_io.i_[t]; |
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268 for (int i = 0; i < dim; ++i) { |
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269 product[i] = f(u[i] / static_cast<TFloat>(INT8_MAX)) * v[i] / INT8_MAX; |
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270 } |
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271 } else { |
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272 const float *u = f_[t]; |
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273 const float *v = v_io.f_[t]; |
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274 for (int i = 0; i < dim; ++i) { |
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275 product[i] = f(u[i]) * v[i]; |
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276 } |
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277 } |
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278 } |
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279 // Applies Func to *this (u) at u_t, and multiplies the result by v[v_t] * w, |
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280 // component-wise, putting the product in *product. |
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281 // All NetworkIOs are assumed to be float. |
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282 template <class Func> |
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283 void FuncMultiply3(int u_t, const NetworkIO &v_io, int v_t, const TFloat *w, |
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284 TFloat *product) const { |
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285 ASSERT_HOST(!int_mode_); |
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286 ASSERT_HOST(!v_io.int_mode_); |
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287 Func f; |
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288 const float *u = f_[u_t]; |
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289 const float *v = v_io.f_[v_t]; |
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290 int dim = f_.dim2(); |
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291 for (int i = 0; i < dim; ++i) { |
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292 product[i] = f(u[i]) * v[i] * w[i]; |
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293 } |
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294 } |
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295 // Applies Func to *this (u) at u_t, and multiplies the result by v[v_t] * w, |
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296 // component-wise, adding the product to *product. |
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297 // All NetworkIOs are assumed to be float. |
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298 template <class Func> |
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299 void FuncMultiply3Add(const NetworkIO &v_io, int t, const TFloat *w, TFloat *product) const { |
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300 ASSERT_HOST(!int_mode_); |
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301 ASSERT_HOST(!v_io.int_mode_); |
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302 Func f; |
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303 const float *u = f_[t]; |
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304 const float *v = v_io.f_[t]; |
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305 int dim = f_.dim2(); |
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306 for (int i = 0; i < dim; ++i) { |
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307 product[i] += f(u[i]) * v[i] * w[i]; |
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308 } |
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309 } |
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310 // Applies Func1 to *this (u), Func2 to v, and multiplies the result by w, |
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311 // component-wise, putting the product in product, all at timestep t, except |
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312 // w, which is a simple array. All NetworkIOs are assumed to be float. |
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313 template <class Func1, class Func2> |
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314 void Func2Multiply3(const NetworkIO &v_io, int t, const TFloat *w, TFloat *product) const { |
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315 ASSERT_HOST(!int_mode_); |
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316 ASSERT_HOST(!v_io.int_mode_); |
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317 Func1 f; |
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318 Func2 g; |
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319 const float *u = f_[t]; |
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320 const float *v = v_io.f_[t]; |
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321 int dim = f_.dim2(); |
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322 for (int i = 0; i < dim; ++i) { |
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323 product[i] = f(u[i]) * g(v[i]) * w[i]; |
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324 } |
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325 } |
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326 |
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327 private: |
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328 // Returns the padding required for the given number of features in order |
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329 // for the SIMD operations to be safe. |
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330 static int GetPadding(int num_features); |
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331 |
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332 // Choice of float vs 8 bit int for data. |
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333 GENERIC_2D_ARRAY<float> f_; |
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334 GENERIC_2D_ARRAY<int8_t> i_; |
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335 // Which of f_ and i_ are we actually using. |
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336 bool int_mode_; |
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337 // Stride for 2d input data. |
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338 StrideMap stride_map_; |
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339 }; |
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340 |
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341 } // namespace tesseract. |
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342 |
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343 #endif // TESSERACT_LSTM_NETWORKIO_H_ |
