Mercurial > hgrepos > Python2 > PyMuPDF

annotate mupdf-source/thirdparty/tesseract/src/lstm/parallel.cpp @ 40:aa33339d6b8a upstream

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ADD: MuPDF v1.26.10: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.5.
author Franz Glasner <fzglas.hg@dom66.de>
date Sat, 11 Oct 2025 11:31:38 +0200
parents b50eed0cc0ef
children
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1 /////////////////////////////////////////////////////////////////////////
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2 // File: parallel.cpp
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3 // Description: Runs networks in parallel on the same input.
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4 // Author: Ray Smith
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5 //
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6 // (C) Copyright 2013, 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 #ifdef HAVE_CONFIG_H
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19 # include "config_auto.h"
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20 #endif
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22 #include "parallel.h"
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24 #ifdef _OPENMP
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25 # include <omp.h>
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26 #endif
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27
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28 #include "functions.h" // For conditional undef of _OPENMP.
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29 #include "networkscratch.h"
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31 namespace tesseract {
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32
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33 // ni_ and no_ will be set by AddToStack.
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34 Parallel::Parallel(const std::string &name, NetworkType type) : Plumbing(name) {
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35 type_ = type;
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36 }
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37
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38 // Returns the shape output from the network given an input shape (which may
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39 // be partially unknown ie zero).
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40 StaticShape Parallel::OutputShape(const StaticShape &input_shape) const {
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41 StaticShape result = stack_[0]->OutputShape(input_shape);
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42 int stack_size = stack_.size();
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43 for (int i = 1; i < stack_size; ++i) {
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44 StaticShape shape = stack_[i]->OutputShape(input_shape);
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45 result.set_depth(result.depth() + shape.depth());
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46 }
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47 return result;
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48 }
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49
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50 // Runs forward propagation of activations on the input line.
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51 // See NetworkCpp for a detailed discussion of the arguments.
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52 void Parallel::Forward(bool debug, const NetworkIO &input, const TransposedArray *input_transpose,
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53 NetworkScratch *scratch, NetworkIO *output) {
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54 bool parallel_debug = false;
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55 // If this parallel is a replicator of convolvers, or holds a 1-d LSTM pair,
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56 // or a 2-d LSTM quad, do debug locally, and don't pass the flag on.
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57 if (debug && type_ != NT_PARALLEL) {
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58 parallel_debug = true;
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59 debug = false;
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60 }
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61 int stack_size = stack_.size();
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62 if (type_ == NT_PAR_2D_LSTM) {
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63 // Special case, run parallel in parallel.
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64 std::vector<NetworkScratch::IO> results(stack_size);
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65 for (int i = 0; i < stack_size; ++i) {
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66 results[i].Resize(input, stack_[i]->NumOutputs(), scratch);
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67 }
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68 #ifdef _OPENMP
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69 # pragma omp parallel for num_threads(stack_size)
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70 #endif
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71 for (int i = 0; i < stack_size; ++i) {
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72 stack_[i]->Forward(debug, input, nullptr, scratch, results[i]);
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73 }
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74 // Now pack all the results (serially) into the output.
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75 int out_offset = 0;
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76 output->Resize(*results[0], NumOutputs());
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77 for (int i = 0; i < stack_size; ++i) {
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78 out_offset = output->CopyPacking(*results[i], out_offset);
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79 }
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80 } else {
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81 // Revolving intermediate result.
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82 NetworkScratch::IO result(input, scratch);
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83 // Source for divided replicated.
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84 NetworkScratch::IO source_part;
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85 TransposedArray *src_transpose = nullptr;
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86 if (IsTraining() && type_ == NT_REPLICATED) {
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87 // Make a transposed copy of the input.
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88 input.Transpose(&transposed_input_);
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89 src_transpose = &transposed_input_;
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90 }
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91 // Run each network, putting the outputs into result.
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92 int out_offset = 0;
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93 for (int i = 0; i < stack_size; ++i) {
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94 stack_[i]->Forward(debug, input, src_transpose, scratch, result);
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95 // All networks must have the same output width
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96 if (i == 0) {
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97 output->Resize(*result, NumOutputs());
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98 } else {
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99 ASSERT_HOST(result->Width() == output->Width());
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100 }
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101 out_offset = output->CopyPacking(*result, out_offset);
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102 }
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103 }
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104 #ifndef GRAPHICS_DISABLED
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105 if (parallel_debug) {
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106 DisplayForward(*output);
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107 }
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108 #endif
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109 }
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110
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111 // Runs backward propagation of errors on the deltas line.
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112 // See NetworkCpp for a detailed discussion of the arguments.
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113 bool Parallel::Backward(bool debug, const NetworkIO &fwd_deltas, NetworkScratch *scratch,
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114 NetworkIO *back_deltas) {
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115 // If this parallel is a replicator of convolvers, or holds a 1-d LSTM pair,
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116 // or a 2-d LSTM quad, do debug locally, and don't pass the flag on.
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117 if (debug && type_ != NT_PARALLEL) {
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118 #ifndef GRAPHICS_DISABLED
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119 DisplayBackward(fwd_deltas);
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120 #endif
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121 debug = false;
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122 }
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123 auto stack_size = stack_.size();
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124 if (type_ == NT_PAR_2D_LSTM) {
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125 // Special case, run parallel in parallel.
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126 std::vector<NetworkScratch::IO> in_deltas(stack_size);
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127 std::vector<NetworkScratch::IO> out_deltas(stack_size);
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128 // Split the forward deltas for each stack element.
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129 int feature_offset = 0;
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130 for (unsigned i = 0; i < stack_.size(); ++i) {
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131 int num_features = stack_[i]->NumOutputs();
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132 in_deltas[i].Resize(fwd_deltas, num_features, scratch);
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133 out_deltas[i].Resize(fwd_deltas, stack_[i]->NumInputs(), scratch);
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134 in_deltas[i]->CopyUnpacking(fwd_deltas, feature_offset, num_features);
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135 feature_offset += num_features;
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136 }
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137 #ifdef _OPENMP
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138 # pragma omp parallel for num_threads(stack_size)
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139 #endif
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140 for (unsigned i = 0; i < stack_size; ++i) {
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141 stack_[i]->Backward(debug, *in_deltas[i], scratch, i == 0 ? back_deltas : out_deltas[i]);
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142 }
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143 if (needs_to_backprop_) {
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144 for (unsigned i = 1; i < stack_size; ++i) {
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145 back_deltas->AddAllToFloat(*out_deltas[i]);
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146 }
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147 }
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148 } else {
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149 // Revolving partial deltas.
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150 NetworkScratch::IO in_deltas(fwd_deltas, scratch);
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151 // The sum of deltas from different sources, which will eventually go into
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152 // back_deltas.
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153 NetworkScratch::IO out_deltas;
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154 int feature_offset = 0;
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155 for (unsigned i = 0; i < stack_.size(); ++i) {
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156 int num_features = stack_[i]->NumOutputs();
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157 in_deltas->CopyUnpacking(fwd_deltas, feature_offset, num_features);
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158 feature_offset += num_features;
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159 if (stack_[i]->Backward(debug, *in_deltas, scratch, back_deltas)) {
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160 if (i == 0) {
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161 out_deltas.ResizeFloat(*back_deltas, back_deltas->NumFeatures(), scratch);
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162 out_deltas->CopyAll(*back_deltas);
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163 } else if (back_deltas->NumFeatures() == out_deltas->NumFeatures()) {
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164 // Widths are allowed to be different going back, as we may have
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165 // input nets, so only accumulate the deltas if the widths are the
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166 // same.
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167 out_deltas->AddAllToFloat(*back_deltas);
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168 }
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169 }
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170 }
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171 if (needs_to_backprop_) {
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172 back_deltas->CopyAll(*out_deltas);
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173 }
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174 }
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175 if (needs_to_backprop_) {
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176 back_deltas->ScaleFloatBy(1.0f / stack_size);
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177 }
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178 return needs_to_backprop_;
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179 }
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180
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181 } // namespace tesseract.