Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/lstm/fullyconnected.cpp @ 21:2f43e400f144
Provide an "all" target to build both the sdist and the wheel
| author | Franz Glasner <fzglas.hg@dom66.de> |
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| date | Fri, 19 Sep 2025 10:28:53 +0200 |
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1 /////////////////////////////////////////////////////////////////////// |
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2 // File: fullyconnected.cpp |
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3 // Description: Simple feed-forward layer with various non-linearities. |
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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 #ifdef HAVE_CONFIG_H |
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19 # include "config_auto.h" |
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20 #endif |
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21 |
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22 #include "fullyconnected.h" |
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23 |
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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 #include <cstdio> |
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28 #include <cstdlib> |
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29 |
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30 #include "functions.h" |
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31 #include "networkscratch.h" |
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32 |
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33 // Number of threads to use for parallel calculation of Forward and Backward. |
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34 #ifdef _OPENMP |
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35 const int kNumThreads = 4; |
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36 #else |
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37 const int kNumThreads = 1; |
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38 #endif |
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39 |
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40 namespace tesseract { |
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41 |
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42 FullyConnected::FullyConnected(const std::string &name, int ni, int no, NetworkType type) |
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43 : Network(type, name, ni, no), external_source_(nullptr), int_mode_(false) {} |
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44 |
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45 // Returns the shape output from the network given an input shape (which may |
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46 // be partially unknown ie zero). |
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47 StaticShape FullyConnected::OutputShape(const StaticShape &input_shape) const { |
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48 LossType loss_type = LT_NONE; |
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49 if (type_ == NT_SOFTMAX) { |
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50 loss_type = LT_CTC; |
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51 } else if (type_ == NT_SOFTMAX_NO_CTC) { |
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52 loss_type = LT_SOFTMAX; |
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53 } else if (type_ == NT_LOGISTIC) { |
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54 loss_type = LT_LOGISTIC; |
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55 } |
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56 StaticShape result(input_shape); |
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57 result.set_depth(no_); |
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58 result.set_loss_type(loss_type); |
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59 return result; |
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60 } |
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61 |
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62 // Suspends/Enables training by setting the training_ flag. |
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63 void FullyConnected::SetEnableTraining(TrainingState state) { |
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64 if (state == TS_RE_ENABLE) { |
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65 // Enable only from temp disabled. |
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66 if (training_ == TS_TEMP_DISABLE) { |
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67 training_ = TS_ENABLED; |
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68 } |
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69 } else if (state == TS_TEMP_DISABLE) { |
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70 // Temp disable only from enabled. |
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71 if (training_ == TS_ENABLED) { |
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72 training_ = state; |
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73 } |
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74 } else { |
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75 if (state == TS_ENABLED && training_ != TS_ENABLED) { |
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76 weights_.InitBackward(); |
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77 } |
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78 training_ = state; |
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79 } |
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80 } |
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81 |
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82 // Sets up the network for training. Initializes weights using weights of |
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83 // scale `range` picked according to the random number generator `randomizer`. |
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84 int FullyConnected::InitWeights(float range, TRand *randomizer) { |
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85 Network::SetRandomizer(randomizer); |
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86 num_weights_ = weights_.InitWeightsFloat(no_, ni_ + 1, TestFlag(NF_ADAM), range, randomizer); |
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87 return num_weights_; |
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88 } |
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89 |
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90 // Recursively searches the network for softmaxes with old_no outputs, |
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91 // and remaps their outputs according to code_map. See network.h for details. |
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92 |
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93 int FullyConnected::RemapOutputs(int old_no, const std::vector<int> &code_map) { |
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94 if (type_ == NT_SOFTMAX && no_ == old_no) { |
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95 num_weights_ = weights_.RemapOutputs(code_map); |
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96 no_ = code_map.size(); |
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97 } |
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98 return num_weights_; |
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99 } |
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100 |
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101 // Converts a float network to an int network. |
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102 void FullyConnected::ConvertToInt() { |
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103 weights_.ConvertToInt(); |
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104 } |
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105 |
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106 // Provides debug output on the weights. |
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107 void FullyConnected::DebugWeights() { |
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108 weights_.Debug2D(name_.c_str()); |
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109 } |
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110 |
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111 // Writes to the given file. Returns false in case of error. |
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112 bool FullyConnected::Serialize(TFile *fp) const { |
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113 if (!Network::Serialize(fp)) { |
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114 return false; |
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115 } |
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116 if (!weights_.Serialize(IsTraining(), fp)) { |
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117 return false; |
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118 } |
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119 return true; |
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120 } |
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121 |
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122 // Reads from the given file. Returns false in case of error. |
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123 bool FullyConnected::DeSerialize(TFile *fp) { |
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124 return weights_.DeSerialize(IsTraining(), fp); |
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125 } |
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126 |
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127 // Runs forward propagation of activations on the input line. |
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128 // See NetworkCpp for a detailed discussion of the arguments. |
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129 void FullyConnected::Forward(bool debug, const NetworkIO &input, |
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130 const TransposedArray *input_transpose, NetworkScratch *scratch, |
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131 NetworkIO *output) { |
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132 int width = input.Width(); |
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133 if (type_ == NT_SOFTMAX) { |
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134 output->ResizeFloat(input, no_); |
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135 } else { |
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136 output->Resize(input, no_); |
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137 } |
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138 SetupForward(input, input_transpose); |
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139 std::vector<NetworkScratch::FloatVec> temp_lines(kNumThreads); |
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140 std::vector<NetworkScratch::FloatVec> curr_input(kNumThreads); |
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141 int ro = no_; |
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142 if (IntSimdMatrix::intSimdMatrix) { |
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143 ro = IntSimdMatrix::intSimdMatrix->RoundOutputs(ro); |
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144 } |
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145 for (int i = 0; i < kNumThreads; ++i) { |
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146 temp_lines[i].Init(ro, scratch); |
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147 curr_input[i].Init(ni_, scratch); |
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148 } |
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149 #ifdef _OPENMP |
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150 # pragma omp parallel for num_threads(kNumThreads) |
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151 for (int t = 0; t < width; ++t) { |
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152 // Thread-local pointer to temporary storage. |
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153 int thread_id = omp_get_thread_num(); |
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154 #else |
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155 for (int t = 0; t < width; ++t) { |
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156 // Thread-local pointer to temporary storage. |
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157 int thread_id = 0; |
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158 #endif |
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159 TFloat *temp_line = temp_lines[thread_id]; |
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160 if (input.int_mode()) { |
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161 ForwardTimeStep(input.i(t), t, temp_line); |
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162 } else { |
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163 input.ReadTimeStep(t, curr_input[thread_id]); |
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164 ForwardTimeStep(curr_input[thread_id], t, temp_line); |
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165 } |
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166 output->WriteTimeStep(t, temp_line); |
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167 if (IsTraining() && type_ != NT_SOFTMAX) { |
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168 acts_.CopyTimeStepFrom(t, *output, t); |
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169 } |
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170 } |
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171 // Zero all the elements that are in the padding around images that allows |
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172 // multiple different-sized images to exist in a single array. |
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173 // acts_ is only used if this is not a softmax op. |
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174 if (IsTraining() && type_ != NT_SOFTMAX) { |
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175 acts_.ZeroInvalidElements(); |
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176 } |
|
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177 output->ZeroInvalidElements(); |
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178 #if DEBUG_DETAIL > 0 |
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179 tprintf("F Output:%s\n", name_.c_str()); |
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180 output->Print(10); |
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181 #endif |
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182 #ifndef GRAPHICS_DISABLED |
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183 if (debug) { |
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184 DisplayForward(*output); |
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185 } |
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186 #endif |
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187 } |
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188 |
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189 // Components of Forward so FullyConnected can be reused inside LSTM. |
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190 void FullyConnected::SetupForward(const NetworkIO &input, const TransposedArray *input_transpose) { |
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191 // Softmax output is always float, so save the input type. |
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192 int_mode_ = input.int_mode(); |
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193 if (IsTraining()) { |
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194 acts_.Resize(input, no_); |
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195 // Source_ is a transposed copy of input. It isn't needed if provided. |
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196 external_source_ = input_transpose; |
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197 if (external_source_ == nullptr) { |
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198 source_t_.ResizeNoInit(ni_, input.Width()); |
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199 } |
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200 } |
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201 } |
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202 |
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203 void FullyConnected::ForwardTimeStep(int t, TFloat *output_line) { |
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204 if (type_ == NT_TANH) { |
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205 FuncInplace<GFunc>(no_, output_line); |
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206 } else if (type_ == NT_LOGISTIC) { |
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207 FuncInplace<FFunc>(no_, output_line); |
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208 } else if (type_ == NT_POSCLIP) { |
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209 FuncInplace<ClipFFunc>(no_, output_line); |
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210 } else if (type_ == NT_SYMCLIP) { |
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|
211 FuncInplace<ClipGFunc>(no_, output_line); |
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212 } else if (type_ == NT_RELU) { |
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213 FuncInplace<Relu>(no_, output_line); |
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214 } else if (type_ == NT_SOFTMAX || type_ == NT_SOFTMAX_NO_CTC) { |
|
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215 SoftmaxInPlace(no_, output_line); |
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216 } else if (type_ != NT_LINEAR) { |
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217 ASSERT_HOST("Invalid fully-connected type!" == nullptr); |
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218 } |
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219 } |
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220 |
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221 void FullyConnected::ForwardTimeStep(const TFloat *d_input, int t, TFloat *output_line) { |
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222 // input is copied to source_ line-by-line for cache coherency. |
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223 if (IsTraining() && external_source_ == nullptr) { |
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224 source_t_.WriteStrided(t, d_input); |
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225 } |
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226 weights_.MatrixDotVector(d_input, output_line); |
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227 ForwardTimeStep(t, output_line); |
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228 } |
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229 |
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230 void FullyConnected::ForwardTimeStep(const int8_t *i_input, int t, TFloat *output_line) { |
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231 // input is copied to source_ line-by-line for cache coherency. |
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|
232 weights_.MatrixDotVector(i_input, output_line); |
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233 ForwardTimeStep(t, output_line); |
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234 } |
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235 |
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236 // Runs backward propagation of errors on the deltas line. |
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|
237 // See NetworkCpp for a detailed discussion of the arguments. |
|
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|
238 bool FullyConnected::Backward(bool debug, const NetworkIO &fwd_deltas, NetworkScratch *scratch, |
|
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|
239 NetworkIO *back_deltas) { |
|
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240 #ifndef GRAPHICS_DISABLED |
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241 if (debug) { |
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242 DisplayBackward(fwd_deltas); |
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243 } |
|
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|
244 #endif |
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245 back_deltas->Resize(fwd_deltas, ni_); |
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246 std::vector<NetworkScratch::FloatVec> errors(kNumThreads); |
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247 for (int i = 0; i < kNumThreads; ++i) { |
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|
248 errors[i].Init(no_, scratch); |
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249 } |
|
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|
250 std::vector<NetworkScratch::FloatVec> temp_backprops; |
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251 if (needs_to_backprop_) { |
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252 temp_backprops.resize(kNumThreads); |
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253 for (int i = 0; i < kNumThreads; ++i) { |
|
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|
254 temp_backprops[i].Init(ni_, scratch); |
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255 } |
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256 } |
|
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|
257 int width = fwd_deltas.Width(); |
|
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|
258 NetworkScratch::GradientStore errors_t; |
|
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259 errors_t.Init(no_, width, scratch); |
|
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|
260 #ifdef _OPENMP |
|
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|
261 # pragma omp parallel for num_threads(kNumThreads) |
|
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|
262 for (int t = 0; t < width; ++t) { |
|
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|
263 int thread_id = omp_get_thread_num(); |
|
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264 #else |
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265 for (int t = 0; t < width; ++t) { |
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266 int thread_id = 0; |
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267 #endif |
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268 TFloat *backprop = nullptr; |
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269 if (needs_to_backprop_) { |
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270 backprop = temp_backprops[thread_id]; |
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271 } |
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272 TFloat *curr_errors = errors[thread_id]; |
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273 BackwardTimeStep(fwd_deltas, t, curr_errors, errors_t.get(), backprop); |
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274 if (backprop != nullptr) { |
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275 back_deltas->WriteTimeStep(t, backprop); |
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276 } |
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277 } |
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278 FinishBackward(*errors_t.get()); |
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279 if (needs_to_backprop_) { |
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280 back_deltas->ZeroInvalidElements(); |
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281 #if DEBUG_DETAIL > 0 |
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282 tprintf("F Backprop:%s\n", name_.c_str()); |
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283 back_deltas->Print(10); |
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284 #endif |
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285 return true; |
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286 } |
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287 return false; // No point going further back. |
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288 } |
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289 |
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290 void FullyConnected::BackwardTimeStep(const NetworkIO &fwd_deltas, int t, TFloat *curr_errors, |
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291 TransposedArray *errors_t, TFloat *backprop) { |
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292 if (type_ == NT_TANH) { |
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293 acts_.FuncMultiply<GPrime>(fwd_deltas, t, curr_errors); |
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294 } else if (type_ == NT_LOGISTIC) { |
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295 acts_.FuncMultiply<FPrime>(fwd_deltas, t, curr_errors); |
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296 } else if (type_ == NT_POSCLIP) { |
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297 acts_.FuncMultiply<ClipFPrime>(fwd_deltas, t, curr_errors); |
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298 } else if (type_ == NT_SYMCLIP) { |
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299 acts_.FuncMultiply<ClipGPrime>(fwd_deltas, t, curr_errors); |
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300 } else if (type_ == NT_RELU) { |
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301 acts_.FuncMultiply<ReluPrime>(fwd_deltas, t, curr_errors); |
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302 } else if (type_ == NT_SOFTMAX || type_ == NT_SOFTMAX_NO_CTC || type_ == NT_LINEAR) { |
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303 fwd_deltas.ReadTimeStep(t, curr_errors); // fwd_deltas are the errors. |
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304 } else { |
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305 ASSERT_HOST("Invalid fully-connected type!" == nullptr); |
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306 } |
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307 // Generate backprop only if needed by the lower layer. |
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308 if (backprop != nullptr) { |
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309 weights_.VectorDotMatrix(curr_errors, backprop); |
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310 } |
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311 errors_t->WriteStrided(t, curr_errors); |
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312 } |
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313 |
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314 void FullyConnected::FinishBackward(const TransposedArray &errors_t) { |
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315 if (external_source_ == nullptr) { |
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316 weights_.SumOuterTransposed(errors_t, source_t_, true); |
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317 } else { |
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318 weights_.SumOuterTransposed(errors_t, *external_source_, true); |
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319 } |
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320 } |
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321 |
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322 // Updates the weights using the given learning rate, momentum and adam_beta. |
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323 // num_samples is used in the adam computation iff use_adam_ is true. |
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324 void FullyConnected::Update(float learning_rate, float momentum, float adam_beta, int num_samples) { |
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325 weights_.Update(learning_rate, momentum, adam_beta, num_samples); |
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326 } |
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327 |
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328 // Sums the products of weight updates in *this and other, splitting into |
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329 // positive (same direction) in *same and negative (different direction) in |
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330 // *changed. |
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331 void FullyConnected::CountAlternators(const Network &other, TFloat *same, TFloat *changed) const { |
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332 ASSERT_HOST(other.type() == type_); |
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333 const auto *fc = static_cast<const FullyConnected *>(&other); |
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334 weights_.CountAlternators(fc->weights_, same, changed); |
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335 } |
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336 |
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337 } // namespace tesseract. |
