Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/lstm/plumbing.cpp @ 2:b50eed0cc0ef upstream
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| author | Franz Glasner <fzglas.hg@dom66.de> |
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| date | Mon, 15 Sep 2025 11:43:07 +0200 |
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1 /////////////////////////////////////////////////////////////////////// |
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2 // File: plumbing.cpp |
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3 // Description: Base class for networks that organize other networks |
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4 // eg series or parallel. |
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5 // Author: Ray Smith |
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6 // |
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7 // (C) Copyright 2014, Google Inc. |
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8 // Licensed under the Apache License, Version 2.0 (the "License"); |
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9 // you may not use this file except in compliance with the License. |
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10 // You may obtain a copy of the License at |
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11 // http://www.apache.org/licenses/LICENSE-2.0 |
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12 // Unless required by applicable law or agreed to in writing, software |
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13 // distributed under the License is distributed on an "AS IS" BASIS, |
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14 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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15 // See the License for the specific language governing permissions and |
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16 // limitations under the License. |
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17 /////////////////////////////////////////////////////////////////////// |
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18 |
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19 #include "plumbing.h" |
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20 |
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21 namespace tesseract { |
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22 |
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23 // ni_ and no_ will be set by AddToStack. |
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24 Plumbing::Plumbing(const std::string &name) : Network(NT_PARALLEL, name, 0, 0) {} |
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25 |
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26 // Suspends/Enables training by setting the training_ flag. Serialize and |
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27 // DeSerialize only operate on the run-time data if state is false. |
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28 void Plumbing::SetEnableTraining(TrainingState state) { |
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29 Network::SetEnableTraining(state); |
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30 for (auto &i : stack_) { |
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31 i->SetEnableTraining(state); |
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32 } |
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33 } |
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34 |
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35 // Sets flags that control the action of the network. See NetworkFlags enum |
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36 // for bit values. |
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37 void Plumbing::SetNetworkFlags(uint32_t flags) { |
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38 Network::SetNetworkFlags(flags); |
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39 for (auto &i : stack_) { |
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40 i->SetNetworkFlags(flags); |
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41 } |
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42 } |
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43 |
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44 // Sets up the network for training. Initializes weights using weights of |
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45 // scale `range` picked according to the random number generator `randomizer`. |
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46 // Note that randomizer is a borrowed pointer that should outlive the network |
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47 // and should not be deleted by any of the networks. |
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48 // Returns the number of weights initialized. |
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49 int Plumbing::InitWeights(float range, TRand *randomizer) { |
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50 num_weights_ = 0; |
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51 for (auto &i : stack_) { |
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52 num_weights_ += i->InitWeights(range, randomizer); |
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53 } |
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54 return num_weights_; |
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55 } |
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56 |
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57 // Recursively searches the network for softmaxes with old_no outputs, |
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58 // and remaps their outputs according to code_map. See network.h for details. |
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59 int Plumbing::RemapOutputs(int old_no, const std::vector<int> &code_map) { |
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60 num_weights_ = 0; |
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61 for (auto &i : stack_) { |
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62 num_weights_ += i->RemapOutputs(old_no, code_map); |
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63 } |
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64 return num_weights_; |
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65 } |
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66 |
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67 // Converts a float network to an int network. |
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68 void Plumbing::ConvertToInt() { |
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69 for (auto &i : stack_) { |
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70 i->ConvertToInt(); |
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71 } |
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72 } |
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73 |
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74 // Provides a pointer to a TRand for any networks that care to use it. |
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75 // Note that randomizer is a borrowed pointer that should outlive the network |
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76 // and should not be deleted by any of the networks. |
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77 void Plumbing::SetRandomizer(TRand *randomizer) { |
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78 for (auto &i : stack_) { |
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79 i->SetRandomizer(randomizer); |
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80 } |
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81 } |
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82 |
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83 // Adds the given network to the stack. |
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84 void Plumbing::AddToStack(Network *network) { |
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85 if (stack_.empty()) { |
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86 ni_ = network->NumInputs(); |
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87 no_ = network->NumOutputs(); |
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88 } else if (type_ == NT_SERIES) { |
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89 // ni is input of first, no output of last, others match output to input. |
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90 ASSERT_HOST(no_ == network->NumInputs()); |
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91 no_ = network->NumOutputs(); |
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92 } else { |
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93 // All parallel types. Output is sum of outputs, inputs all match. |
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94 ASSERT_HOST(ni_ == network->NumInputs()); |
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95 no_ += network->NumOutputs(); |
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96 } |
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97 stack_.push_back(network); |
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98 } |
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99 |
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100 // Sets needs_to_backprop_ to needs_backprop and calls on sub-network |
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101 // according to needs_backprop || any weights in this network. |
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102 bool Plumbing::SetupNeedsBackprop(bool needs_backprop) { |
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103 if (IsTraining()) { |
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104 needs_to_backprop_ = needs_backprop; |
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105 bool retval = needs_backprop; |
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106 for (auto &i : stack_) { |
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107 if (i->SetupNeedsBackprop(needs_backprop)) { |
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108 retval = true; |
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109 } |
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110 } |
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111 return retval; |
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112 } |
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113 // Frozen networks don't do backprop. |
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114 needs_to_backprop_ = false; |
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115 return false; |
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116 } |
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117 |
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118 // Returns an integer reduction factor that the network applies to the |
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119 // time sequence. Assumes that any 2-d is already eliminated. Used for |
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120 // scaling bounding boxes of truth data. |
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121 // WARNING: if GlobalMinimax is used to vary the scale, this will return |
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122 // the last used scale factor. Call it before any forward, and it will return |
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123 // the minimum scale factor of the paths through the GlobalMinimax. |
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124 int Plumbing::XScaleFactor() const { |
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125 return stack_[0]->XScaleFactor(); |
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126 } |
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127 |
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128 // Provides the (minimum) x scale factor to the network (of interest only to |
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129 // input units) so they can determine how to scale bounding boxes. |
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130 void Plumbing::CacheXScaleFactor(int factor) { |
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131 for (auto &i : stack_) { |
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132 i->CacheXScaleFactor(factor); |
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133 } |
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134 } |
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135 |
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136 // Provides debug output on the weights. |
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137 void Plumbing::DebugWeights() { |
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138 for (auto &i : stack_) { |
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139 i->DebugWeights(); |
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140 } |
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141 } |
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142 |
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143 // Returns a set of strings representing the layer-ids of all layers below. |
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144 void Plumbing::EnumerateLayers(const std::string *prefix, std::vector<std::string> &layers) const { |
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145 for (size_t i = 0; i < stack_.size(); ++i) { |
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146 std::string layer_name; |
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147 if (prefix) { |
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148 layer_name = *prefix; |
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149 } |
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150 layer_name += ":" + std::to_string(i); |
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151 if (stack_[i]->IsPlumbingType()) { |
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152 auto *plumbing = static_cast<Plumbing *>(stack_[i]); |
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153 plumbing->EnumerateLayers(&layer_name, layers); |
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154 } else { |
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155 layers.push_back(layer_name); |
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156 } |
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157 } |
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158 } |
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159 |
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160 // Returns a pointer to the network layer corresponding to the given id. |
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161 Network *Plumbing::GetLayer(const char *id) const { |
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162 char *next_id; |
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163 int index = strtol(id, &next_id, 10); |
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164 if (index < 0 || static_cast<unsigned>(index) >= stack_.size()) { |
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165 return nullptr; |
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166 } |
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167 if (stack_[index]->IsPlumbingType()) { |
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168 auto *plumbing = static_cast<Plumbing *>(stack_[index]); |
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169 ASSERT_HOST(*next_id == ':'); |
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170 return plumbing->GetLayer(next_id + 1); |
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171 } |
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172 return stack_[index]; |
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173 } |
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174 |
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175 // Returns a pointer to the learning rate for the given layer id. |
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176 float *Plumbing::LayerLearningRatePtr(const char *id) { |
|
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|
177 char *next_id; |
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178 int index = strtol(id, &next_id, 10); |
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179 if (index < 0 || static_cast<unsigned>(index) >= stack_.size()) { |
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180 return nullptr; |
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181 } |
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182 if (stack_[index]->IsPlumbingType()) { |
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183 auto *plumbing = static_cast<Plumbing *>(stack_[index]); |
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184 ASSERT_HOST(*next_id == ':'); |
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185 return plumbing->LayerLearningRatePtr(next_id + 1); |
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186 } |
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187 if (static_cast<unsigned>(index) >= learning_rates_.size()) { |
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188 return nullptr; |
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189 } |
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190 return &learning_rates_[index]; |
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191 } |
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192 |
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193 // Writes to the given file. Returns false in case of error. |
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194 bool Plumbing::Serialize(TFile *fp) const { |
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195 if (!Network::Serialize(fp)) { |
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196 return false; |
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197 } |
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198 uint32_t size = stack_.size(); |
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199 // Can't use PointerVector::Serialize here as we need a special DeSerialize. |
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200 if (!fp->Serialize(&size)) { |
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201 return false; |
|
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202 } |
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203 for (uint32_t i = 0; i < size; ++i) { |
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204 if (!stack_[i]->Serialize(fp)) { |
|
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205 return false; |
|
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206 } |
|
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207 } |
|
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208 if ((network_flags_ & NF_LAYER_SPECIFIC_LR) && !fp->Serialize(learning_rates_)) { |
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209 return false; |
|
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210 } |
|
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211 return true; |
|
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212 } |
|
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213 |
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214 // Reads from the given file. Returns false in case of error. |
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215 bool Plumbing::DeSerialize(TFile *fp) { |
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216 for (auto data : stack_) { |
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217 delete data; |
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218 } |
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219 stack_.clear(); |
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220 no_ = 0; // We will be modifying this as we AddToStack. |
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221 uint32_t size; |
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222 if (!fp->DeSerialize(&size)) { |
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223 return false; |
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224 } |
|
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225 for (uint32_t i = 0; i < size; ++i) { |
|
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226 Network *network = CreateFromFile(fp); |
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227 if (network == nullptr) { |
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228 return false; |
|
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229 } |
|
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230 AddToStack(network); |
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231 } |
|
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232 if ((network_flags_ & NF_LAYER_SPECIFIC_LR) && !fp->DeSerialize(learning_rates_)) { |
|
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233 return false; |
|
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234 } |
|
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235 return true; |
|
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236 } |
|
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237 |
|
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238 // Updates the weights using the given learning rate, momentum and adam_beta. |
|
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239 // num_samples is used in the adam computation iff use_adam_ is true. |
|
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240 void Plumbing::Update(float learning_rate, float momentum, float adam_beta, int num_samples) { |
|
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241 for (size_t i = 0; i < stack_.size(); ++i) { |
|
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|
242 if (network_flags_ & NF_LAYER_SPECIFIC_LR) { |
|
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|
243 if (i < learning_rates_.size()) { |
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|
244 learning_rate = learning_rates_[i]; |
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245 } else { |
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|
246 learning_rates_.push_back(learning_rate); |
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247 } |
|
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248 } |
|
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|
249 if (stack_[i]->IsTraining()) { |
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parents:
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|
250 stack_[i]->Update(learning_rate, momentum, adam_beta, num_samples); |
|
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251 } |
|
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252 } |
|
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253 } |
|
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254 |
|
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parents:
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255 // Sums the products of weight updates in *this and other, splitting into |
|
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Franz Glasner <fzglas.hg@dom66.de>
parents:
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256 // positive (same direction) in *same and negative (different direction) in |
|
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Franz Glasner <fzglas.hg@dom66.de>
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257 // *changed. |
|
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parents:
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258 void Plumbing::CountAlternators(const Network &other, TFloat *same, TFloat *changed) const { |
|
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259 ASSERT_HOST(other.type() == type_); |
|
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Franz Glasner <fzglas.hg@dom66.de>
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|
260 const auto *plumbing = static_cast<const Plumbing *>(&other); |
|
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Franz Glasner <fzglas.hg@dom66.de>
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|
261 ASSERT_HOST(plumbing->stack_.size() == stack_.size()); |
|
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Franz Glasner <fzglas.hg@dom66.de>
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|
262 for (size_t i = 0; i < stack_.size(); ++i) { |
|
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Franz Glasner <fzglas.hg@dom66.de>
parents:
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|
263 stack_[i]->CountAlternators(*plumbing->stack_[i], same, changed); |
|
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Franz Glasner <fzglas.hg@dom66.de>
parents:
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|
264 } |
|
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Franz Glasner <fzglas.hg@dom66.de>
parents:
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|
265 } |
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b50eed0cc0ef
ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4.
Franz Glasner <fzglas.hg@dom66.de>
parents:
diff
changeset
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266 |
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b50eed0cc0ef
ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4.
Franz Glasner <fzglas.hg@dom66.de>
parents:
diff
changeset
|
267 } // namespace tesseract. |
