Mercurial > hgrepos > Python2 > PyMuPDF

annotate mupdf-source/thirdparty/tesseract/src/lstm/plumbing.h @ 2:b50eed0cc0ef upstream

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ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4. The directory name has changed: no version number in the expanded directory now.
author Franz Glasner <fzglas.hg@dom66.de>
date Mon, 15 Sep 2025 11:43:07 +0200
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1 ///////////////////////////////////////////////////////////////////////
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2 // File: plumbing.h
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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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19 #ifndef TESSERACT_LSTM_PLUMBING_H_
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20 #define TESSERACT_LSTM_PLUMBING_H_
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22 #include "matrix.h"
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23 #include "network.h"
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25 namespace tesseract {
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27 // Holds a collection of other networks and forwards calls to each of them.
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28 class TESS_API Plumbing : public Network {
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29 public:
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30 // ni_ and no_ will be set by AddToStack.
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31 explicit Plumbing(const std::string &name);
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32 ~Plumbing() override {
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33 for (auto data : stack_) {
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34 delete data;
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35 }
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36 }
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38 // Returns the required shape input to the network.
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39 StaticShape InputShape() const override {
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40 return stack_[0]->InputShape();
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41 }
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42 std::string spec() const override {
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43 return "Sub-classes of Plumbing must implement spec()!";
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44 }
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46 // Returns true if the given type is derived from Plumbing, and thus contains
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47 // multiple sub-networks that can have their own learning rate.
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48 bool IsPlumbingType() const override {
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49 return true;
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50 }
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52 // Suspends/Enables training by setting the training_ flag. Serialize and
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53 // DeSerialize only operate on the run-time data if state is false.
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54 void SetEnableTraining(TrainingState state) override;
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56 // Sets flags that control the action of the network. See NetworkFlags enum
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57 // for bit values.
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58 void SetNetworkFlags(uint32_t flags) override;
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60 // Sets up the network for training. Initializes weights using weights of
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61 // scale `range` picked according to the random number generator `randomizer`.
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62 // Note that randomizer is a borrowed pointer that should outlive the network
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63 // and should not be deleted by any of the networks.
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64 // Returns the number of weights initialized.
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65 int InitWeights(float range, TRand *randomizer) override;
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66 // Recursively searches the network for softmaxes with old_no outputs,
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67 // and remaps their outputs according to code_map. See network.h for details.
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68 int RemapOutputs(int old_no, const std::vector<int> &code_map) override;
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70 // Converts a float network to an int network.
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71 void ConvertToInt() override;
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73 // Provides a pointer to a TRand for any networks that care to use it.
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74 // Note that randomizer is a borrowed pointer that should outlive the network
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75 // and should not be deleted by any of the networks.
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76 void SetRandomizer(TRand *randomizer) override;
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78 // Adds the given network to the stack.
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79 virtual void AddToStack(Network *network);
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81 // Sets needs_to_backprop_ to needs_backprop and returns true if
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82 // needs_backprop || any weights in this network so the next layer forward
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83 // can be told to produce backprop for this layer if needed.
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84 bool SetupNeedsBackprop(bool needs_backprop) override;
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85
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86 // Returns an integer reduction factor that the network applies to the
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87 // time sequence. Assumes that any 2-d is already eliminated. Used for
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88 // scaling bounding boxes of truth data.
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89 // WARNING: if GlobalMinimax is used to vary the scale, this will return
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90 // the last used scale factor. Call it before any forward, and it will return
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91 // the minimum scale factor of the paths through the GlobalMinimax.
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92 int XScaleFactor() const override;
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94 // Provides the (minimum) x scale factor to the network (of interest only to
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95 // input units) so they can determine how to scale bounding boxes.
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96 void CacheXScaleFactor(int factor) override;
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98 // Provides debug output on the weights.
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99 void DebugWeights() override;
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100
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101 // Returns the current stack.
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102 const std::vector<Network *> &stack() const {
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103 return stack_;
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104 }
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105 // Returns a set of strings representing the layer-ids of all layers below.
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106 void EnumerateLayers(const std::string *prefix, std::vector<std::string> &layers) const;
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107 // Returns a pointer to the network layer corresponding to the given id.
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108 Network *GetLayer(const char *id) const;
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109 // Returns the learning rate for a specific layer of the stack.
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110 float LayerLearningRate(const char *id) {
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111 const float *lr_ptr = LayerLearningRatePtr(id);
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112 ASSERT_HOST(lr_ptr != nullptr);
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113 return *lr_ptr;
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114 }
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115 // Scales the learning rate for a specific layer of the stack.
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116 void ScaleLayerLearningRate(const char *id, double factor) {
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117 float *lr_ptr = LayerLearningRatePtr(id);
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118 ASSERT_HOST(lr_ptr != nullptr);
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119 *lr_ptr *= factor;
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120 }
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121
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122 // Set the learning rate for a specific layer of the stack to the given value.
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123 void SetLayerLearningRate(const char *id, float learning_rate) {
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124 float *lr_ptr = LayerLearningRatePtr(id);
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125 ASSERT_HOST(lr_ptr != nullptr);
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126 *lr_ptr = learning_rate;
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127 }
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128
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129 // Returns a pointer to the learning rate for the given layer id.
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130 float *LayerLearningRatePtr(const char *id);
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131
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132 // Writes to the given file. Returns false in case of error.
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133 bool Serialize(TFile *fp) const override;
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134 // Reads from the given file. Returns false in case of error.
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135 bool DeSerialize(TFile *fp) override;
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136
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137 // Updates the weights using the given learning rate, momentum and adam_beta.
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138 // num_samples is used in the adam computation iff use_adam_ is true.
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139 void Update(float learning_rate, float momentum, float adam_beta, int num_samples) override;
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140 // Sums the products of weight updates in *this and other, splitting into
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141 // positive (same direction) in *same and negative (different direction) in
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142 // *changed.
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143 void CountAlternators(const Network &other, TFloat *same, TFloat *changed) const override;
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144
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145 protected:
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146 // The networks.
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147 std::vector<Network *> stack_;
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148 // Layer-specific learning rate iff network_flags_ & NF_LAYER_SPECIFIC_LR.
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149 // One element for each element of stack_.
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150 std::vector<float> learning_rates_;
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151 };
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152
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153 } // namespace tesseract.
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154
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155 #endif // TESSERACT_LSTM_PLUMBING_H_