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

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: fullyconnected.h
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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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18 #ifndef TESSERACT_LSTM_FULLYCONNECTED_H_
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19 #define TESSERACT_LSTM_FULLYCONNECTED_H_
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21 #include "network.h"
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22 #include "networkscratch.h"
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23 #include "tesstypes.h"
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25 namespace tesseract {
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27 // C++ Implementation of the Softmax (output) class from lstm.py.
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28 class FullyConnected : public Network {
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29 public:
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30 TESS_API
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31 FullyConnected(const std::string &name, int ni, int no, NetworkType type);
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32 ~FullyConnected() override = default;
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34 // Returns the shape output from the network given an input shape (which may
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35 // be partially unknown ie zero).
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36 StaticShape OutputShape(const StaticShape &input_shape) const override;
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38 std::string spec() const override {
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39 std::string spec;
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40 if (type_ == NT_TANH) {
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41 spec += "Ft" + std::to_string(no_);
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42 } else if (type_ == NT_LOGISTIC) {
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43 spec += "Fs" + std::to_string(no_);
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44 } else if (type_ == NT_RELU) {
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45 spec += "Fr" + std::to_string(no_);
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46 } else if (type_ == NT_LINEAR) {
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47 spec += "Fl" + std::to_string(no_);
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48 } else if (type_ == NT_POSCLIP) {
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49 spec += "Fp" + std::to_string(no_);
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50 } else if (type_ == NT_SYMCLIP) {
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51 spec += "Fn" + std::to_string(no_);
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52 } else if (type_ == NT_SOFTMAX) {
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53 spec += "Fc" + std::to_string(no_);
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54 } else {
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55 spec += "Fm" + std::to_string(no_);
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56 }
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57 return spec;
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58 }
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59
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60 // Changes the type to the given type. Used to commute a softmax to a
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61 // non-output type for adding on other networks.
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62 void ChangeType(NetworkType type) {
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63 type_ = type;
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64 }
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65
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66 // Suspends/Enables training by setting the training_ flag. Serialize and
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67 // DeSerialize only operate on the run-time data if state is false.
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68 void SetEnableTraining(TrainingState state) override;
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69
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70 // Sets up the network for training. Initializes weights using weights of
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71 // scale `range` picked according to the random number generator `randomizer`.
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72 int InitWeights(float range, TRand *randomizer) override;
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73 // Recursively searches the network for softmaxes with old_no outputs,
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74 // and remaps their outputs according to code_map. See network.h for details.
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75 int RemapOutputs(int old_no, const std::vector<int> &code_map) override;
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76
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77 // Converts a float network to an int network.
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78 void ConvertToInt() override;
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79
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80 // Provides debug output on the weights.
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81 void DebugWeights() override;
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82
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83 // Writes to the given file. Returns false in case of error.
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84 bool Serialize(TFile *fp) const override;
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85 // Reads from the given file. Returns false in case of error.
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86 bool DeSerialize(TFile *fp) override;
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87
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88 // Runs forward propagation of activations on the input line.
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89 // See Network for a detailed discussion of the arguments.
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90 void Forward(bool debug, const NetworkIO &input, const TransposedArray *input_transpose,
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91 NetworkScratch *scratch, NetworkIO *output) override;
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92 // Components of Forward so FullyConnected can be reused inside LSTM.
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93 void SetupForward(const NetworkIO &input, const TransposedArray *input_transpose);
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94 void ForwardTimeStep(int t, TFloat *output_line);
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95 void ForwardTimeStep(const TFloat *d_input, int t, TFloat *output_line);
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96 void ForwardTimeStep(const int8_t *i_input, int t, TFloat *output_line);
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97
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98 // Runs backward propagation of errors on the deltas line.
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99 // See Network for a detailed discussion of the arguments.
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100 bool Backward(bool debug, const NetworkIO &fwd_deltas, NetworkScratch *scratch,
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101 NetworkIO *back_deltas) override;
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102 // Components of Backward so FullyConnected can be reused inside LSTM.
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103 void BackwardTimeStep(const NetworkIO &fwd_deltas, int t, TFloat *curr_errors,
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104 TransposedArray *errors_t, TFloat *backprop);
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105 void FinishBackward(const TransposedArray &errors_t);
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106
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107 // Updates the weights using the given learning rate, momentum and adam_beta.
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108 // num_samples is used in the adam computation iff use_adam_ is true.
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109 void Update(float learning_rate, float momentum, float adam_beta, int num_samples) override;
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110 // Sums the products of weight updates in *this and other, splitting into
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111 // positive (same direction) in *same and negative (different direction) in
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112 // *changed.
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113 void CountAlternators(const Network &other, TFloat *same, TFloat *changed) const override;
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114
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115 protected:
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116 // Weight arrays of size [no, ni + 1].
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117 WeightMatrix weights_;
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118 // Transposed copy of input used during training of size [ni, width].
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119 TransposedArray source_t_;
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120 // Pointer to transposed input stored elsewhere. If not null, this is used
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121 // in preference to calculating the transpose and storing it in source_t_.
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122 const TransposedArray *external_source_;
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123 // Activations from forward pass of size [width, no].
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124 NetworkIO acts_;
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125 // Memory of the integer mode input to forward as softmax always outputs
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126 // float, so the information is otherwise lost.
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127 bool int_mode_;
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128 };
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129
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130 } // namespace tesseract.
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131
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132 #endif // TESSERACT_LSTM_FULLYCONNECTED_H_