Mercurial > hgrepos > Python2 > PyMuPDF

annotate mupdf-source/thirdparty/tesseract/src/lstm/lstm.h @ 46:7ee69f120f19 default tip

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>>>>> tag v1.26.5+1 for changeset b74429b0f5c4
author Franz Glasner <fzglas.hg@dom66.de>
date Sat, 11 Oct 2025 17:17:30 +0200
parents b50eed0cc0ef
children
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1 ///////////////////////////////////////////////////////////////////////
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2 // File: lstm.h
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3 // Description: Long-term-short-term-memory Recurrent neural network.
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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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18 #ifndef TESSERACT_LSTM_LSTM_H_
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19 #define TESSERACT_LSTM_LSTM_H_
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21 #include "fullyconnected.h"
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22 #include "network.h"
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24 namespace tesseract {
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26 // C++ Implementation of the LSTM class from lstm.py.
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27 class LSTM : public Network {
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28 public:
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29 // Enum for the different weights in LSTM, to reduce some of the I/O and
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30 // setup code to loops. The elements of the enum correspond to elements of an
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31 // array of WeightMatrix or a corresponding array of NetworkIO.
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32 enum WeightType {
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33 CI, // Cell Inputs.
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34 GI, // Gate at the input.
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35 GF1, // Forget gate at the memory (1-d or looking back 1 timestep).
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36 GO, // Gate at the output.
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37 GFS, // Forget gate at the memory, looking back in the other dimension.
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39 WT_COUNT // Number of WeightTypes.
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40 };
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42 // Constructor for NT_LSTM (regular 1 or 2-d LSTM), NT_LSTM_SOFTMAX (LSTM with
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43 // additional softmax layer included and fed back into the input at the next
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44 // timestep), or NT_LSTM_SOFTMAX_ENCODED (as LSTM_SOFTMAX, but the feedback
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45 // is binary encoded instead of categorical) only.
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46 // 2-d and bidi softmax LSTMs are not rejected, but are impossible to build
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47 // in the conventional way because the output feedback both forwards and
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48 // backwards in time does become impossible.
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49 TESS_API
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50 LSTM(const std::string &name, int num_inputs, int num_states, int num_outputs,
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51 bool two_dimensional, NetworkType type);
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52 ~LSTM() override;
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53
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54 // Returns the shape output from the network given an input shape (which may
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55 // be partially unknown ie zero).
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56 StaticShape OutputShape(const StaticShape &input_shape) const override;
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58 std::string spec() const override {
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59 std::string spec;
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60 if (type_ == NT_LSTM) {
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61 spec += "Lfx" + std::to_string(ns_);
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62 } else if (type_ == NT_LSTM_SUMMARY) {
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63 spec += "Lfxs" + std::to_string(ns_);
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64 } else if (type_ == NT_LSTM_SOFTMAX) {
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65 spec += "LS" + std::to_string(ns_);
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66 } else if (type_ == NT_LSTM_SOFTMAX_ENCODED) {
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67 spec += "LE" + std::to_string(ns_);
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68 }
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69 if (softmax_ != nullptr) {
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70 spec += softmax_->spec();
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71 }
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72 return spec;
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73 }
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74
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75 // Suspends/Enables training by setting the training_ flag. Serialize and
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76 // DeSerialize only operate on the run-time data if state is false.
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77 void SetEnableTraining(TrainingState state) override;
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78
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79 // Sets up the network for training. Initializes weights using weights of
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80 // scale `range` picked according to the random number generator `randomizer`.
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81 int InitWeights(float range, TRand *randomizer) override;
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82 // Recursively searches the network for softmaxes with old_no outputs,
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83 // and remaps their outputs according to code_map. See network.h for details.
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84 int RemapOutputs(int old_no, const std::vector<int> &code_map) override;
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85
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86 // Converts a float network to an int network.
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87 void ConvertToInt() override;
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88
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89 // Provides debug output on the weights.
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90 void DebugWeights() override;
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91
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92 // Writes to the given file. Returns false in case of error.
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93 bool Serialize(TFile *fp) const override;
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94 // Reads from the given file. Returns false in case of error.
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95 bool DeSerialize(TFile *fp) override;
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96
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97 // Runs forward propagation of activations on the input line.
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98 // See Network for a detailed discussion of the arguments.
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99 void Forward(bool debug, const NetworkIO &input, const TransposedArray *input_transpose,
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100 NetworkScratch *scratch, NetworkIO *output) override;
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101
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102 // Runs backward propagation of errors on the deltas line.
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103 // See Network for a detailed discussion of the arguments.
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104 bool Backward(bool debug, const NetworkIO &fwd_deltas, NetworkScratch *scratch,
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105 NetworkIO *back_deltas) override;
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106 // Updates the weights using the given learning rate, momentum and adam_beta.
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107 // num_samples is used in the adam computation iff use_adam_ is true.
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108 void Update(float learning_rate, float momentum, float adam_beta, int num_samples) override;
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109 // Sums the products of weight updates in *this and other, splitting into
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110 // positive (same direction) in *same and negative (different direction) in
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111 // *changed.
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112 void CountAlternators(const Network &other, TFloat *same, TFloat *changed) const override;
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113 // Prints the weights for debug purposes.
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114 void PrintW();
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115 // Prints the weight deltas for debug purposes.
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116 void PrintDW();
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117
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118 // Returns true of this is a 2-d lstm.
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119 bool Is2D() const {
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120 return is_2d_;
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121 }
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122
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123 private:
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124 // Resizes forward data to cope with an input image of the given width.
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125 void ResizeForward(const NetworkIO &input);
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126
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127 private:
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128 // Size of padded input to weight matrices = ni_ + no_ for 1-D operation
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129 // and ni_ + 2 * no_ for 2-D operation. Note that there is a phantom 1 input
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130 // for the bias that makes the weight matrices of size [na + 1][no].
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131 int32_t na_;
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132 // Number of internal states. Equal to no_ except for a softmax LSTM.
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133 // ns_ is NOT serialized, but is calculated from gate_weights_.
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134 int32_t ns_;
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135 // Number of additional feedback states. The softmax types feed back
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136 // additional output information on top of the ns_ internal states.
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137 // In the case of a binary-coded (EMBEDDED) softmax, nf_ < no_.
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138 int32_t nf_;
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139 // Flag indicating 2-D operation.
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140 bool is_2d_;
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141
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142 // Gate weight arrays of size [na + 1, no].
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143 WeightMatrix gate_weights_[WT_COUNT];
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144 // Used only if this is a softmax LSTM.
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145 FullyConnected *softmax_;
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146 // Input padded with previous output of size [width, na].
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147 NetworkIO source_;
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148 // Internal state used during forward operation, of size [width, ns].
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149 NetworkIO state_;
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150 // State of the 2-d maxpool, generated during forward, used during backward.
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151 GENERIC_2D_ARRAY<int8_t> which_fg_;
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152 // Internal state saved from forward, but used only during backward.
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153 NetworkIO node_values_[WT_COUNT];
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154 // Preserved input stride_map used for Backward when NT_LSTM_SQUASHED.
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155 StrideMap input_map_;
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156 int input_width_;
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157 };
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158
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159 } // namespace tesseract.
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160
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161 #endif // TESSERACT_LSTM_LSTM_H_