Mercurial > hgrepos > Python2 > PyMuPDF

annotate mupdf-source/thirdparty/tesseract/src/lstm/network.h @ 17:dd9cdb856310

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Remove PKG-INFO from the because it is regenerated automatically for the sdist
author Franz Glasner <fzglas.hg@dom66.de>
date Thu, 18 Sep 2025 17:40:40 +0200
parents b50eed0cc0ef
children
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1 ///////////////////////////////////////////////////////////////////////
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2 // File: network.h
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3 // Description: Base class for neural network implementations.
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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_NETWORK_H_
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19 #define TESSERACT_LSTM_NETWORK_H_
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21 #include "helpers.h"
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22 #include "matrix.h"
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23 #include "networkio.h"
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24 #include "serialis.h"
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25 #include "static_shape.h"
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26 #include "tprintf.h"
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28 #include <cmath>
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29 #include <cstdio>
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31 struct Pix;
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33 namespace tesseract {
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35 class ScrollView;
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36 class TBOX;
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37 class ImageData;
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38 class NetworkScratch;
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40 // Enum to store the run-time type of a Network. Keep in sync with kTypeNames.
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41 enum NetworkType {
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42 NT_NONE, // The naked base class.
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43 NT_INPUT, // Inputs from an image.
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44 // Plumbing networks combine other networks or rearrange the inputs.
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45 NT_CONVOLVE, // Duplicates inputs in a sliding window neighborhood.
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46 NT_MAXPOOL, // Chooses the max result from a rectangle.
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47 NT_PARALLEL, // Runs networks in parallel.
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48 NT_REPLICATED, // Runs identical networks in parallel.
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49 NT_PAR_RL_LSTM, // Runs LTR and RTL LSTMs in parallel.
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50 NT_PAR_UD_LSTM, // Runs Up and Down LSTMs in parallel.
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51 NT_PAR_2D_LSTM, // Runs 4 LSTMs in parallel.
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52 NT_SERIES, // Executes a sequence of layers.
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53 NT_RECONFIG, // Scales the time/y size but makes the output deeper.
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54 NT_XREVERSED, // Reverses the x direction of the inputs/outputs.
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55 NT_YREVERSED, // Reverses the y-direction of the inputs/outputs.
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56 NT_XYTRANSPOSE, // Transposes x and y (for just a single op).
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57 // Functional networks actually calculate stuff.
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58 NT_LSTM, // Long-Short-Term-Memory block.
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59 NT_LSTM_SUMMARY, // LSTM that only keeps its last output.
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60 NT_LOGISTIC, // Fully connected logistic nonlinearity.
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61 NT_POSCLIP, // Fully connected rect lin version of logistic.
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62 NT_SYMCLIP, // Fully connected rect lin version of tanh.
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63 NT_TANH, // Fully connected with tanh nonlinearity.
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64 NT_RELU, // Fully connected with rectifier nonlinearity.
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65 NT_LINEAR, // Fully connected with no nonlinearity.
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66 NT_SOFTMAX, // Softmax uses exponential normalization, with CTC.
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67 NT_SOFTMAX_NO_CTC, // Softmax uses exponential normalization, no CTC.
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68 // The SOFTMAX LSTMs both have an extra softmax layer on top, but inside, with
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69 // the outputs fed back to the input of the LSTM at the next timestep.
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70 // The ENCODED version binary encodes the softmax outputs, providing log2 of
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71 // the number of outputs as additional inputs, and the other version just
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72 // provides all the softmax outputs as additional inputs.
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73 NT_LSTM_SOFTMAX, // 1-d LSTM with built-in fully connected softmax.
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74 NT_LSTM_SOFTMAX_ENCODED, // 1-d LSTM with built-in binary encoded softmax.
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75 // A TensorFlow graph encapsulated as a Tesseract network.
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76 NT_TENSORFLOW,
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77
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78 NT_COUNT // Array size.
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79 };
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80
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81 // Enum of Network behavior flags. Can in theory be set for each individual
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82 // network element.
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83 enum NetworkFlags {
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84 // Network forward/backprop behavior.
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85 NF_LAYER_SPECIFIC_LR = 64, // Separate learning rate for each layer.
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86 NF_ADAM = 128, // Weight-specific learning rate.
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87 };
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88
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89 // State of training and desired state used in SetEnableTraining.
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90 enum TrainingState {
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91 // Valid states of training_.
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92 TS_DISABLED, // Disabled permanently.
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93 TS_ENABLED, // Enabled for backprop and to write a training dump.
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94 // Re-enable from ANY disabled state.
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95 TS_TEMP_DISABLE, // Temporarily disabled to write a recognition dump.
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96 // Valid only for SetEnableTraining.
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97 TS_RE_ENABLE, // Re-Enable from TS_TEMP_DISABLE, but not TS_DISABLED.
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98 };
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99
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100 // Base class for network types. Not quite an abstract base class, but almost.
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101 // Most of the time no isolated Network exists, except prior to
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102 // deserialization.
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103 class TESS_API Network {
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104 public:
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105 Network();
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106 Network(NetworkType type, const std::string &name, int ni, int no);
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107 virtual ~Network() = default;
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108
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109 // Accessors.
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110 NetworkType type() const {
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111 return type_;
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112 }
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113 bool IsTraining() const {
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114 return training_ == TS_ENABLED;
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115 }
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116 bool needs_to_backprop() const {
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117 return needs_to_backprop_;
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118 }
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119 int num_weights() const {
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120 return num_weights_;
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121 }
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122 int NumInputs() const {
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123 return ni_;
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124 }
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125 int NumOutputs() const {
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126 return no_;
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127 }
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128 // Returns the required shape input to the network.
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129 virtual StaticShape InputShape() const {
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130 StaticShape result;
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131 return result;
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132 }
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133 // Returns the shape output from the network given an input shape (which may
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134 // be partially unknown ie zero).
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135 virtual StaticShape OutputShape(const StaticShape &input_shape) const {
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136 StaticShape result(input_shape);
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137 result.set_depth(no_);
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138 return result;
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139 }
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140 const std::string &name() const {
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141 return name_;
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142 }
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143 virtual std::string spec() const = 0;
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144 bool TestFlag(NetworkFlags flag) const {
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145 return (network_flags_ & flag) != 0;
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146 }
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147
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148 // Initialization and administrative functions that are mostly provided
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149 // by Plumbing.
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150 // Returns true if the given type is derived from Plumbing, and thus contains
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151 // multiple sub-networks that can have their own learning rate.
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152 virtual bool IsPlumbingType() const {
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153 return false;
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154 }
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155
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156 // Suspends/Enables/Permanently disables training by setting the training_
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157 // flag. Serialize and DeSerialize only operate on the run-time data if state
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158 // is TS_DISABLED or TS_TEMP_DISABLE. Specifying TS_TEMP_DISABLE will
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159 // temporarily disable layers in state TS_ENABLED, allowing a trainer to
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160 // serialize as if it were a recognizer.
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161 // TS_RE_ENABLE will re-enable layers that were previously in any disabled
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162 // state. If in TS_TEMP_DISABLE then the flag is just changed, but if in
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163 // TS_DISABLED, the deltas in the weight matrices are reinitialized so that a
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164 // recognizer can be converted back to a trainer.
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165 virtual void SetEnableTraining(TrainingState state);
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166
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167 // Sets flags that control the action of the network. See NetworkFlags enum
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168 // for bit values.
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169 virtual void SetNetworkFlags(uint32_t flags);
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170
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171 // Sets up the network for training. Initializes weights using weights of
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172 // scale `range` picked according to the random number generator `randomizer`.
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173 // Note that randomizer is a borrowed pointer that should outlive the network
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174 // and should not be deleted by any of the networks.
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175 // Returns the number of weights initialized.
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176 virtual int InitWeights(float range, TRand *randomizer);
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177 // Changes the number of outputs to the outside world to the size of the given
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178 // code_map. Recursively searches the entire network for Softmax layers that
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179 // have exactly old_no outputs, and operates only on those, leaving all others
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180 // unchanged. This enables networks with multiple output layers to get all
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181 // their softmaxes updated, but if an internal layer, uses one of those
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182 // softmaxes for input, then the inputs will effectively be scrambled.
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183 // TODO(rays) Fix this before any such network is implemented.
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184 // The softmaxes are resized by copying the old weight matrix entries for each
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185 // output from code_map[output] where non-negative, and uses the mean (over
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186 // all outputs) of the existing weights for all outputs with negative code_map
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187 // entries. Returns the new number of weights.
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188 virtual int RemapOutputs([[maybe_unused]] int old_no,
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189 [[maybe_unused]] const std::vector<int> &code_map) {
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190 return 0;
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191 }
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192
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193 // Converts a float network to an int network.
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194 virtual void ConvertToInt() {}
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195
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196 // Provides a pointer to a TRand for any networks that care to use it.
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197 // Note that randomizer is a borrowed pointer that should outlive the network
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198 // and should not be deleted by any of the networks.
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199 virtual void SetRandomizer(TRand *randomizer);
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200
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201 // Sets needs_to_backprop_ to needs_backprop and returns true if
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202 // needs_backprop || any weights in this network so the next layer forward
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203 // can be told to produce backprop for this layer if needed.
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204 virtual bool SetupNeedsBackprop(bool needs_backprop);
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205
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206 // Returns the most recent reduction factor that the network applied to the
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207 // time sequence. Assumes that any 2-d is already eliminated. Used for
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208 // scaling bounding boxes of truth data and calculating result bounding boxes.
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209 // WARNING: if GlobalMinimax is used to vary the scale, this will return
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210 // the last used scale factor. Call it before any forward, and it will return
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211 // the minimum scale factor of the paths through the GlobalMinimax.
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212 virtual int XScaleFactor() const {
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213 return 1;
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214 }
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215
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216 // Provides the (minimum) x scale factor to the network (of interest only to
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217 // input units) so they can determine how to scale bounding boxes.
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218 virtual void CacheXScaleFactor([[maybe_unused]] int factor) {}
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219
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220 // Provides debug output on the weights.
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221 virtual void DebugWeights() = 0;
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222
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223 // Writes to the given file. Returns false in case of error.
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224 // Should be overridden by subclasses, but called by their Serialize.
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225 virtual bool Serialize(TFile *fp) const;
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226 // Reads from the given file. Returns false in case of error.
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227 // Should be overridden by subclasses, but NOT called by their DeSerialize.
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228 virtual bool DeSerialize(TFile *fp) = 0;
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229
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230 public:
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231 // Updates the weights using the given learning rate, momentum and adam_beta.
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232 // num_samples is used in the adam computation iff use_adam_ is true.
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233 virtual void Update([[maybe_unused]] float learning_rate,
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234 [[maybe_unused]] float momentum,
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235 [[maybe_unused]] float adam_beta,
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236 [[maybe_unused]] int num_samples) {}
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237 // Sums the products of weight updates in *this and other, splitting into
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238 // positive (same direction) in *same and negative (different direction) in
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239 // *changed.
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240 virtual void CountAlternators([[maybe_unused]] const Network &other,
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241 [[maybe_unused]] TFloat *same,
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242 [[maybe_unused]] TFloat *changed) const {}
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243
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244 // Reads from the given file. Returns nullptr in case of error.
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245 // Determines the type of the serialized class and calls its DeSerialize
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246 // on a new object of the appropriate type, which is returned.
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247 static Network *CreateFromFile(TFile *fp);
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248
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249 // Runs forward propagation of activations on the input line.
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250 // Note that input and output are both 2-d arrays.
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251 // The 1st index is the time element. In a 1-d network, it might be the pixel
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252 // position on the textline. In a 2-d network, the linearization is defined
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253 // by the stride_map. (See networkio.h).
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254 // The 2nd index of input is the network inputs/outputs, and the dimension
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255 // of the input must match NumInputs() of this network.
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256 // The output array will be resized as needed so that its 1st dimension is
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257 // always equal to the number of output values, and its second dimension is
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258 // always NumOutputs(). Note that all this detail is encapsulated away inside
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259 // NetworkIO, as are the internals of the scratch memory space used by the
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260 // network. See networkscratch.h for that.
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261 // If input_transpose is not nullptr, then it contains the transpose of input,
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262 // and the caller guarantees that it will still be valid on the next call to
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263 // backward. The callee is therefore at liberty to save the pointer and
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264 // reference it on a call to backward. This is a bit ugly, but it makes it
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265 // possible for a replicating parallel to calculate the input transpose once
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266 // instead of all the replicated networks having to do it.
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267 virtual void Forward(bool debug, const NetworkIO &input,
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268 const TransposedArray *input_transpose,
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269 NetworkScratch *scratch, NetworkIO *output) = 0;
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270
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271 // Runs backward propagation of errors on fwdX_deltas.
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272 // Note that fwd_deltas and back_deltas are both 2-d arrays as with Forward.
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273 // Returns false if back_deltas was not set, due to there being no point in
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274 // propagating further backwards. Thus most complete networks will always
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275 // return false from Backward!
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276 virtual bool Backward(bool debug, const NetworkIO &fwd_deltas,
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277 NetworkScratch *scratch, NetworkIO *back_deltas) = 0;
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278
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279 // === Debug image display methods. ===
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280 // Displays the image of the matrix to the forward window.
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281 void DisplayForward(const NetworkIO &matrix);
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282 // Displays the image of the matrix to the backward window.
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283 void DisplayBackward(const NetworkIO &matrix);
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284
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285 // Creates the window if needed, otherwise clears it.
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286 static void ClearWindow(bool tess_coords, const char *window_name, int width,
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287 int height, ScrollView **window);
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288
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289 // Displays the pix in the given window. and returns the height of the pix.
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290 // The pix is pixDestroyed.
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291 static int DisplayImage(Image pix, ScrollView *window);
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292
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293 protected:
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294 // Returns a random number in [-range, range].
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295 TFloat Random(TFloat range);
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296
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297 protected:
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298 NetworkType type_; // Type of the derived network class.
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299 TrainingState training_; // Are we currently training?
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300 bool needs_to_backprop_; // This network needs to output back_deltas.
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301 int32_t network_flags_; // Behavior control flags in NetworkFlags.
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302 int32_t ni_; // Number of input values.
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303 int32_t no_; // Number of output values.
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304 int32_t num_weights_; // Number of weights in this and sub-network.
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305 std::string name_; // A unique name for this layer.
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306
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307 // NOT-serialized debug data.
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308 ScrollView *forward_win_; // Recognition debug display window.
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309 ScrollView *backward_win_; // Training debug display window.
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310 TRand *randomizer_; // Random number generator.
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311 };
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312
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313 } // namespace tesseract.
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314
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315 #endif // TESSERACT_LSTM_NETWORK_H_