Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/lstm/fullyconnected.h @ 21:2f43e400f144
Provide an "all" target to build both the sdist and the wheel
| author | Franz Glasner <fzglas.hg@dom66.de> |
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| date | Fri, 19 Sep 2025 10:28:53 +0200 |
| parents | b50eed0cc0ef |
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| rev | line source |
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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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17 |
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18 #ifndef TESSERACT_LSTM_FULLYCONNECTED_H_ |
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19 #define TESSERACT_LSTM_FULLYCONNECTED_H_ |
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20 |
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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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24 |
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25 namespace tesseract { |
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26 |
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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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33 |
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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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37 |
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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_ |
