Mercurial > hgrepos > Python2 > PyMuPDF

annotate mupdf-source/thirdparty/tesseract/src/training/common/networkbuilder.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: networkbuilder.h
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3 // Description: Class to parse the network description language and
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4 // build a corresponding network.
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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_NETWORKBUILDER_H_
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20 #define TESSERACT_LSTM_NETWORKBUILDER_H_
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22 #include "export.h"
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23 #include "static_shape.h"
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24 #include "stridemap.h"
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26 class UNICHARSET;
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28 namespace tesseract {
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30 class Input;
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31 class Network;
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32 class Parallel;
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33 class TRand;
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35 class TESS_COMMON_TRAINING_API NetworkBuilder {
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36 public:
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37 explicit NetworkBuilder(int num_softmax_outputs) : num_softmax_outputs_(num_softmax_outputs) {}
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39 // Builds a network with a network_spec in the network description
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40 // language, to recognize a character set of num_outputs size.
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41 // If append_index is non-negative, then *network must be non-null and the
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42 // given network_spec will be appended to *network AFTER append_index, with
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43 // the top of the input *network discarded.
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44 // Note that network_spec is call by value to allow a non-const char* pointer
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45 // into the string for BuildFromString.
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46 // net_flags control network behavior according to the NetworkFlags enum.
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47 // The resulting network is returned via **network.
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48 // Returns false if something failed.
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49 static bool InitNetwork(int num_outputs, const char *network_spec, int append_index,
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50 int net_flags, float weight_range, TRand *randomizer, Network **network);
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51
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52 // Parses the given string and returns a network according to the following
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53 // language:
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54 // ============ Syntax of description below: ============
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55 // <d> represents a number.
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56 // <net> represents any single network element, including (recursively) a
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57 // [...] series or (...) parallel construct.
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58 // (s|t|r|l|m) (regex notation) represents a single required letter.
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59 // NOTE THAT THROUGHOUT, x and y are REVERSED from conventional mathematics,
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60 // to use the same convention as Tensor Flow. The reason TF adopts this
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61 // convention is to eliminate the need to transpose images on input, since
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62 // adjacent memory locations in images increase x and then y, while adjacent
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63 // memory locations in tensors in TF, and NetworkIO in tesseract increase the
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64 // rightmost index first, then the next-left and so-on, like C arrays.
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65 // ============ INPUTS ============
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66 // <b>,<h>,<w>,<d> A batch of b images with height h, width w, and depth d.
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67 // b, h and/or w may be zero, to indicate variable size. Some network layer
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68 // (summarizing LSTM) must be used to make a variable h known.
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69 // d may be 1 for greyscale, 3 for color.
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70 // NOTE that throughout the constructed network, the inputs/outputs are all of
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71 // the same [batch,height,width,depth] dimensions, even if a different size.
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72 // ============ PLUMBING ============
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73 // [...] Execute ... networks in series (layers).
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74 // (...) Execute ... networks in parallel, with their output depths added.
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75 // R<d><net> Execute d replicas of net in parallel, with their output depths
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76 // added.
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77 // Rx<net> Execute <net> with x-dimension reversal.
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78 // Ry<net> Execute <net> with y-dimension reversal.
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79 // S<y>,<x> Rescale 2-D input by shrink factor x,y, rearranging the data by
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80 // increasing the depth of the input by factor xy.
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81 // Mp<y>,<x> Maxpool the input, reducing the size by an (x,y) rectangle.
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82 // ============ FUNCTIONAL UNITS ============
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83 // C(s|t|r|l|m)<y>,<x>,<d> Convolves using a (x,y) window, with no shrinkage,
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84 // random infill, producing d outputs, then applies a non-linearity:
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85 // s: Sigmoid, t: Tanh, r: Relu, l: Linear, m: Softmax.
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86 // F(s|t|r|l|m)<d> Truly fully-connected with s|t|r|l|m non-linearity and d
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87 // outputs. Connects to every x,y,depth position of the input, reducing
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88 // height, width to 1, producing a single <d> vector as the output.
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89 // Input height and width must be constant.
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90 // For a sliding-window linear or non-linear map that connects just to the
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91 // input depth, and leaves the input image size as-is, use a 1x1 convolution
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92 // eg. Cr1,1,64 instead of Fr64.
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93 // L(f|r|b)(x|y)[s]<n> LSTM cell with n states/outputs.
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94 // The LSTM must have one of:
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95 // f runs the LSTM forward only.
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96 // r runs the LSTM reversed only.
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97 // b runs the LSTM bidirectionally.
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98 // It will operate on either the x- or y-dimension, treating the other
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99 // dimension independently (as if part of the batch).
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100 // s (optional) summarizes the output in the requested dimension,
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101 // outputting only the final step, collapsing the dimension to a
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102 // single element.
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103 // LS<n> Forward-only LSTM cell in the x-direction, with built-in Softmax.
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104 // LE<n> Forward-only LSTM cell in the x-direction, with built-in softmax,
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105 // with binary Encoding.
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106 // L2xy<n> Full 2-d LSTM operating in quad-directions (bidi in x and y) and
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107 // all the output depths added.
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108 // ============ OUTPUTS ============
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109 // The network description must finish with an output specification:
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110 // O(2|1|0)(l|s|c)<n> output layer with n classes
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111 // 2 (heatmap) Output is a 2-d vector map of the input (possibly at
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112 // different scale).
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113 // 1 (sequence) Output is a 1-d sequence of vector values.
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114 // 0 (category) Output is a 0-d single vector value.
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115 // l uses a logistic non-linearity on the output, allowing multiple
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116 // hot elements in any output vector value.
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117 // s uses a softmax non-linearity, with one-hot output in each value.
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118 // c uses a softmax with CTC. Can only be used with s (sequence).
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119 // NOTE1: Only O1s and O1c are currently supported.
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120 // NOTE2: n is totally ignored, and for compatibility purposes only. The
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121 // output number of classes is obtained automatically from the
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122 // unicharset.
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123 Network *BuildFromString(const StaticShape &input_shape, const char **str);
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124
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125 private:
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126 // Parses an input specification and returns the result, which may include a
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127 // series.
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128 Network *ParseInput(const char **str);
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129 // Parses a sequential series of networks, defined by [<net><net>...].
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130 Network *ParseSeries(const StaticShape &input_shape, Input *input_layer, const char **str);
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131 // Parses a parallel set of networks, defined by (<net><net>...).
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132 Network *ParseParallel(const StaticShape &input_shape, const char **str);
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133 // Parses a network that begins with 'R'.
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134 Network *ParseR(const StaticShape &input_shape, const char **str);
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135 // Parses a network that begins with 'S'.
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136 Network *ParseS(const StaticShape &input_shape, const char **str);
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137 // Parses a network that begins with 'C'.
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138 Network *ParseC(const StaticShape &input_shape, const char **str);
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139 // Parses a network that begins with 'M'.
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140 Network *ParseM(const StaticShape &input_shape, const char **str);
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141 // Parses an LSTM network, either individual, bi- or quad-directional.
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142 Network *ParseLSTM(const StaticShape &input_shape, const char **str);
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143 // Builds a set of 4 lstms with t and y reversal, running in true parallel.
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144 static Network *BuildLSTMXYQuad(int num_inputs, int num_states);
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145 // Parses a Fully connected network.
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146 Network *ParseFullyConnected(const StaticShape &input_shape, const char **str);
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147 // Parses an Output spec.
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148 Network *ParseOutput(const StaticShape &input_shape, const char **str);
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149
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150 private:
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151 int num_softmax_outputs_;
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152 };
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153
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154 } // namespace tesseract.
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155
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156 #endif // TESSERACT_LSTM_NETWORKBUILDER_H_