Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/lstm/series.cpp @ 40:aa33339d6b8a upstream
ADD: MuPDF v1.26.10: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.5.
| author | Franz Glasner <fzglas.hg@dom66.de> |
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| date | Sat, 11 Oct 2025 11:31:38 +0200 |
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| rev | line source |
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1 /////////////////////////////////////////////////////////////////////// |
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2 // File: series.cpp |
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3 // Description: Runs networks in series on the same input. |
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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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17 |
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18 #include "series.h" |
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19 |
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20 #include "fullyconnected.h" |
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21 #include "networkscratch.h" |
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22 #include "scrollview.h" |
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23 #include "tesserrstream.h" // for tesserr |
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24 #include "tprintf.h" |
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25 |
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26 namespace tesseract { |
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27 |
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28 // ni_ and no_ will be set by AddToStack. |
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29 Series::Series(const std::string &name) : Plumbing(name) { |
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30 type_ = NT_SERIES; |
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31 } |
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32 |
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33 // Returns the shape output from the network given an input shape (which may |
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34 // be partially unknown ie zero). |
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35 StaticShape Series::OutputShape(const StaticShape &input_shape) const { |
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36 StaticShape result(input_shape); |
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37 int stack_size = stack_.size(); |
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38 for (int i = 0; i < stack_size; ++i) { |
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39 result = stack_[i]->OutputShape(result); |
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40 } |
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41 return result; |
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42 } |
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43 |
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44 // Sets up the network for training. Initializes weights using weights of |
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45 // scale `range` picked according to the random number generator `randomizer`. |
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46 // Note that series has its own implementation just for debug purposes. |
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47 int Series::InitWeights(float range, TRand *randomizer) { |
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48 num_weights_ = 0; |
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49 tprintf("Num outputs,weights in Series:\n"); |
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50 for (auto &i : stack_) { |
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51 int weights = i->InitWeights(range, randomizer); |
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52 tprintf(" %s:%d, %d\n", i->spec().c_str(), i->NumOutputs(), weights); |
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53 num_weights_ += weights; |
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54 } |
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55 tprintf("Total weights = %d\n", num_weights_); |
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56 return num_weights_; |
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57 } |
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58 |
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59 // Recursively searches the network for softmaxes with old_no outputs, |
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60 // and remaps their outputs according to code_map. See network.h for details. |
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61 int Series::RemapOutputs(int old_no, const std::vector<int> &code_map) { |
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62 num_weights_ = 0; |
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63 tprintf("Num (Extended) outputs,weights in Series:\n"); |
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64 for (auto &i : stack_) { |
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65 int weights = i->RemapOutputs(old_no, code_map); |
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66 tprintf(" %s:%d, %d\n", i->spec().c_str(), i->NumOutputs(), weights); |
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67 num_weights_ += weights; |
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68 } |
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69 tprintf("Total weights = %d\n", num_weights_); |
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70 no_ = stack_.back()->NumOutputs(); |
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71 return num_weights_; |
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72 } |
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73 |
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74 // Sets needs_to_backprop_ to needs_backprop and returns true if |
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75 // needs_backprop || any weights in this network so the next layer forward |
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76 // can be told to produce backprop for this layer if needed. |
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77 bool Series::SetupNeedsBackprop(bool needs_backprop) { |
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78 needs_to_backprop_ = needs_backprop; |
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79 for (auto &i : stack_) { |
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80 needs_backprop = i->SetupNeedsBackprop(needs_backprop); |
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81 } |
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82 return needs_backprop; |
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83 } |
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84 |
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85 // Returns an integer reduction factor that the network applies to the |
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86 // time sequence. Assumes that any 2-d is already eliminated. Used for |
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parents:
diff
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|
87 // scaling bounding boxes of truth data. |
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ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4.
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88 // WARNING: if GlobalMinimax is used to vary the scale, this will return |
|
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Franz Glasner <fzglas.hg@dom66.de>
parents:
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|
89 // the last used scale factor. Call it before any forward, and it will return |
|
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ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4.
Franz Glasner <fzglas.hg@dom66.de>
parents:
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|
90 // the minimum scale factor of the paths through the GlobalMinimax. |
|
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ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4.
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|
91 int Series::XScaleFactor() const { |
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|
92 int factor = 1; |
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|
93 for (auto i : stack_) { |
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|
94 factor *= i->XScaleFactor(); |
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95 } |
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|
96 return factor; |
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97 } |
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|
98 |
|
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|
99 // Provides the (minimum) x scale factor to the network (of interest only to |
|
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ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4.
Franz Glasner <fzglas.hg@dom66.de>
parents:
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|
100 // input units) so they can determine how to scale bounding boxes. |
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ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4.
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|
101 void Series::CacheXScaleFactor(int factor) { |
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|
102 stack_[0]->CacheXScaleFactor(factor); |
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103 } |
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104 |
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105 // Runs forward propagation of activations on the input line. |
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106 // See NetworkCpp for a detailed discussion of the arguments. |
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107 void Series::Forward(bool debug, const NetworkIO &input, const TransposedArray *input_transpose, |
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108 NetworkScratch *scratch, NetworkIO *output) { |
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109 int stack_size = stack_.size(); |
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110 ASSERT_HOST(stack_size > 1); |
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111 // Revolving intermediate buffers. |
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|
112 NetworkScratch::IO buffer1(input, scratch); |
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|
113 NetworkScratch::IO buffer2(input, scratch); |
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114 // Run each network in turn, giving the output of n as the input to n + 1, |
|
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115 // with the final network providing the real output. |
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116 stack_[0]->Forward(debug, input, input_transpose, scratch, buffer1); |
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117 for (int i = 1; i < stack_size; i += 2) { |
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118 stack_[i]->Forward(debug, *buffer1, nullptr, scratch, i + 1 < stack_size ? buffer2 : output); |
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119 if (i + 1 == stack_size) { |
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120 return; |
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121 } |
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122 stack_[i + 1]->Forward(debug, *buffer2, nullptr, scratch, |
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123 i + 2 < stack_size ? buffer1 : output); |
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124 } |
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125 } |
|
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126 |
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127 // Runs backward propagation of errors on the deltas line. |
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128 // See NetworkCpp for a detailed discussion of the arguments. |
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129 bool Series::Backward(bool debug, const NetworkIO &fwd_deltas, NetworkScratch *scratch, |
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130 NetworkIO *back_deltas) { |
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131 if (!IsTraining()) { |
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132 return false; |
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133 } |
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134 int stack_size = stack_.size(); |
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135 ASSERT_HOST(stack_size > 1); |
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136 // Revolving intermediate buffers. |
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parents:
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|
137 NetworkScratch::IO buffer1(fwd_deltas, scratch); |
|
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138 NetworkScratch::IO buffer2(fwd_deltas, scratch); |
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139 // Run each network in reverse order, giving the back_deltas output of n as |
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140 // the fwd_deltas input to n-1, with the 0 network providing the real output. |
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parents:
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|
141 if (!stack_.back()->IsTraining() || |
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142 !stack_.back()->Backward(debug, fwd_deltas, scratch, buffer1)) { |
|
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parents:
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|
143 return false; |
|
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144 } |
|
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|
145 for (int i = stack_size - 2; i >= 0; i -= 2) { |
|
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|
146 if (!stack_[i]->IsTraining() || |
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147 !stack_[i]->Backward(debug, *buffer1, scratch, i > 0 ? buffer2 : back_deltas)) { |
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148 return false; |
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149 } |
|
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150 if (i == 0) { |
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|
151 return needs_to_backprop_; |
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152 } |
|
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|
153 if (!stack_[i - 1]->IsTraining() || |
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|
154 !stack_[i - 1]->Backward(debug, *buffer2, scratch, i > 1 ? buffer1 : back_deltas)) { |
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|
155 return false; |
|
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156 } |
|
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parents:
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157 } |
|
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|
158 return needs_to_backprop_; |
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159 } |
|
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|
160 |
|
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161 // Splits the series after the given index, returning the two parts and |
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162 // deletes itself. The first part, up to network with index last_start, goes |
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|
163 // into start, and the rest goes into end. |
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|
164 void Series::SplitAt(unsigned last_start, Series **start, Series **end) { |
|
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165 *start = nullptr; |
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|
166 *end = nullptr; |
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|
167 if (last_start >= stack_.size()) { |
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|
168 tesserr << "Invalid split index " << last_start |
|
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|
169 << " must be in range [0," << stack_.size() - 1 << "]!\n"; |
|
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170 return; |
|
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171 } |
|
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|
172 auto *master_series = new Series("MasterSeries"); |
|
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|
173 auto *boosted_series = new Series("BoostedSeries"); |
|
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174 for (unsigned s = 0; s <= last_start; ++s) { |
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175 if (s + 1 == stack_.size() && stack_[s]->type() == NT_SOFTMAX) { |
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176 // Change the softmax to a tanh. |
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177 auto *fc = static_cast<FullyConnected *>(stack_[s]); |
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178 fc->ChangeType(NT_TANH); |
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179 } |
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180 master_series->AddToStack(stack_[s]); |
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181 stack_[s] = nullptr; |
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182 } |
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183 for (unsigned s = last_start + 1; s < stack_.size(); ++s) { |
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184 boosted_series->AddToStack(stack_[s]); |
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185 stack_[s] = nullptr; |
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186 } |
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187 *start = master_series; |
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188 *end = boosted_series; |
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189 delete this; |
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190 } |
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191 |
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192 // Appends the elements of the src series to this, removing from src and |
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193 // deleting it. |
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194 void Series::AppendSeries(Network *src) { |
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195 ASSERT_HOST(src->type() == NT_SERIES); |
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196 auto *src_series = static_cast<Series *>(src); |
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197 for (auto &s : src_series->stack_) { |
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198 AddToStack(s); |
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199 s = nullptr; |
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200 } |
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201 delete src; |
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202 } |
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203 |
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204 } // namespace tesseract. |
