Mercurial > hgrepos > Python2 > PyMuPDF

annotate mupdf-source/thirdparty/tesseract/src/lstm/static_shape.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
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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2
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1 ///////////////////////////////////////////////////////////////////////
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2 // File: static_shape.h
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3 // Description: Defines the size of the 4-d tensor input/output from a network.
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4 // Author: Ray Smith
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5 // Created: Fri Oct 14 09:07:31 PST 2016
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6 //
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7 // (C) Copyright 2016, 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_STATIC_SHAPE_H_
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20 #define TESSERACT_LSTM_STATIC_SHAPE_H_
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21
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22 #include "serialis.h" // for TFile
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23 #include "tprintf.h" // for tprintf
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25 namespace tesseract {
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26
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27 // Enum describing the loss function to apply during training and/or the
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28 // decoding method to apply at runtime.
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29 enum LossType {
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30 LT_NONE, // Undefined.
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31 LT_CTC, // Softmax with standard CTC for training/decoding.
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32 LT_SOFTMAX, // Outputs sum to 1 in fixed positions.
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33 LT_LOGISTIC, // Logistic outputs with independent values.
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34 };
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36 // Simple class to hold the tensor shape that is known at network build time
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37 // and the LossType of the loss function.
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38 class StaticShape {
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39 public:
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40 StaticShape() : batch_(0), height_(0), width_(0), depth_(0), loss_type_(LT_NONE) {}
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41 int batch() const {
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42 return batch_;
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43 }
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44 void set_batch(int value) {
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45 batch_ = value;
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46 }
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47 int height() const {
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48 return height_;
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49 }
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50 void set_height(int value) {
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51 height_ = value;
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52 }
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53 int width() const {
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54 return width_;
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55 }
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56 void set_width(int value) {
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57 width_ = value;
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58 }
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59 int depth() const {
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60 return depth_;
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61 }
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62 void set_depth(int value) {
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63 depth_ = value;
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64 }
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65 LossType loss_type() const {
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66 return loss_type_;
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67 }
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68 void set_loss_type(LossType value) {
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69 loss_type_ = value;
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70 }
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71 void SetShape(int batch, int height, int width, int depth) {
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72 batch_ = batch;
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73 height_ = height;
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74 width_ = width;
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75 depth_ = depth;
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76 }
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77
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78 void Print() const {
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79 tprintf("Batch=%d, Height=%d, Width=%d, Depth=%d, loss=%d\n", batch_, height_, width_, depth_,
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80 loss_type_);
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81 }
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82
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83 bool DeSerialize(TFile *fp) {
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84 int32_t tmp = LT_NONE;
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85 bool result = fp->DeSerialize(&batch_) && fp->DeSerialize(&height_) &&
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86 fp->DeSerialize(&width_) && fp->DeSerialize(&depth_) && fp->DeSerialize(&tmp);
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87 loss_type_ = static_cast<LossType>(tmp);
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88 return result;
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89 }
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90
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91 bool Serialize(TFile *fp) const {
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92 int32_t tmp = loss_type_;
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93 return fp->Serialize(&batch_) && fp->Serialize(&height_) && fp->Serialize(&width_) &&
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94 fp->Serialize(&depth_) && fp->Serialize(&tmp);
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95 }
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96
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97 private:
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98 // Size of the 4-D tensor input/output to a network. A value of zero is
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99 // allowed for all except depth_ and means to be determined at runtime, and
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100 // regarded as variable.
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101 // Number of elements in a batch, or number of frames in a video stream.
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102 int32_t batch_;
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103 // Height of the image.
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104 int32_t height_;
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105 // Width of the image.
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106 int32_t width_;
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107 // Depth of the image. (Number of "nodes").
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108 int32_t depth_;
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109 // How to train/interpret the output.
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110 LossType loss_type_;
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111 };
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112
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113 } // namespace tesseract
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114
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115 #endif // TESSERACT_LSTM_STATIC_SHAPE_H_