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comparison mupdf-source/thirdparty/tesseract/src/lstm/static_shape.h @ 2:b50eed0cc0ef upstream

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ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4. The directory name has changed: no version number in the expanded directory now.
author Franz Glasner <fzglas.hg@dom66.de>
date Mon, 15 Sep 2025 11:43:07 +0200
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1:1d09e1dec1d9 2:b50eed0cc0ef
1 ///////////////////////////////////////////////////////////////////////
2 // File: static_shape.h
3 // Description: Defines the size of the 4-d tensor input/output from a network.
4 // Author: Ray Smith
5 // Created: Fri Oct 14 09:07:31 PST 2016
6 //
7 // (C) Copyright 2016, Google Inc.
8 // Licensed under the Apache License, Version 2.0 (the "License");
9 // you may not use this file except in compliance with the License.
10 // You may obtain a copy of the License at
11 // http://www.apache.org/licenses/LICENSE-2.0
12 // Unless required by applicable law or agreed to in writing, software
13 // distributed under the License is distributed on an "AS IS" BASIS,
14 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15 // See the License for the specific language governing permissions and
16 // limitations under the License.
17 ///////////////////////////////////////////////////////////////////////
18
19 #ifndef TESSERACT_LSTM_STATIC_SHAPE_H_
20 #define TESSERACT_LSTM_STATIC_SHAPE_H_
21
22 #include "serialis.h" // for TFile
23 #include "tprintf.h" // for tprintf
24
25 namespace tesseract {
26
27 // Enum describing the loss function to apply during training and/or the
28 // decoding method to apply at runtime.
29 enum LossType {
30 LT_NONE, // Undefined.
31 LT_CTC, // Softmax with standard CTC for training/decoding.
32 LT_SOFTMAX, // Outputs sum to 1 in fixed positions.
33 LT_LOGISTIC, // Logistic outputs with independent values.
34 };
35
36 // Simple class to hold the tensor shape that is known at network build time
37 // and the LossType of the loss function.
38 class StaticShape {
39 public:
40 StaticShape() : batch_(0), height_(0), width_(0), depth_(0), loss_type_(LT_NONE) {}
41 int batch() const {
42 return batch_;
43 }
44 void set_batch(int value) {
45 batch_ = value;
46 }
47 int height() const {
48 return height_;
49 }
50 void set_height(int value) {
51 height_ = value;
52 }
53 int width() const {
54 return width_;
55 }
56 void set_width(int value) {
57 width_ = value;
58 }
59 int depth() const {
60 return depth_;
61 }
62 void set_depth(int value) {
63 depth_ = value;
64 }
65 LossType loss_type() const {
66 return loss_type_;
67 }
68 void set_loss_type(LossType value) {
69 loss_type_ = value;
70 }
71 void SetShape(int batch, int height, int width, int depth) {
72 batch_ = batch;
73 height_ = height;
74 width_ = width;
75 depth_ = depth;
76 }
77
78 void Print() const {
79 tprintf("Batch=%d, Height=%d, Width=%d, Depth=%d, loss=%d\n", batch_, height_, width_, depth_,
80 loss_type_);
81 }
82
83 bool DeSerialize(TFile *fp) {
84 int32_t tmp = LT_NONE;
85 bool result = fp->DeSerialize(&batch_) && fp->DeSerialize(&height_) &&
86 fp->DeSerialize(&width_) && fp->DeSerialize(&depth_) && fp->DeSerialize(&tmp);
87 loss_type_ = static_cast<LossType>(tmp);
88 return result;
89 }
90
91 bool Serialize(TFile *fp) const {
92 int32_t tmp = loss_type_;
93 return fp->Serialize(&batch_) && fp->Serialize(&height_) && fp->Serialize(&width_) &&
94 fp->Serialize(&depth_) && fp->Serialize(&tmp);
95 }
96
97 private:
98 // Size of the 4-D tensor input/output to a network. A value of zero is
99 // allowed for all except depth_ and means to be determined at runtime, and
100 // regarded as variable.
101 // Number of elements in a batch, or number of frames in a video stream.
102 int32_t batch_;
103 // Height of the image.
104 int32_t height_;
105 // Width of the image.
106 int32_t width_;
107 // Depth of the image. (Number of "nodes").
108 int32_t depth_;
109 // How to train/interpret the output.
110 LossType loss_type_;
111 };
112
113 } // namespace tesseract
114
115 #endif // TESSERACT_LSTM_STATIC_SHAPE_H_