tesseract 4.1.1
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params_model.cpp
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1
2// File: params_model.cpp
3// Description: Trained language model parameters.
4// Author: David Eger
5//
6// (C) Copyright 2012, Google Inc.
7// Licensed under the Apache License, Version 2.0 (the "License");
8// you may not use this file except in compliance with the License.
9// You may obtain a copy of the License at
10// http://www.apache.org/licenses/LICENSE-2.0
11// Unless required by applicable law or agreed to in writing, software
12// distributed under the License is distributed on an "AS IS" BASIS,
13// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14// See the License for the specific language governing permissions and
15// limitations under the License.
16//
18
19#include "params_model.h"
20
21#include <cctype>
22#include <cmath>
23#include <cstdio>
24
25#include "bitvector.h"
26#include "tprintf.h"
27
28namespace tesseract {
29
30// Scale factor to apply to params model scores.
31static const float kScoreScaleFactor = 100.0f;
32// Minimum cost result to return.
33static const float kMinFinalCost = 0.001f;
34// Maximum cost result to return.
35static const float kMaxFinalCost = 100.0f;
36
38 for (int p = 0; p < PTRAIN_NUM_PASSES; ++p) {
39 tprintf("ParamsModel for pass %d lang %s\n", p, lang_.string());
40 for (int i = 0; i < weights_vec_[p].size(); ++i) {
41 tprintf("%s = %g\n", kParamsTrainingFeatureTypeName[i],
42 weights_vec_[p][i]);
43 }
44 }
45}
46
47void ParamsModel::Copy(const ParamsModel &other_model) {
48 for (int p = 0; p < PTRAIN_NUM_PASSES; ++p) {
49 weights_vec_[p] = other_model.weights_for_pass(
50 static_cast<PassEnum>(p));
51 }
52}
53
54// Given a (modifiable) line, parse out a key / value pair.
55// Return true on success.
56bool ParamsModel::ParseLine(char *line, char** key, float *val) {
57 if (line[0] == '#')
58 return false;
59 int end_of_key = 0;
60 while (line[end_of_key] &&
61 !(isascii(line[end_of_key]) && isspace(line[end_of_key]))) {
62 end_of_key++;
63 }
64 if (!line[end_of_key]) {
65 tprintf("ParamsModel::Incomplete line %s\n", line);
66 return false;
67 }
68 line[end_of_key++] = 0;
69 *key = line;
70 if (sscanf(line + end_of_key, " %f", val) != 1)
71 return false;
72 return true;
73}
74
75// Applies params model weights to the given features.
76// Assumes that features is an array of size PTRAIN_NUM_FEATURE_TYPES.
77// The cost is set to a number that can be multiplied by the outline length,
78// as with the old ratings scheme. This enables words of different length
79// and combinations of words to be compared meaningfully.
80float ParamsModel::ComputeCost(const float features[]) const {
81 float unnorm_score = 0.0;
82 for (int f = 0; f < PTRAIN_NUM_FEATURE_TYPES; ++f) {
83 unnorm_score += weights_vec_[pass_][f] * features[f];
84 }
85 return ClipToRange(-unnorm_score / kScoreScaleFactor,
86 kMinFinalCost, kMaxFinalCost);
87}
88
89bool ParamsModel::Equivalent(const ParamsModel &that) const {
90 float epsilon = 0.0001;
91 for (int p = 0; p < PTRAIN_NUM_PASSES; ++p) {
92 if (weights_vec_[p].size() != that.weights_vec_[p].size()) return false;
93 for (int i = 0; i < weights_vec_[p].size(); i++) {
94 if (weights_vec_[p][i] != that.weights_vec_[p][i] &&
95 fabs(weights_vec_[p][i] - that.weights_vec_[p][i]) > epsilon)
96 return false;
97 }
98 }
99 return true;
100}
101
102bool ParamsModel::LoadFromFp(const char *lang, TFile *fp) {
103 const int kMaxLineSize = 100;
104 char line[kMaxLineSize];
105 BitVector present;
107 lang_ = lang;
108 // Load weights for passes with adaption on.
109 GenericVector<float> &weights = weights_vec_[pass_];
111
112 while (fp->FGets(line, kMaxLineSize) != nullptr) {
113 char *key = nullptr;
114 float value;
115 if (!ParseLine(line, &key, &value))
116 continue;
117 int idx = ParamsTrainingFeatureByName(key);
118 if (idx < 0) {
119 tprintf("ParamsModel::Unknown parameter %s\n", key);
120 continue;
121 }
122 if (!present[idx]) {
123 present.SetValue(idx, true);
124 }
125 weights[idx] = value;
126 }
127 bool complete = (present.NumSetBits() == PTRAIN_NUM_FEATURE_TYPES);
128 if (!complete) {
129 for (int i = 0; i < PTRAIN_NUM_FEATURE_TYPES; i++) {
130 if (!present[i]) {
131 tprintf("Missing field %s.\n", kParamsTrainingFeatureTypeName[i]);
132 }
133 }
134 lang_ = "";
135 weights.truncate(0);
136 }
137 return complete;
138}
139
140bool ParamsModel::SaveToFile(const char *full_path) const {
141 const GenericVector<float> &weights = weights_vec_[pass_];
143 tprintf("Refusing to save ParamsModel that has not been initialized.\n");
144 return false;
145 }
146 FILE *fp = fopen(full_path, "wb");
147 if (!fp) {
148 tprintf("Could not open %s for writing.\n", full_path);
149 return false;
150 }
151 bool all_good = true;
152 for (int i = 0; i < weights.size(); i++) {
153 if (fprintf(fp, "%s %f\n", kParamsTrainingFeatureTypeName[i], weights[i])
154 < 0) {
155 all_good = false;
156 }
157 }
158 fclose(fp);
159 return all_good;
160}
161
162} // namespace tesseract
T ClipToRange(const T &x, const T &lower_bound, const T &upper_bound)
Definition: helpers.h:108
DLLSYM void tprintf(const char *format,...)
Definition: tprintf.cpp:35
int ParamsTrainingFeatureByName(const char *name)
void init_to_size(int size, const T &t)
int size() const
Definition: genericvector.h:72
void truncate(int size)
void Init(int length)
Definition: bitvector.cpp:139
void SetValue(int index, bool value)
Definition: bitvector.h:75
int NumSetBits() const
Definition: bitvector.cpp:216
char * FGets(char *buffer, int buffer_size)
Definition: serialis.cpp:249
const char * string() const
Definition: strngs.cpp:194
bool SaveToFile(const char *full_path) const
float ComputeCost(const float features[]) const
const GenericVector< float > & weights_for_pass(PassEnum pass) const
Definition: params_model.h:69
bool LoadFromFp(const char *lang, TFile *fp)
const GenericVector< float > & weights() const
Definition: params_model.h:66
bool Equivalent(const ParamsModel &that) const
void Copy(const ParamsModel &other_model)