copilot/OpenSeeFace
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Switched from LibSVM to ThunderSVM for better performance.

Browse source
This commit is contained in:
Emiliana 2020-08-28 01:14:48 +02:00
parent ac95537694
commit e854036e56
10 changed files with 708 additions and 61 deletions
Download patch file Download diff file Expand all files Collapse all files

216
Source/ThunderSVMWrapper.cpp Normal file
View file

@ -0,0 +1,216 @@
// ThunderSVMWrapper.cpp : Defines the exported functions for the DLL.
//
#include "pch.h"
#include "framework.h"
#include "ThunderSVMWrapper.h"
#include "thundersvm/thundersvm.h"
#include "thundersvm/model/svmmodel.h"
#include "thundersvm/model/svc.h"
#include <cmath>
using namespace std;
extern "C" void __cdecl print_log(const char *str) {
return;
}
struct problem {
int r;
int c;
DataSet::node2d x;
vector<double> y;
DataSet dataset;
};
struct SVMWrapper {
SVC *model;
int cols;
int classes;
double *means;
double *sdevs;
SvmParam param;
struct problem problem;
};
typedef struct svm_node svmnode;
void make_svmnode_array(DataSet::node2d &dataset, float features[], int rows, int cols, double *means, double *sdevs, int rescale) {
double row_factor = 1.0 / (double)rows;
if (rescale) {
for (int c = 0; c < cols; c++) {
means[c] = 0;
sdevs[c] = 0;
}
for (int r = 0; r < rows; r++) {
for (int c = 0; c < cols; c++) {
means[c] += (double)features[r * cols + c];
}
}
for (int c = 0; c < cols; c++) {
means[c] *= row_factor;
}
for (int r = 0; r < rows; r++) {
for (int c = 0; c < cols; c++) {
double sq_diff = ((double)features[r * cols + c] - means[c]);
sq_diff *= sq_diff;
sdevs[c] += sq_diff;
}
}
for (int c = 0; c < cols; c++) {
sdevs[c] *= row_factor;
sdevs[c] = sqrt(sdevs[c]);
sdevs[c] = 1.0 / sdevs[c];
}
}
for (int r = 0; r < rows; r++) {
dataset[r] = vector<DataSet::node>(cols);
int idx = 0;
for (int c = 0; c < cols; c++) {
dataset[r][c].index = c + 1;
dataset[r][c].value = (float)((features[r * cols + c] - means[c]) * sdevs[c]);
}
}
}
void make_problem(struct problem &problem, float features[], float labels[], int rows, int cols, double *means, double *sdevs) {
problem.r = rows;
problem.c = cols;
problem.y = vector<double>(rows);
for (int i = 0; i < rows; i++)
problem.y[i] = (double)labels[i];
problem.x = DataSet::node2d(rows);
make_svmnode_array(problem.x, features, rows, cols, means, sdevs, 1);
problem.dataset = DataSet(problem.x, cols, problem.y);
}
void destroy_problem(struct problem &problem) {
problem.y.clear();
problem.x.clear();
}
extern "C" {
__declspec(dllexport) void *__cdecl trainModel(float features[], float labels[], float weights[], int rows, int cols, int classes, int probability, float C) {
if (!(rows > 0 && cols > 0))
return NULL;
//svm_set_print_string_function(print_log);
struct SVMWrapper *sw = new SVMWrapper();
sw->cols = cols;
sw->classes = classes;
sw->means = new double[cols];
sw->sdevs = new double[cols];
sw->param.svm_type = SvmParam::C_SVC;
sw->param.kernel_type = SvmParam::RBF;
sw->param.degree = 3;
sw->param.gamma = 1.0 / (double)cols;
sw->param.coef0 = 0;
sw->param.nu = 0.5;
sw->param.C = C;
sw->param.epsilon = 0.001;
sw->param.p = 0.1;
sw->param.probability = probability;
sw->param.nr_weight = 0;
sw->param.weight_label = NULL;
sw->param.weight = NULL;
if (weights != NULL) {
sw->param.nr_weight = classes;
sw->param.weight_label = new int[classes];
sw->param.weight = new double[classes];
for (int i = 0; i < classes; i++) {
sw->param.weight_label[i] = i;
sw->param.weight[i] = (double)weights[i];
}
}
make_problem(sw->problem, features, labels, rows, cols, sw->means, sw->sdevs);
sw->model = new SVC();//svm_train(sw->problem, &sw->param);
sw->model->train(sw->problem.dataset, sw->param);
if (weights != NULL) {
delete sw->param.weight_label;
delete sw->param.weight;
}
return sw;
}
__declspec(dllexport) void __cdecl predict(void *ptr, float features[], float predictions[], double probabilities[], int rows) {
struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
int cols = sw->cols;
DataSet::node2d instances = DataSet::node2d(rows);
make_svmnode_array(instances, features, rows, sw->cols, sw->means, sw->sdevs, 0);
vector<double> predicted = sw->model->predict(instances, -1);
instances.clear();
for (int r = 0; r < rows; r++)
predictions[r] = (float)predicted[r];
predicted.clear();
return;
}
__declspec(dllexport) void *__cdecl loadModel(char *filename, int cols, int classes, double means[], double sdevs[]) {
struct SVMWrapper *sw = new SVMWrapper();
sw->cols = cols;
sw->model = new SVC();
sw->model->load_from_file(filename);
sw->classes = classes;
sw->means = new double[cols];
sw->sdevs = new double[cols];
for (int c = 0; c < cols; c++) {
sw->means[c] = means[c];
sw->sdevs[c] = sdevs[c];
}
return sw;
}
__declspec(dllexport) void *__cdecl loadModelString(char *modelString, int cols, int classes, double means[], double sdevs[]) {
struct SVMWrapper *sw = new SVMWrapper();
sw->cols = cols;
sw->model = new SVC();
sw->model->load_from_string(modelString);
sw->classes = classes;
sw->means = new double[cols];
sw->sdevs = new double[cols];
for (int c = 0; c < cols; c++) {
sw->means[c] = means[c];
sw->sdevs[c] = sdevs[c];
}
return sw;
}
__declspec(dllexport) void __cdecl getScales(void *ptr, double means[], double sdevs[]) {
struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
int cols = sw->cols;
for (int c = 0; c < cols; c++) {
means[c] = sw->means[c];
sdevs[c] = sw->sdevs[c];
}
}
__declspec(dllexport) const char *__cdecl saveModelString(void *ptr) {
struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
string model = sw->model->save_to_string();
return _strdup(model.c_str());
}
__declspec(dllexport) int __cdecl saveModel(void *ptr, char *filename) {
struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
sw->model->save_to_file(string(filename));
return 1;
}
__declspec(dllexport) void __cdecl destroyModel(void *ptr) {
struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
delete sw->means;
delete sw->sdevs;
delete sw->model;
destroy_problem(sw->problem);
delete sw;
}
}