bool SVM::train_(ClassificationData &trainingData){
//Clear any previous model
clear();
if( trainingData.getNumSamples() == 0 ){
errorLog << "train_(ClassificationData &trainingData) - Training data has zero samples!" << endl;
return false;
}
//Convert the labelled classification data into the LIBSVM data format
if( !convertClassificationDataToLIBSVMFormat(trainingData) ){
errorLog << "train_(ClassificationData &trainingData) - Failed To Convert Labelled Classification Data To LIBSVM Format!" << endl;
return false;
}
if( useAutoGamma ) param.gamma = 1.0/numInputDimensions;
//Train the model
bool trainingResult = trainSVM();
if(! trainingResult ){
errorLog << "train_(ClassificationData &trainingData) - Failed To Train SVM Model!" << endl;
return false;
}
return true;
}
void mySVM::trainTestSVM(const string& fileName)
{
printf("training SVM, please wait");
trainSVM(fileName);
printf("\r");
showSVMInfo();
testSVM(fileName);
}
void Objectness::trainStageI()
{
vecM pX, nX;
pX.reserve(200000), nX.reserve(200000);
Mat xP1f, xN1f;
CV_Assert(matRead(_modelName + ".xP", xP1f) && matRead(_modelName + ".xN", xN1f));
for (int r = 0; r < xP1f.rows; r++)
pX.push_back(xP1f.row(r));
for (int r = 0; r < xN1f.rows; r++)
nX.push_back(xN1f.row(r));
Mat crntW = trainSVM(pX, nX, L1R_L2LOSS_SVC, 10, 1);
crntW = crntW.colRange(0, crntW.cols - 1).reshape(1, _W);
CV_Assert(crntW.size() == Size(_W, _W));
matWrite(_modelName + ".wS1", crntW);
}
int main() {
cout << "load from file.."<<endl;
map<string,Mat> classes_training_data;
FileStorage fs("training_samples.yml",FileStorage::READ);
vector<string> classes_names;
fs["classes"] >> classes_names;
for (vector<string>::iterator it = classes_names.begin(); it != classes_names.end(); ++it) {
fs[(*it)] >> classes_training_data[*it];
}
cout << "train SVM.." <<endl;
string file_postfix = "with_colors";
Mat& one_class = (*(classes_training_data.begin())).second;
trainSVM(classes_training_data, file_postfix, one_class.cols, one_class.type());
}
// pX1f, nX1f are positive and negative training samples, each is a row vector
Mat Objectness::trainSVM(const vector<Mat> &pX1f, const vector<Mat> &nX1f, int sT, double C, double bias, double eps, int maxTrainNum)
{
vecI ind(nX1f.size());
for (size_t i = 0; i < ind.size(); i++)
ind[i] = i;
int numP = pX1f.size(), feaDim = pX1f[0].cols;
int totalSample = numP + nX1f.size();
if (totalSample > maxTrainNum)
random_shuffle(ind.begin(), ind.end());
totalSample = min(totalSample, maxTrainNum);
Mat X1f(totalSample, feaDim, CV_32F);
vecI Y(totalSample);
for(int i = 0; i < numP; i++) {
pX1f[i].copyTo(X1f.row(i));
Y[i] = 1;
}
for (int i = numP; i < totalSample; i++) {
nX1f[ind[i - numP]].copyTo(X1f.row(i));
Y[i] = -1;
}
return trainSVM(X1f, Y, sT, C, bias, eps);
}
void Objectness::trainStateII(int numPerSz)
{
loadTrainedModel();
const int NUM_TRAIN = _voc.trainNum;
vector<vecI> SZ(NUM_TRAIN), Y(NUM_TRAIN);
vector<vecF> VAL(NUM_TRAIN);
#pragma omp parallel for
for (int i = 0; i < _voc.trainNum; i++) {
const vector<Vec4i> &bbgts = _voc.gtTrainBoxes[i];
ValStructVec<float, Vec4i> valBoxes;
vecI &sz = SZ[i], &y = Y[i];
vecF &val = VAL[i];
CStr imgPath = format(_S(_voc.imgPathW), _S(_voc.trainSet[i]));
predictBBoxSI(imread(imgPath), valBoxes, sz, numPerSz, false);
const int num = valBoxes.size();
CV_Assert(sz.size() == num);
y.resize(num), val.resize(num);
for (int j = 0; j < num; j++) {
Vec4i bb = valBoxes[j];
val[j] = valBoxes(j);
y[j] = maxIntUnion(bb, bbgts) >= 0.5 ? 1 : -1;
}
}
const int NUM_SZ = _svmSzIdxs.size();
const int maxTrainNum = 100000;
vector<vecM> rXP(NUM_SZ), rXN(NUM_SZ);
for (int r = 0; r < NUM_SZ; r++) {
rXP[r].reserve(maxTrainNum);
rXN[r].reserve(1000000);
}
for (int i = 0; i < NUM_TRAIN; i++) {
const vecI &sz = SZ[i], &y = Y[i];
vecF &val = VAL[i];
int num = sz.size();
for (int j = 0; j < num; j++) {
int r = sz[j];
CV_Assert(r >= 0 && r < NUM_SZ);
if (y[j] == 1)
rXP[r].push_back(Mat(1, 1, CV_32F, &val[j]));
else
rXN[r].push_back(Mat(1, 1, CV_32F, &val[j]));
}
}
Mat wMat(NUM_SZ, 2, CV_32F);
for (int i = 0; i < NUM_SZ; i++) {
const vecM &xP = rXP[i], &xN = rXN[i];
if (xP.size() < 10 || xN.size() < 10)
printf("Warning %s:%d not enough training sample for r[%d] = %d. P = %d, N = %d\n", __FILE__, __LINE__, i, _svmSzIdxs[i], xP.size(), xN.size());
for (size_t k = 0; k < xP.size(); k++)
CV_Assert(xP[k].size() == Size(1, 1) && xP[k].type() == CV_32F);
Mat wr = trainSVM(xP, xN, L1R_L2LOSS_SVC, 100, 1);
CV_Assert(wr.size() == Size(2, 1));
wr.copyTo(wMat.row(i));
}
matWrite(_modelName + ".wS2", wMat);
_svmReW1f = wMat;
}
