DeviceMatrixCL::Ptr cell_histogram_dense_cl(
const DeviceMatrixCL3D::Ptr& ff_out,
const int max_bin, const int cell_size,
const int start_y, const int start_x,
const int stop_y, const int stop_x)
{
DeviceMatrixCL::Ptr assignment_mat = makeDeviceMatrixCL(*ff_out, 0);
DeviceMatrixCL::Ptr weight_mat = makeDeviceMatrixCL(*ff_out, 1);
int n_parts_y = (stop_y - start_y) / cell_size;
int n_parts_x = (stop_x - start_x) / cell_size;
#ifdef METHOD_2
n_parts_y += (n_parts_y % 2);
n_parts_x += (n_parts_x % 2);
#endif
DeviceMatrixCL::Ptr histogram = makeDeviceMatrixCL(n_parts_y * n_parts_x, max_bin);
// for(int i=0; i<10000; i++)
cell_histogram_dense_device_cl(
histogram.get(),
assignment_mat.get(),
weight_mat.get(),
max_bin,
cell_size,
start_y, start_x,
n_parts_y, n_parts_x);
return histogram;
}
DeviceMatrixCL::Ptr pwcityblock_cl( const DeviceMatrixCL::Ptr& features_train,
const DeviceMatrixCL::Ptr& features_test){
DeviceMatrixCL::Ptr out = makeDeviceMatrixCL(features_train->height,
features_test->height);
pwdist_genericCL(features_train.get(), features_test.get(), out.get(), CITYBLOCK);
return out;
}
DeviceMatrixCL::Ptr pwchisq_cl( const DeviceMatrixCL::Ptr& features_train,
const DeviceMatrixCL::Ptr& features_test){
DeviceMatrixCL::Ptr out = makeDeviceMatrixCL(features_train->height,
features_test->height);
pwdist_genericCL(features_train.get(), features_test.get(), out.get(), CHISQUARED);
return out;
}
DeviceMatrixCL::Ptr pwabsdot_cl( const DeviceMatrixCL::Ptr& features_train,
const DeviceMatrixCL::Ptr& features_test){
DeviceMatrixCL::Ptr out = makeDeviceMatrixCL(features_train->height,
features_test->height);
pwdist_genericCL(features_train.get(), features_test.get(), out.get(), ABSDOTPRODUCT);
return out;
}
DeviceMatrixCL::Ptr pwdist_cl( const DeviceMatrixCL::Ptr& features_train,
const DeviceMatrixCL::Ptr& features_test){
// double tic = omp_get_wtime();
DeviceMatrixCL::Ptr out = makeDeviceMatrixCL(features_train->height,
features_test->height);
// double tic = omp_get_wtime();
// pwdist_genericCL(features_train.get(), features_test.get(), out.get(), EUCLIDEAN);
// for(int i=0; i<10000; i++)
pwdist_eucCL(features_train.get(), features_test.get(), out.get());
// double toc = omp_get_wtime();
// std::cout << "OpenCL time: " << toc - tic << std::endl;
return out;
}
Classifier(const int window_height, const int window_width, const int cell_size, const int block_size, const int dict_size):
_window_height(window_height), _window_width(window_width), _dict_size(dict_size),
_cell_size(cell_size), _block_size(block_size)
{
_n_cells_x = _window_width / cell_size;
_n_cells_y = _window_height / cell_size;
_n_blocks_x = _n_cells_x - _block_size + 1;
_n_blocks_y = _n_cells_y - _block_size + 1;
_n_total_coeff = _block_size * _block_size * _n_blocks_x * _n_blocks_y, _dict_size;
coefficients = new float[_n_total_coeff];
classifierCL = makeDeviceMatrixCL(_n_total_coeff / _dict_size, _dict_size);
for (int i = 0; i < _n_total_coeff; i++)
{
coefficients[i] = float( std::rand() ) / RAND_MAX;
}
DeviceMatrixCL_copyToDevice(*classifierCL, coefficients);
};
DeviceMatrixCL::Ptr max_cl(const DeviceMatrixCL::Ptr& matrix)
{
DeviceMatrixCL::Ptr out = makeDeviceMatrixCL(matrix->height, 1);
max_cl_local(matrix.get(), out.get());
return out;
}
int main(int argc, char* argv[])
{
if(argc!=2){
printf("Please provide kernel path as argument!");
exit(1);
}
kernelPath=argv[1];
const int height = 640;
const int width = 480;
const int MAX_BIN = 100;
float* weights = new float[height * width];
float* assignments = new float[height * width];
for (int i = 0; i < height * width; i++)
{
weights[i] = float(std::rand()) / RAND_MAX;
assignments[i] = float(std::rand() % MAX_BIN);
}
DeviceMatrixCL::Ptr asCL = makeDeviceMatrixCL(height, width);
DeviceMatrixCL_copyFromDevice(*asCL, assignments);
DeviceMatrixCL::Ptr wtCL = makeDeviceMatrixCL(height, width);
DeviceMatrixCL_copyFromDevice(*wtCL, weights);
DeviceMatrixCL3D::Ptr block_histograms = cell_histogram_dense_cl(
asCL,
wtCL,
MAX_BIN, 8,
0, 0,
height, width);
delete[] weights;
delete[] assignments;
// cv::Mat exampleImage = cv::imread(exampleImagePath, 0);
//
// //convert to float
// exampleImage.convertTo(exampleImage, CV_32FC1);
//
// //pull the data
// float* f_imData = (float*) exampleImage.data;
//
// const int height = exampleImage.size().height;
// const int width = exampleImage.size().width;
//
// //create a random filterbank
// const int num_filters = 100;
// const int filter_dim = 3;
//
// float* filter_bank = new float[num_filters * filter_dim * filter_dim];
//
// for (int i = 0; i < num_filters * filter_dim * filter_dim; i++)
// {
// filter_bank[i] = float( std::rand() ) / RAND_MAX;
// }
//
// //OPENCL Reference
// DeviceMatrixCL::Ptr dmpCL = makeDeviceMatrixCL(height, width);
// DeviceMatrixCL_copyToDevice(*dmpCL, f_imData);
// set_filter_bank_cl(filter_bank, num_filters * filter_dim * filter_dim);
// DeviceMatrixCL3D::Ptr retdm = filter_frame_cl_3(dmpCL, num_filters, 1, FF_OPTYPE_COSINE);
// float* retval = new float[height * width * 2];
// DeviceMatrixCL3D_copyFromDevice(*retdm, retval);
//
// std::ofstream test_out_cl("testcl.out", std::ios_base::out);
// for (int j = 0; j < height; j++)
// {
// for (int i = 0; i < width; i++)
// {
// test_out_cl << retval[j * width + i] << ", ";
// }
//
// test_out_cl << std::endl;
// }
//
// test_out_cl << std::endl << std::endl << std::endl;
//
// for (int j = 0; j < height; j++)
// {
// for (int i = 0; i < width; i++)
// {
// test_out_cl << retval[height * width + j * width + i] << ", ";
// }
// test_out_cl << std::endl;
// }
// test_out_cl.close();
//
// delete[] filter_bank;
// delete[] retval;
// //delete[] retvalCU;
//
// return 0;
}
int main(int argc, char* argv[])
{
device_use = 0;
if(argc>1)
device_use = atoi(argv[1]);
static char* exampleImagePath = "..\\..\\..\\media\\kewell1.jpg";
//create a random filterbank
const int num_filters = 256;
//number of pipeline passes
const int num_iters = 125;
const int filter_dim = 3;
FilterBank fb(filter_dim, num_filters);
fb.set_on_device();
Classifier clf(128, 64, 8, 2, num_filters);
//load the image on device
cv::Mat exampleImage = cv::imread(exampleImagePath, 0);
//convert to float
exampleImage.convertTo(exampleImage, CV_32FC1);
cv::resize(exampleImage, exampleImage, cv::Size(exampleImage.cols, exampleImage.rows));
if(device_use==0)
std::cout << "running on CPU" <<std::endl;
else
std::cout << "running on GPU" <<std::endl;
std::cout << "Image dimensions:" << exampleImage.size().height <<" "<< exampleImage.size().width <<std::endl;
//pull the data
float* f_imData = (float*) exampleImage.data;
DeviceMatrixCL::Ptr dmpCL = makeDeviceMatrixCL(exampleImage.size().height, exampleImage.size().width);
DeviceMatrixCL_copyToDevice(*dmpCL, f_imData);
/* for(int i=0; i<20; i++)
{
DeviceMatrixCL3D::Ptr ff_im = fb.apply_cl(dmpCL);
// tic1= omp_get_wtime();
DeviceMatrixCL::Ptr block_histogram = cell_histogram_dense_cl(
ff_im, num_filters, 8, 0, 0,
exampleImage.size().height, exampleImage.size().width);
// tic2= omp_get_wtime();
DeviceMatrixCL::Ptr result = clf.apply(block_histogram);
}
*/
double tic0, tic1, tic2, tic3;
double tim1 = 0.0;
double tim2 = 0.0;
double tim3 = 0.0;
for(int i=0; i<num_iters; i++)
{
tic0= omp_get_wtime();
DeviceMatrixCL3D::Ptr ff_im = fb.apply_cl(dmpCL);
tic1= omp_get_wtime();
tim1 += tic1 - tic0;
DeviceMatrixCL::Ptr block_histogram = cell_histogram_dense_cl(
ff_im, num_filters, 8, 0, 0,
exampleImage.size().height, exampleImage.size().width);
tic2= omp_get_wtime();
tim2 += tic2 - tic1;
DeviceMatrixCL::Ptr result = clf.apply(block_histogram);
TheContext* tc = new TheContext();
clFinish(tc->getMyContext()->cqCommandQueue);
tic3 = omp_get_wtime();
tim3 += tic3 - tic2;
}
std::cout << "full pipeline time: " << tim1 + tim2 + tim3 << std::endl;
std::cout << "filter pipeline time: " << tim1 << std::endl;
std::cout << "histogram pipeline time: " << tim2 << std::endl;
std::cout << "classifier pipeline time: " << tim3 << std::endl;
return 0;
}