// Copyright (C) 2015 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#include <dlib/matrix.h>
#include <sstream>
#include <string>
#include <cstdlib>
#include <ctime>
#include <vector>
#include "../cuda/tensor_tools.h"
#include "tester.h"
// We only do these tests if CUDA is available to test in the first place.
#ifdef DLIB_USE_CUDA
namespace
{
using namespace test;
using namespace dlib;
using namespace std;
logger dlog("test.cublas");
void test_inv()
{
tt::tensor_rand rnd;
dlib::tt::inv tinv;
dlib::cuda::inv cinv;
resizable_tensor minv1, minv2;
for (int n = 1; n < 20; ++n)
{
print_spinner();
resizable_tensor m(n,n);
rnd.fill_uniform(m);
tinv(m, minv1);
cinv(m, minv2);
matrix<float> mref = inv(mat(m));
DLIB_TEST_MSG(mean(abs(mref-mat(minv1)))/mean(abs(mref)) < 1e-5, mean(abs(mref-mat(minv1)))/mean(abs(mref)) <<" n: " << n);
DLIB_TEST_MSG(mean(abs(mref-mat(minv2)))/mean(abs(mref)) < 1e-5, mean(abs(mref-mat(minv2)))/mean(abs(mref)) <<" n: " << n);
}
}
class cublas_tester : public tester
{
public:
cublas_tester (
) :
tester ("test_cublas",
"Runs tests on the cuBLAS bindings.")
{}
void perform_test (
)
{
{
cuda::cuda_data_ptr<float> nonconst;
cuda::cuda_data_ptr<const float> const_ptr(nonconst);
}
test_inv();
{
resizable_tensor a(4,3), b(3,4), c(3,3);
c = 1;
a = matrix_cast<float>(gaussian_randm(a.num_samples(),a.size()/a.num_samples()));
b = matrix_cast<float>(gaussian_randm(b.num_samples(),b.size()/b.num_samples()));
matrix<float> truth = 2*mat(c)+trans(mat(a))*trans(mat(b));
a.async_copy_to_device(); b.async_copy_to_device(); c.async_copy_to_device();
cuda::gemm(2, c, 1, a, true, b, true);
DLIB_TEST(max(abs(truth-mat(c))) < 1e-6);
}
{
resizable_tensor a(4,3), b(4,3), c(3,3);
c = 1;
a = matrix_cast<float>(gaussian_randm(a.num_samples(),a.size()/a.num_samples()));
b = matrix_cast<float>(gaussian_randm(b.num_samples(),b.size()/b.num_samples()));
matrix<float> truth = 2*mat(c)+trans(mat(a))*mat(b);
a.async_copy_to_device(); b.async_copy_to_device(); c.async_copy_to_device();
cuda::gemm(2, c, 1, a, true, b, false);
DLIB_TEST(max(abs(truth-mat(c))) < 1e-6);
}
{
resizable_tensor a(3,4), b(3,4), c(3,3);
c = 1;
a = matrix_cast<float>(gaussian_randm(a.num_samples(),a.size()/a.num_samples()));
b = matrix_cast<float>(gaussian_randm(b.num_samples(),b.size()/b.num_samples()));
matrix<float> truth = 2*mat(c)+mat(a)*trans(mat(b));
a.async_copy_to_device(); b.async_copy_to_device(); c.async_copy_to_device();
cuda::gemm(2, c, 1, a, false, b, true);
DLIB_TEST(max(abs(truth-mat(c))) < 1e-6);
}
{
resizable_tensor a(3,4), b(3,4), c(3,3);
c = 1;
a = matrix_cast<float>(gaussian_randm(a.num_samples(),a.size()/a.num_samples()));
b = matrix_cast<float>(gaussian_randm(b.num_samples(),b.size()/b.num_samples()));
matrix<float> truth = mat(c)+mat(a)*trans(mat(b));
a.async_copy_to_device(); b.async_copy_to_device(); c.async_copy_to_device();
cuda::gemm(1, c, 1, a, false, b, true);
DLIB_TEST(max(abs(truth-mat(c))) < 1e-6);
}
{
resizable_tensor a(3,4), b(4,3), c(3,3);
c = 1;
a = matrix_cast<float>(gaussian_randm(a.num_samples(),a.size()/a.num_samples()));
b = matrix_cast<float>(gaussian_randm(b.num_samples(),b.size()/b.num_samples()));
matrix<float> truth = 2*mat(c)+mat(a)*mat(b);
a.async_copy_to_device(); b.async_copy_to_device(); c.async_copy_to_device();
cuda::gemm(2, c, 1, a, false, b, false);
DLIB_TEST(max(abs(truth-mat(c))) < 1e-6);
}
{
resizable_tensor a(3,4), b(4,3), c(3,3);
c = std::numeric_limits<float>::infinity();
a = matrix_cast<float>(gaussian_randm(a.num_samples(),a.size()/a.num_samples()));
b = matrix_cast<float>(gaussian_randm(b.num_samples(),b.size()/b.num_samples()));
a.async_copy_to_device(); b.async_copy_to_device(); c.async_copy_to_device();
matrix<float> truth = mat(a)*mat(b);
cuda::gemm(0, c, 1, a, false, b, false);
DLIB_TEST(max(abs(truth-mat(c))) < 1e-6);
}
{
resizable_tensor a(3,4), b(4,4), c(3,4);
c = 1;
a = matrix_cast<float>(gaussian_randm(a.num_samples(),a.size()/a.num_samples()));
b = matrix_cast<float>(gaussian_randm(b.num_samples(),b.size()/b.num_samples()));
matrix<float> truth = 2*mat(c)+mat(a)*mat(b);
cuda::gemm(2, c, 1, a, false, b, false);
DLIB_TEST(get_rect(truth) == get_rect(mat(c)));
DLIB_TEST(max(abs(truth-mat(c))) < 1e-6);
}
{
resizable_tensor a(4,3), b(4,4), c(3,4);
c = 1;
a = matrix_cast<float>(gaussian_randm(a.num_samples(),a.size()/a.num_samples()));
b = matrix_cast<float>(gaussian_randm(b.num_samples(),b.size()/b.num_samples()));
matrix<float> truth = 2*mat(c)+trans(mat(a))*mat(b);
cuda::gemm(2, c, 1, a, true, b, false);
DLIB_TEST(get_rect(truth) == get_rect(mat(c)));
DLIB_TEST(max(abs(truth-mat(c))) < 1e-6);
}
{
resizable_tensor a(4,3), b(4,5), c(3,5);
c = 1;
a = matrix_cast<float>(gaussian_randm(a.num_samples(),a.size()/a.num_samples()));
b = matrix_cast<float>(gaussian_randm(b.num_samples(),b.size()/b.num_samples()));
matrix<float> truth = 2*mat(c)+trans(mat(a))*mat(b);
cuda::gemm(2, c, 1, a, true, b, false);
DLIB_TEST(get_rect(truth) == get_rect(mat(c)));
DLIB_TEST(max(abs(truth-mat(c))) < 1e-6);
}
{
resizable_tensor a(4,3), b(4,5), c(3,5);
c = std::numeric_limits<float>::infinity();
a = matrix_cast<float>(gaussian_randm(a.num_samples(),a.size()/a.num_samples()));
b = matrix_cast<float>(gaussian_randm(b.num_samples(),b.size()/b.num_samples()));
matrix<float> truth = trans(mat(a))*mat(b);
cuda::gemm(0, c, 1, a, true, b, false);
DLIB_TEST(get_rect(truth) == get_rect(mat(c)));
DLIB_TEST(max(abs(truth-mat(c))) < 1e-6);
}
}
} a;
}
#endif // DLIB_USE_CUDA