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