mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-30 00:50:37 +08:00
Joydeep Biswas
344929647a
* Added GPU device and CUDA compute capability identification. * Added GpuMemoryAvailable() to aid downstream optimizations based on GPU memory availability. Change-Id: I326dc1e4b7a6a7f5571b7e5479eb9aa300ad1075
131 lines
4.7 KiB
C++
131 lines
4.7 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2022 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
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#include "ceres/dense_qr.h"
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#include <memory>
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#include <numeric>
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#include <string>
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#include <tuple>
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#include <vector>
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#include "Eigen/Dense"
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#include "ceres/internal/eigen.h"
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#include "ceres/linear_solver.h"
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#include "glog/logging.h"
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#include "gmock/gmock.h"
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#include "gtest/gtest.h"
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namespace ceres::internal {
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using Param = DenseLinearAlgebraLibraryType;
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namespace {
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std::string ParamInfoToString(testing::TestParamInfo<Param> info) {
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return DenseLinearAlgebraLibraryTypeToString(info.param);
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}
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} // namespace
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class DenseQRTest : public ::testing::TestWithParam<Param> {};
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TEST_P(DenseQRTest, FactorAndSolve) {
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// TODO(sameeragarwal): Convert these tests into type parameterized tests so
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// that we can test the single and double precision solvers.
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using Scalar = double;
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using MatrixType = Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic>;
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using VectorType = Eigen::Matrix<Scalar, Eigen::Dynamic, 1>;
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LinearSolver::Options options;
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ContextImpl context;
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#ifndef CERES_NO_CUDA
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options.context = &context;
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std::string error;
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CHECK(context.InitCuda(&error)) << error;
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#endif // CERES_NO_CUDA
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options.dense_linear_algebra_library_type = GetParam();
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const double kEpsilon = std::numeric_limits<double>::epsilon() * 1.5e4;
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std::unique_ptr<DenseQR> dense_qr = DenseQR::Create(options);
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const int kNumTrials = 10;
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const int kMinNumCols = 1;
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const int kMaxNumCols = 10;
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const int kMinRowsFactor = 1;
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const int kMaxRowsFactor = 3;
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for (int num_cols = kMinNumCols; num_cols < kMaxNumCols; ++num_cols) {
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for (int num_rows = kMinRowsFactor * num_cols;
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num_rows < kMaxRowsFactor * num_cols;
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++num_rows) {
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for (int trial = 0; trial < kNumTrials; ++trial) {
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MatrixType lhs = MatrixType::Random(num_rows, num_cols);
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Vector x = VectorType::Random(num_cols);
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Vector rhs = lhs * x;
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Vector actual = Vector::Random(num_cols);
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LinearSolver::Summary summary;
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summary.termination_type = dense_qr->FactorAndSolve(num_rows,
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num_cols,
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lhs.data(),
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rhs.data(),
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actual.data(),
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&summary.message);
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ASSERT_EQ(summary.termination_type,
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LinearSolverTerminationType::SUCCESS);
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ASSERT_NEAR((x - actual).norm() / x.norm(), 0.0, kEpsilon)
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<< "\nexpected: " << x.transpose()
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<< "\nactual : " << actual.transpose();
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}
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}
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}
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}
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namespace {
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// NOTE: preprocessor directives in a macro are not standard conforming
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decltype(auto) MakeValues() {
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return ::testing::Values(EIGEN
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#ifndef CERES_NO_LAPACK
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,
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LAPACK
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#endif
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#ifndef CERES_NO_CUDA
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,
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CUDA
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#endif
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);
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}
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} // namespace
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INSTANTIATE_TEST_SUITE_P(_, DenseQRTest, MakeValues(), ParamInfoToString);
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} // namespace ceres::internal
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