| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2010, 2011, 2012 Google Inc. All rights reserved. |
| // http://code.google.com/p/ceres-solver/ |
| // |
| // Redistribution and use in source and binary forms, with or without |
| // modification, are permitted provided that the following conditions are met: |
| // |
| // * Redistributions of source code must retain the above copyright notice, |
| // this list of conditions and the following disclaimer. |
| // * Redistributions in binary form must reproduce the above copyright notice, |
| // this list of conditions and the following disclaimer in the documentation |
| // and/or other materials provided with the distribution. |
| // * Neither the name of Google Inc. nor the names of its contributors may be |
| // used to endorse or promote products derived from this software without |
| // specific prior written permission. |
| // |
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" |
| // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE |
| // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE |
| // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE |
| // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR |
| // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF |
| // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS |
| // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN |
| // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) |
| // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE |
| // POSSIBILITY OF SUCH DAMAGE. |
| // |
| // Author: sameeragarwal@google.com (Sameer Agarwal) |
| |
| #include "gtest/gtest.h" |
| #include "ceres/autodiff_cost_function.h" |
| #include "ceres/linear_solver.h" |
| #include "ceres/parameter_block.h" |
| #include "ceres/problem_impl.h" |
| #include "ceres/program.h" |
| #include "ceres/residual_block.h" |
| #include "ceres/solver_impl.h" |
| #include "ceres/sized_cost_function.h" |
| |
| namespace ceres { |
| namespace internal { |
| |
| // Templated base class for the CostFunction signatures. |
| template <int kNumResiduals, int N0, int N1, int N2> |
| class MockCostFunctionBase : public |
| SizedCostFunction<kNumResiduals, N0, N1, N2> { |
| public: |
| virtual bool Evaluate(double const* const* parameters, |
| double* residuals, |
| double** jacobians) const { |
| // Do nothing. This is never called. |
| return true; |
| } |
| }; |
| |
| class UnaryCostFunction : public MockCostFunctionBase<2, 1, 0, 0> {}; |
| class BinaryCostFunction : public MockCostFunctionBase<2, 1, 1, 0> {}; |
| class TernaryCostFunction : public MockCostFunctionBase<2, 1, 1, 1> {}; |
| |
| TEST(SolverImpl, RemoveFixedBlocksNothingConstant) { |
| ProblemImpl problem; |
| double x; |
| double y; |
| double z; |
| |
| problem.AddParameterBlock(&x, 1); |
| problem.AddParameterBlock(&y, 1); |
| problem.AddParameterBlock(&z, 1); |
| problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x); |
| problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y); |
| problem.AddResidualBlock(new TernaryCostFunction(), NULL, &x, &y, &z); |
| |
| string error; |
| { |
| int num_eliminate_blocks = 0; |
| Program program(*problem.mutable_program()); |
| EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program, |
| &num_eliminate_blocks, |
| &error)); |
| EXPECT_EQ(program.NumParameterBlocks(), 3); |
| EXPECT_EQ(program.NumResidualBlocks(), 3); |
| EXPECT_EQ(num_eliminate_blocks, 0); |
| } |
| |
| // Check that num_eliminate_blocks is preserved, when it contains |
| // all blocks. |
| { |
| int num_eliminate_blocks = 3; |
| Program program(problem.program()); |
| EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program, |
| &num_eliminate_blocks, |
| &error)); |
| EXPECT_EQ(program.NumParameterBlocks(), 3); |
| EXPECT_EQ(program.NumResidualBlocks(), 3); |
| EXPECT_EQ(num_eliminate_blocks, 3); |
| } |
| } |
| |
| TEST(SolverImpl, RemoveFixedBlocksAllParameterBlocksConstant) { |
| ProblemImpl problem; |
| double x; |
| |
| problem.AddParameterBlock(&x, 1); |
| problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x); |
| problem.SetParameterBlockConstant(&x); |
| |
| int num_eliminate_blocks = 0; |
| Program program(problem.program()); |
| string error; |
| EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program, |
| &num_eliminate_blocks, |
| &error)); |
| EXPECT_EQ(program.NumParameterBlocks(), 0); |
| EXPECT_EQ(program.NumResidualBlocks(), 0); |
| EXPECT_EQ(num_eliminate_blocks, 0); |
| } |
| |
| TEST(SolverImpl, RemoveFixedBlocksNoResidualBlocks) { |
| ProblemImpl problem; |
| double x; |
| double y; |
| double z; |
| |
| problem.AddParameterBlock(&x, 1); |
| problem.AddParameterBlock(&y, 1); |
| problem.AddParameterBlock(&z, 1); |
| |
| int num_eliminate_blocks = 0; |
| Program program(problem.program()); |
| string error; |
| EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program, |
| &num_eliminate_blocks, |
| &error)); |
| EXPECT_EQ(program.NumParameterBlocks(), 0); |
| EXPECT_EQ(program.NumResidualBlocks(), 0); |
| EXPECT_EQ(num_eliminate_blocks, 0); |
| } |
| |
| TEST(SolverImpl, RemoveFixedBlocksOneParameterBlockConstant) { |
| ProblemImpl problem; |
| double x; |
| double y; |
| double z; |
| |
| problem.AddParameterBlock(&x, 1); |
| problem.AddParameterBlock(&y, 1); |
| problem.AddParameterBlock(&z, 1); |
| problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x); |
| problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y); |
| problem.SetParameterBlockConstant(&x); |
| |
| int num_eliminate_blocks = 0; |
| Program program(problem.program()); |
| string error; |
| EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program, |
| &num_eliminate_blocks, |
| &error)); |
| EXPECT_EQ(program.NumParameterBlocks(), 1); |
| EXPECT_EQ(program.NumResidualBlocks(), 1); |
| EXPECT_EQ(num_eliminate_blocks, 0); |
| } |
| |
| TEST(SolverImpl, RemoveFixedBlocksNumEliminateBlocks) { |
| ProblemImpl problem; |
| double x; |
| double y; |
| double z; |
| |
| problem.AddParameterBlock(&x, 1); |
| problem.AddParameterBlock(&y, 1); |
| problem.AddParameterBlock(&z, 1); |
| problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x); |
| problem.AddResidualBlock(new TernaryCostFunction(), NULL, &x, &y, &z); |
| problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y); |
| problem.SetParameterBlockConstant(&x); |
| |
| int num_eliminate_blocks = 2; |
| Program program(problem.program()); |
| string error; |
| EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program, |
| &num_eliminate_blocks, |
| &error)); |
| EXPECT_EQ(program.NumParameterBlocks(), 2); |
| EXPECT_EQ(program.NumResidualBlocks(), 2); |
| EXPECT_EQ(num_eliminate_blocks, 1); |
| } |
| |
| TEST(SolverImpl, ReorderResidualBlockNonSchurSolver) { |
| ProblemImpl problem; |
| double x; |
| double y; |
| double z; |
| |
| problem.AddParameterBlock(&x, 1); |
| problem.AddParameterBlock(&y, 1); |
| problem.AddParameterBlock(&z, 1); |
| problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x); |
| problem.AddResidualBlock(new TernaryCostFunction(), NULL, &x, &y, &z); |
| problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y); |
| |
| const vector<ResidualBlock*>& residual_blocks = |
| problem.program().residual_blocks(); |
| vector<ResidualBlock*> current_residual_blocks(residual_blocks); |
| |
| Solver::Options options; |
| options.linear_solver_type = SPARSE_NORMAL_CHOLESKY; |
| string error; |
| |
| EXPECT_TRUE(SolverImpl::MaybeReorderResidualBlocks(options, |
| problem.mutable_program(), |
| &error)); |
| EXPECT_EQ(current_residual_blocks.size(), residual_blocks.size()); |
| for (int i = 0; i < current_residual_blocks.size(); ++i) { |
| EXPECT_EQ(current_residual_blocks[i], residual_blocks[i]); |
| } |
| } |
| |
| TEST(SolverImpl, ReorderResidualBlockNumEliminateBlockDeathTest) { |
| ProblemImpl problem; |
| double x; |
| double y; |
| double z; |
| |
| problem.AddParameterBlock(&x, 1); |
| problem.AddParameterBlock(&y, 1); |
| problem.AddParameterBlock(&z, 1); |
| problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x); |
| problem.AddResidualBlock(new TernaryCostFunction(), NULL, &x, &y, &z); |
| problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y); |
| |
| Solver::Options options; |
| options.linear_solver_type = DENSE_SCHUR; |
| options.num_eliminate_blocks = 0; |
| string error; |
| #ifndef _WIN32 |
| EXPECT_DEATH( |
| SolverImpl::MaybeReorderResidualBlocks( |
| options, problem.mutable_program(), &error), |
| "Congratulations"); |
| #endif // _WIN32 |
| } |
| |
| TEST(SolverImpl, ReorderResidualBlockNormalFunction) { |
| ProblemImpl problem; |
| double x; |
| double y; |
| double z; |
| |
| problem.AddParameterBlock(&x, 1); |
| problem.AddParameterBlock(&y, 1); |
| problem.AddParameterBlock(&z, 1); |
| |
| problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x); |
| problem.AddResidualBlock(new BinaryCostFunction(), NULL, &z, &x); |
| problem.AddResidualBlock(new BinaryCostFunction(), NULL, &z, &y); |
| problem.AddResidualBlock(new UnaryCostFunction(), NULL, &z); |
| problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y); |
| problem.AddResidualBlock(new UnaryCostFunction(), NULL, &y); |
| |
| Solver::Options options; |
| options.linear_solver_type = DENSE_SCHUR; |
| options.num_eliminate_blocks = 2; |
| |
| const vector<ResidualBlock*>& residual_blocks = |
| problem.program().residual_blocks(); |
| |
| vector<ResidualBlock*> expected_residual_blocks; |
| |
| // This is a bit fragile, but it serves the purpose. We know the |
| // bucketing algorithm that the reordering function uses, so we |
| // expect the order for residual blocks for each e_block to be |
| // filled in reverse. |
| expected_residual_blocks.push_back(residual_blocks[4]); |
| expected_residual_blocks.push_back(residual_blocks[1]); |
| expected_residual_blocks.push_back(residual_blocks[0]); |
| expected_residual_blocks.push_back(residual_blocks[5]); |
| expected_residual_blocks.push_back(residual_blocks[2]); |
| expected_residual_blocks.push_back(residual_blocks[3]); |
| |
| Program* program = problem.mutable_program(); |
| program->SetParameterOffsetsAndIndex(); |
| |
| string error; |
| EXPECT_TRUE(SolverImpl::MaybeReorderResidualBlocks(options, |
| problem.mutable_program(), |
| &error)); |
| EXPECT_EQ(residual_blocks.size(), expected_residual_blocks.size()); |
| for (int i = 0; i < expected_residual_blocks.size(); ++i) { |
| EXPECT_EQ(residual_blocks[i], expected_residual_blocks[i]); |
| } |
| } |
| |
| TEST(SolverImpl, ReorderResidualBlockNormalFunctionWithFixedBlocks) { |
| ProblemImpl problem; |
| double x; |
| double y; |
| double z; |
| |
| problem.AddParameterBlock(&x, 1); |
| problem.AddParameterBlock(&y, 1); |
| problem.AddParameterBlock(&z, 1); |
| |
| // Set one parameter block constant. |
| problem.SetParameterBlockConstant(&z); |
| |
| // Mark residuals for x's row block with "x" for readability. |
| problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x); // 0 x |
| problem.AddResidualBlock(new BinaryCostFunction(), NULL, &z, &x); // 1 x |
| problem.AddResidualBlock(new BinaryCostFunction(), NULL, &z, &y); // 2 |
| problem.AddResidualBlock(new BinaryCostFunction(), NULL, &z, &y); // 3 |
| problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &z); // 4 x |
| problem.AddResidualBlock(new BinaryCostFunction(), NULL, &z, &y); // 5 |
| problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &z); // 6 x |
| problem.AddResidualBlock(new UnaryCostFunction(), NULL, &y); // 7 |
| |
| Solver::Options options; |
| options.linear_solver_type = DENSE_SCHUR; |
| options.num_eliminate_blocks = 2; |
| |
| // Create the reduced program. This should remove the fixed block "z", |
| // marking the index to -1 at the same time. x and y also get indices. |
| string error; |
| scoped_ptr<Program> reduced_program( |
| SolverImpl::CreateReducedProgram(&options, &problem, &error)); |
| |
| const vector<ResidualBlock*>& residual_blocks = |
| problem.program().residual_blocks(); |
| |
| // This is a bit fragile, but it serves the purpose. We know the |
| // bucketing algorithm that the reordering function uses, so we |
| // expect the order for residual blocks for each e_block to be |
| // filled in reverse. |
| |
| vector<ResidualBlock*> expected_residual_blocks; |
| |
| // Row block for residuals involving "x". These are marked "x" in the block |
| // of code calling AddResidual() above. |
| expected_residual_blocks.push_back(residual_blocks[6]); |
| expected_residual_blocks.push_back(residual_blocks[4]); |
| expected_residual_blocks.push_back(residual_blocks[1]); |
| expected_residual_blocks.push_back(residual_blocks[0]); |
| |
| // Row block for residuals involving "y". |
| expected_residual_blocks.push_back(residual_blocks[7]); |
| expected_residual_blocks.push_back(residual_blocks[5]); |
| expected_residual_blocks.push_back(residual_blocks[3]); |
| expected_residual_blocks.push_back(residual_blocks[2]); |
| |
| EXPECT_TRUE(SolverImpl::MaybeReorderResidualBlocks(options, |
| reduced_program.get(), |
| &error)); |
| |
| EXPECT_EQ(reduced_program->residual_blocks().size(), |
| expected_residual_blocks.size()); |
| for (int i = 0; i < expected_residual_blocks.size(); ++i) { |
| EXPECT_EQ(reduced_program->residual_blocks()[i], |
| expected_residual_blocks[i]); |
| } |
| } |
| |
| TEST(SolverImpl, ApplyUserOrderingOrderingTooSmall) { |
| ProblemImpl problem; |
| double x; |
| double y; |
| double z; |
| |
| problem.AddParameterBlock(&x, 1); |
| problem.AddParameterBlock(&y, 1); |
| problem.AddParameterBlock(&z, 1); |
| |
| vector<double*> ordering; |
| ordering.push_back(&x); |
| ordering.push_back(&z); |
| |
| Program program(problem.program()); |
| string error; |
| EXPECT_FALSE(SolverImpl::ApplyUserOrdering(problem, |
| ordering, |
| &program, |
| &error)); |
| } |
| |
| TEST(SolverImpl, ApplyUserOrderingHasDuplicates) { |
| ProblemImpl problem; |
| double x; |
| double y; |
| double z; |
| |
| problem.AddParameterBlock(&x, 1); |
| problem.AddParameterBlock(&y, 1); |
| problem.AddParameterBlock(&z, 1); |
| |
| vector<double*> ordering; |
| ordering.push_back(&x); |
| ordering.push_back(&z); |
| ordering.push_back(&z); |
| |
| Program program(problem.program()); |
| string error; |
| EXPECT_FALSE(SolverImpl::ApplyUserOrdering(problem, |
| ordering, |
| &program, |
| &error)); |
| } |
| |
| |
| TEST(SolverImpl, ApplyUserOrderingNormal) { |
| ProblemImpl problem; |
| double x; |
| double y; |
| double z; |
| |
| problem.AddParameterBlock(&x, 1); |
| problem.AddParameterBlock(&y, 1); |
| problem.AddParameterBlock(&z, 1); |
| |
| vector<double*> ordering; |
| ordering.push_back(&x); |
| ordering.push_back(&z); |
| ordering.push_back(&y); |
| |
| Program* program = problem.mutable_program(); |
| string error; |
| |
| EXPECT_TRUE(SolverImpl::ApplyUserOrdering(problem, |
| ordering, |
| program, |
| &error)); |
| const vector<ParameterBlock*>& parameter_blocks = program->parameter_blocks(); |
| |
| EXPECT_EQ(parameter_blocks.size(), 3); |
| EXPECT_EQ(parameter_blocks[0]->user_state(), &x); |
| EXPECT_EQ(parameter_blocks[1]->user_state(), &z); |
| EXPECT_EQ(parameter_blocks[2]->user_state(), &y); |
| } |
| |
| #if defined(CERES_NO_SUITESPARSE) && defined(CERES_NO_CXSPARSE) |
| TEST(SolverImpl, CreateLinearSolverNoSuiteSparse) { |
| Solver::Options options; |
| options.linear_solver_type = SPARSE_NORMAL_CHOLESKY; |
| string error; |
| EXPECT_FALSE(SolverImpl::CreateLinearSolver(&options, &error)); |
| } |
| #endif |
| |
| TEST(SolverImpl, CreateLinearSolverNegativeMaxNumIterations) { |
| Solver::Options options; |
| options.linear_solver_type = DENSE_QR; |
| options.linear_solver_max_num_iterations = -1; |
| string error; |
| EXPECT_EQ(SolverImpl::CreateLinearSolver(&options, &error), |
| static_cast<LinearSolver*>(NULL)); |
| } |
| |
| TEST(SolverImpl, CreateLinearSolverNegativeMinNumIterations) { |
| Solver::Options options; |
| options.linear_solver_type = DENSE_QR; |
| options.linear_solver_min_num_iterations = -1; |
| string error; |
| EXPECT_EQ(SolverImpl::CreateLinearSolver(&options, &error), |
| static_cast<LinearSolver*>(NULL)); |
| } |
| |
| TEST(SolverImpl, CreateLinearSolverMaxLessThanMinIterations) { |
| Solver::Options options; |
| options.linear_solver_type = DENSE_QR; |
| options.linear_solver_min_num_iterations = 10; |
| options.linear_solver_max_num_iterations = 5; |
| string error; |
| EXPECT_EQ(SolverImpl::CreateLinearSolver(&options, &error), |
| static_cast<LinearSolver*>(NULL)); |
| } |
| |
| TEST(SolverImpl, CreateLinearSolverZeroNumEliminateBlocks) { |
| Solver::Options options; |
| options.num_eliminate_blocks = 0; |
| options.linear_solver_type = DENSE_SCHUR; |
| string error; |
| scoped_ptr<LinearSolver> solver( |
| SolverImpl::CreateLinearSolver(&options, &error)); |
| EXPECT_TRUE(solver != NULL); |
| |
| #if defined(CERES_NO_SUITESPARSE) && defined(CERES_NO_CXSPARSE) |
| EXPECT_EQ(options.linear_solver_type, DENSE_QR); |
| #else |
| EXPECT_EQ(options.linear_solver_type, SPARSE_NORMAL_CHOLESKY); |
| #endif |
| } |
| |
| TEST(SolverImpl, CreateLinearSolverDenseSchurMultipleThreads) { |
| Solver::Options options; |
| options.num_eliminate_blocks = 1; |
| options.linear_solver_type = DENSE_SCHUR; |
| options.num_linear_solver_threads = 2; |
| string error; |
| scoped_ptr<LinearSolver> solver( |
| SolverImpl::CreateLinearSolver(&options, &error)); |
| EXPECT_TRUE(solver != NULL); |
| EXPECT_EQ(options.linear_solver_type, DENSE_SCHUR); |
| EXPECT_EQ(options.num_linear_solver_threads, 1); |
| } |
| |
| TEST(SolverImpl, CreateIterativeLinearSolverForDogleg) { |
| Solver::Options options; |
| options.trust_region_strategy_type = DOGLEG; |
| string error; |
| options.linear_solver_type = ITERATIVE_SCHUR; |
| EXPECT_EQ(SolverImpl::CreateLinearSolver(&options, &error), |
| static_cast<LinearSolver*>(NULL)); |
| |
| options.linear_solver_type = CGNR; |
| EXPECT_EQ(SolverImpl::CreateLinearSolver(&options, &error), |
| static_cast<LinearSolver*>(NULL)); |
| } |
| |
| TEST(SolverImpl, CreateLinearSolverNormalOperation) { |
| Solver::Options options; |
| scoped_ptr<LinearSolver> solver; |
| options.linear_solver_type = DENSE_QR; |
| string error; |
| solver.reset(SolverImpl::CreateLinearSolver(&options, &error)); |
| EXPECT_EQ(options.linear_solver_type, DENSE_QR); |
| EXPECT_TRUE(solver.get() != NULL); |
| |
| #ifndef CERES_NO_SUITESPARSE |
| options.linear_solver_type = SPARSE_NORMAL_CHOLESKY; |
| options.sparse_linear_algebra_library = SUITE_SPARSE; |
| solver.reset(SolverImpl::CreateLinearSolver(&options, &error)); |
| EXPECT_EQ(options.linear_solver_type, SPARSE_NORMAL_CHOLESKY); |
| EXPECT_TRUE(solver.get() != NULL); |
| #endif |
| |
| #ifndef CERES_NO_CXSPARSE |
| options.linear_solver_type = SPARSE_NORMAL_CHOLESKY; |
| options.sparse_linear_algebra_library = CX_SPARSE; |
| solver.reset(SolverImpl::CreateLinearSolver(&options, &error)); |
| EXPECT_EQ(options.linear_solver_type, SPARSE_NORMAL_CHOLESKY); |
| EXPECT_TRUE(solver.get() != NULL); |
| #endif |
| |
| options.linear_solver_type = DENSE_SCHUR; |
| options.num_eliminate_blocks = 2; |
| solver.reset(SolverImpl::CreateLinearSolver(&options, &error)); |
| EXPECT_EQ(options.linear_solver_type, DENSE_SCHUR); |
| EXPECT_TRUE(solver.get() != NULL); |
| |
| options.linear_solver_type = SPARSE_SCHUR; |
| options.num_eliminate_blocks = 2; |
| solver.reset(SolverImpl::CreateLinearSolver(&options, &error)); |
| |
| #if defined(CERES_NO_SUITESPARSE) && defined(CERES_NO_CXSPARSE) |
| EXPECT_TRUE(SolverImpl::CreateLinearSolver(&options, &error) == NULL); |
| #else |
| EXPECT_TRUE(solver.get() != NULL); |
| EXPECT_EQ(options.linear_solver_type, SPARSE_SCHUR); |
| #endif |
| |
| options.linear_solver_type = ITERATIVE_SCHUR; |
| options.num_eliminate_blocks = 2; |
| solver.reset(SolverImpl::CreateLinearSolver(&options, &error)); |
| EXPECT_EQ(options.linear_solver_type, ITERATIVE_SCHUR); |
| EXPECT_TRUE(solver.get() != NULL); |
| } |
| |
| struct QuadraticCostFunction { |
| template <typename T> bool operator()(const T* const x, |
| T* residual) const { |
| residual[0] = T(5.0) - *x; |
| return true; |
| } |
| }; |
| |
| struct RememberingCallback : public IterationCallback { |
| RememberingCallback(double *x) : calls(0), x(x) {} |
| virtual ~RememberingCallback() {} |
| virtual CallbackReturnType operator()(const IterationSummary& summary) { |
| x_values.push_back(*x); |
| return SOLVER_CONTINUE; |
| } |
| int calls; |
| double *x; |
| vector<double> x_values; |
| }; |
| |
| TEST(SolverImpl, UpdateStateEveryIterationOption) { |
| double x = 50.0; |
| const double original_x = x; |
| |
| scoped_ptr<CostFunction> cost_function( |
| new AutoDiffCostFunction<QuadraticCostFunction, 1, 1>( |
| new QuadraticCostFunction)); |
| |
| Problem::Options problem_options; |
| problem_options.cost_function_ownership = DO_NOT_TAKE_OWNERSHIP; |
| ProblemImpl problem(problem_options); |
| problem.AddResidualBlock(cost_function.get(), NULL, &x); |
| |
| Solver::Options options; |
| options.linear_solver_type = DENSE_QR; |
| |
| RememberingCallback callback(&x); |
| options.callbacks.push_back(&callback); |
| |
| Solver::Summary summary; |
| |
| int num_iterations; |
| |
| // First try: no updating. |
| SolverImpl::Solve(options, &problem, &summary); |
| num_iterations = summary.num_successful_steps + |
| summary.num_unsuccessful_steps; |
| EXPECT_GT(num_iterations, 1); |
| for (int i = 0; i < callback.x_values.size(); ++i) { |
| EXPECT_EQ(50.0, callback.x_values[i]); |
| } |
| |
| // Second try: with updating |
| x = 50.0; |
| options.update_state_every_iteration = true; |
| callback.x_values.clear(); |
| SolverImpl::Solve(options, &problem, &summary); |
| num_iterations = summary.num_successful_steps + |
| summary.num_unsuccessful_steps; |
| EXPECT_GT(num_iterations, 1); |
| EXPECT_EQ(original_x, callback.x_values[0]); |
| EXPECT_NE(original_x, callback.x_values[1]); |
| } |
| |
| // The parameters must be in separate blocks so that they can be individually |
| // set constant or not. |
| struct Quadratic4DCostFunction { |
| template <typename T> bool operator()(const T* const x, |
| const T* const y, |
| const T* const z, |
| const T* const w, |
| T* residual) const { |
| // A 4-dimension axis-aligned quadratic. |
| residual[0] = T(10.0) - *x + |
| T(20.0) - *y + |
| T(30.0) - *z + |
| T(40.0) - *w; |
| return true; |
| } |
| }; |
| |
| TEST(SolverImpl, ConstantParameterBlocksDoNotChangeAndStateInvariantKept) { |
| double x = 50.0; |
| double y = 50.0; |
| double z = 50.0; |
| double w = 50.0; |
| const double original_x = 50.0; |
| const double original_y = 50.0; |
| const double original_z = 50.0; |
| const double original_w = 50.0; |
| |
| scoped_ptr<CostFunction> cost_function( |
| new AutoDiffCostFunction<Quadratic4DCostFunction, 1, 1, 1, 1, 1>( |
| new Quadratic4DCostFunction)); |
| |
| Problem::Options problem_options; |
| problem_options.cost_function_ownership = DO_NOT_TAKE_OWNERSHIP; |
| |
| ProblemImpl problem(problem_options); |
| problem.AddResidualBlock(cost_function.get(), NULL, &x, &y, &z, &w); |
| problem.SetParameterBlockConstant(&x); |
| problem.SetParameterBlockConstant(&w); |
| |
| Solver::Options options; |
| options.linear_solver_type = DENSE_QR; |
| |
| Solver::Summary summary; |
| SolverImpl::Solve(options, &problem, &summary); |
| |
| // Verify only the non-constant parameters were mutated. |
| EXPECT_EQ(original_x, x); |
| EXPECT_NE(original_y, y); |
| EXPECT_NE(original_z, z); |
| EXPECT_EQ(original_w, w); |
| |
| // Check that the parameter block state pointers are pointing back at the |
| // user state, instead of inside a random temporary vector made by Solve(). |
| EXPECT_EQ(&x, problem.program().parameter_blocks()[0]->state()); |
| EXPECT_EQ(&y, problem.program().parameter_blocks()[1]->state()); |
| EXPECT_EQ(&z, problem.program().parameter_blocks()[2]->state()); |
| EXPECT_EQ(&w, problem.program().parameter_blocks()[3]->state()); |
| } |
| |
| } // namespace internal |
| } // namespace ceres |
