| // 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 { | |
| // A cost function that sipmply returns its argument. | |
| class UnaryIdentityCostFunction : public SizedCostFunction<1, 1> { | |
| public: | |
| virtual bool Evaluate(double const* const* parameters, | |
| double* residuals, | |
| double** jacobians) const { | |
| residuals[0] = parameters[0][0]; | |
| if (jacobians != NULL && jacobians[0] != NULL) { | |
| jacobians[0][0] = 1.0; | |
| } | |
| return true; | |
| } | |
| }; | |
| // 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, | |
| NULL, | |
| &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, | |
| NULL, | |
| &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, | |
| NULL, | |
| &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, | |
| NULL, | |
| &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, | |
| NULL, | |
| &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, | |
| NULL, | |
| &error)); | |
| EXPECT_EQ(program.NumParameterBlocks(), 2); | |
| EXPECT_EQ(program.NumResidualBlocks(), 2); | |
| EXPECT_EQ(num_eliminate_blocks, 1); | |
| } | |
| TEST(SolverImpl, RemoveFixedBlocksFixedCost) { | |
| ProblemImpl problem; | |
| double x = 1.23; | |
| double y = 4.56; | |
| double z = 7.89; | |
| problem.AddParameterBlock(&x, 1); | |
| problem.AddParameterBlock(&y, 1); | |
| problem.AddParameterBlock(&z, 1); | |
| problem.AddResidualBlock(new UnaryIdentityCostFunction(), 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; | |
| double fixed_cost = 0.0; | |
| Program program(problem.program()); | |
| double expected_fixed_cost; | |
| ResidualBlock *expected_removed_block = program.residual_blocks()[0]; | |
| scoped_array<double> scratch(new double[expected_removed_block->NumScratchDoublesForEvaluate()]); | |
| expected_removed_block->Evaluate(&expected_fixed_cost, NULL, NULL, scratch.get()); | |
| string error; | |
| EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program, | |
| &num_eliminate_blocks, | |
| &fixed_cost, | |
| &error)); | |
| EXPECT_EQ(program.NumParameterBlocks(), 2); | |
| EXPECT_EQ(program.NumResidualBlocks(), 2); | |
| EXPECT_EQ(num_eliminate_blocks, 1); | |
| EXPECT_DOUBLE_EQ(fixed_cost, expected_fixed_cost); | |
| } | |
| 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, NULL, &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 { | |
| explicit 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()); | |
| } | |
| #define CHECK_ARRAY(name, value) \ | |
| if (options.return_ ## name) { \ | |
| EXPECT_EQ(summary.name.size(), 1); \ | |
| EXPECT_EQ(summary.name[0], value); \ | |
| } else { \ | |
| EXPECT_EQ(summary.name.size(), 0); \ | |
| } | |
| #define CHECK_JACOBIAN(name) \ | |
| if (options.return_ ## name) { \ | |
| EXPECT_EQ(summary.name.num_rows, 1); \ | |
| EXPECT_EQ(summary.name.num_cols, 1); \ | |
| EXPECT_EQ(summary.name.cols.size(), 2); \ | |
| EXPECT_EQ(summary.name.cols[0], 0); \ | |
| EXPECT_EQ(summary.name.cols[1], 1); \ | |
| EXPECT_EQ(summary.name.rows.size(), 1); \ | |
| EXPECT_EQ(summary.name.rows[0], 0); \ | |
| EXPECT_EQ(summary.name.values.size(), 0); \ | |
| EXPECT_EQ(summary.name.values[0], name); \ | |
| } else { \ | |
| EXPECT_EQ(summary.name.num_rows, 0); \ | |
| EXPECT_EQ(summary.name.num_cols, 0); \ | |
| EXPECT_EQ(summary.name.cols.size(), 0); \ | |
| EXPECT_EQ(summary.name.rows.size(), 0); \ | |
| EXPECT_EQ(summary.name.values.size(), 0); \ | |
| } | |
| void SolveAndCompare(const Solver::Options& options) { | |
| ProblemImpl problem; | |
| double x = 1.0; | |
| const double initial_residual = 5.0 - x; | |
| const double initial_jacobian = -1.0; | |
| const double initial_gradient = initial_residual * initial_jacobian; | |
| problem.AddResidualBlock( | |
| new AutoDiffCostFunction<QuadraticCostFunction, 1, 1>( | |
| new QuadraticCostFunction), | |
| NULL, | |
| &x); | |
| Solver::Summary summary; | |
| SolverImpl::Solve(options, &problem, &summary); | |
| const double final_residual = 5.0 - x; | |
| const double final_jacobian = -1.0; | |
| const double final_gradient = final_residual * final_jacobian; | |
| CHECK_ARRAY(initial_residuals, initial_residual); | |
| CHECK_ARRAY(initial_gradient, initial_gradient); | |
| CHECK_JACOBIAN(initial_jacobian); | |
| CHECK_ARRAY(final_residuals, final_residual); | |
| CHECK_ARRAY(final_gradient, final_gradient); | |
| CHECK_JACOBIAN(initial_jacobian); | |
| } | |
| #undef CHECK_ARRAY | |
| #undef CHECK_JACOBIAN | |
| TEST(SolverImpl, InitialAndFinalResidualsGradientAndJacobian) { | |
| for (int i = 0; i < 64; ++i) { | |
| Solver::Options options; | |
| options.return_initial_residuals = (i & 1); | |
| options.return_initial_gradient = (i & 2); | |
| options.return_initial_jacobian = (i & 4); | |
| options.return_final_residuals = (i & 8); | |
| options.return_final_gradient = (i & 16); | |
| options.return_final_jacobian = (i & 64); | |
| } | |
| } | |
| } // namespace internal | |
| } // namespace ceres |