mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 16:40:38 +08:00
Keir Mierle
efe7ac60a0
This is a preliminary, but full, port of Ceres to Windows. Currently all tests compile and run, with only system_test failing to work correctly due to a path issue. Change-Id: I4152c1588bf51ffd7f4d9401ef9759f5d28c299c
691 lines
24 KiB
C++
691 lines
24 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
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// http://code.google.com/p/ceres-solver/
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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 "gtest/gtest.h"
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#include "ceres/autodiff_cost_function.h"
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#include "ceres/linear_solver.h"
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#include "ceres/parameter_block.h"
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#include "ceres/problem_impl.h"
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#include "ceres/program.h"
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#include "ceres/residual_block.h"
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#include "ceres/solver_impl.h"
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#include "ceres/sized_cost_function.h"
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namespace ceres {
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namespace internal {
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// Templated base class for the CostFunction signatures.
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template <int kNumResiduals, int N0, int N1, int N2>
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class MockCostFunctionBase : public
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SizedCostFunction<kNumResiduals, N0, N1, N2> {
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public:
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virtual bool Evaluate(double const* const* parameters,
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double* residuals,
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double** jacobians) const {
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// Do nothing. This is never called.
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return true;
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}
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};
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class UnaryCostFunction : public MockCostFunctionBase<2, 1, 0, 0> {};
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class BinaryCostFunction : public MockCostFunctionBase<2, 1, 1, 0> {};
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class TernaryCostFunction : public MockCostFunctionBase<2, 1, 1, 1> {};
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TEST(SolverImpl, RemoveFixedBlocksNothingConstant) {
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ProblemImpl problem;
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double x;
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double y;
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double z;
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problem.AddParameterBlock(&x, 1);
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problem.AddParameterBlock(&y, 1);
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problem.AddParameterBlock(&z, 1);
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problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
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problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y);
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problem.AddResidualBlock(new TernaryCostFunction(), NULL, &x, &y, &z);
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string error;
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{
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int num_eliminate_blocks = 0;
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Program program(*problem.mutable_program());
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EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program,
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&num_eliminate_blocks,
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&error));
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EXPECT_EQ(program.NumParameterBlocks(), 3);
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EXPECT_EQ(program.NumResidualBlocks(), 3);
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EXPECT_EQ(num_eliminate_blocks, 0);
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}
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// Check that num_eliminate_blocks is preserved, when it contains
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// all blocks.
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{
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int num_eliminate_blocks = 3;
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Program program(problem.program());
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EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program,
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&num_eliminate_blocks,
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&error));
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EXPECT_EQ(program.NumParameterBlocks(), 3);
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EXPECT_EQ(program.NumResidualBlocks(), 3);
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EXPECT_EQ(num_eliminate_blocks, 3);
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}
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}
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TEST(SolverImpl, RemoveFixedBlocksAllParameterBlocksConstant) {
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ProblemImpl problem;
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double x;
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problem.AddParameterBlock(&x, 1);
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problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
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problem.SetParameterBlockConstant(&x);
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int num_eliminate_blocks = 0;
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Program program(problem.program());
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string error;
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EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program,
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&num_eliminate_blocks,
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&error));
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EXPECT_EQ(program.NumParameterBlocks(), 0);
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EXPECT_EQ(program.NumResidualBlocks(), 0);
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EXPECT_EQ(num_eliminate_blocks, 0);
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}
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TEST(SolverImpl, RemoveFixedBlocksNoResidualBlocks) {
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ProblemImpl problem;
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double x;
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double y;
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double z;
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problem.AddParameterBlock(&x, 1);
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problem.AddParameterBlock(&y, 1);
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problem.AddParameterBlock(&z, 1);
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int num_eliminate_blocks = 0;
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Program program(problem.program());
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string error;
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EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program,
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&num_eliminate_blocks,
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&error));
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EXPECT_EQ(program.NumParameterBlocks(), 0);
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EXPECT_EQ(program.NumResidualBlocks(), 0);
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EXPECT_EQ(num_eliminate_blocks, 0);
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}
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TEST(SolverImpl, RemoveFixedBlocksOneParameterBlockConstant) {
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ProblemImpl problem;
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double x;
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double y;
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double z;
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problem.AddParameterBlock(&x, 1);
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problem.AddParameterBlock(&y, 1);
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problem.AddParameterBlock(&z, 1);
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problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
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problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y);
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problem.SetParameterBlockConstant(&x);
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int num_eliminate_blocks = 0;
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Program program(problem.program());
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string error;
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EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program,
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&num_eliminate_blocks,
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&error));
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EXPECT_EQ(program.NumParameterBlocks(), 1);
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EXPECT_EQ(program.NumResidualBlocks(), 1);
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EXPECT_EQ(num_eliminate_blocks, 0);
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}
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TEST(SolverImpl, RemoveFixedBlocksNumEliminateBlocks) {
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ProblemImpl problem;
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double x;
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double y;
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double z;
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problem.AddParameterBlock(&x, 1);
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problem.AddParameterBlock(&y, 1);
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problem.AddParameterBlock(&z, 1);
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problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
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problem.AddResidualBlock(new TernaryCostFunction(), NULL, &x, &y, &z);
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problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y);
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problem.SetParameterBlockConstant(&x);
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int num_eliminate_blocks = 2;
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Program program(problem.program());
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string error;
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EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program,
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&num_eliminate_blocks,
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&error));
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EXPECT_EQ(program.NumParameterBlocks(), 2);
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EXPECT_EQ(program.NumResidualBlocks(), 2);
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EXPECT_EQ(num_eliminate_blocks, 1);
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}
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TEST(SolverImpl, ReorderResidualBlockNonSchurSolver) {
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ProblemImpl problem;
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double x;
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double y;
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double z;
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problem.AddParameterBlock(&x, 1);
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problem.AddParameterBlock(&y, 1);
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problem.AddParameterBlock(&z, 1);
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problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
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problem.AddResidualBlock(new TernaryCostFunction(), NULL, &x, &y, &z);
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problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y);
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const vector<ResidualBlock*>& residual_blocks =
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problem.program().residual_blocks();
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vector<ResidualBlock*> current_residual_blocks(residual_blocks);
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Solver::Options options;
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options.linear_solver_type = SPARSE_NORMAL_CHOLESKY;
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string error;
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EXPECT_TRUE(SolverImpl::MaybeReorderResidualBlocks(options,
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problem.mutable_program(),
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&error));
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EXPECT_EQ(current_residual_blocks.size(), residual_blocks.size());
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for (int i = 0; i < current_residual_blocks.size(); ++i) {
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EXPECT_EQ(current_residual_blocks[i], residual_blocks[i]);
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}
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}
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TEST(SolverImpl, ReorderResidualBlockNumEliminateBlockDeathTest) {
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ProblemImpl problem;
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double x;
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double y;
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double z;
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problem.AddParameterBlock(&x, 1);
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problem.AddParameterBlock(&y, 1);
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problem.AddParameterBlock(&z, 1);
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problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
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problem.AddResidualBlock(new TernaryCostFunction(), NULL, &x, &y, &z);
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problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y);
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Solver::Options options;
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options.linear_solver_type = DENSE_SCHUR;
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options.num_eliminate_blocks = 0;
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string error;
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#ifndef _WIN32
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EXPECT_DEATH(
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SolverImpl::MaybeReorderResidualBlocks(
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options, problem.mutable_program(), &error),
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"Congratulations");
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#endif // _WIN32
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}
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TEST(SolverImpl, ReorderResidualBlockNormalFunction) {
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ProblemImpl problem;
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double x;
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double y;
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double z;
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problem.AddParameterBlock(&x, 1);
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problem.AddParameterBlock(&y, 1);
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problem.AddParameterBlock(&z, 1);
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problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
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problem.AddResidualBlock(new BinaryCostFunction(), NULL, &z, &x);
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problem.AddResidualBlock(new BinaryCostFunction(), NULL, &z, &y);
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problem.AddResidualBlock(new UnaryCostFunction(), NULL, &z);
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problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y);
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problem.AddResidualBlock(new UnaryCostFunction(), NULL, &y);
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Solver::Options options;
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options.linear_solver_type = DENSE_SCHUR;
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options.num_eliminate_blocks = 2;
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const vector<ResidualBlock*>& residual_blocks =
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problem.program().residual_blocks();
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vector<ResidualBlock*> expected_residual_blocks;
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// This is a bit fragile, but it serves the purpose. We know the
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// bucketing algorithm that the reordering function uses, so we
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// expect the order for residual blocks for each e_block to be
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// filled in reverse.
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expected_residual_blocks.push_back(residual_blocks[4]);
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expected_residual_blocks.push_back(residual_blocks[1]);
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expected_residual_blocks.push_back(residual_blocks[0]);
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expected_residual_blocks.push_back(residual_blocks[5]);
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expected_residual_blocks.push_back(residual_blocks[2]);
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expected_residual_blocks.push_back(residual_blocks[3]);
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Program* program = problem.mutable_program();
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program->SetParameterOffsetsAndIndex();
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string error;
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EXPECT_TRUE(SolverImpl::MaybeReorderResidualBlocks(options,
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problem.mutable_program(),
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&error));
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EXPECT_EQ(residual_blocks.size(), expected_residual_blocks.size());
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for (int i = 0; i < expected_residual_blocks.size(); ++i) {
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EXPECT_EQ(residual_blocks[i], expected_residual_blocks[i]);
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}
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}
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TEST(SolverImpl, ReorderResidualBlockNormalFunctionWithFixedBlocks) {
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ProblemImpl problem;
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double x;
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double y;
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double z;
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problem.AddParameterBlock(&x, 1);
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problem.AddParameterBlock(&y, 1);
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problem.AddParameterBlock(&z, 1);
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// Set one parameter block constant.
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problem.SetParameterBlockConstant(&z);
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// Mark residuals for x's row block with "x" for readability.
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problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x); // 0 x
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problem.AddResidualBlock(new BinaryCostFunction(), NULL, &z, &x); // 1 x
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problem.AddResidualBlock(new BinaryCostFunction(), NULL, &z, &y); // 2
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problem.AddResidualBlock(new BinaryCostFunction(), NULL, &z, &y); // 3
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problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &z); // 4 x
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problem.AddResidualBlock(new BinaryCostFunction(), NULL, &z, &y); // 5
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problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &z); // 6 x
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problem.AddResidualBlock(new UnaryCostFunction(), NULL, &y); // 7
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Solver::Options options;
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options.linear_solver_type = DENSE_SCHUR;
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options.num_eliminate_blocks = 2;
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// Create the reduced program. This should remove the fixed block "z",
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// marking the index to -1 at the same time. x and y also get indices.
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string error;
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scoped_ptr<Program> reduced_program(
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SolverImpl::CreateReducedProgram(&options, &problem, &error));
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const vector<ResidualBlock*>& residual_blocks =
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problem.program().residual_blocks();
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// This is a bit fragile, but it serves the purpose. We know the
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// bucketing algorithm that the reordering function uses, so we
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// expect the order for residual blocks for each e_block to be
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// filled in reverse.
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vector<ResidualBlock*> expected_residual_blocks;
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// Row block for residuals involving "x". These are marked "x" in the block
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// of code calling AddResidual() above.
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expected_residual_blocks.push_back(residual_blocks[6]);
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expected_residual_blocks.push_back(residual_blocks[4]);
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expected_residual_blocks.push_back(residual_blocks[1]);
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expected_residual_blocks.push_back(residual_blocks[0]);
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// Row block for residuals involving "y".
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expected_residual_blocks.push_back(residual_blocks[7]);
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expected_residual_blocks.push_back(residual_blocks[5]);
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expected_residual_blocks.push_back(residual_blocks[3]);
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expected_residual_blocks.push_back(residual_blocks[2]);
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EXPECT_TRUE(SolverImpl::MaybeReorderResidualBlocks(options,
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reduced_program.get(),
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&error));
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EXPECT_EQ(reduced_program->residual_blocks().size(),
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expected_residual_blocks.size());
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for (int i = 0; i < expected_residual_blocks.size(); ++i) {
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EXPECT_EQ(reduced_program->residual_blocks()[i],
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expected_residual_blocks[i]);
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}
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}
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TEST(SolverImpl, ApplyUserOrderingOrderingTooSmall) {
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ProblemImpl problem;
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double x;
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double y;
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double z;
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problem.AddParameterBlock(&x, 1);
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problem.AddParameterBlock(&y, 1);
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problem.AddParameterBlock(&z, 1);
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vector<double*> ordering;
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ordering.push_back(&x);
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ordering.push_back(&z);
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Program program(problem.program());
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string error;
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EXPECT_FALSE(SolverImpl::ApplyUserOrdering(problem,
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ordering,
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&program,
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&error));
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}
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TEST(SolverImpl, ApplyUserOrderingHasDuplicates) {
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ProblemImpl problem;
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double x;
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double y;
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double z;
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problem.AddParameterBlock(&x, 1);
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problem.AddParameterBlock(&y, 1);
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problem.AddParameterBlock(&z, 1);
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vector<double*> ordering;
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ordering.push_back(&x);
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ordering.push_back(&z);
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ordering.push_back(&z);
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Program program(problem.program());
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string error;
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EXPECT_FALSE(SolverImpl::ApplyUserOrdering(problem,
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ordering,
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&program,
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&error));
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}
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TEST(SolverImpl, ApplyUserOrderingNormal) {
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ProblemImpl problem;
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double x;
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double y;
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double z;
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problem.AddParameterBlock(&x, 1);
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problem.AddParameterBlock(&y, 1);
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problem.AddParameterBlock(&z, 1);
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vector<double*> ordering;
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ordering.push_back(&x);
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ordering.push_back(&z);
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ordering.push_back(&y);
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Program* program = problem.mutable_program();
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string error;
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EXPECT_TRUE(SolverImpl::ApplyUserOrdering(problem,
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ordering,
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program,
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&error));
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const vector<ParameterBlock*>& parameter_blocks = program->parameter_blocks();
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EXPECT_EQ(parameter_blocks.size(), 3);
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EXPECT_EQ(parameter_blocks[0]->user_state(), &x);
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EXPECT_EQ(parameter_blocks[1]->user_state(), &z);
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EXPECT_EQ(parameter_blocks[2]->user_state(), &y);
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}
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#if defined(CERES_NO_SUITESPARSE) && defined(CERES_NO_CXSPARSE)
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TEST(SolverImpl, CreateLinearSolverNoSuiteSparse) {
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Solver::Options options;
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options.linear_solver_type = SPARSE_NORMAL_CHOLESKY;
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string error;
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EXPECT_FALSE(SolverImpl::CreateLinearSolver(&options, &error));
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}
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#endif
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TEST(SolverImpl, CreateLinearSolverNegativeMaxNumIterations) {
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Solver::Options options;
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options.linear_solver_type = DENSE_QR;
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options.linear_solver_max_num_iterations = -1;
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string error;
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EXPECT_EQ(SolverImpl::CreateLinearSolver(&options, &error),
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static_cast<LinearSolver*>(NULL));
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}
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TEST(SolverImpl, CreateLinearSolverNegativeMinNumIterations) {
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Solver::Options options;
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options.linear_solver_type = DENSE_QR;
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options.linear_solver_min_num_iterations = -1;
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string error;
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EXPECT_EQ(SolverImpl::CreateLinearSolver(&options, &error),
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static_cast<LinearSolver*>(NULL));
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}
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TEST(SolverImpl, CreateLinearSolverMaxLessThanMinIterations) {
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Solver::Options options;
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options.linear_solver_type = DENSE_QR;
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options.linear_solver_min_num_iterations = 10;
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options.linear_solver_max_num_iterations = 5;
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string error;
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EXPECT_EQ(SolverImpl::CreateLinearSolver(&options, &error),
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static_cast<LinearSolver*>(NULL));
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}
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TEST(SolverImpl, CreateLinearSolverZeroNumEliminateBlocks) {
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Solver::Options options;
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options.num_eliminate_blocks = 0;
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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
|