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// 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/ordered_groups.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;
{
ParameterBlockOrdering ordering;
ordering.AddElementToGroup(&x, 0);
ordering.AddElementToGroup(&y, 0);
ordering.AddElementToGroup(&z, 0);
Program program(*problem.mutable_program());
EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program,
&ordering,
NULL,
&error));
EXPECT_EQ(program.NumParameterBlocks(), 3);
EXPECT_EQ(program.NumResidualBlocks(), 3);
EXPECT_EQ(ordering.NumElements(), 3);
}
}
TEST(SolverImpl, RemoveFixedBlocksAllParameterBlocksConstant) {
ProblemImpl problem;
double x;
problem.AddParameterBlock(&x, 1);
problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
problem.SetParameterBlockConstant(&x);
ParameterBlockOrdering ordering;
ordering.AddElementToGroup(&x, 0);
Program program(problem.program());
string error;
EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program,
&ordering,
NULL,
&error));
EXPECT_EQ(program.NumParameterBlocks(), 0);
EXPECT_EQ(program.NumResidualBlocks(), 0);
EXPECT_EQ(ordering.NumElements(), 0);
}
TEST(SolverImpl, RemoveFixedBlocksNoResidualBlocks) {
ProblemImpl problem;
double x;
double y;
double z;
problem.AddParameterBlock(&x, 1);
problem.AddParameterBlock(&y, 1);
problem.AddParameterBlock(&z, 1);
ParameterBlockOrdering ordering;
ordering.AddElementToGroup(&x, 0);
ordering.AddElementToGroup(&y, 0);
ordering.AddElementToGroup(&z, 0);
Program program(problem.program());
string error;
EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program,
&ordering,
NULL,
&error));
EXPECT_EQ(program.NumParameterBlocks(), 0);
EXPECT_EQ(program.NumResidualBlocks(), 0);
EXPECT_EQ(ordering.NumElements(), 0);
}
TEST(SolverImpl, RemoveFixedBlocksOneParameterBlockConstant) {
ProblemImpl problem;
double x;
double y;
double z;
problem.AddParameterBlock(&x, 1);
problem.AddParameterBlock(&y, 1);
problem.AddParameterBlock(&z, 1);
ParameterBlockOrdering ordering;
ordering.AddElementToGroup(&x, 0);
ordering.AddElementToGroup(&y, 0);
ordering.AddElementToGroup(&z, 0);
problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y);
problem.SetParameterBlockConstant(&x);
Program program(problem.program());
string error;
EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program,
&ordering,
NULL,
&error));
EXPECT_EQ(program.NumParameterBlocks(), 1);
EXPECT_EQ(program.NumResidualBlocks(), 1);
EXPECT_EQ(ordering.NumElements(), 1);
}
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);
ParameterBlockOrdering ordering;
ordering.AddElementToGroup(&x, 0);
ordering.AddElementToGroup(&y, 0);
ordering.AddElementToGroup(&z, 1);
Program program(problem.program());
string error;
EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program,
&ordering,
NULL,
&error));
EXPECT_EQ(program.NumParameterBlocks(), 2);
EXPECT_EQ(program.NumResidualBlocks(), 2);
EXPECT_EQ(ordering.NumElements(), 2);
EXPECT_EQ(ordering.GroupId(&y), 0);
EXPECT_EQ(ordering.GroupId(&z), 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);
ParameterBlockOrdering ordering;
ordering.AddElementToGroup(&x, 0);
ordering.AddElementToGroup(&y, 0);
ordering.AddElementToGroup(&z, 1);
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,
&ordering,
&fixed_cost,
&error));
EXPECT_EQ(program.NumParameterBlocks(), 2);
EXPECT_EQ(program.NumResidualBlocks(), 2);
EXPECT_EQ(ordering.NumElements(), 2);
EXPECT_EQ(ordering.GroupId(&y), 0);
EXPECT_EQ(ordering.GroupId(&z), 1);
EXPECT_DOUBLE_EQ(fixed_cost, expected_fixed_cost);
}
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);
ParameterBlockOrdering* ordering = new ParameterBlockOrdering;
ordering->AddElementToGroup(&x, 0);
ordering->AddElementToGroup(&y, 0);
ordering->AddElementToGroup(&z, 1);
Solver::Options options;
options.linear_solver_type = DENSE_SCHUR;
options.linear_solver_ordering = ordering;
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::LexicographicallyOrderResidualBlocks(
2,
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
ParameterBlockOrdering* ordering = new ParameterBlockOrdering;
ordering->AddElementToGroup(&x, 0);
ordering->AddElementToGroup(&z, 0);
ordering->AddElementToGroup(&y, 1);
Solver::Options options;
options.linear_solver_type = DENSE_SCHUR;
options.linear_solver_ordering = ordering;
// 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::LexicographicallyOrderResidualBlocks(
2,
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, AutomaticSchurReorderingRespectsConstantBlocks) {
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);
problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
problem.AddResidualBlock(new BinaryCostFunction(), NULL, &z, &x);
problem.AddResidualBlock(new BinaryCostFunction(), NULL, &z, &y);
problem.AddResidualBlock(new BinaryCostFunction(), NULL, &z, &y);
problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &z);
problem.AddResidualBlock(new BinaryCostFunction(), NULL, &z, &y);
problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &z);
problem.AddResidualBlock(new UnaryCostFunction(), NULL, &y);
problem.AddResidualBlock(new UnaryCostFunction(), NULL, &z);
ParameterBlockOrdering* ordering = new ParameterBlockOrdering;
ordering->AddElementToGroup(&x, 0);
ordering->AddElementToGroup(&z, 0);
ordering->AddElementToGroup(&y, 0);
Solver::Options options;
options.linear_solver_type = DENSE_SCHUR;
options.linear_solver_ordering = ordering;
string error;
scoped_ptr<Program> reduced_program(
SolverImpl::CreateReducedProgram(&options, &problem, NULL, &error));
const vector<ResidualBlock*>& residual_blocks =
reduced_program->residual_blocks();
const vector<ParameterBlock*>& parameter_blocks =
reduced_program->parameter_blocks();
const vector<ResidualBlock*>& original_residual_blocks =
problem.program().residual_blocks();
EXPECT_EQ(residual_blocks.size(), 8);
EXPECT_EQ(reduced_program->parameter_blocks().size(), 2);
// Verify that right parmeter block and the residual blocks have
// been removed.
for (int i = 0; i < 8; ++i) {
EXPECT_NE(residual_blocks[i], original_residual_blocks.back());
}
for (int i = 0; i < 2; ++i) {
EXPECT_NE(parameter_blocks[i]->mutable_user_state(), &z);
}
}
TEST(SolverImpl, ApplyUserOrderingOrderingTooSmall) {
ProblemImpl problem;
double x;
double y;
double z;
problem.AddParameterBlock(&x, 1);
problem.AddParameterBlock(&y, 1);
problem.AddParameterBlock(&z, 1);
ParameterBlockOrdering ordering;
ordering.AddElementToGroup(&x, 0);
ordering.AddElementToGroup(&y, 1);
Program program(problem.program());
string error;
EXPECT_FALSE(SolverImpl::ApplyUserOrdering(problem.parameter_map(),
&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);
ParameterBlockOrdering ordering;
ordering.AddElementToGroup(&x, 0);
ordering.AddElementToGroup(&y, 2);
ordering.AddElementToGroup(&z, 1);
Program* program = problem.mutable_program();
string error;
EXPECT_TRUE(SolverImpl::ApplyUserOrdering(problem.parameter_map(),
&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;
// CreateLinearSolver assumes a non-empty ordering.
options.linear_solver_ordering = new ParameterBlockOrdering;
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;
// CreateLinearSolver assumes a non-empty ordering.
options.linear_solver_ordering = new ParameterBlockOrdering;
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;
options.linear_solver_ordering = new ParameterBlockOrdering;
string error;
EXPECT_EQ(SolverImpl::CreateLinearSolver(&options, &error),
static_cast<LinearSolver*>(NULL));
}
TEST(SolverImpl, CreateLinearSolverDenseSchurMultipleThreads) {
Solver::Options options;
options.linear_solver_type = DENSE_SCHUR;
options.num_linear_solver_threads = 2;
// The Schur type solvers can only be created with the Ordering
// contains at least one elimination group.
options.linear_solver_ordering = new ParameterBlockOrdering;
double x;
double y;
options.linear_solver_ordering->AddElementToGroup(&x, 0);
options.linear_solver_ordering->AddElementToGroup(&y, 0);
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;
// CreateLinearSolver assumes a non-empty ordering.
options.linear_solver_ordering = new ParameterBlockOrdering;
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;
// CreateLinearSolver assumes a non-empty ordering.
options.linear_solver_ordering = new ParameterBlockOrdering;
string error;
solver.reset(SolverImpl::CreateLinearSolver(&options, &error));
EXPECT_EQ(options.linear_solver_type, DENSE_QR);
EXPECT_TRUE(solver.get() != NULL);
options.linear_solver_type = DENSE_NORMAL_CHOLESKY;
solver.reset(SolverImpl::CreateLinearSolver(&options, &error));
EXPECT_EQ(options.linear_solver_type, DENSE_NORMAL_CHOLESKY);
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
double x;
double y;
options.linear_solver_ordering->AddElementToGroup(&x, 0);
options.linear_solver_ordering->AddElementToGroup(&y, 0);
options.linear_solver_type = DENSE_SCHUR;
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;
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;
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);
}
}
TEST(SolverImpl, NoParameterBlocks) {
ProblemImpl problem_impl;
Solver::Options options;
Solver::Summary summary;
SolverImpl::Solve(options, &problem_impl, &summary);
EXPECT_EQ(summary.termination_type, DID_NOT_RUN);
EXPECT_EQ(summary.error, "Problem contains no parameter blocks.");
}
TEST(SolverImpl, NoResiduals) {
ProblemImpl problem_impl;
Solver::Options options;
Solver::Summary summary;
double x = 1;
problem_impl.AddParameterBlock(&x, 1);
SolverImpl::Solve(options, &problem_impl, &summary);
EXPECT_EQ(summary.termination_type, DID_NOT_RUN);
EXPECT_EQ(summary.error, "Problem contains no residual blocks.");
}
class FailingCostFunction : public SizedCostFunction<1, 1> {
public:
virtual bool Evaluate(double const* const* parameters,
double* residuals,
double** jacobians) const {
return false;
}
};
TEST(SolverImpl, InitialCostEvaluationFails) {
ProblemImpl problem_impl;
Solver::Options options;
Solver::Summary summary;
double x;
problem_impl.AddResidualBlock(new FailingCostFunction, NULL, &x);
SolverImpl::Solve(options, &problem_impl, &summary);
EXPECT_EQ(summary.termination_type, NUMERICAL_FAILURE);
EXPECT_EQ(summary.error, "Unable to evaluate the initial cost.");
}
TEST(SolverImpl, ProblemIsConstant) {
ProblemImpl problem_impl;
Solver::Options options;
Solver::Summary summary;
double x = 1;
problem_impl.AddResidualBlock(new UnaryIdentityCostFunction, NULL, &x);
problem_impl.SetParameterBlockConstant(&x);
SolverImpl::Solve(options, &problem_impl, &summary);
EXPECT_EQ(summary.termination_type, FUNCTION_TOLERANCE);
EXPECT_EQ(summary.initial_cost, 1.0 / 2.0);
EXPECT_EQ(summary.final_cost, 1.0 / 2.0);
}
} // namespace internal
} // namespace ceres