mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 08:34:37 +08:00
Sameer Agarwal
5f433c8a22
CreateJacobianBlockSparsityTranspose starts with a conservative estimate of the size of the block sparsity pattern of the Jacobian. When the Jacobian has more non-zeros than that, the TripletSparseMatrix being used to store the sparsity has a Reallocate method which allows one to resize the matrix and IF num_nonzeros is set, then the existing values in the array are also copied into the newly allocated memory. Unfortunately the pattern we follow in ceres code is to call set_num_nonzeros after one is done populating the sparsity pattern of a matrix. This does not mix well with Reallocate and results in the matrix having uninitialized memory. This patch fixes this problem and adds a test that verifies the fix. Thanks to Yuliy Schwartzburg for reporting this bug and providing code to reproduce it. Change-Id: I58583714ffaebd880d85af16e3685b2d6ee053e8
996 lines
34 KiB
C++
996 lines
34 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/ordered_groups.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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// A cost function that sipmply returns its argument.
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class UnaryIdentityCostFunction : public SizedCostFunction<1, 1> {
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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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residuals[0] = parameters[0][0];
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if (jacobians != NULL && jacobians[0] != NULL) {
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jacobians[0][0] = 1.0;
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}
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return true;
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}
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};
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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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ParameterBlockOrdering ordering;
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ordering.AddElementToGroup(&x, 0);
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ordering.AddElementToGroup(&y, 0);
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ordering.AddElementToGroup(&z, 0);
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Program program(*problem.mutable_program());
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EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program,
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&ordering,
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NULL,
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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(ordering.NumElements(), 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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ParameterBlockOrdering ordering;
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ordering.AddElementToGroup(&x, 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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&ordering,
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NULL,
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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(ordering.NumElements(), 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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ParameterBlockOrdering ordering;
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ordering.AddElementToGroup(&x, 0);
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ordering.AddElementToGroup(&y, 0);
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ordering.AddElementToGroup(&z, 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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&ordering,
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NULL,
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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(ordering.NumElements(), 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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ParameterBlockOrdering ordering;
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ordering.AddElementToGroup(&x, 0);
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ordering.AddElementToGroup(&y, 0);
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ordering.AddElementToGroup(&z, 0);
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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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Program program(problem.program());
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string error;
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EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program,
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&ordering,
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NULL,
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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(ordering.NumElements(), 1);
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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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ParameterBlockOrdering ordering;
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ordering.AddElementToGroup(&x, 0);
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ordering.AddElementToGroup(&y, 0);
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ordering.AddElementToGroup(&z, 1);
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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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&ordering,
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NULL,
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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(ordering.NumElements(), 2);
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EXPECT_EQ(ordering.GroupId(&y), 0);
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EXPECT_EQ(ordering.GroupId(&z), 1);
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}
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TEST(SolverImpl, RemoveFixedBlocksFixedCost) {
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ProblemImpl problem;
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double x = 1.23;
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double y = 4.56;
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double z = 7.89;
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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 UnaryIdentityCostFunction(), 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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ParameterBlockOrdering ordering;
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ordering.AddElementToGroup(&x, 0);
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ordering.AddElementToGroup(&y, 0);
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ordering.AddElementToGroup(&z, 1);
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double fixed_cost = 0.0;
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Program program(problem.program());
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double expected_fixed_cost;
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ResidualBlock *expected_removed_block = program.residual_blocks()[0];
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scoped_array<double> scratch(
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new double[expected_removed_block->NumScratchDoublesForEvaluate()]);
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expected_removed_block->Evaluate(true,
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&expected_fixed_cost,
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NULL,
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NULL,
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scratch.get());
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string error;
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EXPECT_TRUE(SolverImpl::RemoveFixedBlocksFromProgram(&program,
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&ordering,
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&fixed_cost,
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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(ordering.NumElements(), 2);
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EXPECT_EQ(ordering.GroupId(&y), 0);
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EXPECT_EQ(ordering.GroupId(&z), 1);
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EXPECT_DOUBLE_EQ(fixed_cost, expected_fixed_cost);
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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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ParameterBlockOrdering* ordering = new ParameterBlockOrdering;
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ordering->AddElementToGroup(&x, 0);
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ordering->AddElementToGroup(&y, 0);
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ordering->AddElementToGroup(&z, 1);
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Solver::Options options;
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options.linear_solver_type = DENSE_SCHUR;
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options.linear_solver_ordering = ordering;
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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::LexicographicallyOrderResidualBlocks(
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2,
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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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ParameterBlockOrdering* ordering = new ParameterBlockOrdering;
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ordering->AddElementToGroup(&x, 0);
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ordering->AddElementToGroup(&z, 0);
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ordering->AddElementToGroup(&y, 1);
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Solver::Options options;
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options.linear_solver_type = DENSE_SCHUR;
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options.linear_solver_ordering = ordering;
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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, NULL, &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_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, AutomaticSchurReorderingRespectsConstantBlocks) {
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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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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 BinaryCostFunction(), NULL, &z, &y);
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problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &z);
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problem.AddResidualBlock(new BinaryCostFunction(), NULL, &z, &y);
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problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &z);
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problem.AddResidualBlock(new UnaryCostFunction(), NULL, &y);
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problem.AddResidualBlock(new UnaryCostFunction(), NULL, &z);
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ParameterBlockOrdering* ordering = new ParameterBlockOrdering;
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ordering->AddElementToGroup(&x, 0);
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ordering->AddElementToGroup(&z, 0);
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ordering->AddElementToGroup(&y, 0);
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Solver::Options options;
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options.linear_solver_type = DENSE_SCHUR;
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options.linear_solver_ordering = ordering;
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string error;
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scoped_ptr<Program> reduced_program(
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SolverImpl::CreateReducedProgram(&options, &problem, NULL, &error));
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const vector<ResidualBlock*>& residual_blocks =
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reduced_program->residual_blocks();
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const vector<ParameterBlock*>& parameter_blocks =
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reduced_program->parameter_blocks();
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const vector<ResidualBlock*>& original_residual_blocks =
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problem.program().residual_blocks();
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EXPECT_EQ(residual_blocks.size(), 8);
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EXPECT_EQ(reduced_program->parameter_blocks().size(), 2);
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// Verify that right parmeter block and the residual blocks have
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// been removed.
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for (int i = 0; i < 8; ++i) {
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EXPECT_NE(residual_blocks[i], original_residual_blocks.back());
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}
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for (int i = 0; i < 2; ++i) {
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EXPECT_NE(parameter_blocks[i]->mutable_user_state(), &z);
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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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ParameterBlockOrdering ordering;
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ordering.AddElementToGroup(&x, 0);
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ordering.AddElementToGroup(&y, 1);
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Program program(problem.program());
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string error;
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EXPECT_FALSE(SolverImpl::ApplyUserOrdering(problem.parameter_map(),
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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;
|
|
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;
|
|
// CreateLinearSolver assumes a non-empty ordering.
|
|
options.linear_solver_ordering = new ParameterBlockOrdering;
|
|
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, 2);
|
|
}
|
|
|
|
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());
|
|
}
|
|
|
|
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.");
|
|
}
|
|
|
|
|
|
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);
|
|
}
|
|
|
|
TEST(SolverImpl, AlternateLinearSolverForSchurTypeLinearSolver) {
|
|
Solver::Options options;
|
|
|
|
options.linear_solver_type = DENSE_QR;
|
|
SolverImpl::AlternateLinearSolverForSchurTypeLinearSolver(&options);
|
|
EXPECT_EQ(options.linear_solver_type, DENSE_QR);
|
|
|
|
options.linear_solver_type = DENSE_NORMAL_CHOLESKY;
|
|
SolverImpl::AlternateLinearSolverForSchurTypeLinearSolver(&options);
|
|
EXPECT_EQ(options.linear_solver_type, DENSE_NORMAL_CHOLESKY);
|
|
|
|
options.linear_solver_type = SPARSE_NORMAL_CHOLESKY;
|
|
SolverImpl::AlternateLinearSolverForSchurTypeLinearSolver(&options);
|
|
EXPECT_EQ(options.linear_solver_type, SPARSE_NORMAL_CHOLESKY);
|
|
|
|
options.linear_solver_type = CGNR;
|
|
SolverImpl::AlternateLinearSolverForSchurTypeLinearSolver(&options);
|
|
EXPECT_EQ(options.linear_solver_type, CGNR);
|
|
|
|
options.linear_solver_type = DENSE_SCHUR;
|
|
SolverImpl::AlternateLinearSolverForSchurTypeLinearSolver(&options);
|
|
EXPECT_EQ(options.linear_solver_type, DENSE_QR);
|
|
|
|
options.linear_solver_type = SPARSE_SCHUR;
|
|
SolverImpl::AlternateLinearSolverForSchurTypeLinearSolver(&options);
|
|
EXPECT_EQ(options.linear_solver_type, SPARSE_NORMAL_CHOLESKY);
|
|
|
|
options.linear_solver_type = ITERATIVE_SCHUR;
|
|
options.preconditioner_type = IDENTITY;
|
|
SolverImpl::AlternateLinearSolverForSchurTypeLinearSolver(&options);
|
|
EXPECT_EQ(options.linear_solver_type, CGNR);
|
|
EXPECT_EQ(options.preconditioner_type, IDENTITY);
|
|
|
|
options.linear_solver_type = ITERATIVE_SCHUR;
|
|
options.preconditioner_type = JACOBI;
|
|
SolverImpl::AlternateLinearSolverForSchurTypeLinearSolver(&options);
|
|
EXPECT_EQ(options.linear_solver_type, CGNR);
|
|
EXPECT_EQ(options.preconditioner_type, JACOBI);
|
|
|
|
options.linear_solver_type = ITERATIVE_SCHUR;
|
|
options.preconditioner_type = SCHUR_JACOBI;
|
|
SolverImpl::AlternateLinearSolverForSchurTypeLinearSolver(&options);
|
|
EXPECT_EQ(options.linear_solver_type, CGNR);
|
|
EXPECT_EQ(options.preconditioner_type, JACOBI);
|
|
|
|
options.linear_solver_type = ITERATIVE_SCHUR;
|
|
options.preconditioner_type = CLUSTER_JACOBI;
|
|
SolverImpl::AlternateLinearSolverForSchurTypeLinearSolver(&options);
|
|
EXPECT_EQ(options.linear_solver_type, CGNR);
|
|
EXPECT_EQ(options.preconditioner_type, JACOBI);
|
|
|
|
options.linear_solver_type = ITERATIVE_SCHUR;
|
|
options.preconditioner_type = CLUSTER_TRIDIAGONAL;
|
|
SolverImpl::AlternateLinearSolverForSchurTypeLinearSolver(&options);
|
|
EXPECT_EQ(options.linear_solver_type, CGNR);
|
|
EXPECT_EQ(options.preconditioner_type, JACOBI);
|
|
}
|
|
|
|
TEST(SolverImpl, CreateJacobianBlockSparsityTranspose) {
|
|
ProblemImpl problem;
|
|
double x[2];
|
|
double y[3];
|
|
double z;
|
|
|
|
problem.AddParameterBlock(x, 2);
|
|
problem.AddParameterBlock(y, 3);
|
|
problem.AddParameterBlock(&z, 1);
|
|
|
|
problem.AddResidualBlock(new MockCostFunctionBase<2, 2, 0, 0>(), NULL, x);
|
|
problem.AddResidualBlock(new MockCostFunctionBase<3, 1, 2, 0>(), NULL, &z, x);
|
|
problem.AddResidualBlock(new MockCostFunctionBase<4, 1, 3, 0>(), NULL, &z, y);
|
|
problem.AddResidualBlock(new MockCostFunctionBase<5, 1, 3, 0>(), NULL, &z, y);
|
|
problem.AddResidualBlock(new MockCostFunctionBase<1, 2, 1, 0>(), NULL, x, &z);
|
|
problem.AddResidualBlock(new MockCostFunctionBase<2, 1, 3, 0>(), NULL, &z, y);
|
|
problem.AddResidualBlock(new MockCostFunctionBase<2, 2, 1, 0>(), NULL, x, &z);
|
|
problem.AddResidualBlock(new MockCostFunctionBase<1, 3, 0, 0>(), NULL, y);
|
|
|
|
TripletSparseMatrix expected_block_sparse_jacobian(3, 8, 14);
|
|
{
|
|
int* rows = expected_block_sparse_jacobian.mutable_rows();
|
|
int* cols = expected_block_sparse_jacobian.mutable_cols();
|
|
double* values = expected_block_sparse_jacobian.mutable_values();
|
|
rows[0] = 0;
|
|
cols[0] = 0;
|
|
|
|
rows[1] = 2;
|
|
cols[1] = 1;
|
|
rows[2] = 0;
|
|
cols[2] = 1;
|
|
|
|
rows[3] = 2;
|
|
cols[3] = 2;
|
|
rows[4] = 1;
|
|
cols[4] = 2;
|
|
|
|
rows[5] = 2;
|
|
cols[5] = 3;
|
|
rows[6] = 1;
|
|
cols[6] = 3;
|
|
|
|
rows[7] = 0;
|
|
cols[7] = 4;
|
|
rows[8] = 2;
|
|
cols[8] = 4;
|
|
|
|
rows[9] = 2;
|
|
cols[9] = 5;
|
|
rows[10] = 1;
|
|
cols[10] = 5;
|
|
|
|
rows[11] = 0;
|
|
cols[11] = 6;
|
|
rows[12] = 2;
|
|
cols[12] = 6;
|
|
|
|
rows[13] = 1;
|
|
cols[13] = 7;
|
|
fill(values, values + 14, 1.0);
|
|
expected_block_sparse_jacobian.set_num_nonzeros(14);
|
|
}
|
|
|
|
Program* program = problem.mutable_program();
|
|
program->SetParameterOffsetsAndIndex();
|
|
|
|
scoped_ptr<TripletSparseMatrix> actual_block_sparse_jacobian(
|
|
SolverImpl::CreateJacobianBlockSparsityTranspose(program));
|
|
|
|
Matrix expected_dense_jacobian;
|
|
expected_block_sparse_jacobian.ToDenseMatrix(&expected_dense_jacobian);
|
|
|
|
Matrix actual_dense_jacobian;
|
|
actual_block_sparse_jacobian->ToDenseMatrix(&actual_dense_jacobian);
|
|
EXPECT_EQ((expected_dense_jacobian - actual_dense_jacobian).norm(), 0.0);
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}
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|
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template <int kNumResiduals, int kNumParameterBlocks>
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class NumParameterBlocksCostFunction : public CostFunction {
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public:
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|
NumParameterBlocksCostFunction() {
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set_num_residuals(kNumResiduals);
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|
for (int i = 0; i < kNumParameterBlocks; ++i) {
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|
mutable_parameter_block_sizes()->push_back(1);
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|
}
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|
}
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|
|
|
virtual ~NumParameterBlocksCostFunction() {
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|
}
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|
|
|
virtual bool Evaluate(double const* const* parameters,
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|
double* residuals,
|
|
double** jacobians) const {
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|
return true;
|
|
}
|
|
};
|
|
|
|
TEST(SolverImpl, ReallocationInCreateJacobianBlockSparsityTranspose) {
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|
// CreateJacobianBlockSparsityTranspose starts with a conservative
|
|
// estimate of the size of the sparsity pattern. This test ensures
|
|
// that when those estimates are violated, the reallocation/resizing
|
|
// logic works correctly.
|
|
|
|
ProblemImpl problem;
|
|
double x[20];
|
|
|
|
vector<double*> parameter_blocks;
|
|
for (int i = 0; i < 20; ++i) {
|
|
problem.AddParameterBlock(x + i, 1);
|
|
parameter_blocks.push_back(x + i);
|
|
}
|
|
|
|
problem.AddResidualBlock(new NumParameterBlocksCostFunction<1, 20>(),
|
|
NULL,
|
|
parameter_blocks);
|
|
|
|
TripletSparseMatrix expected_block_sparse_jacobian(20, 1, 20);
|
|
{
|
|
int* rows = expected_block_sparse_jacobian.mutable_rows();
|
|
int* cols = expected_block_sparse_jacobian.mutable_cols();
|
|
for (int i = 0; i < 20; ++i) {
|
|
rows[i] = i;
|
|
cols[i] = 0;
|
|
}
|
|
|
|
double* values = expected_block_sparse_jacobian.mutable_values();
|
|
fill(values, values + 20, 1.0);
|
|
expected_block_sparse_jacobian.set_num_nonzeros(20);
|
|
}
|
|
|
|
Program* program = problem.mutable_program();
|
|
program->SetParameterOffsetsAndIndex();
|
|
|
|
scoped_ptr<TripletSparseMatrix> actual_block_sparse_jacobian(
|
|
SolverImpl::CreateJacobianBlockSparsityTranspose(program));
|
|
|
|
Matrix expected_dense_jacobian;
|
|
expected_block_sparse_jacobian.ToDenseMatrix(&expected_dense_jacobian);
|
|
|
|
Matrix actual_dense_jacobian;
|
|
actual_block_sparse_jacobian->ToDenseMatrix(&actual_dense_jacobian);
|
|
EXPECT_EQ((expected_dense_jacobian - actual_dense_jacobian).norm(), 0.0);
|
|
}
|
|
|
|
} // namespace internal
|
|
} // namespace ceres
|