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ceres-solver/internal/ceres/visibility_based_preconditioner_test.cc
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Alex Stewart ea76585068 Adding autogenerated Ceres config.h to #define Ceres compile options. 
- Previously we passed all compile options to Ceres via add_definitions
  in CMake.  This was fine for private definitions (used only by Ceres)
  but required additional work for public definitions to ensure they
  were correctly propagated to clients via CMake using
  target_compile_definitions() (>= 2.8.11) or add_definitions().
- A drawback to these approaches is that they did not work for chained
  dependencies on Ceres, as in if in the users project B <- A <- Ceres,
  then although the required Ceres public compile definitions would
  be used when compiling A, they would not be propagated to B.

- This patch replaces the addition of compile definitions via
  add_definitions() with an autogenerated config.h header which
  is installed with Ceres and defines all of the enabled Ceres compile
  options.
- This removes the need for the user to propagate any compile
  definitions in their projects, and additionally allows post-install
  inspect of the options with which Ceres was compiled.

Change-Id: Idbdb6abdad0eb31e7540370e301afe87a07f2260
2014-05-09 10:57:31 +01:00

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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)
// This include must come before any #ifndef check on Ceres compile options.
#include "ceres/internal/port.h"
#ifndef CERES_NO_SUITESPARSE
#include "ceres/visibility_based_preconditioner.h"
#include "Eigen/Dense"
#include "ceres/block_random_access_dense_matrix.h"
#include "ceres/block_random_access_sparse_matrix.h"
#include "ceres/block_sparse_matrix.h"
#include "ceres/casts.h"
#include "ceres/collections_port.h"
#include "ceres/file.h"
#include "ceres/internal/eigen.h"
#include "ceres/internal/scoped_ptr.h"
#include "ceres/linear_least_squares_problems.h"
#include "ceres/schur_eliminator.h"
#include "ceres/stringprintf.h"
#include "ceres/types.h"
#include "ceres/test_util.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
namespace ceres {
namespace internal {
// TODO(sameeragarwal): Re-enable this test once serialization is
// working again.
// using testing::AssertionResult;
// using testing::AssertionSuccess;
// using testing::AssertionFailure;
// static const double kTolerance = 1e-12;
// class VisibilityBasedPreconditionerTest : public ::testing::Test {
// public:
// static const int kCameraSize = 9;
// protected:
// void SetUp() {
// string input_file = TestFileAbsolutePath("problem-6-1384-000.lsqp");
// scoped_ptr<LinearLeastSquaresProblem> problem(
// CHECK_NOTNULL(CreateLinearLeastSquaresProblemFromFile(input_file)));
// A_.reset(down_cast<BlockSparseMatrix*>(problem->A.release()));
// b_.reset(problem->b.release());
// D_.reset(problem->D.release());
// const CompressedRowBlockStructure* bs =
// CHECK_NOTNULL(A_->block_structure());
// const int num_col_blocks = bs->cols.size();
// num_cols_ = A_->num_cols();
// num_rows_ = A_->num_rows();
// num_eliminate_blocks_ = problem->num_eliminate_blocks;
// num_camera_blocks_ = num_col_blocks - num_eliminate_blocks_;
// options_.elimination_groups.push_back(num_eliminate_blocks_);
// options_.elimination_groups.push_back(
// A_->block_structure()->cols.size() - num_eliminate_blocks_);
// vector<int> blocks(num_col_blocks - num_eliminate_blocks_, 0);
// for (int i = num_eliminate_blocks_; i < num_col_blocks; ++i) {
// blocks[i - num_eliminate_blocks_] = bs->cols[i].size;
// }
// // The input matrix is a real jacobian and fairly poorly
// // conditioned. Setting D to a large constant makes the normal
// // equations better conditioned and makes the tests below better
// // conditioned.
// VectorRef(D_.get(), num_cols_).setConstant(10.0);
// schur_complement_.reset(new BlockRandomAccessDenseMatrix(blocks));
// Vector rhs(schur_complement_->num_rows());
// scoped_ptr<SchurEliminatorBase> eliminator;
// LinearSolver::Options eliminator_options;
// eliminator_options.elimination_groups = options_.elimination_groups;
// eliminator_options.num_threads = options_.num_threads;
// eliminator.reset(SchurEliminatorBase::Create(eliminator_options));
// eliminator->Init(num_eliminate_blocks_, bs);
// eliminator->Eliminate(A_.get(), b_.get(), D_.get(),
// schur_complement_.get(), rhs.data());
// }
// AssertionResult IsSparsityStructureValid() {
// preconditioner_->InitStorage(*A_->block_structure());
// const HashSet<pair<int, int> >& cluster_pairs = get_cluster_pairs();
// const vector<int>& cluster_membership = get_cluster_membership();
// for (int i = 0; i < num_camera_blocks_; ++i) {
// for (int j = i; j < num_camera_blocks_; ++j) {
// if (cluster_pairs.count(make_pair(cluster_membership[i],
// cluster_membership[j]))) {
// if (!IsBlockPairInPreconditioner(i, j)) {
// return AssertionFailure()
// << "block pair (" << i << "," << j << "missing";
// }
// } else {
// if (IsBlockPairInPreconditioner(i, j)) {
// return AssertionFailure()
// << "block pair (" << i << "," << j << "should not be present";
// }
// }
// }
// }
// return AssertionSuccess();
// }
// AssertionResult PreconditionerValuesMatch() {
// preconditioner_->Update(*A_, D_.get());
// const HashSet<pair<int, int> >& cluster_pairs = get_cluster_pairs();
// const BlockRandomAccessSparseMatrix* m = get_m();
// Matrix preconditioner_matrix;
// m->matrix()->ToDenseMatrix(&preconditioner_matrix);
// ConstMatrixRef full_schur_complement(schur_complement_->values(),
// m->num_rows(),
// m->num_rows());
// const int num_clusters = get_num_clusters();
// const int kDiagonalBlockSize =
// kCameraSize * num_camera_blocks_ / num_clusters;
// for (int i = 0; i < num_clusters; ++i) {
// for (int j = i; j < num_clusters; ++j) {
// double diff = 0.0;
// if (cluster_pairs.count(make_pair(i, j))) {
// diff =
// (preconditioner_matrix.block(kDiagonalBlockSize * i,
// kDiagonalBlockSize * j,
// kDiagonalBlockSize,
// kDiagonalBlockSize) -
// full_schur_complement.block(kDiagonalBlockSize * i,
// kDiagonalBlockSize * j,
// kDiagonalBlockSize,
// kDiagonalBlockSize)).norm();
// } else {
// diff = preconditioner_matrix.block(kDiagonalBlockSize * i,
// kDiagonalBlockSize * j,
// kDiagonalBlockSize,
// kDiagonalBlockSize).norm();
// }
// if (diff > kTolerance) {
// return AssertionFailure()
// << "Preconditioner block " << i << " " << j << " differs "
// << "from expected value by " << diff;
// }
// }
// }
// return AssertionSuccess();
// }
// // Accessors
// int get_num_blocks() { return preconditioner_->num_blocks_; }
// int get_num_clusters() { return preconditioner_->num_clusters_; }
// int* get_mutable_num_clusters() { return &preconditioner_->num_clusters_; }
// const vector<int>& get_block_size() {
// return preconditioner_->block_size_; }
// vector<int>* get_mutable_block_size() {
// return &preconditioner_->block_size_; }
// const vector<int>& get_cluster_membership() {
// return preconditioner_->cluster_membership_;
// }
// vector<int>* get_mutable_cluster_membership() {
// return &preconditioner_->cluster_membership_;
// }
// const set<pair<int, int> >& get_block_pairs() {
// return preconditioner_->block_pairs_;
// }
// set<pair<int, int> >* get_mutable_block_pairs() {
// return &preconditioner_->block_pairs_;
// }
// const HashSet<pair<int, int> >& get_cluster_pairs() {
// return preconditioner_->cluster_pairs_;
// }
// HashSet<pair<int, int> >* get_mutable_cluster_pairs() {
// return &preconditioner_->cluster_pairs_;
// }
// bool IsBlockPairInPreconditioner(const int block1, const int block2) {
// return preconditioner_->IsBlockPairInPreconditioner(block1, block2);
// }
// bool IsBlockPairOffDiagonal(const int block1, const int block2) {
// return preconditioner_->IsBlockPairOffDiagonal(block1, block2);
// }
// const BlockRandomAccessSparseMatrix* get_m() {
// return preconditioner_->m_.get();
// }
// int num_rows_;
// int num_cols_;
// int num_eliminate_blocks_;
// int num_camera_blocks_;
// scoped_ptr<BlockSparseMatrix> A_;
// scoped_array<double> b_;
// scoped_array<double> D_;
// Preconditioner::Options options_;
// scoped_ptr<VisibilityBasedPreconditioner> preconditioner_;
// scoped_ptr<BlockRandomAccessDenseMatrix> schur_complement_;
// };
// TEST_F(VisibilityBasedPreconditionerTest, OneClusterClusterJacobi) {
// options_.type = CLUSTER_JACOBI;
// preconditioner_.reset(
// new VisibilityBasedPreconditioner(*A_->block_structure(), options_));
// // Override the clustering to be a single clustering containing all
// // the cameras.
// vector<int>& cluster_membership = *get_mutable_cluster_membership();
// for (int i = 0; i < num_camera_blocks_; ++i) {
// cluster_membership[i] = 0;
// }
// *get_mutable_num_clusters() = 1;
// HashSet<pair<int, int> >& cluster_pairs = *get_mutable_cluster_pairs();
// cluster_pairs.clear();
// cluster_pairs.insert(make_pair(0, 0));
// EXPECT_TRUE(IsSparsityStructureValid());
// EXPECT_TRUE(PreconditionerValuesMatch());
// // Multiplication by the inverse of the preconditioner.
// const int num_rows = schur_complement_->num_rows();
// ConstMatrixRef full_schur_complement(schur_complement_->values(),
// num_rows,
// num_rows);
// Vector x(num_rows);
// Vector y(num_rows);
// Vector z(num_rows);
// for (int i = 0; i < num_rows; ++i) {
// x.setZero();
// y.setZero();
// z.setZero();
// x[i] = 1.0;
// preconditioner_->RightMultiply(x.data(), y.data());
// z = full_schur_complement
// .selfadjointView<Eigen::Upper>()
// .llt().solve(x);
// double max_relative_difference =
// ((y - z).array() / z.array()).matrix().lpNorm<Eigen::Infinity>();
// EXPECT_NEAR(max_relative_difference, 0.0, kTolerance);
// }
// }
// TEST_F(VisibilityBasedPreconditionerTest, ClusterJacobi) {
// options_.type = CLUSTER_JACOBI;
// preconditioner_.reset(
// new VisibilityBasedPreconditioner(*A_->block_structure(), options_));
// // Override the clustering to be equal number of cameras.
// vector<int>& cluster_membership = *get_mutable_cluster_membership();
// cluster_membership.resize(num_camera_blocks_);
// static const int kNumClusters = 3;
// for (int i = 0; i < num_camera_blocks_; ++i) {
// cluster_membership[i] = (i * kNumClusters) / num_camera_blocks_;
// }
// *get_mutable_num_clusters() = kNumClusters;
// HashSet<pair<int, int> >& cluster_pairs = *get_mutable_cluster_pairs();
// cluster_pairs.clear();
// for (int i = 0; i < kNumClusters; ++i) {
// cluster_pairs.insert(make_pair(i, i));
// }
// EXPECT_TRUE(IsSparsityStructureValid());
// EXPECT_TRUE(PreconditionerValuesMatch());
// }
// TEST_F(VisibilityBasedPreconditionerTest, ClusterTridiagonal) {
// options_.type = CLUSTER_TRIDIAGONAL;
// preconditioner_.reset(
// new VisibilityBasedPreconditioner(*A_->block_structure(), options_));
// static const int kNumClusters = 3;
// // Override the clustering to be 3 clusters.
// vector<int>& cluster_membership = *get_mutable_cluster_membership();
// cluster_membership.resize(num_camera_blocks_);
// for (int i = 0; i < num_camera_blocks_; ++i) {
// cluster_membership[i] = (i * kNumClusters) / num_camera_blocks_;
// }
// *get_mutable_num_clusters() = kNumClusters;
// // Spanning forest has structure 0-1 2
// HashSet<pair<int, int> >& cluster_pairs = *get_mutable_cluster_pairs();
// cluster_pairs.clear();
// for (int i = 0; i < kNumClusters; ++i) {
// cluster_pairs.insert(make_pair(i, i));
// }
// cluster_pairs.insert(make_pair(0, 1));
// EXPECT_TRUE(IsSparsityStructureValid());
// EXPECT_TRUE(PreconditionerValuesMatch());
// }
} // namespace internal
} // namespace ceres
#endif // CERES_NO_SUITESPARSE
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