visibility_based_preconditioner_test.cc revision 79397c21138f54fcff6ec067b44b847f1f7e0e98
1// Ceres Solver - A fast non-linear least squares minimizer 2// Copyright 2010, 2011, 2012 Google Inc. All rights reserved. 3// http://code.google.com/p/ceres-solver/ 4// 5// Redistribution and use in source and binary forms, with or without 6// modification, are permitted provided that the following conditions are met: 7// 8// * Redistributions of source code must retain the above copyright notice, 9// this list of conditions and the following disclaimer. 10// * Redistributions in binary form must reproduce the above copyright notice, 11// this list of conditions and the following disclaimer in the documentation 12// and/or other materials provided with the distribution. 13// * Neither the name of Google Inc. nor the names of its contributors may be 14// used to endorse or promote products derived from this software without 15// specific prior written permission. 16// 17// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 18// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 19// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 20// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE 21// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR 22// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF 23// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS 24// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 25// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) 26// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 27// POSSIBILITY OF SUCH DAMAGE. 28// 29// Author: sameeragarwal@google.com (Sameer Agarwal) 30 31// This include must come before any #ifndef check on Ceres compile options. 32#include "ceres/internal/port.h" 33 34#ifndef CERES_NO_SUITESPARSE 35 36#include "ceres/visibility_based_preconditioner.h" 37 38#include "Eigen/Dense" 39#include "ceres/block_random_access_dense_matrix.h" 40#include "ceres/block_random_access_sparse_matrix.h" 41#include "ceres/block_sparse_matrix.h" 42#include "ceres/casts.h" 43#include "ceres/collections_port.h" 44#include "ceres/file.h" 45#include "ceres/internal/eigen.h" 46#include "ceres/internal/scoped_ptr.h" 47#include "ceres/linear_least_squares_problems.h" 48#include "ceres/schur_eliminator.h" 49#include "ceres/stringprintf.h" 50#include "ceres/types.h" 51#include "ceres/test_util.h" 52#include "glog/logging.h" 53#include "gtest/gtest.h" 54 55namespace ceres { 56namespace internal { 57 58// TODO(sameeragarwal): Re-enable this test once serialization is 59// working again. 60 61// using testing::AssertionResult; 62// using testing::AssertionSuccess; 63// using testing::AssertionFailure; 64 65// static const double kTolerance = 1e-12; 66 67// class VisibilityBasedPreconditionerTest : public ::testing::Test { 68// public: 69// static const int kCameraSize = 9; 70 71// protected: 72// void SetUp() { 73// string input_file = TestFileAbsolutePath("problem-6-1384-000.lsqp"); 74 75// scoped_ptr<LinearLeastSquaresProblem> problem( 76// CHECK_NOTNULL(CreateLinearLeastSquaresProblemFromFile(input_file))); 77// A_.reset(down_cast<BlockSparseMatrix*>(problem->A.release())); 78// b_.reset(problem->b.release()); 79// D_.reset(problem->D.release()); 80 81// const CompressedRowBlockStructure* bs = 82// CHECK_NOTNULL(A_->block_structure()); 83// const int num_col_blocks = bs->cols.size(); 84 85// num_cols_ = A_->num_cols(); 86// num_rows_ = A_->num_rows(); 87// num_eliminate_blocks_ = problem->num_eliminate_blocks; 88// num_camera_blocks_ = num_col_blocks - num_eliminate_blocks_; 89// options_.elimination_groups.push_back(num_eliminate_blocks_); 90// options_.elimination_groups.push_back( 91// A_->block_structure()->cols.size() - num_eliminate_blocks_); 92 93// vector<int> blocks(num_col_blocks - num_eliminate_blocks_, 0); 94// for (int i = num_eliminate_blocks_; i < num_col_blocks; ++i) { 95// blocks[i - num_eliminate_blocks_] = bs->cols[i].size; 96// } 97 98// // The input matrix is a real jacobian and fairly poorly 99// // conditioned. Setting D to a large constant makes the normal 100// // equations better conditioned and makes the tests below better 101// // conditioned. 102// VectorRef(D_.get(), num_cols_).setConstant(10.0); 103 104// schur_complement_.reset(new BlockRandomAccessDenseMatrix(blocks)); 105// Vector rhs(schur_complement_->num_rows()); 106 107// scoped_ptr<SchurEliminatorBase> eliminator; 108// LinearSolver::Options eliminator_options; 109// eliminator_options.elimination_groups = options_.elimination_groups; 110// eliminator_options.num_threads = options_.num_threads; 111 112// eliminator.reset(SchurEliminatorBase::Create(eliminator_options)); 113// eliminator->Init(num_eliminate_blocks_, bs); 114// eliminator->Eliminate(A_.get(), b_.get(), D_.get(), 115// schur_complement_.get(), rhs.data()); 116// } 117 118 119// AssertionResult IsSparsityStructureValid() { 120// preconditioner_->InitStorage(*A_->block_structure()); 121// const HashSet<pair<int, int> >& cluster_pairs = get_cluster_pairs(); 122// const vector<int>& cluster_membership = get_cluster_membership(); 123 124// for (int i = 0; i < num_camera_blocks_; ++i) { 125// for (int j = i; j < num_camera_blocks_; ++j) { 126// if (cluster_pairs.count(make_pair(cluster_membership[i], 127// cluster_membership[j]))) { 128// if (!IsBlockPairInPreconditioner(i, j)) { 129// return AssertionFailure() 130// << "block pair (" << i << "," << j << "missing"; 131// } 132// } else { 133// if (IsBlockPairInPreconditioner(i, j)) { 134// return AssertionFailure() 135// << "block pair (" << i << "," << j << "should not be present"; 136// } 137// } 138// } 139// } 140// return AssertionSuccess(); 141// } 142 143// AssertionResult PreconditionerValuesMatch() { 144// preconditioner_->Update(*A_, D_.get()); 145// const HashSet<pair<int, int> >& cluster_pairs = get_cluster_pairs(); 146// const BlockRandomAccessSparseMatrix* m = get_m(); 147// Matrix preconditioner_matrix; 148// m->matrix()->ToDenseMatrix(&preconditioner_matrix); 149// ConstMatrixRef full_schur_complement(schur_complement_->values(), 150// m->num_rows(), 151// m->num_rows()); 152// const int num_clusters = get_num_clusters(); 153// const int kDiagonalBlockSize = 154// kCameraSize * num_camera_blocks_ / num_clusters; 155 156// for (int i = 0; i < num_clusters; ++i) { 157// for (int j = i; j < num_clusters; ++j) { 158// double diff = 0.0; 159// if (cluster_pairs.count(make_pair(i, j))) { 160// diff = 161// (preconditioner_matrix.block(kDiagonalBlockSize * i, 162// kDiagonalBlockSize * j, 163// kDiagonalBlockSize, 164// kDiagonalBlockSize) - 165// full_schur_complement.block(kDiagonalBlockSize * i, 166// kDiagonalBlockSize * j, 167// kDiagonalBlockSize, 168// kDiagonalBlockSize)).norm(); 169// } else { 170// diff = preconditioner_matrix.block(kDiagonalBlockSize * i, 171// kDiagonalBlockSize * j, 172// kDiagonalBlockSize, 173// kDiagonalBlockSize).norm(); 174// } 175// if (diff > kTolerance) { 176// return AssertionFailure() 177// << "Preconditioner block " << i << " " << j << " differs " 178// << "from expected value by " << diff; 179// } 180// } 181// } 182// return AssertionSuccess(); 183// } 184 185// // Accessors 186// int get_num_blocks() { return preconditioner_->num_blocks_; } 187 188// int get_num_clusters() { return preconditioner_->num_clusters_; } 189// int* get_mutable_num_clusters() { return &preconditioner_->num_clusters_; } 190 191// const vector<int>& get_block_size() { 192// return preconditioner_->block_size_; } 193 194// vector<int>* get_mutable_block_size() { 195// return &preconditioner_->block_size_; } 196 197// const vector<int>& get_cluster_membership() { 198// return preconditioner_->cluster_membership_; 199// } 200 201// vector<int>* get_mutable_cluster_membership() { 202// return &preconditioner_->cluster_membership_; 203// } 204 205// const set<pair<int, int> >& get_block_pairs() { 206// return preconditioner_->block_pairs_; 207// } 208 209// set<pair<int, int> >* get_mutable_block_pairs() { 210// return &preconditioner_->block_pairs_; 211// } 212 213// const HashSet<pair<int, int> >& get_cluster_pairs() { 214// return preconditioner_->cluster_pairs_; 215// } 216 217// HashSet<pair<int, int> >* get_mutable_cluster_pairs() { 218// return &preconditioner_->cluster_pairs_; 219// } 220 221// bool IsBlockPairInPreconditioner(const int block1, const int block2) { 222// return preconditioner_->IsBlockPairInPreconditioner(block1, block2); 223// } 224 225// bool IsBlockPairOffDiagonal(const int block1, const int block2) { 226// return preconditioner_->IsBlockPairOffDiagonal(block1, block2); 227// } 228 229// const BlockRandomAccessSparseMatrix* get_m() { 230// return preconditioner_->m_.get(); 231// } 232 233// int num_rows_; 234// int num_cols_; 235// int num_eliminate_blocks_; 236// int num_camera_blocks_; 237 238// scoped_ptr<BlockSparseMatrix> A_; 239// scoped_array<double> b_; 240// scoped_array<double> D_; 241 242// Preconditioner::Options options_; 243// scoped_ptr<VisibilityBasedPreconditioner> preconditioner_; 244// scoped_ptr<BlockRandomAccessDenseMatrix> schur_complement_; 245// }; 246 247// TEST_F(VisibilityBasedPreconditionerTest, OneClusterClusterJacobi) { 248// options_.type = CLUSTER_JACOBI; 249// preconditioner_.reset( 250// new VisibilityBasedPreconditioner(*A_->block_structure(), options_)); 251 252// // Override the clustering to be a single clustering containing all 253// // the cameras. 254// vector<int>& cluster_membership = *get_mutable_cluster_membership(); 255// for (int i = 0; i < num_camera_blocks_; ++i) { 256// cluster_membership[i] = 0; 257// } 258 259// *get_mutable_num_clusters() = 1; 260 261// HashSet<pair<int, int> >& cluster_pairs = *get_mutable_cluster_pairs(); 262// cluster_pairs.clear(); 263// cluster_pairs.insert(make_pair(0, 0)); 264 265// EXPECT_TRUE(IsSparsityStructureValid()); 266// EXPECT_TRUE(PreconditionerValuesMatch()); 267 268// // Multiplication by the inverse of the preconditioner. 269// const int num_rows = schur_complement_->num_rows(); 270// ConstMatrixRef full_schur_complement(schur_complement_->values(), 271// num_rows, 272// num_rows); 273// Vector x(num_rows); 274// Vector y(num_rows); 275// Vector z(num_rows); 276 277// for (int i = 0; i < num_rows; ++i) { 278// x.setZero(); 279// y.setZero(); 280// z.setZero(); 281// x[i] = 1.0; 282// preconditioner_->RightMultiply(x.data(), y.data()); 283// z = full_schur_complement 284// .selfadjointView<Eigen::Upper>() 285// .llt().solve(x); 286// double max_relative_difference = 287// ((y - z).array() / z.array()).matrix().lpNorm<Eigen::Infinity>(); 288// EXPECT_NEAR(max_relative_difference, 0.0, kTolerance); 289// } 290// } 291 292 293 294// TEST_F(VisibilityBasedPreconditionerTest, ClusterJacobi) { 295// options_.type = CLUSTER_JACOBI; 296// preconditioner_.reset( 297// new VisibilityBasedPreconditioner(*A_->block_structure(), options_)); 298 299// // Override the clustering to be equal number of cameras. 300// vector<int>& cluster_membership = *get_mutable_cluster_membership(); 301// cluster_membership.resize(num_camera_blocks_); 302// static const int kNumClusters = 3; 303 304// for (int i = 0; i < num_camera_blocks_; ++i) { 305// cluster_membership[i] = (i * kNumClusters) / num_camera_blocks_; 306// } 307// *get_mutable_num_clusters() = kNumClusters; 308 309// HashSet<pair<int, int> >& cluster_pairs = *get_mutable_cluster_pairs(); 310// cluster_pairs.clear(); 311// for (int i = 0; i < kNumClusters; ++i) { 312// cluster_pairs.insert(make_pair(i, i)); 313// } 314 315// EXPECT_TRUE(IsSparsityStructureValid()); 316// EXPECT_TRUE(PreconditionerValuesMatch()); 317// } 318 319 320// TEST_F(VisibilityBasedPreconditionerTest, ClusterTridiagonal) { 321// options_.type = CLUSTER_TRIDIAGONAL; 322// preconditioner_.reset( 323// new VisibilityBasedPreconditioner(*A_->block_structure(), options_)); 324// static const int kNumClusters = 3; 325 326// // Override the clustering to be 3 clusters. 327// vector<int>& cluster_membership = *get_mutable_cluster_membership(); 328// cluster_membership.resize(num_camera_blocks_); 329// for (int i = 0; i < num_camera_blocks_; ++i) { 330// cluster_membership[i] = (i * kNumClusters) / num_camera_blocks_; 331// } 332// *get_mutable_num_clusters() = kNumClusters; 333 334// // Spanning forest has structure 0-1 2 335// HashSet<pair<int, int> >& cluster_pairs = *get_mutable_cluster_pairs(); 336// cluster_pairs.clear(); 337// for (int i = 0; i < kNumClusters; ++i) { 338// cluster_pairs.insert(make_pair(i, i)); 339// } 340// cluster_pairs.insert(make_pair(0, 1)); 341 342// EXPECT_TRUE(IsSparsityStructureValid()); 343// EXPECT_TRUE(PreconditionerValuesMatch()); 344// } 345 346} // namespace internal 347} // namespace ceres 348 349#endif // CERES_NO_SUITESPARSE 350