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 (at) google.com (Sameer Agarwal) 30 31 #ifndef CERES_NO_SUITESPARSE 32 33 #include "ceres/visibility_based_preconditioner.h" 34 35 #include "Eigen/Dense" 36 #include "ceres/block_random_access_dense_matrix.h" 37 #include "ceres/block_random_access_sparse_matrix.h" 38 #include "ceres/block_sparse_matrix.h" 39 #include "ceres/casts.h" 40 #include "ceres/collections_port.h" 41 #include "ceres/file.h" 42 #include "ceres/internal/eigen.h" 43 #include "ceres/internal/scoped_ptr.h" 44 #include "ceres/linear_least_squares_problems.h" 45 #include "ceres/schur_eliminator.h" 46 #include "ceres/stringprintf.h" 47 #include "ceres/types.h" 48 #include "ceres/test_util.h" 49 #include "glog/logging.h" 50 #include "gtest/gtest.h" 51 52 namespace ceres { 53 namespace internal { 54 55 // TODO(sameeragarwal): Re-enable this test once serialization is 56 // working again. 57 58 // using testing::AssertionResult; 59 // using testing::AssertionSuccess; 60 // using testing::AssertionFailure; 61 62 // static const double kTolerance = 1e-12; 63 64 // class VisibilityBasedPreconditionerTest : public ::testing::Test { 65 // public: 66 // static const int kCameraSize = 9; 67 68 // protected: 69 // void SetUp() { 70 // string input_file = TestFileAbsolutePath("problem-6-1384-000.lsqp"); 71 72 // scoped_ptr<LinearLeastSquaresProblem> problem( 73 // CHECK_NOTNULL(CreateLinearLeastSquaresProblemFromFile(input_file))); 74 // A_.reset(down_cast<BlockSparseMatrix*>(problem->A.release())); 75 // b_.reset(problem->b.release()); 76 // D_.reset(problem->D.release()); 77 78 // const CompressedRowBlockStructure* bs = 79 // CHECK_NOTNULL(A_->block_structure()); 80 // const int num_col_blocks = bs->cols.size(); 81 82 // num_cols_ = A_->num_cols(); 83 // num_rows_ = A_->num_rows(); 84 // num_eliminate_blocks_ = problem->num_eliminate_blocks; 85 // num_camera_blocks_ = num_col_blocks - num_eliminate_blocks_; 86 // options_.elimination_groups.push_back(num_eliminate_blocks_); 87 // options_.elimination_groups.push_back( 88 // A_->block_structure()->cols.size() - num_eliminate_blocks_); 89 90 // vector<int> blocks(num_col_blocks - num_eliminate_blocks_, 0); 91 // for (int i = num_eliminate_blocks_; i < num_col_blocks; ++i) { 92 // blocks[i - num_eliminate_blocks_] = bs->cols[i].size; 93 // } 94 95 // // The input matrix is a real jacobian and fairly poorly 96 // // conditioned. Setting D to a large constant makes the normal 97 // // equations better conditioned and makes the tests below better 98 // // conditioned. 99 // VectorRef(D_.get(), num_cols_).setConstant(10.0); 100 101 // schur_complement_.reset(new BlockRandomAccessDenseMatrix(blocks)); 102 // Vector rhs(schur_complement_->num_rows()); 103 104 // scoped_ptr<SchurEliminatorBase> eliminator; 105 // LinearSolver::Options eliminator_options; 106 // eliminator_options.elimination_groups = options_.elimination_groups; 107 // eliminator_options.num_threads = options_.num_threads; 108 109 // eliminator.reset(SchurEliminatorBase::Create(eliminator_options)); 110 // eliminator->Init(num_eliminate_blocks_, bs); 111 // eliminator->Eliminate(A_.get(), b_.get(), D_.get(), 112 // schur_complement_.get(), rhs.data()); 113 // } 114 115 116 // AssertionResult IsSparsityStructureValid() { 117 // preconditioner_->InitStorage(*A_->block_structure()); 118 // const HashSet<pair<int, int> >& cluster_pairs = get_cluster_pairs(); 119 // const vector<int>& cluster_membership = get_cluster_membership(); 120 121 // for (int i = 0; i < num_camera_blocks_; ++i) { 122 // for (int j = i; j < num_camera_blocks_; ++j) { 123 // if (cluster_pairs.count(make_pair(cluster_membership[i], 124 // cluster_membership[j]))) { 125 // if (!IsBlockPairInPreconditioner(i, j)) { 126 // return AssertionFailure() 127 // << "block pair (" << i << "," << j << "missing"; 128 // } 129 // } else { 130 // if (IsBlockPairInPreconditioner(i, j)) { 131 // return AssertionFailure() 132 // << "block pair (" << i << "," << j << "should not be present"; 133 // } 134 // } 135 // } 136 // } 137 // return AssertionSuccess(); 138 // } 139 140 // AssertionResult PreconditionerValuesMatch() { 141 // preconditioner_->Update(*A_, D_.get()); 142 // const HashSet<pair<int, int> >& cluster_pairs = get_cluster_pairs(); 143 // const BlockRandomAccessSparseMatrix* m = get_m(); 144 // Matrix preconditioner_matrix; 145 // m->matrix()->ToDenseMatrix(&preconditioner_matrix); 146 // ConstMatrixRef full_schur_complement(schur_complement_->values(), 147 // m->num_rows(), 148 // m->num_rows()); 149 // const int num_clusters = get_num_clusters(); 150 // const int kDiagonalBlockSize = 151 // kCameraSize * num_camera_blocks_ / num_clusters; 152 153 // for (int i = 0; i < num_clusters; ++i) { 154 // for (int j = i; j < num_clusters; ++j) { 155 // double diff = 0.0; 156 // if (cluster_pairs.count(make_pair(i, j))) { 157 // diff = 158 // (preconditioner_matrix.block(kDiagonalBlockSize * i, 159 // kDiagonalBlockSize * j, 160 // kDiagonalBlockSize, 161 // kDiagonalBlockSize) - 162 // full_schur_complement.block(kDiagonalBlockSize * i, 163 // kDiagonalBlockSize * j, 164 // kDiagonalBlockSize, 165 // kDiagonalBlockSize)).norm(); 166 // } else { 167 // diff = preconditioner_matrix.block(kDiagonalBlockSize * i, 168 // kDiagonalBlockSize * j, 169 // kDiagonalBlockSize, 170 // kDiagonalBlockSize).norm(); 171 // } 172 // if (diff > kTolerance) { 173 // return AssertionFailure() 174 // << "Preconditioner block " << i << " " << j << " differs " 175 // << "from expected value by " << diff; 176 // } 177 // } 178 // } 179 // return AssertionSuccess(); 180 // } 181 182 // // Accessors 183 // int get_num_blocks() { return preconditioner_->num_blocks_; } 184 185 // int get_num_clusters() { return preconditioner_->num_clusters_; } 186 // int* get_mutable_num_clusters() { return &preconditioner_->num_clusters_; } 187 188 // const vector<int>& get_block_size() { 189 // return preconditioner_->block_size_; } 190 191 // vector<int>* get_mutable_block_size() { 192 // return &preconditioner_->block_size_; } 193 194 // const vector<int>& get_cluster_membership() { 195 // return preconditioner_->cluster_membership_; 196 // } 197 198 // vector<int>* get_mutable_cluster_membership() { 199 // return &preconditioner_->cluster_membership_; 200 // } 201 202 // const set<pair<int, int> >& get_block_pairs() { 203 // return preconditioner_->block_pairs_; 204 // } 205 206 // set<pair<int, int> >* get_mutable_block_pairs() { 207 // return &preconditioner_->block_pairs_; 208 // } 209 210 // const HashSet<pair<int, int> >& get_cluster_pairs() { 211 // return preconditioner_->cluster_pairs_; 212 // } 213 214 // HashSet<pair<int, int> >* get_mutable_cluster_pairs() { 215 // return &preconditioner_->cluster_pairs_; 216 // } 217 218 // bool IsBlockPairInPreconditioner(const int block1, const int block2) { 219 // return preconditioner_->IsBlockPairInPreconditioner(block1, block2); 220 // } 221 222 // bool IsBlockPairOffDiagonal(const int block1, const int block2) { 223 // return preconditioner_->IsBlockPairOffDiagonal(block1, block2); 224 // } 225 226 // const BlockRandomAccessSparseMatrix* get_m() { 227 // return preconditioner_->m_.get(); 228 // } 229 230 // int num_rows_; 231 // int num_cols_; 232 // int num_eliminate_blocks_; 233 // int num_camera_blocks_; 234 235 // scoped_ptr<BlockSparseMatrix> A_; 236 // scoped_array<double> b_; 237 // scoped_array<double> D_; 238 239 // Preconditioner::Options options_; 240 // scoped_ptr<VisibilityBasedPreconditioner> preconditioner_; 241 // scoped_ptr<BlockRandomAccessDenseMatrix> schur_complement_; 242 // }; 243 244 // TEST_F(VisibilityBasedPreconditionerTest, OneClusterClusterJacobi) { 245 // options_.type = CLUSTER_JACOBI; 246 // preconditioner_.reset( 247 // new VisibilityBasedPreconditioner(*A_->block_structure(), options_)); 248 249 // // Override the clustering to be a single clustering containing all 250 // // the cameras. 251 // vector<int>& cluster_membership = *get_mutable_cluster_membership(); 252 // for (int i = 0; i < num_camera_blocks_; ++i) { 253 // cluster_membership[i] = 0; 254 // } 255 256 // *get_mutable_num_clusters() = 1; 257 258 // HashSet<pair<int, int> >& cluster_pairs = *get_mutable_cluster_pairs(); 259 // cluster_pairs.clear(); 260 // cluster_pairs.insert(make_pair(0, 0)); 261 262 // EXPECT_TRUE(IsSparsityStructureValid()); 263 // EXPECT_TRUE(PreconditionerValuesMatch()); 264 265 // // Multiplication by the inverse of the preconditioner. 266 // const int num_rows = schur_complement_->num_rows(); 267 // ConstMatrixRef full_schur_complement(schur_complement_->values(), 268 // num_rows, 269 // num_rows); 270 // Vector x(num_rows); 271 // Vector y(num_rows); 272 // Vector z(num_rows); 273 274 // for (int i = 0; i < num_rows; ++i) { 275 // x.setZero(); 276 // y.setZero(); 277 // z.setZero(); 278 // x[i] = 1.0; 279 // preconditioner_->RightMultiply(x.data(), y.data()); 280 // z = full_schur_complement 281 // .selfadjointView<Eigen::Upper>() 282 // .llt().solve(x); 283 // double max_relative_difference = 284 // ((y - z).array() / z.array()).matrix().lpNorm<Eigen::Infinity>(); 285 // EXPECT_NEAR(max_relative_difference, 0.0, kTolerance); 286 // } 287 // } 288 289 290 291 // TEST_F(VisibilityBasedPreconditionerTest, ClusterJacobi) { 292 // options_.type = CLUSTER_JACOBI; 293 // preconditioner_.reset( 294 // new VisibilityBasedPreconditioner(*A_->block_structure(), options_)); 295 296 // // Override the clustering to be equal number of cameras. 297 // vector<int>& cluster_membership = *get_mutable_cluster_membership(); 298 // cluster_membership.resize(num_camera_blocks_); 299 // static const int kNumClusters = 3; 300 301 // for (int i = 0; i < num_camera_blocks_; ++i) { 302 // cluster_membership[i] = (i * kNumClusters) / num_camera_blocks_; 303 // } 304 // *get_mutable_num_clusters() = kNumClusters; 305 306 // HashSet<pair<int, int> >& cluster_pairs = *get_mutable_cluster_pairs(); 307 // cluster_pairs.clear(); 308 // for (int i = 0; i < kNumClusters; ++i) { 309 // cluster_pairs.insert(make_pair(i, i)); 310 // } 311 312 // EXPECT_TRUE(IsSparsityStructureValid()); 313 // EXPECT_TRUE(PreconditionerValuesMatch()); 314 // } 315 316 317 // TEST_F(VisibilityBasedPreconditionerTest, ClusterTridiagonal) { 318 // options_.type = CLUSTER_TRIDIAGONAL; 319 // preconditioner_.reset( 320 // new VisibilityBasedPreconditioner(*A_->block_structure(), options_)); 321 // static const int kNumClusters = 3; 322 323 // // Override the clustering to be 3 clusters. 324 // vector<int>& cluster_membership = *get_mutable_cluster_membership(); 325 // cluster_membership.resize(num_camera_blocks_); 326 // for (int i = 0; i < num_camera_blocks_; ++i) { 327 // cluster_membership[i] = (i * kNumClusters) / num_camera_blocks_; 328 // } 329 // *get_mutable_num_clusters() = kNumClusters; 330 331 // // Spanning forest has structure 0-1 2 332 // HashSet<pair<int, int> >& cluster_pairs = *get_mutable_cluster_pairs(); 333 // cluster_pairs.clear(); 334 // for (int i = 0; i < kNumClusters; ++i) { 335 // cluster_pairs.insert(make_pair(i, i)); 336 // } 337 // cluster_pairs.insert(make_pair(0, 1)); 338 339 // EXPECT_TRUE(IsSparsityStructureValid()); 340 // EXPECT_TRUE(PreconditionerValuesMatch()); 341 // } 342 343 } // namespace internal 344 } // namespace ceres 345 346 #endif // CERES_NO_SUITESPARSE 347