| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2014 Google Inc. All rights reserved. |
| // http://code.google.com/p/ceres-solver/ |
| // |
| // Redistribution and use in source and binary forms, with or without |
| // modification, are permitted provided that the following conditions are met: |
| // |
| // * Redistributions of source code must retain the above copyright notice, |
| // this list of conditions and the following disclaimer. |
| // * Redistributions in binary form must reproduce the above copyright notice, |
| // this list of conditions and the following disclaimer in the documentation |
| // and/or other materials provided with the distribution. |
| // * Neither the name of Google Inc. nor the names of its contributors may be |
| // used to endorse or promote products derived from this software without |
| // specific prior written permission. |
| // |
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" |
| // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE |
| // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE |
| // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE |
| // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR |
| // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF |
| // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS |
| // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN |
| // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) |
| // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE |
| // POSSIBILITY OF SUCH DAMAGE. |
| // |
| // Author: sameeragarwal@google.com (Sameer Agarwal) |
| |
| #include "ceres/reorder_program.h" |
| |
| #include <algorithm> |
| #include <numeric> |
| #include <vector> |
| |
| #include "ceres/cxsparse.h" |
| #include "ceres/internal/port.h" |
| #include "ceres/ordered_groups.h" |
| #include "ceres/parameter_block.h" |
| #include "ceres/parameter_block_ordering.h" |
| #include "ceres/problem_impl.h" |
| #include "ceres/program.h" |
| #include "ceres/program.h" |
| #include "ceres/residual_block.h" |
| #include "ceres/solver.h" |
| #include "ceres/suitesparse.h" |
| #include "ceres/triplet_sparse_matrix.h" |
| #include "ceres/types.h" |
| #include "glog/logging.h" |
| |
| namespace ceres { |
| namespace internal { |
| namespace { |
| |
| // Find the minimum index of any parameter block to the given residual. |
| // Parameter blocks that have indices greater than num_eliminate_blocks are |
| // considered to have an index equal to num_eliminate_blocks. |
| static int MinParameterBlock(const ResidualBlock* residual_block, |
| int num_eliminate_blocks) { |
| int min_parameter_block_position = num_eliminate_blocks; |
| for (int i = 0; i < residual_block->NumParameterBlocks(); ++i) { |
| ParameterBlock* parameter_block = residual_block->parameter_blocks()[i]; |
| if (!parameter_block->IsConstant()) { |
| CHECK_NE(parameter_block->index(), -1) |
| << "Did you forget to call Program::SetParameterOffsetsAndIndex()? " |
| << "This is a Ceres bug; please contact the developers!"; |
| min_parameter_block_position = std::min(parameter_block->index(), |
| min_parameter_block_position); |
| } |
| } |
| return min_parameter_block_position; |
| } |
| |
| void OrderingForSparseNormalCholeskyUsingSuiteSparse( |
| const TripletSparseMatrix& tsm_block_jacobian_transpose, |
| const vector<ParameterBlock*>& parameter_blocks, |
| const ParameterBlockOrdering& parameter_block_ordering, |
| int* ordering) { |
| #ifdef CERES_NO_SUITESPARSE |
| LOG(FATAL) << "Congratulations, you found a Ceres bug! " |
| << "Please report this error to the developers."; |
| #else |
| SuiteSparse ss; |
| cholmod_sparse* block_jacobian_transpose = |
| ss.CreateSparseMatrix( |
| const_cast<TripletSparseMatrix*>(&tsm_block_jacobian_transpose)); |
| |
| // No CAMD or the user did not supply a useful ordering, then just |
| // use regular AMD. |
| if (parameter_block_ordering.NumGroups() <= 1 || |
| !SuiteSparse::IsConstrainedApproximateMinimumDegreeOrderingAvailable()) { |
| ss.ApproximateMinimumDegreeOrdering(block_jacobian_transpose, &ordering[0]); |
| } else { |
| vector<int> constraints; |
| for (int i = 0; i < parameter_blocks.size(); ++i) { |
| constraints.push_back( |
| parameter_block_ordering.GroupId( |
| parameter_blocks[i]->mutable_user_state())); |
| } |
| ss.ConstrainedApproximateMinimumDegreeOrdering(block_jacobian_transpose, |
| &constraints[0], |
| ordering); |
| } |
| |
| ss.Free(block_jacobian_transpose); |
| #endif // CERES_NO_SUITESPARSE |
| } |
| |
| void OrderingForSparseNormalCholeskyUsingCXSparse( |
| const TripletSparseMatrix& tsm_block_jacobian_transpose, |
| int* ordering) { |
| #ifdef CERES_NO_CXSPARSE |
| LOG(FATAL) << "Congratulations, you found a Ceres bug! " |
| << "Please report this error to the developers."; |
| #else // CERES_NO_CXSPARSE |
| // CXSparse works with J'J instead of J'. So compute the block |
| // sparsity for J'J and compute an approximate minimum degree |
| // ordering. |
| CXSparse cxsparse; |
| cs_di* block_jacobian_transpose; |
| block_jacobian_transpose = |
| cxsparse.CreateSparseMatrix( |
| const_cast<TripletSparseMatrix*>(&tsm_block_jacobian_transpose)); |
| cs_di* block_jacobian = cxsparse.TransposeMatrix(block_jacobian_transpose); |
| cs_di* block_hessian = |
| cxsparse.MatrixMatrixMultiply(block_jacobian_transpose, block_jacobian); |
| cxsparse.Free(block_jacobian); |
| cxsparse.Free(block_jacobian_transpose); |
| |
| cxsparse.ApproximateMinimumDegreeOrdering(block_hessian, ordering); |
| cxsparse.Free(block_hessian); |
| #endif // CERES_NO_CXSPARSE |
| } |
| |
| } // namespace |
| |
| bool ApplyOrdering(const ProblemImpl::ParameterMap& parameter_map, |
| const ParameterBlockOrdering& ordering, |
| Program* program, |
| string* error) { |
| const int num_parameter_blocks = program->NumParameterBlocks(); |
| if (ordering.NumElements() != num_parameter_blocks) { |
| *error = StringPrintf("User specified ordering does not have the same " |
| "number of parameters as the problem. The problem" |
| "has %d blocks while the ordering has %d blocks.", |
| num_parameter_blocks, |
| ordering.NumElements()); |
| return false; |
| } |
| |
| vector<ParameterBlock*>* parameter_blocks = |
| program->mutable_parameter_blocks(); |
| parameter_blocks->clear(); |
| |
| const map<int, set<double*> >& groups = |
| ordering.group_to_elements(); |
| |
| for (map<int, set<double*> >::const_iterator group_it = groups.begin(); |
| group_it != groups.end(); |
| ++group_it) { |
| const set<double*>& group = group_it->second; |
| for (set<double*>::const_iterator parameter_block_ptr_it = group.begin(); |
| parameter_block_ptr_it != group.end(); |
| ++parameter_block_ptr_it) { |
| ProblemImpl::ParameterMap::const_iterator parameter_block_it = |
| parameter_map.find(*parameter_block_ptr_it); |
| if (parameter_block_it == parameter_map.end()) { |
| *error = StringPrintf("User specified ordering contains a pointer " |
| "to a double that is not a parameter block in " |
| "the problem. The invalid double is in group: %d", |
| group_it->first); |
| return false; |
| } |
| parameter_blocks->push_back(parameter_block_it->second); |
| } |
| } |
| return true; |
| } |
| |
| bool LexicographicallyOrderResidualBlocks(const int num_eliminate_blocks, |
| Program* program, |
| string* error) { |
| CHECK_GE(num_eliminate_blocks, 1) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| |
| // Create a histogram of the number of residuals for each E block. There is an |
| // extra bucket at the end to catch all non-eliminated F blocks. |
| vector<int> residual_blocks_per_e_block(num_eliminate_blocks + 1); |
| vector<ResidualBlock*>* residual_blocks = program->mutable_residual_blocks(); |
| vector<int> min_position_per_residual(residual_blocks->size()); |
| for (int i = 0; i < residual_blocks->size(); ++i) { |
| ResidualBlock* residual_block = (*residual_blocks)[i]; |
| int position = MinParameterBlock(residual_block, num_eliminate_blocks); |
| min_position_per_residual[i] = position; |
| DCHECK_LE(position, num_eliminate_blocks); |
| residual_blocks_per_e_block[position]++; |
| } |
| |
| // Run a cumulative sum on the histogram, to obtain offsets to the start of |
| // each histogram bucket (where each bucket is for the residuals for that |
| // E-block). |
| vector<int> offsets(num_eliminate_blocks + 1); |
| std::partial_sum(residual_blocks_per_e_block.begin(), |
| residual_blocks_per_e_block.end(), |
| offsets.begin()); |
| CHECK_EQ(offsets.back(), residual_blocks->size()) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| |
| CHECK(find(residual_blocks_per_e_block.begin(), |
| residual_blocks_per_e_block.end() - 1, 0) != |
| residual_blocks_per_e_block.end()) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| |
| // Fill in each bucket with the residual blocks for its corresponding E block. |
| // Each bucket is individually filled from the back of the bucket to the front |
| // of the bucket. The filling order among the buckets is dictated by the |
| // residual blocks. This loop uses the offsets as counters; subtracting one |
| // from each offset as a residual block is placed in the bucket. When the |
| // filling is finished, the offset pointerts should have shifted down one |
| // entry (this is verified below). |
| vector<ResidualBlock*> reordered_residual_blocks( |
| (*residual_blocks).size(), static_cast<ResidualBlock*>(NULL)); |
| for (int i = 0; i < residual_blocks->size(); ++i) { |
| int bucket = min_position_per_residual[i]; |
| |
| // Decrement the cursor, which should now point at the next empty position. |
| offsets[bucket]--; |
| |
| // Sanity. |
| CHECK(reordered_residual_blocks[offsets[bucket]] == NULL) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| |
| reordered_residual_blocks[offsets[bucket]] = (*residual_blocks)[i]; |
| } |
| |
| // Sanity check #1: The difference in bucket offsets should match the |
| // histogram sizes. |
| for (int i = 0; i < num_eliminate_blocks; ++i) { |
| CHECK_EQ(residual_blocks_per_e_block[i], offsets[i + 1] - offsets[i]) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| } |
| // Sanity check #2: No NULL's left behind. |
| for (int i = 0; i < reordered_residual_blocks.size(); ++i) { |
| CHECK(reordered_residual_blocks[i] != NULL) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| } |
| |
| // Now that the residuals are collected by E block, swap them in place. |
| swap(*program->mutable_residual_blocks(), reordered_residual_blocks); |
| return true; |
| } |
| |
| void MaybeReorderSchurComplementColumnsUsingSuiteSparse( |
| const ParameterBlockOrdering& parameter_block_ordering, |
| Program* program) { |
| // Pre-order the columns corresponding to the schur complement if |
| // possible. |
| #ifndef CERES_NO_SUITESPARSE |
| SuiteSparse ss; |
| if (!SuiteSparse::IsConstrainedApproximateMinimumDegreeOrderingAvailable()) { |
| return; |
| } |
| |
| vector<int> constraints; |
| vector<ParameterBlock*>& parameter_blocks = |
| *(program->mutable_parameter_blocks()); |
| |
| for (int i = 0; i < parameter_blocks.size(); ++i) { |
| constraints.push_back( |
| parameter_block_ordering.GroupId( |
| parameter_blocks[i]->mutable_user_state())); |
| } |
| |
| // Renumber the entries of constraints to be contiguous integers |
| // as camd requires that the group ids be in the range [0, |
| // parameter_blocks.size() - 1]. |
| MapValuesToContiguousRange(constraints.size(), &constraints[0]); |
| |
| // Set the offsets and index for CreateJacobianSparsityTranspose. |
| program->SetParameterOffsetsAndIndex(); |
| // Compute a block sparse presentation of J'. |
| scoped_ptr<TripletSparseMatrix> tsm_block_jacobian_transpose( |
| program->CreateJacobianBlockSparsityTranspose()); |
| |
| |
| cholmod_sparse* block_jacobian_transpose = |
| ss.CreateSparseMatrix(tsm_block_jacobian_transpose.get()); |
| |
| vector<int> ordering(parameter_blocks.size(), 0); |
| ss.ConstrainedApproximateMinimumDegreeOrdering(block_jacobian_transpose, |
| &constraints[0], |
| &ordering[0]); |
| ss.Free(block_jacobian_transpose); |
| |
| const vector<ParameterBlock*> parameter_blocks_copy(parameter_blocks); |
| for (int i = 0; i < program->NumParameterBlocks(); ++i) { |
| parameter_blocks[i] = parameter_blocks_copy[ordering[i]]; |
| } |
| #endif |
| } |
| |
| bool ReorderProgramForSchurTypeLinearSolver( |
| const LinearSolverType linear_solver_type, |
| const SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type, |
| const ProblemImpl::ParameterMap& parameter_map, |
| ParameterBlockOrdering* parameter_block_ordering, |
| Program* program, |
| string* error) { |
| if (parameter_block_ordering->NumGroups() == 1) { |
| // If the user supplied an parameter_block_ordering with just one |
| // group, it is equivalent to the user supplying NULL as an |
| // parameter_block_ordering. Ceres is completely free to choose the |
| // parameter block ordering as it sees fit. For Schur type solvers, |
| // this means that the user wishes for Ceres to identify the |
| // e_blocks, which we do by computing a maximal independent set. |
| vector<ParameterBlock*> schur_ordering; |
| const int num_eliminate_blocks = |
| ComputeStableSchurOrdering(*program, &schur_ordering); |
| |
| CHECK_EQ(schur_ordering.size(), program->NumParameterBlocks()) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| |
| // Update the parameter_block_ordering object. |
| for (int i = 0; i < schur_ordering.size(); ++i) { |
| double* parameter_block = schur_ordering[i]->mutable_user_state(); |
| const int group_id = (i < num_eliminate_blocks) ? 0 : 1; |
| parameter_block_ordering->AddElementToGroup(parameter_block, group_id); |
| } |
| |
| // We could call ApplyOrdering but this is cheaper and |
| // simpler. |
| swap(*program->mutable_parameter_blocks(), schur_ordering); |
| } else { |
| // The user provided an ordering with more than one elimination |
| // group. Trust the user and apply the ordering. |
| if (!ApplyOrdering(parameter_map, |
| *parameter_block_ordering, |
| program, |
| error)) { |
| return false; |
| } |
| } |
| |
| if (linear_solver_type == SPARSE_SCHUR && |
| sparse_linear_algebra_library_type == SUITE_SPARSE) { |
| MaybeReorderSchurComplementColumnsUsingSuiteSparse( |
| *parameter_block_ordering, |
| program); |
| } |
| |
| program->SetParameterOffsetsAndIndex(); |
| // Schur type solvers also require that their residual blocks be |
| // lexicographically ordered. |
| const int num_eliminate_blocks = |
| parameter_block_ordering->group_to_elements().begin()->second.size(); |
| if (!LexicographicallyOrderResidualBlocks(num_eliminate_blocks, |
| program, |
| error)) { |
| return false; |
| } |
| |
| program->SetParameterOffsetsAndIndex(); |
| return true; |
| } |
| |
| bool ReorderProgramForSparseNormalCholesky( |
| const SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type, |
| const ParameterBlockOrdering& parameter_block_ordering, |
| Program* program, |
| string* error) { |
| |
| if (sparse_linear_algebra_library_type != SUITE_SPARSE && |
| sparse_linear_algebra_library_type != CX_SPARSE && |
| sparse_linear_algebra_library_type != EIGEN_SPARSE) { |
| *error = "Unknown sparse linear algebra library."; |
| return false; |
| } |
| |
| // For Eigen, there is nothing to do. This is because Eigen in its |
| // current stable version does not expose a method for doing |
| // symbolic analysis on pre-ordered matrices, so a block |
| // pre-ordering is a bit pointless. |
| // |
| // The dev version as recently as July 20, 2014 has support for |
| // pre-ordering. Once this becomes more widespread, or we add |
| // support for detecting Eigen versions, we can add support for this |
| // along the lines of CXSparse. |
| if (sparse_linear_algebra_library_type == EIGEN_SPARSE) { |
| program->SetParameterOffsetsAndIndex(); |
| return true; |
| } |
| |
| // Set the offsets and index for CreateJacobianSparsityTranspose. |
| program->SetParameterOffsetsAndIndex(); |
| // Compute a block sparse presentation of J'. |
| scoped_ptr<TripletSparseMatrix> tsm_block_jacobian_transpose( |
| program->CreateJacobianBlockSparsityTranspose()); |
| |
| vector<int> ordering(program->NumParameterBlocks(), 0); |
| vector<ParameterBlock*>& parameter_blocks = |
| *(program->mutable_parameter_blocks()); |
| |
| if (sparse_linear_algebra_library_type == SUITE_SPARSE) { |
| OrderingForSparseNormalCholeskyUsingSuiteSparse( |
| *tsm_block_jacobian_transpose, |
| parameter_blocks, |
| parameter_block_ordering, |
| &ordering[0]); |
| } else if (sparse_linear_algebra_library_type == CX_SPARSE){ |
| OrderingForSparseNormalCholeskyUsingCXSparse( |
| *tsm_block_jacobian_transpose, |
| &ordering[0]); |
| } |
| |
| // Apply ordering. |
| const vector<ParameterBlock*> parameter_blocks_copy(parameter_blocks); |
| for (int i = 0; i < program->NumParameterBlocks(); ++i) { |
| parameter_blocks[i] = parameter_blocks_copy[ordering[i]]; |
| } |
| |
| program->SetParameterOffsetsAndIndex(); |
| return true; |
| } |
| |
| } // namespace internal |
| } // namespace ceres |
