434 lines
17 KiB
C++
434 lines
17 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2014 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 "ceres/reorder_program.h"
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#include <algorithm>
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#include <numeric>
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#include <vector>
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#include "ceres/cxsparse.h"
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#include "ceres/internal/port.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/parameter_block_ordering.h"
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#include "ceres/problem_impl.h"
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#include "ceres/program.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.h"
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#include "ceres/suitesparse.h"
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#include "ceres/triplet_sparse_matrix.h"
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#include "ceres/types.h"
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#include "glog/logging.h"
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namespace ceres {
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namespace internal {
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namespace {
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// Find the minimum index of any parameter block to the given residual.
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// Parameter blocks that have indices greater than num_eliminate_blocks are
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// considered to have an index equal to num_eliminate_blocks.
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static int MinParameterBlock(const ResidualBlock* residual_block,
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int num_eliminate_blocks) {
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int min_parameter_block_position = num_eliminate_blocks;
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for (int i = 0; i < residual_block->NumParameterBlocks(); ++i) {
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ParameterBlock* parameter_block = residual_block->parameter_blocks()[i];
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if (!parameter_block->IsConstant()) {
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CHECK_NE(parameter_block->index(), -1)
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<< "Did you forget to call Program::SetParameterOffsetsAndIndex()? "
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<< "This is a Ceres bug; please contact the developers!";
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min_parameter_block_position = std::min(parameter_block->index(),
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min_parameter_block_position);
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}
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}
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return min_parameter_block_position;
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}
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void OrderingForSparseNormalCholeskyUsingSuiteSparse(
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const TripletSparseMatrix& tsm_block_jacobian_transpose,
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const vector<ParameterBlock*>& parameter_blocks,
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const ParameterBlockOrdering& parameter_block_ordering,
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int* ordering) {
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#ifdef CERES_NO_SUITESPARSE
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LOG(FATAL) << "Congratulations, you found a Ceres bug! "
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<< "Please report this error to the developers.";
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#else
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SuiteSparse ss;
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cholmod_sparse* block_jacobian_transpose =
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ss.CreateSparseMatrix(
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const_cast<TripletSparseMatrix*>(&tsm_block_jacobian_transpose));
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// No CAMD or the user did not supply a useful ordering, then just
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// use regular AMD.
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if (parameter_block_ordering.NumGroups() <= 1 ||
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!SuiteSparse::IsConstrainedApproximateMinimumDegreeOrderingAvailable()) {
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ss.ApproximateMinimumDegreeOrdering(block_jacobian_transpose, &ordering[0]);
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} else {
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vector<int> constraints;
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for (int i = 0; i < parameter_blocks.size(); ++i) {
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constraints.push_back(
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parameter_block_ordering.GroupId(
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parameter_blocks[i]->mutable_user_state()));
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}
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ss.ConstrainedApproximateMinimumDegreeOrdering(block_jacobian_transpose,
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&constraints[0],
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ordering);
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}
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ss.Free(block_jacobian_transpose);
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#endif // CERES_NO_SUITESPARSE
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}
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void OrderingForSparseNormalCholeskyUsingCXSparse(
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const TripletSparseMatrix& tsm_block_jacobian_transpose,
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int* ordering) {
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#ifdef CERES_NO_CXSPARSE
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LOG(FATAL) << "Congratulations, you found a Ceres bug! "
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<< "Please report this error to the developers.";
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#else // CERES_NO_CXSPARSE
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// CXSparse works with J'J instead of J'. So compute the block
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// sparsity for J'J and compute an approximate minimum degree
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// ordering.
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CXSparse cxsparse;
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cs_di* block_jacobian_transpose;
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block_jacobian_transpose =
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cxsparse.CreateSparseMatrix(
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const_cast<TripletSparseMatrix*>(&tsm_block_jacobian_transpose));
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cs_di* block_jacobian = cxsparse.TransposeMatrix(block_jacobian_transpose);
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cs_di* block_hessian =
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cxsparse.MatrixMatrixMultiply(block_jacobian_transpose, block_jacobian);
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cxsparse.Free(block_jacobian);
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cxsparse.Free(block_jacobian_transpose);
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cxsparse.ApproximateMinimumDegreeOrdering(block_hessian, ordering);
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cxsparse.Free(block_hessian);
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#endif // CERES_NO_CXSPARSE
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}
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} // namespace
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bool ApplyOrdering(const ProblemImpl::ParameterMap& parameter_map,
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const ParameterBlockOrdering& ordering,
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Program* program,
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string* error) {
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const int num_parameter_blocks = program->NumParameterBlocks();
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if (ordering.NumElements() != num_parameter_blocks) {
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*error = StringPrintf("User specified ordering does not have the same "
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"number of parameters as the problem. The problem"
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"has %d blocks while the ordering has %d blocks.",
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num_parameter_blocks,
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ordering.NumElements());
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return false;
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}
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vector<ParameterBlock*>* parameter_blocks =
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program->mutable_parameter_blocks();
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parameter_blocks->clear();
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const map<int, set<double*> >& groups =
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ordering.group_to_elements();
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for (map<int, set<double*> >::const_iterator group_it = groups.begin();
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group_it != groups.end();
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++group_it) {
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const set<double*>& group = group_it->second;
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for (set<double*>::const_iterator parameter_block_ptr_it = group.begin();
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parameter_block_ptr_it != group.end();
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++parameter_block_ptr_it) {
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ProblemImpl::ParameterMap::const_iterator parameter_block_it =
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parameter_map.find(*parameter_block_ptr_it);
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if (parameter_block_it == parameter_map.end()) {
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*error = StringPrintf("User specified ordering contains a pointer "
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"to a double that is not a parameter block in "
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"the problem. The invalid double is in group: %d",
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group_it->first);
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return false;
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}
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parameter_blocks->push_back(parameter_block_it->second);
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}
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}
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return true;
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}
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bool LexicographicallyOrderResidualBlocks(const int num_eliminate_blocks,
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Program* program,
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string* error) {
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CHECK_GE(num_eliminate_blocks, 1)
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<< "Congratulations, you found a Ceres bug! Please report this error "
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<< "to the developers.";
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// Create a histogram of the number of residuals for each E block. There is an
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// extra bucket at the end to catch all non-eliminated F blocks.
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vector<int> residual_blocks_per_e_block(num_eliminate_blocks + 1);
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vector<ResidualBlock*>* residual_blocks = program->mutable_residual_blocks();
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vector<int> min_position_per_residual(residual_blocks->size());
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for (int i = 0; i < residual_blocks->size(); ++i) {
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ResidualBlock* residual_block = (*residual_blocks)[i];
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int position = MinParameterBlock(residual_block, num_eliminate_blocks);
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min_position_per_residual[i] = position;
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DCHECK_LE(position, num_eliminate_blocks);
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residual_blocks_per_e_block[position]++;
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}
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// Run a cumulative sum on the histogram, to obtain offsets to the start of
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// each histogram bucket (where each bucket is for the residuals for that
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// E-block).
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vector<int> offsets(num_eliminate_blocks + 1);
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std::partial_sum(residual_blocks_per_e_block.begin(),
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residual_blocks_per_e_block.end(),
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offsets.begin());
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CHECK_EQ(offsets.back(), residual_blocks->size())
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<< "Congratulations, you found a Ceres bug! Please report this error "
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<< "to the developers.";
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CHECK(find(residual_blocks_per_e_block.begin(),
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residual_blocks_per_e_block.end() - 1, 0) !=
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residual_blocks_per_e_block.end())
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<< "Congratulations, you found a Ceres bug! Please report this error "
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<< "to the developers.";
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// Fill in each bucket with the residual blocks for its corresponding E block.
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// Each bucket is individually filled from the back of the bucket to the front
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// of the bucket. The filling order among the buckets is dictated by the
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// residual blocks. This loop uses the offsets as counters; subtracting one
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// from each offset as a residual block is placed in the bucket. When the
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// filling is finished, the offset pointerts should have shifted down one
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// entry (this is verified below).
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vector<ResidualBlock*> reordered_residual_blocks(
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(*residual_blocks).size(), static_cast<ResidualBlock*>(NULL));
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for (int i = 0; i < residual_blocks->size(); ++i) {
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int bucket = min_position_per_residual[i];
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// Decrement the cursor, which should now point at the next empty position.
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offsets[bucket]--;
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// Sanity.
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CHECK(reordered_residual_blocks[offsets[bucket]] == NULL)
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<< "Congratulations, you found a Ceres bug! Please report this error "
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<< "to the developers.";
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reordered_residual_blocks[offsets[bucket]] = (*residual_blocks)[i];
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}
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// Sanity check #1: The difference in bucket offsets should match the
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// histogram sizes.
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for (int i = 0; i < num_eliminate_blocks; ++i) {
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CHECK_EQ(residual_blocks_per_e_block[i], offsets[i + 1] - offsets[i])
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<< "Congratulations, you found a Ceres bug! Please report this error "
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<< "to the developers.";
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}
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// Sanity check #2: No NULL's left behind.
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for (int i = 0; i < reordered_residual_blocks.size(); ++i) {
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CHECK(reordered_residual_blocks[i] != NULL)
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<< "Congratulations, you found a Ceres bug! Please report this error "
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<< "to the developers.";
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}
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// Now that the residuals are collected by E block, swap them in place.
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swap(*program->mutable_residual_blocks(), reordered_residual_blocks);
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return true;
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}
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void MaybeReorderSchurComplementColumnsUsingSuiteSparse(
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const ParameterBlockOrdering& parameter_block_ordering,
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Program* program) {
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// Pre-order the columns corresponding to the schur complement if
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// possible.
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#ifndef CERES_NO_SUITESPARSE
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SuiteSparse ss;
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if (!SuiteSparse::IsConstrainedApproximateMinimumDegreeOrderingAvailable()) {
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return;
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}
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vector<int> constraints;
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vector<ParameterBlock*>& parameter_blocks =
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*(program->mutable_parameter_blocks());
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for (int i = 0; i < parameter_blocks.size(); ++i) {
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constraints.push_back(
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parameter_block_ordering.GroupId(
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parameter_blocks[i]->mutable_user_state()));
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}
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// Renumber the entries of constraints to be contiguous integers
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// as camd requires that the group ids be in the range [0,
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// parameter_blocks.size() - 1].
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MapValuesToContiguousRange(constraints.size(), &constraints[0]);
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// Set the offsets and index for CreateJacobianSparsityTranspose.
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program->SetParameterOffsetsAndIndex();
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// Compute a block sparse presentation of J'.
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scoped_ptr<TripletSparseMatrix> tsm_block_jacobian_transpose(
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program->CreateJacobianBlockSparsityTranspose());
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cholmod_sparse* block_jacobian_transpose =
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ss.CreateSparseMatrix(tsm_block_jacobian_transpose.get());
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vector<int> ordering(parameter_blocks.size(), 0);
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ss.ConstrainedApproximateMinimumDegreeOrdering(block_jacobian_transpose,
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&constraints[0],
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&ordering[0]);
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ss.Free(block_jacobian_transpose);
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const vector<ParameterBlock*> parameter_blocks_copy(parameter_blocks);
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for (int i = 0; i < program->NumParameterBlocks(); ++i) {
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parameter_blocks[i] = parameter_blocks_copy[ordering[i]];
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}
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#endif
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}
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bool ReorderProgramForSchurTypeLinearSolver(
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const LinearSolverType linear_solver_type,
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const SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type,
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const ProblemImpl::ParameterMap& parameter_map,
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ParameterBlockOrdering* parameter_block_ordering,
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Program* program,
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string* error) {
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if (parameter_block_ordering->NumGroups() == 1) {
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// If the user supplied an parameter_block_ordering with just one
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// group, it is equivalent to the user supplying NULL as an
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// parameter_block_ordering. Ceres is completely free to choose the
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// parameter block ordering as it sees fit. For Schur type solvers,
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// this means that the user wishes for Ceres to identify the
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// e_blocks, which we do by computing a maximal independent set.
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vector<ParameterBlock*> schur_ordering;
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const int num_eliminate_blocks =
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ComputeStableSchurOrdering(*program, &schur_ordering);
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CHECK_EQ(schur_ordering.size(), program->NumParameterBlocks())
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<< "Congratulations, you found a Ceres bug! Please report this error "
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<< "to the developers.";
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// Update the parameter_block_ordering object.
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for (int i = 0; i < schur_ordering.size(); ++i) {
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double* parameter_block = schur_ordering[i]->mutable_user_state();
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const int group_id = (i < num_eliminate_blocks) ? 0 : 1;
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parameter_block_ordering->AddElementToGroup(parameter_block, group_id);
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}
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// We could call ApplyOrdering but this is cheaper and
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// simpler.
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swap(*program->mutable_parameter_blocks(), schur_ordering);
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} else {
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// The user provided an ordering with more than one elimination
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// group. Trust the user and apply the ordering.
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if (!ApplyOrdering(parameter_map,
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*parameter_block_ordering,
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program,
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error)) {
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return false;
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}
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}
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if (linear_solver_type == SPARSE_SCHUR &&
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sparse_linear_algebra_library_type == SUITE_SPARSE) {
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MaybeReorderSchurComplementColumnsUsingSuiteSparse(
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*parameter_block_ordering,
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program);
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}
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program->SetParameterOffsetsAndIndex();
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// Schur type solvers also require that their residual blocks be
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// lexicographically ordered.
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const int num_eliminate_blocks =
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parameter_block_ordering->group_to_elements().begin()->second.size();
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if (!LexicographicallyOrderResidualBlocks(num_eliminate_blocks,
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program,
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error)) {
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return false;
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}
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program->SetParameterOffsetsAndIndex();
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return true;
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}
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bool ReorderProgramForSparseNormalCholesky(
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const SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type,
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const ParameterBlockOrdering& parameter_block_ordering,
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Program* program,
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string* error) {
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if (sparse_linear_algebra_library_type != SUITE_SPARSE &&
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sparse_linear_algebra_library_type != CX_SPARSE &&
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sparse_linear_algebra_library_type != EIGEN_SPARSE) {
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*error = "Unknown sparse linear algebra library.";
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return false;
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}
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// For Eigen, there is nothing to do. This is because Eigen in its
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// current stable version does not expose a method for doing
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// symbolic analysis on pre-ordered matrices, so a block
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// pre-ordering is a bit pointless.
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//
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// The dev version as recently as July 20, 2014 has support for
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// pre-ordering. Once this becomes more widespread, or we add
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// support for detecting Eigen versions, we can add support for this
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// along the lines of CXSparse.
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if (sparse_linear_algebra_library_type == EIGEN_SPARSE) {
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program->SetParameterOffsetsAndIndex();
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return true;
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}
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// Set the offsets and index for CreateJacobianSparsityTranspose.
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program->SetParameterOffsetsAndIndex();
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// Compute a block sparse presentation of J'.
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scoped_ptr<TripletSparseMatrix> tsm_block_jacobian_transpose(
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program->CreateJacobianBlockSparsityTranspose());
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vector<int> ordering(program->NumParameterBlocks(), 0);
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vector<ParameterBlock*>& parameter_blocks =
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*(program->mutable_parameter_blocks());
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if (sparse_linear_algebra_library_type == SUITE_SPARSE) {
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OrderingForSparseNormalCholeskyUsingSuiteSparse(
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*tsm_block_jacobian_transpose,
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parameter_blocks,
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parameter_block_ordering,
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&ordering[0]);
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} else if (sparse_linear_algebra_library_type == CX_SPARSE){
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OrderingForSparseNormalCholeskyUsingCXSparse(
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*tsm_block_jacobian_transpose,
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&ordering[0]);
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}
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// Apply ordering.
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const vector<ParameterBlock*> parameter_blocks_copy(parameter_blocks);
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for (int i = 0; i < program->NumParameterBlocks(); ++i) {
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parameter_blocks[i] = parameter_blocks_copy[ordering[i]];
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}
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program->SetParameterOffsetsAndIndex();
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return true;
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}
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} // namespace internal
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} // namespace ceres
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