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eliminate copy/pasta from SparseEigen with generic version of sparseJacobian

release/4.3a0
Gerry Chen 2021-01-19 20:27:30 -05:00
parent e3dd22925a
commit 9584860da0
1 changed files with 6 additions and 71 deletions
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77
gtsam/linear/SparseEigen.h
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@ -37,80 +37,15 @@ typedef Eigen::SparseMatrix<double, Eigen::ColMajor> SparseEigen;
/// Constructs an Eigen-format SparseMatrix of a GaussianFactorGraph /// Constructs an Eigen-format SparseMatrix of a GaussianFactorGraph
SparseEigen sparseJacobianEigen( SparseEigen sparseJacobianEigen(
const GaussianFactorGraph &gfg, const Ordering &ordering) { const GaussianFactorGraph &gfg, const Ordering &ordering) {
// TODO(gerry): eliminate copy/pasta by making GaussianFactorGraph version
// more general, or by creating an Eigen::Triplet compatible wrapper for
// boost::tuple return type
gttic_(SparseEigen_sparseJacobianEigen); gttic_(SparseEigen_sparseJacobianEigen);
// intermediate `entries` vector is kind of unavoidable due to how expensive
// First find dimensions of each variable // factor->rows() is, which prevents us from populating SparseEigen directly
std::map<Key, size_t> dims;
for (const boost::shared_ptr<GaussianFactor> &factor : gfg) {
if (!static_cast<bool>(factor)) continue;
for (auto it = factor->begin(); it != factor->end(); ++it) {
dims[*it] = factor->getDim(it);
}
}
// Compute first scalar column of each variable
size_t currentColIndex = 0;
std::map<Key, size_t> columnIndices;
for (const auto key : ordering) {
columnIndices[key] = currentColIndex;
currentColIndex += dims[key];
}
// Iterate over all factors, adding sparse scalar entries
std::vector<Eigen::Triplet<double>> entries; std::vector<Eigen::Triplet<double>> entries;
entries.reserve(60 * gfg.size()); entries.reserve(60 * gfg.size());
size_t nrows, ncols;
size_t row = 0; gfg.sparseJacobianInPlace(entries, ordering, nrows, ncols);
for (const boost::shared_ptr<GaussianFactor> &factor : gfg) { // create sparse matrix
if (!static_cast<bool>(factor)) continue; SparseEigen Ab(nrows, ncols);
// Convert to JacobianFactor if necessary
JacobianFactor::shared_ptr jacobianFactor(
boost::dynamic_pointer_cast<JacobianFactor>(factor));
if (!jacobianFactor) {
HessianFactor::shared_ptr hessian(
boost::dynamic_pointer_cast<HessianFactor>(factor));
if (hessian)
jacobianFactor.reset(new JacobianFactor(*hessian));
else
throw std::invalid_argument(
"GaussianFactorGraph contains a factor that is neither a "
"JacobianFactor nor a HessianFactor.");
}
// Whiten the factor and add entries for it
// iterate over all variables in the factor
const JacobianFactor whitened(jacobianFactor->whiten());
for (JacobianFactor::const_iterator key = whitened.begin();
key < whitened.end(); ++key) {
JacobianFactor::constABlock whitenedA = whitened.getA(key);
// find first column index for this key
size_t column_start = columnIndices[*key];
for (size_t i = 0; i < (size_t)whitenedA.rows(); i++)
for (size_t j = 0; j < (size_t)whitenedA.cols(); j++) {
double s = whitenedA(i, j);
if (std::abs(s) > 1e-12)
entries.emplace_back(row + i, column_start + j, s);
}
}
JacobianFactor::constBVector whitenedb(whitened.getb());
size_t bcolumn = currentColIndex;
for (size_t i = 0; i < (size_t)whitenedb.size(); i++) {
double s = whitenedb(i);
if (std::abs(s) > 1e-12) entries.emplace_back(row + i, bcolumn, s);
}
// Increment row index
row += jacobianFactor->rows();
}
// ...and make a sparse matrix with it.
SparseEigen Ab(row, currentColIndex + 1);
Ab.setFromTriplets(entries.begin(), entries.end()); Ab.setFromTriplets(entries.begin(), entries.end());
return Ab; return Ab;
} }