Revamped ISAM2::marginalizeLeaves again, more tree algorithm instead of relying on sorted keys, hopefully correct this time.
Richard Roberts
parent
467cd225af
commit
596c5cdae8
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@ -24,8 +24,9 @@ using namespace boost::assign;
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#include <gtsam/base/timing.h>
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#include <gtsam/base/debug.h>
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#include <gtsam/linear/GaussianJunctionTree.h>
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#include <gtsam/inference/BayesTree.h>
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#include <gtsam/linear/GaussianJunctionTree.h>
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#include <gtsam/linear/GaussianSequentialSolver.h>
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#include <gtsam/linear/HessianFactor.h>
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#include <gtsam/linear/GaussianFactorGraph.h>
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@ -792,120 +793,147 @@ void ISAM2::experimentalMarginalizeLeaves(const FastList<Key>& leafKeys)
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BOOST_FOREACH(Key key, leafKeys) {
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indices.insert(ordering_[key]);
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}
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FastSet<Index> origIndices = indices;
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// For each clique containing variables to be marginalized, we need to
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// reeliminate the marginalized variables and add their linear contribution
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// factor into the factor graph. This results in some extra work if we are
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// marginalizing multiple cliques on the same path.
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// To do this, first build a map containing all cliques containing variables to be
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// marginalized that determines the last-ordered variable eliminated in that
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// clique.
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FastMap<sharedClique, Index> cliquesToMarginalize;
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BOOST_FOREACH(Index jI, indices)
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{
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assert(nodes_[jI]); // Assert that the nodes structure contains this index
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pair<FastMap<sharedClique,Index>::iterator, bool> insertResult =
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cliquesToMarginalize.insert(make_pair(nodes_[jI], jI));
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// If insert found a map entry existing, see if our current index is
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// eliminated later and modify the entry if so.
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if(!insertResult.second && jI > insertResult.first->second)
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insertResult.first->second = jI;
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}
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#ifndef NDEBUG
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// Check that the cliques have sorted frontal keys - we assume that here.
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for(FastMap<sharedClique,Index>::const_iterator c = cliquesToMarginalize.begin();
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c != cliquesToMarginalize.end(); ++c)
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{
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FastSet<Key> keys(c->first->conditional()->beginFrontals(), c->first->conditional()->endFrontals());
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assert(std::equal(c->first->conditional()->beginFrontals(), c->first->conditional()->endFrontals(), keys.begin()));
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}
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#endif
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// Now loop over the indices, the iterator jI is advanced inside the loop.
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// Keep track of marginal factors - map from clique to the marginal factors
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// that should be incorporated into it, passed up from it's children.
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multimap<sharedClique, GaussianFactor::shared_ptr> marginalFactors;
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FastSet<size_t> factorIndicesToRemove;
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GaussianFactorGraph factorsToAdd;
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for(FastSet<Index>::iterator jI = indices.begin(); jI != indices.end(); )
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{
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const Index j = *jI;
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// Retrieve the clique and the latest-ordered index corresponding to this index
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FastMap<sharedClique,Index>::iterator clique_lastIndex = cliquesToMarginalize.find(nodes_[*jI]);
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assert(clique_lastIndex != cliquesToMarginalize.end()); // Assert that we indexed the clique
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// Remove each variable and its subtrees
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BOOST_FOREACH(Index j, indices) {
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if(nodes_[j]) { // If the index was not already removed by removing another subtree
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sharedClique clique = nodes_[j];
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const size_t originalnFrontals = clique_lastIndex->first->conditional()->nrFrontals();
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// See if we should remove the whole clique
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bool marginalizeEntireClique = true;
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BOOST_FOREACH(Index frontal, clique->conditional()->frontals()) {
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if(indices.find(frontal) == indices.end()) {
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marginalizeEntireClique = false;
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break; } }
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// Check that the clique has no children
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if(!clique_lastIndex->first->children().empty())
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throw MarginalizeNonleafException(ordering_.key(*jI), params_.keyFormatter);
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// Check that all previous variables in the clique are also being eliminated and no later ones.
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// At the same time, remove the indices marginalized with this clique from the indices set.
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// This is where the iterator j is advanced.
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size_t nFrontals = 0;
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{
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bool foundLast = false;
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BOOST_FOREACH(Index cliqueVar, clique_lastIndex->first->conditional()->frontals()) {
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if(!foundLast && indices.find(cliqueVar) == indices.end())
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throw MarginalizeNonleafException(ordering_.key(j), params_.keyFormatter);
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if(foundLast && indices.find(cliqueVar) != indices.end())
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throw MarginalizeNonleafException(ordering_.key(j), params_.keyFormatter);
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if(!foundLast) {
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++ nFrontals;
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if(cliqueVar == *jI)
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indices.erase(jI++); // j gets advanced here
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else
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indices.erase(cliqueVar);
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}
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if(cliqueVar == clique_lastIndex->second)
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foundLast = true;
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// Mark factors to be removed
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BOOST_FOREACH(size_t i, variableIndex_[cliqueVar]) {
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factorIndicesToRemove.insert(i);
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// Remove either the whole clique or part of it
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if(marginalizeEntireClique) {
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// Remove the whole clique and its subtree, and keep the marginal factor.
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GaussianFactor::shared_ptr marginalFactor = clique->cachedFactor();
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// We do not need the marginal factors associated with this clique
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// because their information is already incorporated in the new
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// marginal factor. So, now associate this marginal factor with the
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// parent of this clique.
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marginalFactors.insert(make_pair(clique->parent(), marginalFactor));
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// Now remove this clique and its subtree - all of its marginal
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// information has been stored in marginalFactors.
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const Cliques removedCliques = this->removeSubtree(clique); // Remove the subtree and throw away the cliques
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BOOST_FOREACH(const sharedClique& removedClique, removedCliques) {
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marginalFactors.erase(removedClique);
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// Mark factors for removal
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BOOST_FOREACH(Index indexInClique, removedClique->conditional()->frontals()) {
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factorIndicesToRemove.insert(variableIndex_[indexInClique].begin(), variableIndex_[indexInClique].end()); }
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}
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}
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}
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else {
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// Reeliminate the current clique and the marginals from its children,
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// then keep only the marginal on the non-marginalized variables. We
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// get the childrens' marginals from any existing children, plus
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// the marginals from the marginalFactors multimap, which come from any
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// subtrees already marginalized out.
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// Add child marginals and remove marginalized subtrees
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GaussianFactorGraph graph;
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Cliques subtreesToRemove;
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BOOST_FOREACH(const sharedClique& child, clique->children()) {
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graph.push_back(child->cachedFactor()); // Add child marginal
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// Remove subtree if child depends on any marginalized keys
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BOOST_FOREACH(Index parentIndex, child->conditional()->parents()) {
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if(indices.find(parentIndex) != indices.end()) {
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subtreesToRemove.push_back(child);
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break;
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}
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}
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}
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BOOST_FOREACH(const sharedClique& childToRemove, subtreesToRemove) {
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const Cliques removedCliques = this->removeSubtree(childToRemove); // Remove the subtree and throw away the cliques
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BOOST_FOREACH(const sharedClique& removedClique, removedCliques) {
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marginalFactors.erase(removedClique); }
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// Mark factors for removal
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BOOST_FOREACH(Index indexInClique, childToRemove->conditional()->frontals()) {
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factorIndicesToRemove.insert(variableIndex_[indexInClique].begin(), variableIndex_[indexInClique].end()); }
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}
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// Reeliminate the factored conditional + marginal that was previously making up the clique
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GaussianFactorGraph cliqueGraph;
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cliqueGraph.push_back(clique_lastIndex->first->conditional()->toFactor());
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assert(clique_lastIndex->first->cachedFactor()); // Assert that we have the cached marginal piece
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cliqueGraph.push_back(clique_lastIndex->first->cachedFactor());
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pair<Clique::sharedConditional, GaussianFactor::shared_ptr> eliminationResult =
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params_.factorization==ISAM2Params::QR ?
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EliminateQR(cliqueGraph, nFrontals) :
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EliminatePreferCholesky(cliqueGraph, nFrontals);
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// Gather remaining children after we removed marginalized subtrees
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vector<sharedClique> orphans(clique->children().begin(), clique->children().end());
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// Now we discard the conditional part and add the marginal part back into
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// the graph. Also we need to rebuild the leaf clique using the marginal.
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// Add a factor for the current clique to the linear graph to reeliminate
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graph.push_back(clique->conditional()->toFactor());
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graph.push_back(clique->cachedFactor());
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// Add the marginal into the factor graph
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factorsToAdd.push_back(eliminationResult.second);
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// Remove the current clique
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sharedClique parent = clique->parent();
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this->removeClique(clique);
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// Mark factors for removal
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BOOST_FOREACH(Index indexInClique, clique->conditional()->frontals()) {
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factorIndicesToRemove.insert(variableIndex_[indexInClique].begin(), variableIndex_[indexInClique].end()); }
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// Get the parent of the clique to be removed
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sharedClique parent = clique_lastIndex->first->parent();
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// Reeliminate the linear graph to get the marginal and discard the conditional
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const FastSet<Index> cliqueFrontals(clique->conditional()->beginFrontals(), clique->conditional()->endFrontals());
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FastSet<Index> cliqueFrontalsToEliminate;
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std::set_intersection(cliqueFrontals.begin(), cliqueFrontals.end(), indices.begin(), indices.end(),
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std::inserter(cliqueFrontalsToEliminate, cliqueFrontalsToEliminate.end()));
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vector<Index> cliqueFrontalsToEliminateV(cliqueFrontalsToEliminate.begin(), cliqueFrontalsToEliminate.end());
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pair<GaussianConditional::shared_ptr, GaussianFactorGraph> eliminationResult1 =
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graph.eliminate(cliqueFrontalsToEliminateV,
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params_.factorization==ISAM2Params::QR ? EliminateQR : EliminatePreferCholesky);
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// Remove the clique
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this->removeClique(clique_lastIndex->first);
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// Add the resulting marginal
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BOOST_FOREACH(const GaussianFactor::shared_ptr& marginal, eliminationResult1.second) {
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if(marginal)
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marginalFactors.insert(make_pair(clique, marginal)); }
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// Add a new leaf clique if necessary
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const size_t newnFrontals = originalnFrontals - nFrontals;
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if(newnFrontals > 0) {
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// Do the elimination for the new leaf clique
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GaussianFactorGraph newCliqueGraph;
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newCliqueGraph.push_back(eliminationResult.second);
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pair<Clique::sharedConditional, GaussianFactor::shared_ptr> newEliminationResult =
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params_.factorization==ISAM2Params::QR ?
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EliminateQR(newCliqueGraph, newnFrontals) :
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EliminatePreferCholesky(newCliqueGraph, newnFrontals);
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// Create and add the new clique
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this->addClique(ISAM2Clique::Create(newEliminationResult), parent);
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// Recover the conditional on the remaining subset of frontal variables
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// of this clique being martially marginalized.
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vector<Index> newFrontals(
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std::find(clique->conditional()->beginFrontals(), clique->conditional()->endFrontals(), j) + 1,
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clique->conditional()->endFrontals()); // New frontals are all of those *after* the marginalized key
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pair<GaussianConditional::shared_ptr, GaussianFactorGraph> eliminationResult2 =
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eliminationResult1.second.eliminate(newFrontals,
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params_.factorization==ISAM2Params::QR ? EliminateQR : EliminatePreferCholesky);
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// Construct the marginalized clique
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sharedClique newClique;
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if(params_.factorization == ISAM2Params::QR)
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newClique.reset(new Clique(GaussianFactorGraph::EliminationResult(
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eliminationResult2.first, boost::make_shared<JacobianFactor>(eliminationResult2.second))));
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else
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newClique.reset(new Clique(GaussianFactorGraph::EliminationResult(
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eliminationResult2.first, boost::make_shared<HessianFactor>(eliminationResult2.second))));
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// Add the marginalized clique to the BayesTree
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this->addClique(newClique, parent);
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// Add the orphans
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BOOST_FOREACH(const sharedClique& orphan, orphans) {
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this->addClique(orphan, newClique); }
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}
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}
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}
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// Remove any factors touching the marginalized-out variables
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// At this point we have updated the BayesTree, now update the remaining iSAM2 data structures
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// Gather factors to add - the new marginal factors
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GaussianFactorGraph factorsToAdd;
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typedef pair<sharedClique, GaussianFactor::shared_ptr> Clique_Factor;
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BOOST_FOREACH(const Clique_Factor& clique_factor, marginalFactors) {
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if(clique_factor.second)
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factorsToAdd.push_back(clique_factor.second);
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nonlinearFactors_.push_back(boost::make_shared<LinearContainerFactor>(
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clique_factor.second, ordering_));
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if(params_.cacheLinearizedFactors)
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linearFactors_.push_back(clique_factor.second);
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BOOST_FOREACH(Index factorIndex, *clique_factor.second) {
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fixedVariables_.insert(ordering_.key(factorIndex)); }
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}
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variableIndex_.augment(factorsToAdd); // Augment the variable index
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// Remove the factors to remove that have been summarized in the newly-added marginal factors
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vector<size_t> removedFactorIndices;
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SymbolicFactorGraph removedFactors;
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BOOST_FOREACH(size_t i, factorIndicesToRemove) {
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@ -917,18 +945,6 @@ void ISAM2::experimentalMarginalizeLeaves(const FastList<Key>& leafKeys)
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}
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variableIndex_.remove(removedFactorIndices, removedFactors);
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// Add the new factors and fix linearization points of involved variables
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BOOST_FOREACH(const GaussianFactor::shared_ptr& factor, factorsToAdd) {
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nonlinearFactors_.push_back(boost::make_shared<LinearContainerFactor>(
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factor, ordering_));
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if(params_.cacheLinearizedFactors)
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linearFactors_.push_back(factor);
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BOOST_FOREACH(Index factorIndex, *factor) {
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fixedVariables_.insert(ordering_.key(factorIndex));
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}
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}
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variableIndex_.augment(factorsToAdd); // Augment the variable index
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// Remove the marginalized variables
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Impl::RemoveVariables(FastSet<Key>(leafKeys.begin(), leafKeys.end()), root_, theta_, variableIndex_, delta_, deltaNewton_, RgProd_,
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deltaReplacedMask_, ordering_, nodes_, linearFactors_, fixedVariables_);
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@ -884,7 +884,68 @@ namespace {
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}
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/* ************************************************************************* */
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TEST_UNSAFE(ISAM2, marginalizeLeaves)
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TEST_UNSAFE(ISAM2, marginalizeLeaves1)
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{
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ISAM2 isam;
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NonlinearFactorGraph factors;
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factors.add(PriorFactor<LieVector>(0, LieVector(0.0), noiseModel::Unit::Create(1)));
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factors.add(BetweenFactor<LieVector>(0, 1, LieVector(0.0), noiseModel::Unit::Create(1)));
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factors.add(BetweenFactor<LieVector>(1, 2, LieVector(0.0), noiseModel::Unit::Create(1)));
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factors.add(BetweenFactor<LieVector>(0, 2, LieVector(0.0), noiseModel::Unit::Create(1)));
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Values values;
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values.insert(0, LieVector(0.0));
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values.insert(1, LieVector(0.0));
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values.insert(2, LieVector(0.0));
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FastMap<Key,int> constrainedKeys;
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constrainedKeys.insert(make_pair(0,0));
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constrainedKeys.insert(make_pair(1,1));
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constrainedKeys.insert(make_pair(2,2));
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isam.update(factors, values, FastVector<size_t>(), constrainedKeys);
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FastList<Key> leafKeys;
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leafKeys.push_back(isam.getOrdering().key(0));
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EXPECT(checkMarginalizeLeaves(isam, leafKeys));
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}
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/* ************************************************************************* */
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TEST_UNSAFE(ISAM2, marginalizeLeaves2)
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{
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ISAM2 isam;
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NonlinearFactorGraph factors;
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factors.add(PriorFactor<LieVector>(0, LieVector(0.0), noiseModel::Unit::Create(1)));
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factors.add(BetweenFactor<LieVector>(0, 1, LieVector(0.0), noiseModel::Unit::Create(1)));
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factors.add(BetweenFactor<LieVector>(1, 2, LieVector(0.0), noiseModel::Unit::Create(1)));
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factors.add(BetweenFactor<LieVector>(0, 2, LieVector(0.0), noiseModel::Unit::Create(1)));
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factors.add(BetweenFactor<LieVector>(2, 3, LieVector(0.0), noiseModel::Unit::Create(1)));
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Values values;
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values.insert(0, LieVector(0.0));
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values.insert(1, LieVector(0.0));
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values.insert(2, LieVector(0.0));
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values.insert(3, LieVector(0.0));
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FastMap<Key,int> constrainedKeys;
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constrainedKeys.insert(make_pair(0,0));
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constrainedKeys.insert(make_pair(1,1));
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constrainedKeys.insert(make_pair(2,2));
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constrainedKeys.insert(make_pair(3,3));
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isam.update(factors, values, FastVector<size_t>(), constrainedKeys);
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FastList<Key> leafKeys;
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leafKeys.push_back(isam.getOrdering().key(0));
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EXPECT(checkMarginalizeLeaves(isam, leafKeys));
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}
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/* ************************************************************************* */
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TEST_UNSAFE(ISAM2, marginalizeLeaves3)
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{
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ISAM2 isam;
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@ -909,15 +970,65 @@ TEST_UNSAFE(ISAM2, marginalizeLeaves)
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values.insert(4, LieVector(0.0));
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values.insert(5, LieVector(0.0));
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isam.update(factors, values);
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FastMap<Key,int> constrainedKeys;
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constrainedKeys.insert(make_pair(0,0));
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constrainedKeys.insert(make_pair(1,1));
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constrainedKeys.insert(make_pair(2,2));
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constrainedKeys.insert(make_pair(3,3));
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constrainedKeys.insert(make_pair(4,4));
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constrainedKeys.insert(make_pair(5,5));
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isam.update(factors, values, FastVector<size_t>(), constrainedKeys);
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FastList<Key> leafKeys;
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leafKeys.push_back(0);
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leafKeys.push_back(isam.getOrdering().key(0));
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EXPECT(checkMarginalizeLeaves(isam, leafKeys));
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}
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/* ************************************************************************* */
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TEST_UNSAFE(ISAM2, marginalizeLeaves2)
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TEST_UNSAFE(ISAM2, marginalizeLeaves4)
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{
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ISAM2 isam;
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NonlinearFactorGraph factors;
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factors.add(PriorFactor<LieVector>(0, LieVector(0.0), noiseModel::Unit::Create(1)));
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factors.add(BetweenFactor<LieVector>(0, 1, LieVector(0.0), noiseModel::Unit::Create(1)));
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factors.add(BetweenFactor<LieVector>(1, 2, LieVector(0.0), noiseModel::Unit::Create(1)));
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factors.add(BetweenFactor<LieVector>(0, 2, LieVector(0.0), noiseModel::Unit::Create(1)));
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factors.add(BetweenFactor<LieVector>(2, 3, LieVector(0.0), noiseModel::Unit::Create(1)));
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factors.add(BetweenFactor<LieVector>(3, 4, LieVector(0.0), noiseModel::Unit::Create(1)));
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factors.add(BetweenFactor<LieVector>(4, 5, LieVector(0.0), noiseModel::Unit::Create(1)));
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factors.add(BetweenFactor<LieVector>(3, 5, LieVector(0.0), noiseModel::Unit::Create(1)));
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Values values;
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values.insert(0, LieVector(0.0));
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values.insert(1, LieVector(0.0));
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||||
values.insert(2, LieVector(0.0));
|
||||
values.insert(3, LieVector(0.0));
|
||||
values.insert(4, LieVector(0.0));
|
||||
values.insert(5, LieVector(0.0));
|
||||
|
||||
FastMap<Key,int> constrainedKeys;
|
||||
constrainedKeys.insert(make_pair(0,0));
|
||||
constrainedKeys.insert(make_pair(1,1));
|
||||
constrainedKeys.insert(make_pair(2,2));
|
||||
constrainedKeys.insert(make_pair(3,3));
|
||||
constrainedKeys.insert(make_pair(4,4));
|
||||
constrainedKeys.insert(make_pair(5,5));
|
||||
|
||||
isam.update(factors, values, FastVector<size_t>(), constrainedKeys);
|
||||
|
||||
FastList<Key> leafKeys;
|
||||
leafKeys.push_back(isam.getOrdering().key(0));
|
||||
leafKeys.push_back(isam.getOrdering().key(1));
|
||||
EXPECT(checkMarginalizeLeaves(isam, leafKeys));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST_UNSAFE(ISAM2, marginalizeLeaves5)
|
||||
{
|
||||
// Create isam2
|
||||
ISAM2 isam = createSlamlikeISAM2();
|
||||
|
|
|
|||
Loading…
Reference in New Issue