gtsam/gtsam/inference/ClusterTree-inst.h

320 lines
12 KiB
C++

/**
* @file ClusterTree-inst.h
* @date Oct 8, 2013
* @author Kai Ni
* @author Richard Roberts
* @author Frank Dellaert
* @brief Collects factorgraph fragments defined on variable clusters, arranged in a tree
*/
#pragma once
#include <gtsam/inference/ClusterTree.h>
#include <gtsam/inference/BayesTree.h>
#include <gtsam/inference/Ordering.h>
#include <gtsam/base/timing.h>
#include <gtsam/base/treeTraversal-inst.h>
#ifdef GTSAM_USE_TBB
#include <mutex>
#endif
#include <queue>
namespace gtsam {
/* ************************************************************************* */
template<class GRAPH>
void ClusterTree<GRAPH>::Cluster::print(const std::string& s,
const KeyFormatter& keyFormatter) const {
std::cout << s << " (" << problemSize_ << ")";
PrintKeyVector(orderedFrontalKeys);
}
/* ************************************************************************* */
template <class GRAPH>
std::vector<size_t> ClusterTree<GRAPH>::Cluster::nrFrontalsOfChildren() const {
std::vector<size_t> nrFrontals;
nrFrontals.reserve(nrChildren());
for (const sharedNode& child : children)
nrFrontals.push_back(child->nrFrontals());
return nrFrontals;
}
/* ************************************************************************* */
template <class GRAPH>
void ClusterTree<GRAPH>::Cluster::merge(const std::shared_ptr<Cluster>& cluster) {
// Merge keys. For efficiency, we add keys in reverse order at end, calling reverse after..
orderedFrontalKeys.insert(orderedFrontalKeys.end(), cluster->orderedFrontalKeys.rbegin(),
cluster->orderedFrontalKeys.rend());
factors.push_back(cluster->factors);
children.insert(children.end(), cluster->children.begin(), cluster->children.end());
// Increment problem size
problemSize_ = std::max(problemSize_, cluster->problemSize_);
}
/* ************************************************************************* */
template<class GRAPH>
void ClusterTree<GRAPH>::Cluster::mergeChildren(
const std::vector<bool>& merge) {
gttic(Cluster_mergeChildren);
assert(merge.size() == this->children.size());
// Count how many keys, factors and children we'll end up with
size_t nrKeys = orderedFrontalKeys.size();
size_t nrFactors = factors.size();
size_t nrNewChildren = 0;
// Loop over children
size_t i = 0;
for(const sharedNode& child: this->children) {
if (merge[i]) {
nrKeys += child->orderedFrontalKeys.size();
nrFactors += child->factors.size();
nrNewChildren += child->nrChildren();
} else {
nrNewChildren += 1; // we keep the child
}
++i;
}
// now reserve space, and really merge
auto oldChildren = this->children;
this->children.clear();
this->children.reserve(nrNewChildren);
orderedFrontalKeys.reserve(nrKeys);
factors.reserve(nrFactors);
i = 0;
for (const sharedNode& child : oldChildren) {
if (merge[i]) {
this->merge(child);
} else {
this->addChild(child); // we keep the child
}
++i;
}
std::reverse(orderedFrontalKeys.begin(), orderedFrontalKeys.end());
}
/* ************************************************************************* */
template <class GRAPH>
void ClusterTree<GRAPH>::print(const std::string& s, const KeyFormatter& keyFormatter) const {
treeTraversal::PrintForest(*this, s, keyFormatter);
}
/* ************************************************************************* */
/* Destructor.
* Using default destructor causes stack overflow for large trees due to recursive destruction of nodes;
* so we manually decrease the reference count of each node in the tree through a BFS, and the nodes with
* reference count 0 will be deleted. Please see [PR-1441](https://github.com/borglab/gtsam/pull/1441) for more details.
*/
template <class GRAPH>
ClusterTree<GRAPH>::~ClusterTree() {
// For each tree, we first move the root into a queue; then we do a BFS on the tree with the queue;
for (auto&& root : roots_) {
std::queue<sharedNode> bfs_queue;
// first, move the root to the queue
bfs_queue.push(root);
root = nullptr; // now the root node is owned by the queue
// for each node iterated, if its reference count is 1, it will be deleted while its children are still in the queue.
// so that the recursive deletion will not happen.
while (!bfs_queue.empty()) {
// move the ownership of the front node from the queue to the current variable
auto node = bfs_queue.front();
bfs_queue.pop();
// add the children of the current node to the queue, so that the queue will also own the children nodes.
for (auto child : node->children) {
bfs_queue.push(child);
} // leaving the scope of current will decrease the reference count of the current node by 1, and if the reference count is 0,
// the node will be deleted. Because the children are in the queue, the deletion of the node will not trigger a recursive
// deletion of the children.
}
}
}
/* ************************************************************************* */
template <class GRAPH>
ClusterTree<GRAPH>& ClusterTree<GRAPH>::operator=(const This& other) {
// Start by duplicating the tree.
roots_ = treeTraversal::CloneForest(other);
return *this;
}
/* ************************************************************************* */
// Elimination traversal data - stores a pointer to the parent data and collects
// the factors resulting from elimination of the children. Also sets up BayesTree
// cliques with parent and child pointers.
template<class CLUSTERTREE>
struct EliminationData {
// Typedefs
typedef typename CLUSTERTREE::sharedFactor sharedFactor;
typedef typename CLUSTERTREE::FactorType FactorType;
typedef typename CLUSTERTREE::FactorGraphType FactorGraphType;
typedef typename CLUSTERTREE::ConditionalType ConditionalType;
typedef typename CLUSTERTREE::BayesTreeType::Node BTNode;
EliminationData* const parentData;
size_t myIndexInParent;
FastVector<sharedFactor> childFactors;
std::shared_ptr<BTNode> bayesTreeNode;
#ifdef GTSAM_USE_TBB
std::shared_ptr<std::mutex> writeLock;
#endif
EliminationData(EliminationData* _parentData, size_t nChildren) :
parentData(_parentData), bayesTreeNode(std::make_shared<BTNode>())
#ifdef GTSAM_USE_TBB
, writeLock(std::make_shared<std::mutex>())
#endif
{
if (parentData) {
#ifdef GTSAM_USE_TBB
parentData->writeLock->lock();
#endif
myIndexInParent = parentData->childFactors.size();
parentData->childFactors.push_back(sharedFactor());
#ifdef GTSAM_USE_TBB
parentData->writeLock->unlock();
#endif
} else {
myIndexInParent = 0;
}
// Set up BayesTree parent and child pointers
if (parentData) {
if (parentData->parentData) // If our parent is not the dummy node
bayesTreeNode->parent_ = parentData->bayesTreeNode;
parentData->bayesTreeNode->children.push_back(bayesTreeNode);
}
}
// Elimination pre-order visitor - creates the EliminationData structure for the visited node.
static EliminationData EliminationPreOrderVisitor(
const typename CLUSTERTREE::sharedNode& node,
EliminationData& parentData) {
assert(node);
EliminationData myData(&parentData, node->nrChildren());
myData.bayesTreeNode->problemSize_ = node->problemSize();
return myData;
}
// Elimination post-order visitor - combine the child factors with our own factors, add the
// resulting conditional to the BayesTree, and add the remaining factor to the parent.
class EliminationPostOrderVisitor {
const typename CLUSTERTREE::Eliminate& eliminationFunction_;
typename CLUSTERTREE::BayesTreeType::Nodes& nodesIndex_;
public:
// Construct functor
EliminationPostOrderVisitor(
const typename CLUSTERTREE::Eliminate& eliminationFunction,
typename CLUSTERTREE::BayesTreeType::Nodes& nodesIndex) :
eliminationFunction_(eliminationFunction), nodesIndex_(nodesIndex) {
}
// Function that does the HEAVY lifting
void operator()(const typename CLUSTERTREE::sharedNode& node, EliminationData& myData) {
assert(node);
// Gather factors
FactorGraphType gatheredFactors;
gatheredFactors.reserve(node->factors.size() + node->nrChildren());
gatheredFactors.push_back(node->factors);
gatheredFactors.push_back(myData.childFactors);
// Check for Bayes tree orphan subtrees, and add them to our children
// TODO(frank): should this really happen here?
for (const sharedFactor& factor: node->factors) {
auto asSubtree = dynamic_cast<const BayesTreeOrphanWrapper<BTNode>*>(factor.get());
if (asSubtree) {
myData.bayesTreeNode->children.push_back(asSubtree->clique);
asSubtree->clique->parent_ = myData.bayesTreeNode;
}
}
// >>>>>>>>>>>>>> Do dense elimination step >>>>>>>>>>>>>>>>>>>>>>>>>>>>>
auto eliminationResult = eliminationFunction_(gatheredFactors, node->orderedFrontalKeys);
// <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
// Store conditional in BayesTree clique, and in the case of ISAM2Clique also store the
// remaining factor
myData.bayesTreeNode->setEliminationResult(eliminationResult);
// Fill nodes index - we do this here instead of calling insertRoot at the end to avoid
// putting orphan subtrees in the index - they'll already be in the index of the ISAM2
// object they're added to.
for (const Key& j : myData.bayesTreeNode->conditional()->frontals()) {
#ifdef GTSAM_USE_TBB
nodesIndex_.insert({j, myData.bayesTreeNode});
#else
nodesIndex_.emplace(j, myData.bayesTreeNode);
#endif
}
// Store remaining factor in parent's gathered factors
if (!eliminationResult.second->empty()) {
#ifdef GTSAM_USE_TBB
myData.parentData->writeLock->lock();
#endif
myData.parentData->childFactors[myData.myIndexInParent] = eliminationResult.second;
#ifdef GTSAM_USE_TBB
myData.parentData->writeLock->unlock();
#endif
}
}
};
};
/* ************************************************************************* */
template<class BAYESTREE, class GRAPH>
EliminatableClusterTree<BAYESTREE, GRAPH>& EliminatableClusterTree<BAYESTREE, GRAPH>::operator=(
const This& other) {
ClusterTree<GRAPH>::operator=(other);
// Assign the remaining factors - these are pointers to factors in the original factor graph and
// we do not clone them.
remainingFactors_ = other.remainingFactors_;
return *this;
}
/* ************************************************************************* */
template <class BAYESTREE, class GRAPH>
std::pair<std::shared_ptr<BAYESTREE>, std::shared_ptr<GRAPH> >
EliminatableClusterTree<BAYESTREE, GRAPH>::eliminate(const Eliminate& function) const {
gttic(ClusterTree_eliminate);
// Do elimination (depth-first traversal). The rootsContainer stores a 'dummy' BayesTree node
// that contains all of the roots as its children. rootsContainer also stores the remaining
// un-eliminated factors passed up from the roots.
std::shared_ptr<BayesTreeType> result = std::make_shared<BayesTreeType>();
typedef EliminationData<This> Data;
Data rootsContainer(0, this->nrRoots());
typename Data::EliminationPostOrderVisitor visitorPost(function, result->nodes_);
{
TbbOpenMPMixedScope threadLimiter; // Limits OpenMP threads since we're mixing TBB and OpenMP
treeTraversal::DepthFirstForestParallel(*this, rootsContainer, Data::EliminationPreOrderVisitor,
visitorPost, 10);
}
// Create BayesTree from roots stored in the dummy BayesTree node.
result->roots_.insert(result->roots_.end(), rootsContainer.bayesTreeNode->children.begin(),
rootsContainer.bayesTreeNode->children.end());
// Add remaining factors that were not involved with eliminated variables
std::shared_ptr<FactorGraphType> remaining = std::make_shared<FactorGraphType>();
remaining->reserve(remainingFactors_.size() + rootsContainer.childFactors.size());
remaining->push_back(remainingFactors_.begin(), remainingFactors_.end());
for (const sharedFactor& factor : rootsContainer.childFactors) {
if (factor)
remaining->push_back(factor);
}
// Return result
return {result, remaining};
}
} // namespace gtsam