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#ifndef OPENGM_LOC_HXX
#define OPENGM_LOC_HXX
#include <vector>
#include <algorithm>
#include <string>
#include <iostream>
#include <iomanip>
#include <cstdlib>
#include <cmath>
#include <queue>
#include <deque>
#include "opengm/opengm.hxx"
#include "opengm/utilities/random.hxx"
#include "opengm/inference/inference.hxx"
#include "opengm/inference/movemaker.hxx"
#include "opengm/inference/external/ad3.hxx"
#include <cmath>
#include <algorithm>
#include <sstream>
#include "opengm/inference/auxiliary/submodel/submodel_builder.hxx"
// internal
#include "opengm/inference/dynamicprogramming.hxx"
#include "opengm/inference/astar.hxx"
#include "opengm/inference/lazyflipper.hxx"
#include <opengm/inference/messagepassing/messagepassing.hxx>
#include "opengm/inference/visitors/visitors.hxx"
// external (autoinc)
#include "opengm/inference/external/ad3.hxx"
// external (inclued by with)
#ifdef WITH_CPLEX
#include "opengm/inference/lpcplex.hxx"
#endif
namespace opengm {
/// \ingroup inference
/// LOC Algorithm\n\n
/// K. Jung, P. Kohli and D. Shah, "Local Rules for Global MAP: When Do They Work?", NIPS 2009
///
/// In this implementation, the user needs to set the parameter of the
/// truncated geometric distribution by hand. Depending on the size of
/// the subgraph, either A* or exhaustive search is used for MAP
/// estimation on the subgraph
/// \ingroup inference
template<class GM, class ACC>
class LOC : public Inference<GM, ACC> {
public:
typedef ACC AccumulationType;
typedef GM GraphicalModelType;
OPENGM_GM_TYPE_TYPEDEFS;
typedef Movemaker<GraphicalModelType> MovemakerType;
typedef opengm::visitors::VerboseVisitor<LOC<GM,ACC> > VerboseVisitorType;
typedef opengm::visitors::EmptyVisitor<LOC<GM,ACC> > EmptyVisitorType;
typedef opengm::visitors::TimingVisitor<LOC<GM,ACC> > TimingVisitorType;
typedef SubmodelOptimizer<GM,ACC> SubOptimizer;
typedef typename SubOptimizer::SubGmType SubGmType;
// subsolvers
typedef opengm::DynamicProgramming<SubGmType,AccumulationType> DpSubInf;
typedef opengm::AStar<SubGmType,AccumulationType> AStarSubInf;
typedef opengm::LazyFlipper<SubGmType,AccumulationType> LfSubInf;
typedef opengm::BeliefPropagationUpdateRules<SubGmType,AccumulationType> UpdateRulesTypeBp;
typedef opengm::TrbpUpdateRules<SubGmType,AccumulationType> UpdateRulesTypeTrbp;
typedef opengm::MessagePassing<SubGmType, AccumulationType,UpdateRulesTypeBp , opengm::MaxDistance> BpSubInf;
typedef opengm::MessagePassing<SubGmType, AccumulationType,UpdateRulesTypeTrbp, opengm::MaxDistance> TrBpSubInf;
// external (autoincluded)
typedef opengm::external::AD3Inf<SubGmType,AccumulationType> Ad3SubInf;
#ifdef WITH_CPLEX
typedef opengm::LPCplex<SubGmType,AccumulationType> LpCplexSubInf;
#endif
template<class _GM>
struct RebindGm{
typedef LOC<_GM, ACC> type;
};
template<class _GM,class _ACC>
struct RebindGmAndAcc{
typedef LOC<_GM, _ACC > type;
};
class Parameter {
public:
/// constuctor
/// \param phi parameter of the truncated geometric distribution is used to select a certain subgraph radius with a certain probability
/// \param maxRadius maximum radius for the subgraphes which are optimized within opengm:::LOC
/// \param maxIteration maximum number of iterations (in one iteration on subgraph gets) optimized
/// \param ad3Threshold if the subgraph size is bigger than ad3Threshold opengm::external::Ad3Inf is used to optimize the subgraphes
/// \param stopAfterNBadIterations stop after n iterations without improvement
Parameter
(
const std::string solver="ad3",
const double phi = 0.3,
const size_t maxBlockRadius = 50,
const size_t maxTreeRadius = 50,
const double pFastHeuristic = 0.9,
const size_t maxIterations = 100000,
const size_t stopAfterNBadIterations=10000,
const size_t maxBlockSize = 0,
const size_t maxTreeSize =0,
const int treeRuns =1
)
: solver_(solver),
phi_(phi),
maxBlockRadius_(maxBlockRadius),
maxTreeRadius_(maxTreeRadius),
pFastHeuristic_(pFastHeuristic),
maxIterations_(maxIterations),
stopAfterNBadIterations_(stopAfterNBadIterations),
maxBlockSize_(maxBlockSize),
treeRuns_(treeRuns)
{
}
template<class P>
Parameter
(
const P & p
)
: solver_(p.solver_),
phi_(p.phi_),
maxBlockRadius_(p.maxBlockRadius_),
maxTreeRadius_(p.maxTreeRadius_),
pFastHeuristic_(p.pFastHeuristic_),
maxIterations_(p.maxIterations_),
stopAfterNBadIterations_(p.stopAfterNBadIterations_),
maxBlockSize_(p.maxBlockSize_),
treeRuns_(p.treeRuns_)
{
}
// subsolver used for submodel ("ad3" or "astar" so far)
std::string solver_;
/// phi of the truncated geometric distribution is used to select a certain subgraph radius with a certain probability
double phi_;
/// maximum subgraph radius
size_t maxBlockRadius_;
size_t maxTreeRadius_;
/// prob. of f
double pFastHeuristic_;
/// maximum number of iterations
size_t maxIterations_;
// stop after n iterations without improvement
size_t stopAfterNBadIterations_;
// max allowed subgraph size (0 means any is allowed)
size_t maxBlockSize_;
size_t maxTreeSize_;
int treeRuns_;
};
LOC(const GraphicalModelType&, const Parameter& param = Parameter());
std::string name() const;
const GraphicalModelType& graphicalModel() const;
InferenceTermination infer();
void reset();
template<class VisitorType>
InferenceTermination infer(VisitorType&);
void setStartingPoint(typename std::vector<LabelType>::const_iterator);
InferenceTermination arg(std::vector<LabelType>&, const size_t = 1) const;
ValueType value() const;
template<class VI_ITER>
void setBorderDirty(VI_ITER begin,VI_ITER end){
const IndexType nVis=std::distance(begin,end);
OPENGM_CHECK_OP(subOptimizer_.submodelSize(),==,nVis,"");
for(IndexType v=0;v<nVis;++v){
const IndexType vi=begin[v];
const IndexType nNVar = viAdjacency_[vi].size();
for(IndexType vo=0;vo<nNVar;++vo){
const IndexType vio=viAdjacency_[vi][vo];
if( subOptimizer_.inSubmodel(vio)==false){
cleanRegion_[vio]=false;
}
}
}
}
template<class VI_ITER>
void setInsideClean(VI_ITER begin,VI_ITER end){
const IndexType nVis=std::distance(begin,end);
OPENGM_CHECK_OP(subOptimizer_.submodelSize(),==,nVis,"");
for(IndexType v=0;v<nVis;++v){
const IndexType vi=begin[v];
cleanRegion_[vi]=true;
}
}
template<class VI_ITER>
bool hasDirtyInsideVariables(VI_ITER begin,VI_ITER end){
const IndexType nVis=std::distance(begin,end);
OPENGM_CHECK_OP(subOptimizer_.submodelSize(),==,nVis,"");
for(IndexType v=0;v<nVis;++v){
const IndexType vi=begin[v];
if(cleanRegion_[vi]==false){
return true;
}
}
return false;
}
private:
void getSubgraphVis(const size_t, const size_t, std::vector<size_t>&);
void getSubgraphTreeVis(const size_t, const size_t, std::vector<size_t>&);
void inline initializeProbabilities(std::vector<double>&,const size_t maxRadius);
const GraphicalModelType& gm_;
MovemakerType movemaker_;
Parameter param_;
std::vector< RandomAccessSet<IndexType> > viAdjacency_;
std::vector<bool> usedVi_;
std::vector<bool> checkedVi_;
std::vector<UInt64Type> distance_;
// submodel
SubOptimizer subOptimizer_;
// clean region
std::vector<bool> cleanRegion_;
bool optimizeSubmodel(std::vector<size_t> & subgraphVi,const bool);
};
template<class GM, class ACC>
LOC<GM, ACC>::LOC
(
const GraphicalModelType& gm,
const Parameter& parameter
)
: gm_(gm),
movemaker_(gm),
param_(parameter),
viAdjacency_(gm.numberOfVariables()),
usedVi_(gm.numberOfVariables(), false),
checkedVi_(gm.numberOfVariables(), false),
distance_(gm.numberOfVariables()),
subOptimizer_(gm),
cleanRegion_(gm.numberOfVariables(),false)
{
// compute variable adjacency
gm.variableAdjacencyList(viAdjacency_);
if(this->param_.maxIterations_==0)
param_.maxIterations_ = gm_.numberOfVariables() *
log(double(gm_.numberOfVariables()))*log(double(gm_.numberOfVariables()));
}
template<class GM, class ACC>
void
LOC<GM, ACC>::reset()
{
movemaker_.reset();
std::fill(usedVi_.begin(),usedVi_.end(),false);
// compute variable adjacency is not nessesary
// since reset assumes that the structure of
// the graphical model has not changed
if(this->param_.maxIterations_==0)
param_.maxIterations_ = gm_.numberOfVariables() *
log(double(gm_.numberOfVariables()))*log(double(gm_.numberOfVariables()));
}
template<class GM, class ACC>
inline void
LOC<GM,ACC>::setStartingPoint
(
typename std::vector<typename LOC<GM,ACC>::LabelType>::const_iterator begin
) {
try{
movemaker_.initialize(begin);
}
catch(...) {
throw RuntimeError("unsuitable starting point");
}
}
template<class GM, class ACC>
inline typename LOC<GM, ACC>::ValueType
LOC<GM, ACC>::value()const
{
return this->movemaker_.value();
}
template<class GM, class ACC>
void inline
LOC<GM, ACC>::initializeProbabilities
(
std::vector<double>& prob,const size_t maxRadius
)
{
if(maxRadius!=0){
const double phi = param_.phi_;
prob.resize(maxRadius);
for(size_t i=0;i<maxRadius-1;++i) {
prob[i] = phi * pow((1.0-phi), static_cast<double>(i));
}
prob[maxRadius-1]= pow((1.0-phi), static_cast<double>(maxRadius));
}
}
template<class GM, class ACC>
inline std::string
LOC<GM, ACC>::name() const {
return "LOC";
}
template<class GM, class ACC>
inline const typename LOC<GM, ACC>::GraphicalModelType&
LOC<GM, ACC>::graphicalModel() const {
return gm_;
}
template<class GM, class ACC>
void LOC<GM, ACC>::getSubgraphVis
(
const size_t startVi,
const size_t radius,
std::vector<size_t>& vis
) {
std::fill(usedVi_.begin(),usedVi_.end(),false);
vis.clear();
vis.push_back(startVi);
usedVi_[startVi]=true;
std::queue<size_t> viQueue;
viQueue.push(startVi);
std::fill(distance_.begin(),distance_.begin()+gm_.numberOfVariables(),0);
const size_t maxSgSize = (param_.maxBlockSize_==0? gm_.numberOfVariables() :param_.maxBlockSize_);
while(viQueue.size()!=0 && vis.size()<=maxSgSize) {
size_t cvi=viQueue.front();
viQueue.pop();
// for each neigbour of cvi
for(size_t vni=0;vni<viAdjacency_[cvi].size();++vni) {
// if neighbour has not been visited
const size_t vn=viAdjacency_[cvi][vni];
if(usedVi_[vn]==false) {
// set as visited
usedVi_[vn]=true;
// insert into the subgraph vis
distance_[vn]=distance_[cvi]+1;
if(distance_[vn]<=radius){
if(vis.size()<maxSgSize){
vis.push_back(vn);
viQueue.push(vn);
}
else{
break;
}
}
}
}
}
}
template<class GM, class ACC>
void LOC<GM, ACC>::getSubgraphTreeVis
(
const size_t startVi,
const size_t radius,
std::vector<size_t>& vis
) {
//std::cout<<"build tree\n";
std::fill(usedVi_.begin(),usedVi_.end(),false);
std::fill(checkedVi_.begin(),checkedVi_.end(),false);
vis.clear();
vis.push_back(startVi);
usedVi_[startVi]=true;
checkedVi_[startVi]=true;
std::deque<IndexType> viQueue;
viQueue.push_back(startVi);
bool first=true;
const size_t maxSgSize = (param_.maxTreeSize_==0? gm_.numberOfVariables() :param_.maxTreeSize_);
std::fill(distance_.begin(),distance_.begin()+gm_.numberOfVariables(),0);
//std::fill(distance_.begin(),distance_.begin()+vis.size(),0);
while(viQueue.size()!=0 && /*r<radius &&*/ vis.size()<=maxSgSize) {
IndexType cvi=viQueue.front();
OPENGM_CHECK(usedVi_[cvi]==false || vis.size()==1,"");
//std::cout<<"cvi "<<cvi<<" size "<<viQueue.size()<<" vis size "<<vis.size()<<"\n";
viQueue.pop_front();
if(checkedVi_[cvi]==true && first ==false){
continue;
}
first=false;
size_t includeInTree=0;
// for each neigbour of cvi
for(size_t vni=0;vni<viAdjacency_[cvi].size();++vni) {
const IndexType vn=viAdjacency_[cvi][vni];
if(usedVi_[vn]==true) {
++includeInTree;
}
}
//std::cout<<"inlcuded in tree "<<includeInTree<<"\n";
OPENGM_CHECK_OP(includeInTree,<=,vis.size(),"");
//OPENGM_CHECK_OP(includeInTree,<=,2,"");
checkedVi_[cvi]=true;
//std::cout<<"icn in tree "<<includeInTree<<"\n";
OPENGM_CHECK(includeInTree>0 || (vis.size()==1 && includeInTree==0),"");
//if (usedVi_[cvi]==false && includeInTree<=1){
if (includeInTree<=1){
//std::cout<<"in 1....\n";
// insert into the subgraph vis
if(usedVi_[cvi]==false){
vis.push_back(cvi);
// set as visited
usedVi_[cvi]=true;
if(vis.size()>=maxSgSize){
//std::cout<<"max size exit\n";
}
}
std::vector<IndexType> adjVis(viAdjacency_[cvi].size());
for(size_t vni=0;vni<viAdjacency_[cvi].size();++vni) {
const size_t vn=viAdjacency_[cvi][vni];
adjVis[vni]=vn;
}
std::random_shuffle(adjVis.begin(),adjVis.end());
// for each neigbour of cvi
for(size_t vni=0;vni<viAdjacency_[cvi].size();++vni) {
//std::cout<<"hello\n";
// if neighbour has not been visited
const size_t vn=adjVis[vni];
//std::cout<<"in 2....\n";
if(usedVi_[vn]==false && checkedVi_[vn]==false) {
//std::cout<<"in 3....\n";
// insert into queue
distance_[vn]=distance_[cvi]+1;
if(distance_[vn]<=radius)
viQueue.push_back(vn);
}
}
}
else{
//usedVi_[cvi]=true;
}
}
}
template<class GM, class ACC>
inline InferenceTermination
LOC<GM, ACC>::infer() {
EmptyVisitorType v;
return infer(v);
}
template<class GM, class ACC>
template<class VisitorType>
InferenceTermination
LOC<GM, ACC>::infer
(
VisitorType& visitor
) {
//const UInt64Type autoStop = param_.stopAfterNBadIterations_==0 ? gm_.numberOfVariables() : param_.stopAfterNBadIterations_;
const bool useTrees = param_.maxTreeRadius_ > 0;
const bool useBlocks = param_.maxBlockRadius_ > 0;
visitor.begin(*this);
// create random generators
opengm::RandomUniform<size_t> randomVariable(0, gm_.numberOfVariables());
opengm::RandomUniform<double> random01(0.0, 1.0);
std::vector<double> probBlock,probTree;
opengm::RandomDiscreteWeighted<size_t, double> randomRadiusBlock,randomRadiusTree;
if(useBlocks){
this->initializeProbabilities(probBlock,param_.maxBlockRadius_);
randomRadiusBlock =opengm::RandomDiscreteWeighted<size_t, double> (probBlock.begin(), probBlock.end());
}
if(useTrees){
this->initializeProbabilities(probTree,param_.maxTreeRadius_);
randomRadiusTree= opengm::RandomDiscreteWeighted<size_t, double> (probTree.begin(), probTree.end());
}
std::vector<size_t> subgGraphViBLock;
std::vector<size_t> subgGraphViTree;
// all iterations, usualy n*log(n)
//ValueType e1 = movemaker_.value(),e2;
//size_t badIter=0;
for(IndexType vi=0;vi<gm_.numberOfVariables();++vi){
subOptimizer_.setLabel(vi,movemaker_.state(vi));
}
for(IndexType run=0;run<2;++run){
std::vector<bool> coverdVar(gm_.numberOfVariables(),false);
for(IndexType vi=0;vi<gm_.numberOfVariables();++vi){
if(coverdVar[vi]==false){
size_t viStart = vi;
// select random radius block and tree
size_t radiusBlock = (useBlocks ? randomRadiusBlock()+1 : 0);
size_t radiusTree = (useTrees ? randomRadiusTree()+1 : 0);
//std::cout<<"viStart "<<viStart<<" rt "<<radiusTree<<" rb "<<radiusBlock<<"\n";
if(useTrees){
//std::cout<<"get'n optimize tree model\n";
if(param_.treeRuns_>0){
for(size_t tr=0;tr<(size_t)(param_.treeRuns_);++tr){
this->getSubgraphTreeVis(viStart, radiusTree, subgGraphViTree);
std::sort(subgGraphViTree.begin(), subgGraphViTree.end());
optimizeSubmodel(subgGraphViTree,true);
}
}
else{
size_t nTr=(param_.treeRuns_==0? 1: std::abs(param_.treeRuns_));
bool changes=true;
while(changes){
this->getSubgraphTreeVis(viStart, radiusTree, subgGraphViTree);
std::sort(subgGraphViTree.begin(), subgGraphViTree.end());
changes=false;
for(size_t tr=0;tr<nTr;++tr){
this->getSubgraphTreeVis(viStart, radiusTree, subgGraphViTree);
std::sort(subgGraphViTree.begin(), subgGraphViTree.end());
bool c=optimizeSubmodel(subgGraphViTree,true);
if(c){
changes=true;
}
}
}
}
}
//std::cout<<"bevore block "<<movemaker_.value()<<"\n";
if(useBlocks){
this->getSubgraphVis(viStart, radiusBlock, subgGraphViBLock);
std::sort(subgGraphViBLock.begin(), subgGraphViBLock.end());
optimizeSubmodel(subgGraphViBLock,false);
for(IndexType lvi=0;lvi<subgGraphViBLock.size();++lvi){
coverdVar[subgGraphViBLock[lvi]]=true;
}
}
//std::cout<<"after block "<<movemaker_.value()<<"\n";
//std::cout<<"after tree "<<movemaker_.value()<<"\n";
visitor(*this);
}
}
}
for(size_t i=0;i<0;++i) {
//for(size_t i=0;i<param_.maxIterations_;++i) {
//std::cout<<i<<" "<<param_.maxIterations_<<"\n";
// select random variable
size_t viStart = randomVariable();
// select random radius block and tree
size_t radiusBlock = (useBlocks ? randomRadiusBlock()+1 : 0);
size_t radiusTree = (useTrees ? randomRadiusTree()+1 : 0);
//std::cout<<"viStart "<<viStart<<" rt "<<radiusTree<<" rb "<<radiusBlock<<"\n";
if(useTrees){
//std::cout<<"get'n optimize tree model\n";
if(param_.treeRuns_>0){
for(size_t tr=0;tr<(size_t)(param_.treeRuns_);++tr){
this->getSubgraphTreeVis(viStart, radiusTree, subgGraphViTree);
std::sort(subgGraphViTree.begin(), subgGraphViTree.end());
optimizeSubmodel(subgGraphViTree,true);
}
}
else{
size_t nTr=(param_.treeRuns_==0? 1: std::abs(param_.treeRuns_));
bool changes=true;
while(changes){
this->getSubgraphTreeVis(viStart, radiusTree, subgGraphViTree);
std::sort(subgGraphViTree.begin(), subgGraphViTree.end());
changes=false;
for(size_t tr=0;tr<nTr;++tr){
this->getSubgraphTreeVis(viStart, radiusTree, subgGraphViTree);
std::sort(subgGraphViTree.begin(), subgGraphViTree.end());
bool c=optimizeSubmodel(subgGraphViTree,true);
if(c){
changes=true;
}
}
}
}
}
//std::cout<<"bevore block "<<movemaker_.value()<<"\n";
if(useBlocks){
this->getSubgraphVis(viStart, radiusBlock, subgGraphViBLock);
std::sort(subgGraphViBLock.begin(), subgGraphViBLock.end());
optimizeSubmodel(subgGraphViBLock,false);
}
//std::cout<<"after block "<<movemaker_.value()<<"\n";
//std::cout<<"after tree "<<movemaker_.value()<<"\n";
visitor(*this);
}
std::cout<<"basic inference is done\n";
visitor.end(*this);
return NORMAL;
}
template<class GM, class ACC>
bool LOC<GM, ACC>::optimizeSubmodel(std::vector<size_t> & subgGraphVi,const bool useTrees){
bool changes=false;
std::vector<LabelType> states;
if(subgGraphVi.size()>2){
subOptimizer_.setVariableIndices(subgGraphVi.begin(), subgGraphVi.end());
if (useTrees){
//std::cout<<"infer with tres\n";
changes = subOptimizer_.mergeFactorsAndInferDp(states);
//changes = subOptimizer_. template inferSubmodel<BpSubInf>(typename BpSubInf::Parameter() ,states);
//changes = subOptimizer_. template inferSubmodel<DpSubInf>(typename DpSubInf::Parameter() ,states);
//std::cout<<"infer with tress\n";
}
// OPTIMAL OR MONOTON MOVERS
else if(param_.solver_==std::string("ad3")){
changes = subOptimizer_. template inferSubmodelInplace<Ad3SubInf>(typename Ad3SubInf::Parameter(Ad3SubInf::AD3_ILP) ,states);
}
else if (param_.solver_==std::string("astar")){
//changes = subOptimizer_. template inferSubmodel<AStarSubInf>(typename AStarSubInf::Parameter() ,states);
}
else if (param_.solver_==std::string("cplex")){
#ifdef WITH_CPLEX
//typedef opengm::LPCplex<SubGmType,AccumulationType> LpCplexSubInf;
typename LpCplexSubInf::Parameter subParam;
subParam.integerConstraint_=true;
changes = subOptimizer_. template inferSubmodel<LpCplexSubInf>(subParam ,states);
#else
throw RuntimeError("solver cplex needs flag WITH_CPLEX defined bevore the #include of LOC sovler");
#endif
}
// MONOTON MOVERS
else if(param_.solver_[0]=='l' && param_.solver_[1]=='f'){
std::stringstream ss;
for(size_t i=2;i<param_.solver_.size();++i){
ss<<param_.solver_[i];
}
size_t maxSgSize;
ss>>maxSgSize;
changes = subOptimizer_. template inferSubmodel<LfSubInf>(typename LfSubInf::Parameter(maxSgSize) ,states,true,true);
}
subOptimizer_.unsetVariableIndices();
if(changes){
movemaker_.move(subgGraphVi.begin(), subgGraphVi.end(), states.begin());
for(IndexType v=0;v<subgGraphVi.size();++v){
subOptimizer_.setLabel(subgGraphVi[v],movemaker_.state(subgGraphVi[v]));
}
}
}
else{
// do nothing
}
return changes;
}
template<class GM, class ACC>
inline InferenceTermination
LOC<GM, ACC>::arg
(
std::vector<LabelType>& x,
const size_t N
) const {
if(N == 1) {
x.resize(gm_.numberOfVariables());
for(size_t j = 0; j < x.size(); ++j) {
x[j] = movemaker_.state(j);
}
return NORMAL;
}
else
return UNKNOWN;
}
} // namespace opengm
#endif // #ifndef OPENGM_LOC_HXX
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