/usr/include/opengm/inference/external/mplp.hxx is in libopengm-dev 2.3.6-2.
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#define OPENGM_EXTERNAL_MPLP_HXX_
#include <algorithm>
#include <sstream>
#include <opengm/inference/inference.hxx>
#include <opengm/graphicalmodel/graphicalmodel.hxx>
#include <opengm/operations/minimizer.hxx>
#include <opengm/inference/inference.hxx>
#include "opengm/inference/visitors/visitors.hxx"
// mplp logic
#include <cycle.h>
#undef eps
#undef Inf
namespace opengm {
namespace external {
/// MPLP
/// MPLP inference algorithm class
/// \ingroup inference
/// \ingroup external_inference
///
// MPLP
/// - cite :[?]
/// - Maximum factor order : ?
/// - Maximum number of labels : ?
/// - Restrictions : ?
/// - Convergent : ?
template<class GM>
class MPLP : public Inference<GM, opengm::Minimizer> {
public:
typedef GM GraphicalModelType;
typedef opengm::Minimizer AccumulationType;
OPENGM_GM_TYPE_TYPEDEFS;
typedef visitors::VerboseVisitor<MPLP<GM> > VerboseVisitorType;
typedef visitors::EmptyVisitor<MPLP<GM> > EmptyVisitorType;
typedef visitors::TimingVisitor<MPLP<GM> > TimingVisitorType;
struct Parameter {
/// \brief Constructor
Parameter(
//const size_t maxTightIter = 1000000,
//const size_t numIter = 1000,
//const size_t numIterLater = 20,
const size_t maxIterLP = 1000,
const size_t maxIterTight = 100000,
const size_t maxIterLater = 20,
const double maxTime = 3600,
const double maxTimeLP = 1200,
const size_t numClusToAddMin = 5,
const size_t numClusToAddMax = 20,
const double objDelThr = 0.0002,
const double intGapThr = 0.0002,
const bool UAIsettings = false,
const bool addEdgeIntersections = true,
const bool doGlobalDecoding = false,
const bool useDecimation=false,
const bool lookForCSPs = false,
//const double infTime = 0.0,
const bool logMode = false,
const std::string& logFile = std::string(),
const int seed = 0,
const std::string& inputFile = std::string(),
const std::string& evidenceFile = std::string()
)
: //maxTightIter_(maxTightIter),
//numIter_(numIter),
//numIterLater_(numIterLater),
maxIterLP_(maxIterLP),
maxIterTight_(maxIterTight),
maxIterLater_(maxIterLater),
maxTime_(maxTime),
maxTimeLP_(maxTimeLP),
numClusToAddMin_(numClusToAddMin),
numClusToAddMax_(numClusToAddMax),
objDelThr_(objDelThr),
intGapThr_(intGapThr),
UAIsettings_(UAIsettings),
addEdgeIntersections_(addEdgeIntersections),
doGlobalDecoding_(doGlobalDecoding),
useDecimation_(useDecimation),
lookForCSPs_(lookForCSPs),
//infTime_(infTime),
logFile_(logFile),
seed_(seed),
inputFile_(inputFile),
evidenceFile_(evidenceFile)
{ }
// new parameters
size_t maxIterLP_; //maximum number of iterrations for the initial LP
size_t maxIterTight_; //maximum number of rounds for tightening
size_t maxIterLater_; //maximum number of iterrations after each tightening
double maxTime_; //overall time limit in seconds
double maxTimeLP_; //time limit for the initial LP in seconds
//size_t maxTightIter_;
//size_t numIter_;
//size_t numIterLater_;
size_t numClusToAddMin_;
size_t numClusToAddMax_;
double objDelThr_;
double intGapThr_;
// Settings for UAI inference competition override all others
bool UAIsettings_;
// defaults. UAIsettings modulates the value of many of these
bool addEdgeIntersections_ ;
bool doGlobalDecoding_;
bool useDecimation_;
bool lookForCSPs_;
/* Note:
* Setting infTime_ to the total number of seconds allowed to run will
* result in global decoding being called once 1/3 through, and (if turned
* on) decimation being called 2/3 through (very helpful for CSP intances).
*/
double infTime_;
std::string logFile_;
int seed_;
std::string inputFile_;
std::string evidenceFile_;
};
// construction
MPLP(const GraphicalModelType& gm, const Parameter& para = Parameter());
// destruction
~MPLP();
// query
std::string name() const;
const GraphicalModelType& graphicalModel() const;
// inference
template<class VISITOR>
InferenceTermination infer(VISITOR & visitor);
InferenceTermination infer();
InferenceTermination arg(std::vector<LabelType>&, const size_t& = 1) const;
typename GM::ValueType bound() const;
typename GM::ValueType value() const;
protected:
const GraphicalModelType& gm_;
Parameter parameter_;
FILE* mplpLogFile_;
//double mplpTimeLimit_;
clock_t mplpStart_;
MPLPAlg* mplp_;
bool valueCheck() const;
};
template<class GM>
inline MPLP<GM>::MPLP(const GraphicalModelType& gm, const Parameter& para)
: gm_(gm), parameter_(para), mplpLogFile_(NULL), mplp_(NULL) {
if(parameter_.UAIsettings_) {
parameter_.doGlobalDecoding_ = true;
parameter_.useDecimation_=true;
parameter_.lookForCSPs_ = true;
}
// Log file
if(!parameter_.logFile_.empty()) {
mplpLogFile_ = fopen(parameter_.logFile_.c_str(), "w");
}
if (parameter_.maxTime_ <= 0.0) {
parameter_.maxTime_ = 3600*24*30; //30 days
}
if(parameter_.maxTime_< parameter_.maxTimeLP_){
parameter_.maxTimeLP_= parameter_.maxTime_;
}
if(MPLP_DEBUG_MODE) {
std::cout << "Time limit = " << parameter_.maxTime_ << std::endl;
}
mplpStart_ = clock();
// Load in the MRF and initialize GMPLP state
if(!parameter_.inputFile_.empty()) {
//mplp_ = new MPLPAlg(mplpStart_, mplpTimeLimit_, parameter_.inputFile_, parameter_.evidenceFile_, mplpLogFile_, parameter_.lookForCSPs_);
mplp_ = new MPLPAlg(mplpStart_, parameter_.maxTime_, parameter_.inputFile_, parameter_.evidenceFile_, mplpLogFile_, parameter_.lookForCSPs_);
} else {
// fill vectors from opengm model
std::vector<int> var_sizes(gm_.numberOfVariables());
for(IndexType var = 0; var < gm_.numberOfVariables(); ++var){
var_sizes[var] = static_cast<int>(gm_.numberOfLabels(var));
}
std::vector< std::vector<int> > all_factors(gm_.numberOfFactors());
for(IndexType f = 0; f < gm_.numberOfFactors(); ++f){
all_factors[f].resize(gm_[f].numberOfVariables());
for(IndexType i = 0; i < gm_[f].numberOfVariables(); ++i){
all_factors[f][i] = static_cast<int>(gm_[f].variableIndex(i));
}
}
std::vector< std::vector<double> > all_lambdas(gm_.numberOfFactors());
for(IndexType f = 0; f < gm_.numberOfFactors(); ++f){
all_lambdas[f].resize(gm_[f].size());
//gm_[f].copyValues(all_lambdas[f].begin());
gm_[f].copyValuesSwitchedOrder(all_lambdas[f].begin());
// TODO check if value transform (log or exp) is needed
for(size_t i = 0; i < all_lambdas[f].size(); i++) {
all_lambdas[f][i] = -all_lambdas[f][i];
}
}
//mplp_ = new MPLPAlg(mplpStart_, mplpTimeLimit_, var_sizes, all_factors, all_lambdas, mplpLogFile_, parameter_.lookForCSPs_);
mplp_ = new MPLPAlg(mplpStart_, parameter_.maxTime_, var_sizes, all_factors, all_lambdas, mplpLogFile_, parameter_.lookForCSPs_);
}
}
template<class GM>
inline MPLP<GM>::~MPLP() {
if(mplp_) {
delete mplp_;
}
}
template<class GM>
inline std::string MPLP<GM>::name() const {
return "MPLP";
}
template<class GM>
inline const typename MPLP<GM>::GraphicalModelType& MPLP<GM>::graphicalModel() const {
return gm_;
}
template<class GM>
inline InferenceTermination MPLP<GM>::infer() {
EmptyVisitorType visitor;
return this->infer(visitor);
}
template<class GM>
template<class VISITOR>
inline InferenceTermination MPLP<GM>::infer(VISITOR & visitor) {
visitor.begin(*this);
bool decimation_has_started = false;
bool force_decimation = false;
bool prevGlobalDecodingWas1 = true;
// Keep track of triplets added so far
std::map<std::vector<int>, bool> triplet_set;
//if(MPLP_DEBUG_MODE) std::cout << "Random seed = " << parameter_.seed_ << std::endl;
srand(parameter_.seed_);
/*
if(!parameter_.logFile_.empty()) {
std::stringstream stream;
stream << "I niter=" << parameter_.numIter_ << ", niter_later=" << parameter_.numIterLater_ << ", nclus_to_add_min=" << parameter_.numClusToAddMin_ << ", nclus_to_add_max=" << parameter_.numClusToAddMax_ << ", obj_del_thr=" << parameter_.objDelThr_ << ", int_gap_thr=" << parameter_.intGapThr_ << "\n";
fprintf(mplpLogFile_, "%s", stream.str().c_str());
}
if (MPLP_DEBUG_MODE) {
std::cout << "niter=" << parameter_.numIter_ << "\nniter_later=" << parameter_.numIterLater_ << "\nnclus_to_add=" << parameter_.numClusToAddMin_ << "\nobj_del_thr=" << parameter_.objDelThr_ << "\nint_gap_thr=" << parameter_.intGapThr_ << "\n";
std::cout << "Initially running MPLP for " << parameter_.numIter_ << " iterations\n";
}
*/
double value_old;
for (size_t i=0; i<=parameter_.maxIterLP_;++i){
value_old = mplp_->last_obj;
mplp_->RunMPLP(1, parameter_.objDelThr_, parameter_.intGapThr_);
if( visitor(*this) != visitors::VisitorReturnFlag::ContinueInf ){
if(!parameter_.logFile_.empty()) fflush(mplpLogFile_);
if(!parameter_.logFile_.empty()) fclose(mplpLogFile_);
visitor.end(*this);
return NORMAL;
}
if(((double)(clock() - mplpStart_) / CLOCKS_PER_SEC) > parameter_.maxTimeLP_){
std::cout << "stop because of timelimit for LP switching to tightening" <<std::endl;
break;
}
if(((double)(clock() - mplpStart_) / CLOCKS_PER_SEC) > parameter_.maxTime_){
std::cout << "stop because of timelimit" <<std::endl;
break;
}
if (std::fabs(value_old- mplp_->last_obj)<parameter_.objDelThr_ && i > 16){
std::cout << "stop because small progress" <<std::endl;
break;
}
if(std::fabs(value()-bound())<parameter_.intGapThr_){
std::cout << "stop because small gap" <<std::endl;
break;
}
}
for(size_t iter=1; iter<=parameter_.maxIterTight_; iter++){ // Break when problem is solved
// if(!parameter_.logFile_.empty()) fflush(mplpLogFile_);
// if (MPLP_DEBUG_MODE) std::cout << "\n\nOuter loop iteration " << iter << "\n----------------------\n";
// Is problem solved? If so, break.
double int_gap = mplp_->last_obj - mplp_->m_best_val;
if(int_gap < parameter_.intGapThr_){
if (MPLP_DEBUG_MODE) std::cout << "Done! Integrality gap less than " << parameter_.intGapThr_ << "\n";
break;
}
double time_elapsed = (double)(clock() - mplpStart_)/ CLOCKS_PER_SEC;
if (time_elapsed > parameter_.maxTime_) {
break; // terminates if alreay running past time limit (this should be very conservative)
}
// Heuristic: when the integrality gap is sufficiently small, allow the algorithm
// more time to run till convergence
if(int_gap < 1){
parameter_.maxIterLater_ = std::max(parameter_.maxIterLater_, static_cast<size_t>(600)); // TODO opt: don't hard code
parameter_.objDelThr_ = std::min(parameter_.objDelThr_, 1e-5);
if (MPLP_DEBUG_MODE) std::cout << "Int gap small, so setting niter_later to " << parameter_.maxIterLater_ << " and obj_del_thr to " << parameter_.objDelThr_ << "\n";
}
// Keep track of global decoding time and run this frequently, but at most 20% of total runtime
if(parameter_.doGlobalDecoding_ && (((double)clock() - mplp_->last_global_decoding_end_time)/CLOCKS_PER_SEC >= mplp_->last_global_decoding_total_time*4)) {
// Alternate between global decoding methods
if(prevGlobalDecodingWas1) {
mplp_->RunGlobalDecoding(false);
prevGlobalDecodingWas1 = false;
} else {
mplp_->RunGlobalDecoding2(false);
prevGlobalDecodingWas1 = true;
}
}
// Tighten LP
if (MPLP_DEBUG_MODE) std::cout << "Now attempting to tighten LP relaxation..." << std::endl;
clock_t tightening_start_time = clock();
double bound=0; double bound2 = 0;
int nClustersAdded = 0;
nClustersAdded += TightenTriplet(*mplp_, parameter_.numClusToAddMin_, parameter_.numClusToAddMax_, triplet_set, bound);
nClustersAdded += TightenCycle(*mplp_, parameter_.numClusToAddMin_, triplet_set, bound2, 1);
if(std::max(bound, bound2) < CLUSTER_THR) {
if(MPLP_DEBUG_MODE) std::cout << "TightenCycle did not find anything useful! Re-running with FindPartition." << std::endl;
nClustersAdded += TightenCycle(*mplp_, parameter_.numClusToAddMin_, triplet_set, bound2, 2);
}
// Check to see if guaranteed bound criterion was non-trivial.
// TODO: these bounds are not for the cycles actually added (since many of the top ones are skipped, already being in the relaxation). Modify it to be so.
bool noprogress = false;
if(std::max(bound, bound2) < CLUSTER_THR) noprogress = true;
clock_t tightening_end_time = clock();
double tightening_total_time = (double)(tightening_end_time - tightening_start_time)/CLOCKS_PER_SEC;
if (MPLP_DEBUG_MODE) {
std::cout << " -- Added " << nClustersAdded << " clusters to relaxation. Took " << tightening_total_time << " seconds\n";
}
if(!parameter_.logFile_.empty()) {
std::stringstream stream;
stream << "I added " << nClustersAdded << " clusters. Took " << tightening_total_time << " seconds\n";
fprintf(mplpLogFile_, "%s", stream.str().c_str());
}
// For CSP instances, 2/3 through run time, start decimation -- OR, when no progress being made
if((mplp_->CSP_instance || noprogress) && ((double)(clock() - mplpStart_) / CLOCKS_PER_SEC) > parameter_.maxTimeLP_ + (parameter_.maxTime_-parameter_.maxTimeLP_)/2){
force_decimation = true;
}
/*
We have done as much as we can with the existing edge intersection sets. Now
add in all new edge intersection sets for large clusters.
*/
if(nClustersAdded == 0 && parameter_.addEdgeIntersections_) {
mplp_->AddAllEdgeIntersections();
parameter_.addEdgeIntersections_ = false; // only makes sense to run this code once
}
// Not able to tighten relaxation further, so try to see if decoding is the problem
// Do not run this too often!
else if((!parameter_.addEdgeIntersections_ && nClustersAdded == 0) || force_decimation) {
// Do one last push to try to find the global assignment!
if(parameter_.doGlobalDecoding_ && (!parameter_.useDecimation_ || !decimation_has_started)) mplp_->RunGlobalDecoding3();
// Do one step of decimation
if (parameter_.useDecimation_) {
decimation_has_started = true;
bool fixed_node = mplp_->RunDecimation();
if(!fixed_node) {
if(MPLP_DEBUG_MODE) std::cout << "Decimation fixed all of the nodes it could... quiting." << std::endl;
break;
}
}
}
if (MPLP_DEBUG_MODE) std::cout << "Running MPLP again for " << parameter_.maxIterLater_ << " more iterations\n";
mplp_->RunMPLP(parameter_.maxIterLater_, parameter_.objDelThr_, parameter_.intGapThr_);
if(parameter_.UAIsettings_) {
// For UAI competition: time limit can be up to 1 hour, so kill process if still running.
//double time_elapsed, /*time,*/ time_limit;
double time_elapsed = (double)(clock() - mplpStart_)/ CLOCKS_PER_SEC;
if (time_elapsed > 4000 && time_elapsed > parameter_.maxTime_) {
break; // terminates if alreay running past time limit (this should be very conservative)
}
}
if(!parameter_.logFile_.empty()) fflush(mplpLogFile_);
if( visitor(*this) != visitors::VisitorReturnFlag::ContinueInf ){
break;
}
}
if(!parameter_.logFile_.empty()) fflush(mplpLogFile_);
if(!parameter_.logFile_.empty()) fclose(mplpLogFile_);
visitor.end(*this);
return NORMAL;
}
template<class GM>
inline InferenceTermination MPLP<GM>::arg(std::vector<LabelType>& arg, const size_t& n) const {
if(n > 1) {
return UNKNOWN;
}
else {
if(parameter_.inputFile_.empty()) {
OPENGM_ASSERT(mplp_->m_decoded_res.size() == gm_.numberOfVariables());
}
arg.resize(mplp_->m_decoded_res.size());
for(size_t i = 0; i < arg.size(); i++) {
arg[i] = static_cast<LabelType>(mplp_->m_decoded_res[i]);
}
return NORMAL;
}
}
template<class GM>
inline typename GM::ValueType MPLP<GM>::bound() const {
return -mplp_->last_obj;
//return -mplp_->m_best_val;
}
template<class GM>
inline typename GM::ValueType MPLP<GM>::value() const {
std::vector<LabelType> state;
arg(state);
return gm_.evaluate(state);
// -mplp_->m_best_val is the best value so far and not the value of the current configuration!
//OPENGM_ASSERT(valueCheck());
//return -mplp_->m_best_val;
}
template<class GM>
inline bool MPLP<GM>::valueCheck() const {
if(!parameter_.inputFile_.empty()) {
return true;
} else {
static bool visited = false;
if(visited) {
std::vector<LabelType> state;
arg(state);
if(fabs(-mplp_->m_best_val - gm_.evaluate(state)) < OPENGM_FLOAT_TOL) {
return true;
} else {
std::cout << "state: ";
for(size_t i = 0; i < state.size(); i++) {
std::cout << state[i] << "; ";
}
std::cout << std::endl;
std::cout << "value: " << -mplp_->m_best_val << std::endl;
std::cout << "expected: " << gm_.evaluate(state) << std::endl;
return false;
}
} else {
visited = true;
return true;
}
}
}
} // namespace external
} // namespace opengm
#endif /* OPENGM_EXTERNAL_MPLP_HXX_ */
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