/usr/include/opengm/inference/external/fastPD.hxx is in libopengm-dev 2.3.6-2.
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#define FASTPD_HXX_
#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"
#include "opengm/inference/auxiliary/lp_reparametrization.hxx"
#include "Fast_PD.h"
namespace opengm {
namespace external {
/// FastPD
/// FastPD inference algorithm class
/// \ingroup inference
/// \ingroup external_inference
///
// FastPD
/// - cite :[?]
/// - Maximum factor order : ?
/// - Maximum number of labels : ?
/// - Restrictions : ?
/// - Convergent : ?
template<class GM>
class FastPD : public Inference<GM, opengm::Minimizer> {
public:
typedef GM GraphicalModelType;
typedef opengm::Minimizer AccumulationType;
OPENGM_GM_TYPE_TYPEDEFS;
typedef visitors::VerboseVisitor<FastPD<GM> > VerboseVisitorType;
typedef visitors::EmptyVisitor<FastPD<GM> > EmptyVisitorType;
typedef visitors::TimingVisitor<FastPD<GM> > TimingVisitorType;
///Parameter
struct Parameter {
/// \brief Constructor
Parameter() : numberOfIterations_(1000) {
}
/// number of iterations
size_t numberOfIterations_;
};
// construction
FastPD(const GraphicalModelType& gm, const Parameter& para = Parameter());
// destruction
~FastPD();
// 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;
typedef LPReparametrizer<GM,opengm::Minimizer> ReparametrizerType;
ReparametrizerType * getReparametrizer(const typename ReparametrizerType::Parameter& params=typename ReparametrizerType::Parameter())const;
protected:
const GraphicalModelType& gm_;
Parameter parameter_;
fastPDLib::CV_Fast_PD* pdInference_;
ValueType value_;
ValueType lowerBound_;
fastPDLib::CV_Fast_PD::Real* labelCosts_;
int numPairs_;
int* pairs_;
fastPDLib::CV_Fast_PD::Real* distance_;
fastPDLib::CV_Fast_PD::Real* weights_;
bool sameNumberOfLabels() const;
void setLabelCosts();
void getNumPairs();
void setPairs();
void setDistance();
void setWeights();
bool sameEnergyTable();
};
template<class GM>
FastPD<GM>::FastPD(const typename FastPD<GM>::GraphicalModelType& gm, const Parameter& para)
: gm_(gm), parameter_(para), pdInference_(NULL), labelCosts_(NULL),
numPairs_(0), pairs_(NULL), distance_(NULL), weights_(NULL) {
OPENGM_ASSERT(sameNumberOfLabels());
OPENGM_ASSERT(gm_.maxFactorOrder(2));
setLabelCosts();
getNumPairs();
setPairs();
setDistance();
setWeights();
if(sameEnergyTable()==false){
throw std::runtime_error("Error: Tables are not proportional");
}
pdInference_ = new fastPDLib::CV_Fast_PD(
gm_.numberOfVariables(),
gm_.numberOfLabels(0),
labelCosts_,
numPairs_,
pairs_,
distance_,
parameter_.numberOfIterations_,
weights_
);
// set initial value and lower bound
AccumulationType::neutral(value_);
AccumulationType::ineutral(lowerBound_);
}
template<class GM>
FastPD<GM>::~FastPD() {
if(pdInference_) {
delete pdInference_;
}
if(labelCosts_) {
delete[] labelCosts_;
}
if(pairs_) {
delete[] pairs_;
}
if(distance_) {
delete[] distance_;
}
if(weights_) {
delete[] weights_;
}
}
template<class GM>
inline std::string FastPD<GM>::name() const {
return "FastPD";
}
template<class GM>
inline const typename FastPD<GM>::GraphicalModelType& FastPD<GM>::graphicalModel() const {
return gm_;
}
template<class GM>
inline InferenceTermination FastPD<GM>::infer() {
EmptyVisitorType visitor;
return this->infer(visitor);
}
template<class GM>
template<class VISITOR>
inline InferenceTermination FastPD<GM>::infer(VISITOR & visitor) {
visitor.begin(*this);
// TODO check for possible visitor injection method
// TODO this is slow, check if fast_pd allows energy extraction
if(pdInference_ != NULL) {
pdInference_->run();
std::vector<LabelType> result;
arg(result);
value_ = gm_.evaluate(result);
}
visitor.end(*this);
return NORMAL;
}
template<class GM>
inline InferenceTermination FastPD<GM>::arg(std::vector<LabelType>& arg, const size_t& n) const {
OPENGM_ASSERT(pdInference_ != NULL);
arg.resize(gm_.numberOfVariables());
for(IndexType i = 0; i < gm_.numberOfVariables(); i++) {
arg[i] = pdInference_->_pinfo[i].label;
}
return NORMAL;
}
// == OLD ==
//template<class GM>
//inline typename GM::ValueType FastPD<GM>::bound() const {
// return lowerBound_;
//}
template<class GM>
typename GM::ValueType FastPD<GM>::bound()const
{
ValueType boundValue=0;
IndexType pwId=0;
std::vector<IndexType> factorId2pwId(gm_.numberOfFactors(),std::numeric_limits<IndexType>::max());
for (IndexType factorId=0;factorId<gm_.numberOfFactors();++factorId)
if (gm_[factorId].numberOfVariables()==2)
factorId2pwId[factorId]=pwId++;
for (IndexType factorId=0;factorId<gm_.numberOfFactors();++factorId)
{
const typename GM::FactorType& f=gm_[factorId];
ValueType res=std::numeric_limits<ValueType>::infinity(), res1;
if (f.numberOfVariables()==1)
{
IndexType varId=f.variableIndex(0);
for (LabelType label=0;label<gm_.numberOfLabels(varId);++label)
{
res1=f(&label);
for (IndexType i=0;i<gm_.numberOfFactors(varId);++i)
{
IndexType fId=gm_.factorOfVariable(varId,i);
if (gm_[fId].numberOfVariables()==2)
{
OPENGM_ASSERT(factorId2pwId[fId]<std::numeric_limits<IndexType>::max());
if (gm_[fId].variableIndex(0)==varId)
res1+=pdInference_->_y[label*numPairs_+factorId2pwId[fId]];
else
res1-=pdInference_->_y[label*numPairs_+factorId2pwId[fId]];
}
}
res=std::min(res,res1);
}
}else if (f.numberOfVariables()==2)
{
pwId=factorId2pwId[factorId];
OPENGM_ASSERT(pwId<std::numeric_limits<IndexType>::max());
IndexType varId0=f.variableIndex(0),varId1=f.variableIndex(1);
for (LabelType label0=0;label0<gm_.numberOfLabels(varId0);++label0)
for (LabelType label1=0;label1<gm_.numberOfLabels(varId1);++label1)
{
std::vector<LabelType> labels(2); labels[0]=label0; labels[1]=label1;
res1=f(labels.begin())-pdInference_->_y[label0*numPairs_+pwId]+pdInference_->_y[label1*numPairs_+pwId];
res=std::min(res,res1);
}
}else{
AccumulationType::ineutral(boundValue);
return boundValue;
//throw std::runtime_error("FastPD: only factors of order 1 and 2 are supported!");
}
boundValue+=res;
}
return boundValue;
}
template<class GM>
inline typename GM::ValueType FastPD<GM>::value() const {
return value_;
}
template<class GM>
inline bool FastPD<GM>::sameNumberOfLabels() const {
OPENGM_ASSERT(gm_.numberOfVariables() > 0);
LabelType numLabels = gm_.numberOfLabels(0);
for(IndexType i = 1; i < gm_.numberOfVariables(); i++) {
if(gm_.numberOfLabels(i) != numLabels) {
return false;
}
}
return true;
}
template<class GM>
inline void FastPD<GM>::setLabelCosts() {
labelCosts_ = new fastPDLib::CV_Fast_PD::Real[gm_.numberOfVariables() * gm_.numberOfLabels(0)];
for(IndexType i = 0; i < gm_.numberOfVariables() * gm_.numberOfLabels(0); i++) {
labelCosts_[i] = 0;
}
for(IndexType i = 0; i < gm_.numberOfVariables(); i++) {
for(IndexType j = 0; j < gm_.numberOfFactors(i); j++) {
IndexType gmFactorIndex = gm_.factorOfVariable(i, j);
if(gm_.numberOfVariables(gmFactorIndex) == 1) {
for(IndexType k = 0; k < gm_.numberOfLabels(0); k++) {
labelCosts_[k * gm_.numberOfVariables() + i ] += gm_[gmFactorIndex](&k);
}
}
}
}
}
template<class GM>
inline void FastPD<GM>::getNumPairs() {
for(IndexType i = 0; i < gm_.numberOfFactors(); i++) {
if(gm_.numberOfVariables(i) == 2) {
numPairs_++;
}
}
}
template<class GM>
inline void FastPD<GM>::setPairs() {
pairs_ = new int[numPairs_ * 2];
int currentPair = 0;
for(IndexType i = 0; i < gm_.numberOfFactors(); i++) {
if(gm_.numberOfVariables(i) == 2) {
pairs_[currentPair * 2] = gm_[i].variableIndex(0);
pairs_[(currentPair * 2) + 1] = gm_[i].variableIndex(1);
currentPair++;
}
}
}
template<class GM>
inline void FastPD<GM>::setDistance() {
distance_ = new fastPDLib::CV_Fast_PD::Real[gm_.numberOfLabels(0) * gm_.numberOfLabels(0)];
for(IndexType k = 0; k < gm_.numberOfLabels(0); k++) {
for(IndexType l = 0; l < gm_.numberOfLabels(0); l++) {
distance_[(l * gm_.numberOfLabels(0)) + k] = 0;
}
}
for(IndexType i = 0; i < gm_.numberOfFactors(); i++) {
if(gm_.numberOfVariables(i) == 2) {
for(IndexType k = 0; k < gm_.numberOfLabels(0); k++) {
for(IndexType l = 0; l < gm_.numberOfLabels(0); l++) {
IndexType index[] = {k, l};
distance_[(l * gm_.numberOfLabels(0)) + k] = gm_[i](index);
}
}
break;
}
}
}
template<class GM>
inline void FastPD<GM>::setWeights() {
weights_ = new fastPDLib::CV_Fast_PD::Real[numPairs_];
int currentPair = 0;
for(IndexType i = 0; i < gm_.numberOfFactors(); i++) {
if(gm_.numberOfVariables(i) == 2) {
OPENGM_ASSERT(currentPair < numPairs_);
IndexType k;
for(k = 0; k < gm_.numberOfLabels(0); k++) {
IndexType l;
for(l = 0; l < gm_.numberOfLabels(0); l++) {
IndexType index[] = {k, l};
if((gm_[i](index) != 0) && (distance_[(l * gm_.numberOfLabels(0)) + k] != 0)) {
double currentWeight = static_cast<double>(gm_[i](index)) / static_cast<double>(distance_[(l * gm_.numberOfLabels(0)) + k]);
weights_[currentPair] = static_cast<fastPDLib::CV_Fast_PD::Real>(currentWeight);
if(fabs(currentWeight - static_cast<double>(weights_[currentPair])) > OPENGM_FLOAT_TOL) {
throw(RuntimeError("Function not supported"));
}
currentPair++;
break;
}
}
if(l != gm_.numberOfLabels(0)) {
break;
}
}
if(k == gm_.numberOfLabels(0)) {
weights_[currentPair] = 0;
currentPair++;
}
}
}
OPENGM_ASSERT(currentPair == numPairs_);
}
template<class GM>
inline bool FastPD<GM>::sameEnergyTable() {
int currentPair = 0;
for(IndexType i = 0; i < gm_.numberOfFactors(); i++) {
if(gm_.numberOfVariables(i) == 2) {
for(IndexType k = 0; k < gm_.numberOfLabels(0); k++) {
for(IndexType l = 0; l < gm_.numberOfLabels(0); l++) {
IndexType index[] = {k, l};
if(fabs(gm_[i](index) - (distance_[(l * gm_.numberOfLabels(0)) + k] * weights_[currentPair])) > OPENGM_FLOAT_TOL) {
return false;
}
}
}
currentPair++;
}
}
OPENGM_ASSERT(currentPair == numPairs_);
return true;
}
template<class GM>
inline typename FastPD<GM>::ReparametrizerType * FastPD<GM>::getReparametrizer(const typename ReparametrizerType::Parameter& params)const
{
ReparametrizerType* pReparametrizer=new ReparametrizerType(gm_);
ReparametrizerType lpreparametrizer(gm_);
typename ReparametrizerType::RepaStorageType& reparametrization=pReparametrizer->Reparametrization();
typedef typename ReparametrizerType::RepaStorageType::uIterator uIterator;
//counting p/w factors
IndexType pwNum=0;
for (IndexType factorId=0;factorId<gm_.numberOfFactors();++factorId)
if (gm_[factorId].numberOfVariables()==2) ++pwNum;
const fastPDLib::CV_Fast_PD::Real* y=pdInference_->_y;
IndexType pwId=0;
for (IndexType factorId=0;factorId<gm_.numberOfFactors();++factorId)
{
if (gm_[factorId].numberOfVariables()!=2) continue;
for (IndexType i=0;i<2;++i)
{
std::pair<uIterator,uIterator> iter=reparametrization.getIterators(factorId,i);
LabelType label=0;
ValueType mul=(i==0 ? 1 : -1);
for (;iter.first!=iter.second;++iter.first)
{
*iter.first=-mul*y[label*pwNum+pwId];
++label;
}
}
++pwId;
}
return pReparametrizer;
}
} // namespace external
} // namespace opengm
#endif /* FASTPD_HXX_ */
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