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#include "incremental.h"
#include "hg.h"
#include "fdict.h"
#include "tdict.h"
#include "lm/enumerate_vocab.hh"
#include "lm/model.hh"
#include "search/applied.hh"
#include "search/config.hh"
#include "search/context.hh"
#include "search/edge.hh"
#include "search/edge_generator.hh"
#include "search/rule.hh"
#include "search/vertex.hh"
#include "search/vertex_generator.hh"
#include "util/exception.hh"
#include <boost/scoped_ptr.hpp>
#include <boost/scoped_array.hpp>
#include <iostream>
#include <vector>
namespace {
struct MapVocab : public lm::EnumerateVocab {
public:
MapVocab() {}
// Do not call after Lookup.
void Add(lm::WordIndex index, const StringPiece &str) {
const WordID cdec_id = TD::Convert(str.as_string());
if (cdec_id >= out_.size()) out_.resize(cdec_id + 1);
out_[cdec_id] = index;
}
// Assumes Add has been called and will never be called again.
lm::WordIndex FromCDec(WordID id) const {
return out_[out_.size() > id ? id : 0];
}
private:
std::vector<lm::WordIndex> out_;
};
template <class Model> class Incremental : public IncrementalBase {
public:
Incremental(const char *model_file, const std::vector<weight_t> &weights) :
IncrementalBase(weights),
m_(model_file, GetConfig()),
lm_(weights[FD::Convert("KLanguageModel")]),
oov_(weights[FD::Convert("KLanguageModel_OOV")]),
word_penalty_(weights[FD::Convert("WordPenalty")]) {
std::cerr << "Weights KLanguageModel " << lm_ << " KLanguageModel_OOV " << oov_ << " WordPenalty " << word_penalty_ << std::endl;
}
void Search(unsigned int pop_limit, const Hypergraph &hg) const;
private:
void ConvertEdge(const search::Context<Model> &context, search::Vertex *vertices, const Hypergraph::Edge &in, search::EdgeGenerator &gen) const;
lm::ngram::Config GetConfig() {
lm::ngram::Config ret;
ret.enumerate_vocab = &vocab_;
return ret;
}
MapVocab vocab_;
const Model m_;
const float lm_, oov_, word_penalty_;
};
void PrintApplied(const Hypergraph &hg, const search::Applied final) {
const std::vector<WordID> &words = static_cast<const Hypergraph::Edge*>(final.GetNote().vp)->rule_->e();
const search::Applied *child(final.Children());
for (std::vector<WordID>::const_iterator i = words.begin(); i != words.end(); ++i) {
if (*i > 0) {
std::cout << TD::Convert(*i) << ' ';
} else {
PrintApplied(hg, *child++);
}
}
}
template <class Model> void Incremental<Model>::Search(unsigned int pop_limit, const Hypergraph &hg) const {
boost::scoped_array<search::Vertex> out_vertices(new search::Vertex[hg.nodes_.size()]);
search::Config config(lm_, pop_limit, search::NBestConfig(1));
search::Context<Model> context(config, m_);
search::SingleBest best;
for (unsigned int i = 0; i < hg.nodes_.size() - 1; ++i) {
search::EdgeGenerator gen;
const Hypergraph::EdgesVector &down_edges = hg.nodes_[i].in_edges_;
for (unsigned int j = 0; j < down_edges.size(); ++j) {
unsigned int edge_index = down_edges[j];
ConvertEdge(context, out_vertices.get(), hg.edges_[edge_index], gen);
}
search::VertexGenerator<search::SingleBest> vertex_gen(context, out_vertices[i], best);
gen.Search(context, vertex_gen);
}
const search::Applied top = out_vertices[hg.nodes_.size() - 2].BestChild();
if (!top.Valid()) {
std::cout << "NO PATH FOUND" << std::endl;
} else {
PrintApplied(hg, top);
std::cout << "||| " << top.GetScore() << std::endl;
}
}
template <class Model> void Incremental<Model>::ConvertEdge(const search::Context<Model> &context, search::Vertex *vertices, const Hypergraph::Edge &in, search::EdgeGenerator &gen) const {
const std::vector<WordID> &e = in.rule_->e();
std::vector<lm::WordIndex> words;
words.reserve(e.size());
std::vector<search::PartialVertex> nts;
unsigned int terminals = 0;
float score = 0.0;
for (std::vector<WordID>::const_iterator word = e.begin(); word != e.end(); ++word) {
if (*word <= 0) {
nts.push_back(vertices[in.tail_nodes_[-*word]].RootPartial());
if (nts.back().Empty()) return;
score += nts.back().Bound();
words.push_back(lm::kMaxWordIndex);
} else {
++terminals;
words.push_back(vocab_.FromCDec(*word));
}
}
search::PartialEdge out(gen.AllocateEdge(nts.size()));
memcpy(out.NT(), &nts[0], sizeof(search::PartialVertex) * nts.size());
search::Note note;
note.vp = ∈
out.SetNote(note);
score += in.rule_->GetFeatureValues().dot(cdec_weights_);
score -= static_cast<float>(terminals) * word_penalty_ / M_LN10;
search::ScoreRuleRet res(search::ScoreRule(context.LanguageModel(), words, out.Between()));
score += res.prob * lm_ + static_cast<float>(res.oov) * oov_;
out.SetScore(score);
gen.AddEdge(out);
}
} // namespace
IncrementalBase *IncrementalBase::Load(const char *model_file, const std::vector<weight_t> &weights) {
lm::ngram::ModelType model_type;
if (!lm::ngram::RecognizeBinary(model_file, model_type)) model_type = lm::ngram::PROBING;
switch (model_type) {
case lm::ngram::PROBING:
return new Incremental<lm::ngram::ProbingModel>(model_file, weights);
case lm::ngram::REST_PROBING:
return new Incremental<lm::ngram::RestProbingModel>(model_file, weights);
default:
UTIL_THROW(util::Exception, "Sorry this lm type isn't supported yet.");
}
}
IncrementalBase::~IncrementalBase() {}
IncrementalBase::IncrementalBase(const std::vector<weight_t> &weights) : cdec_weights_(weights) {}
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