moses-smt/mosesdecoder
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fix cleanup()

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This commit is contained in:
Eva Hasler 2012-04-02 14:31:57 +01:00
parent 1b1459283c
commit 9587b7be5b
3 changed files with 19 additions and 14 deletions
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8
mira/Decoder.cpp
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@ -75,10 +75,12 @@ namespace Mira {
m_bleuScoreFeature = staticData.GetBleuScoreFeature(); m_bleuScoreFeature = staticData.GetBleuScoreFeature();
} }
void MosesDecoder::cleanup() { void MosesDecoder::cleanup(bool chartDecoding) {
delete m_manager; delete m_manager;
delete m_chartManager; if (chartDecoding)
delete m_sentence; delete m_chartManager;
else
delete m_sentence;
} }
vector< vector<const Word*> > MosesDecoder::getNBest(const std::string& source, vector< vector<const Word*> > MosesDecoder::getNBest(const std::string& source,

2
mira/Decoder.h
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@ -115,7 +115,7 @@ class MosesDecoder {
void setAvgInputLength (float l) { m_bleuScoreFeature->SetAvgInputLength(l); } void setAvgInputLength (float l) { m_bleuScoreFeature->SetAvgInputLength(l); }
Moses::ScoreComponentCollection getWeights(); Moses::ScoreComponentCollection getWeights();
void setWeights(const Moses::ScoreComponentCollection& weights); void setWeights(const Moses::ScoreComponentCollection& weights);
void cleanup(); void cleanup(bool chartDecoding);
float getSourceLengthHistory() { return m_bleuScoreFeature->GetSourceLengthHistory(); } float getSourceLengthHistory() { return m_bleuScoreFeature->GetSourceLengthHistory(); }
float getTargetLengthHistory() { return m_bleuScoreFeature->GetTargetLengthHistory(); } float getTargetLengthHistory() { return m_bleuScoreFeature->GetTargetLengthHistory(); }

23
mira/Main.cpp
View File

@ -408,6 +408,9 @@ int main(int argc, char** argv) {
decoder->setBleuParameters(sentenceLevelBleu, scaleByInputLength, scaleByAvgInputLength, decoder->setBleuParameters(sentenceLevelBleu, scaleByInputLength, scaleByAvgInputLength,
scaleByInverseLength, scaleByAvgInverseLength, scaleByInverseLength, scaleByAvgInverseLength,
scaleByX, historySmoothing, bleu_smoothing_scheme, relax_BP, useSourceLengthHistory); scaleByX, historySmoothing, bleu_smoothing_scheme, relax_BP, useSourceLengthHistory);
SearchAlgorithm searchAlgorithm = staticData.GetSearchAlgorithm();
bool chartDecoding = (searchAlgorithm == ChartDecoding);
if (normaliseWeights) { if (normaliseWeights) {
ScoreComponentCollection startWeights = decoder->getWeights(); ScoreComponentCollection startWeights = decoder->getWeights();
startWeights.L1Normalise(); startWeights.L1Normalise();
@ -756,7 +759,7 @@ int main(int argc, char** argv) {
featureValuesHope[batchPosition], bleuScoresHope[batchPosition], modelScoresHope[batchPosition], featureValuesHope[batchPosition], bleuScoresHope[batchPosition], modelScoresHope[batchPosition],
1, distinctNbest, avgRefLength, rank, epoch); 1, distinctNbest, avgRefLength, rank, epoch);
vector<const Word*> oracle = outputHope[0]; vector<const Word*> oracle = outputHope[0];
decoder->cleanup(); decoder->cleanup(chartDecoding);
ref_length = decoder->getClosestReferenceLength(*sid, oracle.size()); ref_length = decoder->getClosestReferenceLength(*sid, oracle.size());
avg_ref_length = ref_length; avg_ref_length = ref_length;
float hope_length_ratio = (float)oracle.size()/ref_length; float hope_length_ratio = (float)oracle.size()/ref_length;
@ -806,7 +809,7 @@ int main(int argc, char** argv) {
dummyFeatureValues[batchPosition], dummyBleuScores[batchPosition], dummyModelScores[batchPosition], dummyFeatureValues[batchPosition], dummyBleuScores[batchPosition], dummyModelScores[batchPosition],
1, distinctNbest, avgRefLength, rank, epoch); 1, distinctNbest, avgRefLength, rank, epoch);
bestModel = outputModel[0]; bestModel = outputModel[0];
decoder->cleanup(); decoder->cleanup(chartDecoding);
cerr << endl; cerr << endl;
ref_length = decoder->getClosestReferenceLength(*sid, bestModel.size()); ref_length = decoder->getClosestReferenceLength(*sid, bestModel.size());
} }
@ -821,7 +824,7 @@ int main(int argc, char** argv) {
featureValuesFear[batchPosition], bleuScoresFear[batchPosition], modelScoresFear[batchPosition], featureValuesFear[batchPosition], bleuScoresFear[batchPosition], modelScoresFear[batchPosition],
1, distinctNbest, avgRefLength, rank, epoch); 1, distinctNbest, avgRefLength, rank, epoch);
vector<const Word*> fear = outputFear[0]; vector<const Word*> fear = outputFear[0];
decoder->cleanup(); decoder->cleanup(chartDecoding);
ref_length = decoder->getClosestReferenceLength(*sid, fear.size()); ref_length = decoder->getClosestReferenceLength(*sid, fear.size());
avg_ref_length += ref_length; avg_ref_length += ref_length;
avg_ref_length /= 2; avg_ref_length /= 2;
@ -885,7 +888,7 @@ int main(int argc, char** argv) {
featureValuesHope[batchPosition], bleuScoresHope[batchPosition], modelScoresHope[batchPosition], featureValuesHope[batchPosition], bleuScoresHope[batchPosition], modelScoresHope[batchPosition],
1, distinctNbest, avgRefLength, rank, epoch); 1, distinctNbest, avgRefLength, rank, epoch);
vector<const Word*> oracle = outputHope[0]; vector<const Word*> oracle = outputHope[0];
decoder->cleanup(); decoder->cleanup(chartDecoding);
cerr << endl; cerr << endl;
// count sparse features occurring in hope translation // count sparse features occurring in hope translation
@ -898,7 +901,7 @@ int main(int argc, char** argv) {
featureValuesFear[batchPosition], bleuScoresFear[batchPosition], modelScoresFear[batchPosition], featureValuesFear[batchPosition], bleuScoresFear[batchPosition], modelScoresFear[batchPosition],
1, distinctNbest, avgRefLength, rank, epoch); 1, distinctNbest, avgRefLength, rank, epoch);
bestModel = outputModel[0]; bestModel = outputModel[0];
decoder->cleanup(); decoder->cleanup(chartDecoding);
cerr << endl; cerr << endl;
// needed for history // needed for history
@ -919,7 +922,7 @@ int main(int argc, char** argv) {
featureValues[batchPosition], bleuScores[batchPosition], modelScores[batchPosition], featureValues[batchPosition], bleuScores[batchPosition], modelScores[batchPosition],
1, distinctNbest, avgRefLength, rank, epoch); 1, distinctNbest, avgRefLength, rank, epoch);
vector<const Word*> bestModel = outputModel[0]; vector<const Word*> bestModel = outputModel[0];
decoder->cleanup(); decoder->cleanup(chartDecoding);
oneBests.push_back(bestModel); oneBests.push_back(bestModel);
ref_length = decoder->getClosestReferenceLength(*sid, bestModel.size()); ref_length = decoder->getClosestReferenceLength(*sid, bestModel.size());
float model_length_ratio = (float)bestModel.size()/ref_length; float model_length_ratio = (float)bestModel.size()/ref_length;
@ -941,7 +944,7 @@ int main(int argc, char** argv) {
// needed for history // needed for history
inputLengths.push_back(current_input_length); inputLengths.push_back(current_input_length);
ref_ids.push_back(*sid); ref_ids.push_back(*sid);
decoder->cleanup(); decoder->cleanup(chartDecoding);
oracles.push_back(oracle); oracles.push_back(oracle);
ref_length = decoder->getClosestReferenceLength(*sid, oracle.size()); ref_length = decoder->getClosestReferenceLength(*sid, oracle.size());
float hope_length_ratio = (float)oracle.size()/ref_length; float hope_length_ratio = (float)oracle.size()/ref_length;
@ -960,7 +963,7 @@ int main(int argc, char** argv) {
featureValues[batchPosition], bleuScores[batchPosition], modelScores[batchPosition], featureValues[batchPosition], bleuScores[batchPosition], modelScores[batchPosition],
1, distinctNbest, avgRefLength, rank, epoch); 1, distinctNbest, avgRefLength, rank, epoch);
vector<const Word*> bestModel = outputModel[0]; vector<const Word*> bestModel = outputModel[0];
decoder->cleanup(); decoder->cleanup(chartDecoding);
oneBests.push_back(bestModel); oneBests.push_back(bestModel);
ref_length = decoder->getClosestReferenceLength(*sid, bestModel.size()); ref_length = decoder->getClosestReferenceLength(*sid, bestModel.size());
float model_length_ratio = (float)bestModel.size()/ref_length; float model_length_ratio = (float)bestModel.size()/ref_length;
@ -973,7 +976,7 @@ int main(int argc, char** argv) {
featureValues[batchPosition], bleuScores[batchPosition], modelScores[batchPosition], featureValues[batchPosition], bleuScores[batchPosition], modelScores[batchPosition],
1, distinctNbest, avgRefLength, rank, epoch); 1, distinctNbest, avgRefLength, rank, epoch);
vector<const Word*> fear = outputFear[0]; vector<const Word*> fear = outputFear[0];
decoder->cleanup(); decoder->cleanup(chartDecoding);
ref_length = decoder->getClosestReferenceLength(*sid, fear.size()); ref_length = decoder->getClosestReferenceLength(*sid, fear.size());
float fear_length_ratio = (float)fear.size()/ref_length; float fear_length_ratio = (float)fear.size()/ref_length;
cerr << ", l-ratio fear: " << fear_length_ratio << endl; cerr << ", l-ratio fear: " << fear_length_ratio << endl;
@ -1701,7 +1704,7 @@ void decodeHopeOrFear(size_t rank, size_t size, size_t decode, string filename,
vector< vector<const Word*> > nbestOutput = decoder->getNBest(input, sid, n, factor, 1, dummyFeatureValues[0], vector< vector<const Word*> > nbestOutput = decoder->getNBest(input, sid, n, factor, 1, dummyFeatureValues[0],
dummyBleuScores[0], dummyModelScores[0], n, true, false, rank, 0); dummyBleuScores[0], dummyModelScores[0], n, true, false, rank, 0);
cerr << endl; cerr << endl;
decoder->cleanup(); decoder->cleanup(StaticData::Instance().GetSearchAlgorithm() == ChartDecoding);
for (size_t i = 0; i < nbestOutput.size(); ++i) { for (size_t i = 0; i < nbestOutput.size(); ++i) {
vector<const Word*> output = nbestOutput[i]; vector<const Word*> output = nbestOutput[i];