merge

1 files changed, 101 insertions, 107 deletions

diff --git a/src/amun/cpu/decoder/best_hyps.h b/src/amun/cpu/decoder/best_hyps.h
index 8ebc9ce6..82d0f3e1 100644
--- a/src/amun/cpu/decoder/best_hyps.h
+++ b/src/amun/cpu/decoder/best_hyps.h
@@ -5,7 +5,6 @@
 
 #include "common/scorer.h"
 #include "common/god.h"
-#include "common/utils.h"
 #include "common/exception.h"
 #include "cpu/mblas/matrix.h"
 
@@ -24,134 +23,129 @@ struct ProbCompare {
 
 class BestHyps : public BestHypsBase
 {
-public:
-  void CalcBeam(
-      const God &god,
-      const Beam& prevHyps,
-      const std::vector<ScorerPtr>& scorers,
-      const Words& filterIndices,
-      bool returnAlignment,
-      std::vector<Beam>& beams,
-      std::vector<uint>& beamSizes
-      )
-  {
-    using namespace mblas;
-
-    auto& weights = god.GetScorerWeights();
-
-    mblas::ArrayMatrix& Probs = static_cast<mblas::ArrayMatrix&>(scorers[0]->GetProbs());
-
-    mblas::ArrayMatrix Costs(Probs.rows(), 1);
-    for (size_t i = 0; i < prevHyps.size(); ++i) {
-      Costs.data()[i] = prevHyps[i]->GetCost();
-    }
-
-    Probs *= weights.at(scorers[0]->GetName());
-    AddBiasVector<byColumn>(Probs, Costs);
-
-    for (size_t i = 1; i < scorers.size(); ++i) {
-      mblas::ArrayMatrix &currProb = static_cast<mblas::ArrayMatrix&>(scorers[i]->GetProbs());
-
-      Probs += weights.at(scorers[i]->GetName()) * currProb;
-    }
-
-    size_t size = Probs.rows() * Probs.columns(); // Probs.size();
-    std::vector<size_t> keys(size);
-    for (size_t i = 0; i < keys.size(); ++i) {
-      keys[i] = i;
-    }
+  public:
+    BestHyps(const God &god)
+      : BestHypsBase(
+          !god.Get<bool>("allow-unk"),
+          god.Get<bool>("n-best"),
+          god.Get<std::vector<std::string>>("softmax-filter").size(),
+          god.Get<bool>("return-alignment") || god.Get<bool>("return-soft-alignment"),
+          god.GetScorerWeights())
+    {}
+
+    void CalcBeam(
+        const Beam& prevHyps,
+        const std::vector<ScorerPtr>& scorers,
+        const Words& filterIndices,
+        std::vector<Beam>& beams,
+        std::vector<uint>& beamSizes)
+    {
+      using namespace mblas;
+
+      mblas::ArrayMatrix& Probs = static_cast<mblas::ArrayMatrix&>(scorers[0]->GetProbs());
+
+      mblas::ArrayMatrix Costs(Probs.rows(), 1);
+      for (size_t i = 0; i < prevHyps.size(); ++i) {
+        Costs.data()[i] = prevHyps[i]->GetCost();
+      }
 
-    size_t beamSize = beamSizes[0];
+      Probs *= weights_.at(scorers[0]->GetName());
+      AddBiasVector<byColumn>(Probs, Costs);
 
-    std::vector<size_t> bestKeys(beamSize);
-    std::vector<float> bestCosts(beamSize);
+      for (size_t i = 1; i < scorers.size(); ++i) {
+        mblas::ArrayMatrix &currProb = static_cast<mblas::ArrayMatrix&>(scorers[i]->GetProbs());
 
-    if (!god.Get<bool>("allow-unk")) {
-      blaze::column(Probs, UNK_ID) = std::numeric_limits<float>::lowest();
-    }
+        Probs += weights_.at(scorers[i]->GetName()) * currProb;
+      }
 
-    //std::cerr << "2Probs=" << Probs.Debug(1) << std::endl;
-    //std::cerr << "beamSizes=" << amunmt::Debug(beamSizes, 2) << " " << std::endl;
+      size_t size = Probs.rows() * Probs.columns(); // Probs.size();
+      std::vector<size_t> keys(size);
+      for (size_t i = 0; i < keys.size(); ++i) {
+        keys[i] = i;
+      }
 
-    std::nth_element(keys.begin(), keys.begin() + beamSize, keys.end(),
-                 ProbCompare(Probs.data()));
+      size_t beamSize = beamSizes[0];
 
-    for (size_t i = 0; i < beamSize; ++i) {
-      bestKeys[i] = keys[i];
-      bestCosts[i] = Probs.data()[keys[i]];
-    }
+      std::vector<size_t> bestKeys(beamSize);
+      std::vector<float> bestCosts(beamSize);
 
-    //std::cerr << "bestCosts=" << amunmt::Debug(bestCosts, 2) << " " << std::endl;
-    //std::cerr << "bestKeys=" << amunmt::Debug(bestKeys, 2) << std::endl;
+      if (forbidUNK_) {
+        blaze::column(Probs, UNK_ID) = std::numeric_limits<float>::lowest();
+      }
 
-    std::vector<std::vector<float>> breakDowns;
-    bool doBreakdown = god.Get<bool>("n-best");
-    if (doBreakdown) {
-      breakDowns.push_back(bestCosts);
-      for (auto& scorer : scorers) {
-        std::vector<float> modelCosts(beamSize);
-        mblas::ArrayMatrix &currProb = static_cast<mblas::ArrayMatrix&>(scorer->GetProbs());
+      std::nth_element(keys.begin(), keys.begin() + beamSize, keys.end(),
+                       ProbCompare(Probs.data()));
 
-        auto it = boost::make_permutation_iterator(currProb.begin(), keys.begin());
-        std::copy(it, it + beamSize, modelCosts.begin());
-        breakDowns.push_back(modelCosts);
+      for (size_t i = 0; i < beamSize; ++i) {
+        bestKeys[i] = keys[i];
+        bestCosts[i] = Probs.data()[keys[i]];
       }
-    }
-
-    bool filter = god.Get<std::vector<std::string>>("softmax-filter").size();
 
-    for (size_t i = 0; i < beamSize; i++) {
-      size_t wordIndex = bestKeys[i] % Probs.columns();
+      std::vector<std::vector<float>> breakDowns;
+      if (returnNBestList_) {
+        breakDowns.push_back(bestCosts);
+        for (auto& scorer : scorers) {
+          std::vector<float> modelCosts(beamSize);
+          mblas::ArrayMatrix &currProb = static_cast<mblas::ArrayMatrix&>(scorer->GetProbs());
 
-      if (filter) {
-        wordIndex = filterIndices[wordIndex];
+          auto it = boost::make_permutation_iterator(currProb.begin(), keys.begin());
+          std::copy(it, it + beamSize, modelCosts.begin());
+          breakDowns.push_back(modelCosts);
+        }
       }
 
-      size_t hypIndex  = bestKeys[i] / Probs.columns();
-      float cost = bestCosts[i];
+      for (size_t i = 0; i < beamSize; i++) {
+        size_t wordIndex = bestKeys[i] % Probs.columns();
 
-      HypothesisPtr hyp;
-      if (returnAlignment) {
-        std::vector<SoftAlignmentPtr> alignments;
-        for (auto& scorer : scorers) {
-          if (CPU::EncoderDecoder* encdec = dynamic_cast<CPU::EncoderDecoder*>(scorer.get())) {
-            auto& attention = encdec->GetAttention();
-            alignments.emplace_back(new SoftAlignment(attention.begin(hypIndex),
-                                                      attention.end(hypIndex)));
-          } else {
-            amunmt_UTIL_THROW2("Return Alignment is allowed only with Nematus scorer.");
-          }
+        if (isInputFiltered_) {
+          wordIndex = filterIndices[wordIndex];
         }
 
-        hyp.reset(new Hypothesis(prevHyps[hypIndex], wordIndex, hypIndex, cost, alignments));
-      } else {
-        hyp.reset(new Hypothesis(prevHyps[hypIndex], wordIndex, hypIndex, cost));
-      }
+        size_t hypIndex  = bestKeys[i] / Probs.columns();
+        float cost = bestCosts[i];
+
+        HypothesisPtr hyp;
+        if (returnAttentionWeights_) {
+          std::vector<SoftAlignmentPtr> alignments;
+          for (auto& scorer : scorers) {
+            if (CPU::EncoderDecoder* encdec = dynamic_cast<CPU::EncoderDecoder*>(scorer.get())) {
+              auto& attention = encdec->GetAttention();
+              alignments.emplace_back(new SoftAlignment(attention.begin(hypIndex),
+                                                        attention.end(hypIndex)));
+            } else {
+              amunmt_UTIL_THROW2("Return Alignment is allowed only with Nematus scorer.");
+            }
+          }
 
-      if (doBreakdown) {
-        hyp->GetCostBreakdown().resize(scorers.size());
-        float sum = 0;
-        for(size_t j = 0; j < scorers.size(); ++j) {
-          if (j == 0) {
-            hyp->GetCostBreakdown()[0] = breakDowns[0][i];
-          } else {
-            float cost = 0;
-            if (j < scorers.size()) {
-              if (prevHyps[hypIndex]->GetCostBreakdown().size() < scorers.size())
-                const_cast<HypothesisPtr&>(prevHyps[hypIndex])->GetCostBreakdown().resize(scorers.size(), 0.0);
-              cost = breakDowns[j][i] + const_cast<HypothesisPtr&>(prevHyps[hypIndex])->GetCostBreakdown()[j];
+          hyp.reset(new Hypothesis(prevHyps[hypIndex], wordIndex, hypIndex, cost, alignments));
+        } else {
+          hyp.reset(new Hypothesis(prevHyps[hypIndex], wordIndex, hypIndex, cost));
+        }
+
+        if (returnNBestList_) {
+          hyp->GetCostBreakdown().resize(scorers.size());
+          float sum = 0;
+          for(size_t j = 0; j < scorers.size(); ++j) {
+            if (j == 0) {
+              hyp->GetCostBreakdown()[0] = breakDowns[0][i];
+            } else {
+              float cost = 0;
+              if (j < scorers.size()) {
+                if (prevHyps[hypIndex]->GetCostBreakdown().size() < scorers.size())
+                  const_cast<HypothesisPtr&>(prevHyps[hypIndex])->GetCostBreakdown().resize(scorers.size(), 0.0);
+                cost = breakDowns[j][i] + const_cast<HypothesisPtr&>(prevHyps[hypIndex])->GetCostBreakdown()[j];
+              }
+              sum += weights_.at(scorers[j]->GetName()) * cost;
+              hyp->GetCostBreakdown()[j] = cost;
             }
-            sum += weights.at(scorers[j]->GetName()) * cost;
-            hyp->GetCostBreakdown()[j] = cost;
           }
+          hyp->GetCostBreakdown()[0] -= sum;
+          hyp->GetCostBreakdown()[0] /= weights_.at(scorers[0]->GetName());
         }
-        hyp->GetCostBreakdown()[0] -= sum;
-        hyp->GetCostBreakdown()[0] /= weights.at(scorers[0]->GetName());
+        beams[0].push_back(hyp);
       }
-      beams[0].push_back(hyp);
     }
-  }
 };
 
-} // namespace
-}
+}  // namespace CPU
+}  // namespace amunmt