Added focusedStackIndexes to CNNNetwork

torch_modules/include/CNNNetwork.hpp

@@ -8,6 +8,7 @@ class CNNNetworkImpl : public NeuralNetworkImpl
   private :
 
   static inline std::vector<long> focusedBufferIndexes{0,1};
+  static inline std::vector<long> focusedStackIndexes{0,1};
   static inline std::vector<long> windowSizes{2,3,4};
   static constexpr unsigned int maxNbLetters = 10;
 

torch_modules/src/CNNNetwork.cpp

@@ -13,7 +13,7 @@ CNNNetworkImpl::CNNNetworkImpl(int nbOutputs, int leftBorder, int rightBorder, i
   setColumns({"FORM", "UPOS"});
 
   wordEmbeddings = register_module("word_embeddings", torch::nn::Embedding(torch::nn::EmbeddingOptions(50000, embeddingsSize)));
-  linear1 = register_module("linear1", torch::nn::Linear(nbFilters*windowSizes.size()+nbFiltersLetters*windowSizes.size()*focusedBufferIndexes.size(), hiddenSize));
+  linear1 = register_module("linear1", torch::nn::Linear(nbFilters*windowSizes.size()+nbFiltersLetters*windowSizes.size()*(focusedBufferIndexes.size()+focusedStackIndexes.size()), hiddenSize));
   linear2 = register_module("linear2", torch::nn::Linear(hiddenSize, nbOutputs));
   for (auto & windowSize : windowSizes)
   {
@@ -28,7 +28,7 @@ torch::Tensor CNNNetworkImpl::forward(torch::Tensor input)
     input = input.unsqueeze(0);
 
   auto wordIndexes = input.narrow(1, 0, columns.size()*(1+leftBorder+rightBorder));
-  auto wordLetters = input.narrow(1, columns.size()*(1+leftBorder+rightBorder), maxNbLetters*focusedBufferIndexes.size());
+  auto wordLetters = input.narrow(1, columns.size()*(1+leftBorder+rightBorder), maxNbLetters*(focusedBufferIndexes.size()+focusedStackIndexes.size()));
 
   auto embeddings = wordEmbeddings(wordIndexes).view({wordIndexes.size(0), wordIndexes.size(1)/(int)columns.size(), (int)columns.size()*(int)wordEmbeddings->options.embedding_dim()}).unsqueeze(1);
   auto lettersEmbeddings = wordEmbeddings(wordLetters).view({wordLetters.size(0), wordLetters.size(1)/maxNbLetters, maxNbLetters, wordEmbeddings->options.embedding_dim()}).unsqueeze(1);
@@ -43,6 +43,14 @@ torch::Tensor CNNNetworkImpl::forward(torch::Tensor input)
       auto pooled = torch::max_pool1d(convOut, convOut.size(2));
       windows.emplace_back(pooled);
     }
+  for (unsigned int word = 0; word < focusedStackIndexes.size(); word++)
+    for (unsigned int i = 0; i < lettersCNNs.size(); i++)
+    {
+      auto input = permuted[focusedBufferIndexes.size()+word];
+      auto convOut = torch::relu(lettersCNNs[i](input).squeeze(-1));
+      auto pooled = torch::max_pool1d(convOut, convOut.size(2));
+      windows.emplace_back(pooled);
+    }
   auto lettersCnnOut = torch::cat(windows, 2);
   lettersCnnOut = lettersCnnOut.view({lettersCnnOut.size(0), -1});
 
@@ -133,6 +141,25 @@ std::vector<long> CNNNetworkImpl::extractContext(Config & config, Dict & dict) c
     }
   }
 
+  for (auto index : focusedStackIndexes)
+  {
+    util::utf8string letters;
+    if (config.hasStack(index) and config.has("FORM", config.getStack(index),0))
+      letters = util::splitAsUtf8(config.getAsFeature("FORM", config.getStack(index)).get());
+    for (unsigned int i = 0; i < maxNbLetters; i++)
+    {
+      if (i < letters.size())
+      {
+        std::string sLetter = fmt::format("Letter({})", letters[i]);
+        context.emplace_back(dict.getIndexOrInsert(sLetter));
+      }
+      else
+      {
+        context.emplace_back(dict.getIndexOrInsert(Dict::nullValueStr));
+      }
+    }
+  }
+
   return context;
 }