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ContextModule.cpp 5.59 KiB
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  • #include "ContextModule.hpp"
    
    
    ContextModuleImpl::ContextModuleImpl(std::string name, const std::string & definition, std::filesystem::path path) : path(path)
    
      setName(name);
    
      std::regex regex("(?:(?:\\s|\\t)*)Targets\\{(.*)\\}(?:(?:\\s|\\t)*)Columns\\{(.*)\\}(?:(?:\\s|\\t)*)(\\S+)\\{(.*)\\}(?:(?:\\s|\\t)*)In\\{(.*)\\}(?:(?:\\s|\\t)*)Out\\{(.*)\\}(?:(?:\\s|\\t)*)w2v\\{(.*)\\}(?:(?:\\s|\\t)*)");
    
      if (!util::doIfNameMatch(regex, definition, [this,&definition](auto sm)
            {
              try
              {
    
                for (auto & target : util::split(sm.str(1), ' '))
                {
                  auto splited = util::split(target, '.');
                  if (splited.size() != 2 and splited.size() != 3)
                    util::myThrow(fmt::format("invalid target '{}' expected 'object.index(.childIndex)'", target));
                  targets.emplace_back(std::make_tuple(Config::str2object(splited[0]), std::stoi(splited[1]), splited.size() == 3 ? std::optional<int>(std::stoi(splited[2])) : std::optional<int>()));
                }
    
                auto funcColumns = util::split(sm.str(2), ' ');
    
                columns.clear();
                for (auto & funcCol : funcColumns)
                {
    
                  functions.emplace_back() = getFunction(funcCol);
                  columns.emplace_back(util::split(funcCol, ':').back());
    
                auto subModuleType = sm.str(3);
                auto subModuleArguments = util::split(sm.str(4), ' ');
    
    
                auto options = MyModule::ModuleOptions(true)
                  .bidirectional(std::stoi(subModuleArguments[0]))
                  .num_layers(std::stoi(subModuleArguments[1]))
                  .dropout(std::stof(subModuleArguments[2]))
                  .complete(std::stoi(subModuleArguments[3]));
    
    
                inSize = std::stoi(sm.str(5));
                int outSize = std::stoi(sm.str(6));
    
    
                if (subModuleType == "LSTM")
                  myModule = register_module("myModule", LSTM(columns.size()*inSize, outSize, options));
                else if (subModuleType == "GRU")
                  myModule = register_module("myModule", GRU(columns.size()*inSize, outSize, options));
    
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                else if (subModuleType == "Concat")
                  myModule = register_module("myModule", Concat(inSize));
    
                else
                  util::myThrow(fmt::format("unknown sumodule type '{}'", subModuleType));
    
    
                w2vFile = sm.str(7);
    
                  getDict().loadWord2Vec(this->path / w2vFile);
    
                  getDict().setState(Dict::State::Closed);
                  dictSetPretrained(true);
                }
    
    
              } catch (std::exception & e) {util::myThrow(fmt::format("{} in '{}'",e.what(),definition));}
            }))
        util::myThrow(fmt::format("invalid definition '{}'", definition));
    }
    
    std::size_t ContextModuleImpl::getOutputSize()
    {
    
      return myModule->getOutputSize(targets.size());
    
    }
    
    std::size_t ContextModuleImpl::getInputSize()
    {
    
      return columns.size()*(targets.size());
    
    void ContextModuleImpl::addToContext(std::vector<std::vector<long>> & context, const Config & config)
    
      auto & dict = getDict();
    
      std::vector<long> contextIndexes;
    
    
      for (auto & target : targets)
        if (config.hasRelativeWordIndex(std::get<0>(target), std::get<1>(target)))
        {
          int baseIndex = config.getRelativeWordIndex(std::get<0>(target), std::get<1>(target));
          if (!std::get<2>(target))
            contextIndexes.emplace_back(baseIndex);
          else
          {
            int childIndex = *std::get<2>(target);
            auto childs = util::split(config.getAsFeature(Config::childsColName, baseIndex).get(), '|');
            if (childIndex >= 0 and childIndex < (int)childs.size())
              contextIndexes.emplace_back(std::stoi(childs[childIndex]));
            else if (childIndex < 0 and ((int)childs.size())+childIndex >= 0)
              contextIndexes.emplace_back(std::stoi(childs[childs.size()+childIndex]));
            else
              contextIndexes.emplace_back(-1);
          }
        }
    
        else
          contextIndexes.emplace_back(-1);
    
      for (auto index : contextIndexes)
    
        for (unsigned int colIndex = 0; colIndex < columns.size(); colIndex++)
        {
          auto & col = columns[colIndex];
    
          if (index == -1)
          {
            for (auto & contextElement : context)
              contextElement.push_back(dict.getIndexOrInsert(Dict::nullValueStr));
          }
          else
          {
    
            int dictIndex;
            if (col == Config::idColName)
            {
              std::string value;
              if (config.isCommentPredicted(index))
                value = "ID(comment)";
              else if (config.isMultiwordPredicted(index))
                value = "ID(multiword)";
              else if (config.isTokenPredicted(index))
                value = "ID(token)";
              dictIndex = dict.getIndexOrInsert(value);
            }
            else if (col == Config::EOSColName)
            {
              dictIndex = dict.getIndexOrInsert(fmt::format("EOS({})", config.getAsFeature(col, index)));
            }
            else
              dictIndex = dict.getIndexOrInsert(functions[colIndex](config.getAsFeature(col, index)));
    
    
            for (auto & contextElement : context)
              contextElement.push_back(dictIndex);
          }
    
    }
    
    torch::Tensor ContextModuleImpl::forward(torch::Tensor input)
    {
      auto context = wordEmbeddings(input.narrow(1, firstInputIndex, getInputSize()));
    
      context = context.view({context.size(0), context.size(1)/(int)columns.size(), (int)columns.size()*context.size(2)});
    
      return myModule->forward(context);
    }
    
    
    void ContextModuleImpl::registerEmbeddings()
    
      wordEmbeddings = register_module("embeddings", torch::nn::Embedding(torch::nn::EmbeddingOptions(getDict().size(), inSize)));
    
      loadPretrainedW2vEmbeddings(wordEmbeddings, w2vFile.empty() ? "" : path / w2vFile);