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#include "CNNNetwork.hpp"
CNNNetworkImpl::CNNNetworkImpl(int nbOutputs, int leftBorder, int rightBorder, int nbStackElements)
{
constexpr int embeddingsSize = 64;
constexpr int hiddenSize = 512;
constexpr int nbFilters = 512;
constexpr int nbFiltersLetters = 64;
setLeftBorder(leftBorder);
setRightBorder(rightBorder);
setNbStackElements(nbStackElements);
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));
linear2 = register_module("linear2", torch::nn::Linear(hiddenSize, nbOutputs));
for (auto & windowSize : windowSizes)
{
CNNs.emplace_back(register_module(fmt::format("cnn_context_{}", windowSize), torch::nn::Conv2d(torch::nn::Conv2dOptions(1, nbFilters, torch::ExpandingArray<2>({windowSize,2*embeddingsSize})).padding({windowSize-1, 0}))));
lettersCNNs.emplace_back(register_module(fmt::format("cnn_letters_{}", windowSize), torch::nn::Conv2d(torch::nn::Conv2dOptions(1, nbFiltersLetters, torch::ExpandingArray<2>({windowSize,embeddingsSize})).padding({windowSize-1, 0}))));
}
}
torch::Tensor CNNNetworkImpl::forward(torch::Tensor input)
{
if (input.dim() == 1)
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 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);
auto permuted = lettersEmbeddings.permute({2,0,1,3,4});
std::vector<torch::Tensor> windows;
for (unsigned int word = 0; word < focusedBufferIndexes.size(); word++)
for (unsigned int i = 0; i < lettersCNNs.size(); i++)
{
auto input = permuted[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});
windows.clear();
for (unsigned int i = 0; i < CNNs.size(); i++)
{
auto convOut = torch::relu(CNNs[i](embeddings).squeeze(-1));
auto pooled = torch::max_pool1d(convOut, convOut.size(2));
windows.emplace_back(pooled);
}
auto cnnOut = torch::cat(windows, 2);
cnnOut = cnnOut.view({cnnOut.size(0), -1});
auto totalInput = torch::cat({cnnOut, lettersCnnOut}, 1);
return linear2(torch::relu(linear1(totalInput)));
}
std::vector<long> CNNNetworkImpl::extractContext(Config & config, Dict & dict) const
{
std::stack<int> leftContext;
std::stack<std::string> leftForms;
for (int index = config.getWordIndex()-1; config.has(0,index,0) && leftContext.size() < columns.size()*leftBorder; --index)
if (config.isToken(index))
for (auto & column : columns)
{
leftContext.push(dict.getIndexOrInsert(config.getAsFeature(column, index)));
if (column == "FORM")
leftForms.push(config.getAsFeature(column, index));
}
std::vector<long> context;
std::vector<std::string> forms;
while ((int)context.size() < (int)columns.size()*(leftBorder-(int)leftContext.size()))
context.emplace_back(dict.getIndexOrInsert(Dict::nullValueStr));
while (forms.size() < leftBorder-leftForms.size())
forms.emplace_back("");
while (!leftForms.empty())
{
forms.emplace_back(leftForms.top());
leftForms.pop();
}
while (!leftContext.empty())
{
context.emplace_back(leftContext.top());
leftContext.pop();
}
for (int index = config.getWordIndex(); config.has(0,index,0) && context.size() < columns.size()*(leftBorder+rightBorder+1); ++index)
if (config.isToken(index))
for (auto & column : columns)
{
context.emplace_back(dict.getIndexOrInsert(config.getAsFeature(column, index)));
if (column == "FORM")
forms.emplace_back(config.getAsFeature(column, index));
}
while (context.size() < columns.size()*(leftBorder+rightBorder+1))
context.emplace_back(dict.getIndexOrInsert(Dict::nullValueStr));
while ((int)forms.size() < leftBorder+rightBorder+1)
forms.emplace_back("");
for (int i = 0; i < nbStackElements; i++)
for (auto & column : columns)
if (config.hasStack(i))
context.emplace_back(dict.getIndexOrInsert(config.getAsFeature(column, config.getStack(i))));
else
context.emplace_back(dict.getIndexOrInsert(Dict::nullValueStr));
for (auto index : focusedBufferIndexes)
{
util::utf8string letters;
if (leftBorder+index >= 0 && leftBorder+index < (int)forms.size() && !forms[leftBorder+index].empty())
letters = util::splitAsUtf8(forms[leftBorder+index]);
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;
}