diff --git a/common/include/util.hpp b/common/include/util.hpp
index efe509a0a176af75fdc0e0947c4ffad65074d774..1ab894349364cbe1dc29bcff089937c2d0ee885f 100644
--- a/common/include/util.hpp
+++ b/common/include/util.hpp
@@ -38,7 +38,7 @@ std::vector<std::filesystem::path> findFilesByExtension(std::filesystem::path di
std::string_view getFilenameFromPath(std::string_view s);
-std::vector<std::string_view> split(std::string_view s, char delimiter);
+std::vector<std::string> split(std::string_view s, char delimiter);
utf8string splitAsUtf8(std::string_view s);
diff --git a/common/src/util.cpp b/common/src/util.cpp
index 13d412282aab1d3e14a232dd0b4e7f8ec4c7a18c..23f5a6e0af99c9066d90518a08bb5773e225a82f 100644
--- a/common/src/util.cpp
+++ b/common/src/util.cpp
@@ -32,9 +32,9 @@ bool util::isIllegal(utf8char c)
return c == '\n' || c == '\t';
}
-std::vector<std::string_view> util::split(std::string_view remaining, char delimiter)
+std::vector<std::string> util::split(std::string_view remaining, char delimiter)
{
- std::vector<std::string_view> result;
+ std::vector<std::string> result;
for (auto firstDelimiterIndex = remaining.find_first_of(delimiter); firstDelimiterIndex != std::string_view::npos; firstDelimiterIndex = remaining.find_first_of(delimiter))
{
diff --git a/reading_machine/include/Config.hpp b/reading_machine/include/Config.hpp
index 13e1806f5afce69d2c3f475c79d2f3f0cced6242..1e6833e016f5df74968b62b3a5354f9709ca8ed5 100644
--- a/reading_machine/include/Config.hpp
+++ b/reading_machine/include/Config.hpp
@@ -109,6 +109,7 @@ class Config
bool rawInputOnlySeparatorsLeft() const;
std::size_t getWordIndex() const;
std::size_t getCharacterIndex() const;
+ long getRelativeWordIndex(int relativeIndex) const;
const String & getHistory(int relativeIndex) const;
std::size_t getStack(int relativeIndex) const;
bool hasHistory(int relativeIndex) const;
diff --git a/reading_machine/src/Classifier.cpp b/reading_machine/src/Classifier.cpp
index 7c96c7fc09c8d3ce96a3f36df1ec33a951e894b6..5cba1706646caf2ec4142b70b893abc313ad5dde 100644
--- a/reading_machine/src/Classifier.cpp
+++ b/reading_machine/src/Classifier.cpp
@@ -37,59 +37,72 @@ void Classifier::initNeuralNetwork(const std::string & topology)
"OneWord(focusedIndex) : Only use the word embedding of the focused word.",
[this,topology](auto sm)
{
- this->nn.reset(new OneWordNetworkImpl(this->transitionSet->size(), std::stoi(sm[1])));
+ this->nn.reset(new OneWordNetworkImpl(this->transitionSet->size(), std::stoi(sm.str(1))));
}
},
{
- std::regex("ConcatWords\\(([+\\-]?\\d+),([+\\-]?\\d+),([+\\-]?\\d+)\\)"),
- "ConcatWords(leftBorder,rightBorder,nbStack) : Concatenate embeddings of words in context.",
+ std::regex("ConcatWords\\(\\{(.*)\\},\\{(.*)\\}\\)"),
+ "ConcatWords({bufferContext},{stackContext}) : Concatenate embeddings of words in context.",
[this,topology](auto sm)
{
- this->nn.reset(new ConcatWordsNetworkImpl(this->transitionSet->size(), std::stoi(sm[1]), std::stoi(sm[2]), std::stoi(sm[3])));
+ std::vector<int> bufferContext, stackContext;
+ for (auto s : util::split(sm.str(1), ','))
+ bufferContext.emplace_back(std::stoi(s));
+ for (auto s : util::split(sm.str(2), ','))
+ stackContext.emplace_back(std::stoi(s));
+ this->nn.reset(new ConcatWordsNetworkImpl(this->transitionSet->size(), bufferContext, stackContext));
}
},
{
- std::regex("CNN\\(([+\\-]?\\d+),(\\d+),(\\d+),(\\d+),\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\}\\,([+\\-]?\\d+)\\,([+\\-]?\\d+)\\)"),
- "CNN(unknownValueThreshold,leftBorder,rightBorder,nbStack,{columns},{focusedBuffer},{focusedStack},{focusedColumns},{maxNbElements},leftBorderRawInput, rightBorderRawInput) : CNN to capture context.",
+ std::regex("CNN\\(([+\\-]?\\d+),\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\}\\,([+\\-]?\\d+)\\,([+\\-]?\\d+)\\)"),
+ "CNN(unknownValueThreshold,{bufferContext},{stackContext},{columns},{focusedBuffer},{focusedStack},{focusedColumns},{maxNbElements},leftBorderRawInput, rightBorderRawInput) : CNN to capture context.",
[this,topology](auto sm)
{
- std::vector<int> focusedBuffer, focusedStack, maxNbElements;
+ std::vector<int> focusedBuffer, focusedStack, maxNbElements, bufferContext, stackContext;
std::vector<std::string> focusedColumns, columns;
- for (auto s : util::split(std::string(sm[5]), ','))
+ for (auto s : util::split(sm.str(2), ','))
+ bufferContext.emplace_back(std::stoi(s));
+ for (auto s : util::split(sm.str(3), ','))
+ stackContext.emplace_back(std::stoi(s));
+ for (auto s : util::split(sm.str(4), ','))
columns.emplace_back(s);
- for (auto s : util::split(std::string(sm[6]), ','))
- focusedBuffer.push_back(std::stoi(std::string(s)));
- for (auto s : util::split(std::string(sm[7]), ','))
- focusedStack.push_back(std::stoi(std::string(s)));
- for (auto s : util::split(std::string(sm[8]), ','))
+ for (auto s : util::split(sm.str(5), ','))
+ focusedBuffer.push_back(std::stoi(s));
+ for (auto s : util::split(sm.str(6), ','))
+ focusedStack.push_back(std::stoi(s));
+ for (auto s : util::split(sm.str(7), ','))
focusedColumns.emplace_back(s);
- for (auto s : util::split(std::string(sm[9]), ','))
- maxNbElements.push_back(std::stoi(std::string(s)));
+ for (auto s : util::split(sm.str(8), ','))
+ maxNbElements.push_back(std::stoi(s));
if (focusedColumns.size() != maxNbElements.size())
util::myThrow("focusedColumns.size() != maxNbElements.size()");
- this->nn.reset(new CNNNetworkImpl(this->transitionSet->size(), std::stoi(sm[1]), std::stoi(sm[2]), std::stoi(sm[3]), std::stoi(sm[4]), columns, focusedBuffer, focusedStack, focusedColumns, maxNbElements, std::stoi(sm[10]), std::stoi(sm[11])));
+ this->nn.reset(new CNNNetworkImpl(this->transitionSet->size(), std::stoi(sm.str(1)), bufferContext, stackContext, columns, focusedBuffer, focusedStack, focusedColumns, maxNbElements, std::stoi(sm.str(9)), std::stoi(sm.str(10))));
}
},
{
- std::regex("LSTM\\(([+\\-]?\\d+),(\\d+),(\\d+),(\\d+),\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\}\\,([+\\-]?\\d+)\\,([+\\-]?\\d+)\\)"),
- "LSTM(unknownValueThreshold,leftBorder,rightBorder,nbStack,{columns},{focusedBuffer},{focusedStack},{focusedColumns},{maxNbElements},leftBorderRawInput, rightBorderRawInput) : CNN to capture context.",
+ std::regex("LSTM\\(([+\\-]?\\d+),\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\},\\{(.*)\\}\\,([+\\-]?\\d+)\\,([+\\-]?\\d+)\\)"),
+ "LSTM(unknownValueThreshold,{bufferContext},{stackContext},{columns},{focusedBuffer},{focusedStack},{focusedColumns},{maxNbElements},leftBorderRawInput, rightBorderRawInput) : CNN to capture context.",
[this,topology](auto sm)
{
- std::vector<int> focusedBuffer, focusedStack, maxNbElements;
+ std::vector<int> focusedBuffer, focusedStack, maxNbElements, bufferContext, stackContext;
std::vector<std::string> focusedColumns, columns;
- for (auto s : util::split(std::string(sm[5]), ','))
+ for (auto s : util::split(sm.str(2), ','))
+ bufferContext.emplace_back(std::stoi(s));
+ for (auto s : util::split(sm.str(3), ','))
+ stackContext.emplace_back(std::stoi(s));
+ for (auto s : util::split(sm.str(4), ','))
columns.emplace_back(s);
- for (auto s : util::split(std::string(sm[6]), ','))
- focusedBuffer.push_back(std::stoi(std::string(s)));
- for (auto s : util::split(std::string(sm[7]), ','))
- focusedStack.push_back(std::stoi(std::string(s)));
- for (auto s : util::split(std::string(sm[8]), ','))
+ for (auto s : util::split(sm.str(5), ','))
+ focusedBuffer.push_back(std::stoi(s));
+ for (auto s : util::split(sm.str(6), ','))
+ focusedStack.push_back(std::stoi(s));
+ for (auto s : util::split(sm.str(7), ','))
focusedColumns.emplace_back(s);
- for (auto s : util::split(std::string(sm[9]), ','))
- maxNbElements.push_back(std::stoi(std::string(s)));
+ for (auto s : util::split(sm.str(8), ','))
+ maxNbElements.push_back(std::stoi(s));
if (focusedColumns.size() != maxNbElements.size())
util::myThrow("focusedColumns.size() != maxNbElements.size()");
- this->nn.reset(new LSTMNetworkImpl(this->transitionSet->size(), std::stoi(sm[1]), std::stoi(sm[2]), std::stoi(sm[3]), std::stoi(sm[4]), columns, focusedBuffer, focusedStack, focusedColumns, maxNbElements, std::stoi(sm[10]), std::stoi(sm[11])));
+ this->nn.reset(new LSTMNetworkImpl(this->transitionSet->size(), std::stoi(sm.str(1)), bufferContext, stackContext, columns, focusedBuffer, focusedStack, focusedColumns, maxNbElements, std::stoi(sm.str(9)), std::stoi(sm.str(10))));
}
},
{
@@ -97,19 +110,28 @@ void Classifier::initNeuralNetwork(const std::string & topology)
"RLT(leftBorder,rightBorder,nbStack) : Recursive tree LSTM.",
[this,topology](auto sm)
{
- this->nn.reset(new RLTNetworkImpl(this->transitionSet->size(), std::stoi(sm[1]), std::stoi(sm[2]), std::stoi(sm[3])));
+ this->nn.reset(new RLTNetworkImpl(this->transitionSet->size(), std::stoi(sm.str(1)), std::stoi(sm.str(2)), std::stoi(sm.str(3))));
}
},
};
+ std::string errorMessage = fmt::format("Unknown neural network '{}', available networks :\n", topology);
+
for (auto & initializer : initializers)
- if (util::doIfNameMatch(std::get<0>(initializer),topology,std::get<2>(initializer)))
+ try
{
- this->nn->to(NeuralNetworkImpl::device);
- return;
+ if (util::doIfNameMatch(std::get<0>(initializer),topology,std::get<2>(initializer)))
+ {
+ this->nn->to(NeuralNetworkImpl::device);
+ return;
+ }
+ }
+ catch (std::exception & e)
+ {
+ errorMessage = fmt::format("Caught({}) {}", e.what(), errorMessage);
+ break;
}
- std::string errorMessage = fmt::format("Unknown neural network '{}', available networks :\n", topology);
for (auto & initializer : initializers)
errorMessage += std::get<1>(initializer) + "\n";
diff --git a/reading_machine/src/Config.cpp b/reading_machine/src/Config.cpp
index ab35b3a58218bacbcc51e40fd063d50317a040a3..03179a020661091f63d51606249c106e4fd55074 100644
--- a/reading_machine/src/Config.cpp
+++ b/reading_machine/src/Config.cpp
@@ -573,3 +573,29 @@ void Config::addMissingColumns()
}
}
+long Config::getRelativeWordIndex(int relativeIndex) const
+{
+ if (relativeIndex < 0)
+ {
+ for (int index = getWordIndex()-1, counter = 0; has(0,index,0); --index)
+ if (!isCommentPredicted(index))
+ {
+ --counter;
+ if (counter == relativeIndex)
+ return index;
+ }
+ }
+ else
+ {
+ for (int index = getWordIndex(), counter = 0; has(0,index,0); ++index)
+ if (!isCommentPredicted(index))
+ {
+ if (counter == relativeIndex)
+ return index;
+ ++counter;
+ }
+ }
+
+ return -1;
+}
+
diff --git a/torch_modules/include/CNNNetwork.hpp b/torch_modules/include/CNNNetwork.hpp
index 0cd54b8f087034863e1fd8dbb2e07089be221a56..a036e7622940d5eb22f2c8f820df0b0063f19d0c 100644
--- a/torch_modules/include/CNNNetwork.hpp
+++ b/torch_modules/include/CNNNetwork.hpp
@@ -11,8 +11,6 @@ class CNNNetworkImpl : public NeuralNetworkImpl
static constexpr int maxNbEmbeddings = 50000;
int unknownValueThreshold;
- std::vector<int> focusedBufferIndexes;
- std::vector<int> focusedStackIndexes;
std::vector<std::string> focusedColumns;
std::vector<int> maxNbElements;
int leftWindowRawInput;
@@ -31,7 +29,7 @@ class CNNNetworkImpl : public NeuralNetworkImpl
public :
- CNNNetworkImpl(int nbOutputs, int unknownValueThreshold, int leftBorder, int rightBorder, int nbStackElements, std::vector<std::string> columns, std::vector<int> focusedBufferIndexes, std::vector<int> focusedStackIndexes, std::vector<std::string> focusedColumns, std::vector<int> maxNbElements, int leftWindowRawInput, int rightWindowRawInput);
+ CNNNetworkImpl(int nbOutputs, int unknownValueThreshold, std::vector<int> bufferContext, std::vector<int> stackContext, std::vector<std::string> columns, std::vector<int> focusedBufferIndexes, std::vector<int> focusedStackIndexes, std::vector<std::string> focusedColumns, std::vector<int> maxNbElements, int leftWindowRawInput, int rightWindowRawInput);
torch::Tensor forward(torch::Tensor input) override;
std::vector<std::vector<long>> extractContext(Config & config, Dict & dict) const override;
};
diff --git a/torch_modules/include/ConcatWordsNetwork.hpp b/torch_modules/include/ConcatWordsNetwork.hpp
index 7152eba2e06aa74b909b2136514d64120b6c0b8d..b2c9691dd3a062cf600e99ce1f7ec9ca4d478643 100644
--- a/torch_modules/include/ConcatWordsNetwork.hpp
+++ b/torch_modules/include/ConcatWordsNetwork.hpp
@@ -14,7 +14,7 @@ class ConcatWordsNetworkImpl : public NeuralNetworkImpl
public :
- ConcatWordsNetworkImpl(int nbOutputs, int leftBorder, int rightBorder, int nbStackElements);
+ ConcatWordsNetworkImpl(int nbOutputs, const std::vector<int> & bufferContext, const std::vector<int> & stackContext);
torch::Tensor forward(torch::Tensor input) override;
};
diff --git a/torch_modules/include/LSTMNetwork.hpp b/torch_modules/include/LSTMNetwork.hpp
index 5fe8de8461320a83b59537a6fae0660f16a3c334..e276eb23f786bc3e80b7004cb67f893b347fe5cf 100644
--- a/torch_modules/include/LSTMNetwork.hpp
+++ b/torch_modules/include/LSTMNetwork.hpp
@@ -10,8 +10,6 @@ class LSTMNetworkImpl : public NeuralNetworkImpl
static constexpr int maxNbEmbeddings = 50000;
int unknownValueThreshold;
- std::vector<int> focusedBufferIndexes;
- std::vector<int> focusedStackIndexes;
std::vector<std::string> focusedColumns;
std::vector<int> maxNbElements;
int leftWindowRawInput;
@@ -30,7 +28,7 @@ class LSTMNetworkImpl : public NeuralNetworkImpl
public :
- LSTMNetworkImpl(int nbOutputs, int unknownValueThreshold, int leftBorder, int rightBorder, int nbStackElements, std::vector<std::string> columns, std::vector<int> focusedBufferIndexes, std::vector<int> focusedStackIndexes, std::vector<std::string> focusedColumns, std::vector<int> maxNbElements, int leftWindowRawInput, int rightWindowRawInput);
+ LSTMNetworkImpl(int nbOutputs, int unknownValueThreshold, std::vector<int> bufferContext, std::vector<int> stackContext, std::vector<std::string> columns, std::vector<int> focusedBufferIndexes, std::vector<int> focusedStackIndexes, std::vector<std::string> focusedColumns, std::vector<int> maxNbElements, int leftWindowRawInput, int rightWindowRawInput);
torch::Tensor forward(torch::Tensor input) override;
std::vector<std::vector<long>> extractContext(Config & config, Dict & dict) const override;
};
diff --git a/torch_modules/include/NeuralNetwork.hpp b/torch_modules/include/NeuralNetwork.hpp
index 34bf14b632cd912e1d0743fc667a14ed49e667c2..8ffa7349d35b2b50816f5574a13a0e6c096c2e9c 100644
--- a/torch_modules/include/NeuralNetwork.hpp
+++ b/torch_modules/include/NeuralNetwork.hpp
@@ -13,22 +13,25 @@ class NeuralNetworkImpl : public torch::nn::Module
protected :
- unsigned leftBorder{5};
- unsigned rightBorder{5};
- unsigned nbStackElements{2};
std::vector<std::string> columns{"FORM"};
+ std::vector<int> bufferContext{-3,-2,-1,0,1};
+ std::vector<int> stackContext{};
+ std::vector<int> bufferFocused{};
+ std::vector<int> stackFocused{};
protected :
- void setRightBorder(int rightBorder);
- void setLeftBorder(int leftBorder);
- void setNbStackElements(int nbStackElements);
+ void setBufferContext(const std::vector<int> & bufferContext);
+ void setStackContext(const std::vector<int> & stackContext);
+ void setBufferFocused(const std::vector<int> & bufferFocused);
+ void setStackFocused(const std::vector<int> & stackFocused);
public :
virtual torch::Tensor forward(torch::Tensor input) = 0;
virtual std::vector<std::vector<long>> extractContext(Config & config, Dict & dict) const;
std::vector<long> extractContextIndexes(const Config & config) const;
+ std::vector<long> extractFocusedIndexes(const Config & config) const;
int getContextSize() const;
void setColumns(const std::vector<std::string> & columns);
};
diff --git a/torch_modules/include/OneWordNetwork.hpp b/torch_modules/include/OneWordNetwork.hpp
index b4ad4753b8bb57a35afa15851742551f7227e0c3..9882b620187fcfaaebff33f1400b98ddc3446aae 100644
--- a/torch_modules/include/OneWordNetwork.hpp
+++ b/torch_modules/include/OneWordNetwork.hpp
@@ -9,7 +9,6 @@ class OneWordNetworkImpl : public NeuralNetworkImpl
torch::nn::Embedding wordEmbeddings{nullptr};
torch::nn::Linear linear{nullptr};
- int focusedIndex;
public :
diff --git a/torch_modules/include/RLTNetwork.hpp b/torch_modules/include/RLTNetwork.hpp
index b996def57a5e540d738bd9db0874a5d8511d2983..71b8aa55dce4740c1f08fd8ecb343826dedd8989 100644
--- a/torch_modules/include/RLTNetwork.hpp
+++ b/torch_modules/include/RLTNetwork.hpp
@@ -11,6 +11,8 @@ class RLTNetworkImpl : public NeuralNetworkImpl
static inline std::vector<long> focusedBufferIndexes{0,1,2};
static inline std::vector<long> focusedStackIndexes{0,1};
+ int leftBorder, rightBorder;
+
torch::nn::Embedding wordEmbeddings{nullptr};
torch::nn::Linear linear1{nullptr};
torch::nn::Linear linear2{nullptr};
diff --git a/torch_modules/src/CNNNetwork.cpp b/torch_modules/src/CNNNetwork.cpp
index 68bd9749cd78ed97687a80543e8109a583b2d3da..c03b347ee55f71d3e130be22f4770d3000904847 100644
--- a/torch_modules/src/CNNNetwork.cpp
+++ b/torch_modules/src/CNNNetwork.cpp
@@ -1,16 +1,17 @@
#include "CNNNetwork.hpp"
-CNNNetworkImpl::CNNNetworkImpl(int nbOutputs, int unknownValueThreshold, int leftBorder, int rightBorder, int nbStackElements, std::vector<std::string> columns, std::vector<int> focusedBufferIndexes, std::vector<int> focusedStackIndexes, std::vector<std::string> focusedColumns, std::vector<int> maxNbElements, int leftWindowRawInput, int rightWindowRawInput) : unknownValueThreshold(unknownValueThreshold), focusedBufferIndexes(focusedBufferIndexes), focusedStackIndexes(focusedStackIndexes), focusedColumns(focusedColumns), maxNbElements(maxNbElements), leftWindowRawInput(leftWindowRawInput), rightWindowRawInput(rightWindowRawInput)
+CNNNetworkImpl::CNNNetworkImpl(int nbOutputs, int unknownValueThreshold, std::vector<int> bufferContext, std::vector<int> stackContext, std::vector<std::string> columns, std::vector<int> focusedBufferIndexes, std::vector<int> focusedStackIndexes, std::vector<std::string> focusedColumns, std::vector<int> maxNbElements, int leftWindowRawInput, int rightWindowRawInput) : unknownValueThreshold(unknownValueThreshold), focusedColumns(focusedColumns), maxNbElements(maxNbElements), leftWindowRawInput(leftWindowRawInput), rightWindowRawInput(rightWindowRawInput)
{
constexpr int embeddingsSize = 64;
constexpr int hiddenSize = 1024;
constexpr int nbFiltersContext = 512;
constexpr int nbFiltersFocused = 64;
- setLeftBorder(leftBorder);
- setRightBorder(rightBorder);
- setNbStackElements(nbStackElements);
+ setBufferContext(bufferContext);
+ setStackContext(stackContext);
setColumns(columns);
+ setBufferFocused(focusedBufferIndexes);
+ setStackFocused(focusedStackIndexes);
rawInputSize = leftWindowRawInput + rightWindowRawInput + 1;
if (leftWindowRawInput < 0 or rightWindowRawInput < 0)
@@ -42,7 +43,7 @@ torch::Tensor CNNNetworkImpl::forward(torch::Tensor input)
auto embeddings = embeddingsDropout(wordEmbeddings(input));
- auto context = embeddings.narrow(1, rawInputSize, columns.size()*(1+leftBorder+rightBorder));
+ auto context = embeddings.narrow(1, rawInputSize, getContextSize());
context = context.view({context.size(0), context.size(1)/(int)columns.size(), (int)columns.size()*(int)wordEmbeddings->options.embedding_dim()});
auto elementsEmbeddings = embeddings.narrow(1, rawInputSize+context.size(1), input.size(1)-(rawInputSize+context.size(1)));
@@ -59,7 +60,7 @@ torch::Tensor CNNNetworkImpl::forward(torch::Tensor input)
for (unsigned int i = 0; i < focusedColumns.size(); i++)
{
long nbElements = maxNbElements[i];
- for (unsigned int focused = 0; focused < focusedBufferIndexes.size()+focusedStackIndexes.size(); focused++)
+ for (unsigned int focused = 0; focused < bufferFocused.size()+stackFocused.size(); focused++)
{
auto cnnInput = elementsEmbeddings.narrow(1, curIndex, nbElements).unsqueeze(1);
curIndex += nbElements;
@@ -119,30 +120,13 @@ std::vector<std::vector<long>> CNNNetworkImpl::extractContext(Config & config, D
}
}
+ std::vector<long> focusedIndexes = extractFocusedIndexes(config);
+
for (auto & contextElement : context)
for (unsigned int colIndex = 0; colIndex < focusedColumns.size(); colIndex++)
{
auto & col = focusedColumns[colIndex];
- std::vector<int> focusedIndexes;
- for (auto relIndex : focusedBufferIndexes)
- {
- int index = relIndex + leftBorder;
- if (index < 0 || index >= (int)contextIndexes.size())
- focusedIndexes.push_back(-1);
- else
- focusedIndexes.push_back(contextIndexes[index]);
- }
- for (auto index : focusedStackIndexes)
- {
- if (!config.hasStack(index))
- focusedIndexes.push_back(-1);
- else if (!config.has(col, config.getStack(index), 0))
- focusedIndexes.push_back(-1);
- else
- focusedIndexes.push_back(config.getStack(index));
- }
-
for (auto index : focusedIndexes)
{
if (index == -1)
diff --git a/torch_modules/src/ConcatWordsNetwork.cpp b/torch_modules/src/ConcatWordsNetwork.cpp
index 81ee6252f29ac1b1f676f25557cf64da3a413331..b03b8493b0af20679e70cea6cd73af59e4658588 100644
--- a/torch_modules/src/ConcatWordsNetwork.cpp
+++ b/torch_modules/src/ConcatWordsNetwork.cpp
@@ -1,26 +1,25 @@
#include "ConcatWordsNetwork.hpp"
-ConcatWordsNetworkImpl::ConcatWordsNetworkImpl(int nbOutputs, int leftBorder, int rightBorder, int nbStackElements)
+ConcatWordsNetworkImpl::ConcatWordsNetworkImpl(int nbOutputs, const std::vector<int> & bufferContext, const std::vector<int> & stackContext)
{
- constexpr int embeddingsSize = 100;
- setLeftBorder(leftBorder);
- setRightBorder(rightBorder);
- setNbStackElements(nbStackElements);
+ constexpr int embeddingsSize = 64;
+ constexpr int hiddenSize = 500;
+
+ setBufferContext(bufferContext);
+ setStackContext(stackContext);
setColumns({"FORM", "UPOS"});
wordEmbeddings = register_module("word_embeddings", torch::nn::Embedding(torch::nn::EmbeddingOptions(50000, embeddingsSize)));
- linear1 = register_module("linear1", torch::nn::Linear(getContextSize()*embeddingsSize, 500));
- linear2 = register_module("linear2", torch::nn::Linear(500, nbOutputs));
+ linear1 = register_module("linear1", torch::nn::Linear(getContextSize()*embeddingsSize, hiddenSize));
+ linear2 = register_module("linear2", torch::nn::Linear(hiddenSize, nbOutputs));
dropout = register_module("dropout", torch::nn::Dropout(0.3));
}
torch::Tensor ConcatWordsNetworkImpl::forward(torch::Tensor input)
{
- // input dim = {batch, sequence, embeddings}
- auto wordsAsEmb = dropout(wordEmbeddings(input));
- // reshaped dim = {batch, sequence of embeddings}
- auto reshaped = wordsAsEmb.dim() == 3 ? torch::reshape(wordsAsEmb, {wordsAsEmb.size(0), wordsAsEmb.size(1)*wordsAsEmb.size(2)}) : torch::reshape(wordsAsEmb, {wordsAsEmb.size(0)*wordsAsEmb.size(1)});
-
- return linear2(torch::relu(linear1(reshaped)));
+ if (input.dim() == 1)
+ input = input.unsqueeze(0);
+ auto wordsAsEmb = dropout(wordEmbeddings(input).view({input.size(0), -1}));
+ return linear2(torch::relu(linear1(wordsAsEmb)));
}
diff --git a/torch_modules/src/LSTMNetwork.cpp b/torch_modules/src/LSTMNetwork.cpp
index 84a36f20c292a95500262607c8767fa7494187d7..0ae4cd10cd7866b8d182bfb4ea60da3f47178efc 100644
--- a/torch_modules/src/LSTMNetwork.cpp
+++ b/torch_modules/src/LSTMNetwork.cpp
@@ -1,6 +1,6 @@
#include "LSTMNetwork.hpp"
-LSTMNetworkImpl::LSTMNetworkImpl(int nbOutputs, int unknownValueThreshold, int leftBorder, int rightBorder, int nbStackElements, std::vector<std::string> columns, std::vector<int> focusedBufferIndexes, std::vector<int> focusedStackIndexes, std::vector<std::string> focusedColumns, std::vector<int> maxNbElements, int leftWindowRawInput, int rightWindowRawInput) : unknownValueThreshold(unknownValueThreshold), focusedBufferIndexes(focusedBufferIndexes), focusedStackIndexes(focusedStackIndexes), focusedColumns(focusedColumns), maxNbElements(maxNbElements), leftWindowRawInput(leftWindowRawInput), rightWindowRawInput(rightWindowRawInput)
+LSTMNetworkImpl::LSTMNetworkImpl(int nbOutputs, int unknownValueThreshold, std::vector<int> bufferContext, std::vector<int> stackContext, std::vector<std::string> columns, std::vector<int> focusedBufferIndexes, std::vector<int> focusedStackIndexes, std::vector<std::string> focusedColumns, std::vector<int> maxNbElements, int leftWindowRawInput, int rightWindowRawInput) : unknownValueThreshold(unknownValueThreshold), focusedColumns(focusedColumns), maxNbElements(maxNbElements), leftWindowRawInput(leftWindowRawInput), rightWindowRawInput(rightWindowRawInput)
{
constexpr int embeddingsSize = 64;
constexpr int hiddenSize = 1024;
@@ -8,10 +8,11 @@ LSTMNetworkImpl::LSTMNetworkImpl(int nbOutputs, int unknownValueThreshold, int l
constexpr int focusedLSTMSize = 64;
constexpr int rawInputLSTMSize = 16;
- setLeftBorder(leftBorder);
- setRightBorder(rightBorder);
- setNbStackElements(nbStackElements);
+ setBufferContext(bufferContext);
+ setStackContext(stackContext);
setColumns(columns);
+ setBufferFocused(focusedBufferIndexes);
+ setStackFocused(focusedStackIndexes);
rawInputSize = leftWindowRawInput + rightWindowRawInput + 1;
if (leftWindowRawInput < 0 or rightWindowRawInput < 0)
@@ -34,7 +35,7 @@ LSTMNetworkImpl::LSTMNetworkImpl(int nbOutputs, int unknownValueThreshold, int l
for (auto & col : focusedColumns)
{
lstms.emplace_back(register_module(fmt::format("LSTM_{}", col), torch::nn::LSTM(torch::nn::LSTMOptions(embeddingsSize, focusedLSTMSize).batch_first(true).bidirectional(true))));
- totalLSTMOutputSize += lstms.back()->options.hidden_size() * (lstms.back()->options.bidirectional() ? 4 : 1) * (focusedBufferIndexes.size()+focusedStackIndexes.size());
+ totalLSTMOutputSize += lstms.back()->options.hidden_size() * (lstms.back()->options.bidirectional() ? 4 : 1) * (bufferFocused.size()+stackFocused.size());
}
linear1 = register_module("linear1", torch::nn::Linear(totalLSTMOutputSize, hiddenSize));
@@ -48,7 +49,7 @@ torch::Tensor LSTMNetworkImpl::forward(torch::Tensor input)
auto embeddings = embeddingsDropout(wordEmbeddings(input));
- auto context = embeddings.narrow(1, rawInputSize, columns.size()*(1+leftBorder+rightBorder));
+ auto context = embeddings.narrow(1, rawInputSize, getContextSize());
context = context.view({context.size(0), context.size(1)/(int)columns.size(), (int)columns.size()*(int)wordEmbeddings->options.embedding_dim()});
@@ -67,7 +68,7 @@ torch::Tensor LSTMNetworkImpl::forward(torch::Tensor input)
for (unsigned int i = 0; i < focusedColumns.size(); i++)
{
long nbElements = maxNbElements[i];
- for (unsigned int focused = 0; focused < focusedBufferIndexes.size()+focusedStackIndexes.size(); focused++)
+ for (unsigned int focused = 0; focused < bufferFocused.size()+stackFocused.size(); focused++)
{
auto lstmInput = elementsEmbeddings.narrow(1, curIndex, nbElements);
curIndex += nbElements;
@@ -136,30 +137,13 @@ std::vector<std::vector<long>> LSTMNetworkImpl::extractContext(Config & config,
}
}
+ std::vector<long> focusedIndexes = extractFocusedIndexes(config);
+
for (auto & contextElement : context)
for (unsigned int colIndex = 0; colIndex < focusedColumns.size(); colIndex++)
{
auto & col = focusedColumns[colIndex];
- std::vector<int> focusedIndexes;
- for (auto relIndex : focusedBufferIndexes)
- {
- int index = relIndex + leftBorder;
- if (index < 0 || index >= (int)contextIndexes.size())
- focusedIndexes.push_back(-1);
- else
- focusedIndexes.push_back(contextIndexes[index]);
- }
- for (auto index : focusedStackIndexes)
- {
- if (!config.hasStack(index))
- focusedIndexes.push_back(-1);
- else if (!config.has(col, config.getStack(index), 0))
- focusedIndexes.push_back(-1);
- else
- focusedIndexes.push_back(config.getStack(index));
- }
-
for (auto index : focusedIndexes)
{
if (index == -1)
diff --git a/torch_modules/src/NeuralNetwork.cpp b/torch_modules/src/NeuralNetwork.cpp
index fef5519623d91e48bbac5dead3dcd575216c4121..088e5c0260f38fd5bc05df067b344b3e59e349e2 100644
--- a/torch_modules/src/NeuralNetwork.cpp
+++ b/torch_modules/src/NeuralNetwork.cpp
@@ -4,31 +4,30 @@ torch::Device NeuralNetworkImpl::device(torch::cuda::is_available() ? torch::kCU
std::vector<long> NeuralNetworkImpl::extractContextIndexes(const Config & config) const
{
- std::stack<long> leftContext;
- for (int index = config.getWordIndex()-1; config.has(0,index,0) && leftContext.size() < leftBorder; --index)
- if (!config.isCommentPredicted(index))
- leftContext.push(index);
-
std::vector<long> context;
- while (context.size() < leftBorder-leftContext.size())
- context.emplace_back(-1);
- while (!leftContext.empty())
- {
- context.emplace_back(leftContext.top());
- leftContext.pop();
- }
+ for (int index : bufferContext)
+ context.emplace_back(config.getRelativeWordIndex(index));
+
+ for (int index : stackContext)
+ if (config.hasStack(index))
+ context.emplace_back(config.getStack(index));
+ else
+ context.emplace_back(-1);
+
+ return context;
+}
- for (int index = config.getWordIndex(); config.has(0,index,0) && context.size() < leftBorder+rightBorder+1; ++index)
- if (!config.isCommentPredicted(index))
- context.emplace_back(index);
+std::vector<long> NeuralNetworkImpl::extractFocusedIndexes(const Config & config) const
+{
+ std::vector<long> context;
- while (context.size() < leftBorder+rightBorder+1)
- context.emplace_back(-1);
+ for (int index : bufferFocused)
+ context.emplace_back(config.getRelativeWordIndex(index));
- for (unsigned int i = 0; i < nbStackElements; i++)
- if (config.hasStack(i))
- context.emplace_back(config.getStack(i));
+ for (int index : stackFocused)
+ if (config.hasStack(index))
+ context.emplace_back(config.getStack(index));
else
context.emplace_back(-1);
@@ -52,22 +51,27 @@ std::vector<std::vector<long>> NeuralNetworkImpl::extractContext(Config & config
int NeuralNetworkImpl::getContextSize() const
{
- return columns.size()*(1 + leftBorder + rightBorder + nbStackElements);
+ return columns.size()*(bufferContext.size()+stackContext.size());
+}
+
+void NeuralNetworkImpl::setBufferContext(const std::vector<int> & bufferContext)
+{
+ this->bufferContext = bufferContext;
}
-void NeuralNetworkImpl::setRightBorder(int rightBorder)
+void NeuralNetworkImpl::setStackContext(const std::vector<int> & stackContext)
{
- this->rightBorder = rightBorder;
+ this->stackContext = stackContext;
}
-void NeuralNetworkImpl::setLeftBorder(int leftBorder)
+void NeuralNetworkImpl::setBufferFocused(const std::vector<int> & bufferFocused)
{
- this->leftBorder = leftBorder;
+ this->bufferFocused = bufferFocused;
}
-void NeuralNetworkImpl::setNbStackElements(int nbStackElements)
+void NeuralNetworkImpl::setStackFocused(const std::vector<int> & stackFocused)
{
- this->nbStackElements = nbStackElements;
+ this->stackFocused = stackFocused;
}
void NeuralNetworkImpl::setColumns(const std::vector<std::string> & columns)
diff --git a/torch_modules/src/OneWordNetwork.cpp b/torch_modules/src/OneWordNetwork.cpp
index d2d796693c88e2c019c0067b0801d3a69a0d0d2c..e3ed3d57c31387bea2d62dc5e230db79f092ec6e 100644
--- a/torch_modules/src/OneWordNetwork.cpp
+++ b/torch_modules/src/OneWordNetwork.cpp
@@ -2,36 +2,22 @@
OneWordNetworkImpl::OneWordNetworkImpl(int nbOutputs, int focusedIndex)
{
- constexpr int embeddingsSize = 30;
+ constexpr int embeddingsSize = 64;
- wordEmbeddings = register_module("word_embeddings", torch::nn::Embedding(torch::nn::EmbeddingOptions(200000, embeddingsSize)));
- linear = register_module("linear", torch::nn::Linear(embeddingsSize, nbOutputs));
-
- int leftBorder = 0;
- int rightBorder = 0;
- if (focusedIndex < 0)
- leftBorder = -focusedIndex;
- if (focusedIndex > 0)
- rightBorder = focusedIndex;
-
- this->focusedIndex = focusedIndex <= 0 ? 0 : focusedIndex;
-
- setLeftBorder(leftBorder);
- setRightBorder(rightBorder);
- setNbStackElements(0);
+ setBufferContext({focusedIndex});
+ setStackContext({});
setColumns({"FORM", "UPOS"});
+
+ wordEmbeddings = register_module("word_embeddings", torch::nn::Embedding(torch::nn::EmbeddingOptions(50000, embeddingsSize)));
+ linear = register_module("linear", torch::nn::Linear(getContextSize()*embeddingsSize, nbOutputs));
}
torch::Tensor OneWordNetworkImpl::forward(torch::Tensor input)
{
- // input dim = {batch, sequence, embeddings}
- auto wordsAsEmb = wordEmbeddings(input);
- auto reshaped = wordsAsEmb;
- // reshaped dim = {sequence, batch, embeddings}
- if (reshaped.dim() == 3)
- reshaped = wordsAsEmb.permute({1,0,2});
-
- auto res = linear(reshaped[focusedIndex]);
+ if (input.dim() == 1)
+ input = input.unsqueeze(0);
+ auto wordAsEmb = wordEmbeddings(input).view({input.size(0),-1});
+ auto res = linear(wordAsEmb);
return res;
}
diff --git a/torch_modules/src/RLTNetwork.cpp b/torch_modules/src/RLTNetwork.cpp
index 38fe64203cbdb0f9546e96cd1b6ac758265af364..a9a346f635a2f35f10aea8ff6743a320bbc537f6 100644
--- a/torch_modules/src/RLTNetwork.cpp
+++ b/torch_modules/src/RLTNetwork.cpp
@@ -7,9 +7,11 @@ RLTNetworkImpl::RLTNetworkImpl(int nbOutputs, int leftBorder, int rightBorder, i
constexpr int treeEmbeddingsSize = 256;
constexpr int hiddenSize = 500;
- setLeftBorder(leftBorder);
- setRightBorder(rightBorder);
- setNbStackElements(nbStackElements);
+ //TODO gerer ces context
+ this->leftBorder = leftBorder;
+ this->rightBorder = rightBorder;
+ setBufferContext({});
+ setStackContext({});
setColumns({"FORM", "UPOS"});
wordEmbeddings = register_module("word_embeddings", torch::nn::Embedding(torch::nn::EmbeddingOptions(50000, embeddingsSize)));
@@ -27,10 +29,10 @@ torch::Tensor RLTNetworkImpl::forward(torch::Tensor input)
input = input.unsqueeze(0);
auto focusedIndexes = input.narrow(1, 0, focusedBufferIndexes.size()+focusedStackIndexes.size());
- auto computeOrder = input.narrow(1, focusedIndexes.size(1), leftBorder+rightBorder+1);
- auto childsFlat = input.narrow(1, focusedIndexes.size(1)+computeOrder.size(1), maxNbChilds*(leftBorder+rightBorder+1));
+ auto computeOrder = input.narrow(1, focusedIndexes.size(1), getContextSize()/columns.size());
+ auto childsFlat = input.narrow(1, focusedIndexes.size(1)+computeOrder.size(1), maxNbChilds*(getContextSize()/columns.size()));
auto childs = torch::reshape(childsFlat, {childsFlat.size(0), computeOrder.size(1), maxNbChilds});
- auto wordIndexes = input.narrow(1, focusedIndexes.size(1)+computeOrder.size(1)+childsFlat.size(1), columns.size()*(leftBorder+rightBorder+1));
+ auto wordIndexes = input.narrow(1, focusedIndexes.size(1)+computeOrder.size(1)+childsFlat.size(1), getContextSize());
auto baseEmbeddings = wordEmbeddings(wordIndexes);
auto concatBaseEmbeddings = torch::reshape(baseEmbeddings, {baseEmbeddings.size(0), (int)baseEmbeddings.size(1)/(int)columns.size(), (int)baseEmbeddings.size(2)*(int)columns.size()});
auto vectorRepresentations = vectorBiLSTM(concatBaseEmbeddings).output;