From 26cc83e4c8331984320522e5d091098f760374d6 Mon Sep 17 00:00:00 2001
From: Franck Dary <franck.dary@lis-lab.fr>
Date: Sun, 15 Mar 2020 13:34:31 +0100
Subject: [PATCH] NeuralNetwork::extractContext can now generate multiple
variants of context
---
decoder/src/Decoder.cpp | 4 +-
torch_modules/include/CNNNetwork.hpp | 2 +-
torch_modules/include/NeuralNetwork.hpp | 2 +-
torch_modules/include/RLTNetwork.hpp | 2 +-
torch_modules/src/CNNNetwork.cpp | 163 +++++++++++++-----------
torch_modules/src/NeuralNetwork.cpp | 4 +-
torch_modules/src/RLTNetwork.cpp | 4 +-
trainer/src/Trainer.cpp | 12 +-
8 files changed, 104 insertions(+), 89 deletions(-)
diff --git a/decoder/src/Decoder.cpp b/decoder/src/Decoder.cpp
index 1d81309..44a8edc 100644
--- a/decoder/src/Decoder.cpp
+++ b/decoder/src/Decoder.cpp
@@ -25,10 +25,10 @@ void Decoder::decode(BaseConfig & config, std::size_t beamSize, bool debug, bool
config.printForDebug(stderr);
auto dictState = machine.getDict(config.getState()).getState();
- auto context = machine.getClassifier()->getNN()->extractContext(config, machine.getDict(config.getState()));
+ auto context = machine.getClassifier()->getNN()->extractContext(config, machine.getDict(config.getState())).back();
machine.getDict(config.getState()).setState(dictState);
- auto neuralInput = torch::from_blob(context.data(), {(long)context.size()}, at::kLong).clone().to(NeuralNetworkImpl::device);
+ auto neuralInput = torch::from_blob(context.data(), {(long)context.size()}, torch::kLong).clone().to(NeuralNetworkImpl::device);
auto prediction = machine.getClassifier()->getNN()(neuralInput).squeeze();
int chosenTransition = -1;
diff --git a/torch_modules/include/CNNNetwork.hpp b/torch_modules/include/CNNNetwork.hpp
index 2edac49..0cd54b8 100644
--- a/torch_modules/include/CNNNetwork.hpp
+++ b/torch_modules/include/CNNNetwork.hpp
@@ -33,7 +33,7 @@ class CNNNetworkImpl : public NeuralNetworkImpl
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);
torch::Tensor forward(torch::Tensor input) override;
- std::vector<long> extractContext(Config & config, Dict & dict) const override;
+ std::vector<std::vector<long>> extractContext(Config & config, Dict & dict) const override;
};
#endif
diff --git a/torch_modules/include/NeuralNetwork.hpp b/torch_modules/include/NeuralNetwork.hpp
index 1ca0919..34bf14b 100644
--- a/torch_modules/include/NeuralNetwork.hpp
+++ b/torch_modules/include/NeuralNetwork.hpp
@@ -27,7 +27,7 @@ class NeuralNetworkImpl : public torch::nn::Module
public :
virtual torch::Tensor forward(torch::Tensor input) = 0;
- virtual std::vector<long> extractContext(Config & config, Dict & dict) const;
+ virtual std::vector<std::vector<long>> extractContext(Config & config, Dict & dict) const;
std::vector<long> extractContextIndexes(const Config & config) const;
int getContextSize() const;
void setColumns(const std::vector<std::string> & columns);
diff --git a/torch_modules/include/RLTNetwork.hpp b/torch_modules/include/RLTNetwork.hpp
index 7d350b3..b996def 100644
--- a/torch_modules/include/RLTNetwork.hpp
+++ b/torch_modules/include/RLTNetwork.hpp
@@ -23,7 +23,7 @@ class RLTNetworkImpl : public NeuralNetworkImpl
RLTNetworkImpl(int nbOutputs, int leftBorder, int rightBorder, int nbStackElements);
torch::Tensor forward(torch::Tensor input) override;
- std::vector<long> extractContext(Config & config, Dict & dict) const override;
+ std::vector<std::vector<long>> extractContext(Config & config, Dict & dict) const override;
};
#endif
diff --git a/torch_modules/src/CNNNetwork.cpp b/torch_modules/src/CNNNetwork.cpp
index 5e9696e..9f9d6a1 100644
--- a/torch_modules/src/CNNNetwork.cpp
+++ b/torch_modules/src/CNNNetwork.cpp
@@ -74,118 +74,129 @@ torch::Tensor CNNNetworkImpl::forward(torch::Tensor input)
return linear2(hiddenDropout(torch::relu(linear1(totalInput))));
}
-std::vector<long> CNNNetworkImpl::extractContext(Config & config, Dict & dict) const
+std::vector<std::vector<long>> CNNNetworkImpl::extractContext(Config & config, Dict & dict) const
{
if (dict.size() >= maxNbEmbeddings)
util::warning(fmt::format("dict.size()={} > maxNbEmbeddings={}", dict.size(), maxNbEmbeddings));
std::vector<long> contextIndexes = extractContextIndexes(config);
- std::vector<long> context;
+ std::vector<std::vector<long>> context;
+ context.emplace_back();
if (rawInputSize > 0)
{
for (int i = 0; i < leftWindowRawInput; i++)
if (config.hasCharacter(config.getCharacterIndex()-leftWindowRawInput+i))
- context.push_back(dict.getIndexOrInsert(fmt::format("Letter({})", config.getLetter(config.getCharacterIndex()-leftWindowRawInput+i))));
+ context.back().push_back(dict.getIndexOrInsert(fmt::format("Letter({})", config.getLetter(config.getCharacterIndex()-leftWindowRawInput+i))));
else
- context.push_back(dict.getIndexOrInsert(Dict::nullValueStr));
+ context.back().push_back(dict.getIndexOrInsert(Dict::nullValueStr));
for (int i = 0; i <= rightWindowRawInput; i++)
if (config.hasCharacter(config.getCharacterIndex()+i))
-
- context.push_back(dict.getIndexOrInsert(fmt::format("Letter({})", config.getLetter(config.getCharacterIndex()+i))));
+ context.back().push_back(dict.getIndexOrInsert(fmt::format("Letter({})", config.getLetter(config.getCharacterIndex()+i))));
else
- context.push_back(dict.getIndexOrInsert(Dict::nullValueStr));
+ context.back().push_back(dict.getIndexOrInsert(Dict::nullValueStr));
}
for (auto index : contextIndexes)
for (auto & col : columns)
if (index == -1)
- context.push_back(dict.getIndexOrInsert(Dict::nullValueStr));
+ for (auto & contextElement : context)
+ contextElement.push_back(dict.getIndexOrInsert(Dict::nullValueStr));
else
{
int dictIndex = dict.getIndexOrInsert(config.getAsFeature(col, index));
- if (col == "FORM" || col == "LEMMA")
- if (dict.getNbOccs(dictIndex) < unknownValueThreshold)
- dictIndex = dict.getIndexOrInsert(Dict::unknownValueStr);
- context.push_back(dictIndex);
- }
+ for (auto & contextElement : context)
+ contextElement.push_back(dictIndex);
- for (unsigned int colIndex = 0; colIndex < focusedColumns.size(); colIndex++)
- {
- auto & col = focusedColumns[colIndex];
+ if (is_training())
+ if (col == "FORM" || col == "LEMMA")
+ if (dict.getNbOccs(dictIndex) < unknownValueThreshold)
+ {
+ context.emplace_back(context.back());
+ context.back().back() = dict.getIndexOrInsert(Dict::unknownValueStr);
+ }
+ }
- std::vector<int> focusedIndexes;
- for (auto relIndex : focusedBufferIndexes)
+ for (auto & contextElement : context)
+ for (unsigned int colIndex = 0; colIndex < focusedColumns.size(); colIndex++)
{
- 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));
- }
+ auto & col = focusedColumns[colIndex];
- for (auto index : focusedIndexes)
- {
- if (index == -1)
+ std::vector<int> focusedIndexes;
+ for (auto relIndex : focusedBufferIndexes)
{
- for (int i = 0; i < maxNbElements[colIndex]; i++)
- context.emplace_back(dict.getIndexOrInsert(Dict::nullValueStr));
- continue;
+ int index = relIndex + leftBorder;
+ if (index < 0 || index >= (int)contextIndexes.size())
+ focusedIndexes.push_back(-1);
+ else
+ focusedIndexes.push_back(contextIndexes[index]);
}
-
- std::vector<std::string> elements;
- if (col == "FORM")
+ for (auto index : focusedStackIndexes)
{
- auto asUtf8 = util::splitAsUtf8(config.getAsFeature(col, index).get());
-
- for (int i = 0; i < maxNbElements[colIndex]; i++)
- if (i < (int)asUtf8.size())
- elements.emplace_back(fmt::format("Letter({})", asUtf8[i]));
- else
- elements.emplace_back(Dict::nullValueStr);
+ 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));
}
- else if (col == "FEATS")
- {
- auto splited = util::split(config.getAsFeature(col, index).get(), '|');
- for (int i = 0; i < maxNbElements[colIndex]; i++)
- if (i < (int)splited.size())
- elements.emplace_back(fmt::format("FEATS({})", splited[i]));
- else
- elements.emplace_back(Dict::nullValueStr);
- }
- else if (col == "ID")
+ for (auto index : focusedIndexes)
{
- if (config.isTokenPredicted(index))
- elements.emplace_back("ID(TOKEN)");
- else if (config.isMultiwordPredicted(index))
- elements.emplace_back("ID(MULTIWORD)");
- else if (config.isEmptyNodePredicted(index))
- elements.emplace_back("ID(EMPTYNODE)");
+ if (index == -1)
+ {
+ for (int i = 0; i < maxNbElements[colIndex]; i++)
+ contextElement.emplace_back(dict.getIndexOrInsert(Dict::nullValueStr));
+ continue;
+ }
+
+ std::vector<std::string> elements;
+ if (col == "FORM")
+ {
+ auto asUtf8 = util::splitAsUtf8(config.getAsFeature(col, index).get());
+
+ for (int i = 0; i < maxNbElements[colIndex]; i++)
+ if (i < (int)asUtf8.size())
+ elements.emplace_back(fmt::format("Letter({})", asUtf8[i]));
+ else
+ elements.emplace_back(Dict::nullValueStr);
+ }
+ else if (col == "FEATS")
+ {
+ auto splited = util::split(config.getAsFeature(col, index).get(), '|');
+
+ for (int i = 0; i < maxNbElements[colIndex]; i++)
+ if (i < (int)splited.size())
+ elements.emplace_back(fmt::format("FEATS({})", splited[i]));
+ else
+ elements.emplace_back(Dict::nullValueStr);
+ }
+ else if (col == "ID")
+ {
+ if (config.isTokenPredicted(index))
+ elements.emplace_back("ID(TOKEN)");
+ else if (config.isMultiwordPredicted(index))
+ elements.emplace_back("ID(MULTIWORD)");
+ else if (config.isEmptyNodePredicted(index))
+ elements.emplace_back("ID(EMPTYNODE)");
+ }
+ else
+ {
+ elements.emplace_back(config.getAsFeature(col, index));
+ }
+
+ if ((int)elements.size() != maxNbElements[colIndex])
+ util::myThrow(fmt::format("elements.size ({}) != maxNbElements[colIndex ({},{})]", elements.size(), maxNbElements[colIndex], col));
+
+ for (auto & element : elements)
+ contextElement.emplace_back(dict.getIndexOrInsert(element));
}
- else
- {
- elements.emplace_back(config.getAsFeature(col, index));
- }
-
- if ((int)elements.size() != maxNbElements[colIndex])
- util::myThrow(fmt::format("elements.size ({}) != maxNbElements[colIndex ({},{})]", elements.size(), maxNbElements[colIndex], col));
-
- for (auto & element : elements)
- context.emplace_back(dict.getIndexOrInsert(element));
}
- }
+
+ if (!is_training() && context.size() > 1)
+ util::myThrow(fmt::format("Not in training mode, yet context yields multiple variants (size={})", context.size()));
return context;
}
diff --git a/torch_modules/src/NeuralNetwork.cpp b/torch_modules/src/NeuralNetwork.cpp
index 37c206c..fef5519 100644
--- a/torch_modules/src/NeuralNetwork.cpp
+++ b/torch_modules/src/NeuralNetwork.cpp
@@ -35,7 +35,7 @@ std::vector<long> NeuralNetworkImpl::extractContextIndexes(const Config & config
return context;
}
-std::vector<long> NeuralNetworkImpl::extractContext(Config & config, Dict & dict) const
+std::vector<std::vector<long>> NeuralNetworkImpl::extractContext(Config & config, Dict & dict) const
{
std::vector<long> indexes = extractContextIndexes(config);
std::vector<long> context;
@@ -47,7 +47,7 @@ std::vector<long> NeuralNetworkImpl::extractContext(Config & config, Dict & dict
else
context.push_back(dict.getIndexOrInsert(config.getAsFeature(col, index)));
- return context;
+ return {context};
}
int NeuralNetworkImpl::getContextSize() const
diff --git a/torch_modules/src/RLTNetwork.cpp b/torch_modules/src/RLTNetwork.cpp
index 85223e7..38fe642 100644
--- a/torch_modules/src/RLTNetwork.cpp
+++ b/torch_modules/src/RLTNetwork.cpp
@@ -79,7 +79,7 @@ torch::Tensor RLTNetworkImpl::forward(torch::Tensor input)
return linear2(torch::relu(linear1(representation)));
}
-std::vector<long> RLTNetworkImpl::extractContext(Config & config, Dict & dict) const
+std::vector<std::vector<long>> RLTNetworkImpl::extractContext(Config & config, Dict & dict) const
{
std::vector<long> contextIndexes;
std::stack<int> leftContext;
@@ -183,6 +183,6 @@ std::vector<long> RLTNetworkImpl::extractContext(Config & config, Dict & dict) c
else
context.push_back(dict.getIndexOrInsert(config.getAsFeature(col, l)));
- return context;
+ return {context};
}
diff --git a/trainer/src/Trainer.cpp b/trainer/src/Trainer.cpp
index 2963701..501af8e 100644
--- a/trainer/src/Trainer.cpp
+++ b/trainer/src/Trainer.cpp
@@ -48,20 +48,24 @@ void Trainer::extractExamples(SubConfig & config, bool debug, std::vector<torch:
util::myThrow("No transition appliable !");
}
+ std::vector<std::vector<long>> context;
+
try
{
- auto context = machine.getClassifier()->getNN()->extractContext(config,machine.getDict(config.getState()));
- contexts.emplace_back(torch::from_blob(context.data(), {(long)context.size()}, torch::TensorOptions(at::kLong)).clone().to(NeuralNetworkImpl::device));
+ context = machine.getClassifier()->getNN()->extractContext(config,machine.getDict(config.getState()));
+ for (auto & element : context)
+ contexts.emplace_back(torch::from_blob(element.data(), {(long)element.size()}, torch::TensorOptions(torch::kLong)).clone().to(NeuralNetworkImpl::device));
} catch(std::exception & e)
{
util::myThrow(fmt::format("Failed to extract context : {}", e.what()));
}
int goldIndex = machine.getTransitionSet().getTransitionIndex(transition);
- auto gold = torch::zeros(1, torch::TensorOptions(at::kLong).device(NeuralNetworkImpl::device));
+ auto gold = torch::zeros(1, torch::TensorOptions(torch::kLong).device(NeuralNetworkImpl::device));
gold[0] = goldIndex;
- classes.emplace_back(gold);
+ for (auto & element : context)
+ classes.emplace_back(gold);
transition->apply(config);
config.addToHistory(transition->getName());
--
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