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Commit b743e4d3 authored by Franck Dary's avatar Franck Dary
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Added option to decide if dev must be evaluated or not

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decoder/src/Decoder.cpp
+ 1
− 3
View file @ b743e4d3
......@@ -7,7 +7,7 @@ Decoder::Decoder(ReadingMachine & machine) : machine(machine)
void Decoder::decode(BaseConfig & config, std::size_t beamSize, bool debug, bool printAdvancement)
{
machine.getClassifier()->getNN()->train(false);
torch::AutoGradMode useGrad(false);
config.addPredicted(machine.getPredicted());
constexpr int printInterval = 50;
......@@ -88,8 +88,6 @@ void Decoder::decode(BaseConfig & config, std::size_t beamSize, bool debug, bool
if (debug)
fmt::print(stderr, "Forcing EOS transition\n");
}
machine.getClassifier()->getNN()->train(true);
}
float Decoder::getMetricScore(const std::string & metric, std::size_t scoreIndex) const
......
trainer/include/Trainer.hpp
+ 8
− 0
View file @ b743e4d3
......@@ -16,16 +16,24 @@ class Trainer
ReadingMachine & machine;
DataLoader dataLoader{nullptr};
DataLoader devDataLoader{nullptr};
std::unique_ptr<torch::optim::Adam> optimizer;
std::size_t epochNumber{0};
int batchSize{50};
int nbExamples{0};
private :
void extractExamples(SubConfig & config, bool debug, std::vector<torch::Tensor> & contexts, std::vector<torch::Tensor> & classes);
float processDataset(DataLoader & loader, bool train, bool printAdvancement);
public :
Trainer(ReadingMachine & machine);
void createDataset(SubConfig & goldConfig, bool debug);
void createDevDataset(SubConfig & goldConfig, bool debug);
float epoch(bool printAdvancement);
float evalOnDev(bool printAdvancement);
};
#endif
trainer/src/Trainer.cpp
+ 44
− 13
View file @ b743e4d3
......@@ -7,12 +7,33 @@ Trainer::Trainer(ReadingMachine & machine) : machine(machine)
void Trainer::createDataset(SubConfig & config, bool debug)
{
config.addPredicted(machine.getPredicted());
config.setState(machine.getStrategy().getInitialState());
std::vector<torch::Tensor> contexts;
std::vector<torch::Tensor> classes;
extractExamples(config, debug, contexts, classes);
nbExamples = classes.size();
dataLoader = torch::data::make_data_loader(Dataset(contexts, classes).map(torch::data::transforms::Stack<>()), torch::data::DataLoaderOptions(batchSize).workers(0).max_jobs(0));
optimizer.reset(new torch::optim::Adam(machine.getClassifier()->getNN()->parameters(), torch::optim::AdamOptions(0.001).amsgrad(true).beta1(0.9).beta2(0.999)));
}
void Trainer::createDevDataset(SubConfig & config, bool debug)
{
std::vector<torch::Tensor> contexts;
std::vector<torch::Tensor> classes;
extractExamples(config, debug, contexts, classes);
devDataLoader = torch::data::make_data_loader(Dataset(contexts, classes).map(torch::data::transforms::Stack<>()), torch::data::DataLoaderOptions(batchSize).workers(0).max_jobs(0));
}
void Trainer::extractExamples(SubConfig & config, bool debug, std::vector<torch::Tensor> & contexts, std::vector<torch::Tensor> & classes)
{
config.addPredicted(machine.getPredicted());
config.setState(machine.getStrategy().getInitialState());
while (true)
{
if (debug)
......@@ -59,15 +80,9 @@ void Trainer::createDataset(SubConfig & config, bool debug)
if (config.needsUpdate())
config.update();
}
nbExamples = classes.size();
dataLoader = torch::data::make_data_loader(Dataset(contexts, classes).map(torch::data::transforms::Stack<>()), torch::data::DataLoaderOptions(batchSize).workers(0).max_jobs(0));
optimizer.reset(new torch::optim::Adam(machine.getClassifier()->getNN()->parameters(), torch::optim::AdamOptions(0.001).amsgrad(true).beta1(0.9).beta2(0.999)));
}
float Trainer::epoch(bool printAdvancement)
float Trainer::processDataset(DataLoader & loader, bool train, bool printAdvancement)
{
constexpr int printInterval = 50;
int nbExamplesProcessed = 0;
......@@ -75,13 +90,16 @@ float Trainer::epoch(bool printAdvancement)
float lossSoFar = 0.0;
int currentBatchNumber = 0;
torch::AutoGradMode useGrad(train);
auto lossFct = torch::nn::CrossEntropyLoss();
auto pastTime = std::chrono::high_resolution_clock::now();
for (auto & batch : *dataLoader)
for (auto & batch : *loader)
{
optimizer->zero_grad();
if (train)
optimizer->zero_grad();
auto data = batch.data;
auto labels = batch.target.squeeze();
......@@ -99,8 +117,11 @@ float Trainer::epoch(bool printAdvancement)
lossSoFar += loss.item<float>();
} catch(std::exception & e) {util::myThrow(e.what());}
loss.backward();
optimizer->step();
if (train)
{
loss.backward();
optimizer->step();
}
if (printAdvancement)
{
......@@ -122,3 +143,13 @@ float Trainer::epoch(bool printAdvancement)
return totalLoss;
}
float Trainer::epoch(bool printAdvancement)
{
return processDataset(dataLoader, true, printAdvancement);
}
float Trainer::evalOnDev(bool printAdvancement)
{
return processDataset(devDataLoader, false, printAdvancement);
}
trainer/src/macaon_train.cpp
+ 30
− 8
View file @ b743e4d3
......@@ -24,6 +24,7 @@ po::options_description getOptionsDescription()
opt.add_options()
("debug,d", "Print debuging infos on stderr")
("silent", "Don't print speed and progress")
("devScore", "Compute score on dev instead of loss (slower)")
("trainTXT", po::value<std::string>()->default_value(""),
"Raw text file of the training corpus")
("devTSV", po::value<std::string>()->default_value(""),
......@@ -75,6 +76,7 @@ int main(int argc, char * argv[])
auto nbEpoch = variables["nbEpochs"].as<int>();
bool debug = variables.count("debug") == 0 ? false : true;
bool printAdvancement = !debug && variables.count("silent") == 0 ? true : false;
bool computeDevScore = variables.count("devScore") == 0 ? false : true;
fmt::print(stderr, "Training using device : {}\n", NeuralNetworkImpl::device.str());
......@@ -84,38 +86,58 @@ int main(int argc, char * argv[])
ReadingMachine machine(machinePath.string());
BaseConfig goldConfig(mcdFile, trainTsvFile, trainRawFile);
BaseConfig devGoldConfig(mcdFile, devTsvFile, devRawFile);
SubConfig config(goldConfig);
Trainer trainer(machine);
trainer.createDataset(config, debug);
if (!computeDevScore)
{
SubConfig devConfig(devGoldConfig);
trainer.createDevDataset(devConfig, debug);
}
Decoder decoder(machine);
BaseConfig devGoldConfig(mcdFile, devTsvFile, devRawFile);
float bestDevScore = 0;
float bestDevScore = computeDevScore ? 0 : 100;
for (int i = 0; i < nbEpoch; i++)
{
float loss = trainer.epoch(printAdvancement);
machine.getStrategy().reset();
auto devConfig = devGoldConfig;
if (debug)
fmt::print(stderr, "Decoding dev :\n");
decoder.decode(devConfig, 1, debug, printAdvancement);
machine.getStrategy().reset();
decoder.evaluate(devConfig, modelPath, devTsvFile);
std::vector<std::pair<float,std::string>> devScores = decoder.getF1Scores(machine.getPredicted());
std::vector<std::pair<float,std::string>> devScores;
if (computeDevScore)
{
auto devConfig = devGoldConfig;
decoder.decode(devConfig, 1, debug, printAdvancement);
machine.getStrategy().reset();
decoder.evaluate(devConfig, modelPath, devTsvFile);
devScores = decoder.getF1Scores(machine.getPredicted());
}
else
{
float devLoss = trainer.evalOnDev(printAdvancement);
devScores.emplace_back(std::make_pair(devLoss, "Loss"));
}
std::string devScoresStr = "";
float devScoreMean = 0;
for (auto & score : devScores)
{
devScoresStr += fmt::format("{}({:5.2f}%),", score.second, score.first);
if (computeDevScore)
devScoresStr += fmt::format("{}({:5.2f}{}),", score.second, score.first, computeDevScore ? "%" : "");
else
devScoresStr += fmt::format("{}({:6.1f}{}),", score.second, score.first, computeDevScore ? "%" : "");
devScoreMean += score.first;
}
if (!devScoresStr.empty())
devScoresStr.pop_back();
devScoreMean /= devScores.size();
bool saved = devScoreMean > bestDevScore;
if (!computeDevScore)
saved = devScoreMean < bestDevScore;
if (saved)
{
bestDevScore = devScoreMean;
......
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