diff --git a/decoder/src/Decoder.cpp b/decoder/src/Decoder.cpp
index 147396e0bd5eccb60c4740bca66f58eedc1a56a5..cb9937edbf78a1bb657a81c544468a8257f3c708 100644
--- a/decoder/src/Decoder.cpp
+++ b/decoder/src/Decoder.cpp
@@ -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
diff --git a/trainer/include/Trainer.hpp b/trainer/include/Trainer.hpp
index 6e889171c5f1fa461f333605446fb8545d287270..e04f3e37dcee7bd29ea47dd226d144d9574d1e40 100644
--- a/trainer/include/Trainer.hpp
+++ b/trainer/include/Trainer.hpp
@@ -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
diff --git a/trainer/src/Trainer.cpp b/trainer/src/Trainer.cpp
index 106df3ffbdf3d90ab397d78a6fbff11619e77dc9..e2572872814364cb643ace0a882f8ec984eb4be9 100644
--- a/trainer/src/Trainer.cpp
+++ b/trainer/src/Trainer.cpp
@@ -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);
+}
+
diff --git a/trainer/src/macaon_train.cpp b/trainer/src/macaon_train.cpp
index dc48e459b3357c20e07ad772acc7af839ea262b5..602553786a7c2d9b199914113ca45ad12e89d735 100644
--- a/trainer/src/macaon_train.cpp
+++ b/trainer/src/macaon_train.cpp
@@ -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;