From be63334bb4116b31c4b0464dde155d2b5633e9bc Mon Sep 17 00:00:00 2001
From: Franck Dary <franck.dary@lis-lab.fr>
Date: Wed, 22 Jan 2020 22:26:50 +0100
Subject: [PATCH] tests
---
dev/src/dev.cpp | 15 +++++++++++----
torch_modules/src/TestNetwork.cpp | 2 +-
2 files changed, 12 insertions(+), 5 deletions(-)
diff --git a/dev/src/dev.cpp b/dev/src/dev.cpp
index b5bd81f..2aac98c 100644
--- a/dev/src/dev.cpp
+++ b/dev/src/dev.cpp
@@ -67,18 +67,20 @@ int main(int argc, char * argv[])
auto dataset = ConfigDataset(contexts, classes).map(torch::data::transforms::Stack<>());
- fmt::print("Done! size={}\n", *dataset.size());
+ int nbExamples = *dataset.size();
+ fmt::print("Done! size={}\n", nbExamples);
int batchSize = 100;
- auto dataLoader = torch::data::make_data_loader(std::move(dataset), torch::data::DataLoaderOptions(batchSize));
+ auto dataLoader = torch::data::make_data_loader(std::move(dataset), torch::data::DataLoaderOptions(batchSize).workers(0).max_jobs(0));
TestNetwork nn(machine.getTransitionSet().size(), 5);
torch::optim::Adam optimizer(nn->parameters(), torch::optim::AdamOptions(2e-4).beta1(0.5));
- for (int epoch = 1; epoch <= 5; ++epoch)
+ for (int epoch = 1; epoch <= 1; ++epoch)
{
float totalLoss = 0.0;
torch::Tensor example;
+ int currentBatchNumber = 0;
for (auto & batch : *dataLoader)
{
@@ -94,10 +96,15 @@ int main(int argc, char * argv[])
totalLoss += loss.item<float>();
loss.backward();
optimizer.step();
+
+ if (++currentBatchNumber*batchSize % 1000 == 0)
+ {
+ fmt::print("\rcurrent epoch : {:6.2f}%", 100.0*currentBatchNumber*batchSize/nbExamples);
+ std::fflush(stdout);
+ }
}
fmt::print("Epoch {} : loss={:.2f}\n", epoch, totalLoss);
- std::cout << example << std::endl;
}
return 0;
diff --git a/torch_modules/src/TestNetwork.cpp b/torch_modules/src/TestNetwork.cpp
index 3e3c010..f379c73 100644
--- a/torch_modules/src/TestNetwork.cpp
+++ b/torch_modules/src/TestNetwork.cpp
@@ -2,7 +2,7 @@
TestNetworkImpl::TestNetworkImpl(int nbOutputs, int focusedIndex)
{
- constexpr int embeddingsSize = 100;
+ constexpr int embeddingsSize = 30;
wordEmbeddings = register_module("word_embeddings", torch::nn::Embedding(200000, embeddingsSize));
linear = register_module("linear", torch::nn::Linear(embeddingsSize, nbOutputs));
this->focusedIndex = focusedIndex;
--
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