tests

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;

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;