Corrected wrong device error when using from_blob

decoder/src/Beam.cpp

@@ -48,7 +48,7 @@ void Beam::update(ReadingMachine & machine, bool debug)
     elements[index].config.setAppliableTransitions(appliableTransitions);
 
     auto context = classifier.getNN()->extractContext(elements[index].config).back();
-    auto neuralInput = torch::from_blob(context.data(), {(long)context.size()}, torch::TensorOptions(torch::kLong).device(NeuralNetworkImpl::device));
+    auto neuralInput = torch::from_blob(context.data(), {(long)context.size()}, torch::TensorOptions(torch::kLong)).clone().to(NeuralNetworkImpl::device);
 
     auto prediction = classifier.isRegression() ? classifier.getNN()(neuralInput).squeeze(0) : torch::softmax(classifier.getNN()(neuralInput).squeeze(0), 0);
     float entropy = classifier.isRegression() ? 0.0 : NeuralNetworkImpl::entropy(prediction);

trainer/src/Trainer.cpp

@@ -93,7 +93,7 @@ void Trainer::extractExamples(std::vector<SubConfig> & configs, bool debug, std:
       if (dynamicOracle and util::choiceWithProbability(1.0) and config.getState() != "tokenizer" and config.getState() != "segmenter")
       {
         auto & classifier = *machine.getClassifier(config.getState());
-        auto neuralInput = torch::from_blob(context.data(), {(long)context.size()}, torch::TensorOptions(torch::kLong).device(NeuralNetworkImpl::device));
+        auto neuralInput = torch::from_blob(context.data(), {(long)context.size()}, torch::TensorOptions(torch::kLong)).clone().to(NeuralNetworkImpl::device);
         auto prediction = classifier.isRegression() ? classifier.getNN()(neuralInput).squeeze(0) : torch::softmax(classifier.getNN()(neuralInput).squeeze(0), 0);
         entropy  = NeuralNetworkImpl::entropy(prediction);