Improvements before MAX IV test

resonet/NeuralNetResPredictor.cpp

@@ -6,14 +6,19 @@
 #include "../common/JFJochException.h"
 
 NeuralNetResPredictor::NeuralNetResPredictor(const std::string& model_path)
-        : model_input(512*512)
+        : model_input(512*512),
+          enable(!model_path.empty())
 #ifdef JFJOCH_USE_TORCH
-          , device(torch::kCUDA)
+        , device(torch::kCUDA)
 #endif
-        {
+{
 #ifdef JFJOCH_USE_TORCH
-    module = torch::jit::load(model_path);
-    module.to(device);
+    if (enable) {
+        module = torch::jit::load(model_path);
+        module.to(device);
+    }
+#else
+    enable = false;
 #endif
 }
 
@@ -65,13 +70,15 @@ size_t NeuralNetResPredictor::GetMaxPoolFactor(const DiffractionExperiment& expe
     return pool_factor;
 }
 
-float NeuralNetResPredictor::Inference(const DiffractionExperiment& experiment, const void *image) {
+std::optional<float> NeuralNetResPredictor::Inference(const DiffractionExperiment& experiment, const void *image) {
+    if (!enable)
+        return {};
+#ifdef JFJOCH_USE_TORCH
     if (experiment.GetPixelDepth() == 2)
         Prepare(experiment, (int16_t *) image);
     else
         Prepare(experiment, (int32_t *) image);
 
-#ifdef JFJOCH_USE_TORCH
     auto options = torch::TensorOptions().dtype(at::kFloat);
     auto model_input_tensor = torch::from_blob(model_input.data(), {1,1,512,512}, options).to(device);
 
@@ -83,10 +90,10 @@ float NeuralNetResPredictor::Inference(const DiffractionExperiment& experiment,
     float two_theta = atanf(((2.0f * experiment.GetPixelSize_mm() / experiment.GetDetectorDistance_mm()) * tensor_output));
     float stheta = sinf(two_theta * 0.5f);
     float resolution = experiment.GetWavelength_A() / (2.0f * stheta);
-#else
-    float resolution = 50.0;
-#endif
     return resolution;
+#else
+    return {};
+#endif
 }
 
 const std::vector<float> &NeuralNetResPredictor::GetModelInput() const {