simplify vector load from json (#105)

this reduces number of memory allocations during model load

NAM/get_dsp.cpp

@@ -68,13 +68,10 @@ void verify_config_version(const std::string versionStr)
 
 std::vector<float> GetWeights(nlohmann::json const& j, const std::filesystem::path config_path)
 {
-  if (j.find("weights") != j.end())
+  auto it = j.find("weights");
+  if (it != j.end())
   {
-    auto weight_list = j["weights"];
-    std::vector<float> weights;
-    for (auto it = weight_list.begin(); it != weight_list.end(); ++it)
-      weights.push_back(*it);
-    return weights;
+    return *it;
   }
   else
     throw std::runtime_error("Corrupted model file is missing weights.");
@@ -153,9 +150,7 @@ std::unique_ptr<DSP> get_dsp(dspData& conf)
   {
     const int channels = config["channels"];
     const bool batchnorm = config["batchnorm"];
-    std::vector<int> dilations;
-    for (size_t i = 0; i < config["dilations"].size(); i++)
-      dilations.push_back(config["dilations"][i]);
+    std::vector<int> dilations = config["dilations"];
     const std::string activation = config["activation"];
     out = std::make_unique<convnet::ConvNet>(channels, dilations, batchnorm, activation, weights, expectedSampleRate);
   }
@@ -172,12 +167,9 @@ std::unique_ptr<DSP> get_dsp(dspData& conf)
     for (size_t i = 0; i < config["layers"].size(); i++)
     {
       nlohmann::json layer_config = config["layers"][i];
-      std::vector<int> dilations;
-      for (size_t j = 0; j < layer_config["dilations"].size(); j++)
-        dilations.push_back(layer_config["dilations"][j]);
       layer_array_params.push_back(
         wavenet::LayerArrayParams(layer_config["input_size"], layer_config["condition_size"], layer_config["head_size"],
-                                  layer_config["channels"], layer_config["kernel_size"], dilations,
+                                  layer_config["channels"], layer_config["kernel_size"], layer_config["dilations"],
                                   layer_config["activation"], layer_config["gated"], layer_config["head_bias"]));
     }
     const bool with_head = config["head"] == NULL;

NAM/wavenet.h

@@ -58,20 +58,19 @@ class LayerArrayParams
 {
 public:
   LayerArrayParams(const int input_size_, const int condition_size_, const int head_size_, const int channels_,
-                   const int kernel_size_, const std::vector<int>& dilations_, const std::string activation_,
+                   const int kernel_size_, const std::vector<int>&& dilations_, const std::string activation_,
                    const bool gated_, const bool head_bias_)
   : input_size(input_size_)
   , condition_size(condition_size_)
   , head_size(head_size_)
   , channels(channels_)
   , kernel_size(kernel_size_)
+  , dilations(std::move(dilations_))
   , activation(activation_)
   , gated(gated_)
   , head_bias(head_bias_)
   {
-    for (size_t i = 0; i < dilations_.size(); i++)
-      this->dilations.push_back(dilations_[i]);
-  };
+  }
 
   const int input_size;
   const int condition_size;