From 86a9ca43f8af6a7d0b9dbc4fd6c3bc5fff50b01d Mon Sep 17 00:00:00 2001
From: Oli Larkin <olilarkin@googlemail.com>
Date: Fri, 19 Jan 2024 23:51:14 +0100
Subject: [PATCH] receptive_field

---
 NAM/dsp.cpp     | 14 +++++++-------
 NAM/dsp.h       |  4 ++--
 NAM/get_dsp.cpp |  4 ++--
 NAM/wavenet.cpp |  4 ++--
 4 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/NAM/dsp.cpp b/NAM/dsp.cpp
index 906aa9f..8b3b871 100644
--- a/NAM/dsp.cpp
+++ b/NAM/dsp.cpp
@@ -62,10 +62,10 @@ void nam::DSP::Finalize(const int numFrames) {}
 
 // Buffer =====================================================================
 
-nam::Buffer::Buffer(const int receptive_field, const double expected_sample_rate)
+nam::Buffer::Buffer(const int receptiveField, const double expected_sample_rate)
 : nam::DSP(expected_sample_rate)
 {
-  this->SetReceptiveField(receptive_field);
+  this->SetReceptiveField(receptiveField);
 }
 
 void nam::Buffer::SetReceptiveField(const int newReceptiveField)
@@ -136,11 +136,11 @@ void nam::Buffer::Finalize(const int numFrames)
 
 // Linear =====================================================================
 
-nam::Linear::Linear(const int receptive_field, const bool _bias, const std::vector<float>& weights,
+nam::Linear::Linear(const int receptiveField, const bool _bias, const std::vector<float>& weights,
                     const double expected_sample_rate)
-: nam::Buffer(receptive_field, expected_sample_rate)
+: nam::Buffer(receptiveField, expected_sample_rate)
 {
-  if ((int)weights.size() != (receptive_field + (_bias ? 1 : 0)))
+  if ((int)weights.size() != (receptiveField + (_bias ? 1 : 0)))
     throw std::runtime_error(
       "Params vector does not match expected size based "
       "on architecture parameters");
@@ -148,8 +148,8 @@ nam::Linear::Linear(const int receptive_field, const bool _bias, const std::vect
   this->_weight.resize(this->mReceptiveField);
   // Pass in in reverse order so that dot products work out of the box.
   for (int i = 0; i < this->mReceptiveField; i++)
-    this->_weight(i) = weights[receptive_field - 1 - i];
-  this->_bias = _bias ? weights[receptive_field] : (float)0.0;
+    this->_weight(i) = weights[receptiveField - 1 - i];
+  this->_bias = _bias ? weights[receptiveField] : (float)0.0;
 }
 
 void nam::Linear::Process(float* input, float* output, const int numFrames)
diff --git a/NAM/dsp.h b/NAM/dsp.h
index 197930d..3ad4213 100644
--- a/NAM/dsp.h
+++ b/NAM/dsp.h
@@ -82,7 +82,7 @@ class DSP
 class Buffer : public DSP
 {
 public:
-  Buffer(const int receptive_field, const double expected_sample_rate = -1.0);
+  Buffer(const int receptiveField, const double expected_sample_rate = -1.0);
   void Finalize(const int numFrames);
 
 protected:
@@ -104,7 +104,7 @@ class Buffer : public DSP
 class Linear : public Buffer
 {
 public:
-  Linear(const int receptive_field, const bool _bias, const std::vector<float>& weights,
+  Linear(const int receptiveField, const bool _bias, const std::vector<float>& weights,
          const double expected_sample_rate = -1.0);
   void Process(float* input, float* output, const int numFrames) override;
 
diff --git a/NAM/get_dsp.cpp b/NAM/get_dsp.cpp
index d848046..08acec4 100644
--- a/NAM/get_dsp.cpp
+++ b/NAM/get_dsp.cpp
@@ -145,9 +145,9 @@ std::unique_ptr<DSP> get_dsp(dspData& conf)
   std::unique_ptr<DSP> out = nullptr;
   if (architecture == "Linear")
   {
-    const int receptive_field = config["receptive_field"];
+    const int receptiveField = config["receptiveField"];
     const bool _bias = config["bias"];
-    out = std::make_unique<Linear>(receptive_field, _bias, weights, expectedSampleRate);
+    out = std::make_unique<Linear>(receptiveField, _bias, weights, expectedSampleRate);
   }
   else if (architecture == "ConvNet")
   {
diff --git a/NAM/wavenet.cpp b/NAM/wavenet.cpp
index f806a54..1ce5ecc 100644
--- a/NAM/wavenet.cpp
+++ b/NAM/wavenet.cpp
@@ -67,10 +67,10 @@ nam::wavenet::_LayerArray::_LayerArray(const int input_size, const int condition
 {
   for (size_t i = 0; i < dilations.size(); i++)
     this->_layers.push_back(_Layer(condition_size, channels, kernel_size, dilations[i], activation, gated));
-  const long receptive_field = this->_get_receptive_field();
+  const long receptiveField = this->_get_receptive_field();
   for (size_t i = 0; i < dilations.size(); i++)
   {
-    this->_layer_buffers.push_back(Eigen::MatrixXf(channels, LAYER_ARRAY_BUFFER_SIZE + receptive_field - 1));
+    this->_layer_buffers.push_back(Eigen::MatrixXf(channels, LAYER_ARRAY_BUFFER_SIZE + receptiveField - 1));
     this->_layer_buffers[i].setZero();
   }
   this->_buffer_start = this->_get_receptive_field() - 1;