Using std::integral_constant for model_detail::forEachInTuple

RTNeural/ModelT.h

@@ -29,9 +29,9 @@ namespace modelt_detail
 
     /** Functions to do a function for each element in the tuple */
     template <typename Fn, typename Tuple, size_t... Ix>
-    constexpr void forEachInTuple(Fn&& fn, Tuple&& tuple, std::index_sequence<Ix...>) noexcept(noexcept(std::initializer_list<int> { (fn(std::get<Ix>(tuple), Ix), 0)... }))
+    constexpr void forEachInTuple(Fn&& fn, Tuple&& tuple, std::index_sequence<Ix...>) noexcept(noexcept(std::initializer_list<int> { (fn(std::get<Ix>(tuple), std::integral_constant<size_t, Ix>()), 0)... }))
     {
-        (void)std::initializer_list<int> { ((void)fn(std::get<Ix>(tuple), Ix), 0)... };
+        (void)std::initializer_list<int> { ((void)fn(std::get<Ix>(tuple), std::integral_constant<size_t, Ix>()), 0)... };
     }
 
     template <typename T>

RTNeural/RTNeural.h

@@ -6,8 +6,8 @@
 #include <limits>
 
 // RTNeural includes:
-#include "config.h"
 #include "Model.h"
 #include "ModelT.h"
+#include "config.h"
 #include "model_loader.h"
 #include "torch_helpers.h"

RTNeural/config.h

@@ -31,7 +31,7 @@
     for which instructions may be found in the RADSan repository above.
 */
 #ifdef RTNEURAL_RADSAN_ENABLED
-  #define RTNEURAL_REALTIME [[clang::realtime]]
+#define RTNEURAL_REALTIME [[clang::realtime]]
 #else
-  #define RTNEURAL_REALTIME
+#define RTNEURAL_REALTIME
 #endif

RTNeural/conv1d/conv1d.h

@@ -59,7 +59,7 @@ class Conv1D final : public Layer<T>
         // set state pointers to particular columns of the buffer
         setStatePointers();
 
-        if (groups == 1)
+        if(groups == 1)
         {
             // copy selected columns to a helper variable
             for(int k = 0; k < kernel_size; ++k)
@@ -198,7 +198,7 @@ class Conv1DT
     /** Resets the layer state. */
     RTNEURAL_REALTIME void reset();
 
-    template<int _groups = groups, std::enable_if_t<_groups == 1, bool> = true>
+    template <int _groups = groups, std::enable_if_t<_groups == 1, bool> = true>
     /** Performs forward propagation for this layer. */
     RTNEURAL_REALTIME inline void forward(const T (&ins)[in_size]) noexcept
     {
@@ -230,7 +230,7 @@ class Conv1DT
         state_ptr = (state_ptr == state_size - 1 ? 0 : state_ptr + 1); // iterate state pointer forwards
     }
 
-    template<int _groups = groups, std::enable_if_t<_groups != 1, bool> = true>
+    template <int _groups = groups, std::enable_if_t<_groups != 1, bool> = true>
     /** Performs forward propagation for this layer. */
     inline void forward(const T (&ins)[in_size]) noexcept
     {

RTNeural/conv1d/conv1d_eigen.h

@@ -52,7 +52,7 @@ class Conv1D : public Layer<T>
         // set state pointers to the particular columns of the buffer
         setStatePointers();
 
-        if (groups == 1)
+        if(groups == 1)
         {
             // copy selected columns to a helper variable
             for(int k = 0; k < kernel_size; ++k)
@@ -175,7 +175,7 @@ class Conv1DT
     RTNEURAL_REALTIME void reset();
 
     /** Performs forward propagation for this layer. */
-    template<int _groups = groups, std::enable_if_t<_groups == 1, bool> = true>
+    template <int _groups = groups, std::enable_if_t<_groups == 1, bool> = true>
     RTNEURAL_REALTIME inline void forward(const Eigen::Matrix<T, in_size, 1>& ins) noexcept
     {
         // insert input into a circular buffer
@@ -196,7 +196,7 @@ class Conv1DT
     }
 
     /** Performs forward propagation for this layer (groups > 1). */
-    template<int _groups = groups, std::enable_if_t<_groups != 1, bool> = true>
+    template <int _groups = groups, std::enable_if_t<_groups != 1, bool> = true>
     RTNEURAL_REALTIME inline void forward(const Eigen::Matrix<T, in_size, 1>& ins) noexcept
     {
         // insert input into a circular buffer

RTNeural/conv1d/conv1d_xsimd.h

@@ -4,9 +4,9 @@
 #include "../Layer.h"
 #include "../common.h"
 #include "../config.h"
+#include <iostream>
 #include <numeric>
 #include <vector>
-#include <iostream>
 
 namespace RTNEURAL_NAMESPACE
 {
@@ -54,7 +54,7 @@ class Conv1D : public Layer<T>
         // set state pointers to particular columns of the buffer
         setStatePointers();
 
-        if (groups == 1)
+        if(groups == 1)
         {
             // copy selected columns to a helper variable
             for(int k = 0; k < kernel_size; ++k)
@@ -220,10 +220,10 @@ class Conv1DT
                 {
                     // copy selected columns to a helper variable
                     // @TODO: I'm not sure the reinterpret_casts are 100% safe here, but they seem to work in testing!
-                    const auto& column = reinterpret_cast<std::array<T, in_size>&> (state[state_ptrs[j]]);
+                    const auto& column = reinterpret_cast<std::array<T, in_size>&>(state[state_ptrs[j]]);
                     const auto column_begin = column.begin() + ii;
                     const auto column_end = column_begin + filters_per_group;
-                    std::copy(column_begin, column_end, reinterpret_cast<std::array<T, filters_per_group>&> (state_cols[j]).begin());
+                    std::copy(column_begin, column_end, reinterpret_cast<std::array<T, filters_per_group>&>(state_cols[j]).begin());
 
                     accum += std::inner_product(
                         subWeights[j].begin(),

RTNeural/dense/dense_xsimd.h

@@ -338,7 +338,7 @@ class DenseT<T, 1, out_sizet>
         for(int i = 0; i < v_out_size; ++i)
             outs[i] = bias[i];
 
-        const auto in = ins[0].get (0);
+        const auto in = ins[0].get(0);
         for(int i = 0; i < v_out_size; ++i)
             outs[i] += in * weights[i];
     }

tests/functional/torch_conv1d_test.cpp

@@ -37,7 +37,7 @@ void testTorchConv1DModel()
     }
 
     std::ifstream modelOutputsFile { std::string { RTNEURAL_ROOT_DIR } + "test_data/conv1d_torch_y_python.csv" };
-    const auto expected_y = RTNeural::torch_helpers::detail::transpose(load_csv::loadFile2d<T> (modelOutputsFile));
+    const auto expected_y = RTNeural::torch_helpers::detail::transpose(load_csv::loadFile2d<T>(modelOutputsFile));
 
     for(size_t n = 0; n < expected_y.size(); ++n)
     {

tests/functional/torch_microtcn_test.cpp

@@ -3,7 +3,7 @@
 #include "RTNeural/RTNeural.h"
 #include "load_csv.hpp"
 
-#if ! (RTNEURAL_USE_EIGEN || RTNEURAL_USE_XSIMD)
+#if !(RTNEURAL_USE_EIGEN || RTNEURAL_USE_XSIMD)
 
 namespace
 {