Address 'google-*' clang-tidy remarks

.clang-tidy

@@ -1,13 +1,12 @@
 Checks: >
   bugprone-*,
+  google-*,
   modernize-*,
   misc-*,
   performance-*,
   portability-*,
   readability-*,
-  -google-readability-braces-around-statements,
-  -google-readability-namespace-comments,
-  -google-runtime-references,
+  -google-build-using-namespace,
   -misc-non-private-member-variables-in-classes,
   -misc-const-correctness,
   -misc-include-cleaner,

app/Graph/build.cpp

@@ -138,16 +138,18 @@ void build_graph_linear(it_lab_ai::Graph& graph, it_lab_ai::Tensor& input,
   if (comments) std::cout << "Input set in graph." << '\n';
 
   graph.makeConnection(a1, layers[0]);
-  if (comments)
+  if (comments) {
     std::cout << "Connection made between InputLayer and first layer." << '\n';
+  }
 
   for (size_t i = 0; i < layers.size() - 1; ++i) {
     if (layerpostop[i]) {
       layers[i - 1]->postops.layers.push_back(layers[i]);
       layers[i - 1]->postops.count++;
       graph.makeConnection(layers[i - 1], layers[i + 1]);
-    } else if (!layerpostop[i + 1])
+    } else if (!layerpostop[i + 1]) {
       graph.makeConnection(layers[i], layers[i + 1]);
+    }
   }
 
   graph.setOutput(layers.back(), output);

include/layers/InputLayer.hpp

@@ -1,6 +1,7 @@
 #pragma once
 #include <algorithm>
 #include <cmath>
+#include <cstdint>
 
 #include "layers/Layer.hpp"
 
@@ -76,10 +77,8 @@ class InputLayer : public Layer {
               }
             }
           }
-          Shape sh1({static_cast<unsigned long long>(n),
-                     static_cast<unsigned long long>(h),
-                     static_cast<unsigned long long>(w),
-                     static_cast<unsigned long long>(c)});
+          Shape sh1({static_cast<uint64_t>(n), static_cast<uint64_t>(h),
+                     static_cast<uint64_t>(w), static_cast<uint64_t>(c)});
           output[0] = make_tensor<int>(res, sh1);
           break;
         }
@@ -103,10 +102,8 @@ class InputLayer : public Layer {
               }
             }
           }
-          Shape sh1({static_cast<unsigned long long>(n),
-                     static_cast<unsigned long long>(c),
-                     static_cast<unsigned long long>(h),
-                     static_cast<unsigned long long>(w)});
+          Shape sh1({static_cast<uint64_t>(n), static_cast<uint64_t>(c),
+                     static_cast<uint64_t>(h), static_cast<uint64_t>(w)});
           output[0] = make_tensor<int>(res, sh1);
           break;
         }
@@ -144,10 +141,8 @@ class InputLayer : public Layer {
               }
             }
           }
-          Shape sh1({static_cast<unsigned long long>(n),
-                     static_cast<unsigned long long>(h),
-                     static_cast<unsigned long long>(w),
-                     static_cast<unsigned long long>(c)});
+          Shape sh1({static_cast<uint64_t>(n), static_cast<uint64_t>(h),
+                     static_cast<uint64_t>(w), static_cast<uint64_t>(c)});
           output[0] = make_tensor<float>(res, sh1);
           break;
         }
@@ -171,10 +166,8 @@ class InputLayer : public Layer {
               }
             }
           }
-          Shape sh1({static_cast<unsigned long long>(n),
-                     static_cast<unsigned long long>(c),
-                     static_cast<unsigned long long>(h),
-                     static_cast<unsigned long long>(w)});
+          Shape sh1({static_cast<uint64_t>(n), static_cast<uint64_t>(c),
+                     static_cast<uint64_t>(h), static_cast<uint64_t>(w)});
           output[0] = make_tensor<float>(res, sh1);
           break;
         }

src/Weights_Reader/reader_weights.cpp

@@ -57,7 +57,8 @@ json read_json(const std::string& filename) {
     return json{};
   }
 
-  char* data = (char*)mmap(nullptr, sb.st_size, PROT_READ, MAP_PRIVATE, fd, 0);
+  char* data = static_cast<char*>(
+      mmap(nullptr, sb.st_size, PROT_READ, MAP_PRIVATE, fd, 0));
   json result = json::parse(data, data + sb.st_size);
 
   munmap(data, sb.st_size);

src/layers_oneDNN/ConvLayer.cpp

@@ -249,12 +249,14 @@ void ConvLayerOneDnn::initialize_convolution(const Shape& input_shape,
     src_memory_ = dnnl::memory(conv_pd.src_desc(), *engine_);
     weights_memory_ = dnnl::memory(conv_pd.weights_desc(), *engine_);
     dst_memory_ = dnnl::memory(conv_pd.dst_desc(), *engine_);
-    if (!bias_->empty())
+    if (!bias_->empty()) {
       bias_memory_ = dnnl::memory(conv_pd.bias_desc(), *engine_);
+    }
 
     fill_memory_with_tensor(weights_memory_, *kernel_, data_type);
-    if (!bias_->empty())
+    if (!bias_->empty()) {
       fill_memory_with_tensor(bias_memory_, *bias_, data_type);
+    }
 
     conv_prim_ = std::make_unique<dnnl::convolution_forward>(conv_pd);
     initialized_ = true;