Fix clang

Author: NeiroYT

include/layers_oneDNN/ConvLayer.hpp

@@ -16,10 +16,10 @@ class ConvLayerOneDnn : public Layer {
         stride_(1),
         pads_(0),
         dilations_(1),
-        group_(1),
-        use_legacy_(false),
         kernel_(nullptr),
-        bias_(nullptr) {}
+        bias_(nullptr),
+        group_(1),
+        use_legacy_(false) {}
 
   ConvLayerOneDnn(size_t stride, size_t pads, size_t dilations, Tensor& kernel,
                   Tensor& bias = *std::make_shared<Tensor>(), size_t group = 1,
@@ -33,7 +33,7 @@ class ConvLayerOneDnn : public Layer {
         group_(group),
         use_legacy_(use_legacy) {}
 
-    ConvLayerOneDnn(size_t stride, size_t pads, size_t dilations,
+  ConvLayerOneDnn(size_t stride, size_t pads, size_t dilations,
                   std::shared_ptr<Tensor> kernel,
                   std::shared_ptr<Tensor> bias = std::make_shared<Tensor>(),
                   size_t group = 1, bool use_legacy = false)

src/layers/ConvLayer.cpp

@@ -21,7 +21,8 @@ void ConvolutionalLayer::run(const std::vector<Tensor>& input,
   ParBackend backend = options.par_backend;
 
   if (group_ > 1) {
-    if (group_ == input[0].get_shape()[1] && group_ == kernel_->get_shape()[0]) {
+    if (group_ == input[0].get_shape()[1] &&
+        group_ == kernel_->get_shape()[0]) {
       switch (input[0].get_type()) {
         case Type::kFloat:
           DepthwiseConv4D<float>(input[0], *kernel_, *bias_, output[0], stride_,
@@ -83,9 +84,11 @@ void ConvolutionalLayer::run(const std::vector<Tensor>& input,
                         dilations_ +
                     kernel_->get_shape()[kernel_->get_shape().dims() - 1],
                 static_cast<int>(
-                    ((1 + kernel_->get_shape()[kernel_->get_shape().dims() - 1]) *
+                    ((1 +
+                      kernel_->get_shape()[kernel_->get_shape().dims() - 1]) *
                          dilations_ +
-                     kernel_->get_shape()[kernel_->get_shape().dims() - 1] - 1) /
+                     kernel_->get_shape()[kernel_->get_shape().dims() - 1] -
+                     1) /
                     2)),
             sh);
       } else {
@@ -137,9 +140,11 @@ void ConvolutionalLayer::run(const std::vector<Tensor>& input,
                         dilations_ +
                     kernel_->get_shape()[kernel_->get_shape().dims() - 1],
                 static_cast<int>(
-                    ((1 + kernel_->get_shape()[kernel_->get_shape().dims() - 1]) *
+                    ((1 +
+                      kernel_->get_shape()[kernel_->get_shape().dims() - 1]) *
                          dilations_ +
-                     kernel_->get_shape()[kernel_->get_shape().dims() - 1] - 1) /
+                     kernel_->get_shape()[kernel_->get_shape().dims() - 1] -
+                     1) /
                     2)),
             sh);
       } else {

src/layers/FCLayer.cpp

@@ -38,8 +38,8 @@ void FCLayer::run(const std::vector<Tensor>& input,
       break;
     }
     case Type::kFloat: {
-      FCLayerImpl<float> used_impl(*weights_->as<float>(), weights_->get_shape(),
-                                   *bias_->as<float>());
+      FCLayerImpl<float> used_impl(*weights_->as<float>(),
+                                   weights_->get_shape(), *bias_->as<float>());
       auto result = used_impl.run(*input[0].as<float>());
       output[0] = make_tensor(result, {batch_size, output_size});
       break;