COMPMID-1995: Fix clang-tidy warnings

- Remove VirtualCall checks
- Fix some unused variables errors
- Use std::array insted of C style arrays
- Various fixes

Change-Id: Ife6170b7102de42b8f04e298dcf8476bf90779f0
Signed-off-by: Michalis Spyrou <michalis.spyrou@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1049
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>

5 files changed, 21 insertions, 10 deletions

diff --git a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
index 5133756993..3bb69b1ffc 100644
--- a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
@@ -112,7 +112,8 @@ void NEDepthwiseConvolutionLayer3x3::configure_generic(ITensor
         const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info();
 
         float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale;
-        int   output_multiplier, output_shift;
+        int   output_multiplier;
+        int   output_shift;
         quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
         _output_stage_kernel.configure(&_accumulator, biases, _is_nchw ? output : &_permuted_output, output_multiplier, output_shift, output_quant_info.offset);
         _accumulator.allocator()->allocate();
@@ -461,7 +462,8 @@ void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weigh
         const QuantizationInfo output_quant_info = output->info()->quantization_info();
 
         float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale;
-        int   output_multiplier, output_shift;
+        int   output_multiplier;
+        int   output_shift;
         quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
         _output_stage_kernel.configure(&_output_reshaped, biases, output_to_use, output_multiplier, output_shift, output_quant_info.offset);
         _output_reshaped.allocator()->allocate();
diff --git a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
index 273da278e8..e1a17db6d4 100644
--- a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
+++ b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
@@ -228,7 +228,8 @@ void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weigh
     if(_is_quantized)
     {
         float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output->info()->quantization_info().scale;
-        int   output_multiplier, output_shift;
+        int   output_multiplier;
+        int   output_shift;
         quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
         _gemmlowp_output_stage.configure(&_gemmlowp_output, biases, output, output_multiplier, output_shift, output->info()->quantization_info().offset);
         _gemmlowp_output.allocator()->allocate();
diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
index beac4ffe5f..a2c4e8a8b1 100644
--- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -118,7 +118,8 @@ void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *w
         const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input_quantization_info : output->info()->quantization_info();
 
         float multiplier = input_quantization_info.scale * weights->info()->quantization_info().scale / output_quant_info.scale;
-        int   output_multiplier, output_shift;
+        int   output_multiplier;
+        int   output_shift;
         quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
 
         // Merge activation with output stage
@@ -184,7 +185,8 @@ Status NEGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITens
         const QuantizationInfo output_quant_info = (output->total_size() == 0) ? input_quantization_info : output->quantization_info();
 
         float multiplier = input_quantization_info.scale * weights->quantization_info().scale / output_quant_info.scale;
-        int   output_multiplier, output_shift;
+        int   output_multiplier;
+        int   output_shift;
         quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
 
         // Merge activation with output stage
@@ -412,7 +414,10 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
     const unsigned int kernel_width  = weights->dimension(idx_width);
     const unsigned int kernel_height = weights->dimension(idx_height);
 
-    TensorInfo         im2col_reshaped_info, info_gemm, tmp_info, weights_reshaped_info;
+    TensorInfo         im2col_reshaped_info{};
+    TensorInfo         info_gemm{};
+    TensorInfo         tmp_info{};
+    TensorInfo         weights_reshaped_info{};
     const ITensorInfo *gemm_input_to_use  = input;
     const ITensorInfo *gemm_output_to_use = output;
     const ITensorInfo *weights_to_use     = weights;
diff --git a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
index 5ce7bc5f2c..54f49a6707 100644
--- a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
+++ b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
@@ -287,7 +287,8 @@ Status NEGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITenso
         }
     }
 
-    TensorInfo info_vector_sum_col, info_vector_sum_row;
+    TensorInfo info_vector_sum_col{};
+    TensorInfo info_vector_sum_row{};
 
     // Validate matrix B reduction kernel only if _a_offset is not equal to 0
     if(a_offset != 0)
diff --git a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
index 2332450c04..1513786ae5 100644
--- a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
@@ -162,7 +162,7 @@ inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input)
     const int        in_channels = input->info()->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
     const int        in_batches  = input->info()->dimension(3);
 
-    return Tensor4DShape({ in_batches, in_height, in_width, in_channels });
+    return Tensor4DShape{ in_batches, in_height, in_width, in_channels };
 }
 
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
@@ -234,7 +234,7 @@ bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_siz
 
 } //namespace
 
-NEWinogradConvolutionLayer::NEWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
+NEWinogradConvolutionLayer::NEWinogradConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager)
     : _memory_group(memory_manager), _gemm_function(memory_manager), _transform_input_kernel(nullptr), _transform_output_kernel(nullptr), _transform_weights_kernel(nullptr), _activationlayer_function(),
       _permute_input(), _permute_weights(), _permute_output(), _input_transformed(), _output_transformed(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(),
       _weights_hwio(), _input(), _weights(), _output(), _is_prepared(false), _is_activationlayer_enabled(false)
@@ -428,7 +428,9 @@ void NEWinogradConvolutionLayer::configure(const ITensor *input, const ITensor *
     d_strides.set(2, 0);
     d_strides.set(3, data_type_size * output_matrix_stride);
 
-    TensorInfo a_info, b_info, d_info;
+    TensorInfo a_info{};
+    TensorInfo b_info{};
+    TensorInfo d_info{};
     a_info.init(a_shape, 1, data_type, a_strides, 0, input_storage_size);
     b_info.init(b_shape, 1, data_type, b_strides, 0, kernel_storage_size);
     d_info.init(d_shape, 1, data_type, d_strides, 0, output_storage_size);