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>

8 files changed, 52 insertions, 35 deletions

diff --git a/src/runtime/CL/functions/CLCropResize.cpp b/src/runtime/CL/functions/CLCropResize.cpp
index 2cacef1bb1..b22809eb09 100644
--- a/src/runtime/CL/functions/CLCropResize.cpp
+++ b/src/runtime/CL/functions/CLCropResize.cpp
@@ -57,9 +57,9 @@ inline void run_crop(const ICLTensor *input, ICLTensor *output, uint32_t batch_i
     bool is_width_flipped  = end[0] < start[0];
     bool is_height_flipped = end[1] < start[1];
     /** The number of rows out of bounds at the start and end of output. */
-    int32_t rows_out_of_bounds[2];
+    std::array<int32_t, 2> rows_out_of_bounds{ 0 };
     /** The number of columns out of bounds at the start and end of output. */
-    int32_t cols_out_of_bounds[2];
+    std::array<int32_t, 2> cols_out_of_bounds{ 0 };
     if(is_height_flipped)
     {
         rows_out_of_bounds[0] = start[1] >= static_cast<int32_t>(input->info()->dimension(2)) ? std::min(start[1] - input->info()->dimension(2) + 1, output->info()->dimension(2)) : 0;
@@ -164,7 +164,7 @@ inline void run_crop(const ICLTensor *input, ICLTensor *output, uint32_t batch_i
 } // namespace
 
 CLCropResize::CLCropResize()
-    : _input(nullptr), _boxes(nullptr), _box_ind(nullptr), _output(nullptr), _num_boxes(0), _method(), _extrapolation_value(0), _scale(), _copy()
+    : _input(nullptr), _boxes(nullptr), _box_ind(nullptr), _output(nullptr), _num_boxes(0), _method(), _extrapolation_value(0), _scale(), _copy(), _crop_results(), _scaled_results()
 {
 }
 
@@ -210,20 +210,19 @@ void CLCropResize::configure(const ICLTensor *input, ICLTensor *boxes, ICLTensor
     // - A scale function is used to resize the cropped image to the size specified by crop_size.
     // - A tensor is required to hold the final scaled image before it is copied into the 4D output
     //   that will hold all final cropped and scaled 3D images using CLCopyKernel.
-    _crop_results   = arm_compute::support::cpp14::make_unique<CLTensor[]>(_num_boxes);
-    _scale          = arm_compute::support::cpp14::make_unique<CLScale[]>(_num_boxes);
-    _scaled_results = arm_compute::support::cpp14::make_unique<CLTensor[]>(_num_boxes);
-    _copy           = arm_compute::support::cpp14::make_unique<CLCopyKernel[]>(_num_boxes);
-
     for(unsigned int i = 0; i < _num_boxes; ++i)
     {
+        auto       crop_tensor = support::cpp14::make_unique<CLTensor>();
         TensorInfo crop_result_info(1, DataType::F32);
         crop_result_info.set_data_layout(DataLayout::NHWC);
-        _crop_results[i].allocator()->init(crop_result_info);
+        crop_tensor->allocator()->init(crop_result_info);
+        _crop_results.emplace_back(std::move(crop_tensor));
 
+        auto       scale_tensor = support::cpp14::make_unique<CLTensor>();
         TensorInfo scaled_result_info(out_shape, 1, DataType::F32);
         scaled_result_info.set_data_layout(DataLayout::NHWC);
-        _scaled_results[i].allocator()->init(scaled_result_info);
+        scale_tensor->allocator()->init(scaled_result_info);
+        _scaled_results.emplace_back(std::move(scale_tensor));
     }
 }
 
@@ -240,32 +239,37 @@ void CLCropResize::run()
         // Size of the crop box in _boxes and thus the shape of _crop_results[i]
         // may not be known until run-time and so the kernels cannot be configured until then.
         uint32_t    batch_index;
-        Coordinates start, end;
-        configure_crop(_input, _boxes, _box_ind, &_crop_results[i], i, start, end, batch_index);
-        _scale[i].configure(&_crop_results[i], &_scaled_results[i], _method, BorderMode::CONSTANT, PixelValue(_extrapolation_value), SamplingPolicy::TOP_LEFT);
+        Coordinates start{};
+        Coordinates end{};
+        configure_crop(_input, _boxes, _box_ind, _crop_results[i].get(), i, start, end, batch_index);
+
+        auto scale_kernel = support::cpp14::make_unique<CLScale>();
+        scale_kernel->configure(_crop_results[i].get(), _scaled_results[i].get(), _method, BorderMode::CONSTANT, PixelValue(_extrapolation_value), SamplingPolicy::TOP_LEFT);
+        _scale.emplace_back(std::move(scale_kernel));
 
         Window win = calculate_max_window(*_output->info());
         win.set(3, Window::Dimension(i, i + 1, 1));
-        _copy[i].configure(&_scaled_results[i], _output, PaddingList(), &win);
 
-        _crop_results[i].allocator()->allocate();
-        _scaled_results[i].allocator()->allocate();
+        auto copy_kernel = support::cpp14::make_unique<CLCopyKernel>();
+        copy_kernel->configure(_scaled_results[i].get(), _output, PaddingList(), &win);
+        _copy.emplace_back(std::move(copy_kernel));
+
+        _crop_results[i]->allocator()->allocate();
+        _scaled_results[i]->allocator()->allocate();
 
-        run_crop(_input, &_crop_results[i], batch_index, start, end, _extrapolation_value);
+        run_crop(_input, _crop_results[i].get(), batch_index, start, end, _extrapolation_value);
     }
     _boxes->unmap(CLScheduler::get().queue());
     _box_ind->unmap(CLScheduler::get().queue());
     CLScheduler::get().sync();
-    for(unsigned int i = 0; i < _num_boxes; ++i)
+    for(auto &kernel : _scale)
     {
-        // Scale the cropped image
-        _scale[i].run();
+        kernel->run();
     }
     CLScheduler::get().sync();
-    for(unsigned int i = 0; i < _num_boxes; ++i)
+    for(auto &kernel : _copy)
     {
-        // Copy scaled image into output.
-        CLScheduler::get().enqueue(_copy[i]);
+        CLScheduler::get().enqueue(*kernel, true);
     }
     CLScheduler::get().sync();
 }
diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
index add5e16498..7b9229c4ae 100644
--- a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
+++ b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
@@ -231,7 +231,8 @@ void CLFullyConnectedLayer::configure(const ICLTensor *input, const ICLTensor *w
     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/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
index cd537087d3..049db1d461 100644
--- a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
+++ b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
@@ -249,7 +249,8 @@ Status CLGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITenso
         ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info));
     }
 
-    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/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);