Apply clang-format on repository

Code is formatted as per a revised clang format configuration
file(not part of this delivery). Version 14.0.6 is used.

Exclusion List:
- files with .cl extension
- files that are not strictly C/C++ (e.g. Android.bp, Sconscript ...)
And the following directories
- compute_kernel_writer/validation/
- tests/
- include/
- src/core/NEON/kernels/convolution/
- src/core/NEON/kernels/arm_gemm/
- src/core/NEON/kernels/arm_conv/
- data/

There will be a follow up for formatting of .cl files and the
files under tests/ and compute_kernel_writer/validation/.

Signed-off-by: Felix Thomasmathibalan <felixjohnny.thomasmathibalan@arm.com>
Change-Id: Ib7eb1fcf4e7537b9feaefcfc15098a804a3fde0a
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10391
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>

1 files changed, 128 insertions, 71 deletions

diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp
index e34b6929e7..b95abe795f 100644
--- a/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp
@@ -23,16 +23,17 @@
  */
 #include "src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h"
 
-#include "arm_compute/core/utils/ActivationFunctionUtils.h"
 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/utils/ActivationFunctionUtils.h"
 #include "arm_compute/core/utils/helpers/AdjustVecSize.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "arm_compute/core/utils/StringUtils.h"
+
 #include "src/core/CL/CLUtils.h"
 #include "src/core/CL/CLValidate.h"
 #include "src/core/CL/ICLKernel.h"
@@ -45,12 +46,18 @@ namespace arm_compute
 {
 namespace
 {
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const DWCComputeKernelInfo &dwc_info,
-                          const ConvolutionInfo &conv_info, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
+Status validate_arguments(const ITensorInfo          *input,
+                          const ITensorInfo          *weights,
+                          const ITensorInfo          *biases,
+                          const ITensorInfo          *output,
+                          const DWCComputeKernelInfo &dwc_info,
+                          const ConvolutionInfo      &conv_info,
+                          const ITensorInfo          *output_multipliers,
+                          const ITensorInfo          *output_shifts)
 {
     ARM_COMPUTE_UNUSED(dwc_info);
     bool in_place = false;
-    if(output == nullptr || output == input)
+    if (output == nullptr || output == input)
     {
         in_place = true;
         output   = input;
@@ -58,11 +65,14 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights,
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights);
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(input, DataLayout::NHWC);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
+                                                         DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride().first > 1 && dwc_info.m0 != 1);
     ARM_COMPUTE_RETURN_ERROR_ON(conv_info.dilation.x() > 1 && dwc_info.m0 != 1);
     ARM_COMPUTE_RETURN_ERROR_ON((dwc_info.export_input_to_cl_image == true));
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((dwc_info.export_weights_to_cl_image == true) && (export_to_cl_image(weights) == false), "Weights cannot be exported to cl_image!");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((dwc_info.export_weights_to_cl_image == true) &&
+                                        (export_to_cl_image(weights) == false),
+                                    "Weights cannot be exported to cl_image!");
     ARM_COMPUTE_RETURN_ERROR_ON((dwc_info.export_weights_to_cl_image == true) && ((dwc_info.n0 % 4) != 0));
     ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride().first < 1);
     ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride().second < 1);
@@ -72,33 +82,40 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights,
     ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_c) != (input->dimension(idx_c) * conv_info.depth_multiplier));
 
     // In place restrictions
-    if(in_place)
+    if (in_place)
     {
-        const int weights_width_idx  = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::WIDTH);
-        const int weights_height_idx = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::HEIGHT);
-        ARM_COMPUTE_RETURN_ERROR_ON(weights->tensor_shape()[weights_width_idx] != 1U || weights->tensor_shape()[weights_height_idx] != 1U);
+        const int weights_width_idx =
+            get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::WIDTH);
+        const int weights_height_idx =
+            get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::HEIGHT);
+        ARM_COMPUTE_RETURN_ERROR_ON(weights->tensor_shape()[weights_width_idx] != 1U ||
+                                    weights->tensor_shape()[weights_height_idx] != 1U);
         ARM_COMPUTE_RETURN_ERROR_ON(conv_info.depth_multiplier != 1U);
         ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride() != std::make_pair(1U, 1U));
         ARM_COMPUTE_RETURN_ERROR_ON(conv_info.dilation != Size2D(1U, 1U));
-        ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.has_padding()); // Note that in princple padding can be supported with in_place but we choose not to support it
+        ARM_COMPUTE_RETURN_ERROR_ON(
+            conv_info.pad_stride_info
+                .has_padding()); // Note that in princple padding can be supported with in_place but we choose not to support it
     }
 
-    const ConvolutionInfo info{ conv_info.pad_stride_info, conv_info.depth_multiplier, ActivationLayerInfo(), conv_info.dilation };
-    const TensorShape     output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info);
+    const ConvolutionInfo info{conv_info.pad_stride_info, conv_info.depth_multiplier, ActivationLayerInfo(),
+                               conv_info.dilation};
+    const TensorShape     output_shape =
+        arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info);
 
-    if(conv_info.depth_multiplier > 1 && dwc_info.n0 > 1)
+    if (conv_info.depth_multiplier > 1 && dwc_info.n0 > 1)
     {
         ARM_COMPUTE_RETURN_ERROR_ON((conv_info.depth_multiplier % dwc_info.n0) != 0);
     }
 
     const bool is_quantized = is_data_type_quantized(input->data_type());
 
-    if(biases != nullptr)
+    if (biases != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != output_shape[idx_c]);
         ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
 
-        if(is_quantized)
+        if (is_quantized)
         {
             ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
         }
@@ -108,7 +125,7 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights,
         }
     }
 
-    if(is_quantized)
+    if (is_quantized)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output_multipliers, output_shifts);
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_multipliers, 1, DataType::S32);
@@ -116,7 +133,7 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights,
         ARM_COMPUTE_RETURN_ERROR_ON(output_multipliers->num_dimensions() > 1);
         ARM_COMPUTE_RETURN_ERROR_ON(output_shifts->num_dimensions() > 1);
 
-        if(is_data_type_quantized_per_channel(weights->data_type()))
+        if (is_data_type_quantized_per_channel(weights->data_type()))
         {
             ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL);
             ARM_COMPUTE_RETURN_ERROR_ON(output_shape[idx_c] != output_multipliers->dimension(0));
@@ -134,22 +151,24 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights,
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
     }
 
-    if(output->total_size() != 0)
+    if (output->total_size() != 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
     }
 
-    if(is_data_type_quantized(input->data_type()))
+    if (is_data_type_quantized(input->data_type()))
     {
         const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
         const UniformQuantizationInfo wq_info = weights->quantization_info().uniform();
-        const UniformQuantizationInfo oq_info = (output->total_size() != 0) ? output->quantization_info().uniform() : iq_info;
+        const UniformQuantizationInfo oq_info =
+            (output->total_size() != 0) ? output->quantization_info().uniform() : iq_info;
 
         float multiplier        = iq_info.scale * wq_info.scale / oq_info.scale;
         int   output_multiplier = 0;
         int   output_shift      = 0;
-        ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift));
+        ARM_COMPUTE_RETURN_ON_ERROR(
+            quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift));
     }
 
     return Status{};
@@ -171,30 +190,48 @@ CLDepthwiseConvolutionLayerNativeKernel::CLDepthwiseConvolutionLayerNativeKernel
     _type = CLKernelType::DEPTHWISE;
 }
 
-void CLDepthwiseConvolutionLayerNativeKernel::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output,
-                                                        const DWCComputeKernelInfo &dwc_info, const ConvolutionInfo &conv_info,
-                                                        const ICLTensor *output_multipliers, const ICLTensor *output_shifts)
+void CLDepthwiseConvolutionLayerNativeKernel::configure(ICLTensor                  *input,
+                                                        const ICLTensor            *weights,
+                                                        const ICLTensor            *biases,
+                                                        ICLTensor                  *output,
+                                                        const DWCComputeKernelInfo &dwc_info,
+                                                        const ConvolutionInfo      &conv_info,
+                                                        const ICLTensor            *output_multipliers,
+                                                        const ICLTensor            *output_shifts)
 {
-    configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, dwc_info, conv_info, output_multipliers, output_shifts);
+    configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, dwc_info, conv_info,
+              output_multipliers, output_shifts);
 }
 
-void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output,
-                                                        const DWCComputeKernelInfo &dwc_info, const ConvolutionInfo &conv_info,
-                                                        const ICLTensor *output_multipliers, const ICLTensor *output_shifts)
+void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext     &compile_context,
+                                                        ICLTensor                  *input,
+                                                        const ICLTensor            *weights,
+                                                        const ICLTensor            *biases,
+                                                        ICLTensor                  *output,
+                                                        const DWCComputeKernelInfo &dwc_info,
+                                                        const ConvolutionInfo      &conv_info,
+                                                        const ICLTensor            *output_multipliers,
+                                                        const ICLTensor            *output_shifts)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
-    if(output == nullptr)
+    if (output == nullptr)
     {
         // In-place
         output = input;
     }
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(),
-                                                  dwc_info, conv_info, (output_multipliers != nullptr) ? output_multipliers->info() : nullptr, (output_shifts != nullptr) ? output_shifts->info() : nullptr));
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(
+        input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), dwc_info,
+        conv_info, (output_multipliers != nullptr) ? output_multipliers->info() : nullptr,
+        (output_shifts != nullptr) ? output_shifts->info() : nullptr));
 
-    auto padding_info = get_padding_info({ input, output });
+    auto padding_info = get_padding_info({input, output});
 
-    const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*(input->info()), *(weights->info()), conv_info);
-    auto_init_if_empty(*(output->info()), input->info()->clone()->set_tensor_shape(output_shape).set_quantization_info(output->info()->quantization_info()));
+    const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(
+        *(input->info()), *(weights->info()), conv_info);
+    auto_init_if_empty(*(output->info()), input->info()
+                                              ->clone()
+                                              ->set_tensor_shape(output_shape)
+                                              .set_quantization_info(output->info()->quantization_info()));
 
     _input                      = input;
     _output                     = output;
@@ -214,12 +251,12 @@ void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext &
     CLBuildOptions build_opts;
 
     // Update the padding for the input/weights tensor if we can export to cl_image
-    if(_export_input_to_cl_image)
+    if (_export_input_to_cl_image)
     {
         arm_compute::opencl::kernels::gemm::update_padding_for_cl_image(input->info());
     }
 
-    if(_export_weights_to_cl_image)
+    if (_export_weights_to_cl_image)
     {
         arm_compute::opencl::kernels::gemm::update_padding_for_cl_image(weights->info());
     }
@@ -229,9 +266,10 @@ void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext &
     const auto      act_function  = conv_info.act_info.activation();
     const auto      dst_data_type = _output->info()->data_type();
 
-    if((gpu_target != GPUTarget::G71 && (gpu_target & GPUTarget::GPU_ARCH_MASK) == GPUTarget::BIFROST)
-       && (act_function == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU || act_function == ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU)
-       && (dst_data_type == DataType::F32 || dst_data_type == DataType::F16))
+    if ((gpu_target != GPUTarget::G71 && (gpu_target & GPUTarget::GPU_ARCH_MASK) == GPUTarget::BIFROST) &&
+        (act_function == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU ||
+         act_function == ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU) &&
+        (dst_data_type == DataType::F32 || dst_data_type == DataType::F16))
     {
         // -cl-fast-relaxed-math also sets -cl-finite-math-only and -cl-unsafe-math-optimizations
         // to disable -cl-finite-math-only, we only include -cl-unsafe-math-optimizations
@@ -268,23 +306,24 @@ void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext &
     build_opts.add_option("-DN0=" + support::cpp11::to_string(n0));
     build_opts.add_option("-DM0=" + support::cpp11::to_string(m0));
     build_opts.add_option("-DM0_A=" + support::cpp11::to_string(_weights->info()->dimension(1) + m0 - 1));
-    build_opts.add_option_if_else(conv_info.depth_multiplier > 1, "-DN0_A=1", "-DN0_A=" + support::cpp11::to_string(n0));
+    build_opts.add_option_if_else(conv_info.depth_multiplier > 1, "-DN0_A=1",
+                                  "-DN0_A=" + support::cpp11::to_string(n0));
     build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(_output->info()->dimension(0) % n0));
     build_opts.add_option_if(_input->info()->num_dimensions() > 3, "-DBATCHED_EXECUTION");
 
     // Force unroll with pragma when any of the following values exceed the maximum number of manual unroll
-    set_unroll_with_pragma(build_opts, { static_cast<int>(_weights->info()->dimension(1) + m0 - 1),
-                                         static_cast<int>(_weights->info()->dimension(1)),
-                                         static_cast<int>(_weights->info()->dimension(2))
-                                       });
+    set_unroll_with_pragma(build_opts, {static_cast<int>(_weights->info()->dimension(1) + m0 - 1),
+                                        static_cast<int>(_weights->info()->dimension(1)),
+                                        static_cast<int>(_weights->info()->dimension(2))});
 
-    if(biases != nullptr)
+    if (biases != nullptr)
     {
         build_opts.add_option(std::string("-DHAS_BIAS"));
-        build_opts.add_option(std::string("-DBIA_DATA_TYPE=" + get_cl_type_from_data_type(biases->info()->data_type())));
+        build_opts.add_option(
+            std::string("-DBIA_DATA_TYPE=" + get_cl_type_from_data_type(biases->info()->data_type())));
     }
 
-    if(_is_quantized)
+    if (_is_quantized)
     {
         kernel_name                          = "dwc_native_quantized_nhwc";
         const UniformQuantizationInfo iqinfo = input->info()->quantization_info().uniform();
@@ -306,13 +345,17 @@ void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext &
         build_opts.add_option("-DDST_OFFSET=" + support::cpp11::to_string(oqinfo.offset));
         build_opts.add_option("-DZERO_VALUE=" + support::cpp11::to_string(zero_value_s32));
         build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(DataType::S32));
-        build_opts.add_option("-DDST_MULTIPLIERS_DATA_TYPE=" + get_cl_type_from_data_type(_output_multipliers->info()->data_type()));
-        build_opts.add_option("-DDST_SHIFTS_DATA_TYPE=" + get_cl_type_from_data_type(_output_shifts->info()->data_type()));
-        build_opts.add_option_if_else(weights->info()->data_type() == DataType::QSYMM8_PER_CHANNEL, "-DQUANTIZATION_TYPE=PER_CHANNEL", "-DQUANTIZATION_TYPE=PER_TENSOR");
+        build_opts.add_option("-DDST_MULTIPLIERS_DATA_TYPE=" +
+                              get_cl_type_from_data_type(_output_multipliers->info()->data_type()));
+        build_opts.add_option("-DDST_SHIFTS_DATA_TYPE=" +
+                              get_cl_type_from_data_type(_output_shifts->info()->data_type()));
+        build_opts.add_option_if_else(weights->info()->data_type() == DataType::QSYMM8_PER_CHANNEL,
+                                      "-DQUANTIZATION_TYPE=PER_CHANNEL", "-DQUANTIZATION_TYPE=PER_TENSOR");
         // Note: We expect the input and output tensors to always adopt a per-tensor quantization approach
         int a_val{};
         int b_val{};
-        std::tie(b_val, a_val) = get_quantized_activation_min_max(conv_info.act_info, input->info()->data_type(), oqinfo);
+        std::tie(b_val, a_val) =
+            get_quantized_activation_min_max(conv_info.act_info, input->info()->data_type(), oqinfo);
 
         build_opts.add_option_if(conv_info.act_info.enabled(), "-DA_VAL=" + support::cpp11::to_string(a_val));
         build_opts.add_option_if(conv_info.act_info.enabled(), "-DB_VAL=" + support::cpp11::to_string(b_val));
@@ -321,8 +364,10 @@ void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext &
     {
         kernel_name = "dwc_native_fp_nhwc";
         build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
-        build_opts.add_option_if(conv_info.act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(conv_info.act_info.a()));
-        build_opts.add_option_if(conv_info.act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(conv_info.act_info.b()));
+        build_opts.add_option_if(conv_info.act_info.enabled(),
+                                 "-DA_VAL=" + float_to_string_with_full_precision(conv_info.act_info.a()));
+        build_opts.add_option_if(conv_info.act_info.enabled(),
+                                 "-DB_VAL=" + float_to_string_with_full_precision(conv_info.act_info.b()));
     }
 
     Window win = calculate_max_window(*(output->info()), Steps(n0, m0));
@@ -350,10 +395,17 @@ void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext &
     _config_id += string_from_data_type(input->info()->data_type());
 }
 
-Status CLDepthwiseConvolutionLayerNativeKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
-                                                         const DWCComputeKernelInfo &dwc_info, const ConvolutionInfo &conv_info, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
+Status CLDepthwiseConvolutionLayerNativeKernel::validate(const ITensorInfo          *input,
+                                                         const ITensorInfo          *weights,
+                                                         const ITensorInfo          *biases,
+                                                         const ITensorInfo          *output,
+                                                         const DWCComputeKernelInfo &dwc_info,
+                                                         const ConvolutionInfo      &conv_info,
+                                                         const ITensorInfo          *output_multipliers,
+                                                         const ITensorInfo          *output_shifts)
 {
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, dwc_info, conv_info, output_multipliers, output_shifts));
+    ARM_COMPUTE_RETURN_ON_ERROR(
+        validate_arguments(input, weights, biases, output, dwc_info, conv_info, output_multipliers, output_shifts));
     return Status{};
 }
 
@@ -370,47 +422,52 @@ void CLDepthwiseConvolutionLayerNativeKernel::run(const Window &window, cl::Comm
     cl::Image2D input_cl_image;
     cl::Image2D weights_cl_image;
 
-    if(_export_input_to_cl_image || _export_weights_to_cl_image)
+    if (_export_input_to_cl_image || _export_weights_to_cl_image)
     {
         // Export cl_buffer to cl_image
-        if(_export_input_to_cl_image)
+        if (_export_input_to_cl_image)
         {
-            const size_t      image_w = _input->info()->dimension(0) / 4;
-            const size_t      image_h = _input->info()->dimension(1) * _input->info()->dimension(2) * _input->info()->dimension(3);
+            const size_t image_w = _input->info()->dimension(0) / 4;
+            const size_t image_h =
+                _input->info()->dimension(1) * _input->info()->dimension(2) * _input->info()->dimension(3);
             const TensorShape shape2d(image_w, image_h);
             const size_t      image_row_pitch = _input->info()->strides_in_bytes()[1];
-            input_cl_image                    = create_image2d_from_buffer(CLKernelLibrary::get().context(), _input->cl_buffer(), shape2d, _input->info()->data_type(), image_row_pitch, CLImage2DType::ReadOnly);
+            input_cl_image =
+                create_image2d_from_buffer(CLKernelLibrary::get().context(), _input->cl_buffer(), shape2d,
+                                           _input->info()->data_type(), image_row_pitch, CLImage2DType::ReadOnly);
         }
 
-        if(_export_weights_to_cl_image)
+        if (_export_weights_to_cl_image)
         {
-            const size_t      image_w = _weights->info()->dimension(0) / 4;
-            const size_t      image_h = _weights->info()->dimension(1) * _weights->info()->dimension(2) * _weights->info()->dimension(3);
+            const size_t image_w = _weights->info()->dimension(0) / 4;
+            const size_t image_h =
+                _weights->info()->dimension(1) * _weights->info()->dimension(2) * _weights->info()->dimension(3);
             const TensorShape shape2d(image_w, image_h);
             const size_t      image_row_pitch = _weights->info()->strides_in_bytes()[1];
-            weights_cl_image                  = create_image2d_from_buffer(CLKernelLibrary::get().context(), _weights->cl_buffer(), shape2d, _weights->info()->data_type(), image_row_pitch,
-                                                                           CLImage2DType::ReadOnly);
+            weights_cl_image =
+                create_image2d_from_buffer(CLKernelLibrary::get().context(), _weights->cl_buffer(), shape2d,
+                                           _weights->info()->data_type(), image_row_pitch, CLImage2DType::ReadOnly);
         }
     }
 
     unsigned int idx = 0;
-    if(_export_input_to_cl_image)
+    if (_export_input_to_cl_image)
     {
         _kernel.setArg(idx++, input_cl_image);
     }
     add_4d_tensor_nhwc_argument(idx, _input);
     add_4d_tensor_nhwc_argument(idx, _output);
-    if(_export_weights_to_cl_image)
+    if (_export_weights_to_cl_image)
     {
         _kernel.setArg(idx++, weights_cl_image);
     }
     add_4d_tensor_nhwc_argument(idx, _weights);
-    if(_is_quantized)
+    if (_is_quantized)
     {
         add_1D_tensor_argument(idx, _output_multipliers, slice);
         add_1D_tensor_argument(idx, _output_shifts, slice);
     }
-    if(_biases != nullptr)
+    if (_biases != nullptr)
     {
         add_1D_tensor_argument(idx, _biases, slice);
     }