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, 24 insertions, 12 deletions

diff --git a/src/runtime/CL/functions/CLInstanceNormalizationLayer.cpp b/src/runtime/CL/functions/CLInstanceNormalizationLayer.cpp
index 5feafe19db..0e994e1aee 100644
--- a/src/runtime/CL/functions/CLInstanceNormalizationLayer.cpp
+++ b/src/runtime/CL/functions/CLInstanceNormalizationLayer.cpp
@@ -27,50 +27,62 @@
 #include "arm_compute/core/Types.h"
 #include "arm_compute/runtime/CL/CLHelpers.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"
+
+#include "src/common/utils/Log.h"
 #include "src/core/CL/ICLKernel.h"
 #include "src/core/CL/kernels/CLFillBorderKernel.h"
 #include "src/core/CL/kernels/CLInstanceNormalizationLayerKernel.h"
 
-#include "src/common/utils/Log.h"
-
 namespace arm_compute
 {
 CLInstanceNormalizationLayer::CLInstanceNormalizationLayer(CLRuntimeContext *ctx) // NOLINT
-    : _inst_norm_kernel(),
-      _mean_var_kernel(),
-      _mean_var_tensor(),
-      _ctx(ctx)
+    : _inst_norm_kernel(), _mean_var_kernel(), _mean_var_tensor(), _ctx(ctx)
 {
 }
 CLInstanceNormalizationLayer::~CLInstanceNormalizationLayer()
 {
 }
 
-void CLInstanceNormalizationLayer::configure(ICLTensor *input, ICLTensor *output, float gamma, float beta, float epsilon, bool use_mixed_precision)
+void CLInstanceNormalizationLayer::configure(
+    ICLTensor *input, ICLTensor *output, float gamma, float beta, float epsilon, bool use_mixed_precision)
 {
     configure(CLKernelLibrary::get().get_compile_context(), input, output, gamma, beta, epsilon, use_mixed_precision);
 }
 
-void CLInstanceNormalizationLayer::configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *output, float gamma, float beta, float epsilon, bool use_mixed_precision)
+void CLInstanceNormalizationLayer::configure(const CLCompileContext &compile_context,
+                                             ICLTensor              *input,
+                                             ICLTensor              *output,
+                                             float                   gamma,
+                                             float                   beta,
+                                             float                   epsilon,
+                                             bool                    use_mixed_precision)
 {
     ARM_COMPUTE_LOG_PARAMS(input, output, gamma, beta, epsilon, use_mixed_precision);
     auto w = std::make_unique<CLComputeMeanVariance>();
     w->configure(compile_context, input, &_mean_var_tensor, use_mixed_precision);
     _mean_var_kernel = std::move(w);
     auto k           = std::make_unique<CLInstanceNormalizationLayerKernel>();
-    k->configure(compile_context, input, &_mean_var_tensor, output, InstanceNormalizationLayerKernelInfo(gamma, beta, epsilon, use_mixed_precision));
+    k->configure(compile_context, input, &_mean_var_tensor, output,
+                 InstanceNormalizationLayerKernelInfo(gamma, beta, epsilon, use_mixed_precision));
     _inst_norm_kernel = std::move(k);
     _mean_var_tensor.allocator()->allocate();
 }
 
-Status CLInstanceNormalizationLayer::validate(const ITensorInfo *input, const ITensorInfo *output, float gamma, float beta, float epsilon, bool use_mixed_precision)
+Status CLInstanceNormalizationLayer::validate(const ITensorInfo *input,
+                                              const ITensorInfo *output,
+                                              float              gamma,
+                                              float              beta,
+                                              float              epsilon,
+                                              bool               use_mixed_precision)
 {
-    return CLInstanceNormalizationLayerKernel::validate(input, output, InstanceNormalizationLayerKernelInfo(gamma, beta, epsilon, use_mixed_precision));
+    return CLInstanceNormalizationLayerKernel::validate(
+        input, output, InstanceNormalizationLayerKernelInfo(gamma, beta, epsilon, use_mixed_precision));
 }
 
 void CLInstanceNormalizationLayer::run()
 {
-    ARM_COMPUTE_ERROR_ON_MSG(!_inst_norm_kernel, "The child class didn't set the CL kernel or function isn't configured");
+    ARM_COMPUTE_ERROR_ON_MSG(!_inst_norm_kernel,
+                             "The child class didn't set the CL kernel or function isn't configured");
     schedule_kernel_on_ctx(_ctx, _mean_var_kernel.get());
     schedule_kernel_on_ctx(_ctx, _inst_norm_kernel.get());
 }