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, 21 insertions, 15 deletions

diff --git a/arm_compute/graph/backends/FusedDepthwiseConvolutionBatchNormalizationFunction.h b/arm_compute/graph/backends/FusedDepthwiseConvolutionBatchNormalizationFunction.h
index 4f8a8da1fb..07a2cdd8b8 100644
--- a/arm_compute/graph/backends/FusedDepthwiseConvolutionBatchNormalizationFunction.h
+++ b/arm_compute/graph/backends/FusedDepthwiseConvolutionBatchNormalizationFunction.h
@@ -67,15 +67,18 @@ public:
      * @param[in]  fused_act        Activation layer information in case of a fused activation.
      *
      */
-    void configure(TensorType       *input,
-                   TensorType       *weights,
-                   TensorType       *bias,
-                   TensorType       *output,
-                   const TensorType *mean,
-                   const TensorType *var,
-                   const TensorType *beta,
-                   const TensorType *gamma,
-                   float epsilon, const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo const &fused_act)
+    void configure(TensorType                *input,
+                   TensorType                *weights,
+                   TensorType                *bias,
+                   TensorType                *output,
+                   const TensorType          *mean,
+                   const TensorType          *var,
+                   const TensorType          *beta,
+                   const TensorType          *gamma,
+                   float                      epsilon,
+                   const PadStrideInfo       &conv_info,
+                   unsigned int               depth_multiplier,
+                   ActivationLayerInfo const &fused_act)
     {
         // We don't run any validate, as we assume that the layers have been already validated
         const bool        has_bias = (bias != nullptr);
@@ -83,20 +86,23 @@ public:
 
         // We check if the layer has a bias. If yes, use it in-place. If not, we need to create one
         // as batch normalization might end up with a bias != 0
-        if(has_bias)
+        if (has_bias)
         {
-            _fused_batch_norm_layer.configure(weights, mean, var, nullptr, nullptr, bias, beta, gamma, epsilon, FuseBatchNormalizationType::DEPTHWISECONVOLUTION);
+            _fused_batch_norm_layer.configure(weights, mean, var, nullptr, nullptr, bias, beta, gamma, epsilon,
+                                              FuseBatchNormalizationType::DEPTHWISECONVOLUTION);
             bias_to_use = bias;
         }
         else
         {
-            _fused_batch_norm_layer.configure(weights, mean, var, nullptr, &_fused_bias, nullptr, beta, gamma, epsilon, FuseBatchNormalizationType::DEPTHWISECONVOLUTION);
+            _fused_batch_norm_layer.configure(weights, mean, var, nullptr, &_fused_bias, nullptr, beta, gamma, epsilon,
+                                              FuseBatchNormalizationType::DEPTHWISECONVOLUTION);
             bias_to_use = &_fused_bias;
         }
 
-        _depth_conv_layer.configure(input, weights, bias_to_use, output, conv_info, depth_multiplier, fused_act.enabled() ? fused_act : ActivationLayerInfo());
+        _depth_conv_layer.configure(input, weights, bias_to_use, output, conv_info, depth_multiplier,
+                                    fused_act.enabled() ? fused_act : ActivationLayerInfo());
 
-        if(!has_bias)
+        if (!has_bias)
         {
             _fused_bias.allocator()->allocate();
         }
@@ -111,7 +117,7 @@ public:
 
     void prepare()
     {
-        if(!_is_prepared)
+        if (!_is_prepared)
         {
             _fused_batch_norm_layer.run();
             _is_prepared = true;