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, 65 insertions, 54 deletions

diff --git a/examples/graph_resnext50.cpp b/examples/graph_resnext50.cpp
index 6378f6c741..12a1507c4c 100644
--- a/examples/graph_resnext50.cpp
+++ b/examples/graph_resnext50.cpp
@@ -22,6 +22,7 @@
  * SOFTWARE.
  */
 #include "arm_compute/graph.h"
+
 #include "support/ToolchainSupport.h"
 #include "utils/CommonGraphOptions.h"
 #include "utils/GraphUtils.h"
@@ -35,8 +36,7 @@ using namespace arm_compute::graph_utils;
 class GraphResNeXt50Example : public Example
 {
 public:
-    GraphResNeXt50Example()
-        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNeXt50")
+    GraphResNeXt50Example() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNeXt50")
     {
     }
     bool do_setup(int argc, char **argv) override
@@ -49,14 +49,15 @@ public:
         common_params = consume_common_graph_parameters(common_opts);
 
         // Return when help menu is requested
-        if(common_params.help)
+        if (common_params.help)
         {
             cmd_parser.print_help(argv[0]);
             return false;
         }
 
         // Checks
-        ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");
+        ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type),
+                                "QASYMM8 not supported for this graph");
 
         // Print parameter values
         std::cout << common_params << std::endl;
@@ -66,28 +67,33 @@ public:
 
         // Create input descriptor
         const auto        operation_layout = common_params.data_layout;
-        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, common_params.batches), DataLayout::NCHW, operation_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
+        const TensorShape tensor_shape =
+            permute_shape(TensorShape(224U, 224U, 3U, common_params.batches), DataLayout::NCHW, operation_layout);
+        TensorDescriptor input_descriptor =
+            TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
 
-        graph << common_params.target
-              << common_params.fast_math_hint
+        graph << common_params.target << common_params.fast_math_hint
               << InputLayer(input_descriptor, get_input_accessor(common_params))
               << ScaleLayer(get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_mul.npy"),
                             get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_add.npy"))
-              .set_name("bn_data/Scale")
+                     .set_name("bn_data/Scale")
               << ConvolutionLayer(
-                  7U, 7U, 64U,
-                  get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy", weights_layout),
-                  get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_biases.npy"),
-                  PadStrideInfo(2, 2, 2, 3, 2, 3, DimensionRoundingType::FLOOR))
-              .set_name("conv0/Convolution")
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool0");
-
-        add_residual_block(data_path, weights_layout, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, /*stride_conv_unit1*/ 1);
+                     7U, 7U, 64U,
+                     get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy", weights_layout),
+                     get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_biases.npy"),
+                     PadStrideInfo(2, 2, 2, 3, 2, 3, DimensionRoundingType::FLOOR))
+                     .set_name("conv0/Convolution")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+                     .set_name("conv0/Relu")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout,
+                                               PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)))
+                     .set_name("pool0");
+
+        add_residual_block(data_path, weights_layout, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3,
+                           /*stride_conv_unit1*/ 1);
         add_residual_block(data_path, weights_layout, 512, 2, 4, 2);
         add_residual_block(data_path, weights_layout, 1024, 3, 6, 2);
         add_residual_block(data_path, weights_layout, 2048, 4, 3, 2);
@@ -121,10 +127,14 @@ private:
     CommonGraphParams  common_params;
     Stream             graph;
 
-    void add_residual_block(const std::string &data_path, DataLayout weights_layout,
-                            unsigned int base_depth, unsigned int stage, unsigned int num_units, unsigned int stride_conv_unit1)
+    void add_residual_block(const std::string &data_path,
+                            DataLayout         weights_layout,
+                            unsigned int       base_depth,
+                            unsigned int       stage,
+                            unsigned int       num_units,
+                            unsigned int       stride_conv_unit1)
     {
-        for(unsigned int i = 0; i < num_units; ++i)
+        for (unsigned int i = 0; i < num_units; ++i)
         {
             std::stringstream unit_path_ss;
             unit_path_ss << "/cnn_data/resnext50_model/stage" << stage << "_unit" << (i + 1) << "_";
@@ -135,54 +145,55 @@ private:
             std::string unit_name = unit_name_ss.str();
 
             PadStrideInfo pad_grouped_conv(1, 1, 1, 1);
-            if(i == 0)
+            if (i == 0)
             {
-                pad_grouped_conv = (stage == 1) ? PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 1, 1) : PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 1, 0, 1, DimensionRoundingType::FLOOR);
+                pad_grouped_conv = (stage == 1) ? PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 1, 1)
+                                                : PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 1, 0, 1,
+                                                                DimensionRoundingType::FLOOR);
             }
 
             SubStream right(graph);
-            right << ConvolutionLayer(
-                      1U, 1U, base_depth / 2,
-                      get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout),
-                      get_weights_accessor(data_path, unit_path + "conv1_biases.npy"),
-                      PadStrideInfo(1, 1, 0, 0))
-                  .set_name(unit_name + "conv1/convolution")
-                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu")
-
-                  << ConvolutionLayer(
-                      3U, 3U, base_depth / 2,
-                      get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout),
-                      std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                      pad_grouped_conv, 32)
-                  .set_name(unit_name + "conv2/convolution")
+            right << ConvolutionLayer(1U, 1U, base_depth / 2,
+                                      get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout),
+                                      get_weights_accessor(data_path, unit_path + "conv1_biases.npy"),
+                                      PadStrideInfo(1, 1, 0, 0))
+                         .set_name(unit_name + "conv1/convolution")
+                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+                         .set_name(unit_name + "conv1/Relu")
+
+                  << ConvolutionLayer(3U, 3U, base_depth / 2,
+                                      get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout),
+                                      std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), pad_grouped_conv,
+                                      32)
+                         .set_name(unit_name + "conv2/convolution")
                   << ScaleLayer(get_weights_accessor(data_path, unit_path + "bn2_mul.npy"),
                                 get_weights_accessor(data_path, unit_path + "bn2_add.npy"))
-                  .set_name(unit_name + "conv1/Scale")
-                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv2/Relu")
+                         .set_name(unit_name + "conv1/Scale")
+                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+                         .set_name(unit_name + "conv2/Relu")
 
-                  << ConvolutionLayer(
-                      1U, 1U, base_depth,
-                      get_weights_accessor(data_path, unit_path + "conv3_weights.npy", weights_layout),
-                      get_weights_accessor(data_path, unit_path + "conv3_biases.npy"),
-                      PadStrideInfo(1, 1, 0, 0))
-                  .set_name(unit_name + "conv3/convolution");
+                  << ConvolutionLayer(1U, 1U, base_depth,
+                                      get_weights_accessor(data_path, unit_path + "conv3_weights.npy", weights_layout),
+                                      get_weights_accessor(data_path, unit_path + "conv3_biases.npy"),
+                                      PadStrideInfo(1, 1, 0, 0))
+                         .set_name(unit_name + "conv3/convolution");
 
             SubStream left(graph);
-            if(i == 0)
+            if (i == 0)
             {
-                left << ConvolutionLayer(
-                         1U, 1U, base_depth,
-                         get_weights_accessor(data_path, unit_path + "sc_weights.npy", weights_layout),
-                         std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                         PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 0))
-                     .set_name(unit_name + "sc/convolution")
+                left << ConvolutionLayer(1U, 1U, base_depth,
+                                         get_weights_accessor(data_path, unit_path + "sc_weights.npy", weights_layout),
+                                         std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                         PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 0))
+                            .set_name(unit_name + "sc/convolution")
                      << ScaleLayer(get_weights_accessor(data_path, unit_path + "sc_bn_mul.npy"),
                                    get_weights_accessor(data_path, unit_path + "sc_bn_add.npy"))
-                     .set_name(unit_name + "sc/scale");
+                            .set_name(unit_name + "sc/scale");
             }
 
             graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(unit_name + "add");
-            graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu");
+            graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+                         .set_name(unit_name + "Relu");
         }
     }
 };