Update default C++ standard to C++14

(3RDPARTY_UPDATE)

Resolves: COMPMID-3849

Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Change-Id: I6369f112337310140e2d6c8e79630cd11138dfa0
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4544
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

22 files changed, 33 insertions, 33 deletions

diff --git a/examples/gemm_tuner/cl_gemmlowp_reshaped_rhs_only_fused_output_stage_fixedpoint.cpp b/examples/gemm_tuner/cl_gemmlowp_reshaped_rhs_only_fused_output_stage_fixedpoint.cpp
index c6818e48b0..8323bbd971 100644
--- a/examples/gemm_tuner/cl_gemmlowp_reshaped_rhs_only_fused_output_stage_fixedpoint.cpp
+++ b/examples/gemm_tuner/cl_gemmlowp_reshaped_rhs_only_fused_output_stage_fixedpoint.cpp
@@ -280,7 +280,7 @@ public:
             const TensorInfo info_vector_sum_row(compute_reductionB_shape(*lhs.info()), 1, DataType::S32);
             vector_sum_row.allocator()->init(info_vector_sum_row);
 
-            mtx_a_reduction = support::cpp14::make_unique<CLGEMMLowpMatrixAReduction>();
+            mtx_a_reduction = std::make_unique<CLGEMMLowpMatrixAReduction>();
 
             if(!mtx_a_reduction->validate(lhs.info(), vector_sum_row.info(), GEMMLowpReductionKernelInfo{}))
             {
diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
index 40bbee1d68..ce398be6cf 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -70,7 +70,7 @@ public:
 
         // Create a preprocessor object
         const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<CaffePreproccessor>(mean_rgb);
 
         // Create input descriptor
         const auto        operation_layout = common_params.data_layout;
diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp
index ed5cbd5120..0a53355611 100644
--- a/examples/graph_googlenet.cpp
+++ b/examples/graph_googlenet.cpp
@@ -66,7 +66,7 @@ public:
 
         // Create a preprocessor object
         const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<CaffePreproccessor>(mean_rgb);
 
         // Create input descriptor
         const auto        operation_layout = common_params.data_layout;
diff --git a/examples/graph_inception_resnet_v1.cpp b/examples/graph_inception_resnet_v1.cpp
index 7c0bb0ce48..7a55733a20 100644
--- a/examples/graph_inception_resnet_v1.cpp
+++ b/examples/graph_inception_resnet_v1.cpp
@@ -92,7 +92,7 @@ public:
         }
 
         // Create a preprocessor object
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(0.f, 1.f);
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>(0.f, 1.f);
 
         // Create input descriptor
         const auto        operation_layout = common_params.data_layout;
@@ -207,7 +207,7 @@ public:
                   get_weights_accessor(data_path, "Logits_Logits_weights.npy", weights_layout),
                   get_weights_accessor(data_path, "Logits_Logits_biases.npy"))
               .set_name("Logits/Logits")
-              << OutputLayer(arm_compute::support::cpp14::make_unique<DummyAccessor>(0));
+              << OutputLayer(std::make_unique<DummyAccessor>(0));
 
         // Finalize graph
         GraphConfig config;
diff --git a/examples/graph_inception_resnet_v2.cpp b/examples/graph_inception_resnet_v2.cpp
index d14c34eb9d..60236d0780 100644
--- a/examples/graph_inception_resnet_v2.cpp
+++ b/examples/graph_inception_resnet_v2.cpp
@@ -76,7 +76,7 @@ public:
         }
 
         // Create a preprocessor object
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(0.f, 1.f);
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>(0.f, 1.f);
 
         // Create input descriptor
         const auto        operation_layout = common_params.data_layout;
diff --git a/examples/graph_inception_v3.cpp b/examples/graph_inception_v3.cpp
index 4b6dc8d296..5cacbcb6e1 100644
--- a/examples/graph_inception_v3.cpp
+++ b/examples/graph_inception_v3.cpp
@@ -62,7 +62,7 @@ public:
         std::string data_path = common_params.data_path;
 
         // Create a preprocessor object
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>();
 
         // Create input descriptor
         const auto        operation_layout = common_params.data_layout;
diff --git a/examples/graph_inception_v4.cpp b/examples/graph_inception_v4.cpp
index 553c96d3e4..db2a31047e 100644
--- a/examples/graph_inception_v4.cpp
+++ b/examples/graph_inception_v4.cpp
@@ -66,7 +66,7 @@ public:
         std::string data_path = common_params.data_path;
 
         // Create a preprocessor object
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>();
 
         // Create input descriptor
         const auto        operation_layout = common_params.data_layout;
diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp
index f74d25189d..b73f7a2abd 100644
--- a/examples/graph_mobilenet.cpp
+++ b/examples/graph_mobilenet.cpp
@@ -124,7 +124,7 @@ private:
         std::string model_path  = (model_id == 0) ? "/cnn_data/mobilenet_v1_1_224_model/" : "/cnn_data/mobilenet_v1_075_160_model/";
 
         // Create a preprocessor object
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>();
 
         // Get trainable parameters data path
         std::string data_path = common_params.data_path;
diff --git a/examples/graph_mobilenet_v2.cpp b/examples/graph_mobilenet_v2.cpp
index 5ee1f7e52a..fa16c94645 100644
--- a/examples/graph_mobilenet_v2.cpp
+++ b/examples/graph_mobilenet_v2.cpp
@@ -129,7 +129,7 @@ private:
         const std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_model/";
 
         // Create a preprocessor object
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>();
 
         // Get trainable parameters data path
         std::string data_path = common_params.data_path;
diff --git a/examples/graph_resnet12.cpp b/examples/graph_resnet12.cpp
index badcaec107..ebd2e5dd16 100644
--- a/examples/graph_resnet12.cpp
+++ b/examples/graph_resnet12.cpp
@@ -81,7 +81,7 @@ public:
         const std::string model_path = "/cnn_data/resnet12_model/";
 
         // Create a preprocessor object
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>();
 
         // Create input descriptor
         const TensorShape tensor_shape     = permute_shape(TensorShape(image_width, image_height, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
@@ -128,7 +128,7 @@ public:
               .set_name("conv12/convolution")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH)).set_name("conv12/Tanh")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 0.58f, 0.5f)).set_name("conv12/Linear")
-              << OutputLayer(arm_compute::support::cpp14::make_unique<DummyAccessor>(0));
+              << OutputLayer(std::make_unique<DummyAccessor>(0));
 
         // Finalize graph
         GraphConfig config;
diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp
index 2939ee40c4..47d258ede7 100644
--- a/examples/graph_resnet50.cpp
+++ b/examples/graph_resnet50.cpp
@@ -63,8 +63,8 @@ public:
 
         // Create a preprocessor object
         const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb,
-                                                                                                                   false /* Do not convert to BGR */);
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<CaffePreproccessor>(mean_rgb,
+                                                                                           false /* Do not convert to BGR */);
 
         // Create input descriptor
         const auto        operation_layout = common_params.data_layout;
diff --git a/examples/graph_resnet_v2_50.cpp b/examples/graph_resnet_v2_50.cpp
index 32434f55dd..921fb145d6 100644
--- a/examples/graph_resnet_v2_50.cpp
+++ b/examples/graph_resnet_v2_50.cpp
@@ -67,7 +67,7 @@ public:
         }
 
         // Create a preprocessor object
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>();
 
         // Create input descriptor
         const auto        operation_layout = common_params.data_layout;
diff --git a/examples/graph_shufflenet.cpp b/examples/graph_shufflenet.cpp
index 08f884b75f..300d0f15a1 100644
--- a/examples/graph_shufflenet.cpp
+++ b/examples/graph_shufflenet.cpp
@@ -89,7 +89,7 @@ public:
         const DataLayout weights_layout = DataLayout::NCHW;
 
         // Create preprocessor
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(0);
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>(0);
 
         graph << common_params.target
               << common_params.fast_math_hint
diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp
index f0d620c67d..2e72c14763 100644
--- a/examples/graph_squeezenet.cpp
+++ b/examples/graph_squeezenet.cpp
@@ -63,7 +63,7 @@ public:
 
         // Create a preprocessor object
         const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<CaffePreproccessor>(mean_rgb);
 
         // Create input descriptor
         const auto        operation_layout = common_params.data_layout;
diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp
index c60448639d..1708ac2f5a 100644
--- a/examples/graph_squeezenet_v1_1.cpp
+++ b/examples/graph_squeezenet_v1_1.cpp
@@ -63,7 +63,7 @@ public:
 
         // Create a preprocessor object
         const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<CaffePreproccessor>(mean_rgb);
 
         // Create input descriptor
         const auto        operation_layout = common_params.data_layout;
diff --git a/examples/graph_srcnn955.cpp b/examples/graph_srcnn955.cpp
index a95f0c1d25..bcc3824c60 100644
--- a/examples/graph_srcnn955.cpp
+++ b/examples/graph_srcnn955.cpp
@@ -78,7 +78,7 @@ public:
         const std::string model_path = "/cnn_data/srcnn955_model/";
 
         // Create a preprocessor object
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>();
 
         // Create input descriptor
         const TensorShape tensor_shape     = permute_shape(TensorShape(image_width, image_height, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
@@ -111,7 +111,7 @@ public:
                   PadStrideInfo(1, 1, 2, 2))
               .set_name("conv3/convolution")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3/Relu")
-              << OutputLayer(arm_compute::support::cpp14::make_unique<DummyAccessor>(0));
+              << OutputLayer(std::make_unique<DummyAccessor>(0));
 
         // Finalize graph
         GraphConfig config;
diff --git a/examples/graph_ssd_mobilenet.cpp b/examples/graph_ssd_mobilenet.cpp
index edd4c94d02..f5af84f4d4 100644
--- a/examples/graph_ssd_mobilenet.cpp
+++ b/examples/graph_ssd_mobilenet.cpp
@@ -216,7 +216,7 @@ private:
     {
         // Create a preprocessor object
         const std::array<float, 3> mean_rgb{ { 127.5f, 127.5f, 127.5f } };
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb, true, 0.007843f);
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<CaffePreproccessor>(mean_rgb, true, 0.007843f);
 
         // Get trainable parameters data path
         std::string data_path = common_params.data_path;
diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp
index 990040b5ef..a4c5e6bbd2 100644
--- a/examples/graph_vgg16.cpp
+++ b/examples/graph_vgg16.cpp
@@ -63,7 +63,7 @@ public:
 
         // Create a preprocessor object
         const std::array<float, 3> mean_rgb{ { 123.68f, 116.779f, 103.939f } };
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<CaffePreproccessor>(mean_rgb);
 
         // Create input descriptor
         const auto        operation_layout = common_params.data_layout;
diff --git a/examples/graph_vgg19.cpp b/examples/graph_vgg19.cpp
index 9215ba7b61..c95fb03368 100644
--- a/examples/graph_vgg19.cpp
+++ b/examples/graph_vgg19.cpp
@@ -62,7 +62,7 @@ public:
 
         // Create a preprocessor object
         const std::array<float, 3> mean_rgb{ { 123.68f, 116.779f, 103.939f } };
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<CaffePreproccessor>(mean_rgb);
 
         // Create input descriptor
         const auto        operation_layout = common_params.data_layout;
diff --git a/examples/graph_vgg_vdsr.cpp b/examples/graph_vgg_vdsr.cpp
index 65c0642485..3fa7dd1330 100644
--- a/examples/graph_vgg_vdsr.cpp
+++ b/examples/graph_vgg_vdsr.cpp
@@ -79,7 +79,7 @@ public:
         const std::string model_path = "/cnn_data/vdsr_model/";
 
         // Create a preprocessor object
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>();
 
         // Create input descriptor
         const TensorShape tensor_shape     = permute_shape(TensorShape(image_width, image_height, 1U, 1U), DataLayout::NCHW, common_params.data_layout);
@@ -132,7 +132,7 @@ public:
 
         // Add residual to input
         graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name("add")
-              << OutputLayer(arm_compute::support::cpp14::make_unique<DummyAccessor>(0));
+              << OutputLayer(std::make_unique<DummyAccessor>(0));
 
         // Finalize graph
         GraphConfig config;
diff --git a/examples/graph_yolov3.cpp b/examples/graph_yolov3.cpp
index c7f917ba6e..79d891a308 100644
--- a/examples/graph_yolov3.cpp
+++ b/examples/graph_yolov3.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2019 Arm Limited.
+ * Copyright (c) 2018-2020 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -66,7 +66,7 @@ public:
         std::string data_path = common_params.data_path;
 
         // Create a preprocessor object
-        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(0.f);
+        std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>(0.f);
 
         // Create input descriptor
         const TensorShape tensor_shape     = permute_shape(TensorShape(608U, 608U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
diff --git a/examples/neon_cnn.cpp b/examples/neon_cnn.cpp
index 85f8792b9c..339c8c1a81 100644
--- a/examples/neon_cnn.cpp
+++ b/examples/neon_cnn.cpp
@@ -53,10 +53,10 @@ public:
         // The weights and biases tensors should be initialized with the values inferred with the training
 
         // Set memory manager where allowed to manage internal memory requirements
-        conv0   = arm_compute::support::cpp14::make_unique<NEConvolutionLayer>(mm_layers);
-        conv1   = arm_compute::support::cpp14::make_unique<NEConvolutionLayer>(mm_layers);
-        fc0     = arm_compute::support::cpp14::make_unique<NEFullyConnectedLayer>(mm_layers);
-        softmax = arm_compute::support::cpp14::make_unique<NESoftmaxLayer>(mm_layers);
+        conv0   = std::make_unique<NEConvolutionLayer>(mm_layers);
+        conv1   = std::make_unique<NEConvolutionLayer>(mm_layers);
+        fc0     = std::make_unique<NEFullyConnectedLayer>(mm_layers);
+        softmax = std::make_unique<NESoftmaxLayer>(mm_layers);
 
         /* [Initialize tensors] */
 
@@ -170,8 +170,8 @@ public:
 
         // We need 2 memory groups for handling the input and output
         // We call explicitly allocate after manage() in order to avoid overlapping lifetimes
-        memory_group0 = arm_compute::support::cpp14::make_unique<MemoryGroup>(mm_transitions);
-        memory_group1 = arm_compute::support::cpp14::make_unique<MemoryGroup>(mm_transitions);
+        memory_group0 = std::make_unique<MemoryGroup>(mm_transitions);
+        memory_group1 = std::make_unique<MemoryGroup>(mm_transitions);
 
         memory_group0->manage(&out_conv0);
         out_conv0.allocator()->allocate();