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>
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 c6818e4..8323bbd 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 @@
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 40bbee1..ce398be 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -70,7 +70,7 @@
// 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 ed5cbd5..0a53355 100644
--- a/examples/graph_googlenet.cpp
+++ b/examples/graph_googlenet.cpp
@@ -66,7 +66,7 @@
// 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 7c0bb0c..7a55733 100644
--- a/examples/graph_inception_resnet_v1.cpp
+++ b/examples/graph_inception_resnet_v1.cpp
@@ -92,7 +92,7 @@
}
// 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 @@
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 d14c34e..60236d0 100644
--- a/examples/graph_inception_resnet_v2.cpp
+++ b/examples/graph_inception_resnet_v2.cpp
@@ -76,7 +76,7 @@
}
// 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 4b6dc8d..5cacbcb 100644
--- a/examples/graph_inception_v3.cpp
+++ b/examples/graph_inception_v3.cpp
@@ -62,7 +62,7 @@
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 553c96d..db2a310 100644
--- a/examples/graph_inception_v4.cpp
+++ b/examples/graph_inception_v4.cpp
@@ -66,7 +66,7 @@
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 f74d251..b73f7a2 100644
--- a/examples/graph_mobilenet.cpp
+++ b/examples/graph_mobilenet.cpp
@@ -124,7 +124,7 @@
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 5ee1f7e..fa16c94 100644
--- a/examples/graph_mobilenet_v2.cpp
+++ b/examples/graph_mobilenet_v2.cpp
@@ -129,7 +129,7 @@
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 badcaec..ebd2e5d 100644
--- a/examples/graph_resnet12.cpp
+++ b/examples/graph_resnet12.cpp
@@ -81,7 +81,7 @@
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 @@
.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 2939ee4..47d258e 100644
--- a/examples/graph_resnet50.cpp
+++ b/examples/graph_resnet50.cpp
@@ -63,8 +63,8 @@
// 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 32434f5..921fb14 100644
--- a/examples/graph_resnet_v2_50.cpp
+++ b/examples/graph_resnet_v2_50.cpp
@@ -67,7 +67,7 @@
}
// 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 08f884b..300d0f1 100644
--- a/examples/graph_shufflenet.cpp
+++ b/examples/graph_shufflenet.cpp
@@ -89,7 +89,7 @@
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 f0d620c..2e72c14 100644
--- a/examples/graph_squeezenet.cpp
+++ b/examples/graph_squeezenet.cpp
@@ -63,7 +63,7 @@
// 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 c604486..1708ac2 100644
--- a/examples/graph_squeezenet_v1_1.cpp
+++ b/examples/graph_squeezenet_v1_1.cpp
@@ -63,7 +63,7 @@
// 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 a95f0c1..bcc3824 100644
--- a/examples/graph_srcnn955.cpp
+++ b/examples/graph_srcnn955.cpp
@@ -78,7 +78,7 @@
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 @@
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 edd4c94..f5af84f 100644
--- a/examples/graph_ssd_mobilenet.cpp
+++ b/examples/graph_ssd_mobilenet.cpp
@@ -216,7 +216,7 @@
{
// 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 990040b..a4c5e6b 100644
--- a/examples/graph_vgg16.cpp
+++ b/examples/graph_vgg16.cpp
@@ -63,7 +63,7 @@
// 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 9215ba7..c95fb03 100644
--- a/examples/graph_vgg19.cpp
+++ b/examples/graph_vgg19.cpp
@@ -62,7 +62,7 @@
// 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 65c0642..3fa7dd1 100644
--- a/examples/graph_vgg_vdsr.cpp
+++ b/examples/graph_vgg_vdsr.cpp
@@ -79,7 +79,7 @@
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 @@
// 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 c7f917b..79d891a 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 @@
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 85f8792..339c8c1 100644
--- a/examples/neon_cnn.cpp
+++ b/examples/neon_cnn.cpp
@@ -53,10 +53,10 @@
// 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 @@
// 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();