From 40f51a63c8e7258db15269427ae4fe1ad199c550 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Sat, 21 Nov 2020 03:04:18 +0000 Subject: Update default C++ standard to C++14 (3RDPARTY_UPDATE) Resolves: COMPMID-3849 Signed-off-by: Georgios Pinitas Change-Id: I6369f112337310140e2d6c8e79630cd11138dfa0 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4544 Tested-by: Arm Jenkins Reviewed-by: Michalis Spyrou Comments-Addressed: Arm Jenkins --- ...mlowp_reshaped_rhs_only_fused_output_stage_fixedpoint.cpp | 2 +- examples/graph_alexnet.cpp | 2 +- examples/graph_googlenet.cpp | 2 +- examples/graph_inception_resnet_v1.cpp | 4 ++-- examples/graph_inception_resnet_v2.cpp | 2 +- examples/graph_inception_v3.cpp | 2 +- examples/graph_inception_v4.cpp | 2 +- examples/graph_mobilenet.cpp | 2 +- examples/graph_mobilenet_v2.cpp | 2 +- examples/graph_resnet12.cpp | 4 ++-- examples/graph_resnet50.cpp | 4 ++-- examples/graph_resnet_v2_50.cpp | 2 +- examples/graph_shufflenet.cpp | 2 +- examples/graph_squeezenet.cpp | 2 +- examples/graph_squeezenet_v1_1.cpp | 2 +- examples/graph_srcnn955.cpp | 4 ++-- examples/graph_ssd_mobilenet.cpp | 2 +- examples/graph_vgg16.cpp | 2 +- examples/graph_vgg19.cpp | 2 +- examples/graph_vgg_vdsr.cpp | 4 ++-- examples/graph_yolov3.cpp | 4 ++-- examples/neon_cnn.cpp | 12 ++++++------ 22 files changed, 33 insertions(+), 33 deletions(-) (limited to 'examples') 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(); + mtx_a_reduction = std::make_unique(); 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 mean_rgb{ { 122.68f, 116.67f, 104.01f } }; - std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(mean_rgb); + std::unique_ptr preprocessor = std::make_unique(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 mean_rgb{ { 122.68f, 116.67f, 104.01f } }; - std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(mean_rgb); + std::unique_ptr preprocessor = std::make_unique(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 preprocessor = arm_compute::support::cpp14::make_unique(0.f, 1.f); + std::unique_ptr preprocessor = std::make_unique(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(0)); + << OutputLayer(std::make_unique(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 preprocessor = arm_compute::support::cpp14::make_unique(0.f, 1.f); + std::unique_ptr preprocessor = std::make_unique(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 preprocessor = arm_compute::support::cpp14::make_unique(); + std::unique_ptr preprocessor = std::make_unique(); // 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 preprocessor = arm_compute::support::cpp14::make_unique(); + std::unique_ptr preprocessor = std::make_unique(); // 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 preprocessor = arm_compute::support::cpp14::make_unique(); + std::unique_ptr preprocessor = std::make_unique(); // 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 preprocessor = arm_compute::support::cpp14::make_unique(); + std::unique_ptr preprocessor = std::make_unique(); // 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 preprocessor = arm_compute::support::cpp14::make_unique(); + std::unique_ptr preprocessor = std::make_unique(); // 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(0)); + << OutputLayer(std::make_unique(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 mean_rgb{ { 122.68f, 116.67f, 104.01f } }; - std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(mean_rgb, - false /* Do not convert to BGR */); + std::unique_ptr preprocessor = std::make_unique(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 preprocessor = arm_compute::support::cpp14::make_unique(); + std::unique_ptr preprocessor = std::make_unique(); // 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 preprocessor = arm_compute::support::cpp14::make_unique(0); + std::unique_ptr preprocessor = std::make_unique(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 mean_rgb{ { 122.68f, 116.67f, 104.01f } }; - std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(mean_rgb); + std::unique_ptr preprocessor = std::make_unique(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 mean_rgb{ { 122.68f, 116.67f, 104.01f } }; - std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(mean_rgb); + std::unique_ptr preprocessor = std::make_unique(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 preprocessor = arm_compute::support::cpp14::make_unique(); + std::unique_ptr preprocessor = std::make_unique(); // 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(0)); + << OutputLayer(std::make_unique(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 mean_rgb{ { 127.5f, 127.5f, 127.5f } }; - std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(mean_rgb, true, 0.007843f); + std::unique_ptr preprocessor = std::make_unique(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 mean_rgb{ { 123.68f, 116.779f, 103.939f } }; - std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(mean_rgb); + std::unique_ptr preprocessor = std::make_unique(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 mean_rgb{ { 123.68f, 116.779f, 103.939f } }; - std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(mean_rgb); + std::unique_ptr preprocessor = std::make_unique(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 preprocessor = arm_compute::support::cpp14::make_unique(); + std::unique_ptr preprocessor = std::make_unique(); // 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(0)); + << OutputLayer(std::make_unique(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 preprocessor = arm_compute::support::cpp14::make_unique(0.f); + std::unique_ptr preprocessor = std::make_unique(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(mm_layers); - conv1 = arm_compute::support::cpp14::make_unique(mm_layers); - fc0 = arm_compute::support::cpp14::make_unique(mm_layers); - softmax = arm_compute::support::cpp14::make_unique(mm_layers); + conv0 = std::make_unique(mm_layers); + conv1 = std::make_unique(mm_layers); + fc0 = std::make_unique(mm_layers); + softmax = std::make_unique(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(mm_transitions); - memory_group1 = arm_compute::support::cpp14::make_unique(mm_transitions); + memory_group0 = std::make_unique(mm_transitions); + memory_group1 = std::make_unique(mm_transitions); memory_group0->manage(&out_conv0); out_conv0.allocator()->allocate(); -- cgit v1.2.1