From 500d6792a4427d1bc5644b7f49c278932242bc27 Mon Sep 17 00:00:00 2001 From: =?utf8?q?=EB=B0=95=EC=A2=85=ED=98=84/=EB=8F=99=EC=9E=91=EC=A0=9C?= =?utf8?q?=EC=96=B4Lab=28SR=29/Senior=20Engineer/=EC=82=BC=EC=84=B1?= =?utf8?q?=EC=A0=84=EC=9E=90?= Date: Thu, 29 Mar 2018 08:46:22 +0900 Subject: [PATCH] Introduce 'Inception v3' benchmark (#283) This commit introduces 'inception v3' benchmark (which is derived from inception v3 ACL Graph API example). Signed-off-by: Jonghyun Park --- benchmark/acl/CMakeLists.txt | 4 + benchmark/acl/benchmark_inception_v3.cpp | 743 +++++++++++++++++++++++++++++++ 2 files changed, 747 insertions(+) create mode 100644 benchmark/acl/benchmark_inception_v3.cpp diff --git a/benchmark/acl/CMakeLists.txt b/benchmark/acl/CMakeLists.txt index e9977af..b5c4aad 100644 --- a/benchmark/acl/CMakeLists.txt +++ b/benchmark/acl/CMakeLists.txt @@ -6,6 +6,10 @@ target_link_libraries(arm_compute_benchmark arm_compute_graph) add_executable(benchmark_googlenet "benchmark_googlenet.cpp") target_link_libraries(benchmark_googlenet arm_compute_benchmark) +# GoogLeNet benchmark +add_executable(benchmark_inception_v3 "benchmark_inception_v3.cpp") +target_link_libraries(benchmark_inception_v3 arm_compute_benchmark) + # MobileNet benchmark add_executable(benchmark_mobilenet "benchmark_mobilenet.cpp") target_link_libraries(benchmark_mobilenet arm_compute_benchmark) diff --git a/benchmark/acl/benchmark_inception_v3.cpp b/benchmark/acl/benchmark_inception_v3.cpp new file mode 100644 index 0000000..3e5df2f --- /dev/null +++ b/benchmark/acl/benchmark_inception_v3.cpp @@ -0,0 +1,743 @@ +/* + * Copyright (c) 2017-2018 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/graph/Graph.h" +#include "arm_compute/graph/Nodes.h" +#include "arm_compute/graph/SubGraph.h" + +#include "Benchmark.h" + +#include +#include + +using namespace arm_compute::graph; + +inline std::unique_ptr get_input_accessor(void) +{ + return get_accessor(); +} + +inline std::unique_ptr get_random_accessor(float lower, float upper) +{ + return get_accessor(); +} + +inline std::unique_ptr get_weights_accessor(const std::string &path, const std::string &data_file) +{ + return get_accessor(); +} + +inline std::unique_ptr get_output_accessor(void) +{ + return get_accessor(); +} + +/** Example demonstrating how to implement InceptionV3's network using the Compute Library's graph API + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels ) + */ +class InceptionV3Example +{ +public: + void do_setup(int argc, char **argv) + { + std::string data_path; /* Path to the trainable data */ + std::string image; /* Image data */ + std::string label; /* Label data */ + + // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON + const int int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; + TargetHint target_hint = set_target_hint(int_target_hint); + + // Parse arguments + if(argc < 2) + { + // Print help + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 2) + { + std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 3) + { + data_path = argv[2]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n"; + std::cout << "No image provided: using random values\n\n"; + } + else if(argc == 4) + { + data_path = argv[2]; + image = argv[3]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n"; + std::cout << "No text file with labels provided: skipping output accessor\n\n"; + } + else + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + } + + graph << target_hint << Tensor(TensorInfo(TensorShape(299U, 299U, 3U, 1U), 1, DataType::F32), + get_input_accessor()) + + << ConvolutionLayer(3U, 3U, 32U, + get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_weights.npy"), + std::unique_ptr(nullptr), PadStrideInfo(2, 2, 0, 0)) + << BatchNormalizationLayer(get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + + << ConvolutionLayer(3U, 3U, 32U, + get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_weights.npy"), + std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer(get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + + << ConvolutionLayer(3U, 3U, 64U, + get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_weights.npy"), + std::unique_ptr(nullptr), PadStrideInfo(1, 1, 1, 1)) + << BatchNormalizationLayer(get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) + + << ConvolutionLayer(1U, 1U, 80U, + get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_weights.npy"), + std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer(get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + + << ConvolutionLayer(3U, 3U, 192U, + get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_weights.npy"), + std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer(get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) + + << get_inception_node_A(data_path, "Mixed_5b", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), + 32U) + << get_inception_node_A(data_path, "Mixed_5c", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), + 64U, true) + << get_inception_node_A(data_path, "Mixed_5d", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), + 64U) + + << get_inception_node_B(data_path, "Mixed_6a", 384U, std::make_tuple(64U, 96U, 96U)) + + << get_inception_node_C(data_path, "Mixed_6b", 192U, std::make_tuple(128U, 128U, 192U), + std::make_tuple(128U, 128U, 128U, 128U, 192U), 192U) + << get_inception_node_C(data_path, "Mixed_6c", 192U, std::make_tuple(160U, 160U, 192U), + std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U) + << get_inception_node_C(data_path, "Mixed_6d", 192U, std::make_tuple(160U, 160U, 192U), + std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U) + << get_inception_node_C(data_path, "Mixed_6e", 192U, std::make_tuple(192U, 192U, 192U), + std::make_tuple(192U, 192U, 192U, 192U, 192U), 192U) + + << get_inception_node_D(data_path, "Mixed_7a", std::make_tuple(192U, 320U), + std::make_tuple(192U, 192U, 192U, 192U)) + + << get_inception_node_E(data_path, "Mixed_7b", 320U, std::make_tuple(384U, 384U, 384U), + std::make_tuple(448U, 384U, 384U, 384U), 192U) + << get_inception_node_E(data_path, "Mixed_7c", 320U, std::make_tuple(384U, 384U, 384U), + std::make_tuple(448U, 384U, 384U, 384U), 192U, true) + + << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 8, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))) + << ConvolutionLayer(1U, 1U, 1001U, get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_weights.npy"), + get_weights_accessor(data_path, + "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_biases.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ReshapeLayer(TensorShape(1001U)) << SoftmaxLayer() + << Tensor(get_output_accessor()); + + // In order to enable the OpenCL tuner, graph_init() has to be called only when all nodes have been instantiated + graph.graph_init(int_target_hint == 2); + } + + void do_run() + { + run_benchmark(graph); + } + +private: + Graph graph{}; + +private: + BranchLayer get_inception_node_A(const std::string &data_path, std::string &¶m_path, + unsigned int a_filt, + std::tuple b_filters, + std::tuple c_filters, + unsigned int d_filt, + bool is_name_different = false) + { + std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; + + // This is due to a naming issue in the tf model + std::string conv_id0 = "_0a_"; + std::string conv_id1 = "2d_0b_"; + if(is_name_different) + { + conv_id0 = "_0b_"; + conv_id1 = "_1_0c_"; + } + + SubGraph i_a; + i_a << ConvolutionLayer( + 1U, 1U, a_filt, + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubGraph i_b; + i_b << ConvolutionLayer( + 1U, 1U, std::get<0>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 5U, 5U, std::get<1>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 2, 2)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubGraph i_c; + i_c << ConvolutionLayer( + 1U, 1U, std::get<0>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, std::get<1>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, std::get<2>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubGraph i_d; + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)) + << ConvolutionLayer( + 1U, 1U, d_filt, + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); + } + + BranchLayer get_inception_node_B(const std::string &data_path, std::string &¶m_path, + unsigned int a_filt, + std::tuple b_filters) + { + std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; + SubGraph i_a; + i_a << ConvolutionLayer( + 3U, 3U, a_filt, + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubGraph i_b; + i_b << ConvolutionLayer( + 1U, 1U, std::get<0>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, std::get<1>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, std::get<2>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubGraph i_c; + i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f, 0.f)); + + return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c)); + } + + BranchLayer get_inception_node_C(const std::string &data_path, std::string &¶m_path, + unsigned int a_filt, + std::tuple b_filters, + std::tuple c_filters, + unsigned int d_filt) + { + std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; + SubGraph i_a; + i_a << ConvolutionLayer( + 1U, 1U, a_filt, + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubGraph i_b; + i_b << ConvolutionLayer( + 1U, 1U, std::get<0>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 7U, 1U, std::get<1>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 3, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 1U, 7U, std::get<2>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 3)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubGraph i_c; + i_c << ConvolutionLayer( + 1U, 1U, std::get<0>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 1U, 7U, std::get<1>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 3)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 7U, 1U, std::get<2>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 3, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 1U, 7U, std::get<3>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 3)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 7U, 1U, std::get<4>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 3, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubGraph i_d; + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)) + << ConvolutionLayer( + 1U, 1U, d_filt, + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); + } + + BranchLayer get_inception_node_D(const std::string &data_path, std::string &¶m_path, + std::tuple a_filters, + std::tuple b_filters) + { + std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; + SubGraph i_a; + i_a << ConvolutionLayer( + 1U, 1U, std::get<0>(a_filters), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, std::get<1>(a_filters), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubGraph i_b; + i_b << ConvolutionLayer( + 1U, 1U, std::get<0>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 7U, 1U, std::get<1>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 3, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 1U, 7U, std::get<2>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 3)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, std::get<3>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubGraph i_c; + i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f, 0.f)); + + return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c)); + } + + BranchLayer get_inception_node_E(const std::string &data_path, std::string &¶m_path, + unsigned int a_filt, + std::tuple b_filters, + std::tuple c_filters, + unsigned int d_filt, + bool is_name_different = false) + { + // This is due to a naming issue in the tf model + std::string conv_id = "_0b_"; + if(is_name_different) + { + conv_id = "_0c_"; + } + + std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; + SubGraph i_a; + i_a << ConvolutionLayer( + 1U, 1U, a_filt, + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubGraph i_b1; + i_b1 << ConvolutionLayer( + 3U, 1U, std::get<1>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubGraph i_b2; + i_b2 << ConvolutionLayer( + 1U, 3U, std::get<2>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 1)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubGraph i_b; + i_b << ConvolutionLayer( + 1U, 1U, std::get<0>(b_filters), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_b1), std::move(i_b2)); + + SubGraph i_c1; + i_c1 << ConvolutionLayer( + 3U, 1U, std::get<2>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubGraph i_c2; + i_c2 << ConvolutionLayer( + 1U, 3U, std::get<3>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 1)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubGraph i_c; + i_c << ConvolutionLayer( + 1U, 1U, std::get<0>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << ConvolutionLayer( + 3U, 3U, std::get<1>(c_filters), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_c1), std::move(i_c2)); + + SubGraph i_d; + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)) + << ConvolutionLayer( + 1U, 1U, d_filt, + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"), + 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); + } +}; + +/** Main program for Inception V3 + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels ) + */ +int main(int argc, char **argv) +{ + InceptionV3Example example; + + example.do_setup(argc, argv); + example.do_run(); + + return 0; +} -- 2.7.4