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Diffstat (limited to 'contrib/benchmark_acl/src/benchmark_inception_v3.cpp')
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1 files changed, 0 insertions, 891 deletions
diff --git a/contrib/benchmark_acl/src/benchmark_inception_v3.cpp b/contrib/benchmark_acl/src/benchmark_inception_v3.cpp deleted file mode 100644 index 382851f50..000000000 --- a/contrib/benchmark_acl/src/benchmark_inception_v3.cpp +++ /dev/null @@ -1,891 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * 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.h" - -#include "Benchmark.h" - -#include <cstdlib> -#include <tuple> - -using namespace arm_compute::graph::frontend; - -inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_input_accessor(void) -{ - return get_accessor<InputAccessor>(); -} - -inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_random_accessor(float lower, float upper) -{ - return get_accessor<InputAccessor>(); -} - -inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_weights_accessor(const std::string &path, const std::string &data_file, DataLayout file_layout = DataLayout::NCHW) -{ - return get_accessor<InputAccessor>(); -} - -inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_output_accessor(void) -{ - return get_accessor<OutputAccessor>(); -} - -/** 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 target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; - Target target_hint = set_target_hint(target); - FastMathHint fast_math_hint = FastMathHint::Disabled; - - // Parse arguments - if(argc < 2) - { - // Print help - std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\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] [fast_math_hint]\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] [fast_math_hint]\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] [fast_math_hint]\n\n"; - std::cout << "No text file with labels provided: skipping output accessor\n\n"; - } - else if(argc == 5) - { - data_path = argv[2]; - image = argv[3]; - label = argv[4]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; - std::cout << "No fast math info provided: disabling fast math\n\n"; - } - else - { - data_path = argv[2]; - image = argv[3]; - label = argv[4]; - fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::Disabled : FastMathHint::Enabled; - } - - graph << target_hint - << fast_math_hint - << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), 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<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0)) - .set_name("Conv2d_1a_3x3/convolution") - << 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) - .set_name("Conv2d_1a_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_1a_3x3/Relu") - << ConvolutionLayer(3U, 3U, 32U, - get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) - .set_name("Conv2d_2a_3x3/convolution") - << 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) - .set_name("Conv2d_2a_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2a_3x3/Relu") - - << ConvolutionLayer(3U, 3U, 64U, - get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1)) - .set_name("Conv2d_2b_3x3/convolution") - << 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) - .set_name("Conv2d_2b_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu") - - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_3a_3x3/MaxPool") - - << ConvolutionLayer(1U, 1U, 80U, - get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) - .set_name("Conv2d_3b_1x1/convolution") - << 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) - .set_name("Conv2d_3b_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_3b_1x1/Relu") - - << ConvolutionLayer(3U, 3U, 192U, - get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) - .set_name("Conv2d_4a_3x3/convolution") - << 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) - .set_name("Conv2d_4a_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4a_3x3/Relu") - - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_5a_3x3/MaxPool"); - - graph << get_inception_node_A(data_path, "Mixed_5b", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), - 32U) - .set_name("Mixed_5b/concat"); - graph << get_inception_node_A(data_path, "Mixed_5c", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), - 64U, true) - .set_name("Mixed_5c/concat"); - graph << get_inception_node_A(data_path, "Mixed_5d", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), - 64U) - .set_name("Mixed_5d/concat"); - - graph << get_inception_node_B(data_path, "Mixed_6a", 384U, std::make_tuple(64U, 96U, 96U)).set_name("Mixed_6a/concat"); - - graph << get_inception_node_C(data_path, "Mixed_6b", 192U, std::make_tuple(128U, 128U, 192U), - std::make_tuple(128U, 128U, 128U, 128U, 192U), 192U) - .set_name("Mixed_6b/concat"); - graph << get_inception_node_C(data_path, "Mixed_6c", 192U, std::make_tuple(160U, 160U, 192U), - std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U) - .set_name("Mixed_6c/concat"); - graph << get_inception_node_C(data_path, "Mixed_6d", 192U, std::make_tuple(160U, 160U, 192U), - std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U) - .set_name("Mixed_6d/concat"); - graph << get_inception_node_C(data_path, "Mixed_6e", 192U, std::make_tuple(192U, 192U, 192U), - std::make_tuple(192U, 192U, 192U, 192U, 192U), 192U) - .set_name("Mixed_6e/concat"); - - graph << get_inception_node_D(data_path, "Mixed_7a", std::make_tuple(192U, 320U), - std::make_tuple(192U, 192U, 192U, 192U)) - .set_name("Mixed_7a/concat"); - - graph << get_inception_node_E(data_path, "Mixed_7b", 320U, std::make_tuple(384U, 384U, 384U), - std::make_tuple(448U, 384U, 384U, 384U), 192U) - .set_name("Mixed_7b/concat"); - graph << get_inception_node_E(data_path, "Mixed_7c", 320U, std::make_tuple(384U, 384U, 384U), - std::make_tuple(448U, 384U, 384U, 384U), 192U, true) - .set_name("Mixed_7c/concat"); - - graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 8, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("Logits/AvgPool_1a_8x8/AvgPool") - << 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)) - .set_name("Logits/Conv2d_1c_1x1/convolution") - << ReshapeLayer(TensorShape(1001U)).set_name("Predictions/Reshape") - << SoftmaxLayer().set_name("Predictions/Softmax") - << OutputLayer(get_output_accessor()); - - // Finalize graph - GraphConfig config; - config.use_tuner = (target == 2); - graph.finalize(target_hint, config); - } - - void do_run() - { - run_benchmark(graph); - } - -private: - Stream graph{ 0, "InceptionV3" }; - -private: - ConcatLayer get_inception_node_A(const std::string &data_path, std::string &¶m_path, - unsigned int a_filt, - std::tuple<unsigned int, unsigned int> b_filters, - std::tuple<unsigned int, unsigned int, unsigned int> 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_"; - } - - SubStream i_a(graph); - i_a << ConvolutionLayer( - 1U, 1U, a_filt, - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") - << 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) - .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu"); - - SubStream i_b(graph); - 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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/convolution") - << 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) - .set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 2, 2)) - .set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/convolution") - << 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) - .set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/Relu"); - - SubStream i_c(graph); - 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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution") - << 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) - .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/convolution") - << 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) - .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/convolution") - << 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) - .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/BatchNorm/batcnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_3x3/Relu"); - - SubStream i_d(graph); - i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") - << ConvolutionLayer( - 1U, 1U, d_filt, - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution") - << 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) - .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu"); - - return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); - } - - ConcatLayer get_inception_node_B(const std::string &data_path, std::string &¶m_path, - unsigned int a_filt, - std::tuple<unsigned int, unsigned int, unsigned int> b_filters) - { - std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; - SubStream i_a(graph); - i_a << ConvolutionLayer( - 3U, 3U, a_filt, - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(2, 2, 0, 0)) - .set_name(param_path + "/Branch_0/Conv2d_1a_1x1/convolution") - << 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) - .set_name(param_path + "/Branch_0/Conv2d_1a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_1a_1x1/Relu"); - - SubStream i_b(graph); - 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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") - << 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) - .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/convolution") - << 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) - .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_3x3/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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(2, 2, 0, 0)) - .set_name(param_path + "/Branch_1/Conv2d_1a_1x1/convolution") - << 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) - .set_name(param_path + "/Branch_1/Conv2d_1a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_1x1/Relu"); - - SubStream i_c(graph); - i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool"); - - return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)); - } - - ConcatLayer get_inception_node_C(const std::string &data_path, std::string &¶m_path, - unsigned int a_filt, - std::tuple<unsigned int, unsigned int, unsigned int> b_filters, - std::tuple<unsigned int, unsigned int, unsigned int, unsigned int, unsigned int> c_filters, - unsigned int d_filt) - { - std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; - SubStream i_a(graph); - i_a << ConvolutionLayer( - 1U, 1U, a_filt, - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") - << 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) - .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu"); - - SubStream i_b(graph); - 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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") - << 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) - .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 3, 0)) - .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/convolution") - << 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) - .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 3)) - .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/convolution") - << 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) - .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0c_7x1/Relu"); - - SubStream i_c(graph); - 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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution") - << 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) - .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 3)) - .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/convolution") - << 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) - .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_7x1/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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 3, 0)) - .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/convolution") - << 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) - .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x7/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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 3)) - .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/convolution") - << 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) - .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_7x1/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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 3, 0)) - .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/convolution") - << 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) - .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0e_1x7/Relu"); - - SubStream i_d(graph); - i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") - << ConvolutionLayer( - 1U, 1U, d_filt, - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution") - << 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) - .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu"); - - return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); - } - - ConcatLayer get_inception_node_D(const std::string &data_path, std::string &¶m_path, - std::tuple<unsigned int, unsigned int> a_filters, - std::tuple<unsigned int, unsigned int, unsigned int, unsigned int> b_filters) - { - std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; - SubStream i_a(graph); - 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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") - << 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) - .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(2, 2, 0, 0)) - .set_name(param_path + "/Branch_0/Conv2d_1a_3x3/convolution") - << 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) - .set_name(param_path + "/Branch_0/Conv2d_1a_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_1a_3x3/Relu"); - - SubStream i_b(graph); - 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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") - << 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) - .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 3, 0)) - .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/convolution") - << 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) - .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 3)) - .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/convolution") - << 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) - .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0c_7x1/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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(2, 2, 0, 0)) - .set_name(param_path + "/Branch_1/Conv2d_1a_3x3/convolution") - << 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) - .set_name(param_path + "/Branch_1/Conv2d_1a_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_3x3/Relu"); - - SubStream i_c(graph); - i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool"); - - return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)); - } - - ConcatLayer get_inception_node_E(const std::string &data_path, std::string &¶m_path, - unsigned int a_filt, - std::tuple<unsigned int, unsigned int, unsigned int> b_filters, - std::tuple<unsigned int, unsigned int, unsigned int, unsigned int> 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 + "_"; - SubStream i_a(graph); - i_a << ConvolutionLayer( - 1U, 1U, a_filt, - get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") - << 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) - .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu"); - - SubStream i_b(graph); - 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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") - << 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) - .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu"); - - SubStream i_b1(static_cast<IStream &>(i_b)); - 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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 0)) - .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/convolution") - << 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) - .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x3/Relu"); - - SubStream i_b2(static_cast<IStream &>(i_b)); - 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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 1)) - .set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/convolution") - << 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) - .set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/Relu"); - - // Merge b1 and b2 - i_b << ConcatLayer(std::move(i_b1), std::move(i_b2)).set_name(param_path + "/Branch_1/concat"); - - SubStream i_c(graph); - 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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution") - << 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) - .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 1)) - .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/convolution") - << 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) - .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu"); - - SubStream i_c1(static_cast<IStream &>(i_c)); - 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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 1, 0)) - .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/convolution") - << 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) - .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x3/Relu"); - - SubStream i_c2(static_cast<IStream &>(i_c)); - 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<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 1)) - .set_name(param_path + "/Branch_2/Conv2d_0d_3x1/convolution") - << 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) - .set_name(param_path + "/Branch_2/Conv2d_0d_3x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_3x1/Relu"); - - // Merge i_c1 and i_c2 - i_c << ConcatLayer(std::move(i_c1), std::move(i_c2)).set_name(param_path + "/Branch_2/concat"); - - SubStream i_d(graph); - i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") - << ConvolutionLayer( - 1U, 1U, d_filt, - get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(1, 1, 0, 0)) - .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution") - << 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) - .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu"); - - return ConcatLayer(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, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) - */ -int main(int argc, char **argv) -{ - InceptionV3Example example; - - example.do_setup(argc, argv); - example.do_run(); - - return 0; -} |