| /* |
| * 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 "support/ToolchainSupport.h" |
| #include "utils/GraphUtils.h" |
| #include "utils/Utils.h" |
| |
| #include <cstdlib> |
| #include <tuple> |
| |
| using namespace arm_compute::utils; |
| using namespace arm_compute::graph::frontend; |
| using namespace arm_compute::graph_utils; |
| |
| /** 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] Path to the weights folder, [optional] image, [optional] labels ) |
| */ |
| class InceptionV3Example : public Example |
| { |
| public: |
| void do_setup(int argc, char **argv) override |
| { |
| std::string data_path; /* Path to the trainable data */ |
| std::string image; /* Image data */ |
| std::string label; /* Label data */ |
| |
| // Create a preprocessor object |
| std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(); |
| |
| // 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); |
| |
| ConvolutionMethod convolution_hint = (target_hint == Target::CL) ? ConvolutionMethod::WINOGRAD : ConvolutionMethod::GEMM; |
| |
| // 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 << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32), |
| get_input_accessor(image, std::move(preprocessor), false)) |
| << 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)) |
| << 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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) |
| << convolution_hint |
| << 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)) |
| << 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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(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() |
| << OutputLayer(get_output_accessor(label, 5)); |
| |
| // Finalize graph |
| GraphConfig config; |
| config.use_function_memory_manager = true; |
| config.use_tuner = (target == 2); |
| graph.finalize(target_hint, config); |
| } |
| |
| void do_run() override |
| { |
| graph.run(); |
| } |
| |
| private: |
| Stream graph{ 0, "InceptionV3" }; |
| |
| private: |
| BranchLayer 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)) |
| << 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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::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)) |
| << 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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::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)) |
| << 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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); |
| |
| SubStream i_d(graph); |
| 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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(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<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)) |
| << 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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::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)) |
| << 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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); |
| |
| SubStream i_c(graph); |
| i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))); |
| |
| 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<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)) |
| << 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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::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)) |
| << 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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::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)) |
| << 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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); |
| |
| SubStream i_d(graph); |
| 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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(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<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)) |
| << 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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::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)) |
| << 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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); |
| |
| SubStream i_c(graph); |
| i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))); |
| |
| 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<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)) |
| << 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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::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)) |
| << 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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::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)) |
| << 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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::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)) |
| << 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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); |
| |
| // Merge b1 and b2 |
| i_b << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_b1), std::move(i_b2)); |
| |
| 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)) |
| << 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) |
| << ActivationLayer(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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::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)) |
| << 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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::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)) |
| << 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) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); |
| |
| // Merge i_c1 and i_c2 |
| i_c << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_c1), std::move(i_c2)); |
| |
| SubStream i_d(graph); |
| 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<arm_compute::graph::ITensorAccessor>(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) |
| << ActivationLayer(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) |
| { |
| return arm_compute::utils::run_example<InceptionV3Example>(argc, argv); |
| } |