| /* | |
| * Copyright (c) 2017-2021 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/CommonGraphOptions.h" | |
| #include "utils/GraphUtils.h" | |
| #include "utils/Utils.h" | |
| using namespace arm_compute; | |
| using namespace arm_compute::utils; | |
| using namespace arm_compute::graph::frontend; | |
| using namespace arm_compute::graph_utils; | |
| /** Example demonstrating how to implement MobileNet's network using the Compute Library's graph API */ | |
| class GraphMobilenetExample : public Example | |
| { | |
| public: | |
| GraphMobilenetExample() | |
| : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetV1") | |
| { | |
| // Add model id option | |
| model_id_opt = cmd_parser.add_option<SimpleOption<int>>("model-id", 0); | |
| model_id_opt->set_help("Mobilenet model id (0: 1.0_224, else: 0.75_160"); | |
| } | |
| GraphMobilenetExample(const GraphMobilenetExample &) = delete; | |
| GraphMobilenetExample &operator=(const GraphMobilenetExample &) = delete; | |
| ~GraphMobilenetExample() override = default; | |
| bool do_setup(int argc, char **argv) override | |
| { | |
| // Parse arguments | |
| cmd_parser.parse(argc, argv); | |
| cmd_parser.validate(); | |
| // Consume common parameters | |
| common_params = consume_common_graph_parameters(common_opts); | |
| // Return when help menu is requested | |
| if(common_params.help) | |
| { | |
| cmd_parser.print_help(argv[0]); | |
| return false; | |
| } | |
| // Print parameter values | |
| std::cout << common_params << std::endl; | |
| // Get model parameters | |
| int model_id = model_id_opt->value(); | |
| // Create input descriptor | |
| unsigned int spatial_size = (model_id == 0 || common_params.data_type == DataType::QASYMM8) ? 224 : 160; | |
| // Create input descriptor | |
| const TensorShape tensor_shape = permute_shape(TensorShape(spatial_size, spatial_size, 3U, common_params.batches), DataLayout::NCHW, common_params.data_layout); | |
| TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout); | |
| // Set graph hints | |
| graph << common_params.target | |
| << common_params.fast_math_hint; | |
| // Create core graph | |
| if(arm_compute::is_data_type_float(common_params.data_type)) | |
| { | |
| create_graph_float(input_descriptor, model_id); | |
| } | |
| else | |
| { | |
| create_graph_qasymm(input_descriptor); | |
| } | |
| // Create common tail | |
| graph << ReshapeLayer(TensorShape(1001U)).set_name("Reshape") | |
| << SoftmaxLayer().set_name("Softmax") | |
| << OutputLayer(get_output_accessor(common_params, 5)); | |
| // Finalize graph | |
| GraphConfig config; | |
| config.num_threads = common_params.threads; | |
| config.use_tuner = common_params.enable_tuner; | |
| config.tuner_mode = common_params.tuner_mode; | |
| config.tuner_file = common_params.tuner_file; | |
| config.mlgo_file = common_params.mlgo_file; | |
| graph.finalize(common_params.target, config); | |
| return true; | |
| } | |
| void do_run() override | |
| { | |
| // Run graph | |
| graph.run(); | |
| } | |
| private: | |
| CommandLineParser cmd_parser; | |
| CommonGraphOptions common_opts; | |
| SimpleOption<int> *model_id_opt{ nullptr }; | |
| CommonGraphParams common_params; | |
| Stream graph; | |
| void create_graph_float(TensorDescriptor &input_descriptor, int model_id) | |
| { | |
| float depth_scale = (model_id == 0) ? 1.f : 0.75; | |
| std::string model_path = (model_id == 0) ? "/cnn_data/mobilenet_v1_1_224_model/" : "/cnn_data/mobilenet_v1_075_160_model/"; | |
| // Create a preprocessor object | |
| std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>(); | |
| // Get trainable parameters data path | |
| std::string data_path = common_params.data_path; | |
| // Add model path to data path | |
| if(!data_path.empty()) | |
| { | |
| data_path += model_path; | |
| } | |
| graph << InputLayer(input_descriptor, | |
| get_input_accessor(common_params, std::move(preprocessor), false)) | |
| << ConvolutionLayer( | |
| 3U, 3U, 32U * depth_scale, | |
| get_weights_accessor(data_path, "Conv2d_0_weights.npy", DataLayout::NCHW), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)) | |
| .set_name("Conv2d_0") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_variance.npy"), | |
| get_weights_accessor(data_path, "Conv2d_0_BatchNorm_gamma.npy"), | |
| get_weights_accessor(data_path, "Conv2d_0_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name("Conv2d_0/BatchNorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name("Conv2d_0/Relu6"); | |
| graph << get_dwsc_node_float(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); | |
| graph << get_dwsc_node_float(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); | |
| graph << get_dwsc_node_float(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); | |
| graph << get_dwsc_node_float(data_path, "Conv2d_4", 256 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); | |
| graph << get_dwsc_node_float(data_path, "Conv2d_5", 256 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); | |
| graph << get_dwsc_node_float(data_path, "Conv2d_6", 512 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); | |
| graph << get_dwsc_node_float(data_path, "Conv2d_7", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); | |
| graph << get_dwsc_node_float(data_path, "Conv2d_8", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); | |
| graph << get_dwsc_node_float(data_path, "Conv2d_9", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); | |
| graph << get_dwsc_node_float(data_path, "Conv2d_10", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); | |
| graph << get_dwsc_node_float(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); | |
| graph << get_dwsc_node_float(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); | |
| graph << get_dwsc_node_float(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); | |
| graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool_1a") | |
| << ConvolutionLayer( | |
| 1U, 1U, 1001U, | |
| get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW), | |
| get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name("Logits/Conv2d_1c_1x1"); | |
| } | |
| void create_graph_qasymm(TensorDescriptor &input_descriptor) | |
| { | |
| // Get trainable parameters data path | |
| std::string data_path = common_params.data_path; | |
| // Add model path to data path | |
| if(!data_path.empty()) | |
| { | |
| data_path += "/cnn_data/mobilenet_qasymm8_model/"; | |
| } | |
| // Quantization info taken from the AndroidNN QASYMM8 MobileNet example | |
| const QuantizationInfo in_quant_info = QuantizationInfo(0.0078125f, 128); | |
| const std::vector<QuantizationInfo> conv_weights_quant_info = | |
| { | |
| QuantizationInfo(0.02182667888700962f, 151), // conv0 | |
| QuantizationInfo(0.004986600950360298f, 74) // conv14 | |
| }; | |
| const std::vector<QuantizationInfo> conv_out_quant_info = | |
| { | |
| QuantizationInfo(0.023528477177023888f, 0), // conv0 | |
| QuantizationInfo(0.16609922051429749f, 66) // conv14 | |
| }; | |
| const std::vector<QuantizationInfo> depth_weights_quant_info = | |
| { | |
| QuantizationInfo(0.29219913482666016f, 110), // dwsc1 | |
| QuantizationInfo(0.40277284383773804f, 130), // dwsc2 | |
| QuantizationInfo(0.06053730100393295f, 160), // dwsc3 | |
| QuantizationInfo(0.01675807684659958f, 123), // dwsc4 | |
| QuantizationInfo(0.04105526953935623f, 129), // dwsc5 | |
| QuantizationInfo(0.013460792601108551f, 122), // dwsc6 | |
| QuantizationInfo(0.036934755742549896f, 132), // dwsc7 | |
| QuantizationInfo(0.042609862983226776f, 94), // dwsc8 | |
| QuantizationInfo(0.028358859941363335f, 127), // dwsc9 | |
| QuantizationInfo(0.024329448118805885f, 134), // dwsc10 | |
| QuantizationInfo(0.019366811960935593f, 106), // dwsc11 | |
| QuantizationInfo(0.007835594937205315f, 126), // dwsc12 | |
| QuantizationInfo(0.12616927921772003f, 211) // dwsc13 | |
| }; | |
| const std::vector<QuantizationInfo> point_weights_quant_info = | |
| { | |
| QuantizationInfo(0.030420949682593346f, 121), // dwsc1 | |
| QuantizationInfo(0.015148180536925793f, 104), // dwsc2 | |
| QuantizationInfo(0.013755458407104015f, 94), // dwsc3 | |
| QuantizationInfo(0.007601846940815449f, 151), // dwsc4 | |
| QuantizationInfo(0.006431614048779011f, 122), // dwsc5 | |
| QuantizationInfo(0.00917122047394514f, 109), // dwsc6 | |
| QuantizationInfo(0.005300046876072884f, 140), // dwsc7 | |
| QuantizationInfo(0.0049632852897048f, 127), // dwsc8 | |
| QuantizationInfo(0.007770895957946777f, 89), // dwsc9 | |
| QuantizationInfo(0.009658650495111942f, 99), // dwsc10 | |
| QuantizationInfo(0.005446993745863438f, 153), // dwsc11 | |
| QuantizationInfo(0.00817922968417406f, 130), // dwsc12 | |
| QuantizationInfo(0.018048152327537537f, 95) // dwsc13 | |
| }; | |
| graph << InputLayer(input_descriptor.set_quantization_info(in_quant_info), | |
| get_input_accessor(common_params, nullptr, false)) | |
| << ConvolutionLayer( | |
| 3U, 3U, 32U, | |
| get_weights_accessor(data_path, "Conv2d_0_weights.npy"), | |
| get_weights_accessor(data_path, "Conv2d_0_bias.npy"), | |
| PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), | |
| 1, conv_weights_quant_info.at(0), conv_out_quant_info.at(0)) | |
| .set_name("Conv2d_0") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv2d_0/Relu6"); | |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_1", 64U, PadStrideInfo(1U, 1U, 1U, 1U), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(0), point_weights_quant_info.at(0)); | |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_2", 128U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(1), | |
| point_weights_quant_info.at(1)); | |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_3", 128U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(2), | |
| point_weights_quant_info.at(2)); | |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_4", 256U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(3), | |
| point_weights_quant_info.at(3)); | |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_5", 256U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(4), | |
| point_weights_quant_info.at(4)); | |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_6", 512U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(5), | |
| point_weights_quant_info.at(5)); | |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_7", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(6), | |
| point_weights_quant_info.at(6)); | |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_8", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(7), | |
| point_weights_quant_info.at(7)); | |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_9", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(8), | |
| point_weights_quant_info.at(8)); | |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_10", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(9), | |
| point_weights_quant_info.at(9)); | |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_11", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(10), | |
| point_weights_quant_info.at(10)); | |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_12", 1024U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(11), | |
| point_weights_quant_info.at(11)); | |
| graph << get_dwsc_node_qasymm(data_path, "Conv2d_13", 1024U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(12), | |
| point_weights_quant_info.at(12)) | |
| << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool_1a") | |
| << ConvolutionLayer( | |
| 1U, 1U, 1001U, | |
| get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"), | |
| get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_bias.npy"), | |
| PadStrideInfo(1U, 1U, 0U, 0U), 1, conv_weights_quant_info.at(1), conv_out_quant_info.at(1)) | |
| .set_name("Logits/Conv2d_1c_1x1"); | |
| } | |
| ConcatLayer get_dwsc_node_float(const std::string &data_path, std::string &¶m_path, | |
| unsigned int conv_filt, | |
| PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info) | |
| { | |
| std::string total_path = param_path + "_"; | |
| SubStream sg(graph); | |
| sg << DepthwiseConvolutionLayer( | |
| 3U, 3U, | |
| get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| dwc_pad_stride_info) | |
| .set_name(total_path + "depthwise/depthwise") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"), | |
| get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"), | |
| get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(total_path + "depthwise/BatchNorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "depthwise/Relu6") | |
| << ConvolutionLayer( | |
| 1U, 1U, conv_filt, | |
| get_weights_accessor(data_path, total_path + "pointwise_weights.npy", DataLayout::NCHW), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| conv_pad_stride_info) | |
| .set_name(total_path + "pointwise/Conv2D") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"), | |
| get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"), | |
| get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(total_path + "pointwise/BatchNorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "pointwise/Relu6"); | |
| return ConcatLayer(std::move(sg)); | |
| } | |
| ConcatLayer get_dwsc_node_qasymm(const std::string &data_path, std::string &¶m_path, | |
| const unsigned int conv_filt, | |
| PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info, | |
| QuantizationInfo depth_weights_quant_info, QuantizationInfo point_weights_quant_info) | |
| { | |
| std::string total_path = param_path + "_"; | |
| SubStream sg(graph); | |
| sg << DepthwiseConvolutionLayer( | |
| 3U, 3U, | |
| get_weights_accessor(data_path, total_path + "depthwise_weights.npy"), | |
| get_weights_accessor(data_path, total_path + "depthwise_bias.npy"), | |
| dwc_pad_stride_info, 1, std::move(depth_weights_quant_info)) | |
| .set_name(total_path + "depthwise/depthwise") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(total_path + "depthwise/Relu6") | |
| << ConvolutionLayer( | |
| 1U, 1U, conv_filt, | |
| get_weights_accessor(data_path, total_path + "pointwise_weights.npy"), | |
| get_weights_accessor(data_path, total_path + "pointwise_bias.npy"), | |
| conv_pad_stride_info, 1, std::move(point_weights_quant_info)) | |
| .set_name(total_path + "pointwise/Conv2D") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(total_path + "pointwise/Relu6"); | |
| return ConcatLayer(std::move(sg)); | |
| } | |
| }; | |
| /** Main program for MobileNetV1 | |
| * | |
| * Model is based on: | |
| * https://arxiv.org/abs/1704.04861 | |
| * "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" | |
| * Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam | |
| * | |
| * Provenance: download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz | |
| * download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_160.tgz | |
| * | |
| * @note To list all the possible arguments execute the binary appended with the --help option | |
| * | |
| * @param[in] argc Number of arguments | |
| * @param[in] argv Arguments | |
| */ | |
| int main(int argc, char **argv) | |
| { | |
| return arm_compute::utils::run_example<GraphMobilenetExample>(argc, argv); | |
| } |