examples/graph_mobilenet.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to
  * deal in the Software without restriction, including without limitation the
  * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
  * sell copies of the Software, and to permit persons to whom the Software is
  * furnished to do so, subject to the following conditions:
  *
  * The above copyright notice and this permission notice shall be included in all
  * copies or substantial portions of the Software.
  *
  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
 #include "arm_compute/graph/Graph.h"
 #include "arm_compute/graph/Nodes.h"
 #include "support/ToolchainSupport.h"
 #include "utils/GraphUtils.h"
 #include "utils/Utils.h"

 #include <cstdlib>

 using namespace arm_compute::utils;
 using namespace arm_compute::graph;
 using namespace arm_compute::graph_utils;

 /** Example demonstrating how to implement MobileNet'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), [optional] Path to the weights folder, [optional] image, [optional] labels )
  */
 class GraphMobilenetExample : 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 */

         constexpr float mean = 0.f;   /* Mean value to subtract from the channels */
         constexpr float std  = 255.f; /* Standard deviation value to divide from the channels */

         // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
         TargetHint            target_hint      = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
         ConvolutionMethodHint convolution_hint = ConvolutionMethodHint::GEMM;

         // Set model to execute. 0 (MobileNetV1_1.0_224), 1 (MobileNetV1_0.75_160)
         int model_id = (argc > 2) ? std::strtol(argv[2], nullptr, 10) : 0;
         ARM_COMPUTE_ERROR_ON_MSG(model_id > 1, "Invalid model ID. Model must be 0 (MobileNetV1_1.0_224) or 1 (MobileNetV1_0.75_160)");
         float        depth_scale  = (model_id == 0) ? 1.f : 0.75;
         unsigned int spatial_size = (model_id == 0) ? 224 : 160;
         std::string  model_path   = (model_id == 0) ? "/cnn_data/mobilenet_v1_1_224_model/" : "/cnn_data/mobilenet_v1_075_160_model/";

         // Parse arguments
         if(argc < 2)
         {
             // Print help
             std::cout << "Usage: " << argv[0] << " [target] [model] [path_to_data] [image] [labels]\n\n";
             std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 2)
         {
             std::cout << "Usage: " << argv[0] << " " << argv[1] << " [model] [path_to_data] [image] [labels]\n\n";
             std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 3)
         {
             std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [path_to_data] [image] [labels]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 4)
         {
             data_path = argv[3];
             std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [image] [labels]\n\n";
             std::cout << "No image provided: using random values\n\n";
         }
         else if(argc == 5)
         {
             data_path = argv[3];
             image     = argv[4];
             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[3];
             image     = argv[4];
             label     = argv[5];
         }

         // Add model path to data path
         if(!data_path.empty())
         {
             data_path += model_path;
         }

         graph << target_hint
               << convolution_hint
               << Tensor(TensorInfo(TensorShape(spatial_size, spatial_size, 3U, 1U), 1, DataType::F32),
                         get_input_accessor(image,
                                            mean, mean, mean,
                                            std, std, std, false /* Do not convert to BGR */))
               << ConvolutionLayer(
                   3U, 3U, 32U * depth_scale,
                   get_weights_accessor(data_path, "Conv2d_0_weights.npy"),
                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                   PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))
               << 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)
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))

               << get_dwsc_node(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0))
               << get_dwsc_node(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
               << get_dwsc_node(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
               << get_dwsc_node(data_path, "Conv2d_4", 256 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
               << get_dwsc_node(data_path, "Conv2d_5", 256 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
               << get_dwsc_node(data_path, "Conv2d_6", 512 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
               << get_dwsc_node(data_path, "Conv2d_7", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
               << get_dwsc_node(data_path, "Conv2d_8", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
               << get_dwsc_node(data_path, "Conv2d_9", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
               << get_dwsc_node(data_path, "Conv2d_10", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
               << get_dwsc_node(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
               << get_dwsc_node(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
               << get_dwsc_node(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
               << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
               << ConvolutionLayer(
                   1U, 1U, 1001U,
                   get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"),
                   get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"),
                   PadStrideInfo(1, 1, 0, 0))
               << ReshapeLayer(TensorShape(1001U))
               << SoftmaxLayer()
               << Tensor(get_output_accessor(label, 5));
     }
     void do_run() override
     {
         // Run graph
         graph.run();
     }

 private:
     Graph graph{};

     BranchLayer get_dwsc_node(const std::string &data_path, std::string &&param_path,
                               unsigned int  conv_filt,
                               PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info)
     {
         std::string total_path = param_path + "_";
         SubGraph    sg;
         sg << DepthwiseConvolutionLayer(
                3U, 3U,
                get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy"),
                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                dwc_pad_stride_info,
                true)
            << 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)
            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
            << ConvolutionLayer(
                1U, 1U, conv_filt,
                get_weights_accessor(data_path, total_path + "pointwise_weights.npy"),
                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                conv_pad_stride_info)
            << 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)
            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));

         return BranchLayer(std::move(sg));
     }
 };

 /** Main program for MobileNetV1
  *
  * @param[in] argc Number of arguments
  * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL),
  *                             [optional] Model ID (0 = MobileNetV1_1.0_224, 1 = MobileNetV1_0.75_160),
  *                             [optional] Path to the weights folder,
  *                             [optional] image,
  *                             [optional] labels )
  */
 int main(int argc, char **argv)
 {
     return arm_compute::utils::run_example<GraphMobilenetExample>(argc, argv);
 }
	/*
	* Copyright (c) 2017-2018 ARM Limited.
	*
	* SPDX-License-Identifier: MIT
	*
	* Permission is hereby granted, free of charge, to any person obtaining a copy
	* of this software and associated documentation files (the "Software"), to
	* deal in the Software without restriction, including without limitation the
	* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
	* sell copies of the Software, and to permit persons to whom the Software is
	* furnished to do so, subject to the following conditions:
	*
	* The above copyright notice and this permission notice shall be included in all
	* copies or substantial portions of the Software.
	*
	* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
	* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
	* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
	* SOFTWARE.
	*/
	#include "arm_compute/graph/Graph.h"
	#include "arm_compute/graph/Nodes.h"
	#include "support/ToolchainSupport.h"
	#include "utils/GraphUtils.h"
	#include "utils/Utils.h"

	#include <cstdlib>

	using namespace arm_compute::utils;
	using namespace arm_compute::graph;
	using namespace arm_compute::graph_utils;

	/** Example demonstrating how to implement MobileNet'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), [optional] Path to the weights folder, [optional] image, [optional] labels )
	*/
	class GraphMobilenetExample : 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 */

	constexpr float mean = 0.f; /* Mean value to subtract from the channels */
	constexpr float std = 255.f; /* Standard deviation value to divide from the channels */

	// Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
	TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
	ConvolutionMethodHint convolution_hint = ConvolutionMethodHint::GEMM;

	// Set model to execute. 0 (MobileNetV1_1.0_224), 1 (MobileNetV1_0.75_160)
	int model_id = (argc > 2) ? std::strtol(argv[2], nullptr, 10) : 0;
	ARM_COMPUTE_ERROR_ON_MSG(model_id > 1, "Invalid model ID. Model must be 0 (MobileNetV1_1.0_224) or 1 (MobileNetV1_0.75_160)");
	float depth_scale = (model_id == 0) ? 1.f : 0.75;
	unsigned int spatial_size = (model_id == 0) ? 224 : 160;
	std::string model_path = (model_id == 0) ? "/cnn_data/mobilenet_v1_1_224_model/" : "/cnn_data/mobilenet_v1_075_160_model/";

	// Parse arguments
	if(argc < 2)
	{
	// Print help
	std::cout << "Usage: " << argv[0] << " [target] [model] [path_to_data] [image] [labels]\n\n";
	std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n";
	std::cout << "No data folder provided: using random values\n\n";
	}
	else if(argc == 2)
	{
	std::cout << "Usage: " << argv[0] << " " << argv[1] << " [model] [path_to_data] [image] [labels]\n\n";
	std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n";
	std::cout << "No data folder provided: using random values\n\n";
	}
	else if(argc == 3)
	{
	std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [path_to_data] [image] [labels]\n\n";
	std::cout << "No data folder provided: using random values\n\n";
	}
	else if(argc == 4)
	{
	data_path = argv[3];
	std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [image] [labels]\n\n";
	std::cout << "No image provided: using random values\n\n";
	}
	else if(argc == 5)
	{
	data_path = argv[3];
	image = argv[4];
	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[3];
	image = argv[4];
	label = argv[5];
	}

	// Add model path to data path
	if(!data_path.empty())
	{
	data_path += model_path;
	}

	graph << target_hint
	<< convolution_hint
	<< Tensor(TensorInfo(TensorShape(spatial_size, spatial_size, 3U, 1U), 1, DataType::F32),
	get_input_accessor(image,
	mean, mean, mean,
	std, std, std, false /* Do not convert to BGR */))
	<< ConvolutionLayer(
	3U, 3U, 32U * depth_scale,
	get_weights_accessor(data_path, "Conv2d_0_weights.npy"),
	std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
	PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))
	<< 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)
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))

	<< get_dwsc_node(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0))
	<< get_dwsc_node(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
	<< get_dwsc_node(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
	<< get_dwsc_node(data_path, "Conv2d_4", 256 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
	<< get_dwsc_node(data_path, "Conv2d_5", 256 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
	<< get_dwsc_node(data_path, "Conv2d_6", 512 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
	<< get_dwsc_node(data_path, "Conv2d_7", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
	<< get_dwsc_node(data_path, "Conv2d_8", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
	<< get_dwsc_node(data_path, "Conv2d_9", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
	<< get_dwsc_node(data_path, "Conv2d_10", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
	<< get_dwsc_node(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
	<< get_dwsc_node(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
	<< get_dwsc_node(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
	<< PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
	<< ConvolutionLayer(
	1U, 1U, 1001U,
	get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"),
	get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"),
	PadStrideInfo(1, 1, 0, 0))
	<< ReshapeLayer(TensorShape(1001U))
	<< SoftmaxLayer()
	<< Tensor(get_output_accessor(label, 5));
	}
	void do_run() override
	{
	// Run graph
	graph.run();
	}

	private:
	Graph graph{};

	BranchLayer get_dwsc_node(const std::string &data_path, std::string &&param_path,
	unsigned int conv_filt,
	PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info)
	{
	std::string total_path = param_path + "_";
	SubGraph sg;
	sg << DepthwiseConvolutionLayer(
	3U, 3U,
	get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy"),
	std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
	dwc_pad_stride_info,
	true)
	<< 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)
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
	<< ConvolutionLayer(
	1U, 1U, conv_filt,
	get_weights_accessor(data_path, total_path + "pointwise_weights.npy"),
	std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
	conv_pad_stride_info)
	<< 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)
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));

	return BranchLayer(std::move(sg));
	}
	};

	/** Main program for MobileNetV1
	*
	* @param[in] argc Number of arguments
	* @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL),
	* [optional] Model ID (0 = MobileNetV1_1.0_224, 1 = MobileNetV1_0.75_160),
	* [optional] Path to the weights folder,
	* [optional] image,
	* [optional] labels )
	*/
	int main(int argc, char **argv)
	{
	return arm_compute::utils::run_example<GraphMobilenetExample>(argc, argv);
	}