examples/graph_mobilenet_qasymm8.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.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::frontend;
 using namespace arm_compute::graph_utils;

 /** Example demonstrating how to implement QASYMM8 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, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] npy_input, [optional] labels )
  */
 class GraphMobileNetQASYMM8Example : public Example
 {
 public:
     //FIXME: Missing quantization info to the tensor descriptor (Giorgio is working on it)
 #if 0
     void do_setup(int argc, char **argv) override
     {
         std::string data_path; /* Path to the trainable data */
         std::string input;     /* Image data */
         std::string label;     /* Label data */

         // Quantization info taken from the AndroidNN QASYMM8 MobileNet example
         const QuantizationInfo in_quant_info  = QuantizationInfo(0.0078125f, 128);
         const QuantizationInfo mid_quant_info = QuantizationInfo(0.0784313753247f, 128);

         const std::vector<QuantizationInfo> conv_weights_quant_info =
         {
             QuantizationInfo(0.031778190285f, 156), // conv0
             QuantizationInfo(0.00604454148561f, 66) // conv14
         };

         const std::vector<QuantizationInfo> depth_weights_quant_info =
         {
             QuantizationInfo(0.254282623529f, 129),  // dwsc1
             QuantizationInfo(0.12828284502f, 172),   // dwsc2
             QuantizationInfo(0.265911251307f, 83),   // dwsc3
             QuantizationInfo(0.0985597148538f, 30),  // dwsc4
             QuantizationInfo(0.0631204470992f, 54),  // dwsc5
             QuantizationInfo(0.0137207424268f, 141), // dwsc6
             QuantizationInfo(0.0817828401923f, 125), // dwsc7
             QuantizationInfo(0.0393880493939f, 164), // dwsc8
             QuantizationInfo(0.211694166064f, 129),  // dwsc9
             QuantizationInfo(0.158015936613f, 103),  // dwsc10
             QuantizationInfo(0.0182712618262f, 137), // dwsc11
             QuantizationInfo(0.0127998134121f, 134), // dwsc12
             QuantizationInfo(0.299285322428f, 161)   // dwsc13
         };

         const std::vector<QuantizationInfo> point_weights_quant_info =
         {
             QuantizationInfo(0.0425766184926f, 129),  // dwsc1
             QuantizationInfo(0.0250773020089f, 94),   // dwsc2
             QuantizationInfo(0.015851572156f, 93),    // dwsc3
             QuantizationInfo(0.0167811904103f, 98),   // dwsc4
             QuantizationInfo(0.00951790809631f, 135), // dwsc5
             QuantizationInfo(0.00999817531556f, 128), // dwsc6
             QuantizationInfo(0.00590536883101f, 126), // dwsc7
             QuantizationInfo(0.00576109671965f, 133), // dwsc8
             QuantizationInfo(0.00830461271107f, 142), // dwsc9
             QuantizationInfo(0.0152327232063f, 72),   // dwsc10
             QuantizationInfo(0.00741417845711f, 125), // dwsc11
             QuantizationInfo(0.0135628981516f, 142),  // dwsc12
             QuantizationInfo(0.0338749065995f, 140)   // dwsc13
         };

         // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
         const int  int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
         TargetHint target_hint     = set_target_hint(int_target_hint);

         // Parse arguments
         if(argc < 2)
         {
             // Print help
             std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_input] [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] [npy_input] [labels]\n\n";
             std::cout << "No input provided: using random values\n\n";
         }
         else if(argc == 4)
         {
             data_path = argv[2];
             input     = 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];
             input     = argv[3];
             label     = argv[4];
         }

         graph << target_hint
               << arm_compute::graph::Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::QASYMM8, in_quant_info),
                                             get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/" + input))
               << ConvolutionLayer(
                   3U, 3U, 32U,
                   get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Conv2d_0_weights.npy"),
                   get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Conv2d_0_bias.npy"),
                   PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR),
                   1, WeightsInfo(),
                   conv_weights_quant_info.at(0),
                   mid_quant_info)
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f))
               << get_dwsc_node(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))
               << get_dwsc_node(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))
               << get_dwsc_node(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))
               << get_dwsc_node(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))
               << get_dwsc_node(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))
               << get_dwsc_node(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))
               << get_dwsc_node(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))
               << get_dwsc_node(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))
               << get_dwsc_node(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))
               << get_dwsc_node(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))
               << get_dwsc_node(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))
               << get_dwsc_node(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))
               << get_dwsc_node(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))
               << ConvolutionLayer(
                   1U, 1U, 1001U,
                   get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Logits_Conv2d_1c_1x1_weights.npy"),
                   get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Logits_Conv2d_1c_1x1_bias.npy"),
                   PadStrideInfo(1U, 1U, 0U, 0U), 1, WeightsInfo(), conv_weights_quant_info.at(1))
               << ReshapeLayer(TensorShape(1001U))
               << SoftmaxLayer()
               << arm_compute::graph::Tensor(get_output_accessor(label, 5));

         // In order to enable the OpenCL tuner, graph_init() has to be called only when all nodes have been instantiated
         graph.graph_init(int_target_hint == 2);
     }
     void do_run() override
     {
         // Run graph
         graph.run();
     }

 private:
     Graph graph{};

     /** This function produces a depthwise separable convolution node (i.e. depthwise + pointwise layers) with ReLU6 activation after each layer.
      *
      * @param[in] data_path                Path to trainable data folder
      * @param[in] param_path               Prefix of specific set of weights/biases data
      * @param[in] conv_filt                Filters depths for pointwise convolution
      * @param[in] dwc_pad_stride_info      PadStrideInfo for depthwise convolution
      * @param[in] conv_pad_stride_info     PadStrideInfo for pointwise convolution
      * @param[in] depth_weights_quant_info QuantizationInfo for depthwise convolution's weights
      * @param[in] point_weights_quant_info QuantizationInfo for pointwise convolution's weights
      *
      * @return The complete dwsc node
      */
     BranchLayer get_dwsc_node(const std::string &data_path, std::string &&param_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 = "/cnn_data/mobilenet_qasymm8_model/" + param_path + "_";
         SubGraph    sg;

         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,
                true,
                depth_weights_quant_info)
            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f))
            << 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, WeightsInfo(),
                point_weights_quant_info)
            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f));

         return BranchLayer(std::move(sg));
     }
 #endif /* if 0 */
     Stream graph { 0, "MobileNetV1_QASYMM8" };
 };
 /** Main program for MobileNetQASYMM8
  *
  * @param[in] argc Number of arguments
  * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] npy_input, [optional] labels )
  */
 int main(int argc, char **argv)
 {
     return arm_compute::utils::run_example<GraphMobileNetQASYMM8Example>(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.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::frontend;
	using namespace arm_compute::graph_utils;

	/** Example demonstrating how to implement QASYMM8 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, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] npy_input, [optional] labels )
	*/
	class GraphMobileNetQASYMM8Example : public Example
	{
	public:
	//FIXME: Missing quantization info to the tensor descriptor (Giorgio is working on it)
	#if 0
	void do_setup(int argc, char **argv) override
	{
	std::string data_path; /* Path to the trainable data */
	std::string input; /* Image data */
	std::string label; /* Label data */

	// Quantization info taken from the AndroidNN QASYMM8 MobileNet example
	const QuantizationInfo in_quant_info = QuantizationInfo(0.0078125f, 128);
	const QuantizationInfo mid_quant_info = QuantizationInfo(0.0784313753247f, 128);

	const std::vector<QuantizationInfo> conv_weights_quant_info =
	{
	QuantizationInfo(0.031778190285f, 156), // conv0
	QuantizationInfo(0.00604454148561f, 66) // conv14
	};

	const std::vector<QuantizationInfo> depth_weights_quant_info =
	{
	QuantizationInfo(0.254282623529f, 129), // dwsc1
	QuantizationInfo(0.12828284502f, 172), // dwsc2
	QuantizationInfo(0.265911251307f, 83), // dwsc3
	QuantizationInfo(0.0985597148538f, 30), // dwsc4
	QuantizationInfo(0.0631204470992f, 54), // dwsc5
	QuantizationInfo(0.0137207424268f, 141), // dwsc6
	QuantizationInfo(0.0817828401923f, 125), // dwsc7
	QuantizationInfo(0.0393880493939f, 164), // dwsc8
	QuantizationInfo(0.211694166064f, 129), // dwsc9
	QuantizationInfo(0.158015936613f, 103), // dwsc10
	QuantizationInfo(0.0182712618262f, 137), // dwsc11
	QuantizationInfo(0.0127998134121f, 134), // dwsc12
	QuantizationInfo(0.299285322428f, 161) // dwsc13
	};

	const std::vector<QuantizationInfo> point_weights_quant_info =
	{
	QuantizationInfo(0.0425766184926f, 129), // dwsc1
	QuantizationInfo(0.0250773020089f, 94), // dwsc2
	QuantizationInfo(0.015851572156f, 93), // dwsc3
	QuantizationInfo(0.0167811904103f, 98), // dwsc4
	QuantizationInfo(0.00951790809631f, 135), // dwsc5
	QuantizationInfo(0.00999817531556f, 128), // dwsc6
	QuantizationInfo(0.00590536883101f, 126), // dwsc7
	QuantizationInfo(0.00576109671965f, 133), // dwsc8
	QuantizationInfo(0.00830461271107f, 142), // dwsc9
	QuantizationInfo(0.0152327232063f, 72), // dwsc10
	QuantizationInfo(0.00741417845711f, 125), // dwsc11
	QuantizationInfo(0.0135628981516f, 142), // dwsc12
	QuantizationInfo(0.0338749065995f, 140) // dwsc13
	};

	// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
	const int int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
	TargetHint target_hint = set_target_hint(int_target_hint);

	// Parse arguments
	if(argc < 2)
	{
	// Print help
	std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_input] [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] [npy_input] [labels]\n\n";
	std::cout << "No input provided: using random values\n\n";
	}
	else if(argc == 4)
	{
	data_path = argv[2];
	input = 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];
	input = argv[3];
	label = argv[4];
	}

	graph << target_hint
	<< arm_compute::graph::Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::QASYMM8, in_quant_info),
	get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/" + input))
	<< ConvolutionLayer(
	3U, 3U, 32U,
	get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Conv2d_0_weights.npy"),
	get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Conv2d_0_bias.npy"),
	PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR),
	1, WeightsInfo(),
	conv_weights_quant_info.at(0),
	mid_quant_info)
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f))
	<< get_dwsc_node(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))
	<< get_dwsc_node(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))
	<< get_dwsc_node(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))
	<< get_dwsc_node(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))
	<< get_dwsc_node(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))
	<< get_dwsc_node(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))
	<< get_dwsc_node(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))
	<< get_dwsc_node(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))
	<< get_dwsc_node(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))
	<< get_dwsc_node(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))
	<< get_dwsc_node(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))
	<< get_dwsc_node(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))
	<< get_dwsc_node(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))
	<< ConvolutionLayer(
	1U, 1U, 1001U,
	get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Logits_Conv2d_1c_1x1_weights.npy"),
	get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Logits_Conv2d_1c_1x1_bias.npy"),
	PadStrideInfo(1U, 1U, 0U, 0U), 1, WeightsInfo(), conv_weights_quant_info.at(1))
	<< ReshapeLayer(TensorShape(1001U))
	<< SoftmaxLayer()
	<< arm_compute::graph::Tensor(get_output_accessor(label, 5));

	// In order to enable the OpenCL tuner, graph_init() has to be called only when all nodes have been instantiated
	graph.graph_init(int_target_hint == 2);
	}
	void do_run() override
	{
	// Run graph
	graph.run();
	}

	private:
	Graph graph{};

	/** This function produces a depthwise separable convolution node (i.e. depthwise + pointwise layers) with ReLU6 activation after each layer.
	*
	* @param[in] data_path Path to trainable data folder
	* @param[in] param_path Prefix of specific set of weights/biases data
	* @param[in] conv_filt Filters depths for pointwise convolution
	* @param[in] dwc_pad_stride_info PadStrideInfo for depthwise convolution
	* @param[in] conv_pad_stride_info PadStrideInfo for pointwise convolution
	* @param[in] depth_weights_quant_info QuantizationInfo for depthwise convolution's weights
	* @param[in] point_weights_quant_info QuantizationInfo for pointwise convolution's weights
	*
	* @return The complete dwsc node
	*/
	BranchLayer get_dwsc_node(const std::string &data_path, std::string &&param_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 = "/cnn_data/mobilenet_qasymm8_model/" + param_path + "_";
	SubGraph sg;

	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,
	true,
	depth_weights_quant_info)
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f))
	<< 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, WeightsInfo(),
	point_weights_quant_info)
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f));

	return BranchLayer(std::move(sg));
	}
	#endif /* if 0 */
	Stream graph { 0, "MobileNetV1_QASYMM8" };
	};
	/** Main program for MobileNetQASYMM8
	*
	* @param[in] argc Number of arguments
	* @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] npy_input, [optional] labels )
	*/
	int main(int argc, char **argv)
	{
	return arm_compute::utils::run_example<GraphMobileNetQASYMM8Example>(argc, argv);
	}