From 4d5e7db268a4f816e24449e8ad011e35890f0c7e Mon Sep 17 00:00:00 2001
From: Qin Su <qsu@ti.com>
Date: Fri, 22 Feb 2019 13:39:09 -0500
Subject: [PATCH] armnn mobilenet test example
 
Upstream-Status: Inappropriate [TI only test code]
Signed-off-by: Qin Su <qsu@ti.com>
---
 tests/ArmnnExamples/ArmnnExamples.cpp | 654 ++++++++++++++++++++++++++++++++++
 1 file changed, 654 insertions(+)
 create mode 100644 tests/ArmnnExamples/ArmnnExamples.cpp
 
diff --git a/tests/ArmnnExamples/ArmnnExamples.cpp b/tests/ArmnnExamples/ArmnnExamples.cpp
new file mode 100644
index 0000000..53a11cc
--- /dev/null
+++ b/tests/ArmnnExamples/ArmnnExamples.cpp
@@ -0,0 +1,654 @@
+/******************************************************************************
+ * Copyright (c) 2018, Texas Instruments Incorporated - http://www.ti.com/
+ *   All rights reserved.
+ *
+ *   Redistribution and use in source and binary forms, with or without
+ *   modification, are permitted provided that the following conditions are met:
+ *       * Redistributions of source code must retain the above copyright
+ *         notice, this list of conditions and the following disclaimer.
+ *       * Redistributions in binary form must reproduce the above copyright
+ *         notice, this list of conditions and the following disclaimer in the
+ *         documentation and/or other materials provided with the distribution.
+ *       * Neither the name of Texas Instruments Incorporated nor the
+ *         names of its contributors may be used to endorse or promote products
+ *         derived from this software without specific prior written permission.
+ *
+ *   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ *   AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ *   IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ *   ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+ *   LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+ *   CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+ *   SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+ *   INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+ *   CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ *   ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+ *   THE POSSIBILITY OF SUCH DAMAGE.
+ *****************************************************************************///
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+#include <armnn/ArmNN.hpp>
+
+#include <utility>
+#include <armnn/TypesUtils.hpp>
+
+#if defined(ARMNN_CAFFE_PARSER)
+#include "armnnCaffeParser/ICaffeParser.hpp"
+#endif
+#if defined(ARMNN_TF_PARSER)
+#include "armnnTfParser/ITfParser.hpp"
+#endif
+#if defined(ARMNN_TF_LITE_PARSER)
+#include "armnnTfLiteParser/ITfLiteParser.hpp"
+#endif
+#if defined(ARMNN_ONNX_PARSER)
+#include "armnnOnnxParser/IOnnxParser.hpp"
+#endif
+#include "CsvReader.hpp"
+#include "../InferenceTest.hpp"
+#include <Logging.hpp>
+#include <Profiling.hpp>
+
+#include <boost/algorithm/string/trim.hpp>
+#include <boost/algorithm/string/split.hpp>
+#include <boost/algorithm/string/classification.hpp>
+#include <boost/program_options.hpp>
+
+#include <iostream>
+#include <fstream>
+#include <functional>
+#include <future>
+#include <algorithm>
+#include <iterator>
+#include<vector>
+
+#include <signal.h>
+#include "opencv2/core.hpp"
+#include "opencv2/imgproc.hpp"
+#include "opencv2/highgui.hpp"
+#include "opencv2/videoio.hpp"
+#include <time.h>
+
+using namespace cv;
+
+#define INPUT_IMAGE  0
+#define INPUT_VIDEO  1
+#define INPUT_CAMERA 2
+
+Mat test_image;
+Rect rectCrop;
+
+time_point<high_resolution_clock> predictStart;
+time_point<high_resolution_clock> predictEnd;
+
+void imagenetCallBackFunc(int event, int x, int y, int flags, void* userdata)
+{
+  if  ( event == EVENT_RBUTTONDOWN )
+  {
+    std::cout << "Right button of the mouse is clicked - position (" << x << ", " << y << ")" << " ... prepare to exit!" << std::endl;
+    exit(0);
+  }
+}
+
+inline float Lerpfloat(float a, float b, float w)
+{
+  return w * b + (1.f - w) * a;
+}
+
+// Load a single image
+struct ImageData
+{
+  unsigned int m_width;
+  unsigned int m_height;
+  unsigned int m_chnum;
+  unsigned int m_size;
+  std::vector<uint8_t> m_image;
+};
+// Load a single image
+std::unique_ptr<ImageData> loadImageData(std::string image_path, VideoCapture &cap, cv::Mat img, int input_type)
+{
+  //cv::Mat img;
+  if (input_type == INPUT_IMAGE)
+  {
+    /* use OpenCV to get the image */
+    img = cv::imread(image_path, CV_LOAD_IMAGE_COLOR);
+  }
+  cv::cvtColor(img, img, CV_BGR2RGB); //convert image format from BGR(openCV format) to RGB (armnn required format).
+
+  // store image and label in output Image
+  std::unique_ptr<ImageData> ret(new ImageData);
+  ret->m_width = static_cast<unsigned int>(img.cols);
+  ret->m_height = static_cast<unsigned int>(img.rows);
+  ret->m_chnum = static_cast<unsigned int>(img.channels());
+  ret->m_size = static_cast<unsigned int>(img.cols*img.rows*img.channels());
+  ret->m_image.resize(ret->m_size);
+
+  for (unsigned int i = 0; i < ret->m_size; i++)
+  {
+    ret->m_image[i] = static_cast<uint8_t>(img.data[i]);
+  }
+  return ret;
+}
+// to resize input tensor size
+std::vector<float> ResizeBilinear(std::vector<uint8_t> input,
+                                    const unsigned int inWidth,
+                                    const unsigned int inHeight,
+                                    const unsigned int inChnum,
+                                    const unsigned int outputWidth,
+                                    const unsigned int outputHeight)
+{
+  std::vector<float> out;
+  out.resize(outputWidth * outputHeight * 3);
+
+  // We follow the definition of TensorFlow and AndroidNN: the top-left corner of a texel in the output
+  // image is projected into the input image to figure out the interpolants and weights. Note that this
+  // will yield different results than if projecting the centre of output texels.
+
+  const unsigned int inputWidth = inWidth;
+  const unsigned int inputHeight = inHeight;
+
+  // How much to scale pixel coordinates in the output image to get the corresponding pixel coordinates
+  // in the input image.
+  const float scaleY = boost::numeric_cast<float>(inputHeight) / boost::numeric_cast<float>(outputHeight);
+  const float scaleX = boost::numeric_cast<float>(inputWidth) / boost::numeric_cast<float>(outputWidth);
+
+  uint8_t rgb_x0y0[3];
+  uint8_t rgb_x1y0[3];
+  uint8_t rgb_x0y1[3];
+  uint8_t rgb_x1y1[3];
+  unsigned int pixelOffset00, pixelOffset10, pixelOffset01, pixelOffset11;
+  for (unsigned int y = 0; y < outputHeight; ++y)
+  {
+    // Corresponding real-valued height coordinate in input image.
+    const float iy = boost::numeric_cast<float>(y) * scaleY;
+    // Discrete height coordinate of top-left texel (in the 2x2 texel area used for interpolation).
+    const float fiy = floorf(iy);
+    const unsigned int y0 = boost::numeric_cast<unsigned int>(fiy);
+
+    // Interpolation weight (range [0,1])
+    const float yw = iy - fiy;
+
+    for (unsigned int x = 0; x < outputWidth; ++x)
+    {
+      // Real-valued and discrete width coordinates in input image.
+      const float ix = boost::numeric_cast<float>(x) * scaleX;
+      const float fix = floorf(ix);
+      const unsigned int x0 = boost::numeric_cast<unsigned int>(fix);
+
+      // Interpolation weight (range [0,1]).
+      const float xw = ix - fix;
+
+      // Discrete width/height coordinates of texels below and to the right of (x0, y0).
+      const unsigned int x1 = std::min(x0 + 1, inputWidth - 1u);
+      const unsigned int y1 = std::min(y0 + 1, inputHeight - 1u);
+
+      pixelOffset00 = x0 * inChnum + y0 * inputWidth * inChnum;
+      pixelOffset10 = x1 * inChnum + y0 * inputWidth * inChnum;
+      pixelOffset01 = x0 * inChnum + y1 * inputWidth * inChnum;
+      pixelOffset11 = x1 * inChnum + y1 * inputWidth * inChnum;
+      for (unsigned int c = 0; c < 3; ++c)
+      {
+        rgb_x0y0[c] = input[pixelOffset00+c];
+        rgb_x1y0[c] = input[pixelOffset10+c];
+        rgb_x0y1[c] = input[pixelOffset01+c];
+        rgb_x1y1[c] = input[pixelOffset11+c];
+      }
+
+      for (unsigned c=0; c<3; ++c)
+      {
+        const float ly0 = Lerpfloat(float(rgb_x0y0[c]), float(rgb_x1y0[c]), xw);
+        const float ly1 = Lerpfloat(float(rgb_x0y1[c]), float(rgb_x1y1[c]), xw);
+        const float l = Lerpfloat(ly0, ly1, yw);
+        out[(3*((y*outputWidth)+x)) + c] = static_cast<float>(l)/255.0f;
+      }
+    }
+  }
+  return out;
+}
+
+namespace
+{
+
+  // Configure boost::program_options for command-line parsing and validation.
+  namespace po = boost::program_options;
+
+  template<typename T, typename TParseElementFunc>
+  std::vector<T> ParseArrayImpl(std::istream& stream, TParseElementFunc parseElementFunc)
+  {
+    std::vector<T> result;
+    // Processes line-by-line.
+    std::string line;
+    while (std::getline(stream, line))
+    {
+      std::vector<std::string> tokens;
+      try
+      {
+        // Coverity fix: boost::split() may throw an exception of type boost::bad_function_call.
+        boost::split(tokens, line, boost::algorithm::is_any_of("\t ,;:"), boost::token_compress_on);
+      }
+      catch (const std::exception& e)
+      {
+        BOOST_LOG_TRIVIAL(error) << "An error occurred when splitting tokens: " << e.what();
+        continue;
+      }
+      for (const std::string& token : tokens)
+      {
+        if (!token.empty())
+        {
+          try
+          {
+            result.push_back(parseElementFunc(token));
+          }
+          catch (const std::exception&)
+          {
+            BOOST_LOG_TRIVIAL(error) << "'" << token << "' is not a valid number. It has been ignored.";
+          }
+        }
+      }
+    }
+
+    return result;
+  }
+
+  template<typename T>
+  std::vector<T> ParseArray(std::istream& stream);
+  template<>
+  std::vector<unsigned int> ParseArray(std::istream& stream)
+  {
+    return ParseArrayImpl<unsigned int>(stream,
+      [](const std::string& s) { return boost::numeric_cast<unsigned int>(std::stoi(s)); });
+  }
+  void RemoveDuplicateDevices(std::vector<armnn::BackendId>& computeDevices)
+  {
+    // Mark the duplicate devices as 'Undefined'.
+    for (auto i = computeDevices.begin(); i != computeDevices.end(); ++i)
+    {
+      for (auto j = std::next(i); j != computeDevices.end(); ++j)
+      {
+        if (*j == *i)
+        {
+          *j = armnn::Compute::Undefined;
+        }
+      }
+    }
+
+    // Remove 'Undefined' devices.
+    computeDevices.erase(std::remove(computeDevices.begin(), computeDevices.end(), armnn::Compute::Undefined),
+    computeDevices.end());
+  }
+} // namespace
+
+template<typename TParser, typename TDataType>
+int MainImpl(const char* modelPath,
+             bool isModelBinary,
+             const std::vector<armnn::BackendId>& computeDevices,
+             const char* inputName,
+             const armnn::TensorShape* inputTensorShape,
+             const char* inputTensorDataFilePath,
+             const char* outputName,
+             bool enableProfiling,
+             const size_t number_frame,
+             const std::shared_ptr<armnn::IRuntime>& runtime = nullptr)
+{
+  // Loads input tensor.
+  std::vector<uint8_t> input;
+  std::vector<float> input_resized;
+  using TContainer = boost::variant<std::vector<float>, std::vector<int>, std::vector<unsigned char>>;
+
+  try
+  {
+    // Creates an InferenceModel, which will parse the model and load it into an IRuntime.
+    typename InferenceModel<TParser, TDataType>::Params params;
+    //const armnn::TensorShape inputTensorShape({ 1, 224, 224 3});
+
+    params.m_ModelPath = modelPath;
+    params.m_IsModelBinary = isModelBinary;
+    params.m_ComputeDevices = computeDevices;
+    params.m_InputBindings = { inputName };
+    params.m_InputShapes = { *inputTensorShape };
+    params.m_OutputBindings = { outputName };
+    //params.m_EnableProfiling = enableProfiling;
+    params.m_SubgraphId = 0;
+    InferenceModel<TParser, TDataType> model(params, enableProfiling, runtime);
+
+    VideoCapture cap;
+    int input_type = INPUT_IMAGE;
+    std::string filename = inputTensorDataFilePath;
+
+    size_t i = filename.rfind("camera_live_input", filename.length());
+    if (i != string::npos)
+    {
+      cap = VideoCapture(1);
+      namedWindow("ARMNN MobileNet Example", WINDOW_AUTOSIZE | CV_GUI_NORMAL);
+      input_type = INPUT_CAMERA; //camera input
+    }
+    else if((filename.substr(filename.find_last_of(".") + 1) == "mp4") ||
+            (filename.substr(filename.find_last_of(".") + 1) == "mov") ||
+            (filename.substr(filename.find_last_of(".") + 1) == "avi") )
+    {
+      cap = VideoCapture(inputTensorDataFilePath);
+      if (! cap.isOpened())
+      {
+        std::cout << "Cannot open video input: " << inputTensorDataFilePath << std::endl;
+        return (-1);
+      }
+
+      namedWindow("ARMNN MobileNet Example", WINDOW_AUTOSIZE | CV_GUI_NORMAL);
+      input_type = INPUT_VIDEO; //video clip input
+    }
+    if (input_type != INPUT_IMAGE)
+    {
+      //set the callback function for any mouse event. Used for right click mouse to exit the program.
+      setMouseCallback("ARMNN MobileNet Example", imagenetCallBackFunc, NULL);
+    }
+
+    for (unsigned int i=0; i < number_frame; i++)
+    {
+      if (input_type != INPUT_IMAGE)
+      {
+        cap.grab();
+        cap.retrieve(test_image);
+      }
+      std::unique_ptr<ImageData> inputData = loadImageData(inputTensorDataFilePath, cap, test_image, input_type);
+      input.resize(inputData->m_size);
+
+      input = std::move(inputData->m_image);
+      input_resized = ResizeBilinear(input, inputData->m_width, inputData->m_height, inputData->m_chnum, 224, 224);
+	
+      // Set up input data container
+      std::vector<TContainer> inputDataContainer(1, std::move(input_resized));
+      	
+      // Set up output data container
+	  std::vector<TContainer> outputDataContainers;
+      outputDataContainers.push_back(std::vector<float>(model.GetOutputSize()));
+ 
+      //profile start
+      predictStart = high_resolution_clock::now();
+      // Execute model
+      model.Run(inputDataContainer, outputDataContainers);
+      //profile end
+      predictEnd = high_resolution_clock::now();
+
+      double timeTakenS = duration<double>(predictEnd - predictStart).count();
+      double preformance_ret = static_cast<double>(1.0/timeTakenS);
+
+      //retrieve output
+      std::vector<float>& outputData = (boost::get<std::vector<float>>(outputDataContainers[0]));
+	  //output TOP predictions
+      std::string predict_target_name;
+      // find the out with the highest confidence
+      int label = static_cast<int>(std::distance(outputData.begin(), std::max_element(outputData.begin(), outputData.end())));
+      std::fstream file("/usr/share/arm/armnn/models/labels.txt");
+      //std::string predict_target_name;
+      for (int i=0; i <= label; i++)
+      {
+        std::getline(file, predict_target_name);
+      }
+      //get the probability of the top prediction
+	  float prob = 100*outputData.data()[label];
+	  //clean the top one so as to find the second top prediction
+      outputData.data()[label] = 0;
+      std::cout << "Top(1) prediction is " << predict_target_name << " with confidence: " << prob << "%" << std::endl;
+	  //output next TOP 4 predictions
+      for (int ii=1; ii<5; ii++)
+      {
+        std::string predict_target_name_n;
+        // find the out with the highest confidence
+        int label = static_cast<int>(std::distance(outputData.begin(), std::max_element(outputData.begin(), outputData.end())));
+        std::fstream file("/usr/share/arm/armnn/models/labels.txt");
+        //std::string predict_target_name;
+        for (int i=0; i <= label; i++)
+        {
+          std::getline(file, predict_target_name_n);
+        }
+        //get the probability of the prediction
+	    float prob = 100*outputData.data()[label];
+		//clean the top one so as to find the second top prediction
+        outputData.data()[label] = 0;
+
+        std::cout << "Top(" << (ii+1) << ") prediction is " << predict_target_name_n << " with confidence:  " << prob << "%" << std::endl;
+      }
+      std::cout << "Performance (FPS): " << preformance_ret << std::endl;
+
+      if (input_type != INPUT_IMAGE)
+      {
+        //convert image format back to BGR for OpenCV imshow from RGB format required by armnn.
+        cv::cvtColor(test_image, test_image, CV_RGB2BGR);
+        // output identified object name on top of input image
+        cv::putText(test_image, predict_target_name,
+                    cv::Point(rectCrop.x + 5,rectCrop.y + 20), // Coordinates
+                    cv::FONT_HERSHEY_COMPLEX_SMALL, // Font
+                    1.0, // Scale. 2.0 = 2x bigger
+                    cv::Scalar(0,0,255), // Color
+                    1, // Thickness
+                    8); // Line type
+
+        // output preformance in FPS on top of input image
+        std::string preformance_ret_string = "Performance (FPS): " + boost::lexical_cast<std::string>(preformance_ret);				
+        cv::putText(test_image, preformance_ret_string,
+                    cv::Point(rectCrop.x + 5,rectCrop.y + 40), // Coordinates
+                    cv::FONT_HERSHEY_COMPLEX_SMALL, // Font
+                    1.0, // Scale. 2.0 = 2x bigger
+                    cv::Scalar(0,0,255), // Color
+                    1, // Thickness
+                    8); // Line type
+
+        cv::imshow("ARMNN MobileNet Example", test_image);
+        waitKey(2);
+      }
+    }
+  }
+  catch (armnn::Exception const& e)
+  {
+    BOOST_LOG_TRIVIAL(fatal) << "Armnn Error: " << e.what();
+    return EXIT_FAILURE;
+  }
+  return EXIT_SUCCESS;
+}
+
+// This will run a test
+int RunTest(const std::string& modelFormat,
+            const std::string& inputTensorShapeStr,
+            const vector<armnn::BackendId>& computeDevice,
+            const std::string& modelPath,
+            const std::string& inputName,
+            const std::string& inputTensorDataFilePath,
+            const std::string& outputName,
+            bool enableProfiling,
+            const size_t subgraphId,
+            const std::shared_ptr<armnn::IRuntime>& runtime = nullptr)
+{
+  // Parse model binary flag from the model-format string we got from the command-line
+  bool isModelBinary;
+  if (modelFormat.find("bin") != std::string::npos)
+  {
+    isModelBinary = true;
+  }
+  else if (modelFormat.find("txt") != std::string::npos || modelFormat.find("text") != std::string::npos)
+  {
+    isModelBinary = false;
+  }
+  else
+  {
+    BOOST_LOG_TRIVIAL(fatal) << "Unknown model format: '" << modelFormat << "'. Please include 'binary' or 'text'";
+    return EXIT_FAILURE;
+  }
+
+  // Parse input tensor shape from the string we got from the command-line.
+  std::unique_ptr<armnn::TensorShape> inputTensorShape;
+  if (!inputTensorShapeStr.empty())
+  {
+    std::stringstream ss(inputTensorShapeStr);
+    std::vector<unsigned int> dims = ParseArray<unsigned int>(ss);
+    try
+    {
+      // Coverity fix: An exception of type armnn::InvalidArgumentException is thrown and never caught.
+      inputTensorShape = std::make_unique<armnn::TensorShape>(dims.size(), dims.data());
+    }
+    catch (const armnn::InvalidArgumentException& e)
+    {
+      BOOST_LOG_TRIVIAL(fatal) << "Cannot create tensor shape: " << e.what();
+      return EXIT_FAILURE;
+    }
+  }
+  // Forward to implementation based on the parser type
+  if (modelFormat.find("caffe") != std::string::npos)
+  {
+#if defined(ARMNN_CAFFE_PARSER)
+    return MainImpl<armnnCaffeParser::ICaffeParser, float>(modelPath.c_str(), isModelBinary, computeDevice,
+                                                           inputName.c_str(), inputTensorShape.get(),
+                                                           inputTensorDataFilePath.c_str(), outputName.c_str(),
+                                                           enableProfiling, subgraphId, runtime);
+#else
+    BOOST_LOG_TRIVIAL(fatal) << "Not built with Caffe parser support.";
+    return EXIT_FAILURE;
+#endif
+  }
+  else if (modelFormat.find("onnx") != std::string::npos)
+  {
+#if defined(ARMNN_ONNX_PARSER)
+    return MainImpl<armnnOnnxParser::IOnnxParser, float>(modelPath.c_str(), isModelBinary, computeDevice,
+                                                         inputName.c_str(), inputTensorShape.get(),
+                                                         inputTensorDataFilePath.c_str(), outputName.c_str(),
+                                                         enableProfiling, subgraphId, runtime);
+#else
+    BOOST_LOG_TRIVIAL(fatal) << "Not built with Onnx parser support.";
+    return EXIT_FAILURE;
+#endif
+  }
+  else if (modelFormat.find("tensorflow") != std::string::npos)
+  {
+#if defined(ARMNN_TF_PARSER)
+    return MainImpl<armnnTfParser::ITfParser, float>(modelPath.c_str(), isModelBinary, computeDevice,
+                                                     inputName.c_str(), inputTensorShape.get(),
+                                                     inputTensorDataFilePath.c_str(), outputName.c_str(),
+                                                     enableProfiling, subgraphId, runtime);
+#else
+    BOOST_LOG_TRIVIAL(fatal) << "Not built with Tensorflow parser support.";
+    return EXIT_FAILURE;
+#endif
+  }
+  else if(modelFormat.find("tflite") != std::string::npos)
+  {
+#if defined(ARMNN_TF_LITE_PARSER)
+    if (! isModelBinary)
+    {
+      BOOST_LOG_TRIVIAL(fatal) << "Unknown model format: '" << modelFormat << "'. Only 'binary' format supported \
+             for tflite files";
+      return EXIT_FAILURE;
+    }
+    return MainImpl<armnnTfLiteParser::ITfLiteParser, float>(modelPath.c_str(), isModelBinary, computeDevice,
+                                                             inputName.c_str(), inputTensorShape.get(),
+                                                             inputTensorDataFilePath.c_str(), outputName.c_str(),
+                                                             enableProfiling, subgraphId, runtime);
+#else
+    BOOST_LOG_TRIVIAL(fatal) << "Unknown model format: '" << modelFormat <<
+            "'. Please include 'caffe', 'tensorflow', 'tflite' or 'onnx'";
+    return EXIT_FAILURE;
+#endif
+  }
+  else
+  {
+    BOOST_LOG_TRIVIAL(fatal) << "Unknown model format: '" << modelFormat <<
+                                 "'. Please include 'caffe', 'tensorflow', 'tflite' or 'onnx'";
+    return EXIT_FAILURE;
+  }
+}
+
+int main(int argc, const char* argv[])
+{
+    // Configures logging for both the ARMNN library and this test program.
+#ifdef NDEBUG
+  armnn::LogSeverity level = armnn::LogSeverity::Info;
+#else
+  armnn::LogSeverity level = armnn::LogSeverity::Debug;
+#endif
+  armnn::ConfigureLogging(true, true, level);
+  armnnUtils::ConfigureLogging(boost::log::core::get().get(), true, true, level);
+
+  std::string testCasesFile;
+
+  std::string modelFormat = "tensorflow-binary";
+  std::string modelPath = "/usr/share/arm/armnn/models/mobilenet_v1_1.0_224_frozen.pb";
+  std::string inputName = "input";
+  std::string inputTensorShapeStr = "1 224 224 3";
+  std::string inputTensorDataFilePath = "/usr/share/arm/armnn/testvecs/test2.mp4";
+  std::string outputName = "MobilenetV1/Predictions/Reshape_1";
+  std::vector<armnn::BackendId> computeDevices = {armnn::Compute::CpuAcc};
+  // Catch ctrl-c to ensure a clean exit
+  signal(SIGABRT, exit);
+  signal(SIGTERM, exit);
+
+  if (argc == 1)
+  {
+	return RunTest(modelFormat, inputTensorShapeStr, computeDevices,
+                       modelPath, inputName, inputTensorDataFilePath, outputName, false, 1000);
+  }
+  else
+  {
+    size_t subgraphId = 0;
+    po::options_description desc("Options");
+    try
+    {
+      desc.add_options()
+      ("help", "Display usage information")
+      ("test-cases,t", po::value(&testCasesFile), "Path to a CSV file containing test cases to run. "
+       "If set, further parameters -- with the exception of compute device and concurrency -- will be ignored, "
+       "as they are expected to be defined in the file for each test in particular.")
+      ("concurrent,n", po::bool_switch()->default_value(false),
+       "Whether or not the test cases should be executed in parallel")
+      ("model-format,f", po::value(&modelFormat),
+       "caffe-binary, caffe-text, onnx-binary, onnx-text, tflite-binary, tensorflow-binary or tensorflow-text.")
+      ("model-path,m", po::value(&modelPath), "Path to model file, e.g. .caffemodel, .prototxt,"
+       " .tflite, .onnx")
+      ("compute,c", po::value<std::vector<armnn::BackendId>>()->multitoken(),
+       "The preferred order of devices to run layers on by default. Possible choices: CpuAcc, CpuRef, GpuAcc")
+      ("input-name,i", po::value(&inputName), "Identifier of the input tensor in the network.")
+      ("input-tensor-shape,s", po::value(&inputTensorShapeStr),
+       "The shape of the input tensor in the network as a flat array of integers separated by whitespace. "
+       "This parameter is optional, depending on the network.")
+      ("input-tensor-data,d", po::value(&inputTensorDataFilePath),
+       "Input test file name. It can be image/video clip file name or use 'camera_live_input' to select camera input.")
+      ("output-name,o", po::value(&outputName), "Identifier of the output tensor in the network.")
+      ("event-based-profiling,e", po::bool_switch()->default_value(false),
+       "Enables built in profiler. If unset, defaults to off.")
+      ("number_frame", po::value<size_t>(&subgraphId)->default_value(1), "Number of frames to process.");
+    }
+    catch (const std::exception& e)
+    {
+      // Coverity points out that default_value(...) can throw a bad_lexical_cast,
+      // and that desc.add_options() can throw boost::io::too_few_args.
+      // They really won't in any of these cases.
+      BOOST_ASSERT_MSG(false, "Caught unexpected exception");
+      BOOST_LOG_TRIVIAL(fatal) << "Fatal internal error: " << e.what();
+      return EXIT_FAILURE;
+    }
+
+    // Parses the command-line.
+    po::variables_map vm;
+    try
+    {
+      po::store(po::parse_command_line(argc, argv, desc), vm);
+      po::notify(vm);
+    }
+    catch (const po::error& e)
+    {
+      std::cerr << e.what() << std::endl << std::endl;
+      std::cerr << desc << std::endl;
+      return EXIT_FAILURE;
+    }
+
+    // Run single test
+    // Get the preferred order of compute devices.
+    std::vector<armnn::BackendId> computeDevices = vm["compute"].as<std::vector<armnn::BackendId>>();
+	bool enableProfiling = vm["event-based-profiling"].as<bool>();
+
+    // Remove duplicates from the list of compute devices.
+    RemoveDuplicateDevices(computeDevices);
+
+    return RunTest(modelFormat, inputTensorShapeStr, computeDevices,
+                   modelPath, inputName, inputTensorDataFilePath, outputName, enableProfiling, subgraphId);
+  }
+}
+
-- 
1.9.1