Accepting request 1295108 from science:machinelearning

OBS-URL: https://build.opensuse.org/request/show/1295108
OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/armnn?expand=0&rev=15

0003-add-more-test-command-line-arguments.patch

@@ -1,76 +0,0 @@
-From 964cb82f3b811aec6663255ab0aa589f0a3be0ee Mon Sep 17 00:00:00 2001
-From: Qin Su <qsu@ti.com>
-Date: Fri, 22 Feb 2019 14:10:07 -0500
-Subject: [PATCH] add more test command line arguments
-
-Updated by Guillaume_G to apply properly (s/BOOST_ASSERT/ARMNN_ASSERT/)
-
-Upstream-Status: Inappropriate [TI only test code]
-Signed-off-by: Qin Su <qsu@ti.com>
----
- tests/InferenceTest.inl | 49 +++++++++++++++++++++++++++++++++++++++++++++++++
- 1 file changed, 49 insertions(+)
-
-diff --git a/tests/InferenceTest.inl b/tests/InferenceTest.inl
-index 538720b..6fd21b8 100644
---- a/tests/InferenceTest.inl
-+++ b/tests/InferenceTest.inl
-@@ -326,6 +326,55 @@ int ClassifierInferenceTestMain(int argc,
-     ARMNN_ASSERT(modelFilename);
-     ARMNN_ASSERT(inputBindingName);
-     ARMNN_ASSERT(outputBindingName);
-+    int count;
-+    const char *p_input;
-+    char inmodelname[500];
-+    char outtensorname[500];
-+
-+    /* parse command line */
-+    for (count = 1; count < argc; count++)
-+    {
-+        if (*(argv[count]) == '+')
-+        {
-+            p_input = argv[count] + 1;
-+            switch (*(p_input))
-+            {
-+                case 'i':
-+                case 'I':
-+                    strcpy(inmodelname, p_input + 2);
-+                    modelFilename = &inmodelname[0];
-+                    std::cout << "Input model = " << modelFilename << std::endl;
-+                    break;
-+                case 'o':
-+                case 'O':
-+                    strcpy(outtensorname, p_input + 2);
-+                    outputBindingName = &outtensorname[0];
-+                    std::cout << "out tensor name = " << outputBindingName << std::endl;
-+                    break;
-+                default:
-+                    break;
-+            }
-+        }
-+        else if (*(argv[count]) == '-')
-+        {
-+            p_input = argv[count] + 1;
-+            switch (*(p_input))
-+            {
-+                case '-':
-+                    p_input = argv[count] + 2;
-+                case 'h':
-+                case 'H':
-+                    std::cout <<"\nAdditional Options: " << std::endl;
-+                    std::cout <<"  +i                                    Set user specified inference model name." << std::endl;
-+                    std::cout <<"                                        If not set, default name is used." << std::endl;
-+                    std::cout <<"  +o                                    Set user specified output tensor name." << std::endl;
-+                    std::cout <<"                                        If not set, default name is used.\n" << std::endl;
-+                    break;
-+                default:
-+                    break;
-+            }
-+        }
-+    }
- 
-     return InferenceTestMain(argc, argv, defaultTestCaseIds,
-         [=]
--- 
-1.9.1
-

0005-add-armnn-mobilenet-test-example.patch

@@ -1,71 +0,0 @@
-From 99a6c339f1828d3cd1b193cf702bada9011d900b Mon Sep 17 00:00:00 2001
-From: Djordje Senicic <x0157990@ti.com>
-Date: Mon, 24 Jun 2019 14:29:19 -0400
-Subject: [PATCH] add armnn mobilenet test example
-
-Upstream-Status: Inappropriate [TI only test code]
-Signed-off-by: Qin Su <qsu@ti.com>
-Signed-off-by: Djordje Senicic <x0157990@ti.com>
-[Guillaume's update: Add boost_log dep]
-[Guillaume's update: Update to apply on top of 20.08]
----
- tests/CMakeLists.txt | 41 +++++++++++++++++++++++++++++++++++++++++
- 1 file changed, 41 insertions(+)
-
-diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
-index dfcf4b48..5a78d3a6 100644
---- a/tests/CMakeLists.txt
-+++ b/tests/CMakeLists.txt
-@@ -1,3 +1,6 @@
-+find_package( OpenCV REQUIRED )
-+include_directories( ${OpenCV_INCLUDE_DIRS} )
-+
- # UnitTests
- include(CheckIncludeFiles)
- 
-@@ -348,3 +351,42 @@ if(BUILD_ARMNN_QUANTIZER)
-     target_include_directories(ImageCSVFileGenerator PRIVATE ../src/armnnUtils)
-     ImageTensorExecutor(ImageCSVFileGenerator)
- endif()
-+
-+if (BUILD_ARMNN_EXAMPLES)
-+    set(ArmnnExamples_sources
-+        ArmnnExamples/ArmnnExamples.cpp)
-+
-+    add_executable_ex(ArmnnExamples ${ArmnnExamples_sources})
-+
-+    target_include_directories(ArmnnExamples PRIVATE ../src/armnnUtils)
-+    target_include_directories(ArmnnExamples PRIVATE ../src/armnn)
-+    target_include_directories(ArmnnExamples PRIVATE ../src/backends)
-+
-+    if (BUILD_CAFFE_PARSER)
-+        target_link_libraries(ArmnnExamples armnnCaffeParser)
-+    endif()
-+    if (BUILD_TF_PARSER)
-+        target_link_libraries(ArmnnExamples armnnTfParser)
-+    endif()
-+
-+    if (BUILD_TF_LITE_PARSER)
-+        target_link_libraries(ArmnnExamples armnnTfLiteParser)
-+    endif()
-+    if (BUILD_ONNX_PARSER)
-+            target_link_libraries(ArmnnExamples armnnOnnxParser)
-+    endif()
-+
-+    target_link_libraries(ArmnnExamples armnn)
-+    target_link_libraries(ArmnnExamples ${CMAKE_THREAD_LIBS_INIT})
-+    if(OPENCL_LIBRARIES)
-+        target_link_libraries(ArmnnExamples ${OPENCL_LIBRARIES})
-+    endif()
-+
-+    target_link_libraries(ArmnnExamples
-+        ${Boost_LOG_LIBRARY}
-+        ${Boost_SYSTEM_LIBRARY}
-+        ${Boost_FILESYSTEM_LIBRARY}
-+        ${Boost_PROGRAM_OPTIONS_LIBRARY}
-+		${OpenCV_LIBS})
-+    addDllCopyCommands(ArmnnExamples)
-+endif()
--- 
-2.17.1
-

0006-armnn-mobilenet-test-example.patch

@@ -1,680 +0,0 @@
-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>
-
-[Guillaume's update: s#Logging.hpp#armnn/Logging.hpp#]
-[Guillaume's update: Add #include <boost/log/trivial.hpp>]
-[Guillaume's update: Drop armnnUtils::ConfigureLogging(...)]
-[Guillaume's update: Handle boost::variant to mapbox::util::variant update]
----
- 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 <boost/log/trivial.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 <mapbox/variant.hpp> /*#include "CsvReader.hpp"*/
-+#include "../InferenceTest.hpp"
-+#include <armnn/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 = mapbox::util::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 = (mapbox::util::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);
-+
-+  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
-

0009-command-line-options-for-video-port-selection.patch

@@ -1,60 +0,0 @@
-From ee152f3b68f91c5fff336306d011becdcf3a6b17 Mon Sep 17 00:00:00 2001
-From: Djordje Senicic <x0157990@ti.com>
-Date: Sat, 24 Aug 2019 17:58:38 -0400
-Subject: [PATCH] command line options for video port selection
-
-- Add command line selection <0|1|2|3> of video port used for live camera input 
-
-Upstream-Status: Inappropriate [TI only test code]
-
-Signed-off-by: Djordje Senicic <x0157990@ti.com>
----
- tests/ArmnnExamples/ArmnnExamples.cpp | 23 ++++++++++++++++++++---
- 1 file changed, 20 insertions(+), 3 deletions(-)
-
-diff --git a/tests/ArmnnExamples/ArmnnExamples.cpp b/tests/ArmnnExamples/ArmnnExamples.cpp
-index 638fc145..d1526539 100644
---- a/tests/ArmnnExamples/ArmnnExamples.cpp
-+++ b/tests/ArmnnExamples/ArmnnExamples.cpp
-@@ -316,10 +316,27 @@ int MainImpl(const char* modelPath,
-     int input_type = INPUT_IMAGE;
-     std::string filename = inputTensorDataFilePath;
- 
--    size_t i = filename.rfind("camera_live_input", filename.length());
-+    size_t i  = filename.rfind("camera_live_input", filename.length());
-     if (i != string::npos)
-     {
--      cap = VideoCapture(1);
-+      int vport = 1;
-+      size_t loc_i = filename.rfind("camera_live_input0", filename.length());
-+      if(loc_i != string::npos) vport = 0;
-+      else {
-+         loc_i = filename.rfind("camera_live_input1", filename.length());
-+         if(loc_i != string::npos) vport = 1;
-+         else {
-+           loc_i = filename.rfind("camera_live_input2", filename.length());
-+           if(loc_i != string::npos) vport = 2;
-+           else {
-+             loc_i = filename.rfind("camera_live_input3", filename.length());
-+             if(loc_i != string::npos) vport = 3;
-+             else std::cout << "Setting ports beyond 3 not supported - using default!" << std::endl;
-+           }
-+         }
-+      }
-+      std::cout << "Using video" << vport << std::endl;
-+      cap = VideoCapture(vport);
-       namedWindow("ARMNN MobileNet Example", WINDOW_AUTOSIZE | CV_GUI_NORMAL);
-       input_type = INPUT_CAMERA; //camera input
-     }
-@@ -609,7 +626,7 @@ int main(int argc, const char* argv[])
-        "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.")
-+       "Input test file name. It can be image/video clip file name or 'camera_live_input or camera_live_input<0|1|2|3>' 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.")
--- 
-2.17.1
-

0010-armnnexamples-update-for-19.08-modifications.patch

@@ -1,28 +0,0 @@
-From a3e266a2de7c45116428f4e21645a2657534191b Mon Sep 17 00:00:00 2001
-From: Djordje Senicic <x0157990@ti.com>
-Date: Mon, 26 Aug 2019 03:51:39 -0400
-Subject: [PATCH] armnnexamples: update for 19.08 modifications
-
-Upstream-Status: Inappropriate [TI only test code]
-
-Signed-off-by: Djordje Senicic <x0157990@ti.com>
----
- tests/ArmnnExamples/ArmnnExamples.cpp | 2 +-
- 1 file changed, 1 insertion(+), 1 deletion(-)
-
-diff --git a/tests/ArmnnExamples/ArmnnExamples.cpp b/tests/ArmnnExamples/ArmnnExamples.cpp
-index d1526539..c10a4fc0 100644
---- a/tests/ArmnnExamples/ArmnnExamples.cpp
-+++ b/tests/ArmnnExamples/ArmnnExamples.cpp
-@@ -310,7 +310,7 @@ int MainImpl(const char* modelPath,
-     params.m_OutputBindings = { outputName };
-     //params.m_EnableProfiling = enableProfiling;
-     params.m_SubgraphId = 0;
--    InferenceModel<TParser, TDataType> model(params, enableProfiling, runtime);
-+    InferenceModel<TParser, TDataType> model(params, enableProfiling, "", runtime);
- 
-     VideoCapture cap;
-     int input_type = INPUT_IMAGE;
--- 
-2.17.1
-

armnn-24.05.tar.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b06c0b03d1447fb1c9222a8dc1f7fc0aac8dbd9defdf67087de2d305b1dbd323
-size 28629675

armnn-24.08.tar.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6af3453b6a0238f9734bbeb13e006f07f7a7a459a978a21423555819415fa328
-size 28695424

armnn-24.11.tar.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ca85052373c19d6816e9842b732b5b3433fefddc302621adac897b1f5b64487a
-size 29099331

armnn-fix-armv7.patch

@@ -1,18 +0,0 @@
---- armnn-24.08/include/armnn/Numpy.hpp.orig	2024-09-06 11:05:16.800066800 +0200
-+++ armnn-24.08/include/armnn/Numpy.hpp	2024-09-06 11:05:58.717592900 +0200
-@@ -157,7 +157,7 @@ namespace armnnNumpy
-     inline void CreateHeader(std::ifstream& ifStream, HeaderInfo& headerInfo, Header& header)
-     {
-         char stringBuffer[headerInfo.m_HeaderLen];
--        ifStream.read(stringBuffer, headerInfo.m_HeaderLen);
-+        ifStream.read(stringBuffer, static_cast<std::streamsize>(headerInfo.m_HeaderLen));
- 
-         header.m_HeaderString = std::string(stringBuffer, headerInfo.m_HeaderLen);
-         // Remove new line character at the end of the string
-@@ -403,4 +403,4 @@ namespace armnnNumpy
-     }
- }
- 
--#endif // NUMPY_HPP
-\ No newline at end of file
-+#endif // NUMPY_HPP

armnn-fix_find_opencv.patch

@@ -1,19 +0,0 @@
---- armnn-19.08.orig/tests/CMakeLists.txt	2019-10-17 09:11:02.836949176 +0200
-+++ armnn-19.08/tests/CMakeLists.txt	2019-10-17 09:10:50.384869262 +0200
-@@ -1,6 +1,3 @@
--find_package( OpenCV REQUIRED )
--include_directories( ${OpenCV_INCLUDE_DIRS} )
--
- # UnitTests
- include(CheckIncludeFiles)
- 
-@@ -368,6 +365,9 @@ if(BUILD_ARMNN_QUANTIZER)
- endif()
- 
- if (BUILD_ARMNN_EXAMPLES)
-+    find_package( OpenCV REQUIRED )
-+    include_directories( ${OpenCV_INCLUDE_DIRS} )
-+
-     set(ArmnnExamples_sources
-         ArmnnExamples/ArmnnExamples.cpp)
-