| // |
| // Copyright © 2020 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "armnnTfLiteParser/ITfLiteParser.hpp" |
| |
| #include "NMS.hpp" |
| |
| #include <stb/stb_image.h> |
| |
| #include <armnn/INetwork.hpp> |
| #include <armnn/IRuntime.hpp> |
| #include <armnn/Logging.hpp> |
| #include <armnn/utility/IgnoreUnused.hpp> |
| |
| #include <cxxopts/cxxopts.hpp> |
| #include <ghc/filesystem.hpp> |
| |
| #include <chrono> |
| #include <fstream> |
| #include <iostream> |
| #include <iterator> |
| #include <cmath> |
| |
| using namespace armnnTfLiteParser; |
| using namespace armnn; |
| |
| static const int OPEN_FILE_ERROR = -2; |
| static const int OPTIMIZE_NETWORK_ERROR = -3; |
| static const int LOAD_NETWORK_ERROR = -4; |
| static const int LOAD_IMAGE_ERROR = -5; |
| static const int GENERAL_ERROR = -100; |
| |
| #define CHECK_OK(v) \ |
| do { \ |
| try { \ |
| auto r_local = v; \ |
| if (r_local != 0) { return r_local;} \ |
| } \ |
| catch (const armnn::Exception& e) \ |
| { \ |
| ARMNN_LOG(error) << "Oops: " << e.what(); \ |
| return GENERAL_ERROR; \ |
| } \ |
| } while(0) |
| |
| |
| |
| template<typename TContainer> |
| inline armnn::InputTensors MakeInputTensors(const std::vector<armnn::BindingPointInfo>& inputBindings, |
| const std::vector<std::reference_wrapper<TContainer>>& inputDataContainers) |
| { |
| armnn::InputTensors inputTensors; |
| |
| const size_t numInputs = inputBindings.size(); |
| if (numInputs != inputDataContainers.size()) |
| { |
| throw armnn::Exception("Mismatching vectors"); |
| } |
| |
| for (size_t i = 0; i < numInputs; i++) |
| { |
| const armnn::BindingPointInfo& inputBinding = inputBindings[i]; |
| const TContainer& inputData = inputDataContainers[i].get(); |
| |
| armnn::ConstTensor inputTensor(inputBinding.second, inputData.data()); |
| inputTensors.push_back(std::make_pair(inputBinding.first, inputTensor)); |
| } |
| |
| return inputTensors; |
| } |
| |
| template<typename TContainer> |
| inline armnn::OutputTensors MakeOutputTensors( |
| const std::vector<armnn::BindingPointInfo>& outputBindings, |
| const std::vector<std::reference_wrapper<TContainer>>& outputDataContainers) |
| { |
| armnn::OutputTensors outputTensors; |
| |
| const size_t numOutputs = outputBindings.size(); |
| if (numOutputs != outputDataContainers.size()) |
| { |
| throw armnn::Exception("Mismatching vectors"); |
| } |
| |
| outputTensors.reserve(numOutputs); |
| |
| for (size_t i = 0; i < numOutputs; i++) |
| { |
| const armnn::BindingPointInfo& outputBinding = outputBindings[i]; |
| const TContainer& outputData = outputDataContainers[i].get(); |
| |
| armnn::Tensor outputTensor(outputBinding.second, const_cast<float*>(outputData.data())); |
| outputTensors.push_back(std::make_pair(outputBinding.first, outputTensor)); |
| } |
| |
| return outputTensors; |
| } |
| |
| #define S_BOOL(name) enum class name {False=0, True=1}; |
| |
| S_BOOL(ImportMemory) |
| S_BOOL(DumpToDot) |
| S_BOOL(ExpectFile) |
| S_BOOL(OptionalArg) |
| |
| int LoadModel(const char* filename, |
| ITfLiteParser& parser, |
| IRuntime& runtime, |
| NetworkId& networkId, |
| const std::vector<BackendId>& backendPreferences, |
| ImportMemory enableImport, |
| DumpToDot dumpToDot) |
| { |
| std::ifstream stream(filename, std::ios::in | std::ios::binary); |
| if (!stream.is_open()) |
| { |
| ARMNN_LOG(error) << "Could not open model: " << filename; |
| return OPEN_FILE_ERROR; |
| } |
| |
| std::vector<uint8_t> contents((std::istreambuf_iterator<char>(stream)), std::istreambuf_iterator<char>()); |
| stream.close(); |
| |
| auto model = parser.CreateNetworkFromBinary(contents); |
| contents.clear(); |
| ARMNN_LOG(debug) << "Model loaded ok: " << filename; |
| |
| // Optimize backbone model |
| OptimizerOptions options; |
| options.m_ImportEnabled = enableImport != ImportMemory::False; |
| auto optimizedModel = Optimize(*model, backendPreferences, runtime.GetDeviceSpec(), options); |
| if (!optimizedModel) |
| { |
| ARMNN_LOG(fatal) << "Could not optimize the model:" << filename; |
| return OPTIMIZE_NETWORK_ERROR; |
| } |
| |
| if (dumpToDot != DumpToDot::False) |
| { |
| std::stringstream ss; |
| ss << filename << ".dot"; |
| std::ofstream dotStream(ss.str().c_str(), std::ofstream::out); |
| optimizedModel->SerializeToDot(dotStream); |
| dotStream.close(); |
| } |
| // Load model into runtime |
| { |
| std::string errorMessage; |
| |
| armnn::MemorySource memSource = options.m_ImportEnabled ? armnn::MemorySource::Malloc |
| : armnn::MemorySource::Undefined; |
| INetworkProperties modelProps(false, memSource, memSource); |
| Status status = runtime.LoadNetwork(networkId, std::move(optimizedModel), errorMessage, modelProps); |
| if (status != Status::Success) |
| { |
| ARMNN_LOG(fatal) << "Could not load " << filename << " model into runtime: " << errorMessage; |
| return LOAD_NETWORK_ERROR; |
| } |
| } |
| |
| return 0; |
| } |
| |
| std::vector<float> LoadImage(const char* filename) |
| { |
| if (strlen(filename) == 0) |
| { |
| return std::vector<float>(1920*10180*3, 0.0f); |
| } |
| struct Memory |
| { |
| ~Memory() {stbi_image_free(m_Data);} |
| bool IsLoaded() const { return m_Data != nullptr;} |
| |
| unsigned char* m_Data; |
| }; |
| |
| std::vector<float> image; |
| |
| int width; |
| int height; |
| int channels; |
| |
| Memory mem = {stbi_load(filename, &width, &height, &channels, 3)}; |
| if (!mem.IsLoaded()) |
| { |
| ARMNN_LOG(error) << "Could not load input image file: " << filename; |
| return image; |
| } |
| |
| if (width != 1920 || height != 1080 || channels != 3) |
| { |
| ARMNN_LOG(error) << "Input image has wong dimension: " << width << "x" << height << "x" << channels << ". " |
| " Expected 1920x1080x3."; |
| return image; |
| } |
| |
| image.resize(1920*1080*3); |
| |
| // Expand to float. Does this need de-gamma? |
| for (unsigned int idx=0; idx <= 1920*1080*3; idx++) |
| { |
| image[idx] = static_cast<float>(mem.m_Data[idx]) /255.0f; |
| } |
| |
| return image; |
| } |
| |
| |
| bool ValidateFilePath(std::string& file, ExpectFile expectFile) |
| { |
| if (!ghc::filesystem::exists(file)) |
| { |
| std::cerr << "Given file path " << file << " does not exist" << std::endl; |
| return false; |
| } |
| if (!ghc::filesystem::is_regular_file(file) && expectFile == ExpectFile::True) |
| { |
| std::cerr << "Given file path " << file << " is not a regular file" << std::endl; |
| return false; |
| } |
| return true; |
| } |
| |
| void CheckAccuracy(std::vector<float>* toDetector0, std::vector<float>* toDetector1, |
| std::vector<float>* toDetector2, std::vector<float>* detectorOutput, |
| const std::vector<yolov3::Detection>& nmsOut, const std::vector<std::string>& filePaths) |
| { |
| std::ifstream pathStream; |
| std::vector<float> expected; |
| std::vector<std::vector<float>*> outputs; |
| float compare = 0; |
| unsigned int count = 0; |
| |
| //Push back output vectors from inference for use in loop |
| outputs.push_back(toDetector0); |
| outputs.push_back(toDetector1); |
| outputs.push_back(toDetector2); |
| outputs.push_back(detectorOutput); |
| |
| for (unsigned int i = 0; i < outputs.size(); ++i) |
| { |
| // Reading expected output files and assigning them to @expected. Close and Clear to reuse stream and clean RAM |
| pathStream.open(filePaths[i]); |
| if (!pathStream.is_open()) |
| { |
| ARMNN_LOG(error) << "Expected output file can not be opened: " << filePaths[i]; |
| continue; |
| } |
| |
| expected.assign(std::istream_iterator<float>(pathStream), {}); |
| pathStream.close(); |
| pathStream.clear(); |
| |
| // Ensure each vector is the same length |
| if (expected.size() != outputs[i]->size()) |
| { |
| ARMNN_LOG(error) << "Expected output size does not match actual output size: " << filePaths[i]; |
| } |
| else |
| { |
| count = 0; |
| |
| // Compare abs(difference) with tolerance to check for value by value equality |
| for (unsigned int j = 0; j < outputs[i]->size(); ++j) |
| { |
| compare = std::abs(expected[j] - outputs[i]->at(j)); |
| if (compare > 0.001f) |
| { |
| count++; |
| } |
| } |
| if (count > 0) |
| { |
| ARMNN_LOG(error) << count << " output(s) do not match expected values in: " << filePaths[i]; |
| } |
| } |
| } |
| |
| pathStream.open(filePaths[4]); |
| if (!pathStream.is_open()) |
| { |
| ARMNN_LOG(error) << "Expected output file can not be opened: " << filePaths[4]; |
| } |
| else |
| { |
| expected.assign(std::istream_iterator<float>(pathStream), {}); |
| pathStream.close(); |
| pathStream.clear(); |
| unsigned int y = 0; |
| unsigned int numOfMember = 6; |
| std::vector<float> intermediate; |
| |
| for (auto& detection: nmsOut) |
| { |
| for (unsigned int x = y * numOfMember; x < ((y * numOfMember) + numOfMember); ++x) |
| { |
| intermediate.push_back(expected[x]); |
| } |
| if (!yolov3::compare_detection(detection, intermediate)) |
| { |
| ARMNN_LOG(error) << "Expected NMS output does not match: Detection " << y + 1; |
| } |
| intermediate.clear(); |
| y++; |
| } |
| } |
| } |
| |
| struct ParseArgs |
| { |
| ParseArgs(int ac, char *av[]) : options{"TfLiteYoloV3Big-Armnn", |
| "Executes YoloV3Big using ArmNN. YoloV3Big consists " |
| "of 3 parts: A backbone TfLite model, a detector TfLite " |
| "model, and None Maximum Suppression. All parts are " |
| "executed successively."} |
| { |
| options.add_options() |
| ("b,backbone-path", |
| "File path where the TfLite model for the yoloV3big backbone " |
| "can be found e.g. mydir/yoloV3big_backbone.tflite", |
| cxxopts::value<std::string>()) |
| |
| ("c,comparison-files", |
| "Defines the expected outputs for the model " |
| "of yoloV3big e.g. 'mydir/file1.txt,mydir/file2.txt,mydir/file3.txt,mydir/file4.txt'->InputToDetector1" |
| " will be tried first then InputToDetector2 then InputToDetector3 then the Detector Output and finally" |
| " the NMS output. NOTE: Files are passed as comma separated list without whitespaces.", |
| cxxopts::value<std::vector<std::string>>()->default_value({})) |
| |
| ("d,detector-path", |
| "File path where the TfLite model for the yoloV3big " |
| "detector can be found e.g.'mydir/yoloV3big_detector.tflite'", |
| cxxopts::value<std::string>()) |
| |
| ("h,help", "Produce help message") |
| |
| ("i,image-path", |
| "File path to a 1080x1920 jpg image that should be " |
| "processed e.g. 'mydir/example_img_180_1920.jpg'", |
| cxxopts::value<std::string>()) |
| |
| ("B,preferred-backends-backbone", |
| "Defines the preferred backends to run the backbone model " |
| "of yoloV3big e.g. 'GpuAcc,CpuRef' -> GpuAcc will be tried " |
| "first before falling back to CpuRef. NOTE: Backends are passed " |
| "as comma separated list without whitespaces.", |
| cxxopts::value<std::vector<std::string>>()->default_value("GpuAcc,CpuRef")) |
| |
| ("D,preferred-backends-detector", |
| "Defines the preferred backends to run the detector model " |
| "of yoloV3big e.g. 'CpuAcc,CpuRef' -> CpuAcc will be tried " |
| "first before falling back to CpuRef. NOTE: Backends are passed " |
| "as comma separated list without whitespaces.", |
| cxxopts::value<std::vector<std::string>>()->default_value("CpuAcc,CpuRef")) |
| |
| ("M, model-to-dot", |
| "Dump the optimized model to a dot file for debugging/analysis", |
| cxxopts::value<bool>()->default_value("false")) |
| |
| ("Y, dynamic-backends-path", |
| "Define a path from which to load any dynamic backends.", |
| cxxopts::value<std::string>()); |
| |
| auto result = options.parse(ac, av); |
| |
| if (result.count("help")) |
| { |
| std::cout << options.help() << "\n"; |
| exit(EXIT_SUCCESS); |
| } |
| |
| |
| backboneDir = GetPathArgument(result, "backbone-path", ExpectFile::True, OptionalArg::False); |
| |
| comparisonFiles = GetPathArgument(result["comparison-files"].as<std::vector<std::string>>(), OptionalArg::True); |
| |
| detectorDir = GetPathArgument(result, "detector-path", ExpectFile::True, OptionalArg::False); |
| |
| imageDir = GetPathArgument(result, "image-path", ExpectFile::True, OptionalArg::True); |
| |
| dynamicBackendPath = GetPathArgument(result, "dynamic-backends-path", ExpectFile::False, OptionalArg::True); |
| |
| prefBackendsBackbone = GetBackendIDs(result["preferred-backends-backbone"].as<std::vector<std::string>>()); |
| LogBackendsInfo(prefBackendsBackbone, "Backbone"); |
| prefBackendsDetector = GetBackendIDs(result["preferred-backends-detector"].as<std::vector<std::string>>()); |
| LogBackendsInfo(prefBackendsDetector, "detector"); |
| |
| dumpToDot = result["model-to-dot"].as<bool>() ? DumpToDot::True : DumpToDot::False; |
| } |
| |
| /// Takes a vector of backend strings and returns a vector of backendIDs |
| std::vector<BackendId> GetBackendIDs(const std::vector<std::string>& backendStrings) |
| { |
| std::vector<BackendId> backendIDs; |
| for (const auto& b : backendStrings) |
| { |
| backendIDs.push_back(BackendId(b)); |
| } |
| return backendIDs; |
| } |
| |
| /// Verifies if the program argument with the name argName contains a valid file path. |
| /// Returns the valid file path string if given argument is associated a valid file path. |
| /// Otherwise throws an exception. |
| std::string GetPathArgument(cxxopts::ParseResult& result, |
| std::string&& argName, |
| ExpectFile expectFile, |
| OptionalArg isOptionalArg) |
| { |
| if (result.count(argName)) |
| { |
| std::string path = result[argName].as<std::string>(); |
| if (!ValidateFilePath(path, expectFile)) |
| { |
| std::stringstream ss; |
| ss << "Argument given to" << argName << "is not a valid file path"; |
| throw cxxopts::option_syntax_exception(ss.str().c_str()); |
| } |
| return path; |
| } |
| else |
| { |
| if (isOptionalArg == OptionalArg::True) |
| { |
| return ""; |
| } |
| |
| throw cxxopts::missing_argument_exception(argName); |
| } |
| } |
| |
| /// Assigns vector of strings to struct member variable |
| std::vector<std::string> GetPathArgument(const std::vector<std::string>& pathStrings, OptionalArg isOptional) |
| { |
| if (pathStrings.size() < 5){ |
| if (isOptional == OptionalArg::True) |
| { |
| return std::vector<std::string>(); |
| } |
| throw cxxopts::option_syntax_exception("Comparison files requires 5 file paths."); |
| } |
| |
| std::vector<std::string> filePaths; |
| for (auto& path : pathStrings) |
| { |
| filePaths.push_back(path); |
| if (!ValidateFilePath(filePaths.back(), ExpectFile::True)) |
| { |
| throw cxxopts::option_syntax_exception("Argument given to Comparison Files is not a valid file path"); |
| } |
| } |
| return filePaths; |
| } |
| |
| /// Log info about assigned backends |
| void LogBackendsInfo(std::vector<BackendId>& backends, std::string&& modelName) |
| { |
| std::string info; |
| info = "Preferred backends for " + modelName + " set to [ "; |
| for (auto const &backend : backends) |
| { |
| info = info + std::string(backend) + " "; |
| } |
| ARMNN_LOG(info) << info << "]"; |
| } |
| |
| // Member variables |
| std::string backboneDir; |
| std::vector<std::string> comparisonFiles; |
| std::string detectorDir; |
| std::string imageDir; |
| std::string dynamicBackendPath; |
| |
| std::vector<BackendId> prefBackendsBackbone; |
| std::vector<BackendId> prefBackendsDetector; |
| |
| cxxopts::Options options; |
| |
| DumpToDot dumpToDot; |
| }; |
| |
| int main(int argc, char* argv[]) |
| { |
| // Configure logging |
| SetAllLoggingSinks(true, true, true); |
| SetLogFilter(LogSeverity::Trace); |
| |
| // Check and get given program arguments |
| ParseArgs progArgs = ParseArgs(argc, argv); |
| |
| // Create runtime |
| IRuntime::CreationOptions runtimeOptions; // default |
| |
| if (!progArgs.dynamicBackendPath.empty()) |
| { |
| std::cout << "Loading backends from" << progArgs.dynamicBackendPath << "\n"; |
| runtimeOptions.m_DynamicBackendsPath = progArgs.dynamicBackendPath; |
| } |
| |
| auto runtime = IRuntime::Create(runtimeOptions); |
| if (!runtime) |
| { |
| ARMNN_LOG(fatal) << "Could not create runtime."; |
| return -1; |
| } |
| |
| // Create TfLite Parsers |
| ITfLiteParser::TfLiteParserOptions parserOptions; |
| auto parser = ITfLiteParser::Create(parserOptions); |
| |
| // Load backbone model |
| ARMNN_LOG(info) << "Loading backbone..."; |
| NetworkId backboneId; |
| const DumpToDot dumpToDot = progArgs.dumpToDot; |
| CHECK_OK(LoadModel(progArgs.backboneDir.c_str(), |
| *parser, |
| *runtime, |
| backboneId, |
| progArgs.prefBackendsBackbone, |
| ImportMemory::False, |
| dumpToDot)); |
| auto inputId = parser->GetNetworkInputBindingInfo(0, "inputs"); |
| auto bbOut0Id = parser->GetNetworkOutputBindingInfo(0, "input_to_detector_1"); |
| auto bbOut1Id = parser->GetNetworkOutputBindingInfo(0, "input_to_detector_2"); |
| auto bbOut2Id = parser->GetNetworkOutputBindingInfo(0, "input_to_detector_3"); |
| auto backboneProfile = runtime->GetProfiler(backboneId); |
| backboneProfile->EnableProfiling(true); |
| |
| |
| // Load detector model |
| ARMNN_LOG(info) << "Loading detector..."; |
| NetworkId detectorId; |
| CHECK_OK(LoadModel(progArgs.detectorDir.c_str(), |
| *parser, |
| *runtime, |
| detectorId, |
| progArgs.prefBackendsDetector, |
| ImportMemory::True, |
| dumpToDot)); |
| auto detectIn0Id = parser->GetNetworkInputBindingInfo(0, "input_to_detector_1"); |
| auto detectIn1Id = parser->GetNetworkInputBindingInfo(0, "input_to_detector_2"); |
| auto detectIn2Id = parser->GetNetworkInputBindingInfo(0, "input_to_detector_3"); |
| auto outputBoxesId = parser->GetNetworkOutputBindingInfo(0, "output_boxes"); |
| auto detectorProfile = runtime->GetProfiler(detectorId); |
| |
| // Load input from file |
| ARMNN_LOG(info) << "Loading test image..."; |
| auto image = LoadImage(progArgs.imageDir.c_str()); |
| if (image.empty()) |
| { |
| return LOAD_IMAGE_ERROR; |
| } |
| |
| // Allocate the intermediate tensors |
| std::vector<float> intermediateMem0(bbOut0Id.second.GetNumElements()); |
| std::vector<float> intermediateMem1(bbOut1Id.second.GetNumElements()); |
| std::vector<float> intermediateMem2(bbOut2Id.second.GetNumElements()); |
| std::vector<float> intermediateMem3(outputBoxesId.second.GetNumElements()); |
| |
| // Setup inputs and outputs |
| using BindingInfos = std::vector<armnn::BindingPointInfo>; |
| using FloatTensors = std::vector<std::reference_wrapper<std::vector<float>>>; |
| |
| InputTensors bbInputTensors = MakeInputTensors(BindingInfos{ inputId }, |
| FloatTensors{ image }); |
| OutputTensors bbOutputTensors = MakeOutputTensors(BindingInfos{ bbOut0Id, bbOut1Id, bbOut2Id }, |
| FloatTensors{ intermediateMem0, |
| intermediateMem1, |
| intermediateMem2 }); |
| InputTensors detectInputTensors = MakeInputTensors(BindingInfos{ detectIn0Id, |
| detectIn1Id, |
| detectIn2Id } , |
| FloatTensors{ intermediateMem0, |
| intermediateMem1, |
| intermediateMem2 }); |
| OutputTensors detectOutputTensors = MakeOutputTensors(BindingInfos{ outputBoxesId }, |
| FloatTensors{ intermediateMem3 }); |
| |
| static const int numIterations=2; |
| using DurationUS = std::chrono::duration<double, std::micro>; |
| std::vector<DurationUS> nmsDurations(0); |
| std::vector<yolov3::Detection> filtered_boxes; |
| nmsDurations.reserve(numIterations); |
| for (int i=0; i < numIterations; i++) |
| { |
| // Execute backbone |
| ARMNN_LOG(info) << "Running backbone..."; |
| runtime->EnqueueWorkload(backboneId, bbInputTensors, bbOutputTensors); |
| |
| // Execute detector |
| ARMNN_LOG(info) << "Running detector..."; |
| runtime->EnqueueWorkload(detectorId, detectInputTensors, detectOutputTensors); |
| |
| // Execute NMS |
| ARMNN_LOG(info) << "Running nms..."; |
| using clock = std::chrono::steady_clock; |
| auto nmsStartTime = clock::now(); |
| yolov3::NMSConfig config; |
| config.num_boxes = 127800; |
| config.num_classes = 80; |
| config.confidence_threshold = 0.9f; |
| config.iou_threshold = 0.5f; |
| filtered_boxes = yolov3::nms(config, intermediateMem3); |
| auto nmsEndTime = clock::now(); |
| |
| // Enable the profiling after the warm-up run |
| if (i>0) |
| { |
| print_detection(std::cout, filtered_boxes); |
| |
| const auto nmsDuration = DurationUS(nmsStartTime - nmsEndTime); |
| nmsDurations.push_back(nmsDuration); |
| } |
| backboneProfile->EnableProfiling(true); |
| detectorProfile->EnableProfiling(true); |
| } |
| // Log timings to file |
| std::ofstream backboneProfileStream("backbone.json"); |
| backboneProfile->Print(backboneProfileStream); |
| backboneProfileStream.close(); |
| |
| std::ofstream detectorProfileStream("detector.json"); |
| detectorProfile->Print(detectorProfileStream); |
| detectorProfileStream.close(); |
| |
| // Manually construct the json output |
| std::ofstream nmsProfileStream("nms.json"); |
| nmsProfileStream << "{" << "\n"; |
| nmsProfileStream << R"( "NmsTimings": {)" << "\n"; |
| nmsProfileStream << R"( "raw": [)" << "\n"; |
| bool isFirst = true; |
| for (auto duration : nmsDurations) |
| { |
| if (!isFirst) |
| { |
| nmsProfileStream << ",\n"; |
| } |
| |
| nmsProfileStream << " " << duration.count(); |
| isFirst = false; |
| } |
| nmsProfileStream << "\n"; |
| nmsProfileStream << R"( "units": "us")" << "\n"; |
| nmsProfileStream << " ]" << "\n"; |
| nmsProfileStream << " }" << "\n"; |
| nmsProfileStream << "}" << "\n"; |
| nmsProfileStream.close(); |
| |
| if (progArgs.comparisonFiles.size() > 0) |
| { |
| CheckAccuracy(&intermediateMem0, |
| &intermediateMem1, |
| &intermediateMem2, |
| &intermediateMem3, |
| filtered_boxes, |
| progArgs.comparisonFiles); |
| } |
| |
| ARMNN_LOG(info) << "Run completed"; |
| return 0; |
| } |