| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #include <armnn/ArmNN.hpp> |
| |
| #if defined(ARMNN_CAFFE_PARSER) |
| #include <armnnCaffeParser/ICaffeParser.hpp> |
| #endif |
| #if defined(ARMNN_ONNX_PARSER) |
| #include <armnnOnnxParser/IOnnxParser.hpp> |
| #endif |
| #if defined(ARMNN_SERIALIZER) |
| #include <armnnSerializer/ISerializer.hpp> |
| #endif |
| #if defined(ARMNN_TF_PARSER) |
| #include <armnnTfParser/ITfParser.hpp> |
| #endif |
| #if defined(ARMNN_TF_LITE_PARSER) |
| #include <armnnTfLiteParser/ITfLiteParser.hpp> |
| #endif |
| |
| #include <Logging.hpp> |
| #include <HeapProfiling.hpp> |
| |
| #include <boost/format.hpp> |
| #include <boost/algorithm/string/split.hpp> |
| #include <boost/algorithm/string/classification.hpp> |
| #include <boost/program_options.hpp> |
| |
| #include <cstdlib> |
| #include <fstream> |
| #include <iostream> |
| |
| namespace |
| { |
| |
| namespace po = boost::program_options; |
| |
| armnn::TensorShape ParseTensorShape(std::istream& stream) |
| { |
| std::vector<unsigned int> result; |
| 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(","), 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(boost::numeric_cast<unsigned int>(std::stoi((token)))); |
| } |
| catch (const std::exception&) |
| { |
| BOOST_LOG_TRIVIAL(error) << "'" << token << "' is not a valid number. It has been ignored."; |
| } |
| } |
| } |
| } |
| |
| return armnn::TensorShape(boost::numeric_cast<unsigned int>(result.size()), result.data()); |
| } |
| |
| bool CheckOption(const po::variables_map& vm, |
| const char* option) |
| { |
| if (option == nullptr) |
| { |
| return false; |
| } |
| |
| // Check whether 'option' is provided. |
| return vm.find(option) != vm.end(); |
| } |
| |
| void CheckOptionDependency(const po::variables_map& vm, |
| const char* option, |
| const char* required) |
| { |
| if (option == nullptr || required == nullptr) |
| { |
| throw po::error("Invalid option to check dependency for"); |
| } |
| |
| // Check that if 'option' is provided, 'required' is also provided. |
| if (CheckOption(vm, option) && !vm[option].defaulted()) |
| { |
| if (CheckOption(vm, required) == 0 || vm[required].defaulted()) |
| { |
| throw po::error(std::string("Option '") + option + "' requires option '" + required + "'."); |
| } |
| } |
| } |
| |
| void CheckOptionDependencies(const po::variables_map& vm) |
| { |
| CheckOptionDependency(vm, "model-path", "model-format"); |
| CheckOptionDependency(vm, "model-path", "input-name"); |
| CheckOptionDependency(vm, "model-path", "output-name"); |
| CheckOptionDependency(vm, "input-tensor-shape", "model-path"); |
| } |
| |
| int ParseCommandLineArgs(int argc, const char* argv[], |
| std::string& modelFormat, |
| std::string& modelPath, |
| std::vector<std::string>& inputNames, |
| std::vector<std::string>& inputTensorShapeStrs, |
| std::vector<std::string>& outputNames, |
| std::string& outputPath, bool& isModelBinary) |
| { |
| po::options_description desc("Options"); |
| |
| desc.add_options() |
| ("help", "Display usage information") |
| ("model-format,f", po::value(&modelFormat)->required(),"Format of the model file" |
| #if defined(ARMNN_CAFFE_PARSER) |
| ", caffe-binary, caffe-text" |
| #endif |
| #if defined(ARMNN_ONNX_PARSER) |
| ", onnx-binary, onnx-text" |
| #endif |
| #if defined(ARMNN_TF_PARSER) |
| ", tensorflow-binary, tensorflow-text" |
| #endif |
| #if defined(ARMNN_TF_LITE_PARSER) |
| ", tflite-binary" |
| #endif |
| ".") |
| ("model-path,m", po::value(&modelPath)->required(), "Path to model file.") |
| ("input-name,i", po::value<std::vector<std::string>>()->multitoken(), |
| "Identifier of the input tensors in the network, separated by whitespace.") |
| ("input-tensor-shape,s", po::value<std::vector<std::string>>()->multitoken(), |
| "The shape of the input tensor in the network as a flat array of integers, separated by comma." |
| " Multiple shapes are separated by whitespace." |
| " This parameter is optional, depending on the network.") |
| ("output-name,o", po::value<std::vector<std::string>>()->multitoken(), |
| "Identifier of the output tensor in the network.") |
| ("output-path,p", po::value(&outputPath)->required(), "Path to serialize the network to."); |
| |
| po::variables_map vm; |
| try |
| { |
| po::store(po::parse_command_line(argc, argv, desc), vm); |
| |
| if (CheckOption(vm, "help") || argc <= 1) |
| { |
| std::cout << "Convert a neural network model from provided file to ArmNN format." << std::endl; |
| std::cout << std::endl; |
| std::cout << desc << std::endl; |
| exit(EXIT_SUCCESS); |
| } |
| po::notify(vm); |
| } |
| catch (const po::error& e) |
| { |
| std::cerr << e.what() << std::endl << std::endl; |
| std::cerr << desc << std::endl; |
| return EXIT_FAILURE; |
| } |
| |
| try |
| { |
| CheckOptionDependencies(vm); |
| } |
| catch (const po::error& e) |
| { |
| std::cerr << e.what() << std::endl << std::endl; |
| std::cerr << desc << std::endl; |
| return EXIT_FAILURE; |
| } |
| |
| if (modelFormat.find("bin") != std::string::npos) |
| { |
| isModelBinary = true; |
| } |
| else if (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; |
| } |
| |
| if (!vm["input-tensor-shape"].empty()) |
| { |
| inputTensorShapeStrs = vm["input-tensor-shape"].as<std::vector<std::string>>(); |
| } |
| |
| inputNames = vm["input-name"].as<std::vector<std::string>>(); |
| outputNames = vm["output-name"].as<std::vector<std::string>>(); |
| |
| return EXIT_SUCCESS; |
| } |
| |
| template<typename T> |
| struct ParserType |
| { |
| typedef T parserType; |
| }; |
| |
| class ArmnnConverter |
| { |
| public: |
| ArmnnConverter(const std::string& modelPath, |
| const std::vector<std::string>& inputNames, |
| const std::vector<armnn::TensorShape>& inputShapes, |
| const std::vector<std::string>& outputNames, |
| const std::string& outputPath, |
| bool isModelBinary) |
| : m_NetworkPtr(armnn::INetworkPtr(nullptr, [](armnn::INetwork *){})), |
| m_ModelPath(modelPath), |
| m_InputNames(inputNames), |
| m_InputShapes(inputShapes), |
| m_OutputNames(outputNames), |
| m_OutputPath(outputPath), |
| m_IsModelBinary(isModelBinary) {} |
| |
| bool Serialize() |
| { |
| if (m_NetworkPtr.get() == nullptr) |
| { |
| return false; |
| } |
| |
| auto serializer(armnnSerializer::ISerializer::Create()); |
| |
| serializer->Serialize(*m_NetworkPtr); |
| |
| std::ofstream file(m_OutputPath, std::ios::out | std::ios::binary); |
| |
| bool retVal = serializer->SaveSerializedToStream(file); |
| |
| return retVal; |
| } |
| |
| template <typename IParser> |
| bool CreateNetwork () |
| { |
| return CreateNetwork (ParserType<IParser>()); |
| } |
| |
| private: |
| armnn::INetworkPtr m_NetworkPtr; |
| std::string m_ModelPath; |
| std::vector<std::string> m_InputNames; |
| std::vector<armnn::TensorShape> m_InputShapes; |
| std::vector<std::string> m_OutputNames; |
| std::string m_OutputPath; |
| bool m_IsModelBinary; |
| |
| template <typename IParser> |
| bool CreateNetwork (ParserType<IParser>) |
| { |
| // Create a network from a file on disk |
| auto parser(IParser::Create()); |
| |
| std::map<std::string, armnn::TensorShape> inputShapes; |
| if (!m_InputShapes.empty()) |
| { |
| const size_t numInputShapes = m_InputShapes.size(); |
| const size_t numInputBindings = m_InputNames.size(); |
| if (numInputShapes < numInputBindings) |
| { |
| throw armnn::Exception(boost::str(boost::format( |
| "Not every input has its tensor shape specified: expected=%1%, got=%2%") |
| % numInputBindings % numInputShapes)); |
| } |
| |
| for (size_t i = 0; i < numInputShapes; i++) |
| { |
| inputShapes[m_InputNames[i]] = m_InputShapes[i]; |
| } |
| } |
| |
| { |
| ARMNN_SCOPED_HEAP_PROFILING("Parsing"); |
| m_NetworkPtr = (m_IsModelBinary ? |
| parser->CreateNetworkFromBinaryFile(m_ModelPath.c_str(), inputShapes, m_OutputNames) : |
| parser->CreateNetworkFromTextFile(m_ModelPath.c_str(), inputShapes, m_OutputNames)); |
| } |
| |
| return m_NetworkPtr.get() != nullptr; |
| } |
| |
| #if defined(ARMNN_TF_LITE_PARSER) |
| bool CreateNetwork (ParserType<armnnTfLiteParser::ITfLiteParser>) |
| { |
| // Create a network from a file on disk |
| auto parser(armnnTfLiteParser::ITfLiteParser::Create()); |
| |
| if (!m_InputShapes.empty()) |
| { |
| const size_t numInputShapes = m_InputShapes.size(); |
| const size_t numInputBindings = m_InputNames.size(); |
| if (numInputShapes < numInputBindings) |
| { |
| throw armnn::Exception(boost::str(boost::format( |
| "Not every input has its tensor shape specified: expected=%1%, got=%2%") |
| % numInputBindings % numInputShapes)); |
| } |
| } |
| |
| { |
| ARMNN_SCOPED_HEAP_PROFILING("Parsing"); |
| m_NetworkPtr = parser->CreateNetworkFromBinaryFile(m_ModelPath.c_str()); |
| } |
| |
| return m_NetworkPtr.get() != nullptr; |
| } |
| #endif |
| |
| #if defined(ARMNN_ONNX_PARSER) |
| bool CreateNetwork (ParserType<armnnOnnxParser::IOnnxParser>) |
| { |
| // Create a network from a file on disk |
| auto parser(armnnOnnxParser::IOnnxParser::Create()); |
| |
| if (!m_InputShapes.empty()) |
| { |
| const size_t numInputShapes = m_InputShapes.size(); |
| const size_t numInputBindings = m_InputNames.size(); |
| if (numInputShapes < numInputBindings) |
| { |
| throw armnn::Exception(boost::str(boost::format( |
| "Not every input has its tensor shape specified: expected=%1%, got=%2%") |
| % numInputBindings % numInputShapes)); |
| } |
| } |
| |
| { |
| ARMNN_SCOPED_HEAP_PROFILING("Parsing"); |
| m_NetworkPtr = (m_IsModelBinary ? |
| parser->CreateNetworkFromBinaryFile(m_ModelPath.c_str()) : |
| parser->CreateNetworkFromTextFile(m_ModelPath.c_str())); |
| } |
| |
| return m_NetworkPtr.get() != nullptr; |
| } |
| #endif |
| |
| }; |
| |
| } // anonymous namespace |
| |
| int main(int argc, const char* argv[]) |
| { |
| |
| #if (!defined(ARMNN_CAFFE_PARSER) \ |
| && !defined(ARMNN_ONNX_PARSER) \ |
| && !defined(ARMNN_TF_PARSER) \ |
| && !defined(ARMNN_TF_LITE_PARSER)) |
| BOOST_LOG_TRIVIAL(fatal) << "Not built with any of the supported parsers, Caffe, Onnx, Tensorflow, or TfLite."; |
| return EXIT_FAILURE; |
| #endif |
| |
| #if !defined(ARMNN_SERIALIZER) |
| BOOST_LOG_TRIVIAL(fatal) << "Not built with Serializer support."; |
| return EXIT_FAILURE; |
| #endif |
| |
| #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 modelFormat; |
| std::string modelPath; |
| |
| std::vector<std::string> inputNames; |
| std::vector<std::string> inputTensorShapeStrs; |
| std::vector<armnn::TensorShape> inputTensorShapes; |
| |
| std::vector<std::string> outputNames; |
| std::string outputPath; |
| |
| bool isModelBinary = true; |
| |
| if (ParseCommandLineArgs( |
| argc, argv, modelFormat, modelPath, inputNames, inputTensorShapeStrs, outputNames, outputPath, isModelBinary) |
| != EXIT_SUCCESS) |
| { |
| return EXIT_FAILURE; |
| } |
| |
| for (const std::string& shapeStr : inputTensorShapeStrs) |
| { |
| if (!shapeStr.empty()) |
| { |
| std::stringstream ss(shapeStr); |
| |
| try |
| { |
| armnn::TensorShape shape = ParseTensorShape(ss); |
| inputTensorShapes.push_back(shape); |
| } |
| catch (const armnn::InvalidArgumentException& e) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Cannot create tensor shape: " << e.what(); |
| return EXIT_FAILURE; |
| } |
| } |
| } |
| |
| ArmnnConverter converter(modelPath, inputNames, inputTensorShapes, outputNames, outputPath, isModelBinary); |
| |
| if (modelFormat.find("caffe") != std::string::npos) |
| { |
| #if defined(ARMNN_CAFFE_PARSER) |
| if (!converter.CreateNetwork<armnnCaffeParser::ICaffeParser>()) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Failed to load model from file"; |
| return EXIT_FAILURE; |
| } |
| #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) |
| if (!converter.CreateNetwork<armnnOnnxParser::IOnnxParser>()) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Failed to load model from file"; |
| return EXIT_FAILURE; |
| } |
| #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) |
| if (!converter.CreateNetwork<armnnTfParser::ITfParser>()) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Failed to load model from file"; |
| return EXIT_FAILURE; |
| } |
| #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; |
| } |
| |
| if (!converter.CreateNetwork<armnnTfLiteParser::ITfLiteParser>()) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Failed to load model from file"; |
| return EXIT_FAILURE; |
| } |
| #else |
| BOOST_LOG_TRIVIAL(fatal) << "Not built with TfLite parser support."; |
| return EXIT_FAILURE; |
| #endif |
| } |
| else |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Unknown model format: '" << modelFormat << "'"; |
| return EXIT_FAILURE; |
| } |
| |
| if (!converter.Serialize()) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Failed to serialize model"; |
| return EXIT_FAILURE; |
| } |
| |
| return EXIT_SUCCESS; |
| } |