| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #include <armnn/Logging.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 <HeapProfiling.hpp> |
| #include <armnn/utility/NumericCast.hpp> |
| #include <armnn/utility/StringUtils.hpp> |
| |
| /* |
| * Historically we use the ',' character to separate dimensions in a tensor shape. However, cxxopts will read this |
| * an an array of values which is fine until we have multiple tensors specified. This lumps the values of all shapes |
| * together in a single array and we cannot break it up again. We'll change the vector delimiter to a '.'. We do this |
| * as close as possible to the usage of cxxopts to avoid polluting other possible uses. |
| */ |
| #define CXXOPTS_VECTOR_DELIMITER '.' |
| #include <cxxopts/cxxopts.hpp> |
| |
| #include <fmt/format.h> |
| |
| #include <cstdlib> |
| #include <fstream> |
| #include <iostream> |
| |
| namespace |
| { |
| |
| armnn::TensorShape ParseTensorShape(std::istream& stream) |
| { |
| std::vector<unsigned int> result; |
| std::string line; |
| |
| while (std::getline(stream, line)) |
| { |
| std::vector<std::string> tokens = armnn::stringUtils::StringTokenizer(line, ","); |
| for (const std::string& token : tokens) |
| { |
| if (!token.empty()) |
| { |
| try |
| { |
| result.push_back(armnn::numeric_cast<unsigned int>(std::stoi((token)))); |
| } |
| catch (const std::exception&) |
| { |
| ARMNN_LOG(error) << "'" << token << "' is not a valid number. It has been ignored."; |
| } |
| } |
| } |
| } |
| |
| return armnn::TensorShape(armnn::numeric_cast<unsigned int>(result.size()), result.data()); |
| } |
| |
| int ParseCommandLineArgs(int argc, 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) |
| { |
| cxxopts::Options options("ArmNNConverter", "Convert a neural network model from provided file to ArmNN format."); |
| try |
| { |
| std::string modelFormatDescription("Format of the model file"); |
| #if defined(ARMNN_CAFFE_PARSER) |
| modelFormatDescription += ", caffe-binary, caffe-text"; |
| #endif |
| #if defined(ARMNN_ONNX_PARSER) |
| modelFormatDescription += ", onnx-binary, onnx-text"; |
| #endif |
| #if defined(ARMNN_TF_PARSER) |
| modelFormatDescription += ", tensorflow-binary, tensorflow-text"; |
| #endif |
| #if defined(ARMNN_TF_LITE_PARSER) |
| modelFormatDescription += ", tflite-binary"; |
| #endif |
| modelFormatDescription += "."; |
| options.add_options() |
| ("help", "Display usage information") |
| ("f,model-format", modelFormatDescription, cxxopts::value<std::string>(modelFormat)) |
| ("m,model-path", "Path to model file.", cxxopts::value<std::string>(modelPath)) |
| |
| ("i,input-name", "Identifier of the input tensors in the network. " |
| "Each input must be specified separately.", |
| cxxopts::value<std::vector<std::string>>(inputNames)) |
| ("s,input-tensor-shape", |
| "The shape of the input tensor in the network as a flat array of integers, " |
| "separated by comma. Each input shape must be specified separately after the input name. " |
| "This parameter is optional, depending on the network.", |
| cxxopts::value<std::vector<std::string>>(inputTensorShapeStrs)) |
| |
| ("o,output-name", "Identifier of the output tensor in the network.", |
| cxxopts::value<std::vector<std::string>>(outputNames)) |
| ("p,output-path", |
| "Path to serialize the network to.", cxxopts::value<std::string>(outputPath)); |
| } |
| catch (const std::exception& e) |
| { |
| std::cerr << e.what() << std::endl << options.help() << std::endl; |
| return EXIT_FAILURE; |
| } |
| try |
| { |
| cxxopts::ParseResult result = options.parse(argc, argv); |
| if (result.count("help")) |
| { |
| std::cerr << options.help() << std::endl; |
| return EXIT_SUCCESS; |
| } |
| // Check for mandatory single options. |
| std::string mandatorySingleParameters[] = { "model-format", "model-path", "output-name", "output-path" }; |
| bool somethingsMissing = false; |
| for (auto param : mandatorySingleParameters) |
| { |
| if (result.count(param) != 1) |
| { |
| std::cerr << "Parameter \'--" << param << "\' is required but missing." << std::endl; |
| somethingsMissing = true; |
| } |
| } |
| // Check at least one "input-name" option. |
| if (result.count("input-name") == 0) |
| { |
| std::cerr << "Parameter \'--" << "input-name" << "\' must be specified at least once." << std::endl; |
| somethingsMissing = true; |
| } |
| // If input-tensor-shape is specified then there must be a 1:1 match with input-name. |
| if (result.count("input-tensor-shape") > 0) |
| { |
| if (result.count("input-tensor-shape") != result.count("input-name")) |
| { |
| std::cerr << "When specifying \'input-tensor-shape\' a matching number of \'input-name\' parameters " |
| "must be specified." << std::endl; |
| somethingsMissing = true; |
| } |
| } |
| |
| if (somethingsMissing) |
| { |
| std::cerr << options.help() << std::endl; |
| return EXIT_FAILURE; |
| } |
| } |
| catch (const cxxopts::OptionException& e) |
| { |
| std::cerr << e.what() << std::endl << 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 |
| { |
| ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat << "'. Please include 'binary' or 'text'"; |
| return EXIT_FAILURE; |
| } |
| |
| 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(fmt::format( |
| "Not every input has its tensor shape specified: expected={0}, got={1}", |
| 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(fmt::format( |
| "Not every input has its tensor shape specified: expected={0}, got={1}", |
| 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(fmt::format( |
| "Not every input has its tensor shape specified: expected={0}, got={1}", |
| 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, char* argv[]) |
| { |
| |
| #if (!defined(ARMNN_CAFFE_PARSER) \ |
| && !defined(ARMNN_ONNX_PARSER) \ |
| && !defined(ARMNN_TF_PARSER) \ |
| && !defined(ARMNN_TF_LITE_PARSER)) |
| ARMNN_LOG(fatal) << "Not built with any of the supported parsers, Caffe, Onnx, Tensorflow, or TfLite."; |
| return EXIT_FAILURE; |
| #endif |
| |
| #if !defined(ARMNN_SERIALIZER) |
| ARMNN_LOG(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); |
| |
| 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) |
| { |
| ARMNN_LOG(fatal) << "Cannot create tensor shape: " << e.what(); |
| return EXIT_FAILURE; |
| } |
| } |
| } |
| |
| ArmnnConverter converter(modelPath, inputNames, inputTensorShapes, outputNames, outputPath, isModelBinary); |
| |
| try |
| { |
| if (modelFormat.find("caffe") != std::string::npos) |
| { |
| #if defined(ARMNN_CAFFE_PARSER) |
| if (!converter.CreateNetwork<armnnCaffeParser::ICaffeParser>()) |
| { |
| ARMNN_LOG(fatal) << "Failed to load model from file"; |
| return EXIT_FAILURE; |
| } |
| #else |
| ARMNN_LOG(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>()) |
| { |
| ARMNN_LOG(fatal) << "Failed to load model from file"; |
| return EXIT_FAILURE; |
| } |
| #else |
| ARMNN_LOG(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>()) |
| { |
| ARMNN_LOG(fatal) << "Failed to load model from file"; |
| return EXIT_FAILURE; |
| } |
| #else |
| ARMNN_LOG(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) |
| { |
| ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat << "'. Only 'binary' format supported \ |
| for tflite files"; |
| return EXIT_FAILURE; |
| } |
| |
| if (!converter.CreateNetwork<armnnTfLiteParser::ITfLiteParser>()) |
| { |
| ARMNN_LOG(fatal) << "Failed to load model from file"; |
| return EXIT_FAILURE; |
| } |
| #else |
| ARMNN_LOG(fatal) << "Not built with TfLite parser support."; |
| return EXIT_FAILURE; |
| #endif |
| } |
| else |
| { |
| ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat << "'"; |
| return EXIT_FAILURE; |
| } |
| } |
| catch(armnn::Exception& e) |
| { |
| ARMNN_LOG(fatal) << "Failed to load model from file: " << e.what(); |
| return EXIT_FAILURE; |
| } |
| |
| if (!converter.Serialize()) |
| { |
| ARMNN_LOG(fatal) << "Failed to serialize model"; |
| return EXIT_FAILURE; |
| } |
| |
| return EXIT_SUCCESS; |
| } |