Nattapat Chaimanowong | 4fbae33 | 2019-02-14 15:28:02 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | #include <armnn/ArmNN.hpp> |
| 6 | |
| 7 | #include <armnnSerializer/ISerializer.hpp> |
| 8 | #include <armnnTfParser/ITfParser.hpp> |
| 9 | |
| 10 | #include <Logging.hpp> |
| 11 | #include <HeapProfiling.hpp> |
| 12 | |
| 13 | #include <boost/format.hpp> |
| 14 | #include <boost/algorithm/string/split.hpp> |
| 15 | #include <boost/algorithm/string/classification.hpp> |
| 16 | #include <boost/program_options.hpp> |
| 17 | |
| 18 | #include <iostream> |
| 19 | #include <fstream> |
| 20 | |
| 21 | namespace |
| 22 | { |
| 23 | |
| 24 | namespace po = boost::program_options; |
| 25 | |
| 26 | armnn::TensorShape ParseTensorShape(std::istream& stream) |
| 27 | { |
| 28 | std::vector<unsigned int> result; |
| 29 | std::string line; |
| 30 | |
| 31 | while (std::getline(stream, line)) |
| 32 | { |
| 33 | std::vector<std::string> tokens; |
| 34 | try |
| 35 | { |
| 36 | // Coverity fix: boost::split() may throw an exception of type boost::bad_function_call. |
| 37 | boost::split(tokens, line, boost::algorithm::is_any_of(","), boost::token_compress_on); |
| 38 | } |
| 39 | catch (const std::exception& e) |
| 40 | { |
| 41 | BOOST_LOG_TRIVIAL(error) << "An error occurred when splitting tokens: " << e.what(); |
| 42 | continue; |
| 43 | } |
| 44 | for (const std::string& token : tokens) |
| 45 | { |
| 46 | if (!token.empty()) |
| 47 | { |
| 48 | try |
| 49 | { |
| 50 | result.push_back(boost::numeric_cast<unsigned int>(std::stoi((token)))); |
| 51 | } |
| 52 | catch (const std::exception&) |
| 53 | { |
| 54 | BOOST_LOG_TRIVIAL(error) << "'" << token << "' is not a valid number. It has been ignored."; |
| 55 | } |
| 56 | } |
| 57 | } |
| 58 | } |
| 59 | |
| 60 | return armnn::TensorShape(boost::numeric_cast<unsigned int>(result.size()), result.data()); |
| 61 | } |
| 62 | |
| 63 | bool CheckOption(const po::variables_map& vm, |
| 64 | const char* option) |
| 65 | { |
| 66 | if (option == nullptr) |
| 67 | { |
| 68 | return false; |
| 69 | } |
| 70 | |
| 71 | // Check whether 'option' is provided. |
| 72 | return vm.find(option) != vm.end(); |
| 73 | } |
| 74 | |
| 75 | void CheckOptionDependency(const po::variables_map& vm, |
| 76 | const char* option, |
| 77 | const char* required) |
| 78 | { |
| 79 | if (option == nullptr || required == nullptr) |
| 80 | { |
| 81 | throw po::error("Invalid option to check dependency for"); |
| 82 | } |
| 83 | |
| 84 | // Check that if 'option' is provided, 'required' is also provided. |
| 85 | if (CheckOption(vm, option) && !vm[option].defaulted()) |
| 86 | { |
| 87 | if (CheckOption(vm, required) == 0 || vm[required].defaulted()) |
| 88 | { |
| 89 | throw po::error(std::string("Option '") + option + "' requires option '" + required + "'."); |
| 90 | } |
| 91 | } |
| 92 | } |
| 93 | |
| 94 | void CheckOptionDependencies(const po::variables_map& vm) |
| 95 | { |
| 96 | CheckOptionDependency(vm, "model-path", "model-format"); |
| 97 | CheckOptionDependency(vm, "model-path", "input-name"); |
| 98 | CheckOptionDependency(vm, "model-path", "output-name"); |
| 99 | CheckOptionDependency(vm, "input-tensor-shape", "model-path"); |
| 100 | } |
| 101 | |
| 102 | int ParseCommandLineArgs(int argc, const char* argv[], |
| 103 | std::string& modelFormat, |
| 104 | std::string& modelPath, |
| 105 | std::vector<std::string>& inputNames, |
| 106 | std::vector<std::string>& inputTensorShapeStrs, |
| 107 | std::vector<std::string>& outputNames, |
| 108 | std::string& outputPath, bool& isModelBinary) |
| 109 | { |
| 110 | po::options_description desc("Options"); |
| 111 | |
| 112 | desc.add_options() |
| 113 | ("help", "Display usage information") |
| 114 | ("model-format,f", po::value(&modelFormat)->required(),"tensorflow-binary or tensorflow-text.") |
| 115 | ("model-path,m", po::value(&modelPath)->required(), "Path to model file") |
| 116 | ("input-name,i", po::value<std::vector<std::string>>()->multitoken(), |
| 117 | "Identifier of the input tensors in the network separated by whitespace") |
| 118 | ("input-tensor-shape,s", po::value<std::vector<std::string>>()->multitoken(), |
| 119 | "The shape of the input tensor in the network as a flat array of integers separated by comma" |
| 120 | "Multiple shapes are separated by whitespace" |
| 121 | "This parameter is optional, depending on the network.") |
| 122 | ("output-name,o", po::value<std::vector<std::string>>()->multitoken(), |
| 123 | "Identifier of the output tensor in the network.") |
| 124 | ("output-path,p", po::value(&outputPath)->required(), "Path to serialize the network to."); |
| 125 | |
| 126 | po::variables_map vm; |
| 127 | try |
| 128 | { |
| 129 | po::store(po::parse_command_line(argc, argv, desc), vm); |
| 130 | |
| 131 | if (CheckOption(vm, "help") || argc <= 1) |
| 132 | { |
| 133 | std::cout << "Convert a neural network model from provided file to ArmNN format " << std::endl; |
| 134 | std::cout << std::endl; |
| 135 | std::cout << desc << std::endl; |
| 136 | return EXIT_SUCCESS; |
| 137 | } |
| 138 | |
| 139 | po::notify(vm); |
| 140 | } |
| 141 | catch (const po::error& e) |
| 142 | { |
| 143 | std::cerr << e.what() << std::endl << std::endl; |
| 144 | std::cerr << desc << std::endl; |
| 145 | return EXIT_FAILURE; |
| 146 | } |
| 147 | |
| 148 | try |
| 149 | { |
| 150 | CheckOptionDependencies(vm); |
| 151 | } |
| 152 | catch (const po::error& e) |
| 153 | { |
| 154 | std::cerr << e.what() << std::endl << std::endl; |
| 155 | std::cerr << desc << std::endl; |
| 156 | return EXIT_FAILURE; |
| 157 | } |
| 158 | |
| 159 | if (modelFormat.find("bin") != std::string::npos) |
| 160 | { |
| 161 | isModelBinary = true; |
| 162 | } |
| 163 | else if (modelFormat.find("txt") != std::string::npos || modelFormat.find("text") != std::string::npos) |
| 164 | { |
| 165 | isModelBinary = false; |
| 166 | } |
| 167 | else |
| 168 | { |
| 169 | BOOST_LOG_TRIVIAL(fatal) << "Unknown model format: '" << modelFormat << "'. Please include 'binary' or 'text'"; |
| 170 | return EXIT_FAILURE; |
| 171 | } |
| 172 | |
| 173 | inputNames = vm["input-name"].as<std::vector<std::string>>(); |
| 174 | inputTensorShapeStrs = vm["input-tensor-shape"].as<std::vector<std::string>>(); |
| 175 | outputNames = vm["output-name"].as<std::vector<std::string>>(); |
| 176 | |
| 177 | return EXIT_SUCCESS; |
| 178 | } |
| 179 | |
| 180 | class ArmnnConverter |
| 181 | { |
| 182 | public: |
| 183 | ArmnnConverter(const std::string& modelPath, |
| 184 | const std::vector<std::string>& inputNames, |
| 185 | const std::vector<armnn::TensorShape>& inputShapes, |
| 186 | const std::vector<std::string>& outputNames, |
| 187 | const std::string& outputPath, |
| 188 | bool isModelBinary) |
| 189 | : m_NetworkPtr(armnn::INetworkPtr(nullptr, [](armnn::INetwork *){})), |
| 190 | m_ModelPath(modelPath), |
| 191 | m_InputNames(inputNames), |
| 192 | m_InputShapes(inputShapes), |
| 193 | m_OutputNames(outputNames), |
| 194 | m_OutputPath(outputPath), |
| 195 | m_IsModelBinary(isModelBinary) {} |
| 196 | |
| 197 | bool Serialize() |
| 198 | { |
| 199 | if (m_NetworkPtr.get() == nullptr) |
| 200 | { |
| 201 | return false; |
| 202 | } |
| 203 | |
| 204 | auto serializer(armnnSerializer::ISerializer::Create()); |
| 205 | |
| 206 | serializer->Serialize(*m_NetworkPtr); |
| 207 | |
| 208 | std::ofstream file(m_OutputPath, std::ios::out | std::ios::binary); |
| 209 | |
| 210 | bool retVal = serializer->SaveSerializedToStream(file); |
| 211 | |
| 212 | return retVal; |
| 213 | } |
| 214 | |
| 215 | template <typename IParser> |
| 216 | bool CreateNetwork () |
| 217 | { |
| 218 | // Create a network from a file on disk |
| 219 | auto parser(IParser::Create()); |
| 220 | |
| 221 | std::map<std::string, armnn::TensorShape> inputShapes; |
| 222 | if (!m_InputShapes.empty()) |
| 223 | { |
| 224 | const size_t numInputShapes = m_InputShapes.size(); |
| 225 | const size_t numInputBindings = m_InputNames.size(); |
| 226 | if (numInputShapes < numInputBindings) |
| 227 | { |
| 228 | throw armnn::Exception(boost::str(boost::format( |
| 229 | "Not every input has its tensor shape specified: expected=%1%, got=%2%") |
| 230 | % numInputBindings % numInputShapes)); |
| 231 | } |
| 232 | |
| 233 | for (size_t i = 0; i < numInputShapes; i++) |
| 234 | { |
| 235 | inputShapes[m_InputNames[i]] = m_InputShapes[i]; |
| 236 | } |
| 237 | } |
| 238 | |
| 239 | { |
| 240 | ARMNN_SCOPED_HEAP_PROFILING("Parsing"); |
| 241 | m_NetworkPtr = (m_IsModelBinary ? |
| 242 | parser->CreateNetworkFromBinaryFile(m_ModelPath.c_str(), inputShapes, m_OutputNames) : |
| 243 | parser->CreateNetworkFromTextFile(m_ModelPath.c_str(), inputShapes, m_OutputNames)); |
| 244 | } |
| 245 | |
| 246 | return m_NetworkPtr.get() != nullptr; |
| 247 | } |
| 248 | |
| 249 | private: |
| 250 | armnn::INetworkPtr m_NetworkPtr; |
| 251 | std::string m_ModelPath; |
| 252 | std::vector<std::string> m_InputNames; |
| 253 | std::vector<armnn::TensorShape> m_InputShapes; |
| 254 | std::vector<std::string> m_OutputNames; |
| 255 | std::string m_OutputPath; |
| 256 | bool m_IsModelBinary; |
| 257 | }; |
| 258 | |
| 259 | } // anonymous namespace |
| 260 | |
| 261 | int main(int argc, const char* argv[]) |
| 262 | { |
| 263 | |
| 264 | #if !defined(ARMNN_TF_PARSER) |
| 265 | BOOST_LOG_TRIVIAL(fatal) << "Not built with Tensorflow parser support."; |
| 266 | return EXIT_FAILURE; |
| 267 | #endif |
| 268 | |
| 269 | #if !defined(ARMNN_SERIALIZER) |
| 270 | BOOST_LOG_TRIVIAL(fatal) << "Not built with Serializer support."; |
| 271 | return EXIT_FAILURE; |
| 272 | #endif |
| 273 | |
| 274 | #ifdef NDEBUG |
| 275 | armnn::LogSeverity level = armnn::LogSeverity::Info; |
| 276 | #else |
| 277 | armnn::LogSeverity level = armnn::LogSeverity::Debug; |
| 278 | #endif |
| 279 | |
| 280 | armnn::ConfigureLogging(true, true, level); |
| 281 | armnnUtils::ConfigureLogging(boost::log::core::get().get(), true, true, level); |
| 282 | |
| 283 | std::string modelFormat; |
| 284 | std::string modelPath; |
| 285 | |
| 286 | std::vector<std::string> inputNames; |
| 287 | std::vector<std::string> inputTensorShapeStrs; |
| 288 | std::vector<armnn::TensorShape> inputTensorShapes; |
| 289 | |
| 290 | std::vector<std::string> outputNames; |
| 291 | std::string outputPath; |
| 292 | |
| 293 | bool isModelBinary = true; |
| 294 | |
| 295 | if (ParseCommandLineArgs( |
| 296 | argc, argv, modelFormat, modelPath, inputNames, inputTensorShapeStrs, outputNames, outputPath, isModelBinary) |
| 297 | != EXIT_SUCCESS) |
| 298 | { |
| 299 | return EXIT_FAILURE; |
| 300 | } |
| 301 | |
| 302 | for (const std::string& shapeStr : inputTensorShapeStrs) |
| 303 | { |
| 304 | if (!shapeStr.empty()) |
| 305 | { |
| 306 | std::stringstream ss(shapeStr); |
| 307 | |
| 308 | try |
| 309 | { |
| 310 | armnn::TensorShape shape = ParseTensorShape(ss); |
| 311 | inputTensorShapes.push_back(shape); |
| 312 | } |
| 313 | catch (const armnn::InvalidArgumentException& e) |
| 314 | { |
| 315 | BOOST_LOG_TRIVIAL(fatal) << "Cannot create tensor shape: " << e.what(); |
| 316 | return EXIT_FAILURE; |
| 317 | } |
| 318 | } |
| 319 | } |
| 320 | |
| 321 | ArmnnConverter converter(modelPath, inputNames, inputTensorShapes, outputNames, outputPath, isModelBinary); |
| 322 | |
| 323 | if (modelFormat.find("tensorflow") != std::string::npos) |
| 324 | { |
| 325 | if (!converter.CreateNetwork<armnnTfParser::ITfParser>()) |
| 326 | { |
| 327 | BOOST_LOG_TRIVIAL(fatal) << "Failed to load model from file"; |
| 328 | return EXIT_FAILURE; |
| 329 | } |
| 330 | } |
| 331 | else |
| 332 | { |
| 333 | BOOST_LOG_TRIVIAL(fatal) << "Unknown model format: '" << modelFormat; |
| 334 | return EXIT_FAILURE; |
| 335 | } |
| 336 | |
| 337 | if (!converter.Serialize()) |
| 338 | { |
| 339 | BOOST_LOG_TRIVIAL(fatal) << "Failed to serialize model"; |
| 340 | return EXIT_FAILURE; |
| 341 | } |
| 342 | |
| 343 | return EXIT_SUCCESS; |
| 344 | } |