telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame^] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // See LICENSE file in the project root for full license information. |
| 4 | // |
| 5 | #include "armnn/ArmNN.hpp" |
| 6 | #if defined(ARMNN_CAFFE_PARSER) |
| 7 | #include "armnnCaffeParser/ICaffeParser.hpp" |
| 8 | #endif |
| 9 | #include "Logging.hpp" |
| 10 | #include "../InferenceTest.hpp" |
| 11 | |
| 12 | #include <boost/program_options.hpp> |
| 13 | #include <boost/algorithm/string/split.hpp> |
| 14 | #include <boost/algorithm/string/classification.hpp> |
| 15 | |
| 16 | #include <iostream> |
| 17 | #include <fstream> |
| 18 | |
| 19 | namespace |
| 20 | { |
| 21 | |
| 22 | template<typename T, typename TParseElementFunc> |
| 23 | std::vector<T> ParseArrayImpl(std::istream& stream, TParseElementFunc parseElementFunc) |
| 24 | { |
| 25 | std::vector<T> result; |
| 26 | // Process line-by-line |
| 27 | std::string line; |
| 28 | while (std::getline(stream, line)) |
| 29 | { |
| 30 | std::vector<std::string> tokens; |
| 31 | boost::split(tokens, line, boost::algorithm::is_any_of("\t ,;:"), boost::token_compress_on); |
| 32 | for (const std::string& token : tokens) |
| 33 | { |
| 34 | if (!token.empty()) // See https://stackoverflow.com/questions/10437406/ |
| 35 | { |
| 36 | try |
| 37 | { |
| 38 | result.push_back(parseElementFunc(token)); |
| 39 | } |
| 40 | catch (const std::exception&) |
| 41 | { |
| 42 | BOOST_LOG_TRIVIAL(error) << "'" << token << "' is not a valid number. It has been ignored."; |
| 43 | } |
| 44 | } |
| 45 | } |
| 46 | } |
| 47 | |
| 48 | return result; |
| 49 | } |
| 50 | |
| 51 | } |
| 52 | |
| 53 | template<typename T> |
| 54 | std::vector<T> ParseArray(std::istream& stream); |
| 55 | |
| 56 | template<> |
| 57 | std::vector<float> ParseArray(std::istream& stream) |
| 58 | { |
| 59 | return ParseArrayImpl<float>(stream, [](const std::string& s) { return std::stof(s); }); |
| 60 | } |
| 61 | |
| 62 | template<> |
| 63 | std::vector<unsigned int> ParseArray(std::istream& stream) |
| 64 | { |
| 65 | return ParseArrayImpl<unsigned int>(stream, |
| 66 | [](const std::string& s) { return boost::numeric_cast<unsigned int>(std::stoi(s)); }); |
| 67 | } |
| 68 | |
| 69 | void PrintArray(const std::vector<float>& v) |
| 70 | { |
| 71 | for (size_t i = 0; i < v.size(); i++) |
| 72 | { |
| 73 | printf("%f ", v[i]); |
| 74 | } |
| 75 | printf("\n"); |
| 76 | } |
| 77 | |
| 78 | template<typename TParser, typename TDataType> |
| 79 | int MainImpl(const char* modelPath, bool isModelBinary, armnn::Compute computeDevice, |
| 80 | const char* inputName, const armnn::TensorShape* inputTensorShape, const char* inputTensorDataFilePath, |
| 81 | const char* outputName) |
| 82 | { |
| 83 | // Load input tensor |
| 84 | std::vector<TDataType> input; |
| 85 | { |
| 86 | std::ifstream inputTensorFile(inputTensorDataFilePath); |
| 87 | if (!inputTensorFile.good()) |
| 88 | { |
| 89 | BOOST_LOG_TRIVIAL(fatal) << "Failed to load input tensor data file from " << inputTensorDataFilePath; |
| 90 | return 1; |
| 91 | } |
| 92 | input = ParseArray<TDataType>(inputTensorFile); |
| 93 | } |
| 94 | |
| 95 | try |
| 96 | { |
| 97 | // Create an InferenceModel, which will parse the model and load it into an IRuntime |
| 98 | typename InferenceModel<TParser, TDataType>::Params params; |
| 99 | params.m_ModelPath = modelPath; |
| 100 | params.m_IsModelBinary = isModelBinary; |
| 101 | params.m_ComputeDevice = computeDevice; |
| 102 | params.m_InputBinding = inputName; |
| 103 | params.m_InputTensorShape = inputTensorShape; |
| 104 | params.m_OutputBinding = outputName; |
| 105 | InferenceModel<TParser, TDataType> model(params); |
| 106 | |
| 107 | // Execute the model |
| 108 | std::vector<TDataType> output(model.GetOutputSize()); |
| 109 | model.Run(input, output); |
| 110 | |
| 111 | // Print the output tensor |
| 112 | PrintArray(output); |
| 113 | } |
| 114 | catch (armnn::Exception const& e) |
| 115 | { |
| 116 | BOOST_LOG_TRIVIAL(fatal) << "Armnn Error: " << e.what(); |
| 117 | return 1; |
| 118 | } |
| 119 | |
| 120 | return 0; |
| 121 | } |
| 122 | |
| 123 | int main(int argc, char* argv[]) |
| 124 | { |
| 125 | // Configure logging for both the ARMNN library and this test program |
| 126 | #ifdef NDEBUG |
| 127 | armnn::LogSeverity level = armnn::LogSeverity::Info; |
| 128 | #else |
| 129 | armnn::LogSeverity level = armnn::LogSeverity::Debug; |
| 130 | #endif |
| 131 | armnn::ConfigureLogging(true, true, level); |
| 132 | armnnUtils::ConfigureLogging(boost::log::core::get().get(), true, true, level); |
| 133 | |
| 134 | // Configure boost::program_options for command-line parsing |
| 135 | namespace po = boost::program_options; |
| 136 | |
| 137 | std::string modelFormat; |
| 138 | std::string modelPath; |
| 139 | std::string inputName; |
| 140 | std::string inputTensorShapeStr; |
| 141 | std::string inputTensorDataFilePath; |
| 142 | std::string outputName; |
| 143 | armnn::Compute computeDevice; |
| 144 | |
| 145 | po::options_description desc("Options"); |
| 146 | try |
| 147 | { |
| 148 | desc.add_options() |
| 149 | ("help", "Display usage information") |
| 150 | ("model-format,f", po::value(&modelFormat)->required(), |
| 151 | "caffe-binary, caffe-text, tensorflow-binary or tensorflow-text.") |
| 152 | ("model-path,m", po::value(&modelPath)->required(), "Path to model file, e.g. .caffemodel, .prototxt") |
| 153 | ("compute,c", po::value<armnn::Compute>(&computeDevice)->required(), |
| 154 | "Which device to run layers on by default. Possible choices: CpuAcc, CpuRef, GpuAcc") |
| 155 | ("input-name,i", po::value(&inputName)->required(), "Identifier of the input tensor in the network.") |
| 156 | ("input-tensor-shape,s", po::value(&inputTensorShapeStr), |
| 157 | "The shape of the input tensor in the network as a flat array of integers separated by whitespace. " |
| 158 | "This parameter is optional, depending on the network.") |
| 159 | ("input-tensor-data,d", po::value(&inputTensorDataFilePath)->required(), |
| 160 | "Path to a file containing the input data as a flat array separated by whitespace.") |
| 161 | ("output-name,o", po::value(&outputName)->required(), "Identifier of the output tensor in the network."); |
| 162 | } |
| 163 | catch (const std::exception& e) |
| 164 | { |
| 165 | // Coverity points out that default_value(...) can throw a bad_lexical_cast, |
| 166 | // and that desc.add_options() can throw boost::io::too_few_args. |
| 167 | // They really won't in any of these cases. |
| 168 | BOOST_ASSERT_MSG(false, "Caught unexpected exception"); |
| 169 | BOOST_LOG_TRIVIAL(fatal) << "Fatal internal error: " << e.what(); |
| 170 | return 1; |
| 171 | } |
| 172 | |
| 173 | // Parse the command-line |
| 174 | po::variables_map vm; |
| 175 | try |
| 176 | { |
| 177 | po::store(po::parse_command_line(argc, argv, desc), vm); |
| 178 | |
| 179 | if (vm.count("help") || argc <= 1) |
| 180 | { |
| 181 | std::cout << "Executes a neural network model using the provided input tensor. " << std::endl; |
| 182 | std::cout << "Prints the resulting output tensor." << std::endl; |
| 183 | std::cout << std::endl; |
| 184 | std::cout << desc << std::endl; |
| 185 | return 1; |
| 186 | } |
| 187 | |
| 188 | po::notify(vm); |
| 189 | } |
| 190 | catch (po::error& e) |
| 191 | { |
| 192 | std::cerr << e.what() << std::endl << std::endl; |
| 193 | std::cerr << desc << std::endl; |
| 194 | return 1; |
| 195 | } |
| 196 | |
| 197 | // Parse model binary flag from the model-format string we got from the command-line |
| 198 | bool isModelBinary; |
| 199 | if (modelFormat.find("bin") != std::string::npos) |
| 200 | { |
| 201 | isModelBinary = true; |
| 202 | } |
| 203 | else if (modelFormat.find("txt") != std::string::npos || modelFormat.find("text") != std::string::npos) |
| 204 | { |
| 205 | isModelBinary = false; |
| 206 | } |
| 207 | else |
| 208 | { |
| 209 | BOOST_LOG_TRIVIAL(fatal) << "Unknown model format: '" << modelFormat << "'. Please include 'binary' or 'text'"; |
| 210 | return 1; |
| 211 | } |
| 212 | |
| 213 | // Parse input tensor shape from the string we got from the command-line. |
| 214 | std::unique_ptr<armnn::TensorShape> inputTensorShape; |
| 215 | if (!inputTensorShapeStr.empty()) |
| 216 | { |
| 217 | std::stringstream ss(inputTensorShapeStr); |
| 218 | std::vector<unsigned int> dims = ParseArray<unsigned int>(ss); |
| 219 | inputTensorShape = std::make_unique<armnn::TensorShape>(dims.size(), dims.data()); |
| 220 | } |
| 221 | |
| 222 | // Forward to implementation based on the parser type |
| 223 | if (modelFormat.find("caffe") != std::string::npos) |
| 224 | { |
| 225 | #if defined(ARMNN_CAFFE_PARSER) |
| 226 | return MainImpl<armnnCaffeParser::ICaffeParser, float>(modelPath.c_str(), isModelBinary, computeDevice, |
| 227 | inputName.c_str(), inputTensorShape.get(), inputTensorDataFilePath.c_str(), outputName.c_str()); |
| 228 | #else |
| 229 | BOOST_LOG_TRIVIAL(fatal) << "Not built with Caffe parser support."; |
| 230 | return 1; |
| 231 | #endif |
| 232 | } |
| 233 | else if (modelFormat.find("tensorflow") != std::string::npos) |
| 234 | { |
| 235 | BOOST_LOG_TRIVIAL(fatal) << "Not built with Tensorflow parser support."; |
| 236 | return 1; |
| 237 | } |
| 238 | else |
| 239 | { |
| 240 | BOOST_LOG_TRIVIAL(fatal) << "Unknown model format: '" << modelFormat << |
| 241 | "'. Please include 'caffe' or 'tensorflow'"; |
| 242 | return 1; |
| 243 | } |
| 244 | } |