| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #include <armnn/ArmNN.hpp> |
| #include <armnn/TypesUtils.hpp> |
| |
| #if defined(ARMNN_SERIALIZER) |
| #include "armnnDeserializer/IDeserializer.hpp" |
| #endif |
| #if defined(ARMNN_CAFFE_PARSER) |
| #include "armnnCaffeParser/ICaffeParser.hpp" |
| #endif |
| #if defined(ARMNN_TF_PARSER) |
| #include "armnnTfParser/ITfParser.hpp" |
| #endif |
| #if defined(ARMNN_TF_LITE_PARSER) |
| #include "armnnTfLiteParser/ITfLiteParser.hpp" |
| #endif |
| #if defined(ARMNN_ONNX_PARSER) |
| #include "armnnOnnxParser/IOnnxParser.hpp" |
| #endif |
| #include "CsvReader.hpp" |
| #include "../InferenceTest.hpp" |
| |
| #include <Logging.hpp> |
| #include <Profiling.hpp> |
| |
| #include <boost/algorithm/string/trim.hpp> |
| #include <boost/algorithm/string/split.hpp> |
| #include <boost/algorithm/string/classification.hpp> |
| #include <boost/program_options.hpp> |
| #include <boost/variant.hpp> |
| |
| #include <iostream> |
| #include <fstream> |
| #include <functional> |
| #include <future> |
| #include <algorithm> |
| #include <iterator> |
| |
| namespace |
| { |
| |
| // Configure boost::program_options for command-line parsing and validation. |
| namespace po = boost::program_options; |
| |
| template<typename T, typename TParseElementFunc> |
| std::vector<T> ParseArrayImpl(std::istream& stream, TParseElementFunc parseElementFunc, const char * chars = "\t ,:") |
| { |
| std::vector<T> result; |
| // Processes line-by-line. |
| 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(chars), 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()) // See https://stackoverflow.com/questions/10437406/ |
| { |
| try |
| { |
| result.push_back(parseElementFunc(token)); |
| } |
| catch (const std::exception&) |
| { |
| BOOST_LOG_TRIVIAL(error) << "'" << token << "' is not a valid number. It has been ignored."; |
| } |
| } |
| } |
| } |
| |
| return result; |
| } |
| |
| bool CheckOption(const po::variables_map& vm, |
| const char* option) |
| { |
| // Check that the given option is valid. |
| 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) |
| { |
| // Check that the given options are valid. |
| 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", "input-tensor-data"); |
| CheckOptionDependency(vm, "model-path", "output-name"); |
| CheckOptionDependency(vm, "input-tensor-shape", "model-path"); |
| } |
| |
| template<armnn::DataType NonQuantizedType> |
| auto ParseDataArray(std::istream & stream); |
| |
| template<armnn::DataType QuantizedType> |
| auto ParseDataArray(std::istream& stream, |
| const float& quantizationScale, |
| const int32_t& quantizationOffset); |
| |
| template<> |
| auto ParseDataArray<armnn::DataType::Float32>(std::istream & stream) |
| { |
| return ParseArrayImpl<float>(stream, [](const std::string& s) { return std::stof(s); }); |
| } |
| |
| template<> |
| auto ParseDataArray<armnn::DataType::Signed32>(std::istream & stream) |
| { |
| return ParseArrayImpl<int>(stream, [](const std::string & s) { return std::stoi(s); }); |
| } |
| |
| template<> |
| auto ParseDataArray<armnn::DataType::QuantisedAsymm8>(std::istream& stream, |
| const float& quantizationScale, |
| const int32_t& quantizationOffset) |
| { |
| return ParseArrayImpl<uint8_t>(stream, |
| [&quantizationScale, &quantizationOffset](const std::string & s) |
| { |
| return boost::numeric_cast<uint8_t>( |
| armnn::Quantize<u_int8_t>(std::stof(s), |
| quantizationScale, |
| quantizationOffset)); |
| }); |
| } |
| |
| std::vector<unsigned int> ParseArray(std::istream& stream) |
| { |
| return ParseArrayImpl<unsigned int>(stream, |
| [](const std::string& s) { return boost::numeric_cast<unsigned int>(std::stoi(s)); }); |
| } |
| |
| std::vector<std::string> ParseStringList(const std::string & inputString, const char * delimiter) |
| { |
| std::stringstream stream(inputString); |
| return ParseArrayImpl<std::string>(stream, [](const std::string& s) { return boost::trim_copy(s); }, delimiter); |
| } |
| |
| void RemoveDuplicateDevices(std::vector<armnn::BackendId>& computeDevices) |
| { |
| // Mark the duplicate devices as 'Undefined'. |
| for (auto i = computeDevices.begin(); i != computeDevices.end(); ++i) |
| { |
| for (auto j = std::next(i); j != computeDevices.end(); ++j) |
| { |
| if (*j == *i) |
| { |
| *j = armnn::Compute::Undefined; |
| } |
| } |
| } |
| |
| // Remove 'Undefined' devices. |
| computeDevices.erase(std::remove(computeDevices.begin(), computeDevices.end(), armnn::Compute::Undefined), |
| computeDevices.end()); |
| } |
| |
| } // namespace |
| |
| template<typename TParser, typename TDataType> |
| int MainImpl(const char* modelPath, |
| bool isModelBinary, |
| const std::vector<armnn::BackendId>& computeDevices, |
| const std::vector<string>& inputNames, |
| const std::vector<std::unique_ptr<armnn::TensorShape>>& inputTensorShapes, |
| const std::vector<string>& inputTensorDataFilePaths, |
| const std::vector<string>& inputTypes, |
| const std::vector<string>& outputTypes, |
| const std::vector<string>& outputNames, |
| bool enableProfiling, |
| bool enableFp16TurboMode, |
| const double& thresholdTime, |
| const size_t subgraphId, |
| const std::shared_ptr<armnn::IRuntime>& runtime = nullptr) |
| { |
| using TContainer = boost::variant<std::vector<float>, std::vector<int>, std::vector<unsigned char>>; |
| |
| std::vector<TContainer> inputDataContainers; |
| |
| try |
| { |
| // Creates an InferenceModel, which will parse the model and load it into an IRuntime. |
| typename InferenceModel<TParser, TDataType>::Params params; |
| params.m_ModelPath = modelPath; |
| params.m_IsModelBinary = isModelBinary; |
| params.m_ComputeDevices = computeDevices; |
| |
| for(const std::string& inputName: inputNames) |
| { |
| params.m_InputBindings.push_back(inputName); |
| } |
| |
| for(unsigned int i = 0; i < inputTensorShapes.size(); ++i) |
| { |
| params.m_InputShapes.push_back(*inputTensorShapes[i]); |
| } |
| |
| for(const std::string& outputName: outputNames) |
| { |
| params.m_OutputBindings.push_back(outputName); |
| } |
| |
| params.m_SubgraphId = subgraphId; |
| params.m_EnableFp16TurboMode = enableFp16TurboMode; |
| InferenceModel<TParser, TDataType> model(params, enableProfiling, runtime); |
| |
| for(unsigned int i = 0; i < inputTensorDataFilePaths.size(); ++i) |
| { |
| std::ifstream inputTensorFile(inputTensorDataFilePaths[i]); |
| |
| if (inputTypes[i].compare("float") == 0) |
| { |
| inputDataContainers.push_back( |
| ParseDataArray<armnn::DataType::Float32>(inputTensorFile)); |
| } |
| else if (inputTypes[i].compare("int") == 0) |
| { |
| inputDataContainers.push_back( |
| ParseDataArray<armnn::DataType::Signed32>(inputTensorFile)); |
| } |
| else if (inputTypes[i].compare("qasymm8") == 0) |
| { |
| auto inputBinding = model.GetInputBindingInfo(); |
| inputDataContainers.push_back( |
| ParseDataArray<armnn::DataType::QuantisedAsymm8>(inputTensorFile, |
| inputBinding.second.GetQuantizationScale(), |
| inputBinding.second.GetQuantizationOffset())); |
| } |
| else |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Unsupported tensor data type \"" << inputTypes[i] << "\". "; |
| return EXIT_FAILURE; |
| } |
| |
| inputTensorFile.close(); |
| } |
| |
| const size_t numOutputs = params.m_OutputBindings.size(); |
| std::vector<TContainer> outputDataContainers; |
| |
| for (unsigned int i = 0; i < numOutputs; ++i) |
| { |
| if (outputTypes[i].compare("float") == 0) |
| { |
| outputDataContainers.push_back(std::vector<float>(model.GetOutputSize(i))); |
| } |
| else if (outputTypes[i].compare("int") == 0) |
| { |
| outputDataContainers.push_back(std::vector<int>(model.GetOutputSize(i))); |
| } |
| else if (outputTypes[i].compare("qasymm8") == 0) |
| { |
| outputDataContainers.push_back(std::vector<uint8_t>(model.GetOutputSize(i))); |
| } |
| else |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Unsupported tensor data type \"" << outputTypes[i] << "\". "; |
| return EXIT_FAILURE; |
| } |
| } |
| |
| // model.Run returns the inference time elapsed in EnqueueWorkload (in milliseconds) |
| auto inference_duration = model.Run(inputDataContainers, outputDataContainers); |
| |
| // Print output tensors |
| for (size_t i = 0; i < numOutputs; i++) |
| { |
| boost::apply_visitor([&](auto&& value) |
| { |
| std::cout << params.m_OutputBindings[i] << ": "; |
| for (size_t i = 0; i < value.size(); ++i) |
| { |
| printf("%f ", static_cast<float>(value[i])); |
| } |
| printf("\n"); |
| }, |
| outputDataContainers[i]); |
| } |
| |
| BOOST_LOG_TRIVIAL(info) << "\nInference time: " << std::setprecision(2) |
| << std::fixed << inference_duration.count() << " ms"; |
| |
| // If thresholdTime == 0.0 (default), then it hasn't been supplied at command line |
| if (thresholdTime != 0.0) |
| { |
| BOOST_LOG_TRIVIAL(info) << "Threshold time: " << std::setprecision(2) |
| << std::fixed << thresholdTime << " ms"; |
| auto thresholdMinusInference = thresholdTime - inference_duration.count(); |
| BOOST_LOG_TRIVIAL(info) << "Threshold time - Inference time: " << std::setprecision(2) |
| << std::fixed << thresholdMinusInference << " ms" << "\n"; |
| |
| if (thresholdMinusInference < 0) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Elapsed inference time is greater than provided threshold time.\n"; |
| return EXIT_FAILURE; |
| } |
| } |
| |
| |
| } |
| catch (armnn::Exception const& e) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Armnn Error: " << e.what(); |
| return EXIT_FAILURE; |
| } |
| |
| return EXIT_SUCCESS; |
| } |
| |
| // This will run a test |
| int RunTest(const std::string& format, |
| const std::string& inputTensorShapesStr, |
| const vector<armnn::BackendId>& computeDevice, |
| const std::string& path, |
| const std::string& inputNames, |
| const std::string& inputTensorDataFilePaths, |
| const std::string& inputTypes, |
| const std::string& outputTypes, |
| const std::string& outputNames, |
| bool enableProfiling, |
| bool enableFp16TurboMode, |
| const double& thresholdTime, |
| const size_t subgraphId, |
| const std::shared_ptr<armnn::IRuntime>& runtime = nullptr) |
| { |
| std::string modelFormat = boost::trim_copy(format); |
| std::string modelPath = boost::trim_copy(path); |
| std::vector<std::string> inputNamesVector = ParseStringList(inputNames, ","); |
| std::vector<std::string> inputTensorShapesVector = ParseStringList(inputTensorShapesStr, ";"); |
| std::vector<std::string> inputTensorDataFilePathsVector = ParseStringList( |
| inputTensorDataFilePaths, ","); |
| std::vector<std::string> outputNamesVector = ParseStringList(outputNames, ","); |
| std::vector<std::string> inputTypesVector = ParseStringList(inputTypes, ","); |
| std::vector<std::string> outputTypesVector = ParseStringList(outputTypes, ","); |
| |
| // Parse model binary flag from the model-format string we got from the command-line |
| bool isModelBinary; |
| if (modelFormat.find("bin") != std::string::npos) |
| { |
| isModelBinary = true; |
| } |
| else if (modelFormat.find("txt") != std::string::npos || 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 ((inputTensorShapesVector.size() != 0) && (inputTensorShapesVector.size() != inputNamesVector.size())) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "input-name and input-tensor-shape must have the same amount of elements."; |
| return EXIT_FAILURE; |
| } |
| |
| if ((inputTensorDataFilePathsVector.size() != 0) && |
| (inputTensorDataFilePathsVector.size() != inputNamesVector.size())) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "input-name and input-tensor-data must have the same amount of elements."; |
| return EXIT_FAILURE; |
| } |
| |
| if (inputTypesVector.size() == 0) |
| { |
| //Defaults the value of all inputs to "float" |
| inputTypesVector.assign(inputNamesVector.size(), "float"); |
| } |
| if (outputTypesVector.size() == 0) |
| { |
| //Defaults the value of all outputs to "float" |
| outputTypesVector.assign(outputNamesVector.size(), "float"); |
| } |
| else if ((inputTypesVector.size() != 0) && (inputTypesVector.size() != inputNamesVector.size())) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "input-name and input-type must have the same amount of elements."; |
| return EXIT_FAILURE; |
| } |
| |
| // Parse input tensor shape from the string we got from the command-line. |
| std::vector<std::unique_ptr<armnn::TensorShape>> inputTensorShapes; |
| |
| if (!inputTensorShapesVector.empty()) |
| { |
| inputTensorShapes.reserve(inputTensorShapesVector.size()); |
| |
| for(const std::string& shape : inputTensorShapesVector) |
| { |
| std::stringstream ss(shape); |
| std::vector<unsigned int> dims = ParseArray(ss); |
| |
| try |
| { |
| // Coverity fix: An exception of type armnn::InvalidArgumentException is thrown and never caught. |
| inputTensorShapes.push_back(std::make_unique<armnn::TensorShape>(dims.size(), dims.data())); |
| } |
| catch (const armnn::InvalidArgumentException& e) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Cannot create tensor shape: " << e.what(); |
| return EXIT_FAILURE; |
| } |
| } |
| } |
| |
| // Check that threshold time is not less than zero |
| if (thresholdTime < 0) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Threshold time supplied as a commoand line argument is less than zero."; |
| return EXIT_FAILURE; |
| } |
| |
| // Forward to implementation based on the parser type |
| if (modelFormat.find("armnn") != std::string::npos) |
| { |
| #if defined(ARMNN_SERIALIZER) |
| return MainImpl<armnnDeserializer::IDeserializer, float>( |
| modelPath.c_str(), isModelBinary, computeDevice, |
| inputNamesVector, inputTensorShapes, |
| inputTensorDataFilePathsVector, inputTypesVector, |
| outputTypesVector, outputNamesVector, enableProfiling, |
| enableFp16TurboMode, thresholdTime, subgraphId, runtime); |
| #else |
| BOOST_LOG_TRIVIAL(fatal) << "Not built with serialization support."; |
| return EXIT_FAILURE; |
| #endif |
| } |
| else if (modelFormat.find("caffe") != std::string::npos) |
| { |
| #if defined(ARMNN_CAFFE_PARSER) |
| return MainImpl<armnnCaffeParser::ICaffeParser, float>(modelPath.c_str(), isModelBinary, computeDevice, |
| inputNamesVector, inputTensorShapes, |
| inputTensorDataFilePathsVector, inputTypesVector, |
| outputTypesVector, outputNamesVector, enableProfiling, |
| enableFp16TurboMode, thresholdTime, subgraphId, runtime); |
| #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) |
| return MainImpl<armnnOnnxParser::IOnnxParser, float>(modelPath.c_str(), isModelBinary, computeDevice, |
| inputNamesVector, inputTensorShapes, |
| inputTensorDataFilePathsVector, inputTypesVector, |
| outputTypesVector, outputNamesVector, enableProfiling, |
| enableFp16TurboMode, thresholdTime, subgraphId, runtime); |
| #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) |
| return MainImpl<armnnTfParser::ITfParser, float>(modelPath.c_str(), isModelBinary, computeDevice, |
| inputNamesVector, inputTensorShapes, |
| inputTensorDataFilePathsVector, inputTypesVector, |
| outputTypesVector, outputNamesVector, enableProfiling, |
| enableFp16TurboMode, thresholdTime, subgraphId, runtime); |
| #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; |
| } |
| return MainImpl<armnnTfLiteParser::ITfLiteParser, float>(modelPath.c_str(), isModelBinary, computeDevice, |
| inputNamesVector, inputTensorShapes, |
| inputTensorDataFilePathsVector, inputTypesVector, |
| outputTypesVector, outputNamesVector, enableProfiling, |
| enableFp16TurboMode, thresholdTime, subgraphId, |
| runtime); |
| #else |
| BOOST_LOG_TRIVIAL(fatal) << "Unknown model format: '" << modelFormat << |
| "'. Please include 'caffe', 'tensorflow', 'tflite' or 'onnx'"; |
| return EXIT_FAILURE; |
| #endif |
| } |
| else |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Unknown model format: '" << modelFormat << |
| "'. Please include 'caffe', 'tensorflow', 'tflite' or 'onnx'"; |
| return EXIT_FAILURE; |
| } |
| } |
| |
| int RunCsvTest(const armnnUtils::CsvRow &csvRow, const std::shared_ptr<armnn::IRuntime>& runtime, |
| const bool enableProfiling, const bool enableFp16TurboMode, const double& thresholdTime) |
| { |
| std::string modelFormat; |
| std::string modelPath; |
| std::string inputNames; |
| std::string inputTensorShapes; |
| std::string inputTensorDataFilePaths; |
| std::string outputNames; |
| std::string inputTypes; |
| std::string outputTypes; |
| |
| size_t subgraphId = 0; |
| |
| const std::string backendsMessage = std::string("The preferred order of devices to run layers on by default. ") |
| + std::string("Possible choices: ") |
| + armnn::BackendRegistryInstance().GetBackendIdsAsString(); |
| |
| po::options_description desc("Options"); |
| try |
| { |
| desc.add_options() |
| ("model-format,f", po::value(&modelFormat), |
| "armnn-binary, caffe-binary, caffe-text, tflite-binary, onnx-binary, onnx-text, tensorflow-binary or " |
| "tensorflow-text.") |
| ("model-path,m", po::value(&modelPath), "Path to model file, e.g. .armnn, .caffemodel, .prototxt, " |
| ".tflite, .onnx") |
| ("compute,c", po::value<std::vector<armnn::BackendId>>()->multitoken(), |
| backendsMessage.c_str()) |
| ("input-name,i", po::value(&inputNames), "Identifier of the input tensors in the network separated by comma.") |
| ("subgraph-number,n", po::value<size_t>(&subgraphId)->default_value(0), "Id of the subgraph to be " |
| "executed. Defaults to 0.") |
| ("input-tensor-shape,s", po::value(&inputTensorShapes), |
| "The shape of the input tensors in the network as a flat array of integers separated by comma. " |
| "Several shapes can be passed separating them by semicolon. " |
| "This parameter is optional, depending on the network.") |
| ("input-tensor-data,d", po::value(&inputTensorDataFilePaths), |
| "Path to files containing the input data as a flat array separated by whitespace. " |
| "Several paths can be passed separating them by comma.") |
| ("input-type,y",po::value(&inputTypes), "The type of the input tensors in the network separated by comma. " |
| "If unset, defaults to \"float\" for all defined inputs. " |
| "Accepted values (float, int or qasymm8).") |
| ("output-type,z",po::value(&outputTypes), "The type of the output tensors in the network separated by comma. " |
| "If unset, defaults to \"float\" for all defined outputs. " |
| "Accepted values (float, int or qasymm8).") |
| ("output-name,o", po::value(&outputNames), |
| "Identifier of the output tensors in the network separated by comma."); |
| } |
| catch (const std::exception& e) |
| { |
| // Coverity points out that default_value(...) can throw a bad_lexical_cast, |
| // and that desc.add_options() can throw boost::io::too_few_args. |
| // They really won't in any of these cases. |
| BOOST_ASSERT_MSG(false, "Caught unexpected exception"); |
| BOOST_LOG_TRIVIAL(fatal) << "Fatal internal error: " << e.what(); |
| return EXIT_FAILURE; |
| } |
| |
| std::vector<const char*> clOptions; |
| clOptions.reserve(csvRow.values.size()); |
| for (const std::string& value : csvRow.values) |
| { |
| clOptions.push_back(value.c_str()); |
| } |
| |
| po::variables_map vm; |
| try |
| { |
| po::store(po::parse_command_line(static_cast<int>(clOptions.size()), clOptions.data(), desc), vm); |
| |
| po::notify(vm); |
| |
| CheckOptionDependencies(vm); |
| } |
| catch (const po::error& e) |
| { |
| std::cerr << e.what() << std::endl << std::endl; |
| std::cerr << desc << std::endl; |
| return EXIT_FAILURE; |
| } |
| |
| // Get the preferred order of compute devices. |
| std::vector<armnn::BackendId> computeDevices = vm["compute"].as<std::vector<armnn::BackendId>>(); |
| |
| // Remove duplicates from the list of compute devices. |
| RemoveDuplicateDevices(computeDevices); |
| |
| // Check that the specified compute devices are valid. |
| std::string invalidBackends; |
| if (!CheckRequestedBackendsAreValid(computeDevices, armnn::Optional<std::string&>(invalidBackends))) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "The list of preferred devices contains invalid backend IDs: " |
| << invalidBackends; |
| return EXIT_FAILURE; |
| } |
| |
| return RunTest(modelFormat, inputTensorShapes, computeDevices, modelPath, inputNames, |
| inputTensorDataFilePaths, inputTypes, outputTypes, outputNames, |
| enableProfiling, enableFp16TurboMode, thresholdTime, subgraphId); |
| } |
| |
| // MAIN |
| int main(int argc, const char* argv[]) |
| { |
| // Configures logging for both the ARMNN library and this test program. |
| #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 testCasesFile; |
| |
| std::string modelFormat; |
| std::string modelPath; |
| std::string inputNames; |
| std::string inputTensorShapes; |
| std::string inputTensorDataFilePaths; |
| std::string outputNames; |
| std::string inputTypes; |
| std::string outputTypes; |
| |
| double thresholdTime = 0.0; |
| |
| size_t subgraphId = 0; |
| |
| const std::string backendsMessage = "Which device to run layers on by default. Possible choices: " |
| + armnn::BackendRegistryInstance().GetBackendIdsAsString(); |
| |
| po::options_description desc("Options"); |
| try |
| { |
| desc.add_options() |
| ("help", "Display usage information") |
| ("test-cases,t", po::value(&testCasesFile), "Path to a CSV file containing test cases to run. " |
| "If set, further parameters -- with the exception of compute device and concurrency -- will be ignored, " |
| "as they are expected to be defined in the file for each test in particular.") |
| ("concurrent,n", po::bool_switch()->default_value(false), |
| "Whether or not the test cases should be executed in parallel") |
| ("model-format,f", po::value(&modelFormat)->required(), |
| "armnn-binary, caffe-binary, caffe-text, onnx-binary, onnx-text, tflite-binary, tensorflow-binary or " |
| "tensorflow-text.") |
| ("model-path,m", po::value(&modelPath)->required(), "Path to model file, e.g. .armnn, .caffemodel, " |
| ".prototxt, .tflite, .onnx") |
| ("compute,c", po::value<std::vector<std::string>>()->multitoken(), |
| backendsMessage.c_str()) |
| ("input-name,i", po::value(&inputNames), |
| "Identifier of the input tensors in the network separated by comma.") |
| ("subgraph-number,x", po::value<size_t>(&subgraphId)->default_value(0), "Id of the subgraph to be executed." |
| "Defaults to 0") |
| ("input-tensor-shape,s", po::value(&inputTensorShapes), |
| "The shape of the input tensors in the network as a flat array of integers separated by comma. " |
| "Several shapes can be passed separating them by semicolon. " |
| "This parameter is optional, depending on the network.") |
| ("input-tensor-data,d", po::value(&inputTensorDataFilePaths), |
| "Path to files containing the input data as a flat array separated by whitespace. " |
| "Several paths can be passed separating them by comma. ") |
| ("input-type,y",po::value(&inputTypes), "The type of the input tensors in the network separated by comma. " |
| "If unset, defaults to \"float\" for all defined inputs. " |
| "Accepted values (float, int or qasymm8)") |
| ("output-type,z",po::value(&outputTypes), |
| "The type of the output tensors in the network separated by comma. " |
| "If unset, defaults to \"float\" for all defined outputs. " |
| "Accepted values (float, int or qasymm8).") |
| ("output-name,o", po::value(&outputNames), |
| "Identifier of the output tensors in the network separated by comma.") |
| ("event-based-profiling,e", po::bool_switch()->default_value(false), |
| "Enables built in profiler. If unset, defaults to off.") |
| ("fp16-turbo-mode,h", po::bool_switch()->default_value(false), "If this option is enabled, FP32 layers, " |
| "weights and biases will be converted to FP16 where the backend supports it") |
| ("threshold-time,r", po::value<double>(&thresholdTime)->default_value(0.0), |
| "Threshold time is the maximum allowed time for inference measured in milliseconds. If the actual " |
| "inference time is greater than the threshold time, the test will fail. By default, no threshold " |
| "time is used."); |
| } |
| catch (const std::exception& e) |
| { |
| // Coverity points out that default_value(...) can throw a bad_lexical_cast, |
| // and that desc.add_options() can throw boost::io::too_few_args. |
| // They really won't in any of these cases. |
| BOOST_ASSERT_MSG(false, "Caught unexpected exception"); |
| BOOST_LOG_TRIVIAL(fatal) << "Fatal internal error: " << e.what(); |
| return EXIT_FAILURE; |
| } |
| |
| // Parses the command-line. |
| po::variables_map vm; |
| try |
| { |
| po::store(po::parse_command_line(argc, argv, desc), vm); |
| |
| if (CheckOption(vm, "help") || argc <= 1) |
| { |
| std::cout << "Executes a neural network model using the provided input tensor. " << std::endl; |
| std::cout << "Prints the resulting output tensor." << std::endl; |
| std::cout << std::endl; |
| std::cout << desc << std::endl; |
| return 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; |
| } |
| |
| // Get the value of the switch arguments. |
| bool concurrent = vm["concurrent"].as<bool>(); |
| bool enableProfiling = vm["event-based-profiling"].as<bool>(); |
| bool enableFp16TurboMode = vm["fp16-turbo-mode"].as<bool>(); |
| |
| // Check whether we have to load test cases from a file. |
| if (CheckOption(vm, "test-cases")) |
| { |
| // Check that the file exists. |
| if (!boost::filesystem::exists(testCasesFile)) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Given file \"" << testCasesFile << "\" does not exist"; |
| return EXIT_FAILURE; |
| } |
| |
| // Parse CSV file and extract test cases |
| armnnUtils::CsvReader reader; |
| std::vector<armnnUtils::CsvRow> testCases = reader.ParseFile(testCasesFile); |
| |
| // Check that there is at least one test case to run |
| if (testCases.empty()) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Given file \"" << testCasesFile << "\" has no test cases"; |
| return EXIT_FAILURE; |
| } |
| |
| // Create runtime |
| armnn::IRuntime::CreationOptions options; |
| options.m_EnableGpuProfiling = enableProfiling; |
| |
| std::shared_ptr<armnn::IRuntime> runtime(armnn::IRuntime::Create(options)); |
| |
| const std::string executableName("ExecuteNetwork"); |
| |
| // Check whether we need to run the test cases concurrently |
| if (concurrent) |
| { |
| std::vector<std::future<int>> results; |
| results.reserve(testCases.size()); |
| |
| // Run each test case in its own thread |
| for (auto& testCase : testCases) |
| { |
| testCase.values.insert(testCase.values.begin(), executableName); |
| results.push_back(std::async(std::launch::async, RunCsvTest, std::cref(testCase), std::cref(runtime), |
| enableProfiling, enableFp16TurboMode, thresholdTime)); |
| } |
| |
| // Check results |
| for (auto& result : results) |
| { |
| if (result.get() != EXIT_SUCCESS) |
| { |
| return EXIT_FAILURE; |
| } |
| } |
| } |
| else |
| { |
| // Run tests sequentially |
| for (auto& testCase : testCases) |
| { |
| testCase.values.insert(testCase.values.begin(), executableName); |
| if (RunCsvTest(testCase, runtime, enableProfiling, enableFp16TurboMode, thresholdTime) != EXIT_SUCCESS) |
| { |
| return EXIT_FAILURE; |
| } |
| } |
| } |
| |
| return EXIT_SUCCESS; |
| } |
| else // Run single test |
| { |
| // Get the preferred order of compute devices. If none are specified, default to using CpuRef |
| const std::string computeOption("compute"); |
| std::vector<std::string> computeDevicesAsStrings = CheckOption(vm, computeOption.c_str()) ? |
| vm[computeOption].as<std::vector<std::string>>() : |
| std::vector<std::string>({ "CpuRef" }); |
| std::vector<armnn::BackendId> computeDevices(computeDevicesAsStrings.begin(), computeDevicesAsStrings.end()); |
| |
| // Remove duplicates from the list of compute devices. |
| RemoveDuplicateDevices(computeDevices); |
| |
| // Check that the specified compute devices are valid. |
| std::string invalidBackends; |
| if (!CheckRequestedBackendsAreValid(computeDevices, armnn::Optional<std::string&>(invalidBackends))) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "The list of preferred devices contains invalid backend IDs: " |
| << invalidBackends; |
| 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; |
| } |
| |
| return RunTest(modelFormat, inputTensorShapes, computeDevices, modelPath, inputNames, |
| inputTensorDataFilePaths, inputTypes, outputTypes, outputNames, |
| enableProfiling, enableFp16TurboMode, thresholdTime, subgraphId); |
| } |
| } |
