| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "../ImageTensorGenerator/ImageTensorGenerator.hpp" |
| #include "../InferenceTest.hpp" |
| #include "ModelAccuracyChecker.hpp" |
| #include "armnnDeserializer/IDeserializer.hpp" |
| |
| #include <boost/filesystem.hpp> |
| #include <boost/program_options/variables_map.hpp> |
| #include <boost/range/iterator_range.hpp> |
| |
| #include <map> |
| |
| using namespace armnn::test; |
| |
| map<std::string, int> LoadValidationLabels(const string & validationLabelPath); |
| |
| int main(int argc, char* argv[]) |
| { |
| try |
| { |
| using namespace boost::filesystem; |
| armnn::LogSeverity level = armnn::LogSeverity::Debug; |
| armnn::ConfigureLogging(true, true, level); |
| armnnUtils::ConfigureLogging(boost::log::core::get().get(), true, true, level); |
| |
| // Set-up program Options |
| namespace po = boost::program_options; |
| |
| std::vector<armnn::BackendId> computeDevice; |
| std::vector<armnn::BackendId> defaultBackends = {armnn::Compute::CpuAcc, armnn::Compute::CpuRef}; |
| std::string modelPath; |
| std::string modelFormat; |
| std::string dataDir; |
| std::string inputName; |
| std::string inputLayout; |
| std::string outputName; |
| std::string validationLabelPath; |
| |
| const std::string backendsMessage = "Which device to run layers on by default. Possible choices: " |
| + armnn::BackendRegistryInstance().GetBackendIdsAsString(); |
| |
| po::options_description desc("Options"); |
| try |
| { |
| // Adds generic options needed to run Accuracy Tool. |
| desc.add_options() |
| ("help,h", "Display help messages") |
| ("model-path,m", po::value<std::string>(&modelPath)->required(), "Path to armnn format model file") |
| ("model-format,f", po::value<std::string>(&modelFormat)->required(), |
| "The model format. Supported values: caffe, tensorflow, tflite") |
| ("input-name,i", po::value<std::string>(&inputName)->required(), |
| "Identifier of the input tensors in the network separated by comma.") |
| ("output-name,o", po::value<std::string>(&outputName)->required(), |
| "Identifier of the output tensors in the network separated by comma.") |
| ("data-dir,d", po::value<std::string>(&dataDir)->required(), |
| "Path to directory containing the ImageNet test data") |
| ("validation-labels-path,v", po::value<std::string>(&validationLabelPath)->required(), |
| "Path to ImageNet Validation Label file") |
| ("data-layout,l", po::value<std::string>(&inputLayout)->default_value("NHWC"), |
| "Data layout. Supported value: NHWC, NCHW. Default: NHCW") |
| ("compute,c", po::value<std::vector<armnn::BackendId>>(&computeDevice)->default_value(defaultBackends), |
| backendsMessage.c_str()); |
| } |
| 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"); |
| std::cerr << "Fatal internal error: " << e.what() << std::endl; |
| return 1; |
| } |
| |
| po::variables_map vm; |
| try |
| { |
| po::store(po::parse_command_line(argc, argv, desc), vm); |
| |
| if (vm.count("help")) |
| { |
| std::cout << desc << std::endl; |
| return 1; |
| } |
| po::notify(vm); |
| } |
| catch (po::error& e) |
| { |
| std::cerr << e.what() << std::endl << std::endl; |
| std::cerr << desc << std::endl; |
| return 1; |
| } |
| |
| // Check if the requested backend are all valid |
| std::string invalidBackends; |
| if (!CheckRequestedBackendsAreValid(computeDevice, armnn::Optional<std::string&>(invalidBackends))) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "The list of preferred devices contains invalid backend IDs: " |
| << invalidBackends; |
| return EXIT_FAILURE; |
| } |
| armnn::Status status; |
| |
| // Create runtime |
| armnn::IRuntime::CreationOptions options; |
| armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| std::ifstream file(modelPath); |
| |
| // Create Parser |
| using IParser = armnnDeserializer::IDeserializer; |
| auto armnnparser(IParser::Create()); |
| |
| // Create a network |
| armnn::INetworkPtr network = armnnparser->CreateNetworkFromBinary(file); |
| |
| // Optimizes the network. |
| armnn::IOptimizedNetworkPtr optimizedNet(nullptr, nullptr); |
| try |
| { |
| optimizedNet = armnn::Optimize(*network, computeDevice, runtime->GetDeviceSpec()); |
| } |
| catch (armnn::Exception& e) |
| { |
| std::stringstream message; |
| message << "armnn::Exception (" << e.what() << ") caught from optimize."; |
| BOOST_LOG_TRIVIAL(fatal) << message.str(); |
| return 1; |
| } |
| |
| // Loads the network into the runtime. |
| armnn::NetworkId networkId; |
| status = runtime->LoadNetwork(networkId, std::move(optimizedNet)); |
| if (status == armnn::Status::Failure) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "armnn::IRuntime: Failed to load network"; |
| return 1; |
| } |
| |
| // Set up Network |
| using BindingPointInfo = InferenceModelInternal::BindingPointInfo; |
| |
| const armnnDeserializer::BindingPointInfo& |
| inputBindingInfo = armnnparser->GetNetworkInputBindingInfo(0, inputName); |
| |
| std::pair<armnn::LayerBindingId, armnn::TensorInfo> |
| m_InputBindingInfo(inputBindingInfo.m_BindingId, inputBindingInfo.m_TensorInfo); |
| std::vector<BindingPointInfo> inputBindings = { m_InputBindingInfo }; |
| |
| const armnnDeserializer::BindingPointInfo& |
| outputBindingInfo = armnnparser->GetNetworkOutputBindingInfo(0, outputName); |
| |
| std::pair<armnn::LayerBindingId, armnn::TensorInfo> |
| m_OutputBindingInfo(outputBindingInfo.m_BindingId, outputBindingInfo.m_TensorInfo); |
| std::vector<BindingPointInfo> outputBindings = { m_OutputBindingInfo }; |
| |
| path pathToDataDir(dataDir); |
| map<string, int> validationLabels = LoadValidationLabels(validationLabelPath); |
| armnnUtils::ModelAccuracyChecker checker(validationLabels); |
| using TContainer = boost::variant<std::vector<float>, std::vector<int>, std::vector<uint8_t>>; |
| |
| if (ValidateDirectory(dataDir)) |
| { |
| InferenceModel<armnnDeserializer::IDeserializer, float>::Params params; |
| params.m_ModelPath = modelPath; |
| params.m_IsModelBinary = true; |
| params.m_ComputeDevices = computeDevice; |
| params.m_InputBindings.push_back(inputName); |
| params.m_OutputBindings.push_back(outputName); |
| |
| using TParser = armnnDeserializer::IDeserializer; |
| InferenceModel<TParser, float> model(params, false); |
| // Get input tensor information |
| const armnn::TensorInfo& inputTensorInfo = model.GetInputBindingInfo().second; |
| const armnn::TensorShape& inputTensorShape = inputTensorInfo.GetShape(); |
| const armnn::DataType& inputTensorDataType = inputTensorInfo.GetDataType(); |
| armnn::DataLayout inputTensorDataLayout; |
| if (inputLayout == "NCHW") |
| { |
| inputTensorDataLayout = armnn::DataLayout::NCHW; |
| } |
| else if (inputLayout == "NHWC") |
| { |
| inputTensorDataLayout = armnn::DataLayout::NHWC; |
| } |
| else |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Invalid Data layout: " << inputLayout; |
| return 1; |
| } |
| const unsigned int inputTensorWidth = |
| inputTensorDataLayout == armnn::DataLayout::NCHW ? inputTensorShape[3] : inputTensorShape[2]; |
| const unsigned int inputTensorHeight = |
| inputTensorDataLayout == armnn::DataLayout::NCHW ? inputTensorShape[2] : inputTensorShape[1]; |
| // Get output tensor info |
| const unsigned int outputNumElements = model.GetOutputSize(); |
| |
| const unsigned int batchSize = 1; |
| // Get normalisation parameters |
| SupportedFrontend modelFrontend; |
| if (modelFormat == "caffe") |
| { |
| modelFrontend = SupportedFrontend::Caffe; |
| } |
| else if (modelFormat == "tensorflow") |
| { |
| modelFrontend = SupportedFrontend::TensorFlow; |
| } |
| else if (modelFormat == "tflite") |
| { |
| modelFrontend = SupportedFrontend::TFLite; |
| } |
| else |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "Unsupported frontend: " << modelFormat; |
| return 1; |
| } |
| const NormalizationParameters& normParams = GetNormalizationParameters(modelFrontend, inputTensorDataType); |
| for (auto& imageEntry : boost::make_iterator_range(directory_iterator(pathToDataDir), {})) |
| { |
| cout << "Processing image: " << imageEntry << "\n"; |
| |
| vector<TContainer> inputDataContainers; |
| vector<TContainer> outputDataContainers; |
| |
| const string& imagePath = imageEntry.path().string(); |
| switch (inputTensorDataType) |
| { |
| case armnn::DataType::Signed32: |
| inputDataContainers.push_back( |
| PrepareImageTensor<int>(imagePath, |
| inputTensorWidth, inputTensorHeight, |
| normParams, |
| batchSize, |
| inputTensorDataLayout)); |
| outputDataContainers = { vector<int>(outputNumElements) }; |
| break; |
| case armnn::DataType::QuantisedAsymm8: |
| inputDataContainers.push_back( |
| PrepareImageTensor<uint8_t>(imagePath, |
| inputTensorWidth, inputTensorHeight, |
| normParams, |
| batchSize, |
| inputTensorDataLayout)); |
| outputDataContainers = { vector<uint8_t>(outputNumElements) }; |
| break; |
| case armnn::DataType::Float32: |
| default: |
| inputDataContainers.push_back( |
| PrepareImageTensor<float>(imagePath, |
| inputTensorWidth, inputTensorHeight, |
| normParams, |
| batchSize, |
| inputTensorDataLayout)); |
| outputDataContainers = { vector<float>(outputNumElements) }; |
| break; |
| } |
| |
| status = runtime->EnqueueWorkload(networkId, |
| armnnUtils::MakeInputTensors(inputBindings, inputDataContainers), |
| armnnUtils::MakeOutputTensors(outputBindings, outputDataContainers)); |
| |
| if (status == armnn::Status::Failure) |
| { |
| BOOST_LOG_TRIVIAL(fatal) << "armnn::IRuntime: Failed to enqueue workload for image: " << imageEntry; |
| } |
| |
| const std::string imageName = imageEntry.path().filename().string(); |
| checker.AddImageResult<TContainer>(imageName, outputDataContainers); |
| } |
| } |
| else |
| { |
| return 1; |
| } |
| |
| for(unsigned int i = 1; i <= 5; ++i) |
| { |
| std::cout << "Top " << i << " Accuracy: " << checker.GetAccuracy(i) << "%" << "\n"; |
| } |
| |
| BOOST_LOG_TRIVIAL(info) << "Accuracy Tool ran successfully!"; |
| return 0; |
| } |
| catch (armnn::Exception const & e) |
| { |
| // Coverity fix: BOOST_LOG_TRIVIAL (typically used to report errors) may throw an |
| // exception of type std::length_error. |
| // Using stderr instead in this context as there is no point in nesting try-catch blocks here. |
| std::cerr << "Armnn Error: " << e.what() << std::endl; |
| return 1; |
| } |
| catch (const std::exception & e) |
| { |
| // Coverity fix: various boost exceptions can be thrown by methods called by this test. |
| std::cerr << "WARNING: ModelAccuracyTool-Armnn: An error has occurred when running the " |
| "Accuracy Tool: " << e.what() << std::endl; |
| return 1; |
| } |
| } |
| |
| map<std::string, int> LoadValidationLabels(const string & validationLabelPath) |
| { |
| std::string imageName; |
| int classification; |
| map<std::string, int> validationLabel; |
| ifstream infile(validationLabelPath); |
| while (infile >> imageName >> classification) |
| { |
| std::string trimmedName; |
| size_t lastindex = imageName.find_last_of("."); |
| if(lastindex != std::string::npos) |
| { |
| trimmedName = imageName.substr(0, lastindex); |
| } |
| validationLabel.insert(pair<string, int>(trimmedName, classification)); |
| } |
| return validationLabel; |
| } |