ModelAccuracyTool-Armnn.cpp File Reference

#include "../ImageTensorGenerator/ImageTensorGenerator.hpp"
#include "../InferenceTest.hpp"
#include "ModelAccuracyChecker.hpp"
#include "armnnDeserializer/IDeserializer.hpp"
#include <Filesystem.hpp>
#include <boost/program_options/variables_map.hpp>
#include <boost/range/iterator_range.hpp>
#include <map>

Go to the source code of this file.

Functions
map< std::string, std::string >	LoadValidationImageFilenamesAndLabels (const string &validationLabelPath, const string &imageDirectoryPath, size_t begIndex=0, size_t endIndex=0, const string &blacklistPath="")
	Load image names and ground-truth labels from the image directory and the ground truth label file. More...
std::vector< armnnUtils::LabelCategoryNames >	LoadModelOutputLabels (const std::string &modelOutputLabelsPath)
	Load model output labels from file. More...
int	main (int argc, char *argv[])

Function Documentation

LoadModelOutputLabels()

std::vector< armnnUtils::LabelCategoryNames > LoadModelOutputLabels

(

const std::string &

modelOutputLabelsPath

)

Load model output labels from file.

Precondition

modelOutputLabelsPath exists and is a regular file

Parameters

[in]

modelOutputLabelsPath

path to model output labels file

Returns

A vector of labels, which in turn is described by a list of category names

Definition at line 464 of file ModelAccuracyTool-Armnn.cpp.

References armnnUtils::SplitBy(), and armnnUtils::Strip().

Referenced by main().

{
    std::vector<armnnUtils::LabelCategoryNames> modelOutputLabels;
    ifstream modelOutputLablesFile(modelOutputLabelsPath);
    std::string line;
    while (std::getline(modelOutputLablesFile, line))
    {
        armnnUtils::LabelCategoryNames tokens                  = armnnUtils::SplitBy(line, ":");
        armnnUtils::LabelCategoryNames predictionCategoryNames = armnnUtils::SplitBy(tokens.back(), ",");
        std::transform(predictionCategoryNames.begin(), predictionCategoryNames.end(), predictionCategoryNames.begin(),
                       [](const std::string& category) { return armnnUtils::Strip(category); });
        modelOutputLabels.push_back(predictionCategoryNames);
    }
    return modelOutputLabels;
}

LoadValidationImageFilenamesAndLabels()

map< std::string, std::string > LoadValidationImageFilenamesAndLabels	(	const string &	validationLabelPath,
		const string &	imageDirectoryPath,
		size_t	begIndex = 0,
		size_t	endIndex = 0,
		const string &	blacklistPath = ""
	)

Load image names and ground-truth labels from the image directory and the ground truth label file.

Precondition

validationLabelPath exists and is valid regular file

imageDirectoryPath exists and is valid directory

labels in validation file correspond to images which are in lexicographical order with the image name

image index starts at 1

begIndex and endIndex are end-inclusive

Parameters

[in]	validationLabelPath	Path to validation label file
[in]	imageDirectoryPath	Path to directory containing validation images
[in]	begIndex	Begin index of images to be loaded. Inclusive
[in]	endIndex	End index of images to be loaded. Inclusive
[in]	blacklistPath	Path to blacklist file

Returns

A map mapping image file names to their corresponding ground-truth labels

Definition at line 386 of file ModelAccuracyTool-Armnn.cpp.

Referenced by main().

{
    // Populate imageFilenames with names of all .JPEG, .PNG images
    std::vector<std::string> imageFilenames;
    for (const auto& imageEntry :
         boost::make_iterator_range(fs::directory_iterator(fs::path(imageDirectoryPath))))
    {
        fs::path imagePath = imageEntry.path();
        std::string imageExtension        = boost::to_upper_copy<std::string>(imagePath.extension().string());
        if (fs::is_regular_file(imagePath) && (imageExtension == ".JPEG" || imageExtension == ".PNG"))
        {
            imageFilenames.push_back(imagePath.filename().string());
        }
    }
    if (imageFilenames.empty())
    {
        throw armnn::Exception("No image file (JPEG, PNG) found at " + imageDirectoryPath);
    }

    // Sort the image filenames lexicographically
    std::sort(imageFilenames.begin(), imageFilenames.end());

    std::cout << imageFilenames.size() << " images found at " << imageDirectoryPath << std::endl;

    // Get default end index
    if (begIndex < 1 || endIndex > imageFilenames.size())
    {
        throw armnn::Exception("Invalid image index range");
    }
    endIndex = endIndex == 0 ? imageFilenames.size() : endIndex;
    if (begIndex > endIndex)
    {
        throw armnn::Exception("Invalid image index range");
    }

    // Load blacklist if there is one
    std::vector<unsigned int> blacklist;
    if (!blacklistPath.empty())
    {
        std::ifstream blacklistFile(blacklistPath);
        unsigned int index;
        while (blacklistFile >> index)
        {
            blacklist.push_back(index);
        }
    }

    // Load ground truth labels and pair them with corresponding image names
    std::string classification;
    map<std::string, std::string> imageNameToLabel;
    ifstream infile(validationLabelPath);
    size_t imageIndex          = begIndex;
    size_t blacklistIndexCount = 0;
    while (std::getline(infile, classification))
    {
        if (imageIndex > endIndex)
        {
            break;
        }
        // If current imageIndex is included in blacklist, skip the current image
        if (blacklistIndexCount < blacklist.size() && imageIndex == blacklist[blacklistIndexCount])
        {
            ++imageIndex;
            ++blacklistIndexCount;
            continue;
        }
        imageNameToLabel.insert(std::pair<std::string, std::string>(imageFilenames[imageIndex - 1], classification));
        ++imageIndex;
    }
    std::cout << blacklistIndexCount << " images blacklisted" << std::endl;
    std::cout << imageIndex - begIndex - blacklistIndexCount << " images to be loaded" << std::endl;
    return imageNameToLabel;
}

main()

int main	(	int	argc,
		char *	argv[]
	)

Definition at line 48 of file ModelAccuracyTool-Armnn.cpp.

References ARMNN_ASSERT_MSG, ARMNN_LOG, armnn::BackendRegistryInstance(), Caffe, armnn::ConfigureLogging(), armnn::CpuAcc, armnn::CpuRef, IRuntime::Create(), armnn::Debug, defaultBackends, armnn::error, armnn::Failure, armnn::Float32, BackendRegistry::GetBackendIdsAsString(), TensorInfo::GetDataType(), InferenceModel< IParser, TDataType >::GetInputBindingInfo(), GetNormalizationParameters(), InferenceModel< IParser, TDataType >::GetOutputSize(), TensorInfo::GetShape(), LoadModelOutputLabels(), LoadValidationImageFilenamesAndLabels(), BindingPointInfo::m_BindingId, Params::m_ComputeDevices, Params::m_InputBindings, Params::m_IsModelBinary, Params::m_ModelPath, Params::m_OutputBindings, BindingPointInfo::m_TensorInfo, armnnUtils::MakeInputTensors(), armnnUtils::MakeOutputTensors(), armnn::NCHW, armnn::NHWC, armnn::Optimize(), PrepareImageTensor< float >(), PrepareImageTensor< int >(), PrepareImageTensor< uint8_t >(), armnn::QAsymmU8, armnn::Signed32, armnnUtils::SplitBy(), TensorFlow, TFLite, armnn::test::ValidateDirectory(), and Exception::what().

{
    try
    {
        armnn::LogSeverity level = armnn::LogSeverity::Debug;
        armnn::ConfigureLogging(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 modelOutputLabelsPath;
        std::string validationLabelPath;
        std::string validationRange;
        std::string blacklistPath;

        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")
                ("model-output-labels,p", po::value<std::string>(&modelOutputLabelsPath)->required(),
                 "Path to model output labels file.")
                ("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: NHWC")
                ("compute,c", po::value<std::vector<armnn::BackendId>>(&computeDevice)->default_value(defaultBackends),
                 backendsMessage.c_str())
                ("validation-range,r", po::value<std::string>(&validationRange)->default_value("1:0"),
                 "The range of the images to be evaluated. Specified in the form <begin index>:<end index>."
                 "The index starts at 1 and the range is inclusive."
                 "By default the evaluation will be performed on all images.")
                ("blacklist-path,b", po::value<std::string>(&blacklistPath)->default_value(""),
                 "Path to a blacklist file where each line denotes the index of an image to be "
                 "excluded from evaluation.");
        }
        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.
            ARMNN_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)))
        {
            ARMNN_LOG(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 (const armnn::Exception& e)
        {
            std::stringstream message;
            message << "armnn::Exception (" << e.what() << ") caught from optimize.";
            ARMNN_LOG(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)
        {
            ARMNN_LOG(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 };

        // Load model output labels
        if (modelOutputLabelsPath.empty() || !fs::exists(modelOutputLabelsPath) ||
            !fs::is_regular_file(modelOutputLabelsPath))
        {
            ARMNN_LOG(fatal) << "Invalid model output labels path at " << modelOutputLabelsPath;
        }
        const std::vector<armnnUtils::LabelCategoryNames> modelOutputLabels =
            LoadModelOutputLabels(modelOutputLabelsPath);

        // Parse begin and end image indices
        std::vector<std::string> imageIndexStrs = armnnUtils::SplitBy(validationRange, ":");
        size_t imageBegIndex;
        size_t imageEndIndex;
        if (imageIndexStrs.size() != 2)
        {
            ARMNN_LOG(fatal) << "Invalid validation range specification: Invalid format " << validationRange;
            return 1;
        }
        try
        {
            imageBegIndex = std::stoul(imageIndexStrs[0]);
            imageEndIndex = std::stoul(imageIndexStrs[1]);
        }
        catch (const std::exception& e)
        {
            ARMNN_LOG(fatal) << "Invalid validation range specification: " << validationRange;
            return 1;
        }

        // Validate  blacklist file if it's specified
        if (!blacklistPath.empty() &&
            !(fs::exists(blacklistPath) && fs::is_regular_file(blacklistPath)))
        {
            ARMNN_LOG(fatal) << "Invalid path to blacklist file at " << blacklistPath;
            return 1;
        }

        path pathToDataDir(dataDir);
        const map<std::string, std::string> imageNameToLabel = LoadValidationImageFilenamesAndLabels(
            validationLabelPath, pathToDataDir.string(), imageBegIndex, imageEndIndex, blacklistPath);
        armnnUtils::ModelAccuracyChecker checker(imageNameToLabel, modelOutputLabels);
        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
            {
                ARMNN_LOG(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();
            // Check output tensor shape is valid
            if (modelOutputLabels.size() != outputNumElements)
            {
                ARMNN_LOG(fatal) << "Number of output elements: " << outputNumElements
                                         << " , mismatches the number of output labels: " << modelOutputLabels.size();
                return 1;
            }

            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
            {
                ARMNN_LOG(fatal) << "Unsupported frontend: " << modelFormat;
                return 1;
            }
            const NormalizationParameters& normParams = GetNormalizationParameters(modelFrontend, inputTensorDataType);
            for (const auto& imageEntry : imageNameToLabel)
            {
                const std::string imageName = imageEntry.first;
                std::cout << "Processing image: " << imageName << "\n";

                vector<TContainer> inputDataContainers;
                vector<TContainer> outputDataContainers;

                auto imagePath = pathToDataDir / fs::path(imageName);
                switch (inputTensorDataType)
                {
                    case armnn::DataType::Signed32:
                        inputDataContainers.push_back(
                            PrepareImageTensor<int>(imagePath.string(),
                            inputTensorWidth, inputTensorHeight,
                            normParams,
                            batchSize,
                            inputTensorDataLayout));
                        outputDataContainers = { vector<int>(outputNumElements) };
                        break;
                    case armnn::DataType::QAsymmU8:
                        inputDataContainers.push_back(
                            PrepareImageTensor<uint8_t>(imagePath.string(),
                            inputTensorWidth, inputTensorHeight,
                            normParams,
                            batchSize,
                            inputTensorDataLayout));
                        outputDataContainers = { vector<uint8_t>(outputNumElements) };
                        break;
                    case armnn::DataType::Float32:
                    default:
                        inputDataContainers.push_back(
                            PrepareImageTensor<float>(imagePath.string(),
                            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)
                {
                    ARMNN_LOG(fatal) << "armnn::IRuntime: Failed to enqueue workload for image: " << imageName;
                }

                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";
        }

        ARMNN_LOG(info) << "Accuracy Tool ran successfully!";
        return 0;
    }
    catch (const armnn::Exception& 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;
    }
}