YoloTestCase< Model > Class Template Reference

#include <YoloInferenceTest.hpp>

Inheritance diagram for YoloTestCase< Model >:

Public Member Functions
	YoloTestCase (Model &model, unsigned int testCaseId, YoloTestCaseData &testCaseData)
virtual TestCaseResult	ProcessResult (const InferenceTestOptions &options) override
Public Member Functions inherited from InferenceModelTestCase< Model >
	InferenceModelTestCase (Model &model, unsigned int testCaseId, const std::vector< TContainer > &inputs, const std::vector< unsigned int > &outputSizes)
virtual void	Run () override
Public Member Functions inherited from IInferenceTestCase
virtual	~IInferenceTestCase ()

Additional Inherited Members
Public Types inherited from InferenceModelTestCase< Model >
using	TContainer = mapbox::util::variant< std::vector< float >, std::vector< int >, std::vector< unsigned char > >
Protected Member Functions inherited from InferenceModelTestCase< Model >
unsigned int	GetTestCaseId () const
const std::vector< TContainer > &	GetOutputs () const

Detailed Description

template<typename Model>
class YoloTestCase< Model >

Definition at line 21 of file YoloInferenceTest.hpp.

Constructor & Destructor Documentation

YoloTestCase()

YoloTestCase ( Model &  model, unsigned int  testCaseId, YoloTestCaseData &  testCaseData  )

inline

Definition at line 24 of file YoloInferenceTest.hpp.

References YoloTestCaseData::m_InputImage, and YoloOutputSize.

     : InferenceModelTestCase<Model>(model, testCaseId, { std::move(testCaseData.m_InputImage) }, { YoloOutputSize })
     , m_TopObjectDetections(std::move(testCaseData.m_TopObjectDetections))
    {
    }

Member Function Documentation

ProcessResult()

virtual TestCaseResult ProcessResult ( const InferenceTestOptions &  options )

inlineoverridevirtual

Implements IInferenceTestCase.

Definition at line 32 of file YoloInferenceTest.hpp.

References ARMNN_ASSERT, ARMNN_LOG, InferenceModelTestCase< Model >::GetOutputs(), InferenceModelTestCase< Model >::GetTestCaseId(), armnn::IgnoreUnused(), YoloDetectedObject::m_Box, YoloDetectedObject::m_Class, YoloDetectedObject::m_Confidence, YoloBoundingBox::m_H, YoloBoundingBox::m_W, YoloBoundingBox::m_X, YoloBoundingBox::m_Y, armnnUtils::within_percentage_tolerance(), YoloImageHeight, YoloImageWidth, and YoloOutputSize.

    {
        armnn::IgnoreUnused(options);

        const std::vector<float>& output = mapbox::util::get<std::vector<float>>(this->GetOutputs()[0]);
        ARMNN_ASSERT(output.size() == YoloOutputSize);

        constexpr unsigned int gridSize = 7;
        constexpr unsigned int numClasses = 20;
        constexpr unsigned int numScales = 2;

        const float* outputPtr =  output.data();

        // Range 0-980. Class probabilities. 7x7x20
        vector<vector<vector<float>>> classProbabilities(gridSize, vector<vector<float>>(gridSize,
                                                         vector<float>(numClasses)));
        for (unsigned int y = 0; y < gridSize; ++y)
        {
            for (unsigned int x = 0; x < gridSize; ++x)
            {
                for (unsigned int c = 0; c < numClasses; ++c)
                {
                    classProbabilities[y][x][c] = *outputPtr++;
                }
            }
        }

        // Range 980-1078. Scales. 7x7x2
        vector<vector<vector<float>>> scales(gridSize, vector<vector<float>>(gridSize, vector<float>(numScales)));
        for (unsigned int y = 0; y < gridSize; ++y)
        {
            for (unsigned int x = 0; x < gridSize; ++x)
            {
                for (unsigned int s = 0; s < numScales; ++s)
                {
                    scales[y][x][s] = *outputPtr++;
                }
            }
        }

        // Range 1078-1469. Bounding boxes. 7x7x2x4
        constexpr float imageWidthAsFloat = static_cast<float>(YoloImageWidth);
        constexpr float imageHeightAsFloat = static_cast<float>(YoloImageHeight);

        vector<vector<vector<vector<float>>>> boxes(gridSize, vector<vector<vector<float>>>
            (gridSize, vector<vector<float>>(numScales, vector<float>(4))));
        for (unsigned int y = 0; y < gridSize; ++y)
        {
            for (unsigned int x = 0; x < gridSize; ++x)
            {
                for (unsigned int s = 0; s < numScales; ++s)
                {
                    float bx = *outputPtr++;
                    float by = *outputPtr++;
                    float bw = *outputPtr++;
                    float bh = *outputPtr++;

                    boxes[y][x][s][0] = ((bx + static_cast<float>(x)) / 7.0f) * imageWidthAsFloat;
                    boxes[y][x][s][1] = ((by + static_cast<float>(y)) / 7.0f) * imageHeightAsFloat;
                    boxes[y][x][s][2] = bw * bw * static_cast<float>(imageWidthAsFloat);
                    boxes[y][x][s][3] = bh * bh * static_cast<float>(imageHeightAsFloat);
                }
            }
        }
        ARMNN_ASSERT(output.data() + YoloOutputSize == outputPtr);

        std::vector<YoloDetectedObject> detectedObjects;
        detectedObjects.reserve(gridSize * gridSize * numScales * numClasses);

        for (unsigned int y = 0; y < gridSize; ++y)
        {
            for (unsigned int x = 0; x < gridSize; ++x)
            {
                for (unsigned int s = 0; s < numScales; ++s)
                {
                    for (unsigned int c = 0; c < numClasses; ++c)
                    {
                        // Resolved confidence: class probabilities * scales.
                        const float confidence = classProbabilities[y][x][c] * scales[y][x][s];

                        // Resolves bounding box and stores.
                        YoloBoundingBox box;
                        box.m_X = boxes[y][x][s][0];
                        box.m_Y = boxes[y][x][s][1];
                        box.m_W = boxes[y][x][s][2];
                        box.m_H = boxes[y][x][s][3];

                        detectedObjects.emplace_back(c, box, confidence);
                    }
                }
            }
        }

        // Sorts detected objects by confidence.
        std::sort(detectedObjects.begin(), detectedObjects.end(),
            [](const YoloDetectedObject& a, const YoloDetectedObject& b)
            {
                // Sorts by largest confidence first, then by class.
                return a.m_Confidence > b.m_Confidence
                    || (a.m_Confidence == b.m_Confidence && a.m_Class > b.m_Class);
            });

        // Checks the top N detections.
        auto outputIt  = detectedObjects.begin();
        auto outputEnd = detectedObjects.end();

        for (const YoloDetectedObject& expectedDetection : m_TopObjectDetections)
        {
            if (outputIt == outputEnd)
            {
                // Somehow expected more things to check than detections found by the model.
                return TestCaseResult::Abort;
            }

            const YoloDetectedObject& detectedObject = *outputIt;
            if (detectedObject.m_Class != expectedDetection.m_Class)
            {
                ARMNN_LOG(error) << "Prediction for test case " << this->GetTestCaseId() <<
                    " is incorrect: Expected (" << expectedDetection.m_Class << ")" <<
                    " but predicted (" << detectedObject.m_Class << ")";
                return TestCaseResult::Failed;
            }

            if (!armnnUtils::within_percentage_tolerance(detectedObject.m_Box.m_X, expectedDetection.m_Box.m_X) ||
                !armnnUtils::within_percentage_tolerance(detectedObject.m_Box.m_Y, expectedDetection.m_Box.m_Y) ||
                !armnnUtils::within_percentage_tolerance(detectedObject.m_Box.m_W, expectedDetection.m_Box.m_W) ||
                !armnnUtils::within_percentage_tolerance(detectedObject.m_Box.m_H, expectedDetection.m_Box.m_H) ||
                !armnnUtils::within_percentage_tolerance(detectedObject.m_Confidence, expectedDetection.m_Confidence))
            {
                ARMNN_LOG(error) << "Detected bounding box for test case " << this->GetTestCaseId() <<
                    " is incorrect";
                return TestCaseResult::Failed;
            }

            ++outputIt;
        }

        return TestCaseResult::Ok;
    }

---

The documentation for this class was generated from the following file:

• tests/YoloInferenceTest.hpp