TransposeConvolution2dTestImpl.hpp File Reference

#include <armnnTestUtils/LayerTestResult.hpp>
#include <ResolveType.hpp>
#include <armnn/backends/IBackendInternal.hpp>
#include <armnn/backends/WorkloadFactory.hpp>

Go to the source code of this file.

Functions
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult< T, 4 >	SimpleTransposeConvolution2dTest (armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool biasEnabled, const armnn::DataLayout layout)
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult< T, 4 >	PaddedTransposeConvolution2dTest (armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool biasEnabled, const armnn::DataLayout layout)
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult< T, 4 >	StridedTransposeConvolution2dTest (armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool biasEnabled, const armnn::DataLayout layout)
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult< T, 4 >	MultiChannelTransposeConvolution2dTest (armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, const armnn::DataLayout layout)
LayerTestResult< uint8_t, 4 >	TransposeConvolution2dPerAxisQuantTest (armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, const armnn::DataLayout layout)

Function Documentation

MultiChannelTransposeConvolution2dTest()

LayerTestResult<T, 4> MultiChannelTransposeConvolution2dTest	(	armnn::IWorkloadFactory &	workloadFactory,
		const armnn::IBackendInternal::IMemoryManagerSharedPtr &	memoryManager,
		const armnn::ITensorHandleFactory &	tensorHandleFactory,
		const armnn::DataLayout	layout
	)

Definition at line 491 of file TransposeConvolution2dTestImpl.cpp.

References TransposeConvolution2dDescriptor::m_BiasEnabled, TransposeConvolution2dDescriptor::m_DataLayout, TransposeConvolution2dDescriptor::m_StrideX, TransposeConvolution2dDescriptor::m_StrideY, and armnn::NHWC.

{
    using namespace armnn;

    TensorShape inputShape   = { 1, 1, 2, 2 };
    TensorShape outputShape  = { 1, 2, 5, 5 };

    // OIHW for NCHW; OHWI for NHWC
    TensorShape weightsShape = { 2, 1, 3, 3 };
    TensorShape biasesShape  = { 2 };

    TensorInfo inputInfo(inputShape, ArmnnType);
    TensorInfo outputInfo(outputShape, ArmnnType);
    TensorInfo weightsInfo(weightsShape, ArmnnType);
    TensorInfo biasesInfo(biasesShape, ArmnnBType);

    std::vector<float> inputData =
    {
        1.f, 2.f,
        3.f, 4.f,
    };

    std::vector<float> weightsData =
    {
         1.f,  3.f,  5.f,
         7.f,  9.f, 11.f,
        13.f, 15.f, 17.f,

         2.f,  4.f,  6.f,
         8.f, 10.f, 12.f,
        14.f, 16.f, 18.f
    };

    std::vector<float> biasesData = { -1.5f, -2.0f };

    std::vector<float> expectedOutputData =
    {
        -0.5f,  1.5f,   5.5f,  4.5f,  8.5f,
         5.5f,  7.5f,  23.5f, 16.5f, 20.5f,
        14.5f, 22.5f,  60.5f, 40.5f, 52.5f,
        19.5f, 25.5f,  59.5f, 34.5f, 42.5f,
        37.5f, 43.5f, 101.5f, 58.5f, 66.5f,

         0.0f,  2.0f,   8.0f,  6.0f, 10.0f,
         6.0f,  8.0f,  26.0f, 18.0f, 22.0f,
        18.0f, 26.0f,  70.0f, 46.0f, 58.0f,
        22.0f, 28.0f,  66.0f, 38.0f, 46.0f,
        40.0f, 46.0f, 108.0f, 62.0f, 70.0f
    };

    TransposeConvolution2dDescriptor descriptor;
    descriptor.m_StrideX     = 2;
    descriptor.m_StrideY     = 2;
    descriptor.m_BiasEnabled = true;
    descriptor.m_DataLayout  = layout;

    // swizzle data if needed
    if (layout == armnn::DataLayout::NHWC)
    {
        SwizzleData(inputInfo, inputData, outputInfo, expectedOutputData, weightsInfo, weightsData);
    }

    return TransposeConvolution2dTest<ArmnnType, ArmnnBType>(workloadFactory,
                                                             memoryManager,
                                                             tensorHandleFactory,
                                                             descriptor,
                                                             inputInfo,
                                                             inputData,
                                                             outputInfo,
                                                             expectedOutputData,
                                                             weightsInfo,
                                                             weightsData,
                                                             biasesInfo,
                                                             biasesData);
}

PaddedTransposeConvolution2dTest()

LayerTestResult<T, 4> PaddedTransposeConvolution2dTest	(	armnn::IWorkloadFactory &	workloadFactory,
		const armnn::IBackendInternal::IMemoryManagerSharedPtr &	memoryManager,
		const armnn::ITensorHandleFactory &	tensorHandleFactory,
		bool	biasEnabled,
		const armnn::DataLayout	layout
	)

Definition at line 309 of file TransposeConvolution2dTestImpl.cpp.

References TransposeConvolution2dDescriptor::m_PadLeft, and armnn::NHWC.

{
    using namespace armnn;

    constexpr unsigned int batches  = 1u;
    constexpr unsigned int channels = 1u;

    constexpr unsigned int wInput = 4u;
    constexpr unsigned int hInput = wInput;

    constexpr unsigned int wOutput = 2u;
    constexpr unsigned int hOutput = wOutput;

    constexpr unsigned int wWeights = 3u;
    constexpr unsigned int hWeights = wWeights;

    TensorShape inputShape   = { batches, channels, hInput,   wInput   };
    TensorShape outputShape  = { batches, channels, hOutput,  wOutput  };
    TensorShape weightsShape = { batches, channels, hWeights, wWeights };

    TensorInfo inputInfo(inputShape, ArmnnType);
    TensorInfo outputInfo(outputShape, ArmnnType);
    TensorInfo weightsInfo(weightsShape, ArmnnType);
    TensorInfo biasesInfo({ channels }, ArmnnBType);

    std::vector<float> inputData =
    {
       1.f, 3.f, 2.f, 1.f,
       1.f, 3.f, 3.f, 1.f,
       2.f, 1.f, 1.f, 3.f,
       3.f, 2.f, 3.f, 3.f
    };

    std::vector<float> weightsData =
    {
        1.f, 2.f, 3.f,
        0.f, 1.f, 0.f,
        2.f, 1.f, 2.f
    };

    std::vector<float> biasesData = { 1.f };

    std::vector<float> expectedOutputData =
    {
         21.f, 21.f,
         28.f, 27.f
    };

    if (biasEnabled)
    {
        // apply bias to expected output data
        std::transform(expectedOutputData.begin(), expectedOutputData.end(), expectedOutputData.begin(),
                       [&](float f) -> float { return f + biasesData[0]; });
    }

    TransposeConvolution2dDescriptor descriptor;
    descriptor.m_PadLeft     = 2;
    descriptor.m_PadRight    = 2;
    descriptor.m_PadTop      = 2;
    descriptor.m_PadBottom   = 2;
    descriptor.m_StrideX     = 1;
    descriptor.m_StrideY     = 1;
    descriptor.m_BiasEnabled = biasEnabled;
    descriptor.m_DataLayout  = layout;

    // swizzle data if needed
    if (layout == armnn::DataLayout::NHWC)
    {
        SwizzleData(inputInfo, inputData, outputInfo, expectedOutputData, weightsInfo, weightsData);
    }

    return TransposeConvolution2dTest<ArmnnType, ArmnnBType>(workloadFactory,
                                                             memoryManager,
                                                             tensorHandleFactory,
                                                             descriptor,
                                                             inputInfo,
                                                             inputData,
                                                             outputInfo,
                                                             expectedOutputData,
                                                             weightsInfo,
                                                             weightsData,
                                                             biasesInfo,
                                                             biasesData);
}

SimpleTransposeConvolution2dTest()

LayerTestResult<T, 4> SimpleTransposeConvolution2dTest	(	armnn::IWorkloadFactory &	workloadFactory,
		const armnn::IBackendInternal::IMemoryManagerSharedPtr &	memoryManager,
		const armnn::ITensorHandleFactory &	tensorHandleFactory,
		bool	biasEnabled,
		const armnn::DataLayout	layout
	)

Definition at line 220 of file TransposeConvolution2dTestImpl.cpp.

References TransposeConvolution2dDescriptor::m_StrideX, and armnn::NHWC.

{
    using namespace armnn;

    constexpr unsigned int batches  = 1u;
    constexpr unsigned int channels = 1u;

    constexpr unsigned int wInput = 3u;
    constexpr unsigned int hInput = wInput;

    constexpr unsigned int wOutput = 5u;
    constexpr unsigned int hOutput = wOutput;

    constexpr unsigned int wWeights = 3u;
    constexpr unsigned int hWeights = wWeights;

    TensorShape inputShape   = { batches, channels, hInput,   wInput   };
    TensorShape outputShape  = { batches, channels, hOutput,  wOutput  };
    TensorShape weightsShape = { batches, channels, hWeights, wWeights };

    TensorInfo inputInfo(inputShape, ArmnnType);
    TensorInfo outputInfo(outputShape, ArmnnType);
    TensorInfo weightsInfo(weightsShape, ArmnnType);
    TensorInfo biasesInfo({ channels }, ArmnnBType);

    std::vector<float> inputData =
    {
       1.f, 1.f, 1.f,
       1.f, 1.f, 1.f,
       1.f, 1.f, 1.f
    };

    std::vector<float> weightsData =
    {
        1.f, 2.f, 3.f,
        4.f, 5.f, 6.f,
        7.f, 8.f, 9.f
    };

    std::vector<float> biasesData = { 1.f };

    std::vector<float> expectedOutputData =
    {
         1.f,  3.f,  6.f,  5.f,  3.f,
         5.f, 12.f, 21.f, 16.f,  9.f,
        12.f, 27.f, 45.f, 33.f, 18.f,
        11.f, 24.f, 39.f, 28.f, 15.f,
         7.f, 15.f, 24.f, 17.f,  9.f
    };

    if (biasEnabled)
    {
        // apply bias to expected output data
        std::transform(expectedOutputData.begin(), expectedOutputData.end(), expectedOutputData.begin(),
                       [&](float f) -> float { return f + biasesData[0]; });
    }

    TransposeConvolution2dDescriptor descriptor;
    descriptor.m_StrideX     = 1;
    descriptor.m_StrideY     = 1;
    descriptor.m_BiasEnabled = biasEnabled;
    descriptor.m_DataLayout  = layout;

    // swizzle data if needed
    if (layout == armnn::DataLayout::NHWC)
    {
       SwizzleData(inputInfo, inputData, outputInfo, expectedOutputData, weightsInfo, weightsData);
    }

    return TransposeConvolution2dTest<ArmnnType, ArmnnBType>(workloadFactory,
                                                             memoryManager,
                                                             tensorHandleFactory,
                                                             descriptor,
                                                             inputInfo,
                                                             inputData,
                                                             outputInfo,
                                                             expectedOutputData,
                                                             weightsInfo,
                                                             weightsData,
                                                             biasesInfo,
                                                             biasesData);
}

StridedTransposeConvolution2dTest()

LayerTestResult<T, 4> StridedTransposeConvolution2dTest	(	armnn::IWorkloadFactory &	workloadFactory,
		const armnn::IBackendInternal::IMemoryManagerSharedPtr &	memoryManager,
		const armnn::ITensorHandleFactory &	tensorHandleFactory,
		bool	biasEnabled,
		const armnn::DataLayout	layout
	)

Definition at line 400 of file TransposeConvolution2dTestImpl.cpp.

References TransposeConvolution2dDescriptor::m_StrideX, and armnn::NHWC.

{
    using namespace armnn;

    constexpr unsigned int batches  = 1u;
    constexpr unsigned int channels = 1u;

    constexpr unsigned int wInput = 3u;
    constexpr unsigned int hInput = wInput;

    constexpr unsigned int wOutput = 7u;
    constexpr unsigned int hOutput = wOutput;

    constexpr unsigned int wWeights = 3u;
    constexpr unsigned int hWeights = wWeights;

    TensorShape inputShape   = { batches, channels, hInput,   wInput   };
    TensorShape outputShape  = { batches, channels, hOutput,  wOutput  };
    TensorShape weightsShape = { batches, channels, hWeights, wWeights };

    TensorInfo inputInfo(inputShape, ArmnnType);
    TensorInfo outputInfo(outputShape, ArmnnType);
    TensorInfo weightsInfo(weightsShape, ArmnnType);
    TensorInfo biasesInfo({ channels }, ArmnnBType);

    std::vector<float> inputData =
    {
        1.f, 1.f, 1.f,
        1.f, 1.f, 1.f,
        1.f, 1.f, 1.f
    };

    std::vector<float> weightsData =
    {
        1.f, 2.f, 3.f,
        4.f, 5.f, 6.f,
        7.f, 8.f, 9.f
    };

    std::vector<float> biasesData = { 1.f };

    std::vector<float> expectedOutputData =
    {
        1.f,  2.f,  4.f,  2.f,  4.f,  2.f,  3.f,
        4.f,  5.f, 10.f,  5.f, 10.f,  5.f,  6.f,
        8.f, 10.f, 20.f, 10.f, 20.f, 10.f, 12.f,
        4.f,  5.f, 10.f,  5.f, 10.f,  5.f,  6.f,
        8.f, 10.f, 20.f, 10.f, 20.f, 10.f, 12.f,
        4.f,  5.f, 10.f,  5.f, 10.f,  5.f,  6.f,
        7.f,  8.f, 16.f,  8.f, 16.f,  8.f,  9.f
    };

    if (biasEnabled)
    {
        // apply bias to expected output data
        std::transform(expectedOutputData.begin(), expectedOutputData.end(), expectedOutputData.begin(),
                    [&](float f) -> float { return f + biasesData[0]; });
    }

    TransposeConvolution2dDescriptor descriptor;
    descriptor.m_StrideX     = 2;
    descriptor.m_StrideY     = 2;
    descriptor.m_BiasEnabled = biasEnabled;
    descriptor.m_DataLayout  = layout;

    // swizzle data if needed
    if (layout == armnn::DataLayout::NHWC)
    {
        SwizzleData(inputInfo, inputData, outputInfo, expectedOutputData, weightsInfo, weightsData);
    }

    return TransposeConvolution2dTest<ArmnnType, ArmnnBType>(workloadFactory,
                                                             memoryManager,
                                                             tensorHandleFactory,
                                                             descriptor,
                                                             inputInfo,
                                                             inputData,
                                                             outputInfo,
                                                             expectedOutputData,
                                                             weightsInfo,
                                                             weightsData,
                                                             biasesInfo,
                                                             biasesData);
}

TransposeConvolution2dPerAxisQuantTest()

LayerTestResult<uint8_t, 4> TransposeConvolution2dPerAxisQuantTest	(	armnn::IWorkloadFactory &	workloadFactory,
		const armnn::IBackendInternal::IMemoryManagerSharedPtr &	memoryManager,
		const armnn::ITensorHandleFactory &	tensorHandleFactory,
		const armnn::DataLayout	layout
	)

Definition at line 571 of file TransposeConvolution2dTestImpl.cpp.

References AllocateAndCopyDataToITensorHandle(), CopyDataFromITensorHandle(), CopyDataToITensorHandle(), ITensorHandleFactory::CreateTensorHandle(), IWorkloadFactory::CreateWorkload(), TensorInfo::GetNumElements(), TensorInfo::GetShape(), TransposeConvolution2dDescriptor::m_BiasEnabled, TransposeConvolution2dDescriptor::m_DataLayout, QueueDescriptorWithParameters< LayerDescriptor >::m_Parameters, TransposeConvolution2dDescriptor::m_StrideX, TransposeConvolution2dDescriptor::m_StrideY, armnn::NHWC, PermuteTensorNchwToNhwc(), armnn::QAsymmU8, armnn::QSymmS8, armnn::Signed32, and armnn::TransposeConvolution2d.

Referenced by TEST_SUITE().

{
   using namespace armnn;

    const DataType inputType  = DataType::QAsymmU8;
    const DataType kernelType = DataType::QSymmS8;
    const DataType biasType   = DataType::Signed32;

    TensorInfo inputInfo ({ 1, 1, 2, 2 }, inputType, 0.50f, 10);
    TensorInfo outputInfo({ 1, 2, 5, 5 }, inputType, 0.50f, 10);

    const std::vector<float> quantScales{ 0.25f, 0.5f };
    constexpr unsigned int quantDimension = 0;

    TensorInfo kernelInfo({ 2, 1, 3, 3 }, kernelType, quantScales, quantDimension);

    const std::vector<float> biasQuantScales{ 0.125f, 0.25f };
    TensorInfo biasInfo({ 2 }, biasType, biasQuantScales, quantDimension);

    std::vector<uint8_t> inputData =
    {
        12, 14,
        16, 18
    };

    std::vector<int8_t> kernelData =
    {
         4, 12, 20,
        28, 36, 44,
        52, 60, 68,

         4,  8, 12,
        16, 20, 24,
        28, 32, 36
    };

    std::vector<int32_t> biasData = { -12, -8 };

    std::vector<uint8_t> actualOutput(outputInfo.GetNumElements());

    std::vector<uint8_t> expectedOutputData =
    {
         9,  13,  21,  19,  27,
        21,  25,  57,  43,  51,
        39,  55, 131,  91, 115,
        49,  61, 129,  79,  95,
        85,  97, 213, 127, 143,

        10,  14,  26,  22,  30,
        22,  26,  62,  46,  54,
        46,  62, 150, 102, 126,
        54,  66, 142,  86, 102,
        90, 102, 226, 134, 150
    };

    if (layout == DataLayout::NHWC)
    {
        PermuteTensorNchwToNhwc(inputInfo, inputData);
        PermuteTensorNchwToNhwc(kernelInfo, kernelData);
        PermuteTensorNchwToNhwc(outputInfo, expectedOutputData);
    }

    TransposeConvolution2dDescriptor descriptor;
    descriptor.m_StrideX     = 2;
    descriptor.m_StrideY     = 2;
    descriptor.m_BiasEnabled = true;
    descriptor.m_DataLayout  = layout;

    std::unique_ptr<ITensorHandle> inputHandle  = tensorHandleFactory.CreateTensorHandle(inputInfo);
    std::unique_ptr<ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputInfo);

    WorkloadInfo workloadInfo;
    ScopedTensorHandle weightTensor(kernelInfo);
    ScopedTensorHandle biasTensor(biasInfo);

    AllocateAndCopyDataToITensorHandle(&weightTensor, kernelData.data());
    AllocateAndCopyDataToITensorHandle(&biasTensor, biasData.data());

    TransposeConvolution2dQueueDescriptor queueDescriptor;
    queueDescriptor.m_Parameters = descriptor;
    queueDescriptor.m_Weight     = &weightTensor;
    queueDescriptor.m_Bias       = &biasTensor;

    AddInputToWorkload(queueDescriptor, workloadInfo, inputInfo, inputHandle.get());
    AddOutputToWorkload(queueDescriptor, workloadInfo, outputInfo, outputHandle.get());

    std::unique_ptr<IWorkload> workload = workloadFactory.CreateWorkload(armnn::LayerType::TransposeConvolution2d,
                                                                         queueDescriptor,
                                                                         workloadInfo);
    inputHandle->Allocate();
    outputHandle->Allocate();

    CopyDataToITensorHandle(inputHandle.get(), inputData.data());

    ExecuteWorkload(*workload, memoryManager);

    CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());

    return LayerTestResult<uint8_t, 4>(actualOutput,
                                       expectedOutputData,
                                       outputHandle->GetShape(),
                                       outputInfo.GetShape());
}