path: root/include/armnn/Descriptors.hpp

//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once

#include "Deprecated.hpp"
#include "DescriptorsFwd.hpp"

#include <cstdint>
#include <initializer_list>

#include "Tensor.hpp"
#include "Types.hpp"

namespace armnn
{

/// An ActivationDescriptor for the ActivationLayer.
struct ActivationDescriptor
{
    ActivationDescriptor() : m_Function(ActivationFunction::Sigmoid), m_A(0), m_B(0) {}

    /// @brief The activation function to use
    /// (Sigmoid, TanH, Linear, ReLu, BoundedReLu, SoftReLu, LeakyReLu, Abs, Sqrt, Square).
    ActivationFunction m_Function;
    /// Alpha upper bound value used by the activation functions. (BoundedReLu, Linear, TanH).
    float              m_A;
    /// Beta lower bound value used by the activation functions. (BoundedReLu, Linear, TanH).
    float              m_B;
};

/// An ArgMinMaxDescriptor for ArgMinMaxLayer
struct ArgMinMaxDescriptor
{
    ArgMinMaxDescriptor()
    : m_Function(ArgMinMaxFunction::Min)
    , m_Axis(-1) {}

    /// Specify if the function is to find Min or Max.
    ArgMinMaxFunction m_Function;
    /// Axis to reduce across the input tensor.
    int m_Axis;
};

/// A PermuteDescriptor for the PermuteLayer.
struct PermuteDescriptor
{
    PermuteDescriptor()
        : m_DimMappings{}
    {
    }
    PermuteDescriptor(const PermutationVector& dimMappings)
        : m_DimMappings(dimMappings)
    {
    }
    /// @brief Indicates how to translate tensor elements from a given source into the target destination, when
    /// source and target potentially have different memory layouts e.g. {0U, 3U, 1U, 2U}.
    PermutationVector m_DimMappings;
};

/// A SoftmaxDescriptor for the SoftmaxLayer.
struct SoftmaxDescriptor
{
    SoftmaxDescriptor()
    : m_Beta(1.0f)
    , m_Axis(-1)
    {}

    /// Exponentiation value.
    float m_Beta;
    /// Scalar, defaulted to the last index (-1), specifying the dimension the activation will be performed on.
    int m_Axis;
};

/// @brief An OriginsDescriptor for the ConcatLayer.
/// Descriptor to configure the concatenation process. Number of views must be equal to the number of inputs, and
/// their order must match - e.g. first view corresponds to the first input, second view to the second input, etc.
struct OriginsDescriptor
{
    OriginsDescriptor();
    OriginsDescriptor(uint32_t numViews, uint32_t numDimensions = 4);
    OriginsDescriptor(const OriginsDescriptor& other);
    OriginsDescriptor(OriginsDescriptor&& other);

    ~OriginsDescriptor();

    OriginsDescriptor& operator=(OriginsDescriptor rhs);

    /// @Brief Set the view origin coordinates. The arguments are: view, dimension, value.
    /// If the view is greater than or equal to GetNumViews(), then the view argument is out of range.
    /// If the coord is greater than or equal to GetNumDimensions(), then the coord argument is out of range.
    Status SetViewOriginCoord(uint32_t view, uint32_t coord, uint32_t value);
    /// Get the number of views.
    uint32_t GetNumViews() const;
    /// Get the number of dimensions.
    uint32_t GetNumDimensions() const;
    /// Return the view origin at the int value idx.
    const uint32_t* GetViewOrigin(uint32_t idx) const;
    /// @brief Reorders the viewOrigins in accordance with the indices presented in newOrdering array.
    /// The number of views must match number of elements in the new ordering array.
    void ReorderOrigins(unsigned int*  newOrdering, unsigned int numNewOrdering);
    /// Swap the ViewsDescriptor values first and second.
    friend void swap(OriginsDescriptor& first, OriginsDescriptor& second);
    /// Set the concatenation axis value.
    void SetConcatAxis(unsigned int concatAxis);
    /// Get the concatenation axis value.
    unsigned int GetConcatAxis() const;

private:
    unsigned int m_ConcatAxis;
    uint32_t     m_NumViews;
    uint32_t     m_NumDimensions;
    uint32_t**   m_ViewOrigins;
};

/// @brief A ViewsDescriptor for the SplitterLayer.
/// Descriptor to configure the splitting process. Number of Views must be equal to the number of outputs, and
/// their order must match - e.g. first view corresponds to the first output, second view to the second output, etc.
struct ViewsDescriptor
{
    ViewsDescriptor(uint32_t numViews, uint32_t numDimensions = 4);
    ViewsDescriptor(const ViewsDescriptor& other);
    ViewsDescriptor();
    ViewsDescriptor(ViewsDescriptor&& other);

    ~ViewsDescriptor();

    ViewsDescriptor& operator=(ViewsDescriptor rhs);
    /// @Brief Set the view origin coordinates. The arguments are: view, dimension, value.
    /// If the view is greater than or equal to GetNumViews(), then the view argument is out of range.
    /// If the coord is greater than or equal to GetNumDimensions(), then the coord argument is out of range.
    Status SetViewOriginCoord(uint32_t view, uint32_t coord, uint32_t value);
    /// @brief Set the size of the views. The arguments are: view, dimension, value.
    /// If the view is greater than or equal to GetNumViews(), then the view argument is out of range.
    /// If the coord is greater than or equal to GetNumDimensions(), then the coord argument is out of range.
    Status SetViewSize(uint32_t view, uint32_t coord, uint32_t value);

    /// Get the number of views.
    uint32_t GetNumViews() const;
    /// Get the number of dimensions.
    uint32_t GetNumDimensions() const;
    /// Get the view origin at the int value idx.
    const uint32_t* GetViewOrigin(uint32_t idx) const;
    /// Get the view sizes at the int value idx.
    const uint32_t* GetViewSizes(uint32_t idx) const;
    /// Get the View Origins
    const OriginsDescriptor& GetOrigins() const;

    /// Swap the ViewsDescriptor value first and second.
    friend void swap(ViewsDescriptor& first, ViewsDescriptor& second);
private:
    OriginsDescriptor m_Origins;
    uint32_t**        m_ViewSizes;
};

template <typename TensorShapeIt>
ARMNN_DEPRECATED_MSG("Use CreateDescriptorForConcatenation instead")
OriginsDescriptor CreateMergerDescriptorForConcatenation(TensorShapeIt first,
                                                         TensorShapeIt last,
                                                         unsigned int concatenationDimension)
{
    return CreateDescriptorForConcatenation(first, last, concatenationDimension);
}

/// @brief Convenience template to create an OriginsDescriptor to use when creating a ConcatLayer for performing
/// concatenation of a number of input tensors.
template <typename TensorShapeIt>
OriginsDescriptor CreateDescriptorForConcatenation(TensorShapeIt first,
                                                   TensorShapeIt last,
                                                   unsigned int concatenationDimension)
{
    auto numInputs = std::distance(first, last);

    if (numInputs < 2)
    {
        throw InvalidArgumentException("Concatenation requires at least 2 inputs");
    }

    const auto& firstInputShape = *first;

    const unsigned int numDimensions = firstInputShape.GetNumDimensions();
    for (auto it = first + 1; it != last; ++it)
    {
        if (it->GetNumDimensions() != numDimensions)
        {
            throw InvalidArgumentException("All inputs to concatenation must have the same number of dimensions");
        }
    }

    if (concatenationDimension >= numDimensions)
    {
        throw InvalidArgumentException("concatenationDimension must be between 0 and the number of dimensions.");
    }

    for (auto it = first; it != last; ++it)
    {
        for (unsigned int d = 0; d < numDimensions; ++d)
        {
            const bool dimSizeOk = (d == concatenationDimension) || (firstInputShape[d] == (*it)[d]);
            if (!dimSizeOk)
            {
                throw InvalidArgumentException("All inputs to concatenation must be the same size along all dimensions "
                    " except the concatenation dimension");
            }
        }
    }

    OriginsDescriptor viewsDescriptor(static_cast<uint32_t>(numInputs), numDimensions);
    viewsDescriptor.SetConcatAxis(concatenationDimension);

    uint32_t viewIndex = 0u;
    uint32_t coordAlongConcatDim = 0u;
    for (auto it = first; it != last; ++it)
    {
        const auto& inputShape = *it;

        for (unsigned int i = 0; i < concatenationDimension; ++i)
        {
            viewsDescriptor.SetViewOriginCoord(viewIndex, i, 0);
        }

        viewsDescriptor.SetViewOriginCoord(viewIndex, concatenationDimension, coordAlongConcatDim);
        unsigned int dimSize = inputShape[concatenationDimension];
        coordAlongConcatDim += dimSize;


        for (unsigned int i = concatenationDimension + 1; i < numDimensions; ++i)
        {
            viewsDescriptor.SetViewOriginCoord(viewIndex, i, 0);
        }

        ++viewIndex;
    }

    return viewsDescriptor;
}

/// A Pooling2dDescriptor for the Pooling2dLayer.
struct Pooling2dDescriptor
{
    Pooling2dDescriptor()
    : m_PoolType(PoolingAlgorithm::Max)
    , m_PadLeft(0)
    , m_PadRight(0)
    , m_PadTop(0)
    , m_PadBottom(0)
    , m_PoolWidth(0)
    , m_PoolHeight(0)
    , m_StrideX(0)
    , m_StrideY(0)
    , m_OutputShapeRounding(OutputShapeRounding::Floor)
    , m_PaddingMethod(PaddingMethod::Exclude)
    , m_DataLayout(DataLayout::NCHW)
    {}

    /// The pooling algorithm to use (Max. Average, L2).
    PoolingAlgorithm    m_PoolType;
    /// Padding left value in the width dimension.
    uint32_t            m_PadLeft;
    /// Padding right value in the width dimension.
    uint32_t            m_PadRight;
    /// Padding top value in the height dimension.
    uint32_t            m_PadTop;
    /// Padding bottom value in the height dimension.
    uint32_t            m_PadBottom;
    /// Pooling width value.
    uint32_t            m_PoolWidth;
    /// Pooling height value.
    uint32_t            m_PoolHeight;
    /// Stride value when proceeding through input for the width dimension.
    uint32_t            m_StrideX;
    /// Stride value when proceeding through input for the height dimension.
    uint32_t            m_StrideY;
    /// The rounding method for the output shape. (Floor, Ceiling).
    OutputShapeRounding m_OutputShapeRounding;
    /// The padding method to be used. (Exclude, IgnoreValue).
    PaddingMethod       m_PaddingMethod;
    /// The data layout to be used (NCHW, NHWC).
    DataLayout   m_DataLayout;
};

/// A FullyConnectedDescriptor for the FullyConnectedLayer.
struct FullyConnectedDescriptor
{
    FullyConnectedDescriptor()
    : m_BiasEnabled(false)
    , m_TransposeWeightMatrix(false)
    {}

    /// Enable/disable bias.
    bool m_BiasEnabled;
    /// Enable/disable transpose weight matrix.
    bool m_TransposeWeightMatrix;
};

/// A Convolution2dDescriptor for the Convolution2dLayer.
struct Convolution2dDescriptor
{
    Convolution2dDescriptor()
    : m_PadLeft(0)
    , m_PadRight(0)
    , m_PadTop(0)
    , m_PadBottom(0)
    , m_StrideX(0)
    , m_StrideY(0)
    , m_DilationX(1)
    , m_DilationY(1)
    , m_BiasEnabled(false)
    , m_DataLayout(DataLayout::NCHW)
    {}

    /// Padding left value in the width dimension.
    uint32_t             m_PadLeft;
    /// Padding right value in the width dimension.
    uint32_t             m_PadRight;
    /// Padding top value in the height dimension.
    uint32_t             m_PadTop;
    /// Padding bottom value in the height dimension.
    uint32_t             m_PadBottom;
    /// Stride value when proceeding through input for the width dimension.
    uint32_t             m_StrideX;
    /// Stride value when proceeding through input for the height dimension.
    uint32_t             m_StrideY;
    /// Dilation along x axis
    uint32_t             m_DilationX;
    /// Dilation along y axis
    uint32_t             m_DilationY;
    /// Enable/disable bias.
    bool                 m_BiasEnabled;
    /// The data layout to be used (NCHW, NHWC).
    DataLayout           m_DataLayout;
};

/// A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer.
struct DepthwiseConvolution2dDescriptor
{
    DepthwiseConvolution2dDescriptor()
    :   m_PadLeft(0)
    ,   m_PadRight(0)
    ,   m_PadTop(0)
    ,   m_PadBottom(0)
    ,   m_StrideX(0)
    ,   m_StrideY(0)
    ,   m_DilationX(1)
    ,   m_DilationY(1)
    ,   m_BiasEnabled(false)
    ,   m_DataLayout(DataLayout::NCHW)
    {}

    /// Padding left value in the width dimension.
    uint32_t   m_PadLeft;
    /// Padding right value in the width dimension.
    uint32_t   m_PadRight;
    /// Padding top value in the height dimension.
    uint32_t   m_PadTop;
    /// Padding bottom value in the height dimension.
    uint32_t   m_PadBottom;
    /// Stride value when proceeding through input for the width dimension.
    uint32_t   m_StrideX;
    /// Stride value when proceeding through input for the height dimension.
    uint32_t   m_StrideY;
    /// Dilation factor value for width dimension.
    uint32_t   m_DilationX;
    /// Dilation factor value for height dimension.
    uint32_t   m_DilationY;
    /// Enable/disable bias.
    bool       m_BiasEnabled;
    /// The data layout to be used (NCHW, NHWC).
    DataLayout m_DataLayout;
};

struct DetectionPostProcessDescriptor
{
    DetectionPostProcessDescriptor()
    : m_MaxDetections(0)
    , m_MaxClassesPerDetection(1)
    , m_DetectionsPerClass(1)
    , m_NmsScoreThreshold(0)
    , m_NmsIouThreshold(0)
    , m_NumClasses(0)
    , m_UseRegularNms(false)
    , m_ScaleX(0)
    , m_ScaleY(0)
    , m_ScaleW(0)
    , m_ScaleH(0)
    {}

    /// Maximum numbers of detections.
    uint32_t m_MaxDetections;
    /// Maximum numbers of classes per detection, used in Fast NMS.
    uint32_t m_MaxClassesPerDetection;
    /// Detections per classes, used in Regular NMS.
    uint32_t m_DetectionsPerClass;
    /// NMS score threshold.
    float m_NmsScoreThreshold;
    /// Intersection over union threshold.
    float m_NmsIouThreshold;
    /// Number of classes.
    uint32_t m_NumClasses;
    /// Use Regular NMS.
    bool m_UseRegularNms;
    /// Center size encoding scale x.
    float m_ScaleX;
    /// Center size encoding scale y.
    float m_ScaleY;
    /// Center size encoding scale weight.
    float m_ScaleW;
    /// Center size encoding scale height.
    float m_ScaleH;
};

/// A NormalizationDescriptor for the NormalizationLayer.
struct NormalizationDescriptor
{
    NormalizationDescriptor()
    : m_NormChannelType(NormalizationAlgorithmChannel::Across)
    , m_NormMethodType(NormalizationAlgorithmMethod::LocalBrightness)
    , m_NormSize(0)
    , m_Alpha(0.f)
    , m_Beta(0.f)
    , m_K(0.f)
    , m_DataLayout(DataLayout::NCHW)
    {}

    /// Normalization channel algorithm to use (Across, Within).
    NormalizationAlgorithmChannel m_NormChannelType;
    /// Normalization method algorithm to use (LocalBrightness, LocalContrast).
    NormalizationAlgorithmMethod  m_NormMethodType;
    /// Depth radius value.
    uint32_t                      m_NormSize;
    /// Alpha value for the normalization equation.
    float                         m_Alpha;
    /// Beta value for the normalization equation.
    float                         m_Beta;
    /// Kappa value used for the across channel normalization equation.
    float                         m_K;
    /// The data layout to be used (NCHW, NHWC).
    DataLayout                    m_DataLayout;
};

/// A L2NormalizationDescriptor for the L2NormalizationLayer.
struct L2NormalizationDescriptor
{
    L2NormalizationDescriptor()
    : m_Eps(1e-12f)
    , m_DataLayout(DataLayout::NCHW)
    {}

    /// Used to avoid dividing by zero.
    float m_Eps;
    /// The data layout to be used (NCHW, NHWC).
    DataLayout m_DataLayout;
};

/// A BatchNormalizationDescriptor for the BatchNormalizationLayer.
struct BatchNormalizationDescriptor
{
    BatchNormalizationDescriptor()
    : m_Eps(0.0001f)
    , m_DataLayout(DataLayout::NCHW)
    {}

    /// Value to add to the variance. Used to avoid dividing by zero.
    float m_Eps;
    /// The data layout to be used (NCHW, NHWC).
    DataLayout m_DataLayout;
};

/// A BatchToSpaceNdDescriptor for the BatchToSpaceNdLayer.
struct BatchToSpaceNdDescriptor
{
    BatchToSpaceNdDescriptor()
        : m_BlockShape({1, 1})
        , m_Crops({{0, 0}, {0, 0}})
        , m_DataLayout(DataLayout::NCHW)
    {}

    BatchToSpaceNdDescriptor(std::vector<unsigned int> blockShape,
                             std::vector<std::pair<unsigned int, unsigned int>> crops)
        : m_BlockShape(blockShape)
        , m_Crops(crops)
        , m_DataLayout(DataLayout::NCHW)
    {}

    /// Block shape values.
    std::vector<unsigned int> m_BlockShape;
    /// The values to crop from the input dimension.
    std::vector<std::pair<unsigned int, unsigned int>> m_Crops;
    /// The data layout to be used (NCHW, NHWC).
    DataLayout m_DataLayout;
};

/// A FakeQuantizationDescriptor for the FakeQuantizationLayer.
struct FakeQuantizationDescriptor
{
    FakeQuantizationDescriptor()
    : m_Min(-6.0f)
    , m_Max(6.0f)
    {}

    /// Minimum value.
    float m_Min;
    /// Maximum value.
    float m_Max;
};

/// A ResizeBilinearDescriptor for the ResizeBilinearLayer.
struct ResizeBilinearDescriptor
{
    ResizeBilinearDescriptor()
    : m_TargetWidth(0)
    , m_TargetHeight(0)
    , m_DataLayout(DataLayout::NCHW)
    {}

    /// Target width value.
    uint32_t          m_TargetWidth;
    /// Target height value.
    uint32_t          m_TargetHeight;
    /// The data layout to be used (NCHW, NHWC).
    DataLayout m_DataLayout;
};

/// A ResizeDescriptor for the ResizeLayer.
struct ResizeDescriptor
{
    ResizeDescriptor()
            : m_TargetWidth(0)
            , m_TargetHeight(0)
            , m_Method(ResizeMethod::NearestNeighbor)
            , m_DataLayout(DataLayout::NCHW)
    {}

    /// Target width value.
    uint32_t m_TargetWidth;
    /// Target height value.
    uint32_t m_TargetHeight;
    /// The Interpolation method to use
    /// (Bilinear, NearestNeighbor).
    ResizeMethod m_Method;
    /// The data layout to be used (NCHW, NHWC).
    DataLayout m_DataLayout;
};


/// A ReshapeDescriptor for the ReshapeLayer.
struct ReshapeDescriptor
{
    ReshapeDescriptor()
    : m_TargetShape()
    {}

    ReshapeDescriptor(const TensorShape& shape)
    : m_TargetShape(shape)
    {}

    /// Target shape value.
    TensorShape m_TargetShape;
};

/// A SpaceToBatchNdDescriptor for the SpaceToBatchNdLayer.
struct SpaceToBatchNdDescriptor
{
    SpaceToBatchNdDescriptor()
    : m_BlockShape({1, 1})
    , m_PadList({{0, 0}, {0, 0}})
    , m_DataLayout(DataLayout::NCHW)
    {}

    SpaceToBatchNdDescriptor(const std::vector<unsigned int>& blockShape,
                             const std::vector<std::pair<unsigned int, unsigned int>>& padList)
    : m_BlockShape(blockShape)
    , m_PadList(padList)
    , m_DataLayout(DataLayout::NCHW)
    {}

    /// Block shape value.
    std::vector<unsigned int> m_BlockShape;
    /// @brief Specifies the padding values for the input dimension:
    /// heightPad{top, bottom} widthPad{left, right}.
    std::vector<std::pair<unsigned int, unsigned int>> m_PadList;
    /// The data layout to be used (NCHW, NHWC).
    DataLayout m_DataLayout;
};

/// A SpaceToDepthDescriptor for the SpaceToDepthLayer
struct SpaceToDepthDescriptor
{
    SpaceToDepthDescriptor()
    : m_BlockSize(1u)
    , m_DataLayout(DataLayout::NHWC)
    {}

    /// Scalar specifying the input block size. It must be >= 1
    unsigned int m_BlockSize;
    /// The data layout to be used (NCHW, NHWC).
    DataLayout m_DataLayout;
};

/// An LstmDescriptor for the LstmLayer.
struct LstmDescriptor
{
    LstmDescriptor()
    : m_ActivationFunc(1) // 0: None, 1: Relu, 3: Relu6, 4: Tanh, 6: Sigmoid
    , m_ClippingThresCell(0.0)
    , m_ClippingThresProj(0.0)
    , m_CifgEnabled(true)
    , m_PeepholeEnabled(false)
    , m_ProjectionEnabled(false)
    , m_LayerNormEnabled(false)
    {}

    /// @brief The activation function to use.
    /// 0: None, 1: Relu, 3: Relu6, 4: Tanh, 6: Sigmoid.
    uint32_t m_ActivationFunc;
    /// Clipping threshold value for the cell state.
    float m_ClippingThresCell;
    /// Clipping threshold value for the projection.
    float m_ClippingThresProj;
    /// Enable/disable cifg (coupled input & forget gate).
    bool m_CifgEnabled;
    /// Enable/disable peephole.
    bool m_PeepholeEnabled;
    /// Enable/disable the projection layer.
    bool m_ProjectionEnabled;
    /// Enable/disable layer normalization
    bool m_LayerNormEnabled;
};

/// A MeanDescriptor for the MeanLayer.
struct MeanDescriptor
{
    MeanDescriptor()
    : m_Axis()
    , m_KeepDims(false)
    {}

    MeanDescriptor(const std::vector<unsigned int>& axis, bool keepDims)
    : m_Axis(axis)
    , m_KeepDims(keepDims)
    {}

    /// Values for the dimensions to reduce.
    std::vector<unsigned int> m_Axis;
    /// Enable/disable keep dimensions. If true, then the reduced dimensions that are of length 1 are kept.
    bool m_KeepDims;
};

/// A PadDescriptor for the PadLayer.
struct PadDescriptor
{
    PadDescriptor() : m_PadValue(0)
    {}

    PadDescriptor(const std::vector<std::pair<unsigned int, unsigned int>>& padList, const float& padValue = 0)
    : m_PadList(padList), m_PadValue(padValue)
    {}

    /// @brief Specifies the padding for input dimension.
    /// First is the number of values to add before the tensor in the dimension.
    /// Second is the number of values to add after the tensor in the dimension.
    /// The number of pairs should match the number of dimensions in the input tensor.
    std::vector<std::pair<unsigned int, unsigned int>> m_PadList;

    /// Optional value to use for padding, defaults to 0
    float m_PadValue;
};

/// A StackDescriptor for the StackLayer.
struct StackDescriptor
{
    StackDescriptor()
    : m_Axis(0)
    , m_NumInputs(0)
    , m_InputShape()
    {}

    StackDescriptor(uint32_t axis, uint32_t numInputs, const TensorShape& inputShape)
    : m_Axis(axis)
    , m_NumInputs(numInputs)
    , m_InputShape(inputShape)
    {}

    /// 0-based axis along which to stack the input tensors.
    uint32_t m_Axis;
    /// Number of input tensors.
    uint32_t m_NumInputs;
    /// Required shape of all input tensors.
    TensorShape m_InputShape;
};

/// A StridedSliceDescriptor for the StridedSliceLayer.
struct StridedSliceDescriptor
{
    StridedSliceDescriptor(const std::vector<int>& begin,
                           const std::vector<int>& end,
                           const std::vector<int>& stride)
    : m_Begin(begin)
    , m_End(end)
    , m_Stride(stride)
    , m_BeginMask(0)
    , m_EndMask(0)
    , m_ShrinkAxisMask(0)
    , m_EllipsisMask(0)
    , m_NewAxisMask(0)
    , m_DataLayout(DataLayout::NCHW)
    {}

    StridedSliceDescriptor()
    : StridedSliceDescriptor({}, {}, {})
    {}

    int GetStartForAxis(const TensorShape& inputShape, unsigned int axis) const;
    int GetStopForAxis(const TensorShape& inputShape,
                       unsigned int axis,
                       int startForAxis) const;

    /// Begin values for the input that will be sliced.
    std::vector<int> m_Begin;
    /// End values for the input that will be sliced.
    std::vector<int> m_End;
    /// Stride values for the input that will be sliced.
    std::vector<int> m_Stride;

    /// @brief Begin mask value. If set, then the begin is disregarded and the fullest
    /// range is used for the dimension.
    int32_t m_BeginMask;
    /// @brief End mask value. If set, then the end is disregarded and the fullest range
    /// is used for the dimension.
    int32_t m_EndMask;
    /// Shrink axis mask value. If set, the nth specification shrinks the dimensionality by 1.
    int32_t m_ShrinkAxisMask;
    /// Ellipsis mask value.
    int32_t m_EllipsisMask;
    /// @brief New axis mask value. If set, the begin, end and stride is disregarded and
    /// a new 1 dimension is inserted to this location of the output tensor.
    int32_t m_NewAxisMask;

    /// The data layout to be used (NCHW, NHWC).
    DataLayout m_DataLayout;
};

/// A PreCompiledDescriptor for the PreCompiledLayer.
struct PreCompiledDescriptor
{
    PreCompiledDescriptor(unsigned int numInputSlots = 1u, unsigned int numOutputSlots = 1u)
        : m_NumInputSlots(numInputSlots), m_NumOutputSlots(numOutputSlots)
    {}

    ~PreCompiledDescriptor() = default;

    unsigned int m_NumInputSlots;
    unsigned int m_NumOutputSlots;
};

/// A TransposeConvolution2dDescriptor for the TransposeConvolution2dLayer.
struct TransposeConvolution2dDescriptor
{
    TransposeConvolution2dDescriptor() :
        m_PadLeft(0),
        m_PadRight(0),
        m_PadTop(0),
        m_PadBottom(0),
        m_StrideX(0),
        m_StrideY(0),
        m_BiasEnabled(false),
        m_DataLayout(DataLayout::NCHW)
    {}

    /// Padding left value in the width dimension.
    uint32_t   m_PadLeft;
    /// Padding right value in the width dimension.
    uint32_t   m_PadRight;
    /// Padding top value in the height dimension.
    uint32_t   m_PadTop;
    /// Padding bottom value in the height dimension.
    uint32_t   m_PadBottom;
    /// Stride value when proceeding through input for the width dimension.
    uint32_t   m_StrideX;
    /// Stride value when proceeding through input for the height dimension.
    uint32_t   m_StrideY;
    /// Enable/disable bias.
    bool       m_BiasEnabled;
    /// The data layout to be used (NCHW, NHWC).
    DataLayout m_DataLayout;
};

} // namespace armnn