arm_compute/core/Types.h

/*
 * Copyright (c) 2016-2022 Arm Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#ifndef ARM_COMPUTE_TYPES_H
#define ARM_COMPUTE_TYPES_H

#include "arm_compute/core/Coordinates.h"
#include "arm_compute/core/QuantizationInfo.h"
#include "arm_compute/core/Size2D.h"
#include "arm_compute/core/Size3D.h"
#include "arm_compute/core/Strides.h"
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/experimental/IPostOp.h"
#include "arm_compute/core/utils/misc/Macros.h"
#include "support/Bfloat16.h"
#include "support/Half.h"

#include <cmath>
#include <cstddef>
#include <cstdint>
#include <map>
#include <string>
#include <utility>

namespace arm_compute
{
/** 16-bit floating point type */
using half = half_float::half;

/** Permutation vector */
using PermutationVector = Strides;
/** Bidirectional strides */
using BiStrides = Coordinates;

/** Image colour formats */
enum class Format
{
    UNKNOWN,  /**< Unknown image format */
    U8,       /**< 1 channel, 1 U8 per channel */
    S16,      /**< 1 channel, 1 S16 per channel */
    U16,      /**< 1 channel, 1 U16 per channel */
    S32,      /**< 1 channel, 1 S32 per channel */
    U32,      /**< 1 channel, 1 U32 per channel */
    BFLOAT16, /**< 16-bit brain floating-point number */
    F16,      /**< 1 channel, 1 F16 per channel */
    F32,      /**< 1 channel, 1 F32 per channel */
    UV88,     /**< 2 channel, 1 U8 per channel */
    RGB888,   /**< 3 channels, 1 U8 per channel */
    RGBA8888, /**< 4 channels, 1 U8 per channel */
    YUV444,   /**< A 3 plane of 8 bit 4:4:4 sampled Y, U, V planes */
    YUYV422,  /**< A single plane of 32-bit macro pixel of Y0, U0, Y1, V0 bytes */
    NV12,     /**< A 2 plane YUV format of Luma (Y) and interleaved UV data at 4:2:0 sampling */
    NV21,     /**< A 2 plane YUV format of Luma (Y) and interleaved VU data at 4:2:0 sampling */
    IYUV,     /**< A 3 plane of 8-bit 4:2:0 sampled Y, U, V planes */
    UYVY422   /**< A single plane of 32-bit macro pixel of U0, Y0, V0, Y1 byte */
};

/** Available data types */
enum class DataType
{
    UNKNOWN,            /**< Unknown data type */
    U8,                 /**< unsigned 8-bit number */
    S8,                 /**< signed 8-bit number */
    QSYMM8,             /**< quantized, symmetric fixed-point 8-bit number */
    QASYMM8,            /**< quantized, asymmetric fixed-point 8-bit number unsigned */
    QASYMM8_SIGNED,     /**< quantized, asymmetric fixed-point 8-bit number signed */
    QSYMM8_PER_CHANNEL, /**< quantized, symmetric per channel fixed-point 8-bit number */
    U16,                /**< unsigned 16-bit number */
    S16,                /**< signed 16-bit number */
    QSYMM16,            /**< quantized, symmetric fixed-point 16-bit number */
    QASYMM16,           /**< quantized, asymmetric fixed-point 16-bit number */
    U32,                /**< unsigned 32-bit number */
    S32,                /**< signed 32-bit number */
    U64,                /**< unsigned 64-bit number */
    S64,                /**< signed 64-bit number */
    BFLOAT16,           /**< 16-bit brain floating-point number */
    F16,                /**< 16-bit floating-point number */
    F32,                /**< 32-bit floating-point number */
    F64,                /**< 64-bit floating-point number */
    SIZET               /**< size_t */
};

/** Available Sampling Policies */
enum class SamplingPolicy
{
    CENTER,  /**< Samples are taken at pixel center */
    TOP_LEFT /**< Samples are taken at pixel top left corner */
};

/** [DataLayout enum definition] **/

/** Supported tensor data layouts */
enum class DataLayout
{
    UNKNOWN, /**< Unknown data layout */
    NCHW,    /**< Num samples, channels, height, width */
    NHWC,    /**< Num samples, height, width, channels */
    NCDHW,   /**< Num samples, channels, depth, height, width */
    NDHWC    /**< Num samples, depth, height, width, channels */
};
/** [DataLayout enum definition] **/

/** Supported tensor data layout dimensions */
enum class DataLayoutDimension
{
    CHANNEL, /**< channel */
    HEIGHT,  /**< height */
    WIDTH,   /**< width */
    DEPTH,   /**< depth */
    BATCHES  /**< batches */
};

/** Available ConvolutionMethod*/
enum class ConvolutionMethod
{
    GEMM,        /**< Convolution using GEMM */
    GEMM_CONV2D, /**< Direct 2D GEMM convolution */
    DIRECT,      /**< Direct convolution */
    WINOGRAD,    /**< Convolution using Winograd */
    FFT          /**< Convolution using FFT */
};

/** Available DepthwiseConvolutionFunction*/
enum class DepthwiseConvolutionFunction
{
    OPTIMIZED, /**< Optimized Depthwise Convolution */
    GENERIC,   /**< Generic Depthwise Convolution */
};

/** Available DeconvolutionMethod*/
enum class DeconvolutionMethod
{
    GEMM,   /**< Deconvolution using GEMM */
    DIRECT, /**< Direct deconvolution */
};

/** Available FuseBatchNormalizationType*/
enum class FuseBatchNormalizationType
{
    CONVOLUTION,         /**< For Convolution weights */
    DEPTHWISECONVOLUTION /**< For Depthwise Convolution weights*/
};

/** Padding mode to use for PadLayer */
enum class PaddingMode
{
    CONSTANT,
    REFLECT,
    SYMMETRIC
};

/** Supported comparison operations */
enum class ComparisonOperation
{
    Equal,        /**< Equal comparison ( \f$ x == y \f$ ) */
    NotEqual,     /**< NotEqual comparison ( \f$ x != y \f$ ) */
    Greater,      /**< Greater comparison ( \f$ x > y \f$ ) */
    GreaterEqual, /**< Greater equal comparison ( \f$ x >= y \f$ ) */
    Less,         /**< Less comparison ( \f$ x < y \f$ ) */
    LessEqual     /**< Less equal comparison ( \f$ x <= y \f$ ) */
};

/** Container for valid region of a window */
struct ValidRegion
{
    /** Default constructor */
    ValidRegion()
        : anchor{}, shape{}
    {
    }

    /** Allow instances of this class to be copy constructed */
    ValidRegion(const ValidRegion &) = default;
    /** Allow instances of this class to be move constructed */
    ValidRegion(ValidRegion &&) = default;
    /** Allow instances of this class to be copied */
    ValidRegion &operator=(const ValidRegion &) = default;
    /** Allow instances of this class to be moved */
    ValidRegion &operator=(ValidRegion &&) = default;
    /** Default destructor */
    ~ValidRegion() = default;

    /** Constructor for a valid region with default number of dimensions
     *
     * @param[in] an_anchor Anchor for the start of the valid region.
     * @param[in] a_shape   Shape of the valid region.
     *
     */
    ValidRegion(const Coordinates &an_anchor, const TensorShape &a_shape)
        : anchor{ an_anchor }, shape{ a_shape }
    {
        anchor.set_num_dimensions(std::max(anchor.num_dimensions(), shape.num_dimensions()));
    }

    /** Constructor for a valid region with specified number of dimensions
     *
     * @param[in] an_anchor      Anchor for the start of the valid region.
     * @param[in] a_shape        Shape of the valid region.
     * @param[in] num_dimensions Number of dimensions (must be >= number of dimensions of anchor and shape).
     *
     */
    ValidRegion(const Coordinates &an_anchor, const TensorShape &a_shape, size_t num_dimensions)
        : anchor{ an_anchor }, shape{ a_shape }
    {
        ARM_COMPUTE_ERROR_ON(num_dimensions < std::max(anchor.num_dimensions(), shape.num_dimensions()));
        anchor.set_num_dimensions(num_dimensions);
    }

    /** Return the start of the valid region for the given dimension @p d */
    int start(unsigned int d) const
    {
        return anchor[d];
    }

    /** Return the end of the valid region for the given dimension @p d */
    int end(unsigned int d) const
    {
        return anchor[d] + shape[d];
    }

    /** Accessor to set the value of anchor and shape for one of the dimensions.
     *
     * @param[in] dimension Dimension for which the value is set.
     * @param[in] start     Value to be set in anchor for the dimension.
     * @param[in] size      Value to be set in shape for the dimension.
     *
     * @return *this.
     */
    ValidRegion &set(size_t dimension, int start, size_t size)
    {
        anchor.set(dimension, start);
        shape.set(dimension, size);
        return *this;
    }

    /** Check whether two valid regions are equal.
     *
     * @param[in] lhs LHS valid region
     * @param[in] rhs RHS valid region
     *
     * @return True if the valid regions are the same.
     */
    inline friend bool operator==(const ValidRegion &lhs, const ValidRegion &rhs);

    Coordinates anchor; /**< Anchor for the start of the valid region. */
    TensorShape shape;  /**< Shape of the valid region. */
};
inline bool operator==(const ValidRegion &lhs, const ValidRegion &rhs)
{
    return (lhs.anchor == rhs.anchor) && (lhs.shape == rhs.shape);
}

/** Methods available to handle borders */
enum class BorderMode
{
    UNDEFINED, /**< Borders are left undefined */
    CONSTANT,  /**< Pixels outside the image are assumed to have a constant value */
    REPLICATE  /**< Pixels outside the image are assumed to have the same value as the closest image pixel */
};

/** Container for 2D border size */
struct BorderSize
{
    /** Empty border, i.e. no border */
    constexpr BorderSize() noexcept
        : top{ 0 },
    right{ 0 },
    bottom{ 0 },
    left{ 0 }
    {
    }

    /** Border with equal size around the 2D plane */
    explicit constexpr BorderSize(unsigned int size) noexcept
        : top{ size },
    right{ size },
    bottom{ size },
    left{ size }
    {
    }

    /** Border with same size for top/bottom and left/right */
    constexpr BorderSize(unsigned int top_bottom, unsigned int left_right)
        : top{ top_bottom }, right{ left_right }, bottom{ top_bottom }, left{ left_right }
    {
    }

    /** Border with different sizes */
    constexpr BorderSize(unsigned int top, unsigned int right, unsigned int bottom, unsigned int left)
        : top{ top }, right{ right }, bottom{ bottom }, left{ left }
    {
    }

    /** Check if the entire border is zero */
    constexpr bool empty() const
    {
        return top == 0 && right == 0 && bottom == 0 && left == 0;
    }

    /** Check if the border is the same size on all sides */
    constexpr bool uniform() const
    {
        return top == right && top == bottom && top == left;
    }

    /** Scale this border size.
     *
     * @param[in] scale Scale to multiply border size by.
     *
     * @return *this.
     */
    BorderSize &operator*=(float scale)
    {
        top *= scale;
        right *= scale;
        bottom *= scale;
        left *= scale;

        return *this;
    }

    /** Scale a copy of this border size.
     *
     * @param[in] scale Scale to multiply border size by.
     *
     * @return a scaled copy of this.
     */
    BorderSize operator*(float scale)
    {
        BorderSize size = *this;
        size *= scale;

        return size;
    }

    /** Check equality with another BorderSize struct
     *
     * @param[in] rhs other struct to check against
     *
     * @return true if they are equal
     */
    bool operator==(const BorderSize &rhs) const
    {
        return (top == rhs.top) && (right == rhs.right) && (bottom == rhs.bottom) && (left == rhs.left);
    }

    /** Check non-equality with another BorderSize struct
     *
     * @param[in] rhs other struct to check against
     *
     * @return true if they are different
     */
    bool operator!=(const BorderSize &rhs) const
    {
        return !(*this == rhs);
    }

    /** Limit this border size.
     *
     * @param[in] limit Border size to limit this border size to.
     */
    void limit(const BorderSize &limit)
    {
        top    = std::min(top, limit.top);
        right  = std::min(right, limit.right);
        bottom = std::min(bottom, limit.bottom);
        left   = std::min(left, limit.left);
    }

    unsigned int top;    /**< top of the border */
    unsigned int right;  /**< right of the border */
    unsigned int bottom; /**< bottom of the border */
    unsigned int left;   /**< left of the border */
};

/** Container for 2D padding size */
using PaddingSize = BorderSize;

/** Policy to handle integer overflow
 *  @note: This is ignored by floating point operations where the overflow behavior adheres to the IEEE-754 standard
 *         which states that in case of overflow ±infinity is returned for the round-to-nearest modes (and follows the
 *         rounding rules for the directed rounding modes) by default.
 */
enum class ConvertPolicy
{
    WRAP,    /**< Wrap around */
    SATURATE /**< Saturate */
};

/** Interpolation method */
enum class InterpolationPolicy
{
    NEAREST_NEIGHBOR, /**< Output values are defined to match the source pixel whose center is nearest to the sample position */
    BILINEAR,         /**< Output values are defined by bilinear interpolation between the pixels */
    AREA,             /**< Output values are determined by averaging the source pixels whose areas fall under the area of the destination pixel, projected onto the source image */
};

/** Bilinear Interpolation method used by LKTracker */
enum class BilinearInterpolation
{
    BILINEAR_OLD_NEW, /**< Old-new method */
    BILINEAR_SCHARR   /**< Scharr method */
};

/** Rectangle type */
struct Rectangle
{
    uint16_t x;      /**< Top-left x coordinate */
    uint16_t y;      /**< Top-left y coordinate */
    uint16_t width;  /**< Width of the rectangle */
    uint16_t height; /**< Height of the rectangle */
};

/** Coordinate type */
struct Coordinates2D
{
    int32_t x; /**< X coordinates */
    int32_t y; /**< Y coordinates */
};

/** Coordinate type */
struct Coordinates3D
{
    uint32_t x; /**< X coordinates */
    uint32_t y; /**< Y coordinates */
    uint32_t z; /**< Z coordinates */
};

/** Padding information as a pair of unsigned int start/end */
using PaddingInfo = std::pair<uint32_t, uint32_t>;

/** List of padding information */
using PaddingList = std::vector<PaddingInfo>;

/** Information to produce a tiled version of a Tensor */
using Multiples = std::vector<uint32_t>;

/** Available channels */
enum class Channel
{
    UNKNOWN, /** Unknown channel format */
    C0,      /**< First channel (used by formats with unknown channel types). */
    C1,      /**< Second channel (used by formats with unknown channel types). */
    C2,      /**< Third channel (used by formats with unknown channel types). */
    C3,      /**< Fourth channel (used by formats with unknown channel types). */
    R,       /**< Red channel. */
    G,       /**< Green channel. */
    B,       /**< Blue channel. */
    A,       /**< Alpha channel. */
    Y,       /**< Luma channel. */
    U,       /**< Cb/U channel. */
    V        /**< Cr/V/Value channel. */
};

/** Available reduction operations */
enum class ReductionOperation
{
    ARG_IDX_MAX, /**< Index of the max value */
    ARG_IDX_MIN, /**< Index of the min value */
    MEAN_SUM,    /**< Mean of sum */
    PROD,        /**< Product */
    SUM_SQUARE,  /**< Sum of squares */
    SUM,         /**< Sum */
    MIN,         /**< Min */
    MAX,         /**< Max */
};

/** Available element-wise operations */
enum class ArithmeticOperation
{
    ADD,          /**< (x + y) */
    SUB,          /**< (x  - y) */
    DIV,          /**< (x / y) */
    MIN,          /**< Min(x, y) */
    MAX,          /**< Max(x, y) */
    SQUARED_DIFF, /**< (x - y)^2 */
    POWER,        /**< x ^ y */
    PRELU,        /**< y*x if x < 0, x otherwise */
};

/** Available element wise unary operations */
enum class ElementWiseUnary
{
    RSQRT,       /**< Reverse square root */
    EXP,         /**< Exponential */
    NEG,         /**< Negate */
    LOG,         /**< Natural Logarithm */
    ABS,         /**< Absolute value */
    SIN,         /**< Sine */
    ROUND,       /**< Round */
    LOGICAL_NOT, /**< Logical Not */
};

/** Available bitwise operations */
enum class BitwiseOperation
{
    AND, /**< Bitwise AND operation */
    NOT, /**< Bitwise NOT operation */
    OR,  /**< Bitwise OR operation  */
    XOR, /**< Bitwise XOR operation  */
};

/** The normalization type used for the normalization layer */
enum class NormType
{
    IN_MAP_1D, /**< Normalization applied within the same map in 1D region */
    IN_MAP_2D, /**< Normalization applied within the same map in 2D region */
    CROSS_MAP  /**< Normalization applied cross maps */
};

/** Detection window used for the object detection. The detection window keeps the following information:
 *
 *  -# Geometry of the rectangular window (x/y of top-left corner and width/height)
 *  -# Index of the class used for evaluating which class the detection window belongs to
 *  -# Confidence value (score) obtained with the classifier
 */
struct DetectionWindow
{
    uint16_t x{ 0 };         /**< Top-left x coordinate */
    uint16_t y{ 0 };         /**< Top-left y coordinate */
    uint16_t width{ 0 };     /**< Width of the detection window */
    uint16_t height{ 0 };    /**< Height of the detection window */
    uint16_t idx_class{ 0 }; /**< Index of the class */
    float    score{ 0.f };   /**< Confidence value for the detection window */
};

/** Dimension rounding type when down-scaling on CNNs
 * @note Used in pooling and convolution layer
 */
enum class DimensionRoundingType
{
    FLOOR, /**< Floor rounding */
    CEIL   /**< Ceil rounding */
};

/** Available pooling types */
enum class PoolingType
{
    MAX, /**< Max Pooling */
    AVG, /**< Average Pooling */
    L2   /**< L2 Pooling */
};

/** Available non maxima suppression types */
enum class NMSType
{
    LINEAR,   /**< Linear NMS */
    GAUSSIAN, /**< Gaussian NMS */
    ORIGINAL  /**< Original NMS */
};

/** BoxWithNonMaximaSuppressionLimit Information class */
class BoxNMSLimitInfo final
{
public:
    /** Constructor
     *
     * @param[in] score_thresh             (Optional) Score threshold.
     * @param[in] nms                      (Optional) NMS value
     * @param[in] detections               (Optional) Number of detections
     * @param[in] soft_nms_enabled         (Optional) Enable SoftNMS
     * @param[in] soft_nms_method          (Optional) Soft NMS method
     * @param[in] soft_nms_sigma           (Optional) Soft NMS sigma value
     * @param[in] soft_nms_min_score_thres (Optional) Soft NMS minimum score threshold
     * @param[in] suppress_size            (Optional) Filter out boxes based on their size. Defaults to false
     * @param[in] min_size                 (Optional) Smaller boxes than min_size will be filtered out. Defaults to 1
     * @param[in] im_width                 (Optional) Boxes whose centers (on the x axis) is beyond im_width will be filtered. Defaults to 1
     * @param[in] im_height                (Optional) Boxes whose centers (on the y axis) is beyond im_height will be filtered. Defaults to 1
     */
    BoxNMSLimitInfo(float score_thresh = 0.05f, float nms = 0.3f,
                    int detections = 100, bool soft_nms_enabled = false,
                    NMSType soft_nms_method = NMSType::LINEAR,
                    float soft_nms_sigma = 0.5f, float soft_nms_min_score_thres = 0.001f, bool suppress_size = false, float min_size = 1.0f, float im_width = 1.0f, float im_height = 1.0f)
        : _score_thresh(score_thresh), _nms(nms), _detections_per_im(detections), _soft_nms_enabled(soft_nms_enabled), _soft_nms_method(soft_nms_method), _soft_nms_sigma(soft_nms_sigma),
          _soft_nms_min_score_thres(soft_nms_min_score_thres), _suppress_size(suppress_size), _min_size(min_size), _im_width(im_width), _im_height(im_height)
    {
    }
    /** Get the score threshold */
    float score_thresh() const
    {
        return _score_thresh;
    }
    /** Get the NMS */
    float nms() const
    {
        return _nms;
    }
    /** Get the number of detections */
    int detections_per_im() const
    {
        return _detections_per_im;
    }
    /** Check if soft NMS is enabled */
    bool soft_nms_enabled() const
    {
        return _soft_nms_enabled;
    }
    /** Get soft NMS method */
    NMSType soft_nms_method() const
    {
        return _soft_nms_method;
    }
    /** Get soft NMS sigma */
    float soft_nms_sigma() const
    {
        return _soft_nms_sigma;
    }
    /** Get soft nms min score threshold */
    float soft_nms_min_score_thres() const
    {
        return _soft_nms_min_score_thres;
    }
    /** Get if NMS will suppress boxes based on their size/position */
    bool suppress_size() const
    {
        return _suppress_size;
    }
    /** Get size suppression threshold */
    float min_size() const
    {
        return _min_size;
    }
    /** Get image width (NMS may suppress boxes whose center sits beyond the image width) */
    float im_width() const
    {
        return _im_width;
    }
    /** Get image height (NMS may suppress boxes whose center sits beyond the image height) */
    float im_height() const
    {
        return _im_height;
    }

private:
    float   _score_thresh;
    float   _nms;
    int     _detections_per_im;
    bool    _soft_nms_enabled;
    NMSType _soft_nms_method;
    float   _soft_nms_sigma;
    float   _soft_nms_min_score_thres;
    bool    _suppress_size;
    float   _min_size;
    float   _im_width;
    float   _im_height;
};

/** Padding and stride information class */
class PadStrideInfo
{
public:
    /** Constructor
     *
     * @param[in] stride_x (Optional) Stride, in elements, across x. Defaults to 1.
     * @param[in] stride_y (Optional) Stride, in elements, across y. Defaults to 1.
     * @param[in] pad_x    (Optional) Padding, in elements, across x. Defaults to 0.
     * @param[in] pad_y    (Optional) Padding, in elements, across y. Defaults to 0.
     * @param[in] round    (Optional) Dimensions rounding. Defaults to @ref FLOOR.
     */
    PadStrideInfo(unsigned int stride_x = 1, unsigned int stride_y = 1,
                  unsigned int pad_x = 0, unsigned int pad_y = 0,
                  DimensionRoundingType round = DimensionRoundingType::FLOOR)
        : _stride(std::make_pair(stride_x, stride_y)),
          _pad_left(pad_x),
          _pad_top(pad_y),
          _pad_right(pad_x),
          _pad_bottom(pad_y),
          _round_type(round)
    {
    }
    /** Constructor
     *
     * @param[in] stride_x   Stride, in elements, across x.
     * @param[in] stride_y   Stride, in elements, across y.
     * @param[in] pad_left   Padding across x on the left, in elements.
     * @param[in] pad_right  Padding across x on the right, in elements.
     * @param[in] pad_top    Padding across y on the top, in elements.
     * @param[in] pad_bottom Padding across y on the bottom, in elements.
     * @param[in] round      Dimensions rounding.
     */
    PadStrideInfo(unsigned int stride_x, unsigned int stride_y,
                  unsigned int pad_left, unsigned int pad_right,
                  unsigned int pad_top, unsigned int pad_bottom,
                  DimensionRoundingType round)
        : _stride(std::make_pair(stride_x, stride_y)),
          _pad_left(pad_left),
          _pad_top(pad_top),
          _pad_right(pad_right),
          _pad_bottom(pad_bottom),
          _round_type(round)
    {
    }
    /** Get the stride.
     *
     * @return a pair: stride x, stride y.
     */
    std::pair<unsigned int, unsigned int> stride() const
    {
        return _stride;
    }
    /** Check whether the padding is symmetric.
     *
     * @return True if the padding is symmetric.
     */
    bool padding_is_symmetric() const
    {
        return (_pad_left == _pad_right) && (_pad_top == _pad_bottom);
    }
    /** Get the padding.
     *
     * @note This should only be used when the padding is symmetric.
     *
     * @return a pair: padding left/right, padding top/bottom
     */
    std::pair<unsigned int, unsigned int> pad() const
    {
        //this accessor should be used only when padding is symmetric
        ARM_COMPUTE_ERROR_ON(!padding_is_symmetric());
        return std::make_pair(_pad_left, _pad_top);
    }

    /** Get the left padding */
    unsigned int pad_left() const
    {
        return _pad_left;
    }
    /** Get the right padding */
    unsigned int pad_right() const
    {
        return _pad_right;
    }
    /** Get the top padding */
    unsigned int pad_top() const
    {
        return _pad_top;
    }
    /** Get the bottom padding */
    unsigned int pad_bottom() const
    {
        return _pad_bottom;
    }

    /** Get the rounding type */
    DimensionRoundingType round() const
    {
        return _round_type;
    }

    /** Check whether this has any padding */
    bool has_padding() const
    {
        return (_pad_left != 0 || _pad_top != 0 || _pad_right != 0 || _pad_bottom != 0);
    }

private:
    std::pair<unsigned int, unsigned int> _stride;
    unsigned int _pad_left;
    unsigned int _pad_top;
    unsigned int _pad_right;
    unsigned int _pad_bottom;

    DimensionRoundingType _round_type;
};

/** Padding information for 3D operations like Conv3d */
struct Padding3D
{
    Padding3D() noexcept
    {
    }

    Padding3D(size_t pad_x, size_t pad_y, size_t pad_z)
        : left(pad_x), right(pad_x), top(pad_y), bottom(pad_y), front(pad_z), back(pad_z)
    {
    }

    Padding3D(size_t left, size_t right, size_t top, size_t bottom, size_t front, size_t back)
        : left(left), right(right), top(top), bottom(bottom), front(front), back(back)
    {
    }

    size_t left   = { 0 }; /**<  Padding across the width dimenstion on the left, in elements. */
    size_t right  = { 0 }; /**<  Padding across the width dimenstion on the right, in elements. */
    size_t top    = { 0 }; /**<  Padding across the height dimenstion  on the top, in elements. */
    size_t bottom = { 0 }; /**<  Padding across the height dimenstion on the bottom, in elements. */
    size_t front  = { 0 }; /**<  Padding across the depth dimenstion on the front, in elements. */
    size_t back   = { 0 }; /**<  Padding across the depth dimenstion on the back, in elements. */
};

/** PriorBox layer info */
class PriorBoxLayerInfo final
{
public:
    /** Default Constructor */
    PriorBoxLayerInfo()
        : _min_sizes(),
          _variances(),
          _offset(),
          _flip(true),
          _clip(false),
          _max_sizes(),
          _aspect_ratios(),
          _img_size(),
          _steps()
    {
    }
    /** Constructor
     *
     * @param[in] min_sizes     Min sizes vector.
     * @param[in] variances     Variances vector.
     * @param[in] offset        Offset value.
     * @param[in] flip          (Optional) Flip the aspect ratios.
     * @param[in] clip          (Optional) Clip coordinates so that they're within [0,1].
     * @param[in] max_sizes     (Optional) Max sizes vector.
     * @param[in] aspect_ratios (Optional) Aspect ratios of the boxes.
     * @param[in] img_size      (Optional) Image size.
     * @param[in] steps         (Optional) Step values.
     */
    PriorBoxLayerInfo(const std::vector<float> &min_sizes, const std::vector<float> &variances, float offset, bool flip = true, bool clip = false,
                      const std::vector<float> &max_sizes = {}, const std::vector<float> &aspect_ratios = {},
    const Coordinates2D &img_size = Coordinates2D{ 0, 0 }, const std::array<float, 2> &steps = { { 0.f, 0.f } })
        : _min_sizes(min_sizes),
          _variances(variances),
          _offset(offset),
          _flip(flip),
          _clip(clip),
          _max_sizes(max_sizes),
          _aspect_ratios(),
          _img_size(img_size),
          _steps(steps)
    {
        _aspect_ratios.push_back(1.);
        for(unsigned int i = 0; i < aspect_ratios.size(); ++i)
        {
            float ar            = aspect_ratios[i];
            bool  already_exist = false;
            for(auto ar_new : _aspect_ratios)
            {
                if(fabs(ar - ar_new) < 1e-6)
                {
                    already_exist = true;
                    break;
                }
            }
            if(!already_exist)
            {
                _aspect_ratios.push_back(ar);
                if(flip)
                {
                    _aspect_ratios.push_back(1.f / ar);
                }
            }
        }
    }
    /** Get min sizes. */
    std::vector<float> min_sizes() const
    {
        return _min_sizes;
    }
    /** Get min variances. */
    std::vector<float> variances() const
    {
        return _variances;
    }
    /** Get the step coordinates */
    std::array<float, 2> steps() const
    {
        return _steps;
    }
    /** Get the image size coordinates */
    Coordinates2D img_size() const
    {
        return _img_size;
    }
    /** Get the offset */
    float offset() const
    {
        return _offset;
    }
    /** Get the flip value */
    bool flip() const
    {
        return _flip;
    }
    /** Get the clip value */
    bool clip() const
    {
        return _clip;
    }
    /** Get max sizes. */
    std::vector<float> max_sizes() const
    {
        return _max_sizes;
    }
    /** Get aspect ratios. */
    std::vector<float> aspect_ratios() const
    {
        return _aspect_ratios;
    }

private:
    std::vector<float> _min_sizes;
    std::vector<float> _variances;
    float              _offset;
    bool               _flip;
    bool               _clip;
    std::vector<float> _max_sizes;
    std::vector<float> _aspect_ratios;
    Coordinates2D      _img_size;
    std::array<float, 2> _steps;
};

// Bounding Box [xmin, ymin, xmax, ymax]
using BBox = std::array<float, 4>;
// LabelBBox used for map label and bounding box
using LabelBBox = std::map<int, std::vector<BBox>>;

/** Available Detection Output code types */
enum class DetectionOutputLayerCodeType
{
    CORNER,      /**< Use box corners */
    CENTER_SIZE, /**< Use box centers and size */
    CORNER_SIZE, /**< Use box centers and size */
    TF_CENTER    /**< Use box centers and size but flip x and y co-ordinates */
};

/** Detection Output layer info */
class DetectionOutputLayerInfo final
{
public:
    /** Default Constructor */
    DetectionOutputLayerInfo()
        : _num_classes(),
          _share_location(),
          _code_type(DetectionOutputLayerCodeType::CORNER),
          _keep_top_k(),
          _nms_threshold(),
          _top_k(),
          _background_label_id(),
          _confidence_threshold(),
          _variance_encoded_in_target(false),
          _eta(),
          _num_loc_classes()
    {
        _num_loc_classes = _share_location ? 1 : _num_classes;
    }
    /** Constructor
     *
     * @param[in] num_classes                Number of classes to be predicted.
     * @param[in] share_location             If true, bounding box are shared among different classes.
     * @param[in] code_type                  Type of coding method for bbox.
     * @param[in] keep_top_k                 Number of total bounding boxes to be kept per image after NMS step.
     * @param[in] nms_threshold              Threshold to be used in NMS.
     * @param[in] top_k                      (Optional) Number of boxes per image with top confidence scores that are fed into the NMS algorithm. Default set to -1.
     * @param[in] background_label_id        (Optional) Background label ID. If there is no background class, set it as -1.
     * @param[in] confidence_threshold       (Optional) Only consider detections whose confidences are larger than a threshold. Default set to -FLT_MAX.
     * @param[in] variance_encoded_in_target (Optional) If true, variance is encoded in target. Otherwise we need to adjust the predicted offset accordingly.Default set to false.
     * @param[in] eta                        (Optional) Eta.
     */
    DetectionOutputLayerInfo(int num_classes, bool share_location, DetectionOutputLayerCodeType code_type, int keep_top_k, float nms_threshold, int top_k = -1, int background_label_id = -1,
                             float confidence_threshold = std::numeric_limits<float>::lowest(), bool variance_encoded_in_target = false, float eta = 1)
        : _num_classes(num_classes),
          _share_location(share_location),
          _code_type(code_type),
          _keep_top_k(keep_top_k),
          _nms_threshold(nms_threshold),
          _top_k(top_k),
          _background_label_id(background_label_id),
          _confidence_threshold(confidence_threshold),
          _variance_encoded_in_target(variance_encoded_in_target),
          _eta(eta),
          _num_loc_classes()
    {
        _num_loc_classes = _share_location ? 1 : _num_classes;
    }
    /** Get num classes. */
    int num_classes() const
    {
        return _num_classes;
    }
    /** Get share location. */
    bool share_location() const
    {
        return _share_location;
    }
    /** Get detection output code type. */
    DetectionOutputLayerCodeType code_type() const
    {
        return _code_type;
    }
    /** Get if variance encoded in target. */
    bool variance_encoded_in_target() const
    {
        return _variance_encoded_in_target;
    }
    /** Get the number of total bounding boxes to be kept per image. */
    int keep_top_k() const
    {
        return _keep_top_k;
    }
    /** Get nms threshold. */
    float nms_threshold() const
    {
        return _nms_threshold;
    }
    /** Get eta. */
    float eta() const
    {
        return _eta;
    }
    /** Get background label ID. */
    int background_label_id() const
    {
        return _background_label_id;
    }
    /** Get confidence threshold. */
    float confidence_threshold() const
    {
        return _confidence_threshold;
    }
    /** Get top K. */
    int top_k() const
    {
        return _top_k;
    }
    /** Get number of location classes. */
    int num_loc_classes() const
    {
        return _num_loc_classes;
    }

private:
    int                          _num_classes;
    bool                         _share_location;
    DetectionOutputLayerCodeType _code_type;
    int                          _keep_top_k;
    float                        _nms_threshold;
    int                          _top_k;
    int                          _background_label_id;
    float                        _confidence_threshold;
    bool                         _variance_encoded_in_target;
    float                        _eta;
    int                          _num_loc_classes;
};

/** Detection Output layer info */
class DetectionPostProcessLayerInfo final
{
public:
    /** Default Constructor */
    DetectionPostProcessLayerInfo()
        : _max_detections(),
          _max_classes_per_detection(),
          _nms_score_threshold(),
          _iou_threshold(),
          _num_classes(),
          _scales_values(),
          _use_regular_nms(),
          _detection_per_class(),
          _dequantize_scores()
    {
    }
    /** Constructor
     *
     * @param[in] max_detections            Number of total detection.
     * @param[in] max_classes_per_detection Number of total classes to be kept after NMS step. Used in the Fast Non-Max-Suppression
     * @param[in] nms_score_threshold       Threshold to be used in NMS
     * @param[in] iou_threshold             Threshold to be used during the intersection over union.
     * @param[in] num_classes               Number of classes.
     * @param[in] scales_values             Scales values used for decode center size boxes.
     * @param[in] use_regular_nms           (Optional) Boolean to determinate if use regular or fast nms. Defaults to false.
     * @param[in] detection_per_class       (Optional) Number of detection per class. Used in the Regular Non-Max-Suppression. Defaults to 100.
     * @param[in] dequantize_scores         (Optional) If the scores need to be dequantized. Defaults to true.
     */
    DetectionPostProcessLayerInfo(unsigned int max_detections, unsigned int max_classes_per_detection, float nms_score_threshold, float iou_threshold, unsigned int num_classes,
                                  std::array<float, 4> scales_values, bool use_regular_nms = false, unsigned int detection_per_class = 100, bool dequantize_scores = true)
        : _max_detections(max_detections),
          _max_classes_per_detection(max_classes_per_detection),
          _nms_score_threshold(nms_score_threshold),
          _iou_threshold(iou_threshold),
          _num_classes(num_classes),
          _scales_values(scales_values),
          _use_regular_nms(use_regular_nms),
          _detection_per_class(detection_per_class),
          _dequantize_scores(dequantize_scores)
    {
    }
    /** Get max detections. */
    unsigned int max_detections() const
    {
        return _max_detections;
    }
    /** Get max_classes per detection. Used in the Fast Non-Max-Suppression.*/
    unsigned int max_classes_per_detection() const
    {
        return _max_classes_per_detection;
    }
    /** Get detection per class. Used in the Regular Non-Max-Suppression */
    unsigned int detection_per_class() const
    {
        return _detection_per_class;
    }
    /** Get nms threshold. */
    float nms_score_threshold() const
    {
        return _nms_score_threshold;
    }
    /** Get intersection over union threshold. */
    float iou_threshold() const
    {
        return _iou_threshold;
    }
    /** Get num classes. */
    unsigned int num_classes() const
    {
        return _num_classes;
    }
    /** Get if use regular nms. */
    bool use_regular_nms() const
    {
        return _use_regular_nms;
    }
    /** Get y scale value. */
    float scale_value_y() const
    {
        // Saved as [y,x,h,w]
        return _scales_values[0];
    }
    /** Get x scale value. */
    float scale_value_x() const
    {
        // Saved as [y,x,h,w]
        return _scales_values[1];
    }
    /** Get h scale value. */
    float scale_value_h() const
    {
        // Saved as [y,x,h,w]
        return _scales_values[2];
    }
    /** Get w scale value. */
    float scale_value_w() const
    {
        // Saved as [y,x,h,w]
        return _scales_values[3];
    }
    /** Get dequantize_scores value. */
    bool dequantize_scores() const
    {
        return _dequantize_scores;
    }

private:
    unsigned int _max_detections;
    unsigned int _max_classes_per_detection;
    float        _nms_score_threshold;
    float        _iou_threshold;
    unsigned int _num_classes;
    std::array<float, 4> _scales_values;
    bool         _use_regular_nms;
    unsigned int _detection_per_class;
    bool         _dequantize_scores;
};

/** Pooling Layer Information struct*/
struct PoolingLayerInfo
{
    /** Default Constructor */
    PoolingLayerInfo()
        : pool_type(PoolingType::MAX),
          pool_size(Size2D()),
          data_layout(DataLayout::UNKNOWN),
          pad_stride_info(PadStrideInfo()),
          exclude_padding(false),
          is_global_pooling(false),
          fp_mixed_precision(false)
    {
    }
    /** Constructor
     *
     * @param[in] pool_type          Pooling type @ref PoolingType.
     * @param[in] pool_size          Pooling size, in elements, across  x and y.
     * @param[in] data_layout        Data layout used by the layer @ref DataLayout
     * @param[in] pad_stride_info    (Optional) Padding and stride information @ref PadStrideInfo
     * @param[in] exclude_padding    (Optional) Strategy when accounting padding in calculations.
     *                               True will exclude padding while false will not (Used in AVG/L2 pooling to determine the pooling area).
     *                               Defaults to false;
     * @param[in] fp_mixed_precision (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy.
     */
    explicit PoolingLayerInfo(PoolingType   pool_type,
                              unsigned int  pool_size,
                              DataLayout    data_layout,
                              PadStrideInfo pad_stride_info    = PadStrideInfo(),
                              bool          exclude_padding    = false,
                              bool          fp_mixed_precision = false)
        : pool_type(pool_type),
          pool_size(Size2D(pool_size, pool_size)),
          data_layout(data_layout),
          pad_stride_info(pad_stride_info),
          exclude_padding(exclude_padding),
          is_global_pooling(false),
          fp_mixed_precision(fp_mixed_precision)
    {
    }

    /** Constructor
     *
     * @param[in] pool_type          Pooling type @ref PoolingType.
     * @param[in] pool_size          Pooling size, in elements, across  x and y.
     * @param[in] data_layout        Data layout used by the layer @ref DataLayout
     * @param[in] pad_stride_info    (Optional) Padding and stride information @ref PadStrideInfo
     * @param[in] exclude_padding    (Optional) Strategy when accounting padding in calculations.
     *                               True will exclude padding while false will not (Used in AVG/L2 pooling to determine the pooling area).
     *                               Defaults to false;
     * @param[in] fp_mixed_precision (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy.
     */
    explicit PoolingLayerInfo(PoolingType   pool_type,
                              Size2D        pool_size,
                              DataLayout    data_layout,
                              PadStrideInfo pad_stride_info    = PadStrideInfo(),
                              bool          exclude_padding    = false,
                              bool          fp_mixed_precision = false)
        : pool_type(pool_type),
          pool_size(pool_size),
          data_layout(data_layout),
          pad_stride_info(pad_stride_info),
          exclude_padding(exclude_padding),
          is_global_pooling(false),
          fp_mixed_precision(fp_mixed_precision)
    {
    }

    /** Constructor
     *
     * @note This constructor is used for global pooling
     *
     * @param[in] pool_type   Pooling type @ref PoolingType.
     * @param[in] data_layout Data layout used by the layer @ref DataLayout
     */
    explicit PoolingLayerInfo(PoolingType pool_type, DataLayout data_layout)
        : pool_type(pool_type),
          pool_size(Size2D()),
          data_layout(data_layout),
          pad_stride_info(PadStrideInfo(1, 1, 0, 0)),
          exclude_padding(false),
          is_global_pooling(true),
          fp_mixed_precision(false)
    {
    }

    PoolingType   pool_type;
    Size2D        pool_size;
    DataLayout    data_layout;
    PadStrideInfo pad_stride_info;
    bool          exclude_padding;
    bool          is_global_pooling;
    bool          fp_mixed_precision;
};

/** Pooling Layer Information struct*/
struct Pooling3dLayerInfo
{
    /** Default Constructor */
    Pooling3dLayerInfo() noexcept
        : pool_type(PoolingType::MAX),
          pool_size(Size3D()),
          stride(Size3D()),
          padding(Padding3D()),
          exclude_padding(false),
          is_global_pooling(false),
          fp_mixed_precision(false),
          round_type(DimensionRoundingType::FLOOR)
    {
    }
    /** Constructor
     *
     * @param[in] pool_type          Pooling type @ref PoolingType.
     * @param[in] pool_size          Pooling size, in elements, across x, y and z.
     * @param[in] stride             (Optional) stride information @ref Size3D
     * @param[in] padding            (Optional) padding information @ref Padding3D
     * @param[in] exclude_padding    (Optional) Strategy when accounting padding in calculations.
     *                               True will exclude padding while false will not (Used in AVG/L2 pooling to determine the pooling area).
     *                               Defaults to false;
     * @param[in] fp_mixed_precision (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy.
     * @param[in] round_type         (Optional) Dimensions rounding. Defaults to @ref FLOOR
     */
    explicit Pooling3dLayerInfo(PoolingType           pool_type,
                                unsigned int          pool_size,
                                Size3D                stride             = Size3D(1U, 1U, 1U),
                                Padding3D             padding            = Padding3D(),
                                bool                  exclude_padding    = false,
                                bool                  fp_mixed_precision = false,
                                DimensionRoundingType round_type         = DimensionRoundingType::FLOOR)
        : pool_type(pool_type),
          pool_size(Size3D(pool_size, pool_size, pool_size)),
          stride(stride),
          padding(padding),
          exclude_padding(exclude_padding),
          is_global_pooling(false),
          fp_mixed_precision(fp_mixed_precision),
          round_type(round_type)
    {
    }

    /** Constructor
     *
     * @param[in] pool_type          Pooling type @ref PoolingType.
     * @param[in] pool_size          Pooling size, in elements, across  x, y and z.
     * @param[in] stride             (Optional) stride information @ref Size3D
     * @param[in] padding            (Optional) padding information @ref Padding3D
     * @param[in] exclude_padding    (Optional) Strategy when accounting padding in calculations.
     *                               True will exclude padding while false will not (Used in AVG/L2 pooling to determine the pooling area).
     *                               Defaults to false;
     * @param[in] fp_mixed_precision (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy.
     * @param[in] round_type         (Optional) Dimensions rounding. Defaults to @ref FLOOR
     */
    explicit Pooling3dLayerInfo(PoolingType           pool_type,
                                Size3D                pool_size,
                                Size3D                stride             = Size3D(1U, 1U, 1U),
                                Padding3D             padding            = Padding3D(),
                                bool                  exclude_padding    = false,
                                bool                  fp_mixed_precision = false,
                                DimensionRoundingType round_type         = DimensionRoundingType::FLOOR)
        : pool_type(pool_type),
          pool_size(pool_size),
          stride(stride),
          padding(padding),
          exclude_padding(exclude_padding),
          is_global_pooling(false),
          fp_mixed_precision(fp_mixed_precision),
          round_type(round_type)
    {
    }

    /** Constructor
     *
     * @note This constructor is used for global pooling
     *
     * @param[in] pool_type Pooling type @ref PoolingType.
     */
    explicit Pooling3dLayerInfo(PoolingType pool_type)
        : pool_type(pool_type),
          pool_size(Size3D()),
          stride(Size3D(1U, 1U, 1U)),
          padding(Padding3D(0, 0, 0)),
          exclude_padding(false),
          is_global_pooling(true),
          fp_mixed_precision(false),
          round_type(DimensionRoundingType::FLOOR)
    {
    }

    PoolingType           pool_type;
    Size3D                pool_size;
    Size3D                stride;
    Padding3D             padding;
    bool                  exclude_padding;
    bool                  is_global_pooling;
    bool                  fp_mixed_precision;
    DimensionRoundingType round_type;
};

/** ROI Pooling Layer Information class */
class ROIPoolingLayerInfo final
{
public:
    /** Constructor
     *
     * @param[in] pooled_width   Pooled width of the layer.
     * @param[in] pooled_height  Pooled height of the layer.
     * @param[in] spatial_scale  Spatial scale to be applied to the ROI coordinates and dimensions.
     * @param[in] sampling_ratio Number of samples to include in each pooling region (if set to zero, a ceil(roi_dims/pooling_dims))
     */
    ROIPoolingLayerInfo(unsigned int pooled_width, unsigned int pooled_height, float spatial_scale, unsigned int sampling_ratio = 0)
        : _pooled_width(pooled_width), _pooled_height(pooled_height), _spatial_scale(spatial_scale), _sampling_ratio(sampling_ratio)
    {
    }
    /** Get the pooled width of the layer */
    unsigned int pooled_width() const
    {
        return _pooled_width;
    }
    /** Get the pooled height of the layer */
    unsigned int pooled_height() const
    {
        return _pooled_height;
    }
    /** Get the spatial scale */
    float spatial_scale() const
    {
        return _spatial_scale;
    }
    /** Get sampling ratio */
    unsigned int sampling_ratio() const
    {
        return _sampling_ratio;
    }

private:
    unsigned int _pooled_width;
    unsigned int _pooled_height;
    float        _spatial_scale;
    unsigned int _sampling_ratio;
};

/** Generate Proposals Information class */
class GenerateProposalsInfo
{
public:
    /** Constructor
     *
     * @param[in] im_width       Width of the original image
     * @param[in] im_height      Height of the original image
     * @param[in] im_scale       Scale applied to the original image
     * @param[in] spatial_scale  (Optional)Scale applied to the feature map. Defaults to 1.0
     * @param[in] pre_nms_topN   (Optional)Number of the best scores to be selected from the transformations. Defaults to 6000.
     * @param[in] post_nms_topN  (Optional)Number of the best scores to be selected from the NMS operation. Defaults to 300.
     * @param[in] nms_thres      (Optional)NMS overlap threshold. Defaults to 0.7.
     * @param[in] min_size       (Optional)Size used to validate the anchors produced. Defaults to 16.
     * @param[in] values_per_roi (Optional)Values used to represent a ROI(Region of interest). Defaults to 4.
     */
    GenerateProposalsInfo(float im_width, float im_height, float im_scale, float spatial_scale = 1.0, int pre_nms_topN = 6000, int post_nms_topN = 300, float nms_thres = 0.7, float min_size = 16.0,
                          size_t values_per_roi = 4)
        : _im_height(im_height), _im_width(im_width), _im_scale(im_scale), _spatial_scale(spatial_scale), _pre_nms_topN(pre_nms_topN), _post_nms_topN(post_nms_topN), _nms_thres(nms_thres),
          _min_size(min_size), _values_per_roi(values_per_roi)
    {
    }

    /* Get the original height */
    float im_height() const
    {
        return _im_height;
    }
    /* Get the original width */
    float im_width() const
    {
        return _im_width;
    }
    /* Get the image scale */
    float im_scale() const
    {
        return _im_scale;
    }
    /* Get the value of how many best scores to select (before NMS) */
    int pre_nms_topN() const
    {
        return _pre_nms_topN;
    }
    /* Get the value of how many best scores to select (after NMS) */
    int post_nms_topN() const
    {
        return _post_nms_topN;
    }
    /* Get the NMS overlap threshold */
    float nms_thres() const
    {
        return _nms_thres;
    }
    /* Get the minimal size */
    float min_size() const
    {
        return _min_size;
    }
    /* Get the spatial scale to be applied to the feature maps */
    float spatial_scale() const
    {
        return _spatial_scale;
    }
    /* Get the values used to represent a ROI(Region of interest)*/
    size_t values_per_roi() const
    {
        return _values_per_roi;
    }

private:
    float  _im_height;
    float  _im_width;
    float  _im_scale;
    float  _spatial_scale;
    int    _pre_nms_topN;
    int    _post_nms_topN;
    float  _nms_thres;
    float  _min_size;
    size_t _values_per_roi;
};

/** ComputeAnchors information class */
class ComputeAnchorsInfo
{
public:
    /** Constructor
     *
     * @param[in] feat_width     Feature map width
     * @param[in] feat_height    Feature map height
     * @param[in] spatial_scale  Feature map scale
     * @param[in] values_per_roi (Optional)Values used to represent a ROI(Region Of Interest). Defaults to 4
     */
    ComputeAnchorsInfo(float feat_width, float feat_height, float spatial_scale, size_t values_per_roi = 4)
        : _feat_height(feat_height),
          _feat_width(feat_width),
          _spatial_scale(spatial_scale),
          _values_per_roi(values_per_roi)
    {
    }

    /* Get the height of the feature map */
    float feat_height() const
    {
        return _feat_height;
    }

    /* Get the width of the feature map */
    float feat_width() const
    {
        return _feat_width;
    }

    /* Get the scale of the feature map */
    float spatial_scale() const
    {
        return _spatial_scale;
    }

    /* Get the values used to represent a ROI(Region Of Interest)*/
    size_t values_per_roi() const
    {
        return _values_per_roi;
    }

private:
    float  _feat_height;
    float  _feat_width;
    float  _spatial_scale;
    size_t _values_per_roi;
};

/** Bounding Box Transform information class */
class BoundingBoxTransformInfo final
{
public:
    /** Constructor
     *
     * @param[in] img_width                Width of the original image
     * @param[in] img_height               Height, of the original image
     * @param[in] scale                    Scale of the original image
     * @param[in] apply_scale              (Optional)Re-apply scaling after transforming the boxes. Defaults to false
     * @param[in] weights                  (Optional)Weights [wx, wy, ww, wh] for the deltas. Defaults to all ones
     * @param[in] correct_transform_coords (Optional)Correct bounding box transform coordinates. Defaults to false
     * @param[in] bbox_xform_clip          (Optional)Minimum bounding box width and height after bounding box transformation in log-space. Defaults to log(1000/16)
     */
    BoundingBoxTransformInfo(float img_width, float img_height, float scale, bool apply_scale = false, const std::array<float, 4> weights = { { 1.f, 1.f, 1.f, 1.f } }, bool correct_transform_coords =
    false,
    float bbox_xform_clip =
        4.135166556742356f)
        : _img_width(img_width), _img_height(img_height), _scale(scale), _apply_scale(apply_scale), _correct_transform_coords(correct_transform_coords), _weights(weights), _bbox_xform_clip(bbox_xform_clip)
    {
    }

    std::array<float, 4> weights() const
    {
        return _weights;
    }

    float bbox_xform_clip() const
    {
        return _bbox_xform_clip;
    }

    float img_height() const
    {
        return _img_height;
    }

    float img_width() const
    {
        return _img_width;
    }

    float scale() const
    {
        return _scale;
    }

    bool apply_scale() const
    {
        return _apply_scale;
    }

    bool correct_transform_coords() const
    {
        return _correct_transform_coords;
    }

private:
    float _img_width;
    float _img_height;
    float _scale;
    bool  _apply_scale;
    bool  _correct_transform_coords;
    std::array<float, 4> _weights;
    float _bbox_xform_clip;
};

/** Activation Layer Information class */
class ActivationLayerInfo
{
public:
    /** Available activation functions */
    enum class ActivationFunction
    {
        LOGISTIC,        /**< Logistic ( \f$ f(x) = \frac{1}{1 + e^{-x}} \f$ ) */
        TANH,            /**< Hyperbolic tangent ( \f$ f(x) = a \cdot tanh(b \cdot x) \f$ ) */
        RELU,            /**< Rectifier ( \f$ f(x) = max(0,x) \f$ ) */
        BOUNDED_RELU,    /**< Upper Bounded Rectifier ( \f$ f(x) = min(a, max(0,x)) \f$ ) */
        LU_BOUNDED_RELU, /**< Lower and Upper Bounded Rectifier ( \f$ f(x) = min(a, max(b,x)) \f$ ) */
        LEAKY_RELU,      /**< Leaky Rectifier ( \f$ f(x) = \begin{cases}  \alpha x & \quad \text{if } x \text{ < 0}\\  x & \quad \text{if } x \geq \text{ 0 } \end{cases} \f$ ) */
        SOFT_RELU,       /**< Soft Rectifier ( \f$ f(x)= log(1+e^x) \f$ ) */
        ELU,             /**< Exponential Linear Unit ( \f$ f(x) = \begin{cases}  \alpha (exp(x) - 1) & \quad \text{if } x \text{ < 0}\\  x & \quad \text{if } x \geq \text{ 0 } \end{cases} \f$ ) */
        ABS,             /**< Absolute ( \f$ f(x)= |x| \f$ ) */
        SQUARE,          /**< Square ( \f$ f(x)= x^2 \f$ )*/
        SQRT,            /**< Square root ( \f$ f(x) = \sqrt{x} \f$ )*/
        LINEAR,          /**< Linear ( \f$ f(x)= ax + b \f$ ) */
        IDENTITY,        /**< Identity ( \f$ f(x)= x \f$ ) */
        HARD_SWISH,      /**< Hard-swish ( \f$ f(x) = (x * relu6(x+3))/6 \f$ ) */
        GELU             /**< GELU ( \f$ f(x) = x * 1/2 * 1 + erf(x / \sqrt{2}) \f$ ) */
    };

    /** Lookup table  */
    using LookupTable256 = std::array<qasymm8_t, 256>;

    ActivationLayerInfo() = default;
    /** Default Constructor
     *
     * @param[in] f The activation function to use.
     * @param[in] a (Optional) The alpha parameter used by some activation functions
     *              (@ref ActivationFunction::BOUNDED_RELU, @ref ActivationFunction::LU_BOUNDED_RELU, @ref ActivationFunction::LINEAR, @ref ActivationFunction::TANH).
     * @param[in] b (Optional) The beta parameter used by some activation functions (@ref ActivationFunction::LINEAR, @ref ActivationFunction::LU_BOUNDED_RELU, @ref ActivationFunction::TANH).
     */
    ActivationLayerInfo(ActivationFunction f, float a = 0.0f, float b = 0.0f)
        : _act(f), _a(a), _b(b), _enabled(true)
    {
    }
    /** Get the type of activation function */
    ActivationFunction activation() const
    {
        return _act;
    }
    /** Get the alpha value */
    float a() const
    {
        return _a;
    }
    /** Get the beta value */
    float b() const
    {
        return _b;
    }
    /** Check if initialised */
    bool enabled() const
    {
        return _enabled;
    }

#ifdef __aarch64__
    const LookupTable256 &lut() const
    {
        return _lut;
    }

    void init_lut(DataType data_type, const UniformQuantizationInfo &qi_in, const UniformQuantizationInfo &qi_out)
    {
        if(_act == ActivationFunction::HARD_SWISH)
        {
            if(data_type == DataType::QASYMM8)
            {
                qasymm8_hard_swish_populate_table(_lut, qi_in, qi_out);
            }
            else
            {
                qasymm8_signed_hard_swish_populate_table(_lut, qi_in, qi_out);
            }
        }
        else if(_act == ActivationFunction::LEAKY_RELU)
        {
            qasymm8_leaky_relu_populate_table(_lut, qi_in, qi_out, _a);
        }
        else if(_act == ActivationFunction::LOGISTIC)
        {
            if(data_type == DataType::QASYMM8)
            {
                qasymm8_logistic_populate_table(_lut, qi_in, qi_out);
            }
            else
            {
                qasymm8_signed_logistic_populate_table(_lut, qi_in, qi_out);
            }
        }
    }
#endif // __aarch64__

    static inline bool is_lut_supported(ActivationFunction act_func, DataType data_type)
    {
#ifdef __aarch64__
        switch(act_func)
        {
            case ActivationFunction::HARD_SWISH:
                return data_type == DataType::QASYMM8 || data_type == DataType::QASYMM8_SIGNED;
            case ActivationFunction::LEAKY_RELU:
                return data_type == DataType::QASYMM8;
            case ActivationFunction::LOGISTIC:
                return data_type == DataType::QASYMM8 || data_type == DataType::QASYMM8_SIGNED;
            default:
                return false;
        }
#else  // __aarch64__
        ARM_COMPUTE_UNUSED(act_func);
        ARM_COMPUTE_UNUSED(data_type);
        return false;
#endif // __aarch64__
    }

private:
    ActivationFunction _act     = { ActivationLayerInfo::ActivationFunction::IDENTITY };
    float              _a       = {};
    float              _b       = {};
    bool               _enabled = { false };

#ifdef __aarch64__
    LookupTable256 _lut = {};

    static inline void qasymm8_hard_swish_populate_table(LookupTable256 &lut, const UniformQuantizationInfo &qi_in, const UniformQuantizationInfo &qi_out)
    {
        for(size_t i = 0; i < lut.size(); ++i)
        {
            lut[i] = qasymm8_hard_swish(i, qi_in, qi_out);
        }
    }

    static inline void qasymm8_signed_hard_swish_populate_table(LookupTable256 &lut, const UniformQuantizationInfo &qi_in, const UniformQuantizationInfo &qi_out)
    {
        for(size_t i = 0; i < lut.size(); ++i)
        {
            lut[i] = qasymm8_signed_hard_swish(i, qi_in, qi_out);
        }
    }

    static inline void qasymm8_leaky_relu_populate_table(LookupTable256 &lut, const UniformQuantizationInfo &qi_in, const UniformQuantizationInfo &qi_out, float alpha)
    {
        for(size_t i = 0; i < lut.size(); ++i)
        {
            lut[i] = qasymm8_leaky_relu(i, qi_in, qi_out, alpha);
        }
    }

    static inline void qasymm8_logistic_populate_table(LookupTable256 &lut, const UniformQuantizationInfo &qi_in, const UniformQuantizationInfo &qi_out)
    {
        for(size_t i = 0; i < lut.size(); ++i)
        {
            lut[i] = qasymm8_logistic(i, qi_in, qi_out);
        }
    }

    static inline void qasymm8_signed_logistic_populate_table(LookupTable256 &lut, const UniformQuantizationInfo &qi_in, const UniformQuantizationInfo &qi_out)
    {
        for(size_t i = 0; i < lut.size(); ++i)
        {
            lut[i] = qasymm8_signed_logistic(static_cast<int8_t>(i), qi_in, qi_out);
        }
    }
#endif // __aarch64__
};

/** Fully connected layer info */
struct FullyConnectedLayerInfo
{
    /* Fused-activation parameters */
    ActivationLayerInfo activation_info{}; /**<  Fused activation to apply after the matrix multiplication. */
    /* Information about weights */
    DataLayout weights_trained_layout{ DataLayout::NCHW }; /**<  Layout that the weights have been trained with. */
    bool       transpose_weights{ true };                  /**<  Transpose weights if true. */
    bool       are_weights_reshaped{ false };              /**<  Reshape the weights tensor if false. */
    bool       retain_internal_weights{ false };           /**<  Retain internal reshaped weights. */
    bool       enable_fast_math{ false };                  /**<  Enable fast math computation. */
    /* Other parameters */
    bool fp_mixed_precision{ false }; /**<  Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy. */

    /** Sets the weights trained data layout
     *
     * @param[in] layout Data layout that the weights were trained with
     *
     * @return Updated object
     */
    FullyConnectedLayerInfo &set_weights_trained_layout(DataLayout layout)
    {
        weights_trained_layout = layout;
        return *this;
    }
    /** Sets the transpose weights flag
     *
     * @param[in] should_transpose_weights Boolean flag indicating if weights should be transposed
     *
     * @return Updated object
     */
    FullyConnectedLayerInfo &set_transpose_weights(bool should_transpose_weights)
    {
        transpose_weights = should_transpose_weights;
        return *this;
    }
};

/** Normalization Layer Information class */
class NormalizationLayerInfo
{
public:
    /** Default Constructor
     *
     * @param[in] type      The normalization type. Can be @ref NormType::IN_MAP_1D, @ref NormType::IN_MAP_2D or @ref NormType::CROSS_MAP
     * @param[in] norm_size The normalization size is the number of elements to normalize across. Defaults to 5.
     * @param[in] alpha     (Optional) Alpha parameter used by normalization equation. Defaults to 0.0001.
     * @param[in] beta      (Optional) Beta parameter used by normalization equation. Defaults to 0.5.
     * @param[in] kappa     (Optional) Kappa parameter used by [Krichevksy 2012] Across Channel Local Brightness Normalization equation.
     * @param[in] is_scaled (Optional) Boolean that specifies if alpha will be scaled by the normalization size or not.
     *                      Should be false to follow [Krichevksy 2012].
     */
    NormalizationLayerInfo(NormType type, uint32_t norm_size = 5, float alpha = 0.0001f, float beta = 0.5f, float kappa = 1.f, bool is_scaled = true)
        : _type(type), _norm_size(norm_size), _alpha(alpha), _beta(beta), _kappa(kappa), _is_scaled(is_scaled)
    {
    }
    /** Get the normalization type */
    NormType type() const
    {
        return _type;
    }
    /** Get the normalization size */
    uint32_t norm_size() const
    {
        return _norm_size;
    }
    /** Get the alpha value */
    float alpha() const
    {
        return _alpha;
    }
    /** Get the beta value */
    float beta() const
    {
        return _beta;
    }
    /** Get the kappa value */
    float kappa() const
    {
        return _kappa;
    }
    /** Get the is_scaled value */
    bool is_scaled() const
    {
        return _is_scaled;
    }
    /** Check if normalization is cross map */
    bool is_cross_map() const
    {
        return _type == NormType::CROSS_MAP;
    }
    /** Check if normalization is not cross map */
    bool is_in_map() const
    {
        return !is_cross_map();
    }
    /** Return the scaling factor of the normalization function.
     *
     * If is_scaled is set to false then [Krichevksy 2012] normalization scaling is performed,
     * where alpha is returned plainly, else alpha is scaled by the total number of elements used for the normalization.
     *
     * @return The normalization scaling factor.
     */
    float scale_coeff() const
    {
        const uint32_t size = (_type == NormType::IN_MAP_2D) ? _norm_size * _norm_size : _norm_size;
        return (_is_scaled) ? (_alpha / size) : _alpha;
    }

private:
    NormType _type;
    uint32_t _norm_size;
    float    _alpha;
    float    _beta;
    float    _kappa;
    bool     _is_scaled;
};

class StridedSliceLayerInfo
{
public:
    /** Default Constructor
     *
     * @param[in] begin_mask       (Optional) If the ith bit of begin_mask is set, starts[i] is ignored and the fullest possible range in that dimension is used instead.
     * @param[in] end_mask         (Optional) If the ith bit of end_mask is set, ends[i] is ignored and the fullest possible range in that dimension is used instead.
     * @param[in] shrink_axis_mask (Optional) If the ith bit of shrink_axis_mask is set, it implies that the ith specification shrinks the dimensionality by 1.
     */
    StridedSliceLayerInfo(int32_t begin_mask = 0, int32_t end_mask = 0, int32_t shrink_axis_mask = 0)
        : _begin_mask(begin_mask), _end_mask(end_mask), _shrink_axis_mask(shrink_axis_mask)
    {
    }

    /* Get the begin mask value */
    int32_t begin_mask() const
    {
        return _begin_mask;
    }

    /* Get the end mask value */
    int32_t end_mask() const
    {
        return _end_mask;
    }

    /* Get the shrink axis mask value */
    int32_t shrink_axis_mask() const
    {
        return _shrink_axis_mask;
    }

private:
    int32_t _begin_mask;
    int32_t _end_mask;
    int32_t _shrink_axis_mask;
};

/** Memory layouts for the weights tensor.
  *
  * * UNSPECIFIED is used to select kernels that do not run in
  *    variable weights mode.
  *
  * * ANY is used to query the kernel database to retrieve any of the
  *   kernels that runs in variable weights mode. Once a kernel is
  *   found, the specific format expected by the kernel can be
  *   retrieved by the user for reordering the weights tensor
  *   accordingly.
  *
  * The other values OHWIo{interleave_by}i{block_by} describe the
  * memory layout of a 4D tensor with layout OHWI that has been
  * transformed into a 4D tensor with dimensions O'HWI' where:
  *
  * O' = first multiple of {interleave_by} s.t. O<=O'
  * I' = first multiple of {block_by} s.t. I<=I'
  *
  * The total size of the dst tensor is O' x H x W x I'
  *
  * The access function of the tensor with layout
  * OHWIo{interleave_by}i{block_by} and size O'HWI' is a 6-parameter
  * access function, where the 6 parameters are computed as follows:
  *
  * x5 = floor(o/{interleave_by}) RANGE [0, O'/{interleave_by} -1] SIZE: O'/{interleave_by}
  *
  * x4 = h                        RANGE [0, H-1]                   SIZE: H
  * x3 = w                        RANGE [0, W-1]                   SIZE: W
  * x2 = floor(i/{block_by})      RANGE [0, I'/{block_by} -1]      SIZE: I'/{block_by}
  * x1 = o%{interleave_by}        RANGE [0, {interleave_by} -1]    SIZE: {interleave_by}
  * x0 = i%{block_by}             RANGE [0, {block_by} -1]         SIZE: {block_by}
  *                                                          TOTAL SIZE: O' * H * W * I'
  *
  *        4D                       6D
  * -----------------   -----------------------------------
  * value(o, h, w, i) =   x5 * H * W * I' * {interleave_by}
  *                     + x4 * W * I' * {interleave_by}
  *                     + x3 * I' * {interleave_by}
  *                     + x2 * {interleave_by} * {block_by}
  *                     + x1 * {block_by}
  *                     + x0
  *
  * Notice that in arm_gemm the 4D tensor of dimension O'HWI' created
  * for the OHWIo{interleave_by}i{block_by} format is in reality seen
  * as a 2D tensor, where the number of rows is O'/{interleave_by}
  * and the number of columns is {interleave_by} * H * W * I'.
  *
  * The postfix *_bf16 is for the memory layout needed for the
  * fast-mode kernels, in which the weights are passed in bfloat16
  * format.
  */
enum class WeightFormat
{
    UNSPECIFIED    = 0x1,
    ANY            = 0x2,
    OHWI           = 0x100100,
    OHWIo2         = 0x100200,
    OHWIo4         = 0x100400,
    OHWIo8         = 0x100800,
    OHWIo16        = 0x101000,
    OHWIo32        = 0x102000,
    OHWIo64        = 0x104000,
    OHWIo128       = 0x108000,
    OHWIo4i2       = 0x200400,
    OHWIo4i2_bf16  = 0x200410,
    OHWIo8i2       = 0x200800,
    OHWIo8i2_bf16  = 0x200810,
    OHWIo16i2      = 0x201000,
    OHWIo16i2_bf16 = 0x201010,
    OHWIo32i2      = 0x202000,
    OHWIo32i2_bf16 = 0x202010,
    OHWIo64i2      = 0x204000,
    OHWIo64i2_bf16 = 0x204010,
    OHWIo4i4       = 0x400400,
    OHWIo4i4_bf16  = 0x400410,
    OHWIo8i4       = 0x400800,
    OHWIo8i4_bf16  = 0x400810,
    OHWIo16i4      = 0x401000,
    OHWIo16i4_bf16 = 0x401010,
    OHWIo32i4      = 0x402000,
    OHWIo32i4_bf16 = 0x402010,
    OHWIo64i4      = 0x404000,
    OHWIo64i4_bf16 = 0x404010,
    OHWIo2i8       = 0x800200,
    OHWIo4i8       = 0x800400,
    OHWIo8i8       = 0x800800,
    OHWIo16i8      = 0x801000,
    OHWIo32i8      = 0x802000,
    OHWIo64i8      = 0x804000
};
// OHWIo<interleave_by>i<block_by>
inline int interleave_by(const WeightFormat wf)
{
    return (static_cast<int>(wf) >> 8) & 0xFFF;
}
inline int block_by(const WeightFormat wf)
{
    return (static_cast<int>(wf) >> 20) & 0xF;
}
inline bool is_fixed_format(const WeightFormat &wf)
{
    return wf != WeightFormat::UNSPECIFIED && wf != WeightFormat::ANY;
}
inline bool is_fixed_format_fast_math(const WeightFormat &wf)
{
    return (static_cast<int>(wf) >> 4) & 0x1;
}

/** Convolution Layer Weights Information class. This class stores the necessary information to compute convolution layer when the weights are already reshaped */
class WeightsInfo
{
public:
    /** Default constructor */
    WeightsInfo()
        : _are_reshaped(false), _kernel_width(0), _kernel_height(0), _num_kernels(0), _retain_internal_weights(false), _weight_format(arm_compute::WeightFormat::UNSPECIFIED)
    {
    }
    /** Constructor
     *
     * @param[in] are_reshaped            True if the weights have been reshaped
     * @param[in] kernel_width            Kernel width.
     * @param[in] kernel_height           Kernel height.
     * @param[in] num_kernels             Number of convolution kernels.
     * @param[in] retain_internal_weights (Optional) True if internal reshaped weights must be retained. Used for reconfiguration purposes. Default is false.
     * @param[in] weight_format           (Optional) arm_gemm:WeightFormat enumeration requested by the user. Default is arm_compute::WeightFormat::UNSPECIFIED.
     */
    WeightsInfo(bool are_reshaped, unsigned int kernel_width, unsigned int kernel_height, unsigned int num_kernels, bool retain_internal_weights = false,
                arm_compute::WeightFormat weight_format = arm_compute::WeightFormat::UNSPECIFIED)
        : _are_reshaped(are_reshaped), _kernel_width(kernel_width), _kernel_height(kernel_height), _num_kernels(num_kernels), _retain_internal_weights(retain_internal_weights), _weight_format(weight_format)
    {
    }
    /** Flag which specifies if the weights tensor has been reshaped.
     *
     * @return True if the weights tensors has been reshaped
     */
    bool are_reshaped() const
    {
        return _are_reshaped;
    };
    /** Return the number of convolution kernels
     *
     * @return The number of convolution kernels
     */
    unsigned int num_kernels() const
    {
        return _num_kernels;
    };
    /** Return the width and height of the kernel
     *
     * @return The width and height of the kernel
     */
    std::pair<unsigned int, unsigned int> kernel_size() const
    {
        return std::make_pair(_kernel_width, _kernel_height);
    }
    bool retain_internal_weights() const
    {
        return _retain_internal_weights;
    }
    arm_compute::WeightFormat weight_format() const
    {
        return _weight_format;
    }
    void set_weight_format(arm_compute::WeightFormat weight_format)
    {
        _weight_format = weight_format;
    }

    unsigned int kernel_width() const
    {
        return _kernel_width;
    }
    unsigned int kernel_height() const
    {
        return _kernel_height;
    }

private:
    bool                      _are_reshaped;
    unsigned int              _kernel_width;
    unsigned int              _kernel_height;
    unsigned int              _num_kernels;
    bool                      _retain_internal_weights;
    arm_compute::WeightFormat _weight_format;
};

/** GEMM reshape information class. This class stores the necessary information about matrix A and matrix B reshape.
 *
 * The matrix A can only be reshaped through @ref opencl::kernels::ClGemmReshapeLhsMatrixKernel or  @ref cpu::kernels::CpuGemmInterleave4x4Kernel
 * Note: Optionally just for @ref opencl::kernels::ClGemmReshapeLhsMatrixKernel is it possible to set mult_interleave4x4_height, the multiplication factor for the height of the 4x4 interleaved block
 *
 * The matrix B can only be reshaped through @ref opencl::kernels::ClGemmReshapeRhsMatrixKernel or  @ref cpu::kernels::CpuGemmTranspose1xWKernel
 * Note: Optionally just for @ref opencl::kernels::ClGemmReshapeRhsMatrixKernel is it possible to set mult_transpose1xW_width, the multiplication factor for the width of the 1xW transposed block
 *
 */
class GEMMReshapeInfo final
{
public:
    /** Default constructor */
    GEMMReshapeInfo()
        : _m(1), _n(1), _k(1), _mult_transpose1xW_width(1), _mult_interleave4x4_height(1), _depth_output_gemm3d(0), _reinterpret_input_as_3d(false), _broadcast_bias(false)
    {
    }
    /** Constructor
     *
     * @param[in] m                         Number of matrix A rows
     * @param[in] n                         Number of matrix B columns
     * @param[in] k                         Number of matrix A columns or matrix B rows
     * @param[in] mult_transpose1xW_width   (Optional) Multiplication factor for the width of the 1xW transposed block
     * @param[in] mult_interleave4x4_height (Optional) Multiplication factor for the height of the 4x4 interleaved block
     * @param[in] depth_output_gemm3d       (Optional) Depth (third dimension) of the output tensor to be used with the GEMM3D kernel.
     *                                      If 0 the output will not be reinterpreted as 3D. Default 0
     * @param[in] reinterpret_input_as_3d   (Optional) Reinterpret the input as 3D tensor. (i.e. this flag should be set to true when GEMM is used
     *                                      to perform 1x1 convolutions with the NHWC data layout)
     * @param[in] broadcast_bias            (Optional) Broadcast the shape of the bias tensor from a vector to a matrix.
     */
    GEMMReshapeInfo(int m, int n, int k, int mult_transpose1xW_width = 1, int mult_interleave4x4_height = 1, int depth_output_gemm3d = 0, bool reinterpret_input_as_3d = false, bool broadcast_bias = false)
        : _m(m), _n(n), _k(k), _mult_transpose1xW_width(mult_transpose1xW_width), _mult_interleave4x4_height(mult_interleave4x4_height), _depth_output_gemm3d(depth_output_gemm3d),
          _reinterpret_input_as_3d(reinterpret_input_as_3d), _broadcast_bias(broadcast_bias)
    {
    }
    /** Number of matrix A rows
     *
     * @return the number of matrix A rows
     */
    int m() const
    {
        return _m;
    }
    /** Number of matrix B columns
     *
     * @return the number of matrix B columns
     */
    int n() const
    {
        return _n;
    }
    /** Number of matrix A columns or matrix B rows
     *
     * @return the number of matrix A columns or matrix B rows
     */
    int k() const
    {
        return _k;
    }
    /** Multiplication factor for the width of the 1xW transposed block
     *
     * @return the multiplication factor for the width of the 1xW transposed block
     */
    int mult_transpose1xW_width() const
    {
        return _mult_transpose1xW_width;
    }
    /** Multiplication factor for the height of the 4x4 interleaved block
     *
     * @return the multiplication factor for the height of the 4x4 interleaved block
     */
    int mult_interleave4x4_height() const
    {
        return _mult_interleave4x4_height;
    }
    /** Depth (third dimension) of the output tensor to be used with the GEMM3D kernel
     *
     * @note GEMM3D kernel is used when the output has to be reinterpret as 3D tensor. In that case:
     *       m = depth_output_gemm3d * output_height
     *
     * @return the depth of the output tensor to be used with the GEMM3D kernel
     */
    int depth_output_gemm3d() const
    {
        return _depth_output_gemm3d;
    }
    /** Flag which specifies if the input tensor has to be reinterpreted as 3D
     *
     * @return True if the input tensor has to be reinterpreted as 3D tensor
     */
    bool reinterpret_input_as_3d() const
    {
        return _reinterpret_input_as_3d;
    };
    /** Flag which specifies whether to broadcast the shape of the bias tensor.
     *
     * @return True if the shape of the bias tensor is to be broadcasted.
     */
    bool broadcast_bias() const
    {
        return _broadcast_bias;
    };

private:
    int  _m;
    int  _n;
    int  _k;
    int  _mult_transpose1xW_width;
    int  _mult_interleave4x4_height;
    int  _depth_output_gemm3d;
    bool _reinterpret_input_as_3d;
    bool _broadcast_bias;
};

struct ConvolutionInfo
{
    ConvolutionInfo() = default;
    ConvolutionInfo(const PadStrideInfo &pad_stride_info, unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation)
        : pad_stride_info(pad_stride_info), depth_multiplier(depth_multiplier), act_info(act_info), dilation(dilation)
    {
    }
    PadStrideInfo       pad_stride_info{};        /**< Convolution info (Pads, strides,...) */
    unsigned int        depth_multiplier{ 1 };    /**< Multiplier to apply to input's depth to retrieve the output depth. Defaults to 1 */
    ActivationLayerInfo act_info{};               /**< Fused activation to apply after convolution. */
    Size2D              dilation{ Size2D(1, 1) }; /**< Dilation, in elements, across x and y. Defaults to (1, 1). */
};

/** GEMMLowp output stage type */
enum class GEMMLowpOutputStageType
{
    NONE,                     /**< No quantization */
    QUANTIZE_DOWN,            /**< Quantize using an integer multiplication */
    QUANTIZE_DOWN_FIXEDPOINT, /**< Quantize using a fixed point multiplication */
    QUANTIZE_DOWN_FLOAT       /**< Quantize using a floating point multiplication */
};

/** GEMMLowp output stage info */
struct GEMMLowpOutputStageInfo
{
    GEMMLowpOutputStageType type{ GEMMLowpOutputStageType::NONE };                        /**< GEMMLowp output stage type */
    int32_t                 gemmlowp_offset{ 0 };                                         /**< GEMMLowp output stage offset used for quantizing to QASYMM8 */
    int32_t                 gemmlowp_multiplier{ 0 };                                     /**< GEMMLowp output stage multiplier used for quantizing to QASYMM8 */
    int32_t                 gemmlowp_shift{ 0 };                                          /**< GEMMLowp output stage shift used for quantizing to uint8 */
    int32_t                 gemmlowp_min_bound{ std::numeric_limits<int32_t>::lowest() }; /**< GEMMLowp min value used to saturate down the output result before converting back to QASYMM8 */
    int32_t                 gemmlowp_max_bound{ std::numeric_limits<int32_t>::max() };    /**< GEMMLowp max value used to saturate down the output result before converting back to QASYMM8 */
    std::vector<int32_t>    gemmlowp_multipliers{};                                       /**< GEMMLowp output stage multiplier used for quantizing to QASYMM8 */
    std::vector<int32_t>    gemmlowp_shifts{};                                            /**< GEMMLowp output stage multiplier used for quantizing to QASYMM8 */
    float                   gemmlowp_real_multiplier{ 0 };                                /**< GEMMLowp output stage real multiplier used for quantizing to QASYMM8 */
    bool                    is_quantized_per_channel{ false };                            /**< GEMMLowp quantized per-channel flag */
    DataType                output_data_type{ DataType::UNKNOWN };                        /**< Output tensor data type to use if the output is not initialized */
};

/** GEMM LHS (Left Hand Side) matrix information */
struct GEMMLHSMatrixInfo
{
    GEMMLHSMatrixInfo() = default;
    GEMMLHSMatrixInfo(unsigned int m, unsigned int k, unsigned int v, bool trans, bool inter)
        : m0(m), k0(k), v0(v), transpose(trans), interleave(inter)
    {
    }
    unsigned int m0{ 1 };            /**< Number of rows processed by the matrix multiplication */
    unsigned int k0{ 1 };            /**< Number of partial accumulations performed by the matrix multiplication */
    unsigned int v0{ 1 };            /**< Number of vertical blocks of size (m0xk0) stored on the same output row */
    bool         transpose{ true };  /**< True if the (m0xk0) block has to be transposed before been stored */
    bool         interleave{ true }; /**< True if the v0 (m0xk0) blocks have to be interleaved in the output row */
};

/** GEMM RHS (Right Hand Side) matrix information */
struct GEMMRHSMatrixInfo
{
    GEMMRHSMatrixInfo() = default;
    GEMMRHSMatrixInfo(unsigned int n, unsigned int k, unsigned int h, bool trans, bool inter, bool export_to_cl_img)
        : n0(n), k0(k), h0(h), transpose(trans), interleave(inter), export_to_cl_image(export_to_cl_img)
    {
    }
    unsigned int n0{ 1 };                     /**< Number of columns processed by the matrix multiplication */
    unsigned int k0{ 1 };                     /**< Number of partial accumulations performed by the matrix multiplication */
    unsigned int h0{ 1 };                     /**< Number of horizontal blocks of size (k0xn0) stored on the same output row */
    bool         transpose{ true };           /**< True if the (k0xn0) block has to be transposed before been stored */
    bool         interleave{ true };          /**< True if the h0 (k0xn0) blocks have to be interleaved in the output row */
    bool         export_to_cl_image{ false }; /**< True if the reshaped rhs has to be exported to cl_image. n0 must be equal to 4 */
};

class ITensorInfo;
/** GEMM information class. This class stores the necessary information to compute GEMM functions
 *
 * This object also contains the information about how matrix A and matrix B have been reshaped
 *
 */
class GEMMInfo
{
public:
    /** Default constructor */
    GEMMInfo() noexcept
        : _is_a_reshaped(false),
          _is_b_reshaped(false),
          _reshape_b_only_on_first_run(true),
          _depth_output_gemm3d(0),
          _reinterpret_input_as_3d(false),
          _retain_internal_weights(false),
          _gemmlowp_output_stage(),
          _fast_math(false),
          _fp_mixed_precision(false),
          _broadcast_bias(false),
          _pretranspose_A(false),
          _pretranspose_B(false),
          _activation_info(),
          _post_ops(),
          _fixed_format(false),
          _weight_format(arm_compute::WeightFormat::UNSPECIFIED)
    {
    }
    /** Constructor
     *
     * @param[in] is_a_reshaped               True if the matrix A has been reshaped
     * @param[in] is_b_reshaped               True if the matrix B has been reshaped
     * @param[in] reshape_b_only_on_first_run Reshape matrix B only for the first run
     * @param[in] depth_output_gemm3d         (Optional) Depth (third dimension) of the output tensor to be used with the GEMM3D kernel
     *                                        If 0 the output will not be reinterpreted as 3D. Default 0
     * @param[in] reinterpret_input_as_3d     (Optional) Reinterpret the input as 3D tensor. (i.e. this flag should be set to true when GEMM is used
     *                                        to perform 1x1 convolutions with the NHWC data layout)
     * @param[in] retain_internal_weights     (Optional) Retain the weights tensor from previous run
     * @param[in] gemmlowp_output_stage       (Optional) GEMMLowp Output stage info
     * @param[in] fp_mixed_precision          (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy.
     * @param[in] fast_math                   (Optional) Use a data type of shorter width to improve performance
     * @param[in] broadcast_bias              (Optional) Broadcast the shape of the bias tensor from a vector to a matrix.
     * @param[in] activation_info             (Optional) Activation to apply after the matrix multiplication
     * @param[in] post_ops                    (Optional) A sequence of post operations that are performed after the main operation.
     * @param[in] fixed_format                (Optional) Specify the selection of fixed format kernels for variable weights support in GEMM. These kernels expect the weights tensor to be in amemory format that is fixed by the kernel itself. For more information, see arm_compute::WeightFormat.
     * @param[in] weight_format               (Optional) arm_gemm:WeightFormat enumeration requested by the user. Default is arm_compute::WeightFormat::UNSPECIFIED.
     */
    GEMMInfo(bool is_a_reshaped, bool is_b_reshaped, bool reshape_b_only_on_first_run, int depth_output_gemm3d = 0, bool reinterpret_input_as_3d = false, bool retain_internal_weights = false,
             GEMMLowpOutputStageInfo gemmlowp_output_stage = GEMMLowpOutputStageInfo(), bool fp_mixed_precision = false, bool fast_math = false, bool broadcast_bias = false,
             const ActivationLayerInfo &activation_info = ActivationLayerInfo(), const experimental::PostOpList<ITensorInfo *> &post_ops = experimental::PostOpList<ITensorInfo *>(),
             bool fixed_format = false, arm_compute::WeightFormat weight_format = arm_compute::WeightFormat::UNSPECIFIED) noexcept
        : _is_a_reshaped(is_a_reshaped),
          _is_b_reshaped(is_b_reshaped),
          _reshape_b_only_on_first_run(reshape_b_only_on_first_run),
          _depth_output_gemm3d(depth_output_gemm3d),
          _reinterpret_input_as_3d(reinterpret_input_as_3d),
          _retain_internal_weights(retain_internal_weights),
          _gemmlowp_output_stage(gemmlowp_output_stage),
          _fast_math(fast_math),
          _fp_mixed_precision(fp_mixed_precision),
          _broadcast_bias(broadcast_bias),
          _pretranspose_A(false),
          _pretranspose_B(false),
          _activation_info(activation_info),
          _post_ops(post_ops),
          _fixed_format(fixed_format),
          _weight_format(weight_format)
    {
    }
    /** Flag which specifies if the matrix A has been reshaped
     *
     * @return True if the matrix A has been reshaped
     */
    bool is_a_reshaped() const
    {
        return _is_a_reshaped;
    };
    /** Flag which specifies if the matrix B has been reshaped
     *
     * @return True if the matrix B has been reshaped
     */
    bool is_b_reshaped() const
    {
        return _is_b_reshaped;
    };
    /** Flag which specifies if the reshape of matrix B should executed only for the first
     *
     * @note This flag could be set to TRUE when GEMM is used to accelerate convolution layer
     *
     * @return True if the reshaped of matrix B happens only for the first run
     */
    bool reshape_b_only_on_first_run() const
    {
        return _reshape_b_only_on_first_run;
    };
    /** Depth of the output when GEMM output is reinterpreted as 3D tensor
     *
     * @return the depth of the output tensor
     */
    int depth_output_gemm3d() const
    {
        return _depth_output_gemm3d;
    };
    /** Flag which specifies if the input tensor has to be reinterpreted as 3D
     *
     * @return True if the input tensor has to be reinterpreted as 3D tensor
     */
    bool reinterpret_input_as_3d() const
    {
        return _reinterpret_input_as_3d;
    };
    /** Flag which specifies if the weights tensor has to be retained from previous run
     *
     * @return True if the weights tensor has to be retained
     */
    bool retain_internal_weights() const
    {
        return _retain_internal_weights;
    };
    /** GEMMLowp output stage
     *
     * @return the GEMMLowp output stage info
     */
    GEMMLowpOutputStageInfo gemmlowp_output_stage() const
    {
        return _gemmlowp_output_stage;
    };
    /** Sets GEMMLowp output stage
     *
     * @param[in] output_stage Output stage to set
     */
    void set_gemmlowp_output_stage(GEMMLowpOutputStageInfo &output_stage)
    {
        _gemmlowp_output_stage = output_stage;
    };
    /** Flag which specifies if a wider accumulator should be used.
     *
     * @return True if a wider accumulator has to be used
     */
    bool fp_mixed_precision() const
    {
        return _fp_mixed_precision;
    };
    /** Flag which specifies if a shorter accumulator to be used.
     *
     * @return True if a shorter accumulator has to be used
     */
    bool fast_math() const
    {
        return _fast_math;
    };
    /** Set fast math flag
     *
     * @param[in] fast_math Flag to set
     */
    void set_fast_math(bool fast_math)
    {
        _fast_math = fast_math;
    }
    /** Flag which specifies whether to broadcast the shape of the bias tensor.
     *
     * @return True if the shape of the bias tensor is to be broadcasted.
     */
    bool broadcast_bias() const
    {
        return _broadcast_bias;
    };
    /** Flag which specifies whether A should be pre-transposed if supported.
     *
     * @return True if A should be pre-transposed else false.
     */
    bool pretranspose_A() const
    {
        return _pretranspose_A;
    };
    /** Set pre-transpose A flag
     *
     * @param[in] flag Flag to set
     */
    void set_pretranspose_A(bool flag)
    {
        _pretranspose_A = flag;
    }
    /** Flag which specifies whether b should be pre-transposed if supported.
     *
     * @return True if b should be pre-transposed else false.
     */
    bool pretranspose_B() const
    {
        return _pretranspose_B;
    };
    /** Set pre-transpose b flag
     *
     * @param[in] flag Flag to set
     */
    void set_pretranspose_B(bool flag)
    {
        _pretranspose_B = flag;
    }
    /** Activation layer to apply after the matrix multiplication
     *
     * @return ActivationLayerInfo object
     */
    ActivationLayerInfo activation_info() const
    {
        return _activation_info;
    }
    /** Set activation layer info
     *
     * @param[in] activation_info ActivationLayerInfo object to set
     */
    void set_activation_info(const ActivationLayerInfo &activation_info)
    {
        _activation_info = activation_info;
    }
    /** Post operations to apply after the matrix multiplication
     *
     * @return experimental::PostOpList object
     */
    const experimental::PostOpList<ITensorInfo *> &post_ops() const
    {
        return _post_ops;
    }
    /** Set post ops
     *
     * @param[in] post_ops experimental::PostOpList object to set
     */
    void set_post_ops(const experimental::PostOpList<ITensorInfo *> &post_ops)
    {
        _post_ops = post_ops;
    }
    /** Flag which specifies if the GEMM operation is running fixed-format kernels.
     *
     * @return True if the GEMM operation is running fixed-format kernel else false.
     */
    bool fixed_format() const
    {
        return _fixed_format;
    }

    /** Set fixed-format flag
     *
     * @param[in] fixed_format sets whether or not to use fixed-format kernels
     */
    void set_fixed_format(bool fixed_format)
    {
        _fixed_format = fixed_format;
    }

    arm_compute::WeightFormat weight_format() const
    {
        return _weight_format;
    }

    /** Set weight format to be used
     *
     * @param[in] weight_format arm_compute::WeightFormat enumeration
     */
    void set_weight_format(arm_compute::WeightFormat weight_format)
    {
        _weight_format = weight_format;
    }

private:
    bool                                    _is_a_reshaped;
    bool                                    _is_b_reshaped;
    bool                                    _reshape_b_only_on_first_run;
    int                                     _depth_output_gemm3d;
    bool                                    _reinterpret_input_as_3d;
    bool                                    _retain_internal_weights;
    GEMMLowpOutputStageInfo                 _gemmlowp_output_stage;
    bool                                    _fast_math;
    bool                                    _fp_mixed_precision;
    bool                                    _broadcast_bias;
    bool                                    _pretranspose_A;
    bool                                    _pretranspose_B;
    ActivationLayerInfo                     _activation_info;
    experimental::PostOpList<ITensorInfo *> _post_ops;
    bool                                    _fixed_format;
    arm_compute::WeightFormat               _weight_format;
};

/** Winograd information */
struct WinogradInfo
{
    /** Default constructor
     *
     * @param[in] output_tile_sz Width and height of the output tile
     * @param[in] kernel_sz      Width and height of the kernel
     * @param[in] input_dims     Width and height of the input tensor before the convolution is applied
     * @param[in] conv_info      Convolution info (Pads, strides)
     * @param[in] data_layout    Data layout to use for the output tensor once the convolution has been applied
     */
    WinogradInfo(Size2D output_tile_sz, Size2D kernel_sz, Size2D input_dims, PadStrideInfo conv_info, DataLayout data_layout)
        : output_tile_size(output_tile_sz), kernel_size(kernel_sz), input_dimensions(input_dims), convolution_info(conv_info), output_data_layout(data_layout)
    {
    }

    Size2D        output_tile_size{};                     /**< Width and height of the output tile */
    Size2D        kernel_size{};                          /**< Width and height of the kernel*/
    Size2D        input_dimensions{};                     /**< Width and height of the input tensor before the convolution is applied */
    PadStrideInfo convolution_info{};                     /**< Convolution info (Pads, strides,...) */
    DataLayout    output_data_layout{ DataLayout::NCHW }; /**< Data layout to use for the output tensor once the convolution has been applied (NCHW or NHWC) */
};

/** IO formatting information class*/
struct IOFormatInfo
{
    /** Precision type used when printing floating point numbers */
    enum class PrecisionType
    {
        Default, /**< Default precision to the one that the current stream has */
        Custom,  /**< Custom precision specified by the user using the precision parameter */
        Full     /**< The maximum precision of the floating point representation */
    };

    /** Specifies the area to be printed, used by Tensor objects */
    enum class PrintRegion
    {
        ValidRegion, /**< Prints the valid region of the Tensor object */
        NoPadding,   /**< Prints the Tensor object without the padding */
        Full         /**< Print the tensor object including padding */
    };

    /** Construct a set of IO formatting information.
     *
     * @param[in] print_region   Area to be printed. Used by Tensor objects. Default: ValidRegion.
     * @param[in] precision_type Precision type for floating point numbers. Default: stream default.
     * @param[in] precision      Precision value for float point numbers. Default: 10.
     * @param[in] align_columns  Whether to align columns when printed. Default: true.
     * @param[in] element_delim  Delimeter between elements. Default: " ".
     * @param[in] row_delim      Delimenter between rows. Default: "\n".
     */
    IOFormatInfo(PrintRegion   print_region   = PrintRegion::ValidRegion,
                 PrecisionType precision_type = PrecisionType::Default,
                 unsigned int  precision      = 10,
                 bool          align_columns  = true,
                 std::string   element_delim  = " ",
                 std::string   row_delim      = "\n")
        : print_region(print_region),
          precision_type(precision_type),
          precision(precision),
          element_delim(element_delim),
          row_delim(row_delim),
          align_columns(align_columns)
    {
    }

    /** Area to be printed by Tensor objects */
    PrintRegion print_region;
    /** Floating point precision type */
    PrecisionType precision_type;
    /** Floating point precision */
    unsigned int precision;
    /** Element delimeter */
    std::string element_delim;
    /** Row delimeter */
    std::string row_delim;
    /** Align columns */
    bool align_columns;
};
} // namespace arm_compute
#endif /* ARM_COMPUTE_TYPES_H */