text: /*
   * Copyright (c) 2017-2021 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_NEGEMMWINOGRADCONVOLUTIONLAYERKERNEL_H
  #define ARM_COMPUTE_NEGEMMWINOGRADCONVOLUTIONLAYERKERNEL_H
  
  #include "src/core/NEON/INEKernel.h"
  #include "src/core/NEON/kernels/convolution/common/convolution.hpp"
  #include "src/core/NEON/kernels/convolution/common/tensor.hpp"
  
  #include "src/core/NEON/kernels/convolution/winograd/winograd_layer.hpp"
  
  namespace arm_compute
  {
  // Forward declarations
  class ITensor;
  
  /** Interface for the Neon kernel to perform Winograd input transform. */
  class INEWinogradLayerTransformInputKernel : public INEKernel
  {
  public:
      /** Get the working space required to perform the transformation.
       *
       * Note, the working space is only required when performing the
       * transformation - hence it can be reused whenever the transformation is
       * not running.
       *
       * @param num_threads The greatest number of threads that will be used to execute the transform.
       * @return Size of working space required in bytes.
       */
      virtual unsigned int get_working_space_size(unsigned int num_threads) const = 0;
  
      /** Determine how much memory (in units of TIn) to allocate for the
       * transformed input.
       *
       * @param[in] num_batches  Number of batches in the input tensor.
       * @param[in] num_channels Number of feature maps in the input tensor.
       * @param[in] num_rows     Number of rows in each feature map.
       * @param[in] num_cols     Number of columns in each feature map.
       * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
       *
       * @return Storage size (in units of TIn) required.
       */
      virtual unsigned int get_input_storage_size(int num_batches, int num_channels, int num_rows, int num_cols, bool same_padding) const = 0;
  
      /** Gets the stride between matrices in the input worspace
       *
       * @param[in] num_batches  Number of batches in the input tensor.
       * @param[in] num_channels Number of feature maps in the input tensor.
       * @param[in] num_rows     Number of rows in each feature map.
       * @param[in] num_cols     Number of columns in each feature map.
       * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
       *
       * @return Stride expressed in bytes.
       */
      virtual int get_matrix_stride(int num_batches, int num_channels, int num_rows, int num_cols, bool same_padding) const = 0;
  
      /** Configure the output transform kernel.
       *
       * @param[in]  input_nhwc    Input tensor in NHWC data layout format.
       * @param[in]  num_batches   Number of batches in input tensor.
       * @param[in]  num_rows      Number of rows in input tensor.
       * @param[in]  num_cols      Number of columns in input tensor.
       * @param[in]  num_channels  Number of channels in input tensor.
       * @param[in]  padding       Padding type.
       * @param[out] output        Base of output matrices.
       * @param[in]  matrix_stride Stride between output matrices.
       * @param[in]  workspace     Tensor to be used as the working space during the computation.
       */
      virtual void configure(const ITensor *input_nhwc, const int num_batches, const int num_rows, const int num_cols, const int num_channels,
                             const PaddingType padding, ITensor *output, const int matrix_stride, ITensor *workspace) = 0;
  
      /** Destructor */
      virtual ~INEWinogradLayerTransformInputKernel()
      {
      }
  };
  
  /** Neon kernel to perform Winograd input transform. */
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  class NEWinogradLayerTransformInputKernel : public INEWinogradLayerTransformInputKernel
  {
  public:
      /** Prevent instances of this class from being copied (As this class contains pointers) */
      NEWinogradLayerTransformInputKernel(const NEWinogradLayerTransformInputKernel &) = delete;
      /** Prevent instances of this class from being copied (As this class contains pointers) */
      NEWinogradLayerTransformInputKernel &operator=(const NEWinogradLayerTransformInputKernel &) = delete;
      /** Allow instances of this class to be moved */
      NEWinogradLayerTransformInputKernel(NEWinogradLayerTransformInputKernel &&) = default;
      /** Allow instances of this class to be moved */
      NEWinogradLayerTransformInputKernel &operator=(NEWinogradLayerTransformInputKernel &&) = default;
      /** Default destructor */
      ~NEWinogradLayerTransformInputKernel() = default;
  
      /** Determine how much memory (in units of TIn) to allocate for the
       * transformed input.
       *
       * @param[in] num_batches  Number of batches in the input tensor.
       * @param[in] num_channels Number of feature maps in the input tensor.
       * @param[in] num_rows     Number of rows in each feature map.
       * @param[in] num_cols     Number of columns in each feature map.
       * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
       *
       * @return Storage size (in units of TIn) required.
       */
      unsigned int get_input_storage_size(
          int  num_batches,
          int  num_channels,
          int  num_rows,
          int  num_cols,
          bool same_padding) const override;
  
      /** Get the working space required to perform the transformation.
       *
       * Note, the working space is only required when performing the
       * transformation - hence it can be reused whenever the transformation is
       * not running.
       *
       * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
       *
       * @return Size of working space required in bytes.
       */
      unsigned int get_working_space_size(unsigned int num_threads) const override;
  
      /** Gets the stride between matrices in the input worspace
       *
       * @param[in] num_batches  Number of batches in the input tensor.
       * @param[in] num_channels Number of feature maps in the input tensor.
       * @param[in] num_rows     Number of rows in each feature map.
       * @param[in] num_cols     Number of columns in each feature map.
       * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
       *
       * @return Stride expressed in bytes.
       */
      int get_matrix_stride(
          int  num_batches,
          int  num_channels,
          int  num_rows,
          int  num_cols,
          bool same_padding) const override;
  
      /** Default constructor */
      NEWinogradLayerTransformInputKernel();
  
      const char *name() const override
      {
          return "NEWinogradLayerTransformInputKernel";
      }
  
      /** Configure the output transform kernel.
       *
       * @param[in]  input_nhwc    Input tensor.  Data types supported: F16/F32. Layout supported NHWC.
       * @param[in]  num_batches   Number of batches in input tensor.
       * @param[in]  num_rows      Number of rows in input tensor.
       * @param[in]  num_cols      Number of columns in input tensor.
       * @param[in]  num_channels  Number of channels in input tensor.
       * @param[in]  padding       Padding type.
       * @param[out] output        Base of output matrices.
       * @param[in]  matrix_stride Stride between output matrices.
       * @param[in]  workspace     Tensor to be used as the working space during the computation.
       */
      void configure(
          const ITensor    *input_nhwc,
          const int         num_batches,
          const int         num_rows,
          const int         num_cols,
          const int         num_channels,
          const PaddingType padding,
          ITensor          *output,
          const int         matrix_stride,
          ITensor          *workspace) override;
  
      // Inherited methods overridden:
      void run(const Window &window, const ThreadInfo &info) override;
  
      /** Winograd base kernel */
      using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
      /** Winograd convolution kernel */
      using WinogradConv = typename WinogradBase::template Convolution<T, T>;
  
      /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformInputKernel
       *
       * @param[in] input         First tensor input info. Data types supported: F16/F32.
       * @param[in] output        Output tensor info. Data types supported: same as @p input.
       * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo
       *
       * @return a status
       */
      static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info);
  
  private:
      using InputTransform = typename WinogradBase::template InputTransform<T, T>;
  
      std::unique_ptr<InputTransform> _transform{ nullptr };
      const ITensor                  *_input_nhwc;
      int                             _num_batches;    /**< Number of batches in input tensor. */
      int                             _num_rows;       /**< Number of rows in input tensor. */
      int                             _num_cols;       /**< Number of columns in input tensor. */
      int                             _num_channels;   /**< Number of channels in input tensor. */
      PaddingType                     _padding;        /**< Padding type. */
      ITensor                        *_output;         /**< Base of output matrices. */
      int                             _matrix_stride;  /**< Stride between output matrices. */
      int                             _padding_top;    /**< Padding to apply to the top of the image. */
      int                             _padding_left;   /**< Padding to apply to the left of the image. */
      int                             _padding_right;  /**< Padding to apply to the right of the image. */
      int                             _padding_bottom; /**< Padding to apply to the bottom of the image. */
      ITensor                        *_workspace;
  };
  
  /** Interface for the Neon kernel to perform Winograd output transform. */
  class INEWinogradLayerTransformOutputKernel : public INEKernel
  {
  public:
      /** Get the working space required to perform the transformation.
       *
       * Note, the working space is only required when performing the
       * transformation - hence it can be reused whenever the transformation is
       * not running.
       *
       * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
       *
       * @return Size of working space required in bytes.
       */
      virtual unsigned int get_working_space_size(unsigned int num_threads) const = 0;
  
      /** Determine how much memory (in units of TOut) to allocate for the
       * (Winograd domain) output.
       *
       * @param[in] num_batches         Number of batches in the output tensor.
       * @param[in] num_rows            Number of rows in each feature map of the input tensor.
       * @param[in] num_cols            Number of columns in each feature map of the input tensor.
       * @param[in] num_output_channels Number of feature maps in the output tensor.
       *
       * @return Storage size (in units of TOut) required.
       */
      virtual unsigned int get_output_storage_size(int num_batches, int num_rows, int num_cols, int num_output_channels) const = 0;
  
      /** Gets the stride between matrices in the output worspace
       *
       * @param[in] num_batches         Number of batches in the output tensor.
       * @param[in] num_rows            Number of rows in each feature map of the input tensor.
       * @param[in] num_cols            Number of columns in each feature map of the input tensor.
       * @param[in] num_output_channels Number of feature maps in the output tensor.
       *
       * @return Stride expressed in bytes.
       */
      virtual int get_matrix_stride(int num_batches, int num_rows, int num_cols, int num_output_channels) const = 0;
  
      /** Get the output shape of a convolution.
       *
       * @param[in] num_rows     Number of rows in each feature map of the input tensor.
       * @param[in] num_cols     Number of columns in each feature map of the input tensor.
       * @param[in] padding_same True if padding is SAME, false otherwise
       *
       * @return Shape of the output tensor
       */
      virtual std::pair<unsigned int, unsigned int> get_output_shape(
          int  num_rows,    /* Number of rows in each feature map of the input tensor. */
          int  num_cols,    /* Number of columns in each feature map of the input tensor. */
          bool padding_same /* True if padding is SAME, false otherwise */
      ) const = 0;
  
      /** Configure the output transform kernel.
       *
       * @param[in]  biases             Pointer to the biases tensor.
       * @param[in]  transformed_output Pointer to working space for the output tensor in the Winograd domain.
       * @param[in]  matrix_stride      Output matrix stride, can be computed with winograd::WinogradGEMM<2, 2, 3, 3>::Convolution<float, float>::get_output_matrix_stride()
       * @param[out] output_nhwc        Pointer to a tensor in NHWC data layout ordered output tensor, in the spatial domain.
       * @param[in]  num_batches        Number of batches in the input tensor.
       * @param[in]  num_rows           Number of rows in output tensor.
       * @param[in]  num_cols           Number of columns in output tensor.
       * @param[in]  num_channels       Number of feature maps in the output tensor.
       * @param[in]  workspace          Tensor to be used as the working space during the computation.
       * @param[in]  activation         Activation to be used
       */
      virtual void configure(
          const ITensor              *biases,
          const ITensor              *transformed_output,
          const int                   matrix_stride,
          ITensor                    *output_nhwc,
          const int                   num_batches,
          const int                   num_rows,
          const int                   num_cols,
          const int                   num_channels,
          ITensor                    *workspace,
          const arm_gemm::Activation &activation) = 0;
  
      virtual ~INEWinogradLayerTransformOutputKernel()
      {
      }
  };
  
  /** Neon kernel to perform Winograd output transform. */
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  class NEWinogradLayerTransformOutputKernel : public INEWinogradLayerTransformOutputKernel
  {
  public:
      const char *name() const override
      {
          return "NEWinogradLayerTransformOutputKernel";
      }
      /** Constructor */
      NEWinogradLayerTransformOutputKernel();
  
      /** Prevent instances of this class from being copied (As this class contains pointers) */
      NEWinogradLayerTransformOutputKernel(const NEWinogradLayerTransformOutputKernel &) = delete;
      /** Prevent instances of this class from being copied (As this class contains pointers) */
      NEWinogradLayerTransformOutputKernel &operator=(const NEWinogradLayerTransformOutputKernel &) = delete;
      /** Allow instances of this class to be moved */
      NEWinogradLayerTransformOutputKernel(NEWinogradLayerTransformOutputKernel &&) = default;
      /** Allow instances of this class to be moved */
      NEWinogradLayerTransformOutputKernel &operator=(NEWinogradLayerTransformOutputKernel &&) = default;
      /** Default destructor */
      ~NEWinogradLayerTransformOutputKernel() = default;
  
      // Inherited methods overridden:
      /** Determine how much memory (in units of TOut) to allocate for the
       * (Winograd domain) output.
       *
       * @param[in] num_batches         Number of batches in the output tensor.
       * @param[in] num_rows            Number of rows in each feature map of the input tensor.
       * @param[in] num_cols            Number of columns in each feature map of the input tensor.
       * @param[in] num_output_channels Number of feature maps in the output tensor.
       *
       * @return Storage size (in units of TOut) required.
       */
      unsigned int get_output_storage_size(int num_batches, int num_rows, int num_cols, int num_output_channels) const override;
  
      /** Gets the stride between matrices in the output worspace
       *
       * @param[in] num_batches         Number of batches in the output tensor.
       * @param[in] num_rows            Number of rows in each feature map of the input tensor.
       * @param[in] num_cols            Number of columns in each feature map of the input tensor.
       * @param[in] num_output_channels Number of feature maps in the output tensor.
       *
       * @return Stride expressed in bytes.
       */
      int get_matrix_stride(int num_batches, int num_rows, int num_cols, int num_output_channels) const override;
      /** Get the output shape of a convolution.
       *
       * @param[in] num_rows     Number of rows in each feature map of the input tensor.
       * @param[in] num_cols     Number of columns in each feature map of the input tensor.
       * @param[in] padding_same True if padding is SAME, false otherwise
       *
       * @return Shape of the output tensor
       */
      std::pair<unsigned int, unsigned int> get_output_shape(
          int  num_rows, /* Number of rows in each feature map of the input tensor. */
          int  num_cols, /* Number of columns in each feature map of the input tensor. */
          bool padding_same) const override;
  
      /** Get the working space required to perform the transformation.
       *
       * Note, the working space is only required when performing the
       * transformation - hence it can be reused whenever the transformation is
       * not running.
       *
       * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
       *
       * @return Size of working space required in bytes.
       */
      unsigned int get_working_space_size(unsigned int num_threads) const override;
  
      /** Configure the output transform kernel.
       *
       * @param[in]  biases             Pointer to the biases tensor.
       * @param[in]  transformed_output Pointer to working space for the output tensor in the Winograd domain.
       * @param[in]  matrix_stride      Output matrix stride, can be computed with winograd::WinogradGEMM<2, 2, 3, 3>::Convolution<float, float>::get_output_matrix_stride()
       * @param[out] output_nhwc        Pointer to a tensor with NHWC data layout, in the spatial domain.
       * @param[in]  num_batches        Number of batches in the input tensor.
       * @param[in]  num_rows           Number of rows in output tensor.
       * @param[in]  num_cols           Number of columns in output tensor.
       * @param[in]  num_channels       Number of feature maps in the output tensor.
       * @param[in]  workspace          Tensor to be used as the working space during the computation.
       * @param[in]  activation         Activation to be used
       */
      void configure(
          const ITensor              *biases,
          const ITensor              *transformed_output,
          const int                   matrix_stride,
          ITensor                    *output_nhwc,
          const int                   num_batches,
          const int                   num_rows,
          const int                   num_cols,
          const int                   num_channels,
          ITensor                    *workspace,
          const arm_gemm::Activation &activation) override;
  
      void run(const Window &window, const ThreadInfo &info) override;
  
      /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformOutputKernel
       *
       * @param[in] input         Source tensor info with shape [C, N, 16, batches] or [C, N, 36, batches]. Data types supported: F16/F32.
       * @param[in] bias          Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
       * @param[in] output        Destination tensor info with shape [output_convolved_dims.width, output_convolved_dims.height, C, batches]. Data type supported: same as @p input
       * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo
       *
       * @return a status
       */
      static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info);
  
  private:
      using WinogradBase    = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
      using WinogradConv    = typename WinogradBase::template Convolution<T, T>;
      using OutputTransform = typename WinogradBase::template OutputTransform<T, T>;
  
      std::unique_ptr<OutputTransform> _transform{ nullptr };
      const ITensor                   *_biases;
      const ITensor                   *_transformed_output;
      ITensor                         *_workspace;
      int                              _matrix_stride;
      int                              _matrix_row_stride;
      ITensor                         *_output_nhwc;
      int                              _num_batches;
      int                              _num_rows;
      int                              _num_cols;
      int                              _num_channels;
  };
  
  /** Interface for the Neon kernel to perform Winograd weights transform. */
  class INEWinogradLayerTransformWeightsKernel : public INEKernel
  {
  public:
      /** Prevent instances of this class from being copied (As this class contains pointers) */
      INEWinogradLayerTransformWeightsKernel(const INEWinogradLayerTransformWeightsKernel &) = default;
      /** Prevent instances of this class from being copied (As this class contains pointers) */
      INEWinogradLayerTransformWeightsKernel &operator=(const INEWinogradLayerTransformWeightsKernel &) = default;
      /** Allow instances of this class to be moved */
      INEWinogradLayerTransformWeightsKernel(INEWinogradLayerTransformWeightsKernel &&) = default;
      /** Allow instances of this class to be moved */
      INEWinogradLayerTransformWeightsKernel &operator=(INEWinogradLayerTransformWeightsKernel &&) = default;
  
      INEWinogradLayerTransformWeightsKernel()
      {
      }
      virtual ~INEWinogradLayerTransformWeightsKernel()
      {
      }
      /** Determine how much memory (in units of T) to allocate for the
       * transformed weights.
       *
       * @param[in] num_output_channels Number of output feature maps.
       * @param[in] num_input_channels  Number of input feature maps.
       *
       * @return Storage size (in units of T) required.
       */
      virtual unsigned int get_weight_storage_size(int num_output_channels, int num_input_channels) const = 0;
      /** Gets the stride between matrices in the kernel worspace
       *
       * @param[in] num_output_channels Number of output feature maps.
       * @param[in] num_input_channels  Number of input feature maps.
       *
       * @return Stride expressed in bytes.
       */
      virtual int get_matrix_stride(int num_output_channels, int num_input_channels) const = 0;
  
      /** Configure the weights transform kernel.
       *
       * @param[in]  weights_hwio        Pointer to the weights tensor
       * @param[out] output              Pointer to working space for the output tensor in the Winograd domain.
       * @param[in]  matrix_stride       Stride across matrices in the output workspace.
       * @param[in]  num_output_channels Number of filters.
       * @param[in]  num_input_channels  Number of channels in each filter.
       */
  
      virtual void configure(const ITensor *weights_hwio, ITensor *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) = 0;
  
      /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformWeightsKernel
       *
       * @param[in] input   First tensor input info. Data types supported: F16/F32.
       * @param[in] weights Weights tensor info. Data types supported: same as @p input.
       *
       * @return a status
       */
      static Status validate(const ITensorInfo *input, const ITensorInfo *weights);
  };
  
  /** Neon kernel to perform Winograd weights transform. */
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  class NEWinogradLayerTransformWeightsKernel final : public INEWinogradLayerTransformWeightsKernel
  {
  public:
      /** Prevent instances of this class from being copied (As this class contains pointers) */
      NEWinogradLayerTransformWeightsKernel(const NEWinogradLayerTransformWeightsKernel &) = delete;
      /** Prevent instances of this class from being copied (As this class contains pointers) */
      NEWinogradLayerTransformWeightsKernel &operator=(const NEWinogradLayerTransformWeightsKernel &) = delete;
      /** Allow instances of this class to be moved */
      NEWinogradLayerTransformWeightsKernel(NEWinogradLayerTransformWeightsKernel &&) = default;
      /** Allow instances of this class to be moved */
      NEWinogradLayerTransformWeightsKernel &operator=(NEWinogradLayerTransformWeightsKernel &&) = default;
      /** Default destructor */
      ~NEWinogradLayerTransformWeightsKernel() = default;
  
      /** Default constructor. */
      NEWinogradLayerTransformWeightsKernel();
      const char *name() const override
      {
          return "NEWinogradLayerTransformWeightsKernel";
      }
  
      /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformWeightsKernel
       *
       * @param[in] input         Source tensor info. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout).
       *                          kernel_x must be 3 and equal to kernel_y. Data types supported: F16/F32.
       * @param[in] output        Destination tensor info. The output is a 3D tensor with dimensions [OFM, IFM, 16] or [OFM, IFM, 36]. Data type supported: same as @p input
       * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo
       *
       * @return a status
       */
      static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info);
  
      // Inherited methods overridden:
  
  #ifndef DOXYGEN_SKIP_THIS
      /** Configure the weights transform kernel.
       *
       * @param[in]  weights_hwio        Pointer to the weights tensor
       * @param[out] output              Pointer to working space for the output tensor in the Winograd domain.
       * @param[in]  matrix_stride       Stride across matrices in the output workspace.
       * @param[in]  num_output_channels Number of filters.
       * @param[in]  num_input_channels  Number of channels in each filter.
       */
      void configure(const ITensor *weights_hwio, ITensor *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) override;
  #endif /* DOXYGEN_SKIP_THIS */
  
      /** Determine how much memory (in units of T) to allocate for the
       * transformed weights.
       *
       * @param[in] num_output_channels Number of output feature maps.
       * @param[in] num_input_channels  Number of input feature maps.
       *
       * @return Storage size (in units of T) required.
       */
      unsigned int get_weight_storage_size(int num_output_channels, int num_input_channels) const override;
  
      /** Gets the stride between matrices in the input worspace
       *
       * @param[in] num_output_channels Number of output feature maps.
       * @param[in] num_input_channels  Number of input feature maps.
       *
       * @return Stride expressed in bytes.
       */
      int get_matrix_stride(int num_output_channels, int num_input_channels) const override;
      void run(const Window &window, const ThreadInfo &info) override;
      bool is_parallelisable() const override;
  
  private:
      using WinogradBase     = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
      using WinogradConv     = typename WinogradBase::template Convolution<T, T>;
      using WeightsTransform = typename WinogradBase::template WeightsTransform<T, T>;
  
      std::unique_ptr<WeightsTransform> _transform{ nullptr };
      const ITensor                    *_weights_hwio;
      ITensor                          *_output;
      int                               _matrix_stride;
      int                               _num_output_channels;
      int                               _num_input_channels;
  };
  
  /** Neon kernel to perform Winograd. */
  template <typename TIn, typename TOut, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  class NEWinogradLayerConfiguration
  {
  public:
      /** Winograd base kernel */
      using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
      /** Winograd convolution kernel */
  
      using WinogradConv = typename WinogradBase::template Convolution<TIn, TOut>;
  
      using TransformInputKernel   = NEWinogradLayerTransformInputKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
      using TransformWeightsKernel = NEWinogradLayerTransformWeightsKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
      using TransformOutputKernel  = NEWinogradLayerTransformOutputKernel<TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
  };
  
  } // namespace arm_compute
  #endif /*ARM_COMPUTE_NEGEMMWINOGRADCONVOLUTIONLAYERKERNEL_H*/
  