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Diffstat (limited to 'src/core/cpu/kernels/CpuWinogradConv2dKernel.h')
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diff --git a/src/core/cpu/kernels/CpuWinogradConv2dKernel.h b/src/core/cpu/kernels/CpuWinogradConv2dKernel.h deleted file mode 100644 index b5a29ffd02..0000000000 --- a/src/core/cpu/kernels/CpuWinogradConv2dKernel.h +++ /dev/null @@ -1,575 +0,0 @@ -/* - * 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_CPUWINOGRADCONV2DKERNEL_H -#define ARM_COMPUTE_CPUWINOGRADCONV2DKERNEL_H - -#include "src/core/NEON/kernels/convolution/common/convolution.hpp" -#include "src/core/NEON/kernels/convolution/common/tensor.hpp" -#include "src/core/cpu/ICpuKernel.h" - -#include "src/core/NEON/kernels/convolution/winograd/winograd_layer.hpp" - -namespace arm_compute -{ -namespace cpu -{ -/** Interface for the kernel to perform Winograd input transform. */ -class ICpuWinogradConv2dTransformInputKernel : public ICpuKernel -{ -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 ITensorInfo *input_nhwc, const int num_batches, const int num_rows, const int num_cols, const int num_channels, - const PaddingType padding, ITensorInfo *output, const int matrix_stride, ITensorInfo *workspace) = 0; - - /** Destructor */ - virtual ~ICpuWinogradConv2dTransformInputKernel() - { - } -}; - -/** Kernel to perform Winograd input transform. */ -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -class CpuWinogradConv2dTransformInputKernel : public ICpuWinogradConv2dTransformInputKernel -{ -public: - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CpuWinogradConv2dTransformInputKernel(const CpuWinogradConv2dTransformInputKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CpuWinogradConv2dTransformInputKernel &operator=(const CpuWinogradConv2dTransformInputKernel &) = delete; - /** Allow instances of this class to be moved */ - CpuWinogradConv2dTransformInputKernel(CpuWinogradConv2dTransformInputKernel &&) = default; - /** Allow instances of this class to be moved */ - CpuWinogradConv2dTransformInputKernel &operator=(CpuWinogradConv2dTransformInputKernel &&) = default; - /** Default destructor */ - ~CpuWinogradConv2dTransformInputKernel() = 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 */ - CpuWinogradConv2dTransformInputKernel(); - - const char *name() const override - { - return "CpuWinogradConv2dTransformInputKernel"; - } - - /** 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 ITensorInfo *input_nhwc, - const int num_batches, - const int num_rows, - const int num_cols, - const int num_channels, - const PaddingType padding, - ITensorInfo *output, - const int matrix_stride, - ITensorInfo *workspace) override; - - // Inherited methods overridden: - void run_op(ITensorPack &tensors, 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 CpuWinogradConv2dTransformInputKernel - * - * @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 }; - int _num_channels; /**< Number of channels in input tensor. */ - int _matrix_stride; /**< Stride between output matrices. */ -}; - -/** Interface for the kernel to perform Winograd output transform. */ -class ICpuWinogradConv2dTransformOutputKernel : public ICpuKernel -{ -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 ITensorInfo *biases, - const ITensorInfo *transformed_output, - const int matrix_stride, - ITensorInfo *output_nhwc, - const int num_batches, - const int num_rows, - const int num_cols, - const int num_channels, - ITensorInfo *workspace, - const arm_gemm::Activation &activation) = 0; - - virtual ~ICpuWinogradConv2dTransformOutputKernel() - { - } -}; - -/** Kernel to perform Winograd output transform. */ -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -class CpuWinogradConv2dTransformOutputKernel : public ICpuWinogradConv2dTransformOutputKernel -{ -public: - const char *name() const override - { - return "CpuWinogradConv2dTransformOutputKernel"; - } - /** Constructor */ - CpuWinogradConv2dTransformOutputKernel(); - - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CpuWinogradConv2dTransformOutputKernel(const CpuWinogradConv2dTransformOutputKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CpuWinogradConv2dTransformOutputKernel &operator=(const CpuWinogradConv2dTransformOutputKernel &) = delete; - /** Allow instances of this class to be moved */ - CpuWinogradConv2dTransformOutputKernel(CpuWinogradConv2dTransformOutputKernel &&) = default; - /** Allow instances of this class to be moved */ - CpuWinogradConv2dTransformOutputKernel &operator=(CpuWinogradConv2dTransformOutputKernel &&) = default; - /** Default destructor */ - ~CpuWinogradConv2dTransformOutputKernel() = 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 ITensorInfo *biases, - const ITensorInfo *transformed_output, - const int matrix_stride, - ITensorInfo *output_nhwc, - const int num_batches, - const int num_rows, - const int num_cols, - const int num_channels, - ITensorInfo *workspace, - const arm_gemm::Activation &activation) override; - - void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override; - - /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformOutputKernel - * - * @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 }; - int _matrix_stride; - int _matrix_row_stride; -}; - -/** Interface for the kernel to perform Winograd weights transform. */ -class ICpuWinogradConv2dTransformWeightsKernel : public ICpuKernel -{ -public: - /** Prevent instances of this class from being copied (As this class contains pointers) */ - ICpuWinogradConv2dTransformWeightsKernel(const ICpuWinogradConv2dTransformWeightsKernel &) = default; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - ICpuWinogradConv2dTransformWeightsKernel &operator=(const ICpuWinogradConv2dTransformWeightsKernel &) = default; - /** Allow instances of this class to be moved */ - ICpuWinogradConv2dTransformWeightsKernel(ICpuWinogradConv2dTransformWeightsKernel &&) = default; - /** Allow instances of this class to be moved */ - ICpuWinogradConv2dTransformWeightsKernel &operator=(ICpuWinogradConv2dTransformWeightsKernel &&) = default; - - ICpuWinogradConv2dTransformWeightsKernel() - { - } - virtual ~ICpuWinogradConv2dTransformWeightsKernel() - { - } - /** 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 info - * @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 ITensorInfo *weights_hwio, ITensorInfo *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 CpuWinogradConv2dTransformWeightsKernel - * - * @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); -}; - -/** Kernel to perform Winograd weights transform. */ -template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -class CpuWinogradConv2dTransformWeightsKernel final : public ICpuWinogradConv2dTransformWeightsKernel -{ -public: - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CpuWinogradConv2dTransformWeightsKernel(const CpuWinogradConv2dTransformWeightsKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CpuWinogradConv2dTransformWeightsKernel &operator=(const CpuWinogradConv2dTransformWeightsKernel &) = delete; - /** Allow instances of this class to be moved */ - CpuWinogradConv2dTransformWeightsKernel(CpuWinogradConv2dTransformWeightsKernel &&) = default; - /** Allow instances of this class to be moved */ - CpuWinogradConv2dTransformWeightsKernel &operator=(CpuWinogradConv2dTransformWeightsKernel &&) = default; - /** Default destructor */ - ~CpuWinogradConv2dTransformWeightsKernel() = default; - - /** Default constructor. */ - CpuWinogradConv2dTransformWeightsKernel(); - const char *name() const override - { - return "CpuWinogradConv2dTransformWeightsKernel"; - } - - /** Static function to check if given info will lead to a valid configuration of @ref CpuWinogradConv2dTransformWeightsKernel - * - * @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 info - * @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 ITensorInfo *weights_hwio, ITensorInfo *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_op(ITensorPack &tensors, 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 }; - int _num_output_channels; - int _matrix_stride; -}; - -/** Kernel to perform Winograd. */ -template <typename TIn, typename TOut, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -class CpuWinogradConv2dConfiguration -{ -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 = CpuWinogradConv2dTransformInputKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>; - using TransformWeightsKernel = CpuWinogradConv2dTransformWeightsKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>; - using TransformOutputKernel = CpuWinogradConv2dTransformOutputKernel<TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>; -}; - -} // namespace cpu -} // namespace arm_compute -#endif /*ARM_COMPUTE_CPUWINOGRADCONV2DKERNEL_H*/ |