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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 new file mode 100644 index 0000000000..b5a29ffd02 --- /dev/null +++ b/src/core/cpu/kernels/CpuWinogradConv2dKernel.h @@ -0,0 +1,575 @@ +/* + * 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*/ |