| /* |
| * Copyright (c) 2017-2022 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_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/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 NewICpuKernel<ICpuWinogradConv2dTransformInputKernel> |
| { |
| 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 NewICpuKernel<ICpuWinogradConv2dTransformOutputKernel> |
| { |
| 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 NewICpuKernel<ICpuWinogradConv2dTransformWeightsKernel> |
| { |
| 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*/ |