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Diffstat (limited to 'arm_compute/core/NEON/kernels/convolution/winograd/winograd_input_transform.hpp')
-rw-r--r-- | arm_compute/core/NEON/kernels/convolution/winograd/winograd_input_transform.hpp | 184 |
1 files changed, 184 insertions, 0 deletions
diff --git a/arm_compute/core/NEON/kernels/convolution/winograd/winograd_input_transform.hpp b/arm_compute/core/NEON/kernels/convolution/winograd/winograd_input_transform.hpp new file mode 100644 index 0000000000..abcda53534 --- /dev/null +++ b/arm_compute/core/NEON/kernels/convolution/winograd/winograd_input_transform.hpp @@ -0,0 +1,184 @@ +/* + * Copyright (c) 2018 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. + */ + +#pragma once + +namespace winograd +{ + +namespace +{ + +template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T> +class InputTransformImpl +{ + public: + /** Apply the transform to a tensor. */ + static void execute( + const T* const input, /** Input tensor data */ + const int n_batches, /** Number of batches in input tensor. */ + const int in_batch_stride, /** Stride between batches of the input. */ + const int n_rows, /** Number of rows in input tensor. */ + const int in_row_stride, /** Stride between rows of the input. */ + const int n_cols, /** Number of columns in input tensor. */ + const int in_col_stride, /** Stride between columns of the input. */ + const int n_channels, /** Number of channels in input tensor. */ + const PaddingType padding, /** Padding type. */ + const int tile_M, + const int tile_N, + T* const output, /** Base of output matrices. */ + const int matrix_stride, /** Stride between output matrices. */ + const int matrix_batch_stride, /** Stride between batches within the matrix. */ + const int matrix_row_stride /** Stride within matrices. */ + ); + + private: + static void process_tile_row( + const int tile_N, + int n_channels, + const T* const input_base, + const int input_row_stride, + const int input_col_stride, + T* const matrix_base, + const int matrix_stride, + const int matrix_row_stride, + const int row_pad_top, + const int row_pad_left, + const int row_pad_bottom, + const int n_cols + ); + + // Tile overlaps + static constexpr int overlap_rows = KernelRows - 1; + static constexpr int overlap_cols = KernelCols - 1; + + // Maximum padding and number of distinct paddings + static constexpr int max_pad_top = KernelRows / 2; + static constexpr int n_pad_top = 1 + iceildiv(max_pad_top, InnerTileRows - overlap_rows); + + static constexpr int max_pad_left = KernelCols / 2; + static constexpr int n_pad_left = 1 + iceildiv(max_pad_left, InnerTileCols - overlap_cols); + + static constexpr int n_pad_bottom = InnerTileRows; + static constexpr int n_pad_right = InnerTileCols; + + /** Process a single tile of the input tensor. */ + template <int pad_top, int pad_left, int pad_bottom, int pad_right> + static void process_tile(int, const T*, int, int, T*, int); + + // Array of methods to transform tiles of the input tensor. + typedef void (*TileFn)(int, const T*, int, int, T*, int); + static const TileFn + tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right]; +}; + + +template <int KernelRows, int InnerTileRows, typename T> +class InputTransformImpl<KernelRows, 1, InnerTileRows, 1, T> +{ + public: + /** Apply the transform to a tensor. */ + static void execute( + const T* const input, /** Input tensor data */ + const int n_batches, /** Number of batches in input tensor. */ + const int in_batch_stride, /** Stride between batches of the input. */ + const int n_rows, /** Number of rows in input tensor. */ + const int in_row_stride, /** Stride between rows of the input. */ + const int n_cols, /** Number of columns in input tensor. */ + const int in_col_stride, /** Stride between columns of the input. */ + const int n_channels, /** Number of channels in input tensor. */ + const PaddingType padding, /** Padding type. */ + const int tile_M, + const int tile_N, + T* const output, /** Base of output matrices. */ + const int matrix_stride, /** Stride between output matrices. */ + const int matrix_batch_stride, /** Stride between batches within the matrix. */ + const int matrix_row_stride /** Stride within matrices. */ + ); +}; + +} // namespace (anonymous) + +template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T> +class InputTransform +{ + public: + /***********************************************************************/ + /** Create an InputTransform operator fixed on a given problem and set of + * pointers. + */ + InputTransform( + const T* const input, /** Input tensor data */ + const int n_batches, /** Number of batches in input tensor. */ + const int n_rows, /** Number of rows in input tensor. */ + const int n_cols, /** Number of columns in input tensor. */ + const int n_channels, /** Number of channels in input tensor. */ + const PaddingType padding, /** Padding type. */ + T* const output, /** Base of output matrices. */ + const int matrix_stride, /** Stride between output matrices. */ + const int matrix_row_stride, /** Stride within matrices. */ + const int in_batch_stride=0, /** Stride between input batches. */ + const int in_row_stride=0, /** Stride between input rows. */ + const int in_col_stride=0 /** Stride between input columns. */ + ); + + /** Get the window of work a given operator can perform. */ + unsigned int get_window() const; + static constexpr unsigned int WINDOW_BLOCK = 16; // Base size of window + + /** Perform work upon a window of the input. */ + void run(const unsigned int start, const unsigned int stop); + + /** Apply the transform to a tensor. */ + static void execute( + const T* const input, /** Input tensor data */ + const int n_batches, /** Number of batches in input tensor. */ + const int in_batch_stride, /** Stride between batches of the input. */ + const int n_rows, /** Number of rows in input tensor. */ + const int in_row_stride, /** Stride between rows of the input. */ + const int n_cols, /** Number of columns in input tensor. */ + const int in_col_stride, /** Stride between columns of the input. */ + const int n_channels, /** Number of channels in input tensor. */ + const PaddingType padding, /** Padding type. */ + const int tile_M, + const int tile_N, + T* const output, /** Base of output matrices. */ + const int matrix_stride, /** Stride between output matrices. */ + const int matrix_batch_stride, /** Stride between batches within the matrix. */ + const int matrix_row_stride /** Stride within matrices. */ + ); + + protected: + using Transform = InputTransformImpl<KernelRows, KernelCols, InnerTileRows, InnerTileCols, T>; + + /* Member values for instance-based API. */ + const T* const _inptr; + T* const _outptr; + const int _n_batches, _n_rows, _n_cols, _n_channels, _matrix_stride, + _matrix_row_stride, _tiles_M, _tiles_N; + const int _in_col_stride, _in_row_stride, _in_batch_stride; + const PaddingType _padding_type; +}; + +} // namespace winograd |