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Diffstat (limited to 'arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp')
| -rw-r--r-- | arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp | 278 |
1 files changed, 0 insertions, 278 deletions
diff --git a/arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp b/arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp deleted file mode 100644 index 77cd9de513..0000000000 --- a/arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp +++ /dev/null @@ -1,278 +0,0 @@ -/* - * Copyright (c) 2017 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 -#include "../winograd_gemm.hpp" - -namespace winograd -{ -/***************************************************************************/ - /* Instance-less API */ - template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T> - void OutputTransformImpl<KernelRows, KernelCols, InnerTileRows, InnerTileCols, T>::execute( - const int n_batches, - const int output_batch_stride, - const int n_rows, - const int output_row_stride, - const int n_cols, - const int output_col_stride, - const int n_channels, - const T* const matrix_base, - const int matrix_stride, - const int matrix_row_stride, - const T* const biases, - T* const output - ) - { - // Compute the number of tiles and hence the padding required on the bottom - // and right of the image. - const int tile_M = iceildiv(n_rows, OutputTileRows); - const int tile_N = iceildiv(n_cols, OutputTileCols); - const int pad_bottom = OutputTileRows*tile_M - n_rows; - const int pad_right = OutputTileCols*tile_N - n_cols; - - const int matrix_tile_row_stride = tile_N * matrix_row_stride; - const int matrix_batch_stride = tile_M * matrix_tile_row_stride; - - // Perform the output transformation for each batch - for (int batch = 0; batch < n_batches; batch++) - { - // Get batch offset for input and outputs. - const T* const matrix_batch = matrix_base + batch*matrix_batch_stride; - T* const outptr_batch = output + batch*output_batch_stride; - - // Perform the output transformation for each row of the output tensor. - for (int tile_i = 0; tile_i < tile_M; tile_i++) - { - // Compute properties of this row of output tiles - const int row_pad_bottom = (tile_i < tile_M - 1) ? 0: pad_bottom; - const T* const matrix_tile_row = matrix_batch + tile_i * matrix_tile_row_stride; - T* const outptr_row = outptr_batch + OutputTileRows*tile_i*output_row_stride; - - // Process the row - process_tile_row( - tile_N, n_channels, matrix_tile_row, matrix_stride, - matrix_row_stride, biases, - outptr_row, output_row_stride, output_col_stride, row_pad_bottom, - pad_right - ); - } - } - } - -template <int KernelRows, int InnerTileRows, typename T> - void OutputTransformImpl<KernelRows, 1, InnerTileRows, 1, T>::execute( - const int n_batches, - const int output_batch_stride, - const int n_rows, - const int output_row_stride, - const int n_cols, - const int output_col_stride, - const int n_channels, - const T* const matrix_base, - const int matrix_stride, - const int matrix_row_stride, - const T* const biases, - T* const output - ) - { - // If an Nx1 kernel then transpose and redirect to the 1xN implementation. - OutputTransformImpl<1, KernelRows, 1, InnerTileRows, T>::execute( - n_batches, - output_batch_stride, - n_cols, output_col_stride, - n_rows, output_row_stride, - n_channels, - matrix_base, matrix_stride, matrix_row_stride, - biases, output - ); - } - - template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T> - void OutputTransformImpl<KernelRows, KernelCols, InnerTileRows, InnerTileCols, T>::process_tile_row( - const int tile_N, - const int n_channels, - const T* const matrix_base, - const int matrix_stride, - const int matrix_row_stride, - const T* const biases, - T* const output, - const int output_row_stride, - const int output_col_stride, - const int row_pad_bottom, - const int row_pad_right - ) - { - // Loop over columns of tiles - for (int tile_j = 0; tile_j < tile_N; tile_j++) - { - // Properties of this tile - const int tile_pad_right = (tile_j < tile_N - 1) ? 0 : row_pad_right; - const T* const matrix_row = matrix_base + tile_j * matrix_row_stride; - T* const outptr = output + OutputTileCols *tile_j*output_col_stride; - - // Perform the output transformation - const typename Tiles::TileFn tilefn = Tiles::get_tile_specialization(row_pad_bottom, tile_pad_right); - tilefn( - n_channels, matrix_row, matrix_stride, biases, - outptr, output_row_stride, output_col_stride, - row_pad_bottom, tile_pad_right - ); - } - } - -/***************************************************************************/ - template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T> - OutputTransform<KernelRows, KernelCols, InnerTileRows, InnerTileCols, T>::OutputTransform( - const T* const matrix_base, - const int matrix_stride, - const int matrix_row_stride, - const T* const biases, - T* const output, - const int n_batches, - const int n_rows, - const int n_cols, - const int n_channels, - const int out_batch_stride, - const int out_row_stride, - const int out_col_stride - ) : _matrix_base(matrix_base), _biases(biases), - _matrix_stride(matrix_stride), _matrix_row_stride(matrix_row_stride), - _outptr(output), _n_batches(n_batches), _n_rows(n_rows), _n_cols(n_cols), - _n_channels(n_channels), _tile_M(iceildiv(n_rows, OutputTileRows)), - _tile_N(iceildiv(n_cols, OutputTileCols)), - _out_col_stride(out_col_stride ? out_col_stride : n_channels), - _out_row_stride(out_row_stride ? out_row_stride : n_cols * _out_col_stride), - _out_batch_stride(out_batch_stride ? out_batch_stride : n_rows * _out_row_stride) - { - } - - template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T> - unsigned int OutputTransform<KernelRows, KernelCols, InnerTileRows, InnerTileCols, T>::get_window() const - { - // The final window includes the tail, all other windows will be a multiple - // of the window block in size. - return iceildiv(_n_channels, WINDOW_BLOCK); - } - -template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T> - void OutputTransform<KernelRows, KernelCols, InnerTileRows, InnerTileCols, T>::run( - const unsigned int start, const unsigned int stop - ) - { - if (start >= get_window()) - { - return; - } - - // Determine the window of work to perform - const unsigned int start_channel = start * WINDOW_BLOCK; - const unsigned int stop_channel = std::min<const unsigned int>( - stop * WINDOW_BLOCK, _n_channels - ); - const unsigned int n_channels = stop_channel - start_channel; - - execute( - _n_batches, - _out_batch_stride, - _n_rows, - _out_row_stride, - _n_cols, - _out_col_stride, - n_channels, - _matrix_base + start_channel, - _matrix_stride, - _matrix_row_stride, - (_biases != nullptr) ? _biases + start_channel : nullptr, - _outptr + start_channel - ); - } - - template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T> - void OutputTransform<KernelRows, KernelCols, InnerTileRows, InnerTileCols, T>::execute( - const int n_batches, - const int out_batch_stride, - const int n_rows, - const int out_row_stride, - const int n_cols, - const int out_col_stride, - const int n_channels, - const T* const matrix_base, - const int matrix_stride, - const int matrix_row_stride, - const T* const biases, - T* const output - ) - { - Transform::execute( - n_batches, out_batch_stride, - n_rows, out_row_stride, - n_cols, out_col_stride, n_channels, - matrix_base, matrix_stride, matrix_row_stride, - biases, output - ); - } - - template <int KernelCols, int InnerTileCols, typename T> - typename OutputTransformImplTiles<1, KernelCols, 1, InnerTileCols, T>::TileFn - OutputTransformImplTiles<1, KernelCols, 1, InnerTileCols, T>:: - get_tile_specialization(const int pad_bottom, const int pad_right) - { - (void) pad_bottom; - - if (!pad_right) - { - // No padding, return unpadded specialisation - return tilefn_unpadded; - } - else - { - return tilefn_right_padded[pad_right - 1]; - } - } - - template <int KernelRows, int KernelCols, int InnerTileRows, int InnerTileCols, typename T> - typename OutputTransformImplTiles<KernelRows, KernelCols, InnerTileRows, InnerTileCols, T>::TileFn - OutputTransformImplTiles<KernelRows, KernelCols, InnerTileRows, InnerTileCols, T>:: - get_tile_specialization(const int pad_bottom, const int pad_right) - { - if (!(pad_bottom || pad_right)) - { - // No padding, return unpadded specialisation - return tilefn_unpadded; - } - else if (pad_bottom && !pad_right) - { - return tilefn_bottom_padded[pad_bottom - 1]; - } - else if (!pad_bottom && pad_right) - { - return tilefn_right_padded[pad_right - 1]; - } - else - { - return tilefn_generic; - } - } -} // namespace winograd |