588 lines
27 KiB
C++
588 lines
27 KiB
C++
/*
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* Copyright (c) 2018-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "Winograd.h"
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#include "tests/validation/Helpers.h"
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#include "tests/validation/reference/Utils.h"
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#include "arm_compute/core/Types.h"
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#include <algorithm>
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#include <cmath>
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace reference
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{
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namespace
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{
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template <typename T>
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void initialize_matrix_transform(SimpleTensor<T> &src, const Size2D &output_tile_size, const Size2D &kernel_size, WinogradTransformType winograd_transform_type)
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{
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// Winograd input transform matrices
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static const std::array<float, 16> imatrix2x2_3x3 =
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{
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1.0f, 0.0f, -1.0f, 0.0f,
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0.0f, 1.0f, 1.0f, 0.0f,
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0.0f, -1.0f, 1.0f, 0.0f,
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0.0f, 1.0f, 0.0f, -1.0f
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};
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static const std::array<float, 36> imatrix4x4_3x3 =
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{
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4.0f, 0.0f, -5.0f, 0.0f, 1.0f, 0.0f,
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0.0f, -4.0f, -4.0f, 1.0f, 1.0f, 0.0f,
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0.0f, 4.0f, -4.0f, -1.0f, 1.0f, 0.0f,
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0.0f, -2.0f, -1.0f, 2.0f, 1.0f, 0.0f,
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0.0f, 2.0f, -1.0f, -2.0f, 1.0f, 0.0f,
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0.0f, 4.0f, 0.0f, -5.0f, 0.0f, 1.0f,
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};
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static const std::array<float, 64> imatrix4x4_5x5 =
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{
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1.f, 0.f, -21.f / 4.f, 0.f, 21.f / 4.f, 0.f, -1.f, 0.f,
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0.f, 1.f, 1.f, -17.f / 4.f, -17.f / 4.f, 1.f, 1.f, 0.f,
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0.f, -1.f, 1.f, 17.f / 4.f, -17.f / 4.f, -1.f, 1.f, 0.f,
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0.f, 1.f / 2.f, 1.f / 4.f, -5.f / 2.f, -5.f / 4.f, 2.f, 1.f, 0.f,
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0.f, -1.f / 2.f, 1.f / 4.f, 5.f / 2.f, -5.f / 4.f, -2.f, 1.f, 0.f,
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0.f, 2.f, 4.f, -5.f / 2.f, -5.f, 1.f / 2.f, 1.f, 0.f,
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0.f, -2.f, 4.f, 5.f / 2.f, -5.f, -1.f / 2.f, 1.f, 0.f,
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0.f, -1.f, 0.f, 21.f / 4.f, 0.f, -21.f / 4.f, 0.f, 1.f
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};
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static const std::array<float, 64> imatrix2x1_7x7 =
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{
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-36.0f, 0.0f, 49.0f, 0.0f, -14.0f, 0.0f, 1.0f, 0.0f,
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0.0f, -36.0f, 36.0f, 13.0f, -13.0f, -1.0f, 1.0f, 0.0f,
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0.0f, 36.0f, 36.0f, -13.0f, -13.0f, 1.0f, 1.0f, 0.0f,
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0.0f, -18.0f, 9.0f, 20.0f, -10.0f, -2.0f, 1.0f, 0.0f,
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0.0f, 18.0f, 9.0f, -20.0f, -10.0f, 2.0f, 1.0f, 0.0f,
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0.0f, -12.0f, 4.0f, 15.0f, -5.0f, -3.0f, 1.0f, 0.0f,
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0.0f, 12.0f, 4.0f, -15.0f, -5.0f, 3.0f, 1.0f, 0.0f,
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0.0f, -36.0f, 0.0f, 49.0f, 0.0f, -14.0f, 0.0f, 1.0f
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};
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// ------------------------------------------
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// Winograd filter transform matrices
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static const std::array<float, 12> fmatrix2x2_3x3 =
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{
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1.0f, 0.0f, 0.0f,
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0.5f, 0.5f, 0.5f,
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0.5f, -0.5f, 0.5f,
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0.0f, 0.0f, 1.0f
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};
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static const std::array<float, 18> fmatrix4x4_3x3 =
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{
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0.25f, 0.0f, 0.0f,
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-1.0f / 6.0f, -1.0f / 6.0f, -1.0f / 6.0f,
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-1.0f / 6.0f, 1.0f / 6.0f, -1.0f / 6.0f,
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1.0f / 24.0f, 1.0f / 12.0f, 1.0f / 6.0f,
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1.0f / 24.0f, -1.0f / 12.0f, 1.0f / 6.0f,
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0.0f, 0.0f, 1.0f
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};
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static const std::array<float, 40> fmatrix4x4_5x5 =
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{
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1.0f, 0.0f, 0.0f, 0.0f, 0.0f,
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-2.0f / 9.0f, -2.0f / 9.0f, -2.0f / 9.0f, -2.0f / 9.0f, -2.0f / 9.0f,
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-2.0f / 9.0f, 2.0f / 9.0f, -2.0f / 9.0f, 2.0f / 9.0f, -2.0f / 9.0f,
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1.0f / 90.0f, 1.0f / 45.0f, 2.0f / 45.0f, 4.0f / 45.0f, 8.0f / 45.0f,
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1.0f / 90.0f, -1.0f / 45.0f, 2.0f / 45.0f, -4.0f / 45.0f, 8.0f / 45.0f,
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4.0f / 45.0f, 2.0f / 45.0f, 1.0f / 45.0f, 1.0f / 90.0f, 1.0f / 180.0f,
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4.0f / 45.0f, -2.0f / 45.0f, 1.0f / 45.0f, -1.0f / 90.0f, 1.0f / 180.0f,
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0.0f, 0.0f, 0.0f, 0.0f, 1.0f
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};
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static const std::array<float, 56> fmatrix2x1_7x7 =
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{
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-1.0f / 36.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
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1.0f / 48.0f, -1.0f / 48.0f, 1.0f / 48.0f, -1.0f / 48.0f, 1.0f / 48.0f, -1.0f / 48.0f, 1.0f / 48.0f,
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1.0f / 48.0f, 1.0f / 48.0f, 1.0f / 48.0f, 1.0f / 48.0f, 1.0f / 48.0f, 1.0f / 48.0f, 1.0f / 48.0f,
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-1.0f / 120.0f, 1.0f / 60.0f, -1.0f / 30.0f, 1.0f / 15.0f, -2.0f / 15.0f, 4.0f / 15.0f, -8.0f / 15.0f,
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-1.0f / 120.0f, -1.0f / 60.0f, -1.0f / 30.0f, -1.0f / 15.0f, -2.0f / 15.0f, -4.0f / 15.0f, -8.0f / 15.0f,
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1.0f / 720.0f, -1.0f / 240.0f, 1.0f / 80.0f, -3.0f / 80.0f, 9.0f / 80.0f, -27.0f / 80.0f, 81.0f / 80.0f,
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1.0f / 720.0f, 1.0f / 240.0f, 1.0f / 80.0f, 3.0f / 80.0f, 9.0f / 80.0f, 27.0f / 80.0f, 81.0f / 80.0f,
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0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f
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};
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// ------------------------------------------
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// Winograd output transform matrices
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static const std::array<float, 8> omatrix2x2_3x3 =
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{
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1.0f, 1.0f, 1.0f, 0.0f,
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0.0f, 1.0f, -1.0f, -1.0f
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};
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static const std::array<float, 24> omatrix4x4_3x3 =
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{
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 0.0f,
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0.0f, 1.0f, -1.0f, 2.0f, -2.0f, 0.0f,
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0.0f, 1.0f, 1.0f, 4.0f, 4.0f, 0.0f,
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0.0f, 1.0f, -1.0f, 8.0f, -8.0f, 1.0f
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};
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static const std::array<float, 36> omatrix4x4_5x5 =
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{
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 8.0f, 8.0f, 0.0f,
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0.0f, 1.0f, -1.0f, 2.0f, -2.0f, 4.0f, -4.0f, 0.0f,
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0.0f, 1.0f, 1.0f, 4.0f, 4.0f, 2.0f, 2.0f, 0.0f,
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0.0f, 1.0f, -1.0f, 8.0f, -8.0f, 1.0f, -1.0f, 1.0f
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};
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static const std::array<float, 16> omatrix2x1_7x7 =
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{
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 0.0f,
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0.0f, -1.0f, 1.0f, -2.0f, 2.0f, -3.0f, 3.0f, 1.0f
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};
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// ------------------------------------------
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using WinogradKey = std::tuple<std::pair<int, int>, std::pair<int, int>, WinogradTransformType>;
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// Key = (Output tile size, Kernel size, Winograd transform type)
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static std::map<WinogradKey, const float *> matrix_map =
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{
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{ WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3), WinogradTransformType::INPUT), imatrix2x2_3x3.data() },
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{ WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3), WinogradTransformType::INPUT), imatrix4x4_3x3.data() },
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{ WinogradKey(std::pair<int, int>(2, 1), std::pair<int, int>(3, 1), WinogradTransformType::INPUT), imatrix2x2_3x3.data() },
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{ WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(3, 1), WinogradTransformType::INPUT), imatrix4x4_3x3.data() },
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{ WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 3), WinogradTransformType::INPUT), imatrix2x2_3x3.data() },
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{ WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 3), WinogradTransformType::INPUT), imatrix4x4_3x3.data() },
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{ WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5), WinogradTransformType::INPUT), imatrix4x4_5x5.data() },
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{ WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(5, 1), WinogradTransformType::INPUT), imatrix4x4_5x5.data() },
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{ WinogradKey(std::pair<int, int>(2, 1), std::pair<int, int>(7, 1), WinogradTransformType::INPUT), imatrix2x1_7x7.data() },
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{ WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 7), WinogradTransformType::INPUT), imatrix2x1_7x7.data() },
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{ WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(7, 7), WinogradTransformType::INPUT), imatrix2x1_7x7.data() },
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{ WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 5), WinogradTransformType::INPUT), imatrix4x4_5x5.data() },
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{ WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3), WinogradTransformType::FILTER), fmatrix2x2_3x3.data() },
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{ WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3), WinogradTransformType::FILTER), fmatrix4x4_3x3.data() },
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{ WinogradKey(std::pair<int, int>(2, 1), std::pair<int, int>(3, 1), WinogradTransformType::FILTER), fmatrix2x2_3x3.data() },
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{ WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(3, 1), WinogradTransformType::FILTER), fmatrix4x4_3x3.data() },
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{ WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 3), WinogradTransformType::FILTER), fmatrix2x2_3x3.data() },
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{ WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 3), WinogradTransformType::FILTER), fmatrix4x4_3x3.data() },
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{ WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5), WinogradTransformType::FILTER), fmatrix4x4_5x5.data() },
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{ WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(5, 1), WinogradTransformType::FILTER), fmatrix4x4_5x5.data() },
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{ WinogradKey(std::pair<int, int>(2, 1), std::pair<int, int>(7, 1), WinogradTransformType::FILTER), fmatrix2x1_7x7.data() },
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{ WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 7), WinogradTransformType::FILTER), fmatrix2x1_7x7.data() },
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{ WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(7, 7), WinogradTransformType::FILTER), fmatrix2x1_7x7.data() },
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{ WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 5), WinogradTransformType::FILTER), fmatrix4x4_5x5.data() },
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{ WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3), WinogradTransformType::OUTPUT), omatrix2x2_3x3.data() },
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{ WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3), WinogradTransformType::OUTPUT), omatrix4x4_3x3.data() },
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{ WinogradKey(std::pair<int, int>(2, 1), std::pair<int, int>(3, 1), WinogradTransformType::OUTPUT), omatrix2x2_3x3.data() },
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{ WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(3, 1), WinogradTransformType::OUTPUT), omatrix4x4_3x3.data() },
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{ WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 3), WinogradTransformType::OUTPUT), omatrix2x2_3x3.data() },
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{ WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 3), WinogradTransformType::OUTPUT), omatrix4x4_3x3.data() },
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{ WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5), WinogradTransformType::OUTPUT), omatrix4x4_5x5.data() },
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{ WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(5, 1), WinogradTransformType::OUTPUT), omatrix4x4_5x5.data() },
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{ WinogradKey(std::pair<int, int>(2, 1), std::pair<int, int>(7, 1), WinogradTransformType::OUTPUT), omatrix2x1_7x7.data() },
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{ WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 7), WinogradTransformType::OUTPUT), omatrix2x1_7x7.data() },
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{ WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(7, 7), WinogradTransformType::OUTPUT), omatrix2x1_7x7.data() },
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{ WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 5), WinogradTransformType::OUTPUT), omatrix4x4_5x5.data() },
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};
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// Find transformation matrix
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std::map<WinogradKey, const float *>::iterator it;
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it = matrix_map.find(WinogradKey(std::pair<int, int>(output_tile_size.width, output_tile_size.height),
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std::pair<int, int>(kernel_size.width, kernel_size.height),
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winograd_transform_type));
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float const *matrix_values = nullptr;
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if(it != matrix_map.end())
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{
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// Get matrix pointer
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matrix_values = it->second;
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}
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else
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{
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ARM_COMPUTE_ERROR("Winograd configuration not supported");
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}
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// Copy values
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std::copy(&matrix_values[0], &matrix_values[0] + src.num_elements(), &src[0]);
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}
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} // namespace
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template <typename T>
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SimpleTensor<T> winograd_input_transform(const SimpleTensor<T> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info)
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{
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ARM_COMPUTE_ERROR_ON(in.data_layout() != DataLayout::NCHW);
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const PadStrideInfo conv_info = winograd_info.convolution_info;
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const Size2D output_tile_size = winograd_info.output_tile_size;
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const Size2D kernel_size = winograd_info.kernel_size;
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SimpleTensor<T> out{ output_shape, in.data_type() };
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// Calculate dimensions for the tile
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const unsigned int tile_w = output_tile_size.width + kernel_size.width - 1;
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const unsigned int tile_h = output_tile_size.height + kernel_size.height - 1;
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// Get the maximum dimension from the tile size
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const unsigned int tile_max_dim = std::max(tile_w, tile_h);
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TensorShape tile_dims(tile_max_dim, tile_max_dim);
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// Simple tensor for the input tile
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SimpleTensor<T> src_tile{ tile_dims, in.data_type() };
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// Simple tensor for the temporary tile
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SimpleTensor<T> tmp_tile{ tile_dims, in.data_type() };
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// Simple tensor for the output tile
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SimpleTensor<T> dst_tile{ tile_dims, in.data_type() };
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// Simple tensor for the transformation matrix
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SimpleTensor<T> matrix{ tile_dims, in.data_type() };
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// Simple tensor for the transformation matrix transposed
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SimpleTensor<T> matrix_transposed{ tile_dims, in.data_type() };
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// Initialize matrix for the input transform
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initialize_matrix_transform(matrix, output_tile_size, kernel_size, WinogradTransformType::INPUT);
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// Transpose matrix
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transpose_matrix<T>(matrix, matrix_transposed);
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const int in_w = in.shape().x();
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const int in_h = in.shape().y();
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const int in_d = in.shape().z();
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const int out_d = out.shape().z();
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const int num_batches = in.shape().total_size() / (in_w * in_h * in_d);
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const int step_x = output_tile_size.width;
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const int step_y = output_tile_size.height;
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// Compute the number of output tiles along the x and y direction of size "output_tile_size"
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const Size2D num_tiles = compute_winograd_convolution_tiles(Size2D(in_w, in_h),
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kernel_size,
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output_tile_size,
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conv_info);
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const int num_tiles_x = num_tiles.width;
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const int num_tiles_y = num_tiles.height;
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// In case of 1D convolution, the input tile has to be partially filled with zeros
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int start_x_zero = 0;
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int start_y_zero = 0;
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|
int end_x_zero = 0;
|
|
int end_y_zero = 0;
|
|
|
|
if(output_tile_size.width == 1)
|
|
{
|
|
start_x_zero = 1;
|
|
start_y_zero = 0;
|
|
end_x_zero = tile_max_dim - 1;
|
|
end_y_zero = tile_max_dim;
|
|
}
|
|
else if(output_tile_size.height == 1)
|
|
{
|
|
start_x_zero = 0;
|
|
start_y_zero = 1;
|
|
end_x_zero = tile_max_dim;
|
|
end_y_zero = tile_max_dim - 1;
|
|
}
|
|
|
|
// Set the anchor and shape of the zeros area
|
|
const Coordinates anchor_zeros(start_x_zero, start_y_zero);
|
|
const TensorShape shape_zeros(end_x_zero, end_y_zero);
|
|
|
|
// If we have a vertical filter (i.e. 1x3, 1x5,..), we need to take the elements along the y direction (step = width of the output tile)
|
|
const int step_y_transf_tile = kernel_size.width == 1 ? tile_max_dim : 1;
|
|
|
|
ARM_COMPUTE_ERROR_ON((num_tiles_x * num_tiles_y) != static_cast<int>(out.shape().y()));
|
|
|
|
for(int b = 0; b < num_batches; ++b)
|
|
{
|
|
for(int z = 0; z < in_d; ++z)
|
|
{
|
|
for(int y = 0; y < num_tiles_y; ++y)
|
|
{
|
|
for(int x = 0; x < num_tiles_x; ++x)
|
|
{
|
|
int xi = x * step_x - conv_info.pad_left();
|
|
int yi = y * step_y - conv_info.pad_top();
|
|
|
|
// Get the tile from the input tensor
|
|
get_tile<T>(in, src_tile, Coordinates(xi, yi, z, b));
|
|
|
|
// Fill partially with zeros in case of 1D convolution
|
|
zeros<T>(src_tile, anchor_zeros, shape_zeros);
|
|
|
|
// Compute the transformation
|
|
matrix_multiply<T>(matrix, src_tile, tmp_tile);
|
|
matrix_multiply<T>(tmp_tile, matrix_transposed, dst_tile);
|
|
|
|
// Store the output tile across the channels
|
|
for(int i = 0; i < out_d; ++i)
|
|
{
|
|
int xo = z;
|
|
int yo = x + y * num_tiles_x;
|
|
out[coords2index(out.shape(), Coordinates(xo, yo, i, b))] = dst_tile[i * step_y_transf_tile];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return out;
|
|
}
|
|
|
|
template <typename T>
|
|
SimpleTensor<T> winograd_filter_transform(const SimpleTensor<T> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info)
|
|
{
|
|
ARM_COMPUTE_ERROR_ON_MSG(in.data_layout() != DataLayout::NCHW, "Only supported NCHW data format");
|
|
|
|
// Create reference
|
|
SimpleTensor<T> out{ output_shape, in.data_type(), 1 };
|
|
|
|
const Size2D output_tile_size = winograd_info.output_tile_size;
|
|
const Size2D kernel_size = winograd_info.kernel_size;
|
|
|
|
// Calculate dimensions for the tile
|
|
const unsigned int input_tile_w = output_tile_size.width + kernel_size.width - 1;
|
|
const unsigned int input_tile_h = output_tile_size.height + kernel_size.height - 1;
|
|
const unsigned int input_tile_area = input_tile_w * input_tile_h;
|
|
|
|
// Get the maximum dimension from the filter size
|
|
const unsigned int kernel_max_dim = std::max(kernel_size.width, kernel_size.height);
|
|
|
|
// Get the maximum dimension from the input tile
|
|
const unsigned int input_tile_max_dim = std::max(input_tile_w, input_tile_h);
|
|
|
|
// Simple tensor for the input tile
|
|
SimpleTensor<T> input_tile{ TensorShape(kernel_max_dim, kernel_max_dim), in.data_type(), 1 };
|
|
|
|
// Simple tensor for the transformation matrix
|
|
SimpleTensor<T> trans_matrix{ TensorShape(kernel_max_dim, input_tile_max_dim), in.data_type(), 1 };
|
|
|
|
// Simple tensor for the transformation matrix transpose
|
|
SimpleTensor<T> trans_matrix_transposed{ TensorShape(input_tile_max_dim, kernel_max_dim), in.data_type(), 1 };
|
|
|
|
// Simple tensor for the temporary tile
|
|
SimpleTensor<T> tmp_tile{ TensorShape(kernel_max_dim, input_tile_max_dim), in.data_type(), 1 };
|
|
|
|
// Simple tensor for the output tile
|
|
SimpleTensor<T> transf_tile{ TensorShape(input_tile_max_dim, input_tile_max_dim), in.data_type(), 1 };
|
|
|
|
// Initialize matrix for the filter transform
|
|
initialize_matrix_transform(trans_matrix, output_tile_size, kernel_size, WinogradTransformType::FILTER);
|
|
|
|
// Transpose the transformation matrix
|
|
transpose_matrix<T>(trans_matrix, trans_matrix_transposed);
|
|
|
|
const int num_channels = in.shape()[2];
|
|
const int num_filters = in.shape()[3];
|
|
const int num_batches = in.shape().total_size() / (kernel_size.area() * num_channels * num_filters);
|
|
|
|
// If we have a vertical filter (i.e. 1x3, 1x5,..), we need to take the elements along the y direction (step_y_transf_tile = width of the output tile)
|
|
const int step_y_transf_tile = kernel_size.width == 1 ? input_tile_max_dim : 1;
|
|
|
|
for(int n = 0; n < num_batches; ++n)
|
|
{
|
|
for(int w = 0; w < num_filters; ++w)
|
|
{
|
|
for(int z = 0; z < num_channels; ++z)
|
|
{
|
|
// Load the tile from the input tensor
|
|
get_tile<T>(in, input_tile, Coordinates(0, 0, z, w, n));
|
|
|
|
// First transformation
|
|
matrix_multiply<T>(trans_matrix, input_tile, tmp_tile);
|
|
|
|
// Second transformation
|
|
matrix_multiply<T>(tmp_tile, trans_matrix_transposed, transf_tile);
|
|
|
|
// Store the output tile across the channels
|
|
const int output_offset = w + z * num_filters;
|
|
|
|
// Store the values across the channels
|
|
for(unsigned int i = 0; i < input_tile_area; ++i)
|
|
{
|
|
out[output_offset + i * num_filters * num_channels] = transf_tile[i * step_y_transf_tile];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return out;
|
|
}
|
|
|
|
template <typename T>
|
|
SimpleTensor<T> winograd_output_transform(const SimpleTensor<T> &in, const SimpleTensor<T> &b, const TensorShape &output_shape, const WinogradInfo &winograd_info)
|
|
{
|
|
const PadStrideInfo conv_info = winograd_info.convolution_info;
|
|
const Size2D input_dimensions = winograd_info.input_dimensions;
|
|
const Size2D output_tile_size = winograd_info.output_tile_size;
|
|
const Size2D kernel_size = winograd_info.kernel_size;
|
|
|
|
// Create reference
|
|
SimpleTensor<T> out{ output_shape, in.data_type(), 1 };
|
|
|
|
// Calculate dimensions for the tiles
|
|
const unsigned int in_tile_w = output_tile_size.width + kernel_size.width - 1;
|
|
const unsigned int in_tile_h = output_tile_size.height + kernel_size.height - 1;
|
|
const unsigned int out_tile_w = output_tile_size.width;
|
|
const unsigned int out_tile_h = output_tile_size.height;
|
|
|
|
ARM_COMPUTE_ERROR_ON(in.shape()[2] != (in_tile_w * in_tile_h));
|
|
ARM_COMPUTE_ERROR_ON(in.shape()[0] != out.shape()[get_data_layout_dimension_index(winograd_info.output_data_layout, DataLayoutDimension::CHANNEL)]);
|
|
|
|
// Get the maximum dimension from the tile size
|
|
const unsigned int in_tile_max_dim = std::max(in_tile_w, in_tile_h);
|
|
const unsigned int out_tile_max_dim = std::max(output_tile_size.width, output_tile_size.height);
|
|
|
|
// Compute tile dimensions
|
|
// Input tile dimensions
|
|
TensorShape in_tile_dims(in_tile_max_dim, in_tile_max_dim);
|
|
|
|
// Output tile dimensions
|
|
TensorShape out_tile_dims(out_tile_max_dim, out_tile_max_dim);
|
|
|
|
// Transformation matrix dimensions
|
|
TensorShape tr_tile_dims(in_tile_max_dim, out_tile_max_dim);
|
|
|
|
// Create tensors
|
|
// Simple tensor for the input tile
|
|
SimpleTensor<T> input_tile{ in_tile_dims, in.data_type(), 1 };
|
|
|
|
// Simple tensor for the transformation matrix
|
|
SimpleTensor<T> trans_matrix{ tr_tile_dims, in.data_type(), 1 };
|
|
|
|
// Simple tensor for the transformation matrix transpose
|
|
SimpleTensor<T> trans_matrix_transposed{ TensorShape(tr_tile_dims[1], tr_tile_dims[0]), in.data_type(), 1 };
|
|
|
|
// Simple tensor for the temporary tile
|
|
SimpleTensor<T> tmp_tile{ tr_tile_dims, in.data_type(), 1 };
|
|
|
|
// Simple tensor for the output tile
|
|
SimpleTensor<T> output_tile{ out_tile_dims, in.data_type(), 1 };
|
|
|
|
// Initialize matrix for the output transform
|
|
initialize_matrix_transform(trans_matrix, output_tile_size, kernel_size, WinogradTransformType::OUTPUT);
|
|
|
|
// Transpose the transformation matrix
|
|
transpose_matrix<T>(trans_matrix, trans_matrix_transposed);
|
|
|
|
const int w_in = in.shape()[0];
|
|
const int h_in = in.shape()[1];
|
|
const int c_in = in.shape()[2];
|
|
const int w_out = out.shape()[0];
|
|
const int h_out = out.shape()[1];
|
|
const int c_out = out.shape()[2];
|
|
const int num_batches = in.shape().total_size() / (w_in * h_in * c_in);
|
|
|
|
// Input strides
|
|
const int stridey_in = w_in;
|
|
const int stridez_in = stridey_in * h_in;
|
|
const int stridew_in = stridez_in * c_in;
|
|
|
|
// Output strides
|
|
const int stridey_out = w_out;
|
|
const int stridez_out = stridey_out * h_out;
|
|
const int stridew_out = stridez_out * c_out;
|
|
|
|
// Compute the number of output tiles along the x and y direction of size "output_tile_size"
|
|
const Size2D num_tiles = compute_winograd_convolution_tiles(Size2D(input_dimensions.width, input_dimensions.height),
|
|
kernel_size,
|
|
output_tile_size,
|
|
conv_info);
|
|
|
|
const int num_tiles_x = num_tiles.width;
|
|
const int num_tiles_y = num_tiles.height;
|
|
|
|
ARM_COMPUTE_UNUSED(num_tiles_y);
|
|
ARM_COMPUTE_ERROR_ON(in.shape()[1] != static_cast<unsigned int>(num_tiles_x * num_tiles_y));
|
|
|
|
// If we have a vertical filter (i.e. 1x3, 1x5,..), we still need to take the elements along the x direction (step_y_transf_tile = 1)
|
|
const int step_y_transf_tile = kernel_size.width == 1 ? 1 : output_tile.shape()[0];
|
|
|
|
// Initialize with zeros the input tile
|
|
zeros<T>(input_tile, Coordinates(0, 0), input_tile.shape());
|
|
|
|
for(int n = 0; n < num_batches; ++n)
|
|
{
|
|
for(int y = 0; y < h_in; ++y)
|
|
{
|
|
for(int x = 0; x < w_in; ++x)
|
|
{
|
|
// Load the input tile tile across the channels of the input tensor
|
|
for(int z = 0; z < c_in; ++z)
|
|
{
|
|
input_tile[z] = in[x + (y * stridey_in) + (z * stridez_in) + (n * stridew_in)];
|
|
}
|
|
|
|
// First transformation
|
|
matrix_multiply<T>(trans_matrix, input_tile, tmp_tile);
|
|
|
|
// Second transformation
|
|
matrix_multiply<T>(tmp_tile, trans_matrix_transposed, output_tile);
|
|
|
|
// Store the output tile
|
|
const int xo = (y % num_tiles_x) * out_tile_w;
|
|
const int yo = (y / num_tiles_x) * out_tile_h;
|
|
const int zo = x;
|
|
|
|
const int output_offset = xo + (yo * stridey_out) + (zo * stridez_out) + (n * stridew_out);
|
|
|
|
for(int yi = 0; yi < static_cast<int>(out_tile_h); ++yi)
|
|
{
|
|
for(int xi = 0; xi < static_cast<int>(out_tile_w); ++xi)
|
|
{
|
|
// Check out-of-bound writes
|
|
if((xo + xi < w_out) && (yo + yi < h_out))
|
|
{
|
|
out[output_offset + yi * stridey_out + xi] = output_tile[xi + yi * step_y_transf_tile];
|
|
|
|
// Add bias
|
|
out[output_offset + yi * stridey_out + xi] += b[zo];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return out;
|
|
}
|
|
|
|
template SimpleTensor<float> winograd_filter_transform(const SimpleTensor<float> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info);
|
|
template SimpleTensor<float> winograd_input_transform(const SimpleTensor<float> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info);
|
|
template SimpleTensor<float> winograd_output_transform(const SimpleTensor<float> &in, const SimpleTensor<float> &b, const TensorShape &output_shape, const WinogradInfo &winograd_info);
|
|
template SimpleTensor<half> winograd_filter_transform(const SimpleTensor<half> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info);
|
|
template SimpleTensor<half> winograd_input_transform(const SimpleTensor<half> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info);
|
|
template SimpleTensor<half> winograd_output_transform(const SimpleTensor<half> &in, const SimpleTensor<half> &b, const TensorShape &output_shape, const WinogradInfo &winograd_info);
|
|
|
|
} // namespace reference
|
|
} // namespace validation
|
|
} // namespace test
|
|
} // namespace arm_compute
|