| /* | |
| * Copyright (c) 2018-2020 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. | |
| */ | |
| #include "Winograd.h" | |
| #include "tests/validation/Helpers.h" | |
| #include "tests/validation/reference/Utils.h" | |
| #include "arm_compute/core/Types.h" | |
| #include <algorithm> | |
| #include <cmath> | |
| namespace arm_compute | |
| { | |
| namespace test | |
| { | |
| namespace validation | |
| { | |
| namespace reference | |
| { | |
| namespace | |
| { | |
| template <typename T> | |
| void initialize_matrix_transform(SimpleTensor<T> &src, const Size2D &output_tile_size, const Size2D &kernel_size, WinogradTransformType winograd_transform_type) | |
| { | |
| // Winograd input transform matrices | |
| static const std::array<float, 16> imatrix2x2_3x3 = | |
| { | |
| 1.0f, 0.0f, -1.0f, 0.0f, | |
| 0.0f, 1.0f, 1.0f, 0.0f, | |
| 0.0f, -1.0f, 1.0f, 0.0f, | |
| 0.0f, 1.0f, 0.0f, -1.0f | |
| }; | |
| static const std::array<float, 36> imatrix4x4_3x3 = | |
| { | |
| 4.0f, 0.0f, -5.0f, 0.0f, 1.0f, 0.0f, | |
| 0.0f, -4.0f, -4.0f, 1.0f, 1.0f, 0.0f, | |
| 0.0f, 4.0f, -4.0f, -1.0f, 1.0f, 0.0f, | |
| 0.0f, -2.0f, -1.0f, 2.0f, 1.0f, 0.0f, | |
| 0.0f, 2.0f, -1.0f, -2.0f, 1.0f, 0.0f, | |
| 0.0f, 4.0f, 0.0f, -5.0f, 0.0f, 1.0f, | |
| }; | |
| static const std::array<float, 64> imatrix4x4_5x5 = | |
| { | |
| 1.f, 0.f, -21.f / 4.f, 0.f, 21.f / 4.f, 0.f, -1.f, 0.f, | |
| 0.f, 1.f, 1.f, -17.f / 4.f, -17.f / 4.f, 1.f, 1.f, 0.f, | |
| 0.f, -1.f, 1.f, 17.f / 4.f, -17.f / 4.f, -1.f, 1.f, 0.f, | |
| 0.f, 1.f / 2.f, 1.f / 4.f, -5.f / 2.f, -5.f / 4.f, 2.f, 1.f, 0.f, | |
| 0.f, -1.f / 2.f, 1.f / 4.f, 5.f / 2.f, -5.f / 4.f, -2.f, 1.f, 0.f, | |
| 0.f, 2.f, 4.f, -5.f / 2.f, -5.f, 1.f / 2.f, 1.f, 0.f, | |
| 0.f, -2.f, 4.f, 5.f / 2.f, -5.f, -1.f / 2.f, 1.f, 0.f, | |
| 0.f, -1.f, 0.f, 21.f / 4.f, 0.f, -21.f / 4.f, 0.f, 1.f | |
| }; | |
| static const std::array<float, 64> imatrix2x1_7x7 = | |
| { | |
| -36.0f, 0.0f, 49.0f, 0.0f, -14.0f, 0.0f, 1.0f, 0.0f, | |
| 0.0f, -36.0f, 36.0f, 13.0f, -13.0f, -1.0f, 1.0f, 0.0f, | |
| 0.0f, 36.0f, 36.0f, -13.0f, -13.0f, 1.0f, 1.0f, 0.0f, | |
| 0.0f, -18.0f, 9.0f, 20.0f, -10.0f, -2.0f, 1.0f, 0.0f, | |
| 0.0f, 18.0f, 9.0f, -20.0f, -10.0f, 2.0f, 1.0f, 0.0f, | |
| 0.0f, -12.0f, 4.0f, 15.0f, -5.0f, -3.0f, 1.0f, 0.0f, | |
| 0.0f, 12.0f, 4.0f, -15.0f, -5.0f, 3.0f, 1.0f, 0.0f, | |
| 0.0f, -36.0f, 0.0f, 49.0f, 0.0f, -14.0f, 0.0f, 1.0f | |
| }; | |
| // ------------------------------------------ | |
| // Winograd filter transform matrices | |
| static const std::array<float, 12> fmatrix2x2_3x3 = | |
| { | |
| 1.0f, 0.0f, 0.0f, | |
| 0.5f, 0.5f, 0.5f, | |
| 0.5f, -0.5f, 0.5f, | |
| 0.0f, 0.0f, 1.0f | |
| }; | |
| static const std::array<float, 18> fmatrix4x4_3x3 = | |
| { | |
| 0.25f, 0.0f, 0.0f, | |
| -1.0f / 6.0f, -1.0f / 6.0f, -1.0f / 6.0f, | |
| -1.0f / 6.0f, 1.0f / 6.0f, -1.0f / 6.0f, | |
| 1.0f / 24.0f, 1.0f / 12.0f, 1.0f / 6.0f, | |
| 1.0f / 24.0f, -1.0f / 12.0f, 1.0f / 6.0f, | |
| 0.0f, 0.0f, 1.0f | |
| }; | |
| static const std::array<float, 40> fmatrix4x4_5x5 = | |
| { | |
| 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, | |
| -2.0f / 9.0f, -2.0f / 9.0f, -2.0f / 9.0f, -2.0f / 9.0f, -2.0f / 9.0f, | |
| -2.0f / 9.0f, 2.0f / 9.0f, -2.0f / 9.0f, 2.0f / 9.0f, -2.0f / 9.0f, | |
| 1.0f / 90.0f, 1.0f / 45.0f, 2.0f / 45.0f, 4.0f / 45.0f, 8.0f / 45.0f, | |
| 1.0f / 90.0f, -1.0f / 45.0f, 2.0f / 45.0f, -4.0f / 45.0f, 8.0f / 45.0f, | |
| 4.0f / 45.0f, 2.0f / 45.0f, 1.0f / 45.0f, 1.0f / 90.0f, 1.0f / 180.0f, | |
| 4.0f / 45.0f, -2.0f / 45.0f, 1.0f / 45.0f, -1.0f / 90.0f, 1.0f / 180.0f, | |
| 0.0f, 0.0f, 0.0f, 0.0f, 1.0f | |
| }; | |
| static const std::array<float, 56> fmatrix2x1_7x7 = | |
| { | |
| -1.0f / 36.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, | |
| 1.0f / 48.0f, -1.0f / 48.0f, 1.0f / 48.0f, -1.0f / 48.0f, 1.0f / 48.0f, -1.0f / 48.0f, 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, 1.0f / 48.0f, | |
| -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, | |
| -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, | |
| 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, | |
| 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, | |
| 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f | |
| }; | |
| // ------------------------------------------ | |
| // Winograd output transform matrices | |
| static const std::array<float, 8> omatrix2x2_3x3 = | |
| { | |
| 1.0f, 1.0f, 1.0f, 0.0f, | |
| 0.0f, 1.0f, -1.0f, -1.0f | |
| }; | |
| static const std::array<float, 24> omatrix4x4_3x3 = | |
| { | |
| 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 0.0f, | |
| 0.0f, 1.0f, -1.0f, 2.0f, -2.0f, 0.0f, | |
| 0.0f, 1.0f, 1.0f, 4.0f, 4.0f, 0.0f, | |
| 0.0f, 1.0f, -1.0f, 8.0f, -8.0f, 1.0f | |
| }; | |
| static const std::array<float, 36> omatrix4x4_5x5 = | |
| { | |
| 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 8.0f, 8.0f, 0.0f, | |
| 0.0f, 1.0f, -1.0f, 2.0f, -2.0f, 4.0f, -4.0f, 0.0f, | |
| 0.0f, 1.0f, 1.0f, 4.0f, 4.0f, 2.0f, 2.0f, 0.0f, | |
| 0.0f, 1.0f, -1.0f, 8.0f, -8.0f, 1.0f, -1.0f, 1.0f | |
| }; | |
| static const std::array<float, 16> omatrix2x1_7x7 = | |
| { | |
| 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 0.0f, | |
| 0.0f, -1.0f, 1.0f, -2.0f, 2.0f, -3.0f, 3.0f, 1.0f | |
| }; | |
| // ------------------------------------------ | |
| using WinogradKey = std::tuple<std::pair<int, int>, std::pair<int, int>, WinogradTransformType>; | |
| // Key = (Output tile size, Kernel size, Winograd transform type) | |
| static std::map<WinogradKey, const float *> matrix_map = | |
| { | |
| { WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3), WinogradTransformType::INPUT), imatrix2x2_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3), WinogradTransformType::INPUT), imatrix4x4_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(2, 1), std::pair<int, int>(3, 1), WinogradTransformType::INPUT), imatrix2x2_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(3, 1), WinogradTransformType::INPUT), imatrix4x4_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 3), WinogradTransformType::INPUT), imatrix2x2_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 3), WinogradTransformType::INPUT), imatrix4x4_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5), WinogradTransformType::INPUT), imatrix4x4_5x5.data() }, | |
| { WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(5, 1), WinogradTransformType::INPUT), imatrix4x4_5x5.data() }, | |
| { WinogradKey(std::pair<int, int>(2, 1), std::pair<int, int>(7, 1), WinogradTransformType::INPUT), imatrix2x1_7x7.data() }, | |
| { WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 7), WinogradTransformType::INPUT), imatrix2x1_7x7.data() }, | |
| { WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(7, 7), WinogradTransformType::INPUT), imatrix2x1_7x7.data() }, | |
| { WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 5), WinogradTransformType::INPUT), imatrix4x4_5x5.data() }, | |
| { WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3), WinogradTransformType::FILTER), fmatrix2x2_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3), WinogradTransformType::FILTER), fmatrix4x4_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(2, 1), std::pair<int, int>(3, 1), WinogradTransformType::FILTER), fmatrix2x2_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(3, 1), WinogradTransformType::FILTER), fmatrix4x4_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 3), WinogradTransformType::FILTER), fmatrix2x2_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 3), WinogradTransformType::FILTER), fmatrix4x4_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5), WinogradTransformType::FILTER), fmatrix4x4_5x5.data() }, | |
| { WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(5, 1), WinogradTransformType::FILTER), fmatrix4x4_5x5.data() }, | |
| { WinogradKey(std::pair<int, int>(2, 1), std::pair<int, int>(7, 1), WinogradTransformType::FILTER), fmatrix2x1_7x7.data() }, | |
| { WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 7), WinogradTransformType::FILTER), fmatrix2x1_7x7.data() }, | |
| { WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(7, 7), WinogradTransformType::FILTER), fmatrix2x1_7x7.data() }, | |
| { WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 5), WinogradTransformType::FILTER), fmatrix4x4_5x5.data() }, | |
| { WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3), WinogradTransformType::OUTPUT), omatrix2x2_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3), WinogradTransformType::OUTPUT), omatrix4x4_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(2, 1), std::pair<int, int>(3, 1), WinogradTransformType::OUTPUT), omatrix2x2_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(3, 1), WinogradTransformType::OUTPUT), omatrix4x4_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 3), WinogradTransformType::OUTPUT), omatrix2x2_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 3), WinogradTransformType::OUTPUT), omatrix4x4_3x3.data() }, | |
| { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5), WinogradTransformType::OUTPUT), omatrix4x4_5x5.data() }, | |
| { WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(5, 1), WinogradTransformType::OUTPUT), omatrix4x4_5x5.data() }, | |
| { WinogradKey(std::pair<int, int>(2, 1), std::pair<int, int>(7, 1), WinogradTransformType::OUTPUT), omatrix2x1_7x7.data() }, | |
| { WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 7), WinogradTransformType::OUTPUT), omatrix2x1_7x7.data() }, | |
| { WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(7, 7), WinogradTransformType::OUTPUT), omatrix2x1_7x7.data() }, | |
| { WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 5), WinogradTransformType::OUTPUT), omatrix4x4_5x5.data() }, | |
| }; | |
| // Find transformation matrix | |
| std::map<WinogradKey, const float *>::iterator it; | |
| it = matrix_map.find(WinogradKey(std::pair<int, int>(output_tile_size.width, output_tile_size.height), | |
| std::pair<int, int>(kernel_size.width, kernel_size.height), | |
| winograd_transform_type)); | |
| float const *matrix_values = nullptr; | |
| if(it != matrix_map.end()) | |
| { | |
| // Get matrix pointer | |
| matrix_values = it->second; | |
| } | |
| else | |
| { | |
| ARM_COMPUTE_ERROR("Winograd configuration not supported"); | |
| } | |
| // Copy values | |
| std::copy(&matrix_values[0], &matrix_values[0] + src.num_elements(), &src[0]); | |
| } | |
| } // namespace | |
| template <typename T> | |
| SimpleTensor<T> winograd_input_transform(const SimpleTensor<T> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info) | |
| { | |
| ARM_COMPUTE_ERROR_ON(in.data_layout() != DataLayout::NCHW); | |
| const PadStrideInfo conv_info = winograd_info.convolution_info; | |
| const Size2D output_tile_size = winograd_info.output_tile_size; | |
| const Size2D kernel_size = winograd_info.kernel_size; | |
| SimpleTensor<T> out{ output_shape, in.data_type() }; | |
| // Calculate dimensions for the tile | |
| const unsigned int tile_w = output_tile_size.width + kernel_size.width - 1; | |
| const unsigned int tile_h = output_tile_size.height + kernel_size.height - 1; | |
| // Get the maximum dimension from the tile size | |
| const unsigned int tile_max_dim = std::max(tile_w, tile_h); | |
| TensorShape tile_dims(tile_max_dim, tile_max_dim); | |
| // Simple tensor for the input tile | |
| SimpleTensor<T> src_tile{ tile_dims, in.data_type() }; | |
| // Simple tensor for the temporary tile | |
| SimpleTensor<T> tmp_tile{ tile_dims, in.data_type() }; | |
| // Simple tensor for the output tile | |
| SimpleTensor<T> dst_tile{ tile_dims, in.data_type() }; | |
| // Simple tensor for the transformation matrix | |
| SimpleTensor<T> matrix{ tile_dims, in.data_type() }; | |
| // Simple tensor for the transformation matrix transposed | |
| SimpleTensor<T> matrix_transposed{ tile_dims, in.data_type() }; | |
| // Initialize matrix for the input transform | |
| initialize_matrix_transform(matrix, output_tile_size, kernel_size, WinogradTransformType::INPUT); | |
| // Transpose matrix | |
| transpose_matrix<T>(matrix, matrix_transposed); | |
| const int in_w = in.shape().x(); | |
| const int in_h = in.shape().y(); | |
| const int in_d = in.shape().z(); | |
| const int out_d = out.shape().z(); | |
| const int num_batches = in.shape().total_size() / (in_w * in_h * in_d); | |
| const int step_x = output_tile_size.width; | |
| const int step_y = output_tile_size.height; | |
| // 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(in_w, in_h), | |
| kernel_size, | |
| output_tile_size, | |
| conv_info); | |
| const int num_tiles_x = num_tiles.width; | |
| const int num_tiles_y = num_tiles.height; | |
| // In case of 1D convolution, the input tile has to be partially filled with zeros | |
| int start_x_zero = 0; | |
| int start_y_zero = 0; | |
| 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 |