| /* |
| * 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. |
| */ |
| #include "Winograd.h" |
| |
| #include "tests/validation/Helpers.h" |
| #include "tests/validation/reference/Utils.h" |
| |
| #include "arm_compute/core/Types.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace reference |
| { |
| namespace |
| { |
| template <typename T> |
| void winograd_filter_transform3x3(const SimpleTensor<T> &in, SimpleTensor<T> &out, const Size2D &output_tile) |
| { |
| const bool is_2x2 = (output_tile.width == 2); |
| const unsigned int transf_side = is_2x2 ? 4u : 6u; |
| |
| // Simple tensor for the 3x3 input tile |
| SimpleTensor<T> input_tile{ TensorShape(3u, 3u), in.data_type(), 1 }; |
| |
| // Simple tensor for the transformation matrix |
| SimpleTensor<T> trans_matrix{ TensorShape(3u, transf_side), in.data_type(), 1 }; |
| |
| // Simple tensor for the transformation matrix transpose |
| SimpleTensor<T> trans_matrix_transposed{ TensorShape(transf_side, 3u), in.data_type(), 1 }; |
| |
| // Simple tensor for the 3xSide temporary tile |
| SimpleTensor<T> tmp_tile{ TensorShape(3u, transf_side), in.data_type(), 1 }; |
| |
| // Simple tensor for the SidexSide output tile |
| SimpleTensor<T> transf_tile{ TensorShape(transf_side, transf_side), in.data_type(), 1 }; |
| |
| if(is_2x2) |
| { |
| // Initialize 3x4 transformation matrix |
| // 1 | 0 | 0 |
| // 0.5 | 0.5 | 0.5 |
| // 0.5 |-0.5 | 0.5 |
| // 0 | 0 | 1 |
| trans_matrix[0 + 0 * 3] = 1.0f; |
| trans_matrix[1 + 0 * 3] = 0.0f; |
| trans_matrix[2 + 0 * 3] = 0.0f; |
| trans_matrix[0 + 1 * 3] = 0.5f; |
| trans_matrix[1 + 1 * 3] = 0.5f; |
| trans_matrix[2 + 1 * 3] = 0.5f; |
| trans_matrix[0 + 2 * 3] = 0.5f; |
| trans_matrix[1 + 2 * 3] = -0.5f; |
| trans_matrix[2 + 2 * 3] = 0.5f; |
| trans_matrix[0 + 3 * 3] = 0.0f; |
| trans_matrix[1 + 3 * 3] = 0.0f; |
| trans_matrix[2 + 3 * 3] = 1.0f; |
| } |
| else |
| { |
| // Initialize 3x6 transformation matrix |
| // 1/4 | 0 | 0 |
| // -1/6 | -1/6 | -1/6 |
| // -1/6 | 1/6 | -1/6 |
| // 1/24 | 1/12 | 1/6 |
| // 1/24 | -1/12 | 1/6 |
| // 0 | 0 | 1 |
| trans_matrix[0 + 0 * 3] = 1.0f / 4.0f; |
| trans_matrix[1 + 0 * 3] = 0.0f; |
| trans_matrix[2 + 0 * 3] = 0.0f; |
| trans_matrix[0 + 1 * 3] = -1.0f / 6.0f; |
| trans_matrix[1 + 1 * 3] = -1.0f / 6.0f; |
| trans_matrix[2 + 1 * 3] = -1.0f / 6.0f; |
| trans_matrix[0 + 2 * 3] = -1.0f / 6.0f; |
| trans_matrix[1 + 2 * 3] = 1.0f / 6.0f; |
| trans_matrix[2 + 2 * 3] = -1.0f / 6.0f; |
| trans_matrix[0 + 3 * 3] = 1.0f / 24.0f; |
| trans_matrix[1 + 3 * 3] = 1.0f / 12.0f; |
| trans_matrix[2 + 3 * 3] = 1.0f / 6.0f; |
| trans_matrix[0 + 4 * 3] = 1.0f / 24.0f; |
| trans_matrix[1 + 4 * 3] = -1.0f / 12.0f; |
| trans_matrix[2 + 4 * 3] = 1.0f / 6.0f; |
| trans_matrix[0 + 5 * 3] = 0.0f; |
| trans_matrix[1 + 5 * 3] = 0.0f; |
| trans_matrix[2 + 5 * 3] = 1.0f; |
| } |
| |
| // Transpose the transformation matrix |
| transpose_matrix(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() / (9 * num_channels * num_filters); |
| |
| 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 3x3 tile from the input tensor |
| get_tile(in, input_tile, Coordinates(0, 0, z, w, n)); |
| |
| // First transformation |
| matrix_multiply(trans_matrix, input_tile, tmp_tile); |
| |
| // Second transformation |
| matrix_multiply(tmp_tile, trans_matrix_transposed, transf_tile); |
| |
| // Store the 4x4 output tile across the 16 channels |
| const int output_offset = w + z * num_filters; |
| |
| for(unsigned int out_h = 0, out_pos = 0; out_h < transf_side; ++out_h) |
| { |
| for(unsigned int out_w = 0; out_w < transf_side; ++out_w, ++out_pos) |
| { |
| out[output_offset + out_pos * num_filters * num_channels] = transf_tile[out_w + out_h * transf_side]; |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| template <typename T> |
| void winograd_input_transform3x3(const SimpleTensor<T> &src, SimpleTensor<T> &dst, const PadStrideInfo &conv_info) |
| { |
| TensorShape shape4x4(4u, 4u); |
| |
| // Simple tensor for the 4x4 input tile |
| SimpleTensor<T> src_tile{ shape4x4, src.data_type() }; |
| |
| // Simple tensor for the 4x4 temporary tile |
| SimpleTensor<T> tmp_tile{ shape4x4, src.data_type() }; |
| |
| // Simple tensor for the 4x4 output tile |
| SimpleTensor<T> dst_tile{ shape4x4, src.data_type() }; |
| |
| // Simple tensor for the transformation matrix |
| SimpleTensor<T> matrix{ shape4x4, src.data_type() }; |
| |
| // Simple tensor for the transformation matrix transposed |
| SimpleTensor<T> matrix_transposed{ shape4x4, src.data_type() }; |
| |
| const float matrix_values[] = { 1.f, 0.f, -1.f, 0.f, |
| 0.f, 1.f, 1.f, 0.f, |
| 0.f, -1.f, 1.f, 0.f, |
| 0.f, 1.f, 0.f, -1.f |
| }; |
| |
| for(int i = 0; i < matrix.num_elements(); ++i) |
| { |
| matrix[i] = matrix_values[i]; |
| } |
| |
| transpose_matrix(matrix, matrix_transposed); |
| |
| const int in_w = src.shape().x(); |
| const int in_h = src.shape().y(); |
| const int in_d = src.shape().z(); |
| const int num_batches = src.shape().total_size() / (in_w * in_h * in_d); |
| const int num_tiles_x = std::ceil((in_w - 2 + conv_info.pad_left() + conv_info.pad_right()) / 2.0f); |
| const int num_tiles_y = std::ceil((in_h - 2 + conv_info.pad_top() + conv_info.pad_bottom()) / 2.0f); |
| |
| ARM_COMPUTE_ERROR_ON((num_tiles_x * num_tiles_y) != static_cast<int>(dst.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 * 2 - conv_info.pad_left(); |
| int yi = y * 2 - conv_info.pad_top(); |
| |
| // Get the 4x4 tile from the input tensor |
| get_tile(src, src_tile, Coordinates(xi, yi, z, b)); |
| |
| // Compute the transformation |
| matrix_multiply(matrix, src_tile, tmp_tile); |
| matrix_multiply(tmp_tile, matrix_transposed, dst_tile); |
| |
| // Store the 4x4 output tile across the 16 channels |
| for(int i = 0; i < 16; ++i) |
| { |
| int xo = z; |
| int yo = x + y * num_tiles_x; |
| dst[coords2index(dst.shape(), Coordinates(xo, yo, i, b))] = dst_tile[i]; |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| template <typename T> |
| void winograd_output_transform3x3(const SimpleTensor<T> &in, SimpleTensor<T> &out, int num_tiles_x) |
| { |
| ARM_COMPUTE_ERROR_ON(in.shape()[2] != 16); |
| ARM_COMPUTE_ERROR_ON(in.shape()[0] != out.shape()[2]); |
| |
| // Simple tensor for the 3x3 input tile |
| SimpleTensor<T> input_tile{ TensorShape(4u, 4u), in.data_type(), 1 }; |
| |
| // Simple tensor for the transformation matrix |
| SimpleTensor<T> trans_matrix{ TensorShape(4u, 2u), in.data_type(), 1 }; |
| |
| // Simple tensor for the transformation matrix transpose |
| SimpleTensor<T> trans_matrix_transposed{ TensorShape(2u, 4u), in.data_type(), 1 }; |
| |
| // Simple tensor for the 4x3 temporary tile |
| SimpleTensor<T> tmp_tile{ TensorShape(4u, 2u), in.data_type(), 1 }; |
| |
| // Simple tensor for the 4x4 output tile |
| SimpleTensor<T> output_tile{ TensorShape(2u, 2u), in.data_type(), 1 }; |
| |
| // Initialize transformation matrix |
| // 1 | 1 | 1 | 1 |
| // 0 | 1 | -1 | -1 |
| trans_matrix[0 + 0 * 4] = 1.0f; |
| trans_matrix[1 + 0 * 4] = 1.0f; |
| trans_matrix[2 + 0 * 4] = 1.0f; |
| trans_matrix[3 + 0 * 4] = 0.0f; |
| trans_matrix[0 + 1 * 4] = 0.0f; |
| trans_matrix[1 + 1 * 4] = 1.0f; |
| trans_matrix[2 + 1 * 4] = -1.0f; |
| trans_matrix[3 + 1 * 4] = -1.0f; |
| |
| // Transpose the transformation matrix |
| transpose_matrix(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; |
| |
| 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 4x4 tile across the 16 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(trans_matrix, input_tile, tmp_tile); |
| |
| // Second transformation |
| matrix_multiply(tmp_tile, trans_matrix_transposed, output_tile); |
| |
| // Store the 2x2 output tile |
| const int xo = (y % num_tiles_x) * 2; |
| const int yo = (y / num_tiles_x) * 2; |
| const int zo = x; |
| |
| const int output_offset = xo + (yo * stridey_out) + (zo * stridez_out) + (n * stridew_out); |
| out[output_offset + 0 * stridey_out + 0] = output_tile[0 + 0 * 2]; |
| |
| // Check out-of-bound writes |
| if(xo + 1 < w_out) |
| { |
| out[output_offset + 0 * stridey_out + 1] = output_tile[1 + 0 * 2]; |
| } |
| |
| if(yo + 1 < h_out) |
| { |
| out[output_offset + 1 * stridey_out + 0] = output_tile[0 + 1 * 2]; |
| } |
| |
| if((yo + 1 < h_out) && (xo + 1 < w_out)) |
| { |
| out[output_offset + 1 * stridey_out + 1] = output_tile[1 + 1 * 2]; |
| } |
| } |
| } |
| } |
| } |
| } // namespace |
| |
| template <typename T> |
| SimpleTensor<T> winograd_input_transform(const SimpleTensor<T> &src, const TensorShape &dst_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims) |
| { |
| ARM_COMPUTE_ERROR_ON(kernel_dims.width != kernel_dims.height); |
| ARM_COMPUTE_ERROR_ON(src.data_layout() != DataLayout::NCHW); |
| |
| SimpleTensor<T> dst{ dst_shape, src.data_type() }; |
| |
| switch(kernel_dims.width) |
| { |
| case 3: |
| winograd_input_transform3x3(src, dst, conv_info); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Only 3x3 kernels are supported"); |
| } |
| |
| return dst; |
| } |
| |
| template <typename T> |
| SimpleTensor<T> winograd_filter_transform(const SimpleTensor<T> &in, const TensorShape &output_shape, const Size2D &output_tile) |
| { |
| 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 }; |
| |
| switch(in.shape()[0]) |
| { |
| case 3: |
| winograd_filter_transform3x3(in, out, output_tile); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Only supported 3x3 kernel"); |
| break; |
| } |
| |
| return out; |
| } |
| |
| template <typename T> |
| SimpleTensor<T> winograd_output_transform(const SimpleTensor<T> &in, const TensorShape &output_shape, const Size2D &kernel_dims, const Size2D &num_tiles) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(in.data_layout() != DataLayout::NCHW, "Only supported NCHW data format"); |
| ARM_COMPUTE_ERROR_ON(kernel_dims.width != kernel_dims.height); |
| ARM_COMPUTE_ERROR_ON(in.shape()[1] != num_tiles.area()); |
| |
| // Create reference |
| SimpleTensor<T> out{ output_shape, in.data_type(), 1 }; |
| |
| switch(kernel_dims.width) |
| { |
| case 3: |
| winograd_output_transform3x3(in, out, num_tiles.width); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Only supported 3x3 kernel"); |
| break; |
| } |
| |
| return out; |
| } |
| |
| template SimpleTensor<float> winograd_input_transform(const SimpleTensor<float> &src, const TensorShape &dst_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims); |
| template SimpleTensor<float> winograd_filter_transform(const SimpleTensor<float> &in, const TensorShape &output_shape, const Size2D &output_tile); |
| template SimpleTensor<float> winograd_output_transform(const SimpleTensor<float> &in, const TensorShape &output_shape, const Size2D &kernel_dims, const Size2D &num_tiles); |
| } // namespace reference |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |