diff options
Diffstat (limited to 'src/core/NEON/kernels/convolution/winograd/transforms/weights_4_5_fp32.cpp')
-rw-r--r-- | src/core/NEON/kernels/convolution/winograd/transforms/weights_4_5_fp32.cpp | 124 |
1 files changed, 0 insertions, 124 deletions
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/weights_4_5_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/weights_4_5_fp32.cpp deleted file mode 100644 index 2f14e20142..0000000000 --- a/src/core/NEON/kernels/convolution/winograd/transforms/weights_4_5_fp32.cpp +++ /dev/null @@ -1,124 +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. - */ - -#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/kernel.hpp" - -namespace winograd -{ - template <> - template <> - void WinogradGEMM<1, 4, 1, 5>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, // NOTE: Data in HWIO order - float* const output, - const int matrix_stride, - const int matrix_row_stride - ) - { - // Get pointers to each cell of the weight tensor - const auto weight_col_stride = n_input_channels * n_output_channels; - const float *inptrs[kernel_cols]; - for (int j = 0; j < kernel_cols; j++) - { - inptrs[j] = input + j*weight_col_stride; - } - - // For each input channel - for (int ic = 0; ic < n_input_channels; ic++) - { - float *outptr = output + ic * matrix_row_stride; - - // For each output channel - int channels_remaining = n_output_channels; - for (; channels_remaining; channels_remaining--) - { - // Matrices used and computed in this kernel - float w[kernel_cols], V[inner_tile_cols]; - - // Read weights - for (int j = 0; j < kernel_cols; j++) - { - w[j] = *(inptrs[j]++); - } - - // Compute V = w WT - V[0] = (w[0]*-1) / 36; - V[1] = (w[1]*-1 + w[3]*-1 + w[0]*1 + w[2]*1 + w[4]*1) / 48; - V[2] = (w[0]*1 + w[1]*1 + w[2]*1 + w[3]*1 + w[4]*1) / 48; - V[3] = (w[0]*-1 + w[4]*-16 + w[2]*-4 + w[1]*2 + w[3]*8) / 120; - V[4] = (w[0]*-1 + w[4]*-16 + w[3]*-8 + w[2]*-4 + w[1]*-2) / 120; - V[5] = (w[3]*-27 + w[1]*-3 + w[2]*9 + w[4]*81 + w[0]*1) / 720; - V[6] = (w[1]*3 + w[2]*9 + w[3]*27 + w[4]*81 + w[0]*1) / 720; - V[7] = (w[4]*1) / 1; - - // Store the transformed weights - for (int j = 0; j < inner_tile_cols; j++) - { - *(outptr + j*matrix_stride) = V[j]; - } - outptr++; - } - } - } - - template <> - template <> - int WinogradGEMM<1, 4, 1, 5>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - (void) shape; - return 0; // TODO - } - - template <> - template <> - void WinogradGEMM<4, 1, 5, 1>::WeightsTransform<float>::execute( - const int n_output_channels, - const int n_input_channels, - const float* const input, // NOTE: Data in HWIO order - float* const output, - const int matrix_stride, - const int matrix_row_stride - ) - { - // Redirect to the 1xN implementation - WinogradGEMM<1, 4, 1, 5>::template WeightsTransform<float>::execute( - n_output_channels, n_input_channels, input, output, matrix_stride, - matrix_row_stride - ); - } - - template <> - template <> - int WinogradGEMM<4, 1, 5, 1>::WeightsTransform<float>::ops_performed(const KernelShape &shape) - { - (void) shape; - return 0; // TODO - } - - template struct WinogradGEMM<1, 4, 1, 5>::WeightsTransform<float>; - template struct WinogradGEMM<4, 1, 5, 1>::WeightsTransform<float>; -} |