| /* |
| * Copyright (c) 2017-2022 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 "src/cpu/kernels/CpuWinogradConv2dKernel.h" |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| CpuWinogradConv2dTransformInputKernel::CpuWinogradConv2dTransformInputKernel(arm_conv::winograd::WinogradImpl &w_impl, arm_conv::ConvolutionArgs &_c_args, uint32_t nthreads) |
| : _winograd_impl{ w_impl }, _conv_args{ _c_args }, _nthreads{ nthreads } |
| { |
| } |
| |
| void CpuWinogradConv2dTransformInputKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(window); |
| const ITensor *input_nhwc = tensors.get_const_tensor(TensorType::ACL_SRC); |
| const ITensor *winograd_input_transform = tensors.get_const_tensor(TensorType::ACL_DST); |
| const ITensor *workspace = tensors.get_const_tensor(TensorType::ACL_INT); |
| |
| const unsigned int width_idx = 1; |
| const unsigned int height_idx = 2; |
| const unsigned int batch_idx = 3; |
| int element_size_in_bytes = input_nhwc->info()->element_size(); |
| const auto src_strides = input_nhwc->info()->strides_in_bytes(); |
| |
| const size_t input_row_stride = src_strides[height_idx] / element_size_in_bytes; |
| const size_t input_col_stride = src_strides[width_idx] / element_size_in_bytes; |
| const size_t input_batch_stride = src_strides[batch_idx] / element_size_in_bytes; |
| const auto input_nhwc_ptr = reinterpret_cast<const void *>(input_nhwc->buffer() + input_nhwc->info()->offset_first_element_in_bytes()); |
| auto win_transf_ptr = reinterpret_cast<void *>(winograd_input_transform->buffer() + winograd_input_transform->info()->offset_first_element_in_bytes()); |
| |
| _winograd_impl.input_transform->execute( |
| _conv_args, |
| input_nhwc_ptr, |
| input_batch_stride, |
| input_row_stride, |
| input_col_stride, |
| win_transf_ptr, |
| _winograd_impl.winograd_spec, |
| workspace->buffer(), |
| info.thread_id, |
| _nthreads); |
| } |
| |
| CpuWinogradConv2dTransformOutputKernel::CpuWinogradConv2dTransformOutputKernel(arm_conv::winograd::WinogradImpl &w_impl, arm_conv::ConvolutionArgs &_c_args, uint32_t nthreads) |
| : _winograd_impl{ w_impl }, _conv_args{ _c_args }, _nthreads{ nthreads } |
| { |
| } |
| |
| // Inherited methods overridden: |
| void CpuWinogradConv2dTransformOutputKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(window); |
| const ITensor *dst_nhwc = tensors.get_const_tensor(TensorType::ACL_DST); |
| const ITensor *winograd_output_transform = tensors.get_const_tensor(TensorType::ACL_SRC_0); |
| const ITensor *biases = tensors.get_const_tensor(TensorType::ACL_SRC_1); |
| const ITensor *workspace = tensors.get_tensor(TensorType::ACL_INT); |
| |
| const unsigned int width_idx = 1; |
| const unsigned int height_idx = 2; |
| const unsigned int batch_idx = 3; |
| const int element_size_in_bytes = dst_nhwc->info()->element_size(); |
| const auto dst_strides = dst_nhwc->info()->strides_in_bytes(); |
| |
| const size_t out_row_stride = dst_strides[height_idx] / element_size_in_bytes; |
| const size_t out_col_stride = dst_strides[width_idx] / element_size_in_bytes; |
| const size_t out_batch_stride = dst_strides[batch_idx] / element_size_in_bytes; |
| const auto wout_transf_ptr = reinterpret_cast<const void *>(winograd_output_transform->buffer() + winograd_output_transform->info()->offset_first_element_in_bytes()); |
| auto dst_nhwc_ptr = reinterpret_cast<void *>(dst_nhwc->buffer() + dst_nhwc->info()->offset_first_element_in_bytes()); |
| void *biases_data_ptr = nullptr; |
| if(biases != nullptr) |
| { |
| biases_data_ptr = reinterpret_cast<void *>(biases->buffer() + biases->info()->offset_first_element_in_bytes()); |
| } |
| |
| // Output transform |
| _winograd_impl.output_transform->execute( |
| _conv_args, |
| wout_transf_ptr, |
| _winograd_impl.winograd_spec, |
| biases_data_ptr, |
| dst_nhwc_ptr, |
| out_batch_stride, |
| out_row_stride, |
| out_col_stride, |
| workspace->buffer(), |
| info.thread_id, |
| _nthreads); |
| } |
| |
| } // namespace cpu |
| } // namespace arm_compute |