/******************************************************************************* * Copyright (c) 2015-2018 Skymind, Inc. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at * https://www.apache.org/licenses/LICENSE-2.0. * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the * License for the specific language governing permissions and limitations * under the License. * * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ // // // #include #include namespace sd { namespace ops { namespace helpers { template static void _spaceTodepth_(const NDArray &input, NDArray *output, int block_size, bool isNHWC) { auto input_ptr = reinterpret_cast(input.buffer()); auto output_ptr = reinterpret_cast(output->buffer()); const int batch_size = input.sizeAt(0); const int input_depth = isNHWC ? input.sizeAt(3) : input.sizeAt(1); const int input_height = isNHWC ? input.sizeAt(1) : input.sizeAt(2); const int input_width = isNHWC ? input.sizeAt(2) : input.sizeAt(3); const int output_depth = isNHWC ? output->sizeAt(3) : output->sizeAt(1); const int output_height = isNHWC ? output->sizeAt(1) : output->sizeAt(2); const int output_width = isNHWC ? output->sizeAt(2) : output->sizeAt(3); const int input_depth_by_output_height = input_depth * output_height; const int output_area = output_width * output_height; const int output_depth_by_output_area = output_depth * output_area; if (isNHWC) { const int total_count = batch_size * input_height * input_width * input_depth; auto func = PRAGMA_THREADS_FOR { for (auto inp_idx = start; inp_idx < stop; inp_idx++) { // inp_idx = d + input_depth * (w + input_width * (h + input_height * b)) const int d = inp_idx % input_depth; const int inp_idx2 = inp_idx / input_depth; const int w = inp_idx2 % input_width; const int inp_idx3 = inp_idx2 / input_width; const int h = inp_idx3 % input_height; const int b = inp_idx3 / input_height; const int out_h = h / block_size; const int offset_h = h % block_size; const int out_w = w / block_size; const int offset_w = w % block_size; const int offset_d = (offset_h * block_size + offset_w) * input_depth; const int out_d = d + offset_d; const int out_idx = out_d + output_depth * (out_w + output_width * (out_h + output_height * b)); *(output_ptr + out_idx) = *(input_ptr + inp_idx); } }; samediff::Threads::parallel_for(func, 0, total_count); } else { const int total_count = batch_size * output_depth_by_output_area; auto func = PRAGMA_THREADS_FOR { for (auto inp_idx = start; inp_idx < stop; inp_idx++) { const int n_iC_oY_bY_oX = inp_idx / block_size; const int bX = inp_idx - n_iC_oY_bY_oX * block_size; const int n_iC_oY_bY = n_iC_oY_bY_oX / output_width; const int oX = n_iC_oY_bY_oX - n_iC_oY_bY * output_width; const int n_iC_oY = n_iC_oY_bY / block_size; const int bY = n_iC_oY_bY - n_iC_oY * block_size; const int n = n_iC_oY / input_depth_by_output_height; const int iC_oY = n_iC_oY - n * input_depth_by_output_height; const int output_idx = oX + (((n * block_size + bY) * block_size + bX) * input_depth_by_output_height + iC_oY) * output_width; *(output_ptr + output_idx) = *(input_ptr + inp_idx); } }; samediff::Threads::parallel_for(func, 0, total_count); } } void _spaceTodepth(sd::LaunchContext * context, const NDArray &input, NDArray *output, int block_size, bool isNHWC) { BUILD_SINGLE_SELECTOR(input.dataType(), _spaceTodepth_, (input, output, block_size, isNHWC), LIBND4J_TYPES); } BUILD_SINGLE_TEMPLATE(template void _spaceTodepth_, (const NDArray &input, NDArray *output, int block_size, bool isNHWC), LIBND4J_TYPES); } } }