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