99 lines
4.3 KiB
C++
99 lines
4.3 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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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* 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/d_t_s.h>
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namespace nd4j {
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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 __depthToSpace(NDArray *input, NDArray *output, int block_size, bool isNHWC) {
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T *input_ptr = reinterpret_cast<T *>(input->buffer());
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T *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_area = input_width * input_height;
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const int input_depth_by_input_area = input_depth * input_area;
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const int output_depth_by_input_height = output_depth * input_height;
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if (isNHWC) {
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const int total_count = batch_size * output_height * output_width * output_depth;
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PRAGMA_OMP_PARALLEL_FOR_SIMD
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for (int out_idx = 0; out_idx < total_count; out_idx++) {
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const int d = out_idx % output_depth;
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const int out_idx2 = out_idx / output_depth;
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const int w = out_idx2 % output_width;
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const int out_idx3 = out_idx2 / output_width;
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const int h = out_idx3 % output_height;
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const int b = out_idx3 / output_height;
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const int in_h = h / block_size;
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const int offset_h = h % block_size;
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const int in_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) * output_depth;
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const int in_d = d + offset_d;
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const int inp_idx = in_d + input_depth * (in_w + input_width * (in_h + input_height * b));
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(output_ptr + out_idx)[0] = (input_ptr + inp_idx)[0];
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}
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} else {
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const int total_count = batch_size * input_depth_by_input_area;
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PRAGMA_OMP_PARALLEL_FOR_SIMD
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for (int input_idx = 0; input_idx < total_count; input_idx++) {
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const int n_bY_bX_oC_iY = input_idx / input_width;
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const int iX = input_idx - n_bY_bX_oC_iY * input_width;
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const int n_bY_bX = n_bY_bX_oC_iY / output_depth_by_input_height;
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const int oC_iY = n_bY_bX_oC_iY - n_bY_bX * output_depth_by_input_height;
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const int n_bY = n_bY_bX / block_size;
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const int bX = n_bY_bX - n_bY * block_size;
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const int n = n_bY / block_size;
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const int bY = n_bY - n * block_size;
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const int output_idx = bX + block_size * (iX + input_width * (bY + block_size * (oC_iY + n * output_depth_by_input_height)));
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(output_ptr + output_idx)[0] = (input_ptr + input_idx)[0];
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}
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}
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}
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void _depthToSpace(nd4j::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC) {
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auto xType = input->dataType();
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BUILD_SINGLE_SELECTOR(xType, __depthToSpace, (input, output, block_size, isNHWC), LIBND4J_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void __depthToSpace, (NDArray *input, NDArray *output, int block_size, bool isNHWC);, LIBND4J_TYPES);
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}
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}
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} |