wehub-resource-sync
162 lines
6.8 KiB
C++
162 lines
6.8 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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// @author Yurii Shyrma (iuriish@yahoo.com), created on 18.09.2018
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//
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#include <execution/Threads.h>
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#include <ops/declarable/helpers/convolutions.h>
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namespace sd {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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// [bS, iC, kD, kH, kW, oD, oH, oW] is de-convoluted to [bS, iC, iD, iH, iW]
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template <typename T>
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static void col2vol_(NDArray& columns, NDArray& volume, const LongType sD, const LongType sH, const LongType sW, const LongType pD,
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const LongType pH, const LongType pW, const LongType dD, const LongType dH, const LongType dW) {
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// initial zeroing of volume content
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volume.nullify();
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const LongType bS = volume.sizeAt(0);
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const LongType iC = volume.sizeAt(1);
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const LongType iD = volume.sizeAt(2);
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const LongType iH = volume.sizeAt(3);
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const LongType iW = volume.sizeAt(4);
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const LongType kD = columns.sizeAt(2);
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const LongType kH = columns.sizeAt(3);
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const LongType kW = columns.sizeAt(4);
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const LongType oD = columns.sizeAt(5);
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const LongType oH = columns.sizeAt(6);
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const LongType oW = columns.sizeAt(7);
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const sd::LongType colStride0 = columns.stridesOf()[0];
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const sd::LongType colStride1 = columns.stridesOf()[1];
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const sd::LongType colStride2 = columns.stridesOf()[2];
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const sd::LongType colStride3 = columns.stridesOf()[3];
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const sd::LongType colStride4 = columns.stridesOf()[4];
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const sd::LongType colStride5 = columns.stridesOf()[5];
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const sd::LongType colStride6 = columns.stridesOf()[6];
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const sd::LongType colStride7 = columns.stridesOf()[7];
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const sd::LongType volStride0 = volume.stridesOf()[0];
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const sd::LongType volStride1 = volume.stridesOf()[1];
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const sd::LongType volStride2 = volume.stridesOf()[2];
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const sd::LongType volStride3 = volume.stridesOf()[3];
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const sd::LongType volStride4 = volume.stridesOf()[4];
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T* volBuff = volume.bufferAsT<T>();
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T* colBuff = const_cast<NDArray&>(columns).bufferAsT<T>();
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if (volume.ordering() == 'c' && columns.ordering() == 'c' && shape::strideDescendingCAscendingF(volume.shapeInfo()) &&
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shape::strideDescendingCAscendingF(columns.shapeInfo())) {
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auto func = PRAGMA_THREADS_FOR {
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T *col, *vol;
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sd::LongType volDep, volRow, volCol;
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for (sd::LongType b = start; b < stop; b++) {
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for (sd::LongType c = 0; c < iC; c++) {
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for (sd::LongType kDep = 0; kDep < kD; ++kDep) {
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for (sd::LongType kRow = 0; kRow < kH; ++kRow) {
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for (sd::LongType kCol = 0; kCol < kW; ++kCol) {
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for (sd::LongType colD = 0; colD < oD; ++colD) {
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for (sd::LongType colH = 0; colH < oH; ++colH) {
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for (sd::LongType colW = 0; colW < oW; ++colW) {
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volDep = (-pD + kDep * dD) + colD * sD;
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volRow = (-pH + kRow * dH) + colH * sH;
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volCol = (-pW + kCol * dW) + colW * sW;
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if (volDep >= 0 && volDep < iD &&
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volRow >= 0 && volRow < iH &&
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volCol >= 0 && volCol < iW) {
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auto colIndex = b * colStride0 + c * colStride1 + kDep * colStride2 + kRow * colStride3 +
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kCol * colStride4 + colD * colStride5 + colH * colStride6 + colW * colStride7;
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auto volIndex = b * volStride0 + c * volStride1 + volDep * volStride2 + volRow * volStride3 +
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volCol * volStride4;
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col = colBuff + colIndex;
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vol = volBuff + volIndex;
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*vol += *col;
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}
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}
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}
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}
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}
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}
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}
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}
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}
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};
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samediff::Threads::parallel_tad(func, 0, bS);
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} else {
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auto func = PRAGMA_THREADS_FOR {
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T *col, *vol;
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sd::LongType volDep, volRow, volCol;
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for (sd::LongType b = start; b < stop; b++) {
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for (sd::LongType colD = 0; colD < oD; colD++) {
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for (sd::LongType colH = 0; colH < oH; ++colH) {
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for (sd::LongType colW = 0; colW < oW; ++colW) {
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for (sd::LongType c = 0; c < iC; ++c) {
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for (sd::LongType kDep = 0; kDep < kD; ++kDep) {
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for (sd::LongType kRow = 0; kRow < kH; ++kRow) {
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for (sd::LongType kCol = 0; kCol < kW; ++kCol) {
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volDep = (-pD + kDep * dD) + colD * sD;
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volRow = (-pH + kRow * dH) + colH * sH;
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volCol = (-pW + kCol * dW) + colW * sW;
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if (volDep >= 0 && volDep < iD &&
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volRow >= 0 && volRow < iH &&
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volCol >= 0 && volCol < iW) {
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auto colIndex = b * colStride0 + c * colStride1 + kDep * colStride2 + kRow * colStride3 +
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kCol * colStride4 + colD * colStride5 + colH * colStride6 + colW * colStride7;
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auto volIndex = b * volStride0 + c * volStride1 + volDep * volStride2 + volRow * volStride3 +
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volCol * volStride4;
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col = colBuff + colIndex;
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vol = volBuff + volIndex;
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*vol += *col;
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}
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}
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}
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}
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}
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}
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}
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}
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}
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};
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samediff::Threads::parallel_tad(func, 0, bS);
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}
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}
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void ConvolutionUtils::col2vol(sd::graph::Context& block, NDArray& columns, NDArray& volume, const LongType sD,
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const LongType sH, const LongType sW, const LongType pD, const LongType pH, const LongType pW, const LongType dD,
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const LongType dH, const LongType dW) {
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BUILD_SINGLE_SELECTOR(volume.dataType(), col2vol_, (columns, volume, sD, sH, sW, pD, pH, pW, dD, dH, dW),
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SD_FLOAT_TYPES);
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}
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} // namespace ops
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} // namespace sd
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