147 lines
7.8 KiB
C++
147 lines
7.8 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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// @author Yurii Shyrma (iuriish@yahoo.com), created on 18.09.2018
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//
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#include <ops/declarable/helpers/convolutions.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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//////////////////////////////////////////////////////////////////////////
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// [bS, iC, iD, iH, iW] is convoluted to [bS, iC, kD, kH, kW, oD, oH, oW]
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template <typename T>
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static void vol2col_(const NDArray& volume, NDArray& columns, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) {
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const int bS = volume.sizeAt(0);
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const int iC = volume.sizeAt(1);
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const int iD = volume.sizeAt(2);
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const int iH = volume.sizeAt(3);
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const int iW = volume.sizeAt(4);
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const int kD = columns.sizeAt(2);
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const int kH = columns.sizeAt(3);
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const int kW = columns.sizeAt(4);
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const int oD = columns.sizeAt(5);
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const int oH = columns.sizeAt(6);
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const int oW = columns.sizeAt(7);
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const Nd4jLong colStride0 = columns.stridesOf()[0];
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const Nd4jLong colStride1 = columns.stridesOf()[1];
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const Nd4jLong colStride2 = columns.stridesOf()[2];
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const Nd4jLong colStride3 = columns.stridesOf()[3];
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const Nd4jLong colStride4 = columns.stridesOf()[4];
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const Nd4jLong colStride5 = columns.stridesOf()[5];
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const Nd4jLong colStride6 = columns.stridesOf()[6];
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const Nd4jLong colStride7 = columns.stridesOf()[7];
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const Nd4jLong volStride0 = volume.stridesOf()[0];
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const Nd4jLong volStride1 = volume.stridesOf()[1];
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const Nd4jLong volStride2 = volume.stridesOf()[2];
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const Nd4jLong volStride3 = volume.stridesOf()[3];
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const Nd4jLong volStride4 = volume.stridesOf()[4];
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T* colBuff = columns.bufferAsT<T>();
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T* volBuff = const_cast<NDArray&>(volume).bufferAsT<T>();
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if (volume.ordering() == 'c' && columns.ordering() == 'c' && shape::strideDescendingCAscendingF(volume.getShapeInfo()) && shape::strideDescendingCAscendingF(columns.getShapeInfo())) {
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auto func = PRAGMA_THREADS_FOR_3D {
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T *col, *vol;
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int volDep, volRow, volCol;
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for (int b = start_x; b < stop_x; b += inc_x) {
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for (int c = start_y; c < stop_y; c += inc_y) {
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for (int kDep = start_z; kDep < stop_z; kDep += inc_z) {
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for (int kRow = 0; kRow < kH; ++kRow) {
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for (int kCol = 0; kCol < kW; ++kCol) {
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for (int colD = 0; colD < oD; ++colD) {
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for (int colH = 0; colH < oH; ++colH) {
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for (int 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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col = colBuff + b * colStride0 + c * colStride1 + kDep * colStride2 + kRow * colStride3 + kCol * colStride4 + colD * colStride5 + colH * colStride6 + colW * colStride7;
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if (static_cast<unsigned>(volDep) >= static_cast<unsigned>(iD) || static_cast<unsigned>(volRow) >= static_cast<unsigned>(iH) || static_cast<unsigned>(volCol) >= static_cast<unsigned>(iW))
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*col = static_cast<T>(0.);
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else {
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vol = volBuff + b * volStride0 + c * volStride1 + volDep * volStride2 + volRow * volStride3 + volCol * volStride4;
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*col = *vol;
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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_for(func, 0, bS, 1, 0, iC, 1, 0, kD, 1);
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} else {
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auto func = PRAGMA_THREADS_FOR_2D {
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T *col, *vol;
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int volDep, volRow, volCol;
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for (int b = start_x; b < stop_x; b++) {
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for (int colD = start_y; colD < stop_y; colD++) {
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for (int colH = 0; colH < oH; ++colH) {
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for (int colW = 0; colW < oW; ++colW) {
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for (int c = 0; c < iC; ++c) {
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for (int kDep = 0; kDep < kD; ++kDep) {
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for (int kRow = 0; kRow < kH; ++kRow) {
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for (int 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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col = colBuff + b * colStride0 + c * colStride1 + kDep * colStride2 + kRow * colStride3 + kCol * colStride4 + colD * colStride5 + colH * colStride6 + colW * colStride7;
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if (static_cast<unsigned>(volDep) >= static_cast<unsigned>(iD) || static_cast<unsigned>(volRow) >= static_cast<unsigned>(iH) || static_cast<unsigned>(volCol) >= static_cast<unsigned>(iW))
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*col = static_cast<T>(0.f);
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else {
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vol = volBuff + b * volStride0 + c * volStride1 + volDep * volStride2 + volRow * volStride3 + volCol * volStride4;
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*col = *vol;
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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_for(func, 0, bS, 1, 0, oD, 1);
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//func(0, 0, bS, 1, 0, oD, 1);
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}
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}
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void ConvolutionUtils::vol2col(sd::graph::Context& block, const NDArray& volume, NDArray& columns, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) {
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BUILD_SINGLE_SELECTOR(volume.dataType(), vol2col_, (volume, columns, sD, sH, sW, pD, pH, pW, dD, dH, dW), FLOAT_TYPES);
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}
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}
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} |