wehub-resource-sync
95 lines
3.8 KiB
C++
95 lines
3.8 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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template <typename T>
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static void upsampling3dBP_(NDArray& gradO, NDArray& gradI, const bool isNCDHW) {
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// input has shape [bS, iC, iD, iH, iW] (NCDHW) or [bS, iD, iH, iW, iC] (NDHWC)
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// output has shape [bS, iC, factorD*iD, factorH*iH, factorW*iW ] (NCDHW) or [bS, factorD*iD, factorH*iH, factorW*iW,
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// iC] (NDHWC)
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const T* x = gradO.bufferAsT<T>();
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T* z = gradI.bufferAsT<T>();
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const sd::LongType dimID = isNCDHW ? 2 : 1;
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const sd::LongType dimIC = isNCDHW ? 1 : 4;
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const sd::LongType bS = gradI.sizeAt(0);
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const sd::LongType iC = gradI.sizeAt(dimIC);
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const sd::LongType iD = gradI.sizeAt(dimID);
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const sd::LongType iH = gradI.sizeAt(dimID + 1);
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const sd::LongType iW = gradI.sizeAt(dimID + 2);
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const sd::LongType factorD = gradO.sizeAt(dimID) / iD;
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const sd::LongType factorH = gradO.sizeAt(dimID + 1) / iH;
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const sd::LongType factorW = gradO.sizeAt(dimID + 2) / iW;
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const sd::LongType xStride0 = gradO.stridesOf()[0];
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const sd::LongType xStride1 = gradO.stridesOf()[dimIC];
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const sd::LongType xStride2 = gradO.stridesOf()[dimID];
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const sd::LongType xStride3 = gradO.stridesOf()[dimID + 1];
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const sd::LongType xStride4 = gradO.stridesOf()[dimID + 2];
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const sd::LongType zStride0 = gradI.stridesOf()[0];
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const sd::LongType zStride1 = gradI.stridesOf()[dimIC];
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const sd::LongType zStride2 = gradI.stridesOf()[dimID];
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const sd::LongType zStride3 = gradI.stridesOf()[dimID + 1];
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const sd::LongType zStride4 = gradI.stridesOf()[dimID + 2];
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// loop through output array
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auto func = PRAGMA_THREADS_FOR_3D {
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for (sd::LongType b = start_x; b < stop_x; b += inc_x) {
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for (sd::LongType c = start_y; c < stop_y; c += inc_y) {
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for (sd::LongType d = start_z; d < stop_z; d += inc_z) {
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for (sd::LongType h = 0; h < iH; ++h) {
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for (sd::LongType w = 0; w < iW; ++w) {
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const auto zOffset = b * zStride0 + c * zStride1 + d * zStride2 + h * zStride3 + w * zStride4;
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z[zOffset] = 0;
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for (sd::LongType xd = d * factorD; xd < d * factorD + factorD; ++xd)
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for (sd::LongType xh = h * factorH; xh < h * factorH + factorH; ++xh)
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for (sd::LongType xw = w * factorW; xw < w * factorW + factorW; ++xw)
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z[zOffset] += x[b * xStride0 + c * xStride1 + xd * xStride2 + xh * xStride3 + xw * xStride4];
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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, iD, 1);
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}
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void ConvolutionUtils::upsampling3dBP(sd::graph::Context& block, NDArray& gradO, NDArray& gradI,
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const bool isNCHW) {
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BUILD_SINGLE_SELECTOR(gradO.dataType(), upsampling3dBP_, (gradO, gradI, isNCHW), SD_FLOAT_TYPES);
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}
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} // namespace ops
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} // namespace sd
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