wehub-resource-sync
93 lines
3.7 KiB
C++
93 lines
3.7 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 upsampling3d_(NDArray& input, NDArray& output, const LongType factorD, const LongType factorH,
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const LongType factorW, 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 = input.bufferAsT<T>();
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T* z = output.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 = input.sizeAt(0);
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const sd::LongType iC = input.sizeAt(dimIC);
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const sd::LongType oD = output.sizeAt(dimID);
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const sd::LongType oH = output.sizeAt(dimID + 1);
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const sd::LongType oW = output.sizeAt(dimID + 2);
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const sd::LongType xStride0 = input.stridesOf()[0];
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const sd::LongType xStride1 = input.stridesOf()[dimIC];
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const sd::LongType xStride2 = input.stridesOf()[dimID];
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const sd::LongType xStride3 = input.stridesOf()[dimID + 1];
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const sd::LongType xStride4 = input.stridesOf()[dimID + 2];
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const sd::LongType zStride0 = output.stridesOf()[0];
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const sd::LongType zStride1 = output.stridesOf()[dimIC];
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const sd::LongType zStride2 = output.stridesOf()[dimID];
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const sd::LongType zStride3 = output.stridesOf()[dimID + 1];
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const sd::LongType zStride4 = output.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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sd::LongType xCoord2, xCoord3, xCoord4;
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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 < oH; ++h) {
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for (sd::LongType w = 0; w < oW; ++w) {
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xCoord2 = d / factorD;
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xCoord3 = h / factorH;
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xCoord4 = w / factorW;
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z[b * zStride0 + c * zStride1 + d * zStride2 + h * zStride3 + w * zStride4] =
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x[b * xStride0 + c * xStride1 + xCoord2 * xStride2 + xCoord3 * xStride3 + xCoord4 * 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, oD, 1);
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}
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void ConvolutionUtils::upsampling3d(sd::graph::Context& block, NDArray& input, NDArray& output, const LongType factorD,
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const LongType factorH, const LongType factorW, const bool isNCDHW) {
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BUILD_SINGLE_SELECTOR(input.dataType(), upsampling3d_, (input, output, factorD, factorH, factorW, isNCDHW),
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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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