/* ****************************************************************************** * * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at * https://www.apache.org/licenses/LICENSE-2.0. * * See the NOTICE file distributed with this work for additional * information regarding copyright ownership. * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the * License for the specific language governing permissions and limitations * under the License. * * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ // // @author Yurii Shyrma (iuriish@yahoo.com), created on 18.09.2018 // #include #include namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// template static void upsampling3d_(const NDArray& input, NDArray& output, const int factorD, const int factorH, const int factorW, const bool isNCDHW) { // input has shape [bS, iC, iD, iH, iW] (NCDHW) or [bS, iD, iH, iW, iC] (NDHWC) // output has shape [bS, iC, factorD*iD, factorH*iH, factorW*iW ] (NCDHW) or [bS, factorD*iD, factorH*iH, factorW*iW, iC] (NDHWC) const T* x = input.bufferAsT(); T* z = output.bufferAsT(); const uint dimID = isNCDHW ? 2 : 1; const uint dimIC = isNCDHW ? 1 : 4; const uint bS = input.sizeAt(0); const uint iC = input.sizeAt(dimIC); const uint oD = output.sizeAt(dimID); const uint oH = output.sizeAt(dimID + 1); const uint oW = output.sizeAt(dimID + 2); const Nd4jLong xStride0 = input.stridesOf()[0]; const Nd4jLong xStride1 = input.stridesOf()[dimIC]; const Nd4jLong xStride2 = input.stridesOf()[dimID]; const Nd4jLong xStride3 = input.stridesOf()[dimID + 1]; const Nd4jLong xStride4 = input.stridesOf()[dimID + 2]; const Nd4jLong zStride0 = output.stridesOf()[0]; const Nd4jLong zStride1 = output.stridesOf()[dimIC]; const Nd4jLong zStride2 = output.stridesOf()[dimID]; const Nd4jLong zStride3 = output.stridesOf()[dimID + 1]; const Nd4jLong zStride4 = output.stridesOf()[dimID + 2]; // loop through output array auto func = PRAGMA_THREADS_FOR_3D { uint xCoord2, xCoord3, xCoord4; for (uint b = start_x; b < stop_x; b += inc_x) { for (uint c = start_y; c < stop_y; c += inc_y) { for (uint d = start_z; d < stop_z; d += inc_z) { for (uint h = 0; h < oH; ++h) { for (uint w = 0; w < oW; ++w) { xCoord2 = d / factorD; xCoord3 = h / factorH; xCoord4 = w / factorW; z[b * zStride0 + c * zStride1 + d * zStride2 + h * zStride3 + w * zStride4] = x[ b * xStride0 + c * xStride1 + xCoord2 * xStride2 + xCoord3 * xStride3 + xCoord4 * xStride4]; } } } } } }; samediff::Threads::parallel_for(func, 0, bS, 1, 0, iC, 1, 0, oD, 1); } void ConvolutionUtils::upsampling3d(sd::graph::Context& block, const NDArray& input, NDArray& output, const int factorD, const int factorH, const int factorW, const bool isNCDHW) { BUILD_SINGLE_SELECTOR(input.dataType(), upsampling3d_, (input, output, factorD, factorH, factorW, isNCDHW), FLOAT_TYPES); } } }